tuple:\n if self.probability_links:\n return tuple(self.probability_links.values())\n return tuple()","def make_footnote(doc: nodes.document, label: str, uri: str) -> nodes.footnote:\n footnote = nodes.footnote(uri)\n para = nodes.paragraph()\n para.append(nodes.Text(uri))\n footnote.append(para)\n footnote.insert(0, nodes.label('', label))\n doc.note_autofootnote(footnote)\n return footnote","def fm_all_children(self):\n return self._relation_lst[self.CHILD].copy()","def get_reference_node_parents(ref):\n parents = []\n return parents","def add_notes(self, notes):\n if hasattr(notes, \"notes\"):\n for x in notes.notes:\n self.add_note(x)\n return self.notes\n elif hasattr(notes, \"name\"):\n self.add_note(notes)\n return self.notes\n elif isinstance(notes, six.string_types):\n self.add_note(notes)\n return self.notes\n for x in notes:\n if isinstance(x, list) and len(x) != 1:\n if len(x) == 2:\n self.add_note(x[0], x[1])\n else:\n self.add_note(x[0], x[1], x[2])\n else:\n self.add_note(x)\n return self.notes","def initialize_children(self, bibs):\n\t\tpass","def get_nodes(self):\n if self.inode_ref is None or self.jnode_ref is None:\n raise ValueError('Node references have not been set.')\n return self.inode_ref, self.jnode_ref","def test_headlines_predecessors(self):\n headline_str = \"* One\\n** Two\\n*** Three\\n** Two\\n*** Three\\n* One\"\n\n doc = parser.parse(headline_str)\n self.assertEqual(len(doc.children()), 2)\n\n h1_1 = doc.children()[0]\n h1_2 = doc.children()[1]\n\n self.assertEqual(len(h1_1.children), 2)\n self.assertEqual(len(h1_2.children), 0)\n\n h2_1 = h1_1.children[0]\n h2_2 = h1_1.children[1]\n\n self.assertEqual(len(h2_1.children), 1)\n self.assertEqual(len(h2_2.children), 1)","def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes","def get_children(ignored_fields, node):\n if isinstance(node, Task):\n return [node.root()]\n elif isinstance(node, WrittenAction):\n return filter(None,\n message_fields(node.start_message, ignored_fields) +\n list(node.children) +\n [node.end_message])\n elif isinstance(node, WrittenMessage):\n return message_fields(node, ignored_fields)\n elif isinstance(node, tuple):\n value = node[1]\n if isinstance(value, dict):\n return sorted(value.items())\n elif isinstance(value, list):\n return enumerate(value)\n return []","def children(self):\n return self._children","def children(self):\n return self._children","def make_footnote_ref(doc: nodes.document, label: str) -> nodes.footnote_reference:\n footnote_ref = nodes.footnote_reference('[#]_')\n footnote_ref.append(nodes.Text(label))\n doc.note_autofootnote_ref(footnote_ref)\n return footnote_ref","def containments(self):\n return self.edges_to_contained + self.edges_to_containers","def notes(self):\n return Notes(self)"],"string":"[\n \"def makeFootnotesDiv(self, root):\\n\\n if not list(self.footnotes.keys()):\\n return None\\n\\n div = etree.Element(\\\"div\\\")\\n div.set('class', 'footnote')\\n etree.SubElement(div, \\\"hr\\\")\\n ol = etree.SubElement(div, \\\"ol\\\")\\n\\n for id in self.footnotes.keys():\\n li = etree.SubElement(ol, \\\"li\\\")\\n li.set(\\\"id\\\", self.makeFootnoteId(id))\\n self.parser.parseChunk(li, self.footnotes[id])\\n backlink = etree.Element(\\\"a\\\")\\n backlink.set(\\\"href\\\", \\\"#\\\" + self.makeFootnoteRefId(id))\\n if self.md.output_format not in ['html5', 'xhtml5']:\\n backlink.set(\\\"rev\\\", \\\"footnote\\\") # Invalid in HTML5\\n backlink.set(\\\"class\\\", \\\"footnote-backref\\\")\\n backlink.set(\\\"title\\\", \\\"Jump back to footnote %d in the text\\\" % \\\\\\n (self.footnotes.index(id)+1))\\n backlink.text = FN_BACKLINK_TEXT\\n\\n if li.getchildren():\\n node = li[-1]\\n if node.tag == \\\"p\\\":\\n node.text = node.text + NBSP_PLACEHOLDER\\n node.append(backlink)\\n else:\\n p = etree.SubElement(li, \\\"p\\\")\\n p.append(backlink)\\n return div\",\n \"def add_footnotes(notice, notice_xml):\\n notice['footnotes'] = {}\\n for child in notice_xml.xpath('//FTNT/*'):\\n spaces_then_remove(child, 'PRTPAGE')\\n swap_emphasis_tags(child)\\n\\n ref = child.xpath('.//SU')\\n if ref:\\n child.text = ref[0].tail\\n child.remove(ref[0])\\n content = child.text\\n for cc in child:\\n content += etree.tostring(cc)\\n if child.tail:\\n content += child.tail\\n notice['footnotes'][ref[0].text] = content.strip()\",\n \"def add_footnotes(notice, notice_xml):\\n notice['footnotes'] = {}\\n for child in notice_xml.xpath('//FTNT/*'):\\n spaces_then_remove(child, 'PRTPAGE')\\n swap_emphasis_tags(child)\\n\\n ref = child.xpath('.//SU')\\n if ref:\\n child.text = ref[0].tail\\n child.remove(ref[0])\\n content = child.text\\n for cc in child:\\n content += etree.tounicode(cc)\\n if child.tail:\\n content += child.tail\\n notice['footnotes'][ref[0].text] = content.strip()\",\n \"def footnote_spot(tree: nodes.document) -> tuple[Element, int]:\\n # The code uses the following heuristic:\\n # a) place them after the last existing footnote\\n # b) place them after an (empty) Footnotes rubric\\n # c) create an empty Footnotes rubric at the end of the document\\n fns = list(tree.findall(nodes.footnote))\\n if fns:\\n fn = fns[-1]\\n return fn.parent, fn.parent.index(fn) + 1\\n for node in tree.findall(nodes.rubric):\\n if len(node) == 1 and node.astext() == FOOTNOTES_RUBRIC_NAME:\\n return node.parent, node.parent.index(node) + 1\\n doc = next(tree.findall(nodes.document))\\n rub = nodes.rubric()\\n rub.append(nodes.Text(FOOTNOTES_RUBRIC_NAME))\\n doc.append(rub)\\n return doc, doc.index(rub) + 1\",\n \"def fix_footnotes(case_el, warnings):\\n case_pq = PyQuery(case_el)\\n # fix footnotes\\n # footnotes look like this (since <small> is already stripped)\\n # <p>--------</p>\\n # <p>Notes:</p>\\n # <p>\\n # <sup>\\n # <a href=\\\"#fn1\\\" name=\\\"fr1\\\">1</a>\\n # </sup> text text text </p>\\n # notes label can look like `<strong><br/> --------</strong>` -- NE2d/990/990ne2d139_12.xml\\n notes_el = case_pq('p:contains(\\\"Notes:\\\")').filter(lambda i, el: strip_tags(PyQuery(el).text()).strip() == 'Notes:')\\n refs = {}\\n notes_section = None\\n if notes_el:\\n notes_section = notes_el.closest('article, section')\\n footnote_index = 0\\n opinion_index = 1\\n footnote_el = None\\n\\n # before and after footnote sections there is a paragraph of either 8 or 15 hyphens\\n footnote_breaks = ['-' * 8, '-' * 15]\\n\\n # remove footnote break before footnote section\\n # can have tags in the footnote break -- A3d/50/50a3d607_29.xml\\n prev_notes_el = notes_el.prev()\\n if strip_tags(prev_notes_el.text()).strip() not in footnote_breaks:\\n warnings.append(\\\"Unexpected element before notes el.\\\")\\n else:\\n prev_notes_el.remove()\\n\\n # remove \\\"Notes:\\\"\\n old_footnote_el = notes_el.next()\\n notes_el.remove()\\n\\n # step through each footnote element\\n while old_footnote_el:\\n # sometimes <a> tag gets out of <p> tag -- SE2d/590/590SE2d53.xml\\n # put it inside a new <p>\\n if old_footnote_el[0].tag == 'a':\\n old_footnote_el = wrap_with(old_footnote_el, PyQuery(etree.Element('p')))\\n\\n link_el = old_footnote_el('a').eq(0)\\n if not link_el:\\n # this could be the end of footnotes, in which case stop\\n if strip_tags(old_footnote_el.text()).strip() in footnote_breaks:\\n old_footnote_el.remove()\\n break\\n # or could be a second paragraph of the previous footnote, in which case append\\n if footnote_el:\\n footnote_el.append(old_footnote_el)\\n old_footnote_el = footnote_el.next()\\n continue\\n else:\\n # if there's a non-footnote before the first footnote, we don't know what's going on,\\n # so quit processing\\n warnings.append(\\\"Unexpected non-footnote element.\\\")\\n break\\n label = link_el.text()\\n footnote_index += 1\\n footnote_id = f'footnote_{opinion_index}_{footnote_index}'\\n footnote_el = PyQuery(renderer.make_footnote_el(id=footnote_id, label=label))\\n refs[link_el.attr('href').lstrip('#')] = [footnote_id, footnote_el]\\n while link_el.parent()[0].tag == 'sup':\\n link_el = link_el.parent()\\n link_el.remove()\\n\\n # remove space at beginning of footnote left over from removing footnote number\\n if old_footnote_el[0].text:\\n old_footnote_el[0].text = old_footnote_el[0].text.lstrip()\\n\\n wrap_with(old_footnote_el, footnote_el)\\n old_footnote_el = footnote_el.next()\\n\\n # fix footnote references (<small> is already stripped)\\n # ...<sup><a href=\\\"#fr1\\\" name=\\\"fn1\\\">1</a></sup>... typical\\n # ...<sup id=\\\"co_fnRef_B00012045229866_ID0E4F\\\">1</sup> BR/590/590 B.R. 577.xml\\n # ...<a href=\\\"#1\\\" name=\\\"fn1\\\" id=\\\"fn1\\\">1</a>... NW2d/781/781NW2d5512010WIApp33_29.xml\\n for section in case_pq('.head-matter, .opinion').items():\\n for old_ref_pq in section('a, sup[id]').items():\\n label = old_ref_pq.text()\\n if old_ref_pq[0].tag == 'a':\\n ref_name = old_ref_pq.attr('name')\\n if not (ref_name and ref_name.startswith('fn')):\\n continue\\n else:\\n ref_name = \\\"fn\\\" + label\\n ref, footnote_el = refs.get(ref_name, ['orphan', None])\\n if footnote_el:\\n # move footnotes from end of document to correct section -- see NW2d/906/906 N.W.2d 436_Replace.xml\\n if section != notes_section:\\n section.append(footnote_el)\\n else:\\n warnings.append(f\\\"Unmatched ref {repr(str(old_ref_pq))}\\\")\\n ref_el = etree.Element('a', {'class': 'footnotemark', 'href': '#' + ref, 'id': 'ref_' + ref})\\n ref_el.text = label\\n while old_ref_pq.parent()[0].tag == 'sup':\\n old_ref_pq = old_ref_pq.parent()\\n PyQuery(ref_el).insert_before(old_ref_pq)\\n old_ref_pq.remove()\",\n \"def findFootnotesPlaceholder(self, root):\\n def finder(element):\\n for child in element:\\n if child.text:\\n if child.text.find(self.getConfig(\\\"PLACE_MARKER\\\")) > -1:\\n return child, element, True\\n if child.tail:\\n if child.tail.find(self.getConfig(\\\"PLACE_MARKER\\\")) > -1:\\n return child, element, False\\n finder(child)\\n return None\\n \\n res = finder(root)\\n return res\",\n \"def parse_footnote(self, footelem) -> FootNote:\\n\\n fn = FootNote()\\n if footelem.text is None:\\n fn.footnote = ''\\n else:\\n fn.footnote = footelem.text.strip()\\n\\n fn.footnoteid = footelem.attrib['{%s}label' % footelem.nsmap['xlink']]\\n\\n return fn\",\n \"def footnotes(self, text):\\n html = '<div class=\\\"footnotes\\\">\\\\n%s<ol>%s</ol>\\\\n</div>\\\\n'\\n return html % (self.hrule(), text)\",\n \"def children(node):\\n\\n return snd(node)\",\n \"def run(self):\\n env = cast(\\\"BuildEnvironment\\\", self.state.document.settings.env)\\n foot_old_refs = env.temp_data.setdefault(\\\"bibtex_foot_old_refs\\\", set())\\n foot_new_refs = env.temp_data.setdefault(\\\"bibtex_foot_new_refs\\\", set())\\n footbibliography_count = env.temp_data[\\\"bibtex_footbibliography_count\\\"] = (\\n env.temp_data.get(\\\"bibtex_footbibliography_count\\\", 0) + 1\\n )\\n if not foot_new_refs:\\n return []\\n else:\\n foot_old_refs |= foot_new_refs\\n foot_new_refs.clear()\\n # bibliography stored in env.temp_data[\\\"bibtex_foot_bibliography\\\"]\\n foot_domain = cast(\\\"BibtexFootDomain\\\", env.get_domain(\\\"footcite\\\"))\\n foot_bibliography, env.temp_data[\\\"bibtex_foot_bibliography\\\"] = (\\n env.temp_data[\\\"bibtex_foot_bibliography\\\"],\\n foot_domain.bibliography_header.deepcopy(),\\n )\\n domain = cast(\\\"BibtexDomain\\\", env.get_domain(\\\"cite\\\"))\\n for bibfile in domain.bibdata.bibfiles:\\n env.note_dependency(bibfile)\\n foot_bibliography[\\\"ids\\\"] += _make_ids(\\n docname=env.docname,\\n lineno=self.lineno,\\n ids=set(self.state.document.ids.keys()),\\n raw_id=env.app.config.bibtex_footbibliography_id.format(\\n footbibliography_count=footbibliography_count\\n ),\\n )\\n self.state.document.note_explicit_target(\\n foot_bibliography, foot_bibliography\\n )\\n return [foot_bibliography]\",\n \"def references(self):\\n ref_nodes = self.root.xpath(\\\".//bib-reference\\\")\\n return list(\\n itertools.chain.from_iterable(\\n self.get_reference_iter(node) for node in ref_nodes\\n )\\n )\",\n \"def nodes(self):\\r\\n return (node.content for node in self.traverse())\",\n \"def get_note_child_list(self):\\n return self.address_list\",\n \"def getNotes(self):\\n return self._nednotes, self._ongcnotes\",\n \"def footnote_item(self, key, text):\\n back = (\\n '<a href=\\\"#fnref-%s\\\" class=\\\"footnote\\\">↩</a>'\\n ) % escape(key)\\n text = text.rstrip()\\n if text.endswith('</p>'):\\n text = re.sub(r'<\\\\/p>$', r'%s</p>' % back, text)\\n else:\\n text = '%s<p>%s</p>' % (text, back)\\n html = '<li id=\\\"fn-%s\\\">%s</li>\\\\n' % (escape(key), text)\\n return html\",\n \"def fParseNoteRefs(self, match):\\n atts, label, nolink = match.groups()\\n atts = self.pba(atts)\\n nolink = nolink == '!'\\n\\n # Assign a sequence number to this reference if there isn't one already\\n if label in self.notes:\\n num = self.notes[label]['seq']\\n else:\\n self.notes[label] = {\\n 'seq': self.note_index, 'refids': [], 'id': ''\\n }\\n num = self.note_index\\n self.note_index += 1\\n\\n # Make our anchor point and stash it for possible use in backlinks when\\n # the note list is generated later...\\n refid = str(uuid.uuid4()).replace('-', '')\\n self.notes[label]['refids'].append(refid)\\n\\n # If we are referencing a note that hasn't had the definition parsed\\n # yet, then assign it an ID...\\n if not self.notes[label]['id']:\\n self.notes[label]['id'] = str(uuid.uuid4()).replace('-', '')\\n labelid = self.notes[label]['id']\\n\\n # Build the link (if any)...\\n result = '<span id=\\\"noteref%s\\\">%s</span>' % (refid, num)\\n if not nolink:\\n result = \\\"\\\"\\\"<a href=\\\"#note%s\\\">%s</a>\\\"\\\"\\\" % (labelid, result)\\n\\n # Build the reference...\\n result = '<sup%s>%s</sup>' % (atts, result)\\n return result\",\n \"def get_handle_referents(self):\\n return self.get_citation_child_list()\",\n \"def Children(self) -> _n_1_t_2:\",\n \"def getNotes(self, *args):\\n return _libsbml.SBase_getNotes(self, *args)\",\n \"def children(self):\\n return self.contents\",\n \"def children(self):\\n return self.contents\",\n \"def get_refs(self): \\n for row in self._get_references_node():\\n yield row.fetch_all_fields()\",\n \"def children(self):\\n return tuple(getattr(self, i) for i in self._traversable)\",\n \"def fNoteLists(self, match):\\n att, start_char, g_links, extras = match.groups()\\n start_char = start_char or 'a'\\n index = '%s%s%s' % (g_links, extras, start_char)\\n result = ''\\n\\n if index not in self.notelist_cache:\\n o = []\\n if self.notes:\\n for seq, info in self.notes.items():\\n links = self.makeBackrefLink(info, g_links, start_char)\\n atts = ''\\n if 'def' in info:\\n infoid = info['id']\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s<span id=\\\"note%s\\\"> </span>%s</li>\\\"\\\"\\\"\\n % (atts, links, infoid, content))\\n else:\\n li = (\\\"\\\"\\\"\\\\t<li%s>%s Undefined Note [#%s].<li>\\\"\\\"\\\" %\\n (atts, links, info['seq']))\\n o.append(li)\\n if '+' == extras and self.unreferencedNotes:\\n for seq, info in self.unreferencedNotes.items():\\n if info['def']:\\n atts = info['def']['atts']\\n content = info['def']['content']\\n li = \\\"\\\"\\\"\\\\t<li%s>%s</li>\\\"\\\"\\\" % (atts, content)\\n o.append(li)\\n self.notelist_cache[index] = u\\\"\\\\n\\\".join(o)\\n result = self.notelist_cache[index]\\n if result:\\n list_atts = self.pba(att)\\n result = \\\"\\\"\\\"<ol%s>\\\\n%s\\\\n</ol>\\\"\\\"\\\" % (list_atts, result)\\n return result\",\n \"def _get_footnote_par(self, id):\\n start = self._current_body_par\\n if start is None:\\n start = self.parsed\\n link = start.find_next(id=id)\\n if link is None:\\n raise NoFootnoteError(f\\\"Could not find id {id}\\\")\\n foot_par = link.parent.find_next_sibling('p')\\n if not self.is_footnote_text(foot_par):\\n raise NoFootnoteError(f\\\"Failed to find adjacent link paragraph for footnote {id}.\\\")\\n return foot_par\",\n \"def get_children(self):\\n children = []\\n for i in self.children_ids:\\n child = Comment(self.articleID, self.children_ids[i])\\n children.append(child)\\n children.extend(child.get_children())\\n return children\",\n \"def remove_footnotes(self, xml):\\n text_nodes = xml.find_all('text')\\n\\n # Find the most commonly occurring height of text nodes. We'll assume this is\\n # the height of the law text itself.\\n heights = {}\\n for node in text_nodes:\\n if not node.has_attr(\\\"height\\\"):\\n continue\\n height = int(node[\\\"height\\\"])\\n heights[height] = ((heights[height] + 1) if heights.has_key(height) else 1)\\n most_common_height = max(heights, key = heights.get)\\n\\n # Remove all text nodes whose height is lower than most_common_height.\\n for node in text_nodes:\\n if node.has_attr(\\\"height\\\") and (int(node[\\\"height\\\"]) < most_common_height):\\n node.extract()\",\n \"def get_all_children(self):\\n return tuple(self.children)\",\n \"def linked_text_paragraphs(self):\\n for par in self._main_paragraphs_raw():\\n par_links = par.find_all('a')\\n if len(par_links) == 0:\\n self.main_count += len(par.text)\\n yield par.text\\n else:\\n for el in par.contents:\\n if el.name is None:\\n #this is plain text\\n self.main_count += len(str(el))\\n yield str(el)\\n elif el.name == \\\"a\\\" and \\\"href\\\" in el.attrs:\\n id = el[\\\"href\\\"].lstrip('#')\\n try:\\n foot_par = self._get_footnote_par(id)\\n except NoFootnoteError:\\n self.log(f\\\"Could not find footnote for {id}, skipping.\\\")\\n self.footnote_count += len(foot_par.text)\\n yield foot_par.text\",\n \"def notes(self):\\n return reapy.NoteList(self)\",\n \"def _get_child_text(node):\\n for child in node.children:\\n if isinstance(child, NavigableString) and not isinstance(child, Comment):\\n yield child.split()\",\n \"def parse_references(article):\\n reference_list = []\\n references = article.find(\\\"text\\\").find(\\\"div\\\", attrs={\\\"type\\\": \\\"references\\\"})\\n references = references.find_all(\\\"biblstruct\\\") if references is not None else []\\n reference_list = []\\n for reference in references:\\n title = reference.find(\\\"title\\\", attrs={\\\"level\\\": \\\"a\\\"})\\n if title is None:\\n title = reference.find(\\\"title\\\", attrs={\\\"level\\\": \\\"m\\\"})\\n title = title.text if title is not None else \\\"\\\"\\n journal = reference.find(\\\"title\\\", attrs={\\\"level\\\": \\\"j\\\"})\\n journal = journal.text if journal is not None else \\\"\\\"\\n if journal is \\\"\\\":\\n journal = reference.find(\\\"publisher\\\")\\n journal = journal.text if journal is not None else \\\"\\\"\\n year = reference.find(\\\"date\\\")\\n year = year.attrs.get(\\\"when\\\") if year is not None else \\\"\\\"\\n authors = []\\n for author in reference.find_all(\\\"author\\\"):\\n firstname = author.find(\\\"forename\\\", {\\\"type\\\": \\\"first\\\"})\\n firstname = firstname.text.strip() if firstname is not None else \\\"\\\"\\n middlename = author.find(\\\"forename\\\", {\\\"type\\\": \\\"middle\\\"})\\n middlename = middlename.text.strip() if middlename is not None else \\\"\\\"\\n lastname = author.find(\\\"surname\\\")\\n lastname = lastname.text.strip() if lastname is not None else \\\"\\\"\\n if middlename is not \\\"\\\":\\n authors.append(firstname + \\\" \\\" + middlename + \\\" \\\" + lastname)\\n else:\\n authors.append(firstname + \\\" \\\" + lastname)\\n authors = \\\"; \\\".join(authors)\\n reference_list.append(\\n {\\\"title\\\": title, \\\"journal\\\": journal, \\\"year\\\": year, \\\"authors\\\": authors}\\n )\\n return reference_list\",\n \"def notes(self):\\n return self._notes\",\n \"def get_node_children(self, node):\\n return node.children\",\n \"def get_children(cls, node: ast.AST) -> Iterable[ast.AST]:\\n body: Optional[Sequence[ast.AST]] = getattr(node, 'body', None)\\n if body is not None:\\n for child in body:\\n yield child\",\n \"def footnote(self) -> str:\\n return self._footnote\",\n \"def children(self):\\n l = []\\n n = self.node.firstChild\\n while n:\\n l.append(XmlWrapper(n))\\n n = n.nextSibling\\n return l\",\n \"def children(self):\\n l = []\\n n = self.node.firstChild\\n while n:\\n l.append(XmlWrapper(n))\\n n = n.nextSibling\\n return l\",\n \"def get_children(self):\\n return self.children\",\n \"def getnotes():\",\n \"def get_children(self):\\n return []\",\n \"def visit_children(node):\\n output_fragments = []\\n for child in node.children_not_empty:\\n fragment = child.value\\n text = fragment.text_fragment\\n output_fragments.append({\\n \\\"id\\\": text.identifier,\\n \\\"language\\\": text.language,\\n \\\"lines\\\": text.lines,\\n \\\"begin\\\": gf.time_to_ssmmm(fragment.begin),\\n \\\"end\\\": gf.time_to_ssmmm(fragment.end),\\n \\\"children\\\": visit_children(child)\\n })\\n return output_fragments\",\n \"def info(self):\\n return nx.info(self.tree)\\n\\n\\n # def children(self):\\n \\\"\\\"\\\" Return the children of the current node.\\n\\n \\\"\\\"\\\"\\n # return self.left, self.right\",\n \"def get_children(self):\\n return NodeList(self._my_map['childNodes'])\",\n \"def notes(self):\\n return self.__notes\",\n \"def fBlock(self, tag, atts, ext, cite, content):\\n att = atts\\n atts = self.pba(atts)\\n o1 = o2 = c2 = c1 = ''\\n eat = False\\n\\n if tag == 'p':\\n # is this an anonymous block with a note definition?\\n notedef_re = re.compile(r\\\"\\\"\\\"\\n ^note\\\\# # start of note def marker\\n ([^%%<*!@#^([{ \\\\s.]+) # !label\\n ([*!^]?) # !link\\n (%s) # !att\\n \\\\.? # optional period.\\n [\\\\s]+ # whitespace ends def marker\\n (.*)$ # !content\\\"\\\"\\\" % (self.c), re.X)\\n notedef = notedef_re.sub(self.fParseNoteDefs, content)\\n\\n # It will be empty if the regex matched and ate it.\\n if '' == notedef:\\n return o1, o2, notedef, c2, c1, True\\n\\n m = re.search(r'fn(\\\\d+)', tag)\\n if m:\\n tag = 'p'\\n if m.group(1) in self.fn:\\n fnid = self.fn[m.group(1)]\\n else:\\n fnid = m.group(1)\\n\\n # If there is an author-specified ID goes on the wrapper & the\\n # auto-id gets pushed to the <sup>\\n supp_id = ''\\n\\n # if class has not been previously specified, set it to \\\"footnote\\\"\\n if atts.find('class=') < 0:\\n atts = atts + ' class=\\\"footnote\\\"'\\n\\n # if there's no specified id, use the generated one.\\n if atts.find('id=') < 0:\\n atts = atts + ' id=\\\"fn%s\\\"' % fnid\\n else:\\n supp_id = ' id=\\\"fn%s\\\"' % fnid\\n\\n if att.find('^') < 0:\\n sup = self.formatFootnote(m.group(1), supp_id)\\n else:\\n fnrev = '<a href=\\\"#fnrev%s\\\">%s</a>' % (fnid, m.group(1))\\n sup = self.formatFootnote(fnrev, supp_id)\\n\\n content = sup + ' ' + content\\n\\n if tag == 'bq':\\n cite = self.checkRefs(cite)\\n if cite:\\n cite = ' cite=\\\"%s\\\"' % cite\\n else:\\n cite = ''\\n o1 = \\\"\\\\t<blockquote%s%s>\\\\n\\\" % (cite, atts)\\n o2 = \\\"\\\\t\\\\t<p%s>\\\" % atts\\n c2 = \\\"</p>\\\"\\n c1 = \\\"\\\\n\\\\t</blockquote>\\\"\\n\\n elif tag == 'bc':\\n o1 = \\\"<pre%s>\\\" % atts\\n o2 = \\\"<code%s>\\\" % atts\\n c2 = \\\"</code>\\\"\\n c1 = \\\"</pre>\\\"\\n content = self.shelve(self.encode_html(content.rstrip(\\\"\\\\n\\\") +\\n \\\"\\\\n\\\"))\\n\\n elif tag == 'notextile':\\n content = self.shelve(content)\\n o1 = o2 = ''\\n c1 = c2 = ''\\n\\n elif tag == 'pre':\\n content = self.shelve(self.encode_html(content.rstrip(\\\"\\\\n\\\") +\\n \\\"\\\\n\\\"))\\n o1 = \\\"<pre%s>\\\" % atts\\n o2 = c2 = ''\\n c1 = '</pre>'\\n\\n elif tag == '###':\\n eat = True\\n\\n else:\\n o2 = \\\"\\\\t<%s%s>\\\" % (tag, atts)\\n c2 = \\\"</%s>\\\" % tag\\n\\n if not eat:\\n content = self.graf(content)\\n else:\\n content = ''\\n return o1, o2, content, c2, c1, eat\",\n \"def _get_children(self, x):\\n try:\\n return x._pfp__children\\n\\n except AttributeError:\\n return []\",\n \"def footnoteRef(self, text):\\n return re.compile(r'(?<=\\\\S)\\\\[(\\\\d+)(!?)\\\\](\\\\s)?', re.U).sub(\\n self.footnoteID, text\\n )\",\n \"def get_child_nodes(node):\\r\\n return list(iter_child_nodes(node))\",\n \"def reference_multi():\\n content = textwrap.dedent(\\\"\\\"\\\"\\n ; Some line before the first molecule.\\n ; Just because.\\n\\n [ molecule ]\\n dummy_0\\n\\n [ atoms ]\\n 0 X1 A\\n 1 X2 B\\n\\n ; Some mess between two molecules.\\n\\n [ molecule ]\\n ; A comment just after the molecule section.\\n dummy_1\\n\\n [ atoms ]\\n 0 X2 C\\n 1 X3 D\\n \\\"\\\"\\\").split('\\\\n')\\n reference = {'universal': {'martini22': {\\n 'dummy_0': (\\n {(0, 'X1'): [(0, 'A')], (0, 'X2'): [(0, 'B')]},\\n {(0, 'A'): {(0, 'X1'): 1.0}, (0, 'B'): {(0, 'X2'): 1.0}},\\n [],\\n ),\\n 'dummy_1': (\\n {(0, 'X2'): [(0, 'C')], (0, 'X3'): [(0, 'D')]},\\n {(0, 'C'): {(0, 'X2'): 1.0}, (0, 'D'): {(0, 'X3'): 1.0}},\\n [],\\n ),\\n }}}\\n return content, reference\",\n \"def children(phi):\\n ret = []\\n if hasattr(phi, 'x'):\\n ret.append(phi.x)\\n if hasattr(phi, 'y'):\\n assert isinstance(phi, amnet.Mu) or \\\\\\n isinstance(phi, amnet.Stack)\\n ret.append(phi.y)\\n if hasattr(phi, 'z'):\\n assert isinstance(phi, amnet.Mu)\\n ret.append(phi.z)\\n\\n assert len(ret) <= 3\\n return ret\",\n \"def get_artists(parse_class):\\n # note temp[0].contents returns [u'\\\\nShape Of You\\\\n']\\n text = parse_class.text.strip()\\n # We strip white spaces, line split, and ignore empty lines hence \\\"if s\\\"\\n info = [s.strip() for s in text.splitlines() if s] \\n if len(info) is not 2:\\n print('There might be something wrong here...', info)\\n return info\",\n \"def get_children(self):\\r\\n return self.children\",\n \"def getRtf(self):\\n self.pieces = []\\n for node in self.root.findall(\\\"MiscellaneousDocumentText\\\"):\\n for child in node:\\n if child.tag == \\\"Para\\\":\\n self.__addPara(child)\\n elif child.tag in (\\\"ItemizedList\\\", \\\"OrderedList\\\"):\\n self.__addList(child, child.tag)\\n return \\\"\\\".join(self.pieces)\",\n \"def notes(self):\\n return notes.Notes(self)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def notes(self):\\r\\n return notes.Notes(self)\",\n \"def getVisitableNodes(self):\\n\\n value = self.subnode_loop_body\\n\\n if value is None:\\n return ()\\n else:\\n return (value,)\",\n \"def add_visible_links(self, tree: nodes.document, show_urls: str = 'inline') -> None:\\n\\n def make_footnote_ref(doc: nodes.document, label: str) -> nodes.footnote_reference:\\n \\\"\\\"\\\"Create a footnote_reference node with children\\\"\\\"\\\"\\n footnote_ref = nodes.footnote_reference('[#]_')\\n footnote_ref.append(nodes.Text(label))\\n doc.note_autofootnote_ref(footnote_ref)\\n return footnote_ref\\n\\n def make_footnote(doc: nodes.document, label: str, uri: str) -> nodes.footnote:\\n \\\"\\\"\\\"Create a footnote node with children\\\"\\\"\\\"\\n footnote = nodes.footnote(uri)\\n para = nodes.paragraph()\\n para.append(nodes.Text(uri))\\n footnote.append(para)\\n footnote.insert(0, nodes.label('', label))\\n doc.note_autofootnote(footnote)\\n return footnote\\n\\n def footnote_spot(tree: nodes.document) -> tuple[Element, int]:\\n \\\"\\\"\\\"Find or create a spot to place footnotes.\\n\\n The function returns the tuple (parent, index).\\\"\\\"\\\"\\n # The code uses the following heuristic:\\n # a) place them after the last existing footnote\\n # b) place them after an (empty) Footnotes rubric\\n # c) create an empty Footnotes rubric at the end of the document\\n fns = list(tree.findall(nodes.footnote))\\n if fns:\\n fn = fns[-1]\\n return fn.parent, fn.parent.index(fn) + 1\\n for node in tree.findall(nodes.rubric):\\n if len(node) == 1 and node.astext() == FOOTNOTES_RUBRIC_NAME:\\n return node.parent, node.parent.index(node) + 1\\n doc = next(tree.findall(nodes.document))\\n rub = nodes.rubric()\\n rub.append(nodes.Text(FOOTNOTES_RUBRIC_NAME))\\n doc.append(rub)\\n return doc, doc.index(rub) + 1\\n\\n if show_urls == 'no':\\n return\\n if show_urls == 'footnote':\\n doc = next(tree.findall(nodes.document))\\n fn_spot, fn_idx = footnote_spot(tree)\\n nr = 1\\n for node in list(tree.findall(nodes.reference)):\\n uri = node.get('refuri', '')\\n if uri.startswith(('http:', 'https:', 'ftp:')) and uri not in node.astext():\\n idx = node.parent.index(node) + 1\\n if show_urls == 'inline':\\n uri = self.link_target_template % {'uri': uri}\\n link = nodes.inline(uri, uri)\\n link['classes'].append(self.css_link_target_class)\\n node.parent.insert(idx, link)\\n elif show_urls == 'footnote':\\n label = FOOTNOTE_LABEL_TEMPLATE % nr\\n nr += 1\\n footnote_ref = make_footnote_ref(doc, label)\\n node.parent.insert(idx, footnote_ref)\\n footnote = make_footnote(doc, label, uri)\\n fn_spot.insert(fn_idx, footnote)\\n footnote_ref['refid'] = footnote['ids'][0]\\n footnote.add_backref(footnote_ref['ids'][0])\\n fn_idx += 1\",\n \"def get_children(self):\\n children = []\\n for node in self.node.get_children():\\n children.append(Geom(node))\\n return children\",\n \"def get_children(self):\\n\\n pass\",\n \"def get_child_nodes(self):\\n parts = []\\n for child in sorted(self.children, key=lambda c: c.start):\\n child_part = (child.start, child.end, child)\\n parts.append(child_part)\\n return parts\",\n \"def get_children(self):\\n raise NotImplementedError()\",\n \"def get_items(self):\\n export_file = self.cmdline_args.file # see setup_parser\\n for a in get_articles(export_file):\\n yield node(\\n heading=dt_heading(\\n a.added,\\n # 'pocket' permalink is pretty convenient to jump straight into Pocket app\\n link(title='pocket', url=a.pocket_link) + ' · ' + link(title=a.title, url=a.url),\\n ),\\n children=[node( # comments are displayed as org-mode child entries\\n heading=dt_heading(hl.created, hl.text)\\n ) for hl in a.highlights]\\n )\",\n \"def paragraphs(self, data=True):\\n return self.nodes(self.max_depth, data)\",\n \"def get_children(self):\\n return self.children\",\n \"def get_children(self):\\n return self.children\",\n \"def get_children(self):\\n return self.children\",\n \"def get_children(self):\\r\\n return self._children\",\n \"def is_footnote(self):\\n return self.style['float'] == 'footnote'\",\n \"def get_notes(score, start_offset, end_offset):\\n scoreTree = m21.tree.fromStream.asTimespans(score, flatten=True, classList=(m21.note.Note, m21.chord.Chord))\\n\\n current_offset = start_offset\\n notes_by_offset = {}\\n\\n while current_offset is not None and current_offset <= end_offset:\\n verticality = scoreTree.getVerticalityAt(current_offset)\\n notes_by_offset[current_offset] = []\\n if current_offset == start_offset:\\n elements = [timespan.element for timespan in verticality.startAndOverlapTimespans]\\n if elements == []:\\n start_offset = verticality.nextStartOffset\\n else:\\n elements = [timespan.element for timespan in verticality.startTimespans]\\n for element in elements:\\n if isinstance(element, m21.chord.Chord):\\n for note in element:\\n note.offset = current_offset\\n notes_by_offset[current_offset].append(note)\\n else:\\n element.offset = current_offset\\n notes_by_offset[current_offset].append(element)\\n current_offset = verticality.nextStartOffset\\n\\n return (start_offset, current_offset, notes_by_offset)\",\n \"def getNotes(self):\\n return self.__notes\",\n \"def references(self):\\n return tuple(self.__references)\",\n \"def get_refnodes(\\n self, doctree: Node, result: list[dict[str, Any]],\\n ) -> list[dict[str, Any]]:\\n # XXX: is there a better way than checking the attribute\\n # toctree-l[1-8] on the parent node?\\n if isinstance(doctree, nodes.reference) and doctree.get('refuri'):\\n refuri = doctree['refuri']\\n if refuri.startswith(('http://', 'https://', 'irc:', 'mailto:')):\\n return result\\n classes = doctree.parent.attributes['classes']\\n for level in range(8, 0, -1): # or range(1, 8)?\\n if (self.toctree_template % level) in classes:\\n result.append({\\n 'level': level,\\n 'refuri': html.escape(refuri),\\n 'text': ssp(html.escape(doctree.astext())),\\n })\\n break\\n elif isinstance(doctree, nodes.Element):\\n for elem in doctree:\\n result = self.get_refnodes(elem, result)\\n return result\",\n \"def get_children(self, item, level):\\n return item.children\",\n \"def stringify_children(node):\\n parts = ([node.text] +\\n list(chain(*([c.text, c.tail] for c in node.getchildren()))) +\\n [node.tail])\\n return ''.join(filter(None, parts))\",\n \"def get_children(self):\\n return self._children\",\n \"def render_footnote_ref(self, token: SyntaxTreeNode) -> None:\\n target = token.meta[\\\"label\\\"]\\n\\n refnode = nodes.footnote_reference(\\\"[^{}]\\\".format(target))\\n self.add_line_and_source_path(refnode, token)\\n if not target.isdigit():\\n refnode[\\\"auto\\\"] = 1\\n self.document.note_autofootnote_ref(refnode)\\n else:\\n refnode += nodes.Text(target)\\n\\n refnode[\\\"refname\\\"] = target\\n self.document.note_footnote_ref(refnode)\\n\\n self.current_node.append(refnode)\",\n \"def postorder(self):\\n return (node for node in self.get_postorder(self.root))\",\n \"def get_children(self):\\n return self.items\",\n \"def children(self) -> Iterable[Heirarchical]:\\n return []\",\n \"def getchildren(self):\\n return self.root.getchildren()\",\n \"def get_children(self):\\r\\n\\r\\n # FIXME: Expose iteration from CIndex, PR6125.\\r\\n def visitor(child, parent, children):\\r\\n # FIXME: Document this assertion in API.\\r\\n # FIXME: There should just be an isNull method.\\r\\n assert child != conf.lib.clang_getNullCursor()\\r\\n\\r\\n # Create reference to TU so it isn't GC'd before Cursor.\\r\\n child._tu = self._tu\\r\\n children.append(child)\\r\\n return 1 # continue\\r\\n children = []\\r\\n conf.lib.clang_visitChildren(self, callbacks['cursor_visit'](visitor),\\r\\n children)\\r\\n return iter(children)\",\n \"def get_children_elements(self):\\n\\n pass\",\n \"def notes(self):\\n return NotesTable(self.rpc, self.name)\",\n \"def get_dependencies(self) -> tuple:\\n if self.probability_links:\\n return tuple(self.probability_links.values())\\n return tuple()\",\n \"def make_footnote(doc: nodes.document, label: str, uri: str) -> nodes.footnote:\\n footnote = nodes.footnote(uri)\\n para = nodes.paragraph()\\n para.append(nodes.Text(uri))\\n footnote.append(para)\\n footnote.insert(0, nodes.label('', label))\\n doc.note_autofootnote(footnote)\\n return footnote\",\n \"def fm_all_children(self):\\n return self._relation_lst[self.CHILD].copy()\",\n \"def get_reference_node_parents(ref):\\n parents = []\\n return parents\",\n \"def add_notes(self, notes):\\n if hasattr(notes, \\\"notes\\\"):\\n for x in notes.notes:\\n self.add_note(x)\\n return self.notes\\n elif hasattr(notes, \\\"name\\\"):\\n self.add_note(notes)\\n return self.notes\\n elif isinstance(notes, six.string_types):\\n self.add_note(notes)\\n return self.notes\\n for x in notes:\\n if isinstance(x, list) and len(x) != 1:\\n if len(x) == 2:\\n self.add_note(x[0], x[1])\\n else:\\n self.add_note(x[0], x[1], x[2])\\n else:\\n self.add_note(x)\\n return self.notes\",\n \"def initialize_children(self, bibs):\\n\\t\\tpass\",\n \"def get_nodes(self):\\n if self.inode_ref is None or self.jnode_ref is None:\\n raise ValueError('Node references have not been set.')\\n return self.inode_ref, self.jnode_ref\",\n \"def test_headlines_predecessors(self):\\n headline_str = \\\"* One\\\\n** Two\\\\n*** Three\\\\n** Two\\\\n*** Three\\\\n* One\\\"\\n\\n doc = parser.parse(headline_str)\\n self.assertEqual(len(doc.children()), 2)\\n\\n h1_1 = doc.children()[0]\\n h1_2 = doc.children()[1]\\n\\n self.assertEqual(len(h1_1.children), 2)\\n self.assertEqual(len(h1_2.children), 0)\\n\\n h2_1 = h1_1.children[0]\\n h2_2 = h1_1.children[1]\\n\\n self.assertEqual(len(h2_1.children), 1)\\n self.assertEqual(len(h2_2.children), 1)\",\n \"def getNotes(self):\\n logger.debug(\\\"Func: getNotes\\\")\\n\\n return self._currentNotes\",\n \"def get_children(ignored_fields, node):\\n if isinstance(node, Task):\\n return [node.root()]\\n elif isinstance(node, WrittenAction):\\n return filter(None,\\n message_fields(node.start_message, ignored_fields) +\\n list(node.children) +\\n [node.end_message])\\n elif isinstance(node, WrittenMessage):\\n return message_fields(node, ignored_fields)\\n elif isinstance(node, tuple):\\n value = node[1]\\n if isinstance(value, dict):\\n return sorted(value.items())\\n elif isinstance(value, list):\\n return enumerate(value)\\n return []\",\n \"def children(self):\\n return self._children\",\n \"def children(self):\\n return self._children\",\n \"def make_footnote_ref(doc: nodes.document, label: str) -> nodes.footnote_reference:\\n footnote_ref = nodes.footnote_reference('[#]_')\\n footnote_ref.append(nodes.Text(label))\\n doc.note_autofootnote_ref(footnote_ref)\\n return footnote_ref\",\n \"def containments(self):\\n return self.edges_to_contained + self.edges_to_containers\",\n \"def notes(self):\\n return Notes(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6317781","0.61900926","0.6149558","0.6146513","0.5762061","0.56409013","0.5610856","0.54278535","0.5426516","0.53579605","0.52695143","0.51903385","0.51892966","0.51519656","0.5131307","0.5113108","0.5091132","0.5089666","0.5081637","0.50717264","0.50717264","0.504744","0.5027004","0.5004364","0.5001188","0.49908495","0.49851847","0.49842426","0.49702066","0.4965579","0.4949466","0.49449372","0.49277875","0.49233907","0.49155393","0.48939642","0.4893615","0.4893615","0.489222","0.48813102","0.48739824","0.4867434","0.48558915","0.4852184","0.48474422","0.4842479","0.48338124","0.48057145","0.48010415","0.48000717","0.47922406","0.47708526","0.4768945","0.47599527","0.47593367","0.47590768","0.47590768","0.47574598","0.47555456","0.47527248","0.4730922","0.47274536","0.4726891","0.4724549","0.4717117","0.47122547","0.47122547","0.47122547","0.46793792","0.46695966","0.46652585","0.4644656","0.4634289","0.46325743","0.46223575","0.46199992","0.46169966","0.4600204","0.45932418","0.45880458","0.45833546","0.45822707","0.45805","0.4579835","0.4573714","0.4568467","0.45637655","0.4561968","0.45596546","0.45546752","0.45479327","0.45367336","0.45333937","0.45290193","0.45286718","0.45172906","0.45172906","0.45167634","0.4515103","0.45101085"],"string":"[\n \"0.6317781\",\n \"0.61900926\",\n \"0.6149558\",\n \"0.6146513\",\n \"0.5762061\",\n \"0.56409013\",\n \"0.5610856\",\n \"0.54278535\",\n \"0.5426516\",\n \"0.53579605\",\n \"0.52695143\",\n \"0.51903385\",\n \"0.51892966\",\n \"0.51519656\",\n \"0.5131307\",\n \"0.5113108\",\n \"0.5091132\",\n \"0.5089666\",\n \"0.5081637\",\n \"0.50717264\",\n \"0.50717264\",\n \"0.504744\",\n \"0.5027004\",\n \"0.5004364\",\n \"0.5001188\",\n \"0.49908495\",\n \"0.49851847\",\n \"0.49842426\",\n \"0.49702066\",\n \"0.4965579\",\n \"0.4949466\",\n \"0.49449372\",\n \"0.49277875\",\n \"0.49233907\",\n \"0.49155393\",\n \"0.48939642\",\n \"0.4893615\",\n \"0.4893615\",\n \"0.489222\",\n \"0.48813102\",\n \"0.48739824\",\n \"0.4867434\",\n \"0.48558915\",\n \"0.4852184\",\n \"0.48474422\",\n \"0.4842479\",\n \"0.48338124\",\n \"0.48057145\",\n \"0.48010415\",\n \"0.48000717\",\n \"0.47922406\",\n \"0.47708526\",\n \"0.4768945\",\n \"0.47599527\",\n \"0.47593367\",\n \"0.47590768\",\n \"0.47590768\",\n \"0.47574598\",\n \"0.47555456\",\n \"0.47527248\",\n \"0.4730922\",\n \"0.47274536\",\n \"0.4726891\",\n \"0.4724549\",\n \"0.4717117\",\n \"0.47122547\",\n \"0.47122547\",\n \"0.47122547\",\n \"0.46793792\",\n \"0.46695966\",\n \"0.46652585\",\n \"0.4644656\",\n \"0.4634289\",\n \"0.46325743\",\n \"0.46223575\",\n \"0.46199992\",\n \"0.46169966\",\n \"0.4600204\",\n \"0.45932418\",\n \"0.45880458\",\n \"0.45833546\",\n \"0.45822707\",\n \"0.45805\",\n \"0.4579835\",\n \"0.4573714\",\n \"0.4568467\",\n \"0.45637655\",\n \"0.4561968\",\n \"0.45596546\",\n \"0.45546752\",\n \"0.45479327\",\n \"0.45367336\",\n \"0.45333937\",\n \"0.45290193\",\n \"0.45286718\",\n \"0.45172906\",\n \"0.45172906\",\n \"0.45167634\",\n \"0.4515103\",\n \"0.45101085\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5484363"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":85679,"cells":{"query":{"kind":"string","value":"Print status each time."},"document":{"kind":"string","value":"def on_status(self, status):\n if self._counter < self._limit:\n if re.match(r'^en(-gb)?$', status.lang): # english tweets only\n with open(self._file, \"a+\") as f:\n f.write(\n json.dumps(self._map_status_fields(status)) + ',\\n')\n print('Status ' + str(self._counter) +\n ': ' + status.text)\n self._counter += 1\n else:\n with open(self._file, \"a+\") as f:\n f.write(\n json.dumps(self._map_status_fields(status)) + ']}')\n print('Status ' + str(self._counter) +\n ': ' + status.text)\n print('Output saved at ' + self._file)\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _print_status(self):","def status(s):\n print('\\033[1m{0}\\033[0m'.format(s))\n time.sleep(2)","def _PrintStatus(self):\n comment = 'iter %i' % (self.n_iter)\n self.gfile.write(fileio.GetPrintCoordsXyzString(\n self.mol.atoms, comment, 14, 8))\n self.PrintEnergy(self.n_iter)\n self.gfile.flush()\n self.efile.flush()","def printStatus(level,text):\n\tglobal t0\n\tpre = \"[{0:>7.2f}] \".format(time.time()-t0)\n\tfor x in range(0,level):\n\t\tpre += \"-\"\n\tpre += \"> \"\n\tprint(pre+text)","def outputStatus(self, line):\r\n for l in line.strip('\\r\\n').split('\\n'):\r\n self.output('%s: %s' % (ctime(), l), 0)","def display_status(self):\n time = float2str(self.scheduler.time, '10.2f')\n tx = float2str(self.tx_total, '10g')\n rx = float2str(self.rx_total, '10g')\n dup = float2str(self.dup_total, '10g')\n uniq_total = float2str(self.uniq_total, '10g')\n delivered_total = float2str(self.delivered_total, '10g')\n uniq_delivered_total = float2str(self.uniq_delivered_total, '10g')\n print(\n 'define status_l text Time:{},____TX:{},____RX:{},____DUP:{},____Delivered:{}__/__{},____Arrived:{} 14 white 0.5 0.05'\n .format(time, tx, rx, dup, uniq_delivered_total, uniq_total,\n delivered_total))","def status(s):\n print('\\033[1m{0}\\033[0m'.format(s))","def status(s):\n print('\\033[1m{0}\\033[0m'.format(s))","def status(s):\n print('\\033[1m{0}\\033[0m'.format(s))","def status(s):\n print('\\033[1m{0}\\033[0m'.format(s))","def show_status():\n\n pass","def status(s):\n print(\"\\033[1m{0}\\033[0m\".format(s))","def status(s):\n print(\"\\033[1m{0}\\033[0m\".format(s))","def status(s):\n print(\"\\033[1m{0}\\033[0m\".format(s))","def status(s):\n print(\"\\033[1m{0}\\033[0m\".format(s))","def print_status(self):\n currentTime = time.time()\n elapsedTime = (currentTime - self.startTime)/60.\n print('Elapsed time/Estimated time: {:.2f}/{:.2f} mins'.format(elapsedTime, self.estTime))","def status(s: str):\n print(\"\\033[1m{0}\\033[0m\".format(s))","def status():\n pass","def emit_status(self):\n next_job_count = len(self.fe.get_next_jobs())\n if next_job_count:\n emoji = \"🤔\"\n else:\n emoji = \"👌\"\n remaining = len(self.fe.get_current_network())\n\n pct = (self.total_job_count - remaining) / self.total_job_count\n print(\n f\"{emoji} ———— {next_job_count} jobs running, {remaining} remaining ({int(100*pct)}%). \",\n end=\"\\r\",\n )","def printStatus(self, status):\n \"\"\" This probably breaks SOI \"\"\"\n\n if hasattr(self, 'show_state') and self.show_state:\n print(\"=============== \" + str(status[\"count\"]) + \" ===============\")\n print(\"Current best energy: \" + str(status[\"bestEnergy\"]) + \" from state: \" + str(status[\"bestState\"]))\n print(\"last accepted energy: \" + str(status[\"energy\"]) + \" from state: \" + str(status[\"state\"]))\n print(\"current temperature: \" + str(status[\"temperature\"]))\n else:\n print(\"=============== \" + str(status[\"count\"]) + \" ===============\")\n print(\"Current best energy: \" + str(status[\"bestEnergy\"]) )\n print(\"last accepted energy: \" + str(status[\"energy\"]) )\n print(\"current temperature: \" + str(status[\"temperature\"]))","def main():\n print \"Printing Sample Status\"","def status(s):\n print(\"\\033 {}\".format(s))#print(\"\\033[1m{0}\\033[0m\".format(s))","def print_status(self):\n print \"Zombie has\" + super(Zombie, self).print_status()","def do_status(self, *arg):\n for sensor_name in ['environment', 'weather', 'webcams']:\n if sensor_name in self.active_sensors:\n console.color_print(\"{:>12s}: \".format(sensor_name.title()), \"default\", \"active\", \"lightgreen\")\n else:\n console.color_print(\"{:>12s}: \".format(sensor_name.title()), \"default\", \"inactive\", \"yellow\")","def put_status(status='successed'):\n if not proxy_config.OUTPUT_DEBUG_INFO:\n return\n status_code=''\n status_color=()\n if status.strip()=='successed':\n status_code='*'\n status_color=(Color.Green,None,[Color.Bold])\n elif status.strip()=='warning':\n status_code='-'\n status_color=(Color.Yellow,None,[Color.Bold])\n else:\n status_code='!'\n status_color=(Color.Red,None,[Color.Bold])\n print(colored(f'[{status_code}]',*status_color),end=' ')","def print_status(self):\r\n\t\tif VERBOSE:\r\n\r\n\t\t\tprint( 'Player : ')\r\n\t\t\tfor h in self.hands:\r\n\t\t\t\tprint('\\t' + str(h))\r\n\t\t\tprint( 'Dealer:\\n\\t' + str(self.dealer))\r\n\t\t\tprint( '-----------------------')","def status(self):","def print_status(data):\n print \"0x%x:0x%x:0x%x:0x%x:0x%x:%d:%d:%d:%d:%d:%d:%d:%d\" % (\n data['sysVer'], data['barVer'], data['uvVer'], data['rccVer'],\n data['windVer'], data['batteryRain'], data['batteryUV'],\n data['batteryWind'], data['battery5'], data['battery4'],\n data['battery3'], data['battery2'], data['battery1'])","def print_status(self, id, pr, package, version, filename):\n\n\t\tprogress = self.get_progress(pr, id);\n\t\t\n\t\twhile progress != 100:\n\t\t\tself.write_line(\"STATUS {0} {1}%\".format(package, progress))\n\t\t\ttime.sleep(1)\n\t\t\tprogress = self.get_progress(pr, id);\n\n\t\tself.write_line(\"STATUS {0} {1}%\".format(package, progress))","def print_status(status):\n for success, term in status:\n if success:\n print \"Added: %s\" % term\n else:\n if term:\n print \"Not added: %s\" % term","def _print_status(self, stream, count, final=False):\n twidth = shutil.get_terminal_size()[0]\n msg_width = min((twidth // 2) - 5, 20)\n if stream == 'in':\n self._last_in_count = count\n if final:\n self._in_finished = True\n else:\n self._last_out_count = count\n if final:\n self._out_finished = True\n\n in_msg = '{: <{}}'.format('in: {}'.format(self._last_in_count),\n msg_width)\n out_msg = '{: <{}}'.format('out: {}'.format(self._last_out_count),\n msg_width)\n print('\\r{} -> {}'.format(in_msg, out_msg), end='', flush=True)\n if self._in_finished and self._out_finished:\n print()","def status(self) -> NoReturn:\n\n curr_status= self.percent_done()\n while(curr_status < 100):\n\n update_status(name=self.name, status=curr_status)\n time.sleep(0.5)\n\n curr_status = self.percent_done()\n\n update_status(name=self.name, status=curr_status)","def status(self):\n logging.debug(\"%s entered status\" % self)\n # print_config(self.infra)\n # print self.images\n # headers = [\"Machine Name\", \"Flavor\", \"IP Addresses\", \"Image Name\", \"Status\"]\n # pt = prettytable.PrettyTable(headers)\n # pt.align[\"Machine Name\"]=\"l\"\n # pt.align[\"IP Addresses\"] = \"l\"\n # pt.align[\"Image Name\"] = \"l\"\n # pt.align[\"Status\"] = \"r\"\n \n print \"Checking status of %s\" % self.footprint_name\n # tmpl = \"%(machine_name)-20s%(flavor)5s%(status)-30s\"\n tmpl1 = \"\"\"%-20s%-52s[%s]\"\"\"\n tmpl2 = \"\"\"%-20s%-60s\\n\"\"\"\n print tmpl1 % (\"Machine Name\", \"IP Addresses\", \"Status\")\n print 80 * \"-\"\n \n for machine in self.machines.keys():\n m = self.machines[machine]\n # machine_name = m.machine_name\n # ips = str(m.ip_addresses)\n # flavor = str(m.flavor)\n # img = str(m.image_id)\n # status = str(m.status)\n # pt.add_row([m, ips, status, img, status])\n # print \"FFF\", m, ips, flavor, img, status\n # print tmpl % locals()\n print m.status\n \n return \"%s is currently: %s\" % (self.footprint_name, self.footprint_status)","def status(self):\n # pdb.set_trace()\n print(\"\\u001b[4m\\t\\tTimer Status\\u001b[0m\")\n print(\"\\n\\t# active timers: {}\".format(len(self.timer_threads)))\n for n in range(0, len(self.timer_threads)):\n active = True\n print(\"\\n\\tID: {}\\n\\tName: {}\\n\\tInit: {} AlarmT: {}\\n\\tTest?: {}\\n\\tTarget: {}\"\n .format(n, self.tuplist[n][1]['name'],\n self.tuplist[n][1]['init_time'].strftime(\"(%m-%d-%y)%H:%M:%S\"),\n self.tuplist[n][1]['alarm_time'],\n self.tuplist[n][1]['testmode'],\n self.tuplist[n][1]['todo']))\n return True","def StatusUpdate(msg):\r\n if verbosity > 0:\r\n print msg","def _print_progress(self):\n \n print 'Completed %d of %d' %(self.progress_id, self.total_work)\n self.progress_id += 1","def do_status(self,line):\n print \"\"\"\n Battery: %f\n X: %f.3 Y: %f.3 Th: %f.3\n Last tank command: %s\n Serial status: %s\n \"\"\" % (self.robotWrap.chargeLevel,self.robotWrap.x,self.robotWrap.y \\\n ,self.robotWrap.th,self.robot.lastTank,self.robot.port)","def StatusUpdate(msg):\n if verbosity > 0:\n print msg","def printstatus(self):\n data = self.statuslist()\n if not data:\n print(\n \"Unable to communicate to the OpenSprinkler \"\n \"at %s\" % self.hostname\n )\n return None\n print('Station\\t%-15.15s\\tStatus' % 'Name')\n for item in data:\n print('%d\\t%-15.15s\\t%s' % (item[0], item[1], item[2]))\n return","def status(self):\n self.scion_sh('status')","def status():\n _request('worklog/status/')","def getStatus():","def print_status(self, current_interval_end: datetime, force_status: bool=False):\n\n # Only update if we've reached the next threshold:\n if self.num_events_since_last_status > self.status_num_events_interval or force_status:\n first_stamp_str = self.first_event_stamp.strftime(\"%Y-%m-%d %H:%M:%S.%f\") \\\n if self.first_event_stamp is not None else \"?\"\n current_stamp_str = current_interval_end.strftime(\"%Y-%m-%d %H:%M:%S.%f\")\n\n # Print status over previous status:\n print(\n f\"Processed {self.num_input_events_processed} events ({first_stamp_str} to {current_stamp_str}) \",\n end='\\r'\n )\n\n self.num_events_since_last_status = 0","def standard_status():\n errors, warnings, infos = THE_LOGGER.status()\n info(errors, \"errors\")\n info(warnings, \"warnings\")\n info(infos, \"infos\")","def setstatus(self, text):\n if type(text) in (bytes, str):\n T = text\n else:\n # list probably:\n T = '\\n'.join(text)\n print(('-'*60))\n print(T)\n print(('='*60))","def simpleStatusPrint(self, i=1, sparse=1):\n if i % sparse == 0:\n print(\"*\", end='', flush=True)\n if i % (50 * sparse) == 0 and i > 0:\n print(\"(\"+str(i)+\")\")","def status_update(frame_number, tot_frames):\r\n if frame_number == 1:\r\n print(\"Starting analysis of %d frames...\" %tot_frames)\r\n\r\n if frame_number % 100 == 0:\r\n print(\"%d\" %frame_number, end='')\r\n sys.stdout.flush()\r\n elif frame_number % 10 == 0:\r\n print(\".\", end='')\r\n sys.stdout.flush()\r\n\r\n if frame_number == tot_frames:\r\n print (\"End of video reached successfully.\")","def _print_progress(counter):\n\tif(slogviz.config.interactive):\n\t\tprint('parse log file entry nr: {}'.format(counter),end='\\r')","def printMCLStatus(self):\n print()\n print(\"====================\")\n print(\"Cycle\", self.countCycles)\n self._printHist()","def print_progress(self, info_dict):\n if self.n_print != 0:\n t = info_dict['t']\n if t == 1 or t % self.n_print == 0:\n string = 'Iteration {0}'.format(str(t).rjust(len(str(self.n_iter))))\n string += ' [{0}%]'.format(str(int(t / self.n_iter * 100)).rjust(3))\n print(string)","def status(self, message=None):\n self.i += 1\n self.last_message = message\n\n if self.silent:\n return\n\n if self.skip > 1 and self.i % self.skip != 0:\n return\n\n if self.adapt:\n # Set skip to the reciprocal of per-instance time to schedule\n # status updates at approximately 1/sec.\n # We use i-1 instead of i to compensate for cases in which\n # status() is called prior to slow processing, resulting in\n # skip getting set to a large value from the outset.\n self.skip = int((self.i - 1) / (time.time() - self.start))\n\n self.display(\"[{0:.2g}s{1}]\".format(\n (time.time() - self.start) / self.i,\n ' on avg' if self.i > 1 else ''),\n message=str(message) if message is not None else None,\n msg_color=self.active_color,\n timing_color=self.active_color)","def status(self):\n pass","def status(self):\n pass","def output_status(self):\n raise NotImplementedError","def status(self):\n\n for index, x in enumerate(self.lot):\n print('|', end='')\n for spot, value in enumerate(x):\n if value == 1:\n print(\"|\", end='')\n if value == 2:\n print(\" |\", end='')\n if value == 3:\n print(\" |\", end='')\n if value == -1:\n print(\"X|\", end='')\n if value == -2:\n print(\"XXX|\", end='')\n if value == -3:\n print(\"XXXXX|\", end='')\n print()","def _update_status(self, message):\n message = \"[{}] {}\".format(strftime(\"%H:%M:%S\", localtime()), message)\n self.tracker.write_log(message)\n self.ui.update_status(message)","def refresh_status() -> None:\n ...","def _print_stat(self):\n if 0 < self.print_stats_interval < time.monotonic() - self.last_stat_print_time:\n if self._file_paths:\n self._log_file_processing_stats(self._file_paths)\n self.last_stat_print_time = time.monotonic()","def print_status(status):\n print(\"This is your status:\")\n print(\"\\n---\\n\")\n print(\"\\n\".join(l.strip(\"\\n\") for l in status))","def dump_pre_iteration(self,status):\n pass","def print_status(self, status):\n\n print \"Status (%X): \" % status\n if (status & STATUS_MEM_0_EMPTY) > 0:\n print \"\\tMEMORY 0 IS EMPTY\"\n if (status & STATUS_MEM_1_EMPTY) > 0:\n print \"\\tMEMORY 1 IS EMPTY\"","def status(self, msg):\n oscid = self.app.global_osc_id()\n print(\"STATUS : /Llia/%s : %s\" % (oscid, msg))","def printStatus(self):\n output = StringIO.StringIO()\n # use a csv writer to write out each row\n writer = csv.writer(output, lineterminator = '\\n')\n \n # write the header\n writer.writerow(['Server','Ping Interval','Status'])\n \n # write out the online servers\n for server, interval in self.online_servers.iteritems():\n writer.writerow([server, interval[1], 'Online'])\n \n # write out the offline servers\n for server, interval in self.offline_servers.iteritems():\n writer.writerow([server, interval[1], 'Offline'])\n \n return output.getvalue()","def status(self, **options):\n pass","def print_status(self, state, auction_round):\n\n if state['bid_winner'] is None:\n self.log('No bidder on this round {}.\\n'.format(auction_round))\n else:\n self.log('Player {} won {} on this round {} with bid amount {}.\\n'.format(\n state['bid_winner'], state['bid_item'], auction_round, state['winning_bid']))\n\n self.log('Remaining time:')\n for idx in range(len(self.players)):\n if self.players[idx]['valid']:\n self.log('\\t{} has {} seconds remaining'\n .format(self.players[idx]['name'], self.players[idx]['remain_time']))\n\n self.log('Remaining wealth:')\n for idx in range(len(self.players)):\n if self.players[idx]['valid']:\n self.log('\\t{} has {} dollars remaining'.format(self.players[idx]['name'], self.players[idx]['wealth']))\n\n self.log('------------------------------------\\n')\n\n if state['finished']:\n self.log('Game over\\n{}\\n'.format(state['reason']))","def status(args):\n print('Yields HPC fleet \"{}\" status'.format(args))","def do_status(self, *arg):\n if self.pocs is None:\n print_warning('Please run `setup_pocs` before trying to run')\n return\n if self.msg_subscriber is None:\n self.do_start_messaging()\n status = self.pocs.status()\n print()\n pprint(status)\n print()","def print_status(self, epoch, iteration, prefix=\"\",\n mode=\"train\", is_main_net=True):\n if mode == \"train\":\n log = getattr(self.logger[\"train\"], \"info\")\n else:\n log = getattr(self.logger[\"train\"], \"debug\")\n\n if is_main_net:\n # prepare txt to print\n jump = 3\n txt = \"epoch {} step {} \".format(epoch, iteration)\n for i, (k,v) in enumerate(self.status.items()):\n if (i+1) % jump == 0:\n txt += \", {} = {:.3f}\".format(k, v)\n log(txt)\n txt = \"\"\n elif (i+1) % jump == 1:\n txt += \"{} = {:.3f}\".format(k, v)\n else:\n txt += \", {} = {:.3f}\".format(k, v)\n\n txt += \" ({}->{}/{})\".format(\"|\".join(pred for pred in self.basenet_pred),\n utils.label2string(self.itoa, self.top1_predictions[0]), self.top1_gt)\n\n # print learning information\n log(txt)","def printStatus(self, verbose=False):\n\n status = \"\"\n display = \"\"\n stash = \"\"\n forward = \"\"\n\n display = \">> \" + self.path\n\n if self.globalStatus():\n status = \"[ \"+self.bcolors.OKGREEN+\"OK\"+self.bcolors.ENDC+\" ]\"\n else:\n status = self.bcolors.bold() + \"[ \" + self.bcolors.bold(\"FAIL\") +\\\n \"NO\" + self.bcolors.bold(\"ENDC\") + self.bcolors.bold() + \" ]\" +\\\n self.bcolors.bold(\"ENDC\")\n display = self.bcolors.bold() + display + self.bcolors.ENDC\n\n ## Get stash\n if self.stashed:\n stash = \"(\" + self.bcolors.WARNING + \"stash\" + self.bcolors.ENDC + \")\"\n\n if self.forward:\n forward = \"(\"+ \",\".join([branch+\":\"+str(self.forward[branch]) for branch in self.forward ]) + \")\"\n\n ## Get forward\n if verbose:\n verboseString = \"\\n\".join(self.verbose)\n\n ## Filling with whitespace\n # Full string = display + whitespace + stash + status\n # Get lenght of string without whitespace\n str_len = self._get_str_len(display) + self._get_str_len(forward) + self._get_str_len(stash)\\\n + self._get_str_len(status)\n nb_whitespace = int(columns) - str_len\n whitespace = \"\".join([\" \"]*nb_whitespace)\n\n ## Show display\n if verbose and verbose != \"\":\n display += whitespace + stash + forward + status + \"\\n\" + verboseString\n else:\n display += whitespace + stash + forward + status\n print display","def after_epoch(self):\n line = ' '.join([str(k) + ': ' + str(v) for k, v in self.trainer.status.items()])\n with open(os.path.join(self.root_path, 'log.txt'), 'a+') as fout:\n fout.write(line + '\\n')","async def status(self, ctx):\n self.logger.info(misolog.format_log(ctx, f\"\"))\n up_time = time.time() - self.start_time\n m, s = divmod(up_time, 60)\n h, m = divmod(m, 60)\n d, h = divmod(h, 24)\n uptime_string = \"%d days %d hours %d minutes %d seconds\" % (d, h, m, s)\n\n stime = time.time() - psutil.boot_time()\n m, s = divmod(stime, 60)\n h, m = divmod(m, 60)\n d, h = divmod(h, 24)\n system_uptime_string = \"%d days %d hours %d minutes %d seconds\" % (d, h, m, s)\n\n mem = psutil.virtual_memory()\n\n pid = os.getpid()\n memory_use = psutil.Process(pid).memory_info()[0]\n\n content = discord.Embed(title=f\"Miso Bot | version {main.version}\")\n content.set_thumbnail(url=self.client.user.avatar_url)\n\n content.add_field(name=\"Bot process uptime\", value=uptime_string)\n content.add_field(name=\"System CPU Usage\", value=f\"{psutil.cpu_percent()}%\")\n content.add_field(name=\"System uptime\", value=system_uptime_string)\n\n content.add_field(name=\"System RAM Usage\", value=f\"{mem.percent}%\")\n content.add_field(name=\"Bot memory usage\", value=f\"{memory_use/math.pow(1024, 2):.2f}MB\")\n\n await ctx.send(embed=content)","def __output(self,msg,status):\n status = int(status)\n if status:\n print \"%s-----------\\033[1;37;42m%s\\033[0m\" % (format(msg,\"<15\"),\"OK\")\n else:\n print \"%s***********\\033[1;37;41m%s\\033[0m\" % (format(msg,\"<15\"),\"ERROR\")","def status(self, loop=False, delay=10):\n if not self._submit:\n raise Exception('The workfow has not started its execution yet.\\n'\n 'Please, check if the workflow is planned and submitted for execution.')\n seq = False\n\n while True:\n out, err = subprocess.Popen('pegasus-status -l %s' % self.submit_dir, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE, shell=True, cwd=self.base_dir).communicate()\n if err:\n raise Exception(err)\n\n for line in out.decode('utf8').split('\\n'):\n if 'UNRDY' in line:\n seq = True\n elif seq:\n seq = False\n v = line.split()\n\n state = v[8]\n if state == 'Success':\n state = '\\x1b[1;32m' + state + '\\x1b[0m'\n elif state == 'Failure':\n state = '\\x1b[1;31m' + state + '\\x1b[0m'\n\n progress = '\\x1b[1;34m' + 'Progress: ' + v[7] + '%\\x1b[0m (' + state + ')'\n completed = '\\x1b[1;32mCompleted: ' + v[5] + '\\x1b[0m'\n queued = '\\x1b[1;33mQueued: ' + v[1] + '\\x1b[0m'\n running = '\\x1b[1;36mRunning: ' + v[3] + '\\x1b[0m'\n fail = '\\x1b[1;31mFailed: ' + v[6] + '\\x1b[0m'\n\n st = progress + '\\t(' + completed + ', ' + queued + ', ' + running + ', ' + fail + ')'\n print('%s\\r' % st, end='')\n break\n\n if not loop or 'Success' in out.decode('utf8') or 'Failure' in out.decode('utf8'):\n break\n time.sleep(delay)","def print_counter(counter):\n while True:\n print(\"Counter is now: \", counter.value, \"\\r\", end = \"\")\n time.sleep(0.25)","def launch_status(self):\n print(\n f\"Starting job with {len(self.fe.get_network())} jobs total. \",\n end=\"\\r\",\n )","def present_status(self):\n output = ''\n if self.stats['hand']:\n output += 'Ready: \\n'\n for card in sorted(self.stats['hand'], key=itemgetter('name')):\n output += card_format(card) + '\\n'\n output += '\\n'\n if self.stats['active']:\n output += 'Active: \\n'\n for card in self.stats['active']:\n output += card_format(card) + '\\n'\n if self.stats['discard']:\n output += '\\nSpent: \\n'\n for card in self.stats['discard']:\n output += card_format(card) + '\\n'\n output += '\\n'\n output += 'Spells: \\n'\n for power in self.stats['powers']:\n output += '%s x %d\\n' % (power, self.stats['powers'][power])\n if self.stats['opponent']:\n output += '\\nCurrent Activity:\\n'\n output += '%s' % (card_format(self.stats['opponent']))\n header_print('Status')\n print(output)","def printReport(self):\n\t\tself.app.printflush('Fetched: ' + str(self.fetched_count), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Processes: ' + str(self.processes), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Updated: ' + str(self.updated_count), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Average page load time: ' + str(self.average_time), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Returned with code: ' + repr(self.code_statistics), self.app.IGNORE_EXIT_FLAG)\n\t\tself.app.printflush('Closing Processes... ', self.app.IGNORE_EXIT_FLAG)","def _repr_pretty_(self, pp, cycle):\n try:\n status_text = self.format_status_info(self.status_info())\n except Exception:\n status_text = (f'{self}: Error showing status information. '\n 'Check IOC connection and device health.')\n logger.debug(status_text, exc_info=True)\n raise\n pp.text(status_text)","def status():\n return 'OK'","def show_status(left_t='', right_t=''):\n print(' %-30s %48s\\r'%(left_t[:30], right_t[:48]), end=' ')","def show_game_status(self, game, diff, step):\n if self.verbose:\n print('========== Step {} =========='.format(step))\n print('Time cost ===> {:.3f}s'.format(diff))\n game.print_game()","def status(self):\n return ''","async def status(self, context):\n await self.send_message(context, await self.status_msg_packed(context))","def status_update(frame_number, tot_frames):\r\n if frame_number == 1:\r\n sys.stdout.write(\"Starting analysis of %d frames...\\n\" %tot_frames)\r\n sys.stdout.flush()\r\n\r\n if frame_number % 100 == 0:\r\n sys.stdout.write(\"%d\" %frame_number)\r\n sys.stdout.flush()\r\n elif frame_number % 10 == 0:\r\n sys.stdout.write(\".\")\r\n sys.stdout.flush()\r\n\r\n if frame_number == tot_frames:\r\n print (\"End of video reached successfully.\")","def log_server_status(self):\n cmd = '{}logServerStatus'.format(self.console)\n self.write_command(cmd)","def refresh_status(self):\n\n pass","def render(self):\n logging.info(self._get_status())\n pass","def _status_command(cast, ramp):\n if ramp.is_playing:\n play_symbol = u'\\u25B6'\n else:\n play_symbol = u'\\u2759\\u2759'\n\n print u' %s %s by %s from %s via %s, %s of %s' % (\n play_symbol,\n ramp.title,\n ramp.artist,\n ramp.album,\n cast.app.app_id,\n _to_minutes(ramp.current_time),\n _to_minutes(ramp.duration)\n )","def status(self, *args):\n for k, v in self.processers.items():\n if v:\n if v.poll() is None:\n status = 'running'\n else:\n status = 'dead'\n else:\n status = 'stoped'\n print '%s - %s' % (k, status)","def dump_iteration(self,status):\n L = self.level\n stats.add_to_stats(step=status.step, time=status.time, iter=status.iter, type='residual',\n value=L.status.residual)\n pass","def status():\n with spinner():\n is_enabled = is_witness_enabled()\n signing_key = current_signing_key()\n misses = total_missed()\n\n t = PrettyTable([\"Enabled\", \"Misses\", \"Key\"])\n t.align = \"l\"\n t.add_row([is_enabled, misses, signing_key])\n\n output(t, 'Status')\n output(get_config(), 'Configuration')","def print_status(self):\n wlan = ''\n ip = ''\n mac = ''\n homepage = ''\n pairing_code = ''\n\n ssid = helpers.get_ssid()\n wlan = '\\nWireless network:\\n%s\\n\\n' % ssid\n\n interfaces = ni.interfaces()\n ips = []\n for iface_id in interfaces:\n iface_obj = ni.ifaddresses(iface_id)\n ifconfigs = iface_obj.get(ni.AF_INET, [])\n for conf in ifconfigs:\n if conf.get('addr') and conf.get('addr'):\n ips.append(conf.get('addr'))\n if len(ips) == 0:\n ip = '\\nERROR: Could not connect to LAN\\n\\nPlease check that the IoTBox is correc-\\ntly connected with a network cable,\\n that the LAN is setup with DHCP, and\\nthat network addresses are available'\n elif len(ips) == 1:\n ip = '\\nIP Address:\\n%s\\n' % ips[0]\n else:\n ip = '\\nIP Addresses:\\n%s\\n' % '\\n'.join(ips)\n\n if len(ips) >= 1:\n ips_filtered = [i for i in ips if i != '127.0.0.1']\n main_ips = ips_filtered and ips_filtered[0] or '127.0.0.1'\n mac = '\\nMAC Address:\\n%s\\n' % helpers.get_mac_address()\n homepage = '\\nHomepage:\\nhttp://%s:8069\\n\\n' % main_ips\n\n code = connection_manager.pairing_code\n if code:\n pairing_code = '\\nPairing Code:\\n%s\\n' % code\n\n commands = RECEIPT_PRINTER_COMMANDS[self.receipt_protocol]\n title = commands['title'] % b'IoTBox Status'\n self.print_raw(commands['center'] + title + b'\\n' + wlan.encode() + mac.encode() + ip.encode() + homepage.encode() + pairing_code.encode() + commands['cut'])","def _drawstatus(self):\n (y, x) = self.chatscreen.getmaxyx()\n\n fillchar = '*' if self.busy > 0 else '-'\n form = '{:'+ fillchar +'^' + str(x - 1) + '}'\n\n self.chatscreen.addstr(y-1, 0, form.format('%s' % self.status()))","def status(msg, options):\n if options.verbose:\n sys.stderr.write(msg)","def printStatus(self,mod=\"\"):\n dims = \"\"\n corner_labels = {\"back_right\":\"br\",\"back_left\":\"bl\",\"front_right\":\"fr\",\\\n \"front_left\":\"fl\"}\n for x in self.four_corners:\n dims += \"{}({},{}), \".format(corner_labels[x],self.four_corners[x][0],\\\n self.four_corners[x][1])\n print(\"{}{}\\tIN: {}\\tOUT: {}\\tWIDTH: {}\\tHEIGHT: {}\".format(mod,\\\n self.label,[entry.label for entry in self.in_lanes],\\\n [entry.label for entry in self.out_lanes],\\\n round(self.width,2),round(self.length,2)))\n print(\"{}{}\\t{}\".format(mod,self.label,dims))","def printStatus(self,mod=\"\"):\n dims = \"\"\n corner_labels = {\"back_right\":\"br\",\"back_left\":\"bl\",\"front_right\":\"fr\",\\\n \"front_left\":\"fl\"}\n for x in self.four_corners:\n dims += \"{}({},{}), \".format(corner_labels[x],self.four_corners[x][0],\\\n self.four_corners[x][1])\n print(\"{}{}\\tLEN: {}\\tLANES: ({},{})\".format(mod,\\\n self.label,round(self.length,2), self.top_up_lane.label,\\\n self.bottom_down_lane.label))\n print(\"{}{}\\t{}\\n\".format(mod,self.label,dims))","def report_status(**kwargs):\n\n print(\"\\nBEGIN: REPORT\\n\")\n\n # Print a formatted status report\n for key, value in kwargs.items():\n print(key + \": \" + value)\n\n print(\"\\nEND REPORT\")","def report_status(**kwargs):\n\n print(\"\\nBEGIN: REPORT\\n\")\n\n # Iterate over the key-value pairs of kwargs\n for key, values in kwargs.items():\n # Print out the keys and values, separated by a colon ':'\n print(key + \": \" + values)\n\n print(\"\\nEND REPORT\")","def display_status(self):\n connected = _check_connection()\n status = (\n f'WiFi: {\"connected\" if connected else \"disconnected\"}\\n'\n f'MQTT host: {self.mqtt.host if self.mqtt else \"N/A\"}\\n'\n f'Packets sent: {self.mqtt.n_sent if self.mqtt else 0}\\n'\n f'Uptime: {self.uptime()}'\n )\n self.display.draw_text_box(status, bg_color=BG_CYAN if connected else BG_RED)","def _print_send_status(event_data):\n message_count = (int(event_data[\"name\"]) - 1000) + 1\n\n if message_count % 5 == 0:\n print(f\"Sent {message_count} messages.\", end=\"\\r\")","async def monitor():\n\n for n in range(6):\n await asyncio.sleep(2)\n print(\"monitor status:\", n, await ps.status())"],"string":"[\n \"def _print_status(self):\",\n \"def status(s):\\n print('\\\\033[1m{0}\\\\033[0m'.format(s))\\n time.sleep(2)\",\n \"def _PrintStatus(self):\\n comment = 'iter %i' % (self.n_iter)\\n self.gfile.write(fileio.GetPrintCoordsXyzString(\\n self.mol.atoms, comment, 14, 8))\\n self.PrintEnergy(self.n_iter)\\n self.gfile.flush()\\n self.efile.flush()\",\n \"def printStatus(level,text):\\n\\tglobal t0\\n\\tpre = \\\"[{0:>7.2f}] \\\".format(time.time()-t0)\\n\\tfor x in range(0,level):\\n\\t\\tpre += \\\"-\\\"\\n\\tpre += \\\"> \\\"\\n\\tprint(pre+text)\",\n \"def outputStatus(self, line):\\r\\n for l in line.strip('\\\\r\\\\n').split('\\\\n'):\\r\\n self.output('%s: %s' % (ctime(), l), 0)\",\n \"def display_status(self):\\n time = float2str(self.scheduler.time, '10.2f')\\n tx = float2str(self.tx_total, '10g')\\n rx = float2str(self.rx_total, '10g')\\n dup = float2str(self.dup_total, '10g')\\n uniq_total = float2str(self.uniq_total, '10g')\\n delivered_total = float2str(self.delivered_total, '10g')\\n uniq_delivered_total = float2str(self.uniq_delivered_total, '10g')\\n print(\\n 'define status_l text Time:{},____TX:{},____RX:{},____DUP:{},____Delivered:{}__/__{},____Arrived:{} 14 white 0.5 0.05'\\n .format(time, tx, rx, dup, uniq_delivered_total, uniq_total,\\n delivered_total))\",\n \"def status(s):\\n print('\\\\033[1m{0}\\\\033[0m'.format(s))\",\n \"def status(s):\\n print('\\\\033[1m{0}\\\\033[0m'.format(s))\",\n \"def status(s):\\n print('\\\\033[1m{0}\\\\033[0m'.format(s))\",\n \"def status(s):\\n print('\\\\033[1m{0}\\\\033[0m'.format(s))\",\n \"def show_status():\\n\\n pass\",\n \"def status(s):\\n print(\\\"\\\\033[1m{0}\\\\033[0m\\\".format(s))\",\n \"def status(s):\\n print(\\\"\\\\033[1m{0}\\\\033[0m\\\".format(s))\",\n \"def status(s):\\n print(\\\"\\\\033[1m{0}\\\\033[0m\\\".format(s))\",\n \"def status(s):\\n print(\\\"\\\\033[1m{0}\\\\033[0m\\\".format(s))\",\n \"def print_status(self):\\n currentTime = time.time()\\n elapsedTime = (currentTime - self.startTime)/60.\\n print('Elapsed time/Estimated time: {:.2f}/{:.2f} mins'.format(elapsedTime, self.estTime))\",\n \"def status(s: str):\\n print(\\\"\\\\033[1m{0}\\\\033[0m\\\".format(s))\",\n \"def status():\\n pass\",\n \"def emit_status(self):\\n next_job_count = len(self.fe.get_next_jobs())\\n if next_job_count:\\n emoji = \\\"🤔\\\"\\n else:\\n emoji = \\\"👌\\\"\\n remaining = len(self.fe.get_current_network())\\n\\n pct = (self.total_job_count - remaining) / self.total_job_count\\n print(\\n f\\\"{emoji} ———— {next_job_count} jobs running, {remaining} remaining ({int(100*pct)}%). \\\",\\n end=\\\"\\\\r\\\",\\n )\",\n \"def printStatus(self, status):\\n \\\"\\\"\\\" This probably breaks SOI \\\"\\\"\\\"\\n\\n if hasattr(self, 'show_state') and self.show_state:\\n print(\\\"=============== \\\" + str(status[\\\"count\\\"]) + \\\" ===============\\\")\\n print(\\\"Current best energy: \\\" + str(status[\\\"bestEnergy\\\"]) + \\\" from state: \\\" + str(status[\\\"bestState\\\"]))\\n print(\\\"last accepted energy: \\\" + str(status[\\\"energy\\\"]) + \\\" from state: \\\" + str(status[\\\"state\\\"]))\\n print(\\\"current temperature: \\\" + str(status[\\\"temperature\\\"]))\\n else:\\n print(\\\"=============== \\\" + str(status[\\\"count\\\"]) + \\\" ===============\\\")\\n print(\\\"Current best energy: \\\" + str(status[\\\"bestEnergy\\\"]) )\\n print(\\\"last accepted energy: \\\" + str(status[\\\"energy\\\"]) )\\n print(\\\"current temperature: \\\" + str(status[\\\"temperature\\\"]))\",\n \"def main():\\n print \\\"Printing Sample Status\\\"\",\n \"def status(s):\\n print(\\\"\\\\033 {}\\\".format(s))#print(\\\"\\\\033[1m{0}\\\\033[0m\\\".format(s))\",\n \"def print_status(self):\\n print \\\"Zombie has\\\" + super(Zombie, self).print_status()\",\n \"def do_status(self, *arg):\\n for sensor_name in ['environment', 'weather', 'webcams']:\\n if sensor_name in self.active_sensors:\\n console.color_print(\\\"{:>12s}: \\\".format(sensor_name.title()), \\\"default\\\", \\\"active\\\", \\\"lightgreen\\\")\\n else:\\n console.color_print(\\\"{:>12s}: \\\".format(sensor_name.title()), \\\"default\\\", \\\"inactive\\\", \\\"yellow\\\")\",\n \"def put_status(status='successed'):\\n if not proxy_config.OUTPUT_DEBUG_INFO:\\n return\\n status_code=''\\n status_color=()\\n if status.strip()=='successed':\\n status_code='*'\\n status_color=(Color.Green,None,[Color.Bold])\\n elif status.strip()=='warning':\\n status_code='-'\\n status_color=(Color.Yellow,None,[Color.Bold])\\n else:\\n status_code='!'\\n status_color=(Color.Red,None,[Color.Bold])\\n print(colored(f'[{status_code}]',*status_color),end=' ')\",\n \"def print_status(self):\\r\\n\\t\\tif VERBOSE:\\r\\n\\r\\n\\t\\t\\tprint( 'Player : ')\\r\\n\\t\\t\\tfor h in self.hands:\\r\\n\\t\\t\\t\\tprint('\\\\t' + str(h))\\r\\n\\t\\t\\tprint( 'Dealer:\\\\n\\\\t' + str(self.dealer))\\r\\n\\t\\t\\tprint( '-----------------------')\",\n \"def status(self):\",\n \"def print_status(data):\\n print \\\"0x%x:0x%x:0x%x:0x%x:0x%x:%d:%d:%d:%d:%d:%d:%d:%d\\\" % (\\n data['sysVer'], data['barVer'], data['uvVer'], data['rccVer'],\\n data['windVer'], data['batteryRain'], data['batteryUV'],\\n data['batteryWind'], data['battery5'], data['battery4'],\\n data['battery3'], data['battery2'], data['battery1'])\",\n \"def print_status(self, id, pr, package, version, filename):\\n\\n\\t\\tprogress = self.get_progress(pr, id);\\n\\t\\t\\n\\t\\twhile progress != 100:\\n\\t\\t\\tself.write_line(\\\"STATUS {0} {1}%\\\".format(package, progress))\\n\\t\\t\\ttime.sleep(1)\\n\\t\\t\\tprogress = self.get_progress(pr, id);\\n\\n\\t\\tself.write_line(\\\"STATUS {0} {1}%\\\".format(package, progress))\",\n \"def print_status(status):\\n for success, term in status:\\n if success:\\n print \\\"Added: %s\\\" % term\\n else:\\n if term:\\n print \\\"Not added: %s\\\" % term\",\n \"def _print_status(self, stream, count, final=False):\\n twidth = shutil.get_terminal_size()[0]\\n msg_width = min((twidth // 2) - 5, 20)\\n if stream == 'in':\\n self._last_in_count = count\\n if final:\\n self._in_finished = True\\n else:\\n self._last_out_count = count\\n if final:\\n self._out_finished = True\\n\\n in_msg = '{: <{}}'.format('in: {}'.format(self._last_in_count),\\n msg_width)\\n out_msg = '{: <{}}'.format('out: {}'.format(self._last_out_count),\\n msg_width)\\n print('\\\\r{} -> {}'.format(in_msg, out_msg), end='', flush=True)\\n if self._in_finished and self._out_finished:\\n print()\",\n \"def status(self) -> NoReturn:\\n\\n curr_status= self.percent_done()\\n while(curr_status < 100):\\n\\n update_status(name=self.name, status=curr_status)\\n time.sleep(0.5)\\n\\n curr_status = self.percent_done()\\n\\n update_status(name=self.name, status=curr_status)\",\n \"def status(self):\\n logging.debug(\\\"%s entered status\\\" % self)\\n # print_config(self.infra)\\n # print self.images\\n # headers = [\\\"Machine Name\\\", \\\"Flavor\\\", \\\"IP Addresses\\\", \\\"Image Name\\\", \\\"Status\\\"]\\n # pt = prettytable.PrettyTable(headers)\\n # pt.align[\\\"Machine Name\\\"]=\\\"l\\\"\\n # pt.align[\\\"IP Addresses\\\"] = \\\"l\\\"\\n # pt.align[\\\"Image Name\\\"] = \\\"l\\\"\\n # pt.align[\\\"Status\\\"] = \\\"r\\\"\\n \\n print \\\"Checking status of %s\\\" % self.footprint_name\\n # tmpl = \\\"%(machine_name)-20s%(flavor)5s%(status)-30s\\\"\\n tmpl1 = \\\"\\\"\\\"%-20s%-52s[%s]\\\"\\\"\\\"\\n tmpl2 = \\\"\\\"\\\"%-20s%-60s\\\\n\\\"\\\"\\\"\\n print tmpl1 % (\\\"Machine Name\\\", \\\"IP Addresses\\\", \\\"Status\\\")\\n print 80 * \\\"-\\\"\\n \\n for machine in self.machines.keys():\\n m = self.machines[machine]\\n # machine_name = m.machine_name\\n # ips = str(m.ip_addresses)\\n # flavor = str(m.flavor)\\n # img = str(m.image_id)\\n # status = str(m.status)\\n # pt.add_row([m, ips, status, img, status])\\n # print \\\"FFF\\\", m, ips, flavor, img, status\\n # print tmpl % locals()\\n print m.status\\n \\n return \\\"%s is currently: %s\\\" % (self.footprint_name, self.footprint_status)\",\n \"def status(self):\\n # pdb.set_trace()\\n print(\\\"\\\\u001b[4m\\\\t\\\\tTimer Status\\\\u001b[0m\\\")\\n print(\\\"\\\\n\\\\t# active timers: {}\\\".format(len(self.timer_threads)))\\n for n in range(0, len(self.timer_threads)):\\n active = True\\n print(\\\"\\\\n\\\\tID: {}\\\\n\\\\tName: {}\\\\n\\\\tInit: {} AlarmT: {}\\\\n\\\\tTest?: {}\\\\n\\\\tTarget: {}\\\"\\n .format(n, self.tuplist[n][1]['name'],\\n self.tuplist[n][1]['init_time'].strftime(\\\"(%m-%d-%y)%H:%M:%S\\\"),\\n self.tuplist[n][1]['alarm_time'],\\n self.tuplist[n][1]['testmode'],\\n self.tuplist[n][1]['todo']))\\n return True\",\n \"def StatusUpdate(msg):\\r\\n if verbosity > 0:\\r\\n print msg\",\n \"def _print_progress(self):\\n \\n print 'Completed %d of %d' %(self.progress_id, self.total_work)\\n self.progress_id += 1\",\n \"def do_status(self,line):\\n print \\\"\\\"\\\"\\n Battery: %f\\n X: %f.3 Y: %f.3 Th: %f.3\\n Last tank command: %s\\n Serial status: %s\\n \\\"\\\"\\\" % (self.robotWrap.chargeLevel,self.robotWrap.x,self.robotWrap.y \\\\\\n ,self.robotWrap.th,self.robot.lastTank,self.robot.port)\",\n \"def StatusUpdate(msg):\\n if verbosity > 0:\\n print msg\",\n \"def printstatus(self):\\n data = self.statuslist()\\n if not data:\\n print(\\n \\\"Unable to communicate to the OpenSprinkler \\\"\\n \\\"at %s\\\" % self.hostname\\n )\\n return None\\n print('Station\\\\t%-15.15s\\\\tStatus' % 'Name')\\n for item in data:\\n print('%d\\\\t%-15.15s\\\\t%s' % (item[0], item[1], item[2]))\\n return\",\n \"def status(self):\\n self.scion_sh('status')\",\n \"def status():\\n _request('worklog/status/')\",\n \"def getStatus():\",\n \"def print_status(self, current_interval_end: datetime, force_status: bool=False):\\n\\n # Only update if we've reached the next threshold:\\n if self.num_events_since_last_status > self.status_num_events_interval or force_status:\\n first_stamp_str = self.first_event_stamp.strftime(\\\"%Y-%m-%d %H:%M:%S.%f\\\") \\\\\\n if self.first_event_stamp is not None else \\\"?\\\"\\n current_stamp_str = current_interval_end.strftime(\\\"%Y-%m-%d %H:%M:%S.%f\\\")\\n\\n # Print status over previous status:\\n print(\\n f\\\"Processed {self.num_input_events_processed} events ({first_stamp_str} to {current_stamp_str}) \\\",\\n end='\\\\r'\\n )\\n\\n self.num_events_since_last_status = 0\",\n \"def standard_status():\\n errors, warnings, infos = THE_LOGGER.status()\\n info(errors, \\\"errors\\\")\\n info(warnings, \\\"warnings\\\")\\n info(infos, \\\"infos\\\")\",\n \"def setstatus(self, text):\\n if type(text) in (bytes, str):\\n T = text\\n else:\\n # list probably:\\n T = '\\\\n'.join(text)\\n print(('-'*60))\\n print(T)\\n print(('='*60))\",\n \"def simpleStatusPrint(self, i=1, sparse=1):\\n if i % sparse == 0:\\n print(\\\"*\\\", end='', flush=True)\\n if i % (50 * sparse) == 0 and i > 0:\\n print(\\\"(\\\"+str(i)+\\\")\\\")\",\n \"def status_update(frame_number, tot_frames):\\r\\n if frame_number == 1:\\r\\n print(\\\"Starting analysis of %d frames...\\\" %tot_frames)\\r\\n\\r\\n if frame_number % 100 == 0:\\r\\n print(\\\"%d\\\" %frame_number, end='')\\r\\n sys.stdout.flush()\\r\\n elif frame_number % 10 == 0:\\r\\n print(\\\".\\\", end='')\\r\\n sys.stdout.flush()\\r\\n\\r\\n if frame_number == tot_frames:\\r\\n print (\\\"End of video reached successfully.\\\")\",\n \"def _print_progress(counter):\\n\\tif(slogviz.config.interactive):\\n\\t\\tprint('parse log file entry nr: {}'.format(counter),end='\\\\r')\",\n \"def printMCLStatus(self):\\n print()\\n print(\\\"====================\\\")\\n print(\\\"Cycle\\\", self.countCycles)\\n self._printHist()\",\n \"def print_progress(self, info_dict):\\n if self.n_print != 0:\\n t = info_dict['t']\\n if t == 1 or t % self.n_print == 0:\\n string = 'Iteration {0}'.format(str(t).rjust(len(str(self.n_iter))))\\n string += ' [{0}%]'.format(str(int(t / self.n_iter * 100)).rjust(3))\\n print(string)\",\n \"def status(self, message=None):\\n self.i += 1\\n self.last_message = message\\n\\n if self.silent:\\n return\\n\\n if self.skip > 1 and self.i % self.skip != 0:\\n return\\n\\n if self.adapt:\\n # Set skip to the reciprocal of per-instance time to schedule\\n # status updates at approximately 1/sec.\\n # We use i-1 instead of i to compensate for cases in which\\n # status() is called prior to slow processing, resulting in\\n # skip getting set to a large value from the outset.\\n self.skip = int((self.i - 1) / (time.time() - self.start))\\n\\n self.display(\\\"[{0:.2g}s{1}]\\\".format(\\n (time.time() - self.start) / self.i,\\n ' on avg' if self.i > 1 else ''),\\n message=str(message) if message is not None else None,\\n msg_color=self.active_color,\\n timing_color=self.active_color)\",\n \"def status(self):\\n pass\",\n \"def status(self):\\n pass\",\n \"def output_status(self):\\n raise NotImplementedError\",\n \"def status(self):\\n\\n for index, x in enumerate(self.lot):\\n print('|', end='')\\n for spot, value in enumerate(x):\\n if value == 1:\\n print(\\\"|\\\", end='')\\n if value == 2:\\n print(\\\" |\\\", end='')\\n if value == 3:\\n print(\\\" |\\\", end='')\\n if value == -1:\\n print(\\\"X|\\\", end='')\\n if value == -2:\\n print(\\\"XXX|\\\", end='')\\n if value == -3:\\n print(\\\"XXXXX|\\\", end='')\\n print()\",\n \"def _update_status(self, message):\\n message = \\\"[{}] {}\\\".format(strftime(\\\"%H:%M:%S\\\", localtime()), message)\\n self.tracker.write_log(message)\\n self.ui.update_status(message)\",\n \"def refresh_status() -> None:\\n ...\",\n \"def _print_stat(self):\\n if 0 < self.print_stats_interval < time.monotonic() - self.last_stat_print_time:\\n if self._file_paths:\\n self._log_file_processing_stats(self._file_paths)\\n self.last_stat_print_time = time.monotonic()\",\n \"def print_status(status):\\n print(\\\"This is your status:\\\")\\n print(\\\"\\\\n---\\\\n\\\")\\n print(\\\"\\\\n\\\".join(l.strip(\\\"\\\\n\\\") for l in status))\",\n \"def dump_pre_iteration(self,status):\\n pass\",\n \"def print_status(self, status):\\n\\n print \\\"Status (%X): \\\" % status\\n if (status & STATUS_MEM_0_EMPTY) > 0:\\n print \\\"\\\\tMEMORY 0 IS EMPTY\\\"\\n if (status & STATUS_MEM_1_EMPTY) > 0:\\n print \\\"\\\\tMEMORY 1 IS EMPTY\\\"\",\n \"def status(self, msg):\\n oscid = self.app.global_osc_id()\\n print(\\\"STATUS : /Llia/%s : %s\\\" % (oscid, msg))\",\n \"def printStatus(self):\\n output = StringIO.StringIO()\\n # use a csv writer to write out each row\\n writer = csv.writer(output, lineterminator = '\\\\n')\\n \\n # write the header\\n writer.writerow(['Server','Ping Interval','Status'])\\n \\n # write out the online servers\\n for server, interval in self.online_servers.iteritems():\\n writer.writerow([server, interval[1], 'Online'])\\n \\n # write out the offline servers\\n for server, interval in self.offline_servers.iteritems():\\n writer.writerow([server, interval[1], 'Offline'])\\n \\n return output.getvalue()\",\n \"def status(self, **options):\\n pass\",\n \"def print_status(self, state, auction_round):\\n\\n if state['bid_winner'] is None:\\n self.log('No bidder on this round {}.\\\\n'.format(auction_round))\\n else:\\n self.log('Player {} won {} on this round {} with bid amount {}.\\\\n'.format(\\n state['bid_winner'], state['bid_item'], auction_round, state['winning_bid']))\\n\\n self.log('Remaining time:')\\n for idx in range(len(self.players)):\\n if self.players[idx]['valid']:\\n self.log('\\\\t{} has {} seconds remaining'\\n .format(self.players[idx]['name'], self.players[idx]['remain_time']))\\n\\n self.log('Remaining wealth:')\\n for idx in range(len(self.players)):\\n if self.players[idx]['valid']:\\n self.log('\\\\t{} has {} dollars remaining'.format(self.players[idx]['name'], self.players[idx]['wealth']))\\n\\n self.log('------------------------------------\\\\n')\\n\\n if state['finished']:\\n self.log('Game over\\\\n{}\\\\n'.format(state['reason']))\",\n \"def status(args):\\n print('Yields HPC fleet \\\"{}\\\" status'.format(args))\",\n \"def do_status(self, *arg):\\n if self.pocs is None:\\n print_warning('Please run `setup_pocs` before trying to run')\\n return\\n if self.msg_subscriber is None:\\n self.do_start_messaging()\\n status = self.pocs.status()\\n print()\\n pprint(status)\\n print()\",\n \"def print_status(self, epoch, iteration, prefix=\\\"\\\",\\n mode=\\\"train\\\", is_main_net=True):\\n if mode == \\\"train\\\":\\n log = getattr(self.logger[\\\"train\\\"], \\\"info\\\")\\n else:\\n log = getattr(self.logger[\\\"train\\\"], \\\"debug\\\")\\n\\n if is_main_net:\\n # prepare txt to print\\n jump = 3\\n txt = \\\"epoch {} step {} \\\".format(epoch, iteration)\\n for i, (k,v) in enumerate(self.status.items()):\\n if (i+1) % jump == 0:\\n txt += \\\", {} = {:.3f}\\\".format(k, v)\\n log(txt)\\n txt = \\\"\\\"\\n elif (i+1) % jump == 1:\\n txt += \\\"{} = {:.3f}\\\".format(k, v)\\n else:\\n txt += \\\", {} = {:.3f}\\\".format(k, v)\\n\\n txt += \\\" ({}->{}/{})\\\".format(\\\"|\\\".join(pred for pred in self.basenet_pred),\\n utils.label2string(self.itoa, self.top1_predictions[0]), self.top1_gt)\\n\\n # print learning information\\n log(txt)\",\n \"def printStatus(self, verbose=False):\\n\\n status = \\\"\\\"\\n display = \\\"\\\"\\n stash = \\\"\\\"\\n forward = \\\"\\\"\\n\\n display = \\\">> \\\" + self.path\\n\\n if self.globalStatus():\\n status = \\\"[ \\\"+self.bcolors.OKGREEN+\\\"OK\\\"+self.bcolors.ENDC+\\\" ]\\\"\\n else:\\n status = self.bcolors.bold() + \\\"[ \\\" + self.bcolors.bold(\\\"FAIL\\\") +\\\\\\n \\\"NO\\\" + self.bcolors.bold(\\\"ENDC\\\") + self.bcolors.bold() + \\\" ]\\\" +\\\\\\n self.bcolors.bold(\\\"ENDC\\\")\\n display = self.bcolors.bold() + display + self.bcolors.ENDC\\n\\n ## Get stash\\n if self.stashed:\\n stash = \\\"(\\\" + self.bcolors.WARNING + \\\"stash\\\" + self.bcolors.ENDC + \\\")\\\"\\n\\n if self.forward:\\n forward = \\\"(\\\"+ \\\",\\\".join([branch+\\\":\\\"+str(self.forward[branch]) for branch in self.forward ]) + \\\")\\\"\\n\\n ## Get forward\\n if verbose:\\n verboseString = \\\"\\\\n\\\".join(self.verbose)\\n\\n ## Filling with whitespace\\n # Full string = display + whitespace + stash + status\\n # Get lenght of string without whitespace\\n str_len = self._get_str_len(display) + self._get_str_len(forward) + self._get_str_len(stash)\\\\\\n + self._get_str_len(status)\\n nb_whitespace = int(columns) - str_len\\n whitespace = \\\"\\\".join([\\\" \\\"]*nb_whitespace)\\n\\n ## Show display\\n if verbose and verbose != \\\"\\\":\\n display += whitespace + stash + forward + status + \\\"\\\\n\\\" + verboseString\\n else:\\n display += whitespace + stash + forward + status\\n print display\",\n \"def after_epoch(self):\\n line = ' '.join([str(k) + ': ' + str(v) for k, v in self.trainer.status.items()])\\n with open(os.path.join(self.root_path, 'log.txt'), 'a+') as fout:\\n fout.write(line + '\\\\n')\",\n \"async def status(self, ctx):\\n self.logger.info(misolog.format_log(ctx, f\\\"\\\"))\\n up_time = time.time() - self.start_time\\n m, s = divmod(up_time, 60)\\n h, m = divmod(m, 60)\\n d, h = divmod(h, 24)\\n uptime_string = \\\"%d days %d hours %d minutes %d seconds\\\" % (d, h, m, s)\\n\\n stime = time.time() - psutil.boot_time()\\n m, s = divmod(stime, 60)\\n h, m = divmod(m, 60)\\n d, h = divmod(h, 24)\\n system_uptime_string = \\\"%d days %d hours %d minutes %d seconds\\\" % (d, h, m, s)\\n\\n mem = psutil.virtual_memory()\\n\\n pid = os.getpid()\\n memory_use = psutil.Process(pid).memory_info()[0]\\n\\n content = discord.Embed(title=f\\\"Miso Bot | version {main.version}\\\")\\n content.set_thumbnail(url=self.client.user.avatar_url)\\n\\n content.add_field(name=\\\"Bot process uptime\\\", value=uptime_string)\\n content.add_field(name=\\\"System CPU Usage\\\", value=f\\\"{psutil.cpu_percent()}%\\\")\\n content.add_field(name=\\\"System uptime\\\", value=system_uptime_string)\\n\\n content.add_field(name=\\\"System RAM Usage\\\", value=f\\\"{mem.percent}%\\\")\\n content.add_field(name=\\\"Bot memory usage\\\", value=f\\\"{memory_use/math.pow(1024, 2):.2f}MB\\\")\\n\\n await ctx.send(embed=content)\",\n \"def __output(self,msg,status):\\n status = int(status)\\n if status:\\n print \\\"%s-----------\\\\033[1;37;42m%s\\\\033[0m\\\" % (format(msg,\\\"<15\\\"),\\\"OK\\\")\\n else:\\n print \\\"%s***********\\\\033[1;37;41m%s\\\\033[0m\\\" % (format(msg,\\\"<15\\\"),\\\"ERROR\\\")\",\n \"def status(self, loop=False, delay=10):\\n if not self._submit:\\n raise Exception('The workfow has not started its execution yet.\\\\n'\\n 'Please, check if the workflow is planned and submitted for execution.')\\n seq = False\\n\\n while True:\\n out, err = subprocess.Popen('pegasus-status -l %s' % self.submit_dir, stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE, shell=True, cwd=self.base_dir).communicate()\\n if err:\\n raise Exception(err)\\n\\n for line in out.decode('utf8').split('\\\\n'):\\n if 'UNRDY' in line:\\n seq = True\\n elif seq:\\n seq = False\\n v = line.split()\\n\\n state = v[8]\\n if state == 'Success':\\n state = '\\\\x1b[1;32m' + state + '\\\\x1b[0m'\\n elif state == 'Failure':\\n state = '\\\\x1b[1;31m' + state + '\\\\x1b[0m'\\n\\n progress = '\\\\x1b[1;34m' + 'Progress: ' + v[7] + '%\\\\x1b[0m (' + state + ')'\\n completed = '\\\\x1b[1;32mCompleted: ' + v[5] + '\\\\x1b[0m'\\n queued = '\\\\x1b[1;33mQueued: ' + v[1] + '\\\\x1b[0m'\\n running = '\\\\x1b[1;36mRunning: ' + v[3] + '\\\\x1b[0m'\\n fail = '\\\\x1b[1;31mFailed: ' + v[6] + '\\\\x1b[0m'\\n\\n st = progress + '\\\\t(' + completed + ', ' + queued + ', ' + running + ', ' + fail + ')'\\n print('%s\\\\r' % st, end='')\\n break\\n\\n if not loop or 'Success' in out.decode('utf8') or 'Failure' in out.decode('utf8'):\\n break\\n time.sleep(delay)\",\n \"def print_counter(counter):\\n while True:\\n print(\\\"Counter is now: \\\", counter.value, \\\"\\\\r\\\", end = \\\"\\\")\\n time.sleep(0.25)\",\n \"def launch_status(self):\\n print(\\n f\\\"Starting job with {len(self.fe.get_network())} jobs total. \\\",\\n end=\\\"\\\\r\\\",\\n )\",\n \"def present_status(self):\\n output = ''\\n if self.stats['hand']:\\n output += 'Ready: \\\\n'\\n for card in sorted(self.stats['hand'], key=itemgetter('name')):\\n output += card_format(card) + '\\\\n'\\n output += '\\\\n'\\n if self.stats['active']:\\n output += 'Active: \\\\n'\\n for card in self.stats['active']:\\n output += card_format(card) + '\\\\n'\\n if self.stats['discard']:\\n output += '\\\\nSpent: \\\\n'\\n for card in self.stats['discard']:\\n output += card_format(card) + '\\\\n'\\n output += '\\\\n'\\n output += 'Spells: \\\\n'\\n for power in self.stats['powers']:\\n output += '%s x %d\\\\n' % (power, self.stats['powers'][power])\\n if self.stats['opponent']:\\n output += '\\\\nCurrent Activity:\\\\n'\\n output += '%s' % (card_format(self.stats['opponent']))\\n header_print('Status')\\n print(output)\",\n \"def printReport(self):\\n\\t\\tself.app.printflush('Fetched: ' + str(self.fetched_count), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Processes: ' + str(self.processes), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Updated: ' + str(self.updated_count), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Average page load time: ' + str(self.average_time), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Returned with code: ' + repr(self.code_statistics), self.app.IGNORE_EXIT_FLAG)\\n\\t\\tself.app.printflush('Closing Processes... ', self.app.IGNORE_EXIT_FLAG)\",\n \"def _repr_pretty_(self, pp, cycle):\\n try:\\n status_text = self.format_status_info(self.status_info())\\n except Exception:\\n status_text = (f'{self}: Error showing status information. '\\n 'Check IOC connection and device health.')\\n logger.debug(status_text, exc_info=True)\\n raise\\n pp.text(status_text)\",\n \"def status():\\n return 'OK'\",\n \"def show_status(left_t='', right_t=''):\\n print(' %-30s %48s\\\\r'%(left_t[:30], right_t[:48]), end=' ')\",\n \"def show_game_status(self, game, diff, step):\\n if self.verbose:\\n print('========== Step {} =========='.format(step))\\n print('Time cost ===> {:.3f}s'.format(diff))\\n game.print_game()\",\n \"def status(self):\\n return ''\",\n \"async def status(self, context):\\n await self.send_message(context, await self.status_msg_packed(context))\",\n \"def status_update(frame_number, tot_frames):\\r\\n if frame_number == 1:\\r\\n sys.stdout.write(\\\"Starting analysis of %d frames...\\\\n\\\" %tot_frames)\\r\\n sys.stdout.flush()\\r\\n\\r\\n if frame_number % 100 == 0:\\r\\n sys.stdout.write(\\\"%d\\\" %frame_number)\\r\\n sys.stdout.flush()\\r\\n elif frame_number % 10 == 0:\\r\\n sys.stdout.write(\\\".\\\")\\r\\n sys.stdout.flush()\\r\\n\\r\\n if frame_number == tot_frames:\\r\\n print (\\\"End of video reached successfully.\\\")\",\n \"def log_server_status(self):\\n cmd = '{}logServerStatus'.format(self.console)\\n self.write_command(cmd)\",\n \"def refresh_status(self):\\n\\n pass\",\n \"def render(self):\\n logging.info(self._get_status())\\n pass\",\n \"def _status_command(cast, ramp):\\n if ramp.is_playing:\\n play_symbol = u'\\\\u25B6'\\n else:\\n play_symbol = u'\\\\u2759\\\\u2759'\\n\\n print u' %s %s by %s from %s via %s, %s of %s' % (\\n play_symbol,\\n ramp.title,\\n ramp.artist,\\n ramp.album,\\n cast.app.app_id,\\n _to_minutes(ramp.current_time),\\n _to_minutes(ramp.duration)\\n )\",\n \"def status(self, *args):\\n for k, v in self.processers.items():\\n if v:\\n if v.poll() is None:\\n status = 'running'\\n else:\\n status = 'dead'\\n else:\\n status = 'stoped'\\n print '%s - %s' % (k, status)\",\n \"def dump_iteration(self,status):\\n L = self.level\\n stats.add_to_stats(step=status.step, time=status.time, iter=status.iter, type='residual',\\n value=L.status.residual)\\n pass\",\n \"def status():\\n with spinner():\\n is_enabled = is_witness_enabled()\\n signing_key = current_signing_key()\\n misses = total_missed()\\n\\n t = PrettyTable([\\\"Enabled\\\", \\\"Misses\\\", \\\"Key\\\"])\\n t.align = \\\"l\\\"\\n t.add_row([is_enabled, misses, signing_key])\\n\\n output(t, 'Status')\\n output(get_config(), 'Configuration')\",\n \"def print_status(self):\\n wlan = ''\\n ip = ''\\n mac = ''\\n homepage = ''\\n pairing_code = ''\\n\\n ssid = helpers.get_ssid()\\n wlan = '\\\\nWireless network:\\\\n%s\\\\n\\\\n' % ssid\\n\\n interfaces = ni.interfaces()\\n ips = []\\n for iface_id in interfaces:\\n iface_obj = ni.ifaddresses(iface_id)\\n ifconfigs = iface_obj.get(ni.AF_INET, [])\\n for conf in ifconfigs:\\n if conf.get('addr') and conf.get('addr'):\\n ips.append(conf.get('addr'))\\n if len(ips) == 0:\\n ip = '\\\\nERROR: Could not connect to LAN\\\\n\\\\nPlease check that the IoTBox is correc-\\\\ntly connected with a network cable,\\\\n that the LAN is setup with DHCP, and\\\\nthat network addresses are available'\\n elif len(ips) == 1:\\n ip = '\\\\nIP Address:\\\\n%s\\\\n' % ips[0]\\n else:\\n ip = '\\\\nIP Addresses:\\\\n%s\\\\n' % '\\\\n'.join(ips)\\n\\n if len(ips) >= 1:\\n ips_filtered = [i for i in ips if i != '127.0.0.1']\\n main_ips = ips_filtered and ips_filtered[0] or '127.0.0.1'\\n mac = '\\\\nMAC Address:\\\\n%s\\\\n' % helpers.get_mac_address()\\n homepage = '\\\\nHomepage:\\\\nhttp://%s:8069\\\\n\\\\n' % main_ips\\n\\n code = connection_manager.pairing_code\\n if code:\\n pairing_code = '\\\\nPairing Code:\\\\n%s\\\\n' % code\\n\\n commands = RECEIPT_PRINTER_COMMANDS[self.receipt_protocol]\\n title = commands['title'] % b'IoTBox Status'\\n self.print_raw(commands['center'] + title + b'\\\\n' + wlan.encode() + mac.encode() + ip.encode() + homepage.encode() + pairing_code.encode() + commands['cut'])\",\n \"def _drawstatus(self):\\n (y, x) = self.chatscreen.getmaxyx()\\n\\n fillchar = '*' if self.busy > 0 else '-'\\n form = '{:'+ fillchar +'^' + str(x - 1) + '}'\\n\\n self.chatscreen.addstr(y-1, 0, form.format('%s' % self.status()))\",\n \"def status(msg, options):\\n if options.verbose:\\n sys.stderr.write(msg)\",\n \"def printStatus(self,mod=\\\"\\\"):\\n dims = \\\"\\\"\\n corner_labels = {\\\"back_right\\\":\\\"br\\\",\\\"back_left\\\":\\\"bl\\\",\\\"front_right\\\":\\\"fr\\\",\\\\\\n \\\"front_left\\\":\\\"fl\\\"}\\n for x in self.four_corners:\\n dims += \\\"{}({},{}), \\\".format(corner_labels[x],self.four_corners[x][0],\\\\\\n self.four_corners[x][1])\\n print(\\\"{}{}\\\\tIN: {}\\\\tOUT: {}\\\\tWIDTH: {}\\\\tHEIGHT: {}\\\".format(mod,\\\\\\n self.label,[entry.label for entry in self.in_lanes],\\\\\\n [entry.label for entry in self.out_lanes],\\\\\\n round(self.width,2),round(self.length,2)))\\n print(\\\"{}{}\\\\t{}\\\".format(mod,self.label,dims))\",\n \"def printStatus(self,mod=\\\"\\\"):\\n dims = \\\"\\\"\\n corner_labels = {\\\"back_right\\\":\\\"br\\\",\\\"back_left\\\":\\\"bl\\\",\\\"front_right\\\":\\\"fr\\\",\\\\\\n \\\"front_left\\\":\\\"fl\\\"}\\n for x in self.four_corners:\\n dims += \\\"{}({},{}), \\\".format(corner_labels[x],self.four_corners[x][0],\\\\\\n self.four_corners[x][1])\\n print(\\\"{}{}\\\\tLEN: {}\\\\tLANES: ({},{})\\\".format(mod,\\\\\\n self.label,round(self.length,2), self.top_up_lane.label,\\\\\\n self.bottom_down_lane.label))\\n print(\\\"{}{}\\\\t{}\\\\n\\\".format(mod,self.label,dims))\",\n \"def report_status(**kwargs):\\n\\n print(\\\"\\\\nBEGIN: REPORT\\\\n\\\")\\n\\n # Print a formatted status report\\n for key, value in kwargs.items():\\n print(key + \\\": \\\" + value)\\n\\n print(\\\"\\\\nEND REPORT\\\")\",\n \"def report_status(**kwargs):\\n\\n print(\\\"\\\\nBEGIN: REPORT\\\\n\\\")\\n\\n # Iterate over the key-value pairs of kwargs\\n for key, values in kwargs.items():\\n # Print out the keys and values, separated by a colon ':'\\n print(key + \\\": \\\" + values)\\n\\n print(\\\"\\\\nEND REPORT\\\")\",\n \"def display_status(self):\\n connected = _check_connection()\\n status = (\\n f'WiFi: {\\\"connected\\\" if connected else \\\"disconnected\\\"}\\\\n'\\n f'MQTT host: {self.mqtt.host if self.mqtt else \\\"N/A\\\"}\\\\n'\\n f'Packets sent: {self.mqtt.n_sent if self.mqtt else 0}\\\\n'\\n f'Uptime: {self.uptime()}'\\n )\\n self.display.draw_text_box(status, bg_color=BG_CYAN if connected else BG_RED)\",\n \"def _print_send_status(event_data):\\n message_count = (int(event_data[\\\"name\\\"]) - 1000) + 1\\n\\n if message_count % 5 == 0:\\n print(f\\\"Sent {message_count} messages.\\\", end=\\\"\\\\r\\\")\",\n \"async def monitor():\\n\\n for n in range(6):\\n await asyncio.sleep(2)\\n print(\\\"monitor status:\\\", n, await ps.status())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8277072","0.75176513","0.7487483","0.7366643","0.7340062","0.7316466","0.73164415","0.73164415","0.73164415","0.73164415","0.7304564","0.7295404","0.7295404","0.7295404","0.7295404","0.7240836","0.72358745","0.72280544","0.7167335","0.71368235","0.71220034","0.7113043","0.70085293","0.69840115","0.69716865","0.6956981","0.6953398","0.69181895","0.68936706","0.6892076","0.6861942","0.68099856","0.68039596","0.68006563","0.6799532","0.67877877","0.67371595","0.672057","0.66853","0.66525334","0.66347575","0.66164315","0.6563318","0.65570533","0.65524805","0.65361655","0.6536058","0.653416","0.65247655","0.6516926","0.65111774","0.6500547","0.6500547","0.6492073","0.6470904","0.6470644","0.64660466","0.6457858","0.6448688","0.64450884","0.6435337","0.6426645","0.6424274","0.64225566","0.6421478","0.641627","0.64126307","0.6397351","0.6393006","0.6380494","0.637249","0.63651407","0.6359888","0.63586056","0.63372636","0.63186127","0.6318028","0.63130736","0.6305685","0.63016385","0.6298974","0.6291565","0.62910783","0.62879455","0.6281073","0.62810296","0.6264198","0.625853","0.6254991","0.62544876","0.62492853","0.62458646","0.6241643","0.6235807","0.6234473","0.6227887","0.61996394","0.619336","0.61879885","0.6186302","0.6186128"],"string":"[\n \"0.8277072\",\n \"0.75176513\",\n \"0.7487483\",\n \"0.7366643\",\n \"0.7340062\",\n \"0.7316466\",\n \"0.73164415\",\n \"0.73164415\",\n \"0.73164415\",\n \"0.73164415\",\n \"0.7304564\",\n \"0.7295404\",\n \"0.7295404\",\n \"0.7295404\",\n \"0.7295404\",\n \"0.7240836\",\n \"0.72358745\",\n \"0.72280544\",\n \"0.7167335\",\n \"0.71368235\",\n \"0.71220034\",\n \"0.7113043\",\n \"0.70085293\",\n \"0.69840115\",\n \"0.69716865\",\n \"0.6956981\",\n \"0.6953398\",\n \"0.69181895\",\n \"0.68936706\",\n \"0.6892076\",\n \"0.6861942\",\n \"0.68099856\",\n \"0.68039596\",\n \"0.68006563\",\n \"0.6799532\",\n \"0.67877877\",\n \"0.67371595\",\n \"0.672057\",\n \"0.66853\",\n \"0.66525334\",\n \"0.66347575\",\n \"0.66164315\",\n \"0.6563318\",\n \"0.65570533\",\n \"0.65524805\",\n \"0.65361655\",\n \"0.6536058\",\n \"0.653416\",\n \"0.65247655\",\n \"0.6516926\",\n \"0.65111774\",\n \"0.6500547\",\n \"0.6500547\",\n \"0.6492073\",\n \"0.6470904\",\n \"0.6470644\",\n \"0.64660466\",\n \"0.6457858\",\n \"0.6448688\",\n \"0.64450884\",\n \"0.6435337\",\n \"0.6426645\",\n \"0.6424274\",\n \"0.64225566\",\n \"0.6421478\",\n \"0.641627\",\n \"0.64126307\",\n \"0.6397351\",\n \"0.6393006\",\n \"0.6380494\",\n \"0.637249\",\n \"0.63651407\",\n \"0.6359888\",\n \"0.63586056\",\n \"0.63372636\",\n \"0.63186127\",\n \"0.6318028\",\n \"0.63130736\",\n \"0.6305685\",\n \"0.63016385\",\n \"0.6298974\",\n \"0.6291565\",\n \"0.62910783\",\n \"0.62879455\",\n \"0.6281073\",\n \"0.62810296\",\n \"0.6264198\",\n \"0.625853\",\n \"0.6254991\",\n \"0.62544876\",\n \"0.62492853\",\n \"0.62458646\",\n \"0.6241643\",\n \"0.6235807\",\n \"0.6234473\",\n \"0.6227887\",\n \"0.61996394\",\n \"0.619336\",\n \"0.61879885\",\n \"0.6186302\",\n \"0.6186128\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":85680,"cells":{"query":{"kind":"string","value":"Extract fields from status to save to json."},"document":{"kind":"string","value":"def _map_status_fields(self, tweet):\n data = {\n # status\n \"date\": tweet.created_at.strftime('%Y-%m-%d %H:%M:%S'),\n \"id\": tweet.id_str,\n \"text\": tweet.text,\n \"truncated\": tweet.truncated,\n \"lang\": tweet.lang,\n # user\n \"user_id\": tweet.user.id_str,\n \"user_screen_name\": tweet.user.screen_name,\n \"user_verified\": tweet.user.verified,\n \"user_lang\": tweet.user.lang,\n # reply\n \"reply_to_id\": tweet.in_reply_to_status_id_str,\n # quote\n \"quoted_id\": None,\n \"quoted_text\": None,\n # retweet\n \"retweeted_id\": None,\n \"retweeted_text\": None\n }\n # full text\n try:\n data.update({\n \"text\": tweet.extended_tweet['full_text']\n })\n except AttributeError:\n pass\n # quote\n if hasattr(tweet, \"quoted_status\"):\n data.update({\"quoted_id\": tweet.quoted_status.id_str})\n try:\n data.update({\n \"quoted_text\":\n tweet.quoted_status.extended_tweet['full_text']\n })\n except AttributeError:\n data.update({\n \"quoted_text\":\n tweet.quoted_status.text\n })\n # retweet\n if hasattr(tweet, \"retweeted_status\"):\n data.update({\"retweeted_id\": tweet.retweeted_status.id_str})\n try:\n data.update({\n \"retweeted_text\":\n tweet.retweeted_status.extended_tweet['full_text']\n })\n except AttributeError:\n data.update({\n \"retweeted_text\":\n tweet.retweeted_status.text\n })\n data.update({\n \"tweet_url\":\n \"https://twitter.com/%s/status/%s\" %\n (tweet.user.screen_name, tweet.id_str)\n })\n return(data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def extract_status(self, status) -> None:\r\n if \"VehicleInfo\" in status:\r\n if \"RemoteHvacInfo\" in status[\"VehicleInfo\"]:\r\n self.hvac = status[\"VehicleInfo\"][\"RemoteHvacInfo\"]\r\n\r\n if \"ChargeInfo\" in status[\"VehicleInfo\"]:\r\n self.battery = status[\"VehicleInfo\"][\"ChargeInfo\"]","def get_status(self, torrent_id, fields):\n status = {}\n for field in fields:\n try:\n status[field] = self.status_fields[field](torrent_id)\n except KeyError:\n pass\n return status","def status(self, status: dict):\n pass","def status(self, additional=[]): # pylint: disable=dangerous-default-value\n self.manager.refresh_client()\n fields = [\"batteryLevel\", \"deviceDisplayName\", \"deviceStatus\", \"name\"]\n fields += additional\n properties = {}\n for field in fields:\n properties[field] = self.content.get(field)\n return properties","def _extract_info(self, data):\n if 'status' in data:\n if data['status'] == 'Already Downloaded':\n # Set self._return_code to already downloaded\n # and trash that key\n self._set_returncode(self.ALREADY)\n data['status'] = None\n\n if data['status'] == 'Filesize Abort':\n # Set self._return_code to filesize abort\n # and trash that key\n self._set_returncode(self.FILESIZE_ABORT)\n data['status'] = None","def status() -> Dict[str, Any]:","def as_dict(self):\n return {\"status\": self.status.AsDict(),\n \"metadata\": self.metadata.as_dict}","def on_status(self, status):\n\n status = status._json\n\n # Only worry about original tweets\n if not status[\"is_quote_status\"] and \"retweeted_status\" not in status \\\n and not status[\"in_reply_to_status_id\"]:\n\n # Take only a subset of fields\n doc = {\n \"text\": status[\"full_text\"] if \"full_text\" in status else status[\"text\"],\n \"screen_name\": status[\"user\"][\"screen_name\"],\n \"verified\": status[\"user\"][\"verified\"],\n \"created_at\": status[\"created_at\"]\n }\n\n # Just to check\n print(doc['text'])\n\n # Store it\n self.write_to_db(doc)","def data_record_to_api_status(data):\r\n\tdata_web_view = data_record_to_web_view(data)\r\n\tdata = {\r\n\t\t\"request_id\": data[\"request_id\"],\r\n\t\t\"url\": data[\"url\"],\r\n\t\t\"status\": data_web_view[\"status\"],\r\n\t\t\"screenshot_available\": data_web_view[\"screenshot_available\"],\r\n\t\t\"proof_available\": data_web_view[\"proof_available\"],\r\n\t\t\"timestamp\": data[\"timestamp\"],\r\n\t\t\"prune_timestamp\": data[\"prune_timestamp\"]\r\n\t}\r\n\treturn data","def convert_dicts_in_status_to_obj(status: Status) -> Status:\n keys_to_update = [\"urls\", \"user\", \"user_mentions\", \"quoted_status\"]\n for key in keys_to_update:\n if key == \"urls\":\n status.urls = [Url(**url) for url in status.__getattribute__(key)]\n elif key == \"user\":\n status.user = User(**status.__getattribute__(key))\n elif key == \"user_mentions\":\n status.user_mentions = [\n User(**user) for user in status.__getattribute__(key)\n ]\n elif key == \"quoted_status\":\n status.quoted_status = (\n convert_dicts_in_status_to_obj(\n status=Status(**status.__getattribute__(key))\n )\n if status.__getattribute__(key)\n else None\n )\n return status","def to_dict(self) -> Dict[str, Any]:\n return {\"status\": self.status.name, \"message\": self.message}","def _do_status(self) -> Dict[str, Any]:\n return {}","def _do_status(self) -> Dict[str, Any]:\n return {}","def additional_data(self, request, qs):\n status_stat = get_status_stat(qs)\n return { 'task_stat' : status_stat }","def get_status_data():\n\n return dict(\n celery=_get_celery_status(),\n users=_get_users_stats(),\n daemons=_get_daemons_status(),\n settings=_get_settings(),\n files=_get_files_status(),\n languages=_get_languages_status(),\n sets=_get_sets_status(),\n )","def load_status(self):\n # jIn = os.path.join(self.meta[\"wdir\"], meta_file)\n with open(self.meta_filepath, \"r\") as f:\n tmp = json.load(f)\n if \"status\" in tmp.keys():\n # self.meta[\"status\"] = tmp[\"status\"]\n return tmp[\"status\"]","def status():\n statuses = get_all_statuses()\n return json.dumps(statuses, indent=4)","def as_dict(self) -> dict:\r\n return {\r\n \"alias\": self.alias,\r\n \"vin\": self.vin,\r\n \"details\": self.details,\r\n \"status\": {\r\n \"battery\": self.battery,\r\n \"hvac\": self.hvac,\r\n \"odometer\": self.odometer,\r\n \"parking\": self.parking,\r\n },\r\n \"servicesEnabled\": self.services,\r\n }","def get_status(self):\n\n status = \"\"\"\n-----------------GENERAL USER INFO-----------------\n Name: {name}\n Age: {age}\n Age Range: {age_range}\n Height: {height}\n Weight: {weight}\n Pregnancy Status: {pregnancy_status}\n Breastfeeding Status: {breastfeeding_status}\n\n-----------------USER'S RECOMMENDED DAILY VITAMIN AND MINERAL COUNT-------------\n (in milligrams, mg)\n Vitamin A: {user_vitamin_a}\n Vitamin B6: {user_vitamin_b_6}\n Vitamin B12: {user_vitamin_b_12}\n Vitamin C: {user_vitamin_c}\n Vitamin D: {user_vitamin_d}\n Vitamin E: {user_vitamin_e}\n \n Calcium: {user_calcium}\n Iron: {user_iron}\n Magnesium: {user_magnesium}\n Potassium: {user_potassium}\n Zinc: {user_zinc}\n \"\"\"\n return status.format(name=self.name, age=self.age, age_range=self.age_range, height=self.height, weight=self.weight, pregnancy_status=self.pregnancy_status, \n breastfeeding_status=self.breastfeeding_status, user_vitamin_a=self.user_vitamin_a,\n user_vitamin_b_6=self.user_vitamin_b_6, user_vitamin_b_12=self.user_vitamin_b_12, user_vitamin_c=self.user_vitamin_c, user_vitamin_d=self.user_vitamin_d, user_vitamin_e=self.user_vitamin_e,\n user_calcium=self.user_calcium, user_iron=self.user_iron, user_magnesium=self.user_magnesium, user_potassium=self.uesr_potassium, user_zinc=self.user_zinc)","def lineup_status_json() -> Response:\n if station_scan:\n lineup_status = {\n \"ScanInProgress\": True,\n \"Progress\": 50,\n \"Found\": 5\n }\n else:\n lineup_status = {\n \"ScanInProgress\": False,\n \"ScanPossible\": True,\n \"Source\": \"Antenna\",\n \"SourceList\": [\"Antenna\"]\n }\n return jsonify(lineup_status)","def status_to_query(status):\n if status == 'changed':\n return {\n 'status': 'ok',\n 'changed': True,\n }\n else:\n return {\n 'status': status,\n }","def status_feedback(self):\n return {\n TAG_ADDRESS: self.address,\n ATTR_STATUS: self._status,\n ATTR_INIT: self._init,\n ATTR_PRECISION: self._prec,\n ATTR_TS: self._last_edited,\n ATTR_LAST_CHANGED: self._last_changed,\n ATTR_LAST_UPDATE: self._last_update,\n }","def to_json_dict(self):\n result = {\"code\": self.code, \"state\": self.state, \"message\": self.message}\n if self.extra_fields:\n result.update(self.extra_fields)\n return result","def extract_fields(self, json_dict):\n raise NotImplementedError()","def _get_details(self, details):\n details['DoT'] = \"Yes\" if self.static else \"No\"\n details['device'] = self.device\n details['volume_id'] = self.volume_id\n details['from_snap'] = \"No\" if not self.from_snapshot_id else self.from_snapshot_id\n details['from_archive'] = \"No\" if not self.from_archive else self.from_archive['url']\n details['snapshot_progress'] = self.snapshot_progress\n details['snapshot_status'] = self.snapshot_status\n # TODO: keep track of any errors\n details['err_msg'] = None if details.get('err_msg', '') == '' else details['err_msg']\n details['snapshots_created'] = self.snapshots_created\n return details","def get_server_status(fields=[]):\n return get_dict_from_db(key='status', fields=fields)","def getStatus():\n return json.dumps({'camera': Camera.status(), 'rover': rover.status()}), 200","def rest_status():\n my_dict = set_status(None)\n return my_dict","def serialize(self):\n return {\n 'id': self.id,\n 'code': self.code,\n 'email': self.email,\n 'status': self.status.value,\n 'organization_id': self.organization_id\n }","def status(self) -> dict[str, str] | None:\n return self._status","def base_json(self):\n\n return {\n \"valid\": self.is_valid,\n \"action\": self.action,\n \"type\": self.entity_type,\n \"id\": self.entity_id,\n \"errors\": self.errors,\n \"warnings\": self.warnings,\n \"unique_keys\": self.secondary_keys_dicts,\n }","def serialize(self):\n return {\n 'id' : self.id,\n 'sid' : self.sid,\n 'status' : self.status,\n 'duration' : self.duration,\n 'recurl' : self.recurl,\n 'dateTime' : self.dateTime\n }","def _get_status(self):\n return self.__status","def status(self):\n return {\n 'id': 'status',\n 'protocol_version': 'PV62',\n 'network': self.origin_node.network.name,\n 'td': self.origin_node.chain.head.header.difficulty,\n 'best_hash': self.origin_node.chain.head.header.hash,\n 'genesis_hash': self.origin_node.chain.genesis.header.hash,\n 'size': kB_to_MB(self._message_size['status'])\n }","def collect(self):\n self.status['serial'] = self.config.get('dlmconfig', 'serial')\n self.status['timestamp'] = time.strftime('%Y/%m/%d %H:%M:%S', time.localtime())\n self.status['uptime'] = system.stats.uptime()\n self.status['free_disk_space_sdcard'] = system.stats.disk_usage('root')\n self.status['free_disk_space_stick'] = system.stats.disk_usage('sda1')\n self.status['wwan_reception'] = system.interfaces.WwanInterface.signal_strength(self.config.get('network', 'iface'))","def _process_info(resp: suds.sudsobject) -> dict:\n last = resp.ultimoValor\n return dict(fonte = str(resp.fonte),\n gestor = str(resp.gestorProprietario),\n freq = str(resp.periodicidadeSigla),\n nome = str(resp.nomeCompleto),\n number = int(resp.oid),\n final = dt(last.ano, last.mes, last.dia))","def status(_):\n return {\"status\": \"ok\"}","def as_dict(self):\n if self._last_changed:\n last_changed_isoformat = self._last_changed.isoformat()\n else:\n last_changed_isoformat = process_timestamp_to_utc_isoformat(\n self._row.last_changed\n )\n if self._last_updated:\n last_updated_isoformat = self._last_updated.isoformat()\n else:\n last_updated_isoformat = process_timestamp_to_utc_isoformat(\n self._row.last_updated\n )\n return {\n \"entity_id\": self.entity_id,\n \"state\": self.state,\n \"attributes\": self._attributes or self.attributes,\n \"last_changed\": last_changed_isoformat,\n \"last_updated\": last_updated_isoformat,\n }","def test_get_status(self):\n resp = self.build_api.getStatus().json()\n assert 'status' in resp\n assert 'message' in resp","def save_object(self, data):\n return Status(**data)","def json_dump(self):\n return {\n \"id\":self.id,\n \"firstname\": self.firstname,\n \"lastname\": self.lastname,\n \"othername\": self.othername,\n \"email\": self.email,\n \"phonenumber\": self.phonenumber,\n \"passporturl\": self.passporturl,\n \"roles\": self.roles,\n \"nationalid\": self.nationalid,\n \"county\": self.county,\n \"date_created\": self.date_created,\n \"date_modified\": self.date_modified\n }","async def get_status(self):\n data = await self.gen_req(self.status_url)\n return data['data']['attributes']['releasedAt'], data['data']['attributes']['version']","def to_json(self):\n return {\n 'revision': self.revision,\n 'active': self.active,\n 'archived_at': self.archived_at\n }","def _init_status(self):\n\n status = self._get_status_obj()\n\n for i, step in enumerate(self._run_list):\n\n for module in step.keys():\n module_dict = {module: {'pipeline_index': i}}\n status.data = status.update_dict(status.data, module_dict)\n\n status._dump()","def status(self):\n return STATUS[self.fields['status']]","def _get_status(self):\n if self._state in [\"processed\", \"error\"]:\n return self._state\n \n get_resp = requests.get(self.location, cookies={\"session\": self.session})\n\n self._state = get_resp.json()[\"status\"]\n self.slice_time = get_resp.json()[\"slice_time\"]\n \n return self._state","def _export(self, filled_only=False, without_id=False):\n response = dict()\n for field in self.fields:\n if filled_only and not self.fields.get(field).get():\n continue\n if field == '_id' and without_id:\n continue\n response[field] = self.fields.get(field).get()\n return response","def _update(self, data):\n self.status = data['status']\n self.progress = data['progress']","def success_rep2(self)->str:\n strings = []\n json_dict = self.to_json()\n for fld in fields(self):\n # val = getattr(self, fld.name)\n val = json_dict.get(fld.name, None)\n if val:\n str1 = f\"{fld.name.title().replace('_',' ')}: {val}\"\n strings.append(str1)\n return \", \".join(strings)","def UpdateFromResponse(self, response):\n for key in self.status:\n self.status[key] = response[key]","def status(self):\n status = {\n 'driver':self.driver.status,\n 'arduino':self.arduino.status,\n 'sensors':[]}\n\n for name, sensor in self.sensors.items():\n sensor_status = sensor.status\n sensor_status['name'] = name\n status['sensors'].append(sensor_status)\n\n return status","def to_dict(self) -> Dict[str, Any]:\n\n fields: Dict[str, Any] = {}\n if hasattr(self, \"url\"):\n fields[\"url\"] = self.url\n if hasattr(self, \"last_check\"):\n fields[\"last_check\"] = self.last_check\n if hasattr(self, \"param1\"):\n fields[\"param1\"] = self.param1\n return fields","def check_status():\n logger.debug(\"Starting the check_status() routine.\")\n\n url = \"https://www.toggl.com/api/v8/time_entries/current\"\n token = os.environ[\"TOGGL_API_TOKEN\"]\n auth_token = base64.b64encode(f\"{token}:api_token\".encode()).decode()\n resp = requests.get(url, headers={\"Authorization\": \"Basic \" + auth_token})\n\n cols = \"id\", \"duration\", \"description\"\n status = {k: v for k, v in (resp.json()[\"data\"] or {}).items() if k in cols}\n logger.debug(f\"{'Something' if 'id' in status else 'Something'} is being tracked.\")\n\n return status","def get_status(self):\n r = requests.get(self.base_url + '/status')\n return r.json()","def __repr__(self):\n return {\n 'username': self.createdBy,\n 'createdOn': self.createdOn,\n 'status': self.status\n }","def new_substatus_json_for_operation(operation_name, status=\"Transitioning\", code=Constants.ExitCode.Okay, message=json.dumps(\"{}\")):\n # NOTE: Todo Function is same for assessment and install, can be generalized later\n return {\n \"name\": str(operation_name),\n \"status\": str(status).lower(),\n \"code\": code,\n \"formattedMessage\": {\n \"lang\": \"en-US\",\n \"message\": str(message)\n }\n }","def _extract_relevant_fields(self, obj):\n try:\n self.uuid = obj.getUUID()\n except Exception:\n pass\n try:\n self.meta_type = obj.meta_type\n except Exception:\n pass\n try:\n self.name = obj.titleOrId()\n except Exception:\n pass\n\n from Products.ZenModel.Device import Device\n\n if isinstance(obj, Device):\n self.is_device = True\n try:\n self.mem_capacity = obj.hw.totalMemory\n except Exception:\n pass\n\n from Products.ZenModel.DeviceComponent import DeviceComponent\n\n if isinstance(obj, DeviceComponent):\n self.is_device_component = True\n\n # Get extra context from IObjectMapContextProvider adapters.\n for provider in subscribers([obj], IObjectMapContextProvider):\n try:\n merge_fields(self.dimensions, provider.get_dimensions(obj))\n except Exception as e:\n log.error(\n \"%s failed to get dimensions for %s: %s\", provider, obj, e\n )\n\n try:\n merge_fields(self.metadata, provider.get_metadata(obj))\n except Exception as e:\n log.error(\n \"%s failed to get metadata for %s: %s\", provider, obj, e\n )","def status(self):\n\n resp = requests.get(\"http://{0}/get_status.cgi\".format(self.address))\n data = resp.text.replace(\";\", \"\")\n data = data.replace(\"var\", \"\")\n data_s = data.split(\"\\n\")\n # Last is an empty line \n data_s.pop()\n data_array = [s.split(\"=\") for s in data_s]\n return dict(data_array)","def json(self) -> dict:\n return {\n 'id': self.id,\n 'requestType': self.request_type.name,\n 'isProcessed': self.is_processed,\n 'serviceName': self.service_name.name,\n 'isAdmin': self.is_admin,\n 'creationDate': LegislationDatetime.as_legislation_timezone(self.creation_date).isoformat()\n }","def get_json(self):\n return {'name': self.name, \n 'path': self.path, \n 'enabled': self.enabled}","def status(self):\n r = requests.get('/'.join([self.base_url, self.ENDPOINT_STATUS]))\n return r.json()","def get_text_status(json):\n if json is None:\n return None\n elif 'statusdetail' in json:\n return json['statusdetail']\n elif 'status' in json:\n return json['status']\n else:\n return None","def json(self):\n data = {\n f: getattr(self, self._custom_key_transform(self._from_api_conversion(f))) for f in self.fields\n }\n if \"timestamp\" in data and not isinstance(data[\"timestamp\"], str):\n data[\"timestamp\"] = data[\"timestamp\"].strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\")\n return data","def get_status():\n data = {\n 'status': 'up',\n }\n jsn = json.dumps(data)\n\n resp = Response(jsn, status=200, mimetype='application/json')\n\n return resp","def dump_api(self, add_config=False):\n\n retour = {}\n\n for val in ['reference', 'extra_data', 'amount', 'postfinance_id', 'postfinance_status', 'internal_status', 'ipn_needed', 'brand', 'card']:\n retour[val] = str(getattr(self, val))\n\n for val in ['creation_date', 'last_userforwarded_date', 'last_user_back_from_postfinance_date', 'last_postfinance_ipn_date', 'last_ipn_date']:\n if getattr(self, val):\n retour[val] = str(localtime(getattr(self, val)))\n else:\n retour[val] = ''\n\n for cal, name in [('get_postfinance_status_display', 'postfinance_status_text'), ('get_internal_status_display', 'internal_status_text'), ('amount_chf', 'amount_chf')]:\n retour[name] = getattr(self, cal)()\n\n if add_config:\n retour['config'] = self.config.name\n\n return retour","def status(self):\n return self._data['status']","def progress_data(self) -> Dict[str, Any]:\n result: Dict[str, Union[Dict[str, Union[str, int]], List[Tuple[str, str]], str]] = {\n 'progress': {\n 'state': 'error',\n 'step': -1\n },\n 'stages': TestStatus.stages(),\n 'start': '-',\n 'end': '-'\n }\n\n if len(self.progress) > 0:\n result['start'] = self.progress[0].timestamp\n last_status = self.progress[-1]\n\n if last_status.status in [TestStatus.completed, TestStatus.canceled]:\n result['end'] = last_status.timestamp\n\n if last_status.status == TestStatus.canceled:\n if len(self.progress) > 1:\n result['progress']['step'] = TestStatus.progress_step(self.progress[-2].status) # type: ignore\n\n else:\n result['progress']['state'] = 'ok' # type: ignore\n result['progress']['step'] = TestStatus.progress_step(last_status.status) # type: ignore\n\n return result","def extra_dejson(self):\n obj = {}\n if self.extra:\n try:\n obj = json.loads(self.extra)\n except Exception as e:\n self.log.exception(e)\n self.log.error(\"Failed parsing the json for conn_id %s\", self.conn_id)\n\n return obj","def json(self):\n return {\n 'id': self.id,\n 'id_bank_data': self.id_bank_data,\n 'national_id_document': self.national_id_document,\n 'country': self.country,\n 'name': self.name,\n 'surname': self.surname,\n 'mail': self.mail,\n 'google_token': self.google_token,\n 'role': self.role\n }","def extract_status(self, status_headers):\n self.status = status_headers.get_statuscode()\n if not self.status:\n self.status = '-'","def status_json():\n status_map = {key : service.current() for key, service in SERVICES.items()}\n return jsonify({\n \"statuses\": [{\"key\": key, \"status\": item[0], \"health\": str(item[1])} for key, item in status_map.items()]\n })","def dump_api(self):\n\n retour = {}\n\n for val in ['when']:\n if getattr(self, val):\n retour[val] = str(localtime(getattr(self, val)))\n else:\n retour[val] = ''\n\n for val in ['extra_data', 'log_type']:\n retour[val] = str(getattr(self, val))\n\n for cal, name in [('get_log_type_display', 'log_type_text')]:\n retour[name] = getattr(self, cal)()\n\n return retour","def status(self, job_id: str) -> dict:\n session = self._session()\n response = session.get(self._status_url(job_id))\n if response.ok:\n fields = [\n 'status', 'message', 'progress', 'createdAt', 'updatedAt', 'request',\n 'numInputGranules'\n ]\n status_subset = {k: v for k, v in response.json().items() if k in fields}\n return {\n 'status': status_subset['status'],\n 'message': status_subset['message'],\n 'progress': status_subset['progress'],\n 'created_at': dateutil.parser.parse(status_subset['createdAt']),\n 'updated_at': dateutil.parser.parse(status_subset['updatedAt']),\n 'request': status_subset['request'],\n 'num_input_granules': int(status_subset['numInputGranules']),\n }\n else:\n response.raise_for_status()","def _get_status(self, context, object_list=None):\n status = self.request.GET.get(\"status\", \"\").upper()\n\n if object_list is not None:\n return self._get_object_list(\n object_list, status != \"\" and JobStatus.is_member(status), status=status\n )\n\n options = list(map(lambda s: (s.name, s.value), JobStatus))\n\n return {\n **context,\n \"status_options\": sorted(options, key=lambda x: x[0]),\n \"status\": status,\n }","def serialize(cls, status=None):\n if status is None:\n tasks = cls.objects.order_by('entry')\n else:\n tasks = cls.objects.filter(status=status).order_by('entry')\n\n data = ''\n for task in tasks:\n data += encode_task(task.todict())\n\n return data","def get_status_stat(qs):\n return [ { 'status':'total', 'count':qs.count() } ] +\\\n [ { 'status':str(x['status']), 'count':str(x['count']) }\n for x in qs.values('status').annotate(count=Count('id')) ]","def getStatus():","def GetStatus(self):\r\n return self.status","def serialize(self):\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"detail\": self.detail,\n \"date_on\": self.date_on,\n }","def status_detail(self):\n return self._status_detail","def dump_pre(self,status):\n pass","def _decode_sensor_status(self, status: str) -> str:\n k = int(status)\n return self.SENSOR_STATUSES[k]","def set_properties_from_api_repr(self, resource):\n self.name = resource.get('name')\n self.number = resource['projectNumber']\n self.labels = resource.get('labels', {})\n self.status = resource['lifecycleState']","def status(self):","def _get_log_status(self):\n log_status = rdBase.LogStatus()\n log_status = {st.split(\":\")[0]: st.split(\":\")[1] for st in log_status.split(\"\\n\")}\n log_status = {k: True if v == \"enabled\" else False for k, v in log_status.items()}\n return log_status","def serialize(self):\n\t\treturn { 'client_id': self.client_id, 'is_in_error' : self.is_in_error, 'error_status' : self.error_status }","def extra_state_attributes(self):\n return {\n \"stage_coct\": self.coordinator.data.get(\"stage\"),\n \"stage_eskom\": self.coordinator.data.get(\"stage_eskom\"),\n \"coct_area\": self.coordinator.data.get(\"coct_area\"),\n \"load_shedding_active\": self.coordinator.data.get(\"load_shedding_active\"),\n \"next_load_shedding_slot\": self.coordinator.data.get(\"next_load_shedding_slot\"),\n \"next_stage\": self.coordinator.data.get(\"next_stage\"),\n \"next_stage_start_time\": self.coordinator.data.get(\"next_stage_start_time\"),\n \"last_updated\": self.coordinator.data.get(\"last_updated\"),\n \"today_slots\": self.coordinator.data.get(\"today_slots\"),\n \"tomorrow_slots\": self.coordinator.data.get(\"tomorrow_slots\"),\n }","def json(self):\n\n json = {}\n json['type'] = self.type\n json['value'] = self.value\n json['status'] = self.status\n json['tags'] = list(set(self.tags))\n json['relationships'] = list(set(self.relationships))\n json['whitelisted'] = self.whitelisted\n json['path'] = self.path\n\n return json","def get_status_from_instructor_task(instructor_task):\r\n status = {}\r\n\r\n if instructor_task is not None:\r\n # status basic information matching what's stored in InstructorTask:\r\n status['task_id'] = instructor_task.task_id\r\n status['task_state'] = instructor_task.task_state\r\n status['in_progress'] = instructor_task.task_state not in READY_STATES\r\n if instructor_task.task_output is not None:\r\n status['task_progress'] = json.loads(instructor_task.task_output)\r\n\r\n return status","def _add_std_fields(self, json):\n\t\tif \"created_by\" in json and json[\"created_by\"] is not None:\n\t\t\tself.creator = json[\"created_by\"][\"name\"]\n\n\t\tif \"created_on\" in json and json[\"created_on\"] is not None:\n\t\t\tcreated_field = json[\"created_on\"]\n\t\t\tif isinstance(created_field, dict):\n\t\t\t\tself.created_on = created_field[\"formatted\"]\n\t\t\telif isinstance(created_field, basestring):\n\t\t\t\tself.created_on = created_field","def _write_status(self, status, cls=MySQLStatus):","def _user_status():\n rv = {\n 'messages': map(unicode, get_flashed_messages()),\n }\n if current_user.is_anonymous:\n rv.update({\n 'logged_id': False\n })\n else:\n rv.update({\n 'logged_in': True,\n 'name': current_user.display_name\n })\n return rv","def getInfoOnStatus(self):\n raise NotImplementedError();","def to_representation(self, object):\n\n finished = object.finished or None\n\n if finished is not None:\n finished = str(finished)\n\n return {\n 'id': object.id,\n 'barcode': object.barcode,\n 'started': str(object.started),\n 'finished': finished,\n 'properties': object.properties\n }","def get_object_status(obj):\n return get_object_parameter(obj, 'status')","def __getstate__(self):\n\n # Get data in long format\n long = self.long_format()\n return {\"date\": self.date,\n \"persons\": long\n }","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(PrecisEngineTaskStatusProgress, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def _GetStatusFromOp(op):\n for prop in op.response.additionalProperties:\n if prop.key == 'status':\n return prop.value.string_value\n return 'UNKNOWN'","def add_statistics_to_status(status):\n return [{\n 'host': h['host'],\n 'status': h['status'],\n 'stats': get_statistics_for_host(h['host']),\n } for h in status]","def printStatus(self, status):\n \"\"\" This probably breaks SOI \"\"\"\n\n if hasattr(self, 'show_state') and self.show_state:\n print(\"=============== \" + str(status[\"count\"]) + \" ===============\")\n print(\"Current best energy: \" + str(status[\"bestEnergy\"]) + \" from state: \" + str(status[\"bestState\"]))\n print(\"last accepted energy: \" + str(status[\"energy\"]) + \" from state: \" + str(status[\"state\"]))\n print(\"current temperature: \" + str(status[\"temperature\"]))\n else:\n print(\"=============== \" + str(status[\"count\"]) + \" ===============\")\n print(\"Current best energy: \" + str(status[\"bestEnergy\"]) )\n print(\"last accepted energy: \" + str(status[\"energy\"]) )\n print(\"current temperature: \" + str(status[\"temperature\"]))"],"string":"[\n \"def extract_status(self, status) -> None:\\r\\n if \\\"VehicleInfo\\\" in status:\\r\\n if \\\"RemoteHvacInfo\\\" in status[\\\"VehicleInfo\\\"]:\\r\\n self.hvac = status[\\\"VehicleInfo\\\"][\\\"RemoteHvacInfo\\\"]\\r\\n\\r\\n if \\\"ChargeInfo\\\" in status[\\\"VehicleInfo\\\"]:\\r\\n self.battery = status[\\\"VehicleInfo\\\"][\\\"ChargeInfo\\\"]\",\n \"def get_status(self, torrent_id, fields):\\n status = {}\\n for field in fields:\\n try:\\n status[field] = self.status_fields[field](torrent_id)\\n except KeyError:\\n pass\\n return status\",\n \"def status(self, status: dict):\\n pass\",\n \"def status(self, additional=[]): # pylint: disable=dangerous-default-value\\n self.manager.refresh_client()\\n fields = [\\\"batteryLevel\\\", \\\"deviceDisplayName\\\", \\\"deviceStatus\\\", \\\"name\\\"]\\n fields += additional\\n properties = {}\\n for field in fields:\\n properties[field] = self.content.get(field)\\n return properties\",\n \"def _extract_info(self, data):\\n if 'status' in data:\\n if data['status'] == 'Already Downloaded':\\n # Set self._return_code to already downloaded\\n # and trash that key\\n self._set_returncode(self.ALREADY)\\n data['status'] = None\\n\\n if data['status'] == 'Filesize Abort':\\n # Set self._return_code to filesize abort\\n # and trash that key\\n self._set_returncode(self.FILESIZE_ABORT)\\n data['status'] = None\",\n \"def status() -> Dict[str, Any]:\",\n \"def as_dict(self):\\n return {\\\"status\\\": self.status.AsDict(),\\n \\\"metadata\\\": self.metadata.as_dict}\",\n \"def on_status(self, status):\\n\\n status = status._json\\n\\n # Only worry about original tweets\\n if not status[\\\"is_quote_status\\\"] and \\\"retweeted_status\\\" not in status \\\\\\n and not status[\\\"in_reply_to_status_id\\\"]:\\n\\n # Take only a subset of fields\\n doc = {\\n \\\"text\\\": status[\\\"full_text\\\"] if \\\"full_text\\\" in status else status[\\\"text\\\"],\\n \\\"screen_name\\\": status[\\\"user\\\"][\\\"screen_name\\\"],\\n \\\"verified\\\": status[\\\"user\\\"][\\\"verified\\\"],\\n \\\"created_at\\\": status[\\\"created_at\\\"]\\n }\\n\\n # Just to check\\n print(doc['text'])\\n\\n # Store it\\n self.write_to_db(doc)\",\n \"def data_record_to_api_status(data):\\r\\n\\tdata_web_view = data_record_to_web_view(data)\\r\\n\\tdata = {\\r\\n\\t\\t\\\"request_id\\\": data[\\\"request_id\\\"],\\r\\n\\t\\t\\\"url\\\": data[\\\"url\\\"],\\r\\n\\t\\t\\\"status\\\": data_web_view[\\\"status\\\"],\\r\\n\\t\\t\\\"screenshot_available\\\": data_web_view[\\\"screenshot_available\\\"],\\r\\n\\t\\t\\\"proof_available\\\": data_web_view[\\\"proof_available\\\"],\\r\\n\\t\\t\\\"timestamp\\\": data[\\\"timestamp\\\"],\\r\\n\\t\\t\\\"prune_timestamp\\\": data[\\\"prune_timestamp\\\"]\\r\\n\\t}\\r\\n\\treturn data\",\n \"def convert_dicts_in_status_to_obj(status: Status) -> Status:\\n keys_to_update = [\\\"urls\\\", \\\"user\\\", \\\"user_mentions\\\", \\\"quoted_status\\\"]\\n for key in keys_to_update:\\n if key == \\\"urls\\\":\\n status.urls = [Url(**url) for url in status.__getattribute__(key)]\\n elif key == \\\"user\\\":\\n status.user = User(**status.__getattribute__(key))\\n elif key == \\\"user_mentions\\\":\\n status.user_mentions = [\\n User(**user) for user in status.__getattribute__(key)\\n ]\\n elif key == \\\"quoted_status\\\":\\n status.quoted_status = (\\n convert_dicts_in_status_to_obj(\\n status=Status(**status.__getattribute__(key))\\n )\\n if status.__getattribute__(key)\\n else None\\n )\\n return status\",\n \"def to_dict(self) -> Dict[str, Any]:\\n return {\\\"status\\\": self.status.name, \\\"message\\\": self.message}\",\n \"def _do_status(self) -> Dict[str, Any]:\\n return {}\",\n \"def _do_status(self) -> Dict[str, Any]:\\n return {}\",\n \"def additional_data(self, request, qs):\\n status_stat = get_status_stat(qs)\\n return { 'task_stat' : status_stat }\",\n \"def get_status_data():\\n\\n return dict(\\n celery=_get_celery_status(),\\n users=_get_users_stats(),\\n daemons=_get_daemons_status(),\\n settings=_get_settings(),\\n files=_get_files_status(),\\n languages=_get_languages_status(),\\n sets=_get_sets_status(),\\n )\",\n \"def load_status(self):\\n # jIn = os.path.join(self.meta[\\\"wdir\\\"], meta_file)\\n with open(self.meta_filepath, \\\"r\\\") as f:\\n tmp = json.load(f)\\n if \\\"status\\\" in tmp.keys():\\n # self.meta[\\\"status\\\"] = tmp[\\\"status\\\"]\\n return tmp[\\\"status\\\"]\",\n \"def status():\\n statuses = get_all_statuses()\\n return json.dumps(statuses, indent=4)\",\n \"def as_dict(self) -> dict:\\r\\n return {\\r\\n \\\"alias\\\": self.alias,\\r\\n \\\"vin\\\": self.vin,\\r\\n \\\"details\\\": self.details,\\r\\n \\\"status\\\": {\\r\\n \\\"battery\\\": self.battery,\\r\\n \\\"hvac\\\": self.hvac,\\r\\n \\\"odometer\\\": self.odometer,\\r\\n \\\"parking\\\": self.parking,\\r\\n },\\r\\n \\\"servicesEnabled\\\": self.services,\\r\\n }\",\n \"def get_status(self):\\n\\n status = \\\"\\\"\\\"\\n-----------------GENERAL USER INFO-----------------\\n Name: {name}\\n Age: {age}\\n Age Range: {age_range}\\n Height: {height}\\n Weight: {weight}\\n Pregnancy Status: {pregnancy_status}\\n Breastfeeding Status: {breastfeeding_status}\\n\\n-----------------USER'S RECOMMENDED DAILY VITAMIN AND MINERAL COUNT-------------\\n (in milligrams, mg)\\n Vitamin A: {user_vitamin_a}\\n Vitamin B6: {user_vitamin_b_6}\\n Vitamin B12: {user_vitamin_b_12}\\n Vitamin C: {user_vitamin_c}\\n Vitamin D: {user_vitamin_d}\\n Vitamin E: {user_vitamin_e}\\n \\n Calcium: {user_calcium}\\n Iron: {user_iron}\\n Magnesium: {user_magnesium}\\n Potassium: {user_potassium}\\n Zinc: {user_zinc}\\n \\\"\\\"\\\"\\n return status.format(name=self.name, age=self.age, age_range=self.age_range, height=self.height, weight=self.weight, pregnancy_status=self.pregnancy_status, \\n breastfeeding_status=self.breastfeeding_status, user_vitamin_a=self.user_vitamin_a,\\n user_vitamin_b_6=self.user_vitamin_b_6, user_vitamin_b_12=self.user_vitamin_b_12, user_vitamin_c=self.user_vitamin_c, user_vitamin_d=self.user_vitamin_d, user_vitamin_e=self.user_vitamin_e,\\n user_calcium=self.user_calcium, user_iron=self.user_iron, user_magnesium=self.user_magnesium, user_potassium=self.uesr_potassium, user_zinc=self.user_zinc)\",\n \"def lineup_status_json() -> Response:\\n if station_scan:\\n lineup_status = {\\n \\\"ScanInProgress\\\": True,\\n \\\"Progress\\\": 50,\\n \\\"Found\\\": 5\\n }\\n else:\\n lineup_status = {\\n \\\"ScanInProgress\\\": False,\\n \\\"ScanPossible\\\": True,\\n \\\"Source\\\": \\\"Antenna\\\",\\n \\\"SourceList\\\": [\\\"Antenna\\\"]\\n }\\n return jsonify(lineup_status)\",\n \"def status_to_query(status):\\n if status == 'changed':\\n return {\\n 'status': 'ok',\\n 'changed': True,\\n }\\n else:\\n return {\\n 'status': status,\\n }\",\n \"def status_feedback(self):\\n return {\\n TAG_ADDRESS: self.address,\\n ATTR_STATUS: self._status,\\n ATTR_INIT: self._init,\\n ATTR_PRECISION: self._prec,\\n ATTR_TS: self._last_edited,\\n ATTR_LAST_CHANGED: self._last_changed,\\n ATTR_LAST_UPDATE: self._last_update,\\n }\",\n \"def to_json_dict(self):\\n result = {\\\"code\\\": self.code, \\\"state\\\": self.state, \\\"message\\\": self.message}\\n if self.extra_fields:\\n result.update(self.extra_fields)\\n return result\",\n \"def extract_fields(self, json_dict):\\n raise NotImplementedError()\",\n \"def _get_details(self, details):\\n details['DoT'] = \\\"Yes\\\" if self.static else \\\"No\\\"\\n details['device'] = self.device\\n details['volume_id'] = self.volume_id\\n details['from_snap'] = \\\"No\\\" if not self.from_snapshot_id else self.from_snapshot_id\\n details['from_archive'] = \\\"No\\\" if not self.from_archive else self.from_archive['url']\\n details['snapshot_progress'] = self.snapshot_progress\\n details['snapshot_status'] = self.snapshot_status\\n # TODO: keep track of any errors\\n details['err_msg'] = None if details.get('err_msg', '') == '' else details['err_msg']\\n details['snapshots_created'] = self.snapshots_created\\n return details\",\n \"def get_server_status(fields=[]):\\n return get_dict_from_db(key='status', fields=fields)\",\n \"def getStatus():\\n return json.dumps({'camera': Camera.status(), 'rover': rover.status()}), 200\",\n \"def rest_status():\\n my_dict = set_status(None)\\n return my_dict\",\n \"def serialize(self):\\n return {\\n 'id': self.id,\\n 'code': self.code,\\n 'email': self.email,\\n 'status': self.status.value,\\n 'organization_id': self.organization_id\\n }\",\n \"def status(self) -> dict[str, str] | None:\\n return self._status\",\n \"def base_json(self):\\n\\n return {\\n \\\"valid\\\": self.is_valid,\\n \\\"action\\\": self.action,\\n \\\"type\\\": self.entity_type,\\n \\\"id\\\": self.entity_id,\\n \\\"errors\\\": self.errors,\\n \\\"warnings\\\": self.warnings,\\n \\\"unique_keys\\\": self.secondary_keys_dicts,\\n }\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'sid' : self.sid,\\n 'status' : self.status,\\n 'duration' : self.duration,\\n 'recurl' : self.recurl,\\n 'dateTime' : self.dateTime\\n }\",\n \"def _get_status(self):\\n return self.__status\",\n \"def status(self):\\n return {\\n 'id': 'status',\\n 'protocol_version': 'PV62',\\n 'network': self.origin_node.network.name,\\n 'td': self.origin_node.chain.head.header.difficulty,\\n 'best_hash': self.origin_node.chain.head.header.hash,\\n 'genesis_hash': self.origin_node.chain.genesis.header.hash,\\n 'size': kB_to_MB(self._message_size['status'])\\n }\",\n \"def collect(self):\\n self.status['serial'] = self.config.get('dlmconfig', 'serial')\\n self.status['timestamp'] = time.strftime('%Y/%m/%d %H:%M:%S', time.localtime())\\n self.status['uptime'] = system.stats.uptime()\\n self.status['free_disk_space_sdcard'] = system.stats.disk_usage('root')\\n self.status['free_disk_space_stick'] = system.stats.disk_usage('sda1')\\n self.status['wwan_reception'] = system.interfaces.WwanInterface.signal_strength(self.config.get('network', 'iface'))\",\n \"def _process_info(resp: suds.sudsobject) -> dict:\\n last = resp.ultimoValor\\n return dict(fonte = str(resp.fonte),\\n gestor = str(resp.gestorProprietario),\\n freq = str(resp.periodicidadeSigla),\\n nome = str(resp.nomeCompleto),\\n number = int(resp.oid),\\n final = dt(last.ano, last.mes, last.dia))\",\n \"def status(_):\\n return {\\\"status\\\": \\\"ok\\\"}\",\n \"def as_dict(self):\\n if self._last_changed:\\n last_changed_isoformat = self._last_changed.isoformat()\\n else:\\n last_changed_isoformat = process_timestamp_to_utc_isoformat(\\n self._row.last_changed\\n )\\n if self._last_updated:\\n last_updated_isoformat = self._last_updated.isoformat()\\n else:\\n last_updated_isoformat = process_timestamp_to_utc_isoformat(\\n self._row.last_updated\\n )\\n return {\\n \\\"entity_id\\\": self.entity_id,\\n \\\"state\\\": self.state,\\n \\\"attributes\\\": self._attributes or self.attributes,\\n \\\"last_changed\\\": last_changed_isoformat,\\n \\\"last_updated\\\": last_updated_isoformat,\\n }\",\n \"def test_get_status(self):\\n resp = self.build_api.getStatus().json()\\n assert 'status' in resp\\n assert 'message' in resp\",\n \"def save_object(self, data):\\n return Status(**data)\",\n \"def json_dump(self):\\n return {\\n \\\"id\\\":self.id,\\n \\\"firstname\\\": self.firstname,\\n \\\"lastname\\\": self.lastname,\\n \\\"othername\\\": self.othername,\\n \\\"email\\\": self.email,\\n \\\"phonenumber\\\": self.phonenumber,\\n \\\"passporturl\\\": self.passporturl,\\n \\\"roles\\\": self.roles,\\n \\\"nationalid\\\": self.nationalid,\\n \\\"county\\\": self.county,\\n \\\"date_created\\\": self.date_created,\\n \\\"date_modified\\\": self.date_modified\\n }\",\n \"async def get_status(self):\\n data = await self.gen_req(self.status_url)\\n return data['data']['attributes']['releasedAt'], data['data']['attributes']['version']\",\n \"def to_json(self):\\n return {\\n 'revision': self.revision,\\n 'active': self.active,\\n 'archived_at': self.archived_at\\n }\",\n \"def _init_status(self):\\n\\n status = self._get_status_obj()\\n\\n for i, step in enumerate(self._run_list):\\n\\n for module in step.keys():\\n module_dict = {module: {'pipeline_index': i}}\\n status.data = status.update_dict(status.data, module_dict)\\n\\n status._dump()\",\n \"def status(self):\\n return STATUS[self.fields['status']]\",\n \"def _get_status(self):\\n if self._state in [\\\"processed\\\", \\\"error\\\"]:\\n return self._state\\n \\n get_resp = requests.get(self.location, cookies={\\\"session\\\": self.session})\\n\\n self._state = get_resp.json()[\\\"status\\\"]\\n self.slice_time = get_resp.json()[\\\"slice_time\\\"]\\n \\n return self._state\",\n \"def _export(self, filled_only=False, without_id=False):\\n response = dict()\\n for field in self.fields:\\n if filled_only and not self.fields.get(field).get():\\n continue\\n if field == '_id' and without_id:\\n continue\\n response[field] = self.fields.get(field).get()\\n return response\",\n \"def _update(self, data):\\n self.status = data['status']\\n self.progress = data['progress']\",\n \"def success_rep2(self)->str:\\n strings = []\\n json_dict = self.to_json()\\n for fld in fields(self):\\n # val = getattr(self, fld.name)\\n val = json_dict.get(fld.name, None)\\n if val:\\n str1 = f\\\"{fld.name.title().replace('_',' ')}: {val}\\\"\\n strings.append(str1)\\n return \\\", \\\".join(strings)\",\n \"def UpdateFromResponse(self, response):\\n for key in self.status:\\n self.status[key] = response[key]\",\n \"def status(self):\\n status = {\\n 'driver':self.driver.status,\\n 'arduino':self.arduino.status,\\n 'sensors':[]}\\n\\n for name, sensor in self.sensors.items():\\n sensor_status = sensor.status\\n sensor_status['name'] = name\\n status['sensors'].append(sensor_status)\\n\\n return status\",\n \"def to_dict(self) -> Dict[str, Any]:\\n\\n fields: Dict[str, Any] = {}\\n if hasattr(self, \\\"url\\\"):\\n fields[\\\"url\\\"] = self.url\\n if hasattr(self, \\\"last_check\\\"):\\n fields[\\\"last_check\\\"] = self.last_check\\n if hasattr(self, \\\"param1\\\"):\\n fields[\\\"param1\\\"] = self.param1\\n return fields\",\n \"def check_status():\\n logger.debug(\\\"Starting the check_status() routine.\\\")\\n\\n url = \\\"https://www.toggl.com/api/v8/time_entries/current\\\"\\n token = os.environ[\\\"TOGGL_API_TOKEN\\\"]\\n auth_token = base64.b64encode(f\\\"{token}:api_token\\\".encode()).decode()\\n resp = requests.get(url, headers={\\\"Authorization\\\": \\\"Basic \\\" + auth_token})\\n\\n cols = \\\"id\\\", \\\"duration\\\", \\\"description\\\"\\n status = {k: v for k, v in (resp.json()[\\\"data\\\"] or {}).items() if k in cols}\\n logger.debug(f\\\"{'Something' if 'id' in status else 'Something'} is being tracked.\\\")\\n\\n return status\",\n \"def get_status(self):\\n r = requests.get(self.base_url + '/status')\\n return r.json()\",\n \"def __repr__(self):\\n return {\\n 'username': self.createdBy,\\n 'createdOn': self.createdOn,\\n 'status': self.status\\n }\",\n \"def new_substatus_json_for_operation(operation_name, status=\\\"Transitioning\\\", code=Constants.ExitCode.Okay, message=json.dumps(\\\"{}\\\")):\\n # NOTE: Todo Function is same for assessment and install, can be generalized later\\n return {\\n \\\"name\\\": str(operation_name),\\n \\\"status\\\": str(status).lower(),\\n \\\"code\\\": code,\\n \\\"formattedMessage\\\": {\\n \\\"lang\\\": \\\"en-US\\\",\\n \\\"message\\\": str(message)\\n }\\n }\",\n \"def _extract_relevant_fields(self, obj):\\n try:\\n self.uuid = obj.getUUID()\\n except Exception:\\n pass\\n try:\\n self.meta_type = obj.meta_type\\n except Exception:\\n pass\\n try:\\n self.name = obj.titleOrId()\\n except Exception:\\n pass\\n\\n from Products.ZenModel.Device import Device\\n\\n if isinstance(obj, Device):\\n self.is_device = True\\n try:\\n self.mem_capacity = obj.hw.totalMemory\\n except Exception:\\n pass\\n\\n from Products.ZenModel.DeviceComponent import DeviceComponent\\n\\n if isinstance(obj, DeviceComponent):\\n self.is_device_component = True\\n\\n # Get extra context from IObjectMapContextProvider adapters.\\n for provider in subscribers([obj], IObjectMapContextProvider):\\n try:\\n merge_fields(self.dimensions, provider.get_dimensions(obj))\\n except Exception as e:\\n log.error(\\n \\\"%s failed to get dimensions for %s: %s\\\", provider, obj, e\\n )\\n\\n try:\\n merge_fields(self.metadata, provider.get_metadata(obj))\\n except Exception as e:\\n log.error(\\n \\\"%s failed to get metadata for %s: %s\\\", provider, obj, e\\n )\",\n \"def status(self):\\n\\n resp = requests.get(\\\"http://{0}/get_status.cgi\\\".format(self.address))\\n data = resp.text.replace(\\\";\\\", \\\"\\\")\\n data = data.replace(\\\"var\\\", \\\"\\\")\\n data_s = data.split(\\\"\\\\n\\\")\\n # Last is an empty line \\n data_s.pop()\\n data_array = [s.split(\\\"=\\\") for s in data_s]\\n return dict(data_array)\",\n \"def json(self) -> dict:\\n return {\\n 'id': self.id,\\n 'requestType': self.request_type.name,\\n 'isProcessed': self.is_processed,\\n 'serviceName': self.service_name.name,\\n 'isAdmin': self.is_admin,\\n 'creationDate': LegislationDatetime.as_legislation_timezone(self.creation_date).isoformat()\\n }\",\n \"def get_json(self):\\n return {'name': self.name, \\n 'path': self.path, \\n 'enabled': self.enabled}\",\n \"def status(self):\\n r = requests.get('/'.join([self.base_url, self.ENDPOINT_STATUS]))\\n return r.json()\",\n \"def get_text_status(json):\\n if json is None:\\n return None\\n elif 'statusdetail' in json:\\n return json['statusdetail']\\n elif 'status' in json:\\n return json['status']\\n else:\\n return None\",\n \"def json(self):\\n data = {\\n f: getattr(self, self._custom_key_transform(self._from_api_conversion(f))) for f in self.fields\\n }\\n if \\\"timestamp\\\" in data and not isinstance(data[\\\"timestamp\\\"], str):\\n data[\\\"timestamp\\\"] = data[\\\"timestamp\\\"].strftime(\\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n return data\",\n \"def get_status():\\n data = {\\n 'status': 'up',\\n }\\n jsn = json.dumps(data)\\n\\n resp = Response(jsn, status=200, mimetype='application/json')\\n\\n return resp\",\n \"def dump_api(self, add_config=False):\\n\\n retour = {}\\n\\n for val in ['reference', 'extra_data', 'amount', 'postfinance_id', 'postfinance_status', 'internal_status', 'ipn_needed', 'brand', 'card']:\\n retour[val] = str(getattr(self, val))\\n\\n for val in ['creation_date', 'last_userforwarded_date', 'last_user_back_from_postfinance_date', 'last_postfinance_ipn_date', 'last_ipn_date']:\\n if getattr(self, val):\\n retour[val] = str(localtime(getattr(self, val)))\\n else:\\n retour[val] = ''\\n\\n for cal, name in [('get_postfinance_status_display', 'postfinance_status_text'), ('get_internal_status_display', 'internal_status_text'), ('amount_chf', 'amount_chf')]:\\n retour[name] = getattr(self, cal)()\\n\\n if add_config:\\n retour['config'] = self.config.name\\n\\n return retour\",\n \"def status(self):\\n return self._data['status']\",\n \"def progress_data(self) -> Dict[str, Any]:\\n result: Dict[str, Union[Dict[str, Union[str, int]], List[Tuple[str, str]], str]] = {\\n 'progress': {\\n 'state': 'error',\\n 'step': -1\\n },\\n 'stages': TestStatus.stages(),\\n 'start': '-',\\n 'end': '-'\\n }\\n\\n if len(self.progress) > 0:\\n result['start'] = self.progress[0].timestamp\\n last_status = self.progress[-1]\\n\\n if last_status.status in [TestStatus.completed, TestStatus.canceled]:\\n result['end'] = last_status.timestamp\\n\\n if last_status.status == TestStatus.canceled:\\n if len(self.progress) > 1:\\n result['progress']['step'] = TestStatus.progress_step(self.progress[-2].status) # type: ignore\\n\\n else:\\n result['progress']['state'] = 'ok' # type: ignore\\n result['progress']['step'] = TestStatus.progress_step(last_status.status) # type: ignore\\n\\n return result\",\n \"def extra_dejson(self):\\n obj = {}\\n if self.extra:\\n try:\\n obj = json.loads(self.extra)\\n except Exception as e:\\n self.log.exception(e)\\n self.log.error(\\\"Failed parsing the json for conn_id %s\\\", self.conn_id)\\n\\n return obj\",\n \"def json(self):\\n return {\\n 'id': self.id,\\n 'id_bank_data': self.id_bank_data,\\n 'national_id_document': self.national_id_document,\\n 'country': self.country,\\n 'name': self.name,\\n 'surname': self.surname,\\n 'mail': self.mail,\\n 'google_token': self.google_token,\\n 'role': self.role\\n }\",\n \"def extract_status(self, status_headers):\\n self.status = status_headers.get_statuscode()\\n if not self.status:\\n self.status = '-'\",\n \"def status_json():\\n status_map = {key : service.current() for key, service in SERVICES.items()}\\n return jsonify({\\n \\\"statuses\\\": [{\\\"key\\\": key, \\\"status\\\": item[0], \\\"health\\\": str(item[1])} for key, item in status_map.items()]\\n })\",\n \"def dump_api(self):\\n\\n retour = {}\\n\\n for val in ['when']:\\n if getattr(self, val):\\n retour[val] = str(localtime(getattr(self, val)))\\n else:\\n retour[val] = ''\\n\\n for val in ['extra_data', 'log_type']:\\n retour[val] = str(getattr(self, val))\\n\\n for cal, name in [('get_log_type_display', 'log_type_text')]:\\n retour[name] = getattr(self, cal)()\\n\\n return retour\",\n \"def status(self, job_id: str) -> dict:\\n session = self._session()\\n response = session.get(self._status_url(job_id))\\n if response.ok:\\n fields = [\\n 'status', 'message', 'progress', 'createdAt', 'updatedAt', 'request',\\n 'numInputGranules'\\n ]\\n status_subset = {k: v for k, v in response.json().items() if k in fields}\\n return {\\n 'status': status_subset['status'],\\n 'message': status_subset['message'],\\n 'progress': status_subset['progress'],\\n 'created_at': dateutil.parser.parse(status_subset['createdAt']),\\n 'updated_at': dateutil.parser.parse(status_subset['updatedAt']),\\n 'request': status_subset['request'],\\n 'num_input_granules': int(status_subset['numInputGranules']),\\n }\\n else:\\n response.raise_for_status()\",\n \"def _get_status(self, context, object_list=None):\\n status = self.request.GET.get(\\\"status\\\", \\\"\\\").upper()\\n\\n if object_list is not None:\\n return self._get_object_list(\\n object_list, status != \\\"\\\" and JobStatus.is_member(status), status=status\\n )\\n\\n options = list(map(lambda s: (s.name, s.value), JobStatus))\\n\\n return {\\n **context,\\n \\\"status_options\\\": sorted(options, key=lambda x: x[0]),\\n \\\"status\\\": status,\\n }\",\n \"def serialize(cls, status=None):\\n if status is None:\\n tasks = cls.objects.order_by('entry')\\n else:\\n tasks = cls.objects.filter(status=status).order_by('entry')\\n\\n data = ''\\n for task in tasks:\\n data += encode_task(task.todict())\\n\\n return data\",\n \"def get_status_stat(qs):\\n return [ { 'status':'total', 'count':qs.count() } ] +\\\\\\n [ { 'status':str(x['status']), 'count':str(x['count']) }\\n for x in qs.values('status').annotate(count=Count('id')) ]\",\n \"def getStatus():\",\n \"def GetStatus(self):\\r\\n return self.status\",\n \"def serialize(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"detail\\\": self.detail,\\n \\\"date_on\\\": self.date_on,\\n }\",\n \"def status_detail(self):\\n return self._status_detail\",\n \"def dump_pre(self,status):\\n pass\",\n \"def _decode_sensor_status(self, status: str) -> str:\\n k = int(status)\\n return self.SENSOR_STATUSES[k]\",\n \"def set_properties_from_api_repr(self, resource):\\n self.name = resource.get('name')\\n self.number = resource['projectNumber']\\n self.labels = resource.get('labels', {})\\n self.status = resource['lifecycleState']\",\n \"def status(self):\",\n \"def _get_log_status(self):\\n log_status = rdBase.LogStatus()\\n log_status = {st.split(\\\":\\\")[0]: st.split(\\\":\\\")[1] for st in log_status.split(\\\"\\\\n\\\")}\\n log_status = {k: True if v == \\\"enabled\\\" else False for k, v in log_status.items()}\\n return log_status\",\n \"def serialize(self):\\n\\t\\treturn { 'client_id': self.client_id, 'is_in_error' : self.is_in_error, 'error_status' : self.error_status }\",\n \"def extra_state_attributes(self):\\n return {\\n \\\"stage_coct\\\": self.coordinator.data.get(\\\"stage\\\"),\\n \\\"stage_eskom\\\": self.coordinator.data.get(\\\"stage_eskom\\\"),\\n \\\"coct_area\\\": self.coordinator.data.get(\\\"coct_area\\\"),\\n \\\"load_shedding_active\\\": self.coordinator.data.get(\\\"load_shedding_active\\\"),\\n \\\"next_load_shedding_slot\\\": self.coordinator.data.get(\\\"next_load_shedding_slot\\\"),\\n \\\"next_stage\\\": self.coordinator.data.get(\\\"next_stage\\\"),\\n \\\"next_stage_start_time\\\": self.coordinator.data.get(\\\"next_stage_start_time\\\"),\\n \\\"last_updated\\\": self.coordinator.data.get(\\\"last_updated\\\"),\\n \\\"today_slots\\\": self.coordinator.data.get(\\\"today_slots\\\"),\\n \\\"tomorrow_slots\\\": self.coordinator.data.get(\\\"tomorrow_slots\\\"),\\n }\",\n \"def json(self):\\n\\n json = {}\\n json['type'] = self.type\\n json['value'] = self.value\\n json['status'] = self.status\\n json['tags'] = list(set(self.tags))\\n json['relationships'] = list(set(self.relationships))\\n json['whitelisted'] = self.whitelisted\\n json['path'] = self.path\\n\\n return json\",\n \"def get_status_from_instructor_task(instructor_task):\\r\\n status = {}\\r\\n\\r\\n if instructor_task is not None:\\r\\n # status basic information matching what's stored in InstructorTask:\\r\\n status['task_id'] = instructor_task.task_id\\r\\n status['task_state'] = instructor_task.task_state\\r\\n status['in_progress'] = instructor_task.task_state not in READY_STATES\\r\\n if instructor_task.task_output is not None:\\r\\n status['task_progress'] = json.loads(instructor_task.task_output)\\r\\n\\r\\n return status\",\n \"def _add_std_fields(self, json):\\n\\t\\tif \\\"created_by\\\" in json and json[\\\"created_by\\\"] is not None:\\n\\t\\t\\tself.creator = json[\\\"created_by\\\"][\\\"name\\\"]\\n\\n\\t\\tif \\\"created_on\\\" in json and json[\\\"created_on\\\"] is not None:\\n\\t\\t\\tcreated_field = json[\\\"created_on\\\"]\\n\\t\\t\\tif isinstance(created_field, dict):\\n\\t\\t\\t\\tself.created_on = created_field[\\\"formatted\\\"]\\n\\t\\t\\telif isinstance(created_field, basestring):\\n\\t\\t\\t\\tself.created_on = created_field\",\n \"def _write_status(self, status, cls=MySQLStatus):\",\n \"def _user_status():\\n rv = {\\n 'messages': map(unicode, get_flashed_messages()),\\n }\\n if current_user.is_anonymous:\\n rv.update({\\n 'logged_id': False\\n })\\n else:\\n rv.update({\\n 'logged_in': True,\\n 'name': current_user.display_name\\n })\\n return rv\",\n \"def getInfoOnStatus(self):\\n raise NotImplementedError();\",\n \"def to_representation(self, object):\\n\\n finished = object.finished or None\\n\\n if finished is not None:\\n finished = str(finished)\\n\\n return {\\n 'id': object.id,\\n 'barcode': object.barcode,\\n 'started': str(object.started),\\n 'finished': finished,\\n 'properties': object.properties\\n }\",\n \"def get_object_status(obj):\\n return get_object_parameter(obj, 'status')\",\n \"def __getstate__(self):\\n\\n # Get data in long format\\n long = self.long_format()\\n return {\\\"date\\\": self.date,\\n \\\"persons\\\": long\\n }\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(PrecisEngineTaskStatusProgress, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def _GetStatusFromOp(op):\\n for prop in op.response.additionalProperties:\\n if prop.key == 'status':\\n return prop.value.string_value\\n return 'UNKNOWN'\",\n \"def add_statistics_to_status(status):\\n return [{\\n 'host': h['host'],\\n 'status': h['status'],\\n 'stats': get_statistics_for_host(h['host']),\\n } for h in status]\",\n \"def printStatus(self, status):\\n \\\"\\\"\\\" This probably breaks SOI \\\"\\\"\\\"\\n\\n if hasattr(self, 'show_state') and self.show_state:\\n print(\\\"=============== \\\" + str(status[\\\"count\\\"]) + \\\" ===============\\\")\\n print(\\\"Current best energy: \\\" + str(status[\\\"bestEnergy\\\"]) + \\\" from state: \\\" + str(status[\\\"bestState\\\"]))\\n print(\\\"last accepted energy: \\\" + str(status[\\\"energy\\\"]) + \\\" from state: \\\" + str(status[\\\"state\\\"]))\\n print(\\\"current temperature: \\\" + str(status[\\\"temperature\\\"]))\\n else:\\n print(\\\"=============== \\\" + str(status[\\\"count\\\"]) + \\\" ===============\\\")\\n print(\\\"Current best energy: \\\" + str(status[\\\"bestEnergy\\\"]) )\\n print(\\\"last accepted energy: \\\" + str(status[\\\"energy\\\"]) )\\n print(\\\"current temperature: \\\" + str(status[\\\"temperature\\\"]))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6647689","0.6349667","0.60837454","0.6059531","0.6050074","0.5994271","0.59692353","0.59652483","0.59057873","0.59021086","0.58989656","0.5840351","0.5840351","0.5793476","0.57565075","0.567152","0.5638876","0.5635663","0.55974156","0.556527","0.5554442","0.5547243","0.55404836","0.55298173","0.5517178","0.5516947","0.5515729","0.5445762","0.54237026","0.53723747","0.5368714","0.5361061","0.5320117","0.5309704","0.52988946","0.5298758","0.5283391","0.5282944","0.52740335","0.52630967","0.52624005","0.52337927","0.5226831","0.52146626","0.5210169","0.5208887","0.520445","0.51995486","0.51944447","0.5192501","0.51911944","0.51777864","0.51776904","0.5176558","0.51756907","0.5159836","0.5151281","0.5150644","0.51501495","0.5149739","0.5149003","0.51475286","0.5142921","0.5134842","0.5133683","0.5127616","0.5126523","0.5116078","0.51089776","0.510406","0.5100894","0.51001215","0.50942886","0.5093438","0.50866395","0.50590724","0.5057981","0.50529355","0.5051324","0.5050696","0.50505924","0.5048351","0.50431144","0.50387657","0.5029708","0.5025222","0.50242627","0.5019579","0.50169283","0.50131226","0.50131124","0.5010421","0.5006078","0.50023896","0.5001043","0.49996102","0.4998718","0.49986297","0.49874538","0.49870512"],"string":"[\n \"0.6647689\",\n \"0.6349667\",\n \"0.60837454\",\n \"0.6059531\",\n \"0.6050074\",\n \"0.5994271\",\n \"0.59692353\",\n \"0.59652483\",\n \"0.59057873\",\n \"0.59021086\",\n \"0.58989656\",\n \"0.5840351\",\n \"0.5840351\",\n \"0.5793476\",\n \"0.57565075\",\n \"0.567152\",\n \"0.5638876\",\n \"0.5635663\",\n \"0.55974156\",\n \"0.556527\",\n \"0.5554442\",\n \"0.5547243\",\n \"0.55404836\",\n \"0.55298173\",\n \"0.5517178\",\n \"0.5516947\",\n \"0.5515729\",\n \"0.5445762\",\n \"0.54237026\",\n \"0.53723747\",\n \"0.5368714\",\n \"0.5361061\",\n \"0.5320117\",\n \"0.5309704\",\n \"0.52988946\",\n \"0.5298758\",\n \"0.5283391\",\n \"0.5282944\",\n \"0.52740335\",\n \"0.52630967\",\n \"0.52624005\",\n \"0.52337927\",\n \"0.5226831\",\n \"0.52146626\",\n \"0.5210169\",\n \"0.5208887\",\n \"0.520445\",\n \"0.51995486\",\n \"0.51944447\",\n \"0.5192501\",\n \"0.51911944\",\n \"0.51777864\",\n \"0.51776904\",\n \"0.5176558\",\n \"0.51756907\",\n \"0.5159836\",\n \"0.5151281\",\n \"0.5150644\",\n \"0.51501495\",\n \"0.5149739\",\n \"0.5149003\",\n \"0.51475286\",\n \"0.5142921\",\n \"0.5134842\",\n \"0.5133683\",\n \"0.5127616\",\n \"0.5126523\",\n \"0.5116078\",\n \"0.51089776\",\n \"0.510406\",\n \"0.5100894\",\n \"0.51001215\",\n \"0.50942886\",\n \"0.5093438\",\n \"0.50866395\",\n \"0.50590724\",\n \"0.5057981\",\n \"0.50529355\",\n \"0.5051324\",\n \"0.5050696\",\n \"0.50505924\",\n \"0.5048351\",\n \"0.50431144\",\n \"0.50387657\",\n \"0.5029708\",\n \"0.5025222\",\n \"0.50242627\",\n \"0.5019579\",\n \"0.50169283\",\n \"0.50131226\",\n \"0.50131124\",\n \"0.5010421\",\n \"0.5006078\",\n \"0.50023896\",\n \"0.5001043\",\n \"0.49996102\",\n \"0.4998718\",\n \"0.49986297\",\n \"0.49874538\",\n \"0.49870512\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6915547"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85681,"cells":{"query":{"kind":"string","value":"Cleanup the test byproducts."},"document":{"kind":"string","value":"def classCleanup(cls):\n cls.RemoveTempFile(SettingsCommandTestCase.output_file_name)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def tearDown(self):\n self.tmp.cleanup()","def teardown(self):\n\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\n\n return","def cleanup(self):\n super(Test200SmartSanityDownload004, self).cleanup()","def teardown(self):\n del self.testInst, self.dname, self.test_vals, self.test_fracs\n\n return","def tearDown(self):\n self.remove_test_files()","def teardown(self):","def teardown(self):","def teardown(self):","def teardown(self):\n pass","def teardown(self):\n pass","def teardown(self):\n pass","def teardown(self) -> None:","def teardown(self) -> None:","def teardown(self) -> None:","def teardown(self):\n del self.testInst, self.dname\n\n return","def tearDown(cls):\n\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\n # reusable data struct\n cls.test_mmp_series_object.clean_out_data_seriesobj()\n cls.test_dataset_testresults.clear()\n # reusable results file\n # os.remove(cls.temp_file_output_series.name)","def teardown(self):\n del self.testInst, self.dname, self.test_val_length\n\n return","def teardown(self):\n del self.testInst, self.dname, self.test_val_length\n\n return","def tearDown(self):\n self._s1ap_wrapper.cleanup()","def tearDown(self):\n self._s1ap_wrapper.cleanup()","def teardown(self):\n\n del self.testInst, self.test_bins, self.test_label, self.test_data\n del self.out_keys, self.out_data\n\n return","def tearDown(self):\n tests.utils.cleanup_environment()","def tearDown(self):\n tests.utils.cleanup_environment()","def tearDown(self):\n del self.review","def cleanup():","def tearDown(self):\n if self.flavor_creator:\n self.flavor_creator.clean()\n\n super(self.__class__, self).__clean__()","def tearDown(self):\n test_env_teardown()","def cleanup(self):\n\n pass","def tearDown(self):\n ProcessRequest(f\"products/{self.product_id}.json\").send_request(\n 'DELETE', continue_on_error=True)\n super().tearDown()","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def cleanup(self):\n pass","def tearDown(self):\n util.delete_mocks()","def tearDown(self):\n util.delete_mocks()","def tearDown(self):\n util.delete_mocks()","def tearDown(self):\n util.delete_mocks()","def tearDown(self):\n util.delete_mocks()","def tearDown(self):\n util.delete_mocks()","def cleanup(self):","def cleanup(self):","def cleanup(self):","def cleanup (self):\n pass","def tearDown(self):\n if os.path.isdir('/tmp/remote_pacha'):\n shutil.rmtree('/tmp/remote_pacha')\n if os.path.isdir('/tmp/localhost'):\n shutil.rmtree('/tmp/localhost')\n if os.path.isdir('/tmp/test_pacha'):\n shutil.rmtree('/tmp/test_pacha')\n if os.path.isdir('/tmp/pacha_test'):\n shutil.rmtree('/tmp/pacha_test')\n if os.path.isdir('/tmp/pacha_bucket'):\n shutil.rmtree('/tmp/pacha_bucket')\n\n try:\n shutil.rmtree('/tmp/test_pacha')\n shutil.rmtree('/tmp/remote_pacha')\n shutil.rmtree('/tmp/localhost')\n shutil.rmtree('/tmp/single_dir')\n except OSError:\n pass # nevermind if you could not delete this guy","def teardown(self,**kwargs):\n pass","def tearDown(self):\n zope.component.testing.tearDown()","def tearDown(self):\n log.warning(\"Cleaning up after test...\")\n # All of the following artefacts are created: AWSSnapshot, AWSS3Object,\n # GCSObject, GCEImage, GCEDisk\n for c in self.test_state.GetContainers(containers.AWSSnapshot):\n self._removeAWSSnapshot(c.id)\n for c in self.test_state.GetContainers(containers.AWSS3Object):\n self._removeAWSS3Object(c.path)\n for c in self.test_state.GetContainers(containers.GCSObject):\n self._removeGCSObject(c.path)\n for c in self.test_state.GetContainers(containers.GCEImage):\n self._removeGCEImage(c.name)\n for c in self.test_state.GetContainers(containers.GCEDisk):\n self._removeGCEDisk(c.name)","def tearDown(self):\n self.testInit.clearDatabase()\n self.testInit.tearDownCouch()\n self.specGen.removeSpecs()\n return","def test_z_cleanup(self):\n\n TestController.tearDown(\n self,\n clear_all_tables=True,\n del_global_app_set=True,\n dirs_to_destroy=['user', 'morphology', 'corpus', 'morphological_parser'])","def trial_clean_up(self):\n pass","def tearDown(self):\n del self.a","def teardown(self) -> None:\n pass","def teardown(self) -> None:\n pass","def cleanup(self):\r\n pass","def cleanup(self):\r\n pass","def tearDown(self):\n self.clean()\n self.clean_s3()\n super().tearDown()","def tearDown(self):\n server.Inventory.remove_all()","def setUp(self):\n Pet.remove_all()","def cleanUp(self):\r\n pass","def tearDown(self):\n Pics.objects.all().delete()\n Category.objects.all().delete()\n Location.objects.all().delete()","def tearDown(self):\n del self.business_item_class","def cleanup(self, *args, **kwargs):","def tearDown(self):\n self.tempdir.cleanup()","def vtest_ut_cleanup(self):\n shutil.rmtree(self.get_test_file_path(), ignore_errors=True)","def teardown_class(self):\n\n # TODO: If environment variable is set keep the workspace\n # and print out the path.\n global TEST_WORKSPACE\n\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\n 'codechecker_cfg']['check_env']\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\n\n print(\"Removing: \" + TEST_WORKSPACE)\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)","def teardown_class(self):\n\n # TODO: If environment variable is set keep the workspace\n # and print out the path.\n global TEST_WORKSPACE\n\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\n 'codechecker_cfg']['check_env']\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\n\n print(\"Removing: \" + TEST_WORKSPACE)\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)","def cleanUp():\n pass","def tearDown(self):\n test_utils.delete_test_config()","def tearDown(self):\n del self.pop","def cleanup(self):\n del self.quester.db.test_quest_counter","def cleanup(self):\r\n pass","def tearDown(self):\n pass\n # teardown called after each test\n # e.g. maybe write test results to some text file","def tearDown(self):\n\t\tdel self.user\n\t\tdel self.image","def tearDown(self):\r\n testing.tearDown()","def tearDown(self):\n testing_dir = os.path.split(os.path.realpath(__file__))[0]\n for f in glob.glob(os.path.join(testing_dir, \"*\")):\n if f.split(\".\")[-1] in [\"o\", \"out\", \"pyc\", \"log\"]:\n subprocess.call(['rm', f])","def teardown(self):\n del self.testC, self.testI, self.bounds\n\n return","def _clean_up(self):","def tearDown(self):\n super(TestSelectAPI, self).tearDown()\n self.destroy_fixtures()","def teardown(self):\n pass # pylint: disable=unnecessary-pass","def tearDown(self):\n account_models.User.objects.all().delete()\n photo_models.PhotoFeed.objects.all().delete()\n photo_models.PhotoClassification.objects.filter(name__in=[\"Rural\", \"Abstract\", \"City\"]).delete()\n test_helpers.clear_directory('backend/media/', '*.jpg')","def tearDown(self):\n self.teardown_beets()","def cleanup(self):\n self._tmp_obj.cleanup()","def tearDown(self):\n print(\n \"\\nDeleting temporary files...\\n\")\n try:\n shutil.rmtree(TEST_DIR)\n except OSError:\n pass","def tearDown(self) -> None:\n filtered = [f for f in glob.glob('steps/tests/test_output/*') if not re.match(r'\\.keep', f)]\n for file in filtered:\n try:\n if Path(file).is_dir():\n shutil.rmtree(file)\n else:\n os.remove(file)\n except PermissionError as pe:\n # We don't necessarily care that much\n continue","def tearDownClass(self):\n self.product_id = None","def tearDown(self):\n if 'turbinia-test-tmp' in self.tmp_dir:\n shutil.rmtree(self.tmp_dir)","def teardown(self):\n super(TestCisPlyOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def teardown(self):\n del self.testC, self.bounds, self.one_d_vars, self.unequal_one_d_vars\n del self.testI\n\n return","def clean_up(self) -> None:\n print('Doing some clean-up work...')","def teardown_method(self, test_method):\n self.wo_obj = None\n self.config_data = None","def tearDown(self):\n\n rmtree(self.test_output_dir)\n\n return","def cleanup(self):\n\n self.PLC['1'].set_plc_mode(0)\n self.PLC['1'].plc_clear('all')\n super(Test200SmartSanityClear005, self).cleanup()","def tearDown(self):\n User.objects.all().delete()\n Project.objects.all().delete()\n Review.objects.all().delete()","def cleanup(self):\r\n print(\"Cleanup not implemented\")"],"string":"[\n \"def tearDown(self):\\n self.tmp.cleanup()\",\n \"def teardown(self):\\n\\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\\n\\n return\",\n \"def cleanup(self):\\n super(Test200SmartSanityDownload004, self).cleanup()\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_vals, self.test_fracs\\n\\n return\",\n \"def tearDown(self):\\n self.remove_test_files()\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def teardown(self):\\n del self.testInst, self.dname\\n\\n return\",\n \"def tearDown(cls):\\n\\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\\n # reusable data struct\\n cls.test_mmp_series_object.clean_out_data_seriesobj()\\n cls.test_dataset_testresults.clear()\\n # reusable results file\\n # os.remove(cls.temp_file_output_series.name)\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_val_length\\n\\n return\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_val_length\\n\\n return\",\n \"def tearDown(self):\\n self._s1ap_wrapper.cleanup()\",\n \"def tearDown(self):\\n self._s1ap_wrapper.cleanup()\",\n \"def teardown(self):\\n\\n del self.testInst, self.test_bins, self.test_label, self.test_data\\n del self.out_keys, self.out_data\\n\\n return\",\n \"def tearDown(self):\\n tests.utils.cleanup_environment()\",\n \"def tearDown(self):\\n tests.utils.cleanup_environment()\",\n \"def tearDown(self):\\n del self.review\",\n \"def cleanup():\",\n \"def tearDown(self):\\n if self.flavor_creator:\\n self.flavor_creator.clean()\\n\\n super(self.__class__, self).__clean__()\",\n \"def tearDown(self):\\n test_env_teardown()\",\n \"def cleanup(self):\\n\\n pass\",\n \"def tearDown(self):\\n ProcessRequest(f\\\"products/{self.product_id}.json\\\").send_request(\\n 'DELETE', continue_on_error=True)\\n super().tearDown()\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def cleanup(self):\\n pass\",\n \"def tearDown(self):\\n util.delete_mocks()\",\n \"def tearDown(self):\\n util.delete_mocks()\",\n \"def tearDown(self):\\n util.delete_mocks()\",\n \"def tearDown(self):\\n util.delete_mocks()\",\n \"def tearDown(self):\\n util.delete_mocks()\",\n \"def tearDown(self):\\n util.delete_mocks()\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def cleanup (self):\\n pass\",\n \"def tearDown(self):\\n if os.path.isdir('/tmp/remote_pacha'):\\n shutil.rmtree('/tmp/remote_pacha')\\n if os.path.isdir('/tmp/localhost'):\\n shutil.rmtree('/tmp/localhost')\\n if os.path.isdir('/tmp/test_pacha'):\\n shutil.rmtree('/tmp/test_pacha')\\n if os.path.isdir('/tmp/pacha_test'):\\n shutil.rmtree('/tmp/pacha_test')\\n if os.path.isdir('/tmp/pacha_bucket'):\\n shutil.rmtree('/tmp/pacha_bucket')\\n\\n try:\\n shutil.rmtree('/tmp/test_pacha')\\n shutil.rmtree('/tmp/remote_pacha')\\n shutil.rmtree('/tmp/localhost')\\n shutil.rmtree('/tmp/single_dir')\\n except OSError:\\n pass # nevermind if you could not delete this guy\",\n \"def teardown(self,**kwargs):\\n pass\",\n \"def tearDown(self):\\n zope.component.testing.tearDown()\",\n \"def tearDown(self):\\n log.warning(\\\"Cleaning up after test...\\\")\\n # All of the following artefacts are created: AWSSnapshot, AWSS3Object,\\n # GCSObject, GCEImage, GCEDisk\\n for c in self.test_state.GetContainers(containers.AWSSnapshot):\\n self._removeAWSSnapshot(c.id)\\n for c in self.test_state.GetContainers(containers.AWSS3Object):\\n self._removeAWSS3Object(c.path)\\n for c in self.test_state.GetContainers(containers.GCSObject):\\n self._removeGCSObject(c.path)\\n for c in self.test_state.GetContainers(containers.GCEImage):\\n self._removeGCEImage(c.name)\\n for c in self.test_state.GetContainers(containers.GCEDisk):\\n self._removeGCEDisk(c.name)\",\n \"def tearDown(self):\\n self.testInit.clearDatabase()\\n self.testInit.tearDownCouch()\\n self.specGen.removeSpecs()\\n return\",\n \"def test_z_cleanup(self):\\n\\n TestController.tearDown(\\n self,\\n clear_all_tables=True,\\n del_global_app_set=True,\\n dirs_to_destroy=['user', 'morphology', 'corpus', 'morphological_parser'])\",\n \"def trial_clean_up(self):\\n pass\",\n \"def tearDown(self):\\n del self.a\",\n \"def teardown(self) -> None:\\n pass\",\n \"def teardown(self) -> None:\\n pass\",\n \"def cleanup(self):\\r\\n pass\",\n \"def cleanup(self):\\r\\n pass\",\n \"def tearDown(self):\\n self.clean()\\n self.clean_s3()\\n super().tearDown()\",\n \"def tearDown(self):\\n server.Inventory.remove_all()\",\n \"def setUp(self):\\n Pet.remove_all()\",\n \"def cleanUp(self):\\r\\n pass\",\n \"def tearDown(self):\\n Pics.objects.all().delete()\\n Category.objects.all().delete()\\n Location.objects.all().delete()\",\n \"def tearDown(self):\\n del self.business_item_class\",\n \"def cleanup(self, *args, **kwargs):\",\n \"def tearDown(self):\\n self.tempdir.cleanup()\",\n \"def vtest_ut_cleanup(self):\\n shutil.rmtree(self.get_test_file_path(), ignore_errors=True)\",\n \"def teardown_class(self):\\n\\n # TODO: If environment variable is set keep the workspace\\n # and print out the path.\\n global TEST_WORKSPACE\\n\\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\\n 'codechecker_cfg']['check_env']\\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\\n\\n print(\\\"Removing: \\\" + TEST_WORKSPACE)\\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)\",\n \"def teardown_class(self):\\n\\n # TODO: If environment variable is set keep the workspace\\n # and print out the path.\\n global TEST_WORKSPACE\\n\\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\\n 'codechecker_cfg']['check_env']\\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\\n\\n print(\\\"Removing: \\\" + TEST_WORKSPACE)\\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)\",\n \"def cleanUp():\\n pass\",\n \"def tearDown(self):\\n test_utils.delete_test_config()\",\n \"def tearDown(self):\\n del self.pop\",\n \"def cleanup(self):\\n del self.quester.db.test_quest_counter\",\n \"def cleanup(self):\\r\\n pass\",\n \"def tearDown(self):\\n pass\\n # teardown called after each test\\n # e.g. maybe write test results to some text file\",\n \"def tearDown(self):\\n\\t\\tdel self.user\\n\\t\\tdel self.image\",\n \"def tearDown(self):\\r\\n testing.tearDown()\",\n \"def tearDown(self):\\n testing_dir = os.path.split(os.path.realpath(__file__))[0]\\n for f in glob.glob(os.path.join(testing_dir, \\\"*\\\")):\\n if f.split(\\\".\\\")[-1] in [\\\"o\\\", \\\"out\\\", \\\"pyc\\\", \\\"log\\\"]:\\n subprocess.call(['rm', f])\",\n \"def teardown(self):\\n del self.testC, self.testI, self.bounds\\n\\n return\",\n \"def _clean_up(self):\",\n \"def tearDown(self):\\n super(TestSelectAPI, self).tearDown()\\n self.destroy_fixtures()\",\n \"def teardown(self):\\n pass # pylint: disable=unnecessary-pass\",\n \"def tearDown(self):\\n account_models.User.objects.all().delete()\\n photo_models.PhotoFeed.objects.all().delete()\\n photo_models.PhotoClassification.objects.filter(name__in=[\\\"Rural\\\", \\\"Abstract\\\", \\\"City\\\"]).delete()\\n test_helpers.clear_directory('backend/media/', '*.jpg')\",\n \"def tearDown(self):\\n self.teardown_beets()\",\n \"def cleanup(self):\\n self._tmp_obj.cleanup()\",\n \"def tearDown(self):\\n print(\\n \\\"\\\\nDeleting temporary files...\\\\n\\\")\\n try:\\n shutil.rmtree(TEST_DIR)\\n except OSError:\\n pass\",\n \"def tearDown(self) -> None:\\n filtered = [f for f in glob.glob('steps/tests/test_output/*') if not re.match(r'\\\\.keep', f)]\\n for file in filtered:\\n try:\\n if Path(file).is_dir():\\n shutil.rmtree(file)\\n else:\\n os.remove(file)\\n except PermissionError as pe:\\n # We don't necessarily care that much\\n continue\",\n \"def tearDownClass(self):\\n self.product_id = None\",\n \"def tearDown(self):\\n if 'turbinia-test-tmp' in self.tmp_dir:\\n shutil.rmtree(self.tmp_dir)\",\n \"def teardown(self):\\n super(TestCisPlyOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def teardown(self):\\n del self.testC, self.bounds, self.one_d_vars, self.unequal_one_d_vars\\n del self.testI\\n\\n return\",\n \"def clean_up(self) -> None:\\n print('Doing some clean-up work...')\",\n \"def teardown_method(self, test_method):\\n self.wo_obj = None\\n self.config_data = None\",\n \"def tearDown(self):\\n\\n rmtree(self.test_output_dir)\\n\\n return\",\n \"def cleanup(self):\\n\\n self.PLC['1'].set_plc_mode(0)\\n self.PLC['1'].plc_clear('all')\\n super(Test200SmartSanityClear005, self).cleanup()\",\n \"def tearDown(self):\\n User.objects.all().delete()\\n Project.objects.all().delete()\\n Review.objects.all().delete()\",\n \"def cleanup(self):\\r\\n print(\\\"Cleanup not implemented\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7640776","0.74029046","0.7368922","0.7343883","0.7318036","0.7304285","0.7304285","0.7304285","0.7271605","0.7271605","0.7271605","0.7263641","0.7263641","0.7263641","0.7233525","0.72201914","0.72102296","0.72102296","0.7205624","0.7205624","0.71853197","0.71313596","0.71313596","0.71107036","0.7104896","0.70999026","0.7092305","0.7082488","0.7076573","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7073861","0.7070391","0.7070391","0.7070391","0.7070391","0.7070391","0.7070391","0.70675015","0.70675015","0.70675015","0.70652974","0.70411277","0.7027731","0.70265263","0.70250475","0.70179236","0.7005346","0.7000421","0.69993293","0.6985338","0.6985338","0.69777","0.69777","0.6977557","0.6969296","0.6967402","0.6960706","0.6945101","0.6940956","0.6935157","0.69340456","0.69200224","0.6910221","0.6910221","0.69055843","0.68991685","0.68986356","0.6889163","0.6888159","0.68681306","0.6865267","0.68587196","0.6852821","0.6851752","0.68438846","0.6833822","0.68297035","0.68048906","0.6804284","0.68020236","0.67974925","0.6795091","0.6794267","0.6793424","0.67909694","0.6788742","0.6783955","0.6774304","0.67710537","0.67705697","0.6762855","0.6760553"],"string":"[\n \"0.7640776\",\n \"0.74029046\",\n \"0.7368922\",\n \"0.7343883\",\n \"0.7318036\",\n \"0.7304285\",\n \"0.7304285\",\n \"0.7304285\",\n \"0.7271605\",\n \"0.7271605\",\n \"0.7271605\",\n \"0.7263641\",\n \"0.7263641\",\n \"0.7263641\",\n \"0.7233525\",\n \"0.72201914\",\n \"0.72102296\",\n \"0.72102296\",\n \"0.7205624\",\n \"0.7205624\",\n \"0.71853197\",\n \"0.71313596\",\n \"0.71313596\",\n \"0.71107036\",\n \"0.7104896\",\n \"0.70999026\",\n \"0.7092305\",\n \"0.7082488\",\n \"0.7076573\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7073861\",\n \"0.7070391\",\n \"0.7070391\",\n \"0.7070391\",\n \"0.7070391\",\n \"0.7070391\",\n \"0.7070391\",\n \"0.70675015\",\n \"0.70675015\",\n \"0.70675015\",\n \"0.70652974\",\n \"0.70411277\",\n \"0.7027731\",\n \"0.70265263\",\n \"0.70250475\",\n \"0.70179236\",\n \"0.7005346\",\n \"0.7000421\",\n \"0.69993293\",\n \"0.6985338\",\n \"0.6985338\",\n \"0.69777\",\n \"0.69777\",\n \"0.6977557\",\n \"0.6969296\",\n \"0.6967402\",\n \"0.6960706\",\n \"0.6945101\",\n \"0.6940956\",\n \"0.6935157\",\n \"0.69340456\",\n \"0.69200224\",\n \"0.6910221\",\n \"0.6910221\",\n \"0.69055843\",\n \"0.68991685\",\n \"0.68986356\",\n \"0.6889163\",\n \"0.6888159\",\n \"0.68681306\",\n \"0.6865267\",\n \"0.68587196\",\n \"0.6852821\",\n \"0.6851752\",\n \"0.68438846\",\n \"0.6833822\",\n \"0.68297035\",\n \"0.68048906\",\n \"0.6804284\",\n \"0.68020236\",\n \"0.67974925\",\n \"0.6795091\",\n \"0.6794267\",\n \"0.6793424\",\n \"0.67909694\",\n \"0.6788742\",\n \"0.6783955\",\n \"0.6774304\",\n \"0.67710537\",\n \"0.67705697\",\n \"0.6762855\",\n \"0.6760553\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":85682,"cells":{"query":{"kind":"string","value":"Test argument parsing. Run the program with args_in. The program dumps its arguments to stdout. Compare the stdout with args_out."},"document":{"kind":"string","value":"def expect_args(self, args_in, args_out):\n\n filename = SettingsCommandTestCase.output_file_name\n outfile = self.getBuildArtifact(filename)\n\n if lldb.remote_platform:\n outfile_arg = os.path.join(\n lldb.remote_platform.GetWorkingDirectory(), filename\n )\n else:\n outfile_arg = outfile\n\n self.runCmd(\"process launch -- %s %s\" % (outfile_arg, args_in))\n\n if lldb.remote_platform:\n src_file_spec = lldb.SBFileSpec(outfile_arg, False)\n dst_file_spec = lldb.SBFileSpec(outfile, True)\n lldb.remote_platform.Get(src_file_spec, dst_file_spec)\n\n with open(outfile, \"r\") as f:\n output = f.read()\n\n self.RemoveTempFile(outfile)\n self.assertEqual(output, args_out)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main_parse_args(args):\n # Must return so that check command return value is passed back to calling routine\n # otherwise py.test will fail\n return main(parse_args(args))","def main_parse_args(args):\n # Must return so that check command return value is passed back to calling routine\n # otherwise py.test will fail\n return main(parse_args(args))","def test_args(self):\n parser = argparse.ArgumentParser(\n prog=\"sysbottle\", description=\"sysbottle is parsed\"\n )\n subparsers = parser.add_subparsers()\n sysbottle.build(subparsers)\n args = parser.parse_args(\n [\n \"sysbottle\",\n \"abc.txt\",\n \"-c\",\n \"90\",\n \"-q\",\n \"1\",\n \"-d\",\n \"sda\",\n \"-i\",\n \"5\",\n \"-t\",\n \"3\",\n ]\n )\n self.assertTrue(hasattr(args, \"file\"))\n self.assertTrue(hasattr(args, \"cpu\"))\n self.assertTrue(hasattr(args, \"diskQ\"))\n self.assertTrue(hasattr(args, \"disks\"))\n self.assertTrue(hasattr(args, \"iowait\"))\n self.assertTrue(hasattr(args, \"throughput\"))","def test_check_args(caplog, tmpdir, monkeypatch):\n\n # Check existing input file\n argv = [\"-i\", f\"{proj_dir}/data/input.json\"]\n check_args(parse_args(argv))\n\n # Check non-existent input file\n caplog.clear()\n argv = [\"-i\", \"foo.bar\"]\n with pytest.raises(SystemExit):\n check_args(parse_args(argv))\n assert caplog.records[0].message == 'Cannot find input file: foo.bar'\n\n # Check non-existent output file\n ofile = tmpdir.join(\"foo.bar\")\n argv = [\"-o\", ofile.strpath]\n check_args(parse_args(argv))\n\n # Check existing output file\n ofile = tmpdir.join(\"output.txt\")\n ofile.write(\"Test\")\n argv = [\"-o\", ofile.strpath]\n\n monkeypatch.setattr('sys.stdin', StringIO('Y\\n'))\n check_args(parse_args(argv))\n\n monkeypatch.setattr('sys.stdin', StringIO('N\\n'))\n with pytest.raises(SystemExit):\n check_args(parse_args(argv))\n ## Check unacceptable user input\n monkeypatch.setattr('sys.stdin', StringIO('A\\nB\\nC\\nN'))\n with pytest.raises(SystemExit):\n check_args(parse_args(argv))","def main():\n args = parse_args()\n process_args(args)","def test_validargs(clickrunner):\n for args in maincli.valid_args:\n result = clickrunner.invoke(maincli.entrypoint, args)\n assert result.exit_code == 2\n assert \"Missing command\" in result.output","def test_arg_parser_arg(self):\n\n parsed_args = self.parser.parse_args(['accu', '--debug', '--refresh',\n '-v'])\n\n self.assertEqual(parsed_args.command, 'accu')\n self.assertTrue(parsed_args.debug)\n self.assertEqual(parsed_args.formatter, 'table')\n self.assertTrue(parsed_args.refresh)\n self.assertEqual(parsed_args.verbose_level, 1)","def test_parse_arguments2():\n args = ['--file', 'data.csv', '--debug']\n parsed_args = parse_arguments.parse_arguments(args)\n assert parsed_args.file == 'data.csv'\n assert parsed_args.logging_level == logging.DEBUG","def runTool(self, filename, expected_out, args):\n\n input_path = os.path.join(self.inputs_dir, filename)\n return_value, actual_output = create_subprocess(self.executable_binary, args + [input_path] + ['--'])\n actual_output = actual_output.decode('utf-8')\n\n self.assertEqual(return_value, 0)\n self.evaluate(expected_out, actual_output, command=f'{[self.executable_binary] + args} {filename}')","def test_cli_argparse(\n config,\n):\n args = CLI.parse_args([\"-t\", \"0664123456\"])\n assert args.recipient == \"0664123456\"\n\n args = CLI.parse_args([\"--to\", \"0664123456\"])\n assert args.recipient == \"0664123456\"\n\n args = CLI.parse_args([\"-l\", \"0676456789123\"])\n assert args.login == \"0676456789123\"\n\n args = CLI.parse_args([\"--login\", \"0676456789123\"])\n assert args.login == \"0676456789123\"\n\n args = CLI.parse_args([\"-p\", \"s3cret..11\"])\n assert args.password == \"s3cret..11\"\n\n args = CLI.parse_args([\"--password\", \"s3cret..11\"])\n assert args.password == \"s3cret..11\"\n\n args = CLI.parse_args([\"-c\", \".yessssms.config\"])\n assert args.configfile == \".yessssms.config\"\n\n args = CLI.parse_args([\"--configfile\", \".yessssms.config\"])\n assert args.configfile == \".yessssms.config\"\n\n args = CLI.parse_args([\"--message\", \"testmessage 123 - can you see this?\"])\n assert args.message == \"testmessage 123 - can you see this?\"\n\n args = CLI.parse_args([\"-m\", \"testmessage 123 - can you see this?\"])\n assert args.message == \"testmessage 123 - can you see this?\"\n\n args = CLI.parse_args([\"--mvno\", \"YESSS\"])\n assert args.provider == \"YESSS\"\n\n args = CLI.parse_args([\"--mvno\", \"EDUCOM\"])\n assert args.provider == \"EDUCOM\"\n\n args = CLI.parse_args([\"--mvno\", \"SIMfonie\"])\n assert args.provider == \"SIMfonie\"\n\n args = CLI.parse_args([\"--mvno\", \"BLABLABLA\"])\n assert args.provider == \"BLABLABLA\"","def test_with_explicit_sample_args(self):\n test_dict = CliArgs('sample', ['-a', '26', '-s', 'somefile', '-n', '-u', 'foo', '-v']).__dict__\n self.assertEqual('foo', test_dict['user'])\n self.assertEqual(1, test_dict['verbosity'])\n self.assertEqual('26', test_dict['analyzer_profile'])","def handle_options( args ):\n parser = optparse.OptionParser( version = \"BitTest Bitbake Testing Tool version %s\" % __version__,\n usage = \"\"\"%prog [options] [test ...]\nExecutes the specified tests or if none are given all tests. The result is printed\non the cmd line\n\"\"\" )\n\n parser.add_option( \"-o\", \"--output\", help = \"print the output to the file\",\n action = \"store\", dest = \"output\", default = None )\n parser.add_option( \"-f\", \"--format\", help = \"print the output in the specified format\",\n action = \"store\", dest = \"format\", default = None )\n\n options, args = parser.parse_args( args )\n return options, args[1:]","def test_with_command_line_arguments(self, arguments):\n fixed_arguments = self.get_argument_string(arguments)\n result = self.run(\n arguments=fixed_arguments,\n timeout=self.full_timeout,\n use_fresh_profile=True)\n return self._handle_test_result(result)","def check_argv():\n parser = argparse.ArgumentParser(description=__doc__.strip().split(\"\\n\")[0], add_help=False)\n parser.add_argument(\"data_dir\", type=str, help=\"data directory\")\n parser.add_argument(\n \"--init_am_n_iter\", type=int, help=\"default: %(default)s\",\n default=default_options_dict[\"init_am_n_iter\"]\n )\n parser.add_argument(\n \"--segment_n_iter\", type=int, help=\"default: %(default)s\",\n default=default_options_dict[\"segment_n_iter\"]\n )\n parser.add_argument(\n \"--K_max\", type=str, help=\"default: %(default)s\",\n default=default_options_dict[\"K_max\"]\n )\n parser.add_argument(\n \"--min_duration\", type=int, help=\"default: %(default)s\",\n default=default_options_dict[\"min_duration\"]\n )\n parser.add_argument(\n \"--n_slices_max\", type=int, help=\"default: %(default)s\",\n default=default_options_dict[\"n_slices_max\"]\n )\n parser.add_argument(\n \"--p_boundary_init\", type=float, help=\"default: %(default)s\",\n default=default_options_dict[\"p_boundary_init\"]\n ) \n parser.add_argument(\n \"--n_cpus\", type=int, help=\"default: %(default)s\",\n default=default_options_dict[\"n_cpus\"]\n )\n parser.add_argument(\n \"--n_batches\", type=int, help=\"default: %(default)s\",\n default=default_options_dict[\"n_batches\"]\n )\n if len(sys.argv) == 1:\n parser.print_help()\n sys.exit(1)\n return parser.parse_args()","def old_main():\n valid_formats = ('pre', 'table', 'numeric')\n from optparse import OptionParser\n parser = OptionParser()\n #parser.add_option('-v', '--verbose', action='store_true', dest='verbose', default=False)\n parser.add_option('-o', '--output', dest='output_filename', metavar='FILE', default='-', help='output file')\n parser.add_option('--format', dest='format', default='pre', help='one of ' + str(valid_formats))\n parser.add_option('--test', action='store_true', dest='test', default=False, help='run some unit tests')\n options, args = parser.parse_args()\n # validate the format option\n if options.format not in valid_formats:\n print 'invalid --format parameter:', options.format, 'is not in', valid_formats\n return\n # run a test or run a demo\n if options.output_filename == '-':\n out = sys.stdout\n else:\n out = open(options.output_filename, 'w')\n run(out, options.format)\n if out is not sys.stdout:\n out.close()","def command_test(args):\n\tfrom inquisitor.configs.resolver import (\n\t\tconfig_path,\n\t\tCONFIG_DATA, data_path,\n\t\tCONFIG_SOURCES, source_path,\n\t\tCONFIG_CACHE, cache_path,\n\t\tCONFIG_LOGFILE, log_file,\n\t\tCONFIG_VERBOSE, is_verbose,\n\t\tCONFIG_SUBFEEDS, subfeeds,\n\t)\n\tsubfeeds = '; '.join(\n\t\t'{0}: {1}'.format(\n\t\t\tsf_name,\n\t\t\t' '.join(sf_sources)\n\t\t)\n\t\tfor sf_name, sf_sources\n\t\tin subfeeds.items()\n\t) if subfeeds else ''\n\tprint(f'Inquisitor configured from {config_path}')\n\tprint(f' {CONFIG_DATA} = {data_path}')\n\tprint(f' {CONFIG_SOURCES} = {source_path}')\n\tprint(f' {CONFIG_CACHE} = {cache_path}')\n\tprint(f' {CONFIG_LOGFILE} = {log_file}')\n\tprint(f' {CONFIG_VERBOSE} = {is_verbose}')\n\tprint(f' {CONFIG_SUBFEEDS} = {subfeeds}')\n\treturn 0","def test_options2args():\n args, kwargs = util.options2args([\"--arg1\", \"-arg2\", \"--arg3=10\"])\n assert all([\"--arg1\" in args, \"-arg2\" in args, \"arg3\" in kwargs.keys()])","def main(args):\n cli = CLI()\n # Check arguments\n cli.parse_arguments(args)","def test_atleast_two_arguments_needed_one():\n cli_result = subprocess.run(\n ['kaiba', 'config.js'],\n capture_output=True,\n )\n assert b'the following arguments are required: input' in cli_result.stderr\n assert cli_result.returncode == 2","def run(self):\n if len(sys.argv[1:]) == 0:\n arg_parse.print_help()\n arg_parse.exit()\n\n try:\n if args.unix:\n self.from_unix_sec()\n print (\"Unix Seconds: \" + self.in_unix_sec + \" UTC\")\n elif args.umil:\n self.from_unix_milli()\n print (\"Unix Milliseconds: \" + self.in_unix_milli + \" UTC\")\n elif args.wh:\n self.from_win_64_hex()\n print (\"Windows 64 bit Hex BE: \" + self.in_windows_hex_64 + \" UTC\")\n elif args.whle:\n self.from_win_64_hexle()\n print (\"Windows 64 bit Hex LE: \" + self.in_windows_hex_le + \" UTC\")\n elif args.goog:\n self.from_chrome()\n print (\"Google Chrome Time: \" + self.in_chrome + \" UTC\")\n elif args.active:\n self.from_ad()\n print (\"Active Directory Timestamp: \" + self.in_ad + \" UTC\")\n elif args.uhbe:\n self.from_unix_hex_32be()\n print (\"Unix Hex 32 bit BE: \" + self.in_unix_hex_32 + \" UTC\")\n elif args.uhle:\n self.from_unix_hex_32le()\n print (\"Unix Hex 32 bit LE: \" + self.in_unix_hex_32le + \" UTC\")\n elif args.cookie:\n self.from_cookie()\n print (\"Windows Cookie Date: \" + self.in_cookie + \" UTC\")\n elif args.oleb:\n self.from_ole_be()\n print (\"Windows OLE 64 bit double BE: \" + self.in_ole_be + \" UTC\")\n elif args.olel:\n self.from_ole_le()\n print (\"Windows OLE 64 bit double LE: \" + self.in_ole_le + \" UTC\")\n elif args.mac:\n self.from_mac()\n print (\"Mac Absolute Time: \" + self.in_mac + \" UTC\")\n elif args.hfsdec:\n self.from_hfs_dec()\n print (\"Mac OS/HFS+ Decimal Date: \" + self.in_hfs_dec + \" UTC\")\n elif args.hfsbe:\n self.from_hfs_be()\n print (\"HFS/HFS+ 32 bit Hex BE: \" + self.in_hfs_be + \" HFS Local / HFS+ UTC\")\n elif args.hfsle:\n self.from_hfs_le()\n print (\"HFS/HFS+ 32 big Hex LE: \" + self.in_hfs_le + \" HFS Local / HFS+ UTC\")\n elif args.msdos:\n self.from_msdos()\n print (\"MS-DOS 32 bit Hex Value: \" + self.in_msdos + \" Local\")\n elif args.fat:\n self.from_fat()\n print (\"FAT Date + Time: \" + self.in_fat + \" Local\")\n elif args.sys:\n self.from_systime()\n print (\"Microsoft 128 bit SYSTEMTIME: \" + self.in_systemtime + \" UTC\")\n elif args.ft:\n self.from_filetime()\n print (\"Microsoft FILETIME/LDAP time: \" + self.in_filetime + \" UTC\")\n elif args.pr:\n self.from_prtime()\n print (\"Mozilla PRTime: \" + self.in_prtime + \" UTC\")\n elif args.auto:\n self.from_ole_auto()\n print (\"OLE Automation Date: \" + self.in_ole_auto + \" UTC\")\n elif args.ios:\n self.from_ios_time()\n print (\"iOS 11 Date: \" + self.in_iostime)\n elif args.sym:\n self.from_sym_time()\n print (\"Symantec AV Timestamp: \" + self.in_symtime)\n elif args.gps:\n self.from_gps_time()\n print (\"GPS Timestamp: \" + self.in_gpstime)\n elif args.timestamp:\n self.to_timestamps()\n elif args.guess:\n self.from_all()\n except Exception as e:\n logging.error(str(type(e)) + \",\" + str(e))","def test_cli_with_test_args(\n config,\n):\n testargs = [\n \"yessssms\",\n \"--test\",\n \"-l\",\n \"06641234567\",\n \"-p\",\n \"passw0rd\",\n \"-t\",\n \"+43676564736\",\n ]\n with mock.patch.object(sys, \"argv\", testargs):\n with requests_mock.Mocker() as m:\n m.register_uri(\n \"POST\",\n _LOGIN_URL,\n status_code=302,\n # pylint: disable=protected-access\n headers={\"location\": _KONTOMANAGER_URL},\n )\n m.register_uri(\"GET\", _KONTOMANAGER_URL, status_code=200)\n m.register_uri(\n \"GET\",\n # pylint: disable=protected-access\n _SMS_FORM_URL,\n status_code=200,\n text=TEST_FORM_TOKEN_SAMPLE,\n )\n m.register_uri(\n \"POST\",\n _SEND_SMS_URL,\n status_code=200,\n text=\"<h1>Ihre SMS wurde erfolgreich \" + \"verschickt!</h1>\",\n )\n m.register_uri(\"GET\", _LOGOUT_URL, status_code=200)\n val = CLI().exit_status\n assert val == 0","def ReadArguments():\n\n args = ParseArguments()\n\n logging.info('Command line arguments...')\n for arg in vars(args):\n logging.info(str(arg) + ': ' + str(getattr(args, arg)))\n logging.info('')\n\n IsTest(args)\n ProcessCacheSize(args)\n ProcessLineSize(args)\n ProcessMulti(args)\n ProcessMemPattern(args)\n ProcessMemFile(args)","def parse_args(args):\n\n parser = argparse.ArgumentParser(\n description=\"\"\"Generates and runs an afni_proc.py script to preprocess resting state fMRI data\"\"\",\n formatter_class=argparse.RawDescriptionHelpFormatter)\n\n # Optional Flags\n parser.add_argument(\"-t\", \"--trs_remove\", action=\"store\", default=5, type=int, metavar='TRs',\n help=\"\"\"number of trs to remove at the beginning of the epi data\n (default = 5 trs)\"\"\")\n parser.add_argument(\"-d\", \"--dim_voxel\", action=\"store\", default=2.0, type=float, metavar='MM',\n help=\"voxel dimensions in mm that processed epi will be resampled to (default = 2.0 mm)\")\n parser.add_argument(\"-b\", \"--bandpass\", action=\"store\", default=[0.01, 0.25], nargs=2, type=float, metavar=\"F\",\n help=\"bandpass frequencies lower and upper limits (default = 0.01 0.25)\")\n parser.add_argument(\"-v\", \"--volumes\", action=\"store\", default=0, type=int, metavar=\"V\",\n help=\"\"\"truncate the epi data to the inputted number of volumes, useful if subjects have data \n with different numbers of volumes (default = no truncation)\"\"\")\n parser.add_argument(\"-f\", \"--fwhm\", action=\"store\", default=5.0, type=float, metavar=\"MM\",\n help=\"the full width half maximum that is used when blurring (default = 5.0 mm)\")\n parser.add_argument(\"-c\", \"--cores\", action=\"store\", default=cpu_count(), type=int, metavar=\"C\",\n help=\"number of cores supplied to 3dDeconvolve (default = all cores)\")\n parser.add_argument(\"-s\", \"--subj_id\", action=\"store\", default=\"sub\", metavar=\"SUB\",\n help=\"text file of subject ids (default = sub)\")\n parser.add_argument(\"-T\", \"--time_step\", action=\"store\", default=0, type=float, metavar=\"TS\",\n help=\"set the time step for bandpassing (default = ts in header info\")\n\n parser.add_argument(\"-g\", \"--global_signal_regression\", action=\"store_false\", default=True,\n help=\"do not perform global signal regression (default = perform gsr)\")\n\n parser.add_argument(\"-r\", \"--rerun\", action=\"store_true\", default=False,\n help=\"\"\"rerun preprocessing, override and delete previous results in \n 'Processed' folder (default = don't override)\"\"\")\n parser.add_argument(\"-m\", \"--motion_param\", action=\"store_true\", default=False,\n help=\"use 12 motion parameters for regression (default = 6 motion parameters)\")\n parser.add_argument(\"-G\", \"--gm_blur\", action=\"store_true\", default=False,\n help=\"blur only in grey matter mask (default = blur in whole brain)\")\n parser.add_argument(\"-n\", \"--nl_reg\", action=\"store_true\", default=False,\n help=\"use non-linear warp between anatomical and MNI template (default = linear warp)\")\n\n # Required Inputs\n required = parser.add_argument_group(\"required arguments\")\n required.add_argument(\"-e\", \"--epi\", action=\"store\", required=True,\n help=\"text file of paths to raw epi data\")\n required.add_argument(\"-a\", \"--anat\", action=\"store\", required=True,\n help=\"text file of paths to raw anatomical data\")\n required.add_argument(\"-o\", \"--out_dir\", action=\"store\", required=True, metavar=\"OUT\",\n help=\"text file of paths to output directory\")\n result = parser.parse_args(args)\n\n # Make sure inputted parameters are legal\n assert (os.path.isfile(result.epi)), \"{} does not exist or is not a file\".format(result.epi)\n assert (os.path.isfile(result.anat)), \"{} does not exist or is not a file\".format(result.ant)\n assert (result.trs_remove >= 0), \"Cannot remove negative trs\"\n assert (result.dim_voxel >= 0), \"Cannot have a negative voxel dimension\"\n assert (np.all(np.array(result.bandpass) > 0)), \"Cannot have a negative frequency limit for bandpassing\"\n assert (result.volumes > -1), \"Number of volumes must be greater than 0\"\n assert (result.cores > 0), \"Number of cores used must be greater than 0\"\n assert (result.time_step > -1), \"Time step must be greater than 0\"\n\n return result","def test_parse_arguments1():\n args = []\n parsed_args = parse_arguments.parse_arguments(args)\n assert parsed_args.logging_level == logging.ERROR\n assert parsed_args.group_size == defaults.DEFAULT_GRPSIZE\n assert parsed_args.students_file == defaults.DEFAULT_CSVFILE\n assert (parsed_args.grouping_method == group_random) is False","def parse_arguments(args):","def test_cli_boolean_args(\n config,\n):\n args = CLI.parse_args([\"--version\"])\n assert args.version is True\n\n args = CLI.parse_args([\"--test\"])\n assert args.test is True\n\n args = CLI.parse_args([\"--print-config-file\"])\n assert args.print_config_file is True\n\n args = CLI.parse_args([\"-T\"])\n assert args.check_login is True","def test_cli_args():\n expected = dict(\n paths=[\"path1\", \"path2\"],\n exclude=[\"file*.py\", \"dir/\"],\n ignore_decorators=[\"deco1\", \"deco2\"],\n ignore_names=[\"name1\", \"name2\"],\n make_whitelist=True,\n min_confidence=10,\n sort_by_size=True,\n verbose=True,\n )\n result = _parse_args(\n [\n \"--exclude=file*.py,dir/\",\n \"--ignore-decorators=deco1,deco2\",\n \"--ignore-names=name1,name2\",\n \"--make-whitelist\",\n \"--min-confidence=10\",\n \"--sort-by-size\",\n \"--verbose\",\n \"path1\",\n \"path2\",\n ]\n )\n assert isinstance(result, dict)\n assert result == expected","def parse_args(self):\n parser = argparse.ArgumentParser()\n parser.add_argument('-d', '--data', dest='data',\n help='Generate requested amount of test data.',\n type=int, nargs='+')\n parser.add_argument('-c', '--check', action='store_true',\n dest='check', help='Check files without modifying them.',\n default=False)\n args = parser.parse_args()\n self.arg_data = args.data\n self.arg_check = args.check\n\n if xc.arg_data: # did the user request to generate test data?\n choice = input(Fore.YELLOW + 'This option will ' + Fore.RED +\n '*OVERWRITE ALL FILES* ' + Fore.YELLOW + 'you sure (y/n)? ')\n if choice.upper() == 'Y':\n self.test_data_row_count = int(self.arg_data[0])\n xc.generate_test_data()\n else:\n xc.arg_data = False\n else:\n self.process_dump_files()","def test_args_combination():\n args = cli.parse_args(['-cp'])\n assert args.copy\n assert args.paste\n args = cli.parse_args(['-c', '-p'])\n assert args.copy\n assert args.paste","def check_argv():\n parser = argparse.ArgumentParser(\n description=__doc__.strip().split(\"\\n\")[0], add_help=False\n )\n parser.add_argument(\"npz_fn\", type=str, help=\"NumPy archive\")\n parser.add_argument(\n \"--metric\", choices=[\"cosine\", \"euclidean\", \"hamming\", \"chebyshev\"],\n default=\"cosine\",\n help=\"distance metric (default: %(default)s)\"\n )\n parser.add_argument(\n \"--mean_ap\", dest=\"mean_ap\", action=\"store_true\",\n help=\"also compute mean average precision (this is significantly \"\n \"more resource intensive)\"\n )\n parser.add_argument(\n \"--mvn\", action=\"store_true\",\n help=\"mean and variance normalise (default: False)\"\n )\n if len(sys.argv) == 1:\n parser.print_help()\n sys.exit(1)\n return parser.parse_args()","def test_0_args(library_db):\n import argparse\n\n namespace = app.main([])\n assert namespace.__class__ == argparse.Namespace","def test_main_arguments():\n args = argparse.Namespace(url=RANDOM_URL,\n username=RANDOM_USERNAME,\n password=RANDOM_PASSWORD,\n tenantcode=RANDOM_TENANTCODE)\n result = Config(\"wso_args.json\").main(args)\n\n assert result is True","def main():\n program_name = os.path.basename(sys.argv[0])\n\n _initialize_debugging(program_name)\n _handle_signals()\n _process_environment_variables()\n arguments = _process_command_line()\n\n exit_status = 1 # no match\n if arguments:\n for filename in arguments:\n if os.path.isfile(filename):\n if not parameters[\"No formatting\"]:\n print(filename + \":\")\n for _, printable_string in strings.strings(filename):\n if what_in_string(printable_string):\n exit_status = 0 # match found\n if parameters[\"First match only\"]:\n break\n else:\n logging.error('\"%s\": No such file or directory', filename)\n elif parameters[\"Stdin unused\"]:\n logging.critical(\"At least one filename expected\")\n else:\n for _, printable_string in strings.strings():\n if what_in_string(printable_string):\n exit_status = 0 # match found\n if parameters[\"First match only\"]:\n break\n\n sys.exit(exit_status)","def test_parse_arguments_default(self):\n self.assertEqual(self.args.python, sys.executable)\n self.assertEqual(self.args.pip_packages, [])\n self.assertEqual(self.args.number_of_threads, multiprocessing.cpu_count())\n self.assertEqual(self.args.cmake_installer,\n CMAKE_INSTALLER_URL_BY_HOST_PLATFORM.get(sys.platform))\n self.assertEqual(self.args.cmake_source_project_root,\n os.path.join(os.getcwd(), 'git', 'falken'))\n self.assertEqual(self.args.cmake_copybara_variable, 'FALKEN_DIR')\n self.assertIsNone(self.args.cmake_configure_args)\n self.assertEqual(self.args.cmake_generator,\n cmake_runner.CMakeRunner.default_generator())\n self.assertEqual(\n self.args.cmake_target_architecture,\n cmake_runner.CMakeRunner.default_architecture(\n cmake_runner.CMakeRunner.default_generator()))\n self.assertEqual(self.args.cmake_build_dir,\n os.path.join(os.getcwd(), 'build'))\n self.assertEqual(self.args.cmake_build_configs, CMAKE_DEFAULT_BUILD_CONFIGS)\n self.assertIsNone(self.args.cmake_package_configs)\n self.assertEqual(self.args.cmake_package_generator, 'ZIP')\n self.assertIsNone(self.args.cmake_test_regex)\n self.assertEqual(self.args.output_dir, 'output')\n self.assertIsNone(self.args.copy_artifacts)\n self.assertIsNone(self.args.zip_artifacts)","def test_parser_all(self):\n args = [\"directory\", \"-n\", \"-f\", \"-r\"]\n parser = setup_parser()\n output = parser.parse_args(args)\n self.assertEqual(output.directory, \"directory\")\n self.assertFalse(output.colorize)\n self.assertTrue(output.fancy)\n self.assertTrue(output.reverse)","def parse_args(args):\n \n # Construct the parser (which is stored in parser)\n # Module docstring lives in __doc__\n # See http://python-forum.com/pythonforum/viewtopic.php?f=3&t=36847\n # And a formatter class so our examples in the docstring look good. Isn't it\n # convenient how we already wrapped it to 80 characters?\n # See http://docs.python.org/library/argparse.html#formatter-class\n parser = argparse.ArgumentParser(description=__doc__, \n formatter_class=argparse.RawDescriptionHelpFormatter)\n \n # General options\n parser.add_argument(\"hal\",\n help=\"HAL file to evaluate\")\n parser.add_argument(\"--truth\",\n help=\"MAF file of a true alignment for precision and recall\")\n parser.add_argument(\"--beds\", nargs=\"*\",\n help=\"BED file(s) of genes on the genomes in the HAL\")\n parser.add_argument(\"--coverage_file\", type=argparse.FileType(\"w\"),\n default = sys.stdout,\n help=\"file to save average coverage vs. the reference in (one number)\")\n parser.add_argument(\"--precision_recall_file\", type=argparse.FileType(\"w\"),\n default = sys.stdout,\n help=\"TSV file to save precision and recall in (two numbers)\")\n parser.add_argument(\"--gene_category_file\", type=argparse.FileType(\"w\"),\n default = sys.stdout,\n help=\"file to save categories and counts for genes in\")\n parser.add_argument(\"--tag\", nargs=\"*\", default=[],\n help=\"extra columns to tag all result TSV lines with at the front\")\n \n # The command line arguments start with the program name, which we don't\n # want to treat as an argument for argparse. So we remove it.\n args = args[1:]\n \n return parser.parse_args(args)","def parse_args():\n parser = argparse.ArgumentParser(description=\"evaluate the recovered derg by comparing with ground truth mapping file\")\n parser.add_argument(\"-mapping\", action=\"store\", dest=\"mapping_file\",\n required=True, help=\"path to proguard-generated mapping.txt\")\n parser.add_argument(\"-recovered_derg\", action=\"store\", dest=\"recovered_derg\",\n required=True, help=\"path to recovered derg\")\n parser.add_argument(\"-nice2predict_mapping\", action=\"store\", dest=\"nice2predict_mapping_file\",\n help=\"path to nice2predict-generated mapping.txt\")\n parser.add_argument(\"-o\", action=\"store\", dest=\"report_dir\",\n default=\".\", help=\"directory of report files\")\n parser.add_argument(\"-report_name\", action=\"store\", dest=\"report_name\",\n default=DEFAULT_REPORT_NAME, help=\"name of report file\")\n parser.add_argument(\"-match_mode\", action=\"store\", dest=\"match_mode\",\n default=MATCH_MODE_EXACT, help=\"match mode\")\n\n options = parser.parse_args()\n print options\n return options","def parse_args():\n parser = argparse.ArgumentParser(description=__doc__)\n # If user doesn't specify an input file, read from standard input. Since\n # encodings are the worst thing, we're explicitly expecting std\n parser.add_argument('-i', '--infile',\n type=lambda x: open(x, encoding=ENCODE_IN),\n default=io.TextIOWrapper(\n sys.stdin.buffer, encoding=ENCODE_IN)\n )\n # Same thing goes with the output file.\n parser.add_argument('-o', '--outfile',\n type=lambda x: open(x, 'w', encoding=ENCODE_OUT),\n default=io.TextIOWrapper(\n sys.stdout.buffer, encoding=ENCODE_OUT)\n )\n # Set the verbosity level for the logger. The `-v` option will set it to\n # the debug level, while the `-q` will set it to the warning level.\n # Otherwise use the info level.\n verbosity = parser.add_mutually_exclusive_group()\n verbosity.add_argument('-v', '--verbose', action='store_const',\n const=logging.DEBUG, default=logging.INFO)\n verbosity.add_argument('-q', '--quiet', dest='verbose',\n action='store_const', const=logging.WARNING)\n return parser.parse_args()","def parse_stdin_args():\n \n #Initialize parser\n parser = argparse.ArgumentParser(description = \"Creates synthetic paired end DNA from given fraction of Human, Bacteria, Phix174 and Virus/Phage. \\\n Note: List of Human, Bacteria, Phix174 and Virus/Phage fractions should be space separated. Example: -hu 0.5 0.2 \\\n -b 0.3 0.01 -x 0.1 0.01. The Virus/Phage fraction is taken 1 - Human - Bacteria - Phix174.\")\n \n #Required command line arguments\n parser.add_argument(\"-s\", help=\"Number of total reads\", required = True, type = int)\n parser.add_argument(\"-p\", help=\"Path to directory for output files\", required = True)\n parser.add_argument(\"-hu\", help=\"Human DNA percentage. Default: [0.5, 0.1, 0.01, 0.001]\", required = False, type= float, nargs = \"+\", default = [0.5, 0.1, 0.01, 0.001])\n parser.add_argument(\"-b\", help=\"Bacterial DNA percentage. Default: [0.4, 0.25, 0.1, 0.5]\", required = False, type= float, nargs = \"+\", default = [0.4, 0.25, 0.1, 0.5])\n parser.add_argument(\"-x\", help=\"Phix174 DNA percentage. Default: [0.01, 0.001]\", required = False, type= float, nargs = \"+\", default = [0.01, 0.001])\n parser.add_argument(\"-isfastq\", help=\"Specify output format 1=FASTQ 0=FASTA (Default: 1)\", required = False, type= int, default = 1)\n parser.add_argument(\"-n\", help=\"Read length. Default: 250\", required = False, type= int, default = 250)\n parser.add_argument(\"-err\", help=\"Error rate. Default: 0\", required = False, type= int, default = 0)\n \n #Read the command line arguments\n args = parser.parse_args()\n \n #check parameters\n if args.s < 100:\n print \"Number of total reads must be greater than 100.\"\n sys.exit(0)\n \n if args.n < 35:\n print \"Read length should be greater than 35\"\n sys.exit(0)\n\n return args","def test_compare(self):\n parser = parse_args(['-g', '10', '-s', 'bubble', '-c'])\n self.assertTrue(parser.compare)\n self.assertEqual(True, parser.compare)\n\n parser = parse_args(['-g', '10', '-s', 'bubble'])\n self.assertEqual(False, parser.compare)","def test_arg_parse(test):\n print(test)\n testargs = ['']\n with mock.patch.object(sys, 'argv', testargs):\n assert gc.arg_parse().interactive","def verify(args):\n # assume that the arguments are not valid and prove otherwise\n verified_arguments = False\n # TOP-LEVEL VERIFIED:\n # both a file and a directory were specified and a command is not given\n # pylint: disable=bad-continuation\n if is_valid_file_and_directory(args) and (\n not is_command_ancillary(args)\n and not is_valid_command(args)\n and not is_valid_commits(args)\n ):\n # track how many of the sub-arguments separately verified\n file_verified = []\n # VERIFIED: correct check for existence of a file in a directory\n if is_valid_exists(args):\n # verified_arguments = True\n file_verified.append(True)\n # VERIFIED: correct check for comments with language in a file in a directory\n if is_valid_comments(args) and is_valid_language(args):\n # verified_arguments = True\n file_verified.append(True)\n # VERIFIED: correct check for paragraphs in a file in a directory\n if is_valid_paragraphs(args):\n # verified_arguments = True\n file_verified.append(True)\n # VERIFIED: correct check for words in a file in a directory\n if is_valid_words(args):\n # verified_arguments = True\n file_verified.append(True)\n # VERIFIED: correct check for fragments in a file in a directory\n if is_valid_fragment(args):\n # verified_arguments = True\n file_verified.append(True)\n # VERIFIED: correct check for line count of a file in a directory\n if is_valid_count(args):\n # verified_arguments = True\n file_verified.append(True)\n # VERIFIED: only one of prior valid options specified\n if file_verified.count(True) == 1:\n verified_arguments = True\n # TOP-LEVEL VERIFIED:\n # no file or directory details were specified and a command given\n # pylint: disable=bad-continuation\n elif is_valid_command(args) and (\n not is_valid_file_or_directory(args)\n and not is_file_ancillary(args)\n and not is_valid_commits(args)\n ):\n # track how many of the sub-arguments separately verified\n command_verified = []\n # VERIFIED: correct check for existence of a file in a directory\n if is_valid_executes(args):\n command_verified.append(True)\n # verified_arguments = True\n # VERIFIED: correct check for fragments in a file in a directory\n if is_valid_fragment(args):\n # verified_arguments = True\n command_verified.append(True)\n # VERIFIED: correct check for line count of a file in a directory\n if is_valid_count(args):\n # verified_arguments = True\n command_verified.append(True)\n # VERIFIED: only one of prior valid options specified\n if command_verified.count(True) == 1:\n verified_arguments = True\n # TOP-LEVEL VERIFIED:\n # no file or directory details were specified or a command given\n # and the argument is a request to check the count of Git commits\n # pylint: disable=bad-continuation\n elif is_valid_commits(args) and (\n not is_valid_file_or_directory(args)\n and not is_file_ancillary(args)\n and not is_valid_command(args)\n and not is_command_ancillary(args)\n ):\n verified_arguments = True\n return verified_arguments","def check_args():\n schema = Schema({\n 'FOLDREC': Use(open, error='FOLDREC file should be readable'),\n 'CLUSTAL': Use(open, error='CLUSTAL file should be readable'),\n 'CCMPRED': Use(open, error='CCMPRED file should be readable'),\n '--metafold': Use(open, error='METAFOLD_FILE should be readable'),\n '--nb_pdb': And(Use(int), lambda n: 1 <= n <= 405,\n error='--nb_pdb=NUM should be integer 1 <= N <= 405'),\n '--dssp': Use(open, error='dssp/mkdssp should be readable'),\n '--dope': Use(open, error='dope file should be readable'),\n '--benchmark': Use(open, error='BENCHMARK_FILE should be readable'),\n '--cpu': And(Use(int), lambda n: 0 <= n <= cpu_count(),\n error='--cpus=NUM should be integer 1 <= N <= ' + str(cpu_count())),\n # The output PATH is created (if not exists) at the end of the program\n # so we skip the check.\n object: object})\n try:\n schema.validate(ARGUMENTS)\n except SchemaError as err:\n exit(err)","def parse_args():\n default_file_name = 'test.db'\n default_table_name = 'test'\n default_location = 'hasan_bedroom'\n default_node_id = 'SecuritySystem_1'\n\n parser = argparse.ArgumentParser(\n description='Run the SecuritySystemDB test program')\n\n parser.add_argument('-v',\n '--verbose',\n default=False,\n action='store_true',\n help='Print all debug logs')\n\n parser.add_argument('-ss',\n '--security_system',\n default=False,\n action='store_true',\n help='Run SecuritySystemDB test')\n\n parser.add_argument('-f',\n '--file_name',\n type=str,\n default=default_file_name,\n metavar='<file_name.db>',\n help='Specify file name of SQL db (Relative to pwd)')\n\n parser.add_argument('-t',\n '--table_name',\n type=str,\n default=default_table_name,\n metavar='<test_table_name>',\n help='Specify table name of SQL db')\n\n parser.add_argument('-l',\n '--location',\n type=str,\n default=default_location,\n metavar='<owner_room>',\n help='Specify owner and room')\n\n parser.add_argument('-id',\n '--node_id',\n type=str,\n default=default_node_id,\n metavar='<node_id>',\n help='Specify node ID')\n\n args = parser.parse_args()\n return args","def test_cli__passes__with_version_option(args: str) -> None:\n\n runner = CliRunner()\n result = runner.invoke(\n cli,\n args=args,\n )\n\n assert_that(result.exit_code).is_equal_to(0)\n assert_that(result.output).is_instance_of(str)\n assert_that(result.output).is_equal_to(f\"cli, version {__version__}\\n\")","def main(args=None):\n if args is None:\n args = _parse_arguments()\n\n # Handling the arguments prematurely. If this begins running away\n # in complexity do this and maybe even parse_args in a separate function\n if args.log_level is None:\n # no LOG_LEVEL specified but -l was specified\n if args.log is True:\n LOG_LEVEL = 30\n else:\n # Don't log\n LOG_LEVEL = 99\n else:\n LOG_LEVEL = args.log_level\n\n logging.basicConfig(level=LOG_LEVEL)\n if args.yaml is True:\n plaintext = convert_to_yaml(args.yaml)\n else:\n plaintext = sort_json(args.input)\n\n logging.debug(\"Plaintext is: \" + str(plaintext))\n text_writer(plaintext, args.output)","def parse_args():\n parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')\n parser.add_argument('--dataset', help='training dataset')\n parser.add_argument('--cfg', dest='cfg_file', required=True, help='optional config file')\n\n parser.add_argument('--load_ckpt', help='path of checkpoint to load')\n parser.add_argument('--load_detectron', help='path to the detectron weight pickle file')\n\n parser.add_argument('--output_dir', help='output directory to save the testing results. If not provided, defaults to [args.load_ckpt|args.load_detectron]/../test.')\n\n parser.add_argument('--set', dest='set_cfgs', help='set config keys, will overwrite config in the cfg_file. See lib/core/config.py for all options', default=[], nargs='*')\n parser.add_argument('--range', help='start (inclusive) and end (exclusive) indices', type=int, nargs=2)\n parser.add_argument('--multi-gpu-testing', help='using multiple gpus for inference', action='store_true')\n parser.add_argument('--vis', dest='vis', help='visualize detections', action='store_true')\n parser.add_argument('--model', help='Set model', type=str)\n\n parser.add_argument('--use_matlab', help='use matlab?', action='store_true')\n parser.add_argument('--early_stop', help='run eval only to 10 images', action='store_true')\n \n\n\n\n return parser.parse_args()","def test_main_minimal(self, capsys):\n UI.main(**self.args)\n captured = capsys.readouterr().out\n assert self.stdout_output in captured","def run(self, args):\n args_count = len(args)\n if args_count == 0:\n self.parser.print_help()\n sys.exit(1)\n\n return self.parse_args(self.parser.parse_args(args))","def analyzeCommandLineArguments(options):\n print(options)\n flag=0\n if os.path.exists(options.output_directory)==False:\n cmd=\"mkdir \"+options.output_directory\n os.system(cmd)\n else:\n if options.force==0:\n os.system(\"echo \\\"Output directory already exists. Please re-run the program with -f 1 to enforce rewrite of the directory \\\" >> \"+options.output_directory+\"/Log.out\")\n flag=1\n else:\n cmd=\"rm -rf \"+options.output_directory\n os.system(cmd)\n cmd=\"mkdir \"+options.output_directory\n os.system(cmd)\n \n cmd=\"touch \"+options.output_directory+\"/Log.out\"\n os.system(cmd)\n if options.bowtie_index == None:\n os.system(\"echo \\\"No bowtie index provided. Proceeding to building index\\\" >> \"+options.output_directory+\"/Log.out\")\n if os.path.exists(options.genome)==False:\n os.system(\"echo \\\"The genome file you provided does not exist\\\" >> \"+options.output_directory+\"/Log.out\")\n flag=1\n if options.input_library==None:\n os.system(\"echo \\\"The input file \"+options.input_library+\" does not exist\\\" >> \"+options.output_directory+\"/Log.out\")\n flag=1\n \n if flag==1:\n print(\"The program had to terminate prematurely....Please check \"+options.output_directory+\"/Log.out file for more details\")\n sys.exit()\n for ele in options.number_of_cycles:\n if ele not in [\"9\",\"10\",\"11\",\"12\",\"13\"]:\n os.system(\"echo \\\"Incorrect number of cycles have been entered. Valid choices are 9, 10, 11, 12 and 13 \\\" >> \"+options.output_directory+\"/Log.out\")\n flag=1\n \n if options.input_library.split(\".\")[-1]==\"fq\" or options.input_library.split(\".\")[-1]==\"fastq\":\n options.input_filename=options.output_directory+\"/\"+options.input_library.split(\"/\")[-1].split(\".\")[0]+\".fa\"\n \n options.input_path=\"/\".join(options.input_library.split(\"/\")[:-1])\n options.input_filename=options.input_library.split(\"/\")[-1].split(\".\")[0]\n options.consolidated_filename=options.output_directory+\"/\"+options.input_filename+\".consolidated.fasta\"\n options.adapter_trimmed_filename=options.output_directory+\"/\"+options.input_filename+\"_adapter_trimmed.fastq\"\n \n options.small_rna_size=list(map(int,options.small_rna_size))\n options.number_of_cycles=list(map(int,options.number_of_cycles))\n options.pvalue_cutoff=float(options.pvalue_cutoff)\n options.map_limit=int(options.map_limit)\n return options","def main(cli_args=None):\n # build an arg parser\n parser = get_arg_parser()\n\n # run the parser on cli args\n args = parser.parse_args(cli_args)\n\n print(f\"Running script with arguments: {args}\")\n test_input(args.raw_training_data)\n test_input(args.raw_testing_data)\n test_output(args.train_output)\n test_output(args.test_output)","def main():\n\tparser = setup_argument_parser()\n\targuments = parser.parse_args()\n\tto_print = arguments.to_print\n\techo(to_print)","def check_input(args):\n\n # Defaults\n option = ''\n fh = sys.stdin # file handle\n\n if not len(args):\n # Reading from pipe with default option\n if sys.stdin.isatty():\n sys.stderr.write(__doc__)\n sys.exit(1)\n\n elif len(args) == 1:\n # One of two options: option & Pipe OR file & default option\n if args[0].startswith('-'):\n option = args[0][1:]\n if sys.stdin.isatty(): # ensure the PDB data is streamed in\n emsg = 'ERROR!! No data to process!\\n'\n sys.stderr.write(emsg)\n sys.stderr.write(__doc__)\n sys.exit(1)\n\n else:\n if not os.path.isfile(args[0]):\n emsg = 'ERROR!! File not found or not readable: \\'{}\\'\\n'\n sys.stderr.write(emsg.format(args[0]))\n sys.stderr.write(__doc__)\n sys.exit(1)\n\n fh = open(args[0], 'r')\n\n elif len(args) == 2:\n # Two options: option & File\n if not args[0].startswith('-'):\n emsg = 'ERROR! First argument is not an option: \\'{}\\'\\n'\n sys.stderr.write(emsg.format(args[0]))\n sys.stderr.write(__doc__)\n sys.exit(1)\n\n if not os.path.isfile(args[1]):\n emsg = 'ERROR!! File not found or not readable: \\'{}\\'\\n'\n sys.stderr.write(emsg.format(args[1]))\n sys.stderr.write(__doc__)\n sys.exit(1)\n\n option = args[0][1:]\n fh = open(args[1], 'r')\n\n else: # Whatever ...\n sys.stderr.write(__doc__)\n sys.exit(1)\n\n # Validate option\n if len(option) > 1:\n emsg = 'ERROR!! Alternate location identifiers must be single '\n emsg += 'characters: \\'{}\\''\n sys.stderr.write(emsg.format(option))\n sys.exit(1)\n\n return (option, fh)","def _run_args(cls, args: Optional[List[str]] = None):\n parser = cls.setup_args()\n opt = parser.parse_args(args=args)\n return cls._run_from_parser_and_opt(opt, parser)","def test_args(self):\n self.assertEqual(self.parser.N_particles, 500)\n self.assertEqual(self.parser.reduced_T, 0.9)\n self.assertEqual(self.parser.reduced_rho, 0.9)\n self.assertEqual(self.parser.n_steps, 1000000)\n self.assertEqual(self.parser.freq_ener, 1000)\n self.assertEqual(self.parser.freq_traj, 1000)\n self.assertEqual(self.parser.max_d, 0.1)\n self.assertEqual(self.parser.energy, 'UnitlessLJ')","def main(argv):\n\n if len(argv) != 3:\n print 'ERROR: usage %s infile outfile' % argv[0]\n sys.exit(1)\n\n in_fname = argv[1]\n out_fname = argv[2]\n\n # TODO: normalize the paths and make sure that they're not the same.\n # It's generally a bad idea to overwrite your input\n\n if in_fname == '-':\n fin = sys.stdin\n else:\n try:\n fin = open(in_fname, 'r')\n except BaseException, exc:\n print 'ERROR: %s: %s' % (argv[0], str(exc))\n sys.exit(1)\n\n in_lines = fin.readlines()\n if fin != sys.stdin:\n fin.close()\n\n try:\n out_lines = process_lines(in_lines)\n except ValueError, exc:\n print 'ERROR: %s: %s' % (argv[0], str(exc))\n sys.exit(1)\n\n if out_fname == '-':\n fout = sys.stdout\n else:\n try:\n fout = open(out_fname, 'w+')\n except BaseException, exc:\n print 'ERROR: %s: %s' % (argv[0], str(exc))\n sys.exit(1)\n\n fout.write(''.join(out_lines))\n if fout != sys.stdout:\n fout.close()","def check_arguments(self):\n ## only four test operation is permitted, if given anything apart from this, then it should print error message\n if (self.args.snap is False and self.args.snapcheck is False and self.args.check is False and self.args.diff is False and self.args.version is False):\n self.logger.error(colorama.Fore.RED +\n \"Arguments not given correctly, Please refer help message\", extra=self.log_detail)\n self.parser.print_help()\n sys.exit(1)\n\n if(((self.args.snap is True and (self.args.pre_snapfile is None or self.args.file is None)) or\n (self.args.snapcheck is True and self.args.file is None) or\n (self.args.check is True and self.args.file is None)) and \n (self.args.testfiles is None or self.args.hostname is None)\n ):\n self.logger.error(colorama.Fore.RED +\n \"Arguments not given correctly, Please refer help message\", extra=self.log_detail)\n self.parser.print_help()\n sys.exit(1)\n if self.args.diff is True:\n if (self.args.pre_snapfile is not None and os.path.isfile(self.args.pre_snapfile)) and (\n self.args.post_snapfile is not None and os.path.isfile(self.args.post_snapfile)):\n comp = Comparator()\n comp.compare_diff(\n self.args.pre_snapfile,\n self.args.post_snapfile,\n None)\n sys.exit(1)\n else:\n if (self.args.file is None) and (\n self.args.testfiles is None or self.args.hostname is None):\n self.parser.print_help()\n sys.exit(1)","def parse_args():\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"-i\", \"--input\", required=True, action=\"store\", dest=\"f_in\", help=\"input file\"\n )\n parser.add_argument(\n \"-o\",\n \"--output\",\n required=True,\n action=\"store\",\n dest=\"f_out\",\n help=\"stem of output file\",\n )\n\n parser.add_argument(\n \"-d\",\n \"--dir\",\n required=True,\n action=\"store\",\n dest=\"dir\",\n help=\"directory to save output files\",\n )\n\n parser.add_argument(\n \"-t\",\n \"--threshold\",\n required=False,\n action=\"store\",\n dest=\"thres\",\n default=0.85,\n help=\"threshold for the scoring function\",\n )\n\n parser.add_argument(\n \"-l\",\n \"--language\",\n required=True,\n action=\"store\",\n dest=\"lang\",\n help=\"provide language in order to set stop words\",\n )\n\n parser.add_argument(\n \"-min\",\n \"--minimum\",\n required=False,\n action=\"store\",\n dest=\"min\",\n default=100,\n help=\"minimum number of occurrences to be considered as ngram\",\n )\n\n parser.add_argument(\n \"--trigram\",\n required=False,\n action=\"store_true\",\n dest=\"trigram\",\n help=\"extracting trigrams in addition to bigrams\",\n )\n\n return parser.parse_args()","def check_args(argv):\n arg_length = len(argv)\n\n usage_str = '\\nUSAGE:\\npython {} [ls_hostname] [ls_listen_port]\\npython {} [ls_hostname] [ls_listen_port] [input_file_name]\\npython {} [ls_hostname] [ls_listen_port] [input_file_name] [output_file_name]\\n'.format(argv[0], argv[0], argv[0])\n\n ls_hostname = DEFAULT_HOSTNAME_LS\n ls_portno = DEFAULT_PORTNO_LS\n\n input_file_str = DEFAULT_INPUT_FILE_STR_HNS\n output_file_str = DEFAULT_OUTPUT_FILE_STR_RESOLVED\n\n # debugging args\n if arg_length is 1:\n pass\n elif arg_length is 2:\n ls_hostname = argv[1]\n # end debugging args\n elif arg_length is 3:\n ls_hostname = argv[1]\n ls_portno = int(argv[2])\n elif arg_length is 4:\n ls_hostname = argv[1]\n ls_portno = int(argv[2])\n\n input_file_str = argv[3]\n elif arg_length is 5:\n ls_hostname = argv[1]\n ls_portno = int(argv[2])\n\n input_file_str = argv[3]\n output_file_str = argv[4]\n else:\n print(usage_str)\n exit()\n\n return (ls_hostname, ls_portno), (input_file_str, output_file_str)","def parse_args():\n\tparser = argparse.ArgumentParser(\n\t\tformatter_class=argparse.RawTextHelpFormatter,\n\t\tusage=argparse.SUPPRESS)\n\n\tparser.add_argument('--edge', type=str, dest='edge_path', required=True,\n\t\t\thelp=\"\"\"Path to edge file as input in a special space seperated format, e.g. node_1 (pp) node2 = 1.00\"\"\")\n\tparser.add_argument('--otu', type=str, dest='otu_path', required=True,\n\t\t\thelp=\"\"\"Path to otu abundance file in a tab seperated format. Header and row name present as the first row and first column from the 2nd row\"\"\")\n\tparser.add_argument('--env', type=str, dest='env_path', required=True,\n\t\t\thelp=\"\"\"Path to env variable data file in a tab seperated format. Header and row name present as the first row and first column from the 2nd row\"\"\")\n\tparser.add_argument('--r2', type=float, dest='th_r2', default=0.6,\n\t\t\thelp=\"\"\"Specify a r2 value as correlation threshold (Pearson) for identify significant links between env variables and OTUs. All links below this value will not be considered. Default: 0.6\"\"\")\n\tparser.add_argument('--pval', type=float, dest='th_p', default=0.05,\n\t\t\thelp=\"\"\"Specify a p-value as threshold for testing whether the links between env variables and OTU passed significant level of correlation. All links tested with p-value higher than this value will not be considered. Default: 0.05\"\"\")\n\tparser.add_argument('--out', type=str, dest='out', default=\"/dev/stdout\",\n\t\t\thelp=\"\"\"Path to output which includes a succinct summary of valid links. Default: standard output\"\"\")\n\tparser.add_argument('--verbose', action='store_true', dest='verbose', \n\t\t\thelp=\"\"\"Toggle to output details for each env dependent link. Default: False\"\"\")\n\n\treturn vars(parser.parse_args())","def test_cli_parser_good(self):\n ARGV = ([],)\n for argv in ARGV:\n opts, args = self.cmd.OptionParser.parse_args(argv)\n self.cmd.ValidateOptions(opts, args)","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"in_fq\", help=\"The fastq file containing Hi-C reads.\")\n parser.add_argument(\n \"-r\",\n \"--reference\",\n required=True,\n help=\"Path to the reference genome, in FASTA format.\",\n )\n parser.add_argument(\n \"-p\",\n \"--nb_processors\",\n default=1,\n type=int,\n help=\"number of CPUs used for alignment.\",\n )\n parser.add_argument(\n \"-o\",\n \"--out_sam\",\n help=\"Path to the output SAM file for the alignment of in_fq.\",\n )\n parser.add_argument(\n \"-T\",\n \"--tempdir\",\n default=\".\",\n help=\"Directory to write temporary files. Defaults to current directory.\",\n )\n parser.add_argument(\n \"-m\",\n \"--minimap2\",\n default=False,\n action=\"store_true\",\n help=\"Use minimap2 instead of bowtie for the alignment.\",\n )\n parser.add_argument(\n \"-l\",\n \"--min_len\",\n type=int,\n default=20,\n help=\"Minimum length to which reads should be truncated.\",\n )\n return parser.parse_args()","def test_main_output(self, capsys):\n args = self.args.copy()\n args[\"out_file\"] = \"text.txt\"\n UI.main(**args)\n captured = capsys.readouterr().out\n assert \"Results written to text.txt\" in captured","def parse_arguments(args):\n\n parser = argparse.ArgumentParser(\n description=\"Text2Text: Read input text training files, output item files and train a model\"\n )\n\n parser.add_argument(\n \"-i\",\n \"--input-text-path\",\n type=str,\n required=True,\n metavar=\"INPUT_TEXT_PATH\",\n help=\"Text input file name. Format: in each line, OUTPUT_ID1,OUTPUT_ID2,OUTPUT_ID3,...\\t INPUT_TEXT \\\n where OUTPUT_IDs are the zero-based output item indices corresponding to the line numbers of OUTPUT_ITEM_PATH. We assume utf-8 encoding for text.\",\n )\n\n parser.add_argument(\n \"-q\",\n \"--output-item-path\",\n type=str,\n required=True,\n metavar=\"OUTPUT_ITEM_PATH\",\n help=\"Output item file name. Format: each line corresponds to a representation of the output item. We assume utf-8 encoding for text.\",\n )\n\n parser.add_argument(\n \"-m\",\n \"--model-folder\",\n type=str,\n required=True,\n metavar=\"MODEL_FOLDER\",\n help=\"Output model folder name\",\n )\n\n parser.add_argument(\n \"--workspace-folder\",\n type=str,\n default=None,\n metavar=\"WORKSPACE_FOLDER\",\n help=\"A folder name for storing intermediate variables during training\",\n )\n\n vectorizer_config_group_parser = parser.add_mutually_exclusive_group()\n vectorizer_config_group_parser.add_argument(\n \"--vectorizer-config-path\",\n type=str,\n default=None,\n metavar=\"VECTORIZER_CONFIG_PATH\",\n help=\"Json file for vectorizer config (default tfidf vectorizer)\",\n )\n\n vectorizer_config_group_parser.add_argument(\n \"--vectorizer-config-json\",\n type=str,\n default='{\"type\":\"tfidf\", \"kwargs\":{}}',\n metavar=\"VECTORIZER_CONFIG_JSON\",\n help='Json-format string for vectorizer config (default {\"type\":\"tfidf\", \"kwargs\":{}})',\n )\n\n parser.add_argument(\n \"--dtype\",\n type=lambda x: np.float32 if \"32\" in x else np.float64,\n default=np.float32,\n help=\"data type for the csr matrix. float32 | float64. (default float32)\",\n )\n\n parser.add_argument(\n \"--max-leaf-size\",\n type=cli.comma_separated_type(int),\n default=[100],\n metavar=\"INT-LIST\",\n help=\"The max size of the leaf nodes of hierarchical 2-means clustering. Multiple values (separated by comma) are supported and will lead to different individual models for ensembling. (default [100])\",\n )\n\n parser.add_argument(\n \"--nr-splits\",\n type=int,\n default=2,\n metavar=\"INT\",\n help=\"number of splits used to construct hierarchy (a power of 2 is recommended, default 2)\",\n )\n\n parser.add_argument(\n \"--imbalanced-ratio\",\n type=float,\n default=0.0,\n metavar=\"FLOAT\",\n help=\"Value between 0.0 and 0.5 (inclusive). Indicates how relaxed the balancedness constraint of 2-means can be. Specifically, if an iteration of 2-means is clustering L labels, the size of the output 2 clusters will be within approx imbalanced_ratio * 2 * L of each other. (default 0.0)\",\n )\n\n parser.add_argument(\n \"--imbalanced-depth\",\n type=int,\n default=100,\n metavar=\"INT\",\n help=\"After hierarchical 2-means clustering has reached this depth, it will continue clustering as if --imbalanced-ratio is set to 0.0. (default 100)\",\n )\n\n parser.add_argument(\n \"--label-embed-type\",\n type=cli.comma_separated_type(str),\n default=\"pifa\",\n metavar=\"STR-LIST\",\n help=\"Label embedding types. (default pifa).\\\n We support pifa, pifa_lf_concat::Z=path, and pifa_lf_convex_combine::Z=path::alpha=scalar_value,\\\n where path is the additional user-porivded label embedding path and alpha is the scalar value for convex combination.\\\n Multiple values (separated by comma) are supported and will lead to different individual models for ensembling.\",\n )\n\n parser.add_argument(\n \"--indexer\",\n choices=Indexer.indexer_dict.keys(),\n default=\"hierarchicalkmeans\",\n metavar=\"STR\",\n help=f\"Indexer algorithm (default hierarchicalkmeans). Available choices are {', '.join(Indexer.indexer_dict.keys())}\",\n )\n\n parser.add_argument(\n \"--no-spherical\",\n action=\"store_true\",\n default=False,\n help=\"Do not l2-normalize cluster centers while clustering\",\n )\n\n parser.add_argument(\n \"--seed\",\n type=cli.comma_separated_type(int),\n default=[0],\n metavar=\"INT-LIST\",\n help=\"Random seeds (default 0). Multiple values (separated by comma) are supported and will lead to different individual models for ensembling.\",\n )\n\n parser.add_argument(\n \"--max-iter\",\n type=int,\n default=20,\n metavar=\"INT\",\n help=\"The max iteration for indexing (default 20)\",\n )\n\n parser.add_argument(\n \"-n\",\n \"--threads\",\n type=int,\n default=-1,\n metavar=\"INT\",\n help=\"Number of threads to use (default -1 to denote all the CPUs)\",\n )\n\n # Linear matching/ranking parameters\n parser.add_argument(\n \"-s\",\n \"--solver-type\",\n type=str,\n default=\"L2R_L2LOSS_SVC_DUAL\",\n metavar=\"STR\",\n help=\"{} (default L2R_L2LOSS_SVC_DUAL)\".format(\" | \".join(XLINEAR_SOLVERS.keys())),\n )\n\n parser.add_argument(\n \"--Cp\",\n type=float,\n default=1.0,\n metavar=\"VAL\",\n help=\"Coefficient for positive class in the loss function (default 1.0)\",\n )\n\n parser.add_argument(\n \"--Cn\",\n type=float,\n default=1.0,\n metavar=\"VAL\",\n help=\"Coefficient for negative class in the loss function (default 1.0)\",\n )\n\n parser.add_argument(\n \"--bias\", type=float, default=1.0, metavar=\"VAL\", help=\"bias term (default 1.0)\"\n )\n\n parser.add_argument(\n \"-ns\",\n \"--negative-sampling\",\n type=str,\n choices=[\"tfn\", \"man\", \"tfn+man\"],\n default=\"tfn\",\n metavar=\"STR\",\n help=\"Negative Sampling Schemes\",\n )\n\n parser.add_argument(\n \"-t\",\n \"--threshold\",\n type=float,\n default=0.1,\n metavar=\"VAL\",\n help=\"Threshold to sparsify the model weights (default 0.1)\",\n )\n\n # Prediction kwargs\n parser.add_argument(\n \"-k\",\n \"--only-topk\",\n type=int,\n default=20,\n metavar=\"INT\",\n help=\"the default number of top labels used in the prediction\",\n )\n\n parser.add_argument(\n \"-b\",\n \"--beam-size\",\n type=int,\n default=10,\n metavar=\"INT\",\n help=\"the default size of beam search used in the prediction\",\n )\n\n parser.add_argument(\n \"-pp\",\n \"--post-processor\",\n type=str,\n choices=PostProcessor.valid_list(),\n default=\"l3-hinge\",\n metavar=\"STR\",\n help=\"the default post processor used in the prediction\",\n )\n\n parser.add_argument(\n \"--verbose-level\",\n type=int,\n choices=logging_util.log_levels.keys(),\n default=1,\n metavar=\"INT\",\n help=f\"the verbose level, {', '.join([str(k) + ' for ' + logging.getLevelName(v) for k, v in logging_util.log_levels.items()])}, default 1\",\n )\n\n parsed_args = parser.parse_args(args)\n return parsed_args","def handle_args():\n parser = argparse.ArgumentParser(\n description=\"\"\"Script to download archives from the NLM public\n FTP server.\n \"\"\")\n # Server settings\n server_settings = parser.add_argument_group('FTP SERVER SETTINGS', '')\n server_settings.add_argument(\n '-n', '--netrc', default='~/.netrc',\n help=\"\"\"netrc file containing login parameters for the NLM\n server. See `man 5 netrc` for details on generating this\n file or read nlm_data_import/netrc/example.netrc.\n \"\"\")\n server_settings.add_argument(\n 'server_data_dir',\n help='Directory containing desired files on the NLM FTP server')\n server_settings.add_argument(\n '-l', '--limit', type=int, default=0,\n help='Only download LIMIT files.')\n\n # Download settings\n local_settings = parser.add_argument_group('LOCAL SETTINGS', '')\n local_settings.add_argument(\n '-d', '--download_database', default='~/.ftp_download_db',\n help='Path to SQLite database detailing past downloads')\n local_settings.add_argument(\n '-o', '--output_dir', default='~/medline_data',\n help='Directory where downloads will be saved')\n local_settings.add_argument(\n '-x', '--export_dir', default='~/medline_data_exports',\n help=\"\"\"Directory where data to be retrieved by the\n `hypothesis_graph application server are staged.\n \"\"\")\n # Sending debug emails (requires the send_ses_messages module - see\n # setup.py)\n debugging_settings = parser.add_argument_group('DEBUGGING SETTINGS', '')\n debugging_settings.add_argument(\n '--email_debugging', default=False, action='store_true',\n help=\"Send debugging emails. Defaults to FALSE.\")\n debugging_settings.add_argument(\n '--from_email', required=False, help=\"FROM field for debugging emails\")\n debugging_settings.add_argument(\n '--to_email', required=False, help=\"TO field for debugging emails\")\n\n return parser.parse_args()","def test_args_debug():\n args = cli.parse_args(['-d'])\n assert args.debug\n args = cli.parse_args(['--debug'])\n assert args.debug","def check_argv():\n parser = argparse.ArgumentParser(description=__doc__.strip().split(\"\\n\")[0], add_help=False)\n parser.add_argument(\"input_pairs_fn\", type=str)\n parser.add_argument(\"output_pairs_fn\", type=str)\n if len(sys.argv) == 1:\n parser.print_help()\n sys.exit(1)\n return parser.parse_args()","def parse_args():\n\n op = OptionParser(usage=\"usage: %prog [opts] input_folder\")\n\n op.add_option(\"--test\",\n dest=\"test\",\n default=False,\n action=\"store_true\",\n help=\"executes the test suite\")\n\n return op.parse_args()","def parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--length', required=True, type=int, help='Test length in seconds')\n parser.add_argument('--txs-per-ledger', required=True, type=int, help='Transaction rate to submit (spam) in parallel for every ledger round')\n parser.add_argument('--prioritizer-seeds-file', required=True, type=str, help='File path to prioritizer seeds file')\n parser.add_argument('--spammer-seeds-file', required=True, type=str, help='File path to spammer seeds file')\n parser.add_argument('--out', default='spam-results-{}.json'.format(str(int(time.time()))), type=str, help='Spam results JSON output')\n parser.add_argument('--avg-block-time', type=int, default=5, help='Average block time. Controls the time delay between every spam round and the one just after that')\n\n parser.add_argument('--passphrase', type=str, help='Network passphrase')\n parser.add_argument('--horizon', action='append',\n help='Horizon endpoint URL (use multiple --horizon flags for multiple addresses)')\n\n return parser.parse_args()","def main(args):\n\n if 'log' in args and args['log'] is not None:\n logging.basicConfig(level=LOGGING_LEVELS.get(args['log'].lower(), logging.NOTSET))\n\n test_structure = read_test_file(args['test'])\n tests = build_testsets(args['url'], test_structure)\n\n # Override configs from command line if config set\n for t in tests:\n if 'print_bodies' in args and args['print_bodies'] is not None:\n t.config.print_bodies = safe_to_bool(args['print_bodies'])\n\n if 'interactive' in args and args['interactive'] is not None:\n t.config.interactive = safe_to_bool(args['interactive'])\n\n # Execute all testsets\n failures = execute_testsets(tests)\n\n sys.exit(failures)","def test_cmdlineproc_test2():\n\n parameters = {\n \"debug\": False,\n \"disconnect\": False,\n \"executable\": \"\",\n \"executableargs\": \"\",\n \"hosts\": \"\",\n \"job\": \"\",\n \"jobname\": \"\",\n \"log\": \"\",\n \"recover\": \"\",\n \"resource\": \"\",\n \"replicates\": \"\",\n \"verbose\": False\n }\n\n commandlineargs = [\"-about\"]\n\n longbowargs = _commandlineproc(ALLLONGBOWARGS, commandlineargs, parameters)\n\n assert parameters[\"executable\"] == \"\"\n assert parameters[\"executableargs\"] == \"\"\n assert longbowargs == [\"-about\"]","def execute_program(args: List[str], mode: TestMode) -> None:\n args_str = ' '.join(args)\n\n def check_output(result: CompletedProcess):\n stdout = strip_ascii_codes(result.stdout.decode())\n error_string = f'Failure:\\nMode: {mode.value}\\nCommand: {args_str}\\nExit code: {result.returncode:X}\\nOutput:\\n{stdout}\\n'\n if mode == TestMode.STUB:\n if result.returncode not in (1, 2) or 'Test FAILED' not in stdout:\n raise RuntimeError(error_string)\n elif mode == TestMode.SOLUTION:\n if (result.returncode != 0 or\n \"*** You've passed ALL tests. Congratulations! ***\" not in stdout):\n raise RuntimeError(error_string)\n else:\n print('Invalid mode: {}'.format(mode))\n sys.exit(1)\n\n try:\n result = subprocess.run(args, stdout=subprocess.PIPE, timeout=300)\n check_output(result)\n except RuntimeError as e:\n print(e)\n sys.exit(1)\n except TimeoutExpired:\n print(f\"{mode} > {args_str}: TIMEOUT\")\n sys.exit(1)","def parseArguments():\n # Create argument parser\n parser = argparse.ArgumentParser()\n\n # Optional arguments\n parser.add_argument(\"-t\", \"--test\", help=\"Optionally test algorithm on subsample of the data. Set to 1 for testing\", type=int, default=0)\n\n parser.add_argument(\"--cores\", help=\"Optimized code for a server with a lot of RAM, set to the number of available cores\", type=int, default=40)\n\n\n # Print version\n parser.add_argument(\"--version\", action=\"version\", version='%(prog)s - Version 2.0') #version 1.0 is for the observations in June 2018\n #version 1.1 contains the optimizations made after the june observations (mainly the switch to stackmags)\n #version 1.2 changed sim class to NOT include the list of failed candidates (not qsos)\n #... copied changes made to crossval version\n #version 1.5 added check for duplicate quasars and remove them\n #version 1.6 new simulated quasars (december)\n ##-------------------\n #version 2.0: combined training of classifier and regressor, streamlined input\n #version 2.1: Tryied to updates excluded area to a little more than stripe 82 but decided not to keep it, so no change\n\n # Parse arguments\n args = parser.parse_args()\n\n return args","def testParseArguments(self):\n de_object = de.DockerExplorerTool()\n\n prog = sys.argv[0]\n\n expected_docker_root = os.path.join('test_data', 'docker')\n\n args = [prog, '-r', expected_docker_root, 'list', 'repositories']\n sys.argv = args\n\n options = de_object.ParseArguments()\n usage_string = de_object._argument_parser.format_usage()\n expected_usage = '[-h] [-d] [-r DOCKER_DIRECTORY] [-V]'\n expected_usage_commands = '{download,mount,list,history}'\n self.assertIn(expected_usage, usage_string)\n self.assertIn(expected_usage_commands, usage_string)\n self.assertEqual(expected_docker_root, options.docker_directory)","def parse_args():\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=\"\"\"\nNenG - Nash Equilibrium Noncooperative games.\nTool for computing Nash equilibria in noncooperative games.\nSpecifically:\nAll pure Nash equilibria in all games (--method=pne).\nAll mixed Nash equilibria in two-players games (--method=support_enumeration).\nOne sample mixed Nash equilibria in n-players games (--method={CMAES,L-BFGS-B,SLSQP}).\n\"\"\")\n pa = parser.add_argument\n pa('-f', '--file', required=True, help=\"File where game in nfg format is saved.\")\n pa('-m', '--method', default='CMAES', choices=game.Game.METHODS,\n help=\"Method to use for computing Nash equlibria.\")\n pa('-e', '--elimination', action='store_true', default=False,\n help=\"Use Iterative Elimination of Strictly Dominated Strategies before computing NE.\")\n pa('-p', '--payoff', action='store_true', default=False,\n help=\"Print also players payoff with each Nash equilibrium.\")\n pa('-c', '--checkNE', action='store_true', default=False,\n help=\"After computation check if found strategy profile is really Nash equilibrium.\")\n pa('-t', '--trim', choices=('normalization', 'penalization'), default='normalization',\n help=\"Method for keeping strategy profile in probability distribution universum.\")\n pa('-l', '--log', default=\"WARNING\",\n choices=(\"DEBUG\", \"INFO\", \"WARNING\", \"ERROR\", \"CRITICAL\"),\n help=\"Level of logs to save/print\")\n pa('--log-file', default=None, help='Log file. If omitted log is printed to stdout.')\n return parser.parse_args()","def handle_cmdline_args():\n\n parser = argparse.ArgumentParser(\n description='Generate synthetic data from a specification in a json '\n 'file using the \"synth-method\" described in the json file. ')\n\n parser.add_argument(\n '-i', dest='infile', required=True,\n help='The input json file. Must contain a \"synth-method\" property')\n\n parser.add_argument(\n '-o', dest='outfile_prefix', required=True, help='The prefix of the output paths (data json and csv), relative to the QUIPP-pipeline root directory')\n\n args = parser.parse_args()\n return args","def main(args):\n options = parse_cmd_parameters_(args)\n execute_(options)","def verify_args(opts):\n special_opts = (opts.initialize, opts.sigtool, opts.scanner)\n list_opts = (opts.listUnable, opts.listMoved, opts.listInfected, opts.listFailed)\n if any(list_opts):\n return\n special_opts_flags = [\"-g/--sigtool\", \"-i/--initialize\", \"-s/--scan\"]\n total = 0\n for opt in special_opts:\n if opt:\n total += 1\n if total == 0:\n log.error(\n \"must pass a required argument to begin, required arguments can be found in the help menu (penneav -h)\"\n )\n sys.exit(1)\n if total != 1:\n log.error(\n \"can only pass a total of 1 of the required arguments at a time ({}). You have passed a total of {}. \"\n \"Please verify your arguments and rerun Penne.\".format(\n \", \".join(special_opts_flags), total\n )\n )\n sys.exit(1)\n if opts.sigtool and opts.filename is None:\n log.error(\"must supply a filename with sigtool to generate the signature\")\n sys.exit(1)\n if opts.threadNum > 30:\n log.warning(\"max amount of threads is 30 (you passed {}), defaulting down to 30 threads\".format(opts.threadNum))\n opts.threadNum = 30\n print(WELCOME_BANNER)","def test_cmdlineproc_test11():\n\n parameters = {\n \"debug\": False,\n \"disconnect\": False,\n \"executable\": \"\",\n \"executableargs\": \"\",\n \"hosts\": \"\",\n \"job\": \"\",\n \"jobname\": \"\",\n \"log\": \"\",\n \"recover\": \"\",\n \"resource\": \"\",\n \"replicates\": \"\",\n \"verbose\": False\n }\n\n commandlineargs = [\"test.exe\", \"-i\", \"input.file\", \"param1\", \"--someflag\"]\n\n longbowargs = _commandlineproc(ALLLONGBOWARGS, commandlineargs, parameters)\n\n assert parameters[\"executable\"] == \"test.exe\"\n assert parameters[\"executableargs\"] == \"-i input.file param1 --someflag\"\n assert longbowargs == []","def test_invalid_arguments(self):\n # More than two arguments should report an error.\n exit_code, output = run_cli('a', 'b', 'c')\n assert exit_code != 0\n assert \"Error\" in output\n # Invalid `ionice' values should report an error.\n exit_code, output = run_cli('--ionice=foo')\n assert exit_code != 0\n assert \"Error\" in output","def main(args):\n # Results: print to console and also write to output file\n pass","def test_with_empty_sample_args(self):\n with self.assertRaises(SystemExit):\n CliArgs('sample')","def test_atleast_two_arguments_needed_none():\n cli_result = subprocess.run(\n ['kaiba'],\n capture_output=True,\n )\n assert b'the following arguments are required: config' in cli_result.stderr\n assert cli_result.returncode == 2","def parse_input(command_input=None):\n parser = argparse.ArgumentParser(\n prog='python3 ok',\n description=__doc__,\n usage='%(prog)s [--help] [options]',\n formatter_class=argparse.RawDescriptionHelpFormatter)\n\n testing = parser.add_argument_group('running tests')\n testing.add_argument('-q', '--question', type=str, action='append',\n help=\"run tests for a specific question\")\n testing.add_argument('--suite', type=str, default=None,\n help=\"run cases from a specific suite\")\n testing.add_argument('--case', type=str, action='append',\n help=\"run specific cases\")\n testing.add_argument('-u', '--unlock', action='store_true',\n help=\"unlock tests interactively\")\n testing.add_argument('-i', '--interactive', action='store_true',\n help=\"start the Python interpreter after a failed test\")\n testing.add_argument('-v', '--verbose', action='store_true',\n help=\"show all tests (not just passing tests) up to failing line (if any)\")\n testing.add_argument('-t', '--testing', nargs='?', type=str, const='mytests.rst',\n help='run tests from rst file (default: mytests.rst)')\n testing.add_argument('--all', action='store_true',\n help=\"run tests for all questions in config file\")\n testing.add_argument('--submit', action='store_true',\n help=\"submit the assignment\")\n testing.add_argument('--backup', action='store_true',\n help=\"attempt to reliably backup your work\")\n testing.add_argument('--revise', action='store_true',\n help=\"submit composition revision\")\n testing.add_argument('--timeout', type=int, default=10,\n help=\"set the timeout duration (in seconds) for running tests\")\n testing.add_argument('-cov', '--coverage', action='store_true',\n help=\"get suggestions on what lines to add tests for\")\n testing.add_argument('--autobackup', action='store_true',\n help=\"back up your work every minute in the background\")\n # runs an autobackup in the foreground. Used by `--autobackup`,\n # if you specify this other options will be ignored.\n testing.add_argument('--autobackup-actual-run-sync', action='store_true',\n help=argparse.SUPPRESS)\n # Debugging\n debugging = parser.add_argument_group('debugging tools for students')\n\n debugging.add_argument('--trace', action='store_true',\n help=\"trace code and launch python tutor\")\n debugging.add_argument('--trace-print', action='store_true',\n help=\"print the trace instead of visualizing it\")\n\n # Experiments\n experiment = parser.add_argument_group('experiment options')\n experiment.add_argument('--no-experiments', action='store_true',\n help=\"do not run experimental features\")\n experiment.add_argument('--hint', action='store_true',\n help=\"give a hint (if available)\")\n experiment.add_argument('--style', action='store_true',\n help=\"run AutoStyle feedback system\")\n experiment.add_argument('--collab', action='store_true',\n help=\"launch collaborative programming environment\")\n\n # Debug information\n debug = parser.add_argument_group('ok developer debugging options')\n debug.add_argument('--version', action='store_true',\n help=\"print the version number and exit\")\n debug.add_argument('--tests', action='store_true',\n help=\"display a list of all available tests\")\n debug.add_argument('--debug', action='store_true',\n help=\"show debugging output\")\n\n # Grading\n grading = parser.add_argument_group('grading options')\n grading.add_argument('--lock', action='store_true',\n help=\"lock the tests in a directory\")\n grading.add_argument('--score', action='store_true',\n help=\"score the assignment\")\n grading.add_argument('--score-out', type=str,\n nargs='?', const=None, default=None,\n help=\"write scores to a file\")\n grading.add_argument('--config', type=str,\n help=\"use a specific configuration file\")\n grading.add_argument('--ignore-empty', action='store_true',\n help=\"ignore empty doctests\")\n\n # Encrypt\n crypt = parser.add_argument_group('encryption')\n crypt.add_argument('--generate-encryption-key', type=str,\n help='generates a JSON file containing a list of [(file, key)] pairs. Path is a keyfile')\n crypt.add_argument('--encrypt', type=str,\n help='encrypt each problem. provide a path to a keyfile generated by --generate-encryption-key')\n crypt.add_argument('--encrypt-padding', type=int,\n help='If provided, pads all plaintexts to this size in bytes.'\n 'Errors if any of the files are longer than this')\n crypt.add_argument('--decrypt', type=str, nargs='*',\n help='decrypt all problems where the given keys apply')\n\n # Server parameters\n server = parser.add_argument_group('server options')\n server.add_argument('--local', action='store_true',\n help=\"disable any network activity\")\n server.add_argument('--nointeract', action='store_true',\n help=\"disable prompts to user\")\n server.add_argument('--server', type=str,\n default='okpy.org',\n help=\"set the server address\")\n server.add_argument('--authenticate', action='store_true',\n help=\"authenticate, ignoring previous authentication\")\n server.add_argument('--no-browser', action='store_true',\n help=\"do not use a web browser for authentication\")\n server.add_argument('--get-token', action='store_true',\n help=\"get ok access token\")\n server.add_argument('--insecure', action='store_true',\n help=\"use http instead of https\")\n server.add_argument('--no-update', action='store_true',\n help=\"do not check for ok updates\")\n server.add_argument('--update', action='store_true',\n help=\"update ok and exit\")\n # used in faded-parsons-frontend repo\n server.add_argument('--parsons', action='store_true', \n help=\"run parsons problems in browser\") \n\n return parser.parse_args(command_input)","def cli_main():\n\n\n if len(sys.argv) > 1 and sys.argv[1].endswith('.xmind'):\n xmind_file = sys.argv[1]\n xmind_file = get_absolute_path(xmind_file)\n logging.info('Start to convert XMind file: %s', xmind_file)\n\n if len(sys.argv) == 3 and sys.argv[2] == '-json':\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\n logging.info('Convert XMind file to testcase json file successfully: %s', testlink_json_file)\n elif len(sys.argv) == 3 and sys.argv[2] == '-xml':\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\n logging.info('Convert XMind file to testlink xml files successfully: %s', testlink_xml_file)\n elif len(sys.argv) == 3 and sys.argv[2] == '-csv':\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\n logging.info('Convert XMind file to zentao csv file successfully: %s', zentao_csv_file)\n elif len(sys.argv) == 3 and sys.argv[2] == '-xlsx':\n excel_xlsx_file = xmind_to_xlsx_file(xmind_file)\n logging.info('Convert XMind file to zentao csv file successfully: %s', excel_xlsx_file)\n else:\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\n logging.info('Convert XMind file successfully: \\n'\n '1、 testcase json file(%s)\\n'\n '2、 testlink xml file(%s)\\n'\n '3、 zentao csv file(%s)',\n testlink_json_file,\n testlink_xml_file,\n zentao_csv_file)\n\n\n else:\n print(__doc__)\n logging.error('%s', __doc__)","def test_cli(args, env, use_defaultenv, expect, defaultenv):\n # use --dump-config by default to make sure that the postgrest process will terminate for sure\n args = args or [\"--dump-config\"]\n\n env = env or {}\n if use_defaultenv:\n env = {**defaultenv, **env}\n\n if expect == \"error\":\n with pytest.raises(PostgrestError):\n print(cli(args, env=env))\n else:\n dump = cli(args, env=env).split(\"\\n\")\n if expect:\n assert expect in dump","def parse_args():\n parser = argparse.ArgumentParser(description='VCF to Typhi genotypes')\n parser.add_argument('--mode', choices=('vcf', 'bam', 'vcf_parsnp'), default='bam',\n help='Mode to run in based on input files (vcf, bam, or vcf_parsnp)')\n parser.add_argument('--vcf', nargs='+', type=str, required=False,\n help='VCF file(s) to genotype (Mapping MUST have been done using CT18 as a reference sequence)')\n parser.add_argument('--bam', nargs='+', type=str, required=False,\n help='BAM file(s) to genotype (Mapping MUST have been done using CT18 as a reference sequence)')\n parser.add_argument('--ref_id', type=str, required=False,\n help='Name of the reference in the VCF file (#CHROM column) or fasta file. Note that CT18 has '\n 'genotype 3.2.1. If all your strains return this genotype, it is likely you have '\n 'specified the name of the refrence sequence incorrectly; please check your VCFs.')\n parser.add_argument('--phred', type=int, required=False, default=20,\n help='Minimum phred quality to count a variant call vs CT18 as a true SNP (default 20)')\n parser.add_argument('--min_prop', type=float, required=False, default=0.1,\n help='Minimum proportion of reads required to call a SNP (default 0.1)')\n parser.add_argument('--ref', type=str, required=False,\n help='Reference sequence in fasta format. Required if bam files provided.')\n parser.add_argument('--output', type=str, required=False, default=None,\n help='Location and name for output file (default=stdout)')\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(\n description=\"Visualize the output of the model.\")\n\n parser.add_argument(\n \"-m\", \"--model\",\n required=True,\n help=\"Path to the model.\")\n parser.add_argument(\n \"-i\", \"--input\",\n required=True,\n help=\"Input file. Can be a '.npy', '.png', '.jpg', '.jpeg' or '.tif'.\")\n parser.add_argument(\n \"-s\", \"--sigma\",\n required=True,\n type=float,\n help=\"Standard deviation of the gaussian noise.\")\n\n parser.add_argument(\n \"-p\", \"--patch\",\n default=256,\n type=int,\n help=\"Optional. Patch size. Default is 256.\")\n parser.add_argument(\n \"-o\", \"--output\",\n default=\"\",\n help=\"Optional. Output file. A '.npz' file will save reshaped input \" \\\n \"image, noisy image and prediction. A '.npy' will save the \" \\\n \"prediction array. A '.png', '.jpg', '.jpeg' or '.tif' will \" \\\n \"save the prediction, but only if the input is a single image\")\n parser.add_argument(\n \"-v\", \"--visualize\",\n default=False,\n action='store_true',\n help=\"Optional. Boolean. Visualize the prediction.\")\n\n return parser.parse_args()","def main(args=None):\n\n args, _ = root_parser.parse_known_args(args=args)\n import_path = ENTRY_POINTS[args.test]\n module = import_module(import_path)\n main_fnc = getattr(module, \"main\")\n _check_main(main_fnc)\n if args.dry:\n return\n main_fnc()","def test_with_empty_args(self):\n parser = Parser()\n args = parser.parser.parse_args([])\n self.assertFalse(args.config)\n self.assertFalse(args.verbose)\n self.assertFalse(args.quiet)","def parse_args():\n\n op = OptionParser(usage=\"usage: %prog [opts] output_folder label\")\n\n ########################################\n\n op.add_option(\n \"--test\",\n dest=\"test\",\n default=False,\n action=\"store_true\",\n help=\"executes the test suite\")\n\n ########################################\n\n return op.parse_args()","def test_parse_arguments1():\n args = []\n parsed_args = parse_arguments.parse_arguments(args)\n assert parsed_args.logging_level == logging.ERROR","def parse_args():\n parser = argparse.ArgumentParser(description='use STAR for mapping reads')\n #input files\n parser.add_argument('-s', '--sequences',\n help='number of sequences to take for testing')\n parser.add_argument('--tmpdir',\n help='tmp directory',default=\"/tmp/\")\n parser.add_argument('--input_dir',\n help='optional: Choose input directory')\n parser.add_argument('--reads_R1',\n help='Forward unmerged reads')\n parser.add_argument('--reads_R2',\n help='Reverse unmerged reads')\n parser.add_argument('--merged',\n help='merged watson and crick fastq')\n parser.add_argument('--reference',\n help='reference clusters')\n parser.add_argument('--refgenome',\n help='reference clusters')\n parser.add_argument('--barcodes',\n help='Barcodes used in output')\n parser.add_argument('--species',\n help='Species: if selected only that species will be put in BAM RG header')\n parser.add_argument('--threads',\n help='Number of threads to used where multithreading is possible')\n parser.add_argument('--output_dir',\n help='Choose output directory')\n parser.add_argument('--extraflags',\n help='extra flags for testing')\n args = parser.parse_args()\n if args.input_dir:\n args.reads_R1 = os.path.join(args.input_dir,'Unassembled.R1.watson.fq.gz')\n args.reads_R2 = os.path.join(args.input_dir,'Unassembled.R2.crick.fq.gz')\n args.merged = os.path.join(args.input_dir,'Assembled.fq.gz')\n args.reference = os.path.join(args.input_dir,'consensus_cluster.renamed.fa')\n if args.output_dir:\n if not os.path.exists(args.output_dir):\n os.mkdir(args.output_dir)\n if 'log' not in args:\n args.log = os.path.join(args.output_dir,'mapping_variantcalling.log')\n args.watson_vcf = os.path.join(args.output_dir,'watson.vcf')\n args.crick_vcf = os.path.join(args.output_dir,'crick.vcf')\n args.snp_vcf = os.path.join(args.output_dir,'snp.vcf')\n args.methylation_vcf = os.path.join(args.output_dir,'methylation.vcf')\n args.heatmap = os.path.join(args.output_dir,'heatmap.igv')\n #2 bed files should be made for subsequent analysis using Rnbeads or other software\n args.mastermeth = os.path.join(args.output_dir,'methylation.bed')\n args.tmpdir = tempfile.mkdtemp(suffix='STAR', prefix='tmp', dir=args.tmpdir)\n return args","def test_run(prog, correct, inp=None):\n print(f\"Testing [{prog2str(prog)}] (inp={inp})...\", end=\"\")\n out = list()\n run_prog(prog, inp, out)\n if (out and out == correct) or (not out and prog == correct):\n print(\" OK!\")\n else:\n fail_str = prog2str(out if out else prog)\n print(f\" Failed: [{fail_str}] != [{prog2str(correct)}]!\")","def test_create_parser() -> None:\n\n def check(arg: List[str], expected_key: str, expected_value: Any) -> None:\n parsed = ParamClass.parse_args(arg)\n assert getattr(parsed, expected_key) == expected_value\n\n def check_fails(arg: List[str]) -> None:\n with pytest.raises(SystemExit) as e:\n ParamClass.parse_args(arg)\n assert e.type == SystemExit\n assert e.value.code == 2\n\n check([\"--name=foo\"], \"name\", \"foo\")\n check([\"--seed\", \"42\"], \"seed\", 42)\n check([\"--seed\", \"\"], \"seed\", 42)\n check([\"--number\", \"2.17\"], \"number\", 2.17)\n check([\"--number\", \"\"], \"number\", 3.14)\n check([\"--integers\", \"1,2,3\"], \"integers\", [1, 2, 3])\n check([\"--optional_int\", \"\"], \"optional_int\", None)\n check([\"--optional_int\", \"2\"], \"optional_int\", 2)\n check([\"--optional_float\", \"\"], \"optional_float\", None)\n check([\"--optional_float\", \"3.14\"], \"optional_float\", 3.14)\n check([\"--tuple1\", \"1,2\"], \"tuple1\", (1, 2.0))\n check([\"--int_tuple\", \"1,2,3\"], \"int_tuple\", (1, 2, 3))\n check([\"--enum=2\"], \"enum\", ParamEnum.EnumValue2)\n check([\"--floats=1,2,3.14\"], \"floats\", [1., 2., 3.14])\n check([\"--integers=1,2,3\"], \"integers\", [1, 2, 3])\n # Check all the ways of passing in True, with and without the first letter capitialized\n for flag in ('on', 't', 'true', 'y', 'yes', '1'):\n check([f\"--flag={flag}\"], \"flag\", True)\n check([f\"--flag={flag.capitalize()}\"], \"flag\", True)\n check([f\"--not_flag={flag}\"], \"not_flag\", True)\n check([f\"--not_flag={flag.capitalize()}\"], \"not_flag\", True)\n # Check all the ways of passing in False, with and without the first letter capitialized\n for flag in ('off', 'f', 'false', 'n', 'no', '0'):\n check([f\"--flag={flag}\"], \"flag\", False)\n check([f\"--flag={flag.capitalize()}\"], \"flag\", False)\n check([f\"--not_flag={flag}\"], \"not_flag\", False)\n check([f\"--not_flag={flag.capitalize()}\"], \"not_flag\", False)\n # Check that passing no value to flag sets it to True (the opposite of its default)\n check([\"--flag\"], \"flag\", True)\n # Check that no-flag is not an option\n check_fails([\"--no-flag\"])\n # Check that passing no value to not_flag fails\n check_fails([\"--not_flag\"])\n # Check that --no-not_flag is an option and sets it to False (the opposite of its default)\n check([\"--no-not_flag\"], \"not_flag\", False)\n # Check that both not_flag and no-not_flag cannot be passed at the same time\n check_fails([\"--not_flag=false\", \"--no-not_flag\"])\n # Check that invalid bools are caught\n check_fails([\"--flag=Falsf\"])\n check_fails([\"--flag=Truf\"])\n # Check that default values are created as expected, and that the non-overridable parameters\n # are omitted.\n defaults = vars(ParamClass.create_argparser().parse_args([]))\n assert defaults[\"seed\"] == 42\n assert defaults[\"tuple1\"] == (1, 2.3)\n assert defaults[\"int_tuple\"] == (1, 1, 1)\n assert defaults[\"enum\"] == ParamEnum.EnumValue1\n assert not defaults[\"flag\"]\n assert defaults[\"not_flag\"]\n assert \"readonly\" not in defaults\n assert \"constant\" not in defaults\n assert \"_non_override\" not in defaults\n # We can't test if all invalid cases are handled because argparse call sys.exit\n # upon errors.","def parse_cmd_arguments():\n parser = argparse.ArgumentParser(description='Process some integers.')\n parser.add_argument('-i', '--input', required=True, help='input JSON file')\n parser.add_argument('-o', '--output', required=True,\n help='ouput JSON file')\n parser.add_argument('-d', '--debug', required=False,\n help='log level. Can be 0-3. Defaults to 0')\n\n return parser.parse_args()","def test_main(cmd_map, argv, action, debug, alias):\n cmd_map.return_value = mock.Mock()\n app.main(argv)\n cmd_map.assert_called_once_with(argparse.Namespace(alias=alias, cmd_args=[], color=False, action=action, debug=debug))","def run(*args, **kwargs):\n kwargs[\"check\"] = True\n print(\"+\", \" \".join(args[0]))\n return subprocess.run(*args, **kwargs)","def parse_args(args):\n parser = argparse.ArgumentParser(prog=\"cc.py fuzzer\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=\"A fuzzer for the CAN bus\",\n epilog=\"\"\"Example usage:\n ./cc.py fuzzer -alg random\n ./cc.py fuzzer -alg ring_bf -id 244 -payload_bitmap 0000001 \n -file example.txt\n \"\"\"\n\n + \"\"\"\\nCurrently supported algorithms:\n random - Send random or static CAN payloads to \n random or static arbitration ids.\n linear - Use a given input file to send can packets.\n replay - Use the linear algorithm but also attempt \n to find a specific payload response.\n ring_bf - Attempts to brute force a static id \n using a ring based brute force algorithm.\n mutate - Mutates (hex) bits in the given id/payload.\n The mutation bits are specified \n in the id/payload bitmaps.\"\"\")\n\n parser.add_argument(\"-alg\", type=str, help=\"What fuzzing algorithm to use.\")\n parser.add_argument(\"-log\", type=int, default=0,\n help=\"How many cansend directives must be kept in memory at a time (default is 0)\")\n\n parser.add_argument(\"-file\", type=str, help=\"Specify a file to where the fuzzer should write\"\n \"the cansend directives it uses. \"\n \"This is required for the linear algorithm.\")\n\n parser.add_argument(\"-id\", type=str, help=\"Specify an id to use. \"\n \" Use the following syntax: 123\")\n parser.add_argument(\"-id_bitmap\", type=list, help=\"Override the default id bitmap with a different id bitmap. \"\n \"Use the following syntax: 0100 \"\n \"(with 1 a digit that can be overriden)\")\n\n parser.add_argument(\"-payload\", type=str, help=\"Specify a payload to use. \"\n \"Use the following syntax: FFFFFFFF\")\n parser.add_argument(\"-payload_bitmap\", type=list,\n help=\"Override the default payload bitmap with a different payload bitmap. \"\n \"Use the following syntax: 0100 (with 1 a digit that can be overriden)\")\n\n args = parser.parse_args(args)\n return args","def main(cls, args=None):\n parser = cls.CreateParser()\n cls.AddCommandLineArgs(parser, None)\n options, extra_args = parser.parse_known_args(args=args)\n cls.ProcessCommandLineArgs(parser, options, extra_args, None)\n return min(cls().Run(options, extra_args), 255)"],"string":"[\n \"def main_parse_args(args):\\n # Must return so that check command return value is passed back to calling routine\\n # otherwise py.test will fail\\n return main(parse_args(args))\",\n \"def main_parse_args(args):\\n # Must return so that check command return value is passed back to calling routine\\n # otherwise py.test will fail\\n return main(parse_args(args))\",\n \"def test_args(self):\\n parser = argparse.ArgumentParser(\\n prog=\\\"sysbottle\\\", description=\\\"sysbottle is parsed\\\"\\n )\\n subparsers = parser.add_subparsers()\\n sysbottle.build(subparsers)\\n args = parser.parse_args(\\n [\\n \\\"sysbottle\\\",\\n \\\"abc.txt\\\",\\n \\\"-c\\\",\\n \\\"90\\\",\\n \\\"-q\\\",\\n \\\"1\\\",\\n \\\"-d\\\",\\n \\\"sda\\\",\\n \\\"-i\\\",\\n \\\"5\\\",\\n \\\"-t\\\",\\n \\\"3\\\",\\n ]\\n )\\n self.assertTrue(hasattr(args, \\\"file\\\"))\\n self.assertTrue(hasattr(args, \\\"cpu\\\"))\\n self.assertTrue(hasattr(args, \\\"diskQ\\\"))\\n self.assertTrue(hasattr(args, \\\"disks\\\"))\\n self.assertTrue(hasattr(args, \\\"iowait\\\"))\\n self.assertTrue(hasattr(args, \\\"throughput\\\"))\",\n \"def test_check_args(caplog, tmpdir, monkeypatch):\\n\\n # Check existing input file\\n argv = [\\\"-i\\\", f\\\"{proj_dir}/data/input.json\\\"]\\n check_args(parse_args(argv))\\n\\n # Check non-existent input file\\n caplog.clear()\\n argv = [\\\"-i\\\", \\\"foo.bar\\\"]\\n with pytest.raises(SystemExit):\\n check_args(parse_args(argv))\\n assert caplog.records[0].message == 'Cannot find input file: foo.bar'\\n\\n # Check non-existent output file\\n ofile = tmpdir.join(\\\"foo.bar\\\")\\n argv = [\\\"-o\\\", ofile.strpath]\\n check_args(parse_args(argv))\\n\\n # Check existing output file\\n ofile = tmpdir.join(\\\"output.txt\\\")\\n ofile.write(\\\"Test\\\")\\n argv = [\\\"-o\\\", ofile.strpath]\\n\\n monkeypatch.setattr('sys.stdin', StringIO('Y\\\\n'))\\n check_args(parse_args(argv))\\n\\n monkeypatch.setattr('sys.stdin', StringIO('N\\\\n'))\\n with pytest.raises(SystemExit):\\n check_args(parse_args(argv))\\n ## Check unacceptable user input\\n monkeypatch.setattr('sys.stdin', StringIO('A\\\\nB\\\\nC\\\\nN'))\\n with pytest.raises(SystemExit):\\n check_args(parse_args(argv))\",\n \"def main():\\n args = parse_args()\\n process_args(args)\",\n \"def test_validargs(clickrunner):\\n for args in maincli.valid_args:\\n result = clickrunner.invoke(maincli.entrypoint, args)\\n assert result.exit_code == 2\\n assert \\\"Missing command\\\" in result.output\",\n \"def test_arg_parser_arg(self):\\n\\n parsed_args = self.parser.parse_args(['accu', '--debug', '--refresh',\\n '-v'])\\n\\n self.assertEqual(parsed_args.command, 'accu')\\n self.assertTrue(parsed_args.debug)\\n self.assertEqual(parsed_args.formatter, 'table')\\n self.assertTrue(parsed_args.refresh)\\n self.assertEqual(parsed_args.verbose_level, 1)\",\n \"def test_parse_arguments2():\\n args = ['--file', 'data.csv', '--debug']\\n parsed_args = parse_arguments.parse_arguments(args)\\n assert parsed_args.file == 'data.csv'\\n assert parsed_args.logging_level == logging.DEBUG\",\n \"def runTool(self, filename, expected_out, args):\\n\\n input_path = os.path.join(self.inputs_dir, filename)\\n return_value, actual_output = create_subprocess(self.executable_binary, args + [input_path] + ['--'])\\n actual_output = actual_output.decode('utf-8')\\n\\n self.assertEqual(return_value, 0)\\n self.evaluate(expected_out, actual_output, command=f'{[self.executable_binary] + args} {filename}')\",\n \"def test_cli_argparse(\\n config,\\n):\\n args = CLI.parse_args([\\\"-t\\\", \\\"0664123456\\\"])\\n assert args.recipient == \\\"0664123456\\\"\\n\\n args = CLI.parse_args([\\\"--to\\\", \\\"0664123456\\\"])\\n assert args.recipient == \\\"0664123456\\\"\\n\\n args = CLI.parse_args([\\\"-l\\\", \\\"0676456789123\\\"])\\n assert args.login == \\\"0676456789123\\\"\\n\\n args = CLI.parse_args([\\\"--login\\\", \\\"0676456789123\\\"])\\n assert args.login == \\\"0676456789123\\\"\\n\\n args = CLI.parse_args([\\\"-p\\\", \\\"s3cret..11\\\"])\\n assert args.password == \\\"s3cret..11\\\"\\n\\n args = CLI.parse_args([\\\"--password\\\", \\\"s3cret..11\\\"])\\n assert args.password == \\\"s3cret..11\\\"\\n\\n args = CLI.parse_args([\\\"-c\\\", \\\".yessssms.config\\\"])\\n assert args.configfile == \\\".yessssms.config\\\"\\n\\n args = CLI.parse_args([\\\"--configfile\\\", \\\".yessssms.config\\\"])\\n assert args.configfile == \\\".yessssms.config\\\"\\n\\n args = CLI.parse_args([\\\"--message\\\", \\\"testmessage 123 - can you see this?\\\"])\\n assert args.message == \\\"testmessage 123 - can you see this?\\\"\\n\\n args = CLI.parse_args([\\\"-m\\\", \\\"testmessage 123 - can you see this?\\\"])\\n assert args.message == \\\"testmessage 123 - can you see this?\\\"\\n\\n args = CLI.parse_args([\\\"--mvno\\\", \\\"YESSS\\\"])\\n assert args.provider == \\\"YESSS\\\"\\n\\n args = CLI.parse_args([\\\"--mvno\\\", \\\"EDUCOM\\\"])\\n assert args.provider == \\\"EDUCOM\\\"\\n\\n args = CLI.parse_args([\\\"--mvno\\\", \\\"SIMfonie\\\"])\\n assert args.provider == \\\"SIMfonie\\\"\\n\\n args = CLI.parse_args([\\\"--mvno\\\", \\\"BLABLABLA\\\"])\\n assert args.provider == \\\"BLABLABLA\\\"\",\n \"def test_with_explicit_sample_args(self):\\n test_dict = CliArgs('sample', ['-a', '26', '-s', 'somefile', '-n', '-u', 'foo', '-v']).__dict__\\n self.assertEqual('foo', test_dict['user'])\\n self.assertEqual(1, test_dict['verbosity'])\\n self.assertEqual('26', test_dict['analyzer_profile'])\",\n \"def handle_options( args ):\\n parser = optparse.OptionParser( version = \\\"BitTest Bitbake Testing Tool version %s\\\" % __version__,\\n usage = \\\"\\\"\\\"%prog [options] [test ...]\\nExecutes the specified tests or if none are given all tests. The result is printed\\non the cmd line\\n\\\"\\\"\\\" )\\n\\n parser.add_option( \\\"-o\\\", \\\"--output\\\", help = \\\"print the output to the file\\\",\\n action = \\\"store\\\", dest = \\\"output\\\", default = None )\\n parser.add_option( \\\"-f\\\", \\\"--format\\\", help = \\\"print the output in the specified format\\\",\\n action = \\\"store\\\", dest = \\\"format\\\", default = None )\\n\\n options, args = parser.parse_args( args )\\n return options, args[1:]\",\n \"def test_with_command_line_arguments(self, arguments):\\n fixed_arguments = self.get_argument_string(arguments)\\n result = self.run(\\n arguments=fixed_arguments,\\n timeout=self.full_timeout,\\n use_fresh_profile=True)\\n return self._handle_test_result(result)\",\n \"def check_argv():\\n parser = argparse.ArgumentParser(description=__doc__.strip().split(\\\"\\\\n\\\")[0], add_help=False)\\n parser.add_argument(\\\"data_dir\\\", type=str, help=\\\"data directory\\\")\\n parser.add_argument(\\n \\\"--init_am_n_iter\\\", type=int, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"init_am_n_iter\\\"]\\n )\\n parser.add_argument(\\n \\\"--segment_n_iter\\\", type=int, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"segment_n_iter\\\"]\\n )\\n parser.add_argument(\\n \\\"--K_max\\\", type=str, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"K_max\\\"]\\n )\\n parser.add_argument(\\n \\\"--min_duration\\\", type=int, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"min_duration\\\"]\\n )\\n parser.add_argument(\\n \\\"--n_slices_max\\\", type=int, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"n_slices_max\\\"]\\n )\\n parser.add_argument(\\n \\\"--p_boundary_init\\\", type=float, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"p_boundary_init\\\"]\\n ) \\n parser.add_argument(\\n \\\"--n_cpus\\\", type=int, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"n_cpus\\\"]\\n )\\n parser.add_argument(\\n \\\"--n_batches\\\", type=int, help=\\\"default: %(default)s\\\",\\n default=default_options_dict[\\\"n_batches\\\"]\\n )\\n if len(sys.argv) == 1:\\n parser.print_help()\\n sys.exit(1)\\n return parser.parse_args()\",\n \"def old_main():\\n valid_formats = ('pre', 'table', 'numeric')\\n from optparse import OptionParser\\n parser = OptionParser()\\n #parser.add_option('-v', '--verbose', action='store_true', dest='verbose', default=False)\\n parser.add_option('-o', '--output', dest='output_filename', metavar='FILE', default='-', help='output file')\\n parser.add_option('--format', dest='format', default='pre', help='one of ' + str(valid_formats))\\n parser.add_option('--test', action='store_true', dest='test', default=False, help='run some unit tests')\\n options, args = parser.parse_args()\\n # validate the format option\\n if options.format not in valid_formats:\\n print 'invalid --format parameter:', options.format, 'is not in', valid_formats\\n return\\n # run a test or run a demo\\n if options.output_filename == '-':\\n out = sys.stdout\\n else:\\n out = open(options.output_filename, 'w')\\n run(out, options.format)\\n if out is not sys.stdout:\\n out.close()\",\n \"def command_test(args):\\n\\tfrom inquisitor.configs.resolver import (\\n\\t\\tconfig_path,\\n\\t\\tCONFIG_DATA, data_path,\\n\\t\\tCONFIG_SOURCES, source_path,\\n\\t\\tCONFIG_CACHE, cache_path,\\n\\t\\tCONFIG_LOGFILE, log_file,\\n\\t\\tCONFIG_VERBOSE, is_verbose,\\n\\t\\tCONFIG_SUBFEEDS, subfeeds,\\n\\t)\\n\\tsubfeeds = '; '.join(\\n\\t\\t'{0}: {1}'.format(\\n\\t\\t\\tsf_name,\\n\\t\\t\\t' '.join(sf_sources)\\n\\t\\t)\\n\\t\\tfor sf_name, sf_sources\\n\\t\\tin subfeeds.items()\\n\\t) if subfeeds else ''\\n\\tprint(f'Inquisitor configured from {config_path}')\\n\\tprint(f' {CONFIG_DATA} = {data_path}')\\n\\tprint(f' {CONFIG_SOURCES} = {source_path}')\\n\\tprint(f' {CONFIG_CACHE} = {cache_path}')\\n\\tprint(f' {CONFIG_LOGFILE} = {log_file}')\\n\\tprint(f' {CONFIG_VERBOSE} = {is_verbose}')\\n\\tprint(f' {CONFIG_SUBFEEDS} = {subfeeds}')\\n\\treturn 0\",\n \"def test_options2args():\\n args, kwargs = util.options2args([\\\"--arg1\\\", \\\"-arg2\\\", \\\"--arg3=10\\\"])\\n assert all([\\\"--arg1\\\" in args, \\\"-arg2\\\" in args, \\\"arg3\\\" in kwargs.keys()])\",\n \"def main(args):\\n cli = CLI()\\n # Check arguments\\n cli.parse_arguments(args)\",\n \"def test_atleast_two_arguments_needed_one():\\n cli_result = subprocess.run(\\n ['kaiba', 'config.js'],\\n capture_output=True,\\n )\\n assert b'the following arguments are required: input' in cli_result.stderr\\n assert cli_result.returncode == 2\",\n \"def run(self):\\n if len(sys.argv[1:]) == 0:\\n arg_parse.print_help()\\n arg_parse.exit()\\n\\n try:\\n if args.unix:\\n self.from_unix_sec()\\n print (\\\"Unix Seconds: \\\" + self.in_unix_sec + \\\" UTC\\\")\\n elif args.umil:\\n self.from_unix_milli()\\n print (\\\"Unix Milliseconds: \\\" + self.in_unix_milli + \\\" UTC\\\")\\n elif args.wh:\\n self.from_win_64_hex()\\n print (\\\"Windows 64 bit Hex BE: \\\" + self.in_windows_hex_64 + \\\" UTC\\\")\\n elif args.whle:\\n self.from_win_64_hexle()\\n print (\\\"Windows 64 bit Hex LE: \\\" + self.in_windows_hex_le + \\\" UTC\\\")\\n elif args.goog:\\n self.from_chrome()\\n print (\\\"Google Chrome Time: \\\" + self.in_chrome + \\\" UTC\\\")\\n elif args.active:\\n self.from_ad()\\n print (\\\"Active Directory Timestamp: \\\" + self.in_ad + \\\" UTC\\\")\\n elif args.uhbe:\\n self.from_unix_hex_32be()\\n print (\\\"Unix Hex 32 bit BE: \\\" + self.in_unix_hex_32 + \\\" UTC\\\")\\n elif args.uhle:\\n self.from_unix_hex_32le()\\n print (\\\"Unix Hex 32 bit LE: \\\" + self.in_unix_hex_32le + \\\" UTC\\\")\\n elif args.cookie:\\n self.from_cookie()\\n print (\\\"Windows Cookie Date: \\\" + self.in_cookie + \\\" UTC\\\")\\n elif args.oleb:\\n self.from_ole_be()\\n print (\\\"Windows OLE 64 bit double BE: \\\" + self.in_ole_be + \\\" UTC\\\")\\n elif args.olel:\\n self.from_ole_le()\\n print (\\\"Windows OLE 64 bit double LE: \\\" + self.in_ole_le + \\\" UTC\\\")\\n elif args.mac:\\n self.from_mac()\\n print (\\\"Mac Absolute Time: \\\" + self.in_mac + \\\" UTC\\\")\\n elif args.hfsdec:\\n self.from_hfs_dec()\\n print (\\\"Mac OS/HFS+ Decimal Date: \\\" + self.in_hfs_dec + \\\" UTC\\\")\\n elif args.hfsbe:\\n self.from_hfs_be()\\n print (\\\"HFS/HFS+ 32 bit Hex BE: \\\" + self.in_hfs_be + \\\" HFS Local / HFS+ UTC\\\")\\n elif args.hfsle:\\n self.from_hfs_le()\\n print (\\\"HFS/HFS+ 32 big Hex LE: \\\" + self.in_hfs_le + \\\" HFS Local / HFS+ UTC\\\")\\n elif args.msdos:\\n self.from_msdos()\\n print (\\\"MS-DOS 32 bit Hex Value: \\\" + self.in_msdos + \\\" Local\\\")\\n elif args.fat:\\n self.from_fat()\\n print (\\\"FAT Date + Time: \\\" + self.in_fat + \\\" Local\\\")\\n elif args.sys:\\n self.from_systime()\\n print (\\\"Microsoft 128 bit SYSTEMTIME: \\\" + self.in_systemtime + \\\" UTC\\\")\\n elif args.ft:\\n self.from_filetime()\\n print (\\\"Microsoft FILETIME/LDAP time: \\\" + self.in_filetime + \\\" UTC\\\")\\n elif args.pr:\\n self.from_prtime()\\n print (\\\"Mozilla PRTime: \\\" + self.in_prtime + \\\" UTC\\\")\\n elif args.auto:\\n self.from_ole_auto()\\n print (\\\"OLE Automation Date: \\\" + self.in_ole_auto + \\\" UTC\\\")\\n elif args.ios:\\n self.from_ios_time()\\n print (\\\"iOS 11 Date: \\\" + self.in_iostime)\\n elif args.sym:\\n self.from_sym_time()\\n print (\\\"Symantec AV Timestamp: \\\" + self.in_symtime)\\n elif args.gps:\\n self.from_gps_time()\\n print (\\\"GPS Timestamp: \\\" + self.in_gpstime)\\n elif args.timestamp:\\n self.to_timestamps()\\n elif args.guess:\\n self.from_all()\\n except Exception as e:\\n logging.error(str(type(e)) + \\\",\\\" + str(e))\",\n \"def test_cli_with_test_args(\\n config,\\n):\\n testargs = [\\n \\\"yessssms\\\",\\n \\\"--test\\\",\\n \\\"-l\\\",\\n \\\"06641234567\\\",\\n \\\"-p\\\",\\n \\\"passw0rd\\\",\\n \\\"-t\\\",\\n \\\"+43676564736\\\",\\n ]\\n with mock.patch.object(sys, \\\"argv\\\", testargs):\\n with requests_mock.Mocker() as m:\\n m.register_uri(\\n \\\"POST\\\",\\n _LOGIN_URL,\\n status_code=302,\\n # pylint: disable=protected-access\\n headers={\\\"location\\\": _KONTOMANAGER_URL},\\n )\\n m.register_uri(\\\"GET\\\", _KONTOMANAGER_URL, status_code=200)\\n m.register_uri(\\n \\\"GET\\\",\\n # pylint: disable=protected-access\\n _SMS_FORM_URL,\\n status_code=200,\\n text=TEST_FORM_TOKEN_SAMPLE,\\n )\\n m.register_uri(\\n \\\"POST\\\",\\n _SEND_SMS_URL,\\n status_code=200,\\n text=\\\"<h1>Ihre SMS wurde erfolgreich \\\" + \\\"verschickt!</h1>\\\",\\n )\\n m.register_uri(\\\"GET\\\", _LOGOUT_URL, status_code=200)\\n val = CLI().exit_status\\n assert val == 0\",\n \"def ReadArguments():\\n\\n args = ParseArguments()\\n\\n logging.info('Command line arguments...')\\n for arg in vars(args):\\n logging.info(str(arg) + ': ' + str(getattr(args, arg)))\\n logging.info('')\\n\\n IsTest(args)\\n ProcessCacheSize(args)\\n ProcessLineSize(args)\\n ProcessMulti(args)\\n ProcessMemPattern(args)\\n ProcessMemFile(args)\",\n \"def parse_args(args):\\n\\n parser = argparse.ArgumentParser(\\n description=\\\"\\\"\\\"Generates and runs an afni_proc.py script to preprocess resting state fMRI data\\\"\\\"\\\",\\n formatter_class=argparse.RawDescriptionHelpFormatter)\\n\\n # Optional Flags\\n parser.add_argument(\\\"-t\\\", \\\"--trs_remove\\\", action=\\\"store\\\", default=5, type=int, metavar='TRs',\\n help=\\\"\\\"\\\"number of trs to remove at the beginning of the epi data\\n (default = 5 trs)\\\"\\\"\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--dim_voxel\\\", action=\\\"store\\\", default=2.0, type=float, metavar='MM',\\n help=\\\"voxel dimensions in mm that processed epi will be resampled to (default = 2.0 mm)\\\")\\n parser.add_argument(\\\"-b\\\", \\\"--bandpass\\\", action=\\\"store\\\", default=[0.01, 0.25], nargs=2, type=float, metavar=\\\"F\\\",\\n help=\\\"bandpass frequencies lower and upper limits (default = 0.01 0.25)\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--volumes\\\", action=\\\"store\\\", default=0, type=int, metavar=\\\"V\\\",\\n help=\\\"\\\"\\\"truncate the epi data to the inputted number of volumes, useful if subjects have data \\n with different numbers of volumes (default = no truncation)\\\"\\\"\\\")\\n parser.add_argument(\\\"-f\\\", \\\"--fwhm\\\", action=\\\"store\\\", default=5.0, type=float, metavar=\\\"MM\\\",\\n help=\\\"the full width half maximum that is used when blurring (default = 5.0 mm)\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--cores\\\", action=\\\"store\\\", default=cpu_count(), type=int, metavar=\\\"C\\\",\\n help=\\\"number of cores supplied to 3dDeconvolve (default = all cores)\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--subj_id\\\", action=\\\"store\\\", default=\\\"sub\\\", metavar=\\\"SUB\\\",\\n help=\\\"text file of subject ids (default = sub)\\\")\\n parser.add_argument(\\\"-T\\\", \\\"--time_step\\\", action=\\\"store\\\", default=0, type=float, metavar=\\\"TS\\\",\\n help=\\\"set the time step for bandpassing (default = ts in header info\\\")\\n\\n parser.add_argument(\\\"-g\\\", \\\"--global_signal_regression\\\", action=\\\"store_false\\\", default=True,\\n help=\\\"do not perform global signal regression (default = perform gsr)\\\")\\n\\n parser.add_argument(\\\"-r\\\", \\\"--rerun\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"\\\"\\\"rerun preprocessing, override and delete previous results in \\n 'Processed' folder (default = don't override)\\\"\\\"\\\")\\n parser.add_argument(\\\"-m\\\", \\\"--motion_param\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"use 12 motion parameters for regression (default = 6 motion parameters)\\\")\\n parser.add_argument(\\\"-G\\\", \\\"--gm_blur\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"blur only in grey matter mask (default = blur in whole brain)\\\")\\n parser.add_argument(\\\"-n\\\", \\\"--nl_reg\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"use non-linear warp between anatomical and MNI template (default = linear warp)\\\")\\n\\n # Required Inputs\\n required = parser.add_argument_group(\\\"required arguments\\\")\\n required.add_argument(\\\"-e\\\", \\\"--epi\\\", action=\\\"store\\\", required=True,\\n help=\\\"text file of paths to raw epi data\\\")\\n required.add_argument(\\\"-a\\\", \\\"--anat\\\", action=\\\"store\\\", required=True,\\n help=\\\"text file of paths to raw anatomical data\\\")\\n required.add_argument(\\\"-o\\\", \\\"--out_dir\\\", action=\\\"store\\\", required=True, metavar=\\\"OUT\\\",\\n help=\\\"text file of paths to output directory\\\")\\n result = parser.parse_args(args)\\n\\n # Make sure inputted parameters are legal\\n assert (os.path.isfile(result.epi)), \\\"{} does not exist or is not a file\\\".format(result.epi)\\n assert (os.path.isfile(result.anat)), \\\"{} does not exist or is not a file\\\".format(result.ant)\\n assert (result.trs_remove >= 0), \\\"Cannot remove negative trs\\\"\\n assert (result.dim_voxel >= 0), \\\"Cannot have a negative voxel dimension\\\"\\n assert (np.all(np.array(result.bandpass) > 0)), \\\"Cannot have a negative frequency limit for bandpassing\\\"\\n assert (result.volumes > -1), \\\"Number of volumes must be greater than 0\\\"\\n assert (result.cores > 0), \\\"Number of cores used must be greater than 0\\\"\\n assert (result.time_step > -1), \\\"Time step must be greater than 0\\\"\\n\\n return result\",\n \"def test_parse_arguments1():\\n args = []\\n parsed_args = parse_arguments.parse_arguments(args)\\n assert parsed_args.logging_level == logging.ERROR\\n assert parsed_args.group_size == defaults.DEFAULT_GRPSIZE\\n assert parsed_args.students_file == defaults.DEFAULT_CSVFILE\\n assert (parsed_args.grouping_method == group_random) is False\",\n \"def parse_arguments(args):\",\n \"def test_cli_boolean_args(\\n config,\\n):\\n args = CLI.parse_args([\\\"--version\\\"])\\n assert args.version is True\\n\\n args = CLI.parse_args([\\\"--test\\\"])\\n assert args.test is True\\n\\n args = CLI.parse_args([\\\"--print-config-file\\\"])\\n assert args.print_config_file is True\\n\\n args = CLI.parse_args([\\\"-T\\\"])\\n assert args.check_login is True\",\n \"def test_cli_args():\\n expected = dict(\\n paths=[\\\"path1\\\", \\\"path2\\\"],\\n exclude=[\\\"file*.py\\\", \\\"dir/\\\"],\\n ignore_decorators=[\\\"deco1\\\", \\\"deco2\\\"],\\n ignore_names=[\\\"name1\\\", \\\"name2\\\"],\\n make_whitelist=True,\\n min_confidence=10,\\n sort_by_size=True,\\n verbose=True,\\n )\\n result = _parse_args(\\n [\\n \\\"--exclude=file*.py,dir/\\\",\\n \\\"--ignore-decorators=deco1,deco2\\\",\\n \\\"--ignore-names=name1,name2\\\",\\n \\\"--make-whitelist\\\",\\n \\\"--min-confidence=10\\\",\\n \\\"--sort-by-size\\\",\\n \\\"--verbose\\\",\\n \\\"path1\\\",\\n \\\"path2\\\",\\n ]\\n )\\n assert isinstance(result, dict)\\n assert result == expected\",\n \"def parse_args(self):\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-d', '--data', dest='data',\\n help='Generate requested amount of test data.',\\n type=int, nargs='+')\\n parser.add_argument('-c', '--check', action='store_true',\\n dest='check', help='Check files without modifying them.',\\n default=False)\\n args = parser.parse_args()\\n self.arg_data = args.data\\n self.arg_check = args.check\\n\\n if xc.arg_data: # did the user request to generate test data?\\n choice = input(Fore.YELLOW + 'This option will ' + Fore.RED +\\n '*OVERWRITE ALL FILES* ' + Fore.YELLOW + 'you sure (y/n)? ')\\n if choice.upper() == 'Y':\\n self.test_data_row_count = int(self.arg_data[0])\\n xc.generate_test_data()\\n else:\\n xc.arg_data = False\\n else:\\n self.process_dump_files()\",\n \"def test_args_combination():\\n args = cli.parse_args(['-cp'])\\n assert args.copy\\n assert args.paste\\n args = cli.parse_args(['-c', '-p'])\\n assert args.copy\\n assert args.paste\",\n \"def check_argv():\\n parser = argparse.ArgumentParser(\\n description=__doc__.strip().split(\\\"\\\\n\\\")[0], add_help=False\\n )\\n parser.add_argument(\\\"npz_fn\\\", type=str, help=\\\"NumPy archive\\\")\\n parser.add_argument(\\n \\\"--metric\\\", choices=[\\\"cosine\\\", \\\"euclidean\\\", \\\"hamming\\\", \\\"chebyshev\\\"],\\n default=\\\"cosine\\\",\\n help=\\\"distance metric (default: %(default)s)\\\"\\n )\\n parser.add_argument(\\n \\\"--mean_ap\\\", dest=\\\"mean_ap\\\", action=\\\"store_true\\\",\\n help=\\\"also compute mean average precision (this is significantly \\\"\\n \\\"more resource intensive)\\\"\\n )\\n parser.add_argument(\\n \\\"--mvn\\\", action=\\\"store_true\\\",\\n help=\\\"mean and variance normalise (default: False)\\\"\\n )\\n if len(sys.argv) == 1:\\n parser.print_help()\\n sys.exit(1)\\n return parser.parse_args()\",\n \"def test_0_args(library_db):\\n import argparse\\n\\n namespace = app.main([])\\n assert namespace.__class__ == argparse.Namespace\",\n \"def test_main_arguments():\\n args = argparse.Namespace(url=RANDOM_URL,\\n username=RANDOM_USERNAME,\\n password=RANDOM_PASSWORD,\\n tenantcode=RANDOM_TENANTCODE)\\n result = Config(\\\"wso_args.json\\\").main(args)\\n\\n assert result is True\",\n \"def main():\\n program_name = os.path.basename(sys.argv[0])\\n\\n _initialize_debugging(program_name)\\n _handle_signals()\\n _process_environment_variables()\\n arguments = _process_command_line()\\n\\n exit_status = 1 # no match\\n if arguments:\\n for filename in arguments:\\n if os.path.isfile(filename):\\n if not parameters[\\\"No formatting\\\"]:\\n print(filename + \\\":\\\")\\n for _, printable_string in strings.strings(filename):\\n if what_in_string(printable_string):\\n exit_status = 0 # match found\\n if parameters[\\\"First match only\\\"]:\\n break\\n else:\\n logging.error('\\\"%s\\\": No such file or directory', filename)\\n elif parameters[\\\"Stdin unused\\\"]:\\n logging.critical(\\\"At least one filename expected\\\")\\n else:\\n for _, printable_string in strings.strings():\\n if what_in_string(printable_string):\\n exit_status = 0 # match found\\n if parameters[\\\"First match only\\\"]:\\n break\\n\\n sys.exit(exit_status)\",\n \"def test_parse_arguments_default(self):\\n self.assertEqual(self.args.python, sys.executable)\\n self.assertEqual(self.args.pip_packages, [])\\n self.assertEqual(self.args.number_of_threads, multiprocessing.cpu_count())\\n self.assertEqual(self.args.cmake_installer,\\n CMAKE_INSTALLER_URL_BY_HOST_PLATFORM.get(sys.platform))\\n self.assertEqual(self.args.cmake_source_project_root,\\n os.path.join(os.getcwd(), 'git', 'falken'))\\n self.assertEqual(self.args.cmake_copybara_variable, 'FALKEN_DIR')\\n self.assertIsNone(self.args.cmake_configure_args)\\n self.assertEqual(self.args.cmake_generator,\\n cmake_runner.CMakeRunner.default_generator())\\n self.assertEqual(\\n self.args.cmake_target_architecture,\\n cmake_runner.CMakeRunner.default_architecture(\\n cmake_runner.CMakeRunner.default_generator()))\\n self.assertEqual(self.args.cmake_build_dir,\\n os.path.join(os.getcwd(), 'build'))\\n self.assertEqual(self.args.cmake_build_configs, CMAKE_DEFAULT_BUILD_CONFIGS)\\n self.assertIsNone(self.args.cmake_package_configs)\\n self.assertEqual(self.args.cmake_package_generator, 'ZIP')\\n self.assertIsNone(self.args.cmake_test_regex)\\n self.assertEqual(self.args.output_dir, 'output')\\n self.assertIsNone(self.args.copy_artifacts)\\n self.assertIsNone(self.args.zip_artifacts)\",\n \"def test_parser_all(self):\\n args = [\\\"directory\\\", \\\"-n\\\", \\\"-f\\\", \\\"-r\\\"]\\n parser = setup_parser()\\n output = parser.parse_args(args)\\n self.assertEqual(output.directory, \\\"directory\\\")\\n self.assertFalse(output.colorize)\\n self.assertTrue(output.fancy)\\n self.assertTrue(output.reverse)\",\n \"def parse_args(args):\\n \\n # Construct the parser (which is stored in parser)\\n # Module docstring lives in __doc__\\n # See http://python-forum.com/pythonforum/viewtopic.php?f=3&t=36847\\n # And a formatter class so our examples in the docstring look good. Isn't it\\n # convenient how we already wrapped it to 80 characters?\\n # See http://docs.python.org/library/argparse.html#formatter-class\\n parser = argparse.ArgumentParser(description=__doc__, \\n formatter_class=argparse.RawDescriptionHelpFormatter)\\n \\n # General options\\n parser.add_argument(\\\"hal\\\",\\n help=\\\"HAL file to evaluate\\\")\\n parser.add_argument(\\\"--truth\\\",\\n help=\\\"MAF file of a true alignment for precision and recall\\\")\\n parser.add_argument(\\\"--beds\\\", nargs=\\\"*\\\",\\n help=\\\"BED file(s) of genes on the genomes in the HAL\\\")\\n parser.add_argument(\\\"--coverage_file\\\", type=argparse.FileType(\\\"w\\\"),\\n default = sys.stdout,\\n help=\\\"file to save average coverage vs. the reference in (one number)\\\")\\n parser.add_argument(\\\"--precision_recall_file\\\", type=argparse.FileType(\\\"w\\\"),\\n default = sys.stdout,\\n help=\\\"TSV file to save precision and recall in (two numbers)\\\")\\n parser.add_argument(\\\"--gene_category_file\\\", type=argparse.FileType(\\\"w\\\"),\\n default = sys.stdout,\\n help=\\\"file to save categories and counts for genes in\\\")\\n parser.add_argument(\\\"--tag\\\", nargs=\\\"*\\\", default=[],\\n help=\\\"extra columns to tag all result TSV lines with at the front\\\")\\n \\n # The command line arguments start with the program name, which we don't\\n # want to treat as an argument for argparse. So we remove it.\\n args = args[1:]\\n \\n return parser.parse_args(args)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"evaluate the recovered derg by comparing with ground truth mapping file\\\")\\n parser.add_argument(\\\"-mapping\\\", action=\\\"store\\\", dest=\\\"mapping_file\\\",\\n required=True, help=\\\"path to proguard-generated mapping.txt\\\")\\n parser.add_argument(\\\"-recovered_derg\\\", action=\\\"store\\\", dest=\\\"recovered_derg\\\",\\n required=True, help=\\\"path to recovered derg\\\")\\n parser.add_argument(\\\"-nice2predict_mapping\\\", action=\\\"store\\\", dest=\\\"nice2predict_mapping_file\\\",\\n help=\\\"path to nice2predict-generated mapping.txt\\\")\\n parser.add_argument(\\\"-o\\\", action=\\\"store\\\", dest=\\\"report_dir\\\",\\n default=\\\".\\\", help=\\\"directory of report files\\\")\\n parser.add_argument(\\\"-report_name\\\", action=\\\"store\\\", dest=\\\"report_name\\\",\\n default=DEFAULT_REPORT_NAME, help=\\\"name of report file\\\")\\n parser.add_argument(\\\"-match_mode\\\", action=\\\"store\\\", dest=\\\"match_mode\\\",\\n default=MATCH_MODE_EXACT, help=\\\"match mode\\\")\\n\\n options = parser.parse_args()\\n print options\\n return options\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=__doc__)\\n # If user doesn't specify an input file, read from standard input. Since\\n # encodings are the worst thing, we're explicitly expecting std\\n parser.add_argument('-i', '--infile',\\n type=lambda x: open(x, encoding=ENCODE_IN),\\n default=io.TextIOWrapper(\\n sys.stdin.buffer, encoding=ENCODE_IN)\\n )\\n # Same thing goes with the output file.\\n parser.add_argument('-o', '--outfile',\\n type=lambda x: open(x, 'w', encoding=ENCODE_OUT),\\n default=io.TextIOWrapper(\\n sys.stdout.buffer, encoding=ENCODE_OUT)\\n )\\n # Set the verbosity level for the logger. The `-v` option will set it to\\n # the debug level, while the `-q` will set it to the warning level.\\n # Otherwise use the info level.\\n verbosity = parser.add_mutually_exclusive_group()\\n verbosity.add_argument('-v', '--verbose', action='store_const',\\n const=logging.DEBUG, default=logging.INFO)\\n verbosity.add_argument('-q', '--quiet', dest='verbose',\\n action='store_const', const=logging.WARNING)\\n return parser.parse_args()\",\n \"def parse_stdin_args():\\n \\n #Initialize parser\\n parser = argparse.ArgumentParser(description = \\\"Creates synthetic paired end DNA from given fraction of Human, Bacteria, Phix174 and Virus/Phage. \\\\\\n Note: List of Human, Bacteria, Phix174 and Virus/Phage fractions should be space separated. Example: -hu 0.5 0.2 \\\\\\n -b 0.3 0.01 -x 0.1 0.01. The Virus/Phage fraction is taken 1 - Human - Bacteria - Phix174.\\\")\\n \\n #Required command line arguments\\n parser.add_argument(\\\"-s\\\", help=\\\"Number of total reads\\\", required = True, type = int)\\n parser.add_argument(\\\"-p\\\", help=\\\"Path to directory for output files\\\", required = True)\\n parser.add_argument(\\\"-hu\\\", help=\\\"Human DNA percentage. Default: [0.5, 0.1, 0.01, 0.001]\\\", required = False, type= float, nargs = \\\"+\\\", default = [0.5, 0.1, 0.01, 0.001])\\n parser.add_argument(\\\"-b\\\", help=\\\"Bacterial DNA percentage. Default: [0.4, 0.25, 0.1, 0.5]\\\", required = False, type= float, nargs = \\\"+\\\", default = [0.4, 0.25, 0.1, 0.5])\\n parser.add_argument(\\\"-x\\\", help=\\\"Phix174 DNA percentage. Default: [0.01, 0.001]\\\", required = False, type= float, nargs = \\\"+\\\", default = [0.01, 0.001])\\n parser.add_argument(\\\"-isfastq\\\", help=\\\"Specify output format 1=FASTQ 0=FASTA (Default: 1)\\\", required = False, type= int, default = 1)\\n parser.add_argument(\\\"-n\\\", help=\\\"Read length. Default: 250\\\", required = False, type= int, default = 250)\\n parser.add_argument(\\\"-err\\\", help=\\\"Error rate. Default: 0\\\", required = False, type= int, default = 0)\\n \\n #Read the command line arguments\\n args = parser.parse_args()\\n \\n #check parameters\\n if args.s < 100:\\n print \\\"Number of total reads must be greater than 100.\\\"\\n sys.exit(0)\\n \\n if args.n < 35:\\n print \\\"Read length should be greater than 35\\\"\\n sys.exit(0)\\n\\n return args\",\n \"def test_compare(self):\\n parser = parse_args(['-g', '10', '-s', 'bubble', '-c'])\\n self.assertTrue(parser.compare)\\n self.assertEqual(True, parser.compare)\\n\\n parser = parse_args(['-g', '10', '-s', 'bubble'])\\n self.assertEqual(False, parser.compare)\",\n \"def test_arg_parse(test):\\n print(test)\\n testargs = ['']\\n with mock.patch.object(sys, 'argv', testargs):\\n assert gc.arg_parse().interactive\",\n \"def verify(args):\\n # assume that the arguments are not valid and prove otherwise\\n verified_arguments = False\\n # TOP-LEVEL VERIFIED:\\n # both a file and a directory were specified and a command is not given\\n # pylint: disable=bad-continuation\\n if is_valid_file_and_directory(args) and (\\n not is_command_ancillary(args)\\n and not is_valid_command(args)\\n and not is_valid_commits(args)\\n ):\\n # track how many of the sub-arguments separately verified\\n file_verified = []\\n # VERIFIED: correct check for existence of a file in a directory\\n if is_valid_exists(args):\\n # verified_arguments = True\\n file_verified.append(True)\\n # VERIFIED: correct check for comments with language in a file in a directory\\n if is_valid_comments(args) and is_valid_language(args):\\n # verified_arguments = True\\n file_verified.append(True)\\n # VERIFIED: correct check for paragraphs in a file in a directory\\n if is_valid_paragraphs(args):\\n # verified_arguments = True\\n file_verified.append(True)\\n # VERIFIED: correct check for words in a file in a directory\\n if is_valid_words(args):\\n # verified_arguments = True\\n file_verified.append(True)\\n # VERIFIED: correct check for fragments in a file in a directory\\n if is_valid_fragment(args):\\n # verified_arguments = True\\n file_verified.append(True)\\n # VERIFIED: correct check for line count of a file in a directory\\n if is_valid_count(args):\\n # verified_arguments = True\\n file_verified.append(True)\\n # VERIFIED: only one of prior valid options specified\\n if file_verified.count(True) == 1:\\n verified_arguments = True\\n # TOP-LEVEL VERIFIED:\\n # no file or directory details were specified and a command given\\n # pylint: disable=bad-continuation\\n elif is_valid_command(args) and (\\n not is_valid_file_or_directory(args)\\n and not is_file_ancillary(args)\\n and not is_valid_commits(args)\\n ):\\n # track how many of the sub-arguments separately verified\\n command_verified = []\\n # VERIFIED: correct check for existence of a file in a directory\\n if is_valid_executes(args):\\n command_verified.append(True)\\n # verified_arguments = True\\n # VERIFIED: correct check for fragments in a file in a directory\\n if is_valid_fragment(args):\\n # verified_arguments = True\\n command_verified.append(True)\\n # VERIFIED: correct check for line count of a file in a directory\\n if is_valid_count(args):\\n # verified_arguments = True\\n command_verified.append(True)\\n # VERIFIED: only one of prior valid options specified\\n if command_verified.count(True) == 1:\\n verified_arguments = True\\n # TOP-LEVEL VERIFIED:\\n # no file or directory details were specified or a command given\\n # and the argument is a request to check the count of Git commits\\n # pylint: disable=bad-continuation\\n elif is_valid_commits(args) and (\\n not is_valid_file_or_directory(args)\\n and not is_file_ancillary(args)\\n and not is_valid_command(args)\\n and not is_command_ancillary(args)\\n ):\\n verified_arguments = True\\n return verified_arguments\",\n \"def check_args():\\n schema = Schema({\\n 'FOLDREC': Use(open, error='FOLDREC file should be readable'),\\n 'CLUSTAL': Use(open, error='CLUSTAL file should be readable'),\\n 'CCMPRED': Use(open, error='CCMPRED file should be readable'),\\n '--metafold': Use(open, error='METAFOLD_FILE should be readable'),\\n '--nb_pdb': And(Use(int), lambda n: 1 <= n <= 405,\\n error='--nb_pdb=NUM should be integer 1 <= N <= 405'),\\n '--dssp': Use(open, error='dssp/mkdssp should be readable'),\\n '--dope': Use(open, error='dope file should be readable'),\\n '--benchmark': Use(open, error='BENCHMARK_FILE should be readable'),\\n '--cpu': And(Use(int), lambda n: 0 <= n <= cpu_count(),\\n error='--cpus=NUM should be integer 1 <= N <= ' + str(cpu_count())),\\n # The output PATH is created (if not exists) at the end of the program\\n # so we skip the check.\\n object: object})\\n try:\\n schema.validate(ARGUMENTS)\\n except SchemaError as err:\\n exit(err)\",\n \"def parse_args():\\n default_file_name = 'test.db'\\n default_table_name = 'test'\\n default_location = 'hasan_bedroom'\\n default_node_id = 'SecuritySystem_1'\\n\\n parser = argparse.ArgumentParser(\\n description='Run the SecuritySystemDB test program')\\n\\n parser.add_argument('-v',\\n '--verbose',\\n default=False,\\n action='store_true',\\n help='Print all debug logs')\\n\\n parser.add_argument('-ss',\\n '--security_system',\\n default=False,\\n action='store_true',\\n help='Run SecuritySystemDB test')\\n\\n parser.add_argument('-f',\\n '--file_name',\\n type=str,\\n default=default_file_name,\\n metavar='<file_name.db>',\\n help='Specify file name of SQL db (Relative to pwd)')\\n\\n parser.add_argument('-t',\\n '--table_name',\\n type=str,\\n default=default_table_name,\\n metavar='<test_table_name>',\\n help='Specify table name of SQL db')\\n\\n parser.add_argument('-l',\\n '--location',\\n type=str,\\n default=default_location,\\n metavar='<owner_room>',\\n help='Specify owner and room')\\n\\n parser.add_argument('-id',\\n '--node_id',\\n type=str,\\n default=default_node_id,\\n metavar='<node_id>',\\n help='Specify node ID')\\n\\n args = parser.parse_args()\\n return args\",\n \"def test_cli__passes__with_version_option(args: str) -> None:\\n\\n runner = CliRunner()\\n result = runner.invoke(\\n cli,\\n args=args,\\n )\\n\\n assert_that(result.exit_code).is_equal_to(0)\\n assert_that(result.output).is_instance_of(str)\\n assert_that(result.output).is_equal_to(f\\\"cli, version {__version__}\\\\n\\\")\",\n \"def main(args=None):\\n if args is None:\\n args = _parse_arguments()\\n\\n # Handling the arguments prematurely. If this begins running away\\n # in complexity do this and maybe even parse_args in a separate function\\n if args.log_level is None:\\n # no LOG_LEVEL specified but -l was specified\\n if args.log is True:\\n LOG_LEVEL = 30\\n else:\\n # Don't log\\n LOG_LEVEL = 99\\n else:\\n LOG_LEVEL = args.log_level\\n\\n logging.basicConfig(level=LOG_LEVEL)\\n if args.yaml is True:\\n plaintext = convert_to_yaml(args.yaml)\\n else:\\n plaintext = sort_json(args.input)\\n\\n logging.debug(\\\"Plaintext is: \\\" + str(plaintext))\\n text_writer(plaintext, args.output)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')\\n parser.add_argument('--dataset', help='training dataset')\\n parser.add_argument('--cfg', dest='cfg_file', required=True, help='optional config file')\\n\\n parser.add_argument('--load_ckpt', help='path of checkpoint to load')\\n parser.add_argument('--load_detectron', help='path to the detectron weight pickle file')\\n\\n parser.add_argument('--output_dir', help='output directory to save the testing results. If not provided, defaults to [args.load_ckpt|args.load_detectron]/../test.')\\n\\n parser.add_argument('--set', dest='set_cfgs', help='set config keys, will overwrite config in the cfg_file. See lib/core/config.py for all options', default=[], nargs='*')\\n parser.add_argument('--range', help='start (inclusive) and end (exclusive) indices', type=int, nargs=2)\\n parser.add_argument('--multi-gpu-testing', help='using multiple gpus for inference', action='store_true')\\n parser.add_argument('--vis', dest='vis', help='visualize detections', action='store_true')\\n parser.add_argument('--model', help='Set model', type=str)\\n\\n parser.add_argument('--use_matlab', help='use matlab?', action='store_true')\\n parser.add_argument('--early_stop', help='run eval only to 10 images', action='store_true')\\n \\n\\n\\n\\n return parser.parse_args()\",\n \"def test_main_minimal(self, capsys):\\n UI.main(**self.args)\\n captured = capsys.readouterr().out\\n assert self.stdout_output in captured\",\n \"def run(self, args):\\n args_count = len(args)\\n if args_count == 0:\\n self.parser.print_help()\\n sys.exit(1)\\n\\n return self.parse_args(self.parser.parse_args(args))\",\n \"def analyzeCommandLineArguments(options):\\n print(options)\\n flag=0\\n if os.path.exists(options.output_directory)==False:\\n cmd=\\\"mkdir \\\"+options.output_directory\\n os.system(cmd)\\n else:\\n if options.force==0:\\n os.system(\\\"echo \\\\\\\"Output directory already exists. Please re-run the program with -f 1 to enforce rewrite of the directory \\\\\\\" >> \\\"+options.output_directory+\\\"/Log.out\\\")\\n flag=1\\n else:\\n cmd=\\\"rm -rf \\\"+options.output_directory\\n os.system(cmd)\\n cmd=\\\"mkdir \\\"+options.output_directory\\n os.system(cmd)\\n \\n cmd=\\\"touch \\\"+options.output_directory+\\\"/Log.out\\\"\\n os.system(cmd)\\n if options.bowtie_index == None:\\n os.system(\\\"echo \\\\\\\"No bowtie index provided. Proceeding to building index\\\\\\\" >> \\\"+options.output_directory+\\\"/Log.out\\\")\\n if os.path.exists(options.genome)==False:\\n os.system(\\\"echo \\\\\\\"The genome file you provided does not exist\\\\\\\" >> \\\"+options.output_directory+\\\"/Log.out\\\")\\n flag=1\\n if options.input_library==None:\\n os.system(\\\"echo \\\\\\\"The input file \\\"+options.input_library+\\\" does not exist\\\\\\\" >> \\\"+options.output_directory+\\\"/Log.out\\\")\\n flag=1\\n \\n if flag==1:\\n print(\\\"The program had to terminate prematurely....Please check \\\"+options.output_directory+\\\"/Log.out file for more details\\\")\\n sys.exit()\\n for ele in options.number_of_cycles:\\n if ele not in [\\\"9\\\",\\\"10\\\",\\\"11\\\",\\\"12\\\",\\\"13\\\"]:\\n os.system(\\\"echo \\\\\\\"Incorrect number of cycles have been entered. Valid choices are 9, 10, 11, 12 and 13 \\\\\\\" >> \\\"+options.output_directory+\\\"/Log.out\\\")\\n flag=1\\n \\n if options.input_library.split(\\\".\\\")[-1]==\\\"fq\\\" or options.input_library.split(\\\".\\\")[-1]==\\\"fastq\\\":\\n options.input_filename=options.output_directory+\\\"/\\\"+options.input_library.split(\\\"/\\\")[-1].split(\\\".\\\")[0]+\\\".fa\\\"\\n \\n options.input_path=\\\"/\\\".join(options.input_library.split(\\\"/\\\")[:-1])\\n options.input_filename=options.input_library.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\n options.consolidated_filename=options.output_directory+\\\"/\\\"+options.input_filename+\\\".consolidated.fasta\\\"\\n options.adapter_trimmed_filename=options.output_directory+\\\"/\\\"+options.input_filename+\\\"_adapter_trimmed.fastq\\\"\\n \\n options.small_rna_size=list(map(int,options.small_rna_size))\\n options.number_of_cycles=list(map(int,options.number_of_cycles))\\n options.pvalue_cutoff=float(options.pvalue_cutoff)\\n options.map_limit=int(options.map_limit)\\n return options\",\n \"def main(cli_args=None):\\n # build an arg parser\\n parser = get_arg_parser()\\n\\n # run the parser on cli args\\n args = parser.parse_args(cli_args)\\n\\n print(f\\\"Running script with arguments: {args}\\\")\\n test_input(args.raw_training_data)\\n test_input(args.raw_testing_data)\\n test_output(args.train_output)\\n test_output(args.test_output)\",\n \"def main():\\n\\tparser = setup_argument_parser()\\n\\targuments = parser.parse_args()\\n\\tto_print = arguments.to_print\\n\\techo(to_print)\",\n \"def check_input(args):\\n\\n # Defaults\\n option = ''\\n fh = sys.stdin # file handle\\n\\n if not len(args):\\n # Reading from pipe with default option\\n if sys.stdin.isatty():\\n sys.stderr.write(__doc__)\\n sys.exit(1)\\n\\n elif len(args) == 1:\\n # One of two options: option & Pipe OR file & default option\\n if args[0].startswith('-'):\\n option = args[0][1:]\\n if sys.stdin.isatty(): # ensure the PDB data is streamed in\\n emsg = 'ERROR!! No data to process!\\\\n'\\n sys.stderr.write(emsg)\\n sys.stderr.write(__doc__)\\n sys.exit(1)\\n\\n else:\\n if not os.path.isfile(args[0]):\\n emsg = 'ERROR!! File not found or not readable: \\\\'{}\\\\'\\\\n'\\n sys.stderr.write(emsg.format(args[0]))\\n sys.stderr.write(__doc__)\\n sys.exit(1)\\n\\n fh = open(args[0], 'r')\\n\\n elif len(args) == 2:\\n # Two options: option & File\\n if not args[0].startswith('-'):\\n emsg = 'ERROR! First argument is not an option: \\\\'{}\\\\'\\\\n'\\n sys.stderr.write(emsg.format(args[0]))\\n sys.stderr.write(__doc__)\\n sys.exit(1)\\n\\n if not os.path.isfile(args[1]):\\n emsg = 'ERROR!! File not found or not readable: \\\\'{}\\\\'\\\\n'\\n sys.stderr.write(emsg.format(args[1]))\\n sys.stderr.write(__doc__)\\n sys.exit(1)\\n\\n option = args[0][1:]\\n fh = open(args[1], 'r')\\n\\n else: # Whatever ...\\n sys.stderr.write(__doc__)\\n sys.exit(1)\\n\\n # Validate option\\n if len(option) > 1:\\n emsg = 'ERROR!! Alternate location identifiers must be single '\\n emsg += 'characters: \\\\'{}\\\\''\\n sys.stderr.write(emsg.format(option))\\n sys.exit(1)\\n\\n return (option, fh)\",\n \"def _run_args(cls, args: Optional[List[str]] = None):\\n parser = cls.setup_args()\\n opt = parser.parse_args(args=args)\\n return cls._run_from_parser_and_opt(opt, parser)\",\n \"def test_args(self):\\n self.assertEqual(self.parser.N_particles, 500)\\n self.assertEqual(self.parser.reduced_T, 0.9)\\n self.assertEqual(self.parser.reduced_rho, 0.9)\\n self.assertEqual(self.parser.n_steps, 1000000)\\n self.assertEqual(self.parser.freq_ener, 1000)\\n self.assertEqual(self.parser.freq_traj, 1000)\\n self.assertEqual(self.parser.max_d, 0.1)\\n self.assertEqual(self.parser.energy, 'UnitlessLJ')\",\n \"def main(argv):\\n\\n if len(argv) != 3:\\n print 'ERROR: usage %s infile outfile' % argv[0]\\n sys.exit(1)\\n\\n in_fname = argv[1]\\n out_fname = argv[2]\\n\\n # TODO: normalize the paths and make sure that they're not the same.\\n # It's generally a bad idea to overwrite your input\\n\\n if in_fname == '-':\\n fin = sys.stdin\\n else:\\n try:\\n fin = open(in_fname, 'r')\\n except BaseException, exc:\\n print 'ERROR: %s: %s' % (argv[0], str(exc))\\n sys.exit(1)\\n\\n in_lines = fin.readlines()\\n if fin != sys.stdin:\\n fin.close()\\n\\n try:\\n out_lines = process_lines(in_lines)\\n except ValueError, exc:\\n print 'ERROR: %s: %s' % (argv[0], str(exc))\\n sys.exit(1)\\n\\n if out_fname == '-':\\n fout = sys.stdout\\n else:\\n try:\\n fout = open(out_fname, 'w+')\\n except BaseException, exc:\\n print 'ERROR: %s: %s' % (argv[0], str(exc))\\n sys.exit(1)\\n\\n fout.write(''.join(out_lines))\\n if fout != sys.stdout:\\n fout.close()\",\n \"def check_arguments(self):\\n ## only four test operation is permitted, if given anything apart from this, then it should print error message\\n if (self.args.snap is False and self.args.snapcheck is False and self.args.check is False and self.args.diff is False and self.args.version is False):\\n self.logger.error(colorama.Fore.RED +\\n \\\"Arguments not given correctly, Please refer help message\\\", extra=self.log_detail)\\n self.parser.print_help()\\n sys.exit(1)\\n\\n if(((self.args.snap is True and (self.args.pre_snapfile is None or self.args.file is None)) or\\n (self.args.snapcheck is True and self.args.file is None) or\\n (self.args.check is True and self.args.file is None)) and \\n (self.args.testfiles is None or self.args.hostname is None)\\n ):\\n self.logger.error(colorama.Fore.RED +\\n \\\"Arguments not given correctly, Please refer help message\\\", extra=self.log_detail)\\n self.parser.print_help()\\n sys.exit(1)\\n if self.args.diff is True:\\n if (self.args.pre_snapfile is not None and os.path.isfile(self.args.pre_snapfile)) and (\\n self.args.post_snapfile is not None and os.path.isfile(self.args.post_snapfile)):\\n comp = Comparator()\\n comp.compare_diff(\\n self.args.pre_snapfile,\\n self.args.post_snapfile,\\n None)\\n sys.exit(1)\\n else:\\n if (self.args.file is None) and (\\n self.args.testfiles is None or self.args.hostname is None):\\n self.parser.print_help()\\n sys.exit(1)\",\n \"def parse_args():\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument(\\n \\\"-i\\\", \\\"--input\\\", required=True, action=\\\"store\\\", dest=\\\"f_in\\\", help=\\\"input file\\\"\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--output\\\",\\n required=True,\\n action=\\\"store\\\",\\n dest=\\\"f_out\\\",\\n help=\\\"stem of output file\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--dir\\\",\\n required=True,\\n action=\\\"store\\\",\\n dest=\\\"dir\\\",\\n help=\\\"directory to save output files\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--threshold\\\",\\n required=False,\\n action=\\\"store\\\",\\n dest=\\\"thres\\\",\\n default=0.85,\\n help=\\\"threshold for the scoring function\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--language\\\",\\n required=True,\\n action=\\\"store\\\",\\n dest=\\\"lang\\\",\\n help=\\\"provide language in order to set stop words\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-min\\\",\\n \\\"--minimum\\\",\\n required=False,\\n action=\\\"store\\\",\\n dest=\\\"min\\\",\\n default=100,\\n help=\\\"minimum number of occurrences to be considered as ngram\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--trigram\\\",\\n required=False,\\n action=\\\"store_true\\\",\\n dest=\\\"trigram\\\",\\n help=\\\"extracting trigrams in addition to bigrams\\\",\\n )\\n\\n return parser.parse_args()\",\n \"def check_args(argv):\\n arg_length = len(argv)\\n\\n usage_str = '\\\\nUSAGE:\\\\npython {} [ls_hostname] [ls_listen_port]\\\\npython {} [ls_hostname] [ls_listen_port] [input_file_name]\\\\npython {} [ls_hostname] [ls_listen_port] [input_file_name] [output_file_name]\\\\n'.format(argv[0], argv[0], argv[0])\\n\\n ls_hostname = DEFAULT_HOSTNAME_LS\\n ls_portno = DEFAULT_PORTNO_LS\\n\\n input_file_str = DEFAULT_INPUT_FILE_STR_HNS\\n output_file_str = DEFAULT_OUTPUT_FILE_STR_RESOLVED\\n\\n # debugging args\\n if arg_length is 1:\\n pass\\n elif arg_length is 2:\\n ls_hostname = argv[1]\\n # end debugging args\\n elif arg_length is 3:\\n ls_hostname = argv[1]\\n ls_portno = int(argv[2])\\n elif arg_length is 4:\\n ls_hostname = argv[1]\\n ls_portno = int(argv[2])\\n\\n input_file_str = argv[3]\\n elif arg_length is 5:\\n ls_hostname = argv[1]\\n ls_portno = int(argv[2])\\n\\n input_file_str = argv[3]\\n output_file_str = argv[4]\\n else:\\n print(usage_str)\\n exit()\\n\\n return (ls_hostname, ls_portno), (input_file_str, output_file_str)\",\n \"def parse_args():\\n\\tparser = argparse.ArgumentParser(\\n\\t\\tformatter_class=argparse.RawTextHelpFormatter,\\n\\t\\tusage=argparse.SUPPRESS)\\n\\n\\tparser.add_argument('--edge', type=str, dest='edge_path', required=True,\\n\\t\\t\\thelp=\\\"\\\"\\\"Path to edge file as input in a special space seperated format, e.g. node_1 (pp) node2 = 1.00\\\"\\\"\\\")\\n\\tparser.add_argument('--otu', type=str, dest='otu_path', required=True,\\n\\t\\t\\thelp=\\\"\\\"\\\"Path to otu abundance file in a tab seperated format. Header and row name present as the first row and first column from the 2nd row\\\"\\\"\\\")\\n\\tparser.add_argument('--env', type=str, dest='env_path', required=True,\\n\\t\\t\\thelp=\\\"\\\"\\\"Path to env variable data file in a tab seperated format. Header and row name present as the first row and first column from the 2nd row\\\"\\\"\\\")\\n\\tparser.add_argument('--r2', type=float, dest='th_r2', default=0.6,\\n\\t\\t\\thelp=\\\"\\\"\\\"Specify a r2 value as correlation threshold (Pearson) for identify significant links between env variables and OTUs. All links below this value will not be considered. Default: 0.6\\\"\\\"\\\")\\n\\tparser.add_argument('--pval', type=float, dest='th_p', default=0.05,\\n\\t\\t\\thelp=\\\"\\\"\\\"Specify a p-value as threshold for testing whether the links between env variables and OTU passed significant level of correlation. All links tested with p-value higher than this value will not be considered. Default: 0.05\\\"\\\"\\\")\\n\\tparser.add_argument('--out', type=str, dest='out', default=\\\"/dev/stdout\\\",\\n\\t\\t\\thelp=\\\"\\\"\\\"Path to output which includes a succinct summary of valid links. Default: standard output\\\"\\\"\\\")\\n\\tparser.add_argument('--verbose', action='store_true', dest='verbose', \\n\\t\\t\\thelp=\\\"\\\"\\\"Toggle to output details for each env dependent link. Default: False\\\"\\\"\\\")\\n\\n\\treturn vars(parser.parse_args())\",\n \"def test_cli_parser_good(self):\\n ARGV = ([],)\\n for argv in ARGV:\\n opts, args = self.cmd.OptionParser.parse_args(argv)\\n self.cmd.ValidateOptions(opts, args)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"in_fq\\\", help=\\\"The fastq file containing Hi-C reads.\\\")\\n parser.add_argument(\\n \\\"-r\\\",\\n \\\"--reference\\\",\\n required=True,\\n help=\\\"Path to the reference genome, in FASTA format.\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--nb_processors\\\",\\n default=1,\\n type=int,\\n help=\\\"number of CPUs used for alignment.\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--out_sam\\\",\\n help=\\\"Path to the output SAM file for the alignment of in_fq.\\\",\\n )\\n parser.add_argument(\\n \\\"-T\\\",\\n \\\"--tempdir\\\",\\n default=\\\".\\\",\\n help=\\\"Directory to write temporary files. Defaults to current directory.\\\",\\n )\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--minimap2\\\",\\n default=False,\\n action=\\\"store_true\\\",\\n help=\\\"Use minimap2 instead of bowtie for the alignment.\\\",\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--min_len\\\",\\n type=int,\\n default=20,\\n help=\\\"Minimum length to which reads should be truncated.\\\",\\n )\\n return parser.parse_args()\",\n \"def test_main_output(self, capsys):\\n args = self.args.copy()\\n args[\\\"out_file\\\"] = \\\"text.txt\\\"\\n UI.main(**args)\\n captured = capsys.readouterr().out\\n assert \\\"Results written to text.txt\\\" in captured\",\n \"def parse_arguments(args):\\n\\n parser = argparse.ArgumentParser(\\n description=\\\"Text2Text: Read input text training files, output item files and train a model\\\"\\n )\\n\\n parser.add_argument(\\n \\\"-i\\\",\\n \\\"--input-text-path\\\",\\n type=str,\\n required=True,\\n metavar=\\\"INPUT_TEXT_PATH\\\",\\n help=\\\"Text input file name. Format: in each line, OUTPUT_ID1,OUTPUT_ID2,OUTPUT_ID3,...\\\\t INPUT_TEXT \\\\\\n where OUTPUT_IDs are the zero-based output item indices corresponding to the line numbers of OUTPUT_ITEM_PATH. We assume utf-8 encoding for text.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-q\\\",\\n \\\"--output-item-path\\\",\\n type=str,\\n required=True,\\n metavar=\\\"OUTPUT_ITEM_PATH\\\",\\n help=\\\"Output item file name. Format: each line corresponds to a representation of the output item. We assume utf-8 encoding for text.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--model-folder\\\",\\n type=str,\\n required=True,\\n metavar=\\\"MODEL_FOLDER\\\",\\n help=\\\"Output model folder name\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--workspace-folder\\\",\\n type=str,\\n default=None,\\n metavar=\\\"WORKSPACE_FOLDER\\\",\\n help=\\\"A folder name for storing intermediate variables during training\\\",\\n )\\n\\n vectorizer_config_group_parser = parser.add_mutually_exclusive_group()\\n vectorizer_config_group_parser.add_argument(\\n \\\"--vectorizer-config-path\\\",\\n type=str,\\n default=None,\\n metavar=\\\"VECTORIZER_CONFIG_PATH\\\",\\n help=\\\"Json file for vectorizer config (default tfidf vectorizer)\\\",\\n )\\n\\n vectorizer_config_group_parser.add_argument(\\n \\\"--vectorizer-config-json\\\",\\n type=str,\\n default='{\\\"type\\\":\\\"tfidf\\\", \\\"kwargs\\\":{}}',\\n metavar=\\\"VECTORIZER_CONFIG_JSON\\\",\\n help='Json-format string for vectorizer config (default {\\\"type\\\":\\\"tfidf\\\", \\\"kwargs\\\":{}})',\\n )\\n\\n parser.add_argument(\\n \\\"--dtype\\\",\\n type=lambda x: np.float32 if \\\"32\\\" in x else np.float64,\\n default=np.float32,\\n help=\\\"data type for the csr matrix. float32 | float64. (default float32)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--max-leaf-size\\\",\\n type=cli.comma_separated_type(int),\\n default=[100],\\n metavar=\\\"INT-LIST\\\",\\n help=\\\"The max size of the leaf nodes of hierarchical 2-means clustering. Multiple values (separated by comma) are supported and will lead to different individual models for ensembling. (default [100])\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--nr-splits\\\",\\n type=int,\\n default=2,\\n metavar=\\\"INT\\\",\\n help=\\\"number of splits used to construct hierarchy (a power of 2 is recommended, default 2)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--imbalanced-ratio\\\",\\n type=float,\\n default=0.0,\\n metavar=\\\"FLOAT\\\",\\n help=\\\"Value between 0.0 and 0.5 (inclusive). Indicates how relaxed the balancedness constraint of 2-means can be. Specifically, if an iteration of 2-means is clustering L labels, the size of the output 2 clusters will be within approx imbalanced_ratio * 2 * L of each other. (default 0.0)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--imbalanced-depth\\\",\\n type=int,\\n default=100,\\n metavar=\\\"INT\\\",\\n help=\\\"After hierarchical 2-means clustering has reached this depth, it will continue clustering as if --imbalanced-ratio is set to 0.0. (default 100)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--label-embed-type\\\",\\n type=cli.comma_separated_type(str),\\n default=\\\"pifa\\\",\\n metavar=\\\"STR-LIST\\\",\\n help=\\\"Label embedding types. (default pifa).\\\\\\n We support pifa, pifa_lf_concat::Z=path, and pifa_lf_convex_combine::Z=path::alpha=scalar_value,\\\\\\n where path is the additional user-porivded label embedding path and alpha is the scalar value for convex combination.\\\\\\n Multiple values (separated by comma) are supported and will lead to different individual models for ensembling.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--indexer\\\",\\n choices=Indexer.indexer_dict.keys(),\\n default=\\\"hierarchicalkmeans\\\",\\n metavar=\\\"STR\\\",\\n help=f\\\"Indexer algorithm (default hierarchicalkmeans). Available choices are {', '.join(Indexer.indexer_dict.keys())}\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--no-spherical\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"Do not l2-normalize cluster centers while clustering\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--seed\\\",\\n type=cli.comma_separated_type(int),\\n default=[0],\\n metavar=\\\"INT-LIST\\\",\\n help=\\\"Random seeds (default 0). Multiple values (separated by comma) are supported and will lead to different individual models for ensembling.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--max-iter\\\",\\n type=int,\\n default=20,\\n metavar=\\\"INT\\\",\\n help=\\\"The max iteration for indexing (default 20)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--threads\\\",\\n type=int,\\n default=-1,\\n metavar=\\\"INT\\\",\\n help=\\\"Number of threads to use (default -1 to denote all the CPUs)\\\",\\n )\\n\\n # Linear matching/ranking parameters\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--solver-type\\\",\\n type=str,\\n default=\\\"L2R_L2LOSS_SVC_DUAL\\\",\\n metavar=\\\"STR\\\",\\n help=\\\"{} (default L2R_L2LOSS_SVC_DUAL)\\\".format(\\\" | \\\".join(XLINEAR_SOLVERS.keys())),\\n )\\n\\n parser.add_argument(\\n \\\"--Cp\\\",\\n type=float,\\n default=1.0,\\n metavar=\\\"VAL\\\",\\n help=\\\"Coefficient for positive class in the loss function (default 1.0)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--Cn\\\",\\n type=float,\\n default=1.0,\\n metavar=\\\"VAL\\\",\\n help=\\\"Coefficient for negative class in the loss function (default 1.0)\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--bias\\\", type=float, default=1.0, metavar=\\\"VAL\\\", help=\\\"bias term (default 1.0)\\\"\\n )\\n\\n parser.add_argument(\\n \\\"-ns\\\",\\n \\\"--negative-sampling\\\",\\n type=str,\\n choices=[\\\"tfn\\\", \\\"man\\\", \\\"tfn+man\\\"],\\n default=\\\"tfn\\\",\\n metavar=\\\"STR\\\",\\n help=\\\"Negative Sampling Schemes\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--threshold\\\",\\n type=float,\\n default=0.1,\\n metavar=\\\"VAL\\\",\\n help=\\\"Threshold to sparsify the model weights (default 0.1)\\\",\\n )\\n\\n # Prediction kwargs\\n parser.add_argument(\\n \\\"-k\\\",\\n \\\"--only-topk\\\",\\n type=int,\\n default=20,\\n metavar=\\\"INT\\\",\\n help=\\\"the default number of top labels used in the prediction\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-b\\\",\\n \\\"--beam-size\\\",\\n type=int,\\n default=10,\\n metavar=\\\"INT\\\",\\n help=\\\"the default size of beam search used in the prediction\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-pp\\\",\\n \\\"--post-processor\\\",\\n type=str,\\n choices=PostProcessor.valid_list(),\\n default=\\\"l3-hinge\\\",\\n metavar=\\\"STR\\\",\\n help=\\\"the default post processor used in the prediction\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--verbose-level\\\",\\n type=int,\\n choices=logging_util.log_levels.keys(),\\n default=1,\\n metavar=\\\"INT\\\",\\n help=f\\\"the verbose level, {', '.join([str(k) + ' for ' + logging.getLevelName(v) for k, v in logging_util.log_levels.items()])}, default 1\\\",\\n )\\n\\n parsed_args = parser.parse_args(args)\\n return parsed_args\",\n \"def handle_args():\\n parser = argparse.ArgumentParser(\\n description=\\\"\\\"\\\"Script to download archives from the NLM public\\n FTP server.\\n \\\"\\\"\\\")\\n # Server settings\\n server_settings = parser.add_argument_group('FTP SERVER SETTINGS', '')\\n server_settings.add_argument(\\n '-n', '--netrc', default='~/.netrc',\\n help=\\\"\\\"\\\"netrc file containing login parameters for the NLM\\n server. See `man 5 netrc` for details on generating this\\n file or read nlm_data_import/netrc/example.netrc.\\n \\\"\\\"\\\")\\n server_settings.add_argument(\\n 'server_data_dir',\\n help='Directory containing desired files on the NLM FTP server')\\n server_settings.add_argument(\\n '-l', '--limit', type=int, default=0,\\n help='Only download LIMIT files.')\\n\\n # Download settings\\n local_settings = parser.add_argument_group('LOCAL SETTINGS', '')\\n local_settings.add_argument(\\n '-d', '--download_database', default='~/.ftp_download_db',\\n help='Path to SQLite database detailing past downloads')\\n local_settings.add_argument(\\n '-o', '--output_dir', default='~/medline_data',\\n help='Directory where downloads will be saved')\\n local_settings.add_argument(\\n '-x', '--export_dir', default='~/medline_data_exports',\\n help=\\\"\\\"\\\"Directory where data to be retrieved by the\\n `hypothesis_graph application server are staged.\\n \\\"\\\"\\\")\\n # Sending debug emails (requires the send_ses_messages module - see\\n # setup.py)\\n debugging_settings = parser.add_argument_group('DEBUGGING SETTINGS', '')\\n debugging_settings.add_argument(\\n '--email_debugging', default=False, action='store_true',\\n help=\\\"Send debugging emails. Defaults to FALSE.\\\")\\n debugging_settings.add_argument(\\n '--from_email', required=False, help=\\\"FROM field for debugging emails\\\")\\n debugging_settings.add_argument(\\n '--to_email', required=False, help=\\\"TO field for debugging emails\\\")\\n\\n return parser.parse_args()\",\n \"def test_args_debug():\\n args = cli.parse_args(['-d'])\\n assert args.debug\\n args = cli.parse_args(['--debug'])\\n assert args.debug\",\n \"def check_argv():\\n parser = argparse.ArgumentParser(description=__doc__.strip().split(\\\"\\\\n\\\")[0], add_help=False)\\n parser.add_argument(\\\"input_pairs_fn\\\", type=str)\\n parser.add_argument(\\\"output_pairs_fn\\\", type=str)\\n if len(sys.argv) == 1:\\n parser.print_help()\\n sys.exit(1)\\n return parser.parse_args()\",\n \"def parse_args():\\n\\n op = OptionParser(usage=\\\"usage: %prog [opts] input_folder\\\")\\n\\n op.add_option(\\\"--test\\\",\\n dest=\\\"test\\\",\\n default=False,\\n action=\\\"store_true\\\",\\n help=\\\"executes the test suite\\\")\\n\\n return op.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('--length', required=True, type=int, help='Test length in seconds')\\n parser.add_argument('--txs-per-ledger', required=True, type=int, help='Transaction rate to submit (spam) in parallel for every ledger round')\\n parser.add_argument('--prioritizer-seeds-file', required=True, type=str, help='File path to prioritizer seeds file')\\n parser.add_argument('--spammer-seeds-file', required=True, type=str, help='File path to spammer seeds file')\\n parser.add_argument('--out', default='spam-results-{}.json'.format(str(int(time.time()))), type=str, help='Spam results JSON output')\\n parser.add_argument('--avg-block-time', type=int, default=5, help='Average block time. Controls the time delay between every spam round and the one just after that')\\n\\n parser.add_argument('--passphrase', type=str, help='Network passphrase')\\n parser.add_argument('--horizon', action='append',\\n help='Horizon endpoint URL (use multiple --horizon flags for multiple addresses)')\\n\\n return parser.parse_args()\",\n \"def main(args):\\n\\n if 'log' in args and args['log'] is not None:\\n logging.basicConfig(level=LOGGING_LEVELS.get(args['log'].lower(), logging.NOTSET))\\n\\n test_structure = read_test_file(args['test'])\\n tests = build_testsets(args['url'], test_structure)\\n\\n # Override configs from command line if config set\\n for t in tests:\\n if 'print_bodies' in args and args['print_bodies'] is not None:\\n t.config.print_bodies = safe_to_bool(args['print_bodies'])\\n\\n if 'interactive' in args and args['interactive'] is not None:\\n t.config.interactive = safe_to_bool(args['interactive'])\\n\\n # Execute all testsets\\n failures = execute_testsets(tests)\\n\\n sys.exit(failures)\",\n \"def test_cmdlineproc_test2():\\n\\n parameters = {\\n \\\"debug\\\": False,\\n \\\"disconnect\\\": False,\\n \\\"executable\\\": \\\"\\\",\\n \\\"executableargs\\\": \\\"\\\",\\n \\\"hosts\\\": \\\"\\\",\\n \\\"job\\\": \\\"\\\",\\n \\\"jobname\\\": \\\"\\\",\\n \\\"log\\\": \\\"\\\",\\n \\\"recover\\\": \\\"\\\",\\n \\\"resource\\\": \\\"\\\",\\n \\\"replicates\\\": \\\"\\\",\\n \\\"verbose\\\": False\\n }\\n\\n commandlineargs = [\\\"-about\\\"]\\n\\n longbowargs = _commandlineproc(ALLLONGBOWARGS, commandlineargs, parameters)\\n\\n assert parameters[\\\"executable\\\"] == \\\"\\\"\\n assert parameters[\\\"executableargs\\\"] == \\\"\\\"\\n assert longbowargs == [\\\"-about\\\"]\",\n \"def execute_program(args: List[str], mode: TestMode) -> None:\\n args_str = ' '.join(args)\\n\\n def check_output(result: CompletedProcess):\\n stdout = strip_ascii_codes(result.stdout.decode())\\n error_string = f'Failure:\\\\nMode: {mode.value}\\\\nCommand: {args_str}\\\\nExit code: {result.returncode:X}\\\\nOutput:\\\\n{stdout}\\\\n'\\n if mode == TestMode.STUB:\\n if result.returncode not in (1, 2) or 'Test FAILED' not in stdout:\\n raise RuntimeError(error_string)\\n elif mode == TestMode.SOLUTION:\\n if (result.returncode != 0 or\\n \\\"*** You've passed ALL tests. Congratulations! ***\\\" not in stdout):\\n raise RuntimeError(error_string)\\n else:\\n print('Invalid mode: {}'.format(mode))\\n sys.exit(1)\\n\\n try:\\n result = subprocess.run(args, stdout=subprocess.PIPE, timeout=300)\\n check_output(result)\\n except RuntimeError as e:\\n print(e)\\n sys.exit(1)\\n except TimeoutExpired:\\n print(f\\\"{mode} > {args_str}: TIMEOUT\\\")\\n sys.exit(1)\",\n \"def parseArguments():\\n # Create argument parser\\n parser = argparse.ArgumentParser()\\n\\n # Optional arguments\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", help=\\\"Optionally test algorithm on subsample of the data. Set to 1 for testing\\\", type=int, default=0)\\n\\n parser.add_argument(\\\"--cores\\\", help=\\\"Optimized code for a server with a lot of RAM, set to the number of available cores\\\", type=int, default=40)\\n\\n\\n # Print version\\n parser.add_argument(\\\"--version\\\", action=\\\"version\\\", version='%(prog)s - Version 2.0') #version 1.0 is for the observations in June 2018\\n #version 1.1 contains the optimizations made after the june observations (mainly the switch to stackmags)\\n #version 1.2 changed sim class to NOT include the list of failed candidates (not qsos)\\n #... copied changes made to crossval version\\n #version 1.5 added check for duplicate quasars and remove them\\n #version 1.6 new simulated quasars (december)\\n ##-------------------\\n #version 2.0: combined training of classifier and regressor, streamlined input\\n #version 2.1: Tryied to updates excluded area to a little more than stripe 82 but decided not to keep it, so no change\\n\\n # Parse arguments\\n args = parser.parse_args()\\n\\n return args\",\n \"def testParseArguments(self):\\n de_object = de.DockerExplorerTool()\\n\\n prog = sys.argv[0]\\n\\n expected_docker_root = os.path.join('test_data', 'docker')\\n\\n args = [prog, '-r', expected_docker_root, 'list', 'repositories']\\n sys.argv = args\\n\\n options = de_object.ParseArguments()\\n usage_string = de_object._argument_parser.format_usage()\\n expected_usage = '[-h] [-d] [-r DOCKER_DIRECTORY] [-V]'\\n expected_usage_commands = '{download,mount,list,history}'\\n self.assertIn(expected_usage, usage_string)\\n self.assertIn(expected_usage_commands, usage_string)\\n self.assertEqual(expected_docker_root, options.docker_directory)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n description=\\\"\\\"\\\"\\nNenG - Nash Equilibrium Noncooperative games.\\nTool for computing Nash equilibria in noncooperative games.\\nSpecifically:\\nAll pure Nash equilibria in all games (--method=pne).\\nAll mixed Nash equilibria in two-players games (--method=support_enumeration).\\nOne sample mixed Nash equilibria in n-players games (--method={CMAES,L-BFGS-B,SLSQP}).\\n\\\"\\\"\\\")\\n pa = parser.add_argument\\n pa('-f', '--file', required=True, help=\\\"File where game in nfg format is saved.\\\")\\n pa('-m', '--method', default='CMAES', choices=game.Game.METHODS,\\n help=\\\"Method to use for computing Nash equlibria.\\\")\\n pa('-e', '--elimination', action='store_true', default=False,\\n help=\\\"Use Iterative Elimination of Strictly Dominated Strategies before computing NE.\\\")\\n pa('-p', '--payoff', action='store_true', default=False,\\n help=\\\"Print also players payoff with each Nash equilibrium.\\\")\\n pa('-c', '--checkNE', action='store_true', default=False,\\n help=\\\"After computation check if found strategy profile is really Nash equilibrium.\\\")\\n pa('-t', '--trim', choices=('normalization', 'penalization'), default='normalization',\\n help=\\\"Method for keeping strategy profile in probability distribution universum.\\\")\\n pa('-l', '--log', default=\\\"WARNING\\\",\\n choices=(\\\"DEBUG\\\", \\\"INFO\\\", \\\"WARNING\\\", \\\"ERROR\\\", \\\"CRITICAL\\\"),\\n help=\\\"Level of logs to save/print\\\")\\n pa('--log-file', default=None, help='Log file. If omitted log is printed to stdout.')\\n return parser.parse_args()\",\n \"def handle_cmdline_args():\\n\\n parser = argparse.ArgumentParser(\\n description='Generate synthetic data from a specification in a json '\\n 'file using the \\\"synth-method\\\" described in the json file. ')\\n\\n parser.add_argument(\\n '-i', dest='infile', required=True,\\n help='The input json file. Must contain a \\\"synth-method\\\" property')\\n\\n parser.add_argument(\\n '-o', dest='outfile_prefix', required=True, help='The prefix of the output paths (data json and csv), relative to the QUIPP-pipeline root directory')\\n\\n args = parser.parse_args()\\n return args\",\n \"def main(args):\\n options = parse_cmd_parameters_(args)\\n execute_(options)\",\n \"def verify_args(opts):\\n special_opts = (opts.initialize, opts.sigtool, opts.scanner)\\n list_opts = (opts.listUnable, opts.listMoved, opts.listInfected, opts.listFailed)\\n if any(list_opts):\\n return\\n special_opts_flags = [\\\"-g/--sigtool\\\", \\\"-i/--initialize\\\", \\\"-s/--scan\\\"]\\n total = 0\\n for opt in special_opts:\\n if opt:\\n total += 1\\n if total == 0:\\n log.error(\\n \\\"must pass a required argument to begin, required arguments can be found in the help menu (penneav -h)\\\"\\n )\\n sys.exit(1)\\n if total != 1:\\n log.error(\\n \\\"can only pass a total of 1 of the required arguments at a time ({}). You have passed a total of {}. \\\"\\n \\\"Please verify your arguments and rerun Penne.\\\".format(\\n \\\", \\\".join(special_opts_flags), total\\n )\\n )\\n sys.exit(1)\\n if opts.sigtool and opts.filename is None:\\n log.error(\\\"must supply a filename with sigtool to generate the signature\\\")\\n sys.exit(1)\\n if opts.threadNum > 30:\\n log.warning(\\\"max amount of threads is 30 (you passed {}), defaulting down to 30 threads\\\".format(opts.threadNum))\\n opts.threadNum = 30\\n print(WELCOME_BANNER)\",\n \"def test_cmdlineproc_test11():\\n\\n parameters = {\\n \\\"debug\\\": False,\\n \\\"disconnect\\\": False,\\n \\\"executable\\\": \\\"\\\",\\n \\\"executableargs\\\": \\\"\\\",\\n \\\"hosts\\\": \\\"\\\",\\n \\\"job\\\": \\\"\\\",\\n \\\"jobname\\\": \\\"\\\",\\n \\\"log\\\": \\\"\\\",\\n \\\"recover\\\": \\\"\\\",\\n \\\"resource\\\": \\\"\\\",\\n \\\"replicates\\\": \\\"\\\",\\n \\\"verbose\\\": False\\n }\\n\\n commandlineargs = [\\\"test.exe\\\", \\\"-i\\\", \\\"input.file\\\", \\\"param1\\\", \\\"--someflag\\\"]\\n\\n longbowargs = _commandlineproc(ALLLONGBOWARGS, commandlineargs, parameters)\\n\\n assert parameters[\\\"executable\\\"] == \\\"test.exe\\\"\\n assert parameters[\\\"executableargs\\\"] == \\\"-i input.file param1 --someflag\\\"\\n assert longbowargs == []\",\n \"def test_invalid_arguments(self):\\n # More than two arguments should report an error.\\n exit_code, output = run_cli('a', 'b', 'c')\\n assert exit_code != 0\\n assert \\\"Error\\\" in output\\n # Invalid `ionice' values should report an error.\\n exit_code, output = run_cli('--ionice=foo')\\n assert exit_code != 0\\n assert \\\"Error\\\" in output\",\n \"def main(args):\\n # Results: print to console and also write to output file\\n pass\",\n \"def test_with_empty_sample_args(self):\\n with self.assertRaises(SystemExit):\\n CliArgs('sample')\",\n \"def test_atleast_two_arguments_needed_none():\\n cli_result = subprocess.run(\\n ['kaiba'],\\n capture_output=True,\\n )\\n assert b'the following arguments are required: config' in cli_result.stderr\\n assert cli_result.returncode == 2\",\n \"def parse_input(command_input=None):\\n parser = argparse.ArgumentParser(\\n prog='python3 ok',\\n description=__doc__,\\n usage='%(prog)s [--help] [options]',\\n formatter_class=argparse.RawDescriptionHelpFormatter)\\n\\n testing = parser.add_argument_group('running tests')\\n testing.add_argument('-q', '--question', type=str, action='append',\\n help=\\\"run tests for a specific question\\\")\\n testing.add_argument('--suite', type=str, default=None,\\n help=\\\"run cases from a specific suite\\\")\\n testing.add_argument('--case', type=str, action='append',\\n help=\\\"run specific cases\\\")\\n testing.add_argument('-u', '--unlock', action='store_true',\\n help=\\\"unlock tests interactively\\\")\\n testing.add_argument('-i', '--interactive', action='store_true',\\n help=\\\"start the Python interpreter after a failed test\\\")\\n testing.add_argument('-v', '--verbose', action='store_true',\\n help=\\\"show all tests (not just passing tests) up to failing line (if any)\\\")\\n testing.add_argument('-t', '--testing', nargs='?', type=str, const='mytests.rst',\\n help='run tests from rst file (default: mytests.rst)')\\n testing.add_argument('--all', action='store_true',\\n help=\\\"run tests for all questions in config file\\\")\\n testing.add_argument('--submit', action='store_true',\\n help=\\\"submit the assignment\\\")\\n testing.add_argument('--backup', action='store_true',\\n help=\\\"attempt to reliably backup your work\\\")\\n testing.add_argument('--revise', action='store_true',\\n help=\\\"submit composition revision\\\")\\n testing.add_argument('--timeout', type=int, default=10,\\n help=\\\"set the timeout duration (in seconds) for running tests\\\")\\n testing.add_argument('-cov', '--coverage', action='store_true',\\n help=\\\"get suggestions on what lines to add tests for\\\")\\n testing.add_argument('--autobackup', action='store_true',\\n help=\\\"back up your work every minute in the background\\\")\\n # runs an autobackup in the foreground. Used by `--autobackup`,\\n # if you specify this other options will be ignored.\\n testing.add_argument('--autobackup-actual-run-sync', action='store_true',\\n help=argparse.SUPPRESS)\\n # Debugging\\n debugging = parser.add_argument_group('debugging tools for students')\\n\\n debugging.add_argument('--trace', action='store_true',\\n help=\\\"trace code and launch python tutor\\\")\\n debugging.add_argument('--trace-print', action='store_true',\\n help=\\\"print the trace instead of visualizing it\\\")\\n\\n # Experiments\\n experiment = parser.add_argument_group('experiment options')\\n experiment.add_argument('--no-experiments', action='store_true',\\n help=\\\"do not run experimental features\\\")\\n experiment.add_argument('--hint', action='store_true',\\n help=\\\"give a hint (if available)\\\")\\n experiment.add_argument('--style', action='store_true',\\n help=\\\"run AutoStyle feedback system\\\")\\n experiment.add_argument('--collab', action='store_true',\\n help=\\\"launch collaborative programming environment\\\")\\n\\n # Debug information\\n debug = parser.add_argument_group('ok developer debugging options')\\n debug.add_argument('--version', action='store_true',\\n help=\\\"print the version number and exit\\\")\\n debug.add_argument('--tests', action='store_true',\\n help=\\\"display a list of all available tests\\\")\\n debug.add_argument('--debug', action='store_true',\\n help=\\\"show debugging output\\\")\\n\\n # Grading\\n grading = parser.add_argument_group('grading options')\\n grading.add_argument('--lock', action='store_true',\\n help=\\\"lock the tests in a directory\\\")\\n grading.add_argument('--score', action='store_true',\\n help=\\\"score the assignment\\\")\\n grading.add_argument('--score-out', type=str,\\n nargs='?', const=None, default=None,\\n help=\\\"write scores to a file\\\")\\n grading.add_argument('--config', type=str,\\n help=\\\"use a specific configuration file\\\")\\n grading.add_argument('--ignore-empty', action='store_true',\\n help=\\\"ignore empty doctests\\\")\\n\\n # Encrypt\\n crypt = parser.add_argument_group('encryption')\\n crypt.add_argument('--generate-encryption-key', type=str,\\n help='generates a JSON file containing a list of [(file, key)] pairs. Path is a keyfile')\\n crypt.add_argument('--encrypt', type=str,\\n help='encrypt each problem. provide a path to a keyfile generated by --generate-encryption-key')\\n crypt.add_argument('--encrypt-padding', type=int,\\n help='If provided, pads all plaintexts to this size in bytes.'\\n 'Errors if any of the files are longer than this')\\n crypt.add_argument('--decrypt', type=str, nargs='*',\\n help='decrypt all problems where the given keys apply')\\n\\n # Server parameters\\n server = parser.add_argument_group('server options')\\n server.add_argument('--local', action='store_true',\\n help=\\\"disable any network activity\\\")\\n server.add_argument('--nointeract', action='store_true',\\n help=\\\"disable prompts to user\\\")\\n server.add_argument('--server', type=str,\\n default='okpy.org',\\n help=\\\"set the server address\\\")\\n server.add_argument('--authenticate', action='store_true',\\n help=\\\"authenticate, ignoring previous authentication\\\")\\n server.add_argument('--no-browser', action='store_true',\\n help=\\\"do not use a web browser for authentication\\\")\\n server.add_argument('--get-token', action='store_true',\\n help=\\\"get ok access token\\\")\\n server.add_argument('--insecure', action='store_true',\\n help=\\\"use http instead of https\\\")\\n server.add_argument('--no-update', action='store_true',\\n help=\\\"do not check for ok updates\\\")\\n server.add_argument('--update', action='store_true',\\n help=\\\"update ok and exit\\\")\\n # used in faded-parsons-frontend repo\\n server.add_argument('--parsons', action='store_true', \\n help=\\\"run parsons problems in browser\\\") \\n\\n return parser.parse_args(command_input)\",\n \"def cli_main():\\n\\n\\n if len(sys.argv) > 1 and sys.argv[1].endswith('.xmind'):\\n xmind_file = sys.argv[1]\\n xmind_file = get_absolute_path(xmind_file)\\n logging.info('Start to convert XMind file: %s', xmind_file)\\n\\n if len(sys.argv) == 3 and sys.argv[2] == '-json':\\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\\n logging.info('Convert XMind file to testcase json file successfully: %s', testlink_json_file)\\n elif len(sys.argv) == 3 and sys.argv[2] == '-xml':\\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\\n logging.info('Convert XMind file to testlink xml files successfully: %s', testlink_xml_file)\\n elif len(sys.argv) == 3 and sys.argv[2] == '-csv':\\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\\n logging.info('Convert XMind file to zentao csv file successfully: %s', zentao_csv_file)\\n elif len(sys.argv) == 3 and sys.argv[2] == '-xlsx':\\n excel_xlsx_file = xmind_to_xlsx_file(xmind_file)\\n logging.info('Convert XMind file to zentao csv file successfully: %s', excel_xlsx_file)\\n else:\\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\\n logging.info('Convert XMind file successfully: \\\\n'\\n '1、 testcase json file(%s)\\\\n'\\n '2、 testlink xml file(%s)\\\\n'\\n '3、 zentao csv file(%s)',\\n testlink_json_file,\\n testlink_xml_file,\\n zentao_csv_file)\\n\\n\\n else:\\n print(__doc__)\\n logging.error('%s', __doc__)\",\n \"def test_cli(args, env, use_defaultenv, expect, defaultenv):\\n # use --dump-config by default to make sure that the postgrest process will terminate for sure\\n args = args or [\\\"--dump-config\\\"]\\n\\n env = env or {}\\n if use_defaultenv:\\n env = {**defaultenv, **env}\\n\\n if expect == \\\"error\\\":\\n with pytest.raises(PostgrestError):\\n print(cli(args, env=env))\\n else:\\n dump = cli(args, env=env).split(\\\"\\\\n\\\")\\n if expect:\\n assert expect in dump\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description='VCF to Typhi genotypes')\\n parser.add_argument('--mode', choices=('vcf', 'bam', 'vcf_parsnp'), default='bam',\\n help='Mode to run in based on input files (vcf, bam, or vcf_parsnp)')\\n parser.add_argument('--vcf', nargs='+', type=str, required=False,\\n help='VCF file(s) to genotype (Mapping MUST have been done using CT18 as a reference sequence)')\\n parser.add_argument('--bam', nargs='+', type=str, required=False,\\n help='BAM file(s) to genotype (Mapping MUST have been done using CT18 as a reference sequence)')\\n parser.add_argument('--ref_id', type=str, required=False,\\n help='Name of the reference in the VCF file (#CHROM column) or fasta file. Note that CT18 has '\\n 'genotype 3.2.1. If all your strains return this genotype, it is likely you have '\\n 'specified the name of the refrence sequence incorrectly; please check your VCFs.')\\n parser.add_argument('--phred', type=int, required=False, default=20,\\n help='Minimum phred quality to count a variant call vs CT18 as a true SNP (default 20)')\\n parser.add_argument('--min_prop', type=float, required=False, default=0.1,\\n help='Minimum proportion of reads required to call a SNP (default 0.1)')\\n parser.add_argument('--ref', type=str, required=False,\\n help='Reference sequence in fasta format. Required if bam files provided.')\\n parser.add_argument('--output', type=str, required=False, default=None,\\n help='Location and name for output file (default=stdout)')\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n description=\\\"Visualize the output of the model.\\\")\\n\\n parser.add_argument(\\n \\\"-m\\\", \\\"--model\\\",\\n required=True,\\n help=\\\"Path to the model.\\\")\\n parser.add_argument(\\n \\\"-i\\\", \\\"--input\\\",\\n required=True,\\n help=\\\"Input file. Can be a '.npy', '.png', '.jpg', '.jpeg' or '.tif'.\\\")\\n parser.add_argument(\\n \\\"-s\\\", \\\"--sigma\\\",\\n required=True,\\n type=float,\\n help=\\\"Standard deviation of the gaussian noise.\\\")\\n\\n parser.add_argument(\\n \\\"-p\\\", \\\"--patch\\\",\\n default=256,\\n type=int,\\n help=\\\"Optional. Patch size. Default is 256.\\\")\\n parser.add_argument(\\n \\\"-o\\\", \\\"--output\\\",\\n default=\\\"\\\",\\n help=\\\"Optional. Output file. A '.npz' file will save reshaped input \\\" \\\\\\n \\\"image, noisy image and prediction. A '.npy' will save the \\\" \\\\\\n \\\"prediction array. A '.png', '.jpg', '.jpeg' or '.tif' will \\\" \\\\\\n \\\"save the prediction, but only if the input is a single image\\\")\\n parser.add_argument(\\n \\\"-v\\\", \\\"--visualize\\\",\\n default=False,\\n action='store_true',\\n help=\\\"Optional. Boolean. Visualize the prediction.\\\")\\n\\n return parser.parse_args()\",\n \"def main(args=None):\\n\\n args, _ = root_parser.parse_known_args(args=args)\\n import_path = ENTRY_POINTS[args.test]\\n module = import_module(import_path)\\n main_fnc = getattr(module, \\\"main\\\")\\n _check_main(main_fnc)\\n if args.dry:\\n return\\n main_fnc()\",\n \"def test_with_empty_args(self):\\n parser = Parser()\\n args = parser.parser.parse_args([])\\n self.assertFalse(args.config)\\n self.assertFalse(args.verbose)\\n self.assertFalse(args.quiet)\",\n \"def parse_args():\\n\\n op = OptionParser(usage=\\\"usage: %prog [opts] output_folder label\\\")\\n\\n ########################################\\n\\n op.add_option(\\n \\\"--test\\\",\\n dest=\\\"test\\\",\\n default=False,\\n action=\\\"store_true\\\",\\n help=\\\"executes the test suite\\\")\\n\\n ########################################\\n\\n return op.parse_args()\",\n \"def test_parse_arguments1():\\n args = []\\n parsed_args = parse_arguments.parse_arguments(args)\\n assert parsed_args.logging_level == logging.ERROR\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description='use STAR for mapping reads')\\n #input files\\n parser.add_argument('-s', '--sequences',\\n help='number of sequences to take for testing')\\n parser.add_argument('--tmpdir',\\n help='tmp directory',default=\\\"/tmp/\\\")\\n parser.add_argument('--input_dir',\\n help='optional: Choose input directory')\\n parser.add_argument('--reads_R1',\\n help='Forward unmerged reads')\\n parser.add_argument('--reads_R2',\\n help='Reverse unmerged reads')\\n parser.add_argument('--merged',\\n help='merged watson and crick fastq')\\n parser.add_argument('--reference',\\n help='reference clusters')\\n parser.add_argument('--refgenome',\\n help='reference clusters')\\n parser.add_argument('--barcodes',\\n help='Barcodes used in output')\\n parser.add_argument('--species',\\n help='Species: if selected only that species will be put in BAM RG header')\\n parser.add_argument('--threads',\\n help='Number of threads to used where multithreading is possible')\\n parser.add_argument('--output_dir',\\n help='Choose output directory')\\n parser.add_argument('--extraflags',\\n help='extra flags for testing')\\n args = parser.parse_args()\\n if args.input_dir:\\n args.reads_R1 = os.path.join(args.input_dir,'Unassembled.R1.watson.fq.gz')\\n args.reads_R2 = os.path.join(args.input_dir,'Unassembled.R2.crick.fq.gz')\\n args.merged = os.path.join(args.input_dir,'Assembled.fq.gz')\\n args.reference = os.path.join(args.input_dir,'consensus_cluster.renamed.fa')\\n if args.output_dir:\\n if not os.path.exists(args.output_dir):\\n os.mkdir(args.output_dir)\\n if 'log' not in args:\\n args.log = os.path.join(args.output_dir,'mapping_variantcalling.log')\\n args.watson_vcf = os.path.join(args.output_dir,'watson.vcf')\\n args.crick_vcf = os.path.join(args.output_dir,'crick.vcf')\\n args.snp_vcf = os.path.join(args.output_dir,'snp.vcf')\\n args.methylation_vcf = os.path.join(args.output_dir,'methylation.vcf')\\n args.heatmap = os.path.join(args.output_dir,'heatmap.igv')\\n #2 bed files should be made for subsequent analysis using Rnbeads or other software\\n args.mastermeth = os.path.join(args.output_dir,'methylation.bed')\\n args.tmpdir = tempfile.mkdtemp(suffix='STAR', prefix='tmp', dir=args.tmpdir)\\n return args\",\n \"def test_run(prog, correct, inp=None):\\n print(f\\\"Testing [{prog2str(prog)}] (inp={inp})...\\\", end=\\\"\\\")\\n out = list()\\n run_prog(prog, inp, out)\\n if (out and out == correct) or (not out and prog == correct):\\n print(\\\" OK!\\\")\\n else:\\n fail_str = prog2str(out if out else prog)\\n print(f\\\" Failed: [{fail_str}] != [{prog2str(correct)}]!\\\")\",\n \"def test_create_parser() -> None:\\n\\n def check(arg: List[str], expected_key: str, expected_value: Any) -> None:\\n parsed = ParamClass.parse_args(arg)\\n assert getattr(parsed, expected_key) == expected_value\\n\\n def check_fails(arg: List[str]) -> None:\\n with pytest.raises(SystemExit) as e:\\n ParamClass.parse_args(arg)\\n assert e.type == SystemExit\\n assert e.value.code == 2\\n\\n check([\\\"--name=foo\\\"], \\\"name\\\", \\\"foo\\\")\\n check([\\\"--seed\\\", \\\"42\\\"], \\\"seed\\\", 42)\\n check([\\\"--seed\\\", \\\"\\\"], \\\"seed\\\", 42)\\n check([\\\"--number\\\", \\\"2.17\\\"], \\\"number\\\", 2.17)\\n check([\\\"--number\\\", \\\"\\\"], \\\"number\\\", 3.14)\\n check([\\\"--integers\\\", \\\"1,2,3\\\"], \\\"integers\\\", [1, 2, 3])\\n check([\\\"--optional_int\\\", \\\"\\\"], \\\"optional_int\\\", None)\\n check([\\\"--optional_int\\\", \\\"2\\\"], \\\"optional_int\\\", 2)\\n check([\\\"--optional_float\\\", \\\"\\\"], \\\"optional_float\\\", None)\\n check([\\\"--optional_float\\\", \\\"3.14\\\"], \\\"optional_float\\\", 3.14)\\n check([\\\"--tuple1\\\", \\\"1,2\\\"], \\\"tuple1\\\", (1, 2.0))\\n check([\\\"--int_tuple\\\", \\\"1,2,3\\\"], \\\"int_tuple\\\", (1, 2, 3))\\n check([\\\"--enum=2\\\"], \\\"enum\\\", ParamEnum.EnumValue2)\\n check([\\\"--floats=1,2,3.14\\\"], \\\"floats\\\", [1., 2., 3.14])\\n check([\\\"--integers=1,2,3\\\"], \\\"integers\\\", [1, 2, 3])\\n # Check all the ways of passing in True, with and without the first letter capitialized\\n for flag in ('on', 't', 'true', 'y', 'yes', '1'):\\n check([f\\\"--flag={flag}\\\"], \\\"flag\\\", True)\\n check([f\\\"--flag={flag.capitalize()}\\\"], \\\"flag\\\", True)\\n check([f\\\"--not_flag={flag}\\\"], \\\"not_flag\\\", True)\\n check([f\\\"--not_flag={flag.capitalize()}\\\"], \\\"not_flag\\\", True)\\n # Check all the ways of passing in False, with and without the first letter capitialized\\n for flag in ('off', 'f', 'false', 'n', 'no', '0'):\\n check([f\\\"--flag={flag}\\\"], \\\"flag\\\", False)\\n check([f\\\"--flag={flag.capitalize()}\\\"], \\\"flag\\\", False)\\n check([f\\\"--not_flag={flag}\\\"], \\\"not_flag\\\", False)\\n check([f\\\"--not_flag={flag.capitalize()}\\\"], \\\"not_flag\\\", False)\\n # Check that passing no value to flag sets it to True (the opposite of its default)\\n check([\\\"--flag\\\"], \\\"flag\\\", True)\\n # Check that no-flag is not an option\\n check_fails([\\\"--no-flag\\\"])\\n # Check that passing no value to not_flag fails\\n check_fails([\\\"--not_flag\\\"])\\n # Check that --no-not_flag is an option and sets it to False (the opposite of its default)\\n check([\\\"--no-not_flag\\\"], \\\"not_flag\\\", False)\\n # Check that both not_flag and no-not_flag cannot be passed at the same time\\n check_fails([\\\"--not_flag=false\\\", \\\"--no-not_flag\\\"])\\n # Check that invalid bools are caught\\n check_fails([\\\"--flag=Falsf\\\"])\\n check_fails([\\\"--flag=Truf\\\"])\\n # Check that default values are created as expected, and that the non-overridable parameters\\n # are omitted.\\n defaults = vars(ParamClass.create_argparser().parse_args([]))\\n assert defaults[\\\"seed\\\"] == 42\\n assert defaults[\\\"tuple1\\\"] == (1, 2.3)\\n assert defaults[\\\"int_tuple\\\"] == (1, 1, 1)\\n assert defaults[\\\"enum\\\"] == ParamEnum.EnumValue1\\n assert not defaults[\\\"flag\\\"]\\n assert defaults[\\\"not_flag\\\"]\\n assert \\\"readonly\\\" not in defaults\\n assert \\\"constant\\\" not in defaults\\n assert \\\"_non_override\\\" not in defaults\\n # We can't test if all invalid cases are handled because argparse call sys.exit\\n # upon errors.\",\n \"def parse_cmd_arguments():\\n parser = argparse.ArgumentParser(description='Process some integers.')\\n parser.add_argument('-i', '--input', required=True, help='input JSON file')\\n parser.add_argument('-o', '--output', required=True,\\n help='ouput JSON file')\\n parser.add_argument('-d', '--debug', required=False,\\n help='log level. Can be 0-3. Defaults to 0')\\n\\n return parser.parse_args()\",\n \"def test_main(cmd_map, argv, action, debug, alias):\\n cmd_map.return_value = mock.Mock()\\n app.main(argv)\\n cmd_map.assert_called_once_with(argparse.Namespace(alias=alias, cmd_args=[], color=False, action=action, debug=debug))\",\n \"def run(*args, **kwargs):\\n kwargs[\\\"check\\\"] = True\\n print(\\\"+\\\", \\\" \\\".join(args[0]))\\n return subprocess.run(*args, **kwargs)\",\n \"def parse_args(args):\\n parser = argparse.ArgumentParser(prog=\\\"cc.py fuzzer\\\",\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n description=\\\"A fuzzer for the CAN bus\\\",\\n epilog=\\\"\\\"\\\"Example usage:\\n ./cc.py fuzzer -alg random\\n ./cc.py fuzzer -alg ring_bf -id 244 -payload_bitmap 0000001 \\n -file example.txt\\n \\\"\\\"\\\"\\n\\n + \\\"\\\"\\\"\\\\nCurrently supported algorithms:\\n random - Send random or static CAN payloads to \\n random or static arbitration ids.\\n linear - Use a given input file to send can packets.\\n replay - Use the linear algorithm but also attempt \\n to find a specific payload response.\\n ring_bf - Attempts to brute force a static id \\n using a ring based brute force algorithm.\\n mutate - Mutates (hex) bits in the given id/payload.\\n The mutation bits are specified \\n in the id/payload bitmaps.\\\"\\\"\\\")\\n\\n parser.add_argument(\\\"-alg\\\", type=str, help=\\\"What fuzzing algorithm to use.\\\")\\n parser.add_argument(\\\"-log\\\", type=int, default=0,\\n help=\\\"How many cansend directives must be kept in memory at a time (default is 0)\\\")\\n\\n parser.add_argument(\\\"-file\\\", type=str, help=\\\"Specify a file to where the fuzzer should write\\\"\\n \\\"the cansend directives it uses. \\\"\\n \\\"This is required for the linear algorithm.\\\")\\n\\n parser.add_argument(\\\"-id\\\", type=str, help=\\\"Specify an id to use. \\\"\\n \\\" Use the following syntax: 123\\\")\\n parser.add_argument(\\\"-id_bitmap\\\", type=list, help=\\\"Override the default id bitmap with a different id bitmap. \\\"\\n \\\"Use the following syntax: 0100 \\\"\\n \\\"(with 1 a digit that can be overriden)\\\")\\n\\n parser.add_argument(\\\"-payload\\\", type=str, help=\\\"Specify a payload to use. \\\"\\n \\\"Use the following syntax: FFFFFFFF\\\")\\n parser.add_argument(\\\"-payload_bitmap\\\", type=list,\\n help=\\\"Override the default payload bitmap with a different payload bitmap. \\\"\\n \\\"Use the following syntax: 0100 (with 1 a digit that can be overriden)\\\")\\n\\n args = parser.parse_args(args)\\n return args\",\n \"def main(cls, args=None):\\n parser = cls.CreateParser()\\n cls.AddCommandLineArgs(parser, None)\\n options, extra_args = parser.parse_known_args(args=args)\\n cls.ProcessCommandLineArgs(parser, options, extra_args, None)\\n return min(cls().Run(options, extra_args), 255)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70857084","0.70857084","0.6719469","0.6544911","0.64907324","0.6395866","0.6362561","0.632354","0.63156325","0.62827814","0.6258688","0.6254855","0.6252494","0.62392056","0.6207981","0.61993474","0.61984104","0.61981714","0.6184711","0.61758745","0.6159631","0.61503065","0.61470115","0.6137951","0.6131081","0.6127437","0.6112099","0.6102443","0.60991746","0.60969317","0.6079416","0.60785294","0.60723025","0.60717505","0.60693187","0.6065854","0.60632896","0.6045401","0.6043539","0.6040715","0.60353076","0.6027484","0.6019444","0.601098","0.60024524","0.59891784","0.59852254","0.5980472","0.5976005","0.5969716","0.59690773","0.59688336","0.5965744","0.5964746","0.5964681","0.59644437","0.59621584","0.5954215","0.59515744","0.5948447","0.59242266","0.592297","0.59149945","0.59134823","0.5912481","0.5904408","0.58969384","0.58963525","0.58909076","0.5887699","0.58865535","0.5884456","0.58815604","0.5880723","0.5872849","0.5870313","0.5864687","0.5863793","0.5861596","0.58610344","0.5860596","0.5855599","0.58544487","0.58467805","0.5836458","0.58355254","0.58308864","0.58304036","0.58252215","0.581486","0.5814111","0.58135414","0.5810359","0.580887","0.580831","0.58067197","0.57924694","0.5788139","0.57831657","0.5782237"],"string":"[\n \"0.70857084\",\n \"0.70857084\",\n \"0.6719469\",\n \"0.6544911\",\n \"0.64907324\",\n \"0.6395866\",\n \"0.6362561\",\n \"0.632354\",\n \"0.63156325\",\n \"0.62827814\",\n \"0.6258688\",\n \"0.6254855\",\n \"0.6252494\",\n \"0.62392056\",\n \"0.6207981\",\n \"0.61993474\",\n \"0.61984104\",\n \"0.61981714\",\n \"0.6184711\",\n \"0.61758745\",\n \"0.6159631\",\n \"0.61503065\",\n \"0.61470115\",\n \"0.6137951\",\n \"0.6131081\",\n \"0.6127437\",\n \"0.6112099\",\n \"0.6102443\",\n \"0.60991746\",\n \"0.60969317\",\n \"0.6079416\",\n \"0.60785294\",\n \"0.60723025\",\n \"0.60717505\",\n \"0.60693187\",\n \"0.6065854\",\n \"0.60632896\",\n \"0.6045401\",\n \"0.6043539\",\n \"0.6040715\",\n \"0.60353076\",\n \"0.6027484\",\n \"0.6019444\",\n \"0.601098\",\n \"0.60024524\",\n \"0.59891784\",\n \"0.59852254\",\n \"0.5980472\",\n \"0.5976005\",\n \"0.5969716\",\n \"0.59690773\",\n \"0.59688336\",\n \"0.5965744\",\n \"0.5964746\",\n \"0.5964681\",\n \"0.59644437\",\n \"0.59621584\",\n \"0.5954215\",\n \"0.59515744\",\n \"0.5948447\",\n \"0.59242266\",\n \"0.592297\",\n \"0.59149945\",\n \"0.59134823\",\n \"0.5912481\",\n \"0.5904408\",\n \"0.58969384\",\n \"0.58963525\",\n \"0.58909076\",\n \"0.5887699\",\n \"0.58865535\",\n \"0.5884456\",\n \"0.58815604\",\n \"0.5880723\",\n \"0.5872849\",\n \"0.5870313\",\n \"0.5864687\",\n \"0.5863793\",\n \"0.5861596\",\n \"0.58610344\",\n \"0.5860596\",\n \"0.5855599\",\n \"0.58544487\",\n \"0.58467805\",\n \"0.5836458\",\n \"0.58355254\",\n \"0.58308864\",\n \"0.58304036\",\n \"0.58252215\",\n \"0.581486\",\n \"0.5814111\",\n \"0.58135414\",\n \"0.5810359\",\n \"0.580887\",\n \"0.580831\",\n \"0.58067197\",\n \"0.57924694\",\n \"0.5788139\",\n \"0.57831657\",\n \"0.5782237\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7031648"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":85683,"cells":{"query":{"kind":"string","value":"Responsible for verifying techniques directory and generating techniques index markdown"},"document":{"kind":"string","value":"def generate():\n\n # Verify if directory exists\n if not os.path.isdir(config.techniques_markdown_path):\n os.mkdir(config.techniques_markdown_path)\n\n #Write the technique index.html page\n with open(os.path.join(config.techniques_markdown_path, \"overview.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(config.technique_overview_md)\n\n for domain in config.domains:\n generate_domain_markdown(domain)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def walkthrough(software_map):\n\n for i in software_map:\n\n if not i[\"is_file\"]:\n\n # for each directory: make a index.md\n dname = \"./docs/\" + i[\"name\"]\n index = \"./docs/\" + i[\"name\"] + \"/index.md\"\n print(index)\n os.mkdir(dname)\n\n with open(index, \"w+\") as f:\n\n children = i[\"children\"]\n\n # list files\n f.write(\"Files:\\n\\n\")\n for i in children:\n if i[\"is_file\"]:\n\n fname = i[\"name\"]\n fext = fname.split(\".\")\n if len(fext) == 2:\n fext = fext[1]\n else:\n fext = \"none\"\n # for each file, note name and extension\n f.write(fname + \" : \" + fext + \"\\n\")\n\n # list subdirectories\n f.write(\"\\nSubdirectories:\\n\\n\")\n for i in children:\n if not i[\"is_file\"]:\n\n dirname = i[\"name\"]\n\n # note the number of files and subdirs in it\n num_files, num_dirs = 0, 0\n for child in i[\"children\"]:\n if child[\"is_file\"]:\n num_files += 1\n elif not child[\"is_file\"]:\n num_dirs += 1\n\n # note down name and numbers for each dir\n f.write(dirname + \" : \" + str(num_files) + \" files, \" +\n str(num_dirs) + \" directories\\n\")\n\n # goto subdir\n if len(i[\"children\"]) > 0:\n walkthrough(i[\"children\"])","def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)","def main():\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \"original\")\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \"annotated\")\n\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\n os.mkdir(ANNOTATED_LESSONS_PATH)\n\n print(\"Scanning original lessons in %s...\" % ORIGINAL_LESSONS_PATH)\n\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\n if item == \".DS_Store\": continue\n\n print(\" found: %s\" % item)\n\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\n\n lesson_number = None\n lesson_description = None\n mobj = re.search(r'^AY\\s+(\\d+)\\s*-\\s*(.+)\\.txt$', item)\n if mobj:\n lesson_number = mobj.group(1)\n lesson_description = mobj.group(2)\n\n print(\" number: %s\" % lesson_number)\n print(\" description: %s\" % lesson_description)\n\n lesson = dict()\n lesson['number'] = lesson_number\n lesson['description'] = lesson_description\n\n fh = open(item_path)\n lesson_raw_text = fh.read()\n fh.close()\n lesson_text = re.split(r'\\n', lesson_raw_text)\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\n# lesson_text = re.split(r'\\n', lesson_raw_text_reencoded)\n\n lesson['text'] = lesson_text\n lesson['parsed'] = parseLesson(lesson_text)\n\n if lesson['parsed']['end_of_lesson'] is None:\n print(\" lesson has no 'end of lesson' marker\")\n\n lesson_json = json.dumps(lesson, indent=4)\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \"ay_%04d.json\" % int(lesson_number))\n fh = open(annotated_lesson_path, \"w\")\n fh.write(lesson_json)\n fh.close()\n\n else:\n print(\"ERROR: File name not understood: %s\" % item)\n\n return 0","def main():\n\n dofile = \"thebook\"\n\n #spellcheck()\n\n common_options = '--encoding=utf-8 --examples_as_exercises '\n\n # --- HTML ---\n\n common_html_options = ' '\n\n # HTML Bootstrap\n bootstrap_options = ' --html_style=bootswatch_readable --html_code_style=inherit --html_pre_style=inherit --toc_depth=2 --pygments_html_style=default --html_template=template_bootstrap_wtoc.html --html_figure_caption=bottom --html_figure_hrule=top+bottom' \n\n html(\n dofile,\n options=common_options + common_html_options + bootstrap_options,\n split=True)\n\n # One long HTML file\n #html(dofile, options=common_options + common_html_options + ' --html_style=bloodish --html_output=%s-1' % dofile, split=False)\n\n # Solarized HTML\n #html(dofile, options=common_options + common_html_options + ' --html_style=solarized3 --html_output=%s-solarized' % dofile, split=True)\n\n mksnippets()\n sys.exit(1)\n\n # --- latex ---\n\n common_latex_options = ' --latex_code_style=vrb'\n\n for version in 'paper', 'screen': # , 'A4', '2up', 'A4-2up':\n latex(\n dofile,\n latex_program='pdflatex',\n options=common_options + common_latex_options,\n version=version,\n postfix='auto')\n\n # --- Sphinx ---\n\n# sphinx_themes = ['pyramid',]\n# for theme in sphinx_themes:\n# dirname = 'sphinx-rootdir' if len(sphinx_themes) == 1 else 'sphinx-rootdir-%s' % theme\n# sphinx(\n# dofile,\n# options=common_options + '',\n# dirname=dirname,\n# theme=theme,\n# automake_sphinx_options='',\n# split=False)\n\n # Dump all Unix commands run above as a Bash script\n bash = open('tmp_make.sh', 'w')\n print 'see tmp_make.sh for an equivalent auto-generated unix script'\n bash.write('''#!/bin/bash\nset -x # display all commands in output\n\n# Safe execution of a Unix command: exit if failure\nfunction system {\n \"$@\"\n if [ $? -ne 0 ]; then\n echo \"make.sh: unsuccessful command $@\"\n echo \"abort!\"\n exit 1\n fi\n}\n''')\n for cmd in unix_command_recorder:\n if cmd.startswith('doconce format') or cmd.startswith('rm '):\n bash.write('\\n') # delimiter line in script\n bash.write('system ' + cmd + '\\n')\n bash.close()\n\n print 'see tmp_output.log for the output of all the commands'","def check_data():\n check_docs(\"Training\")\n check_docs(\"dev\")\n check_docs(\"Test\")","def gen_tutorials(repo_dir: str) -> None:\n with open(os.path.join(repo_dir, \"website\", \"tutorials.json\"), \"r\") as infile:\n tutorial_config = json.loads(infile.read())\n\n tutorial_ids = [x[\"id\"] for v in tutorial_config.values() for x in v]\n tutorial_dirs = [x.get(\"dir\") for v in tutorial_config.values() for x in v]\n\n for tid, t_dir in zip(tutorial_ids, tutorial_dirs):\n print(\"Generating {} tutorial\".format(tid))\n\n if t_dir is not None:\n tutorial_dir = os.path.join(repo_dir, \"tutorials\", t_dir)\n html_dir = os.path.join(repo_dir, \"website\", \"_tutorials\", t_dir)\n js_dir = os.path.join(repo_dir, \"website\", \"pages\", \"tutorials\", t_dir)\n py_dir = os.path.join(repo_dir, \"website\", \"static\", \"files\", t_dir)\n\n for d in [tutorial_dir, html_dir, js_dir, py_dir]:\n os.makedirs(d, exist_ok=True)\n\n tutorial_path = os.path.join(tutorial_dir, \"{}.ipynb\".format(tid))\n html_path = os.path.join(html_dir, \"{}.html\".format(tid))\n js_path = os.path.join(js_dir, \"{}.js\".format(tid))\n ipynb_path = os.path.join(py_dir, \"{}.ipynb\".format(tid))\n py_path = os.path.join(py_dir, \"{}.py\".format(tid))\n tar_path = os.path.join(py_dir, \"{}.tar.gz\".format(tid))\n else:\n tutorial_path = os.path.join(repo_dir, \"tutorials\", \"{}.ipynb\".format(tid))\n html_path = os.path.join(\n repo_dir, \"website\", \"_tutorials\", \"{}.html\".format(tid)\n )\n js_path = os.path.join(\n repo_dir, \"website\", \"pages\", \"tutorials\", \"{}.js\".format(tid)\n )\n ipynb_path = os.path.join(\n repo_dir, \"website\", \"static\", \"files\", \"{}.ipynb\".format(tid)\n )\n py_path = os.path.join(\n repo_dir, \"website\", \"static\", \"files\", \"{}.py\".format(tid)\n )\n\n # convert notebook to HTML\n with open(tutorial_path, \"r\") as infile:\n nb_str = infile.read()\n nb = nbformat.reads(nb_str, nbformat.NO_CONVERT)\n\n exporter = HTMLExporter()\n html, meta = exporter.from_notebook_node(nb)\n\n # pull out html div for notebook\n soup = BeautifulSoup(html, \"html.parser\")\n nb_meat = soup.find(\"div\", {\"id\": \"notebook-container\"})\n del nb_meat.attrs[\"id\"]\n nb_meat.attrs[\"class\"] = [\"notebook\"]\n\n # when output html, iframe it (useful for Ax reports)\n for html_div in nb_meat.findAll(\"div\", {\"class\": \"output_html\"}):\n if html_div.html is not None:\n iframe = soup.new_tag(\"iframe\")\n iframe.attrs[\"src\"] = \"data:text/html;charset=utf-8,\" + str(\n html_div.html\n )\n # replace `#` in CSS\n iframe.attrs[\"src\"] = iframe.attrs[\"src\"].replace(\"#\", \"%23\")\n html_div.contents = [iframe]\n\n html_out = MOCK_JS_REQUIRES + str(nb_meat)\n\n # generate HTML file\n with open(html_path, \"w\") as html_outfile:\n html_outfile.write(html_out)\n\n # generate JS file\n t_dir_js = t_dir if t_dir else \"\"\n script = TEMPLATE.format(t_dir=t_dir_js, tid=tid)\n with open(js_path, \"w\") as js_outfile:\n js_outfile.write(script)\n\n # output tutorial in both ipynb & py form\n with open(ipynb_path, \"w\") as ipynb_outfile:\n ipynb_outfile.write(nb_str)\n exporter = ScriptExporter()\n script, meta = exporter.from_notebook_node(nb)\n with open(py_path, \"w\") as py_outfile:\n py_outfile.write(script)\n\n # create .tar archive (if necessary)\n if t_dir is not None:\n with tarfile.open(tar_path, \"w:gz\") as tar:\n tar.add(tutorial_dir, arcname=os.path.basename(tutorial_dir))","def index():\n with open(\"PROJECT.md\", \"r\") as project_file:\n md_template_string = markdown.markdown(\n project_file.read(), extensions=[\"fenced_code\"]\n )\n return md_template_string","def verifyBuilt(self):\n htmlDir = self.sphinxDir.sibling(\"doc\")\n self.assertTrue(htmlDir.isdir())\n doctreeDir = htmlDir.child(\"doctrees\")\n self.assertFalse(doctreeDir.exists())\n\n self.verifyFileExists(htmlDir, \"index.html\")\n self.verifyFileExists(htmlDir, \"genindex.html\")\n self.verifyFileExists(htmlDir, \"objects.inv\")\n self.verifyFileExists(htmlDir, \"search.html\")\n self.verifyFileExists(htmlDir, \"searchindex.js\")","def workbench_scenarios():\n return [\n (\"Markdown\",\n \"\"\"<markdowna />\n \"\"\")\n ]","def helper():\n \n import webbrowser, os.path\n \n path = os.path.splitext(__file__)[0]\n helpspec = \"file://\" + path + os.path.sep + \\\n \"markdown.html\"\n \n # webbrowser.open seems not to work well\n browser = webbrowser.get()\n if not browser.open_new(helpspec):\n print((\"Help file not found:\" + helpspec))","def helper():\n \n import webbrowser, os.path\n \n path = os.path.splitext(__file__)[0]\n helpspec = \"file://\" + path + os.path.sep + \\\n \"markdown.html\"\n \n # webbrowser.open seems not to work well\n browser = webbrowser.get()\n if not browser.open_new(helpspec):\n print((\"Help file not found:\" + helpspec))","def _setup(self):\n self._raw_dir = os.path.join(self._snippets_dir,\"raw\",\"static\")\n if not os.path.exists(self._raw_dir):\n os.mkdir(self._raw_dir)\n\n self._trec_dir = os.path.join(self._snippets_dir,\"trec\",\"static\")\n if not os.path.exists(self._trec_dir):\n os.mkdir(self._trec_dir)\n\n self._temp_dir = os.path.join(self._snippets_dir,\"temp\",\"static\")\n if not os.path.exists(self._temp_dir):\n os.mkdir(self._temp_dir)\n\n self._para_dir = os.path.join(self._snippets_dir,\"para\",\"static\")\n if not os.path.exists(self._para_dir):\n os.mkdir(self._para_dir)\n\n self._snippet_result_dir = os.path.join(self._snippets_dir,\"result\",\"static\")\n if not os.path.exists(self._snippet_result_dir):\n os.mkdir(self._snippet_result_dir)\n\n self._snippet_index_dir = os.path.join(self._snippets_dir,\"index\",\"static\")\n if not os.path.exists(self._snippet_index_dir):\n os.mkdir(self._snippet_index_dir)\n\n \n\n\n self._index_para = os.path.join(self._para_dir,\"index_para\")\n\n self._temp_query_para = os.path.join(self._para_dir,\"temp_query_para\")\n\n self._index_list = os.path.join(self._para_dir,\"static_index_list\")\n \n self._orf = os.path.join(self._snippet_result_dir,\"orf\")\n\n self._oqf = os.path.join(self._temp_dir,\"oqf\")\n \n self._temp_output = os.path.join(self._temp_dir,\"temp_output\")\n\n with open(self._index_list,\"w\") as f:\n f.write(self._snippet_index_dir+\"\\n\")\n\n self._temp_query_builder = IndriQueryFactory(count=10000,\n rule=self._retrieval_method)\n\n self._oqf_builder = IndriQueryFactory(count=30,\n rule=self._retrieval_method)","def test_docs_paths():\n assert os.path.exists('test/examples/docs/paths-root-api.md')\n assert os.path.exists('test/examples/docs/paths-subpath1.md')\n assert os.path.exists('test/examples/docs/paths-subpath1.md')","def _markdown(checklist):\n\n checklist = json.load(open(checklist), object_pairs_hook=OrderedDict)\n\n mdFile = MdUtils(file_name='Ikigai-Checklist',\n title='PDP 2019 Checklist')\n\n\n mdFile.new_paragraph(checklist['overview'])\n\n sections = sorted(checklist['checklist'].values(),\n key=lambda s: int(s['no']))\n for section in sections:\n mdFile.new_header(level=1, title=section['title'])\n for subject in section['subjects'].values():\n mdFile.new_header(level=2, title=subject['title'])\n mdFile.new_paragraph(subject['description'])\n mdFile.new_paragraph(\"Reference: \" + subject['provisions'])\n mdFile.new_paragraph(\"Functions: \" + \", \".join(subject['functions']))\n mdFile.new_paragraph(\"Groups: \" + \", \".join(subject['groups']))\n\n actions = [['No','Description', 'Tags', 'Check']]\n actions += [[a['no'], a['description'], \", \".join(a['tags']),''] for a in subject['actions'].values()]\n rows = len(actions) \n actions = flatten(actions)\n mdFile.new_table(columns=4,\n rows=rows,\n text=actions,\n text_align='left')\n\n mdFile.create_md_file()","def main(base_path):\n current = os.getcwd()\n try:\n if not(os.path.exists(base_path)):\n ans = 'y'\n if p_out:\n print(\"Do you want to create \" + base_path + \"?(y/n)\")\n ans = sys.stdin.read(1)\n print(\"\")\n if ans in ('y', 'Y'):\n pass\n elif ans in ('n', 'N'):\n raise NoneOutput\n else:\n raise InputError\n else:\n m_path = os.path.join(base_path, 'nzmath/manual')\n if os.path.exists(m_path):\n ans = 'y'\n if p_out:\n print(\"Do you want to remove \" + m_path + \"?(y/n)\")\n ans = sys.stdin.read(1)\n print(\"\")\n if ans in ('y', 'Y'):\n for root, dirs, files in os.walk(m_path, topdown=False):\n for name in files:\n os.remove(os.path.join(root, name))\n for name in dirs:\n os.rmdir(os.path.join(root, name))\n elif ans in ('n', 'N'):\n raise NoneOutput\n else:\n raise InputError\n dirname = os.path.join(base_path, 'nzmath/manual/modules')\n if not(os.path.exists(dirname)):\n os.makedirs(dirname)\n os.chdir(os.path.join(base_path, 'nzmath/manual/'))\n csspage = convertHPURL('manual/default.css')\n if p_out:\n print(\"get css from \" + csspage)\n retryConnection(urllib.request.urlretrieve, csspage, 'default.css')\n while ad_list:\n files = ad_list.pop()\n MyWikiParser(files).feeds()\n if p_out:\n print(\"\\n\" + \"All process is done!\" + \"\\n\")\n print(\"Ok, now created nzmath-current manual located to\")\n print(os.path.join(base_path, \"nzmath\"))\n print(\"if you check difference between nzmath-cvs manual, with GNU diff,\")\n print(\"$ diff -ubBr /tmp/nzmath/manual {your-nzmathcvs-repo}/manual\")\n print(\"or you check only new version files,\")\n print(\"$ diff -r --brief /tmp/nzmath/manual {your-nzmathcvs-repo}/manual .\")\n except NoneOutput:\n if p_out:\n print('end.')\n except InputError:\n print(\"Error: Invalid input!\")\n except LookupError:\n print(\"Error: Maybe, Japanese encodings(ex.euc_jp) is not supported.\")\n except:\n if p_out:\n print(\"Check \" + base_path + \" (dir? truly path? and so on.)\")\n print(\"Delete \" + base_path + \" and try again.\")\n print(\"(Maybe, caused by problem of network connection)\\n\")\n print(sys.exc_info()[0])\n os.chdir(current)","def test_md(tmp_path) -> Path:\n yield Path(tmp_path)/\"test.md\"","def generate_markdown(self, outdir, definitions):\n doc_cases = defaultdict(lambda: defaultdict(list))\n\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n for category in definitions.categories:\n cat_outdir = os.path.join(outdir, category.replace(\" \", \"_\").lower())\n if not os.path.exists(cat_outdir):\n os.makedirs(cat_outdir)\n\n for case in definitions.case_set:\n if case[\"category\"] == category:\n doc_cases[category][case[\"name\"]] = case\n\n for technique in doc_cases[category]:\n rendered = self._generate_markdown(doc_cases[category][technique])\n md_filename = technique.replace(\" \", \"_\").lower() + \".md\"\n filename = os.path.join(cat_outdir, md_filename)\n mdoutfile = open(filename, \"w\")\n mdoutfile.write(rendered)\n mdoutfile.close()","def test_markdown(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.example.Foo.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n for item in data['items']:\n if item['uid'] == 'example.example.Foo.method_markdown':\n self.assertEqual(\n item['summary'],\n 'Check out our '\n '[site](http://sphinx-docfx-yaml.readthedocs.io/en/latest/)'\n ' for more info.',\n )","def index():\n mongo_collection = mongo_database[\"settings\"]\n doc_instructions = mongo_collection.find_one({\"id\": \"instructions\"})\n instructions = markdown.markdown(doc_instructions['text'])\n return render_template(\"index.html\", instructions=instructions)","def generate_domain_markdown(domain):\n\n #Reads the STIX and creates a list of the ATT&CK Techniques\n full_techniques = stixhelpers.get_techniques(config.ms[domain])\n tactic_list = stixhelpers.get_tactic_list(config.ms[domain])\n\n data = {}\n \n technique_list = get_techniques_list(full_techniques)\n\n data['domain'] = domain.split(\"-\")[0]\n\n # Get technique table data and number of techniques\n data['technique_table'] = util.get_technique_table_data(None, full_techniques)\n data['technique_list_len'] = str(len(full_techniques))\n side_menu_data = get_technique_side_menu_data(domain, technique_list, tactic_list)\n\n # Get tactic-techniques table\n data['menu'] = side_menu_data\n\n subs = config.technique_domain_md.substitute(data)\n subs = subs + json.dumps(data)\n\n with open(os.path.join(config.techniques_markdown_path, data['domain'] + \"-techniques.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)\n\n # Create the markdown for the enterprise groups in the STIX\n\n for technique in full_techniques:\n if 'revoked' not in technique or technique['revoked'] is False:\n generate_technique_md(technique, domain, side_menu_data, tactic_list)","def markov_story():\n return render_template(\"markovstory.html\")","def test_get_analyzed_recipe_instructions(self):\n pass","def home() :\n st.markdown(\"This application provides 3 mains modules :\")\n st.markdown(\"* **The prediction module :** enables you to assess client's liability based on its file\")\n st.markdown(\"* **The explorer module :** enables you to dig deeper into your client informations,\"\n \" particularly historcial data coming from federal loan bureau and historical\"\n \" Home Credit's data if available.\")\n st.markdown(\"* **The statistics module** : enables you to explore the database at a macro scale :\"\n \" understand how variables such as age, sex and income impact probability of repayment\")","def test_existing_current_files(self):\n\n m = Mothur(**self.init_vars)\n self.set_current_dirs(m)\n m.summary.seqs(fasta='test_fasta_1.fasta')\n m.summary.seqs()\n\n return","def idempotence(args):\n title, posts = parse_markdown(os.path.join(args.root, 'index.md'))\n print_markdown(posts, title, os.path.join(args.dest, 'index.md'))","def test_htmldir(self):\n self.chck_triple('htmldir')","def test_incorrect_case_file_index(data: TestData) -> None:\n req = install_req_from_line(\"dinner\")\n finder = make_test_finder(index_urls=[data.find_links3])\n found = finder.find_requirement(req, False)\n assert found is not None\n assert found.link.url.endswith(\"Dinner-2.0.tar.gz\")","def files_exist(self):\n\n passed = []\n warned = []\n failed = []\n ignored = []\n\n # NB: Should all be files, not directories\n # List of lists. Passes if any of the files in the sublist are found.\n #: test autodoc\n try:\n _, short_name = self.nf_config[\"manifest.name\"].strip(\"\\\"'\").split(\"/\")\n except ValueError:\n log.warning(\"Expected manifest.name to be in the format '<repo>/<pipeline>'. Will assume it is '<pipeline>'.\")\n short_name = self.nf_config[\"manifest.name\"].strip(\"\\\"'\").split(\"/\")\n\n files_fail = [\n [\".gitattributes\"],\n [\".gitignore\"],\n [\".nf-core.yml\"],\n [\".editorconfig\"],\n [\".prettierignore\"],\n [\".prettierrc.yml\"],\n [\"CHANGELOG.md\"],\n [\"CITATIONS.md\"],\n [\"CODE_OF_CONDUCT.md\"],\n [\"CODE_OF_CONDUCT.md\"],\n [\"LICENSE\", \"LICENSE.md\", \"LICENCE\", \"LICENCE.md\"], # NB: British / American spelling\n [\"nextflow_schema.json\"],\n [\"nextflow.config\"],\n [\"README.md\"],\n [os.path.join(\".github\", \".dockstore.yml\")],\n [os.path.join(\".github\", \"CONTRIBUTING.md\")],\n [os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"bug_report.yml\")],\n [os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"config.yml\")],\n [os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"feature_request.yml\")],\n [os.path.join(\".github\", \"PULL_REQUEST_TEMPLATE.md\")],\n [os.path.join(\".github\", \"workflows\", \"branch.yml\")],\n [os.path.join(\".github\", \"workflows\", \"ci.yml\")],\n [os.path.join(\".github\", \"workflows\", \"linting_comment.yml\")],\n [os.path.join(\".github\", \"workflows\", \"linting.yml\")],\n [os.path.join(\"assets\", \"email_template.html\")],\n [os.path.join(\"assets\", \"email_template.txt\")],\n [os.path.join(\"assets\", \"sendmail_template.txt\")],\n [os.path.join(\"assets\", f\"nf-core-{short_name}_logo_light.png\")],\n [os.path.join(\"conf\", \"modules.config\")],\n [os.path.join(\"conf\", \"test.config\")],\n [os.path.join(\"conf\", \"test_full.config\")],\n [os.path.join(\"docs\", \"images\", f\"nf-core-{short_name}_logo_light.png\")],\n [os.path.join(\"docs\", \"images\", f\"nf-core-{short_name}_logo_dark.png\")],\n [os.path.join(\"docs\", \"output.md\")],\n [os.path.join(\"docs\", \"README.md\")],\n [os.path.join(\"docs\", \"README.md\")],\n [os.path.join(\"docs\", \"usage.md\")],\n [os.path.join(\"lib\", \"nfcore_external_java_deps.jar\")],\n [os.path.join(\"lib\", \"NfcoreTemplate.groovy\")],\n [os.path.join(\"lib\", \"Utils.groovy\")],\n [os.path.join(\"lib\", \"WorkflowMain.groovy\")],\n ]\n\n files_warn = [\n [\"main.nf\"],\n [os.path.join(\"assets\", \"multiqc_config.yml\")],\n [os.path.join(\"conf\", \"base.config\")],\n [os.path.join(\"conf\", \"igenomes.config\")],\n [os.path.join(\".github\", \"workflows\", \"awstest.yml\")],\n [os.path.join(\".github\", \"workflows\", \"awsfulltest.yml\")],\n [os.path.join(\"lib\", f\"Workflow{short_name[0].upper()}{short_name[1:]}.groovy\")],\n [\"modules.json\"],\n [\"pyproject.toml\"],\n ]\n\n # List of strings. Fails / warns if any of the strings exist.\n files_fail_ifexists = [\n \"Singularity\",\n \"parameters.settings.json\",\n \".nf-core.yaml\", # yml not yaml\n os.path.join(\"bin\", \"markdown_to_html.r\"),\n os.path.join(\"conf\", \"aws.config\"),\n os.path.join(\".github\", \"workflows\", \"push_dockerhub.yml\"),\n os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"bug_report.md\"),\n os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"feature_request.md\"),\n os.path.join(\"docs\", \"images\", f\"nf-core-{short_name}_logo.png\"),\n \".markdownlint.yml\",\n \".yamllint.yml\",\n os.path.join(\"lib\", \"Checks.groovy\"),\n os.path.join(\"lib\", \"Completion.groovy\"),\n os.path.join(\"lib\", \"Workflow.groovy\"),\n ]\n files_warn_ifexists = [\".travis.yml\"]\n\n # Remove files that should be ignored according to the linting config\n ignore_files = self.lint_config.get(\"files_exist\", [])\n\n def pf(file_path):\n return os.path.join(self.wf_path, file_path)\n\n # First - critical files. Check that this is actually a Nextflow pipeline\n if not os.path.isfile(pf(\"nextflow.config\")) and not os.path.isfile(pf(\"main.nf\")):\n failed.append(\"File not found: nextflow.config or main.nf\")\n raise AssertionError(\"Neither nextflow.config or main.nf found! Is this a Nextflow pipeline?\")\n\n # Files that cause an error if they don't exist\n for files in files_fail:\n if any([f in ignore_files for f in files]):\n continue\n if any([os.path.isfile(pf(f)) for f in files]):\n passed.append(f\"File found: {self._wrap_quotes(files)}\")\n else:\n failed.append(f\"File not found: {self._wrap_quotes(files)}\")\n\n # Files that cause a warning if they don't exist\n for files in files_warn:\n if any([f in ignore_files for f in files]):\n continue\n if any([os.path.isfile(pf(f)) for f in files]):\n passed.append(f\"File found: {self._wrap_quotes(files)}\")\n else:\n warned.append(f\"File not found: {self._wrap_quotes(files)}\")\n\n # Files that cause an error if they exist\n for file in files_fail_ifexists:\n if file in ignore_files:\n continue\n if os.path.isfile(pf(file)):\n failed.append(f\"File must be removed: {self._wrap_quotes(file)}\")\n else:\n passed.append(f\"File not found check: {self._wrap_quotes(file)}\")\n\n # Files that cause a warning if they exist\n for file in files_warn_ifexists:\n if file in ignore_files:\n continue\n if os.path.isfile(pf(file)):\n warned.append(f\"File should be removed: {self._wrap_quotes(file)}\")\n else:\n passed.append(f\"File not found check: {self._wrap_quotes(file)}\")\n\n # Files that are ignoed\n for file in ignore_files:\n ignored.append(f\"File is ignored: {self._wrap_quotes(file)}\")\n\n return {\"passed\": passed, \"warned\": warned, \"failed\": failed, \"ignored\": ignored}","def main():\n\n # Remove the files if existing before creating new ones\n OUT_VOCAB = \"results/vocabs.txt\"\n OUT_SYN = \"results/syn.txt\"\n OUT_EXCP = \"results/exception.txt\"\n for f in [OUT_SYN, OUT_VOCAB, OUT_EXCP]:\n if os.path.isfile(f):\n os.remove(f)\n\n \"\"\"\n Now we do the recursion:\n Step 1: Find the children link;\n Step 2: Add to the url_list;\n Step: Move to next entry of the url_list and Go to Step 1;\n \"\"\"\n with open(DATA, \"r\") as fin:\n url_list = fin.readlines()\n\n # Write down the urls as tuples, 0 as the first level\n url_list = [((PREFIX_URL + subURL).strip(), 0) for subURL in url_list]\n\n with open(OUT_VOCAB, \"w\") as vocab_file, \\\n open(OUT_SYN, \"w\") as syn_file, \\\n open(OUT_EXCP, \"w\") as exp_file:\n cur_index = 0\n while cur_index < len(url_list):\n # Current url of the term, current level number (hierarchy) of the term\n cur_url, cur_hrc = url_list[cur_index]\n try:\n # Get the current sub-url\n time.sleep(0.1)\n r = requests.get(cur_url + \"?\" + API_KEY)\n source_dict = json.loads(r.text)\n\n # Getting the vocabulary and the synonym from this page.\n vocab, syn = parse_words(source_dict=source_dict)\n if cur_hrc + 1 < int(level):\n # Fetch all the children's name and url\n children_urls = fetch_children_url(source_dict=source_dict, hrc=cur_hrc + 1)\n # print children_urls\n url_list = url_list + children_urls # Concatenate the lists\n\n if vocab is not None:\n syn_file.write(json.dumps({vocab: syn}) + \"\\n\")\n vocab_file.write(vocab + \"\\n\")\n\n if cur_index % 100 == 0:\n print \"\\tNow at index # {}, {} in total.\".format(str(cur_index), str(len(url_list)))\n except UnicodeEncodeError as unicode_err:\n exp_file.write(str(unicode_err))\n except KeyError as key_err:\n exp_file.write(str(key_err))\n except:\n pass\n cur_index += 1","def main():\n # %%\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\n print( f'{len(fpaths)} htmls found' )\n # %%\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\n # %%\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\n # %%\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\n # %%\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\n # %%\n dics = {}\n # %%\n\n for i, fpath in enumerate(fpaths):\n if fpath in dics:\n continue\n\n with fpath.open('rt') as f_in:\n html = f_in.read()\n\n print( f'\\n***{i+1}/{len(fpaths)} {fpath.name}:')\n dic = extract_one( html, fpath )\n dic['linkedin_url'] = f\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\"\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\n # pprint(dic['work_stats'])\n dics[fpath] = dic\n\n dics_arr = list(dics.values())\n # %%\n del dics\n # %%\n\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\n # %%\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\n yaml.safe_dump( dics_arr, f_out )\n # %%\n df = produce_summary_table( dics_arr )\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\n index=False)\n # %%","def md_writer(clf, features, outcome, eval_folder,\n config_file, summary_df, i=''):\n if config_file.endswith('.xlsx'):\n config = pd.read_excel(config_file, sheetname='Sheet1')\n elif config_file.endswith('.csv'):\n config = pd.read_csv(config_file)\n\n clf_params = clf.get_params()\n clf_name = str(clf)[:str(clf).index('(')]\n clf_img = clf_name+str(i)\n\n file_name = clf_name+str(i)+'_Evaluation.md'\n\n save_file = open(eval_folder+file_name, 'w')\n\n def new_line():\n save_file.write('\\n')\n\n save_file.write('<link rel=\"stylesheet\" href=\"style.css\" type=\"text/css\" />\\n')\n save_file.write('# Model Evaluation Report\\n')\n new_line()\n\n save_file.write('## Data Configuration:\\n')\n new_line()\n save_file.write(config.to_html(na_rep='', index=False).replace('NaT', ''))\n new_line()\n\n save_file.write('## Classifier Parameters: '+clf_name+'\\n')\n new_line()\n for elem in clf_params:\n save_file.write('* {}: {}\\n'.format(elem, clf_params[elem]))\n new_line()\n\n summary_df = summary_df.T\n summary_df.columns = ['value']\n\n save_file.write('## Evaluation Metrics; Summary\\n')\n new_line()\n save_file.write(summary_df.to_html())\n new_line()\n\n save_file.write('## ROC Curve\\n')\n new_line()\n save_file.write('\\n')\n new_line()\n\n save_file.write('## Precision-Recall Curve\\n')\n new_line()\n save_file.write('\\n')\n new_line()\n\n save_file.write('## Precision, Recall vs % Population\\n')\n new_line()\n save_file.write('\\n')\n\n if clf_name in ['LogisticRegression']:\n save_file.write('## Coefficients\\n')\n new_line()\n for i,coef in enumerate(clf.coef_[0]):\n save_file.write('*<b>{}: {}</b>\\n'.format(features[i], round(coef,4)))\n new_line()\n\n if clf_name in ['WeightedQuestions']:\n save_file.write('## Weights\\n')\n new_line()\n for i,wt in enumerate(clf.weights):\n save_file.write('*<b>{}: {}</b>\\n'.format(features[i], wt))\n new_line()\n\n save_file.close()\n\n def markdown_to_html(md_file, out_file_name=None):\n import markdown\n\n with open(md_file, 'r') as f:\n html = markdown.markdown(f.read())\n\n if out_file_name is None:\n out_file_name = md_file.split('.')[0]+'.html'\n with open(out_file_name, 'w') as f:\n f.write(html)\n\n markdown_to_html(eval_folder+file_name)","def test_pdfdir(self):\n self.chck_triple('pdfdir')","def test_docdir(self):\n self.chck_triple('docdir')","def main(\n files: List[Path] = typer.Argument(default=None, dir_okay=False, exists=True),\n template: Optional[str] = typer.Option(\n None, '--template', help='Name of template file'\n ),\n logo: Optional[str] = typer.Option(None, '--logo', help='Name of logo file'),\n logo_width: Optional[str] = typer.Option(\n None, '--logo-width', help='Logo width (default 35mm)'\n ),\n highlight_style: Optional[str] = typer.Option(None, '--highlight-style',\n help='Specify coloring style to be used in highlighting source code'),\n syntax_definition: Optional[str] = typer.Option(None, '--syntax-definition',\n help='Specify a directory which contains syntax definition files'),\n no_toc: bool = typer.Option(\n False, '--no-toc', help='table of contents in PDF document'\n ),\n no_number_sections: bool = typer.Option(False, '--no-number-sections', help='no section numbering'),\n\n no_titlepage: bool = typer.Option(False, '--no-titlepage', help='title in PDF document'),\n tex_file: bool = typer.Option(\n False, '--tex', help='create TeX file instead of PDF document'\n ),\n email: Optional[str] = typer.Option(None, '--email', help='Author email'),\n company: Optional[str] = typer.Option(None, '--company', help='Name of company'),\n department: Optional[str] = typer.Option(\n None, '--department', help='Name of department'\n ),\n confidential: bool = typer.Option(\n False, '--confidential', help='indicate confidential'\n ),\n debug: bool = typer.Option(False, '--debug', help='turns debugging on'),\n pdf_engine: str = typer.Option(\n 'xelatex',\n '--pdf-engine',\n help='Specify pdf engine, one of lualatex, xelatex or tectonic ',\n ),\n _version: bool = typer.Option(\n None, '-V', '--version', callback=version_callback, help='Show version and exit'\n ),\n):\n\n if not files:\n typer.echo('Error: Must specify at least one .md file.')\n raise typer.Abort()\n\n mdfiles: List[str] = [str(md) for md in files]\n\n template = template or os.environ.get('MD2PDF_TEMPLATE')\n if template is None:\n print('No template specified')\n sys.exit(1)\n\n email = email or os.environ.get('MD2PDF_AUTHOR_EMAIL')\n footer_center = ''\n\n # command line overwrites `MD2PDF_PDF_ENGINE`. if both are not given\n # then `xelatex` is the default\n pdf_engine = pdf_engine or os.environ.get('MD2PDF_PDF_ENGINE') or 'xelatex'\n # check that pdf-engine is one of the following\n if pdf_engine not in ['xelatex', 'lualatex', 'tectonic']:\n print('--pdf-engine must be one of \"xelatex\", \"lualatex\", \"tectonic\"')\n sys.exit(1)\n\n ext = '.pdf'\n if tex_file:\n ext = '.tex'\n\n if len(mdfiles) == 1:\n toml_file = os.path.splitext(mdfiles[0])[0] + '.toml'\n\n if os.path.exists(toml_file):\n print(f'TOML file {toml_file} found')\n parsed_toml = toml.load(toml_file)\n default_val = parsed_toml.get('default')\n if default_val is None:\n print(f'No file names found in {toml_file}')\n else:\n mdfiles = default_val.get('files')\n\n for mdf in mdfiles:\n print(f'Compiling {mdf}')\n\n main_mdfile = os.path.realpath(mdfiles[0])\n\n outfile = Path(main_mdfile).stem + ext\n\n year = date.today().year\n\n company = company or os.environ.get('MD2PDF_COMPANY')\n department = department or os.environ.get('MD2PDF_DEPARTMENT')\n\n if company:\n if confidential:\n footer_center = f'© Copyright {year} {company}'\n else:\n footer_center = f'{year} {company}'\n\n pdcmd = PandocCmd(outfile)\n pdcmd.append(f'--template={template}')\n pdcmd.append(f'--pdf-engine={pdf_engine}')\n\n pdcmd.set_v('footer-center', footer_center)\n pdcmd.set_v('company', company)\n pdcmd.set_v('department', department)\n\n syntax_definition = syntax_definition or os.environ.get('MD2PDF_SYNTAX_DEFINITION_DIR')\n if syntax_definition is not None:\n add_syntax_definition(pdcmd, syntax_definition)\n\n pdcmd.append('--highlight-style')\n highlight_style = highlight_style or os.environ.get('MD2PDF_HIGHLIGHT_STYLE')\n if highlight_style is None:\n pdcmd.append('pygments')\n else:\n check_highlight_style(highlight_style)\n pdcmd.append(highlight_style)\n\n if not no_number_sections:\n pdcmd.append('--number-sections')\n\n if no_titlepage:\n pdcmd.set_m('titlepage', 'false')\n\n logo = logo or os.environ.get('MD2PDF_LOGO')\n pdcmd.set_v('logo', logo)\n\n logo_width = logo_width or os.environ.get('MD2PDF_LOGO_WIDTH')\n pdcmd.set_v('logo-width', logo_width)\n\n pdcmd.set_m('email', email)\n\n if not no_toc:\n pdcmd.append('--toc')\n\n pdcmd.extend(mdfiles)\n\n if debug:\n print(' '.join(pdcmd.pandoc))\n\n\n pdcmd.run()","def test_examples():\n tests = [d for d in listdir(ex) if path.isdir(path.join(ex, d))]\n for d in tests:\n yield check_examples, d","def test_man1dir(self):\n self.chck_triple('man1dir')","def _docs():\n url = \"https://vanheeringen-lab.github.io/seq2science\"\n if not webbrowser.open(url):\n print(url)","def test_analyze_recipe_instructions(self):\n pass","def test_path04(self):\n index_file = os.path.join(self.test_input, 'index.html')\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n</body>\n</html>\n\"\"\")\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_item, = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}/index.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n })","def test_path02(self):\n index_file = os.path.join(self.test_input, 'folder1#中文', 'folder2', 'mypage.html')\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n</body>\n</html>\n\"\"\")\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_folder1, id_folder2, id_item = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_folder1: {\n 'title': 'folder1#中文',\n 'type': 'folder',\n 'create': id_folder1,\n 'modify': id_folder1,\n },\n id_folder2: {\n 'title': 'folder2',\n 'type': 'folder',\n 'create': id_folder2,\n 'modify': id_folder2,\n },\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}/index.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_folder1,\n ],\n id_folder1: [\n id_folder2,\n ],\n id_folder2: [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n os.path.join(self.test_output, id_item, 'mypage.html'),\n })","def get_example():\n ex_dir = os.path.join(app.config['TEMPLATE_DIR'], \"inline_code\")\n app.logger.debug(\"Example directory is {}\".format(ex_dir))\n return ex_dir","def test_path01(self):\n index_file = os.path.join(self.test_input, 'folder1#中文', 'folder2', 'folder_data', 'index.html')\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n</body>\n</html>\n\"\"\")\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_folder1, id_folder2, id_item = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_folder1: {\n 'title': 'folder1#中文',\n 'type': 'folder',\n 'create': id_folder1,\n 'modify': id_folder1,\n },\n id_folder2: {\n 'title': 'folder2',\n 'type': 'folder',\n 'create': id_folder2,\n 'modify': id_folder2,\n },\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}/index.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_folder1,\n ],\n id_folder1: [\n id_folder2,\n ],\n id_folder2: [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n })","def read_hansard(train_dir, num_sentences):\n \"\"\"\n # TODO\n # Get starting files from directory:\n # Get file num\n # If file language is english get french with same num, & vice versa\n # load files into two lists: curr_english, curr_french\n # while count < num_sentences:\n # if index >= len(curr_english):\n # load two new files into curr_english and curr_french\n # make sure to keep track of files already read\n # index = 0 \n # sentences['e'][count] = preprocess(curr_english[index])\n # sentences['f'][count] = preprocess(curr_french[index])\n\n #====================================\n # Return (eng, fre) version:\n # Get starting files from directory:\n # Get file num\n # If file language is english get french with same num, & vice versa\n # load files into two lists: curr_english, curr_french\n # while count < num_sentences:\n # if index >= min(len(curr_english), len(curr_french)):\n # load two new files into curr_english and curr_french\n # make sure to keep track of files already read\n # index = 0\n # preprocess and remove SENTSTART and SENTEND from the sentences\n # eng[count] = eng_sentence.split()\n # fre[count] = fre_sentence.split()\n # return (eng, fre)\n \"\"\"\n\n files_examined = set()\n count = 0\n eng = []\n fre = []\n\n # for subdir, dirs, files in os.walk(train_dir):\n # for file in files:\n\n files = os.listdir(train_dir)\n for file in files:\n\n # First set up and validate the files\n file_name, extension = os.path.splitext(file)\n file_name, file_id = os.path.splitext(file_name)\n\n # Skip if not .e or .f file\n if not (extension == '.f' or extension == '.e'):\n continue\n\n # Skip if already examined this file pair\n if file_id in files_examined:\n continue\n\n # Skip if either language file is not available\n eng_file = file_name + file_id + '.e'\n fre_file = file_name + file_id + '.f'\n if eng_file not in files or fre_file not in files:\n continue\n\n # If it reaches here we know we can process it\n files_examined.add(file_id)\n print( \"Reading \" + str(count+1))\n\n # Finally open files and iterate simultaneously\n eng_path = os.path.join(train_dir, eng_file)\n fre_path = os.path.join(train_dir, fre_file)\n with open(eng_path) as english:\n with open(fre_path) as french:\n for E, F in zip(english, french):\n\n # Stop when limit reached\n if count >= num_sentences:\n return (eng, fre)\n\n # Process and split sentences\n E = preprocess(E.rstrip(), 'e')\n F = preprocess(F.rstrip(), 'f')\n\n E_words = E.split()\n F_words = F.split()\n\n eng.append(E_words)\n fre.append(F_words)\n\n count += 1\n\n return (eng, fre)","def test_examples(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.nap.Base.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n for item in data['items']:\n if item['uid'] == 'example.nap.Base.ref':\n self.assertEqual(\n item['example'].split('\\n')[2],\n \"\"\">>> print('docblock 1')\"\"\"\n )\n self.assertEqual(\n item['example'].split('\\n')[7],\n \"\"\">>> print('docblock 2')\"\"\"\n )","def do_docs(self, path):\n print(\"scaraping documentation\")\n for p in path.glob(\"**/*\"):\n if p.is_file():\n parts = p.relative_to(path).parts\n if parts[-1].endswith(\"rst\"):\n data = tsparse(p.read_bytes())\n blob = DocBlob()\n blob.arbitrary = data\n blob.content = {}\n\n blob.ordered_sections = []\n blob.item_file = None\n blob.item_line = None\n blob.item_type = None\n blob.aliases = []\n blob.example_section_data = Section()\n blob.see_also = []\n blob.signature = None\n blob.references = None\n blob.refs = []\n\n self.docs[parts] = json.dumps(blob.to_json(), indent=2)\n else:\n pass\n # data = p.read_bytes()","def test_noDocumentsFound(self):\n self.assertRaises(NoDocumentsFound, self.builder.build, self.manDir)","def test_man4dir(self):\n self.chck_triple('man4dir')","def problem4() -> None:\n \n # Obtain the dataframe containing lines from the articles and the keyword\n # with which the article is associated.\n try:\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \n except FileNotFoundError:\n raise Exception(\"[ERROR] The 'articles' folder was not found. Please \"\n \"ensure that the functions for problems 1 and 2 have \"\n \"been applied first.\")\n \n keywords, corpus = separate_keywords_corpus(kw_df)\n \n # Complete the preprocessing for the corpus for comparison's sake\n w2vp = W2VPreprocessor(keywords, corpus)\n \n # Display the counts of sentences collected associated with each keyword\n keywords_countplot(kw_df)\n \n # Create displots for the lengths of sentences in the corpus\n displot_all_sentence_lengths(kw_df)\n displot_sentence_lengths_per_keyword(kw_df)\n \n # Visualise the semantic distances\n try:\n distances = read_in_distance_matrix()\n except FileNotFoundError:\n raise Exception(\"[ERROR] The distance matrix excel file was could not \"\n \"be found! Please ensure the methods for the \"\n \"preceding problems have been run first.\")\n visualise_distances(distances)\n \n # Visualise word frequency before and after preprocessing\n visualise_initial_most_frequent_words(kw_df)\n visualise_preprocessed_most_frequent_words(w2vp.corpus)","def testExampleFileGeneration(ref):\n outdir = ref.tmp_dir\n outpath = os.path.join(outdir, 'file_result.html')\n generate_file(outpath)\n ref.assertTextFileCorrect(outpath, 'file_result.html',\n ignore_substrings=['Copyright', 'Version'])","def test_supporting_folder03(self):\n content = \"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n<img src=\"index.files/picture.bmp\">\n</body>\n</html>\n\"\"\"\n index_file = os.path.join(self.test_input, 'index.html')\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(content)\n img_file = os.path.join(self.test_input, 'index.files', 'picture.bmp')\n os.makedirs(os.path.dirname(img_file), exist_ok=True)\n with open(img_file, 'wb') as fh:\n fh.write(b'dummy')\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_item, = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}/index.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n os.path.join(self.test_output, id_item, 'index.files'),\n os.path.join(self.test_output, id_item, 'index.files', 'picture.bmp'),\n })\n with open(os.path.join(self.test_output, id_item, 'index.html'), encoding='UTF-8') as fh:\n self.assertEqual(fh.read(), content)","def cli(input_directory: str, output_directory: str = 'docs',\n style: str = 'md'):\n input_path = Path(input_directory)\n output_path = Path(output_directory)\n parsed_modules = find_and_extract(input_path)\n builder = MarkdownBuilder()\n builder.setting()\n builder.build(parsed_modules)\n builder.save(output_path)","def machinelearn():\n return render_template('machinelearn.html')","def evaluation():\n file = open('./contest/content/evaluation.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Evaluation', \n content=content)","def test_session10_readme_proper_description():\n READMELOOKSGOOD = True\n f = open(\"README.md\", \"r\", encoding=\"utf-8\")\n content = f.read()\n f.close()\n for c in README_CONTENT_CHECK_FOR:\n if c not in content:\n print(c)\n READMELOOKSGOOD = False\n pass\n assert READMELOOKSGOOD == True, \"You have not described all the functions/class well in your README.md file\"","def test_pep8_conformance_example(self):\n\n print(\"\\r\\n\")\n\n # Get the path to current directory\n path = os.path.dirname(os.path.realpath(__file__))\n path += \"/../docs/examples/\"\n\n # Find all the examples files\n file_paths = []\n for root, dirnames, filenames in os.walk(path):\n for file_path in fnmatch.filter(filenames, '*.py'):\n file_paths.append(os.path.join(root, file_path))\n\n for path in file_paths:\n self.run_check(path)","def rules():\n file = open('./contest/content/rules.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Rules', \n content=content)","def main():\n for file_name in os.listdir(CONTENT_FOLDER):\n if file_name.endswith('.html'):\n try_generate_page(file_name)","def test_other01(self):\n index_file = os.path.join(self.test_input, 'folder1#中文', 'mypage.txt')\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write('ABC 中文')\n ts = datetime(2020, 1, 2, 3, 4, 5, 67000, tzinfo=timezone.utc).timestamp()\n os.utime(index_file, (ts, ts))\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_folder1, id_item = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_folder1: {\n 'title': 'folder1#中文',\n 'type': 'folder',\n 'create': id_folder1,\n 'modify': id_folder1,\n },\n id_item: {\n 'title': 'mypage.txt',\n 'type': 'file',\n 'index': f'{id_item}/index.html',\n 'create': id_item,\n 'modify': '20200102030405067',\n 'source': '',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_folder1,\n ],\n id_folder1: [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n os.path.join(self.test_output, id_item, 'mypage.txt'),\n })","def analyse() -> 'html':\n storeFileName = ''\n if request.method == 'POST':\n \n if 'file' not in request.files:\n return render_mainpageerror('No file selected')\n \n file = request.files['file']\n if file.filename == '':\n return render_mainpageerror('Empty file name Please select a File to upload')\n \n if file and not allowed_file(file.filename):\n storeFileName = filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n return render_mainpageerror('Not allowed file format')\n \n if ' ' in file.filename:\n return render_mainpageerror('Please remove withespace from file name before uploading')\n \n if file and allowed_file(file.filename):\n originalFilename = file.filename\n storeFileName = filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n \n \n if len(storeFileName) == 0:\n return render_template('mainpage.html',\n title='Word association')\n else:\n wordsfromfile = readFile.red_uploaded_file(storeFileName)\n #print(wordsfromfile, 'The ans from pdf')\n if wordsfromfile == 'STOP':\n return render_mainpageerror('Cant find Encoding on PDF document')\n size = get_store()\n if not check_fileName(size):\n \"\"\"Analyse first time building up JSON file\"\"\"\n keyWords = readFile.read_JSON()\n allJson = readFile.load_JSON()\n data = Counter(wordsfromfile)\n printResultsExluds = []\n container = {}\n container['size'] = size\n container['file'] = storeFileName\n toDB = []\n for k, v in sorted(data.items()):\n if k in keyWords:\n string = allJson[k][:3]\n toDB.append({k : v})\n toDB.append(string)\n else:\n value = str(v)\n keystr = k + '(' + value + ')'\n printResultsExluds.append(keystr)\n for w in printResultsExluds:\n toDB.append({w:'was not found in the associations list'})\n\n container['files'] = toDB \n js = json.dumps(container)\n print(js)\n c = get_client()\n c.insert(json.loads(js))\n print('insert')\n \"\"\"If analysed first time\"\"\"\n return pr(size, 'Analysed first time')\n else:\n \"\"\"If already Analysed\"\"\"\n return pr(size,'Already analysed')","def test_path03(self):\n index_file = os.path.join(self.test_input, 'folder1#中文', 'folder2', 'mypage.html')\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n</body>\n</html>\n\"\"\")\n\n for _info in file2wsb.run(self.test_input, self.test_output, no_preserve_filename=True):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_folder1, id_folder2, id_item = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_folder1: {\n 'title': 'folder1#中文',\n 'type': 'folder',\n 'create': id_folder1,\n 'modify': id_folder1,\n },\n id_folder2: {\n 'title': 'folder2',\n 'type': 'folder',\n 'create': id_folder2,\n 'modify': id_folder2,\n },\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_folder1,\n ],\n id_folder1: [\n id_folder2,\n ],\n id_folder2: [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, f'{id_item}.html'),\n })","def analyze_test_image_corpus(args):\n\n # First: index the existing corpus\n\n name_to_refs = {}\n\n for refdir in glob(os.path.join(DATA, 'refimg_*')):\n name = os.path.basename(refdir).replace('refimg_', '')\n refs = {}\n\n for p in os.listdir(refdir):\n if not p.endswith('.png'):\n continue\n\n refs[p.replace('.png', '')] = set()\n\n name_to_refs[name] = refs\n\n # Now trawl the samples and see which refimgs we hit\n\n def directory_to_paths(dir_path):\n for filename in os.listdir(dir_path):\n name = filename.replace('.png', '')\n if name in name_to_refs:\n yield name, os.path.join(dir_path, filename)\n\n def zip_to_paths(zip_path):\n from zipfile import ZipFile\n\n with tempfile.TemporaryDirectory() as tmpdir, ZipFile(zip_path) as zip:\n for zipname in zip.namelist():\n zip.extract(zipname, tmpdir)\n name = os.path.basename(zipname).replace('.png', '')\n if name in name_to_refs:\n yield name, os.path.join(tmpdir, zipname)\n\n for path in args:\n if os.path.isdir(path):\n paths = directory_to_paths(path)\n sampname = os.path.basename(path)\n elif path.endswith('.zip'):\n paths = zip_to_paths(path)\n sampname = os.path.basename(path).replace('.zip', '')\n else:\n raise Exception(f'don\\'t know how to handle input path `{path}`')\n\n for name, imgpath in paths:\n refs = name_to_refs[name]\n refnames = sorted(refs.keys())\n found_it = False\n results = []\n\n for refname in refnames:\n refpath = os.path.join(DATA, 'refimg_' + name, refname + '.png')\n rv = compare_images(\n refpath,\n imgpath,\n tol=IMAGE_COMPARISON_TOLERANCE,\n in_decorator=True\n )\n\n if rv is None:\n refs[refname].add(sampname)\n found_it = True\n break\n\n failpath = imgpath.replace('.png', '-failed-diff.png')\n os.unlink(failpath)\n results.append((refname, rv['rms']))\n\n if not found_it:\n print(f'no refimg found for {sampname}::{name}:', ', '.join('%s=%.2f' % t for t in results))\n\n # Now report\n\n print()\n print('Report:')\n any_reports = False\n\n for name in sorted(name_to_refs.keys()):\n refs = name_to_refs[name]\n\n for refname in sorted(refs.keys()):\n refdata = refs[refname]\n\n if not refdata:\n print(f'- no hits to {name}::{refname}')\n any_reports = True\n\n if not any_reports:\n print('- no suggested modifications')","def test_html_documentation(self):\n app = Sphinx(\n self.source_dir,\n self.config_dir,\n self.output_dir,\n self.doctree_dir,\n buildername='html',\n warningiserror=True,\n )\n app.build(force_all=self.all_files)","def write_to_md(dictData, outputDirectory):\n\tdic = prepare_hw_dict(dictData)\n\tfor hw in dic:\n\t\tfileout = os.path.join(outputDirectory, hw+'.md')\n\t\t# Prepare the output file\n\t\tfout = codecs.open(fileout, 'w', 'utf-8')\n\t\t# Write frontmatter\n\t\tfout.write('---\\ntitle: \"'+hw+'\"\\n---\\n\\n')\n\t\t# For each (headword, meanings, verseNumber, PageNum) tuples,\n\t\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dic[hw]:\n\t\t\tcommaed = ', '.join(meanings)\n\t\t\tverse = verse.replace('<BR>', '<br />')\n\t\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\t\tfout.write('# ' + hw + '\\n## ' + commaed + '\\n' + verse + '<br />verse ' + verseNumDetails + '<br />page ' + pageNumDetails +'\\n\\n')\n\t\tfout.close()\n\n\t# Give some summary to the user\n\tprint('MD files generated. Success!')\n\tprint('{} separate .md files written, one per headword.'.format(len(dic)))","def test_readme_contents():\n readme = open(\"README.md\", \"r\", encoding=\"utf-8\")\n readme_words = readme.read().split()\n readme.close()\n assert len(readme_words) >= 500, \"Make your README.md file interesting! Add atleast 500 words\"","def collect():\n # catch possible bug with --dashes.md\n os.chdir(pathlib.Path(__file__).resolve().parent / 'test')\n for filename in glob.glob('*.md'):\n stem, _ = os.path.splitext(filename)\n args = ['../readme.py', '--timeout=1', '--', filename]\n result = cmd(args)\n obj = normalize(result['output'])\n tmp = obj['tmp']\n lisp = slurp(tmp) if tmp else None\n yield stem, result['exit_code'], obj['out'], lisp","def scan_sample_directory(sample_dir: Path) -> None:\n if not (sample_dir / 'README.md').is_file():\n print(f\"WARNING ({sample_dir}): No README.md file\")\n if not (sample_dir / 'main.py').is_file():\n print(f\"ERROR ({sample_dir}): No main.py file\")","def test_yaml_tech_file(self) -> None:\n tech_yaml = \"\"\"\nname: My Technology Library\ninstalls:\n - path: test\n base var: \"\" # means relative to tech dir\nlibraries: []\n \"\"\"\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n\n tech_yaml_filename = os.path.join(tech_dir, \"dummy28.tech.yml\")\n with open(tech_yaml_filename, \"w\") as f: # pylint: disable=invalid-name\n f.write(tech_yaml)\n sys.path.append(tech_dir_base)\n tech_opt = hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir)\n self.assertFalse(tech_opt is None, \"Unable to load technology\")\n\n # Cleanup\n shutil.rmtree(tech_dir_base)","def test_malware_have_techniques(attck_fixture):\n for malware in attck_fixture.enterprise.malwares:\n if malware.techniques:\n assert getattr(malware,'techniques')","def readme_tests(readme=None):\n if readme is None:\n readme = Path('..', 'readme.md')\n with open(readme) as target:\n for line in target:\n if line.startswith(' ') and 'test_utils' not in line:\n print()\n print(line)\n input('Press Enter to execute')\n sb.run(line, shell=True, check=True)","def __init__(self, path: str, tg: bool) -> None:\n if tg:\n self.path = os.path.join(path, 'processed_corpus')\n self.path_in_tokens = os.path.join(self.path, 'pp_token_corpus.txt')\n self.path_in_lemmas = os.path.join(self.path, 'pp_lemma_corpus.txt')\n self.path_out_corpus = os.path.join(path, 'processed_corpus')\n self.path_out_idx_tokens = os.path.join(\n self.path_out_corpus, 'token_idx_corpus.txt')\n self.path_out_idx_lemmas = os.path.join(\n self.path_out_corpus, 'lemma_idx_corpus.txt')\n self.path_out_indexing = os.path.join(path, 'indexing')\n self.path_token_to_idx = os.path.join(\n self.path_out_indexing, 'token_to_idx.json')\n self.path_lemma_to_idx = os.path.join(\n self.path_out_indexing, 'lemma_to_idx.json')\n self.path_idx_to_token = os.path.join(\n self.path_out_indexing, 'idx_to_token.json')\n self.path_idx_to_lemma = os.path.join(\n self.path_out_indexing, 'idx_to_lemma.json')\n\n self.path_token_terms = os.path.join(\n self.path_out_corpus, 'token_terms.txt')\n self.path_lemma_terms = os.path.join(\n self.path_out_corpus, 'lemma_terms.txt')\n\n self.path_idx_token_terms = os.path.join(\n self.path_out_corpus, 'token_terms_idxs.txt')\n self.path_idx_lemma_terms = os.path.join(\n self.path_out_corpus, 'lemma_terms_idxs.txt')\n\n self.path_token_contains = os.path.join(\n self.path_out_corpus, 'token_contains.json')\n self.path_lemma_contains = os.path.join(\n self.path_out_corpus, 'lemma_contains.json')\n\n self.path_hierarch_rels_tokens = os.path.join(\n path, 'hierarchy/hierarchical_relations_tokens.json')\n self.path_hierarch_rels_lemmas = os.path.join(\n path, 'hierarchy/hierarchical_relations_lemmas.json')\n self.path_hierarch_rels_tokens_idx = os.path.join(\n path, 'hierarchy/hierarch_rels_tokens_idx.json')\n self.path_hierarch_rels_lemmas_idx = os.path.join(\n path, 'hierarchy/hierarch_rels_lemmas_idx.json')\n else:\n self.path = os.path.join(path, 'processed_corpus')\n self.path_in_tokens = os.path.join(self.path,\n 'pp_token_corpus_non_tg.txt')\n self.path_in_lemmas = os.path.join(self.path,\n 'pp_lemma_corpus_non_tg.txt')\n self.path_out_corpus = os.path.join(path, 'processed_corpus')\n self.path_out_idx_tokens = os.path.join(\n self.path_out_corpus, 'token_idx_corpus_non_tg.txt')\n self.path_out_idx_lemmas = os.path.join(\n self.path_out_corpus, 'lemma_idx_corpus_non_tg.txt')\n self.path_out_indexing = os.path.join(path, 'indexing')\n self.path_token_to_idx = os.path.join(\n self.path_out_indexing, 'token_to_idx_non_tg.json')\n self.path_lemma_to_idx = os.path.join(\n self.path_out_indexing, 'lemma_to_idx_non_tg.json')\n self.path_idx_to_token = os.path.join(\n self.path_out_indexing, 'idx_to_token_non_tg.json')\n self.path_idx_to_lemma = os.path.join(\n self.path_out_indexing, 'idx_to_lemma_non_tg.json')\n\n self.path_token_terms = os.path.join(\n self.path_out_corpus, 'token_terms_non_tg.txt')\n self.path_lemma_terms = os.path.join(\n self.path_out_corpus, 'lemma_terms_non_tg.txt')\n\n self.path_idx_token_terms = os.path.join(\n self.path_out_corpus, 'token_terms_idxs_non_tg.txt')\n self.path_idx_lemma_terms = os.path.join(\n self.path_out_corpus, 'lemma_terms_idxs_non_tg.txt')\n\n self.path_token_contains = os.path.join(\n self.path_out_corpus, 'token_contains_non_tg.json')\n self.path_lemma_contains = os.path.join(\n self.path_out_corpus, 'lemma_contains_non_tg.json')\n\n self.path_hierarch_rels_tokens = os.path.join(\n path, 'hierarchy/hierarchical_relations_tokens_non_tg.json')\n self.path_hierarch_rels_lemmas = os.path.join(\n path, 'hierarchy/hierarchical_relations_lemmas_non_tg.json')\n self.path_hierarch_rels_tokens_idx = os.path.join(\n path, 'hierarchy/hierarch_rels_tokens_idx_non_tg.json')\n self.path_hierarch_rels_lemmas_idx = os.path.join(\n path, 'hierarchy/hierarch_rels_lemmas_idx_non_tg.json')\n\n self._file_write_threshhold = 10000\n self._docs_processed = 0\n self._already_written_to_file = False\n self._start_time = 0\n self._upper_bound = None\n self.subtoken_index = {}\n self.sublemma_index = {}","def _create_index_file(\n root_dir, location, image_files, video_files, dirs, force_no_processing=False):\n # Put together HTML as a list of the lines we'll want to include\n # Issue #2 exists to do this better than HTML in-code\n header_text = 'imageMe: {0} [{1} image(s)] [{2} video(s)]'.format(\n location, str(len(image_files)), str(len(video_files))\n )\n html = [\n '<!DOCTYPE html>',\n '<html>',\n ' <head>',\n ' <title>imageMe</title>'\n ' <style>',\n ' html, body {margin: 0; padding: 0;}',\n ' .table {align: center;}',\n ' .content {',\n ' padding: 3em;',\n ' padding-left: 4em;',\n ' padding-right: 4em;',\n ' }',\n ' .image {max-width: 100%; border-radius: 0.3em;}',\n ' td {width: ' + str(100.0 / args.column) + '%;}',\n ' </style>',\n ' </head>',\n ' <body>',\n ' <div class=\"content\">',\n ' <h2 class=\"header\">' + header_text + '</h2>'\n ]\n\n # Populate the present subdirectories - this includes '..' unless we're at\n # the top level\n directories = []\n if root_dir != location:\n directories = ['..']\n directories += dirs\n if len(directories) > 0:\n html.append('<hr>')\n # For each subdirectory, include a link to its index file\n for directory in directories:\n link = directory + '/' + args.index_file_name\n html += [\n ' <h3>',\n ' <a href=\"' + link + '\">' + directory + '</a>',\n ' </h3>'\n ]\n\n files = sorted(image_files + video_files)\n if args.separate_image_and_video:\n files = image_files + [None] + video_files\n\n # Populate the gallery table\n if files:\n # Counter to cycle down through table rows\n table_column_count = 1\n html += ['<hr>', '<table>']\n\n # For each file, potentially create a new <tr> and create a new <td>\n for file in files:\n if table_column_count == 1:\n html.append('<tr>')\n\n if file in video_files:\n html += [\n '<td>',\n ' <video controls preload width=\"100%\">',\n ' <source src=\"' + file + '\">',\n ' Your browser does not support HTML5 video.'\n ' </video>',\n '</td>'\n ]\n\n if file in image_files:\n img_src = _get_thumbnail_src_from_file(\n location, file, force_no_processing\n )\n link_target = _get_image_link_target_from_file(\n location, file, force_no_processing\n )\n html += [\n '<td>',\n ' <a href=\"' + link_target + '\">',\n ' <img class=\"image\" src=\"' + img_src + '\">',\n ' </a>',\n '</td>'\n ]\n\n if table_column_count == args.column or file == None:\n table_column_count = 0\n html.append('</tr>')\n\n table_column_count += 1\n\n if table_column_count != 1:\n html += ['</tr>']\n html += ['</table>']\n\n html += [\n ' </div>',\n ' </body>',\n '</html>'\n ]\n\n # Actually create the file, now we've put together the HTML content\n index_file_path = _get_index_file_path(location)\n print('Creating index file %s' % index_file_path)\n index_file = open(index_file_path, 'w')\n index_file.write('\\n'.join(html))\n index_file.close()\n\n # Return the path for cleaning up later\n return index_file_path","def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLinks()\n part3 = \"\"\"</div>\n <div id=\"results\">\n <iframe id=\"loadHTMLResults\" name=\"loadHTMLResults\" frameborder=\"0\" src=\"statistics.html\" style=\"height:100%;width:100%;\"></iframe>\n </div>\n <div id=\"footer\">Test Engineer(s) :\"\"\"+testEngineers+\"\"\"</div>\n </div>\n </body>\n </html>\n\t\t<style>\n\t\tbody{\n margin:0;\n }\n\t\t#pg_body{\n\t\twidth=100%;\n\t\ttext-align:center;\n\t\t}\n\t\t#testSuitesTitle{\n\t\twidth:25%;\n\t\tfloat:left;\n\t\tbackground-color:#6495ED;\n\t\tfont-weight:bold;\n\t\tcolor:white;\n\t\t}\n\t\t#resultsTitle{\n\t\twidth:75%;\n\t\tfloat:right;\n\t\tbackground-color:#6495ED;\n\t\tfont-weight:bold;\n\t\tcolor:white;\n\t\t}\n\t\t#testSuites{\n\t\twidth:25%;\n\t\tfloat:left;\n\t\tbackground-color:lightgrey;\n\t\tfont-weight:bold;\n\t\ttext-align:left;\n\t\theight:94%;\n\t\toverflow:scroll;\n\t\t}\n\t\t#results{\n\t\twidth:75%;\n\t\tbackground-color:white;\n\t\tfloat:right;\n\t\ttext-align:left;\n\t\theight:94%;\n\t\toverflow:scroll;\n\t\t}\n\t\t#footer{\n\t\twidth:100%;\n\t\ttext-align:left;\n\t\tcolor:lightgrey;\n\t\tbackground-color:#6495ED;\n\t\t}\n\t\t</style>\n \"\"\"\n \n page = (part1+part2+part3)\n f = open(self.dir+'/index.html','w')\n f.write(page)\n f.close","def test_readme_proper_description():\n READMELOOKSGOOD = True\n f = open(\"README.md\", \"r\", encoding=\"utf-8\")\n content = f.read()\n f.close()\n for c in README_CONTENT_CHECK_FOR:\n if c not in content:\n print(c)\n READMELOOKSGOOD = False\n break\n\n assert READMELOOKSGOOD is True, \"You have not described all the functions/class well in your README.md file\"","def test_text_classifier_create_from_path(self):\n pass","def test_path05(self):\n index_file = os.path.join(self.test_input, 'index.html')\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n</body>\n</html>\n\"\"\")\n\n for _info in file2wsb.run(self.test_input, self.test_output, no_preserve_filename=True):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_item, = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, f'{id_item}.html'),\n })","def test_other02(self):\n index_file = os.path.join(self.test_input, 'folder1#中文', 'mypage.txt')\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write('ABC 中文')\n ts = datetime(2020, 1, 2, 3, 4, 5, 67000, tzinfo=timezone.utc).timestamp()\n os.utime(index_file, (ts, ts))\n\n for _info in file2wsb.run(self.test_input, self.test_output, no_preserve_filename=True):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_folder1, id_item = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_folder1: {\n 'title': 'folder1#中文',\n 'type': 'folder',\n 'create': id_folder1,\n 'modify': id_folder1,\n },\n id_item: {\n 'title': 'mypage.txt',\n 'type': 'file',\n 'index': f'{id_item}.txt',\n 'create': id_item,\n 'modify': '20200102030405067',\n 'source': '',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_folder1,\n ],\n id_folder1: [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, f'{id_item}.txt'),\n })","def test_api_document_create_student(self):\n markdown_document = MarkdownDocumentFactory()\n\n jwt_token = StudentLtiTokenFactory(\n playlist=markdown_document.playlist,\n permissions__can_update=True,\n )\n\n response = self.client.post(\n \"/api/markdown-documents/\", HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\"\n )\n self.assertEqual(response.status_code, 403)","def main():\r\n\tlang = get_arguments()\r\n\twiki_analyzer(lang)","def test_man7dir(self):\n self.chck_triple('man7dir')","def docs():","def scan_path(path,ext_lst=['md','markdown']):\n if not os.path.exists(path):\n logger.debug('scan_path: invalid path : %s' %(path,files))\n return -1\n pages={}\n files=[]\n for item in glob.glob(os.path.join(path,'*.*')):\n if string.split(os.path.basename(item),'.')[-1] in ext_lst:\n files.append(item)\n logger.debug('scan_path: %s : %s' %(path,files))\n res = None\n pagelist=[]\n articledict={}\n for item in files:\n res=os.path.basename(item).split()\n res=string.split(os.path.basename(item),'.')\n #print 'scan_path: res : %s' %res \n if res[0] not in pagelist:\n logger.debug('scan_path: add page : %s' %res[0]) \n pagelist.append(res[0])\n articledict[res[0]]=[] #or add itself?\n articledict[res[0]].append(item) \n pages['pagelist'] = pagelist\n if len(pagelist)>0:\n for page in pagelist: \n logger.debug('scan_path: pages= %s' %page)\n logger.debug('scan_path: articles= %s' %articledict[page]) \n pages[page]=articledict[page] \n #look for templates/layout... To ENHANCE...\n pages['template']=glob.glob(os.path.join(path,'*.tpl')) \n if os.path.isfile(os.path.join(path,'layout.tpl')):\n logger.info('scan_path: found general layout file : %s' %os.path.join(path,'layout.tpl'))\n pages['layout']=os.path.join(path,'layout.tpl')\n return pages","def _analyze_file(self) -> None:\n\n md_reader = MarkdownReader.from_file(self.path)\n for md_line in md_reader.lines:\n if len(md_line.current_section) == 1:\n if self.title and self.title != md_line.current_section[0]:\n logger.warning(f\"{self.path} Warning: Multiple titles. \")\n self.title = md_line.current_section[0]\n if (\n not md_line.is_code_block\n and md_line.text.lower().strip().startswith(\"tags: \")\n ):\n if self.tags:\n logger.warning(\n f\"{self.path} Warning: Tags were already set.\"\n )\n self.tags = self._read_tags(md_line.text)\n if (\n len(md_line.current_section) >= 2\n and md_line.current_section[1].lower().strip() == \"backlinks\"\n ):\n pass\n else:\n self.links.extend(self.id_link_regex.findall(md_line.text))","def main():\n\n parser = argparse.ArgumentParser(\n description='Convert collection of bibtex files to clean markdown script.')\n parser.add_argument('-i', '--input', nargs='+', required=False,\n help='Input bibtex files. Defaults to bib/*')\n parser.add_argument('-o', '--output', default='publications.md', required=False,\n help='Output markdown floadile.')\n args = parser.parse_args()\n\n # Build setup\n os.makedirs(BUILD_ROOT, exist_ok=True)\n build_dir = os.path.join(\n BUILD_ROOT,\n datetime.now().strftime(datetime.now().strftime('%Y_%m_%d_%H_%M_%S_%f')))\n os.makedirs(build_dir)\n\n # Find and merge all bibtex files\n input_files = args.input\n if input_files is None:\n input_files = [os.path.join(BIB_ROOT, f) for f in os.listdir(BIB_ROOT)\n if os.path.isfile(os.path.join(BIB_ROOT, f))]\n merge_file = merge_bibtex(input_files, build_dir)\n\n # Parse merged bibtex file with custom options\n parse_file = parse_bibtex(merge_file, build_dir)\n\n # Render bibtex to markdown\n render_file = render_bibtex(parse_file, build_dir, args.output)","def techniques(self):\n return self._get_child_page_of_type(LearningTechniquesPage)","def test_landing_page(self, doi_dataset, doi_bib, orcid, metadata_parser):\n apply_mock(doi_dataset, doi_bib, orcid, metadata_parser)\n os.chdir(\"input/\")\n _set_args(\"-i\",\"yamls/paper.yaml\",\"-o\",\"../out\",\"-ns\")\n with HiddenPrints():\n ya2ro.main()\n\n _set_args(\"-i\",\"yamls/paper_doi.yaml\",\"-o\",\"../out\",\"-ns\")\n with HiddenPrints():\n ya2ro.main()\n\n _set_args(\"-i\",\"yamls/project.yaml\",\"-o\",\"../out\",\"-ns\")\n with HiddenPrints():\n ya2ro.main()\n\n global parent_dir\n os.chdir(parent_dir)\n _set_args(\"-l\",\"out\")\n with HiddenPrints():\n ya2ro.main()\n\n with open(\"out/landing_page.html\") as landing:\n web = landing.read()\n \n n_webs = web.count('</details>')\n self.assertEquals(3, n_webs)","def Subtask4_pre_train_3():\n train_data = np.load(PATH + 'pre_train_2_Subtask4.npy', allow_pickle=True).item()\n\n evidence = []\n for d in train_data.items():\n for i in range(5):\n evidence.append(d[1][i])\n\n files = os.listdir(PATH + 'data/wiki-pages/wiki-pages/')\n documents = {}\n for i in files:\n with open(os.path.join(PATH + 'data/wiki-pages/wiki-pages/', i)) as fp:\n lines = fp.readlines()\n for line in lines:\n line = eval(line)\n if line['id'] in evidence:\n text = line['lines']\n documents[line['id']] = text\n with open(PATH + 'pre_train_3_Subtask4.txt', 'w', encoding='utf-8') as f:\n f.write(str(documents))","def tplans_content(ident, args):\n\n struct = {\n \"root\": [\n (\"descr\", True),\n (\"descr_long\", False),\n (\"hooks\", False),\n (\"evars\", False),\n (\"testsuites\", True)\n ],\n \"suites\": [\n (\"name\", True),\n (\"alias\", False),\n (\"hooks\", False),\n (\"hooks_pr_tcase\", False),\n (\"evars\", False),\n (\"evars_pr_tcase\", False)\n ]\n }\n\n violations = []\n\n\n tplans = _index(args.testplans_root, \"TPLAN\")\n\n for tp_fname in tplans:\n tp_fpath = os.sep.join([args.testplans_root, tp_fname])\n\n suites = []\n hooks = []\n\n tplan = None\n try:\n with open(tp_fpath) as tp_fd:\n tplan = yaml.load(tp_fd)\n\n except IOError as exc:\n continue\n except Exception as exc:\n continue\n\n for k in list(set(tplan.keys()) - set([k for k, _ in struct[\"root\"]])):\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"invalid key: %r\" % k\n ))\n\n for k in (k for k, req in struct[\"root\"] if req):\n if k not in tplan.keys():\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"missing required key: %r\" % k\n ))\n\n hooks += tplan[\"hooks\"] if \"hooks\" in tplan else []\n suites += []\n\n if \"testsuites\" not in tplan:\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"missing key 'testsuites'\"\n ))\n continue\n\n for suite in tplan[\"testsuites\"]:\n for k in list(set(suite.keys()) - set([k for k, _ in struct[\"suites\"]])):\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"invalid key: %r\" % k\n ))\n\n for k in (k for k, req in struct[\"suites\"] if req):\n if k not in suite.keys():\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"missing required key: %r\" % k\n ))\n\n if \"name\" in suite:\n suites.append(suite[\"name\"])\n\n if \"hooks\" in suite:\n hooks += suite[\"hooks\"]\n\n if \"hooks_pr_tcase\" in suite:\n hooks += suite[\"hooks_pr_tcase\"]\n\n # Check for existence of suites\n suites = list(set(suites))\n for suite_name in suites:\n suite_fpath = os.sep.join([\n args.testsuites_root,\n \"%s.suite\" % suite_name\n ])\n\n if not os.path.exists(suite_fpath):\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"testsuite: %r, does not exist\" % suite_fpath\n ))\n\n # Check for existence of hooks\n hooks = list(set(hooks))\n for hook_name in hooks:\n\n exists = []\n for tmpl in [\"%s.sh\", \"%s_enter.sh\", \"%s_exit.sh\"]:\n hook_fpath = os.sep.join([\n args.hooks_root,\n tmpl % hook_name\n ])\n exists.append(os.path.exists(hook_fpath))\n\n if not sum(exists):\n violations.append(MESSAGES[ident] % (\n tp_fname,\n \"hook: %r, does not exist\" % hook_name\n ))\n\n return violations","def test_supporting_folder02(self):\n index_file = os.path.join(self.test_input, 'mypage.html')\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n<img src=\"mypage_files/picture.bmp\">\n</body>\n</html>\n\"\"\")\n img_file = os.path.join(self.test_input, 'mypage_files', 'picture.bmp')\n os.makedirs(os.path.dirname(img_file), exist_ok=True)\n with open(img_file, 'wb') as fh:\n fh.write(b'dummy')\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_item, = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}/index.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n os.path.join(self.test_output, id_item, 'mypage.html'),\n os.path.join(self.test_output, id_item, 'mypage_files'),\n os.path.join(self.test_output, id_item, 'mypage_files', 'picture.bmp'),\n })","def test_supporting_folder01(self):\n index_file = os.path.join(self.test_input, 'mypage.html')\n with open(index_file, 'w', encoding='UTF-8') as fh:\n fh.write(\"\"\"\\\n<!DOCTYPE html>\n<html\n data-scrapbook-create=\"20200101000000000\"\n data-scrapbook-modify=\"20200101000000000\"\n data-scrapbook-source=\"http://example.com\">\n<head>\n<meta charset=\"UTF-8\">\n<title>MyTitle 中文</title>\n</head>\n<body>\npage content\n<img src=\"mypage.files/picture.bmp\">\n</body>\n</html>\n\"\"\")\n img_file = os.path.join(self.test_input, 'mypage.files', 'picture.bmp')\n os.makedirs(os.path.dirname(img_file), exist_ok=True)\n with open(img_file, 'wb') as fh:\n fh.write(b'dummy')\n\n for _info in file2wsb.run(self.test_input, self.test_output):\n pass\n\n book = Host(self.test_output).books['']\n book.load_meta_files()\n book.load_toc_files()\n\n id_item, = book.meta.keys()\n self.assertDictEqual(book.meta, {\n id_item: {\n 'title': 'MyTitle 中文',\n 'type': '',\n 'index': f'{id_item}/index.html',\n 'create': '20200101000000000',\n 'modify': '20200101000000000',\n 'source': 'http://example.com',\n 'icon': '',\n 'comment': '',\n },\n })\n self.assertDictEqual(book.toc, {\n 'root': [\n id_item,\n ],\n })\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\n os.path.join(self.test_output, ''),\n os.path.join(self.test_output, id_item),\n os.path.join(self.test_output, id_item, 'index.html'),\n os.path.join(self.test_output, id_item, 'mypage.html'),\n os.path.join(self.test_output, id_item, 'mypage.files'),\n os.path.join(self.test_output, id_item, 'mypage.files', 'picture.bmp'),\n })","def extract_json_to_files(input_dir,output_dir):\n files={}\n files['train']='train-v1.1.json'\n files['dev']='dev-v1.1.json'\n\n for file in files:\n filename=os.path.join(input_dir,files[file])\n with open(filename,'r',encoding='utf-8') as data_file:\n examples = []\n dataset=json.load(data_file)\n count_total=total_exs(dataset)\n count_mapping_problem=0\n count_token_problem=0\n count_ansspan_problem=0\n count_examples=0\n for article_id in tqdm(range(len(dataset['data'])), desc=\"Preprocessing {}\".format(file)):\n article_paragraph=dataset['data'][article_id]['paragraphs']\n for paragraph_id in range(len(article_paragraph)):\n context=article_paragraph[paragraph_id]['context']\n context=context.replace(\"''\",'\"').replace(\"``\",'\"')\n context = context.replace('\\u3000', ' ').replace('\\u202f',' ').replace('\\u2009', ' ')#.replace(\"'\",\"'\")\n context=context.replace('\\-',' ')\n context_tokens=tokenize_sequence(context)\n context=context.lower()\n qas=article_paragraph[paragraph_id]['qas']\n charloc2wordloc=get_char_word_loc_mapping(context, context_tokens)\n if charloc2wordloc is None:\n count_mapping_problem+=len(qas)\n continue\n for qa in qas:\n question=qa['question'].lower()\n question_tokens=tokenize_sequence(question)\n\n ans_text=qa['answers'][0]['text'].lower()\n ans_text=ans_text.replace('\\u3000', ' ').replace('\\u202f', ' ').replace('\\u2009', ' ')\n ans_start_loc=qa['answers'][0]['answer_start']\n if qa['id'] in ['5706baed2eaba6190074aca5','57269c73708984140094cbb5','57269c73708984140094cbb7','572a11661d04691400779721','572a11661d04691400779722','572a11661d04691400779723','572a11661d04691400779724','572a11661d04691400779725','572a2cfc1d0469140077981b','572a3a453f37b319004787e9','572a84d3f75d5e190021fb3c']:\n ans_start_loc+=1\n if qa['id'] in ['572a5df77a1753140016aedf','572a5df77a1753140016aee0','572a84d3f75d5e190021fb38','572a84d3f75d5e190021fb39','572a84d3f75d5e190021fb3a','572a84d3f75d5e190021fb3b','572a85df111d821400f38bad','572a85df111d821400f38bae','572a85df111d821400f38baf','572a85df111d821400f38bb0']:\n ans_start_loc+=2\n if qa['id'] in ['572a5df77a1753140016aee1','572a5df77a1753140016aee2']:\n ans_start_loc+=3\n if qa['id'] in ['57286bf84b864d19001649d6','57286bf84b864d19001649d5']:\n ans_start_loc-=1\n if qa['id'] in ['5726bee5f1498d1400e8e9f3','5726bee5f1498d1400e8e9f4']:\n ans_start_loc-=2\n ans_end_loc=ans_start_loc+len(ans_text)\n\n if context[ans_start_loc:ans_end_loc]!=ans_text:\n count_ansspan_problem+=1\n continue\n ans_start_wordloc = charloc2wordloc[ans_start_loc][1] # answer start word loc\n ans_end_wordloc = charloc2wordloc[ans_end_loc-1][1] # answer end word loc\n assert ans_start_wordloc <= ans_end_wordloc\n\n ans_tokens = context_tokens[ans_start_wordloc:ans_end_wordloc + 1]\n if \"\".join(ans_tokens) != \"\".join(ans_text.split()):\n count_token_problem += 1\n #print(ans_text)\n #print(ans_tokens)\n continue # skip this question/answer pair\n examples.append((' '.join(context_tokens),' '.join(question_tokens),' '.join(ans_tokens),' '.join([str(ans_start_wordloc),str(ans_end_wordloc)])))\n print(\"Number of (context, question, answer) triples discarded due to char -> token mapping problems: \", count_mapping_problem)\n print(\"Number of (context, question, answer) triples discarded because character-based answer span is unaligned with tokenization: \",count_token_problem)\n print(\"Number of (context, question, answer) triples discarded due character span alignment problems (usually Unicode problems): \",count_ansspan_problem)\n print(\"Processed %i examples of total %i\\n\" % (len(examples), len(examples)+count_mapping_problem+count_token_problem+count_ansspan_problem))\n indices = list(range(len(examples)))\n np.random.shuffle(indices)\n with open(os.path.join(output_dir,file+'.context'),'w',encoding='utf-8') as context_file, \\\n open(os.path.join(output_dir,file+'.question'),'w',encoding='utf-8') as question_file, \\\n open(os.path.join(output_dir,file+'.answer'),'w',encoding='utf-8') as answer_file, \\\n open(os.path.join(output_dir,file+'.span'),'w',encoding='utf-8') as span_file:\n for i in indices:\n (context,question,answer,span)=examples[i]\n context_file.write(context+'\\n')\n question_file.write(question+'\\n')\n answer_file.write(answer+'\\n')\n span_file.write(span+'\\n')","def index():\n\n # open the README file\n with open(os.path.dirname(app.root_path) + '/README.md',\n 'r', encoding=\"utf-8\") as markdown_file:\n\n # Read the content of the file\n content = markdown_file.read()\n\n # convert to html\n return markdown.markdown(content)","def opendocs():\n _open_file('_build/index.html')","def test_man9dir(self):\n self.chck_triple('man9dir')","def write_index_html(self):\n print(\"- writing index.md\")\n index_toc = [f\"### [Table of Contents]({config['github_pages_url']}/toc.html)\"] if self.notebooks else []\n if os.path.isfile(os.path.join(self.dst_dir, \"data_index.html\")):\n index_toc += [f\"### [Data Index]({config['github_pages_url']}/data_index.html)\"]\n if os.path.isfile(os.path.join(self.dst_dir, \"figure_index.html\")):\n index_toc += [f\"### [Figure Index]({config['github_pages_url']}/figure_index.html)\"]\n if os.path.isfile(os.path.join(self.dst_dir, \"python_index.html\")):\n index_toc += [f\"### [Python Module Index]({config['github_pages_url']}/python_index.html)\"]\n if os.path.isfile(os.path.join(self.dst_dir, \"tag_index.html\")):\n index_toc += [f\"### [Tag Index]({config['github_pages_url']}/tag_index.html)\"]\n index_toc += [f\"- {nb.link}\" if type(nb) == Section else f\"\\n### {nb.link}\" for nb in self.notebooks]\n env = Environment(loader=FileSystemLoader(\"templates\"))\n with open(os.path.join(self.dst_dir, \"index.md\"), 'w') as f:\n f.write(env.get_template('index.md.tpl').render(\n readme_toc=index_toc, page_title=config['github_repo_name'], github_url=config['github_repo_url']))","def setup():\n if not os.path.isfile(etymology_file):\n page = re.compile(r'index.php\\?l=\\w+&p=\\d+&allowed_in_frame=0.html')\n pages = list(find_files(directory=site, pattern=page, recursive=False))\n etymology = etymologies(pages)\n dump(etymology, etymology_file)\n for affix, dictionary in affixes(etymology):\n affix_file = os.path.join('resources', '{}.json'.format(affix))\n if not os.path.isfile(affix_file):\n dump(dictionary, affix_file)","def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\n def is_whitespace(c):\n if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n return True\n return False\n\n if is_training:\n subset_list = ['train']\n else:\n subset_list = ['dev', 'test']\n\n examples, features = {}, {}\n for subset in subset_list:\n level_example_dict = {\"high\": None, \"middle\": None}\n level_features_dict = {\"high\": None, \"middle\": None}\n\n for level in ['high', 'middle']:\n subset_dir = os.path.join(data_dir, subset)\n file_list = os.listdir(subset_dir)\n file_list = [file for file in file_list if file.endswith('txt')]\n file_list = sorted(file_list)\n\n alldata = []\n for file in file_list:\n data = json.load(open(os.path.join(subset_dir, file)))\n alldata.append(data)\n \n examples_list = []\n for data in alldata:\n doc_id = data['id']\n doc = data[\"article\"].replace('\\\\n', '\\n')\n doc_token = []\n prev_is_whitespace = True\n for c in doc:\n if is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n doc_token.append(c)\n else:\n doc_token[-1] += c\n prev_is_whitespace = False\n\n for i, answer in enumerate(data[\"answers\"]):\n if answer == \" \":\n answer = \"C\"\n example = InputExample(\n guid=doc_id + '-%d' % i,\n doc_token=doc_token,\n question_text=\"\",\n options=data[\"options\"],\n answer=answer)\n examples_list.append(example)\n\n level_example_dict[level] = examples_list\n level_features_dict[level] = convert_examples_to_features(\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\n \n print(\"MuTuaL Data Loader....\")\n print(level_example_dict[level][-1])\n print(level_features_dict[level][-1])\n examples[subset] = level_example_dict\n features[subset] = level_features_dict\n\n return examples, features","def run(self):\n make_sure_path_exists(OUT_FOLDER)\n\n if self.config['type'] == 'website':\n make_sure_path_exists(self.config['out_folder'])\n\n\n for file in self.config['bodies']:\n if file['type'] == 'content':\n self.pandoc_file(file)\n if self.config['type'] == 'website':\n shutil.copyfile(file['generated'], os.path.join(self.config['out_folder'], os.path.basename(file['source'])))\n\n if self.config['type'] == 'website':\n return\n\n for file in self.config['abstract']:\n self.pandoc_file(file)\n for file in self.config['summary']:\n self.pandoc_file(file)\n\n template = LATEX_JINJA_ENV.get_template(self.config['template_file'])\n\n logging.info('Rendering template')\n out = template.render(**self.config)\n with open(self.config['name'] + self.get_file_extension(), 'w') as file:\n file.write(out)\n\n if not self.args.pandoc:\n logging.info('Rendering latex')\n self.write()\n if not self.args.fast:\n logging.info('Rendering latex, again')\n self.write() # twice for the toc\n\n logging.info('Done!')","def test_darknet_txt_paths(path_to_samples_list: str):\n with open(path_to_samples_list, \"r\") as f:\n data = f.readlines()\n for img_path in [path[:-1] for path in data]:\n assert os.path.isfile(img_path)\n assert os.path.isfile(img_path.replace(\"/images/\", \"/labels/\").replace(\".jpg\", \".txt\"))\n print(f\"All files listed in '{path_to_samples_list}' exists.\")","def check_init_files_and_folders():\n\t#['cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', 'cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', 'color_detect_2.py', 'dedupe.py', 'detect_image_group_ku.py', 'detect_shape_5.py', 'get_cam_id_2.py', 'get_image_8.py', 'gui_hsv.py', 'knaps.py', 'knapsack_2.py', 'maps.html', 'program_detect_rectangle.zip', 'start_capture.py']\n\tfile_list=[\n\t#'cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', \n\t'models/cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', \n\t#'color_detect_2.py', \n\t#'dedupe.py', \n\t'detect_bus_haar_group.py', \n\t#'detect_shape_5.py', \n\t'get_cam_detail.py', \n\t'get_image.py', \n\t#'gui_hsv.py', \n\t#'knaps.py', \n\t#'knapsack_2.py', \n\t#'maps.html', \n\t#'program_detect_rectangle.zip', \n\t'start_wimb.py',\n\t'g.php',\n\t]\n\tdirectory_list=[\n\t'images',\n\t'images_bgs',\n\t'images_bgs_mask',\n\t#'images_bgs_result',\n\t'images_color',\n\t'images_haar',\n\t'images_haar_result',\n\t'images_number',\n\t'images_number_result',\n\t'models',\n\t'images_old',\n\t'text_number',\n\t]\n\t\n\tfor file_name in file_list: print 'file '+file_name+' existed: '+str(os.path.isfile(file_name))\n\tfor directory_name in directory_list: \n\t\tprint 'directory '+directory_name+' existed: '+str(os.path.isdir(directory_name))\n\t\tif not os.path.isdir(directory_name): \n\t\t\tos.makedirs(directory_name)\n\t\tif \"images\" in directory_name: shutil.copy(path+'/g.php',path+'/'+directory_name+'/g.php')","def test_doc():\n pass"],"string":"[\n \"def walkthrough(software_map):\\n\\n for i in software_map:\\n\\n if not i[\\\"is_file\\\"]:\\n\\n # for each directory: make a index.md\\n dname = \\\"./docs/\\\" + i[\\\"name\\\"]\\n index = \\\"./docs/\\\" + i[\\\"name\\\"] + \\\"/index.md\\\"\\n print(index)\\n os.mkdir(dname)\\n\\n with open(index, \\\"w+\\\") as f:\\n\\n children = i[\\\"children\\\"]\\n\\n # list files\\n f.write(\\\"Files:\\\\n\\\\n\\\")\\n for i in children:\\n if i[\\\"is_file\\\"]:\\n\\n fname = i[\\\"name\\\"]\\n fext = fname.split(\\\".\\\")\\n if len(fext) == 2:\\n fext = fext[1]\\n else:\\n fext = \\\"none\\\"\\n # for each file, note name and extension\\n f.write(fname + \\\" : \\\" + fext + \\\"\\\\n\\\")\\n\\n # list subdirectories\\n f.write(\\\"\\\\nSubdirectories:\\\\n\\\\n\\\")\\n for i in children:\\n if not i[\\\"is_file\\\"]:\\n\\n dirname = i[\\\"name\\\"]\\n\\n # note the number of files and subdirs in it\\n num_files, num_dirs = 0, 0\\n for child in i[\\\"children\\\"]:\\n if child[\\\"is_file\\\"]:\\n num_files += 1\\n elif not child[\\\"is_file\\\"]:\\n num_dirs += 1\\n\\n # note down name and numbers for each dir\\n f.write(dirname + \\\" : \\\" + str(num_files) + \\\" files, \\\" +\\n str(num_dirs) + \\\" directories\\\\n\\\")\\n\\n # goto subdir\\n if len(i[\\\"children\\\"]) > 0:\\n walkthrough(i[\\\"children\\\"])\",\n \"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\\n\\n attack_id = util.get_attack_id(technique)\\n\\n # Only add technique if the attack id was found\\n if attack_id:\\n\\n technique_dict = {}\\n\\n technique_dict['attack_id'] = attack_id\\n technique_dict['domain'] = domain.split(\\\"-\\\")[0]\\n technique_dict['menu'] = side_menu_data\\n technique_dict['name'] = technique.get('name')\\n\\n # Get capecs and mtcs\\n for ref in technique['external_references']:\\n if ref.get('source_name'):\\n if ref['source_name'] == \\\"capec\\\":\\n if technique_dict.get('capecs') is None:\\n technique_dict['capecs'] = []\\n capec_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['capecs'].append(capec_dict)\\n\\n if ref['source_name'] == \\\"NIST Mobile Threat Catalogue\\\":\\n if technique_dict.get('mtcs') is None:\\n technique_dict['mtcs'] = []\\n\\n mtcs_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['mtcs'].append(mtcs_dict)\\n\\n # Get technique external references \\n ext_ref = technique[\\\"external_references\\\"]\\n\\n # Get initial reference list\\n reference_list = []\\n # Decleared as an object to be able to pass by reference\\n next_reference_number = {}\\n next_reference_number['value'] = 1\\n reference_list = util.update_reference_list(reference_list, technique)\\n\\n # Get technique description\\n if technique.get(\\\"description\\\"):\\n citations_from_descr = util.get_citations_from_descr(technique['description'])\\n\\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\\n \\n if 'x_mitre_deprecated' in technique:\\n technique_dict['deprecated'] = True\\n \\n # Get mitigation table\\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\\n \\n # Get related techniques\\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\\n\\n # Get examples\\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\\n\\n # Get technique version\\n if technique.get(\\\"x_mitre_version\\\"):\\n technique_dict['version'] = technique[\\\"x_mitre_version\\\"]\\n\\n # Get tactics of technique\\n if technique.get('kill_chain_phases'):\\n technique_dict['tactics'] = []\\n for elem in technique['kill_chain_phases']:\\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\\n\\n # Get platforms that technique uses\\n if technique.get('x_mitre_platforms'):\\n technique_dict['platforms'] = \\\", \\\".join(technique['x_mitre_platforms'])\\n\\n # Get system requirements\\n if technique.get('x_mitre_system_requirements'):\\n technique_dict['sysreqs'] = \\\", \\\".join(technique['x_mitre_system_requirements'])\\n technique_dict['sysreqs'] = re.sub(\\\"\\\\.?\\\\\\\\n+\\\", \\\"; \\\", technique_dict['sysreqs'])\\n\\n # Get permissions required\\n if technique.get('x_mitre_permissions_required'):\\n technique_dict['perms'] = \\\", \\\".join(technique['x_mitre_permissions_required'])\\n\\n # Get effective permissions\\n if technique.get('x_mitre_effective_permissions'):\\n technique_dict['eff_perms'] = \\\", \\\".join(technique['x_mitre_effective_permissions'])\\n\\n # Get data sources\\n if technique.get('x_mitre_data_sources'):\\n technique_dict['data_sources'] = \\\", \\\".join(technique['x_mitre_data_sources'])\\n\\n # Get if technique supports remote\\n if technique.get('x_mitre_remote_support'):\\n if technique['x_mitre_remote_support']:\\n technique_dict['supports_remote'] = \\\" Yes\\\"\\n else:\\n technique_dict['supports_remote'] = \\\" No\\\"\\n\\n # Get network requirements\\n if technique.get('x_mitre_network_requirements'):\\n if technique['x_mitre_network_requirements']:\\n technique_dict['network_reqs'] = \\\" Yes\\\"\\n else:\\n technique_dict['network_reqs'] = \\\" No\\\"\\n\\n # Get list of impacts\\n if technique.get('x_mitre_impact_type'):\\n technique_dict['impact_type'] = \\\", \\\".join(technique['x_mitre_impact_type'])\\n\\n # Get list of defenses bypassed\\n if technique.get('x_mitre_defense_bypassed'):\\n technique_dict['def_bypass'] = \\\", \\\".join(technique['x_mitre_defense_bypassed'])\\n\\n # Get list of contributors \\n if technique.get('x_mitre_contributors'):\\n technique_dict['contributors'] = \\\"; \\\".join(technique['x_mitre_contributors'])\\n\\n # Get list of tactic types\\n if technique.get('x_mitre_tactic_type'):\\n technique_dict['tactic_type'] = \\\", \\\".join(technique['x_mitre_tactic_type'])\\n\\n # Get detection data\\n if technique.get('x_mitre_detection'):\\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\\n\\n # Get if technique is detectable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses'):\\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\\n\\n # Get explanation of detecatable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\\n\\n # Get diffulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary'):\\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\\n\\n # Get explanation of difficulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \\n \\n # Add reference for bottom part of technique page\\n if reference_list:\\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\\n\\n subs = config.technique_md.substitute(technique_dict)\\n subs = subs + json.dumps(technique_dict)\\n\\n #Write out the markdown file\\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\\\".md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\",\n \"def main():\\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"original\\\")\\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"annotated\\\")\\n\\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\\n os.mkdir(ANNOTATED_LESSONS_PATH)\\n\\n print(\\\"Scanning original lessons in %s...\\\" % ORIGINAL_LESSONS_PATH)\\n\\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\\n if item == \\\".DS_Store\\\": continue\\n\\n print(\\\" found: %s\\\" % item)\\n\\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\\n\\n lesson_number = None\\n lesson_description = None\\n mobj = re.search(r'^AY\\\\s+(\\\\d+)\\\\s*-\\\\s*(.+)\\\\.txt$', item)\\n if mobj:\\n lesson_number = mobj.group(1)\\n lesson_description = mobj.group(2)\\n\\n print(\\\" number: %s\\\" % lesson_number)\\n print(\\\" description: %s\\\" % lesson_description)\\n\\n lesson = dict()\\n lesson['number'] = lesson_number\\n lesson['description'] = lesson_description\\n\\n fh = open(item_path)\\n lesson_raw_text = fh.read()\\n fh.close()\\n lesson_text = re.split(r'\\\\n', lesson_raw_text)\\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\\n# lesson_text = re.split(r'\\\\n', lesson_raw_text_reencoded)\\n\\n lesson['text'] = lesson_text\\n lesson['parsed'] = parseLesson(lesson_text)\\n\\n if lesson['parsed']['end_of_lesson'] is None:\\n print(\\\" lesson has no 'end of lesson' marker\\\")\\n\\n lesson_json = json.dumps(lesson, indent=4)\\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \\\"ay_%04d.json\\\" % int(lesson_number))\\n fh = open(annotated_lesson_path, \\\"w\\\")\\n fh.write(lesson_json)\\n fh.close()\\n\\n else:\\n print(\\\"ERROR: File name not understood: %s\\\" % item)\\n\\n return 0\",\n \"def main():\\n\\n dofile = \\\"thebook\\\"\\n\\n #spellcheck()\\n\\n common_options = '--encoding=utf-8 --examples_as_exercises '\\n\\n # --- HTML ---\\n\\n common_html_options = ' '\\n\\n # HTML Bootstrap\\n bootstrap_options = ' --html_style=bootswatch_readable --html_code_style=inherit --html_pre_style=inherit --toc_depth=2 --pygments_html_style=default --html_template=template_bootstrap_wtoc.html --html_figure_caption=bottom --html_figure_hrule=top+bottom' \\n\\n html(\\n dofile,\\n options=common_options + common_html_options + bootstrap_options,\\n split=True)\\n\\n # One long HTML file\\n #html(dofile, options=common_options + common_html_options + ' --html_style=bloodish --html_output=%s-1' % dofile, split=False)\\n\\n # Solarized HTML\\n #html(dofile, options=common_options + common_html_options + ' --html_style=solarized3 --html_output=%s-solarized' % dofile, split=True)\\n\\n mksnippets()\\n sys.exit(1)\\n\\n # --- latex ---\\n\\n common_latex_options = ' --latex_code_style=vrb'\\n\\n for version in 'paper', 'screen': # , 'A4', '2up', 'A4-2up':\\n latex(\\n dofile,\\n latex_program='pdflatex',\\n options=common_options + common_latex_options,\\n version=version,\\n postfix='auto')\\n\\n # --- Sphinx ---\\n\\n# sphinx_themes = ['pyramid',]\\n# for theme in sphinx_themes:\\n# dirname = 'sphinx-rootdir' if len(sphinx_themes) == 1 else 'sphinx-rootdir-%s' % theme\\n# sphinx(\\n# dofile,\\n# options=common_options + '',\\n# dirname=dirname,\\n# theme=theme,\\n# automake_sphinx_options='',\\n# split=False)\\n\\n # Dump all Unix commands run above as a Bash script\\n bash = open('tmp_make.sh', 'w')\\n print 'see tmp_make.sh for an equivalent auto-generated unix script'\\n bash.write('''#!/bin/bash\\nset -x # display all commands in output\\n\\n# Safe execution of a Unix command: exit if failure\\nfunction system {\\n \\\"$@\\\"\\n if [ $? -ne 0 ]; then\\n echo \\\"make.sh: unsuccessful command $@\\\"\\n echo \\\"abort!\\\"\\n exit 1\\n fi\\n}\\n''')\\n for cmd in unix_command_recorder:\\n if cmd.startswith('doconce format') or cmd.startswith('rm '):\\n bash.write('\\\\n') # delimiter line in script\\n bash.write('system ' + cmd + '\\\\n')\\n bash.close()\\n\\n print 'see tmp_output.log for the output of all the commands'\",\n \"def check_data():\\n check_docs(\\\"Training\\\")\\n check_docs(\\\"dev\\\")\\n check_docs(\\\"Test\\\")\",\n \"def gen_tutorials(repo_dir: str) -> None:\\n with open(os.path.join(repo_dir, \\\"website\\\", \\\"tutorials.json\\\"), \\\"r\\\") as infile:\\n tutorial_config = json.loads(infile.read())\\n\\n tutorial_ids = [x[\\\"id\\\"] for v in tutorial_config.values() for x in v]\\n tutorial_dirs = [x.get(\\\"dir\\\") for v in tutorial_config.values() for x in v]\\n\\n for tid, t_dir in zip(tutorial_ids, tutorial_dirs):\\n print(\\\"Generating {} tutorial\\\".format(tid))\\n\\n if t_dir is not None:\\n tutorial_dir = os.path.join(repo_dir, \\\"tutorials\\\", t_dir)\\n html_dir = os.path.join(repo_dir, \\\"website\\\", \\\"_tutorials\\\", t_dir)\\n js_dir = os.path.join(repo_dir, \\\"website\\\", \\\"pages\\\", \\\"tutorials\\\", t_dir)\\n py_dir = os.path.join(repo_dir, \\\"website\\\", \\\"static\\\", \\\"files\\\", t_dir)\\n\\n for d in [tutorial_dir, html_dir, js_dir, py_dir]:\\n os.makedirs(d, exist_ok=True)\\n\\n tutorial_path = os.path.join(tutorial_dir, \\\"{}.ipynb\\\".format(tid))\\n html_path = os.path.join(html_dir, \\\"{}.html\\\".format(tid))\\n js_path = os.path.join(js_dir, \\\"{}.js\\\".format(tid))\\n ipynb_path = os.path.join(py_dir, \\\"{}.ipynb\\\".format(tid))\\n py_path = os.path.join(py_dir, \\\"{}.py\\\".format(tid))\\n tar_path = os.path.join(py_dir, \\\"{}.tar.gz\\\".format(tid))\\n else:\\n tutorial_path = os.path.join(repo_dir, \\\"tutorials\\\", \\\"{}.ipynb\\\".format(tid))\\n html_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"_tutorials\\\", \\\"{}.html\\\".format(tid)\\n )\\n js_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"pages\\\", \\\"tutorials\\\", \\\"{}.js\\\".format(tid)\\n )\\n ipynb_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"static\\\", \\\"files\\\", \\\"{}.ipynb\\\".format(tid)\\n )\\n py_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"static\\\", \\\"files\\\", \\\"{}.py\\\".format(tid)\\n )\\n\\n # convert notebook to HTML\\n with open(tutorial_path, \\\"r\\\") as infile:\\n nb_str = infile.read()\\n nb = nbformat.reads(nb_str, nbformat.NO_CONVERT)\\n\\n exporter = HTMLExporter()\\n html, meta = exporter.from_notebook_node(nb)\\n\\n # pull out html div for notebook\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n nb_meat = soup.find(\\\"div\\\", {\\\"id\\\": \\\"notebook-container\\\"})\\n del nb_meat.attrs[\\\"id\\\"]\\n nb_meat.attrs[\\\"class\\\"] = [\\\"notebook\\\"]\\n\\n # when output html, iframe it (useful for Ax reports)\\n for html_div in nb_meat.findAll(\\\"div\\\", {\\\"class\\\": \\\"output_html\\\"}):\\n if html_div.html is not None:\\n iframe = soup.new_tag(\\\"iframe\\\")\\n iframe.attrs[\\\"src\\\"] = \\\"data:text/html;charset=utf-8,\\\" + str(\\n html_div.html\\n )\\n # replace `#` in CSS\\n iframe.attrs[\\\"src\\\"] = iframe.attrs[\\\"src\\\"].replace(\\\"#\\\", \\\"%23\\\")\\n html_div.contents = [iframe]\\n\\n html_out = MOCK_JS_REQUIRES + str(nb_meat)\\n\\n # generate HTML file\\n with open(html_path, \\\"w\\\") as html_outfile:\\n html_outfile.write(html_out)\\n\\n # generate JS file\\n t_dir_js = t_dir if t_dir else \\\"\\\"\\n script = TEMPLATE.format(t_dir=t_dir_js, tid=tid)\\n with open(js_path, \\\"w\\\") as js_outfile:\\n js_outfile.write(script)\\n\\n # output tutorial in both ipynb & py form\\n with open(ipynb_path, \\\"w\\\") as ipynb_outfile:\\n ipynb_outfile.write(nb_str)\\n exporter = ScriptExporter()\\n script, meta = exporter.from_notebook_node(nb)\\n with open(py_path, \\\"w\\\") as py_outfile:\\n py_outfile.write(script)\\n\\n # create .tar archive (if necessary)\\n if t_dir is not None:\\n with tarfile.open(tar_path, \\\"w:gz\\\") as tar:\\n tar.add(tutorial_dir, arcname=os.path.basename(tutorial_dir))\",\n \"def index():\\n with open(\\\"PROJECT.md\\\", \\\"r\\\") as project_file:\\n md_template_string = markdown.markdown(\\n project_file.read(), extensions=[\\\"fenced_code\\\"]\\n )\\n return md_template_string\",\n \"def verifyBuilt(self):\\n htmlDir = self.sphinxDir.sibling(\\\"doc\\\")\\n self.assertTrue(htmlDir.isdir())\\n doctreeDir = htmlDir.child(\\\"doctrees\\\")\\n self.assertFalse(doctreeDir.exists())\\n\\n self.verifyFileExists(htmlDir, \\\"index.html\\\")\\n self.verifyFileExists(htmlDir, \\\"genindex.html\\\")\\n self.verifyFileExists(htmlDir, \\\"objects.inv\\\")\\n self.verifyFileExists(htmlDir, \\\"search.html\\\")\\n self.verifyFileExists(htmlDir, \\\"searchindex.js\\\")\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Markdown\\\",\\n \\\"\\\"\\\"<markdowna />\\n \\\"\\\"\\\")\\n ]\",\n \"def helper():\\n \\n import webbrowser, os.path\\n \\n path = os.path.splitext(__file__)[0]\\n helpspec = \\\"file://\\\" + path + os.path.sep + \\\\\\n \\\"markdown.html\\\"\\n \\n # webbrowser.open seems not to work well\\n browser = webbrowser.get()\\n if not browser.open_new(helpspec):\\n print((\\\"Help file not found:\\\" + helpspec))\",\n \"def helper():\\n \\n import webbrowser, os.path\\n \\n path = os.path.splitext(__file__)[0]\\n helpspec = \\\"file://\\\" + path + os.path.sep + \\\\\\n \\\"markdown.html\\\"\\n \\n # webbrowser.open seems not to work well\\n browser = webbrowser.get()\\n if not browser.open_new(helpspec):\\n print((\\\"Help file not found:\\\" + helpspec))\",\n \"def _setup(self):\\n self._raw_dir = os.path.join(self._snippets_dir,\\\"raw\\\",\\\"static\\\")\\n if not os.path.exists(self._raw_dir):\\n os.mkdir(self._raw_dir)\\n\\n self._trec_dir = os.path.join(self._snippets_dir,\\\"trec\\\",\\\"static\\\")\\n if not os.path.exists(self._trec_dir):\\n os.mkdir(self._trec_dir)\\n\\n self._temp_dir = os.path.join(self._snippets_dir,\\\"temp\\\",\\\"static\\\")\\n if not os.path.exists(self._temp_dir):\\n os.mkdir(self._temp_dir)\\n\\n self._para_dir = os.path.join(self._snippets_dir,\\\"para\\\",\\\"static\\\")\\n if not os.path.exists(self._para_dir):\\n os.mkdir(self._para_dir)\\n\\n self._snippet_result_dir = os.path.join(self._snippets_dir,\\\"result\\\",\\\"static\\\")\\n if not os.path.exists(self._snippet_result_dir):\\n os.mkdir(self._snippet_result_dir)\\n\\n self._snippet_index_dir = os.path.join(self._snippets_dir,\\\"index\\\",\\\"static\\\")\\n if not os.path.exists(self._snippet_index_dir):\\n os.mkdir(self._snippet_index_dir)\\n\\n \\n\\n\\n self._index_para = os.path.join(self._para_dir,\\\"index_para\\\")\\n\\n self._temp_query_para = os.path.join(self._para_dir,\\\"temp_query_para\\\")\\n\\n self._index_list = os.path.join(self._para_dir,\\\"static_index_list\\\")\\n \\n self._orf = os.path.join(self._snippet_result_dir,\\\"orf\\\")\\n\\n self._oqf = os.path.join(self._temp_dir,\\\"oqf\\\")\\n \\n self._temp_output = os.path.join(self._temp_dir,\\\"temp_output\\\")\\n\\n with open(self._index_list,\\\"w\\\") as f:\\n f.write(self._snippet_index_dir+\\\"\\\\n\\\")\\n\\n self._temp_query_builder = IndriQueryFactory(count=10000,\\n rule=self._retrieval_method)\\n\\n self._oqf_builder = IndriQueryFactory(count=30,\\n rule=self._retrieval_method)\",\n \"def test_docs_paths():\\n assert os.path.exists('test/examples/docs/paths-root-api.md')\\n assert os.path.exists('test/examples/docs/paths-subpath1.md')\\n assert os.path.exists('test/examples/docs/paths-subpath1.md')\",\n \"def _markdown(checklist):\\n\\n checklist = json.load(open(checklist), object_pairs_hook=OrderedDict)\\n\\n mdFile = MdUtils(file_name='Ikigai-Checklist',\\n title='PDP 2019 Checklist')\\n\\n\\n mdFile.new_paragraph(checklist['overview'])\\n\\n sections = sorted(checklist['checklist'].values(),\\n key=lambda s: int(s['no']))\\n for section in sections:\\n mdFile.new_header(level=1, title=section['title'])\\n for subject in section['subjects'].values():\\n mdFile.new_header(level=2, title=subject['title'])\\n mdFile.new_paragraph(subject['description'])\\n mdFile.new_paragraph(\\\"Reference: \\\" + subject['provisions'])\\n mdFile.new_paragraph(\\\"Functions: \\\" + \\\", \\\".join(subject['functions']))\\n mdFile.new_paragraph(\\\"Groups: \\\" + \\\", \\\".join(subject['groups']))\\n\\n actions = [['No','Description', 'Tags', 'Check']]\\n actions += [[a['no'], a['description'], \\\", \\\".join(a['tags']),''] for a in subject['actions'].values()]\\n rows = len(actions) \\n actions = flatten(actions)\\n mdFile.new_table(columns=4,\\n rows=rows,\\n text=actions,\\n text_align='left')\\n\\n mdFile.create_md_file()\",\n \"def main(base_path):\\n current = os.getcwd()\\n try:\\n if not(os.path.exists(base_path)):\\n ans = 'y'\\n if p_out:\\n print(\\\"Do you want to create \\\" + base_path + \\\"?(y/n)\\\")\\n ans = sys.stdin.read(1)\\n print(\\\"\\\")\\n if ans in ('y', 'Y'):\\n pass\\n elif ans in ('n', 'N'):\\n raise NoneOutput\\n else:\\n raise InputError\\n else:\\n m_path = os.path.join(base_path, 'nzmath/manual')\\n if os.path.exists(m_path):\\n ans = 'y'\\n if p_out:\\n print(\\\"Do you want to remove \\\" + m_path + \\\"?(y/n)\\\")\\n ans = sys.stdin.read(1)\\n print(\\\"\\\")\\n if ans in ('y', 'Y'):\\n for root, dirs, files in os.walk(m_path, topdown=False):\\n for name in files:\\n os.remove(os.path.join(root, name))\\n for name in dirs:\\n os.rmdir(os.path.join(root, name))\\n elif ans in ('n', 'N'):\\n raise NoneOutput\\n else:\\n raise InputError\\n dirname = os.path.join(base_path, 'nzmath/manual/modules')\\n if not(os.path.exists(dirname)):\\n os.makedirs(dirname)\\n os.chdir(os.path.join(base_path, 'nzmath/manual/'))\\n csspage = convertHPURL('manual/default.css')\\n if p_out:\\n print(\\\"get css from \\\" + csspage)\\n retryConnection(urllib.request.urlretrieve, csspage, 'default.css')\\n while ad_list:\\n files = ad_list.pop()\\n MyWikiParser(files).feeds()\\n if p_out:\\n print(\\\"\\\\n\\\" + \\\"All process is done!\\\" + \\\"\\\\n\\\")\\n print(\\\"Ok, now created nzmath-current manual located to\\\")\\n print(os.path.join(base_path, \\\"nzmath\\\"))\\n print(\\\"if you check difference between nzmath-cvs manual, with GNU diff,\\\")\\n print(\\\"$ diff -ubBr /tmp/nzmath/manual {your-nzmathcvs-repo}/manual\\\")\\n print(\\\"or you check only new version files,\\\")\\n print(\\\"$ diff -r --brief /tmp/nzmath/manual {your-nzmathcvs-repo}/manual .\\\")\\n except NoneOutput:\\n if p_out:\\n print('end.')\\n except InputError:\\n print(\\\"Error: Invalid input!\\\")\\n except LookupError:\\n print(\\\"Error: Maybe, Japanese encodings(ex.euc_jp) is not supported.\\\")\\n except:\\n if p_out:\\n print(\\\"Check \\\" + base_path + \\\" (dir? truly path? and so on.)\\\")\\n print(\\\"Delete \\\" + base_path + \\\" and try again.\\\")\\n print(\\\"(Maybe, caused by problem of network connection)\\\\n\\\")\\n print(sys.exc_info()[0])\\n os.chdir(current)\",\n \"def test_md(tmp_path) -> Path:\\n yield Path(tmp_path)/\\\"test.md\\\"\",\n \"def generate_markdown(self, outdir, definitions):\\n doc_cases = defaultdict(lambda: defaultdict(list))\\n\\n if not os.path.exists(outdir):\\n os.makedirs(outdir)\\n for category in definitions.categories:\\n cat_outdir = os.path.join(outdir, category.replace(\\\" \\\", \\\"_\\\").lower())\\n if not os.path.exists(cat_outdir):\\n os.makedirs(cat_outdir)\\n\\n for case in definitions.case_set:\\n if case[\\\"category\\\"] == category:\\n doc_cases[category][case[\\\"name\\\"]] = case\\n\\n for technique in doc_cases[category]:\\n rendered = self._generate_markdown(doc_cases[category][technique])\\n md_filename = technique.replace(\\\" \\\", \\\"_\\\").lower() + \\\".md\\\"\\n filename = os.path.join(cat_outdir, md_filename)\\n mdoutfile = open(filename, \\\"w\\\")\\n mdoutfile.write(rendered)\\n mdoutfile.close()\",\n \"def test_markdown(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.example.Foo.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n for item in data['items']:\\n if item['uid'] == 'example.example.Foo.method_markdown':\\n self.assertEqual(\\n item['summary'],\\n 'Check out our '\\n '[site](http://sphinx-docfx-yaml.readthedocs.io/en/latest/)'\\n ' for more info.',\\n )\",\n \"def index():\\n mongo_collection = mongo_database[\\\"settings\\\"]\\n doc_instructions = mongo_collection.find_one({\\\"id\\\": \\\"instructions\\\"})\\n instructions = markdown.markdown(doc_instructions['text'])\\n return render_template(\\\"index.html\\\", instructions=instructions)\",\n \"def generate_domain_markdown(domain):\\n\\n #Reads the STIX and creates a list of the ATT&CK Techniques\\n full_techniques = stixhelpers.get_techniques(config.ms[domain])\\n tactic_list = stixhelpers.get_tactic_list(config.ms[domain])\\n\\n data = {}\\n \\n technique_list = get_techniques_list(full_techniques)\\n\\n data['domain'] = domain.split(\\\"-\\\")[0]\\n\\n # Get technique table data and number of techniques\\n data['technique_table'] = util.get_technique_table_data(None, full_techniques)\\n data['technique_list_len'] = str(len(full_techniques))\\n side_menu_data = get_technique_side_menu_data(domain, technique_list, tactic_list)\\n\\n # Get tactic-techniques table\\n data['menu'] = side_menu_data\\n\\n subs = config.technique_domain_md.substitute(data)\\n subs = subs + json.dumps(data)\\n\\n with open(os.path.join(config.techniques_markdown_path, data['domain'] + \\\"-techniques.md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\\n\\n # Create the markdown for the enterprise groups in the STIX\\n\\n for technique in full_techniques:\\n if 'revoked' not in technique or technique['revoked'] is False:\\n generate_technique_md(technique, domain, side_menu_data, tactic_list)\",\n \"def markov_story():\\n return render_template(\\\"markovstory.html\\\")\",\n \"def test_get_analyzed_recipe_instructions(self):\\n pass\",\n \"def home() :\\n st.markdown(\\\"This application provides 3 mains modules :\\\")\\n st.markdown(\\\"* **The prediction module :** enables you to assess client's liability based on its file\\\")\\n st.markdown(\\\"* **The explorer module :** enables you to dig deeper into your client informations,\\\"\\n \\\" particularly historcial data coming from federal loan bureau and historical\\\"\\n \\\" Home Credit's data if available.\\\")\\n st.markdown(\\\"* **The statistics module** : enables you to explore the database at a macro scale :\\\"\\n \\\" understand how variables such as age, sex and income impact probability of repayment\\\")\",\n \"def test_existing_current_files(self):\\n\\n m = Mothur(**self.init_vars)\\n self.set_current_dirs(m)\\n m.summary.seqs(fasta='test_fasta_1.fasta')\\n m.summary.seqs()\\n\\n return\",\n \"def idempotence(args):\\n title, posts = parse_markdown(os.path.join(args.root, 'index.md'))\\n print_markdown(posts, title, os.path.join(args.dest, 'index.md'))\",\n \"def test_htmldir(self):\\n self.chck_triple('htmldir')\",\n \"def test_incorrect_case_file_index(data: TestData) -> None:\\n req = install_req_from_line(\\\"dinner\\\")\\n finder = make_test_finder(index_urls=[data.find_links3])\\n found = finder.find_requirement(req, False)\\n assert found is not None\\n assert found.link.url.endswith(\\\"Dinner-2.0.tar.gz\\\")\",\n \"def files_exist(self):\\n\\n passed = []\\n warned = []\\n failed = []\\n ignored = []\\n\\n # NB: Should all be files, not directories\\n # List of lists. Passes if any of the files in the sublist are found.\\n #: test autodoc\\n try:\\n _, short_name = self.nf_config[\\\"manifest.name\\\"].strip(\\\"\\\\\\\"'\\\").split(\\\"/\\\")\\n except ValueError:\\n log.warning(\\\"Expected manifest.name to be in the format '<repo>/<pipeline>'. Will assume it is '<pipeline>'.\\\")\\n short_name = self.nf_config[\\\"manifest.name\\\"].strip(\\\"\\\\\\\"'\\\").split(\\\"/\\\")\\n\\n files_fail = [\\n [\\\".gitattributes\\\"],\\n [\\\".gitignore\\\"],\\n [\\\".nf-core.yml\\\"],\\n [\\\".editorconfig\\\"],\\n [\\\".prettierignore\\\"],\\n [\\\".prettierrc.yml\\\"],\\n [\\\"CHANGELOG.md\\\"],\\n [\\\"CITATIONS.md\\\"],\\n [\\\"CODE_OF_CONDUCT.md\\\"],\\n [\\\"CODE_OF_CONDUCT.md\\\"],\\n [\\\"LICENSE\\\", \\\"LICENSE.md\\\", \\\"LICENCE\\\", \\\"LICENCE.md\\\"], # NB: British / American spelling\\n [\\\"nextflow_schema.json\\\"],\\n [\\\"nextflow.config\\\"],\\n [\\\"README.md\\\"],\\n [os.path.join(\\\".github\\\", \\\".dockstore.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"CONTRIBUTING.md\\\")],\\n [os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"bug_report.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"config.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"feature_request.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"PULL_REQUEST_TEMPLATE.md\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"branch.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"ci.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"linting_comment.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"linting.yml\\\")],\\n [os.path.join(\\\"assets\\\", \\\"email_template.html\\\")],\\n [os.path.join(\\\"assets\\\", \\\"email_template.txt\\\")],\\n [os.path.join(\\\"assets\\\", \\\"sendmail_template.txt\\\")],\\n [os.path.join(\\\"assets\\\", f\\\"nf-core-{short_name}_logo_light.png\\\")],\\n [os.path.join(\\\"conf\\\", \\\"modules.config\\\")],\\n [os.path.join(\\\"conf\\\", \\\"test.config\\\")],\\n [os.path.join(\\\"conf\\\", \\\"test_full.config\\\")],\\n [os.path.join(\\\"docs\\\", \\\"images\\\", f\\\"nf-core-{short_name}_logo_light.png\\\")],\\n [os.path.join(\\\"docs\\\", \\\"images\\\", f\\\"nf-core-{short_name}_logo_dark.png\\\")],\\n [os.path.join(\\\"docs\\\", \\\"output.md\\\")],\\n [os.path.join(\\\"docs\\\", \\\"README.md\\\")],\\n [os.path.join(\\\"docs\\\", \\\"README.md\\\")],\\n [os.path.join(\\\"docs\\\", \\\"usage.md\\\")],\\n [os.path.join(\\\"lib\\\", \\\"nfcore_external_java_deps.jar\\\")],\\n [os.path.join(\\\"lib\\\", \\\"NfcoreTemplate.groovy\\\")],\\n [os.path.join(\\\"lib\\\", \\\"Utils.groovy\\\")],\\n [os.path.join(\\\"lib\\\", \\\"WorkflowMain.groovy\\\")],\\n ]\\n\\n files_warn = [\\n [\\\"main.nf\\\"],\\n [os.path.join(\\\"assets\\\", \\\"multiqc_config.yml\\\")],\\n [os.path.join(\\\"conf\\\", \\\"base.config\\\")],\\n [os.path.join(\\\"conf\\\", \\\"igenomes.config\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"awstest.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"awsfulltest.yml\\\")],\\n [os.path.join(\\\"lib\\\", f\\\"Workflow{short_name[0].upper()}{short_name[1:]}.groovy\\\")],\\n [\\\"modules.json\\\"],\\n [\\\"pyproject.toml\\\"],\\n ]\\n\\n # List of strings. Fails / warns if any of the strings exist.\\n files_fail_ifexists = [\\n \\\"Singularity\\\",\\n \\\"parameters.settings.json\\\",\\n \\\".nf-core.yaml\\\", # yml not yaml\\n os.path.join(\\\"bin\\\", \\\"markdown_to_html.r\\\"),\\n os.path.join(\\\"conf\\\", \\\"aws.config\\\"),\\n os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"push_dockerhub.yml\\\"),\\n os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"bug_report.md\\\"),\\n os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"feature_request.md\\\"),\\n os.path.join(\\\"docs\\\", \\\"images\\\", f\\\"nf-core-{short_name}_logo.png\\\"),\\n \\\".markdownlint.yml\\\",\\n \\\".yamllint.yml\\\",\\n os.path.join(\\\"lib\\\", \\\"Checks.groovy\\\"),\\n os.path.join(\\\"lib\\\", \\\"Completion.groovy\\\"),\\n os.path.join(\\\"lib\\\", \\\"Workflow.groovy\\\"),\\n ]\\n files_warn_ifexists = [\\\".travis.yml\\\"]\\n\\n # Remove files that should be ignored according to the linting config\\n ignore_files = self.lint_config.get(\\\"files_exist\\\", [])\\n\\n def pf(file_path):\\n return os.path.join(self.wf_path, file_path)\\n\\n # First - critical files. Check that this is actually a Nextflow pipeline\\n if not os.path.isfile(pf(\\\"nextflow.config\\\")) and not os.path.isfile(pf(\\\"main.nf\\\")):\\n failed.append(\\\"File not found: nextflow.config or main.nf\\\")\\n raise AssertionError(\\\"Neither nextflow.config or main.nf found! Is this a Nextflow pipeline?\\\")\\n\\n # Files that cause an error if they don't exist\\n for files in files_fail:\\n if any([f in ignore_files for f in files]):\\n continue\\n if any([os.path.isfile(pf(f)) for f in files]):\\n passed.append(f\\\"File found: {self._wrap_quotes(files)}\\\")\\n else:\\n failed.append(f\\\"File not found: {self._wrap_quotes(files)}\\\")\\n\\n # Files that cause a warning if they don't exist\\n for files in files_warn:\\n if any([f in ignore_files for f in files]):\\n continue\\n if any([os.path.isfile(pf(f)) for f in files]):\\n passed.append(f\\\"File found: {self._wrap_quotes(files)}\\\")\\n else:\\n warned.append(f\\\"File not found: {self._wrap_quotes(files)}\\\")\\n\\n # Files that cause an error if they exist\\n for file in files_fail_ifexists:\\n if file in ignore_files:\\n continue\\n if os.path.isfile(pf(file)):\\n failed.append(f\\\"File must be removed: {self._wrap_quotes(file)}\\\")\\n else:\\n passed.append(f\\\"File not found check: {self._wrap_quotes(file)}\\\")\\n\\n # Files that cause a warning if they exist\\n for file in files_warn_ifexists:\\n if file in ignore_files:\\n continue\\n if os.path.isfile(pf(file)):\\n warned.append(f\\\"File should be removed: {self._wrap_quotes(file)}\\\")\\n else:\\n passed.append(f\\\"File not found check: {self._wrap_quotes(file)}\\\")\\n\\n # Files that are ignoed\\n for file in ignore_files:\\n ignored.append(f\\\"File is ignored: {self._wrap_quotes(file)}\\\")\\n\\n return {\\\"passed\\\": passed, \\\"warned\\\": warned, \\\"failed\\\": failed, \\\"ignored\\\": ignored}\",\n \"def main():\\n\\n # Remove the files if existing before creating new ones\\n OUT_VOCAB = \\\"results/vocabs.txt\\\"\\n OUT_SYN = \\\"results/syn.txt\\\"\\n OUT_EXCP = \\\"results/exception.txt\\\"\\n for f in [OUT_SYN, OUT_VOCAB, OUT_EXCP]:\\n if os.path.isfile(f):\\n os.remove(f)\\n\\n \\\"\\\"\\\"\\n Now we do the recursion:\\n Step 1: Find the children link;\\n Step 2: Add to the url_list;\\n Step: Move to next entry of the url_list and Go to Step 1;\\n \\\"\\\"\\\"\\n with open(DATA, \\\"r\\\") as fin:\\n url_list = fin.readlines()\\n\\n # Write down the urls as tuples, 0 as the first level\\n url_list = [((PREFIX_URL + subURL).strip(), 0) for subURL in url_list]\\n\\n with open(OUT_VOCAB, \\\"w\\\") as vocab_file, \\\\\\n open(OUT_SYN, \\\"w\\\") as syn_file, \\\\\\n open(OUT_EXCP, \\\"w\\\") as exp_file:\\n cur_index = 0\\n while cur_index < len(url_list):\\n # Current url of the term, current level number (hierarchy) of the term\\n cur_url, cur_hrc = url_list[cur_index]\\n try:\\n # Get the current sub-url\\n time.sleep(0.1)\\n r = requests.get(cur_url + \\\"?\\\" + API_KEY)\\n source_dict = json.loads(r.text)\\n\\n # Getting the vocabulary and the synonym from this page.\\n vocab, syn = parse_words(source_dict=source_dict)\\n if cur_hrc + 1 < int(level):\\n # Fetch all the children's name and url\\n children_urls = fetch_children_url(source_dict=source_dict, hrc=cur_hrc + 1)\\n # print children_urls\\n url_list = url_list + children_urls # Concatenate the lists\\n\\n if vocab is not None:\\n syn_file.write(json.dumps({vocab: syn}) + \\\"\\\\n\\\")\\n vocab_file.write(vocab + \\\"\\\\n\\\")\\n\\n if cur_index % 100 == 0:\\n print \\\"\\\\tNow at index # {}, {} in total.\\\".format(str(cur_index), str(len(url_list)))\\n except UnicodeEncodeError as unicode_err:\\n exp_file.write(str(unicode_err))\\n except KeyError as key_err:\\n exp_file.write(str(key_err))\\n except:\\n pass\\n cur_index += 1\",\n \"def main():\\n # %%\\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\\n print( f'{len(fpaths)} htmls found' )\\n # %%\\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\\n # %%\\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\\n # %%\\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\\n # %%\\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\\n # %%\\n dics = {}\\n # %%\\n\\n for i, fpath in enumerate(fpaths):\\n if fpath in dics:\\n continue\\n\\n with fpath.open('rt') as f_in:\\n html = f_in.read()\\n\\n print( f'\\\\n***{i+1}/{len(fpaths)} {fpath.name}:')\\n dic = extract_one( html, fpath )\\n dic['linkedin_url'] = f\\\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\\\"\\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\\n # pprint(dic['work_stats'])\\n dics[fpath] = dic\\n\\n dics_arr = list(dics.values())\\n # %%\\n del dics\\n # %%\\n\\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\\n # %%\\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\\n yaml.safe_dump( dics_arr, f_out )\\n # %%\\n df = produce_summary_table( dics_arr )\\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\\n index=False)\\n # %%\",\n \"def md_writer(clf, features, outcome, eval_folder,\\n config_file, summary_df, i=''):\\n if config_file.endswith('.xlsx'):\\n config = pd.read_excel(config_file, sheetname='Sheet1')\\n elif config_file.endswith('.csv'):\\n config = pd.read_csv(config_file)\\n\\n clf_params = clf.get_params()\\n clf_name = str(clf)[:str(clf).index('(')]\\n clf_img = clf_name+str(i)\\n\\n file_name = clf_name+str(i)+'_Evaluation.md'\\n\\n save_file = open(eval_folder+file_name, 'w')\\n\\n def new_line():\\n save_file.write('\\\\n')\\n\\n save_file.write('<link rel=\\\"stylesheet\\\" href=\\\"style.css\\\" type=\\\"text/css\\\" />\\\\n')\\n save_file.write('# Model Evaluation Report\\\\n')\\n new_line()\\n\\n save_file.write('## Data Configuration:\\\\n')\\n new_line()\\n save_file.write(config.to_html(na_rep='', index=False).replace('NaT', ''))\\n new_line()\\n\\n save_file.write('## Classifier Parameters: '+clf_name+'\\\\n')\\n new_line()\\n for elem in clf_params:\\n save_file.write('* {}: {}\\\\n'.format(elem, clf_params[elem]))\\n new_line()\\n\\n summary_df = summary_df.T\\n summary_df.columns = ['value']\\n\\n save_file.write('## Evaluation Metrics; Summary\\\\n')\\n new_line()\\n save_file.write(summary_df.to_html())\\n new_line()\\n\\n save_file.write('## ROC Curve\\\\n')\\n new_line()\\n save_file.write('\\\\n')\\n new_line()\\n\\n save_file.write('## Precision-Recall Curve\\\\n')\\n new_line()\\n save_file.write('\\\\n')\\n new_line()\\n\\n save_file.write('## Precision, Recall vs % Population\\\\n')\\n new_line()\\n save_file.write('\\\\n')\\n\\n if clf_name in ['LogisticRegression']:\\n save_file.write('## Coefficients\\\\n')\\n new_line()\\n for i,coef in enumerate(clf.coef_[0]):\\n save_file.write('*<b>{}: {}</b>\\\\n'.format(features[i], round(coef,4)))\\n new_line()\\n\\n if clf_name in ['WeightedQuestions']:\\n save_file.write('## Weights\\\\n')\\n new_line()\\n for i,wt in enumerate(clf.weights):\\n save_file.write('*<b>{}: {}</b>\\\\n'.format(features[i], wt))\\n new_line()\\n\\n save_file.close()\\n\\n def markdown_to_html(md_file, out_file_name=None):\\n import markdown\\n\\n with open(md_file, 'r') as f:\\n html = markdown.markdown(f.read())\\n\\n if out_file_name is None:\\n out_file_name = md_file.split('.')[0]+'.html'\\n with open(out_file_name, 'w') as f:\\n f.write(html)\\n\\n markdown_to_html(eval_folder+file_name)\",\n \"def test_pdfdir(self):\\n self.chck_triple('pdfdir')\",\n \"def test_docdir(self):\\n self.chck_triple('docdir')\",\n \"def main(\\n files: List[Path] = typer.Argument(default=None, dir_okay=False, exists=True),\\n template: Optional[str] = typer.Option(\\n None, '--template', help='Name of template file'\\n ),\\n logo: Optional[str] = typer.Option(None, '--logo', help='Name of logo file'),\\n logo_width: Optional[str] = typer.Option(\\n None, '--logo-width', help='Logo width (default 35mm)'\\n ),\\n highlight_style: Optional[str] = typer.Option(None, '--highlight-style',\\n help='Specify coloring style to be used in highlighting source code'),\\n syntax_definition: Optional[str] = typer.Option(None, '--syntax-definition',\\n help='Specify a directory which contains syntax definition files'),\\n no_toc: bool = typer.Option(\\n False, '--no-toc', help='table of contents in PDF document'\\n ),\\n no_number_sections: bool = typer.Option(False, '--no-number-sections', help='no section numbering'),\\n\\n no_titlepage: bool = typer.Option(False, '--no-titlepage', help='title in PDF document'),\\n tex_file: bool = typer.Option(\\n False, '--tex', help='create TeX file instead of PDF document'\\n ),\\n email: Optional[str] = typer.Option(None, '--email', help='Author email'),\\n company: Optional[str] = typer.Option(None, '--company', help='Name of company'),\\n department: Optional[str] = typer.Option(\\n None, '--department', help='Name of department'\\n ),\\n confidential: bool = typer.Option(\\n False, '--confidential', help='indicate confidential'\\n ),\\n debug: bool = typer.Option(False, '--debug', help='turns debugging on'),\\n pdf_engine: str = typer.Option(\\n 'xelatex',\\n '--pdf-engine',\\n help='Specify pdf engine, one of lualatex, xelatex or tectonic ',\\n ),\\n _version: bool = typer.Option(\\n None, '-V', '--version', callback=version_callback, help='Show version and exit'\\n ),\\n):\\n\\n if not files:\\n typer.echo('Error: Must specify at least one .md file.')\\n raise typer.Abort()\\n\\n mdfiles: List[str] = [str(md) for md in files]\\n\\n template = template or os.environ.get('MD2PDF_TEMPLATE')\\n if template is None:\\n print('No template specified')\\n sys.exit(1)\\n\\n email = email or os.environ.get('MD2PDF_AUTHOR_EMAIL')\\n footer_center = ''\\n\\n # command line overwrites `MD2PDF_PDF_ENGINE`. if both are not given\\n # then `xelatex` is the default\\n pdf_engine = pdf_engine or os.environ.get('MD2PDF_PDF_ENGINE') or 'xelatex'\\n # check that pdf-engine is one of the following\\n if pdf_engine not in ['xelatex', 'lualatex', 'tectonic']:\\n print('--pdf-engine must be one of \\\"xelatex\\\", \\\"lualatex\\\", \\\"tectonic\\\"')\\n sys.exit(1)\\n\\n ext = '.pdf'\\n if tex_file:\\n ext = '.tex'\\n\\n if len(mdfiles) == 1:\\n toml_file = os.path.splitext(mdfiles[0])[0] + '.toml'\\n\\n if os.path.exists(toml_file):\\n print(f'TOML file {toml_file} found')\\n parsed_toml = toml.load(toml_file)\\n default_val = parsed_toml.get('default')\\n if default_val is None:\\n print(f'No file names found in {toml_file}')\\n else:\\n mdfiles = default_val.get('files')\\n\\n for mdf in mdfiles:\\n print(f'Compiling {mdf}')\\n\\n main_mdfile = os.path.realpath(mdfiles[0])\\n\\n outfile = Path(main_mdfile).stem + ext\\n\\n year = date.today().year\\n\\n company = company or os.environ.get('MD2PDF_COMPANY')\\n department = department or os.environ.get('MD2PDF_DEPARTMENT')\\n\\n if company:\\n if confidential:\\n footer_center = f'© Copyright {year} {company}'\\n else:\\n footer_center = f'{year} {company}'\\n\\n pdcmd = PandocCmd(outfile)\\n pdcmd.append(f'--template={template}')\\n pdcmd.append(f'--pdf-engine={pdf_engine}')\\n\\n pdcmd.set_v('footer-center', footer_center)\\n pdcmd.set_v('company', company)\\n pdcmd.set_v('department', department)\\n\\n syntax_definition = syntax_definition or os.environ.get('MD2PDF_SYNTAX_DEFINITION_DIR')\\n if syntax_definition is not None:\\n add_syntax_definition(pdcmd, syntax_definition)\\n\\n pdcmd.append('--highlight-style')\\n highlight_style = highlight_style or os.environ.get('MD2PDF_HIGHLIGHT_STYLE')\\n if highlight_style is None:\\n pdcmd.append('pygments')\\n else:\\n check_highlight_style(highlight_style)\\n pdcmd.append(highlight_style)\\n\\n if not no_number_sections:\\n pdcmd.append('--number-sections')\\n\\n if no_titlepage:\\n pdcmd.set_m('titlepage', 'false')\\n\\n logo = logo or os.environ.get('MD2PDF_LOGO')\\n pdcmd.set_v('logo', logo)\\n\\n logo_width = logo_width or os.environ.get('MD2PDF_LOGO_WIDTH')\\n pdcmd.set_v('logo-width', logo_width)\\n\\n pdcmd.set_m('email', email)\\n\\n if not no_toc:\\n pdcmd.append('--toc')\\n\\n pdcmd.extend(mdfiles)\\n\\n if debug:\\n print(' '.join(pdcmd.pandoc))\\n\\n\\n pdcmd.run()\",\n \"def test_examples():\\n tests = [d for d in listdir(ex) if path.isdir(path.join(ex, d))]\\n for d in tests:\\n yield check_examples, d\",\n \"def test_man1dir(self):\\n self.chck_triple('man1dir')\",\n \"def _docs():\\n url = \\\"https://vanheeringen-lab.github.io/seq2science\\\"\\n if not webbrowser.open(url):\\n print(url)\",\n \"def test_analyze_recipe_instructions(self):\\n pass\",\n \"def test_path04(self):\\n index_file = os.path.join(self.test_input, 'index.html')\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_item, = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}/index.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n })\",\n \"def test_path02(self):\\n index_file = os.path.join(self.test_input, 'folder1#中文', 'folder2', 'mypage.html')\\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_folder1, id_folder2, id_item = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_folder1: {\\n 'title': 'folder1#中文',\\n 'type': 'folder',\\n 'create': id_folder1,\\n 'modify': id_folder1,\\n },\\n id_folder2: {\\n 'title': 'folder2',\\n 'type': 'folder',\\n 'create': id_folder2,\\n 'modify': id_folder2,\\n },\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}/index.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_folder1,\\n ],\\n id_folder1: [\\n id_folder2,\\n ],\\n id_folder2: [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n os.path.join(self.test_output, id_item, 'mypage.html'),\\n })\",\n \"def get_example():\\n ex_dir = os.path.join(app.config['TEMPLATE_DIR'], \\\"inline_code\\\")\\n app.logger.debug(\\\"Example directory is {}\\\".format(ex_dir))\\n return ex_dir\",\n \"def test_path01(self):\\n index_file = os.path.join(self.test_input, 'folder1#中文', 'folder2', 'folder_data', 'index.html')\\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_folder1, id_folder2, id_item = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_folder1: {\\n 'title': 'folder1#中文',\\n 'type': 'folder',\\n 'create': id_folder1,\\n 'modify': id_folder1,\\n },\\n id_folder2: {\\n 'title': 'folder2',\\n 'type': 'folder',\\n 'create': id_folder2,\\n 'modify': id_folder2,\\n },\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}/index.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_folder1,\\n ],\\n id_folder1: [\\n id_folder2,\\n ],\\n id_folder2: [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n })\",\n \"def read_hansard(train_dir, num_sentences):\\n \\\"\\\"\\\"\\n # TODO\\n # Get starting files from directory:\\n # Get file num\\n # If file language is english get french with same num, & vice versa\\n # load files into two lists: curr_english, curr_french\\n # while count < num_sentences:\\n # if index >= len(curr_english):\\n # load two new files into curr_english and curr_french\\n # make sure to keep track of files already read\\n # index = 0 \\n # sentences['e'][count] = preprocess(curr_english[index])\\n # sentences['f'][count] = preprocess(curr_french[index])\\n\\n #====================================\\n # Return (eng, fre) version:\\n # Get starting files from directory:\\n # Get file num\\n # If file language is english get french with same num, & vice versa\\n # load files into two lists: curr_english, curr_french\\n # while count < num_sentences:\\n # if index >= min(len(curr_english), len(curr_french)):\\n # load two new files into curr_english and curr_french\\n # make sure to keep track of files already read\\n # index = 0\\n # preprocess and remove SENTSTART and SENTEND from the sentences\\n # eng[count] = eng_sentence.split()\\n # fre[count] = fre_sentence.split()\\n # return (eng, fre)\\n \\\"\\\"\\\"\\n\\n files_examined = set()\\n count = 0\\n eng = []\\n fre = []\\n\\n # for subdir, dirs, files in os.walk(train_dir):\\n # for file in files:\\n\\n files = os.listdir(train_dir)\\n for file in files:\\n\\n # First set up and validate the files\\n file_name, extension = os.path.splitext(file)\\n file_name, file_id = os.path.splitext(file_name)\\n\\n # Skip if not .e or .f file\\n if not (extension == '.f' or extension == '.e'):\\n continue\\n\\n # Skip if already examined this file pair\\n if file_id in files_examined:\\n continue\\n\\n # Skip if either language file is not available\\n eng_file = file_name + file_id + '.e'\\n fre_file = file_name + file_id + '.f'\\n if eng_file not in files or fre_file not in files:\\n continue\\n\\n # If it reaches here we know we can process it\\n files_examined.add(file_id)\\n print( \\\"Reading \\\" + str(count+1))\\n\\n # Finally open files and iterate simultaneously\\n eng_path = os.path.join(train_dir, eng_file)\\n fre_path = os.path.join(train_dir, fre_file)\\n with open(eng_path) as english:\\n with open(fre_path) as french:\\n for E, F in zip(english, french):\\n\\n # Stop when limit reached\\n if count >= num_sentences:\\n return (eng, fre)\\n\\n # Process and split sentences\\n E = preprocess(E.rstrip(), 'e')\\n F = preprocess(F.rstrip(), 'f')\\n\\n E_words = E.split()\\n F_words = F.split()\\n\\n eng.append(E_words)\\n fre.append(F_words)\\n\\n count += 1\\n\\n return (eng, fre)\",\n \"def test_examples(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.nap.Base.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n for item in data['items']:\\n if item['uid'] == 'example.nap.Base.ref':\\n self.assertEqual(\\n item['example'].split('\\\\n')[2],\\n \\\"\\\"\\\">>> print('docblock 1')\\\"\\\"\\\"\\n )\\n self.assertEqual(\\n item['example'].split('\\\\n')[7],\\n \\\"\\\"\\\">>> print('docblock 2')\\\"\\\"\\\"\\n )\",\n \"def do_docs(self, path):\\n print(\\\"scaraping documentation\\\")\\n for p in path.glob(\\\"**/*\\\"):\\n if p.is_file():\\n parts = p.relative_to(path).parts\\n if parts[-1].endswith(\\\"rst\\\"):\\n data = tsparse(p.read_bytes())\\n blob = DocBlob()\\n blob.arbitrary = data\\n blob.content = {}\\n\\n blob.ordered_sections = []\\n blob.item_file = None\\n blob.item_line = None\\n blob.item_type = None\\n blob.aliases = []\\n blob.example_section_data = Section()\\n blob.see_also = []\\n blob.signature = None\\n blob.references = None\\n blob.refs = []\\n\\n self.docs[parts] = json.dumps(blob.to_json(), indent=2)\\n else:\\n pass\\n # data = p.read_bytes()\",\n \"def test_noDocumentsFound(self):\\n self.assertRaises(NoDocumentsFound, self.builder.build, self.manDir)\",\n \"def test_man4dir(self):\\n self.chck_triple('man4dir')\",\n \"def problem4() -> None:\\n \\n # Obtain the dataframe containing lines from the articles and the keyword\\n # with which the article is associated.\\n try:\\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \\n except FileNotFoundError:\\n raise Exception(\\\"[ERROR] The 'articles' folder was not found. Please \\\"\\n \\\"ensure that the functions for problems 1 and 2 have \\\"\\n \\\"been applied first.\\\")\\n \\n keywords, corpus = separate_keywords_corpus(kw_df)\\n \\n # Complete the preprocessing for the corpus for comparison's sake\\n w2vp = W2VPreprocessor(keywords, corpus)\\n \\n # Display the counts of sentences collected associated with each keyword\\n keywords_countplot(kw_df)\\n \\n # Create displots for the lengths of sentences in the corpus\\n displot_all_sentence_lengths(kw_df)\\n displot_sentence_lengths_per_keyword(kw_df)\\n \\n # Visualise the semantic distances\\n try:\\n distances = read_in_distance_matrix()\\n except FileNotFoundError:\\n raise Exception(\\\"[ERROR] The distance matrix excel file was could not \\\"\\n \\\"be found! Please ensure the methods for the \\\"\\n \\\"preceding problems have been run first.\\\")\\n visualise_distances(distances)\\n \\n # Visualise word frequency before and after preprocessing\\n visualise_initial_most_frequent_words(kw_df)\\n visualise_preprocessed_most_frequent_words(w2vp.corpus)\",\n \"def testExampleFileGeneration(ref):\\n outdir = ref.tmp_dir\\n outpath = os.path.join(outdir, 'file_result.html')\\n generate_file(outpath)\\n ref.assertTextFileCorrect(outpath, 'file_result.html',\\n ignore_substrings=['Copyright', 'Version'])\",\n \"def test_supporting_folder03(self):\\n content = \\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n<img src=\\\"index.files/picture.bmp\\\">\\n</body>\\n</html>\\n\\\"\\\"\\\"\\n index_file = os.path.join(self.test_input, 'index.html')\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(content)\\n img_file = os.path.join(self.test_input, 'index.files', 'picture.bmp')\\n os.makedirs(os.path.dirname(img_file), exist_ok=True)\\n with open(img_file, 'wb') as fh:\\n fh.write(b'dummy')\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_item, = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}/index.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n os.path.join(self.test_output, id_item, 'index.files'),\\n os.path.join(self.test_output, id_item, 'index.files', 'picture.bmp'),\\n })\\n with open(os.path.join(self.test_output, id_item, 'index.html'), encoding='UTF-8') as fh:\\n self.assertEqual(fh.read(), content)\",\n \"def cli(input_directory: str, output_directory: str = 'docs',\\n style: str = 'md'):\\n input_path = Path(input_directory)\\n output_path = Path(output_directory)\\n parsed_modules = find_and_extract(input_path)\\n builder = MarkdownBuilder()\\n builder.setting()\\n builder.build(parsed_modules)\\n builder.save(output_path)\",\n \"def machinelearn():\\n return render_template('machinelearn.html')\",\n \"def evaluation():\\n file = open('./contest/content/evaluation.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Evaluation', \\n content=content)\",\n \"def test_session10_readme_proper_description():\\n READMELOOKSGOOD = True\\n f = open(\\\"README.md\\\", \\\"r\\\", encoding=\\\"utf-8\\\")\\n content = f.read()\\n f.close()\\n for c in README_CONTENT_CHECK_FOR:\\n if c not in content:\\n print(c)\\n READMELOOKSGOOD = False\\n pass\\n assert READMELOOKSGOOD == True, \\\"You have not described all the functions/class well in your README.md file\\\"\",\n \"def test_pep8_conformance_example(self):\\n\\n print(\\\"\\\\r\\\\n\\\")\\n\\n # Get the path to current directory\\n path = os.path.dirname(os.path.realpath(__file__))\\n path += \\\"/../docs/examples/\\\"\\n\\n # Find all the examples files\\n file_paths = []\\n for root, dirnames, filenames in os.walk(path):\\n for file_path in fnmatch.filter(filenames, '*.py'):\\n file_paths.append(os.path.join(root, file_path))\\n\\n for path in file_paths:\\n self.run_check(path)\",\n \"def rules():\\n file = open('./contest/content/rules.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Rules', \\n content=content)\",\n \"def main():\\n for file_name in os.listdir(CONTENT_FOLDER):\\n if file_name.endswith('.html'):\\n try_generate_page(file_name)\",\n \"def test_other01(self):\\n index_file = os.path.join(self.test_input, 'folder1#中文', 'mypage.txt')\\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write('ABC 中文')\\n ts = datetime(2020, 1, 2, 3, 4, 5, 67000, tzinfo=timezone.utc).timestamp()\\n os.utime(index_file, (ts, ts))\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_folder1, id_item = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_folder1: {\\n 'title': 'folder1#中文',\\n 'type': 'folder',\\n 'create': id_folder1,\\n 'modify': id_folder1,\\n },\\n id_item: {\\n 'title': 'mypage.txt',\\n 'type': 'file',\\n 'index': f'{id_item}/index.html',\\n 'create': id_item,\\n 'modify': '20200102030405067',\\n 'source': '',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_folder1,\\n ],\\n id_folder1: [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n os.path.join(self.test_output, id_item, 'mypage.txt'),\\n })\",\n \"def analyse() -> 'html':\\n storeFileName = ''\\n if request.method == 'POST':\\n \\n if 'file' not in request.files:\\n return render_mainpageerror('No file selected')\\n \\n file = request.files['file']\\n if file.filename == '':\\n return render_mainpageerror('Empty file name Please select a File to upload')\\n \\n if file and not allowed_file(file.filename):\\n storeFileName = filename = secure_filename(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n return render_mainpageerror('Not allowed file format')\\n \\n if ' ' in file.filename:\\n return render_mainpageerror('Please remove withespace from file name before uploading')\\n \\n if file and allowed_file(file.filename):\\n originalFilename = file.filename\\n storeFileName = filename = secure_filename(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n \\n \\n if len(storeFileName) == 0:\\n return render_template('mainpage.html',\\n title='Word association')\\n else:\\n wordsfromfile = readFile.red_uploaded_file(storeFileName)\\n #print(wordsfromfile, 'The ans from pdf')\\n if wordsfromfile == 'STOP':\\n return render_mainpageerror('Cant find Encoding on PDF document')\\n size = get_store()\\n if not check_fileName(size):\\n \\\"\\\"\\\"Analyse first time building up JSON file\\\"\\\"\\\"\\n keyWords = readFile.read_JSON()\\n allJson = readFile.load_JSON()\\n data = Counter(wordsfromfile)\\n printResultsExluds = []\\n container = {}\\n container['size'] = size\\n container['file'] = storeFileName\\n toDB = []\\n for k, v in sorted(data.items()):\\n if k in keyWords:\\n string = allJson[k][:3]\\n toDB.append({k : v})\\n toDB.append(string)\\n else:\\n value = str(v)\\n keystr = k + '(' + value + ')'\\n printResultsExluds.append(keystr)\\n for w in printResultsExluds:\\n toDB.append({w:'was not found in the associations list'})\\n\\n container['files'] = toDB \\n js = json.dumps(container)\\n print(js)\\n c = get_client()\\n c.insert(json.loads(js))\\n print('insert')\\n \\\"\\\"\\\"If analysed first time\\\"\\\"\\\"\\n return pr(size, 'Analysed first time')\\n else:\\n \\\"\\\"\\\"If already Analysed\\\"\\\"\\\"\\n return pr(size,'Already analysed')\",\n \"def test_path03(self):\\n index_file = os.path.join(self.test_input, 'folder1#中文', 'folder2', 'mypage.html')\\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n\\n for _info in file2wsb.run(self.test_input, self.test_output, no_preserve_filename=True):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_folder1, id_folder2, id_item = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_folder1: {\\n 'title': 'folder1#中文',\\n 'type': 'folder',\\n 'create': id_folder1,\\n 'modify': id_folder1,\\n },\\n id_folder2: {\\n 'title': 'folder2',\\n 'type': 'folder',\\n 'create': id_folder2,\\n 'modify': id_folder2,\\n },\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_folder1,\\n ],\\n id_folder1: [\\n id_folder2,\\n ],\\n id_folder2: [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, f'{id_item}.html'),\\n })\",\n \"def analyze_test_image_corpus(args):\\n\\n # First: index the existing corpus\\n\\n name_to_refs = {}\\n\\n for refdir in glob(os.path.join(DATA, 'refimg_*')):\\n name = os.path.basename(refdir).replace('refimg_', '')\\n refs = {}\\n\\n for p in os.listdir(refdir):\\n if not p.endswith('.png'):\\n continue\\n\\n refs[p.replace('.png', '')] = set()\\n\\n name_to_refs[name] = refs\\n\\n # Now trawl the samples and see which refimgs we hit\\n\\n def directory_to_paths(dir_path):\\n for filename in os.listdir(dir_path):\\n name = filename.replace('.png', '')\\n if name in name_to_refs:\\n yield name, os.path.join(dir_path, filename)\\n\\n def zip_to_paths(zip_path):\\n from zipfile import ZipFile\\n\\n with tempfile.TemporaryDirectory() as tmpdir, ZipFile(zip_path) as zip:\\n for zipname in zip.namelist():\\n zip.extract(zipname, tmpdir)\\n name = os.path.basename(zipname).replace('.png', '')\\n if name in name_to_refs:\\n yield name, os.path.join(tmpdir, zipname)\\n\\n for path in args:\\n if os.path.isdir(path):\\n paths = directory_to_paths(path)\\n sampname = os.path.basename(path)\\n elif path.endswith('.zip'):\\n paths = zip_to_paths(path)\\n sampname = os.path.basename(path).replace('.zip', '')\\n else:\\n raise Exception(f'don\\\\'t know how to handle input path `{path}`')\\n\\n for name, imgpath in paths:\\n refs = name_to_refs[name]\\n refnames = sorted(refs.keys())\\n found_it = False\\n results = []\\n\\n for refname in refnames:\\n refpath = os.path.join(DATA, 'refimg_' + name, refname + '.png')\\n rv = compare_images(\\n refpath,\\n imgpath,\\n tol=IMAGE_COMPARISON_TOLERANCE,\\n in_decorator=True\\n )\\n\\n if rv is None:\\n refs[refname].add(sampname)\\n found_it = True\\n break\\n\\n failpath = imgpath.replace('.png', '-failed-diff.png')\\n os.unlink(failpath)\\n results.append((refname, rv['rms']))\\n\\n if not found_it:\\n print(f'no refimg found for {sampname}::{name}:', ', '.join('%s=%.2f' % t for t in results))\\n\\n # Now report\\n\\n print()\\n print('Report:')\\n any_reports = False\\n\\n for name in sorted(name_to_refs.keys()):\\n refs = name_to_refs[name]\\n\\n for refname in sorted(refs.keys()):\\n refdata = refs[refname]\\n\\n if not refdata:\\n print(f'- no hits to {name}::{refname}')\\n any_reports = True\\n\\n if not any_reports:\\n print('- no suggested modifications')\",\n \"def test_html_documentation(self):\\n app = Sphinx(\\n self.source_dir,\\n self.config_dir,\\n self.output_dir,\\n self.doctree_dir,\\n buildername='html',\\n warningiserror=True,\\n )\\n app.build(force_all=self.all_files)\",\n \"def write_to_md(dictData, outputDirectory):\\n\\tdic = prepare_hw_dict(dictData)\\n\\tfor hw in dic:\\n\\t\\tfileout = os.path.join(outputDirectory, hw+'.md')\\n\\t\\t# Prepare the output file\\n\\t\\tfout = codecs.open(fileout, 'w', 'utf-8')\\n\\t\\t# Write frontmatter\\n\\t\\tfout.write('---\\\\ntitle: \\\"'+hw+'\\\"\\\\n---\\\\n\\\\n')\\n\\t\\t# For each (headword, meanings, verseNumber, PageNum) tuples,\\n\\t\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dic[hw]:\\n\\t\\t\\tcommaed = ', '.join(meanings)\\n\\t\\t\\tverse = verse.replace('<BR>', '<br />')\\n\\t\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\t\\tfout.write('# ' + hw + '\\\\n## ' + commaed + '\\\\n' + verse + '<br />verse ' + verseNumDetails + '<br />page ' + pageNumDetails +'\\\\n\\\\n')\\n\\t\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('MD files generated. Success!')\\n\\tprint('{} separate .md files written, one per headword.'.format(len(dic)))\",\n \"def test_readme_contents():\\n readme = open(\\\"README.md\\\", \\\"r\\\", encoding=\\\"utf-8\\\")\\n readme_words = readme.read().split()\\n readme.close()\\n assert len(readme_words) >= 500, \\\"Make your README.md file interesting! Add atleast 500 words\\\"\",\n \"def collect():\\n # catch possible bug with --dashes.md\\n os.chdir(pathlib.Path(__file__).resolve().parent / 'test')\\n for filename in glob.glob('*.md'):\\n stem, _ = os.path.splitext(filename)\\n args = ['../readme.py', '--timeout=1', '--', filename]\\n result = cmd(args)\\n obj = normalize(result['output'])\\n tmp = obj['tmp']\\n lisp = slurp(tmp) if tmp else None\\n yield stem, result['exit_code'], obj['out'], lisp\",\n \"def scan_sample_directory(sample_dir: Path) -> None:\\n if not (sample_dir / 'README.md').is_file():\\n print(f\\\"WARNING ({sample_dir}): No README.md file\\\")\\n if not (sample_dir / 'main.py').is_file():\\n print(f\\\"ERROR ({sample_dir}): No main.py file\\\")\",\n \"def test_yaml_tech_file(self) -> None:\\n tech_yaml = \\\"\\\"\\\"\\nname: My Technology Library\\ninstalls:\\n - path: test\\n base var: \\\"\\\" # means relative to tech dir\\nlibraries: []\\n \\\"\\\"\\\"\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n\\n tech_yaml_filename = os.path.join(tech_dir, \\\"dummy28.tech.yml\\\")\\n with open(tech_yaml_filename, \\\"w\\\") as f: # pylint: disable=invalid-name\\n f.write(tech_yaml)\\n sys.path.append(tech_dir_base)\\n tech_opt = hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir)\\n self.assertFalse(tech_opt is None, \\\"Unable to load technology\\\")\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\",\n \"def test_malware_have_techniques(attck_fixture):\\n for malware in attck_fixture.enterprise.malwares:\\n if malware.techniques:\\n assert getattr(malware,'techniques')\",\n \"def readme_tests(readme=None):\\n if readme is None:\\n readme = Path('..', 'readme.md')\\n with open(readme) as target:\\n for line in target:\\n if line.startswith(' ') and 'test_utils' not in line:\\n print()\\n print(line)\\n input('Press Enter to execute')\\n sb.run(line, shell=True, check=True)\",\n \"def __init__(self, path: str, tg: bool) -> None:\\n if tg:\\n self.path = os.path.join(path, 'processed_corpus')\\n self.path_in_tokens = os.path.join(self.path, 'pp_token_corpus.txt')\\n self.path_in_lemmas = os.path.join(self.path, 'pp_lemma_corpus.txt')\\n self.path_out_corpus = os.path.join(path, 'processed_corpus')\\n self.path_out_idx_tokens = os.path.join(\\n self.path_out_corpus, 'token_idx_corpus.txt')\\n self.path_out_idx_lemmas = os.path.join(\\n self.path_out_corpus, 'lemma_idx_corpus.txt')\\n self.path_out_indexing = os.path.join(path, 'indexing')\\n self.path_token_to_idx = os.path.join(\\n self.path_out_indexing, 'token_to_idx.json')\\n self.path_lemma_to_idx = os.path.join(\\n self.path_out_indexing, 'lemma_to_idx.json')\\n self.path_idx_to_token = os.path.join(\\n self.path_out_indexing, 'idx_to_token.json')\\n self.path_idx_to_lemma = os.path.join(\\n self.path_out_indexing, 'idx_to_lemma.json')\\n\\n self.path_token_terms = os.path.join(\\n self.path_out_corpus, 'token_terms.txt')\\n self.path_lemma_terms = os.path.join(\\n self.path_out_corpus, 'lemma_terms.txt')\\n\\n self.path_idx_token_terms = os.path.join(\\n self.path_out_corpus, 'token_terms_idxs.txt')\\n self.path_idx_lemma_terms = os.path.join(\\n self.path_out_corpus, 'lemma_terms_idxs.txt')\\n\\n self.path_token_contains = os.path.join(\\n self.path_out_corpus, 'token_contains.json')\\n self.path_lemma_contains = os.path.join(\\n self.path_out_corpus, 'lemma_contains.json')\\n\\n self.path_hierarch_rels_tokens = os.path.join(\\n path, 'hierarchy/hierarchical_relations_tokens.json')\\n self.path_hierarch_rels_lemmas = os.path.join(\\n path, 'hierarchy/hierarchical_relations_lemmas.json')\\n self.path_hierarch_rels_tokens_idx = os.path.join(\\n path, 'hierarchy/hierarch_rels_tokens_idx.json')\\n self.path_hierarch_rels_lemmas_idx = os.path.join(\\n path, 'hierarchy/hierarch_rels_lemmas_idx.json')\\n else:\\n self.path = os.path.join(path, 'processed_corpus')\\n self.path_in_tokens = os.path.join(self.path,\\n 'pp_token_corpus_non_tg.txt')\\n self.path_in_lemmas = os.path.join(self.path,\\n 'pp_lemma_corpus_non_tg.txt')\\n self.path_out_corpus = os.path.join(path, 'processed_corpus')\\n self.path_out_idx_tokens = os.path.join(\\n self.path_out_corpus, 'token_idx_corpus_non_tg.txt')\\n self.path_out_idx_lemmas = os.path.join(\\n self.path_out_corpus, 'lemma_idx_corpus_non_tg.txt')\\n self.path_out_indexing = os.path.join(path, 'indexing')\\n self.path_token_to_idx = os.path.join(\\n self.path_out_indexing, 'token_to_idx_non_tg.json')\\n self.path_lemma_to_idx = os.path.join(\\n self.path_out_indexing, 'lemma_to_idx_non_tg.json')\\n self.path_idx_to_token = os.path.join(\\n self.path_out_indexing, 'idx_to_token_non_tg.json')\\n self.path_idx_to_lemma = os.path.join(\\n self.path_out_indexing, 'idx_to_lemma_non_tg.json')\\n\\n self.path_token_terms = os.path.join(\\n self.path_out_corpus, 'token_terms_non_tg.txt')\\n self.path_lemma_terms = os.path.join(\\n self.path_out_corpus, 'lemma_terms_non_tg.txt')\\n\\n self.path_idx_token_terms = os.path.join(\\n self.path_out_corpus, 'token_terms_idxs_non_tg.txt')\\n self.path_idx_lemma_terms = os.path.join(\\n self.path_out_corpus, 'lemma_terms_idxs_non_tg.txt')\\n\\n self.path_token_contains = os.path.join(\\n self.path_out_corpus, 'token_contains_non_tg.json')\\n self.path_lemma_contains = os.path.join(\\n self.path_out_corpus, 'lemma_contains_non_tg.json')\\n\\n self.path_hierarch_rels_tokens = os.path.join(\\n path, 'hierarchy/hierarchical_relations_tokens_non_tg.json')\\n self.path_hierarch_rels_lemmas = os.path.join(\\n path, 'hierarchy/hierarchical_relations_lemmas_non_tg.json')\\n self.path_hierarch_rels_tokens_idx = os.path.join(\\n path, 'hierarchy/hierarch_rels_tokens_idx_non_tg.json')\\n self.path_hierarch_rels_lemmas_idx = os.path.join(\\n path, 'hierarchy/hierarch_rels_lemmas_idx_non_tg.json')\\n\\n self._file_write_threshhold = 10000\\n self._docs_processed = 0\\n self._already_written_to_file = False\\n self._start_time = 0\\n self._upper_bound = None\\n self.subtoken_index = {}\\n self.sublemma_index = {}\",\n \"def _create_index_file(\\n root_dir, location, image_files, video_files, dirs, force_no_processing=False):\\n # Put together HTML as a list of the lines we'll want to include\\n # Issue #2 exists to do this better than HTML in-code\\n header_text = 'imageMe: {0} [{1} image(s)] [{2} video(s)]'.format(\\n location, str(len(image_files)), str(len(video_files))\\n )\\n html = [\\n '<!DOCTYPE html>',\\n '<html>',\\n ' <head>',\\n ' <title>imageMe</title>'\\n ' <style>',\\n ' html, body {margin: 0; padding: 0;}',\\n ' .table {align: center;}',\\n ' .content {',\\n ' padding: 3em;',\\n ' padding-left: 4em;',\\n ' padding-right: 4em;',\\n ' }',\\n ' .image {max-width: 100%; border-radius: 0.3em;}',\\n ' td {width: ' + str(100.0 / args.column) + '%;}',\\n ' </style>',\\n ' </head>',\\n ' <body>',\\n ' <div class=\\\"content\\\">',\\n ' <h2 class=\\\"header\\\">' + header_text + '</h2>'\\n ]\\n\\n # Populate the present subdirectories - this includes '..' unless we're at\\n # the top level\\n directories = []\\n if root_dir != location:\\n directories = ['..']\\n directories += dirs\\n if len(directories) > 0:\\n html.append('<hr>')\\n # For each subdirectory, include a link to its index file\\n for directory in directories:\\n link = directory + '/' + args.index_file_name\\n html += [\\n ' <h3>',\\n ' <a href=\\\"' + link + '\\\">' + directory + '</a>',\\n ' </h3>'\\n ]\\n\\n files = sorted(image_files + video_files)\\n if args.separate_image_and_video:\\n files = image_files + [None] + video_files\\n\\n # Populate the gallery table\\n if files:\\n # Counter to cycle down through table rows\\n table_column_count = 1\\n html += ['<hr>', '<table>']\\n\\n # For each file, potentially create a new <tr> and create a new <td>\\n for file in files:\\n if table_column_count == 1:\\n html.append('<tr>')\\n\\n if file in video_files:\\n html += [\\n '<td>',\\n ' <video controls preload width=\\\"100%\\\">',\\n ' <source src=\\\"' + file + '\\\">',\\n ' Your browser does not support HTML5 video.'\\n ' </video>',\\n '</td>'\\n ]\\n\\n if file in image_files:\\n img_src = _get_thumbnail_src_from_file(\\n location, file, force_no_processing\\n )\\n link_target = _get_image_link_target_from_file(\\n location, file, force_no_processing\\n )\\n html += [\\n '<td>',\\n ' <a href=\\\"' + link_target + '\\\">',\\n ' <img class=\\\"image\\\" src=\\\"' + img_src + '\\\">',\\n ' </a>',\\n '</td>'\\n ]\\n\\n if table_column_count == args.column or file == None:\\n table_column_count = 0\\n html.append('</tr>')\\n\\n table_column_count += 1\\n\\n if table_column_count != 1:\\n html += ['</tr>']\\n html += ['</table>']\\n\\n html += [\\n ' </div>',\\n ' </body>',\\n '</html>'\\n ]\\n\\n # Actually create the file, now we've put together the HTML content\\n index_file_path = _get_index_file_path(location)\\n print('Creating index file %s' % index_file_path)\\n index_file = open(index_file_path, 'w')\\n index_file.write('\\\\n'.join(html))\\n index_file.close()\\n\\n # Return the path for cleaning up later\\n return index_file_path\",\n \"def makeHTMLIndexFile(self):\\n part1 = \\\"\\\"\\\"<html>\\n <body>\\n <title>Index</title>\\n <div id=\\\"pg_body\\\">\\n <div id=\\\"testSuitesTitle\\\">TestSuites</div>\\n <div id=\\\"resultsTitle\\\">Results</div>\\n <div id=\\\"testSuites\\\">\\n \\\"\\\"\\\"\\n part2 = self.makeLinks()\\n part3 = \\\"\\\"\\\"</div>\\n <div id=\\\"results\\\">\\n <iframe id=\\\"loadHTMLResults\\\" name=\\\"loadHTMLResults\\\" frameborder=\\\"0\\\" src=\\\"statistics.html\\\" style=\\\"height:100%;width:100%;\\\"></iframe>\\n </div>\\n <div id=\\\"footer\\\">Test Engineer(s) :\\\"\\\"\\\"+testEngineers+\\\"\\\"\\\"</div>\\n </div>\\n </body>\\n </html>\\n\\t\\t<style>\\n\\t\\tbody{\\n margin:0;\\n }\\n\\t\\t#pg_body{\\n\\t\\twidth=100%;\\n\\t\\ttext-align:center;\\n\\t\\t}\\n\\t\\t#testSuitesTitle{\\n\\t\\twidth:25%;\\n\\t\\tfloat:left;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\tfont-weight:bold;\\n\\t\\tcolor:white;\\n\\t\\t}\\n\\t\\t#resultsTitle{\\n\\t\\twidth:75%;\\n\\t\\tfloat:right;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\tfont-weight:bold;\\n\\t\\tcolor:white;\\n\\t\\t}\\n\\t\\t#testSuites{\\n\\t\\twidth:25%;\\n\\t\\tfloat:left;\\n\\t\\tbackground-color:lightgrey;\\n\\t\\tfont-weight:bold;\\n\\t\\ttext-align:left;\\n\\t\\theight:94%;\\n\\t\\toverflow:scroll;\\n\\t\\t}\\n\\t\\t#results{\\n\\t\\twidth:75%;\\n\\t\\tbackground-color:white;\\n\\t\\tfloat:right;\\n\\t\\ttext-align:left;\\n\\t\\theight:94%;\\n\\t\\toverflow:scroll;\\n\\t\\t}\\n\\t\\t#footer{\\n\\t\\twidth:100%;\\n\\t\\ttext-align:left;\\n\\t\\tcolor:lightgrey;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\t}\\n\\t\\t</style>\\n \\\"\\\"\\\"\\n \\n page = (part1+part2+part3)\\n f = open(self.dir+'/index.html','w')\\n f.write(page)\\n f.close\",\n \"def test_readme_proper_description():\\n READMELOOKSGOOD = True\\n f = open(\\\"README.md\\\", \\\"r\\\", encoding=\\\"utf-8\\\")\\n content = f.read()\\n f.close()\\n for c in README_CONTENT_CHECK_FOR:\\n if c not in content:\\n print(c)\\n READMELOOKSGOOD = False\\n break\\n\\n assert READMELOOKSGOOD is True, \\\"You have not described all the functions/class well in your README.md file\\\"\",\n \"def test_text_classifier_create_from_path(self):\\n pass\",\n \"def test_path05(self):\\n index_file = os.path.join(self.test_input, 'index.html')\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n\\n for _info in file2wsb.run(self.test_input, self.test_output, no_preserve_filename=True):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_item, = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, f'{id_item}.html'),\\n })\",\n \"def test_other02(self):\\n index_file = os.path.join(self.test_input, 'folder1#中文', 'mypage.txt')\\n os.makedirs(os.path.dirname(index_file), exist_ok=True)\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write('ABC 中文')\\n ts = datetime(2020, 1, 2, 3, 4, 5, 67000, tzinfo=timezone.utc).timestamp()\\n os.utime(index_file, (ts, ts))\\n\\n for _info in file2wsb.run(self.test_input, self.test_output, no_preserve_filename=True):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_folder1, id_item = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_folder1: {\\n 'title': 'folder1#中文',\\n 'type': 'folder',\\n 'create': id_folder1,\\n 'modify': id_folder1,\\n },\\n id_item: {\\n 'title': 'mypage.txt',\\n 'type': 'file',\\n 'index': f'{id_item}.txt',\\n 'create': id_item,\\n 'modify': '20200102030405067',\\n 'source': '',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_folder1,\\n ],\\n id_folder1: [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, f'{id_item}.txt'),\\n })\",\n \"def test_api_document_create_student(self):\\n markdown_document = MarkdownDocumentFactory()\\n\\n jwt_token = StudentLtiTokenFactory(\\n playlist=markdown_document.playlist,\\n permissions__can_update=True,\\n )\\n\\n response = self.client.post(\\n \\\"/api/markdown-documents/\\\", HTTP_AUTHORIZATION=f\\\"Bearer {jwt_token}\\\"\\n )\\n self.assertEqual(response.status_code, 403)\",\n \"def main():\\r\\n\\tlang = get_arguments()\\r\\n\\twiki_analyzer(lang)\",\n \"def test_man7dir(self):\\n self.chck_triple('man7dir')\",\n \"def docs():\",\n \"def scan_path(path,ext_lst=['md','markdown']):\\n if not os.path.exists(path):\\n logger.debug('scan_path: invalid path : %s' %(path,files))\\n return -1\\n pages={}\\n files=[]\\n for item in glob.glob(os.path.join(path,'*.*')):\\n if string.split(os.path.basename(item),'.')[-1] in ext_lst:\\n files.append(item)\\n logger.debug('scan_path: %s : %s' %(path,files))\\n res = None\\n pagelist=[]\\n articledict={}\\n for item in files:\\n res=os.path.basename(item).split()\\n res=string.split(os.path.basename(item),'.')\\n #print 'scan_path: res : %s' %res \\n if res[0] not in pagelist:\\n logger.debug('scan_path: add page : %s' %res[0]) \\n pagelist.append(res[0])\\n articledict[res[0]]=[] #or add itself?\\n articledict[res[0]].append(item) \\n pages['pagelist'] = pagelist\\n if len(pagelist)>0:\\n for page in pagelist: \\n logger.debug('scan_path: pages= %s' %page)\\n logger.debug('scan_path: articles= %s' %articledict[page]) \\n pages[page]=articledict[page] \\n #look for templates/layout... To ENHANCE...\\n pages['template']=glob.glob(os.path.join(path,'*.tpl')) \\n if os.path.isfile(os.path.join(path,'layout.tpl')):\\n logger.info('scan_path: found general layout file : %s' %os.path.join(path,'layout.tpl'))\\n pages['layout']=os.path.join(path,'layout.tpl')\\n return pages\",\n \"def _analyze_file(self) -> None:\\n\\n md_reader = MarkdownReader.from_file(self.path)\\n for md_line in md_reader.lines:\\n if len(md_line.current_section) == 1:\\n if self.title and self.title != md_line.current_section[0]:\\n logger.warning(f\\\"{self.path} Warning: Multiple titles. \\\")\\n self.title = md_line.current_section[0]\\n if (\\n not md_line.is_code_block\\n and md_line.text.lower().strip().startswith(\\\"tags: \\\")\\n ):\\n if self.tags:\\n logger.warning(\\n f\\\"{self.path} Warning: Tags were already set.\\\"\\n )\\n self.tags = self._read_tags(md_line.text)\\n if (\\n len(md_line.current_section) >= 2\\n and md_line.current_section[1].lower().strip() == \\\"backlinks\\\"\\n ):\\n pass\\n else:\\n self.links.extend(self.id_link_regex.findall(md_line.text))\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(\\n description='Convert collection of bibtex files to clean markdown script.')\\n parser.add_argument('-i', '--input', nargs='+', required=False,\\n help='Input bibtex files. Defaults to bib/*')\\n parser.add_argument('-o', '--output', default='publications.md', required=False,\\n help='Output markdown floadile.')\\n args = parser.parse_args()\\n\\n # Build setup\\n os.makedirs(BUILD_ROOT, exist_ok=True)\\n build_dir = os.path.join(\\n BUILD_ROOT,\\n datetime.now().strftime(datetime.now().strftime('%Y_%m_%d_%H_%M_%S_%f')))\\n os.makedirs(build_dir)\\n\\n # Find and merge all bibtex files\\n input_files = args.input\\n if input_files is None:\\n input_files = [os.path.join(BIB_ROOT, f) for f in os.listdir(BIB_ROOT)\\n if os.path.isfile(os.path.join(BIB_ROOT, f))]\\n merge_file = merge_bibtex(input_files, build_dir)\\n\\n # Parse merged bibtex file with custom options\\n parse_file = parse_bibtex(merge_file, build_dir)\\n\\n # Render bibtex to markdown\\n render_file = render_bibtex(parse_file, build_dir, args.output)\",\n \"def techniques(self):\\n return self._get_child_page_of_type(LearningTechniquesPage)\",\n \"def test_landing_page(self, doi_dataset, doi_bib, orcid, metadata_parser):\\n apply_mock(doi_dataset, doi_bib, orcid, metadata_parser)\\n os.chdir(\\\"input/\\\")\\n _set_args(\\\"-i\\\",\\\"yamls/paper.yaml\\\",\\\"-o\\\",\\\"../out\\\",\\\"-ns\\\")\\n with HiddenPrints():\\n ya2ro.main()\\n\\n _set_args(\\\"-i\\\",\\\"yamls/paper_doi.yaml\\\",\\\"-o\\\",\\\"../out\\\",\\\"-ns\\\")\\n with HiddenPrints():\\n ya2ro.main()\\n\\n _set_args(\\\"-i\\\",\\\"yamls/project.yaml\\\",\\\"-o\\\",\\\"../out\\\",\\\"-ns\\\")\\n with HiddenPrints():\\n ya2ro.main()\\n\\n global parent_dir\\n os.chdir(parent_dir)\\n _set_args(\\\"-l\\\",\\\"out\\\")\\n with HiddenPrints():\\n ya2ro.main()\\n\\n with open(\\\"out/landing_page.html\\\") as landing:\\n web = landing.read()\\n \\n n_webs = web.count('</details>')\\n self.assertEquals(3, n_webs)\",\n \"def Subtask4_pre_train_3():\\n train_data = np.load(PATH + 'pre_train_2_Subtask4.npy', allow_pickle=True).item()\\n\\n evidence = []\\n for d in train_data.items():\\n for i in range(5):\\n evidence.append(d[1][i])\\n\\n files = os.listdir(PATH + 'data/wiki-pages/wiki-pages/')\\n documents = {}\\n for i in files:\\n with open(os.path.join(PATH + 'data/wiki-pages/wiki-pages/', i)) as fp:\\n lines = fp.readlines()\\n for line in lines:\\n line = eval(line)\\n if line['id'] in evidence:\\n text = line['lines']\\n documents[line['id']] = text\\n with open(PATH + 'pre_train_3_Subtask4.txt', 'w', encoding='utf-8') as f:\\n f.write(str(documents))\",\n \"def tplans_content(ident, args):\\n\\n struct = {\\n \\\"root\\\": [\\n (\\\"descr\\\", True),\\n (\\\"descr_long\\\", False),\\n (\\\"hooks\\\", False),\\n (\\\"evars\\\", False),\\n (\\\"testsuites\\\", True)\\n ],\\n \\\"suites\\\": [\\n (\\\"name\\\", True),\\n (\\\"alias\\\", False),\\n (\\\"hooks\\\", False),\\n (\\\"hooks_pr_tcase\\\", False),\\n (\\\"evars\\\", False),\\n (\\\"evars_pr_tcase\\\", False)\\n ]\\n }\\n\\n violations = []\\n\\n\\n tplans = _index(args.testplans_root, \\\"TPLAN\\\")\\n\\n for tp_fname in tplans:\\n tp_fpath = os.sep.join([args.testplans_root, tp_fname])\\n\\n suites = []\\n hooks = []\\n\\n tplan = None\\n try:\\n with open(tp_fpath) as tp_fd:\\n tplan = yaml.load(tp_fd)\\n\\n except IOError as exc:\\n continue\\n except Exception as exc:\\n continue\\n\\n for k in list(set(tplan.keys()) - set([k for k, _ in struct[\\\"root\\\"]])):\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"invalid key: %r\\\" % k\\n ))\\n\\n for k in (k for k, req in struct[\\\"root\\\"] if req):\\n if k not in tplan.keys():\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"missing required key: %r\\\" % k\\n ))\\n\\n hooks += tplan[\\\"hooks\\\"] if \\\"hooks\\\" in tplan else []\\n suites += []\\n\\n if \\\"testsuites\\\" not in tplan:\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"missing key 'testsuites'\\\"\\n ))\\n continue\\n\\n for suite in tplan[\\\"testsuites\\\"]:\\n for k in list(set(suite.keys()) - set([k for k, _ in struct[\\\"suites\\\"]])):\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"invalid key: %r\\\" % k\\n ))\\n\\n for k in (k for k, req in struct[\\\"suites\\\"] if req):\\n if k not in suite.keys():\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"missing required key: %r\\\" % k\\n ))\\n\\n if \\\"name\\\" in suite:\\n suites.append(suite[\\\"name\\\"])\\n\\n if \\\"hooks\\\" in suite:\\n hooks += suite[\\\"hooks\\\"]\\n\\n if \\\"hooks_pr_tcase\\\" in suite:\\n hooks += suite[\\\"hooks_pr_tcase\\\"]\\n\\n # Check for existence of suites\\n suites = list(set(suites))\\n for suite_name in suites:\\n suite_fpath = os.sep.join([\\n args.testsuites_root,\\n \\\"%s.suite\\\" % suite_name\\n ])\\n\\n if not os.path.exists(suite_fpath):\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"testsuite: %r, does not exist\\\" % suite_fpath\\n ))\\n\\n # Check for existence of hooks\\n hooks = list(set(hooks))\\n for hook_name in hooks:\\n\\n exists = []\\n for tmpl in [\\\"%s.sh\\\", \\\"%s_enter.sh\\\", \\\"%s_exit.sh\\\"]:\\n hook_fpath = os.sep.join([\\n args.hooks_root,\\n tmpl % hook_name\\n ])\\n exists.append(os.path.exists(hook_fpath))\\n\\n if not sum(exists):\\n violations.append(MESSAGES[ident] % (\\n tp_fname,\\n \\\"hook: %r, does not exist\\\" % hook_name\\n ))\\n\\n return violations\",\n \"def test_supporting_folder02(self):\\n index_file = os.path.join(self.test_input, 'mypage.html')\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n<img src=\\\"mypage_files/picture.bmp\\\">\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n img_file = os.path.join(self.test_input, 'mypage_files', 'picture.bmp')\\n os.makedirs(os.path.dirname(img_file), exist_ok=True)\\n with open(img_file, 'wb') as fh:\\n fh.write(b'dummy')\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_item, = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}/index.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n os.path.join(self.test_output, id_item, 'mypage.html'),\\n os.path.join(self.test_output, id_item, 'mypage_files'),\\n os.path.join(self.test_output, id_item, 'mypage_files', 'picture.bmp'),\\n })\",\n \"def test_supporting_folder01(self):\\n index_file = os.path.join(self.test_input, 'mypage.html')\\n with open(index_file, 'w', encoding='UTF-8') as fh:\\n fh.write(\\\"\\\"\\\"\\\\\\n<!DOCTYPE html>\\n<html\\n data-scrapbook-create=\\\"20200101000000000\\\"\\n data-scrapbook-modify=\\\"20200101000000000\\\"\\n data-scrapbook-source=\\\"http://example.com\\\">\\n<head>\\n<meta charset=\\\"UTF-8\\\">\\n<title>MyTitle 中文</title>\\n</head>\\n<body>\\npage content\\n<img src=\\\"mypage.files/picture.bmp\\\">\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n img_file = os.path.join(self.test_input, 'mypage.files', 'picture.bmp')\\n os.makedirs(os.path.dirname(img_file), exist_ok=True)\\n with open(img_file, 'wb') as fh:\\n fh.write(b'dummy')\\n\\n for _info in file2wsb.run(self.test_input, self.test_output):\\n pass\\n\\n book = Host(self.test_output).books['']\\n book.load_meta_files()\\n book.load_toc_files()\\n\\n id_item, = book.meta.keys()\\n self.assertDictEqual(book.meta, {\\n id_item: {\\n 'title': 'MyTitle 中文',\\n 'type': '',\\n 'index': f'{id_item}/index.html',\\n 'create': '20200101000000000',\\n 'modify': '20200101000000000',\\n 'source': 'http://example.com',\\n 'icon': '',\\n 'comment': '',\\n },\\n })\\n self.assertDictEqual(book.toc, {\\n 'root': [\\n id_item,\\n ],\\n })\\n self.assertEqual(set(glob.iglob(os.path.join(self.test_output, '**'), recursive=True)), {\\n os.path.join(self.test_output, ''),\\n os.path.join(self.test_output, id_item),\\n os.path.join(self.test_output, id_item, 'index.html'),\\n os.path.join(self.test_output, id_item, 'mypage.html'),\\n os.path.join(self.test_output, id_item, 'mypage.files'),\\n os.path.join(self.test_output, id_item, 'mypage.files', 'picture.bmp'),\\n })\",\n \"def extract_json_to_files(input_dir,output_dir):\\n files={}\\n files['train']='train-v1.1.json'\\n files['dev']='dev-v1.1.json'\\n\\n for file in files:\\n filename=os.path.join(input_dir,files[file])\\n with open(filename,'r',encoding='utf-8') as data_file:\\n examples = []\\n dataset=json.load(data_file)\\n count_total=total_exs(dataset)\\n count_mapping_problem=0\\n count_token_problem=0\\n count_ansspan_problem=0\\n count_examples=0\\n for article_id in tqdm(range(len(dataset['data'])), desc=\\\"Preprocessing {}\\\".format(file)):\\n article_paragraph=dataset['data'][article_id]['paragraphs']\\n for paragraph_id in range(len(article_paragraph)):\\n context=article_paragraph[paragraph_id]['context']\\n context=context.replace(\\\"''\\\",'\\\"').replace(\\\"``\\\",'\\\"')\\n context = context.replace('\\\\u3000', ' ').replace('\\\\u202f',' ').replace('\\\\u2009', ' ')#.replace(\\\"'\\\",\\\"'\\\")\\n context=context.replace('\\\\-',' ')\\n context_tokens=tokenize_sequence(context)\\n context=context.lower()\\n qas=article_paragraph[paragraph_id]['qas']\\n charloc2wordloc=get_char_word_loc_mapping(context, context_tokens)\\n if charloc2wordloc is None:\\n count_mapping_problem+=len(qas)\\n continue\\n for qa in qas:\\n question=qa['question'].lower()\\n question_tokens=tokenize_sequence(question)\\n\\n ans_text=qa['answers'][0]['text'].lower()\\n ans_text=ans_text.replace('\\\\u3000', ' ').replace('\\\\u202f', ' ').replace('\\\\u2009', ' ')\\n ans_start_loc=qa['answers'][0]['answer_start']\\n if qa['id'] in ['5706baed2eaba6190074aca5','57269c73708984140094cbb5','57269c73708984140094cbb7','572a11661d04691400779721','572a11661d04691400779722','572a11661d04691400779723','572a11661d04691400779724','572a11661d04691400779725','572a2cfc1d0469140077981b','572a3a453f37b319004787e9','572a84d3f75d5e190021fb3c']:\\n ans_start_loc+=1\\n if qa['id'] in ['572a5df77a1753140016aedf','572a5df77a1753140016aee0','572a84d3f75d5e190021fb38','572a84d3f75d5e190021fb39','572a84d3f75d5e190021fb3a','572a84d3f75d5e190021fb3b','572a85df111d821400f38bad','572a85df111d821400f38bae','572a85df111d821400f38baf','572a85df111d821400f38bb0']:\\n ans_start_loc+=2\\n if qa['id'] in ['572a5df77a1753140016aee1','572a5df77a1753140016aee2']:\\n ans_start_loc+=3\\n if qa['id'] in ['57286bf84b864d19001649d6','57286bf84b864d19001649d5']:\\n ans_start_loc-=1\\n if qa['id'] in ['5726bee5f1498d1400e8e9f3','5726bee5f1498d1400e8e9f4']:\\n ans_start_loc-=2\\n ans_end_loc=ans_start_loc+len(ans_text)\\n\\n if context[ans_start_loc:ans_end_loc]!=ans_text:\\n count_ansspan_problem+=1\\n continue\\n ans_start_wordloc = charloc2wordloc[ans_start_loc][1] # answer start word loc\\n ans_end_wordloc = charloc2wordloc[ans_end_loc-1][1] # answer end word loc\\n assert ans_start_wordloc <= ans_end_wordloc\\n\\n ans_tokens = context_tokens[ans_start_wordloc:ans_end_wordloc + 1]\\n if \\\"\\\".join(ans_tokens) != \\\"\\\".join(ans_text.split()):\\n count_token_problem += 1\\n #print(ans_text)\\n #print(ans_tokens)\\n continue # skip this question/answer pair\\n examples.append((' '.join(context_tokens),' '.join(question_tokens),' '.join(ans_tokens),' '.join([str(ans_start_wordloc),str(ans_end_wordloc)])))\\n print(\\\"Number of (context, question, answer) triples discarded due to char -> token mapping problems: \\\", count_mapping_problem)\\n print(\\\"Number of (context, question, answer) triples discarded because character-based answer span is unaligned with tokenization: \\\",count_token_problem)\\n print(\\\"Number of (context, question, answer) triples discarded due character span alignment problems (usually Unicode problems): \\\",count_ansspan_problem)\\n print(\\\"Processed %i examples of total %i\\\\n\\\" % (len(examples), len(examples)+count_mapping_problem+count_token_problem+count_ansspan_problem))\\n indices = list(range(len(examples)))\\n np.random.shuffle(indices)\\n with open(os.path.join(output_dir,file+'.context'),'w',encoding='utf-8') as context_file, \\\\\\n open(os.path.join(output_dir,file+'.question'),'w',encoding='utf-8') as question_file, \\\\\\n open(os.path.join(output_dir,file+'.answer'),'w',encoding='utf-8') as answer_file, \\\\\\n open(os.path.join(output_dir,file+'.span'),'w',encoding='utf-8') as span_file:\\n for i in indices:\\n (context,question,answer,span)=examples[i]\\n context_file.write(context+'\\\\n')\\n question_file.write(question+'\\\\n')\\n answer_file.write(answer+'\\\\n')\\n span_file.write(span+'\\\\n')\",\n \"def index():\\n\\n # open the README file\\n with open(os.path.dirname(app.root_path) + '/README.md',\\n 'r', encoding=\\\"utf-8\\\") as markdown_file:\\n\\n # Read the content of the file\\n content = markdown_file.read()\\n\\n # convert to html\\n return markdown.markdown(content)\",\n \"def opendocs():\\n _open_file('_build/index.html')\",\n \"def test_man9dir(self):\\n self.chck_triple('man9dir')\",\n \"def write_index_html(self):\\n print(\\\"- writing index.md\\\")\\n index_toc = [f\\\"### [Table of Contents]({config['github_pages_url']}/toc.html)\\\"] if self.notebooks else []\\n if os.path.isfile(os.path.join(self.dst_dir, \\\"data_index.html\\\")):\\n index_toc += [f\\\"### [Data Index]({config['github_pages_url']}/data_index.html)\\\"]\\n if os.path.isfile(os.path.join(self.dst_dir, \\\"figure_index.html\\\")):\\n index_toc += [f\\\"### [Figure Index]({config['github_pages_url']}/figure_index.html)\\\"]\\n if os.path.isfile(os.path.join(self.dst_dir, \\\"python_index.html\\\")):\\n index_toc += [f\\\"### [Python Module Index]({config['github_pages_url']}/python_index.html)\\\"]\\n if os.path.isfile(os.path.join(self.dst_dir, \\\"tag_index.html\\\")):\\n index_toc += [f\\\"### [Tag Index]({config['github_pages_url']}/tag_index.html)\\\"]\\n index_toc += [f\\\"- {nb.link}\\\" if type(nb) == Section else f\\\"\\\\n### {nb.link}\\\" for nb in self.notebooks]\\n env = Environment(loader=FileSystemLoader(\\\"templates\\\"))\\n with open(os.path.join(self.dst_dir, \\\"index.md\\\"), 'w') as f:\\n f.write(env.get_template('index.md.tpl').render(\\n readme_toc=index_toc, page_title=config['github_repo_name'], github_url=config['github_repo_url']))\",\n \"def setup():\\n if not os.path.isfile(etymology_file):\\n page = re.compile(r'index.php\\\\?l=\\\\w+&p=\\\\d+&allowed_in_frame=0.html')\\n pages = list(find_files(directory=site, pattern=page, recursive=False))\\n etymology = etymologies(pages)\\n dump(etymology, etymology_file)\\n for affix, dictionary in affixes(etymology):\\n affix_file = os.path.join('resources', '{}.json'.format(affix))\\n if not os.path.isfile(affix_file):\\n dump(dictionary, affix_file)\",\n \"def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\\n def is_whitespace(c):\\n if c == \\\" \\\" or c == \\\"\\\\t\\\" or c == \\\"\\\\r\\\" or c == \\\"\\\\n\\\" or ord(c) == 0x202F:\\n return True\\n return False\\n\\n if is_training:\\n subset_list = ['train']\\n else:\\n subset_list = ['dev', 'test']\\n\\n examples, features = {}, {}\\n for subset in subset_list:\\n level_example_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n level_features_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n\\n for level in ['high', 'middle']:\\n subset_dir = os.path.join(data_dir, subset)\\n file_list = os.listdir(subset_dir)\\n file_list = [file for file in file_list if file.endswith('txt')]\\n file_list = sorted(file_list)\\n\\n alldata = []\\n for file in file_list:\\n data = json.load(open(os.path.join(subset_dir, file)))\\n alldata.append(data)\\n \\n examples_list = []\\n for data in alldata:\\n doc_id = data['id']\\n doc = data[\\\"article\\\"].replace('\\\\\\\\n', '\\\\n')\\n doc_token = []\\n prev_is_whitespace = True\\n for c in doc:\\n if is_whitespace(c):\\n prev_is_whitespace = True\\n else:\\n if prev_is_whitespace:\\n doc_token.append(c)\\n else:\\n doc_token[-1] += c\\n prev_is_whitespace = False\\n\\n for i, answer in enumerate(data[\\\"answers\\\"]):\\n if answer == \\\" \\\":\\n answer = \\\"C\\\"\\n example = InputExample(\\n guid=doc_id + '-%d' % i,\\n doc_token=doc_token,\\n question_text=\\\"\\\",\\n options=data[\\\"options\\\"],\\n answer=answer)\\n examples_list.append(example)\\n\\n level_example_dict[level] = examples_list\\n level_features_dict[level] = convert_examples_to_features(\\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\\n \\n print(\\\"MuTuaL Data Loader....\\\")\\n print(level_example_dict[level][-1])\\n print(level_features_dict[level][-1])\\n examples[subset] = level_example_dict\\n features[subset] = level_features_dict\\n\\n return examples, features\",\n \"def run(self):\\n make_sure_path_exists(OUT_FOLDER)\\n\\n if self.config['type'] == 'website':\\n make_sure_path_exists(self.config['out_folder'])\\n\\n\\n for file in self.config['bodies']:\\n if file['type'] == 'content':\\n self.pandoc_file(file)\\n if self.config['type'] == 'website':\\n shutil.copyfile(file['generated'], os.path.join(self.config['out_folder'], os.path.basename(file['source'])))\\n\\n if self.config['type'] == 'website':\\n return\\n\\n for file in self.config['abstract']:\\n self.pandoc_file(file)\\n for file in self.config['summary']:\\n self.pandoc_file(file)\\n\\n template = LATEX_JINJA_ENV.get_template(self.config['template_file'])\\n\\n logging.info('Rendering template')\\n out = template.render(**self.config)\\n with open(self.config['name'] + self.get_file_extension(), 'w') as file:\\n file.write(out)\\n\\n if not self.args.pandoc:\\n logging.info('Rendering latex')\\n self.write()\\n if not self.args.fast:\\n logging.info('Rendering latex, again')\\n self.write() # twice for the toc\\n\\n logging.info('Done!')\",\n \"def test_darknet_txt_paths(path_to_samples_list: str):\\n with open(path_to_samples_list, \\\"r\\\") as f:\\n data = f.readlines()\\n for img_path in [path[:-1] for path in data]:\\n assert os.path.isfile(img_path)\\n assert os.path.isfile(img_path.replace(\\\"/images/\\\", \\\"/labels/\\\").replace(\\\".jpg\\\", \\\".txt\\\"))\\n print(f\\\"All files listed in '{path_to_samples_list}' exists.\\\")\",\n \"def check_init_files_and_folders():\\n\\t#['cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', 'cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', 'color_detect_2.py', 'dedupe.py', 'detect_image_group_ku.py', 'detect_shape_5.py', 'get_cam_id_2.py', 'get_image_8.py', 'gui_hsv.py', 'knaps.py', 'knapsack_2.py', 'maps.html', 'program_detect_rectangle.zip', 'start_capture.py']\\n\\tfile_list=[\\n\\t#'cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', \\n\\t'models/cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', \\n\\t#'color_detect_2.py', \\n\\t#'dedupe.py', \\n\\t'detect_bus_haar_group.py', \\n\\t#'detect_shape_5.py', \\n\\t'get_cam_detail.py', \\n\\t'get_image.py', \\n\\t#'gui_hsv.py', \\n\\t#'knaps.py', \\n\\t#'knapsack_2.py', \\n\\t#'maps.html', \\n\\t#'program_detect_rectangle.zip', \\n\\t'start_wimb.py',\\n\\t'g.php',\\n\\t]\\n\\tdirectory_list=[\\n\\t'images',\\n\\t'images_bgs',\\n\\t'images_bgs_mask',\\n\\t#'images_bgs_result',\\n\\t'images_color',\\n\\t'images_haar',\\n\\t'images_haar_result',\\n\\t'images_number',\\n\\t'images_number_result',\\n\\t'models',\\n\\t'images_old',\\n\\t'text_number',\\n\\t]\\n\\t\\n\\tfor file_name in file_list: print 'file '+file_name+' existed: '+str(os.path.isfile(file_name))\\n\\tfor directory_name in directory_list: \\n\\t\\tprint 'directory '+directory_name+' existed: '+str(os.path.isdir(directory_name))\\n\\t\\tif not os.path.isdir(directory_name): \\n\\t\\t\\tos.makedirs(directory_name)\\n\\t\\tif \\\"images\\\" in directory_name: shutil.copy(path+'/g.php',path+'/'+directory_name+'/g.php')\",\n \"def test_doc():\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58886623","0.58457476","0.57502025","0.5703051","0.56759125","0.56134313","0.5500864","0.5470556","0.5454886","0.5448571","0.5448571","0.54260826","0.54148185","0.54075974","0.53453696","0.53054494","0.5277186","0.52735656","0.5244364","0.5243433","0.5226075","0.5219602","0.52148926","0.5214649","0.5197863","0.51880234","0.5185965","0.5175208","0.51460224","0.5145093","0.5106675","0.5104142","0.5103613","0.5098035","0.50969654","0.5092976","0.5078017","0.5075796","0.5072092","0.50703835","0.5065264","0.506326","0.50606227","0.5051767","0.505136","0.5045635","0.5036444","0.5034471","0.5034222","0.5034132","0.5027977","0.50258696","0.502078","0.5019459","0.5017428","0.50173146","0.5015134","0.5013049","0.5000823","0.49989927","0.49960604","0.499392","0.49930012","0.4991891","0.49871886","0.4986921","0.49821946","0.4978651","0.49764082","0.49738634","0.49688837","0.49653754","0.4960139","0.49575216","0.4951792","0.4947505","0.49468434","0.49398607","0.49398386","0.49366632","0.49363965","0.49232477","0.49211743","0.4915458","0.4913929","0.49095768","0.4908608","0.49072576","0.49005818","0.489665","0.4890305","0.48850885","0.48747075","0.48735043","0.4872349","0.4863338","0.48568502","0.48557743","0.48557255","0.4848805"],"string":"[\n \"0.58886623\",\n \"0.58457476\",\n \"0.57502025\",\n \"0.5703051\",\n \"0.56759125\",\n \"0.56134313\",\n \"0.5500864\",\n \"0.5470556\",\n \"0.5454886\",\n \"0.5448571\",\n \"0.5448571\",\n \"0.54260826\",\n \"0.54148185\",\n \"0.54075974\",\n \"0.53453696\",\n \"0.53054494\",\n \"0.5277186\",\n \"0.52735656\",\n \"0.5244364\",\n \"0.5243433\",\n \"0.5226075\",\n \"0.5219602\",\n \"0.52148926\",\n \"0.5214649\",\n \"0.5197863\",\n \"0.51880234\",\n \"0.5185965\",\n \"0.5175208\",\n \"0.51460224\",\n \"0.5145093\",\n \"0.5106675\",\n \"0.5104142\",\n \"0.5103613\",\n \"0.5098035\",\n \"0.50969654\",\n \"0.5092976\",\n \"0.5078017\",\n \"0.5075796\",\n \"0.5072092\",\n \"0.50703835\",\n \"0.5065264\",\n \"0.506326\",\n \"0.50606227\",\n \"0.5051767\",\n \"0.505136\",\n \"0.5045635\",\n \"0.5036444\",\n \"0.5034471\",\n \"0.5034222\",\n \"0.5034132\",\n \"0.5027977\",\n \"0.50258696\",\n \"0.502078\",\n \"0.5019459\",\n \"0.5017428\",\n \"0.50173146\",\n \"0.5015134\",\n \"0.5013049\",\n \"0.5000823\",\n \"0.49989927\",\n \"0.49960604\",\n \"0.499392\",\n \"0.49930012\",\n \"0.4991891\",\n \"0.49871886\",\n \"0.4986921\",\n \"0.49821946\",\n \"0.4978651\",\n \"0.49764082\",\n \"0.49738634\",\n \"0.49688837\",\n \"0.49653754\",\n \"0.4960139\",\n \"0.49575216\",\n \"0.4951792\",\n \"0.4947505\",\n \"0.49468434\",\n \"0.49398607\",\n \"0.49398386\",\n \"0.49366632\",\n \"0.49363965\",\n \"0.49232477\",\n \"0.49211743\",\n \"0.4915458\",\n \"0.4913929\",\n \"0.49095768\",\n \"0.4908608\",\n \"0.49072576\",\n \"0.49005818\",\n \"0.489665\",\n \"0.4890305\",\n \"0.48850885\",\n \"0.48747075\",\n \"0.48735043\",\n \"0.4872349\",\n \"0.4863338\",\n \"0.48568502\",\n \"0.48557743\",\n \"0.48557255\",\n \"0.4848805\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7166579"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85684,"cells":{"query":{"kind":"string","value":"Generate technique index markdown for each domain and generates shared data for techniques"},"document":{"kind":"string","value":"def generate_domain_markdown(domain):\n\n #Reads the STIX and creates a list of the ATT&CK Techniques\n full_techniques = stixhelpers.get_techniques(config.ms[domain])\n tactic_list = stixhelpers.get_tactic_list(config.ms[domain])\n\n data = {}\n \n technique_list = get_techniques_list(full_techniques)\n\n data['domain'] = domain.split(\"-\")[0]\n\n # Get technique table data and number of techniques\n data['technique_table'] = util.get_technique_table_data(None, full_techniques)\n data['technique_list_len'] = str(len(full_techniques))\n side_menu_data = get_technique_side_menu_data(domain, technique_list, tactic_list)\n\n # Get tactic-techniques table\n data['menu'] = side_menu_data\n\n subs = config.technique_domain_md.substitute(data)\n subs = subs + json.dumps(data)\n\n with open(os.path.join(config.techniques_markdown_path, data['domain'] + \"-techniques.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)\n\n # Create the markdown for the enterprise groups in the STIX\n\n for technique in full_techniques:\n if 'revoked' not in technique or technique['revoked'] is False:\n generate_technique_md(technique, domain, side_menu_data, tactic_list)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate():\n\n # Verify if directory exists\n if not os.path.isdir(config.techniques_markdown_path):\n os.mkdir(config.techniques_markdown_path)\n\n #Write the technique index.html page\n with open(os.path.join(config.techniques_markdown_path, \"overview.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(config.technique_overview_md)\n\n for domain in config.domains:\n generate_domain_markdown(domain)","def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)","def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.groups_using_technique }\n ]\n\n example_data = []\n\n # Get malware or tools used by group\n for examples in examples_map:\n if examples['example_type'].get(technique.get('id')):\n for example in examples['example_type'][technique['id']]:\n\n attack_id = util.get_attack_id(example['object'])\n\n # Only add example data if the attack id is found \n if attack_id:\n row = {}\n\n row['id'] = attack_id\n\n if attack_id.startswith('S'):\n row['path'] = \"software\"\n else:\n row['path'] = \"groups\"\n\n row['name'] = example['object']['name']\n\n if example['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\n\n example_data.append(row)\n \n if example_data:\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\n return example_data","def index():\n import numpy as np\n import random\n\n total_gender = {}\n total_gender['Male'] = db(db.patient.sex == 'Male').count()\n total_gender['Female'] = db(db.patient.sex == 'Female').count()\n total_gender['Undeclared'] = db(db.patient.sex == 'Undeclared').count()\n\n groups = db(db.groups).select()\n freq_groups = {}\n grp_gender = {}\n for g in groups:\n freq_groups[g.code] = db(db.patient.groups.contains(g.id)).count()\n grp_gender[g.code] = {}\n grp_gender[g.code]['Male'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Male')).count()\n grp_gender[g.code]['Female'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Female')).count()\n grp_gender[g.code]['Undeclared'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Undeclared')).count()\n\n experiments = db(db.experiments).select()\n freq_experiments = {}\n exp_gender = {}\n for e in experiments:\n freq_experiments[e.code] = db(db.patient.experiments.contains(e.id)).count()\n exp_gender[e.code] = {}\n exp_gender[e.code]['Male'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Male')).count()\n exp_gender[e.code]['Female'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Female')).count()\n exp_gender[e.code]['Undeclared'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Undeclared')).count()\n\n grp_exp = {}\n for e in experiments:\n grp_exp[e.code] = {}\n for g in groups:\n grp_exp[e.code][g.code] = db(db.patient.experiments.contains(e.id) & db.patient.groups.contains(g.id)).count()\n\n return dict(message=T('Pain Network: A web-based tool for diagnosis of the Chronic Pain.'),\n freq_gender=total_gender,freq_groups=freq_groups,freq_experiments=freq_experiments,\n exp_gender=exp_gender,grp_gender=grp_gender,grp_exp=grp_exp)","def get_technique_side_menu_data(domain, technique_list, tactic_list):\n\n # Get tactics techniques side menu data to fill out with jinja\n tactics_techniques_menu_data = []\n\n for tactic in tactic_list:\n tactic_row = {}\n \n tactic_row['name'] = tactic['name']\n tactic_row['id'] = util.get_attack_id(tactic)\n tactic_row['path'] = \"/tactics/{}\".format(util.get_attack_id(tactic))\n \n tactic_row['children'] = []\n for technique in technique_list[tactic['x_mitre_shortname']]:\n technique_row = {}\n # Get technique id and name for each technique\n technique_row['name'] = technique['name']\n technique_row['id'] = technique['id']\n technique_row['path'] = \"/techniques/{}/\".format(technique['id'])\n technique_row['children'] = []\n # Add technique data to tactic\n tactic_row['children'].append(technique_row)\n \n # Add tactic to table\n tactics_techniques_menu_data.append(tactic_row)\n \n return {\n \"name\": \"techniques\",\n \"id\": \"techniques\",\n \"path\": \"/techniques/{}/\".format(domain.split(\"-\")[0]), # root level doesn't get a path\n \"children\": tactics_techniques_menu_data\n }","def build_index():\n for site in get_sites():\n text = read_site(site)\n while text == False:\n text = read_site(site) # keep attempting to read until successful\n index_site(site, text)","def build_index():\n for site in get_sites():\n text = read_site(site)\n while text == False:\n text = read_site(site) # keep attempting to read until successful\n index_site(site, text)","def homepage():\n\n pagesClassIDs = {\n \"index\": {\n \"bannertitle\": [],\n \"subtitle\": [],\n \"firstText\": [],\n \"secondText\": []\n }\n }\n for key in pagesClassIDs[\"index\"].keys():\n pagesClassIDs[\"index\"][key].append(\n str(\n pageTexts.query.filter_by(pageID=key,\n htmlName=\"index\").first()))\n\n experts = Expert.query.all()\n data = {\n \"modules\": [],\n \"discipline\": [],\n \"subdivision\": [],\n \"publications\": [],\n \"People\": []\n }\n for expert in experts:\n if not expert.is_admin and str(expert.accepted) == \"Yes\":\n data[\"modules\"].append(str(expert.affiliation))\n data[\"discipline\"].append(str(expert.discipline))\n data[\"subdivision\"].append(str(expert.specialization))\n data[\"publications\"].append(0)\n if str(expert.core_exp) == \"Yes\":\n data[\"People\"].append(\n str(expert.title) + \" \" + str(expert.first_name) + \" \" +\n str(expert.last_name) + \"(T)\")\n else:\n data[\"People\"].append(\n str(expert.title) + \" \" + str(expert.first_name) + \" \" +\n str(expert.last_name) + \"(N)\")\n\n crdata = pd.DataFrame(data).groupby(\n ['modules', 'discipline', 'subdivision'], as_index=False).agg({\n 'publications':\n 'sum',\n 'People':\n lambda x: ','.join(x)\n })\n circleData = createJSON(crdata)\n\n return render_template(\n 'home/index.html',\n pageDicts=pagesClassIDs,\n bannertitle=\"Introduction to Research\",\n subtitle=\"Interactive Causal Mapping\",\n title=\"Home\",\n circleData=circleData)","def generate(softwaretype=\"software\"):\n # import the STIX data from MITRE/CTI\n stix = requests.get(\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\").json()\n ms = MemoryStore(stix_data=stix[\"objects\"])\n # software includes malware and tool types so perform two queries and merge the results\n software_filters = []\n if softwaretype == \"malware\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'malware') ] )\n if softwaretype == \"tool\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'tool') ] )\n \n software = list(chain.from_iterable(\n ms.query(f) for f in software_filters\n ))\n\n # build a list of techniques used by software\n techniques_used = {} #attackID => using software names\n for thesoftware in software:\n # filter out revoked and deprecated software\n if (\"x_mitre_deprecated\" in thesoftware and thesoftware[\"x_mitre_deprecated\"]) or (\"revoked\" in thesoftware and thesoftware[\"revoked\"]): continue\n for relationship in ms.relationships(thesoftware[\"id\"]):\n # skip all non-technique relationships\n if \"attack-pattern\" not in relationship[\"target_ref\"]: continue\n technique = ms.get(relationship[\"target_ref\"])\n # filter out deprecated and revoked techniques\n if (\"x_mitre_deprecated\" in technique and technique[\"x_mitre_deprecated\"]) or (\"revoked\" in technique and technique[\"revoked\"]): continue\n techniqueID = technique[\"external_references\"][0][\"external_id\"]\n # store usage in techniques_used struct\n if techniqueID in techniques_used:\n techniques_used[techniqueID].append(thesoftware[\"name\"])\n else:\n techniques_used[techniqueID] = [thesoftware[\"name\"]]\n\n # format the techniques for the output layer\n techniques_list = []\n highest_usage = 0\n lowest_usage = 1\n for techniqueID in techniques_used:\n # determine the number of used techniques for the score\n count = len(techniques_used[techniqueID])\n highest_usage = max(highest_usage, count)\n lowest_usage = min(lowest_usage, count)\n # append technique struct to list of layer-formatted techniques\n techniques_list.append({\n \"techniqueID\": techniqueID,\n \"comment\": \"executed by \" + \", \".join(techniques_used[techniqueID]),\n \"score\": count,\n })\n # set up layer name and desc according to softwaretype\n if softwaretype != \"software\": \n plural = \"tools\" if softwaretype == \"tool\" else \"malware\"\n layername = f\"Software ({softwaretype}) Execution\"\n layerdescription = f\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\"\n else: \n layername = \"Software Execution\"\n layerdescription = f\"All techniques that can be executed by software, where the score is the count of software using the technique\"\n\n # construct and return the layer as a dict\n return {\n \"name\": layername,\n \"description\": layerdescription,\n \"versions\": {\n \"layer\": \"4.1\",\n \"navigator\": \"4.1\"\n },\n \"domain\": \"enterprise-attack\",\n \"techniques\": techniques_list,\n \"sorting\": 3, # order in descending order of score (count)\n \"gradient\": {\n \"colors\": [\n \"#fff7b3\", # low counts are yellow\n \"#ff6666\", # high counts are red\n ],\n \"minValue\": lowest_usage,\n \"maxValue\": highest_usage\n },\n }","def task_generate_sc_html():\n for dept in Department.list():\n command = (f\"pandoc --self-contained\"\n f\" -o {dept.sc_html_path}\"\n f\" {dept.sc_markdown_path}\")\n\n yield {\n 'name': dept.name,\n 'file_dep': [\n dept.sc_figure1_path,\n dept.sc_figure2_path,\n dept.sc_figure3_path,\n dept.sc_figure4_path,\n dept.sc_figure5_path,\n dept.sc_markdown_path,\n ],\n 'targets': [dept.sc_html_path],\n 'actions': [command],\n 'clean': True,\n }","def cluster_and_render(conf, dbname, outname=\"./text.html\", nclusters=8):\n\n\n db = sqlite3.connect(dbname)\n r = db.execute(\"select min(year), max(year) from counts where conf=?\", (conf,))\n minyear, maxyear = r.fetchone()\n\n # total words per year for normalization purposes\n r = db.execute(\"select year, count(*) from counts where conf=? order by year\", (conf,))\n year2c = dict([(year, c) for year, c in r])\n yearcounts = dict2arr(year2c, range(minyear, maxyear+1), 1)\n\n\n def add_content(subcluster, content, suffix):\n \"\"\"\n Render the cluster as an image\n \"\"\"\n\n fname = './plots/%s_%s.png' % (conf, suffix)\n\n # pick the top 10 terms\n subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\n subcluster = subcluster[:10]\n\n words = np.array(subcluster)[:,0]\n ys = np.array(subcluster)[:,1:].astype(float)\n mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]\n maxmean = max(mean)\n idx = mean.index(maxmean)\n\n # this is used to make the top-k list in the HTML later\n content.append(('', words, fname, idx))\n\n\n data = []\n for arr in subcluster:\n word = arr[0]\n for x, y in enumerate(map(float, arr[1:])):\n data.append(dict(\n group=\"normal\",\n word=word,\n x=xs[x],\n y=y, \n alpha=0.3\n ))\n\n # add a line for the mean\n for x, y in enumerate(mean):\n data.append(dict(group=\"aggregate\", word='___mean___', x=xs[x], y=y, alpha=1))\n\n if 1:\n maxy = max(10, max(pluckone(data, 'y')))\n if maxy <= 10:\n breaks = [0, 5, 10]\n\n\n # pygg lets you write ggplot2 syntax in python\n p = ggplot(data, aes(x='x', y='y', group='word', color='group', alpha='alpha'))\n p += geom_line(size=1)\n p += scale_color_manual(values=\"c('normal' = '#7777dd','aggregate' = 'black')\", guide=\"FALSE\")\n p += scale_alpha_continuous(guide=\"FALSE\")\n if 1:\n if maxy <= 10:\n p += scale_y_continuous(lim=[0, maxy], breaks=breaks, labels = \"function (x) as.integer(x)\")\n else:\n p += scale_y_continuous(lim=[0, maxy], labels = \"function (x) as.integer(x)\")\n p += legend_bottom\n p += theme(**{\n \"axis.title\":element_blank()\n })\n ggsave(fname, p, width=10, height=4, libs=['grid'])\n \n\n\n def vectors():\n \"\"\"\n Extract a matrix of term count vectors\n\n Return: [\n [word, count1, count2, ...],\n ...\n ]\n \"\"\"\n r = db.execute(\"select word, year, c from counts where conf=? order by word, year\", (conf,))\n vects = defaultdict(dict)\n for w,y,c in r:\n l = vects[w]\n l[y] = float(c) \n\n\n ret = []\n for w in vects:\n d = vects[w]\n\n # if word is super uncommon, skip it\n if (max(d.values()) <= 3):\n continue\n if (max([v / (1.+year2c.get(y,0)) for y, v in d.items()]) < .1): \n continue\n\n # some years may not have the word\n counts = dict2arr(d, xrange(minyear, maxyear+1), 1.0)\n\n \n # naive window averaging smoothing over the trend curve\n smooth = []\n for i in xrange(len(counts)):\n smooth.append(np.mean(counts[max(0,i-2):i+2]))\n if max(smooth) > 2:\n ret.append([w] + smooth)\n return np.array(ret)\n\n\n vects = vectors()\n # dimensions: words (row) x year (col)\n data = vects[:,1:].astype(float)\n\n # there's a bajillion ways to normalize the counts before clustering.\n # we do the following:\n\n # 1. divide by the total number of words in that year\n # (normalize by column)\n for idx, base in enumerate(yearcounts):\n data[:,idx] /= float(base)\n\n # 2. ensure zero mean and 1 std\n # (normalize by row)\n data = np.array([(l - np.mean(l)) / (max(l)) for l in data ])\n\n\n clusterer = KMeans(nclusters, n_init=50, init='k-means++')\n clusterer.fit(data) \n labels = clusterer.labels_\n xs = np.array(range(minyear, maxyear+1))\n\n content = []\n\n # each label is a cluster\n for label in set(labels):\n idxs = labels == label\n cluster = vects[idxs]\n\n # sort the words/clusters by their max count\n cluster = sorted(cluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\n if not len(cluster): continue\n cluster = np.array(cluster)\n words = cluster[:,0]\n words = list(words)\n\n add_content(cluster, content, label)\n\n content.sort(key=lambda c: c[-1])\n\n\n\n # make HTML\n from jinja2 import Template\n template = Template(file('./clustertemplate.html').read())\n\n with file(outname, 'w') as f:\n f.write( template.render(content=content))","def generate_document(query_df): # sourcery skip: identity-comprehension\n doc_lines = [\n \"Data Queries Reference\",\n \"=\" * len(\"Data Queries Reference\"),\n \"\",\n \"\",\n ]\n\n # This line fails if re-written as dict(query_df.groupby(\"Environment\"))\n # pylint: disable=unnecessary-comprehension\n group_dict = {name: group for name, group in query_df.groupby(\"Environment\")}\n for name, friendly_name in PROVIDERS.items():\n if name not in group_dict:\n continue\n grp = group_dict[name]\n doc_lines.append(f\"Queries for {friendly_name}\")\n doc_lines.append(\"-\" * len(f\"Queries for {friendly_name}\"))\n doc_lines.append(f\"\\nData Environment identifier: {name}\\n\")\n tbl_txt = tabulate(\n grp.drop(columns=\"Environment\"),\n headers=\"keys\",\n showindex=\"never\",\n tablefmt=\"rst\",\n )\n tbl_txt = [line.strip() for line in tbl_txt.split(\"\\n\")]\n doc_lines.extend(tbl_txt)\n doc_lines.append(\"\\n\\n\")\n return \"\\n\".join(doc_lines)","def build_md_once(self, index, tid):\n url = self.joint_url(tid)\n title_json = proxy_req(url, 1)\n if not title_json:\n if can_retry(url, index):\n self.build_md_once(index, tid)\n return\n content = BeautifulSoup(\n title_json['content'], 'html.parser').find_all('div')\n text = []\n img_href = []\n img_id = 1\n ttid = 1\n img_title = self.find_title(index).split('/')[1][:-3]\n for word in content:\n temp_text = ''\n if word.span and len(word.span.text) and not word.span.text[0].isdigit:\n temp_text = '## ' + word.span.text\n ttid = 1\n if word.img:\n temp_text = ' + '.jpg)'\n img_href.append(word.img['src'].replace('https', 'http'))\n img_id += 1\n\n if not len(temp_text):\n temp_text = word.text\n if len(temp_text) and temp_text[0].isdigit():\n temp_text = str(ttid) + '. **' + \\\n ' '.join(temp_text.split('\\xa0')[1:]).strip() + '**'\n ttid += 1\n if len(temp_text) and temp_text[0:2] == '//':\n temp_text = str(ttid) + '. **' + \\\n ' '.join(temp_text.split('\\xa0')[2:]).strip() + '**'\n ttid += 1\n if len(temp_text) and (temp_text[0] == '￥' or temp_text[0] == '€'):\n temp_text = '<a>' + temp_text + '</a>'\n text.append(temp_text)\n with codecs.open(data_dir + self.find_title(index), 'w', encoding='utf-8') as f:\n f.write('\\n'.join(text))\n self.img_map[index] = img_href\n print(index, len(img_href))","def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\n\n examples = []\n total_answerable = 0\n\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\n guid = \"%s-%s-%s\" % (set_type, line['qid'], str(i))\n src = []\n has_pos = 0\n\n if passage_type == 'both' and set_type != 'train':\n\n # rank by bert score in val and test\n score_key = 'rank_score'\n # score_key = \"nr_score\"\n both_sources = line[\"passages\"][:2*cand_for_each_source] + line[\"tables\"][:2*cand_for_each_source]\n all_scores = np.array([x[score_key] for x in both_sources])\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\n\n for idx in sorted_index:\n pas = both_sources[idx]\n if idx < 2 * cand_for_each_source:\n pas_type = 'passage'\n title = pas[\"article_title\"]\n else:\n pas_type = 'table'\n title = \"table_\" + pas[\"uid\"].split('-split')[0]\n\n src.append(\"question: \" + line[\"question\"].strip() + f\" </s> {pas_type} title: \" + title + f\" </s> {pas_type} content: \" + pas[\"text\"] + \" </s>\")\n\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n else:\n\n if passage_type in ['textual', 'hybrid', 'both']:\n for pas in line[\"passages\"][:cand_for_each_source]:\n # 10 passages\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> passage title: \" + pas[\"article_title\"] + \" </s> passage content: \" + pas[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\"ZAPPAtite\\\". All songs written and performed ... </s>\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n if passage_type in ['tabular', 'hybrid', 'both']:\n for tab in line[\"tables\"][:cand_for_each_source]:\n # 10 tables\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> table title: \" + \"table_\" + tab[\"uid\"].split('-split')[0] + \" </s> table content: \" + tab[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\n if isinstance(tab['judge'], dict):\n has_pos += int(tab['judge']['judge_contain_all'])\n else:\n has_pos += int(tab['judge'])\n\n const = 2 if passage_type in ['hybrid', 'both'] else 1\n if len(src) != const * cand_for_each_source:\n logger.info(line)\n src = src + [\"\"] * (const * cand_for_each_source - len(src))\n\n if has_pos > 0:\n total_answerable += 1\n\n if enable_sql_supervision and 'true_sql' in line:\n tgt = \"sql: \" + line[\"true_sql\"] + \" </s>\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\"Butler CC (KS)\\\" </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'denotation' in line:\n tgt = \"answer: \" + str(line[\"denotation\"][0]) + \" </s>\"\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'answers' in line:\n tgt = \"answer: \" + str(line[\"answers\"][0]) + \" </s>\" # e.g. answer: no slogan on current series </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt + \"\\n\")\n\n logger.info(f\"Total answerable in {set_type} split is {total_answerable / len(lines)}\")\n\n return examples","def generateMapping(text, extensions):\n extensions = filter(lambda e: e != 'markdown.extensions.footnotes', extensions)\n\n text_index, modified = preprocess(text)\n html_modified = markdown(modified, extensions=extensions)\n modified_index = findPlaceholderIndex(html_modified, len(text_index))\n return zip(text_index, modified_index)","def get_technique_stats(db):\n \n techniques = {}\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n temp_dict = {technique: [{u'id': element['project_no'], \n u'name': element['project_name'], u'description': element['short_description']}]}\n techniques.update(temp_dict)\n else:\n temp_dict = {u'id': element['project_no'],\n u'name': element['project_name'], u'description': element['short_description']}\n techniques[technique].append(temp_dict)\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\n\n return techniques","def dump_designs(self):\n param_dict = {'startkey': '\"_design/\"',\n 'endkey': '\"_design0\"',\n 'include_docs': 'true'} \n r = requests.get('/'.join([self.url, self.db, \"_all_docs\"]), params=param_dict)\n # Write out the JSON document itself\n for row in r.json()['rows']:\n design_str = row['id'].replace('/', '%2F')\n json.dump(row, open(design_str + '.json', 'w'), indent=4)\n # Also create separate files for the map/reduce javascript\n for view in row[\"doc\"][\"views\"]:\n view_str = '%2F'.join(['_view', view])\n f = open('%2F'.join([design_str, view_str + \".js\"]), 'w')\n for func in row[\"doc\"][\"views\"][view]:\n f.write(func + \" = \" + row[\"doc\"][\"views\"][view][func] + \"\\n\")\n f.close()","def iterate_data(self):\n if \"single\" in self.dataset_name:\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for answer_id in self.data:\n summary = self.data[answer_id]['summary']\n articles = self.data[answer_id]['articles']\n question = self.data[answer_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(articles, 'article')\n self._get_token_cnts(question, 'question')\n self._write_stats(\"token_counts\")\n\n if \"multi\" in self.dataset_name:\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for q_id in self.data:\n summary = self.data[q_id]['summary']\n question = self.data[q_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(question, 'question')\n question = self.data[q_id]['question']\n for answer_id in self.data[q_id]['articles']:\n articles = self.data[q_id]['articles'][answer_id][0]\n if args.tokenize:\n self._get_token_cnts(articles, 'article')\n self._write_stats(\"token_counts\")\n\n if self.dataset_name == \"complete_dataset\":\n self.stat_dict = {'urls': [], 'sites': []}\n article_dict = {}\n print(\"Counting answers, sites, unique urls, and tokenized counts of unique articles\")\n answer_cnt = 0\n for q_id in self.data:\n for a_id in self.data[q_id]['answers']:\n answer_cnt += 1\n url = self.data[q_id]['answers'][a_id]['url']\n article = self.data[q_id]['answers'][a_id]['article']\n if url not in article_dict:\n article_dict[url] = article\n self.stat_dict['urls'].append(url)\n assert \"//\" in url, url\n site = url.split(\"//\")[1].split(\"/\")\n self.stat_dict['sites'].append(site[0])\n print(\"# of Answers:\", answer_cnt)\n print(\"Unique articles: \", len(article_dict)) # This should match up with count written to file\n self._write_stats(\"full collection\")\n\n # Get token/sent averages of unique articles\n if args.tokenize:\n self.stat_dict = {'article': [[], []]}\n for a in article_dict:\n self._get_token_cnts(article_dict[a], 'article')\n self._write_stats(\"token_counts\")","def index():\n response = \"\"\n for table in config.TABLE_SCHEMA.keys():\n response = response + disco.examples(table)\n return response","def tex(self):\n self.cardinality = self.question.sorted_answers_cardinality(\n self.min_cardinality,\n self.group_together,\n self.group_by_letter_case,\n self.group_by_slugify,\n self.filter,\n self.sort_answer,\n other_question=self.other_question,\n )\n q1 = []\n q2 = []\n for answer_to_q1, cardinality_to_q2 in list(self.cardinality.items()):\n for answer_to_q2, number_of_time in list(cardinality_to_q2.items()):\n q1 += [answer_to_q1] * number_of_time\n q2 += [answer_to_q2] * number_of_time\n df = DataFrame(data={self.question.text: q1, self.other_question.text: q2})\n name = \"tex/q{}_vs_q{}\".format(self.question.pk, self.other_question.pk)\n sankey(df[self.question.text], df[self.other_question.text], aspect=20, fontsize=10, figureName=name)\n return Question2TexSankey.TEX_SKELETON % (\n name[4:],\n self.question.pk,\n self.other_question.pk,\n self.get_caption(),\n )","def corpus_group():","def extract_json_to_files(input_dir,output_dir):\n files={}\n files['train']='train-v1.1.json'\n files['dev']='dev-v1.1.json'\n\n for file in files:\n filename=os.path.join(input_dir,files[file])\n with open(filename,'r',encoding='utf-8') as data_file:\n examples = []\n dataset=json.load(data_file)\n count_total=total_exs(dataset)\n count_mapping_problem=0\n count_token_problem=0\n count_ansspan_problem=0\n count_examples=0\n for article_id in tqdm(range(len(dataset['data'])), desc=\"Preprocessing {}\".format(file)):\n article_paragraph=dataset['data'][article_id]['paragraphs']\n for paragraph_id in range(len(article_paragraph)):\n context=article_paragraph[paragraph_id]['context']\n context=context.replace(\"''\",'\"').replace(\"``\",'\"')\n context = context.replace('\\u3000', ' ').replace('\\u202f',' ').replace('\\u2009', ' ')#.replace(\"'\",\"'\")\n context=context.replace('\\-',' ')\n context_tokens=tokenize_sequence(context)\n context=context.lower()\n qas=article_paragraph[paragraph_id]['qas']\n charloc2wordloc=get_char_word_loc_mapping(context, context_tokens)\n if charloc2wordloc is None:\n count_mapping_problem+=len(qas)\n continue\n for qa in qas:\n question=qa['question'].lower()\n question_tokens=tokenize_sequence(question)\n\n ans_text=qa['answers'][0]['text'].lower()\n ans_text=ans_text.replace('\\u3000', ' ').replace('\\u202f', ' ').replace('\\u2009', ' ')\n ans_start_loc=qa['answers'][0]['answer_start']\n if qa['id'] in ['5706baed2eaba6190074aca5','57269c73708984140094cbb5','57269c73708984140094cbb7','572a11661d04691400779721','572a11661d04691400779722','572a11661d04691400779723','572a11661d04691400779724','572a11661d04691400779725','572a2cfc1d0469140077981b','572a3a453f37b319004787e9','572a84d3f75d5e190021fb3c']:\n ans_start_loc+=1\n if qa['id'] in ['572a5df77a1753140016aedf','572a5df77a1753140016aee0','572a84d3f75d5e190021fb38','572a84d3f75d5e190021fb39','572a84d3f75d5e190021fb3a','572a84d3f75d5e190021fb3b','572a85df111d821400f38bad','572a85df111d821400f38bae','572a85df111d821400f38baf','572a85df111d821400f38bb0']:\n ans_start_loc+=2\n if qa['id'] in ['572a5df77a1753140016aee1','572a5df77a1753140016aee2']:\n ans_start_loc+=3\n if qa['id'] in ['57286bf84b864d19001649d6','57286bf84b864d19001649d5']:\n ans_start_loc-=1\n if qa['id'] in ['5726bee5f1498d1400e8e9f3','5726bee5f1498d1400e8e9f4']:\n ans_start_loc-=2\n ans_end_loc=ans_start_loc+len(ans_text)\n\n if context[ans_start_loc:ans_end_loc]!=ans_text:\n count_ansspan_problem+=1\n continue\n ans_start_wordloc = charloc2wordloc[ans_start_loc][1] # answer start word loc\n ans_end_wordloc = charloc2wordloc[ans_end_loc-1][1] # answer end word loc\n assert ans_start_wordloc <= ans_end_wordloc\n\n ans_tokens = context_tokens[ans_start_wordloc:ans_end_wordloc + 1]\n if \"\".join(ans_tokens) != \"\".join(ans_text.split()):\n count_token_problem += 1\n #print(ans_text)\n #print(ans_tokens)\n continue # skip this question/answer pair\n examples.append((' '.join(context_tokens),' '.join(question_tokens),' '.join(ans_tokens),' '.join([str(ans_start_wordloc),str(ans_end_wordloc)])))\n print(\"Number of (context, question, answer) triples discarded due to char -> token mapping problems: \", count_mapping_problem)\n print(\"Number of (context, question, answer) triples discarded because character-based answer span is unaligned with tokenization: \",count_token_problem)\n print(\"Number of (context, question, answer) triples discarded due character span alignment problems (usually Unicode problems): \",count_ansspan_problem)\n print(\"Processed %i examples of total %i\\n\" % (len(examples), len(examples)+count_mapping_problem+count_token_problem+count_ansspan_problem))\n indices = list(range(len(examples)))\n np.random.shuffle(indices)\n with open(os.path.join(output_dir,file+'.context'),'w',encoding='utf-8') as context_file, \\\n open(os.path.join(output_dir,file+'.question'),'w',encoding='utf-8') as question_file, \\\n open(os.path.join(output_dir,file+'.answer'),'w',encoding='utf-8') as answer_file, \\\n open(os.path.join(output_dir,file+'.span'),'w',encoding='utf-8') as span_file:\n for i in indices:\n (context,question,answer,span)=examples[i]\n context_file.write(context+'\\n')\n question_file.write(question+'\\n')\n answer_file.write(answer+'\\n')\n span_file.write(span+'\\n')","def write_data_index(self):\n content = \"\"\n self.remove(os.path.join(config[\"data_subdir\"], \"*\"))\n if self.data_index:\n print(\"- writing data index\")\n content += f\"# [{config['github_repo_name']}]({config['github_pages_url']})\\n\"\n content += \"\\n## Index of Data files in this Repository\\n\"\n for data, links in sorted(self.data_index.items(), key=lambda x: natsort(x[0])):\n if links:\n content += f\"\\n### {data}\\n\"\n content += f\"\\n\"\n for link in links:\n content += f\"* {link}\\n\"\n data_src = os.path.join(self.src_dir, config[\"data_subdir\"], data)\n data_dst = os.path.join(self.dst_dir, config[\"data_subdir\"], data)\n print(f\"- copying {data_src} to {data_dst}\")\n shutil.copy(data_src, data_dst)\n self.write_md2html(\"data_index\", content)","def load_demos():\n for index in range(len(feconf.DEMO_EXPLORATIONS)):\n load_demo(str(index))","def write_index_html(wk_dir,region_dict,metrics_filename,ext=\"png\"):\n # Make lists of the metrics and figure files to display\n metrics_dir = os.path.join(wk_dir,metrics_dir_name)\n metric_list = sorted([\n f for f in os.listdir(metrics_dir) if f.endswith('_summary.csv')])\n plot_list=[]\n fig_list=sorted([f for f in os.listdir(wk_dir+'/'+figure_dir_name)])\n for keyword in ['lag','correlations','twodpdf']:\n plot_list.append([f for f in fig_list if (keyword in f)]) # sort datasets\n subtitle_list=['Autocorrelation','2D Histograms','Correlation maps']\n\n # Start working on html text. Each line is appened to a list that\n # is then written to file.\n html_file=['<html>\\n',\n '<body>','<head><title>ASoP-Coherence</title></head>\\n',\n '<br><h1>ASoP-Coherence results</h1>\\n','<h2>Contents</h2>\\n',\n '<dl>\\n','<dt><a href=\"#Metrics\">Metrics</a></dt>\\n',\n '<dt><a href=\"#Figures\">Figures</a></dt>\\n',\n '<dd><a href=\"#Autocorrelation\">Autocorrelation</a></dd>\\n',\n '<dd><a href=\"#2D-Histograms\">2D Histograms</a></dd>\\n',\n '<dd><a href=\"#Correlation-maps\">Correlation Maps</a></dd>\\n',\n '</dl>\\n''<section id=\"Metrics\">\\n','<br><h2>Metrics</h2>\\n']\n html_file.append('<h3>Intermittency Metrics</h3>\\n')\n\n # Display metrics JSON in dashboard option\n metrics_json = os.path.basename(metrics_filename)\n metrics_relocated = os.path.join(metrics_dir_name,metrics_json)\n tmp='<p><a href=\"'+metrics_relocated+'\" target=\"_blank\">'+metrics_json+'</a></p>\\n'\n html_file.append(tmp)\n\n # Link CSV tables for download\n html_file.append('<h3>Tables</h3>\\n')\n for metric_file in metric_list:\n metric_path = os.path.join(metrics_dir_name,metric_file)\n html_file.append('<p><a href=\"{0}\">{1}</a></p>\\n'.format(metric_path,metric_file))\n html_file.append('<br>\\n')\n html_file.append('</section>\\n')\n\n # Add figures\n html_file.append('<section id=\"Figures\">\\n')\n html_file.append('<h2>Figures</h2>\\n')\n for title,category in zip(subtitle_list,plot_list):\n html_file.append('<section id='+title.replace(' ','-')+'>\\n')\n html_file.append('<h3>{0}</h3>\\n'.format(title))\n # Adjust figure width for autocorrelation\n fwidth = \"647\"\n if title==\"Autocorrelation\":\n fwidth=\"450\"\n for region in region_dict:\n html_file.append('<h4>{0}</h4>\\n'.format(region.replace('_',' ')))\n region_fig = [f for f in category if (region.replace(\" \",\"_\") in f)]\n for fig in region_fig:\n tmp = '<p><a href=\"{0}\" target=\"_blank\" alt={0}>' + \\\n '<img src=\"{0}\" width={1} alt=\"{0}\"></a></p>\\n'\n html_file.append(\n tmp.format(os.path.join(figure_dir_name,fig),fwidth))\n html_file.append('</section>\\n')\n html_file.append('</section>\\n')\n\n html_file.append('</body>\\n</html>\\n')\n filename=wk_dir+'/index.html'\n with open(filename,'w') as html_page:\n html_page.writelines(html_file)","def display_wmd(google_snippets, my_snippets):\r\n for i in range(len(google_snippets)):\r\n print(i+1, '.', urls[i])\r\n print('Google Snippet:', google_snippets[i])\r\n print('Generated Snippet:', my_snippets[i])\r\n print(\"Word Mover's Distance:\", get_wmd_dist(google_snippets[i], my_snippets[i], model))\r\n print()","def index():\n with open(\"PROJECT.md\", \"r\") as project_file:\n md_template_string = markdown.markdown(\n project_file.read(), extensions=[\"fenced_code\"]\n )\n return md_template_string","def task_generate_sc_markdown():\n for dept in Department.list():\n yield {\n 'name': dept.name,\n 'file_dep': [],\n 'targets': [dept.sc_markdown_path],\n 'actions': [dept.generate_sc_markdown],\n 'clean': True,\n 'uptodate': [False],\n }","def create_explanations_csv():\n with open('output/' + dataset_name + '_' + model_name + '.csv', mode='w', newline='') as file:\n writer = csv.writer(file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n writer.writerow(\n [\"index\", \"original text\", \"true class\", \"decoded text\", \"black box prediction\",\n \"decision tree prediction\", \"fidelity\", \"exemplars\", \"counter exemplars\", \"top exemplar words\",\n \"top counter exemplar words\"])\n for i in range(len(idx)):\n writer.writerow(\n [idx[i], X_original[i], y_original[i], final_decoded_sentences[i][0], bbpreds[i], dtpreds[i],\n fidelities[i], exemplars[i], counter_exemplars[i], top_exemplar_words_dict_list[i],\n top_counter_exemplar_words_dict_list[i]])","def Subtask4_pre_train_3():\n train_data = np.load(PATH + 'pre_train_2_Subtask4.npy', allow_pickle=True).item()\n\n evidence = []\n for d in train_data.items():\n for i in range(5):\n evidence.append(d[1][i])\n\n files = os.listdir(PATH + 'data/wiki-pages/wiki-pages/')\n documents = {}\n for i in files:\n with open(os.path.join(PATH + 'data/wiki-pages/wiki-pages/', i)) as fp:\n lines = fp.readlines()\n for line in lines:\n line = eval(line)\n if line['id'] in evidence:\n text = line['lines']\n documents[line['id']] = text\n with open(PATH + 'pre_train_3_Subtask4.txt', 'w', encoding='utf-8') as f:\n f.write(str(documents))","def index_page(data):\n main_index = [html.H1(\"INDEX\"),\n html.Div([html.A(\"Characters\", href=\"/character\"),\n \" | \", html.A(\"Search\", href=\"/search\")]),\n dcc.Loading(html.Img(src=create_wordcloud(data[\"tokens\"]))),\n get_random_quotes(data, 5)]\n\n return main_index","def generate_excerpt():","def to_markdown(self, experiment_name : str):\n experiment = self.get_experiment(experiment_name)\n f = open(experiment.root_dir + \"/\" + experiment_name + \".md\", 'w')\n # Create a markdown object\n markdown = Markdown()\n\n for img in experiment.images:\n mkimg = Markdown.Image(img.title, img.filename, img.description)\n markdown.add_image(img.filename)\n\n table = Markdown.Table([\"Parameter Name\", \"Input/Output\", \"Value\", \"Title\", \"Description\"])\n for param in experiment.parameters:\n table.add_row([param.name, ])\n\n markdown.add_table(table)","def generateHtml(self):\n # only the master processor needs to do this\n if not self.master: return\n\n for page in self.layout.pages:\n \n # build the metric dictionary\n metrics = {}\n page.models = []\n for fname in glob.glob(os.path.join(self.output_path,\"*.nc\")):\n with Dataset(fname) as dataset:\n mname = dataset.getncattr(\"name\")\n if mname != \"Benchmark\": page.models.append(mname)\n if not dataset.groups.has_key(page.name): continue\n group = dataset.groups[page.name]\n\n # if the dataset opens, we need to add the model (table row)\n metrics[mname] = {}\n \n # each model will need to have all regions\n for region in self.regions: metrics[mname][region] = {}\n \n # columns in the table will be in the scalars group\n if not group.groups.has_key(\"scalars\"): continue\n \n # we add scalars to the model/region based on the region\n # name being in the variable name. If no region is found,\n # we assume it is the global region.\n grp = group.groups[\"scalars\"]\n for vname in grp.variables.keys():\n found = False\n for region in self.regions:\n if region in vname: \n found = True\n var = grp.variables[vname]\n name = vname.replace(region,\"\")\n metrics[mname][region][name] = Variable(name = name,\n unit = var.units,\n data = var[...])\n if not found:\n var = grp.variables[vname]\n metrics[mname][\"global\"][vname] = Variable(name = vname,\n unit = var.units,\n data = var[...])\n page.setMetrics(metrics)\n \n # write the HTML page\n f = file(os.path.join(self.output_path,\"%s.html\" % (self.name)),\"w\")\n f.write(str(self.layout))\n f.close()","def get_designs(self):","def _setup(self):\n self._raw_dir = os.path.join(self._snippets_dir,\"raw\",\"static\")\n if not os.path.exists(self._raw_dir):\n os.mkdir(self._raw_dir)\n\n self._trec_dir = os.path.join(self._snippets_dir,\"trec\",\"static\")\n if not os.path.exists(self._trec_dir):\n os.mkdir(self._trec_dir)\n\n self._temp_dir = os.path.join(self._snippets_dir,\"temp\",\"static\")\n if not os.path.exists(self._temp_dir):\n os.mkdir(self._temp_dir)\n\n self._para_dir = os.path.join(self._snippets_dir,\"para\",\"static\")\n if not os.path.exists(self._para_dir):\n os.mkdir(self._para_dir)\n\n self._snippet_result_dir = os.path.join(self._snippets_dir,\"result\",\"static\")\n if not os.path.exists(self._snippet_result_dir):\n os.mkdir(self._snippet_result_dir)\n\n self._snippet_index_dir = os.path.join(self._snippets_dir,\"index\",\"static\")\n if not os.path.exists(self._snippet_index_dir):\n os.mkdir(self._snippet_index_dir)\n\n \n\n\n self._index_para = os.path.join(self._para_dir,\"index_para\")\n\n self._temp_query_para = os.path.join(self._para_dir,\"temp_query_para\")\n\n self._index_list = os.path.join(self._para_dir,\"static_index_list\")\n \n self._orf = os.path.join(self._snippet_result_dir,\"orf\")\n\n self._oqf = os.path.join(self._temp_dir,\"oqf\")\n \n self._temp_output = os.path.join(self._temp_dir,\"temp_output\")\n\n with open(self._index_list,\"w\") as f:\n f.write(self._snippet_index_dir+\"\\n\")\n\n self._temp_query_builder = IndriQueryFactory(count=10000,\n rule=self._retrieval_method)\n\n self._oqf_builder = IndriQueryFactory(count=30,\n rule=self._retrieval_method)","def write_to_md(dictData, outputDirectory):\n\tdic = prepare_hw_dict(dictData)\n\tfor hw in dic:\n\t\tfileout = os.path.join(outputDirectory, hw+'.md')\n\t\t# Prepare the output file\n\t\tfout = codecs.open(fileout, 'w', 'utf-8')\n\t\t# Write frontmatter\n\t\tfout.write('---\\ntitle: \"'+hw+'\"\\n---\\n\\n')\n\t\t# For each (headword, meanings, verseNumber, PageNum) tuples,\n\t\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dic[hw]:\n\t\t\tcommaed = ', '.join(meanings)\n\t\t\tverse = verse.replace('<BR>', '<br />')\n\t\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\t\tfout.write('# ' + hw + '\\n## ' + commaed + '\\n' + verse + '<br />verse ' + verseNumDetails + '<br />page ' + pageNumDetails +'\\n\\n')\n\t\tfout.close()\n\n\t# Give some summary to the user\n\tprint('MD files generated. Success!')\n\tprint('{} separate .md files written, one per headword.'.format(len(dic)))","def get_data():\n wga_df = pd.read_csv(os.path.join(MODEL_DIR, 'wga/sculptures/wga_sculpture_periods.csv'), index_col=0)\n wikiart_df = pd.read_csv(os.path.join(MODEL_DIR, 'wikiart/sculptures/wikiart_sculpture_periods.csv'), index_col=0)\n nga_df = pd.read_csv(os.path.join(MODEL_DIR, 'nga/sculptures/nga_sculpture_periods.csv'), index_col=0)\n\n ######## Fix name for WGA and WikiaRt ###########\n wga_df['Author'] = wga_df.apply(lambda x: fix_name_wga(x['Author']), axis=1)\n wikiart_df['Author'] = wikiart_df.apply(lambda x: fix_name_wiki(x['Author']), axis=1)\n nga_df['Author'] = nga_df.apply(lambda x: fix_name_nga(x['Author']), axis=1)\n\n df = pd.concat([wga_df, wikiart_df, nga_df], ignore_index=True, sort=True)\n\n df['Author_Fixed'] = df.apply(lambda x: fix_text(x['Author']), axis=1)\n df['title_fixed'] = df.apply(lambda x: fix_text(x['title']), axis=1)\n\n periods = [\"BAROQUE\", \"EARLY RENAISSANCE\", \"MEDIEVAL\", \"NEOCLASSICISM\", \"HIGH RENAISSANCE\", \"MINIMALISM\", \"REALISM\",\n \"IMPRESSIONISM\", \"ROCOCO\", \"SURREALISM\", \"MANNERISM\", \"ROMANTICISM\",\n ]\n df['Period'] = df.apply(lambda row: row['Period'].upper(), axis=1)\n\n # Get Desired Periods\n df['Period'] = df.apply(lambda x: \"SURREALISM\" if \"SURREALISM\" in x['Period'] else x['Period'], axis=1)\n df = df[(df['Period'].isin(periods))]\n df = df.sort_values(['Author_Fixed', 'title_fixed'])\n\n #print(\"Combined Drop Rows:\", df.shape[0] - df.drop_duplicates(subset=['Author_Fixed', 'title_fixed']).shape[0])\n\n df = df.drop_duplicates(subset=['Author_Fixed', 'title_fixed'], keep='last')\n\n # Drop Duplicate Sculptures\n df = df[~df['file'].isin(DUP_SCULPTURES)].reset_index(drop=True)\n\n #print(df['Period'].value_counts())\n\n return df","def main() -> Dict[int, int]:\n # DOCUMENTS DIRECTORY\n # Path of the directory that contains the .txt documents. One .txt document by company. IT NEEDS TO BE \"/data\" when you upload it in data challenge platform. For test in local, you can modifiy to match your data path.\n if dev:\n documents_directory = \"../example_dataset/data\"\n else: \n documents_directory = \"/data\"\n\n path_to_files: List[str] = [os.path.join(documents_directory, file) for file in os.listdir(documents_directory)]\n assert len(path_to_files) == 10 # 10 files in documents directory\n path_to_files.sort() # Sort list of path file by alphabetical order to match ground truth annotations order : IT IS ESSENTIAL.\n\n # INITIALIZATION OF YOUR OBJECTS\n data_model = FormDataModel.from_json_file(\n os.path.join(os.path.dirname(__file__), \"resources\", \"data-model.json\")\n )\n country_referential = CountryReferential.from_csv(\n os.path.join(os.path.dirname(__file__), \"resources\", \"countries_code.csv\")\n )\n form_company_filling = FormCompanyFilling([\n BasicExtractor(\n question_ids=NOT_COUNTRY_QUESTIONS_NUMBERS,\n form_data_model=data_model,\n ),\n BasicCountryExtractor(\n question_ids=COUNTRY_QUESTIONS_NUMBERS,\n form_data_model=data_model,\n country_code_referential=country_referential,\n\n )\n ])\n\n # COMPUTE PREDICTION BY FILE (ie company)\n print(\"##################################\")\n print(\"RUNNING PREDICTION\")\n results: Dict[int, int] = {}\n start_time = time.time()\n for i, path in enumerate(path_to_files):\n start = time.time()\n print(f\"File : {path}\")\n with open(path, \"r\") as input_file:\n text = input_file.read()\n print(\"... Encoding ...\")\n start_encoding = time.time()\n embeddings = model.encode(prepare_sentences(text))\n # embeddings = []\n print(\"Successfully encoded\")\n print(\"Encoding time : \", time.time() - start_encoding)\n\n form_company_response = form_company_filling.fill(text, embeddings, model)\n form_company_response.sort_by_question_id() # ESSENTIAL : Sort the response by question number for each company\n for answer in form_company_response.answers:\n question_number = answer.question_id + i * 22 # ESSENTIAL : each company has 22 questions. Each question_number in results should be unique\n results[question_number] = answer.answer_id\n # gc.collect()\n print(\"File time :\", time.time()-start, '\\n')\n # CHECK FORMAT RESULTS IS DATACHALLENGE PLATFORM COMPATIBLE\n assert len(results) == len(path_to_files) * (len(COUNTRY_QUESTIONS_NUMBERS) + len(NOT_COUNTRY_QUESTIONS_NUMBERS))\n assert set(list(results.keys())) == {i for i in range(1,221)}\n print(\"Full Time\", time.time()-start_time)\n return results","def generate_markdown(self, outdir, definitions):\n doc_cases = defaultdict(lambda: defaultdict(list))\n\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n for category in definitions.categories:\n cat_outdir = os.path.join(outdir, category.replace(\" \", \"_\").lower())\n if not os.path.exists(cat_outdir):\n os.makedirs(cat_outdir)\n\n for case in definitions.case_set:\n if case[\"category\"] == category:\n doc_cases[category][case[\"name\"]] = case\n\n for technique in doc_cases[category]:\n rendered = self._generate_markdown(doc_cases[category][technique])\n md_filename = technique.replace(\" \", \"_\").lower() + \".md\"\n filename = os.path.join(cat_outdir, md_filename)\n mdoutfile = open(filename, \"w\")\n mdoutfile.write(rendered)\n mdoutfile.close()","def get_structure():\n\n _articles = []\n _categories = []\n\n def get_article(article_filename, general_category, sep='|||'):\n \"\"\"\n Adds the given article to the \"articles, categories, general\" current structure\n \"\"\"\n category = []\n with open(article_filename) as _f:\n for _row in _f:\n if len(_row):\n if _row[0] == '=':\n # new category\n k = 0\n while _row[k] == '=':\n k += 1\n if k > 1:\n category = category[:k - 1]\n category += [clean_text(_row)]\n sub_category = []\n elif _row[0] == '#':\n # new entry\n _articles.append(clean_text(_row))\n k = 0\n while _row[k] == '#':\n k += 1\n sub_category = sub_category[:k - 1] + [clean_text(_row)]\n if category[0] == general_category:\n _categories.append(sep.join(category + sub_category[:-1]))\n else:\n _categories.append(sep.join([general_category] + category + sub_category[:-1]))\n\n categories_dict = get_categories('https://en.wikipedia.org/wiki/Wikipedia:Vital_articles/Level/5')\n _general = {k: v.split('/')[5] for k, v in categories_dict.items()}\n filenames = list(categories_dict.keys())\n\n if not os.path.exists('wikivitals/data/mds/'):\n os.makedirs('wikivitals/data/mds/')\n\n for k, v in categories_dict.items(): # saves the category pages' text\n with open('wikivitals/data/mds/{}'.format(k), 'w', encoding='utf8') as f:\n url = \"https://en.wikipedia.org/w/index.php?title={}&action=edit\".format(v[6:])\n page = requests.get(url)\n soup = BeautifulSoup(page.text, 'html.parser')\n f.write(soup.find('textarea').text)\n\n for filename in filenames:\n get_article('wikivitals/data/mds/' + filename, _general[filename])\n\n with open('wikivitals/data/en-categories.txt', 'w', encoding='utf8') as file:\n for cat in _categories:\n file.write(cat + \"\\n\")\n\n with open('wikivitals/data/en-articles.txt', 'w', encoding='utf8') as file:\n for name in _articles:\n file.write(name + \"\\n\")\n\n return _articles, _categories, _general","def dataset_summary(request, project, data_type, week):\n logger = logging.getLogger('ibdmdb')\n\n file_cache_dict = map(str.strip, open(public_cache_file).readlines())\n summary_groups = itertools.groupby(file_cache_dict, lambda x: 'summary' in x and project in x and data_type in x and week in x)\n\n anadama2_static_base = settings.STATIC_ROOT\n\n logger.info(\"Summary groups:\", summary_groups)\n\n # If this is a list > 1 than something went wrong here\n summary_files = []\n\n for (group, items) in summary_groups:\n if not group:\n continue\n\n summary_files.extend(list(items))\n\n if summary_files:\n # We should have a summary.html file, a summary.json file and a\n # collection of images here that we will want to handle.\n #\n # The images and json file should go to our AnADAMA2 static directory\n # to be served with our static content.\n #\n # Quick note here over the adding of the \"/tunnel/dataset_summary\"\n # If we are operating in our current production setting we are proxy'ing \n # requests between a protected server and an unprotected server so we \n # need to add these all for the requests to pass through to the internal \n # server. Messy but needed for the time being.\n anadama2_static_dir = os.path.join(anadama2_static_base,\n 'tunnel',\n 'dataset_summary',\n project,\n data_type, week)\n\n if not os.path.isdir(anadama2_static_dir):\n logger.info('Static dir being created %s' % anadama2_static_dir)\n os.makedirs(anadama2_static_dir)\n \n complete_file = os.path.join(anadama2_static_dir, 'summary', 'complete')\n if not os.path.exists(complete_file):\n logger.info('Creating symylink of all files')\n\n # Pull out the first file; get the basename and symlink that into the static dir folder\n # TODO: Add a check here to see if we retrieve nothing -- which means something bad happened..\n summary_html = next((x for x in summary_files if \"summary.html\" in x), None)\n summary_base = os.path.dirname(summary_html)\n os.symlink(summary_base, os.path.join(anadama2_static_dir, 'summary'))\n open(complete_file, 'a').close()\n\n # Need to add a check in here to make sure that our summary.html file does exist.\n summary_url = os.path.join('/tunnel', 'dataset_summary', \n project, data_type, week, 'summary', 'summary.html')\n\n return HttpResponseRedirect(summary_url)\n return HttpResponse(status=500)","def build_dataset(token_seq, token_seq_index, term_to_index, char_to_index, max_token_length=15, leading_context_size=10, trailing_context_size=10, leading_separator='{', trailing_separator='}', n_examples_per_context=10, n_errors_per_token=[1], n_errors_per_context=[0], dictionary=enchant.Dict('en_US'), seed=17):\n if isinstance(seed, RandomState):\n rng = seed\n else:\n rng = RandomState(seed=seed)\n\n n_contexts = sum([len(token_seq_index[t]) for t in token_seq_index.keys() if len(t) <= max_token_length])\n n_examples = n_examples_per_context * n_contexts\n max_inserts_per_token = max(n_errors_per_token)\n max_inserts_per_context = 2*max(n_errors_per_context)\n max_chars_in_window = leading_context_size + max_token_length + trailing_context_size + len(leading_separator) + len(trailing_separator) + max_inserts_per_token + max_inserts_per_context\n\n error_examples = np.zeros((n_examples, max_chars_in_window), dtype=np.int32)\n error_examples.fill(char_to_index[' '])\n\n leading_contexts = np.zeros((n_examples, leading_context_size + max_inserts_per_context))\n leading_contexts.fill(char_to_index[' '])\n spelling_errors = np.zeros((n_examples, max_token_length + max_inserts_per_token))\n spelling_errors.fill(char_to_index[' '])\n trailing_contexts = np.zeros((n_examples, trailing_context_size + max_inserts_per_context))\n trailing_contexts.fill(char_to_index[' '])\n\n correct_tokens = []\n corrupt_tokens = []\n corrections = np.zeros(n_examples, dtype=np.int32)\n error_types = []\n context_ids = []\n example_ids = []\n term_lens = []\n edit_distance_to_nearest_term = []\n nearest_term = []\n\n print('starting to construct {n_examples} examples'.format(\n n_examples=n_examples))\n\n pbar = progressbar.ProgressBar(term_width=40,\n widgets=[' ', progressbar.Percentage(),\n ' ', progressbar.ETA()],\n maxval=n_examples).start()\n n = 0\n\n index_to_char = dict((i,c) for c,i in char_to_index.iteritems())\n if 63 not in index_to_char.keys():\n # A workaround for now. Delete ASAP.\n index_to_char[63] = '^'\n\n def add_chars_to_array(n, array, chars, default_char=' '):\n for k, ch in enumerate(chars):\n try:\n array[n, k] = char_to_index[ch]\n except KeyError, e:\n array[n, k] = char_to_index[default_char]\n\n for token in token_seq_index.keys():\n if len(token) > max_token_length:\n continue\n\n term_len = len(token)\n distances = min_dictionary_edit_distance([token], dictionary)\n distance = distances[token]['distance']\n term = distances[token]['accepted']\n\n for i in token_seq_index[token]:\n for j in six.moves.range(n_examples_per_context):\n pbar.update(n+1)\n corruptor = rng.choice([insert_characters, delete_characters,\n replace_characters, transpose_characters])\n n_token_errors = rng.choice(n_errors_per_token)\n corrupt_token = corruptor(token, index_to_char, n=n_token_errors, seed=rng)\n\n n_context_errors = rng.choice(n_errors_per_context)\n leading = leading_context(token_seq, i, leading_context_size)\n leading = corruptor(leading, index_to_char, n=n_context_errors, seed=rng)\n trailing = trailing_context(token_seq, i, trailing_context_size)\n trailing = corruptor(trailing, index_to_char, n=n_context_errors, seed=rng)\n\n window = ''.join([leading, leading_separator,\n corrupt_token, trailing_separator, trailing])\n\n add_chars_to_array(n, error_examples, window)\n add_chars_to_array(n, leading_contexts, leading)\n add_chars_to_array(n, spelling_errors, corrupt_token)\n add_chars_to_array(n, trailing_contexts, trailing)\n\n correct_tokens.append(token)\n corrections[n] = term_to_index[token]\n corrupt_tokens.append(corrupt_token)\n error_types.append(corruptor.__name__)\n context_ids.append(i)\n example_ids.append(n)\n edit_distance_to_nearest_term.append(distance)\n nearest_term.append(term)\n term_lens.append(term_len)\n\n n += 1\n\n pbar.finish()\n\n df = pd.DataFrame({\n 'correction': corrections,\n 'error_type': error_types,\n 'context_id': context_ids,\n 'example_id': example_ids,\n 'edit_distance_to_nearest_term': edit_distance_to_nearest_term,\n 'nearest': nearest_term,\n 'term_length': term_lens,\n 'correct_token': correct_tokens,\n 'corrupt_token': corrupt_tokens\n })\n\n def add_columns(df, array, colname_prefix):\n colwidth = int(np.log10(array.shape[1]))+1\n colfmt = colname_prefix + '{col:0' + str(colwidth) + 'd}'\n for col in np.arange(array.shape[1]):\n colname = colfmt.format(col=col)\n df[colname] = array[:, col]\n\n add_columns(df, error_examples, 'full_error')\n add_columns(df, leading_contexts, 'leading_context')\n add_columns(df, spelling_errors, 'spelling_error')\n add_columns(df, trailing_contexts, 'trailing_context')\n\n return df","def gen_tutorials(repo_dir: str) -> None:\n with open(os.path.join(repo_dir, \"website\", \"tutorials.json\"), \"r\") as infile:\n tutorial_config = json.loads(infile.read())\n\n tutorial_ids = [x[\"id\"] for v in tutorial_config.values() for x in v]\n tutorial_dirs = [x.get(\"dir\") for v in tutorial_config.values() for x in v]\n\n for tid, t_dir in zip(tutorial_ids, tutorial_dirs):\n print(\"Generating {} tutorial\".format(tid))\n\n if t_dir is not None:\n tutorial_dir = os.path.join(repo_dir, \"tutorials\", t_dir)\n html_dir = os.path.join(repo_dir, \"website\", \"_tutorials\", t_dir)\n js_dir = os.path.join(repo_dir, \"website\", \"pages\", \"tutorials\", t_dir)\n py_dir = os.path.join(repo_dir, \"website\", \"static\", \"files\", t_dir)\n\n for d in [tutorial_dir, html_dir, js_dir, py_dir]:\n os.makedirs(d, exist_ok=True)\n\n tutorial_path = os.path.join(tutorial_dir, \"{}.ipynb\".format(tid))\n html_path = os.path.join(html_dir, \"{}.html\".format(tid))\n js_path = os.path.join(js_dir, \"{}.js\".format(tid))\n ipynb_path = os.path.join(py_dir, \"{}.ipynb\".format(tid))\n py_path = os.path.join(py_dir, \"{}.py\".format(tid))\n tar_path = os.path.join(py_dir, \"{}.tar.gz\".format(tid))\n else:\n tutorial_path = os.path.join(repo_dir, \"tutorials\", \"{}.ipynb\".format(tid))\n html_path = os.path.join(\n repo_dir, \"website\", \"_tutorials\", \"{}.html\".format(tid)\n )\n js_path = os.path.join(\n repo_dir, \"website\", \"pages\", \"tutorials\", \"{}.js\".format(tid)\n )\n ipynb_path = os.path.join(\n repo_dir, \"website\", \"static\", \"files\", \"{}.ipynb\".format(tid)\n )\n py_path = os.path.join(\n repo_dir, \"website\", \"static\", \"files\", \"{}.py\".format(tid)\n )\n\n # convert notebook to HTML\n with open(tutorial_path, \"r\") as infile:\n nb_str = infile.read()\n nb = nbformat.reads(nb_str, nbformat.NO_CONVERT)\n\n exporter = HTMLExporter()\n html, meta = exporter.from_notebook_node(nb)\n\n # pull out html div for notebook\n soup = BeautifulSoup(html, \"html.parser\")\n nb_meat = soup.find(\"div\", {\"id\": \"notebook-container\"})\n del nb_meat.attrs[\"id\"]\n nb_meat.attrs[\"class\"] = [\"notebook\"]\n\n # when output html, iframe it (useful for Ax reports)\n for html_div in nb_meat.findAll(\"div\", {\"class\": \"output_html\"}):\n if html_div.html is not None:\n iframe = soup.new_tag(\"iframe\")\n iframe.attrs[\"src\"] = \"data:text/html;charset=utf-8,\" + str(\n html_div.html\n )\n # replace `#` in CSS\n iframe.attrs[\"src\"] = iframe.attrs[\"src\"].replace(\"#\", \"%23\")\n html_div.contents = [iframe]\n\n html_out = MOCK_JS_REQUIRES + str(nb_meat)\n\n # generate HTML file\n with open(html_path, \"w\") as html_outfile:\n html_outfile.write(html_out)\n\n # generate JS file\n t_dir_js = t_dir if t_dir else \"\"\n script = TEMPLATE.format(t_dir=t_dir_js, tid=tid)\n with open(js_path, \"w\") as js_outfile:\n js_outfile.write(script)\n\n # output tutorial in both ipynb & py form\n with open(ipynb_path, \"w\") as ipynb_outfile:\n ipynb_outfile.write(nb_str)\n exporter = ScriptExporter()\n script, meta = exporter.from_notebook_node(nb)\n with open(py_path, \"w\") as py_outfile:\n py_outfile.write(script)\n\n # create .tar archive (if necessary)\n if t_dir is not None:\n with tarfile.open(tar_path, \"w:gz\") as tar:\n tar.add(tutorial_dir, arcname=os.path.basename(tutorial_dir))","def update_demo(self):\n debug = False\n if not self.settings_data:\n lib.OutputLib.update_html_ctrl(self.html, WAITING_MSG)\n return\n if debug: print(self.settings_data)\n ## 1) part before the table-specific items e.g. column names and data\n html = [mg.DEFAULT_HDR % {'title': 'Demonstration table',\n 'css': self.styles, 'dojo_insert': ''}]\n html.append(\"<table cellspacing='0'>\\n<thead>\\n<tr>\")\n ## 2) the table-specific items (inc column labels)\n ## list based on sequence of fields in underlying table\n db_flds_orig_names = [] ## will be the key for any dicts taken from db\n ## lists based on sequence of fields in (re)design\n design_flds_orig_names = [] ## NB will be None for new or inserted flds.\n ## Ordered as per list of variables in design.\n design_flds_new_names = []\n design_flds_col_labels = []\n design_flds_types = []\n for data_dict in self.settings_data:\n ## all must have same num of elements (even if a None) in same order\n fldname = data_dict[mg.TBL_FLDNAME]\n design_flds_new_names.append(fldname)\n design_flds_col_labels.append(\n self.var_labels.get(fldname, fldname.title()))\n design_flds_orig_names.append(data_dict.get(mg.TBL_FLDNAME_ORIG))\n design_flds_types.append(data_dict[mg.TBL_FLDTYPE]) \n if data_dict.get(mg.TBL_FLDNAME_ORIG) is not None:\n db_flds_orig_names.append(data_dict[mg.TBL_FLDNAME_ORIG]) \n if debug:\n print(db_flds_orig_names)\n print(design_flds_orig_names)\n print(design_flds_new_names)\n print(design_flds_col_labels)\n print(design_flds_types)\n ## column names\n for col_label in design_flds_col_labels:\n html.append(f'<th>{col_label}</th>')\n ## get data rows (list of lists)\n display_n = 4 ## demo rows to display\n if self.new:\n rows = []\n for i in range(display_n):\n row_lst = self.get_demo_row_lst(\n i, design_flds_col_labels, design_flds_types)\n rows.append(row_lst)\n else:\n rows = self.get_real_demo_data(display_n, db_flds_orig_names,\n design_flds_orig_names, design_flds_new_names,\n design_flds_col_labels, design_flds_types)\n ## data rows into html\n for row in rows:\n html.append('</tr>\\n</thead>\\n<tbody><tr>')\n for raw_val in row:\n html.append(f'<td>{raw_val}</td>')\n html.append('</tr>')\n html.append('\\n</tbody>\\n</table></body></html>')\n str_content = ''.join(html)\n lib.OutputLib.update_html_ctrl(self.html, str_content)","def _generate_examples(self, data_dir=None, archive=None, label_path=None, process=None):\n if process == 'train':\n abnormal_train = pd.read_csv(os.path.join(data_dir, \"train-abnormal.csv\"), names=[\"Patient_Number\", \"abnormal\"])\n ACL_train = pd.read_csv(os.path.join(data_dir, \"train-acl.csv\"), names=[\"Patient_Number\", \"ACL\"])\n meniscus_train = pd.read_csv(os.path.join(data_dir, \"train-meniscus.csv\"), names=[\"Patient_Number\", \"meniscus\"])\n abnormal_acl_train = abnormal_train.merge(ACL_train, on=\"Patient_Number\")\n ab_acl_meni_train = abnormal_acl_train.merge(meniscus_train, on=\"Patient_Number\")\n else:\n abnormal_test = pd.read_csv(os.path.join(data_dir, \"valid-abnormal.csv\"), names=[\"Patient_Number\", \"abnormal\"])\n ACL_test = pd.read_csv(os.path.join(data_dir, \"valid-acl.csv\"), names=[\"Patient_Number\", \"ACL\"])\n meniscus_test = pd.read_csv(os.path.join(data_dir, \"valid-meniscus.csv\"), names=[\"Patient_Number\", \"meniscus\"])\n abnormal_acl_test = abnormal_test.merge(ACL_test, on=\"Patient_Number\")\n ab_acl_meni_test = abnormal_acl_test.merge(meniscus_test, on=\"Patient_Number\")\n\n def sumup(row):\n if (row['abnormal'] == 1) & (row[\"ACL\"] == 1) & (row[\"meniscus\"] == 1):\n val = \"Both_ACL_Meniscus\"\n elif (row['abnormal'] == 1) & (row[\"ACL\"] == 1) & (row[\"meniscus\"] == 0):\n val = \"ACL\"\n elif (row['abnormal'] == 1) & (row[\"ACL\"] == 0) & (row[\"meniscus\"] == 1):\n val = \"Meniscus\"\n elif (row['abnormal'] == 1) & (row[\"ACL\"] == 0) & (row[\"meniscus\"] == 0):\n val = \"abnormal\"\n elif row['abnormal'] == 0:\n val = \"normal\"\n return val\n if process == 'train':\n ab_acl_meni_train['sumup'] = ab_acl_meni_train.apply(sumup, axis=1)\n ab_acl_meni_train[['Patient_Number', \"sumup\"]].to_csv(os.path.join(data_dir, \"train_labels.csv\"), header=False)\n else:\n ab_acl_meni_test['sumup'] = ab_acl_meni_test.apply(sumup, axis=1)\n ab_acl_meni_test[['Patient_Number', \"sumup\"]].to_csv(os.path.join(data_dir, \"valid_labels.csv\"), header=False)\n\n all_labels = pd.read_csv(label_path, names=[\"Patient_Number\", \"label\"])\n\n count = 0\n archive_list = [os.path.join(archive, \"axial\"), os.path.join(archive, \"coronal\"), os.path.join(archive, \"sagittal\")]\n for direction in archive_list:\n files = []\n for r, d, f in tf.io.gfile.walk(direction):\n for file in f:\n if '.npy' in file:\n files.append(os.path.join(r, file))\n sorted_files = sorted(files)\n\n for path_cube in sorted_files:\n cube = np.load(tf.io.gfile.GFile(path_cube, mode='rb'))\n for picture in cube:\n example_image = picture[..., np.newaxis]\n example_label = (all_labels.loc[all_labels['Patient_Number'] ==\n int(os.path.splitext(os.path.basename(path_cube))[0][:4]),\n 'label'].iloc[0])\n count += 1\n yield count, {\n 'image': example_image,\n 'label': example_label,\n }","def _generate_examples(self, files):\n idx = 0\n for filename in files:\n with open(filename) as file:\n for line in file:\n yield idx, {\"text\": line}\n idx += 1","def build_index(path, limit=None):\n\n documents = {}\n doc_lengths = {}\n index = {}\n j = 0 # Counter for articles\n for i in range(0, 22):\n if i >= 10:\n file = open(path + \"reut2-0\" + str(i) + \".sgm\", encoding='latin-1')\n else:\n file = open(path + \"reut2-00\" + str(i) + \".sgm\", encoding='latin-1')\n\n # Parsing html pages and getting reuters tagged once\n soup = BeautifulSoup(file, \"html.parser\")\n articles = soup.find_all('reuters')\n\n for article in articles:\n\n body = \"\"\n title = \"\"\n newid = int(article['newid'])\n\n try:\n body = article.body.get_text()\n except AttributeError:\n pass\n\n try:\n title = article.title.get_text()\n except AttributeError:\n pass\n\n words_list = title + \"\\n\" + body\n\n # Adding title+body to documents dictionary\n documents[newid] = words_list\n\n # Processing document and adding document lengths to dictionary\n processed_doc = preprocess(documents[newid])\n doc_lengths[newid] = len(processed_doc)\n\n # Adding word to index\n for term in processed_doc:\n if term in index:\n term_freq, docs_dict = index[term]\n\n term_freq += 1\n if newid in docs_dict:\n docs_dict[newid] += 1\n else:\n docs_dict[newid] = 1\n\n index[term] = (term_freq, docs_dict)\n else:\n docs_dict = {newid: 1}\n index[term] = (1, docs_dict)\n j += 1\n # Checking limit on articles\n if limit is not None:\n if j == limit:\n break\n\n # Checking limit on articles\n if limit is not None:\n if j == limit:\n break\n\n for term in index:\n term_freq, docs_dict = index[term]\n index[term] = [term_freq] + list(docs_dict.items())\n\n if limit is None:\n save_obj(index, \"reuters_index\")\n save_obj(documents, \"reuters_documents\")\n save_obj(doc_lengths, \"reuters_doc_length\")\n\n return index","def generatePerWordHTML(testDataWithFeatures, \n allLabels, classifications, colors):\n \n assert (len(testDataWithFeatures)-1) % len(allLabels) == 0\n numWords = (len(testDataWithFeatures) - 1) / len(allLabels) + 1 \n \n allLabelsDict = dict((label,index) for index,label in enumerate(allLabels)) \n \n perWordHTML = []\n for i in xrange(numWords):\n if classifications:\n word, correctClass, guessClass = classifications[i]\n color = colors[(correctClass, guessClass)]\n else:\n datum = getDatum(testDataWithFeatures, \n allLabelsDict, i, allLabels[0])\n word = datum.word\n color = colors[datum.label, datum.label]\n html = [('<a href=\"#\" onmouseover=\"a({0});\" '\n + 'onmouseout=\"b({0});\">').format(i), \n '<span style=\"color:{}\">{}</span>'.format(\n color, escape(word)),\n '</a>']\n \n if word in \".!?\":\n html.append(\"<br/>\")\n \n perWordHTML.append(\"\".join(html)) \n return perWordHTML","def process_lab_prn(self):\n self.gen_hts_lab_fts()\n self.gen_hts_lab_full()","def build_model(num_topics=30):\n data = utils.read_wiki(\"wiki.train.tokens\")\n\n # preprocessing: remove too frequent words, stopwords ...\n logger.info(\"Start preprocessing, this will take quite some time ...\")\n list_of_tokens, bigrams = preprocess(data)\n\n id2word = corpora.Dictionary(list_of_tokens)\n id2word.filter_extremes(no_below=5, no_above=0.6, keep_n=VOCAB_SIZE)\n logger.info(f\"Done processing dataset len, vocab len {len(id2word.keys())}, {len(list_of_tokens)}\")\n \n # convert data into df vectors\n corpus = [id2word.doc2bow(tokens) for tokens in list_of_tokens]\n\n for num_topics in range(10, 100, 6):\n lda_model = LdaModel(corpus, num_topics=num_topics,\n id2word=id2word,\n passes=20,\n iterations=400,\n # alpha=[0.01]*num_topics,\n alpha=\"auto\",\n # eta=[0.01] * VOCAB_SIZE,\n eta=\"auto\")\n \n # save the model\n path = pathlib.Path(f\"{SAVING_DIR}/lda_topic_{num_topics}\")\n path.mkdir(parents=True, exist_ok=True)\n path = path / \"lda.model\"\n lda_model.save(str(path.absolute()))\n id2word.save(UNIGRAM_FILE)\n bigrams.save(BIGRAM_FILE)\n\n # visualize topics by LDAviz\n vis = gensimvis.prepare(topic_model=lda_model, corpus=corpus, dictionary=id2word)\n pathlib.Path(\"lda_vizs\").mkdir(parents=True, exist_ok=True)\n pyLDAvis.save_html(vis, f'lda_vizs/lda_visualization_{num_topics}.html')\n return id2word, bigrams, lda_model","def create_index_tfidf(lines, numDocuments):\n \n index=collections.defaultdict(list)\n tf=collections.defaultdict(list) #term frequencies of terms in documents (documents in the same order as in the main index)\n df=collections.defaultdict(int) #document frequencies of terms in the corpus\n idf=collections.defaultdict(float)\n with Bar('Creating tf-idf index', max=len(lines)) as bar:\n for key in lines:\n page_id = key \n terms = getTerms(lines[key]) \n\n ## create the index for the **current page** and store it in termdictPage\n ## termdictPage in form ==> { ‘term1’: [currentdoc, [list of positions]], ...,‘termn’: [currentdoc, [list of positions]]}\n\n termdictPage={}\n\n for position, term in enumerate(terms): \n try:\n # if the term is already in the dict append the position to the corrisponding list\n termdictPage[term][1].append(position) \n except:\n # Add the new term as dict key and initialize the array of positions and add the position\n termdictPage[term]=[page_id, array('I',[position])] \n\n #normalize term frequencies\n norm=0\n for term, posting in termdictPage.items(): \n # posting ==> [currentdoc, [list of positions]] \n norm+=len(posting[1])**2\n norm=math.sqrt(norm)\n\n\n #calculate the tf(dividing the term frequency by the above computed norm) and df weights\n for term, posting in termdictPage.items(): \n # append the tf for current term (tf = term frequency in current doc/norm)\n tf[term].append(np.round(len(posting[1])/norm,4)) ## SEE formula (1) above\n #increment the document frequency of current term (number of documents containing the current term)\n df[term] += 1 \n\n #merge the current page index with the main index\n for termpage, postingpage in termdictPage.items():\n index[termpage].append(postingpage)\n\n # Compute idf following the formula (3) above. HINT: use np.log\n bar.next()\n for term in df:\n idf[term] = np.round(np.log(float(numDocuments/df[term])),4)\n \n return (index, tf, df, idf)","def main():\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \"original\")\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \"annotated\")\n\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\n os.mkdir(ANNOTATED_LESSONS_PATH)\n\n print(\"Scanning original lessons in %s...\" % ORIGINAL_LESSONS_PATH)\n\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\n if item == \".DS_Store\": continue\n\n print(\" found: %s\" % item)\n\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\n\n lesson_number = None\n lesson_description = None\n mobj = re.search(r'^AY\\s+(\\d+)\\s*-\\s*(.+)\\.txt$', item)\n if mobj:\n lesson_number = mobj.group(1)\n lesson_description = mobj.group(2)\n\n print(\" number: %s\" % lesson_number)\n print(\" description: %s\" % lesson_description)\n\n lesson = dict()\n lesson['number'] = lesson_number\n lesson['description'] = lesson_description\n\n fh = open(item_path)\n lesson_raw_text = fh.read()\n fh.close()\n lesson_text = re.split(r'\\n', lesson_raw_text)\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\n# lesson_text = re.split(r'\\n', lesson_raw_text_reencoded)\n\n lesson['text'] = lesson_text\n lesson['parsed'] = parseLesson(lesson_text)\n\n if lesson['parsed']['end_of_lesson'] is None:\n print(\" lesson has no 'end of lesson' marker\")\n\n lesson_json = json.dumps(lesson, indent=4)\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \"ay_%04d.json\" % int(lesson_number))\n fh = open(annotated_lesson_path, \"w\")\n fh.write(lesson_json)\n fh.close()\n\n else:\n print(\"ERROR: File name not understood: %s\" % item)\n\n return 0","def write_txt(self):\n random.seed(self.seed)\n sites = ['BIDMC', 'HK', 'I2CVB', 'ISBI', 'ISBI_15', 'UCL']\n site_files, site_masks = self.read_file_names()\n\n for idx in range(len(site_files)):\n # get files name\n files = site_files[idx]\n segmentation = site_masks[idx]\n len_files = len(files)\n site_label = sites[idx]\n # shuffle the patient's file\n indices = list(range(len_files))\n random.shuffle(indices)\n\n if self.split_te == None:\n # get list of indices\n split = int(np.floor(self.split_val*len_files))\n train_idx = indices[split:]\n valid_idx = indices[:split]\n split_un_sup = int(self.split_sup * len(train_idx))\n train_sup_idx = train_idx[split_un_sup:]\n train_unsup_idx = train_idx[:split_un_sup]\n # slice the original list with the indices\n files_train_sup = [files[i] for i in train_sup_idx]\n files_train_unsup = [files[i] for i in train_unsup_idx]\n masks_train_sup = [segmentation[i] for i in train_sup_idx]\n masks_train_unsup = [segmentation[i] for i in train_unsup_idx]\n files_val = [files[i] for i in valid_idx]\n masks_val = [segmentation[i] for i in valid_idx]\n # write txt file\n self.generate_txt('train_supervised_files_tr_val_' + site_label + '.txt', files_train_sup, masks_train_sup)\n self.generate_txt('train_unsupervised_files_tr_val_' + site_label + '.txt', files_train_unsup, masks_train_unsup)\n self.generate_txt('val_files_tr_val_' + site_label + '.txt', files_val, masks_val)\n else:\n # get list of indices\n split_tr_te = int(np.floor(self.split_te*len_files))\n train_idx_tmp = indices[split_tr_te:]\n test_idx = indices[:split_tr_te]\n split_tr_val = int(self.split_val*len(train_idx_tmp))\n train_idx = train_idx_tmp[split_tr_val:]\n valid_idx = train_idx_tmp[:split_tr_val]\n split_un_sup = int(self.split_sup * len(train_idx))\n train_sup_idx = train_idx[split_un_sup:]\n train_unsup_idx = train_idx[:split_un_sup]\n # slice the original list with the indices\n files_train_sup = [files[i] for i in train_sup_idx]\n files_train_unsup = [files[i] for i in train_unsup_idx]\n masks_train_sup = [segmentation[i] for i in train_sup_idx]\n masks_train_unsup = [segmentation[i] for i in train_unsup_idx]\n files_test = [files[i] for i in test_idx]\n masks_test = [segmentation[i] for i in test_idx]\n files_val = [files[i] for i in valid_idx]\n masks_val = [segmentation[i] for i in valid_idx]\n # write txt file\n self.generate_txt('train_supervised_files_tr_te_val_' + site_label + '.txt', files_train_sup, masks_train_sup)\n self.generate_txt('train_unsupervised_files_tr_te_val_' + site_label + '.txt', files_train_unsup, masks_train_unsup)\n self.generate_txt('test_files_tr_te_val_'+site_label+'.txt', files_test, masks_test)\n self.generate_txt('val_files_tr_te_val_'+site_label+'.txt', files_val, masks_val)","def dataframe_for_domain(domain, algorithm):\n df = []\n files = glob.glob(os.path.join(outdir, algorithm, domain, '*', '*.txt'))\n for filename in files:\n query, reference = os.path.basename(filename).replace('.txt', '').split('_vs_')\n try:\n _df = pd.read_csv(filename, comment='#', sep='\\t')\n except pd.errors.EmptyDataError:\n _df = pd.DataFrame([dict(value=np.nan)])\n\n _df['query'] = query\n _df['reference'] = reference\n df.append(\n _df.iloc[0].to_dict()\n )\n return pd.DataFrame(df)","def walk_experimental(project_id, version=None):\n from urllib import parse\n doc = IndexedPublication.from_id(project_id, revision=version)\n relation_map = []\n\n project_meta = format_metadata_for_fedora(project_id, version=version)\n if version:\n project_id = '{}v{}'.format(project_id, str(version))\n license = project_meta.get('license', None)\n full_author_list = []\n project_map = {\n 'uuid': doc.project.uuid,\n 'container_path': project_id,\n 'fedora_mapping': {**project_meta, 'generated': [], 'license': None},\n 'fileObjs': []\n }\n\n experiments_list = doc.experimentsList\n for expt in experiments_list:\n # Do stuff with experiment.\n expt_container_path = \"{}/{}\".format(project_id, parse.quote(expt.value.title))\n print('experiment ' + expt.value.title)\n exp_doi = expt.doi\n project_map['fedora_mapping']['generated'].append('Experiment: {}'.format(exp_doi))\n\n experiment_map = {\n 'uuid': expt.uuid,\n 'container_path': expt_container_path,\n 'fedora_mapping': {**format_experiment(expt), 'license': license, 'wasGeneratedBy': project_id, 'generated': []},\n 'fileObjs': expt.fileObjs\n }\n\n full_author_list += experiment_map['fedora_mapping']['creator']\n\n reports = filter(\n lambda report: expt.uuid in report.value.experiments,\n getattr(doc, 'reportsList', []))\n for report in reports:\n # Do stuff with report.\n report_container_path = \"{}/{}\".format(expt_container_path, parse.quote(report.value.title))\n print('\\treport ' + report.value.title)\n experiment_map['fedora_mapping']['generated'].append('Report: {}'.format(report.value.title))\n\n report_map = {\n 'uuid': report.uuid,\n 'fileObjs': report.fileObjs,\n 'container_path': report_container_path,\n 'fedora_mapping': {**format_report(report), 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi)}\n }\n relation_map.append(report_map)\n\n analysis_list = filter(\n lambda analysis: expt.uuid in analysis.value.experiments,\n getattr(doc, 'analysisList', []))\n for analysis in analysis_list:\n # Do stuff with analysis.\n analysis_container_path = \"{}/{}\".format(expt_container_path, parse.quote(analysis.value.title))\n print('\\tanalysis ' + analysis.value.title)\n experiment_map['fedora_mapping']['generated'].append('Analysis: {}'.format(analysis.value.title))\n\n analysis_map = {\n 'uuid': analysis.uuid,\n 'fileObjs': analysis.fileObjs,\n 'container_path': analysis_container_path,\n 'fedora_mapping': {**format_analysis(analysis), 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi)}\n\n }\n relation_map.append(analysis_map)\n\n model_configs = filter(\n lambda model_config: expt.uuid in model_config.value.experiments,\n getattr(doc, 'modelConfigs', []))\n for mc in model_configs:\n # Do stuff with model config.\n configs_container_path = \"{}/{}\".format(expt_container_path, parse.quote(mc.value.title))\n print('\\tmodel config ' + mc.value.title)\n experiment_map['fedora_mapping']['generated'].append('Model Configuration: {}'.format(mc.value.title))\n\n mc_map = {\n 'uuid': mc.uuid,\n 'fileObjs': mc.fileObjs,\n 'container_path': configs_container_path,\n 'fedora_mapping': {**format_model_config(mc), 'wasGeneratedBy': exp_doi}\n }\n\n sensor_lists = filter(\n lambda sensor_list: mc.uuid in sensor_list.value.modelConfigs and expt.uuid in sensor_list.associationIds,\n getattr(doc, 'sensorLists', []))\n for sl in sensor_lists:\n # Do stuff with sensor list.\n sl_container_path = \"{}/{}\".format(configs_container_path, parse.quote(sl.value.title))\n print('\\t\\tsensor list ' + sl.value.title)\n experiment_map['fedora_mapping']['generated'].append('Sensor: {}'.format(sl.value.title))\n\n sl_map = {\n 'uuid': sl.uuid,\n 'fileObjs': sl.fileObjs,\n 'container_path': sl_container_path,\n 'fedora_mapping': {**format_sensor_info(sl),\n 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi),\n 'wasDerivedFrom': 'Model Configuration: {}'.format(mc.value.title),\n 'influenced': []}\n }\n\n events = filter(\n lambda event: sl.uuid in event.value.sensorLists and expt.uuid in event.associationIds and mc.uuid in event.associationIds,\n getattr(doc, 'eventsList', []))\n for event in events:\n # Do stuff with events.\n evt_container_path = \"{}/{}\".format(sl_container_path, parse.quote(event.value.title))\n print('\\t\\t\\tevent ' + event.value.title)\n sl_map['fedora_mapping']['influenced'].append('Event: {}'.format(event.value.title))\n experiment_map['fedora_mapping']['generated'].append('Event: {}'.format(event.value.title))\n\n event_map = {\n 'uuid': event.uuid,\n 'fileObjs': event.fileObjs,\n 'container_path': evt_container_path,\n 'fedora_mapping': {**format_event(event),\n 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi),\n 'wasDerivedFrom': 'Model Configuration: {}'.format(mc.value.title),\n 'wasInfluencedBy': 'Sensor: {}'.format(sl.value.title)}\n }\n relation_map.append(event_map)\n relation_map.append(sl_map)\n relation_map.append(mc_map)\n relation_map.append(experiment_map)\n project_map['fedora_mapping']['creator'] = list(set(full_author_list))\n relation_map.append(project_map)\n\n return relation_map[::-1]","def main():\n # %%\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\n print( f'{len(fpaths)} htmls found' )\n # %%\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\n # %%\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\n # %%\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\n # %%\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\n # %%\n dics = {}\n # %%\n\n for i, fpath in enumerate(fpaths):\n if fpath in dics:\n continue\n\n with fpath.open('rt') as f_in:\n html = f_in.read()\n\n print( f'\\n***{i+1}/{len(fpaths)} {fpath.name}:')\n dic = extract_one( html, fpath )\n dic['linkedin_url'] = f\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\"\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\n # pprint(dic['work_stats'])\n dics[fpath] = dic\n\n dics_arr = list(dics.values())\n # %%\n del dics\n # %%\n\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\n # %%\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\n yaml.safe_dump( dics_arr, f_out )\n # %%\n df = produce_summary_table( dics_arr )\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\n index=False)\n # %%","def per_sample_dot_files(self):\n # The output directory #\n directory = DirectoryPath(self.a.out_dir+'per_sample_ontology/')\n directory.create_if_not_exists()\n # Main loop #\n for i, sample in self.df_sample_concepts.iteritems():\n # File path #\n sanitized_name = \"\".join([c for c in sample.name if re.match(r'\\w', c)])\n dot_path = directory + sanitized_name +'.dot'\n pdf_path = directory + sanitized_name +'.pdf'\n # Counts #\n counts = sample / sample.sum()\n counts = dict(counts)\n envos = counts.keys()\n # Skip sample if it has no counts #\n if sample.sum() == 0: continue\n # Make graph #\n graph = self.a.ontology.get_subgraph(envos)\n graph = self.a.ontology.add_weights(graph, counts)\n graph = self.a.ontology.add_style(graph)\n # Write output #\n self.a.ontology.write_to_dot(graph, dot_path)\n self.a.ontology.add_legend(dot_path)\n self.a.ontology.draw_to_pdf(dot_path, pdf_path)","def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLinks()\n part3 = \"\"\"</div>\n <div id=\"results\">\n <iframe id=\"loadHTMLResults\" name=\"loadHTMLResults\" frameborder=\"0\" src=\"statistics.html\" style=\"height:100%;width:100%;\"></iframe>\n </div>\n <div id=\"footer\">Test Engineer(s) :\"\"\"+testEngineers+\"\"\"</div>\n </div>\n </body>\n </html>\n\t\t<style>\n\t\tbody{\n margin:0;\n }\n\t\t#pg_body{\n\t\twidth=100%;\n\t\ttext-align:center;\n\t\t}\n\t\t#testSuitesTitle{\n\t\twidth:25%;\n\t\tfloat:left;\n\t\tbackground-color:#6495ED;\n\t\tfont-weight:bold;\n\t\tcolor:white;\n\t\t}\n\t\t#resultsTitle{\n\t\twidth:75%;\n\t\tfloat:right;\n\t\tbackground-color:#6495ED;\n\t\tfont-weight:bold;\n\t\tcolor:white;\n\t\t}\n\t\t#testSuites{\n\t\twidth:25%;\n\t\tfloat:left;\n\t\tbackground-color:lightgrey;\n\t\tfont-weight:bold;\n\t\ttext-align:left;\n\t\theight:94%;\n\t\toverflow:scroll;\n\t\t}\n\t\t#results{\n\t\twidth:75%;\n\t\tbackground-color:white;\n\t\tfloat:right;\n\t\ttext-align:left;\n\t\theight:94%;\n\t\toverflow:scroll;\n\t\t}\n\t\t#footer{\n\t\twidth:100%;\n\t\ttext-align:left;\n\t\tcolor:lightgrey;\n\t\tbackground-color:#6495ED;\n\t\t}\n\t\t</style>\n \"\"\"\n \n page = (part1+part2+part3)\n f = open(self.dir+'/index.html','w')\n f.write(page)\n f.close","def walkthrough(software_map):\n\n for i in software_map:\n\n if not i[\"is_file\"]:\n\n # for each directory: make a index.md\n dname = \"./docs/\" + i[\"name\"]\n index = \"./docs/\" + i[\"name\"] + \"/index.md\"\n print(index)\n os.mkdir(dname)\n\n with open(index, \"w+\") as f:\n\n children = i[\"children\"]\n\n # list files\n f.write(\"Files:\\n\\n\")\n for i in children:\n if i[\"is_file\"]:\n\n fname = i[\"name\"]\n fext = fname.split(\".\")\n if len(fext) == 2:\n fext = fext[1]\n else:\n fext = \"none\"\n # for each file, note name and extension\n f.write(fname + \" : \" + fext + \"\\n\")\n\n # list subdirectories\n f.write(\"\\nSubdirectories:\\n\\n\")\n for i in children:\n if not i[\"is_file\"]:\n\n dirname = i[\"name\"]\n\n # note the number of files and subdirs in it\n num_files, num_dirs = 0, 0\n for child in i[\"children\"]:\n if child[\"is_file\"]:\n num_files += 1\n elif not child[\"is_file\"]:\n num_dirs += 1\n\n # note down name and numbers for each dir\n f.write(dirname + \" : \" + str(num_files) + \" files, \" +\n str(num_dirs) + \" directories\\n\")\n\n # goto subdir\n if len(i[\"children\"]) > 0:\n walkthrough(i[\"children\"])","def create_trianing_instances(document_files, mentions_files, tokenizer, max_seq_length,\n\t\t\t\t\t\t\t\trng, is_training=True):\n\tdocuments = {}\n\tfor input_file in document_files:\n\t\twith open(documents_file + \"/\"+ input_file, \"r\") as reader:\n\t\t\twhile True:\n\t\t\t\tline = reader.readline().strip()\n\t\t\t\tif not line:\n\t\t\t\t\tbreak\n\t\t\t\tline = json.loads(line)\n\t\t\t\tdocuments[line['document_id']] = line\n\n\tmentions = []\n\tfor input_file in mentions_files:\n\t\twith open(input_file, \"r\") as reader:\n\t\t\twhile True:\n\t\t\t\tline = reader.readline().strip()\n\t\t\t\t\n\t\t\t\tif not line:\n\t\t\t\t\tbreak\n\t\t\t\tline = json.loads(line)\n\t\t\t\tmentions.append(line)\n\n\n\ttfidf_candidates = {}\n\twith open(candidates_file, \"r\") as reader:\n\t\twhile True:\n\t\t\tline = reader.readline().strip()\n\n\t\t\tif not line:\n\t\t\t\tbreak\n\t\t\td = json.loads(line)\n\t\t\ttfidf_candidates[d['mention_id']] = d['tfidf_candidates']\n\n\tif split_by_domain:\n\t\tinstances = {}\n\telse:\n\t\tinstances = [] \n\n\tprint(\"--- Total {} mention links ---\".format(len(mentions))) \n\tfor i, mention in enumerate(mentions):\n\t\t# print(\"mention {} \".format(i))\n\t\tinstance = create_instances_from_mention_link(\n\t\t\t\tmention, documents, tfidf_candidates, tokenizer, max_seq_length,\n\t\t\t\trng, is_training=is_training)\n\n\t\tif instance:\n\t\t\tif split_by_domain:\n\t\t\t\tcorpus = mention['corpus']\n\t\t\t\tif corpus not in instances:\n\t\t\t\t\tinstances[corpus] = []\n\t\t\t\tinstances[corpus].append(instance)\n\t\t\telse:\n\t\t\t\tinstances.append(instance)\n\t\t# else:\n\t\t# \tprint(i)\n\n\t\tif i > 0 and i % 1000 == 0:\n\t\t\tprint(\"Instance: %d\" % i)\n\n\tif is_training:\n\t\tif split_by_domain:\n\t\t\tfor corpus in instances:\n\t\t\t\trng.shuffle(instances[corpus])\n\t\telse:\n\t\t\trng.shuffle(instances)\n\n\treturn instances","def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\n def is_whitespace(c):\n if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n return True\n return False\n\n if is_training:\n subset_list = ['train']\n else:\n subset_list = ['dev', 'test']\n\n examples, features = {}, {}\n for subset in subset_list:\n level_example_dict = {\"high\": None, \"middle\": None}\n level_features_dict = {\"high\": None, \"middle\": None}\n\n for level in ['high', 'middle']:\n subset_dir = os.path.join(data_dir, subset)\n file_list = os.listdir(subset_dir)\n file_list = [file for file in file_list if file.endswith('txt')]\n file_list = sorted(file_list)\n\n alldata = []\n for file in file_list:\n data = json.load(open(os.path.join(subset_dir, file)))\n alldata.append(data)\n \n examples_list = []\n for data in alldata:\n doc_id = data['id']\n doc = data[\"article\"].replace('\\\\n', '\\n')\n doc_token = []\n prev_is_whitespace = True\n for c in doc:\n if is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n doc_token.append(c)\n else:\n doc_token[-1] += c\n prev_is_whitespace = False\n\n for i, answer in enumerate(data[\"answers\"]):\n if answer == \" \":\n answer = \"C\"\n example = InputExample(\n guid=doc_id + '-%d' % i,\n doc_token=doc_token,\n question_text=\"\",\n options=data[\"options\"],\n answer=answer)\n examples_list.append(example)\n\n level_example_dict[level] = examples_list\n level_features_dict[level] = convert_examples_to_features(\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\n \n print(\"MuTuaL Data Loader....\")\n print(level_example_dict[level][-1])\n print(level_features_dict[level][-1])\n examples[subset] = level_example_dict\n features[subset] = level_features_dict\n\n return examples, features","def problem4() -> None:\n \n # Obtain the dataframe containing lines from the articles and the keyword\n # with which the article is associated.\n try:\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \n except FileNotFoundError:\n raise Exception(\"[ERROR] The 'articles' folder was not found. Please \"\n \"ensure that the functions for problems 1 and 2 have \"\n \"been applied first.\")\n \n keywords, corpus = separate_keywords_corpus(kw_df)\n \n # Complete the preprocessing for the corpus for comparison's sake\n w2vp = W2VPreprocessor(keywords, corpus)\n \n # Display the counts of sentences collected associated with each keyword\n keywords_countplot(kw_df)\n \n # Create displots for the lengths of sentences in the corpus\n displot_all_sentence_lengths(kw_df)\n displot_sentence_lengths_per_keyword(kw_df)\n \n # Visualise the semantic distances\n try:\n distances = read_in_distance_matrix()\n except FileNotFoundError:\n raise Exception(\"[ERROR] The distance matrix excel file was could not \"\n \"be found! Please ensure the methods for the \"\n \"preceding problems have been run first.\")\n visualise_distances(distances)\n \n # Visualise word frequency before and after preprocessing\n visualise_initial_most_frequent_words(kw_df)\n visualise_preprocessed_most_frequent_words(w2vp.corpus)","def generate_figures_and_xls_all_strains(outdir, cols_starts, region2data, ext, xls, group2pos, feature_names, samples):\n all_freqs = []\n # concatenate all pos and samples into one dataframe\n dframes = []\n for ri, (ref, pos) in enumerate(region2data.keys()): #regions): #[3]#; print(ref, pos, mt)\n mer, calls = region2data[(ref, pos)]\n for c, s in zip(calls, samples): \n df = pd.DataFrame(c, columns=feature_names)\n df[\"Strain\"] = s\n df[\"chr_pos\"] = \"%s:%s\"%(ref, pos)\n dframes.append(df)\n # read all tsv files\n df = pd.concat(dframes).dropna().reset_index()\n chr_pos, strains = df[\"chr_pos\"].unique(), df[\"Strain\"].unique() \n # compare individual methods\n for clf, method in (\n (KMeans(n_clusters=2), \"KMeans\"), \n (KNeighborsClassifier(), \"KNN\"), \n #(iso_new.iForest(ntrees=100, random_state=0), \"GMM+eIF\"), \n (GaussianMixture(random_state=0, n_components=2), \"GMM\"), \n (AgglomerativeClustering(n_clusters=2), \"AggClust\"), \n #(OneClassSVM(), \"OCSVM\"), \n (IsolationForest(random_state=0), \"IF\"), \n #(iso_new.iForest(ntrees=100, random_state=0), \"eIF\"), \n (RandomForestClassifier(), \"RF\"), \n ):\n fname = method\n for i, cols_start in enumerate(cols_starts, 1):\n results = []\n feat_name = \"_\".join(cols_start)\n fname = \"%s.%s\"%(method, feat_name); print(fname)\n outfn = os.path.join(outdir, \"%s.%s\"%(fname, ext))\n # narrow down the features to only signal intensity & trace\n cols = list(filter(lambda n: n.startswith(cols_start), feature_names))#; print(cols) #, \"DT\"\n # compare all samples to 0%\n s0 = samples[0]\n for s in samples[3:]: \n with np.errstate(under='ignore'):\n if \"+\" in method:\n clf2_name = method.split(\"+\")[-1]\n results += get_mod_freq_two_step(df, cols, chr_pos, [s0, s], feat_name, \n OFFSET=0.5, clf2_name=clf2_name, clf2=clf)\n elif method in (\"KNN\", \"RF\"):\n results += get_mod_freq_clf_train_test(df, cols, chr_pos, [s0, s], samples[1:3], clf, feat_name)\n else:\n results += get_mod_freq_clf(df, cols, chr_pos, [s0, s], clf, feat_name)\n \n # and store mod_freq predicted by various methods\n freqs = pd.DataFrame(results, columns=[\"chr_pos\", \"features\", \"mod_freq wt\", \"mod_freq strain\", \"strain\"])\n freqs[\"diff\"] = freqs.max(axis=1)-freqs.min(axis=1); freqs\n for name, pos in group2pos.items(): #((\"negative\", negatives), (\"pU\", pU_pos), (\"Nm\", Nm_pos)):\n freqs.loc[freqs[\"chr_pos\"].isin(pos), \"group\"] = name\n #freqs.to_csv(outfn, sep=\"\\t\"); freqs.head()\n freqs.to_excel(xls, fname, index=False)\n # plot differences between methods\n for group, pos in group2pos.items():\n freqs.loc[freqs[\"chr_pos\"].isin(pos), \"modification\"] = group\n #return freqs\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))#, sharey=\"all\")\n sns.barplot(x=\"chr_pos\", y=\"mod_freq strain\", hue=\"strain\", edgecolor=\"white\", palette=[\"#f8786fff\", \"#7aae02ff\", \"#00bfc2ff\", \"#c67afeff\"], \n data=freqs[(freqs[\"features\"]==feat_name)&(freqs[\"group\"]==\"pU\")], ax=ax1)\n sns.barplot(x=\"chr_pos\", y=\"mod_freq strain\", hue=\"strain\", edgecolor=\"white\", palette=[\"#ed823aff\", \"#1c6ca9ff\", \"#35d1bbff\", \"#c978fdff\"], \n data=freqs[(freqs[\"features\"]==feat_name)&(freqs[\"group\"]==\"Nm\")], ax=ax2)\n ax1.set_ylabel(\"Per-site stoichiometry\"); ax2.set_ylabel(\"\")\n ax1.get_legend().remove(); ax2.get_legend().remove()#ax1.legend([]); ax2.legend([])\n ax1.set_ylim(0, 1); ax2.set_ylim(0, 1); #ax2.set(aspect=1.7)\n ax1.set_title(\"pU modifications\"); ax2.set_title(\"Nm modifications\")\n fig.suptitle(fname)\n fig.savefig(outfn)\n plt.close() # clear axis\n freqs[\"name\"] = fname\n all_freqs.append(freqs)\n return all_freqs","def test_snippet_1(self):\n\n text = \"Asimov also wrote mysteries and fantasy, as well as much nonfiction. Most of his popular science books explain concepts in a historical way, going as far back as possible to a time when the science in question was at its simplest stage. Examples include Guide to Science, the three-volume set Understanding Physics, and Asimov's Chronology of Science and Discovery. He wrote on numerous other scientific and non-scientific topics, such as chemistry, astronomy, mathematics, history, biblical exegesis, and literary criticism.\"\n drs = Drs.create_from_natural_language(text)\n expected_drs = Drs.create_from_predicates_string(\"\"\"\n {'word': 'wrote', 'tag': 'v', 'compound': 'wrote', 'entity': '', 'lemma': 'write', 'gender_guess': None,\n 'is_head_token': True, 'refers_to': None, 'negated': 'false', 'type': None}(v2),\n {'word': 'Asimov', 'tag': 'n', 'compound': 'Asimov', 'entity': 'PERSON', 'lemma': 'Asimov',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v0),\n {'word': 'also', 'tag': 'RB', 'compound': 'also', 'entity': '', 'lemma': 'also', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v1),\n {'word': 'mysteries', 'tag': 'n', 'compound': 'mysteries', 'entity': '', 'lemma': 'mystery',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v3),\n {'word': 'fantasy', 'tag': 'n', 'compound': 'fantasy', 'entity': '', 'lemma': 'fantasy',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v5),\n {'word': 'nonfiction', 'tag': 'n', 'compound': 'nonfiction', 'entity': '', 'lemma': 'nonfiction',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v11),\n {'word': 'as', 'tag': 'RB', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v7),\n {'word': 'well', 'tag': 'RB', 'compound': 'well', 'entity': '', 'lemma': 'well', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v8),\n {'word': 'much', 'tag': 'j', 'compound': 'much', 'entity': '', 'lemma': 'much', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v10),\n {'word': 'Most', 'tag': 'j', 'compound': 'Most', 'entity': '', 'lemma': 'Most', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v13),\n {'word': 'books', 'tag': 'n', 'compound': 'books', 'entity': '', 'lemma': 'book', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v18),\n {'word': 'he', 'tag': 'PRP$', 'compound': 'he', 'entity': '', 'lemma': 'he', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v15),\n {'word': 'popular', 'tag': 'j', 'compound': 'popular', 'entity': '', 'lemma': 'popular',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v16),\n {'word': 'science', 'tag': 'n', 'compound': 'science', 'entity': '', 'lemma': 'science',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v17),\n {'word': 'explain', 'tag': 'v', 'compound': 'explain', 'entity': '', 'lemma': 'explain',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v19),\n {'word': 'concepts', 'tag': 'n', 'compound': 'concepts', 'entity': '', 'lemma': 'concept',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v20),\n {'word': 'going', 'tag': 'v', 'compound': 'going', 'entity': '', 'lemma': 'go', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v26),\n {'word': 'way', 'tag': 'n', 'compound': 'way', 'entity': '', 'lemma': 'way', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v24),\n {'word': 'historical', 'tag': 'j', 'compound': 'historical', 'entity': '', 'lemma': 'historical',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v23),\n {'word': 'back', 'tag': 'RB', 'compound': 'back', 'entity': '', 'lemma': 'back', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v29),\n {'word': 'to', 'tag': 'IN', 'compound': 'to', 'entity': '', 'lemma': 'to', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'to'}(v32),\n {'word': 'far', 'tag': 'RB', 'compound': 'far', 'entity': '', 'lemma': 'far', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v28),\n {'word': 'as', 'tag': 'RB', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v27),\n {'word': 'as', 'tag': 'IN', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'as'}(v30),\n {'word': 'possible', 'tag': 'j', 'compound': 'possible', 'entity': '', 'lemma': 'possible',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v31),\n {'word': 'time', 'tag': 'n', 'compound': 'time', 'entity': '', 'lemma': 'time', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v34),\n {'word': 'was', 'tag': 'v', 'compound': 'was', 'entity': '', 'lemma': 'be', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v40),\n {'word': 'science', 'tag': 'n', 'compound': 'science', 'entity': '', 'lemma': 'science',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v37),\n {'word': 'in', 'tag': 'IN', 'compound': 'in', 'entity': '', 'lemma': 'in', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'in'}(v38),\n {'word': 'question', 'tag': 'n', 'compound': 'question', 'entity': '', 'lemma': 'question',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v39),\n {'word': 'when', 'tag': 'WRB', 'compound': 'when', 'entity': '', 'lemma': 'when', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v35),\n {'word': 'at', 'tag': 'IN', 'compound': 'at', 'entity': '', 'lemma': 'at', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'at'}(v41),\n {'word': 'stage', 'tag': 'n', 'compound': 'stage', 'entity': '', 'lemma': 'stage', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v44),\n {'word': 'its', 'tag': 'PRP$', 'compound': 'its', 'entity': '', 'lemma': 'its', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v42),\n {'word': 'simplest', 'tag': 'j', 'compound': 'simplest', 'entity': '', 'lemma': 'simple',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v43),\n {'word': 'include', 'tag': 'v', 'compound': 'include', 'entity': '', 'lemma': 'include',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v47),\n {'word': 'Examples', 'tag': 'n', 'compound': 'Examples', 'entity': '', 'lemma': 'example',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v46),\n {'word': 'Guide', 'tag': 'n', 'compound': 'Guide', 'entity': '', 'lemma': 'Guide', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v48),\n {'word': '.', 'tag': '.', 'compound': '.', 'entity': '', 'lemma': '.', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v68),\n {'word': 'to', 'tag': 'IN', 'compound': 'to', 'entity': '', 'lemma': 'to', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v49),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v51),\n {'word': 'set', 'tag': 'v', 'compound': 'set', 'entity': '', 'lemma': 'set', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v56),\n {'word': 'Science', 'tag': 'n', 'compound': 'Science', 'entity': '', 'lemma': 'Science',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v50),\n {'word': 'volume', 'tag': 'n', 'compound': 'volume', 'entity': '', 'lemma': 'volume', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v55),\n {'word': 'three', 'tag': 'CD', 'compound': 'three', 'entity': 'CARDINAL', 'lemma': 'three',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v53),\n {'word': '-', 'tag': 'HYPH', 'compound': '-', 'entity': '', 'lemma': '-', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v54),\n {'word': 'Physics', 'tag': 'n', 'compound': 'Understanding_Physics', 'entity': 'ORG', 'lemma': 'Physics',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v58),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v59),\n {'word': 'and', 'tag': 'CC', 'compound': 'and', 'entity': '', 'lemma': 'and', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v60),\n {'word': 'Chronology', 'tag': 'n', 'compound': 'Chronology', 'entity': 'WORK_OF_ART', 'lemma': 'Chronology',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v63),\n {'word': 'Asimov', 'tag': 'n', 'compound': 'Asimov', 'entity': 'PERSON', 'lemma': 'Asimov',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v61),\n {'word': 'of', 'tag': 'IN', 'compound': 'of', 'entity': 'WORK_OF_ART', 'lemma': 'of', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v64),\n {'word': 'Science', 'tag': 'n', 'compound': 'Science', 'entity': 'WORK_OF_ART', 'lemma': 'Science',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v65),\n {'word': 'and', 'tag': 'CC', 'compound': 'and', 'entity': 'WORK_OF_ART', 'lemma': 'and',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v66),\n {'word': 'Discovery', 'tag': 'n', 'compound': 'Discovery', 'entity': 'WORK_OF_ART', 'lemma': 'Discovery',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v67),\n {'word': 'wrote', 'tag': 'v', 'compound': 'wrote', 'entity': '', 'lemma': 'write', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v70),\n {'word': 'He', 'tag': 'PRP', 'compound': 'He', 'entity': 'PERSON', 'lemma': 'he', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v69),\n {'word': 'on', 'tag': 'IN', 'compound': 'on', 'entity': '', 'lemma': 'on', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v71),\n {'word': '.', 'tag': '.', 'compound': '.', 'entity': '', 'lemma': '.', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v97),\n {'word': 'topics', 'tag': 'n', 'compound': 'topics', 'entity': '', 'lemma': 'topic', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v79),\n {'word': 'scientific', 'tag': 'j', 'compound': 'scientific', 'entity': '', 'lemma': 'scientific',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v74),\n {'word': '-', 'tag': 'j', 'compound': '-', 'entity': '', 'lemma': '-', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v77),\n {'word': 'non', 'tag': 'j', 'compound': 'non', 'entity': '', 'lemma': 'non', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v76),\n {'word': 'scientific', 'tag': 'j', 'compound': 'scientific', 'entity': '', 'lemma': 'scientific',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v78),\n {'word': 'numerous', 'tag': 'j', 'compound': 'numerous', 'entity': '', 'lemma': 'numerous',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v72),\n {'word': 'other', 'tag': 'j', 'compound': 'other', 'entity': '', 'lemma': 'other', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v73),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v80),\n {'word': 'as', 'tag': 'IN', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'as'}(v82),\n {'word': 'such', 'tag': 'j', 'compound': 'such', 'entity': '', 'lemma': 'such', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v81),\n {'word': 'chemistry', 'tag': 'n', 'compound': 'chemistry', 'entity': '', 'lemma': 'chemistry',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v83),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v84),\n {'word': 'astronomy', 'tag': 'n', 'compound': 'astronomy', 'entity': '', 'lemma': 'astronomy',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v85),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v86),\n {'word': 'mathematics', 'tag': 'n', 'compound': 'mathematics', 'entity': '', 'lemma': 'mathematic',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v87),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v88),\n {'word': 'history', 'tag': 'n', 'compound': 'history', 'entity': '', 'lemma': 'history',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v89),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v90),\n {'word': 'exegesis', 'tag': 'n', 'compound': 'exegesis', 'entity': '', 'lemma': 'exegesis',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v92),\n {'word': 'biblical', 'tag': 'j', 'compound': 'biblical', 'entity': '', 'lemma': 'biblical',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v91),\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v93),\n {'word': 'criticism', 'tag': 'n', 'compound': 'criticism', 'entity': '', 'lemma': 'criticism',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v96),\n {'word': 'literary', 'tag': 'j', 'compound': 'literary', 'entity': '', 'lemma': 'literary',\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v95),\n {'type': 'AMOD'}(v24, v23), {'type': 'ADVMOD'}(v26, v29), {'type': 'ADVMOD'}(v28, v27),\n {'type': 'ADVMOD'}(v29, v28), {'type': 'NSUBJ'}(v40, v37), {'type': 'POSS'}(v44, v42),\n {'type': 'AMOD'}(v44, v43), {'type': 'NSUBJ'}(v47, v46), {'type': 'DOBJ'}(v47, v48),\n {'type': 'PUNCT'}(v47, v68), {'type': 'PREP'}(v48, v49), {'type': 'POBJ'}(v49, v50),\n {'type': 'PUNCT'}(v55, v54), {'type': 'NPADVMOD'}(v56, v55), {'type': 'DOBJ'}(v56, v58),\n {'type': 'PUNCT'}(v56, v59), {'type': 'POSS'}(v63, v61), {'type': 'PREP'}(v63, v64),\n {'type': 'POBJ'}(v64, v65), {'type': 'NSUBJ'}(v70, v69), {'type': 'PREP'}(v70, v71),\n {'type': 'PUNCT'}(v70, v97), {'type': 'POBJ'}(v71, v79), {'type': 'SUBTOK'}(v77, v76),\n {'type': 'SUBTOK'}(v78, v77), {'type': 'AMOD'}(v79, v72), {'type': 'AMOD'}(v79, v73),\n {'type': 'AMOD'}(v79, v74), {'type': 'AMOD'}(v79, v78), {'type': 'PUNCT'}(v79, v80),\n {'type': 'POBJ'}(v82, v83), {'type': 'PUNCT'}(v85, v86), {'type': 'CONJ'}(v85, v87),\n {'type': 'PUNCT'}(v87, v88), {'type': 'CONJ'}(v87, v89), {'type': 'PUNCT'}(v89, v90),\n {'type': 'CONJ'}(v89, v92), {'type': 'AMOD'}(v92, v91), {'type': 'PUNCT'}(v92, v93),\n {'type': 'CONJ'}(v92, v96), {'type': 'AMOD'}(v96, v95), {'type': 'AGENT'}(v2, v0),\n {'type': 'ADVOCATIVE_CLAUSE'}(v19, v26), {'type': 'when'}(v34, v40), {'type': 'of'}(v13, v18),\n {'type': 'AGENT'}(v19, v13), {'type': 'PATIENT'}(v19, v20), {'type': 'in'}(v19, v24),\n {'type': 'NUMBER'}(v55, v53), {'type': 'OWNS'}(v61, v63), {'type': 'OWNS'}(v15, v18),\n {'type': 'PATIENT'}(v2, v5), {'type': 'and'}(v56, v63), {'type': 'and'}(v48, v56),\n {'type': 'and'}(v65, v67), {'type': 'and'}(v83, v85), {'type': 'in'}(v37, v39), {'type': 'to'}(v26, v34),\n {'type': 'as'}(v29, v31), {'type': 'at'}(v40, v44), {'type': 'as'}(v79, v81), {'type': 'of'}(v18, v17),\n {'type': 'ADJECTIVE'}(v11, v10), {'type': 'ADVERB'}(v2, v1), {'type': 'ADJECTIVE'}(v18, v16),\n {'type': 'as'}(v2, v3), {'type': 'as'}(v2, v5)\"\"\")\n lst = drs.apply(DrsMatcher(expected_drs, metric))\n is_match = len(lst) > 1\n self.assertTrue(is_match)","def index():\n mongo_collection = mongo_database[\"settings\"]\n doc_instructions = mongo_collection.find_one({\"id\": \"instructions\"})\n instructions = markdown.markdown(doc_instructions['text'])\n return render_template(\"index.html\", instructions=instructions)","def get_wordlists():\n\n\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\n\t\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\n\t\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\n\t\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\n\t\t'Language Processing', 'Internet', 'Intelligent Systems',\n\t\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\n\t\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \n\t\t'Computational Intelligence', 'Computational Linguistics',\n\t\t'Computational linguistics','Algorithm','Computer','ITiCSE',\n\t\t'ITICSE','Machine learning','Learning','learning',\n\t\t'Artificial intelligence','CIVR','Document Analysis'}\n\n\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\n\t\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\n\t\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\n\t\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\n\t\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\n\t\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\n\t\t'Neuro','Psych',\n\t\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\n\t\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\n\t\t'Neurobiological'}\n\n\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\n\t\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\n\t\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\n\t\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\n\t\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\n\t\t'mathemati','Kernel'}\n\n\tlinguistics = {}\n\n\tcomputer_vision = {}\n\n\tchemistry = {}\n\n\tphysics = {}\n\n\t# Rename \"Computer Vision\" to \"Image Processing\"?\n\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\n\t\t'Physics','Computer Vision','Natural Language Processing']\n\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\n\n\treturn {topic_names[i]:topics[i] for i in range(len(topics))}","def demo1():\n \n print \"loading the NIPS publications dataset...\"\n pubs = loadPubs('pubs_nips')\n \n # get all papers that mention mnist\n p = [x for x in pubs if 'mnist' in x.get('pdf_text',{})]\n print \"titles of papers that mention MNIST dataset:\"\n for x in p:\n print x['title']\n print \"total of %d publications mention MNIST.\" %(len(p),)\n \n # sort by number of occurences\n occ = [(x['year'], x['pdf']) for i,x in enumerate(p) if x['pdf_text']['mnist']>1]\n occ.sort(reverse = True)\n \n # open the top 3 latest in browser\n print \"opening the top 3...\"\n openPDFs([x for year,x in occ[:3]])","def _generate_examples(self, filepath, split):\r\n if self.config.name == \"trex\":\r\n paths = filepath\r\n relations_path = paths[0]\r\n paths = paths[1:]\r\n all_rels = {}\r\n with open(relations_path, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n all_rels[data[\"relation\"]] = data\r\n id_ = -1\r\n for filepath in paths:\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n pred = all_rels.get(data[\"predicate_id\"], {})\r\n for evidences in data[\"evidences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"obj_uri\": str(data[\"obj_uri\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"sub_uri\": str(data[\"sub_uri\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"predicate_id\": str(data[\"predicate_id\"]),\r\n \"sub_surface\": str(evidences[\"sub_surface\"]),\r\n \"obj_surface\": str(evidences[\"obj_surface\"]),\r\n \"masked_sentence\": str(evidences[\"masked_sentence\"]),\r\n \"template\": str(pred.get(\"template\", \"\")),\r\n \"template_negated\": str(pred.get(\"template_negated\", \"\")),\r\n \"label\": str(pred.get(\"label\", \"\")),\r\n \"description\": str(pred.get(\"description\", \"\")),\r\n \"type\": str(pred.get(\"type\", \"\")),\r\n }\r\n elif self.config.name == \"conceptnet\":\r\n id_ = -1\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n if data.get(\"negated\") is not None:\r\n for masked_sentence, negated in zip(data[\"masked_sentences\"], data[\"negated\"]):\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"sub\": str(data.get(\"sub\", \"\")),\r\n \"obj\": str(data.get(\"obj\", \"\")),\r\n \"pred\": str(data[\"pred\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"negated\": str(negated),\r\n }\r\n else:\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"sub\": str(data.get(\"sub\", \"\")),\r\n \"obj\": str(data.get(\"obj\", \"\")),\r\n \"pred\": str(data[\"pred\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"negated\": str(\"\"),\r\n }\r\n elif self.config.name == \"squad\":\r\n id_ = -1\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"id\": str(data[\"id\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"negated\": str(data.get(\"negated\", \"\")),\r\n \"masked_sentence\": str(masked_sentence),\r\n }\r\n elif self.config.name == \"google_re\":\r\n id_ = -1\r\n paths = filepath\r\n for filepath in paths:\r\n # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py\r\n if \"place_of_birth\" in filepath:\r\n pred = {\r\n \"relation\": \"place_of_birth\",\r\n \"template\": \"[X] was born in [Y] .\",\r\n \"template_negated\": \"[X] was not born in [Y] .\",\r\n }\r\n elif \"date_of_birth\" in filepath:\r\n pred = {\r\n \"relation\": \"date_of_birth\",\r\n \"template\": \"[X] (born [Y]).\",\r\n \"template_negated\": \"[X] (not born [Y]).\",\r\n }\r\n else:\r\n pred = {\r\n \"relation\": \"place_of_death\",\r\n \"template\": \"[X] died in [Y] .\",\r\n \"template_negated\": \"[X] did not die in [Y] .\",\r\n }\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"pred\": str(data[\"pred\"]),\r\n \"sub\": str(data[\"sub\"]),\r\n \"obj\": str(data[\"obj\"]),\r\n \"evidences\": str(data[\"evidences\"]),\r\n \"judgments\": str(data[\"judgments\"]),\r\n \"sub_w\": str(data[\"sub_w\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"sub_aliases\": str(data[\"sub_aliases\"]),\r\n \"obj_w\": str(data[\"obj_w\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"obj_aliases\": str(data[\"obj_aliases\"]),\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"template\": str(pred[\"template\"]),\r\n \"template_negated\": str(pred[\"template_negated\"]),\r\n }","def prime_cache(cfg):\n from dictionaria.util import add_links2\n\n labels = {}\n for type_, cls in [('source', common.Source), ('unit', common.Unit)]:\n labels[type_] = defaultdict(set)\n for r in DBSession.query(cls.id):\n sid, _, lid = r[0].partition('-')\n labels[type_][sid].add(lid)\n\n for d in DBSession.query(Dictionary):\n for type_ in ['source', 'unit']:\n d.description = add_links2(d.id, labels[type_][d.id], d.description, type_)\n\n for meaning in DBSession.query(ComparisonMeaning).options(\n joinedload_all(common.Parameter.valuesets, common.ValueSet.values)\n ):\n meaning.representation = sum([len(vs.values) for vs in meaning.valuesets])\n if meaning.representation == 0:\n meaning.active = False\n\n def joined(iterable):\n return ' / '.join(sorted(nfilter(set(iterable))))\n\n q = DBSession.query(Word)\\\n .order_by(Word.dictionary_pk, common.Unit.name, common.Unit.pk)\\\n .options(joinedload(Word.meanings), joinedload(Word.dictionary))\n for _, words in groupby(q, lambda u: u.name):\n words = list(words)\n for i, word in enumerate(words):\n word.description = ' / '.join(m.name for m in word.meanings)\n word.comparison_meanings = joined(m.reverse for m in word.meanings)\n word.semantic_domain = joined(m.semantic_domain for m in word.meanings)\n word.number = i + 1 if len(words) > 1 else 0\n\n for suffix in ['1', '2']:\n alt_t, alt_l = [], []\n for m in word.meanings:\n if getattr(m, 'alt_translation' + suffix):\n alt_l.append(getattr(m, 'alt_translation_language' + suffix))\n alt_t.append(getattr(m, 'alt_translation' + suffix))\n if alt_t and len(set(alt_l)) == 1:\n DBSession.add(common.Unit_data(\n object_pk=word.pk, key='lang-' + alt_l.pop(), value=join(alt_t)))\n\n def count_unit_media_files(contrib, mtype):\n return DBSession.query(common.Unit_files)\\\n .join(Word, common.Unit_files.object_pk == Word.pk)\\\n .filter(Word.dictionary_pk == contrib.pk)\\\n .filter(common.Unit_files.mime_type.ilike(mtype + '/%'))\\\n .count() + \\\n DBSession.query(Meaning_files)\\\n .join(Meaning, Meaning_files.object_pk == Meaning.pk)\\\n .join(Word, Meaning.word_pk == Word.pk)\\\n .filter(Word.dictionary_pk == contrib.pk)\\\n .filter(Meaning_files.mime_type.ilike(mtype + '/%'))\\\n .count()\n\n for d in DBSession.query(Dictionary).options(joinedload(Dictionary.words)):\n d.count_words = len(d.words)\n sds = set(chain(*[w.semantic_domain_list for w in d.words]))\n d.semantic_domains = join(sorted(sds))\n d.count_audio = count_unit_media_files(d, 'audio')\n d.count_image = count_unit_media_files(d, 'image')\n\n word_pks = [w.pk for w in d.words]\n choices = {}\n for col in d.jsondata.get('custom_fields', []):\n values = [\n r[0] for r in DBSession.query(common.Unit_data.value)\n .filter(common.Unit_data.object_pk.in_(word_pks))\n .filter(common.Unit_data.key == col)\n .distinct()]\n if len(values) < 40:\n choices[col] = sorted(values)\n d.update_jsondata(choices=choices)\n\n DBSession.execute(\"\"\"\n UPDATE word\n SET example_count = s.c \n FROM (\n SELECT m.word_pk AS wpk, count(ms.sentence_pk) AS c\n FROM meaning AS m, meaningsentence AS ms\n WHERE m.pk = ms.meaning_pk\n GROUP BY m.word_pk\n ) AS s\n WHERE word.pk = s.wpk\n \"\"\")","def dvi():\n\n return M(c=\"dvi\")(\n #M(\"Home\", f=\"index\"),\n M(\"Recovery Requests\", f=\"recreq\")(\n M(\"New Request\", m=\"create\"),\n M(\"List Current\",\n vars={\"recreq.status\":\"1,2,3\"}),\n ),\n M(\"Dead Bodies\", f=\"body\")(\n M(\"Add\", m=\"create\"),\n M(\"List unidentified\",\n vars={\"identification.status\": \"None\"}),\n M(\"Report by Age/Gender\", m=\"report\",\n vars=dict(rows=\"age_group\",\n cols=\"gender\",\n fact=\"count(pe_label)\",\n ),\n ),\n ),\n #M(\"Missing Persons\", f=\"person\")(\n # M(\"List all\"),\n #),\n M(\"Morgues\", f=\"morgue\")(\n M(\"Create\", m=\"create\"),\n ),\n M(\"Dashboard\", f=\"index\"),\n )","def rf_tf_idf_train(df, dominio):\n df_final = obtain_train_corpus()\n # Puedo separarlo en distintos df segun el dominio\n df_domain_total = [{category:df_domain} for category, df_domain in df_final.groupby('category')]\n \n if dominio == \"entidad\":\n # Tambien puedo separar a nivel de dominio y entity\n df_domain_total_entity = {}\n for df in df_domain_total:\n category = list(df.keys())[0]\n df = list(df.values())[0]\n df_entities = [{entity:df_entity} for entity, df_entity in df.groupby('entity_name')]\n df_domain_total_entity.update({category:df_entities})\n \n vocabulario = corpus_generation(df_domain_total_entity, \"entidad\")\n entidades = list(vocabulario.keys())\n categorias = list(df_domain_total_entity.keys())\n \n i = 1\n total = len(entidades)\n for categoria in categorias:\n for df in df_domain_total_entity[categoria]:\n \n print(\"Entrendando modelo \" + str(i) + \"/\" + str(total))\n \n entidad = list(df.keys())[0]\n df = list(df.values())[0]\n df = df.reset_index()\n \n X = list(df['text'])\n print(\"Entidad: \", entidad)\n\n words, words_tot, median, df_pattern, df_suma = word_tf_idf(X)\n df_classificacion = df_suma.join(df, how=\"outer\") # Join por los index\n \n X_tf_idf = list(df_classificacion['tf-idf'])\n y_tf_idf = list(df_classificacion['topic'])\n \n # Encoding a numerico\n labelencoder_X = LabelEncoder()\n y_tf_idf=labelencoder_X.fit_transform(y_tf_idf) # Codifico en valores numericos las clases que hay\n\n \n # Train/validation split\n X_train, X_val, y_train, y_val = train_test_split(X_tf_idf, y_tf_idf, \n test_size = 0.1, random_state = 0)\n \n \n # Menor distancia cuadratica de TF\n y_pred = []\n for x_ref in X_val:\n ref = 999\n i = 0\n for x in X_train:\n \n diff = (x_ref - x)**2\n diff = np.sqrt(diff)\n print(diff)\n \n if diff < ref:\n i = X_train.index(x)\n ref = diff\n\n y_pred.append(y_train[i]) # Identifico con la clase de menor distancia cuadratica TF-IDF\n \n \n # Making the Confusion Matrix\n cm = confusion_matrix(y_val, y_pred)\n \n # Accuracy\n accuracy = accuracy_score(y_val, y_pred)\n \n # Precision\n average_precision = precision_score(y_val, y_pred, average = \"macro\")\n \n # Recall\n recall = recall_score(y_val, y_pred, average='macro')\n \n print(\"Modelo \"+str(i)+\" resultados\")\n print(\"accuracy \", accuracy, \" precision \", average_precision, \" recall \", recall) # Se ve que los resultados son muy malos","def identify_datasets(self, language_objects, context):\n\n datasets, new_sen = self.extractor.extract_all_templates(\n language_objects, context\n )\n context[\"datasets\"] = datasets\n return {'type': 'result', 'result': (new_sen, context[\"datasets\"])}","def generate_data():\n for subdir, dirs, files in os.walk(legend_images_dir):\n for _file in files:\n getTables(_file)\n\n file_list = []\n for subdir, dirs, files in os.walk(pdf_output_dir):\n for _file in files:\n if _file.endswith('.pdf'):\n file_list.append(_file)\n\n print (\"Writing merged output in Output.pdf...\")\n current_dir = os.getcwd()\n mergeOutput(file_list, current_dir + \"/Output.pdf\")\n\n clean()","def generate_examples(self, docname):\n fake_factory = faker.Factory.create()\n for name, language, parent in self.data['examples']:\n props = self.get_object(name)\n sample_data = props.generate_sample_data(self.data['all_objects'],\n fake_factory)\n if language == 'yaml' and yaml is not None:\n title = 'YAML Example'\n code_text = yaml.safe_dump(sample_data, indent=4,\n default_flow_style=False,\n explicit_start=True,\n version=(1, 2))\n else:\n if language == 'yaml':\n self.env.warn(docname,\n 'YAML support is disabled, pip install yaml '\n 'to enable.')\n title = 'JSON Example'\n language = 'json'\n code_text = json.dumps(sample_data, indent=4,\n ensure_ascii=False)\n\n example = nodes.literal_block(code_text, code_text)\n example['language'] = language\n parent.append(nodes.strong(title, title))\n parent.append(example)","def _generate_markdown(self, case):\n # Lucene query generation\n lucene_dict = {\n \"sources\": case[\"detection\"][\"sources\"],\n \"data\": case[\"input_arguments\"],\n }\n case[\"lucene_query\"] = self.templates[\"lucene\"].render(lucene_dict)\n # AWS CLI command generation\n command_template = jinja2.Template(case[\"executors\"][\"sh\"][\"code\"])\n if case[\"input_arguments\"]:\n aws_cli_render_args = {}\n for arg in case[\"input_arguments\"]:\n aws_cli_render_args[arg] = case[\"input_arguments\"][arg][\"value\"]\n case[\"compiled_command\"] = command_template.render(aws_cli_render_args)\n else:\n case[\"compiled_command\"] = command_template.render()\n\n render_dict = {\"case\": case}\n return self.templates[\"markdown\"].render(render_dict)","def explain ():\n\n print(\"\"\"\n Isis Web Spider\\n\n\n Isis Web Spider take some parameters:\n ./isis-web-spider -t targets.txt -s strings.txt [-o output.txt]\n Note that each file must contain one information by line\n \"\"\")","def build_index():\n pass","def get_data_loaders_1sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1_sentence_final_generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+1):]\n #history_complete.append(history)\n if len(history) > 3:\n history_chatbot = history[1]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def write_analysis(path, dataset_dict, datasettype, mask_part, start_time, supervised=True):\n for mask_el in mask_part:\n if mask_el == 'podocytes':\n filename = datasettype + '_podos.txt'\n filestr = 'podos images'\n elif mask_el == 'glomerulus':\n filename = datasettype + '_gloms.txt'\n filestr = 'gloms images'\n else:\n filename = datasettype + 'unknown.txt'\n filestr = 'unknown type'\n\n write_txt = open(str(os.path.join(path, filename)), \"w\")\n\n if supervised:\n dc_mean = np.sum(np.array(dataset_dict['dice_coeffs_%s' % mask_el])) / len(dataset_dict['dice_coeffs_%s'\n % mask_el])\n dc_min = np.min(np.array(dataset_dict['dice_coeffs_%s' % mask_el]))\n dc_max = np.max(np.array(dataset_dict['dice_coeffs_%s' % mask_el]))\n object_dc_mean = np.sum(np.array(dataset_dict['object_dc_%s' % mask_el])) / len(dataset_dict['object_dc_%s'\n % mask_el])\n object_dc_min = np.min(np.array(dataset_dict['object_dc_%s' % mask_el]))\n object_dc_max = np.max(np.array(dataset_dict['object_dc_%s' % mask_el]))\n pearson = calculate_pearson(dataset_dict['count_masks_%s' % mask_el], dataset_dict['count_preds_%s'\n % mask_el])\n\n write_txt.write(str(\"Mean dice coefficient on pixels of \" + filestr + \" compared to groundtruth: \") +\n str(dc_mean) + '\\n')\n write_txt.write(str(\"Min dice coefficient on pixels of \" + filestr + \" compared to groundtruth: \") +\n str(dc_min) + '\\n')\n write_txt.write(str(\"Max dice coefficient on pixels of \" + filestr + \" compared to groundtruth: \") +\n str(dc_max) + '\\n')\n write_txt.write(str(\"Pearson correlation coefficient on objects of \" + filestr +\n \" compared to groundtruth: \") + str(pearson) + '\\n')\n write_txt.write(str(\"Mean dice coeff on objects of \" + filestr + \" compared to groundtruth: \") +\n str(object_dc_mean) + '\\n')\n write_txt.write(str(\"Min dice coeff on objects of \" + filestr + \" compared to groundtruth: \") +\n str(object_dc_min) + '\\n')\n write_txt.write(str(\"Max dice coeff on objects of \" + filestr + \" compared to groundtruth: \") +\n str(object_dc_max) + '\\n')\n write_txt.write('\\n')\n\n duration = time.time() - start_time\n duration_std = int(duration / 3600)\n duration_min = int((duration % 3600) / 60)\n duration_sec = int(duration % 60)\n\n write_txt.write(str(\"Test time: \") + str(duration_std) + \"h \" + str(duration_min)\n + \"min \" + str(duration_sec) + 'sec \\n')\n write_txt.close()\n return","def get_data_loaders_4sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"]\n #history_complete.append(history)\n if len(history_splitted) > (len(persona)-1):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def generate(self, params):\n size = params.size\n n_answers = params.n_answers\n max_strokes = params.max_strokes\n min_strokes = params.min_strokes\n kanji_similarity = params.kanji_similarity\n answer_similarity = params.answer_similarity\n grade = params.grade\n allow_duplicates = params.allow_duplicates\n reversed_bool = params.reversed_bool\n question_type = params.question_type #kanji, vocab \n\n chunk = Chunk(params)\n\n # List of potential indices into the kanji list, indices can be removed\n # to disallow duplicates.\n kanji_indices = list(range(len(self.kanji[grade])))\n \n # Filter out all kanji with a stroke_count up to max_strokes.\n # Horrendous piece of code. Don't show your kids.\n new_kanji_indices = []\n for index in kanji_indices:\n _,_,stroke_count = self.kanji[grade][index]\n if int(stroke_count) <= max_strokes and int(stroke_count) >= min_strokes:\n new_kanji_indices.append(index)\n kanji_indices = new_kanji_indices\n\n if len(kanji_indices) < size:\n raise Exception(\"Not enough kanji found for these parameters\")\n\n # translate the answer_similarity measure to an easy/normal/hard scale\n # as used by compareKanji.py\n answer_difficulty = ('easy' if answer_similarity <= 0.33 else 'normal'\n if answer_similarity <= 0.66 else 'hard')\n\n for _ in range(size):\n if question_type == \"kanji\":\n index = random.choice(kanji_indices)\n kanji, meaning, stroke_count = self.kanji[grade][index]\n options = self.__choice_list(kanji, answer_difficulty, grade,\n n_answers - 1, reversed_bool)\n hint = self.__hint(reversed_bool, question_type, kanji, meaning, grade)\n if not reversed_bool:\n question = \"What is the meaning of the following kanji?\"\n options += [meaning]\n random.shuffle(options)\n chunk.add_question(question, kanji, meaning, options, hint)\n else:\n question = \"Which kanji belongs to the following meaning(s)?\"\n options += [kanji]\n random.shuffle(options)\n chunk.add_question(question, meaning, kanji, options, hint)\n else: #question_type == vocab\n kanji = \"\"\n while not kanji in self.vocab_dict or self.vocab_dict[kanji] == []:\n index = random.choice(kanji_indices)\n kanji, meaning, stroke_count = self.kanji[grade][index]\n compound_choice = self.vocab_dict[kanji]\n choice = random.choice(compound_choice)\n item, _, item_meaning = choice \n\n def check_compatibility(element0):\n if element0[0] in [x[0] for x in selection] + [item]:\n return False\n if element0[2].lower() in [x[2].lower() for x in selection] + [item_meaning]:\n return False\n if \"(surname)\" in element0[2].lower():\n return False \n if \"～\" in element0[0] or \"~\" in element0[0] or \"～\" in element0[0]:\n return False\n return True\n\n selection = []\n for x in compound_choice:\n if x != choice and check_compatibility(x):\n selection.append(x)\n random.shuffle(selection) \n \n #easy: option compounds\n #normal: option compounds and 0.5 item compounds\n #hard: option compounds and all item compounds\n if answer_difficulty == \"easy\":\n selection = []\n elif answer_similarity == \"normal\":\n selection = selection[::2]\n\n options = self.__choice_list(kanji, answer_difficulty, grade,\n n_answers - 1, reversed_bool)\n\n for option in options:\n if option in self.vocab_dict:\n for vocab in self.vocab_dict[option]:\n if check_compatibility(vocab):\n selection.append(vocab)\n\n #make we have enough anwsers to present\n while len(selection) < n_answers - 1:\n kanji, _, _ = self.kanji[grade][random.choice(kanji_indices)]\n if kanji in self.vocab_dict:\n for vocab in self.vocab_dict[kanji]:\n if check_compatibility(vocab):\n selection.append(vocab)\n\n sampled_selection = random.sample(selection,n_answers - 1)\n\n #remove dublicates: on kanji and meaning\n hint = self.__hint(reversed_bool, question_type, item, item_meaning, grade) \n if not reversed_bool:\n question = \"What is the meaning of the following word?\"\n options = [x[2] for x in sampled_selection] + [item_meaning]\n random.shuffle(options)\n chunk.add_question(question, item, item_meaning, options, hint)\n else:\n question = \"Which word belongs to the following meaning(s)?\"\n options = [x[0] for x in sampled_selection] + [item]\n random.shuffle(options)\n chunk.add_question(question, item_meaning, item, options, hint)\n if not allow_duplicates:\n kanji_indices.remove(index)\n return chunk","def data():\n file = open('./contest/content/data.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Data', \n content=content)","def generateAll(self):\n\n dsource = DataSource.DataSource('summer533moon')\n data = dsource.getKeyList()\n replacements = {}\n\n # Generates the champion list to be displayed.\n result = '<p class=\"champ-display\">'\n line_break_counter = 0\n for champ in data:\n if line_break_counter % 10 == 0 and line_break_counter != 0:\n result += '<br />'\n result += '<a href=\"index.py?champ_name=%s\"><img src=\"http://ddra\\\ngon.leagueoflegends.com/cdn/5.7.2/img/champion/%s.png\"></a>' %\\\n (champ, champ)\n line_break_counter += 1\n result += '</p>'\n\n replacements['champ_display'] = result\n self.content = self.content.format(**replacements)","def get_examples():\n\n base_query = \"[[Medium::TCG]] [[Database ID::+]]\"\n base_printouts = [\n \"Password\",\n \"English name\",\n \"Card type\",\n ]\n all_res = {}\n\n n = len(ALL_DISPLAYS)\n for i, x in enumerate(ALL_DISPLAYS):\n query = f\"{base_query} [[{x}::+]]\"\n print(i, n, query)\n try:\n data = ask_yugipedia(query, base_printouts + [x])\n except:\n print(\"ERROR\", i, x)\n continue\n all_res[x] = data\n return all_res","def generate_training_set(individual, db_name):\n\n db = connect(db_name)\n for miller_indices in individual:\n\n # Do MD\n slab = generate_slab(miller_indices, 5)\n slab = make_supercell(slab, P=[[3, 1, 1], [1, 3, 1], [1, 1, 1]])\n\n slab.set_calculator(EMT())\n slab.get_potential_energy()\n\n db.write(slab)\n\n MaxwellBoltzmannDistribution(slab, 300. * units.kB)\n dyn = VelocityVerlet(slab, dt=1. * units.fs)\n for i in range(1, 10):\n dyn.run(10)\n db.write(slab)","def workbench_scenarios(): \n return [\n (\"TermsXBlock\",\n \"\"\"<terms/>\n \"\"\"),\n (\"Multiple TermsXBlock\",\n \"\"\"<vertical_demo>\n <terms/>\n <terms/>\n <terms/>\n </vertical_demo>\n \"\"\"),\n ]","def summarize_corpus():\n\t\n\t# get metadata\n\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \"metadata\", md_mode)\n\t\n\t# visualize some metadata\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"author-continent\")\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"author-country\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"language\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_hist\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_x\")\n\tvisualize_metadata.plot_pie(wdir, md_csv, \"subgenre\")\n\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"subgenre\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"gender\")\n\t\n\t# make some counts\n\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\n\tnum_texts = len(md_table)\n\t#num_language = len(md_table.groupby([\"language\"]))\n\t#num_continent = len(md_table.groupby([\"author-continent\"]))\n\t#num_countries = len(md_table.groupby([\"author-country\"]))\n\t#num_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_subgenre = len(md_table.groupby([\"subgenre\"]))\n\t#num_subgenre_x = len(md_table.groupby([\"subgenre_x\"]))\n\t#fr_subgenre_hist = md_table.groupby([\"subgenre_hist\"]).count()\n\t#num_historical = fr_subgenre_hist[\"idno\"][\"historical\"]\n\t#num_not_historical = fr_subgenre_hist[\"idno\"][\"not_historical\"]\n\t\n\t\n\td = {\"texts\":[num_texts], \n\t#\"languages\":[num_language],\n\t#\"continents\":[num_continent],\n\t#\"countries\":[num_countries],\n\t\"authors\":[num_authors],\n\t#\"subgenre_x\":[num_subgenre_x],\n\t\"subgenre\":[num_subgenre]}\n\t#\"num_historical\":[num_historical],\n\t#\"num_not_historical\":[num_not_historical]}\n\t\n\t\n\t\n\tcount_fr = pd.DataFrame(d)\n\tcount_fr.to_csv(os.path.join(wdir, \"corpus-description.csv\"), sep=\",\", header=True)\n\tprint(\"Done: summarize corpus\")","def splits(cls, search_query = 'all', max_results = 300, start = 0, reduced_words=1000, savepath='.data', split_ratio=0.7, stratified=False, strata_field='abstract',\n random_state=None, **kwargs):\n\n # initialize text field\n text_field = torchtext.data.Field(tokenize=get_tokenizer(\"spacy\"), init_token='<sos>', eos_token='<eos>', lower=True)\n fields = [('abstract', text_field), ('paper', text_field)]\n examples = []\n\n # Create new dataset by downloading from arxiv or open dataset from folder\n if not os.path.exists(savepath):\n os.mkdir(savepath)\n # create directories for saving the data set\n if not os.path.exists(os.path.join(savepath, 'temp')):\n os.mkdir(os.path.join(savepath, 'temp'))\n if not os.path.exists(os.path.join(savepath, 'abstracts')):\n os.mkdir(os.path.join(savepath, 'abstracts'))\n if not os.path.exists(os.path.join(savepath, 'paper')):\n os.mkdir(os.path.join(savepath, 'paper'))\n data = cls.download(search_query=search_query, max_results=max_results)\n abstracts, papers = cls.extract_paper_and_abstract(data, savepath=savepath)\n\n # generate all examples\n for i, (abstract, paper) in enumerate(zip(abstracts, papers)):\n paper_tokenized = []\n abstract_tokenized = []\n # rdeduce the number of words with the textrank approach\n textranked_paper = summarizer.summarize(paper, words=reduced_words)\n # add start and end token\n paper_tokenized += [u'<sos>'] + text_field.preprocess(textranked_paper) + [u'<eos>']\n abstract_tokenized += [u'<sos>'] + text_field.preprocess(abstract) + [u'<eos>']\n # initialize examples\n examples.append(torchtext.data.Example.fromlist([abstract_tokenized, paper_tokenized], fields))\n\n # save data samples in txt files\n with open(os.path.join(savepath,'abstracts','abstract_' + str(i) + '.txt'), 'w+', encoding='utf-8') as abstr:\n csvwriter = csv.writer(abstr, delimiter=' ')\n csvwriter.writerow(abstract_tokenized)\n with open(os.path.join(savepath,'paper','paper_' + str(i) + '.txt'), 'w+', encoding='utf-8') as pap:\n csvwriter = csv.writer(pap, delimiter=' ')\n csvwriter.writerow(paper_tokenized)\n\n else:\n # read all files in saved data path\n paper_files = os.listdir(os.path.join(savepath,'paper'))\n abstract_files = os.listdir(os.path.join(savepath,'abstracts'))\n data = [[paper, abstract] for paper, abstract in zip(paper_files, abstract_files)]\n papers_tokenized = []\n abstracts_tokenized = []\n # read paper and abstracts from files\n for paper, abstract in data:\n with open(os.path.join(savepath, 'paper', paper), encoding='utf-8') as csvfile:\n paper = csv.reader(csvfile, delimiter=' ')\n for pap in paper:\n if pap:\n papers_tokenized.append(pap)\n with open(os.path.join(savepath, 'abstracts', abstract), encoding='utf-8') as csvfile:\n abstract = csv.reader(csvfile, delimiter=' ')\n for abstr in abstract:\n if abstr:\n abstracts_tokenized.append(abstr)\n \n # generate all examples\n for abstract_tokenized, paper_tokenized in zip(abstracts_tokenized, papers_tokenized):\n examples.append(torchtext.data.Example.fromlist([abstract_tokenized, paper_tokenized], fields))\n\n # create initial dataset\n dataset = PaperAbstractDataset(examples, fields)\n # split dataset\n splits = dataset.split(split_ratio=split_ratio, stratified=stratified, strata_field=strata_field,\n random_state=random_state)\n # initialize vocabulary\n pre_trained_vector_type = 'glove.6B.300d'\n for d in splits:\n for name, field in d.fields.items():\n field.build_vocab(splits[0], vectors=pre_trained_vector_type)\n d.filter_examples(['abstract', 'paper'])\n return splits","def main():\n\n dofile = \"thebook\"\n\n #spellcheck()\n\n common_options = '--encoding=utf-8 --examples_as_exercises '\n\n # --- HTML ---\n\n common_html_options = ' '\n\n # HTML Bootstrap\n bootstrap_options = ' --html_style=bootswatch_readable --html_code_style=inherit --html_pre_style=inherit --toc_depth=2 --pygments_html_style=default --html_template=template_bootstrap_wtoc.html --html_figure_caption=bottom --html_figure_hrule=top+bottom' \n\n html(\n dofile,\n options=common_options + common_html_options + bootstrap_options,\n split=True)\n\n # One long HTML file\n #html(dofile, options=common_options + common_html_options + ' --html_style=bloodish --html_output=%s-1' % dofile, split=False)\n\n # Solarized HTML\n #html(dofile, options=common_options + common_html_options + ' --html_style=solarized3 --html_output=%s-solarized' % dofile, split=True)\n\n mksnippets()\n sys.exit(1)\n\n # --- latex ---\n\n common_latex_options = ' --latex_code_style=vrb'\n\n for version in 'paper', 'screen': # , 'A4', '2up', 'A4-2up':\n latex(\n dofile,\n latex_program='pdflatex',\n options=common_options + common_latex_options,\n version=version,\n postfix='auto')\n\n # --- Sphinx ---\n\n# sphinx_themes = ['pyramid',]\n# for theme in sphinx_themes:\n# dirname = 'sphinx-rootdir' if len(sphinx_themes) == 1 else 'sphinx-rootdir-%s' % theme\n# sphinx(\n# dofile,\n# options=common_options + '',\n# dirname=dirname,\n# theme=theme,\n# automake_sphinx_options='',\n# split=False)\n\n # Dump all Unix commands run above as a Bash script\n bash = open('tmp_make.sh', 'w')\n print 'see tmp_make.sh for an equivalent auto-generated unix script'\n bash.write('''#!/bin/bash\nset -x # display all commands in output\n\n# Safe execution of a Unix command: exit if failure\nfunction system {\n \"$@\"\n if [ $? -ne 0 ]; then\n echo \"make.sh: unsuccessful command $@\"\n echo \"abort!\"\n exit 1\n fi\n}\n''')\n for cmd in unix_command_recorder:\n if cmd.startswith('doconce format') or cmd.startswith('rm '):\n bash.write('\\n') # delimiter line in script\n bash.write('system ' + cmd + '\\n')\n bash.close()\n\n print 'see tmp_output.log for the output of all the commands'","def workbench_scenarios():\n return [\n (\"Markdown\",\n \"\"\"<markdowna />\n \"\"\")\n ]","def print_output(data,alignments,file):\n print(\"######################################################################\")\n print(\"Task 1 : IBM model 1 and EM algorithm implementation ,with corpus @\",file)\n print(\"######################################################################\")\n\n for i in range(len(data)):\n print(\"English Sentence : \",data[i][\"en\"])\n print(\"Foreign Sentence : \",data[i][\"fr\"])\n print(\"Alignment : \",alignments[i])\n print(\"----------------------------------------------------------------------\")","def domain_parser():\n\tinput_handle=\"blast_hits.fasta\"\n\t# File with: (1) the domains of each hit and (2) domains information\n\tdomains= \"domains_hits.txt\"\n\toutput_handle = open(domains, \"w\")\n\toutput_handle.write(\"#This file contains the domains of each hit.\\n#At the bottom, you will find detail information of all the domains detected.\\n\")\n\toutput_handle.write(\"#We strongly recommend to open this file with Visual Studio Code.\\n#Because when the names of the domains are too large, in regular editors the table looks awful.\\n\")\n\toutput_handle.write(\"#Here it is only showed how many times a pattern is present.\\n#In the figure of the domains you will find the position of each domain.\\n\\n\")\n\taccession_list=[] \t# List of prosite.doc accessions of the domains that had been found\n\tdomains_dict=dict() # dictionary that saves matches\n\tcount=1\n\tmax_seq_len=0 # Keep larger sequence to plot x-axe\n\t# Loop to go through hits\n\tfor seq_record in SeqIO.parse(input_handle, \"fasta\"):\n\t\toutput_handle.write(str(seq_record.id)+\"\\n\") # print identifier of the hit\n\t\toutput_handle.write(str(seq_record.seq)+\"\\n\") # print sequence of the hit\n\t\tif len(seq_record.seq)>max_seq_len:\n\t\t\tmax_seq_len=len(seq_record.seq)\n\t\t# Make a table for each hit with the domains, that contains the following fields: name, accession, description and pattern\n\t\tx=PrettyTable()\n\t\tx.field_names=[\"name\",\"accession\",\"description\",\"pattern\",\"repetitions\"]\n\n\t\t# Loop to go through prosite domains\n\t\thandle = open(\"prosite.dat\",\"r\")\n\t\trecords = Prosite.parse(handle)\n\t\tfor record in records:\n\t\t\t# prosite.dat preparation for parsing\n\t\t\t# {} -> [^]\n\t\t\tpattern = record.pattern.upper()\n\t\t\tpattern = pattern.replace(\"{\", \"[^\")\n\t\t\tpattern = pattern.replace(\"}\", \"]\")\t\n\t\t\t# - -> \"\"\n\t\t\tpattern = pattern.replace(\"-\", \"\")\t\n\t\t\t# . -> \"\"\n\t\t\tpattern = pattern.replace(\".\", \"\")\t\n\t\t\t# X|x -> \"[ARNDCQEGHILKMFPSTWYV]\"\n\t\t\tAAS=\"[ARNDCQEGHILKMFPSTWYV]\"\n\t\t\tpattern = pattern.replace(\"x\", AAS)\n\t\t\tpattern = pattern.replace(\"X\", AAS)\t\n\t\t\t# () -> {}\n\t\t\tpattern = pattern.replace(\"(\", \"{\")\n\t\t\tpattern = pattern.replace(\")\", \"}\")\t\n\n\t\t\t# >] -> ]?$\n\t\t\tpattern = pattern.replace(\">]\", \"]?$\")\t\n\n\t\t\t# < -> ^\n\t\t\t# > -> $\n\t\t\tpattern = pattern.replace(\"<\", \"^\")\t\n\t\t\tpattern = pattern.replace(\">\", \"$\")\t\n\t\t\tif pattern != \"\":\n\t\t\t\t# Look if the hit contains the current patter\n\t\t\t\tif re.search(r\"\"+str(pattern), str(seq_record.seq).upper()): # if found\n\t\t\t\t\tif record.pdoc not in accession_list:\n\t\t\t\t\t\t# Save pdoc accession in the list of prosite.doc accessions\n\t\t\t\t\t\t# if it is not already\n\t\t\t\t\t\taccession_list.append(record.pdoc)\n\t\t\t\t\tmatches = re.finditer(r\"\"+str(pattern), str(seq_record.seq).upper())\n\t\t\t\t\treps=0\n\t\t\t\t\tfor match in matches: # save all matches in a dictionary to plot them later\n\t\t\t\t\t\tdomains_dict[count]=[seq_record.id, len(seq_record.seq),record.name,match.start(),match.end()]\n\t\t\t\t\t\tcount=count+1\n\t\t\t\t\t\treps=reps+1\n\t\t\t\t\tx.add_row([record.name,record.accession,record.description,record.pattern, reps]) # add found domain to table\n\n\t\toutput_handle.write(str(x)+\"\\n\") # add table of hit to domains_hits.txt\n\n\t# At the end of the tables, print information of all the domains that had been found\n\toutput_handle.write(\"\\n\")\n\trecord_text_list=DocParser(accession_list)\n\tfor text in record_text_list:\n\t\toutput_handle.write(text)\n\treturn (domains_dict,max_seq_len,accession_list)","def generate_sentinel_measure(\n data_dict,\n data_dict_practice,\n codelist_dict,\n measure,\n code_column,\n term_column,\n dates_list,\n codelist_links,\n interactive=True,\n):\n df = data_dict[measure]\n\n childs_df, childs_df_with_count = create_child_table(\n df, codelist_dict[measure], code_column, term_column, measure\n )\n\n practices_included = get_number_practices(df)\n practices_included_percent = get_percentage_practices(df)\n\n num_events, num_events_mil = get_number_events_mil(df, measure)\n\n num_patients = get_number_patients(measure)\n\n num_practices_df = pd.DataFrame(\n {\"num_practices_included\": pd.Series([practices_included])}\n )\n num_practices_df.to_csv(\n f\"{OUTPUT_DIR}/num_practices_included_{measure}.csv\", index=False\n )\n\n df = data_dict_practice[measure]\n\n deciles_chart(\n df,\n period_column=\"date\",\n column=\"rate\",\n ylabel=\"rate per 1000\",\n interactive=interactive,\n )\n\n display(\n Markdown(f\"Practices included: {practices_included} ({practices_included_percent:.2f}%)\"),\n )\n\n childs_df = childs_df.rename(columns={code_column: code_column.title()})\n childs_df.to_csv(f\"{OUTPUT_DIR}/code_table_{measure}.csv\")\n\n childs_df_with_count = childs_df_with_count.rename(\n columns={code_column: code_column.title()}\n )\n childs_df_with_count.to_csv(f\"{OUTPUT_DIR}/code_table_{measure}_with_count.csv\")\n\n if len(codelist_links)>1:\n display(\n Markdown(f\"#### Most Common Codes <a href={codelist_links[0]}>(Codelist 1)</a>, <a href={codelist_links[1]}>(Codelist 2)</a>\"),\n HTML(childs_df.to_html(index=False)),\n )\n\n else:\n display(\n Markdown(f\"#### Most Common Codes <a href={codelist_links[0]}>(Codelist)</a>\"),\n HTML(childs_df.to_html(index=False)),\n )\n\n display(\n Markdown(f\"Total patients: {num_patients:.2f}M ({num_events_mil:.2f}M events)\"),\n )\n\n return df, num_events","def create_file(self):\n for data_element in self.data:\n title = data_element['title']\n anchor = data_element['href']\n example = data_element['example']\n content = data_element['content']\n if example:\n abstract = '<section class=\"prog__container\">{}<br>{}</section>'.format(content, example)\n\n list_of_data = [\n title, # api title\n 'A', # type is article\n '', # no redirect data\n '', # ignore\n '', # no categories\n '', # ignore\n '', # no related topics\n '', # ignore\n '', # no external link\n '', # no disambiguation\n '', # images\n abstract, # abstract\n anchor # url to doc\n ]\n self.output_file.write('{}\\n'.format('\\t'.join(list_of_data)))","def generate_features(stances, dataset, name, feature_list, features_dir):\n h, b, y, bodyId, headId = [], [], [], [], []\n\n feature_dict = {'overlap': word_overlap_features,\n 'refuting': refuting_features,\n 'polarity': polarity_features,\n 'hand': hand_features,\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\n 'NMF_fit_all': NMF_fit_all,\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\n 'NMF_cos_50': NMF_cos_50,\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\n 'ppdb': ppdb,\n 'stanford_ppdb': stanford_ppdb_score,\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\n 'stanford_sentiment': stanford_sentiment,\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\n 'stanford_wordsim': stanford_based_verb_noun_sim,\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\n 'stanford_negation': stanford_negation_features,\n 'stanford_negation_1sent': stanford_negation_features_1sent,\n 'stanford_negation_2sent': stanford_negation_features_2sent,\n 'stanford_negation_3sent': stanford_negation_features_3sent,\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\n 'hedging': hedging_features,\n 'sen2sen': sen2sen_similarity_max,\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\n 'wmdsenDoc': word_mover_distance_wholebody,\n 'sdm_sim': sdm_sim,\n 'discuss': discuss_features,\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\n 'lexical_features': lexical_features,\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\n 'mpqa_unigrams': mpqa_unigrams,\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\n 'nrc_emo_lex': nrc_emo_lex,\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\n #'POS_features': POS_features,\n 'readability_features': readability_features,\n 'sentiment140_unigrams': sentiment140_unigrams,\n 'structural_features': structural_features,\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\n 'NMF_cos_300': NMF_cos_300\n }\n\n stanceCounter = 0\n for stance in stances:\n y.append(LABELS.index(stance['Stance']))\n h.append(stance['Headline'])\n b.append(dataset.articles[stance['Body ID']])\n bodyId.append(stance['Body ID'])\n headId.append(name+str(stanceCounter))\n stanceCounter += 1\n\n X_feat = []\n feat_list = []\n last_index = 0\n for feature in feature_list:\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\"/\"+feature+\".\"+name+'.npy', bodyId, feature, headId, fold=name)\n feat_list.append((last_index, last_index+len(feat[0]), str(feature)))\n last_index += len(feat[0])\n X_feat.append(feat)\n X = np.concatenate(X_feat, axis=1)\n\n return X, y, feat_list","def create_index():\n es = Elasticsearch()\n es.indices.delete(index='drugaid-index')\n with open('../data_crawling/demo_drug_data-fixed.json') as f:\n drug_data = json.load(f)\n\n for drug in drug_data:\n es.index(index='drugaid-index', doc_type='drug', body=drug)","def api_index():\n db = get_db()\n datasets = db.execute('SELECT * FROM dataset').fetchall()\n \n responses = {}\n columns = ['name', 'description', 'tags', 'dataset_file_size', 'files']\n\n for dataset in datasets:\n response = dict(zip(columns, [dataset[column] for column in columns]))\n response['link'] = url_for('dataset.api_show', dataset_id=dataset['id'])\n responses[dataset['id']] = response\n\n return jsonify(dict(responses))","def generateSpecified(self):\n\n dsource = DataSource.DataSource('summer533moon')\n replacements = {}\n\n # Split into two cases where the lane parameter is non-trivial\n # and where the category parameter is non-trivial.\n if self.filter['lane'] != '':\n data = dsource.getLaneList(self.filter['lane'])\n result = '<p>'\n line_break_counter = 0\n for champ in data:\n if line_break_counter % 10 == 0 and line_break_counter != 0:\n result += '</p>'\n result += '<a href=\"index.py?champ_name=%s\"><img src=\"http://\\\nddragon.leagueoflegends.com/cdn/5.7.2/img/champion/%s.png\"></a>' %\\\n (champ, champ)\n line_break_counter += 1\n result += '</p>'\n elif self.filter['category'] != '':\n data = dsource.getCategoryList(self.filter['category'])\n result = '<p>'\n line_break_counter = 0\n for champ in data:\n if line_break_counter % 10 == 0 and line_break_counter != 0:\n result += '</p>'\n result += '<a href=\"index.py?champ_name=%s\"><img src=\"http://\\\nddragon.leagueoflegends.com/cdn/5.7.2/img/champion/%s.png\"></a>' %\\\n (champ, champ)\n line_break_counter += 1\n result += '</p>'\n # If somehow parameters do not match the cases above, generate homepage\n # with complete list of champions instead.\n else:\n self.generateAll()\n return\n\n replacements['champ_display'] = result\n self.content = self.content.format(**replacements)","def fase1():\n\n pagesClassIDs = {\n \"fase1\": {\n \"bannertitle\": [],\n \"subtitle\": [],\n \"firstText\": [],\n \"secondText\": []\n }\n }\n for key in pagesClassIDs[\"fase1\"].keys():\n pagesClassIDs[\"fase1\"][key].append(\n str(\n pageTexts.query.filter_by(pageID=key,\n htmlName=\"fase1\").first()))\n\n spat_aspects = Spat_aspect.query.all()\n spat_aspectsList = [\n spat_aspect.__dict__[\"name\"] for spat_aspect in spat_aspects\n ]\n\n temp_aspects = Temp_aspect.query.all()\n temp_aspectsList = [\n temp_aspect.__dict__[\"name\"] for temp_aspect in temp_aspects\n ]\n\n nodes = Node.query.all()\n nodesList = [node.__dict__ for node in nodes]\n for nd in nodesList:\n del nd['_sa_instance_state']\n\n factorDict = {}\n for node in nodesList:\n spat_asp = int(node[\"spat_aspect_id\"]) - 1\n temp_asp = int(node[\"temp_aspect_id\"]) - 1\n factorDict.setdefault(spat_aspectsList[spat_asp], {})\n factorDict[spat_aspectsList[spat_asp]].setdefault(\n temp_aspectsList[temp_asp], [])\n factorDict[spat_aspectsList[spat_asp]][\n temp_aspectsList[temp_asp]].append(node[\"factor\"])\n\n print(nodesList)\n print(factorDict)\n\n return render_template(\n 'home/fase1.html',\n factorDict=factorDict,\n nodes=nodesList,\n spat_aspects=spat_aspectsList,\n temp_aspects=temp_aspectsList,\n pageDicts=pagesClassIDs,\n title=\"fase1\")"],"string":"[\n \"def generate():\\n\\n # Verify if directory exists\\n if not os.path.isdir(config.techniques_markdown_path):\\n os.mkdir(config.techniques_markdown_path)\\n\\n #Write the technique index.html page\\n with open(os.path.join(config.techniques_markdown_path, \\\"overview.md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(config.technique_overview_md)\\n\\n for domain in config.domains:\\n generate_domain_markdown(domain)\",\n \"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\\n\\n attack_id = util.get_attack_id(technique)\\n\\n # Only add technique if the attack id was found\\n if attack_id:\\n\\n technique_dict = {}\\n\\n technique_dict['attack_id'] = attack_id\\n technique_dict['domain'] = domain.split(\\\"-\\\")[0]\\n technique_dict['menu'] = side_menu_data\\n technique_dict['name'] = technique.get('name')\\n\\n # Get capecs and mtcs\\n for ref in technique['external_references']:\\n if ref.get('source_name'):\\n if ref['source_name'] == \\\"capec\\\":\\n if technique_dict.get('capecs') is None:\\n technique_dict['capecs'] = []\\n capec_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['capecs'].append(capec_dict)\\n\\n if ref['source_name'] == \\\"NIST Mobile Threat Catalogue\\\":\\n if technique_dict.get('mtcs') is None:\\n technique_dict['mtcs'] = []\\n\\n mtcs_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['mtcs'].append(mtcs_dict)\\n\\n # Get technique external references \\n ext_ref = technique[\\\"external_references\\\"]\\n\\n # Get initial reference list\\n reference_list = []\\n # Decleared as an object to be able to pass by reference\\n next_reference_number = {}\\n next_reference_number['value'] = 1\\n reference_list = util.update_reference_list(reference_list, technique)\\n\\n # Get technique description\\n if technique.get(\\\"description\\\"):\\n citations_from_descr = util.get_citations_from_descr(technique['description'])\\n\\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\\n \\n if 'x_mitre_deprecated' in technique:\\n technique_dict['deprecated'] = True\\n \\n # Get mitigation table\\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\\n \\n # Get related techniques\\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\\n\\n # Get examples\\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\\n\\n # Get technique version\\n if technique.get(\\\"x_mitre_version\\\"):\\n technique_dict['version'] = technique[\\\"x_mitre_version\\\"]\\n\\n # Get tactics of technique\\n if technique.get('kill_chain_phases'):\\n technique_dict['tactics'] = []\\n for elem in technique['kill_chain_phases']:\\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\\n\\n # Get platforms that technique uses\\n if technique.get('x_mitre_platforms'):\\n technique_dict['platforms'] = \\\", \\\".join(technique['x_mitre_platforms'])\\n\\n # Get system requirements\\n if technique.get('x_mitre_system_requirements'):\\n technique_dict['sysreqs'] = \\\", \\\".join(technique['x_mitre_system_requirements'])\\n technique_dict['sysreqs'] = re.sub(\\\"\\\\.?\\\\\\\\n+\\\", \\\"; \\\", technique_dict['sysreqs'])\\n\\n # Get permissions required\\n if technique.get('x_mitre_permissions_required'):\\n technique_dict['perms'] = \\\", \\\".join(technique['x_mitre_permissions_required'])\\n\\n # Get effective permissions\\n if technique.get('x_mitre_effective_permissions'):\\n technique_dict['eff_perms'] = \\\", \\\".join(technique['x_mitre_effective_permissions'])\\n\\n # Get data sources\\n if technique.get('x_mitre_data_sources'):\\n technique_dict['data_sources'] = \\\", \\\".join(technique['x_mitre_data_sources'])\\n\\n # Get if technique supports remote\\n if technique.get('x_mitre_remote_support'):\\n if technique['x_mitre_remote_support']:\\n technique_dict['supports_remote'] = \\\" Yes\\\"\\n else:\\n technique_dict['supports_remote'] = \\\" No\\\"\\n\\n # Get network requirements\\n if technique.get('x_mitre_network_requirements'):\\n if technique['x_mitre_network_requirements']:\\n technique_dict['network_reqs'] = \\\" Yes\\\"\\n else:\\n technique_dict['network_reqs'] = \\\" No\\\"\\n\\n # Get list of impacts\\n if technique.get('x_mitre_impact_type'):\\n technique_dict['impact_type'] = \\\", \\\".join(technique['x_mitre_impact_type'])\\n\\n # Get list of defenses bypassed\\n if technique.get('x_mitre_defense_bypassed'):\\n technique_dict['def_bypass'] = \\\", \\\".join(technique['x_mitre_defense_bypassed'])\\n\\n # Get list of contributors \\n if technique.get('x_mitre_contributors'):\\n technique_dict['contributors'] = \\\"; \\\".join(technique['x_mitre_contributors'])\\n\\n # Get list of tactic types\\n if technique.get('x_mitre_tactic_type'):\\n technique_dict['tactic_type'] = \\\", \\\".join(technique['x_mitre_tactic_type'])\\n\\n # Get detection data\\n if technique.get('x_mitre_detection'):\\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\\n\\n # Get if technique is detectable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses'):\\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\\n\\n # Get explanation of detecatable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\\n\\n # Get diffulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary'):\\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\\n\\n # Get explanation of difficulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \\n \\n # Add reference for bottom part of technique page\\n if reference_list:\\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\\n\\n subs = config.technique_md.substitute(technique_dict)\\n subs = subs + json.dumps(technique_dict)\\n\\n #Write out the markdown file\\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\\\".md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\",\n \"def get_examples_table_data(technique, reference_list, next_reference_number):\\n\\n # Creating map to avoid repeating the code 3 times\\n examples_map = [\\n { 'example_type': config.tools_using_technique }, \\n { 'example_type': config.malware_using_technique },\\n { 'example_type': config.groups_using_technique }\\n ]\\n\\n example_data = []\\n\\n # Get malware or tools used by group\\n for examples in examples_map:\\n if examples['example_type'].get(technique.get('id')):\\n for example in examples['example_type'][technique['id']]:\\n\\n attack_id = util.get_attack_id(example['object'])\\n\\n # Only add example data if the attack id is found \\n if attack_id:\\n row = {}\\n\\n row['id'] = attack_id\\n\\n if attack_id.startswith('S'):\\n row['path'] = \\\"software\\\"\\n else:\\n row['path'] = \\\"groups\\\"\\n\\n row['name'] = example['object']['name']\\n\\n if example['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\\n\\n example_data.append(row)\\n \\n if example_data:\\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\\n return example_data\",\n \"def index():\\n import numpy as np\\n import random\\n\\n total_gender = {}\\n total_gender['Male'] = db(db.patient.sex == 'Male').count()\\n total_gender['Female'] = db(db.patient.sex == 'Female').count()\\n total_gender['Undeclared'] = db(db.patient.sex == 'Undeclared').count()\\n\\n groups = db(db.groups).select()\\n freq_groups = {}\\n grp_gender = {}\\n for g in groups:\\n freq_groups[g.code] = db(db.patient.groups.contains(g.id)).count()\\n grp_gender[g.code] = {}\\n grp_gender[g.code]['Male'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Male')).count()\\n grp_gender[g.code]['Female'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Female')).count()\\n grp_gender[g.code]['Undeclared'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Undeclared')).count()\\n\\n experiments = db(db.experiments).select()\\n freq_experiments = {}\\n exp_gender = {}\\n for e in experiments:\\n freq_experiments[e.code] = db(db.patient.experiments.contains(e.id)).count()\\n exp_gender[e.code] = {}\\n exp_gender[e.code]['Male'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Male')).count()\\n exp_gender[e.code]['Female'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Female')).count()\\n exp_gender[e.code]['Undeclared'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Undeclared')).count()\\n\\n grp_exp = {}\\n for e in experiments:\\n grp_exp[e.code] = {}\\n for g in groups:\\n grp_exp[e.code][g.code] = db(db.patient.experiments.contains(e.id) & db.patient.groups.contains(g.id)).count()\\n\\n return dict(message=T('Pain Network: A web-based tool for diagnosis of the Chronic Pain.'),\\n freq_gender=total_gender,freq_groups=freq_groups,freq_experiments=freq_experiments,\\n exp_gender=exp_gender,grp_gender=grp_gender,grp_exp=grp_exp)\",\n \"def get_technique_side_menu_data(domain, technique_list, tactic_list):\\n\\n # Get tactics techniques side menu data to fill out with jinja\\n tactics_techniques_menu_data = []\\n\\n for tactic in tactic_list:\\n tactic_row = {}\\n \\n tactic_row['name'] = tactic['name']\\n tactic_row['id'] = util.get_attack_id(tactic)\\n tactic_row['path'] = \\\"/tactics/{}\\\".format(util.get_attack_id(tactic))\\n \\n tactic_row['children'] = []\\n for technique in technique_list[tactic['x_mitre_shortname']]:\\n technique_row = {}\\n # Get technique id and name for each technique\\n technique_row['name'] = technique['name']\\n technique_row['id'] = technique['id']\\n technique_row['path'] = \\\"/techniques/{}/\\\".format(technique['id'])\\n technique_row['children'] = []\\n # Add technique data to tactic\\n tactic_row['children'].append(technique_row)\\n \\n # Add tactic to table\\n tactics_techniques_menu_data.append(tactic_row)\\n \\n return {\\n \\\"name\\\": \\\"techniques\\\",\\n \\\"id\\\": \\\"techniques\\\",\\n \\\"path\\\": \\\"/techniques/{}/\\\".format(domain.split(\\\"-\\\")[0]), # root level doesn't get a path\\n \\\"children\\\": tactics_techniques_menu_data\\n }\",\n \"def build_index():\\n for site in get_sites():\\n text = read_site(site)\\n while text == False:\\n text = read_site(site) # keep attempting to read until successful\\n index_site(site, text)\",\n \"def build_index():\\n for site in get_sites():\\n text = read_site(site)\\n while text == False:\\n text = read_site(site) # keep attempting to read until successful\\n index_site(site, text)\",\n \"def homepage():\\n\\n pagesClassIDs = {\\n \\\"index\\\": {\\n \\\"bannertitle\\\": [],\\n \\\"subtitle\\\": [],\\n \\\"firstText\\\": [],\\n \\\"secondText\\\": []\\n }\\n }\\n for key in pagesClassIDs[\\\"index\\\"].keys():\\n pagesClassIDs[\\\"index\\\"][key].append(\\n str(\\n pageTexts.query.filter_by(pageID=key,\\n htmlName=\\\"index\\\").first()))\\n\\n experts = Expert.query.all()\\n data = {\\n \\\"modules\\\": [],\\n \\\"discipline\\\": [],\\n \\\"subdivision\\\": [],\\n \\\"publications\\\": [],\\n \\\"People\\\": []\\n }\\n for expert in experts:\\n if not expert.is_admin and str(expert.accepted) == \\\"Yes\\\":\\n data[\\\"modules\\\"].append(str(expert.affiliation))\\n data[\\\"discipline\\\"].append(str(expert.discipline))\\n data[\\\"subdivision\\\"].append(str(expert.specialization))\\n data[\\\"publications\\\"].append(0)\\n if str(expert.core_exp) == \\\"Yes\\\":\\n data[\\\"People\\\"].append(\\n str(expert.title) + \\\" \\\" + str(expert.first_name) + \\\" \\\" +\\n str(expert.last_name) + \\\"(T)\\\")\\n else:\\n data[\\\"People\\\"].append(\\n str(expert.title) + \\\" \\\" + str(expert.first_name) + \\\" \\\" +\\n str(expert.last_name) + \\\"(N)\\\")\\n\\n crdata = pd.DataFrame(data).groupby(\\n ['modules', 'discipline', 'subdivision'], as_index=False).agg({\\n 'publications':\\n 'sum',\\n 'People':\\n lambda x: ','.join(x)\\n })\\n circleData = createJSON(crdata)\\n\\n return render_template(\\n 'home/index.html',\\n pageDicts=pagesClassIDs,\\n bannertitle=\\\"Introduction to Research\\\",\\n subtitle=\\\"Interactive Causal Mapping\\\",\\n title=\\\"Home\\\",\\n circleData=circleData)\",\n \"def generate(softwaretype=\\\"software\\\"):\\n # import the STIX data from MITRE/CTI\\n stix = requests.get(\\\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\\\").json()\\n ms = MemoryStore(stix_data=stix[\\\"objects\\\"])\\n # software includes malware and tool types so perform two queries and merge the results\\n software_filters = []\\n if softwaretype == \\\"malware\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'malware') ] )\\n if softwaretype == \\\"tool\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'tool') ] )\\n \\n software = list(chain.from_iterable(\\n ms.query(f) for f in software_filters\\n ))\\n\\n # build a list of techniques used by software\\n techniques_used = {} #attackID => using software names\\n for thesoftware in software:\\n # filter out revoked and deprecated software\\n if (\\\"x_mitre_deprecated\\\" in thesoftware and thesoftware[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in thesoftware and thesoftware[\\\"revoked\\\"]): continue\\n for relationship in ms.relationships(thesoftware[\\\"id\\\"]):\\n # skip all non-technique relationships\\n if \\\"attack-pattern\\\" not in relationship[\\\"target_ref\\\"]: continue\\n technique = ms.get(relationship[\\\"target_ref\\\"])\\n # filter out deprecated and revoked techniques\\n if (\\\"x_mitre_deprecated\\\" in technique and technique[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in technique and technique[\\\"revoked\\\"]): continue\\n techniqueID = technique[\\\"external_references\\\"][0][\\\"external_id\\\"]\\n # store usage in techniques_used struct\\n if techniqueID in techniques_used:\\n techniques_used[techniqueID].append(thesoftware[\\\"name\\\"])\\n else:\\n techniques_used[techniqueID] = [thesoftware[\\\"name\\\"]]\\n\\n # format the techniques for the output layer\\n techniques_list = []\\n highest_usage = 0\\n lowest_usage = 1\\n for techniqueID in techniques_used:\\n # determine the number of used techniques for the score\\n count = len(techniques_used[techniqueID])\\n highest_usage = max(highest_usage, count)\\n lowest_usage = min(lowest_usage, count)\\n # append technique struct to list of layer-formatted techniques\\n techniques_list.append({\\n \\\"techniqueID\\\": techniqueID,\\n \\\"comment\\\": \\\"executed by \\\" + \\\", \\\".join(techniques_used[techniqueID]),\\n \\\"score\\\": count,\\n })\\n # set up layer name and desc according to softwaretype\\n if softwaretype != \\\"software\\\": \\n plural = \\\"tools\\\" if softwaretype == \\\"tool\\\" else \\\"malware\\\"\\n layername = f\\\"Software ({softwaretype}) Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\\\"\\n else: \\n layername = \\\"Software Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software, where the score is the count of software using the technique\\\"\\n\\n # construct and return the layer as a dict\\n return {\\n \\\"name\\\": layername,\\n \\\"description\\\": layerdescription,\\n \\\"versions\\\": {\\n \\\"layer\\\": \\\"4.1\\\",\\n \\\"navigator\\\": \\\"4.1\\\"\\n },\\n \\\"domain\\\": \\\"enterprise-attack\\\",\\n \\\"techniques\\\": techniques_list,\\n \\\"sorting\\\": 3, # order in descending order of score (count)\\n \\\"gradient\\\": {\\n \\\"colors\\\": [\\n \\\"#fff7b3\\\", # low counts are yellow\\n \\\"#ff6666\\\", # high counts are red\\n ],\\n \\\"minValue\\\": lowest_usage,\\n \\\"maxValue\\\": highest_usage\\n },\\n }\",\n \"def task_generate_sc_html():\\n for dept in Department.list():\\n command = (f\\\"pandoc --self-contained\\\"\\n f\\\" -o {dept.sc_html_path}\\\"\\n f\\\" {dept.sc_markdown_path}\\\")\\n\\n yield {\\n 'name': dept.name,\\n 'file_dep': [\\n dept.sc_figure1_path,\\n dept.sc_figure2_path,\\n dept.sc_figure3_path,\\n dept.sc_figure4_path,\\n dept.sc_figure5_path,\\n dept.sc_markdown_path,\\n ],\\n 'targets': [dept.sc_html_path],\\n 'actions': [command],\\n 'clean': True,\\n }\",\n \"def cluster_and_render(conf, dbname, outname=\\\"./text.html\\\", nclusters=8):\\n\\n\\n db = sqlite3.connect(dbname)\\n r = db.execute(\\\"select min(year), max(year) from counts where conf=?\\\", (conf,))\\n minyear, maxyear = r.fetchone()\\n\\n # total words per year for normalization purposes\\n r = db.execute(\\\"select year, count(*) from counts where conf=? order by year\\\", (conf,))\\n year2c = dict([(year, c) for year, c in r])\\n yearcounts = dict2arr(year2c, range(minyear, maxyear+1), 1)\\n\\n\\n def add_content(subcluster, content, suffix):\\n \\\"\\\"\\\"\\n Render the cluster as an image\\n \\\"\\\"\\\"\\n\\n fname = './plots/%s_%s.png' % (conf, suffix)\\n\\n # pick the top 10 terms\\n subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\\n subcluster = subcluster[:10]\\n\\n words = np.array(subcluster)[:,0]\\n ys = np.array(subcluster)[:,1:].astype(float)\\n mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]\\n maxmean = max(mean)\\n idx = mean.index(maxmean)\\n\\n # this is used to make the top-k list in the HTML later\\n content.append(('', words, fname, idx))\\n\\n\\n data = []\\n for arr in subcluster:\\n word = arr[0]\\n for x, y in enumerate(map(float, arr[1:])):\\n data.append(dict(\\n group=\\\"normal\\\",\\n word=word,\\n x=xs[x],\\n y=y, \\n alpha=0.3\\n ))\\n\\n # add a line for the mean\\n for x, y in enumerate(mean):\\n data.append(dict(group=\\\"aggregate\\\", word='___mean___', x=xs[x], y=y, alpha=1))\\n\\n if 1:\\n maxy = max(10, max(pluckone(data, 'y')))\\n if maxy <= 10:\\n breaks = [0, 5, 10]\\n\\n\\n # pygg lets you write ggplot2 syntax in python\\n p = ggplot(data, aes(x='x', y='y', group='word', color='group', alpha='alpha'))\\n p += geom_line(size=1)\\n p += scale_color_manual(values=\\\"c('normal' = '#7777dd','aggregate' = 'black')\\\", guide=\\\"FALSE\\\")\\n p += scale_alpha_continuous(guide=\\\"FALSE\\\")\\n if 1:\\n if maxy <= 10:\\n p += scale_y_continuous(lim=[0, maxy], breaks=breaks, labels = \\\"function (x) as.integer(x)\\\")\\n else:\\n p += scale_y_continuous(lim=[0, maxy], labels = \\\"function (x) as.integer(x)\\\")\\n p += legend_bottom\\n p += theme(**{\\n \\\"axis.title\\\":element_blank()\\n })\\n ggsave(fname, p, width=10, height=4, libs=['grid'])\\n \\n\\n\\n def vectors():\\n \\\"\\\"\\\"\\n Extract a matrix of term count vectors\\n\\n Return: [\\n [word, count1, count2, ...],\\n ...\\n ]\\n \\\"\\\"\\\"\\n r = db.execute(\\\"select word, year, c from counts where conf=? order by word, year\\\", (conf,))\\n vects = defaultdict(dict)\\n for w,y,c in r:\\n l = vects[w]\\n l[y] = float(c) \\n\\n\\n ret = []\\n for w in vects:\\n d = vects[w]\\n\\n # if word is super uncommon, skip it\\n if (max(d.values()) <= 3):\\n continue\\n if (max([v / (1.+year2c.get(y,0)) for y, v in d.items()]) < .1): \\n continue\\n\\n # some years may not have the word\\n counts = dict2arr(d, xrange(minyear, maxyear+1), 1.0)\\n\\n \\n # naive window averaging smoothing over the trend curve\\n smooth = []\\n for i in xrange(len(counts)):\\n smooth.append(np.mean(counts[max(0,i-2):i+2]))\\n if max(smooth) > 2:\\n ret.append([w] + smooth)\\n return np.array(ret)\\n\\n\\n vects = vectors()\\n # dimensions: words (row) x year (col)\\n data = vects[:,1:].astype(float)\\n\\n # there's a bajillion ways to normalize the counts before clustering.\\n # we do the following:\\n\\n # 1. divide by the total number of words in that year\\n # (normalize by column)\\n for idx, base in enumerate(yearcounts):\\n data[:,idx] /= float(base)\\n\\n # 2. ensure zero mean and 1 std\\n # (normalize by row)\\n data = np.array([(l - np.mean(l)) / (max(l)) for l in data ])\\n\\n\\n clusterer = KMeans(nclusters, n_init=50, init='k-means++')\\n clusterer.fit(data) \\n labels = clusterer.labels_\\n xs = np.array(range(minyear, maxyear+1))\\n\\n content = []\\n\\n # each label is a cluster\\n for label in set(labels):\\n idxs = labels == label\\n cluster = vects[idxs]\\n\\n # sort the words/clusters by their max count\\n cluster = sorted(cluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\\n if not len(cluster): continue\\n cluster = np.array(cluster)\\n words = cluster[:,0]\\n words = list(words)\\n\\n add_content(cluster, content, label)\\n\\n content.sort(key=lambda c: c[-1])\\n\\n\\n\\n # make HTML\\n from jinja2 import Template\\n template = Template(file('./clustertemplate.html').read())\\n\\n with file(outname, 'w') as f:\\n f.write( template.render(content=content))\",\n \"def generate_document(query_df): # sourcery skip: identity-comprehension\\n doc_lines = [\\n \\\"Data Queries Reference\\\",\\n \\\"=\\\" * len(\\\"Data Queries Reference\\\"),\\n \\\"\\\",\\n \\\"\\\",\\n ]\\n\\n # This line fails if re-written as dict(query_df.groupby(\\\"Environment\\\"))\\n # pylint: disable=unnecessary-comprehension\\n group_dict = {name: group for name, group in query_df.groupby(\\\"Environment\\\")}\\n for name, friendly_name in PROVIDERS.items():\\n if name not in group_dict:\\n continue\\n grp = group_dict[name]\\n doc_lines.append(f\\\"Queries for {friendly_name}\\\")\\n doc_lines.append(\\\"-\\\" * len(f\\\"Queries for {friendly_name}\\\"))\\n doc_lines.append(f\\\"\\\\nData Environment identifier: {name}\\\\n\\\")\\n tbl_txt = tabulate(\\n grp.drop(columns=\\\"Environment\\\"),\\n headers=\\\"keys\\\",\\n showindex=\\\"never\\\",\\n tablefmt=\\\"rst\\\",\\n )\\n tbl_txt = [line.strip() for line in tbl_txt.split(\\\"\\\\n\\\")]\\n doc_lines.extend(tbl_txt)\\n doc_lines.append(\\\"\\\\n\\\\n\\\")\\n return \\\"\\\\n\\\".join(doc_lines)\",\n \"def build_md_once(self, index, tid):\\n url = self.joint_url(tid)\\n title_json = proxy_req(url, 1)\\n if not title_json:\\n if can_retry(url, index):\\n self.build_md_once(index, tid)\\n return\\n content = BeautifulSoup(\\n title_json['content'], 'html.parser').find_all('div')\\n text = []\\n img_href = []\\n img_id = 1\\n ttid = 1\\n img_title = self.find_title(index).split('/')[1][:-3]\\n for word in content:\\n temp_text = ''\\n if word.span and len(word.span.text) and not word.span.text[0].isdigit:\\n temp_text = '## ' + word.span.text\\n ttid = 1\\n if word.img:\\n temp_text = ' + '.jpg)'\\n img_href.append(word.img['src'].replace('https', 'http'))\\n img_id += 1\\n\\n if not len(temp_text):\\n temp_text = word.text\\n if len(temp_text) and temp_text[0].isdigit():\\n temp_text = str(ttid) + '. **' + \\\\\\n ' '.join(temp_text.split('\\\\xa0')[1:]).strip() + '**'\\n ttid += 1\\n if len(temp_text) and temp_text[0:2] == '//':\\n temp_text = str(ttid) + '. **' + \\\\\\n ' '.join(temp_text.split('\\\\xa0')[2:]).strip() + '**'\\n ttid += 1\\n if len(temp_text) and (temp_text[0] == '￥' or temp_text[0] == '€'):\\n temp_text = '<a>' + temp_text + '</a>'\\n text.append(temp_text)\\n with codecs.open(data_dir + self.find_title(index), 'w', encoding='utf-8') as f:\\n f.write('\\\\n'.join(text))\\n self.img_map[index] = img_href\\n print(index, len(img_href))\",\n \"def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\\n\\n examples = []\\n total_answerable = 0\\n\\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\\n guid = \\\"%s-%s-%s\\\" % (set_type, line['qid'], str(i))\\n src = []\\n has_pos = 0\\n\\n if passage_type == 'both' and set_type != 'train':\\n\\n # rank by bert score in val and test\\n score_key = 'rank_score'\\n # score_key = \\\"nr_score\\\"\\n both_sources = line[\\\"passages\\\"][:2*cand_for_each_source] + line[\\\"tables\\\"][:2*cand_for_each_source]\\n all_scores = np.array([x[score_key] for x in both_sources])\\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\\n\\n for idx in sorted_index:\\n pas = both_sources[idx]\\n if idx < 2 * cand_for_each_source:\\n pas_type = 'passage'\\n title = pas[\\\"article_title\\\"]\\n else:\\n pas_type = 'table'\\n title = \\\"table_\\\" + pas[\\\"uid\\\"].split('-split')[0]\\n\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + f\\\" </s> {pas_type} title: \\\" + title + f\\\" </s> {pas_type} content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\")\\n\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n else:\\n\\n if passage_type in ['textual', 'hybrid', 'both']:\\n for pas in line[\\\"passages\\\"][:cand_for_each_source]:\\n # 10 passages\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> passage title: \\\" + pas[\\\"article_title\\\"] + \\\" </s> passage content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\\\\\"ZAPPAtite\\\\\\\". All songs written and performed ... </s>\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n if passage_type in ['tabular', 'hybrid', 'both']:\\n for tab in line[\\\"tables\\\"][:cand_for_each_source]:\\n # 10 tables\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> table title: \\\" + \\\"table_\\\" + tab[\\\"uid\\\"].split('-split')[0] + \\\" </s> table content: \\\" + tab[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\\n if isinstance(tab['judge'], dict):\\n has_pos += int(tab['judge']['judge_contain_all'])\\n else:\\n has_pos += int(tab['judge'])\\n\\n const = 2 if passage_type in ['hybrid', 'both'] else 1\\n if len(src) != const * cand_for_each_source:\\n logger.info(line)\\n src = src + [\\\"\\\"] * (const * cand_for_each_source - len(src))\\n\\n if has_pos > 0:\\n total_answerable += 1\\n\\n if enable_sql_supervision and 'true_sql' in line:\\n tgt = \\\"sql: \\\" + line[\\\"true_sql\\\"] + \\\" </s>\\\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\\\\\"Butler CC (KS)\\\\\\\" </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'denotation' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"denotation\\\"][0]) + \\\" </s>\\\"\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'answers' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"answers\\\"][0]) + \\\" </s>\\\" # e.g. answer: no slogan on current series </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt + \\\"\\\\n\\\")\\n\\n logger.info(f\\\"Total answerable in {set_type} split is {total_answerable / len(lines)}\\\")\\n\\n return examples\",\n \"def generateMapping(text, extensions):\\n extensions = filter(lambda e: e != 'markdown.extensions.footnotes', extensions)\\n\\n text_index, modified = preprocess(text)\\n html_modified = markdown(modified, extensions=extensions)\\n modified_index = findPlaceholderIndex(html_modified, len(text_index))\\n return zip(text_index, modified_index)\",\n \"def get_technique_stats(db):\\n \\n techniques = {}\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n temp_dict = {technique: [{u'id': element['project_no'], \\n u'name': element['project_name'], u'description': element['short_description']}]}\\n techniques.update(temp_dict)\\n else:\\n temp_dict = {u'id': element['project_no'],\\n u'name': element['project_name'], u'description': element['short_description']}\\n techniques[technique].append(temp_dict)\\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\\n\\n return techniques\",\n \"def dump_designs(self):\\n param_dict = {'startkey': '\\\"_design/\\\"',\\n 'endkey': '\\\"_design0\\\"',\\n 'include_docs': 'true'} \\n r = requests.get('/'.join([self.url, self.db, \\\"_all_docs\\\"]), params=param_dict)\\n # Write out the JSON document itself\\n for row in r.json()['rows']:\\n design_str = row['id'].replace('/', '%2F')\\n json.dump(row, open(design_str + '.json', 'w'), indent=4)\\n # Also create separate files for the map/reduce javascript\\n for view in row[\\\"doc\\\"][\\\"views\\\"]:\\n view_str = '%2F'.join(['_view', view])\\n f = open('%2F'.join([design_str, view_str + \\\".js\\\"]), 'w')\\n for func in row[\\\"doc\\\"][\\\"views\\\"][view]:\\n f.write(func + \\\" = \\\" + row[\\\"doc\\\"][\\\"views\\\"][view][func] + \\\"\\\\n\\\")\\n f.close()\",\n \"def iterate_data(self):\\n if \\\"single\\\" in self.dataset_name:\\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for answer_id in self.data:\\n summary = self.data[answer_id]['summary']\\n articles = self.data[answer_id]['articles']\\n question = self.data[answer_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(articles, 'article')\\n self._get_token_cnts(question, 'question')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if \\\"multi\\\" in self.dataset_name:\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for q_id in self.data:\\n summary = self.data[q_id]['summary']\\n question = self.data[q_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(question, 'question')\\n question = self.data[q_id]['question']\\n for answer_id in self.data[q_id]['articles']:\\n articles = self.data[q_id]['articles'][answer_id][0]\\n if args.tokenize:\\n self._get_token_cnts(articles, 'article')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if self.dataset_name == \\\"complete_dataset\\\":\\n self.stat_dict = {'urls': [], 'sites': []}\\n article_dict = {}\\n print(\\\"Counting answers, sites, unique urls, and tokenized counts of unique articles\\\")\\n answer_cnt = 0\\n for q_id in self.data:\\n for a_id in self.data[q_id]['answers']:\\n answer_cnt += 1\\n url = self.data[q_id]['answers'][a_id]['url']\\n article = self.data[q_id]['answers'][a_id]['article']\\n if url not in article_dict:\\n article_dict[url] = article\\n self.stat_dict['urls'].append(url)\\n assert \\\"//\\\" in url, url\\n site = url.split(\\\"//\\\")[1].split(\\\"/\\\")\\n self.stat_dict['sites'].append(site[0])\\n print(\\\"# of Answers:\\\", answer_cnt)\\n print(\\\"Unique articles: \\\", len(article_dict)) # This should match up with count written to file\\n self._write_stats(\\\"full collection\\\")\\n\\n # Get token/sent averages of unique articles\\n if args.tokenize:\\n self.stat_dict = {'article': [[], []]}\\n for a in article_dict:\\n self._get_token_cnts(article_dict[a], 'article')\\n self._write_stats(\\\"token_counts\\\")\",\n \"def index():\\n response = \\\"\\\"\\n for table in config.TABLE_SCHEMA.keys():\\n response = response + disco.examples(table)\\n return response\",\n \"def tex(self):\\n self.cardinality = self.question.sorted_answers_cardinality(\\n self.min_cardinality,\\n self.group_together,\\n self.group_by_letter_case,\\n self.group_by_slugify,\\n self.filter,\\n self.sort_answer,\\n other_question=self.other_question,\\n )\\n q1 = []\\n q2 = []\\n for answer_to_q1, cardinality_to_q2 in list(self.cardinality.items()):\\n for answer_to_q2, number_of_time in list(cardinality_to_q2.items()):\\n q1 += [answer_to_q1] * number_of_time\\n q2 += [answer_to_q2] * number_of_time\\n df = DataFrame(data={self.question.text: q1, self.other_question.text: q2})\\n name = \\\"tex/q{}_vs_q{}\\\".format(self.question.pk, self.other_question.pk)\\n sankey(df[self.question.text], df[self.other_question.text], aspect=20, fontsize=10, figureName=name)\\n return Question2TexSankey.TEX_SKELETON % (\\n name[4:],\\n self.question.pk,\\n self.other_question.pk,\\n self.get_caption(),\\n )\",\n \"def corpus_group():\",\n \"def extract_json_to_files(input_dir,output_dir):\\n files={}\\n files['train']='train-v1.1.json'\\n files['dev']='dev-v1.1.json'\\n\\n for file in files:\\n filename=os.path.join(input_dir,files[file])\\n with open(filename,'r',encoding='utf-8') as data_file:\\n examples = []\\n dataset=json.load(data_file)\\n count_total=total_exs(dataset)\\n count_mapping_problem=0\\n count_token_problem=0\\n count_ansspan_problem=0\\n count_examples=0\\n for article_id in tqdm(range(len(dataset['data'])), desc=\\\"Preprocessing {}\\\".format(file)):\\n article_paragraph=dataset['data'][article_id]['paragraphs']\\n for paragraph_id in range(len(article_paragraph)):\\n context=article_paragraph[paragraph_id]['context']\\n context=context.replace(\\\"''\\\",'\\\"').replace(\\\"``\\\",'\\\"')\\n context = context.replace('\\\\u3000', ' ').replace('\\\\u202f',' ').replace('\\\\u2009', ' ')#.replace(\\\"'\\\",\\\"'\\\")\\n context=context.replace('\\\\-',' ')\\n context_tokens=tokenize_sequence(context)\\n context=context.lower()\\n qas=article_paragraph[paragraph_id]['qas']\\n charloc2wordloc=get_char_word_loc_mapping(context, context_tokens)\\n if charloc2wordloc is None:\\n count_mapping_problem+=len(qas)\\n continue\\n for qa in qas:\\n question=qa['question'].lower()\\n question_tokens=tokenize_sequence(question)\\n\\n ans_text=qa['answers'][0]['text'].lower()\\n ans_text=ans_text.replace('\\\\u3000', ' ').replace('\\\\u202f', ' ').replace('\\\\u2009', ' ')\\n ans_start_loc=qa['answers'][0]['answer_start']\\n if qa['id'] in ['5706baed2eaba6190074aca5','57269c73708984140094cbb5','57269c73708984140094cbb7','572a11661d04691400779721','572a11661d04691400779722','572a11661d04691400779723','572a11661d04691400779724','572a11661d04691400779725','572a2cfc1d0469140077981b','572a3a453f37b319004787e9','572a84d3f75d5e190021fb3c']:\\n ans_start_loc+=1\\n if qa['id'] in ['572a5df77a1753140016aedf','572a5df77a1753140016aee0','572a84d3f75d5e190021fb38','572a84d3f75d5e190021fb39','572a84d3f75d5e190021fb3a','572a84d3f75d5e190021fb3b','572a85df111d821400f38bad','572a85df111d821400f38bae','572a85df111d821400f38baf','572a85df111d821400f38bb0']:\\n ans_start_loc+=2\\n if qa['id'] in ['572a5df77a1753140016aee1','572a5df77a1753140016aee2']:\\n ans_start_loc+=3\\n if qa['id'] in ['57286bf84b864d19001649d6','57286bf84b864d19001649d5']:\\n ans_start_loc-=1\\n if qa['id'] in ['5726bee5f1498d1400e8e9f3','5726bee5f1498d1400e8e9f4']:\\n ans_start_loc-=2\\n ans_end_loc=ans_start_loc+len(ans_text)\\n\\n if context[ans_start_loc:ans_end_loc]!=ans_text:\\n count_ansspan_problem+=1\\n continue\\n ans_start_wordloc = charloc2wordloc[ans_start_loc][1] # answer start word loc\\n ans_end_wordloc = charloc2wordloc[ans_end_loc-1][1] # answer end word loc\\n assert ans_start_wordloc <= ans_end_wordloc\\n\\n ans_tokens = context_tokens[ans_start_wordloc:ans_end_wordloc + 1]\\n if \\\"\\\".join(ans_tokens) != \\\"\\\".join(ans_text.split()):\\n count_token_problem += 1\\n #print(ans_text)\\n #print(ans_tokens)\\n continue # skip this question/answer pair\\n examples.append((' '.join(context_tokens),' '.join(question_tokens),' '.join(ans_tokens),' '.join([str(ans_start_wordloc),str(ans_end_wordloc)])))\\n print(\\\"Number of (context, question, answer) triples discarded due to char -> token mapping problems: \\\", count_mapping_problem)\\n print(\\\"Number of (context, question, answer) triples discarded because character-based answer span is unaligned with tokenization: \\\",count_token_problem)\\n print(\\\"Number of (context, question, answer) triples discarded due character span alignment problems (usually Unicode problems): \\\",count_ansspan_problem)\\n print(\\\"Processed %i examples of total %i\\\\n\\\" % (len(examples), len(examples)+count_mapping_problem+count_token_problem+count_ansspan_problem))\\n indices = list(range(len(examples)))\\n np.random.shuffle(indices)\\n with open(os.path.join(output_dir,file+'.context'),'w',encoding='utf-8') as context_file, \\\\\\n open(os.path.join(output_dir,file+'.question'),'w',encoding='utf-8') as question_file, \\\\\\n open(os.path.join(output_dir,file+'.answer'),'w',encoding='utf-8') as answer_file, \\\\\\n open(os.path.join(output_dir,file+'.span'),'w',encoding='utf-8') as span_file:\\n for i in indices:\\n (context,question,answer,span)=examples[i]\\n context_file.write(context+'\\\\n')\\n question_file.write(question+'\\\\n')\\n answer_file.write(answer+'\\\\n')\\n span_file.write(span+'\\\\n')\",\n \"def write_data_index(self):\\n content = \\\"\\\"\\n self.remove(os.path.join(config[\\\"data_subdir\\\"], \\\"*\\\"))\\n if self.data_index:\\n print(\\\"- writing data index\\\")\\n content += f\\\"# [{config['github_repo_name']}]({config['github_pages_url']})\\\\n\\\"\\n content += \\\"\\\\n## Index of Data files in this Repository\\\\n\\\"\\n for data, links in sorted(self.data_index.items(), key=lambda x: natsort(x[0])):\\n if links:\\n content += f\\\"\\\\n### {data}\\\\n\\\"\\n content += f\\\"\\\\n\\\"\\n for link in links:\\n content += f\\\"* {link}\\\\n\\\"\\n data_src = os.path.join(self.src_dir, config[\\\"data_subdir\\\"], data)\\n data_dst = os.path.join(self.dst_dir, config[\\\"data_subdir\\\"], data)\\n print(f\\\"- copying {data_src} to {data_dst}\\\")\\n shutil.copy(data_src, data_dst)\\n self.write_md2html(\\\"data_index\\\", content)\",\n \"def load_demos():\\n for index in range(len(feconf.DEMO_EXPLORATIONS)):\\n load_demo(str(index))\",\n \"def write_index_html(wk_dir,region_dict,metrics_filename,ext=\\\"png\\\"):\\n # Make lists of the metrics and figure files to display\\n metrics_dir = os.path.join(wk_dir,metrics_dir_name)\\n metric_list = sorted([\\n f for f in os.listdir(metrics_dir) if f.endswith('_summary.csv')])\\n plot_list=[]\\n fig_list=sorted([f for f in os.listdir(wk_dir+'/'+figure_dir_name)])\\n for keyword in ['lag','correlations','twodpdf']:\\n plot_list.append([f for f in fig_list if (keyword in f)]) # sort datasets\\n subtitle_list=['Autocorrelation','2D Histograms','Correlation maps']\\n\\n # Start working on html text. Each line is appened to a list that\\n # is then written to file.\\n html_file=['<html>\\\\n',\\n '<body>','<head><title>ASoP-Coherence</title></head>\\\\n',\\n '<br><h1>ASoP-Coherence results</h1>\\\\n','<h2>Contents</h2>\\\\n',\\n '<dl>\\\\n','<dt><a href=\\\"#Metrics\\\">Metrics</a></dt>\\\\n',\\n '<dt><a href=\\\"#Figures\\\">Figures</a></dt>\\\\n',\\n '<dd><a href=\\\"#Autocorrelation\\\">Autocorrelation</a></dd>\\\\n',\\n '<dd><a href=\\\"#2D-Histograms\\\">2D Histograms</a></dd>\\\\n',\\n '<dd><a href=\\\"#Correlation-maps\\\">Correlation Maps</a></dd>\\\\n',\\n '</dl>\\\\n''<section id=\\\"Metrics\\\">\\\\n','<br><h2>Metrics</h2>\\\\n']\\n html_file.append('<h3>Intermittency Metrics</h3>\\\\n')\\n\\n # Display metrics JSON in dashboard option\\n metrics_json = os.path.basename(metrics_filename)\\n metrics_relocated = os.path.join(metrics_dir_name,metrics_json)\\n tmp='<p><a href=\\\"'+metrics_relocated+'\\\" target=\\\"_blank\\\">'+metrics_json+'</a></p>\\\\n'\\n html_file.append(tmp)\\n\\n # Link CSV tables for download\\n html_file.append('<h3>Tables</h3>\\\\n')\\n for metric_file in metric_list:\\n metric_path = os.path.join(metrics_dir_name,metric_file)\\n html_file.append('<p><a href=\\\"{0}\\\">{1}</a></p>\\\\n'.format(metric_path,metric_file))\\n html_file.append('<br>\\\\n')\\n html_file.append('</section>\\\\n')\\n\\n # Add figures\\n html_file.append('<section id=\\\"Figures\\\">\\\\n')\\n html_file.append('<h2>Figures</h2>\\\\n')\\n for title,category in zip(subtitle_list,plot_list):\\n html_file.append('<section id='+title.replace(' ','-')+'>\\\\n')\\n html_file.append('<h3>{0}</h3>\\\\n'.format(title))\\n # Adjust figure width for autocorrelation\\n fwidth = \\\"647\\\"\\n if title==\\\"Autocorrelation\\\":\\n fwidth=\\\"450\\\"\\n for region in region_dict:\\n html_file.append('<h4>{0}</h4>\\\\n'.format(region.replace('_',' ')))\\n region_fig = [f for f in category if (region.replace(\\\" \\\",\\\"_\\\") in f)]\\n for fig in region_fig:\\n tmp = '<p><a href=\\\"{0}\\\" target=\\\"_blank\\\" alt={0}>' + \\\\\\n '<img src=\\\"{0}\\\" width={1} alt=\\\"{0}\\\"></a></p>\\\\n'\\n html_file.append(\\n tmp.format(os.path.join(figure_dir_name,fig),fwidth))\\n html_file.append('</section>\\\\n')\\n html_file.append('</section>\\\\n')\\n\\n html_file.append('</body>\\\\n</html>\\\\n')\\n filename=wk_dir+'/index.html'\\n with open(filename,'w') as html_page:\\n html_page.writelines(html_file)\",\n \"def display_wmd(google_snippets, my_snippets):\\r\\n for i in range(len(google_snippets)):\\r\\n print(i+1, '.', urls[i])\\r\\n print('Google Snippet:', google_snippets[i])\\r\\n print('Generated Snippet:', my_snippets[i])\\r\\n print(\\\"Word Mover's Distance:\\\", get_wmd_dist(google_snippets[i], my_snippets[i], model))\\r\\n print()\",\n \"def index():\\n with open(\\\"PROJECT.md\\\", \\\"r\\\") as project_file:\\n md_template_string = markdown.markdown(\\n project_file.read(), extensions=[\\\"fenced_code\\\"]\\n )\\n return md_template_string\",\n \"def task_generate_sc_markdown():\\n for dept in Department.list():\\n yield {\\n 'name': dept.name,\\n 'file_dep': [],\\n 'targets': [dept.sc_markdown_path],\\n 'actions': [dept.generate_sc_markdown],\\n 'clean': True,\\n 'uptodate': [False],\\n }\",\n \"def create_explanations_csv():\\n with open('output/' + dataset_name + '_' + model_name + '.csv', mode='w', newline='') as file:\\n writer = csv.writer(file, delimiter=',', quotechar='\\\"', quoting=csv.QUOTE_MINIMAL)\\n writer.writerow(\\n [\\\"index\\\", \\\"original text\\\", \\\"true class\\\", \\\"decoded text\\\", \\\"black box prediction\\\",\\n \\\"decision tree prediction\\\", \\\"fidelity\\\", \\\"exemplars\\\", \\\"counter exemplars\\\", \\\"top exemplar words\\\",\\n \\\"top counter exemplar words\\\"])\\n for i in range(len(idx)):\\n writer.writerow(\\n [idx[i], X_original[i], y_original[i], final_decoded_sentences[i][0], bbpreds[i], dtpreds[i],\\n fidelities[i], exemplars[i], counter_exemplars[i], top_exemplar_words_dict_list[i],\\n top_counter_exemplar_words_dict_list[i]])\",\n \"def Subtask4_pre_train_3():\\n train_data = np.load(PATH + 'pre_train_2_Subtask4.npy', allow_pickle=True).item()\\n\\n evidence = []\\n for d in train_data.items():\\n for i in range(5):\\n evidence.append(d[1][i])\\n\\n files = os.listdir(PATH + 'data/wiki-pages/wiki-pages/')\\n documents = {}\\n for i in files:\\n with open(os.path.join(PATH + 'data/wiki-pages/wiki-pages/', i)) as fp:\\n lines = fp.readlines()\\n for line in lines:\\n line = eval(line)\\n if line['id'] in evidence:\\n text = line['lines']\\n documents[line['id']] = text\\n with open(PATH + 'pre_train_3_Subtask4.txt', 'w', encoding='utf-8') as f:\\n f.write(str(documents))\",\n \"def index_page(data):\\n main_index = [html.H1(\\\"INDEX\\\"),\\n html.Div([html.A(\\\"Characters\\\", href=\\\"/character\\\"),\\n \\\" | \\\", html.A(\\\"Search\\\", href=\\\"/search\\\")]),\\n dcc.Loading(html.Img(src=create_wordcloud(data[\\\"tokens\\\"]))),\\n get_random_quotes(data, 5)]\\n\\n return main_index\",\n \"def generate_excerpt():\",\n \"def to_markdown(self, experiment_name : str):\\n experiment = self.get_experiment(experiment_name)\\n f = open(experiment.root_dir + \\\"/\\\" + experiment_name + \\\".md\\\", 'w')\\n # Create a markdown object\\n markdown = Markdown()\\n\\n for img in experiment.images:\\n mkimg = Markdown.Image(img.title, img.filename, img.description)\\n markdown.add_image(img.filename)\\n\\n table = Markdown.Table([\\\"Parameter Name\\\", \\\"Input/Output\\\", \\\"Value\\\", \\\"Title\\\", \\\"Description\\\"])\\n for param in experiment.parameters:\\n table.add_row([param.name, ])\\n\\n markdown.add_table(table)\",\n \"def generateHtml(self):\\n # only the master processor needs to do this\\n if not self.master: return\\n\\n for page in self.layout.pages:\\n \\n # build the metric dictionary\\n metrics = {}\\n page.models = []\\n for fname in glob.glob(os.path.join(self.output_path,\\\"*.nc\\\")):\\n with Dataset(fname) as dataset:\\n mname = dataset.getncattr(\\\"name\\\")\\n if mname != \\\"Benchmark\\\": page.models.append(mname)\\n if not dataset.groups.has_key(page.name): continue\\n group = dataset.groups[page.name]\\n\\n # if the dataset opens, we need to add the model (table row)\\n metrics[mname] = {}\\n \\n # each model will need to have all regions\\n for region in self.regions: metrics[mname][region] = {}\\n \\n # columns in the table will be in the scalars group\\n if not group.groups.has_key(\\\"scalars\\\"): continue\\n \\n # we add scalars to the model/region based on the region\\n # name being in the variable name. If no region is found,\\n # we assume it is the global region.\\n grp = group.groups[\\\"scalars\\\"]\\n for vname in grp.variables.keys():\\n found = False\\n for region in self.regions:\\n if region in vname: \\n found = True\\n var = grp.variables[vname]\\n name = vname.replace(region,\\\"\\\")\\n metrics[mname][region][name] = Variable(name = name,\\n unit = var.units,\\n data = var[...])\\n if not found:\\n var = grp.variables[vname]\\n metrics[mname][\\\"global\\\"][vname] = Variable(name = vname,\\n unit = var.units,\\n data = var[...])\\n page.setMetrics(metrics)\\n \\n # write the HTML page\\n f = file(os.path.join(self.output_path,\\\"%s.html\\\" % (self.name)),\\\"w\\\")\\n f.write(str(self.layout))\\n f.close()\",\n \"def get_designs(self):\",\n \"def _setup(self):\\n self._raw_dir = os.path.join(self._snippets_dir,\\\"raw\\\",\\\"static\\\")\\n if not os.path.exists(self._raw_dir):\\n os.mkdir(self._raw_dir)\\n\\n self._trec_dir = os.path.join(self._snippets_dir,\\\"trec\\\",\\\"static\\\")\\n if not os.path.exists(self._trec_dir):\\n os.mkdir(self._trec_dir)\\n\\n self._temp_dir = os.path.join(self._snippets_dir,\\\"temp\\\",\\\"static\\\")\\n if not os.path.exists(self._temp_dir):\\n os.mkdir(self._temp_dir)\\n\\n self._para_dir = os.path.join(self._snippets_dir,\\\"para\\\",\\\"static\\\")\\n if not os.path.exists(self._para_dir):\\n os.mkdir(self._para_dir)\\n\\n self._snippet_result_dir = os.path.join(self._snippets_dir,\\\"result\\\",\\\"static\\\")\\n if not os.path.exists(self._snippet_result_dir):\\n os.mkdir(self._snippet_result_dir)\\n\\n self._snippet_index_dir = os.path.join(self._snippets_dir,\\\"index\\\",\\\"static\\\")\\n if not os.path.exists(self._snippet_index_dir):\\n os.mkdir(self._snippet_index_dir)\\n\\n \\n\\n\\n self._index_para = os.path.join(self._para_dir,\\\"index_para\\\")\\n\\n self._temp_query_para = os.path.join(self._para_dir,\\\"temp_query_para\\\")\\n\\n self._index_list = os.path.join(self._para_dir,\\\"static_index_list\\\")\\n \\n self._orf = os.path.join(self._snippet_result_dir,\\\"orf\\\")\\n\\n self._oqf = os.path.join(self._temp_dir,\\\"oqf\\\")\\n \\n self._temp_output = os.path.join(self._temp_dir,\\\"temp_output\\\")\\n\\n with open(self._index_list,\\\"w\\\") as f:\\n f.write(self._snippet_index_dir+\\\"\\\\n\\\")\\n\\n self._temp_query_builder = IndriQueryFactory(count=10000,\\n rule=self._retrieval_method)\\n\\n self._oqf_builder = IndriQueryFactory(count=30,\\n rule=self._retrieval_method)\",\n \"def write_to_md(dictData, outputDirectory):\\n\\tdic = prepare_hw_dict(dictData)\\n\\tfor hw in dic:\\n\\t\\tfileout = os.path.join(outputDirectory, hw+'.md')\\n\\t\\t# Prepare the output file\\n\\t\\tfout = codecs.open(fileout, 'w', 'utf-8')\\n\\t\\t# Write frontmatter\\n\\t\\tfout.write('---\\\\ntitle: \\\"'+hw+'\\\"\\\\n---\\\\n\\\\n')\\n\\t\\t# For each (headword, meanings, verseNumber, PageNum) tuples,\\n\\t\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dic[hw]:\\n\\t\\t\\tcommaed = ', '.join(meanings)\\n\\t\\t\\tverse = verse.replace('<BR>', '<br />')\\n\\t\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\t\\tfout.write('# ' + hw + '\\\\n## ' + commaed + '\\\\n' + verse + '<br />verse ' + verseNumDetails + '<br />page ' + pageNumDetails +'\\\\n\\\\n')\\n\\t\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('MD files generated. Success!')\\n\\tprint('{} separate .md files written, one per headword.'.format(len(dic)))\",\n \"def get_data():\\n wga_df = pd.read_csv(os.path.join(MODEL_DIR, 'wga/sculptures/wga_sculpture_periods.csv'), index_col=0)\\n wikiart_df = pd.read_csv(os.path.join(MODEL_DIR, 'wikiart/sculptures/wikiart_sculpture_periods.csv'), index_col=0)\\n nga_df = pd.read_csv(os.path.join(MODEL_DIR, 'nga/sculptures/nga_sculpture_periods.csv'), index_col=0)\\n\\n ######## Fix name for WGA and WikiaRt ###########\\n wga_df['Author'] = wga_df.apply(lambda x: fix_name_wga(x['Author']), axis=1)\\n wikiart_df['Author'] = wikiart_df.apply(lambda x: fix_name_wiki(x['Author']), axis=1)\\n nga_df['Author'] = nga_df.apply(lambda x: fix_name_nga(x['Author']), axis=1)\\n\\n df = pd.concat([wga_df, wikiart_df, nga_df], ignore_index=True, sort=True)\\n\\n df['Author_Fixed'] = df.apply(lambda x: fix_text(x['Author']), axis=1)\\n df['title_fixed'] = df.apply(lambda x: fix_text(x['title']), axis=1)\\n\\n periods = [\\\"BAROQUE\\\", \\\"EARLY RENAISSANCE\\\", \\\"MEDIEVAL\\\", \\\"NEOCLASSICISM\\\", \\\"HIGH RENAISSANCE\\\", \\\"MINIMALISM\\\", \\\"REALISM\\\",\\n \\\"IMPRESSIONISM\\\", \\\"ROCOCO\\\", \\\"SURREALISM\\\", \\\"MANNERISM\\\", \\\"ROMANTICISM\\\",\\n ]\\n df['Period'] = df.apply(lambda row: row['Period'].upper(), axis=1)\\n\\n # Get Desired Periods\\n df['Period'] = df.apply(lambda x: \\\"SURREALISM\\\" if \\\"SURREALISM\\\" in x['Period'] else x['Period'], axis=1)\\n df = df[(df['Period'].isin(periods))]\\n df = df.sort_values(['Author_Fixed', 'title_fixed'])\\n\\n #print(\\\"Combined Drop Rows:\\\", df.shape[0] - df.drop_duplicates(subset=['Author_Fixed', 'title_fixed']).shape[0])\\n\\n df = df.drop_duplicates(subset=['Author_Fixed', 'title_fixed'], keep='last')\\n\\n # Drop Duplicate Sculptures\\n df = df[~df['file'].isin(DUP_SCULPTURES)].reset_index(drop=True)\\n\\n #print(df['Period'].value_counts())\\n\\n return df\",\n \"def main() -> Dict[int, int]:\\n # DOCUMENTS DIRECTORY\\n # Path of the directory that contains the .txt documents. One .txt document by company. IT NEEDS TO BE \\\"/data\\\" when you upload it in data challenge platform. For test in local, you can modifiy to match your data path.\\n if dev:\\n documents_directory = \\\"../example_dataset/data\\\"\\n else: \\n documents_directory = \\\"/data\\\"\\n\\n path_to_files: List[str] = [os.path.join(documents_directory, file) for file in os.listdir(documents_directory)]\\n assert len(path_to_files) == 10 # 10 files in documents directory\\n path_to_files.sort() # Sort list of path file by alphabetical order to match ground truth annotations order : IT IS ESSENTIAL.\\n\\n # INITIALIZATION OF YOUR OBJECTS\\n data_model = FormDataModel.from_json_file(\\n os.path.join(os.path.dirname(__file__), \\\"resources\\\", \\\"data-model.json\\\")\\n )\\n country_referential = CountryReferential.from_csv(\\n os.path.join(os.path.dirname(__file__), \\\"resources\\\", \\\"countries_code.csv\\\")\\n )\\n form_company_filling = FormCompanyFilling([\\n BasicExtractor(\\n question_ids=NOT_COUNTRY_QUESTIONS_NUMBERS,\\n form_data_model=data_model,\\n ),\\n BasicCountryExtractor(\\n question_ids=COUNTRY_QUESTIONS_NUMBERS,\\n form_data_model=data_model,\\n country_code_referential=country_referential,\\n\\n )\\n ])\\n\\n # COMPUTE PREDICTION BY FILE (ie company)\\n print(\\\"##################################\\\")\\n print(\\\"RUNNING PREDICTION\\\")\\n results: Dict[int, int] = {}\\n start_time = time.time()\\n for i, path in enumerate(path_to_files):\\n start = time.time()\\n print(f\\\"File : {path}\\\")\\n with open(path, \\\"r\\\") as input_file:\\n text = input_file.read()\\n print(\\\"... Encoding ...\\\")\\n start_encoding = time.time()\\n embeddings = model.encode(prepare_sentences(text))\\n # embeddings = []\\n print(\\\"Successfully encoded\\\")\\n print(\\\"Encoding time : \\\", time.time() - start_encoding)\\n\\n form_company_response = form_company_filling.fill(text, embeddings, model)\\n form_company_response.sort_by_question_id() # ESSENTIAL : Sort the response by question number for each company\\n for answer in form_company_response.answers:\\n question_number = answer.question_id + i * 22 # ESSENTIAL : each company has 22 questions. Each question_number in results should be unique\\n results[question_number] = answer.answer_id\\n # gc.collect()\\n print(\\\"File time :\\\", time.time()-start, '\\\\n')\\n # CHECK FORMAT RESULTS IS DATACHALLENGE PLATFORM COMPATIBLE\\n assert len(results) == len(path_to_files) * (len(COUNTRY_QUESTIONS_NUMBERS) + len(NOT_COUNTRY_QUESTIONS_NUMBERS))\\n assert set(list(results.keys())) == {i for i in range(1,221)}\\n print(\\\"Full Time\\\", time.time()-start_time)\\n return results\",\n \"def generate_markdown(self, outdir, definitions):\\n doc_cases = defaultdict(lambda: defaultdict(list))\\n\\n if not os.path.exists(outdir):\\n os.makedirs(outdir)\\n for category in definitions.categories:\\n cat_outdir = os.path.join(outdir, category.replace(\\\" \\\", \\\"_\\\").lower())\\n if not os.path.exists(cat_outdir):\\n os.makedirs(cat_outdir)\\n\\n for case in definitions.case_set:\\n if case[\\\"category\\\"] == category:\\n doc_cases[category][case[\\\"name\\\"]] = case\\n\\n for technique in doc_cases[category]:\\n rendered = self._generate_markdown(doc_cases[category][technique])\\n md_filename = technique.replace(\\\" \\\", \\\"_\\\").lower() + \\\".md\\\"\\n filename = os.path.join(cat_outdir, md_filename)\\n mdoutfile = open(filename, \\\"w\\\")\\n mdoutfile.write(rendered)\\n mdoutfile.close()\",\n \"def get_structure():\\n\\n _articles = []\\n _categories = []\\n\\n def get_article(article_filename, general_category, sep='|||'):\\n \\\"\\\"\\\"\\n Adds the given article to the \\\"articles, categories, general\\\" current structure\\n \\\"\\\"\\\"\\n category = []\\n with open(article_filename) as _f:\\n for _row in _f:\\n if len(_row):\\n if _row[0] == '=':\\n # new category\\n k = 0\\n while _row[k] == '=':\\n k += 1\\n if k > 1:\\n category = category[:k - 1]\\n category += [clean_text(_row)]\\n sub_category = []\\n elif _row[0] == '#':\\n # new entry\\n _articles.append(clean_text(_row))\\n k = 0\\n while _row[k] == '#':\\n k += 1\\n sub_category = sub_category[:k - 1] + [clean_text(_row)]\\n if category[0] == general_category:\\n _categories.append(sep.join(category + sub_category[:-1]))\\n else:\\n _categories.append(sep.join([general_category] + category + sub_category[:-1]))\\n\\n categories_dict = get_categories('https://en.wikipedia.org/wiki/Wikipedia:Vital_articles/Level/5')\\n _general = {k: v.split('/')[5] for k, v in categories_dict.items()}\\n filenames = list(categories_dict.keys())\\n\\n if not os.path.exists('wikivitals/data/mds/'):\\n os.makedirs('wikivitals/data/mds/')\\n\\n for k, v in categories_dict.items(): # saves the category pages' text\\n with open('wikivitals/data/mds/{}'.format(k), 'w', encoding='utf8') as f:\\n url = \\\"https://en.wikipedia.org/w/index.php?title={}&action=edit\\\".format(v[6:])\\n page = requests.get(url)\\n soup = BeautifulSoup(page.text, 'html.parser')\\n f.write(soup.find('textarea').text)\\n\\n for filename in filenames:\\n get_article('wikivitals/data/mds/' + filename, _general[filename])\\n\\n with open('wikivitals/data/en-categories.txt', 'w', encoding='utf8') as file:\\n for cat in _categories:\\n file.write(cat + \\\"\\\\n\\\")\\n\\n with open('wikivitals/data/en-articles.txt', 'w', encoding='utf8') as file:\\n for name in _articles:\\n file.write(name + \\\"\\\\n\\\")\\n\\n return _articles, _categories, _general\",\n \"def dataset_summary(request, project, data_type, week):\\n logger = logging.getLogger('ibdmdb')\\n\\n file_cache_dict = map(str.strip, open(public_cache_file).readlines())\\n summary_groups = itertools.groupby(file_cache_dict, lambda x: 'summary' in x and project in x and data_type in x and week in x)\\n\\n anadama2_static_base = settings.STATIC_ROOT\\n\\n logger.info(\\\"Summary groups:\\\", summary_groups)\\n\\n # If this is a list > 1 than something went wrong here\\n summary_files = []\\n\\n for (group, items) in summary_groups:\\n if not group:\\n continue\\n\\n summary_files.extend(list(items))\\n\\n if summary_files:\\n # We should have a summary.html file, a summary.json file and a\\n # collection of images here that we will want to handle.\\n #\\n # The images and json file should go to our AnADAMA2 static directory\\n # to be served with our static content.\\n #\\n # Quick note here over the adding of the \\\"/tunnel/dataset_summary\\\"\\n # If we are operating in our current production setting we are proxy'ing \\n # requests between a protected server and an unprotected server so we \\n # need to add these all for the requests to pass through to the internal \\n # server. Messy but needed for the time being.\\n anadama2_static_dir = os.path.join(anadama2_static_base,\\n 'tunnel',\\n 'dataset_summary',\\n project,\\n data_type, week)\\n\\n if not os.path.isdir(anadama2_static_dir):\\n logger.info('Static dir being created %s' % anadama2_static_dir)\\n os.makedirs(anadama2_static_dir)\\n \\n complete_file = os.path.join(anadama2_static_dir, 'summary', 'complete')\\n if not os.path.exists(complete_file):\\n logger.info('Creating symylink of all files')\\n\\n # Pull out the first file; get the basename and symlink that into the static dir folder\\n # TODO: Add a check here to see if we retrieve nothing -- which means something bad happened..\\n summary_html = next((x for x in summary_files if \\\"summary.html\\\" in x), None)\\n summary_base = os.path.dirname(summary_html)\\n os.symlink(summary_base, os.path.join(anadama2_static_dir, 'summary'))\\n open(complete_file, 'a').close()\\n\\n # Need to add a check in here to make sure that our summary.html file does exist.\\n summary_url = os.path.join('/tunnel', 'dataset_summary', \\n project, data_type, week, 'summary', 'summary.html')\\n\\n return HttpResponseRedirect(summary_url)\\n return HttpResponse(status=500)\",\n \"def build_dataset(token_seq, token_seq_index, term_to_index, char_to_index, max_token_length=15, leading_context_size=10, trailing_context_size=10, leading_separator='{', trailing_separator='}', n_examples_per_context=10, n_errors_per_token=[1], n_errors_per_context=[0], dictionary=enchant.Dict('en_US'), seed=17):\\n if isinstance(seed, RandomState):\\n rng = seed\\n else:\\n rng = RandomState(seed=seed)\\n\\n n_contexts = sum([len(token_seq_index[t]) for t in token_seq_index.keys() if len(t) <= max_token_length])\\n n_examples = n_examples_per_context * n_contexts\\n max_inserts_per_token = max(n_errors_per_token)\\n max_inserts_per_context = 2*max(n_errors_per_context)\\n max_chars_in_window = leading_context_size + max_token_length + trailing_context_size + len(leading_separator) + len(trailing_separator) + max_inserts_per_token + max_inserts_per_context\\n\\n error_examples = np.zeros((n_examples, max_chars_in_window), dtype=np.int32)\\n error_examples.fill(char_to_index[' '])\\n\\n leading_contexts = np.zeros((n_examples, leading_context_size + max_inserts_per_context))\\n leading_contexts.fill(char_to_index[' '])\\n spelling_errors = np.zeros((n_examples, max_token_length + max_inserts_per_token))\\n spelling_errors.fill(char_to_index[' '])\\n trailing_contexts = np.zeros((n_examples, trailing_context_size + max_inserts_per_context))\\n trailing_contexts.fill(char_to_index[' '])\\n\\n correct_tokens = []\\n corrupt_tokens = []\\n corrections = np.zeros(n_examples, dtype=np.int32)\\n error_types = []\\n context_ids = []\\n example_ids = []\\n term_lens = []\\n edit_distance_to_nearest_term = []\\n nearest_term = []\\n\\n print('starting to construct {n_examples} examples'.format(\\n n_examples=n_examples))\\n\\n pbar = progressbar.ProgressBar(term_width=40,\\n widgets=[' ', progressbar.Percentage(),\\n ' ', progressbar.ETA()],\\n maxval=n_examples).start()\\n n = 0\\n\\n index_to_char = dict((i,c) for c,i in char_to_index.iteritems())\\n if 63 not in index_to_char.keys():\\n # A workaround for now. Delete ASAP.\\n index_to_char[63] = '^'\\n\\n def add_chars_to_array(n, array, chars, default_char=' '):\\n for k, ch in enumerate(chars):\\n try:\\n array[n, k] = char_to_index[ch]\\n except KeyError, e:\\n array[n, k] = char_to_index[default_char]\\n\\n for token in token_seq_index.keys():\\n if len(token) > max_token_length:\\n continue\\n\\n term_len = len(token)\\n distances = min_dictionary_edit_distance([token], dictionary)\\n distance = distances[token]['distance']\\n term = distances[token]['accepted']\\n\\n for i in token_seq_index[token]:\\n for j in six.moves.range(n_examples_per_context):\\n pbar.update(n+1)\\n corruptor = rng.choice([insert_characters, delete_characters,\\n replace_characters, transpose_characters])\\n n_token_errors = rng.choice(n_errors_per_token)\\n corrupt_token = corruptor(token, index_to_char, n=n_token_errors, seed=rng)\\n\\n n_context_errors = rng.choice(n_errors_per_context)\\n leading = leading_context(token_seq, i, leading_context_size)\\n leading = corruptor(leading, index_to_char, n=n_context_errors, seed=rng)\\n trailing = trailing_context(token_seq, i, trailing_context_size)\\n trailing = corruptor(trailing, index_to_char, n=n_context_errors, seed=rng)\\n\\n window = ''.join([leading, leading_separator,\\n corrupt_token, trailing_separator, trailing])\\n\\n add_chars_to_array(n, error_examples, window)\\n add_chars_to_array(n, leading_contexts, leading)\\n add_chars_to_array(n, spelling_errors, corrupt_token)\\n add_chars_to_array(n, trailing_contexts, trailing)\\n\\n correct_tokens.append(token)\\n corrections[n] = term_to_index[token]\\n corrupt_tokens.append(corrupt_token)\\n error_types.append(corruptor.__name__)\\n context_ids.append(i)\\n example_ids.append(n)\\n edit_distance_to_nearest_term.append(distance)\\n nearest_term.append(term)\\n term_lens.append(term_len)\\n\\n n += 1\\n\\n pbar.finish()\\n\\n df = pd.DataFrame({\\n 'correction': corrections,\\n 'error_type': error_types,\\n 'context_id': context_ids,\\n 'example_id': example_ids,\\n 'edit_distance_to_nearest_term': edit_distance_to_nearest_term,\\n 'nearest': nearest_term,\\n 'term_length': term_lens,\\n 'correct_token': correct_tokens,\\n 'corrupt_token': corrupt_tokens\\n })\\n\\n def add_columns(df, array, colname_prefix):\\n colwidth = int(np.log10(array.shape[1]))+1\\n colfmt = colname_prefix + '{col:0' + str(colwidth) + 'd}'\\n for col in np.arange(array.shape[1]):\\n colname = colfmt.format(col=col)\\n df[colname] = array[:, col]\\n\\n add_columns(df, error_examples, 'full_error')\\n add_columns(df, leading_contexts, 'leading_context')\\n add_columns(df, spelling_errors, 'spelling_error')\\n add_columns(df, trailing_contexts, 'trailing_context')\\n\\n return df\",\n \"def gen_tutorials(repo_dir: str) -> None:\\n with open(os.path.join(repo_dir, \\\"website\\\", \\\"tutorials.json\\\"), \\\"r\\\") as infile:\\n tutorial_config = json.loads(infile.read())\\n\\n tutorial_ids = [x[\\\"id\\\"] for v in tutorial_config.values() for x in v]\\n tutorial_dirs = [x.get(\\\"dir\\\") for v in tutorial_config.values() for x in v]\\n\\n for tid, t_dir in zip(tutorial_ids, tutorial_dirs):\\n print(\\\"Generating {} tutorial\\\".format(tid))\\n\\n if t_dir is not None:\\n tutorial_dir = os.path.join(repo_dir, \\\"tutorials\\\", t_dir)\\n html_dir = os.path.join(repo_dir, \\\"website\\\", \\\"_tutorials\\\", t_dir)\\n js_dir = os.path.join(repo_dir, \\\"website\\\", \\\"pages\\\", \\\"tutorials\\\", t_dir)\\n py_dir = os.path.join(repo_dir, \\\"website\\\", \\\"static\\\", \\\"files\\\", t_dir)\\n\\n for d in [tutorial_dir, html_dir, js_dir, py_dir]:\\n os.makedirs(d, exist_ok=True)\\n\\n tutorial_path = os.path.join(tutorial_dir, \\\"{}.ipynb\\\".format(tid))\\n html_path = os.path.join(html_dir, \\\"{}.html\\\".format(tid))\\n js_path = os.path.join(js_dir, \\\"{}.js\\\".format(tid))\\n ipynb_path = os.path.join(py_dir, \\\"{}.ipynb\\\".format(tid))\\n py_path = os.path.join(py_dir, \\\"{}.py\\\".format(tid))\\n tar_path = os.path.join(py_dir, \\\"{}.tar.gz\\\".format(tid))\\n else:\\n tutorial_path = os.path.join(repo_dir, \\\"tutorials\\\", \\\"{}.ipynb\\\".format(tid))\\n html_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"_tutorials\\\", \\\"{}.html\\\".format(tid)\\n )\\n js_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"pages\\\", \\\"tutorials\\\", \\\"{}.js\\\".format(tid)\\n )\\n ipynb_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"static\\\", \\\"files\\\", \\\"{}.ipynb\\\".format(tid)\\n )\\n py_path = os.path.join(\\n repo_dir, \\\"website\\\", \\\"static\\\", \\\"files\\\", \\\"{}.py\\\".format(tid)\\n )\\n\\n # convert notebook to HTML\\n with open(tutorial_path, \\\"r\\\") as infile:\\n nb_str = infile.read()\\n nb = nbformat.reads(nb_str, nbformat.NO_CONVERT)\\n\\n exporter = HTMLExporter()\\n html, meta = exporter.from_notebook_node(nb)\\n\\n # pull out html div for notebook\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n nb_meat = soup.find(\\\"div\\\", {\\\"id\\\": \\\"notebook-container\\\"})\\n del nb_meat.attrs[\\\"id\\\"]\\n nb_meat.attrs[\\\"class\\\"] = [\\\"notebook\\\"]\\n\\n # when output html, iframe it (useful for Ax reports)\\n for html_div in nb_meat.findAll(\\\"div\\\", {\\\"class\\\": \\\"output_html\\\"}):\\n if html_div.html is not None:\\n iframe = soup.new_tag(\\\"iframe\\\")\\n iframe.attrs[\\\"src\\\"] = \\\"data:text/html;charset=utf-8,\\\" + str(\\n html_div.html\\n )\\n # replace `#` in CSS\\n iframe.attrs[\\\"src\\\"] = iframe.attrs[\\\"src\\\"].replace(\\\"#\\\", \\\"%23\\\")\\n html_div.contents = [iframe]\\n\\n html_out = MOCK_JS_REQUIRES + str(nb_meat)\\n\\n # generate HTML file\\n with open(html_path, \\\"w\\\") as html_outfile:\\n html_outfile.write(html_out)\\n\\n # generate JS file\\n t_dir_js = t_dir if t_dir else \\\"\\\"\\n script = TEMPLATE.format(t_dir=t_dir_js, tid=tid)\\n with open(js_path, \\\"w\\\") as js_outfile:\\n js_outfile.write(script)\\n\\n # output tutorial in both ipynb & py form\\n with open(ipynb_path, \\\"w\\\") as ipynb_outfile:\\n ipynb_outfile.write(nb_str)\\n exporter = ScriptExporter()\\n script, meta = exporter.from_notebook_node(nb)\\n with open(py_path, \\\"w\\\") as py_outfile:\\n py_outfile.write(script)\\n\\n # create .tar archive (if necessary)\\n if t_dir is not None:\\n with tarfile.open(tar_path, \\\"w:gz\\\") as tar:\\n tar.add(tutorial_dir, arcname=os.path.basename(tutorial_dir))\",\n \"def update_demo(self):\\n debug = False\\n if not self.settings_data:\\n lib.OutputLib.update_html_ctrl(self.html, WAITING_MSG)\\n return\\n if debug: print(self.settings_data)\\n ## 1) part before the table-specific items e.g. column names and data\\n html = [mg.DEFAULT_HDR % {'title': 'Demonstration table',\\n 'css': self.styles, 'dojo_insert': ''}]\\n html.append(\\\"<table cellspacing='0'>\\\\n<thead>\\\\n<tr>\\\")\\n ## 2) the table-specific items (inc column labels)\\n ## list based on sequence of fields in underlying table\\n db_flds_orig_names = [] ## will be the key for any dicts taken from db\\n ## lists based on sequence of fields in (re)design\\n design_flds_orig_names = [] ## NB will be None for new or inserted flds.\\n ## Ordered as per list of variables in design.\\n design_flds_new_names = []\\n design_flds_col_labels = []\\n design_flds_types = []\\n for data_dict in self.settings_data:\\n ## all must have same num of elements (even if a None) in same order\\n fldname = data_dict[mg.TBL_FLDNAME]\\n design_flds_new_names.append(fldname)\\n design_flds_col_labels.append(\\n self.var_labels.get(fldname, fldname.title()))\\n design_flds_orig_names.append(data_dict.get(mg.TBL_FLDNAME_ORIG))\\n design_flds_types.append(data_dict[mg.TBL_FLDTYPE]) \\n if data_dict.get(mg.TBL_FLDNAME_ORIG) is not None:\\n db_flds_orig_names.append(data_dict[mg.TBL_FLDNAME_ORIG]) \\n if debug:\\n print(db_flds_orig_names)\\n print(design_flds_orig_names)\\n print(design_flds_new_names)\\n print(design_flds_col_labels)\\n print(design_flds_types)\\n ## column names\\n for col_label in design_flds_col_labels:\\n html.append(f'<th>{col_label}</th>')\\n ## get data rows (list of lists)\\n display_n = 4 ## demo rows to display\\n if self.new:\\n rows = []\\n for i in range(display_n):\\n row_lst = self.get_demo_row_lst(\\n i, design_flds_col_labels, design_flds_types)\\n rows.append(row_lst)\\n else:\\n rows = self.get_real_demo_data(display_n, db_flds_orig_names,\\n design_flds_orig_names, design_flds_new_names,\\n design_flds_col_labels, design_flds_types)\\n ## data rows into html\\n for row in rows:\\n html.append('</tr>\\\\n</thead>\\\\n<tbody><tr>')\\n for raw_val in row:\\n html.append(f'<td>{raw_val}</td>')\\n html.append('</tr>')\\n html.append('\\\\n</tbody>\\\\n</table></body></html>')\\n str_content = ''.join(html)\\n lib.OutputLib.update_html_ctrl(self.html, str_content)\",\n \"def _generate_examples(self, data_dir=None, archive=None, label_path=None, process=None):\\n if process == 'train':\\n abnormal_train = pd.read_csv(os.path.join(data_dir, \\\"train-abnormal.csv\\\"), names=[\\\"Patient_Number\\\", \\\"abnormal\\\"])\\n ACL_train = pd.read_csv(os.path.join(data_dir, \\\"train-acl.csv\\\"), names=[\\\"Patient_Number\\\", \\\"ACL\\\"])\\n meniscus_train = pd.read_csv(os.path.join(data_dir, \\\"train-meniscus.csv\\\"), names=[\\\"Patient_Number\\\", \\\"meniscus\\\"])\\n abnormal_acl_train = abnormal_train.merge(ACL_train, on=\\\"Patient_Number\\\")\\n ab_acl_meni_train = abnormal_acl_train.merge(meniscus_train, on=\\\"Patient_Number\\\")\\n else:\\n abnormal_test = pd.read_csv(os.path.join(data_dir, \\\"valid-abnormal.csv\\\"), names=[\\\"Patient_Number\\\", \\\"abnormal\\\"])\\n ACL_test = pd.read_csv(os.path.join(data_dir, \\\"valid-acl.csv\\\"), names=[\\\"Patient_Number\\\", \\\"ACL\\\"])\\n meniscus_test = pd.read_csv(os.path.join(data_dir, \\\"valid-meniscus.csv\\\"), names=[\\\"Patient_Number\\\", \\\"meniscus\\\"])\\n abnormal_acl_test = abnormal_test.merge(ACL_test, on=\\\"Patient_Number\\\")\\n ab_acl_meni_test = abnormal_acl_test.merge(meniscus_test, on=\\\"Patient_Number\\\")\\n\\n def sumup(row):\\n if (row['abnormal'] == 1) & (row[\\\"ACL\\\"] == 1) & (row[\\\"meniscus\\\"] == 1):\\n val = \\\"Both_ACL_Meniscus\\\"\\n elif (row['abnormal'] == 1) & (row[\\\"ACL\\\"] == 1) & (row[\\\"meniscus\\\"] == 0):\\n val = \\\"ACL\\\"\\n elif (row['abnormal'] == 1) & (row[\\\"ACL\\\"] == 0) & (row[\\\"meniscus\\\"] == 1):\\n val = \\\"Meniscus\\\"\\n elif (row['abnormal'] == 1) & (row[\\\"ACL\\\"] == 0) & (row[\\\"meniscus\\\"] == 0):\\n val = \\\"abnormal\\\"\\n elif row['abnormal'] == 0:\\n val = \\\"normal\\\"\\n return val\\n if process == 'train':\\n ab_acl_meni_train['sumup'] = ab_acl_meni_train.apply(sumup, axis=1)\\n ab_acl_meni_train[['Patient_Number', \\\"sumup\\\"]].to_csv(os.path.join(data_dir, \\\"train_labels.csv\\\"), header=False)\\n else:\\n ab_acl_meni_test['sumup'] = ab_acl_meni_test.apply(sumup, axis=1)\\n ab_acl_meni_test[['Patient_Number', \\\"sumup\\\"]].to_csv(os.path.join(data_dir, \\\"valid_labels.csv\\\"), header=False)\\n\\n all_labels = pd.read_csv(label_path, names=[\\\"Patient_Number\\\", \\\"label\\\"])\\n\\n count = 0\\n archive_list = [os.path.join(archive, \\\"axial\\\"), os.path.join(archive, \\\"coronal\\\"), os.path.join(archive, \\\"sagittal\\\")]\\n for direction in archive_list:\\n files = []\\n for r, d, f in tf.io.gfile.walk(direction):\\n for file in f:\\n if '.npy' in file:\\n files.append(os.path.join(r, file))\\n sorted_files = sorted(files)\\n\\n for path_cube in sorted_files:\\n cube = np.load(tf.io.gfile.GFile(path_cube, mode='rb'))\\n for picture in cube:\\n example_image = picture[..., np.newaxis]\\n example_label = (all_labels.loc[all_labels['Patient_Number'] ==\\n int(os.path.splitext(os.path.basename(path_cube))[0][:4]),\\n 'label'].iloc[0])\\n count += 1\\n yield count, {\\n 'image': example_image,\\n 'label': example_label,\\n }\",\n \"def _generate_examples(self, files):\\n idx = 0\\n for filename in files:\\n with open(filename) as file:\\n for line in file:\\n yield idx, {\\\"text\\\": line}\\n idx += 1\",\n \"def build_index(path, limit=None):\\n\\n documents = {}\\n doc_lengths = {}\\n index = {}\\n j = 0 # Counter for articles\\n for i in range(0, 22):\\n if i >= 10:\\n file = open(path + \\\"reut2-0\\\" + str(i) + \\\".sgm\\\", encoding='latin-1')\\n else:\\n file = open(path + \\\"reut2-00\\\" + str(i) + \\\".sgm\\\", encoding='latin-1')\\n\\n # Parsing html pages and getting reuters tagged once\\n soup = BeautifulSoup(file, \\\"html.parser\\\")\\n articles = soup.find_all('reuters')\\n\\n for article in articles:\\n\\n body = \\\"\\\"\\n title = \\\"\\\"\\n newid = int(article['newid'])\\n\\n try:\\n body = article.body.get_text()\\n except AttributeError:\\n pass\\n\\n try:\\n title = article.title.get_text()\\n except AttributeError:\\n pass\\n\\n words_list = title + \\\"\\\\n\\\" + body\\n\\n # Adding title+body to documents dictionary\\n documents[newid] = words_list\\n\\n # Processing document and adding document lengths to dictionary\\n processed_doc = preprocess(documents[newid])\\n doc_lengths[newid] = len(processed_doc)\\n\\n # Adding word to index\\n for term in processed_doc:\\n if term in index:\\n term_freq, docs_dict = index[term]\\n\\n term_freq += 1\\n if newid in docs_dict:\\n docs_dict[newid] += 1\\n else:\\n docs_dict[newid] = 1\\n\\n index[term] = (term_freq, docs_dict)\\n else:\\n docs_dict = {newid: 1}\\n index[term] = (1, docs_dict)\\n j += 1\\n # Checking limit on articles\\n if limit is not None:\\n if j == limit:\\n break\\n\\n # Checking limit on articles\\n if limit is not None:\\n if j == limit:\\n break\\n\\n for term in index:\\n term_freq, docs_dict = index[term]\\n index[term] = [term_freq] + list(docs_dict.items())\\n\\n if limit is None:\\n save_obj(index, \\\"reuters_index\\\")\\n save_obj(documents, \\\"reuters_documents\\\")\\n save_obj(doc_lengths, \\\"reuters_doc_length\\\")\\n\\n return index\",\n \"def generatePerWordHTML(testDataWithFeatures, \\n allLabels, classifications, colors):\\n \\n assert (len(testDataWithFeatures)-1) % len(allLabels) == 0\\n numWords = (len(testDataWithFeatures) - 1) / len(allLabels) + 1 \\n \\n allLabelsDict = dict((label,index) for index,label in enumerate(allLabels)) \\n \\n perWordHTML = []\\n for i in xrange(numWords):\\n if classifications:\\n word, correctClass, guessClass = classifications[i]\\n color = colors[(correctClass, guessClass)]\\n else:\\n datum = getDatum(testDataWithFeatures, \\n allLabelsDict, i, allLabels[0])\\n word = datum.word\\n color = colors[datum.label, datum.label]\\n html = [('<a href=\\\"#\\\" onmouseover=\\\"a({0});\\\" '\\n + 'onmouseout=\\\"b({0});\\\">').format(i), \\n '<span style=\\\"color:{}\\\">{}</span>'.format(\\n color, escape(word)),\\n '</a>']\\n \\n if word in \\\".!?\\\":\\n html.append(\\\"<br/>\\\")\\n \\n perWordHTML.append(\\\"\\\".join(html)) \\n return perWordHTML\",\n \"def process_lab_prn(self):\\n self.gen_hts_lab_fts()\\n self.gen_hts_lab_full()\",\n \"def build_model(num_topics=30):\\n data = utils.read_wiki(\\\"wiki.train.tokens\\\")\\n\\n # preprocessing: remove too frequent words, stopwords ...\\n logger.info(\\\"Start preprocessing, this will take quite some time ...\\\")\\n list_of_tokens, bigrams = preprocess(data)\\n\\n id2word = corpora.Dictionary(list_of_tokens)\\n id2word.filter_extremes(no_below=5, no_above=0.6, keep_n=VOCAB_SIZE)\\n logger.info(f\\\"Done processing dataset len, vocab len {len(id2word.keys())}, {len(list_of_tokens)}\\\")\\n \\n # convert data into df vectors\\n corpus = [id2word.doc2bow(tokens) for tokens in list_of_tokens]\\n\\n for num_topics in range(10, 100, 6):\\n lda_model = LdaModel(corpus, num_topics=num_topics,\\n id2word=id2word,\\n passes=20,\\n iterations=400,\\n # alpha=[0.01]*num_topics,\\n alpha=\\\"auto\\\",\\n # eta=[0.01] * VOCAB_SIZE,\\n eta=\\\"auto\\\")\\n \\n # save the model\\n path = pathlib.Path(f\\\"{SAVING_DIR}/lda_topic_{num_topics}\\\")\\n path.mkdir(parents=True, exist_ok=True)\\n path = path / \\\"lda.model\\\"\\n lda_model.save(str(path.absolute()))\\n id2word.save(UNIGRAM_FILE)\\n bigrams.save(BIGRAM_FILE)\\n\\n # visualize topics by LDAviz\\n vis = gensimvis.prepare(topic_model=lda_model, corpus=corpus, dictionary=id2word)\\n pathlib.Path(\\\"lda_vizs\\\").mkdir(parents=True, exist_ok=True)\\n pyLDAvis.save_html(vis, f'lda_vizs/lda_visualization_{num_topics}.html')\\n return id2word, bigrams, lda_model\",\n \"def create_index_tfidf(lines, numDocuments):\\n \\n index=collections.defaultdict(list)\\n tf=collections.defaultdict(list) #term frequencies of terms in documents (documents in the same order as in the main index)\\n df=collections.defaultdict(int) #document frequencies of terms in the corpus\\n idf=collections.defaultdict(float)\\n with Bar('Creating tf-idf index', max=len(lines)) as bar:\\n for key in lines:\\n page_id = key \\n terms = getTerms(lines[key]) \\n\\n ## create the index for the **current page** and store it in termdictPage\\n ## termdictPage in form ==> { ‘term1’: [currentdoc, [list of positions]], ...,‘termn’: [currentdoc, [list of positions]]}\\n\\n termdictPage={}\\n\\n for position, term in enumerate(terms): \\n try:\\n # if the term is already in the dict append the position to the corrisponding list\\n termdictPage[term][1].append(position) \\n except:\\n # Add the new term as dict key and initialize the array of positions and add the position\\n termdictPage[term]=[page_id, array('I',[position])] \\n\\n #normalize term frequencies\\n norm=0\\n for term, posting in termdictPage.items(): \\n # posting ==> [currentdoc, [list of positions]] \\n norm+=len(posting[1])**2\\n norm=math.sqrt(norm)\\n\\n\\n #calculate the tf(dividing the term frequency by the above computed norm) and df weights\\n for term, posting in termdictPage.items(): \\n # append the tf for current term (tf = term frequency in current doc/norm)\\n tf[term].append(np.round(len(posting[1])/norm,4)) ## SEE formula (1) above\\n #increment the document frequency of current term (number of documents containing the current term)\\n df[term] += 1 \\n\\n #merge the current page index with the main index\\n for termpage, postingpage in termdictPage.items():\\n index[termpage].append(postingpage)\\n\\n # Compute idf following the formula (3) above. HINT: use np.log\\n bar.next()\\n for term in df:\\n idf[term] = np.round(np.log(float(numDocuments/df[term])),4)\\n \\n return (index, tf, df, idf)\",\n \"def main():\\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"original\\\")\\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"annotated\\\")\\n\\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\\n os.mkdir(ANNOTATED_LESSONS_PATH)\\n\\n print(\\\"Scanning original lessons in %s...\\\" % ORIGINAL_LESSONS_PATH)\\n\\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\\n if item == \\\".DS_Store\\\": continue\\n\\n print(\\\" found: %s\\\" % item)\\n\\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\\n\\n lesson_number = None\\n lesson_description = None\\n mobj = re.search(r'^AY\\\\s+(\\\\d+)\\\\s*-\\\\s*(.+)\\\\.txt$', item)\\n if mobj:\\n lesson_number = mobj.group(1)\\n lesson_description = mobj.group(2)\\n\\n print(\\\" number: %s\\\" % lesson_number)\\n print(\\\" description: %s\\\" % lesson_description)\\n\\n lesson = dict()\\n lesson['number'] = lesson_number\\n lesson['description'] = lesson_description\\n\\n fh = open(item_path)\\n lesson_raw_text = fh.read()\\n fh.close()\\n lesson_text = re.split(r'\\\\n', lesson_raw_text)\\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\\n# lesson_text = re.split(r'\\\\n', lesson_raw_text_reencoded)\\n\\n lesson['text'] = lesson_text\\n lesson['parsed'] = parseLesson(lesson_text)\\n\\n if lesson['parsed']['end_of_lesson'] is None:\\n print(\\\" lesson has no 'end of lesson' marker\\\")\\n\\n lesson_json = json.dumps(lesson, indent=4)\\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \\\"ay_%04d.json\\\" % int(lesson_number))\\n fh = open(annotated_lesson_path, \\\"w\\\")\\n fh.write(lesson_json)\\n fh.close()\\n\\n else:\\n print(\\\"ERROR: File name not understood: %s\\\" % item)\\n\\n return 0\",\n \"def write_txt(self):\\n random.seed(self.seed)\\n sites = ['BIDMC', 'HK', 'I2CVB', 'ISBI', 'ISBI_15', 'UCL']\\n site_files, site_masks = self.read_file_names()\\n\\n for idx in range(len(site_files)):\\n # get files name\\n files = site_files[idx]\\n segmentation = site_masks[idx]\\n len_files = len(files)\\n site_label = sites[idx]\\n # shuffle the patient's file\\n indices = list(range(len_files))\\n random.shuffle(indices)\\n\\n if self.split_te == None:\\n # get list of indices\\n split = int(np.floor(self.split_val*len_files))\\n train_idx = indices[split:]\\n valid_idx = indices[:split]\\n split_un_sup = int(self.split_sup * len(train_idx))\\n train_sup_idx = train_idx[split_un_sup:]\\n train_unsup_idx = train_idx[:split_un_sup]\\n # slice the original list with the indices\\n files_train_sup = [files[i] for i in train_sup_idx]\\n files_train_unsup = [files[i] for i in train_unsup_idx]\\n masks_train_sup = [segmentation[i] for i in train_sup_idx]\\n masks_train_unsup = [segmentation[i] for i in train_unsup_idx]\\n files_val = [files[i] for i in valid_idx]\\n masks_val = [segmentation[i] for i in valid_idx]\\n # write txt file\\n self.generate_txt('train_supervised_files_tr_val_' + site_label + '.txt', files_train_sup, masks_train_sup)\\n self.generate_txt('train_unsupervised_files_tr_val_' + site_label + '.txt', files_train_unsup, masks_train_unsup)\\n self.generate_txt('val_files_tr_val_' + site_label + '.txt', files_val, masks_val)\\n else:\\n # get list of indices\\n split_tr_te = int(np.floor(self.split_te*len_files))\\n train_idx_tmp = indices[split_tr_te:]\\n test_idx = indices[:split_tr_te]\\n split_tr_val = int(self.split_val*len(train_idx_tmp))\\n train_idx = train_idx_tmp[split_tr_val:]\\n valid_idx = train_idx_tmp[:split_tr_val]\\n split_un_sup = int(self.split_sup * len(train_idx))\\n train_sup_idx = train_idx[split_un_sup:]\\n train_unsup_idx = train_idx[:split_un_sup]\\n # slice the original list with the indices\\n files_train_sup = [files[i] for i in train_sup_idx]\\n files_train_unsup = [files[i] for i in train_unsup_idx]\\n masks_train_sup = [segmentation[i] for i in train_sup_idx]\\n masks_train_unsup = [segmentation[i] for i in train_unsup_idx]\\n files_test = [files[i] for i in test_idx]\\n masks_test = [segmentation[i] for i in test_idx]\\n files_val = [files[i] for i in valid_idx]\\n masks_val = [segmentation[i] for i in valid_idx]\\n # write txt file\\n self.generate_txt('train_supervised_files_tr_te_val_' + site_label + '.txt', files_train_sup, masks_train_sup)\\n self.generate_txt('train_unsupervised_files_tr_te_val_' + site_label + '.txt', files_train_unsup, masks_train_unsup)\\n self.generate_txt('test_files_tr_te_val_'+site_label+'.txt', files_test, masks_test)\\n self.generate_txt('val_files_tr_te_val_'+site_label+'.txt', files_val, masks_val)\",\n \"def dataframe_for_domain(domain, algorithm):\\n df = []\\n files = glob.glob(os.path.join(outdir, algorithm, domain, '*', '*.txt'))\\n for filename in files:\\n query, reference = os.path.basename(filename).replace('.txt', '').split('_vs_')\\n try:\\n _df = pd.read_csv(filename, comment='#', sep='\\\\t')\\n except pd.errors.EmptyDataError:\\n _df = pd.DataFrame([dict(value=np.nan)])\\n\\n _df['query'] = query\\n _df['reference'] = reference\\n df.append(\\n _df.iloc[0].to_dict()\\n )\\n return pd.DataFrame(df)\",\n \"def walk_experimental(project_id, version=None):\\n from urllib import parse\\n doc = IndexedPublication.from_id(project_id, revision=version)\\n relation_map = []\\n\\n project_meta = format_metadata_for_fedora(project_id, version=version)\\n if version:\\n project_id = '{}v{}'.format(project_id, str(version))\\n license = project_meta.get('license', None)\\n full_author_list = []\\n project_map = {\\n 'uuid': doc.project.uuid,\\n 'container_path': project_id,\\n 'fedora_mapping': {**project_meta, 'generated': [], 'license': None},\\n 'fileObjs': []\\n }\\n\\n experiments_list = doc.experimentsList\\n for expt in experiments_list:\\n # Do stuff with experiment.\\n expt_container_path = \\\"{}/{}\\\".format(project_id, parse.quote(expt.value.title))\\n print('experiment ' + expt.value.title)\\n exp_doi = expt.doi\\n project_map['fedora_mapping']['generated'].append('Experiment: {}'.format(exp_doi))\\n\\n experiment_map = {\\n 'uuid': expt.uuid,\\n 'container_path': expt_container_path,\\n 'fedora_mapping': {**format_experiment(expt), 'license': license, 'wasGeneratedBy': project_id, 'generated': []},\\n 'fileObjs': expt.fileObjs\\n }\\n\\n full_author_list += experiment_map['fedora_mapping']['creator']\\n\\n reports = filter(\\n lambda report: expt.uuid in report.value.experiments,\\n getattr(doc, 'reportsList', []))\\n for report in reports:\\n # Do stuff with report.\\n report_container_path = \\\"{}/{}\\\".format(expt_container_path, parse.quote(report.value.title))\\n print('\\\\treport ' + report.value.title)\\n experiment_map['fedora_mapping']['generated'].append('Report: {}'.format(report.value.title))\\n\\n report_map = {\\n 'uuid': report.uuid,\\n 'fileObjs': report.fileObjs,\\n 'container_path': report_container_path,\\n 'fedora_mapping': {**format_report(report), 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi)}\\n }\\n relation_map.append(report_map)\\n\\n analysis_list = filter(\\n lambda analysis: expt.uuid in analysis.value.experiments,\\n getattr(doc, 'analysisList', []))\\n for analysis in analysis_list:\\n # Do stuff with analysis.\\n analysis_container_path = \\\"{}/{}\\\".format(expt_container_path, parse.quote(analysis.value.title))\\n print('\\\\tanalysis ' + analysis.value.title)\\n experiment_map['fedora_mapping']['generated'].append('Analysis: {}'.format(analysis.value.title))\\n\\n analysis_map = {\\n 'uuid': analysis.uuid,\\n 'fileObjs': analysis.fileObjs,\\n 'container_path': analysis_container_path,\\n 'fedora_mapping': {**format_analysis(analysis), 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi)}\\n\\n }\\n relation_map.append(analysis_map)\\n\\n model_configs = filter(\\n lambda model_config: expt.uuid in model_config.value.experiments,\\n getattr(doc, 'modelConfigs', []))\\n for mc in model_configs:\\n # Do stuff with model config.\\n configs_container_path = \\\"{}/{}\\\".format(expt_container_path, parse.quote(mc.value.title))\\n print('\\\\tmodel config ' + mc.value.title)\\n experiment_map['fedora_mapping']['generated'].append('Model Configuration: {}'.format(mc.value.title))\\n\\n mc_map = {\\n 'uuid': mc.uuid,\\n 'fileObjs': mc.fileObjs,\\n 'container_path': configs_container_path,\\n 'fedora_mapping': {**format_model_config(mc), 'wasGeneratedBy': exp_doi}\\n }\\n\\n sensor_lists = filter(\\n lambda sensor_list: mc.uuid in sensor_list.value.modelConfigs and expt.uuid in sensor_list.associationIds,\\n getattr(doc, 'sensorLists', []))\\n for sl in sensor_lists:\\n # Do stuff with sensor list.\\n sl_container_path = \\\"{}/{}\\\".format(configs_container_path, parse.quote(sl.value.title))\\n print('\\\\t\\\\tsensor list ' + sl.value.title)\\n experiment_map['fedora_mapping']['generated'].append('Sensor: {}'.format(sl.value.title))\\n\\n sl_map = {\\n 'uuid': sl.uuid,\\n 'fileObjs': sl.fileObjs,\\n 'container_path': sl_container_path,\\n 'fedora_mapping': {**format_sensor_info(sl),\\n 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi),\\n 'wasDerivedFrom': 'Model Configuration: {}'.format(mc.value.title),\\n 'influenced': []}\\n }\\n\\n events = filter(\\n lambda event: sl.uuid in event.value.sensorLists and expt.uuid in event.associationIds and mc.uuid in event.associationIds,\\n getattr(doc, 'eventsList', []))\\n for event in events:\\n # Do stuff with events.\\n evt_container_path = \\\"{}/{}\\\".format(sl_container_path, parse.quote(event.value.title))\\n print('\\\\t\\\\t\\\\tevent ' + event.value.title)\\n sl_map['fedora_mapping']['influenced'].append('Event: {}'.format(event.value.title))\\n experiment_map['fedora_mapping']['generated'].append('Event: {}'.format(event.value.title))\\n\\n event_map = {\\n 'uuid': event.uuid,\\n 'fileObjs': event.fileObjs,\\n 'container_path': evt_container_path,\\n 'fedora_mapping': {**format_event(event),\\n 'wasGeneratedBy': 'Experiment: {}'.format(exp_doi),\\n 'wasDerivedFrom': 'Model Configuration: {}'.format(mc.value.title),\\n 'wasInfluencedBy': 'Sensor: {}'.format(sl.value.title)}\\n }\\n relation_map.append(event_map)\\n relation_map.append(sl_map)\\n relation_map.append(mc_map)\\n relation_map.append(experiment_map)\\n project_map['fedora_mapping']['creator'] = list(set(full_author_list))\\n relation_map.append(project_map)\\n\\n return relation_map[::-1]\",\n \"def main():\\n # %%\\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\\n print( f'{len(fpaths)} htmls found' )\\n # %%\\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\\n # %%\\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\\n # %%\\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\\n # %%\\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\\n # %%\\n dics = {}\\n # %%\\n\\n for i, fpath in enumerate(fpaths):\\n if fpath in dics:\\n continue\\n\\n with fpath.open('rt') as f_in:\\n html = f_in.read()\\n\\n print( f'\\\\n***{i+1}/{len(fpaths)} {fpath.name}:')\\n dic = extract_one( html, fpath )\\n dic['linkedin_url'] = f\\\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\\\"\\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\\n # pprint(dic['work_stats'])\\n dics[fpath] = dic\\n\\n dics_arr = list(dics.values())\\n # %%\\n del dics\\n # %%\\n\\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\\n # %%\\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\\n yaml.safe_dump( dics_arr, f_out )\\n # %%\\n df = produce_summary_table( dics_arr )\\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\\n index=False)\\n # %%\",\n \"def per_sample_dot_files(self):\\n # The output directory #\\n directory = DirectoryPath(self.a.out_dir+'per_sample_ontology/')\\n directory.create_if_not_exists()\\n # Main loop #\\n for i, sample in self.df_sample_concepts.iteritems():\\n # File path #\\n sanitized_name = \\\"\\\".join([c for c in sample.name if re.match(r'\\\\w', c)])\\n dot_path = directory + sanitized_name +'.dot'\\n pdf_path = directory + sanitized_name +'.pdf'\\n # Counts #\\n counts = sample / sample.sum()\\n counts = dict(counts)\\n envos = counts.keys()\\n # Skip sample if it has no counts #\\n if sample.sum() == 0: continue\\n # Make graph #\\n graph = self.a.ontology.get_subgraph(envos)\\n graph = self.a.ontology.add_weights(graph, counts)\\n graph = self.a.ontology.add_style(graph)\\n # Write output #\\n self.a.ontology.write_to_dot(graph, dot_path)\\n self.a.ontology.add_legend(dot_path)\\n self.a.ontology.draw_to_pdf(dot_path, pdf_path)\",\n \"def makeHTMLIndexFile(self):\\n part1 = \\\"\\\"\\\"<html>\\n <body>\\n <title>Index</title>\\n <div id=\\\"pg_body\\\">\\n <div id=\\\"testSuitesTitle\\\">TestSuites</div>\\n <div id=\\\"resultsTitle\\\">Results</div>\\n <div id=\\\"testSuites\\\">\\n \\\"\\\"\\\"\\n part2 = self.makeLinks()\\n part3 = \\\"\\\"\\\"</div>\\n <div id=\\\"results\\\">\\n <iframe id=\\\"loadHTMLResults\\\" name=\\\"loadHTMLResults\\\" frameborder=\\\"0\\\" src=\\\"statistics.html\\\" style=\\\"height:100%;width:100%;\\\"></iframe>\\n </div>\\n <div id=\\\"footer\\\">Test Engineer(s) :\\\"\\\"\\\"+testEngineers+\\\"\\\"\\\"</div>\\n </div>\\n </body>\\n </html>\\n\\t\\t<style>\\n\\t\\tbody{\\n margin:0;\\n }\\n\\t\\t#pg_body{\\n\\t\\twidth=100%;\\n\\t\\ttext-align:center;\\n\\t\\t}\\n\\t\\t#testSuitesTitle{\\n\\t\\twidth:25%;\\n\\t\\tfloat:left;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\tfont-weight:bold;\\n\\t\\tcolor:white;\\n\\t\\t}\\n\\t\\t#resultsTitle{\\n\\t\\twidth:75%;\\n\\t\\tfloat:right;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\tfont-weight:bold;\\n\\t\\tcolor:white;\\n\\t\\t}\\n\\t\\t#testSuites{\\n\\t\\twidth:25%;\\n\\t\\tfloat:left;\\n\\t\\tbackground-color:lightgrey;\\n\\t\\tfont-weight:bold;\\n\\t\\ttext-align:left;\\n\\t\\theight:94%;\\n\\t\\toverflow:scroll;\\n\\t\\t}\\n\\t\\t#results{\\n\\t\\twidth:75%;\\n\\t\\tbackground-color:white;\\n\\t\\tfloat:right;\\n\\t\\ttext-align:left;\\n\\t\\theight:94%;\\n\\t\\toverflow:scroll;\\n\\t\\t}\\n\\t\\t#footer{\\n\\t\\twidth:100%;\\n\\t\\ttext-align:left;\\n\\t\\tcolor:lightgrey;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\t}\\n\\t\\t</style>\\n \\\"\\\"\\\"\\n \\n page = (part1+part2+part3)\\n f = open(self.dir+'/index.html','w')\\n f.write(page)\\n f.close\",\n \"def walkthrough(software_map):\\n\\n for i in software_map:\\n\\n if not i[\\\"is_file\\\"]:\\n\\n # for each directory: make a index.md\\n dname = \\\"./docs/\\\" + i[\\\"name\\\"]\\n index = \\\"./docs/\\\" + i[\\\"name\\\"] + \\\"/index.md\\\"\\n print(index)\\n os.mkdir(dname)\\n\\n with open(index, \\\"w+\\\") as f:\\n\\n children = i[\\\"children\\\"]\\n\\n # list files\\n f.write(\\\"Files:\\\\n\\\\n\\\")\\n for i in children:\\n if i[\\\"is_file\\\"]:\\n\\n fname = i[\\\"name\\\"]\\n fext = fname.split(\\\".\\\")\\n if len(fext) == 2:\\n fext = fext[1]\\n else:\\n fext = \\\"none\\\"\\n # for each file, note name and extension\\n f.write(fname + \\\" : \\\" + fext + \\\"\\\\n\\\")\\n\\n # list subdirectories\\n f.write(\\\"\\\\nSubdirectories:\\\\n\\\\n\\\")\\n for i in children:\\n if not i[\\\"is_file\\\"]:\\n\\n dirname = i[\\\"name\\\"]\\n\\n # note the number of files and subdirs in it\\n num_files, num_dirs = 0, 0\\n for child in i[\\\"children\\\"]:\\n if child[\\\"is_file\\\"]:\\n num_files += 1\\n elif not child[\\\"is_file\\\"]:\\n num_dirs += 1\\n\\n # note down name and numbers for each dir\\n f.write(dirname + \\\" : \\\" + str(num_files) + \\\" files, \\\" +\\n str(num_dirs) + \\\" directories\\\\n\\\")\\n\\n # goto subdir\\n if len(i[\\\"children\\\"]) > 0:\\n walkthrough(i[\\\"children\\\"])\",\n \"def create_trianing_instances(document_files, mentions_files, tokenizer, max_seq_length,\\n\\t\\t\\t\\t\\t\\t\\t\\trng, is_training=True):\\n\\tdocuments = {}\\n\\tfor input_file in document_files:\\n\\t\\twith open(documents_file + \\\"/\\\"+ input_file, \\\"r\\\") as reader:\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\tline = reader.readline().strip()\\n\\t\\t\\t\\tif not line:\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\tline = json.loads(line)\\n\\t\\t\\t\\tdocuments[line['document_id']] = line\\n\\n\\tmentions = []\\n\\tfor input_file in mentions_files:\\n\\t\\twith open(input_file, \\\"r\\\") as reader:\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\tline = reader.readline().strip()\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif not line:\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\tline = json.loads(line)\\n\\t\\t\\t\\tmentions.append(line)\\n\\n\\n\\ttfidf_candidates = {}\\n\\twith open(candidates_file, \\\"r\\\") as reader:\\n\\t\\twhile True:\\n\\t\\t\\tline = reader.readline().strip()\\n\\n\\t\\t\\tif not line:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\td = json.loads(line)\\n\\t\\t\\ttfidf_candidates[d['mention_id']] = d['tfidf_candidates']\\n\\n\\tif split_by_domain:\\n\\t\\tinstances = {}\\n\\telse:\\n\\t\\tinstances = [] \\n\\n\\tprint(\\\"--- Total {} mention links ---\\\".format(len(mentions))) \\n\\tfor i, mention in enumerate(mentions):\\n\\t\\t# print(\\\"mention {} \\\".format(i))\\n\\t\\tinstance = create_instances_from_mention_link(\\n\\t\\t\\t\\tmention, documents, tfidf_candidates, tokenizer, max_seq_length,\\n\\t\\t\\t\\trng, is_training=is_training)\\n\\n\\t\\tif instance:\\n\\t\\t\\tif split_by_domain:\\n\\t\\t\\t\\tcorpus = mention['corpus']\\n\\t\\t\\t\\tif corpus not in instances:\\n\\t\\t\\t\\t\\tinstances[corpus] = []\\n\\t\\t\\t\\tinstances[corpus].append(instance)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tinstances.append(instance)\\n\\t\\t# else:\\n\\t\\t# \\tprint(i)\\n\\n\\t\\tif i > 0 and i % 1000 == 0:\\n\\t\\t\\tprint(\\\"Instance: %d\\\" % i)\\n\\n\\tif is_training:\\n\\t\\tif split_by_domain:\\n\\t\\t\\tfor corpus in instances:\\n\\t\\t\\t\\trng.shuffle(instances[corpus])\\n\\t\\telse:\\n\\t\\t\\trng.shuffle(instances)\\n\\n\\treturn instances\",\n \"def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\\n def is_whitespace(c):\\n if c == \\\" \\\" or c == \\\"\\\\t\\\" or c == \\\"\\\\r\\\" or c == \\\"\\\\n\\\" or ord(c) == 0x202F:\\n return True\\n return False\\n\\n if is_training:\\n subset_list = ['train']\\n else:\\n subset_list = ['dev', 'test']\\n\\n examples, features = {}, {}\\n for subset in subset_list:\\n level_example_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n level_features_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n\\n for level in ['high', 'middle']:\\n subset_dir = os.path.join(data_dir, subset)\\n file_list = os.listdir(subset_dir)\\n file_list = [file for file in file_list if file.endswith('txt')]\\n file_list = sorted(file_list)\\n\\n alldata = []\\n for file in file_list:\\n data = json.load(open(os.path.join(subset_dir, file)))\\n alldata.append(data)\\n \\n examples_list = []\\n for data in alldata:\\n doc_id = data['id']\\n doc = data[\\\"article\\\"].replace('\\\\\\\\n', '\\\\n')\\n doc_token = []\\n prev_is_whitespace = True\\n for c in doc:\\n if is_whitespace(c):\\n prev_is_whitespace = True\\n else:\\n if prev_is_whitespace:\\n doc_token.append(c)\\n else:\\n doc_token[-1] += c\\n prev_is_whitespace = False\\n\\n for i, answer in enumerate(data[\\\"answers\\\"]):\\n if answer == \\\" \\\":\\n answer = \\\"C\\\"\\n example = InputExample(\\n guid=doc_id + '-%d' % i,\\n doc_token=doc_token,\\n question_text=\\\"\\\",\\n options=data[\\\"options\\\"],\\n answer=answer)\\n examples_list.append(example)\\n\\n level_example_dict[level] = examples_list\\n level_features_dict[level] = convert_examples_to_features(\\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\\n \\n print(\\\"MuTuaL Data Loader....\\\")\\n print(level_example_dict[level][-1])\\n print(level_features_dict[level][-1])\\n examples[subset] = level_example_dict\\n features[subset] = level_features_dict\\n\\n return examples, features\",\n \"def problem4() -> None:\\n \\n # Obtain the dataframe containing lines from the articles and the keyword\\n # with which the article is associated.\\n try:\\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \\n except FileNotFoundError:\\n raise Exception(\\\"[ERROR] The 'articles' folder was not found. Please \\\"\\n \\\"ensure that the functions for problems 1 and 2 have \\\"\\n \\\"been applied first.\\\")\\n \\n keywords, corpus = separate_keywords_corpus(kw_df)\\n \\n # Complete the preprocessing for the corpus for comparison's sake\\n w2vp = W2VPreprocessor(keywords, corpus)\\n \\n # Display the counts of sentences collected associated with each keyword\\n keywords_countplot(kw_df)\\n \\n # Create displots for the lengths of sentences in the corpus\\n displot_all_sentence_lengths(kw_df)\\n displot_sentence_lengths_per_keyword(kw_df)\\n \\n # Visualise the semantic distances\\n try:\\n distances = read_in_distance_matrix()\\n except FileNotFoundError:\\n raise Exception(\\\"[ERROR] The distance matrix excel file was could not \\\"\\n \\\"be found! Please ensure the methods for the \\\"\\n \\\"preceding problems have been run first.\\\")\\n visualise_distances(distances)\\n \\n # Visualise word frequency before and after preprocessing\\n visualise_initial_most_frequent_words(kw_df)\\n visualise_preprocessed_most_frequent_words(w2vp.corpus)\",\n \"def generate_figures_and_xls_all_strains(outdir, cols_starts, region2data, ext, xls, group2pos, feature_names, samples):\\n all_freqs = []\\n # concatenate all pos and samples into one dataframe\\n dframes = []\\n for ri, (ref, pos) in enumerate(region2data.keys()): #regions): #[3]#; print(ref, pos, mt)\\n mer, calls = region2data[(ref, pos)]\\n for c, s in zip(calls, samples): \\n df = pd.DataFrame(c, columns=feature_names)\\n df[\\\"Strain\\\"] = s\\n df[\\\"chr_pos\\\"] = \\\"%s:%s\\\"%(ref, pos)\\n dframes.append(df)\\n # read all tsv files\\n df = pd.concat(dframes).dropna().reset_index()\\n chr_pos, strains = df[\\\"chr_pos\\\"].unique(), df[\\\"Strain\\\"].unique() \\n # compare individual methods\\n for clf, method in (\\n (KMeans(n_clusters=2), \\\"KMeans\\\"), \\n (KNeighborsClassifier(), \\\"KNN\\\"), \\n #(iso_new.iForest(ntrees=100, random_state=0), \\\"GMM+eIF\\\"), \\n (GaussianMixture(random_state=0, n_components=2), \\\"GMM\\\"), \\n (AgglomerativeClustering(n_clusters=2), \\\"AggClust\\\"), \\n #(OneClassSVM(), \\\"OCSVM\\\"), \\n (IsolationForest(random_state=0), \\\"IF\\\"), \\n #(iso_new.iForest(ntrees=100, random_state=0), \\\"eIF\\\"), \\n (RandomForestClassifier(), \\\"RF\\\"), \\n ):\\n fname = method\\n for i, cols_start in enumerate(cols_starts, 1):\\n results = []\\n feat_name = \\\"_\\\".join(cols_start)\\n fname = \\\"%s.%s\\\"%(method, feat_name); print(fname)\\n outfn = os.path.join(outdir, \\\"%s.%s\\\"%(fname, ext))\\n # narrow down the features to only signal intensity & trace\\n cols = list(filter(lambda n: n.startswith(cols_start), feature_names))#; print(cols) #, \\\"DT\\\"\\n # compare all samples to 0%\\n s0 = samples[0]\\n for s in samples[3:]: \\n with np.errstate(under='ignore'):\\n if \\\"+\\\" in method:\\n clf2_name = method.split(\\\"+\\\")[-1]\\n results += get_mod_freq_two_step(df, cols, chr_pos, [s0, s], feat_name, \\n OFFSET=0.5, clf2_name=clf2_name, clf2=clf)\\n elif method in (\\\"KNN\\\", \\\"RF\\\"):\\n results += get_mod_freq_clf_train_test(df, cols, chr_pos, [s0, s], samples[1:3], clf, feat_name)\\n else:\\n results += get_mod_freq_clf(df, cols, chr_pos, [s0, s], clf, feat_name)\\n \\n # and store mod_freq predicted by various methods\\n freqs = pd.DataFrame(results, columns=[\\\"chr_pos\\\", \\\"features\\\", \\\"mod_freq wt\\\", \\\"mod_freq strain\\\", \\\"strain\\\"])\\n freqs[\\\"diff\\\"] = freqs.max(axis=1)-freqs.min(axis=1); freqs\\n for name, pos in group2pos.items(): #((\\\"negative\\\", negatives), (\\\"pU\\\", pU_pos), (\\\"Nm\\\", Nm_pos)):\\n freqs.loc[freqs[\\\"chr_pos\\\"].isin(pos), \\\"group\\\"] = name\\n #freqs.to_csv(outfn, sep=\\\"\\\\t\\\"); freqs.head()\\n freqs.to_excel(xls, fname, index=False)\\n # plot differences between methods\\n for group, pos in group2pos.items():\\n freqs.loc[freqs[\\\"chr_pos\\\"].isin(pos), \\\"modification\\\"] = group\\n #return freqs\\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))#, sharey=\\\"all\\\")\\n sns.barplot(x=\\\"chr_pos\\\", y=\\\"mod_freq strain\\\", hue=\\\"strain\\\", edgecolor=\\\"white\\\", palette=[\\\"#f8786fff\\\", \\\"#7aae02ff\\\", \\\"#00bfc2ff\\\", \\\"#c67afeff\\\"], \\n data=freqs[(freqs[\\\"features\\\"]==feat_name)&(freqs[\\\"group\\\"]==\\\"pU\\\")], ax=ax1)\\n sns.barplot(x=\\\"chr_pos\\\", y=\\\"mod_freq strain\\\", hue=\\\"strain\\\", edgecolor=\\\"white\\\", palette=[\\\"#ed823aff\\\", \\\"#1c6ca9ff\\\", \\\"#35d1bbff\\\", \\\"#c978fdff\\\"], \\n data=freqs[(freqs[\\\"features\\\"]==feat_name)&(freqs[\\\"group\\\"]==\\\"Nm\\\")], ax=ax2)\\n ax1.set_ylabel(\\\"Per-site stoichiometry\\\"); ax2.set_ylabel(\\\"\\\")\\n ax1.get_legend().remove(); ax2.get_legend().remove()#ax1.legend([]); ax2.legend([])\\n ax1.set_ylim(0, 1); ax2.set_ylim(0, 1); #ax2.set(aspect=1.7)\\n ax1.set_title(\\\"pU modifications\\\"); ax2.set_title(\\\"Nm modifications\\\")\\n fig.suptitle(fname)\\n fig.savefig(outfn)\\n plt.close() # clear axis\\n freqs[\\\"name\\\"] = fname\\n all_freqs.append(freqs)\\n return all_freqs\",\n \"def test_snippet_1(self):\\n\\n text = \\\"Asimov also wrote mysteries and fantasy, as well as much nonfiction. Most of his popular science books explain concepts in a historical way, going as far back as possible to a time when the science in question was at its simplest stage. Examples include Guide to Science, the three-volume set Understanding Physics, and Asimov's Chronology of Science and Discovery. He wrote on numerous other scientific and non-scientific topics, such as chemistry, astronomy, mathematics, history, biblical exegesis, and literary criticism.\\\"\\n drs = Drs.create_from_natural_language(text)\\n expected_drs = Drs.create_from_predicates_string(\\\"\\\"\\\"\\n {'word': 'wrote', 'tag': 'v', 'compound': 'wrote', 'entity': '', 'lemma': 'write', 'gender_guess': None,\\n 'is_head_token': True, 'refers_to': None, 'negated': 'false', 'type': None}(v2),\\n {'word': 'Asimov', 'tag': 'n', 'compound': 'Asimov', 'entity': 'PERSON', 'lemma': 'Asimov',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v0),\\n {'word': 'also', 'tag': 'RB', 'compound': 'also', 'entity': '', 'lemma': 'also', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v1),\\n {'word': 'mysteries', 'tag': 'n', 'compound': 'mysteries', 'entity': '', 'lemma': 'mystery',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v3),\\n {'word': 'fantasy', 'tag': 'n', 'compound': 'fantasy', 'entity': '', 'lemma': 'fantasy',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v5),\\n {'word': 'nonfiction', 'tag': 'n', 'compound': 'nonfiction', 'entity': '', 'lemma': 'nonfiction',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v11),\\n {'word': 'as', 'tag': 'RB', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v7),\\n {'word': 'well', 'tag': 'RB', 'compound': 'well', 'entity': '', 'lemma': 'well', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v8),\\n {'word': 'much', 'tag': 'j', 'compound': 'much', 'entity': '', 'lemma': 'much', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v10),\\n {'word': 'Most', 'tag': 'j', 'compound': 'Most', 'entity': '', 'lemma': 'Most', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v13),\\n {'word': 'books', 'tag': 'n', 'compound': 'books', 'entity': '', 'lemma': 'book', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v18),\\n {'word': 'he', 'tag': 'PRP$', 'compound': 'he', 'entity': '', 'lemma': 'he', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v15),\\n {'word': 'popular', 'tag': 'j', 'compound': 'popular', 'entity': '', 'lemma': 'popular',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v16),\\n {'word': 'science', 'tag': 'n', 'compound': 'science', 'entity': '', 'lemma': 'science',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v17),\\n {'word': 'explain', 'tag': 'v', 'compound': 'explain', 'entity': '', 'lemma': 'explain',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v19),\\n {'word': 'concepts', 'tag': 'n', 'compound': 'concepts', 'entity': '', 'lemma': 'concept',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v20),\\n {'word': 'going', 'tag': 'v', 'compound': 'going', 'entity': '', 'lemma': 'go', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v26),\\n {'word': 'way', 'tag': 'n', 'compound': 'way', 'entity': '', 'lemma': 'way', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v24),\\n {'word': 'historical', 'tag': 'j', 'compound': 'historical', 'entity': '', 'lemma': 'historical',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v23),\\n {'word': 'back', 'tag': 'RB', 'compound': 'back', 'entity': '', 'lemma': 'back', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v29),\\n {'word': 'to', 'tag': 'IN', 'compound': 'to', 'entity': '', 'lemma': 'to', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'to'}(v32),\\n {'word': 'far', 'tag': 'RB', 'compound': 'far', 'entity': '', 'lemma': 'far', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v28),\\n {'word': 'as', 'tag': 'RB', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v27),\\n {'word': 'as', 'tag': 'IN', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'as'}(v30),\\n {'word': 'possible', 'tag': 'j', 'compound': 'possible', 'entity': '', 'lemma': 'possible',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v31),\\n {'word': 'time', 'tag': 'n', 'compound': 'time', 'entity': '', 'lemma': 'time', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v34),\\n {'word': 'was', 'tag': 'v', 'compound': 'was', 'entity': '', 'lemma': 'be', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v40),\\n {'word': 'science', 'tag': 'n', 'compound': 'science', 'entity': '', 'lemma': 'science',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v37),\\n {'word': 'in', 'tag': 'IN', 'compound': 'in', 'entity': '', 'lemma': 'in', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'in'}(v38),\\n {'word': 'question', 'tag': 'n', 'compound': 'question', 'entity': '', 'lemma': 'question',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v39),\\n {'word': 'when', 'tag': 'WRB', 'compound': 'when', 'entity': '', 'lemma': 'when', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v35),\\n {'word': 'at', 'tag': 'IN', 'compound': 'at', 'entity': '', 'lemma': 'at', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'at'}(v41),\\n {'word': 'stage', 'tag': 'n', 'compound': 'stage', 'entity': '', 'lemma': 'stage', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v44),\\n {'word': 'its', 'tag': 'PRP$', 'compound': 'its', 'entity': '', 'lemma': 'its', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v42),\\n {'word': 'simplest', 'tag': 'j', 'compound': 'simplest', 'entity': '', 'lemma': 'simple',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v43),\\n {'word': 'include', 'tag': 'v', 'compound': 'include', 'entity': '', 'lemma': 'include',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v47),\\n {'word': 'Examples', 'tag': 'n', 'compound': 'Examples', 'entity': '', 'lemma': 'example',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v46),\\n {'word': 'Guide', 'tag': 'n', 'compound': 'Guide', 'entity': '', 'lemma': 'Guide', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v48),\\n {'word': '.', 'tag': '.', 'compound': '.', 'entity': '', 'lemma': '.', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v68),\\n {'word': 'to', 'tag': 'IN', 'compound': 'to', 'entity': '', 'lemma': 'to', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v49),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v51),\\n {'word': 'set', 'tag': 'v', 'compound': 'set', 'entity': '', 'lemma': 'set', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v56),\\n {'word': 'Science', 'tag': 'n', 'compound': 'Science', 'entity': '', 'lemma': 'Science',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v50),\\n {'word': 'volume', 'tag': 'n', 'compound': 'volume', 'entity': '', 'lemma': 'volume', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v55),\\n {'word': 'three', 'tag': 'CD', 'compound': 'three', 'entity': 'CARDINAL', 'lemma': 'three',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v53),\\n {'word': '-', 'tag': 'HYPH', 'compound': '-', 'entity': '', 'lemma': '-', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v54),\\n {'word': 'Physics', 'tag': 'n', 'compound': 'Understanding_Physics', 'entity': 'ORG', 'lemma': 'Physics',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v58),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v59),\\n {'word': 'and', 'tag': 'CC', 'compound': 'and', 'entity': '', 'lemma': 'and', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v60),\\n {'word': 'Chronology', 'tag': 'n', 'compound': 'Chronology', 'entity': 'WORK_OF_ART', 'lemma': 'Chronology',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v63),\\n {'word': 'Asimov', 'tag': 'n', 'compound': 'Asimov', 'entity': 'PERSON', 'lemma': 'Asimov',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v61),\\n {'word': 'of', 'tag': 'IN', 'compound': 'of', 'entity': 'WORK_OF_ART', 'lemma': 'of', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v64),\\n {'word': 'Science', 'tag': 'n', 'compound': 'Science', 'entity': 'WORK_OF_ART', 'lemma': 'Science',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v65),\\n {'word': 'and', 'tag': 'CC', 'compound': 'and', 'entity': 'WORK_OF_ART', 'lemma': 'and',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v66),\\n {'word': 'Discovery', 'tag': 'n', 'compound': 'Discovery', 'entity': 'WORK_OF_ART', 'lemma': 'Discovery',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v67),\\n {'word': 'wrote', 'tag': 'v', 'compound': 'wrote', 'entity': '', 'lemma': 'write', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v70),\\n {'word': 'He', 'tag': 'PRP', 'compound': 'He', 'entity': 'PERSON', 'lemma': 'he', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v69),\\n {'word': 'on', 'tag': 'IN', 'compound': 'on', 'entity': '', 'lemma': 'on', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v71),\\n {'word': '.', 'tag': '.', 'compound': '.', 'entity': '', 'lemma': '.', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v97),\\n {'word': 'topics', 'tag': 'n', 'compound': 'topics', 'entity': '', 'lemma': 'topic', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v79),\\n {'word': 'scientific', 'tag': 'j', 'compound': 'scientific', 'entity': '', 'lemma': 'scientific',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v74),\\n {'word': '-', 'tag': 'j', 'compound': '-', 'entity': '', 'lemma': '-', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v77),\\n {'word': 'non', 'tag': 'j', 'compound': 'non', 'entity': '', 'lemma': 'non', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v76),\\n {'word': 'scientific', 'tag': 'j', 'compound': 'scientific', 'entity': '', 'lemma': 'scientific',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v78),\\n {'word': 'numerous', 'tag': 'j', 'compound': 'numerous', 'entity': '', 'lemma': 'numerous',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v72),\\n {'word': 'other', 'tag': 'j', 'compound': 'other', 'entity': '', 'lemma': 'other', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v73),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v80),\\n {'word': 'as', 'tag': 'IN', 'compound': 'as', 'entity': '', 'lemma': 'as', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'as'}(v82),\\n {'word': 'such', 'tag': 'j', 'compound': 'such', 'entity': '', 'lemma': 'such', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v81),\\n {'word': 'chemistry', 'tag': 'n', 'compound': 'chemistry', 'entity': '', 'lemma': 'chemistry',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v83),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': 'and'}(v84),\\n {'word': 'astronomy', 'tag': 'n', 'compound': 'astronomy', 'entity': '', 'lemma': 'astronomy',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v85),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v86),\\n {'word': 'mathematics', 'tag': 'n', 'compound': 'mathematics', 'entity': '', 'lemma': 'mathematic',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v87),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v88),\\n {'word': 'history', 'tag': 'n', 'compound': 'history', 'entity': '', 'lemma': 'history',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v89),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v90),\\n {'word': 'exegesis', 'tag': 'n', 'compound': 'exegesis', 'entity': '', 'lemma': 'exegesis',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v92),\\n {'word': 'biblical', 'tag': 'j', 'compound': 'biblical', 'entity': '', 'lemma': 'biblical',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v91),\\n {'word': ',', 'tag': ',', 'compound': ',', 'entity': '', 'lemma': ',', 'gender_guess': None,\\n 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v93),\\n {'word': 'criticism', 'tag': 'n', 'compound': 'criticism', 'entity': '', 'lemma': 'criticism',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v96),\\n {'word': 'literary', 'tag': 'j', 'compound': 'literary', 'entity': '', 'lemma': 'literary',\\n 'gender_guess': None, 'is_head_token': False, 'refers_to': None, 'negated': 'false', 'type': None}(v95),\\n {'type': 'AMOD'}(v24, v23), {'type': 'ADVMOD'}(v26, v29), {'type': 'ADVMOD'}(v28, v27),\\n {'type': 'ADVMOD'}(v29, v28), {'type': 'NSUBJ'}(v40, v37), {'type': 'POSS'}(v44, v42),\\n {'type': 'AMOD'}(v44, v43), {'type': 'NSUBJ'}(v47, v46), {'type': 'DOBJ'}(v47, v48),\\n {'type': 'PUNCT'}(v47, v68), {'type': 'PREP'}(v48, v49), {'type': 'POBJ'}(v49, v50),\\n {'type': 'PUNCT'}(v55, v54), {'type': 'NPADVMOD'}(v56, v55), {'type': 'DOBJ'}(v56, v58),\\n {'type': 'PUNCT'}(v56, v59), {'type': 'POSS'}(v63, v61), {'type': 'PREP'}(v63, v64),\\n {'type': 'POBJ'}(v64, v65), {'type': 'NSUBJ'}(v70, v69), {'type': 'PREP'}(v70, v71),\\n {'type': 'PUNCT'}(v70, v97), {'type': 'POBJ'}(v71, v79), {'type': 'SUBTOK'}(v77, v76),\\n {'type': 'SUBTOK'}(v78, v77), {'type': 'AMOD'}(v79, v72), {'type': 'AMOD'}(v79, v73),\\n {'type': 'AMOD'}(v79, v74), {'type': 'AMOD'}(v79, v78), {'type': 'PUNCT'}(v79, v80),\\n {'type': 'POBJ'}(v82, v83), {'type': 'PUNCT'}(v85, v86), {'type': 'CONJ'}(v85, v87),\\n {'type': 'PUNCT'}(v87, v88), {'type': 'CONJ'}(v87, v89), {'type': 'PUNCT'}(v89, v90),\\n {'type': 'CONJ'}(v89, v92), {'type': 'AMOD'}(v92, v91), {'type': 'PUNCT'}(v92, v93),\\n {'type': 'CONJ'}(v92, v96), {'type': 'AMOD'}(v96, v95), {'type': 'AGENT'}(v2, v0),\\n {'type': 'ADVOCATIVE_CLAUSE'}(v19, v26), {'type': 'when'}(v34, v40), {'type': 'of'}(v13, v18),\\n {'type': 'AGENT'}(v19, v13), {'type': 'PATIENT'}(v19, v20), {'type': 'in'}(v19, v24),\\n {'type': 'NUMBER'}(v55, v53), {'type': 'OWNS'}(v61, v63), {'type': 'OWNS'}(v15, v18),\\n {'type': 'PATIENT'}(v2, v5), {'type': 'and'}(v56, v63), {'type': 'and'}(v48, v56),\\n {'type': 'and'}(v65, v67), {'type': 'and'}(v83, v85), {'type': 'in'}(v37, v39), {'type': 'to'}(v26, v34),\\n {'type': 'as'}(v29, v31), {'type': 'at'}(v40, v44), {'type': 'as'}(v79, v81), {'type': 'of'}(v18, v17),\\n {'type': 'ADJECTIVE'}(v11, v10), {'type': 'ADVERB'}(v2, v1), {'type': 'ADJECTIVE'}(v18, v16),\\n {'type': 'as'}(v2, v3), {'type': 'as'}(v2, v5)\\\"\\\"\\\")\\n lst = drs.apply(DrsMatcher(expected_drs, metric))\\n is_match = len(lst) > 1\\n self.assertTrue(is_match)\",\n \"def index():\\n mongo_collection = mongo_database[\\\"settings\\\"]\\n doc_instructions = mongo_collection.find_one({\\\"id\\\": \\\"instructions\\\"})\\n instructions = markdown.markdown(doc_instructions['text'])\\n return render_template(\\\"index.html\\\", instructions=instructions)\",\n \"def get_wordlists():\\n\\n\\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\\n\\t\\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\\n\\t\\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\\n\\t\\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\\n\\t\\t'Language Processing', 'Internet', 'Intelligent Systems',\\n\\t\\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\\n\\t\\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \\n\\t\\t'Computational Intelligence', 'Computational Linguistics',\\n\\t\\t'Computational linguistics','Algorithm','Computer','ITiCSE',\\n\\t\\t'ITICSE','Machine learning','Learning','learning',\\n\\t\\t'Artificial intelligence','CIVR','Document Analysis'}\\n\\n\\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\\n\\t\\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\\n\\t\\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\\n\\t\\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\\n\\t\\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\\n\\t\\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\\n\\t\\t'Neuro','Psych',\\n\\t\\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\\n\\t\\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\\n\\t\\t'Neurobiological'}\\n\\n\\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\\n\\t\\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\\n\\t\\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\\n\\t\\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\\n\\t\\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\\n\\t\\t'mathemati','Kernel'}\\n\\n\\tlinguistics = {}\\n\\n\\tcomputer_vision = {}\\n\\n\\tchemistry = {}\\n\\n\\tphysics = {}\\n\\n\\t# Rename \\\"Computer Vision\\\" to \\\"Image Processing\\\"?\\n\\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\\n\\t\\t'Physics','Computer Vision','Natural Language Processing']\\n\\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\\n\\n\\treturn {topic_names[i]:topics[i] for i in range(len(topics))}\",\n \"def demo1():\\n \\n print \\\"loading the NIPS publications dataset...\\\"\\n pubs = loadPubs('pubs_nips')\\n \\n # get all papers that mention mnist\\n p = [x for x in pubs if 'mnist' in x.get('pdf_text',{})]\\n print \\\"titles of papers that mention MNIST dataset:\\\"\\n for x in p:\\n print x['title']\\n print \\\"total of %d publications mention MNIST.\\\" %(len(p),)\\n \\n # sort by number of occurences\\n occ = [(x['year'], x['pdf']) for i,x in enumerate(p) if x['pdf_text']['mnist']>1]\\n occ.sort(reverse = True)\\n \\n # open the top 3 latest in browser\\n print \\\"opening the top 3...\\\"\\n openPDFs([x for year,x in occ[:3]])\",\n \"def _generate_examples(self, filepath, split):\\r\\n if self.config.name == \\\"trex\\\":\\r\\n paths = filepath\\r\\n relations_path = paths[0]\\r\\n paths = paths[1:]\\r\\n all_rels = {}\\r\\n with open(relations_path, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n all_rels[data[\\\"relation\\\"]] = data\\r\\n id_ = -1\\r\\n for filepath in paths:\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n pred = all_rels.get(data[\\\"predicate_id\\\"], {})\\r\\n for evidences in data[\\\"evidences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"obj_uri\\\": str(data[\\\"obj_uri\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"sub_uri\\\": str(data[\\\"sub_uri\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"predicate_id\\\": str(data[\\\"predicate_id\\\"]),\\r\\n \\\"sub_surface\\\": str(evidences[\\\"sub_surface\\\"]),\\r\\n \\\"obj_surface\\\": str(evidences[\\\"obj_surface\\\"]),\\r\\n \\\"masked_sentence\\\": str(evidences[\\\"masked_sentence\\\"]),\\r\\n \\\"template\\\": str(pred.get(\\\"template\\\", \\\"\\\")),\\r\\n \\\"template_negated\\\": str(pred.get(\\\"template_negated\\\", \\\"\\\")),\\r\\n \\\"label\\\": str(pred.get(\\\"label\\\", \\\"\\\")),\\r\\n \\\"description\\\": str(pred.get(\\\"description\\\", \\\"\\\")),\\r\\n \\\"type\\\": str(pred.get(\\\"type\\\", \\\"\\\")),\\r\\n }\\r\\n elif self.config.name == \\\"conceptnet\\\":\\r\\n id_ = -1\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n if data.get(\\\"negated\\\") is not None:\\r\\n for masked_sentence, negated in zip(data[\\\"masked_sentences\\\"], data[\\\"negated\\\"]):\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"sub\\\": str(data.get(\\\"sub\\\", \\\"\\\")),\\r\\n \\\"obj\\\": str(data.get(\\\"obj\\\", \\\"\\\")),\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"negated\\\": str(negated),\\r\\n }\\r\\n else:\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"sub\\\": str(data.get(\\\"sub\\\", \\\"\\\")),\\r\\n \\\"obj\\\": str(data.get(\\\"obj\\\", \\\"\\\")),\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"negated\\\": str(\\\"\\\"),\\r\\n }\\r\\n elif self.config.name == \\\"squad\\\":\\r\\n id_ = -1\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"id\\\": str(data[\\\"id\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"negated\\\": str(data.get(\\\"negated\\\", \\\"\\\")),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n }\\r\\n elif self.config.name == \\\"google_re\\\":\\r\\n id_ = -1\\r\\n paths = filepath\\r\\n for filepath in paths:\\r\\n # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py\\r\\n if \\\"place_of_birth\\\" in filepath:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"place_of_birth\\\",\\r\\n \\\"template\\\": \\\"[X] was born in [Y] .\\\",\\r\\n \\\"template_negated\\\": \\\"[X] was not born in [Y] .\\\",\\r\\n }\\r\\n elif \\\"date_of_birth\\\" in filepath:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"date_of_birth\\\",\\r\\n \\\"template\\\": \\\"[X] (born [Y]).\\\",\\r\\n \\\"template_negated\\\": \\\"[X] (not born [Y]).\\\",\\r\\n }\\r\\n else:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"place_of_death\\\",\\r\\n \\\"template\\\": \\\"[X] died in [Y] .\\\",\\r\\n \\\"template_negated\\\": \\\"[X] did not die in [Y] .\\\",\\r\\n }\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"sub\\\": str(data[\\\"sub\\\"]),\\r\\n \\\"obj\\\": str(data[\\\"obj\\\"]),\\r\\n \\\"evidences\\\": str(data[\\\"evidences\\\"]),\\r\\n \\\"judgments\\\": str(data[\\\"judgments\\\"]),\\r\\n \\\"sub_w\\\": str(data[\\\"sub_w\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"sub_aliases\\\": str(data[\\\"sub_aliases\\\"]),\\r\\n \\\"obj_w\\\": str(data[\\\"obj_w\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"obj_aliases\\\": str(data[\\\"obj_aliases\\\"]),\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"template\\\": str(pred[\\\"template\\\"]),\\r\\n \\\"template_negated\\\": str(pred[\\\"template_negated\\\"]),\\r\\n }\",\n \"def prime_cache(cfg):\\n from dictionaria.util import add_links2\\n\\n labels = {}\\n for type_, cls in [('source', common.Source), ('unit', common.Unit)]:\\n labels[type_] = defaultdict(set)\\n for r in DBSession.query(cls.id):\\n sid, _, lid = r[0].partition('-')\\n labels[type_][sid].add(lid)\\n\\n for d in DBSession.query(Dictionary):\\n for type_ in ['source', 'unit']:\\n d.description = add_links2(d.id, labels[type_][d.id], d.description, type_)\\n\\n for meaning in DBSession.query(ComparisonMeaning).options(\\n joinedload_all(common.Parameter.valuesets, common.ValueSet.values)\\n ):\\n meaning.representation = sum([len(vs.values) for vs in meaning.valuesets])\\n if meaning.representation == 0:\\n meaning.active = False\\n\\n def joined(iterable):\\n return ' / '.join(sorted(nfilter(set(iterable))))\\n\\n q = DBSession.query(Word)\\\\\\n .order_by(Word.dictionary_pk, common.Unit.name, common.Unit.pk)\\\\\\n .options(joinedload(Word.meanings), joinedload(Word.dictionary))\\n for _, words in groupby(q, lambda u: u.name):\\n words = list(words)\\n for i, word in enumerate(words):\\n word.description = ' / '.join(m.name for m in word.meanings)\\n word.comparison_meanings = joined(m.reverse for m in word.meanings)\\n word.semantic_domain = joined(m.semantic_domain for m in word.meanings)\\n word.number = i + 1 if len(words) > 1 else 0\\n\\n for suffix in ['1', '2']:\\n alt_t, alt_l = [], []\\n for m in word.meanings:\\n if getattr(m, 'alt_translation' + suffix):\\n alt_l.append(getattr(m, 'alt_translation_language' + suffix))\\n alt_t.append(getattr(m, 'alt_translation' + suffix))\\n if alt_t and len(set(alt_l)) == 1:\\n DBSession.add(common.Unit_data(\\n object_pk=word.pk, key='lang-' + alt_l.pop(), value=join(alt_t)))\\n\\n def count_unit_media_files(contrib, mtype):\\n return DBSession.query(common.Unit_files)\\\\\\n .join(Word, common.Unit_files.object_pk == Word.pk)\\\\\\n .filter(Word.dictionary_pk == contrib.pk)\\\\\\n .filter(common.Unit_files.mime_type.ilike(mtype + '/%'))\\\\\\n .count() + \\\\\\n DBSession.query(Meaning_files)\\\\\\n .join(Meaning, Meaning_files.object_pk == Meaning.pk)\\\\\\n .join(Word, Meaning.word_pk == Word.pk)\\\\\\n .filter(Word.dictionary_pk == contrib.pk)\\\\\\n .filter(Meaning_files.mime_type.ilike(mtype + '/%'))\\\\\\n .count()\\n\\n for d in DBSession.query(Dictionary).options(joinedload(Dictionary.words)):\\n d.count_words = len(d.words)\\n sds = set(chain(*[w.semantic_domain_list for w in d.words]))\\n d.semantic_domains = join(sorted(sds))\\n d.count_audio = count_unit_media_files(d, 'audio')\\n d.count_image = count_unit_media_files(d, 'image')\\n\\n word_pks = [w.pk for w in d.words]\\n choices = {}\\n for col in d.jsondata.get('custom_fields', []):\\n values = [\\n r[0] for r in DBSession.query(common.Unit_data.value)\\n .filter(common.Unit_data.object_pk.in_(word_pks))\\n .filter(common.Unit_data.key == col)\\n .distinct()]\\n if len(values) < 40:\\n choices[col] = sorted(values)\\n d.update_jsondata(choices=choices)\\n\\n DBSession.execute(\\\"\\\"\\\"\\n UPDATE word\\n SET example_count = s.c \\n FROM (\\n SELECT m.word_pk AS wpk, count(ms.sentence_pk) AS c\\n FROM meaning AS m, meaningsentence AS ms\\n WHERE m.pk = ms.meaning_pk\\n GROUP BY m.word_pk\\n ) AS s\\n WHERE word.pk = s.wpk\\n \\\"\\\"\\\")\",\n \"def dvi():\\n\\n return M(c=\\\"dvi\\\")(\\n #M(\\\"Home\\\", f=\\\"index\\\"),\\n M(\\\"Recovery Requests\\\", f=\\\"recreq\\\")(\\n M(\\\"New Request\\\", m=\\\"create\\\"),\\n M(\\\"List Current\\\",\\n vars={\\\"recreq.status\\\":\\\"1,2,3\\\"}),\\n ),\\n M(\\\"Dead Bodies\\\", f=\\\"body\\\")(\\n M(\\\"Add\\\", m=\\\"create\\\"),\\n M(\\\"List unidentified\\\",\\n vars={\\\"identification.status\\\": \\\"None\\\"}),\\n M(\\\"Report by Age/Gender\\\", m=\\\"report\\\",\\n vars=dict(rows=\\\"age_group\\\",\\n cols=\\\"gender\\\",\\n fact=\\\"count(pe_label)\\\",\\n ),\\n ),\\n ),\\n #M(\\\"Missing Persons\\\", f=\\\"person\\\")(\\n # M(\\\"List all\\\"),\\n #),\\n M(\\\"Morgues\\\", f=\\\"morgue\\\")(\\n M(\\\"Create\\\", m=\\\"create\\\"),\\n ),\\n M(\\\"Dashboard\\\", f=\\\"index\\\"),\\n )\",\n \"def rf_tf_idf_train(df, dominio):\\n df_final = obtain_train_corpus()\\n # Puedo separarlo en distintos df segun el dominio\\n df_domain_total = [{category:df_domain} for category, df_domain in df_final.groupby('category')]\\n \\n if dominio == \\\"entidad\\\":\\n # Tambien puedo separar a nivel de dominio y entity\\n df_domain_total_entity = {}\\n for df in df_domain_total:\\n category = list(df.keys())[0]\\n df = list(df.values())[0]\\n df_entities = [{entity:df_entity} for entity, df_entity in df.groupby('entity_name')]\\n df_domain_total_entity.update({category:df_entities})\\n \\n vocabulario = corpus_generation(df_domain_total_entity, \\\"entidad\\\")\\n entidades = list(vocabulario.keys())\\n categorias = list(df_domain_total_entity.keys())\\n \\n i = 1\\n total = len(entidades)\\n for categoria in categorias:\\n for df in df_domain_total_entity[categoria]:\\n \\n print(\\\"Entrendando modelo \\\" + str(i) + \\\"/\\\" + str(total))\\n \\n entidad = list(df.keys())[0]\\n df = list(df.values())[0]\\n df = df.reset_index()\\n \\n X = list(df['text'])\\n print(\\\"Entidad: \\\", entidad)\\n\\n words, words_tot, median, df_pattern, df_suma = word_tf_idf(X)\\n df_classificacion = df_suma.join(df, how=\\\"outer\\\") # Join por los index\\n \\n X_tf_idf = list(df_classificacion['tf-idf'])\\n y_tf_idf = list(df_classificacion['topic'])\\n \\n # Encoding a numerico\\n labelencoder_X = LabelEncoder()\\n y_tf_idf=labelencoder_X.fit_transform(y_tf_idf) # Codifico en valores numericos las clases que hay\\n\\n \\n # Train/validation split\\n X_train, X_val, y_train, y_val = train_test_split(X_tf_idf, y_tf_idf, \\n test_size = 0.1, random_state = 0)\\n \\n \\n # Menor distancia cuadratica de TF\\n y_pred = []\\n for x_ref in X_val:\\n ref = 999\\n i = 0\\n for x in X_train:\\n \\n diff = (x_ref - x)**2\\n diff = np.sqrt(diff)\\n print(diff)\\n \\n if diff < ref:\\n i = X_train.index(x)\\n ref = diff\\n\\n y_pred.append(y_train[i]) # Identifico con la clase de menor distancia cuadratica TF-IDF\\n \\n \\n # Making the Confusion Matrix\\n cm = confusion_matrix(y_val, y_pred)\\n \\n # Accuracy\\n accuracy = accuracy_score(y_val, y_pred)\\n \\n # Precision\\n average_precision = precision_score(y_val, y_pred, average = \\\"macro\\\")\\n \\n # Recall\\n recall = recall_score(y_val, y_pred, average='macro')\\n \\n print(\\\"Modelo \\\"+str(i)+\\\" resultados\\\")\\n print(\\\"accuracy \\\", accuracy, \\\" precision \\\", average_precision, \\\" recall \\\", recall) # Se ve que los resultados son muy malos\",\n \"def identify_datasets(self, language_objects, context):\\n\\n datasets, new_sen = self.extractor.extract_all_templates(\\n language_objects, context\\n )\\n context[\\\"datasets\\\"] = datasets\\n return {'type': 'result', 'result': (new_sen, context[\\\"datasets\\\"])}\",\n \"def generate_data():\\n for subdir, dirs, files in os.walk(legend_images_dir):\\n for _file in files:\\n getTables(_file)\\n\\n file_list = []\\n for subdir, dirs, files in os.walk(pdf_output_dir):\\n for _file in files:\\n if _file.endswith('.pdf'):\\n file_list.append(_file)\\n\\n print (\\\"Writing merged output in Output.pdf...\\\")\\n current_dir = os.getcwd()\\n mergeOutput(file_list, current_dir + \\\"/Output.pdf\\\")\\n\\n clean()\",\n \"def generate_examples(self, docname):\\n fake_factory = faker.Factory.create()\\n for name, language, parent in self.data['examples']:\\n props = self.get_object(name)\\n sample_data = props.generate_sample_data(self.data['all_objects'],\\n fake_factory)\\n if language == 'yaml' and yaml is not None:\\n title = 'YAML Example'\\n code_text = yaml.safe_dump(sample_data, indent=4,\\n default_flow_style=False,\\n explicit_start=True,\\n version=(1, 2))\\n else:\\n if language == 'yaml':\\n self.env.warn(docname,\\n 'YAML support is disabled, pip install yaml '\\n 'to enable.')\\n title = 'JSON Example'\\n language = 'json'\\n code_text = json.dumps(sample_data, indent=4,\\n ensure_ascii=False)\\n\\n example = nodes.literal_block(code_text, code_text)\\n example['language'] = language\\n parent.append(nodes.strong(title, title))\\n parent.append(example)\",\n \"def _generate_markdown(self, case):\\n # Lucene query generation\\n lucene_dict = {\\n \\\"sources\\\": case[\\\"detection\\\"][\\\"sources\\\"],\\n \\\"data\\\": case[\\\"input_arguments\\\"],\\n }\\n case[\\\"lucene_query\\\"] = self.templates[\\\"lucene\\\"].render(lucene_dict)\\n # AWS CLI command generation\\n command_template = jinja2.Template(case[\\\"executors\\\"][\\\"sh\\\"][\\\"code\\\"])\\n if case[\\\"input_arguments\\\"]:\\n aws_cli_render_args = {}\\n for arg in case[\\\"input_arguments\\\"]:\\n aws_cli_render_args[arg] = case[\\\"input_arguments\\\"][arg][\\\"value\\\"]\\n case[\\\"compiled_command\\\"] = command_template.render(aws_cli_render_args)\\n else:\\n case[\\\"compiled_command\\\"] = command_template.render()\\n\\n render_dict = {\\\"case\\\": case}\\n return self.templates[\\\"markdown\\\"].render(render_dict)\",\n \"def explain ():\\n\\n print(\\\"\\\"\\\"\\n Isis Web Spider\\\\n\\n\\n Isis Web Spider take some parameters:\\n ./isis-web-spider -t targets.txt -s strings.txt [-o output.txt]\\n Note that each file must contain one information by line\\n \\\"\\\"\\\")\",\n \"def build_index():\\n pass\",\n \"def get_data_loaders_1sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1_sentence_final_generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+1):]\\n #history_complete.append(history)\\n if len(history) > 3:\\n history_chatbot = history[1]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def write_analysis(path, dataset_dict, datasettype, mask_part, start_time, supervised=True):\\n for mask_el in mask_part:\\n if mask_el == 'podocytes':\\n filename = datasettype + '_podos.txt'\\n filestr = 'podos images'\\n elif mask_el == 'glomerulus':\\n filename = datasettype + '_gloms.txt'\\n filestr = 'gloms images'\\n else:\\n filename = datasettype + 'unknown.txt'\\n filestr = 'unknown type'\\n\\n write_txt = open(str(os.path.join(path, filename)), \\\"w\\\")\\n\\n if supervised:\\n dc_mean = np.sum(np.array(dataset_dict['dice_coeffs_%s' % mask_el])) / len(dataset_dict['dice_coeffs_%s'\\n % mask_el])\\n dc_min = np.min(np.array(dataset_dict['dice_coeffs_%s' % mask_el]))\\n dc_max = np.max(np.array(dataset_dict['dice_coeffs_%s' % mask_el]))\\n object_dc_mean = np.sum(np.array(dataset_dict['object_dc_%s' % mask_el])) / len(dataset_dict['object_dc_%s'\\n % mask_el])\\n object_dc_min = np.min(np.array(dataset_dict['object_dc_%s' % mask_el]))\\n object_dc_max = np.max(np.array(dataset_dict['object_dc_%s' % mask_el]))\\n pearson = calculate_pearson(dataset_dict['count_masks_%s' % mask_el], dataset_dict['count_preds_%s'\\n % mask_el])\\n\\n write_txt.write(str(\\\"Mean dice coefficient on pixels of \\\" + filestr + \\\" compared to groundtruth: \\\") +\\n str(dc_mean) + '\\\\n')\\n write_txt.write(str(\\\"Min dice coefficient on pixels of \\\" + filestr + \\\" compared to groundtruth: \\\") +\\n str(dc_min) + '\\\\n')\\n write_txt.write(str(\\\"Max dice coefficient on pixels of \\\" + filestr + \\\" compared to groundtruth: \\\") +\\n str(dc_max) + '\\\\n')\\n write_txt.write(str(\\\"Pearson correlation coefficient on objects of \\\" + filestr +\\n \\\" compared to groundtruth: \\\") + str(pearson) + '\\\\n')\\n write_txt.write(str(\\\"Mean dice coeff on objects of \\\" + filestr + \\\" compared to groundtruth: \\\") +\\n str(object_dc_mean) + '\\\\n')\\n write_txt.write(str(\\\"Min dice coeff on objects of \\\" + filestr + \\\" compared to groundtruth: \\\") +\\n str(object_dc_min) + '\\\\n')\\n write_txt.write(str(\\\"Max dice coeff on objects of \\\" + filestr + \\\" compared to groundtruth: \\\") +\\n str(object_dc_max) + '\\\\n')\\n write_txt.write('\\\\n')\\n\\n duration = time.time() - start_time\\n duration_std = int(duration / 3600)\\n duration_min = int((duration % 3600) / 60)\\n duration_sec = int(duration % 60)\\n\\n write_txt.write(str(\\\"Test time: \\\") + str(duration_std) + \\\"h \\\" + str(duration_min)\\n + \\\"min \\\" + str(duration_sec) + 'sec \\\\n')\\n write_txt.close()\\n return\",\n \"def get_data_loaders_4sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"]\\n #history_complete.append(history)\\n if len(history_splitted) > (len(persona)-1):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def generate(self, params):\\n size = params.size\\n n_answers = params.n_answers\\n max_strokes = params.max_strokes\\n min_strokes = params.min_strokes\\n kanji_similarity = params.kanji_similarity\\n answer_similarity = params.answer_similarity\\n grade = params.grade\\n allow_duplicates = params.allow_duplicates\\n reversed_bool = params.reversed_bool\\n question_type = params.question_type #kanji, vocab \\n\\n chunk = Chunk(params)\\n\\n # List of potential indices into the kanji list, indices can be removed\\n # to disallow duplicates.\\n kanji_indices = list(range(len(self.kanji[grade])))\\n \\n # Filter out all kanji with a stroke_count up to max_strokes.\\n # Horrendous piece of code. Don't show your kids.\\n new_kanji_indices = []\\n for index in kanji_indices:\\n _,_,stroke_count = self.kanji[grade][index]\\n if int(stroke_count) <= max_strokes and int(stroke_count) >= min_strokes:\\n new_kanji_indices.append(index)\\n kanji_indices = new_kanji_indices\\n\\n if len(kanji_indices) < size:\\n raise Exception(\\\"Not enough kanji found for these parameters\\\")\\n\\n # translate the answer_similarity measure to an easy/normal/hard scale\\n # as used by compareKanji.py\\n answer_difficulty = ('easy' if answer_similarity <= 0.33 else 'normal'\\n if answer_similarity <= 0.66 else 'hard')\\n\\n for _ in range(size):\\n if question_type == \\\"kanji\\\":\\n index = random.choice(kanji_indices)\\n kanji, meaning, stroke_count = self.kanji[grade][index]\\n options = self.__choice_list(kanji, answer_difficulty, grade,\\n n_answers - 1, reversed_bool)\\n hint = self.__hint(reversed_bool, question_type, kanji, meaning, grade)\\n if not reversed_bool:\\n question = \\\"What is the meaning of the following kanji?\\\"\\n options += [meaning]\\n random.shuffle(options)\\n chunk.add_question(question, kanji, meaning, options, hint)\\n else:\\n question = \\\"Which kanji belongs to the following meaning(s)?\\\"\\n options += [kanji]\\n random.shuffle(options)\\n chunk.add_question(question, meaning, kanji, options, hint)\\n else: #question_type == vocab\\n kanji = \\\"\\\"\\n while not kanji in self.vocab_dict or self.vocab_dict[kanji] == []:\\n index = random.choice(kanji_indices)\\n kanji, meaning, stroke_count = self.kanji[grade][index]\\n compound_choice = self.vocab_dict[kanji]\\n choice = random.choice(compound_choice)\\n item, _, item_meaning = choice \\n\\n def check_compatibility(element0):\\n if element0[0] in [x[0] for x in selection] + [item]:\\n return False\\n if element0[2].lower() in [x[2].lower() for x in selection] + [item_meaning]:\\n return False\\n if \\\"(surname)\\\" in element0[2].lower():\\n return False \\n if \\\"～\\\" in element0[0] or \\\"~\\\" in element0[0] or \\\"～\\\" in element0[0]:\\n return False\\n return True\\n\\n selection = []\\n for x in compound_choice:\\n if x != choice and check_compatibility(x):\\n selection.append(x)\\n random.shuffle(selection) \\n \\n #easy: option compounds\\n #normal: option compounds and 0.5 item compounds\\n #hard: option compounds and all item compounds\\n if answer_difficulty == \\\"easy\\\":\\n selection = []\\n elif answer_similarity == \\\"normal\\\":\\n selection = selection[::2]\\n\\n options = self.__choice_list(kanji, answer_difficulty, grade,\\n n_answers - 1, reversed_bool)\\n\\n for option in options:\\n if option in self.vocab_dict:\\n for vocab in self.vocab_dict[option]:\\n if check_compatibility(vocab):\\n selection.append(vocab)\\n\\n #make we have enough anwsers to present\\n while len(selection) < n_answers - 1:\\n kanji, _, _ = self.kanji[grade][random.choice(kanji_indices)]\\n if kanji in self.vocab_dict:\\n for vocab in self.vocab_dict[kanji]:\\n if check_compatibility(vocab):\\n selection.append(vocab)\\n\\n sampled_selection = random.sample(selection,n_answers - 1)\\n\\n #remove dublicates: on kanji and meaning\\n hint = self.__hint(reversed_bool, question_type, item, item_meaning, grade) \\n if not reversed_bool:\\n question = \\\"What is the meaning of the following word?\\\"\\n options = [x[2] for x in sampled_selection] + [item_meaning]\\n random.shuffle(options)\\n chunk.add_question(question, item, item_meaning, options, hint)\\n else:\\n question = \\\"Which word belongs to the following meaning(s)?\\\"\\n options = [x[0] for x in sampled_selection] + [item]\\n random.shuffle(options)\\n chunk.add_question(question, item_meaning, item, options, hint)\\n if not allow_duplicates:\\n kanji_indices.remove(index)\\n return chunk\",\n \"def data():\\n file = open('./contest/content/data.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Data', \\n content=content)\",\n \"def generateAll(self):\\n\\n dsource = DataSource.DataSource('summer533moon')\\n data = dsource.getKeyList()\\n replacements = {}\\n\\n # Generates the champion list to be displayed.\\n result = '<p class=\\\"champ-display\\\">'\\n line_break_counter = 0\\n for champ in data:\\n if line_break_counter % 10 == 0 and line_break_counter != 0:\\n result += '<br />'\\n result += '<a href=\\\"index.py?champ_name=%s\\\"><img src=\\\"http://ddra\\\\\\ngon.leagueoflegends.com/cdn/5.7.2/img/champion/%s.png\\\"></a>' %\\\\\\n (champ, champ)\\n line_break_counter += 1\\n result += '</p>'\\n\\n replacements['champ_display'] = result\\n self.content = self.content.format(**replacements)\",\n \"def get_examples():\\n\\n base_query = \\\"[[Medium::TCG]] [[Database ID::+]]\\\"\\n base_printouts = [\\n \\\"Password\\\",\\n \\\"English name\\\",\\n \\\"Card type\\\",\\n ]\\n all_res = {}\\n\\n n = len(ALL_DISPLAYS)\\n for i, x in enumerate(ALL_DISPLAYS):\\n query = f\\\"{base_query} [[{x}::+]]\\\"\\n print(i, n, query)\\n try:\\n data = ask_yugipedia(query, base_printouts + [x])\\n except:\\n print(\\\"ERROR\\\", i, x)\\n continue\\n all_res[x] = data\\n return all_res\",\n \"def generate_training_set(individual, db_name):\\n\\n db = connect(db_name)\\n for miller_indices in individual:\\n\\n # Do MD\\n slab = generate_slab(miller_indices, 5)\\n slab = make_supercell(slab, P=[[3, 1, 1], [1, 3, 1], [1, 1, 1]])\\n\\n slab.set_calculator(EMT())\\n slab.get_potential_energy()\\n\\n db.write(slab)\\n\\n MaxwellBoltzmannDistribution(slab, 300. * units.kB)\\n dyn = VelocityVerlet(slab, dt=1. * units.fs)\\n for i in range(1, 10):\\n dyn.run(10)\\n db.write(slab)\",\n \"def workbench_scenarios(): \\n return [\\n (\\\"TermsXBlock\\\",\\n \\\"\\\"\\\"<terms/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple TermsXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <terms/>\\n <terms/>\\n <terms/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def summarize_corpus():\\n\\t\\n\\t# get metadata\\n\\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \\\"metadata\\\", md_mode)\\n\\t\\n\\t# visualize some metadata\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"author-continent\\\")\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"author-country\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"language\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_hist\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_x\\\")\\n\\tvisualize_metadata.plot_pie(wdir, md_csv, \\\"subgenre\\\")\\n\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"gender\\\")\\n\\t\\n\\t# make some counts\\n\\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\\n\\tnum_texts = len(md_table)\\n\\t#num_language = len(md_table.groupby([\\\"language\\\"]))\\n\\t#num_continent = len(md_table.groupby([\\\"author-continent\\\"]))\\n\\t#num_countries = len(md_table.groupby([\\\"author-country\\\"]))\\n\\t#num_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_subgenre = len(md_table.groupby([\\\"subgenre\\\"]))\\n\\t#num_subgenre_x = len(md_table.groupby([\\\"subgenre_x\\\"]))\\n\\t#fr_subgenre_hist = md_table.groupby([\\\"subgenre_hist\\\"]).count()\\n\\t#num_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"historical\\\"]\\n\\t#num_not_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"not_historical\\\"]\\n\\t\\n\\t\\n\\td = {\\\"texts\\\":[num_texts], \\n\\t#\\\"languages\\\":[num_language],\\n\\t#\\\"continents\\\":[num_continent],\\n\\t#\\\"countries\\\":[num_countries],\\n\\t\\\"authors\\\":[num_authors],\\n\\t#\\\"subgenre_x\\\":[num_subgenre_x],\\n\\t\\\"subgenre\\\":[num_subgenre]}\\n\\t#\\\"num_historical\\\":[num_historical],\\n\\t#\\\"num_not_historical\\\":[num_not_historical]}\\n\\t\\n\\t\\n\\t\\n\\tcount_fr = pd.DataFrame(d)\\n\\tcount_fr.to_csv(os.path.join(wdir, \\\"corpus-description.csv\\\"), sep=\\\",\\\", header=True)\\n\\tprint(\\\"Done: summarize corpus\\\")\",\n \"def splits(cls, search_query = 'all', max_results = 300, start = 0, reduced_words=1000, savepath='.data', split_ratio=0.7, stratified=False, strata_field='abstract',\\n random_state=None, **kwargs):\\n\\n # initialize text field\\n text_field = torchtext.data.Field(tokenize=get_tokenizer(\\\"spacy\\\"), init_token='<sos>', eos_token='<eos>', lower=True)\\n fields = [('abstract', text_field), ('paper', text_field)]\\n examples = []\\n\\n # Create new dataset by downloading from arxiv or open dataset from folder\\n if not os.path.exists(savepath):\\n os.mkdir(savepath)\\n # create directories for saving the data set\\n if not os.path.exists(os.path.join(savepath, 'temp')):\\n os.mkdir(os.path.join(savepath, 'temp'))\\n if not os.path.exists(os.path.join(savepath, 'abstracts')):\\n os.mkdir(os.path.join(savepath, 'abstracts'))\\n if not os.path.exists(os.path.join(savepath, 'paper')):\\n os.mkdir(os.path.join(savepath, 'paper'))\\n data = cls.download(search_query=search_query, max_results=max_results)\\n abstracts, papers = cls.extract_paper_and_abstract(data, savepath=savepath)\\n\\n # generate all examples\\n for i, (abstract, paper) in enumerate(zip(abstracts, papers)):\\n paper_tokenized = []\\n abstract_tokenized = []\\n # rdeduce the number of words with the textrank approach\\n textranked_paper = summarizer.summarize(paper, words=reduced_words)\\n # add start and end token\\n paper_tokenized += [u'<sos>'] + text_field.preprocess(textranked_paper) + [u'<eos>']\\n abstract_tokenized += [u'<sos>'] + text_field.preprocess(abstract) + [u'<eos>']\\n # initialize examples\\n examples.append(torchtext.data.Example.fromlist([abstract_tokenized, paper_tokenized], fields))\\n\\n # save data samples in txt files\\n with open(os.path.join(savepath,'abstracts','abstract_' + str(i) + '.txt'), 'w+', encoding='utf-8') as abstr:\\n csvwriter = csv.writer(abstr, delimiter=' ')\\n csvwriter.writerow(abstract_tokenized)\\n with open(os.path.join(savepath,'paper','paper_' + str(i) + '.txt'), 'w+', encoding='utf-8') as pap:\\n csvwriter = csv.writer(pap, delimiter=' ')\\n csvwriter.writerow(paper_tokenized)\\n\\n else:\\n # read all files in saved data path\\n paper_files = os.listdir(os.path.join(savepath,'paper'))\\n abstract_files = os.listdir(os.path.join(savepath,'abstracts'))\\n data = [[paper, abstract] for paper, abstract in zip(paper_files, abstract_files)]\\n papers_tokenized = []\\n abstracts_tokenized = []\\n # read paper and abstracts from files\\n for paper, abstract in data:\\n with open(os.path.join(savepath, 'paper', paper), encoding='utf-8') as csvfile:\\n paper = csv.reader(csvfile, delimiter=' ')\\n for pap in paper:\\n if pap:\\n papers_tokenized.append(pap)\\n with open(os.path.join(savepath, 'abstracts', abstract), encoding='utf-8') as csvfile:\\n abstract = csv.reader(csvfile, delimiter=' ')\\n for abstr in abstract:\\n if abstr:\\n abstracts_tokenized.append(abstr)\\n \\n # generate all examples\\n for abstract_tokenized, paper_tokenized in zip(abstracts_tokenized, papers_tokenized):\\n examples.append(torchtext.data.Example.fromlist([abstract_tokenized, paper_tokenized], fields))\\n\\n # create initial dataset\\n dataset = PaperAbstractDataset(examples, fields)\\n # split dataset\\n splits = dataset.split(split_ratio=split_ratio, stratified=stratified, strata_field=strata_field,\\n random_state=random_state)\\n # initialize vocabulary\\n pre_trained_vector_type = 'glove.6B.300d'\\n for d in splits:\\n for name, field in d.fields.items():\\n field.build_vocab(splits[0], vectors=pre_trained_vector_type)\\n d.filter_examples(['abstract', 'paper'])\\n return splits\",\n \"def main():\\n\\n dofile = \\\"thebook\\\"\\n\\n #spellcheck()\\n\\n common_options = '--encoding=utf-8 --examples_as_exercises '\\n\\n # --- HTML ---\\n\\n common_html_options = ' '\\n\\n # HTML Bootstrap\\n bootstrap_options = ' --html_style=bootswatch_readable --html_code_style=inherit --html_pre_style=inherit --toc_depth=2 --pygments_html_style=default --html_template=template_bootstrap_wtoc.html --html_figure_caption=bottom --html_figure_hrule=top+bottom' \\n\\n html(\\n dofile,\\n options=common_options + common_html_options + bootstrap_options,\\n split=True)\\n\\n # One long HTML file\\n #html(dofile, options=common_options + common_html_options + ' --html_style=bloodish --html_output=%s-1' % dofile, split=False)\\n\\n # Solarized HTML\\n #html(dofile, options=common_options + common_html_options + ' --html_style=solarized3 --html_output=%s-solarized' % dofile, split=True)\\n\\n mksnippets()\\n sys.exit(1)\\n\\n # --- latex ---\\n\\n common_latex_options = ' --latex_code_style=vrb'\\n\\n for version in 'paper', 'screen': # , 'A4', '2up', 'A4-2up':\\n latex(\\n dofile,\\n latex_program='pdflatex',\\n options=common_options + common_latex_options,\\n version=version,\\n postfix='auto')\\n\\n # --- Sphinx ---\\n\\n# sphinx_themes = ['pyramid',]\\n# for theme in sphinx_themes:\\n# dirname = 'sphinx-rootdir' if len(sphinx_themes) == 1 else 'sphinx-rootdir-%s' % theme\\n# sphinx(\\n# dofile,\\n# options=common_options + '',\\n# dirname=dirname,\\n# theme=theme,\\n# automake_sphinx_options='',\\n# split=False)\\n\\n # Dump all Unix commands run above as a Bash script\\n bash = open('tmp_make.sh', 'w')\\n print 'see tmp_make.sh for an equivalent auto-generated unix script'\\n bash.write('''#!/bin/bash\\nset -x # display all commands in output\\n\\n# Safe execution of a Unix command: exit if failure\\nfunction system {\\n \\\"$@\\\"\\n if [ $? -ne 0 ]; then\\n echo \\\"make.sh: unsuccessful command $@\\\"\\n echo \\\"abort!\\\"\\n exit 1\\n fi\\n}\\n''')\\n for cmd in unix_command_recorder:\\n if cmd.startswith('doconce format') or cmd.startswith('rm '):\\n bash.write('\\\\n') # delimiter line in script\\n bash.write('system ' + cmd + '\\\\n')\\n bash.close()\\n\\n print 'see tmp_output.log for the output of all the commands'\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Markdown\\\",\\n \\\"\\\"\\\"<markdowna />\\n \\\"\\\"\\\")\\n ]\",\n \"def print_output(data,alignments,file):\\n print(\\\"######################################################################\\\")\\n print(\\\"Task 1 : IBM model 1 and EM algorithm implementation ,with corpus @\\\",file)\\n print(\\\"######################################################################\\\")\\n\\n for i in range(len(data)):\\n print(\\\"English Sentence : \\\",data[i][\\\"en\\\"])\\n print(\\\"Foreign Sentence : \\\",data[i][\\\"fr\\\"])\\n print(\\\"Alignment : \\\",alignments[i])\\n print(\\\"----------------------------------------------------------------------\\\")\",\n \"def domain_parser():\\n\\tinput_handle=\\\"blast_hits.fasta\\\"\\n\\t# File with: (1) the domains of each hit and (2) domains information\\n\\tdomains= \\\"domains_hits.txt\\\"\\n\\toutput_handle = open(domains, \\\"w\\\")\\n\\toutput_handle.write(\\\"#This file contains the domains of each hit.\\\\n#At the bottom, you will find detail information of all the domains detected.\\\\n\\\")\\n\\toutput_handle.write(\\\"#We strongly recommend to open this file with Visual Studio Code.\\\\n#Because when the names of the domains are too large, in regular editors the table looks awful.\\\\n\\\")\\n\\toutput_handle.write(\\\"#Here it is only showed how many times a pattern is present.\\\\n#In the figure of the domains you will find the position of each domain.\\\\n\\\\n\\\")\\n\\taccession_list=[] \\t# List of prosite.doc accessions of the domains that had been found\\n\\tdomains_dict=dict() # dictionary that saves matches\\n\\tcount=1\\n\\tmax_seq_len=0 # Keep larger sequence to plot x-axe\\n\\t# Loop to go through hits\\n\\tfor seq_record in SeqIO.parse(input_handle, \\\"fasta\\\"):\\n\\t\\toutput_handle.write(str(seq_record.id)+\\\"\\\\n\\\") # print identifier of the hit\\n\\t\\toutput_handle.write(str(seq_record.seq)+\\\"\\\\n\\\") # print sequence of the hit\\n\\t\\tif len(seq_record.seq)>max_seq_len:\\n\\t\\t\\tmax_seq_len=len(seq_record.seq)\\n\\t\\t# Make a table for each hit with the domains, that contains the following fields: name, accession, description and pattern\\n\\t\\tx=PrettyTable()\\n\\t\\tx.field_names=[\\\"name\\\",\\\"accession\\\",\\\"description\\\",\\\"pattern\\\",\\\"repetitions\\\"]\\n\\n\\t\\t# Loop to go through prosite domains\\n\\t\\thandle = open(\\\"prosite.dat\\\",\\\"r\\\")\\n\\t\\trecords = Prosite.parse(handle)\\n\\t\\tfor record in records:\\n\\t\\t\\t# prosite.dat preparation for parsing\\n\\t\\t\\t# {} -> [^]\\n\\t\\t\\tpattern = record.pattern.upper()\\n\\t\\t\\tpattern = pattern.replace(\\\"{\\\", \\\"[^\\\")\\n\\t\\t\\tpattern = pattern.replace(\\\"}\\\", \\\"]\\\")\\t\\n\\t\\t\\t# - -> \\\"\\\"\\n\\t\\t\\tpattern = pattern.replace(\\\"-\\\", \\\"\\\")\\t\\n\\t\\t\\t# . -> \\\"\\\"\\n\\t\\t\\tpattern = pattern.replace(\\\".\\\", \\\"\\\")\\t\\n\\t\\t\\t# X|x -> \\\"[ARNDCQEGHILKMFPSTWYV]\\\"\\n\\t\\t\\tAAS=\\\"[ARNDCQEGHILKMFPSTWYV]\\\"\\n\\t\\t\\tpattern = pattern.replace(\\\"x\\\", AAS)\\n\\t\\t\\tpattern = pattern.replace(\\\"X\\\", AAS)\\t\\n\\t\\t\\t# () -> {}\\n\\t\\t\\tpattern = pattern.replace(\\\"(\\\", \\\"{\\\")\\n\\t\\t\\tpattern = pattern.replace(\\\")\\\", \\\"}\\\")\\t\\n\\n\\t\\t\\t# >] -> ]?$\\n\\t\\t\\tpattern = pattern.replace(\\\">]\\\", \\\"]?$\\\")\\t\\n\\n\\t\\t\\t# < -> ^\\n\\t\\t\\t# > -> $\\n\\t\\t\\tpattern = pattern.replace(\\\"<\\\", \\\"^\\\")\\t\\n\\t\\t\\tpattern = pattern.replace(\\\">\\\", \\\"$\\\")\\t\\n\\t\\t\\tif pattern != \\\"\\\":\\n\\t\\t\\t\\t# Look if the hit contains the current patter\\n\\t\\t\\t\\tif re.search(r\\\"\\\"+str(pattern), str(seq_record.seq).upper()): # if found\\n\\t\\t\\t\\t\\tif record.pdoc not in accession_list:\\n\\t\\t\\t\\t\\t\\t# Save pdoc accession in the list of prosite.doc accessions\\n\\t\\t\\t\\t\\t\\t# if it is not already\\n\\t\\t\\t\\t\\t\\taccession_list.append(record.pdoc)\\n\\t\\t\\t\\t\\tmatches = re.finditer(r\\\"\\\"+str(pattern), str(seq_record.seq).upper())\\n\\t\\t\\t\\t\\treps=0\\n\\t\\t\\t\\t\\tfor match in matches: # save all matches in a dictionary to plot them later\\n\\t\\t\\t\\t\\t\\tdomains_dict[count]=[seq_record.id, len(seq_record.seq),record.name,match.start(),match.end()]\\n\\t\\t\\t\\t\\t\\tcount=count+1\\n\\t\\t\\t\\t\\t\\treps=reps+1\\n\\t\\t\\t\\t\\tx.add_row([record.name,record.accession,record.description,record.pattern, reps]) # add found domain to table\\n\\n\\t\\toutput_handle.write(str(x)+\\\"\\\\n\\\") # add table of hit to domains_hits.txt\\n\\n\\t# At the end of the tables, print information of all the domains that had been found\\n\\toutput_handle.write(\\\"\\\\n\\\")\\n\\trecord_text_list=DocParser(accession_list)\\n\\tfor text in record_text_list:\\n\\t\\toutput_handle.write(text)\\n\\treturn (domains_dict,max_seq_len,accession_list)\",\n \"def generate_sentinel_measure(\\n data_dict,\\n data_dict_practice,\\n codelist_dict,\\n measure,\\n code_column,\\n term_column,\\n dates_list,\\n codelist_links,\\n interactive=True,\\n):\\n df = data_dict[measure]\\n\\n childs_df, childs_df_with_count = create_child_table(\\n df, codelist_dict[measure], code_column, term_column, measure\\n )\\n\\n practices_included = get_number_practices(df)\\n practices_included_percent = get_percentage_practices(df)\\n\\n num_events, num_events_mil = get_number_events_mil(df, measure)\\n\\n num_patients = get_number_patients(measure)\\n\\n num_practices_df = pd.DataFrame(\\n {\\\"num_practices_included\\\": pd.Series([practices_included])}\\n )\\n num_practices_df.to_csv(\\n f\\\"{OUTPUT_DIR}/num_practices_included_{measure}.csv\\\", index=False\\n )\\n\\n df = data_dict_practice[measure]\\n\\n deciles_chart(\\n df,\\n period_column=\\\"date\\\",\\n column=\\\"rate\\\",\\n ylabel=\\\"rate per 1000\\\",\\n interactive=interactive,\\n )\\n\\n display(\\n Markdown(f\\\"Practices included: {practices_included} ({practices_included_percent:.2f}%)\\\"),\\n )\\n\\n childs_df = childs_df.rename(columns={code_column: code_column.title()})\\n childs_df.to_csv(f\\\"{OUTPUT_DIR}/code_table_{measure}.csv\\\")\\n\\n childs_df_with_count = childs_df_with_count.rename(\\n columns={code_column: code_column.title()}\\n )\\n childs_df_with_count.to_csv(f\\\"{OUTPUT_DIR}/code_table_{measure}_with_count.csv\\\")\\n\\n if len(codelist_links)>1:\\n display(\\n Markdown(f\\\"#### Most Common Codes <a href={codelist_links[0]}>(Codelist 1)</a>, <a href={codelist_links[1]}>(Codelist 2)</a>\\\"),\\n HTML(childs_df.to_html(index=False)),\\n )\\n\\n else:\\n display(\\n Markdown(f\\\"#### Most Common Codes <a href={codelist_links[0]}>(Codelist)</a>\\\"),\\n HTML(childs_df.to_html(index=False)),\\n )\\n\\n display(\\n Markdown(f\\\"Total patients: {num_patients:.2f}M ({num_events_mil:.2f}M events)\\\"),\\n )\\n\\n return df, num_events\",\n \"def create_file(self):\\n for data_element in self.data:\\n title = data_element['title']\\n anchor = data_element['href']\\n example = data_element['example']\\n content = data_element['content']\\n if example:\\n abstract = '<section class=\\\"prog__container\\\">{}<br>{}</section>'.format(content, example)\\n\\n list_of_data = [\\n title, # api title\\n 'A', # type is article\\n '', # no redirect data\\n '', # ignore\\n '', # no categories\\n '', # ignore\\n '', # no related topics\\n '', # ignore\\n '', # no external link\\n '', # no disambiguation\\n '', # images\\n abstract, # abstract\\n anchor # url to doc\\n ]\\n self.output_file.write('{}\\\\n'.format('\\\\t'.join(list_of_data)))\",\n \"def generate_features(stances, dataset, name, feature_list, features_dir):\\n h, b, y, bodyId, headId = [], [], [], [], []\\n\\n feature_dict = {'overlap': word_overlap_features,\\n 'refuting': refuting_features,\\n 'polarity': polarity_features,\\n 'hand': hand_features,\\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\\n 'NMF_fit_all': NMF_fit_all,\\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\\n 'NMF_cos_50': NMF_cos_50,\\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\\n 'ppdb': ppdb,\\n 'stanford_ppdb': stanford_ppdb_score,\\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\\n 'stanford_sentiment': stanford_sentiment,\\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\\n 'stanford_wordsim': stanford_based_verb_noun_sim,\\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\\n 'stanford_negation': stanford_negation_features,\\n 'stanford_negation_1sent': stanford_negation_features_1sent,\\n 'stanford_negation_2sent': stanford_negation_features_2sent,\\n 'stanford_negation_3sent': stanford_negation_features_3sent,\\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\\n 'hedging': hedging_features,\\n 'sen2sen': sen2sen_similarity_max,\\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\\n 'wmdsenDoc': word_mover_distance_wholebody,\\n 'sdm_sim': sdm_sim,\\n 'discuss': discuss_features,\\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\\n 'lexical_features': lexical_features,\\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\\n 'mpqa_unigrams': mpqa_unigrams,\\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\\n 'nrc_emo_lex': nrc_emo_lex,\\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\\n #'POS_features': POS_features,\\n 'readability_features': readability_features,\\n 'sentiment140_unigrams': sentiment140_unigrams,\\n 'structural_features': structural_features,\\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\\n 'NMF_cos_300': NMF_cos_300\\n }\\n\\n stanceCounter = 0\\n for stance in stances:\\n y.append(LABELS.index(stance['Stance']))\\n h.append(stance['Headline'])\\n b.append(dataset.articles[stance['Body ID']])\\n bodyId.append(stance['Body ID'])\\n headId.append(name+str(stanceCounter))\\n stanceCounter += 1\\n\\n X_feat = []\\n feat_list = []\\n last_index = 0\\n for feature in feature_list:\\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\\\"/\\\"+feature+\\\".\\\"+name+'.npy', bodyId, feature, headId, fold=name)\\n feat_list.append((last_index, last_index+len(feat[0]), str(feature)))\\n last_index += len(feat[0])\\n X_feat.append(feat)\\n X = np.concatenate(X_feat, axis=1)\\n\\n return X, y, feat_list\",\n \"def create_index():\\n es = Elasticsearch()\\n es.indices.delete(index='drugaid-index')\\n with open('../data_crawling/demo_drug_data-fixed.json') as f:\\n drug_data = json.load(f)\\n\\n for drug in drug_data:\\n es.index(index='drugaid-index', doc_type='drug', body=drug)\",\n \"def api_index():\\n db = get_db()\\n datasets = db.execute('SELECT * FROM dataset').fetchall()\\n \\n responses = {}\\n columns = ['name', 'description', 'tags', 'dataset_file_size', 'files']\\n\\n for dataset in datasets:\\n response = dict(zip(columns, [dataset[column] for column in columns]))\\n response['link'] = url_for('dataset.api_show', dataset_id=dataset['id'])\\n responses[dataset['id']] = response\\n\\n return jsonify(dict(responses))\",\n \"def generateSpecified(self):\\n\\n dsource = DataSource.DataSource('summer533moon')\\n replacements = {}\\n\\n # Split into two cases where the lane parameter is non-trivial\\n # and where the category parameter is non-trivial.\\n if self.filter['lane'] != '':\\n data = dsource.getLaneList(self.filter['lane'])\\n result = '<p>'\\n line_break_counter = 0\\n for champ in data:\\n if line_break_counter % 10 == 0 and line_break_counter != 0:\\n result += '</p>'\\n result += '<a href=\\\"index.py?champ_name=%s\\\"><img src=\\\"http://\\\\\\nddragon.leagueoflegends.com/cdn/5.7.2/img/champion/%s.png\\\"></a>' %\\\\\\n (champ, champ)\\n line_break_counter += 1\\n result += '</p>'\\n elif self.filter['category'] != '':\\n data = dsource.getCategoryList(self.filter['category'])\\n result = '<p>'\\n line_break_counter = 0\\n for champ in data:\\n if line_break_counter % 10 == 0 and line_break_counter != 0:\\n result += '</p>'\\n result += '<a href=\\\"index.py?champ_name=%s\\\"><img src=\\\"http://\\\\\\nddragon.leagueoflegends.com/cdn/5.7.2/img/champion/%s.png\\\"></a>' %\\\\\\n (champ, champ)\\n line_break_counter += 1\\n result += '</p>'\\n # If somehow parameters do not match the cases above, generate homepage\\n # with complete list of champions instead.\\n else:\\n self.generateAll()\\n return\\n\\n replacements['champ_display'] = result\\n self.content = self.content.format(**replacements)\",\n \"def fase1():\\n\\n pagesClassIDs = {\\n \\\"fase1\\\": {\\n \\\"bannertitle\\\": [],\\n \\\"subtitle\\\": [],\\n \\\"firstText\\\": [],\\n \\\"secondText\\\": []\\n }\\n }\\n for key in pagesClassIDs[\\\"fase1\\\"].keys():\\n pagesClassIDs[\\\"fase1\\\"][key].append(\\n str(\\n pageTexts.query.filter_by(pageID=key,\\n htmlName=\\\"fase1\\\").first()))\\n\\n spat_aspects = Spat_aspect.query.all()\\n spat_aspectsList = [\\n spat_aspect.__dict__[\\\"name\\\"] for spat_aspect in spat_aspects\\n ]\\n\\n temp_aspects = Temp_aspect.query.all()\\n temp_aspectsList = [\\n temp_aspect.__dict__[\\\"name\\\"] for temp_aspect in temp_aspects\\n ]\\n\\n nodes = Node.query.all()\\n nodesList = [node.__dict__ for node in nodes]\\n for nd in nodesList:\\n del nd['_sa_instance_state']\\n\\n factorDict = {}\\n for node in nodesList:\\n spat_asp = int(node[\\\"spat_aspect_id\\\"]) - 1\\n temp_asp = int(node[\\\"temp_aspect_id\\\"]) - 1\\n factorDict.setdefault(spat_aspectsList[spat_asp], {})\\n factorDict[spat_aspectsList[spat_asp]].setdefault(\\n temp_aspectsList[temp_asp], [])\\n factorDict[spat_aspectsList[spat_asp]][\\n temp_aspectsList[temp_asp]].append(node[\\\"factor\\\"])\\n\\n print(nodesList)\\n print(factorDict)\\n\\n return render_template(\\n 'home/fase1.html',\\n factorDict=factorDict,\\n nodes=nodesList,\\n spat_aspects=spat_aspectsList,\\n temp_aspects=temp_aspectsList,\\n pageDicts=pagesClassIDs,\\n title=\\\"fase1\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7529587","0.6753163","0.5811377","0.5526393","0.5446928","0.5422765","0.5422765","0.5392124","0.5317624","0.52876973","0.5264621","0.5209","0.52000964","0.5198834","0.5198507","0.5195234","0.51844853","0.5183828","0.51717633","0.5149823","0.5149334","0.5140577","0.5121049","0.5112023","0.51105785","0.5082116","0.5081299","0.50785035","0.50759506","0.50659865","0.50646436","0.5062489","0.50571144","0.505475","0.5049099","0.5044637","0.50381607","0.50243294","0.5022682","0.50159305","0.5013203","0.50107276","0.5010704","0.4988442","0.49823058","0.49811357","0.4965053","0.4951823","0.49428406","0.49419904","0.494123","0.49361882","0.49330738","0.49293438","0.49263674","0.49218476","0.49208736","0.49186802","0.49180084","0.4915004","0.49113524","0.49099702","0.49058214","0.4904908","0.4904325","0.49029785","0.4896455","0.48816627","0.4868881","0.48602277","0.48584607","0.48482755","0.48454648","0.4840739","0.48342183","0.48270136","0.48261195","0.48252073","0.48247656","0.48215413","0.48189443","0.4818658","0.4816766","0.4816342","0.48124886","0.48117363","0.48075885","0.48069152","0.48023048","0.4802258","0.47969675","0.47946295","0.47939456","0.47916797","0.47902495","0.4788154","0.4784843","0.47795418","0.47788185","0.47785974"],"string":"[\n \"0.7529587\",\n \"0.6753163\",\n \"0.5811377\",\n \"0.5526393\",\n \"0.5446928\",\n \"0.5422765\",\n \"0.5422765\",\n \"0.5392124\",\n \"0.5317624\",\n \"0.52876973\",\n \"0.5264621\",\n \"0.5209\",\n \"0.52000964\",\n \"0.5198834\",\n \"0.5198507\",\n \"0.5195234\",\n \"0.51844853\",\n \"0.5183828\",\n \"0.51717633\",\n \"0.5149823\",\n \"0.5149334\",\n \"0.5140577\",\n \"0.5121049\",\n \"0.5112023\",\n \"0.51105785\",\n \"0.5082116\",\n \"0.5081299\",\n \"0.50785035\",\n \"0.50759506\",\n \"0.50659865\",\n \"0.50646436\",\n \"0.5062489\",\n \"0.50571144\",\n \"0.505475\",\n \"0.5049099\",\n \"0.5044637\",\n \"0.50381607\",\n \"0.50243294\",\n \"0.5022682\",\n \"0.50159305\",\n \"0.5013203\",\n \"0.50107276\",\n \"0.5010704\",\n \"0.4988442\",\n \"0.49823058\",\n \"0.49811357\",\n \"0.4965053\",\n \"0.4951823\",\n \"0.49428406\",\n \"0.49419904\",\n \"0.494123\",\n \"0.49361882\",\n \"0.49330738\",\n \"0.49293438\",\n \"0.49263674\",\n \"0.49218476\",\n \"0.49208736\",\n \"0.49186802\",\n \"0.49180084\",\n \"0.4915004\",\n \"0.49113524\",\n \"0.49099702\",\n \"0.49058214\",\n \"0.4904908\",\n \"0.4904325\",\n \"0.49029785\",\n \"0.4896455\",\n \"0.48816627\",\n \"0.4868881\",\n \"0.48602277\",\n \"0.48584607\",\n \"0.48482755\",\n \"0.48454648\",\n \"0.4840739\",\n \"0.48342183\",\n \"0.48270136\",\n \"0.48261195\",\n \"0.48252073\",\n \"0.48247656\",\n \"0.48215413\",\n \"0.48189443\",\n \"0.4818658\",\n \"0.4816766\",\n \"0.4816342\",\n \"0.48124886\",\n \"0.48117363\",\n \"0.48075885\",\n \"0.48069152\",\n \"0.48023048\",\n \"0.4802258\",\n \"0.47969675\",\n \"0.47946295\",\n \"0.47939456\",\n \"0.47916797\",\n \"0.47902495\",\n \"0.4788154\",\n \"0.4784843\",\n \"0.47795418\",\n \"0.47788185\",\n \"0.47785974\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69349456"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":85685,"cells":{"query":{"kind":"string","value":"Generetes markdown data for given technique"},"document":{"kind":"string","value":"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate():\n\n # Verify if directory exists\n if not os.path.isdir(config.techniques_markdown_path):\n os.mkdir(config.techniques_markdown_path)\n\n #Write the technique index.html page\n with open(os.path.join(config.techniques_markdown_path, \"overview.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(config.technique_overview_md)\n\n for domain in config.domains:\n generate_domain_markdown(domain)","def generate_domain_markdown(domain):\n\n #Reads the STIX and creates a list of the ATT&CK Techniques\n full_techniques = stixhelpers.get_techniques(config.ms[domain])\n tactic_list = stixhelpers.get_tactic_list(config.ms[domain])\n\n data = {}\n \n technique_list = get_techniques_list(full_techniques)\n\n data['domain'] = domain.split(\"-\")[0]\n\n # Get technique table data and number of techniques\n data['technique_table'] = util.get_technique_table_data(None, full_techniques)\n data['technique_list_len'] = str(len(full_techniques))\n side_menu_data = get_technique_side_menu_data(domain, technique_list, tactic_list)\n\n # Get tactic-techniques table\n data['menu'] = side_menu_data\n\n subs = config.technique_domain_md.substitute(data)\n subs = subs + json.dumps(data)\n\n with open(os.path.join(config.techniques_markdown_path, data['domain'] + \"-techniques.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)\n\n # Create the markdown for the enterprise groups in the STIX\n\n for technique in full_techniques:\n if 'revoked' not in technique or technique['revoked'] is False:\n generate_technique_md(technique, domain, side_menu_data, tactic_list)","def _generate_markdown(self, case):\n # Lucene query generation\n lucene_dict = {\n \"sources\": case[\"detection\"][\"sources\"],\n \"data\": case[\"input_arguments\"],\n }\n case[\"lucene_query\"] = self.templates[\"lucene\"].render(lucene_dict)\n # AWS CLI command generation\n command_template = jinja2.Template(case[\"executors\"][\"sh\"][\"code\"])\n if case[\"input_arguments\"]:\n aws_cli_render_args = {}\n for arg in case[\"input_arguments\"]:\n aws_cli_render_args[arg] = case[\"input_arguments\"][arg][\"value\"]\n case[\"compiled_command\"] = command_template.render(aws_cli_render_args)\n else:\n case[\"compiled_command\"] = command_template.render()\n\n render_dict = {\"case\": case}\n return self.templates[\"markdown\"].render(render_dict)","def to_markdown(self, experiment_name : str):\n experiment = self.get_experiment(experiment_name)\n f = open(experiment.root_dir + \"/\" + experiment_name + \".md\", 'w')\n # Create a markdown object\n markdown = Markdown()\n\n for img in experiment.images:\n mkimg = Markdown.Image(img.title, img.filename, img.description)\n markdown.add_image(img.filename)\n\n table = Markdown.Table([\"Parameter Name\", \"Input/Output\", \"Value\", \"Title\", \"Description\"])\n for param in experiment.parameters:\n table.add_row([param.name, ])\n\n markdown.add_table(table)","def gen_markdown_doc(self):\n # Spec for markdown doc\n # vars displayed as \\{ hyperlinked classExpression \\}\n # pattern name\n # label rule\n # def\n # MS fields\n # dicts?\n\n # sprintf subs and MS conversion\n \n out = \"## %s\\n\" % self.pattern['pattern_name'] #\n # sprintf to generate label - Use { var name } in sub\n out += \"__label:__ %s\\n\\n\" % (self._var_quote_sub(self.pattern['name']['text'],self.pattern['name']['vars']))\n # sprintf to generate def - Use { var name } in sub\n out += \"__def:__ %s\\n\\n\" % (self._var_quote_sub(self.pattern['def']['text'],self.pattern['def']['vars']))\n \n if \"EquivalentTo\" in self.pattern: \n out += \"__equivalentTo:__ %s\\n\\n\" % self._ms_sub_and_md('EquivalentTo') # Ugly! Refactor!\n if \"SubClassOf\" in self.pattern:\n out += \"__subClassOf:__ %s\\n\\n\" % self._ms_sub_and_md('SubClassOf')\n if \"GCI\" in self.pattern:\n out += \"__GCI:__ %s\\n\\n\" % self._ms_sub_and_md('GCI')\n return out","def _generate_conclusion_markdown(self, data):\n pass","def data():\n file = open('./contest/content/data.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Data', \n content=content)","def generate_excerpt():","def _markdown(checklist):\n\n checklist = json.load(open(checklist), object_pairs_hook=OrderedDict)\n\n mdFile = MdUtils(file_name='Ikigai-Checklist',\n title='PDP 2019 Checklist')\n\n\n mdFile.new_paragraph(checklist['overview'])\n\n sections = sorted(checklist['checklist'].values(),\n key=lambda s: int(s['no']))\n for section in sections:\n mdFile.new_header(level=1, title=section['title'])\n for subject in section['subjects'].values():\n mdFile.new_header(level=2, title=subject['title'])\n mdFile.new_paragraph(subject['description'])\n mdFile.new_paragraph(\"Reference: \" + subject['provisions'])\n mdFile.new_paragraph(\"Functions: \" + \", \".join(subject['functions']))\n mdFile.new_paragraph(\"Groups: \" + \", \".join(subject['groups']))\n\n actions = [['No','Description', 'Tags', 'Check']]\n actions += [[a['no'], a['description'], \", \".join(a['tags']),''] for a in subject['actions'].values()]\n rows = len(actions) \n actions = flatten(actions)\n mdFile.new_table(columns=4,\n rows=rows,\n text=actions,\n text_align='left')\n\n mdFile.create_md_file()","def convert_to_markdown(self, text: str) -> str:","def _generate_pr_comment_markdown(self, data):\n pass","def get_markdown(tools: List[Dict[str, Any]]) -> str:\n padding = 43\n\n # First Row\n markdown = \"| {name: <{padding}}| PHP \".format(name=\"Tool\", padding=padding)\n markdown += \" | PHP \".join(PHP_VERSIONS)\n markdown += \" |\\n\"\n # Second Row\n markdown += \"|{name:-<{padding}}-|\".format(name=\"\", padding=padding)\n for php in PHP_VERSIONS:\n markdown += \"---------|\"\n markdown += \"\\n\"\n for tool in tools:\n markdown += \"| {name: <{padding}}|\".format(\n name=\"[\" + tool[\"name\"] + \"][lnk_\" + tool[\"name\"] + \"]\", padding=padding\n )\n for php in PHP_VERSIONS:\n if str(php) in tool[\"exclude\"]:\n markdown += \" |\"\n else:\n markdown += \" ✓ |\"\n markdown += \"\\n\"\n\n markdown += \"\\n\"\n for tool in tools:\n markdown += \"[lnk_\" + tool[\"name\"] + \"]: \" + tool[\"dir\"] + \"\\n\"\n\n return markdown","def _repr_markdown_(self):\n mod = f'**{self.__class__.__name__} Model**'\n try:\n mod +=f': {self.filename}'\n except:\n pass\n s = [mod,'']\n if self.metadata:\n s += ['|Parameter|Value|',\n '|:--------|:----:|']\n for name, v in self.metadata.items():\n try: \n s += [f\"|{name} | ${v.value:g}$ {v.unit:latex}|\"]\n except:\n s += [f\"|{name} | {v} |\"]\n return '\\n'.join(s)","def gen_md(self):\n # https://pythonhosted.org/Markdown/extensions/index.html\n extensions = ['extra', 'codehilite', 'admonition',\n 'toc', 'smarty', 'sane_lists', 'wikilinks']\n # TODO\n extension_configs = {'toc': {\n 'anchorlink': False,\n 'permalink': False\n }\n }\n output_format = 'html5'\n md = markdown.Markdown(extensions=extensions,\n extension_configs=extension_configs,\n output_format=output_format)\n html = md.convert(self.md)\n toc = getattr(md, 'toc', '')\n if toc:\n toc = process_toc(toc)\n return html, toc","def description():\n #rule = request.url_rule\n #print(rule)\n file = open('./contest/content/description.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Description', \n content=content)","def task_generate_sc_markdown():\n for dept in Department.list():\n yield {\n 'name': dept.name,\n 'file_dep': [],\n 'targets': [dept.sc_markdown_path],\n 'actions': [dept.generate_sc_markdown],\n 'clean': True,\n 'uptodate': [False],\n }","def markdown(value):\n return Markup(md(value))","def write():\n \n st.title(\"All about me..!!\")\n st.markdown(\n \"\"\"\n\n:large_blue_diamond: **Data Engineer**\\n\nThe current role revolves around working with database management(AWS Redshift), automation of tasks (using Python), and helping out other teams at work with required data for BI reports. This involves me working with various ETL tools like Matillion. Apart from this, I have started some creative learning initiatives at work to develop and grow skills.\n\n:large_blue_diamond: **Speaker | Blogger | Community- Builder**\\n\nI have speaker at various meetups and workshops. Apart from this, I am a Lead organizer at **Kaggle Days Meetups - Surat** and **Pie and AI: Gujarat**. You will often find me on Saturdays at Integrated Mentoring Session - where we as a community believe in More Together Learning. I like to write blogs on data science, NLP, work learnings, and some general topics.\n\n:large_blue_diamond: **NLP- Practitioner**\\n\nSharing the learnings and practices of NLP with the community through various workshops and classes or through LinkedIn posts and videos. I have completed her thesis titled \"Neural Machine Translation on Indian Languages\". Currently, working on a research project. \n\n:large_blue_diamond: **Collaborations**\\n\nIf you are looking to collaborate on Research opportunities in NLP, \nCorporate training in NLP, \nteaching and mentoring students & professionals in NLP.\nPlease\n[email me](mailto:budhraja.manpreet@gmail.com) or reach out \nto me on [LinkedIn](https://www.linkedin.com/in/manpreet-budhraja/) \n\n\n\n\"\"\",\n unsafe_allow_html=True,\n )","def markov_story():\n return render_template(\"markovstory.html\")","def all_markdown_tips(self) -> str:\n\t\tmessage = \"**__Markdown Tips__**\\n\\n\"\n\t\tfor format in self.formats:\n\t\t\ttip = self.formats[format]\n\t\t\tmessage += f\"{tip.preview}\\n\"\n\t\t\tmessage += f\"{blockquote(tip.escaped)}\\n\\n\"\n\t\treturn message","def generate_markdown(self, outdir, definitions):\n doc_cases = defaultdict(lambda: defaultdict(list))\n\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n for category in definitions.categories:\n cat_outdir = os.path.join(outdir, category.replace(\" \", \"_\").lower())\n if not os.path.exists(cat_outdir):\n os.makedirs(cat_outdir)\n\n for case in definitions.case_set:\n if case[\"category\"] == category:\n doc_cases[category][case[\"name\"]] = case\n\n for technique in doc_cases[category]:\n rendered = self._generate_markdown(doc_cases[category][technique])\n md_filename = technique.replace(\" \", \"_\").lower() + \".md\"\n filename = os.path.join(cat_outdir, md_filename)\n mdoutfile = open(filename, \"w\")\n mdoutfile.write(rendered)\n mdoutfile.close()","def workbench_scenarios():\n return [\n (\"Markdown\",\n \"\"\"<markdowna />\n \"\"\")\n ]","def markdown_table(self, which):\n if which == 'C':\n coef = 'C'\n elif which == 'c':\n coef = 'c'\n elif which == 'f':\n coef = 'f'\n str = '|order|'\n for i in range(1,N+1):\n str = str + '$%s_{%d}$ |' % (coef,i)\n str = str + '\\n|'\n for i in range(1,N+1):\n str = str + '-|'\n str = str + '\\n'\n for i in range(1,self.N+1):\n str = str + (self.dat[i]).markdown_row(self.N, which)\n return str","def markdown(text):\n text = gfm(text)\n text = markdown_lib.markdown(text)\n return text","def fixture_ext_markdown(plugin):\n return plugin.md","def markdown(self):\n return db[self.basic_image_id]","def md_writer(clf, features, outcome, eval_folder,\n config_file, summary_df, i=''):\n if config_file.endswith('.xlsx'):\n config = pd.read_excel(config_file, sheetname='Sheet1')\n elif config_file.endswith('.csv'):\n config = pd.read_csv(config_file)\n\n clf_params = clf.get_params()\n clf_name = str(clf)[:str(clf).index('(')]\n clf_img = clf_name+str(i)\n\n file_name = clf_name+str(i)+'_Evaluation.md'\n\n save_file = open(eval_folder+file_name, 'w')\n\n def new_line():\n save_file.write('\\n')\n\n save_file.write('<link rel=\"stylesheet\" href=\"style.css\" type=\"text/css\" />\\n')\n save_file.write('# Model Evaluation Report\\n')\n new_line()\n\n save_file.write('## Data Configuration:\\n')\n new_line()\n save_file.write(config.to_html(na_rep='', index=False).replace('NaT', ''))\n new_line()\n\n save_file.write('## Classifier Parameters: '+clf_name+'\\n')\n new_line()\n for elem in clf_params:\n save_file.write('* {}: {}\\n'.format(elem, clf_params[elem]))\n new_line()\n\n summary_df = summary_df.T\n summary_df.columns = ['value']\n\n save_file.write('## Evaluation Metrics; Summary\\n')\n new_line()\n save_file.write(summary_df.to_html())\n new_line()\n\n save_file.write('## ROC Curve\\n')\n new_line()\n save_file.write('\\n')\n new_line()\n\n save_file.write('## Precision-Recall Curve\\n')\n new_line()\n save_file.write('\\n')\n new_line()\n\n save_file.write('## Precision, Recall vs % Population\\n')\n new_line()\n save_file.write('\\n')\n\n if clf_name in ['LogisticRegression']:\n save_file.write('## Coefficients\\n')\n new_line()\n for i,coef in enumerate(clf.coef_[0]):\n save_file.write('*<b>{}: {}</b>\\n'.format(features[i], round(coef,4)))\n new_line()\n\n if clf_name in ['WeightedQuestions']:\n save_file.write('## Weights\\n')\n new_line()\n for i,wt in enumerate(clf.weights):\n save_file.write('*<b>{}: {}</b>\\n'.format(features[i], wt))\n new_line()\n\n save_file.close()\n\n def markdown_to_html(md_file, out_file_name=None):\n import markdown\n\n with open(md_file, 'r') as f:\n html = markdown.markdown(f.read())\n\n if out_file_name is None:\n out_file_name = md_file.split('.')[0]+'.html'\n with open(out_file_name, 'w') as f:\n f.write(html)\n\n markdown_to_html(eval_folder+file_name)","def enml_to_markdown(enml):\n pass","def pc_md_to_html(data_list):\n pcrenderer = PanelCodeRenderer()\n markdown = mistune.Markdown(renderer=pcrenderer)\n label = '<p style=\"font-size:x-small\"><em>panelcode: markdown processor (mistune)</em></p>\\n'\n return markdown(\"\\n\".join(data_list) + label)","def readme_md(cls):\n\n template = Helpers.File(Settings.readme_me_template).read()\n\n template = Helpers.Regex(\n template, r\"%%version%%\", replace_with=Settings.version\n ).replace()\n template = Helpers.Regex(\n template, r\"%%lenHosts%%\", replace_with=format(len(Settings.domains), \",d\")\n ).replace()\n template = Helpers.Regex(\n template, r\"%%lenIPs%%\", replace_with=format(len(Settings.ips), \",d\")\n ).replace()\n template = Helpers.Regex(\n template,\n r\"%%lenHostsIPs%%\",\n replace_with=format(len(Settings.ips) + len(Settings.domains), \",d\"),\n ).replace()\n\n print(\"Generation of %s\" % Settings.readme_md_file, end=\" \")\n Helpers.File(Settings.readme_md_file).write(template, overwrite=True)\n print(Settings.done)","def json2markdown(json_data):\n with open(path.join(ROOT, 'utils/markdown.jinja2')) as f:\n return Template(f.read()).render(pdfid=json_data.get('pdfid'), streams=json_data.get('streams'))","def process_markdown(input_markdown, output_name, latex_img_dir = \"./\", input_path = \"./\", thumb_size=64):\n\tmd = markdown.Markdown( extensions=[ 'meta'\n\t , 'codehilite'\n\t , 'tables'\n\t , 'def_list'\n\t , 'footnotes'\n\t , ResourceExtractor({ \"resource_dir\": output_name\n\t , \"relative_path\": input_path\n\t })\n\t , AbstractExtractor()\n\t , ToCExtractor()\n\t , MathJaxExtension()\n\t , LaTeX({ \"latex_img_dir\": latex_img_dir\n\t , \"input_path\": input_path\n\t })\n\t ]\n\t )\n\t\n\t# Basic HTML conversion\n\thtml = md.convert(input_markdown)\n\t\n\t# Generate table of contents\n\ttoc = md.toc\n\t\n\t# Choose document title (default to the output name)\n\ttitle = output_name\n\t# Use the first heading if possible\n\tif len(toc) > 0:\n\t\ttitle = toc[0][1]\n\t# Better yet, get the explicitly given metadata\n\ttitle = md.Meta.get(\"title\", [title])[0]\n\t\n\t# Choose document subtitle (only available from metadata)\n\tsubtitle = md.Meta.get(\"subtitle\", [None])[0]\n\t\n\t# Get the image from the metadata\n\timg = md.Meta.get(\"img\", [None])[0]\n\timg_alt = md.Meta.get(\"img_alt\", [title])[0]\n\t\n\t# The abstract should be taken to be the first paragraph.\n\tabstract = md.abstract if md.abstract is not None else \"\"\n\t\n\t# Get the list of tags\n\ttags = md.Meta.get(\"tags\", [])\n\t\n\t# Get the list of files to include\n\tincludes = md.Meta.get(\"include\", [])\n\t\n\t# Get the show option\n\tshow = md.Meta.get(\"show\", [\"True\"])[0] == \"True\"\n\t\n\tfiles = md.resources\n\t\n\t# Add the article image to the list of files and create a thumbnail if\n\t# possible.\n\tif img is not None and img.startswith(\"file://\"):\n\t\timg = os.path.join(input_path, img[len(\"file://\"):])\n\t\timg_output_name = \"%s/%s\"%(output_name,\n\t\t unique(os.path.basename(img),\n\t\t [f.split(\"/\")[-1] for (_,f) in files]))\n\t\t\n\t\timg_thumbnail = \"%s.thumb.png\"%img\n\t\t\n\t\tp = Popen( [\"convert\"\n\t\t , img\n\t\t , \"-thumbnail\", \"%dx%d\"%(thumb_size,thumb_size)\n\t\t , img_thumbnail]\n\t\t , stdin = None\n\t\t , stdout = sys.stderr\n\t\t , stderr = sys.stderr\n\t\t )\n\t\tif p.wait() != 0:\n\t\t\traise Exception(\"Creating img thumbnail failed.\")\n\t\t\n\t\tfiles.append((img_thumbnail, img_output_name))\n\t\timg = img_output_name\n\t\n\t# Generate meta-data\n\tmeta_data = {\n\t\t\"url\" : output_name,\n\t\t\"title\" : title,\n\t\t\"subtitle\" : subtitle,\n\t\t\"img\" : img,\n\t\t\"img_alt\" : img_alt,\n\t\t\"abstract\" : abstract,\n\t\t\"tags\" : tags,\n\t\t\"show\" : show,\n\t}\n\t\n\treturn html, toc, meta_data, files, includes","def _parse_markdown(self):\n renderer = MyRenderer()\n md = mistune.Markdown(renderer=renderer)\n md.render(self._markdown_text)\n self._bash_commands = renderer._bash_commands","def markdown_text(context, text, autoescape=True):\n includes = ''\n if 'render_markdown_src' not in context:\n # The first markdown in the template - include JS.\n context['render_markdown_src'] = True\n # Ask Webpack for the location of the asset.\n includes = '\\n'.join(webpack_utils.get_as_tags('common-render-markdown'))\n result_html = html.format_html(\n '<span class=\"markdown\">{}</span>{}',\n text,\n safestring.mark_safe(includes),\n )\n return result_html","def on_page_markdown(self, markdown, page, config, files):\n listext = self.config['ext']\n src_file_path = page.file.abs_src_path\n prepath, ext = os.path.splitext(src_file_path)\n lang = ext.lstrip('.')\n filename = page.file.name\n if ext in listext:\n new_markdown = \"# {0}\\n\\n```{1}\\n\".format(filename, lang) + markdown + \"\\n```\"\n return new_markdown\n else:\n return markdown","def addDemographics(self):\n p = self.p\n demographics_data = {\n 'dob': p.dob,\n 'gender': p.gender,\n 'email': p.email,\n 'fname': p.fname,\n 'lname': p.lname,\n 'hphone': p.home,\n 'cphone': p.cell,\n 'country': p.country,\n 'city': p.city,\n 'pcode': p.pcode,\n 'region': p.region,\n 'street': p.street,\n }\n self.demographics_doc = DEMOGRAPHICS.sub(demographics_data).done()","def index():\n with open(\"PROJECT.md\", \"r\") as project_file:\n md_template_string = markdown.markdown(\n project_file.read(), extensions=[\"fenced_code\"]\n )\n return md_template_string","def markdown(text, **kwargs):\n import markdown\n return markdown.markdown(text, **kwargs)","def markdown_post(post):\n post['entry'] = markdown(post['entry'].replace(\"\\n\",\" \\n\"), output=\"html5\")\n return post","def markdown(text, *args, **kwargs):\n md = StMarkdown(*args, **kwargs)\n return md.convert(text)","def convert(md_text):\n # separate by line\n md_text = md_text.split('\\n')\n\n # save the html content for return\n html_text = ''\n\n # begin looping from the first line\n index = -1\n while index < len(md_text) - 1:\n index += 1\n line = md_text[index]\n\n # code segment\n if len(line) >= 3 and line[:3] == '```':\n html_line = \"\"\n language = line[3:].replace(' ', '')\n if len(language) == 0:\n language = False\n order_index = index + 1\n find_end = False\n while order_index < len(md_text):\n if md_text[order_index][:3] == '```':\n find_end = True\n break\n else:\n temp_line = md_text[order_index]\n temp_line = temp_line.replace('<', '<')\n temp_line = temp_line.replace('>', '>')\n temp_line = temp_line.replace(' ', ' ')\n html_line += temp_line + '<br />'\n order_index += 1\n\n if find_end:\n # if language is not False:\n # html_text += ('<pre><code class=\"' + language + '\">' + html_line + '</code></pre>')\n # else:\n html_text += ('<code>' + html_line + '</code>')\n # print(language)\n index = order_index\n continue\n\n # inline code\n\n\n # header\n is_header, html_line = check_header(line)\n if is_header:\n html_text = html_text + html_line\n continue\n\n # horizontal rule\n is_horizontal_rule, html_line = check_horizontal_rule(line)\n if is_horizontal_rule:\n html_text = html_text + html_line\n continue\n\n # paragraph\n line = check_paragraph(line)\n\n # deal with ordered list\n if len(line.split('.')) != 0 and '1.' == line[:2]:\n html_line = '<ol>'\n order_index = index\n while order_index < len(md_text)\\\n and len(md_text[order_index].split('.')) != 0\\\n and (str(order_index - index + 1) == md_text[order_index].split('.')[0]\n or '1' == md_text[order_index].split('.')[0]):\n to_replace = [str(order_index - index + 1) + '.', '1.']\n for replace_content in to_replace:\n md_text[order_index] = md_text[order_index].replace(replace_content, '')\n html_line = html_line + '<li>' + md_text[order_index] + '</li>'\n\n order_index += 1\n index = order_index - 1\n html_line = html_line + '</ol>'\n line = html_line\n\n # deal with unordered list\n is_unordered_list, html_line = check_unordered_list(line)\n if is_unordered_list:\n line = html_line\n\n # deal with strong\n line = strong(line)\n\n # Scratch\n line = scratch(line)\n\n # italics\n line = italics(line)\n\n # image\n while len(re.match(r'((?P<pre_text>.*)!\\[(?P<alt_text>.*)\\]\\((?P<link>.*)\\)(?P<after_text>.*))*', line).group())\\\n != 0:\n match = re.match(r'((?P<pre_text>.*)!\\[(?P<alt_text>.*)\\]\\((?P<link>.*)\\)(?P<after_text>.*))*', line)\n pre_text = match.group('pre_text')\n alt_text = match.group('alt_text')\n link = match.group('link')\n after_text = match.group('after_text')\n img_html = '<img src=\"' + link + '\" alt=\"' + alt_text + '\">'\n line = pre_text + img_html + after_text\n\n # link\n while len(re.match(r'((?P<pre_text>.*)\\[(?P<alt_text>.*)\\]\\((?P<link>.*)\\)(?P<after_text>.*))*', line).group())\\\n != 0:\n match = re.match(r'((?P<pre_text>.*)\\[(?P<alt_text>.*)\\]\\((?P<link>.*)\\)(?P<after_text>.*))*', line)\n pre_text = match.group('pre_text')\n alt_text = match.group('alt_text')\n link = match.group('link')\n after_text = match.group('after_text')\n img_html = '<a href=\"' + link + '\">' + alt_text + '</a>'\n line = pre_text + img_html + after_text\n\n html_text = html_text + line\n if not is_unordered_list:\n html_text = html_text + '<br>'\n\n return html_text","def generate_html(self):\n html_text_1 = \"\"\"\n <div class=\"concept\">\n\n \t\t<div class=\"concept-title\">\n\n \t\t\t\t\"\"\" + self.title\n\n html_text_2 = \"\"\"\n \t\t</div>\n\n \t\t<div class=\"concept-description\">\n\n\t\t <p>\n\t\t\t\n \t\t \t\t\"\"\" + self.description + \"\"\" \n \n </p>\"\"\"\n\n html_text_3 = '''\n\n \t\t</div>\n\n </div>'''\n\n return html_text_1 + html_text_2 + html_text_3","def gen_analysis_text(num_data, usage_flag, labelled_landmark, landmark_name, error_summary):\n analysis_text = r'<p style=\"color:red;\">Basic information:</p>'\n analysis_text += '<p style=\"color:black;\">Landmark name: {0}.</p>'.format(landmark_name)\n analysis_text += '<p style=\"color:black;\"># cases in total: {0}.</p>'.format(num_data)\n labelled_landmarks_stat = get_landmarks_stat(labelled_landmark)\n \n analysis_text += r'<p style=\"color:black;\"># cases having this landmark (Pos. cases): {0}.</p>'.format(\n len(labelled_landmarks_stat[landmark_name]['pos']))\n analysis_text += r'<p style=\"color:black;\"># cases missing this landmark (Neg. cases): {}.</p>'.format(\n len(labelled_landmarks_stat[landmark_name]['neg']))\n if len(labelled_landmarks_stat[landmark_name]['neg']) > 0:\n missing_cases = copy.deepcopy(labelled_landmarks_stat[landmark_name]['neg'])\n missing_cases.sort()\n analysis_text += r'{}'.format(missing_cases)\n\n if usage_flag == 2:\n tp_cases = error_summary.tp_cases[landmark_name]\n tn_cases = error_summary.tn_cases[landmark_name]\n fp_cases = error_summary.fp_cases[landmark_name]\n fn_cases = error_summary.fn_cases[landmark_name]\n num_pos_cases = len(tp_cases) + len(fn_cases)\n num_neg_cases = len(tn_cases) + len(fp_cases)\n # compute TPR, TNR, FPR, FNR\n TPR = len(tp_cases) / max(1, num_pos_cases) * 100 \\\n if len(tp_cases) != 0 or num_pos_cases != 0 else 100\n TNR = len(tn_cases) / max(1, num_neg_cases) * 100 \\\n if len(tn_cases) != 0 or num_neg_cases != 0 else 100\n FPR = 100 - TNR\n FNR = 100 - TPR\n mean_error = error_summary.mean_error_tp[landmark_name]\n std_error = error_summary.std_error_tp[landmark_name]\n median_error = error_summary.median_error_tp[landmark_name]\n max_error = error_summary.max_error_tp[landmark_name]\n analysis_text += r'<p style=\"color:red;\"> Landmark classification error: </p>'\n analysis_text += r'<p style=\"color:black;\">TP (TPR): {0} ({1:.2f}%)</p>'.format(\n len(tp_cases), TPR)\n analysis_text += r'<p style=\"color:black;\">TN (TNR): {0} ({1:.2f}%)</p>'.format(\n len(tn_cases), TNR)\n analysis_text += r'<p style=\"color:black;\">FP (FPR): {0} ({1:.2f}%)</p>'.format(\n len(fp_cases), FPR)\n analysis_text += r'<p style=\"color:black;\">FN (FNR): {0} ({1:.2f}%)</p>'.format(\n len(fn_cases), FNR)\n analysis_text += r'<p style=\"color:red;\"> Landmark distance error for the {} TP cases (unit: mm): </p>'.format(\n len(tp_cases))\n analysis_text += r'<p style=\"color:black;\">mean (std): {0:.2f} ({1:.2f})</p>'.format(\n mean_error, std_error)\n analysis_text += r'<p style=\"color:black;\">median: {0:.2f}</p>'.format(median_error)\n analysis_text += r'<p style=\"color:black;\">max: {0:.2f}</p>'.format(max_error)\n\n return analysis_text","def generate_body(issue):\n markdown = \"### {}\\n\".format(issue.pop('title'))\n for k, v in issue.iteritems():\n markdown += \"- {}: {}\\n\".format(k, v)\n return markdown","def write_to_md(dictData, outputDirectory):\n\tdic = prepare_hw_dict(dictData)\n\tfor hw in dic:\n\t\tfileout = os.path.join(outputDirectory, hw+'.md')\n\t\t# Prepare the output file\n\t\tfout = codecs.open(fileout, 'w', 'utf-8')\n\t\t# Write frontmatter\n\t\tfout.write('---\\ntitle: \"'+hw+'\"\\n---\\n\\n')\n\t\t# For each (headword, meanings, verseNumber, PageNum) tuples,\n\t\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dic[hw]:\n\t\t\tcommaed = ', '.join(meanings)\n\t\t\tverse = verse.replace('<BR>', '<br />')\n\t\t\t# Write in babylon format. <BR><BR> is to separate verses.\n\t\t\tfout.write('# ' + hw + '\\n## ' + commaed + '\\n' + verse + '<br />verse ' + verseNumDetails + '<br />page ' + pageNumDetails +'\\n\\n')\n\t\tfout.close()\n\n\t# Give some summary to the user\n\tprint('MD files generated. Success!')\n\tprint('{} separate .md files written, one per headword.'.format(len(dic)))","def gen_flowable(self):\n flow = randint(0,randint(1,2)) ## Weighted random numbers to balance text to image ratio\n if flow == 0:\n try:\n photo = self.photos[randint(0, len(self.photos)-1)]\n img = Image(self.dict['img'][photo]['src'], self.dict['img'][photo]['dimensions'][0], self.dict['img'][photo]['dimensions'][1])\n img.wrap(A6[0],A6[1])\n img.hAlign = ['CENTER', 'LEFT', 'RIGHT'][randint(0,3)]\n return img\n except:\n return Spacer(randint(0, 100), randint(0, 100))\n elif flow == 1:\n try:\n quote = self.quotes[randint(0, len(self.quotes)-1)]\n q_sheet = randint(0, 5)\n #return Paragraph(quote, Stylesheet.stylesheet['quote'][q_sheet])\n return Paragraph(cleanhtml(quote), Stylesheet.stylesheet['title'])\n except:\n return Spacer(randint(0, 100), randint(0, 100))\n elif flow == 2:\n try:\n tainr = self.containers[randint(0, (len(self.containers)-1))]\n c_sheet = randint(0, 1)\n #return Paragraph(self.dict['txt'][tainr]['text'], Stylesheet.stylesheet['quote'][q_sheet])\n return Paragraph(cleanhtml(self.dict['txt'][tainr]['text']), Stylesheet.stylesheet['default'])\n except:\n return Spacer(randint(0, 100), randint(0, 100))\n else:\n return Spacer(randint(0, 100), randint(0, 100))","def _get_delta_markdown_string(self):\n markdown_string = \"\"\n\n if self.is_commit_test is True:\n if len(self.delta_fp_string_dict.delta_dict[\"commits\"]) > 0:\n markdown_string += (\n os.linesep\n + \"## Commit history SHA1 for this analysis:\"\n + os.linesep\n )\n for sha1_commit in self.delta_fp_string_dict.delta_dict[\"commits\"]:\n markdown_string += \"- `\" + sha1_commit + \"`\" + os.linesep\n markdown_string += os.linesep\n elif self.is_branch_test is True:\n if len(self.delta_fp_string_dict.delta_dict[\"branches\"]) > 0:\n markdown_string += (\n os.linesep + \"## Branches under analysis:\" + os.linesep\n )\n for branch in self.delta_fp_string_dict.delta_dict[\"branches\"]:\n markdown_string += \"- \" + branch + os.linesep\n markdown_string += os.linesep\n\n # Added files block\n markdown_string += \"## Added Files\" + os.linesep\n if len(self.delta_fp_string_dict.delta_dict[\"added\"]) > 0:\n for added_file in self.delta_fp_string_dict.delta_dict[\"added\"]:\n markdown_string += \"- \" + added_file + os.linesep\n else:\n markdown_string += \"- None\" + os.linesep\n\n # Deleted files block\n markdown_string += os.linesep + os.linesep + \"## Deleted Files\" + os.linesep\n if len(self.delta_fp_string_dict.delta_dict[\"deleted\"]) > 0:\n for deleted_file in self.delta_fp_string_dict.delta_dict[\"deleted\"]:\n markdown_string += \"- \" + deleted_file + os.linesep\n else:\n markdown_string += \"- None\" + os.linesep\n\n # Modified files block\n markdown_string += os.linesep + os.linesep + \"## Modified Files\" + os.linesep\n if len(self.delta_fp_string_dict.delta_dict[\"modified\"]) > 0:\n for modified_file in self.delta_fp_string_dict.delta_dict[\"modified\"]:\n markdown_string += \"- \" + modified_file + os.linesep\n else:\n markdown_string += \"- None\" + os.linesep\n\n # Project URL + version footer\n markdown_string += (\n os.linesep\n + os.linesep\n + \"---\"\n + os.linesep\n + \"[ufodiff](https://github.com/source-foundry/ufodiff) v\"\n + major_version\n + \".\"\n + minor_version\n + \".\"\n + patch_version\n )\n\n return markdown_string","def render_markdown(text):\n return markdown(text, **MARKDOWN_KWARGS)","def generate_pdf(list,id):\n\n doc = SimpleDocTemplate(settings.STATIC_ROOT+\"/tests/\"+str(id)+\"/\"+str(id)+\".pdf\")\n\n Story = [Spacer(1,2*inch)]\n styles = stylesheet()\n global Title\n\n # Add 10 questions with boxes below\n for i in list:\n if not i[0] in \"skills-scan\" and not i[0] in \"csrfmiddlewaretoken\" and not i[0] in \"titre\" and not i[0] in \"custom\":\n tmp = int(i[0])+1\n bogustext = (str(tmp)+\". %s\" % i[1])\n p = Paragraph(bogustext, styles['default'])\n # Write the paragraph\n\n draw = Drawing()\n # rect(x1,y1,width,height)\n rec = Rect(0, 100, 450, 150)\n rec.fillColor = colors.white\n # draw the rect under each paragraph\n draw.add(rec)\n p.keepWithNext = True\n Story.append(p)\n Story.append(draw)\n Story.append(Spacer(1,-0.9 * inch))\n elif i[0] in \"titre\":\n Title = i[1]\n # build the document by inserting the whole story\n doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages)\n return str(id)+\".pdf\"","def home() :\n st.markdown(\"This application provides 3 mains modules :\")\n st.markdown(\"* **The prediction module :** enables you to assess client's liability based on its file\")\n st.markdown(\"* **The explorer module :** enables you to dig deeper into your client informations,\"\n \" particularly historcial data coming from federal loan bureau and historical\"\n \" Home Credit's data if available.\")\n st.markdown(\"* **The statistics module** : enables you to explore the database at a macro scale :\"\n \" understand how variables such as age, sex and income impact probability of repayment\")","def generate_markdown_table() -> None:\n comments = comments_from_file('skywalking/config.py')\n\n with open('docs/en/setup/Configuration.md', 'w') as plugin_doc:\n plugin_doc.write(DOC_HEAD)\n offset = 0\n for config_index, comment in enumerate(comments):\n if comment.startswith('# BEGIN'):\n # remove `#BEGIN: `\n plugin_doc.write(TABLE_HEAD.format(comment[8:]))\n offset += 1\n else:\n table_entry = create_entry(comment, config_index - offset)\n plugin_doc.write(f'{table_entry}\\n')","def generate_preproc_steps_docstring(\n prepreproc_undergone=\"\",\n tools_used=None,\n dcm2nii=False,\n deleteorient=False,\n fwhm=None, anat_fwhm=None,\n bet=False,\n slice_timing=False,\n realign=False,\n coregister=False,\n coreg_func_to_anat=False,\n segment=False,\n normalize=False,\n func_write_voxel_sizes=None,\n anat_write_voxel_sizes=None,\n dartel=False,\n additional_preproc_undergone=\"\",\n command_line=None,\n details_filename=None,\n has_func=True\n ):\n fwhm = sanitize_fwhm(fwhm)\n anat_fwhm = sanitize_fwhm(anat_fwhm)\n if dartel:\n normalize = False\n segment = False\n\n # which tools were used ?\n if tools_used is None:\n tools_used = (\n 'All preprocessing was done using <a href=\"%s\">pypreprocess</a>,'\n ' a collection of python scripts and modules for '\n 'preprocessing functional and anatomical MRI data.' % (\n PYPREPROCESS_URL))\n preproc_undergone = \"<p>%s</p>\" % tools_used\n\n # what was actually typed at the command line ?\n if not command_line is None:\n preproc_undergone += \"Command-line: <i>%s</i><br/>\" % command_line\n preproc_undergone += (\n \"<br>For each subject, the following preprocessing steps have \"\n \"been done:\")\n\n preproc_undergone += \"<ul>\"\n if prepreproc_undergone:\n preproc_undergone += \"<li>%s</li>\" % prepreproc_undergone\n if dcm2nii:\n preproc_undergone += (\n \"<li>\"\n \"dcm2nii has been used to convert input images from DICOM to nifti\"\n \" format\"\n \"</li>\")\n if deleteorient:\n preproc_undergone += (\n \"<li>\"\n \"Orientation-specific meta-data in the image headers have \"\n \"been suspected as garbage and stripped-off to prevent severe \"\n \"mis-registration problems.\"\n \"</li>\")\n if bet:\n preproc_undergone += (\n \"<li>\"\n \"Brain extraction has been applied to strip-off the skull\"\n \" and other non-brain tissues. This prevents later \"\n \"registration problems like the skull been (mis-)aligned \"\n \"unto the cortical surface, \"\n \"etc.</li>\")\n if slice_timing:\n preproc_undergone += (\n \"<li>\"\n \"Slice-Timing Correction (STC) has been done to interpolate the \"\n \"BOLD signal in time, so that in the sequel we can safely pretend\"\n \" all 3D volumes within a TR (Repetition Time) were \"\n \"acquired simultaneously, an crucial assumption for any further \"\n \"analysis of the data (GLM, ICA, etc.). \"\n \"</li>\"\n )\n if realign:\n preproc_undergone += (\n \"<li>\"\n \"Motion correction has been done so as to estimate, and then \"\n \"correct for, subject's head motion.\"\n \"</li>\"\n )\n if coregister:\n preproc_undergone += \"<li>\"\n if coreg_func_to_anat:\n preproc_undergone += (\n \"The subject's functional images have been coregistered \"\n \"to their anatomical image.\"\n )\n else:\n preproc_undergone += (\n \"The subject's anatomical image has been coregistered \"\n \"against their functional images.\")\n preproc_undergone += (\n \" Coregistration is important as it allows: (1) segmentation of \"\n \"the functional via segmentation of the anatomical brain; \"\n \"(2) inter-subject registration via inter-anatomical registration,\"\n \" a trick referred to as 'Indirect Normalization'; \"\n \"(3) ROIs to be defined on the anatomy, making it \"\n \"possible for activation maps to be projected and appreciated\"\n \" thereupon.\"\n \"</li>\")\n if segment:\n preproc_undergone += (\n \"<li>\"\n \"Tissue Segmentation has been employed to segment the \"\n \"anatomical image into GM, WM, and CSF compartments, using \"\n \"template TPMs (Tissue Probability Maps).</li>\")\n if normalize:\n if segment:\n if has_func:\n salt = (\" The same deformations have been \"\n 'applied to the functional images.')\n else: salt = \"\"\n preproc_undergone += (\n \"<li>\"\n \"The segmented anatomical image has been warped \"\n \"into the MNI template space by applying the deformations \"\n \"learnt during segmentation.%s</li>\" % salt)\n else:\n if coregister:\n preproc_undergone += (\n \"<li>\"\n \"Deformations from native to standard space have been \"\n \"learnt on the anatomically brain. These deformations \"\n \"have been used to warp the functional and anatomical \"\n \"images into standard space.</li>\")\n else:\n preproc_undergone += (\n \"<li>\"\n \"The functional images have been warped from native to \"\n \"standard space via classical normalization.</li>\")\n if dartel:\n preproc_undergone += (\n \"<li>\"\n \"Group/Inter-subject Normalization has been done using the \"\n \"SPM8 <a href='%s'>DARTEL</a> to warp subject brains into \"\n \"MNI space. \"\n \"The idea is to register images by computing a “flow\"\n \" field” which can then be “exponentiated\"\n \"” to generate both forward and backward deformation\"\n \"s. Processing begins with the “import” \"\n \"step. This involves taking the parameter files \"\n \"produced by the segmentation (NewSegment), and writing \"\n \"out rigidly \"\n \"transformed versions of the tissue class images, \"\n \"such that they are in as close alignment as possible with\"\n \" the tissue probability maps. \"\n \"The next step is the registration itself. This involves \"\n \"the simultaneous registration of e.g. GM with GM, \"\n \"WM with WM and 1-(GM+WM) with 1-(GM+WM) (when needed, the\"\n \" 1- (GM+WM) class is generated implicitly, so there \"\n \"is no need to include this class yourself). This \"\n \"procedure begins by creating a mean of all the images, \"\n \"which is used as an initial template. Deformations \"\n \"from this template to each of the individual images \"\n \"are computed, and the template is then re-generated\"\n \" by applying the inverses of the deformations to \"\n \"the images and averaging. This procedure is repeated a \"\n \"number of times. Finally, warped \"\n \"versions of the images (or other images that are in \"\n \"alignment with them) can be generated. \"\n \"</li>\") % DARTEL_URL\n if normalize or dartel:\n if (not func_write_voxel_sizes is None or\n not anat_write_voxel_sizes is None):\n preproc_undergone += \"<li>\"\n sep = \"\"\n if not func_write_voxel_sizes is None:\n preproc_undergone += (\n \"Output functional images have been re-written with voxel \"\n \"size %smm x %smm x %smm.\") % tuple(\n func_write_voxel_sizes)\n sep = \" \"\n if not anat_write_voxel_sizes is None:\n preproc_undergone += (\n \"%sThe output anatomical image has been re-written with \"\n \"voxel \"\n \"size %smm x %smm x %smm.\") % tuple([sep] + list(\n anat_write_voxel_sizes))\n preproc_undergone += \"</li>\"\n\n if additional_preproc_undergone:\n preproc_undergone += additional_preproc_undergone\n if np.sum(fwhm) > 0 and has_func:\n preproc_undergone += (\n \"<li>\"\n \"The functional images have been \"\n \"smoothed with a %smm x %smm x %smm \"\n \"Gaussian kernel.</li>\") % tuple(fwhm)\n if np.sum(anat_fwhm) > 0:\n preproc_undergone += (\n \"<li>\"\n \"The anatomical image has been \"\n \"smoothed with a %smm x %smm x %smm \"\n \"Gaussian kernel.\") % tuple(anat_fwhm)\n if segment:\n preproc_undergone += (\n \" Warped TPMs have been smoothed with the same kernel.\")\n if not details_filename is None:\n preproc_undergone += (\n \" <a href=%s>See complete configuration used for preprocessing\"\n \" here</a>\") % os.path.basename(details_filename)\n preproc_undergone += \"</ul>\"\n\n return preproc_undergone","def task_generate_sc_html():\n for dept in Department.list():\n command = (f\"pandoc --self-contained\"\n f\" -o {dept.sc_html_path}\"\n f\" {dept.sc_markdown_path}\")\n\n yield {\n 'name': dept.name,\n 'file_dep': [\n dept.sc_figure1_path,\n dept.sc_figure2_path,\n dept.sc_figure3_path,\n dept.sc_figure4_path,\n dept.sc_figure5_path,\n dept.sc_markdown_path,\n ],\n 'targets': [dept.sc_html_path],\n 'actions': [command],\n 'clean': True,\n }","def test_markdown(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.example.Foo.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n for item in data['items']:\n if item['uid'] == 'example.example.Foo.method_markdown':\n self.assertEqual(\n item['summary'],\n 'Check out our '\n '[site](http://sphinx-docfx-yaml.readthedocs.io/en/latest/)'\n ' for more info.',\n )","def render_biography(self):\n return markdown2.markdown(self.biography)","def convert(cell):\r\n\r\n markdownResult=\"\"\r\n if cell['cell_type'] == 'code':\r\n markdownResult += '```\\n'\r\n\r\n for line in cell['source']: \r\n markdownResult += line\r\n\r\n if cell['cell_type'] == 'code':\r\n markdownResult += '\\n```'\r\n \r\n debugPrint(markdownResult)\r\n markdownResult += '\\n\\n'\r\n \r\n return markdownResult","def report(params):\n # handle input\n yamlin = params[\"yaml\"]\n indict = yaml.load(open(yamlin))\n\n enrichreportl = get_template(\"enrichreport\")\n out = \"enrichreport.md\"\n cmd = \"%s -t %s -j %s -o %s -y\" % (render,enrichreportl,yamlin,out)\n log.run(\"render enrichreport template\",cmd)\n \n cmd = \"%s %s\" % (md2html,out)\n log.run(\"md2html enrichreport \",cmd, o=[\"html.tgz\"])\n outdict = {}\n outdict[\"enrichreport\"] = out\n yamlout = yamladd(yamlin,outdict)\n yamlout[\"enrichreport_outdir\"] = os.getcwd()\n return yamlout","def gen_rst(txt):\n res = \"\"\n title_block = get_title_block(txt)\n title_rst = gen_title_rst(title_block)\n res += title_rst\n # add a blank line just to be sure\n res += \"\\n\"\n fun_blocks = get_fun_blocks(txt)\n for (name, fun_block) in fun_blocks:\n fun_rst = gen_fun_rst(name, fun_block)\n res += fun_rst\n return res","def evaluation():\n file = open('./contest/content/evaluation.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Evaluation', \n content=content)","def as_markdown(self):\n text = \"|name|value| \\n|-|-| \\n\"\n for attr, value in sorted(vars(self).items()):\n text += \"|{}|{}| \\n\".format(attr, value)\n\n return text","def _convert_to_fancypants(self, markdown_text: str) -> dict: # noqa: ANN001\n text_data = {\"output_mode\": \"rtjson\", \"markdown_text\": markdown_text}\n return self._reddit.post(API_PATH[\"convert_rte_body\"], data=text_data)[\"output\"]","def rules():\n file = open('./contest/content/rules.md', 'r')\n rawText = file.read()\n file.close()\n content = Markup(markdown(rawText, \n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\n return render_template('markdowntemplate.html', \n title='Rules', \n content=content)","def parse(text):\n md = markdown.Markdown(['codehilite', 'tables', ])\n\n for iref in re.findall(img_ref_re, text):\n img_id = iref[7]\n try:\n image = FlatPageImage.objects.get(pk=int(img_id))\n md.references[img_id] = (image.image_path.url, '')\n except ObjectDoesNotExist:\n pass\n\n for lref in re.findall(reference_re, text):\n doc_name = lref[7]\n try:\n doc = File.objects.get(name=doc_name)\n md.references[doc_name]= (doc.url, doc.name)\n except ObjectDoesNotExist:\n pass\n\n return md.convert(text)","def test_description_markdown_with_custom_options() -> None:\n soup = generate_case(\n \"description_markdown\",\n GenerationConfiguration(\n markdown_options={\n \"cuddled-lists\": True,\n }\n ),\n )\n\n assert (\n str(soup.find(\"span\", class_=\"description\"))\n == \"\"\"<span class=\"description\"><p>DOC </p> <ul> <li>List 1</li> <li>List 2</li> </ul> </span>\"\"\"\n )","def as_markdown(self):\n text = \"|name|value| \\n|-|-| \\n\"\n for attr, value in sorted(vars(self).items()):\n text += \"|{}|{}| \\n\".format(attr, value)\n return text","def markdown_figs(self):\n return self.findall_markdown_cells(MARKDOWN_FIG)","def gen_explanation():\n f = self.media['explanation'][0]\n k = self.__type_by_extension(os.path.sep.join(\n os.path.join([self.media_path, f])))\n v = [os.sep.join([self.web_root, expl]) \\\n for expl in self.media['explanation']]\n if v:\n v = v[0]\n else:\n v = \"\"\n return {'explanation': {k: v}}\n #): os.sep.join([self.web_root, f])\n\n #[os.sep.join([self.web_root, expl]) \\\n # for expl in self.media['explanation']]}","def display_wmd(google_snippets, my_snippets):\r\n for i in range(len(google_snippets)):\r\n print(i+1, '.', urls[i])\r\n print('Google Snippet:', google_snippets[i])\r\n print('Generated Snippet:', my_snippets[i])\r\n print(\"Word Mover's Distance:\", get_wmd_dist(google_snippets[i], my_snippets[i], model))\r\n print()","def generate_story():\n\n text = story.generate(request.args)\n \n return render_template(\"story.html\", text = text)","def poem_generation(magnet,topic):\r\n e = inflect.engine()\r\n antonyms = []\r\n synonyms = []\r\n poem = \"\"\r\n verb = \"\"\r\n # plural\r\n if (e.singular_noun(topic) is False):\r\n verb = \"is\"\r\n else:\r\n verb = \"are\"\r\n \r\n for syn in wordnet.synsets(topic):\r\n for l in syn.lemmas():\r\n synonyms.append(l.name())\r\n if l.antonyms():\r\n antonyms.append(l.antonyms()[0].name())\r\n if (len(set(antonyms)) < 1):\r\n for syn in wordnet.synsets(RAND_LIST[random.randint(0,len(RAND_LIST)-1)]):\r\n for l in syn.lemmas():\r\n synonyms.append(l.name())\r\n if l.antonyms():\r\n antonyms.append(l.antonyms()[0].name())\r\n topic = topic.capitalize()\r\n for i in range(0,random.randint(5,15)):\r\n verse = random.randint(0,6)\r\n question = random.randint(0,6)\r\n # structure\r\n # antonyms\r\n if (verse < 2) and len(antonyms) > 0:\r\n ant_magnet = metaphor_magnet(antonyms[random.randint(0,len(antonyms)-1)])\r\n choice = random.randint(0,len(ant_magnet)-1)\r\n detail = ant_magnet[choice].split(\":\")\r\n if (question < 2):\r\n index = random.randint(0,len(QUESTION)-1)\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" not like an \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" not like a \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += topic + \" \" + verb + \" not like an \" + detail[0] + \" \" + detail[1] + \".\\n\" \r\n else:\r\n poem += topic + \" \" + verb + \" not like a \" + detail[0] + \" \" + detail[1] + \".\\n\" \r\n \r\n else:\r\n choice = random.randint(0,len(magnet)-1)\r\n detail = magnet[choice].split(\":\")\r\n if (question < 2):\r\n index = random.randint(0,len(QUESTION)-1)\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" like an \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" like a \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += topic + \" \" + verb + \" like an \" + detail[0] + \" \" + detail[1] + \"\\n\" \r\n else:\r\n poem += topic + \" \" + verb + \" like a \" + detail[0] + \" \" + detail[1] + \"\\n\" \r\n \r\n return poem","def build_md_once(self, index, tid):\n url = self.joint_url(tid)\n title_json = proxy_req(url, 1)\n if not title_json:\n if can_retry(url, index):\n self.build_md_once(index, tid)\n return\n content = BeautifulSoup(\n title_json['content'], 'html.parser').find_all('div')\n text = []\n img_href = []\n img_id = 1\n ttid = 1\n img_title = self.find_title(index).split('/')[1][:-3]\n for word in content:\n temp_text = ''\n if word.span and len(word.span.text) and not word.span.text[0].isdigit:\n temp_text = '## ' + word.span.text\n ttid = 1\n if word.img:\n temp_text = ' + '.jpg)'\n img_href.append(word.img['src'].replace('https', 'http'))\n img_id += 1\n\n if not len(temp_text):\n temp_text = word.text\n if len(temp_text) and temp_text[0].isdigit():\n temp_text = str(ttid) + '. **' + \\\n ' '.join(temp_text.split('\\xa0')[1:]).strip() + '**'\n ttid += 1\n if len(temp_text) and temp_text[0:2] == '//':\n temp_text = str(ttid) + '. **' + \\\n ' '.join(temp_text.split('\\xa0')[2:]).strip() + '**'\n ttid += 1\n if len(temp_text) and (temp_text[0] == '￥' or temp_text[0] == '€'):\n temp_text = '<a>' + temp_text + '</a>'\n text.append(temp_text)\n with codecs.open(data_dir + self.find_title(index), 'w', encoding='utf-8') as f:\n f.write('\\n'.join(text))\n self.img_map[index] = img_href\n print(index, len(img_href))","def paragraph_p12(candidates_tup, return_html=False):\n\n elim_list = \"\"\n for i, c in candidates_tup:\n elim_list += f\"<dt><b>{i:>2}: {c}</b></dt>\"\n \n text = \"\"\"<h3>* Insights from Problems 1 and 2</h3><p style=\"font-size:110%;\">\"\"\"\n text += \"\"\"On the basis of Figures 1 and 2, which show the number of new nodes created, \n and the time spent by each search function, respectively, the searches that are candidates \n for elimination for more complex problems are those at the intersection of the average-ranked \n costliest sets viz new nodes creation and search time.<br>These searches are:</p><pre><dl>\"\"\"\n text += f\"<dl>{elim_list}</dl></p></pre>\"\n \n if return_html:\n return text\n else:\n return Markdown(text)","def phast_cmmd(self):\n temp = '{prog} -R {rho} -C {ecov} -E {elen} -N {chrom} -i MAF {maf} {model} > {wig}\\n'.format(**self.dict)\n return temp.format(fnum=self.fnum)","def pdf_gen(report, summary=None):\n with open(\"report_content.yaml\", \"r\") as stream:\n docs = yaml.safe_load(stream)\n\n style = g_stylesheet.get(\"styles\")\n elems = [] # elements array used to build pdf structure\n pdf = SimpleDocTemplate(\n f\"{report.replay_id}_report.pdf\",\n pagesize=letter,\n leftMargin=0.75 * inch,\n rightMargin=0.75 * inch,\n topMargin=0.75 * inch,\n bottomMargin=0.75 * inch,\n )\n\n # title and subtitle and cluster info table\n elems.append(Paragraph(docs[\"title\"], style[\"Title\"]))\n elems.append(\n Paragraph(sub_yaml_vars(report, docs[\"subtitle\"]), style[\"Heading4\"])\n )\n cluster_info = pd.DataFrame.from_dict(report.cluster_details, orient=\"index\")\n elems.append(\n Table(\n df_to_np(report.cluster_details.keys(), cluster_info.transpose()),\n hAlign=\"LEFT\",\n style=g_stylesheet.get(\"table_style\"),\n )\n )\n # replay summary\n if summary is not None:\n elems.append(Paragraph(f\"Replay Summary\", style[\"Heading4\"]))\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in summary],\n bulletType=\"bullet\",\n )\n )\n elems.append(Spacer(0, 5))\n\n elems.append(Paragraph(docs[\"report_paragraph\"], style[\"Normal\"]))\n\n # glossary section\n elems.append(Paragraph(docs[\"glossary_header\"], style[\"Heading4\"]))\n elems.append(Paragraph(docs[\"glossary_paragraph\"], style[\"Normal\"]))\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in docs[\"glossary\"]],\n bulletType=\"bullet\",\n )\n )\n elems.append(Spacer(0, 5))\n\n # access data section\n elems.append(Paragraph(docs[\"data_header\"], style[\"Heading4\"]))\n elems.append(\n Paragraph(sub_yaml_vars(report, docs[\"data_paragraph\"]), style[\"Normal\"])\n )\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in docs[\"raw_data\"]],\n bulletType=\"bullet\",\n )\n )\n elems.append(Spacer(0, 5))\n elems.append(\n Paragraph(\n sub_yaml_vars(report, docs[\"agg_data_paragraph\"]), style[\"Normal\"]\n )\n )\n\n # notes section\n elems.append(Paragraph(docs[\"notes_header\"], style[\"Heading4\"]))\n elems.append(Paragraph(docs[\"notes_paragraph\"], style[\"Normal\"]))\n elems.append(\n ListFlowable(\n [ListItem(Paragraph(x, style[\"Normal\"])) for x in docs[\"notes\"]],\n bulletType=\"bullet\",\n )\n )\n\n elems.append(PageBreak()) # page 2: cluster details\n\n # query breakdown\n build_pdf_tables(elems, docs[\"query_breakdown\"], report)\n elems.append(Spacer(0, 5))\n\n # histogram and description\n image_path = hist_gen(\n x_data=report.feature_graph[\"sec_start\"],\n y_data=report.feature_graph[\"count\"],\n title=docs[\"graph\"].get(\"title\"),\n x_label=\"Average Elapsed Time (s)\",\n )\n\n desc = Paragraph(docs[\"graph\"].get(\"paragraph\"), style[\"Normal\"])\n data = [[Image(image_path, width=300, height=200, hAlign=\"LEFT\"), desc]]\n elems.append(\n Table(data, style=TableStyle([(\"VALIGN\", (0, 0), (-1, -1), \"MIDDLE\")]))\n )\n elems.append(Spacer(0, 5))\n\n # cluster metrics table\n build_pdf_tables(elems, docs[\"cluster_metrics\"], report)\n\n elems.append(PageBreak()) # page 3+ measure tables\n\n build_pdf_tables(\n elems, docs[\"measure_tables\"], report\n ) # build 5 measure tables all at once\n\n # build pdf\n pdf.build(\n elems,\n onFirstPage=partial(first_page, report=report),\n onLaterPages=partial(later_pages, report=report),\n )\n os.remove(image_path)\n\n return pdf.filename","def ler_markdown(nome_do_arquivo):\n return open(\"teste.md\").read()","def with_markdown(template):\n def decorator(fn):\n @wraps(fn)\n def decorated_fn(*args, **kwargs):\n ctx = fn(*args, **kwargs)\n if ctx is None:\n ctx = {}\n source = render_template(template)\n extensions = ['codehilite', 'fenced_code']\n contents = Markup(md.markdown(source, extensions=extensions))\n ctx['contents'] = contents\n return render_template('_base_md.html', **ctx)\n return decorated_fn\n return decorator","def markdown(self, session):\n if not self.__doc__:\n return None\n orig_text = self.__doc__.strip('\\n').split('\\n')\n colors = session.ath['render'].get_settings()\n bullet_color = colors['help_file_bullet'].value\n heading_color = colors['help_file_header'].value\n emphasized_color = colors['help_file_emphasized'].value\n\n def bullet(find):\n return '%s|%s*|n%s%s' % (find.group('spaces'), bullet_color, find.group('spaces2'), find.group('text'))\n\n def heading(find):\n return '%s|%s%s%s|n' % (find.group('spaces'), heading_color, find.group('spaces2'), find.group('text'))\n\n def emphasized(find):\n return '|%s%s|n' % (emphasized_color, find.group('text'))\n\n output_text = list()\n\n for line in orig_text:\n out_line = HEADING.sub(heading, line)\n out_line = BULLET.sub(bullet, out_line)\n out_line = EMPHASIZED.sub(emphasized, out_line)\n out_line = ANSIString(out_line)\n output_text.append(out_line)\n return '\\n'.join([unicode(l) for l in output_text])","def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.groups_using_technique }\n ]\n\n example_data = []\n\n # Get malware or tools used by group\n for examples in examples_map:\n if examples['example_type'].get(technique.get('id')):\n for example in examples['example_type'][technique['id']]:\n\n attack_id = util.get_attack_id(example['object'])\n\n # Only add example data if the attack id is found \n if attack_id:\n row = {}\n\n row['id'] = attack_id\n\n if attack_id.startswith('S'):\n row['path'] = \"software\"\n else:\n row['path'] = \"groups\"\n\n row['name'] = example['object']['name']\n\n if example['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\n\n example_data.append(row)\n \n if example_data:\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\n return example_data","def _show(path):\n with open(path, 'r') as f:\n content = f.read()\n\n return Markdown(content)","def content(self, datas):\n subject = \"TCC - MLaaS Topzero D+\"\n\n body = f\"\"\"\n <h1>TCC MLaaS</h1>\n <p>Realizamos o treinamento da base de dados que nos foi enviada. Abaixo você pode ver nossa o resultado dos treinos realizados.</p>\n \"\"\"\n\n for data in datas:\n if data is not None:\n for key, info in data['images'].items():\n body += f\"\"\"{info[0]}\"\"\"\n if info[1] is not None:\n body += f\"\"\"<br><img src=\"cid:{info[1]}\" alt=\"{key}\" height=\"300\" width=\"300\"><br>\"\"\"\n\n body += f\"\"\"\n </br>\n Atenciosamente\n <h4><b>IESB</b></h4>\n <h5>created by <b>Allan Kleitson Teotonio</b></h5>\n <h5>Science Computer. 2019</h5>\n \"\"\"\n\n return subject, body","def to_markdown(self):\n s = \"[\" + self.label + \"]\"\n if self.is_reflink:\n s += \": \" + self.url\n else:\n s += \"(\" + self.url + \")\"\n return s","def build(self, verbose: bool = False):\n self.content = \"\"\n for par in self.paragraphs:\n for observ in par.observations:\n\n # save the meta_data\n self.observs_id.append(observ.observ_id)\n self.sit_relev.append(observ.relevance2)\n\n # append the sentence to the article\n self.content += f\"{observ.observation_new} \"\n if verbose:\n print(observ.year, observ.week_number, observ.day_number, observ.pattern, observ.observation_new)\n\n # end of paragraph (add a paragraph divider)\n self.content += self.par_divider","def __html__(self, file_path:str) -> str:\n with open(f\"{file_path}\", \"r\") as mdfile: # Parse markdown file\n text = mdfile.read()\n html = self.md.convert(text) # Convert the markdown content text to hmtl\n return html","def to_markdown(\n self, buf: Optional[Union[IO[str], str]] = None, mode: Optional[str] = None\n ) -> str:\n log_advice(\n \"`to_markdown` loads all data into the driver's memory. \"\n \"It should only be used if the resulting pandas object is expected to be small.\"\n )\n # Make sure locals() call is at the top of the function so we don't capture local variables.\n args = locals()\n psser_or_psdf = self\n internal_pandas = psser_or_psdf._to_internal_pandas()\n return validate_arguments_and_invoke_function(\n internal_pandas, self.to_markdown, type(internal_pandas).to_markdown, args\n )","def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\n\n examples = []\n total_answerable = 0\n\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\n guid = \"%s-%s-%s\" % (set_type, line['qid'], str(i))\n src = []\n has_pos = 0\n\n if passage_type == 'both' and set_type != 'train':\n\n # rank by bert score in val and test\n score_key = 'rank_score'\n # score_key = \"nr_score\"\n both_sources = line[\"passages\"][:2*cand_for_each_source] + line[\"tables\"][:2*cand_for_each_source]\n all_scores = np.array([x[score_key] for x in both_sources])\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\n\n for idx in sorted_index:\n pas = both_sources[idx]\n if idx < 2 * cand_for_each_source:\n pas_type = 'passage'\n title = pas[\"article_title\"]\n else:\n pas_type = 'table'\n title = \"table_\" + pas[\"uid\"].split('-split')[0]\n\n src.append(\"question: \" + line[\"question\"].strip() + f\" </s> {pas_type} title: \" + title + f\" </s> {pas_type} content: \" + pas[\"text\"] + \" </s>\")\n\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n else:\n\n if passage_type in ['textual', 'hybrid', 'both']:\n for pas in line[\"passages\"][:cand_for_each_source]:\n # 10 passages\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> passage title: \" + pas[\"article_title\"] + \" </s> passage content: \" + pas[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\"ZAPPAtite\\\". All songs written and performed ... </s>\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n if passage_type in ['tabular', 'hybrid', 'both']:\n for tab in line[\"tables\"][:cand_for_each_source]:\n # 10 tables\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> table title: \" + \"table_\" + tab[\"uid\"].split('-split')[0] + \" </s> table content: \" + tab[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\n if isinstance(tab['judge'], dict):\n has_pos += int(tab['judge']['judge_contain_all'])\n else:\n has_pos += int(tab['judge'])\n\n const = 2 if passage_type in ['hybrid', 'both'] else 1\n if len(src) != const * cand_for_each_source:\n logger.info(line)\n src = src + [\"\"] * (const * cand_for_each_source - len(src))\n\n if has_pos > 0:\n total_answerable += 1\n\n if enable_sql_supervision and 'true_sql' in line:\n tgt = \"sql: \" + line[\"true_sql\"] + \" </s>\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\"Butler CC (KS)\\\" </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'denotation' in line:\n tgt = \"answer: \" + str(line[\"denotation\"][0]) + \" </s>\"\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'answers' in line:\n tgt = \"answer: \" + str(line[\"answers\"][0]) + \" </s>\" # e.g. answer: no slogan on current series </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt + \"\\n\")\n\n logger.info(f\"Total answerable in {set_type} split is {total_answerable / len(lines)}\")\n\n return examples","def process_md(text_md):\n\tprocessed_text_md = ( pre_proc.replace_br(text_md)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.remove_false_titles)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.remove_blank_lines)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.replace_cid)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.replace_with_dash)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_by_hyphen)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_lines)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_lines)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_et_al)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_beta)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_vs)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.fix_enye)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_ellipsis)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_subtraction)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_by_colon)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.remove_duplicated_dashes)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.fix_marks)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.join_title_questions)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.remove_useless_lines)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.remove_duplicated_whitespaces)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t| p(pre_proc.remove_repeated_strings)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t)\n\treturn processed_text_md","def htmlForMarkdown(md):\n return mdProcessor.convert(md)","def _intermediary_to_markdown(tables, relationships):\n t = '\\n'.join(t.to_markdown() for t in tables)\n r = '\\n'.join(r.to_markdown() for r in relationships)\n return '{}\\n{}'.format(t, r)","def extendMarkdown(self, md, md_globals):\n md.registerExtension(self)\n self.parser = md.parser\n self.md = md\n self.sep = ':'\n if self.md.output_format in ['html5', 'xhtml5']:\n self.sep = '-'\n # Insert a preprocessor before ReferencePreprocessor\n md.preprocessors.add(\"footnote\", FootnotePreprocessor(self),\n \"<reference\")\n # Insert an inline pattern before ImageReferencePattern\n FOOTNOTE_RE = r'\\[\\^([^\\]]*)\\]' # blah blah [^1] blah\n md.inlinePatterns.add(\"footnote\", FootnotePattern(FOOTNOTE_RE, self),\n \"<reference\")\n # Insert a tree-processor that would actually add the footnote div\n # This must be before all other treeprocessors (i.e., inline and \n # codehilite) so they can run on the the contents of the div.\n md.treeprocessors.add(\"footnote\", FootnoteTreeprocessor(self),\n \"_begin\")\n # Insert a postprocessor after amp_substitute oricessor\n md.postprocessors.add(\"footnote\", FootnotePostprocessor(self),\n \">amp_substitute\")","def generate(self, diagram):","def student_view(self, context=None):\n\n data = {}\n data['markdown_text'] = unicode(self.markdown_text)\n\n html = self.resource_string(\"static/html/markdowna_studentview.html\")\n frag = Fragment(unicode(html.format(\n data=unicode(cgi.escape(json.dumps(data))))))\n\n # markdown it\n frag.add_javascript(self.resource_string(\n \"static/js/lib/markdown-it_new.min.js\"))\n frag.add_javascript(self.resource_string(\n \"static/js/lib/markdown-it-front-matter.js\"))\n\n # github markdown css\n frag.add_css(self.resource_string(\n \"static/css/lib/github-markdown_new.css\"))\n\n frag.add_javascript(self.resource_string(\n \"static/js/src/markdowna_studentview.js\"))\n\n frag.initialize_js('MarkdownXBlock')\n return frag","def markdown(self, text):\n\n try:\n html = markdown.markdown(text)\n data = self.convert_content(html)\n return data\n except Exception as e:\n Utils.log(traceback.format_exc())\n Utils.send('markdown.error')\n Utils.error(e.args[0])","def markdown_item(title, url):\n print('* [{0}]({1})'.format(\n markdown_escape(title),\n markdown_escape(url),\n ))","def markdown_cells(notebook):\n cells = all_cells(notebook)\n return [cell[\"source\"] for cell in cells if cell[\"cell_type\"] == \"markdown\"]","def show_text(self, lang):\n return markdown(self.text % dict(\n [(k, e_html(unicode(v), True)) for k, v in self.vars.items()]\n ), output_format=\"html5\")","def get_markdown(self, filters: List[str]) -> str: # type:ignore\n from mkapi.core.renderer import renderer\n\n return renderer.render_module(self, filters) # type:ignore","def generateMapping(text, extensions):\n extensions = filter(lambda e: e != 'markdown.extensions.footnotes', extensions)\n\n text_index, modified = preprocess(text)\n html_modified = markdown(modified, extensions=extensions)\n modified_index = findPlaceholderIndex(html_modified, len(text_index))\n return zip(text_index, modified_index)","def get_description(self):\n text = \"is a student's t distribution; characterised by its degrees of freedom, which here is\"+str(self.dofs)+\".\"\n return text","def Content():\n\tTOPIC_DICT = {\"Basics\": [[\"Introduction to Python\", \"/introduction-to-python/\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Print functions\", \"/python-print-function/\"],\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\"Math basics with Python 3\", \"/math-basics-python-3/\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t ]],\n\t\t\t\t\t\t\t\t\"Web Dev\": [[\"wd1\"]]} #Web dev is now the second topic\n\t\t\t\t\t\t\t\t#Introduction to Python\n\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\n\t#section\n\n\treturn TOPIC_DICT","def print_markdown(text):\n\n md = Padding(Markdown(text), 2)\n console.print(md)"],"string":"[\n \"def generate():\\n\\n # Verify if directory exists\\n if not os.path.isdir(config.techniques_markdown_path):\\n os.mkdir(config.techniques_markdown_path)\\n\\n #Write the technique index.html page\\n with open(os.path.join(config.techniques_markdown_path, \\\"overview.md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(config.technique_overview_md)\\n\\n for domain in config.domains:\\n generate_domain_markdown(domain)\",\n \"def generate_domain_markdown(domain):\\n\\n #Reads the STIX and creates a list of the ATT&CK Techniques\\n full_techniques = stixhelpers.get_techniques(config.ms[domain])\\n tactic_list = stixhelpers.get_tactic_list(config.ms[domain])\\n\\n data = {}\\n \\n technique_list = get_techniques_list(full_techniques)\\n\\n data['domain'] = domain.split(\\\"-\\\")[0]\\n\\n # Get technique table data and number of techniques\\n data['technique_table'] = util.get_technique_table_data(None, full_techniques)\\n data['technique_list_len'] = str(len(full_techniques))\\n side_menu_data = get_technique_side_menu_data(domain, technique_list, tactic_list)\\n\\n # Get tactic-techniques table\\n data['menu'] = side_menu_data\\n\\n subs = config.technique_domain_md.substitute(data)\\n subs = subs + json.dumps(data)\\n\\n with open(os.path.join(config.techniques_markdown_path, data['domain'] + \\\"-techniques.md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\\n\\n # Create the markdown for the enterprise groups in the STIX\\n\\n for technique in full_techniques:\\n if 'revoked' not in technique or technique['revoked'] is False:\\n generate_technique_md(technique, domain, side_menu_data, tactic_list)\",\n \"def _generate_markdown(self, case):\\n # Lucene query generation\\n lucene_dict = {\\n \\\"sources\\\": case[\\\"detection\\\"][\\\"sources\\\"],\\n \\\"data\\\": case[\\\"input_arguments\\\"],\\n }\\n case[\\\"lucene_query\\\"] = self.templates[\\\"lucene\\\"].render(lucene_dict)\\n # AWS CLI command generation\\n command_template = jinja2.Template(case[\\\"executors\\\"][\\\"sh\\\"][\\\"code\\\"])\\n if case[\\\"input_arguments\\\"]:\\n aws_cli_render_args = {}\\n for arg in case[\\\"input_arguments\\\"]:\\n aws_cli_render_args[arg] = case[\\\"input_arguments\\\"][arg][\\\"value\\\"]\\n case[\\\"compiled_command\\\"] = command_template.render(aws_cli_render_args)\\n else:\\n case[\\\"compiled_command\\\"] = command_template.render()\\n\\n render_dict = {\\\"case\\\": case}\\n return self.templates[\\\"markdown\\\"].render(render_dict)\",\n \"def to_markdown(self, experiment_name : str):\\n experiment = self.get_experiment(experiment_name)\\n f = open(experiment.root_dir + \\\"/\\\" + experiment_name + \\\".md\\\", 'w')\\n # Create a markdown object\\n markdown = Markdown()\\n\\n for img in experiment.images:\\n mkimg = Markdown.Image(img.title, img.filename, img.description)\\n markdown.add_image(img.filename)\\n\\n table = Markdown.Table([\\\"Parameter Name\\\", \\\"Input/Output\\\", \\\"Value\\\", \\\"Title\\\", \\\"Description\\\"])\\n for param in experiment.parameters:\\n table.add_row([param.name, ])\\n\\n markdown.add_table(table)\",\n \"def gen_markdown_doc(self):\\n # Spec for markdown doc\\n # vars displayed as \\\\{ hyperlinked classExpression \\\\}\\n # pattern name\\n # label rule\\n # def\\n # MS fields\\n # dicts?\\n\\n # sprintf subs and MS conversion\\n \\n out = \\\"## %s\\\\n\\\" % self.pattern['pattern_name'] #\\n # sprintf to generate label - Use { var name } in sub\\n out += \\\"__label:__ %s\\\\n\\\\n\\\" % (self._var_quote_sub(self.pattern['name']['text'],self.pattern['name']['vars']))\\n # sprintf to generate def - Use { var name } in sub\\n out += \\\"__def:__ %s\\\\n\\\\n\\\" % (self._var_quote_sub(self.pattern['def']['text'],self.pattern['def']['vars']))\\n \\n if \\\"EquivalentTo\\\" in self.pattern: \\n out += \\\"__equivalentTo:__ %s\\\\n\\\\n\\\" % self._ms_sub_and_md('EquivalentTo') # Ugly! Refactor!\\n if \\\"SubClassOf\\\" in self.pattern:\\n out += \\\"__subClassOf:__ %s\\\\n\\\\n\\\" % self._ms_sub_and_md('SubClassOf')\\n if \\\"GCI\\\" in self.pattern:\\n out += \\\"__GCI:__ %s\\\\n\\\\n\\\" % self._ms_sub_and_md('GCI')\\n return out\",\n \"def _generate_conclusion_markdown(self, data):\\n pass\",\n \"def data():\\n file = open('./contest/content/data.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Data', \\n content=content)\",\n \"def generate_excerpt():\",\n \"def _markdown(checklist):\\n\\n checklist = json.load(open(checklist), object_pairs_hook=OrderedDict)\\n\\n mdFile = MdUtils(file_name='Ikigai-Checklist',\\n title='PDP 2019 Checklist')\\n\\n\\n mdFile.new_paragraph(checklist['overview'])\\n\\n sections = sorted(checklist['checklist'].values(),\\n key=lambda s: int(s['no']))\\n for section in sections:\\n mdFile.new_header(level=1, title=section['title'])\\n for subject in section['subjects'].values():\\n mdFile.new_header(level=2, title=subject['title'])\\n mdFile.new_paragraph(subject['description'])\\n mdFile.new_paragraph(\\\"Reference: \\\" + subject['provisions'])\\n mdFile.new_paragraph(\\\"Functions: \\\" + \\\", \\\".join(subject['functions']))\\n mdFile.new_paragraph(\\\"Groups: \\\" + \\\", \\\".join(subject['groups']))\\n\\n actions = [['No','Description', 'Tags', 'Check']]\\n actions += [[a['no'], a['description'], \\\", \\\".join(a['tags']),''] for a in subject['actions'].values()]\\n rows = len(actions) \\n actions = flatten(actions)\\n mdFile.new_table(columns=4,\\n rows=rows,\\n text=actions,\\n text_align='left')\\n\\n mdFile.create_md_file()\",\n \"def convert_to_markdown(self, text: str) -> str:\",\n \"def _generate_pr_comment_markdown(self, data):\\n pass\",\n \"def get_markdown(tools: List[Dict[str, Any]]) -> str:\\n padding = 43\\n\\n # First Row\\n markdown = \\\"| {name: <{padding}}| PHP \\\".format(name=\\\"Tool\\\", padding=padding)\\n markdown += \\\" | PHP \\\".join(PHP_VERSIONS)\\n markdown += \\\" |\\\\n\\\"\\n # Second Row\\n markdown += \\\"|{name:-<{padding}}-|\\\".format(name=\\\"\\\", padding=padding)\\n for php in PHP_VERSIONS:\\n markdown += \\\"---------|\\\"\\n markdown += \\\"\\\\n\\\"\\n for tool in tools:\\n markdown += \\\"| {name: <{padding}}|\\\".format(\\n name=\\\"[\\\" + tool[\\\"name\\\"] + \\\"][lnk_\\\" + tool[\\\"name\\\"] + \\\"]\\\", padding=padding\\n )\\n for php in PHP_VERSIONS:\\n if str(php) in tool[\\\"exclude\\\"]:\\n markdown += \\\" |\\\"\\n else:\\n markdown += \\\" ✓ |\\\"\\n markdown += \\\"\\\\n\\\"\\n\\n markdown += \\\"\\\\n\\\"\\n for tool in tools:\\n markdown += \\\"[lnk_\\\" + tool[\\\"name\\\"] + \\\"]: \\\" + tool[\\\"dir\\\"] + \\\"\\\\n\\\"\\n\\n return markdown\",\n \"def _repr_markdown_(self):\\n mod = f'**{self.__class__.__name__} Model**'\\n try:\\n mod +=f': {self.filename}'\\n except:\\n pass\\n s = [mod,'']\\n if self.metadata:\\n s += ['|Parameter|Value|',\\n '|:--------|:----:|']\\n for name, v in self.metadata.items():\\n try: \\n s += [f\\\"|{name} | ${v.value:g}$ {v.unit:latex}|\\\"]\\n except:\\n s += [f\\\"|{name} | {v} |\\\"]\\n return '\\\\n'.join(s)\",\n \"def gen_md(self):\\n # https://pythonhosted.org/Markdown/extensions/index.html\\n extensions = ['extra', 'codehilite', 'admonition',\\n 'toc', 'smarty', 'sane_lists', 'wikilinks']\\n # TODO\\n extension_configs = {'toc': {\\n 'anchorlink': False,\\n 'permalink': False\\n }\\n }\\n output_format = 'html5'\\n md = markdown.Markdown(extensions=extensions,\\n extension_configs=extension_configs,\\n output_format=output_format)\\n html = md.convert(self.md)\\n toc = getattr(md, 'toc', '')\\n if toc:\\n toc = process_toc(toc)\\n return html, toc\",\n \"def description():\\n #rule = request.url_rule\\n #print(rule)\\n file = open('./contest/content/description.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Description', \\n content=content)\",\n \"def task_generate_sc_markdown():\\n for dept in Department.list():\\n yield {\\n 'name': dept.name,\\n 'file_dep': [],\\n 'targets': [dept.sc_markdown_path],\\n 'actions': [dept.generate_sc_markdown],\\n 'clean': True,\\n 'uptodate': [False],\\n }\",\n \"def markdown(value):\\n return Markup(md(value))\",\n \"def write():\\n \\n st.title(\\\"All about me..!!\\\")\\n st.markdown(\\n \\\"\\\"\\\"\\n\\n:large_blue_diamond: **Data Engineer**\\\\n\\nThe current role revolves around working with database management(AWS Redshift), automation of tasks (using Python), and helping out other teams at work with required data for BI reports. This involves me working with various ETL tools like Matillion. Apart from this, I have started some creative learning initiatives at work to develop and grow skills.\\n\\n:large_blue_diamond: **Speaker | Blogger | Community- Builder**\\\\n\\nI have speaker at various meetups and workshops. Apart from this, I am a Lead organizer at **Kaggle Days Meetups - Surat** and **Pie and AI: Gujarat**. You will often find me on Saturdays at Integrated Mentoring Session - where we as a community believe in More Together Learning. I like to write blogs on data science, NLP, work learnings, and some general topics.\\n\\n:large_blue_diamond: **NLP- Practitioner**\\\\n\\nSharing the learnings and practices of NLP with the community through various workshops and classes or through LinkedIn posts and videos. I have completed her thesis titled \\\"Neural Machine Translation on Indian Languages\\\". Currently, working on a research project. \\n\\n:large_blue_diamond: **Collaborations**\\\\n\\nIf you are looking to collaborate on Research opportunities in NLP, \\nCorporate training in NLP, \\nteaching and mentoring students & professionals in NLP.\\nPlease\\n[email me](mailto:budhraja.manpreet@gmail.com) or reach out \\nto me on [LinkedIn](https://www.linkedin.com/in/manpreet-budhraja/) \\n\\n\\n\\n\\\"\\\"\\\",\\n unsafe_allow_html=True,\\n )\",\n \"def markov_story():\\n return render_template(\\\"markovstory.html\\\")\",\n \"def all_markdown_tips(self) -> str:\\n\\t\\tmessage = \\\"**__Markdown Tips__**\\\\n\\\\n\\\"\\n\\t\\tfor format in self.formats:\\n\\t\\t\\ttip = self.formats[format]\\n\\t\\t\\tmessage += f\\\"{tip.preview}\\\\n\\\"\\n\\t\\t\\tmessage += f\\\"{blockquote(tip.escaped)}\\\\n\\\\n\\\"\\n\\t\\treturn message\",\n \"def generate_markdown(self, outdir, definitions):\\n doc_cases = defaultdict(lambda: defaultdict(list))\\n\\n if not os.path.exists(outdir):\\n os.makedirs(outdir)\\n for category in definitions.categories:\\n cat_outdir = os.path.join(outdir, category.replace(\\\" \\\", \\\"_\\\").lower())\\n if not os.path.exists(cat_outdir):\\n os.makedirs(cat_outdir)\\n\\n for case in definitions.case_set:\\n if case[\\\"category\\\"] == category:\\n doc_cases[category][case[\\\"name\\\"]] = case\\n\\n for technique in doc_cases[category]:\\n rendered = self._generate_markdown(doc_cases[category][technique])\\n md_filename = technique.replace(\\\" \\\", \\\"_\\\").lower() + \\\".md\\\"\\n filename = os.path.join(cat_outdir, md_filename)\\n mdoutfile = open(filename, \\\"w\\\")\\n mdoutfile.write(rendered)\\n mdoutfile.close()\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Markdown\\\",\\n \\\"\\\"\\\"<markdowna />\\n \\\"\\\"\\\")\\n ]\",\n \"def markdown_table(self, which):\\n if which == 'C':\\n coef = 'C'\\n elif which == 'c':\\n coef = 'c'\\n elif which == 'f':\\n coef = 'f'\\n str = '|order|'\\n for i in range(1,N+1):\\n str = str + '$%s_{%d}$ |' % (coef,i)\\n str = str + '\\\\n|'\\n for i in range(1,N+1):\\n str = str + '-|'\\n str = str + '\\\\n'\\n for i in range(1,self.N+1):\\n str = str + (self.dat[i]).markdown_row(self.N, which)\\n return str\",\n \"def markdown(text):\\n text = gfm(text)\\n text = markdown_lib.markdown(text)\\n return text\",\n \"def fixture_ext_markdown(plugin):\\n return plugin.md\",\n \"def markdown(self):\\n return db[self.basic_image_id]\",\n \"def md_writer(clf, features, outcome, eval_folder,\\n config_file, summary_df, i=''):\\n if config_file.endswith('.xlsx'):\\n config = pd.read_excel(config_file, sheetname='Sheet1')\\n elif config_file.endswith('.csv'):\\n config = pd.read_csv(config_file)\\n\\n clf_params = clf.get_params()\\n clf_name = str(clf)[:str(clf).index('(')]\\n clf_img = clf_name+str(i)\\n\\n file_name = clf_name+str(i)+'_Evaluation.md'\\n\\n save_file = open(eval_folder+file_name, 'w')\\n\\n def new_line():\\n save_file.write('\\\\n')\\n\\n save_file.write('<link rel=\\\"stylesheet\\\" href=\\\"style.css\\\" type=\\\"text/css\\\" />\\\\n')\\n save_file.write('# Model Evaluation Report\\\\n')\\n new_line()\\n\\n save_file.write('## Data Configuration:\\\\n')\\n new_line()\\n save_file.write(config.to_html(na_rep='', index=False).replace('NaT', ''))\\n new_line()\\n\\n save_file.write('## Classifier Parameters: '+clf_name+'\\\\n')\\n new_line()\\n for elem in clf_params:\\n save_file.write('* {}: {}\\\\n'.format(elem, clf_params[elem]))\\n new_line()\\n\\n summary_df = summary_df.T\\n summary_df.columns = ['value']\\n\\n save_file.write('## Evaluation Metrics; Summary\\\\n')\\n new_line()\\n save_file.write(summary_df.to_html())\\n new_line()\\n\\n save_file.write('## ROC Curve\\\\n')\\n new_line()\\n save_file.write('\\\\n')\\n new_line()\\n\\n save_file.write('## Precision-Recall Curve\\\\n')\\n new_line()\\n save_file.write('\\\\n')\\n new_line()\\n\\n save_file.write('## Precision, Recall vs % Population\\\\n')\\n new_line()\\n save_file.write('\\\\n')\\n\\n if clf_name in ['LogisticRegression']:\\n save_file.write('## Coefficients\\\\n')\\n new_line()\\n for i,coef in enumerate(clf.coef_[0]):\\n save_file.write('*<b>{}: {}</b>\\\\n'.format(features[i], round(coef,4)))\\n new_line()\\n\\n if clf_name in ['WeightedQuestions']:\\n save_file.write('## Weights\\\\n')\\n new_line()\\n for i,wt in enumerate(clf.weights):\\n save_file.write('*<b>{}: {}</b>\\\\n'.format(features[i], wt))\\n new_line()\\n\\n save_file.close()\\n\\n def markdown_to_html(md_file, out_file_name=None):\\n import markdown\\n\\n with open(md_file, 'r') as f:\\n html = markdown.markdown(f.read())\\n\\n if out_file_name is None:\\n out_file_name = md_file.split('.')[0]+'.html'\\n with open(out_file_name, 'w') as f:\\n f.write(html)\\n\\n markdown_to_html(eval_folder+file_name)\",\n \"def enml_to_markdown(enml):\\n pass\",\n \"def pc_md_to_html(data_list):\\n pcrenderer = PanelCodeRenderer()\\n markdown = mistune.Markdown(renderer=pcrenderer)\\n label = '<p style=\\\"font-size:x-small\\\"><em>panelcode: markdown processor (mistune)</em></p>\\\\n'\\n return markdown(\\\"\\\\n\\\".join(data_list) + label)\",\n \"def readme_md(cls):\\n\\n template = Helpers.File(Settings.readme_me_template).read()\\n\\n template = Helpers.Regex(\\n template, r\\\"%%version%%\\\", replace_with=Settings.version\\n ).replace()\\n template = Helpers.Regex(\\n template, r\\\"%%lenHosts%%\\\", replace_with=format(len(Settings.domains), \\\",d\\\")\\n ).replace()\\n template = Helpers.Regex(\\n template, r\\\"%%lenIPs%%\\\", replace_with=format(len(Settings.ips), \\\",d\\\")\\n ).replace()\\n template = Helpers.Regex(\\n template,\\n r\\\"%%lenHostsIPs%%\\\",\\n replace_with=format(len(Settings.ips) + len(Settings.domains), \\\",d\\\"),\\n ).replace()\\n\\n print(\\\"Generation of %s\\\" % Settings.readme_md_file, end=\\\" \\\")\\n Helpers.File(Settings.readme_md_file).write(template, overwrite=True)\\n print(Settings.done)\",\n \"def json2markdown(json_data):\\n with open(path.join(ROOT, 'utils/markdown.jinja2')) as f:\\n return Template(f.read()).render(pdfid=json_data.get('pdfid'), streams=json_data.get('streams'))\",\n \"def process_markdown(input_markdown, output_name, latex_img_dir = \\\"./\\\", input_path = \\\"./\\\", thumb_size=64):\\n\\tmd = markdown.Markdown( extensions=[ 'meta'\\n\\t , 'codehilite'\\n\\t , 'tables'\\n\\t , 'def_list'\\n\\t , 'footnotes'\\n\\t , ResourceExtractor({ \\\"resource_dir\\\": output_name\\n\\t , \\\"relative_path\\\": input_path\\n\\t })\\n\\t , AbstractExtractor()\\n\\t , ToCExtractor()\\n\\t , MathJaxExtension()\\n\\t , LaTeX({ \\\"latex_img_dir\\\": latex_img_dir\\n\\t , \\\"input_path\\\": input_path\\n\\t })\\n\\t ]\\n\\t )\\n\\t\\n\\t# Basic HTML conversion\\n\\thtml = md.convert(input_markdown)\\n\\t\\n\\t# Generate table of contents\\n\\ttoc = md.toc\\n\\t\\n\\t# Choose document title (default to the output name)\\n\\ttitle = output_name\\n\\t# Use the first heading if possible\\n\\tif len(toc) > 0:\\n\\t\\ttitle = toc[0][1]\\n\\t# Better yet, get the explicitly given metadata\\n\\ttitle = md.Meta.get(\\\"title\\\", [title])[0]\\n\\t\\n\\t# Choose document subtitle (only available from metadata)\\n\\tsubtitle = md.Meta.get(\\\"subtitle\\\", [None])[0]\\n\\t\\n\\t# Get the image from the metadata\\n\\timg = md.Meta.get(\\\"img\\\", [None])[0]\\n\\timg_alt = md.Meta.get(\\\"img_alt\\\", [title])[0]\\n\\t\\n\\t# The abstract should be taken to be the first paragraph.\\n\\tabstract = md.abstract if md.abstract is not None else \\\"\\\"\\n\\t\\n\\t# Get the list of tags\\n\\ttags = md.Meta.get(\\\"tags\\\", [])\\n\\t\\n\\t# Get the list of files to include\\n\\tincludes = md.Meta.get(\\\"include\\\", [])\\n\\t\\n\\t# Get the show option\\n\\tshow = md.Meta.get(\\\"show\\\", [\\\"True\\\"])[0] == \\\"True\\\"\\n\\t\\n\\tfiles = md.resources\\n\\t\\n\\t# Add the article image to the list of files and create a thumbnail if\\n\\t# possible.\\n\\tif img is not None and img.startswith(\\\"file://\\\"):\\n\\t\\timg = os.path.join(input_path, img[len(\\\"file://\\\"):])\\n\\t\\timg_output_name = \\\"%s/%s\\\"%(output_name,\\n\\t\\t unique(os.path.basename(img),\\n\\t\\t [f.split(\\\"/\\\")[-1] for (_,f) in files]))\\n\\t\\t\\n\\t\\timg_thumbnail = \\\"%s.thumb.png\\\"%img\\n\\t\\t\\n\\t\\tp = Popen( [\\\"convert\\\"\\n\\t\\t , img\\n\\t\\t , \\\"-thumbnail\\\", \\\"%dx%d\\\"%(thumb_size,thumb_size)\\n\\t\\t , img_thumbnail]\\n\\t\\t , stdin = None\\n\\t\\t , stdout = sys.stderr\\n\\t\\t , stderr = sys.stderr\\n\\t\\t )\\n\\t\\tif p.wait() != 0:\\n\\t\\t\\traise Exception(\\\"Creating img thumbnail failed.\\\")\\n\\t\\t\\n\\t\\tfiles.append((img_thumbnail, img_output_name))\\n\\t\\timg = img_output_name\\n\\t\\n\\t# Generate meta-data\\n\\tmeta_data = {\\n\\t\\t\\\"url\\\" : output_name,\\n\\t\\t\\\"title\\\" : title,\\n\\t\\t\\\"subtitle\\\" : subtitle,\\n\\t\\t\\\"img\\\" : img,\\n\\t\\t\\\"img_alt\\\" : img_alt,\\n\\t\\t\\\"abstract\\\" : abstract,\\n\\t\\t\\\"tags\\\" : tags,\\n\\t\\t\\\"show\\\" : show,\\n\\t}\\n\\t\\n\\treturn html, toc, meta_data, files, includes\",\n \"def _parse_markdown(self):\\n renderer = MyRenderer()\\n md = mistune.Markdown(renderer=renderer)\\n md.render(self._markdown_text)\\n self._bash_commands = renderer._bash_commands\",\n \"def markdown_text(context, text, autoescape=True):\\n includes = ''\\n if 'render_markdown_src' not in context:\\n # The first markdown in the template - include JS.\\n context['render_markdown_src'] = True\\n # Ask Webpack for the location of the asset.\\n includes = '\\\\n'.join(webpack_utils.get_as_tags('common-render-markdown'))\\n result_html = html.format_html(\\n '<span class=\\\"markdown\\\">{}</span>{}',\\n text,\\n safestring.mark_safe(includes),\\n )\\n return result_html\",\n \"def on_page_markdown(self, markdown, page, config, files):\\n listext = self.config['ext']\\n src_file_path = page.file.abs_src_path\\n prepath, ext = os.path.splitext(src_file_path)\\n lang = ext.lstrip('.')\\n filename = page.file.name\\n if ext in listext:\\n new_markdown = \\\"# {0}\\\\n\\\\n```{1}\\\\n\\\".format(filename, lang) + markdown + \\\"\\\\n```\\\"\\n return new_markdown\\n else:\\n return markdown\",\n \"def addDemographics(self):\\n p = self.p\\n demographics_data = {\\n 'dob': p.dob,\\n 'gender': p.gender,\\n 'email': p.email,\\n 'fname': p.fname,\\n 'lname': p.lname,\\n 'hphone': p.home,\\n 'cphone': p.cell,\\n 'country': p.country,\\n 'city': p.city,\\n 'pcode': p.pcode,\\n 'region': p.region,\\n 'street': p.street,\\n }\\n self.demographics_doc = DEMOGRAPHICS.sub(demographics_data).done()\",\n \"def index():\\n with open(\\\"PROJECT.md\\\", \\\"r\\\") as project_file:\\n md_template_string = markdown.markdown(\\n project_file.read(), extensions=[\\\"fenced_code\\\"]\\n )\\n return md_template_string\",\n \"def markdown(text, **kwargs):\\n import markdown\\n return markdown.markdown(text, **kwargs)\",\n \"def markdown_post(post):\\n post['entry'] = markdown(post['entry'].replace(\\\"\\\\n\\\",\\\" \\\\n\\\"), output=\\\"html5\\\")\\n return post\",\n \"def markdown(text, *args, **kwargs):\\n md = StMarkdown(*args, **kwargs)\\n return md.convert(text)\",\n \"def convert(md_text):\\n # separate by line\\n md_text = md_text.split('\\\\n')\\n\\n # save the html content for return\\n html_text = ''\\n\\n # begin looping from the first line\\n index = -1\\n while index < len(md_text) - 1:\\n index += 1\\n line = md_text[index]\\n\\n # code segment\\n if len(line) >= 3 and line[:3] == '```':\\n html_line = \\\"\\\"\\n language = line[3:].replace(' ', '')\\n if len(language) == 0:\\n language = False\\n order_index = index + 1\\n find_end = False\\n while order_index < len(md_text):\\n if md_text[order_index][:3] == '```':\\n find_end = True\\n break\\n else:\\n temp_line = md_text[order_index]\\n temp_line = temp_line.replace('<', '<')\\n temp_line = temp_line.replace('>', '>')\\n temp_line = temp_line.replace(' ', ' ')\\n html_line += temp_line + '<br />'\\n order_index += 1\\n\\n if find_end:\\n # if language is not False:\\n # html_text += ('<pre><code class=\\\"' + language + '\\\">' + html_line + '</code></pre>')\\n # else:\\n html_text += ('<code>' + html_line + '</code>')\\n # print(language)\\n index = order_index\\n continue\\n\\n # inline code\\n\\n\\n # header\\n is_header, html_line = check_header(line)\\n if is_header:\\n html_text = html_text + html_line\\n continue\\n\\n # horizontal rule\\n is_horizontal_rule, html_line = check_horizontal_rule(line)\\n if is_horizontal_rule:\\n html_text = html_text + html_line\\n continue\\n\\n # paragraph\\n line = check_paragraph(line)\\n\\n # deal with ordered list\\n if len(line.split('.')) != 0 and '1.' == line[:2]:\\n html_line = '<ol>'\\n order_index = index\\n while order_index < len(md_text)\\\\\\n and len(md_text[order_index].split('.')) != 0\\\\\\n and (str(order_index - index + 1) == md_text[order_index].split('.')[0]\\n or '1' == md_text[order_index].split('.')[0]):\\n to_replace = [str(order_index - index + 1) + '.', '1.']\\n for replace_content in to_replace:\\n md_text[order_index] = md_text[order_index].replace(replace_content, '')\\n html_line = html_line + '<li>' + md_text[order_index] + '</li>'\\n\\n order_index += 1\\n index = order_index - 1\\n html_line = html_line + '</ol>'\\n line = html_line\\n\\n # deal with unordered list\\n is_unordered_list, html_line = check_unordered_list(line)\\n if is_unordered_list:\\n line = html_line\\n\\n # deal with strong\\n line = strong(line)\\n\\n # Scratch\\n line = scratch(line)\\n\\n # italics\\n line = italics(line)\\n\\n # image\\n while len(re.match(r'((?P<pre_text>.*)!\\\\[(?P<alt_text>.*)\\\\]\\\\((?P<link>.*)\\\\)(?P<after_text>.*))*', line).group())\\\\\\n != 0:\\n match = re.match(r'((?P<pre_text>.*)!\\\\[(?P<alt_text>.*)\\\\]\\\\((?P<link>.*)\\\\)(?P<after_text>.*))*', line)\\n pre_text = match.group('pre_text')\\n alt_text = match.group('alt_text')\\n link = match.group('link')\\n after_text = match.group('after_text')\\n img_html = '<img src=\\\"' + link + '\\\" alt=\\\"' + alt_text + '\\\">'\\n line = pre_text + img_html + after_text\\n\\n # link\\n while len(re.match(r'((?P<pre_text>.*)\\\\[(?P<alt_text>.*)\\\\]\\\\((?P<link>.*)\\\\)(?P<after_text>.*))*', line).group())\\\\\\n != 0:\\n match = re.match(r'((?P<pre_text>.*)\\\\[(?P<alt_text>.*)\\\\]\\\\((?P<link>.*)\\\\)(?P<after_text>.*))*', line)\\n pre_text = match.group('pre_text')\\n alt_text = match.group('alt_text')\\n link = match.group('link')\\n after_text = match.group('after_text')\\n img_html = '<a href=\\\"' + link + '\\\">' + alt_text + '</a>'\\n line = pre_text + img_html + after_text\\n\\n html_text = html_text + line\\n if not is_unordered_list:\\n html_text = html_text + '<br>'\\n\\n return html_text\",\n \"def generate_html(self):\\n html_text_1 = \\\"\\\"\\\"\\n <div class=\\\"concept\\\">\\n\\n \\t\\t<div class=\\\"concept-title\\\">\\n\\n \\t\\t\\t\\t\\\"\\\"\\\" + self.title\\n\\n html_text_2 = \\\"\\\"\\\"\\n \\t\\t</div>\\n\\n \\t\\t<div class=\\\"concept-description\\\">\\n\\n\\t\\t <p>\\n\\t\\t\\t\\n \\t\\t \\t\\t\\\"\\\"\\\" + self.description + \\\"\\\"\\\" \\n \\n </p>\\\"\\\"\\\"\\n\\n html_text_3 = '''\\n\\n \\t\\t</div>\\n\\n </div>'''\\n\\n return html_text_1 + html_text_2 + html_text_3\",\n \"def gen_analysis_text(num_data, usage_flag, labelled_landmark, landmark_name, error_summary):\\n analysis_text = r'<p style=\\\"color:red;\\\">Basic information:</p>'\\n analysis_text += '<p style=\\\"color:black;\\\">Landmark name: {0}.</p>'.format(landmark_name)\\n analysis_text += '<p style=\\\"color:black;\\\"># cases in total: {0}.</p>'.format(num_data)\\n labelled_landmarks_stat = get_landmarks_stat(labelled_landmark)\\n \\n analysis_text += r'<p style=\\\"color:black;\\\"># cases having this landmark (Pos. cases): {0}.</p>'.format(\\n len(labelled_landmarks_stat[landmark_name]['pos']))\\n analysis_text += r'<p style=\\\"color:black;\\\"># cases missing this landmark (Neg. cases): {}.</p>'.format(\\n len(labelled_landmarks_stat[landmark_name]['neg']))\\n if len(labelled_landmarks_stat[landmark_name]['neg']) > 0:\\n missing_cases = copy.deepcopy(labelled_landmarks_stat[landmark_name]['neg'])\\n missing_cases.sort()\\n analysis_text += r'{}'.format(missing_cases)\\n\\n if usage_flag == 2:\\n tp_cases = error_summary.tp_cases[landmark_name]\\n tn_cases = error_summary.tn_cases[landmark_name]\\n fp_cases = error_summary.fp_cases[landmark_name]\\n fn_cases = error_summary.fn_cases[landmark_name]\\n num_pos_cases = len(tp_cases) + len(fn_cases)\\n num_neg_cases = len(tn_cases) + len(fp_cases)\\n # compute TPR, TNR, FPR, FNR\\n TPR = len(tp_cases) / max(1, num_pos_cases) * 100 \\\\\\n if len(tp_cases) != 0 or num_pos_cases != 0 else 100\\n TNR = len(tn_cases) / max(1, num_neg_cases) * 100 \\\\\\n if len(tn_cases) != 0 or num_neg_cases != 0 else 100\\n FPR = 100 - TNR\\n FNR = 100 - TPR\\n mean_error = error_summary.mean_error_tp[landmark_name]\\n std_error = error_summary.std_error_tp[landmark_name]\\n median_error = error_summary.median_error_tp[landmark_name]\\n max_error = error_summary.max_error_tp[landmark_name]\\n analysis_text += r'<p style=\\\"color:red;\\\"> Landmark classification error: </p>'\\n analysis_text += r'<p style=\\\"color:black;\\\">TP (TPR): {0} ({1:.2f}%)</p>'.format(\\n len(tp_cases), TPR)\\n analysis_text += r'<p style=\\\"color:black;\\\">TN (TNR): {0} ({1:.2f}%)</p>'.format(\\n len(tn_cases), TNR)\\n analysis_text += r'<p style=\\\"color:black;\\\">FP (FPR): {0} ({1:.2f}%)</p>'.format(\\n len(fp_cases), FPR)\\n analysis_text += r'<p style=\\\"color:black;\\\">FN (FNR): {0} ({1:.2f}%)</p>'.format(\\n len(fn_cases), FNR)\\n analysis_text += r'<p style=\\\"color:red;\\\"> Landmark distance error for the {} TP cases (unit: mm): </p>'.format(\\n len(tp_cases))\\n analysis_text += r'<p style=\\\"color:black;\\\">mean (std): {0:.2f} ({1:.2f})</p>'.format(\\n mean_error, std_error)\\n analysis_text += r'<p style=\\\"color:black;\\\">median: {0:.2f}</p>'.format(median_error)\\n analysis_text += r'<p style=\\\"color:black;\\\">max: {0:.2f}</p>'.format(max_error)\\n\\n return analysis_text\",\n \"def generate_body(issue):\\n markdown = \\\"### {}\\\\n\\\".format(issue.pop('title'))\\n for k, v in issue.iteritems():\\n markdown += \\\"- {}: {}\\\\n\\\".format(k, v)\\n return markdown\",\n \"def write_to_md(dictData, outputDirectory):\\n\\tdic = prepare_hw_dict(dictData)\\n\\tfor hw in dic:\\n\\t\\tfileout = os.path.join(outputDirectory, hw+'.md')\\n\\t\\t# Prepare the output file\\n\\t\\tfout = codecs.open(fileout, 'w', 'utf-8')\\n\\t\\t# Write frontmatter\\n\\t\\tfout.write('---\\\\ntitle: \\\"'+hw+'\\\"\\\\n---\\\\n\\\\n')\\n\\t\\t# For each (headword, meanings, verseNumber, PageNum) tuples,\\n\\t\\tfor (hw, meanings, verse, verseNumDetails, pageNumDetails) in dic[hw]:\\n\\t\\t\\tcommaed = ', '.join(meanings)\\n\\t\\t\\tverse = verse.replace('<BR>', '<br />')\\n\\t\\t\\t# Write in babylon format. <BR><BR> is to separate verses.\\n\\t\\t\\tfout.write('# ' + hw + '\\\\n## ' + commaed + '\\\\n' + verse + '<br />verse ' + verseNumDetails + '<br />page ' + pageNumDetails +'\\\\n\\\\n')\\n\\t\\tfout.close()\\n\\n\\t# Give some summary to the user\\n\\tprint('MD files generated. Success!')\\n\\tprint('{} separate .md files written, one per headword.'.format(len(dic)))\",\n \"def gen_flowable(self):\\n flow = randint(0,randint(1,2)) ## Weighted random numbers to balance text to image ratio\\n if flow == 0:\\n try:\\n photo = self.photos[randint(0, len(self.photos)-1)]\\n img = Image(self.dict['img'][photo]['src'], self.dict['img'][photo]['dimensions'][0], self.dict['img'][photo]['dimensions'][1])\\n img.wrap(A6[0],A6[1])\\n img.hAlign = ['CENTER', 'LEFT', 'RIGHT'][randint(0,3)]\\n return img\\n except:\\n return Spacer(randint(0, 100), randint(0, 100))\\n elif flow == 1:\\n try:\\n quote = self.quotes[randint(0, len(self.quotes)-1)]\\n q_sheet = randint(0, 5)\\n #return Paragraph(quote, Stylesheet.stylesheet['quote'][q_sheet])\\n return Paragraph(cleanhtml(quote), Stylesheet.stylesheet['title'])\\n except:\\n return Spacer(randint(0, 100), randint(0, 100))\\n elif flow == 2:\\n try:\\n tainr = self.containers[randint(0, (len(self.containers)-1))]\\n c_sheet = randint(0, 1)\\n #return Paragraph(self.dict['txt'][tainr]['text'], Stylesheet.stylesheet['quote'][q_sheet])\\n return Paragraph(cleanhtml(self.dict['txt'][tainr]['text']), Stylesheet.stylesheet['default'])\\n except:\\n return Spacer(randint(0, 100), randint(0, 100))\\n else:\\n return Spacer(randint(0, 100), randint(0, 100))\",\n \"def _get_delta_markdown_string(self):\\n markdown_string = \\\"\\\"\\n\\n if self.is_commit_test is True:\\n if len(self.delta_fp_string_dict.delta_dict[\\\"commits\\\"]) > 0:\\n markdown_string += (\\n os.linesep\\n + \\\"## Commit history SHA1 for this analysis:\\\"\\n + os.linesep\\n )\\n for sha1_commit in self.delta_fp_string_dict.delta_dict[\\\"commits\\\"]:\\n markdown_string += \\\"- `\\\" + sha1_commit + \\\"`\\\" + os.linesep\\n markdown_string += os.linesep\\n elif self.is_branch_test is True:\\n if len(self.delta_fp_string_dict.delta_dict[\\\"branches\\\"]) > 0:\\n markdown_string += (\\n os.linesep + \\\"## Branches under analysis:\\\" + os.linesep\\n )\\n for branch in self.delta_fp_string_dict.delta_dict[\\\"branches\\\"]:\\n markdown_string += \\\"- \\\" + branch + os.linesep\\n markdown_string += os.linesep\\n\\n # Added files block\\n markdown_string += \\\"## Added Files\\\" + os.linesep\\n if len(self.delta_fp_string_dict.delta_dict[\\\"added\\\"]) > 0:\\n for added_file in self.delta_fp_string_dict.delta_dict[\\\"added\\\"]:\\n markdown_string += \\\"- \\\" + added_file + os.linesep\\n else:\\n markdown_string += \\\"- None\\\" + os.linesep\\n\\n # Deleted files block\\n markdown_string += os.linesep + os.linesep + \\\"## Deleted Files\\\" + os.linesep\\n if len(self.delta_fp_string_dict.delta_dict[\\\"deleted\\\"]) > 0:\\n for deleted_file in self.delta_fp_string_dict.delta_dict[\\\"deleted\\\"]:\\n markdown_string += \\\"- \\\" + deleted_file + os.linesep\\n else:\\n markdown_string += \\\"- None\\\" + os.linesep\\n\\n # Modified files block\\n markdown_string += os.linesep + os.linesep + \\\"## Modified Files\\\" + os.linesep\\n if len(self.delta_fp_string_dict.delta_dict[\\\"modified\\\"]) > 0:\\n for modified_file in self.delta_fp_string_dict.delta_dict[\\\"modified\\\"]:\\n markdown_string += \\\"- \\\" + modified_file + os.linesep\\n else:\\n markdown_string += \\\"- None\\\" + os.linesep\\n\\n # Project URL + version footer\\n markdown_string += (\\n os.linesep\\n + os.linesep\\n + \\\"---\\\"\\n + os.linesep\\n + \\\"[ufodiff](https://github.com/source-foundry/ufodiff) v\\\"\\n + major_version\\n + \\\".\\\"\\n + minor_version\\n + \\\".\\\"\\n + patch_version\\n )\\n\\n return markdown_string\",\n \"def render_markdown(text):\\n return markdown(text, **MARKDOWN_KWARGS)\",\n \"def generate_pdf(list,id):\\n\\n doc = SimpleDocTemplate(settings.STATIC_ROOT+\\\"/tests/\\\"+str(id)+\\\"/\\\"+str(id)+\\\".pdf\\\")\\n\\n Story = [Spacer(1,2*inch)]\\n styles = stylesheet()\\n global Title\\n\\n # Add 10 questions with boxes below\\n for i in list:\\n if not i[0] in \\\"skills-scan\\\" and not i[0] in \\\"csrfmiddlewaretoken\\\" and not i[0] in \\\"titre\\\" and not i[0] in \\\"custom\\\":\\n tmp = int(i[0])+1\\n bogustext = (str(tmp)+\\\". %s\\\" % i[1])\\n p = Paragraph(bogustext, styles['default'])\\n # Write the paragraph\\n\\n draw = Drawing()\\n # rect(x1,y1,width,height)\\n rec = Rect(0, 100, 450, 150)\\n rec.fillColor = colors.white\\n # draw the rect under each paragraph\\n draw.add(rec)\\n p.keepWithNext = True\\n Story.append(p)\\n Story.append(draw)\\n Story.append(Spacer(1,-0.9 * inch))\\n elif i[0] in \\\"titre\\\":\\n Title = i[1]\\n # build the document by inserting the whole story\\n doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages)\\n return str(id)+\\\".pdf\\\"\",\n \"def home() :\\n st.markdown(\\\"This application provides 3 mains modules :\\\")\\n st.markdown(\\\"* **The prediction module :** enables you to assess client's liability based on its file\\\")\\n st.markdown(\\\"* **The explorer module :** enables you to dig deeper into your client informations,\\\"\\n \\\" particularly historcial data coming from federal loan bureau and historical\\\"\\n \\\" Home Credit's data if available.\\\")\\n st.markdown(\\\"* **The statistics module** : enables you to explore the database at a macro scale :\\\"\\n \\\" understand how variables such as age, sex and income impact probability of repayment\\\")\",\n \"def generate_markdown_table() -> None:\\n comments = comments_from_file('skywalking/config.py')\\n\\n with open('docs/en/setup/Configuration.md', 'w') as plugin_doc:\\n plugin_doc.write(DOC_HEAD)\\n offset = 0\\n for config_index, comment in enumerate(comments):\\n if comment.startswith('# BEGIN'):\\n # remove `#BEGIN: `\\n plugin_doc.write(TABLE_HEAD.format(comment[8:]))\\n offset += 1\\n else:\\n table_entry = create_entry(comment, config_index - offset)\\n plugin_doc.write(f'{table_entry}\\\\n')\",\n \"def generate_preproc_steps_docstring(\\n prepreproc_undergone=\\\"\\\",\\n tools_used=None,\\n dcm2nii=False,\\n deleteorient=False,\\n fwhm=None, anat_fwhm=None,\\n bet=False,\\n slice_timing=False,\\n realign=False,\\n coregister=False,\\n coreg_func_to_anat=False,\\n segment=False,\\n normalize=False,\\n func_write_voxel_sizes=None,\\n anat_write_voxel_sizes=None,\\n dartel=False,\\n additional_preproc_undergone=\\\"\\\",\\n command_line=None,\\n details_filename=None,\\n has_func=True\\n ):\\n fwhm = sanitize_fwhm(fwhm)\\n anat_fwhm = sanitize_fwhm(anat_fwhm)\\n if dartel:\\n normalize = False\\n segment = False\\n\\n # which tools were used ?\\n if tools_used is None:\\n tools_used = (\\n 'All preprocessing was done using <a href=\\\"%s\\\">pypreprocess</a>,'\\n ' a collection of python scripts and modules for '\\n 'preprocessing functional and anatomical MRI data.' % (\\n PYPREPROCESS_URL))\\n preproc_undergone = \\\"<p>%s</p>\\\" % tools_used\\n\\n # what was actually typed at the command line ?\\n if not command_line is None:\\n preproc_undergone += \\\"Command-line: <i>%s</i><br/>\\\" % command_line\\n preproc_undergone += (\\n \\\"<br>For each subject, the following preprocessing steps have \\\"\\n \\\"been done:\\\")\\n\\n preproc_undergone += \\\"<ul>\\\"\\n if prepreproc_undergone:\\n preproc_undergone += \\\"<li>%s</li>\\\" % prepreproc_undergone\\n if dcm2nii:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"dcm2nii has been used to convert input images from DICOM to nifti\\\"\\n \\\" format\\\"\\n \\\"</li>\\\")\\n if deleteorient:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Orientation-specific meta-data in the image headers have \\\"\\n \\\"been suspected as garbage and stripped-off to prevent severe \\\"\\n \\\"mis-registration problems.\\\"\\n \\\"</li>\\\")\\n if bet:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Brain extraction has been applied to strip-off the skull\\\"\\n \\\" and other non-brain tissues. This prevents later \\\"\\n \\\"registration problems like the skull been (mis-)aligned \\\"\\n \\\"unto the cortical surface, \\\"\\n \\\"etc.</li>\\\")\\n if slice_timing:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Slice-Timing Correction (STC) has been done to interpolate the \\\"\\n \\\"BOLD signal in time, so that in the sequel we can safely pretend\\\"\\n \\\" all 3D volumes within a TR (Repetition Time) were \\\"\\n \\\"acquired simultaneously, an crucial assumption for any further \\\"\\n \\\"analysis of the data (GLM, ICA, etc.). \\\"\\n \\\"</li>\\\"\\n )\\n if realign:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Motion correction has been done so as to estimate, and then \\\"\\n \\\"correct for, subject's head motion.\\\"\\n \\\"</li>\\\"\\n )\\n if coregister:\\n preproc_undergone += \\\"<li>\\\"\\n if coreg_func_to_anat:\\n preproc_undergone += (\\n \\\"The subject's functional images have been coregistered \\\"\\n \\\"to their anatomical image.\\\"\\n )\\n else:\\n preproc_undergone += (\\n \\\"The subject's anatomical image has been coregistered \\\"\\n \\\"against their functional images.\\\")\\n preproc_undergone += (\\n \\\" Coregistration is important as it allows: (1) segmentation of \\\"\\n \\\"the functional via segmentation of the anatomical brain; \\\"\\n \\\"(2) inter-subject registration via inter-anatomical registration,\\\"\\n \\\" a trick referred to as 'Indirect Normalization'; \\\"\\n \\\"(3) ROIs to be defined on the anatomy, making it \\\"\\n \\\"possible for activation maps to be projected and appreciated\\\"\\n \\\" thereupon.\\\"\\n \\\"</li>\\\")\\n if segment:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Tissue Segmentation has been employed to segment the \\\"\\n \\\"anatomical image into GM, WM, and CSF compartments, using \\\"\\n \\\"template TPMs (Tissue Probability Maps).</li>\\\")\\n if normalize:\\n if segment:\\n if has_func:\\n salt = (\\\" The same deformations have been \\\"\\n 'applied to the functional images.')\\n else: salt = \\\"\\\"\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"The segmented anatomical image has been warped \\\"\\n \\\"into the MNI template space by applying the deformations \\\"\\n \\\"learnt during segmentation.%s</li>\\\" % salt)\\n else:\\n if coregister:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Deformations from native to standard space have been \\\"\\n \\\"learnt on the anatomically brain. These deformations \\\"\\n \\\"have been used to warp the functional and anatomical \\\"\\n \\\"images into standard space.</li>\\\")\\n else:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"The functional images have been warped from native to \\\"\\n \\\"standard space via classical normalization.</li>\\\")\\n if dartel:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"Group/Inter-subject Normalization has been done using the \\\"\\n \\\"SPM8 <a href='%s'>DARTEL</a> to warp subject brains into \\\"\\n \\\"MNI space. \\\"\\n \\\"The idea is to register images by computing a “flow\\\"\\n \\\" field” which can then be “exponentiated\\\"\\n \\\"” to generate both forward and backward deformation\\\"\\n \\\"s. Processing begins with the “import” \\\"\\n \\\"step. This involves taking the parameter files \\\"\\n \\\"produced by the segmentation (NewSegment), and writing \\\"\\n \\\"out rigidly \\\"\\n \\\"transformed versions of the tissue class images, \\\"\\n \\\"such that they are in as close alignment as possible with\\\"\\n \\\" the tissue probability maps. \\\"\\n \\\"The next step is the registration itself. This involves \\\"\\n \\\"the simultaneous registration of e.g. GM with GM, \\\"\\n \\\"WM with WM and 1-(GM+WM) with 1-(GM+WM) (when needed, the\\\"\\n \\\" 1- (GM+WM) class is generated implicitly, so there \\\"\\n \\\"is no need to include this class yourself). This \\\"\\n \\\"procedure begins by creating a mean of all the images, \\\"\\n \\\"which is used as an initial template. Deformations \\\"\\n \\\"from this template to each of the individual images \\\"\\n \\\"are computed, and the template is then re-generated\\\"\\n \\\" by applying the inverses of the deformations to \\\"\\n \\\"the images and averaging. This procedure is repeated a \\\"\\n \\\"number of times. Finally, warped \\\"\\n \\\"versions of the images (or other images that are in \\\"\\n \\\"alignment with them) can be generated. \\\"\\n \\\"</li>\\\") % DARTEL_URL\\n if normalize or dartel:\\n if (not func_write_voxel_sizes is None or\\n not anat_write_voxel_sizes is None):\\n preproc_undergone += \\\"<li>\\\"\\n sep = \\\"\\\"\\n if not func_write_voxel_sizes is None:\\n preproc_undergone += (\\n \\\"Output functional images have been re-written with voxel \\\"\\n \\\"size %smm x %smm x %smm.\\\") % tuple(\\n func_write_voxel_sizes)\\n sep = \\\" \\\"\\n if not anat_write_voxel_sizes is None:\\n preproc_undergone += (\\n \\\"%sThe output anatomical image has been re-written with \\\"\\n \\\"voxel \\\"\\n \\\"size %smm x %smm x %smm.\\\") % tuple([sep] + list(\\n anat_write_voxel_sizes))\\n preproc_undergone += \\\"</li>\\\"\\n\\n if additional_preproc_undergone:\\n preproc_undergone += additional_preproc_undergone\\n if np.sum(fwhm) > 0 and has_func:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"The functional images have been \\\"\\n \\\"smoothed with a %smm x %smm x %smm \\\"\\n \\\"Gaussian kernel.</li>\\\") % tuple(fwhm)\\n if np.sum(anat_fwhm) > 0:\\n preproc_undergone += (\\n \\\"<li>\\\"\\n \\\"The anatomical image has been \\\"\\n \\\"smoothed with a %smm x %smm x %smm \\\"\\n \\\"Gaussian kernel.\\\") % tuple(anat_fwhm)\\n if segment:\\n preproc_undergone += (\\n \\\" Warped TPMs have been smoothed with the same kernel.\\\")\\n if not details_filename is None:\\n preproc_undergone += (\\n \\\" <a href=%s>See complete configuration used for preprocessing\\\"\\n \\\" here</a>\\\") % os.path.basename(details_filename)\\n preproc_undergone += \\\"</ul>\\\"\\n\\n return preproc_undergone\",\n \"def task_generate_sc_html():\\n for dept in Department.list():\\n command = (f\\\"pandoc --self-contained\\\"\\n f\\\" -o {dept.sc_html_path}\\\"\\n f\\\" {dept.sc_markdown_path}\\\")\\n\\n yield {\\n 'name': dept.name,\\n 'file_dep': [\\n dept.sc_figure1_path,\\n dept.sc_figure2_path,\\n dept.sc_figure3_path,\\n dept.sc_figure4_path,\\n dept.sc_figure5_path,\\n dept.sc_markdown_path,\\n ],\\n 'targets': [dept.sc_html_path],\\n 'actions': [command],\\n 'clean': True,\\n }\",\n \"def test_markdown(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.example.Foo.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n for item in data['items']:\\n if item['uid'] == 'example.example.Foo.method_markdown':\\n self.assertEqual(\\n item['summary'],\\n 'Check out our '\\n '[site](http://sphinx-docfx-yaml.readthedocs.io/en/latest/)'\\n ' for more info.',\\n )\",\n \"def render_biography(self):\\n return markdown2.markdown(self.biography)\",\n \"def convert(cell):\\r\\n\\r\\n markdownResult=\\\"\\\"\\r\\n if cell['cell_type'] == 'code':\\r\\n markdownResult += '```\\\\n'\\r\\n\\r\\n for line in cell['source']: \\r\\n markdownResult += line\\r\\n\\r\\n if cell['cell_type'] == 'code':\\r\\n markdownResult += '\\\\n```'\\r\\n \\r\\n debugPrint(markdownResult)\\r\\n markdownResult += '\\\\n\\\\n'\\r\\n \\r\\n return markdownResult\",\n \"def report(params):\\n # handle input\\n yamlin = params[\\\"yaml\\\"]\\n indict = yaml.load(open(yamlin))\\n\\n enrichreportl = get_template(\\\"enrichreport\\\")\\n out = \\\"enrichreport.md\\\"\\n cmd = \\\"%s -t %s -j %s -o %s -y\\\" % (render,enrichreportl,yamlin,out)\\n log.run(\\\"render enrichreport template\\\",cmd)\\n \\n cmd = \\\"%s %s\\\" % (md2html,out)\\n log.run(\\\"md2html enrichreport \\\",cmd, o=[\\\"html.tgz\\\"])\\n outdict = {}\\n outdict[\\\"enrichreport\\\"] = out\\n yamlout = yamladd(yamlin,outdict)\\n yamlout[\\\"enrichreport_outdir\\\"] = os.getcwd()\\n return yamlout\",\n \"def gen_rst(txt):\\n res = \\\"\\\"\\n title_block = get_title_block(txt)\\n title_rst = gen_title_rst(title_block)\\n res += title_rst\\n # add a blank line just to be sure\\n res += \\\"\\\\n\\\"\\n fun_blocks = get_fun_blocks(txt)\\n for (name, fun_block) in fun_blocks:\\n fun_rst = gen_fun_rst(name, fun_block)\\n res += fun_rst\\n return res\",\n \"def evaluation():\\n file = open('./contest/content/evaluation.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Evaluation', \\n content=content)\",\n \"def as_markdown(self):\\n text = \\\"|name|value| \\\\n|-|-| \\\\n\\\"\\n for attr, value in sorted(vars(self).items()):\\n text += \\\"|{}|{}| \\\\n\\\".format(attr, value)\\n\\n return text\",\n \"def _convert_to_fancypants(self, markdown_text: str) -> dict: # noqa: ANN001\\n text_data = {\\\"output_mode\\\": \\\"rtjson\\\", \\\"markdown_text\\\": markdown_text}\\n return self._reddit.post(API_PATH[\\\"convert_rte_body\\\"], data=text_data)[\\\"output\\\"]\",\n \"def rules():\\n file = open('./contest/content/rules.md', 'r')\\n rawText = file.read()\\n file.close()\\n content = Markup(markdown(rawText, \\n extensions=['markdown.extensions.fenced_code', 'markdown.extensions.tables']))\\n return render_template('markdowntemplate.html', \\n title='Rules', \\n content=content)\",\n \"def parse(text):\\n md = markdown.Markdown(['codehilite', 'tables', ])\\n\\n for iref in re.findall(img_ref_re, text):\\n img_id = iref[7]\\n try:\\n image = FlatPageImage.objects.get(pk=int(img_id))\\n md.references[img_id] = (image.image_path.url, '')\\n except ObjectDoesNotExist:\\n pass\\n\\n for lref in re.findall(reference_re, text):\\n doc_name = lref[7]\\n try:\\n doc = File.objects.get(name=doc_name)\\n md.references[doc_name]= (doc.url, doc.name)\\n except ObjectDoesNotExist:\\n pass\\n\\n return md.convert(text)\",\n \"def test_description_markdown_with_custom_options() -> None:\\n soup = generate_case(\\n \\\"description_markdown\\\",\\n GenerationConfiguration(\\n markdown_options={\\n \\\"cuddled-lists\\\": True,\\n }\\n ),\\n )\\n\\n assert (\\n str(soup.find(\\\"span\\\", class_=\\\"description\\\"))\\n == \\\"\\\"\\\"<span class=\\\"description\\\"><p>DOC </p> <ul> <li>List 1</li> <li>List 2</li> </ul> </span>\\\"\\\"\\\"\\n )\",\n \"def as_markdown(self):\\n text = \\\"|name|value| \\\\n|-|-| \\\\n\\\"\\n for attr, value in sorted(vars(self).items()):\\n text += \\\"|{}|{}| \\\\n\\\".format(attr, value)\\n return text\",\n \"def markdown_figs(self):\\n return self.findall_markdown_cells(MARKDOWN_FIG)\",\n \"def gen_explanation():\\n f = self.media['explanation'][0]\\n k = self.__type_by_extension(os.path.sep.join(\\n os.path.join([self.media_path, f])))\\n v = [os.sep.join([self.web_root, expl]) \\\\\\n for expl in self.media['explanation']]\\n if v:\\n v = v[0]\\n else:\\n v = \\\"\\\"\\n return {'explanation': {k: v}}\\n #): os.sep.join([self.web_root, f])\\n\\n #[os.sep.join([self.web_root, expl]) \\\\\\n # for expl in self.media['explanation']]}\",\n \"def display_wmd(google_snippets, my_snippets):\\r\\n for i in range(len(google_snippets)):\\r\\n print(i+1, '.', urls[i])\\r\\n print('Google Snippet:', google_snippets[i])\\r\\n print('Generated Snippet:', my_snippets[i])\\r\\n print(\\\"Word Mover's Distance:\\\", get_wmd_dist(google_snippets[i], my_snippets[i], model))\\r\\n print()\",\n \"def generate_story():\\n\\n text = story.generate(request.args)\\n \\n return render_template(\\\"story.html\\\", text = text)\",\n \"def poem_generation(magnet,topic):\\r\\n e = inflect.engine()\\r\\n antonyms = []\\r\\n synonyms = []\\r\\n poem = \\\"\\\"\\r\\n verb = \\\"\\\"\\r\\n # plural\\r\\n if (e.singular_noun(topic) is False):\\r\\n verb = \\\"is\\\"\\r\\n else:\\r\\n verb = \\\"are\\\"\\r\\n \\r\\n for syn in wordnet.synsets(topic):\\r\\n for l in syn.lemmas():\\r\\n synonyms.append(l.name())\\r\\n if l.antonyms():\\r\\n antonyms.append(l.antonyms()[0].name())\\r\\n if (len(set(antonyms)) < 1):\\r\\n for syn in wordnet.synsets(RAND_LIST[random.randint(0,len(RAND_LIST)-1)]):\\r\\n for l in syn.lemmas():\\r\\n synonyms.append(l.name())\\r\\n if l.antonyms():\\r\\n antonyms.append(l.antonyms()[0].name())\\r\\n topic = topic.capitalize()\\r\\n for i in range(0,random.randint(5,15)):\\r\\n verse = random.randint(0,6)\\r\\n question = random.randint(0,6)\\r\\n # structure\\r\\n # antonyms\\r\\n if (verse < 2) and len(antonyms) > 0:\\r\\n ant_magnet = metaphor_magnet(antonyms[random.randint(0,len(antonyms)-1)])\\r\\n choice = random.randint(0,len(ant_magnet)-1)\\r\\n detail = ant_magnet[choice].split(\\\":\\\")\\r\\n if (question < 2):\\r\\n index = random.randint(0,len(QUESTION)-1)\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" not like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" not like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += topic + \\\" \\\" + verb + \\\" not like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\".\\\\n\\\" \\r\\n else:\\r\\n poem += topic + \\\" \\\" + verb + \\\" not like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\".\\\\n\\\" \\r\\n \\r\\n else:\\r\\n choice = random.randint(0,len(magnet)-1)\\r\\n detail = magnet[choice].split(\\\":\\\")\\r\\n if (question < 2):\\r\\n index = random.randint(0,len(QUESTION)-1)\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += topic + \\\" \\\" + verb + \\\" like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"\\\\n\\\" \\r\\n else:\\r\\n poem += topic + \\\" \\\" + verb + \\\" like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"\\\\n\\\" \\r\\n \\r\\n return poem\",\n \"def build_md_once(self, index, tid):\\n url = self.joint_url(tid)\\n title_json = proxy_req(url, 1)\\n if not title_json:\\n if can_retry(url, index):\\n self.build_md_once(index, tid)\\n return\\n content = BeautifulSoup(\\n title_json['content'], 'html.parser').find_all('div')\\n text = []\\n img_href = []\\n img_id = 1\\n ttid = 1\\n img_title = self.find_title(index).split('/')[1][:-3]\\n for word in content:\\n temp_text = ''\\n if word.span and len(word.span.text) and not word.span.text[0].isdigit:\\n temp_text = '## ' + word.span.text\\n ttid = 1\\n if word.img:\\n temp_text = ' + '.jpg)'\\n img_href.append(word.img['src'].replace('https', 'http'))\\n img_id += 1\\n\\n if not len(temp_text):\\n temp_text = word.text\\n if len(temp_text) and temp_text[0].isdigit():\\n temp_text = str(ttid) + '. **' + \\\\\\n ' '.join(temp_text.split('\\\\xa0')[1:]).strip() + '**'\\n ttid += 1\\n if len(temp_text) and temp_text[0:2] == '//':\\n temp_text = str(ttid) + '. **' + \\\\\\n ' '.join(temp_text.split('\\\\xa0')[2:]).strip() + '**'\\n ttid += 1\\n if len(temp_text) and (temp_text[0] == '￥' or temp_text[0] == '€'):\\n temp_text = '<a>' + temp_text + '</a>'\\n text.append(temp_text)\\n with codecs.open(data_dir + self.find_title(index), 'w', encoding='utf-8') as f:\\n f.write('\\\\n'.join(text))\\n self.img_map[index] = img_href\\n print(index, len(img_href))\",\n \"def paragraph_p12(candidates_tup, return_html=False):\\n\\n elim_list = \\\"\\\"\\n for i, c in candidates_tup:\\n elim_list += f\\\"<dt><b>{i:>2}: {c}</b></dt>\\\"\\n \\n text = \\\"\\\"\\\"<h3>* Insights from Problems 1 and 2</h3><p style=\\\"font-size:110%;\\\">\\\"\\\"\\\"\\n text += \\\"\\\"\\\"On the basis of Figures 1 and 2, which show the number of new nodes created, \\n and the time spent by each search function, respectively, the searches that are candidates \\n for elimination for more complex problems are those at the intersection of the average-ranked \\n costliest sets viz new nodes creation and search time.<br>These searches are:</p><pre><dl>\\\"\\\"\\\"\\n text += f\\\"<dl>{elim_list}</dl></p></pre>\\\"\\n \\n if return_html:\\n return text\\n else:\\n return Markdown(text)\",\n \"def phast_cmmd(self):\\n temp = '{prog} -R {rho} -C {ecov} -E {elen} -N {chrom} -i MAF {maf} {model} > {wig}\\\\n'.format(**self.dict)\\n return temp.format(fnum=self.fnum)\",\n \"def pdf_gen(report, summary=None):\\n with open(\\\"report_content.yaml\\\", \\\"r\\\") as stream:\\n docs = yaml.safe_load(stream)\\n\\n style = g_stylesheet.get(\\\"styles\\\")\\n elems = [] # elements array used to build pdf structure\\n pdf = SimpleDocTemplate(\\n f\\\"{report.replay_id}_report.pdf\\\",\\n pagesize=letter,\\n leftMargin=0.75 * inch,\\n rightMargin=0.75 * inch,\\n topMargin=0.75 * inch,\\n bottomMargin=0.75 * inch,\\n )\\n\\n # title and subtitle and cluster info table\\n elems.append(Paragraph(docs[\\\"title\\\"], style[\\\"Title\\\"]))\\n elems.append(\\n Paragraph(sub_yaml_vars(report, docs[\\\"subtitle\\\"]), style[\\\"Heading4\\\"])\\n )\\n cluster_info = pd.DataFrame.from_dict(report.cluster_details, orient=\\\"index\\\")\\n elems.append(\\n Table(\\n df_to_np(report.cluster_details.keys(), cluster_info.transpose()),\\n hAlign=\\\"LEFT\\\",\\n style=g_stylesheet.get(\\\"table_style\\\"),\\n )\\n )\\n # replay summary\\n if summary is not None:\\n elems.append(Paragraph(f\\\"Replay Summary\\\", style[\\\"Heading4\\\"]))\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in summary],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n elems.append(Spacer(0, 5))\\n\\n elems.append(Paragraph(docs[\\\"report_paragraph\\\"], style[\\\"Normal\\\"]))\\n\\n # glossary section\\n elems.append(Paragraph(docs[\\\"glossary_header\\\"], style[\\\"Heading4\\\"]))\\n elems.append(Paragraph(docs[\\\"glossary_paragraph\\\"], style[\\\"Normal\\\"]))\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in docs[\\\"glossary\\\"]],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n elems.append(Spacer(0, 5))\\n\\n # access data section\\n elems.append(Paragraph(docs[\\\"data_header\\\"], style[\\\"Heading4\\\"]))\\n elems.append(\\n Paragraph(sub_yaml_vars(report, docs[\\\"data_paragraph\\\"]), style[\\\"Normal\\\"])\\n )\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in docs[\\\"raw_data\\\"]],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n elems.append(Spacer(0, 5))\\n elems.append(\\n Paragraph(\\n sub_yaml_vars(report, docs[\\\"agg_data_paragraph\\\"]), style[\\\"Normal\\\"]\\n )\\n )\\n\\n # notes section\\n elems.append(Paragraph(docs[\\\"notes_header\\\"], style[\\\"Heading4\\\"]))\\n elems.append(Paragraph(docs[\\\"notes_paragraph\\\"], style[\\\"Normal\\\"]))\\n elems.append(\\n ListFlowable(\\n [ListItem(Paragraph(x, style[\\\"Normal\\\"])) for x in docs[\\\"notes\\\"]],\\n bulletType=\\\"bullet\\\",\\n )\\n )\\n\\n elems.append(PageBreak()) # page 2: cluster details\\n\\n # query breakdown\\n build_pdf_tables(elems, docs[\\\"query_breakdown\\\"], report)\\n elems.append(Spacer(0, 5))\\n\\n # histogram and description\\n image_path = hist_gen(\\n x_data=report.feature_graph[\\\"sec_start\\\"],\\n y_data=report.feature_graph[\\\"count\\\"],\\n title=docs[\\\"graph\\\"].get(\\\"title\\\"),\\n x_label=\\\"Average Elapsed Time (s)\\\",\\n )\\n\\n desc = Paragraph(docs[\\\"graph\\\"].get(\\\"paragraph\\\"), style[\\\"Normal\\\"])\\n data = [[Image(image_path, width=300, height=200, hAlign=\\\"LEFT\\\"), desc]]\\n elems.append(\\n Table(data, style=TableStyle([(\\\"VALIGN\\\", (0, 0), (-1, -1), \\\"MIDDLE\\\")]))\\n )\\n elems.append(Spacer(0, 5))\\n\\n # cluster metrics table\\n build_pdf_tables(elems, docs[\\\"cluster_metrics\\\"], report)\\n\\n elems.append(PageBreak()) # page 3+ measure tables\\n\\n build_pdf_tables(\\n elems, docs[\\\"measure_tables\\\"], report\\n ) # build 5 measure tables all at once\\n\\n # build pdf\\n pdf.build(\\n elems,\\n onFirstPage=partial(first_page, report=report),\\n onLaterPages=partial(later_pages, report=report),\\n )\\n os.remove(image_path)\\n\\n return pdf.filename\",\n \"def ler_markdown(nome_do_arquivo):\\n return open(\\\"teste.md\\\").read()\",\n \"def with_markdown(template):\\n def decorator(fn):\\n @wraps(fn)\\n def decorated_fn(*args, **kwargs):\\n ctx = fn(*args, **kwargs)\\n if ctx is None:\\n ctx = {}\\n source = render_template(template)\\n extensions = ['codehilite', 'fenced_code']\\n contents = Markup(md.markdown(source, extensions=extensions))\\n ctx['contents'] = contents\\n return render_template('_base_md.html', **ctx)\\n return decorated_fn\\n return decorator\",\n \"def markdown(self, session):\\n if not self.__doc__:\\n return None\\n orig_text = self.__doc__.strip('\\\\n').split('\\\\n')\\n colors = session.ath['render'].get_settings()\\n bullet_color = colors['help_file_bullet'].value\\n heading_color = colors['help_file_header'].value\\n emphasized_color = colors['help_file_emphasized'].value\\n\\n def bullet(find):\\n return '%s|%s*|n%s%s' % (find.group('spaces'), bullet_color, find.group('spaces2'), find.group('text'))\\n\\n def heading(find):\\n return '%s|%s%s%s|n' % (find.group('spaces'), heading_color, find.group('spaces2'), find.group('text'))\\n\\n def emphasized(find):\\n return '|%s%s|n' % (emphasized_color, find.group('text'))\\n\\n output_text = list()\\n\\n for line in orig_text:\\n out_line = HEADING.sub(heading, line)\\n out_line = BULLET.sub(bullet, out_line)\\n out_line = EMPHASIZED.sub(emphasized, out_line)\\n out_line = ANSIString(out_line)\\n output_text.append(out_line)\\n return '\\\\n'.join([unicode(l) for l in output_text])\",\n \"def get_examples_table_data(technique, reference_list, next_reference_number):\\n\\n # Creating map to avoid repeating the code 3 times\\n examples_map = [\\n { 'example_type': config.tools_using_technique }, \\n { 'example_type': config.malware_using_technique },\\n { 'example_type': config.groups_using_technique }\\n ]\\n\\n example_data = []\\n\\n # Get malware or tools used by group\\n for examples in examples_map:\\n if examples['example_type'].get(technique.get('id')):\\n for example in examples['example_type'][technique['id']]:\\n\\n attack_id = util.get_attack_id(example['object'])\\n\\n # Only add example data if the attack id is found \\n if attack_id:\\n row = {}\\n\\n row['id'] = attack_id\\n\\n if attack_id.startswith('S'):\\n row['path'] = \\\"software\\\"\\n else:\\n row['path'] = \\\"groups\\\"\\n\\n row['name'] = example['object']['name']\\n\\n if example['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\\n\\n example_data.append(row)\\n \\n if example_data:\\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\\n return example_data\",\n \"def _show(path):\\n with open(path, 'r') as f:\\n content = f.read()\\n\\n return Markdown(content)\",\n \"def content(self, datas):\\n subject = \\\"TCC - MLaaS Topzero D+\\\"\\n\\n body = f\\\"\\\"\\\"\\n <h1>TCC MLaaS</h1>\\n <p>Realizamos o treinamento da base de dados que nos foi enviada. Abaixo você pode ver nossa o resultado dos treinos realizados.</p>\\n \\\"\\\"\\\"\\n\\n for data in datas:\\n if data is not None:\\n for key, info in data['images'].items():\\n body += f\\\"\\\"\\\"{info[0]}\\\"\\\"\\\"\\n if info[1] is not None:\\n body += f\\\"\\\"\\\"<br><img src=\\\"cid:{info[1]}\\\" alt=\\\"{key}\\\" height=\\\"300\\\" width=\\\"300\\\"><br>\\\"\\\"\\\"\\n\\n body += f\\\"\\\"\\\"\\n </br>\\n Atenciosamente\\n <h4><b>IESB</b></h4>\\n <h5>created by <b>Allan Kleitson Teotonio</b></h5>\\n <h5>Science Computer. 2019</h5>\\n \\\"\\\"\\\"\\n\\n return subject, body\",\n \"def to_markdown(self):\\n s = \\\"[\\\" + self.label + \\\"]\\\"\\n if self.is_reflink:\\n s += \\\": \\\" + self.url\\n else:\\n s += \\\"(\\\" + self.url + \\\")\\\"\\n return s\",\n \"def build(self, verbose: bool = False):\\n self.content = \\\"\\\"\\n for par in self.paragraphs:\\n for observ in par.observations:\\n\\n # save the meta_data\\n self.observs_id.append(observ.observ_id)\\n self.sit_relev.append(observ.relevance2)\\n\\n # append the sentence to the article\\n self.content += f\\\"{observ.observation_new} \\\"\\n if verbose:\\n print(observ.year, observ.week_number, observ.day_number, observ.pattern, observ.observation_new)\\n\\n # end of paragraph (add a paragraph divider)\\n self.content += self.par_divider\",\n \"def __html__(self, file_path:str) -> str:\\n with open(f\\\"{file_path}\\\", \\\"r\\\") as mdfile: # Parse markdown file\\n text = mdfile.read()\\n html = self.md.convert(text) # Convert the markdown content text to hmtl\\n return html\",\n \"def to_markdown(\\n self, buf: Optional[Union[IO[str], str]] = None, mode: Optional[str] = None\\n ) -> str:\\n log_advice(\\n \\\"`to_markdown` loads all data into the driver's memory. \\\"\\n \\\"It should only be used if the resulting pandas object is expected to be small.\\\"\\n )\\n # Make sure locals() call is at the top of the function so we don't capture local variables.\\n args = locals()\\n psser_or_psdf = self\\n internal_pandas = psser_or_psdf._to_internal_pandas()\\n return validate_arguments_and_invoke_function(\\n internal_pandas, self.to_markdown, type(internal_pandas).to_markdown, args\\n )\",\n \"def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\\n\\n examples = []\\n total_answerable = 0\\n\\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\\n guid = \\\"%s-%s-%s\\\" % (set_type, line['qid'], str(i))\\n src = []\\n has_pos = 0\\n\\n if passage_type == 'both' and set_type != 'train':\\n\\n # rank by bert score in val and test\\n score_key = 'rank_score'\\n # score_key = \\\"nr_score\\\"\\n both_sources = line[\\\"passages\\\"][:2*cand_for_each_source] + line[\\\"tables\\\"][:2*cand_for_each_source]\\n all_scores = np.array([x[score_key] for x in both_sources])\\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\\n\\n for idx in sorted_index:\\n pas = both_sources[idx]\\n if idx < 2 * cand_for_each_source:\\n pas_type = 'passage'\\n title = pas[\\\"article_title\\\"]\\n else:\\n pas_type = 'table'\\n title = \\\"table_\\\" + pas[\\\"uid\\\"].split('-split')[0]\\n\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + f\\\" </s> {pas_type} title: \\\" + title + f\\\" </s> {pas_type} content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\")\\n\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n else:\\n\\n if passage_type in ['textual', 'hybrid', 'both']:\\n for pas in line[\\\"passages\\\"][:cand_for_each_source]:\\n # 10 passages\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> passage title: \\\" + pas[\\\"article_title\\\"] + \\\" </s> passage content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\\\\\"ZAPPAtite\\\\\\\". All songs written and performed ... </s>\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n if passage_type in ['tabular', 'hybrid', 'both']:\\n for tab in line[\\\"tables\\\"][:cand_for_each_source]:\\n # 10 tables\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> table title: \\\" + \\\"table_\\\" + tab[\\\"uid\\\"].split('-split')[0] + \\\" </s> table content: \\\" + tab[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\\n if isinstance(tab['judge'], dict):\\n has_pos += int(tab['judge']['judge_contain_all'])\\n else:\\n has_pos += int(tab['judge'])\\n\\n const = 2 if passage_type in ['hybrid', 'both'] else 1\\n if len(src) != const * cand_for_each_source:\\n logger.info(line)\\n src = src + [\\\"\\\"] * (const * cand_for_each_source - len(src))\\n\\n if has_pos > 0:\\n total_answerable += 1\\n\\n if enable_sql_supervision and 'true_sql' in line:\\n tgt = \\\"sql: \\\" + line[\\\"true_sql\\\"] + \\\" </s>\\\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\\\\\"Butler CC (KS)\\\\\\\" </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'denotation' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"denotation\\\"][0]) + \\\" </s>\\\"\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'answers' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"answers\\\"][0]) + \\\" </s>\\\" # e.g. answer: no slogan on current series </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt + \\\"\\\\n\\\")\\n\\n logger.info(f\\\"Total answerable in {set_type} split is {total_answerable / len(lines)}\\\")\\n\\n return examples\",\n \"def process_md(text_md):\\n\\tprocessed_text_md = ( pre_proc.replace_br(text_md)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.remove_false_titles)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.remove_blank_lines)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.replace_cid)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.replace_with_dash)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_by_hyphen)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_lines)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_lines)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_et_al)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_beta)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_vs)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.fix_enye)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_ellipsis)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_subtraction)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_by_colon)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.remove_duplicated_dashes)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.fix_marks)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.join_title_questions)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.remove_useless_lines)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.remove_duplicated_whitespaces)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t| p(pre_proc.remove_repeated_strings)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t)\\n\\treturn processed_text_md\",\n \"def htmlForMarkdown(md):\\n return mdProcessor.convert(md)\",\n \"def _intermediary_to_markdown(tables, relationships):\\n t = '\\\\n'.join(t.to_markdown() for t in tables)\\n r = '\\\\n'.join(r.to_markdown() for r in relationships)\\n return '{}\\\\n{}'.format(t, r)\",\n \"def extendMarkdown(self, md, md_globals):\\n md.registerExtension(self)\\n self.parser = md.parser\\n self.md = md\\n self.sep = ':'\\n if self.md.output_format in ['html5', 'xhtml5']:\\n self.sep = '-'\\n # Insert a preprocessor before ReferencePreprocessor\\n md.preprocessors.add(\\\"footnote\\\", FootnotePreprocessor(self),\\n \\\"<reference\\\")\\n # Insert an inline pattern before ImageReferencePattern\\n FOOTNOTE_RE = r'\\\\[\\\\^([^\\\\]]*)\\\\]' # blah blah [^1] blah\\n md.inlinePatterns.add(\\\"footnote\\\", FootnotePattern(FOOTNOTE_RE, self),\\n \\\"<reference\\\")\\n # Insert a tree-processor that would actually add the footnote div\\n # This must be before all other treeprocessors (i.e., inline and \\n # codehilite) so they can run on the the contents of the div.\\n md.treeprocessors.add(\\\"footnote\\\", FootnoteTreeprocessor(self),\\n \\\"_begin\\\")\\n # Insert a postprocessor after amp_substitute oricessor\\n md.postprocessors.add(\\\"footnote\\\", FootnotePostprocessor(self),\\n \\\">amp_substitute\\\")\",\n \"def generate(self, diagram):\",\n \"def student_view(self, context=None):\\n\\n data = {}\\n data['markdown_text'] = unicode(self.markdown_text)\\n\\n html = self.resource_string(\\\"static/html/markdowna_studentview.html\\\")\\n frag = Fragment(unicode(html.format(\\n data=unicode(cgi.escape(json.dumps(data))))))\\n\\n # markdown it\\n frag.add_javascript(self.resource_string(\\n \\\"static/js/lib/markdown-it_new.min.js\\\"))\\n frag.add_javascript(self.resource_string(\\n \\\"static/js/lib/markdown-it-front-matter.js\\\"))\\n\\n # github markdown css\\n frag.add_css(self.resource_string(\\n \\\"static/css/lib/github-markdown_new.css\\\"))\\n\\n frag.add_javascript(self.resource_string(\\n \\\"static/js/src/markdowna_studentview.js\\\"))\\n\\n frag.initialize_js('MarkdownXBlock')\\n return frag\",\n \"def markdown(self, text):\\n\\n try:\\n html = markdown.markdown(text)\\n data = self.convert_content(html)\\n return data\\n except Exception as e:\\n Utils.log(traceback.format_exc())\\n Utils.send('markdown.error')\\n Utils.error(e.args[0])\",\n \"def markdown_item(title, url):\\n print('* [{0}]({1})'.format(\\n markdown_escape(title),\\n markdown_escape(url),\\n ))\",\n \"def markdown_cells(notebook):\\n cells = all_cells(notebook)\\n return [cell[\\\"source\\\"] for cell in cells if cell[\\\"cell_type\\\"] == \\\"markdown\\\"]\",\n \"def show_text(self, lang):\\n return markdown(self.text % dict(\\n [(k, e_html(unicode(v), True)) for k, v in self.vars.items()]\\n ), output_format=\\\"html5\\\")\",\n \"def get_markdown(self, filters: List[str]) -> str: # type:ignore\\n from mkapi.core.renderer import renderer\\n\\n return renderer.render_module(self, filters) # type:ignore\",\n \"def generateMapping(text, extensions):\\n extensions = filter(lambda e: e != 'markdown.extensions.footnotes', extensions)\\n\\n text_index, modified = preprocess(text)\\n html_modified = markdown(modified, extensions=extensions)\\n modified_index = findPlaceholderIndex(html_modified, len(text_index))\\n return zip(text_index, modified_index)\",\n \"def get_description(self):\\n text = \\\"is a student's t distribution; characterised by its degrees of freedom, which here is\\\"+str(self.dofs)+\\\".\\\"\\n return text\",\n \"def Content():\\n\\tTOPIC_DICT = {\\\"Basics\\\": [[\\\"Introduction to Python\\\", \\\"/introduction-to-python/\\\"], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Print functions\\\", \\\"/python-print-function/\\\"],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t [\\\"Math basics with Python 3\\\", \\\"/math-basics-python-3/\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ]],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"Web Dev\\\": [[\\\"wd1\\\"]]} #Web dev is now the second topic\\n\\t\\t\\t\\t\\t\\t\\t\\t#Introduction to Python\\n\\t#will be the title and the link to that is immediately after. Each inner array will be a specific sub section of the basics\\n\\t#section\\n\\n\\treturn TOPIC_DICT\",\n \"def print_markdown(text):\\n\\n md = Padding(Markdown(text), 2)\\n console.print(md)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73341054","0.7133402","0.6663783","0.659057","0.6576931","0.63710845","0.62863004","0.6106517","0.60687923","0.60239404","0.5961844","0.5960654","0.5940064","0.5900538","0.5900436","0.5838119","0.5826031","0.58112127","0.57876456","0.5779548","0.5773856","0.5744301","0.5718887","0.5692403","0.56577665","0.56343025","0.5591953","0.5562689","0.5557459","0.5539262","0.5491648","0.5485194","0.5479323","0.54787105","0.547187","0.5471777","0.54671025","0.54660964","0.5427823","0.5427303","0.5416669","0.54165316","0.5400391","0.53985065","0.539768","0.53942776","0.53673035","0.5351091","0.5338228","0.5328986","0.52932864","0.52925265","0.5274289","0.52698314","0.5249907","0.5240602","0.52225053","0.52200985","0.5216115","0.52128625","0.5212046","0.5210348","0.52089566","0.52082837","0.5190261","0.5188427","0.5188366","0.5180362","0.51772285","0.51721203","0.51622355","0.51544255","0.5149431","0.5144794","0.51446354","0.5143088","0.51409286","0.5134726","0.5133335","0.5130111","0.5129449","0.5128057","0.51239485","0.51231444","0.51170844","0.5114056","0.5114006","0.5111975","0.51109314","0.5100947","0.5100729","0.50951743","0.5094397","0.5093224","0.5090632","0.50806767","0.50769866","0.5067632","0.50667757","0.5059328"],"string":"[\n \"0.73341054\",\n \"0.7133402\",\n \"0.6663783\",\n \"0.659057\",\n \"0.6576931\",\n \"0.63710845\",\n \"0.62863004\",\n \"0.6106517\",\n \"0.60687923\",\n \"0.60239404\",\n \"0.5961844\",\n \"0.5960654\",\n \"0.5940064\",\n \"0.5900538\",\n \"0.5900436\",\n \"0.5838119\",\n \"0.5826031\",\n \"0.58112127\",\n \"0.57876456\",\n \"0.5779548\",\n \"0.5773856\",\n \"0.5744301\",\n \"0.5718887\",\n \"0.5692403\",\n \"0.56577665\",\n \"0.56343025\",\n \"0.5591953\",\n \"0.5562689\",\n \"0.5557459\",\n \"0.5539262\",\n \"0.5491648\",\n \"0.5485194\",\n \"0.5479323\",\n \"0.54787105\",\n \"0.547187\",\n \"0.5471777\",\n \"0.54671025\",\n \"0.54660964\",\n \"0.5427823\",\n \"0.5427303\",\n \"0.5416669\",\n \"0.54165316\",\n \"0.5400391\",\n \"0.53985065\",\n \"0.539768\",\n \"0.53942776\",\n \"0.53673035\",\n \"0.5351091\",\n \"0.5338228\",\n \"0.5328986\",\n \"0.52932864\",\n \"0.52925265\",\n \"0.5274289\",\n \"0.52698314\",\n \"0.5249907\",\n \"0.5240602\",\n \"0.52225053\",\n \"0.52200985\",\n \"0.5216115\",\n \"0.52128625\",\n \"0.5212046\",\n \"0.5210348\",\n \"0.52089566\",\n \"0.52082837\",\n \"0.5190261\",\n \"0.5188427\",\n \"0.5188366\",\n \"0.5180362\",\n \"0.51772285\",\n \"0.51721203\",\n \"0.51622355\",\n \"0.51544255\",\n \"0.5149431\",\n \"0.5144794\",\n \"0.51446354\",\n \"0.5143088\",\n \"0.51409286\",\n \"0.5134726\",\n \"0.5133335\",\n \"0.5130111\",\n \"0.5129449\",\n \"0.5128057\",\n \"0.51239485\",\n \"0.51231444\",\n \"0.51170844\",\n \"0.5114056\",\n \"0.5114006\",\n \"0.5111975\",\n \"0.51109314\",\n \"0.5100947\",\n \"0.5100729\",\n \"0.50951743\",\n \"0.5094397\",\n \"0.5093224\",\n \"0.5090632\",\n \"0.50806767\",\n \"0.50769866\",\n \"0.5067632\",\n \"0.50667757\",\n \"0.5059328\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73468506"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85686,"cells":{"query":{"kind":"string","value":"Given a technique and a tactic list, return data of related techniques. Data includes technique id/name and tactic name/id for each related technique"},"document":{"kind":"string","value":"def get_related_techniques_data(technique, tactic_list):\n \n technique_data = []\n\n if config.related_techniques.get(technique['id']):\n for rel_tech in config.related_techniques[technique['id']]:\n\n attack_id = util.get_attack_id(rel_tech['object'])\n\n if attack_id:\n row = {}\n row['technique_id'] = attack_id\n\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\n if tactic:\n row['tactic_id'] = util.get_attack_id(tactic)\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\n \n row['technique_name'] = rel_tech['object']['name']\n technique_data.append(row)\n\n if technique_data:\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\n return technique_data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.groups_using_technique }\n ]\n\n example_data = []\n\n # Get malware or tools used by group\n for examples in examples_map:\n if examples['example_type'].get(technique.get('id')):\n for example in examples['example_type'][technique['id']]:\n\n attack_id = util.get_attack_id(example['object'])\n\n # Only add example data if the attack id is found \n if attack_id:\n row = {}\n\n row['id'] = attack_id\n\n if attack_id.startswith('S'):\n row['path'] = \"software\"\n else:\n row['path'] = \"groups\"\n\n row['name'] = example['object']['name']\n\n if example['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\n\n example_data.append(row)\n \n if example_data:\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\n return example_data","def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)","def get_technique_side_menu_data(domain, technique_list, tactic_list):\n\n # Get tactics techniques side menu data to fill out with jinja\n tactics_techniques_menu_data = []\n\n for tactic in tactic_list:\n tactic_row = {}\n \n tactic_row['name'] = tactic['name']\n tactic_row['id'] = util.get_attack_id(tactic)\n tactic_row['path'] = \"/tactics/{}\".format(util.get_attack_id(tactic))\n \n tactic_row['children'] = []\n for technique in technique_list[tactic['x_mitre_shortname']]:\n technique_row = {}\n # Get technique id and name for each technique\n technique_row['name'] = technique['name']\n technique_row['id'] = technique['id']\n technique_row['path'] = \"/techniques/{}/\".format(technique['id'])\n technique_row['children'] = []\n # Add technique data to tactic\n tactic_row['children'].append(technique_row)\n \n # Add tactic to table\n tactics_techniques_menu_data.append(tactic_row)\n \n return {\n \"name\": \"techniques\",\n \"id\": \"techniques\",\n \"path\": \"/techniques/{}/\".format(domain.split(\"-\")[0]), # root level doesn't get a path\n \"children\": tactics_techniques_menu_data\n }","def get_techniques_list(techniques):\n\n technique_list = {}\n \n for technique in techniques:\n if 'revoked' not in technique or technique['revoked'] == False:\n\n attack_id = util.get_attack_id(technique)\n\n if attack_id:\n\n technique_dict = {}\n technique_dict['id'] = attack_id\n \n technique_dict['name'] = technique['name']\n technique_dict['description'] = technique['description']\n\n if technique.get('kill_chain_phases'):\n for elem in technique['kill_chain_phases']:\n # Fill dict\n\n if elem['phase_name'] not in technique_list:\n technique_list[elem['phase_name']] = []\n \n technique_list[elem['phase_name']].append(technique_dict)\n\n for key, value in technique_list.items():\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\n \n return technique_list","def get_technique_related_to_technique():\n global technique_related_to_technique\n\n if not technique_related_to_technique:\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\n\n return technique_related_to_technique","def get_technique_stats(db):\n \n techniques = {}\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n temp_dict = {technique: [{u'id': element['project_no'], \n u'name': element['project_name'], u'description': element['short_description']}]}\n techniques.update(temp_dict)\n else:\n temp_dict = {u'id': element['project_no'],\n u'name': element['project_name'], u'description': element['short_description']}\n techniques[technique].append(temp_dict)\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\n\n return techniques","def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)","def get_mitigations_table_data(technique, reference_list, next_reference_number):\n\n mitigation_data = []\n\n # Check if technique has mitigations\n if config.technique_mitigated.get(technique['id']):\n # Iterate through technique mitigations\n for mitigation in config.technique_mitigated[technique['id']]:\n\n # Do not add deprecated mitigation to table\n if 'x_mitre_deprecated' not in mitigation['object']:\n\n attack_id = util.get_attack_id(mitigation['object'])\n\n # Only add if mitigation attack id is found \n if attack_id:\n\n row = {}\n row['mid'] = attack_id\n row['name'] = mitigation['object']['name']\n if mitigation['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, mitigation)\n \n mitigation_data.append(row)\n \n if mitigation_data:\n mitigation_data = sorted(mitigation_data, key=lambda k: k['name'].lower())\n return mitigation_data","def add_techniques(\n client: Act, matrice: AttckMatrice, output_format: Text = \"json\"\n) -> List:\n\n notify = []\n\n for technique in matrice.techniques:\n notify += handle_techniques(client, technique, None, output_format)\n\n for subtechnique in getattr(technique, \"subtechniques\", []):\n # Pre Attack does not have sub techniques\n notify += handle_techniques(client, subtechnique, technique, output_format)\n\n return notify","def get_technique_mitigated_by_mitigation():\n global technique_mitigated_by_mitigation\n\n if not technique_mitigated_by_mitigation:\n technique_mitigated_by_mitigation = rsh.technique_mitigated_by_mitigation(get_srcs())\n\n return technique_mitigated_by_mitigation","def get_all_techniques(src, source_name, tactic=None):\n filters = [\n Filter(\"type\", \"=\", \"attack-pattern\"),\n Filter(\"external_references.source_name\", \"=\", source_name),\n ]\n if tactic:\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\n\n results = src.query(filters)\n return remove_deprecated(results)","def get_techniques_used_by_tools():\n global techniques_used_by_tools\n\n if not techniques_used_by_tools:\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\n \n return techniques_used_by_tools","def techniques(self):\n return self._get_child_page_of_type(LearningTechniquesPage)","def get_techniques_used_by_groups():\n global techniques_used_by_groups\n\n if not techniques_used_by_groups:\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\n\n return techniques_used_by_groups","def get_technique_by_id(self, id_code):\r\n return self.fs.query([\r\n Filter('type', '=', 'attack-pattern'),\r\n Filter('external_references.external_id', '=', id_code)\r\n ])","def generate_features(stances, dataset, name, feature_list, features_dir):\n h, b, y, bodyId, headId = [], [], [], [], []\n\n feature_dict = {'overlap': word_overlap_features,\n 'refuting': refuting_features,\n 'polarity': polarity_features,\n 'hand': hand_features,\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\n 'NMF_fit_all': NMF_fit_all,\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\n 'NMF_cos_50': NMF_cos_50,\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\n 'ppdb': ppdb,\n 'stanford_ppdb': stanford_ppdb_score,\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\n 'stanford_sentiment': stanford_sentiment,\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\n 'stanford_wordsim': stanford_based_verb_noun_sim,\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\n 'stanford_negation': stanford_negation_features,\n 'stanford_negation_1sent': stanford_negation_features_1sent,\n 'stanford_negation_2sent': stanford_negation_features_2sent,\n 'stanford_negation_3sent': stanford_negation_features_3sent,\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\n 'hedging': hedging_features,\n 'sen2sen': sen2sen_similarity_max,\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\n 'wmdsenDoc': word_mover_distance_wholebody,\n 'sdm_sim': sdm_sim,\n 'discuss': discuss_features,\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\n 'lexical_features': lexical_features,\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\n 'mpqa_unigrams': mpqa_unigrams,\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\n 'nrc_emo_lex': nrc_emo_lex,\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\n #'POS_features': POS_features,\n 'readability_features': readability_features,\n 'sentiment140_unigrams': sentiment140_unigrams,\n 'structural_features': structural_features,\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\n 'NMF_cos_300': NMF_cos_300\n }\n\n stanceCounter = 0\n for stance in stances:\n y.append(LABELS.index(stance['Stance']))\n h.append(stance['Headline'])\n b.append(dataset.articles[stance['Body ID']])\n bodyId.append(stance['Body ID'])\n headId.append(name+str(stanceCounter))\n stanceCounter += 1\n\n X_feat = []\n feat_list = []\n last_index = 0\n for feature in feature_list:\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\"/\"+feature+\".\"+name+'.npy', bodyId, feature, headId, fold=name)\n feat_list.append((last_index, last_index+len(feat[0]), str(feature)))\n last_index += len(feat[0])\n X_feat.append(feat)\n X = np.concatenate(X_feat, axis=1)\n\n return X, y, feat_list","def get_techniques_used_by_malware():\n global techniques_used_by_malware\n \n if not techniques_used_by_malware:\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\n \n return techniques_used_by_malware","def get_skills(lang, spec=None):\n\n if lang.lower() == 'java':\n output = get_java(spec)\n if spec is not None:\n print(f\"Our suggestions for {spec} are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n else:\n print(\"Our suggestions for are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n elif lang.lower() == 'python':\n output = get_python(spec)\n if spec is not None:\n print(f\"Our suggestions for {spec} are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n else:\n print(\"Our suggestions for are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")","def _transform_data(self):\n parser = self.parse_xml(self._data_src, 'Chemical')\n for chemical in parser:\n if 'displayName' not in chemical.attrib:\n continue\n\n # initial setup and get label\n display_name = chemical.attrib['displayName']\n if not display_name or not re.search(TAGS_REGEX, display_name):\n continue\n label = re.sub(TAGS_REGEX, '', display_name)\n params = {\n 'label': label\n }\n\n # get concept ID\n reg_no = chemical.find('NumberList').find(\"CASRegistryNumber\")\n if not reg_no:\n continue\n params['concept_id'] = \\\n f'{NamespacePrefix.CASREGISTRY.value}:{reg_no.text}'\n\n # get aliases\n aliases = []\n label_l = label.lower()\n name_list = chemical.find('NameList')\n if name_list:\n for name in name_list.findall('NameOfSubstance'):\n text = name.text\n if text != display_name and text.lower() != label_l:\n aliases.append(re.sub(TAGS_REGEX, '', text))\n params['aliases'] = aliases\n\n # get xrefs and associated_with\n params['xrefs'] = []\n params['associated_with'] = []\n locator_list = chemical.find('LocatorList')\n if locator_list:\n for loc in locator_list.findall('InternetLocator'):\n if loc.text == 'DrugBank':\n db = f'{NamespacePrefix.DRUGBANK.value}:' \\\n f'{loc.attrib[\"url\"].split(\"/\")[-1]}'\n params['xrefs'].append(db)\n elif loc.text == 'FDA SRS':\n fda = f'{NamespacePrefix.FDA.value}:' \\\n f'{loc.attrib[\"url\"].split(\"/\")[-1]}'\n params['associated_with'].append(fda)\n\n # double-check and load full record\n assert Drug(**params)\n self._load_therapy(params)","def filter_ordered_questions_by_concepts(questions, item_params, target_concept, concept_map): \n \n def get_parents(target, concept_map):\n \"\"\"\n Given a target concept, return a list of concept IDs for parent concepts\n \"\"\"\n parents = []\n target_index = concept_map[CONCEPTS_STR].index(target)\n for row in range(len(concept_map[ADJ_MAT_STR])): \n # get value in adjMat for each row at target concept's col\n val = concept_map[ADJ_MAT_STR][row][target_index] \n if val > 0 and target_index != row: # don't care concepts are their own parents\n # print('parent found at {}, {}'.format(row, target_index)) # TODO remove\n parents.append(concept_map[CONCEPTS_STR][row])\n return parents\n\n def get_children(target, concept_map):\n \"\"\"\n Given a target concept, return a list of concept IDs for all child concepts\n \"\"\"\n child_inds = []\n target_index = concept_map[CONCEPTS_STR].index(target)\n target_row = concept_map[ADJ_MAT_STR][target_index]\n for ind in range(len(target_row)): # for each ind in row of adj mat\n val = target_row[ind]\n if(val>0 and ind != target_index): # don't care concept is child of itself\n child_inds.append(ind)\n return list(map(lambda ind: concept_map[CONCEPTS_STR][ind], child_inds))\n\n def get_concept(eid, item_params):\n \"\"\"\n Given a question/exercise eid (String) and item_params (pd.DataFrame),\n return the concept that exercise corresponds to.\n\n Assumes eid is unique (maps to exactly 1 row)\n \"\"\"\n\n concept = item_params[item_params[EID]==eid][CONCEPT]\n\n if len(concept) < 1: # TODO: make this filter \"!= 1\" to be more strict\n raise Exception(\"Exercise ID does not map to any exercises. eid: {}\".format(eid))\n else:\n return concept.iloc[0] # to get actual value\n \n df_item_params = pd.DataFrame.from_dict(item_params)\n\n rec_eids = []\n\n # get all recommendations from target, its children, parents\n rec_eids += list(filter(lambda q: get_concept(q, df_item_params) in \n [target_concept] + \n get_children(target_concept, concept_map) +\n get_parents(target_concept, concept_map), questions))\n \n # # GET TOP FOR EACH RELATIONSHIP\n # # add max_num_target questions of same concept to rec_eids\n # rec_target = list(filter(lambda q: get_concept(q, df_item_params) == target_concept, \n # questions))[:max_num_target]\n # # print(\"For target, added {}\".format(rec_target))\n # rec_eids += rec_target\n \n\n # # add max_num_child questions of child concepts to rec_eids\n # rec_child = list(filter(lambda q: get_concept(q, df_item_params) in get_children(target_concept, concept_map), \n # questions))[:max_num_child]\n # # print(\"For children, added {}\".format(rec_child ))\n # rec_eids += rec_child\n\n # # add max_num_parent questions of parent concepts to rec_eids\n # rec_parent = list(filter(lambda q: get_concept(q, df_item_params) in get_parents(target_concept, concept_map), \n # questions))[:max_num_parent] \n # # print(\"For parents, added {}\".format(rec_parent))\n # rec_eids += rec_parent \n\n # want order of recommendations to stay same & only grab from top half of recommendations\n # return list(filter(lambda eid: eid in rec_eids, questions[:int(len(questions)/2)+1])) \n\n # want order of recommendations to stay same\n return list(filter(lambda eid: eid in rec_eids, questions))","def get_subtechniques_of():\n global subtechniques_of\n\n if not subtechniques_of:\n subtechniques_of = rsh.subtechniques_of(get_srcs())\n \n return subtechniques_of","def generate_features_test(stances, dataset, name, feature_list, features_dir):\n h, b, bodyId, headId = [], [], [], []\n\n feature_dict = {'overlap': word_overlap_features,\n 'refuting': refuting_features,\n 'polarity': polarity_features,\n 'hand': hand_features,\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\n 'NMF_fit_all': NMF_fit_all,\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\n 'NMF_cos_50': NMF_cos_50,\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\n 'ppdb': ppdb,\n 'stanford_ppdb': stanford_ppdb_score,\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\n 'stanford_sentiment': stanford_sentiment,\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\n 'stanford_wordsim': stanford_based_verb_noun_sim,\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\n 'stanford_negation': stanford_negation_features,\n 'stanford_negation_1sent': stanford_negation_features_1sent,\n 'stanford_negation_2sent': stanford_negation_features_2sent,\n 'stanford_negation_3sent': stanford_negation_features_3sent,\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\n 'hedging': hedging_features,\n 'sen2sen': sen2sen_similarity_max,\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\n 'wmdsenDoc': word_mover_distance_wholebody,\n 'sdm_sim': sdm_sim,\n 'discuss': discuss_features,\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\n 'lexical_features': lexical_features,\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\n 'mpqa_unigrams': mpqa_unigrams,\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\n 'nrc_emo_lex': nrc_emo_lex,\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\n #'POS_features': POS_features,\n 'readability_features': readability_features,\n 'sentiment140_unigrams': sentiment140_unigrams,\n 'structural_features': structural_features,\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\n 'NMF_cos_300': NMF_cos_300\n }\n\n stanceCounter = 0\n for stance in stances:\n h.append(stance['Headline'])\n b.append(dataset.articles[stance['Body ID']])\n bodyId.append(stance['Body ID'])\n headId.append(name+str(stanceCounter))\n stanceCounter += 1\n\n X_feat = []\n for feature in feature_list:\n print(\"calculate feature: \" + str(feature))\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\"/\"+feature+\"_test.\"+name+'.npy', bodyId, feature, headId, fold=name)\n X_feat.append(feat)\n print(len(feat))\n X = np.concatenate(X_feat, axis=1)\n return X","def extract_data_in_pylot_format(actor_list):\n # Note: the output will include the ego vehicle as well.\n vec_actors = actor_list.filter('vehicle.*')\n vehicles = [\n Obstacle.from_carla_actor(vec_actor) for vec_actor in vec_actors\n ]\n\n person_actors = actor_list.filter('walker.pedestrian.*')\n people = [\n Obstacle.from_carla_actor(ped_actor) for ped_actor in person_actors\n ]\n\n tl_actors = actor_list.filter('traffic.traffic_light*')\n traffic_lights = [\n TrafficLight.from_carla_actor(tl_actor) for tl_actor in tl_actors\n ]\n\n speed_limit_actors = actor_list.filter('traffic.speed_limit*')\n speed_limits = [\n SpeedLimitSign.from_carla_actor(ts_actor)\n for ts_actor in speed_limit_actors\n ]\n\n traffic_stop_actors = actor_list.filter('traffic.stop')\n traffic_stops = [\n StopSign.from_carla_actor(ts_actor) for ts_actor in traffic_stop_actors\n ]\n\n return (vehicles, people, traffic_lights, speed_limits, traffic_stops)","def get_mitigation_mitigates_techniques():\n global mitigation_mitigates_techniques\n\n if not mitigation_mitigates_techniques:\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\n\n return mitigation_mitigates_techniques","def example_data():\n return [\n {'id': 'text', 'title': {'en': 'Text'}, 'type': 'resourcetypes'},\n {'id': 'data', 'title': {'en': 'Data'}, 'type': 'resourcetypes',\n 'tags': ['recommended']},\n ]","def process_data_from_input_file(triplet):\n\n sentence = triplet.subject + ' ' + triplet.predicate + ' ' + triplet.object\n doc = nlp(unicode(sentence))\n root = doc[0]\n for t in doc:\n if t.pos_ == 'VERB' and t.head == t:\n root = t\n # elif t.pos_ == 'NOUN'\n\n # also, if only one sentence\n # root = doc[:].root\n\n\n \"\"\"\n CURRENT ASSUMPTIONS:\n - People's names are unique (i.e. there only exists one person with a certain name).\n - Pet's names are unique\n - The only pets are dogs and cats\n - Only one person can own a specific pet\n - A person can own only one pet\n \"\"\"\n\n\n # Process (PERSON, likes, PERSON) relations\n if root.lemma_ == 'like':\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and triplet.object in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and \"n't\" not in triplet.predicate:\n s = add_person(triplet.subject)\n o = add_person(triplet.object)\n s.likes.append(o)\n\n if root.lemma_ == 'be' and triplet.object.startswith('friends with'):\n fw_doc = nlp(unicode(triplet.object))\n with_token = [t for t in fw_doc if t.text == 'with'][0]\n # get text after with\n after_with = fw_doc.text.split(with_token.text+ ' ')[1]\n people = []\n for p in after_with.split(' '):\n if nlp(p)[0].tag_ == 'NNP':\n people.append(nlp(p)[0].text)\n # fw_who = [t for t in with_token.children if t.dep_ == 'pobj'][0].text\n # fw_who = [e for e in fw_doc.ents if e.label_ == 'PERSON'][0].text\n for p in people:\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON']:\n s = add_person(triplet.subject)\n o = add_person(p)\n s.likes.append(o)\n o.likes.append(s)\n if root.lemma_ == 'be' and triplet.object == 'friends':\n fw_doc = nlp(unicode(triplet.subject))\n and_token = [t for t in fw_doc if t.text == 'and']\n if and_token:\n and_token = and_token[0].text\n if and_token == 'and' and fw_doc[0].text in [e.text for e in doc.ents if e.label_ == 'PERSON'] and fw_doc[2].text in [e.text for e in doc.ents if e.label_ == 'PERSON']:\n s = add_person(fw_doc[0].text)\n o = add_person(fw_doc[2].text)\n s.likes.append(o)\n o.likes.append(s)\n\n # Process (PET, has, NAME) Mary's dog's name is Rover\n if triplet.subject.endswith('name') and ('dog' in triplet.subject or 'cat' in triplet.subject):\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\n\n # handle single names, but what about compound names? Noun chunks might help.\n if (len(obj_span) == 1 or len(obj_span) == 2) and obj_span[-1].pos_ == 'PROPN':\n name = triplet.object\n subj_start = sentence.find(triplet.subject)\n subj_doc = doc.char_span(subj_start, subj_start + len(triplet.subject))\n\n s_people = [token.text for token in subj_doc if token.ent_type_ == 'PERSON']\n assert len(s_people) == 1\n s_person = select_person(s_people[0])\n\n pet = get_persons_pet(s_person.name)\n\n pet.name = name\n s_person.has.append(pet)\n\n # Process (Who has dog)\n if root.lemma_ == 'have'and ('dog' in triplet.object or 'cat' in triplet.object):\n # find pets name and instantiate name empty str\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\n name = ''\n\n if obj_span[-1].pos_ == 'PROPN':\n name = obj_span[-1].text\n s = add_person(triplet.subject)\n s_pet_type = 'dog' if 'dog' in triplet.object else 'cat'\n pet = add_pet(s_pet_type, name)\n s.has.append(pet)\n\n date = [e.text for e in doc.ents if e.label_ == 'DATE']\n gpe = [e.text for e in doc.ents if e.label_ == 'GPE']\n person = [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG']\n # if person and GPE exists, we add it into trip(departs_on, departs_to)\n if person and (gpe or date):\n s = add_person(triplet.subject)\n o = add_trip(date, gpe)\n s.travels.append(o)","def get_technique_to_domain():\n global technique_to_domain\n \n if not technique_to_domain:\n technique_to_domain = stixhelpers.get_technique_id_domain_map(get_ms())\n \n return technique_to_domain","def _read_fight_talents(root):\n output_list = []\n for _, value in enumerate(root[0][8]):\n # offensive and defensive test possible, so add every entry twice\n output_list.append(FightTalent(value, \"AT\"))\n output_list.append(FightTalent(value, \"PA\"))\n return output_list","def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\n return sorted(list(set(expert_knowledge)))","def read_squad_examples(input_file, return_answers, context_only=False, question_only=False,\n draft=False, draft_num_examples=12, append_title=False):\n with open(input_file, \"r\") as reader:\n input_data = json.load(reader)[\"data\"]\n\n examples = []\n ans_cnt = 0\n no_ans_cnt = 0\n\n # Only word-based tokenization is peformed (whitespace based)\n for doc_idx, entry in enumerate(input_data):\n title = entry['title'][0] if type(entry['title']) == list else entry['title']\n assert type(title) == str\n\n for par_idx, paragraph in enumerate(entry[\"paragraphs\"]):\n # Do not load context for question only\n if not question_only:\n paragraph_text = paragraph[\"context\"]\n title_offset = 0\n if append_title:\n title_str = '[ ' + ' '.join(title.split('_')) + ' ] '\n title_offset += len(title_str)\n paragraph_text = title_str + paragraph_text\n # Note that we use the term 'word' for whitespace based words, and 'token' for subtokens (for BERT input)\n doc_words, char_to_word_offset = context_to_words_and_offset(paragraph_text)\n\n # 1) Context only ends here\n if context_only:\n metadata = {}\n if \"pubmed_id\" in entry:\n entry_keys = [\n \"pubmed_id\", \"sha\", \"title_original\", \"title_entities\",\n \"journal\", \"authors\", \"article_idx\"\n ]\n para_keys = [\"context_entities\"]\n for entry_key in entry_keys:\n if entry_key in entry:\n metadata[entry_key] = entry[entry_key]\n for para_key in para_keys:\n if para_key in paragraph:\n metadata[para_key] = paragraph[para_key]\n # metadata[\"pubmed_id\"] = (metadata[\"pubmed_id\"] if not pd.isnull(metadata[\"pubmed_id\"])\n # else 'NaN')\n example = SquadExample(\n doc_words=doc_words,\n title=title,\n doc_idx=doc_idx,\n par_idx=par_idx,\n metadata=metadata)\n examples.append(example)\n\n if draft and len(examples) == draft_num_examples:\n return examples\n continue\n\n # 2) Question only or 3) context/question pair\n else:\n for qa in paragraph[\"qas\"]:\n qas_id = str(qa[\"id\"])\n question_text = qa[\"question\"]\n\n # Noisy question skipping\n if len(question_text.split(' ')) == 1:\n logger.info('Skipping a single word question: {}'.format(question_text))\n continue\n if \"I couldn't could up with another question.\" in question_text:\n logger.info('Skipping a strange question: {}'.format(question_text))\n continue\n\n start_position = None\n end_position = None\n orig_answer_text = None\n\n # For pre-processing that should return answers together\n if return_answers:\n assert type(qa[\"answers\"]) == dict or type(qa[\"answers\"]) == list, type(qa[\"answers\"])\n if type(qa[\"answers\"]) == dict:\n qa[\"answers\"] = [qa[\"answers\"]]\n\n # No answers\n if len(qa[\"answers\"]) == 0:\n orig_answer_text = \"\"\n start_position = -1 # Word-level no-answer => -1\n end_position = -1\n no_ans_cnt += 1\n # Answer exists\n else:\n answer = qa[\"answers\"][0]\n ans_cnt += 1\n\n orig_answer_text = answer[\"text\"]\n answer_offset = answer[\"answer_start\"] + title_offset\n answer_length = len(orig_answer_text)\n start_position = char_to_word_offset[answer_offset]\n end_position = char_to_word_offset[answer_offset + answer_length - 1]\n\n # Only add answers where the text can be exactly recovered from the context\n actual_text = \" \".join(doc_words[start_position:(end_position + 1)])\n cleaned_answer_text = \" \".join(\n tokenization.whitespace_tokenize(orig_answer_text)) # word based tokenization\n if actual_text.find(cleaned_answer_text) == -1:\n logger.warning(\"Could not find answer: '%s' vs. '%s'\",\n actual_text, cleaned_answer_text)\n continue\n\n # Question only ends here\n if question_only:\n example = SquadExample(\n qas_id=qas_id,\n question_text=question_text)\n\n # Context/question pair ends here\n else:\n example = SquadExample(\n qas_id=qas_id,\n question_text=question_text,\n paragraph_text=paragraph_text,\n doc_words=doc_words,\n orig_answer_text=orig_answer_text,\n start_position=start_position,\n end_position=end_position,\n title=title,\n doc_idx=doc_idx,\n par_idx=par_idx)\n examples.append(example)\n\n if draft and len(examples) == draft_num_examples:\n return examples\n\n # Testing for shuffled draft (should comment out above 'draft' if-else statements)\n if draft:\n random.shuffle(examples)\n logger.info(str(len(examples)) + ' were collected before draft for shuffling')\n return examples[:draft_num_examples]\n\n logger.info('Answer/no-answer stat: %d vs %d'%(ans_cnt, no_ans_cnt))\n return examples","def skill_pedagogic_ressources(request, type, slug):\n\n if type == 'skill':\n base = get_object_or_404(Skill, code=slug)\n elif type == 'section':\n base = get_object_or_404(Section, id=slug)\n else:\n # type == 'coder'\n base = get_object_or_404(CodeR, id=slug)\n\n personal_resource = base.resource.filter(section=\"personal_resource\")\n lesson_resource = base.resource.filter(section=\"lesson_resource\")\n exercice_resource = base.resource.filter(section=\"exercice_resource\")\n other_resource = base.resource.filter(section=\"other_resource\")\n\n exercice_resource_sesamath = list()\n lesson_resource_sesamath = list()\n lesson_resource_khanacademy = list()\n\n sori_skills_lesson_resources = list()\n sori_skills_exercice_resources = list()\n sori_skills_other_resources = list()\n\n sori_skills_lesson_resource_sesamath = list()\n sori_skills_lesson_resource_khanacademy = list()\n sori_skills_exercice_resource_sesamath = list()\n\n sori_coder_lesson_resources = list()\n sori_coder_exercice_resources = list()\n sori_coder_other_resources = list()\n\n sori_coder_lesson_resource_sesamath = list()\n sori_coder_lesson_resource_khanacademy = list()\n sori_coder_exercice_resource_sesamath = list()\n\n # Sorting the different type of resources by category (personal, lesson, exercice or other)\n # and by type (khanacademy, sesamath or other)\n\n for exo in lesson_resource:\n if exo.content.get('from') and exo.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\n lesson_resource_sesamath.append([exo.pk, resource])\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\n\n elif exo.content.get('from') and exo.content['from'] == \"skills_khanacademyvideoskill\":\n resource = get_object_or_404(KhanAcademy, pk=exo.content['referenced'])\n lesson_resource_khanacademy.append([exo.pk, resource])\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\n\n for exo in exercice_resource:\n if exo.content.get('from') and exo.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\n exercice_resource_sesamath.append([exo.pk, resource])\n exercice_resource = exercice_resource.exclude(pk=exo.pk)\n\n # Do the same operation for similar or identical resources :\n # Some Skill or CodeR can be similar or identical to other Skill or CodeR\n # We have to display the resources of those as well\n\n # We achieve this only if we have either a Skill or a CodeR.\n # If it is a Section, we don't display the similar/identical resources\n # First step : is base a Skill or a CodeR ?\n if isinstance(base, Skill):\n # Begin with the Skills, same step is done for CodeR a bit further\n\n # Retrieve all the similar and identical Relations for Skills\n skills_from = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], from_skill=base.id))\n skills_to = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], to_skill=base.id))\n\n # This list will gather all the similar or 'identic' Skills\n # (similar_or_identic = sori)\n sori_skills = list()\n for skill_from in skills_from:\n sori_skills.append(skill_from.to_skill)\n for skill_to in skills_to:\n sori_skills.append(skill_to.from_skill)\n\n # Get the associated resources for these Skills\n # We need to keep them separated by section to ease their use in the template\n\n for skill in sori_skills:\n lesson = skill.resource.filter(section=\"lesson_resource\")\n if lesson:\n sori_skills_lesson_resources.append([skill, lesson])\n exercice = skill.resource.filter(section=\"exercice_resource\")\n if exercice:\n sori_skills_exercice_resources.append([skill, exercice])\n other = skill.resource.filter(section=\"other_resource\")\n if other:\n sori_skills_other_resources.append([skill, other])\n\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\n for skill in sori_skills_lesson_resources:\n # Lists to regroup resources together\n sesamath_list = list()\n khan_list = list()\n\n for res in skill[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n skill[1] = skill[1].exclude(pk=res.pk)\n\n elif res.content.get('from') and res.content['from'] == \"skills_khanacademyvideoskill\":\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\n khan_list.append([res.pk, resource])\n skill[1] = skill[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the Skill, we remove it from the list\n if not skill[1]:\n sori_skills_lesson_resources.remove(skill)\n\n # Once resources are sorted, we can add them with the associated Skill\n if sesamath_list:\n sori_skills_lesson_resource_sesamath.append([skill[0], sesamath_list])\n if khan_list:\n sori_skills_lesson_resource_khanacademy.append([skill[0], khan_list])\n\n # sori_skills_exercice_resources has resources of different types, we need to distinguish them\n for skill in sori_skills_exercice_resources:\n # List to regroup resources together\n sesamath_list = list()\n\n for res in skill[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n skill[1] = skill[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the Skill, we remove it from the list\n if not skill[1]:\n sori_skills_exercice_resources.remove(skill)\n\n # Once resources are sorted, we can add them with the associated Skill\n if sesamath_list:\n sori_skills_exercice_resource_sesamath.append([skill[0], sesamath_list])\n\n if isinstance(base, CodeR) or isinstance(base, Skill):\n # Repeat this operation for the CodeR\n # TODO : Group these steps as a function for Skill and CodeR\n # Retrieve all the CodeR related to the current Skill or to the current CodeR\n\n if isinstance(base, Skill):\n related_coder = CodeR.objects.filter(skill=base.id)\n else:\n coder_from = CodeR_relations.objects.filter(\n Q(relation_type__in=['similar_to', 'identic_to'], from_coder=base.id))\n coder_to = CodeR_relations.objects.filter(\n Q(relation_type__in=['similar_to', 'identic_to'], to_coder=base.id))\n\n # This list will gather all the similar or 'identic' CodeR\n related_coder = list()\n for coder_from in coder_from:\n related_coder.append(coder_from.to_coder)\n for coder_to in coder_to:\n related_coder.append(coder_to.from_coder)\n # Case where we look for CodeR similar or identic to a CodeR\n\n # Get the associated resources for these CodeR\n\n for coder in related_coder:\n lesson = coder.resource.filter(section=\"lesson_resource\")\n if lesson:\n sori_coder_lesson_resources.append([coder, lesson])\n exercice = coder.resource.filter(section=\"exercice_resource\")\n if exercice:\n sori_coder_exercice_resources.append([coder, exercice])\n other = coder.resource.filter(section=\"other_resource\")\n if other:\n sori_coder_other_resources.append([coder, other])\n\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\n for coder in sori_coder_lesson_resources:\n # Lists to regroup resources together\n sesamath_list = list()\n khan_list = list()\n\n for res in coder[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n coder[1] = coder[1].exclude(pk=res.pk)\n\n elif res.content.get('from') and res.content['from'] == \"skills_khanacademyvideoskill\":\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\n khan_list.append([res.pk, resource])\n coder[1] = coder[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the Skill, we remove it from the list\n if not coder[1]:\n sori_coder_lesson_resources.remove(coder)\n\n # Once resources are sorted, we can add them with the associated Skill\n if sesamath_list:\n sori_coder_lesson_resource_sesamath.append([coder[0], sesamath_list])\n if khan_list:\n sori_coder_lesson_resource_khanacademy.append([coder[0], khan_list])\n\n # sori_coder_exercice_resources has resources of different types, we need to distinguish them\n for coder in sori_coder_exercice_resources:\n # List to regroup resources together\n sesamath_list = list()\n\n for res in coder[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n coder[1] = coder[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the CodeR, we remove it from the list\n if not coder[1]:\n sori_coder_exercice_resources.remove(coder)\n\n # Once resources are sorted, we can add them with the associated CodeR\n if sesamath_list:\n sori_coder_exercice_resource_sesamath.append([coder[0], sesamath_list])\n\n\n sesamath_references_manuals = Sesamath.objects.filter(ressource_kind__iexact=\"Manuel\")\n sesamath_references_cahiers = Sesamath.objects.filter(ressource_kind__iexact=\"Cahier\")\n\n return render(request, \"professor/skill/update_pedagogical_resources.haml\", {\n \"sesamath_references_manuals\": sesamath_references_manuals,\n \"sesamath_references_cahier\": sesamath_references_cahiers,\n \"base\": base,\n \"personal_resources\": personal_resource,\n \"other_resources\": other_resource,\n \"exercice_resources\": exercice_resource,\n \"exercice_resource_sesamath\": exercice_resource_sesamath,\n \"lesson_resources\": lesson_resource,\n \"lesson_resource_sesamath\": lesson_resource_sesamath,\n \"lesson_resource_khanacademy\": lesson_resource_khanacademy,\n \"type\": type,\n \"sori_skills_exercice_resources\": sori_skills_exercice_resources,\n \"sori_skills_lesson_resources\": sori_skills_lesson_resources,\n \"sori_skills_other_resources\": sori_skills_other_resources,\n \"sori_skills_lesson_resource_sesamath\": sori_skills_lesson_resource_sesamath,\n \"sori_skills_lesson_resource_khanacademy\": sori_skills_lesson_resource_khanacademy,\n \"sori_skills_exercice_resource_sesamath\": sori_skills_exercice_resource_sesamath,\n \"sori_coder_exercice_resources\": sori_coder_exercice_resources,\n \"sori_coder_lesson_resources\": sori_coder_lesson_resources,\n \"sori_coder_other_resources\": sori_coder_other_resources,\n \"sori_coder_lesson_resource_sesamath\": sori_coder_lesson_resource_sesamath,\n \"sori_coder_lesson_resource_khanacademy\": sori_coder_lesson_resource_khanacademy,\n \"sori_coder_exercice_resource_sesamath\": sori_coder_exercice_resource_sesamath,\n })","def get_data_loaders_3sentences():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_3generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+3):]\n #history_complete.append(history)\n if len(history) > 6:\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def _add_descriptors(related):\n\n for r in related:\n r[\"descriptors\"] = []\n for edge in G.edges(data=True):\n sibling_idx = _get_connected(edge, r[\"tokenIndex\"])\n if sibling_idx and (A.lookup[int(sibling_idx)][\"pos\"] == \"JJ\" or edge[2][\"dep\"] in [\"amod\", \"compound\"]):\n r[\"descriptors\"].append(\n {\n \"tokenIndex\": sibling_idx,\n \"rawName\": A.lookup[int(sibling_idx)][\"word\"]\n }\n )\n\n if sibling_idx and \"NN\" in A.lookup[int(sibling_idx)][\"pos\"] and \"amod\" in edge[2][\"dep\"]:\n additional_related = _get_cousin(sibling_idx, [\"nmod\"])\n for add in set(additional_related):\n related = _add_related(add, \"nmod\", related, A.index_lookup[add],\n connector=G.nodes[sibling_idx]['word'])\n return related","def data_process(list_of_speeches_pronoun_data):\n speech_list = []\n first_person_singular_proportion_list = []\n first_person_plural_proportion_list = []\n second_person_proportion_list = []\n third_person_singular_proportion_list = []\n third_person_plural_proportion_list = []\n\n for speech_data in list_of_speeches_pronoun_data:\n speech_list.append(speech_data[0].replace('_', ' ').title())\n first_person_singular_proportion_list.append(speech_data[2]['first_person_singular'])\n first_person_plural_proportion_list.append(speech_data[2]['first_person_plural'])\n second_person_proportion_list.append(speech_data[2]['second_person'])\n third_person_singular_proportion_list.append(speech_data[2]['third_person_singular'])\n third_person_plural_proportion_list.append(speech_data[2]['third_person_plural'])\n\n return [\n speech_list,\n first_person_singular_proportion_list,\n first_person_plural_proportion_list,\n second_person_proportion_list,\n third_person_singular_proportion_list,\n third_person_plural_proportion_list\n ]","def _create_examples(self, lines, kb_data, set_type):\n examples = []\n for idx, line in enumerate(lines):\n item = json.loads(line.strip())\n question_id = \"%s-%s\" % (set_type, idx)\n \n context_a_list = kb_data[idx]['answerA']\n context_b_list = kb_data[idx]['answerB']\n context_c_list = kb_data[idx]['answerC']\n\n context_a = \"\"\n for l in context_a_list[:1]:\n context_a += l.replace(\"\\n\",\". \")\n context_a = context_a[:-1]\n\n context_b = \"\"\n for l in context_b_list[:1]:\n context_b += l.replace(\"\\n\",\". \")\n context_b = context_b[:-1]\n\n context_c = \"\"\n for l in context_c_list[:1]:\n context_c += l.replace(\"\\n\",\". \")\n context_c = context_c[:-1]\n \n \n question = item[\"context\"] + item[\"question\"]\n endings = [item[\"answerA\"],item[\"answerB\"],item[\"answerC\"] ]\n label = item[\"correct\"]\n #race_id = \"%s-%s\" % (set_type, data_raw[\"race_id\"])\n #article = data_raw[\"article\"]\n #for i in range(len(data_raw[\"answers\"])):\n #truth = str(ord(data_raw[\"answers\"][i]) - ord(\"A\"))\n #question = data_raw[\"questions\"][i]\n #options = data_raw[\"options\"][i]\n\n examples.append(\n InputExample(\n example_id=question_id,\n question=question,\n contexts=[context_a,context_b,context_c],\n endings=[endings[0], endings[1], endings[2]],#, options[3]\n label=label,\n )\n )\n return examples","def pcw_protocol(DS, prot_list):\n orig_ics = DS.initialconditions.copy()\n t = 0\n for i, stage_dict in enumerate(prot_list):\n if 'pars' in stage_dict:\n DS.set(pars=stage_dict['pars'])\n if 'ics' in stage_dict:\n DS.set(ics=stage_dict['ics'])\n if 'tdata' in stage_dict:\n raise NotImplementedError(\"Replace 'tdata' with 'tdur' float value\")\n if 'tdur' in stage_dict:\n DS.set(tdata=[t, t+stage_dict['tdur']])\n traj = DS.compute('test')\n if i == 0:\n pts = traj.sample()\n t = pts['t'][-1]\n DS.set(ics=pts[-1])\n else:\n new_pts = traj.sample()\n pts.extend(new_pts, skipMatchingIndepvar=True)\n t = new_pts['t'][-1]\n DS.set(ics=pts[-1])\n DS.set(ics=orig_ics)\n return pointset_to_traj(pts, events=DS.eventstruct.events), pts","def create_feature_timex_association(self, featurelist, timexList):\n \n if not timexList or not featurelist:\n return featurelist\n \n text = self.text\n sentences = self.sentences\n \n for sentNum, sentence in enumerate(sentences):\n timexes = [t for t in timexList if t.getSentNum()==sentNum and t.getRole()!='IGNORE']\n features = [feat for feat in featurelist if feat.getSentNum()==sentNum]\n if not timexes or not features:\n continue\n \n timexes_valid = [t for t in timexes if t.getType() in ['DATE', 'REL']]\n tlinks = self.create_tlinks_from_timexes(timexes_valid, sentence)\n featureBundles = []\n nn = len(tlinks)\n if nn==1:\n for feat in features:\n feat.setTlink(tlinks[0].copy())\n elif nn > 1:\n featureBundles = self.get_feature_bundles(features, timexes_valid)\n for i in range(nn-1):\n leftTK = tlinks[i]\n rightTK = tlinks[i+1]\n fbs = [fb for fb in featureBundles if fb[0].getStartPos()>leftTK.getStartPos() \n and fb[-1].getStartPos()<rightTK.getStartPos()]\n \n for fb in fbs:\n segPre = text[leftTK.getEndPos():fb[0].getStartPos()]\n segNext = text[fb[-1].getEndPos():rightTK.getStartPos()]\n \n if leftTK and self.is_next_separated(segPre, segNext, leftTK.getTimexes()[-1], rightTK.getTimexes()[0]):\n chosenTK = leftTK\n else:\n chosenTK = rightTK\n \n for feat in fb:\n ##: set all tlink with copy to avoid the case that two paris share the same tlink.\n ##: this would make an extra copy in the first one, but can reduce the complexity of the code\n feat.setTlink(chosenTK.copy())\n \n ##: assign the first tlink to features in the begining, and last tlink to those at the end\n feats = [feat for feat in features if not feat.getTlink()]\n if feats:\n for ft in feats:\n if ft.getStartPos() < tlinks[0].getStartPos():\n ft.setTlink(tlinks[0].copy())\n else:\n ft.setTlink(tlinks[-1].copy())\n \n ##: assign the duration timex to the feature in front of it, except feature type is BETWEEN already \n timexes_dur = [t for t in timexes if t.getType()=='DUR']\n for t in timexes_dur:\n featsLeft = [ft for ft in features if ft.getEndPos() < t.getStartPos()]\n if featsLeft:\n featleft = featsLeft[-1]\n else:\n break\n \n if featleft.getTlink() and featleft.getTlink().getType()=='BETWEEN': \n continue\n \n featInBundle = False\n for fb in featureBundles:\n if featleft in fb:\n for ft in fb:\n if ft.getTlink():\n ft.getTlink().addTimex(t.copy())\n else:\n ft.setTlink(TLink(t))\n ft.getTlink().setType('DURATION')\n featInBundle = True\n break\n \n if not featInBundle:\n if featleft.getTlink():\n featleft.getTlink().addTimex(t)\n else:\n featleft.setTlink(TLink(t))\n featleft.getTlink().setType('DURATION')\n \n ##: Set flag to indicate if the feature is in a clause \n featurelist = self.flag_features_in_associate_clause(featurelist)\n \n return featurelist","def process_clinical_data(data: pd.DataFrame, grp_var: str) -> Dict:\n\n # split results by concept type (i.e. concepts used in practice, standard concepts)\n grouped_data = data.groupby(grp_var)\n\n # obtain results for groups\n results = {}\n for grp in grouped_data.groups.keys():\n results[grp] = {}\n print('Processing Group: {}'.format(grp))\n\n # get group data\n grp_data = grouped_data.get_group(grp).drop_duplicates()\n results[grp]['grp_full_data'] = grp_data\n # get concept data\n results[grp]['concept_src_code'] = [x for y in grp_data['CONCEPT_SOURCE_CODE'] for x in y.split(' | ')]\n results[grp]['concept_src_label'] = [x for y in grp_data['CONCEPT_SOURCE_LABEL'] for x in y.split(' | ')]\n results[grp]['concept_synonym'] = [x for y in grp_data['CONCEPT_SYNONYM'] for x in y.split(' | ')]\n results[grp]['concept_vocab'] = ', '.join(set([x for y in grp_data['CONCEPT_VOCAB'] for x in y.split(' | ')]))\n # get ancestor data\n results[grp]['anc_concept_id'] = [x for y in grp_data['ANCESTOR_CONCEPT_ID'] for x in y.split(' | ')]\n results[grp]['anc_src_code'] = [x for y in grp_data['ANCESTOR_SOURCE_CODE'] for x in y.split(' | ')]\n results[grp]['anc_label'] = [x for y in grp_data['ANCESTOR_LABEL'] for x in y.split(' | ')]\n results[grp]['anc_vocab'] = ', '.join(set([x for y in grp_data['ANCESTOR_VOCAB'] for x in y.split(' | ')]))\n\n return results","def inferential_analysis(x_arr, y_arr, samples_related):\n delta = np.mean(x_arr) - np.mean(y_arr)\n\n if samples_related:\n _, pval = stats.ttest_rel(\n x_arr, y_arr)\n else:\n _, pval = stats.ttest_ind(\n x_arr, y_arr, equal_var=False) # _ = tstat\n return {\n 'Hypothesis Testing': {\n 'Treatment vs Control': {\n 'Difference': delta,\n 'p_value': pval,\n 'percent_bias': percent_bias(x_arr, y_arr),\n # 'cohen\\'s d effect size': cohen_d(x_arr, y_arr),\n # 'Wilcoxon rank-sum statistic': stats.ranksums(x_arr, y_arr),\n }\n }\n }","def find_related_concepts(text):\n #\n # Find wikipedia terms in text\n # Spot terms in text\n text = text.encode('ascii', 'replace')\n response = TagMeService.check_spotting(text)\n\n results = return_wikipedia_term(response)\n\n #\n # Lookup terms in taxonomy and fetch relatedConcepts (keywords)\n # return related terms if they are found in the taxonomy\n\n return lookup_in_taxonomy(results)","def get_tools_using_technique():\n global tools_using_technique\n\n if not tools_using_technique:\n tools_using_technique = rsh.tools_using_technique(get_srcs())\n \n return tools_using_technique","def get_data_loaders_4sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"]\n #history_complete.append(history)\n if len(history_splitted) > (len(persona)-1):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def _create_examples(self, data_dir, set_type):\n\t\texamples = []\n\t\tinput_file_data = os.path.join(data_dir, \"data.tsv\")\n\t\twith open(input_file_data, \"r\", encoding=\"utf-8-sig\") as f:\n\t\t\tfor i, inp in enumerate(f):\n\t\t\t\tinps = inp.split('\\t') \n\t\t\t\tguid = \"%s-%s\" % (set_type, i)\n\t\t\t\ttext_inp = inps[1].strip()\n\t\t\t\ttext_out = inps[2].strip()\n\t\t\t\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\n\t\t\t\t\n\t\t\t# Sort these out before returning\n\t\t\texamples = sorted(examples, key=sort_inp_len)\n\t\t\treturn examples","async def insert_attack_stix_data(self):\n logging.info('Downloading ATT&CK data from STIX/TAXII...')\n attack = {}\n collection = Collection(\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\")\n tc_source = TAXIICollectionSource(collection)\n filter_objs = {\"techniques\": Filter(\"type\", \"=\", \"attack-pattern\"),\n \"groups\": Filter(\"type\", \"=\", \"intrusion-set\"), \"malware\": Filter(\"type\", \"=\", \"malware\"),\n \"tools\": Filter(\"type\", \"=\", \"tool\"), \"relationships\": Filter(\"type\", \"=\", \"relationship\")}\n for key in filter_objs:\n attack[key] = tc_source.query(filter_objs[key])\n references = {}\n\n # add all of the patterns and dictionary keys/values for each technique and software\n for i in attack[\"techniques\"]:\n if \"description\" in i:\n descr = i['description'].replace('<code>', '').replace('</code>', '').replace(\n '\\n', '').encode('ascii', 'ignore').decode('ascii')\n else:\n descr = \"missing description\"\n references[i[\"id\"]] = {\"name\": i[\"name\"], \"id\": i[\"external_references\"][0][\"external_id\"],\n \"example_uses\": [],\n \"description\": descr,\n \"similar_words\": [i[\"name\"]]}\n\n for i in attack[\"relationships\"]:\n if i[\"relationship_type\"] == 'uses':\n if 'attack-pattern' in i[\"target_ref\"]:\n use = i[\"description\"]\n # remove unnecessary strings, fix unicode errors\n use = use.replace('<code>', '').replace('</code>', '').replace('\"', \"\").replace(',', '').replace(\n '\\t', '').replace(' ', ' ').replace('\\n', '').encode('ascii', 'ignore').decode('ascii')\n find_pattern = re.compile('\\[.*?\\]\\(.*?\\)') # get rid of att&ck reference (name)[link to site]\n m = find_pattern.findall(use)\n if len(m) > 0:\n for j in m:\n use = use.replace(j, '')\n if use[0:2] == '\\'s':\n use = use[3:]\n elif use[0] == ' ':\n use = use[1:]\n # combine all the examples to one list\n references[i[\"target_ref\"]][\"example_uses\"].append(use)\n\n for i in attack[\"malware\"]:\n if 'description' not in i: # some software do not have description, example: darkmoon https://attack.mitre.org/software/S0209\n continue\n else:\n references[i[\"id\"]] = {\"id\": i['id'], \"name\": i[\"name\"], \"description\": i[\"description\"],\n \"examples\": [], \"example_uses\": [], \"similar_words\": [i[\"name\"]]}\n for i in attack[\"tools\"]:\n references[i[\"id\"]] = {\"id\": i['id'], \"name\": i[\"name\"], \"description\": i[\"description\"], \"examples\": [],\n \"example_uses\": [], \"similar_words\": [i[\"name\"]]}\n\n attack_data = references\n logging.info(\"Finished...now creating the database.\")\n\n cur_uids = await self.dao.get('attack_uids') if await self.dao.get('attack_uids') else []\n cur_items = [i['uid'] for i in cur_uids]\n for k, v in attack_data.items():\n if k not in cur_items:\n await self.dao.insert('attack_uids', dict(uid=k, description=defang_text(v['description']), tid=v['id'],\n name=v['name']))\n if 'regex_patterns' in v:\n [await self.dao.insert('regex_patterns', dict(uid=k, regex_pattern=defang_text(x))) for x in\n v['regex_patterns']]\n if 'similar_words' in v:\n [await self.dao.insert('similar_words', dict(uid=k, similar_word=defang_text(x))) for x in\n v['similar_words']]\n if 'false_negatives' in v:\n [await self.dao.insert('false_negatives', dict(uid=k, false_negative=defang_text(x))) for x in\n v['false_negatives']]\n if 'false_positives' in v:\n [await self.dao.insert('false_positives', dict(uid=k, false_positive=defang_text(x))) for x in\n v['false_positives']]\n if 'true_positives' in v:\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\n v['true_positives']]\n if 'example_uses' in v:\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\n v['example_uses']]\n logging.info('[!] DB Item Count: {}'.format(len(await self.dao.get('attack_uids'))))","def get_data_loaders_1sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1_sentence_final_generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+1):]\n #history_complete.append(history)\n if len(history) > 3:\n history_chatbot = history[1]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def get_one_study(study_dir):\n df_columns = ['item_type', 'item_urn', 'content']\n df = pd.DataFrame(columns=df_columns)\n\n # category urn and label\n list_files = os.listdir(study_dir)\n\n instrument_dict = {}\n if 'Instrument.txt' in list_files:\n L = json.load(open(os.path.join(study_dir, 'Instrument.txt')))\n item = item_to_dict(L[0])\n instrument_dict['instrument_urn'] = item['InstrumentURN']\n instrument_dict['instrument_name'] = item['InstrumentName']\n\n if 'Category.txt' in list_files:\n category_dict = generate_category_dict(os.path.join(study_dir, 'Category.txt'))\n else:\n category_dict = []\n\n for input_file in list_files:\n\n filename = os.path.splitext(input_file)[0]\n\n L = json.load(open(os.path.join(study_dir, input_file)))\n\n if filename == 'Question':\n for dict_item in L:\n item = item_to_dict(dict_item)\n if not item['QuestionLiteral'] is None:\n literal = item['QuestionLiteral'].replace('\\n', '')\n else:\n literal = None\n df.loc[len(df)] = ['question', item['QuestionURN'], literal]\n df.loc[len(df)] = ['question name', None, item['QuestionItemName']]\n\n if item['Response'] != {} and item['Response']['response_type'] != 'CodeList':\n df.loc[len(df)] = [item['Response']['response_type'], None, item['Response']['response_label']]\n\n elif filename == 'Interviewer Instruction':\n for dict_item in L:\n item = item_to_dict(dict_item)\n df.loc[len(df)] = ['instruction', item['InstructionURN'], item['InstructionText']]\n\n elif filename == 'Code Set' and category_dict != []:\n for dict_item in L:\n item = item_to_dict(dict_item)\n for code in item['Code']:\n if code['Value'] is None:\n code['Value'] = ''\n cat_urn = 'urn:ddi:' + code['CategoryReference']['Agency'] + ':' + code['CategoryReference']['ID'] + ':' + code['CategoryReference']['Version']\n df.loc[len(df)] = ['codelist', code['URN'], code['Value'] + ', ' + category_dict[cat_urn]]\n\n elif filename == 'Statement':\n for dict_item in L:\n item = item_to_dict(dict_item)\n df.loc[len(df)] = ['statement', item['StatementURN'], item['Literal']]\n\n elif filename == 'Conditional':\n for dict_item in L:\n item = item_to_dict(dict_item)\n df.loc[len(df)] = ['conditional', item['URN'], item['IfCondition']['Description'] + item['IfCondition']['CommandContent'] if not item['IfCondition']['CommandContent'] is None else item['IfCondition']['Description']]\n\n elif filename == 'Loop':\n for dict_item in L:\n item = item_to_dict(dict_item)\n df.loc[len(df)] = ['loop', item['URN'], item['LoopWhile']['CommandContent']]\n\n else:\n # TODO\n print(filename)\n\n df = df.drop_duplicates(keep='first')\n df['instrument_name'] = instrument_dict['instrument_name']\n df['instrument_urn'] = instrument_dict['instrument_urn']\n return df","def tokenize_one_example(self, example):\n index, q_id, question, context, answer_list, answer_start = example\n\n context = context.replace('\\'\\'', '\\\" ').replace(r'``', '\\\" ')\n context_tokens = SQuADDataTokenizer._word_tokenize_spacy(context) if self._use_spacy else \\\n SQuADDataTokenizer._word_tokenize_nltk(context)\n context_chars = [list(token) for token in context_tokens]\n spans = SQuADDataTokenizer._get_token_spans(context, context_tokens)\n\n ques = question.replace('\\'\\'', '\\\" ').replace('``', '\\\" ')\n ques_tokens = SQuADDataTokenizer._word_tokenize_spacy(ques) if self._use_spacy else \\\n SQuADDataTokenizer._word_tokenize_nltk(ques)\n ques_chars = [list(token) for token in ques_tokens]\n\n y1s, y2s = [], []\n answer_texts = []\n\n for answer_text, answer_start in zip(answer_list, answer_start):\n answer_end = answer_start + len(answer_text)\n answer_texts.append(answer_text)\n answer_span = []\n for idx, span in enumerate(spans):\n if not (answer_end <= span[0] or answer_start >= span[1]):\n answer_span.append(idx)\n y1, y2 = answer_span[0], answer_span[-1]\n y1s.append(y1)\n y2s.append(y2)\n\n result = {'context_tokens': context_tokens, 'context_chars': context_chars,\n 'ques_tokens': ques_tokens, 'ques_chars': ques_chars, 'y1s': y1s,\n 'y2s': y2s, 'id': q_id, 'context': context, 'spans': spans, 'record_idx': index}\n return result","def load_dataset(path):\n with open(path) as f:\n data = json.load(f)['data']\n output = {'qids': [], 'questions': [], 'answers': [],\n 'contexts': [], 'qid2cid': []}\n for article in data:\n for paragraph in article['paragraphs']:\n output['contexts'].append(paragraph['context'])\n for qa in paragraph['qas']:\n output['qids'].append(qa['id'])\n output['questions'].append(qa['question'])\n output['qid2cid'].append(len(output['contexts']) - 1)\n if 'answers' in qa:\n output['answers'].append(qa['answers'])\n return output","def get_clarifications_socialiqa(ex, nlp, comet_model):\n context = ex['context']\n question = ex['question']\n\n question_to_comet_relation = {\n \"What will [NAME] want to do next?\": \"xWant\",\n \"What will [NAME] want to do after?\": \"xWant\",\n \"How would [NAME] feel afterwards?\": \"xReact\",\n \"How would [NAME] feel as a result?\": \"xReact\",\n \"What will [NAME] do next?\": \"xReact\",\n \"How would [NAME] feel after?\": \"xReact\",\n \"How would you describe [NAME]?\": \"xAttr\",\n \"What kind of person is [NAME]?\": \"xAttr\",\n \"How would you describe [NAME] as a person?\": \"xAttr\",\n \"Why did [NAME] do that?\": \"xIntent\",\n \"Why did [NAME] do this?\": \"xIntent\",\n \"Why did [NAME] want to do this?\": \"xIntent\",\n \"What does [NAME] need to do beforehand?\": \"xNeed\",\n \"What does [NAME] need to do before?\": \"xNeed\",\n \"What does [NAME] need to do before this?\": \"xNeed\",\n \"What did [NAME] need to do before this?\": \"xNeed\",\n \"What will happen to [NAME]?\": \"xEffect\",\n \"What will happen to [NAME] next?\": \"xEffect\"\n }\n\n clarifications = []\n personx, _ = get_personx(nlp, context)\n relation = question_to_comet_relation.get(re.sub(personx, \"[NAME]\", question, flags=re.I), None)\n\n if relation is not None:\n outputs = {relation: comet_model.predict(context, relation, num_beams=5)}\n\n prefix = CATEGORY_TO_PREFIX[relation]\n for out_event in outputs[relation]:\n if out_event != \"none\" and out_event != \"\":\n if not out_event.lower().startswith(\"person\") and not out_event.lower().startswith(\"other\"):\n out_event = \" \".join((prefix, out_event))\n\n out_event = re.sub(\"personx\", personx, out_event, flags=re.I)\n out_event = re.sub(\"person x\", personx, out_event, flags=re.I)\n out_event = re.sub(\"persony\", \"others\", out_event, flags=re.I)\n out_event = re.sub(\"person y\", \"others\", out_event, flags=re.I)\n\n clarifications.append((question, out_event))\n\n return clarifications","def main(example, df, possible_subjects):\n\n # Drop example tutor if in df\n try:\n df.drop(df[example['url_id']==df['url_id']].index.values, inplace=True)\n df.reset_index(drop=True, inplace=True)\n except:\n pass # Tutor is not in database\n\n # Check for graduate degree\n df = graduate_degrees(example, df)\n\n # Filter by Jaccard index and location.\n sim_tuts = subject_similarity(example, df, possible_subjects)\n sim_tuts = location_overlap(example, sim_tuts)\n\n # Relevant features for computing similarity\n rel_feats = ['avg_review_length',\\\n 'badge_hours',\\\n 'days_since_last_review',\\\n 'has_rating',\\\n 'number_of_ratings',\\\n 'number_of_reviews',\\\n 'profile_picture',\\\n 'rating',\\\n 'has_ivy_degree',\\\n 'has_background_check',\\\n 'response_time',\\\n 'avg_review_sentiment']\n\n # Convert similar tutors to matrix. Normalize features.\n # In parlance of machine learning, X are features, y is hourly rate.\n X = sim_tuts[rel_feats].as_matrix().astype(np.float)\n y = sim_tuts['hourly_rate'].as_matrix().astype(np.float)\n scaler = preprocessing.StandardScaler()\n X = scaler.fit_transform(X)\n\n X_example = example[rel_feats].as_matrix().astype(np.float)\n y_example = np.float(example['hourly_rate'])\n X_example = scaler.transform(X_example)\n\n # Get cosine similarity between example tutor and tutor db.\n cos_tuts = np.empty(X.shape[0])\n for i in xrange(X.shape[0]):\n cos_tuts[i] = cosine_similarity(X[i,:], X_example)\n\n # Sort by similarity\n sorted_idx = np.argsort(cos_tuts)[::-1]\n cos_tuts = cos_tuts[sorted_idx]\n y = y[sorted_idx]\n sim_tuts.reset_index(drop=True, inplace=True)\n\n # Only keep tutors with similarity > 0.5\n sim_tuts = sim_tuts.iloc[sorted_idx][cos_tuts>.5]\n\n # Calculate three outputted tutors.\n nearest_neighbor = sim_tuts.iloc[0] # Highest similarity\n max_tut = sim_tuts[sim_tuts['hourly_rate']==sim_tuts['hourly_rate'].max()].iloc[0]\n min_tut = sim_tuts[sim_tuts['hourly_rate']==sim_tuts['hourly_rate'].min()].iloc[0]\n\n scaling = scale_kde(y, cos_tuts)\n\n kde = gaussian_kde(y[cos_tuts>0], weights=cos_tuts[cos_tuts>0])\n x = np.linspace(0, y.max()+50, y.max()+50+1)\n\n pdf = kde(x)*scaling # Probability density function (estimated)\n\n img_io = make_kde_plot(x, pdf)\n\n return nearest_neighbor, max_tut, min_tut, img_io","def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\n\n examples = []\n total_answerable = 0\n\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\n guid = \"%s-%s-%s\" % (set_type, line['qid'], str(i))\n src = []\n has_pos = 0\n\n if passage_type == 'both' and set_type != 'train':\n\n # rank by bert score in val and test\n score_key = 'rank_score'\n # score_key = \"nr_score\"\n both_sources = line[\"passages\"][:2*cand_for_each_source] + line[\"tables\"][:2*cand_for_each_source]\n all_scores = np.array([x[score_key] for x in both_sources])\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\n\n for idx in sorted_index:\n pas = both_sources[idx]\n if idx < 2 * cand_for_each_source:\n pas_type = 'passage'\n title = pas[\"article_title\"]\n else:\n pas_type = 'table'\n title = \"table_\" + pas[\"uid\"].split('-split')[0]\n\n src.append(\"question: \" + line[\"question\"].strip() + f\" </s> {pas_type} title: \" + title + f\" </s> {pas_type} content: \" + pas[\"text\"] + \" </s>\")\n\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n else:\n\n if passage_type in ['textual', 'hybrid', 'both']:\n for pas in line[\"passages\"][:cand_for_each_source]:\n # 10 passages\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> passage title: \" + pas[\"article_title\"] + \" </s> passage content: \" + pas[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\"ZAPPAtite\\\". All songs written and performed ... </s>\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n if passage_type in ['tabular', 'hybrid', 'both']:\n for tab in line[\"tables\"][:cand_for_each_source]:\n # 10 tables\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> table title: \" + \"table_\" + tab[\"uid\"].split('-split')[0] + \" </s> table content: \" + tab[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\n if isinstance(tab['judge'], dict):\n has_pos += int(tab['judge']['judge_contain_all'])\n else:\n has_pos += int(tab['judge'])\n\n const = 2 if passage_type in ['hybrid', 'both'] else 1\n if len(src) != const * cand_for_each_source:\n logger.info(line)\n src = src + [\"\"] * (const * cand_for_each_source - len(src))\n\n if has_pos > 0:\n total_answerable += 1\n\n if enable_sql_supervision and 'true_sql' in line:\n tgt = \"sql: \" + line[\"true_sql\"] + \" </s>\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\"Butler CC (KS)\\\" </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'denotation' in line:\n tgt = \"answer: \" + str(line[\"denotation\"][0]) + \" </s>\"\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'answers' in line:\n tgt = \"answer: \" + str(line[\"answers\"][0]) + \" </s>\" # e.g. answer: no slogan on current series </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt + \"\\n\")\n\n logger.info(f\"Total answerable in {set_type} split is {total_answerable / len(lines)}\")\n\n return examples","def get_clarifications_winogrande(ex, nlp, comet_model):\n personx, persony = ex['option1'], ex['option2']\n\n # Only extract relations for people\n if personx[0] != personx[0].upper() or persony[0] != persony[0].upper():\n return []\n\n input_event = ex[\"sentence\"]\n outputs = {category: comet_model.predict(input_event, category, num_beams=5) for category in comet_model.categories}\n\n curr_events = []\n for category, prefix in CATEGORY_TO_PREFIX.items():\n for out_event in outputs[category]:\n if out_event != \"none\" and out_event != \"\":\n if not out_event.lower().startswith(\"person\") and not out_event.lower().startswith(\"other\"):\n out_event = \" \".join((prefix, out_event))\n\n out_event = re.sub(\"personx\", personx, out_event, flags=re.I)\n out_event = re.sub(\"person x\", personx, out_event, flags=re.I)\n out_event = re.sub(\"persony\", persony, out_event, flags=re.I)\n out_event = re.sub(\"person y\", persony, out_event, flags=re.I)\n\n question = CATEGORY_TO_QUESTION[category].replace(\"PersonX\", personx)\n curr_events.append((question, out_event))\n\n return curr_events","def process_dataset(data, tokenizer, workers=None):\n tokenizer_class = tokenizers.get_class(tokenizer)\n make_pool = partial(Pool, workers, initializer=init)\n workers = make_pool(initargs=(tokenizer_class, {'annotators': {'lemma'}}))\n q_tokens = workers.map(tokenize, data['questions'])\n workers.close()\n workers.join()\n\n workers = make_pool(\n initargs=(tokenizer_class, {'annotators': {'lemma', 'pos', 'ner'}})\n )\n c_tokens = workers.map(tokenize, data['contexts'])\n workers.close()\n workers.join()\n\n for idx in range(len(data['qids'])):\n question = q_tokens[idx]['words']\n qlemma = q_tokens[idx]['lemma']\n document = c_tokens[data['qid2cid'][idx]]['words']\n offsets = c_tokens[data['qid2cid'][idx]]['offsets']\n lemma = c_tokens[data['qid2cid'][idx]]['lemma']\n pos = c_tokens[data['qid2cid'][idx]]['pos']\n ner = c_tokens[data['qid2cid'][idx]]['ner']\n ans_tokens = []\n if len(data['answers']) > 0:\n for ans in data['answers'][idx]:\n found = find_answer(offsets,\n ans['answer_start'],\n ans['answer_start'] + len(ans['text']))\n if found:\n ans_tokens.append(found)\n yield {\n 'id': data['qids'][idx],\n 'question': question,\n 'document': document,\n 'offsets': offsets,\n 'answers': ans_tokens,\n 'qlemma': qlemma,\n 'lemma': lemma,\n 'pos': pos,\n 'ner': ner,\n }","def Related(self,name):\n # Get related property-entity (property id, property name, entity id, entity name) given entity name\n #\n # Fail to fetch eid would result in empty list\n query = self.entity2id(name)\n # print \"query\" + query\n # if query == \"Not Applicable\": return []\n ans = []\n for i in range(len(query)):\n s_id = query[i][\"s_id\"]\n subject_des = query[i][\"subject_des\"]\n url = \"https://www.wikidata.org/w/api.php?action=wbgetentities&ids=\" + s_id + \"&format=json&languages=en\"\n # print urllib.urlopen(url).read()\n response = json.loads(urllib.urlopen(url).read())\n for p in response[\"entities\"][s_id][\"claims\"]:\n for o in response[\"entities\"][s_id][\"claims\"][p]:\n # Enumerate property & entity (multi-property, multi-entity)\n try:\n # Some properties are not related to entities, thus try & except\n o_id = o[\"mainsnak\"][\"datavalue\"][\"value\"][\"id\"]\n ans.append({\n \"s_id\": s_id,\n \"s_des\": subject_des,\n \"p_id\": p,\n \"property\": self.getp(p),\n \"o_id\": o_id,\n \"object\": self.getc(o_id)\n })\n # ans.append(\"\\\\property\\\\\"+p+\"\\t\"+getp(p)+\"\\t\\\\entity\\\\\"+cid+\"\\t\"+getc(cid))\n # Print in a pid-pname-eid-ename fashion\n except:\n continue\n return ans","def get_scenarios(experiments):\n return {exp.scenario for exp in experiments}","def get_data_loaders():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+5):]\n \n #history_complete.append(history)\n if len(persona) == 4:\n if len(history) > (len(persona)+3):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def _get_related(stats, match, patterns_file):\n all_related = None\n measurement_formats = [\"space_between\", \"attached\", \"hyphenated\"]\n\n all_related = _parse_patterns(match[\"unit_idx\"], match[\"measurement_format\"], patterns_file)\n if all_related == None:\n all_related = _parse_patterns(match[\"unit_idx\"], [\"uncertain\"], patterns_file)\n\n # get words like approximately\n num_adverbs = _parse_patterns([match[\"num_idx\"]], match[\"measurement_format\"], patterns_file)\n unit_adverbs = _parse_patterns([match[\"unit_idx\"]], match[\"measurement_format\"], patterns_file)\n adverbs = num_adverbs + unit_adverbs\n for_removal = []\n for a in adverbs:\n if a[\"relationForm\"] != \"advmod\":\n for_removal.append(a)\n else:\n [a.pop(key, None) for key in [\"descriptors\", \"connector\"]] # not relevant for adverbs\n [adverbs.remove(a) for a in for_removal]\n\n if adverbs:\n match[\"grobid\"][\"adverbs\"] = adverbs\n\n # Check to make sure related isn't already a number, unit, or quantified thing identified by Grobid\n potential_keys = [\"quantity\", \"quantityLeast\", \"quantityMost\", \"quantified\"]\n\n if all_related:\n for key in potential_keys:\n for related in all_related:\n if key in match[\"grobid\"]:\n num, unit, quantified = \"\", \"\", \"\"\n if \"rawValue\" in match[\"grobid\"][key]: num = match[\"grobid\"][key][\"rawValue\"]\n if \"rawUnit\" in match[\"grobid\"][key]: unit = match[\"grobid\"][key][\"rawUnit\"][\"name\"]\n if \"normalizedName\" in match[\"grobid\"][key]: quantified = match[\"grobid\"][key][\"normalizedName\"]\n\n if related[\"rawName\"] in [num, unit, quantified] or related[\"rawName\"] == num + unit or (\n quantified in related[\"rawName\"] and not quantified == \"\"):\n all_related.remove(related)\n\n if related[\"rawName\"] == unit:\n for k in related.keys():\n if not k in match[\"grobid\"][key][\"rawUnit\"]:\n match[\"grobid\"][key][\"rawUnit\"][k] = related[k]\n\n elif related[\"rawName\"] == quantified:\n for k in related.keys():\n if not k in match[\"grobid\"][key]:\n match[\"grobid\"][key][k] = related[k]\n return all_related","def __find_topics(self, concepts):\n\n # Set up\n found_topics = dict() # to store the matched topics\n explanation = dict()\n\n # finding matches\n for concept in concepts:\n evgrams = everygrams(concept.split(), 1, 3) # list of unigrams, bigrams, trigrams\n for grams in evgrams:\n gram = \"_\".join(grams)\n gram_without_underscore = \" \".join(grams)\n #### Finding similar words contained in the model\n\n list_of_matched_topics = []\n\n if self.fast_classification:\n list_of_matched_topics = self.__get_similar_words_from_cached_model(gram,grams)\n else:\n list_of_matched_topics = self.__get_similar_words_from_full_model(gram, grams)\n\n\n for topic_item in list_of_matched_topics:\n\n topic = topic_item[\"topic\"]\n str_sim = topic_item[\"sim_t\"]\n wet = topic_item[\"wet\"]\n sim = topic_item[\"sim_w\"]\n\n\n if str_sim >= self.min_similarity and topic in self.cso.topics_wu:\n\n\n if topic in found_topics:\n #tracking this match\n found_topics[topic][\"times\"] += 1\n\n found_topics[topic][\"gram_similarity\"].append(sim)\n\n #tracking the matched gram\n if gram in found_topics[topic][\"grams\"]:\n found_topics[topic][\"grams\"][gram] += 1\n else:\n found_topics[topic][\"grams\"][gram] = 1\n\n #tracking the most similar gram to the topic\n if str_sim > found_topics[topic][\"embedding_similarity\"]:\n found_topics[topic][\"embedding_similarity\"] = str_sim\n found_topics[topic][\"embedding_matched\"] = wet\n\n else:\n #creating new topic in the result set\n found_topics[topic] = {'grams': {gram:1},\n 'embedding_matched': wet,\n 'embedding_similarity': str_sim,\n 'gram_similarity':[sim],\n 'times': 1,\n 'topic':topic}\n\n\n\n if sim == 1:\n found_topics[topic][\"syntactic\"] = True\n\n\n\n primary_label_topic = self.cso.get_primary_label_wu(topic)\n if primary_label_topic not in explanation:\n explanation[primary_label_topic] = set()\n\n explanation[primary_label_topic].add(gram_without_underscore)\n\n return found_topics, explanation","def watershedlist():\n opts = watersheds_db()\n return [(opts[opt]['name'] + ' (' + opts[opt]['delineation'] + ')', opt) for opt in opts]","def append_direct_component_matrices(technique_data: TransitiveTechniqueData):\n for technique in technique_data.technique.get_component_techniques():\n direct_calculator = DirectTechniqueCalculator(technique.definition)\n similarity_matrix = direct_calculator.calculate_technique_data(\n technique_data.dataset\n ).similarity_matrix\n technique_data.transitive_matrices.append(similarity_matrix)","def meaning_of(word, app_id, app_key):\n \n url = 'https://od-api.oxforddictionaries.com:443/api/v1/entries/' + language + '/' + word.lower()\n r = requests.get(url, headers={\"app_id\":app_id, \"app_key\":app_key})\n\n data = r.json()\n useful_data = {}\n\n for i in data['results'][0]['lexicalEntries'][0]['entries']:\n for j in i:\n for k in i[j][0]:\n try:\n subdata = i[j][0][k]\n if k == 'subsenses':\n useful_data.update({\"meanings\":subdata[0]['definitions']})\n elif k == 'examples':\n useful_data.update({\"examples\":subdata[0]['text']})\n else:\n pass\n except:\n pass\n return useful_data","def get_guides(data):\n return data.groups[\"Frames\"].objects","def extract_features(data, stopwords=STOPWORDS):\n tags = set()\n docs = []\n for document in data:\n doc_data = dict()\n doc_data['pmid'] = document['sourceid']\n text = document['text']\n\n # Insert PubTator annotations inside abstract\n denotations = document['denotations']\n sorted_denotations = []\n for denotation in denotations:\n begin = denotation['span']['begin']\n end = denotation['span']['end']\n obj = denotation['obj']\n for c in punctuation:\n obj = obj.replace(c, '')\n tags.add(obj)\n doc_data[obj] = doc_data.get(obj,0)+1\n sorted_denotations.append([begin,end,obj])\n sorted_denotations.sort()\n sorted_denotations.reverse()\n for begin, end, obj in sorted_denotations:\n text = text[:begin] + obj + ' ' + text[end:]\n\n doc_data['text'] = clean_text(text, stopwords)\n docs.append(doc_data)\n\n return docs","def get_concepts_for_text(f2t, adir, ptext2ptmk, ptmk2sk):\n print \"Getting concepts for texts (returns pointmention pks for a text)\"\n cpco2ptmk = {} # simple hash\n vbco2ptmk = {} # verbose hash\n for idx, fn in enumerate(os.listdir(adir)):\n sfn = fn.replace(\"pointmention_nbr_\", \"\")\n with codecs.open(os.path.join(adir, fn), \"r\", \"utf8\") as ifd:\n jso = js.loads(ifd.read().strip())\n for co in jso[\"concepts\"]:\n txt = f2t[sfn]\n # only value is a set with the pointmention pks\n ptmpks = ptext2ptmk[txt].values()\n assert len(ptmpks) == 1\n assert isinstance(ptmpks[0], set)\n ptmpks = ptmpks[0]\n # smaller hash, all ptmkeys for a concept go in a set\n cpco2ptmk.setdefault(co[\"prefLabel\"].strip(),\n {\"uri\": co[\"uri\"].replace(RPREF, \"\"),\n \"ptmks\": set()})\n cpco2ptmk[co[\"prefLabel\"].strip()][\"ptmks\"].update(ptmpks)\n # verbose hash (with sentence id and score per mention)\n for ptmpk in ptmpks:\n curkey = (co[\"prefLabel\"].strip(),\n co[\"uri\"].replace(RPREF, \"\"))\n curdict = {\"ptmk\": ptmpk, \"spk\": ptmk2sk[ptmpk],\n \"sco\": co[\"score\"] / 100.0}\n vbco2ptmk.setdefault(curkey, [])\n if curdict not in vbco2ptmk[curkey]:\n vbco2ptmk[curkey].append(curdict)\n if idx and not idx % 10000:\n print \"- Done concepts for {} texts, {}\".format(\n idx, time.strftime(\"%H:%M:%S\", time.localtime()))\n return cpco2ptmk, vbco2ptmk","def test_get_skills_multiple_lessons(self):\n skill_graph = SkillGraph.load()\n\n skill_1 = skill_graph.add(Skill.build(SKILL_NAME, SKILL_DESC))\n unit = self.course.add_unit()\n unit.title = 'Test Unit'\n lesson1 = self.course.add_lesson(unit)\n lesson1.title = 'Test Lesson 1'\n lesson2 = self.course.add_lesson(unit)\n lesson2.title = 'Test Lesson 2'\n self.course.save()\n lesson1.properties[SKILLS_KEY] = [skill_1.id]\n lesson2.properties[SKILLS_KEY] = [skill_1.id]\n self.course.save()\n\n actions.login(ADMIN_EMAIL)\n response = transforms.loads(self.get(self.URL).body)\n self.assertEqual(200, response['status'])\n\n skills = transforms.loads(response['payload'])['skills']\n self.assertEqual(1, len(skills))\n # All lessons listed\n self.assertEqual(2, len(skills[0]['lessons']))","def model_cohort(cohort):\n model_traj = []\n for part in cohort:\n for traj in part.trajectories:\n if traj.filter is True:\n model_traj.append(\n modelling.model(x=traj.data.age.tolist(),\n y=traj.data.AF.tolist(),\n mutation=traj.mutation,\n variant_class=traj.variant_class,\n gene=traj.mutation.split()[0],\n id=part.id,\n p_key=traj.p_key))\n return model_traj","def get_groups_using_technique():\n global groups_using_technique\n\n if not groups_using_technique:\n groups_using_technique = rsh.groups_using_technique(get_srcs())\n \n return groups_using_technique","def process(data_item, article_id):\n questions = []\n answers = []\n paragraph = [article_id, data_item['context']]\n\n for item in data_item['qas']:\n question = [item[\"id\"], item[\"question\"], item['is_impossible']]\n questions.append(question)\n if item['is_impossible']:\n continue\n answer_options = item[\"answers\"]\n answer_set = set()\n for option in answer_options:\n answer_tuple = (option['text'], option['answer_start'])\n answer_set.add(answer_tuple)\n for index, answer_tuple in enumerate(answer_set):\n answer = [\"{}_{}\".format(item[\"id\"], index+1), item[\"id\"], answer_tuple[0], answer_tuple[1]]\n answers.append(answer)\n return paragraph, questions, answers","def get_patient(drs):\n for line in drs:\n if line.strip().startswith('sem'):\n datalist = line.split(':')\n for word in datalist: \n if word.count('patient') > 0:\n variable = word[6:7]\n for word in datalist:\n if word.startswith('pred({0}'.format(variable)):\n return word.split(',')[1]","def read(self, input_file, remove_evidence=False, remove_question=False, remove_passage=False, remove_dict=None):\n logger.info('Reading data set from {}...'.format(input_file))\n logger.info('Remove evidence during test: {}'.format(remove_evidence))\n with open(input_file, \"r\", encoding='utf-8') as reader:\n input_data = json.load(reader)\n\n def is_whitespace(ch):\n if ch == \" \" or ch == \"\\t\" or ch == \"\\r\" or ch == \"\\n\" or ord(ch) == 0x202F:\n return True\n return False\n\n examples = []\n for instance in tqdm(input_data):\n sentences = instance['article']\n article_id = instance['id']\n\n sentence_start_list, sentence_end_list = [], []\n passage = ''\n for sentence in sentences:\n sentence = sentence.strip()\n sentence_start_list.append(len(passage))\n passage = passage + sentence + ' '\n sentence_end_list.append(len(passage) - 1)\n assert sentence_start_list[-1] <= sentence_end_list[-1]\n # if len(examples) == 453:\n # print(sentence)\n # print(sentence_start_list[-1], sentence_end_list[-1])\n\n doc_tokens = []\n prev_is_whitespace = True\n char_to_word_offset = []\n for c in passage:\n if is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n doc_tokens.append(c)\n else:\n doc_tokens[-1] += c\n prev_is_whitespace = False\n char_to_word_offset.append(len(doc_tokens) - 1)\n\n # Split context into sentences\n # sentence_start_list, sentence_end_list = utils.split_sentence(passage, self.sentence_tokenizer)\n sentence_span_list = []\n for c_start, c_end in zip(sentence_start_list, sentence_end_list):\n t_start = char_to_word_offset[c_start]\n t_end = char_to_word_offset[c_end]\n sentence_span_list.append((t_start, t_end))\n assert t_start <= t_end\n\n questions = instance['questions']\n answers = instance['answers']\n options = instance['options']\n evidences = instance['evidences']\n # multi_sents = instance['multi_sents']\n\n for q_id, (question, option_list, option_answer_list, evidence) in enumerate(zip(questions, options, answers, evidences)):\n\n if remove_evidence:\n new_doc_tokens, new_sentence_span_list = utils.remove_all_evidence(sentence_span_list, doc_tokens, evidence)\n evidence = []\n else:\n new_doc_tokens = doc_tokens\n new_sentence_span_list = sentence_span_list\n\n # qas_id = f\"{article_id}--{q_id}\"\n for op_id, (option, answer) in enumerate(zip(option_list, option_answer_list)):\n qas_id = f\"{article_id}--{q_id}--{op_id}\"\n example = MultiRCExample(\n qas_id=qas_id,\n # doc_tokens=doc_tokens,\n doc_tokens=new_doc_tokens,\n question_text=question,\n # sentence_span_list=sentence_span_list,\n sentence_span_list=new_sentence_span_list,\n option_text=option,\n answer=answer,\n sentence_ids=evidence\n )\n examples.append(example)\n\n logger.info('Finish reading {} examples from {}'.format(len(examples), input_file))\n return examples","def load_data(squad_per_lang):\n question_set = QuestionSet()\n candidate_set = CandidateSet()\n\n for lang, squad in squad_per_lang.items():\n for question, answer, context, context_sentences, xling_id, context_id in (\n generate_examples(squad, lang)):\n question = Question(question, xling_id, lang)\n question_set.add(question)\n assert answer in context_sentences, (\n \"answer doesn't appear in context_sentences\")\n for sent_pos, sentence in enumerate(context_sentences):\n candidate = Candidate(sentence, context, lang, context_id, sent_pos)\n candidate = candidate_set.add_or_retrieve_candidate(candidate)\n if sentence == answer:\n candidate_set.update_xling_id(candidate, xling_id)\n print(\"Totals across languages: questions={}, candidates={}\".format(\n len(question_set.as_list()), len(candidate_set.as_list())))\n\n return question_set, candidate_set","def _process_examples(self):\n logger = getLogger(\"problog_lfi\")\n # value can be True / False / None\n # ( atom ), ( ( value, ... ), ... )\n\n # Simple implementation: don't add neutral evidence.\n\n # ad_groups is a list of lists where each list contains an AD\n ad_groups = list()\n for ad in self._adatoms:\n # if it's an AD group\n if len(ad[1]) > 1:\n ad_list = []\n for var in ad[1]:\n ad_list.append(Term(self.names[var].functor, *self.names[var].args))\n ad_groups.append(tuple(ad_list))\n logger.debug(\"AD Groups\\t\\t:\" + str(ad_groups))\n\n def all_false(d):\n \"\"\"\n This function recognizes inconsistent evidence s.t. all values are False in AD.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :return: whether all values are False\n \"\"\"\n # false_count should be the same as the length of d\n false_count = sum(v is False for v in d.values())\n return false_count == len(d)\n\n def all_false_except_one(d):\n \"\"\"\n This function recognizes incomplete evidence s.t.\n the non-False value in AD needs to be set to True.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :return: whether all values except one are False\n \"\"\"\n false_count = sum(v is False for v in d.values())\n the_left_is_none = bool(sum(v is None for v in d.values()))\n return (false_count == len(d) - 1) and the_left_is_none\n\n def getADtemplate(d, atom=None):\n \"\"\"\n This function gets atom's complement AD template.\n This should only be used when the AD contains non-ground terms.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :param atom: an evidence\n :return: atom's complement AD template\n \"\"\"\n if atom is not None:\n temp_dict = {\n k: v\n for k, v in d.items()\n if v == \"Template\" and atom.signature != k.signature\n }\n return temp_dict\n else:\n temp_dict = {k: v for k, v in d.items() if v == \"Template\"}\n return temp_dict\n\n def add_to_ad_evidence(pair, l, ADtemplate):\n \"\"\"\n :param pair: a new pair of (atom, value)\n :param l: a list of dictionaries, all dictionaries need to have the same format\n :return:\n \"\"\"\n (k, v) = pair\n # if entry k exists, update the value with k\n for d in l:\n if k in d:\n d[k] = v\n return\n # if entry k does not exist, create a new dictionary from template\n # and instantiate it with k\n new_d = dict()\n for temp_k in ADtemplate.keys():\n new_key = Term(temp_k.functor, *k.args)\n new_d[new_key] = None\n # put v in there\n new_d[k] = v\n l.append(new_d)\n\n if self.infer_AD_values:\n result = ExampleSet()\n inconsistent = False\n # iterate over all examples given in .ev\n for index, example in enumerate(self.examples):\n ad_evidences = []\n non_ad_evidence = {}\n for ad_group in ad_groups:\n # create a dictionary to memorize what evidence is given in AD\n d = dict()\n # TODO: what if the AD contains both ground and non-ground????\n # e.g. t(_)::a; t(_)::b(X)\n for var in ad_group:\n if var.is_ground():\n d[var] = None # for ground unknown evidence\n else:\n d[var] = \"Template\" # for unground unknown evidence\n ad_evidences.append(d)\n\n # add all evidence in the example to ad_evidences\n for atom, value in example:\n # if atom has a tunable probability to learn\n if any([atom.signature == name.signature for name in self.names]):\n if len(atom.args) == 0: # Propositional evidence\n # insert evidence\n is_ad = False\n for d in ad_evidences:\n if atom in d:\n d[atom] = value\n is_ad = True\n if not is_ad:\n non_ad_evidence[\n atom\n ] = value\n else: # First Order evidence\n # find the right AD dictionary : AD_dict\n AD_dict = None\n for d in ad_evidences:\n if any([atom.signature == k.signature for k in d]):\n AD_dict = d\n # if the instantiation is new, add it as a key to the dictionary\n if AD_dict and AD_dict.get(atom) is None:\n AD_dict[atom] = value\n # also add other AD parts in the dictionary with value==None\n other_ADs = getADtemplate(AD_dict, atom)\n for otherAD in other_ADs.keys():\n new_key = Term(otherAD.functor, *atom.args)\n AD_dict[new_key] = AD_dict.get(new_key, None)\n else:\n non_ad_evidence[atom] = value\n else:\n non_ad_evidence[atom] = value\n\n # grounded_ad_evidences contains all usable evidence (gound, not template)\n grounded_ad_evidences = []\n for d in ad_evidences:\n # for first order evidence dictionaries\n if \"Template\" in d.values():\n # new_ad_evidence is a list of dictionaries\n # each dictionary is a group of the AD template instantiation\n new_ad_evidence = list()\n # get template AD evidence\n ADtemplate = getADtemplate(d)\n # group all pairs according to ADtemplate\n for k, v in d.items():\n if v != \"Template\":\n add_to_ad_evidence((k, v), new_ad_evidence, ADtemplate)\n grounded_ad_evidences += new_ad_evidence\n else:\n # simply us them\n grounded_ad_evidences.append(d)\n\n inconsistent_example = False\n for i, d in enumerate(grounded_ad_evidences):\n # inconsistent1 = multiple_true(d)\n inconsistent2 = all_false(d)\n add_compliment = all_false_except_one(d)\n\n if inconsistent2:\n inconsistent_example = True\n continue\n elif add_compliment:\n for key, value in d.items():\n if value is None:\n grounded_ad_evidences[i][key] = True\n\n if not inconsistent_example and len(grounded_ad_evidences) > 0:\n # There are (fully tunable) ADs in the program\n evidence_list = []\n for d in grounded_ad_evidences:\n for key, value in d.items():\n if value is not None:\n if (key, value) not in evidence_list:\n # TODO: Switch to orderedSet so we don't have to check whether it's in already\n evidence_list.append((key, value))\n\n for key, value in non_ad_evidence.items():\n evidence_list.append((key, value))\n\n atoms, values = zip(*evidence_list)\n result.add(index, atoms, values)\n\n else:\n # (No AD case) or (Inconsistent Evidence Case)\n atoms, values = zip(*example)\n result.add(index, atoms, values)\n return result\n else:\n # smarter: compile-once all examples with same atoms\n result = ExampleSet()\n for index, example in enumerate(self.examples):\n atoms, values = zip(*example)\n result.add(index, atoms, values)\n return result","def students_using_relevant_words_by_context_and_question(self):\n relevant_words = [\"dead\", \"death\", \"miscarriage\", \"killed\", \"kill\", \"losing\", \"loss\",\n \"lost\", \"deceased\", \"died\", \"grief\", \"pregnancy\", \"pregnant\"]\n\n question_context_count_map = {}\n\n for row in self.responses:\n question = row.question.text\n context = row.context.text\n if question not in question_context_count_map:\n question_context_count_map[question] = {}\n if context not in question_context_count_map[question]:\n question_context_count_map[question][context] = 0\n\n if string_contains_words(row.response, relevant_words):\n question_context_count_map[question][context] += 1\n\n flattened_data = \\\n PrototypeRereadingAnalysis.transform_nested_dict_to_list(question_context_count_map)\n\n flattened_data.sort()\n return flattened_data","def get_faulty_scenario_data():\n return [\n {\n \"population_count\": 100,\n \"county\": \"oxford\",\n \"season\": \"cold_month\",\n \"year\": 2017,\n },\n {\n \"population_count\": 150,\n \"county\": \"oxford\",\n \"season\": \"spring_month\",\n \"year\": 2017,\n },\n {\n \"population_count\": 200,\n \"county\": \"oxford\",\n \"season\": \"hot_month\",\n \"year\": 2017,\n },\n {\n \"population_count\": 210,\n \"county\": \"oxford\",\n \"season\": \"fall_month\",\n \"year\": 2017,\n },\n ]","def do_mapping(ds, fieldnames, relationship_type, type_filter, source_name, sorting_keys, tactic=None):\n all_attack_patterns = get_all_techniques(ds, source_name, tactic)\n writable_results = []\n\n for attack_pattern in tqdm.tqdm(all_attack_patterns, desc=\"parsing data for techniques\"):\n # Grabs relationships for identified techniques\n relationships = filter_for_term_relationships(ds, relationship_type, attack_pattern.id)\n\n for relationship in relationships:\n # Groups are defined in STIX as intrusion-set objects\n # Mitigations are defined in STIX as course-of-action objects\n # Software are defined in STIX as malware objects\n stix_results = filter_by_type_and_id(ds, type_filter, relationship.source_ref, source_name)\n\n if stix_results:\n row_data = (\n grab_external_id(attack_pattern, source_name),\n attack_pattern.name,\n grab_external_id(stix_results[0], source_name),\n stix_results[0].name,\n escape_chars(stix_results[0].description),\n escape_chars(relationship.description),\n )\n\n writable_results.append(dict(zip(fieldnames, row_data)))\n\n return sorted(writable_results, key=lambda x: (x[sorting_keys[0]], x[sorting_keys[1]]))","def get_feedback_details(queryFeatures, pId, cId, es):\n values = {}\n feedback = []\n \n # get case\n query = {}\n query['query'] = {}\n query['query']['terms'] = {}\n query['query']['terms'][\"_id\"] = []\n query['query']['terms'][\"_id\"].append(cId)\n res = es.search(index=pId, body=query)\n\n # find attributes that are array sbert and store their values, also convert query array to vector array\n for attrib in queryFeatures:\n if attrib['similarity'] == 'Array SBERT':\n temp = {\"values\": res['hits']['hits'][0]['_source'][attrib['name']]['name'], \"vec\": res['hits']['hits'][0]['_source'][attrib['name']]['rep'], \"queryRep\": getVectorSemanticSBERTArray(attrib['value'])}\n values[attrib['name']] = temp\n\n # perform a cosine similarity between query elements and case elements\n for key, value in values.items():\n for idx, vec in enumerate(value['vec']):\n maxsim = 0\n for idx2, vec2 in enumerate(value['queryRep']):\n sim = cosine_similarity(vec['rep'], vec2)\n if sim > maxsim:\n maxsim = sim\n if maxsim < 0.7:\n feedback.append({\"field\": key, \"value\": value['values'][idx], \"similarity\": maxsim})\n \n #print(\"feedback: \", feedback)\n\n return feedback","def load_data(data_links_list=(\n 'https://raw.githubusercontent.com/JanetMatsen/bacteriopop/master'\n '/raw_data/raw_data.csv',\n 'https://raw.githubusercontent.com/JanetMatsen/bacteriopop/master'\n '/raw_data/sample_meta_info.tsv')):\n\n # Reading data sets from the links provided.\n df1 = pd.read_csv(data_links_list[0],\n error_bad_lines=False)\n df2 = pd.read_csv(data_links_list[1],\n sep='\\t')\n df2 = df2.set_index(df2['project'])\n # fill the Nas id df1 as \". Makes the groupbys behave better.\n df1.fillna('', inplace=True)\n # repleace 'genus' = 'other' with an empty string to be consistent.\n df1.replace(to_replace='other', value='', inplace=True)\n # Removing duplicate columns.\n del df2['project']\n del df2['ID']\n df1 = df1.set_index(df1['project'])\n # Removing duplicate column.\n del df1['project']\n # Joining the two datasets.\n df = df1.join(df2)\n # Uniformity in non-capitalization of column names.\n df.rename(columns={'Kingdom': 'kingdom', 'Phylum': 'phylum',\n 'Class': 'class', 'Order': 'order',\n 'Family': 'family', 'Genus': 'genus',\n 'Length': 'length'}, inplace=True)\n df.index.names = ['sampleID']\n # Rearranging columns so that abundance is the last column.\n df = df[['kingdom',\t'phylum', 'class', 'order',\n 'family', 'genus', 'length', 'oxygen',\n 'replicate', 'week', 'abundance']]\n assert isinstance(df, pd.DataFrame)\n return df","def get_teacher_topic_all():\n topic_data = query_db(\n \"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \"\n \"ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n topics = []\n for topic in topic_data:\n topic_dict_teacher = {}\n topic_dict_teacher[\"id\"] = topic[0]\n topic_dict_teacher[\"name\"] = flask.escape(str(topic[1]))\n topic_dict_teacher[\"class\"] = flask.escape(str(topic[2]))\n topics.append(topic_dict_teacher)\n return topics","def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\n def is_whitespace(c):\n if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n return True\n return False\n\n if is_training:\n subset_list = ['train']\n else:\n subset_list = ['dev', 'test']\n\n examples, features = {}, {}\n for subset in subset_list:\n level_example_dict = {\"high\": None, \"middle\": None}\n level_features_dict = {\"high\": None, \"middle\": None}\n\n for level in ['high', 'middle']:\n subset_dir = os.path.join(data_dir, subset)\n file_list = os.listdir(subset_dir)\n file_list = [file for file in file_list if file.endswith('txt')]\n file_list = sorted(file_list)\n\n alldata = []\n for file in file_list:\n data = json.load(open(os.path.join(subset_dir, file)))\n alldata.append(data)\n \n examples_list = []\n for data in alldata:\n doc_id = data['id']\n doc = data[\"article\"].replace('\\\\n', '\\n')\n doc_token = []\n prev_is_whitespace = True\n for c in doc:\n if is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n doc_token.append(c)\n else:\n doc_token[-1] += c\n prev_is_whitespace = False\n\n for i, answer in enumerate(data[\"answers\"]):\n if answer == \" \":\n answer = \"C\"\n example = InputExample(\n guid=doc_id + '-%d' % i,\n doc_token=doc_token,\n question_text=\"\",\n options=data[\"options\"],\n answer=answer)\n examples_list.append(example)\n\n level_example_dict[level] = examples_list\n level_features_dict[level] = convert_examples_to_features(\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\n \n print(\"MuTuaL Data Loader....\")\n print(level_example_dict[level][-1])\n print(level_features_dict[level][-1])\n examples[subset] = level_example_dict\n features[subset] = level_features_dict\n\n return examples, features","def get_wordlists():\n\n\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\n\t\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\n\t\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\n\t\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\n\t\t'Language Processing', 'Internet', 'Intelligent Systems',\n\t\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\n\t\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \n\t\t'Computational Intelligence', 'Computational Linguistics',\n\t\t'Computational linguistics','Algorithm','Computer','ITiCSE',\n\t\t'ITICSE','Machine learning','Learning','learning',\n\t\t'Artificial intelligence','CIVR','Document Analysis'}\n\n\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\n\t\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\n\t\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\n\t\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\n\t\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\n\t\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\n\t\t'Neuro','Psych',\n\t\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\n\t\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\n\t\t'Neurobiological'}\n\n\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\n\t\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\n\t\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\n\t\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\n\t\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\n\t\t'mathemati','Kernel'}\n\n\tlinguistics = {}\n\n\tcomputer_vision = {}\n\n\tchemistry = {}\n\n\tphysics = {}\n\n\t# Rename \"Computer Vision\" to \"Image Processing\"?\n\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\n\t\t'Physics','Computer Vision','Natural Language Processing']\n\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\n\n\treturn {topic_names[i]:topics[i] for i in range(len(topics))}","def get_data_loaders_2sentences():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_2generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+1):]\n #history_complete.append(history)\n if len(history) > 4:\n history_chatbot = history[1::2]\n\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def fetch_technologies(self):\n return self.fetch_levels(\"research\", None, codes.techs)","def concept_list(\n self, params: Optional[Dict] = None, headers: Optional[Dict] = None\n ) -> List[ConceptDetails]:\n method = self._get_method(\"concepts\")\n\n return self.call_api_get(method=method, params=params, headers=headers)","def get_threat_summary_by_id():\n sample_id = demisto.getArg('id')\n request = req('GET', SUB_API + 'samples/' + sample_id + '/threat')\n r = request.json()\n sample = {\n 'ID': sample_id,\n 'MaxSeverity': demisto.get(r, 'data.max-severity'),\n 'Score': demisto.get(r, 'data.score'),\n 'Count': demisto.get(r, 'data.count'),\n 'MaxConfidence': demisto.get(r, 'data.max-confidence'),\n 'ThreatFeeds': demisto.get(r, 'data.bis')\n }\n dbot = {\n 'Vendor': 'ThreatGrid',\n 'Type': 'Sample ID',\n 'Indicator': sample['ID'],\n 'Score': calc_score(sample['Score'])\n }\n md = tableToMarkdown('ThreatGrid - Threat Summary', [sample],\n ['ID', 'MaxSeverity', 'Score', 'Count', 'MaxConfidence'])\n mdTableList = []\n for threatfeed in sample['ThreatFeeds']:\n mdTableList.append({'Threat Feed': threatfeed})\n md += tableToMarkdown('Threat Feeds', mdTableList, ['Threat Feed'])\n md += tableToMarkdown('DBot', [dbot], ['Indicator', 'Score', 'Type', 'Vendor'])\n demisto.results({\n 'Type': entryTypes['note'],\n 'EntryContext': {'ThreatGrid.Sample(val.ID == obj.ID)': sample, 'DBotScore': dbot},\n 'HumanReadable': md,\n 'ContentsFormat': formats['json'],\n 'Contents': r\n })","def tags_data(item):\n\n # loop through tags, building up a nested dict of information as\n # we go. Note, using select_related like this means only one query,\n # but returns lots of redundant data. If performance is a problem,\n # consider separate queries for colloquialism and transcript details\n\n data = {}\n\n # add info on each tag that appears in the transcript\n tags = item.get_tags()\n\n # get tag count by colloquialism id\n tag_counts = tags.get_counts()\n\n # get ordered list of occurrence times with colloquialism type and value\n occ_times = list(\n tags.values('colloquialism__type', 'colloquialism__pk', 'start_exact')\n .order_by('start_exact'))\n\n def get_prev_time(time, c_type=None, c_pk=None):\n \"\"\"Get previous time before the given time matching the type/id. \"\"\"\n\n # TODO if efficiency is an issue here, make occ_times a generator and\n # keep a queue (collections.deque) of the relevant times; this should\n # work because we're moving through them in sequence so could safely\n # discard those before the current time as we go\n\n if c_pk:\n def filter_func(obj):\n return obj['colloquialism__pk'] == c_pk\n elif c_type:\n def filter_func(obj):\n return obj['colloquialism__type'] == c_type\n else:\n def filter_func(obj):\n return True\n\n # filter out relevant times\n filtered = filter(\n lambda obj: filter_func(obj) and obj['start_exact'] < time,\n occ_times)\n\n if not len(filtered):\n return None\n\n # return last filtered time since they are in order\n return filtered[-1]['start_exact']\n\n def get_uniqueness(time, colloquialism):\n # tau_value = time difference between occurrence of same tag.type.value\n # tau_tag = time difference between self and previous tag.\n # x = (tau_value - tau_tag) / tau_tag\n # uniqueness = 1/count(tag.type.value) * (1 - 1/exp(x/10))\n\n count = tag_counts[colloquialism.pk]\n\n if count <= 1:\n return 1\n\n value_time = get_prev_time(time, c_pk=colloquialism.pk)\n if value_time is None:\n return 1.0 / count\n else:\n # assume a previous tag exists, because it's a superset of t_value\n tag_time = get_prev_time(time)\n\n tau_tag = (time - tag_time).total_seconds()\n tau_value = (time - value_time).total_seconds()\n\n x = (tau_value - tau_tag) / tau_tag\n\n coefficient = 10\n uniqueness = 1.0 / count * (1 - 1 / math.exp(x / coefficient))\n return uniqueness\n\n for tag in tags.with_colloquialism():\n colloquialism = tag.colloquialism\n\n # add type details the first time the type is encountered\n if colloquialism.type not in data:\n data[colloquialism.type] = {\n 'displayName': colloquialism.get_type_display(),\n 'items': {},\n }\n items = data[colloquialism.type]['items']\n\n colloquialism_key = colloquialism.normalised_value\n\n # add colloquialism details the first time it appears\n if colloquialism_key not in items:\n items[colloquialism_key] = {\n 'meaning': colloquialism.meaning,\n 'occurrences': [],\n 'related': {},\n }\n\n occ_data = tag.to_json(True)\n occ_data['uniqueness'] = get_uniqueness(tag.start_exact, colloquialism)\n items[colloquialism_key]['occurrences'].append(occ_data)\n\n # add in related tag info. Assume that the tag info is already in the data,\n # this code just adds the related information\n related = item.related_tags()\n\n for tag in related.with_colloquialism().with_transcript():\n colloquialism = tag.colloquialism\n transcript = tag.get_transcript()\n items = data[colloquialism.type]['items']\n related = items[colloquialism.normalised_value]['related']\n\n # add transcript details the first time it appears\n if transcript.pk not in related:\n related[transcript.pk] = transcript.to_json()\n related[transcript.pk]['occurrences'] = []\n\n # append tag details\n related[transcript.pk]['occurrences'].append(tag.to_json())\n\n return render_json(data)","def splits_concept_levels(data: pd.DataFrame, type_col: Optional[str], concept_strings: List) -> List:\n\n con_string, anc_string = concept_strings\n data = data.copy().replace(r'^\\s*$', np.nan, regex=True)\n\n # extract relevant columns\n if type_col is not None:\n all_cols = [x for x in data.columns if type_col not in x]\n conc_type = [x for x in data.columns if con_string.upper() in x.upper() and type_col.upper() in x.upper()]\n conc_type_uri = [x for x in conc_type if x.upper().endswith('URI')][0]\n anc_type = [x for x in data.columns if anc_string.upper() in x.upper() and type_col.upper() in x.upper()]\n anc_type_uri = [x for x in anc_type if x.upper().endswith('URI')][0]\n # extract concept codes from ancestor codes\n concept = data[all_cols + conc_type].dropna(subset=conc_type, how='all').drop_duplicates()\n ancestor = data[all_cols + anc_type].dropna(subset=anc_type, how='all').drop_duplicates()\n # get counts of ontology concepts at each concept level\n concept_ont_codes = [i for j in [x.split(' | ') for x in list(concept[conc_type_uri])] for i in j]\n ancestor_ont_codes = [i for j in [x.split(' | ') for x in list(ancestor[anc_type_uri])] for i in j]\n else:\n concept = data[[x for x in data.columns if x.startswith(con_string)]].dropna(how='all').drop_duplicates()\n ancestor = data[[x for x in data.columns if x.startswith(anc_string)]].dropna(how='all').drop_duplicates()\n concept_ont_codes, ancestor_ont_codes = [], []\n\n return [(concept, concept_ont_codes), (ancestor, ancestor_ont_codes)]","def get_teacher_career_results(self, teacher, career):\n data = []\n\n # Get the active exams of the career.\n exams = EvaluationsExam.objects.filter(\n type__exact=career.type, status=\"ACTIVE\")\n\n # Get the results for each exam.\n for exam in exams:\n\n # Get the signatures of the teacher for the career in the exam.\n signatures_dtl = EvaluationsTeacherSignature.objects.filter(\n fk_teacher__exact=teacher.id, fk_period__exact=exam.fk_period, status=\"ACTIVE\").select_related('fk_signature')\n\n signatures_results = []\n for signature_dtl in signatures_dtl:\n \n # If it raise an exception, it means that the signature isn't evaluated yet or other error.\n try:\n # Get the results of the signature.\n signature_results = EvaluationsSignatureResult.objects.get(\n group=signature_dtl.group,\n fk_signature=signature_dtl.fk_signature.id,\n fk_exam=exam.id,\n status=\"ACTIVE\"\n )\n\n # Get the results for each question in the exam for the signature.\n questions_results = EvaluationsSignatureQuestionResult.objects.filter(\n group=signature_dtl.group,\n fk_signature=signature_dtl.fk_signature.id,\n fk_exam=exam.id,\n fk_question__optional='NO',\n status=\"ACTIVE\"\n ).values_list('fk_question__description', 'result')\n\n # Get the comments of the signature/group.\n comments_result = EvaluationsSignatureQuestionResult.objects.get(\n group=signature_dtl.group,\n fk_signature=signature_dtl.fk_signature.id,\n fk_exam=exam.id,\n fk_question__optional='YES',\n status=\"ACTIVE\"\n ).result\n\n # Split the comments and add them to a list, only the ones that are not empty.\n comments = list(filter(None, comments_result.split('|')))\n\n # Crate a dictionary with the results of the signature and the questions.\n signatures_results.append({\n 'teacher': teacher.name + ' ' + teacher.last_name + ' ' + teacher.last_name_2,\n 'signature': signature_dtl.fk_signature.description,\n 'group': signature_dtl.group,\n 'average': signature_results.average,\n 'comments': comments,\n 'total_evaluated': signature_results.total_evaluated,\n 'questions': questions_results\n })\n except Exception:\n pass\n\n # Add the results to the exam dictionary.\n exam_results = {\n 'exam': exam.description,\n 'career': career.description,\n 'signatures_results': signatures_results,\n 'period': exam.fk_period.period\n }\n\n # Add the exam results to the list that will be returned at the end.\n data.append(exam_results)\n\n return data","def get_datasets(recipe):\n # \"datasets\"\n return {dataset: get_instance(**par) for dataset, par in recipe.items()}","def extract_routine_instances(theories, routines, exec_routine_instruction_index):\n new_theories=[]\n for theory in theories:\n for i in range(len(routines)):\n routine=routines[i]\n theory=replace_instance(theory,routine,[(exec_routine_instruction_index,i)])\n new_theories.append(theory)\n return new_theories","def get_hikedetails_by_feature(feature):\n\n if (feature == \"dog\"):\n npbyfeature = Hike.query.filter(Hike.features.like('%dogs-leash%')).all()\n\n if (feature == \"kid\"):\n npbyfeature = Hike.query.filter(Hike.features.like('%kids%') | Hike.features.like('%strollers%')).all()\n \n if (feature == \"water\"):\n npbyfeature = Hike.query.filter(Hike.features.like('%river%') | Hike.features.like('%beach%')).all()\n\n \n return npbyfeature","def extract_english_raw_texts():\n # conceptnet triples raw text\n cpnet_en_raw_text = []\n\n # conceptnet entity context\n cpnet_en_entity_context = []\n\n with open(conceptnet_path, encoding=\"utf8\") as f:\n for line in f.readlines():\n ls = line.split('\\t')\n if ls[2].startswith('/c/en/') and ls[3].startswith('/c/en/'):\n \"\"\"\n Some preprocessing:\n - Remove part-of-speech encoding.\n - Split(\"/\")[-1] to trim the \"/c/en/\" and just get the entity name, convert all to \n - Lowercase for uniformity.\n \"\"\"\n rel = ls[1].split(\"/\")[-1].lower()\n head = del_pos(ls[2]).split(\"/\")[-1].lower()\n tail = del_pos(ls[3]).split(\"/\")[-1].lower()\n\n if not head.replace(\"_\", \"\").replace(\"-\", \"\").isalpha():\n continue\n\n if not tail.replace(\"_\", \"\").replace(\"-\", \"\").isalpha():\n continue\n\n # transfer to raw text\n head_text = head.replace(\"_\", \" \")\n tail_text = tail.replace(\"_\", \" \")\n\n if rel not in relation_mapping:\n continue\n\n rel_text = relation_mapping[rel]\n raw_text = head_text + \" \" + rel_text + \" \" + tail_text\n cpnet_en_raw_text.append(raw_text)\n \n # split to train and test\n shuffle(cpnet_en_raw_text)\n train_size = int(len(cpnet_en_raw_text) * 0.9)\n cpnet_en_raw_train_text = cpnet_en_raw_text[:train_size]\n cpnet_en_raw_dev_text = cpnet_en_raw_text[train_size:]\n\n with open(conceptnet_en_raw_text_train_path, \"w\", encoding=\"utf8\") as f:\n f.write(\"\\n\".join(cpnet_en_raw_train_text))\n \n with open(conceptnet_en_raw_text_dev_path, \"w\", encoding=\"utf8\") as f:\n f.write(\"\\n\".join(cpnet_en_raw_dev_text))","def concept_details(\n self,\n concept_id: str,\n params: Optional[Dict] = None,\n headers: Optional[Dict] = None,\n ) -> ConceptDetails:\n method = self._get_method(\"concept_details\")\n method = method.format(**{\"id\": concept_id})\n\n return self.call_api_get(method=method, params=params, headers=headers)","def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\n return [\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\n ]","def create_example(line, tfidf_dict, is_training, args):\n sample = json.loads(line, object_pairs_hook=collections.OrderedDict)\n example_id = sample['example_id']\n question_text = sample['question_text']\n ori_doc_tokens = sample['document_text'].split()\n\n # 抽取特定段落list[list]\n tfidf_cands_ids = tfidf_dict[str(example_id)]\n # tfidf并不保证所有段落必定出现在所选段落内\n if is_training:\n long_answer_cand = sample['annotations'][0]['long_answer']['candidate_index']\n\n if long_answer_cand != -1:\n # answer_cand保证top_level是true\n if sample['long_answer_candidates'][long_answer_cand]['top_level'] is False:\n gt_start_token = sample['long_answer_candidates'][long_answer_cand]['start_token']\n gt_end_token = sample['long_answer_candidates'][long_answer_cand]['end_token']\n for il, cand in enumerate(sample['long_answer_candidates']):\n if cand['start_token'] <= gt_start_token and cand['end_token'] >= gt_end_token \\\n and cand['top_level'] is True:\n long_answer_cand = il\n break\n # training的时候当tfidf中没有包含正确答案，且long_answer是存在的时候，tfidf的结果则只选目标段落\n hit_answer = False\n for pids in tfidf_cands_ids:\n if long_answer_cand in pids:\n hit_answer = True\n break\n if hit_answer is False:\n tfidf_cands_ids = [[]]\n token_count = 0\n for ic, cand in enumerate(sample['long_answer_candidates']):\n if cand['top_level'] is True:\n tfidf_cands_ids[-1].append(ic)\n token_count += (cand['end_token'] - cand['start_token'])\n if token_count > 600:\n tfidf_cands_ids.append([])\n token_count = 0\n while len(tfidf_cands_ids[-1]) == 0:\n tfidf_cands_ids.pop(-1)\n # 防止负样本爆炸，只选目标段落\n tfidf_cands_ids = [cands for cands in tfidf_cands_ids if long_answer_cand in cands]\n\n # 由于接下来要对special_tokens排序，所以这里tfidf选择的段落要按照首段排序\n tfidf_cands_ids = sorted(tfidf_cands_ids, key=lambda x: x[0])\n\n if args.do_combine: # 如果do_combine，我们把所有抽取的candidates合并到一起\n tfidf_cands_ids_ = []\n for c in tfidf_cands_ids:\n tfidf_cands_ids_.extend(c)\n tfidf_cands_ids = [tfidf_cands_ids_]\n\n # 获取candidate的type信息，去除HTML符号\n # 保留特殊token到段首\n # 注意table paragraph list最小起步是1\n special_tokens_count = {'ContextId': -1, 'Table': 0, 'Paragraph': 0, 'List': 0}\n\n # 为了保证一致性，TABLE, Paragraph等结构信息还是尽可能保留...\n selected_ps = []\n for i, cand_ids in enumerate(tfidf_cands_ids):\n position_map = [] # 新paragraph到老paragraph的token位置映射\n map_to_origin = {} # 为了保证能够对答案位置进行正确的偏移，这里需要重新搞一波map映射\n p_tokens = []\n for cand_id in cand_ids:\n st = sample['long_answer_candidates'][cand_id]['start_token']\n ed = sample['long_answer_candidates'][cand_id]['end_token']\n ind = st # 追踪pos_map\n ori_cand_tokens = ori_doc_tokens[st:ed]\n # 先加ContextId特殊token\n special_tokens_count['ContextId'] += 1\n special_tokens_count['ContextId'] = min(special_tokens_count['ContextId'], args.max_position)\n p_tokens.append('[ContextId={}]'.format(special_tokens_count['ContextId']))\n position_map.append(ind)\n cand_type = get_candidate_type(ori_cand_tokens)\n if cand_type in special_tokens_count:\n special_tokens_count[cand_type] += 1\n special_tokens_count[cand_type] = min(special_tokens_count[cand_type], args.max_position)\n p_tokens.append('[' + cand_type + '=' + str(special_tokens_count[cand_type]) + ']')\n position_map.append(ind)\n for token in ori_cand_tokens:\n if '<' not in token: # 去除HTML符号\n p_tokens.append(token)\n position_map.append(ind)\n map_to_origin[ind] = len(position_map) - 1\n ind += 1\n assert len(position_map) == len(p_tokens)\n\n selected_ps.append({'paragraph_tokens': p_tokens,\n 'question_text': question_text,\n 'position_map': position_map,\n 'map_to_origin': map_to_origin,\n 'example_id': example_id,\n 'paragraph_id': str(example_id) + '_' + str(i),\n 'answer_type': AnswerType['UNKNOWN'],\n 'long_start': -1,\n 'long_end': -1,\n 'short_start': -1,\n 'short_end': -1,\n 'short_answer_text': None})\n\n answer = None\n answer_text = None\n if is_training and 'annotations' in sample:\n # 答案只取第一个标注\n annotation = sample['annotations'][0]\n if annotation is not None:\n long_answer = annotation['long_answer']\n if long_answer['candidate_index'] != -1:\n answer_type = AnswerType['LONG']\n ori_long_start = long_answer['start_token']\n ori_long_end = long_answer['end_token']\n else:\n answer_type = AnswerType['UNKNOWN']\n ori_long_start = -1\n ori_long_end = -1\n\n assert annotation[\"yes_no_answer\"] in (\"YES\", \"NO\", \"NONE\")\n if annotation[\"yes_no_answer\"] == 'YES':\n answer_text = 'YES'\n answer_type = AnswerType['YES']\n elif annotation[\"yes_no_answer\"] == 'NO':\n answer_text = 'NO'\n answer_type = AnswerType['NO']\n\n short_answers = annotation['short_answers']\n # 这里short answer必须排序\n short_answers = sorted(short_answers, key=lambda x: x['start_token'])\n if len(short_answers) > 0:\n # TODO:可能存在多个short，multi-tag\n answer_type = AnswerType['SHORT']\n short_ans = random.choice(short_answers)\n ori_short_start = short_ans['start_token']\n ori_short_end = short_ans['end_token']\n answer_text = ori_doc_tokens[ori_short_start:ori_short_end]\n answer_text = \" \".join([at for at in answer_text if '<' not in at])\n else:\n ori_short_start = -1\n ori_short_end = -1\n else:\n answer_type = AnswerType['UNKNOWN']\n ori_long_start = -1\n ori_long_end = -1\n ori_short_start = -1\n ori_short_end = -1\n\n answer = {'answer_type': answer_type,\n 'ori_long_start': ori_long_start,\n 'ori_long_end': ori_long_end,\n 'ori_short_start': ori_short_start,\n 'ori_short_end': ori_short_end}\n\n if answer['answer_type'] == AnswerType['SHORT'] and answer_text == \"\":\n print('WRONG SHORT', answer, answer_text)\n answer['answer_type'] = AnswerType['LONG']\n answer['ori_short_start'] = -1\n answer['ori_short_end'] = -1\n\n examples = []\n for p_sample in selected_ps:\n if answer and answer['answer_type'] != AnswerType['UNKNOWN']:\n # 如果长答案在候选里，那么首位必然都在这个候选里，!!!注意这里的ori_long_end必须-1，否则可能会漏!!!\n if answer['ori_long_start'] in p_sample['map_to_origin'] \\\n and answer['ori_long_end'] - 1 in p_sample['map_to_origin']:\n final_long_start = p_sample['map_to_origin'][answer['ori_long_start']]\n final_long_end = p_sample['map_to_origin'][answer['ori_long_end'] - 1] + 1\n long_answer_text = \" \".join(p_sample['paragraph_tokens'][final_long_start:final_long_end])\n\n p_sample['answer_type'] = answer['answer_type']\n p_sample['long_start'] = final_long_start\n p_sample['long_end'] = final_long_end\n\n # 短答案必然在长答案所在段落里面\n if answer['answer_type'] == AnswerType['SHORT']:\n final_short_start = p_sample['map_to_origin'][answer['ori_short_start']]\n final_short_end = p_sample['map_to_origin'][answer['ori_short_end'] - 1] + 1\n p_sample['short_start'] = final_short_start\n p_sample['short_end'] = final_short_end\n\n new_answer_text = \" \".join(p_sample['paragraph_tokens'][final_short_start:final_short_end])\n assert new_answer_text == answer_text, (new_answer_text, answer_text, long_answer_text)\n p_sample['short_answer_text'] = new_answer_text\n\n # 由于negative的段落太多了，所以这里先过滤掉一部分\n elif is_training and random.random() > args.example_neg_filter:\n continue\n\n # 由于negative的段落太多了，所以这里先过滤掉一部分\n elif is_training and random.random() > args.example_neg_filter:\n continue\n\n p_sample.pop('map_to_origin')\n examples.append(p_sample)\n\n return examples","def visualize_dataset(\n dataset_json):\n\n # - Iterate over phenomena, per phenomenon\n # - Iterate over property-sets, per property-set\n # - Iterator over properties, per property\n # - ...\n\n dataset_pathname = os.path.expandvars(dataset_json[\"pathname\"])\n dataset = lue.open_dataset(dataset_pathname)\n\n for phenomenon in dataset_json[\"phenomena\"]:\n visualize_phenomenon(dataset, phenomenon)","def build_feature_target_pair(mts, fun_get_features, fun_get_targets):\r\n features = []\r\n targets = []\r\n for mt in mts:\r\n targets.append(fun_get_targets(mt))\r\n features.append(fun_get_features(mt))\r\n \r\n return features, targets","def get_data():\n wga_df = pd.read_csv(os.path.join(MODEL_DIR, 'wga/sculptures/wga_sculpture_periods.csv'), index_col=0)\n wikiart_df = pd.read_csv(os.path.join(MODEL_DIR, 'wikiart/sculptures/wikiart_sculpture_periods.csv'), index_col=0)\n nga_df = pd.read_csv(os.path.join(MODEL_DIR, 'nga/sculptures/nga_sculpture_periods.csv'), index_col=0)\n\n ######## Fix name for WGA and WikiaRt ###########\n wga_df['Author'] = wga_df.apply(lambda x: fix_name_wga(x['Author']), axis=1)\n wikiart_df['Author'] = wikiart_df.apply(lambda x: fix_name_wiki(x['Author']), axis=1)\n nga_df['Author'] = nga_df.apply(lambda x: fix_name_nga(x['Author']), axis=1)\n\n df = pd.concat([wga_df, wikiart_df, nga_df], ignore_index=True, sort=True)\n\n df['Author_Fixed'] = df.apply(lambda x: fix_text(x['Author']), axis=1)\n df['title_fixed'] = df.apply(lambda x: fix_text(x['title']), axis=1)\n\n periods = [\"BAROQUE\", \"EARLY RENAISSANCE\", \"MEDIEVAL\", \"NEOCLASSICISM\", \"HIGH RENAISSANCE\", \"MINIMALISM\", \"REALISM\",\n \"IMPRESSIONISM\", \"ROCOCO\", \"SURREALISM\", \"MANNERISM\", \"ROMANTICISM\",\n ]\n df['Period'] = df.apply(lambda row: row['Period'].upper(), axis=1)\n\n # Get Desired Periods\n df['Period'] = df.apply(lambda x: \"SURREALISM\" if \"SURREALISM\" in x['Period'] else x['Period'], axis=1)\n df = df[(df['Period'].isin(periods))]\n df = df.sort_values(['Author_Fixed', 'title_fixed'])\n\n #print(\"Combined Drop Rows:\", df.shape[0] - df.drop_duplicates(subset=['Author_Fixed', 'title_fixed']).shape[0])\n\n df = df.drop_duplicates(subset=['Author_Fixed', 'title_fixed'], keep='last')\n\n # Drop Duplicate Sculptures\n df = df[~df['file'].isin(DUP_SCULPTURES)].reset_index(drop=True)\n\n #print(df['Period'].value_counts())\n\n return df","def _create_examples(self, lines, set_type):\n examples = []\n for idx, line in enumerate(lines):\n item = json.loads(line.strip())\n question_id = \"%s-%s\" % (set_type, idx)\n context = item[\"context\"]\n question = item[\"question\"]\n endings = [item[\"answerA\"],item[\"answerB\"],item[\"answerC\"] ]\n label = item[\"correct\"]\n #race_id = \"%s-%s\" % (set_type, data_raw[\"race_id\"])\n #article = data_raw[\"article\"]\n #for i in range(len(data_raw[\"answers\"])):\n #truth = str(ord(data_raw[\"answers\"][i]) - ord(\"A\"))\n #question = data_raw[\"questions\"][i]\n #options = data_raw[\"options\"][i]\n\n examples.append(\n InputExample(\n example_id=question_id,\n question=question,\n contexts=[context,context,context],\n endings=[endings[0], endings[1], endings[2]],#, options[3]\n label=label,\n )\n )\n return examples","def _create_examples(self, lines, set_type):\n examples = []\n for (_, data) in enumerate(lines):\n race_id = \"%s-%s\" % (set_type, data[\"race_id\"])\n article = data[\"article\"]\n for i in range(len(data[\"answers\"])):\n truth = str(ord(data[\"answers\"][i]) - ord(\"A\"))\n question = data[\"questions\"][i]\n options = data[\"options\"][i]\n\n examples.append(\n MCInputExample(\n guid=race_id,\n question=question,\n # this is not efficient but convenient\n contexts=[article, article, article, article],\n endings=[options[0], options[1],\n options[2], options[3]],\n label=truth,\n )\n )\n return examples","def _generate_examples(self, filepath, split):\r\n if self.config.name == \"trex\":\r\n paths = filepath\r\n relations_path = paths[0]\r\n paths = paths[1:]\r\n all_rels = {}\r\n with open(relations_path, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n all_rels[data[\"relation\"]] = data\r\n id_ = -1\r\n for filepath in paths:\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n pred = all_rels.get(data[\"predicate_id\"], {})\r\n for evidences in data[\"evidences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"obj_uri\": str(data[\"obj_uri\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"sub_uri\": str(data[\"sub_uri\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"predicate_id\": str(data[\"predicate_id\"]),\r\n \"sub_surface\": str(evidences[\"sub_surface\"]),\r\n \"obj_surface\": str(evidences[\"obj_surface\"]),\r\n \"masked_sentence\": str(evidences[\"masked_sentence\"]),\r\n \"template\": str(pred.get(\"template\", \"\")),\r\n \"template_negated\": str(pred.get(\"template_negated\", \"\")),\r\n \"label\": str(pred.get(\"label\", \"\")),\r\n \"description\": str(pred.get(\"description\", \"\")),\r\n \"type\": str(pred.get(\"type\", \"\")),\r\n }\r\n elif self.config.name == \"conceptnet\":\r\n id_ = -1\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n if data.get(\"negated\") is not None:\r\n for masked_sentence, negated in zip(data[\"masked_sentences\"], data[\"negated\"]):\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"sub\": str(data.get(\"sub\", \"\")),\r\n \"obj\": str(data.get(\"obj\", \"\")),\r\n \"pred\": str(data[\"pred\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"negated\": str(negated),\r\n }\r\n else:\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"sub\": str(data.get(\"sub\", \"\")),\r\n \"obj\": str(data.get(\"obj\", \"\")),\r\n \"pred\": str(data[\"pred\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"negated\": str(\"\"),\r\n }\r\n elif self.config.name == \"squad\":\r\n id_ = -1\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"id\": str(data[\"id\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"negated\": str(data.get(\"negated\", \"\")),\r\n \"masked_sentence\": str(masked_sentence),\r\n }\r\n elif self.config.name == \"google_re\":\r\n id_ = -1\r\n paths = filepath\r\n for filepath in paths:\r\n # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py\r\n if \"place_of_birth\" in filepath:\r\n pred = {\r\n \"relation\": \"place_of_birth\",\r\n \"template\": \"[X] was born in [Y] .\",\r\n \"template_negated\": \"[X] was not born in [Y] .\",\r\n }\r\n elif \"date_of_birth\" in filepath:\r\n pred = {\r\n \"relation\": \"date_of_birth\",\r\n \"template\": \"[X] (born [Y]).\",\r\n \"template_negated\": \"[X] (not born [Y]).\",\r\n }\r\n else:\r\n pred = {\r\n \"relation\": \"place_of_death\",\r\n \"template\": \"[X] died in [Y] .\",\r\n \"template_negated\": \"[X] did not die in [Y] .\",\r\n }\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"pred\": str(data[\"pred\"]),\r\n \"sub\": str(data[\"sub\"]),\r\n \"obj\": str(data[\"obj\"]),\r\n \"evidences\": str(data[\"evidences\"]),\r\n \"judgments\": str(data[\"judgments\"]),\r\n \"sub_w\": str(data[\"sub_w\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"sub_aliases\": str(data[\"sub_aliases\"]),\r\n \"obj_w\": str(data[\"obj_w\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"obj_aliases\": str(data[\"obj_aliases\"]),\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"template\": str(pred[\"template\"]),\r\n \"template_negated\": str(pred[\"template_negated\"]),\r\n }"],"string":"[\n \"def get_examples_table_data(technique, reference_list, next_reference_number):\\n\\n # Creating map to avoid repeating the code 3 times\\n examples_map = [\\n { 'example_type': config.tools_using_technique }, \\n { 'example_type': config.malware_using_technique },\\n { 'example_type': config.groups_using_technique }\\n ]\\n\\n example_data = []\\n\\n # Get malware or tools used by group\\n for examples in examples_map:\\n if examples['example_type'].get(technique.get('id')):\\n for example in examples['example_type'][technique['id']]:\\n\\n attack_id = util.get_attack_id(example['object'])\\n\\n # Only add example data if the attack id is found \\n if attack_id:\\n row = {}\\n\\n row['id'] = attack_id\\n\\n if attack_id.startswith('S'):\\n row['path'] = \\\"software\\\"\\n else:\\n row['path'] = \\\"groups\\\"\\n\\n row['name'] = example['object']['name']\\n\\n if example['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\\n\\n example_data.append(row)\\n \\n if example_data:\\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\\n return example_data\",\n \"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\\n\\n attack_id = util.get_attack_id(technique)\\n\\n # Only add technique if the attack id was found\\n if attack_id:\\n\\n technique_dict = {}\\n\\n technique_dict['attack_id'] = attack_id\\n technique_dict['domain'] = domain.split(\\\"-\\\")[0]\\n technique_dict['menu'] = side_menu_data\\n technique_dict['name'] = technique.get('name')\\n\\n # Get capecs and mtcs\\n for ref in technique['external_references']:\\n if ref.get('source_name'):\\n if ref['source_name'] == \\\"capec\\\":\\n if technique_dict.get('capecs') is None:\\n technique_dict['capecs'] = []\\n capec_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['capecs'].append(capec_dict)\\n\\n if ref['source_name'] == \\\"NIST Mobile Threat Catalogue\\\":\\n if technique_dict.get('mtcs') is None:\\n technique_dict['mtcs'] = []\\n\\n mtcs_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['mtcs'].append(mtcs_dict)\\n\\n # Get technique external references \\n ext_ref = technique[\\\"external_references\\\"]\\n\\n # Get initial reference list\\n reference_list = []\\n # Decleared as an object to be able to pass by reference\\n next_reference_number = {}\\n next_reference_number['value'] = 1\\n reference_list = util.update_reference_list(reference_list, technique)\\n\\n # Get technique description\\n if technique.get(\\\"description\\\"):\\n citations_from_descr = util.get_citations_from_descr(technique['description'])\\n\\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\\n \\n if 'x_mitre_deprecated' in technique:\\n technique_dict['deprecated'] = True\\n \\n # Get mitigation table\\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\\n \\n # Get related techniques\\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\\n\\n # Get examples\\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\\n\\n # Get technique version\\n if technique.get(\\\"x_mitre_version\\\"):\\n technique_dict['version'] = technique[\\\"x_mitre_version\\\"]\\n\\n # Get tactics of technique\\n if technique.get('kill_chain_phases'):\\n technique_dict['tactics'] = []\\n for elem in technique['kill_chain_phases']:\\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\\n\\n # Get platforms that technique uses\\n if technique.get('x_mitre_platforms'):\\n technique_dict['platforms'] = \\\", \\\".join(technique['x_mitre_platforms'])\\n\\n # Get system requirements\\n if technique.get('x_mitre_system_requirements'):\\n technique_dict['sysreqs'] = \\\", \\\".join(technique['x_mitre_system_requirements'])\\n technique_dict['sysreqs'] = re.sub(\\\"\\\\.?\\\\\\\\n+\\\", \\\"; \\\", technique_dict['sysreqs'])\\n\\n # Get permissions required\\n if technique.get('x_mitre_permissions_required'):\\n technique_dict['perms'] = \\\", \\\".join(technique['x_mitre_permissions_required'])\\n\\n # Get effective permissions\\n if technique.get('x_mitre_effective_permissions'):\\n technique_dict['eff_perms'] = \\\", \\\".join(technique['x_mitre_effective_permissions'])\\n\\n # Get data sources\\n if technique.get('x_mitre_data_sources'):\\n technique_dict['data_sources'] = \\\", \\\".join(technique['x_mitre_data_sources'])\\n\\n # Get if technique supports remote\\n if technique.get('x_mitre_remote_support'):\\n if technique['x_mitre_remote_support']:\\n technique_dict['supports_remote'] = \\\" Yes\\\"\\n else:\\n technique_dict['supports_remote'] = \\\" No\\\"\\n\\n # Get network requirements\\n if technique.get('x_mitre_network_requirements'):\\n if technique['x_mitre_network_requirements']:\\n technique_dict['network_reqs'] = \\\" Yes\\\"\\n else:\\n technique_dict['network_reqs'] = \\\" No\\\"\\n\\n # Get list of impacts\\n if technique.get('x_mitre_impact_type'):\\n technique_dict['impact_type'] = \\\", \\\".join(technique['x_mitre_impact_type'])\\n\\n # Get list of defenses bypassed\\n if technique.get('x_mitre_defense_bypassed'):\\n technique_dict['def_bypass'] = \\\", \\\".join(technique['x_mitre_defense_bypassed'])\\n\\n # Get list of contributors \\n if technique.get('x_mitre_contributors'):\\n technique_dict['contributors'] = \\\"; \\\".join(technique['x_mitre_contributors'])\\n\\n # Get list of tactic types\\n if technique.get('x_mitre_tactic_type'):\\n technique_dict['tactic_type'] = \\\", \\\".join(technique['x_mitre_tactic_type'])\\n\\n # Get detection data\\n if technique.get('x_mitre_detection'):\\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\\n\\n # Get if technique is detectable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses'):\\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\\n\\n # Get explanation of detecatable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\\n\\n # Get diffulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary'):\\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\\n\\n # Get explanation of difficulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \\n \\n # Add reference for bottom part of technique page\\n if reference_list:\\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\\n\\n subs = config.technique_md.substitute(technique_dict)\\n subs = subs + json.dumps(technique_dict)\\n\\n #Write out the markdown file\\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\\\".md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\",\n \"def get_technique_side_menu_data(domain, technique_list, tactic_list):\\n\\n # Get tactics techniques side menu data to fill out with jinja\\n tactics_techniques_menu_data = []\\n\\n for tactic in tactic_list:\\n tactic_row = {}\\n \\n tactic_row['name'] = tactic['name']\\n tactic_row['id'] = util.get_attack_id(tactic)\\n tactic_row['path'] = \\\"/tactics/{}\\\".format(util.get_attack_id(tactic))\\n \\n tactic_row['children'] = []\\n for technique in technique_list[tactic['x_mitre_shortname']]:\\n technique_row = {}\\n # Get technique id and name for each technique\\n technique_row['name'] = technique['name']\\n technique_row['id'] = technique['id']\\n technique_row['path'] = \\\"/techniques/{}/\\\".format(technique['id'])\\n technique_row['children'] = []\\n # Add technique data to tactic\\n tactic_row['children'].append(technique_row)\\n \\n # Add tactic to table\\n tactics_techniques_menu_data.append(tactic_row)\\n \\n return {\\n \\\"name\\\": \\\"techniques\\\",\\n \\\"id\\\": \\\"techniques\\\",\\n \\\"path\\\": \\\"/techniques/{}/\\\".format(domain.split(\\\"-\\\")[0]), # root level doesn't get a path\\n \\\"children\\\": tactics_techniques_menu_data\\n }\",\n \"def get_techniques_list(techniques):\\n\\n technique_list = {}\\n \\n for technique in techniques:\\n if 'revoked' not in technique or technique['revoked'] == False:\\n\\n attack_id = util.get_attack_id(technique)\\n\\n if attack_id:\\n\\n technique_dict = {}\\n technique_dict['id'] = attack_id\\n \\n technique_dict['name'] = technique['name']\\n technique_dict['description'] = technique['description']\\n\\n if technique.get('kill_chain_phases'):\\n for elem in technique['kill_chain_phases']:\\n # Fill dict\\n\\n if elem['phase_name'] not in technique_list:\\n technique_list[elem['phase_name']] = []\\n \\n technique_list[elem['phase_name']].append(technique_dict)\\n\\n for key, value in technique_list.items():\\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\\n \\n return technique_list\",\n \"def get_technique_related_to_technique():\\n global technique_related_to_technique\\n\\n if not technique_related_to_technique:\\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\\n\\n return technique_related_to_technique\",\n \"def get_technique_stats(db):\\n \\n techniques = {}\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n temp_dict = {technique: [{u'id': element['project_no'], \\n u'name': element['project_name'], u'description': element['short_description']}]}\\n techniques.update(temp_dict)\\n else:\\n temp_dict = {u'id': element['project_no'],\\n u'name': element['project_name'], u'description': element['short_description']}\\n techniques[technique].append(temp_dict)\\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\\n\\n return techniques\",\n \"def get_techniques(db):\\n\\n techniques = []\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n techniques.append(technique)\\n \\n return sorted(techniques)\",\n \"def get_mitigations_table_data(technique, reference_list, next_reference_number):\\n\\n mitigation_data = []\\n\\n # Check if technique has mitigations\\n if config.technique_mitigated.get(technique['id']):\\n # Iterate through technique mitigations\\n for mitigation in config.technique_mitigated[technique['id']]:\\n\\n # Do not add deprecated mitigation to table\\n if 'x_mitre_deprecated' not in mitigation['object']:\\n\\n attack_id = util.get_attack_id(mitigation['object'])\\n\\n # Only add if mitigation attack id is found \\n if attack_id:\\n\\n row = {}\\n row['mid'] = attack_id\\n row['name'] = mitigation['object']['name']\\n if mitigation['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, mitigation)\\n \\n mitigation_data.append(row)\\n \\n if mitigation_data:\\n mitigation_data = sorted(mitigation_data, key=lambda k: k['name'].lower())\\n return mitigation_data\",\n \"def add_techniques(\\n client: Act, matrice: AttckMatrice, output_format: Text = \\\"json\\\"\\n) -> List:\\n\\n notify = []\\n\\n for technique in matrice.techniques:\\n notify += handle_techniques(client, technique, None, output_format)\\n\\n for subtechnique in getattr(technique, \\\"subtechniques\\\", []):\\n # Pre Attack does not have sub techniques\\n notify += handle_techniques(client, subtechnique, technique, output_format)\\n\\n return notify\",\n \"def get_technique_mitigated_by_mitigation():\\n global technique_mitigated_by_mitigation\\n\\n if not technique_mitigated_by_mitigation:\\n technique_mitigated_by_mitigation = rsh.technique_mitigated_by_mitigation(get_srcs())\\n\\n return technique_mitigated_by_mitigation\",\n \"def get_all_techniques(src, source_name, tactic=None):\\n filters = [\\n Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n Filter(\\\"external_references.source_name\\\", \\\"=\\\", source_name),\\n ]\\n if tactic:\\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\\n\\n results = src.query(filters)\\n return remove_deprecated(results)\",\n \"def get_techniques_used_by_tools():\\n global techniques_used_by_tools\\n\\n if not techniques_used_by_tools:\\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\\n \\n return techniques_used_by_tools\",\n \"def techniques(self):\\n return self._get_child_page_of_type(LearningTechniquesPage)\",\n \"def get_techniques_used_by_groups():\\n global techniques_used_by_groups\\n\\n if not techniques_used_by_groups:\\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\\n\\n return techniques_used_by_groups\",\n \"def get_technique_by_id(self, id_code):\\r\\n return self.fs.query([\\r\\n Filter('type', '=', 'attack-pattern'),\\r\\n Filter('external_references.external_id', '=', id_code)\\r\\n ])\",\n \"def generate_features(stances, dataset, name, feature_list, features_dir):\\n h, b, y, bodyId, headId = [], [], [], [], []\\n\\n feature_dict = {'overlap': word_overlap_features,\\n 'refuting': refuting_features,\\n 'polarity': polarity_features,\\n 'hand': hand_features,\\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\\n 'NMF_fit_all': NMF_fit_all,\\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\\n 'NMF_cos_50': NMF_cos_50,\\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\\n 'ppdb': ppdb,\\n 'stanford_ppdb': stanford_ppdb_score,\\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\\n 'stanford_sentiment': stanford_sentiment,\\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\\n 'stanford_wordsim': stanford_based_verb_noun_sim,\\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\\n 'stanford_negation': stanford_negation_features,\\n 'stanford_negation_1sent': stanford_negation_features_1sent,\\n 'stanford_negation_2sent': stanford_negation_features_2sent,\\n 'stanford_negation_3sent': stanford_negation_features_3sent,\\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\\n 'hedging': hedging_features,\\n 'sen2sen': sen2sen_similarity_max,\\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\\n 'wmdsenDoc': word_mover_distance_wholebody,\\n 'sdm_sim': sdm_sim,\\n 'discuss': discuss_features,\\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\\n 'lexical_features': lexical_features,\\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\\n 'mpqa_unigrams': mpqa_unigrams,\\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\\n 'nrc_emo_lex': nrc_emo_lex,\\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\\n #'POS_features': POS_features,\\n 'readability_features': readability_features,\\n 'sentiment140_unigrams': sentiment140_unigrams,\\n 'structural_features': structural_features,\\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\\n 'NMF_cos_300': NMF_cos_300\\n }\\n\\n stanceCounter = 0\\n for stance in stances:\\n y.append(LABELS.index(stance['Stance']))\\n h.append(stance['Headline'])\\n b.append(dataset.articles[stance['Body ID']])\\n bodyId.append(stance['Body ID'])\\n headId.append(name+str(stanceCounter))\\n stanceCounter += 1\\n\\n X_feat = []\\n feat_list = []\\n last_index = 0\\n for feature in feature_list:\\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\\\"/\\\"+feature+\\\".\\\"+name+'.npy', bodyId, feature, headId, fold=name)\\n feat_list.append((last_index, last_index+len(feat[0]), str(feature)))\\n last_index += len(feat[0])\\n X_feat.append(feat)\\n X = np.concatenate(X_feat, axis=1)\\n\\n return X, y, feat_list\",\n \"def get_techniques_used_by_malware():\\n global techniques_used_by_malware\\n \\n if not techniques_used_by_malware:\\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\\n \\n return techniques_used_by_malware\",\n \"def get_skills(lang, spec=None):\\n\\n if lang.lower() == 'java':\\n output = get_java(spec)\\n if spec is not None:\\n print(f\\\"Our suggestions for {spec} are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n else:\\n print(\\\"Our suggestions for are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n elif lang.lower() == 'python':\\n output = get_python(spec)\\n if spec is not None:\\n print(f\\\"Our suggestions for {spec} are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n else:\\n print(\\\"Our suggestions for are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\",\n \"def _transform_data(self):\\n parser = self.parse_xml(self._data_src, 'Chemical')\\n for chemical in parser:\\n if 'displayName' not in chemical.attrib:\\n continue\\n\\n # initial setup and get label\\n display_name = chemical.attrib['displayName']\\n if not display_name or not re.search(TAGS_REGEX, display_name):\\n continue\\n label = re.sub(TAGS_REGEX, '', display_name)\\n params = {\\n 'label': label\\n }\\n\\n # get concept ID\\n reg_no = chemical.find('NumberList').find(\\\"CASRegistryNumber\\\")\\n if not reg_no:\\n continue\\n params['concept_id'] = \\\\\\n f'{NamespacePrefix.CASREGISTRY.value}:{reg_no.text}'\\n\\n # get aliases\\n aliases = []\\n label_l = label.lower()\\n name_list = chemical.find('NameList')\\n if name_list:\\n for name in name_list.findall('NameOfSubstance'):\\n text = name.text\\n if text != display_name and text.lower() != label_l:\\n aliases.append(re.sub(TAGS_REGEX, '', text))\\n params['aliases'] = aliases\\n\\n # get xrefs and associated_with\\n params['xrefs'] = []\\n params['associated_with'] = []\\n locator_list = chemical.find('LocatorList')\\n if locator_list:\\n for loc in locator_list.findall('InternetLocator'):\\n if loc.text == 'DrugBank':\\n db = f'{NamespacePrefix.DRUGBANK.value}:' \\\\\\n f'{loc.attrib[\\\"url\\\"].split(\\\"/\\\")[-1]}'\\n params['xrefs'].append(db)\\n elif loc.text == 'FDA SRS':\\n fda = f'{NamespacePrefix.FDA.value}:' \\\\\\n f'{loc.attrib[\\\"url\\\"].split(\\\"/\\\")[-1]}'\\n params['associated_with'].append(fda)\\n\\n # double-check and load full record\\n assert Drug(**params)\\n self._load_therapy(params)\",\n \"def filter_ordered_questions_by_concepts(questions, item_params, target_concept, concept_map): \\n \\n def get_parents(target, concept_map):\\n \\\"\\\"\\\"\\n Given a target concept, return a list of concept IDs for parent concepts\\n \\\"\\\"\\\"\\n parents = []\\n target_index = concept_map[CONCEPTS_STR].index(target)\\n for row in range(len(concept_map[ADJ_MAT_STR])): \\n # get value in adjMat for each row at target concept's col\\n val = concept_map[ADJ_MAT_STR][row][target_index] \\n if val > 0 and target_index != row: # don't care concepts are their own parents\\n # print('parent found at {}, {}'.format(row, target_index)) # TODO remove\\n parents.append(concept_map[CONCEPTS_STR][row])\\n return parents\\n\\n def get_children(target, concept_map):\\n \\\"\\\"\\\"\\n Given a target concept, return a list of concept IDs for all child concepts\\n \\\"\\\"\\\"\\n child_inds = []\\n target_index = concept_map[CONCEPTS_STR].index(target)\\n target_row = concept_map[ADJ_MAT_STR][target_index]\\n for ind in range(len(target_row)): # for each ind in row of adj mat\\n val = target_row[ind]\\n if(val>0 and ind != target_index): # don't care concept is child of itself\\n child_inds.append(ind)\\n return list(map(lambda ind: concept_map[CONCEPTS_STR][ind], child_inds))\\n\\n def get_concept(eid, item_params):\\n \\\"\\\"\\\"\\n Given a question/exercise eid (String) and item_params (pd.DataFrame),\\n return the concept that exercise corresponds to.\\n\\n Assumes eid is unique (maps to exactly 1 row)\\n \\\"\\\"\\\"\\n\\n concept = item_params[item_params[EID]==eid][CONCEPT]\\n\\n if len(concept) < 1: # TODO: make this filter \\\"!= 1\\\" to be more strict\\n raise Exception(\\\"Exercise ID does not map to any exercises. eid: {}\\\".format(eid))\\n else:\\n return concept.iloc[0] # to get actual value\\n \\n df_item_params = pd.DataFrame.from_dict(item_params)\\n\\n rec_eids = []\\n\\n # get all recommendations from target, its children, parents\\n rec_eids += list(filter(lambda q: get_concept(q, df_item_params) in \\n [target_concept] + \\n get_children(target_concept, concept_map) +\\n get_parents(target_concept, concept_map), questions))\\n \\n # # GET TOP FOR EACH RELATIONSHIP\\n # # add max_num_target questions of same concept to rec_eids\\n # rec_target = list(filter(lambda q: get_concept(q, df_item_params) == target_concept, \\n # questions))[:max_num_target]\\n # # print(\\\"For target, added {}\\\".format(rec_target))\\n # rec_eids += rec_target\\n \\n\\n # # add max_num_child questions of child concepts to rec_eids\\n # rec_child = list(filter(lambda q: get_concept(q, df_item_params) in get_children(target_concept, concept_map), \\n # questions))[:max_num_child]\\n # # print(\\\"For children, added {}\\\".format(rec_child ))\\n # rec_eids += rec_child\\n\\n # # add max_num_parent questions of parent concepts to rec_eids\\n # rec_parent = list(filter(lambda q: get_concept(q, df_item_params) in get_parents(target_concept, concept_map), \\n # questions))[:max_num_parent] \\n # # print(\\\"For parents, added {}\\\".format(rec_parent))\\n # rec_eids += rec_parent \\n\\n # want order of recommendations to stay same & only grab from top half of recommendations\\n # return list(filter(lambda eid: eid in rec_eids, questions[:int(len(questions)/2)+1])) \\n\\n # want order of recommendations to stay same\\n return list(filter(lambda eid: eid in rec_eids, questions))\",\n \"def get_subtechniques_of():\\n global subtechniques_of\\n\\n if not subtechniques_of:\\n subtechniques_of = rsh.subtechniques_of(get_srcs())\\n \\n return subtechniques_of\",\n \"def generate_features_test(stances, dataset, name, feature_list, features_dir):\\n h, b, bodyId, headId = [], [], [], []\\n\\n feature_dict = {'overlap': word_overlap_features,\\n 'refuting': refuting_features,\\n 'polarity': polarity_features,\\n 'hand': hand_features,\\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\\n 'NMF_fit_all': NMF_fit_all,\\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\\n 'NMF_cos_50': NMF_cos_50,\\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\\n 'ppdb': ppdb,\\n 'stanford_ppdb': stanford_ppdb_score,\\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\\n 'stanford_sentiment': stanford_sentiment,\\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\\n 'stanford_wordsim': stanford_based_verb_noun_sim,\\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\\n 'stanford_negation': stanford_negation_features,\\n 'stanford_negation_1sent': stanford_negation_features_1sent,\\n 'stanford_negation_2sent': stanford_negation_features_2sent,\\n 'stanford_negation_3sent': stanford_negation_features_3sent,\\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\\n 'hedging': hedging_features,\\n 'sen2sen': sen2sen_similarity_max,\\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\\n 'wmdsenDoc': word_mover_distance_wholebody,\\n 'sdm_sim': sdm_sim,\\n 'discuss': discuss_features,\\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\\n 'lexical_features': lexical_features,\\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\\n 'mpqa_unigrams': mpqa_unigrams,\\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\\n 'nrc_emo_lex': nrc_emo_lex,\\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\\n #'POS_features': POS_features,\\n 'readability_features': readability_features,\\n 'sentiment140_unigrams': sentiment140_unigrams,\\n 'structural_features': structural_features,\\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\\n 'NMF_cos_300': NMF_cos_300\\n }\\n\\n stanceCounter = 0\\n for stance in stances:\\n h.append(stance['Headline'])\\n b.append(dataset.articles[stance['Body ID']])\\n bodyId.append(stance['Body ID'])\\n headId.append(name+str(stanceCounter))\\n stanceCounter += 1\\n\\n X_feat = []\\n for feature in feature_list:\\n print(\\\"calculate feature: \\\" + str(feature))\\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\\\"/\\\"+feature+\\\"_test.\\\"+name+'.npy', bodyId, feature, headId, fold=name)\\n X_feat.append(feat)\\n print(len(feat))\\n X = np.concatenate(X_feat, axis=1)\\n return X\",\n \"def extract_data_in_pylot_format(actor_list):\\n # Note: the output will include the ego vehicle as well.\\n vec_actors = actor_list.filter('vehicle.*')\\n vehicles = [\\n Obstacle.from_carla_actor(vec_actor) for vec_actor in vec_actors\\n ]\\n\\n person_actors = actor_list.filter('walker.pedestrian.*')\\n people = [\\n Obstacle.from_carla_actor(ped_actor) for ped_actor in person_actors\\n ]\\n\\n tl_actors = actor_list.filter('traffic.traffic_light*')\\n traffic_lights = [\\n TrafficLight.from_carla_actor(tl_actor) for tl_actor in tl_actors\\n ]\\n\\n speed_limit_actors = actor_list.filter('traffic.speed_limit*')\\n speed_limits = [\\n SpeedLimitSign.from_carla_actor(ts_actor)\\n for ts_actor in speed_limit_actors\\n ]\\n\\n traffic_stop_actors = actor_list.filter('traffic.stop')\\n traffic_stops = [\\n StopSign.from_carla_actor(ts_actor) for ts_actor in traffic_stop_actors\\n ]\\n\\n return (vehicles, people, traffic_lights, speed_limits, traffic_stops)\",\n \"def get_mitigation_mitigates_techniques():\\n global mitigation_mitigates_techniques\\n\\n if not mitigation_mitigates_techniques:\\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\\n\\n return mitigation_mitigates_techniques\",\n \"def example_data():\\n return [\\n {'id': 'text', 'title': {'en': 'Text'}, 'type': 'resourcetypes'},\\n {'id': 'data', 'title': {'en': 'Data'}, 'type': 'resourcetypes',\\n 'tags': ['recommended']},\\n ]\",\n \"def process_data_from_input_file(triplet):\\n\\n sentence = triplet.subject + ' ' + triplet.predicate + ' ' + triplet.object\\n doc = nlp(unicode(sentence))\\n root = doc[0]\\n for t in doc:\\n if t.pos_ == 'VERB' and t.head == t:\\n root = t\\n # elif t.pos_ == 'NOUN'\\n\\n # also, if only one sentence\\n # root = doc[:].root\\n\\n\\n \\\"\\\"\\\"\\n CURRENT ASSUMPTIONS:\\n - People's names are unique (i.e. there only exists one person with a certain name).\\n - Pet's names are unique\\n - The only pets are dogs and cats\\n - Only one person can own a specific pet\\n - A person can own only one pet\\n \\\"\\\"\\\"\\n\\n\\n # Process (PERSON, likes, PERSON) relations\\n if root.lemma_ == 'like':\\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and triplet.object in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and \\\"n't\\\" not in triplet.predicate:\\n s = add_person(triplet.subject)\\n o = add_person(triplet.object)\\n s.likes.append(o)\\n\\n if root.lemma_ == 'be' and triplet.object.startswith('friends with'):\\n fw_doc = nlp(unicode(triplet.object))\\n with_token = [t for t in fw_doc if t.text == 'with'][0]\\n # get text after with\\n after_with = fw_doc.text.split(with_token.text+ ' ')[1]\\n people = []\\n for p in after_with.split(' '):\\n if nlp(p)[0].tag_ == 'NNP':\\n people.append(nlp(p)[0].text)\\n # fw_who = [t for t in with_token.children if t.dep_ == 'pobj'][0].text\\n # fw_who = [e for e in fw_doc.ents if e.label_ == 'PERSON'][0].text\\n for p in people:\\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON']:\\n s = add_person(triplet.subject)\\n o = add_person(p)\\n s.likes.append(o)\\n o.likes.append(s)\\n if root.lemma_ == 'be' and triplet.object == 'friends':\\n fw_doc = nlp(unicode(triplet.subject))\\n and_token = [t for t in fw_doc if t.text == 'and']\\n if and_token:\\n and_token = and_token[0].text\\n if and_token == 'and' and fw_doc[0].text in [e.text for e in doc.ents if e.label_ == 'PERSON'] and fw_doc[2].text in [e.text for e in doc.ents if e.label_ == 'PERSON']:\\n s = add_person(fw_doc[0].text)\\n o = add_person(fw_doc[2].text)\\n s.likes.append(o)\\n o.likes.append(s)\\n\\n # Process (PET, has, NAME) Mary's dog's name is Rover\\n if triplet.subject.endswith('name') and ('dog' in triplet.subject or 'cat' in triplet.subject):\\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\\n\\n # handle single names, but what about compound names? Noun chunks might help.\\n if (len(obj_span) == 1 or len(obj_span) == 2) and obj_span[-1].pos_ == 'PROPN':\\n name = triplet.object\\n subj_start = sentence.find(triplet.subject)\\n subj_doc = doc.char_span(subj_start, subj_start + len(triplet.subject))\\n\\n s_people = [token.text for token in subj_doc if token.ent_type_ == 'PERSON']\\n assert len(s_people) == 1\\n s_person = select_person(s_people[0])\\n\\n pet = get_persons_pet(s_person.name)\\n\\n pet.name = name\\n s_person.has.append(pet)\\n\\n # Process (Who has dog)\\n if root.lemma_ == 'have'and ('dog' in triplet.object or 'cat' in triplet.object):\\n # find pets name and instantiate name empty str\\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\\n name = ''\\n\\n if obj_span[-1].pos_ == 'PROPN':\\n name = obj_span[-1].text\\n s = add_person(triplet.subject)\\n s_pet_type = 'dog' if 'dog' in triplet.object else 'cat'\\n pet = add_pet(s_pet_type, name)\\n s.has.append(pet)\\n\\n date = [e.text for e in doc.ents if e.label_ == 'DATE']\\n gpe = [e.text for e in doc.ents if e.label_ == 'GPE']\\n person = [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG']\\n # if person and GPE exists, we add it into trip(departs_on, departs_to)\\n if person and (gpe or date):\\n s = add_person(triplet.subject)\\n o = add_trip(date, gpe)\\n s.travels.append(o)\",\n \"def get_technique_to_domain():\\n global technique_to_domain\\n \\n if not technique_to_domain:\\n technique_to_domain = stixhelpers.get_technique_id_domain_map(get_ms())\\n \\n return technique_to_domain\",\n \"def _read_fight_talents(root):\\n output_list = []\\n for _, value in enumerate(root[0][8]):\\n # offensive and defensive test possible, so add every entry twice\\n output_list.append(FightTalent(value, \\\"AT\\\"))\\n output_list.append(FightTalent(value, \\\"PA\\\"))\\n return output_list\",\n \"def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\\n return sorted(list(set(expert_knowledge)))\",\n \"def read_squad_examples(input_file, return_answers, context_only=False, question_only=False,\\n draft=False, draft_num_examples=12, append_title=False):\\n with open(input_file, \\\"r\\\") as reader:\\n input_data = json.load(reader)[\\\"data\\\"]\\n\\n examples = []\\n ans_cnt = 0\\n no_ans_cnt = 0\\n\\n # Only word-based tokenization is peformed (whitespace based)\\n for doc_idx, entry in enumerate(input_data):\\n title = entry['title'][0] if type(entry['title']) == list else entry['title']\\n assert type(title) == str\\n\\n for par_idx, paragraph in enumerate(entry[\\\"paragraphs\\\"]):\\n # Do not load context for question only\\n if not question_only:\\n paragraph_text = paragraph[\\\"context\\\"]\\n title_offset = 0\\n if append_title:\\n title_str = '[ ' + ' '.join(title.split('_')) + ' ] '\\n title_offset += len(title_str)\\n paragraph_text = title_str + paragraph_text\\n # Note that we use the term 'word' for whitespace based words, and 'token' for subtokens (for BERT input)\\n doc_words, char_to_word_offset = context_to_words_and_offset(paragraph_text)\\n\\n # 1) Context only ends here\\n if context_only:\\n metadata = {}\\n if \\\"pubmed_id\\\" in entry:\\n entry_keys = [\\n \\\"pubmed_id\\\", \\\"sha\\\", \\\"title_original\\\", \\\"title_entities\\\",\\n \\\"journal\\\", \\\"authors\\\", \\\"article_idx\\\"\\n ]\\n para_keys = [\\\"context_entities\\\"]\\n for entry_key in entry_keys:\\n if entry_key in entry:\\n metadata[entry_key] = entry[entry_key]\\n for para_key in para_keys:\\n if para_key in paragraph:\\n metadata[para_key] = paragraph[para_key]\\n # metadata[\\\"pubmed_id\\\"] = (metadata[\\\"pubmed_id\\\"] if not pd.isnull(metadata[\\\"pubmed_id\\\"])\\n # else 'NaN')\\n example = SquadExample(\\n doc_words=doc_words,\\n title=title,\\n doc_idx=doc_idx,\\n par_idx=par_idx,\\n metadata=metadata)\\n examples.append(example)\\n\\n if draft and len(examples) == draft_num_examples:\\n return examples\\n continue\\n\\n # 2) Question only or 3) context/question pair\\n else:\\n for qa in paragraph[\\\"qas\\\"]:\\n qas_id = str(qa[\\\"id\\\"])\\n question_text = qa[\\\"question\\\"]\\n\\n # Noisy question skipping\\n if len(question_text.split(' ')) == 1:\\n logger.info('Skipping a single word question: {}'.format(question_text))\\n continue\\n if \\\"I couldn't could up with another question.\\\" in question_text:\\n logger.info('Skipping a strange question: {}'.format(question_text))\\n continue\\n\\n start_position = None\\n end_position = None\\n orig_answer_text = None\\n\\n # For pre-processing that should return answers together\\n if return_answers:\\n assert type(qa[\\\"answers\\\"]) == dict or type(qa[\\\"answers\\\"]) == list, type(qa[\\\"answers\\\"])\\n if type(qa[\\\"answers\\\"]) == dict:\\n qa[\\\"answers\\\"] = [qa[\\\"answers\\\"]]\\n\\n # No answers\\n if len(qa[\\\"answers\\\"]) == 0:\\n orig_answer_text = \\\"\\\"\\n start_position = -1 # Word-level no-answer => -1\\n end_position = -1\\n no_ans_cnt += 1\\n # Answer exists\\n else:\\n answer = qa[\\\"answers\\\"][0]\\n ans_cnt += 1\\n\\n orig_answer_text = answer[\\\"text\\\"]\\n answer_offset = answer[\\\"answer_start\\\"] + title_offset\\n answer_length = len(orig_answer_text)\\n start_position = char_to_word_offset[answer_offset]\\n end_position = char_to_word_offset[answer_offset + answer_length - 1]\\n\\n # Only add answers where the text can be exactly recovered from the context\\n actual_text = \\\" \\\".join(doc_words[start_position:(end_position + 1)])\\n cleaned_answer_text = \\\" \\\".join(\\n tokenization.whitespace_tokenize(orig_answer_text)) # word based tokenization\\n if actual_text.find(cleaned_answer_text) == -1:\\n logger.warning(\\\"Could not find answer: '%s' vs. '%s'\\\",\\n actual_text, cleaned_answer_text)\\n continue\\n\\n # Question only ends here\\n if question_only:\\n example = SquadExample(\\n qas_id=qas_id,\\n question_text=question_text)\\n\\n # Context/question pair ends here\\n else:\\n example = SquadExample(\\n qas_id=qas_id,\\n question_text=question_text,\\n paragraph_text=paragraph_text,\\n doc_words=doc_words,\\n orig_answer_text=orig_answer_text,\\n start_position=start_position,\\n end_position=end_position,\\n title=title,\\n doc_idx=doc_idx,\\n par_idx=par_idx)\\n examples.append(example)\\n\\n if draft and len(examples) == draft_num_examples:\\n return examples\\n\\n # Testing for shuffled draft (should comment out above 'draft' if-else statements)\\n if draft:\\n random.shuffle(examples)\\n logger.info(str(len(examples)) + ' were collected before draft for shuffling')\\n return examples[:draft_num_examples]\\n\\n logger.info('Answer/no-answer stat: %d vs %d'%(ans_cnt, no_ans_cnt))\\n return examples\",\n \"def skill_pedagogic_ressources(request, type, slug):\\n\\n if type == 'skill':\\n base = get_object_or_404(Skill, code=slug)\\n elif type == 'section':\\n base = get_object_or_404(Section, id=slug)\\n else:\\n # type == 'coder'\\n base = get_object_or_404(CodeR, id=slug)\\n\\n personal_resource = base.resource.filter(section=\\\"personal_resource\\\")\\n lesson_resource = base.resource.filter(section=\\\"lesson_resource\\\")\\n exercice_resource = base.resource.filter(section=\\\"exercice_resource\\\")\\n other_resource = base.resource.filter(section=\\\"other_resource\\\")\\n\\n exercice_resource_sesamath = list()\\n lesson_resource_sesamath = list()\\n lesson_resource_khanacademy = list()\\n\\n sori_skills_lesson_resources = list()\\n sori_skills_exercice_resources = list()\\n sori_skills_other_resources = list()\\n\\n sori_skills_lesson_resource_sesamath = list()\\n sori_skills_lesson_resource_khanacademy = list()\\n sori_skills_exercice_resource_sesamath = list()\\n\\n sori_coder_lesson_resources = list()\\n sori_coder_exercice_resources = list()\\n sori_coder_other_resources = list()\\n\\n sori_coder_lesson_resource_sesamath = list()\\n sori_coder_lesson_resource_khanacademy = list()\\n sori_coder_exercice_resource_sesamath = list()\\n\\n # Sorting the different type of resources by category (personal, lesson, exercice or other)\\n # and by type (khanacademy, sesamath or other)\\n\\n for exo in lesson_resource:\\n if exo.content.get('from') and exo.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\\n lesson_resource_sesamath.append([exo.pk, resource])\\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\\n\\n elif exo.content.get('from') and exo.content['from'] == \\\"skills_khanacademyvideoskill\\\":\\n resource = get_object_or_404(KhanAcademy, pk=exo.content['referenced'])\\n lesson_resource_khanacademy.append([exo.pk, resource])\\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\\n\\n for exo in exercice_resource:\\n if exo.content.get('from') and exo.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\\n exercice_resource_sesamath.append([exo.pk, resource])\\n exercice_resource = exercice_resource.exclude(pk=exo.pk)\\n\\n # Do the same operation for similar or identical resources :\\n # Some Skill or CodeR can be similar or identical to other Skill or CodeR\\n # We have to display the resources of those as well\\n\\n # We achieve this only if we have either a Skill or a CodeR.\\n # If it is a Section, we don't display the similar/identical resources\\n # First step : is base a Skill or a CodeR ?\\n if isinstance(base, Skill):\\n # Begin with the Skills, same step is done for CodeR a bit further\\n\\n # Retrieve all the similar and identical Relations for Skills\\n skills_from = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], from_skill=base.id))\\n skills_to = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], to_skill=base.id))\\n\\n # This list will gather all the similar or 'identic' Skills\\n # (similar_or_identic = sori)\\n sori_skills = list()\\n for skill_from in skills_from:\\n sori_skills.append(skill_from.to_skill)\\n for skill_to in skills_to:\\n sori_skills.append(skill_to.from_skill)\\n\\n # Get the associated resources for these Skills\\n # We need to keep them separated by section to ease their use in the template\\n\\n for skill in sori_skills:\\n lesson = skill.resource.filter(section=\\\"lesson_resource\\\")\\n if lesson:\\n sori_skills_lesson_resources.append([skill, lesson])\\n exercice = skill.resource.filter(section=\\\"exercice_resource\\\")\\n if exercice:\\n sori_skills_exercice_resources.append([skill, exercice])\\n other = skill.resource.filter(section=\\\"other_resource\\\")\\n if other:\\n sori_skills_other_resources.append([skill, other])\\n\\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\\n for skill in sori_skills_lesson_resources:\\n # Lists to regroup resources together\\n sesamath_list = list()\\n khan_list = list()\\n\\n for res in skill[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n skill[1] = skill[1].exclude(pk=res.pk)\\n\\n elif res.content.get('from') and res.content['from'] == \\\"skills_khanacademyvideoskill\\\":\\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\\n khan_list.append([res.pk, resource])\\n skill[1] = skill[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the Skill, we remove it from the list\\n if not skill[1]:\\n sori_skills_lesson_resources.remove(skill)\\n\\n # Once resources are sorted, we can add them with the associated Skill\\n if sesamath_list:\\n sori_skills_lesson_resource_sesamath.append([skill[0], sesamath_list])\\n if khan_list:\\n sori_skills_lesson_resource_khanacademy.append([skill[0], khan_list])\\n\\n # sori_skills_exercice_resources has resources of different types, we need to distinguish them\\n for skill in sori_skills_exercice_resources:\\n # List to regroup resources together\\n sesamath_list = list()\\n\\n for res in skill[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n skill[1] = skill[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the Skill, we remove it from the list\\n if not skill[1]:\\n sori_skills_exercice_resources.remove(skill)\\n\\n # Once resources are sorted, we can add them with the associated Skill\\n if sesamath_list:\\n sori_skills_exercice_resource_sesamath.append([skill[0], sesamath_list])\\n\\n if isinstance(base, CodeR) or isinstance(base, Skill):\\n # Repeat this operation for the CodeR\\n # TODO : Group these steps as a function for Skill and CodeR\\n # Retrieve all the CodeR related to the current Skill or to the current CodeR\\n\\n if isinstance(base, Skill):\\n related_coder = CodeR.objects.filter(skill=base.id)\\n else:\\n coder_from = CodeR_relations.objects.filter(\\n Q(relation_type__in=['similar_to', 'identic_to'], from_coder=base.id))\\n coder_to = CodeR_relations.objects.filter(\\n Q(relation_type__in=['similar_to', 'identic_to'], to_coder=base.id))\\n\\n # This list will gather all the similar or 'identic' CodeR\\n related_coder = list()\\n for coder_from in coder_from:\\n related_coder.append(coder_from.to_coder)\\n for coder_to in coder_to:\\n related_coder.append(coder_to.from_coder)\\n # Case where we look for CodeR similar or identic to a CodeR\\n\\n # Get the associated resources for these CodeR\\n\\n for coder in related_coder:\\n lesson = coder.resource.filter(section=\\\"lesson_resource\\\")\\n if lesson:\\n sori_coder_lesson_resources.append([coder, lesson])\\n exercice = coder.resource.filter(section=\\\"exercice_resource\\\")\\n if exercice:\\n sori_coder_exercice_resources.append([coder, exercice])\\n other = coder.resource.filter(section=\\\"other_resource\\\")\\n if other:\\n sori_coder_other_resources.append([coder, other])\\n\\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\\n for coder in sori_coder_lesson_resources:\\n # Lists to regroup resources together\\n sesamath_list = list()\\n khan_list = list()\\n\\n for res in coder[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n coder[1] = coder[1].exclude(pk=res.pk)\\n\\n elif res.content.get('from') and res.content['from'] == \\\"skills_khanacademyvideoskill\\\":\\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\\n khan_list.append([res.pk, resource])\\n coder[1] = coder[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the Skill, we remove it from the list\\n if not coder[1]:\\n sori_coder_lesson_resources.remove(coder)\\n\\n # Once resources are sorted, we can add them with the associated Skill\\n if sesamath_list:\\n sori_coder_lesson_resource_sesamath.append([coder[0], sesamath_list])\\n if khan_list:\\n sori_coder_lesson_resource_khanacademy.append([coder[0], khan_list])\\n\\n # sori_coder_exercice_resources has resources of different types, we need to distinguish them\\n for coder in sori_coder_exercice_resources:\\n # List to regroup resources together\\n sesamath_list = list()\\n\\n for res in coder[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n coder[1] = coder[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the CodeR, we remove it from the list\\n if not coder[1]:\\n sori_coder_exercice_resources.remove(coder)\\n\\n # Once resources are sorted, we can add them with the associated CodeR\\n if sesamath_list:\\n sori_coder_exercice_resource_sesamath.append([coder[0], sesamath_list])\\n\\n\\n sesamath_references_manuals = Sesamath.objects.filter(ressource_kind__iexact=\\\"Manuel\\\")\\n sesamath_references_cahiers = Sesamath.objects.filter(ressource_kind__iexact=\\\"Cahier\\\")\\n\\n return render(request, \\\"professor/skill/update_pedagogical_resources.haml\\\", {\\n \\\"sesamath_references_manuals\\\": sesamath_references_manuals,\\n \\\"sesamath_references_cahier\\\": sesamath_references_cahiers,\\n \\\"base\\\": base,\\n \\\"personal_resources\\\": personal_resource,\\n \\\"other_resources\\\": other_resource,\\n \\\"exercice_resources\\\": exercice_resource,\\n \\\"exercice_resource_sesamath\\\": exercice_resource_sesamath,\\n \\\"lesson_resources\\\": lesson_resource,\\n \\\"lesson_resource_sesamath\\\": lesson_resource_sesamath,\\n \\\"lesson_resource_khanacademy\\\": lesson_resource_khanacademy,\\n \\\"type\\\": type,\\n \\\"sori_skills_exercice_resources\\\": sori_skills_exercice_resources,\\n \\\"sori_skills_lesson_resources\\\": sori_skills_lesson_resources,\\n \\\"sori_skills_other_resources\\\": sori_skills_other_resources,\\n \\\"sori_skills_lesson_resource_sesamath\\\": sori_skills_lesson_resource_sesamath,\\n \\\"sori_skills_lesson_resource_khanacademy\\\": sori_skills_lesson_resource_khanacademy,\\n \\\"sori_skills_exercice_resource_sesamath\\\": sori_skills_exercice_resource_sesamath,\\n \\\"sori_coder_exercice_resources\\\": sori_coder_exercice_resources,\\n \\\"sori_coder_lesson_resources\\\": sori_coder_lesson_resources,\\n \\\"sori_coder_other_resources\\\": sori_coder_other_resources,\\n \\\"sori_coder_lesson_resource_sesamath\\\": sori_coder_lesson_resource_sesamath,\\n \\\"sori_coder_lesson_resource_khanacademy\\\": sori_coder_lesson_resource_khanacademy,\\n \\\"sori_coder_exercice_resource_sesamath\\\": sori_coder_exercice_resource_sesamath,\\n })\",\n \"def get_data_loaders_3sentences():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_3generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+3):]\\n #history_complete.append(history)\\n if len(history) > 6:\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def _add_descriptors(related):\\n\\n for r in related:\\n r[\\\"descriptors\\\"] = []\\n for edge in G.edges(data=True):\\n sibling_idx = _get_connected(edge, r[\\\"tokenIndex\\\"])\\n if sibling_idx and (A.lookup[int(sibling_idx)][\\\"pos\\\"] == \\\"JJ\\\" or edge[2][\\\"dep\\\"] in [\\\"amod\\\", \\\"compound\\\"]):\\n r[\\\"descriptors\\\"].append(\\n {\\n \\\"tokenIndex\\\": sibling_idx,\\n \\\"rawName\\\": A.lookup[int(sibling_idx)][\\\"word\\\"]\\n }\\n )\\n\\n if sibling_idx and \\\"NN\\\" in A.lookup[int(sibling_idx)][\\\"pos\\\"] and \\\"amod\\\" in edge[2][\\\"dep\\\"]:\\n additional_related = _get_cousin(sibling_idx, [\\\"nmod\\\"])\\n for add in set(additional_related):\\n related = _add_related(add, \\\"nmod\\\", related, A.index_lookup[add],\\n connector=G.nodes[sibling_idx]['word'])\\n return related\",\n \"def data_process(list_of_speeches_pronoun_data):\\n speech_list = []\\n first_person_singular_proportion_list = []\\n first_person_plural_proportion_list = []\\n second_person_proportion_list = []\\n third_person_singular_proportion_list = []\\n third_person_plural_proportion_list = []\\n\\n for speech_data in list_of_speeches_pronoun_data:\\n speech_list.append(speech_data[0].replace('_', ' ').title())\\n first_person_singular_proportion_list.append(speech_data[2]['first_person_singular'])\\n first_person_plural_proportion_list.append(speech_data[2]['first_person_plural'])\\n second_person_proportion_list.append(speech_data[2]['second_person'])\\n third_person_singular_proportion_list.append(speech_data[2]['third_person_singular'])\\n third_person_plural_proportion_list.append(speech_data[2]['third_person_plural'])\\n\\n return [\\n speech_list,\\n first_person_singular_proportion_list,\\n first_person_plural_proportion_list,\\n second_person_proportion_list,\\n third_person_singular_proportion_list,\\n third_person_plural_proportion_list\\n ]\",\n \"def _create_examples(self, lines, kb_data, set_type):\\n examples = []\\n for idx, line in enumerate(lines):\\n item = json.loads(line.strip())\\n question_id = \\\"%s-%s\\\" % (set_type, idx)\\n \\n context_a_list = kb_data[idx]['answerA']\\n context_b_list = kb_data[idx]['answerB']\\n context_c_list = kb_data[idx]['answerC']\\n\\n context_a = \\\"\\\"\\n for l in context_a_list[:1]:\\n context_a += l.replace(\\\"\\\\n\\\",\\\". \\\")\\n context_a = context_a[:-1]\\n\\n context_b = \\\"\\\"\\n for l in context_b_list[:1]:\\n context_b += l.replace(\\\"\\\\n\\\",\\\". \\\")\\n context_b = context_b[:-1]\\n\\n context_c = \\\"\\\"\\n for l in context_c_list[:1]:\\n context_c += l.replace(\\\"\\\\n\\\",\\\". \\\")\\n context_c = context_c[:-1]\\n \\n \\n question = item[\\\"context\\\"] + item[\\\"question\\\"]\\n endings = [item[\\\"answerA\\\"],item[\\\"answerB\\\"],item[\\\"answerC\\\"] ]\\n label = item[\\\"correct\\\"]\\n #race_id = \\\"%s-%s\\\" % (set_type, data_raw[\\\"race_id\\\"])\\n #article = data_raw[\\\"article\\\"]\\n #for i in range(len(data_raw[\\\"answers\\\"])):\\n #truth = str(ord(data_raw[\\\"answers\\\"][i]) - ord(\\\"A\\\"))\\n #question = data_raw[\\\"questions\\\"][i]\\n #options = data_raw[\\\"options\\\"][i]\\n\\n examples.append(\\n InputExample(\\n example_id=question_id,\\n question=question,\\n contexts=[context_a,context_b,context_c],\\n endings=[endings[0], endings[1], endings[2]],#, options[3]\\n label=label,\\n )\\n )\\n return examples\",\n \"def pcw_protocol(DS, prot_list):\\n orig_ics = DS.initialconditions.copy()\\n t = 0\\n for i, stage_dict in enumerate(prot_list):\\n if 'pars' in stage_dict:\\n DS.set(pars=stage_dict['pars'])\\n if 'ics' in stage_dict:\\n DS.set(ics=stage_dict['ics'])\\n if 'tdata' in stage_dict:\\n raise NotImplementedError(\\\"Replace 'tdata' with 'tdur' float value\\\")\\n if 'tdur' in stage_dict:\\n DS.set(tdata=[t, t+stage_dict['tdur']])\\n traj = DS.compute('test')\\n if i == 0:\\n pts = traj.sample()\\n t = pts['t'][-1]\\n DS.set(ics=pts[-1])\\n else:\\n new_pts = traj.sample()\\n pts.extend(new_pts, skipMatchingIndepvar=True)\\n t = new_pts['t'][-1]\\n DS.set(ics=pts[-1])\\n DS.set(ics=orig_ics)\\n return pointset_to_traj(pts, events=DS.eventstruct.events), pts\",\n \"def create_feature_timex_association(self, featurelist, timexList):\\n \\n if not timexList or not featurelist:\\n return featurelist\\n \\n text = self.text\\n sentences = self.sentences\\n \\n for sentNum, sentence in enumerate(sentences):\\n timexes = [t for t in timexList if t.getSentNum()==sentNum and t.getRole()!='IGNORE']\\n features = [feat for feat in featurelist if feat.getSentNum()==sentNum]\\n if not timexes or not features:\\n continue\\n \\n timexes_valid = [t for t in timexes if t.getType() in ['DATE', 'REL']]\\n tlinks = self.create_tlinks_from_timexes(timexes_valid, sentence)\\n featureBundles = []\\n nn = len(tlinks)\\n if nn==1:\\n for feat in features:\\n feat.setTlink(tlinks[0].copy())\\n elif nn > 1:\\n featureBundles = self.get_feature_bundles(features, timexes_valid)\\n for i in range(nn-1):\\n leftTK = tlinks[i]\\n rightTK = tlinks[i+1]\\n fbs = [fb for fb in featureBundles if fb[0].getStartPos()>leftTK.getStartPos() \\n and fb[-1].getStartPos()<rightTK.getStartPos()]\\n \\n for fb in fbs:\\n segPre = text[leftTK.getEndPos():fb[0].getStartPos()]\\n segNext = text[fb[-1].getEndPos():rightTK.getStartPos()]\\n \\n if leftTK and self.is_next_separated(segPre, segNext, leftTK.getTimexes()[-1], rightTK.getTimexes()[0]):\\n chosenTK = leftTK\\n else:\\n chosenTK = rightTK\\n \\n for feat in fb:\\n ##: set all tlink with copy to avoid the case that two paris share the same tlink.\\n ##: this would make an extra copy in the first one, but can reduce the complexity of the code\\n feat.setTlink(chosenTK.copy())\\n \\n ##: assign the first tlink to features in the begining, and last tlink to those at the end\\n feats = [feat for feat in features if not feat.getTlink()]\\n if feats:\\n for ft in feats:\\n if ft.getStartPos() < tlinks[0].getStartPos():\\n ft.setTlink(tlinks[0].copy())\\n else:\\n ft.setTlink(tlinks[-1].copy())\\n \\n ##: assign the duration timex to the feature in front of it, except feature type is BETWEEN already \\n timexes_dur = [t for t in timexes if t.getType()=='DUR']\\n for t in timexes_dur:\\n featsLeft = [ft for ft in features if ft.getEndPos() < t.getStartPos()]\\n if featsLeft:\\n featleft = featsLeft[-1]\\n else:\\n break\\n \\n if featleft.getTlink() and featleft.getTlink().getType()=='BETWEEN': \\n continue\\n \\n featInBundle = False\\n for fb in featureBundles:\\n if featleft in fb:\\n for ft in fb:\\n if ft.getTlink():\\n ft.getTlink().addTimex(t.copy())\\n else:\\n ft.setTlink(TLink(t))\\n ft.getTlink().setType('DURATION')\\n featInBundle = True\\n break\\n \\n if not featInBundle:\\n if featleft.getTlink():\\n featleft.getTlink().addTimex(t)\\n else:\\n featleft.setTlink(TLink(t))\\n featleft.getTlink().setType('DURATION')\\n \\n ##: Set flag to indicate if the feature is in a clause \\n featurelist = self.flag_features_in_associate_clause(featurelist)\\n \\n return featurelist\",\n \"def process_clinical_data(data: pd.DataFrame, grp_var: str) -> Dict:\\n\\n # split results by concept type (i.e. concepts used in practice, standard concepts)\\n grouped_data = data.groupby(grp_var)\\n\\n # obtain results for groups\\n results = {}\\n for grp in grouped_data.groups.keys():\\n results[grp] = {}\\n print('Processing Group: {}'.format(grp))\\n\\n # get group data\\n grp_data = grouped_data.get_group(grp).drop_duplicates()\\n results[grp]['grp_full_data'] = grp_data\\n # get concept data\\n results[grp]['concept_src_code'] = [x for y in grp_data['CONCEPT_SOURCE_CODE'] for x in y.split(' | ')]\\n results[grp]['concept_src_label'] = [x for y in grp_data['CONCEPT_SOURCE_LABEL'] for x in y.split(' | ')]\\n results[grp]['concept_synonym'] = [x for y in grp_data['CONCEPT_SYNONYM'] for x in y.split(' | ')]\\n results[grp]['concept_vocab'] = ', '.join(set([x for y in grp_data['CONCEPT_VOCAB'] for x in y.split(' | ')]))\\n # get ancestor data\\n results[grp]['anc_concept_id'] = [x for y in grp_data['ANCESTOR_CONCEPT_ID'] for x in y.split(' | ')]\\n results[grp]['anc_src_code'] = [x for y in grp_data['ANCESTOR_SOURCE_CODE'] for x in y.split(' | ')]\\n results[grp]['anc_label'] = [x for y in grp_data['ANCESTOR_LABEL'] for x in y.split(' | ')]\\n results[grp]['anc_vocab'] = ', '.join(set([x for y in grp_data['ANCESTOR_VOCAB'] for x in y.split(' | ')]))\\n\\n return results\",\n \"def inferential_analysis(x_arr, y_arr, samples_related):\\n delta = np.mean(x_arr) - np.mean(y_arr)\\n\\n if samples_related:\\n _, pval = stats.ttest_rel(\\n x_arr, y_arr)\\n else:\\n _, pval = stats.ttest_ind(\\n x_arr, y_arr, equal_var=False) # _ = tstat\\n return {\\n 'Hypothesis Testing': {\\n 'Treatment vs Control': {\\n 'Difference': delta,\\n 'p_value': pval,\\n 'percent_bias': percent_bias(x_arr, y_arr),\\n # 'cohen\\\\'s d effect size': cohen_d(x_arr, y_arr),\\n # 'Wilcoxon rank-sum statistic': stats.ranksums(x_arr, y_arr),\\n }\\n }\\n }\",\n \"def find_related_concepts(text):\\n #\\n # Find wikipedia terms in text\\n # Spot terms in text\\n text = text.encode('ascii', 'replace')\\n response = TagMeService.check_spotting(text)\\n\\n results = return_wikipedia_term(response)\\n\\n #\\n # Lookup terms in taxonomy and fetch relatedConcepts (keywords)\\n # return related terms if they are found in the taxonomy\\n\\n return lookup_in_taxonomy(results)\",\n \"def get_tools_using_technique():\\n global tools_using_technique\\n\\n if not tools_using_technique:\\n tools_using_technique = rsh.tools_using_technique(get_srcs())\\n \\n return tools_using_technique\",\n \"def get_data_loaders_4sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"]\\n #history_complete.append(history)\\n if len(history_splitted) > (len(persona)-1):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def _create_examples(self, data_dir, set_type):\\n\\t\\texamples = []\\n\\t\\tinput_file_data = os.path.join(data_dir, \\\"data.tsv\\\")\\n\\t\\twith open(input_file_data, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n\\t\\t\\tfor i, inp in enumerate(f):\\n\\t\\t\\t\\tinps = inp.split('\\\\t') \\n\\t\\t\\t\\tguid = \\\"%s-%s\\\" % (set_type, i)\\n\\t\\t\\t\\ttext_inp = inps[1].strip()\\n\\t\\t\\t\\ttext_out = inps[2].strip()\\n\\t\\t\\t\\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\\n\\t\\t\\t\\t\\n\\t\\t\\t# Sort these out before returning\\n\\t\\t\\texamples = sorted(examples, key=sort_inp_len)\\n\\t\\t\\treturn examples\",\n \"async def insert_attack_stix_data(self):\\n logging.info('Downloading ATT&CK data from STIX/TAXII...')\\n attack = {}\\n collection = Collection(\\\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\\\")\\n tc_source = TAXIICollectionSource(collection)\\n filter_objs = {\\\"techniques\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n \\\"groups\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"intrusion-set\\\"), \\\"malware\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"malware\\\"),\\n \\\"tools\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"tool\\\"), \\\"relationships\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"relationship\\\")}\\n for key in filter_objs:\\n attack[key] = tc_source.query(filter_objs[key])\\n references = {}\\n\\n # add all of the patterns and dictionary keys/values for each technique and software\\n for i in attack[\\\"techniques\\\"]:\\n if \\\"description\\\" in i:\\n descr = i['description'].replace('<code>', '').replace('</code>', '').replace(\\n '\\\\n', '').encode('ascii', 'ignore').decode('ascii')\\n else:\\n descr = \\\"missing description\\\"\\n references[i[\\\"id\\\"]] = {\\\"name\\\": i[\\\"name\\\"], \\\"id\\\": i[\\\"external_references\\\"][0][\\\"external_id\\\"],\\n \\\"example_uses\\\": [],\\n \\\"description\\\": descr,\\n \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n\\n for i in attack[\\\"relationships\\\"]:\\n if i[\\\"relationship_type\\\"] == 'uses':\\n if 'attack-pattern' in i[\\\"target_ref\\\"]:\\n use = i[\\\"description\\\"]\\n # remove unnecessary strings, fix unicode errors\\n use = use.replace('<code>', '').replace('</code>', '').replace('\\\"', \\\"\\\").replace(',', '').replace(\\n '\\\\t', '').replace(' ', ' ').replace('\\\\n', '').encode('ascii', 'ignore').decode('ascii')\\n find_pattern = re.compile('\\\\[.*?\\\\]\\\\(.*?\\\\)') # get rid of att&ck reference (name)[link to site]\\n m = find_pattern.findall(use)\\n if len(m) > 0:\\n for j in m:\\n use = use.replace(j, '')\\n if use[0:2] == '\\\\'s':\\n use = use[3:]\\n elif use[0] == ' ':\\n use = use[1:]\\n # combine all the examples to one list\\n references[i[\\\"target_ref\\\"]][\\\"example_uses\\\"].append(use)\\n\\n for i in attack[\\\"malware\\\"]:\\n if 'description' not in i: # some software do not have description, example: darkmoon https://attack.mitre.org/software/S0209\\n continue\\n else:\\n references[i[\\\"id\\\"]] = {\\\"id\\\": i['id'], \\\"name\\\": i[\\\"name\\\"], \\\"description\\\": i[\\\"description\\\"],\\n \\\"examples\\\": [], \\\"example_uses\\\": [], \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n for i in attack[\\\"tools\\\"]:\\n references[i[\\\"id\\\"]] = {\\\"id\\\": i['id'], \\\"name\\\": i[\\\"name\\\"], \\\"description\\\": i[\\\"description\\\"], \\\"examples\\\": [],\\n \\\"example_uses\\\": [], \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n\\n attack_data = references\\n logging.info(\\\"Finished...now creating the database.\\\")\\n\\n cur_uids = await self.dao.get('attack_uids') if await self.dao.get('attack_uids') else []\\n cur_items = [i['uid'] for i in cur_uids]\\n for k, v in attack_data.items():\\n if k not in cur_items:\\n await self.dao.insert('attack_uids', dict(uid=k, description=defang_text(v['description']), tid=v['id'],\\n name=v['name']))\\n if 'regex_patterns' in v:\\n [await self.dao.insert('regex_patterns', dict(uid=k, regex_pattern=defang_text(x))) for x in\\n v['regex_patterns']]\\n if 'similar_words' in v:\\n [await self.dao.insert('similar_words', dict(uid=k, similar_word=defang_text(x))) for x in\\n v['similar_words']]\\n if 'false_negatives' in v:\\n [await self.dao.insert('false_negatives', dict(uid=k, false_negative=defang_text(x))) for x in\\n v['false_negatives']]\\n if 'false_positives' in v:\\n [await self.dao.insert('false_positives', dict(uid=k, false_positive=defang_text(x))) for x in\\n v['false_positives']]\\n if 'true_positives' in v:\\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\\n v['true_positives']]\\n if 'example_uses' in v:\\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\\n v['example_uses']]\\n logging.info('[!] DB Item Count: {}'.format(len(await self.dao.get('attack_uids'))))\",\n \"def get_data_loaders_1sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1_sentence_final_generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+1):]\\n #history_complete.append(history)\\n if len(history) > 3:\\n history_chatbot = history[1]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def get_one_study(study_dir):\\n df_columns = ['item_type', 'item_urn', 'content']\\n df = pd.DataFrame(columns=df_columns)\\n\\n # category urn and label\\n list_files = os.listdir(study_dir)\\n\\n instrument_dict = {}\\n if 'Instrument.txt' in list_files:\\n L = json.load(open(os.path.join(study_dir, 'Instrument.txt')))\\n item = item_to_dict(L[0])\\n instrument_dict['instrument_urn'] = item['InstrumentURN']\\n instrument_dict['instrument_name'] = item['InstrumentName']\\n\\n if 'Category.txt' in list_files:\\n category_dict = generate_category_dict(os.path.join(study_dir, 'Category.txt'))\\n else:\\n category_dict = []\\n\\n for input_file in list_files:\\n\\n filename = os.path.splitext(input_file)[0]\\n\\n L = json.load(open(os.path.join(study_dir, input_file)))\\n\\n if filename == 'Question':\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n if not item['QuestionLiteral'] is None:\\n literal = item['QuestionLiteral'].replace('\\\\n', '')\\n else:\\n literal = None\\n df.loc[len(df)] = ['question', item['QuestionURN'], literal]\\n df.loc[len(df)] = ['question name', None, item['QuestionItemName']]\\n\\n if item['Response'] != {} and item['Response']['response_type'] != 'CodeList':\\n df.loc[len(df)] = [item['Response']['response_type'], None, item['Response']['response_label']]\\n\\n elif filename == 'Interviewer Instruction':\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n df.loc[len(df)] = ['instruction', item['InstructionURN'], item['InstructionText']]\\n\\n elif filename == 'Code Set' and category_dict != []:\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n for code in item['Code']:\\n if code['Value'] is None:\\n code['Value'] = ''\\n cat_urn = 'urn:ddi:' + code['CategoryReference']['Agency'] + ':' + code['CategoryReference']['ID'] + ':' + code['CategoryReference']['Version']\\n df.loc[len(df)] = ['codelist', code['URN'], code['Value'] + ', ' + category_dict[cat_urn]]\\n\\n elif filename == 'Statement':\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n df.loc[len(df)] = ['statement', item['StatementURN'], item['Literal']]\\n\\n elif filename == 'Conditional':\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n df.loc[len(df)] = ['conditional', item['URN'], item['IfCondition']['Description'] + item['IfCondition']['CommandContent'] if not item['IfCondition']['CommandContent'] is None else item['IfCondition']['Description']]\\n\\n elif filename == 'Loop':\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n df.loc[len(df)] = ['loop', item['URN'], item['LoopWhile']['CommandContent']]\\n\\n else:\\n # TODO\\n print(filename)\\n\\n df = df.drop_duplicates(keep='first')\\n df['instrument_name'] = instrument_dict['instrument_name']\\n df['instrument_urn'] = instrument_dict['instrument_urn']\\n return df\",\n \"def tokenize_one_example(self, example):\\n index, q_id, question, context, answer_list, answer_start = example\\n\\n context = context.replace('\\\\'\\\\'', '\\\\\\\" ').replace(r'``', '\\\\\\\" ')\\n context_tokens = SQuADDataTokenizer._word_tokenize_spacy(context) if self._use_spacy else \\\\\\n SQuADDataTokenizer._word_tokenize_nltk(context)\\n context_chars = [list(token) for token in context_tokens]\\n spans = SQuADDataTokenizer._get_token_spans(context, context_tokens)\\n\\n ques = question.replace('\\\\'\\\\'', '\\\\\\\" ').replace('``', '\\\\\\\" ')\\n ques_tokens = SQuADDataTokenizer._word_tokenize_spacy(ques) if self._use_spacy else \\\\\\n SQuADDataTokenizer._word_tokenize_nltk(ques)\\n ques_chars = [list(token) for token in ques_tokens]\\n\\n y1s, y2s = [], []\\n answer_texts = []\\n\\n for answer_text, answer_start in zip(answer_list, answer_start):\\n answer_end = answer_start + len(answer_text)\\n answer_texts.append(answer_text)\\n answer_span = []\\n for idx, span in enumerate(spans):\\n if not (answer_end <= span[0] or answer_start >= span[1]):\\n answer_span.append(idx)\\n y1, y2 = answer_span[0], answer_span[-1]\\n y1s.append(y1)\\n y2s.append(y2)\\n\\n result = {'context_tokens': context_tokens, 'context_chars': context_chars,\\n 'ques_tokens': ques_tokens, 'ques_chars': ques_chars, 'y1s': y1s,\\n 'y2s': y2s, 'id': q_id, 'context': context, 'spans': spans, 'record_idx': index}\\n return result\",\n \"def load_dataset(path):\\n with open(path) as f:\\n data = json.load(f)['data']\\n output = {'qids': [], 'questions': [], 'answers': [],\\n 'contexts': [], 'qid2cid': []}\\n for article in data:\\n for paragraph in article['paragraphs']:\\n output['contexts'].append(paragraph['context'])\\n for qa in paragraph['qas']:\\n output['qids'].append(qa['id'])\\n output['questions'].append(qa['question'])\\n output['qid2cid'].append(len(output['contexts']) - 1)\\n if 'answers' in qa:\\n output['answers'].append(qa['answers'])\\n return output\",\n \"def get_clarifications_socialiqa(ex, nlp, comet_model):\\n context = ex['context']\\n question = ex['question']\\n\\n question_to_comet_relation = {\\n \\\"What will [NAME] want to do next?\\\": \\\"xWant\\\",\\n \\\"What will [NAME] want to do after?\\\": \\\"xWant\\\",\\n \\\"How would [NAME] feel afterwards?\\\": \\\"xReact\\\",\\n \\\"How would [NAME] feel as a result?\\\": \\\"xReact\\\",\\n \\\"What will [NAME] do next?\\\": \\\"xReact\\\",\\n \\\"How would [NAME] feel after?\\\": \\\"xReact\\\",\\n \\\"How would you describe [NAME]?\\\": \\\"xAttr\\\",\\n \\\"What kind of person is [NAME]?\\\": \\\"xAttr\\\",\\n \\\"How would you describe [NAME] as a person?\\\": \\\"xAttr\\\",\\n \\\"Why did [NAME] do that?\\\": \\\"xIntent\\\",\\n \\\"Why did [NAME] do this?\\\": \\\"xIntent\\\",\\n \\\"Why did [NAME] want to do this?\\\": \\\"xIntent\\\",\\n \\\"What does [NAME] need to do beforehand?\\\": \\\"xNeed\\\",\\n \\\"What does [NAME] need to do before?\\\": \\\"xNeed\\\",\\n \\\"What does [NAME] need to do before this?\\\": \\\"xNeed\\\",\\n \\\"What did [NAME] need to do before this?\\\": \\\"xNeed\\\",\\n \\\"What will happen to [NAME]?\\\": \\\"xEffect\\\",\\n \\\"What will happen to [NAME] next?\\\": \\\"xEffect\\\"\\n }\\n\\n clarifications = []\\n personx, _ = get_personx(nlp, context)\\n relation = question_to_comet_relation.get(re.sub(personx, \\\"[NAME]\\\", question, flags=re.I), None)\\n\\n if relation is not None:\\n outputs = {relation: comet_model.predict(context, relation, num_beams=5)}\\n\\n prefix = CATEGORY_TO_PREFIX[relation]\\n for out_event in outputs[relation]:\\n if out_event != \\\"none\\\" and out_event != \\\"\\\":\\n if not out_event.lower().startswith(\\\"person\\\") and not out_event.lower().startswith(\\\"other\\\"):\\n out_event = \\\" \\\".join((prefix, out_event))\\n\\n out_event = re.sub(\\\"personx\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"person x\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"persony\\\", \\\"others\\\", out_event, flags=re.I)\\n out_event = re.sub(\\\"person y\\\", \\\"others\\\", out_event, flags=re.I)\\n\\n clarifications.append((question, out_event))\\n\\n return clarifications\",\n \"def main(example, df, possible_subjects):\\n\\n # Drop example tutor if in df\\n try:\\n df.drop(df[example['url_id']==df['url_id']].index.values, inplace=True)\\n df.reset_index(drop=True, inplace=True)\\n except:\\n pass # Tutor is not in database\\n\\n # Check for graduate degree\\n df = graduate_degrees(example, df)\\n\\n # Filter by Jaccard index and location.\\n sim_tuts = subject_similarity(example, df, possible_subjects)\\n sim_tuts = location_overlap(example, sim_tuts)\\n\\n # Relevant features for computing similarity\\n rel_feats = ['avg_review_length',\\\\\\n 'badge_hours',\\\\\\n 'days_since_last_review',\\\\\\n 'has_rating',\\\\\\n 'number_of_ratings',\\\\\\n 'number_of_reviews',\\\\\\n 'profile_picture',\\\\\\n 'rating',\\\\\\n 'has_ivy_degree',\\\\\\n 'has_background_check',\\\\\\n 'response_time',\\\\\\n 'avg_review_sentiment']\\n\\n # Convert similar tutors to matrix. Normalize features.\\n # In parlance of machine learning, X are features, y is hourly rate.\\n X = sim_tuts[rel_feats].as_matrix().astype(np.float)\\n y = sim_tuts['hourly_rate'].as_matrix().astype(np.float)\\n scaler = preprocessing.StandardScaler()\\n X = scaler.fit_transform(X)\\n\\n X_example = example[rel_feats].as_matrix().astype(np.float)\\n y_example = np.float(example['hourly_rate'])\\n X_example = scaler.transform(X_example)\\n\\n # Get cosine similarity between example tutor and tutor db.\\n cos_tuts = np.empty(X.shape[0])\\n for i in xrange(X.shape[0]):\\n cos_tuts[i] = cosine_similarity(X[i,:], X_example)\\n\\n # Sort by similarity\\n sorted_idx = np.argsort(cos_tuts)[::-1]\\n cos_tuts = cos_tuts[sorted_idx]\\n y = y[sorted_idx]\\n sim_tuts.reset_index(drop=True, inplace=True)\\n\\n # Only keep tutors with similarity > 0.5\\n sim_tuts = sim_tuts.iloc[sorted_idx][cos_tuts>.5]\\n\\n # Calculate three outputted tutors.\\n nearest_neighbor = sim_tuts.iloc[0] # Highest similarity\\n max_tut = sim_tuts[sim_tuts['hourly_rate']==sim_tuts['hourly_rate'].max()].iloc[0]\\n min_tut = sim_tuts[sim_tuts['hourly_rate']==sim_tuts['hourly_rate'].min()].iloc[0]\\n\\n scaling = scale_kde(y, cos_tuts)\\n\\n kde = gaussian_kde(y[cos_tuts>0], weights=cos_tuts[cos_tuts>0])\\n x = np.linspace(0, y.max()+50, y.max()+50+1)\\n\\n pdf = kde(x)*scaling # Probability density function (estimated)\\n\\n img_io = make_kde_plot(x, pdf)\\n\\n return nearest_neighbor, max_tut, min_tut, img_io\",\n \"def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\\n\\n examples = []\\n total_answerable = 0\\n\\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\\n guid = \\\"%s-%s-%s\\\" % (set_type, line['qid'], str(i))\\n src = []\\n has_pos = 0\\n\\n if passage_type == 'both' and set_type != 'train':\\n\\n # rank by bert score in val and test\\n score_key = 'rank_score'\\n # score_key = \\\"nr_score\\\"\\n both_sources = line[\\\"passages\\\"][:2*cand_for_each_source] + line[\\\"tables\\\"][:2*cand_for_each_source]\\n all_scores = np.array([x[score_key] for x in both_sources])\\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\\n\\n for idx in sorted_index:\\n pas = both_sources[idx]\\n if idx < 2 * cand_for_each_source:\\n pas_type = 'passage'\\n title = pas[\\\"article_title\\\"]\\n else:\\n pas_type = 'table'\\n title = \\\"table_\\\" + pas[\\\"uid\\\"].split('-split')[0]\\n\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + f\\\" </s> {pas_type} title: \\\" + title + f\\\" </s> {pas_type} content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\")\\n\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n else:\\n\\n if passage_type in ['textual', 'hybrid', 'both']:\\n for pas in line[\\\"passages\\\"][:cand_for_each_source]:\\n # 10 passages\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> passage title: \\\" + pas[\\\"article_title\\\"] + \\\" </s> passage content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\\\\\"ZAPPAtite\\\\\\\". All songs written and performed ... </s>\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n if passage_type in ['tabular', 'hybrid', 'both']:\\n for tab in line[\\\"tables\\\"][:cand_for_each_source]:\\n # 10 tables\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> table title: \\\" + \\\"table_\\\" + tab[\\\"uid\\\"].split('-split')[0] + \\\" </s> table content: \\\" + tab[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\\n if isinstance(tab['judge'], dict):\\n has_pos += int(tab['judge']['judge_contain_all'])\\n else:\\n has_pos += int(tab['judge'])\\n\\n const = 2 if passage_type in ['hybrid', 'both'] else 1\\n if len(src) != const * cand_for_each_source:\\n logger.info(line)\\n src = src + [\\\"\\\"] * (const * cand_for_each_source - len(src))\\n\\n if has_pos > 0:\\n total_answerable += 1\\n\\n if enable_sql_supervision and 'true_sql' in line:\\n tgt = \\\"sql: \\\" + line[\\\"true_sql\\\"] + \\\" </s>\\\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\\\\\"Butler CC (KS)\\\\\\\" </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'denotation' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"denotation\\\"][0]) + \\\" </s>\\\"\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'answers' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"answers\\\"][0]) + \\\" </s>\\\" # e.g. answer: no slogan on current series </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt + \\\"\\\\n\\\")\\n\\n logger.info(f\\\"Total answerable in {set_type} split is {total_answerable / len(lines)}\\\")\\n\\n return examples\",\n \"def get_clarifications_winogrande(ex, nlp, comet_model):\\n personx, persony = ex['option1'], ex['option2']\\n\\n # Only extract relations for people\\n if personx[0] != personx[0].upper() or persony[0] != persony[0].upper():\\n return []\\n\\n input_event = ex[\\\"sentence\\\"]\\n outputs = {category: comet_model.predict(input_event, category, num_beams=5) for category in comet_model.categories}\\n\\n curr_events = []\\n for category, prefix in CATEGORY_TO_PREFIX.items():\\n for out_event in outputs[category]:\\n if out_event != \\\"none\\\" and out_event != \\\"\\\":\\n if not out_event.lower().startswith(\\\"person\\\") and not out_event.lower().startswith(\\\"other\\\"):\\n out_event = \\\" \\\".join((prefix, out_event))\\n\\n out_event = re.sub(\\\"personx\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"person x\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"persony\\\", persony, out_event, flags=re.I)\\n out_event = re.sub(\\\"person y\\\", persony, out_event, flags=re.I)\\n\\n question = CATEGORY_TO_QUESTION[category].replace(\\\"PersonX\\\", personx)\\n curr_events.append((question, out_event))\\n\\n return curr_events\",\n \"def process_dataset(data, tokenizer, workers=None):\\n tokenizer_class = tokenizers.get_class(tokenizer)\\n make_pool = partial(Pool, workers, initializer=init)\\n workers = make_pool(initargs=(tokenizer_class, {'annotators': {'lemma'}}))\\n q_tokens = workers.map(tokenize, data['questions'])\\n workers.close()\\n workers.join()\\n\\n workers = make_pool(\\n initargs=(tokenizer_class, {'annotators': {'lemma', 'pos', 'ner'}})\\n )\\n c_tokens = workers.map(tokenize, data['contexts'])\\n workers.close()\\n workers.join()\\n\\n for idx in range(len(data['qids'])):\\n question = q_tokens[idx]['words']\\n qlemma = q_tokens[idx]['lemma']\\n document = c_tokens[data['qid2cid'][idx]]['words']\\n offsets = c_tokens[data['qid2cid'][idx]]['offsets']\\n lemma = c_tokens[data['qid2cid'][idx]]['lemma']\\n pos = c_tokens[data['qid2cid'][idx]]['pos']\\n ner = c_tokens[data['qid2cid'][idx]]['ner']\\n ans_tokens = []\\n if len(data['answers']) > 0:\\n for ans in data['answers'][idx]:\\n found = find_answer(offsets,\\n ans['answer_start'],\\n ans['answer_start'] + len(ans['text']))\\n if found:\\n ans_tokens.append(found)\\n yield {\\n 'id': data['qids'][idx],\\n 'question': question,\\n 'document': document,\\n 'offsets': offsets,\\n 'answers': ans_tokens,\\n 'qlemma': qlemma,\\n 'lemma': lemma,\\n 'pos': pos,\\n 'ner': ner,\\n }\",\n \"def Related(self,name):\\n # Get related property-entity (property id, property name, entity id, entity name) given entity name\\n #\\n # Fail to fetch eid would result in empty list\\n query = self.entity2id(name)\\n # print \\\"query\\\" + query\\n # if query == \\\"Not Applicable\\\": return []\\n ans = []\\n for i in range(len(query)):\\n s_id = query[i][\\\"s_id\\\"]\\n subject_des = query[i][\\\"subject_des\\\"]\\n url = \\\"https://www.wikidata.org/w/api.php?action=wbgetentities&ids=\\\" + s_id + \\\"&format=json&languages=en\\\"\\n # print urllib.urlopen(url).read()\\n response = json.loads(urllib.urlopen(url).read())\\n for p in response[\\\"entities\\\"][s_id][\\\"claims\\\"]:\\n for o in response[\\\"entities\\\"][s_id][\\\"claims\\\"][p]:\\n # Enumerate property & entity (multi-property, multi-entity)\\n try:\\n # Some properties are not related to entities, thus try & except\\n o_id = o[\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"][\\\"id\\\"]\\n ans.append({\\n \\\"s_id\\\": s_id,\\n \\\"s_des\\\": subject_des,\\n \\\"p_id\\\": p,\\n \\\"property\\\": self.getp(p),\\n \\\"o_id\\\": o_id,\\n \\\"object\\\": self.getc(o_id)\\n })\\n # ans.append(\\\"\\\\\\\\property\\\\\\\\\\\"+p+\\\"\\\\t\\\"+getp(p)+\\\"\\\\t\\\\\\\\entity\\\\\\\\\\\"+cid+\\\"\\\\t\\\"+getc(cid))\\n # Print in a pid-pname-eid-ename fashion\\n except:\\n continue\\n return ans\",\n \"def get_scenarios(experiments):\\n return {exp.scenario for exp in experiments}\",\n \"def get_data_loaders():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+5):]\\n \\n #history_complete.append(history)\\n if len(persona) == 4:\\n if len(history) > (len(persona)+3):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def _get_related(stats, match, patterns_file):\\n all_related = None\\n measurement_formats = [\\\"space_between\\\", \\\"attached\\\", \\\"hyphenated\\\"]\\n\\n all_related = _parse_patterns(match[\\\"unit_idx\\\"], match[\\\"measurement_format\\\"], patterns_file)\\n if all_related == None:\\n all_related = _parse_patterns(match[\\\"unit_idx\\\"], [\\\"uncertain\\\"], patterns_file)\\n\\n # get words like approximately\\n num_adverbs = _parse_patterns([match[\\\"num_idx\\\"]], match[\\\"measurement_format\\\"], patterns_file)\\n unit_adverbs = _parse_patterns([match[\\\"unit_idx\\\"]], match[\\\"measurement_format\\\"], patterns_file)\\n adverbs = num_adverbs + unit_adverbs\\n for_removal = []\\n for a in adverbs:\\n if a[\\\"relationForm\\\"] != \\\"advmod\\\":\\n for_removal.append(a)\\n else:\\n [a.pop(key, None) for key in [\\\"descriptors\\\", \\\"connector\\\"]] # not relevant for adverbs\\n [adverbs.remove(a) for a in for_removal]\\n\\n if adverbs:\\n match[\\\"grobid\\\"][\\\"adverbs\\\"] = adverbs\\n\\n # Check to make sure related isn't already a number, unit, or quantified thing identified by Grobid\\n potential_keys = [\\\"quantity\\\", \\\"quantityLeast\\\", \\\"quantityMost\\\", \\\"quantified\\\"]\\n\\n if all_related:\\n for key in potential_keys:\\n for related in all_related:\\n if key in match[\\\"grobid\\\"]:\\n num, unit, quantified = \\\"\\\", \\\"\\\", \\\"\\\"\\n if \\\"rawValue\\\" in match[\\\"grobid\\\"][key]: num = match[\\\"grobid\\\"][key][\\\"rawValue\\\"]\\n if \\\"rawUnit\\\" in match[\\\"grobid\\\"][key]: unit = match[\\\"grobid\\\"][key][\\\"rawUnit\\\"][\\\"name\\\"]\\n if \\\"normalizedName\\\" in match[\\\"grobid\\\"][key]: quantified = match[\\\"grobid\\\"][key][\\\"normalizedName\\\"]\\n\\n if related[\\\"rawName\\\"] in [num, unit, quantified] or related[\\\"rawName\\\"] == num + unit or (\\n quantified in related[\\\"rawName\\\"] and not quantified == \\\"\\\"):\\n all_related.remove(related)\\n\\n if related[\\\"rawName\\\"] == unit:\\n for k in related.keys():\\n if not k in match[\\\"grobid\\\"][key][\\\"rawUnit\\\"]:\\n match[\\\"grobid\\\"][key][\\\"rawUnit\\\"][k] = related[k]\\n\\n elif related[\\\"rawName\\\"] == quantified:\\n for k in related.keys():\\n if not k in match[\\\"grobid\\\"][key]:\\n match[\\\"grobid\\\"][key][k] = related[k]\\n return all_related\",\n \"def __find_topics(self, concepts):\\n\\n # Set up\\n found_topics = dict() # to store the matched topics\\n explanation = dict()\\n\\n # finding matches\\n for concept in concepts:\\n evgrams = everygrams(concept.split(), 1, 3) # list of unigrams, bigrams, trigrams\\n for grams in evgrams:\\n gram = \\\"_\\\".join(grams)\\n gram_without_underscore = \\\" \\\".join(grams)\\n #### Finding similar words contained in the model\\n\\n list_of_matched_topics = []\\n\\n if self.fast_classification:\\n list_of_matched_topics = self.__get_similar_words_from_cached_model(gram,grams)\\n else:\\n list_of_matched_topics = self.__get_similar_words_from_full_model(gram, grams)\\n\\n\\n for topic_item in list_of_matched_topics:\\n\\n topic = topic_item[\\\"topic\\\"]\\n str_sim = topic_item[\\\"sim_t\\\"]\\n wet = topic_item[\\\"wet\\\"]\\n sim = topic_item[\\\"sim_w\\\"]\\n\\n\\n if str_sim >= self.min_similarity and topic in self.cso.topics_wu:\\n\\n\\n if topic in found_topics:\\n #tracking this match\\n found_topics[topic][\\\"times\\\"] += 1\\n\\n found_topics[topic][\\\"gram_similarity\\\"].append(sim)\\n\\n #tracking the matched gram\\n if gram in found_topics[topic][\\\"grams\\\"]:\\n found_topics[topic][\\\"grams\\\"][gram] += 1\\n else:\\n found_topics[topic][\\\"grams\\\"][gram] = 1\\n\\n #tracking the most similar gram to the topic\\n if str_sim > found_topics[topic][\\\"embedding_similarity\\\"]:\\n found_topics[topic][\\\"embedding_similarity\\\"] = str_sim\\n found_topics[topic][\\\"embedding_matched\\\"] = wet\\n\\n else:\\n #creating new topic in the result set\\n found_topics[topic] = {'grams': {gram:1},\\n 'embedding_matched': wet,\\n 'embedding_similarity': str_sim,\\n 'gram_similarity':[sim],\\n 'times': 1,\\n 'topic':topic}\\n\\n\\n\\n if sim == 1:\\n found_topics[topic][\\\"syntactic\\\"] = True\\n\\n\\n\\n primary_label_topic = self.cso.get_primary_label_wu(topic)\\n if primary_label_topic not in explanation:\\n explanation[primary_label_topic] = set()\\n\\n explanation[primary_label_topic].add(gram_without_underscore)\\n\\n return found_topics, explanation\",\n \"def watershedlist():\\n opts = watersheds_db()\\n return [(opts[opt]['name'] + ' (' + opts[opt]['delineation'] + ')', opt) for opt in opts]\",\n \"def append_direct_component_matrices(technique_data: TransitiveTechniqueData):\\n for technique in technique_data.technique.get_component_techniques():\\n direct_calculator = DirectTechniqueCalculator(technique.definition)\\n similarity_matrix = direct_calculator.calculate_technique_data(\\n technique_data.dataset\\n ).similarity_matrix\\n technique_data.transitive_matrices.append(similarity_matrix)\",\n \"def meaning_of(word, app_id, app_key):\\n \\n url = 'https://od-api.oxforddictionaries.com:443/api/v1/entries/' + language + '/' + word.lower()\\n r = requests.get(url, headers={\\\"app_id\\\":app_id, \\\"app_key\\\":app_key})\\n\\n data = r.json()\\n useful_data = {}\\n\\n for i in data['results'][0]['lexicalEntries'][0]['entries']:\\n for j in i:\\n for k in i[j][0]:\\n try:\\n subdata = i[j][0][k]\\n if k == 'subsenses':\\n useful_data.update({\\\"meanings\\\":subdata[0]['definitions']})\\n elif k == 'examples':\\n useful_data.update({\\\"examples\\\":subdata[0]['text']})\\n else:\\n pass\\n except:\\n pass\\n return useful_data\",\n \"def get_guides(data):\\n return data.groups[\\\"Frames\\\"].objects\",\n \"def extract_features(data, stopwords=STOPWORDS):\\n tags = set()\\n docs = []\\n for document in data:\\n doc_data = dict()\\n doc_data['pmid'] = document['sourceid']\\n text = document['text']\\n\\n # Insert PubTator annotations inside abstract\\n denotations = document['denotations']\\n sorted_denotations = []\\n for denotation in denotations:\\n begin = denotation['span']['begin']\\n end = denotation['span']['end']\\n obj = denotation['obj']\\n for c in punctuation:\\n obj = obj.replace(c, '')\\n tags.add(obj)\\n doc_data[obj] = doc_data.get(obj,0)+1\\n sorted_denotations.append([begin,end,obj])\\n sorted_denotations.sort()\\n sorted_denotations.reverse()\\n for begin, end, obj in sorted_denotations:\\n text = text[:begin] + obj + ' ' + text[end:]\\n\\n doc_data['text'] = clean_text(text, stopwords)\\n docs.append(doc_data)\\n\\n return docs\",\n \"def get_concepts_for_text(f2t, adir, ptext2ptmk, ptmk2sk):\\n print \\\"Getting concepts for texts (returns pointmention pks for a text)\\\"\\n cpco2ptmk = {} # simple hash\\n vbco2ptmk = {} # verbose hash\\n for idx, fn in enumerate(os.listdir(adir)):\\n sfn = fn.replace(\\\"pointmention_nbr_\\\", \\\"\\\")\\n with codecs.open(os.path.join(adir, fn), \\\"r\\\", \\\"utf8\\\") as ifd:\\n jso = js.loads(ifd.read().strip())\\n for co in jso[\\\"concepts\\\"]:\\n txt = f2t[sfn]\\n # only value is a set with the pointmention pks\\n ptmpks = ptext2ptmk[txt].values()\\n assert len(ptmpks) == 1\\n assert isinstance(ptmpks[0], set)\\n ptmpks = ptmpks[0]\\n # smaller hash, all ptmkeys for a concept go in a set\\n cpco2ptmk.setdefault(co[\\\"prefLabel\\\"].strip(),\\n {\\\"uri\\\": co[\\\"uri\\\"].replace(RPREF, \\\"\\\"),\\n \\\"ptmks\\\": set()})\\n cpco2ptmk[co[\\\"prefLabel\\\"].strip()][\\\"ptmks\\\"].update(ptmpks)\\n # verbose hash (with sentence id and score per mention)\\n for ptmpk in ptmpks:\\n curkey = (co[\\\"prefLabel\\\"].strip(),\\n co[\\\"uri\\\"].replace(RPREF, \\\"\\\"))\\n curdict = {\\\"ptmk\\\": ptmpk, \\\"spk\\\": ptmk2sk[ptmpk],\\n \\\"sco\\\": co[\\\"score\\\"] / 100.0}\\n vbco2ptmk.setdefault(curkey, [])\\n if curdict not in vbco2ptmk[curkey]:\\n vbco2ptmk[curkey].append(curdict)\\n if idx and not idx % 10000:\\n print \\\"- Done concepts for {} texts, {}\\\".format(\\n idx, time.strftime(\\\"%H:%M:%S\\\", time.localtime()))\\n return cpco2ptmk, vbco2ptmk\",\n \"def test_get_skills_multiple_lessons(self):\\n skill_graph = SkillGraph.load()\\n\\n skill_1 = skill_graph.add(Skill.build(SKILL_NAME, SKILL_DESC))\\n unit = self.course.add_unit()\\n unit.title = 'Test Unit'\\n lesson1 = self.course.add_lesson(unit)\\n lesson1.title = 'Test Lesson 1'\\n lesson2 = self.course.add_lesson(unit)\\n lesson2.title = 'Test Lesson 2'\\n self.course.save()\\n lesson1.properties[SKILLS_KEY] = [skill_1.id]\\n lesson2.properties[SKILLS_KEY] = [skill_1.id]\\n self.course.save()\\n\\n actions.login(ADMIN_EMAIL)\\n response = transforms.loads(self.get(self.URL).body)\\n self.assertEqual(200, response['status'])\\n\\n skills = transforms.loads(response['payload'])['skills']\\n self.assertEqual(1, len(skills))\\n # All lessons listed\\n self.assertEqual(2, len(skills[0]['lessons']))\",\n \"def model_cohort(cohort):\\n model_traj = []\\n for part in cohort:\\n for traj in part.trajectories:\\n if traj.filter is True:\\n model_traj.append(\\n modelling.model(x=traj.data.age.tolist(),\\n y=traj.data.AF.tolist(),\\n mutation=traj.mutation,\\n variant_class=traj.variant_class,\\n gene=traj.mutation.split()[0],\\n id=part.id,\\n p_key=traj.p_key))\\n return model_traj\",\n \"def get_groups_using_technique():\\n global groups_using_technique\\n\\n if not groups_using_technique:\\n groups_using_technique = rsh.groups_using_technique(get_srcs())\\n \\n return groups_using_technique\",\n \"def process(data_item, article_id):\\n questions = []\\n answers = []\\n paragraph = [article_id, data_item['context']]\\n\\n for item in data_item['qas']:\\n question = [item[\\\"id\\\"], item[\\\"question\\\"], item['is_impossible']]\\n questions.append(question)\\n if item['is_impossible']:\\n continue\\n answer_options = item[\\\"answers\\\"]\\n answer_set = set()\\n for option in answer_options:\\n answer_tuple = (option['text'], option['answer_start'])\\n answer_set.add(answer_tuple)\\n for index, answer_tuple in enumerate(answer_set):\\n answer = [\\\"{}_{}\\\".format(item[\\\"id\\\"], index+1), item[\\\"id\\\"], answer_tuple[0], answer_tuple[1]]\\n answers.append(answer)\\n return paragraph, questions, answers\",\n \"def get_patient(drs):\\n for line in drs:\\n if line.strip().startswith('sem'):\\n datalist = line.split(':')\\n for word in datalist: \\n if word.count('patient') > 0:\\n variable = word[6:7]\\n for word in datalist:\\n if word.startswith('pred({0}'.format(variable)):\\n return word.split(',')[1]\",\n \"def read(self, input_file, remove_evidence=False, remove_question=False, remove_passage=False, remove_dict=None):\\n logger.info('Reading data set from {}...'.format(input_file))\\n logger.info('Remove evidence during test: {}'.format(remove_evidence))\\n with open(input_file, \\\"r\\\", encoding='utf-8') as reader:\\n input_data = json.load(reader)\\n\\n def is_whitespace(ch):\\n if ch == \\\" \\\" or ch == \\\"\\\\t\\\" or ch == \\\"\\\\r\\\" or ch == \\\"\\\\n\\\" or ord(ch) == 0x202F:\\n return True\\n return False\\n\\n examples = []\\n for instance in tqdm(input_data):\\n sentences = instance['article']\\n article_id = instance['id']\\n\\n sentence_start_list, sentence_end_list = [], []\\n passage = ''\\n for sentence in sentences:\\n sentence = sentence.strip()\\n sentence_start_list.append(len(passage))\\n passage = passage + sentence + ' '\\n sentence_end_list.append(len(passage) - 1)\\n assert sentence_start_list[-1] <= sentence_end_list[-1]\\n # if len(examples) == 453:\\n # print(sentence)\\n # print(sentence_start_list[-1], sentence_end_list[-1])\\n\\n doc_tokens = []\\n prev_is_whitespace = True\\n char_to_word_offset = []\\n for c in passage:\\n if is_whitespace(c):\\n prev_is_whitespace = True\\n else:\\n if prev_is_whitespace:\\n doc_tokens.append(c)\\n else:\\n doc_tokens[-1] += c\\n prev_is_whitespace = False\\n char_to_word_offset.append(len(doc_tokens) - 1)\\n\\n # Split context into sentences\\n # sentence_start_list, sentence_end_list = utils.split_sentence(passage, self.sentence_tokenizer)\\n sentence_span_list = []\\n for c_start, c_end in zip(sentence_start_list, sentence_end_list):\\n t_start = char_to_word_offset[c_start]\\n t_end = char_to_word_offset[c_end]\\n sentence_span_list.append((t_start, t_end))\\n assert t_start <= t_end\\n\\n questions = instance['questions']\\n answers = instance['answers']\\n options = instance['options']\\n evidences = instance['evidences']\\n # multi_sents = instance['multi_sents']\\n\\n for q_id, (question, option_list, option_answer_list, evidence) in enumerate(zip(questions, options, answers, evidences)):\\n\\n if remove_evidence:\\n new_doc_tokens, new_sentence_span_list = utils.remove_all_evidence(sentence_span_list, doc_tokens, evidence)\\n evidence = []\\n else:\\n new_doc_tokens = doc_tokens\\n new_sentence_span_list = sentence_span_list\\n\\n # qas_id = f\\\"{article_id}--{q_id}\\\"\\n for op_id, (option, answer) in enumerate(zip(option_list, option_answer_list)):\\n qas_id = f\\\"{article_id}--{q_id}--{op_id}\\\"\\n example = MultiRCExample(\\n qas_id=qas_id,\\n # doc_tokens=doc_tokens,\\n doc_tokens=new_doc_tokens,\\n question_text=question,\\n # sentence_span_list=sentence_span_list,\\n sentence_span_list=new_sentence_span_list,\\n option_text=option,\\n answer=answer,\\n sentence_ids=evidence\\n )\\n examples.append(example)\\n\\n logger.info('Finish reading {} examples from {}'.format(len(examples), input_file))\\n return examples\",\n \"def load_data(squad_per_lang):\\n question_set = QuestionSet()\\n candidate_set = CandidateSet()\\n\\n for lang, squad in squad_per_lang.items():\\n for question, answer, context, context_sentences, xling_id, context_id in (\\n generate_examples(squad, lang)):\\n question = Question(question, xling_id, lang)\\n question_set.add(question)\\n assert answer in context_sentences, (\\n \\\"answer doesn't appear in context_sentences\\\")\\n for sent_pos, sentence in enumerate(context_sentences):\\n candidate = Candidate(sentence, context, lang, context_id, sent_pos)\\n candidate = candidate_set.add_or_retrieve_candidate(candidate)\\n if sentence == answer:\\n candidate_set.update_xling_id(candidate, xling_id)\\n print(\\\"Totals across languages: questions={}, candidates={}\\\".format(\\n len(question_set.as_list()), len(candidate_set.as_list())))\\n\\n return question_set, candidate_set\",\n \"def _process_examples(self):\\n logger = getLogger(\\\"problog_lfi\\\")\\n # value can be True / False / None\\n # ( atom ), ( ( value, ... ), ... )\\n\\n # Simple implementation: don't add neutral evidence.\\n\\n # ad_groups is a list of lists where each list contains an AD\\n ad_groups = list()\\n for ad in self._adatoms:\\n # if it's an AD group\\n if len(ad[1]) > 1:\\n ad_list = []\\n for var in ad[1]:\\n ad_list.append(Term(self.names[var].functor, *self.names[var].args))\\n ad_groups.append(tuple(ad_list))\\n logger.debug(\\\"AD Groups\\\\t\\\\t:\\\" + str(ad_groups))\\n\\n def all_false(d):\\n \\\"\\\"\\\"\\n This function recognizes inconsistent evidence s.t. all values are False in AD.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :return: whether all values are False\\n \\\"\\\"\\\"\\n # false_count should be the same as the length of d\\n false_count = sum(v is False for v in d.values())\\n return false_count == len(d)\\n\\n def all_false_except_one(d):\\n \\\"\\\"\\\"\\n This function recognizes incomplete evidence s.t.\\n the non-False value in AD needs to be set to True.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :return: whether all values except one are False\\n \\\"\\\"\\\"\\n false_count = sum(v is False for v in d.values())\\n the_left_is_none = bool(sum(v is None for v in d.values()))\\n return (false_count == len(d) - 1) and the_left_is_none\\n\\n def getADtemplate(d, atom=None):\\n \\\"\\\"\\\"\\n This function gets atom's complement AD template.\\n This should only be used when the AD contains non-ground terms.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :param atom: an evidence\\n :return: atom's complement AD template\\n \\\"\\\"\\\"\\n if atom is not None:\\n temp_dict = {\\n k: v\\n for k, v in d.items()\\n if v == \\\"Template\\\" and atom.signature != k.signature\\n }\\n return temp_dict\\n else:\\n temp_dict = {k: v for k, v in d.items() if v == \\\"Template\\\"}\\n return temp_dict\\n\\n def add_to_ad_evidence(pair, l, ADtemplate):\\n \\\"\\\"\\\"\\n :param pair: a new pair of (atom, value)\\n :param l: a list of dictionaries, all dictionaries need to have the same format\\n :return:\\n \\\"\\\"\\\"\\n (k, v) = pair\\n # if entry k exists, update the value with k\\n for d in l:\\n if k in d:\\n d[k] = v\\n return\\n # if entry k does not exist, create a new dictionary from template\\n # and instantiate it with k\\n new_d = dict()\\n for temp_k in ADtemplate.keys():\\n new_key = Term(temp_k.functor, *k.args)\\n new_d[new_key] = None\\n # put v in there\\n new_d[k] = v\\n l.append(new_d)\\n\\n if self.infer_AD_values:\\n result = ExampleSet()\\n inconsistent = False\\n # iterate over all examples given in .ev\\n for index, example in enumerate(self.examples):\\n ad_evidences = []\\n non_ad_evidence = {}\\n for ad_group in ad_groups:\\n # create a dictionary to memorize what evidence is given in AD\\n d = dict()\\n # TODO: what if the AD contains both ground and non-ground????\\n # e.g. t(_)::a; t(_)::b(X)\\n for var in ad_group:\\n if var.is_ground():\\n d[var] = None # for ground unknown evidence\\n else:\\n d[var] = \\\"Template\\\" # for unground unknown evidence\\n ad_evidences.append(d)\\n\\n # add all evidence in the example to ad_evidences\\n for atom, value in example:\\n # if atom has a tunable probability to learn\\n if any([atom.signature == name.signature for name in self.names]):\\n if len(atom.args) == 0: # Propositional evidence\\n # insert evidence\\n is_ad = False\\n for d in ad_evidences:\\n if atom in d:\\n d[atom] = value\\n is_ad = True\\n if not is_ad:\\n non_ad_evidence[\\n atom\\n ] = value\\n else: # First Order evidence\\n # find the right AD dictionary : AD_dict\\n AD_dict = None\\n for d in ad_evidences:\\n if any([atom.signature == k.signature for k in d]):\\n AD_dict = d\\n # if the instantiation is new, add it as a key to the dictionary\\n if AD_dict and AD_dict.get(atom) is None:\\n AD_dict[atom] = value\\n # also add other AD parts in the dictionary with value==None\\n other_ADs = getADtemplate(AD_dict, atom)\\n for otherAD in other_ADs.keys():\\n new_key = Term(otherAD.functor, *atom.args)\\n AD_dict[new_key] = AD_dict.get(new_key, None)\\n else:\\n non_ad_evidence[atom] = value\\n else:\\n non_ad_evidence[atom] = value\\n\\n # grounded_ad_evidences contains all usable evidence (gound, not template)\\n grounded_ad_evidences = []\\n for d in ad_evidences:\\n # for first order evidence dictionaries\\n if \\\"Template\\\" in d.values():\\n # new_ad_evidence is a list of dictionaries\\n # each dictionary is a group of the AD template instantiation\\n new_ad_evidence = list()\\n # get template AD evidence\\n ADtemplate = getADtemplate(d)\\n # group all pairs according to ADtemplate\\n for k, v in d.items():\\n if v != \\\"Template\\\":\\n add_to_ad_evidence((k, v), new_ad_evidence, ADtemplate)\\n grounded_ad_evidences += new_ad_evidence\\n else:\\n # simply us them\\n grounded_ad_evidences.append(d)\\n\\n inconsistent_example = False\\n for i, d in enumerate(grounded_ad_evidences):\\n # inconsistent1 = multiple_true(d)\\n inconsistent2 = all_false(d)\\n add_compliment = all_false_except_one(d)\\n\\n if inconsistent2:\\n inconsistent_example = True\\n continue\\n elif add_compliment:\\n for key, value in d.items():\\n if value is None:\\n grounded_ad_evidences[i][key] = True\\n\\n if not inconsistent_example and len(grounded_ad_evidences) > 0:\\n # There are (fully tunable) ADs in the program\\n evidence_list = []\\n for d in grounded_ad_evidences:\\n for key, value in d.items():\\n if value is not None:\\n if (key, value) not in evidence_list:\\n # TODO: Switch to orderedSet so we don't have to check whether it's in already\\n evidence_list.append((key, value))\\n\\n for key, value in non_ad_evidence.items():\\n evidence_list.append((key, value))\\n\\n atoms, values = zip(*evidence_list)\\n result.add(index, atoms, values)\\n\\n else:\\n # (No AD case) or (Inconsistent Evidence Case)\\n atoms, values = zip(*example)\\n result.add(index, atoms, values)\\n return result\\n else:\\n # smarter: compile-once all examples with same atoms\\n result = ExampleSet()\\n for index, example in enumerate(self.examples):\\n atoms, values = zip(*example)\\n result.add(index, atoms, values)\\n return result\",\n \"def students_using_relevant_words_by_context_and_question(self):\\n relevant_words = [\\\"dead\\\", \\\"death\\\", \\\"miscarriage\\\", \\\"killed\\\", \\\"kill\\\", \\\"losing\\\", \\\"loss\\\",\\n \\\"lost\\\", \\\"deceased\\\", \\\"died\\\", \\\"grief\\\", \\\"pregnancy\\\", \\\"pregnant\\\"]\\n\\n question_context_count_map = {}\\n\\n for row in self.responses:\\n question = row.question.text\\n context = row.context.text\\n if question not in question_context_count_map:\\n question_context_count_map[question] = {}\\n if context not in question_context_count_map[question]:\\n question_context_count_map[question][context] = 0\\n\\n if string_contains_words(row.response, relevant_words):\\n question_context_count_map[question][context] += 1\\n\\n flattened_data = \\\\\\n PrototypeRereadingAnalysis.transform_nested_dict_to_list(question_context_count_map)\\n\\n flattened_data.sort()\\n return flattened_data\",\n \"def get_faulty_scenario_data():\\n return [\\n {\\n \\\"population_count\\\": 100,\\n \\\"county\\\": \\\"oxford\\\",\\n \\\"season\\\": \\\"cold_month\\\",\\n \\\"year\\\": 2017,\\n },\\n {\\n \\\"population_count\\\": 150,\\n \\\"county\\\": \\\"oxford\\\",\\n \\\"season\\\": \\\"spring_month\\\",\\n \\\"year\\\": 2017,\\n },\\n {\\n \\\"population_count\\\": 200,\\n \\\"county\\\": \\\"oxford\\\",\\n \\\"season\\\": \\\"hot_month\\\",\\n \\\"year\\\": 2017,\\n },\\n {\\n \\\"population_count\\\": 210,\\n \\\"county\\\": \\\"oxford\\\",\\n \\\"season\\\": \\\"fall_month\\\",\\n \\\"year\\\": 2017,\\n },\\n ]\",\n \"def do_mapping(ds, fieldnames, relationship_type, type_filter, source_name, sorting_keys, tactic=None):\\n all_attack_patterns = get_all_techniques(ds, source_name, tactic)\\n writable_results = []\\n\\n for attack_pattern in tqdm.tqdm(all_attack_patterns, desc=\\\"parsing data for techniques\\\"):\\n # Grabs relationships for identified techniques\\n relationships = filter_for_term_relationships(ds, relationship_type, attack_pattern.id)\\n\\n for relationship in relationships:\\n # Groups are defined in STIX as intrusion-set objects\\n # Mitigations are defined in STIX as course-of-action objects\\n # Software are defined in STIX as malware objects\\n stix_results = filter_by_type_and_id(ds, type_filter, relationship.source_ref, source_name)\\n\\n if stix_results:\\n row_data = (\\n grab_external_id(attack_pattern, source_name),\\n attack_pattern.name,\\n grab_external_id(stix_results[0], source_name),\\n stix_results[0].name,\\n escape_chars(stix_results[0].description),\\n escape_chars(relationship.description),\\n )\\n\\n writable_results.append(dict(zip(fieldnames, row_data)))\\n\\n return sorted(writable_results, key=lambda x: (x[sorting_keys[0]], x[sorting_keys[1]]))\",\n \"def get_feedback_details(queryFeatures, pId, cId, es):\\n values = {}\\n feedback = []\\n \\n # get case\\n query = {}\\n query['query'] = {}\\n query['query']['terms'] = {}\\n query['query']['terms'][\\\"_id\\\"] = []\\n query['query']['terms'][\\\"_id\\\"].append(cId)\\n res = es.search(index=pId, body=query)\\n\\n # find attributes that are array sbert and store their values, also convert query array to vector array\\n for attrib in queryFeatures:\\n if attrib['similarity'] == 'Array SBERT':\\n temp = {\\\"values\\\": res['hits']['hits'][0]['_source'][attrib['name']]['name'], \\\"vec\\\": res['hits']['hits'][0]['_source'][attrib['name']]['rep'], \\\"queryRep\\\": getVectorSemanticSBERTArray(attrib['value'])}\\n values[attrib['name']] = temp\\n\\n # perform a cosine similarity between query elements and case elements\\n for key, value in values.items():\\n for idx, vec in enumerate(value['vec']):\\n maxsim = 0\\n for idx2, vec2 in enumerate(value['queryRep']):\\n sim = cosine_similarity(vec['rep'], vec2)\\n if sim > maxsim:\\n maxsim = sim\\n if maxsim < 0.7:\\n feedback.append({\\\"field\\\": key, \\\"value\\\": value['values'][idx], \\\"similarity\\\": maxsim})\\n \\n #print(\\\"feedback: \\\", feedback)\\n\\n return feedback\",\n \"def load_data(data_links_list=(\\n 'https://raw.githubusercontent.com/JanetMatsen/bacteriopop/master'\\n '/raw_data/raw_data.csv',\\n 'https://raw.githubusercontent.com/JanetMatsen/bacteriopop/master'\\n '/raw_data/sample_meta_info.tsv')):\\n\\n # Reading data sets from the links provided.\\n df1 = pd.read_csv(data_links_list[0],\\n error_bad_lines=False)\\n df2 = pd.read_csv(data_links_list[1],\\n sep='\\\\t')\\n df2 = df2.set_index(df2['project'])\\n # fill the Nas id df1 as \\\". Makes the groupbys behave better.\\n df1.fillna('', inplace=True)\\n # repleace 'genus' = 'other' with an empty string to be consistent.\\n df1.replace(to_replace='other', value='', inplace=True)\\n # Removing duplicate columns.\\n del df2['project']\\n del df2['ID']\\n df1 = df1.set_index(df1['project'])\\n # Removing duplicate column.\\n del df1['project']\\n # Joining the two datasets.\\n df = df1.join(df2)\\n # Uniformity in non-capitalization of column names.\\n df.rename(columns={'Kingdom': 'kingdom', 'Phylum': 'phylum',\\n 'Class': 'class', 'Order': 'order',\\n 'Family': 'family', 'Genus': 'genus',\\n 'Length': 'length'}, inplace=True)\\n df.index.names = ['sampleID']\\n # Rearranging columns so that abundance is the last column.\\n df = df[['kingdom',\\t'phylum', 'class', 'order',\\n 'family', 'genus', 'length', 'oxygen',\\n 'replicate', 'week', 'abundance']]\\n assert isinstance(df, pd.DataFrame)\\n return df\",\n \"def get_teacher_topic_all():\\n topic_data = query_db(\\n \\\"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \\\"\\n \\\"ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n topics = []\\n for topic in topic_data:\\n topic_dict_teacher = {}\\n topic_dict_teacher[\\\"id\\\"] = topic[0]\\n topic_dict_teacher[\\\"name\\\"] = flask.escape(str(topic[1]))\\n topic_dict_teacher[\\\"class\\\"] = flask.escape(str(topic[2]))\\n topics.append(topic_dict_teacher)\\n return topics\",\n \"def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\\n def is_whitespace(c):\\n if c == \\\" \\\" or c == \\\"\\\\t\\\" or c == \\\"\\\\r\\\" or c == \\\"\\\\n\\\" or ord(c) == 0x202F:\\n return True\\n return False\\n\\n if is_training:\\n subset_list = ['train']\\n else:\\n subset_list = ['dev', 'test']\\n\\n examples, features = {}, {}\\n for subset in subset_list:\\n level_example_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n level_features_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n\\n for level in ['high', 'middle']:\\n subset_dir = os.path.join(data_dir, subset)\\n file_list = os.listdir(subset_dir)\\n file_list = [file for file in file_list if file.endswith('txt')]\\n file_list = sorted(file_list)\\n\\n alldata = []\\n for file in file_list:\\n data = json.load(open(os.path.join(subset_dir, file)))\\n alldata.append(data)\\n \\n examples_list = []\\n for data in alldata:\\n doc_id = data['id']\\n doc = data[\\\"article\\\"].replace('\\\\\\\\n', '\\\\n')\\n doc_token = []\\n prev_is_whitespace = True\\n for c in doc:\\n if is_whitespace(c):\\n prev_is_whitespace = True\\n else:\\n if prev_is_whitespace:\\n doc_token.append(c)\\n else:\\n doc_token[-1] += c\\n prev_is_whitespace = False\\n\\n for i, answer in enumerate(data[\\\"answers\\\"]):\\n if answer == \\\" \\\":\\n answer = \\\"C\\\"\\n example = InputExample(\\n guid=doc_id + '-%d' % i,\\n doc_token=doc_token,\\n question_text=\\\"\\\",\\n options=data[\\\"options\\\"],\\n answer=answer)\\n examples_list.append(example)\\n\\n level_example_dict[level] = examples_list\\n level_features_dict[level] = convert_examples_to_features(\\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\\n \\n print(\\\"MuTuaL Data Loader....\\\")\\n print(level_example_dict[level][-1])\\n print(level_features_dict[level][-1])\\n examples[subset] = level_example_dict\\n features[subset] = level_features_dict\\n\\n return examples, features\",\n \"def get_wordlists():\\n\\n\\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\\n\\t\\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\\n\\t\\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\\n\\t\\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\\n\\t\\t'Language Processing', 'Internet', 'Intelligent Systems',\\n\\t\\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\\n\\t\\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \\n\\t\\t'Computational Intelligence', 'Computational Linguistics',\\n\\t\\t'Computational linguistics','Algorithm','Computer','ITiCSE',\\n\\t\\t'ITICSE','Machine learning','Learning','learning',\\n\\t\\t'Artificial intelligence','CIVR','Document Analysis'}\\n\\n\\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\\n\\t\\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\\n\\t\\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\\n\\t\\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\\n\\t\\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\\n\\t\\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\\n\\t\\t'Neuro','Psych',\\n\\t\\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\\n\\t\\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\\n\\t\\t'Neurobiological'}\\n\\n\\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\\n\\t\\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\\n\\t\\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\\n\\t\\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\\n\\t\\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\\n\\t\\t'mathemati','Kernel'}\\n\\n\\tlinguistics = {}\\n\\n\\tcomputer_vision = {}\\n\\n\\tchemistry = {}\\n\\n\\tphysics = {}\\n\\n\\t# Rename \\\"Computer Vision\\\" to \\\"Image Processing\\\"?\\n\\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\\n\\t\\t'Physics','Computer Vision','Natural Language Processing']\\n\\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\\n\\n\\treturn {topic_names[i]:topics[i] for i in range(len(topics))}\",\n \"def get_data_loaders_2sentences():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_2generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+1):]\\n #history_complete.append(history)\\n if len(history) > 4:\\n history_chatbot = history[1::2]\\n\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def fetch_technologies(self):\\n return self.fetch_levels(\\\"research\\\", None, codes.techs)\",\n \"def concept_list(\\n self, params: Optional[Dict] = None, headers: Optional[Dict] = None\\n ) -> List[ConceptDetails]:\\n method = self._get_method(\\\"concepts\\\")\\n\\n return self.call_api_get(method=method, params=params, headers=headers)\",\n \"def get_threat_summary_by_id():\\n sample_id = demisto.getArg('id')\\n request = req('GET', SUB_API + 'samples/' + sample_id + '/threat')\\n r = request.json()\\n sample = {\\n 'ID': sample_id,\\n 'MaxSeverity': demisto.get(r, 'data.max-severity'),\\n 'Score': demisto.get(r, 'data.score'),\\n 'Count': demisto.get(r, 'data.count'),\\n 'MaxConfidence': demisto.get(r, 'data.max-confidence'),\\n 'ThreatFeeds': demisto.get(r, 'data.bis')\\n }\\n dbot = {\\n 'Vendor': 'ThreatGrid',\\n 'Type': 'Sample ID',\\n 'Indicator': sample['ID'],\\n 'Score': calc_score(sample['Score'])\\n }\\n md = tableToMarkdown('ThreatGrid - Threat Summary', [sample],\\n ['ID', 'MaxSeverity', 'Score', 'Count', 'MaxConfidence'])\\n mdTableList = []\\n for threatfeed in sample['ThreatFeeds']:\\n mdTableList.append({'Threat Feed': threatfeed})\\n md += tableToMarkdown('Threat Feeds', mdTableList, ['Threat Feed'])\\n md += tableToMarkdown('DBot', [dbot], ['Indicator', 'Score', 'Type', 'Vendor'])\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'EntryContext': {'ThreatGrid.Sample(val.ID == obj.ID)': sample, 'DBotScore': dbot},\\n 'HumanReadable': md,\\n 'ContentsFormat': formats['json'],\\n 'Contents': r\\n })\",\n \"def tags_data(item):\\n\\n # loop through tags, building up a nested dict of information as\\n # we go. Note, using select_related like this means only one query,\\n # but returns lots of redundant data. If performance is a problem,\\n # consider separate queries for colloquialism and transcript details\\n\\n data = {}\\n\\n # add info on each tag that appears in the transcript\\n tags = item.get_tags()\\n\\n # get tag count by colloquialism id\\n tag_counts = tags.get_counts()\\n\\n # get ordered list of occurrence times with colloquialism type and value\\n occ_times = list(\\n tags.values('colloquialism__type', 'colloquialism__pk', 'start_exact')\\n .order_by('start_exact'))\\n\\n def get_prev_time(time, c_type=None, c_pk=None):\\n \\\"\\\"\\\"Get previous time before the given time matching the type/id. \\\"\\\"\\\"\\n\\n # TODO if efficiency is an issue here, make occ_times a generator and\\n # keep a queue (collections.deque) of the relevant times; this should\\n # work because we're moving through them in sequence so could safely\\n # discard those before the current time as we go\\n\\n if c_pk:\\n def filter_func(obj):\\n return obj['colloquialism__pk'] == c_pk\\n elif c_type:\\n def filter_func(obj):\\n return obj['colloquialism__type'] == c_type\\n else:\\n def filter_func(obj):\\n return True\\n\\n # filter out relevant times\\n filtered = filter(\\n lambda obj: filter_func(obj) and obj['start_exact'] < time,\\n occ_times)\\n\\n if not len(filtered):\\n return None\\n\\n # return last filtered time since they are in order\\n return filtered[-1]['start_exact']\\n\\n def get_uniqueness(time, colloquialism):\\n # tau_value = time difference between occurrence of same tag.type.value\\n # tau_tag = time difference between self and previous tag.\\n # x = (tau_value - tau_tag) / tau_tag\\n # uniqueness = 1/count(tag.type.value) * (1 - 1/exp(x/10))\\n\\n count = tag_counts[colloquialism.pk]\\n\\n if count <= 1:\\n return 1\\n\\n value_time = get_prev_time(time, c_pk=colloquialism.pk)\\n if value_time is None:\\n return 1.0 / count\\n else:\\n # assume a previous tag exists, because it's a superset of t_value\\n tag_time = get_prev_time(time)\\n\\n tau_tag = (time - tag_time).total_seconds()\\n tau_value = (time - value_time).total_seconds()\\n\\n x = (tau_value - tau_tag) / tau_tag\\n\\n coefficient = 10\\n uniqueness = 1.0 / count * (1 - 1 / math.exp(x / coefficient))\\n return uniqueness\\n\\n for tag in tags.with_colloquialism():\\n colloquialism = tag.colloquialism\\n\\n # add type details the first time the type is encountered\\n if colloquialism.type not in data:\\n data[colloquialism.type] = {\\n 'displayName': colloquialism.get_type_display(),\\n 'items': {},\\n }\\n items = data[colloquialism.type]['items']\\n\\n colloquialism_key = colloquialism.normalised_value\\n\\n # add colloquialism details the first time it appears\\n if colloquialism_key not in items:\\n items[colloquialism_key] = {\\n 'meaning': colloquialism.meaning,\\n 'occurrences': [],\\n 'related': {},\\n }\\n\\n occ_data = tag.to_json(True)\\n occ_data['uniqueness'] = get_uniqueness(tag.start_exact, colloquialism)\\n items[colloquialism_key]['occurrences'].append(occ_data)\\n\\n # add in related tag info. Assume that the tag info is already in the data,\\n # this code just adds the related information\\n related = item.related_tags()\\n\\n for tag in related.with_colloquialism().with_transcript():\\n colloquialism = tag.colloquialism\\n transcript = tag.get_transcript()\\n items = data[colloquialism.type]['items']\\n related = items[colloquialism.normalised_value]['related']\\n\\n # add transcript details the first time it appears\\n if transcript.pk not in related:\\n related[transcript.pk] = transcript.to_json()\\n related[transcript.pk]['occurrences'] = []\\n\\n # append tag details\\n related[transcript.pk]['occurrences'].append(tag.to_json())\\n\\n return render_json(data)\",\n \"def splits_concept_levels(data: pd.DataFrame, type_col: Optional[str], concept_strings: List) -> List:\\n\\n con_string, anc_string = concept_strings\\n data = data.copy().replace(r'^\\\\s*$', np.nan, regex=True)\\n\\n # extract relevant columns\\n if type_col is not None:\\n all_cols = [x for x in data.columns if type_col not in x]\\n conc_type = [x for x in data.columns if con_string.upper() in x.upper() and type_col.upper() in x.upper()]\\n conc_type_uri = [x for x in conc_type if x.upper().endswith('URI')][0]\\n anc_type = [x for x in data.columns if anc_string.upper() in x.upper() and type_col.upper() in x.upper()]\\n anc_type_uri = [x for x in anc_type if x.upper().endswith('URI')][0]\\n # extract concept codes from ancestor codes\\n concept = data[all_cols + conc_type].dropna(subset=conc_type, how='all').drop_duplicates()\\n ancestor = data[all_cols + anc_type].dropna(subset=anc_type, how='all').drop_duplicates()\\n # get counts of ontology concepts at each concept level\\n concept_ont_codes = [i for j in [x.split(' | ') for x in list(concept[conc_type_uri])] for i in j]\\n ancestor_ont_codes = [i for j in [x.split(' | ') for x in list(ancestor[anc_type_uri])] for i in j]\\n else:\\n concept = data[[x for x in data.columns if x.startswith(con_string)]].dropna(how='all').drop_duplicates()\\n ancestor = data[[x for x in data.columns if x.startswith(anc_string)]].dropna(how='all').drop_duplicates()\\n concept_ont_codes, ancestor_ont_codes = [], []\\n\\n return [(concept, concept_ont_codes), (ancestor, ancestor_ont_codes)]\",\n \"def get_teacher_career_results(self, teacher, career):\\n data = []\\n\\n # Get the active exams of the career.\\n exams = EvaluationsExam.objects.filter(\\n type__exact=career.type, status=\\\"ACTIVE\\\")\\n\\n # Get the results for each exam.\\n for exam in exams:\\n\\n # Get the signatures of the teacher for the career in the exam.\\n signatures_dtl = EvaluationsTeacherSignature.objects.filter(\\n fk_teacher__exact=teacher.id, fk_period__exact=exam.fk_period, status=\\\"ACTIVE\\\").select_related('fk_signature')\\n\\n signatures_results = []\\n for signature_dtl in signatures_dtl:\\n \\n # If it raise an exception, it means that the signature isn't evaluated yet or other error.\\n try:\\n # Get the results of the signature.\\n signature_results = EvaluationsSignatureResult.objects.get(\\n group=signature_dtl.group,\\n fk_signature=signature_dtl.fk_signature.id,\\n fk_exam=exam.id,\\n status=\\\"ACTIVE\\\"\\n )\\n\\n # Get the results for each question in the exam for the signature.\\n questions_results = EvaluationsSignatureQuestionResult.objects.filter(\\n group=signature_dtl.group,\\n fk_signature=signature_dtl.fk_signature.id,\\n fk_exam=exam.id,\\n fk_question__optional='NO',\\n status=\\\"ACTIVE\\\"\\n ).values_list('fk_question__description', 'result')\\n\\n # Get the comments of the signature/group.\\n comments_result = EvaluationsSignatureQuestionResult.objects.get(\\n group=signature_dtl.group,\\n fk_signature=signature_dtl.fk_signature.id,\\n fk_exam=exam.id,\\n fk_question__optional='YES',\\n status=\\\"ACTIVE\\\"\\n ).result\\n\\n # Split the comments and add them to a list, only the ones that are not empty.\\n comments = list(filter(None, comments_result.split('|')))\\n\\n # Crate a dictionary with the results of the signature and the questions.\\n signatures_results.append({\\n 'teacher': teacher.name + ' ' + teacher.last_name + ' ' + teacher.last_name_2,\\n 'signature': signature_dtl.fk_signature.description,\\n 'group': signature_dtl.group,\\n 'average': signature_results.average,\\n 'comments': comments,\\n 'total_evaluated': signature_results.total_evaluated,\\n 'questions': questions_results\\n })\\n except Exception:\\n pass\\n\\n # Add the results to the exam dictionary.\\n exam_results = {\\n 'exam': exam.description,\\n 'career': career.description,\\n 'signatures_results': signatures_results,\\n 'period': exam.fk_period.period\\n }\\n\\n # Add the exam results to the list that will be returned at the end.\\n data.append(exam_results)\\n\\n return data\",\n \"def get_datasets(recipe):\\n # \\\"datasets\\\"\\n return {dataset: get_instance(**par) for dataset, par in recipe.items()}\",\n \"def extract_routine_instances(theories, routines, exec_routine_instruction_index):\\n new_theories=[]\\n for theory in theories:\\n for i in range(len(routines)):\\n routine=routines[i]\\n theory=replace_instance(theory,routine,[(exec_routine_instruction_index,i)])\\n new_theories.append(theory)\\n return new_theories\",\n \"def get_hikedetails_by_feature(feature):\\n\\n if (feature == \\\"dog\\\"):\\n npbyfeature = Hike.query.filter(Hike.features.like('%dogs-leash%')).all()\\n\\n if (feature == \\\"kid\\\"):\\n npbyfeature = Hike.query.filter(Hike.features.like('%kids%') | Hike.features.like('%strollers%')).all()\\n \\n if (feature == \\\"water\\\"):\\n npbyfeature = Hike.query.filter(Hike.features.like('%river%') | Hike.features.like('%beach%')).all()\\n\\n \\n return npbyfeature\",\n \"def extract_english_raw_texts():\\n # conceptnet triples raw text\\n cpnet_en_raw_text = []\\n\\n # conceptnet entity context\\n cpnet_en_entity_context = []\\n\\n with open(conceptnet_path, encoding=\\\"utf8\\\") as f:\\n for line in f.readlines():\\n ls = line.split('\\\\t')\\n if ls[2].startswith('/c/en/') and ls[3].startswith('/c/en/'):\\n \\\"\\\"\\\"\\n Some preprocessing:\\n - Remove part-of-speech encoding.\\n - Split(\\\"/\\\")[-1] to trim the \\\"/c/en/\\\" and just get the entity name, convert all to \\n - Lowercase for uniformity.\\n \\\"\\\"\\\"\\n rel = ls[1].split(\\\"/\\\")[-1].lower()\\n head = del_pos(ls[2]).split(\\\"/\\\")[-1].lower()\\n tail = del_pos(ls[3]).split(\\\"/\\\")[-1].lower()\\n\\n if not head.replace(\\\"_\\\", \\\"\\\").replace(\\\"-\\\", \\\"\\\").isalpha():\\n continue\\n\\n if not tail.replace(\\\"_\\\", \\\"\\\").replace(\\\"-\\\", \\\"\\\").isalpha():\\n continue\\n\\n # transfer to raw text\\n head_text = head.replace(\\\"_\\\", \\\" \\\")\\n tail_text = tail.replace(\\\"_\\\", \\\" \\\")\\n\\n if rel not in relation_mapping:\\n continue\\n\\n rel_text = relation_mapping[rel]\\n raw_text = head_text + \\\" \\\" + rel_text + \\\" \\\" + tail_text\\n cpnet_en_raw_text.append(raw_text)\\n \\n # split to train and test\\n shuffle(cpnet_en_raw_text)\\n train_size = int(len(cpnet_en_raw_text) * 0.9)\\n cpnet_en_raw_train_text = cpnet_en_raw_text[:train_size]\\n cpnet_en_raw_dev_text = cpnet_en_raw_text[train_size:]\\n\\n with open(conceptnet_en_raw_text_train_path, \\\"w\\\", encoding=\\\"utf8\\\") as f:\\n f.write(\\\"\\\\n\\\".join(cpnet_en_raw_train_text))\\n \\n with open(conceptnet_en_raw_text_dev_path, \\\"w\\\", encoding=\\\"utf8\\\") as f:\\n f.write(\\\"\\\\n\\\".join(cpnet_en_raw_dev_text))\",\n \"def concept_details(\\n self,\\n concept_id: str,\\n params: Optional[Dict] = None,\\n headers: Optional[Dict] = None,\\n ) -> ConceptDetails:\\n method = self._get_method(\\\"concept_details\\\")\\n method = method.format(**{\\\"id\\\": concept_id})\\n\\n return self.call_api_get(method=method, params=params, headers=headers)\",\n \"def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\\n return [\\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\\n ]\",\n \"def create_example(line, tfidf_dict, is_training, args):\\n sample = json.loads(line, object_pairs_hook=collections.OrderedDict)\\n example_id = sample['example_id']\\n question_text = sample['question_text']\\n ori_doc_tokens = sample['document_text'].split()\\n\\n # 抽取特定段落list[list]\\n tfidf_cands_ids = tfidf_dict[str(example_id)]\\n # tfidf并不保证所有段落必定出现在所选段落内\\n if is_training:\\n long_answer_cand = sample['annotations'][0]['long_answer']['candidate_index']\\n\\n if long_answer_cand != -1:\\n # answer_cand保证top_level是true\\n if sample['long_answer_candidates'][long_answer_cand]['top_level'] is False:\\n gt_start_token = sample['long_answer_candidates'][long_answer_cand]['start_token']\\n gt_end_token = sample['long_answer_candidates'][long_answer_cand]['end_token']\\n for il, cand in enumerate(sample['long_answer_candidates']):\\n if cand['start_token'] <= gt_start_token and cand['end_token'] >= gt_end_token \\\\\\n and cand['top_level'] is True:\\n long_answer_cand = il\\n break\\n # training的时候当tfidf中没有包含正确答案，且long_answer是存在的时候，tfidf的结果则只选目标段落\\n hit_answer = False\\n for pids in tfidf_cands_ids:\\n if long_answer_cand in pids:\\n hit_answer = True\\n break\\n if hit_answer is False:\\n tfidf_cands_ids = [[]]\\n token_count = 0\\n for ic, cand in enumerate(sample['long_answer_candidates']):\\n if cand['top_level'] is True:\\n tfidf_cands_ids[-1].append(ic)\\n token_count += (cand['end_token'] - cand['start_token'])\\n if token_count > 600:\\n tfidf_cands_ids.append([])\\n token_count = 0\\n while len(tfidf_cands_ids[-1]) == 0:\\n tfidf_cands_ids.pop(-1)\\n # 防止负样本爆炸，只选目标段落\\n tfidf_cands_ids = [cands for cands in tfidf_cands_ids if long_answer_cand in cands]\\n\\n # 由于接下来要对special_tokens排序，所以这里tfidf选择的段落要按照首段排序\\n tfidf_cands_ids = sorted(tfidf_cands_ids, key=lambda x: x[0])\\n\\n if args.do_combine: # 如果do_combine，我们把所有抽取的candidates合并到一起\\n tfidf_cands_ids_ = []\\n for c in tfidf_cands_ids:\\n tfidf_cands_ids_.extend(c)\\n tfidf_cands_ids = [tfidf_cands_ids_]\\n\\n # 获取candidate的type信息，去除HTML符号\\n # 保留特殊token到段首\\n # 注意table paragraph list最小起步是1\\n special_tokens_count = {'ContextId': -1, 'Table': 0, 'Paragraph': 0, 'List': 0}\\n\\n # 为了保证一致性，TABLE, Paragraph等结构信息还是尽可能保留...\\n selected_ps = []\\n for i, cand_ids in enumerate(tfidf_cands_ids):\\n position_map = [] # 新paragraph到老paragraph的token位置映射\\n map_to_origin = {} # 为了保证能够对答案位置进行正确的偏移，这里需要重新搞一波map映射\\n p_tokens = []\\n for cand_id in cand_ids:\\n st = sample['long_answer_candidates'][cand_id]['start_token']\\n ed = sample['long_answer_candidates'][cand_id]['end_token']\\n ind = st # 追踪pos_map\\n ori_cand_tokens = ori_doc_tokens[st:ed]\\n # 先加ContextId特殊token\\n special_tokens_count['ContextId'] += 1\\n special_tokens_count['ContextId'] = min(special_tokens_count['ContextId'], args.max_position)\\n p_tokens.append('[ContextId={}]'.format(special_tokens_count['ContextId']))\\n position_map.append(ind)\\n cand_type = get_candidate_type(ori_cand_tokens)\\n if cand_type in special_tokens_count:\\n special_tokens_count[cand_type] += 1\\n special_tokens_count[cand_type] = min(special_tokens_count[cand_type], args.max_position)\\n p_tokens.append('[' + cand_type + '=' + str(special_tokens_count[cand_type]) + ']')\\n position_map.append(ind)\\n for token in ori_cand_tokens:\\n if '<' not in token: # 去除HTML符号\\n p_tokens.append(token)\\n position_map.append(ind)\\n map_to_origin[ind] = len(position_map) - 1\\n ind += 1\\n assert len(position_map) == len(p_tokens)\\n\\n selected_ps.append({'paragraph_tokens': p_tokens,\\n 'question_text': question_text,\\n 'position_map': position_map,\\n 'map_to_origin': map_to_origin,\\n 'example_id': example_id,\\n 'paragraph_id': str(example_id) + '_' + str(i),\\n 'answer_type': AnswerType['UNKNOWN'],\\n 'long_start': -1,\\n 'long_end': -1,\\n 'short_start': -1,\\n 'short_end': -1,\\n 'short_answer_text': None})\\n\\n answer = None\\n answer_text = None\\n if is_training and 'annotations' in sample:\\n # 答案只取第一个标注\\n annotation = sample['annotations'][0]\\n if annotation is not None:\\n long_answer = annotation['long_answer']\\n if long_answer['candidate_index'] != -1:\\n answer_type = AnswerType['LONG']\\n ori_long_start = long_answer['start_token']\\n ori_long_end = long_answer['end_token']\\n else:\\n answer_type = AnswerType['UNKNOWN']\\n ori_long_start = -1\\n ori_long_end = -1\\n\\n assert annotation[\\\"yes_no_answer\\\"] in (\\\"YES\\\", \\\"NO\\\", \\\"NONE\\\")\\n if annotation[\\\"yes_no_answer\\\"] == 'YES':\\n answer_text = 'YES'\\n answer_type = AnswerType['YES']\\n elif annotation[\\\"yes_no_answer\\\"] == 'NO':\\n answer_text = 'NO'\\n answer_type = AnswerType['NO']\\n\\n short_answers = annotation['short_answers']\\n # 这里short answer必须排序\\n short_answers = sorted(short_answers, key=lambda x: x['start_token'])\\n if len(short_answers) > 0:\\n # TODO:可能存在多个short，multi-tag\\n answer_type = AnswerType['SHORT']\\n short_ans = random.choice(short_answers)\\n ori_short_start = short_ans['start_token']\\n ori_short_end = short_ans['end_token']\\n answer_text = ori_doc_tokens[ori_short_start:ori_short_end]\\n answer_text = \\\" \\\".join([at for at in answer_text if '<' not in at])\\n else:\\n ori_short_start = -1\\n ori_short_end = -1\\n else:\\n answer_type = AnswerType['UNKNOWN']\\n ori_long_start = -1\\n ori_long_end = -1\\n ori_short_start = -1\\n ori_short_end = -1\\n\\n answer = {'answer_type': answer_type,\\n 'ori_long_start': ori_long_start,\\n 'ori_long_end': ori_long_end,\\n 'ori_short_start': ori_short_start,\\n 'ori_short_end': ori_short_end}\\n\\n if answer['answer_type'] == AnswerType['SHORT'] and answer_text == \\\"\\\":\\n print('WRONG SHORT', answer, answer_text)\\n answer['answer_type'] = AnswerType['LONG']\\n answer['ori_short_start'] = -1\\n answer['ori_short_end'] = -1\\n\\n examples = []\\n for p_sample in selected_ps:\\n if answer and answer['answer_type'] != AnswerType['UNKNOWN']:\\n # 如果长答案在候选里，那么首位必然都在这个候选里，!!!注意这里的ori_long_end必须-1，否则可能会漏!!!\\n if answer['ori_long_start'] in p_sample['map_to_origin'] \\\\\\n and answer['ori_long_end'] - 1 in p_sample['map_to_origin']:\\n final_long_start = p_sample['map_to_origin'][answer['ori_long_start']]\\n final_long_end = p_sample['map_to_origin'][answer['ori_long_end'] - 1] + 1\\n long_answer_text = \\\" \\\".join(p_sample['paragraph_tokens'][final_long_start:final_long_end])\\n\\n p_sample['answer_type'] = answer['answer_type']\\n p_sample['long_start'] = final_long_start\\n p_sample['long_end'] = final_long_end\\n\\n # 短答案必然在长答案所在段落里面\\n if answer['answer_type'] == AnswerType['SHORT']:\\n final_short_start = p_sample['map_to_origin'][answer['ori_short_start']]\\n final_short_end = p_sample['map_to_origin'][answer['ori_short_end'] - 1] + 1\\n p_sample['short_start'] = final_short_start\\n p_sample['short_end'] = final_short_end\\n\\n new_answer_text = \\\" \\\".join(p_sample['paragraph_tokens'][final_short_start:final_short_end])\\n assert new_answer_text == answer_text, (new_answer_text, answer_text, long_answer_text)\\n p_sample['short_answer_text'] = new_answer_text\\n\\n # 由于negative的段落太多了，所以这里先过滤掉一部分\\n elif is_training and random.random() > args.example_neg_filter:\\n continue\\n\\n # 由于negative的段落太多了，所以这里先过滤掉一部分\\n elif is_training and random.random() > args.example_neg_filter:\\n continue\\n\\n p_sample.pop('map_to_origin')\\n examples.append(p_sample)\\n\\n return examples\",\n \"def visualize_dataset(\\n dataset_json):\\n\\n # - Iterate over phenomena, per phenomenon\\n # - Iterate over property-sets, per property-set\\n # - Iterator over properties, per property\\n # - ...\\n\\n dataset_pathname = os.path.expandvars(dataset_json[\\\"pathname\\\"])\\n dataset = lue.open_dataset(dataset_pathname)\\n\\n for phenomenon in dataset_json[\\\"phenomena\\\"]:\\n visualize_phenomenon(dataset, phenomenon)\",\n \"def build_feature_target_pair(mts, fun_get_features, fun_get_targets):\\r\\n features = []\\r\\n targets = []\\r\\n for mt in mts:\\r\\n targets.append(fun_get_targets(mt))\\r\\n features.append(fun_get_features(mt))\\r\\n \\r\\n return features, targets\",\n \"def get_data():\\n wga_df = pd.read_csv(os.path.join(MODEL_DIR, 'wga/sculptures/wga_sculpture_periods.csv'), index_col=0)\\n wikiart_df = pd.read_csv(os.path.join(MODEL_DIR, 'wikiart/sculptures/wikiart_sculpture_periods.csv'), index_col=0)\\n nga_df = pd.read_csv(os.path.join(MODEL_DIR, 'nga/sculptures/nga_sculpture_periods.csv'), index_col=0)\\n\\n ######## Fix name for WGA and WikiaRt ###########\\n wga_df['Author'] = wga_df.apply(lambda x: fix_name_wga(x['Author']), axis=1)\\n wikiart_df['Author'] = wikiart_df.apply(lambda x: fix_name_wiki(x['Author']), axis=1)\\n nga_df['Author'] = nga_df.apply(lambda x: fix_name_nga(x['Author']), axis=1)\\n\\n df = pd.concat([wga_df, wikiart_df, nga_df], ignore_index=True, sort=True)\\n\\n df['Author_Fixed'] = df.apply(lambda x: fix_text(x['Author']), axis=1)\\n df['title_fixed'] = df.apply(lambda x: fix_text(x['title']), axis=1)\\n\\n periods = [\\\"BAROQUE\\\", \\\"EARLY RENAISSANCE\\\", \\\"MEDIEVAL\\\", \\\"NEOCLASSICISM\\\", \\\"HIGH RENAISSANCE\\\", \\\"MINIMALISM\\\", \\\"REALISM\\\",\\n \\\"IMPRESSIONISM\\\", \\\"ROCOCO\\\", \\\"SURREALISM\\\", \\\"MANNERISM\\\", \\\"ROMANTICISM\\\",\\n ]\\n df['Period'] = df.apply(lambda row: row['Period'].upper(), axis=1)\\n\\n # Get Desired Periods\\n df['Period'] = df.apply(lambda x: \\\"SURREALISM\\\" if \\\"SURREALISM\\\" in x['Period'] else x['Period'], axis=1)\\n df = df[(df['Period'].isin(periods))]\\n df = df.sort_values(['Author_Fixed', 'title_fixed'])\\n\\n #print(\\\"Combined Drop Rows:\\\", df.shape[0] - df.drop_duplicates(subset=['Author_Fixed', 'title_fixed']).shape[0])\\n\\n df = df.drop_duplicates(subset=['Author_Fixed', 'title_fixed'], keep='last')\\n\\n # Drop Duplicate Sculptures\\n df = df[~df['file'].isin(DUP_SCULPTURES)].reset_index(drop=True)\\n\\n #print(df['Period'].value_counts())\\n\\n return df\",\n \"def _create_examples(self, lines, set_type):\\n examples = []\\n for idx, line in enumerate(lines):\\n item = json.loads(line.strip())\\n question_id = \\\"%s-%s\\\" % (set_type, idx)\\n context = item[\\\"context\\\"]\\n question = item[\\\"question\\\"]\\n endings = [item[\\\"answerA\\\"],item[\\\"answerB\\\"],item[\\\"answerC\\\"] ]\\n label = item[\\\"correct\\\"]\\n #race_id = \\\"%s-%s\\\" % (set_type, data_raw[\\\"race_id\\\"])\\n #article = data_raw[\\\"article\\\"]\\n #for i in range(len(data_raw[\\\"answers\\\"])):\\n #truth = str(ord(data_raw[\\\"answers\\\"][i]) - ord(\\\"A\\\"))\\n #question = data_raw[\\\"questions\\\"][i]\\n #options = data_raw[\\\"options\\\"][i]\\n\\n examples.append(\\n InputExample(\\n example_id=question_id,\\n question=question,\\n contexts=[context,context,context],\\n endings=[endings[0], endings[1], endings[2]],#, options[3]\\n label=label,\\n )\\n )\\n return examples\",\n \"def _create_examples(self, lines, set_type):\\n examples = []\\n for (_, data) in enumerate(lines):\\n race_id = \\\"%s-%s\\\" % (set_type, data[\\\"race_id\\\"])\\n article = data[\\\"article\\\"]\\n for i in range(len(data[\\\"answers\\\"])):\\n truth = str(ord(data[\\\"answers\\\"][i]) - ord(\\\"A\\\"))\\n question = data[\\\"questions\\\"][i]\\n options = data[\\\"options\\\"][i]\\n\\n examples.append(\\n MCInputExample(\\n guid=race_id,\\n question=question,\\n # this is not efficient but convenient\\n contexts=[article, article, article, article],\\n endings=[options[0], options[1],\\n options[2], options[3]],\\n label=truth,\\n )\\n )\\n return examples\",\n \"def _generate_examples(self, filepath, split):\\r\\n if self.config.name == \\\"trex\\\":\\r\\n paths = filepath\\r\\n relations_path = paths[0]\\r\\n paths = paths[1:]\\r\\n all_rels = {}\\r\\n with open(relations_path, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n all_rels[data[\\\"relation\\\"]] = data\\r\\n id_ = -1\\r\\n for filepath in paths:\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n pred = all_rels.get(data[\\\"predicate_id\\\"], {})\\r\\n for evidences in data[\\\"evidences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"obj_uri\\\": str(data[\\\"obj_uri\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"sub_uri\\\": str(data[\\\"sub_uri\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"predicate_id\\\": str(data[\\\"predicate_id\\\"]),\\r\\n \\\"sub_surface\\\": str(evidences[\\\"sub_surface\\\"]),\\r\\n \\\"obj_surface\\\": str(evidences[\\\"obj_surface\\\"]),\\r\\n \\\"masked_sentence\\\": str(evidences[\\\"masked_sentence\\\"]),\\r\\n \\\"template\\\": str(pred.get(\\\"template\\\", \\\"\\\")),\\r\\n \\\"template_negated\\\": str(pred.get(\\\"template_negated\\\", \\\"\\\")),\\r\\n \\\"label\\\": str(pred.get(\\\"label\\\", \\\"\\\")),\\r\\n \\\"description\\\": str(pred.get(\\\"description\\\", \\\"\\\")),\\r\\n \\\"type\\\": str(pred.get(\\\"type\\\", \\\"\\\")),\\r\\n }\\r\\n elif self.config.name == \\\"conceptnet\\\":\\r\\n id_ = -1\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n if data.get(\\\"negated\\\") is not None:\\r\\n for masked_sentence, negated in zip(data[\\\"masked_sentences\\\"], data[\\\"negated\\\"]):\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"sub\\\": str(data.get(\\\"sub\\\", \\\"\\\")),\\r\\n \\\"obj\\\": str(data.get(\\\"obj\\\", \\\"\\\")),\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"negated\\\": str(negated),\\r\\n }\\r\\n else:\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"sub\\\": str(data.get(\\\"sub\\\", \\\"\\\")),\\r\\n \\\"obj\\\": str(data.get(\\\"obj\\\", \\\"\\\")),\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"negated\\\": str(\\\"\\\"),\\r\\n }\\r\\n elif self.config.name == \\\"squad\\\":\\r\\n id_ = -1\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"id\\\": str(data[\\\"id\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"negated\\\": str(data.get(\\\"negated\\\", \\\"\\\")),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n }\\r\\n elif self.config.name == \\\"google_re\\\":\\r\\n id_ = -1\\r\\n paths = filepath\\r\\n for filepath in paths:\\r\\n # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py\\r\\n if \\\"place_of_birth\\\" in filepath:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"place_of_birth\\\",\\r\\n \\\"template\\\": \\\"[X] was born in [Y] .\\\",\\r\\n \\\"template_negated\\\": \\\"[X] was not born in [Y] .\\\",\\r\\n }\\r\\n elif \\\"date_of_birth\\\" in filepath:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"date_of_birth\\\",\\r\\n \\\"template\\\": \\\"[X] (born [Y]).\\\",\\r\\n \\\"template_negated\\\": \\\"[X] (not born [Y]).\\\",\\r\\n }\\r\\n else:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"place_of_death\\\",\\r\\n \\\"template\\\": \\\"[X] died in [Y] .\\\",\\r\\n \\\"template_negated\\\": \\\"[X] did not die in [Y] .\\\",\\r\\n }\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"sub\\\": str(data[\\\"sub\\\"]),\\r\\n \\\"obj\\\": str(data[\\\"obj\\\"]),\\r\\n \\\"evidences\\\": str(data[\\\"evidences\\\"]),\\r\\n \\\"judgments\\\": str(data[\\\"judgments\\\"]),\\r\\n \\\"sub_w\\\": str(data[\\\"sub_w\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"sub_aliases\\\": str(data[\\\"sub_aliases\\\"]),\\r\\n \\\"obj_w\\\": str(data[\\\"obj_w\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"obj_aliases\\\": str(data[\\\"obj_aliases\\\"]),\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"template\\\": str(pred[\\\"template\\\"]),\\r\\n \\\"template_negated\\\": str(pred[\\\"template_negated\\\"]),\\r\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6661438","0.6644859","0.66175026","0.6393515","0.6246491","0.61199707","0.60754085","0.5725204","0.5500558","0.5417878","0.533964","0.52860373","0.5199164","0.516859","0.51545167","0.49810523","0.4943632","0.48681778","0.48513177","0.48226207","0.47853136","0.4774503","0.47142214","0.47117168","0.47109213","0.46515298","0.46429527","0.46401066","0.46381462","0.46255025","0.4624221","0.46175975","0.45935938","0.45884538","0.45832744","0.4575373","0.45749113","0.4562428","0.4560874","0.45561832","0.4553022","0.45489737","0.45329696","0.45309526","0.45110127","0.4491451","0.4440646","0.44297203","0.4425671","0.44247058","0.44244435","0.44189373","0.44154394","0.44135794","0.44123775","0.44048575","0.44036838","0.44017836","0.44014207","0.44011998","0.43950638","0.4393332","0.43911424","0.43847862","0.43809822","0.43782","0.43762416","0.43735367","0.4372107","0.4371455","0.43661177","0.4355989","0.43513036","0.4349089","0.43334845","0.4332355","0.4320561","0.43137425","0.43086526","0.43059358","0.4300866","0.4300466","0.42934227","0.42777586","0.42765447","0.42764032","0.42749223","0.42730597","0.42719623","0.4271473","0.42652434","0.42606896","0.42585635","0.4257301","0.42546037","0.42531744","0.42477557","0.4235193","0.4217741","0.42167234"],"string":"[\n \"0.6661438\",\n \"0.6644859\",\n \"0.66175026\",\n \"0.6393515\",\n \"0.6246491\",\n \"0.61199707\",\n \"0.60754085\",\n \"0.5725204\",\n \"0.5500558\",\n \"0.5417878\",\n \"0.533964\",\n \"0.52860373\",\n \"0.5199164\",\n \"0.516859\",\n \"0.51545167\",\n \"0.49810523\",\n \"0.4943632\",\n \"0.48681778\",\n \"0.48513177\",\n \"0.48226207\",\n \"0.47853136\",\n \"0.4774503\",\n \"0.47142214\",\n \"0.47117168\",\n \"0.47109213\",\n \"0.46515298\",\n \"0.46429527\",\n \"0.46401066\",\n \"0.46381462\",\n \"0.46255025\",\n \"0.4624221\",\n \"0.46175975\",\n \"0.45935938\",\n \"0.45884538\",\n \"0.45832744\",\n \"0.4575373\",\n \"0.45749113\",\n \"0.4562428\",\n \"0.4560874\",\n \"0.45561832\",\n \"0.4553022\",\n \"0.45489737\",\n \"0.45329696\",\n \"0.45309526\",\n \"0.45110127\",\n \"0.4491451\",\n \"0.4440646\",\n \"0.44297203\",\n \"0.4425671\",\n \"0.44247058\",\n \"0.44244435\",\n \"0.44189373\",\n \"0.44154394\",\n \"0.44135794\",\n \"0.44123775\",\n \"0.44048575\",\n \"0.44036838\",\n \"0.44017836\",\n \"0.44014207\",\n \"0.44011998\",\n \"0.43950638\",\n \"0.4393332\",\n \"0.43911424\",\n \"0.43847862\",\n \"0.43809822\",\n \"0.43782\",\n \"0.43762416\",\n \"0.43735367\",\n \"0.4372107\",\n \"0.4371455\",\n \"0.43661177\",\n \"0.4355989\",\n \"0.43513036\",\n \"0.4349089\",\n \"0.43334845\",\n \"0.4332355\",\n \"0.4320561\",\n \"0.43137425\",\n \"0.43086526\",\n \"0.43059358\",\n \"0.4300866\",\n \"0.4300466\",\n \"0.42934227\",\n \"0.42777586\",\n \"0.42765447\",\n \"0.42764032\",\n \"0.42749223\",\n \"0.42730597\",\n \"0.42719623\",\n \"0.4271473\",\n \"0.42652434\",\n \"0.42606896\",\n \"0.42585635\",\n \"0.4257301\",\n \"0.42546037\",\n \"0.42531744\",\n \"0.42477557\",\n \"0.4235193\",\n \"0.4217741\",\n \"0.42167234\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85353535"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85687,"cells":{"query":{"kind":"string","value":"Given a technique a reference list, find mitigations that mitigate technique and return list with mitigation data. Also modifies the reference list if it finds a reference that is not on the list"},"document":{"kind":"string","value":"def get_mitigations_table_data(technique, reference_list, next_reference_number):\n\n mitigation_data = []\n\n # Check if technique has mitigations\n if config.technique_mitigated.get(technique['id']):\n # Iterate through technique mitigations\n for mitigation in config.technique_mitigated[technique['id']]:\n\n # Do not add deprecated mitigation to table\n if 'x_mitre_deprecated' not in mitigation['object']:\n\n attack_id = util.get_attack_id(mitigation['object'])\n\n # Only add if mitigation attack id is found \n if attack_id:\n\n row = {}\n row['mid'] = attack_id\n row['name'] = mitigation['object']['name']\n if mitigation['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, mitigation)\n \n mitigation_data.append(row)\n \n if mitigation_data:\n mitigation_data = sorted(mitigation_data, key=lambda k: k['name'].lower())\n return mitigation_data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_techniques_used_by_malware():\n global techniques_used_by_malware\n \n if not techniques_used_by_malware:\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\n \n return techniques_used_by_malware","def get_mitigation_mitigates_techniques():\n global mitigation_mitigates_techniques\n\n if not mitigation_mitigates_techniques:\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\n\n return mitigation_mitigates_techniques","def get_technique_mitigated_by_mitigation():\n global technique_mitigated_by_mitigation\n\n if not technique_mitigated_by_mitigation:\n technique_mitigated_by_mitigation = rsh.technique_mitigated_by_mitigation(get_srcs())\n\n return technique_mitigated_by_mitigation","def get_techniques_list(techniques):\n\n technique_list = {}\n \n for technique in techniques:\n if 'revoked' not in technique or technique['revoked'] == False:\n\n attack_id = util.get_attack_id(technique)\n\n if attack_id:\n\n technique_dict = {}\n technique_dict['id'] = attack_id\n \n technique_dict['name'] = technique['name']\n technique_dict['description'] = technique['description']\n\n if technique.get('kill_chain_phases'):\n for elem in technique['kill_chain_phases']:\n # Fill dict\n\n if elem['phase_name'] not in technique_list:\n technique_list[elem['phase_name']] = []\n \n technique_list[elem['phase_name']].append(technique_dict)\n\n for key, value in technique_list.items():\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\n \n return technique_list","def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)","def add_techniques(\n client: Act, matrice: AttckMatrice, output_format: Text = \"json\"\n) -> List:\n\n notify = []\n\n for technique in matrice.techniques:\n notify += handle_techniques(client, technique, None, output_format)\n\n for subtechnique in getattr(technique, \"subtechniques\", []):\n # Pre Attack does not have sub techniques\n notify += handle_techniques(client, subtechnique, technique, output_format)\n\n return notify","def get_related_techniques_data(technique, tactic_list):\n \n technique_data = []\n\n if config.related_techniques.get(technique['id']):\n for rel_tech in config.related_techniques[technique['id']]:\n\n attack_id = util.get_attack_id(rel_tech['object'])\n\n if attack_id:\n row = {}\n row['technique_id'] = attack_id\n\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\n if tactic:\n row['tactic_id'] = util.get_attack_id(tactic)\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\n \n row['technique_name'] = rel_tech['object']['name']\n technique_data.append(row)\n\n if technique_data:\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\n return technique_data","def get_techniques_used_by_tools():\n global techniques_used_by_tools\n\n if not techniques_used_by_tools:\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\n \n return techniques_used_by_tools","def get_technique_related_to_technique():\n global technique_related_to_technique\n\n if not technique_related_to_technique:\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\n\n return technique_related_to_technique","def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.groups_using_technique }\n ]\n\n example_data = []\n\n # Get malware or tools used by group\n for examples in examples_map:\n if examples['example_type'].get(technique.get('id')):\n for example in examples['example_type'][technique['id']]:\n\n attack_id = util.get_attack_id(example['object'])\n\n # Only add example data if the attack id is found \n if attack_id:\n row = {}\n\n row['id'] = attack_id\n\n if attack_id.startswith('S'):\n row['path'] = \"software\"\n else:\n row['path'] = \"groups\"\n\n row['name'] = example['object']['name']\n\n if example['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\n\n example_data.append(row)\n \n if example_data:\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\n return example_data","def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)","def get_techniques_used_by_groups():\n global techniques_used_by_groups\n\n if not techniques_used_by_groups:\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\n\n return techniques_used_by_groups","def get_all_techniques(src, source_name, tactic=None):\n filters = [\n Filter(\"type\", \"=\", \"attack-pattern\"),\n Filter(\"external_references.source_name\", \"=\", source_name),\n ]\n if tactic:\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\n\n results = src.query(filters)\n return remove_deprecated(results)","def get_malware_using_technique():\n global malware_using_technique\n\n if not malware_using_technique:\n malware_using_technique = rsh.malware_using_technique(get_srcs())\n \n return malware_using_technique","def get_technique_by_id(self, id_code):\r\n return self.fs.query([\r\n Filter('type', '=', 'attack-pattern'),\r\n Filter('external_references.external_id', '=', id_code)\r\n ])","def find_effects(sv): \r\n for eff in sv.Object_list:\r\n nod=sv.Object[eff]\r\n for nam in nod.causes: # look for causes\r\n if not eff in sv.Object[nam].effects: sv.Object[nam].effects+=[eff]","def get_tools_using_technique():\n global tools_using_technique\n\n if not tools_using_technique:\n tools_using_technique = rsh.tools_using_technique(get_srcs())\n \n return tools_using_technique","def import_deep_distortion_by_type(defect_list: list,\r\n fancy_defects: dict,\r\n ):\r\n list_deep_distortion = []\r\n for i in defect_list: \r\n if i['name'] in fancy_defects.keys(): # if defect underwent a deep distortion\r\n defect_name = i['name']\r\n print(defect_name)\r\n i['supercell']['structure'] = fancy_defects[defect_name]['structure'] #structure of E lowering distortion\r\n print(\"Using the distortion found for charge state(s) {} with BDM distortion {}\".format(fancy_defects[defect_name]['charges'],\r\n fancy_defects[defect_name][\"BDM_distortion\"] ) )\r\n #remove the charge state of the E lowering distortion distortion\r\n if len(fancy_defects[i['name']]['charges']) > 1:\r\n print(\"Intial charge states of defect:\", i['charges'], \"Will remove the ones where the distortion was found...\")\r\n [i['charges'].remove(charge) for charge in fancy_defects[defect_name]['charges']]\r\n print(\"Trying distortion for charge states:\", i['charges'])\r\n else: \r\n i['charges'].remove(fancy_defects[defect_name]['charges'][0])\r\n if i['charges']: #if list of charges to try deep distortion not empty, then add defect to the list\r\n list_deep_distortion.append(i)\r\n return list_deep_distortion","def get_groups_using_technique():\n global groups_using_technique\n\n if not groups_using_technique:\n groups_using_technique = rsh.groups_using_technique(get_srcs())\n \n return groups_using_technique","def findShifts(alternatives):\n shifted = []\n samples = {0: \"Acute\", 1:\"Challenge\", 2:\"Chronic\"}\n for item in alternatives:\n isShifted = False\n maxShift = 0.0\n percent_expr = 0.0\n what = \"\"\n p_value = 0\n transcript = 0\n m_samples = item.getSamples()\n for row in range(m_samples.shape[0]):\n if row in item.getNonAnnotated():\n continue\n meanAcute = np.mean(m_samples[row][:SAMPLES_PARTS[0]])\n meanChallenge = np.mean(m_samples[row][SAMPLES_PARTS[0]:SAMPLES_PARTS[1]])\n meanChronic = np.mean(m_samples[row][SAMPLES_PARTS[1]:])\n p = stats.kruskal(m_samples[row][:SAMPLES_PARTS[0]], m_samples[row][SAMPLES_PARTS[0]:SAMPLES_PARTS[1]],\n m_samples[row][SAMPLES_PARTS[1]:])[1]\n means = [meanAcute, meanChronic, meanChallenge]\n for i in range(len(means)):\n mean1 = means[i]\n for j in range(len(means)):\n mean2 = means[j]\n if mean2 > 0 and (mean1 / mean2 >= PERCENT_OF_SHIFT) and \\\n (mean1 >= RATIO_TRESHOLD or mean2 >= RATIO_TRESHOLD)\\\n and (abs(int(item.getCoordinates()[row][1]) - int(item.getCoordinates()[row][0])) <= 500):\n # and counter not in item.getNonAnnotated() \\\n if (mean1 / mean2) > maxShift:\n percent_expr = np.max([mean1, mean2])\n maxShift = (mean1 / mean2)\n transcript = row\n p_value = p\n what = (i, j)\n isShifted = True\n # counter += 1\n if isShifted:\n shifted.append(item)\n item.setMaxShift(maxShift)\n item.setNumTranscript(transcript + 1)\n item.setWhatDiffers(what)\n item.setPValue(p_value)\n item.setPercentOfExpression(percent_expr)\n return shifted","def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\n return [\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\n ]","def find(checkinList):\n return [Inspiration(item) for item in checkinList]","def filter_words_list(words,pattern,wrong_guess_lst):\r\n list_hints = same_length(words,pattern)\r\n list_hints = if_in_wrong_guess(wrong_guess_lst,list_hints)\r\n list_hints = same_pattern(list_hints,pattern)\r\n list_hints = lst_and_pattern(list_hints,pattern)\r\n return list_hints","def calcDistortionList(work_list):\n distortion_list = []\n for swap in work_list:\n distortion_list.append(Cluster.calcDistortion(*swap)) # call calcDistortion with tuple expansion as args\n return distortion_list","def get_technique_stats(db):\n \n techniques = {}\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n temp_dict = {technique: [{u'id': element['project_no'], \n u'name': element['project_name'], u'description': element['short_description']}]}\n techniques.update(temp_dict)\n else:\n temp_dict = {u'id': element['project_no'],\n u'name': element['project_name'], u'description': element['short_description']}\n techniques[technique].append(temp_dict)\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\n\n return techniques","def challenge_functions(input_list, factor, min_don=None, max_don=None):\n\n new_challenge_list = []\n for donor in input_list:\n # print(donor.donations)\n donation = filter_factor_map(factor, donor.donations, min_don, max_don)\n if donation:\n new_challenge_list.append(\n Donor(donor.firstname, donor.lastname, donor.fullname, donation))\n return new_challenge_list","def resolve_vulns(self):\n for vuln in self.vulns:\n for ref in vuln.refs:\n if 'cve' in str.lower(ref):\n results = str(self.client.console_execute('search cve:{0}\\n'.format(ref))[b'data'])\n while not 'Modules' in results:\n sleep(10)\n results = self.client.console_read()\n results = results.split('\\n')\n for line in results:\n if 'exploit' in line:\n self.exploits.append(line.split(' ')[0])","def extract_references(text, preprocessor, model):\n has_reference_section, reference_section, non_reference_section = preprocess_reference_text(text)\n reference_text = reference_section if has_reference_section else non_reference_section\n fragment = preprocessor(reference_text)\n out = model(fragment.idx).argmax(dim=2).cpu().numpy()\n\n buffer = ''\n found_citations = []\n in_citation = False\n for char, pred in zip(fragment.text, out[0, :]):\n if pred == 2:\n in_citation = True\n if pred == 3:\n in_citation = False\n if pred in [2, 3]:\n if buffer:\n found_citations.append(buffer)\n buffer = char\n elif pred == 1:\n buffer += char\n elif pred == 0:\n if in_citation:\n buffer += char\n else:\n if buffer:\n found_citations.append(buffer)\n buffer = ''\n return found_citations","def solutions_for_dep_problem(self, solution: 'DepAlgoSolution', found_problems: Set['DepAlgoFoundProblems'],\n installed_system: 'System', upstream_system: 'System',\n deps_to_deep_check: Set[str]) -> List['DepAlgoSolution']:\n\n def filter_solutions(solutions: Sequence['DepAlgoSolution']) -> List['DepAlgoSolution']:\n \"\"\"\n Filter given solutions so that only valid solutions are left\n or in case of no valid solutions only one invalid solution\n\n :param solutions: The solutions to filter\n :return: The filtered solutions\n \"\"\"\n return_solutions: List['DepAlgoSolution'] = []\n\n for solution in solutions:\n if not return_solutions:\n return_solutions.append(solution)\n continue\n\n first_solution = return_solutions[0]\n if first_solution.is_valid and solution.is_valid:\n return_solutions.append(solution)\n elif first_solution.is_valid:\n continue\n elif solution.is_valid:\n return_solutions = [solution]\n\n return return_solutions\n\n if self in solution.installed_solution_packages:\n return [solution.solution_copy()]\n\n # dep cycle\n # dirty... thanks to dep cycle between mesa and libglvnd\n if self in solution.visited_packages and not (self.type_of is PossibleTypes.REPO_PACKAGE):\n # problem only relevant\n # if the solution is not already invalid\n if solution.is_valid:\n index_of_self = solution.visited_packages.index(self)\n cycle_packages = []\n for i in range(index_of_self, len(solution.visited_packages)):\n cycle_packages.append(solution.visited_packages[i])\n cycle_packages.append(self)\n\n # create the problem\n cycle_problem = DepAlgoCycle(cycle_packages)\n for package in cycle_packages:\n cycle_problem.relevant_packages.add(package)\n cycle_problem.relevant_packages |= set(solution.dict_to_way.get(package.name, []))\n found_problems.add(cycle_problem)\n invalid_sol = solution.solution_copy()\n invalid_sol.is_valid = False\n return [invalid_sol]\n\n # pacman has to handle dep cycles between repo packages\n elif self in solution.visited_packages:\n return [solution.solution_copy()]\n\n # copy solution and add self to visited packages\n solution: 'DepAlgoSolution' = solution.solution_copy()\n is_build_available: bool = self in solution.packages_in_solution\n own_way: List['Package'] = solution.dict_to_way.get(self.name, [])\n own_not_to_delete_deps: Set[str] = set()\n solution.visited_packages.append(self)\n current_solutions: List['DepAlgoSolution'] = [solution]\n\n # filter not fulfillable deps\n relevant_deps = self.relevant_deps()\n for dep in relevant_deps[:]:\n\n # skip since already provided\n if installed_system.provided_by(dep):\n continue\n\n # skip since built package available and dep is not a normal dependency\n # so it's make and/or check dep\n if is_build_available and dep not in self.relevant_deps(only_depends=True):\n continue\n\n # dep not fulfillable, solutions not valid\n if not upstream_system.provided_by(dep):\n for solution in current_solutions:\n solution.is_valid = False\n\n # create problem\n dep_problem = DepAlgoNotProvided(dep, self)\n dep_problem.relevant_packages.add(self)\n dep_problem.relevant_packages |= set(own_way)\n found_problems.add(dep_problem)\n\n relevant_deps.remove(dep)\n\n # AND - every dep has to be fulfilled\n # we filtered the unfulfillable deps,\n # hence at least one dep provider is available\n for dep in relevant_deps:\n\n # skip since already provided\n if installed_system.provided_by(dep):\n continue\n\n # skip since built package available and dep is not a normal dependency\n # so it's make and/or check dep\n if is_build_available and dep not in self.relevant_deps(only_depends=True):\n continue\n\n # fetch dep providers\n dep_providers = upstream_system.provided_by(dep)\n dep_providers_names = [package.name for package in dep_providers]\n dep_stripped_name = strip_versioning_from_name(dep)\n\n # we only need relevant dep providers\n # deps_to_deep_check will be filled\n # when we encounter problems as dep-cycle, conflicts ...\n if dep_stripped_name in dep_providers_names and dep not in deps_to_deep_check:\n dep_providers = [package for package in dep_providers if package.name == dep_stripped_name]\n\n # OR - at least one of the dep providers needs to provide the dep\n finished_solutions = [solution for solution in current_solutions if dep in solution.visited_names]\n not_finished_solutions = [solution for solution in current_solutions if dep not in solution.visited_names]\n\n # check if dep provided by one of the packages already in a solution\n new_not_finished_solutions = []\n for solution in not_finished_solutions:\n if System(list(solution.installed_solution_packages)).provided_by(dep):\n finished_solutions.append(solution)\n else:\n new_not_finished_solutions.append(solution)\n not_finished_solutions = new_not_finished_solutions\n\n # track deps which may not be deleted\n for solution in current_solutions:\n if dep not in solution.not_to_delete_deps:\n solution.not_to_delete_deps.add(dep)\n own_not_to_delete_deps.add(dep)\n\n # calc and append new solutions\n current_solutions = finished_solutions\n # used for tracking problems\n new_problems_master: List[Set['DepAlgoFoundProblems']] = []\n found_problems_copy: Set['DepAlgoFoundProblems'] = set(found_problems)\n for solution in not_finished_solutions:\n\n # add dep to visited names\n # and create another container\n # for problem tracking\n solution.visited_names.add(dep)\n new_problems: List[Set['DepAlgoFoundProblems']] = []\n\n for dep_provider in dep_providers:\n # way to the package being called in the current solution\n if dep_provider.name not in solution.dict_to_way:\n way_added = True\n solution.dict_to_way[dep_provider.name] = own_way[:]\n solution.dict_to_way[dep_provider.name].append(self)\n else:\n way_added = False\n # tracking for which deps the package being called has been chosen as provider\n if dep_provider.name not in solution.dict_to_deps:\n solution.dict_to_deps[dep_provider.name] = set()\n solution.dict_to_deps[dep_provider.name].add(dep)\n\n # call this function recursively on the dep provider\n # and yield an empty found_problems set instance\n found_problems.clear()\n current_solutions.extend(\n dep_provider.solutions_for_dep_problem(\n solution, found_problems, installed_system, upstream_system, deps_to_deep_check\n )\n )\n # save the new problems\n new_problems.append(set(found_problems))\n # remove added things\n solution.dict_to_deps[dep_provider.name].remove(dep)\n if way_added:\n del solution.dict_to_way[dep_provider.name]\n\n # reset the problems to the problems\n # we had before calling the dep\n found_problems.clear()\n for problem in found_problems_copy:\n found_problems.add(problem)\n\n # if there is at least one valid solution\n # problems are not relevant\n # hence add an empty set containing no problems\n for problems in new_problems:\n if not problems:\n new_problems_master.append(set())\n break\n\n # if there are problems contained in all return values\n # show them to the user\n # will most likely be unfulfillable deps in general\n else:\n prob_in_all_ret = set.intersection(*new_problems)\n if prob_in_all_ret:\n new_problems_master.append(prob_in_all_ret)\n # otherwise append all found problems\n else:\n new_problems_master.append(set.union(*new_problems))\n\n # again - at least one valid solution\n # means new problems are not relevant\n if not_finished_solutions:\n for problems in new_problems_master:\n if not problems:\n break\n else:\n for problem in set.union(*new_problems_master):\n found_problems.add(problem)\n\n # filter solutions so that irrelevant solutions are not being\n # used anymore\n # great impact on the performance\n current_solutions = filter_solutions(current_solutions)\n\n # conflict checking\n for solution in current_solutions:\n # as with dep cycles,\n # conflicts are only relevant\n # if the solution is not already invalid\n if not solution.is_valid:\n continue\n\n # generate hypothetic system containing the packages of the current solution\n # and check for conflicts with that system\n installed_packages = list(solution.installed_solution_packages)\n conf_system = System(installed_packages).conflicting_with(self)\n\n # if there are no conflicts, nothing will get deleted, so we may\n # safely assume that we do not get an invalid solution\n if not conf_system:\n continue\n\n # append the whole current solution to the currently\n # installed system\n # may be empty in case of deep_search\n packages_to_append = solution.packages_in_solution[:]\n packages_to_append.append(self)\n new_system = installed_system.hypothetical_append_packages_to_system(packages_to_append)\n\n # prepare message for conflict\n additional_message = \"\"\n\n # if self cannot be added, this solution\n # is clearly not valid\n if self.name not in new_system.all_packages_dict:\n additional_message = \"Tried to install {}, but it was not possible.\".format(\n Colors.BOLD(Colors.LIGHT_MAGENTA(self.name))\n )\n is_possible = False\n else:\n is_possible = True\n\n # these deps have to remain provided,\n # since they are needed for a package which\n # has not been installed yet\n # e.g. A needs B and C, B has been solved with this algo\n # but C not, hence B must remain provided\n # otherwise A cannot be installed\n for dep in solution.not_to_delete_deps:\n if not is_possible:\n break\n if not new_system.provided_by(dep):\n additional_message = \"While trying to install {}, the needed dependency {} has been removed\".format(\n Colors.BOLD(Colors.LIGHT_MAGENTA(self.name)),\n Colors.BOLD(Colors.LIGHT_MAGENTA(dep))\n )\n is_possible = False\n break\n\n # same for packages which have to remain installed\n for package in installed_packages:\n if not is_possible:\n break\n if solution.dict_call_as_needed.get(package.name, False) \\\n and package.name not in new_system.all_packages_dict:\n additional_message = \"The package {} had to remain installed, \" \\\n \"but has been removed.\\n\" \\\n \"The package which lead to the removal is {}\" \\\n \"\".format(Colors.BOLD(Colors.LIGHT_MAGENTA(package.name)),\n Colors.BOLD(Colors.LIGHT_MAGENTA(self.name)))\n\n break\n\n # solution possible at this point if there are no installed packages\n else:\n # check which packages have been removed\n # due to adding the packages\n for package in installed_packages:\n # remove all remainings of the package\n # besides the knowledge that the package\n # has already been built\n if package.name not in new_system.all_packages_dict:\n solution.installed_solution_packages.remove(package)\n if package.name in solution.dict_to_deps:\n for dep in solution.dict_to_deps[package.name]:\n solution.visited_names.remove(dep)\n del solution.dict_to_deps[package.name]\n if package.name in solution.dict_to_way:\n del solution.dict_to_way[package.name]\n\n # for the case that there are no installed packages\n if is_possible:\n continue\n\n # solution not possible!\n solution.is_valid = False\n conflicting_packages = set(conf_system)\n conflicting_packages.add(self)\n ways_to_conflict = []\n for package in conflicting_packages:\n way_to_conflict = solution.dict_to_way.get(package.name, [])[:]\n way_to_conflict.append(package)\n ways_to_conflict.append(way_to_conflict)\n\n # create the problem\n conflict_problem = DepAlgoConflict(conflicting_packages, ways_to_conflict)\n conflict_problem.additional_message = additional_message\n for way_to_conflict in ways_to_conflict:\n for package in way_to_conflict:\n conflict_problem.relevant_packages.add(package)\n found_problems.add(conflict_problem)\n\n # we have valid solutions left, so the problems are not relevant\n if [solution for solution in current_solutions if solution.is_valid]:\n found_problems.clear()\n\n # add self to packages in solution, those are always topologically sorted\n for solution in current_solutions:\n solution.not_to_delete_deps -= own_not_to_delete_deps\n solution.installed_solution_packages.add(self)\n solution.packages_in_solution.append(self)\n solution.visited_packages.remove(self)\n\n # may contain invalid solutions !!!\n # but also filtered\n return filter_solutions(current_solutions)","def deferred_acceptance(men: List[Man], women: List[Woman],\n method='MPDA') -> List[Tuple[Man, Woman]]:\n if method not in ['MPDA', 'WPDA']:\n raise ValueError('Unknown method: must be MPDA or WPDA')\n N = len(men)\n if N == 0:\n raise ValueError('Men list is empty')\n if len(women) == 0:\n raise ValueError('Women list is empty')\n if N != len(women):\n raise ValueError(\"Men and women list don't match length\")\n proposers = men if method == 'MPDA' else women\n proposed_to = women if method == 'MPDA' else men\n unmatched = list(range(len(proposers)))\n for agent in proposers + proposed_to:\n agent.match = None\n while unmatched:\n idx = unmatched[0]\n incr = 0\n while proposers[idx].match is None and incr < N:\n spouse_idx = proposed_to.index(proposers[idx].preferences[incr])\n if proposed_to[spouse_idx].match is None:\n proposed_to[spouse_idx].match = proposers[idx]\n proposers[idx].match = proposed_to[spouse_idx]\n unmatched.pop(0)\n elif proposed_to[spouse_idx].prefers(proposers[idx]):\n proposed_to[spouse_idx].match.match = None\n unmatched.append(proposers.index(\n proposed_to[spouse_idx].match))\n proposed_to[spouse_idx].match = proposers[idx]\n proposers[idx].match = proposed_to[spouse_idx]\n unmatched.pop(0)\n incr += 1\n if any([x.match is None for x in proposers + proposed_to]):\n for agent in proposers + proposed_to:\n print(f\"{agent}, {agent.match}\")\n raise RuntimeError(\n \"Someone is unmatched.\")\n # matchings_1 = sorted([(x, x.match) for x in proposers])\n # matchings_2 = sorted([(x.match, x) for x in proposed_to])\n # if matchings_1 != matchings_2:\n # raise RuntimeError(\n # \"Something went horribly wrong. Matchings don't match\")\n matchings_1 = sorted([(x, x.match)\n for x in proposers], key=lambda x: x[1].id)\n return matchings_1","def get_references(input, verbose=False):\n\n # TODO: Make this a class reference parser\n\n # *** These tags are mobile-site specific\n\n # When we don't have proper access rights, this is present in the html\n GUEST_TAG_TUPLE = (\"li\", {\"id\": \"menuGuest\"})\n\n # Entries are \"li\" tags with classes of the form:\n # article-reference-article\n # article-reference-other-ref\n REFERENCE_TAG_TUPLE = (\"li\", {\"class\": re.compile('article-reference-*')})\n\n # This is the URL to the page that contains the document info, including\n # reference material\n BASE_URL = _SD_URL + '/science/article/pii/'\n\n # This URL was found first via Fiddler, then via closer inspection of the script\n # 'article_catalyst.js' under sciencedirect.com/mobile/js in the function\n # resolveReferences\n REF_RESOLVER_URL = _SD_URL + '/science/referenceResolution/ajaxRefResol'\n\n # Return the BeautifulSoup result, the requests session, and the requests response\n if _is_url(input):\n pii = _extract_pii(input)\n else:\n pii = input\n\n sess = requests.Session()\n\n if verbose:\n print('Requesting main page for pii: %s' % pii)\n resp = sess.get(BASE_URL + pii, cookies={'Site': 'Mobile'})\n\n # Step 2 - Get the reference tags\n\n soup = BeautifulSoup(resp.text)\n\n reference_section = soup.find(\"ol\", {\"class\": \"article-references\"})\n\n if reference_section is None:\n # Then we might be a guest. In other words, we might not have sufficient\n # privileges to access the data we want. Generally this is protected via\n # IP mask. When I'm working from home I need to VPN into work so\n # that I can access the data :/\n print(\"reference_section is None\")\n temp = soup.find(*GUEST_TAG_TUPLE)\n if temp is None:\n # We might have no references ... (Doubtful)\n raise ParseException(\"References were not found ..., code error likely\")\n else:\n raise InsufficientCredentialsException(\n \"Insufficient access rights to get referencs, requires certain IP addresses (e.g. university based IP)\")\n\n ref_tags = reference_section.find_all(*REFERENCE_TAG_TUPLE)\n\n n_refs = len(ref_tags)\n\n if n_refs == 0:\n return None\n\n # Step 3 - Resolve reference links\n # --------------------------------------------------------------------------\n # The returned html code contains javascript which returns more information\n # about each reference, such as:\n #\n # - links to the full text\n # - DOI \n\n\n # Step 3.1 - Make the request for the information\n # --------------------------------------------------------------------------\n # We need the eid of the current entry, it is of the form:\n #\n # SDM.pm.eid = \"1-s2.0-0006899387903726\"\n #\n # * I think this entry gets deleted after the requests so it may not be\n # visible if looking for it in Chrome. \n match = re.search('SDM\\.pm\\.eid\\s*=\\s*\"([^\"]+)\"', resp.text)\n #eid = match.group(1)\n\n # This list comes from the resolveReferences function in article_catalyst.js\n payload = {\n '_pii': pii,\n '_refCnt': n_refs,\n '_docType': 'article', # yikes, this might change ...\n '_refRangeStart': '1',\n '_refRangeCount': str(n_refs)} # This is normally in sets of 20's ...\n # I'm not sure if it is important to limit this. The browser then\n # makes a request fromr 1 count 20, 21 count 20, 41 count 20 etc,\n # It always goes by 20 even if there aren't 20 left\n\n if verbose:\n print('Requesting reference links')\n r2 = sess.get(REF_RESOLVER_URL, params=payload)\n\n # Step 3.2 - Parse the returned information into single entries\n # --------------------------------------------------------------------------\n # This could probably be optimized in terms of execution time. We basically\n # get back a single script tag. Inside is some sort of hash map for links\n # for each reference.\n #\n # The script tag is of the form:\n # myMap['bibsbref11']['refHtml']= \"<some html stuffs>\"; \n # myMap['bibsbref11']['absUrl']= \"http://www.sciencedirect.com/science/absref/sd/0018506X7790068X\";\n # etc.\n #\n # - Each entry is quite long.\n # - Normally contains html\n # - can be empty i.e. myMap['bibsbref11']['refHtml'] = \"\";\n # - the refHtml is quite interesting\n # - the absolute url is not always present (and currently not parsed)\n more_soup = BeautifulSoup(r2.text)\n script_tag = more_soup.find('script')\n\n # We unquote the script text as it is transmitted with characters escaped\n # and we want the parsed data to contain the non-escaped text\n #\n # We might eventually want to move this to being after the regular expression ...\n script_text = urllib_unquote(script_tag.text)\n\n ref_match_result = re.findall(\"myMap\\['bibsbref(\\d+)'\\]\\['refHtml'\\]=\\s?\" + '\"([^\"]*)\";', script_text)\n # Tokens:\n # 0 - the # from bibsbref#\n # 1 - the html content from the 'refHtml' entry\n # \n # NOTE: We don't really use the #, so we might remove the () around\n # \\d+ which would shift the index from 1 to 0\n if verbose:\n print('Creating reference objects')\n\n if len(ref_match_result) > 0:\n zipped = zip(ref_tags, ref_match_result, range(n_refs))\n ref_objects = [ScienceDirectRef(ref_tag, ref_link_info[1], ref_id) for\n ref_tag, ref_link_info, ref_id in zipped]\n else:\n zipped = zip(ref_tags, range(n_refs))\n ref_objects = [ScienceDirectRef(ref_tag, ref_id) for\n ref_tag, ref_id in zipped]\n\n # Step 4:\n # --------------------------------------------------------------------------\n # TODO: Improve documentation for this step\n\n if verbose:\n print('Retrieving Scopus Counts')\n\n ref_scopus_eids = [] # The Scopus IDs of the references to resolve\n # but with a particular formatting ...\n ref_count = 0 # Number of references we haven't resolved\n\n ref_count_list = []\n # NOTE: Browser requests these in the reverse order ...\n for ref_id, ref in enumerate(ref_objects):\n\n if ref._data_sceid is not None:\n ref_scopus_eids.append(ref._data_sceid + ',' + str(ref_id + 1) + '~')\n ref_count += 1\n\n # If we've got enough, then update the counts\n # The 20 may be arbitrary but it was what was used in original JS\n if ref_count > 20:\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\n ref_count = 0\n ref_scopus_eids = []\n\n # Get any remaining reference counts\n if ref_count != 0:\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\n\n # Take the raw data and set the citation count for each object\n for ref_tuple in ref_count_list:\n ref_id = int(ref_tuple[0]) - 1\n ref_count = int(ref_tuple[1])\n ref_objects[ref_id].scopus_cite_count = ref_count\n\n # All done!\n # ---------\n return ref_objects","def get_toolchanges(move: Move, cave: Cave) -> List[Move]:\n distance, point, equipment = move\n tools = [t for t in [\"torch\", \"climbing\", \"neither\"] if t != equipment]\n return [(distance+7, point, e) for e in tools if cave.can_traverse(e, point)]","def _filter_allows(self, ref, filtered_ref):\n for allow in self.allows_:\n if not allow:\n continue\n\n for sub_key, sub_ref in ref.ref_items(allow):\n filtered_ref.setdefault(sub_key).update(sub_ref.config)","def replace_errors(suggestion_num, sequence_switched, end_matches, offset_list):\n if len(end_matches) != 0:\n # Initialize Variables\n new_list = initialize_suggestion_list(suggestion_num)\n tokenizer, model = edf.initialize_tokenizer_variables()\n\n # Swapping masks with corrections section\n if len(end_matches) > 1:\n k = 0\n j = 0\n current = 0\n keep_going = True\n\n while keep_going:\n # Determine what section to locate mask token in\n mask_str = section_decision(j, sequence_switched, offset_list, end_matches)\n\n new_list = replace_tokens(mask_str, sequence_switched, new_list, j, tokenizer, model, suggestion_num)\n\n # If end of sentence, return j to 0, else continue iterations\n if j == (len(end_matches) - 1):\n # Update variables\n j = 0\n current += 1\n keep_going = False\n\n else:\n j += 1\n\n k += 1\n\n else:\n new_list = replace_tokens(sequence_switched, sequence_switched, new_list, 0, tokenizer, model,\n suggestion_num)\n\n return new_list\n\n else:\n return []","def _get_lexicon_update(self, lexicon):\n\n def merge(lists):\n \"\"\"\n Merge the lists so lists with overlap are joined together\n (i.e. [[1,2], [3,4], [2,5]] --> [[1,2,5], [3,4]])\n from: http://stackoverflow.com/a/9400562\n \"\"\"\n newsets, sets = [set(lst) for lst in lists if lst], []\n while len(sets) != len(newsets):\n sets, newsets = newsets, []\n for aset in sets:\n for eachset in newsets:\n if not aset.isdisjoint(eachset):\n eachset.update(aset)\n break\n else:\n newsets.append(aset)\n return newsets\n\n def get_coreferences(coreferences):\n \"\"\"Decode the SAF coreferences as (node: coreferencing_nodes) pairs\"\"\"\n coref_groups = []\n for a, b in coreferences:\n # take only the heads of each coref group\n coref_groups.append([a[0], b[0]])\n for nodes in merge(coref_groups):\n for node in nodes:\n yield node, nodes\n\n coreferences = dict(get_coreferences(self.saf_article.get('coreferences', [])))\n\n classes = defaultdict(set) # token -> classes\n uris = {}\n for uri, token in self.get_tokens().iteritems():\n if 'pos' not in token: continue # not a word\n uris[int(token['id'])] = uri\n pos = token['pos']\n lemma = token['lemma']\n for lex in lexicon:\n if \"pos\" in lex and lex['pos'] != pos:\n continue\n lemmata = lex['lemma']\n lexclass = lex['lexclass']\n if not isinstance(lemmata, list):\n lemmata = [lemmata]\n for target in lemmata:\n if (target == lemma or target == lemma.lower()\n or (target.endswith(\"*\") and lemma.lower().startswith(target[:-1]))):\n id = int(token['id'])\n for coref in coreferences.get(id, [id]):\n classes[coref].add(lexclass)\n inserts = []\n for id, lexclasses in classes.iteritems():\n if id not in uris:\n continue # coref to different sentence\n uri = str(uris[id]).replace(AMCAT, \":\")\n for lexclass in lexclasses:\n inserts.append('{uri} :lexclass \"{lexclass}\"'.format(**locals()))\n return {\"insert\": \".\\n\".join(inserts)}","def filter_mechanism_list(mechanisms, properties, allow_insecure = False,\n server_side = False):\n # pylint: disable=W0212\n result = []\n for mechanism in mechanisms:\n try:\n if server_side:\n klass = SERVER_MECHANISMS_D[mechanism]\n else:\n klass = CLIENT_MECHANISMS_D[mechanism]\n except KeyError:\n logger.debug(\" skipping {0} - not supported\".format(mechanism))\n continue\n secure = properties.get(\"security-layer\")\n if not allow_insecure and not klass._pyxmpp_sasl_secure and not secure:\n logger.debug(\" skipping {0}, as it is not secure\".format(mechanism))\n continue\n if not klass.are_properties_sufficient(properties):\n logger.debug(\" skipping {0}, as the properties are not sufficient\"\n .format(mechanism))\n continue\n result.append(mechanism)\n return result","def build(pattern, words, seen, list):\r\n return [word for word in words\r\n if re.search(pattern, word) and word not in seen.keys() and word not in list]","def getReferenceAnalyses(self):\n return [analysis for analysis in self.getAnalyses() \\\n if analysis.portal_type=='ReferenceAnalysis']","def wrong_reference_system(net):\n check_results = {}\n neg_loads = list(net.load[net.load.p_mw < 0].index)\n neg_gens = list(net.gen[net.gen.p_mw < 0].index)\n neg_sgens = list(net.sgen[net.sgen.p_mw < 0].index)\n\n if neg_loads:\n check_results['loads'] = neg_loads\n if neg_gens:\n check_results['gens'] = neg_gens\n if neg_sgens:\n check_results['sgens'] = neg_sgens\n\n if check_results:\n return check_results","def handle_mitre_attcks(\n actapi: act.api.Act, content: Text, mitre_attcks: List[Dict]\n) -> List[act.api.fact.Fact]:\n\n feeds_facts: List[act.api.fact.Fact] = []\n\n for attck in mitre_attcks:\n chain = []\n\n chain.append(\n actapi.fact(\"classifiedAs\")\n .source(\"content\", content)\n .destination(\"tool\", \"*\")\n )\n chain.append(\n actapi.fact(\"implements\")\n .source(\"tool\", \"*\")\n .destination(\"technique\", attck[\"technique\"])\n )\n\n feeds_facts += act.api.fact.fact_chain(*chain)\n\n return feeds_facts","async def insert_attack_stix_data(self):\n logging.info('Downloading ATT&CK data from STIX/TAXII...')\n attack = {}\n collection = Collection(\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\")\n tc_source = TAXIICollectionSource(collection)\n filter_objs = {\"techniques\": Filter(\"type\", \"=\", \"attack-pattern\"),\n \"groups\": Filter(\"type\", \"=\", \"intrusion-set\"), \"malware\": Filter(\"type\", \"=\", \"malware\"),\n \"tools\": Filter(\"type\", \"=\", \"tool\"), \"relationships\": Filter(\"type\", \"=\", \"relationship\")}\n for key in filter_objs:\n attack[key] = tc_source.query(filter_objs[key])\n references = {}\n\n # add all of the patterns and dictionary keys/values for each technique and software\n for i in attack[\"techniques\"]:\n if \"description\" in i:\n descr = i['description'].replace('<code>', '').replace('</code>', '').replace(\n '\\n', '').encode('ascii', 'ignore').decode('ascii')\n else:\n descr = \"missing description\"\n references[i[\"id\"]] = {\"name\": i[\"name\"], \"id\": i[\"external_references\"][0][\"external_id\"],\n \"example_uses\": [],\n \"description\": descr,\n \"similar_words\": [i[\"name\"]]}\n\n for i in attack[\"relationships\"]:\n if i[\"relationship_type\"] == 'uses':\n if 'attack-pattern' in i[\"target_ref\"]:\n use = i[\"description\"]\n # remove unnecessary strings, fix unicode errors\n use = use.replace('<code>', '').replace('</code>', '').replace('\"', \"\").replace(',', '').replace(\n '\\t', '').replace(' ', ' ').replace('\\n', '').encode('ascii', 'ignore').decode('ascii')\n find_pattern = re.compile('\\[.*?\\]\\(.*?\\)') # get rid of att&ck reference (name)[link to site]\n m = find_pattern.findall(use)\n if len(m) > 0:\n for j in m:\n use = use.replace(j, '')\n if use[0:2] == '\\'s':\n use = use[3:]\n elif use[0] == ' ':\n use = use[1:]\n # combine all the examples to one list\n references[i[\"target_ref\"]][\"example_uses\"].append(use)\n\n for i in attack[\"malware\"]:\n if 'description' not in i: # some software do not have description, example: darkmoon https://attack.mitre.org/software/S0209\n continue\n else:\n references[i[\"id\"]] = {\"id\": i['id'], \"name\": i[\"name\"], \"description\": i[\"description\"],\n \"examples\": [], \"example_uses\": [], \"similar_words\": [i[\"name\"]]}\n for i in attack[\"tools\"]:\n references[i[\"id\"]] = {\"id\": i['id'], \"name\": i[\"name\"], \"description\": i[\"description\"], \"examples\": [],\n \"example_uses\": [], \"similar_words\": [i[\"name\"]]}\n\n attack_data = references\n logging.info(\"Finished...now creating the database.\")\n\n cur_uids = await self.dao.get('attack_uids') if await self.dao.get('attack_uids') else []\n cur_items = [i['uid'] for i in cur_uids]\n for k, v in attack_data.items():\n if k not in cur_items:\n await self.dao.insert('attack_uids', dict(uid=k, description=defang_text(v['description']), tid=v['id'],\n name=v['name']))\n if 'regex_patterns' in v:\n [await self.dao.insert('regex_patterns', dict(uid=k, regex_pattern=defang_text(x))) for x in\n v['regex_patterns']]\n if 'similar_words' in v:\n [await self.dao.insert('similar_words', dict(uid=k, similar_word=defang_text(x))) for x in\n v['similar_words']]\n if 'false_negatives' in v:\n [await self.dao.insert('false_negatives', dict(uid=k, false_negative=defang_text(x))) for x in\n v['false_negatives']]\n if 'false_positives' in v:\n [await self.dao.insert('false_positives', dict(uid=k, false_positive=defang_text(x))) for x in\n v['false_positives']]\n if 'true_positives' in v:\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\n v['true_positives']]\n if 'example_uses' in v:\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\n v['example_uses']]\n logging.info('[!] DB Item Count: {}'.format(len(await self.dao.get('attack_uids'))))","def matching_sites(structure: Structure, \n reference_structure: Structure,\n species: Optional[List[str]] = None) -> List[Tuple[Site, int]]:\n matched_sites = []\n for ref_site in reference_structure:\n dr = [site.distance(ref_site) for site in structure]\n i = int(np.argmin(dr))\n matched_sites.append((structure[i], i))\n if species:\n matched_sites = [(s, i) for s, i in matched_sites if s.species_string in species]\n return matched_sites","def _filter_denies(self, filtered_ref):\n for deny in self.denies_:\n if not deny:\n continue\n\n for ref_key in filtered_ref.ref_keys(deny):\n del filtered_ref[ref_key]","def get_subtechniques_of():\n global subtechniques_of\n\n if not subtechniques_of:\n subtechniques_of = rsh.subtechniques_of(get_srcs())\n \n return subtechniques_of","def test_malware_have_techniques(attck_fixture):\n for malware in attck_fixture.enterprise.malwares:\n if malware.techniques:\n assert getattr(malware,'techniques')","def DiscoverPatterns(parameters, graph):\n patternCount = 0\n # get initial one-edge patterns\n parentPatternList = GetInitialPatterns(graph, parameters.temporal)\n if DEBUGFLAG:\n print(\"Initial patterns (\" + str(len(parentPatternList)) + \"):\")\n for pattern in parentPatternList:\n pattern.print_pattern(' ')\n discoveredPatternList = []\n while ((patternCount < parameters.limit) and parentPatternList):\n print(str(parameters.limit - patternCount) + \" patterns left\")\n childPatternList = []\n # extend each pattern in parent list (***** todo: in parallel)\n while (parentPatternList):\n parentPattern = parentPatternList.pop(0)\n if ((len(parentPattern.instances) > 1) and (patternCount < parameters.limit)):\n patternCount += 1\n extendedPatternList = Pattern.ExtendPattern(parentPattern, parameters.temporal)\n while (extendedPatternList):\n extendedPattern = extendedPatternList.pop(0)\n if DEBUGFLAG:\n print(\"Extended Pattern:\")\n extendedPattern.print_pattern(' ')\n if (len(extendedPattern.definition.edges) <= parameters.maxSize):\n # evaluate each extension and add to child list\n extendedPattern.evaluate(graph)\n if ((not parameters.prune) or (extendedPattern.value >= parentPattern.value)):\n Pattern.PatternListInsert(extendedPattern, childPatternList, parameters.beamWidth, parameters.valueBased)\n # add parent pattern to final discovered list\n if (len(parentPattern.definition.edges) >= parameters.minSize):\n Pattern.PatternListInsert(parentPattern, discoveredPatternList, parameters.numBest, False) # valueBased = False\n parentPatternList = childPatternList\n # insert any remaining patterns in parent list on to discovered list\n while (parentPatternList):\n parentPattern = parentPatternList.pop(0)\n if (len(parentPattern.definition.edges) >= parameters.minSize):\n Pattern.PatternListInsert(parentPattern, discoveredPatternList, parameters.numBest, False) # valueBased = False\n return discoveredPatternList","def test_dont_use_list(self) -> None:\n import hammer_config\n\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n\n def add_dont_use_list(in_dict: Dict[str, Any]) -> Dict[str, Any]:\n out_dict = deepdict(in_dict)\n out_dict.update({\"dont use list\": [\"cell1\", \"cell2\"]})\n return out_dict\n\n HammerToolTestHelpers.write_tech_json(tech_json_filename, add_dont_use_list)\n sys.path.append(tech_dir_base)\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir))\n tech.cache_dir = tech_dir\n\n tool = DummyTool()\n tool.technology = tech\n database = hammer_config.HammerDatabase()\n tool.set_database(database)\n\n # Test that manual mode for dont_use_mode works.\n database.update_project([{\n 'vlsi.inputs.dont_use_mode': 'manual',\n 'vlsi.inputs.dont_use_list': ['cell1']\n }])\n self.assertEqual(tool.get_dont_use_list(), ['cell1'])\n\n # Test that auto mode for dont_use_mode works if the technology has a \"don't use\" list.\n database.update_project([{\n 'vlsi.inputs.dont_use_mode': 'auto',\n 'vlsi.inputs.dont_use_list': []\n }])\n\n self.assertEqual(tool.get_dont_use_list(), tool.technology.config.dont_use_list)\n\n # Test that append mode for dont_use_mode works if the everyone has a \"don't use\" list.\n database.update_project([{\n 'vlsi.inputs.dont_use_mode': 'append',\n 'vlsi.inputs.dont_use_list': ['cell3']\n }])\n\n self.assertEqual(tool.get_dont_use_list(), ['cell1', 'cell2', 'cell3'])\n\n # Cleanup\n shutil.rmtree(tech_dir_base)\n\n # Create a new technology with no dont use list\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n HammerToolTestHelpers.write_tech_json(tech_json_filename)\n sys.path.append(tech_dir_base)\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir))\n tech.cache_dir = tech_dir\n\n tool.technology = tech\n\n # Test that auto mode for don't use list works if the technology has no don't use list file.\n database.update_project([{\n 'vlsi.inputs.dont_use_mode': 'auto',\n 'vlsi.inputs.dont_use_list': []\n }])\n self.assertEqual(tool.get_dont_use_list(), [])\n\n # Cleanup\n shutil.rmtree(tech_dir_base)","def _spacyfy(\n matches: List[List[Optional[Tuple[str, str]]]], pattern: List[Dict[str, Any]]\n) -> List[List[Dict[str, Any]]]:\n new_patterns = []\n if matches:\n for match in matches:\n new_pattern = deepcopy(pattern)\n for i, token in enumerate(match):\n if token:\n del new_pattern[i][token[0]]\n new_pattern[i][\"TEXT\"] = token[1]\n new_patterns.append(new_pattern)\n return new_patterns","def damage_model(skill, personas):\n damages = []\n for persona in personas:\n damage = skill.damage\n for resistance in persona.resistances:\n if skill.element == resistance.element:\n damage *= resistance.resistance.value\n damages.append((damage, resistance.resistance))\n persona.hp = persona.hp - damage\n return damages","def _local_improvement(self, folded_design):\n differing_sites = _string_difference_indices(\n self.target.dot_bracket, folded_design\n )\n hamming_distances = []\n for mutation in product(\"AGCU\", repeat=len(differing_sites)):\n mutated = self.design.get_mutated(mutation, differing_sites)\n folded_mutated, _ = fold(mutated.primary)\n hamming_distance = hamming(folded_mutated, self.target.dot_bracket)\n hamming_distances.append(hamming_distance)\n if hamming_distance == 0: # For better timing results\n return 0\n return min(hamming_distances)","def elimination_technique_2(C):\n rels = C._reidemeister_relators\n rels.sort(reverse=True)\n gens = C._schreier_generators\n for i in range(len(gens) - 1, -1, -1):\n rel = rels[i]\n for j in range(len(gens) - 1, -1, -1):\n gen = gens[j]\n if rel.generator_count(gen) == 1:\n k = rel.exponent_sum(gen)\n gen_index = rel.index(gen**k)\n bk = rel.subword(gen_index + 1, len(rel))\n fw = rel.subword(0, gen_index)\n rep_by = (bk*fw)**(-1*k)\n del rels[i]; del gens[j]\n for l in range(len(rels)):\n rels[l] = rels[l].eliminate_word(gen, rep_by)\n break\n C._reidemeister_relators = rels\n C._schreier_generators = gens\n return C._schreier_generators, C._reidemeister_relators","def get_usable_mal_guard_ases(client_as, all_ases, pfi):\r\n\r\n if client_as in SUSPECTS:\r\n return []\r\n\r\n usable_mal_guard_ases = []\r\n \r\n cnt = 0\r\n for asn in all_ases:\r\n suspects = bidirectional_lookup(client_as, asn, pfi)\r\n\r\n if len(suspects) != 0 and len(suspects & SUSPECTS) == 0:\r\n usable_mal_guard_ases.append(asn)\r\n\r\n cnt += 1\r\n # if cnt % 1000 == 0:\r\n # print(len(usable_mal_guard_ases))\r\n\r\n return usable_mal_guard_ases","def find_exploit_chains(attack_surface_graph, attack_surface, violated_components, target):\n starting_points = []\n admissible_components = []\n admissible_paths = []\n\n for violated_component in attack_surface:\n starting_points.append(violated_component[1])\n\n # removing duplicates\n for violated_component in violated_components:\n admissible_components.append(violated_component[0])\n\n admissible_components = set(admissible_components + starting_points)\n\n for starting_point in starting_points:\n for path in nx.all_simple_paths(attack_surface_graph, source=starting_point, target=target):\n # path - admissible components should result in an empty set\n # because all elements in path need to be in admissible components.\n if not (set(path) - admissible_components):\n if validate_edges(attack_surface_graph, path, starting_points):\n admissible_paths.append(path)\n\n # remove duplicate paths\n admissible_paths_set = set(tuple(x) for x in admissible_paths)\n admissible_paths = [list(x) for x in admissible_paths_set]\n\n return admissible_paths","def prefer_weak_emphathetic_features(self, pos_arr, max):\n log.debug(pos_arr)\n for pos_feature in pos_arr:\n log.debug('loop pos feature \\''+pos_feature+'\\'')\n result = []\n if pos_feature in self.weak_adjs:\n log.debug('--> GOT WEAK MATCH')\n result.append(pos_feature)\n\n if len(result) > max:\n log.debug('Got enough items')\n return result[:max]\n else:\n for pos_feature in pos_arr:\n if pos_feature not in result:\n if pos_feature in self.strong_adjs:\n log.debug('Ignored '+pos_feature+' as a strong one')\n else:\n pos_arr.remove(pos_feature)\n result.append(pos_feature)\n if len(result) >= max:\n return result\n return result","def replace_tokens(the_string, sequence_switched, suggestion_list, indexer, tokenizer, model, suggestion_num):\n # Get top tokens\n top_tokens = get_top_tokens(sequence_switched, indexer, tokenizer, model, suggestion_num)\n\n iterator = 0\n for token in top_tokens:\n suggestion_list[iterator] += the_string.replace(tokenizer.mask_token, tokenizer.decode([token]))\n iterator += 1\n\n return suggestion_list","def enumerate(self, capability: int = Capability.ALL) -> List[Technique]:\n\n # Update the cache for the current user\n self.find_suid()\n\n known_techniques = []\n for user, paths in self.suid_paths.items():\n for path in paths:\n binary = gtfobins.Binary.find(self.pty.which, path=path)\n if binary is not None:\n if (capability & binary.capabilities) == 0:\n continue\n\n known_techniques.append(\n Technique(user, self, binary, binary.capabilities)\n )\n\n return known_techniques","def existing_analysis_sweeps(self):\n setup_list = self.existing_analysis_setups\n sweep_list = []\n s_type = self.solution_type\n for el in setup_list:\n sweep_list.append(el + \" : \" + s_type)\n return sweep_list","def references_list( self, theWeaver ):\n return [ (c.name, c.seq) \n for c in theWeaver.reference_style.chunkReferencedBy( self ) ]","def influence(k,L,n):\n try:\n to_check = L[n-1] #set des indices\n contact_direct=C(k,0)\n return list(to_check.intersection(contact_direct))\n except:\n return []","def filter_ordered_questions_by_concepts(questions, item_params, target_concept, concept_map): \n \n def get_parents(target, concept_map):\n \"\"\"\n Given a target concept, return a list of concept IDs for parent concepts\n \"\"\"\n parents = []\n target_index = concept_map[CONCEPTS_STR].index(target)\n for row in range(len(concept_map[ADJ_MAT_STR])): \n # get value in adjMat for each row at target concept's col\n val = concept_map[ADJ_MAT_STR][row][target_index] \n if val > 0 and target_index != row: # don't care concepts are their own parents\n # print('parent found at {}, {}'.format(row, target_index)) # TODO remove\n parents.append(concept_map[CONCEPTS_STR][row])\n return parents\n\n def get_children(target, concept_map):\n \"\"\"\n Given a target concept, return a list of concept IDs for all child concepts\n \"\"\"\n child_inds = []\n target_index = concept_map[CONCEPTS_STR].index(target)\n target_row = concept_map[ADJ_MAT_STR][target_index]\n for ind in range(len(target_row)): # for each ind in row of adj mat\n val = target_row[ind]\n if(val>0 and ind != target_index): # don't care concept is child of itself\n child_inds.append(ind)\n return list(map(lambda ind: concept_map[CONCEPTS_STR][ind], child_inds))\n\n def get_concept(eid, item_params):\n \"\"\"\n Given a question/exercise eid (String) and item_params (pd.DataFrame),\n return the concept that exercise corresponds to.\n\n Assumes eid is unique (maps to exactly 1 row)\n \"\"\"\n\n concept = item_params[item_params[EID]==eid][CONCEPT]\n\n if len(concept) < 1: # TODO: make this filter \"!= 1\" to be more strict\n raise Exception(\"Exercise ID does not map to any exercises. eid: {}\".format(eid))\n else:\n return concept.iloc[0] # to get actual value\n \n df_item_params = pd.DataFrame.from_dict(item_params)\n\n rec_eids = []\n\n # get all recommendations from target, its children, parents\n rec_eids += list(filter(lambda q: get_concept(q, df_item_params) in \n [target_concept] + \n get_children(target_concept, concept_map) +\n get_parents(target_concept, concept_map), questions))\n \n # # GET TOP FOR EACH RELATIONSHIP\n # # add max_num_target questions of same concept to rec_eids\n # rec_target = list(filter(lambda q: get_concept(q, df_item_params) == target_concept, \n # questions))[:max_num_target]\n # # print(\"For target, added {}\".format(rec_target))\n # rec_eids += rec_target\n \n\n # # add max_num_child questions of child concepts to rec_eids\n # rec_child = list(filter(lambda q: get_concept(q, df_item_params) in get_children(target_concept, concept_map), \n # questions))[:max_num_child]\n # # print(\"For children, added {}\".format(rec_child ))\n # rec_eids += rec_child\n\n # # add max_num_parent questions of parent concepts to rec_eids\n # rec_parent = list(filter(lambda q: get_concept(q, df_item_params) in get_parents(target_concept, concept_map), \n # questions))[:max_num_parent] \n # # print(\"For parents, added {}\".format(rec_parent))\n # rec_eids += rec_parent \n\n # want order of recommendations to stay same & only grab from top half of recommendations\n # return list(filter(lambda eid: eid in rec_eids, questions[:int(len(questions)/2)+1])) \n\n # want order of recommendations to stay same\n return list(filter(lambda eid: eid in rec_eids, questions))","def multiple_and_remover(code):\n list_ands_element =[]\n list_ands_position =[]\n counter = 0\n x=0\n z=0\n while counter + 1 <= len(code): \n and_finder = find_and(code[counter]) #finds the position in the list where the & appears using the find_and function defined in fparsertools\n counter += 1\n list_ands_position.append(and_finder)\n if and_finder != None : \n list_ands_element.append(counter-1) # vector with the numbers of the position of the element in the list where & appear\n n=len(list_ands_element)\n and_position = [] \n for val in list_ands_position: \n if val != None : \n and_position.append(val) # Position in each component of the list where & appears\n counter2 = 0\n clean_code=[]\n\n while counter2+1 <= len(code): \n if counter2 not in list_ands_element: # if the component of the list doesn't have & it is appended\n clean_code.append(code[counter2]) \n \n if counter2 in list_ands_element: # if the component of the list has & \n index=list_ands_element.index(counter2) # search the index in where the & appears in the vector\n if index+1 < len(list_ands_element): \n x = 0+z\n if abs(list_ands_element[index]-list_ands_element[index+1])!=1: # Checks if there are not two consecutive & on the list\n clean_code.append(code[counter2][:and_position[x]] + code[counter2+1]) # if there are not two consecutive & keeps the code until the &, and adds the next line of the code/component of the list\n z+=1\n counter2+=1 # Jumps the next component because it was added to the previous one\n if abs(list_ands_element[index]-list_ands_element[index+1])==1:# Checks if there are two consecutive & on the list\n clean_code.append(code[counter2][:and_position[x]] + code[counter2+1]) # If two consecutive & appear it makes the same as the previous case\n z+=2 # Jumps two lines in the and position vector because one & is included in the previous line\n counter2+=1 # Jumps the next component because it was added to the previous one\n elif index+1 == len(list_ands_element): # This option is defined for the last component in order to avoid problems with indexing \n clean_code.append(code[counter2][:and_position[-1]] + code[counter2+1]) \n z+=1\n counter2+=1\n counter2+=1\n \n if n==0: # Recursive call\n return clean_code\n if n!=0:\n clean_code=(multiple_and_remover(clean_code))\n \n return clean_code","def filter_suggestions(rules, item):\n suggestions = rules.copy()\n for i in suggestions.index:\n # Checks if the consequent already exists in the item. If yes, the rule is dropped.\n if suggestions['consequents'][i][0] in item:\n suggestions.drop([i], inplace=True)\n\n for j in suggestions.index:\n # For every list of properties in the antecedents, check if they are contained in item properties list\n # If no, the rule is dropped.\n if all(props in item for props in list(suggestions['antecedents'][j])) == False:\n suggestions.drop([j], inplace=True)\n\n result = np.unique([*itertools.chain.from_iterable(suggestions.consequents)]).tolist()\n\n return result","def prefer_strong_negative_features(self, neg_arr, max):\n log.debug(neg_arr)\n for neg_feature in neg_arr:\n log.debug('loop neg feature \\''+neg_feature+'\\'')\n result = []\n if neg_feature in self.strong_adjs:\n log.debug('--> GOT STRONG MATCH')\n result.append(neg_feature)\n\n if len(result) > max:\n log.debug('Got enough items')\n return result[:max]\n else:\n for neg_feature in neg_arr:\n if neg_feature not in result:\n if neg_feature in self.strong_adjs:\n log.debug('Ignored '+neg_feature+' as a weak one')\n else:\n neg_arr.remove(neg_feature)\n result.append(neg_feature)\n if len(result) >= max:\n return result\n return result","def _calculate_loss_formula(self) -> List[List[List[Tuple]]]:\n self._formulas = []\n for i in trange(len(self.explanations), desc=\"Setting explanations \"):\n explanation = self.explanations[i]\n if not is_dnf(explanation):\n explanation = str(to_dnf(explanation))\n formulas_terms = []\n terms = explanation.split(\"|\")\n assert len(terms) > 0, f\"Error in explanation formulation. Expected DNF formula but it is {explanation}\"\n for term in terms:\n # excluding trivial explanation\n if term == \"False\" or term == \"True\":\n continue\n # excluding parenthesis\n term = term.replace(\" \", \"\")\n if term[0] == \"(\":\n term = term[1:-1]\n formula_term = []\n attributes = term.split(\"&\")\n for attribute in attributes:\n if attribute[0] == \"~\":\n negated = True\n attribute = attribute[1:]\n else:\n negated = False\n idx = self.attributes.index(attribute)\n formula_term.append((idx, negated))\n formulas_terms.append(formula_term)\n self._formulas.append(formulas_terms)\n return self._formulas","def match_rules(rules, wm):\n res = []\n for r in rules:\n new_patterns = match_rule(r[0],r[1],r[2], wm)\n if new_patterns:\n print(\" Match succeeds\")\n print(\" Adding assertions to WM\")\n else:\n print(\" Match fails\")\n for n in new_patterns:\n if (n not in wm) and (n not in res):\n print(\" \",n)\n res.append(n)\n # print(\"new patterns so far = \", res)\n # print()\n # for testing\n # break\n return res","def solution2(inp):\n rules, mticket, nearby = inp.strip().split(\"\\n\\n\")\n rules = rules.split(\"\\n\")\n nearby = nearby.split(\"\\n\")[1:]\n mticket = list(map(int, mticket.split(\"\\n\")[1].split(\",\")))\n rrules = []\n for rule in rules:\n a, b = rule.split(\" or \")\n name = a.strip().split(\":\")[0]\n r1 = a.strip().split(\" \")[-1]\n r2 = b.strip()\n def to_range(r):\n i, j = list(map(int, r.split(\"-\")))\n return range(i, j + 1)\n rrules.append((to_range(r1), to_range(r2), name))\n\n nearby = [list(map(int, ticket.split(\",\"))) for ticket in nearby]\n s = 0\n to_remove = []\n for i, ticket in enumerate(nearby):\n for v in ticket:\n valid = False\n for r in rrules:\n valid |= v in r[0] or v in r[1]\n if not valid:\n to_remove.append(i)\n nearby = list(map(lambda x: x[1], filter(lambda x: x[0] not in to_remove, enumerate(nearby))))\n indices = list(range(len(rrules)))\n keys = {}\n for rule in rrules:\n rule_idx = []\n for i in indices:\n if all(ticket[i] in rule[0] or ticket[i] in rule[1] for ticket in nearby):\n rule_idx.append(i)\n keys[rule[2]] = rule_idx\n\n stack = list(keys.items())\n def resolve(j, avail):\n f, cand = stack[j]\n for i in avail.intersection(cand):\n if len(avail) == 1:\n return [i]\n avail.remove(i)\n res = resolve(j + 1, avail)\n avail.add(i)\n if res != False:\n return [i] + res\n return False\n solver = resolve(0, set(range(len(rrules))))\n names = list(map(lambda x: x[0], stack))\n return reduce(lambda x, y: x * y, [mticket[v] for k, v in zip(names, solver) if k.startswith(\"departure\")], 1)","def _filter_references_section(self, text):\n references_pattern = r\"(REFERENCE)|(reference)|(Reference)\"\n references_found = [i.start() for i in re.finditer(references_pattern, text)]\n if references_found != []:\n last_reference_index = references_found[-1]\n return text[:last_reference_index]\n else:\n return text","def get_referents(data):\n fakes_group = data.groups.get(\"Fakes\")\n\n return [obj for obj in data.groups[\"Referents\"].objects\n if not obj.hide_render or fakes_group in obj.users_group]","def techniques(self):\n return self._get_child_page_of_type(LearningTechniquesPage)","def find_final_stanzas_from_stanzas(stanzas, adj_list_ids, adj_list_words):\n\n # create combos generator\n all_combos = combinations(stanzas, 3)\n\n # filtered generator (all lines unique)\n combos = (c for c in all_combos if len(set().union(*c)) == 12)\n\n all_valid = []\n for combo in tqdm(combos):\n valid = find_final_stanzas(*combo, adj_list_ids, adj_list_words)\n if valid:\n all_valid.append((combo, valid))\n return all_valid","def matching_site_indices(structure: Structure,\n reference_structure: Structure,\n species: Optional[List[str]] = None) -> List[int]:\n return [m[1] for m in matching_sites(structure, reference_structure, species=species)]","def ref_ex1():\n\n print(\"Look Alikes\")\n print(\"===========\")\n\n lst1 = [7, 3, 2]\n lst2 = [7, 3, 2]\n print(lst1, lst2)\n\n lst1[1] = -8\n print(lst1, lst2)\n\n print(\"\")\n print(\"Aliases\")\n print(\"=======\")\n\n lst3 = [1, 5, 9]\n lst4 = lst3\n print(lst3, lst4)\n\n lst3[1] = 17\n print(lst3, lst4)\n\n print(\"\")\n print(\"Copies\")\n print(\"======\")\n\n lst5 = [8, 9, 4]\n # This makes a shallow copy\n lst6 = list(lst5)\n print(lst5, lst6)\n\n lst5[1] = -2\n print(lst5, lst6)\n\n print(\"\")\n print(\"Function Arguments\")\n print(\"==================\")\n\n\n lst7 = [1, 2, 3]\n print(lst7)\n mutate_list(lst7)\n print(lst7)\n return 0","def update_time_references_with_impact_zones(self, docFeatList, timexList):\n \n if not self.timexImpactZones:\n return self.timeReferences\n \n impactZoneStart = self.timexImpactZones[0][0]\n features = [feat for feat in docFeatList if feat.getStartPos() > impactZoneStart \n and self.sentence_tags[feat.getSentNum()]=='NORMAL' and not feat.inClause() \n and feat.getType() in ['VACCINE', 'DRUG']] \n for feature in features:\n sentnumber = feature.getSentNum()\n if not feature.getTlink() or not feature.getTlink().getDateTime():\n if 'concomitant' in [tg[0] for tg in self.sentence_full_tags[sentnumber]]:\n continue\n feature = self.assign_feature_time_with_impact_zones(feature, self.timexImpactZones)\n tlink = feature.getTlink()\n if tlink:\n if feature.getType()=='VACCINE':\n refType = 'Vaccine'\n confidence = 0.6\n else:\n refType = 'Drug'\n confidence = 0.6\n \n self.timeReferences[(refType, feature.getString(), 0, sentnumber, \n feature.getStartPos(), feature.getEndPos(), confidence)] = tlink.getDateTime()\n \n ##: Too many false alarms. Disable this part for now.\n ##: E.g, in 3603565(4th). .... Before treatment, .... \n# exposureSet = ['Vaccination', 'Injection']\n# for sentnum, sentence in enumerate(self.sentences):\n# timexes = [t for t in timexList if t.getDateTime() and t.getSentNum()==sentnum]\n# if timexes: ##: has been processed for time references\n# continue\n# \n# tags = set([tg[1] for tg in self.taggedSentences[sentnum]])\n# intersect = tags.intersection(exposureSet)\n# if intersect:\n# tgs = [tg for tg in self.taggedSentences[sentnum] if tg[1] in exposureSet]\n# word = tgs[0][0]\n# sentStartPos = self.sentence_startPos[sentnum]\n# dtimes = [r[2] for r in self.timexImpactZones if sentStartPos >=r[0] and sentStartPos<=r[1]]\n# if dtimes:\n# self.timeReferences[(tgs[0][1], word, None, sentnum, sentStartPos, sentStartPos, 0.6)] = dtimes[0]\n# \n return self.timeReferences","def find_transformation(target, reference):\n\n # Brute force search on all possible transformations\n for axis in (None, \"x\", \"y\", \"x=y\"):\n curr = target\n for degrees in (None, 90, 180, 270):\n if axis is not None:\n curr = curr.reflect(axis)\n if degrees is not None:\n curr = curr.rotate(degrees)\n\n # Find the reference points in target by matching them with the colors in the\n # reference collection\n target_reference = Collection([p for p in curr.points if p.color in reference.colors])\n\n # Apply the transformation of one point, to the rest of the points in the target collection\n corresponding = next(p for p in reference.points if p.color == target_reference.points[0].color)\n transformed = target_reference.translate(target_reference.points[0], corresponding)\n\n # If the resulting collection is identical to the reference point collection it is a match\n if transformed == reference:\n return curr.translate(target_reference.points[0], corresponding)\n\n return None","def _find_states(self, lookup, alignment, reference):\n # Get the reference allele, given our contig and position found above\n self._reference = reference[self._contig][self._position - 1] # Subtract one as FASTA is 1-based and Python is 0-based\n if alignment.get_rc(): # If we're reverse complement\n alt, do_rc = lookup.get_alternate(self.reverse_complement(self._reference))\n self._alternate = self.reverse_complement(alt)\n else:\n self._alternate, do_rc = lookup.get_alternate(self._reference) # An 'N' will be returned if the reference allele doesn't match with our IUPAC code\n if do_rc:\n self._reference = self.reverse_complement(self._reference)","def find_adapted_solution(list_of_tuples, module, n_of_players_with_vote):\n\n def malus_roles_left(players_left, roles_left):\n\n \"\"\"\n Checks whether it is possible to deploy all the players by assinging\n a certain number of malus.\n \"\"\"\n\n # Permutations of the players still to be deployed. We do that because\n # we only want that combination of players in which ALL of them are\n # deployed\n players_perm = permutations(players_left, len(players_left))\n\n # Initialize the number of malus (just a number high enough)\n fin_malus = 10\n\n # For each permutation of players to be deployed\n for perm in players_perm:\n\n # Initialize two parameters: a counter and the number of malus for\n # this specific permutation. Counter is used to be sure all the\n # players in the permutation are checked\n count = 0\n temp_malus = 0\n\n # Make a copy of the roles to be covered so we can use it later to\n # delete roles that we are able to cover\n copy_of_adapted_roles = copy.copy(roles_left)\n\n # For each element in the permutation we select the corresponding\n # role and try to cover it\n for i in range(len(perm)):\n role_to_cover = roles_left[i]\n role_cand = perm[i][2]\n\n # If it is possible to cover it with a malus we increase the\n # number of malus and the counter and then remove the role from\n # the list of the roles still uncovered\n if role_to_cover in malus_roles[role_cand]:\n temp_malus += 1\n count += 1\n copy_of_adapted_roles.remove(role_to_cover)\n\n # If it is possible to cover it with no malus we just increase\n # the counter and delete the role\n elif (role_to_cover not in malus_roles[role_cand]\n and role_to_cover in compatible_roles[role_cand]):\n count += 1\n copy_of_adapted_roles.remove(role_to_cover)\n\n # Else we interrupt checking this permutation and go to the\n # one\n else:\n break\n\n # If we checked ALL the elements in the permutation and the number\n # of malus is lower than the previous value we store it\n if count == len(perm) and temp_malus < fin_malus:\n fin_malus = temp_malus\n\n # If this value is different from the default one it means we found a\n # solution and we return it\n if fin_malus != 10:\n return fin_malus\n else:\n return False\n\n def calculate(candidate, roles_of_module):\n\n \"\"\"\n This function applies the deploy_players function to look for the\n solution, if it exists. If all the players are deployed it returns\n True, otherwise False.\n \"\"\"\n\n # See find_solution for explanation on the try method\n try:\n to_deploy_list, roles_left = deploy_players(candidate,\n roles_of_module,\n 'adapted')\n\n # If the roles to deploy can be covered with a malus we return the\n # number of malus assigned\n\n if malus_roles_left(to_deploy_list, roles_left):\n return malus_roles_left(to_deploy_list, roles_left)\n else:\n return False\n\n except TypeError:\n return False\n\n ordered_lineup = order_by_role(list_of_tuples)\n\n all_comb = list(combinations(schemes[module], n_of_players_with_vote))\n\n for comb in all_comb:\n\n # Change from tuple to list and check wings\n comb = transf_wings(list(comb), module)\n\n # If a solution is found we return the number of malus\n if calculate(ordered_lineup, comb):\n return calculate(ordered_lineup, comb)\n\n return False","def _enum_stem_match(enum_hypothesis_list, enum_reference_list, stemmer=PorterStemmer()):\n stemmed_enum_list1 = [(word_pair[0], stemmer.stem(word_pair[1])) \\\n for word_pair in enum_hypothesis_list]\n\n stemmed_enum_list2 = [(word_pair[0], stemmer.stem(word_pair[1])) \\\n for word_pair in enum_reference_list]\n\n word_match, enum_unmat_hypo_list, enum_unmat_ref_list = \\\n _match_enums(stemmed_enum_list1, stemmed_enum_list2)\n\n enum_unmat_hypo_list = list(zip(*enum_unmat_hypo_list)) if len(enum_unmat_hypo_list) > 0 else []\n\n enum_unmat_ref_list = list(zip(*enum_unmat_ref_list)) if len(enum_unmat_ref_list) > 0 else []\n\n enum_hypothesis_list = list(filter(lambda x: x[0] not in enum_unmat_hypo_list,\n enum_hypothesis_list))\n\n enum_reference_list = list(filter(lambda x: x[0] not in enum_unmat_ref_list,\n enum_reference_list))\n\n return word_match, enum_hypothesis_list, enum_reference_list","def coding_problem_42(numbers, target):\n if target == 0:\n return []\n\n valid_numbers = [n for n in numbers if 0 < n <= target]\n for number in sorted(valid_numbers, reverse=True):\n\n remaining_numbers = copy(valid_numbers)\n remaining_numbers.remove(number)\n partial_sum = coding_problem_42(remaining_numbers, target - number)\n if partial_sum is not None:\n return [number] + partial_sum\n\n return None","def repair_fleet(input_fleet):\n logistics = logi_subfleet(input_fleet)\n logi_shield = logistics[0]\n logi_armor = logistics[1]\n if (logi_shield == []) and (logi_armor == []):\n return input_fleet\n\n damaged_shield = []\n\n # I have a bad feeling that this function won't last longer\n # than a single commit :ohdear:\n # also this has a slight bug that ships can rep themselves\n # and a ship might get over repped, but that's actually intended\n\n # shield first\n for x in xrange(len(input_fleet)):\n if input_fleet[x].attributes.shield != input_fleet[x].schema.shield:\n damaged_shield.append(x)\n\n if damaged_shield != []:\n for ship in logi_shield:\n rep_target = random.choice(damaged_shield)\n input_fleet[rep_target] = Ship(\n input_fleet[rep_target].schema,\n ShipAttributes(\n min(input_fleet[rep_target].schema.shield,\n (input_fleet[rep_target].attributes.shield\n + ship.schema.remote_shield)\n ),\n input_fleet[rep_target].attributes.armor,\n input_fleet[rep_target].attributes.hull,\n ),\n input_fleet[rep_target].debuffs,\n )\n\n damaged_armor = []\n\n #armor second\n for x in xrange(len(input_fleet)):\n if input_fleet[x].attributes.armor != input_fleet[x].schema.armor:\n damaged_armor.append(int(x))\n\n if damaged_armor != []:\n for ship in logi_armor:\n rep_target = random.choice(damaged_armor)\n input_fleet[rep_target] = Ship(\n input_fleet[rep_target].schema,\n ShipAttributes(\n input_fleet[rep_target].attributes.shield,\n min(input_fleet[rep_target].schema.armor,\n (input_fleet[rep_target].attributes.armor\n + ship.schema.remote_armor)\n ),\n input_fleet[rep_target].attributes.hull,\n ),\n input_fleet[rep_target].debuffs,\n )\n\n return input_fleet","def generate(softwaretype=\"software\"):\n # import the STIX data from MITRE/CTI\n stix = requests.get(\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\").json()\n ms = MemoryStore(stix_data=stix[\"objects\"])\n # software includes malware and tool types so perform two queries and merge the results\n software_filters = []\n if softwaretype == \"malware\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'malware') ] )\n if softwaretype == \"tool\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'tool') ] )\n \n software = list(chain.from_iterable(\n ms.query(f) for f in software_filters\n ))\n\n # build a list of techniques used by software\n techniques_used = {} #attackID => using software names\n for thesoftware in software:\n # filter out revoked and deprecated software\n if (\"x_mitre_deprecated\" in thesoftware and thesoftware[\"x_mitre_deprecated\"]) or (\"revoked\" in thesoftware and thesoftware[\"revoked\"]): continue\n for relationship in ms.relationships(thesoftware[\"id\"]):\n # skip all non-technique relationships\n if \"attack-pattern\" not in relationship[\"target_ref\"]: continue\n technique = ms.get(relationship[\"target_ref\"])\n # filter out deprecated and revoked techniques\n if (\"x_mitre_deprecated\" in technique and technique[\"x_mitre_deprecated\"]) or (\"revoked\" in technique and technique[\"revoked\"]): continue\n techniqueID = technique[\"external_references\"][0][\"external_id\"]\n # store usage in techniques_used struct\n if techniqueID in techniques_used:\n techniques_used[techniqueID].append(thesoftware[\"name\"])\n else:\n techniques_used[techniqueID] = [thesoftware[\"name\"]]\n\n # format the techniques for the output layer\n techniques_list = []\n highest_usage = 0\n lowest_usage = 1\n for techniqueID in techniques_used:\n # determine the number of used techniques for the score\n count = len(techniques_used[techniqueID])\n highest_usage = max(highest_usage, count)\n lowest_usage = min(lowest_usage, count)\n # append technique struct to list of layer-formatted techniques\n techniques_list.append({\n \"techniqueID\": techniqueID,\n \"comment\": \"executed by \" + \", \".join(techniques_used[techniqueID]),\n \"score\": count,\n })\n # set up layer name and desc according to softwaretype\n if softwaretype != \"software\": \n plural = \"tools\" if softwaretype == \"tool\" else \"malware\"\n layername = f\"Software ({softwaretype}) Execution\"\n layerdescription = f\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\"\n else: \n layername = \"Software Execution\"\n layerdescription = f\"All techniques that can be executed by software, where the score is the count of software using the technique\"\n\n # construct and return the layer as a dict\n return {\n \"name\": layername,\n \"description\": layerdescription,\n \"versions\": {\n \"layer\": \"4.1\",\n \"navigator\": \"4.1\"\n },\n \"domain\": \"enterprise-attack\",\n \"techniques\": techniques_list,\n \"sorting\": 3, # order in descending order of score (count)\n \"gradient\": {\n \"colors\": [\n \"#fff7b3\", # low counts are yellow\n \"#ff6666\", # high counts are red\n ],\n \"minValue\": lowest_usage,\n \"maxValue\": highest_usage\n },\n }","def get_possible_stresses(stress_pattern: str) -> List[str]:\n possible_stresses = []\n for index in range(len(stress_pattern)):\n possible_stresses.append(stress_pattern[:index + 1])\n return possible_stresses","def mutant(self):\n _mutant = []\n _wt = self.wildtype\n for i in range(0, len(self.mutations)):\n site = _wt[i]\n options = self.mutations[i]\n if options is None:\n _mutant.append(_wt[i])\n else:\n for o in options:\n if o != site:\n _mutant.append(o)\n return \"\".join(_mutant)","def replace_derived_rules(derivation):\n\n derivation2 = list()\n formulas_list = [x[0] for x in derivation]\n cts_used = {x for f in formulas_list for x in FOL_individual_constants if x in str(f)}\n cts_unused = list(set(FOL_individual_constants) - cts_used)\n\n # In the first pass it replaces only DM2 (since DM2 uses DM)\n for step in range(len(derivation)):\n step_open_sups = derivation[step][3]\n if derivation[step][1] == \"DM2\":\n # A DM2 step has structure [ [[¬, [p]], or, [¬, [q]]], 'DM2', [[¬, [[p], & [q]]]] ]\n negated_conjunction = derivation[step][2][0]\n disjunction = derivation[step][0]\n\n new_steps = list()\n step_open_sups.append(['¬', disjunction])\n new_steps.append([['¬', disjunction], 'SUP', [], step_open_sups[:]])\n new_steps.append([['¬', disjunction[0]], 'DM', [['¬', disjunction]], step_open_sups[:]])\n new_steps.append([['¬', disjunction[2]], 'DM', [['¬', disjunction]], step_open_sups[:]])\n new_steps.append([disjunction[0][1], 'DN', [['¬', disjunction[0]]], step_open_sups[:]])\n new_steps.append([disjunction[2][1], 'DN', [['¬', disjunction[2]]], step_open_sups[:]])\n new_steps.append([negated_conjunction[1], 'I˄', [disjunction[0][1], disjunction[2][1]], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [negated_conjunction, negated_conjunction[1]], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', ['¬', disjunction]], 'I¬', [['¬', disjunction], ['⊥']], step_open_sups[:]])\n new_steps.append([disjunction, 'DN', [['¬', ['¬', disjunction]]], step_open_sups[:]])\n derivation2.extend(new_steps)\n\n else:\n derivation2.append(derivation[step])\n\n derivation3 = list()\n\n # Second pass: The rest of the rules\n for step in range(len(derivation2)):\n step_open_sups = derivation2[step][3]\n if derivation2[step][1] == \"DM\":\n # A DM steps looks like [ [¬,[p]], DM, [[¬,[[p] v [q]]]] ] or like [ [¬,[q]], DM, [[¬,[[p] v [q]]]] ]\n negated_disjunct = derivation2[step][0]\n disjunct = negated_disjunct[1]\n negated_disjunction = derivation2[step][2][0]\n disjunction = negated_disjunction[1]\n\n new_steps = list()\n step_open_sups.append(disjunct)\n new_steps.append([disjunct, 'SUP', [], step_open_sups[:]])\n new_steps.append([disjunction, 'I˅', [disjunct], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [negated_disjunction, disjunction], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([negated_disjunct, 'I¬', [disjunct, ['⊥']], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n elif derivation2[step][1] == \"NegCond\":\n # A NegCond step looks either like:\n # [ [p], NegCond, [[¬, [[p], then, [q]]]] ] or\n # [ [¬, [q]], NegCond, [[¬, [[p], then, [q]]]] ]\n\n negated_conditional = derivation2[step][2][0]\n conditional = negated_conditional[1]\n antecedent = conditional[0]\n consequent = conditional[2]\n\n if derivation2[step][0] == antecedent:\n\n new_steps = list()\n step_open_sups.append(['¬', antecedent])\n new_steps.append([['¬', antecedent], 'SUP', [], step_open_sups[:]])\n step_open_sups.append(antecedent)\n new_steps.append([antecedent, 'SUP', [], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [['¬', antecedent], antecedent], step_open_sups[:]])\n new_steps.append([consequent, 'EFSQ', [['⊥']], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([conditional, 'I→', [antecedent, consequent], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [negated_conditional, conditional], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', ['¬', antecedent]], 'I¬', [['¬', antecedent], ['⊥']], step_open_sups[:]])\n new_steps.append([antecedent, 'DN', [['¬', ['¬', antecedent]]], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n elif derivation2[step][0] == ['¬', consequent]:\n new_steps = list()\n step_open_sups.append(consequent)\n new_steps.append([consequent, 'SUP', [], step_open_sups[:]])\n step_open_sups.append(antecedent)\n new_steps.append([antecedent, 'SUP', [], step_open_sups[:]])\n new_steps.append([consequent, 'REP', [consequent], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([conditional, 'I→', [consequent, antecedent], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [conditional, negated_conditional], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', consequent], 'I¬', [consequent, ['⊥']], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n elif derivation2[step][1] == \"MT\":\n # A MT step looks either like:\n # [ [¬, [p]], 'MT', [ [[p], then, [q]], [¬, [q]] ] ] or\n # [ [¬, [p]], 'MT', [ [[p], then, [¬ [q]]], [q] ] ]\n\n negated_antecedent = derivation2[step][0]\n antecedent = negated_antecedent[1]\n conditional = derivation2[step][2][0]\n consequent = conditional[2]\n independent_formula = derivation2[step][2][1]\n\n new_steps = list()\n step_open_sups.append(antecedent)\n new_steps.append([antecedent, 'SUP', [], step_open_sups[:]])\n new_steps.append([consequent, 'E→', [conditional, antecedent], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [consequent, independent_formula], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([negated_antecedent, 'I¬', [antecedent, ['⊥']], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n elif derivation2[step][1] == \"SD\":\n # A SD step looks either like:\n # [ [p], 'SD', [ [[p], or, [q]], [¬, [q]] ] ]\n # [ [q], 'SD', [ [[p], or, [q]], [¬, [p]] ] ]\n # [ [p], 'SD', [ [[p], or, [¬, [q]]], [q] ] ]\n # [ [q], 'SD', [ [[¬, [p]], or, [q]], [p] ] ]\n\n disjunction = derivation2[step][2][0]\n derived_disjunct = derivation2[step][0]\n independent_formula = derivation2[step][2][1]\n\n new_steps = list()\n step_open_sups.append(['¬', derived_disjunct])\n new_steps.append([['¬', derived_disjunct], 'SUP', [], step_open_sups[:]])\n step_open_sups.append(disjunction[0])\n new_steps.append([disjunction[0], 'SUP', [], step_open_sups[:]])\n if len(disjunction[0]) == 2 and independent_formula == disjunction[0][1]:\n new_steps.append([['⊥'], 'E¬', [independent_formula, disjunction[0]], step_open_sups[:]])\n else:\n new_steps.append([['⊥'], 'E¬', [disjunction[0], ['¬', disjunction[0]]], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([[disjunction[0], '→', ['⊥']], 'I→', [disjunction[0], ['⊥']], step_open_sups[:]])\n step_open_sups.append(disjunction[2])\n new_steps.append([disjunction[2], 'SUP', [], step_open_sups[:]])\n if len(disjunction[2]) == 2 and independent_formula == disjunction[2][1]:\n new_steps.append([['⊥'], 'E¬', [independent_formula, disjunction[2]], step_open_sups[:]])\n else:\n new_steps.append([['⊥'], 'E¬', [disjunction[2], ['¬', disjunction[2]]], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([[disjunction[2], '→', ['⊥']], 'I→', [disjunction[2], ['⊥']], step_open_sups[:]])\n new_steps.append([['⊥'], 'E˅', [disjunction,\n [disjunction[0], '→', ['⊥']],\n [disjunction[2], '→', ['⊥']]], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', ['¬', derived_disjunct]], 'I¬',\n [['¬', derived_disjunct], ['⊥']], step_open_sups[:]])\n new_steps.append([derived_disjunct, 'DN', [['¬', ['¬', derived_disjunct]]], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n elif derivation2[step][1] == \"NegUniv\":\n # A NegUniv steps looks like [ [E, x, [¬,[Phi]] , NegUniv, [ [¬, [V, x, [Phi]]] ] ]\n negated_universal = derivation2[step][2][0]\n universal = negated_universal[1]\n existential = derivation[step][0]\n variable = universal[1]\n phi = universal[2]\n\n new_steps = list()\n step_open_sups.append(['¬', existential])\n new_steps.append([['¬', existential], 'SUP', [], step_open_sups[:]])\n subst = substitute(phi, variable, cts_unused[0], Classical2)\n del cts_unused[0]\n step_open_sups.append(['¬', subst])\n new_steps.append([['¬', subst], 'SUP', [], step_open_sups[:]])\n new_steps.append([existential, 'I∃', [['¬', subst]], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [['¬', existential], existential], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', ['¬', subst]], 'I¬', [['¬', subst], ['⊥']], step_open_sups[:]])\n new_steps.append([subst, 'DN', [['¬', ['¬', subst]]], step_open_sups[:]])\n new_steps.append([universal, 'I∀', [subst], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [negated_universal, universal], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', ['¬', existential]], 'I¬', [['¬', existential], ['⊥']], step_open_sups[:]])\n new_steps.append([existential, 'DN', [['¬', ['¬', existential]]], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n elif derivation2[step][1] == \"NegExist\":\n # A NegExist steps looks like [ [V, x, [¬,[Phi]] , NegUniv, [ [¬, [E, x, [Phi]]] ] ]\n negated_existential = derivation2[step][2][0]\n existential = negated_existential[1]\n universal = derivation[step][0]\n variable = existential[1]\n phi = existential[2]\n\n new_steps = list()\n subst = substitute(phi, variable, cts_unused[0], Classical2)\n del cts_unused[0]\n step_open_sups.append(subst)\n new_steps.append([subst, 'SUP', [], step_open_sups[:]])\n new_steps.append([existential, 'I∃', [subst], step_open_sups[:]])\n new_steps.append([['⊥'], 'E¬', [negated_existential, existential], step_open_sups[:]])\n del step_open_sups[-1]\n new_steps.append([['¬', subst], 'I¬', [subst, ['⊥']], step_open_sups[:]])\n new_steps.append([universal, 'I∀', [['¬', subst]], step_open_sups[:]])\n derivation3.extend(new_steps)\n\n else:\n derivation3.append(derivation2[step])\n\n return derivation3","def ref_analyzer(citation_informations, initialresult, initial_citationlist,\n initial_referencelist,config, updated_rec_list ):\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attr function\", alert_admin=True)\n return {}\n\n pubrefntag = \"\"\n try:\n pubrefntag = config.get(function, \"reference_via_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_report_number\", alert_admin=True)\n return {}\n\n pubreftag = \"\"\n try:\n pubreftag = config.get(function, \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_pubinfo\", alert_admin=True)\n return {}\n\n #pubrefntag is often 999C5r, pubreftag 999C5s\n if task_get_task_param('verbose') >= 9:\n write_message(\"pubrefntag \"+pubrefntag)\n write_message(\"pubreftag \"+pubreftag)\n\n citation_list = initial_citationlist\n reference_list = initial_referencelist\n result = initialresult\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\n d_references_s = citation_informations[2]\n #dict of recid -> publication_infos_in_its_bibliography\n d_records_s = citation_informations[3] #recid -> its publication inf\n t1 = os.times()[4]\n\n write_message(\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\")\n for somerecid in updated_rec_list:\n try:\n del citation_list[somerecid]\n except KeyError:\n pass\n try:\n del reference_list[somerecid]\n except KeyError:\n pass\n\n write_message(\"Phase 1: d_references_report_numbers\")\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\n #meaning: rec 8 contains these in bibliography\n\n done = 0\n numrecs = len(d_references_report_numbers)\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n done = done+1\n\n for refnumber in refnumbers:\n if refnumber:\n p = refnumber\n f = 'reportnumber'\n #sanitise p\n p.replace(\"\\n\",'')\n #search for \"hep-th/5644654 or such\" in existing records\n rec_ids = get_recids_matching_query(p, f)\n\n if len(rec_ids) > 1:\n msg = \"Whoops: record '%d' report number value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_citer_cited(thisrecid, rec_ids[0])\n remove_from_missing(p)\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n # Citation list should have rec_ids[0] but check anyway\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n #append unless this key already has the item\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid)\n #and update result\n result[rec_ids[0]] += 1\n\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n else:\n #the reference we wanted was not found among our records.\n #put the reference in the \"missing\".. however, it will look\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\n #This should really be done in the next loop d_references_s\n #but the 999C5s fields are not yet normalized\n\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\n rectext = \"\" # print_record() call disabled to speed things up\n lines = rectext.split(\"\\n\")\n rpart = p #to be used..\n for l in lines:\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\n st = l.find('$s')\n if (st > 0):\n end = l.find('$', st)\n if (end == st):\n end = len(l)\n rpart = l[st+2:end]\n insert_into_missing(thisrecid, rpart)\n\n mesg = \"d_references_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t2 = os.times()[4]\n\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\n write_message(\"Phase 2: d_references_s\")\n done = 0\n numrecs = len(d_references_s)\n for thisrecid, refss in d_references_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n\n done = done+1\n\n for refs in refss:\n if refs:\n p = refs\n #remove the latter page number if it is like 67-74\n matches = re.compile(\"(.*)(-\\d+$)\").findall(p)\n if matches and matches[0]:\n p = matches[0][0]\n\n # check reference value to see whether it is well formed:\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"is not well formed; skipping it.\" % (thisrecid, p)\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n continue # skip this ill-formed value\n\n # look for reference value:\n rec_id = None\n try:\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\n except:\n rec_ids = None\n if len(rec_ids) > 1:\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_message(\"These match searching \"+p+\" in journal: \"+repr(rec_ids), verbose=9)\n #the refered publication is in our collection, remove\n #from missing\n remove_from_missing(p)\n else:\n #it was not found so add in missing\n insert_into_missing(thisrecid, p)\n #check citation and reference for this..\n if rec_ids and rec_ids[0]:\n #the above should always hold\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\n result[rec_ids[0]] += 1 #add count for this..\n\n #update reference_list accordingly\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n mesg = \"d_references_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t3 = os.times()[4]\n done = 0\n numrecs = len(d_reports_numbers)\n write_message(\"Phase 3: d_reports_numbers\")\n\n #search for stuff like CERN-TH-4859/87 in list of refs\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n for reportcode in reportcodes:\n if reportcode:\n rec_ids = []\n try:\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\n except:\n rec_ids = []\n\n if rec_ids:\n for recid in rec_ids:\n #normal checks..\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(recid):\n reference_list[recid] = []\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n\n #normal updates\n if not recid in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(recid)\n if not thisrecid in reference_list[recid]:\n reference_list[recid].append(thisrecid)\n\n mesg = \"d_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #find this record's pubinfo in other records' bibliography\n write_message(\"Phase 4: d_records_s\")\n done = 0\n numrecs = len(d_records_s)\n t4 = os.times()[4]\n for thisrecid, recs in d_records_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_records_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n p = recs.replace(\"\\\"\",\"\")\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\n write_message(\"These records match \"+p+\" in \"+pubreftag+\" : \"+str(rec_ids), verbose=9)\n if rec_ids:\n for rec_id in rec_ids:\n #normal checks\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(rec_id):\n reference_list[rec_id] = []\n\n if not rec_id in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(rec_id)\n if not thisrecid in reference_list[rec_id]:\n reference_list[rec_id].append(thisrecid)\n\n mesg = \"d_records_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n write_message(\"Phase 5: reverse lists\")\n\n #remove empty lists in citation and reference\n keys = citation_list.keys()\n for k in keys:\n if not citation_list[k]:\n del citation_list[k]\n\n keys = reference_list.keys()\n for k in keys:\n if not reference_list[k]:\n del reference_list[k]\n\n write_message(\"Phase 6: self-citations\")\n selfdic = {}\n #get the initial self citation dict\n initial_self_dict = get_cit_dict(\"selfcitdict\")\n selfdic = initial_self_dict\n #add new records to selfdic\n acit = task_get_option(\"author-citations\")\n if not acit:\n write_message(\"Self cite processing disabled. Use -A option to enable it.\")\n else:\n write_message(\"self cite and author citations enabled\")\n selfdic = get_self_citations(updated_rec_list, citation_list,\n initial_self_dict, config)\n #selfdic consists of\n #key k -> list of values [v1,v2,..]\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\n\n #create a reverse \"x cited by y\" self cit dict\n selfcitedbydic = {}\n for k in selfdic.keys():\n vlist = selfdic[k]\n for v in vlist:\n if selfcitedbydic.has_key(v):\n tmplist = selfcitedbydic[v]\n if not k in tmplist:\n tmplist.append(k)\n else:\n tmplist = [k]\n selfcitedbydic[v] = tmplist\n\n write_message(\"Getting author citations\")\n\n #get author citations for records in updated_rec_list\n initial_author_dict = get_initial_author_dict()\n authorcitdic = initial_author_dict\n acit = task_get_option(\"author-citations\")\n if not acit:\n print \"Author cites disabled. Use -A option to enable it.\"\n else:\n write_message(\"author citations enabled\")\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\n initial_author_dict, config)\n\n\n if task_get_task_param('verbose') >= 3:\n #print only X first to prevent flood\n tmpdict = {}\n tmp = citation_list.keys()[0:10]\n for t in tmp:\n tmpdict[t] = citation_list[t]\n write_message(\"citation_list (x is cited by y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(citation_list.keys())))\n tmp = reference_list.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = reference_list[t]\n write_message(\"reference_list (x cites y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(reference_list.keys())))\n tmp = selfcitedbydic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfcitedbydic[t]\n mesg = \"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\"\n mesg += str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfcitedbydic.keys())))\n tmp = selfdic.keys()[0:100]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfdic[t]\n mesg = \"selfdic (x cites y and one of the authors of x same as y's): \"+str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfdic.keys())))\n tmp = authorcitdic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = authorcitdic[t]\n write_message(\"authorcitdic (author is cited in recs): \"+str(tmpdict))\n write_message(\"size: \"+str(len(authorcitdic.keys())))\n insert_cit_ref_list_intodb(citation_list, reference_list,\n selfcitedbydic, selfdic, authorcitdic)\n\n t5 = os.times()[4]\n\n write_message(\"Execution time for analyzing the citation information generating the dictionary:\")\n write_message(\"... checking ref number: %.2f sec\" % (t2-t1))\n write_message(\"... checking ref ypvt: %.2f sec\" % (t3-t2))\n write_message(\"... checking rec number: %.2f sec\" % (t4-t3))\n write_message(\"... checking rec ypvt: %.2f sec\" % (t5-t4))\n write_message(\"... total time of ref_analyze: %.2f sec\" % (t5-t1))\n\n return result","def make_contrast_modulation(\n self,\n frequency: int | float,\n sampling_frequency: int,\n target_contrasts: list[float],\n phase: int | float = 0,\n duration: int | float = 1\n ):\n waveform = waves.make_stimulus_waveform(\n frequency,\n sampling_frequency,\n phase,\n duration,\n )\n\n solutions = []\n for point in waveform:\n contrast = [point * c for c in target_contrasts]\n self.target_contrast = contrast\n solutions.append(self.linalg_solve())\n return solutions","def cleanup():\n for s in [missiles, explosions, bonus]:\n\n set_to_remove = set([])\n for m in s:\n if m.isDead:\n set_to_remove.add(m)\n\n s.difference_update(set_to_remove)","def get_pattern(self):\n pattern = list()\n for item in self.gradual_items:\n pattern.append(item.gradual_item.tolist())\n return pattern","def lookup_coarse_stress_patterns_for_word(\n word: Text,\n ) -> Sequence[Sequence[CoarseStress]]:\n return [\n word.pf.coarse_stress_pattern\n for word in EnglishUtils.all_possible_forms_for(word)\n ]","async def check_hacks(self) -> None:\n\n hacks = await self.get_expired_hacks()\n for h in hacks:\n await self.delete_skill_action_by_target_id_and_skill_type(h[3], 'hack')\n\n channel = self.bots_txt\n\n await channel.send(\n content=f\"<@{h[0]}>\",\n embed=discord.Embed(\n description=f\"**<@{h[3]}> updated his firewall so <@{h[0]}>'s hacking has no effect anymore! 💻**\",\n color=discord.Color.red()))","def enrich_textrefs(self, db):\n tr_list = db.select_all(db.TextRef,\n db.TextContent.text_ref_id == db.TextRef.id,\n db.TextContent.source == self.my_source,\n db.TextRef.manuscript_id.is_(None))\n file_list = self.ftp.get_csv_as_dict('filelist.csv', header=0)\n pmcid_mid_dict = {entry['PMCID']: entry['MID'] for entry in file_list}\n pmid_mid_dict = {entry['PMID']: entry['MID'] for entry in file_list\n if entry['PMID'] is not '0'}\n for tr in tr_list:\n if tr.pmcid is not None:\n tr.manuscript_id = pmcid_mid_dict[tr.pmcid]\n elif tr.pmid is not None and tr.pmid in pmid_mid_dict.keys():\n tr.manuscript_id = pmid_mid_dict[tr.pmid]\n db.commit(\"Could not update text refs with manuscript ids.\")\n return","def connect_refs_to_species(species: list, citelist: list) -> dict:\n # create a dictionary with empty reference lists\n species_refs = {s.species: set() for s in species}\n # go through all citations\n # for c in tqdm(citelist):\n for c in citelist:\n if c.actual in species_refs:\n reflist = species_refs[c.actual]\n reflist |= {c.cite_key}\n return species_refs","def where(overstrand_list, where_lists, wall_color_lists, p):\n where_lists = []\n head_lists = [[1, 0, 3, 0], [0, 2, 0, 0]]\n num_crossings = len(overstrand_list)\n head_list_first_crossing = head_lists[0]\n wall_color_first_crossing = wall_color_lists[0]\n indicies_of_heads = match(head_list_first_crossing, wall_color_first_crossing, p)\n new_where_lists = build_where([], indicies_of_heads, wall_color_first_crossing, p)\n for i in range(num_crossings):\n head_list_at_crossing = pass_through(head_list[i])\n indicies_of_heads = match(head_list_at_crossing, wall_color_lists[i], p)\n build_where(new_where_lists, indicies_of_heads, wall_color_at_crossing[i], p)\n # now we match the universes of the last entry in each where_list to the\n # wall_color_lists[i+1] to generate head_list[i+1]\n indicies_of_heads = match(head_list_at_crossing, wall_color_at_crossing[i+1], p)\n return new_where_lists","def get_references(subarray, filter='CLEAR', context='jwst_niriss_0134.imap'):\n # Accepted subarrays\n subarrays = ['SUBSTRIP96', 'SUBSTRIP256', 'FULL']\n if subarray not in subarrays:\n raise ValueError(\"{} is not a supported subarray. Please use {}\".format(subarray, subarrays))\n\n # Accepted filters\n filters = ['CLEAR', 'F277W']\n if filter not in filters:\n raise ValueError(\"{} is not a supported filter. Please use {}\".format(filter, filters))\n\n # F277W not yet supported. Just delete this line when F277W support is added to crds\n filter = 'CLEAR'\n\n params = {\"INSTRUME\": \"NIRISS\",\n \"READPATT\": \"NIS\",\n \"EXP_TYPE\": \"NIS_SOSS\",\n \"DETECTOR\": \"NIS\",\n \"PUPIL\": \"GR700XD\",\n \"DATE-OBS\": \"2020-07-28\",\n \"TIME-OBS\": \"00:00:00\",\n \"INSTRUMENT\": \"NIRISS\",\n \"FILTER\": filter,\n \"SUBARRAY\": subarray}\n\n # Default ref file path\n default_path = resource_filename('awesimsoss', 'files/refs/')\n\n # Collect reference files for subarray+filter combination\n try:\n import crds\n refs = crds.getreferences(params, context=context)\n except:\n\n refs = {'saturation': os.path.join(default_path, 'jwst_niriss_saturation_0010.fits'),\n 'photom': os.path.join(default_path, 'jwst_niriss_photom_0037.fits'),\n 'flat': os.path.join(default_path, 'jwst_niriss_flat_0190.fits'),\n 'gain': os.path.join(default_path, 'jwst_niriss_gain_0005.fits'),\n 'superbias': os.path.join(default_path, 'jwst_niriss_superbias_0120.fits'),\n 'dark': os.path.join(default_path, 'jwst_niriss_dark_0114.fits'),\n 'readnoise': os.path.join(default_path, 'jwst_niriss_readnoise_0001.fits'),\n 'linearity': os.path.join(default_path, 'jwst_niriss_linearity_0011.fits')}\n\n if subarray == 'SUBSTRIP96':\n refs['superbias'] = os.path.join(default_path, 'jwst_niriss_superbias_0111.fits')\n refs['dark'] = os.path.join(default_path, 'jwst_niriss_dark_0111.fits')\n\n if subarray == 'FULL':\n refs['gain'] = os.path.join(default_path, 'jwst_niriss_gain_0002.fits')\n refs['superbias'] = os.path.join(default_path, 'jwst_niriss_superbias_0029.fits')\n refs['dark'] = os.path.join(default_path, 'jwst_niriss_dark_0129.fits')\n\n # Check if reference files exist and load defaults if necessary\n for ref_name, ref_fn in refs.items():\n if not 'NOT FOUND' in ref_fn and not os.path.isfile(refs[ref_name]):\n refs[ref_name] = os.path.join(default_path, ref_fn)\n print(\"Could not get {} reference file from CRDS. Using {}.\".format(ref_name, ref_fn))\n\n return refs","def build_alternatives(replacements):\n alternatives = []\n highest = 0\n prev_lev = 0\n\n for term in replacements:\n # print(\"\\nterm => \" + str(term))\n lev = term[1][0]\n hits = term[1][1]\n\n if lev < 2 or lev == prev_lev:\n if args.all:\n # print(\"\\nterm => \" + str(term))\n alternatives.append(term[0])\n prev_lev = lev\n else:\n if hits > highest:\n highest = hits\n # print(\"\\nterm => \" + str(term))\n alternatives.append(term[0])\n prev_lev = lev\n\n if args.all or args.jaccard:\n # print('!')\n return alternatives\n else:\n # print('?')\n return alternatives[-1]","def extract_references_from_txt(filename):\n file = open(filename)\n references_section = \"\"\n references_section_found = False\n for line in file:\n if \"REFERENCE\" in line:\n references_section_found = True\n if references_section_found:\n references_section+=line + \" \"\n references_section = references_section.replace('\\n','')\n refs = extract_references(references_section)\n return refs","def unlocking_technologies(self) -> Set[Technology]:\n return {technology_table[tech_name] for tech_name in recipe_sources.get(self.name, ())}","def ref_ex2():\n\n print (\"Part 1 - references to two distinct objects\")\n iipp1_instructors = [\"Joe\", \"Scott\", \"John\", \"Stephen\"]\n iipp2_instructors = [\"Joe\", \"Scott\", \"John\", \"Stephen\"]\n print(iipp1_instructors)\n print(iipp2_instructors)\n\n print (\"Mutate one of the two objects\")\n iipp2_instructors.pop()\n print(iipp1_instructors)\n print(iipp2_instructors)\n print()\n\n print (\"Part 2 - two references to the same object\")\n iipp1_instructors = [\"Joe\", \"Scott\", \"John\", \"Stephen\"]\n iipp2_instructors = iipp1_instructors\n print(iipp1_instructors)\n print(iipp2_instructors)\n\n print (\"Mutate the object\")\n iipp2_instructors.pop()\n print(iipp1_instructors)\n print(iipp2_instructors)\n print()\n\n print (\"Part 3 - two references to an object and a copy of the object\")\n iipp1_instructors = [\"Joe\", \"Scott\", \"John\", \"Stephen\"]\n iipp2_instructors = list(iipp1_instructors)\n print(iipp1_instructors)\n print(iipp2_instructors)\n\n print (\"Mutate the one of the objects\")\n iipp2_instructors.pop()\n print(iipp1_instructors)\n print(iipp2_instructors)\n return 0","def get_reference_no_from_narration(narration,validatorlist=[validate_western_code,validate_ria,validate_placid, validate_xpress,validate_moneygram, validate_prabhu_ref, validate_merchantrade_ref, validate_necmoney_ref, validate_necitaly_ref, validate_cbl_ref, swift_re]):\n ref_list = []\n for e in narration.split():\n for fn in validatorlist:\n try:\n fn(e)\n if e not in ref_list:\n ref_list.append(e)\n except ValidationError:\n pass\n return ref_list","async def check_hacks(self) -> None:\n\n hacks = await self.get_expired_hacks()\n for h in hacks:\n await self.delete_skill_action_by_target_id_and_skill_type(h[3], 'hack')\n await self.update_user_is_hacked(h[3], 0)\n\n channel = self.bots_txt\n\n await channel.send(\n content=f\"<@{h[0]}>\",\n embed=discord.Embed(\n description=f\"**<@{h[3]}> updated his firewall so <@{h[0]}>'s hacking has no effect anymore! 💻**\",\n color=discord.Color.red()))","def get_facts(facts, situation, target):\n return [f[2] for f in facts if (f[0], f[1]) == (situation, target)]"],"string":"[\n \"def get_techniques_used_by_malware():\\n global techniques_used_by_malware\\n \\n if not techniques_used_by_malware:\\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\\n \\n return techniques_used_by_malware\",\n \"def get_mitigation_mitigates_techniques():\\n global mitigation_mitigates_techniques\\n\\n if not mitigation_mitigates_techniques:\\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\\n\\n return mitigation_mitigates_techniques\",\n \"def get_technique_mitigated_by_mitigation():\\n global technique_mitigated_by_mitigation\\n\\n if not technique_mitigated_by_mitigation:\\n technique_mitigated_by_mitigation = rsh.technique_mitigated_by_mitigation(get_srcs())\\n\\n return technique_mitigated_by_mitigation\",\n \"def get_techniques_list(techniques):\\n\\n technique_list = {}\\n \\n for technique in techniques:\\n if 'revoked' not in technique or technique['revoked'] == False:\\n\\n attack_id = util.get_attack_id(technique)\\n\\n if attack_id:\\n\\n technique_dict = {}\\n technique_dict['id'] = attack_id\\n \\n technique_dict['name'] = technique['name']\\n technique_dict['description'] = technique['description']\\n\\n if technique.get('kill_chain_phases'):\\n for elem in technique['kill_chain_phases']:\\n # Fill dict\\n\\n if elem['phase_name'] not in technique_list:\\n technique_list[elem['phase_name']] = []\\n \\n technique_list[elem['phase_name']].append(technique_dict)\\n\\n for key, value in technique_list.items():\\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\\n \\n return technique_list\",\n \"def get_techniques(db):\\n\\n techniques = []\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n techniques.append(technique)\\n \\n return sorted(techniques)\",\n \"def add_techniques(\\n client: Act, matrice: AttckMatrice, output_format: Text = \\\"json\\\"\\n) -> List:\\n\\n notify = []\\n\\n for technique in matrice.techniques:\\n notify += handle_techniques(client, technique, None, output_format)\\n\\n for subtechnique in getattr(technique, \\\"subtechniques\\\", []):\\n # Pre Attack does not have sub techniques\\n notify += handle_techniques(client, subtechnique, technique, output_format)\\n\\n return notify\",\n \"def get_related_techniques_data(technique, tactic_list):\\n \\n technique_data = []\\n\\n if config.related_techniques.get(technique['id']):\\n for rel_tech in config.related_techniques[technique['id']]:\\n\\n attack_id = util.get_attack_id(rel_tech['object'])\\n\\n if attack_id:\\n row = {}\\n row['technique_id'] = attack_id\\n\\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\\n if tactic:\\n row['tactic_id'] = util.get_attack_id(tactic)\\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\\n \\n row['technique_name'] = rel_tech['object']['name']\\n technique_data.append(row)\\n\\n if technique_data:\\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\\n return technique_data\",\n \"def get_techniques_used_by_tools():\\n global techniques_used_by_tools\\n\\n if not techniques_used_by_tools:\\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\\n \\n return techniques_used_by_tools\",\n \"def get_technique_related_to_technique():\\n global technique_related_to_technique\\n\\n if not technique_related_to_technique:\\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\\n\\n return technique_related_to_technique\",\n \"def get_examples_table_data(technique, reference_list, next_reference_number):\\n\\n # Creating map to avoid repeating the code 3 times\\n examples_map = [\\n { 'example_type': config.tools_using_technique }, \\n { 'example_type': config.malware_using_technique },\\n { 'example_type': config.groups_using_technique }\\n ]\\n\\n example_data = []\\n\\n # Get malware or tools used by group\\n for examples in examples_map:\\n if examples['example_type'].get(technique.get('id')):\\n for example in examples['example_type'][technique['id']]:\\n\\n attack_id = util.get_attack_id(example['object'])\\n\\n # Only add example data if the attack id is found \\n if attack_id:\\n row = {}\\n\\n row['id'] = attack_id\\n\\n if attack_id.startswith('S'):\\n row['path'] = \\\"software\\\"\\n else:\\n row['path'] = \\\"groups\\\"\\n\\n row['name'] = example['object']['name']\\n\\n if example['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\\n\\n example_data.append(row)\\n \\n if example_data:\\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\\n return example_data\",\n \"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\\n\\n attack_id = util.get_attack_id(technique)\\n\\n # Only add technique if the attack id was found\\n if attack_id:\\n\\n technique_dict = {}\\n\\n technique_dict['attack_id'] = attack_id\\n technique_dict['domain'] = domain.split(\\\"-\\\")[0]\\n technique_dict['menu'] = side_menu_data\\n technique_dict['name'] = technique.get('name')\\n\\n # Get capecs and mtcs\\n for ref in technique['external_references']:\\n if ref.get('source_name'):\\n if ref['source_name'] == \\\"capec\\\":\\n if technique_dict.get('capecs') is None:\\n technique_dict['capecs'] = []\\n capec_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['capecs'].append(capec_dict)\\n\\n if ref['source_name'] == \\\"NIST Mobile Threat Catalogue\\\":\\n if technique_dict.get('mtcs') is None:\\n technique_dict['mtcs'] = []\\n\\n mtcs_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['mtcs'].append(mtcs_dict)\\n\\n # Get technique external references \\n ext_ref = technique[\\\"external_references\\\"]\\n\\n # Get initial reference list\\n reference_list = []\\n # Decleared as an object to be able to pass by reference\\n next_reference_number = {}\\n next_reference_number['value'] = 1\\n reference_list = util.update_reference_list(reference_list, technique)\\n\\n # Get technique description\\n if technique.get(\\\"description\\\"):\\n citations_from_descr = util.get_citations_from_descr(technique['description'])\\n\\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\\n \\n if 'x_mitre_deprecated' in technique:\\n technique_dict['deprecated'] = True\\n \\n # Get mitigation table\\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\\n \\n # Get related techniques\\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\\n\\n # Get examples\\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\\n\\n # Get technique version\\n if technique.get(\\\"x_mitre_version\\\"):\\n technique_dict['version'] = technique[\\\"x_mitre_version\\\"]\\n\\n # Get tactics of technique\\n if technique.get('kill_chain_phases'):\\n technique_dict['tactics'] = []\\n for elem in technique['kill_chain_phases']:\\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\\n\\n # Get platforms that technique uses\\n if technique.get('x_mitre_platforms'):\\n technique_dict['platforms'] = \\\", \\\".join(technique['x_mitre_platforms'])\\n\\n # Get system requirements\\n if technique.get('x_mitre_system_requirements'):\\n technique_dict['sysreqs'] = \\\", \\\".join(technique['x_mitre_system_requirements'])\\n technique_dict['sysreqs'] = re.sub(\\\"\\\\.?\\\\\\\\n+\\\", \\\"; \\\", technique_dict['sysreqs'])\\n\\n # Get permissions required\\n if technique.get('x_mitre_permissions_required'):\\n technique_dict['perms'] = \\\", \\\".join(technique['x_mitre_permissions_required'])\\n\\n # Get effective permissions\\n if technique.get('x_mitre_effective_permissions'):\\n technique_dict['eff_perms'] = \\\", \\\".join(technique['x_mitre_effective_permissions'])\\n\\n # Get data sources\\n if technique.get('x_mitre_data_sources'):\\n technique_dict['data_sources'] = \\\", \\\".join(technique['x_mitre_data_sources'])\\n\\n # Get if technique supports remote\\n if technique.get('x_mitre_remote_support'):\\n if technique['x_mitre_remote_support']:\\n technique_dict['supports_remote'] = \\\" Yes\\\"\\n else:\\n technique_dict['supports_remote'] = \\\" No\\\"\\n\\n # Get network requirements\\n if technique.get('x_mitre_network_requirements'):\\n if technique['x_mitre_network_requirements']:\\n technique_dict['network_reqs'] = \\\" Yes\\\"\\n else:\\n technique_dict['network_reqs'] = \\\" No\\\"\\n\\n # Get list of impacts\\n if technique.get('x_mitre_impact_type'):\\n technique_dict['impact_type'] = \\\", \\\".join(technique['x_mitre_impact_type'])\\n\\n # Get list of defenses bypassed\\n if technique.get('x_mitre_defense_bypassed'):\\n technique_dict['def_bypass'] = \\\", \\\".join(technique['x_mitre_defense_bypassed'])\\n\\n # Get list of contributors \\n if technique.get('x_mitre_contributors'):\\n technique_dict['contributors'] = \\\"; \\\".join(technique['x_mitre_contributors'])\\n\\n # Get list of tactic types\\n if technique.get('x_mitre_tactic_type'):\\n technique_dict['tactic_type'] = \\\", \\\".join(technique['x_mitre_tactic_type'])\\n\\n # Get detection data\\n if technique.get('x_mitre_detection'):\\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\\n\\n # Get if technique is detectable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses'):\\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\\n\\n # Get explanation of detecatable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\\n\\n # Get diffulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary'):\\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\\n\\n # Get explanation of difficulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \\n \\n # Add reference for bottom part of technique page\\n if reference_list:\\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\\n\\n subs = config.technique_md.substitute(technique_dict)\\n subs = subs + json.dumps(technique_dict)\\n\\n #Write out the markdown file\\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\\\".md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\",\n \"def get_techniques_used_by_groups():\\n global techniques_used_by_groups\\n\\n if not techniques_used_by_groups:\\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\\n\\n return techniques_used_by_groups\",\n \"def get_all_techniques(src, source_name, tactic=None):\\n filters = [\\n Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n Filter(\\\"external_references.source_name\\\", \\\"=\\\", source_name),\\n ]\\n if tactic:\\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\\n\\n results = src.query(filters)\\n return remove_deprecated(results)\",\n \"def get_malware_using_technique():\\n global malware_using_technique\\n\\n if not malware_using_technique:\\n malware_using_technique = rsh.malware_using_technique(get_srcs())\\n \\n return malware_using_technique\",\n \"def get_technique_by_id(self, id_code):\\r\\n return self.fs.query([\\r\\n Filter('type', '=', 'attack-pattern'),\\r\\n Filter('external_references.external_id', '=', id_code)\\r\\n ])\",\n \"def find_effects(sv): \\r\\n for eff in sv.Object_list:\\r\\n nod=sv.Object[eff]\\r\\n for nam in nod.causes: # look for causes\\r\\n if not eff in sv.Object[nam].effects: sv.Object[nam].effects+=[eff]\",\n \"def get_tools_using_technique():\\n global tools_using_technique\\n\\n if not tools_using_technique:\\n tools_using_technique = rsh.tools_using_technique(get_srcs())\\n \\n return tools_using_technique\",\n \"def import_deep_distortion_by_type(defect_list: list,\\r\\n fancy_defects: dict,\\r\\n ):\\r\\n list_deep_distortion = []\\r\\n for i in defect_list: \\r\\n if i['name'] in fancy_defects.keys(): # if defect underwent a deep distortion\\r\\n defect_name = i['name']\\r\\n print(defect_name)\\r\\n i['supercell']['structure'] = fancy_defects[defect_name]['structure'] #structure of E lowering distortion\\r\\n print(\\\"Using the distortion found for charge state(s) {} with BDM distortion {}\\\".format(fancy_defects[defect_name]['charges'],\\r\\n fancy_defects[defect_name][\\\"BDM_distortion\\\"] ) )\\r\\n #remove the charge state of the E lowering distortion distortion\\r\\n if len(fancy_defects[i['name']]['charges']) > 1:\\r\\n print(\\\"Intial charge states of defect:\\\", i['charges'], \\\"Will remove the ones where the distortion was found...\\\")\\r\\n [i['charges'].remove(charge) for charge in fancy_defects[defect_name]['charges']]\\r\\n print(\\\"Trying distortion for charge states:\\\", i['charges'])\\r\\n else: \\r\\n i['charges'].remove(fancy_defects[defect_name]['charges'][0])\\r\\n if i['charges']: #if list of charges to try deep distortion not empty, then add defect to the list\\r\\n list_deep_distortion.append(i)\\r\\n return list_deep_distortion\",\n \"def get_groups_using_technique():\\n global groups_using_technique\\n\\n if not groups_using_technique:\\n groups_using_technique = rsh.groups_using_technique(get_srcs())\\n \\n return groups_using_technique\",\n \"def findShifts(alternatives):\\n shifted = []\\n samples = {0: \\\"Acute\\\", 1:\\\"Challenge\\\", 2:\\\"Chronic\\\"}\\n for item in alternatives:\\n isShifted = False\\n maxShift = 0.0\\n percent_expr = 0.0\\n what = \\\"\\\"\\n p_value = 0\\n transcript = 0\\n m_samples = item.getSamples()\\n for row in range(m_samples.shape[0]):\\n if row in item.getNonAnnotated():\\n continue\\n meanAcute = np.mean(m_samples[row][:SAMPLES_PARTS[0]])\\n meanChallenge = np.mean(m_samples[row][SAMPLES_PARTS[0]:SAMPLES_PARTS[1]])\\n meanChronic = np.mean(m_samples[row][SAMPLES_PARTS[1]:])\\n p = stats.kruskal(m_samples[row][:SAMPLES_PARTS[0]], m_samples[row][SAMPLES_PARTS[0]:SAMPLES_PARTS[1]],\\n m_samples[row][SAMPLES_PARTS[1]:])[1]\\n means = [meanAcute, meanChronic, meanChallenge]\\n for i in range(len(means)):\\n mean1 = means[i]\\n for j in range(len(means)):\\n mean2 = means[j]\\n if mean2 > 0 and (mean1 / mean2 >= PERCENT_OF_SHIFT) and \\\\\\n (mean1 >= RATIO_TRESHOLD or mean2 >= RATIO_TRESHOLD)\\\\\\n and (abs(int(item.getCoordinates()[row][1]) - int(item.getCoordinates()[row][0])) <= 500):\\n # and counter not in item.getNonAnnotated() \\\\\\n if (mean1 / mean2) > maxShift:\\n percent_expr = np.max([mean1, mean2])\\n maxShift = (mean1 / mean2)\\n transcript = row\\n p_value = p\\n what = (i, j)\\n isShifted = True\\n # counter += 1\\n if isShifted:\\n shifted.append(item)\\n item.setMaxShift(maxShift)\\n item.setNumTranscript(transcript + 1)\\n item.setWhatDiffers(what)\\n item.setPValue(p_value)\\n item.setPercentOfExpression(percent_expr)\\n return shifted\",\n \"def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\\n return [\\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\\n ]\",\n \"def find(checkinList):\\n return [Inspiration(item) for item in checkinList]\",\n \"def filter_words_list(words,pattern,wrong_guess_lst):\\r\\n list_hints = same_length(words,pattern)\\r\\n list_hints = if_in_wrong_guess(wrong_guess_lst,list_hints)\\r\\n list_hints = same_pattern(list_hints,pattern)\\r\\n list_hints = lst_and_pattern(list_hints,pattern)\\r\\n return list_hints\",\n \"def calcDistortionList(work_list):\\n distortion_list = []\\n for swap in work_list:\\n distortion_list.append(Cluster.calcDistortion(*swap)) # call calcDistortion with tuple expansion as args\\n return distortion_list\",\n \"def get_technique_stats(db):\\n \\n techniques = {}\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n temp_dict = {technique: [{u'id': element['project_no'], \\n u'name': element['project_name'], u'description': element['short_description']}]}\\n techniques.update(temp_dict)\\n else:\\n temp_dict = {u'id': element['project_no'],\\n u'name': element['project_name'], u'description': element['short_description']}\\n techniques[technique].append(temp_dict)\\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\\n\\n return techniques\",\n \"def challenge_functions(input_list, factor, min_don=None, max_don=None):\\n\\n new_challenge_list = []\\n for donor in input_list:\\n # print(donor.donations)\\n donation = filter_factor_map(factor, donor.donations, min_don, max_don)\\n if donation:\\n new_challenge_list.append(\\n Donor(donor.firstname, donor.lastname, donor.fullname, donation))\\n return new_challenge_list\",\n \"def resolve_vulns(self):\\n for vuln in self.vulns:\\n for ref in vuln.refs:\\n if 'cve' in str.lower(ref):\\n results = str(self.client.console_execute('search cve:{0}\\\\n'.format(ref))[b'data'])\\n while not 'Modules' in results:\\n sleep(10)\\n results = self.client.console_read()\\n results = results.split('\\\\n')\\n for line in results:\\n if 'exploit' in line:\\n self.exploits.append(line.split(' ')[0])\",\n \"def extract_references(text, preprocessor, model):\\n has_reference_section, reference_section, non_reference_section = preprocess_reference_text(text)\\n reference_text = reference_section if has_reference_section else non_reference_section\\n fragment = preprocessor(reference_text)\\n out = model(fragment.idx).argmax(dim=2).cpu().numpy()\\n\\n buffer = ''\\n found_citations = []\\n in_citation = False\\n for char, pred in zip(fragment.text, out[0, :]):\\n if pred == 2:\\n in_citation = True\\n if pred == 3:\\n in_citation = False\\n if pred in [2, 3]:\\n if buffer:\\n found_citations.append(buffer)\\n buffer = char\\n elif pred == 1:\\n buffer += char\\n elif pred == 0:\\n if in_citation:\\n buffer += char\\n else:\\n if buffer:\\n found_citations.append(buffer)\\n buffer = ''\\n return found_citations\",\n \"def solutions_for_dep_problem(self, solution: 'DepAlgoSolution', found_problems: Set['DepAlgoFoundProblems'],\\n installed_system: 'System', upstream_system: 'System',\\n deps_to_deep_check: Set[str]) -> List['DepAlgoSolution']:\\n\\n def filter_solutions(solutions: Sequence['DepAlgoSolution']) -> List['DepAlgoSolution']:\\n \\\"\\\"\\\"\\n Filter given solutions so that only valid solutions are left\\n or in case of no valid solutions only one invalid solution\\n\\n :param solutions: The solutions to filter\\n :return: The filtered solutions\\n \\\"\\\"\\\"\\n return_solutions: List['DepAlgoSolution'] = []\\n\\n for solution in solutions:\\n if not return_solutions:\\n return_solutions.append(solution)\\n continue\\n\\n first_solution = return_solutions[0]\\n if first_solution.is_valid and solution.is_valid:\\n return_solutions.append(solution)\\n elif first_solution.is_valid:\\n continue\\n elif solution.is_valid:\\n return_solutions = [solution]\\n\\n return return_solutions\\n\\n if self in solution.installed_solution_packages:\\n return [solution.solution_copy()]\\n\\n # dep cycle\\n # dirty... thanks to dep cycle between mesa and libglvnd\\n if self in solution.visited_packages and not (self.type_of is PossibleTypes.REPO_PACKAGE):\\n # problem only relevant\\n # if the solution is not already invalid\\n if solution.is_valid:\\n index_of_self = solution.visited_packages.index(self)\\n cycle_packages = []\\n for i in range(index_of_self, len(solution.visited_packages)):\\n cycle_packages.append(solution.visited_packages[i])\\n cycle_packages.append(self)\\n\\n # create the problem\\n cycle_problem = DepAlgoCycle(cycle_packages)\\n for package in cycle_packages:\\n cycle_problem.relevant_packages.add(package)\\n cycle_problem.relevant_packages |= set(solution.dict_to_way.get(package.name, []))\\n found_problems.add(cycle_problem)\\n invalid_sol = solution.solution_copy()\\n invalid_sol.is_valid = False\\n return [invalid_sol]\\n\\n # pacman has to handle dep cycles between repo packages\\n elif self in solution.visited_packages:\\n return [solution.solution_copy()]\\n\\n # copy solution and add self to visited packages\\n solution: 'DepAlgoSolution' = solution.solution_copy()\\n is_build_available: bool = self in solution.packages_in_solution\\n own_way: List['Package'] = solution.dict_to_way.get(self.name, [])\\n own_not_to_delete_deps: Set[str] = set()\\n solution.visited_packages.append(self)\\n current_solutions: List['DepAlgoSolution'] = [solution]\\n\\n # filter not fulfillable deps\\n relevant_deps = self.relevant_deps()\\n for dep in relevant_deps[:]:\\n\\n # skip since already provided\\n if installed_system.provided_by(dep):\\n continue\\n\\n # skip since built package available and dep is not a normal dependency\\n # so it's make and/or check dep\\n if is_build_available and dep not in self.relevant_deps(only_depends=True):\\n continue\\n\\n # dep not fulfillable, solutions not valid\\n if not upstream_system.provided_by(dep):\\n for solution in current_solutions:\\n solution.is_valid = False\\n\\n # create problem\\n dep_problem = DepAlgoNotProvided(dep, self)\\n dep_problem.relevant_packages.add(self)\\n dep_problem.relevant_packages |= set(own_way)\\n found_problems.add(dep_problem)\\n\\n relevant_deps.remove(dep)\\n\\n # AND - every dep has to be fulfilled\\n # we filtered the unfulfillable deps,\\n # hence at least one dep provider is available\\n for dep in relevant_deps:\\n\\n # skip since already provided\\n if installed_system.provided_by(dep):\\n continue\\n\\n # skip since built package available and dep is not a normal dependency\\n # so it's make and/or check dep\\n if is_build_available and dep not in self.relevant_deps(only_depends=True):\\n continue\\n\\n # fetch dep providers\\n dep_providers = upstream_system.provided_by(dep)\\n dep_providers_names = [package.name for package in dep_providers]\\n dep_stripped_name = strip_versioning_from_name(dep)\\n\\n # we only need relevant dep providers\\n # deps_to_deep_check will be filled\\n # when we encounter problems as dep-cycle, conflicts ...\\n if dep_stripped_name in dep_providers_names and dep not in deps_to_deep_check:\\n dep_providers = [package for package in dep_providers if package.name == dep_stripped_name]\\n\\n # OR - at least one of the dep providers needs to provide the dep\\n finished_solutions = [solution for solution in current_solutions if dep in solution.visited_names]\\n not_finished_solutions = [solution for solution in current_solutions if dep not in solution.visited_names]\\n\\n # check if dep provided by one of the packages already in a solution\\n new_not_finished_solutions = []\\n for solution in not_finished_solutions:\\n if System(list(solution.installed_solution_packages)).provided_by(dep):\\n finished_solutions.append(solution)\\n else:\\n new_not_finished_solutions.append(solution)\\n not_finished_solutions = new_not_finished_solutions\\n\\n # track deps which may not be deleted\\n for solution in current_solutions:\\n if dep not in solution.not_to_delete_deps:\\n solution.not_to_delete_deps.add(dep)\\n own_not_to_delete_deps.add(dep)\\n\\n # calc and append new solutions\\n current_solutions = finished_solutions\\n # used for tracking problems\\n new_problems_master: List[Set['DepAlgoFoundProblems']] = []\\n found_problems_copy: Set['DepAlgoFoundProblems'] = set(found_problems)\\n for solution in not_finished_solutions:\\n\\n # add dep to visited names\\n # and create another container\\n # for problem tracking\\n solution.visited_names.add(dep)\\n new_problems: List[Set['DepAlgoFoundProblems']] = []\\n\\n for dep_provider in dep_providers:\\n # way to the package being called in the current solution\\n if dep_provider.name not in solution.dict_to_way:\\n way_added = True\\n solution.dict_to_way[dep_provider.name] = own_way[:]\\n solution.dict_to_way[dep_provider.name].append(self)\\n else:\\n way_added = False\\n # tracking for which deps the package being called has been chosen as provider\\n if dep_provider.name not in solution.dict_to_deps:\\n solution.dict_to_deps[dep_provider.name] = set()\\n solution.dict_to_deps[dep_provider.name].add(dep)\\n\\n # call this function recursively on the dep provider\\n # and yield an empty found_problems set instance\\n found_problems.clear()\\n current_solutions.extend(\\n dep_provider.solutions_for_dep_problem(\\n solution, found_problems, installed_system, upstream_system, deps_to_deep_check\\n )\\n )\\n # save the new problems\\n new_problems.append(set(found_problems))\\n # remove added things\\n solution.dict_to_deps[dep_provider.name].remove(dep)\\n if way_added:\\n del solution.dict_to_way[dep_provider.name]\\n\\n # reset the problems to the problems\\n # we had before calling the dep\\n found_problems.clear()\\n for problem in found_problems_copy:\\n found_problems.add(problem)\\n\\n # if there is at least one valid solution\\n # problems are not relevant\\n # hence add an empty set containing no problems\\n for problems in new_problems:\\n if not problems:\\n new_problems_master.append(set())\\n break\\n\\n # if there are problems contained in all return values\\n # show them to the user\\n # will most likely be unfulfillable deps in general\\n else:\\n prob_in_all_ret = set.intersection(*new_problems)\\n if prob_in_all_ret:\\n new_problems_master.append(prob_in_all_ret)\\n # otherwise append all found problems\\n else:\\n new_problems_master.append(set.union(*new_problems))\\n\\n # again - at least one valid solution\\n # means new problems are not relevant\\n if not_finished_solutions:\\n for problems in new_problems_master:\\n if not problems:\\n break\\n else:\\n for problem in set.union(*new_problems_master):\\n found_problems.add(problem)\\n\\n # filter solutions so that irrelevant solutions are not being\\n # used anymore\\n # great impact on the performance\\n current_solutions = filter_solutions(current_solutions)\\n\\n # conflict checking\\n for solution in current_solutions:\\n # as with dep cycles,\\n # conflicts are only relevant\\n # if the solution is not already invalid\\n if not solution.is_valid:\\n continue\\n\\n # generate hypothetic system containing the packages of the current solution\\n # and check for conflicts with that system\\n installed_packages = list(solution.installed_solution_packages)\\n conf_system = System(installed_packages).conflicting_with(self)\\n\\n # if there are no conflicts, nothing will get deleted, so we may\\n # safely assume that we do not get an invalid solution\\n if not conf_system:\\n continue\\n\\n # append the whole current solution to the currently\\n # installed system\\n # may be empty in case of deep_search\\n packages_to_append = solution.packages_in_solution[:]\\n packages_to_append.append(self)\\n new_system = installed_system.hypothetical_append_packages_to_system(packages_to_append)\\n\\n # prepare message for conflict\\n additional_message = \\\"\\\"\\n\\n # if self cannot be added, this solution\\n # is clearly not valid\\n if self.name not in new_system.all_packages_dict:\\n additional_message = \\\"Tried to install {}, but it was not possible.\\\".format(\\n Colors.BOLD(Colors.LIGHT_MAGENTA(self.name))\\n )\\n is_possible = False\\n else:\\n is_possible = True\\n\\n # these deps have to remain provided,\\n # since they are needed for a package which\\n # has not been installed yet\\n # e.g. A needs B and C, B has been solved with this algo\\n # but C not, hence B must remain provided\\n # otherwise A cannot be installed\\n for dep in solution.not_to_delete_deps:\\n if not is_possible:\\n break\\n if not new_system.provided_by(dep):\\n additional_message = \\\"While trying to install {}, the needed dependency {} has been removed\\\".format(\\n Colors.BOLD(Colors.LIGHT_MAGENTA(self.name)),\\n Colors.BOLD(Colors.LIGHT_MAGENTA(dep))\\n )\\n is_possible = False\\n break\\n\\n # same for packages which have to remain installed\\n for package in installed_packages:\\n if not is_possible:\\n break\\n if solution.dict_call_as_needed.get(package.name, False) \\\\\\n and package.name not in new_system.all_packages_dict:\\n additional_message = \\\"The package {} had to remain installed, \\\" \\\\\\n \\\"but has been removed.\\\\n\\\" \\\\\\n \\\"The package which lead to the removal is {}\\\" \\\\\\n \\\"\\\".format(Colors.BOLD(Colors.LIGHT_MAGENTA(package.name)),\\n Colors.BOLD(Colors.LIGHT_MAGENTA(self.name)))\\n\\n break\\n\\n # solution possible at this point if there are no installed packages\\n else:\\n # check which packages have been removed\\n # due to adding the packages\\n for package in installed_packages:\\n # remove all remainings of the package\\n # besides the knowledge that the package\\n # has already been built\\n if package.name not in new_system.all_packages_dict:\\n solution.installed_solution_packages.remove(package)\\n if package.name in solution.dict_to_deps:\\n for dep in solution.dict_to_deps[package.name]:\\n solution.visited_names.remove(dep)\\n del solution.dict_to_deps[package.name]\\n if package.name in solution.dict_to_way:\\n del solution.dict_to_way[package.name]\\n\\n # for the case that there are no installed packages\\n if is_possible:\\n continue\\n\\n # solution not possible!\\n solution.is_valid = False\\n conflicting_packages = set(conf_system)\\n conflicting_packages.add(self)\\n ways_to_conflict = []\\n for package in conflicting_packages:\\n way_to_conflict = solution.dict_to_way.get(package.name, [])[:]\\n way_to_conflict.append(package)\\n ways_to_conflict.append(way_to_conflict)\\n\\n # create the problem\\n conflict_problem = DepAlgoConflict(conflicting_packages, ways_to_conflict)\\n conflict_problem.additional_message = additional_message\\n for way_to_conflict in ways_to_conflict:\\n for package in way_to_conflict:\\n conflict_problem.relevant_packages.add(package)\\n found_problems.add(conflict_problem)\\n\\n # we have valid solutions left, so the problems are not relevant\\n if [solution for solution in current_solutions if solution.is_valid]:\\n found_problems.clear()\\n\\n # add self to packages in solution, those are always topologically sorted\\n for solution in current_solutions:\\n solution.not_to_delete_deps -= own_not_to_delete_deps\\n solution.installed_solution_packages.add(self)\\n solution.packages_in_solution.append(self)\\n solution.visited_packages.remove(self)\\n\\n # may contain invalid solutions !!!\\n # but also filtered\\n return filter_solutions(current_solutions)\",\n \"def deferred_acceptance(men: List[Man], women: List[Woman],\\n method='MPDA') -> List[Tuple[Man, Woman]]:\\n if method not in ['MPDA', 'WPDA']:\\n raise ValueError('Unknown method: must be MPDA or WPDA')\\n N = len(men)\\n if N == 0:\\n raise ValueError('Men list is empty')\\n if len(women) == 0:\\n raise ValueError('Women list is empty')\\n if N != len(women):\\n raise ValueError(\\\"Men and women list don't match length\\\")\\n proposers = men if method == 'MPDA' else women\\n proposed_to = women if method == 'MPDA' else men\\n unmatched = list(range(len(proposers)))\\n for agent in proposers + proposed_to:\\n agent.match = None\\n while unmatched:\\n idx = unmatched[0]\\n incr = 0\\n while proposers[idx].match is None and incr < N:\\n spouse_idx = proposed_to.index(proposers[idx].preferences[incr])\\n if proposed_to[spouse_idx].match is None:\\n proposed_to[spouse_idx].match = proposers[idx]\\n proposers[idx].match = proposed_to[spouse_idx]\\n unmatched.pop(0)\\n elif proposed_to[spouse_idx].prefers(proposers[idx]):\\n proposed_to[spouse_idx].match.match = None\\n unmatched.append(proposers.index(\\n proposed_to[spouse_idx].match))\\n proposed_to[spouse_idx].match = proposers[idx]\\n proposers[idx].match = proposed_to[spouse_idx]\\n unmatched.pop(0)\\n incr += 1\\n if any([x.match is None for x in proposers + proposed_to]):\\n for agent in proposers + proposed_to:\\n print(f\\\"{agent}, {agent.match}\\\")\\n raise RuntimeError(\\n \\\"Someone is unmatched.\\\")\\n # matchings_1 = sorted([(x, x.match) for x in proposers])\\n # matchings_2 = sorted([(x.match, x) for x in proposed_to])\\n # if matchings_1 != matchings_2:\\n # raise RuntimeError(\\n # \\\"Something went horribly wrong. Matchings don't match\\\")\\n matchings_1 = sorted([(x, x.match)\\n for x in proposers], key=lambda x: x[1].id)\\n return matchings_1\",\n \"def get_references(input, verbose=False):\\n\\n # TODO: Make this a class reference parser\\n\\n # *** These tags are mobile-site specific\\n\\n # When we don't have proper access rights, this is present in the html\\n GUEST_TAG_TUPLE = (\\\"li\\\", {\\\"id\\\": \\\"menuGuest\\\"})\\n\\n # Entries are \\\"li\\\" tags with classes of the form:\\n # article-reference-article\\n # article-reference-other-ref\\n REFERENCE_TAG_TUPLE = (\\\"li\\\", {\\\"class\\\": re.compile('article-reference-*')})\\n\\n # This is the URL to the page that contains the document info, including\\n # reference material\\n BASE_URL = _SD_URL + '/science/article/pii/'\\n\\n # This URL was found first via Fiddler, then via closer inspection of the script\\n # 'article_catalyst.js' under sciencedirect.com/mobile/js in the function\\n # resolveReferences\\n REF_RESOLVER_URL = _SD_URL + '/science/referenceResolution/ajaxRefResol'\\n\\n # Return the BeautifulSoup result, the requests session, and the requests response\\n if _is_url(input):\\n pii = _extract_pii(input)\\n else:\\n pii = input\\n\\n sess = requests.Session()\\n\\n if verbose:\\n print('Requesting main page for pii: %s' % pii)\\n resp = sess.get(BASE_URL + pii, cookies={'Site': 'Mobile'})\\n\\n # Step 2 - Get the reference tags\\n\\n soup = BeautifulSoup(resp.text)\\n\\n reference_section = soup.find(\\\"ol\\\", {\\\"class\\\": \\\"article-references\\\"})\\n\\n if reference_section is None:\\n # Then we might be a guest. In other words, we might not have sufficient\\n # privileges to access the data we want. Generally this is protected via\\n # IP mask. When I'm working from home I need to VPN into work so\\n # that I can access the data :/\\n print(\\\"reference_section is None\\\")\\n temp = soup.find(*GUEST_TAG_TUPLE)\\n if temp is None:\\n # We might have no references ... (Doubtful)\\n raise ParseException(\\\"References were not found ..., code error likely\\\")\\n else:\\n raise InsufficientCredentialsException(\\n \\\"Insufficient access rights to get referencs, requires certain IP addresses (e.g. university based IP)\\\")\\n\\n ref_tags = reference_section.find_all(*REFERENCE_TAG_TUPLE)\\n\\n n_refs = len(ref_tags)\\n\\n if n_refs == 0:\\n return None\\n\\n # Step 3 - Resolve reference links\\n # --------------------------------------------------------------------------\\n # The returned html code contains javascript which returns more information\\n # about each reference, such as:\\n #\\n # - links to the full text\\n # - DOI \\n\\n\\n # Step 3.1 - Make the request for the information\\n # --------------------------------------------------------------------------\\n # We need the eid of the current entry, it is of the form:\\n #\\n # SDM.pm.eid = \\\"1-s2.0-0006899387903726\\\"\\n #\\n # * I think this entry gets deleted after the requests so it may not be\\n # visible if looking for it in Chrome. \\n match = re.search('SDM\\\\.pm\\\\.eid\\\\s*=\\\\s*\\\"([^\\\"]+)\\\"', resp.text)\\n #eid = match.group(1)\\n\\n # This list comes from the resolveReferences function in article_catalyst.js\\n payload = {\\n '_pii': pii,\\n '_refCnt': n_refs,\\n '_docType': 'article', # yikes, this might change ...\\n '_refRangeStart': '1',\\n '_refRangeCount': str(n_refs)} # This is normally in sets of 20's ...\\n # I'm not sure if it is important to limit this. The browser then\\n # makes a request fromr 1 count 20, 21 count 20, 41 count 20 etc,\\n # It always goes by 20 even if there aren't 20 left\\n\\n if verbose:\\n print('Requesting reference links')\\n r2 = sess.get(REF_RESOLVER_URL, params=payload)\\n\\n # Step 3.2 - Parse the returned information into single entries\\n # --------------------------------------------------------------------------\\n # This could probably be optimized in terms of execution time. We basically\\n # get back a single script tag. Inside is some sort of hash map for links\\n # for each reference.\\n #\\n # The script tag is of the form:\\n # myMap['bibsbref11']['refHtml']= \\\"<some html stuffs>\\\"; \\n # myMap['bibsbref11']['absUrl']= \\\"http://www.sciencedirect.com/science/absref/sd/0018506X7790068X\\\";\\n # etc.\\n #\\n # - Each entry is quite long.\\n # - Normally contains html\\n # - can be empty i.e. myMap['bibsbref11']['refHtml'] = \\\"\\\";\\n # - the refHtml is quite interesting\\n # - the absolute url is not always present (and currently not parsed)\\n more_soup = BeautifulSoup(r2.text)\\n script_tag = more_soup.find('script')\\n\\n # We unquote the script text as it is transmitted with characters escaped\\n # and we want the parsed data to contain the non-escaped text\\n #\\n # We might eventually want to move this to being after the regular expression ...\\n script_text = urllib_unquote(script_tag.text)\\n\\n ref_match_result = re.findall(\\\"myMap\\\\['bibsbref(\\\\d+)'\\\\]\\\\['refHtml'\\\\]=\\\\s?\\\" + '\\\"([^\\\"]*)\\\";', script_text)\\n # Tokens:\\n # 0 - the # from bibsbref#\\n # 1 - the html content from the 'refHtml' entry\\n # \\n # NOTE: We don't really use the #, so we might remove the () around\\n # \\\\d+ which would shift the index from 1 to 0\\n if verbose:\\n print('Creating reference objects')\\n\\n if len(ref_match_result) > 0:\\n zipped = zip(ref_tags, ref_match_result, range(n_refs))\\n ref_objects = [ScienceDirectRef(ref_tag, ref_link_info[1], ref_id) for\\n ref_tag, ref_link_info, ref_id in zipped]\\n else:\\n zipped = zip(ref_tags, range(n_refs))\\n ref_objects = [ScienceDirectRef(ref_tag, ref_id) for\\n ref_tag, ref_id in zipped]\\n\\n # Step 4:\\n # --------------------------------------------------------------------------\\n # TODO: Improve documentation for this step\\n\\n if verbose:\\n print('Retrieving Scopus Counts')\\n\\n ref_scopus_eids = [] # The Scopus IDs of the references to resolve\\n # but with a particular formatting ...\\n ref_count = 0 # Number of references we haven't resolved\\n\\n ref_count_list = []\\n # NOTE: Browser requests these in the reverse order ...\\n for ref_id, ref in enumerate(ref_objects):\\n\\n if ref._data_sceid is not None:\\n ref_scopus_eids.append(ref._data_sceid + ',' + str(ref_id + 1) + '~')\\n ref_count += 1\\n\\n # If we've got enough, then update the counts\\n # The 20 may be arbitrary but it was what was used in original JS\\n if ref_count > 20:\\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\\n ref_count = 0\\n ref_scopus_eids = []\\n\\n # Get any remaining reference counts\\n if ref_count != 0:\\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\\n\\n # Take the raw data and set the citation count for each object\\n for ref_tuple in ref_count_list:\\n ref_id = int(ref_tuple[0]) - 1\\n ref_count = int(ref_tuple[1])\\n ref_objects[ref_id].scopus_cite_count = ref_count\\n\\n # All done!\\n # ---------\\n return ref_objects\",\n \"def get_toolchanges(move: Move, cave: Cave) -> List[Move]:\\n distance, point, equipment = move\\n tools = [t for t in [\\\"torch\\\", \\\"climbing\\\", \\\"neither\\\"] if t != equipment]\\n return [(distance+7, point, e) for e in tools if cave.can_traverse(e, point)]\",\n \"def _filter_allows(self, ref, filtered_ref):\\n for allow in self.allows_:\\n if not allow:\\n continue\\n\\n for sub_key, sub_ref in ref.ref_items(allow):\\n filtered_ref.setdefault(sub_key).update(sub_ref.config)\",\n \"def replace_errors(suggestion_num, sequence_switched, end_matches, offset_list):\\n if len(end_matches) != 0:\\n # Initialize Variables\\n new_list = initialize_suggestion_list(suggestion_num)\\n tokenizer, model = edf.initialize_tokenizer_variables()\\n\\n # Swapping masks with corrections section\\n if len(end_matches) > 1:\\n k = 0\\n j = 0\\n current = 0\\n keep_going = True\\n\\n while keep_going:\\n # Determine what section to locate mask token in\\n mask_str = section_decision(j, sequence_switched, offset_list, end_matches)\\n\\n new_list = replace_tokens(mask_str, sequence_switched, new_list, j, tokenizer, model, suggestion_num)\\n\\n # If end of sentence, return j to 0, else continue iterations\\n if j == (len(end_matches) - 1):\\n # Update variables\\n j = 0\\n current += 1\\n keep_going = False\\n\\n else:\\n j += 1\\n\\n k += 1\\n\\n else:\\n new_list = replace_tokens(sequence_switched, sequence_switched, new_list, 0, tokenizer, model,\\n suggestion_num)\\n\\n return new_list\\n\\n else:\\n return []\",\n \"def _get_lexicon_update(self, lexicon):\\n\\n def merge(lists):\\n \\\"\\\"\\\"\\n Merge the lists so lists with overlap are joined together\\n (i.e. [[1,2], [3,4], [2,5]] --> [[1,2,5], [3,4]])\\n from: http://stackoverflow.com/a/9400562\\n \\\"\\\"\\\"\\n newsets, sets = [set(lst) for lst in lists if lst], []\\n while len(sets) != len(newsets):\\n sets, newsets = newsets, []\\n for aset in sets:\\n for eachset in newsets:\\n if not aset.isdisjoint(eachset):\\n eachset.update(aset)\\n break\\n else:\\n newsets.append(aset)\\n return newsets\\n\\n def get_coreferences(coreferences):\\n \\\"\\\"\\\"Decode the SAF coreferences as (node: coreferencing_nodes) pairs\\\"\\\"\\\"\\n coref_groups = []\\n for a, b in coreferences:\\n # take only the heads of each coref group\\n coref_groups.append([a[0], b[0]])\\n for nodes in merge(coref_groups):\\n for node in nodes:\\n yield node, nodes\\n\\n coreferences = dict(get_coreferences(self.saf_article.get('coreferences', [])))\\n\\n classes = defaultdict(set) # token -> classes\\n uris = {}\\n for uri, token in self.get_tokens().iteritems():\\n if 'pos' not in token: continue # not a word\\n uris[int(token['id'])] = uri\\n pos = token['pos']\\n lemma = token['lemma']\\n for lex in lexicon:\\n if \\\"pos\\\" in lex and lex['pos'] != pos:\\n continue\\n lemmata = lex['lemma']\\n lexclass = lex['lexclass']\\n if not isinstance(lemmata, list):\\n lemmata = [lemmata]\\n for target in lemmata:\\n if (target == lemma or target == lemma.lower()\\n or (target.endswith(\\\"*\\\") and lemma.lower().startswith(target[:-1]))):\\n id = int(token['id'])\\n for coref in coreferences.get(id, [id]):\\n classes[coref].add(lexclass)\\n inserts = []\\n for id, lexclasses in classes.iteritems():\\n if id not in uris:\\n continue # coref to different sentence\\n uri = str(uris[id]).replace(AMCAT, \\\":\\\")\\n for lexclass in lexclasses:\\n inserts.append('{uri} :lexclass \\\"{lexclass}\\\"'.format(**locals()))\\n return {\\\"insert\\\": \\\".\\\\n\\\".join(inserts)}\",\n \"def filter_mechanism_list(mechanisms, properties, allow_insecure = False,\\n server_side = False):\\n # pylint: disable=W0212\\n result = []\\n for mechanism in mechanisms:\\n try:\\n if server_side:\\n klass = SERVER_MECHANISMS_D[mechanism]\\n else:\\n klass = CLIENT_MECHANISMS_D[mechanism]\\n except KeyError:\\n logger.debug(\\\" skipping {0} - not supported\\\".format(mechanism))\\n continue\\n secure = properties.get(\\\"security-layer\\\")\\n if not allow_insecure and not klass._pyxmpp_sasl_secure and not secure:\\n logger.debug(\\\" skipping {0}, as it is not secure\\\".format(mechanism))\\n continue\\n if not klass.are_properties_sufficient(properties):\\n logger.debug(\\\" skipping {0}, as the properties are not sufficient\\\"\\n .format(mechanism))\\n continue\\n result.append(mechanism)\\n return result\",\n \"def build(pattern, words, seen, list):\\r\\n return [word for word in words\\r\\n if re.search(pattern, word) and word not in seen.keys() and word not in list]\",\n \"def getReferenceAnalyses(self):\\n return [analysis for analysis in self.getAnalyses() \\\\\\n if analysis.portal_type=='ReferenceAnalysis']\",\n \"def wrong_reference_system(net):\\n check_results = {}\\n neg_loads = list(net.load[net.load.p_mw < 0].index)\\n neg_gens = list(net.gen[net.gen.p_mw < 0].index)\\n neg_sgens = list(net.sgen[net.sgen.p_mw < 0].index)\\n\\n if neg_loads:\\n check_results['loads'] = neg_loads\\n if neg_gens:\\n check_results['gens'] = neg_gens\\n if neg_sgens:\\n check_results['sgens'] = neg_sgens\\n\\n if check_results:\\n return check_results\",\n \"def handle_mitre_attcks(\\n actapi: act.api.Act, content: Text, mitre_attcks: List[Dict]\\n) -> List[act.api.fact.Fact]:\\n\\n feeds_facts: List[act.api.fact.Fact] = []\\n\\n for attck in mitre_attcks:\\n chain = []\\n\\n chain.append(\\n actapi.fact(\\\"classifiedAs\\\")\\n .source(\\\"content\\\", content)\\n .destination(\\\"tool\\\", \\\"*\\\")\\n )\\n chain.append(\\n actapi.fact(\\\"implements\\\")\\n .source(\\\"tool\\\", \\\"*\\\")\\n .destination(\\\"technique\\\", attck[\\\"technique\\\"])\\n )\\n\\n feeds_facts += act.api.fact.fact_chain(*chain)\\n\\n return feeds_facts\",\n \"async def insert_attack_stix_data(self):\\n logging.info('Downloading ATT&CK data from STIX/TAXII...')\\n attack = {}\\n collection = Collection(\\\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\\\")\\n tc_source = TAXIICollectionSource(collection)\\n filter_objs = {\\\"techniques\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n \\\"groups\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"intrusion-set\\\"), \\\"malware\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"malware\\\"),\\n \\\"tools\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"tool\\\"), \\\"relationships\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"relationship\\\")}\\n for key in filter_objs:\\n attack[key] = tc_source.query(filter_objs[key])\\n references = {}\\n\\n # add all of the patterns and dictionary keys/values for each technique and software\\n for i in attack[\\\"techniques\\\"]:\\n if \\\"description\\\" in i:\\n descr = i['description'].replace('<code>', '').replace('</code>', '').replace(\\n '\\\\n', '').encode('ascii', 'ignore').decode('ascii')\\n else:\\n descr = \\\"missing description\\\"\\n references[i[\\\"id\\\"]] = {\\\"name\\\": i[\\\"name\\\"], \\\"id\\\": i[\\\"external_references\\\"][0][\\\"external_id\\\"],\\n \\\"example_uses\\\": [],\\n \\\"description\\\": descr,\\n \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n\\n for i in attack[\\\"relationships\\\"]:\\n if i[\\\"relationship_type\\\"] == 'uses':\\n if 'attack-pattern' in i[\\\"target_ref\\\"]:\\n use = i[\\\"description\\\"]\\n # remove unnecessary strings, fix unicode errors\\n use = use.replace('<code>', '').replace('</code>', '').replace('\\\"', \\\"\\\").replace(',', '').replace(\\n '\\\\t', '').replace(' ', ' ').replace('\\\\n', '').encode('ascii', 'ignore').decode('ascii')\\n find_pattern = re.compile('\\\\[.*?\\\\]\\\\(.*?\\\\)') # get rid of att&ck reference (name)[link to site]\\n m = find_pattern.findall(use)\\n if len(m) > 0:\\n for j in m:\\n use = use.replace(j, '')\\n if use[0:2] == '\\\\'s':\\n use = use[3:]\\n elif use[0] == ' ':\\n use = use[1:]\\n # combine all the examples to one list\\n references[i[\\\"target_ref\\\"]][\\\"example_uses\\\"].append(use)\\n\\n for i in attack[\\\"malware\\\"]:\\n if 'description' not in i: # some software do not have description, example: darkmoon https://attack.mitre.org/software/S0209\\n continue\\n else:\\n references[i[\\\"id\\\"]] = {\\\"id\\\": i['id'], \\\"name\\\": i[\\\"name\\\"], \\\"description\\\": i[\\\"description\\\"],\\n \\\"examples\\\": [], \\\"example_uses\\\": [], \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n for i in attack[\\\"tools\\\"]:\\n references[i[\\\"id\\\"]] = {\\\"id\\\": i['id'], \\\"name\\\": i[\\\"name\\\"], \\\"description\\\": i[\\\"description\\\"], \\\"examples\\\": [],\\n \\\"example_uses\\\": [], \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n\\n attack_data = references\\n logging.info(\\\"Finished...now creating the database.\\\")\\n\\n cur_uids = await self.dao.get('attack_uids') if await self.dao.get('attack_uids') else []\\n cur_items = [i['uid'] for i in cur_uids]\\n for k, v in attack_data.items():\\n if k not in cur_items:\\n await self.dao.insert('attack_uids', dict(uid=k, description=defang_text(v['description']), tid=v['id'],\\n name=v['name']))\\n if 'regex_patterns' in v:\\n [await self.dao.insert('regex_patterns', dict(uid=k, regex_pattern=defang_text(x))) for x in\\n v['regex_patterns']]\\n if 'similar_words' in v:\\n [await self.dao.insert('similar_words', dict(uid=k, similar_word=defang_text(x))) for x in\\n v['similar_words']]\\n if 'false_negatives' in v:\\n [await self.dao.insert('false_negatives', dict(uid=k, false_negative=defang_text(x))) for x in\\n v['false_negatives']]\\n if 'false_positives' in v:\\n [await self.dao.insert('false_positives', dict(uid=k, false_positive=defang_text(x))) for x in\\n v['false_positives']]\\n if 'true_positives' in v:\\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\\n v['true_positives']]\\n if 'example_uses' in v:\\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\\n v['example_uses']]\\n logging.info('[!] DB Item Count: {}'.format(len(await self.dao.get('attack_uids'))))\",\n \"def matching_sites(structure: Structure, \\n reference_structure: Structure,\\n species: Optional[List[str]] = None) -> List[Tuple[Site, int]]:\\n matched_sites = []\\n for ref_site in reference_structure:\\n dr = [site.distance(ref_site) for site in structure]\\n i = int(np.argmin(dr))\\n matched_sites.append((structure[i], i))\\n if species:\\n matched_sites = [(s, i) for s, i in matched_sites if s.species_string in species]\\n return matched_sites\",\n \"def _filter_denies(self, filtered_ref):\\n for deny in self.denies_:\\n if not deny:\\n continue\\n\\n for ref_key in filtered_ref.ref_keys(deny):\\n del filtered_ref[ref_key]\",\n \"def get_subtechniques_of():\\n global subtechniques_of\\n\\n if not subtechniques_of:\\n subtechniques_of = rsh.subtechniques_of(get_srcs())\\n \\n return subtechniques_of\",\n \"def test_malware_have_techniques(attck_fixture):\\n for malware in attck_fixture.enterprise.malwares:\\n if malware.techniques:\\n assert getattr(malware,'techniques')\",\n \"def DiscoverPatterns(parameters, graph):\\n patternCount = 0\\n # get initial one-edge patterns\\n parentPatternList = GetInitialPatterns(graph, parameters.temporal)\\n if DEBUGFLAG:\\n print(\\\"Initial patterns (\\\" + str(len(parentPatternList)) + \\\"):\\\")\\n for pattern in parentPatternList:\\n pattern.print_pattern(' ')\\n discoveredPatternList = []\\n while ((patternCount < parameters.limit) and parentPatternList):\\n print(str(parameters.limit - patternCount) + \\\" patterns left\\\")\\n childPatternList = []\\n # extend each pattern in parent list (***** todo: in parallel)\\n while (parentPatternList):\\n parentPattern = parentPatternList.pop(0)\\n if ((len(parentPattern.instances) > 1) and (patternCount < parameters.limit)):\\n patternCount += 1\\n extendedPatternList = Pattern.ExtendPattern(parentPattern, parameters.temporal)\\n while (extendedPatternList):\\n extendedPattern = extendedPatternList.pop(0)\\n if DEBUGFLAG:\\n print(\\\"Extended Pattern:\\\")\\n extendedPattern.print_pattern(' ')\\n if (len(extendedPattern.definition.edges) <= parameters.maxSize):\\n # evaluate each extension and add to child list\\n extendedPattern.evaluate(graph)\\n if ((not parameters.prune) or (extendedPattern.value >= parentPattern.value)):\\n Pattern.PatternListInsert(extendedPattern, childPatternList, parameters.beamWidth, parameters.valueBased)\\n # add parent pattern to final discovered list\\n if (len(parentPattern.definition.edges) >= parameters.minSize):\\n Pattern.PatternListInsert(parentPattern, discoveredPatternList, parameters.numBest, False) # valueBased = False\\n parentPatternList = childPatternList\\n # insert any remaining patterns in parent list on to discovered list\\n while (parentPatternList):\\n parentPattern = parentPatternList.pop(0)\\n if (len(parentPattern.definition.edges) >= parameters.minSize):\\n Pattern.PatternListInsert(parentPattern, discoveredPatternList, parameters.numBest, False) # valueBased = False\\n return discoveredPatternList\",\n \"def test_dont_use_list(self) -> None:\\n import hammer_config\\n\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n\\n def add_dont_use_list(in_dict: Dict[str, Any]) -> Dict[str, Any]:\\n out_dict = deepdict(in_dict)\\n out_dict.update({\\\"dont use list\\\": [\\\"cell1\\\", \\\"cell2\\\"]})\\n return out_dict\\n\\n HammerToolTestHelpers.write_tech_json(tech_json_filename, add_dont_use_list)\\n sys.path.append(tech_dir_base)\\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir))\\n tech.cache_dir = tech_dir\\n\\n tool = DummyTool()\\n tool.technology = tech\\n database = hammer_config.HammerDatabase()\\n tool.set_database(database)\\n\\n # Test that manual mode for dont_use_mode works.\\n database.update_project([{\\n 'vlsi.inputs.dont_use_mode': 'manual',\\n 'vlsi.inputs.dont_use_list': ['cell1']\\n }])\\n self.assertEqual(tool.get_dont_use_list(), ['cell1'])\\n\\n # Test that auto mode for dont_use_mode works if the technology has a \\\"don't use\\\" list.\\n database.update_project([{\\n 'vlsi.inputs.dont_use_mode': 'auto',\\n 'vlsi.inputs.dont_use_list': []\\n }])\\n\\n self.assertEqual(tool.get_dont_use_list(), tool.technology.config.dont_use_list)\\n\\n # Test that append mode for dont_use_mode works if the everyone has a \\\"don't use\\\" list.\\n database.update_project([{\\n 'vlsi.inputs.dont_use_mode': 'append',\\n 'vlsi.inputs.dont_use_list': ['cell3']\\n }])\\n\\n self.assertEqual(tool.get_dont_use_list(), ['cell1', 'cell2', 'cell3'])\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\\n\\n # Create a new technology with no dont use list\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n HammerToolTestHelpers.write_tech_json(tech_json_filename)\\n sys.path.append(tech_dir_base)\\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir))\\n tech.cache_dir = tech_dir\\n\\n tool.technology = tech\\n\\n # Test that auto mode for don't use list works if the technology has no don't use list file.\\n database.update_project([{\\n 'vlsi.inputs.dont_use_mode': 'auto',\\n 'vlsi.inputs.dont_use_list': []\\n }])\\n self.assertEqual(tool.get_dont_use_list(), [])\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\",\n \"def _spacyfy(\\n matches: List[List[Optional[Tuple[str, str]]]], pattern: List[Dict[str, Any]]\\n) -> List[List[Dict[str, Any]]]:\\n new_patterns = []\\n if matches:\\n for match in matches:\\n new_pattern = deepcopy(pattern)\\n for i, token in enumerate(match):\\n if token:\\n del new_pattern[i][token[0]]\\n new_pattern[i][\\\"TEXT\\\"] = token[1]\\n new_patterns.append(new_pattern)\\n return new_patterns\",\n \"def damage_model(skill, personas):\\n damages = []\\n for persona in personas:\\n damage = skill.damage\\n for resistance in persona.resistances:\\n if skill.element == resistance.element:\\n damage *= resistance.resistance.value\\n damages.append((damage, resistance.resistance))\\n persona.hp = persona.hp - damage\\n return damages\",\n \"def _local_improvement(self, folded_design):\\n differing_sites = _string_difference_indices(\\n self.target.dot_bracket, folded_design\\n )\\n hamming_distances = []\\n for mutation in product(\\\"AGCU\\\", repeat=len(differing_sites)):\\n mutated = self.design.get_mutated(mutation, differing_sites)\\n folded_mutated, _ = fold(mutated.primary)\\n hamming_distance = hamming(folded_mutated, self.target.dot_bracket)\\n hamming_distances.append(hamming_distance)\\n if hamming_distance == 0: # For better timing results\\n return 0\\n return min(hamming_distances)\",\n \"def elimination_technique_2(C):\\n rels = C._reidemeister_relators\\n rels.sort(reverse=True)\\n gens = C._schreier_generators\\n for i in range(len(gens) - 1, -1, -1):\\n rel = rels[i]\\n for j in range(len(gens) - 1, -1, -1):\\n gen = gens[j]\\n if rel.generator_count(gen) == 1:\\n k = rel.exponent_sum(gen)\\n gen_index = rel.index(gen**k)\\n bk = rel.subword(gen_index + 1, len(rel))\\n fw = rel.subword(0, gen_index)\\n rep_by = (bk*fw)**(-1*k)\\n del rels[i]; del gens[j]\\n for l in range(len(rels)):\\n rels[l] = rels[l].eliminate_word(gen, rep_by)\\n break\\n C._reidemeister_relators = rels\\n C._schreier_generators = gens\\n return C._schreier_generators, C._reidemeister_relators\",\n \"def get_usable_mal_guard_ases(client_as, all_ases, pfi):\\r\\n\\r\\n if client_as in SUSPECTS:\\r\\n return []\\r\\n\\r\\n usable_mal_guard_ases = []\\r\\n \\r\\n cnt = 0\\r\\n for asn in all_ases:\\r\\n suspects = bidirectional_lookup(client_as, asn, pfi)\\r\\n\\r\\n if len(suspects) != 0 and len(suspects & SUSPECTS) == 0:\\r\\n usable_mal_guard_ases.append(asn)\\r\\n\\r\\n cnt += 1\\r\\n # if cnt % 1000 == 0:\\r\\n # print(len(usable_mal_guard_ases))\\r\\n\\r\\n return usable_mal_guard_ases\",\n \"def find_exploit_chains(attack_surface_graph, attack_surface, violated_components, target):\\n starting_points = []\\n admissible_components = []\\n admissible_paths = []\\n\\n for violated_component in attack_surface:\\n starting_points.append(violated_component[1])\\n\\n # removing duplicates\\n for violated_component in violated_components:\\n admissible_components.append(violated_component[0])\\n\\n admissible_components = set(admissible_components + starting_points)\\n\\n for starting_point in starting_points:\\n for path in nx.all_simple_paths(attack_surface_graph, source=starting_point, target=target):\\n # path - admissible components should result in an empty set\\n # because all elements in path need to be in admissible components.\\n if not (set(path) - admissible_components):\\n if validate_edges(attack_surface_graph, path, starting_points):\\n admissible_paths.append(path)\\n\\n # remove duplicate paths\\n admissible_paths_set = set(tuple(x) for x in admissible_paths)\\n admissible_paths = [list(x) for x in admissible_paths_set]\\n\\n return admissible_paths\",\n \"def prefer_weak_emphathetic_features(self, pos_arr, max):\\n log.debug(pos_arr)\\n for pos_feature in pos_arr:\\n log.debug('loop pos feature \\\\''+pos_feature+'\\\\'')\\n result = []\\n if pos_feature in self.weak_adjs:\\n log.debug('--> GOT WEAK MATCH')\\n result.append(pos_feature)\\n\\n if len(result) > max:\\n log.debug('Got enough items')\\n return result[:max]\\n else:\\n for pos_feature in pos_arr:\\n if pos_feature not in result:\\n if pos_feature in self.strong_adjs:\\n log.debug('Ignored '+pos_feature+' as a strong one')\\n else:\\n pos_arr.remove(pos_feature)\\n result.append(pos_feature)\\n if len(result) >= max:\\n return result\\n return result\",\n \"def replace_tokens(the_string, sequence_switched, suggestion_list, indexer, tokenizer, model, suggestion_num):\\n # Get top tokens\\n top_tokens = get_top_tokens(sequence_switched, indexer, tokenizer, model, suggestion_num)\\n\\n iterator = 0\\n for token in top_tokens:\\n suggestion_list[iterator] += the_string.replace(tokenizer.mask_token, tokenizer.decode([token]))\\n iterator += 1\\n\\n return suggestion_list\",\n \"def enumerate(self, capability: int = Capability.ALL) -> List[Technique]:\\n\\n # Update the cache for the current user\\n self.find_suid()\\n\\n known_techniques = []\\n for user, paths in self.suid_paths.items():\\n for path in paths:\\n binary = gtfobins.Binary.find(self.pty.which, path=path)\\n if binary is not None:\\n if (capability & binary.capabilities) == 0:\\n continue\\n\\n known_techniques.append(\\n Technique(user, self, binary, binary.capabilities)\\n )\\n\\n return known_techniques\",\n \"def existing_analysis_sweeps(self):\\n setup_list = self.existing_analysis_setups\\n sweep_list = []\\n s_type = self.solution_type\\n for el in setup_list:\\n sweep_list.append(el + \\\" : \\\" + s_type)\\n return sweep_list\",\n \"def references_list( self, theWeaver ):\\n return [ (c.name, c.seq) \\n for c in theWeaver.reference_style.chunkReferencedBy( self ) ]\",\n \"def influence(k,L,n):\\n try:\\n to_check = L[n-1] #set des indices\\n contact_direct=C(k,0)\\n return list(to_check.intersection(contact_direct))\\n except:\\n return []\",\n \"def filter_ordered_questions_by_concepts(questions, item_params, target_concept, concept_map): \\n \\n def get_parents(target, concept_map):\\n \\\"\\\"\\\"\\n Given a target concept, return a list of concept IDs for parent concepts\\n \\\"\\\"\\\"\\n parents = []\\n target_index = concept_map[CONCEPTS_STR].index(target)\\n for row in range(len(concept_map[ADJ_MAT_STR])): \\n # get value in adjMat for each row at target concept's col\\n val = concept_map[ADJ_MAT_STR][row][target_index] \\n if val > 0 and target_index != row: # don't care concepts are their own parents\\n # print('parent found at {}, {}'.format(row, target_index)) # TODO remove\\n parents.append(concept_map[CONCEPTS_STR][row])\\n return parents\\n\\n def get_children(target, concept_map):\\n \\\"\\\"\\\"\\n Given a target concept, return a list of concept IDs for all child concepts\\n \\\"\\\"\\\"\\n child_inds = []\\n target_index = concept_map[CONCEPTS_STR].index(target)\\n target_row = concept_map[ADJ_MAT_STR][target_index]\\n for ind in range(len(target_row)): # for each ind in row of adj mat\\n val = target_row[ind]\\n if(val>0 and ind != target_index): # don't care concept is child of itself\\n child_inds.append(ind)\\n return list(map(lambda ind: concept_map[CONCEPTS_STR][ind], child_inds))\\n\\n def get_concept(eid, item_params):\\n \\\"\\\"\\\"\\n Given a question/exercise eid (String) and item_params (pd.DataFrame),\\n return the concept that exercise corresponds to.\\n\\n Assumes eid is unique (maps to exactly 1 row)\\n \\\"\\\"\\\"\\n\\n concept = item_params[item_params[EID]==eid][CONCEPT]\\n\\n if len(concept) < 1: # TODO: make this filter \\\"!= 1\\\" to be more strict\\n raise Exception(\\\"Exercise ID does not map to any exercises. eid: {}\\\".format(eid))\\n else:\\n return concept.iloc[0] # to get actual value\\n \\n df_item_params = pd.DataFrame.from_dict(item_params)\\n\\n rec_eids = []\\n\\n # get all recommendations from target, its children, parents\\n rec_eids += list(filter(lambda q: get_concept(q, df_item_params) in \\n [target_concept] + \\n get_children(target_concept, concept_map) +\\n get_parents(target_concept, concept_map), questions))\\n \\n # # GET TOP FOR EACH RELATIONSHIP\\n # # add max_num_target questions of same concept to rec_eids\\n # rec_target = list(filter(lambda q: get_concept(q, df_item_params) == target_concept, \\n # questions))[:max_num_target]\\n # # print(\\\"For target, added {}\\\".format(rec_target))\\n # rec_eids += rec_target\\n \\n\\n # # add max_num_child questions of child concepts to rec_eids\\n # rec_child = list(filter(lambda q: get_concept(q, df_item_params) in get_children(target_concept, concept_map), \\n # questions))[:max_num_child]\\n # # print(\\\"For children, added {}\\\".format(rec_child ))\\n # rec_eids += rec_child\\n\\n # # add max_num_parent questions of parent concepts to rec_eids\\n # rec_parent = list(filter(lambda q: get_concept(q, df_item_params) in get_parents(target_concept, concept_map), \\n # questions))[:max_num_parent] \\n # # print(\\\"For parents, added {}\\\".format(rec_parent))\\n # rec_eids += rec_parent \\n\\n # want order of recommendations to stay same & only grab from top half of recommendations\\n # return list(filter(lambda eid: eid in rec_eids, questions[:int(len(questions)/2)+1])) \\n\\n # want order of recommendations to stay same\\n return list(filter(lambda eid: eid in rec_eids, questions))\",\n \"def multiple_and_remover(code):\\n list_ands_element =[]\\n list_ands_position =[]\\n counter = 0\\n x=0\\n z=0\\n while counter + 1 <= len(code): \\n and_finder = find_and(code[counter]) #finds the position in the list where the & appears using the find_and function defined in fparsertools\\n counter += 1\\n list_ands_position.append(and_finder)\\n if and_finder != None : \\n list_ands_element.append(counter-1) # vector with the numbers of the position of the element in the list where & appear\\n n=len(list_ands_element)\\n and_position = [] \\n for val in list_ands_position: \\n if val != None : \\n and_position.append(val) # Position in each component of the list where & appears\\n counter2 = 0\\n clean_code=[]\\n\\n while counter2+1 <= len(code): \\n if counter2 not in list_ands_element: # if the component of the list doesn't have & it is appended\\n clean_code.append(code[counter2]) \\n \\n if counter2 in list_ands_element: # if the component of the list has & \\n index=list_ands_element.index(counter2) # search the index in where the & appears in the vector\\n if index+1 < len(list_ands_element): \\n x = 0+z\\n if abs(list_ands_element[index]-list_ands_element[index+1])!=1: # Checks if there are not two consecutive & on the list\\n clean_code.append(code[counter2][:and_position[x]] + code[counter2+1]) # if there are not two consecutive & keeps the code until the &, and adds the next line of the code/component of the list\\n z+=1\\n counter2+=1 # Jumps the next component because it was added to the previous one\\n if abs(list_ands_element[index]-list_ands_element[index+1])==1:# Checks if there are two consecutive & on the list\\n clean_code.append(code[counter2][:and_position[x]] + code[counter2+1]) # If two consecutive & appear it makes the same as the previous case\\n z+=2 # Jumps two lines in the and position vector because one & is included in the previous line\\n counter2+=1 # Jumps the next component because it was added to the previous one\\n elif index+1 == len(list_ands_element): # This option is defined for the last component in order to avoid problems with indexing \\n clean_code.append(code[counter2][:and_position[-1]] + code[counter2+1]) \\n z+=1\\n counter2+=1\\n counter2+=1\\n \\n if n==0: # Recursive call\\n return clean_code\\n if n!=0:\\n clean_code=(multiple_and_remover(clean_code))\\n \\n return clean_code\",\n \"def filter_suggestions(rules, item):\\n suggestions = rules.copy()\\n for i in suggestions.index:\\n # Checks if the consequent already exists in the item. If yes, the rule is dropped.\\n if suggestions['consequents'][i][0] in item:\\n suggestions.drop([i], inplace=True)\\n\\n for j in suggestions.index:\\n # For every list of properties in the antecedents, check if they are contained in item properties list\\n # If no, the rule is dropped.\\n if all(props in item for props in list(suggestions['antecedents'][j])) == False:\\n suggestions.drop([j], inplace=True)\\n\\n result = np.unique([*itertools.chain.from_iterable(suggestions.consequents)]).tolist()\\n\\n return result\",\n \"def prefer_strong_negative_features(self, neg_arr, max):\\n log.debug(neg_arr)\\n for neg_feature in neg_arr:\\n log.debug('loop neg feature \\\\''+neg_feature+'\\\\'')\\n result = []\\n if neg_feature in self.strong_adjs:\\n log.debug('--> GOT STRONG MATCH')\\n result.append(neg_feature)\\n\\n if len(result) > max:\\n log.debug('Got enough items')\\n return result[:max]\\n else:\\n for neg_feature in neg_arr:\\n if neg_feature not in result:\\n if neg_feature in self.strong_adjs:\\n log.debug('Ignored '+neg_feature+' as a weak one')\\n else:\\n neg_arr.remove(neg_feature)\\n result.append(neg_feature)\\n if len(result) >= max:\\n return result\\n return result\",\n \"def _calculate_loss_formula(self) -> List[List[List[Tuple]]]:\\n self._formulas = []\\n for i in trange(len(self.explanations), desc=\\\"Setting explanations \\\"):\\n explanation = self.explanations[i]\\n if not is_dnf(explanation):\\n explanation = str(to_dnf(explanation))\\n formulas_terms = []\\n terms = explanation.split(\\\"|\\\")\\n assert len(terms) > 0, f\\\"Error in explanation formulation. Expected DNF formula but it is {explanation}\\\"\\n for term in terms:\\n # excluding trivial explanation\\n if term == \\\"False\\\" or term == \\\"True\\\":\\n continue\\n # excluding parenthesis\\n term = term.replace(\\\" \\\", \\\"\\\")\\n if term[0] == \\\"(\\\":\\n term = term[1:-1]\\n formula_term = []\\n attributes = term.split(\\\"&\\\")\\n for attribute in attributes:\\n if attribute[0] == \\\"~\\\":\\n negated = True\\n attribute = attribute[1:]\\n else:\\n negated = False\\n idx = self.attributes.index(attribute)\\n formula_term.append((idx, negated))\\n formulas_terms.append(formula_term)\\n self._formulas.append(formulas_terms)\\n return self._formulas\",\n \"def match_rules(rules, wm):\\n res = []\\n for r in rules:\\n new_patterns = match_rule(r[0],r[1],r[2], wm)\\n if new_patterns:\\n print(\\\" Match succeeds\\\")\\n print(\\\" Adding assertions to WM\\\")\\n else:\\n print(\\\" Match fails\\\")\\n for n in new_patterns:\\n if (n not in wm) and (n not in res):\\n print(\\\" \\\",n)\\n res.append(n)\\n # print(\\\"new patterns so far = \\\", res)\\n # print()\\n # for testing\\n # break\\n return res\",\n \"def solution2(inp):\\n rules, mticket, nearby = inp.strip().split(\\\"\\\\n\\\\n\\\")\\n rules = rules.split(\\\"\\\\n\\\")\\n nearby = nearby.split(\\\"\\\\n\\\")[1:]\\n mticket = list(map(int, mticket.split(\\\"\\\\n\\\")[1].split(\\\",\\\")))\\n rrules = []\\n for rule in rules:\\n a, b = rule.split(\\\" or \\\")\\n name = a.strip().split(\\\":\\\")[0]\\n r1 = a.strip().split(\\\" \\\")[-1]\\n r2 = b.strip()\\n def to_range(r):\\n i, j = list(map(int, r.split(\\\"-\\\")))\\n return range(i, j + 1)\\n rrules.append((to_range(r1), to_range(r2), name))\\n\\n nearby = [list(map(int, ticket.split(\\\",\\\"))) for ticket in nearby]\\n s = 0\\n to_remove = []\\n for i, ticket in enumerate(nearby):\\n for v in ticket:\\n valid = False\\n for r in rrules:\\n valid |= v in r[0] or v in r[1]\\n if not valid:\\n to_remove.append(i)\\n nearby = list(map(lambda x: x[1], filter(lambda x: x[0] not in to_remove, enumerate(nearby))))\\n indices = list(range(len(rrules)))\\n keys = {}\\n for rule in rrules:\\n rule_idx = []\\n for i in indices:\\n if all(ticket[i] in rule[0] or ticket[i] in rule[1] for ticket in nearby):\\n rule_idx.append(i)\\n keys[rule[2]] = rule_idx\\n\\n stack = list(keys.items())\\n def resolve(j, avail):\\n f, cand = stack[j]\\n for i in avail.intersection(cand):\\n if len(avail) == 1:\\n return [i]\\n avail.remove(i)\\n res = resolve(j + 1, avail)\\n avail.add(i)\\n if res != False:\\n return [i] + res\\n return False\\n solver = resolve(0, set(range(len(rrules))))\\n names = list(map(lambda x: x[0], stack))\\n return reduce(lambda x, y: x * y, [mticket[v] for k, v in zip(names, solver) if k.startswith(\\\"departure\\\")], 1)\",\n \"def _filter_references_section(self, text):\\n references_pattern = r\\\"(REFERENCE)|(reference)|(Reference)\\\"\\n references_found = [i.start() for i in re.finditer(references_pattern, text)]\\n if references_found != []:\\n last_reference_index = references_found[-1]\\n return text[:last_reference_index]\\n else:\\n return text\",\n \"def get_referents(data):\\n fakes_group = data.groups.get(\\\"Fakes\\\")\\n\\n return [obj for obj in data.groups[\\\"Referents\\\"].objects\\n if not obj.hide_render or fakes_group in obj.users_group]\",\n \"def techniques(self):\\n return self._get_child_page_of_type(LearningTechniquesPage)\",\n \"def find_final_stanzas_from_stanzas(stanzas, adj_list_ids, adj_list_words):\\n\\n # create combos generator\\n all_combos = combinations(stanzas, 3)\\n\\n # filtered generator (all lines unique)\\n combos = (c for c in all_combos if len(set().union(*c)) == 12)\\n\\n all_valid = []\\n for combo in tqdm(combos):\\n valid = find_final_stanzas(*combo, adj_list_ids, adj_list_words)\\n if valid:\\n all_valid.append((combo, valid))\\n return all_valid\",\n \"def matching_site_indices(structure: Structure,\\n reference_structure: Structure,\\n species: Optional[List[str]] = None) -> List[int]:\\n return [m[1] for m in matching_sites(structure, reference_structure, species=species)]\",\n \"def ref_ex1():\\n\\n print(\\\"Look Alikes\\\")\\n print(\\\"===========\\\")\\n\\n lst1 = [7, 3, 2]\\n lst2 = [7, 3, 2]\\n print(lst1, lst2)\\n\\n lst1[1] = -8\\n print(lst1, lst2)\\n\\n print(\\\"\\\")\\n print(\\\"Aliases\\\")\\n print(\\\"=======\\\")\\n\\n lst3 = [1, 5, 9]\\n lst4 = lst3\\n print(lst3, lst4)\\n\\n lst3[1] = 17\\n print(lst3, lst4)\\n\\n print(\\\"\\\")\\n print(\\\"Copies\\\")\\n print(\\\"======\\\")\\n\\n lst5 = [8, 9, 4]\\n # This makes a shallow copy\\n lst6 = list(lst5)\\n print(lst5, lst6)\\n\\n lst5[1] = -2\\n print(lst5, lst6)\\n\\n print(\\\"\\\")\\n print(\\\"Function Arguments\\\")\\n print(\\\"==================\\\")\\n\\n\\n lst7 = [1, 2, 3]\\n print(lst7)\\n mutate_list(lst7)\\n print(lst7)\\n return 0\",\n \"def update_time_references_with_impact_zones(self, docFeatList, timexList):\\n \\n if not self.timexImpactZones:\\n return self.timeReferences\\n \\n impactZoneStart = self.timexImpactZones[0][0]\\n features = [feat for feat in docFeatList if feat.getStartPos() > impactZoneStart \\n and self.sentence_tags[feat.getSentNum()]=='NORMAL' and not feat.inClause() \\n and feat.getType() in ['VACCINE', 'DRUG']] \\n for feature in features:\\n sentnumber = feature.getSentNum()\\n if not feature.getTlink() or not feature.getTlink().getDateTime():\\n if 'concomitant' in [tg[0] for tg in self.sentence_full_tags[sentnumber]]:\\n continue\\n feature = self.assign_feature_time_with_impact_zones(feature, self.timexImpactZones)\\n tlink = feature.getTlink()\\n if tlink:\\n if feature.getType()=='VACCINE':\\n refType = 'Vaccine'\\n confidence = 0.6\\n else:\\n refType = 'Drug'\\n confidence = 0.6\\n \\n self.timeReferences[(refType, feature.getString(), 0, sentnumber, \\n feature.getStartPos(), feature.getEndPos(), confidence)] = tlink.getDateTime()\\n \\n ##: Too many false alarms. Disable this part for now.\\n ##: E.g, in 3603565(4th). .... Before treatment, .... \\n# exposureSet = ['Vaccination', 'Injection']\\n# for sentnum, sentence in enumerate(self.sentences):\\n# timexes = [t for t in timexList if t.getDateTime() and t.getSentNum()==sentnum]\\n# if timexes: ##: has been processed for time references\\n# continue\\n# \\n# tags = set([tg[1] for tg in self.taggedSentences[sentnum]])\\n# intersect = tags.intersection(exposureSet)\\n# if intersect:\\n# tgs = [tg for tg in self.taggedSentences[sentnum] if tg[1] in exposureSet]\\n# word = tgs[0][0]\\n# sentStartPos = self.sentence_startPos[sentnum]\\n# dtimes = [r[2] for r in self.timexImpactZones if sentStartPos >=r[0] and sentStartPos<=r[1]]\\n# if dtimes:\\n# self.timeReferences[(tgs[0][1], word, None, sentnum, sentStartPos, sentStartPos, 0.6)] = dtimes[0]\\n# \\n return self.timeReferences\",\n \"def find_transformation(target, reference):\\n\\n # Brute force search on all possible transformations\\n for axis in (None, \\\"x\\\", \\\"y\\\", \\\"x=y\\\"):\\n curr = target\\n for degrees in (None, 90, 180, 270):\\n if axis is not None:\\n curr = curr.reflect(axis)\\n if degrees is not None:\\n curr = curr.rotate(degrees)\\n\\n # Find the reference points in target by matching them with the colors in the\\n # reference collection\\n target_reference = Collection([p for p in curr.points if p.color in reference.colors])\\n\\n # Apply the transformation of one point, to the rest of the points in the target collection\\n corresponding = next(p for p in reference.points if p.color == target_reference.points[0].color)\\n transformed = target_reference.translate(target_reference.points[0], corresponding)\\n\\n # If the resulting collection is identical to the reference point collection it is a match\\n if transformed == reference:\\n return curr.translate(target_reference.points[0], corresponding)\\n\\n return None\",\n \"def _find_states(self, lookup, alignment, reference):\\n # Get the reference allele, given our contig and position found above\\n self._reference = reference[self._contig][self._position - 1] # Subtract one as FASTA is 1-based and Python is 0-based\\n if alignment.get_rc(): # If we're reverse complement\\n alt, do_rc = lookup.get_alternate(self.reverse_complement(self._reference))\\n self._alternate = self.reverse_complement(alt)\\n else:\\n self._alternate, do_rc = lookup.get_alternate(self._reference) # An 'N' will be returned if the reference allele doesn't match with our IUPAC code\\n if do_rc:\\n self._reference = self.reverse_complement(self._reference)\",\n \"def find_adapted_solution(list_of_tuples, module, n_of_players_with_vote):\\n\\n def malus_roles_left(players_left, roles_left):\\n\\n \\\"\\\"\\\"\\n Checks whether it is possible to deploy all the players by assinging\\n a certain number of malus.\\n \\\"\\\"\\\"\\n\\n # Permutations of the players still to be deployed. We do that because\\n # we only want that combination of players in which ALL of them are\\n # deployed\\n players_perm = permutations(players_left, len(players_left))\\n\\n # Initialize the number of malus (just a number high enough)\\n fin_malus = 10\\n\\n # For each permutation of players to be deployed\\n for perm in players_perm:\\n\\n # Initialize two parameters: a counter and the number of malus for\\n # this specific permutation. Counter is used to be sure all the\\n # players in the permutation are checked\\n count = 0\\n temp_malus = 0\\n\\n # Make a copy of the roles to be covered so we can use it later to\\n # delete roles that we are able to cover\\n copy_of_adapted_roles = copy.copy(roles_left)\\n\\n # For each element in the permutation we select the corresponding\\n # role and try to cover it\\n for i in range(len(perm)):\\n role_to_cover = roles_left[i]\\n role_cand = perm[i][2]\\n\\n # If it is possible to cover it with a malus we increase the\\n # number of malus and the counter and then remove the role from\\n # the list of the roles still uncovered\\n if role_to_cover in malus_roles[role_cand]:\\n temp_malus += 1\\n count += 1\\n copy_of_adapted_roles.remove(role_to_cover)\\n\\n # If it is possible to cover it with no malus we just increase\\n # the counter and delete the role\\n elif (role_to_cover not in malus_roles[role_cand]\\n and role_to_cover in compatible_roles[role_cand]):\\n count += 1\\n copy_of_adapted_roles.remove(role_to_cover)\\n\\n # Else we interrupt checking this permutation and go to the\\n # one\\n else:\\n break\\n\\n # If we checked ALL the elements in the permutation and the number\\n # of malus is lower than the previous value we store it\\n if count == len(perm) and temp_malus < fin_malus:\\n fin_malus = temp_malus\\n\\n # If this value is different from the default one it means we found a\\n # solution and we return it\\n if fin_malus != 10:\\n return fin_malus\\n else:\\n return False\\n\\n def calculate(candidate, roles_of_module):\\n\\n \\\"\\\"\\\"\\n This function applies the deploy_players function to look for the\\n solution, if it exists. If all the players are deployed it returns\\n True, otherwise False.\\n \\\"\\\"\\\"\\n\\n # See find_solution for explanation on the try method\\n try:\\n to_deploy_list, roles_left = deploy_players(candidate,\\n roles_of_module,\\n 'adapted')\\n\\n # If the roles to deploy can be covered with a malus we return the\\n # number of malus assigned\\n\\n if malus_roles_left(to_deploy_list, roles_left):\\n return malus_roles_left(to_deploy_list, roles_left)\\n else:\\n return False\\n\\n except TypeError:\\n return False\\n\\n ordered_lineup = order_by_role(list_of_tuples)\\n\\n all_comb = list(combinations(schemes[module], n_of_players_with_vote))\\n\\n for comb in all_comb:\\n\\n # Change from tuple to list and check wings\\n comb = transf_wings(list(comb), module)\\n\\n # If a solution is found we return the number of malus\\n if calculate(ordered_lineup, comb):\\n return calculate(ordered_lineup, comb)\\n\\n return False\",\n \"def _enum_stem_match(enum_hypothesis_list, enum_reference_list, stemmer=PorterStemmer()):\\n stemmed_enum_list1 = [(word_pair[0], stemmer.stem(word_pair[1])) \\\\\\n for word_pair in enum_hypothesis_list]\\n\\n stemmed_enum_list2 = [(word_pair[0], stemmer.stem(word_pair[1])) \\\\\\n for word_pair in enum_reference_list]\\n\\n word_match, enum_unmat_hypo_list, enum_unmat_ref_list = \\\\\\n _match_enums(stemmed_enum_list1, stemmed_enum_list2)\\n\\n enum_unmat_hypo_list = list(zip(*enum_unmat_hypo_list)) if len(enum_unmat_hypo_list) > 0 else []\\n\\n enum_unmat_ref_list = list(zip(*enum_unmat_ref_list)) if len(enum_unmat_ref_list) > 0 else []\\n\\n enum_hypothesis_list = list(filter(lambda x: x[0] not in enum_unmat_hypo_list,\\n enum_hypothesis_list))\\n\\n enum_reference_list = list(filter(lambda x: x[0] not in enum_unmat_ref_list,\\n enum_reference_list))\\n\\n return word_match, enum_hypothesis_list, enum_reference_list\",\n \"def coding_problem_42(numbers, target):\\n if target == 0:\\n return []\\n\\n valid_numbers = [n for n in numbers if 0 < n <= target]\\n for number in sorted(valid_numbers, reverse=True):\\n\\n remaining_numbers = copy(valid_numbers)\\n remaining_numbers.remove(number)\\n partial_sum = coding_problem_42(remaining_numbers, target - number)\\n if partial_sum is not None:\\n return [number] + partial_sum\\n\\n return None\",\n \"def repair_fleet(input_fleet):\\n logistics = logi_subfleet(input_fleet)\\n logi_shield = logistics[0]\\n logi_armor = logistics[1]\\n if (logi_shield == []) and (logi_armor == []):\\n return input_fleet\\n\\n damaged_shield = []\\n\\n # I have a bad feeling that this function won't last longer\\n # than a single commit :ohdear:\\n # also this has a slight bug that ships can rep themselves\\n # and a ship might get over repped, but that's actually intended\\n\\n # shield first\\n for x in xrange(len(input_fleet)):\\n if input_fleet[x].attributes.shield != input_fleet[x].schema.shield:\\n damaged_shield.append(x)\\n\\n if damaged_shield != []:\\n for ship in logi_shield:\\n rep_target = random.choice(damaged_shield)\\n input_fleet[rep_target] = Ship(\\n input_fleet[rep_target].schema,\\n ShipAttributes(\\n min(input_fleet[rep_target].schema.shield,\\n (input_fleet[rep_target].attributes.shield\\n + ship.schema.remote_shield)\\n ),\\n input_fleet[rep_target].attributes.armor,\\n input_fleet[rep_target].attributes.hull,\\n ),\\n input_fleet[rep_target].debuffs,\\n )\\n\\n damaged_armor = []\\n\\n #armor second\\n for x in xrange(len(input_fleet)):\\n if input_fleet[x].attributes.armor != input_fleet[x].schema.armor:\\n damaged_armor.append(int(x))\\n\\n if damaged_armor != []:\\n for ship in logi_armor:\\n rep_target = random.choice(damaged_armor)\\n input_fleet[rep_target] = Ship(\\n input_fleet[rep_target].schema,\\n ShipAttributes(\\n input_fleet[rep_target].attributes.shield,\\n min(input_fleet[rep_target].schema.armor,\\n (input_fleet[rep_target].attributes.armor\\n + ship.schema.remote_armor)\\n ),\\n input_fleet[rep_target].attributes.hull,\\n ),\\n input_fleet[rep_target].debuffs,\\n )\\n\\n return input_fleet\",\n \"def generate(softwaretype=\\\"software\\\"):\\n # import the STIX data from MITRE/CTI\\n stix = requests.get(\\\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\\\").json()\\n ms = MemoryStore(stix_data=stix[\\\"objects\\\"])\\n # software includes malware and tool types so perform two queries and merge the results\\n software_filters = []\\n if softwaretype == \\\"malware\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'malware') ] )\\n if softwaretype == \\\"tool\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'tool') ] )\\n \\n software = list(chain.from_iterable(\\n ms.query(f) for f in software_filters\\n ))\\n\\n # build a list of techniques used by software\\n techniques_used = {} #attackID => using software names\\n for thesoftware in software:\\n # filter out revoked and deprecated software\\n if (\\\"x_mitre_deprecated\\\" in thesoftware and thesoftware[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in thesoftware and thesoftware[\\\"revoked\\\"]): continue\\n for relationship in ms.relationships(thesoftware[\\\"id\\\"]):\\n # skip all non-technique relationships\\n if \\\"attack-pattern\\\" not in relationship[\\\"target_ref\\\"]: continue\\n technique = ms.get(relationship[\\\"target_ref\\\"])\\n # filter out deprecated and revoked techniques\\n if (\\\"x_mitre_deprecated\\\" in technique and technique[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in technique and technique[\\\"revoked\\\"]): continue\\n techniqueID = technique[\\\"external_references\\\"][0][\\\"external_id\\\"]\\n # store usage in techniques_used struct\\n if techniqueID in techniques_used:\\n techniques_used[techniqueID].append(thesoftware[\\\"name\\\"])\\n else:\\n techniques_used[techniqueID] = [thesoftware[\\\"name\\\"]]\\n\\n # format the techniques for the output layer\\n techniques_list = []\\n highest_usage = 0\\n lowest_usage = 1\\n for techniqueID in techniques_used:\\n # determine the number of used techniques for the score\\n count = len(techniques_used[techniqueID])\\n highest_usage = max(highest_usage, count)\\n lowest_usage = min(lowest_usage, count)\\n # append technique struct to list of layer-formatted techniques\\n techniques_list.append({\\n \\\"techniqueID\\\": techniqueID,\\n \\\"comment\\\": \\\"executed by \\\" + \\\", \\\".join(techniques_used[techniqueID]),\\n \\\"score\\\": count,\\n })\\n # set up layer name and desc according to softwaretype\\n if softwaretype != \\\"software\\\": \\n plural = \\\"tools\\\" if softwaretype == \\\"tool\\\" else \\\"malware\\\"\\n layername = f\\\"Software ({softwaretype}) Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\\\"\\n else: \\n layername = \\\"Software Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software, where the score is the count of software using the technique\\\"\\n\\n # construct and return the layer as a dict\\n return {\\n \\\"name\\\": layername,\\n \\\"description\\\": layerdescription,\\n \\\"versions\\\": {\\n \\\"layer\\\": \\\"4.1\\\",\\n \\\"navigator\\\": \\\"4.1\\\"\\n },\\n \\\"domain\\\": \\\"enterprise-attack\\\",\\n \\\"techniques\\\": techniques_list,\\n \\\"sorting\\\": 3, # order in descending order of score (count)\\n \\\"gradient\\\": {\\n \\\"colors\\\": [\\n \\\"#fff7b3\\\", # low counts are yellow\\n \\\"#ff6666\\\", # high counts are red\\n ],\\n \\\"minValue\\\": lowest_usage,\\n \\\"maxValue\\\": highest_usage\\n },\\n }\",\n \"def get_possible_stresses(stress_pattern: str) -> List[str]:\\n possible_stresses = []\\n for index in range(len(stress_pattern)):\\n possible_stresses.append(stress_pattern[:index + 1])\\n return possible_stresses\",\n \"def mutant(self):\\n _mutant = []\\n _wt = self.wildtype\\n for i in range(0, len(self.mutations)):\\n site = _wt[i]\\n options = self.mutations[i]\\n if options is None:\\n _mutant.append(_wt[i])\\n else:\\n for o in options:\\n if o != site:\\n _mutant.append(o)\\n return \\\"\\\".join(_mutant)\",\n \"def replace_derived_rules(derivation):\\n\\n derivation2 = list()\\n formulas_list = [x[0] for x in derivation]\\n cts_used = {x for f in formulas_list for x in FOL_individual_constants if x in str(f)}\\n cts_unused = list(set(FOL_individual_constants) - cts_used)\\n\\n # In the first pass it replaces only DM2 (since DM2 uses DM)\\n for step in range(len(derivation)):\\n step_open_sups = derivation[step][3]\\n if derivation[step][1] == \\\"DM2\\\":\\n # A DM2 step has structure [ [[¬, [p]], or, [¬, [q]]], 'DM2', [[¬, [[p], & [q]]]] ]\\n negated_conjunction = derivation[step][2][0]\\n disjunction = derivation[step][0]\\n\\n new_steps = list()\\n step_open_sups.append(['¬', disjunction])\\n new_steps.append([['¬', disjunction], 'SUP', [], step_open_sups[:]])\\n new_steps.append([['¬', disjunction[0]], 'DM', [['¬', disjunction]], step_open_sups[:]])\\n new_steps.append([['¬', disjunction[2]], 'DM', [['¬', disjunction]], step_open_sups[:]])\\n new_steps.append([disjunction[0][1], 'DN', [['¬', disjunction[0]]], step_open_sups[:]])\\n new_steps.append([disjunction[2][1], 'DN', [['¬', disjunction[2]]], step_open_sups[:]])\\n new_steps.append([negated_conjunction[1], 'I˄', [disjunction[0][1], disjunction[2][1]], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [negated_conjunction, negated_conjunction[1]], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', ['¬', disjunction]], 'I¬', [['¬', disjunction], ['⊥']], step_open_sups[:]])\\n new_steps.append([disjunction, 'DN', [['¬', ['¬', disjunction]]], step_open_sups[:]])\\n derivation2.extend(new_steps)\\n\\n else:\\n derivation2.append(derivation[step])\\n\\n derivation3 = list()\\n\\n # Second pass: The rest of the rules\\n for step in range(len(derivation2)):\\n step_open_sups = derivation2[step][3]\\n if derivation2[step][1] == \\\"DM\\\":\\n # A DM steps looks like [ [¬,[p]], DM, [[¬,[[p] v [q]]]] ] or like [ [¬,[q]], DM, [[¬,[[p] v [q]]]] ]\\n negated_disjunct = derivation2[step][0]\\n disjunct = negated_disjunct[1]\\n negated_disjunction = derivation2[step][2][0]\\n disjunction = negated_disjunction[1]\\n\\n new_steps = list()\\n step_open_sups.append(disjunct)\\n new_steps.append([disjunct, 'SUP', [], step_open_sups[:]])\\n new_steps.append([disjunction, 'I˅', [disjunct], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [negated_disjunction, disjunction], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([negated_disjunct, 'I¬', [disjunct, ['⊥']], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n elif derivation2[step][1] == \\\"NegCond\\\":\\n # A NegCond step looks either like:\\n # [ [p], NegCond, [[¬, [[p], then, [q]]]] ] or\\n # [ [¬, [q]], NegCond, [[¬, [[p], then, [q]]]] ]\\n\\n negated_conditional = derivation2[step][2][0]\\n conditional = negated_conditional[1]\\n antecedent = conditional[0]\\n consequent = conditional[2]\\n\\n if derivation2[step][0] == antecedent:\\n\\n new_steps = list()\\n step_open_sups.append(['¬', antecedent])\\n new_steps.append([['¬', antecedent], 'SUP', [], step_open_sups[:]])\\n step_open_sups.append(antecedent)\\n new_steps.append([antecedent, 'SUP', [], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [['¬', antecedent], antecedent], step_open_sups[:]])\\n new_steps.append([consequent, 'EFSQ', [['⊥']], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([conditional, 'I→', [antecedent, consequent], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [negated_conditional, conditional], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', ['¬', antecedent]], 'I¬', [['¬', antecedent], ['⊥']], step_open_sups[:]])\\n new_steps.append([antecedent, 'DN', [['¬', ['¬', antecedent]]], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n elif derivation2[step][0] == ['¬', consequent]:\\n new_steps = list()\\n step_open_sups.append(consequent)\\n new_steps.append([consequent, 'SUP', [], step_open_sups[:]])\\n step_open_sups.append(antecedent)\\n new_steps.append([antecedent, 'SUP', [], step_open_sups[:]])\\n new_steps.append([consequent, 'REP', [consequent], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([conditional, 'I→', [consequent, antecedent], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [conditional, negated_conditional], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', consequent], 'I¬', [consequent, ['⊥']], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n elif derivation2[step][1] == \\\"MT\\\":\\n # A MT step looks either like:\\n # [ [¬, [p]], 'MT', [ [[p], then, [q]], [¬, [q]] ] ] or\\n # [ [¬, [p]], 'MT', [ [[p], then, [¬ [q]]], [q] ] ]\\n\\n negated_antecedent = derivation2[step][0]\\n antecedent = negated_antecedent[1]\\n conditional = derivation2[step][2][0]\\n consequent = conditional[2]\\n independent_formula = derivation2[step][2][1]\\n\\n new_steps = list()\\n step_open_sups.append(antecedent)\\n new_steps.append([antecedent, 'SUP', [], step_open_sups[:]])\\n new_steps.append([consequent, 'E→', [conditional, antecedent], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [consequent, independent_formula], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([negated_antecedent, 'I¬', [antecedent, ['⊥']], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n elif derivation2[step][1] == \\\"SD\\\":\\n # A SD step looks either like:\\n # [ [p], 'SD', [ [[p], or, [q]], [¬, [q]] ] ]\\n # [ [q], 'SD', [ [[p], or, [q]], [¬, [p]] ] ]\\n # [ [p], 'SD', [ [[p], or, [¬, [q]]], [q] ] ]\\n # [ [q], 'SD', [ [[¬, [p]], or, [q]], [p] ] ]\\n\\n disjunction = derivation2[step][2][0]\\n derived_disjunct = derivation2[step][0]\\n independent_formula = derivation2[step][2][1]\\n\\n new_steps = list()\\n step_open_sups.append(['¬', derived_disjunct])\\n new_steps.append([['¬', derived_disjunct], 'SUP', [], step_open_sups[:]])\\n step_open_sups.append(disjunction[0])\\n new_steps.append([disjunction[0], 'SUP', [], step_open_sups[:]])\\n if len(disjunction[0]) == 2 and independent_formula == disjunction[0][1]:\\n new_steps.append([['⊥'], 'E¬', [independent_formula, disjunction[0]], step_open_sups[:]])\\n else:\\n new_steps.append([['⊥'], 'E¬', [disjunction[0], ['¬', disjunction[0]]], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([[disjunction[0], '→', ['⊥']], 'I→', [disjunction[0], ['⊥']], step_open_sups[:]])\\n step_open_sups.append(disjunction[2])\\n new_steps.append([disjunction[2], 'SUP', [], step_open_sups[:]])\\n if len(disjunction[2]) == 2 and independent_formula == disjunction[2][1]:\\n new_steps.append([['⊥'], 'E¬', [independent_formula, disjunction[2]], step_open_sups[:]])\\n else:\\n new_steps.append([['⊥'], 'E¬', [disjunction[2], ['¬', disjunction[2]]], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([[disjunction[2], '→', ['⊥']], 'I→', [disjunction[2], ['⊥']], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E˅', [disjunction,\\n [disjunction[0], '→', ['⊥']],\\n [disjunction[2], '→', ['⊥']]], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', ['¬', derived_disjunct]], 'I¬',\\n [['¬', derived_disjunct], ['⊥']], step_open_sups[:]])\\n new_steps.append([derived_disjunct, 'DN', [['¬', ['¬', derived_disjunct]]], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n elif derivation2[step][1] == \\\"NegUniv\\\":\\n # A NegUniv steps looks like [ [E, x, [¬,[Phi]] , NegUniv, [ [¬, [V, x, [Phi]]] ] ]\\n negated_universal = derivation2[step][2][0]\\n universal = negated_universal[1]\\n existential = derivation[step][0]\\n variable = universal[1]\\n phi = universal[2]\\n\\n new_steps = list()\\n step_open_sups.append(['¬', existential])\\n new_steps.append([['¬', existential], 'SUP', [], step_open_sups[:]])\\n subst = substitute(phi, variable, cts_unused[0], Classical2)\\n del cts_unused[0]\\n step_open_sups.append(['¬', subst])\\n new_steps.append([['¬', subst], 'SUP', [], step_open_sups[:]])\\n new_steps.append([existential, 'I∃', [['¬', subst]], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [['¬', existential], existential], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', ['¬', subst]], 'I¬', [['¬', subst], ['⊥']], step_open_sups[:]])\\n new_steps.append([subst, 'DN', [['¬', ['¬', subst]]], step_open_sups[:]])\\n new_steps.append([universal, 'I∀', [subst], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [negated_universal, universal], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', ['¬', existential]], 'I¬', [['¬', existential], ['⊥']], step_open_sups[:]])\\n new_steps.append([existential, 'DN', [['¬', ['¬', existential]]], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n elif derivation2[step][1] == \\\"NegExist\\\":\\n # A NegExist steps looks like [ [V, x, [¬,[Phi]] , NegUniv, [ [¬, [E, x, [Phi]]] ] ]\\n negated_existential = derivation2[step][2][0]\\n existential = negated_existential[1]\\n universal = derivation[step][0]\\n variable = existential[1]\\n phi = existential[2]\\n\\n new_steps = list()\\n subst = substitute(phi, variable, cts_unused[0], Classical2)\\n del cts_unused[0]\\n step_open_sups.append(subst)\\n new_steps.append([subst, 'SUP', [], step_open_sups[:]])\\n new_steps.append([existential, 'I∃', [subst], step_open_sups[:]])\\n new_steps.append([['⊥'], 'E¬', [negated_existential, existential], step_open_sups[:]])\\n del step_open_sups[-1]\\n new_steps.append([['¬', subst], 'I¬', [subst, ['⊥']], step_open_sups[:]])\\n new_steps.append([universal, 'I∀', [['¬', subst]], step_open_sups[:]])\\n derivation3.extend(new_steps)\\n\\n else:\\n derivation3.append(derivation2[step])\\n\\n return derivation3\",\n \"def ref_analyzer(citation_informations, initialresult, initial_citationlist,\\n initial_referencelist,config, updated_rec_list ):\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attr function\\\", alert_admin=True)\\n return {}\\n\\n pubrefntag = \\\"\\\"\\n try:\\n pubrefntag = config.get(function, \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_report_number\\\", alert_admin=True)\\n return {}\\n\\n pubreftag = \\\"\\\"\\n try:\\n pubreftag = config.get(function, \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_pubinfo\\\", alert_admin=True)\\n return {}\\n\\n #pubrefntag is often 999C5r, pubreftag 999C5s\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"pubrefntag \\\"+pubrefntag)\\n write_message(\\\"pubreftag \\\"+pubreftag)\\n\\n citation_list = initial_citationlist\\n reference_list = initial_referencelist\\n result = initialresult\\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\\n d_references_s = citation_informations[2]\\n #dict of recid -> publication_infos_in_its_bibliography\\n d_records_s = citation_informations[3] #recid -> its publication inf\\n t1 = os.times()[4]\\n\\n write_message(\\\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\\\")\\n for somerecid in updated_rec_list:\\n try:\\n del citation_list[somerecid]\\n except KeyError:\\n pass\\n try:\\n del reference_list[somerecid]\\n except KeyError:\\n pass\\n\\n write_message(\\\"Phase 1: d_references_report_numbers\\\")\\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\\n #meaning: rec 8 contains these in bibliography\\n\\n done = 0\\n numrecs = len(d_references_report_numbers)\\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n done = done+1\\n\\n for refnumber in refnumbers:\\n if refnumber:\\n p = refnumber\\n f = 'reportnumber'\\n #sanitise p\\n p.replace(\\\"\\\\n\\\",'')\\n #search for \\\"hep-th/5644654 or such\\\" in existing records\\n rec_ids = get_recids_matching_query(p, f)\\n\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops: record '%d' report number value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_citer_cited(thisrecid, rec_ids[0])\\n remove_from_missing(p)\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n # Citation list should have rec_ids[0] but check anyway\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n #append unless this key already has the item\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid)\\n #and update result\\n result[rec_ids[0]] += 1\\n\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n else:\\n #the reference we wanted was not found among our records.\\n #put the reference in the \\\"missing\\\".. however, it will look\\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\\n #This should really be done in the next loop d_references_s\\n #but the 999C5s fields are not yet normalized\\n\\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\\n rectext = \\\"\\\" # print_record() call disabled to speed things up\\n lines = rectext.split(\\\"\\\\n\\\")\\n rpart = p #to be used..\\n for l in lines:\\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\\n st = l.find('$s')\\n if (st > 0):\\n end = l.find('$', st)\\n if (end == st):\\n end = len(l)\\n rpart = l[st+2:end]\\n insert_into_missing(thisrecid, rpart)\\n\\n mesg = \\\"d_references_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t2 = os.times()[4]\\n\\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\\n write_message(\\\"Phase 2: d_references_s\\\")\\n done = 0\\n numrecs = len(d_references_s)\\n for thisrecid, refss in d_references_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n\\n done = done+1\\n\\n for refs in refss:\\n if refs:\\n p = refs\\n #remove the latter page number if it is like 67-74\\n matches = re.compile(\\\"(.*)(-\\\\d+$)\\\").findall(p)\\n if matches and matches[0]:\\n p = matches[0][0]\\n\\n # check reference value to see whether it is well formed:\\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"is not well formed; skipping it.\\\" % (thisrecid, p)\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n continue # skip this ill-formed value\\n\\n # look for reference value:\\n rec_id = None\\n try:\\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\\n except:\\n rec_ids = None\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_message(\\\"These match searching \\\"+p+\\\" in journal: \\\"+repr(rec_ids), verbose=9)\\n #the refered publication is in our collection, remove\\n #from missing\\n remove_from_missing(p)\\n else:\\n #it was not found so add in missing\\n insert_into_missing(thisrecid, p)\\n #check citation and reference for this..\\n if rec_ids and rec_ids[0]:\\n #the above should always hold\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\\n result[rec_ids[0]] += 1 #add count for this..\\n\\n #update reference_list accordingly\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n mesg = \\\"d_references_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t3 = os.times()[4]\\n done = 0\\n numrecs = len(d_reports_numbers)\\n write_message(\\\"Phase 3: d_reports_numbers\\\")\\n\\n #search for stuff like CERN-TH-4859/87 in list of refs\\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n for reportcode in reportcodes:\\n if reportcode:\\n rec_ids = []\\n try:\\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\\n except:\\n rec_ids = []\\n\\n if rec_ids:\\n for recid in rec_ids:\\n #normal checks..\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(recid):\\n reference_list[recid] = []\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n\\n #normal updates\\n if not recid in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(recid)\\n if not thisrecid in reference_list[recid]:\\n reference_list[recid].append(thisrecid)\\n\\n mesg = \\\"d_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #find this record's pubinfo in other records' bibliography\\n write_message(\\\"Phase 4: d_records_s\\\")\\n done = 0\\n numrecs = len(d_records_s)\\n t4 = os.times()[4]\\n for thisrecid, recs in d_records_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_records_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n p = recs.replace(\\\"\\\\\\\"\\\",\\\"\\\")\\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\\n write_message(\\\"These records match \\\"+p+\\\" in \\\"+pubreftag+\\\" : \\\"+str(rec_ids), verbose=9)\\n if rec_ids:\\n for rec_id in rec_ids:\\n #normal checks\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(rec_id):\\n reference_list[rec_id] = []\\n\\n if not rec_id in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(rec_id)\\n if not thisrecid in reference_list[rec_id]:\\n reference_list[rec_id].append(thisrecid)\\n\\n mesg = \\\"d_records_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n write_message(\\\"Phase 5: reverse lists\\\")\\n\\n #remove empty lists in citation and reference\\n keys = citation_list.keys()\\n for k in keys:\\n if not citation_list[k]:\\n del citation_list[k]\\n\\n keys = reference_list.keys()\\n for k in keys:\\n if not reference_list[k]:\\n del reference_list[k]\\n\\n write_message(\\\"Phase 6: self-citations\\\")\\n selfdic = {}\\n #get the initial self citation dict\\n initial_self_dict = get_cit_dict(\\\"selfcitdict\\\")\\n selfdic = initial_self_dict\\n #add new records to selfdic\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n write_message(\\\"Self cite processing disabled. Use -A option to enable it.\\\")\\n else:\\n write_message(\\\"self cite and author citations enabled\\\")\\n selfdic = get_self_citations(updated_rec_list, citation_list,\\n initial_self_dict, config)\\n #selfdic consists of\\n #key k -> list of values [v1,v2,..]\\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\\n\\n #create a reverse \\\"x cited by y\\\" self cit dict\\n selfcitedbydic = {}\\n for k in selfdic.keys():\\n vlist = selfdic[k]\\n for v in vlist:\\n if selfcitedbydic.has_key(v):\\n tmplist = selfcitedbydic[v]\\n if not k in tmplist:\\n tmplist.append(k)\\n else:\\n tmplist = [k]\\n selfcitedbydic[v] = tmplist\\n\\n write_message(\\\"Getting author citations\\\")\\n\\n #get author citations for records in updated_rec_list\\n initial_author_dict = get_initial_author_dict()\\n authorcitdic = initial_author_dict\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n print \\\"Author cites disabled. Use -A option to enable it.\\\"\\n else:\\n write_message(\\\"author citations enabled\\\")\\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\\n initial_author_dict, config)\\n\\n\\n if task_get_task_param('verbose') >= 3:\\n #print only X first to prevent flood\\n tmpdict = {}\\n tmp = citation_list.keys()[0:10]\\n for t in tmp:\\n tmpdict[t] = citation_list[t]\\n write_message(\\\"citation_list (x is cited by y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(citation_list.keys())))\\n tmp = reference_list.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = reference_list[t]\\n write_message(\\\"reference_list (x cites y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(reference_list.keys())))\\n tmp = selfcitedbydic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfcitedbydic[t]\\n mesg = \\\"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\\\"\\n mesg += str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfcitedbydic.keys())))\\n tmp = selfdic.keys()[0:100]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfdic[t]\\n mesg = \\\"selfdic (x cites y and one of the authors of x same as y's): \\\"+str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfdic.keys())))\\n tmp = authorcitdic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = authorcitdic[t]\\n write_message(\\\"authorcitdic (author is cited in recs): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(authorcitdic.keys())))\\n insert_cit_ref_list_intodb(citation_list, reference_list,\\n selfcitedbydic, selfdic, authorcitdic)\\n\\n t5 = os.times()[4]\\n\\n write_message(\\\"Execution time for analyzing the citation information generating the dictionary:\\\")\\n write_message(\\\"... checking ref number: %.2f sec\\\" % (t2-t1))\\n write_message(\\\"... checking ref ypvt: %.2f sec\\\" % (t3-t2))\\n write_message(\\\"... checking rec number: %.2f sec\\\" % (t4-t3))\\n write_message(\\\"... checking rec ypvt: %.2f sec\\\" % (t5-t4))\\n write_message(\\\"... total time of ref_analyze: %.2f sec\\\" % (t5-t1))\\n\\n return result\",\n \"def make_contrast_modulation(\\n self,\\n frequency: int | float,\\n sampling_frequency: int,\\n target_contrasts: list[float],\\n phase: int | float = 0,\\n duration: int | float = 1\\n ):\\n waveform = waves.make_stimulus_waveform(\\n frequency,\\n sampling_frequency,\\n phase,\\n duration,\\n )\\n\\n solutions = []\\n for point in waveform:\\n contrast = [point * c for c in target_contrasts]\\n self.target_contrast = contrast\\n solutions.append(self.linalg_solve())\\n return solutions\",\n \"def cleanup():\\n for s in [missiles, explosions, bonus]:\\n\\n set_to_remove = set([])\\n for m in s:\\n if m.isDead:\\n set_to_remove.add(m)\\n\\n s.difference_update(set_to_remove)\",\n \"def get_pattern(self):\\n pattern = list()\\n for item in self.gradual_items:\\n pattern.append(item.gradual_item.tolist())\\n return pattern\",\n \"def lookup_coarse_stress_patterns_for_word(\\n word: Text,\\n ) -> Sequence[Sequence[CoarseStress]]:\\n return [\\n word.pf.coarse_stress_pattern\\n for word in EnglishUtils.all_possible_forms_for(word)\\n ]\",\n \"async def check_hacks(self) -> None:\\n\\n hacks = await self.get_expired_hacks()\\n for h in hacks:\\n await self.delete_skill_action_by_target_id_and_skill_type(h[3], 'hack')\\n\\n channel = self.bots_txt\\n\\n await channel.send(\\n content=f\\\"<@{h[0]}>\\\",\\n embed=discord.Embed(\\n description=f\\\"**<@{h[3]}> updated his firewall so <@{h[0]}>'s hacking has no effect anymore! 💻**\\\",\\n color=discord.Color.red()))\",\n \"def enrich_textrefs(self, db):\\n tr_list = db.select_all(db.TextRef,\\n db.TextContent.text_ref_id == db.TextRef.id,\\n db.TextContent.source == self.my_source,\\n db.TextRef.manuscript_id.is_(None))\\n file_list = self.ftp.get_csv_as_dict('filelist.csv', header=0)\\n pmcid_mid_dict = {entry['PMCID']: entry['MID'] for entry in file_list}\\n pmid_mid_dict = {entry['PMID']: entry['MID'] for entry in file_list\\n if entry['PMID'] is not '0'}\\n for tr in tr_list:\\n if tr.pmcid is not None:\\n tr.manuscript_id = pmcid_mid_dict[tr.pmcid]\\n elif tr.pmid is not None and tr.pmid in pmid_mid_dict.keys():\\n tr.manuscript_id = pmid_mid_dict[tr.pmid]\\n db.commit(\\\"Could not update text refs with manuscript ids.\\\")\\n return\",\n \"def connect_refs_to_species(species: list, citelist: list) -> dict:\\n # create a dictionary with empty reference lists\\n species_refs = {s.species: set() for s in species}\\n # go through all citations\\n # for c in tqdm(citelist):\\n for c in citelist:\\n if c.actual in species_refs:\\n reflist = species_refs[c.actual]\\n reflist |= {c.cite_key}\\n return species_refs\",\n \"def where(overstrand_list, where_lists, wall_color_lists, p):\\n where_lists = []\\n head_lists = [[1, 0, 3, 0], [0, 2, 0, 0]]\\n num_crossings = len(overstrand_list)\\n head_list_first_crossing = head_lists[0]\\n wall_color_first_crossing = wall_color_lists[0]\\n indicies_of_heads = match(head_list_first_crossing, wall_color_first_crossing, p)\\n new_where_lists = build_where([], indicies_of_heads, wall_color_first_crossing, p)\\n for i in range(num_crossings):\\n head_list_at_crossing = pass_through(head_list[i])\\n indicies_of_heads = match(head_list_at_crossing, wall_color_lists[i], p)\\n build_where(new_where_lists, indicies_of_heads, wall_color_at_crossing[i], p)\\n # now we match the universes of the last entry in each where_list to the\\n # wall_color_lists[i+1] to generate head_list[i+1]\\n indicies_of_heads = match(head_list_at_crossing, wall_color_at_crossing[i+1], p)\\n return new_where_lists\",\n \"def get_references(subarray, filter='CLEAR', context='jwst_niriss_0134.imap'):\\n # Accepted subarrays\\n subarrays = ['SUBSTRIP96', 'SUBSTRIP256', 'FULL']\\n if subarray not in subarrays:\\n raise ValueError(\\\"{} is not a supported subarray. Please use {}\\\".format(subarray, subarrays))\\n\\n # Accepted filters\\n filters = ['CLEAR', 'F277W']\\n if filter not in filters:\\n raise ValueError(\\\"{} is not a supported filter. Please use {}\\\".format(filter, filters))\\n\\n # F277W not yet supported. Just delete this line when F277W support is added to crds\\n filter = 'CLEAR'\\n\\n params = {\\\"INSTRUME\\\": \\\"NIRISS\\\",\\n \\\"READPATT\\\": \\\"NIS\\\",\\n \\\"EXP_TYPE\\\": \\\"NIS_SOSS\\\",\\n \\\"DETECTOR\\\": \\\"NIS\\\",\\n \\\"PUPIL\\\": \\\"GR700XD\\\",\\n \\\"DATE-OBS\\\": \\\"2020-07-28\\\",\\n \\\"TIME-OBS\\\": \\\"00:00:00\\\",\\n \\\"INSTRUMENT\\\": \\\"NIRISS\\\",\\n \\\"FILTER\\\": filter,\\n \\\"SUBARRAY\\\": subarray}\\n\\n # Default ref file path\\n default_path = resource_filename('awesimsoss', 'files/refs/')\\n\\n # Collect reference files for subarray+filter combination\\n try:\\n import crds\\n refs = crds.getreferences(params, context=context)\\n except:\\n\\n refs = {'saturation': os.path.join(default_path, 'jwst_niriss_saturation_0010.fits'),\\n 'photom': os.path.join(default_path, 'jwst_niriss_photom_0037.fits'),\\n 'flat': os.path.join(default_path, 'jwst_niriss_flat_0190.fits'),\\n 'gain': os.path.join(default_path, 'jwst_niriss_gain_0005.fits'),\\n 'superbias': os.path.join(default_path, 'jwst_niriss_superbias_0120.fits'),\\n 'dark': os.path.join(default_path, 'jwst_niriss_dark_0114.fits'),\\n 'readnoise': os.path.join(default_path, 'jwst_niriss_readnoise_0001.fits'),\\n 'linearity': os.path.join(default_path, 'jwst_niriss_linearity_0011.fits')}\\n\\n if subarray == 'SUBSTRIP96':\\n refs['superbias'] = os.path.join(default_path, 'jwst_niriss_superbias_0111.fits')\\n refs['dark'] = os.path.join(default_path, 'jwst_niriss_dark_0111.fits')\\n\\n if subarray == 'FULL':\\n refs['gain'] = os.path.join(default_path, 'jwst_niriss_gain_0002.fits')\\n refs['superbias'] = os.path.join(default_path, 'jwst_niriss_superbias_0029.fits')\\n refs['dark'] = os.path.join(default_path, 'jwst_niriss_dark_0129.fits')\\n\\n # Check if reference files exist and load defaults if necessary\\n for ref_name, ref_fn in refs.items():\\n if not 'NOT FOUND' in ref_fn and not os.path.isfile(refs[ref_name]):\\n refs[ref_name] = os.path.join(default_path, ref_fn)\\n print(\\\"Could not get {} reference file from CRDS. Using {}.\\\".format(ref_name, ref_fn))\\n\\n return refs\",\n \"def build_alternatives(replacements):\\n alternatives = []\\n highest = 0\\n prev_lev = 0\\n\\n for term in replacements:\\n # print(\\\"\\\\nterm => \\\" + str(term))\\n lev = term[1][0]\\n hits = term[1][1]\\n\\n if lev < 2 or lev == prev_lev:\\n if args.all:\\n # print(\\\"\\\\nterm => \\\" + str(term))\\n alternatives.append(term[0])\\n prev_lev = lev\\n else:\\n if hits > highest:\\n highest = hits\\n # print(\\\"\\\\nterm => \\\" + str(term))\\n alternatives.append(term[0])\\n prev_lev = lev\\n\\n if args.all or args.jaccard:\\n # print('!')\\n return alternatives\\n else:\\n # print('?')\\n return alternatives[-1]\",\n \"def extract_references_from_txt(filename):\\n file = open(filename)\\n references_section = \\\"\\\"\\n references_section_found = False\\n for line in file:\\n if \\\"REFERENCE\\\" in line:\\n references_section_found = True\\n if references_section_found:\\n references_section+=line + \\\" \\\"\\n references_section = references_section.replace('\\\\n','')\\n refs = extract_references(references_section)\\n return refs\",\n \"def unlocking_technologies(self) -> Set[Technology]:\\n return {technology_table[tech_name] for tech_name in recipe_sources.get(self.name, ())}\",\n \"def ref_ex2():\\n\\n print (\\\"Part 1 - references to two distinct objects\\\")\\n iipp1_instructors = [\\\"Joe\\\", \\\"Scott\\\", \\\"John\\\", \\\"Stephen\\\"]\\n iipp2_instructors = [\\\"Joe\\\", \\\"Scott\\\", \\\"John\\\", \\\"Stephen\\\"]\\n print(iipp1_instructors)\\n print(iipp2_instructors)\\n\\n print (\\\"Mutate one of the two objects\\\")\\n iipp2_instructors.pop()\\n print(iipp1_instructors)\\n print(iipp2_instructors)\\n print()\\n\\n print (\\\"Part 2 - two references to the same object\\\")\\n iipp1_instructors = [\\\"Joe\\\", \\\"Scott\\\", \\\"John\\\", \\\"Stephen\\\"]\\n iipp2_instructors = iipp1_instructors\\n print(iipp1_instructors)\\n print(iipp2_instructors)\\n\\n print (\\\"Mutate the object\\\")\\n iipp2_instructors.pop()\\n print(iipp1_instructors)\\n print(iipp2_instructors)\\n print()\\n\\n print (\\\"Part 3 - two references to an object and a copy of the object\\\")\\n iipp1_instructors = [\\\"Joe\\\", \\\"Scott\\\", \\\"John\\\", \\\"Stephen\\\"]\\n iipp2_instructors = list(iipp1_instructors)\\n print(iipp1_instructors)\\n print(iipp2_instructors)\\n\\n print (\\\"Mutate the one of the objects\\\")\\n iipp2_instructors.pop()\\n print(iipp1_instructors)\\n print(iipp2_instructors)\\n return 0\",\n \"def get_reference_no_from_narration(narration,validatorlist=[validate_western_code,validate_ria,validate_placid, validate_xpress,validate_moneygram, validate_prabhu_ref, validate_merchantrade_ref, validate_necmoney_ref, validate_necitaly_ref, validate_cbl_ref, swift_re]):\\n ref_list = []\\n for e in narration.split():\\n for fn in validatorlist:\\n try:\\n fn(e)\\n if e not in ref_list:\\n ref_list.append(e)\\n except ValidationError:\\n pass\\n return ref_list\",\n \"async def check_hacks(self) -> None:\\n\\n hacks = await self.get_expired_hacks()\\n for h in hacks:\\n await self.delete_skill_action_by_target_id_and_skill_type(h[3], 'hack')\\n await self.update_user_is_hacked(h[3], 0)\\n\\n channel = self.bots_txt\\n\\n await channel.send(\\n content=f\\\"<@{h[0]}>\\\",\\n embed=discord.Embed(\\n description=f\\\"**<@{h[3]}> updated his firewall so <@{h[0]}>'s hacking has no effect anymore! 💻**\\\",\\n color=discord.Color.red()))\",\n \"def get_facts(facts, situation, target):\\n return [f[2] for f in facts if (f[0], f[1]) == (situation, target)]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6026351","0.59703493","0.58191","0.5710842","0.56594396","0.55248374","0.5523887","0.54103875","0.5382938","0.5350205","0.52979887","0.52793556","0.51606363","0.5085763","0.49043208","0.47701126","0.47418934","0.47043297","0.47007185","0.46617642","0.46412155","0.46275026","0.46250692","0.46136573","0.46126974","0.45789677","0.45498145","0.4537595","0.45314586","0.45217255","0.44482008","0.4425743","0.44090483","0.43963835","0.43866116","0.43691707","0.43676728","0.4363953","0.4358013","0.43534026","0.43533632","0.43499038","0.43438944","0.4334362","0.43327945","0.43291324","0.43249595","0.43132362","0.43128222","0.43094063","0.43050689","0.42816257","0.4279884","0.42794788","0.42750975","0.4273847","0.42733055","0.42702103","0.42621198","0.42431298","0.42365628","0.4236307","0.42331776","0.4229936","0.4214242","0.42002347","0.41997698","0.41986573","0.41733015","0.41692954","0.41655853","0.41653943","0.41650844","0.41592005","0.41573042","0.4151495","0.41489705","0.41297916","0.41253832","0.41232815","0.41196916","0.4112169","0.41105524","0.41081235","0.41017404","0.4097109","0.40890867","0.40858257","0.40845865","0.40769064","0.40762502","0.40727755","0.40695652","0.40689358","0.40663347","0.40644258","0.4060257","0.4059858","0.40550384","0.40545362"],"string":"[\n \"0.6026351\",\n \"0.59703493\",\n \"0.58191\",\n \"0.5710842\",\n \"0.56594396\",\n \"0.55248374\",\n \"0.5523887\",\n \"0.54103875\",\n \"0.5382938\",\n \"0.5350205\",\n \"0.52979887\",\n \"0.52793556\",\n \"0.51606363\",\n \"0.5085763\",\n \"0.49043208\",\n \"0.47701126\",\n \"0.47418934\",\n \"0.47043297\",\n \"0.47007185\",\n \"0.46617642\",\n \"0.46412155\",\n \"0.46275026\",\n \"0.46250692\",\n \"0.46136573\",\n \"0.46126974\",\n \"0.45789677\",\n \"0.45498145\",\n \"0.4537595\",\n \"0.45314586\",\n \"0.45217255\",\n \"0.44482008\",\n \"0.4425743\",\n \"0.44090483\",\n \"0.43963835\",\n \"0.43866116\",\n \"0.43691707\",\n \"0.43676728\",\n \"0.4363953\",\n \"0.4358013\",\n \"0.43534026\",\n \"0.43533632\",\n \"0.43499038\",\n \"0.43438944\",\n \"0.4334362\",\n \"0.43327945\",\n \"0.43291324\",\n \"0.43249595\",\n \"0.43132362\",\n \"0.43128222\",\n \"0.43094063\",\n \"0.43050689\",\n \"0.42816257\",\n \"0.4279884\",\n \"0.42794788\",\n \"0.42750975\",\n \"0.4273847\",\n \"0.42733055\",\n \"0.42702103\",\n \"0.42621198\",\n \"0.42431298\",\n \"0.42365628\",\n \"0.4236307\",\n \"0.42331776\",\n \"0.4229936\",\n \"0.4214242\",\n \"0.42002347\",\n \"0.41997698\",\n \"0.41986573\",\n \"0.41733015\",\n \"0.41692954\",\n \"0.41655853\",\n \"0.41653943\",\n \"0.41650844\",\n \"0.41592005\",\n \"0.41573042\",\n \"0.4151495\",\n \"0.41489705\",\n \"0.41297916\",\n \"0.41253832\",\n \"0.41232815\",\n \"0.41196916\",\n \"0.4112169\",\n \"0.41105524\",\n \"0.41081235\",\n \"0.41017404\",\n \"0.4097109\",\n \"0.40890867\",\n \"0.40858257\",\n \"0.40845865\",\n \"0.40769064\",\n \"0.40762502\",\n \"0.40727755\",\n \"0.40695652\",\n \"0.40689358\",\n \"0.40663347\",\n \"0.40644258\",\n \"0.4060257\",\n \"0.4059858\",\n \"0.40550384\",\n \"0.40545362\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60934097"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85688,"cells":{"query":{"kind":"string","value":"Given a technique object, find examples in malware using technique, tools using technique and groups using technique. Return list with example data"},"document":{"kind":"string","value":"def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.groups_using_technique }\n ]\n\n example_data = []\n\n # Get malware or tools used by group\n for examples in examples_map:\n if examples['example_type'].get(technique.get('id')):\n for example in examples['example_type'][technique['id']]:\n\n attack_id = util.get_attack_id(example['object'])\n\n # Only add example data if the attack id is found \n if attack_id:\n row = {}\n\n row['id'] = attack_id\n\n if attack_id.startswith('S'):\n row['path'] = \"software\"\n else:\n row['path'] = \"groups\"\n\n row['name'] = example['object']['name']\n\n if example['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\n\n example_data.append(row)\n \n if example_data:\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\n return example_data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)","def get_techniques_used_by_groups():\n global techniques_used_by_groups\n\n if not techniques_used_by_groups:\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\n\n return techniques_used_by_groups","def get_all_techniques(src, source_name, tactic=None):\n filters = [\n Filter(\"type\", \"=\", \"attack-pattern\"),\n Filter(\"external_references.source_name\", \"=\", source_name),\n ]\n if tactic:\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\n\n results = src.query(filters)\n return remove_deprecated(results)","def get_techniques_list(techniques):\n\n technique_list = {}\n \n for technique in techniques:\n if 'revoked' not in technique or technique['revoked'] == False:\n\n attack_id = util.get_attack_id(technique)\n\n if attack_id:\n\n technique_dict = {}\n technique_dict['id'] = attack_id\n \n technique_dict['name'] = technique['name']\n technique_dict['description'] = technique['description']\n\n if technique.get('kill_chain_phases'):\n for elem in technique['kill_chain_phases']:\n # Fill dict\n\n if elem['phase_name'] not in technique_list:\n technique_list[elem['phase_name']] = []\n \n technique_list[elem['phase_name']].append(technique_dict)\n\n for key, value in technique_list.items():\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\n \n return technique_list","def get_techniques_used_by_malware():\n global techniques_used_by_malware\n \n if not techniques_used_by_malware:\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\n \n return techniques_used_by_malware","def get_techniques_used_by_tools():\n global techniques_used_by_tools\n\n if not techniques_used_by_tools:\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\n \n return techniques_used_by_tools","def get_related_techniques_data(technique, tactic_list):\n \n technique_data = []\n\n if config.related_techniques.get(technique['id']):\n for rel_tech in config.related_techniques[technique['id']]:\n\n attack_id = util.get_attack_id(rel_tech['object'])\n\n if attack_id:\n row = {}\n row['technique_id'] = attack_id\n\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\n if tactic:\n row['tactic_id'] = util.get_attack_id(tactic)\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\n \n row['technique_name'] = rel_tech['object']['name']\n technique_data.append(row)\n\n if technique_data:\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\n return technique_data","def get_examples():\n # Get the tjson test cases from a url.\n examples = urlopen(TJSON_EXAMPLES_URL).read()\n\n # Strip comments from the lines.\n examples = re.sub(r\"^#.*\\n?\", \"\", examples.decode(), flags=re.MULTILINE)\n\n # Split a group of examples.\n delimiter = re.escape(EXAMPLES_DELIMITER)\n example_re = re.compile(delimiter + r'(.*?)' + delimiter, re.S)\n split = re.findall(example_re, examples)\n\n # Create the `Example` named tuples.\n return [\n parse_example(example)\n for example in split]","def add_techniques(\n client: Act, matrice: AttckMatrice, output_format: Text = \"json\"\n) -> List:\n\n notify = []\n\n for technique in matrice.techniques:\n notify += handle_techniques(client, technique, None, output_format)\n\n for subtechnique in getattr(technique, \"subtechniques\", []):\n # Pre Attack does not have sub techniques\n notify += handle_techniques(client, subtechnique, technique, output_format)\n\n return notify","def run_examples():\n\n for example in examples:\n\n print(str(example) + \" : \", end=\" \")\n try:\n t, smush = analyse(example, my_env)\n print(lookup(t, smush))\n # print(\"Smush\")\n # for k,v in smush.items():\n # print(f\"\\t{k} : {v}\")\n except (ParseError, InferenceError) as e:\n print(e)","def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)","def get_technique_by_id(self, id_code):\r\n return self.fs.query([\r\n Filter('type', '=', 'attack-pattern'),\r\n Filter('external_references.external_id', '=', id_code)\r\n ])","def get_technique_stats(db):\n \n techniques = {}\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n temp_dict = {technique: [{u'id': element['project_no'], \n u'name': element['project_name'], u'description': element['short_description']}]}\n techniques.update(temp_dict)\n else:\n temp_dict = {u'id': element['project_no'],\n u'name': element['project_name'], u'description': element['short_description']}\n techniques[technique].append(temp_dict)\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\n\n return techniques","def get_groups_using_technique():\n global groups_using_technique\n\n if not groups_using_technique:\n groups_using_technique = rsh.groups_using_technique(get_srcs())\n \n return groups_using_technique","def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\n return [\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\n ]","def get_technique_mitigated_by_mitigation():\n global technique_mitigated_by_mitigation\n\n if not technique_mitigated_by_mitigation:\n technique_mitigated_by_mitigation = rsh.technique_mitigated_by_mitigation(get_srcs())\n\n return technique_mitigated_by_mitigation","def get_scenarios(experiments):\n return {exp.scenario for exp in experiments}","def get_technique_related_to_technique():\n global technique_related_to_technique\n\n if not technique_related_to_technique:\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\n\n return technique_related_to_technique","def test_malware_have_techniques(attck_fixture):\n for malware in attck_fixture.enterprise.malwares:\n if malware.techniques:\n assert getattr(malware,'techniques')","def extract_features(data, stopwords=STOPWORDS):\n tags = set()\n docs = []\n for document in data:\n doc_data = dict()\n doc_data['pmid'] = document['sourceid']\n text = document['text']\n\n # Insert PubTator annotations inside abstract\n denotations = document['denotations']\n sorted_denotations = []\n for denotation in denotations:\n begin = denotation['span']['begin']\n end = denotation['span']['end']\n obj = denotation['obj']\n for c in punctuation:\n obj = obj.replace(c, '')\n tags.add(obj)\n doc_data[obj] = doc_data.get(obj,0)+1\n sorted_denotations.append([begin,end,obj])\n sorted_denotations.sort()\n sorted_denotations.reverse()\n for begin, end, obj in sorted_denotations:\n text = text[:begin] + obj + ' ' + text[end:]\n\n doc_data['text'] = clean_text(text, stopwords)\n docs.append(doc_data)\n\n return docs","def generate(softwaretype=\"software\"):\n # import the STIX data from MITRE/CTI\n stix = requests.get(\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\").json()\n ms = MemoryStore(stix_data=stix[\"objects\"])\n # software includes malware and tool types so perform two queries and merge the results\n software_filters = []\n if softwaretype == \"malware\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'malware') ] )\n if softwaretype == \"tool\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'tool') ] )\n \n software = list(chain.from_iterable(\n ms.query(f) for f in software_filters\n ))\n\n # build a list of techniques used by software\n techniques_used = {} #attackID => using software names\n for thesoftware in software:\n # filter out revoked and deprecated software\n if (\"x_mitre_deprecated\" in thesoftware and thesoftware[\"x_mitre_deprecated\"]) or (\"revoked\" in thesoftware and thesoftware[\"revoked\"]): continue\n for relationship in ms.relationships(thesoftware[\"id\"]):\n # skip all non-technique relationships\n if \"attack-pattern\" not in relationship[\"target_ref\"]: continue\n technique = ms.get(relationship[\"target_ref\"])\n # filter out deprecated and revoked techniques\n if (\"x_mitre_deprecated\" in technique and technique[\"x_mitre_deprecated\"]) or (\"revoked\" in technique and technique[\"revoked\"]): continue\n techniqueID = technique[\"external_references\"][0][\"external_id\"]\n # store usage in techniques_used struct\n if techniqueID in techniques_used:\n techniques_used[techniqueID].append(thesoftware[\"name\"])\n else:\n techniques_used[techniqueID] = [thesoftware[\"name\"]]\n\n # format the techniques for the output layer\n techniques_list = []\n highest_usage = 0\n lowest_usage = 1\n for techniqueID in techniques_used:\n # determine the number of used techniques for the score\n count = len(techniques_used[techniqueID])\n highest_usage = max(highest_usage, count)\n lowest_usage = min(lowest_usage, count)\n # append technique struct to list of layer-formatted techniques\n techniques_list.append({\n \"techniqueID\": techniqueID,\n \"comment\": \"executed by \" + \", \".join(techniques_used[techniqueID]),\n \"score\": count,\n })\n # set up layer name and desc according to softwaretype\n if softwaretype != \"software\": \n plural = \"tools\" if softwaretype == \"tool\" else \"malware\"\n layername = f\"Software ({softwaretype}) Execution\"\n layerdescription = f\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\"\n else: \n layername = \"Software Execution\"\n layerdescription = f\"All techniques that can be executed by software, where the score is the count of software using the technique\"\n\n # construct and return the layer as a dict\n return {\n \"name\": layername,\n \"description\": layerdescription,\n \"versions\": {\n \"layer\": \"4.1\",\n \"navigator\": \"4.1\"\n },\n \"domain\": \"enterprise-attack\",\n \"techniques\": techniques_list,\n \"sorting\": 3, # order in descending order of score (count)\n \"gradient\": {\n \"colors\": [\n \"#fff7b3\", # low counts are yellow\n \"#ff6666\", # high counts are red\n ],\n \"minValue\": lowest_usage,\n \"maxValue\": highest_usage\n },\n }","def fixture_lims_samples(lims_family: dict) -> List[dict]:\n return lims_family[\"samples\"]","def extract_routine_instances(theories, routines, exec_routine_instruction_index):\n new_theories=[]\n for theory in theories:\n for i in range(len(routines)):\n routine=routines[i]\n theory=replace_instance(theory,routine,[(exec_routine_instruction_index,i)])\n new_theories.append(theory)\n return new_theories","def examples(self):\n for obj_ind in range(len(self.objects)):\n yield self.get_object_intent_by_index(obj_ind)","def get_tools_using_technique():\n global tools_using_technique\n\n if not tools_using_technique:\n tools_using_technique = rsh.tools_using_technique(get_srcs())\n \n return tools_using_technique","def get_subtechniques_of():\n global subtechniques_of\n\n if not subtechniques_of:\n subtechniques_of = rsh.subtechniques_of(get_srcs())\n \n return subtechniques_of","def get_skills(lang, spec=None):\n\n if lang.lower() == 'java':\n output = get_java(spec)\n if spec is not None:\n print(f\"Our suggestions for {spec} are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n else:\n print(\"Our suggestions for are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n elif lang.lower() == 'python':\n output = get_python(spec)\n if spec is not None:\n print(f\"Our suggestions for {spec} are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n else:\n print(\"Our suggestions for are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")","def get_malware_using_technique():\n global malware_using_technique\n\n if not malware_using_technique:\n malware_using_technique = rsh.malware_using_technique(get_srcs())\n \n return malware_using_technique","def _process_examples(self):\n logger = getLogger(\"problog_lfi\")\n # value can be True / False / None\n # ( atom ), ( ( value, ... ), ... )\n\n # Simple implementation: don't add neutral evidence.\n\n # ad_groups is a list of lists where each list contains an AD\n ad_groups = list()\n for ad in self._adatoms:\n # if it's an AD group\n if len(ad[1]) > 1:\n ad_list = []\n for var in ad[1]:\n ad_list.append(Term(self.names[var].functor, *self.names[var].args))\n ad_groups.append(tuple(ad_list))\n logger.debug(\"AD Groups\\t\\t:\" + str(ad_groups))\n\n def all_false(d):\n \"\"\"\n This function recognizes inconsistent evidence s.t. all values are False in AD.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :return: whether all values are False\n \"\"\"\n # false_count should be the same as the length of d\n false_count = sum(v is False for v in d.values())\n return false_count == len(d)\n\n def all_false_except_one(d):\n \"\"\"\n This function recognizes incomplete evidence s.t.\n the non-False value in AD needs to be set to True.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :return: whether all values except one are False\n \"\"\"\n false_count = sum(v is False for v in d.values())\n the_left_is_none = bool(sum(v is None for v in d.values()))\n return (false_count == len(d) - 1) and the_left_is_none\n\n def getADtemplate(d, atom=None):\n \"\"\"\n This function gets atom's complement AD template.\n This should only be used when the AD contains non-ground terms.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :param atom: an evidence\n :return: atom's complement AD template\n \"\"\"\n if atom is not None:\n temp_dict = {\n k: v\n for k, v in d.items()\n if v == \"Template\" and atom.signature != k.signature\n }\n return temp_dict\n else:\n temp_dict = {k: v for k, v in d.items() if v == \"Template\"}\n return temp_dict\n\n def add_to_ad_evidence(pair, l, ADtemplate):\n \"\"\"\n :param pair: a new pair of (atom, value)\n :param l: a list of dictionaries, all dictionaries need to have the same format\n :return:\n \"\"\"\n (k, v) = pair\n # if entry k exists, update the value with k\n for d in l:\n if k in d:\n d[k] = v\n return\n # if entry k does not exist, create a new dictionary from template\n # and instantiate it with k\n new_d = dict()\n for temp_k in ADtemplate.keys():\n new_key = Term(temp_k.functor, *k.args)\n new_d[new_key] = None\n # put v in there\n new_d[k] = v\n l.append(new_d)\n\n if self.infer_AD_values:\n result = ExampleSet()\n inconsistent = False\n # iterate over all examples given in .ev\n for index, example in enumerate(self.examples):\n ad_evidences = []\n non_ad_evidence = {}\n for ad_group in ad_groups:\n # create a dictionary to memorize what evidence is given in AD\n d = dict()\n # TODO: what if the AD contains both ground and non-ground????\n # e.g. t(_)::a; t(_)::b(X)\n for var in ad_group:\n if var.is_ground():\n d[var] = None # for ground unknown evidence\n else:\n d[var] = \"Template\" # for unground unknown evidence\n ad_evidences.append(d)\n\n # add all evidence in the example to ad_evidences\n for atom, value in example:\n # if atom has a tunable probability to learn\n if any([atom.signature == name.signature for name in self.names]):\n if len(atom.args) == 0: # Propositional evidence\n # insert evidence\n is_ad = False\n for d in ad_evidences:\n if atom in d:\n d[atom] = value\n is_ad = True\n if not is_ad:\n non_ad_evidence[\n atom\n ] = value\n else: # First Order evidence\n # find the right AD dictionary : AD_dict\n AD_dict = None\n for d in ad_evidences:\n if any([atom.signature == k.signature for k in d]):\n AD_dict = d\n # if the instantiation is new, add it as a key to the dictionary\n if AD_dict and AD_dict.get(atom) is None:\n AD_dict[atom] = value\n # also add other AD parts in the dictionary with value==None\n other_ADs = getADtemplate(AD_dict, atom)\n for otherAD in other_ADs.keys():\n new_key = Term(otherAD.functor, *atom.args)\n AD_dict[new_key] = AD_dict.get(new_key, None)\n else:\n non_ad_evidence[atom] = value\n else:\n non_ad_evidence[atom] = value\n\n # grounded_ad_evidences contains all usable evidence (gound, not template)\n grounded_ad_evidences = []\n for d in ad_evidences:\n # for first order evidence dictionaries\n if \"Template\" in d.values():\n # new_ad_evidence is a list of dictionaries\n # each dictionary is a group of the AD template instantiation\n new_ad_evidence = list()\n # get template AD evidence\n ADtemplate = getADtemplate(d)\n # group all pairs according to ADtemplate\n for k, v in d.items():\n if v != \"Template\":\n add_to_ad_evidence((k, v), new_ad_evidence, ADtemplate)\n grounded_ad_evidences += new_ad_evidence\n else:\n # simply us them\n grounded_ad_evidences.append(d)\n\n inconsistent_example = False\n for i, d in enumerate(grounded_ad_evidences):\n # inconsistent1 = multiple_true(d)\n inconsistent2 = all_false(d)\n add_compliment = all_false_except_one(d)\n\n if inconsistent2:\n inconsistent_example = True\n continue\n elif add_compliment:\n for key, value in d.items():\n if value is None:\n grounded_ad_evidences[i][key] = True\n\n if not inconsistent_example and len(grounded_ad_evidences) > 0:\n # There are (fully tunable) ADs in the program\n evidence_list = []\n for d in grounded_ad_evidences:\n for key, value in d.items():\n if value is not None:\n if (key, value) not in evidence_list:\n # TODO: Switch to orderedSet so we don't have to check whether it's in already\n evidence_list.append((key, value))\n\n for key, value in non_ad_evidence.items():\n evidence_list.append((key, value))\n\n atoms, values = zip(*evidence_list)\n result.add(index, atoms, values)\n\n else:\n # (No AD case) or (Inconsistent Evidence Case)\n atoms, values = zip(*example)\n result.add(index, atoms, values)\n return result\n else:\n # smarter: compile-once all examples with same atoms\n result = ExampleSet()\n for index, example in enumerate(self.examples):\n atoms, values = zip(*example)\n result.add(index, atoms, values)\n return result","def get_mitigation_mitigates_techniques():\n global mitigation_mitigates_techniques\n\n if not mitigation_mitigates_techniques:\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\n\n return mitigation_mitigates_techniques","def get_guides(data):\n return data.groups[\"Frames\"].objects","def examples():\n from matdb import msg\n script = \"MATDB Finder for Groups/Trainers and General Contexts\"\n explain = (\"Each context has a controller that provides a find() method for\"\n \"looking up instances within that context. This script provides \"\n \"a simple interface for finding things.\")\n contents = [((\"Find hessian databases.\"), \n \"matdb_find.py system.yaml -d -p hessian-*/hessian\",\n \"The find function uses the format dbname/group/seed\")]\n required = (\"'matdb.yaml' file with database settings.\")\n output = (\"\")\n details = (\"\")\n outputfmt = (\"\")\n\n msg.example(script, explain, contents, required, output, outputfmt, details)","def get_examples():\n\n base_query = \"[[Medium::TCG]] [[Database ID::+]]\"\n base_printouts = [\n \"Password\",\n \"English name\",\n \"Card type\",\n ]\n all_res = {}\n\n n = len(ALL_DISPLAYS)\n for i, x in enumerate(ALL_DISPLAYS):\n query = f\"{base_query} [[{x}::+]]\"\n print(i, n, query)\n try:\n data = ask_yugipedia(query, base_printouts + [x])\n except:\n print(\"ERROR\", i, x)\n continue\n all_res[x] = data\n return all_res","def patterns() -> List[Dict[str, Any]]:\n patterns = [\n {\"label\": \"DRUG\", \"pattern\": \"Zithromax\", \"type\": \"fuzzy\", \"id\": \"Antibiotic\"},\n {\"label\": \"GPE\", \"pattern\": \"Mahwahe\", \"type\": \"fuzzy\"},\n {\"label\": \"GPE\", \"pattern\": \"Mahwah\", \"type\": \"fuzzy\"},\n {\n \"label\": \"NAME\",\n \"pattern\": \"Grant Andersen\",\n \"type\": \"fuzzy\",\n \"kwargs\": {\"fuzzy_func\": \"token_sort\"},\n \"id\": \"Developer\",\n },\n {\n \"label\": \"NAME\",\n \"pattern\": \"Garth Andersen\",\n \"type\": \"fuzzy\",\n \"kwargs\": {\"fuzzy_func\": \"token_sort\"},\n \"id\": \"Developer\",\n },\n {\n \"label\": \"STREET\",\n \"pattern\": \"street_addresses\",\n \"type\": \"regex\",\n \"kwargs\": {\"predef\": True},\n },\n {\n \"label\": \"GPE\",\n \"pattern\": \"(?i)[U](nited|\\\\.?) ?[S](tates|\\\\.?)\",\n \"type\": \"regex\",\n \"id\": \"USA\",\n },\n {\"label\": \"GPE\", \"pattern\": \"(?:USR){e<=1}\", \"type\": \"regex\", \"id\": \"USA\"},\n {\"label\": \"GPE\", \"pattern\": \"(?:USSR){d<=1, s<=1}\", \"type\": \"regex\"},\n {\n \"label\": \"BAND\",\n \"pattern\": [{\"LOWER\": {\"FREGEX\": \"(converge){e<=1}\"}}],\n \"type\": \"token\",\n },\n {\n \"label\": \"BAND\",\n \"pattern\": [\n {\"TEXT\": {\"FUZZY\": \"Protest\"}},\n {\"IS_STOP\": True},\n {\"TEXT\": {\"FUZZY\": \"Hero\"}},\n ],\n \"type\": \"token\",\n \"id\": \"Metal\",\n },\n ]\n return patterns # type: ignore","def _create_examples(self, data_dir, set_type):\n\t\texamples = []\n\t\tinput_file_data = os.path.join(data_dir, \"data.tsv\")\n\t\twith open(input_file_data, \"r\", encoding=\"utf-8-sig\") as f:\n\t\t\tfor i, inp in enumerate(f):\n\t\t\t\tinps = inp.split('\\t') \n\t\t\t\tguid = \"%s-%s\" % (set_type, i)\n\t\t\t\ttext_inp = inps[1].strip()\n\t\t\t\ttext_out = inps[2].strip()\n\t\t\t\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\n\t\t\t\t\n\t\t\t# Sort these out before returning\n\t\t\texamples = sorted(examples, key=sort_inp_len)\n\t\t\treturn examples","def read_examples(data_dir, data_sign):\n examples = []\n # read src data\n with open(data_dir / f'{data_sign}/sentences.txt', \"r\", encoding='utf-8') as f_sen, \\\n open(data_dir / f'{data_sign}/tags.txt', 'r', encoding='utf-8') as f_tag:\n for sen, tag in zip(f_sen, f_tag):\n example = InputExample(sentence=sen.strip().split(' '), tags=tag.strip().split(' '))\n examples.append(example)\n print(\"InputExamples:\", len(examples))\n return examples","def search_example(self, query: str) -> List[Example]:\n raise NotImplementedError","def get_examples(self, env):\n return self.fam.c_get_examples(self, env)","def convert_examples_to_features(self):\n features = []\n for ex_index, example in enumerate(self.examples):\n taskname = example.taskname\n taskname_ids = self.tokenizer.text_to_ids(taskname)\n processor = example.processor\n if ex_index % 10000 == 0:\n logging.info(f\"Writing example {ex_index} of {len(self.examples)}\")\n enc_query = processor.get_ptune_query(\n example.content,\n self.pseudo_token_id,\n self.max_seq_length - self.max_seq_length_decoder + 1,\n self.templates,\n self.tokenizer,\n )\n features.append([enc_query, taskname_ids])\n return features","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def find_examples_in_schema(path):\n with open(path, 'rb') as fd:\n schema_tree = yaml.load(fd)\n\n for node in treeutil.iter_tree(schema_tree):\n if (isinstance(node, dict) and\n 'examples' in node and\n isinstance(node['examples'], list)):\n for desc, example in node['examples']:\n yield example","def techniques(self):\n return self._get_child_page_of_type(LearningTechniquesPage)","def find_patterns(obs, player_x, player_y):\n for get_group in groups_of_memory_patterns:\n group = get_group(obs, player_x, player_y)\n if group[\"environment_fits\"](obs, player_x, player_y):\n return group[\"get_memory_patterns\"](obs, player_x, player_y)","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def extract_patterns_from_trees(self, important_words, trees, class_value):\n for tree in trees:\n self.extract_patterns(important_words, tree, class_value)","def sample_handling(sample, lexicon, classification):\n\n # We have a list of lists [.... [ [0, 2, 1, 0, 0, ..., 0] [1] ] , .... ] with the bag of words and the class\n featureset = []\n\n # Open the sample text and parse through the document and generate feastures.\n with open(sample, 'r') as f:\n contents = f.readlines()\n for l in contents[:hm_lines]:\n current_words = word_tokenize(l.lower())\n current_words = [lemmatizer.lemmatize(i) for i in current_words]\n features = np.zeros(len(lexicon))\n for word in current_words:\n if word.lower() in lexicon:\n index_value = lexicon.index(word.lower())\n features[index_value] = 1\n features = list(features)\n featureset.append([features, classification])\n\n return featureset","def samples(self, groups=None):\n groups = check_parameters_for_validity(\n groups, \"groups\", self.groups(), self.groups()\n )\n all_samples = []\n\n if \"train\" in groups:\n all_samples.extend(self.background_model_samples())\n groups.remove(\"train\")\n\n for grp in groups:\n all_samples.extend(_sample_sets_to_samples(self.references(grp)))\n all_samples.extend(_sample_sets_to_samples(self.probes(grp)))\n\n # Add znorm samples. Returning znorm samples for one group of dev or\n # eval is enough because they are duplicated.\n for grp in groups:\n all_samples.extend(_sample_sets_to_samples(self.zprobes(grp)))\n break\n\n # Add tnorm samples.\n all_samples.extend(_sample_sets_to_samples(self.treferences()))\n\n return all_samples","def get_dev_examples(self, input_file):\r\n raise NotImplementedError()","def test_filters_with_extra_extraction(self) -> None:\n\n # pylint: disable=too-many-locals\n\n import hammer_config\n\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n\n def add_named_library(in_dict: Dict[str, Any]) -> Dict[str, Any]:\n out_dict = deepdict(in_dict)\n out_dict[\"libraries\"].append({\n \"name\": \"abcdef\",\n \"milkyway techfile\": \"test/abcdef.tf\"\n })\n return out_dict\n\n HammerToolTestHelpers.write_tech_json(tech_json_filename, add_named_library)\n sys.path.append(tech_dir_base)\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir))\n tech.cache_dir = tech_dir\n\n def filter_func(lib: hammer_tech.Library) -> bool:\n return lib.milkyway_techfile is not None\n\n def paths_func(lib: hammer_tech.Library) -> List[str]:\n assert lib.milkyway_techfile is not None\n return [lib.milkyway_techfile]\n\n def extraction_func(lib: hammer_tech.Library, paths: List[str]) -> List[str]:\n assert len(paths) == 1\n if lib.name is None:\n name = \"\"\n else:\n name = str(lib.name)\n return [json.dumps({\"path\": paths[0], \"name\": name}, cls=HammerJSONEncoder, indent=4)]\n\n def sort_func(lib: hammer_tech.Library):\n assert lib.milkyway_techfile is not None\n return lib.milkyway_techfile\n\n test_filter = LibraryFilter.new(\"metatest\", \"Test filter that extracts metadata\",\n is_file=True, filter_func=filter_func,\n paths_func=paths_func,\n extraction_func=extraction_func,\n sort_func=sort_func)\n\n database = hammer_config.HammerDatabase()\n tech.set_database(database)\n raw = tech.process_library_filter(pre_filts=[], filt=test_filter,\n must_exist=False,\n output_func=hammer_tech.HammerTechnologyUtils.to_plain_item)\n\n # Disable false positive from pylint\n outputs = list(map(lambda s: json.loads(s), raw)) # pylint: disable=unnecessary-lambda\n self.assertEqual(outputs,\n [\n {\"path\": tech.prepend_dir_path(\"test/abcdef.tf\"), \"name\": \"abcdef\"},\n {\"path\": tech.prepend_dir_path(\"test/coconut\"), \"name\": \"\"},\n {\"path\": tech.prepend_dir_path(\"test/soy\"), \"name\": \"\"}\n ])\n\n # Cleanup\n shutil.rmtree(tech_dir_base)","def getExamples(query):\n HREF_ITER = 'href'\n UN_URL_PART = '/kanji/details/'\n EX_FOUND = 'Found'\n JISHO_URL = 'http://jisho.org/sentences?jap='\n \n url = JISHO_URL + query #may be kanji or word\n \n try:\n soup = BeautifulSoup(urllib2.urlopen(url))\n \n check = soup.findAll('h2')\n if len(check) > 0 and len(check[0].contents) > 0:\n if EX_FOUND in check[0].contents[0]:\n \n sentences = soup.findAll('a', href=re.compile('/kanji/details'))\n translations = soup.findAll('td', attrs={'class':'english'})\n \n r_sent = []\n r_trans = []\n \n if len(sentences) == len(translations):\n for sentence in sentences:\n r_sent.append(sentence[HREF_ITER].replace(UN_URL_PART, ''))\n for translation in translations:\n r_trans.append(translation.contents[0].replace('\\t',''))\n \n return dict(zip(r_sent, r_trans))\n except Exception:\n return {}","def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\n return sorted(list(set(expert_knowledge)))","def test_extract():\n print(\"Executing test_extract:\")\n\n theory_1=[\n (14,),\n (15,),\n (14,),\n (16,)\n ]\n theory_2=[\n (14,),\n (15,),\n (14,),\n (17,)\n ]\n theory_3=[\n (15,),\n (14,)\n ]\n\n mind=minds.new_mind(theories=[theory_1,theory_2,theory_3])\n\n print(\"Mind initial state:\")\n print(minds.mind_string(mind, show_claims=False, show_problems=False))\n\n minds.extract_new_routines(mind,1)\n print(\"Mind after 1 step of extraction:\")\n print(minds.mind_string(mind, show_claims=False, show_problems=False))\n\n minds.extract_new_routines(mind,1)\n print(\"Mind after 2 steps of extraction:\")\n print(minds.mind_string(mind, show_claims=False, show_problems=False))","def get_dev_examples(self, data_path):\r\n return self.create_examples(self.read_data(data_path), 'dev')","def get_legal_examples(input_filepath):\n solution_filepath = TestSystem.get_solution_filepath(input_filepath)\n with io.open(solution_filepath, 'r') as f:\n solution_examples = f.readlines()\n return [\n {\n \"intent\": ex.split(TestSystem.solution_marker)[0],\n \"text\": ex.split(TestSystem.solution_marker)[1].rstrip()\n }\n for ex in solution_examples\n if (not ex.startswith('#') and not ex.isspace())\n ]","def findFeatures(self):\n\t\tpass","def get_word_examples(text):\n\n # Resulting examples list\n examples = []\n # Multiple quotes escape stack\n stack = []\n # Reverse text for iteration\n reverse = text[::-1].strip()\n # Represents a single example\n example = ''\n\n # Iterates over every character in the text\n for char in reverse:\n # If char is closed brace break\n if char == ')':\n break\n\n # If char is quotes but no quotes in stack add them to stack\n if char == '\"' and len(stack) == 0:\n stack.append(char)\n\n # Add the character to the example string\n example += char\n\n # If char is quotes but there is already quotes inside the stack\n if char == '\"' and len(stack) != 0:\n stack.pop()\n\n example = example[::-1].strip()\n example = example[1: len(example)]\n example = example.capitalize()\n\n examples.append(example)\n\n example = ''\n\n # Clear all empty strings\n for example in examples:\n if example == '' or example == ';':\n examples.remove(example)\n\n return examples","def _get_examples_from_json(self):\n results = []\n found = glob.glob(\n os.path.join(_TEST_DEFS_DIR, \"**\", f\"{self.expectation_type}.json\"),\n recursive=True,\n )\n if found:\n with open(found[0]) as fp:\n data = json.load(fp)\n results = data[\"datasets\"]\n return results","async def insert_attack_stix_data(self):\n logging.info('Downloading ATT&CK data from STIX/TAXII...')\n attack = {}\n collection = Collection(\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\")\n tc_source = TAXIICollectionSource(collection)\n filter_objs = {\"techniques\": Filter(\"type\", \"=\", \"attack-pattern\"),\n \"groups\": Filter(\"type\", \"=\", \"intrusion-set\"), \"malware\": Filter(\"type\", \"=\", \"malware\"),\n \"tools\": Filter(\"type\", \"=\", \"tool\"), \"relationships\": Filter(\"type\", \"=\", \"relationship\")}\n for key in filter_objs:\n attack[key] = tc_source.query(filter_objs[key])\n references = {}\n\n # add all of the patterns and dictionary keys/values for each technique and software\n for i in attack[\"techniques\"]:\n if \"description\" in i:\n descr = i['description'].replace('<code>', '').replace('</code>', '').replace(\n '\\n', '').encode('ascii', 'ignore').decode('ascii')\n else:\n descr = \"missing description\"\n references[i[\"id\"]] = {\"name\": i[\"name\"], \"id\": i[\"external_references\"][0][\"external_id\"],\n \"example_uses\": [],\n \"description\": descr,\n \"similar_words\": [i[\"name\"]]}\n\n for i in attack[\"relationships\"]:\n if i[\"relationship_type\"] == 'uses':\n if 'attack-pattern' in i[\"target_ref\"]:\n use = i[\"description\"]\n # remove unnecessary strings, fix unicode errors\n use = use.replace('<code>', '').replace('</code>', '').replace('\"', \"\").replace(',', '').replace(\n '\\t', '').replace(' ', ' ').replace('\\n', '').encode('ascii', 'ignore').decode('ascii')\n find_pattern = re.compile('\\[.*?\\]\\(.*?\\)') # get rid of att&ck reference (name)[link to site]\n m = find_pattern.findall(use)\n if len(m) > 0:\n for j in m:\n use = use.replace(j, '')\n if use[0:2] == '\\'s':\n use = use[3:]\n elif use[0] == ' ':\n use = use[1:]\n # combine all the examples to one list\n references[i[\"target_ref\"]][\"example_uses\"].append(use)\n\n for i in attack[\"malware\"]:\n if 'description' not in i: # some software do not have description, example: darkmoon https://attack.mitre.org/software/S0209\n continue\n else:\n references[i[\"id\"]] = {\"id\": i['id'], \"name\": i[\"name\"], \"description\": i[\"description\"],\n \"examples\": [], \"example_uses\": [], \"similar_words\": [i[\"name\"]]}\n for i in attack[\"tools\"]:\n references[i[\"id\"]] = {\"id\": i['id'], \"name\": i[\"name\"], \"description\": i[\"description\"], \"examples\": [],\n \"example_uses\": [], \"similar_words\": [i[\"name\"]]}\n\n attack_data = references\n logging.info(\"Finished...now creating the database.\")\n\n cur_uids = await self.dao.get('attack_uids') if await self.dao.get('attack_uids') else []\n cur_items = [i['uid'] for i in cur_uids]\n for k, v in attack_data.items():\n if k not in cur_items:\n await self.dao.insert('attack_uids', dict(uid=k, description=defang_text(v['description']), tid=v['id'],\n name=v['name']))\n if 'regex_patterns' in v:\n [await self.dao.insert('regex_patterns', dict(uid=k, regex_pattern=defang_text(x))) for x in\n v['regex_patterns']]\n if 'similar_words' in v:\n [await self.dao.insert('similar_words', dict(uid=k, similar_word=defang_text(x))) for x in\n v['similar_words']]\n if 'false_negatives' in v:\n [await self.dao.insert('false_negatives', dict(uid=k, false_negative=defang_text(x))) for x in\n v['false_negatives']]\n if 'false_positives' in v:\n [await self.dao.insert('false_positives', dict(uid=k, false_positive=defang_text(x))) for x in\n v['false_positives']]\n if 'true_positives' in v:\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\n v['true_positives']]\n if 'example_uses' in v:\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\n v['example_uses']]\n logging.info('[!] DB Item Count: {}'.format(len(await self.dao.get('attack_uids'))))","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\r\n raise NotImplementedError()","def _create_examples(self, data, set_type):\n examples = []\n for (i, elem) in enumerate(data):\n guid = \"%s-%s\" % (set_type, i)\n text = elem[0]\n label = elem[1]\n examples.append(\n InputExample(guid=guid, text=text, label=label))\n return examples","def get_dev_examples(self, data_dir):\n\t\traise NotImplementedError()","def test_text_classifier_get_testing_samples(self):\n pass","def examples_from_file(path):\n examples = []\n\n # count total lines before loading\n total_lines = int(local('wc -l {}'.format(path), capture=True).split()[0])\n\n with codecs.open(path, 'r', encoding='utf-8') as f:\n for line in verboserate(f, desc='Reading data file.', total=total_lines):\n src, trg = line.strip().lower().split('\\t')\n src_words = src.split(' ')\n trg_words = trg.split(' ')\n assert len(src_words) > 0\n assert len(trg_words) > 0\n\n if use_diff:\n ex = EditExample.salient_diff(src_words, trg_words, free_set)\n else:\n ex = EditExample.whitelist_blacklist(src_words, trg_words)\n examples.append(ex)\n return examples","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def get_dev_examples(self, data_dir):\n raise NotImplementedError()","def search_samples():\n r = req('GET', SUB_API + 'search/samples', params=apply_search_filters())\n samples = []\n for sample in demisto.get(r.json(), 'data.items'):\n samples.append({\n 'ID': demisto.get(sample, 'result'),\n 'Details': demisto.get(sample, 'details')\n })\n demisto.results({\n 'Type': entryTypes['note'],\n 'EntryContext': {'ThreatGrid.Sample': samples},\n 'HumanReadable': tableToMarkdown('ThreatGrid - Sample Search', samples, ['Result', 'Details']),\n 'ContentsFormat': formats['json'],\n 'Contents': r.json()\n })","def examples(self):\n return self._examples","def create_training_examples(statement_list: List[List[str]], trees, training_spans,\n training: bool, max_span_length, num_sentence_words,\n args, pos_tags, constituents, k=1):\n print('building features...')\n\n word_frequencies = FreqDist([w.lower() for w in reuters.words()])\n # list of science tokens & science multiword expressions\n science_tokens = get_science_terms(args.data_path)\n science_expressions = get_science_terms(args.data_path, False)\n stop_words = set(stopwords.words('english'))\n\n\n\n true_examples, false_examples, examples_per_sentence = [], [], []\n span_indexes = []\n\n # loop over all statements\n for i, statement in enumerate(statement_list):\n print(i)\n\n span_index = [] #enumerating all spans for this sentence.\n pos_tags_this_statement = pos_tag(statement)\n sentence_word_frequencies = [word_frequencies.freq(token.lower()) \\\n for token in statement]\n tree = trees[i]\n false_examples_this_instance = []\n sentence_candidate_span_examples = [] # when training.\n\n # loop across different spans for given sentence\n for span in legal_spans(num_sentence_words, max_span_length): #globally legal\n if span[1] > len(statement):\n continue\n if span[0] > len(statement):\n break\n span_index.append(span)\n\n # extract scalar features for this span. [position will not be used]\n f_bias = 1\n f_length = span[1] - span[0]\n #f_begin = span[0]\n #f_end = span[1]\n #f_dist_to_end0 = len(statement) - span[0]\n #f_dist_to_end1 = len(statement) - span[1]\n\n # list of tokens of this span\n span_tokens = statement[span[0]: span[1]]\n\n # feature: span contains at least one science token\n f_science_token = bool( \\\n set(span_tokens).intersection(science_expressions))\n\n\n\n f_science_token_count = 0 # counting # of science tokens in span\n max_token_length = 0 # in this span.\n for token in span_tokens:\n f_science_token_count += int(token in science_tokens)\n max_token_length = max(max_token_length, len(token))\n\n f_max_token_length = np.log(max_token_length)\n\n # feature: relative word frequency average\n # with numerical stability/ avoiding -inf\n f_avg_word_frequency = 1e-10+np.mean(sentence_word_frequencies[span[0]: span[1]])\n f_avg_word_frequency = np.log(f_avg_word_frequency)\n\n # feature: begin with stop word?\n f_stop_word_begin = bool(span_tokens[0] in stop_words)\n\n # POS indicator (one-hot)\n f_pos = np.zeros([len(pos_tags)])\n\n # Bag-of-POS-tags for this span.\n for token, tag in pos_tags_this_statement[span[0]:span[1]]:\n f_pos[pos_tags.index(tag)] += 1.0\n\n # feature: POS indicator for span beginning\n f_pos_beginning = np.zeros([len(pos_tags)])\n f_pos_beginning[pos_tags.index(pos_tags_this_statement[span[0]][1])] = 1.0\n\n # feature: POS indicator for span end\n f_pos_end = np.zeros([len(pos_tags)])\n f_pos_end[pos_tags.index(pos_tags_this_statement[span[1]-1][1])] = 1.0\n\n # feature: POS bigram indicator\n # define extended POS tag set with additional begin and end symbols for bigrams.\n # pos_tags_bigram = pos_tags + [\"POS_BEGIN\", \"POS_END\"]\n\n # for POS bigrams.\n # pos_tags_square = [x for x in product(pos_tags_bigram, pos_tags_bigram)]\n\n # f_pos_bigram = np.zeros([len(pos_tags_square)])\n\n # obtaining the POS bigram\n # for position in range(-1, f_length):\n # boundary cases: start of span and end of span.\n # if position == -1:\n # tag1 = 'POS_BEGIN'\n # _, tag2 = pos_tags_this_statement[span[0]]\n # elif position == f_length -1:\n # _, tag1 = pos_tags_this_statement[span[0]+position]\n # tag2 = 'POS_END'\n # #normal case: inside span.\n # else:\n # _, tag1 = pos_tags_this_statement[span[0] + position]\n # _, tag2 = pos_tags_this_statement[span[0] + position + 1]\n #\n # f_pos_bigram[pos_tags_square.index( ( tag1, tag2 ) )] += 1.0\n\n # constituent tree features\n\n tree_position = tree.treeposition_spanning_leaves(span[0], span[1])\n\n # smallest subtree in constituent parse, containing this span.\n smallest_subtree = tree[tree_position[:-1]]\n constituent_tag = smallest_subtree.label()\n\n # feature: is this span a constituent parse subtree span?\n f_span_match = bool(span[1]-span[0] == len(smallest_subtree))\n\n # constituency parse label indicator\n f_span_constituent = np.zeros([len(constituents)])\n f_span_constituent[constituents.index(constituent_tag)] = 1.0\n\n # constituency parse label indicator with indication for large spans.\n f_span_constituent_big = np.zeros([len(constituents)])\n f_span_constituent_big[constituents.index(constituent_tag)] = (f_length > 2)\n\n\n # leave out position features:\n #### f_begin, f_end, f_dist_to_end0, f_dist_to_end1,\n\n #now collect all features:\n f_scalars = np.array([f_bias, f_span_match, f_length,\n f_science_token, f_avg_word_frequency,\n f_stop_word_begin,\n f_max_token_length,\n f_science_token_count])\n\n # these are all features for this span, in a np array.\n feature_vector = np.concatenate((f_scalars, f_pos, f_pos_beginning,\n f_pos_end, f_span_constituent,\n f_span_constituent_big))\n\n\n\n # provide True/False annotation in case the data is used for training.\n if training:\n if span == training_spans[i]:\n #positive example\n true_examples.append(feature_vector)\n sentence_candidate_span_examples.append((feature_vector, True))\n else:\n #negative example\n false_examples_this_instance.append(feature_vector)\n sentence_candidate_span_examples.append((feature_vector, False))\n else:\n sentence_candidate_span_examples.append(feature_vector)\n\n span_indexes.append(span_index)\n examples_per_sentence.append(sentence_candidate_span_examples)\n\n\n # select at random k negative spans as training examples. default 1:1\n if training:\n for random_index in np.random.randint(0, len(false_examples_this_instance), k):\n false_examples.append(false_examples_this_instance[random_index])\n\n print(len(true_examples), 'True span examples.')\n print(len(false_examples), 'False span examples.')\n\n # collect true and false examples [inputs]\n all_examples = np.concatenate((np.asarray(false_examples), np.asarray(true_examples)))\n\n # collect annotations for each example (True/False target outputs)\n false_span_labels = np.zeros([len(false_examples)])\n true_span_labels = np.ones([len(true_examples)])\n all_labels = np.concatenate((false_span_labels, true_span_labels))\n\n return all_examples, all_labels, examples_per_sentence, span_indexes","def get_extra_facets(project, dataset, mip, short_name, extra_facets_dir):\n project_details = _load_extra_facets(project, extra_facets_dir)\n\n def pattern_filter(patterns, name):\n \"\"\"Get the subset of the list `patterns` that `name` matches.\n\n Parameters\n ----------\n patterns : :obj:`list` of :obj:`str`\n A list of strings that may contain shell-style wildcards.\n name : str\n A string describing the dataset, mip, or short_name.\n\n Returns\n -------\n :obj:`list` of :obj:`str`\n The subset of patterns that `name` matches.\n \"\"\"\n return [pat for pat in patterns if fnmatch.fnmatchcase(name, pat)]\n\n extra_facets = {}\n for dataset_ in pattern_filter(project_details, dataset):\n for mip_ in pattern_filter(project_details[dataset_], mip):\n for var in pattern_filter(project_details[dataset_][mip_],\n short_name):\n facets = project_details[dataset_][mip_][var]\n extra_facets.update(facets)\n\n return extra_facets","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def enumerate(self, capability: int = Capability.ALL) -> List[Technique]:\n\n # Update the cache for the current user\n self.find_suid()\n\n known_techniques = []\n for user, paths in self.suid_paths.items():\n for path in paths:\n binary = gtfobins.Binary.find(self.pty.which, path=path)\n if binary is not None:\n if (capability & binary.capabilities) == 0:\n continue\n\n known_techniques.append(\n Technique(user, self, binary, binary.capabilities)\n )\n\n return known_techniques","def get_test_examples(self, data_path):\r\n return self.create_examples(self.read_data(data_path), 'test')","def _generate_examples_from_file(self, file):\n\n examples = []\n for i, token in enumerate(file):\n if token == -1:\n # Out of dictionary token\n continue\n\n # Generate context tokens for the current token\n context_words = self._generate_contexts(i, file)\n\n # Form Examples:\n # center, context - follows form: (input, target)\n new_examples = [(token, ctxt) for ctxt in context_words if ctxt != -1]\n\n # Add to class\n examples.extend(new_examples)\n return examples","def txt_of_sources_in_run (ins, exp, run) :\n list_of_detectors = detectors(ins, exp, run)\n txt = '\\nList of detectors for inst: %s exp: %s run %d' % (ins, exp, run)\n for det in list_of_detectors :\n txt += '\\n'+det\n return txt","def get_samples(meta, classes_list):\n\n h = list(meta[meta['DiseaseState'].isin(classes_list[0])].index)\n dis = list(meta[meta['DiseaseState'].isin(classes_list[1])].index)\n\n return [h, dis]","def getFeatures(c):\n\n\n feature_list = []\n lc_rc_list = []\n w1 = c.getStack(0)\n w2 = c.getStack(1)\n w3 = c.getStack(2)\n b1 = c.getBuffer(0)\n b2 = c.getBuffer(1)\n b3 = c.getBuffer(2)\n for i in [w1, w2]: #12\n lc = c.getLeftChild(i,1) # 1 st left child of the word on the stack.\n rc = c.getRightChild(i,1) # 1 st right child of the word on the stack.\n lc_rc_list.append(lc)\n lc_rc_list.append(rc)\n lc_rc_list.append(c.getLeftChild(i,2)) # 2 nd left child of the word on the stack\n lc_rc_list.append(c.getRightChild(i,2)) # 2 nd right child of the word on the stack\n lc_rc_list.append(c.getLeftChild(lc,1)) # 1 st left child of the left child of the word on the stack\n lc_rc_list.append(c.getRightChild(rc,1)) # 1 st right child of the right child of the word on the stack\n ########################### 18 Word Features ###########################\n for i in [w1,w2,w3,b1,b2,b3]:\n\n feature_list.append(getWordID(c.getWord(i))) # 6 words of the stack and buffer\n\n for i in lc_rc_list: #12 words of the tree\n feature_list.append(getWordID(c.getWord(i)))\n\n ########################### 18 Tag Features ###########################\n for i in [w1,w2,w3,b1,b2,b3]:\n\n feature_list.append(getPosID(c.getPOS(i))) # 6 tags of the owrds on the stack and the buffer\n\n for i in lc_rc_list:\n feature_list.append(getPosID(c.getPOS(i))) #12 tags of the words onthe stack and the buffer.\n ########################### 12 label Features ###########################\n for i in lc_rc_list:\n feature_list.append(getLabelID(c.getLabel(i))) #12 labels of the words on the stack and the buffer.\n\n\n return feature_list","def fetch_group_lasso_datasets():\n\n # helper functions\n\n def find_interaction_index(seq, subseq,\n alphabet=\"ATGC\",\n all_possible_len_n_interactions=None):\n n = len(subseq)\n alphabet_interactions = \\\n [set(p) for\n p in list(itertools.combinations_with_replacement(alphabet, n))]\n\n num_interactions = len(alphabet_interactions)\n if all_possible_len_n_interactions is None:\n all_possible_len_n_interactions = \\\n [set(interaction) for\n interaction in\n list(itertools.combinations_with_replacement(seq, n))]\n\n subseq = set(subseq)\n\n group_index = num_interactions * \\\n all_possible_len_n_interactions.index(subseq)\n value_index = alphabet_interactions.index(subseq)\n\n final_index = group_index + value_index\n return final_index\n\n def create_group_indicies_list(seqlength=7,\n alphabet=\"ATGC\",\n interactions=[1, 2, 3],\n include_extra=True):\n alphabet_length = len(alphabet)\n index_groups = []\n if include_extra:\n index_groups.append(0)\n group_count = 1\n for inter in interactions:\n n_interactions = comb(seqlength, inter)\n n_alphabet_combos = comb(alphabet_length,\n inter,\n repetition=True)\n\n for x1 in range(int(n_interactions)):\n for x2 in range(int(n_alphabet_combos)):\n index_groups.append(int(group_count))\n\n group_count += 1\n return index_groups\n\n def create_feature_vector_for_sequence(seq,\n alphabet=\"ATGC\",\n interactions=[1, 2, 3]):\n feature_vector_length = \\\n sum([comb(len(seq), inter) *\n comb(len(alphabet), inter, repetition=True)\n for inter in interactions]) + 1\n\n feature_vector = np.zeros(int(feature_vector_length))\n feature_vector[0] = 1.0\n for inter in interactions:\n # interactions at the current level\n cur_interactions = \\\n [set(p) for p in list(itertools.combinations(seq, inter))]\n interaction_idxs = \\\n [find_interaction_index(\n seq, cur_inter,\n all_possible_len_n_interactions=cur_interactions) + 1\n for cur_inter in cur_interactions]\n feature_vector[interaction_idxs] = 1.0\n\n return feature_vector\n\n positive_url = \\\n \"http://genes.mit.edu/burgelab/maxent/ssdata/MEMset/train5_hs\"\n negative_url = \\\n \"http://genes.mit.edu/burgelab/maxent/ssdata/MEMset/train0_5_hs\"\n\n pos_file = tempfile.NamedTemporaryFile() #bufsize=0)\n neg_file = tempfile.NamedTemporaryFile() #bufsize=0)\n\n posreq = urllib.request.Request(positive_url)\n with urllib.request.urlopen(posreq) as posresponse:\n pos_page = posresponse.read().decode(\"utf-8\")\n\n negreq = urllib.request.Request(negative_url)\n with urllib.request.urlopen(negreq) as negresponse:\n neg_page = negresponse.read().decode(\"utf-8\")\n\n positive_sequences = [str(line.strip().upper()) for idx, line in\n enumerate(pos_page.strip().split('\\n'))\n if \">\" not in line and idx < 2 * 8000]\n\n negative_sequences = [str(line.strip().upper()) for idx, line in\n enumerate(neg_page.strip().split('\\n'))\n if \">\" not in line and\n idx < 2 * len(positive_sequences)]\n\n assert len(positive_sequences) == len(negative_sequences), \\\n \"lengths were not the same: p={pos} n={neg}\" \\\n .format(pos=len(positive_sequences), neg=len(negative_sequences))\n\n positive_vector_matrix = np.array([create_feature_vector_for_sequence(s)\n for s in positive_sequences])\n negative_vector_matrix = np.array([create_feature_vector_for_sequence(s)\n for s in negative_sequences])\n\n df = pd.DataFrame(data=np.vstack((positive_vector_matrix,\n negative_vector_matrix)))\n df.loc[0:positive_vector_matrix.shape[0], \"Label\"] = 1.0\n df.loc[positive_vector_matrix.shape[0]:, \"Label\"] = 0.0\n\n design_matrix = df\n groups = create_group_indicies_list()\n\n return design_matrix, groups","def matching_programs(pattern):\n programs = get_programs()\n search_results = []\n\n # For every program, check if it matches by title or by filename.\n for program, data in programs.items():\n title_result = fuzzy_search(pattern, data['Name'])\n filename_result = fuzzy_search(pattern, program)\n title_match = True\n for result in title_result, filename_result:\n if result in (WHOLE_PATTERN_FIRST,\n WHOLE_PATTERN_IN_STRING,\n PATTERN_IN_STRING):\n search_results.append((program, data, title_match, result))\n break\n title_match = False\n\n # Sort the programs by how well they matched and yield them.\n sorted_ = sorted(search_results, key=lambda r: r[3], reverse=True)\n for i, item in enumerate(sorted_):\n program, data, title_match, _ = item\n yield (program, data, title_match)","def keyword_extraction(file_content):\n\n # [question, question....]\n for key, value in file_content.items():\n seg, hidden = ltp.seg([key])\n # ner: [[('Nh', 2, 2)]]\n ner = ltp.ner(hidden)\n # keywords: [('PERSON', \"吴轩\")], tuple_item: ('Nh', 2, 2)\n keywords = [(tag_to_name[tuple_item[0]], to_string(seg[0][tuple_item[1]: tuple_item[2]+1])) for tuple_item in ner[0]]\n file_content[key].keywords = keywords\n\n return file_content","def get_malware_used_by_groups():\n global malware_used_by_groups\n\n if not malware_used_by_groups:\n malware_used_by_groups = rsh.malware_used_by_groups(get_srcs())\n\n return malware_used_by_groups","def _evaluate_examples(self):\n\n getLogger(\"problog_lfi\").debug(\"Evaluating examples:\")\n if self._log:\n evaluator = ExampleEvaluatorLog(self._weights)\n else:\n evaluator = ExampleEvaluator(self._weights)\n\n results = []\n for i, example in enumerate(self._compiled_examples):\n try:\n result = evaluator(example)\n results.append(result)\n getLogger(\"problog_lfi\").debug(\n \"Example \"\n + str(i + 1)\n + \":\\tFrequency = \"\n + str(result[0][0])\n + \"\\tp_evidence = \"\n + str(result[0][1])\n + \"\\tp_queries = \"\n + str(result[0][2])\n )\n except InconsistentEvidenceError:\n # print(\"Ignoring example {}/{}\".format(i + 1, len(self._compiled_examples)))\n getLogger(\"problog_lfi\").warning(\n \"Ignoring example {}/{}\".format(i + 1, len(self._compiled_examples))\n )\n\n return list(chain.from_iterable(results))"],"string":"[\n \"def get_techniques(db):\\n\\n techniques = []\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n techniques.append(technique)\\n \\n return sorted(techniques)\",\n \"def get_techniques_used_by_groups():\\n global techniques_used_by_groups\\n\\n if not techniques_used_by_groups:\\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\\n\\n return techniques_used_by_groups\",\n \"def get_all_techniques(src, source_name, tactic=None):\\n filters = [\\n Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n Filter(\\\"external_references.source_name\\\", \\\"=\\\", source_name),\\n ]\\n if tactic:\\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\\n\\n results = src.query(filters)\\n return remove_deprecated(results)\",\n \"def get_techniques_list(techniques):\\n\\n technique_list = {}\\n \\n for technique in techniques:\\n if 'revoked' not in technique or technique['revoked'] == False:\\n\\n attack_id = util.get_attack_id(technique)\\n\\n if attack_id:\\n\\n technique_dict = {}\\n technique_dict['id'] = attack_id\\n \\n technique_dict['name'] = technique['name']\\n technique_dict['description'] = technique['description']\\n\\n if technique.get('kill_chain_phases'):\\n for elem in technique['kill_chain_phases']:\\n # Fill dict\\n\\n if elem['phase_name'] not in technique_list:\\n technique_list[elem['phase_name']] = []\\n \\n technique_list[elem['phase_name']].append(technique_dict)\\n\\n for key, value in technique_list.items():\\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\\n \\n return technique_list\",\n \"def get_techniques_used_by_malware():\\n global techniques_used_by_malware\\n \\n if not techniques_used_by_malware:\\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\\n \\n return techniques_used_by_malware\",\n \"def get_techniques_used_by_tools():\\n global techniques_used_by_tools\\n\\n if not techniques_used_by_tools:\\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\\n \\n return techniques_used_by_tools\",\n \"def get_related_techniques_data(technique, tactic_list):\\n \\n technique_data = []\\n\\n if config.related_techniques.get(technique['id']):\\n for rel_tech in config.related_techniques[technique['id']]:\\n\\n attack_id = util.get_attack_id(rel_tech['object'])\\n\\n if attack_id:\\n row = {}\\n row['technique_id'] = attack_id\\n\\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\\n if tactic:\\n row['tactic_id'] = util.get_attack_id(tactic)\\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\\n \\n row['technique_name'] = rel_tech['object']['name']\\n technique_data.append(row)\\n\\n if technique_data:\\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\\n return technique_data\",\n \"def get_examples():\\n # Get the tjson test cases from a url.\\n examples = urlopen(TJSON_EXAMPLES_URL).read()\\n\\n # Strip comments from the lines.\\n examples = re.sub(r\\\"^#.*\\\\n?\\\", \\\"\\\", examples.decode(), flags=re.MULTILINE)\\n\\n # Split a group of examples.\\n delimiter = re.escape(EXAMPLES_DELIMITER)\\n example_re = re.compile(delimiter + r'(.*?)' + delimiter, re.S)\\n split = re.findall(example_re, examples)\\n\\n # Create the `Example` named tuples.\\n return [\\n parse_example(example)\\n for example in split]\",\n \"def add_techniques(\\n client: Act, matrice: AttckMatrice, output_format: Text = \\\"json\\\"\\n) -> List:\\n\\n notify = []\\n\\n for technique in matrice.techniques:\\n notify += handle_techniques(client, technique, None, output_format)\\n\\n for subtechnique in getattr(technique, \\\"subtechniques\\\", []):\\n # Pre Attack does not have sub techniques\\n notify += handle_techniques(client, subtechnique, technique, output_format)\\n\\n return notify\",\n \"def run_examples():\\n\\n for example in examples:\\n\\n print(str(example) + \\\" : \\\", end=\\\" \\\")\\n try:\\n t, smush = analyse(example, my_env)\\n print(lookup(t, smush))\\n # print(\\\"Smush\\\")\\n # for k,v in smush.items():\\n # print(f\\\"\\\\t{k} : {v}\\\")\\n except (ParseError, InferenceError) as e:\\n print(e)\",\n \"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\\n\\n attack_id = util.get_attack_id(technique)\\n\\n # Only add technique if the attack id was found\\n if attack_id:\\n\\n technique_dict = {}\\n\\n technique_dict['attack_id'] = attack_id\\n technique_dict['domain'] = domain.split(\\\"-\\\")[0]\\n technique_dict['menu'] = side_menu_data\\n technique_dict['name'] = technique.get('name')\\n\\n # Get capecs and mtcs\\n for ref in technique['external_references']:\\n if ref.get('source_name'):\\n if ref['source_name'] == \\\"capec\\\":\\n if technique_dict.get('capecs') is None:\\n technique_dict['capecs'] = []\\n capec_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['capecs'].append(capec_dict)\\n\\n if ref['source_name'] == \\\"NIST Mobile Threat Catalogue\\\":\\n if technique_dict.get('mtcs') is None:\\n technique_dict['mtcs'] = []\\n\\n mtcs_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['mtcs'].append(mtcs_dict)\\n\\n # Get technique external references \\n ext_ref = technique[\\\"external_references\\\"]\\n\\n # Get initial reference list\\n reference_list = []\\n # Decleared as an object to be able to pass by reference\\n next_reference_number = {}\\n next_reference_number['value'] = 1\\n reference_list = util.update_reference_list(reference_list, technique)\\n\\n # Get technique description\\n if technique.get(\\\"description\\\"):\\n citations_from_descr = util.get_citations_from_descr(technique['description'])\\n\\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\\n \\n if 'x_mitre_deprecated' in technique:\\n technique_dict['deprecated'] = True\\n \\n # Get mitigation table\\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\\n \\n # Get related techniques\\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\\n\\n # Get examples\\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\\n\\n # Get technique version\\n if technique.get(\\\"x_mitre_version\\\"):\\n technique_dict['version'] = technique[\\\"x_mitre_version\\\"]\\n\\n # Get tactics of technique\\n if technique.get('kill_chain_phases'):\\n technique_dict['tactics'] = []\\n for elem in technique['kill_chain_phases']:\\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\\n\\n # Get platforms that technique uses\\n if technique.get('x_mitre_platforms'):\\n technique_dict['platforms'] = \\\", \\\".join(technique['x_mitre_platforms'])\\n\\n # Get system requirements\\n if technique.get('x_mitre_system_requirements'):\\n technique_dict['sysreqs'] = \\\", \\\".join(technique['x_mitre_system_requirements'])\\n technique_dict['sysreqs'] = re.sub(\\\"\\\\.?\\\\\\\\n+\\\", \\\"; \\\", technique_dict['sysreqs'])\\n\\n # Get permissions required\\n if technique.get('x_mitre_permissions_required'):\\n technique_dict['perms'] = \\\", \\\".join(technique['x_mitre_permissions_required'])\\n\\n # Get effective permissions\\n if technique.get('x_mitre_effective_permissions'):\\n technique_dict['eff_perms'] = \\\", \\\".join(technique['x_mitre_effective_permissions'])\\n\\n # Get data sources\\n if technique.get('x_mitre_data_sources'):\\n technique_dict['data_sources'] = \\\", \\\".join(technique['x_mitre_data_sources'])\\n\\n # Get if technique supports remote\\n if technique.get('x_mitre_remote_support'):\\n if technique['x_mitre_remote_support']:\\n technique_dict['supports_remote'] = \\\" Yes\\\"\\n else:\\n technique_dict['supports_remote'] = \\\" No\\\"\\n\\n # Get network requirements\\n if technique.get('x_mitre_network_requirements'):\\n if technique['x_mitre_network_requirements']:\\n technique_dict['network_reqs'] = \\\" Yes\\\"\\n else:\\n technique_dict['network_reqs'] = \\\" No\\\"\\n\\n # Get list of impacts\\n if technique.get('x_mitre_impact_type'):\\n technique_dict['impact_type'] = \\\", \\\".join(technique['x_mitre_impact_type'])\\n\\n # Get list of defenses bypassed\\n if technique.get('x_mitre_defense_bypassed'):\\n technique_dict['def_bypass'] = \\\", \\\".join(technique['x_mitre_defense_bypassed'])\\n\\n # Get list of contributors \\n if technique.get('x_mitre_contributors'):\\n technique_dict['contributors'] = \\\"; \\\".join(technique['x_mitre_contributors'])\\n\\n # Get list of tactic types\\n if technique.get('x_mitre_tactic_type'):\\n technique_dict['tactic_type'] = \\\", \\\".join(technique['x_mitre_tactic_type'])\\n\\n # Get detection data\\n if technique.get('x_mitre_detection'):\\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\\n\\n # Get if technique is detectable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses'):\\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\\n\\n # Get explanation of detecatable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\\n\\n # Get diffulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary'):\\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\\n\\n # Get explanation of difficulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \\n \\n # Add reference for bottom part of technique page\\n if reference_list:\\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\\n\\n subs = config.technique_md.substitute(technique_dict)\\n subs = subs + json.dumps(technique_dict)\\n\\n #Write out the markdown file\\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\\\".md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\",\n \"def get_technique_by_id(self, id_code):\\r\\n return self.fs.query([\\r\\n Filter('type', '=', 'attack-pattern'),\\r\\n Filter('external_references.external_id', '=', id_code)\\r\\n ])\",\n \"def get_technique_stats(db):\\n \\n techniques = {}\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n temp_dict = {technique: [{u'id': element['project_no'], \\n u'name': element['project_name'], u'description': element['short_description']}]}\\n techniques.update(temp_dict)\\n else:\\n temp_dict = {u'id': element['project_no'],\\n u'name': element['project_name'], u'description': element['short_description']}\\n techniques[technique].append(temp_dict)\\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\\n\\n return techniques\",\n \"def get_groups_using_technique():\\n global groups_using_technique\\n\\n if not groups_using_technique:\\n groups_using_technique = rsh.groups_using_technique(get_srcs())\\n \\n return groups_using_technique\",\n \"def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\\n return [\\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\\n ]\",\n \"def get_technique_mitigated_by_mitigation():\\n global technique_mitigated_by_mitigation\\n\\n if not technique_mitigated_by_mitigation:\\n technique_mitigated_by_mitigation = rsh.technique_mitigated_by_mitigation(get_srcs())\\n\\n return technique_mitigated_by_mitigation\",\n \"def get_scenarios(experiments):\\n return {exp.scenario for exp in experiments}\",\n \"def get_technique_related_to_technique():\\n global technique_related_to_technique\\n\\n if not technique_related_to_technique:\\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\\n\\n return technique_related_to_technique\",\n \"def test_malware_have_techniques(attck_fixture):\\n for malware in attck_fixture.enterprise.malwares:\\n if malware.techniques:\\n assert getattr(malware,'techniques')\",\n \"def extract_features(data, stopwords=STOPWORDS):\\n tags = set()\\n docs = []\\n for document in data:\\n doc_data = dict()\\n doc_data['pmid'] = document['sourceid']\\n text = document['text']\\n\\n # Insert PubTator annotations inside abstract\\n denotations = document['denotations']\\n sorted_denotations = []\\n for denotation in denotations:\\n begin = denotation['span']['begin']\\n end = denotation['span']['end']\\n obj = denotation['obj']\\n for c in punctuation:\\n obj = obj.replace(c, '')\\n tags.add(obj)\\n doc_data[obj] = doc_data.get(obj,0)+1\\n sorted_denotations.append([begin,end,obj])\\n sorted_denotations.sort()\\n sorted_denotations.reverse()\\n for begin, end, obj in sorted_denotations:\\n text = text[:begin] + obj + ' ' + text[end:]\\n\\n doc_data['text'] = clean_text(text, stopwords)\\n docs.append(doc_data)\\n\\n return docs\",\n \"def generate(softwaretype=\\\"software\\\"):\\n # import the STIX data from MITRE/CTI\\n stix = requests.get(\\\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\\\").json()\\n ms = MemoryStore(stix_data=stix[\\\"objects\\\"])\\n # software includes malware and tool types so perform two queries and merge the results\\n software_filters = []\\n if softwaretype == \\\"malware\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'malware') ] )\\n if softwaretype == \\\"tool\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'tool') ] )\\n \\n software = list(chain.from_iterable(\\n ms.query(f) for f in software_filters\\n ))\\n\\n # build a list of techniques used by software\\n techniques_used = {} #attackID => using software names\\n for thesoftware in software:\\n # filter out revoked and deprecated software\\n if (\\\"x_mitre_deprecated\\\" in thesoftware and thesoftware[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in thesoftware and thesoftware[\\\"revoked\\\"]): continue\\n for relationship in ms.relationships(thesoftware[\\\"id\\\"]):\\n # skip all non-technique relationships\\n if \\\"attack-pattern\\\" not in relationship[\\\"target_ref\\\"]: continue\\n technique = ms.get(relationship[\\\"target_ref\\\"])\\n # filter out deprecated and revoked techniques\\n if (\\\"x_mitre_deprecated\\\" in technique and technique[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in technique and technique[\\\"revoked\\\"]): continue\\n techniqueID = technique[\\\"external_references\\\"][0][\\\"external_id\\\"]\\n # store usage in techniques_used struct\\n if techniqueID in techniques_used:\\n techniques_used[techniqueID].append(thesoftware[\\\"name\\\"])\\n else:\\n techniques_used[techniqueID] = [thesoftware[\\\"name\\\"]]\\n\\n # format the techniques for the output layer\\n techniques_list = []\\n highest_usage = 0\\n lowest_usage = 1\\n for techniqueID in techniques_used:\\n # determine the number of used techniques for the score\\n count = len(techniques_used[techniqueID])\\n highest_usage = max(highest_usage, count)\\n lowest_usage = min(lowest_usage, count)\\n # append technique struct to list of layer-formatted techniques\\n techniques_list.append({\\n \\\"techniqueID\\\": techniqueID,\\n \\\"comment\\\": \\\"executed by \\\" + \\\", \\\".join(techniques_used[techniqueID]),\\n \\\"score\\\": count,\\n })\\n # set up layer name and desc according to softwaretype\\n if softwaretype != \\\"software\\\": \\n plural = \\\"tools\\\" if softwaretype == \\\"tool\\\" else \\\"malware\\\"\\n layername = f\\\"Software ({softwaretype}) Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\\\"\\n else: \\n layername = \\\"Software Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software, where the score is the count of software using the technique\\\"\\n\\n # construct and return the layer as a dict\\n return {\\n \\\"name\\\": layername,\\n \\\"description\\\": layerdescription,\\n \\\"versions\\\": {\\n \\\"layer\\\": \\\"4.1\\\",\\n \\\"navigator\\\": \\\"4.1\\\"\\n },\\n \\\"domain\\\": \\\"enterprise-attack\\\",\\n \\\"techniques\\\": techniques_list,\\n \\\"sorting\\\": 3, # order in descending order of score (count)\\n \\\"gradient\\\": {\\n \\\"colors\\\": [\\n \\\"#fff7b3\\\", # low counts are yellow\\n \\\"#ff6666\\\", # high counts are red\\n ],\\n \\\"minValue\\\": lowest_usage,\\n \\\"maxValue\\\": highest_usage\\n },\\n }\",\n \"def fixture_lims_samples(lims_family: dict) -> List[dict]:\\n return lims_family[\\\"samples\\\"]\",\n \"def extract_routine_instances(theories, routines, exec_routine_instruction_index):\\n new_theories=[]\\n for theory in theories:\\n for i in range(len(routines)):\\n routine=routines[i]\\n theory=replace_instance(theory,routine,[(exec_routine_instruction_index,i)])\\n new_theories.append(theory)\\n return new_theories\",\n \"def examples(self):\\n for obj_ind in range(len(self.objects)):\\n yield self.get_object_intent_by_index(obj_ind)\",\n \"def get_tools_using_technique():\\n global tools_using_technique\\n\\n if not tools_using_technique:\\n tools_using_technique = rsh.tools_using_technique(get_srcs())\\n \\n return tools_using_technique\",\n \"def get_subtechniques_of():\\n global subtechniques_of\\n\\n if not subtechniques_of:\\n subtechniques_of = rsh.subtechniques_of(get_srcs())\\n \\n return subtechniques_of\",\n \"def get_skills(lang, spec=None):\\n\\n if lang.lower() == 'java':\\n output = get_java(spec)\\n if spec is not None:\\n print(f\\\"Our suggestions for {spec} are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n else:\\n print(\\\"Our suggestions for are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n elif lang.lower() == 'python':\\n output = get_python(spec)\\n if spec is not None:\\n print(f\\\"Our suggestions for {spec} are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n else:\\n print(\\\"Our suggestions for are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\",\n \"def get_malware_using_technique():\\n global malware_using_technique\\n\\n if not malware_using_technique:\\n malware_using_technique = rsh.malware_using_technique(get_srcs())\\n \\n return malware_using_technique\",\n \"def _process_examples(self):\\n logger = getLogger(\\\"problog_lfi\\\")\\n # value can be True / False / None\\n # ( atom ), ( ( value, ... ), ... )\\n\\n # Simple implementation: don't add neutral evidence.\\n\\n # ad_groups is a list of lists where each list contains an AD\\n ad_groups = list()\\n for ad in self._adatoms:\\n # if it's an AD group\\n if len(ad[1]) > 1:\\n ad_list = []\\n for var in ad[1]:\\n ad_list.append(Term(self.names[var].functor, *self.names[var].args))\\n ad_groups.append(tuple(ad_list))\\n logger.debug(\\\"AD Groups\\\\t\\\\t:\\\" + str(ad_groups))\\n\\n def all_false(d):\\n \\\"\\\"\\\"\\n This function recognizes inconsistent evidence s.t. all values are False in AD.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :return: whether all values are False\\n \\\"\\\"\\\"\\n # false_count should be the same as the length of d\\n false_count = sum(v is False for v in d.values())\\n return false_count == len(d)\\n\\n def all_false_except_one(d):\\n \\\"\\\"\\\"\\n This function recognizes incomplete evidence s.t.\\n the non-False value in AD needs to be set to True.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :return: whether all values except one are False\\n \\\"\\\"\\\"\\n false_count = sum(v is False for v in d.values())\\n the_left_is_none = bool(sum(v is None for v in d.values()))\\n return (false_count == len(d) - 1) and the_left_is_none\\n\\n def getADtemplate(d, atom=None):\\n \\\"\\\"\\\"\\n This function gets atom's complement AD template.\\n This should only be used when the AD contains non-ground terms.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :param atom: an evidence\\n :return: atom's complement AD template\\n \\\"\\\"\\\"\\n if atom is not None:\\n temp_dict = {\\n k: v\\n for k, v in d.items()\\n if v == \\\"Template\\\" and atom.signature != k.signature\\n }\\n return temp_dict\\n else:\\n temp_dict = {k: v for k, v in d.items() if v == \\\"Template\\\"}\\n return temp_dict\\n\\n def add_to_ad_evidence(pair, l, ADtemplate):\\n \\\"\\\"\\\"\\n :param pair: a new pair of (atom, value)\\n :param l: a list of dictionaries, all dictionaries need to have the same format\\n :return:\\n \\\"\\\"\\\"\\n (k, v) = pair\\n # if entry k exists, update the value with k\\n for d in l:\\n if k in d:\\n d[k] = v\\n return\\n # if entry k does not exist, create a new dictionary from template\\n # and instantiate it with k\\n new_d = dict()\\n for temp_k in ADtemplate.keys():\\n new_key = Term(temp_k.functor, *k.args)\\n new_d[new_key] = None\\n # put v in there\\n new_d[k] = v\\n l.append(new_d)\\n\\n if self.infer_AD_values:\\n result = ExampleSet()\\n inconsistent = False\\n # iterate over all examples given in .ev\\n for index, example in enumerate(self.examples):\\n ad_evidences = []\\n non_ad_evidence = {}\\n for ad_group in ad_groups:\\n # create a dictionary to memorize what evidence is given in AD\\n d = dict()\\n # TODO: what if the AD contains both ground and non-ground????\\n # e.g. t(_)::a; t(_)::b(X)\\n for var in ad_group:\\n if var.is_ground():\\n d[var] = None # for ground unknown evidence\\n else:\\n d[var] = \\\"Template\\\" # for unground unknown evidence\\n ad_evidences.append(d)\\n\\n # add all evidence in the example to ad_evidences\\n for atom, value in example:\\n # if atom has a tunable probability to learn\\n if any([atom.signature == name.signature for name in self.names]):\\n if len(atom.args) == 0: # Propositional evidence\\n # insert evidence\\n is_ad = False\\n for d in ad_evidences:\\n if atom in d:\\n d[atom] = value\\n is_ad = True\\n if not is_ad:\\n non_ad_evidence[\\n atom\\n ] = value\\n else: # First Order evidence\\n # find the right AD dictionary : AD_dict\\n AD_dict = None\\n for d in ad_evidences:\\n if any([atom.signature == k.signature for k in d]):\\n AD_dict = d\\n # if the instantiation is new, add it as a key to the dictionary\\n if AD_dict and AD_dict.get(atom) is None:\\n AD_dict[atom] = value\\n # also add other AD parts in the dictionary with value==None\\n other_ADs = getADtemplate(AD_dict, atom)\\n for otherAD in other_ADs.keys():\\n new_key = Term(otherAD.functor, *atom.args)\\n AD_dict[new_key] = AD_dict.get(new_key, None)\\n else:\\n non_ad_evidence[atom] = value\\n else:\\n non_ad_evidence[atom] = value\\n\\n # grounded_ad_evidences contains all usable evidence (gound, not template)\\n grounded_ad_evidences = []\\n for d in ad_evidences:\\n # for first order evidence dictionaries\\n if \\\"Template\\\" in d.values():\\n # new_ad_evidence is a list of dictionaries\\n # each dictionary is a group of the AD template instantiation\\n new_ad_evidence = list()\\n # get template AD evidence\\n ADtemplate = getADtemplate(d)\\n # group all pairs according to ADtemplate\\n for k, v in d.items():\\n if v != \\\"Template\\\":\\n add_to_ad_evidence((k, v), new_ad_evidence, ADtemplate)\\n grounded_ad_evidences += new_ad_evidence\\n else:\\n # simply us them\\n grounded_ad_evidences.append(d)\\n\\n inconsistent_example = False\\n for i, d in enumerate(grounded_ad_evidences):\\n # inconsistent1 = multiple_true(d)\\n inconsistent2 = all_false(d)\\n add_compliment = all_false_except_one(d)\\n\\n if inconsistent2:\\n inconsistent_example = True\\n continue\\n elif add_compliment:\\n for key, value in d.items():\\n if value is None:\\n grounded_ad_evidences[i][key] = True\\n\\n if not inconsistent_example and len(grounded_ad_evidences) > 0:\\n # There are (fully tunable) ADs in the program\\n evidence_list = []\\n for d in grounded_ad_evidences:\\n for key, value in d.items():\\n if value is not None:\\n if (key, value) not in evidence_list:\\n # TODO: Switch to orderedSet so we don't have to check whether it's in already\\n evidence_list.append((key, value))\\n\\n for key, value in non_ad_evidence.items():\\n evidence_list.append((key, value))\\n\\n atoms, values = zip(*evidence_list)\\n result.add(index, atoms, values)\\n\\n else:\\n # (No AD case) or (Inconsistent Evidence Case)\\n atoms, values = zip(*example)\\n result.add(index, atoms, values)\\n return result\\n else:\\n # smarter: compile-once all examples with same atoms\\n result = ExampleSet()\\n for index, example in enumerate(self.examples):\\n atoms, values = zip(*example)\\n result.add(index, atoms, values)\\n return result\",\n \"def get_mitigation_mitigates_techniques():\\n global mitigation_mitigates_techniques\\n\\n if not mitigation_mitigates_techniques:\\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\\n\\n return mitigation_mitigates_techniques\",\n \"def get_guides(data):\\n return data.groups[\\\"Frames\\\"].objects\",\n \"def examples():\\n from matdb import msg\\n script = \\\"MATDB Finder for Groups/Trainers and General Contexts\\\"\\n explain = (\\\"Each context has a controller that provides a find() method for\\\"\\n \\\"looking up instances within that context. This script provides \\\"\\n \\\"a simple interface for finding things.\\\")\\n contents = [((\\\"Find hessian databases.\\\"), \\n \\\"matdb_find.py system.yaml -d -p hessian-*/hessian\\\",\\n \\\"The find function uses the format dbname/group/seed\\\")]\\n required = (\\\"'matdb.yaml' file with database settings.\\\")\\n output = (\\\"\\\")\\n details = (\\\"\\\")\\n outputfmt = (\\\"\\\")\\n\\n msg.example(script, explain, contents, required, output, outputfmt, details)\",\n \"def get_examples():\\n\\n base_query = \\\"[[Medium::TCG]] [[Database ID::+]]\\\"\\n base_printouts = [\\n \\\"Password\\\",\\n \\\"English name\\\",\\n \\\"Card type\\\",\\n ]\\n all_res = {}\\n\\n n = len(ALL_DISPLAYS)\\n for i, x in enumerate(ALL_DISPLAYS):\\n query = f\\\"{base_query} [[{x}::+]]\\\"\\n print(i, n, query)\\n try:\\n data = ask_yugipedia(query, base_printouts + [x])\\n except:\\n print(\\\"ERROR\\\", i, x)\\n continue\\n all_res[x] = data\\n return all_res\",\n \"def patterns() -> List[Dict[str, Any]]:\\n patterns = [\\n {\\\"label\\\": \\\"DRUG\\\", \\\"pattern\\\": \\\"Zithromax\\\", \\\"type\\\": \\\"fuzzy\\\", \\\"id\\\": \\\"Antibiotic\\\"},\\n {\\\"label\\\": \\\"GPE\\\", \\\"pattern\\\": \\\"Mahwahe\\\", \\\"type\\\": \\\"fuzzy\\\"},\\n {\\\"label\\\": \\\"GPE\\\", \\\"pattern\\\": \\\"Mahwah\\\", \\\"type\\\": \\\"fuzzy\\\"},\\n {\\n \\\"label\\\": \\\"NAME\\\",\\n \\\"pattern\\\": \\\"Grant Andersen\\\",\\n \\\"type\\\": \\\"fuzzy\\\",\\n \\\"kwargs\\\": {\\\"fuzzy_func\\\": \\\"token_sort\\\"},\\n \\\"id\\\": \\\"Developer\\\",\\n },\\n {\\n \\\"label\\\": \\\"NAME\\\",\\n \\\"pattern\\\": \\\"Garth Andersen\\\",\\n \\\"type\\\": \\\"fuzzy\\\",\\n \\\"kwargs\\\": {\\\"fuzzy_func\\\": \\\"token_sort\\\"},\\n \\\"id\\\": \\\"Developer\\\",\\n },\\n {\\n \\\"label\\\": \\\"STREET\\\",\\n \\\"pattern\\\": \\\"street_addresses\\\",\\n \\\"type\\\": \\\"regex\\\",\\n \\\"kwargs\\\": {\\\"predef\\\": True},\\n },\\n {\\n \\\"label\\\": \\\"GPE\\\",\\n \\\"pattern\\\": \\\"(?i)[U](nited|\\\\\\\\.?) ?[S](tates|\\\\\\\\.?)\\\",\\n \\\"type\\\": \\\"regex\\\",\\n \\\"id\\\": \\\"USA\\\",\\n },\\n {\\\"label\\\": \\\"GPE\\\", \\\"pattern\\\": \\\"(?:USR){e<=1}\\\", \\\"type\\\": \\\"regex\\\", \\\"id\\\": \\\"USA\\\"},\\n {\\\"label\\\": \\\"GPE\\\", \\\"pattern\\\": \\\"(?:USSR){d<=1, s<=1}\\\", \\\"type\\\": \\\"regex\\\"},\\n {\\n \\\"label\\\": \\\"BAND\\\",\\n \\\"pattern\\\": [{\\\"LOWER\\\": {\\\"FREGEX\\\": \\\"(converge){e<=1}\\\"}}],\\n \\\"type\\\": \\\"token\\\",\\n },\\n {\\n \\\"label\\\": \\\"BAND\\\",\\n \\\"pattern\\\": [\\n {\\\"TEXT\\\": {\\\"FUZZY\\\": \\\"Protest\\\"}},\\n {\\\"IS_STOP\\\": True},\\n {\\\"TEXT\\\": {\\\"FUZZY\\\": \\\"Hero\\\"}},\\n ],\\n \\\"type\\\": \\\"token\\\",\\n \\\"id\\\": \\\"Metal\\\",\\n },\\n ]\\n return patterns # type: ignore\",\n \"def _create_examples(self, data_dir, set_type):\\n\\t\\texamples = []\\n\\t\\tinput_file_data = os.path.join(data_dir, \\\"data.tsv\\\")\\n\\t\\twith open(input_file_data, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n\\t\\t\\tfor i, inp in enumerate(f):\\n\\t\\t\\t\\tinps = inp.split('\\\\t') \\n\\t\\t\\t\\tguid = \\\"%s-%s\\\" % (set_type, i)\\n\\t\\t\\t\\ttext_inp = inps[1].strip()\\n\\t\\t\\t\\ttext_out = inps[2].strip()\\n\\t\\t\\t\\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\\n\\t\\t\\t\\t\\n\\t\\t\\t# Sort these out before returning\\n\\t\\t\\texamples = sorted(examples, key=sort_inp_len)\\n\\t\\t\\treturn examples\",\n \"def read_examples(data_dir, data_sign):\\n examples = []\\n # read src data\\n with open(data_dir / f'{data_sign}/sentences.txt', \\\"r\\\", encoding='utf-8') as f_sen, \\\\\\n open(data_dir / f'{data_sign}/tags.txt', 'r', encoding='utf-8') as f_tag:\\n for sen, tag in zip(f_sen, f_tag):\\n example = InputExample(sentence=sen.strip().split(' '), tags=tag.strip().split(' '))\\n examples.append(example)\\n print(\\\"InputExamples:\\\", len(examples))\\n return examples\",\n \"def search_example(self, query: str) -> List[Example]:\\n raise NotImplementedError\",\n \"def get_examples(self, env):\\n return self.fam.c_get_examples(self, env)\",\n \"def convert_examples_to_features(self):\\n features = []\\n for ex_index, example in enumerate(self.examples):\\n taskname = example.taskname\\n taskname_ids = self.tokenizer.text_to_ids(taskname)\\n processor = example.processor\\n if ex_index % 10000 == 0:\\n logging.info(f\\\"Writing example {ex_index} of {len(self.examples)}\\\")\\n enc_query = processor.get_ptune_query(\\n example.content,\\n self.pseudo_token_id,\\n self.max_seq_length - self.max_seq_length_decoder + 1,\\n self.templates,\\n self.tokenizer,\\n )\\n features.append([enc_query, taskname_ids])\\n return features\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def find_examples_in_schema(path):\\n with open(path, 'rb') as fd:\\n schema_tree = yaml.load(fd)\\n\\n for node in treeutil.iter_tree(schema_tree):\\n if (isinstance(node, dict) and\\n 'examples' in node and\\n isinstance(node['examples'], list)):\\n for desc, example in node['examples']:\\n yield example\",\n \"def techniques(self):\\n return self._get_child_page_of_type(LearningTechniquesPage)\",\n \"def find_patterns(obs, player_x, player_y):\\n for get_group in groups_of_memory_patterns:\\n group = get_group(obs, player_x, player_y)\\n if group[\\\"environment_fits\\\"](obs, player_x, player_y):\\n return group[\\\"get_memory_patterns\\\"](obs, player_x, player_y)\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def extract_patterns_from_trees(self, important_words, trees, class_value):\\n for tree in trees:\\n self.extract_patterns(important_words, tree, class_value)\",\n \"def sample_handling(sample, lexicon, classification):\\n\\n # We have a list of lists [.... [ [0, 2, 1, 0, 0, ..., 0] [1] ] , .... ] with the bag of words and the class\\n featureset = []\\n\\n # Open the sample text and parse through the document and generate feastures.\\n with open(sample, 'r') as f:\\n contents = f.readlines()\\n for l in contents[:hm_lines]:\\n current_words = word_tokenize(l.lower())\\n current_words = [lemmatizer.lemmatize(i) for i in current_words]\\n features = np.zeros(len(lexicon))\\n for word in current_words:\\n if word.lower() in lexicon:\\n index_value = lexicon.index(word.lower())\\n features[index_value] = 1\\n features = list(features)\\n featureset.append([features, classification])\\n\\n return featureset\",\n \"def samples(self, groups=None):\\n groups = check_parameters_for_validity(\\n groups, \\\"groups\\\", self.groups(), self.groups()\\n )\\n all_samples = []\\n\\n if \\\"train\\\" in groups:\\n all_samples.extend(self.background_model_samples())\\n groups.remove(\\\"train\\\")\\n\\n for grp in groups:\\n all_samples.extend(_sample_sets_to_samples(self.references(grp)))\\n all_samples.extend(_sample_sets_to_samples(self.probes(grp)))\\n\\n # Add znorm samples. Returning znorm samples for one group of dev or\\n # eval is enough because they are duplicated.\\n for grp in groups:\\n all_samples.extend(_sample_sets_to_samples(self.zprobes(grp)))\\n break\\n\\n # Add tnorm samples.\\n all_samples.extend(_sample_sets_to_samples(self.treferences()))\\n\\n return all_samples\",\n \"def get_dev_examples(self, input_file):\\r\\n raise NotImplementedError()\",\n \"def test_filters_with_extra_extraction(self) -> None:\\n\\n # pylint: disable=too-many-locals\\n\\n import hammer_config\\n\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n\\n def add_named_library(in_dict: Dict[str, Any]) -> Dict[str, Any]:\\n out_dict = deepdict(in_dict)\\n out_dict[\\\"libraries\\\"].append({\\n \\\"name\\\": \\\"abcdef\\\",\\n \\\"milkyway techfile\\\": \\\"test/abcdef.tf\\\"\\n })\\n return out_dict\\n\\n HammerToolTestHelpers.write_tech_json(tech_json_filename, add_named_library)\\n sys.path.append(tech_dir_base)\\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir))\\n tech.cache_dir = tech_dir\\n\\n def filter_func(lib: hammer_tech.Library) -> bool:\\n return lib.milkyway_techfile is not None\\n\\n def paths_func(lib: hammer_tech.Library) -> List[str]:\\n assert lib.milkyway_techfile is not None\\n return [lib.milkyway_techfile]\\n\\n def extraction_func(lib: hammer_tech.Library, paths: List[str]) -> List[str]:\\n assert len(paths) == 1\\n if lib.name is None:\\n name = \\\"\\\"\\n else:\\n name = str(lib.name)\\n return [json.dumps({\\\"path\\\": paths[0], \\\"name\\\": name}, cls=HammerJSONEncoder, indent=4)]\\n\\n def sort_func(lib: hammer_tech.Library):\\n assert lib.milkyway_techfile is not None\\n return lib.milkyway_techfile\\n\\n test_filter = LibraryFilter.new(\\\"metatest\\\", \\\"Test filter that extracts metadata\\\",\\n is_file=True, filter_func=filter_func,\\n paths_func=paths_func,\\n extraction_func=extraction_func,\\n sort_func=sort_func)\\n\\n database = hammer_config.HammerDatabase()\\n tech.set_database(database)\\n raw = tech.process_library_filter(pre_filts=[], filt=test_filter,\\n must_exist=False,\\n output_func=hammer_tech.HammerTechnologyUtils.to_plain_item)\\n\\n # Disable false positive from pylint\\n outputs = list(map(lambda s: json.loads(s), raw)) # pylint: disable=unnecessary-lambda\\n self.assertEqual(outputs,\\n [\\n {\\\"path\\\": tech.prepend_dir_path(\\\"test/abcdef.tf\\\"), \\\"name\\\": \\\"abcdef\\\"},\\n {\\\"path\\\": tech.prepend_dir_path(\\\"test/coconut\\\"), \\\"name\\\": \\\"\\\"},\\n {\\\"path\\\": tech.prepend_dir_path(\\\"test/soy\\\"), \\\"name\\\": \\\"\\\"}\\n ])\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\",\n \"def getExamples(query):\\n HREF_ITER = 'href'\\n UN_URL_PART = '/kanji/details/'\\n EX_FOUND = 'Found'\\n JISHO_URL = 'http://jisho.org/sentences?jap='\\n \\n url = JISHO_URL + query #may be kanji or word\\n \\n try:\\n soup = BeautifulSoup(urllib2.urlopen(url))\\n \\n check = soup.findAll('h2')\\n if len(check) > 0 and len(check[0].contents) > 0:\\n if EX_FOUND in check[0].contents[0]:\\n \\n sentences = soup.findAll('a', href=re.compile('/kanji/details'))\\n translations = soup.findAll('td', attrs={'class':'english'})\\n \\n r_sent = []\\n r_trans = []\\n \\n if len(sentences) == len(translations):\\n for sentence in sentences:\\n r_sent.append(sentence[HREF_ITER].replace(UN_URL_PART, ''))\\n for translation in translations:\\n r_trans.append(translation.contents[0].replace('\\\\t',''))\\n \\n return dict(zip(r_sent, r_trans))\\n except Exception:\\n return {}\",\n \"def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\\n return sorted(list(set(expert_knowledge)))\",\n \"def test_extract():\\n print(\\\"Executing test_extract:\\\")\\n\\n theory_1=[\\n (14,),\\n (15,),\\n (14,),\\n (16,)\\n ]\\n theory_2=[\\n (14,),\\n (15,),\\n (14,),\\n (17,)\\n ]\\n theory_3=[\\n (15,),\\n (14,)\\n ]\\n\\n mind=minds.new_mind(theories=[theory_1,theory_2,theory_3])\\n\\n print(\\\"Mind initial state:\\\")\\n print(minds.mind_string(mind, show_claims=False, show_problems=False))\\n\\n minds.extract_new_routines(mind,1)\\n print(\\\"Mind after 1 step of extraction:\\\")\\n print(minds.mind_string(mind, show_claims=False, show_problems=False))\\n\\n minds.extract_new_routines(mind,1)\\n print(\\\"Mind after 2 steps of extraction:\\\")\\n print(minds.mind_string(mind, show_claims=False, show_problems=False))\",\n \"def get_dev_examples(self, data_path):\\r\\n return self.create_examples(self.read_data(data_path), 'dev')\",\n \"def get_legal_examples(input_filepath):\\n solution_filepath = TestSystem.get_solution_filepath(input_filepath)\\n with io.open(solution_filepath, 'r') as f:\\n solution_examples = f.readlines()\\n return [\\n {\\n \\\"intent\\\": ex.split(TestSystem.solution_marker)[0],\\n \\\"text\\\": ex.split(TestSystem.solution_marker)[1].rstrip()\\n }\\n for ex in solution_examples\\n if (not ex.startswith('#') and not ex.isspace())\\n ]\",\n \"def findFeatures(self):\\n\\t\\tpass\",\n \"def get_word_examples(text):\\n\\n # Resulting examples list\\n examples = []\\n # Multiple quotes escape stack\\n stack = []\\n # Reverse text for iteration\\n reverse = text[::-1].strip()\\n # Represents a single example\\n example = ''\\n\\n # Iterates over every character in the text\\n for char in reverse:\\n # If char is closed brace break\\n if char == ')':\\n break\\n\\n # If char is quotes but no quotes in stack add them to stack\\n if char == '\\\"' and len(stack) == 0:\\n stack.append(char)\\n\\n # Add the character to the example string\\n example += char\\n\\n # If char is quotes but there is already quotes inside the stack\\n if char == '\\\"' and len(stack) != 0:\\n stack.pop()\\n\\n example = example[::-1].strip()\\n example = example[1: len(example)]\\n example = example.capitalize()\\n\\n examples.append(example)\\n\\n example = ''\\n\\n # Clear all empty strings\\n for example in examples:\\n if example == '' or example == ';':\\n examples.remove(example)\\n\\n return examples\",\n \"def _get_examples_from_json(self):\\n results = []\\n found = glob.glob(\\n os.path.join(_TEST_DEFS_DIR, \\\"**\\\", f\\\"{self.expectation_type}.json\\\"),\\n recursive=True,\\n )\\n if found:\\n with open(found[0]) as fp:\\n data = json.load(fp)\\n results = data[\\\"datasets\\\"]\\n return results\",\n \"async def insert_attack_stix_data(self):\\n logging.info('Downloading ATT&CK data from STIX/TAXII...')\\n attack = {}\\n collection = Collection(\\\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\\\")\\n tc_source = TAXIICollectionSource(collection)\\n filter_objs = {\\\"techniques\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n \\\"groups\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"intrusion-set\\\"), \\\"malware\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"malware\\\"),\\n \\\"tools\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"tool\\\"), \\\"relationships\\\": Filter(\\\"type\\\", \\\"=\\\", \\\"relationship\\\")}\\n for key in filter_objs:\\n attack[key] = tc_source.query(filter_objs[key])\\n references = {}\\n\\n # add all of the patterns and dictionary keys/values for each technique and software\\n for i in attack[\\\"techniques\\\"]:\\n if \\\"description\\\" in i:\\n descr = i['description'].replace('<code>', '').replace('</code>', '').replace(\\n '\\\\n', '').encode('ascii', 'ignore').decode('ascii')\\n else:\\n descr = \\\"missing description\\\"\\n references[i[\\\"id\\\"]] = {\\\"name\\\": i[\\\"name\\\"], \\\"id\\\": i[\\\"external_references\\\"][0][\\\"external_id\\\"],\\n \\\"example_uses\\\": [],\\n \\\"description\\\": descr,\\n \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n\\n for i in attack[\\\"relationships\\\"]:\\n if i[\\\"relationship_type\\\"] == 'uses':\\n if 'attack-pattern' in i[\\\"target_ref\\\"]:\\n use = i[\\\"description\\\"]\\n # remove unnecessary strings, fix unicode errors\\n use = use.replace('<code>', '').replace('</code>', '').replace('\\\"', \\\"\\\").replace(',', '').replace(\\n '\\\\t', '').replace(' ', ' ').replace('\\\\n', '').encode('ascii', 'ignore').decode('ascii')\\n find_pattern = re.compile('\\\\[.*?\\\\]\\\\(.*?\\\\)') # get rid of att&ck reference (name)[link to site]\\n m = find_pattern.findall(use)\\n if len(m) > 0:\\n for j in m:\\n use = use.replace(j, '')\\n if use[0:2] == '\\\\'s':\\n use = use[3:]\\n elif use[0] == ' ':\\n use = use[1:]\\n # combine all the examples to one list\\n references[i[\\\"target_ref\\\"]][\\\"example_uses\\\"].append(use)\\n\\n for i in attack[\\\"malware\\\"]:\\n if 'description' not in i: # some software do not have description, example: darkmoon https://attack.mitre.org/software/S0209\\n continue\\n else:\\n references[i[\\\"id\\\"]] = {\\\"id\\\": i['id'], \\\"name\\\": i[\\\"name\\\"], \\\"description\\\": i[\\\"description\\\"],\\n \\\"examples\\\": [], \\\"example_uses\\\": [], \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n for i in attack[\\\"tools\\\"]:\\n references[i[\\\"id\\\"]] = {\\\"id\\\": i['id'], \\\"name\\\": i[\\\"name\\\"], \\\"description\\\": i[\\\"description\\\"], \\\"examples\\\": [],\\n \\\"example_uses\\\": [], \\\"similar_words\\\": [i[\\\"name\\\"]]}\\n\\n attack_data = references\\n logging.info(\\\"Finished...now creating the database.\\\")\\n\\n cur_uids = await self.dao.get('attack_uids') if await self.dao.get('attack_uids') else []\\n cur_items = [i['uid'] for i in cur_uids]\\n for k, v in attack_data.items():\\n if k not in cur_items:\\n await self.dao.insert('attack_uids', dict(uid=k, description=defang_text(v['description']), tid=v['id'],\\n name=v['name']))\\n if 'regex_patterns' in v:\\n [await self.dao.insert('regex_patterns', dict(uid=k, regex_pattern=defang_text(x))) for x in\\n v['regex_patterns']]\\n if 'similar_words' in v:\\n [await self.dao.insert('similar_words', dict(uid=k, similar_word=defang_text(x))) for x in\\n v['similar_words']]\\n if 'false_negatives' in v:\\n [await self.dao.insert('false_negatives', dict(uid=k, false_negative=defang_text(x))) for x in\\n v['false_negatives']]\\n if 'false_positives' in v:\\n [await self.dao.insert('false_positives', dict(uid=k, false_positive=defang_text(x))) for x in\\n v['false_positives']]\\n if 'true_positives' in v:\\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\\n v['true_positives']]\\n if 'example_uses' in v:\\n [await self.dao.insert('true_positives', dict(uid=k, true_positive=defang_text(x))) for x in\\n v['example_uses']]\\n logging.info('[!] DB Item Count: {}'.format(len(await self.dao.get('attack_uids'))))\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def _create_examples(self, data, set_type):\\n examples = []\\n for (i, elem) in enumerate(data):\\n guid = \\\"%s-%s\\\" % (set_type, i)\\n text = elem[0]\\n label = elem[1]\\n examples.append(\\n InputExample(guid=guid, text=text, label=label))\\n return examples\",\n \"def get_dev_examples(self, data_dir):\\n\\t\\traise NotImplementedError()\",\n \"def test_text_classifier_get_testing_samples(self):\\n pass\",\n \"def examples_from_file(path):\\n examples = []\\n\\n # count total lines before loading\\n total_lines = int(local('wc -l {}'.format(path), capture=True).split()[0])\\n\\n with codecs.open(path, 'r', encoding='utf-8') as f:\\n for line in verboserate(f, desc='Reading data file.', total=total_lines):\\n src, trg = line.strip().lower().split('\\\\t')\\n src_words = src.split(' ')\\n trg_words = trg.split(' ')\\n assert len(src_words) > 0\\n assert len(trg_words) > 0\\n\\n if use_diff:\\n ex = EditExample.salient_diff(src_words, trg_words, free_set)\\n else:\\n ex = EditExample.whitelist_blacklist(src_words, trg_words)\\n examples.append(ex)\\n return examples\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_dev_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def search_samples():\\n r = req('GET', SUB_API + 'search/samples', params=apply_search_filters())\\n samples = []\\n for sample in demisto.get(r.json(), 'data.items'):\\n samples.append({\\n 'ID': demisto.get(sample, 'result'),\\n 'Details': demisto.get(sample, 'details')\\n })\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'EntryContext': {'ThreatGrid.Sample': samples},\\n 'HumanReadable': tableToMarkdown('ThreatGrid - Sample Search', samples, ['Result', 'Details']),\\n 'ContentsFormat': formats['json'],\\n 'Contents': r.json()\\n })\",\n \"def examples(self):\\n return self._examples\",\n \"def create_training_examples(statement_list: List[List[str]], trees, training_spans,\\n training: bool, max_span_length, num_sentence_words,\\n args, pos_tags, constituents, k=1):\\n print('building features...')\\n\\n word_frequencies = FreqDist([w.lower() for w in reuters.words()])\\n # list of science tokens & science multiword expressions\\n science_tokens = get_science_terms(args.data_path)\\n science_expressions = get_science_terms(args.data_path, False)\\n stop_words = set(stopwords.words('english'))\\n\\n\\n\\n true_examples, false_examples, examples_per_sentence = [], [], []\\n span_indexes = []\\n\\n # loop over all statements\\n for i, statement in enumerate(statement_list):\\n print(i)\\n\\n span_index = [] #enumerating all spans for this sentence.\\n pos_tags_this_statement = pos_tag(statement)\\n sentence_word_frequencies = [word_frequencies.freq(token.lower()) \\\\\\n for token in statement]\\n tree = trees[i]\\n false_examples_this_instance = []\\n sentence_candidate_span_examples = [] # when training.\\n\\n # loop across different spans for given sentence\\n for span in legal_spans(num_sentence_words, max_span_length): #globally legal\\n if span[1] > len(statement):\\n continue\\n if span[0] > len(statement):\\n break\\n span_index.append(span)\\n\\n # extract scalar features for this span. [position will not be used]\\n f_bias = 1\\n f_length = span[1] - span[0]\\n #f_begin = span[0]\\n #f_end = span[1]\\n #f_dist_to_end0 = len(statement) - span[0]\\n #f_dist_to_end1 = len(statement) - span[1]\\n\\n # list of tokens of this span\\n span_tokens = statement[span[0]: span[1]]\\n\\n # feature: span contains at least one science token\\n f_science_token = bool( \\\\\\n set(span_tokens).intersection(science_expressions))\\n\\n\\n\\n f_science_token_count = 0 # counting # of science tokens in span\\n max_token_length = 0 # in this span.\\n for token in span_tokens:\\n f_science_token_count += int(token in science_tokens)\\n max_token_length = max(max_token_length, len(token))\\n\\n f_max_token_length = np.log(max_token_length)\\n\\n # feature: relative word frequency average\\n # with numerical stability/ avoiding -inf\\n f_avg_word_frequency = 1e-10+np.mean(sentence_word_frequencies[span[0]: span[1]])\\n f_avg_word_frequency = np.log(f_avg_word_frequency)\\n\\n # feature: begin with stop word?\\n f_stop_word_begin = bool(span_tokens[0] in stop_words)\\n\\n # POS indicator (one-hot)\\n f_pos = np.zeros([len(pos_tags)])\\n\\n # Bag-of-POS-tags for this span.\\n for token, tag in pos_tags_this_statement[span[0]:span[1]]:\\n f_pos[pos_tags.index(tag)] += 1.0\\n\\n # feature: POS indicator for span beginning\\n f_pos_beginning = np.zeros([len(pos_tags)])\\n f_pos_beginning[pos_tags.index(pos_tags_this_statement[span[0]][1])] = 1.0\\n\\n # feature: POS indicator for span end\\n f_pos_end = np.zeros([len(pos_tags)])\\n f_pos_end[pos_tags.index(pos_tags_this_statement[span[1]-1][1])] = 1.0\\n\\n # feature: POS bigram indicator\\n # define extended POS tag set with additional begin and end symbols for bigrams.\\n # pos_tags_bigram = pos_tags + [\\\"POS_BEGIN\\\", \\\"POS_END\\\"]\\n\\n # for POS bigrams.\\n # pos_tags_square = [x for x in product(pos_tags_bigram, pos_tags_bigram)]\\n\\n # f_pos_bigram = np.zeros([len(pos_tags_square)])\\n\\n # obtaining the POS bigram\\n # for position in range(-1, f_length):\\n # boundary cases: start of span and end of span.\\n # if position == -1:\\n # tag1 = 'POS_BEGIN'\\n # _, tag2 = pos_tags_this_statement[span[0]]\\n # elif position == f_length -1:\\n # _, tag1 = pos_tags_this_statement[span[0]+position]\\n # tag2 = 'POS_END'\\n # #normal case: inside span.\\n # else:\\n # _, tag1 = pos_tags_this_statement[span[0] + position]\\n # _, tag2 = pos_tags_this_statement[span[0] + position + 1]\\n #\\n # f_pos_bigram[pos_tags_square.index( ( tag1, tag2 ) )] += 1.0\\n\\n # constituent tree features\\n\\n tree_position = tree.treeposition_spanning_leaves(span[0], span[1])\\n\\n # smallest subtree in constituent parse, containing this span.\\n smallest_subtree = tree[tree_position[:-1]]\\n constituent_tag = smallest_subtree.label()\\n\\n # feature: is this span a constituent parse subtree span?\\n f_span_match = bool(span[1]-span[0] == len(smallest_subtree))\\n\\n # constituency parse label indicator\\n f_span_constituent = np.zeros([len(constituents)])\\n f_span_constituent[constituents.index(constituent_tag)] = 1.0\\n\\n # constituency parse label indicator with indication for large spans.\\n f_span_constituent_big = np.zeros([len(constituents)])\\n f_span_constituent_big[constituents.index(constituent_tag)] = (f_length > 2)\\n\\n\\n # leave out position features:\\n #### f_begin, f_end, f_dist_to_end0, f_dist_to_end1,\\n\\n #now collect all features:\\n f_scalars = np.array([f_bias, f_span_match, f_length,\\n f_science_token, f_avg_word_frequency,\\n f_stop_word_begin,\\n f_max_token_length,\\n f_science_token_count])\\n\\n # these are all features for this span, in a np array.\\n feature_vector = np.concatenate((f_scalars, f_pos, f_pos_beginning,\\n f_pos_end, f_span_constituent,\\n f_span_constituent_big))\\n\\n\\n\\n # provide True/False annotation in case the data is used for training.\\n if training:\\n if span == training_spans[i]:\\n #positive example\\n true_examples.append(feature_vector)\\n sentence_candidate_span_examples.append((feature_vector, True))\\n else:\\n #negative example\\n false_examples_this_instance.append(feature_vector)\\n sentence_candidate_span_examples.append((feature_vector, False))\\n else:\\n sentence_candidate_span_examples.append(feature_vector)\\n\\n span_indexes.append(span_index)\\n examples_per_sentence.append(sentence_candidate_span_examples)\\n\\n\\n # select at random k negative spans as training examples. default 1:1\\n if training:\\n for random_index in np.random.randint(0, len(false_examples_this_instance), k):\\n false_examples.append(false_examples_this_instance[random_index])\\n\\n print(len(true_examples), 'True span examples.')\\n print(len(false_examples), 'False span examples.')\\n\\n # collect true and false examples [inputs]\\n all_examples = np.concatenate((np.asarray(false_examples), np.asarray(true_examples)))\\n\\n # collect annotations for each example (True/False target outputs)\\n false_span_labels = np.zeros([len(false_examples)])\\n true_span_labels = np.ones([len(true_examples)])\\n all_labels = np.concatenate((false_span_labels, true_span_labels))\\n\\n return all_examples, all_labels, examples_per_sentence, span_indexes\",\n \"def get_extra_facets(project, dataset, mip, short_name, extra_facets_dir):\\n project_details = _load_extra_facets(project, extra_facets_dir)\\n\\n def pattern_filter(patterns, name):\\n \\\"\\\"\\\"Get the subset of the list `patterns` that `name` matches.\\n\\n Parameters\\n ----------\\n patterns : :obj:`list` of :obj:`str`\\n A list of strings that may contain shell-style wildcards.\\n name : str\\n A string describing the dataset, mip, or short_name.\\n\\n Returns\\n -------\\n :obj:`list` of :obj:`str`\\n The subset of patterns that `name` matches.\\n \\\"\\\"\\\"\\n return [pat for pat in patterns if fnmatch.fnmatchcase(name, pat)]\\n\\n extra_facets = {}\\n for dataset_ in pattern_filter(project_details, dataset):\\n for mip_ in pattern_filter(project_details[dataset_], mip):\\n for var in pattern_filter(project_details[dataset_][mip_],\\n short_name):\\n facets = project_details[dataset_][mip_][var]\\n extra_facets.update(facets)\\n\\n return extra_facets\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def enumerate(self, capability: int = Capability.ALL) -> List[Technique]:\\n\\n # Update the cache for the current user\\n self.find_suid()\\n\\n known_techniques = []\\n for user, paths in self.suid_paths.items():\\n for path in paths:\\n binary = gtfobins.Binary.find(self.pty.which, path=path)\\n if binary is not None:\\n if (capability & binary.capabilities) == 0:\\n continue\\n\\n known_techniques.append(\\n Technique(user, self, binary, binary.capabilities)\\n )\\n\\n return known_techniques\",\n \"def get_test_examples(self, data_path):\\r\\n return self.create_examples(self.read_data(data_path), 'test')\",\n \"def _generate_examples_from_file(self, file):\\n\\n examples = []\\n for i, token in enumerate(file):\\n if token == -1:\\n # Out of dictionary token\\n continue\\n\\n # Generate context tokens for the current token\\n context_words = self._generate_contexts(i, file)\\n\\n # Form Examples:\\n # center, context - follows form: (input, target)\\n new_examples = [(token, ctxt) for ctxt in context_words if ctxt != -1]\\n\\n # Add to class\\n examples.extend(new_examples)\\n return examples\",\n \"def txt_of_sources_in_run (ins, exp, run) :\\n list_of_detectors = detectors(ins, exp, run)\\n txt = '\\\\nList of detectors for inst: %s exp: %s run %d' % (ins, exp, run)\\n for det in list_of_detectors :\\n txt += '\\\\n'+det\\n return txt\",\n \"def get_samples(meta, classes_list):\\n\\n h = list(meta[meta['DiseaseState'].isin(classes_list[0])].index)\\n dis = list(meta[meta['DiseaseState'].isin(classes_list[1])].index)\\n\\n return [h, dis]\",\n \"def getFeatures(c):\\n\\n\\n feature_list = []\\n lc_rc_list = []\\n w1 = c.getStack(0)\\n w2 = c.getStack(1)\\n w3 = c.getStack(2)\\n b1 = c.getBuffer(0)\\n b2 = c.getBuffer(1)\\n b3 = c.getBuffer(2)\\n for i in [w1, w2]: #12\\n lc = c.getLeftChild(i,1) # 1 st left child of the word on the stack.\\n rc = c.getRightChild(i,1) # 1 st right child of the word on the stack.\\n lc_rc_list.append(lc)\\n lc_rc_list.append(rc)\\n lc_rc_list.append(c.getLeftChild(i,2)) # 2 nd left child of the word on the stack\\n lc_rc_list.append(c.getRightChild(i,2)) # 2 nd right child of the word on the stack\\n lc_rc_list.append(c.getLeftChild(lc,1)) # 1 st left child of the left child of the word on the stack\\n lc_rc_list.append(c.getRightChild(rc,1)) # 1 st right child of the right child of the word on the stack\\n ########################### 18 Word Features ###########################\\n for i in [w1,w2,w3,b1,b2,b3]:\\n\\n feature_list.append(getWordID(c.getWord(i))) # 6 words of the stack and buffer\\n\\n for i in lc_rc_list: #12 words of the tree\\n feature_list.append(getWordID(c.getWord(i)))\\n\\n ########################### 18 Tag Features ###########################\\n for i in [w1,w2,w3,b1,b2,b3]:\\n\\n feature_list.append(getPosID(c.getPOS(i))) # 6 tags of the owrds on the stack and the buffer\\n\\n for i in lc_rc_list:\\n feature_list.append(getPosID(c.getPOS(i))) #12 tags of the words onthe stack and the buffer.\\n ########################### 12 label Features ###########################\\n for i in lc_rc_list:\\n feature_list.append(getLabelID(c.getLabel(i))) #12 labels of the words on the stack and the buffer.\\n\\n\\n return feature_list\",\n \"def fetch_group_lasso_datasets():\\n\\n # helper functions\\n\\n def find_interaction_index(seq, subseq,\\n alphabet=\\\"ATGC\\\",\\n all_possible_len_n_interactions=None):\\n n = len(subseq)\\n alphabet_interactions = \\\\\\n [set(p) for\\n p in list(itertools.combinations_with_replacement(alphabet, n))]\\n\\n num_interactions = len(alphabet_interactions)\\n if all_possible_len_n_interactions is None:\\n all_possible_len_n_interactions = \\\\\\n [set(interaction) for\\n interaction in\\n list(itertools.combinations_with_replacement(seq, n))]\\n\\n subseq = set(subseq)\\n\\n group_index = num_interactions * \\\\\\n all_possible_len_n_interactions.index(subseq)\\n value_index = alphabet_interactions.index(subseq)\\n\\n final_index = group_index + value_index\\n return final_index\\n\\n def create_group_indicies_list(seqlength=7,\\n alphabet=\\\"ATGC\\\",\\n interactions=[1, 2, 3],\\n include_extra=True):\\n alphabet_length = len(alphabet)\\n index_groups = []\\n if include_extra:\\n index_groups.append(0)\\n group_count = 1\\n for inter in interactions:\\n n_interactions = comb(seqlength, inter)\\n n_alphabet_combos = comb(alphabet_length,\\n inter,\\n repetition=True)\\n\\n for x1 in range(int(n_interactions)):\\n for x2 in range(int(n_alphabet_combos)):\\n index_groups.append(int(group_count))\\n\\n group_count += 1\\n return index_groups\\n\\n def create_feature_vector_for_sequence(seq,\\n alphabet=\\\"ATGC\\\",\\n interactions=[1, 2, 3]):\\n feature_vector_length = \\\\\\n sum([comb(len(seq), inter) *\\n comb(len(alphabet), inter, repetition=True)\\n for inter in interactions]) + 1\\n\\n feature_vector = np.zeros(int(feature_vector_length))\\n feature_vector[0] = 1.0\\n for inter in interactions:\\n # interactions at the current level\\n cur_interactions = \\\\\\n [set(p) for p in list(itertools.combinations(seq, inter))]\\n interaction_idxs = \\\\\\n [find_interaction_index(\\n seq, cur_inter,\\n all_possible_len_n_interactions=cur_interactions) + 1\\n for cur_inter in cur_interactions]\\n feature_vector[interaction_idxs] = 1.0\\n\\n return feature_vector\\n\\n positive_url = \\\\\\n \\\"http://genes.mit.edu/burgelab/maxent/ssdata/MEMset/train5_hs\\\"\\n negative_url = \\\\\\n \\\"http://genes.mit.edu/burgelab/maxent/ssdata/MEMset/train0_5_hs\\\"\\n\\n pos_file = tempfile.NamedTemporaryFile() #bufsize=0)\\n neg_file = tempfile.NamedTemporaryFile() #bufsize=0)\\n\\n posreq = urllib.request.Request(positive_url)\\n with urllib.request.urlopen(posreq) as posresponse:\\n pos_page = posresponse.read().decode(\\\"utf-8\\\")\\n\\n negreq = urllib.request.Request(negative_url)\\n with urllib.request.urlopen(negreq) as negresponse:\\n neg_page = negresponse.read().decode(\\\"utf-8\\\")\\n\\n positive_sequences = [str(line.strip().upper()) for idx, line in\\n enumerate(pos_page.strip().split('\\\\n'))\\n if \\\">\\\" not in line and idx < 2 * 8000]\\n\\n negative_sequences = [str(line.strip().upper()) for idx, line in\\n enumerate(neg_page.strip().split('\\\\n'))\\n if \\\">\\\" not in line and\\n idx < 2 * len(positive_sequences)]\\n\\n assert len(positive_sequences) == len(negative_sequences), \\\\\\n \\\"lengths were not the same: p={pos} n={neg}\\\" \\\\\\n .format(pos=len(positive_sequences), neg=len(negative_sequences))\\n\\n positive_vector_matrix = np.array([create_feature_vector_for_sequence(s)\\n for s in positive_sequences])\\n negative_vector_matrix = np.array([create_feature_vector_for_sequence(s)\\n for s in negative_sequences])\\n\\n df = pd.DataFrame(data=np.vstack((positive_vector_matrix,\\n negative_vector_matrix)))\\n df.loc[0:positive_vector_matrix.shape[0], \\\"Label\\\"] = 1.0\\n df.loc[positive_vector_matrix.shape[0]:, \\\"Label\\\"] = 0.0\\n\\n design_matrix = df\\n groups = create_group_indicies_list()\\n\\n return design_matrix, groups\",\n \"def matching_programs(pattern):\\n programs = get_programs()\\n search_results = []\\n\\n # For every program, check if it matches by title or by filename.\\n for program, data in programs.items():\\n title_result = fuzzy_search(pattern, data['Name'])\\n filename_result = fuzzy_search(pattern, program)\\n title_match = True\\n for result in title_result, filename_result:\\n if result in (WHOLE_PATTERN_FIRST,\\n WHOLE_PATTERN_IN_STRING,\\n PATTERN_IN_STRING):\\n search_results.append((program, data, title_match, result))\\n break\\n title_match = False\\n\\n # Sort the programs by how well they matched and yield them.\\n sorted_ = sorted(search_results, key=lambda r: r[3], reverse=True)\\n for i, item in enumerate(sorted_):\\n program, data, title_match, _ = item\\n yield (program, data, title_match)\",\n \"def keyword_extraction(file_content):\\n\\n # [question, question....]\\n for key, value in file_content.items():\\n seg, hidden = ltp.seg([key])\\n # ner: [[('Nh', 2, 2)]]\\n ner = ltp.ner(hidden)\\n # keywords: [('PERSON', \\\"吴轩\\\")], tuple_item: ('Nh', 2, 2)\\n keywords = [(tag_to_name[tuple_item[0]], to_string(seg[0][tuple_item[1]: tuple_item[2]+1])) for tuple_item in ner[0]]\\n file_content[key].keywords = keywords\\n\\n return file_content\",\n \"def get_malware_used_by_groups():\\n global malware_used_by_groups\\n\\n if not malware_used_by_groups:\\n malware_used_by_groups = rsh.malware_used_by_groups(get_srcs())\\n\\n return malware_used_by_groups\",\n \"def _evaluate_examples(self):\\n\\n getLogger(\\\"problog_lfi\\\").debug(\\\"Evaluating examples:\\\")\\n if self._log:\\n evaluator = ExampleEvaluatorLog(self._weights)\\n else:\\n evaluator = ExampleEvaluator(self._weights)\\n\\n results = []\\n for i, example in enumerate(self._compiled_examples):\\n try:\\n result = evaluator(example)\\n results.append(result)\\n getLogger(\\\"problog_lfi\\\").debug(\\n \\\"Example \\\"\\n + str(i + 1)\\n + \\\":\\\\tFrequency = \\\"\\n + str(result[0][0])\\n + \\\"\\\\tp_evidence = \\\"\\n + str(result[0][1])\\n + \\\"\\\\tp_queries = \\\"\\n + str(result[0][2])\\n )\\n except InconsistentEvidenceError:\\n # print(\\\"Ignoring example {}/{}\\\".format(i + 1, len(self._compiled_examples)))\\n getLogger(\\\"problog_lfi\\\").warning(\\n \\\"Ignoring example {}/{}\\\".format(i + 1, len(self._compiled_examples))\\n )\\n\\n return list(chain.from_iterable(results))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.628506","0.62599236","0.6136026","0.60789555","0.5992689","0.5951035","0.5889614","0.5713279","0.563185","0.5599353","0.5587775","0.55412316","0.5538815","0.5419111","0.5401357","0.5392721","0.53859246","0.5369623","0.5243388","0.52172226","0.521398","0.52089083","0.5178656","0.5177566","0.5174239","0.5169755","0.51621324","0.5129986","0.5094265","0.5088446","0.50845027","0.5084419","0.50811064","0.5041244","0.50294095","0.5020534","0.50174284","0.50165105","0.5016397","0.4996726","0.49876624","0.4987455","0.49861705","0.49642423","0.49642423","0.49575943","0.4955044","0.49549058","0.49428365","0.4927284","0.4905911","0.48935336","0.48825297","0.4882074","0.48782432","0.48778862","0.4872246","0.48695564","0.48662528","0.48452932","0.48452932","0.48452932","0.48452932","0.48452932","0.48452932","0.48398492","0.48292118","0.4818337","0.48060903","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47979346","0.47969052","0.47937864","0.47905564","0.47747427","0.47744167","0.47744167","0.47724092","0.4771702","0.47710633","0.47697163","0.47462034","0.4737013","0.47255698","0.4723432","0.47113976","0.47074932","0.46985033"],"string":"[\n \"0.628506\",\n \"0.62599236\",\n \"0.6136026\",\n \"0.60789555\",\n \"0.5992689\",\n \"0.5951035\",\n \"0.5889614\",\n \"0.5713279\",\n \"0.563185\",\n \"0.5599353\",\n \"0.5587775\",\n \"0.55412316\",\n \"0.5538815\",\n \"0.5419111\",\n \"0.5401357\",\n \"0.5392721\",\n \"0.53859246\",\n \"0.5369623\",\n \"0.5243388\",\n \"0.52172226\",\n \"0.521398\",\n \"0.52089083\",\n \"0.5178656\",\n \"0.5177566\",\n \"0.5174239\",\n \"0.5169755\",\n \"0.51621324\",\n \"0.5129986\",\n \"0.5094265\",\n \"0.5088446\",\n \"0.50845027\",\n \"0.5084419\",\n \"0.50811064\",\n \"0.5041244\",\n \"0.50294095\",\n \"0.5020534\",\n \"0.50174284\",\n \"0.50165105\",\n \"0.5016397\",\n \"0.4996726\",\n \"0.49876624\",\n \"0.4987455\",\n \"0.49861705\",\n \"0.49642423\",\n \"0.49642423\",\n \"0.49575943\",\n \"0.4955044\",\n \"0.49549058\",\n \"0.49428365\",\n \"0.4927284\",\n \"0.4905911\",\n \"0.48935336\",\n \"0.48825297\",\n \"0.4882074\",\n \"0.48782432\",\n \"0.48778862\",\n \"0.4872246\",\n \"0.48695564\",\n \"0.48662528\",\n \"0.48452932\",\n \"0.48452932\",\n \"0.48452932\",\n \"0.48452932\",\n \"0.48452932\",\n \"0.48452932\",\n \"0.48398492\",\n \"0.48292118\",\n \"0.4818337\",\n \"0.48060903\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47979346\",\n \"0.47969052\",\n \"0.47937864\",\n \"0.47905564\",\n \"0.47747427\",\n \"0.47744167\",\n \"0.47744167\",\n \"0.47724092\",\n \"0.4771702\",\n \"0.47710633\",\n \"0.47697163\",\n \"0.47462034\",\n \"0.4737013\",\n \"0.47255698\",\n \"0.4723432\",\n \"0.47113976\",\n \"0.47074932\",\n \"0.46985033\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.659891"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85689,"cells":{"query":{"kind":"string","value":"Responsible for generating the links that are located on the left side of individual technique domain pages"},"document":{"kind":"string","value":"def get_technique_side_menu_data(domain, technique_list, tactic_list):\n\n # Get tactics techniques side menu data to fill out with jinja\n tactics_techniques_menu_data = []\n\n for tactic in tactic_list:\n tactic_row = {}\n \n tactic_row['name'] = tactic['name']\n tactic_row['id'] = util.get_attack_id(tactic)\n tactic_row['path'] = \"/tactics/{}\".format(util.get_attack_id(tactic))\n \n tactic_row['children'] = []\n for technique in technique_list[tactic['x_mitre_shortname']]:\n technique_row = {}\n # Get technique id and name for each technique\n technique_row['name'] = technique['name']\n technique_row['id'] = technique['id']\n technique_row['path'] = \"/techniques/{}/\".format(technique['id'])\n technique_row['children'] = []\n # Add technique data to tactic\n tactic_row['children'].append(technique_row)\n \n # Add tactic to table\n tactics_techniques_menu_data.append(tactic_row)\n \n return {\n \"name\": \"techniques\",\n \"id\": \"techniques\",\n \"path\": \"/techniques/{}/\".format(domain.split(\"-\")[0]), # root level doesn't get a path\n \"children\": tactics_techniques_menu_data\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getExpandedLinks():","def make_navbar_for_homepage(self):\n links = [\n \"home\", [\"Result Pages\", self._result_page_links()], \"Version\"\n ]\n if len(self.samples) > 1:\n links[1][1] += [\"Comparison\"]\n if self.publication:\n links.insert(2, \"Publication\")\n if self.gwdata is not None:\n links.append([\"Detchar\", [i for i in self.gwdata.keys()]])\n if self.notes is not None:\n links.append(\"Notes\")\n return links","def generate_links():\n start_url = 'https://twigserial.wordpress.com/'\n base_url = start_url + 'category/story/'\n tree = etree.HTML(requests.get(start_url).text)\n xpath = './/*/option[@class=\"level-2\"]/text()'\n return [base_url + suffix.strip() for suffix in tree.xpath(xpath)]","def iter_page_links(self) -> Iterable[str]:\n base_url = 'https://health.mil/About-MHS/OASDHA/Defense-Health-Agency/Resources-and-Management/DHA-Publications'\n yield base_url","def generate_page(links, emails):\n s = ''.join(generate_string(30, valid_domain_name_chars))\n\n for i in links:\n s = s + ' ' + '<a href=\"'+i+'\"></a>' + ' ' +''.join(generate_string(30, valid_domain_name_chars))\n s = s + ' ' +''.join(generate_string(30, valid_domain_name_chars)) \n\n for i in emails:\n s = s + ' ' + i + ' ' +''.join(generate_string(30, valid_domain_name_chars))\n s = s + ' ' +''.join(generate_string(30, valid_domain_name_chars)) \n \n\n return s","def append_links(self, lines, lang):\n lines.append(\"verbatim \")\n lines.append(\"section Links\")\n lines.append(\"external http://polcasaglia.blogspot.com Blog\")\n lines.append(\"external http://www.uisp-fe.it/calcio.php UISP\" )\n lines.append(\"verbatim \")\n return lines","def fix_links():\n pass","def makeLinks(self):\n self.deleteIndexFileIfExists()\n _fileNames = self.getHTMLFileNames()\n _msgPart1 = \"<a href=\\\"\"\n _msgPart2 = \"\\\" target=\\\"loadHTMLResults\\\">\"\n _msgPart3 = \"</a><br>\"\n _link = \"\"\n for _fileName in _fileNames:\n _origFileName = _fileName\n _linkName = _fileName.split('.')[0]\n _createAnchorTag = (_msgPart1+str(_origFileName)+_msgPart2+str(_linkName)+_msgPart3)\n _link = _link + _createAnchorTag\n return _link","def methods():\n list_groups_text = '<a href=\"/groups\">List Groups</a>'\n list_users_text = '<a href=\"/users\">List Users</a>'\n page_links = list_groups_text + \"<br>\" + list_users_text\n return page_links","def getLink(self):","def get_links(self):\n links = \"\"\n if self.title != \"\":\n links += html_link_to_tag(\n plain_to_html(self.title), self.title, self.proc\n )\n return links + \\\n html_unordered_list([x.get_links() for x in self.subsections])","def _generate_links(self):\n index = 0\n links = \"\"\n for ch in self.text:\n if ch == '[':\n links += \"(^\"\n elif ch == ']':\n links += \")$|\"\n index += 1\n elif links[-1:] != '|' and links != \"\":\n links += ch\n self.links = compile(links[:-1].lower())","def links(n):\n return redirect(url_for(\"link_page\", n=n, offset=0))","def makeLandingUrl(self, artMeta):\n return None","def get_links(self):\n soup = BeautifulSoup(requests.get(self.locations_url).text.strip(), features=\"lxml\")\n for region in soup.select('td[class=\"navbox-list navbox-odd\"]'):\n self.links.extend(region.div.find_all('a'))\n\n soup_prague = BeautifulSoup(requests.get(self.url_prague).text.strip(), features=\"lxml\")\n table_prague = soup_prague.findAll('table', {\"class\": \"wikitable\"})[3]\n for prague_parts in table_prague.select(\"tr > td:nth-child(3)\"):\n self.links.extend(prague_parts.find_all('a'))\n\n self.links = [self.url + i['href'] for i in self.links]\n self.links.append(self.url_prague)\n return None","def Site(self) -> str:","def _build_links(links):\n for link in links:\n link['href'] = link['href'].replace('servers', 'instances')\n return links","def renderView(self):\n html = \"\"\n if self.lms.lms == \"other\":\n html += \"_______________________________<br/>\"\n url = \"http://%s\" % self.lms.otherUrl\n html += \"<br/><b>%s </b>\" % self.lms.otherLabel\n html += '<a href=\"%s\">%s</a>' % (url, url) \n return html","def iter_page_links(self) -> Iterable[str]:\n base_url = 'https://www.med.navy.mil'\n r = requests.get(self.starting_url, verify=CERTIFICATE_DIR + '/cat3.pem')\n soup = bs4.BeautifulSoup(r.content, features=\"html.parser\")\n\n # get target column of list items\n issuance_list = soup.find('div', attrs={'class': 'noindex ms-wpContentDivSpace'})\n matches = [\"Publications\", \"BUMEDNotes\", \"BUMEDInstructions\"]\n # extract links\n links = [link for link in issuance_list.find_all('a')]\n for link in links[2:-1]:\n if any(x in str(link) for x in matches):\n if not link['href'].startswith('http'):\n url = base_url + link['href']\n else:\n url = link['href']\n yield url","def gen_links(text):\n return []","def extra_links(request):\n return {'zentral_extra_links': settings.get('extra_links', [])}","def _fix_links(self, text, page_names):\n for n in page_names:\n text = text.replace(f\"]({n})\", f\"]({n}.html)\")\n text = text.replace(f\"]({n}.md)\", f\"]({n}.html)\")\n return text","def test_menu_path_links(self):\r\n main_page = DogMainPage(self.driver)\r\n # Navigate to Title (main) page\r\n title_page = main_page.navigate_main()\r\n title_expected_header = title_page.get_expected_header()\r\n title_header = title_page.get_header()\r\n # Assert the title by sub-string to verify page\r\n self.assertTrue(title_expected_header in title_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (title_expected_header, title_header)))\r\n # Navigate to 'Documentation' page\r\n doc_page = main_page.navigate_documentation()\r\n doc_expected = doc_page.get_expected_header()\r\n doc_header = doc_page.get_header()\r\n # Assert the title to verify page\r\n self.assertEqual(doc_expected, doc_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (doc_expected, doc_header)))\r\n # Navigate to 'Breed list' page\r\n breed_list_page = main_page.navigate_breeds_list()\r\n breed_list_expected_header = breed_list_page.get_expected_header()\r\n breed_list_header = breed_list_page.get_header()\r\n # Assert the title to verify page\r\n self.assertEqual(breed_list_expected_header, breed_list_header,\r\n ('%s expected, instead found: %s. Page is wrong' %\r\n (breed_list_expected_header, breed_list_header)))\r\n # Navigate to 'About' page\r\n about_page = main_page.navigate_about()\r\n about_expected_header = about_page.get_expected_header()\r\n about_header = about_page.get_header()\r\n # Assert the title to verify page\r\n self.assertEqual(about_expected_header, about_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (about_expected_header, about_header)))","def menu_forum_spe(self, event=None):\n self.link('http://www.stani.be/python/spe/page_forum')","def menu_spe_homepage(self, event=None):\n self.link('http://pythonide.stani.be')","def getSubpagesLink(self):\n return self.subpages","def marketing_link_context_processor(request):\r\n return dict(\r\n [\r\n (\"MKTG_URL_\" + k, marketing_link(k))\r\n for k in (\r\n settings.MKTG_URL_LINK_MAP.viewkeys() |\r\n settings.MKTG_URLS.viewkeys()\r\n )\r\n ]\r\n )","def test_page_links(inspire_app):\n create_record(\"lit\", data={\"titles\": [{\"title\": \"Solenoid\"}]})\n create_record(\"lit\", data={\"titles\": [{\"title\": \"Solenoid\"}]})\n with inspire_app.test_client() as client:\n # Limit records\n response = client.get(\n \"/api/literature\", query_string=dict(size=1, page=1, q=\"Solenoid\")\n )\n response_json = response.json\n assert len(response_json[\"hits\"][\"hits\"]) == 1\n\n data = response_json[\"links\"]\n assert \"self\" in data\n assert \"next\" in data\n assert \"prev\" not in data\n\n # Assert next URL before calling it\n first_url = data[\"self\"]\n next_url = data[\"next\"]\n parsed_url = parse_url(next_url)\n assert parsed_url[\"qs\"][\"size\"] == [\"1\"]\n assert parsed_url[\"qs\"][\"page\"] == [\"2\"]\n\n # Access next URL\n response = client.get(next_url)\n response_json = response.json\n assert len(response_json[\"hits\"][\"hits\"]) == 1\n data = response.json[\"links\"]\n assert data[\"self\"] == next_url\n assert \"next\" not in data\n assert \"prev\" in data and data[\"prev\"] == first_url","def _create_links_and_track(self, page_name, category_list):\n env = self.state.document.settings.env\n if not hasattr(env, \"categories\"):\n env.categories = {}\n\n link_rst = \"\"\n ncategs = 0\n for item in category_list:\n has_subcat = False\n if r\"\\\\\" in item: \n categs = item.split(r\"\\\\\")\n has_subcat = True\n else:\n categs = [item]\n # endif\n\n print \n for index, categ_name in enumerate(categs):\n if categ_name not in env.categories:\n category = Category(categ_name)\n env.categories[categ_name] = category\n else:\n category = env.categories[categ_name]\n #endif\n category.pages.append(PageRef(page_name))\n if has_subcat and index > 0:\n category.subcategories.append(PageRef(categ_name))\n #endif\n link_rst += \":ref:`%s` | \" % categ_name\n ncategs += 1\n # endfor\n # endfor\n\n link_rst = \"`%s: <categories.html>`_ \" + link_rst\n if ncategs == 1:\n link_rst = link_rst % \"Category\"\n else:\n link_rst = link_rst % \"Categories\"\n #endif\n\n return link_rst","def render_prependsite(self, ctx, data):\n\t\tctx.tag.children = [base.getConfig(\"web\", \"sitename\")]+ctx.tag.children\n\t\treturn ctx.tag","def page_links(self, title):\n return [l[0] for l in self.page_links_and_labels(title)]","def treat_page(self):\n # let's define some basic variables\n urtext = self.current_page.text\n urlang = self.current_page.site.code\n urtitle = self.current_page.title()\n urcat = []\n eng_site = pywikibot.Site('en')\n eng_title = ''\n \n interDict = {}\n try:\n site = pywikibot.Site('ur', 'wikipedia')\n urpage = pywikibot.Page(site, urtitle)\n langlst = urpage.iterlanglinks()\n\n \n for i in langlst:\n lang = str(i.site).split(':')[1]\n interDict[lang] = i.title\n \n eng_title = interDict['en']\n except:\n pywikibot.output(u'\\03{lightred}Unable to fetch interwiki links!\\03{default}')\n return False\n \n site = pywikibot.Site('en', 'wikipedia')\n enpage = pywikibot.Page(site, eng_title)\n\n wikitext = enpage.get() \n wikicode = mwp.parse(wikitext)\n\n # Extracting sfn templates and converting them in REF tags\n sfnlist = []\n for template in wikicode.filter_templates():\n if template.name in ('sfn', 'sfn'):\n sfnlist.append(template)\n templ_rep = '<ref>' + str(template) + '</ref>'\n wikicode.replace(template , templ_rep)\n\n alltags = wikicode.filter_tags() \n reftags = {}\n \n def search(myDict, search1):\n for key, value in myDict.items():\n if search1 in value: \n return key \n \n i=1\n for tag in alltags:\n if tag.tag=='ref':\n if tag.attributes == []: # check if attributes list is empty\n refval='NoRefName' # Reference has no name so assigning \"NoRefName\"\n else:\n name = tag.attributes[0]\n refval = name.value\n \n if tag.contents is None:\n #conval = search(reftags,refval)\n #reftags[i] = (refval,reftags[conval][1])\n pass\n else: \n reftags[i] = (refval,tag.contents)\n i += 1\n\n dlinks = {}\n for k,v in reftags.items():\n dkey = 'و' + str(k) + 'و'\n dlinks[dkey] = '<ref>' + str(v[1]) + '</ref>'\n\n urtext = urpage.text\n for r in tuple(dlinks.items()):\n urtext = urtext.replace(*r)\n\n # newln = '\\n'\n # Using noreferences to add Reference template if not present\n self.norefbot = noreferences.NoReferencesBot(None)\n if self.norefbot.lacksReferences(urtext):\n urtext = self.norefbot.addReferences(urtext)\n else:\n urpage.text = urtext + '\\n'\n\n print(urpage.text)\n \n # save the page \n urpage.save(summary=self.summary, minor=False)\n #self.put_current(urpage.text, summary=self.summary)","def _gen_link(content):\n text = \"{% load macros %}\" + content\n as_str = Template(text).render(Context({}))\n # Safe because: we've constructed the XML\n as_xml = etree.fromstring(\"<ROOT>{}</ROOT>\".format(as_str)) # nosec\n anchors = as_xml.findall('.//a')\n assert len(anchors) > 0\n return anchors[0]","def createLinkFromWikiWord(word, wikiPage): # normalizeWikiWord\r\n return \"\"","def connect_links(base_url, extensions, wikidir, body):\n if base_url.endswith(\"/\"):\n base_url = base_url[:-1]\n\n i = 0\n body2 = []\n\n for match in WIKILINK.finditer(body):\n body2.append(body[i:match.span(0)[0]])\n \n text = match.group(1)\n\n if \"|\" in text:\n topic, desc = text.split(\"|\")\n topic = topic.strip()\n else:\n topic, desc = (text, text)\n\n fn = os.path.join(wikidir, topic)\n\n ext = tools.what_ext(extensions, fn)\n if not ext:\n body2.append(match.group(0))\n i = match.span(0)[1]\n continue\n\n body2.append(\"<a href=\\\"%s/%s/%s\\\">%s</a>\" % \\\n (base_url, TRIGGER, topic, desc))\n i = match.span(0)[1]\n\n body2.append(body[i:])\n return \"\".join(body2)","def navlist(stu, stulist, rooturl='/dist'):\n parts = []\n for x in stulist:\n if x == stu:\n parts.append('<b>' + x + '</b>')\n else:\n parts.append('<a href=\"' + rooturl + '/' + x + '/\">' + x + '</a>')\n return ' '.join(parts)","def get_front_page_links():\n response = requests.get(\"https://codeup.com/blog/\", headers={\"user-agent\": \"Codeup DS\"})\n soup = BeautifulSoup(response.text)\n links = [link.attrs[\"href\"] for link in soup.select(\".more-link\")]\n return links","def link(self):\n return f\"[{self.numbered_title}]({self.html_url})\"","def get_page_links(): \n\ttitle = driver.find_elements_by_xpath(XPATHS['title'])\n\tlinks = [link.get_attribute('href') for link in title]\n\treturn links","def _create_links_and_track(self, page_name, category_list):\n env = self.state.document.settings.env\n if not hasattr(env, \"categories\"):\n env.categories = {}\n\n link_rst = \"\"\n ncategs = 0\n for categ_name in category_list:\n #categ_name is the full category name - register that\n category = self.register_category(categ_name, env)\n category.pages.add(PageRef(page_name, env.docname))\n\n #now step up a step up each time the category hierarchy\n parent_category = categ_name\n while True:\n if r\"\\\\\" in parent_category:\n categs = parent_category.split(r\"\\\\\")\n else:\n break\n # remove the last item\n subcat = Category(categ_name, env.docname) #create the category with the full name\n subcat.name=categs.pop() # and then replace it with the last token of the name\n parent_category = r\"\\\\\".join(categs)\n\n #register the parent category\n parent = self.register_category(parent_category, env)\n parent.subcategories.add(subcat)\n\n # endwhile\n\n #category should be the last subcategory by this point\n link_rst += \"`%s <%s>`_ | \" % (categ_name, category.link(env.docname))\n ncategs += 1\n # endfor\n\n link_rst = \"**%s**: \" + link_rst.rstrip(\" | \") # remove final separator\n if ncategs == 1:\n link_rst = link_rst % \"Category\"\n else:\n link_rst = link_rst % \"Categories\"\n #endif\n\n return link_rst","def update_links(self):\n for a in self.book.xpath(\"//a[@href]\"):\n href = a.xpath(\"@href\")[0]\n index_list = a.xpath(\"@data-index\")\n \n ### If there is no data-index it is assumed link comes from initial book landing page (the index page)\n if index_list == []:\n index = self.manager.get_page_index(\"index.html\")\n else:\n index = index_list[0]\n \n ### Fix people who are bad at links\n if href.startswith(\"www.\"):\n href = \"https://\" + href\n a.set(\"href\", href)\n \n ## Correct for ambiguity (Naive assumption that this error only occours on index page)\n if href == \"./\":\n href = \"index.html\"\n \n if not href:\n return None\n \n href = self.manager.convert_link(href, index)\n a.set(\"href\", href)","def __url_list(self, page):\n url_list = []\n for tag_a in page.find_all('a'):\n href = str(tag_a.get('href'))\n if self.__verify(href):\n url = parse.quote(self.__add_main_site(href), '/:#')\n url_list.append(url)\n return url_list","def iter_links(self):","def get_course_page_urls(self,soup):\n\t\tcourse_links =[]\n\t\troot_url = 'http://onlinelearning.cornell.edu'\n\t\tfor link in soup.select('span.field-content a[href]'):\n\t\t\tnew_url = root_url + link['href']\n\t\t\tcourse_links.append(new_url)\n\t\t\tcourse_links.append(' \\n')\n\t\t\n\t\tself.new_list.append(course_links)\n\t\treturn course_links","def get_absolute_url(self):\r\n return \"{0}page1/\".format(self.get_short_url())","def html_page_context(app, pagename, templatename, context, doctree):\n site = context[\"SITEMAP_BASE_URL\"]\n version = context[\"version\"]\n app.sitemap_links.add(f\"{site}{version}/{pagename}.html\")","def get_left_panel_links(self):\n status_list = {}\n link_list = (self.reports, self.rewards, self.catalogs, self.campaigns, self.loyalties,\n self.merchants, self.customer_management, self.bulk_actions, self.settings,\n self.business_intelligence)\n for link in link_list:\n if find_element(self.browser, link):\n status_list[link[1]] = 'Access'\n else:\n status_list[link[1]] = 'No Access'\n return status_list","def link_prefix(request):\n return request.application_url","def homepage():\n\n general_news=get_sources('general')\n business_news=get_sources('business')\n sports_news=get_sources('sports')\n return render_template('sources.html',general=general_news, business=business_news, sports=sports_news)","def grab_links(self):\n links = []\n link_char = []\n w_temp = [] #in template?\n par = [] #in parentheses?\n rtag = [] #in <ref> tag?\n dtag = [] #in <div> tag?\n\n skip_char = []\n\n for i, c in enumerate(self.article_xml):\n if i in skip_char: continue #eliminates double counting\n char = self.article_xml[i:i+2]\n tag = self.article_xml[i:i+4]\n \n #wiki template\n w_temp = self.inside_char(char, Article.w_marker, w_temp, i)\n if char in Article.w_marker: skip_char.append(i+1)\n if w_temp:\n continue #doesn't process if inside wiki template\n \n #parentheses\n par = self.inside_char(c, Article.par_marker, par, i)\n if par:\n continue\n \n #<ref> or <div>\n rtag = self.inside_char(tag, Article.rtag_marker, rtag, i)\n dtag = self.inside_char(tag, Article.dtag_marker, dtag, i)\n if rtag or dtag:\n continue\n \n #clear to add outer-most link\n if char == '[[':\n link_char.append(i)\n elif char == ']]' and len(link_char) == 1:\n links.append( self.article_xml[link_char[0]:i+2])\n link_char.pop()\n elif char == ']]' and len(link_char) > 1:\n link_char.pop()\n return links","def links(self) -> str:\n return pulumi.get(self, \"links\")","def menu_authors_homepage(self, event=None):\n self.link('http://www.stani.be')","def middleshow(self):\n self._showitemlist(ITEMLIST_LINKS)","def moreContext(self, app, pagename, templatename, context, doctree):\n links = []\n for name, url, condition in self.toplinks:\n link = self.path_for(url, pagename)\n links.append((name, link, condition.match(pagename)))\n\n context['toplinks'] = links","def get_absolute_url(self):\n\n url = reverse('comicsite.views.page', args=[self.comicsite.short_name,self.title])\n return url","def home_page():\n return (\n f\"Available Routes:<br/>\"\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/>\"\n f\"/api/v1.0/start<br/>\"\n f\"/api/v1.0/start/end<br/>\"\n )","def Prolinks(promotion_label):\n return prolinks","def __add_main_site(self, href):\n if self.main_url[8:] not in href:\n return self.main_url + href\n else:\n return href","def _link_items(self):\n pass","def test_documentation_path_links(self):\r\n main_page = DogMainPage(self.driver)\r\n dog_page = main_page.navigate_documentation()\r\n # Switch to 'List all breeds' tab\r\n all_breeds_page = dog_page.switch_tab(dog_page.ALL_BREEDS)\r\n all_breeds_expected = all_breeds_page.get_expected_header()\r\n all_breeds_header = all_breeds_page.get_header()\r\n # Assert the title to verify the page\r\n self.assertEqual(all_breeds_expected, all_breeds_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (all_breeds_expected, all_breeds_header)))\r\n # Switch to 'Random image' tab\r\n random_page = dog_page.switch_tab(dog_page.RANDOM)\r\n random_expected_header = random_page.get_expected_header()\r\n random_header = random_page.get_header()\r\n # Assert the title to verify the page\r\n self.assertEqual(random_expected_header, random_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (random_expected_header, random_header)))\r\n # Switch to 'By breed' tab\r\n breed_page = dog_page.switch_tab(dog_page.BREED)\r\n breed_expected_header = breed_page.get_expected_header()\r\n breed_header = breed_page.get_header()\r\n # Assert the title to verify the page\r\n self.assertEqual(breed_expected_header, breed_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (breed_expected_header, breed_header)))\r\n # Switch to 'By sub-breed' tab\r\n sub_breed_page = dog_page.switch_tab(dog_page.SUB_BREED)\r\n sub_expected_header = sub_breed_page.get_expected_header()\r\n sub_header = sub_breed_page.get_header()\r\n # Assert the title to verify the page\r\n self.assertEqual(sub_expected_header, sub_header,\r\n ('%s expected, instead found: %s. Page is wrong' % (sub_expected_header, sub_header)))","def generate_slack_links(links):\n return \", \".join(\"<%s|%d>\" % (el, idx + 1) for idx, el in enumerate(links))","def linktrail(self, code: str) -> str:\n site = pywikibot.Site(code, 'wikipedia')\n return site.linktrail()","def page_url(self):\n url = '/plaque/%s' % self.key.urlsafe()\n return url","def link(address):","def theLinky(self):\n theLink = self.absolute_url()\n return theLink","def controls_setup(self):\n\n self.to_homepage = element.Link(self, class_name='nav-home', alias='Return to site Link')\n self.account = element.Link(self, class_name='nav-account', alias='Account Profile Link')\n self.logout = element.Link(self, class_name='nav-logout', alias='Logout Link')","def _link_elements(self):\n raise NotImplementedError(\"Please implement this method\")","def links(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/links.html',\n {\n \t'cart': cart,\n 'title':'Полезные ресурсы',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def getSiteExampleURLs(self):\r\n return 'no such example'","def GetUrlFirst(self):\n self.url = \"https://www.taobao.com/\"\n self.host = \"www.taobao.com\"\n self.referer = \"https://www.taobao.com/\"\n content = self.GetContent()\n __clear__ = '<a href=.*?</a>'\n match = open(self.base_dir_url+\"url_first.html\", 'w')\n try:\n all_link = re.findall(__clear__, content, re.S)\n print \"All links of the web page is: \", len(all_link)\n self.DealUrlFirst(match, all_link)\n except:\n print \"Something wrong is happening!\"\n finally:\n match.close()\n match.close()","def get_menu_urls():\n\n r = requests.get(URL_BASE)\n if r.status_code != 200:\n raise Exception(\"Code {0} downloading base website: {1}\".format(r.status_code, r.text))\n\n soup = bs4.BeautifulSoup(r.text)\n\n file_list = soup.select(\"ul.file_list\")[0]\n file_links = [a['href'] for a in file_list.select('a.pdf_file') if RE_MENU.match(a['href'])]\n\n return [urlparse.urljoin(URL_BASE, fl) for fl in file_links]","def marketing_link(name):\r\n\r\n # link_map maps URLs from the marketing site to the old equivalent on\r\n # the Django site\r\n link_map = settings.MKTG_URL_LINK_MAP\r\n enable_mktg_site = microsite.get_value(\r\n 'ENABLE_MKTG_SITE',\r\n settings.FEATURES.get('ENABLE_MKTG_SITE', False)\r\n )\r\n\r\n if enable_mktg_site and name in settings.MKTG_URLS:\r\n # special case for when we only want the root marketing URL\r\n if name == 'ROOT':\r\n return settings.MKTG_URLS.get('ROOT')\r\n return settings.MKTG_URLS.get('ROOT') + settings.MKTG_URLS.get(name)\r\n # only link to the old pages when the marketing site isn't on\r\n elif not enable_mktg_site and name in link_map:\r\n # don't try to reverse disabled marketing links\r\n if link_map[name] is not None:\r\n return reverse(link_map[name])\r\n else:\r\n log.warning(\"Cannot find corresponding link for name: {name}\".format(name=name))\r\n return '#'","def links(self):\n return self._links_tpl.expand(self._identity, self._record)","def get_links_from_one_page(driver,site,URL_exclusions):\r\n while True:\r\n try:\r\n results = driver.find_elements_by_class_name(\"g\") #Find all elements with class=\"g\". This includes search results.\r\n break\r\n except:\r\n continue \r\n links = []\r\n for result in results:\r\n link = result.find_element_by_tag_name(\"a\") #Hyperlinks are contained under <a> tags\r\n link = link.get_attribute('href') #Retrive link as a string\r\n if link.find(site) != -1: #Some class=\"g\" elements are not search results. Only store links with urls containing \"site\".\r\n links.append(link)\r\n sig_links = [] #Create list of links for pages not from travel sections\r\n for url in links:\r\n find = np.zeros(len(URL_exclusions))\r\n for i in range(len(URL_exclusions)):\r\n find[i] = bool(url.find(URL_exclusions[i]) == -1)\r\n if all(find) == True: #If none of the exclusion words are in url\r\n sig_links.append(url)\r\n return sig_links","def auto_link_urls(self, options = {}):\r\n return self.auto_link_entities(self.extractor.extract_urls_with_indices({'extract_url_without_protocol': False}), options)","def test_url_link_multiple(self):\n content = ('[Link]([url(\\'/content/pages/test1.md\\')])'\n '[Link]([url(\\'/content/pages/test2.md\\')])')\n self.pod.write_file('/content/pages/test.md', content)\n content = '{{doc.html|safe}}'\n self.pod.write_file('/views/base.html', content)\n self.pod.router.add_all(use_cache=False)\n result = testing.render_path(self.pod, '/test/')\n self.assertIn('href=\"/test1/\"', result)\n self.assertIn('href=\"/test2/\"', result)","def generate():\n\n # Verify if directory exists\n if not os.path.isdir(config.techniques_markdown_path):\n os.mkdir(config.techniques_markdown_path)\n\n #Write the technique index.html page\n with open(os.path.join(config.techniques_markdown_path, \"overview.md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(config.technique_overview_md)\n\n for domain in config.domains:\n generate_domain_markdown(domain)","def sitemap_urls():\n for batch in m.Batch.objects.all():\n yield batch.url, batch.released\n yield rdf_uri(batch), batch.released\n for issue in batch.issues.all():\n yield issue.url, batch.released\n yield rdf_uri(issue), batch.released\n for page in issue.pages.all():\n yield page.url, batch.released\n yield rdf_uri(page), batch.released\n\n paginator = Paginator(m.Title.objects.all(), 10000)\n for page_num in range(1, paginator.num_pages + 1):\n page = paginator.page(page_num)\n for title in page.object_list:\n yield title.url, title.created","def mk_htmls(self):\n\n domain_link_list = self.scrape_domain_int_links()\n html_fail_rate = float(self.failed_retrieving_html_counter / len(domain_link_list))\n print \"mk_htmls SUCCES RATE %r\" % html_fail_rate\n if html_fail_rate < (float(0.1)):\n # driving links, to collect htmls from web and create htmls with removed img elements\n for ind in range(len(domain_link_list)):\n page_file_name = \"page_\" + str(ind + 1) + \".html\" # gen file names for html file of page on site\n active_page_link = domain_link_list[ind]\n if \";;;\" in active_page_link: # separator problems hedging\n self.links_contain_seprator.append([active_page_link, page_file_name])\n print \"LINK CONTAINS SEPARATOR, CAN NOT ADD TO PAGE LIST, WILL RUIN DATASYSTEM, WILL BE ADDED TO SEPARATE FILE\"\n else:\n BS_object = self.retrieve_html(active_page_link, self.domain_folder_name, self.html_folder_name, page_file_name)\n self.page_list.append([active_page_link, page_file_name])\n self.bs_object_dict[active_page_link] = BS_object\n # if no \";;;\" separator found in link, then write as usual\n if len(self.links_contain_seprator) > 0:\n c_m.l_of_l_write(self.links_contain_seprator, self.main_path, self.domain_folder_name, self.link_contains_separator)\n else:\n c_m.l_of_l_write(self.page_list, self.main_path, self.domain_folder_name, self.page_list_f_name)\n return self.bs_object_dict\n else:\n return False","def get_or_make_links(self, link_names):\n link_names = link_names if isinstance(link_names, (list, tuple)) else [link_names]\n urls = []\n for name in link_names:\n try:\n url = reverse(name)\n except NoReverseMatch as e:\n print(e)\n url = None\n # if name == 'password_reset':\n # path('test-password/', views.PasswordChangeView.as_view(template_name='update.html'), name=name)\n # else:\n # pass\n urls.append(url)\n # print(urls)\n return urls","def home_page():\n return \"<h4>Welcome !</h4><br><a href='/fetch'>View Results</a>\"","def extractPhysOrgLinks(soup):\n\n # Define 'links' dictionary\n links = {'Related' : set(),\n 'Recommended' : set(),\n 'Other' : set(),\n }\n \n # Get links for 'Related Stories' and 'Recommended for you'\n rrs = soup.find_all(attrs={'class':'news-col'})\n for rs in rrs:\n try:\n if re.search('related', rs.find('h2').text.lower()):\n links['Related'].update(set([cleanPhysOrgLink(a['href']) \\\n for a in rs.find_all('a')]))\n elif re.search('recommended', rs.find('h2').text.lower()):\n links['Recommended'].update(set([cleanPhysOrgLink(a['href']) \\\n for a in rs.find_all('a')]))\n else:\n links['Other'].update(set([cleanPhysOrgLink(a['href']) \\\n for a in rs.find_all('a')]))\n except AttributeError:\n pass\n \n # Get the 'news-box' links\n nb = soup.findAll('article', {'class':'news-box'})\n nb_links = [l for l in filter(physOrgNewsFilter,\n [cleanPhysOrgLink(n.find('a')['href']) \\\n for n in nb])]\n links['Other'].update(set(nb_links))\n \n # Merge for easy use\n links['All'] = links['Related'].copy()\n links['All'].update(links['Recommended'])\n links['All'].update(links['Other'])\n \n return(links)","def html_link(filings_list):\n html_links = {}\n for lista in filings_list:\n \n for e in lista: \n if 'html' in e:\n url = 'https://www.sec.gov/Archives/' + e \n table = pd.read_html(url)[0]\n table = table['Document'][0].split(' ')\n\n url_formatted = url.replace('-', '').replace('index.html', '')\n html_links[lista[3]] = url_formatted+'/'+table[0] \n\n return html_links","def exactor_links(self, response: BeautifulSoup):\n raise NotImplementedError","def create_menu(page):\n menu = []\n # menu.append('<span class=\"header\">Topics</span>')\n for title, target in NAV.items():\n if isinstance(target, str):\n if target.startswith((\"https://\", \"http://\", \"/\")):\n menu.append(f\"<a href='target'>{title}</a>\")\n else:\n menu.append(f\"<a href='{target}.html'>{title}</a>\")\n if target == page.name:\n menu[-1] = menu[-1].replace(\"<a \", '<a class=\"current\" ')\n menu += [\n f\"<a class='sub' href='#{title.lower()}'>{title}</a>\"\n for level, title in page.headers\n if level == 2\n ]\n elif isinstance(target, dict):\n menu.append(f\"<a href='{target['']}.html'>{title}</a>\")\n if target[\"\"] == page.name:\n menu[-1] = menu[-1].replace(\"<a \", '<a class=\"current\" ')\n if any(page.name == subtarget for subtarget in target.values()):\n for subtitle, subtarget in target.items():\n if not subtitle:\n continue\n if subtarget.startswith((\"https://\", \"http://\", \"/\")):\n menu.append(f\"<a class='sub' href='{subtarget}'>{subtitle}</a>\")\n else:\n menu.append(\n f\"<a class='sub' href='{subtarget}.html'>{subtitle}</a>\"\n )\n if subtarget == page.name:\n menu[-1] = menu[-1].replace(\"class='\", \"class='current \")\n else:\n raise RuntimeError(f\"Unexpected NAV entry {type(target)}\")\n\n return \"<br />\".join(menu)","def genAbsoluteLink(plink, link):\n\n\ttoCrawl = True\n\n\tif link != '':\n\t\tif link[0] == '/':\t\t\t\t\t\t\t\t#starts with '/'\n\t\t\tlink = variables.seed + link[1:]\t\t #avoid 2 '/'\n\n\t\telif link not in variables.relatives:\t\t\t#Default.aspx\n\t\t\tvariables.relatives.append(link)\n\t\t\tif plink[-1] == '/':\n\t\t\t\tlink = plink + link\n\t\t\telse:\n\t\t\t\tlink = plink + '/' + link\n\n\t\telse:\n\t\t\ttoCrawl = False\n\n\treturn link, toCrawl","def _parse_links(self, response, start):\n links = self.document_date_map[start.date()]\n for link in response.css(\".agenda-min-pres .field a\"):\n link_url = response.urljoin(link.xpath(\"@href\").extract_first())\n title = link.xpath(\"./text()\").extract_first()\n if title.strip().startswith(\"Agenda\"):\n title = \"Agenda\"\n links.append(\n {\"title\": re.sub(r\"\\s+\", \" \", title).strip(), \"href\": link_url}\n )\n return links","def correct_links(html_file, schema_name):\n return html_file.replace(schema_name.replace(\".\", \"_\") + \"_xsd.html#\", \"#\").replace(\"target=\\\"mainFrame\\\"\", \"\")","def default(self):\n\t\tself.link_words = ['accordingly', 'actually', 'and', 'also', 'although', 'as', 'because', 'besides', 'but', 'consequently', 'conversely', 'eventually', 'firstly', 'furthermore', 'how', 'hence', 'however', 'if', 'meanwhile', 'moreover', 'namely', 'nevertheless', 'next', 'nonetheless', 'once', 'otherwise', 'secondly', 'similarly', 'since', 'so', 'still', 'that', 'then', 'therefore', 'thirdly', 'though', 'thus', 'till', 'tofirst', 'unless', 'unlike', 'until', 'whatever', 'when', 'whenever', 'where', 'whereas', 'whether', 'while', 'while', 'who', 'yet']","def auto_link(self, options = {}):\r\n return self.auto_link_entities(self.extractor.extract_entities_with_indices({'extract_url_without_protocol': False}), options)","def title_page_url(self):\n url = '/plaque/%s' % self.title_url\n return url","def url(self):\n self._current_page += 1\n return URL_TPL.format(self._uid, self._current_page)","def loadLinks(self):\n\t\tmainPageFile = open(self.mainPageFilePath, 'r')\n\t\ttry:\n\t\t\tlines = mainPageFile.readlines()\n\t\t\tif len(lines) > 0:\n\t\t\t\tself.mainPage = lines[0]\n\t\t\telse:\n\t\t\t\tprint(\"No link in : \", self.mainPageFilePath)\n\t\tfinally:\n\t\t mainPageFile.close()\n\n\t\tlinkFile = open(self.linkFilePath, 'r')\n\t\ttry:\n\t\t\t# add all links to a list\n\t\t\tfor line in linkFile.readlines():\n\t\t\t\tif \"http\" in line:\n\t\t\t\t\tsubPageName = line.split(\".de\")[1].replace(\"/\", \"\")\n\t\t\t\t\tself.subpages.append( Subpage(line.strip(' \\t\\n\\r'),subPageName,self.mainPage) )\n\t\tfinally:\n\t\t linkFile.close()","def get_page_links(self, message_list):\n # coerce to list\n if not isinstance(message_list, list):\n message_list = list(message_list)\n first = message_list[0].timestamp.isoformat()\n last = message_list[-1].timestamp.isoformat()\n prev_vars = self.request.GET.copy()\n next_vars = self.request.GET.copy()\n if self.newest_first:\n prev_vars.update({'after': first})\n next_vars.update({'before': last})\n else:\n next_vars.update({'after': last})\n prev_vars.update({'before': first})\n next_page_link = '{0}?{1}'.format(self.page_base_url,\n next_vars.urlencode())\n prev_page_link = '{0}?{1}'.format(self.page_base_url,\n prev_vars.urlencode())\n return prev_page_link, next_page_link","def home():\n tc = totalclicks()\n tl1, tl2, tl3 = topthreelinks()\n bl1, bl2, bl3 = topblomoedlinks()\n return flask.render_template('home.html', tc=tc, tl1=tl1, tl2=tl2, tl3=tl3, bl1=bl1, bl2=bl2, bl3=bl3)","def __build_one_site(root_dir, all_docs, domain_refs, doc_entry_ref, www_root_dir, site_name, github_url, user_group):\n www_build_dir = www_root_dir + site_name + \"/\"\n\n print(\"Reset build directory : \" + www_build_dir)\n shutil.rmtree(www_build_dir, ignore_errors=True)\n try:\n shutil.copytree(\"src/default_www/\", www_build_dir)\n except: # strange behaviour on Windows, try again ...\n shutil.copytree(\"src/default_www/\", www_build_dir)\n \n all_domain_cats = { XML.xpath_plain(all_docs, \".//*[@ref='\"+itm_ref+\"']/@cat\") for itm_ref in domain_refs } - {\"\"}\n \n domain_cats = []\n for cat_ref in all_domain_cats:\n cat_restrict = XML.xpath_plain(all_docs, \".//*[@ref='\"+cat_ref+\"']/@restricted_to\")\n if (cat_restrict == \"\") or (user_group in cat_restrict):\n domain_cats += [cat_ref]\n with_errors = user_group in \"#devs,#admin\" \n path = HTM.store_home_page(www_build_dir, all_docs, \"\", root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\n\n for cat_ref in domain_cats:\n try:\n path = HTM.store_index_page(www_build_dir, all_docs, cat_ref, root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\n except:\n LIB.debug_error()\n LIB.debug(\"### Error for category index : \", cat_ref)\n\n for itm_ref in domain_refs:\n cat_ref = XML.xpath_plain(all_docs, \".//*[@ref='\"+itm_ref+\"']/@cat\")\n if (cat_ref in domain_cats):\n try:\n path = HTM.store_content_page(www_build_dir+cat_ref+\"/\", all_docs, itm_ref, root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\n except:\n LIB.debug_error()\n LIB.debug(\"### Error for file : \", itm)\n\n path = HTM.store_glossary_page(www_build_dir, all_docs, \"\", root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\n\n if with_errors:\n path = HTM.store_error_page(www_build_dir, all_docs, \"\", root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)","def get_url_parts(self, *args, **kwargs):\n url_parts = super().get_url_parts(*args, **kwargs)\n # NOTE evidently this can sometimes be None; unclear why – perhaps it\n # gets called in a context where the request is unavailable? Only\n # happens in QA, not locally.\n if url_parts:\n site_id, root_url, _ = url_parts\n page_path = reverse(\n \"events:detail\",\n kwargs={\n \"year\": self.start_time.year,\n # force two-digit month\n \"month\": \"%02d\" % self.start_time.month,\n \"slug\": self.slug,\n },\n )\n return site_id, root_url, page_path","def _get_links(self, from_year):\n self.links = []\n self.titles = []\n self.speakers = []\n self.dates = []\n\n r = requests.get(self.calendar_url)\n soup = BeautifulSoup(r.text, \"html.parser\")\n\n if self.verbose:\n print(\"Getting links for press conference scripts...\")\n presconfs = soup.find_all(\n \"a\", href=re.compile(\"^/monetarypolicy/fomcpresconf\\d{8}.htm\")\n )\n presconf_urls = [\n self.base_url + presconf.attrs[\"href\"] for presconf in presconfs\n ]\n for presconf_url in presconf_urls:\n r_presconf = requests.get(presconf_url)\n soup_presconf = BeautifulSoup(r_presconf.text, \"html.parser\")\n contents = soup_presconf.find_all(\n \"a\", href=re.compile(\"^/mediacenter/files/FOMCpresconf\\d{8}.pdf\")\n )\n for content in contents:\n # print(content)\n self.links.append(content.attrs[\"href\"])\n self.speakers.append(\n self._speaker_from_date(self._date_from_link(content.attrs[\"href\"]))\n )\n self.titles.append(\"FOMC Press Conference Transcript\")\n self.dates.append(\n datetime.strptime(\n self._date_from_link(content.attrs[\"href\"]), \"%Y-%m-%d\"\n )\n )\n if self.verbose:\n print(\"{} links found in current page.\".format(len(self.links)))\n\n # Archived before 2015\n if from_year <= 2014:\n print(\"Getting links from archive pages...\")\n for year in range(from_year, 2015):\n yearly_contents = []\n fomc_yearly_url = (\n self.base_url\n + \"/monetarypolicy/fomchistorical\"\n + str(year)\n + \".htm\"\n )\n r_year = requests.get(fomc_yearly_url)\n soup_yearly = BeautifulSoup(r_year.text, \"html.parser\")\n\n presconf_hists = soup_yearly.find_all(\n \"a\", href=re.compile(\"^/monetarypolicy/fomcpresconf\\d{8}.htm\")\n )\n presconf_hist_urls = [\n self.base_url + presconf_hist.attrs[\"href\"]\n for presconf_hist in presconf_hists\n ]\n for presconf_hist_url in presconf_hist_urls:\n # print(presconf_hist_url)\n r_presconf_hist = requests.get(presconf_hist_url)\n soup_presconf_hist = BeautifulSoup(\n r_presconf_hist.text, \"html.parser\"\n )\n yearly_contents = soup_presconf_hist.find_all(\n \"a\",\n href=re.compile(\"^/mediacenter/files/FOMCpresconf\\d{8}.pdf\"),\n )\n for yearly_content in yearly_contents:\n # print(yearly_content)\n self.links.append(yearly_content.attrs[\"href\"])\n self.speakers.append(\n self._speaker_from_date(\n self._date_from_link(yearly_content.attrs[\"href\"])\n )\n )\n self.titles.append(\"FOMC Press Conference Transcript\")\n self.dates.append(\n datetime.strptime(\n self._date_from_link(yearly_content.attrs[\"href\"]),\n \"%Y-%m-%d\",\n )\n )\n if self.verbose:\n print(\n \"YEAR: {} - {} links found.\".format(\n year, len(presconf_hist_urls)\n )\n )\n print(\"There are total \", len(self.links), \" links for \", self.content_type)","def makeLandingUrl(self, artMeta):\n self._readIssns()\n issn = getIssn(artMeta)\n if issn in self.highwireIssns:\n baseUrl = self.highwireIssns[issn]\n delayTime = self._highwireDelay(baseUrl)\n vol = artMeta.get('vol', '')\n issue = artMeta.get('issue', '')\n page = artMeta.get('page', '')\n if (vol, issue, page) != ('', '', '') and ' ' not in vol + issue + page:\n url = '%s/content/%s/%s/%s.long' % (baseUrl,\n vol,\n issue,\n page)\n page = httpGetDelay(url, delayTime)\n if page != None:\n return url\n if 'pmid' in artMeta:\n url = '%s/cgi/pmidlookup?view=long&pmid=%s' % (baseUrl, artMeta['pmid'])\n page = httpGetDelay(url, delayTime)\n if page != None:\n return url\n return","def links(self):\n runeIdea = idea.Idea(Rune())\n yield idea.Link(self.thing.idea, runeIdea)","def get_url_parts(self, request=None):\n # Verify the page is routable\n url_parts = super().get_url_parts(request=request)\n\n if not url_parts:\n return None\n\n site_id, site_root, parent_path = self.get_parent().specific.get_url_parts(\n request=request\n )\n page_path = \"\"\n\n # Depending on whether we have trailing slashes or not, build the correct path\n if WAGTAIL_APPEND_SLASH:\n page_path = \"{}{}/\".format(parent_path, self.slug)\n else:\n page_path = \"{}/{}\".format(parent_path, self.slug)\n return (site_id, site_root, page_path)"],"string":"[\n \"def getExpandedLinks():\",\n \"def make_navbar_for_homepage(self):\\n links = [\\n \\\"home\\\", [\\\"Result Pages\\\", self._result_page_links()], \\\"Version\\\"\\n ]\\n if len(self.samples) > 1:\\n links[1][1] += [\\\"Comparison\\\"]\\n if self.publication:\\n links.insert(2, \\\"Publication\\\")\\n if self.gwdata is not None:\\n links.append([\\\"Detchar\\\", [i for i in self.gwdata.keys()]])\\n if self.notes is not None:\\n links.append(\\\"Notes\\\")\\n return links\",\n \"def generate_links():\\n start_url = 'https://twigserial.wordpress.com/'\\n base_url = start_url + 'category/story/'\\n tree = etree.HTML(requests.get(start_url).text)\\n xpath = './/*/option[@class=\\\"level-2\\\"]/text()'\\n return [base_url + suffix.strip() for suffix in tree.xpath(xpath)]\",\n \"def iter_page_links(self) -> Iterable[str]:\\n base_url = 'https://health.mil/About-MHS/OASDHA/Defense-Health-Agency/Resources-and-Management/DHA-Publications'\\n yield base_url\",\n \"def generate_page(links, emails):\\n s = ''.join(generate_string(30, valid_domain_name_chars))\\n\\n for i in links:\\n s = s + ' ' + '<a href=\\\"'+i+'\\\"></a>' + ' ' +''.join(generate_string(30, valid_domain_name_chars))\\n s = s + ' ' +''.join(generate_string(30, valid_domain_name_chars)) \\n\\n for i in emails:\\n s = s + ' ' + i + ' ' +''.join(generate_string(30, valid_domain_name_chars))\\n s = s + ' ' +''.join(generate_string(30, valid_domain_name_chars)) \\n \\n\\n return s\",\n \"def append_links(self, lines, lang):\\n lines.append(\\\"verbatim \\\")\\n lines.append(\\\"section Links\\\")\\n lines.append(\\\"external http://polcasaglia.blogspot.com Blog\\\")\\n lines.append(\\\"external http://www.uisp-fe.it/calcio.php UISP\\\" )\\n lines.append(\\\"verbatim \\\")\\n return lines\",\n \"def fix_links():\\n pass\",\n \"def makeLinks(self):\\n self.deleteIndexFileIfExists()\\n _fileNames = self.getHTMLFileNames()\\n _msgPart1 = \\\"<a href=\\\\\\\"\\\"\\n _msgPart2 = \\\"\\\\\\\" target=\\\\\\\"loadHTMLResults\\\\\\\">\\\"\\n _msgPart3 = \\\"</a><br>\\\"\\n _link = \\\"\\\"\\n for _fileName in _fileNames:\\n _origFileName = _fileName\\n _linkName = _fileName.split('.')[0]\\n _createAnchorTag = (_msgPart1+str(_origFileName)+_msgPart2+str(_linkName)+_msgPart3)\\n _link = _link + _createAnchorTag\\n return _link\",\n \"def methods():\\n list_groups_text = '<a href=\\\"/groups\\\">List Groups</a>'\\n list_users_text = '<a href=\\\"/users\\\">List Users</a>'\\n page_links = list_groups_text + \\\"<br>\\\" + list_users_text\\n return page_links\",\n \"def getLink(self):\",\n \"def get_links(self):\\n links = \\\"\\\"\\n if self.title != \\\"\\\":\\n links += html_link_to_tag(\\n plain_to_html(self.title), self.title, self.proc\\n )\\n return links + \\\\\\n html_unordered_list([x.get_links() for x in self.subsections])\",\n \"def _generate_links(self):\\n index = 0\\n links = \\\"\\\"\\n for ch in self.text:\\n if ch == '[':\\n links += \\\"(^\\\"\\n elif ch == ']':\\n links += \\\")$|\\\"\\n index += 1\\n elif links[-1:] != '|' and links != \\\"\\\":\\n links += ch\\n self.links = compile(links[:-1].lower())\",\n \"def links(n):\\n return redirect(url_for(\\\"link_page\\\", n=n, offset=0))\",\n \"def makeLandingUrl(self, artMeta):\\n return None\",\n \"def get_links(self):\\n soup = BeautifulSoup(requests.get(self.locations_url).text.strip(), features=\\\"lxml\\\")\\n for region in soup.select('td[class=\\\"navbox-list navbox-odd\\\"]'):\\n self.links.extend(region.div.find_all('a'))\\n\\n soup_prague = BeautifulSoup(requests.get(self.url_prague).text.strip(), features=\\\"lxml\\\")\\n table_prague = soup_prague.findAll('table', {\\\"class\\\": \\\"wikitable\\\"})[3]\\n for prague_parts in table_prague.select(\\\"tr > td:nth-child(3)\\\"):\\n self.links.extend(prague_parts.find_all('a'))\\n\\n self.links = [self.url + i['href'] for i in self.links]\\n self.links.append(self.url_prague)\\n return None\",\n \"def Site(self) -> str:\",\n \"def _build_links(links):\\n for link in links:\\n link['href'] = link['href'].replace('servers', 'instances')\\n return links\",\n \"def renderView(self):\\n html = \\\"\\\"\\n if self.lms.lms == \\\"other\\\":\\n html += \\\"_______________________________<br/>\\\"\\n url = \\\"http://%s\\\" % self.lms.otherUrl\\n html += \\\"<br/><b>%s </b>\\\" % self.lms.otherLabel\\n html += '<a href=\\\"%s\\\">%s</a>' % (url, url) \\n return html\",\n \"def iter_page_links(self) -> Iterable[str]:\\n base_url = 'https://www.med.navy.mil'\\n r = requests.get(self.starting_url, verify=CERTIFICATE_DIR + '/cat3.pem')\\n soup = bs4.BeautifulSoup(r.content, features=\\\"html.parser\\\")\\n\\n # get target column of list items\\n issuance_list = soup.find('div', attrs={'class': 'noindex ms-wpContentDivSpace'})\\n matches = [\\\"Publications\\\", \\\"BUMEDNotes\\\", \\\"BUMEDInstructions\\\"]\\n # extract links\\n links = [link for link in issuance_list.find_all('a')]\\n for link in links[2:-1]:\\n if any(x in str(link) for x in matches):\\n if not link['href'].startswith('http'):\\n url = base_url + link['href']\\n else:\\n url = link['href']\\n yield url\",\n \"def gen_links(text):\\n return []\",\n \"def extra_links(request):\\n return {'zentral_extra_links': settings.get('extra_links', [])}\",\n \"def _fix_links(self, text, page_names):\\n for n in page_names:\\n text = text.replace(f\\\"]({n})\\\", f\\\"]({n}.html)\\\")\\n text = text.replace(f\\\"]({n}.md)\\\", f\\\"]({n}.html)\\\")\\n return text\",\n \"def test_menu_path_links(self):\\r\\n main_page = DogMainPage(self.driver)\\r\\n # Navigate to Title (main) page\\r\\n title_page = main_page.navigate_main()\\r\\n title_expected_header = title_page.get_expected_header()\\r\\n title_header = title_page.get_header()\\r\\n # Assert the title by sub-string to verify page\\r\\n self.assertTrue(title_expected_header in title_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (title_expected_header, title_header)))\\r\\n # Navigate to 'Documentation' page\\r\\n doc_page = main_page.navigate_documentation()\\r\\n doc_expected = doc_page.get_expected_header()\\r\\n doc_header = doc_page.get_header()\\r\\n # Assert the title to verify page\\r\\n self.assertEqual(doc_expected, doc_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (doc_expected, doc_header)))\\r\\n # Navigate to 'Breed list' page\\r\\n breed_list_page = main_page.navigate_breeds_list()\\r\\n breed_list_expected_header = breed_list_page.get_expected_header()\\r\\n breed_list_header = breed_list_page.get_header()\\r\\n # Assert the title to verify page\\r\\n self.assertEqual(breed_list_expected_header, breed_list_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' %\\r\\n (breed_list_expected_header, breed_list_header)))\\r\\n # Navigate to 'About' page\\r\\n about_page = main_page.navigate_about()\\r\\n about_expected_header = about_page.get_expected_header()\\r\\n about_header = about_page.get_header()\\r\\n # Assert the title to verify page\\r\\n self.assertEqual(about_expected_header, about_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (about_expected_header, about_header)))\",\n \"def menu_forum_spe(self, event=None):\\n self.link('http://www.stani.be/python/spe/page_forum')\",\n \"def menu_spe_homepage(self, event=None):\\n self.link('http://pythonide.stani.be')\",\n \"def getSubpagesLink(self):\\n return self.subpages\",\n \"def marketing_link_context_processor(request):\\r\\n return dict(\\r\\n [\\r\\n (\\\"MKTG_URL_\\\" + k, marketing_link(k))\\r\\n for k in (\\r\\n settings.MKTG_URL_LINK_MAP.viewkeys() |\\r\\n settings.MKTG_URLS.viewkeys()\\r\\n )\\r\\n ]\\r\\n )\",\n \"def test_page_links(inspire_app):\\n create_record(\\\"lit\\\", data={\\\"titles\\\": [{\\\"title\\\": \\\"Solenoid\\\"}]})\\n create_record(\\\"lit\\\", data={\\\"titles\\\": [{\\\"title\\\": \\\"Solenoid\\\"}]})\\n with inspire_app.test_client() as client:\\n # Limit records\\n response = client.get(\\n \\\"/api/literature\\\", query_string=dict(size=1, page=1, q=\\\"Solenoid\\\")\\n )\\n response_json = response.json\\n assert len(response_json[\\\"hits\\\"][\\\"hits\\\"]) == 1\\n\\n data = response_json[\\\"links\\\"]\\n assert \\\"self\\\" in data\\n assert \\\"next\\\" in data\\n assert \\\"prev\\\" not in data\\n\\n # Assert next URL before calling it\\n first_url = data[\\\"self\\\"]\\n next_url = data[\\\"next\\\"]\\n parsed_url = parse_url(next_url)\\n assert parsed_url[\\\"qs\\\"][\\\"size\\\"] == [\\\"1\\\"]\\n assert parsed_url[\\\"qs\\\"][\\\"page\\\"] == [\\\"2\\\"]\\n\\n # Access next URL\\n response = client.get(next_url)\\n response_json = response.json\\n assert len(response_json[\\\"hits\\\"][\\\"hits\\\"]) == 1\\n data = response.json[\\\"links\\\"]\\n assert data[\\\"self\\\"] == next_url\\n assert \\\"next\\\" not in data\\n assert \\\"prev\\\" in data and data[\\\"prev\\\"] == first_url\",\n \"def _create_links_and_track(self, page_name, category_list):\\n env = self.state.document.settings.env\\n if not hasattr(env, \\\"categories\\\"):\\n env.categories = {}\\n\\n link_rst = \\\"\\\"\\n ncategs = 0\\n for item in category_list:\\n has_subcat = False\\n if r\\\"\\\\\\\\\\\" in item: \\n categs = item.split(r\\\"\\\\\\\\\\\")\\n has_subcat = True\\n else:\\n categs = [item]\\n # endif\\n\\n print \\n for index, categ_name in enumerate(categs):\\n if categ_name not in env.categories:\\n category = Category(categ_name)\\n env.categories[categ_name] = category\\n else:\\n category = env.categories[categ_name]\\n #endif\\n category.pages.append(PageRef(page_name))\\n if has_subcat and index > 0:\\n category.subcategories.append(PageRef(categ_name))\\n #endif\\n link_rst += \\\":ref:`%s` | \\\" % categ_name\\n ncategs += 1\\n # endfor\\n # endfor\\n\\n link_rst = \\\"`%s: <categories.html>`_ \\\" + link_rst\\n if ncategs == 1:\\n link_rst = link_rst % \\\"Category\\\"\\n else:\\n link_rst = link_rst % \\\"Categories\\\"\\n #endif\\n\\n return link_rst\",\n \"def render_prependsite(self, ctx, data):\\n\\t\\tctx.tag.children = [base.getConfig(\\\"web\\\", \\\"sitename\\\")]+ctx.tag.children\\n\\t\\treturn ctx.tag\",\n \"def page_links(self, title):\\n return [l[0] for l in self.page_links_and_labels(title)]\",\n \"def treat_page(self):\\n # let's define some basic variables\\n urtext = self.current_page.text\\n urlang = self.current_page.site.code\\n urtitle = self.current_page.title()\\n urcat = []\\n eng_site = pywikibot.Site('en')\\n eng_title = ''\\n \\n interDict = {}\\n try:\\n site = pywikibot.Site('ur', 'wikipedia')\\n urpage = pywikibot.Page(site, urtitle)\\n langlst = urpage.iterlanglinks()\\n\\n \\n for i in langlst:\\n lang = str(i.site).split(':')[1]\\n interDict[lang] = i.title\\n \\n eng_title = interDict['en']\\n except:\\n pywikibot.output(u'\\\\03{lightred}Unable to fetch interwiki links!\\\\03{default}')\\n return False\\n \\n site = pywikibot.Site('en', 'wikipedia')\\n enpage = pywikibot.Page(site, eng_title)\\n\\n wikitext = enpage.get() \\n wikicode = mwp.parse(wikitext)\\n\\n # Extracting sfn templates and converting them in REF tags\\n sfnlist = []\\n for template in wikicode.filter_templates():\\n if template.name in ('sfn', 'sfn'):\\n sfnlist.append(template)\\n templ_rep = '<ref>' + str(template) + '</ref>'\\n wikicode.replace(template , templ_rep)\\n\\n alltags = wikicode.filter_tags() \\n reftags = {}\\n \\n def search(myDict, search1):\\n for key, value in myDict.items():\\n if search1 in value: \\n return key \\n \\n i=1\\n for tag in alltags:\\n if tag.tag=='ref':\\n if tag.attributes == []: # check if attributes list is empty\\n refval='NoRefName' # Reference has no name so assigning \\\"NoRefName\\\"\\n else:\\n name = tag.attributes[0]\\n refval = name.value\\n \\n if tag.contents is None:\\n #conval = search(reftags,refval)\\n #reftags[i] = (refval,reftags[conval][1])\\n pass\\n else: \\n reftags[i] = (refval,tag.contents)\\n i += 1\\n\\n dlinks = {}\\n for k,v in reftags.items():\\n dkey = 'و' + str(k) + 'و'\\n dlinks[dkey] = '<ref>' + str(v[1]) + '</ref>'\\n\\n urtext = urpage.text\\n for r in tuple(dlinks.items()):\\n urtext = urtext.replace(*r)\\n\\n # newln = '\\\\n'\\n # Using noreferences to add Reference template if not present\\n self.norefbot = noreferences.NoReferencesBot(None)\\n if self.norefbot.lacksReferences(urtext):\\n urtext = self.norefbot.addReferences(urtext)\\n else:\\n urpage.text = urtext + '\\\\n'\\n\\n print(urpage.text)\\n \\n # save the page \\n urpage.save(summary=self.summary, minor=False)\\n #self.put_current(urpage.text, summary=self.summary)\",\n \"def _gen_link(content):\\n text = \\\"{% load macros %}\\\" + content\\n as_str = Template(text).render(Context({}))\\n # Safe because: we've constructed the XML\\n as_xml = etree.fromstring(\\\"<ROOT>{}</ROOT>\\\".format(as_str)) # nosec\\n anchors = as_xml.findall('.//a')\\n assert len(anchors) > 0\\n return anchors[0]\",\n \"def createLinkFromWikiWord(word, wikiPage): # normalizeWikiWord\\r\\n return \\\"\\\"\",\n \"def connect_links(base_url, extensions, wikidir, body):\\n if base_url.endswith(\\\"/\\\"):\\n base_url = base_url[:-1]\\n\\n i = 0\\n body2 = []\\n\\n for match in WIKILINK.finditer(body):\\n body2.append(body[i:match.span(0)[0]])\\n \\n text = match.group(1)\\n\\n if \\\"|\\\" in text:\\n topic, desc = text.split(\\\"|\\\")\\n topic = topic.strip()\\n else:\\n topic, desc = (text, text)\\n\\n fn = os.path.join(wikidir, topic)\\n\\n ext = tools.what_ext(extensions, fn)\\n if not ext:\\n body2.append(match.group(0))\\n i = match.span(0)[1]\\n continue\\n\\n body2.append(\\\"<a href=\\\\\\\"%s/%s/%s\\\\\\\">%s</a>\\\" % \\\\\\n (base_url, TRIGGER, topic, desc))\\n i = match.span(0)[1]\\n\\n body2.append(body[i:])\\n return \\\"\\\".join(body2)\",\n \"def navlist(stu, stulist, rooturl='/dist'):\\n parts = []\\n for x in stulist:\\n if x == stu:\\n parts.append('<b>' + x + '</b>')\\n else:\\n parts.append('<a href=\\\"' + rooturl + '/' + x + '/\\\">' + x + '</a>')\\n return ' '.join(parts)\",\n \"def get_front_page_links():\\n response = requests.get(\\\"https://codeup.com/blog/\\\", headers={\\\"user-agent\\\": \\\"Codeup DS\\\"})\\n soup = BeautifulSoup(response.text)\\n links = [link.attrs[\\\"href\\\"] for link in soup.select(\\\".more-link\\\")]\\n return links\",\n \"def link(self):\\n return f\\\"[{self.numbered_title}]({self.html_url})\\\"\",\n \"def get_page_links(): \\n\\ttitle = driver.find_elements_by_xpath(XPATHS['title'])\\n\\tlinks = [link.get_attribute('href') for link in title]\\n\\treturn links\",\n \"def _create_links_and_track(self, page_name, category_list):\\n env = self.state.document.settings.env\\n if not hasattr(env, \\\"categories\\\"):\\n env.categories = {}\\n\\n link_rst = \\\"\\\"\\n ncategs = 0\\n for categ_name in category_list:\\n #categ_name is the full category name - register that\\n category = self.register_category(categ_name, env)\\n category.pages.add(PageRef(page_name, env.docname))\\n\\n #now step up a step up each time the category hierarchy\\n parent_category = categ_name\\n while True:\\n if r\\\"\\\\\\\\\\\" in parent_category:\\n categs = parent_category.split(r\\\"\\\\\\\\\\\")\\n else:\\n break\\n # remove the last item\\n subcat = Category(categ_name, env.docname) #create the category with the full name\\n subcat.name=categs.pop() # and then replace it with the last token of the name\\n parent_category = r\\\"\\\\\\\\\\\".join(categs)\\n\\n #register the parent category\\n parent = self.register_category(parent_category, env)\\n parent.subcategories.add(subcat)\\n\\n # endwhile\\n\\n #category should be the last subcategory by this point\\n link_rst += \\\"`%s <%s>`_ | \\\" % (categ_name, category.link(env.docname))\\n ncategs += 1\\n # endfor\\n\\n link_rst = \\\"**%s**: \\\" + link_rst.rstrip(\\\" | \\\") # remove final separator\\n if ncategs == 1:\\n link_rst = link_rst % \\\"Category\\\"\\n else:\\n link_rst = link_rst % \\\"Categories\\\"\\n #endif\\n\\n return link_rst\",\n \"def update_links(self):\\n for a in self.book.xpath(\\\"//a[@href]\\\"):\\n href = a.xpath(\\\"@href\\\")[0]\\n index_list = a.xpath(\\\"@data-index\\\")\\n \\n ### If there is no data-index it is assumed link comes from initial book landing page (the index page)\\n if index_list == []:\\n index = self.manager.get_page_index(\\\"index.html\\\")\\n else:\\n index = index_list[0]\\n \\n ### Fix people who are bad at links\\n if href.startswith(\\\"www.\\\"):\\n href = \\\"https://\\\" + href\\n a.set(\\\"href\\\", href)\\n \\n ## Correct for ambiguity (Naive assumption that this error only occours on index page)\\n if href == \\\"./\\\":\\n href = \\\"index.html\\\"\\n \\n if not href:\\n return None\\n \\n href = self.manager.convert_link(href, index)\\n a.set(\\\"href\\\", href)\",\n \"def __url_list(self, page):\\n url_list = []\\n for tag_a in page.find_all('a'):\\n href = str(tag_a.get('href'))\\n if self.__verify(href):\\n url = parse.quote(self.__add_main_site(href), '/:#')\\n url_list.append(url)\\n return url_list\",\n \"def iter_links(self):\",\n \"def get_course_page_urls(self,soup):\\n\\t\\tcourse_links =[]\\n\\t\\troot_url = 'http://onlinelearning.cornell.edu'\\n\\t\\tfor link in soup.select('span.field-content a[href]'):\\n\\t\\t\\tnew_url = root_url + link['href']\\n\\t\\t\\tcourse_links.append(new_url)\\n\\t\\t\\tcourse_links.append(' \\\\n')\\n\\t\\t\\n\\t\\tself.new_list.append(course_links)\\n\\t\\treturn course_links\",\n \"def get_absolute_url(self):\\r\\n return \\\"{0}page1/\\\".format(self.get_short_url())\",\n \"def html_page_context(app, pagename, templatename, context, doctree):\\n site = context[\\\"SITEMAP_BASE_URL\\\"]\\n version = context[\\\"version\\\"]\\n app.sitemap_links.add(f\\\"{site}{version}/{pagename}.html\\\")\",\n \"def get_left_panel_links(self):\\n status_list = {}\\n link_list = (self.reports, self.rewards, self.catalogs, self.campaigns, self.loyalties,\\n self.merchants, self.customer_management, self.bulk_actions, self.settings,\\n self.business_intelligence)\\n for link in link_list:\\n if find_element(self.browser, link):\\n status_list[link[1]] = 'Access'\\n else:\\n status_list[link[1]] = 'No Access'\\n return status_list\",\n \"def link_prefix(request):\\n return request.application_url\",\n \"def homepage():\\n\\n general_news=get_sources('general')\\n business_news=get_sources('business')\\n sports_news=get_sources('sports')\\n return render_template('sources.html',general=general_news, business=business_news, sports=sports_news)\",\n \"def grab_links(self):\\n links = []\\n link_char = []\\n w_temp = [] #in template?\\n par = [] #in parentheses?\\n rtag = [] #in <ref> tag?\\n dtag = [] #in <div> tag?\\n\\n skip_char = []\\n\\n for i, c in enumerate(self.article_xml):\\n if i in skip_char: continue #eliminates double counting\\n char = self.article_xml[i:i+2]\\n tag = self.article_xml[i:i+4]\\n \\n #wiki template\\n w_temp = self.inside_char(char, Article.w_marker, w_temp, i)\\n if char in Article.w_marker: skip_char.append(i+1)\\n if w_temp:\\n continue #doesn't process if inside wiki template\\n \\n #parentheses\\n par = self.inside_char(c, Article.par_marker, par, i)\\n if par:\\n continue\\n \\n #<ref> or <div>\\n rtag = self.inside_char(tag, Article.rtag_marker, rtag, i)\\n dtag = self.inside_char(tag, Article.dtag_marker, dtag, i)\\n if rtag or dtag:\\n continue\\n \\n #clear to add outer-most link\\n if char == '[[':\\n link_char.append(i)\\n elif char == ']]' and len(link_char) == 1:\\n links.append( self.article_xml[link_char[0]:i+2])\\n link_char.pop()\\n elif char == ']]' and len(link_char) > 1:\\n link_char.pop()\\n return links\",\n \"def links(self) -> str:\\n return pulumi.get(self, \\\"links\\\")\",\n \"def menu_authors_homepage(self, event=None):\\n self.link('http://www.stani.be')\",\n \"def middleshow(self):\\n self._showitemlist(ITEMLIST_LINKS)\",\n \"def moreContext(self, app, pagename, templatename, context, doctree):\\n links = []\\n for name, url, condition in self.toplinks:\\n link = self.path_for(url, pagename)\\n links.append((name, link, condition.match(pagename)))\\n\\n context['toplinks'] = links\",\n \"def get_absolute_url(self):\\n\\n url = reverse('comicsite.views.page', args=[self.comicsite.short_name,self.title])\\n return url\",\n \"def home_page():\\n return (\\n f\\\"Available Routes:<br/>\\\"\\n f\\\"/api/v1.0/precipitation<br/>\\\"\\n f\\\"/api/v1.0/stations<br/>\\\"\\n f\\\"/api/v1.0/tobs<br/>\\\"\\n f\\\"/api/v1.0/start<br/>\\\"\\n f\\\"/api/v1.0/start/end<br/>\\\"\\n )\",\n \"def Prolinks(promotion_label):\\n return prolinks\",\n \"def __add_main_site(self, href):\\n if self.main_url[8:] not in href:\\n return self.main_url + href\\n else:\\n return href\",\n \"def _link_items(self):\\n pass\",\n \"def test_documentation_path_links(self):\\r\\n main_page = DogMainPage(self.driver)\\r\\n dog_page = main_page.navigate_documentation()\\r\\n # Switch to 'List all breeds' tab\\r\\n all_breeds_page = dog_page.switch_tab(dog_page.ALL_BREEDS)\\r\\n all_breeds_expected = all_breeds_page.get_expected_header()\\r\\n all_breeds_header = all_breeds_page.get_header()\\r\\n # Assert the title to verify the page\\r\\n self.assertEqual(all_breeds_expected, all_breeds_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (all_breeds_expected, all_breeds_header)))\\r\\n # Switch to 'Random image' tab\\r\\n random_page = dog_page.switch_tab(dog_page.RANDOM)\\r\\n random_expected_header = random_page.get_expected_header()\\r\\n random_header = random_page.get_header()\\r\\n # Assert the title to verify the page\\r\\n self.assertEqual(random_expected_header, random_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (random_expected_header, random_header)))\\r\\n # Switch to 'By breed' tab\\r\\n breed_page = dog_page.switch_tab(dog_page.BREED)\\r\\n breed_expected_header = breed_page.get_expected_header()\\r\\n breed_header = breed_page.get_header()\\r\\n # Assert the title to verify the page\\r\\n self.assertEqual(breed_expected_header, breed_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (breed_expected_header, breed_header)))\\r\\n # Switch to 'By sub-breed' tab\\r\\n sub_breed_page = dog_page.switch_tab(dog_page.SUB_BREED)\\r\\n sub_expected_header = sub_breed_page.get_expected_header()\\r\\n sub_header = sub_breed_page.get_header()\\r\\n # Assert the title to verify the page\\r\\n self.assertEqual(sub_expected_header, sub_header,\\r\\n ('%s expected, instead found: %s. Page is wrong' % (sub_expected_header, sub_header)))\",\n \"def generate_slack_links(links):\\n return \\\", \\\".join(\\\"<%s|%d>\\\" % (el, idx + 1) for idx, el in enumerate(links))\",\n \"def linktrail(self, code: str) -> str:\\n site = pywikibot.Site(code, 'wikipedia')\\n return site.linktrail()\",\n \"def page_url(self):\\n url = '/plaque/%s' % self.key.urlsafe()\\n return url\",\n \"def link(address):\",\n \"def theLinky(self):\\n theLink = self.absolute_url()\\n return theLink\",\n \"def controls_setup(self):\\n\\n self.to_homepage = element.Link(self, class_name='nav-home', alias='Return to site Link')\\n self.account = element.Link(self, class_name='nav-account', alias='Account Profile Link')\\n self.logout = element.Link(self, class_name='nav-logout', alias='Logout Link')\",\n \"def _link_elements(self):\\n raise NotImplementedError(\\\"Please implement this method\\\")\",\n \"def links(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/links.html',\\n {\\n \\t'cart': cart,\\n 'title':'Полезные ресурсы',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def getSiteExampleURLs(self):\\r\\n return 'no such example'\",\n \"def GetUrlFirst(self):\\n self.url = \\\"https://www.taobao.com/\\\"\\n self.host = \\\"www.taobao.com\\\"\\n self.referer = \\\"https://www.taobao.com/\\\"\\n content = self.GetContent()\\n __clear__ = '<a href=.*?</a>'\\n match = open(self.base_dir_url+\\\"url_first.html\\\", 'w')\\n try:\\n all_link = re.findall(__clear__, content, re.S)\\n print \\\"All links of the web page is: \\\", len(all_link)\\n self.DealUrlFirst(match, all_link)\\n except:\\n print \\\"Something wrong is happening!\\\"\\n finally:\\n match.close()\\n match.close()\",\n \"def get_menu_urls():\\n\\n r = requests.get(URL_BASE)\\n if r.status_code != 200:\\n raise Exception(\\\"Code {0} downloading base website: {1}\\\".format(r.status_code, r.text))\\n\\n soup = bs4.BeautifulSoup(r.text)\\n\\n file_list = soup.select(\\\"ul.file_list\\\")[0]\\n file_links = [a['href'] for a in file_list.select('a.pdf_file') if RE_MENU.match(a['href'])]\\n\\n return [urlparse.urljoin(URL_BASE, fl) for fl in file_links]\",\n \"def marketing_link(name):\\r\\n\\r\\n # link_map maps URLs from the marketing site to the old equivalent on\\r\\n # the Django site\\r\\n link_map = settings.MKTG_URL_LINK_MAP\\r\\n enable_mktg_site = microsite.get_value(\\r\\n 'ENABLE_MKTG_SITE',\\r\\n settings.FEATURES.get('ENABLE_MKTG_SITE', False)\\r\\n )\\r\\n\\r\\n if enable_mktg_site and name in settings.MKTG_URLS:\\r\\n # special case for when we only want the root marketing URL\\r\\n if name == 'ROOT':\\r\\n return settings.MKTG_URLS.get('ROOT')\\r\\n return settings.MKTG_URLS.get('ROOT') + settings.MKTG_URLS.get(name)\\r\\n # only link to the old pages when the marketing site isn't on\\r\\n elif not enable_mktg_site and name in link_map:\\r\\n # don't try to reverse disabled marketing links\\r\\n if link_map[name] is not None:\\r\\n return reverse(link_map[name])\\r\\n else:\\r\\n log.warning(\\\"Cannot find corresponding link for name: {name}\\\".format(name=name))\\r\\n return '#'\",\n \"def links(self):\\n return self._links_tpl.expand(self._identity, self._record)\",\n \"def get_links_from_one_page(driver,site,URL_exclusions):\\r\\n while True:\\r\\n try:\\r\\n results = driver.find_elements_by_class_name(\\\"g\\\") #Find all elements with class=\\\"g\\\". This includes search results.\\r\\n break\\r\\n except:\\r\\n continue \\r\\n links = []\\r\\n for result in results:\\r\\n link = result.find_element_by_tag_name(\\\"a\\\") #Hyperlinks are contained under <a> tags\\r\\n link = link.get_attribute('href') #Retrive link as a string\\r\\n if link.find(site) != -1: #Some class=\\\"g\\\" elements are not search results. Only store links with urls containing \\\"site\\\".\\r\\n links.append(link)\\r\\n sig_links = [] #Create list of links for pages not from travel sections\\r\\n for url in links:\\r\\n find = np.zeros(len(URL_exclusions))\\r\\n for i in range(len(URL_exclusions)):\\r\\n find[i] = bool(url.find(URL_exclusions[i]) == -1)\\r\\n if all(find) == True: #If none of the exclusion words are in url\\r\\n sig_links.append(url)\\r\\n return sig_links\",\n \"def auto_link_urls(self, options = {}):\\r\\n return self.auto_link_entities(self.extractor.extract_urls_with_indices({'extract_url_without_protocol': False}), options)\",\n \"def test_url_link_multiple(self):\\n content = ('[Link]([url(\\\\'/content/pages/test1.md\\\\')])'\\n '[Link]([url(\\\\'/content/pages/test2.md\\\\')])')\\n self.pod.write_file('/content/pages/test.md', content)\\n content = '{{doc.html|safe}}'\\n self.pod.write_file('/views/base.html', content)\\n self.pod.router.add_all(use_cache=False)\\n result = testing.render_path(self.pod, '/test/')\\n self.assertIn('href=\\\"/test1/\\\"', result)\\n self.assertIn('href=\\\"/test2/\\\"', result)\",\n \"def generate():\\n\\n # Verify if directory exists\\n if not os.path.isdir(config.techniques_markdown_path):\\n os.mkdir(config.techniques_markdown_path)\\n\\n #Write the technique index.html page\\n with open(os.path.join(config.techniques_markdown_path, \\\"overview.md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(config.technique_overview_md)\\n\\n for domain in config.domains:\\n generate_domain_markdown(domain)\",\n \"def sitemap_urls():\\n for batch in m.Batch.objects.all():\\n yield batch.url, batch.released\\n yield rdf_uri(batch), batch.released\\n for issue in batch.issues.all():\\n yield issue.url, batch.released\\n yield rdf_uri(issue), batch.released\\n for page in issue.pages.all():\\n yield page.url, batch.released\\n yield rdf_uri(page), batch.released\\n\\n paginator = Paginator(m.Title.objects.all(), 10000)\\n for page_num in range(1, paginator.num_pages + 1):\\n page = paginator.page(page_num)\\n for title in page.object_list:\\n yield title.url, title.created\",\n \"def mk_htmls(self):\\n\\n domain_link_list = self.scrape_domain_int_links()\\n html_fail_rate = float(self.failed_retrieving_html_counter / len(domain_link_list))\\n print \\\"mk_htmls SUCCES RATE %r\\\" % html_fail_rate\\n if html_fail_rate < (float(0.1)):\\n # driving links, to collect htmls from web and create htmls with removed img elements\\n for ind in range(len(domain_link_list)):\\n page_file_name = \\\"page_\\\" + str(ind + 1) + \\\".html\\\" # gen file names for html file of page on site\\n active_page_link = domain_link_list[ind]\\n if \\\";;;\\\" in active_page_link: # separator problems hedging\\n self.links_contain_seprator.append([active_page_link, page_file_name])\\n print \\\"LINK CONTAINS SEPARATOR, CAN NOT ADD TO PAGE LIST, WILL RUIN DATASYSTEM, WILL BE ADDED TO SEPARATE FILE\\\"\\n else:\\n BS_object = self.retrieve_html(active_page_link, self.domain_folder_name, self.html_folder_name, page_file_name)\\n self.page_list.append([active_page_link, page_file_name])\\n self.bs_object_dict[active_page_link] = BS_object\\n # if no \\\";;;\\\" separator found in link, then write as usual\\n if len(self.links_contain_seprator) > 0:\\n c_m.l_of_l_write(self.links_contain_seprator, self.main_path, self.domain_folder_name, self.link_contains_separator)\\n else:\\n c_m.l_of_l_write(self.page_list, self.main_path, self.domain_folder_name, self.page_list_f_name)\\n return self.bs_object_dict\\n else:\\n return False\",\n \"def get_or_make_links(self, link_names):\\n link_names = link_names if isinstance(link_names, (list, tuple)) else [link_names]\\n urls = []\\n for name in link_names:\\n try:\\n url = reverse(name)\\n except NoReverseMatch as e:\\n print(e)\\n url = None\\n # if name == 'password_reset':\\n # path('test-password/', views.PasswordChangeView.as_view(template_name='update.html'), name=name)\\n # else:\\n # pass\\n urls.append(url)\\n # print(urls)\\n return urls\",\n \"def home_page():\\n return \\\"<h4>Welcome !</h4><br><a href='/fetch'>View Results</a>\\\"\",\n \"def extractPhysOrgLinks(soup):\\n\\n # Define 'links' dictionary\\n links = {'Related' : set(),\\n 'Recommended' : set(),\\n 'Other' : set(),\\n }\\n \\n # Get links for 'Related Stories' and 'Recommended for you'\\n rrs = soup.find_all(attrs={'class':'news-col'})\\n for rs in rrs:\\n try:\\n if re.search('related', rs.find('h2').text.lower()):\\n links['Related'].update(set([cleanPhysOrgLink(a['href']) \\\\\\n for a in rs.find_all('a')]))\\n elif re.search('recommended', rs.find('h2').text.lower()):\\n links['Recommended'].update(set([cleanPhysOrgLink(a['href']) \\\\\\n for a in rs.find_all('a')]))\\n else:\\n links['Other'].update(set([cleanPhysOrgLink(a['href']) \\\\\\n for a in rs.find_all('a')]))\\n except AttributeError:\\n pass\\n \\n # Get the 'news-box' links\\n nb = soup.findAll('article', {'class':'news-box'})\\n nb_links = [l for l in filter(physOrgNewsFilter,\\n [cleanPhysOrgLink(n.find('a')['href']) \\\\\\n for n in nb])]\\n links['Other'].update(set(nb_links))\\n \\n # Merge for easy use\\n links['All'] = links['Related'].copy()\\n links['All'].update(links['Recommended'])\\n links['All'].update(links['Other'])\\n \\n return(links)\",\n \"def html_link(filings_list):\\n html_links = {}\\n for lista in filings_list:\\n \\n for e in lista: \\n if 'html' in e:\\n url = 'https://www.sec.gov/Archives/' + e \\n table = pd.read_html(url)[0]\\n table = table['Document'][0].split(' ')\\n\\n url_formatted = url.replace('-', '').replace('index.html', '')\\n html_links[lista[3]] = url_formatted+'/'+table[0] \\n\\n return html_links\",\n \"def exactor_links(self, response: BeautifulSoup):\\n raise NotImplementedError\",\n \"def create_menu(page):\\n menu = []\\n # menu.append('<span class=\\\"header\\\">Topics</span>')\\n for title, target in NAV.items():\\n if isinstance(target, str):\\n if target.startswith((\\\"https://\\\", \\\"http://\\\", \\\"/\\\")):\\n menu.append(f\\\"<a href='target'>{title}</a>\\\")\\n else:\\n menu.append(f\\\"<a href='{target}.html'>{title}</a>\\\")\\n if target == page.name:\\n menu[-1] = menu[-1].replace(\\\"<a \\\", '<a class=\\\"current\\\" ')\\n menu += [\\n f\\\"<a class='sub' href='#{title.lower()}'>{title}</a>\\\"\\n for level, title in page.headers\\n if level == 2\\n ]\\n elif isinstance(target, dict):\\n menu.append(f\\\"<a href='{target['']}.html'>{title}</a>\\\")\\n if target[\\\"\\\"] == page.name:\\n menu[-1] = menu[-1].replace(\\\"<a \\\", '<a class=\\\"current\\\" ')\\n if any(page.name == subtarget for subtarget in target.values()):\\n for subtitle, subtarget in target.items():\\n if not subtitle:\\n continue\\n if subtarget.startswith((\\\"https://\\\", \\\"http://\\\", \\\"/\\\")):\\n menu.append(f\\\"<a class='sub' href='{subtarget}'>{subtitle}</a>\\\")\\n else:\\n menu.append(\\n f\\\"<a class='sub' href='{subtarget}.html'>{subtitle}</a>\\\"\\n )\\n if subtarget == page.name:\\n menu[-1] = menu[-1].replace(\\\"class='\\\", \\\"class='current \\\")\\n else:\\n raise RuntimeError(f\\\"Unexpected NAV entry {type(target)}\\\")\\n\\n return \\\"<br />\\\".join(menu)\",\n \"def genAbsoluteLink(plink, link):\\n\\n\\ttoCrawl = True\\n\\n\\tif link != '':\\n\\t\\tif link[0] == '/':\\t\\t\\t\\t\\t\\t\\t\\t#starts with '/'\\n\\t\\t\\tlink = variables.seed + link[1:]\\t\\t #avoid 2 '/'\\n\\n\\t\\telif link not in variables.relatives:\\t\\t\\t#Default.aspx\\n\\t\\t\\tvariables.relatives.append(link)\\n\\t\\t\\tif plink[-1] == '/':\\n\\t\\t\\t\\tlink = plink + link\\n\\t\\t\\telse:\\n\\t\\t\\t\\tlink = plink + '/' + link\\n\\n\\t\\telse:\\n\\t\\t\\ttoCrawl = False\\n\\n\\treturn link, toCrawl\",\n \"def _parse_links(self, response, start):\\n links = self.document_date_map[start.date()]\\n for link in response.css(\\\".agenda-min-pres .field a\\\"):\\n link_url = response.urljoin(link.xpath(\\\"@href\\\").extract_first())\\n title = link.xpath(\\\"./text()\\\").extract_first()\\n if title.strip().startswith(\\\"Agenda\\\"):\\n title = \\\"Agenda\\\"\\n links.append(\\n {\\\"title\\\": re.sub(r\\\"\\\\s+\\\", \\\" \\\", title).strip(), \\\"href\\\": link_url}\\n )\\n return links\",\n \"def correct_links(html_file, schema_name):\\n return html_file.replace(schema_name.replace(\\\".\\\", \\\"_\\\") + \\\"_xsd.html#\\\", \\\"#\\\").replace(\\\"target=\\\\\\\"mainFrame\\\\\\\"\\\", \\\"\\\")\",\n \"def default(self):\\n\\t\\tself.link_words = ['accordingly', 'actually', 'and', 'also', 'although', 'as', 'because', 'besides', 'but', 'consequently', 'conversely', 'eventually', 'firstly', 'furthermore', 'how', 'hence', 'however', 'if', 'meanwhile', 'moreover', 'namely', 'nevertheless', 'next', 'nonetheless', 'once', 'otherwise', 'secondly', 'similarly', 'since', 'so', 'still', 'that', 'then', 'therefore', 'thirdly', 'though', 'thus', 'till', 'tofirst', 'unless', 'unlike', 'until', 'whatever', 'when', 'whenever', 'where', 'whereas', 'whether', 'while', 'while', 'who', 'yet']\",\n \"def auto_link(self, options = {}):\\r\\n return self.auto_link_entities(self.extractor.extract_entities_with_indices({'extract_url_without_protocol': False}), options)\",\n \"def title_page_url(self):\\n url = '/plaque/%s' % self.title_url\\n return url\",\n \"def url(self):\\n self._current_page += 1\\n return URL_TPL.format(self._uid, self._current_page)\",\n \"def loadLinks(self):\\n\\t\\tmainPageFile = open(self.mainPageFilePath, 'r')\\n\\t\\ttry:\\n\\t\\t\\tlines = mainPageFile.readlines()\\n\\t\\t\\tif len(lines) > 0:\\n\\t\\t\\t\\tself.mainPage = lines[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"No link in : \\\", self.mainPageFilePath)\\n\\t\\tfinally:\\n\\t\\t mainPageFile.close()\\n\\n\\t\\tlinkFile = open(self.linkFilePath, 'r')\\n\\t\\ttry:\\n\\t\\t\\t# add all links to a list\\n\\t\\t\\tfor line in linkFile.readlines():\\n\\t\\t\\t\\tif \\\"http\\\" in line:\\n\\t\\t\\t\\t\\tsubPageName = line.split(\\\".de\\\")[1].replace(\\\"/\\\", \\\"\\\")\\n\\t\\t\\t\\t\\tself.subpages.append( Subpage(line.strip(' \\\\t\\\\n\\\\r'),subPageName,self.mainPage) )\\n\\t\\tfinally:\\n\\t\\t linkFile.close()\",\n \"def get_page_links(self, message_list):\\n # coerce to list\\n if not isinstance(message_list, list):\\n message_list = list(message_list)\\n first = message_list[0].timestamp.isoformat()\\n last = message_list[-1].timestamp.isoformat()\\n prev_vars = self.request.GET.copy()\\n next_vars = self.request.GET.copy()\\n if self.newest_first:\\n prev_vars.update({'after': first})\\n next_vars.update({'before': last})\\n else:\\n next_vars.update({'after': last})\\n prev_vars.update({'before': first})\\n next_page_link = '{0}?{1}'.format(self.page_base_url,\\n next_vars.urlencode())\\n prev_page_link = '{0}?{1}'.format(self.page_base_url,\\n prev_vars.urlencode())\\n return prev_page_link, next_page_link\",\n \"def home():\\n tc = totalclicks()\\n tl1, tl2, tl3 = topthreelinks()\\n bl1, bl2, bl3 = topblomoedlinks()\\n return flask.render_template('home.html', tc=tc, tl1=tl1, tl2=tl2, tl3=tl3, bl1=bl1, bl2=bl2, bl3=bl3)\",\n \"def __build_one_site(root_dir, all_docs, domain_refs, doc_entry_ref, www_root_dir, site_name, github_url, user_group):\\n www_build_dir = www_root_dir + site_name + \\\"/\\\"\\n\\n print(\\\"Reset build directory : \\\" + www_build_dir)\\n shutil.rmtree(www_build_dir, ignore_errors=True)\\n try:\\n shutil.copytree(\\\"src/default_www/\\\", www_build_dir)\\n except: # strange behaviour on Windows, try again ...\\n shutil.copytree(\\\"src/default_www/\\\", www_build_dir)\\n \\n all_domain_cats = { XML.xpath_plain(all_docs, \\\".//*[@ref='\\\"+itm_ref+\\\"']/@cat\\\") for itm_ref in domain_refs } - {\\\"\\\"}\\n \\n domain_cats = []\\n for cat_ref in all_domain_cats:\\n cat_restrict = XML.xpath_plain(all_docs, \\\".//*[@ref='\\\"+cat_ref+\\\"']/@restricted_to\\\")\\n if (cat_restrict == \\\"\\\") or (user_group in cat_restrict):\\n domain_cats += [cat_ref]\\n with_errors = user_group in \\\"#devs,#admin\\\" \\n path = HTM.store_home_page(www_build_dir, all_docs, \\\"\\\", root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\\n\\n for cat_ref in domain_cats:\\n try:\\n path = HTM.store_index_page(www_build_dir, all_docs, cat_ref, root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\\n except:\\n LIB.debug_error()\\n LIB.debug(\\\"### Error for category index : \\\", cat_ref)\\n\\n for itm_ref in domain_refs:\\n cat_ref = XML.xpath_plain(all_docs, \\\".//*[@ref='\\\"+itm_ref+\\\"']/@cat\\\")\\n if (cat_ref in domain_cats):\\n try:\\n path = HTM.store_content_page(www_build_dir+cat_ref+\\\"/\\\", all_docs, itm_ref, root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\\n except:\\n LIB.debug_error()\\n LIB.debug(\\\"### Error for file : \\\", itm)\\n\\n path = HTM.store_glossary_page(www_build_dir, all_docs, \\\"\\\", root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\\n\\n if with_errors:\\n path = HTM.store_error_page(www_build_dir, all_docs, \\\"\\\", root_dir, github_url, doc_entry_ref, domain_cats, domain_refs, with_errors)\",\n \"def get_url_parts(self, *args, **kwargs):\\n url_parts = super().get_url_parts(*args, **kwargs)\\n # NOTE evidently this can sometimes be None; unclear why – perhaps it\\n # gets called in a context where the request is unavailable? Only\\n # happens in QA, not locally.\\n if url_parts:\\n site_id, root_url, _ = url_parts\\n page_path = reverse(\\n \\\"events:detail\\\",\\n kwargs={\\n \\\"year\\\": self.start_time.year,\\n # force two-digit month\\n \\\"month\\\": \\\"%02d\\\" % self.start_time.month,\\n \\\"slug\\\": self.slug,\\n },\\n )\\n return site_id, root_url, page_path\",\n \"def _get_links(self, from_year):\\n self.links = []\\n self.titles = []\\n self.speakers = []\\n self.dates = []\\n\\n r = requests.get(self.calendar_url)\\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n\\n if self.verbose:\\n print(\\\"Getting links for press conference scripts...\\\")\\n presconfs = soup.find_all(\\n \\\"a\\\", href=re.compile(\\\"^/monetarypolicy/fomcpresconf\\\\d{8}.htm\\\")\\n )\\n presconf_urls = [\\n self.base_url + presconf.attrs[\\\"href\\\"] for presconf in presconfs\\n ]\\n for presconf_url in presconf_urls:\\n r_presconf = requests.get(presconf_url)\\n soup_presconf = BeautifulSoup(r_presconf.text, \\\"html.parser\\\")\\n contents = soup_presconf.find_all(\\n \\\"a\\\", href=re.compile(\\\"^/mediacenter/files/FOMCpresconf\\\\d{8}.pdf\\\")\\n )\\n for content in contents:\\n # print(content)\\n self.links.append(content.attrs[\\\"href\\\"])\\n self.speakers.append(\\n self._speaker_from_date(self._date_from_link(content.attrs[\\\"href\\\"]))\\n )\\n self.titles.append(\\\"FOMC Press Conference Transcript\\\")\\n self.dates.append(\\n datetime.strptime(\\n self._date_from_link(content.attrs[\\\"href\\\"]), \\\"%Y-%m-%d\\\"\\n )\\n )\\n if self.verbose:\\n print(\\\"{} links found in current page.\\\".format(len(self.links)))\\n\\n # Archived before 2015\\n if from_year <= 2014:\\n print(\\\"Getting links from archive pages...\\\")\\n for year in range(from_year, 2015):\\n yearly_contents = []\\n fomc_yearly_url = (\\n self.base_url\\n + \\\"/monetarypolicy/fomchistorical\\\"\\n + str(year)\\n + \\\".htm\\\"\\n )\\n r_year = requests.get(fomc_yearly_url)\\n soup_yearly = BeautifulSoup(r_year.text, \\\"html.parser\\\")\\n\\n presconf_hists = soup_yearly.find_all(\\n \\\"a\\\", href=re.compile(\\\"^/monetarypolicy/fomcpresconf\\\\d{8}.htm\\\")\\n )\\n presconf_hist_urls = [\\n self.base_url + presconf_hist.attrs[\\\"href\\\"]\\n for presconf_hist in presconf_hists\\n ]\\n for presconf_hist_url in presconf_hist_urls:\\n # print(presconf_hist_url)\\n r_presconf_hist = requests.get(presconf_hist_url)\\n soup_presconf_hist = BeautifulSoup(\\n r_presconf_hist.text, \\\"html.parser\\\"\\n )\\n yearly_contents = soup_presconf_hist.find_all(\\n \\\"a\\\",\\n href=re.compile(\\\"^/mediacenter/files/FOMCpresconf\\\\d{8}.pdf\\\"),\\n )\\n for yearly_content in yearly_contents:\\n # print(yearly_content)\\n self.links.append(yearly_content.attrs[\\\"href\\\"])\\n self.speakers.append(\\n self._speaker_from_date(\\n self._date_from_link(yearly_content.attrs[\\\"href\\\"])\\n )\\n )\\n self.titles.append(\\\"FOMC Press Conference Transcript\\\")\\n self.dates.append(\\n datetime.strptime(\\n self._date_from_link(yearly_content.attrs[\\\"href\\\"]),\\n \\\"%Y-%m-%d\\\",\\n )\\n )\\n if self.verbose:\\n print(\\n \\\"YEAR: {} - {} links found.\\\".format(\\n year, len(presconf_hist_urls)\\n )\\n )\\n print(\\\"There are total \\\", len(self.links), \\\" links for \\\", self.content_type)\",\n \"def makeLandingUrl(self, artMeta):\\n self._readIssns()\\n issn = getIssn(artMeta)\\n if issn in self.highwireIssns:\\n baseUrl = self.highwireIssns[issn]\\n delayTime = self._highwireDelay(baseUrl)\\n vol = artMeta.get('vol', '')\\n issue = artMeta.get('issue', '')\\n page = artMeta.get('page', '')\\n if (vol, issue, page) != ('', '', '') and ' ' not in vol + issue + page:\\n url = '%s/content/%s/%s/%s.long' % (baseUrl,\\n vol,\\n issue,\\n page)\\n page = httpGetDelay(url, delayTime)\\n if page != None:\\n return url\\n if 'pmid' in artMeta:\\n url = '%s/cgi/pmidlookup?view=long&pmid=%s' % (baseUrl, artMeta['pmid'])\\n page = httpGetDelay(url, delayTime)\\n if page != None:\\n return url\\n return\",\n \"def links(self):\\n runeIdea = idea.Idea(Rune())\\n yield idea.Link(self.thing.idea, runeIdea)\",\n \"def get_url_parts(self, request=None):\\n # Verify the page is routable\\n url_parts = super().get_url_parts(request=request)\\n\\n if not url_parts:\\n return None\\n\\n site_id, site_root, parent_path = self.get_parent().specific.get_url_parts(\\n request=request\\n )\\n page_path = \\\"\\\"\\n\\n # Depending on whether we have trailing slashes or not, build the correct path\\n if WAGTAIL_APPEND_SLASH:\\n page_path = \\\"{}{}/\\\".format(parent_path, self.slug)\\n else:\\n page_path = \\\"{}/{}\\\".format(parent_path, self.slug)\\n return (site_id, site_root, page_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.603881","0.59611195","0.5923129","0.5906821","0.5832609","0.58210886","0.5725273","0.5673449","0.56281406","0.558823","0.55820566","0.5566538","0.5542917","0.55155784","0.5482084","0.547729","0.5472593","0.54505503","0.5410811","0.5399908","0.5361225","0.53339195","0.5317359","0.53172374","0.5312524","0.5287","0.52824664","0.5278461","0.5256317","0.52227044","0.5220251","0.52121913","0.51965225","0.51909274","0.5190861","0.51714206","0.5170607","0.5160885","0.51548487","0.51299775","0.51232314","0.51103485","0.5102878","0.5101076","0.5095635","0.5090911","0.5059381","0.5056235","0.50474757","0.50470287","0.5036282","0.5035317","0.50185734","0.501095","0.4996807","0.49960017","0.49888873","0.49875188","0.49866837","0.49812788","0.49767467","0.49655953","0.49642462","0.49639505","0.495568","0.49548283","0.49544975","0.4953359","0.49510875","0.4950024","0.4945679","0.49409366","0.4937381","0.49310315","0.49247256","0.49226454","0.49224","0.49126303","0.49081412","0.4907637","0.49023998","0.48934487","0.48860133","0.4882015","0.4881853","0.48813713","0.4878539","0.4873854","0.48633844","0.4861207","0.48608994","0.48593906","0.48587233","0.48543027","0.48469403","0.48465428","0.48450613","0.48414314","0.48409185","0.4838996","0.48379853"],"string":"[\n \"0.603881\",\n \"0.59611195\",\n \"0.5923129\",\n \"0.5906821\",\n \"0.5832609\",\n \"0.58210886\",\n \"0.5725273\",\n \"0.5673449\",\n \"0.56281406\",\n \"0.558823\",\n \"0.55820566\",\n \"0.5566538\",\n \"0.5542917\",\n \"0.55155784\",\n \"0.5482084\",\n \"0.547729\",\n \"0.5472593\",\n \"0.54505503\",\n \"0.5410811\",\n \"0.5399908\",\n \"0.5361225\",\n \"0.53339195\",\n \"0.5317359\",\n \"0.53172374\",\n \"0.5312524\",\n \"0.5287\",\n \"0.52824664\",\n \"0.5278461\",\n \"0.5256317\",\n \"0.52227044\",\n \"0.5220251\",\n \"0.52121913\",\n \"0.51965225\",\n \"0.51909274\",\n \"0.5190861\",\n \"0.51714206\",\n \"0.5170607\",\n \"0.5160885\",\n \"0.51548487\",\n \"0.51299775\",\n \"0.51232314\",\n \"0.51103485\",\n \"0.5102878\",\n \"0.5101076\",\n \"0.5095635\",\n \"0.5090911\",\n \"0.5059381\",\n \"0.5056235\",\n \"0.50474757\",\n \"0.50470287\",\n \"0.5036282\",\n \"0.5035317\",\n \"0.50185734\",\n \"0.501095\",\n \"0.4996807\",\n \"0.49960017\",\n \"0.49888873\",\n \"0.49875188\",\n \"0.49866837\",\n \"0.49812788\",\n \"0.49767467\",\n \"0.49655953\",\n \"0.49642462\",\n \"0.49639505\",\n \"0.495568\",\n \"0.49548283\",\n \"0.49544975\",\n \"0.4953359\",\n \"0.49510875\",\n \"0.4950024\",\n \"0.4945679\",\n \"0.49409366\",\n \"0.4937381\",\n \"0.49310315\",\n \"0.49247256\",\n \"0.49226454\",\n \"0.49224\",\n \"0.49126303\",\n \"0.49081412\",\n \"0.4907637\",\n \"0.49023998\",\n \"0.48934487\",\n \"0.48860133\",\n \"0.4882015\",\n \"0.4881853\",\n \"0.48813713\",\n \"0.4878539\",\n \"0.4873854\",\n \"0.48633844\",\n \"0.4861207\",\n \"0.48608994\",\n \"0.48593906\",\n \"0.48587233\",\n \"0.48543027\",\n \"0.48469403\",\n \"0.48465428\",\n \"0.48450613\",\n \"0.48414314\",\n \"0.48409185\",\n \"0.4838996\",\n \"0.48379853\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":85690,"cells":{"query":{"kind":"string","value":"This method is used to generate a list of techniques"},"document":{"kind":"string","value":"def get_techniques_list(techniques):\n\n technique_list = {}\n \n for technique in techniques:\n if 'revoked' not in technique or technique['revoked'] == False:\n\n attack_id = util.get_attack_id(technique)\n\n if attack_id:\n\n technique_dict = {}\n technique_dict['id'] = attack_id\n \n technique_dict['name'] = technique['name']\n technique_dict['description'] = technique['description']\n\n if technique.get('kill_chain_phases'):\n for elem in technique['kill_chain_phases']:\n # Fill dict\n\n if elem['phase_name'] not in technique_list:\n technique_list[elem['phase_name']] = []\n \n technique_list[elem['phase_name']].append(technique_dict)\n\n for key, value in technique_list.items():\n technique_list[key] = sorted(technique_list[key], key=lambda k: k['name'].lower())\n \n return technique_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)","def techniques(self):\n return self._get_child_page_of_type(LearningTechniquesPage)","def add_techniques(\n client: Act, matrice: AttckMatrice, output_format: Text = \"json\"\n) -> List:\n\n notify = []\n\n for technique in matrice.techniques:\n notify += handle_techniques(client, technique, None, output_format)\n\n for subtechnique in getattr(technique, \"subtechniques\", []):\n # Pre Attack does not have sub techniques\n notify += handle_techniques(client, subtechnique, technique, output_format)\n\n return notify","def generate_technique_md(technique, domain, side_menu_data, tactic_list):\n\n attack_id = util.get_attack_id(technique)\n\n # Only add technique if the attack id was found\n if attack_id:\n\n technique_dict = {}\n\n technique_dict['attack_id'] = attack_id\n technique_dict['domain'] = domain.split(\"-\")[0]\n technique_dict['menu'] = side_menu_data\n technique_dict['name'] = technique.get('name')\n\n # Get capecs and mtcs\n for ref in technique['external_references']:\n if ref.get('source_name'):\n if ref['source_name'] == \"capec\":\n if technique_dict.get('capecs') is None:\n technique_dict['capecs'] = []\n capec_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['capecs'].append(capec_dict)\n\n if ref['source_name'] == \"NIST Mobile Threat Catalogue\":\n if technique_dict.get('mtcs') is None:\n technique_dict['mtcs'] = []\n\n mtcs_dict = {\n 'id': ref['external_id'],\n 'url': ref['url']\n }\n technique_dict['mtcs'].append(mtcs_dict)\n\n # Get technique external references \n ext_ref = technique[\"external_references\"]\n\n # Get initial reference list\n reference_list = []\n # Decleared as an object to be able to pass by reference\n next_reference_number = {}\n next_reference_number['value'] = 1\n reference_list = util.update_reference_list(reference_list, technique)\n\n # Get technique description\n if technique.get(\"description\"):\n citations_from_descr = util.get_citations_from_descr(technique['description'])\n\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\n \n if 'x_mitre_deprecated' in technique:\n technique_dict['deprecated'] = True\n \n # Get mitigation table\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\n \n # Get related techniques\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\n\n # Get examples\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\n\n # Get technique version\n if technique.get(\"x_mitre_version\"):\n technique_dict['version'] = technique[\"x_mitre_version\"]\n\n # Get tactics of technique\n if technique.get('kill_chain_phases'):\n technique_dict['tactics'] = []\n for elem in technique['kill_chain_phases']:\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\n\n # Get platforms that technique uses\n if technique.get('x_mitre_platforms'):\n technique_dict['platforms'] = \", \".join(technique['x_mitre_platforms'])\n\n # Get system requirements\n if technique.get('x_mitre_system_requirements'):\n technique_dict['sysreqs'] = \", \".join(technique['x_mitre_system_requirements'])\n technique_dict['sysreqs'] = re.sub(\"\\.?\\\\n+\", \"; \", technique_dict['sysreqs'])\n\n # Get permissions required\n if technique.get('x_mitre_permissions_required'):\n technique_dict['perms'] = \", \".join(technique['x_mitre_permissions_required'])\n\n # Get effective permissions\n if technique.get('x_mitre_effective_permissions'):\n technique_dict['eff_perms'] = \", \".join(technique['x_mitre_effective_permissions'])\n\n # Get data sources\n if technique.get('x_mitre_data_sources'):\n technique_dict['data_sources'] = \", \".join(technique['x_mitre_data_sources'])\n\n # Get if technique supports remote\n if technique.get('x_mitre_remote_support'):\n if technique['x_mitre_remote_support']:\n technique_dict['supports_remote'] = \" Yes\"\n else:\n technique_dict['supports_remote'] = \" No\"\n\n # Get network requirements\n if technique.get('x_mitre_network_requirements'):\n if technique['x_mitre_network_requirements']:\n technique_dict['network_reqs'] = \" Yes\"\n else:\n technique_dict['network_reqs'] = \" No\"\n\n # Get list of impacts\n if technique.get('x_mitre_impact_type'):\n technique_dict['impact_type'] = \", \".join(technique['x_mitre_impact_type'])\n\n # Get list of defenses bypassed\n if technique.get('x_mitre_defense_bypassed'):\n technique_dict['def_bypass'] = \", \".join(technique['x_mitre_defense_bypassed'])\n\n # Get list of contributors \n if technique.get('x_mitre_contributors'):\n technique_dict['contributors'] = \"; \".join(technique['x_mitre_contributors'])\n\n # Get list of tactic types\n if technique.get('x_mitre_tactic_type'):\n technique_dict['tactic_type'] = \", \".join(technique['x_mitre_tactic_type'])\n\n # Get detection data\n if technique.get('x_mitre_detection'):\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\n\n # Get if technique is detectable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses'):\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\n\n # Get explanation of detecatable by common defenses\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\n\n # Get diffulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary'):\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\n\n # Get explanation of difficulty for adversaries\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \n \n # Add reference for bottom part of technique page\n if reference_list:\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\n\n subs = config.technique_md.substitute(technique_dict)\n subs = subs + json.dumps(technique_dict)\n\n #Write out the markdown file\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\".md\"), \"w\", encoding='utf8') as md_file:\n md_file.write(subs)","def do_make_(self):\n global g_list_of_classifier\n\n for ite_clf in g_list_of_classifier:\n ite_clf.learn()\n return ''","def get_techniques_used_by_groups():\n global techniques_used_by_groups\n\n if not techniques_used_by_groups:\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\n\n return techniques_used_by_groups","def get_techniques_used_by_tools():\n global techniques_used_by_tools\n\n if not techniques_used_by_tools:\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\n \n return techniques_used_by_tools","def get_related_techniques_data(technique, tactic_list):\n \n technique_data = []\n\n if config.related_techniques.get(technique['id']):\n for rel_tech in config.related_techniques[technique['id']]:\n\n attack_id = util.get_attack_id(rel_tech['object'])\n\n if attack_id:\n row = {}\n row['technique_id'] = attack_id\n\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\n if tactic:\n row['tactic_id'] = util.get_attack_id(tactic)\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\n \n row['technique_name'] = rel_tech['object']['name']\n technique_data.append(row)\n\n if technique_data:\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\n return technique_data","def generate(softwaretype=\"software\"):\n # import the STIX data from MITRE/CTI\n stix = requests.get(\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\").json()\n ms = MemoryStore(stix_data=stix[\"objects\"])\n # software includes malware and tool types so perform two queries and merge the results\n software_filters = []\n if softwaretype == \"malware\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'malware') ] )\n if softwaretype == \"tool\" or softwaretype == \"software\":\n software_filters.append( [ Filter('type', '=', 'tool') ] )\n \n software = list(chain.from_iterable(\n ms.query(f) for f in software_filters\n ))\n\n # build a list of techniques used by software\n techniques_used = {} #attackID => using software names\n for thesoftware in software:\n # filter out revoked and deprecated software\n if (\"x_mitre_deprecated\" in thesoftware and thesoftware[\"x_mitre_deprecated\"]) or (\"revoked\" in thesoftware and thesoftware[\"revoked\"]): continue\n for relationship in ms.relationships(thesoftware[\"id\"]):\n # skip all non-technique relationships\n if \"attack-pattern\" not in relationship[\"target_ref\"]: continue\n technique = ms.get(relationship[\"target_ref\"])\n # filter out deprecated and revoked techniques\n if (\"x_mitre_deprecated\" in technique and technique[\"x_mitre_deprecated\"]) or (\"revoked\" in technique and technique[\"revoked\"]): continue\n techniqueID = technique[\"external_references\"][0][\"external_id\"]\n # store usage in techniques_used struct\n if techniqueID in techniques_used:\n techniques_used[techniqueID].append(thesoftware[\"name\"])\n else:\n techniques_used[techniqueID] = [thesoftware[\"name\"]]\n\n # format the techniques for the output layer\n techniques_list = []\n highest_usage = 0\n lowest_usage = 1\n for techniqueID in techniques_used:\n # determine the number of used techniques for the score\n count = len(techniques_used[techniqueID])\n highest_usage = max(highest_usage, count)\n lowest_usage = min(lowest_usage, count)\n # append technique struct to list of layer-formatted techniques\n techniques_list.append({\n \"techniqueID\": techniqueID,\n \"comment\": \"executed by \" + \", \".join(techniques_used[techniqueID]),\n \"score\": count,\n })\n # set up layer name and desc according to softwaretype\n if softwaretype != \"software\": \n plural = \"tools\" if softwaretype == \"tool\" else \"malware\"\n layername = f\"Software ({softwaretype}) Execution\"\n layerdescription = f\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\"\n else: \n layername = \"Software Execution\"\n layerdescription = f\"All techniques that can be executed by software, where the score is the count of software using the technique\"\n\n # construct and return the layer as a dict\n return {\n \"name\": layername,\n \"description\": layerdescription,\n \"versions\": {\n \"layer\": \"4.1\",\n \"navigator\": \"4.1\"\n },\n \"domain\": \"enterprise-attack\",\n \"techniques\": techniques_list,\n \"sorting\": 3, # order in descending order of score (count)\n \"gradient\": {\n \"colors\": [\n \"#fff7b3\", # low counts are yellow\n \"#ff6666\", # high counts are red\n ],\n \"minValue\": lowest_usage,\n \"maxValue\": highest_usage\n },\n }","def get_technique_stats(db):\n \n techniques = {}\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n temp_dict = {technique: [{u'id': element['project_no'], \n u'name': element['project_name'], u'description': element['short_description']}]}\n techniques.update(temp_dict)\n else:\n temp_dict = {u'id': element['project_no'],\n u'name': element['project_name'], u'description': element['short_description']}\n techniques[technique].append(temp_dict)\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\n\n return techniques","def get_techniques_used_by_malware():\n global techniques_used_by_malware\n \n if not techniques_used_by_malware:\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\n \n return techniques_used_by_malware","def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.groups_using_technique }\n ]\n\n example_data = []\n\n # Get malware or tools used by group\n for examples in examples_map:\n if examples['example_type'].get(technique.get('id')):\n for example in examples['example_type'][technique['id']]:\n\n attack_id = util.get_attack_id(example['object'])\n\n # Only add example data if the attack id is found \n if attack_id:\n row = {}\n\n row['id'] = attack_id\n\n if attack_id.startswith('S'):\n row['path'] = \"software\"\n else:\n row['path'] = \"groups\"\n\n row['name'] = example['object']['name']\n\n if example['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\n\n example_data.append(row)\n \n if example_data:\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\n return example_data","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # Add are you a lucker strategies\n strategies.extend(\n generate_meta_strategy_pair(AreYouALuckerStrategy))\n\n # Add RPS Meta Fix strategies\n strategies.extend(\n generate_meta_strategy_pair(RPSMetaFixStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # Add are you a lucker strategies\n strategies.extend(\n generate_meta_strategy_pair(AreYouALuckerStrategy))\n\n # Add Greenberg strategies\n strategies.extend(\n generate_meta_strategy_pair(GreenbergStrategy))\n\n # Add RPS Meta Fix strategies\n strategies.extend(\n generate_meta_strategy_pair(RPSMetaFixStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def get_wordlists():\n\n\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\n\t\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\n\t\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\n\t\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\n\t\t'Language Processing', 'Internet', 'Intelligent Systems',\n\t\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\n\t\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \n\t\t'Computational Intelligence', 'Computational Linguistics',\n\t\t'Computational linguistics','Algorithm','Computer','ITiCSE',\n\t\t'ITICSE','Machine learning','Learning','learning',\n\t\t'Artificial intelligence','CIVR','Document Analysis'}\n\n\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\n\t\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\n\t\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\n\t\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\n\t\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\n\t\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\n\t\t'Neuro','Psych',\n\t\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\n\t\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\n\t\t'Neurobiological'}\n\n\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\n\t\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\n\t\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\n\t\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\n\t\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\n\t\t'mathemati','Kernel'}\n\n\tlinguistics = {}\n\n\tcomputer_vision = {}\n\n\tchemistry = {}\n\n\tphysics = {}\n\n\t# Rename \"Computer Vision\" to \"Image Processing\"?\n\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\n\t\t'Physics','Computer Vision','Natural Language Processing']\n\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\n\n\treturn {topic_names[i]:topics[i] for i in range(len(topics))}","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # Add are you a lucker strategies\n strategies.extend(\n generate_meta_strategy_pair(AreYouALuckerStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generateFeatures(self, data):\n pass","def generate_solutions(possible_words, labels):\r\n return []","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate_clips_kwds(base_path='../datasets/TIMIT/TRAIN/'):\n if not os.path.exists(kw_path):\n os.mkdir(kw_path)\n else:\n shutil.rmtree(kw_path)\n os.mkdir(kw_path)\n\n non_kw_clips = []\n kw_clips = {k: [] for k in keywords}\n\n for dialect in listdir(base_path):\n print(\"Dialect:\", dialect)\n\n for speaker in listdir(base_path + dialect):\n bp = base_path + dialect + '/' + speaker + '/'\n\n for file in listdir(bp):\n if file.split('.')[-1] == 'WRD':\n with open(bp + file, 'r') as f:\n dat = f.readlines()\n\n # read the word level annotations and crop out the keywords\n all_dat = [x.strip().split(' ') for x in dat]\n words = [x[-1] for x in all_dat]\n\n # check if words in utterance and keyword have ONE and ONLY ONE keyword\n inter = set(keywords).intersection(set(words))\n if len(inter) == 1:\n cur_keyword = inter.pop()\n word_line = all_dat[words.index(cur_keyword)]\n kw_clips[cur_keyword].append(\n (bp + file, (int(word_line[0]) // downsample_rate, int(word_line[1]) // downsample_rate))\n )\n else:\n non_kw_clips.append(bp + file)\n\n chosen_kw_clips = {k: [] for k in keywords}\n kw_templates = {k: [] for k in keywords}\n\n for word, l in kw_clips.items():\n # choose desired number fo templates and keyword utterances\n idxs = np.random.choice(len(l), utter_per_kw + templates_per_kw)\n for idx in idxs[:utter_per_kw]:\n chosen_kw_clips[word].append(l[idx][0])\n for idx in idxs[utter_per_kw:]:\n kw_templates[word].append(l[idx])\n\n # Save keyword wav files\n for word, l in kw_templates.items():\n for idx in range(len(l)):\n save_name = kw_path + word + '_' + str(idx) + '.wav'\n start_t, stop_t = l[idx][1]\n wav_path = l[idx][0][:-3] + 'wav'\n (rate, sig) = wav.read(wav_path)\n only_kw = sig[start_t:stop_t]\n wav.write(save_name, rate, only_kw)\n\n np.random.shuffle(non_kw_clips)\n print(\n \"non-kw clips: {0} ; kw-clips: {1}\".format(len(non_kw_clips), sum([len(x) for x in chosen_kw_clips.values()])))\n return non_kw_clips[:total_nonkw_utter], chosen_kw_clips","def generate_models(clf_library):\n clf_list = []\n for i in clf_library:\n param_dict = i['param_dict']\n dict_list = [dict(izip_longest(param_dict, v)) for v in product(*param_dict.values())]\n clf_list = clf_list+[i['clf'](**param_set) for param_set in dict_list]\n return clf_list","def create_training_examples(statement_list: List[List[str]], trees, training_spans,\n training: bool, max_span_length, num_sentence_words,\n args, pos_tags, constituents, k=1):\n print('building features...')\n\n word_frequencies = FreqDist([w.lower() for w in reuters.words()])\n # list of science tokens & science multiword expressions\n science_tokens = get_science_terms(args.data_path)\n science_expressions = get_science_terms(args.data_path, False)\n stop_words = set(stopwords.words('english'))\n\n\n\n true_examples, false_examples, examples_per_sentence = [], [], []\n span_indexes = []\n\n # loop over all statements\n for i, statement in enumerate(statement_list):\n print(i)\n\n span_index = [] #enumerating all spans for this sentence.\n pos_tags_this_statement = pos_tag(statement)\n sentence_word_frequencies = [word_frequencies.freq(token.lower()) \\\n for token in statement]\n tree = trees[i]\n false_examples_this_instance = []\n sentence_candidate_span_examples = [] # when training.\n\n # loop across different spans for given sentence\n for span in legal_spans(num_sentence_words, max_span_length): #globally legal\n if span[1] > len(statement):\n continue\n if span[0] > len(statement):\n break\n span_index.append(span)\n\n # extract scalar features for this span. [position will not be used]\n f_bias = 1\n f_length = span[1] - span[0]\n #f_begin = span[0]\n #f_end = span[1]\n #f_dist_to_end0 = len(statement) - span[0]\n #f_dist_to_end1 = len(statement) - span[1]\n\n # list of tokens of this span\n span_tokens = statement[span[0]: span[1]]\n\n # feature: span contains at least one science token\n f_science_token = bool( \\\n set(span_tokens).intersection(science_expressions))\n\n\n\n f_science_token_count = 0 # counting # of science tokens in span\n max_token_length = 0 # in this span.\n for token in span_tokens:\n f_science_token_count += int(token in science_tokens)\n max_token_length = max(max_token_length, len(token))\n\n f_max_token_length = np.log(max_token_length)\n\n # feature: relative word frequency average\n # with numerical stability/ avoiding -inf\n f_avg_word_frequency = 1e-10+np.mean(sentence_word_frequencies[span[0]: span[1]])\n f_avg_word_frequency = np.log(f_avg_word_frequency)\n\n # feature: begin with stop word?\n f_stop_word_begin = bool(span_tokens[0] in stop_words)\n\n # POS indicator (one-hot)\n f_pos = np.zeros([len(pos_tags)])\n\n # Bag-of-POS-tags for this span.\n for token, tag in pos_tags_this_statement[span[0]:span[1]]:\n f_pos[pos_tags.index(tag)] += 1.0\n\n # feature: POS indicator for span beginning\n f_pos_beginning = np.zeros([len(pos_tags)])\n f_pos_beginning[pos_tags.index(pos_tags_this_statement[span[0]][1])] = 1.0\n\n # feature: POS indicator for span end\n f_pos_end = np.zeros([len(pos_tags)])\n f_pos_end[pos_tags.index(pos_tags_this_statement[span[1]-1][1])] = 1.0\n\n # feature: POS bigram indicator\n # define extended POS tag set with additional begin and end symbols for bigrams.\n # pos_tags_bigram = pos_tags + [\"POS_BEGIN\", \"POS_END\"]\n\n # for POS bigrams.\n # pos_tags_square = [x for x in product(pos_tags_bigram, pos_tags_bigram)]\n\n # f_pos_bigram = np.zeros([len(pos_tags_square)])\n\n # obtaining the POS bigram\n # for position in range(-1, f_length):\n # boundary cases: start of span and end of span.\n # if position == -1:\n # tag1 = 'POS_BEGIN'\n # _, tag2 = pos_tags_this_statement[span[0]]\n # elif position == f_length -1:\n # _, tag1 = pos_tags_this_statement[span[0]+position]\n # tag2 = 'POS_END'\n # #normal case: inside span.\n # else:\n # _, tag1 = pos_tags_this_statement[span[0] + position]\n # _, tag2 = pos_tags_this_statement[span[0] + position + 1]\n #\n # f_pos_bigram[pos_tags_square.index( ( tag1, tag2 ) )] += 1.0\n\n # constituent tree features\n\n tree_position = tree.treeposition_spanning_leaves(span[0], span[1])\n\n # smallest subtree in constituent parse, containing this span.\n smallest_subtree = tree[tree_position[:-1]]\n constituent_tag = smallest_subtree.label()\n\n # feature: is this span a constituent parse subtree span?\n f_span_match = bool(span[1]-span[0] == len(smallest_subtree))\n\n # constituency parse label indicator\n f_span_constituent = np.zeros([len(constituents)])\n f_span_constituent[constituents.index(constituent_tag)] = 1.0\n\n # constituency parse label indicator with indication for large spans.\n f_span_constituent_big = np.zeros([len(constituents)])\n f_span_constituent_big[constituents.index(constituent_tag)] = (f_length > 2)\n\n\n # leave out position features:\n #### f_begin, f_end, f_dist_to_end0, f_dist_to_end1,\n\n #now collect all features:\n f_scalars = np.array([f_bias, f_span_match, f_length,\n f_science_token, f_avg_word_frequency,\n f_stop_word_begin,\n f_max_token_length,\n f_science_token_count])\n\n # these are all features for this span, in a np array.\n feature_vector = np.concatenate((f_scalars, f_pos, f_pos_beginning,\n f_pos_end, f_span_constituent,\n f_span_constituent_big))\n\n\n\n # provide True/False annotation in case the data is used for training.\n if training:\n if span == training_spans[i]:\n #positive example\n true_examples.append(feature_vector)\n sentence_candidate_span_examples.append((feature_vector, True))\n else:\n #negative example\n false_examples_this_instance.append(feature_vector)\n sentence_candidate_span_examples.append((feature_vector, False))\n else:\n sentence_candidate_span_examples.append(feature_vector)\n\n span_indexes.append(span_index)\n examples_per_sentence.append(sentence_candidate_span_examples)\n\n\n # select at random k negative spans as training examples. default 1:1\n if training:\n for random_index in np.random.randint(0, len(false_examples_this_instance), k):\n false_examples.append(false_examples_this_instance[random_index])\n\n print(len(true_examples), 'True span examples.')\n print(len(false_examples), 'False span examples.')\n\n # collect true and false examples [inputs]\n all_examples = np.concatenate((np.asarray(false_examples), np.asarray(true_examples)))\n\n # collect annotations for each example (True/False target outputs)\n false_span_labels = np.zeros([len(false_examples)])\n true_span_labels = np.ones([len(true_examples)])\n all_labels = np.concatenate((false_span_labels, true_span_labels))\n\n return all_examples, all_labels, examples_per_sentence, span_indexes","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(\n GeometryV4Strategy))\n\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate_features(stances, dataset, name, feature_list, features_dir):\n h, b, y, bodyId, headId = [], [], [], [], []\n\n feature_dict = {'overlap': word_overlap_features,\n 'refuting': refuting_features,\n 'polarity': polarity_features,\n 'hand': hand_features,\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\n 'NMF_fit_all': NMF_fit_all,\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\n 'NMF_cos_50': NMF_cos_50,\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\n 'ppdb': ppdb,\n 'stanford_ppdb': stanford_ppdb_score,\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\n 'stanford_sentiment': stanford_sentiment,\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\n 'stanford_wordsim': stanford_based_verb_noun_sim,\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\n 'stanford_negation': stanford_negation_features,\n 'stanford_negation_1sent': stanford_negation_features_1sent,\n 'stanford_negation_2sent': stanford_negation_features_2sent,\n 'stanford_negation_3sent': stanford_negation_features_3sent,\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\n 'hedging': hedging_features,\n 'sen2sen': sen2sen_similarity_max,\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\n 'wmdsenDoc': word_mover_distance_wholebody,\n 'sdm_sim': sdm_sim,\n 'discuss': discuss_features,\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\n 'lexical_features': lexical_features,\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\n 'mpqa_unigrams': mpqa_unigrams,\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\n 'nrc_emo_lex': nrc_emo_lex,\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\n #'POS_features': POS_features,\n 'readability_features': readability_features,\n 'sentiment140_unigrams': sentiment140_unigrams,\n 'structural_features': structural_features,\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\n 'NMF_cos_300': NMF_cos_300\n }\n\n stanceCounter = 0\n for stance in stances:\n y.append(LABELS.index(stance['Stance']))\n h.append(stance['Headline'])\n b.append(dataset.articles[stance['Body ID']])\n bodyId.append(stance['Body ID'])\n headId.append(name+str(stanceCounter))\n stanceCounter += 1\n\n X_feat = []\n feat_list = []\n last_index = 0\n for feature in feature_list:\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\"/\"+feature+\".\"+name+'.npy', bodyId, feature, headId, fold=name)\n feat_list.append((last_index, last_index+len(feat[0]), str(feature)))\n last_index += len(feat[0])\n X_feat.append(feat)\n X = np.concatenate(X_feat, axis=1)\n\n return X, y, feat_list","def algorithms():\n algorith_paradigms = ['Divide-and-conquer', 'Backtrackig', 'Greedy-Algorithms', 'Dynamic-programming']\n return algorith_paradigms","def generate_non_bleeding_features(fold_stances, hold_out_stances, no_folds, BOW_feature_list, features_dir, d):\n\n # holds all bag of words features and their feature extraction methods\n non_bleeding_feature_dict = {}\n\n def generate_holdout_BOW_features():\n for fold in range(no_folds):\n ids = list(range(no_folds))\n merged = []\n for i in ids:\n merged.extend(fold_stances[i])\n\n h = []\n b = []\n for stance in merged:\n h.append(stance['Headline'])\n b.append(d.articles[stance['Body ID']])\n\n h_test = []\n b_test = []\n for stance in hold_out_stances:\n h_test.append(stance['Headline'])\n b_test.append(d.articles[stance['Body ID']])\n\n gen_non_bleeding_feats(non_bleeding_feature_dict[feature], h, b, h_test, b_test,\n features_dir, feature, 'holdout')\n\n def generate_fold_BOW_features():\n for fold in range(no_folds):\n ids = list(range(no_folds))\n del ids[fold]\n\n merged = []\n for i in ids:\n merged.extend(fold_stances[i])\n\n # 9 of 10 folds merged for training BOW features\n h = []\n b = []\n for stance in merged:\n h.append(stance['Headline'])\n b.append(d.articles[stance['Body ID']])\n\n # 1 fold (test) to extract features out of the generated BOW\n h_test = []\n b_test = []\n for stance in fold_stances[fold]:\n h_test.append(stance['Headline'])\n b_test.append(d.articles[stance['Body ID']])\n\n gen_non_bleeding_feats(non_bleeding_feature_dict[feature],\n h, b, h_test, b_test, features_dir, feature,\n fold)\n\n for feature in BOW_feature_list:\n generate_fold_BOW_features()\n generate_holdout_BOW_features()","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generateISTemplates(course, wordlist, charLevels, translateDict, similarCharsList, supportsUppercase):\n\n levelsDict= {}\n stageByNumber = 1 # represents key of wordsByLevelDict to get wordlist value\n wordsByLengthDict = {}\n wordsByLevelDict = {}\n swappedTranslateDict = dict((v,k) for k,v in translateDict.iteritems()) # Keys are latin, values non-latin\n\n for word in wordlist:\n wordsByLengthDict = wordsByLength(word, wordsByLengthDict)\n wordsByLevelDict = wordsByLevel(word, charLevels, wordsByLevelDict)\n\n for stage in charLevels:\n\n if not wordsByLevelDict.has_key(stageByNumber): # Skip the uppercase stages in charLevels\n stageByNumber += 1\n continue\n\n charLevelsWordList = wordsByLevelDict[stageByNumber] # contains list of words suitable for current stage\n selectedOptions = makeWordOptions(charLevelsWordList, similarCharsList) # List of 3 sublists containing options or None\n\n if selectedOptions is not None:\n\n for i in range(len(selectedOptions)):\n generateLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\".qdef\")\n generateLevelAlternative(selectedOptions[i], translateDict, similarCharsList, name+str(stageByNumber)+\".qdef\")\n\n if supportsUppercase:\n generateUppercaseLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\".qdef\")\n\n generateEndOfQuiz(name+str(stageByNumber)+\".qdef\")\n stageByNumber += 1\n\n # generate practice levels\n for i in range(30):\n selectedWords = wordsByLengthDict[random.choice(wordsByLengthDict.keys())]\n if len(selectedWords) >= 3:\n quizRandomSelection = random.sample(selectedWords,3) # take random 4 items from list\n\n for word in quizRandomSelection:\n trickWord = findAndReplaceSimilarChar(word, quizRandomSelection, similarCharsList)\n if trickWord:\n quizRandomSelection.append(trickWord)\n break\n\n generateLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\"Practice.qdef\")\n generateLevelAlternative(quizRandomSelection, translateDict, similarCharsList, name+\"Practice.qdef\")\n\n if supportsUppercase:\n generateUppercaseLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\"Practice.qdef\")\n\n generateEndOfQuiz(name+\"Practice.qdef\")\n\n print \"New MUNI ROPOT templates has been created in: \" + target","def get_technique_side_menu_data(domain, technique_list, tactic_list):\n\n # Get tactics techniques side menu data to fill out with jinja\n tactics_techniques_menu_data = []\n\n for tactic in tactic_list:\n tactic_row = {}\n \n tactic_row['name'] = tactic['name']\n tactic_row['id'] = util.get_attack_id(tactic)\n tactic_row['path'] = \"/tactics/{}\".format(util.get_attack_id(tactic))\n \n tactic_row['children'] = []\n for technique in technique_list[tactic['x_mitre_shortname']]:\n technique_row = {}\n # Get technique id and name for each technique\n technique_row['name'] = technique['name']\n technique_row['id'] = technique['id']\n technique_row['path'] = \"/techniques/{}/\".format(technique['id'])\n technique_row['children'] = []\n # Add technique data to tactic\n tactic_row['children'].append(technique_row)\n \n # Add tactic to table\n tactics_techniques_menu_data.append(tactic_row)\n \n return {\n \"name\": \"techniques\",\n \"id\": \"techniques\",\n \"path\": \"/techniques/{}/\".format(domain.split(\"-\")[0]), # root level doesn't get a path\n \"children\": tactics_techniques_menu_data\n }","def get_mitigation_mitigates_techniques():\n global mitigation_mitigates_techniques\n\n if not mitigation_mitigates_techniques:\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\n\n return mitigation_mitigates_techniques","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n return strategies, do_rotations","def generate():\n strategies, do_rotations = [], []\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(\n CentrifugalBumblepuppy16h,\n mirroring=False))\n do_rotations.extend([False])\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n do_rotations.extend([True, True])\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n do_rotations.extend([True, True])\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n do_rotations.extend([True, True])\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def setup_random_opinions_representatives():\r\n global CATEGORIES\r\n \r\n ideas_dic = {}\r\n \r\n for i in CATEGORIES:\r\n #idea = Idea(1,\"\",i, 1-random.expovariate(6))\r\n if i == 1:\r\n idea = Idea(1,\"\",i, random.uniform(-1,-0.5))\r\n elif i == 2:\r\n idea = Idea(1,\"\",i, random.uniform(-1,-0.5))\r\n elif i == 3:\r\n idea = Idea(1,\"\",i, random.uniform(0.5,1))\r\n \r\n# idea = Idea(1,\"\",i, random.uniform(0.5,1))\r\n ideas_dic[i] = idea\r\n \r\n return ideas_dic","def get_scenarios(experiments):\n return {exp.scenario for exp in experiments}","def generate_features_test(stances, dataset, name, feature_list, features_dir):\n h, b, bodyId, headId = [], [], [], []\n\n feature_dict = {'overlap': word_overlap_features,\n 'refuting': refuting_features,\n 'polarity': polarity_features,\n 'hand': hand_features,\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\n 'NMF_fit_all': NMF_fit_all,\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\n 'NMF_cos_50': NMF_cos_50,\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\n 'ppdb': ppdb,\n 'stanford_ppdb': stanford_ppdb_score,\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\n 'stanford_sentiment': stanford_sentiment,\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\n 'stanford_wordsim': stanford_based_verb_noun_sim,\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\n 'stanford_negation': stanford_negation_features,\n 'stanford_negation_1sent': stanford_negation_features_1sent,\n 'stanford_negation_2sent': stanford_negation_features_2sent,\n 'stanford_negation_3sent': stanford_negation_features_3sent,\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\n 'hedging': hedging_features,\n 'sen2sen': sen2sen_similarity_max,\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\n 'wmdsenDoc': word_mover_distance_wholebody,\n 'sdm_sim': sdm_sim,\n 'discuss': discuss_features,\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\n 'lexical_features': lexical_features,\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\n 'mpqa_unigrams': mpqa_unigrams,\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\n 'nrc_emo_lex': nrc_emo_lex,\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\n #'POS_features': POS_features,\n 'readability_features': readability_features,\n 'sentiment140_unigrams': sentiment140_unigrams,\n 'structural_features': structural_features,\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\n 'NMF_cos_300': NMF_cos_300\n }\n\n stanceCounter = 0\n for stance in stances:\n h.append(stance['Headline'])\n b.append(dataset.articles[stance['Body ID']])\n bodyId.append(stance['Body ID'])\n headId.append(name+str(stanceCounter))\n stanceCounter += 1\n\n X_feat = []\n for feature in feature_list:\n print(\"calculate feature: \" + str(feature))\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\"/\"+feature+\"_test.\"+name+'.npy', bodyId, feature, headId, fold=name)\n X_feat.append(feat)\n print(len(feat))\n X = np.concatenate(X_feat, axis=1)\n return X","def generate_features(paraphrases, pos2index, prep2index, model, wv, word2index, UNK):\n features = []\n for (w1, w2), curr_paraphrases in tqdm.tqdm(paraphrases.items()):\n features.append([extract_paraphrase_features(w1, w2, paraphrase, pos2index, prep2index,\n model, wv, word2index, UNK)\n for paraphrase in curr_paraphrases.keys()])\n\n scores = [list(curr_paraphrases.values()) for curr_paraphrases in paraphrases.values()]\n return features, scores","def variations():","def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\n return [\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\n ]","def get_groups_using_technique():\n global groups_using_technique\n\n if not groups_using_technique:\n groups_using_technique = rsh.groups_using_technique(get_srcs())\n \n return groups_using_technique","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add memory patterns v7\n strategies.extend(\n generate_meta_strategy_pair(MemoryPatternsV7Strategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def get_subtechniques_of():\n global subtechniques_of\n\n if not subtechniques_of:\n subtechniques_of = rsh.subtechniques_of(get_srcs())\n \n return subtechniques_of","def get_all_techniques(src, source_name, tactic=None):\n filters = [\n Filter(\"type\", \"=\", \"attack-pattern\"),\n Filter(\"external_references.source_name\", \"=\", source_name),\n ]\n if tactic:\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\n\n results = src.query(filters)\n return remove_deprecated(results)","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(MemoryPatternsV7Strategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def make_raw_skill_sets(skills):\n\n possible_sizes_in_list_of_things = range(4)\n basic_skills = [list(combinations_of_a_certain_size)\n for each_possible_size_of_combination in possible_sizes_in_list_of_things\n for combinations_of_a_certain_size in itertools.combinations(skills,\n each_possible_size_of_combination)]\n\n return basic_skills","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n\n strategies.extend([\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False)[0]])\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def sample_handling(sample, lexicon, classification):\n\n # We have a list of lists [.... [ [0, 2, 1, 0, 0, ..., 0] [1] ] , .... ] with the bag of words and the class\n featureset = []\n\n # Open the sample text and parse through the document and generate feastures.\n with open(sample, 'r') as f:\n contents = f.readlines()\n for l in contents[:hm_lines]:\n current_words = word_tokenize(l.lower())\n current_words = [lemmatizer.lemmatize(i) for i in current_words]\n features = np.zeros(len(lexicon))\n for word in current_words:\n if word.lower() in lexicon:\n index_value = lexicon.index(word.lower())\n features[index_value] = 1\n features = list(features)\n featureset.append([features, classification])\n\n return featureset","def generate_advice(it, transforms=lambda x: (x, 1)):\n if type(transforms) is not list:\n transforms = [transforms]\n advice = [LookupTableAdvice() for i in range(len(transforms))]\n for example in it:\n for i, tr in enumerate(transforms):\n key, value = tr(example)\n advice[i].process(key, value)\n return advice","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def build_data_cv(train_set, cuisine_classes, frequent_ingredients, cv=10, shuffle=1):\n data_cv = []\n # print \"Number of shuffle %d\" % shuffle\n\n for item in train_set:\n for i in range(shuffle + 1):\n ingredients = []\n random.shuffle(item[\"ingredients\"])\n for ingredient in item[\"ingredients\"]:\n if ingredient.replace(\" \", \"_\") in frequent_ingredients:\n ingredients.append(ingredient.replace(\" \", \"_\"))\n datum = {\"y\": cuisine_classes.index(item[\"cuisine\"]),\n \"recipe\": \" \".join(ingredients),\n \"num_ingredients\": len(ingredients),\n \"split\": np.random.randint(0, cv)}\n data_cv.append(datum)\n\n return data_cv","def get_mitigations_table_data(technique, reference_list, next_reference_number):\n\n mitigation_data = []\n\n # Check if technique has mitigations\n if config.technique_mitigated.get(technique['id']):\n # Iterate through technique mitigations\n for mitigation in config.technique_mitigated[technique['id']]:\n\n # Do not add deprecated mitigation to table\n if 'x_mitre_deprecated' not in mitigation['object']:\n\n attack_id = util.get_attack_id(mitigation['object'])\n\n # Only add if mitigation attack id is found \n if attack_id:\n\n row = {}\n row['mid'] = attack_id\n row['name'] = mitigation['object']['name']\n if mitigation['relationship'].get('description'):\n # Get filtered description\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, mitigation)\n \n mitigation_data.append(row)\n \n if mitigation_data:\n mitigation_data = sorted(mitigation_data, key=lambda k: k['name'].lower())\n return mitigation_data","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def _create_examples(self, lines, set_type):\n examples = []\n for (i, line) in enumerate(tqdm(lines)):\n if i == 0:\n continue\n gold_label_id = self.label2id(line[0])\n several_labels = [self.label2id(line[-5]), self.label2id(line[-4]), self.label2id(line[-3]),\n self.label2id(line[-2]), self.label2id(line[-1])]\n\n pair_id = line[-6]\n premise = line[5]\n hypothesis = line[6]\n premise_bp = line[1]\n hypothesis_bp = line[2]\n premise_p = line[3]\n hypothesis_p = line[4]\n # premise_length = len(premise)\n # hypothesis_length = len(hypothesis)\n guid = \"%s-%s\" % (set_type, i)\n\n ex = dict()\n for k in self.data_keys:\n ex[k] = eval(k)\n examples.append(ex)\n logger.info(\" {} examples\".format(len(examples)))\n return examples","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def get_random_all_sklearn():\n models = []\n import sklearn.linear_model\n import sklearn.svm\n import sklearn.neighbors\n import sklearn.gaussian_process\n import sklearn.naive_bayes\n import sklearn.tree\n import sklearn.ensemble\n import sklearn.neural_network\n import xgboost\n\n models.extend(get_all_sklearn_in_module(sklearn.linear_model))\n # http://scikit-learn.org/stable/modules/lda_qda.html\n models.extend([\n 'sklearn.discriminant_analysis.LineadDiscriminantAnalysis',\n 'sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis'\n ])\n # http://scikit-learn.org/stable/modules/kernel_ridge.html\n models.extend(['sklearn.kernel_ridge.KernelRidge'])\n models.extend(get_all_sklearn_in_module(sklearn.svm))\n models.extend(get_all_sklearn_in_module(sklearn.neighbors))\n models.extend(get_all_sklearn_in_module(sklearn.gaussian_process))\n models.extend(get_all_sklearn_in_module(sklearn.naive_bayes))\n models.extend(get_all_sklearn_in_module(sklearn.tree))\n models.extend(get_all_sklearn_in_module(sklearn.ensemble))\n models.extend(get_all_sklearn_in_module(sklearn.neural_network))\n # add in xgboost\n models.extend([\n 'xgboost.sklearn.XGBClassifier', 'xgboost.sklearn.XGBRegressor'\n ])\n\n transforms = []\n import sklearn.preprocessing\n import sklearn.feature_selection\n import sklearn.feature_extraction\n import sklearn.feature_extraction.text\n import sklearn.decomposition\n transforms.extend(get_all_sklearn_in_module(sklearn.preprocessing))\n transforms.extend(get_all_sklearn_in_module(sklearn.feature_selection))\n transforms.extend(get_all_sklearn_in_module(sklearn.feature_extraction))\n transforms.extend(\n get_all_sklearn_in_module(sklearn.feature_extraction.text)\n )\n transforms.extend(get_all_sklearn_in_module(sklearn.decomposition))\n\n transform_label_encoder = LabelEncoder().fit(transforms)\n model_label_encoder = LabelEncoder().fit(models)\n return RandomScorer(transform_label_encoder, model_label_encoder)","def eval_all_combinations(docs, labels, punct_vals,\n feature_fns, min_freqs):\n# result = []\n\n# for punct_val in punct_vals:\n# tokens = [tokenize(doc, punct_val) for doc in docs]\n# for f in [comb for i in range(len(feature_fns)) for comb in combinations(feature_fns, i+1)]:\n# feature = list(f)\n\n# for min_freq in min_freqs:\n# clf = LogisticRegression()\n# X, vocab = vectorize(tokens, feature, min_freq)\n# accuracy = cross_validation_accuracy(clf, X, labels, 5)\n# result.append(dict(punct= punct_val, features= feature, min_freq= min_freq, accuracy = accuracy))\n\n# return sorted(result, key=lambda x:(-x['accuracy'],-x['min_freq']))\n clf = LogisticRegression()\n result = []\n output = []\n for l in range(1, len(feature_fns)+1):\n for c in combinations(feature_fns,l):\n result.append(c)\n\n for p in punct_vals:\n list_tok = [tokenize(d,p) for d in docs]\n for fl in result:\n for mf in min_freqs:\n dict_output = {}\n matrix,vocab = vectorize(list_tok, fl, mf)\n average_value = cross_validation_accuracy(clf, matrix, labels, 5)\n dict_output['features'] = fl\n dict_output['punct'] = p\n dict_output['accuracy'] = average_value\n dict_output['min_freq'] = mf\n output.append(dict_output)\n\n return sorted(output, key=lambda x: (-x['accuracy'], -x['min_freq']))","def prepare_for_supervised(embeddings):\n \n X, y = [], []\n for problem in embeddings:\n pairs = [np.concatenate((problem['question'], problem[i])) \n for i in ('choice_0', 'choice_1', 'choice_2', 'choice_3')]\n correct_answer = problem['correct_answer']\n \n labels = [0, 0, 0, 0]\n labels[correct_answer] = 1\n X.extend(pairs)\n y.extend(labels)\n return X, y","def _generate_evaluaters(self):\n evaluators = []\n for para_key in self.parameter[1]:\n for value in self.parameter[1][para_key]:\n evaluators.append(evaluaterSearch.evaluaterSearch(self.parameter[2], [para_key, value]))\n self.evaluators = evaluators","def _create_examples(self, data_dir, set_type):\n\t\texamples = []\n\t\tinput_file_data = os.path.join(data_dir, \"data.tsv\")\n\t\twith open(input_file_data, \"r\", encoding=\"utf-8-sig\") as f:\n\t\t\tfor i, inp in enumerate(f):\n\t\t\t\tinps = inp.split('\\t') \n\t\t\t\tguid = \"%s-%s\" % (set_type, i)\n\t\t\t\ttext_inp = inps[1].strip()\n\t\t\t\ttext_out = inps[2].strip()\n\t\t\t\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\n\t\t\t\t\n\t\t\t# Sort these out before returning\n\t\t\texamples = sorted(examples, key=sort_inp_len)\n\t\t\treturn examples","def get_technique_related_to_technique():\n global technique_related_to_technique\n\n if not technique_related_to_technique:\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\n\n return technique_related_to_technique","def eval_all_combinations(docs, labels, punct_vals,\n feature_fns, min_freqs):\n ###TODO\n \n # gettting feature's 7 combinations\n feature_comb = [] \n i = 1\n \n while i <= len (feature_fns) :\n comb = combinations(feature_fns,i)\n i += 1 \n \n for c in comb:\n feature_comb.append(c) \n #print(c)\n\n #for option in feature_comb:\n #print(option)\n \n # LogisticRegression object\n \n\n keys = ['punct','features','min_freq','accuracy']\n results = []\n dicts = [] \n feature_dict = {}\n \n # setting on punct,mi_freq and conmbination of feature\n for punct in punct_vals: \n tokens_list = [tokenize(d,punct) for d in docs]\n \n for freq in min_freqs : \n\n for features in feature_comb:\n \n #print('MinFreq = %d Punct = %s fetures = %s'%(freq,punct,features))\n X, vocabulary = vectorize(tokens_list,features,freq)\n clf = LogisticRegression()\n avg_acc = cross_validation_accuracy(clf, X, labels, k=5)\n #print('Avg accuracy = %f'%(avg_acc))\n #print('vocab size =',len(vocabulary)) \n \n result = [punct,features,freq,avg_acc]\n\n feature_dict = dict(zip(keys, result))\n\n dicts.append(feature_dict)\n \n \n # sort dict on accuracy \n dicts.sort(key=lambda x:(-x['accuracy'],-x['min_freq']))\n #print('dicts = ',dicts)\n \n return(dicts)","def workbench_scenarios(): \n return [\n (\"TermsXBlock\",\n \"\"\"<terms/>\n \"\"\"),\n (\"Multiple TermsXBlock\",\n \"\"\"<vertical_demo>\n <terms/>\n <terms/>\n <terms/>\n </vertical_demo>\n \"\"\"),\n ]","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n strategies.extend(\n generate_meta_strategy_pair(\n WrappedRFindStrategy,\n limits=limits,\n sources=sources,\n shenanigans=False))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add memory pattern strategies\n strategies.extend(\n generate_meta_strategy_pair(MemoryPatternsV7Strategy))\n\n do_rotations = [True for _ in strategies]\n return strategies, do_rotations","def build_vocabulary(image_paths, vocab_size):\n n_image = len(image_paths)\n\n # Since want to sample tens of thousands of SIFT descriptors from different images, we\n # calculate the number of SIFT descriptors we need to sample from each image.\n n_each = int(np.ceil(40000 / n_image)) # You can adjust 10000 if more is desired\n\n # Initialize an array of features, which will store the sampled descriptors\n features = np.zeros((n_image * n_each, 128))\n j=0\n for i, path in enumerate(image_paths):\n # Load SIFT features from path\n descriptors = np.loadtxt(path, delimiter=',',dtype=float)\n\n # TODO: Randomly sample n_each features from descriptors, and store them in features\n #use the randomizer in numpy library to make n_each random index\n idx= np.array(np.random.randint(0,len(descriptors),n_each))\n\n # choose randomly n_each number of discriptor to train K-mean classifier\n for k in idx:\n\n features[j] = descriptors[k,:]\n j = j+1\n # TODO: pefrom k-means clustering to cluster sampled SIFT features into vocab_size regions.\n # You can use KMeans from sci-kit learn.\n # Reference: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n\n #use K_mean classifier to make Bag of visual words represantation for SIFT features\n kmeans = KMeans(n_clusters=250).fit(features)\n #kmeans= clustering = AgglomerativeClustering().fit(features)\n\n\n return kmeans","def featurize_1(list_of_demonstrations, kinematics, sr):\n\tprint \"FEATURIZATION 1\"\n\n\tdata_X_1 = {}\n\tdata_X_2 = {}\n\tfor demonstration in list_of_demonstrations:\n\t\tprint \"SIFT for \", demonstration\n\t\tstart, end = parser.get_start_end_annotations(constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER\n\t\t\t\t\t\t+ demonstration + \"_\" + constants.CAMERA +\".p\")\n\n\t\tW = kinematics[demonstration]\n\t\tW_sampled = utils.sample_matrix(W, sampling_rate = sr)\n\n\n\t\tPATH_TO_SIFT = constants.PATH_TO_DATA + \"sift_FCED/SIFT_\"+ demonstration\n\t\tZ = pickle.load(open(PATH_TO_SIFT + \"_1.p\", \"rb\"))\n\t\tZ = Z[start:end + 1]\n\t\tZ_sampled_1 = utils.sample_matrix(Z, sampling_rate = sr)\n\n\t\tZ = pickle.load(open(PATH_TO_SIFT + \"_2.p\", \"rb\"))\n\t\tZ = Z[start:end + 1]\n\t\tZ_sampled_2 = utils.sample_matrix(Z, sampling_rate = sr)\n\n\t\tassert Z_sampled_1.shape[0] == W_sampled.shape[0]\n\t\tassert Z_sampled_2.shape[0] == W_sampled.shape[0]\n\n\t\tdata_X_1[demonstration] = np.concatenate((W_sampled, Z_sampled_1), axis = 1)\n\t\tdata_X_2[demonstration] = np.concatenate((W_sampled, Z_sampled_2), axis = 1)\n\n\tpickle.dump(data_X_1, open(PATH_TO_FEATURES + \"SIFT_1.p\", \"wb\"))\n\tpickle.dump(data_X_2, open(PATH_TO_FEATURES + \"SIFT_2.p\", \"wb\"))","def get_bests(self):\n set_names = [\"training\", \"hp_selection\", \"validation\"]\n run_tec_conf_set = recursivedict()\n validation = self._campaign_configuration['General']['validation']\n hp_selection = self._campaign_configuration['General']['hp_selection']\n if (validation, hp_selection) in {(\"All\", \"All\"), (\"Extrapolation\", \"All\"), (\"All\", \"HoldOut\"), (\"HoldOut\", \"All\"), (\"HoldOut\", \"HoldOut\"), (\"Extrapolation\", \"HoldOut\")}:\n # For each run, for each technique the best configuration\n run_tec_best_conf = recursivedict()\n\n # Hyperparameter search\n for conf in self._exp_confs:\n run = int(conf.get_signature()[0].replace(\"run_\", \"\"))\n technique = conf.technique\n run_tec_conf_set[run][technique][str(conf.get_signature()[4:])] = conf.mapes\n # First experiment for this technique or better than the current best\n if technique not in run_tec_best_conf[run] or conf.mapes[\"hp_selection\"] < run_tec_best_conf[run][technique].mapes[\"hp_selection\"]:\n run_tec_best_conf[run][technique] = conf\n\n # Print results for each run\n for run in range(0, self._campaign_configuration['General']['run_num']):\n self._logger.info(\"-->Printing results for run %s\", str(run))\n overall_run_best = None\n # Print data of single techniques\n for technique in run_tec_best_conf[run]:\n temp = run_tec_best_conf[run][technique]\n self._logger.info(\"---Best result for %s - Configuration is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", technique, temp.get_signature()[4:], temp.mapes[\"training\"], temp.mapes[\"hp_selection\"], temp.mapes[\"validation\"])\n\n # Compute which is the best technique\n if not overall_run_best or temp.mapes[\"hp_selection\"] < overall_run_best.mapes[\"hp_selection\"]:\n overall_run_best = temp\n best_model_description = overall_run_best.print_model()\n self._logger.info(\"<--Overall best result is %s %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", overall_run_best.get_signature()[3:], \"(\" + best_model_description + \")\" if best_model_description else \"\", overall_run_best.mapes[\"training\"], overall_run_best.mapes[\"hp_selection\"], overall_run_best.mapes[\"validation\"])\n\n elif (validation, hp_selection) in {(\"KFold\", \"All\"), (\"KFold\", \"HoldOut\")}:\n folds = float(self._campaign_configuration['General']['folds'])\n # For each run, for each fold, for each technique, the best configuration\n run_fold_tec_best_conf = recursivedict()\n\n # Hyperparameter search inside each fold\n for conf in self._exp_confs:\n run = int(conf.get_signature()[0].replace(\"run_\", \"\"))\n fold = int(conf.get_signature()[1].replace(\"f\", \"\"))\n technique = conf.technique\n if \"hp_selection\" not in run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])]:\n for set_name in set_names:\n run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])][set_name] = 0\n for set_name in set_names:\n run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])][set_name] = run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])][set_name] + conf.mapes[set_name] / folds\n # First experiment for this fold+technique or better than the current best\n if technique not in run_fold_tec_best_conf[run][fold] or conf.mapes[\"hp_selection\"] < run_fold_tec_best_conf[run][fold][technique].mapes[\"hp_selection\"]:\n run_fold_tec_best_conf[run][fold][technique] = conf\n\n # Aggregate different folds (only the value of the mapes)\n run_tec_set = recursivedict()\n for run in run_fold_tec_best_conf:\n for fold in run_fold_tec_best_conf[run]:\n for tec in run_fold_tec_best_conf[run][fold]:\n if \"hp_selection\" not in run_tec_set[run][technique]:\n for set_name in set_names:\n run_tec_set[run][tec][set_name] = 0\n for set_name in set_names:\n run_tec_set[run][tec][set_name] = run_fold_tec_best_conf[run][fold][tec].mapes[set_name]\n # Print results for each run\n for run in range(0, self._campaign_configuration['General']['run_num']):\n self._logger.info(\"Printing results for run %s\", str(run))\n overall_run_best = ()\n # Print data of single techniques\n for technique in run_tec_set[run]:\n self._logger.info(\"---Best result for %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", technique, run_tec_set[run][technique][\"training\"], run_tec_set[run][technique][\"hp_selection\"], run_tec_set[run][technique][\"validation\"])\n\n # Compute which is the best technique\n if not overall_run_best or run_tec_set[run][technique][\"hp_selection\"] < overall_run_best[1][\"hp_selection\"]:\n overall_run_best = (technique, run_tec_set[run][technique])\n\n self._logger.info(\"---Overall best result is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", overall_run_best[0], overall_run_best[1][\"training\"], overall_run_best[1][\"hp_selection\"], overall_run_best[1][\"validation\"])\n\n # Overall best will contain as first argument the technique with the best (across runs) average (across folds) mape on validation; now we consider on all the runs and on all the folds the configuraiton of this technique with best validation mape\n\n elif (validation, hp_selection) in {(\"All\", \"KFold\"), (\"HoldOut\", \"KFold\"), (\"Extrapolation\", \"KFold\")}:\n folds = float(self._campaign_configuration['General']['folds'])\n # For each run, for each technique, for each configuration, the aggregated mape\n run_tec_conf_set = recursivedict()\n\n # Hyperparameter search aggregating over folders\n for conf in self._exp_confs:\n run = int(conf.get_signature()[0].replace(\"run_\", \"\"))\n fold = int(conf.get_signature()[2].replace(\"f\", \"\"))\n technique = conf.technique\n configuration = str(conf.get_signature()[4:])\n if \"hp_selection\" not in run_tec_conf_set[run][technique][configuration]:\n for set_name in set_names:\n run_tec_conf_set[run][technique][configuration][set_name] = 0\n for set_name in set_names:\n run_tec_conf_set[run][technique][configuration][set_name] = run_tec_conf_set[run][technique][configuration][set_name] + conf.mapes[set_name] / folds\n\n # Select the best configuration for each technique across different folders\n run_tec_best_conf = recursivedict()\n for run in run_tec_conf_set:\n for tec in run_tec_conf_set[run]:\n for conf in run_tec_conf_set[run][tec]:\n if tec not in run_tec_best_conf[run] or run_tec_conf_set[run][tec][conf][\"hp_selection\"] < run_tec_best_conf[run][tec][1][\"hp_selection\"]:\n run_tec_best_conf[run][tec] = (conf, run_tec_conf_set[run][tec][conf])\n\n # Print results for each run\n for run in range(0, self._campaign_configuration['General']['run_num']):\n self._logger.info(\"Printing results for run %s\", run)\n overall_run_best = () # (technique, configuration, mapes)\n # Print data of single techniques\n for technique in run_tec_best_conf[run]:\n temp = run_tec_best_conf[run][technique]\n self._logger.info(\"---Best result for %s - Configuration is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", technique, temp[0], temp[1][\"training\"], temp[1][\"hp_selection\"], temp[1][\"validation\"])\n\n # Compute which is the best technique\n if not overall_run_best or temp[1][\"hp_selection\"] < overall_run_best[2][\"hp_selection\"]:\n overall_run_best = (technique, temp[0], temp[1])\n\n self._logger.info(\"---Overall best result is %s %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", overall_run_best[0], overall_run_best[1], overall_run_best[2][\"training\"], overall_run_best[2][\"hp_selection\"], overall_run_best[2][\"validation\"])\n\n elif (validation, hp_selection) in {(\"KFold\", \"KFold\")}:\n folds = float(self._campaign_configuration['General']['folds'])\n # For each run, for each external fold, for each technique, the aggregated mape\n run_efold_tec_conf_set = recursivedict()\n\n # Hyperparameter search aggregating over internal folders\n for conf in self._exp_confs:\n run = int(conf.get_signature()[0].replace(\"run_\", \"\"))\n ext_fold = int(conf.get_signature()[2].replace(\"f\", \"\"))\n technique = conf.technique\n configuration = str(conf.get_signature()[4:])\n if \"hp_selection\" not in run_tec_conf_set[run][technique][configuration]:\n for set_name in set_names:\n run_tec_conf_set[run][technique][configuration][set_name] = 0\n for set_name in set_names:\n run_tec_conf_set[run][technique][configuration][set_name] = run_tec_conf_set[run][technique][configuration][set_name] + (conf.mapes[set_name] / (folds * folds))\n if configuration not in run_efold_tec_conf_set[run][ext_fold][technique]:\n for set_name in set_names:\n run_efold_tec_conf_set[run][ext_fold][technique][configuration][set_name] = 0\n for set_name in set_names:\n run_efold_tec_conf_set[run][ext_fold][technique][configuration][set_name] = run_efold_tec_conf_set[run][ext_fold][technique][configuration][set_name] + (conf.mapes[set_name] / (folds * folds))\n\n # Select the best configuration for each technique in each external fold across different internal folders\n run_efold_tec_best_conf = recursivedict()\n for run in run_efold_tec_conf_set:\n for efold in run_efold_tec_conf_set[run]:\n for tec in run_efold_tec_conf_set[run][efold]:\n for conf in run_efold_tec_conf_set[run][efold][tec]:\n if conf not in run_efold_tec_best_conf[run][efold][tec] or run_efold_tec_conf_set[run][efold][tec][conf][\"hp_selection\"] < run_efold_tec_best_conf[run][efold][tec][1][\"hp_selection\"]:\n run_efold_tec_best_conf[run][efold][tec] = (conf, run_efold_tec_conf_set[run][efold][tec][conf], run_efold_tec_conf_set[run][efold][tec][conf])\n\n # Aggregate on external folds\n run_tec_set = recursivedict()\n for run in run_efold_tec_best_conf:\n for efold in run_efold_tec_best_conf[run]:\n for tec in run_efold_tec_best_conf[run][efold]:\n if \"hp_selection\" not in run_tec_set[run][tec]:\n for set_name in set_names:\n run_tec_set[run][tec][set_name] = 0\n for set_name in set_names:\n run_tec_set[run][tec][set_name] = run_tec_set[run][tec][set_name] + run_efold_tec_best_conf[run][efold][tec][1][set_name]\n\n # Print results for each run\n for run in range(0, self._campaign_configuration['General']['run_num']):\n self._logger.info(\"Printing results for run %s\", run)\n overall_run_best = ()\n # Print data of single techniques\n for technique in run_tec_set[run]:\n self._logger.info(\"---Best result for %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", technique, run_tec_set[run][technique][\"training\"], run_tec_set[run][technique][\"hp_selection\"], run_tec_set[run][technique][\"validation\"])\n\n # Compute which is the best technique\n if not overall_run_best or run_tec_set[run][technique][\"hp_selection\"] < overall_run_best[1][\"hp_selection\"]:\n overall_run_best = (technique, run_tec_set[run][technique])\n\n self._logger.info(\"---Overall best result is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\", overall_run_best[0], overall_run_best[1][\"training\"], overall_run_best[1][\"hp_selection\"], overall_run_best[1][\"validation\"])\n\n else:\n self._logger.error(\"Unexpected combination: %s\", str((validation, hp_selection)))\n sys.exit(1)\n best_confs = {}\n best_technique = None\n for conf in self._exp_confs:\n technique = conf.technique\n if technique not in best_confs or conf.mapes[\"validation\"] < best_confs[technique].mapes[\"validation\"]:\n best_confs[technique] = conf\n for technique in best_confs:\n if not best_technique or best_confs[technique].mapes[\"validation\"] < best_confs[best_technique].mapes[\"validation\"]:\n best_technique = technique\n if bool(self._campaign_configuration['General']['details']):\n for run in run_tec_conf_set:\n for tec in run_tec_conf_set[run]:\n for conf in run_tec_conf_set[run][tec]:\n assert \"hp_selection\" in run_tec_conf_set[run][tec][conf]\n assert \"validation\" in run_tec_conf_set[run][tec][conf], \"training MAPE not found for \" + str(run) + str(tec) + str(conf)\n self._logger.info(\"Run %s - Technique %s - Conf %s - Training MAPE %f - Test MAPE %f\", str(run), ec.enum_to_configuration_label[tec], str(conf), run_tec_conf_set[run][tec][conf][\"hp_selection\"], run_tec_conf_set[run][tec][conf][\"validation\"])\n return best_confs, best_technique","def _generate_variations(self):\n words = list(self.solutions)\n letter_choices = tuple(self._data_words[np.where(self._data_words != '')])\n\n self._data_variations[:] = ''\n while np.sum(self._data_variations != '') < self._data_variations.size * self.difficulty.density_target_variations:\n word = random.choice(words)\n chrs = list(word)\n\n # Replace random letters in word\n # eg. word = ward, wore, wond, qerd\n for _ in range(random.randint(0, len(chrs) // 2)):\n if letter_choices and random.uniform(0, 1) > self.difficulty.copy_letter_chance:\n chrs[random.randint(0, len(chrs) - 1)] = random.choice(letter_choices)\n else:\n chrs[random.randint(0, len(chrs) - 1)] = random.choice(string.ascii_lowercase)\n\n # Remove random letters from the word\n # eg. word = wrd, wod\n for _ in range(random.randint(0, len(chrs) // 2)):\n del chrs[random.randint(0, len(chrs) - 1)]\n\n # Add the word to the variations data\n direction = self.difficulty.generate_direction(self.dimensions)\n location = tuple(random.randint(0, size - 1) for size in self.shape)\n for char in chrs:\n if not all(-1 <= a + b < c + 1 for a, b, c in zip(location, direction, self.shape)):\n break\n self._data_variations[location] = char\n location = tuple(map(sum, zip(location, direction)))\n\n self._req_update_variation = False","def make_classifiers(NAMES) :\r\n\r\n# if len(data_shape) != 2:\r\n# raise ValueError(\"Only 2-d data allowed (samples by dimension).\")\r\n\r\n classifiers = {\r\n \"Chance\": DummyClassifier(strategy=\"most_frequent\"),\r\n \"Nearest Neighbors\": KNeighborsClassifier(3),\r\n \"Linear SVM\": LinearSVC(penalty='l2', C=1,# probability=True,\r\n class_weight='balanced'),\r\n # sahil changed the configuration from \"probability\" True to False (probability\r\n # based inference doesn't work well in SVM models from part experiences,\r\n # as SVM original algorithm just split the data with no probablistic notion of inference.)\r\n \"RBF SVM\": SVC(gamma=2, C=1, probability=False),\r\n \"Decision Tree\": DecisionTreeClassifier(max_depth=None,\r\n max_features=\"auto\"),\r\n \"Random Forest\": RandomForestClassifier(max_depth=None,\r\n n_estimators=20,\r\n max_features=\"auto\",\r\n n_jobs=PROCESSORS),\r\n \"Logistic Regression\": LogisticRegression(penalty='l1',\r\n class_weight='balanced'),\r\n \"Naive Bayes\": GaussianNB(),\r\n \"LDA\": LDA(),\r\n \"SGD_logL1\": SGDClassifier(random_state=1952,loss='log', average = 3,\r\n penalty='l1',\r\n alpha=1e-3,\r\n class_weight='balanced'),\r\n \"SGD_log_elastic\": SGDClassifier(random_state=1952,loss='log',\r\n class_weight='balanced',\r\n alpha=1e-3,\r\n average = 3,\r\n penalty='elasticnet'),\r\n \"SGD_SVM_elastic\": SGDClassifier(random_state=1952,loss='log',\r\n class_weight='balanced',\r\n average = 3,\r\n alpha=1e-3,\r\n penalty='elasticnet'),\r\n\r\n # Sahil commented the two classiifer below as not able to install the packages\r\n # \"CGC_log_L1\": CDClassifier(penalty=\"l1\",\r\n # loss=\"log\",\r\n # multiclass=False,\r\n # max_iter=200,\r\n # C=1,\r\n # tol=1e-3),\r\n # \"SDCA_SVM_elastic\": SDCAClassifier(\r\n # loss=\"hinge\",\r\n # max_iter=200,\r\n # tol=1e-3)\r\n #\r\n }\r\n\r\n params = {\r\n \"Chance\": {},\r\n # 3, 4, 5\r\n # , 6, 7, 8, 10, 12, 15, 20, 30, 50, 75, 100\r\n #\r\n #\r\n \"Nearest Neighbors\": {\"n_neighbors\": [1, 2, 3, 5, 10, 20, 50, 75, 100, 150, 200, 250]},\r\n \"Linear SVM\": {\"C\": [0.1, 0.2, 0.3, 0.4, 0.5, 1, 1.5, 2],\r\n \"loss\":['hinge', 'squared_hinge']},\r\n \"RBF SVM\": {\"kernel\": [\"rbf\"],\r\n \"gamma\": np.logspace(-2, 0, 6).tolist() + \\\r\n np.logspace(0,1,5)[1:].tolist(),\r\n \"C\": np.logspace(-2, 2, 5).tolist()},\r\n \"Decision Tree\": {},\r\n \"Random Forest\": {\"max_depth\": np.round(np.logspace(np.log10(2), \\\r\n 1.2, 6)).astype(int).tolist()},\r\n \"Logistic Regression\": {\"C\": np.logspace(-2, 3, 6).tolist()},\r\n \"Naive Bayes\": {},\r\n \"LDA\": {},\r\n \"SGD_logL1\": {\"alpha\": np.logspace(-5, 2, 7)},\r\n \"SGD_log_elastic\": {\"alpha\": np.logspace(-5, 2, 6),\r\n \"l1_ratio\": 10**np.array([-2, -1, -.5, -.25,\r\n -.12, -.06, -.01])},\r\n \"SGD_SVM_elastic\": {\"alpha\": np.logspace(-5, 2, 6),\r\n \"l1_ratio\": 10**np.array([-2, -1, -.5, -.25,\r\n -.12, -.06, -.01])},\r\n \"CGC_log_L1\": {\"alpha\": np.logspace(-5, 2, 6)},\r\n \"SDCA_SVM_elastic\": {\"alpha\": np.logspace(-4, 4, 5),\r\n \"l1_ratio\": 10**np.array([-3,-2, -1, np.log10(.5),\r\n np.log10(.9)])}\r\n }\r\n out_classifiers = {cname: classifiers[cname] for cname in NAMES}\r\n out_params = {cname: params[cname] for cname in NAMES}\r\n logging.info(\"Using classifiers %r with params %r\" % (out_classifiers,\r\n out_params))\r\n return classifiers, params","def generate_products():\n # initialize list of noun and adj\n num_products = 30\n products = [0] * num_products\n prices = [0] * num_products\n weights = [0] * num_products\n flammabilities = [0] * num_products\n\n # initlize random word object\n random = RandomWords()\n\n adj = [random.get_random_word(includePartOfSpeech=\"adjective\")\n for product in products]\n noun = [random.get_random_word(includePartOfSpeech=\"noun\")\n for product in products]\n products = [noun + \" \" + adj for noun, adj in zip(adj, noun)]\n\n prices = [random.randint(5, 100) for price in prices]\n weights = [random.randint(5, 100) for weight in weights]\n flammabilities = [random.randint(0.0, 2.5)\n for flammability in flammabilities]\n\n return products, prices, weights, flammabilities","def test_malware_have_techniques(attck_fixture):\n for malware in attck_fixture.enterprise.malwares:\n if malware.techniques:\n assert getattr(malware,'techniques')","def getAllDecisionRules(self):\n\n #check this shit lol?\n thetas = self.getAllTheta()\n human_actions = self.getAllHumanActions()\n return [list(zip(thetas, item)) for item in itertools.product(human_actions, repeat=len(thetas))]","def generateSupplies(self):\n typePart = ['wrench','resistor','bulb','mushroom','coin']\n chosenPart = []\n for i in range(3):\n randomPart = choice(typePart)\n chosenPart.append(randomPart)\n typePart.remove(randomPart)\n for part in chosenPart:\n amount = randint(1,3)\n self._supplies.append(Node(part,amount))","def construct_powerset(feature_list):\n\n # Sort for easily reproducible order\n feature_names = sorted(list(feature_list))\n\n feature_combinations = [list(x) for l in range(1, len(feature_names) +1) for x in combinations(feature_names, l)]\n\n return feature_combinations","def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\n\n examples = []\n total_answerable = 0\n\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\n guid = \"%s-%s-%s\" % (set_type, line['qid'], str(i))\n src = []\n has_pos = 0\n\n if passage_type == 'both' and set_type != 'train':\n\n # rank by bert score in val and test\n score_key = 'rank_score'\n # score_key = \"nr_score\"\n both_sources = line[\"passages\"][:2*cand_for_each_source] + line[\"tables\"][:2*cand_for_each_source]\n all_scores = np.array([x[score_key] for x in both_sources])\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\n\n for idx in sorted_index:\n pas = both_sources[idx]\n if idx < 2 * cand_for_each_source:\n pas_type = 'passage'\n title = pas[\"article_title\"]\n else:\n pas_type = 'table'\n title = \"table_\" + pas[\"uid\"].split('-split')[0]\n\n src.append(\"question: \" + line[\"question\"].strip() + f\" </s> {pas_type} title: \" + title + f\" </s> {pas_type} content: \" + pas[\"text\"] + \" </s>\")\n\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n else:\n\n if passage_type in ['textual', 'hybrid', 'both']:\n for pas in line[\"passages\"][:cand_for_each_source]:\n # 10 passages\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> passage title: \" + pas[\"article_title\"] + \" </s> passage content: \" + pas[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\"ZAPPAtite\\\". All songs written and performed ... </s>\n if isinstance(pas['judge'], dict):\n has_pos += int(pas['judge']['judge_contain_all'])\n else:\n has_pos += int(pas['judge'])\n\n if passage_type in ['tabular', 'hybrid', 'both']:\n for tab in line[\"tables\"][:cand_for_each_source]:\n # 10 tables\n src.append(\"question: \" + line[\"question\"].strip() + \" </s> table title: \" + \"table_\" + tab[\"uid\"].split('-split')[0] + \" </s> table content: \" + tab[\"text\"] + \" </s>\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\n if isinstance(tab['judge'], dict):\n has_pos += int(tab['judge']['judge_contain_all'])\n else:\n has_pos += int(tab['judge'])\n\n const = 2 if passage_type in ['hybrid', 'both'] else 1\n if len(src) != const * cand_for_each_source:\n logger.info(line)\n src = src + [\"\"] * (const * cand_for_each_source - len(src))\n\n if has_pos > 0:\n total_answerable += 1\n\n if enable_sql_supervision and 'true_sql' in line:\n tgt = \"sql: \" + line[\"true_sql\"] + \" </s>\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\"Butler CC (KS)\\\" </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'denotation' in line:\n tgt = \"answer: \" + str(line[\"denotation\"][0]) + \" </s>\"\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt)\n if 'answers' in line:\n tgt = \"answer: \" + str(line[\"answers\"][0]) + \" </s>\" # e.g. answer: no slogan on current series </s>\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\n if i % 1000 == 0:\n logger.info(src[0] + \" \" + tgt + \"\\n\")\n\n logger.info(f\"Total answerable in {set_type} split is {total_answerable / len(lines)}\")\n\n return examples","def learn(self):\n pass","def learn(self):\n pass","def get_tools_using_technique():\n global tools_using_technique\n\n if not tools_using_technique:\n tools_using_technique = rsh.tools_using_technique(get_srcs())\n \n return tools_using_technique","def generateAssociationRule(freqSet):","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=True,\n ))\n do_rotations = [True for _ in strategies]\n return strategies, do_rotations","def build_train_set(trajectories):\n\n observes = np.concatenate([t['observes'] for t in trajectories])\n actions = np.concatenate([t['actions'] for t in trajectories])\n disc_sum_rew = np.concatenate([t['disc_sum_rew'] for t in trajectories])\n advantages = np.concatenate([t['advantages'] for t in trajectories])\n # normalize advantages\n advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-6)\n learning_signals = np.zeros((len(trajectories), len(trajectories[0]['advantages'])))\n\n # TODO：check if it is something wrong for computing learning signals\n for i in range(0, learning_signals.shape[0]):\n for j in range(0, learning_signals.shape[1]):\n learning_signals[i][j] = trajectories[i]['advantages'][j]\n return observes, actions, advantages, learning_signals, disc_sum_rew","def classifiers():\n return [(\"LogisticRegression\", LogisticRegression(C=1.0,\n class_weight=None,\n dual=False,\n fit_intercept=True,\n intercept_scaling=1,\n penalty='l2',\n random_state=None,\n tol=0.0001)),\n\n (\"RandomForests\", RandomForestClassifier(n_jobs=-1,\n n_estimators = 15,\n max_features = 'sqrt')),\n (\"Gaussian NaiveBayes\", GaussianNB())] #,\n #(\"LinearSVM\", svm.LinearSVC())]","def makeRandomSet(weightedSet): \n\tnewtrainingSet = []\n\t\n\t#Make a list starting with zero and ending with MAX_INT\n\t#Where all of values inbetween are the probs of choosing this element\n\tprobs = [0]\n\tfor ex in weightedSet:\t\t\n\t\tprobs.append(ex.weight + probs[-1])\n\t\n\tprobs.append(_MAX_INT)\n\t\t\n\tfor i in range(len(weightedSet)): #Get N new smaples\n\t\trandNum = _random()\n\t\tfor j in range(len(probs) - 1 ):\t\n\t\t\tif randNum >= probs[j] and probs[j+1] > randNum : \n\t\t\t\tnewtrainingSet.append(boostExample(\n\t\t\t\t\tLabeledExample(weightedSet[j-1],label=weightedSet[j-1].label) ,weightedSet[j-1].weight) )\n\t\t\t\tbreak\n\t\t\n\t\n\treturn newtrainingSet","def main():\n\n args = get_args()\n random.seed(args.seed)\n wod = []\n\n for name, low, high in read_csv(args.file):\n reps = random.randint(low, high)\n if args.easy:\n reps = int(reps / 2)\n wod.append((name, reps))\n\n wod = random.sample(wod, k=args.num_exercises)\n print(tabulate(wod, headers=('Exercise', 'Reps')))","def test_generate_all_training(self):\n facade = ChatetteFacade.get_or_create()\n\n input_dir_path = \"tests/system-testing/inputs/generate-all/\"\n input_filenames = [\n \"simplest.chatette\", \"only-words.chatette\",\n \"words-and-groups.chatette\", \"alias.chatette\", \"include.chatette\",\n \"slot.chatette\", \"slotrolegroup.chatette\"\n ]\n for filename in input_filenames:\n file_path = os.path.join(input_dir_path, filename)\n facade.run(file_path)\n if not TestSystem.check_no_duplicates(facade.train_examples):\n pytest.fail(\n \"Some examples were generated several times \" +\n \"when dealing with file '\" + filename + \"'.\\nGenerated: \" + \\\n str(facade.train_examples)\n )\n legal_examples = TestSystem.get_legal_examples(file_path)\n for ex in facade.train_examples:\n formatted_ex = {\"intent\": ex.intent_name, \"text\": ex.text}\n if formatted_ex not in legal_examples:\n pytest.fail(\n str(formatted_ex) + \" is not a legal example for '\" + \\\n file_path + \"'\"\n )\n if len(legal_examples) != len(facade.train_examples):\n training_texts = [ex.text for ex in facade.train_examples]\n for legal_ex in legal_examples:\n if legal_ex[\"text\"] not in training_texts:\n pytest.fail(\n \"Example '\" + legal_ex[\"text\"] + \\\n \"' was not generated.\"\n )\n pytest.fail(\n \"An unknown example was not generated (\" + \\\n str(len(facade.train_examples)) + \\\n \" generated instead of \" + str(len(legal_examples)) + \\\n \").\\nGenerated: \" + str(facade.train_examples)\n )\n legal_syn = TestSystem.get_legal_synonyms(file_path)\n if legal_syn is not None:\n synonyms = AST.get_or_create().get_entities_synonyms()\n for key in synonyms:\n if key not in legal_syn:\n pytest.fail(\n \"'\" + key + \"' shouldn't have any synonyms.\"\n )\n for syn in synonyms[key]:\n if syn not in legal_syn[key]:\n pytest.fail(\n \"'\" + syn + \"' shouldn't be a synonym of '\" + \\\n key + \"'\"\n )","def generate_folds(self) -> list[dict]:\n pass","def do_training(self):\n json_data = request.data\n global g_list_of_classifier\n\n datas = json.loads(json_data.decode('UTF-8')) #datas = liste\n\n for ite_clf in g_list_of_classifier:\n for data in datas:\n ite_clf.add_data(data['score'], data['answer'])\n print(ite_clf.get_info())\n return ''","def classifier_set(tuning=False):\n\tif tuning==False:\n\t\tclassifiers = [\n\t\t\tKNeighborsClassifier(50),\n\t\t\tSVC(kernel=\"linear\", C=0.025, probability=True),\n\t\t\tSVC(gamma=1, C=1, probability=True),\n\t\t\tGaussianProcessClassifier(1.0 * RBF(1.0)),\n\t\t\tDecisionTreeClassifier(criterion= 'entropy', min_samples_leaf= 30, min_samples_split= 10, splitter= 'random'),\n\t\t\tRandomForestClassifier(n_estimators=50, min_samples_leaf=30, min_samples_split=2),\n\t\t\tMLPClassifier(early_stopping=True, hidden_layer_sizes=100,learning_rate_init=0.1),\n\t\t\tAdaBoostClassifier(n_estimators= 50),\n\t\t\tGaussianNB(),\n\t\t\tLogisticRegression()\n\t\t\t]\n\t\tnames = [\"KNN\",\n\t\t\t \t\"L SVM\",\n\t\t\t \t\"RBF SVM\", \n\t\t\t \t\"GP\",\n\t\t\t\t\"DT\",\n\t\t\t\t\"RF\",\n\t\t\t\t\"NN\", \n\t\t\t\t\"AB\",\n\t\t\t\t\"NB\",\n\t\t\t\t\"LR\"\n\t\t\t\t]\n\treturn classifiers, names","def _create_examples(self, data, set_type):\n examples = []\n for (i, elem) in enumerate(data):\n guid = \"%s-%s\" % (set_type, i)\n text = elem[0]\n label = elem[1]\n examples.append(\n InputExample(guid=guid, text=text, label=label))\n return examples","def create_new_features(self):\n train = self.train\n \n train['is_context'] = train['context_type'].isin(CONTEXT_TYPE_TEST)\n train['is_context_flow'] = train['listen_type'] * train['is_context']\n \n train['is_listened_context'] = train['is_listened'] * train['is_context']\n train['is_listened_flow'] = train['is_listened'] * train['listen_type']\n train['is_listened_context_flow'] = train['is_listened'] * train['is_context_flow']\n \n for feature in self.categorize_features:\n gby_feat = train.groupby(feature)\n new_features(train, gby_feat, feature, feature in self.listen_type_features, self.context_features, self.flow_features, self.fillna)\n \n # Variable combinations\n for feat1 in self.combo_features1:\n for feat2 in self.combo_features2:\n gby_feat = train.groupby([feat1, feat2])\n name = feat1 + '_' + feat2\n new_features(train, gby_feat, name, feat1 in self.listen_type_features, self.context_features, self.flow_features, self.fillna)","def generate_models(x, y, degs):\n import numpy as np\n rslt = []\n for deg in degs:\n coefs = np.polyfit(x, y, deg)\n rslt.append(coefs)\n return rslt","def generate(dictalg):\n\n # dsList, sortedAlgs, dictAlg = processInputArgs(args, verbose=verbose)\n res = {}\n for f, i in pproc.dictAlgByFun(dictalg).iteritems():\n for d, j in pproc.dictAlgByDim(i).iteritems():\n tmp = BestAlgSet(j)\n res[(d, f)] = tmp\n return res","def _create_examples(self, lines, set_type):\n examples = []\n \n for (i, line) in enumerate(lines):\n sentence_number = 0\n premise_text = line[\"premise\"]\n modified_premise_text = re.sub(self.stage_name_pattern,\"\",premise_text)\n modified_premise_text = re.sub(self.w_patterns,\"\",modified_premise_text)\n hypothesis_text = line[\"hypothesis\"]\n hypothesis_text = re.sub(self.w_patterns,\"\",hypothesis_text)\n a_label = int(line[\"label\"])\n\n sentences = modified_premise_text.split('.')\n\n for j, sentence in enumerate(sentences):\n guid = \"\" + str(sentence_number) + \"\\t\" + str(i) + \"\\t\" + str(len(sentences)) + \"\\t\" + str(a_label)\n text_a = sentence\n text_b = hypothesis_text\n label = a_label\n sentence_number += 1\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n #print(\"16th sentence::\",sentences[16])\n\n return examples","def get_skills(lang, spec=None):\n\n if lang.lower() == 'java':\n output = get_java(spec)\n if spec is not None:\n print(f\"Our suggestions for {spec} are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n else:\n print(\"Our suggestions for are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n elif lang.lower() == 'python':\n output = get_python(spec)\n if spec is not None:\n print(f\"Our suggestions for {spec} are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")\n else:\n print(\"Our suggestions for are: \", end='')\n print(*output[:-1], sep=', ', end='')\n print(f\" and {output[-1]}.\\n\")"],"string":"[\n \"def get_techniques(db):\\n\\n techniques = []\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n techniques.append(technique)\\n \\n return sorted(techniques)\",\n \"def techniques(self):\\n return self._get_child_page_of_type(LearningTechniquesPage)\",\n \"def add_techniques(\\n client: Act, matrice: AttckMatrice, output_format: Text = \\\"json\\\"\\n) -> List:\\n\\n notify = []\\n\\n for technique in matrice.techniques:\\n notify += handle_techniques(client, technique, None, output_format)\\n\\n for subtechnique in getattr(technique, \\\"subtechniques\\\", []):\\n # Pre Attack does not have sub techniques\\n notify += handle_techniques(client, subtechnique, technique, output_format)\\n\\n return notify\",\n \"def generate_technique_md(technique, domain, side_menu_data, tactic_list):\\n\\n attack_id = util.get_attack_id(technique)\\n\\n # Only add technique if the attack id was found\\n if attack_id:\\n\\n technique_dict = {}\\n\\n technique_dict['attack_id'] = attack_id\\n technique_dict['domain'] = domain.split(\\\"-\\\")[0]\\n technique_dict['menu'] = side_menu_data\\n technique_dict['name'] = technique.get('name')\\n\\n # Get capecs and mtcs\\n for ref in technique['external_references']:\\n if ref.get('source_name'):\\n if ref['source_name'] == \\\"capec\\\":\\n if technique_dict.get('capecs') is None:\\n technique_dict['capecs'] = []\\n capec_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['capecs'].append(capec_dict)\\n\\n if ref['source_name'] == \\\"NIST Mobile Threat Catalogue\\\":\\n if technique_dict.get('mtcs') is None:\\n technique_dict['mtcs'] = []\\n\\n mtcs_dict = {\\n 'id': ref['external_id'],\\n 'url': ref['url']\\n }\\n technique_dict['mtcs'].append(mtcs_dict)\\n\\n # Get technique external references \\n ext_ref = technique[\\\"external_references\\\"]\\n\\n # Get initial reference list\\n reference_list = []\\n # Decleared as an object to be able to pass by reference\\n next_reference_number = {}\\n next_reference_number['value'] = 1\\n reference_list = util.update_reference_list(reference_list, technique)\\n\\n # Get technique description\\n if technique.get(\\\"description\\\"):\\n citations_from_descr = util.get_citations_from_descr(technique['description'])\\n\\n technique_dict['descr'] = util.replace_html_chars(markdown.markdown(technique['description']))\\n technique_dict['descr'] = util.filter_urls(technique_dict['descr'])\\n technique_dict['descr'] = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, technique_dict['descr'])\\n \\n if 'x_mitre_deprecated' in technique:\\n technique_dict['deprecated'] = True\\n \\n # Get mitigation table\\n technique_dict['mitigation_table'] = get_mitigations_table_data(technique, reference_list, next_reference_number)\\n \\n # Get related techniques\\n technique_dict['rel_techniques_table'] = get_related_techniques_data(technique, tactic_list)\\n\\n # Get examples\\n technique_dict['examples_table'] = get_examples_table_data(technique, reference_list, next_reference_number)\\n\\n # Get technique version\\n if technique.get(\\\"x_mitre_version\\\"):\\n technique_dict['version'] = technique[\\\"x_mitre_version\\\"]\\n\\n # Get tactics of technique\\n if technique.get('kill_chain_phases'):\\n technique_dict['tactics'] = []\\n for elem in technique['kill_chain_phases']:\\n technique_dict['tactics'].append(elem['phase_name'].title().replace('-', ' '))\\n\\n # Get platforms that technique uses\\n if technique.get('x_mitre_platforms'):\\n technique_dict['platforms'] = \\\", \\\".join(technique['x_mitre_platforms'])\\n\\n # Get system requirements\\n if technique.get('x_mitre_system_requirements'):\\n technique_dict['sysreqs'] = \\\", \\\".join(technique['x_mitre_system_requirements'])\\n technique_dict['sysreqs'] = re.sub(\\\"\\\\.?\\\\\\\\n+\\\", \\\"; \\\", technique_dict['sysreqs'])\\n\\n # Get permissions required\\n if technique.get('x_mitre_permissions_required'):\\n technique_dict['perms'] = \\\", \\\".join(technique['x_mitre_permissions_required'])\\n\\n # Get effective permissions\\n if technique.get('x_mitre_effective_permissions'):\\n technique_dict['eff_perms'] = \\\", \\\".join(technique['x_mitre_effective_permissions'])\\n\\n # Get data sources\\n if technique.get('x_mitre_data_sources'):\\n technique_dict['data_sources'] = \\\", \\\".join(technique['x_mitre_data_sources'])\\n\\n # Get if technique supports remote\\n if technique.get('x_mitre_remote_support'):\\n if technique['x_mitre_remote_support']:\\n technique_dict['supports_remote'] = \\\" Yes\\\"\\n else:\\n technique_dict['supports_remote'] = \\\" No\\\"\\n\\n # Get network requirements\\n if technique.get('x_mitre_network_requirements'):\\n if technique['x_mitre_network_requirements']:\\n technique_dict['network_reqs'] = \\\" Yes\\\"\\n else:\\n technique_dict['network_reqs'] = \\\" No\\\"\\n\\n # Get list of impacts\\n if technique.get('x_mitre_impact_type'):\\n technique_dict['impact_type'] = \\\", \\\".join(technique['x_mitre_impact_type'])\\n\\n # Get list of defenses bypassed\\n if technique.get('x_mitre_defense_bypassed'):\\n technique_dict['def_bypass'] = \\\", \\\".join(technique['x_mitre_defense_bypassed'])\\n\\n # Get list of contributors \\n if technique.get('x_mitre_contributors'):\\n technique_dict['contributors'] = \\\"; \\\".join(technique['x_mitre_contributors'])\\n\\n # Get list of tactic types\\n if technique.get('x_mitre_tactic_type'):\\n technique_dict['tactic_type'] = \\\", \\\".join(technique['x_mitre_tactic_type'])\\n\\n # Get detection data\\n if technique.get('x_mitre_detection'):\\n technique_dict['detection'] = get_detection_string(technique['x_mitre_detection'], reference_list, next_reference_number)\\n\\n # Get if technique is detectable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses'):\\n technique_dict['detectable'] = technique.get('x_mitre_detectable_by_common_defenses')\\n\\n # Get explanation of detecatable by common defenses\\n if technique.get('x_mitre_detectable_by_common_defenses_explanation'):\\n technique_dict['detectable_exp'] = util.replace_html_chars(technique['x_mitre_detectable_by_common_defenses_explanation'])\\n\\n # Get diffulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary'):\\n technique_dict['diff_for_adv'] = technique['x_mitre_difficulty_for_adversary']\\n\\n # Get explanation of difficulty for adversaries\\n if technique.get('x_mitre_difficulty_for_adversary_explanation'):\\n technique_dict['diff_for_adv_exp'] = util.replace_html_chars(technique['x_mitre_difficulty_for_adversary_explanation']) \\n \\n # Add reference for bottom part of technique page\\n if reference_list:\\n technique_dict['bottom_ref'] = util.sort_reference_list(reference_list)\\n\\n subs = config.technique_md.substitute(technique_dict)\\n subs = subs + json.dumps(technique_dict)\\n\\n #Write out the markdown file\\n with open(os.path.join(config.techniques_markdown_path, technique_dict['attack_id'] +\\\".md\\\"), \\\"w\\\", encoding='utf8') as md_file:\\n md_file.write(subs)\",\n \"def do_make_(self):\\n global g_list_of_classifier\\n\\n for ite_clf in g_list_of_classifier:\\n ite_clf.learn()\\n return ''\",\n \"def get_techniques_used_by_groups():\\n global techniques_used_by_groups\\n\\n if not techniques_used_by_groups:\\n techniques_used_by_groups = rsh.techniques_used_by_groups(get_srcs())\\n\\n return techniques_used_by_groups\",\n \"def get_techniques_used_by_tools():\\n global techniques_used_by_tools\\n\\n if not techniques_used_by_tools:\\n techniques_used_by_tools = rsh.techniques_used_by_tools(get_srcs())\\n \\n return techniques_used_by_tools\",\n \"def get_related_techniques_data(technique, tactic_list):\\n \\n technique_data = []\\n\\n if config.related_techniques.get(technique['id']):\\n for rel_tech in config.related_techniques[technique['id']]:\\n\\n attack_id = util.get_attack_id(rel_tech['object'])\\n\\n if attack_id:\\n row = {}\\n row['technique_id'] = attack_id\\n\\n tactic = [x for x in tactic_list if x['x_mitre_shortname'] == rel_tech['object']['kill_chain_phases'][0]['phase_name']][0]\\n if tactic:\\n row['tactic_id'] = util.get_attack_id(tactic)\\n row['tactic_name'] = rel_tech['object']['kill_chain_phases'][0]['phase_name'].title().replace('-', ' ')\\n \\n row['technique_name'] = rel_tech['object']['name']\\n technique_data.append(row)\\n\\n if technique_data:\\n technique_data = sorted(technique_data, key=lambda k: k['tactic_name'].lower())\\n return technique_data\",\n \"def generate(softwaretype=\\\"software\\\"):\\n # import the STIX data from MITRE/CTI\\n stix = requests.get(\\\"https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json\\\").json()\\n ms = MemoryStore(stix_data=stix[\\\"objects\\\"])\\n # software includes malware and tool types so perform two queries and merge the results\\n software_filters = []\\n if softwaretype == \\\"malware\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'malware') ] )\\n if softwaretype == \\\"tool\\\" or softwaretype == \\\"software\\\":\\n software_filters.append( [ Filter('type', '=', 'tool') ] )\\n \\n software = list(chain.from_iterable(\\n ms.query(f) for f in software_filters\\n ))\\n\\n # build a list of techniques used by software\\n techniques_used = {} #attackID => using software names\\n for thesoftware in software:\\n # filter out revoked and deprecated software\\n if (\\\"x_mitre_deprecated\\\" in thesoftware and thesoftware[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in thesoftware and thesoftware[\\\"revoked\\\"]): continue\\n for relationship in ms.relationships(thesoftware[\\\"id\\\"]):\\n # skip all non-technique relationships\\n if \\\"attack-pattern\\\" not in relationship[\\\"target_ref\\\"]: continue\\n technique = ms.get(relationship[\\\"target_ref\\\"])\\n # filter out deprecated and revoked techniques\\n if (\\\"x_mitre_deprecated\\\" in technique and technique[\\\"x_mitre_deprecated\\\"]) or (\\\"revoked\\\" in technique and technique[\\\"revoked\\\"]): continue\\n techniqueID = technique[\\\"external_references\\\"][0][\\\"external_id\\\"]\\n # store usage in techniques_used struct\\n if techniqueID in techniques_used:\\n techniques_used[techniqueID].append(thesoftware[\\\"name\\\"])\\n else:\\n techniques_used[techniqueID] = [thesoftware[\\\"name\\\"]]\\n\\n # format the techniques for the output layer\\n techniques_list = []\\n highest_usage = 0\\n lowest_usage = 1\\n for techniqueID in techniques_used:\\n # determine the number of used techniques for the score\\n count = len(techniques_used[techniqueID])\\n highest_usage = max(highest_usage, count)\\n lowest_usage = min(lowest_usage, count)\\n # append technique struct to list of layer-formatted techniques\\n techniques_list.append({\\n \\\"techniqueID\\\": techniqueID,\\n \\\"comment\\\": \\\"executed by \\\" + \\\", \\\".join(techniques_used[techniqueID]),\\n \\\"score\\\": count,\\n })\\n # set up layer name and desc according to softwaretype\\n if softwaretype != \\\"software\\\": \\n plural = \\\"tools\\\" if softwaretype == \\\"tool\\\" else \\\"malware\\\"\\n layername = f\\\"Software ({softwaretype}) Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software of subtype {softwaretype}, where the score is the count of {plural} using the technique\\\"\\n else: \\n layername = \\\"Software Execution\\\"\\n layerdescription = f\\\"All techniques that can be executed by software, where the score is the count of software using the technique\\\"\\n\\n # construct and return the layer as a dict\\n return {\\n \\\"name\\\": layername,\\n \\\"description\\\": layerdescription,\\n \\\"versions\\\": {\\n \\\"layer\\\": \\\"4.1\\\",\\n \\\"navigator\\\": \\\"4.1\\\"\\n },\\n \\\"domain\\\": \\\"enterprise-attack\\\",\\n \\\"techniques\\\": techniques_list,\\n \\\"sorting\\\": 3, # order in descending order of score (count)\\n \\\"gradient\\\": {\\n \\\"colors\\\": [\\n \\\"#fff7b3\\\", # low counts are yellow\\n \\\"#ff6666\\\", # high counts are red\\n ],\\n \\\"minValue\\\": lowest_usage,\\n \\\"maxValue\\\": highest_usage\\n },\\n }\",\n \"def get_technique_stats(db):\\n \\n techniques = {}\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n temp_dict = {technique: [{u'id': element['project_no'], \\n u'name': element['project_name'], u'description': element['short_description']}]}\\n techniques.update(temp_dict)\\n else:\\n temp_dict = {u'id': element['project_no'],\\n u'name': element['project_name'], u'description': element['short_description']}\\n techniques[technique].append(temp_dict)\\n techniques[technique] = sorted(techniques[technique], key=itemgetter('name'))\\n\\n return techniques\",\n \"def get_techniques_used_by_malware():\\n global techniques_used_by_malware\\n \\n if not techniques_used_by_malware:\\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\\n \\n return techniques_used_by_malware\",\n \"def get_examples_table_data(technique, reference_list, next_reference_number):\\n\\n # Creating map to avoid repeating the code 3 times\\n examples_map = [\\n { 'example_type': config.tools_using_technique }, \\n { 'example_type': config.malware_using_technique },\\n { 'example_type': config.groups_using_technique }\\n ]\\n\\n example_data = []\\n\\n # Get malware or tools used by group\\n for examples in examples_map:\\n if examples['example_type'].get(technique.get('id')):\\n for example in examples['example_type'][technique['id']]:\\n\\n attack_id = util.get_attack_id(example['object'])\\n\\n # Only add example data if the attack id is found \\n if attack_id:\\n row = {}\\n\\n row['id'] = attack_id\\n\\n if attack_id.startswith('S'):\\n row['path'] = \\\"software\\\"\\n else:\\n row['path'] = \\\"groups\\\"\\n\\n row['name'] = example['object']['name']\\n\\n if example['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, example)\\n\\n example_data.append(row)\\n \\n if example_data:\\n example_data = sorted(example_data, key=lambda k: k['name'].lower())\\n return example_data\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # Add are you a lucker strategies\\n strategies.extend(\\n generate_meta_strategy_pair(AreYouALuckerStrategy))\\n\\n # Add RPS Meta Fix strategies\\n strategies.extend(\\n generate_meta_strategy_pair(RPSMetaFixStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # Add are you a lucker strategies\\n strategies.extend(\\n generate_meta_strategy_pair(AreYouALuckerStrategy))\\n\\n # Add Greenberg strategies\\n strategies.extend(\\n generate_meta_strategy_pair(GreenbergStrategy))\\n\\n # Add RPS Meta Fix strategies\\n strategies.extend(\\n generate_meta_strategy_pair(RPSMetaFixStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def get_wordlists():\\n\\n\\tCS = {'ACM', 'IEEE', 'Computer Science', 'Artificial Intelligence',\\n\\t\\t'Pattern Recognition', 'Computer Vision', 'Machine Learning',\\n\\t\\t'Signal Processing', 'Electrical Engineering', 'Image Processing',\\n\\t\\t'Data Mining', 'Neural Networks', 'Computer Graphics', 'Graphics',\\n\\t\\t'Language Processing', 'Internet', 'Intelligent Systems',\\n\\t\\t'Robotic','Data','Software', 'Machine Vision', 'Image Analysis',\\n\\t\\t'Scientific Computing', 'SIAM', 'Malware','World Wide Web', \\n\\t\\t'Computational Intelligence', 'Computational Linguistics',\\n\\t\\t'Computational linguistics','Algorithm','Computer','ITiCSE',\\n\\t\\t'ITICSE','Machine learning','Learning','learning',\\n\\t\\t'Artificial intelligence','CIVR','Document Analysis'}\\n\\n\\tbio = {'Biology', 'Microbiology', 'Molecular', 'Medical', 'Biological',\\n\\t\\t'Cancer', 'Genome', 'Bioinformatics', 'Protein', 'Biocomputing',\\n\\t\\t'Biomedical', 'biology', 'Medicine', 'Biosystems', 'Virology',\\n\\t\\t'Brain', 'Psychology', 'Genetics', 'Bioengineering', 'Cell',\\n\\t\\t'Cardiology', 'Metabolic', 'Biotechnology', 'Pathogens',\\n\\t\\t'Pathology', 'Plant', 'PLANT', 'Virus', 'Drug','Medicinal',\\n\\t\\t'Neuro','Psych',\\n\\t\\t'Genomic','Diseases','Endocrinology', 'Epidemiology',\\n\\t\\t'Proteom','Biochem', 'DNA', 'Pharma', 'Biomedic', 'biomedica',\\n\\t\\t'Neurobiological'}\\n\\n\\tmath = {'Mathemati','Markov','Probability','Algebra','Network',\\n\\t\\t'Topology','Optimization', 'Geometr','Statistic','Algorithm',\\n\\t\\t'Graph ','Graphs','Combinatori','Riemann Surfaces','Permutation Groups',\\n\\t\\t'Functional Analysis', 'SIAM','Fixed Point','Wavelet','Statistics',\\n\\t\\t'Linear Regression','Fractal','geometry','Multivariate','Chaos',\\n\\t\\t'mathemati','Kernel'}\\n\\n\\tlinguistics = {}\\n\\n\\tcomputer_vision = {}\\n\\n\\tchemistry = {}\\n\\n\\tphysics = {}\\n\\n\\t# Rename \\\"Computer Vision\\\" to \\\"Image Processing\\\"?\\n\\ttopic_names = ['Computer Science','Biology','Mathematics','Chemistry',\\n\\t\\t'Physics','Computer Vision','Natural Language Processing']\\n\\ttopics = [CS, bio, math]#, linguistics, computer_vision, chemistry, physics]\\n\\n\\treturn {topic_names[i]:topics[i] for i in range(len(topics))}\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # Add are you a lucker strategies\\n strategies.extend(\\n generate_meta_strategy_pair(AreYouALuckerStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generateFeatures(self, data):\\n pass\",\n \"def generate_solutions(possible_words, labels):\\r\\n return []\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate_clips_kwds(base_path='../datasets/TIMIT/TRAIN/'):\\n if not os.path.exists(kw_path):\\n os.mkdir(kw_path)\\n else:\\n shutil.rmtree(kw_path)\\n os.mkdir(kw_path)\\n\\n non_kw_clips = []\\n kw_clips = {k: [] for k in keywords}\\n\\n for dialect in listdir(base_path):\\n print(\\\"Dialect:\\\", dialect)\\n\\n for speaker in listdir(base_path + dialect):\\n bp = base_path + dialect + '/' + speaker + '/'\\n\\n for file in listdir(bp):\\n if file.split('.')[-1] == 'WRD':\\n with open(bp + file, 'r') as f:\\n dat = f.readlines()\\n\\n # read the word level annotations and crop out the keywords\\n all_dat = [x.strip().split(' ') for x in dat]\\n words = [x[-1] for x in all_dat]\\n\\n # check if words in utterance and keyword have ONE and ONLY ONE keyword\\n inter = set(keywords).intersection(set(words))\\n if len(inter) == 1:\\n cur_keyword = inter.pop()\\n word_line = all_dat[words.index(cur_keyword)]\\n kw_clips[cur_keyword].append(\\n (bp + file, (int(word_line[0]) // downsample_rate, int(word_line[1]) // downsample_rate))\\n )\\n else:\\n non_kw_clips.append(bp + file)\\n\\n chosen_kw_clips = {k: [] for k in keywords}\\n kw_templates = {k: [] for k in keywords}\\n\\n for word, l in kw_clips.items():\\n # choose desired number fo templates and keyword utterances\\n idxs = np.random.choice(len(l), utter_per_kw + templates_per_kw)\\n for idx in idxs[:utter_per_kw]:\\n chosen_kw_clips[word].append(l[idx][0])\\n for idx in idxs[utter_per_kw:]:\\n kw_templates[word].append(l[idx])\\n\\n # Save keyword wav files\\n for word, l in kw_templates.items():\\n for idx in range(len(l)):\\n save_name = kw_path + word + '_' + str(idx) + '.wav'\\n start_t, stop_t = l[idx][1]\\n wav_path = l[idx][0][:-3] + 'wav'\\n (rate, sig) = wav.read(wav_path)\\n only_kw = sig[start_t:stop_t]\\n wav.write(save_name, rate, only_kw)\\n\\n np.random.shuffle(non_kw_clips)\\n print(\\n \\\"non-kw clips: {0} ; kw-clips: {1}\\\".format(len(non_kw_clips), sum([len(x) for x in chosen_kw_clips.values()])))\\n return non_kw_clips[:total_nonkw_utter], chosen_kw_clips\",\n \"def generate_models(clf_library):\\n clf_list = []\\n for i in clf_library:\\n param_dict = i['param_dict']\\n dict_list = [dict(izip_longest(param_dict, v)) for v in product(*param_dict.values())]\\n clf_list = clf_list+[i['clf'](**param_set) for param_set in dict_list]\\n return clf_list\",\n \"def create_training_examples(statement_list: List[List[str]], trees, training_spans,\\n training: bool, max_span_length, num_sentence_words,\\n args, pos_tags, constituents, k=1):\\n print('building features...')\\n\\n word_frequencies = FreqDist([w.lower() for w in reuters.words()])\\n # list of science tokens & science multiword expressions\\n science_tokens = get_science_terms(args.data_path)\\n science_expressions = get_science_terms(args.data_path, False)\\n stop_words = set(stopwords.words('english'))\\n\\n\\n\\n true_examples, false_examples, examples_per_sentence = [], [], []\\n span_indexes = []\\n\\n # loop over all statements\\n for i, statement in enumerate(statement_list):\\n print(i)\\n\\n span_index = [] #enumerating all spans for this sentence.\\n pos_tags_this_statement = pos_tag(statement)\\n sentence_word_frequencies = [word_frequencies.freq(token.lower()) \\\\\\n for token in statement]\\n tree = trees[i]\\n false_examples_this_instance = []\\n sentence_candidate_span_examples = [] # when training.\\n\\n # loop across different spans for given sentence\\n for span in legal_spans(num_sentence_words, max_span_length): #globally legal\\n if span[1] > len(statement):\\n continue\\n if span[0] > len(statement):\\n break\\n span_index.append(span)\\n\\n # extract scalar features for this span. [position will not be used]\\n f_bias = 1\\n f_length = span[1] - span[0]\\n #f_begin = span[0]\\n #f_end = span[1]\\n #f_dist_to_end0 = len(statement) - span[0]\\n #f_dist_to_end1 = len(statement) - span[1]\\n\\n # list of tokens of this span\\n span_tokens = statement[span[0]: span[1]]\\n\\n # feature: span contains at least one science token\\n f_science_token = bool( \\\\\\n set(span_tokens).intersection(science_expressions))\\n\\n\\n\\n f_science_token_count = 0 # counting # of science tokens in span\\n max_token_length = 0 # in this span.\\n for token in span_tokens:\\n f_science_token_count += int(token in science_tokens)\\n max_token_length = max(max_token_length, len(token))\\n\\n f_max_token_length = np.log(max_token_length)\\n\\n # feature: relative word frequency average\\n # with numerical stability/ avoiding -inf\\n f_avg_word_frequency = 1e-10+np.mean(sentence_word_frequencies[span[0]: span[1]])\\n f_avg_word_frequency = np.log(f_avg_word_frequency)\\n\\n # feature: begin with stop word?\\n f_stop_word_begin = bool(span_tokens[0] in stop_words)\\n\\n # POS indicator (one-hot)\\n f_pos = np.zeros([len(pos_tags)])\\n\\n # Bag-of-POS-tags for this span.\\n for token, tag in pos_tags_this_statement[span[0]:span[1]]:\\n f_pos[pos_tags.index(tag)] += 1.0\\n\\n # feature: POS indicator for span beginning\\n f_pos_beginning = np.zeros([len(pos_tags)])\\n f_pos_beginning[pos_tags.index(pos_tags_this_statement[span[0]][1])] = 1.0\\n\\n # feature: POS indicator for span end\\n f_pos_end = np.zeros([len(pos_tags)])\\n f_pos_end[pos_tags.index(pos_tags_this_statement[span[1]-1][1])] = 1.0\\n\\n # feature: POS bigram indicator\\n # define extended POS tag set with additional begin and end symbols for bigrams.\\n # pos_tags_bigram = pos_tags + [\\\"POS_BEGIN\\\", \\\"POS_END\\\"]\\n\\n # for POS bigrams.\\n # pos_tags_square = [x for x in product(pos_tags_bigram, pos_tags_bigram)]\\n\\n # f_pos_bigram = np.zeros([len(pos_tags_square)])\\n\\n # obtaining the POS bigram\\n # for position in range(-1, f_length):\\n # boundary cases: start of span and end of span.\\n # if position == -1:\\n # tag1 = 'POS_BEGIN'\\n # _, tag2 = pos_tags_this_statement[span[0]]\\n # elif position == f_length -1:\\n # _, tag1 = pos_tags_this_statement[span[0]+position]\\n # tag2 = 'POS_END'\\n # #normal case: inside span.\\n # else:\\n # _, tag1 = pos_tags_this_statement[span[0] + position]\\n # _, tag2 = pos_tags_this_statement[span[0] + position + 1]\\n #\\n # f_pos_bigram[pos_tags_square.index( ( tag1, tag2 ) )] += 1.0\\n\\n # constituent tree features\\n\\n tree_position = tree.treeposition_spanning_leaves(span[0], span[1])\\n\\n # smallest subtree in constituent parse, containing this span.\\n smallest_subtree = tree[tree_position[:-1]]\\n constituent_tag = smallest_subtree.label()\\n\\n # feature: is this span a constituent parse subtree span?\\n f_span_match = bool(span[1]-span[0] == len(smallest_subtree))\\n\\n # constituency parse label indicator\\n f_span_constituent = np.zeros([len(constituents)])\\n f_span_constituent[constituents.index(constituent_tag)] = 1.0\\n\\n # constituency parse label indicator with indication for large spans.\\n f_span_constituent_big = np.zeros([len(constituents)])\\n f_span_constituent_big[constituents.index(constituent_tag)] = (f_length > 2)\\n\\n\\n # leave out position features:\\n #### f_begin, f_end, f_dist_to_end0, f_dist_to_end1,\\n\\n #now collect all features:\\n f_scalars = np.array([f_bias, f_span_match, f_length,\\n f_science_token, f_avg_word_frequency,\\n f_stop_word_begin,\\n f_max_token_length,\\n f_science_token_count])\\n\\n # these are all features for this span, in a np array.\\n feature_vector = np.concatenate((f_scalars, f_pos, f_pos_beginning,\\n f_pos_end, f_span_constituent,\\n f_span_constituent_big))\\n\\n\\n\\n # provide True/False annotation in case the data is used for training.\\n if training:\\n if span == training_spans[i]:\\n #positive example\\n true_examples.append(feature_vector)\\n sentence_candidate_span_examples.append((feature_vector, True))\\n else:\\n #negative example\\n false_examples_this_instance.append(feature_vector)\\n sentence_candidate_span_examples.append((feature_vector, False))\\n else:\\n sentence_candidate_span_examples.append(feature_vector)\\n\\n span_indexes.append(span_index)\\n examples_per_sentence.append(sentence_candidate_span_examples)\\n\\n\\n # select at random k negative spans as training examples. default 1:1\\n if training:\\n for random_index in np.random.randint(0, len(false_examples_this_instance), k):\\n false_examples.append(false_examples_this_instance[random_index])\\n\\n print(len(true_examples), 'True span examples.')\\n print(len(false_examples), 'False span examples.')\\n\\n # collect true and false examples [inputs]\\n all_examples = np.concatenate((np.asarray(false_examples), np.asarray(true_examples)))\\n\\n # collect annotations for each example (True/False target outputs)\\n false_span_labels = np.zeros([len(false_examples)])\\n true_span_labels = np.ones([len(true_examples)])\\n all_labels = np.concatenate((false_span_labels, true_span_labels))\\n\\n return all_examples, all_labels, examples_per_sentence, span_indexes\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy4))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(\\n GeometryV4Strategy))\\n\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate_features(stances, dataset, name, feature_list, features_dir):\\n h, b, y, bodyId, headId = [], [], [], [], []\\n\\n feature_dict = {'overlap': word_overlap_features,\\n 'refuting': refuting_features,\\n 'polarity': polarity_features,\\n 'hand': hand_features,\\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\\n 'NMF_fit_all': NMF_fit_all,\\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\\n 'NMF_cos_50': NMF_cos_50,\\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\\n 'ppdb': ppdb,\\n 'stanford_ppdb': stanford_ppdb_score,\\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\\n 'stanford_sentiment': stanford_sentiment,\\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\\n 'stanford_wordsim': stanford_based_verb_noun_sim,\\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\\n 'stanford_negation': stanford_negation_features,\\n 'stanford_negation_1sent': stanford_negation_features_1sent,\\n 'stanford_negation_2sent': stanford_negation_features_2sent,\\n 'stanford_negation_3sent': stanford_negation_features_3sent,\\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\\n 'hedging': hedging_features,\\n 'sen2sen': sen2sen_similarity_max,\\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\\n 'wmdsenDoc': word_mover_distance_wholebody,\\n 'sdm_sim': sdm_sim,\\n 'discuss': discuss_features,\\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\\n 'lexical_features': lexical_features,\\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\\n 'mpqa_unigrams': mpqa_unigrams,\\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\\n 'nrc_emo_lex': nrc_emo_lex,\\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\\n #'POS_features': POS_features,\\n 'readability_features': readability_features,\\n 'sentiment140_unigrams': sentiment140_unigrams,\\n 'structural_features': structural_features,\\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\\n 'NMF_cos_300': NMF_cos_300\\n }\\n\\n stanceCounter = 0\\n for stance in stances:\\n y.append(LABELS.index(stance['Stance']))\\n h.append(stance['Headline'])\\n b.append(dataset.articles[stance['Body ID']])\\n bodyId.append(stance['Body ID'])\\n headId.append(name+str(stanceCounter))\\n stanceCounter += 1\\n\\n X_feat = []\\n feat_list = []\\n last_index = 0\\n for feature in feature_list:\\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\\\"/\\\"+feature+\\\".\\\"+name+'.npy', bodyId, feature, headId, fold=name)\\n feat_list.append((last_index, last_index+len(feat[0]), str(feature)))\\n last_index += len(feat[0])\\n X_feat.append(feat)\\n X = np.concatenate(X_feat, axis=1)\\n\\n return X, y, feat_list\",\n \"def algorithms():\\n algorith_paradigms = ['Divide-and-conquer', 'Backtrackig', 'Greedy-Algorithms', 'Dynamic-programming']\\n return algorith_paradigms\",\n \"def generate_non_bleeding_features(fold_stances, hold_out_stances, no_folds, BOW_feature_list, features_dir, d):\\n\\n # holds all bag of words features and their feature extraction methods\\n non_bleeding_feature_dict = {}\\n\\n def generate_holdout_BOW_features():\\n for fold in range(no_folds):\\n ids = list(range(no_folds))\\n merged = []\\n for i in ids:\\n merged.extend(fold_stances[i])\\n\\n h = []\\n b = []\\n for stance in merged:\\n h.append(stance['Headline'])\\n b.append(d.articles[stance['Body ID']])\\n\\n h_test = []\\n b_test = []\\n for stance in hold_out_stances:\\n h_test.append(stance['Headline'])\\n b_test.append(d.articles[stance['Body ID']])\\n\\n gen_non_bleeding_feats(non_bleeding_feature_dict[feature], h, b, h_test, b_test,\\n features_dir, feature, 'holdout')\\n\\n def generate_fold_BOW_features():\\n for fold in range(no_folds):\\n ids = list(range(no_folds))\\n del ids[fold]\\n\\n merged = []\\n for i in ids:\\n merged.extend(fold_stances[i])\\n\\n # 9 of 10 folds merged for training BOW features\\n h = []\\n b = []\\n for stance in merged:\\n h.append(stance['Headline'])\\n b.append(d.articles[stance['Body ID']])\\n\\n # 1 fold (test) to extract features out of the generated BOW\\n h_test = []\\n b_test = []\\n for stance in fold_stances[fold]:\\n h_test.append(stance['Headline'])\\n b_test.append(d.articles[stance['Body ID']])\\n\\n gen_non_bleeding_feats(non_bleeding_feature_dict[feature],\\n h, b, h_test, b_test, features_dir, feature,\\n fold)\\n\\n for feature in BOW_feature_list:\\n generate_fold_BOW_features()\\n generate_holdout_BOW_features()\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generateISTemplates(course, wordlist, charLevels, translateDict, similarCharsList, supportsUppercase):\\n\\n levelsDict= {}\\n stageByNumber = 1 # represents key of wordsByLevelDict to get wordlist value\\n wordsByLengthDict = {}\\n wordsByLevelDict = {}\\n swappedTranslateDict = dict((v,k) for k,v in translateDict.iteritems()) # Keys are latin, values non-latin\\n\\n for word in wordlist:\\n wordsByLengthDict = wordsByLength(word, wordsByLengthDict)\\n wordsByLevelDict = wordsByLevel(word, charLevels, wordsByLevelDict)\\n\\n for stage in charLevels:\\n\\n if not wordsByLevelDict.has_key(stageByNumber): # Skip the uppercase stages in charLevels\\n stageByNumber += 1\\n continue\\n\\n charLevelsWordList = wordsByLevelDict[stageByNumber] # contains list of words suitable for current stage\\n selectedOptions = makeWordOptions(charLevelsWordList, similarCharsList) # List of 3 sublists containing options or None\\n\\n if selectedOptions is not None:\\n\\n for i in range(len(selectedOptions)):\\n generateLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\\\".qdef\\\")\\n generateLevelAlternative(selectedOptions[i], translateDict, similarCharsList, name+str(stageByNumber)+\\\".qdef\\\")\\n\\n if supportsUppercase:\\n generateUppercaseLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\\\".qdef\\\")\\n\\n generateEndOfQuiz(name+str(stageByNumber)+\\\".qdef\\\")\\n stageByNumber += 1\\n\\n # generate practice levels\\n for i in range(30):\\n selectedWords = wordsByLengthDict[random.choice(wordsByLengthDict.keys())]\\n if len(selectedWords) >= 3:\\n quizRandomSelection = random.sample(selectedWords,3) # take random 4 items from list\\n\\n for word in quizRandomSelection:\\n trickWord = findAndReplaceSimilarChar(word, quizRandomSelection, similarCharsList)\\n if trickWord:\\n quizRandomSelection.append(trickWord)\\n break\\n\\n generateLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\\\"Practice.qdef\\\")\\n generateLevelAlternative(quizRandomSelection, translateDict, similarCharsList, name+\\\"Practice.qdef\\\")\\n\\n if supportsUppercase:\\n generateUppercaseLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\\\"Practice.qdef\\\")\\n\\n generateEndOfQuiz(name+\\\"Practice.qdef\\\")\\n\\n print \\\"New MUNI ROPOT templates has been created in: \\\" + target\",\n \"def get_technique_side_menu_data(domain, technique_list, tactic_list):\\n\\n # Get tactics techniques side menu data to fill out with jinja\\n tactics_techniques_menu_data = []\\n\\n for tactic in tactic_list:\\n tactic_row = {}\\n \\n tactic_row['name'] = tactic['name']\\n tactic_row['id'] = util.get_attack_id(tactic)\\n tactic_row['path'] = \\\"/tactics/{}\\\".format(util.get_attack_id(tactic))\\n \\n tactic_row['children'] = []\\n for technique in technique_list[tactic['x_mitre_shortname']]:\\n technique_row = {}\\n # Get technique id and name for each technique\\n technique_row['name'] = technique['name']\\n technique_row['id'] = technique['id']\\n technique_row['path'] = \\\"/techniques/{}/\\\".format(technique['id'])\\n technique_row['children'] = []\\n # Add technique data to tactic\\n tactic_row['children'].append(technique_row)\\n \\n # Add tactic to table\\n tactics_techniques_menu_data.append(tactic_row)\\n \\n return {\\n \\\"name\\\": \\\"techniques\\\",\\n \\\"id\\\": \\\"techniques\\\",\\n \\\"path\\\": \\\"/techniques/{}/\\\".format(domain.split(\\\"-\\\")[0]), # root level doesn't get a path\\n \\\"children\\\": tactics_techniques_menu_data\\n }\",\n \"def get_mitigation_mitigates_techniques():\\n global mitigation_mitigates_techniques\\n\\n if not mitigation_mitigates_techniques:\\n mitigation_mitigates_techniques = rsh.mitigation_mitigates_techniques(get_srcs())\\n\\n return mitigation_mitigates_techniques\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n return strategies, do_rotations\",\n \"def generate():\\n strategies, do_rotations = [], []\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(\\n CentrifugalBumblepuppy16h,\\n mirroring=False))\\n do_rotations.extend([False])\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n do_rotations.extend([True, True])\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n do_rotations.extend([True, True])\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n do_rotations.extend([True, True])\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def setup_random_opinions_representatives():\\r\\n global CATEGORIES\\r\\n \\r\\n ideas_dic = {}\\r\\n \\r\\n for i in CATEGORIES:\\r\\n #idea = Idea(1,\\\"\\\",i, 1-random.expovariate(6))\\r\\n if i == 1:\\r\\n idea = Idea(1,\\\"\\\",i, random.uniform(-1,-0.5))\\r\\n elif i == 2:\\r\\n idea = Idea(1,\\\"\\\",i, random.uniform(-1,-0.5))\\r\\n elif i == 3:\\r\\n idea = Idea(1,\\\"\\\",i, random.uniform(0.5,1))\\r\\n \\r\\n# idea = Idea(1,\\\"\\\",i, random.uniform(0.5,1))\\r\\n ideas_dic[i] = idea\\r\\n \\r\\n return ideas_dic\",\n \"def get_scenarios(experiments):\\n return {exp.scenario for exp in experiments}\",\n \"def generate_features_test(stances, dataset, name, feature_list, features_dir):\\n h, b, bodyId, headId = [], [], [], []\\n\\n feature_dict = {'overlap': word_overlap_features,\\n 'refuting': refuting_features,\\n 'polarity': polarity_features,\\n 'hand': hand_features,\\n 'word_unigrams_5000_concat_tf_l2_holdout_unlbled_test': word_unigrams_5000_concat_tf_l2_holdout_unlbled_test,\\n 'NMF_cos_300_holdout_unlbled_test': NMF_cos_300_holdout_unlbled_test,\\n 'NMF_concat_300_holdout_unlbled_test': NMF_concat_300_holdout_unlbled_test,\\n 'latent_dirichlet_allocation_25_holdout_unlbled_test': latent_dirichlet_allocation_25_holdout_unlbled_test,\\n 'latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test': latent_semantic_indexing_gensim_300_concat_holdout_unlbled_test,\\n 'NMF_fit_all_incl_holdout_and_test': NMF_fit_all_incl_holdout_and_test,\\n 'latent_dirichlet_allocation_incl_holdout_and_test': latent_dirichlet_allocation_incl_holdout_and_test,\\n 'latent_semantic_indexing_gensim_holdout_and_test': latent_semantic_indexing_gensim_holdout_and_test,\\n 'NMF_fit_all_concat_300_and_test': NMF_fit_all_concat_300_and_test,\\n 'word_ngrams_concat_tf5000_l2_w_holdout_and_test': word_ngrams_concat_tf5000_l2_w_holdout_and_test,\\n 'NMF_fit_all': NMF_fit_all,\\n 'word_ngrams_concat_tf5000_l2_w_holdout': word_ngrams_concat_tf5000_l2_w_holdout,\\n 'latent_dirichlet_allocation': latent_dirichlet_allocation,\\n 'latent_semantic_indexing_gensim_test': latent_semantic_indexing_gensim_test,\\n 'NMF_fit_all_concat_300': NMF_fit_all_concat_300,\\n 'NMF_cos_50': NMF_cos_50,\\n 'latent_dirichlet_allocation_25': latent_dirichlet_allocation_25,\\n 'latent_semantic_indexing_gensim_300_concat_holdout': latent_semantic_indexing_gensim_300_concat_holdout,\\n 'NMF_concat_300_holdout': NMF_concat_300_holdout,\\n 'word_unigrams_5000_concat_tf_l2_holdout': word_unigrams_5000_concat_tf_l2_holdout,\\n 'ppdb': ppdb,\\n 'stanford_ppdb': stanford_ppdb_score,\\n 'stanford_ppdb_1sent': stanford_ppdb_score_1sent,\\n 'stanford_ppdb_2sent': stanford_ppdb_score_2sent,\\n 'stanford_ppdb_3sent': stanford_ppdb_score_3sent,\\n 'stanford_sentiment': stanford_sentiment,\\n 'stanford_sentiment_1sent': stanford_sentiment_1sent,\\n 'stanford_sentiment_2sent': stanford_sentiment_2sent,\\n 'stanford_sentiment_3sent': stanford_sentiment_3sent,\\n 'stanford_wordsim': stanford_based_verb_noun_sim,\\n 'stanford_wordsim_1sent': stanford_based_verb_noun_sim_1sent,\\n 'stanford_wordsim_2sent': stanford_based_verb_noun_sim_2sent,\\n 'stanford_wordsim_3sent': stanford_based_verb_noun_sim_3sent,\\n 'stanford_negation': stanford_negation_features,\\n 'stanford_negation_1sent': stanford_negation_features_1sent,\\n 'stanford_negation_2sent': stanford_negation_features_2sent,\\n 'stanford_negation_3sent': stanford_negation_features_3sent,\\n 'stanford_avg_words_per_sent': stanford_avg_words_per_sent,\\n 'stanford_avg_words_per_sent_1sent': stanford_avg_words_per_sent_1sent,\\n 'stanford_avg_words_per_sent_2sent': stanford_avg_words_per_sent_2sent,\\n 'stanford_avg_words_per_sent_3sent': stanford_avg_words_per_sent_3sent,\\n 'hedging': hedging_features,\\n 'sen2sen': sen2sen_similarity_max,\\n 'wmdsenSen': word_mover_distance_similarity_sentence_min,\\n 'wmdsenDoc': word_mover_distance_wholebody,\\n 'sdm_sim': sdm_sim,\\n 'discuss': discuss_features,\\n 'single_flat_LSTM_50d_100': single_flat_LSTM_50d_100,\\n 'char_3grams_5000_concat_all_data': char_3grams_5000_concat_all_data,\\n 'lexical_features': lexical_features,\\n 'max_diff_twitter_uni_bigrams': max_diff_twitter_uni_bigrams,\\n 'mpqa_unigrams': mpqa_unigrams,\\n 'negated_context_word_12grams_concat_tf5000_l2_all_data': negated_context_word_12grams_concat_tf5000_l2_all_data,\\n 'nrc_emo_lex': nrc_emo_lex,\\n 'nrc_hashtag_sentiment_unigram': nrc_hashtag_sentiment_unigram,\\n 'nrc_hashtag_sentiment_unigram_POS': nrc_hashtag_sentiment_unigram_POS,\\n #'POS_features': POS_features,\\n 'readability_features': readability_features,\\n 'sentiment140_unigrams': sentiment140_unigrams,\\n 'structural_features': structural_features,\\n 'latent_dirichlet_allocation_300': latent_dirichlet_allocation_300,\\n 'NMF_cos_300': NMF_cos_300\\n }\\n\\n stanceCounter = 0\\n for stance in stances:\\n h.append(stance['Headline'])\\n b.append(dataset.articles[stance['Body ID']])\\n bodyId.append(stance['Body ID'])\\n headId.append(name+str(stanceCounter))\\n stanceCounter += 1\\n\\n X_feat = []\\n for feature in feature_list:\\n print(\\\"calculate feature: \\\" + str(feature))\\n feat = gen_or_load_feats(feature_dict[feature], h, b, features_dir+\\\"/\\\"+feature+\\\"_test.\\\"+name+'.npy', bodyId, feature, headId, fold=name)\\n X_feat.append(feat)\\n print(len(feat))\\n X = np.concatenate(X_feat, axis=1)\\n return X\",\n \"def generate_features(paraphrases, pos2index, prep2index, model, wv, word2index, UNK):\\n features = []\\n for (w1, w2), curr_paraphrases in tqdm.tqdm(paraphrases.items()):\\n features.append([extract_paraphrase_features(w1, w2, paraphrase, pos2index, prep2index,\\n model, wv, word2index, UNK)\\n for paraphrase in curr_paraphrases.keys()])\\n\\n scores = [list(curr_paraphrases.values()) for curr_paraphrases in paraphrases.values()]\\n return features, scores\",\n \"def variations():\",\n \"def lookup_stress_patterns_for_word(word: Text) -> Sequence[Sequence[Stress]]:\\n return [\\n word.pf.stress_pattern for word in EnglishUtils.all_possible_forms_for(word)\\n ]\",\n \"def get_groups_using_technique():\\n global groups_using_technique\\n\\n if not groups_using_technique:\\n groups_using_technique = rsh.groups_using_technique(get_srcs())\\n \\n return groups_using_technique\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add memory patterns v7\\n strategies.extend(\\n generate_meta_strategy_pair(MemoryPatternsV7Strategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def get_subtechniques_of():\\n global subtechniques_of\\n\\n if not subtechniques_of:\\n subtechniques_of = rsh.subtechniques_of(get_srcs())\\n \\n return subtechniques_of\",\n \"def get_all_techniques(src, source_name, tactic=None):\\n filters = [\\n Filter(\\\"type\\\", \\\"=\\\", \\\"attack-pattern\\\"),\\n Filter(\\\"external_references.source_name\\\", \\\"=\\\", source_name),\\n ]\\n if tactic:\\n filters.append(Filter('kill_chain_phases.phase_name', '=', tactic))\\n\\n results = src.query(filters)\\n return remove_deprecated(results)\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(MemoryPatternsV7Strategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def make_raw_skill_sets(skills):\\n\\n possible_sizes_in_list_of_things = range(4)\\n basic_skills = [list(combinations_of_a_certain_size)\\n for each_possible_size_of_combination in possible_sizes_in_list_of_things\\n for combinations_of_a_certain_size in itertools.combinations(skills,\\n each_possible_size_of_combination)]\\n\\n return basic_skills\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n\\n strategies.extend([\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False)[0]])\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def sample_handling(sample, lexicon, classification):\\n\\n # We have a list of lists [.... [ [0, 2, 1, 0, 0, ..., 0] [1] ] , .... ] with the bag of words and the class\\n featureset = []\\n\\n # Open the sample text and parse through the document and generate feastures.\\n with open(sample, 'r') as f:\\n contents = f.readlines()\\n for l in contents[:hm_lines]:\\n current_words = word_tokenize(l.lower())\\n current_words = [lemmatizer.lemmatize(i) for i in current_words]\\n features = np.zeros(len(lexicon))\\n for word in current_words:\\n if word.lower() in lexicon:\\n index_value = lexicon.index(word.lower())\\n features[index_value] = 1\\n features = list(features)\\n featureset.append([features, classification])\\n\\n return featureset\",\n \"def generate_advice(it, transforms=lambda x: (x, 1)):\\n if type(transforms) is not list:\\n transforms = [transforms]\\n advice = [LookupTableAdvice() for i in range(len(transforms))]\\n for example in it:\\n for i, tr in enumerate(transforms):\\n key, value = tr(example)\\n advice[i].process(key, value)\\n return advice\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def build_data_cv(train_set, cuisine_classes, frequent_ingredients, cv=10, shuffle=1):\\n data_cv = []\\n # print \\\"Number of shuffle %d\\\" % shuffle\\n\\n for item in train_set:\\n for i in range(shuffle + 1):\\n ingredients = []\\n random.shuffle(item[\\\"ingredients\\\"])\\n for ingredient in item[\\\"ingredients\\\"]:\\n if ingredient.replace(\\\" \\\", \\\"_\\\") in frequent_ingredients:\\n ingredients.append(ingredient.replace(\\\" \\\", \\\"_\\\"))\\n datum = {\\\"y\\\": cuisine_classes.index(item[\\\"cuisine\\\"]),\\n \\\"recipe\\\": \\\" \\\".join(ingredients),\\n \\\"num_ingredients\\\": len(ingredients),\\n \\\"split\\\": np.random.randint(0, cv)}\\n data_cv.append(datum)\\n\\n return data_cv\",\n \"def get_mitigations_table_data(technique, reference_list, next_reference_number):\\n\\n mitigation_data = []\\n\\n # Check if technique has mitigations\\n if config.technique_mitigated.get(technique['id']):\\n # Iterate through technique mitigations\\n for mitigation in config.technique_mitigated[technique['id']]:\\n\\n # Do not add deprecated mitigation to table\\n if 'x_mitre_deprecated' not in mitigation['object']:\\n\\n attack_id = util.get_attack_id(mitigation['object'])\\n\\n # Only add if mitigation attack id is found \\n if attack_id:\\n\\n row = {}\\n row['mid'] = attack_id\\n row['name'] = mitigation['object']['name']\\n if mitigation['relationship'].get('description'):\\n # Get filtered description\\n row['descr'] = util.get_filtered_description(reference_list, next_reference_number, mitigation)\\n \\n mitigation_data.append(row)\\n \\n if mitigation_data:\\n mitigation_data = sorted(mitigation_data, key=lambda k: k['name'].lower())\\n return mitigation_data\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def _create_examples(self, lines, set_type):\\n examples = []\\n for (i, line) in enumerate(tqdm(lines)):\\n if i == 0:\\n continue\\n gold_label_id = self.label2id(line[0])\\n several_labels = [self.label2id(line[-5]), self.label2id(line[-4]), self.label2id(line[-3]),\\n self.label2id(line[-2]), self.label2id(line[-1])]\\n\\n pair_id = line[-6]\\n premise = line[5]\\n hypothesis = line[6]\\n premise_bp = line[1]\\n hypothesis_bp = line[2]\\n premise_p = line[3]\\n hypothesis_p = line[4]\\n # premise_length = len(premise)\\n # hypothesis_length = len(hypothesis)\\n guid = \\\"%s-%s\\\" % (set_type, i)\\n\\n ex = dict()\\n for k in self.data_keys:\\n ex[k] = eval(k)\\n examples.append(ex)\\n logger.info(\\\" {} examples\\\".format(len(examples)))\\n return examples\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def get_random_all_sklearn():\\n models = []\\n import sklearn.linear_model\\n import sklearn.svm\\n import sklearn.neighbors\\n import sklearn.gaussian_process\\n import sklearn.naive_bayes\\n import sklearn.tree\\n import sklearn.ensemble\\n import sklearn.neural_network\\n import xgboost\\n\\n models.extend(get_all_sklearn_in_module(sklearn.linear_model))\\n # http://scikit-learn.org/stable/modules/lda_qda.html\\n models.extend([\\n 'sklearn.discriminant_analysis.LineadDiscriminantAnalysis',\\n 'sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis'\\n ])\\n # http://scikit-learn.org/stable/modules/kernel_ridge.html\\n models.extend(['sklearn.kernel_ridge.KernelRidge'])\\n models.extend(get_all_sklearn_in_module(sklearn.svm))\\n models.extend(get_all_sklearn_in_module(sklearn.neighbors))\\n models.extend(get_all_sklearn_in_module(sklearn.gaussian_process))\\n models.extend(get_all_sklearn_in_module(sklearn.naive_bayes))\\n models.extend(get_all_sklearn_in_module(sklearn.tree))\\n models.extend(get_all_sklearn_in_module(sklearn.ensemble))\\n models.extend(get_all_sklearn_in_module(sklearn.neural_network))\\n # add in xgboost\\n models.extend([\\n 'xgboost.sklearn.XGBClassifier', 'xgboost.sklearn.XGBRegressor'\\n ])\\n\\n transforms = []\\n import sklearn.preprocessing\\n import sklearn.feature_selection\\n import sklearn.feature_extraction\\n import sklearn.feature_extraction.text\\n import sklearn.decomposition\\n transforms.extend(get_all_sklearn_in_module(sklearn.preprocessing))\\n transforms.extend(get_all_sklearn_in_module(sklearn.feature_selection))\\n transforms.extend(get_all_sklearn_in_module(sklearn.feature_extraction))\\n transforms.extend(\\n get_all_sklearn_in_module(sklearn.feature_extraction.text)\\n )\\n transforms.extend(get_all_sklearn_in_module(sklearn.decomposition))\\n\\n transform_label_encoder = LabelEncoder().fit(transforms)\\n model_label_encoder = LabelEncoder().fit(models)\\n return RandomScorer(transform_label_encoder, model_label_encoder)\",\n \"def eval_all_combinations(docs, labels, punct_vals,\\n feature_fns, min_freqs):\\n# result = []\\n\\n# for punct_val in punct_vals:\\n# tokens = [tokenize(doc, punct_val) for doc in docs]\\n# for f in [comb for i in range(len(feature_fns)) for comb in combinations(feature_fns, i+1)]:\\n# feature = list(f)\\n\\n# for min_freq in min_freqs:\\n# clf = LogisticRegression()\\n# X, vocab = vectorize(tokens, feature, min_freq)\\n# accuracy = cross_validation_accuracy(clf, X, labels, 5)\\n# result.append(dict(punct= punct_val, features= feature, min_freq= min_freq, accuracy = accuracy))\\n\\n# return sorted(result, key=lambda x:(-x['accuracy'],-x['min_freq']))\\n clf = LogisticRegression()\\n result = []\\n output = []\\n for l in range(1, len(feature_fns)+1):\\n for c in combinations(feature_fns,l):\\n result.append(c)\\n\\n for p in punct_vals:\\n list_tok = [tokenize(d,p) for d in docs]\\n for fl in result:\\n for mf in min_freqs:\\n dict_output = {}\\n matrix,vocab = vectorize(list_tok, fl, mf)\\n average_value = cross_validation_accuracy(clf, matrix, labels, 5)\\n dict_output['features'] = fl\\n dict_output['punct'] = p\\n dict_output['accuracy'] = average_value\\n dict_output['min_freq'] = mf\\n output.append(dict_output)\\n\\n return sorted(output, key=lambda x: (-x['accuracy'], -x['min_freq']))\",\n \"def prepare_for_supervised(embeddings):\\n \\n X, y = [], []\\n for problem in embeddings:\\n pairs = [np.concatenate((problem['question'], problem[i])) \\n for i in ('choice_0', 'choice_1', 'choice_2', 'choice_3')]\\n correct_answer = problem['correct_answer']\\n \\n labels = [0, 0, 0, 0]\\n labels[correct_answer] = 1\\n X.extend(pairs)\\n y.extend(labels)\\n return X, y\",\n \"def _generate_evaluaters(self):\\n evaluators = []\\n for para_key in self.parameter[1]:\\n for value in self.parameter[1][para_key]:\\n evaluators.append(evaluaterSearch.evaluaterSearch(self.parameter[2], [para_key, value]))\\n self.evaluators = evaluators\",\n \"def _create_examples(self, data_dir, set_type):\\n\\t\\texamples = []\\n\\t\\tinput_file_data = os.path.join(data_dir, \\\"data.tsv\\\")\\n\\t\\twith open(input_file_data, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n\\t\\t\\tfor i, inp in enumerate(f):\\n\\t\\t\\t\\tinps = inp.split('\\\\t') \\n\\t\\t\\t\\tguid = \\\"%s-%s\\\" % (set_type, i)\\n\\t\\t\\t\\ttext_inp = inps[1].strip()\\n\\t\\t\\t\\ttext_out = inps[2].strip()\\n\\t\\t\\t\\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\\n\\t\\t\\t\\t\\n\\t\\t\\t# Sort these out before returning\\n\\t\\t\\texamples = sorted(examples, key=sort_inp_len)\\n\\t\\t\\treturn examples\",\n \"def get_technique_related_to_technique():\\n global technique_related_to_technique\\n\\n if not technique_related_to_technique:\\n technique_related_to_technique = rsh.technique_related_to_technique(get_srcs())\\n\\n return technique_related_to_technique\",\n \"def eval_all_combinations(docs, labels, punct_vals,\\n feature_fns, min_freqs):\\n ###TODO\\n \\n # gettting feature's 7 combinations\\n feature_comb = [] \\n i = 1\\n \\n while i <= len (feature_fns) :\\n comb = combinations(feature_fns,i)\\n i += 1 \\n \\n for c in comb:\\n feature_comb.append(c) \\n #print(c)\\n\\n #for option in feature_comb:\\n #print(option)\\n \\n # LogisticRegression object\\n \\n\\n keys = ['punct','features','min_freq','accuracy']\\n results = []\\n dicts = [] \\n feature_dict = {}\\n \\n # setting on punct,mi_freq and conmbination of feature\\n for punct in punct_vals: \\n tokens_list = [tokenize(d,punct) for d in docs]\\n \\n for freq in min_freqs : \\n\\n for features in feature_comb:\\n \\n #print('MinFreq = %d Punct = %s fetures = %s'%(freq,punct,features))\\n X, vocabulary = vectorize(tokens_list,features,freq)\\n clf = LogisticRegression()\\n avg_acc = cross_validation_accuracy(clf, X, labels, k=5)\\n #print('Avg accuracy = %f'%(avg_acc))\\n #print('vocab size =',len(vocabulary)) \\n \\n result = [punct,features,freq,avg_acc]\\n\\n feature_dict = dict(zip(keys, result))\\n\\n dicts.append(feature_dict)\\n \\n \\n # sort dict on accuracy \\n dicts.sort(key=lambda x:(-x['accuracy'],-x['min_freq']))\\n #print('dicts = ',dicts)\\n \\n return(dicts)\",\n \"def workbench_scenarios(): \\n return [\\n (\\\"TermsXBlock\\\",\\n \\\"\\\"\\\"<terms/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple TermsXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <terms/>\\n <terms/>\\n <terms/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n strategies.extend(\\n generate_meta_strategy_pair(\\n WrappedRFindStrategy,\\n limits=limits,\\n sources=sources,\\n shenanigans=False))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add memory pattern strategies\\n strategies.extend(\\n generate_meta_strategy_pair(MemoryPatternsV7Strategy))\\n\\n do_rotations = [True for _ in strategies]\\n return strategies, do_rotations\",\n \"def build_vocabulary(image_paths, vocab_size):\\n n_image = len(image_paths)\\n\\n # Since want to sample tens of thousands of SIFT descriptors from different images, we\\n # calculate the number of SIFT descriptors we need to sample from each image.\\n n_each = int(np.ceil(40000 / n_image)) # You can adjust 10000 if more is desired\\n\\n # Initialize an array of features, which will store the sampled descriptors\\n features = np.zeros((n_image * n_each, 128))\\n j=0\\n for i, path in enumerate(image_paths):\\n # Load SIFT features from path\\n descriptors = np.loadtxt(path, delimiter=',',dtype=float)\\n\\n # TODO: Randomly sample n_each features from descriptors, and store them in features\\n #use the randomizer in numpy library to make n_each random index\\n idx= np.array(np.random.randint(0,len(descriptors),n_each))\\n\\n # choose randomly n_each number of discriptor to train K-mean classifier\\n for k in idx:\\n\\n features[j] = descriptors[k,:]\\n j = j+1\\n # TODO: pefrom k-means clustering to cluster sampled SIFT features into vocab_size regions.\\n # You can use KMeans from sci-kit learn.\\n # Reference: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\\n\\n #use K_mean classifier to make Bag of visual words represantation for SIFT features\\n kmeans = KMeans(n_clusters=250).fit(features)\\n #kmeans= clustering = AgglomerativeClustering().fit(features)\\n\\n\\n return kmeans\",\n \"def featurize_1(list_of_demonstrations, kinematics, sr):\\n\\tprint \\\"FEATURIZATION 1\\\"\\n\\n\\tdata_X_1 = {}\\n\\tdata_X_2 = {}\\n\\tfor demonstration in list_of_demonstrations:\\n\\t\\tprint \\\"SIFT for \\\", demonstration\\n\\t\\tstart, end = parser.get_start_end_annotations(constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER\\n\\t\\t\\t\\t\\t\\t+ demonstration + \\\"_\\\" + constants.CAMERA +\\\".p\\\")\\n\\n\\t\\tW = kinematics[demonstration]\\n\\t\\tW_sampled = utils.sample_matrix(W, sampling_rate = sr)\\n\\n\\n\\t\\tPATH_TO_SIFT = constants.PATH_TO_DATA + \\\"sift_FCED/SIFT_\\\"+ demonstration\\n\\t\\tZ = pickle.load(open(PATH_TO_SIFT + \\\"_1.p\\\", \\\"rb\\\"))\\n\\t\\tZ = Z[start:end + 1]\\n\\t\\tZ_sampled_1 = utils.sample_matrix(Z, sampling_rate = sr)\\n\\n\\t\\tZ = pickle.load(open(PATH_TO_SIFT + \\\"_2.p\\\", \\\"rb\\\"))\\n\\t\\tZ = Z[start:end + 1]\\n\\t\\tZ_sampled_2 = utils.sample_matrix(Z, sampling_rate = sr)\\n\\n\\t\\tassert Z_sampled_1.shape[0] == W_sampled.shape[0]\\n\\t\\tassert Z_sampled_2.shape[0] == W_sampled.shape[0]\\n\\n\\t\\tdata_X_1[demonstration] = np.concatenate((W_sampled, Z_sampled_1), axis = 1)\\n\\t\\tdata_X_2[demonstration] = np.concatenate((W_sampled, Z_sampled_2), axis = 1)\\n\\n\\tpickle.dump(data_X_1, open(PATH_TO_FEATURES + \\\"SIFT_1.p\\\", \\\"wb\\\"))\\n\\tpickle.dump(data_X_2, open(PATH_TO_FEATURES + \\\"SIFT_2.p\\\", \\\"wb\\\"))\",\n \"def get_bests(self):\\n set_names = [\\\"training\\\", \\\"hp_selection\\\", \\\"validation\\\"]\\n run_tec_conf_set = recursivedict()\\n validation = self._campaign_configuration['General']['validation']\\n hp_selection = self._campaign_configuration['General']['hp_selection']\\n if (validation, hp_selection) in {(\\\"All\\\", \\\"All\\\"), (\\\"Extrapolation\\\", \\\"All\\\"), (\\\"All\\\", \\\"HoldOut\\\"), (\\\"HoldOut\\\", \\\"All\\\"), (\\\"HoldOut\\\", \\\"HoldOut\\\"), (\\\"Extrapolation\\\", \\\"HoldOut\\\")}:\\n # For each run, for each technique the best configuration\\n run_tec_best_conf = recursivedict()\\n\\n # Hyperparameter search\\n for conf in self._exp_confs:\\n run = int(conf.get_signature()[0].replace(\\\"run_\\\", \\\"\\\"))\\n technique = conf.technique\\n run_tec_conf_set[run][technique][str(conf.get_signature()[4:])] = conf.mapes\\n # First experiment for this technique or better than the current best\\n if technique not in run_tec_best_conf[run] or conf.mapes[\\\"hp_selection\\\"] < run_tec_best_conf[run][technique].mapes[\\\"hp_selection\\\"]:\\n run_tec_best_conf[run][technique] = conf\\n\\n # Print results for each run\\n for run in range(0, self._campaign_configuration['General']['run_num']):\\n self._logger.info(\\\"-->Printing results for run %s\\\", str(run))\\n overall_run_best = None\\n # Print data of single techniques\\n for technique in run_tec_best_conf[run]:\\n temp = run_tec_best_conf[run][technique]\\n self._logger.info(\\\"---Best result for %s - Configuration is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", technique, temp.get_signature()[4:], temp.mapes[\\\"training\\\"], temp.mapes[\\\"hp_selection\\\"], temp.mapes[\\\"validation\\\"])\\n\\n # Compute which is the best technique\\n if not overall_run_best or temp.mapes[\\\"hp_selection\\\"] < overall_run_best.mapes[\\\"hp_selection\\\"]:\\n overall_run_best = temp\\n best_model_description = overall_run_best.print_model()\\n self._logger.info(\\\"<--Overall best result is %s %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", overall_run_best.get_signature()[3:], \\\"(\\\" + best_model_description + \\\")\\\" if best_model_description else \\\"\\\", overall_run_best.mapes[\\\"training\\\"], overall_run_best.mapes[\\\"hp_selection\\\"], overall_run_best.mapes[\\\"validation\\\"])\\n\\n elif (validation, hp_selection) in {(\\\"KFold\\\", \\\"All\\\"), (\\\"KFold\\\", \\\"HoldOut\\\")}:\\n folds = float(self._campaign_configuration['General']['folds'])\\n # For each run, for each fold, for each technique, the best configuration\\n run_fold_tec_best_conf = recursivedict()\\n\\n # Hyperparameter search inside each fold\\n for conf in self._exp_confs:\\n run = int(conf.get_signature()[0].replace(\\\"run_\\\", \\\"\\\"))\\n fold = int(conf.get_signature()[1].replace(\\\"f\\\", \\\"\\\"))\\n technique = conf.technique\\n if \\\"hp_selection\\\" not in run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])]:\\n for set_name in set_names:\\n run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])][set_name] = 0\\n for set_name in set_names:\\n run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])][set_name] = run_tec_conf_set[run][technique][str(conf.get_signature_string()[4:])][set_name] + conf.mapes[set_name] / folds\\n # First experiment for this fold+technique or better than the current best\\n if technique not in run_fold_tec_best_conf[run][fold] or conf.mapes[\\\"hp_selection\\\"] < run_fold_tec_best_conf[run][fold][technique].mapes[\\\"hp_selection\\\"]:\\n run_fold_tec_best_conf[run][fold][technique] = conf\\n\\n # Aggregate different folds (only the value of the mapes)\\n run_tec_set = recursivedict()\\n for run in run_fold_tec_best_conf:\\n for fold in run_fold_tec_best_conf[run]:\\n for tec in run_fold_tec_best_conf[run][fold]:\\n if \\\"hp_selection\\\" not in run_tec_set[run][technique]:\\n for set_name in set_names:\\n run_tec_set[run][tec][set_name] = 0\\n for set_name in set_names:\\n run_tec_set[run][tec][set_name] = run_fold_tec_best_conf[run][fold][tec].mapes[set_name]\\n # Print results for each run\\n for run in range(0, self._campaign_configuration['General']['run_num']):\\n self._logger.info(\\\"Printing results for run %s\\\", str(run))\\n overall_run_best = ()\\n # Print data of single techniques\\n for technique in run_tec_set[run]:\\n self._logger.info(\\\"---Best result for %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", technique, run_tec_set[run][technique][\\\"training\\\"], run_tec_set[run][technique][\\\"hp_selection\\\"], run_tec_set[run][technique][\\\"validation\\\"])\\n\\n # Compute which is the best technique\\n if not overall_run_best or run_tec_set[run][technique][\\\"hp_selection\\\"] < overall_run_best[1][\\\"hp_selection\\\"]:\\n overall_run_best = (technique, run_tec_set[run][technique])\\n\\n self._logger.info(\\\"---Overall best result is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", overall_run_best[0], overall_run_best[1][\\\"training\\\"], overall_run_best[1][\\\"hp_selection\\\"], overall_run_best[1][\\\"validation\\\"])\\n\\n # Overall best will contain as first argument the technique with the best (across runs) average (across folds) mape on validation; now we consider on all the runs and on all the folds the configuraiton of this technique with best validation mape\\n\\n elif (validation, hp_selection) in {(\\\"All\\\", \\\"KFold\\\"), (\\\"HoldOut\\\", \\\"KFold\\\"), (\\\"Extrapolation\\\", \\\"KFold\\\")}:\\n folds = float(self._campaign_configuration['General']['folds'])\\n # For each run, for each technique, for each configuration, the aggregated mape\\n run_tec_conf_set = recursivedict()\\n\\n # Hyperparameter search aggregating over folders\\n for conf in self._exp_confs:\\n run = int(conf.get_signature()[0].replace(\\\"run_\\\", \\\"\\\"))\\n fold = int(conf.get_signature()[2].replace(\\\"f\\\", \\\"\\\"))\\n technique = conf.technique\\n configuration = str(conf.get_signature()[4:])\\n if \\\"hp_selection\\\" not in run_tec_conf_set[run][technique][configuration]:\\n for set_name in set_names:\\n run_tec_conf_set[run][technique][configuration][set_name] = 0\\n for set_name in set_names:\\n run_tec_conf_set[run][technique][configuration][set_name] = run_tec_conf_set[run][technique][configuration][set_name] + conf.mapes[set_name] / folds\\n\\n # Select the best configuration for each technique across different folders\\n run_tec_best_conf = recursivedict()\\n for run in run_tec_conf_set:\\n for tec in run_tec_conf_set[run]:\\n for conf in run_tec_conf_set[run][tec]:\\n if tec not in run_tec_best_conf[run] or run_tec_conf_set[run][tec][conf][\\\"hp_selection\\\"] < run_tec_best_conf[run][tec][1][\\\"hp_selection\\\"]:\\n run_tec_best_conf[run][tec] = (conf, run_tec_conf_set[run][tec][conf])\\n\\n # Print results for each run\\n for run in range(0, self._campaign_configuration['General']['run_num']):\\n self._logger.info(\\\"Printing results for run %s\\\", run)\\n overall_run_best = () # (technique, configuration, mapes)\\n # Print data of single techniques\\n for technique in run_tec_best_conf[run]:\\n temp = run_tec_best_conf[run][technique]\\n self._logger.info(\\\"---Best result for %s - Configuration is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", technique, temp[0], temp[1][\\\"training\\\"], temp[1][\\\"hp_selection\\\"], temp[1][\\\"validation\\\"])\\n\\n # Compute which is the best technique\\n if not overall_run_best or temp[1][\\\"hp_selection\\\"] < overall_run_best[2][\\\"hp_selection\\\"]:\\n overall_run_best = (technique, temp[0], temp[1])\\n\\n self._logger.info(\\\"---Overall best result is %s %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", overall_run_best[0], overall_run_best[1], overall_run_best[2][\\\"training\\\"], overall_run_best[2][\\\"hp_selection\\\"], overall_run_best[2][\\\"validation\\\"])\\n\\n elif (validation, hp_selection) in {(\\\"KFold\\\", \\\"KFold\\\")}:\\n folds = float(self._campaign_configuration['General']['folds'])\\n # For each run, for each external fold, for each technique, the aggregated mape\\n run_efold_tec_conf_set = recursivedict()\\n\\n # Hyperparameter search aggregating over internal folders\\n for conf in self._exp_confs:\\n run = int(conf.get_signature()[0].replace(\\\"run_\\\", \\\"\\\"))\\n ext_fold = int(conf.get_signature()[2].replace(\\\"f\\\", \\\"\\\"))\\n technique = conf.technique\\n configuration = str(conf.get_signature()[4:])\\n if \\\"hp_selection\\\" not in run_tec_conf_set[run][technique][configuration]:\\n for set_name in set_names:\\n run_tec_conf_set[run][technique][configuration][set_name] = 0\\n for set_name in set_names:\\n run_tec_conf_set[run][technique][configuration][set_name] = run_tec_conf_set[run][technique][configuration][set_name] + (conf.mapes[set_name] / (folds * folds))\\n if configuration not in run_efold_tec_conf_set[run][ext_fold][technique]:\\n for set_name in set_names:\\n run_efold_tec_conf_set[run][ext_fold][technique][configuration][set_name] = 0\\n for set_name in set_names:\\n run_efold_tec_conf_set[run][ext_fold][technique][configuration][set_name] = run_efold_tec_conf_set[run][ext_fold][technique][configuration][set_name] + (conf.mapes[set_name] / (folds * folds))\\n\\n # Select the best configuration for each technique in each external fold across different internal folders\\n run_efold_tec_best_conf = recursivedict()\\n for run in run_efold_tec_conf_set:\\n for efold in run_efold_tec_conf_set[run]:\\n for tec in run_efold_tec_conf_set[run][efold]:\\n for conf in run_efold_tec_conf_set[run][efold][tec]:\\n if conf not in run_efold_tec_best_conf[run][efold][tec] or run_efold_tec_conf_set[run][efold][tec][conf][\\\"hp_selection\\\"] < run_efold_tec_best_conf[run][efold][tec][1][\\\"hp_selection\\\"]:\\n run_efold_tec_best_conf[run][efold][tec] = (conf, run_efold_tec_conf_set[run][efold][tec][conf], run_efold_tec_conf_set[run][efold][tec][conf])\\n\\n # Aggregate on external folds\\n run_tec_set = recursivedict()\\n for run in run_efold_tec_best_conf:\\n for efold in run_efold_tec_best_conf[run]:\\n for tec in run_efold_tec_best_conf[run][efold]:\\n if \\\"hp_selection\\\" not in run_tec_set[run][tec]:\\n for set_name in set_names:\\n run_tec_set[run][tec][set_name] = 0\\n for set_name in set_names:\\n run_tec_set[run][tec][set_name] = run_tec_set[run][tec][set_name] + run_efold_tec_best_conf[run][efold][tec][1][set_name]\\n\\n # Print results for each run\\n for run in range(0, self._campaign_configuration['General']['run_num']):\\n self._logger.info(\\\"Printing results for run %s\\\", run)\\n overall_run_best = ()\\n # Print data of single techniques\\n for technique in run_tec_set[run]:\\n self._logger.info(\\\"---Best result for %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", technique, run_tec_set[run][technique][\\\"training\\\"], run_tec_set[run][technique][\\\"hp_selection\\\"], run_tec_set[run][technique][\\\"validation\\\"])\\n\\n # Compute which is the best technique\\n if not overall_run_best or run_tec_set[run][technique][\\\"hp_selection\\\"] < overall_run_best[1][\\\"hp_selection\\\"]:\\n overall_run_best = (technique, run_tec_set[run][technique])\\n\\n self._logger.info(\\\"---Overall best result is %s - (Training MAPE is %f - HP Selection MAPE is %f) - Validation MAPE is %f\\\", overall_run_best[0], overall_run_best[1][\\\"training\\\"], overall_run_best[1][\\\"hp_selection\\\"], overall_run_best[1][\\\"validation\\\"])\\n\\n else:\\n self._logger.error(\\\"Unexpected combination: %s\\\", str((validation, hp_selection)))\\n sys.exit(1)\\n best_confs = {}\\n best_technique = None\\n for conf in self._exp_confs:\\n technique = conf.technique\\n if technique not in best_confs or conf.mapes[\\\"validation\\\"] < best_confs[technique].mapes[\\\"validation\\\"]:\\n best_confs[technique] = conf\\n for technique in best_confs:\\n if not best_technique or best_confs[technique].mapes[\\\"validation\\\"] < best_confs[best_technique].mapes[\\\"validation\\\"]:\\n best_technique = technique\\n if bool(self._campaign_configuration['General']['details']):\\n for run in run_tec_conf_set:\\n for tec in run_tec_conf_set[run]:\\n for conf in run_tec_conf_set[run][tec]:\\n assert \\\"hp_selection\\\" in run_tec_conf_set[run][tec][conf]\\n assert \\\"validation\\\" in run_tec_conf_set[run][tec][conf], \\\"training MAPE not found for \\\" + str(run) + str(tec) + str(conf)\\n self._logger.info(\\\"Run %s - Technique %s - Conf %s - Training MAPE %f - Test MAPE %f\\\", str(run), ec.enum_to_configuration_label[tec], str(conf), run_tec_conf_set[run][tec][conf][\\\"hp_selection\\\"], run_tec_conf_set[run][tec][conf][\\\"validation\\\"])\\n return best_confs, best_technique\",\n \"def _generate_variations(self):\\n words = list(self.solutions)\\n letter_choices = tuple(self._data_words[np.where(self._data_words != '')])\\n\\n self._data_variations[:] = ''\\n while np.sum(self._data_variations != '') < self._data_variations.size * self.difficulty.density_target_variations:\\n word = random.choice(words)\\n chrs = list(word)\\n\\n # Replace random letters in word\\n # eg. word = ward, wore, wond, qerd\\n for _ in range(random.randint(0, len(chrs) // 2)):\\n if letter_choices and random.uniform(0, 1) > self.difficulty.copy_letter_chance:\\n chrs[random.randint(0, len(chrs) - 1)] = random.choice(letter_choices)\\n else:\\n chrs[random.randint(0, len(chrs) - 1)] = random.choice(string.ascii_lowercase)\\n\\n # Remove random letters from the word\\n # eg. word = wrd, wod\\n for _ in range(random.randint(0, len(chrs) // 2)):\\n del chrs[random.randint(0, len(chrs) - 1)]\\n\\n # Add the word to the variations data\\n direction = self.difficulty.generate_direction(self.dimensions)\\n location = tuple(random.randint(0, size - 1) for size in self.shape)\\n for char in chrs:\\n if not all(-1 <= a + b < c + 1 for a, b, c in zip(location, direction, self.shape)):\\n break\\n self._data_variations[location] = char\\n location = tuple(map(sum, zip(location, direction)))\\n\\n self._req_update_variation = False\",\n \"def make_classifiers(NAMES) :\\r\\n\\r\\n# if len(data_shape) != 2:\\r\\n# raise ValueError(\\\"Only 2-d data allowed (samples by dimension).\\\")\\r\\n\\r\\n classifiers = {\\r\\n \\\"Chance\\\": DummyClassifier(strategy=\\\"most_frequent\\\"),\\r\\n \\\"Nearest Neighbors\\\": KNeighborsClassifier(3),\\r\\n \\\"Linear SVM\\\": LinearSVC(penalty='l2', C=1,# probability=True,\\r\\n class_weight='balanced'),\\r\\n # sahil changed the configuration from \\\"probability\\\" True to False (probability\\r\\n # based inference doesn't work well in SVM models from part experiences,\\r\\n # as SVM original algorithm just split the data with no probablistic notion of inference.)\\r\\n \\\"RBF SVM\\\": SVC(gamma=2, C=1, probability=False),\\r\\n \\\"Decision Tree\\\": DecisionTreeClassifier(max_depth=None,\\r\\n max_features=\\\"auto\\\"),\\r\\n \\\"Random Forest\\\": RandomForestClassifier(max_depth=None,\\r\\n n_estimators=20,\\r\\n max_features=\\\"auto\\\",\\r\\n n_jobs=PROCESSORS),\\r\\n \\\"Logistic Regression\\\": LogisticRegression(penalty='l1',\\r\\n class_weight='balanced'),\\r\\n \\\"Naive Bayes\\\": GaussianNB(),\\r\\n \\\"LDA\\\": LDA(),\\r\\n \\\"SGD_logL1\\\": SGDClassifier(random_state=1952,loss='log', average = 3,\\r\\n penalty='l1',\\r\\n alpha=1e-3,\\r\\n class_weight='balanced'),\\r\\n \\\"SGD_log_elastic\\\": SGDClassifier(random_state=1952,loss='log',\\r\\n class_weight='balanced',\\r\\n alpha=1e-3,\\r\\n average = 3,\\r\\n penalty='elasticnet'),\\r\\n \\\"SGD_SVM_elastic\\\": SGDClassifier(random_state=1952,loss='log',\\r\\n class_weight='balanced',\\r\\n average = 3,\\r\\n alpha=1e-3,\\r\\n penalty='elasticnet'),\\r\\n\\r\\n # Sahil commented the two classiifer below as not able to install the packages\\r\\n # \\\"CGC_log_L1\\\": CDClassifier(penalty=\\\"l1\\\",\\r\\n # loss=\\\"log\\\",\\r\\n # multiclass=False,\\r\\n # max_iter=200,\\r\\n # C=1,\\r\\n # tol=1e-3),\\r\\n # \\\"SDCA_SVM_elastic\\\": SDCAClassifier(\\r\\n # loss=\\\"hinge\\\",\\r\\n # max_iter=200,\\r\\n # tol=1e-3)\\r\\n #\\r\\n }\\r\\n\\r\\n params = {\\r\\n \\\"Chance\\\": {},\\r\\n # 3, 4, 5\\r\\n # , 6, 7, 8, 10, 12, 15, 20, 30, 50, 75, 100\\r\\n #\\r\\n #\\r\\n \\\"Nearest Neighbors\\\": {\\\"n_neighbors\\\": [1, 2, 3, 5, 10, 20, 50, 75, 100, 150, 200, 250]},\\r\\n \\\"Linear SVM\\\": {\\\"C\\\": [0.1, 0.2, 0.3, 0.4, 0.5, 1, 1.5, 2],\\r\\n \\\"loss\\\":['hinge', 'squared_hinge']},\\r\\n \\\"RBF SVM\\\": {\\\"kernel\\\": [\\\"rbf\\\"],\\r\\n \\\"gamma\\\": np.logspace(-2, 0, 6).tolist() + \\\\\\r\\n np.logspace(0,1,5)[1:].tolist(),\\r\\n \\\"C\\\": np.logspace(-2, 2, 5).tolist()},\\r\\n \\\"Decision Tree\\\": {},\\r\\n \\\"Random Forest\\\": {\\\"max_depth\\\": np.round(np.logspace(np.log10(2), \\\\\\r\\n 1.2, 6)).astype(int).tolist()},\\r\\n \\\"Logistic Regression\\\": {\\\"C\\\": np.logspace(-2, 3, 6).tolist()},\\r\\n \\\"Naive Bayes\\\": {},\\r\\n \\\"LDA\\\": {},\\r\\n \\\"SGD_logL1\\\": {\\\"alpha\\\": np.logspace(-5, 2, 7)},\\r\\n \\\"SGD_log_elastic\\\": {\\\"alpha\\\": np.logspace(-5, 2, 6),\\r\\n \\\"l1_ratio\\\": 10**np.array([-2, -1, -.5, -.25,\\r\\n -.12, -.06, -.01])},\\r\\n \\\"SGD_SVM_elastic\\\": {\\\"alpha\\\": np.logspace(-5, 2, 6),\\r\\n \\\"l1_ratio\\\": 10**np.array([-2, -1, -.5, -.25,\\r\\n -.12, -.06, -.01])},\\r\\n \\\"CGC_log_L1\\\": {\\\"alpha\\\": np.logspace(-5, 2, 6)},\\r\\n \\\"SDCA_SVM_elastic\\\": {\\\"alpha\\\": np.logspace(-4, 4, 5),\\r\\n \\\"l1_ratio\\\": 10**np.array([-3,-2, -1, np.log10(.5),\\r\\n np.log10(.9)])}\\r\\n }\\r\\n out_classifiers = {cname: classifiers[cname] for cname in NAMES}\\r\\n out_params = {cname: params[cname] for cname in NAMES}\\r\\n logging.info(\\\"Using classifiers %r with params %r\\\" % (out_classifiers,\\r\\n out_params))\\r\\n return classifiers, params\",\n \"def generate_products():\\n # initialize list of noun and adj\\n num_products = 30\\n products = [0] * num_products\\n prices = [0] * num_products\\n weights = [0] * num_products\\n flammabilities = [0] * num_products\\n\\n # initlize random word object\\n random = RandomWords()\\n\\n adj = [random.get_random_word(includePartOfSpeech=\\\"adjective\\\")\\n for product in products]\\n noun = [random.get_random_word(includePartOfSpeech=\\\"noun\\\")\\n for product in products]\\n products = [noun + \\\" \\\" + adj for noun, adj in zip(adj, noun)]\\n\\n prices = [random.randint(5, 100) for price in prices]\\n weights = [random.randint(5, 100) for weight in weights]\\n flammabilities = [random.randint(0.0, 2.5)\\n for flammability in flammabilities]\\n\\n return products, prices, weights, flammabilities\",\n \"def test_malware_have_techniques(attck_fixture):\\n for malware in attck_fixture.enterprise.malwares:\\n if malware.techniques:\\n assert getattr(malware,'techniques')\",\n \"def getAllDecisionRules(self):\\n\\n #check this shit lol?\\n thetas = self.getAllTheta()\\n human_actions = self.getAllHumanActions()\\n return [list(zip(thetas, item)) for item in itertools.product(human_actions, repeat=len(thetas))]\",\n \"def generateSupplies(self):\\n typePart = ['wrench','resistor','bulb','mushroom','coin']\\n chosenPart = []\\n for i in range(3):\\n randomPart = choice(typePart)\\n chosenPart.append(randomPart)\\n typePart.remove(randomPart)\\n for part in chosenPart:\\n amount = randint(1,3)\\n self._supplies.append(Node(part,amount))\",\n \"def construct_powerset(feature_list):\\n\\n # Sort for easily reproducible order\\n feature_names = sorted(list(feature_list))\\n\\n feature_combinations = [list(x) for l in range(1, len(feature_names) +1) for x in combinations(feature_names, l)]\\n\\n return feature_combinations\",\n \"def _create_examples(self, lines, set_type, passage_type, enable_sql_supervision, cand_for_each_source):\\n\\n examples = []\\n total_answerable = 0\\n\\n for (i, line) in tqdm(enumerate(lines), total=len(lines)):\\n guid = \\\"%s-%s-%s\\\" % (set_type, line['qid'], str(i))\\n src = []\\n has_pos = 0\\n\\n if passage_type == 'both' and set_type != 'train':\\n\\n # rank by bert score in val and test\\n score_key = 'rank_score'\\n # score_key = \\\"nr_score\\\"\\n both_sources = line[\\\"passages\\\"][:2*cand_for_each_source] + line[\\\"tables\\\"][:2*cand_for_each_source]\\n all_scores = np.array([x[score_key] for x in both_sources])\\n sorted_index = all_scores.argsort()[::-1][:2*cand_for_each_source]\\n\\n for idx in sorted_index:\\n pas = both_sources[idx]\\n if idx < 2 * cand_for_each_source:\\n pas_type = 'passage'\\n title = pas[\\\"article_title\\\"]\\n else:\\n pas_type = 'table'\\n title = \\\"table_\\\" + pas[\\\"uid\\\"].split('-split')[0]\\n\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + f\\\" </s> {pas_type} title: \\\" + title + f\\\" </s> {pas_type} content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\")\\n\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n else:\\n\\n if passage_type in ['textual', 'hybrid', 'both']:\\n for pas in line[\\\"passages\\\"][:cand_for_each_source]:\\n # 10 passages\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> passage title: \\\" + pas[\\\"article_title\\\"] + \\\" </s> passage content: \\\" + pas[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> passage title: Strictly Commercial </s> passage content: album \\\\\\\"ZAPPAtite\\\\\\\". All songs written and performed ... </s>\\n if isinstance(pas['judge'], dict):\\n has_pos += int(pas['judge']['judge_contain_all'])\\n else:\\n has_pos += int(pas['judge'])\\n\\n if passage_type in ['tabular', 'hybrid', 'both']:\\n for tab in line[\\\"tables\\\"][:cand_for_each_source]:\\n # 10 tables\\n src.append(\\\"question: \\\" + line[\\\"question\\\"].strip() + \\\" </s> table title: \\\" + \\\"table_\\\" + tab[\\\"uid\\\"].split('-split')[0] + \\\" </s> table content: \\\" + tab[\\\"text\\\"] + \\\" </s>\\\") # e.g. question: Tell me what the notes are for South Australia </s> table title: From Nashville to Memphis: The Essential '60s Masters ; Disc Two ; Disc Tw </s> table content: ... </s>\\n if isinstance(tab['judge'], dict):\\n has_pos += int(tab['judge']['judge_contain_all'])\\n else:\\n has_pos += int(tab['judge'])\\n\\n const = 2 if passage_type in ['hybrid', 'both'] else 1\\n if len(src) != const * cand_for_each_source:\\n logger.info(line)\\n src = src + [\\\"\\\"] * (const * cand_for_each_source - len(src))\\n\\n if has_pos > 0:\\n total_answerable += 1\\n\\n if enable_sql_supervision and 'true_sql' in line:\\n tgt = \\\"sql: \\\" + line[\\\"true_sql\\\"] + \\\" </s>\\\" # e.g. sql: SELECT Position FROM table_1-10015132-11 WHERE School/Club Team = \\\\\\\"Butler CC (KS)\\\\\\\" </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'denotation' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"denotation\\\"][0]) + \\\" </s>\\\"\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt)\\n if 'answers' in line:\\n tgt = \\\"answer: \\\" + str(line[\\\"answers\\\"][0]) + \\\" </s>\\\" # e.g. answer: no slogan on current series </s>\\n examples.append(InputExamples(guid=guid, source=src, target=tgt))\\n if i % 1000 == 0:\\n logger.info(src[0] + \\\" \\\" + tgt + \\\"\\\\n\\\")\\n\\n logger.info(f\\\"Total answerable in {set_type} split is {total_answerable / len(lines)}\\\")\\n\\n return examples\",\n \"def learn(self):\\n pass\",\n \"def learn(self):\\n pass\",\n \"def get_tools_using_technique():\\n global tools_using_technique\\n\\n if not tools_using_technique:\\n tools_using_technique = rsh.tools_using_technique(get_srcs())\\n \\n return tools_using_technique\",\n \"def generateAssociationRule(freqSet):\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=True,\\n ))\\n do_rotations = [True for _ in strategies]\\n return strategies, do_rotations\",\n \"def build_train_set(trajectories):\\n\\n observes = np.concatenate([t['observes'] for t in trajectories])\\n actions = np.concatenate([t['actions'] for t in trajectories])\\n disc_sum_rew = np.concatenate([t['disc_sum_rew'] for t in trajectories])\\n advantages = np.concatenate([t['advantages'] for t in trajectories])\\n # normalize advantages\\n advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-6)\\n learning_signals = np.zeros((len(trajectories), len(trajectories[0]['advantages'])))\\n\\n # TODO：check if it is something wrong for computing learning signals\\n for i in range(0, learning_signals.shape[0]):\\n for j in range(0, learning_signals.shape[1]):\\n learning_signals[i][j] = trajectories[i]['advantages'][j]\\n return observes, actions, advantages, learning_signals, disc_sum_rew\",\n \"def classifiers():\\n return [(\\\"LogisticRegression\\\", LogisticRegression(C=1.0,\\n class_weight=None,\\n dual=False,\\n fit_intercept=True,\\n intercept_scaling=1,\\n penalty='l2',\\n random_state=None,\\n tol=0.0001)),\\n\\n (\\\"RandomForests\\\", RandomForestClassifier(n_jobs=-1,\\n n_estimators = 15,\\n max_features = 'sqrt')),\\n (\\\"Gaussian NaiveBayes\\\", GaussianNB())] #,\\n #(\\\"LinearSVM\\\", svm.LinearSVC())]\",\n \"def makeRandomSet(weightedSet): \\n\\tnewtrainingSet = []\\n\\t\\n\\t#Make a list starting with zero and ending with MAX_INT\\n\\t#Where all of values inbetween are the probs of choosing this element\\n\\tprobs = [0]\\n\\tfor ex in weightedSet:\\t\\t\\n\\t\\tprobs.append(ex.weight + probs[-1])\\n\\t\\n\\tprobs.append(_MAX_INT)\\n\\t\\t\\n\\tfor i in range(len(weightedSet)): #Get N new smaples\\n\\t\\trandNum = _random()\\n\\t\\tfor j in range(len(probs) - 1 ):\\t\\n\\t\\t\\tif randNum >= probs[j] and probs[j+1] > randNum : \\n\\t\\t\\t\\tnewtrainingSet.append(boostExample(\\n\\t\\t\\t\\t\\tLabeledExample(weightedSet[j-1],label=weightedSet[j-1].label) ,weightedSet[j-1].weight) )\\n\\t\\t\\t\\tbreak\\n\\t\\t\\n\\t\\n\\treturn newtrainingSet\",\n \"def main():\\n\\n args = get_args()\\n random.seed(args.seed)\\n wod = []\\n\\n for name, low, high in read_csv(args.file):\\n reps = random.randint(low, high)\\n if args.easy:\\n reps = int(reps / 2)\\n wod.append((name, reps))\\n\\n wod = random.sample(wod, k=args.num_exercises)\\n print(tabulate(wod, headers=('Exercise', 'Reps')))\",\n \"def test_generate_all_training(self):\\n facade = ChatetteFacade.get_or_create()\\n\\n input_dir_path = \\\"tests/system-testing/inputs/generate-all/\\\"\\n input_filenames = [\\n \\\"simplest.chatette\\\", \\\"only-words.chatette\\\",\\n \\\"words-and-groups.chatette\\\", \\\"alias.chatette\\\", \\\"include.chatette\\\",\\n \\\"slot.chatette\\\", \\\"slotrolegroup.chatette\\\"\\n ]\\n for filename in input_filenames:\\n file_path = os.path.join(input_dir_path, filename)\\n facade.run(file_path)\\n if not TestSystem.check_no_duplicates(facade.train_examples):\\n pytest.fail(\\n \\\"Some examples were generated several times \\\" +\\n \\\"when dealing with file '\\\" + filename + \\\"'.\\\\nGenerated: \\\" + \\\\\\n str(facade.train_examples)\\n )\\n legal_examples = TestSystem.get_legal_examples(file_path)\\n for ex in facade.train_examples:\\n formatted_ex = {\\\"intent\\\": ex.intent_name, \\\"text\\\": ex.text}\\n if formatted_ex not in legal_examples:\\n pytest.fail(\\n str(formatted_ex) + \\\" is not a legal example for '\\\" + \\\\\\n file_path + \\\"'\\\"\\n )\\n if len(legal_examples) != len(facade.train_examples):\\n training_texts = [ex.text for ex in facade.train_examples]\\n for legal_ex in legal_examples:\\n if legal_ex[\\\"text\\\"] not in training_texts:\\n pytest.fail(\\n \\\"Example '\\\" + legal_ex[\\\"text\\\"] + \\\\\\n \\\"' was not generated.\\\"\\n )\\n pytest.fail(\\n \\\"An unknown example was not generated (\\\" + \\\\\\n str(len(facade.train_examples)) + \\\\\\n \\\" generated instead of \\\" + str(len(legal_examples)) + \\\\\\n \\\").\\\\nGenerated: \\\" + str(facade.train_examples)\\n )\\n legal_syn = TestSystem.get_legal_synonyms(file_path)\\n if legal_syn is not None:\\n synonyms = AST.get_or_create().get_entities_synonyms()\\n for key in synonyms:\\n if key not in legal_syn:\\n pytest.fail(\\n \\\"'\\\" + key + \\\"' shouldn't have any synonyms.\\\"\\n )\\n for syn in synonyms[key]:\\n if syn not in legal_syn[key]:\\n pytest.fail(\\n \\\"'\\\" + syn + \\\"' shouldn't be a synonym of '\\\" + \\\\\\n key + \\\"'\\\"\\n )\",\n \"def generate_folds(self) -> list[dict]:\\n pass\",\n \"def do_training(self):\\n json_data = request.data\\n global g_list_of_classifier\\n\\n datas = json.loads(json_data.decode('UTF-8')) #datas = liste\\n\\n for ite_clf in g_list_of_classifier:\\n for data in datas:\\n ite_clf.add_data(data['score'], data['answer'])\\n print(ite_clf.get_info())\\n return ''\",\n \"def classifier_set(tuning=False):\\n\\tif tuning==False:\\n\\t\\tclassifiers = [\\n\\t\\t\\tKNeighborsClassifier(50),\\n\\t\\t\\tSVC(kernel=\\\"linear\\\", C=0.025, probability=True),\\n\\t\\t\\tSVC(gamma=1, C=1, probability=True),\\n\\t\\t\\tGaussianProcessClassifier(1.0 * RBF(1.0)),\\n\\t\\t\\tDecisionTreeClassifier(criterion= 'entropy', min_samples_leaf= 30, min_samples_split= 10, splitter= 'random'),\\n\\t\\t\\tRandomForestClassifier(n_estimators=50, min_samples_leaf=30, min_samples_split=2),\\n\\t\\t\\tMLPClassifier(early_stopping=True, hidden_layer_sizes=100,learning_rate_init=0.1),\\n\\t\\t\\tAdaBoostClassifier(n_estimators= 50),\\n\\t\\t\\tGaussianNB(),\\n\\t\\t\\tLogisticRegression()\\n\\t\\t\\t]\\n\\t\\tnames = [\\\"KNN\\\",\\n\\t\\t\\t \\t\\\"L SVM\\\",\\n\\t\\t\\t \\t\\\"RBF SVM\\\", \\n\\t\\t\\t \\t\\\"GP\\\",\\n\\t\\t\\t\\t\\\"DT\\\",\\n\\t\\t\\t\\t\\\"RF\\\",\\n\\t\\t\\t\\t\\\"NN\\\", \\n\\t\\t\\t\\t\\\"AB\\\",\\n\\t\\t\\t\\t\\\"NB\\\",\\n\\t\\t\\t\\t\\\"LR\\\"\\n\\t\\t\\t\\t]\\n\\treturn classifiers, names\",\n \"def _create_examples(self, data, set_type):\\n examples = []\\n for (i, elem) in enumerate(data):\\n guid = \\\"%s-%s\\\" % (set_type, i)\\n text = elem[0]\\n label = elem[1]\\n examples.append(\\n InputExample(guid=guid, text=text, label=label))\\n return examples\",\n \"def create_new_features(self):\\n train = self.train\\n \\n train['is_context'] = train['context_type'].isin(CONTEXT_TYPE_TEST)\\n train['is_context_flow'] = train['listen_type'] * train['is_context']\\n \\n train['is_listened_context'] = train['is_listened'] * train['is_context']\\n train['is_listened_flow'] = train['is_listened'] * train['listen_type']\\n train['is_listened_context_flow'] = train['is_listened'] * train['is_context_flow']\\n \\n for feature in self.categorize_features:\\n gby_feat = train.groupby(feature)\\n new_features(train, gby_feat, feature, feature in self.listen_type_features, self.context_features, self.flow_features, self.fillna)\\n \\n # Variable combinations\\n for feat1 in self.combo_features1:\\n for feat2 in self.combo_features2:\\n gby_feat = train.groupby([feat1, feat2])\\n name = feat1 + '_' + feat2\\n new_features(train, gby_feat, name, feat1 in self.listen_type_features, self.context_features, self.flow_features, self.fillna)\",\n \"def generate_models(x, y, degs):\\n import numpy as np\\n rslt = []\\n for deg in degs:\\n coefs = np.polyfit(x, y, deg)\\n rslt.append(coefs)\\n return rslt\",\n \"def generate(dictalg):\\n\\n # dsList, sortedAlgs, dictAlg = processInputArgs(args, verbose=verbose)\\n res = {}\\n for f, i in pproc.dictAlgByFun(dictalg).iteritems():\\n for d, j in pproc.dictAlgByDim(i).iteritems():\\n tmp = BestAlgSet(j)\\n res[(d, f)] = tmp\\n return res\",\n \"def _create_examples(self, lines, set_type):\\n examples = []\\n \\n for (i, line) in enumerate(lines):\\n sentence_number = 0\\n premise_text = line[\\\"premise\\\"]\\n modified_premise_text = re.sub(self.stage_name_pattern,\\\"\\\",premise_text)\\n modified_premise_text = re.sub(self.w_patterns,\\\"\\\",modified_premise_text)\\n hypothesis_text = line[\\\"hypothesis\\\"]\\n hypothesis_text = re.sub(self.w_patterns,\\\"\\\",hypothesis_text)\\n a_label = int(line[\\\"label\\\"])\\n\\n sentences = modified_premise_text.split('.')\\n\\n for j, sentence in enumerate(sentences):\\n guid = \\\"\\\" + str(sentence_number) + \\\"\\\\t\\\" + str(i) + \\\"\\\\t\\\" + str(len(sentences)) + \\\"\\\\t\\\" + str(a_label)\\n text_a = sentence\\n text_b = hypothesis_text\\n label = a_label\\n sentence_number += 1\\n examples.append(\\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\\n #print(\\\"16th sentence::\\\",sentences[16])\\n\\n return examples\",\n \"def get_skills(lang, spec=None):\\n\\n if lang.lower() == 'java':\\n output = get_java(spec)\\n if spec is not None:\\n print(f\\\"Our suggestions for {spec} are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n else:\\n print(\\\"Our suggestions for are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n elif lang.lower() == 'python':\\n output = get_python(spec)\\n if spec is not None:\\n print(f\\\"Our suggestions for {spec} are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\\n else:\\n print(\\\"Our suggestions for are: \\\", end='')\\n print(*output[:-1], sep=', ', end='')\\n print(f\\\" and {output[-1]}.\\\\n\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6610979","0.6524354","0.62929714","0.61466","0.61216724","0.61157763","0.59929365","0.5992363","0.5967745","0.59375405","0.59261817","0.5862121","0.5525391","0.5521534","0.5519102","0.5513661","0.5487172","0.5474245","0.54732275","0.54732275","0.5459485","0.5455644","0.54510695","0.54392344","0.5418748","0.5418036","0.5411953","0.5409308","0.53961873","0.5392366","0.5385584","0.53827864","0.5372129","0.536119","0.5346429","0.5317915","0.53127074","0.5310682","0.5309662","0.5300334","0.5293586","0.5273807","0.52710503","0.52549356","0.5254196","0.5248668","0.5238245","0.523658","0.52339065","0.52174336","0.5212906","0.52034354","0.5198467","0.51962453","0.519381","0.5191741","0.5189366","0.517731","0.51722753","0.5167855","0.5166821","0.51649576","0.51556605","0.51508075","0.5146908","0.51450115","0.51432264","0.51406276","0.5134746","0.51329345","0.5116268","0.5113859","0.5104889","0.5102092","0.50939757","0.50887436","0.50689524","0.5066908","0.50643075","0.5062804","0.5050511","0.5047941","0.5047941","0.50460917","0.50249493","0.5019629","0.501807","0.5010319","0.50054675","0.49967226","0.49957442","0.49905923","0.49897572","0.4983346","0.4982077","0.49798024","0.49796042","0.49709958","0.49706876","0.4969274"],"string":"[\n \"0.6610979\",\n \"0.6524354\",\n \"0.62929714\",\n \"0.61466\",\n \"0.61216724\",\n \"0.61157763\",\n \"0.59929365\",\n \"0.5992363\",\n \"0.5967745\",\n \"0.59375405\",\n \"0.59261817\",\n \"0.5862121\",\n \"0.5525391\",\n \"0.5521534\",\n \"0.5519102\",\n \"0.5513661\",\n \"0.5487172\",\n \"0.5474245\",\n \"0.54732275\",\n \"0.54732275\",\n \"0.5459485\",\n \"0.5455644\",\n \"0.54510695\",\n \"0.54392344\",\n \"0.5418748\",\n \"0.5418036\",\n \"0.5411953\",\n \"0.5409308\",\n \"0.53961873\",\n \"0.5392366\",\n \"0.5385584\",\n \"0.53827864\",\n \"0.5372129\",\n \"0.536119\",\n \"0.5346429\",\n \"0.5317915\",\n \"0.53127074\",\n \"0.5310682\",\n \"0.5309662\",\n \"0.5300334\",\n \"0.5293586\",\n \"0.5273807\",\n \"0.52710503\",\n \"0.52549356\",\n \"0.5254196\",\n \"0.5248668\",\n \"0.5238245\",\n \"0.523658\",\n \"0.52339065\",\n \"0.52174336\",\n \"0.5212906\",\n \"0.52034354\",\n \"0.5198467\",\n \"0.51962453\",\n \"0.519381\",\n \"0.5191741\",\n \"0.5189366\",\n \"0.517731\",\n \"0.51722753\",\n \"0.5167855\",\n \"0.5166821\",\n \"0.51649576\",\n \"0.51556605\",\n \"0.51508075\",\n \"0.5146908\",\n \"0.51450115\",\n \"0.51432264\",\n \"0.51406276\",\n \"0.5134746\",\n \"0.51329345\",\n \"0.5116268\",\n \"0.5113859\",\n \"0.5104889\",\n \"0.5102092\",\n \"0.50939757\",\n \"0.50887436\",\n \"0.50689524\",\n \"0.5066908\",\n \"0.50643075\",\n \"0.5062804\",\n \"0.5050511\",\n \"0.5047941\",\n \"0.5047941\",\n \"0.50460917\",\n \"0.50249493\",\n \"0.5019629\",\n \"0.501807\",\n \"0.5010319\",\n \"0.50054675\",\n \"0.49967226\",\n \"0.49957442\",\n \"0.49905923\",\n \"0.49897572\",\n \"0.4983346\",\n \"0.4982077\",\n \"0.49798024\",\n \"0.49796042\",\n \"0.49709958\",\n \"0.49706876\",\n \"0.4969274\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6863604"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85691,"cells":{"query":{"kind":"string","value":"Given the technique's detection string, replace the citations and add them to the reference_list is they are not found. Return modified detection string"},"document":{"kind":"string","value":"def get_detection_string(detection, reference_list, next_reference_number):\n\n citations_from_descr = util.get_citations_from_descr(detection)\n\n filtered_detection = util.replace_html_chars(markdown.markdown(detection))\n filtered_detection = util.filter_urls(filtered_detection)\n filtered_detection = util.get_descr_reference_sect(citations_from_descr, reference_list, next_reference_number, filtered_detection)\n \n return filtered_detection"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ref_tag_preprocess(inp_tag_text):\n\n # some words in references\n inp_tag_text = inp_tag_text.replace(\"до римлян\", \"Римл\")\n inp_tag_text = inp_tag_text.replace(\" и \", \"; \")\n inp_tag_text = inp_tag_text.replace(\" і \", \"; \")\n inp_tag_text = inp_tag_text.replace(\"–\", \"-\")\n # replacing \"'\" to \"’\", it is similar in Ukrainian\n inp_tag_text = inp_tag_text.replace(\"'\", \"’\")\n return inp_tag_text","def extract_references(text, preprocessor, model):\n has_reference_section, reference_section, non_reference_section = preprocess_reference_text(text)\n reference_text = reference_section if has_reference_section else non_reference_section\n fragment = preprocessor(reference_text)\n out = model(fragment.idx).argmax(dim=2).cpu().numpy()\n\n buffer = ''\n found_citations = []\n in_citation = False\n for char, pred in zip(fragment.text, out[0, :]):\n if pred == 2:\n in_citation = True\n if pred == 3:\n in_citation = False\n if pred in [2, 3]:\n if buffer:\n found_citations.append(buffer)\n buffer = char\n elif pred == 1:\n buffer += char\n elif pred == 0:\n if in_citation:\n buffer += char\n else:\n if buffer:\n found_citations.append(buffer)\n buffer = ''\n return found_citations","def process_reference(ref):\n logger.debug(\"==================================================\")\n logger.debug(\"'{}'\".format(ref))\n stripped = ref.lower().strip()\n stripped = str(unidecode.unidecode(unicode(stripped, 'utf8')))\n\n conf_inds = []\n for conf_str in conf_strings:\n found_ind = stripped.find(conf_str)\n\n if found_ind > 0:\n logger.debug(\"found conf string {} at ind {}\".format(conf_str, found_ind))\n conf_inds.append(found_ind)\n\n if not conf_inds:\n logger.debug(\"found no conference strings\")\n conf_strip = stripped\n else:\n # Only consider conference strings in the second half of the reference.\n str_start = 0\n for ind in conf_inds:\n if ind > len(stripped)/2:\n str_start = ind\n\n\n if str_start == 0:\n logger.debug(\"No conf string found in the second half of the given reference\")\n conf_strip = stripped\n else:\n last_dot = stripped.rfind(\".\", 0, str_start) + 1\n last_comma = stripped.rfind(\",\", 0, str_start) + 1\n\n conf_strip = stripped[:max(last_dot, last_comma)]\n logger.debug(\"conf string started at {}, last dot at {}, last comma at {}\".format(str_start, last_dot, last_comma))\n logger.debug(\"all after latest punctuation: {}\".format(conf_strip))\n\n sep_inds, sep_inds_rev = punctuation_density_separate(conf_strip)\n if not sep_inds: # if it's empty this is usually just a spurious reference\n return ()\n\n # If there are two or fewer separation indices then the separation\n # wasn't able to get good separation for authors, title and journal,\n # so we don't change anything\n if len(sep_inds) > 2:\n # if there are 3 or more sep inds, we want to get all the text in the middle part\n punc_sep = conf_strip[sep_inds[0]:sep_inds_rev[-2]].strip()\n elif len(sep_inds) == 2:\n punc_sep = conf_strip[sep_inds[0]:sep_inds_rev[-1]].strip()\n else:\n punc_sep = conf_strip\n\n logger.debug(\"conf_strip: {}\\npunc_sep: {}\\n\".format(conf_strip, punc_sep))\n\n full_strip = punc_sep\n\n ref_stopped = [word for word in full_strip.split(\" \") if word not in stopwords and len(word) > 2]\n ref_nopunc = \" \".join(ref_stopped).translate(None, punctuation).split(\" \")\n ref_set = set(ref_nopunc)\n return (ref.strip(), ref_stopped, ref_nopunc, ref_set)","def __citation_correction(self, bs, ground_truth):\n bs_ref = bs.findNext('bibl')\n gt_ref = ground_truth.findNext('ref')\n while gt_ref is not None:\n if gt_ref.find('article-title') != bs_ref.title:\n pass\n gt_ref = gt_ref.findNext('ref')","def noteRef(self, text):\n text_re = re.compile(r\"\"\"\n \\[ # start\n (%s) # !atts\n \\#\n ([^\\]!]+) # !label\n ([!]?) # !nolink\n \\]\"\"\" % self.c, re.X)\n text = text_re.sub(self.fParseNoteRefs, text)\n return text","def replace_by_canonical_referent(self, cross_sentence=True):\n replaced_count = 0\n\n # replace the participant in their containers\n def replace_in_containers(myobj, replacing):\n containers = {\n \"event\": \"participants\",\n \"sentiment_expression\": \"targets\",\n \"sentence\": \"participants\",\n }\n for c_name, attrib_n in containers.items():\n c_name = f\"in_{c_name}\"\n if hasattr(myobj, c_name):\n for c in getattr(myobj, c_name):\n to_replace_in = getattr(c, attrib_n)\n to_replace_in.append(replacing)\n if myobj in to_replace_in:\n to_replace_in.remove(myobj)\n to_replace_in.sort(key=lambda x: x.begin)\n\n if self.canonical_referents and self.canonical_referents != \"from_canonref\":\n # sometimes there are multiple canonrefs tagged\n # this can be a) annotation mistake or\n # b) multiple reference to a group, e.g. \"all\" refers to three companies.\n for canonref in self.canonical_referents:\n # check whether canonical referent is in same sentence\n same_sentence = [s.element_id for s in self.in_sentence] == [\n s.element_id for s in canonref.in_sentence\n ]\n # always replace when cross_sentence is true\n # if cross_sentence is False (disallowed) only replace when canonref is in same sentence\n if cross_sentence or same_sentence:\n # replace element\n replacing_participant = Participant(\n canonref.text,\n canonref.begin,\n canonref.end,\n canonref.element_id,\n canonref.annotator_id,\n canonref.document_title,\n canonref.in_document,\n self.role,\n \"from_canonref\",\n self.link_id,\n canonref.tokens,\n )\n replacing_participant.in_sentence = self.in_sentence\n replacing_participant.in_document = self.in_document\n if hasattr(self, \"in_sentiment_expression\"):\n replacing_participant.in_sentiment_expression = (\n self.in_sentiment_expression\n )\n replacing_participant.from_original_participant = copy(self)\n print(\n f\"Replaced {self} with {canonref}. (in same sentence:{same_sentence})\"\n )\n\n replace_in_containers(self, replacing_participant)\n\n # replace on document\n self.in_document.participants.append(replacing_participant)\n if self in self.in_document.participants:\n self.in_document.participants.remove(self)\n\n replaced_count += 1\n return replaced_count","def _filter_references_section(self, text):\n references_pattern = r\"(REFERENCE)|(reference)|(Reference)\"\n references_found = [i.start() for i in re.finditer(references_pattern, text)]\n if references_found != []:\n last_reference_index = references_found[-1]\n return text[:last_reference_index]\n else:\n return text","def patch(text):\n if match := re.search(r'\\d+__\\d+__\\d+', text):\n tag = match.group(0)\n if tag not in ocds_tags:\n if ocds_version or not use_development_version:\n text = text.replace(tag, ocds_tag)\n else:\n text = text.replace(ocds_schema_base_url + tag, development_base_url)\n return text","def fix_references(self) -> None:\n\n\t\tself.log_info(\"--- Searching for references to variants ---\")\n\t\tfor publication in Publication.objects.filter(variant_of__isnull=False):\n\t\t\tvariant = publication.variant_of\n\t\t\torigs = PublicationReference.objects.filter(reference=publication)\n\t\t\tfor orig in origs:\n\t\t\t\tif PublicationReference.objects.filter(reference=variant, publication=orig.publication).exists():\n\t\t\t\t\tcontinue\n\t\t\t\tfixed = PublicationReference(\n\t\t\t\t\treference=variant,\n\t\t\t\t\tpublication=orig.publication,\n\t\t\t\t\tidentifier=('' if orig.identifier is None else orig.identifier) + \"*\",\n\t\t\t\t)\n\t\t\t\ttry:\n\t\t\t\t\tfixed.full_clean()\n\t\t\t\t\tfixed.save()\n\t\t\t\t\tself.log_success(f\"Added reference: {publication} -- {fixed.identifier} -> {variant}\")\n\t\t\t\texcept ValidationError as e:\n\t\t\t\t\traise CommandError(f\"{publication} -- {fixed.identifier} -> {variant}: {e}\")","def add_ref(doc_name, bib_name, keep_label=False, slides=False):\n bib_path = bib_name # os.path.join(static_path, \"tex\", bib_name)\n if not slides:\n keep_label = True\n with open(doc_name, \"r\") as f:\n _file = f.read()\n # find citations\n match = re.findall(r\"\\[@.*?\\]\", _file)\n references = []\n # print(match)\n for cit in match:\n tmp_cite_html = tempfile.NamedTemporaryFile(suffix=\".html\", delete=True)\n tmp = tempfile.NamedTemporaryFile(suffix=\".md\", delete=True)\n with open(tmp.name, \"w\") as md_file:\n # Open the file for writing.\n md_file.write(cit)\n\n pandoc_command = [\n \"pandoc\",\n \"-C\",\n tmp.name,\n \"--bibliography\",\n bib_path,\n \"--csl\",\n \"./templates/my-csl.csl\",\n \"-o\",\n tmp_cite_html.name,\n ]\n if not slides:\n for tag in [\"--metadata\", \"link-citations=true\"]:\n pandoc_command.append(tag)\n output = subprocess.check_output(\n pandoc_command, stderr=subprocess.STDOUT\n ).decode(\"utf-8\")\n print(output.rstrip())\n tmp.close()\n with open(tmp_cite_html.name, \"r\") as cite_html:\n # Open the file for writing.\n cit_new = cite_html.read()\n # print(cit_new)\n if not keep_label:\n # remove first line\n cit_new = \"\\n\".join(cit_new.splitlines()[1:])\n else:\n lines = cit_new.splitlines()\n # remove the <p> tags\n lines[0] = lines[0][3:-4]\n cit_new = \"\\n\".join(lines)\n if slides:\n _file = _file.replace(cit, cit_new)\n else:\n _file = _file.replace(cit, lines[0])\n # collect the references\n references.append(\"<p>\")\n references.append(\"\\n\".join(lines))\n references.append(\"</p>\")\n # paste_refs_end()\n tmp_cite_html.close()\n if slides:\n with open(doc_name, \"w\") as f:\n f.write(_file)\n if not slides:\n if references == []:\n _file=clean_html_refs(_file)\n else:\n _file = _file.replace(\"<bibliography-placeholder>\", \"\\n\".join(references))\n with open(doc_name, \"w\") as f:\n f.write(_file)\n return","def complete_description(description):\n local_find_all_re = find_all_re\n local_find_one_re = find_one_re\n local_find_series_2_re = find_series_2_re\n local_find_series_3_re = find_series_3_re\n local_course_re = course_re\n local_three_digits = three_digits\n\n find_all = re.findall(local_find_all_re, description)\n find_one = re.findall(local_find_one_re, description)\n \n def remove_in(a, b):\n remove = [y for x in a for y in b if y in x]\n a.extend(b)\n for x in remove: a.remove(x)\n return a\n \n if find_one:\n find_all = remove_in(find_all, find_one)\n for i, x in enumerate(find_all):\n completed = []\n find_all[i] = x.replace(' ', '')\n number = re.search(local_three_digits, find_all[i]).group(0)\n for crs in re.findall(local_course_re, find_all[i]):\n completed.append(f'{crs}{number}')\n description = description.replace(x, ' {}'.format('/'.join(completed)), 1)\n\n find_series_2 = re.findall(local_find_series_2_re, description)\n find_series_3 = re.findall(local_find_series_3_re, description)\n if find_series_2 or find_series_3:\n find_series_2 = remove_in(find_series_3, find_series_2)\n for i, series in enumerate(find_series_2):\n find_series_2[i] = series.replace(' ', '')\n course_dep = re.search(local_course_re, find_series_2[i])\n if course_dep: \n depmnt = course_dep.group(0)\n description = description.replace(series, f'{depmnt}{find_series_2[i][-3:]}', 1)\n return description","def replace_contractions(text):\r\n return contractions.fix(text)","def _replace_contractions(text):\n return contractions.fix(text)","def preprocess_reference_text(text):\n try:\n splitpoint = text.lower().rindex('reference')\n except ValueError:\n splitpoint = False\n while splitpoint and len(text) - splitpoint < 100 and 'reference' in text[:splitpoint].lower():\n text = text[:splitpoint]\n splitpoint = text.lower().rindex('reference')\n if not splitpoint:\n has_reference_section = False\n non_reference_section, reference_section = text, ''\n else:\n has_reference_section = True\n non_reference_section, reference_section = text[:splitpoint], text[splitpoint:]\n return has_reference_section, reference_section, non_reference_section","def ref_to_link(txt):\n text = txt.group(1) # because it was a match in a regular expression\n\n thecite, everythingelse = first_bracketed_string(text)\n thecite = thecite[1:-1] # strip curly brackets\n thecite = thecite.replace(\"\\\\\",\"\") # \\href --> href\n\n refs = thecite.split(\",\")\n ans = \"\"\n\n # print \"refs\",refs\n\n for ref in refs:\n ref = ref.strip() # because \\cite{A, B, C,D} can have spaces\n this_link = \"\"\n if ref.startswith(\"href\"):\n the_link = re.sub(r\".*{([^}]+)}{.*\", r\"\\1\", ref)\n click_on = re.sub(r\".*}{([^}]+)}\\s*\", r\"\\1\", ref)\n this_link = '{{ LINK_EXT(\"' + click_on + '\",\"' + the_link + '\") | safe}}'\n elif ref.startswith(\"doi\"):\n ref = ref.replace(\":\",\"\") # could be doi:: or doi: or doi\n the_doi = ref[3:] # remove the \"doi\"\n this_link = '{{ LINK_EXT(\"' + the_doi + '\",\"https://doi.org/' + the_doi + '\")| safe }}'\n elif ref.lower().startswith(\"mr\"):\n ref = ref.replace(\":\",\"\")\n the_mr = ref[2:] # remove the \"MR\"\n this_link = '{{ LINK_EXT(\"' + 'MR:' + the_mr + '\", '\n this_link += '\"http://www.ams.org/mathscinet/search/publdoc.html?pg1=MR&s1='\n this_link += the_mr + '\") | safe}}'\n elif ref.lower().startswith(\"arxiv\"):\n ref = ref.replace(\":\",\"\")\n the_arx = ref[5:] # remove the \"arXiv\"\n this_link = '{{ LINK_EXT(\"' + 'arXiv:' + the_arx + '\", '\n this_link += '\"http://arxiv.org/abs/'\n this_link += the_arx + '\")| safe}}'\n\n\n if this_link:\n if ans:\n ans += \", \"\n ans += this_link\n\n return '[' + ans + ']' + everythingelse","def replace_words_fun(self):\n\n cleaned_doc = []\n for word in str(self.doc).split():\n if word.lower() in self.replacement_list.keys():\n cleaned_doc.append(self.replacement_list[word.lower()])\n else:\n cleaned_doc.append(word)\n self.doc = ' '.join(cleaned_doc)","def replace_bibliography(self, text):\n\n indexBegin = 0\n indexEnd = 0\n indexBegin = text.find('\\\\bibliography{', indexBegin+1)\n indexEnd = text.find('}', indexBegin+1)\n text_to_replace = text[indexBegin:indexEnd+1]\n new_text = self.dir_helper.read_file(file_name = self.temp_dir + self.config.BBL_FILE)\n bbl_text = \"\"\n for line in new_text.split('\\n'):\n bbl_text = bbl_text + '\\t' + line + '\\n'\n text = text.replace(text_to_replace, bbl_text)\n\n return text","def replace_contractions(text):\n return contractions.fix(text)","def replace_contractions(text):\n return contractions.fix(text)","def find_replace(text, list):\r\n\tfor pair in list:\r\n\t\tif pair[0] in text:\r\n\t\t\ttext = text.replace(pair[0], pair[1])\r\n\treturn text","def delexicaliseReferenceNumber(sent, turn):\n domains = ['restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'] # , 'police']\n if turn['metadata']:\n for domain in domains:\n if turn['metadata'][domain]['book']['booked']:\n for slot in turn['metadata'][domain]['book']['booked'][0]:\n if slot == 'reference':\n val = '[' + domain + '_' + slot + ']'\n else:\n val = '[' + domain + '_' + slot + ']'\n key = normalize(turn['metadata'][domain]['book']['booked'][0][slot])\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\n\n # try reference with hashtag\n key = normalize(\"#\" + turn['metadata'][domain]['book']['booked'][0][slot])\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\n\n # try reference with ref#\n key = normalize(\"ref#\" + turn['metadata'][domain]['book']['booked'][0][slot])\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\n return sent","def getRefs(self, text):\n pattern = re.compile(r'(?:(?<=^)|(?<=\\s))\\[(.+)\\]((?:http(?:s?):\\/\\/|\\/)\\S+)(?=\\s|$)',\n re.U)\n text = pattern.sub(self.refs, text)\n return text","def remove_citations(text: str) -> str:\n text = re.sub(\"\\[[a-zA-Z]\\]\", \"\", text)\n return re.sub(r\"\\[(\\s|\\w)*\\d+(\\s|\\w)*\\]\", \"\", text)","def compute_species_from_citation_linking(citelist: list) -> None:\n # for i, cite in enumerate(tqdm(citelist)):\n unrecorded_xrefs = []\n recorded_refs = set()\n for i, cite in enumerate(citelist):\n recorded_refs.add(cite.cite_key)\n if cite.actual == \"=\":\n cname = \"\"\n crossnames = collections.Counter()\n for j in range(i): # only look at entries up to the current one\n tmp = citelist[j]\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\n cname = tmp.name\n crossnames.update([tmp.actual])\n if len(crossnames) == 0:\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\n elif len(crossnames) == 1:\n cite.actual = list(crossnames.keys())[0]\n else:\n # find name(s) with largest count\n mcnt = max(crossnames.values())\n keylist = []\n for key in crossnames:\n if crossnames[key] == mcnt:\n keylist.append(key)\n\n if len(keylist) == 1:\n cite.actual = keylist[0]\n else:\n cite_name = cite.name.lower()\n while cite_name.find(\" \") > -1:\n cite_name = cite_name[cite_name.find(\" \")+1:]\n while cname.find(\" \") > -1:\n cname = cname[cname.find(\" \")+1:]\n\n if cite_name in keylist:\n cite.actual = cite_name\n elif cname in keylist:\n cite.actual = cname\n else:\n cite.actual = keylist[0]\n\n if cite.name_note == \".\":\n cite.name_note = \"in part\"\n else:\n cite.name_note = \"in part; \" + cite.name_note\n\n for x in unrecorded_xrefs:\n if x[1] in recorded_refs:\n report_error(\"Reference {} ({}) does not appear until after citation from {} ({})\".format(x[1], x[3],\n x[0], x[2]))","def ref_analyzer(citation_informations, initialresult, initial_citationlist,\n initial_referencelist,config, updated_rec_list ):\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attr function\", alert_admin=True)\n return {}\n\n pubrefntag = \"\"\n try:\n pubrefntag = config.get(function, \"reference_via_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_report_number\", alert_admin=True)\n return {}\n\n pubreftag = \"\"\n try:\n pubreftag = config.get(function, \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_pubinfo\", alert_admin=True)\n return {}\n\n #pubrefntag is often 999C5r, pubreftag 999C5s\n if task_get_task_param('verbose') >= 9:\n write_message(\"pubrefntag \"+pubrefntag)\n write_message(\"pubreftag \"+pubreftag)\n\n citation_list = initial_citationlist\n reference_list = initial_referencelist\n result = initialresult\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\n d_references_s = citation_informations[2]\n #dict of recid -> publication_infos_in_its_bibliography\n d_records_s = citation_informations[3] #recid -> its publication inf\n t1 = os.times()[4]\n\n write_message(\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\")\n for somerecid in updated_rec_list:\n try:\n del citation_list[somerecid]\n except KeyError:\n pass\n try:\n del reference_list[somerecid]\n except KeyError:\n pass\n\n write_message(\"Phase 1: d_references_report_numbers\")\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\n #meaning: rec 8 contains these in bibliography\n\n done = 0\n numrecs = len(d_references_report_numbers)\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n done = done+1\n\n for refnumber in refnumbers:\n if refnumber:\n p = refnumber\n f = 'reportnumber'\n #sanitise p\n p.replace(\"\\n\",'')\n #search for \"hep-th/5644654 or such\" in existing records\n rec_ids = get_recids_matching_query(p, f)\n\n if len(rec_ids) > 1:\n msg = \"Whoops: record '%d' report number value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_citer_cited(thisrecid, rec_ids[0])\n remove_from_missing(p)\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n # Citation list should have rec_ids[0] but check anyway\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n #append unless this key already has the item\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid)\n #and update result\n result[rec_ids[0]] += 1\n\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n else:\n #the reference we wanted was not found among our records.\n #put the reference in the \"missing\".. however, it will look\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\n #This should really be done in the next loop d_references_s\n #but the 999C5s fields are not yet normalized\n\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\n rectext = \"\" # print_record() call disabled to speed things up\n lines = rectext.split(\"\\n\")\n rpart = p #to be used..\n for l in lines:\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\n st = l.find('$s')\n if (st > 0):\n end = l.find('$', st)\n if (end == st):\n end = len(l)\n rpart = l[st+2:end]\n insert_into_missing(thisrecid, rpart)\n\n mesg = \"d_references_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t2 = os.times()[4]\n\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\n write_message(\"Phase 2: d_references_s\")\n done = 0\n numrecs = len(d_references_s)\n for thisrecid, refss in d_references_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n\n done = done+1\n\n for refs in refss:\n if refs:\n p = refs\n #remove the latter page number if it is like 67-74\n matches = re.compile(\"(.*)(-\\d+$)\").findall(p)\n if matches and matches[0]:\n p = matches[0][0]\n\n # check reference value to see whether it is well formed:\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"is not well formed; skipping it.\" % (thisrecid, p)\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n continue # skip this ill-formed value\n\n # look for reference value:\n rec_id = None\n try:\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\n except:\n rec_ids = None\n if len(rec_ids) > 1:\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_message(\"These match searching \"+p+\" in journal: \"+repr(rec_ids), verbose=9)\n #the refered publication is in our collection, remove\n #from missing\n remove_from_missing(p)\n else:\n #it was not found so add in missing\n insert_into_missing(thisrecid, p)\n #check citation and reference for this..\n if rec_ids and rec_ids[0]:\n #the above should always hold\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\n result[rec_ids[0]] += 1 #add count for this..\n\n #update reference_list accordingly\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n mesg = \"d_references_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t3 = os.times()[4]\n done = 0\n numrecs = len(d_reports_numbers)\n write_message(\"Phase 3: d_reports_numbers\")\n\n #search for stuff like CERN-TH-4859/87 in list of refs\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n for reportcode in reportcodes:\n if reportcode:\n rec_ids = []\n try:\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\n except:\n rec_ids = []\n\n if rec_ids:\n for recid in rec_ids:\n #normal checks..\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(recid):\n reference_list[recid] = []\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n\n #normal updates\n if not recid in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(recid)\n if not thisrecid in reference_list[recid]:\n reference_list[recid].append(thisrecid)\n\n mesg = \"d_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #find this record's pubinfo in other records' bibliography\n write_message(\"Phase 4: d_records_s\")\n done = 0\n numrecs = len(d_records_s)\n t4 = os.times()[4]\n for thisrecid, recs in d_records_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_records_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n p = recs.replace(\"\\\"\",\"\")\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\n write_message(\"These records match \"+p+\" in \"+pubreftag+\" : \"+str(rec_ids), verbose=9)\n if rec_ids:\n for rec_id in rec_ids:\n #normal checks\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(rec_id):\n reference_list[rec_id] = []\n\n if not rec_id in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(rec_id)\n if not thisrecid in reference_list[rec_id]:\n reference_list[rec_id].append(thisrecid)\n\n mesg = \"d_records_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n write_message(\"Phase 5: reverse lists\")\n\n #remove empty lists in citation and reference\n keys = citation_list.keys()\n for k in keys:\n if not citation_list[k]:\n del citation_list[k]\n\n keys = reference_list.keys()\n for k in keys:\n if not reference_list[k]:\n del reference_list[k]\n\n write_message(\"Phase 6: self-citations\")\n selfdic = {}\n #get the initial self citation dict\n initial_self_dict = get_cit_dict(\"selfcitdict\")\n selfdic = initial_self_dict\n #add new records to selfdic\n acit = task_get_option(\"author-citations\")\n if not acit:\n write_message(\"Self cite processing disabled. Use -A option to enable it.\")\n else:\n write_message(\"self cite and author citations enabled\")\n selfdic = get_self_citations(updated_rec_list, citation_list,\n initial_self_dict, config)\n #selfdic consists of\n #key k -> list of values [v1,v2,..]\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\n\n #create a reverse \"x cited by y\" self cit dict\n selfcitedbydic = {}\n for k in selfdic.keys():\n vlist = selfdic[k]\n for v in vlist:\n if selfcitedbydic.has_key(v):\n tmplist = selfcitedbydic[v]\n if not k in tmplist:\n tmplist.append(k)\n else:\n tmplist = [k]\n selfcitedbydic[v] = tmplist\n\n write_message(\"Getting author citations\")\n\n #get author citations for records in updated_rec_list\n initial_author_dict = get_initial_author_dict()\n authorcitdic = initial_author_dict\n acit = task_get_option(\"author-citations\")\n if not acit:\n print \"Author cites disabled. Use -A option to enable it.\"\n else:\n write_message(\"author citations enabled\")\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\n initial_author_dict, config)\n\n\n if task_get_task_param('verbose') >= 3:\n #print only X first to prevent flood\n tmpdict = {}\n tmp = citation_list.keys()[0:10]\n for t in tmp:\n tmpdict[t] = citation_list[t]\n write_message(\"citation_list (x is cited by y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(citation_list.keys())))\n tmp = reference_list.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = reference_list[t]\n write_message(\"reference_list (x cites y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(reference_list.keys())))\n tmp = selfcitedbydic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfcitedbydic[t]\n mesg = \"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\"\n mesg += str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfcitedbydic.keys())))\n tmp = selfdic.keys()[0:100]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfdic[t]\n mesg = \"selfdic (x cites y and one of the authors of x same as y's): \"+str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfdic.keys())))\n tmp = authorcitdic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = authorcitdic[t]\n write_message(\"authorcitdic (author is cited in recs): \"+str(tmpdict))\n write_message(\"size: \"+str(len(authorcitdic.keys())))\n insert_cit_ref_list_intodb(citation_list, reference_list,\n selfcitedbydic, selfdic, authorcitdic)\n\n t5 = os.times()[4]\n\n write_message(\"Execution time for analyzing the citation information generating the dictionary:\")\n write_message(\"... checking ref number: %.2f sec\" % (t2-t1))\n write_message(\"... checking ref ypvt: %.2f sec\" % (t3-t2))\n write_message(\"... checking rec number: %.2f sec\" % (t4-t3))\n write_message(\"... checking rec ypvt: %.2f sec\" % (t5-t4))\n write_message(\"... total time of ref_analyze: %.2f sec\" % (t5-t1))\n\n return result","def figure_id_filter(text):\n def fn(m):\n s = m.group(1) or \"\"\n s += m.group(2)\n body = m.group(4)\n s += replace_body(body, type=m.group(3))\n s += m.group(5) or \"\" # Close parens if any\n return s\n\n return __ref_pattern.sub(fn,text)","def replace_contractions(sample):\n sample[\"full_text\"] = contractions.fix(sample[\"full_text\"])\n return sample","def clean_reference_html(ref: str) -> str:\n # replace hyphens with an n-dash in page ranges\n sstr = r\"((?:Pp\\. |:)[\\D]?[\\d]+)(-)([\\D]?[\\d]+)\"\n ref = re.sub(sstr, r\"\\1–\\3\", ref)\n\n # replace hyphens with an n-dash in volume ranges\n sstr = r\"(\\([\\d]+)(-)([\\d]+\\))\"\n ref = re.sub(sstr, r\"\\1–\\3\", ref)\n\n # remove (?) from entries with unknown years\n ref = ref.replace(\" (?) \", \" \")\n return ref","def process_citation(style, reference_list, citation):\n processed_citation = Element(\"span\", attrib={\"class\":\"citation\"})\n\n for reference in reference_list:\n citeref = SubElement(processed_citation, \"span\")\n for style_node in style.citation.layout:\n process_node(citeref, style_node, style.macros, reference) \n\n return(processed_citation)","def replace(orig, new, text):\n return([word.replace(orig, new) for word in containing(orig, text)])","def replace_all(sentence_string_list,input_word, user_ans):\r\n\tindex = 0\r\n\twhile index < len(sentence_string_list):\r\n\t\tif input_word in sentence_string_list[index]:\r\n\t\t\tsentence_string_list[index] = sentence_string_list[index].replace(input_word, user_ans)\r\n\t\tindex += 1","def rep_legtags(text, footnotes):\n textstring = text\n tagsintext = []\n taggedtextlist = []\n tagtextpat = re.compile(r'\\[/[a-z]\\]')\n tagtextitir = tagtextpat.finditer(textstring)\n for tagfound in tagtextitir:\n closetag = tagfound.group()\n opentag = \"[\" + closetag[2:]\n tag = opentag[1:-1]\n tagsintext.append(tag)\n tagtextlist = get_tagtext(textstring, tag)\n for taggedtext in tagtextlist:\n tagstring = opentag + taggedtext + closetag\n taggedtextlist.append(tagstring)\n for tag in tagsintext:\n tagplace = tagsintext.index(tag)\n replacetext = taggedtextlist[tagplace]\n for footnote in footnotes:\n if footnote[:2] == tag + \" \":\n if \"[/LRep]\" in footnote:\n replacementlist = get_tagtext(footnote, \"LRep\")\n repstring = \"[LRep]\" + replacementlist[0] + \"[/LRep]\"\n textstringlist = textstring.split(replacetext)\n textstring = repstring.join(textstringlist)\n return textstring","def citeproc(style, reference_list):\n pass","def parsingconvtext(retrievedtext,customtextlist):\r\n if not retrievedtext: #in case empty text \r\n retrievedtext=changenonetostr(retrievedtext)\r\n newtext=BeautifulSoup(retrievedtext).get_text() \r\n #newtext=changenonetostr(retrievedtext)\r\n #newtext=BeautifulSoup(newtext).get_text() \r\n #remove http links\r\n newtext=re.sub(r'http\\S+', '', newtext)\r\n newtext=re.sub(r'\\r\\r\\r\\n', ' ', newtext)\r\n #remove LL specific text\r\n if customtextlist:\r\n for i in customtextlist:\r\n newtext=re.sub(i, '', newtext)\r\n return newtext","def main(bib_fpath=None):\n\n if bib_fpath is None:\n bib_fpath = 'My Library.bib'\n\n # DEBUG = ub.argflag('--debug')\n # Read in text and ensure ascii format\n dirty_text = ut.readfrom(bib_fpath)\n\n from fixtex.fix_tex import find_used_citations, testdata_fpaths\n\n if exists('custom_extra.bib'):\n extra_parser = bparser.BibTexParser(ignore_nonstandard_types=False)\n parser = bparser.BibTexParser()\n ut.delete_keys(parser.alt_dict, ['url', 'urls'])\n print('Parsing extra bibtex file')\n extra_text = ut.readfrom('custom_extra.bib')\n extra_database = extra_parser.parse(extra_text, partial=False)\n print('Finished parsing extra')\n extra_dict = extra_database.get_entry_dict()\n else:\n extra_dict = None\n\n #udata = dirty_text.decode(\"utf-8\")\n #dirty_text = udata.encode(\"ascii\", \"ignore\")\n #dirty_text = udata\n\n # parser = bparser.BibTexParser()\n # bib_database = parser.parse(dirty_text)\n # d = bib_database.get_entry_dict()\n\n print('BIBTEXPARSER LOAD')\n parser = bparser.BibTexParser(ignore_nonstandard_types=False,\n common_strings=True)\n ut.delete_keys(parser.alt_dict, ['url', 'urls'])\n print('Parsing bibtex file')\n bib_database = parser.parse(dirty_text, partial=False)\n print('Finished parsing')\n\n bibtex_dict = bib_database.get_entry_dict()\n old_keys = list(bibtex_dict.keys())\n new_keys = []\n for key in ub.ProgIter(old_keys, label='fixing keys'):\n new_key = key\n new_key = new_key.replace(':', '')\n new_key = new_key.replace('-', '_')\n new_key = re.sub('__*', '_', new_key)\n new_keys.append(new_key)\n\n # assert len(ut.find_duplicate_items(new_keys)) == 0, 'new keys created conflict'\n assert len(ub.find_duplicates(new_keys)) == 0, 'new keys created conflict'\n\n for key, new_key in zip(old_keys, new_keys):\n if key != new_key:\n entry = bibtex_dict[key]\n entry['ID'] = new_key\n bibtex_dict[new_key] = entry\n del bibtex_dict[key]\n\n # The bibtext is now clean. Print it to stdout\n #print(clean_text)\n verbose = None\n if verbose is None:\n verbose = 1\n\n # Find citations from the tex documents\n key_list = None\n if key_list is None:\n cacher = ub.Cacher('texcite1', enabled=0)\n data = cacher.tryload()\n if data is None:\n fpaths = testdata_fpaths()\n key_list, inverse = find_used_citations(fpaths, return_inverse=True)\n # ignore = ['JP', '?', 'hendrick']\n # for item in ignore:\n # try:\n # key_list.remove(item)\n # except ValueError:\n # pass\n if verbose:\n print('Found %d citations used in the document' % (len(key_list),))\n data = key_list, inverse\n cacher.save(data)\n key_list, inverse = data\n\n # else:\n # key_list = None\n\n unknown_pubkeys = []\n debug_author = ub.argval('--debug-author', default=None)\n # ./fix_bib.py --debug_author=Kappes\n\n if verbose:\n print('Fixing %d/%d bibtex entries' % (len(key_list), len(bibtex_dict)))\n\n # debug = True\n debug = False\n if debug_author is not None:\n debug = False\n\n known_keys = list(bibtex_dict.keys())\n missing_keys = set(key_list) - set(known_keys)\n if extra_dict is not None:\n missing_keys.difference_update(set(extra_dict.keys()))\n\n if missing_keys:\n print('The library is missing keys found in tex files %s' % (\n ub.repr2(missing_keys),))\n\n # Search for possible typos:\n candidate_typos = {}\n sedlines = []\n for key in missing_keys:\n candidates = ut.closet_words(key, known_keys, num=3, subset=True)\n if len(candidates) > 1:\n top = candidates[0]\n if ut.edit_distance(key, top) == 1:\n # \"sed -i -e 's/{}/{}/g' *.tex\".format(key, top)\n import os\n replpaths = ' '.join([relpath(p, os.getcwd()) for p in inverse[key]])\n sedlines.append(\"sed -i -e 's/{}/{}/g' {}\".format(key, top, replpaths))\n candidate_typos[key] = candidates\n print('Cannot find key = %r' % (key,))\n print('Did you mean? %r' % (candidates,))\n\n print('Quick fixes')\n print('\\n'.join(sedlines))\n\n # group by file\n just = max([0] + list(map(len, missing_keys)))\n missing_fpaths = [inverse[key] for key in missing_keys]\n for fpath in sorted(set(ub.flatten(missing_fpaths))):\n # ut.fix_embed_globals()\n subkeys = [k for k in missing_keys if fpath in inverse[k]]\n print('')\n ut.cprint('--- Missing Keys ---', 'blue')\n ut.cprint('fpath = %r' % (fpath,), 'blue')\n ut.cprint('{} | {}'.format('Missing'.ljust(just), 'Did you mean?'), 'blue')\n for key in subkeys:\n print('{} | {}'.format(\n ut.highlight_text(key.ljust(just), 'red'),\n ' '.join(candidate_typos[key]))\n )\n\n # for key in list(bibtex_dict.keys()):\n\n if extra_dict is not None:\n # Extra database takes precidence over regular\n key_list = list(ut.unique(key_list + list(extra_dict.keys())))\n for k, v in extra_dict.items():\n bibtex_dict[k] = v\n\n full = ub.argflag('--full')\n\n for key in key_list:\n try:\n entry = bibtex_dict[key]\n except KeyError:\n continue\n self = BibTexCleaner(key, entry, full=full)\n\n if debug_author is not None:\n debug = debug_author in entry.get('author', '')\n\n if debug:\n ut.cprint(' --- ENTRY ---', 'yellow')\n print(ub.repr2(entry, nl=1))\n\n entry = self.fix()\n # self.clip_abstract()\n # self.shorten_keys()\n # self.fix_authors()\n # self.fix_year()\n # old_pubval = self.fix_pubkey()\n # if old_pubval:\n # unknown_pubkeys.append(old_pubval)\n # self.fix_arxiv()\n # self.fix_general()\n # self.fix_paper_types()\n\n if debug:\n print(ub.repr2(entry, nl=1))\n ut.cprint(' --- END ENTRY ---', 'yellow')\n bibtex_dict[key] = entry\n\n unwanted_keys = set(bibtex_dict.keys()) - set(key_list)\n if verbose:\n print('Removing unwanted %d entries' % (len(unwanted_keys)))\n ut.delete_dict_keys(bibtex_dict, unwanted_keys)\n\n if 0:\n d1 = bibtex_dict.copy()\n full = True\n for key, entry in d1.items():\n self = BibTexCleaner(key, entry, full=full)\n pub = self.publication()\n if pub is None:\n print(self.entry['ENTRYTYPE'])\n\n old = self.fix_pubkey()\n x1 = self._pubval()\n x2 = self.standard_pubval(full=full)\n # if x2 is not None and len(x2) > 5:\n # print(ub.repr2(self.entry))\n\n if x1 != x2:\n print('x2 = %r' % (x2,))\n print('x1 = %r' % (x1,))\n print(ub.repr2(self.entry))\n\n # if 'CVPR' in self.entry.get('booktitle', ''):\n # if 'CVPR' != self.entry.get('booktitle', ''):\n # break\n if old:\n print('old = %r' % (old,))\n d1[key] = self.entry\n\n if full:\n d1 = bibtex_dict.copy()\n\n import numpy as np\n import pandas as pd\n df = pd.DataFrame.from_dict(d1, orient='index')\n\n paged_items = df[~pd.isnull(df['pub_accro'])]\n has_pages = ~pd.isnull(paged_items['pages'])\n print('have pages {} / {}'.format(has_pages.sum(), len(has_pages)))\n print(ub.repr2(paged_items[~has_pages]['title'].values.tolist()))\n\n entrytypes = dict(list(df.groupby('pub_type')))\n if False:\n # entrytypes['misc']\n g = entrytypes['online']\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\n\n entrytypes['book']\n entrytypes['thesis']\n g = entrytypes['article']\n g = entrytypes['incollection']\n g = entrytypes['conference']\n\n def lookup_pub(e):\n if e == 'article':\n return 'journal', 'journal'\n elif e == 'incollection':\n return 'booksection', 'booktitle'\n elif e == 'conference':\n return 'conference', 'booktitle'\n return None, None\n\n for e, g in entrytypes.items():\n print('e = %r' % (e,))\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\n if 'pub_full' in g.columns:\n place_title = g['pub_full'].tolist()\n print(ub.repr2(ub.dict_hist(place_title)))\n else:\n print('Unknown publications')\n\n if 'report' in entrytypes:\n g = entrytypes['report']\n missing = g[pd.isnull(g['title'])]\n if len(missing):\n print('Missing Title')\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\n\n if 'journal' in entrytypes:\n g = entrytypes['journal']\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\n\n missing = g[pd.isnull(g['journal'])]\n if len(missing):\n print('Missing Journal')\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\n\n if 'conference' in entrytypes:\n g = entrytypes['conference']\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\n\n missing = g[pd.isnull(g['booktitle'])]\n if len(missing):\n print('Missing Booktitle')\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\n\n if 'incollection' in entrytypes:\n g = entrytypes['incollection']\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\n\n missing = g[pd.isnull(g['booktitle'])]\n if len(missing):\n print('Missing Booktitle')\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\n\n if 'thesis' in entrytypes:\n g = entrytypes['thesis']\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\n missing = g[pd.isnull(g['institution'])]\n if len(missing):\n print('Missing Institution')\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\n\n # import utool\n # utool.embed()\n\n # Overwrite BibDatabase structure\n bib_database._entries_dict = bibtex_dict\n bib_database.entries = list(bibtex_dict.values())\n\n #conftitle_to_types_set_hist = {key: set(val) for key, val in conftitle_to_types_hist.items()}\n #print(ub.repr2(conftitle_to_types_set_hist))\n\n print('Unknown conference keys:')\n print(ub.repr2(sorted(unknown_pubkeys)))\n print('len(unknown_pubkeys) = %r' % (len(unknown_pubkeys),))\n\n writer = BibTexWriter()\n writer.contents = ['comments', 'entries']\n writer.indent = ' '\n writer.order_entries_by = ('type', 'author', 'year')\n\n new_bibtex_str = bibtexparser.dumps(bib_database, writer)\n\n # Need to check\n #jegou_aggregating_2012\n\n # Fix the Journal Abreviations\n # References:\n # https://www.ieee.org/documents/trans_journal_names.pdf\n\n # Write out clean bibfile in ascii format\n clean_bib_fpath = ub.augpath(bib_fpath.replace(' ', '_'), suffix='_clean')\n\n if not ub.argflag('--dryrun'):\n ut.writeto(clean_bib_fpath, new_bibtex_str)","def replace_tokens(the_string, sequence_switched, suggestion_list, indexer, tokenizer, model, suggestion_num):\n # Get top tokens\n top_tokens = get_top_tokens(sequence_switched, indexer, tokenizer, model, suggestion_num)\n\n iterator = 0\n for token in top_tokens:\n suggestion_list[iterator] += the_string.replace(tokenizer.mask_token, tokenizer.decode([token]))\n iterator += 1\n\n return suggestion_list","def preprocessing(sample):\n\n content = ' '.join(sample)\n content = re.sub(r\"\\'ll\", \" will\", content)\n content = re.sub(r\"\\'d\", \" would\", content)\n content = re.sub(r\"\\'s\", \" is\", content)\n content = re.sub(r\"\\'m\", \" am\", content)\n content = re.sub(r\"\\'ve\", \" have\", content)\n content = re.sub(r\"\\'re\", \" are\", content)\n content = content.replace('&', 'and')\n content = content.replace('$', '')\n content = content.split()\n return content","def __call__(self, text):\n return extract_references(text, self.preprocessor, self.model)","def replace_canonical_referents(self, **kwargs):\n replaced_count = 0\n for doc in self.annotation_documents:\n for sen in doc.sentences:\n for part in sen.participants:\n replaced_count += part.replace_by_canonical_referent(**kwargs) #this will replace in-place in project, but returns a count too.\n\n return replaced_count","def updateUncertain(update_read, my_ref):\n # get positions of repeats in the ref_seq\n # (\\w) catures a 'word' char, \\1 back-reference, {1,} reps \\1 >= 1 times\n match = re.finditer(r'(\\w)\\1{1,}', my_ref)\n rep_pos = [xrange(*m.span()) for m in match]\n\n # make sure the indexing matches that of the reference sequence\n # (updateRead will output 1 based, but we need 0 based) we will do this\n # in-place for now\n update_read = [(x[0], x[1] - 1, x[2], x[3]) for x in update_read]\n\n # split update read into tuples that are in repeat regions or are not\n # make sure good numbering returns to a 1-index\n rep_set = set(flatten(rep_pos))\n uncertain = [x for x in update_read\n if x[1] in rep_set and x[2] in set(['D', 'I'])]\n good = ((x[0], x[1] + 1, x[2], x[3]) for x in update_read\n if x[1] not in rep_set or x[2] not in set(['D', 'I']))\n\n # find the repeat region for each tuple in uncertain and package each\n # position and the length of the range so we can do everything in one pass\n # remember list comp loops go outer then inner\n rng_gen = (rng for rng in rep_pos for tup in uncertain if tup[1] in rng)\n rng_pack = [zip(rng, itertools.repeat(len(rng), len(rng))) for rng in rng_gen]\n\n # create new tuples with uncertain tag in first pass\n # add length of unncertain on the second pass\n # return numbering to 1-based\n new_tups = ((tup[0], x[0] + 1, 'U' + tup[2], str(x[1]) + tup[3])\n for i, tup in enumerate(uncertain) for x in rng_pack[i])\n\n # combine the new tuples with the good ones and sort on the index\n return sorted(itertools.chain(good, new_tups), key=lambda x: x[1])","def lookup(text):\n ret = basic(text)\n ret.update(basic(text, \"gwas\"))\n return ret","def extract_references(content):\n references = {}\n content = content.replace(\"\\n\", \"\\\\n\")\n matches = re.findall('(\\[[0-9]+\\][^\\[]*?(?=\\[|Acknowledge|Fig|Table|Conclusion|pdf))', content)\n if matches:\n for match in matches:\n ref_id = get_reference_id(match)\n # No reference id exist -- skip it\n if ref_id != -1:\n value = match.replace('\\\\n', '\\n')\n references[ref_id] = value\n return references","def replace_images(md):\n\n return re.sub(r'\\!\\[(.*?)\\]\\((.*?)\\)', replace_image, md)","def enrich_textrefs(self, db):\n tr_list = db.select_all(db.TextRef,\n db.TextContent.text_ref_id == db.TextRef.id,\n db.TextContent.source == self.my_source,\n db.TextRef.manuscript_id.is_(None))\n file_list = self.ftp.get_csv_as_dict('filelist.csv', header=0)\n pmcid_mid_dict = {entry['PMCID']: entry['MID'] for entry in file_list}\n pmid_mid_dict = {entry['PMID']: entry['MID'] for entry in file_list\n if entry['PMID'] is not '0'}\n for tr in tr_list:\n if tr.pmcid is not None:\n tr.manuscript_id = pmcid_mid_dict[tr.pmcid]\n elif tr.pmid is not None and tr.pmid in pmid_mid_dict.keys():\n tr.manuscript_id = pmid_mid_dict[tr.pmid]\n db.commit(\"Could not update text refs with manuscript ids.\")\n return","def make_inter_wiki_links(string):\n\n _inter_wiki = _inter_wiki_re.get(config.get_option('language'))\n\n if _inter_wiki is None:\n return string\n\n mapping = _inter_wiki_map.get(config.get_option('language'))\n\n for i, regex in enumerate(_inter_wiki):\n out = []\n last_index = 0\n for match in regex.finditer(string):\n text = match.group('text')\n # Offset by 1 to account for text group\n index = match.groups().index(text, 1)-1\n data = mapping[i*_MAX_RE+index][1]\n\n out.append(string[last_index:match.start('text')])\n if text == data['link']:\n out.append('[[%s]]' % data['link'])\n else:\n out.append('[[%s|%s]]' % (data['link'], text))\n\n last_index = match.end('text')\n\n out.append(string[last_index:])\n string = ''.join(out)\n\n return string","def get_references(input, verbose=False):\n\n # TODO: Make this a class reference parser\n\n # *** These tags are mobile-site specific\n\n # When we don't have proper access rights, this is present in the html\n GUEST_TAG_TUPLE = (\"li\", {\"id\": \"menuGuest\"})\n\n # Entries are \"li\" tags with classes of the form:\n # article-reference-article\n # article-reference-other-ref\n REFERENCE_TAG_TUPLE = (\"li\", {\"class\": re.compile('article-reference-*')})\n\n # This is the URL to the page that contains the document info, including\n # reference material\n BASE_URL = _SD_URL + '/science/article/pii/'\n\n # This URL was found first via Fiddler, then via closer inspection of the script\n # 'article_catalyst.js' under sciencedirect.com/mobile/js in the function\n # resolveReferences\n REF_RESOLVER_URL = _SD_URL + '/science/referenceResolution/ajaxRefResol'\n\n # Return the BeautifulSoup result, the requests session, and the requests response\n if _is_url(input):\n pii = _extract_pii(input)\n else:\n pii = input\n\n sess = requests.Session()\n\n if verbose:\n print('Requesting main page for pii: %s' % pii)\n resp = sess.get(BASE_URL + pii, cookies={'Site': 'Mobile'})\n\n # Step 2 - Get the reference tags\n\n soup = BeautifulSoup(resp.text)\n\n reference_section = soup.find(\"ol\", {\"class\": \"article-references\"})\n\n if reference_section is None:\n # Then we might be a guest. In other words, we might not have sufficient\n # privileges to access the data we want. Generally this is protected via\n # IP mask. When I'm working from home I need to VPN into work so\n # that I can access the data :/\n print(\"reference_section is None\")\n temp = soup.find(*GUEST_TAG_TUPLE)\n if temp is None:\n # We might have no references ... (Doubtful)\n raise ParseException(\"References were not found ..., code error likely\")\n else:\n raise InsufficientCredentialsException(\n \"Insufficient access rights to get referencs, requires certain IP addresses (e.g. university based IP)\")\n\n ref_tags = reference_section.find_all(*REFERENCE_TAG_TUPLE)\n\n n_refs = len(ref_tags)\n\n if n_refs == 0:\n return None\n\n # Step 3 - Resolve reference links\n # --------------------------------------------------------------------------\n # The returned html code contains javascript which returns more information\n # about each reference, such as:\n #\n # - links to the full text\n # - DOI \n\n\n # Step 3.1 - Make the request for the information\n # --------------------------------------------------------------------------\n # We need the eid of the current entry, it is of the form:\n #\n # SDM.pm.eid = \"1-s2.0-0006899387903726\"\n #\n # * I think this entry gets deleted after the requests so it may not be\n # visible if looking for it in Chrome. \n match = re.search('SDM\\.pm\\.eid\\s*=\\s*\"([^\"]+)\"', resp.text)\n #eid = match.group(1)\n\n # This list comes from the resolveReferences function in article_catalyst.js\n payload = {\n '_pii': pii,\n '_refCnt': n_refs,\n '_docType': 'article', # yikes, this might change ...\n '_refRangeStart': '1',\n '_refRangeCount': str(n_refs)} # This is normally in sets of 20's ...\n # I'm not sure if it is important to limit this. The browser then\n # makes a request fromr 1 count 20, 21 count 20, 41 count 20 etc,\n # It always goes by 20 even if there aren't 20 left\n\n if verbose:\n print('Requesting reference links')\n r2 = sess.get(REF_RESOLVER_URL, params=payload)\n\n # Step 3.2 - Parse the returned information into single entries\n # --------------------------------------------------------------------------\n # This could probably be optimized in terms of execution time. We basically\n # get back a single script tag. Inside is some sort of hash map for links\n # for each reference.\n #\n # The script tag is of the form:\n # myMap['bibsbref11']['refHtml']= \"<some html stuffs>\"; \n # myMap['bibsbref11']['absUrl']= \"http://www.sciencedirect.com/science/absref/sd/0018506X7790068X\";\n # etc.\n #\n # - Each entry is quite long.\n # - Normally contains html\n # - can be empty i.e. myMap['bibsbref11']['refHtml'] = \"\";\n # - the refHtml is quite interesting\n # - the absolute url is not always present (and currently not parsed)\n more_soup = BeautifulSoup(r2.text)\n script_tag = more_soup.find('script')\n\n # We unquote the script text as it is transmitted with characters escaped\n # and we want the parsed data to contain the non-escaped text\n #\n # We might eventually want to move this to being after the regular expression ...\n script_text = urllib_unquote(script_tag.text)\n\n ref_match_result = re.findall(\"myMap\\['bibsbref(\\d+)'\\]\\['refHtml'\\]=\\s?\" + '\"([^\"]*)\";', script_text)\n # Tokens:\n # 0 - the # from bibsbref#\n # 1 - the html content from the 'refHtml' entry\n # \n # NOTE: We don't really use the #, so we might remove the () around\n # \\d+ which would shift the index from 1 to 0\n if verbose:\n print('Creating reference objects')\n\n if len(ref_match_result) > 0:\n zipped = zip(ref_tags, ref_match_result, range(n_refs))\n ref_objects = [ScienceDirectRef(ref_tag, ref_link_info[1], ref_id) for\n ref_tag, ref_link_info, ref_id in zipped]\n else:\n zipped = zip(ref_tags, range(n_refs))\n ref_objects = [ScienceDirectRef(ref_tag, ref_id) for\n ref_tag, ref_id in zipped]\n\n # Step 4:\n # --------------------------------------------------------------------------\n # TODO: Improve documentation for this step\n\n if verbose:\n print('Retrieving Scopus Counts')\n\n ref_scopus_eids = [] # The Scopus IDs of the references to resolve\n # but with a particular formatting ...\n ref_count = 0 # Number of references we haven't resolved\n\n ref_count_list = []\n # NOTE: Browser requests these in the reverse order ...\n for ref_id, ref in enumerate(ref_objects):\n\n if ref._data_sceid is not None:\n ref_scopus_eids.append(ref._data_sceid + ',' + str(ref_id + 1) + '~')\n ref_count += 1\n\n # If we've got enough, then update the counts\n # The 20 may be arbitrary but it was what was used in original JS\n if ref_count > 20:\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\n ref_count = 0\n ref_scopus_eids = []\n\n # Get any remaining reference counts\n if ref_count != 0:\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\n\n # Take the raw data and set the citation count for each object\n for ref_tuple in ref_count_list:\n ref_id = int(ref_tuple[0]) - 1\n ref_count = int(ref_tuple[1])\n ref_objects[ref_id].scopus_cite_count = ref_count\n\n # All done!\n # ---------\n return ref_objects","def __find_and_replace_words(text=str, word_list=list, replacement_text=str):\n word_set = set()\n tokenized_text = nltk.word_tokenize(text)\n # loop through all the words to see if it contains any of the words in the word list\n for word in tokenized_text:\n if word in word_list:\n word_set.add(word)\n # convert the set to list to make it sortable\n word_set = list(word_set)\n word_set.sort(key=len, reverse=True)\n # replace those words, if any, with the replacement words\n for word in word_set:\n if len(word) == 1:\n # if its only one character (e.g. 'C'), ensure that it is a singular word by using regex\n text = re.sub(r\"\\b%s\\b\" % word, replacement_text, text, flags=re.IGNORECASE)\n else:\n text = text.replace(word, replacement_text)\n return text","def _fix_evidence_refs(db, rid_stmt_trios):\n rid_set = {rid for rid, _, _ in rid_stmt_trios if rid is not None}\n logger.info(\"Getting text refs for %d readings.\" % len(rid_set))\n if rid_set:\n rid_tr_pairs = db.select_all([db.Reading.id, db.TextRef],\n db.Reading.id.in_(rid_set),\n *db.join(db.TextRef, db.Reading))\n rid_tr_dict = {rid: tr for rid, tr in rid_tr_pairs}\n for rid, sid, stmt in rid_stmt_trios:\n if rid is None:\n # This means this statement came from a database, not reading.\n continue\n assert len(stmt.evidence) == 1, \\\n \"Only raw statements can have their refs fixed.\"\n _set_evidence_text_ref(stmt, rid_tr_dict[rid])\n return","def replacingBasicElementsFromEpubLibre(text):\n text = re.sub(r'<div class=\"vineta\"><img[^<]*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n \n \n # Replace some elements with atributes with other cleaner elements\n\n text = re.sub(r'<p class=\"ind\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\n \n text = re.sub(r'<p class=\"cursiva\\d*\">(.*?)</p>', r'<p><seg rend=\"italic\">\\1</seg></p>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"calibre[0-9]+\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"saltoinicio\\d*\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"salto[0-9]+\">', r'<milestone unit=\"section\"/>\\n<p>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"saltoc\">', r'<milestone unit=\"section\"/>\\n<p>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"sinopsis\">.*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"ilustra\">.*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE) \n text = re.sub(r'<div class=\"info\">.*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<br class=\"calibre9\" />', r' ', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<sup(|[^>]*)>(<a[^>]*?>)?\\[.*?\\](</a>)?</sup>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span(|[^>]*)>\\s*</span>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<a(|[^>]*)>\\s*</a>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"(tlogo|tautor|ttitulo|trevision|tfirma)\">.*?</p>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<h1 class=\"ttitulo\">(.*?)</h1>\\s*(<p class=\"titulo\">.*?</p>)', r'<head>\\1: \\2</head>', text, flags=re.DOTALL|re.IGNORECASE)\n \n text = re.sub(r'<img[^>]*?>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"(bloque|ala)?derecha[0-9]*\">(.*?)</p>', r'<ab>\\2</ab>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"asangre[0-9]?\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\n #text = re.sub(r'<p class=\"centrado1\">(.*?)</p>', r'<quote>\\n<p>\\1</p>\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n #text = re.sub(r'<p class=\"extenso\">(.*?)</p>', r'<quote>\\n<p>\\1</p>\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span class=\"(normal|calibre)[0-9]*\">(.*?)</span>', r'<seg rend=\"italic\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<p><span class=\"(cap[0-9]*|versalita|smallcaps)\">(..?)</span>', r'<p>\\2', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span class=\"(versalita|smallcaps)[0-9]*\">(.*?)</span>', r'<seg rend=\"smallcaps\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<div class=\"(?:poema[0-9]*|poe|versos|estrofas|citar\\d*|verso\\d*|versosinpie\\d*|centro|centrado|poemaizqcursi\\d*|poemaizq|poemacursi\\d*)\">(.*?)</div>', r'<lg>\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"center\">\\s+<table class=\"text\">\\s+<tr class=\"calibre\\d*\">\\s+<td class=\"calibre\\d*\">(\\s+.*?)</td>\\s+</tr>\\s+</table>\\s+</div>', r'<lg>\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE) \n\n text = re.sub(r'<blockquote class=\"(?:versos?\\d*)\">(.*?)</blockquote>', r'<lg>\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE)\n\n \n text = re.sub(r'(<lg>\\s*)<p class=\"izq\">', r'\\1<l>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<p class=\"poemaizqcursi\">(.*?)</p>', r'<lg>\\n<l>\\1</l>\\n</lg>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"verso\">(.*?)</p>', r'<l>\\1</l>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"normal1\">(.*?)</p>', r'<l>\\1</l>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"cursiva\">(.*?)</p>', r'<p><seg rend=\"italic\">\\1</seg></p>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<em(|[^>]*)>(.*?)</em>', r'<seg rend=\"italic\">\\2</seg>', text, flags=re.IGNORECASE)\n\n text = re.sub(r'<p class=\"estrofa1\">', r'\\n\\n<p>', text, flags=re.DOTALL|re.IGNORECASE)\n \n text = re.sub(r'<i(|[^>]*)>(.*?)</i>', r'<seg rend=\"italic\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<b(|[^>]*)>(.*?)</b>', r'<seg rend=\"bold\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<sup(|[^>]*)>(.*?)</sup>', r'<seg rend=\"sup\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<span class=\"cursiva[0-9]*\">(.*?)</span>', r'<seg rend=\"italic\">\\1</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<sub(|[^>]*)>(.*?)</sub>', r'<seg rend=\"subscript\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<small(|[^>]*)>(.*?)</small>', r'<seg rend=\"small\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<span class=\"nosep\\d*\">(.*?)</span>', r'<seg rend=\"italic\">\\1</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<span class=\"nosep\\d*\">(.*?)</span>', r'<seg rend=\"italic\">\\1</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<strong(|[^>]*)>(.*?)</strong>', r'<seg rend=\"bold\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<p class=\"cita\">(.*?)</p>', r'<quote>\\n<p>\\1</p>\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"micita\">(.*?)</p>', r'<quote>\\n<p>\\1</p>\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"acotacion\\d*\">(.*?)</div>', r'<quote>\\n\\1\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<cite.*?>(.*?)</cite>', r'<seg type=\"cite\">\\1</seg>', text, flags=re.DOTALL|re.IGNORECASE)\n \n # Setting quotes in floatingText element\n text = re.sub(r'<div class=\"(?:mi)?cita\">(.*?)</div>', r'<floatingText>\\n<body>\\n<div type=\"section\">\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<blockquote class=\"bloque\">(.*?)</blockquote>', r'<floatingText>\\n<body>\\n<div type=\"section\">\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<p class=\"salto25x\">([IVXCL\\d]+)</p>', r'<head>\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<p class=\"salto25x\\d+\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\n \n \n text = re.sub(r'<div class=\"bloquederecha1\">(.+?)</div>', r'<quote>\\1</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"cursiva[0-9]*\">', r'<div rend=\"italic\">', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"calibre\" id=\"calibre_link-[0-9]+\">', r'<div>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"centrado\">[… ]+?</p>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"asteriscos\">[\\* ∗]+</p>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"centrado\">\\[…\\]</p>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"asteriscos\\d*\">[\\. ]+</p>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<hr (class=\"calibre\\d*\" )?/>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(<milestone unit=\"section\"/>\\s+)+', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<div class=\"(?:banner|text\\d+)\">(.*?)</div>', r'<floatingText>\\n<body>\\n<div type=\"section\">\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<blockquote class=\"banner\\d*\">(.*?)</blockquote>', r'<floatingText>\\n<body>\\n<div type=\"section\">\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<blockquote class=\"carta\\d*\">(.*?)</blockquote>', r'<floatingText>\\n<body>\\n<div type=\"section\">\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n #text = re.sub(r'<blockquote class=\"cita\">(.*?)</blockquote>', r'<floatingText>\\n<body>\\n<div>\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<blockquote class=\"calibre\\d*\">(.*?)</blockquote>', r'<floatingText>\\n<body>\\n<div type=\"section\">\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<html.*?<body>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'</body></html>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(</p>)\\s*(<head>.*?</head>)', r'\\1</div>\\n<div>\\n\\2', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<p class=\"(?:subh?|inicio|numtitulo|subt)\">(.*?)</p>', r'<head>\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<h[1-6][^>]*>(.*?)</h[1-6]>', r'<head>\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"(tsub|sub)?tit(ulo)?[0-9]*\">(.*?)</p>', r'<head>\\3</head>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"(subcapitulo|capitulonombre|h\\d+)\">(.*?)</p>', r'<head>\\2</head>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"tit-cap\">(.*?)</p>', r'<head>\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'</head>\\s*<p class=\"(subtil|subcap)\">(.*?)</p>', r': \\2</head>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<span class=\"ncap\">(.*?)</span>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span class=\"ncap\">(.*?)</span>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span class=\"palabra\">(.*?)</span>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span class=\"sigla\\d*\">(.*?)</span>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(<p>—?)<span class=\"inicial\">(.*?)</span>', r'\\1\\2', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(<p>—?¿?)<big class=\"calibre\\d*\">(.*?)</big>', r'\\1\\2', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<head>\\s*</head>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p [^>]*?>\\s*\\* *\\* *\\*</p>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"sep\">\\* \\* \\*</p>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'</head>\\s*<head>', r': ', text, flags=re.DOTALL|re.IGNORECASE)\n #text = re.sub(r'(</head>)(\\s*<head>)', r'\\1\\n<div>\\n\\2', text, flags=re.DOTALL|re.IGNORECASE)\n\n\n \"\"\"\n #Setting divs after ps. Used for 219\n text = re.sub(r'(</p>|</div>)(\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\1\\n<div>\\2</div>\\n\\3', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(</p>|</div>)(\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\1\\n<div>\\2</div>\\n\\3', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(</p>|</div>)(\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\1\\n<div>\\2</div>\\n\\3', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(</p>|</div>)(\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\1\\n<div>\\2</div>\\n\\3', text, flags=re.DOTALL|re.IGNORECASE)\n \"\"\"\n\n # Cleaning some <milestone />\n text = re.sub(r'(<div>\\s*)<milestone unit=\"section\"/>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(</div>\\s*)<milestone unit=\"section\"/>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(</head>\\s*)<milestone unit=\"section\"/>', r'\\1', text, flags=re.DOTALL|re.IGNORECASE)\n\n # Setting some floatingTexts\n #text = re.sub(r'(<div(| [^>]*?)>((?!<div).)*</div>)(\\s*<p)', r'<floatingText>\\n<body>\\n\\1\\n</body>\\n</floatingText>\\n\\4', text, flags=re.DOTALL|re.IGNORECASE)\n\n #text = re.sub(r'(<div(|[^>]+?)>.*?</div>)(\\s*<p)', r'<floatingText>\\n<body>\\n<div>\\n\\1\\n</div>\\n</body>\\n</floatingText>\\n\\3', text, flags=re.DOTALL|re.IGNORECASE)\n \n # Floating Texts\n\n text = re.sub(r'<p(| [^>]+)>[\\s\\r\\n]*</p>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'\\n[ \\t]+', r'\\n', text, flags=re.IGNORECASE)\n text = re.sub(r'[\\r\\n]+', r'\\r\\n', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div>[\\r?\\n]*</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'(<div>((?!<div>).)*</div>\\r?\\n)\\Z', r'<!--\\1-->', text, flags=re.DOTALL|re.IGNORECASE)\n \n\n text = re.sub(r'<div class=\"cita?[0-9]*\">(.*?)</div>', r'<quote>\\1</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<p class=\"cita[0-9]*\">(.*?</p>)', r'<quote>\\n<p>\\1</quote>', text, flags=re.DOTALL|re.IGNORECASE)\n\n \n text = re.sub(r'<br class=\"calibre[0-9]*\" />', r'', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'(<p>—?. ?)<span class=\"palabra\">(.*?)</span>', r'\\1<seg rend=\"small\">\\2</seg>', text, flags=re.IGNORECASE)\n text = re.sub(r'<p class=\"saltoinicio\"><span class=\"inicial\">(.*)</span><span class=\"palabra\">(.*?)</span>', r'<p>\\1\\2', text, flags=re.IGNORECASE)\n text = re.sub(r'<p class=\"saltoinicio\"><span class=\"inicial\">(.*)</span>', r'<p>\\1', text, flags=re.IGNORECASE)\n text = re.sub(r'<p>(.)<span>(.*)</span>', r'<p>\\1\\2', text, flags=re.IGNORECASE)\n text = re.sub(r'<span class=\"inicial\">(.*)</span>', r'\\1', text, flags=re.IGNORECASE)\n\n \n text = re.sub(r'(<head>.*?)<seg rend=\"(smallcaps|small)\">(.*?)</seg>(.*?</head>)', r'\\1\\3\\4', text, flags=re.DOTALL|re.IGNORECASE)\n \n text = re.sub(r'<div[^>]*?>\\s*</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n \n text = re.sub(r'<div class=\"(?:tablacentro\\d*|center)\">\\s*<table class=\"text\\d*\">\\s*<tr class=\"calibre\\d*\">\\s*<td class=\"calibre\\d*\">(.*?)\\s*</td>\\s*</tr>\\s*</table>\\s*</div>', r'<lg>\\1\\n</lg>', text, flags=re.DOTALL|re.IGNORECASE)\n\n text = re.sub(r'<p class=\"traduccion\">(.*?)</p>', r'<ab type=\"translation\">\\1</ab>', text, flags=re.IGNORECASE)\n\n #text = re.sub(r'<p class=\"centrado\">(.*?)</p>', r'<head>\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"centrado\\d*\">(.*?)</p>', r'<ab>\\1</ab>', text, flags=re.DOTALL|re.IGNORECASE)\n \n #text = re.sub(r'<blockquote class=\"calibre18\">(.*?)</blockquote>', r'<floatingText>\\n<body>\\n\\1\\n</body>\\n</floatingText>\\n', text, flags=re.DOTALL|re.IGNORECASE)\n\n \"\"\"\n #The next lines were used for 232, a theater-like novel\n \n text = re.sub(r'<p>(.*?</seg>)(\\.\\—.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.IGNORECASE)\n \"\"\"\n \"\"\"\n #The next lines were used for 109, a theater-like novel\n text = re.sub(r'<p>(.*?</seg>\\.)(.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.IGNORECASE)\n text = re.sub(r'<p>(.*?</seg>)( +<seg rend=\"italic\">.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.IGNORECASE)\n text = re.sub(r'<seg rend=\"italic\">(\\(.*?)</seg>', r'<stage>\\1</stage>', text, flags=re.IGNORECASE)\n \"\"\"\n \"\"\"\n #The next lines were used for 224, a theater-like novel\n text = re.sub(r'<p>(.<seg rend=\"small\">.*?</seg>\\.)(.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.IGNORECASE)\n #text = re.sub(r'<p>(.*?</seg>)( +<seg rend=\"italic\">.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.IGNORECASE)\n text = re.sub(r'<seg rend=\"italic\">(\\(.*?)</seg>', r'<stage>\\1</stage>', text, flags=re.IGNORECASE)\n \"\"\"\n \"\"\"\n # Code used for paradox rey\n text = re.sub(r'<div class=\"clear\">\\s*</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"banner\\d*\">(.*?)</p>', r'<ab>\\1</ab>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"francesa\\d*\">(.*?)</p>', r'<ab>\\1</ab>', text, flags=re.DOTALL|re.IGNORECASE)\n \"\"\"\n \"\"\"\n text = re.sub(r'<p class=\"personaje\">(.*?)</p>\\s*<p>(.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<span class=\"descrip\">(.*?)</span>', r'<stage>\\1</stage>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"(?:descrip\\d*|entrada|fin)\">(.*?)</p>', r'<stage>\\1</stage>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<p class=\"cuadro\">(.*?)</p>', r'<head>\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"descrip1\">(.*?)</div>', r'<stage>\\1</stage>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"puntos\">.*?</div>', r'<milestone unit=\"section\"/>', text, flags=re.DOTALL|re.IGNORECASE)\n text = re.sub(r'<div class=\"tablacentro\">(.*?)</div>', r'<lg>\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE)\n \"\"\"\n text = re.sub(r'<p class=\"decla\">(.*?\\. )(.*?)</p>', r'<sp>\\n<speaker>\\1</speaker>\\n<p>\\2</p>\\n</sp>', text, flags=re.IGNORECASE)\n text = re.sub(r'<p class=\"(?:descrip|decla1)\">(.*?)</p>', r'<stage>\\1</stage>', text, flags=re.IGNORECASE)\n\n\n text = re.sub(r'(<seg\\s+rend\\s*=\\s*\"small(?:caps)?\">)(.*?)(?=</seg>)', lambda m: m.group(1) + m.group(2).lower(), text)\n \n text = re.sub(r'(<seg\\s+rend\\s*=\\s*\"small(?:caps)?\">)(.*?)(</seg>)', r'\\2', text, flags=re.IGNORECASE)\n \n text = re.sub(r'<big[^>]*?>(.*?)</big>', r'\\1', text, flags=re.IGNORECASE) \n text = re.sub(r'<div class=\"serif\" id=\"calibre_link-\\d+\">', r'<div>', text, flags=re.IGNORECASE) \n text = re.sub(r'<div class=\"sinmargen\" id=\"calibre_link-\\d+\">', r'<div>', text, flags=re.IGNORECASE) \n\n text = re.sub(r'<p class=\"(?:carta|firma\\d+)\">(.*?)</p>', r'<ab>\\1</ab>', text, flags=re.IGNORECASE) \n\n\n\n return text","def replace_redacted(text):\n pat = re.compile(r'\\[\\*\\*(.*?)\\*\\*\\]', re.IGNORECASE)\n \n # replace name types\n text = pat.sub(replace_names, text)\n \n # replace place types\n text = pat.sub(replace_places, text)\n \n # replace person identifier types\n text = pat.sub(replace_identifiers, text) \n \n # replace date types\n text = pat.sub(replace_dates, text)\n \n # replace remaining digits\n text = pat.sub(replace_digits, text)\n return text","def _fix_links(self, text, page_names):\n for n in page_names:\n text = text.replace(f\"]({n})\", f\"]({n}.html)\")\n text = text.replace(f\"]({n}.md)\", f\"]({n}.html)\")\n return text","def format_i94prtl(string, former_countries_dict, countries_ref_list):\n to_replace = ['INVALID - ', ' (BPS)', ' (I-91)', ' (RT. 5)', ' (BP - SECTOR HQ)', ' #ARPT', '#ARPT', '-ARPT', 'ARPT']\n\n for char in to_replace:\n string = string.strip().replace(char, '')\n\n string = string.replace('NETH ANTILLES', 'Netherlands Antilles')\\\n .replace('WASHINGTON #INTL', 'WA')\\\n .replace('MEXICO Land (Banco de Mexico)', 'Mexico City, Mexico')\\\n .replace(\"PASO DEL NORTE,TX\", \"PASO DEL NORTE, TX\")\\\n .replace(\"WASHINGTON DC\", \"WASHINGTON, DC\")\\\n .replace(\"MARIPOSA AZ\", \"MARIPOSA, AZ\")\\\n .replace(\"SAIPAN, SPN\", \"SAIPAN, MP\")\\\n .replace(\"Abu Dhabi\", \"ABU DHABI, UNITED ARAB EMIRATES\")\\\n .replace(\"SEOUL KOREA\", \"SEOUL, SOUTH KOREA\")\\\n .replace(\"PRC\", \"CHINA\")\\\n .replace(\"TURK & CAIMAN\", \"Turks and Caicos Islands\")\\\n .replace(\"PAPUA, NEW GUINEA\", \"SEPIK PLAINS, PAPUA NEW GUINEA\")\\\n .replace(\"ENGLAND\", \"UNITED KINGDOM\")\\\n .replace(\"UNIDENTIFED AIR / SEAPORT\", \"Unknown code\")\\\n .replace(\"UNKNOWN POE\", \"Unknown code\")\\\n .replace(\"NOT REPORTED/UNKNOWN\", \"Unknown code\")\n\n if string.find(',') != -1:\n if (len(string.split(', ')) > 2) & (len(string.strip(', ')[-1]) == 2):\n # eg. JUAREZ INTL, MEXICO CITY, MX\n\n result = string.split(', ')\n return result[0].title().strip() + ', ' + result[-1].strip()\n\n elif (len(string.split(', ')) > 2) & (len(string.strip(', ')[-1]) > 2):\n # eg. GUARULHOS INTL, SAO PAULO, BRAZIL\n\n result = string.split(', ')\n\n # find country name in former country name\n # and standardise country name based on countries_ref_list\n find_former_country = process.extract(result[-1], list(former_countries_dict.keys()))[0]\n\n if find_former_country[1] == 100:\n country = former_countries_dict[find_former_country[0]]\n else:\n country = result[-1]\n\n country = process.extract(country, countries_ref_list)[0][0]\n\n return result[0].title().strip() + ', ' + country\n\n elif len(string.split(', ')[-1]) > 2:\n # eg. HAKAI PASS, CANADA\n\n result = string.split(', ')\n\n # find country name in former country name\n # and standardise country name based on countries_ref_list\n find_former_country = process.extract(result[-1], list(former_countries_dict.keys()))[0]\n\n if find_former_country[1] == 100:\n country = former_countries_dict[find_former_country[0]]\n else:\n country = result[-1]\n\n country = process.extract(country, countries_ref_list)[0][0]\n\n return result[0].title().strip() + ', ' + country\n\n elif len(string.split(', ')[-1]) == 2:\n # eg. TAMPA, FL\n return string.split(',')[0].title() + ',' + string.split(',')[-1]\n else:\n return string","def replace(text, max_width=MAX_WIDTH, max_height=MAX_HEIGHT):\r\n rules = list(ProviderRule.objects.all())\r\n patterns = [re.compile(r.regex) for r in rules] # Compiled patterns from the rules\r\n parts = [] # The parts that we will assemble into the final return value.\r\n indices = [] # List of indices of parts that need to be replaced with OEmbed stuff.\r\n indices_rules = [] # List of indices into the rules in order for which index was gotten by.\r\n urls = set() # A set of URLs to try to lookup from the database.\r\n stored = {} # A mapping of URLs to StoredOEmbed objects.\r\n index = 0\r\n # First we pass through the text, populating our data structures.\r\n for i, part in re_parts(patterns, text):\r\n if i == -1:\r\n parts.append(part)\r\n index += 1\r\n else:\r\n to_append = \"\"\r\n # If the link ends with one of our overrides, build a list\r\n while part[-1] in END_OVERRIDES:\r\n to_append += part[-1]\r\n part = part[:-1]\r\n indices.append(index)\r\n urls.add(part)\r\n indices_rules.append(i)\r\n parts.append(part)\r\n index += 1\r\n if to_append:\r\n parts.append(to_append)\r\n index += 1\r\n # Now we fetch a list of all stored patterns, and put it in a dictionary \r\n # mapping the URL to to the stored model instance.\r\n for stored_embed in StoredOEmbed.objects.filter(match__in=urls, max_width=max_width, max_height = max_height):\r\n stored[stored_embed.match] = stored_embed\r\n # Now we're going to do the actual replacement of URL to embed.\r\n for i, id_to_replace in enumerate(indices):\r\n rule = rules[indices_rules[i]]\r\n part = parts[id_to_replace]\r\n try:\r\n # Try to grab the stored model instance from our dictionary, and\r\n # use the stored HTML fragment as a replacement.\r\n parts[id_to_replace] = stored[part].html\r\n except KeyError:\r\n try:\r\n # Build the URL based on the properties defined in the OEmbed spec.\r\n url = u\"%s?url=%s&maxwidth=%s&maxheight=%s&format=%s\" % (\r\n rule.endpoint, part, max_width, max_height, FORMAT\r\n )\r\n # Fetch the link and parse the JSON.\r\n resp = simplejson.loads(fetch(url))\r\n \r\n # link types that don't have html elements aren't dealt with right now.\r\n if resp['type'] == 'link' and 'html' not in resp:\r\n raise ValueError\r\n \r\n # Depending on the embed type, grab the associated template and\r\n # pass it the parsed JSON response as context.\r\n replacement = render_to_string('oembed/%s.html' % resp['type'], {'response': resp})\r\n if replacement:\r\n stored_embed = StoredOEmbed.objects.create(\r\n match = part,\r\n max_width = max_width,\r\n max_height = max_height,\r\n html = replacement,\r\n )\r\n stored[stored_embed.match] = stored_embed\r\n parts[id_to_replace] = replacement\r\n else:\r\n raise ValueError\r\n except ValueError:\r\n parts[id_to_replace] = part\r\n except KeyError:\r\n parts[id_to_replace] = part\r\n except urllib2.HTTPError:\r\n parts[id_to_replace] = part\r\n # Combine the list into one string and return it.\r\n return mark_safe(u''.join(parts))","def replace_strings(text: str, replacement_pair_list):\n\n new_text = text\n for pair in replacement_pair_list:\n old, new = pair\n new_text = new_text.replace(old, new)\n return new_text","def _remove_invalid_links(text):\n\n for reply_number in re.finditer(REGEX_REPLY, text):\n post_id = reply_number.group(1)\n post = Post.objects.filter(id=post_id)\n if not post.exists():\n text = string.replace(text, REFLINK_PREFIX + post_id, post_id)\n\n return text","def compute_applied_name_contexts(citelist: list) -> None:\n for i, cite in enumerate(citelist):\n if (cite.context == \"specimen\") or (cite.context == \"location\") or (cite.context == \"sequence\"):\n cite.applied_cites = {cite}\n elif cite.context == \"citation\":\n for j in range(i): # only look at entries up to the current one\n tmp = citelist[j]\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\n if (tmp.context == \"specimen\") or (tmp.context == \"location\") or (tmp.context == \"sequence\"):\n cite.applied_cites |= {tmp}\n if len(cite.applied_cites) == 0:\n cite.applied_cites = None\n\n # for testing purposes only\n # if cite.applied_cites is None:\n # print(cite.cite_key, cite.name_key, cite.context, \"No application\")\n # else:\n # for c in cite.applied_cites:\n # print(cite.cite_key, cite.name_key, cite.context, c.cite_key, c.name_key, c.context)\n # input()","def replace(self, string):\n for i, j in self.defs.items():\n string = string.replace(i, j)\n return string","def refined_text(text):\n import re\n text = text.replace('<e1>','')\n text = text.replace('</e1>','')\n text = text.replace('<e2>','')\n text = text.replace('</e2>','')\n\n text = text[1:-1] # trim quotes\n # text = text.replace('\"','')\n # text = text.replace(',','')\n # text = text.replace('.','')\n # text = text.replace(';','')\n # text = text.replace('`','')\n # text = text.replace('\\'','')\n # text = text.replace('(','')\n # text = text.replace(')','')\n # text = text.replace('/','')\n\n return text","def texify(value):\n for k, v in REPLACEMENTS.items():\n value = value.replace(k, v)\n return mark_safe(value)","def rep(self,tag,nstr):\n tmp = []\n for line in self.content: \n if tag in line:\n tmp.append(line.replace(tag,nstr))\n else:\n tmp.append(line)\n self.content = tmp","def process_bibliography(style, reference_list):\n processed_bibliography = Element(\"ol\", attrib={\"class\":\"bibliography\"})\n\n for reference in reference_list:\n ref = SubElement(processed_bibliography, \"li\", \n attrib={\"property\":\"dc:references\"})\n\n for style_node in style.bibliography.layout:\n process_node(ref, style_node, style.macros, reference) \n\n return(processed_bibliography)","def shorten_in_text(text):\n replacements = {} #URL -> is.gd URL\n #Only check for urls that start with \"http://\" for now\n for m in re.finditer(\"http://[^ \\n\\r]*\", text):\n try:\n replacements[m.group()] = shorten_with_is_gd(m.group())\n except:\n replacements[m.group()] = m.group()\n for url,replacement in replacements.items():\n text = text.replace(url, replacement)\n return text","def add_references_to_papers(infile, dir):\n papers = json.load(open(infile))\n for paper in papers:\n for file in os.listdir(dir):\n if file.split(\".txt\")[0] == paper['doi']: # Must find the correct file to parse\n filename = TEXT_DIR+file\n refs =extract_references_from_txt(filename) #Uses the text files to find references\n paper['references']=refs\n return papers","def fix_errors_in_citation(citation):\n result = regex.sub(r\"\\s+\", \" \", citation)\n result = regex.sub(r\"§(?=\\d)\", \"§ \", result)\n result = regex.sub(r\",\\sbis\\s\", \" bis \", result)\n return result","def process_citations(conn: Connection, path: Path) -> None:\n sql = \"INSERT OR IGNORE INTO Citations (note, reference) VALUES (?, ?)\"\n run_sql_on_csv(conn, path, sql, (int, str))","def make_unparsed_comments(self, replacements):\n if not hasattr(self, 'cleaned_html'):\n self.cleaned_html = self.clean_html()\n \n self.basic_comments = self.cleaned_html\n for d in self.replacements:\n if d['regex']:\n self.basic_comments = re.sub(d['pattern'], d['replacement'], self.basic_comments)\n else:\n self.basic_comments = self.basic_comments.replace(d['pattern'], d['replacement'])\n return self.basic_comments","def filter_profanities(text,replace_handler=profanity_word_handler):\n profanities=CensoredWord.objects.get_profanities_wordlist()\n return word_filter(text,profanities,replace_handler)","def lf_findall_interp_with_discont(report):\n\n if 'interpretation' in report.sections.keys():\n interpretation = report.sections['interpretation']\n interp_text = interpretation['text']\n return abnormal_interp_with_discont(interp_text)\n else:\n candtext = get_section_with_name(SEIZURE_SECTION_NAMES_LOWER, report)\n if candtext:\n return abnormal_interp_with_discont(candtext)\n else:\n return ABSTAIN_VAL","def convert_true_support_to_relevance():\n clm_as_key_sen_support_score_val_wiki = read_pickle(\"clm_as_key_sen_support_score_val_wiki\") #key is a claim num, value is a list of tuple - sen and their 0/1/2 support score)\n claim_sen_true_relevance_dict = {}\n claim_dict = read_pickle(\"claim_dict\")\n #t Dead Poets Society , Peter Weir , Steven HaftPaurn clm_as_key_sen_support_score_val_wiki to a relevance dict\n for (claim_num,list_sen_support_score) in clm_as_key_sen_support_score_val_wiki.items():\n new_relevance_list = []\n for (sen,support_score) in list_sen_support_score:\n if support_score !=0:\n new_relevance_list.append((sen,1))\n else:\n new_relevance_list.append((sen,0))\n claim_sen_true_relevance_dict[claim_dict[str(claim_num)]] = new_relevance_list\n save_pickle(\"claim_sen_relevance_dict_\"+curr_source, claim_sen_true_relevance_dict)","def sanitize_text(text: str) -> str:\n for r in [RE_NOISE, RE_EMAIL, RE_REFERENCE]:\n text = r.sub(\"\", text)\n return text","def update_refseq_info(ensembl_info, word, value):\n if \"refseq\" in word:\n if \"mrna\" in word:\n if \"predicted\" in word:\n ensembl_info[\"refseq_mrna_predicted\"] = value\n else:\n ensembl_info[\"refseq_mrna\"] = value\n\n if \"ncrna\" in word:\n ensembl_info[\"refseq_ncrna\"] = value\n return ensembl_info","def _process_replacements(input_text):\n original_input = list(input_text) # Immutable\n replaced = list(input_text)\n for index, char in enumerate(original_input):\n if char.isalpha() or char.isdigit():\n replacement = helpers.get_character(char)\n if replacement:\n replaced[index] = ' ' + replacement + ' '\n return helpers.remove_excess_spaces(''.join(replaced))","def replace(word, replacements):\n for old, new in replacements:\n if word == old:\n return new\n return word","def preprocess_note(text):\n # replace redacted info with tokens\n text = replace_redacted(text)\n \n # misc scrubbing\n text = replace_misc(text) \n return text","def corrected(text):\n # Init SpellChecker\n spell = SpellChecker(language='fr', distance=1, case_sensitive=False)\n # Get misspellings and corrections\n text_splitted = text.split()\n misspelled = spell.unknown(text_splitted)\n correction = {word:spell.correction(word) for word in misspelled}\n\n # Replace misspellings in text\n for k,v in correction.items():\n text = text.replace(k,v)\n return text","def process_text(text):\n fix_dict = {'fig.': 'fig', 'fig .': 'fig ', 'Fig.': 'Fig', 'Fig .': 'Fig ',\n 'figure.': 'figure', 'figure .': 'figure ', 'Figure.': 'Fig', 'Figure .': 'Fig ',\n 'et al.': 'et al', 'III': '3', 'II': '2', 'I': '1'}\n\n for old_pattern in fix_dict.keys():\n text = text.replace(old_pattern, fix_dict[old_pattern])\n return text","def citation_validation(self):\n for file in filter(lambda x: x.startswith('citation'), listdir(bs_directory)):\n print \"Processing\", file\n bs = BeautifulSoup(open(file), 'xml')\n # find ground_truth file\n ground_truth = BeautifulSoup(open(self.ground_truth_directory + file.split(\".\")[0] + '.xml'), 'xml')\n self.__citation_correction(ground_truth, bs)\n file = open(self.citation_output + file, \"wb\")\n file.write(bs.prettify().encode('utf-8'))","def store_orig_strings(self, example_list):\n\t\tself.original_articles = [ex.original_article for ex in example_list] # list of lists\n\t\tself.original_abstracts = [ex.original_abstract for ex in example_list] # list of lists\n\t\tself.original_abstracts_sents = [ex.original_abstract_sents for ex in example_list] # list of list of lists","def synonym_sentence_append(tokens, ignored_tokens=stopwords.words('english'), excluded_token_regex=None,\n max_frequency=None, min_occurrences=None,\n # min_frequency=None, max_occurrences=None,\n # min_document_frequency=None, max_document_frequency=None,\n # min_document_occurrences=None, max_document_occurrences=None,\n num_candidates=25, tokens_to_replace=None, replace_probability=0.3):\n\n # check if tokens to be replaced are supplied, if not, choose tokens depending on parameters from user\n if not tokens_to_replace:\n # flatten tuple of tuples and get token dictionary\n token_dict, token_freq, n_tokens = vectorizers._vectorizers.construct_token_dictionary_and_frequency(\n vectorizers.utils.flatten(tokens))\n\n # prune token dictionary depending on parameters supplied by user\n # returns a dictionary of candidate tokens for replacement\n candidate_dict, candidate_freq = vectorizers._vectorizers.prune_token_dictionary(\n token_dict,\n token_freq,\n ignored_tokens=ignored_tokens,\n excluded_token_regex=excluded_token_regex,\n min_frequency=(min_occurrences / n_tokens),\n max_frequency=max_frequency,\n # min_occurrences=min_occurrences,\n # max_occurrences=max_occurrences,\n # min_document_frequency=min_document_frequency,\n # max_document_frequency=max_document_frequency,\n # min_document_occurrences=min_document_occurrences,\n # max_document_occurrences=max_document_occurrences,\n total_tokens=n_tokens,\n total_documents=len(tokens),\n )\n\n # take a random sample of tokens from the candidate dictionary\n tokens_to_replace = random.sample(list(candidate_dict.keys()), num_candidates)\n\n print(\"Tokens for replacement:\")\n print(tokens_to_replace)\n\n new_tokens = []\n for sent in tokens:\n word_changed = False\n sent = list(sent)\n # check each token by index and create a deep copy with the changed word at that index and add new sentence\n # to new corpus\n for idx, token in enumerate(sent):\n if token in tokens_to_replace:\n new_sent = copy.deepcopy(sent)\n new_sent[idx] = f\"{token}_$$0\"\n new_tokens.append(new_sent)\n word_changed = True\n # depending on probability, add another copy of the new sentence with the second replacement synonym\n if random.random() <= replace_probability:\n added_sent = copy.deepcopy(sent)\n added_sent[idx] = f\"{token}_$$1\"\n new_tokens.append(added_sent)\n # if no words were changed, just add the original sentence to the new corpus\n if not word_changed:\n new_tokens.append(sent)\n\n # change dataset back to tuple of tuples before returning\n new_tokens_tuple = tuple(tuple(sent) for sent in new_tokens)\n\n return tokens_to_replace, new_tokens_tuple","def prepare_nlu_text(example: Text, entities: List[Dict]):\n if not Utility.check_empty_string(example):\n if entities:\n for entity in entities:\n example = example.replace(\n entity[\"value\"],\n \"[\" + entity[\"value\"] + \"](\" + entity[\"entity\"] + \")\",\n )\n return example","def gen_enzyme_religation_regex(enzyme):\n\n # Split the str on the comma to separate the different enzymes.\n enzyme = enzyme.split(\",\")\n\n # Check on Biopython dictionnary the enzyme.\n rb = RestrictionBatch(enzyme)\n\n # Initiation:\n give_list = []\n accept_list = []\n ligation_list = []\n\n # Iterates on the enzymes.\n for enz in rb:\n\n # Extract restriction sites and look for cut sites.\n site = enz.elucidate()\n fw_cut = site.find(\"^\")\n rev_cut = site.find(\"_\")\n\n # Process \"give\" site. Remove N on the left (useless).\n give_site = site[:rev_cut].replace(\"^\", \"\")\n while give_site[0] == \"N\":\n give_site = give_site[1:]\n give_list.append(give_site)\n\n # Process \"accept\" site. Remove N on the rigth (useless).\n accept_site = site[fw_cut + 1 :].replace(\"_\", \"\")\n while accept_site[-1] == \"N\":\n accept_site = accept_site[:-1]\n accept_list.append(accept_site)\n\n # Iterates on the two list to build all the possible HiC ligation sites.\n for give_site in give_list:\n for accept_site in accept_list:\n # Replace \"N\" by \".\" for regex searching of the sites\n ligation_list.append((give_site + accept_site).replace(\"N\", \".\"))\n ligation_list.append(\n str(Seq(give_site + accept_site).reverse_complement()).replace(\n \"N\", \".\"\n )\n )\n\n # Build the regex for any ligation sites.\n pattern = \"|\".join(sorted(list(set(ligation_list))))\n return re.compile(pattern)","def getBibTeX(bibref,tag_suf,outFile):\n if bibref == '1988iras....1.....B':\n bibtex = ['>@article{1988iras....1.....B,\\n',\n ' title={Infrared astronomical satellite (IRAS) catalogs and atlases. Volume 1: Explanatory supplement},\\n',\n ' keywords = {All Sky Photography, Catalogs, Indexes (Documentation), Infrared Astronomy Satellite, Cosmology, Galaxies, Star Formation, Stellar Evolution, Astrophysics},\\n',\n ' author={Beichman, CA and Neugebauer, G and Habing, HJ and Clegg, PE and Chester, Thomas J},\\n',\n ' year=1988,\\n',\n ' volume = {1},\\n', \n ' month = jan,\\n', \n ' adsurl = {https://ui.adsabs.harvard.edu/abs/1988iras....1.....B},\\n'\n '}\\n']\n else:\n baseURL = 'https://ui.adsabs.harvard.edu/abs/'\n suf = '/exportcitation'\n lines = urllib.request.urlopen(baseURL+bibref+suf).readlines()\n lines = [l.decode('utf-8') for l in lines] # remove additional webpage encoding\n \n bibtex = []\n for l in range(0, len(lines)):\n if 'export-textarea ' in str(lines[l]):\n bibtex.append(str(lines[l]))\n t = l+1\n \n while '</textarea>' not in str(lines[t+1]):\n bibtex.append(str(lines[t])) \n t += 1\n \n for item in bibtex:\n if 'author' in item.split('=')[0]:\n auth = item.split('=')[1].split(',')[0]\n for i in string.punctuation:\n auth = auth.replace(i, '')\n auth = auth.replace(' ', '')\n if 'year' in item.split('=')[0]:\n yr = item.split('=')[1].split(',')[0]\n yr = yr.replace(' ', '')\n \n try:\n bibtex[0] = bibtex[0].split('>')[1].split('{')[0]+'{'+auth+yr+tag_suf+',\\n'\n except UnboundLocalError as ule:\n print(bibtex)\n print('')\n print(ule)\n sys.exit()\n \n with open(outFile, 'a') as o:\n for item in bibtex:\n item = item.replace('"', '\"')\n item = item.replace(''', \"'\")\n item = item.replace('&', \"&\")\n o.write(item)\n o.write('\\n')\n \n return auth+yr+tag_suf","def replaceContractions(self, text):\n\t\treturn contractions.fix(text)","def replace_misc(text): \n # replace different types of \"year old\" with \n # matches: y.o., y/o, years old. year old, yearold\n text = re.sub(r'-?\\byears? ?-?old\\b|\\by(?:o|r)*[ ./-]*o(?:ld)?\\b', ' yo', text, flags=re.IGNORECASE)\n\n # Does the same thing as above but copied from https://arxiv.org/abs/1808.02622v1\n text = re.sub(r'(\\d+)\\s*(year\\s*old|y.\\s*o.|yo|year\\s*old|year-old|-year-old|-year old)', r'\\1 yo', text, flags=re.IGNORECASE)\n \n # replaces yr, yr's, yrs with years\n text = re.sub(r'\\byr[\\'s]*\\b', 'years', text, re.IGNORECASE)\n \n # replace Pt and pt with patient, and IN/OUT/OT PT with patient\n # Note: PT also refers to physical therapy and physical therapist\n text = re.sub(r'\\b[P|p]t.?|\\b(IN|OU?T) PT\\b', 'patient ', text)\n\n # replace sex with consistant token\n text = re.sub(r'\\b(gentlman|male|man|m|M)(?!\\S)\\b', 'male', text)\n text = re.sub(r'\\b(female|woman|f|F)(?!\\S)\\b', 'female', text)\n \n # replace time types\n text = re.sub(r'\\d{0,2}:\\d{0,2} \\b[A|P]\\.?M\\.?\\b', replace_time, text, flags=re.IGNORECASE)\n text = re.sub(r'\\[\\*\\*(\\d{2})\\*\\*\\] \\b[a|p].?m.?\\b', replace_time, text, flags=re.IGNORECASE)\n \n # finally remove leftover redacted stuff (mostly empty)\n text = re.sub(r'\\[\\*\\*(.*?)\\*\\*\\]', '', text, flags=re.IGNORECASE)\n\n return text","def _clean_text(text, website):\n site_list = ['menafn_algeria', 'menafn_bahrain', 'menafn_egypt',\n 'menafn_iraq', 'menafn_jordan', 'menafn_kuwait',\n 'menafn_lebanon', 'menafn_morocco', 'menafn_oman',\n 'menafn_palestine', 'menafn_qatar', 'menafn_saudi',\n 'menafn_syria', 'menafn_tunisia', 'menafn_turkey',\n 'menafn_uae', 'menafn_yemen']\n\n if website == 'bbc':\n text = text.replace(\n \"This page is best viewed in an up-to-date web browser with style sheets (CSS) \"\n \"enabled. While you will be able to view the content of this page in your current \"\n \"browser, you will not be able to get the full visual experience. Please consider \"\n \"upgrading your browser software or enabling style sheets (CSS) if you are able to do \"\n \"so.\",\n '')\n if website == 'almonitor':\n text = re.sub(\"^.*?\\(photo by REUTERS.*?\\)\", \"\", text)\n if website in site_list:\n text = re.sub(\"^\\(.*?MENAFN.*?\\)\", \"\", text)\n elif website == 'upi':\n text = text.replace(\n \"Since 1907, United Press International (UPI) has been a leading provider of critical \"\n \"information to media outlets, businesses, governments and researchers worldwide. UPI \"\n \"is a global operation with offices in Beirut, Hong Kong, London, Santiago, Seoul and \"\n \"Tokyo. Our headquarters is located in downtown Washington, DC, surrounded by major \"\n \"international policy-making governmental and non-governmental organizations. UPI \"\n \"licenses content directly to print outlets, online media and institutions of all \"\n \"types. In addition, UPI's distribution partners provide our content to thousands of \"\n \"businesses, policy groups and academic institutions worldwide. Our audience consists \"\n \"of millions of decision-makers who depend on UPI's insightful and analytical stories \"\n \"to make better business or policy decisions. In the year of our 107th anniversary, \"\n \"our company strives to continue being a leading and trusted source for news, \"\n \"analysis and insight for readers around the world.\",\n '')\n\n text = text.replace('\\n', ' ')\n\n return text","def replaceMultiExclamationMark(text):\n text = re.sub(r\"(\\!)\\1+\", ' multiExclamation ', text)\n return text","def variables_referenced(text):\n return set(substitution_pattern.findall(text))","def process_and_dispatch(self):\n references = []\n for raw_block_references in self.raw_references:\n bibcode = raw_block_references['bibcode']\n block_references = raw_block_references['block_references']\n item_nums = raw_block_references.get('item_nums', [])\n\n parsed_references = []\n for i, reference in enumerate(block_references):\n reference = self.latex_reference.cleanup(reference)\n logger.debug(\"confTEX: parsing %s\" % reference)\n parsed_references.append(self.merge({'refstr': reference, 'refraw': reference}, self.any_item_num(item_nums, i)))\n\n references.append({'bibcode': bibcode, 'references': parsed_references})\n logger.debug(\"%s: parsed %d references\" % (bibcode, len(references)))\n\n return references","def replace_delicious_tags(generator):\n from jinja2 import Template\n template = Template(generator.context.get('DELICIOUS_TEMPLATE'))\n\n for page in generator.pages:\n for match in delicious_regex.findall(page._content):\n bookmarks = sorted([bookmark for bookmark\n in generator.context.get('DELICIOUS_BOOKMARKS')\n if set(match.split()).issubset(bookmark.tags)])\n\n # Create a context to render with\n context = generator.context.copy()\n context.update({'bookmarks': bookmarks})\n\n replacement = template.render(context)\n\n page._content = page._content.replace(\n \"[delicious \" + match + \"]\", replacement)","def _replace_links(input_string,link_dict={}):\n\n # Dictionary to hold link string/target pairs\n target_dict = {}\n\n # Strip white space from string\n this_string = input_string.strip()\n\n # If we have a string stub, return it\n if len(input_string) < 2:\n return input_string, {}\n\n # Look for patterns: \";_\" OR \",_\" OR \" _\" OR \":_\"\n search_pattern = re.compile(\"[\\,\\s\\;\\:]\\_|\\A\\_\")\n\n # Look for place to break link: \";\" OR \",\" OR \":\" OR \" \" OR \".\"\n end_pattern = re.compile(\"[\\,\\s\\;\\:\\.]\")\n\n # Look for a match\n match = search_pattern.search(this_string)\n while match:\n\n # If started with \"_\", link_alias starts at match.span()[0]\n if this_string[match.span()[0]] == \"_\":\n start = match.span()[0]\n\n # If started with \",_\" or the like, link_alias starts at match.span()[0] + 1\n else:\n start = match.span()[0] + 1\n\n # Chop string into before and after _ in link (front and back)\n front = this_string[:start]\n back = this_string[start:]\n\n # Look for end of the link\n link_end = end_pattern.search(back)\n\n # If we find the end, split back into link_alias and trailing\n if link_end:\n link_alias = back[:link_end.span()[0]]\n trailing = back[link_end.span()[0]:]\n\n # If we do not find the end, the whole back is link_alias ... no trailing\n else:\n link_alias = back[:]\n trailing = \"\"\n\n # Extract url and text for constructing the link text\n try:\n url, text = link_dict[link_alias]\n\n if url == \"\":\n if text == \"\":\n raise KeyError\n else:\n # Replace the link_alias with the text, no url\n link_string = text\n else:\n if text == \"\":\n # Replace the link_alias with link_alias[1:] -> url\n label = link_alias[1:]\n else:\n # Replace the link_alias with the text -> url\n label = text\n\n link_string = \"`{}`_\".format(label)\n url_string = \".. _`{}`: {}\".format(label,url)\n try:\n already_seen = target_dict[label]\n if already_seen != url_string:\n err = \"The same link_text '{}' corresponds to more than one url\\n\".format(text)\n raise ValueError(err)\n except KeyError:\n target_dict[label] = url_string\n\n except KeyError:\n\n # Replace the link_alias with link_alias[1:]\n link_string = link_alias[1:]\n\n # Rebuild this_string with front + new link + trailing\n this_string = \"{}{}{}\".format(front,link_string,trailing)\n\n # Look for another link\n match = search_pattern.search(this_string)\n\n return this_string, target_dict","def display_abstract( id, highlights=[]):\n a = Abstracts[ id ]\n for h in highlights:\n a = re.sub(r'\\b(%s)\\b'%h,'<mark>\\\\1</mark>',a, flags=re.IGNORECASE)\n print ('<blockquote>%s</blockquote>' % a)","def filter_substitution_image_links(links):\n return [link for link in links if '{' not in link]","def find_replace_all(self):\n old_term = self.text_find.get()\n new_term = self.text_replace.get()\n while True:\n idx = '1.0'\n idx = self.text.search(old_term, idx, nocase=1,\n stopindex=tk.END)\n lastidx = '% s+% dc' % (idx, len(old_term))\n if not idx:\n break\n self.text.delete(idx, lastidx)\n self.text.insert(idx, new_term)","def journal_booktitle_unify(entries: t.List[t.Dict]) -> t.List[t.Dict[str, str]]:\n\n def _process(\n entries: t.List[t.Dict], etype: str, val: str, replacing_guide_file: str\n ) -> t.List[t.Dict[str, str]]:\n rep_rules = {}\n with codecs.open(replacing_guide_file, \"r\", \"utf-8\") as gf:\n for line in gf:\n line = line.strip()\n raw, sep, rep = line.partition(\"=\")\n logging.debug(f'{line}')\n if len(sep) != 0:\n rep_rules[raw] = rep\n logging.debug(f'{rep_rules}')\n for e in entries:\n if e[\"ENTRYTYPE\"].lower() == etype.lower():\n value = e.get(val, \"\")\n if value in rep_rules:\n logging.debug(\"Before: %s\", e[val])\n e[val] = rep_rules[value]\n logging.debug(\"After: %s\", e[val])\n return entries\n\n entries = _process(entries, \"InProceedings\", \"booktitle\", \"conferences.txt\")\n return _process(entries, \"Article\", \"journal\", \"journals.txt\")","def find_references(doi):\n if doi is None:\n return None\n\n references = []\n if doi:\n response = requests.get(f\"https://opencitations.net/index/api/v1/references/{doi}\").json()\n if response:\n references = [{\"doi\": r['cited'].replace(\"coci =>\", \"\")} for r in response]\n\n if references:\n return references\n else:\n return None","def convert_text(s):\n for d in config.repl: # loaded from config.py\n if \"flags\" in d:\n s = re.sub(d[\"ptrn\"], d[\"repl\"], s, flags=d[\"flags\"])\n else:\n s = re.sub(d[\"ptrn\"], d[\"repl\"], s)\n return s","def placeNoteLists(self, text):\n if self.notes:\n o = OrderedDict()\n for label, info in self.notes.items():\n if 'seq' in info:\n i = info['seq']\n info['seq'] = label\n o[i] = info\n else:\n self.unreferencedNotes[label] = info\n\n if o:\n # sort o by key\n o = OrderedDict(sorted(o.items(), key=lambda t: t[0]))\n self.notes = o\n text_re = re.compile('<p>notelist(%s)(?:\\:([\\w|%s]))?([\\^!]?)(\\+?)\\.?[\\s]*</p>'\n % (self.c, self.syms), re.U)\n text = text_re.sub(self.fNoteLists, text)\n return text","def update_time_references_with_impact_zones(self, docFeatList, timexList):\n \n if not self.timexImpactZones:\n return self.timeReferences\n \n impactZoneStart = self.timexImpactZones[0][0]\n features = [feat for feat in docFeatList if feat.getStartPos() > impactZoneStart \n and self.sentence_tags[feat.getSentNum()]=='NORMAL' and not feat.inClause() \n and feat.getType() in ['VACCINE', 'DRUG']] \n for feature in features:\n sentnumber = feature.getSentNum()\n if not feature.getTlink() or not feature.getTlink().getDateTime():\n if 'concomitant' in [tg[0] for tg in self.sentence_full_tags[sentnumber]]:\n continue\n feature = self.assign_feature_time_with_impact_zones(feature, self.timexImpactZones)\n tlink = feature.getTlink()\n if tlink:\n if feature.getType()=='VACCINE':\n refType = 'Vaccine'\n confidence = 0.6\n else:\n refType = 'Drug'\n confidence = 0.6\n \n self.timeReferences[(refType, feature.getString(), 0, sentnumber, \n feature.getStartPos(), feature.getEndPos(), confidence)] = tlink.getDateTime()\n \n ##: Too many false alarms. Disable this part for now.\n ##: E.g, in 3603565(4th). .... Before treatment, .... \n# exposureSet = ['Vaccination', 'Injection']\n# for sentnum, sentence in enumerate(self.sentences):\n# timexes = [t for t in timexList if t.getDateTime() and t.getSentNum()==sentnum]\n# if timexes: ##: has been processed for time references\n# continue\n# \n# tags = set([tg[1] for tg in self.taggedSentences[sentnum]])\n# intersect = tags.intersection(exposureSet)\n# if intersect:\n# tgs = [tg for tg in self.taggedSentences[sentnum] if tg[1] in exposureSet]\n# word = tgs[0][0]\n# sentStartPos = self.sentence_startPos[sentnum]\n# dtimes = [r[2] for r in self.timexImpactZones if sentStartPos >=r[0] and sentStartPos<=r[1]]\n# if dtimes:\n# self.timeReferences[(tgs[0][1], word, None, sentnum, sentStartPos, sentStartPos, 0.6)] = dtimes[0]\n# \n return self.timeReferences","def multiwordReplace(text, wordDic):\r\n rc = re.compile('|'.join(map(re.escape, wordDic)))\r\n def translate(match):\r\n return wordDic[match.group(0)]\r\n return rc.sub(translate, text)","def process_bug_links(text):\n text = LP_RE.sub(r\"[\\1](https://bugs.launchpad.net/bugs/\\2)\", text)\n text = BDO_RE.sub(r\"[\\1](https://bugs.debian.org/\\2)\", text)\n text = ISSUE_RE.sub(r\"[\\1](https://github.com/gammu/gammu/\\2)\", text)\n return BUG_RE.sub(r\"[\\1](https://bugs.cihar.com/\\2)\", text)"],"string":"[\n \"def ref_tag_preprocess(inp_tag_text):\\n\\n # some words in references\\n inp_tag_text = inp_tag_text.replace(\\\"до римлян\\\", \\\"Римл\\\")\\n inp_tag_text = inp_tag_text.replace(\\\" и \\\", \\\"; \\\")\\n inp_tag_text = inp_tag_text.replace(\\\" і \\\", \\\"; \\\")\\n inp_tag_text = inp_tag_text.replace(\\\"–\\\", \\\"-\\\")\\n # replacing \\\"'\\\" to \\\"’\\\", it is similar in Ukrainian\\n inp_tag_text = inp_tag_text.replace(\\\"'\\\", \\\"’\\\")\\n return inp_tag_text\",\n \"def extract_references(text, preprocessor, model):\\n has_reference_section, reference_section, non_reference_section = preprocess_reference_text(text)\\n reference_text = reference_section if has_reference_section else non_reference_section\\n fragment = preprocessor(reference_text)\\n out = model(fragment.idx).argmax(dim=2).cpu().numpy()\\n\\n buffer = ''\\n found_citations = []\\n in_citation = False\\n for char, pred in zip(fragment.text, out[0, :]):\\n if pred == 2:\\n in_citation = True\\n if pred == 3:\\n in_citation = False\\n if pred in [2, 3]:\\n if buffer:\\n found_citations.append(buffer)\\n buffer = char\\n elif pred == 1:\\n buffer += char\\n elif pred == 0:\\n if in_citation:\\n buffer += char\\n else:\\n if buffer:\\n found_citations.append(buffer)\\n buffer = ''\\n return found_citations\",\n \"def process_reference(ref):\\n logger.debug(\\\"==================================================\\\")\\n logger.debug(\\\"'{}'\\\".format(ref))\\n stripped = ref.lower().strip()\\n stripped = str(unidecode.unidecode(unicode(stripped, 'utf8')))\\n\\n conf_inds = []\\n for conf_str in conf_strings:\\n found_ind = stripped.find(conf_str)\\n\\n if found_ind > 0:\\n logger.debug(\\\"found conf string {} at ind {}\\\".format(conf_str, found_ind))\\n conf_inds.append(found_ind)\\n\\n if not conf_inds:\\n logger.debug(\\\"found no conference strings\\\")\\n conf_strip = stripped\\n else:\\n # Only consider conference strings in the second half of the reference.\\n str_start = 0\\n for ind in conf_inds:\\n if ind > len(stripped)/2:\\n str_start = ind\\n\\n\\n if str_start == 0:\\n logger.debug(\\\"No conf string found in the second half of the given reference\\\")\\n conf_strip = stripped\\n else:\\n last_dot = stripped.rfind(\\\".\\\", 0, str_start) + 1\\n last_comma = stripped.rfind(\\\",\\\", 0, str_start) + 1\\n\\n conf_strip = stripped[:max(last_dot, last_comma)]\\n logger.debug(\\\"conf string started at {}, last dot at {}, last comma at {}\\\".format(str_start, last_dot, last_comma))\\n logger.debug(\\\"all after latest punctuation: {}\\\".format(conf_strip))\\n\\n sep_inds, sep_inds_rev = punctuation_density_separate(conf_strip)\\n if not sep_inds: # if it's empty this is usually just a spurious reference\\n return ()\\n\\n # If there are two or fewer separation indices then the separation\\n # wasn't able to get good separation for authors, title and journal,\\n # so we don't change anything\\n if len(sep_inds) > 2:\\n # if there are 3 or more sep inds, we want to get all the text in the middle part\\n punc_sep = conf_strip[sep_inds[0]:sep_inds_rev[-2]].strip()\\n elif len(sep_inds) == 2:\\n punc_sep = conf_strip[sep_inds[0]:sep_inds_rev[-1]].strip()\\n else:\\n punc_sep = conf_strip\\n\\n logger.debug(\\\"conf_strip: {}\\\\npunc_sep: {}\\\\n\\\".format(conf_strip, punc_sep))\\n\\n full_strip = punc_sep\\n\\n ref_stopped = [word for word in full_strip.split(\\\" \\\") if word not in stopwords and len(word) > 2]\\n ref_nopunc = \\\" \\\".join(ref_stopped).translate(None, punctuation).split(\\\" \\\")\\n ref_set = set(ref_nopunc)\\n return (ref.strip(), ref_stopped, ref_nopunc, ref_set)\",\n \"def __citation_correction(self, bs, ground_truth):\\n bs_ref = bs.findNext('bibl')\\n gt_ref = ground_truth.findNext('ref')\\n while gt_ref is not None:\\n if gt_ref.find('article-title') != bs_ref.title:\\n pass\\n gt_ref = gt_ref.findNext('ref')\",\n \"def noteRef(self, text):\\n text_re = re.compile(r\\\"\\\"\\\"\\n \\\\[ # start\\n (%s) # !atts\\n \\\\#\\n ([^\\\\]!]+) # !label\\n ([!]?) # !nolink\\n \\\\]\\\"\\\"\\\" % self.c, re.X)\\n text = text_re.sub(self.fParseNoteRefs, text)\\n return text\",\n \"def replace_by_canonical_referent(self, cross_sentence=True):\\n replaced_count = 0\\n\\n # replace the participant in their containers\\n def replace_in_containers(myobj, replacing):\\n containers = {\\n \\\"event\\\": \\\"participants\\\",\\n \\\"sentiment_expression\\\": \\\"targets\\\",\\n \\\"sentence\\\": \\\"participants\\\",\\n }\\n for c_name, attrib_n in containers.items():\\n c_name = f\\\"in_{c_name}\\\"\\n if hasattr(myobj, c_name):\\n for c in getattr(myobj, c_name):\\n to_replace_in = getattr(c, attrib_n)\\n to_replace_in.append(replacing)\\n if myobj in to_replace_in:\\n to_replace_in.remove(myobj)\\n to_replace_in.sort(key=lambda x: x.begin)\\n\\n if self.canonical_referents and self.canonical_referents != \\\"from_canonref\\\":\\n # sometimes there are multiple canonrefs tagged\\n # this can be a) annotation mistake or\\n # b) multiple reference to a group, e.g. \\\"all\\\" refers to three companies.\\n for canonref in self.canonical_referents:\\n # check whether canonical referent is in same sentence\\n same_sentence = [s.element_id for s in self.in_sentence] == [\\n s.element_id for s in canonref.in_sentence\\n ]\\n # always replace when cross_sentence is true\\n # if cross_sentence is False (disallowed) only replace when canonref is in same sentence\\n if cross_sentence or same_sentence:\\n # replace element\\n replacing_participant = Participant(\\n canonref.text,\\n canonref.begin,\\n canonref.end,\\n canonref.element_id,\\n canonref.annotator_id,\\n canonref.document_title,\\n canonref.in_document,\\n self.role,\\n \\\"from_canonref\\\",\\n self.link_id,\\n canonref.tokens,\\n )\\n replacing_participant.in_sentence = self.in_sentence\\n replacing_participant.in_document = self.in_document\\n if hasattr(self, \\\"in_sentiment_expression\\\"):\\n replacing_participant.in_sentiment_expression = (\\n self.in_sentiment_expression\\n )\\n replacing_participant.from_original_participant = copy(self)\\n print(\\n f\\\"Replaced {self} with {canonref}. (in same sentence:{same_sentence})\\\"\\n )\\n\\n replace_in_containers(self, replacing_participant)\\n\\n # replace on document\\n self.in_document.participants.append(replacing_participant)\\n if self in self.in_document.participants:\\n self.in_document.participants.remove(self)\\n\\n replaced_count += 1\\n return replaced_count\",\n \"def _filter_references_section(self, text):\\n references_pattern = r\\\"(REFERENCE)|(reference)|(Reference)\\\"\\n references_found = [i.start() for i in re.finditer(references_pattern, text)]\\n if references_found != []:\\n last_reference_index = references_found[-1]\\n return text[:last_reference_index]\\n else:\\n return text\",\n \"def patch(text):\\n if match := re.search(r'\\\\d+__\\\\d+__\\\\d+', text):\\n tag = match.group(0)\\n if tag not in ocds_tags:\\n if ocds_version or not use_development_version:\\n text = text.replace(tag, ocds_tag)\\n else:\\n text = text.replace(ocds_schema_base_url + tag, development_base_url)\\n return text\",\n \"def fix_references(self) -> None:\\n\\n\\t\\tself.log_info(\\\"--- Searching for references to variants ---\\\")\\n\\t\\tfor publication in Publication.objects.filter(variant_of__isnull=False):\\n\\t\\t\\tvariant = publication.variant_of\\n\\t\\t\\torigs = PublicationReference.objects.filter(reference=publication)\\n\\t\\t\\tfor orig in origs:\\n\\t\\t\\t\\tif PublicationReference.objects.filter(reference=variant, publication=orig.publication).exists():\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tfixed = PublicationReference(\\n\\t\\t\\t\\t\\treference=variant,\\n\\t\\t\\t\\t\\tpublication=orig.publication,\\n\\t\\t\\t\\t\\tidentifier=('' if orig.identifier is None else orig.identifier) + \\\"*\\\",\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tfixed.full_clean()\\n\\t\\t\\t\\t\\tfixed.save()\\n\\t\\t\\t\\t\\tself.log_success(f\\\"Added reference: {publication} -- {fixed.identifier} -> {variant}\\\")\\n\\t\\t\\t\\texcept ValidationError as e:\\n\\t\\t\\t\\t\\traise CommandError(f\\\"{publication} -- {fixed.identifier} -> {variant}: {e}\\\")\",\n \"def add_ref(doc_name, bib_name, keep_label=False, slides=False):\\n bib_path = bib_name # os.path.join(static_path, \\\"tex\\\", bib_name)\\n if not slides:\\n keep_label = True\\n with open(doc_name, \\\"r\\\") as f:\\n _file = f.read()\\n # find citations\\n match = re.findall(r\\\"\\\\[@.*?\\\\]\\\", _file)\\n references = []\\n # print(match)\\n for cit in match:\\n tmp_cite_html = tempfile.NamedTemporaryFile(suffix=\\\".html\\\", delete=True)\\n tmp = tempfile.NamedTemporaryFile(suffix=\\\".md\\\", delete=True)\\n with open(tmp.name, \\\"w\\\") as md_file:\\n # Open the file for writing.\\n md_file.write(cit)\\n\\n pandoc_command = [\\n \\\"pandoc\\\",\\n \\\"-C\\\",\\n tmp.name,\\n \\\"--bibliography\\\",\\n bib_path,\\n \\\"--csl\\\",\\n \\\"./templates/my-csl.csl\\\",\\n \\\"-o\\\",\\n tmp_cite_html.name,\\n ]\\n if not slides:\\n for tag in [\\\"--metadata\\\", \\\"link-citations=true\\\"]:\\n pandoc_command.append(tag)\\n output = subprocess.check_output(\\n pandoc_command, stderr=subprocess.STDOUT\\n ).decode(\\\"utf-8\\\")\\n print(output.rstrip())\\n tmp.close()\\n with open(tmp_cite_html.name, \\\"r\\\") as cite_html:\\n # Open the file for writing.\\n cit_new = cite_html.read()\\n # print(cit_new)\\n if not keep_label:\\n # remove first line\\n cit_new = \\\"\\\\n\\\".join(cit_new.splitlines()[1:])\\n else:\\n lines = cit_new.splitlines()\\n # remove the <p> tags\\n lines[0] = lines[0][3:-4]\\n cit_new = \\\"\\\\n\\\".join(lines)\\n if slides:\\n _file = _file.replace(cit, cit_new)\\n else:\\n _file = _file.replace(cit, lines[0])\\n # collect the references\\n references.append(\\\"<p>\\\")\\n references.append(\\\"\\\\n\\\".join(lines))\\n references.append(\\\"</p>\\\")\\n # paste_refs_end()\\n tmp_cite_html.close()\\n if slides:\\n with open(doc_name, \\\"w\\\") as f:\\n f.write(_file)\\n if not slides:\\n if references == []:\\n _file=clean_html_refs(_file)\\n else:\\n _file = _file.replace(\\\"<bibliography-placeholder>\\\", \\\"\\\\n\\\".join(references))\\n with open(doc_name, \\\"w\\\") as f:\\n f.write(_file)\\n return\",\n \"def complete_description(description):\\n local_find_all_re = find_all_re\\n local_find_one_re = find_one_re\\n local_find_series_2_re = find_series_2_re\\n local_find_series_3_re = find_series_3_re\\n local_course_re = course_re\\n local_three_digits = three_digits\\n\\n find_all = re.findall(local_find_all_re, description)\\n find_one = re.findall(local_find_one_re, description)\\n \\n def remove_in(a, b):\\n remove = [y for x in a for y in b if y in x]\\n a.extend(b)\\n for x in remove: a.remove(x)\\n return a\\n \\n if find_one:\\n find_all = remove_in(find_all, find_one)\\n for i, x in enumerate(find_all):\\n completed = []\\n find_all[i] = x.replace(' ', '')\\n number = re.search(local_three_digits, find_all[i]).group(0)\\n for crs in re.findall(local_course_re, find_all[i]):\\n completed.append(f'{crs}{number}')\\n description = description.replace(x, ' {}'.format('/'.join(completed)), 1)\\n\\n find_series_2 = re.findall(local_find_series_2_re, description)\\n find_series_3 = re.findall(local_find_series_3_re, description)\\n if find_series_2 or find_series_3:\\n find_series_2 = remove_in(find_series_3, find_series_2)\\n for i, series in enumerate(find_series_2):\\n find_series_2[i] = series.replace(' ', '')\\n course_dep = re.search(local_course_re, find_series_2[i])\\n if course_dep: \\n depmnt = course_dep.group(0)\\n description = description.replace(series, f'{depmnt}{find_series_2[i][-3:]}', 1)\\n return description\",\n \"def replace_contractions(text):\\r\\n return contractions.fix(text)\",\n \"def _replace_contractions(text):\\n return contractions.fix(text)\",\n \"def preprocess_reference_text(text):\\n try:\\n splitpoint = text.lower().rindex('reference')\\n except ValueError:\\n splitpoint = False\\n while splitpoint and len(text) - splitpoint < 100 and 'reference' in text[:splitpoint].lower():\\n text = text[:splitpoint]\\n splitpoint = text.lower().rindex('reference')\\n if not splitpoint:\\n has_reference_section = False\\n non_reference_section, reference_section = text, ''\\n else:\\n has_reference_section = True\\n non_reference_section, reference_section = text[:splitpoint], text[splitpoint:]\\n return has_reference_section, reference_section, non_reference_section\",\n \"def ref_to_link(txt):\\n text = txt.group(1) # because it was a match in a regular expression\\n\\n thecite, everythingelse = first_bracketed_string(text)\\n thecite = thecite[1:-1] # strip curly brackets\\n thecite = thecite.replace(\\\"\\\\\\\\\\\",\\\"\\\") # \\\\href --> href\\n\\n refs = thecite.split(\\\",\\\")\\n ans = \\\"\\\"\\n\\n # print \\\"refs\\\",refs\\n\\n for ref in refs:\\n ref = ref.strip() # because \\\\cite{A, B, C,D} can have spaces\\n this_link = \\\"\\\"\\n if ref.startswith(\\\"href\\\"):\\n the_link = re.sub(r\\\".*{([^}]+)}{.*\\\", r\\\"\\\\1\\\", ref)\\n click_on = re.sub(r\\\".*}{([^}]+)}\\\\s*\\\", r\\\"\\\\1\\\", ref)\\n this_link = '{{ LINK_EXT(\\\"' + click_on + '\\\",\\\"' + the_link + '\\\") | safe}}'\\n elif ref.startswith(\\\"doi\\\"):\\n ref = ref.replace(\\\":\\\",\\\"\\\") # could be doi:: or doi: or doi\\n the_doi = ref[3:] # remove the \\\"doi\\\"\\n this_link = '{{ LINK_EXT(\\\"' + the_doi + '\\\",\\\"https://doi.org/' + the_doi + '\\\")| safe }}'\\n elif ref.lower().startswith(\\\"mr\\\"):\\n ref = ref.replace(\\\":\\\",\\\"\\\")\\n the_mr = ref[2:] # remove the \\\"MR\\\"\\n this_link = '{{ LINK_EXT(\\\"' + 'MR:' + the_mr + '\\\", '\\n this_link += '\\\"http://www.ams.org/mathscinet/search/publdoc.html?pg1=MR&s1='\\n this_link += the_mr + '\\\") | safe}}'\\n elif ref.lower().startswith(\\\"arxiv\\\"):\\n ref = ref.replace(\\\":\\\",\\\"\\\")\\n the_arx = ref[5:] # remove the \\\"arXiv\\\"\\n this_link = '{{ LINK_EXT(\\\"' + 'arXiv:' + the_arx + '\\\", '\\n this_link += '\\\"http://arxiv.org/abs/'\\n this_link += the_arx + '\\\")| safe}}'\\n\\n\\n if this_link:\\n if ans:\\n ans += \\\", \\\"\\n ans += this_link\\n\\n return '[' + ans + ']' + everythingelse\",\n \"def replace_words_fun(self):\\n\\n cleaned_doc = []\\n for word in str(self.doc).split():\\n if word.lower() in self.replacement_list.keys():\\n cleaned_doc.append(self.replacement_list[word.lower()])\\n else:\\n cleaned_doc.append(word)\\n self.doc = ' '.join(cleaned_doc)\",\n \"def replace_bibliography(self, text):\\n\\n indexBegin = 0\\n indexEnd = 0\\n indexBegin = text.find('\\\\\\\\bibliography{', indexBegin+1)\\n indexEnd = text.find('}', indexBegin+1)\\n text_to_replace = text[indexBegin:indexEnd+1]\\n new_text = self.dir_helper.read_file(file_name = self.temp_dir + self.config.BBL_FILE)\\n bbl_text = \\\"\\\"\\n for line in new_text.split('\\\\n'):\\n bbl_text = bbl_text + '\\\\t' + line + '\\\\n'\\n text = text.replace(text_to_replace, bbl_text)\\n\\n return text\",\n \"def replace_contractions(text):\\n return contractions.fix(text)\",\n \"def replace_contractions(text):\\n return contractions.fix(text)\",\n \"def find_replace(text, list):\\r\\n\\tfor pair in list:\\r\\n\\t\\tif pair[0] in text:\\r\\n\\t\\t\\ttext = text.replace(pair[0], pair[1])\\r\\n\\treturn text\",\n \"def delexicaliseReferenceNumber(sent, turn):\\n domains = ['restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'] # , 'police']\\n if turn['metadata']:\\n for domain in domains:\\n if turn['metadata'][domain]['book']['booked']:\\n for slot in turn['metadata'][domain]['book']['booked'][0]:\\n if slot == 'reference':\\n val = '[' + domain + '_' + slot + ']'\\n else:\\n val = '[' + domain + '_' + slot + ']'\\n key = normalize(turn['metadata'][domain]['book']['booked'][0][slot])\\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\\n\\n # try reference with hashtag\\n key = normalize(\\\"#\\\" + turn['metadata'][domain]['book']['booked'][0][slot])\\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\\n\\n # try reference with ref#\\n key = normalize(\\\"ref#\\\" + turn['metadata'][domain]['book']['booked'][0][slot])\\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\\n return sent\",\n \"def getRefs(self, text):\\n pattern = re.compile(r'(?:(?<=^)|(?<=\\\\s))\\\\[(.+)\\\\]((?:http(?:s?):\\\\/\\\\/|\\\\/)\\\\S+)(?=\\\\s|$)',\\n re.U)\\n text = pattern.sub(self.refs, text)\\n return text\",\n \"def remove_citations(text: str) -> str:\\n text = re.sub(\\\"\\\\[[a-zA-Z]\\\\]\\\", \\\"\\\", text)\\n return re.sub(r\\\"\\\\[(\\\\s|\\\\w)*\\\\d+(\\\\s|\\\\w)*\\\\]\\\", \\\"\\\", text)\",\n \"def compute_species_from_citation_linking(citelist: list) -> None:\\n # for i, cite in enumerate(tqdm(citelist)):\\n unrecorded_xrefs = []\\n recorded_refs = set()\\n for i, cite in enumerate(citelist):\\n recorded_refs.add(cite.cite_key)\\n if cite.actual == \\\"=\\\":\\n cname = \\\"\\\"\\n crossnames = collections.Counter()\\n for j in range(i): # only look at entries up to the current one\\n tmp = citelist[j]\\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\\n cname = tmp.name\\n crossnames.update([tmp.actual])\\n if len(crossnames) == 0:\\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\\n elif len(crossnames) == 1:\\n cite.actual = list(crossnames.keys())[0]\\n else:\\n # find name(s) with largest count\\n mcnt = max(crossnames.values())\\n keylist = []\\n for key in crossnames:\\n if crossnames[key] == mcnt:\\n keylist.append(key)\\n\\n if len(keylist) == 1:\\n cite.actual = keylist[0]\\n else:\\n cite_name = cite.name.lower()\\n while cite_name.find(\\\" \\\") > -1:\\n cite_name = cite_name[cite_name.find(\\\" \\\")+1:]\\n while cname.find(\\\" \\\") > -1:\\n cname = cname[cname.find(\\\" \\\")+1:]\\n\\n if cite_name in keylist:\\n cite.actual = cite_name\\n elif cname in keylist:\\n cite.actual = cname\\n else:\\n cite.actual = keylist[0]\\n\\n if cite.name_note == \\\".\\\":\\n cite.name_note = \\\"in part\\\"\\n else:\\n cite.name_note = \\\"in part; \\\" + cite.name_note\\n\\n for x in unrecorded_xrefs:\\n if x[1] in recorded_refs:\\n report_error(\\\"Reference {} ({}) does not appear until after citation from {} ({})\\\".format(x[1], x[3],\\n x[0], x[2]))\",\n \"def ref_analyzer(citation_informations, initialresult, initial_citationlist,\\n initial_referencelist,config, updated_rec_list ):\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attr function\\\", alert_admin=True)\\n return {}\\n\\n pubrefntag = \\\"\\\"\\n try:\\n pubrefntag = config.get(function, \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_report_number\\\", alert_admin=True)\\n return {}\\n\\n pubreftag = \\\"\\\"\\n try:\\n pubreftag = config.get(function, \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_pubinfo\\\", alert_admin=True)\\n return {}\\n\\n #pubrefntag is often 999C5r, pubreftag 999C5s\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"pubrefntag \\\"+pubrefntag)\\n write_message(\\\"pubreftag \\\"+pubreftag)\\n\\n citation_list = initial_citationlist\\n reference_list = initial_referencelist\\n result = initialresult\\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\\n d_references_s = citation_informations[2]\\n #dict of recid -> publication_infos_in_its_bibliography\\n d_records_s = citation_informations[3] #recid -> its publication inf\\n t1 = os.times()[4]\\n\\n write_message(\\\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\\\")\\n for somerecid in updated_rec_list:\\n try:\\n del citation_list[somerecid]\\n except KeyError:\\n pass\\n try:\\n del reference_list[somerecid]\\n except KeyError:\\n pass\\n\\n write_message(\\\"Phase 1: d_references_report_numbers\\\")\\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\\n #meaning: rec 8 contains these in bibliography\\n\\n done = 0\\n numrecs = len(d_references_report_numbers)\\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n done = done+1\\n\\n for refnumber in refnumbers:\\n if refnumber:\\n p = refnumber\\n f = 'reportnumber'\\n #sanitise p\\n p.replace(\\\"\\\\n\\\",'')\\n #search for \\\"hep-th/5644654 or such\\\" in existing records\\n rec_ids = get_recids_matching_query(p, f)\\n\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops: record '%d' report number value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_citer_cited(thisrecid, rec_ids[0])\\n remove_from_missing(p)\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n # Citation list should have rec_ids[0] but check anyway\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n #append unless this key already has the item\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid)\\n #and update result\\n result[rec_ids[0]] += 1\\n\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n else:\\n #the reference we wanted was not found among our records.\\n #put the reference in the \\\"missing\\\".. however, it will look\\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\\n #This should really be done in the next loop d_references_s\\n #but the 999C5s fields are not yet normalized\\n\\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\\n rectext = \\\"\\\" # print_record() call disabled to speed things up\\n lines = rectext.split(\\\"\\\\n\\\")\\n rpart = p #to be used..\\n for l in lines:\\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\\n st = l.find('$s')\\n if (st > 0):\\n end = l.find('$', st)\\n if (end == st):\\n end = len(l)\\n rpart = l[st+2:end]\\n insert_into_missing(thisrecid, rpart)\\n\\n mesg = \\\"d_references_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t2 = os.times()[4]\\n\\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\\n write_message(\\\"Phase 2: d_references_s\\\")\\n done = 0\\n numrecs = len(d_references_s)\\n for thisrecid, refss in d_references_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n\\n done = done+1\\n\\n for refs in refss:\\n if refs:\\n p = refs\\n #remove the latter page number if it is like 67-74\\n matches = re.compile(\\\"(.*)(-\\\\d+$)\\\").findall(p)\\n if matches and matches[0]:\\n p = matches[0][0]\\n\\n # check reference value to see whether it is well formed:\\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"is not well formed; skipping it.\\\" % (thisrecid, p)\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n continue # skip this ill-formed value\\n\\n # look for reference value:\\n rec_id = None\\n try:\\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\\n except:\\n rec_ids = None\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_message(\\\"These match searching \\\"+p+\\\" in journal: \\\"+repr(rec_ids), verbose=9)\\n #the refered publication is in our collection, remove\\n #from missing\\n remove_from_missing(p)\\n else:\\n #it was not found so add in missing\\n insert_into_missing(thisrecid, p)\\n #check citation and reference for this..\\n if rec_ids and rec_ids[0]:\\n #the above should always hold\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\\n result[rec_ids[0]] += 1 #add count for this..\\n\\n #update reference_list accordingly\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n mesg = \\\"d_references_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t3 = os.times()[4]\\n done = 0\\n numrecs = len(d_reports_numbers)\\n write_message(\\\"Phase 3: d_reports_numbers\\\")\\n\\n #search for stuff like CERN-TH-4859/87 in list of refs\\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n for reportcode in reportcodes:\\n if reportcode:\\n rec_ids = []\\n try:\\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\\n except:\\n rec_ids = []\\n\\n if rec_ids:\\n for recid in rec_ids:\\n #normal checks..\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(recid):\\n reference_list[recid] = []\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n\\n #normal updates\\n if not recid in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(recid)\\n if not thisrecid in reference_list[recid]:\\n reference_list[recid].append(thisrecid)\\n\\n mesg = \\\"d_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #find this record's pubinfo in other records' bibliography\\n write_message(\\\"Phase 4: d_records_s\\\")\\n done = 0\\n numrecs = len(d_records_s)\\n t4 = os.times()[4]\\n for thisrecid, recs in d_records_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_records_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n p = recs.replace(\\\"\\\\\\\"\\\",\\\"\\\")\\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\\n write_message(\\\"These records match \\\"+p+\\\" in \\\"+pubreftag+\\\" : \\\"+str(rec_ids), verbose=9)\\n if rec_ids:\\n for rec_id in rec_ids:\\n #normal checks\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(rec_id):\\n reference_list[rec_id] = []\\n\\n if not rec_id in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(rec_id)\\n if not thisrecid in reference_list[rec_id]:\\n reference_list[rec_id].append(thisrecid)\\n\\n mesg = \\\"d_records_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n write_message(\\\"Phase 5: reverse lists\\\")\\n\\n #remove empty lists in citation and reference\\n keys = citation_list.keys()\\n for k in keys:\\n if not citation_list[k]:\\n del citation_list[k]\\n\\n keys = reference_list.keys()\\n for k in keys:\\n if not reference_list[k]:\\n del reference_list[k]\\n\\n write_message(\\\"Phase 6: self-citations\\\")\\n selfdic = {}\\n #get the initial self citation dict\\n initial_self_dict = get_cit_dict(\\\"selfcitdict\\\")\\n selfdic = initial_self_dict\\n #add new records to selfdic\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n write_message(\\\"Self cite processing disabled. Use -A option to enable it.\\\")\\n else:\\n write_message(\\\"self cite and author citations enabled\\\")\\n selfdic = get_self_citations(updated_rec_list, citation_list,\\n initial_self_dict, config)\\n #selfdic consists of\\n #key k -> list of values [v1,v2,..]\\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\\n\\n #create a reverse \\\"x cited by y\\\" self cit dict\\n selfcitedbydic = {}\\n for k in selfdic.keys():\\n vlist = selfdic[k]\\n for v in vlist:\\n if selfcitedbydic.has_key(v):\\n tmplist = selfcitedbydic[v]\\n if not k in tmplist:\\n tmplist.append(k)\\n else:\\n tmplist = [k]\\n selfcitedbydic[v] = tmplist\\n\\n write_message(\\\"Getting author citations\\\")\\n\\n #get author citations for records in updated_rec_list\\n initial_author_dict = get_initial_author_dict()\\n authorcitdic = initial_author_dict\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n print \\\"Author cites disabled. Use -A option to enable it.\\\"\\n else:\\n write_message(\\\"author citations enabled\\\")\\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\\n initial_author_dict, config)\\n\\n\\n if task_get_task_param('verbose') >= 3:\\n #print only X first to prevent flood\\n tmpdict = {}\\n tmp = citation_list.keys()[0:10]\\n for t in tmp:\\n tmpdict[t] = citation_list[t]\\n write_message(\\\"citation_list (x is cited by y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(citation_list.keys())))\\n tmp = reference_list.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = reference_list[t]\\n write_message(\\\"reference_list (x cites y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(reference_list.keys())))\\n tmp = selfcitedbydic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfcitedbydic[t]\\n mesg = \\\"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\\\"\\n mesg += str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfcitedbydic.keys())))\\n tmp = selfdic.keys()[0:100]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfdic[t]\\n mesg = \\\"selfdic (x cites y and one of the authors of x same as y's): \\\"+str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfdic.keys())))\\n tmp = authorcitdic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = authorcitdic[t]\\n write_message(\\\"authorcitdic (author is cited in recs): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(authorcitdic.keys())))\\n insert_cit_ref_list_intodb(citation_list, reference_list,\\n selfcitedbydic, selfdic, authorcitdic)\\n\\n t5 = os.times()[4]\\n\\n write_message(\\\"Execution time for analyzing the citation information generating the dictionary:\\\")\\n write_message(\\\"... checking ref number: %.2f sec\\\" % (t2-t1))\\n write_message(\\\"... checking ref ypvt: %.2f sec\\\" % (t3-t2))\\n write_message(\\\"... checking rec number: %.2f sec\\\" % (t4-t3))\\n write_message(\\\"... checking rec ypvt: %.2f sec\\\" % (t5-t4))\\n write_message(\\\"... total time of ref_analyze: %.2f sec\\\" % (t5-t1))\\n\\n return result\",\n \"def figure_id_filter(text):\\n def fn(m):\\n s = m.group(1) or \\\"\\\"\\n s += m.group(2)\\n body = m.group(4)\\n s += replace_body(body, type=m.group(3))\\n s += m.group(5) or \\\"\\\" # Close parens if any\\n return s\\n\\n return __ref_pattern.sub(fn,text)\",\n \"def replace_contractions(sample):\\n sample[\\\"full_text\\\"] = contractions.fix(sample[\\\"full_text\\\"])\\n return sample\",\n \"def clean_reference_html(ref: str) -> str:\\n # replace hyphens with an n-dash in page ranges\\n sstr = r\\\"((?:Pp\\\\. |:)[\\\\D]?[\\\\d]+)(-)([\\\\D]?[\\\\d]+)\\\"\\n ref = re.sub(sstr, r\\\"\\\\1–\\\\3\\\", ref)\\n\\n # replace hyphens with an n-dash in volume ranges\\n sstr = r\\\"(\\\\([\\\\d]+)(-)([\\\\d]+\\\\))\\\"\\n ref = re.sub(sstr, r\\\"\\\\1–\\\\3\\\", ref)\\n\\n # remove (?) from entries with unknown years\\n ref = ref.replace(\\\" (?) \\\", \\\" \\\")\\n return ref\",\n \"def process_citation(style, reference_list, citation):\\n processed_citation = Element(\\\"span\\\", attrib={\\\"class\\\":\\\"citation\\\"})\\n\\n for reference in reference_list:\\n citeref = SubElement(processed_citation, \\\"span\\\")\\n for style_node in style.citation.layout:\\n process_node(citeref, style_node, style.macros, reference) \\n\\n return(processed_citation)\",\n \"def replace(orig, new, text):\\n return([word.replace(orig, new) for word in containing(orig, text)])\",\n \"def replace_all(sentence_string_list,input_word, user_ans):\\r\\n\\tindex = 0\\r\\n\\twhile index < len(sentence_string_list):\\r\\n\\t\\tif input_word in sentence_string_list[index]:\\r\\n\\t\\t\\tsentence_string_list[index] = sentence_string_list[index].replace(input_word, user_ans)\\r\\n\\t\\tindex += 1\",\n \"def rep_legtags(text, footnotes):\\n textstring = text\\n tagsintext = []\\n taggedtextlist = []\\n tagtextpat = re.compile(r'\\\\[/[a-z]\\\\]')\\n tagtextitir = tagtextpat.finditer(textstring)\\n for tagfound in tagtextitir:\\n closetag = tagfound.group()\\n opentag = \\\"[\\\" + closetag[2:]\\n tag = opentag[1:-1]\\n tagsintext.append(tag)\\n tagtextlist = get_tagtext(textstring, tag)\\n for taggedtext in tagtextlist:\\n tagstring = opentag + taggedtext + closetag\\n taggedtextlist.append(tagstring)\\n for tag in tagsintext:\\n tagplace = tagsintext.index(tag)\\n replacetext = taggedtextlist[tagplace]\\n for footnote in footnotes:\\n if footnote[:2] == tag + \\\" \\\":\\n if \\\"[/LRep]\\\" in footnote:\\n replacementlist = get_tagtext(footnote, \\\"LRep\\\")\\n repstring = \\\"[LRep]\\\" + replacementlist[0] + \\\"[/LRep]\\\"\\n textstringlist = textstring.split(replacetext)\\n textstring = repstring.join(textstringlist)\\n return textstring\",\n \"def citeproc(style, reference_list):\\n pass\",\n \"def parsingconvtext(retrievedtext,customtextlist):\\r\\n if not retrievedtext: #in case empty text \\r\\n retrievedtext=changenonetostr(retrievedtext)\\r\\n newtext=BeautifulSoup(retrievedtext).get_text() \\r\\n #newtext=changenonetostr(retrievedtext)\\r\\n #newtext=BeautifulSoup(newtext).get_text() \\r\\n #remove http links\\r\\n newtext=re.sub(r'http\\\\S+', '', newtext)\\r\\n newtext=re.sub(r'\\\\r\\\\r\\\\r\\\\n', ' ', newtext)\\r\\n #remove LL specific text\\r\\n if customtextlist:\\r\\n for i in customtextlist:\\r\\n newtext=re.sub(i, '', newtext)\\r\\n return newtext\",\n \"def main(bib_fpath=None):\\n\\n if bib_fpath is None:\\n bib_fpath = 'My Library.bib'\\n\\n # DEBUG = ub.argflag('--debug')\\n # Read in text and ensure ascii format\\n dirty_text = ut.readfrom(bib_fpath)\\n\\n from fixtex.fix_tex import find_used_citations, testdata_fpaths\\n\\n if exists('custom_extra.bib'):\\n extra_parser = bparser.BibTexParser(ignore_nonstandard_types=False)\\n parser = bparser.BibTexParser()\\n ut.delete_keys(parser.alt_dict, ['url', 'urls'])\\n print('Parsing extra bibtex file')\\n extra_text = ut.readfrom('custom_extra.bib')\\n extra_database = extra_parser.parse(extra_text, partial=False)\\n print('Finished parsing extra')\\n extra_dict = extra_database.get_entry_dict()\\n else:\\n extra_dict = None\\n\\n #udata = dirty_text.decode(\\\"utf-8\\\")\\n #dirty_text = udata.encode(\\\"ascii\\\", \\\"ignore\\\")\\n #dirty_text = udata\\n\\n # parser = bparser.BibTexParser()\\n # bib_database = parser.parse(dirty_text)\\n # d = bib_database.get_entry_dict()\\n\\n print('BIBTEXPARSER LOAD')\\n parser = bparser.BibTexParser(ignore_nonstandard_types=False,\\n common_strings=True)\\n ut.delete_keys(parser.alt_dict, ['url', 'urls'])\\n print('Parsing bibtex file')\\n bib_database = parser.parse(dirty_text, partial=False)\\n print('Finished parsing')\\n\\n bibtex_dict = bib_database.get_entry_dict()\\n old_keys = list(bibtex_dict.keys())\\n new_keys = []\\n for key in ub.ProgIter(old_keys, label='fixing keys'):\\n new_key = key\\n new_key = new_key.replace(':', '')\\n new_key = new_key.replace('-', '_')\\n new_key = re.sub('__*', '_', new_key)\\n new_keys.append(new_key)\\n\\n # assert len(ut.find_duplicate_items(new_keys)) == 0, 'new keys created conflict'\\n assert len(ub.find_duplicates(new_keys)) == 0, 'new keys created conflict'\\n\\n for key, new_key in zip(old_keys, new_keys):\\n if key != new_key:\\n entry = bibtex_dict[key]\\n entry['ID'] = new_key\\n bibtex_dict[new_key] = entry\\n del bibtex_dict[key]\\n\\n # The bibtext is now clean. Print it to stdout\\n #print(clean_text)\\n verbose = None\\n if verbose is None:\\n verbose = 1\\n\\n # Find citations from the tex documents\\n key_list = None\\n if key_list is None:\\n cacher = ub.Cacher('texcite1', enabled=0)\\n data = cacher.tryload()\\n if data is None:\\n fpaths = testdata_fpaths()\\n key_list, inverse = find_used_citations(fpaths, return_inverse=True)\\n # ignore = ['JP', '?', 'hendrick']\\n # for item in ignore:\\n # try:\\n # key_list.remove(item)\\n # except ValueError:\\n # pass\\n if verbose:\\n print('Found %d citations used in the document' % (len(key_list),))\\n data = key_list, inverse\\n cacher.save(data)\\n key_list, inverse = data\\n\\n # else:\\n # key_list = None\\n\\n unknown_pubkeys = []\\n debug_author = ub.argval('--debug-author', default=None)\\n # ./fix_bib.py --debug_author=Kappes\\n\\n if verbose:\\n print('Fixing %d/%d bibtex entries' % (len(key_list), len(bibtex_dict)))\\n\\n # debug = True\\n debug = False\\n if debug_author is not None:\\n debug = False\\n\\n known_keys = list(bibtex_dict.keys())\\n missing_keys = set(key_list) - set(known_keys)\\n if extra_dict is not None:\\n missing_keys.difference_update(set(extra_dict.keys()))\\n\\n if missing_keys:\\n print('The library is missing keys found in tex files %s' % (\\n ub.repr2(missing_keys),))\\n\\n # Search for possible typos:\\n candidate_typos = {}\\n sedlines = []\\n for key in missing_keys:\\n candidates = ut.closet_words(key, known_keys, num=3, subset=True)\\n if len(candidates) > 1:\\n top = candidates[0]\\n if ut.edit_distance(key, top) == 1:\\n # \\\"sed -i -e 's/{}/{}/g' *.tex\\\".format(key, top)\\n import os\\n replpaths = ' '.join([relpath(p, os.getcwd()) for p in inverse[key]])\\n sedlines.append(\\\"sed -i -e 's/{}/{}/g' {}\\\".format(key, top, replpaths))\\n candidate_typos[key] = candidates\\n print('Cannot find key = %r' % (key,))\\n print('Did you mean? %r' % (candidates,))\\n\\n print('Quick fixes')\\n print('\\\\n'.join(sedlines))\\n\\n # group by file\\n just = max([0] + list(map(len, missing_keys)))\\n missing_fpaths = [inverse[key] for key in missing_keys]\\n for fpath in sorted(set(ub.flatten(missing_fpaths))):\\n # ut.fix_embed_globals()\\n subkeys = [k for k in missing_keys if fpath in inverse[k]]\\n print('')\\n ut.cprint('--- Missing Keys ---', 'blue')\\n ut.cprint('fpath = %r' % (fpath,), 'blue')\\n ut.cprint('{} | {}'.format('Missing'.ljust(just), 'Did you mean?'), 'blue')\\n for key in subkeys:\\n print('{} | {}'.format(\\n ut.highlight_text(key.ljust(just), 'red'),\\n ' '.join(candidate_typos[key]))\\n )\\n\\n # for key in list(bibtex_dict.keys()):\\n\\n if extra_dict is not None:\\n # Extra database takes precidence over regular\\n key_list = list(ut.unique(key_list + list(extra_dict.keys())))\\n for k, v in extra_dict.items():\\n bibtex_dict[k] = v\\n\\n full = ub.argflag('--full')\\n\\n for key in key_list:\\n try:\\n entry = bibtex_dict[key]\\n except KeyError:\\n continue\\n self = BibTexCleaner(key, entry, full=full)\\n\\n if debug_author is not None:\\n debug = debug_author in entry.get('author', '')\\n\\n if debug:\\n ut.cprint(' --- ENTRY ---', 'yellow')\\n print(ub.repr2(entry, nl=1))\\n\\n entry = self.fix()\\n # self.clip_abstract()\\n # self.shorten_keys()\\n # self.fix_authors()\\n # self.fix_year()\\n # old_pubval = self.fix_pubkey()\\n # if old_pubval:\\n # unknown_pubkeys.append(old_pubval)\\n # self.fix_arxiv()\\n # self.fix_general()\\n # self.fix_paper_types()\\n\\n if debug:\\n print(ub.repr2(entry, nl=1))\\n ut.cprint(' --- END ENTRY ---', 'yellow')\\n bibtex_dict[key] = entry\\n\\n unwanted_keys = set(bibtex_dict.keys()) - set(key_list)\\n if verbose:\\n print('Removing unwanted %d entries' % (len(unwanted_keys)))\\n ut.delete_dict_keys(bibtex_dict, unwanted_keys)\\n\\n if 0:\\n d1 = bibtex_dict.copy()\\n full = True\\n for key, entry in d1.items():\\n self = BibTexCleaner(key, entry, full=full)\\n pub = self.publication()\\n if pub is None:\\n print(self.entry['ENTRYTYPE'])\\n\\n old = self.fix_pubkey()\\n x1 = self._pubval()\\n x2 = self.standard_pubval(full=full)\\n # if x2 is not None and len(x2) > 5:\\n # print(ub.repr2(self.entry))\\n\\n if x1 != x2:\\n print('x2 = %r' % (x2,))\\n print('x1 = %r' % (x1,))\\n print(ub.repr2(self.entry))\\n\\n # if 'CVPR' in self.entry.get('booktitle', ''):\\n # if 'CVPR' != self.entry.get('booktitle', ''):\\n # break\\n if old:\\n print('old = %r' % (old,))\\n d1[key] = self.entry\\n\\n if full:\\n d1 = bibtex_dict.copy()\\n\\n import numpy as np\\n import pandas as pd\\n df = pd.DataFrame.from_dict(d1, orient='index')\\n\\n paged_items = df[~pd.isnull(df['pub_accro'])]\\n has_pages = ~pd.isnull(paged_items['pages'])\\n print('have pages {} / {}'.format(has_pages.sum(), len(has_pages)))\\n print(ub.repr2(paged_items[~has_pages]['title'].values.tolist()))\\n\\n entrytypes = dict(list(df.groupby('pub_type')))\\n if False:\\n # entrytypes['misc']\\n g = entrytypes['online']\\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\\n\\n entrytypes['book']\\n entrytypes['thesis']\\n g = entrytypes['article']\\n g = entrytypes['incollection']\\n g = entrytypes['conference']\\n\\n def lookup_pub(e):\\n if e == 'article':\\n return 'journal', 'journal'\\n elif e == 'incollection':\\n return 'booksection', 'booktitle'\\n elif e == 'conference':\\n return 'conference', 'booktitle'\\n return None, None\\n\\n for e, g in entrytypes.items():\\n print('e = %r' % (e,))\\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\\n if 'pub_full' in g.columns:\\n place_title = g['pub_full'].tolist()\\n print(ub.repr2(ub.dict_hist(place_title)))\\n else:\\n print('Unknown publications')\\n\\n if 'report' in entrytypes:\\n g = entrytypes['report']\\n missing = g[pd.isnull(g['title'])]\\n if len(missing):\\n print('Missing Title')\\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\\n\\n if 'journal' in entrytypes:\\n g = entrytypes['journal']\\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\\n\\n missing = g[pd.isnull(g['journal'])]\\n if len(missing):\\n print('Missing Journal')\\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\\n\\n if 'conference' in entrytypes:\\n g = entrytypes['conference']\\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\\n\\n missing = g[pd.isnull(g['booktitle'])]\\n if len(missing):\\n print('Missing Booktitle')\\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\\n\\n if 'incollection' in entrytypes:\\n g = entrytypes['incollection']\\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\\n\\n missing = g[pd.isnull(g['booktitle'])]\\n if len(missing):\\n print('Missing Booktitle')\\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\\n\\n if 'thesis' in entrytypes:\\n g = entrytypes['thesis']\\n g = g[g.columns[~np.all(pd.isnull(g), axis=0)]]\\n missing = g[pd.isnull(g['institution'])]\\n if len(missing):\\n print('Missing Institution')\\n print(ub.repr2(missing[['title', 'author']].values.tolist()))\\n\\n # import utool\\n # utool.embed()\\n\\n # Overwrite BibDatabase structure\\n bib_database._entries_dict = bibtex_dict\\n bib_database.entries = list(bibtex_dict.values())\\n\\n #conftitle_to_types_set_hist = {key: set(val) for key, val in conftitle_to_types_hist.items()}\\n #print(ub.repr2(conftitle_to_types_set_hist))\\n\\n print('Unknown conference keys:')\\n print(ub.repr2(sorted(unknown_pubkeys)))\\n print('len(unknown_pubkeys) = %r' % (len(unknown_pubkeys),))\\n\\n writer = BibTexWriter()\\n writer.contents = ['comments', 'entries']\\n writer.indent = ' '\\n writer.order_entries_by = ('type', 'author', 'year')\\n\\n new_bibtex_str = bibtexparser.dumps(bib_database, writer)\\n\\n # Need to check\\n #jegou_aggregating_2012\\n\\n # Fix the Journal Abreviations\\n # References:\\n # https://www.ieee.org/documents/trans_journal_names.pdf\\n\\n # Write out clean bibfile in ascii format\\n clean_bib_fpath = ub.augpath(bib_fpath.replace(' ', '_'), suffix='_clean')\\n\\n if not ub.argflag('--dryrun'):\\n ut.writeto(clean_bib_fpath, new_bibtex_str)\",\n \"def replace_tokens(the_string, sequence_switched, suggestion_list, indexer, tokenizer, model, suggestion_num):\\n # Get top tokens\\n top_tokens = get_top_tokens(sequence_switched, indexer, tokenizer, model, suggestion_num)\\n\\n iterator = 0\\n for token in top_tokens:\\n suggestion_list[iterator] += the_string.replace(tokenizer.mask_token, tokenizer.decode([token]))\\n iterator += 1\\n\\n return suggestion_list\",\n \"def preprocessing(sample):\\n\\n content = ' '.join(sample)\\n content = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", content)\\n content = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", content)\\n content = re.sub(r\\\"\\\\'s\\\", \\\" is\\\", content)\\n content = re.sub(r\\\"\\\\'m\\\", \\\" am\\\", content)\\n content = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", content)\\n content = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", content)\\n content = content.replace('&', 'and')\\n content = content.replace('$', '')\\n content = content.split()\\n return content\",\n \"def __call__(self, text):\\n return extract_references(text, self.preprocessor, self.model)\",\n \"def replace_canonical_referents(self, **kwargs):\\n replaced_count = 0\\n for doc in self.annotation_documents:\\n for sen in doc.sentences:\\n for part in sen.participants:\\n replaced_count += part.replace_by_canonical_referent(**kwargs) #this will replace in-place in project, but returns a count too.\\n\\n return replaced_count\",\n \"def updateUncertain(update_read, my_ref):\\n # get positions of repeats in the ref_seq\\n # (\\\\w) catures a 'word' char, \\\\1 back-reference, {1,} reps \\\\1 >= 1 times\\n match = re.finditer(r'(\\\\w)\\\\1{1,}', my_ref)\\n rep_pos = [xrange(*m.span()) for m in match]\\n\\n # make sure the indexing matches that of the reference sequence\\n # (updateRead will output 1 based, but we need 0 based) we will do this\\n # in-place for now\\n update_read = [(x[0], x[1] - 1, x[2], x[3]) for x in update_read]\\n\\n # split update read into tuples that are in repeat regions or are not\\n # make sure good numbering returns to a 1-index\\n rep_set = set(flatten(rep_pos))\\n uncertain = [x for x in update_read\\n if x[1] in rep_set and x[2] in set(['D', 'I'])]\\n good = ((x[0], x[1] + 1, x[2], x[3]) for x in update_read\\n if x[1] not in rep_set or x[2] not in set(['D', 'I']))\\n\\n # find the repeat region for each tuple in uncertain and package each\\n # position and the length of the range so we can do everything in one pass\\n # remember list comp loops go outer then inner\\n rng_gen = (rng for rng in rep_pos for tup in uncertain if tup[1] in rng)\\n rng_pack = [zip(rng, itertools.repeat(len(rng), len(rng))) for rng in rng_gen]\\n\\n # create new tuples with uncertain tag in first pass\\n # add length of unncertain on the second pass\\n # return numbering to 1-based\\n new_tups = ((tup[0], x[0] + 1, 'U' + tup[2], str(x[1]) + tup[3])\\n for i, tup in enumerate(uncertain) for x in rng_pack[i])\\n\\n # combine the new tuples with the good ones and sort on the index\\n return sorted(itertools.chain(good, new_tups), key=lambda x: x[1])\",\n \"def lookup(text):\\n ret = basic(text)\\n ret.update(basic(text, \\\"gwas\\\"))\\n return ret\",\n \"def extract_references(content):\\n references = {}\\n content = content.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n matches = re.findall('(\\\\[[0-9]+\\\\][^\\\\[]*?(?=\\\\[|Acknowledge|Fig|Table|Conclusion|pdf))', content)\\n if matches:\\n for match in matches:\\n ref_id = get_reference_id(match)\\n # No reference id exist -- skip it\\n if ref_id != -1:\\n value = match.replace('\\\\\\\\n', '\\\\n')\\n references[ref_id] = value\\n return references\",\n \"def replace_images(md):\\n\\n return re.sub(r'\\\\!\\\\[(.*?)\\\\]\\\\((.*?)\\\\)', replace_image, md)\",\n \"def enrich_textrefs(self, db):\\n tr_list = db.select_all(db.TextRef,\\n db.TextContent.text_ref_id == db.TextRef.id,\\n db.TextContent.source == self.my_source,\\n db.TextRef.manuscript_id.is_(None))\\n file_list = self.ftp.get_csv_as_dict('filelist.csv', header=0)\\n pmcid_mid_dict = {entry['PMCID']: entry['MID'] for entry in file_list}\\n pmid_mid_dict = {entry['PMID']: entry['MID'] for entry in file_list\\n if entry['PMID'] is not '0'}\\n for tr in tr_list:\\n if tr.pmcid is not None:\\n tr.manuscript_id = pmcid_mid_dict[tr.pmcid]\\n elif tr.pmid is not None and tr.pmid in pmid_mid_dict.keys():\\n tr.manuscript_id = pmid_mid_dict[tr.pmid]\\n db.commit(\\\"Could not update text refs with manuscript ids.\\\")\\n return\",\n \"def make_inter_wiki_links(string):\\n\\n _inter_wiki = _inter_wiki_re.get(config.get_option('language'))\\n\\n if _inter_wiki is None:\\n return string\\n\\n mapping = _inter_wiki_map.get(config.get_option('language'))\\n\\n for i, regex in enumerate(_inter_wiki):\\n out = []\\n last_index = 0\\n for match in regex.finditer(string):\\n text = match.group('text')\\n # Offset by 1 to account for text group\\n index = match.groups().index(text, 1)-1\\n data = mapping[i*_MAX_RE+index][1]\\n\\n out.append(string[last_index:match.start('text')])\\n if text == data['link']:\\n out.append('[[%s]]' % data['link'])\\n else:\\n out.append('[[%s|%s]]' % (data['link'], text))\\n\\n last_index = match.end('text')\\n\\n out.append(string[last_index:])\\n string = ''.join(out)\\n\\n return string\",\n \"def get_references(input, verbose=False):\\n\\n # TODO: Make this a class reference parser\\n\\n # *** These tags are mobile-site specific\\n\\n # When we don't have proper access rights, this is present in the html\\n GUEST_TAG_TUPLE = (\\\"li\\\", {\\\"id\\\": \\\"menuGuest\\\"})\\n\\n # Entries are \\\"li\\\" tags with classes of the form:\\n # article-reference-article\\n # article-reference-other-ref\\n REFERENCE_TAG_TUPLE = (\\\"li\\\", {\\\"class\\\": re.compile('article-reference-*')})\\n\\n # This is the URL to the page that contains the document info, including\\n # reference material\\n BASE_URL = _SD_URL + '/science/article/pii/'\\n\\n # This URL was found first via Fiddler, then via closer inspection of the script\\n # 'article_catalyst.js' under sciencedirect.com/mobile/js in the function\\n # resolveReferences\\n REF_RESOLVER_URL = _SD_URL + '/science/referenceResolution/ajaxRefResol'\\n\\n # Return the BeautifulSoup result, the requests session, and the requests response\\n if _is_url(input):\\n pii = _extract_pii(input)\\n else:\\n pii = input\\n\\n sess = requests.Session()\\n\\n if verbose:\\n print('Requesting main page for pii: %s' % pii)\\n resp = sess.get(BASE_URL + pii, cookies={'Site': 'Mobile'})\\n\\n # Step 2 - Get the reference tags\\n\\n soup = BeautifulSoup(resp.text)\\n\\n reference_section = soup.find(\\\"ol\\\", {\\\"class\\\": \\\"article-references\\\"})\\n\\n if reference_section is None:\\n # Then we might be a guest. In other words, we might not have sufficient\\n # privileges to access the data we want. Generally this is protected via\\n # IP mask. When I'm working from home I need to VPN into work so\\n # that I can access the data :/\\n print(\\\"reference_section is None\\\")\\n temp = soup.find(*GUEST_TAG_TUPLE)\\n if temp is None:\\n # We might have no references ... (Doubtful)\\n raise ParseException(\\\"References were not found ..., code error likely\\\")\\n else:\\n raise InsufficientCredentialsException(\\n \\\"Insufficient access rights to get referencs, requires certain IP addresses (e.g. university based IP)\\\")\\n\\n ref_tags = reference_section.find_all(*REFERENCE_TAG_TUPLE)\\n\\n n_refs = len(ref_tags)\\n\\n if n_refs == 0:\\n return None\\n\\n # Step 3 - Resolve reference links\\n # --------------------------------------------------------------------------\\n # The returned html code contains javascript which returns more information\\n # about each reference, such as:\\n #\\n # - links to the full text\\n # - DOI \\n\\n\\n # Step 3.1 - Make the request for the information\\n # --------------------------------------------------------------------------\\n # We need the eid of the current entry, it is of the form:\\n #\\n # SDM.pm.eid = \\\"1-s2.0-0006899387903726\\\"\\n #\\n # * I think this entry gets deleted after the requests so it may not be\\n # visible if looking for it in Chrome. \\n match = re.search('SDM\\\\.pm\\\\.eid\\\\s*=\\\\s*\\\"([^\\\"]+)\\\"', resp.text)\\n #eid = match.group(1)\\n\\n # This list comes from the resolveReferences function in article_catalyst.js\\n payload = {\\n '_pii': pii,\\n '_refCnt': n_refs,\\n '_docType': 'article', # yikes, this might change ...\\n '_refRangeStart': '1',\\n '_refRangeCount': str(n_refs)} # This is normally in sets of 20's ...\\n # I'm not sure if it is important to limit this. The browser then\\n # makes a request fromr 1 count 20, 21 count 20, 41 count 20 etc,\\n # It always goes by 20 even if there aren't 20 left\\n\\n if verbose:\\n print('Requesting reference links')\\n r2 = sess.get(REF_RESOLVER_URL, params=payload)\\n\\n # Step 3.2 - Parse the returned information into single entries\\n # --------------------------------------------------------------------------\\n # This could probably be optimized in terms of execution time. We basically\\n # get back a single script tag. Inside is some sort of hash map for links\\n # for each reference.\\n #\\n # The script tag is of the form:\\n # myMap['bibsbref11']['refHtml']= \\\"<some html stuffs>\\\"; \\n # myMap['bibsbref11']['absUrl']= \\\"http://www.sciencedirect.com/science/absref/sd/0018506X7790068X\\\";\\n # etc.\\n #\\n # - Each entry is quite long.\\n # - Normally contains html\\n # - can be empty i.e. myMap['bibsbref11']['refHtml'] = \\\"\\\";\\n # - the refHtml is quite interesting\\n # - the absolute url is not always present (and currently not parsed)\\n more_soup = BeautifulSoup(r2.text)\\n script_tag = more_soup.find('script')\\n\\n # We unquote the script text as it is transmitted with characters escaped\\n # and we want the parsed data to contain the non-escaped text\\n #\\n # We might eventually want to move this to being after the regular expression ...\\n script_text = urllib_unquote(script_tag.text)\\n\\n ref_match_result = re.findall(\\\"myMap\\\\['bibsbref(\\\\d+)'\\\\]\\\\['refHtml'\\\\]=\\\\s?\\\" + '\\\"([^\\\"]*)\\\";', script_text)\\n # Tokens:\\n # 0 - the # from bibsbref#\\n # 1 - the html content from the 'refHtml' entry\\n # \\n # NOTE: We don't really use the #, so we might remove the () around\\n # \\\\d+ which would shift the index from 1 to 0\\n if verbose:\\n print('Creating reference objects')\\n\\n if len(ref_match_result) > 0:\\n zipped = zip(ref_tags, ref_match_result, range(n_refs))\\n ref_objects = [ScienceDirectRef(ref_tag, ref_link_info[1], ref_id) for\\n ref_tag, ref_link_info, ref_id in zipped]\\n else:\\n zipped = zip(ref_tags, range(n_refs))\\n ref_objects = [ScienceDirectRef(ref_tag, ref_id) for\\n ref_tag, ref_id in zipped]\\n\\n # Step 4:\\n # --------------------------------------------------------------------------\\n # TODO: Improve documentation for this step\\n\\n if verbose:\\n print('Retrieving Scopus Counts')\\n\\n ref_scopus_eids = [] # The Scopus IDs of the references to resolve\\n # but with a particular formatting ...\\n ref_count = 0 # Number of references we haven't resolved\\n\\n ref_count_list = []\\n # NOTE: Browser requests these in the reverse order ...\\n for ref_id, ref in enumerate(ref_objects):\\n\\n if ref._data_sceid is not None:\\n ref_scopus_eids.append(ref._data_sceid + ',' + str(ref_id + 1) + '~')\\n ref_count += 1\\n\\n # If we've got enough, then update the counts\\n # The 20 may be arbitrary but it was what was used in original JS\\n if ref_count > 20:\\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\\n ref_count = 0\\n ref_scopus_eids = []\\n\\n # Get any remaining reference counts\\n if ref_count != 0:\\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\\n\\n # Take the raw data and set the citation count for each object\\n for ref_tuple in ref_count_list:\\n ref_id = int(ref_tuple[0]) - 1\\n ref_count = int(ref_tuple[1])\\n ref_objects[ref_id].scopus_cite_count = ref_count\\n\\n # All done!\\n # ---------\\n return ref_objects\",\n \"def __find_and_replace_words(text=str, word_list=list, replacement_text=str):\\n word_set = set()\\n tokenized_text = nltk.word_tokenize(text)\\n # loop through all the words to see if it contains any of the words in the word list\\n for word in tokenized_text:\\n if word in word_list:\\n word_set.add(word)\\n # convert the set to list to make it sortable\\n word_set = list(word_set)\\n word_set.sort(key=len, reverse=True)\\n # replace those words, if any, with the replacement words\\n for word in word_set:\\n if len(word) == 1:\\n # if its only one character (e.g. 'C'), ensure that it is a singular word by using regex\\n text = re.sub(r\\\"\\\\b%s\\\\b\\\" % word, replacement_text, text, flags=re.IGNORECASE)\\n else:\\n text = text.replace(word, replacement_text)\\n return text\",\n \"def _fix_evidence_refs(db, rid_stmt_trios):\\n rid_set = {rid for rid, _, _ in rid_stmt_trios if rid is not None}\\n logger.info(\\\"Getting text refs for %d readings.\\\" % len(rid_set))\\n if rid_set:\\n rid_tr_pairs = db.select_all([db.Reading.id, db.TextRef],\\n db.Reading.id.in_(rid_set),\\n *db.join(db.TextRef, db.Reading))\\n rid_tr_dict = {rid: tr for rid, tr in rid_tr_pairs}\\n for rid, sid, stmt in rid_stmt_trios:\\n if rid is None:\\n # This means this statement came from a database, not reading.\\n continue\\n assert len(stmt.evidence) == 1, \\\\\\n \\\"Only raw statements can have their refs fixed.\\\"\\n _set_evidence_text_ref(stmt, rid_tr_dict[rid])\\n return\",\n \"def replacingBasicElementsFromEpubLibre(text):\\n text = re.sub(r'<div class=\\\"vineta\\\"><img[^<]*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n \\n # Replace some elements with atributes with other cleaner elements\\n\\n text = re.sub(r'<p class=\\\"ind\\\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n text = re.sub(r'<p class=\\\"cursiva\\\\d*\\\">(.*?)</p>', r'<p><seg rend=\\\"italic\\\">\\\\1</seg></p>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"calibre[0-9]+\\\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"saltoinicio\\\\d*\\\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"salto[0-9]+\\\">', r'<milestone unit=\\\"section\\\"/>\\\\n<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"saltoc\\\">', r'<milestone unit=\\\"section\\\"/>\\\\n<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"sinopsis\\\">.*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"ilustra\\\">.*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE) \\n text = re.sub(r'<div class=\\\"info\\\">.*?</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<br class=\\\"calibre9\\\" />', r' ', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<sup(|[^>]*)>(<a[^>]*?>)?\\\\[.*?\\\\](</a>)?</sup>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span(|[^>]*)>\\\\s*</span>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<a(|[^>]*)>\\\\s*</a>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"(tlogo|tautor|ttitulo|trevision|tfirma)\\\">.*?</p>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<h1 class=\\\"ttitulo\\\">(.*?)</h1>\\\\s*(<p class=\\\"titulo\\\">.*?</p>)', r'<head>\\\\1: \\\\2</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n text = re.sub(r'<img[^>]*?>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"(bloque|ala)?derecha[0-9]*\\\">(.*?)</p>', r'<ab>\\\\2</ab>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"asangre[0-9]?\\\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n #text = re.sub(r'<p class=\\\"centrado1\\\">(.*?)</p>', r'<quote>\\\\n<p>\\\\1</p>\\\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n #text = re.sub(r'<p class=\\\"extenso\\\">(.*?)</p>', r'<quote>\\\\n<p>\\\\1</p>\\\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"(normal|calibre)[0-9]*\\\">(.*?)</span>', r'<seg rend=\\\"italic\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p><span class=\\\"(cap[0-9]*|versalita|smallcaps)\\\">(..?)</span>', r'<p>\\\\2', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"(versalita|smallcaps)[0-9]*\\\">(.*?)</span>', r'<seg rend=\\\"smallcaps\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"(?:poema[0-9]*|poe|versos|estrofas|citar\\\\d*|verso\\\\d*|versosinpie\\\\d*|centro|centrado|poemaizqcursi\\\\d*|poemaizq|poemacursi\\\\d*)\\\">(.*?)</div>', r'<lg>\\\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"center\\\">\\\\s+<table class=\\\"text\\\">\\\\s+<tr class=\\\"calibre\\\\d*\\\">\\\\s+<td class=\\\"calibre\\\\d*\\\">(\\\\s+.*?)</td>\\\\s+</tr>\\\\s+</table>\\\\s+</div>', r'<lg>\\\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE) \\n\\n text = re.sub(r'<blockquote class=\\\"(?:versos?\\\\d*)\\\">(.*?)</blockquote>', r'<lg>\\\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n \\n text = re.sub(r'(<lg>\\\\s*)<p class=\\\"izq\\\">', r'\\\\1<l>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"poemaizqcursi\\\">(.*?)</p>', r'<lg>\\\\n<l>\\\\1</l>\\\\n</lg>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"verso\\\">(.*?)</p>', r'<l>\\\\1</l>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"normal1\\\">(.*?)</p>', r'<l>\\\\1</l>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"cursiva\\\">(.*?)</p>', r'<p><seg rend=\\\"italic\\\">\\\\1</seg></p>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<em(|[^>]*)>(.*?)</em>', r'<seg rend=\\\"italic\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"estrofa1\\\">', r'\\\\n\\\\n<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n text = re.sub(r'<i(|[^>]*)>(.*?)</i>', r'<seg rend=\\\"italic\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<b(|[^>]*)>(.*?)</b>', r'<seg rend=\\\"bold\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<sup(|[^>]*)>(.*?)</sup>', r'<seg rend=\\\"sup\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"cursiva[0-9]*\\\">(.*?)</span>', r'<seg rend=\\\"italic\\\">\\\\1</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<sub(|[^>]*)>(.*?)</sub>', r'<seg rend=\\\"subscript\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<small(|[^>]*)>(.*?)</small>', r'<seg rend=\\\"small\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"nosep\\\\d*\\\">(.*?)</span>', r'<seg rend=\\\"italic\\\">\\\\1</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"nosep\\\\d*\\\">(.*?)</span>', r'<seg rend=\\\"italic\\\">\\\\1</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<strong(|[^>]*)>(.*?)</strong>', r'<seg rend=\\\"bold\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"cita\\\">(.*?)</p>', r'<quote>\\\\n<p>\\\\1</p>\\\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"micita\\\">(.*?)</p>', r'<quote>\\\\n<p>\\\\1</p>\\\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"acotacion\\\\d*\\\">(.*?)</div>', r'<quote>\\\\n\\\\1\\\\n</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<cite.*?>(.*?)</cite>', r'<seg type=\\\"cite\\\">\\\\1</seg>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n # Setting quotes in floatingText element\\n text = re.sub(r'<div class=\\\"(?:mi)?cita\\\">(.*?)</div>', r'<floatingText>\\\\n<body>\\\\n<div type=\\\"section\\\">\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<blockquote class=\\\"bloque\\\">(.*?)</blockquote>', r'<floatingText>\\\\n<body>\\\\n<div type=\\\"section\\\">\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"salto25x\\\">([IVXCL\\\\d]+)</p>', r'<head>\\\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"salto25x\\\\d+\\\">', r'<p>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n \\n text = re.sub(r'<div class=\\\"bloquederecha1\\\">(.+?)</div>', r'<quote>\\\\1</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"cursiva[0-9]*\\\">', r'<div rend=\\\"italic\\\">', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"calibre\\\" id=\\\"calibre_link-[0-9]+\\\">', r'<div>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"centrado\\\">[… ]+?</p>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"asteriscos\\\">[\\\\* ∗]+</p>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"centrado\\\">\\\\[…\\\\]</p>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"asteriscos\\\\d*\\\">[\\\\. ]+</p>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<hr (class=\\\"calibre\\\\d*\\\" )?/>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(<milestone unit=\\\"section\\\"/>\\\\s+)+', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<div class=\\\"(?:banner|text\\\\d+)\\\">(.*?)</div>', r'<floatingText>\\\\n<body>\\\\n<div type=\\\"section\\\">\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<blockquote class=\\\"banner\\\\d*\\\">(.*?)</blockquote>', r'<floatingText>\\\\n<body>\\\\n<div type=\\\"section\\\">\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<blockquote class=\\\"carta\\\\d*\\\">(.*?)</blockquote>', r'<floatingText>\\\\n<body>\\\\n<div type=\\\"section\\\">\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n #text = re.sub(r'<blockquote class=\\\"cita\\\">(.*?)</blockquote>', r'<floatingText>\\\\n<body>\\\\n<div>\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<blockquote class=\\\"calibre\\\\d*\\\">(.*?)</blockquote>', r'<floatingText>\\\\n<body>\\\\n<div type=\\\"section\\\">\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<html.*?<body>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'</body></html>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(</p>)\\\\s*(<head>.*?</head>)', r'\\\\1</div>\\\\n<div>\\\\n\\\\2', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"(?:subh?|inicio|numtitulo|subt)\\\">(.*?)</p>', r'<head>\\\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<h[1-6][^>]*>(.*?)</h[1-6]>', r'<head>\\\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"(tsub|sub)?tit(ulo)?[0-9]*\\\">(.*?)</p>', r'<head>\\\\3</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"(subcapitulo|capitulonombre|h\\\\d+)\\\">(.*?)</p>', r'<head>\\\\2</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"tit-cap\\\">(.*?)</p>', r'<head>\\\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'</head>\\\\s*<p class=\\\"(subtil|subcap)\\\">(.*?)</p>', r': \\\\2</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<span class=\\\"ncap\\\">(.*?)</span>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"ncap\\\">(.*?)</span>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"palabra\\\">(.*?)</span>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"sigla\\\\d*\\\">(.*?)</span>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(<p>—?)<span class=\\\"inicial\\\">(.*?)</span>', r'\\\\1\\\\2', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(<p>—?¿?)<big class=\\\"calibre\\\\d*\\\">(.*?)</big>', r'\\\\1\\\\2', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<head>\\\\s*</head>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p [^>]*?>\\\\s*\\\\* *\\\\* *\\\\*</p>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"sep\\\">\\\\* \\\\* \\\\*</p>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'</head>\\\\s*<head>', r': ', text, flags=re.DOTALL|re.IGNORECASE)\\n #text = re.sub(r'(</head>)(\\\\s*<head>)', r'\\\\1\\\\n<div>\\\\n\\\\2', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n\\n \\\"\\\"\\\"\\n #Setting divs after ps. Used for 219\\n text = re.sub(r'(</p>|</div>)(\\\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\\\1\\\\n<div>\\\\2</div>\\\\n\\\\3', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(</p>|</div>)(\\\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\\\1\\\\n<div>\\\\2</div>\\\\n\\\\3', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(</p>|</div>)(\\\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\\\1\\\\n<div>\\\\2</div>\\\\n\\\\3', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(</p>|</div>)(\\\\s*<head>.*?</head>.*?)(<head>|</div>|<div>)', r'\\\\1\\\\n<div>\\\\2</div>\\\\n\\\\3', text, flags=re.DOTALL|re.IGNORECASE)\\n \\\"\\\"\\\"\\n\\n # Cleaning some <milestone />\\n text = re.sub(r'(<div>\\\\s*)<milestone unit=\\\"section\\\"/>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(</div>\\\\s*)<milestone unit=\\\"section\\\"/>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(</head>\\\\s*)<milestone unit=\\\"section\\\"/>', r'\\\\1', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n # Setting some floatingTexts\\n #text = re.sub(r'(<div(| [^>]*?)>((?!<div).)*</div>)(\\\\s*<p)', r'<floatingText>\\\\n<body>\\\\n\\\\1\\\\n</body>\\\\n</floatingText>\\\\n\\\\4', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n #text = re.sub(r'(<div(|[^>]+?)>.*?</div>)(\\\\s*<p)', r'<floatingText>\\\\n<body>\\\\n<div>\\\\n\\\\1\\\\n</div>\\\\n</body>\\\\n</floatingText>\\\\n\\\\3', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n # Floating Texts\\n\\n text = re.sub(r'<p(| [^>]+)>[\\\\s\\\\r\\\\n]*</p>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'\\\\n[ \\\\t]+', r'\\\\n', text, flags=re.IGNORECASE)\\n text = re.sub(r'[\\\\r\\\\n]+', r'\\\\r\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div>[\\\\r?\\\\n]*</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'(<div>((?!<div>).)*</div>\\\\r?\\\\n)\\\\Z', r'<!--\\\\1-->', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n\\n text = re.sub(r'<div class=\\\"cita?[0-9]*\\\">(.*?)</div>', r'<quote>\\\\1</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"cita[0-9]*\\\">(.*?</p>)', r'<quote>\\\\n<p>\\\\1</quote>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n \\n text = re.sub(r'<br class=\\\"calibre[0-9]*\\\" />', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'(<p>—?. ?)<span class=\\\"palabra\\\">(.*?)</span>', r'\\\\1<seg rend=\\\"small\\\">\\\\2</seg>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"saltoinicio\\\"><span class=\\\"inicial\\\">(.*)</span><span class=\\\"palabra\\\">(.*?)</span>', r'<p>\\\\1\\\\2', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"saltoinicio\\\"><span class=\\\"inicial\\\">(.*)</span>', r'<p>\\\\1', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p>(.)<span>(.*)</span>', r'<p>\\\\1\\\\2', text, flags=re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"inicial\\\">(.*)</span>', r'\\\\1', text, flags=re.IGNORECASE)\\n\\n \\n text = re.sub(r'(<head>.*?)<seg rend=\\\"(smallcaps|small)\\\">(.*?)</seg>(.*?</head>)', r'\\\\1\\\\3\\\\4', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n text = re.sub(r'<div[^>]*?>\\\\s*</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n text = re.sub(r'<div class=\\\"(?:tablacentro\\\\d*|center)\\\">\\\\s*<table class=\\\"text\\\\d*\\\">\\\\s*<tr class=\\\"calibre\\\\d*\\\">\\\\s*<td class=\\\"calibre\\\\d*\\\">(.*?)\\\\s*</td>\\\\s*</tr>\\\\s*</table>\\\\s*</div>', r'<lg>\\\\1\\\\n</lg>', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n text = re.sub(r'<p class=\\\"traduccion\\\">(.*?)</p>', r'<ab type=\\\"translation\\\">\\\\1</ab>', text, flags=re.IGNORECASE)\\n\\n #text = re.sub(r'<p class=\\\"centrado\\\">(.*?)</p>', r'<head>\\\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"centrado\\\\d*\\\">(.*?)</p>', r'<ab>\\\\1</ab>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\n #text = re.sub(r'<blockquote class=\\\"calibre18\\\">(.*?)</blockquote>', r'<floatingText>\\\\n<body>\\\\n\\\\1\\\\n</body>\\\\n</floatingText>\\\\n', text, flags=re.DOTALL|re.IGNORECASE)\\n\\n \\\"\\\"\\\"\\n #The next lines were used for 232, a theater-like novel\\n \\n text = re.sub(r'<p>(.*?</seg>)(\\\\.\\\\—.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.IGNORECASE)\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n #The next lines were used for 109, a theater-like novel\\n text = re.sub(r'<p>(.*?</seg>\\\\.)(.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p>(.*?</seg>)( +<seg rend=\\\"italic\\\">.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<seg rend=\\\"italic\\\">(\\\\(.*?)</seg>', r'<stage>\\\\1</stage>', text, flags=re.IGNORECASE)\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n #The next lines were used for 224, a theater-like novel\\n text = re.sub(r'<p>(.<seg rend=\\\"small\\\">.*?</seg>\\\\.)(.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.IGNORECASE)\\n #text = re.sub(r'<p>(.*?</seg>)( +<seg rend=\\\"italic\\\">.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<seg rend=\\\"italic\\\">(\\\\(.*?)</seg>', r'<stage>\\\\1</stage>', text, flags=re.IGNORECASE)\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n # Code used for paradox rey\\n text = re.sub(r'<div class=\\\"clear\\\">\\\\s*</div>', r'', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"banner\\\\d*\\\">(.*?)</p>', r'<ab>\\\\1</ab>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"francesa\\\\d*\\\">(.*?)</p>', r'<ab>\\\\1</ab>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n text = re.sub(r'<p class=\\\"personaje\\\">(.*?)</p>\\\\s*<p>(.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<span class=\\\"descrip\\\">(.*?)</span>', r'<stage>\\\\1</stage>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"(?:descrip\\\\d*|entrada|fin)\\\">(.*?)</p>', r'<stage>\\\\1</stage>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"cuadro\\\">(.*?)</p>', r'<head>\\\\1</head>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"descrip1\\\">(.*?)</div>', r'<stage>\\\\1</stage>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"puntos\\\">.*?</div>', r'<milestone unit=\\\"section\\\"/>', text, flags=re.DOTALL|re.IGNORECASE)\\n text = re.sub(r'<div class=\\\"tablacentro\\\">(.*?)</div>', r'<lg>\\\\1</lg>', text, flags=re.DOTALL|re.IGNORECASE)\\n \\\"\\\"\\\"\\n text = re.sub(r'<p class=\\\"decla\\\">(.*?\\\\. )(.*?)</p>', r'<sp>\\\\n<speaker>\\\\1</speaker>\\\\n<p>\\\\2</p>\\\\n</sp>', text, flags=re.IGNORECASE)\\n text = re.sub(r'<p class=\\\"(?:descrip|decla1)\\\">(.*?)</p>', r'<stage>\\\\1</stage>', text, flags=re.IGNORECASE)\\n\\n\\n text = re.sub(r'(<seg\\\\s+rend\\\\s*=\\\\s*\\\"small(?:caps)?\\\">)(.*?)(?=</seg>)', lambda m: m.group(1) + m.group(2).lower(), text)\\n \\n text = re.sub(r'(<seg\\\\s+rend\\\\s*=\\\\s*\\\"small(?:caps)?\\\">)(.*?)(</seg>)', r'\\\\2', text, flags=re.IGNORECASE)\\n \\n text = re.sub(r'<big[^>]*?>(.*?)</big>', r'\\\\1', text, flags=re.IGNORECASE) \\n text = re.sub(r'<div class=\\\"serif\\\" id=\\\"calibre_link-\\\\d+\\\">', r'<div>', text, flags=re.IGNORECASE) \\n text = re.sub(r'<div class=\\\"sinmargen\\\" id=\\\"calibre_link-\\\\d+\\\">', r'<div>', text, flags=re.IGNORECASE) \\n\\n text = re.sub(r'<p class=\\\"(?:carta|firma\\\\d+)\\\">(.*?)</p>', r'<ab>\\\\1</ab>', text, flags=re.IGNORECASE) \\n\\n\\n\\n return text\",\n \"def replace_redacted(text):\\n pat = re.compile(r'\\\\[\\\\*\\\\*(.*?)\\\\*\\\\*\\\\]', re.IGNORECASE)\\n \\n # replace name types\\n text = pat.sub(replace_names, text)\\n \\n # replace place types\\n text = pat.sub(replace_places, text)\\n \\n # replace person identifier types\\n text = pat.sub(replace_identifiers, text) \\n \\n # replace date types\\n text = pat.sub(replace_dates, text)\\n \\n # replace remaining digits\\n text = pat.sub(replace_digits, text)\\n return text\",\n \"def _fix_links(self, text, page_names):\\n for n in page_names:\\n text = text.replace(f\\\"]({n})\\\", f\\\"]({n}.html)\\\")\\n text = text.replace(f\\\"]({n}.md)\\\", f\\\"]({n}.html)\\\")\\n return text\",\n \"def format_i94prtl(string, former_countries_dict, countries_ref_list):\\n to_replace = ['INVALID - ', ' (BPS)', ' (I-91)', ' (RT. 5)', ' (BP - SECTOR HQ)', ' #ARPT', '#ARPT', '-ARPT', 'ARPT']\\n\\n for char in to_replace:\\n string = string.strip().replace(char, '')\\n\\n string = string.replace('NETH ANTILLES', 'Netherlands Antilles')\\\\\\n .replace('WASHINGTON #INTL', 'WA')\\\\\\n .replace('MEXICO Land (Banco de Mexico)', 'Mexico City, Mexico')\\\\\\n .replace(\\\"PASO DEL NORTE,TX\\\", \\\"PASO DEL NORTE, TX\\\")\\\\\\n .replace(\\\"WASHINGTON DC\\\", \\\"WASHINGTON, DC\\\")\\\\\\n .replace(\\\"MARIPOSA AZ\\\", \\\"MARIPOSA, AZ\\\")\\\\\\n .replace(\\\"SAIPAN, SPN\\\", \\\"SAIPAN, MP\\\")\\\\\\n .replace(\\\"Abu Dhabi\\\", \\\"ABU DHABI, UNITED ARAB EMIRATES\\\")\\\\\\n .replace(\\\"SEOUL KOREA\\\", \\\"SEOUL, SOUTH KOREA\\\")\\\\\\n .replace(\\\"PRC\\\", \\\"CHINA\\\")\\\\\\n .replace(\\\"TURK & CAIMAN\\\", \\\"Turks and Caicos Islands\\\")\\\\\\n .replace(\\\"PAPUA, NEW GUINEA\\\", \\\"SEPIK PLAINS, PAPUA NEW GUINEA\\\")\\\\\\n .replace(\\\"ENGLAND\\\", \\\"UNITED KINGDOM\\\")\\\\\\n .replace(\\\"UNIDENTIFED AIR / SEAPORT\\\", \\\"Unknown code\\\")\\\\\\n .replace(\\\"UNKNOWN POE\\\", \\\"Unknown code\\\")\\\\\\n .replace(\\\"NOT REPORTED/UNKNOWN\\\", \\\"Unknown code\\\")\\n\\n if string.find(',') != -1:\\n if (len(string.split(', ')) > 2) & (len(string.strip(', ')[-1]) == 2):\\n # eg. JUAREZ INTL, MEXICO CITY, MX\\n\\n result = string.split(', ')\\n return result[0].title().strip() + ', ' + result[-1].strip()\\n\\n elif (len(string.split(', ')) > 2) & (len(string.strip(', ')[-1]) > 2):\\n # eg. GUARULHOS INTL, SAO PAULO, BRAZIL\\n\\n result = string.split(', ')\\n\\n # find country name in former country name\\n # and standardise country name based on countries_ref_list\\n find_former_country = process.extract(result[-1], list(former_countries_dict.keys()))[0]\\n\\n if find_former_country[1] == 100:\\n country = former_countries_dict[find_former_country[0]]\\n else:\\n country = result[-1]\\n\\n country = process.extract(country, countries_ref_list)[0][0]\\n\\n return result[0].title().strip() + ', ' + country\\n\\n elif len(string.split(', ')[-1]) > 2:\\n # eg. HAKAI PASS, CANADA\\n\\n result = string.split(', ')\\n\\n # find country name in former country name\\n # and standardise country name based on countries_ref_list\\n find_former_country = process.extract(result[-1], list(former_countries_dict.keys()))[0]\\n\\n if find_former_country[1] == 100:\\n country = former_countries_dict[find_former_country[0]]\\n else:\\n country = result[-1]\\n\\n country = process.extract(country, countries_ref_list)[0][0]\\n\\n return result[0].title().strip() + ', ' + country\\n\\n elif len(string.split(', ')[-1]) == 2:\\n # eg. TAMPA, FL\\n return string.split(',')[0].title() + ',' + string.split(',')[-1]\\n else:\\n return string\",\n \"def replace(text, max_width=MAX_WIDTH, max_height=MAX_HEIGHT):\\r\\n rules = list(ProviderRule.objects.all())\\r\\n patterns = [re.compile(r.regex) for r in rules] # Compiled patterns from the rules\\r\\n parts = [] # The parts that we will assemble into the final return value.\\r\\n indices = [] # List of indices of parts that need to be replaced with OEmbed stuff.\\r\\n indices_rules = [] # List of indices into the rules in order for which index was gotten by.\\r\\n urls = set() # A set of URLs to try to lookup from the database.\\r\\n stored = {} # A mapping of URLs to StoredOEmbed objects.\\r\\n index = 0\\r\\n # First we pass through the text, populating our data structures.\\r\\n for i, part in re_parts(patterns, text):\\r\\n if i == -1:\\r\\n parts.append(part)\\r\\n index += 1\\r\\n else:\\r\\n to_append = \\\"\\\"\\r\\n # If the link ends with one of our overrides, build a list\\r\\n while part[-1] in END_OVERRIDES:\\r\\n to_append += part[-1]\\r\\n part = part[:-1]\\r\\n indices.append(index)\\r\\n urls.add(part)\\r\\n indices_rules.append(i)\\r\\n parts.append(part)\\r\\n index += 1\\r\\n if to_append:\\r\\n parts.append(to_append)\\r\\n index += 1\\r\\n # Now we fetch a list of all stored patterns, and put it in a dictionary \\r\\n # mapping the URL to to the stored model instance.\\r\\n for stored_embed in StoredOEmbed.objects.filter(match__in=urls, max_width=max_width, max_height = max_height):\\r\\n stored[stored_embed.match] = stored_embed\\r\\n # Now we're going to do the actual replacement of URL to embed.\\r\\n for i, id_to_replace in enumerate(indices):\\r\\n rule = rules[indices_rules[i]]\\r\\n part = parts[id_to_replace]\\r\\n try:\\r\\n # Try to grab the stored model instance from our dictionary, and\\r\\n # use the stored HTML fragment as a replacement.\\r\\n parts[id_to_replace] = stored[part].html\\r\\n except KeyError:\\r\\n try:\\r\\n # Build the URL based on the properties defined in the OEmbed spec.\\r\\n url = u\\\"%s?url=%s&maxwidth=%s&maxheight=%s&format=%s\\\" % (\\r\\n rule.endpoint, part, max_width, max_height, FORMAT\\r\\n )\\r\\n # Fetch the link and parse the JSON.\\r\\n resp = simplejson.loads(fetch(url))\\r\\n \\r\\n # link types that don't have html elements aren't dealt with right now.\\r\\n if resp['type'] == 'link' and 'html' not in resp:\\r\\n raise ValueError\\r\\n \\r\\n # Depending on the embed type, grab the associated template and\\r\\n # pass it the parsed JSON response as context.\\r\\n replacement = render_to_string('oembed/%s.html' % resp['type'], {'response': resp})\\r\\n if replacement:\\r\\n stored_embed = StoredOEmbed.objects.create(\\r\\n match = part,\\r\\n max_width = max_width,\\r\\n max_height = max_height,\\r\\n html = replacement,\\r\\n )\\r\\n stored[stored_embed.match] = stored_embed\\r\\n parts[id_to_replace] = replacement\\r\\n else:\\r\\n raise ValueError\\r\\n except ValueError:\\r\\n parts[id_to_replace] = part\\r\\n except KeyError:\\r\\n parts[id_to_replace] = part\\r\\n except urllib2.HTTPError:\\r\\n parts[id_to_replace] = part\\r\\n # Combine the list into one string and return it.\\r\\n return mark_safe(u''.join(parts))\",\n \"def replace_strings(text: str, replacement_pair_list):\\n\\n new_text = text\\n for pair in replacement_pair_list:\\n old, new = pair\\n new_text = new_text.replace(old, new)\\n return new_text\",\n \"def _remove_invalid_links(text):\\n\\n for reply_number in re.finditer(REGEX_REPLY, text):\\n post_id = reply_number.group(1)\\n post = Post.objects.filter(id=post_id)\\n if not post.exists():\\n text = string.replace(text, REFLINK_PREFIX + post_id, post_id)\\n\\n return text\",\n \"def compute_applied_name_contexts(citelist: list) -> None:\\n for i, cite in enumerate(citelist):\\n if (cite.context == \\\"specimen\\\") or (cite.context == \\\"location\\\") or (cite.context == \\\"sequence\\\"):\\n cite.applied_cites = {cite}\\n elif cite.context == \\\"citation\\\":\\n for j in range(i): # only look at entries up to the current one\\n tmp = citelist[j]\\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\\n if (tmp.context == \\\"specimen\\\") or (tmp.context == \\\"location\\\") or (tmp.context == \\\"sequence\\\"):\\n cite.applied_cites |= {tmp}\\n if len(cite.applied_cites) == 0:\\n cite.applied_cites = None\\n\\n # for testing purposes only\\n # if cite.applied_cites is None:\\n # print(cite.cite_key, cite.name_key, cite.context, \\\"No application\\\")\\n # else:\\n # for c in cite.applied_cites:\\n # print(cite.cite_key, cite.name_key, cite.context, c.cite_key, c.name_key, c.context)\\n # input()\",\n \"def replace(self, string):\\n for i, j in self.defs.items():\\n string = string.replace(i, j)\\n return string\",\n \"def refined_text(text):\\n import re\\n text = text.replace('<e1>','')\\n text = text.replace('</e1>','')\\n text = text.replace('<e2>','')\\n text = text.replace('</e2>','')\\n\\n text = text[1:-1] # trim quotes\\n # text = text.replace('\\\"','')\\n # text = text.replace(',','')\\n # text = text.replace('.','')\\n # text = text.replace(';','')\\n # text = text.replace('`','')\\n # text = text.replace('\\\\'','')\\n # text = text.replace('(','')\\n # text = text.replace(')','')\\n # text = text.replace('/','')\\n\\n return text\",\n \"def texify(value):\\n for k, v in REPLACEMENTS.items():\\n value = value.replace(k, v)\\n return mark_safe(value)\",\n \"def rep(self,tag,nstr):\\n tmp = []\\n for line in self.content: \\n if tag in line:\\n tmp.append(line.replace(tag,nstr))\\n else:\\n tmp.append(line)\\n self.content = tmp\",\n \"def process_bibliography(style, reference_list):\\n processed_bibliography = Element(\\\"ol\\\", attrib={\\\"class\\\":\\\"bibliography\\\"})\\n\\n for reference in reference_list:\\n ref = SubElement(processed_bibliography, \\\"li\\\", \\n attrib={\\\"property\\\":\\\"dc:references\\\"})\\n\\n for style_node in style.bibliography.layout:\\n process_node(ref, style_node, style.macros, reference) \\n\\n return(processed_bibliography)\",\n \"def shorten_in_text(text):\\n replacements = {} #URL -> is.gd URL\\n #Only check for urls that start with \\\"http://\\\" for now\\n for m in re.finditer(\\\"http://[^ \\\\n\\\\r]*\\\", text):\\n try:\\n replacements[m.group()] = shorten_with_is_gd(m.group())\\n except:\\n replacements[m.group()] = m.group()\\n for url,replacement in replacements.items():\\n text = text.replace(url, replacement)\\n return text\",\n \"def add_references_to_papers(infile, dir):\\n papers = json.load(open(infile))\\n for paper in papers:\\n for file in os.listdir(dir):\\n if file.split(\\\".txt\\\")[0] == paper['doi']: # Must find the correct file to parse\\n filename = TEXT_DIR+file\\n refs =extract_references_from_txt(filename) #Uses the text files to find references\\n paper['references']=refs\\n return papers\",\n \"def fix_errors_in_citation(citation):\\n result = regex.sub(r\\\"\\\\s+\\\", \\\" \\\", citation)\\n result = regex.sub(r\\\"§(?=\\\\d)\\\", \\\"§ \\\", result)\\n result = regex.sub(r\\\",\\\\sbis\\\\s\\\", \\\" bis \\\", result)\\n return result\",\n \"def process_citations(conn: Connection, path: Path) -> None:\\n sql = \\\"INSERT OR IGNORE INTO Citations (note, reference) VALUES (?, ?)\\\"\\n run_sql_on_csv(conn, path, sql, (int, str))\",\n \"def make_unparsed_comments(self, replacements):\\n if not hasattr(self, 'cleaned_html'):\\n self.cleaned_html = self.clean_html()\\n \\n self.basic_comments = self.cleaned_html\\n for d in self.replacements:\\n if d['regex']:\\n self.basic_comments = re.sub(d['pattern'], d['replacement'], self.basic_comments)\\n else:\\n self.basic_comments = self.basic_comments.replace(d['pattern'], d['replacement'])\\n return self.basic_comments\",\n \"def filter_profanities(text,replace_handler=profanity_word_handler):\\n profanities=CensoredWord.objects.get_profanities_wordlist()\\n return word_filter(text,profanities,replace_handler)\",\n \"def lf_findall_interp_with_discont(report):\\n\\n if 'interpretation' in report.sections.keys():\\n interpretation = report.sections['interpretation']\\n interp_text = interpretation['text']\\n return abnormal_interp_with_discont(interp_text)\\n else:\\n candtext = get_section_with_name(SEIZURE_SECTION_NAMES_LOWER, report)\\n if candtext:\\n return abnormal_interp_with_discont(candtext)\\n else:\\n return ABSTAIN_VAL\",\n \"def convert_true_support_to_relevance():\\n clm_as_key_sen_support_score_val_wiki = read_pickle(\\\"clm_as_key_sen_support_score_val_wiki\\\") #key is a claim num, value is a list of tuple - sen and their 0/1/2 support score)\\n claim_sen_true_relevance_dict = {}\\n claim_dict = read_pickle(\\\"claim_dict\\\")\\n #t Dead Poets Society , Peter Weir , Steven HaftPaurn clm_as_key_sen_support_score_val_wiki to a relevance dict\\n for (claim_num,list_sen_support_score) in clm_as_key_sen_support_score_val_wiki.items():\\n new_relevance_list = []\\n for (sen,support_score) in list_sen_support_score:\\n if support_score !=0:\\n new_relevance_list.append((sen,1))\\n else:\\n new_relevance_list.append((sen,0))\\n claim_sen_true_relevance_dict[claim_dict[str(claim_num)]] = new_relevance_list\\n save_pickle(\\\"claim_sen_relevance_dict_\\\"+curr_source, claim_sen_true_relevance_dict)\",\n \"def sanitize_text(text: str) -> str:\\n for r in [RE_NOISE, RE_EMAIL, RE_REFERENCE]:\\n text = r.sub(\\\"\\\", text)\\n return text\",\n \"def update_refseq_info(ensembl_info, word, value):\\n if \\\"refseq\\\" in word:\\n if \\\"mrna\\\" in word:\\n if \\\"predicted\\\" in word:\\n ensembl_info[\\\"refseq_mrna_predicted\\\"] = value\\n else:\\n ensembl_info[\\\"refseq_mrna\\\"] = value\\n\\n if \\\"ncrna\\\" in word:\\n ensembl_info[\\\"refseq_ncrna\\\"] = value\\n return ensembl_info\",\n \"def _process_replacements(input_text):\\n original_input = list(input_text) # Immutable\\n replaced = list(input_text)\\n for index, char in enumerate(original_input):\\n if char.isalpha() or char.isdigit():\\n replacement = helpers.get_character(char)\\n if replacement:\\n replaced[index] = ' ' + replacement + ' '\\n return helpers.remove_excess_spaces(''.join(replaced))\",\n \"def replace(word, replacements):\\n for old, new in replacements:\\n if word == old:\\n return new\\n return word\",\n \"def preprocess_note(text):\\n # replace redacted info with tokens\\n text = replace_redacted(text)\\n \\n # misc scrubbing\\n text = replace_misc(text) \\n return text\",\n \"def corrected(text):\\n # Init SpellChecker\\n spell = SpellChecker(language='fr', distance=1, case_sensitive=False)\\n # Get misspellings and corrections\\n text_splitted = text.split()\\n misspelled = spell.unknown(text_splitted)\\n correction = {word:spell.correction(word) for word in misspelled}\\n\\n # Replace misspellings in text\\n for k,v in correction.items():\\n text = text.replace(k,v)\\n return text\",\n \"def process_text(text):\\n fix_dict = {'fig.': 'fig', 'fig .': 'fig ', 'Fig.': 'Fig', 'Fig .': 'Fig ',\\n 'figure.': 'figure', 'figure .': 'figure ', 'Figure.': 'Fig', 'Figure .': 'Fig ',\\n 'et al.': 'et al', 'III': '3', 'II': '2', 'I': '1'}\\n\\n for old_pattern in fix_dict.keys():\\n text = text.replace(old_pattern, fix_dict[old_pattern])\\n return text\",\n \"def citation_validation(self):\\n for file in filter(lambda x: x.startswith('citation'), listdir(bs_directory)):\\n print \\\"Processing\\\", file\\n bs = BeautifulSoup(open(file), 'xml')\\n # find ground_truth file\\n ground_truth = BeautifulSoup(open(self.ground_truth_directory + file.split(\\\".\\\")[0] + '.xml'), 'xml')\\n self.__citation_correction(ground_truth, bs)\\n file = open(self.citation_output + file, \\\"wb\\\")\\n file.write(bs.prettify().encode('utf-8'))\",\n \"def store_orig_strings(self, example_list):\\n\\t\\tself.original_articles = [ex.original_article for ex in example_list] # list of lists\\n\\t\\tself.original_abstracts = [ex.original_abstract for ex in example_list] # list of lists\\n\\t\\tself.original_abstracts_sents = [ex.original_abstract_sents for ex in example_list] # list of list of lists\",\n \"def synonym_sentence_append(tokens, ignored_tokens=stopwords.words('english'), excluded_token_regex=None,\\n max_frequency=None, min_occurrences=None,\\n # min_frequency=None, max_occurrences=None,\\n # min_document_frequency=None, max_document_frequency=None,\\n # min_document_occurrences=None, max_document_occurrences=None,\\n num_candidates=25, tokens_to_replace=None, replace_probability=0.3):\\n\\n # check if tokens to be replaced are supplied, if not, choose tokens depending on parameters from user\\n if not tokens_to_replace:\\n # flatten tuple of tuples and get token dictionary\\n token_dict, token_freq, n_tokens = vectorizers._vectorizers.construct_token_dictionary_and_frequency(\\n vectorizers.utils.flatten(tokens))\\n\\n # prune token dictionary depending on parameters supplied by user\\n # returns a dictionary of candidate tokens for replacement\\n candidate_dict, candidate_freq = vectorizers._vectorizers.prune_token_dictionary(\\n token_dict,\\n token_freq,\\n ignored_tokens=ignored_tokens,\\n excluded_token_regex=excluded_token_regex,\\n min_frequency=(min_occurrences / n_tokens),\\n max_frequency=max_frequency,\\n # min_occurrences=min_occurrences,\\n # max_occurrences=max_occurrences,\\n # min_document_frequency=min_document_frequency,\\n # max_document_frequency=max_document_frequency,\\n # min_document_occurrences=min_document_occurrences,\\n # max_document_occurrences=max_document_occurrences,\\n total_tokens=n_tokens,\\n total_documents=len(tokens),\\n )\\n\\n # take a random sample of tokens from the candidate dictionary\\n tokens_to_replace = random.sample(list(candidate_dict.keys()), num_candidates)\\n\\n print(\\\"Tokens for replacement:\\\")\\n print(tokens_to_replace)\\n\\n new_tokens = []\\n for sent in tokens:\\n word_changed = False\\n sent = list(sent)\\n # check each token by index and create a deep copy with the changed word at that index and add new sentence\\n # to new corpus\\n for idx, token in enumerate(sent):\\n if token in tokens_to_replace:\\n new_sent = copy.deepcopy(sent)\\n new_sent[idx] = f\\\"{token}_$$0\\\"\\n new_tokens.append(new_sent)\\n word_changed = True\\n # depending on probability, add another copy of the new sentence with the second replacement synonym\\n if random.random() <= replace_probability:\\n added_sent = copy.deepcopy(sent)\\n added_sent[idx] = f\\\"{token}_$$1\\\"\\n new_tokens.append(added_sent)\\n # if no words were changed, just add the original sentence to the new corpus\\n if not word_changed:\\n new_tokens.append(sent)\\n\\n # change dataset back to tuple of tuples before returning\\n new_tokens_tuple = tuple(tuple(sent) for sent in new_tokens)\\n\\n return tokens_to_replace, new_tokens_tuple\",\n \"def prepare_nlu_text(example: Text, entities: List[Dict]):\\n if not Utility.check_empty_string(example):\\n if entities:\\n for entity in entities:\\n example = example.replace(\\n entity[\\\"value\\\"],\\n \\\"[\\\" + entity[\\\"value\\\"] + \\\"](\\\" + entity[\\\"entity\\\"] + \\\")\\\",\\n )\\n return example\",\n \"def gen_enzyme_religation_regex(enzyme):\\n\\n # Split the str on the comma to separate the different enzymes.\\n enzyme = enzyme.split(\\\",\\\")\\n\\n # Check on Biopython dictionnary the enzyme.\\n rb = RestrictionBatch(enzyme)\\n\\n # Initiation:\\n give_list = []\\n accept_list = []\\n ligation_list = []\\n\\n # Iterates on the enzymes.\\n for enz in rb:\\n\\n # Extract restriction sites and look for cut sites.\\n site = enz.elucidate()\\n fw_cut = site.find(\\\"^\\\")\\n rev_cut = site.find(\\\"_\\\")\\n\\n # Process \\\"give\\\" site. Remove N on the left (useless).\\n give_site = site[:rev_cut].replace(\\\"^\\\", \\\"\\\")\\n while give_site[0] == \\\"N\\\":\\n give_site = give_site[1:]\\n give_list.append(give_site)\\n\\n # Process \\\"accept\\\" site. Remove N on the rigth (useless).\\n accept_site = site[fw_cut + 1 :].replace(\\\"_\\\", \\\"\\\")\\n while accept_site[-1] == \\\"N\\\":\\n accept_site = accept_site[:-1]\\n accept_list.append(accept_site)\\n\\n # Iterates on the two list to build all the possible HiC ligation sites.\\n for give_site in give_list:\\n for accept_site in accept_list:\\n # Replace \\\"N\\\" by \\\".\\\" for regex searching of the sites\\n ligation_list.append((give_site + accept_site).replace(\\\"N\\\", \\\".\\\"))\\n ligation_list.append(\\n str(Seq(give_site + accept_site).reverse_complement()).replace(\\n \\\"N\\\", \\\".\\\"\\n )\\n )\\n\\n # Build the regex for any ligation sites.\\n pattern = \\\"|\\\".join(sorted(list(set(ligation_list))))\\n return re.compile(pattern)\",\n \"def getBibTeX(bibref,tag_suf,outFile):\\n if bibref == '1988iras....1.....B':\\n bibtex = ['>@article{1988iras....1.....B,\\\\n',\\n ' title={Infrared astronomical satellite (IRAS) catalogs and atlases. Volume 1: Explanatory supplement},\\\\n',\\n ' keywords = {All Sky Photography, Catalogs, Indexes (Documentation), Infrared Astronomy Satellite, Cosmology, Galaxies, Star Formation, Stellar Evolution, Astrophysics},\\\\n',\\n ' author={Beichman, CA and Neugebauer, G and Habing, HJ and Clegg, PE and Chester, Thomas J},\\\\n',\\n ' year=1988,\\\\n',\\n ' volume = {1},\\\\n', \\n ' month = jan,\\\\n', \\n ' adsurl = {https://ui.adsabs.harvard.edu/abs/1988iras....1.....B},\\\\n'\\n '}\\\\n']\\n else:\\n baseURL = 'https://ui.adsabs.harvard.edu/abs/'\\n suf = '/exportcitation'\\n lines = urllib.request.urlopen(baseURL+bibref+suf).readlines()\\n lines = [l.decode('utf-8') for l in lines] # remove additional webpage encoding\\n \\n bibtex = []\\n for l in range(0, len(lines)):\\n if 'export-textarea ' in str(lines[l]):\\n bibtex.append(str(lines[l]))\\n t = l+1\\n \\n while '</textarea>' not in str(lines[t+1]):\\n bibtex.append(str(lines[t])) \\n t += 1\\n \\n for item in bibtex:\\n if 'author' in item.split('=')[0]:\\n auth = item.split('=')[1].split(',')[0]\\n for i in string.punctuation:\\n auth = auth.replace(i, '')\\n auth = auth.replace(' ', '')\\n if 'year' in item.split('=')[0]:\\n yr = item.split('=')[1].split(',')[0]\\n yr = yr.replace(' ', '')\\n \\n try:\\n bibtex[0] = bibtex[0].split('>')[1].split('{')[0]+'{'+auth+yr+tag_suf+',\\\\n'\\n except UnboundLocalError as ule:\\n print(bibtex)\\n print('')\\n print(ule)\\n sys.exit()\\n \\n with open(outFile, 'a') as o:\\n for item in bibtex:\\n item = item.replace('"', '\\\"')\\n item = item.replace(''', \\\"'\\\")\\n item = item.replace('&', \\\"&\\\")\\n o.write(item)\\n o.write('\\\\n')\\n \\n return auth+yr+tag_suf\",\n \"def replaceContractions(self, text):\\n\\t\\treturn contractions.fix(text)\",\n \"def replace_misc(text): \\n # replace different types of \\\"year old\\\" with \\n # matches: y.o., y/o, years old. year old, yearold\\n text = re.sub(r'-?\\\\byears? ?-?old\\\\b|\\\\by(?:o|r)*[ ./-]*o(?:ld)?\\\\b', ' yo', text, flags=re.IGNORECASE)\\n\\n # Does the same thing as above but copied from https://arxiv.org/abs/1808.02622v1\\n text = re.sub(r'(\\\\d+)\\\\s*(year\\\\s*old|y.\\\\s*o.|yo|year\\\\s*old|year-old|-year-old|-year old)', r'\\\\1 yo', text, flags=re.IGNORECASE)\\n \\n # replaces yr, yr's, yrs with years\\n text = re.sub(r'\\\\byr[\\\\'s]*\\\\b', 'years', text, re.IGNORECASE)\\n \\n # replace Pt and pt with patient, and IN/OUT/OT PT with patient\\n # Note: PT also refers to physical therapy and physical therapist\\n text = re.sub(r'\\\\b[P|p]t.?|\\\\b(IN|OU?T) PT\\\\b', 'patient ', text)\\n\\n # replace sex with consistant token\\n text = re.sub(r'\\\\b(gentlman|male|man|m|M)(?!\\\\S)\\\\b', 'male', text)\\n text = re.sub(r'\\\\b(female|woman|f|F)(?!\\\\S)\\\\b', 'female', text)\\n \\n # replace time types\\n text = re.sub(r'\\\\d{0,2}:\\\\d{0,2} \\\\b[A|P]\\\\.?M\\\\.?\\\\b', replace_time, text, flags=re.IGNORECASE)\\n text = re.sub(r'\\\\[\\\\*\\\\*(\\\\d{2})\\\\*\\\\*\\\\] \\\\b[a|p].?m.?\\\\b', replace_time, text, flags=re.IGNORECASE)\\n \\n # finally remove leftover redacted stuff (mostly empty)\\n text = re.sub(r'\\\\[\\\\*\\\\*(.*?)\\\\*\\\\*\\\\]', '', text, flags=re.IGNORECASE)\\n\\n return text\",\n \"def _clean_text(text, website):\\n site_list = ['menafn_algeria', 'menafn_bahrain', 'menafn_egypt',\\n 'menafn_iraq', 'menafn_jordan', 'menafn_kuwait',\\n 'menafn_lebanon', 'menafn_morocco', 'menafn_oman',\\n 'menafn_palestine', 'menafn_qatar', 'menafn_saudi',\\n 'menafn_syria', 'menafn_tunisia', 'menafn_turkey',\\n 'menafn_uae', 'menafn_yemen']\\n\\n if website == 'bbc':\\n text = text.replace(\\n \\\"This page is best viewed in an up-to-date web browser with style sheets (CSS) \\\"\\n \\\"enabled. While you will be able to view the content of this page in your current \\\"\\n \\\"browser, you will not be able to get the full visual experience. Please consider \\\"\\n \\\"upgrading your browser software or enabling style sheets (CSS) if you are able to do \\\"\\n \\\"so.\\\",\\n '')\\n if website == 'almonitor':\\n text = re.sub(\\\"^.*?\\\\(photo by REUTERS.*?\\\\)\\\", \\\"\\\", text)\\n if website in site_list:\\n text = re.sub(\\\"^\\\\(.*?MENAFN.*?\\\\)\\\", \\\"\\\", text)\\n elif website == 'upi':\\n text = text.replace(\\n \\\"Since 1907, United Press International (UPI) has been a leading provider of critical \\\"\\n \\\"information to media outlets, businesses, governments and researchers worldwide. UPI \\\"\\n \\\"is a global operation with offices in Beirut, Hong Kong, London, Santiago, Seoul and \\\"\\n \\\"Tokyo. Our headquarters is located in downtown Washington, DC, surrounded by major \\\"\\n \\\"international policy-making governmental and non-governmental organizations. UPI \\\"\\n \\\"licenses content directly to print outlets, online media and institutions of all \\\"\\n \\\"types. In addition, UPI's distribution partners provide our content to thousands of \\\"\\n \\\"businesses, policy groups and academic institutions worldwide. Our audience consists \\\"\\n \\\"of millions of decision-makers who depend on UPI's insightful and analytical stories \\\"\\n \\\"to make better business or policy decisions. In the year of our 107th anniversary, \\\"\\n \\\"our company strives to continue being a leading and trusted source for news, \\\"\\n \\\"analysis and insight for readers around the world.\\\",\\n '')\\n\\n text = text.replace('\\\\n', ' ')\\n\\n return text\",\n \"def replaceMultiExclamationMark(text):\\n text = re.sub(r\\\"(\\\\!)\\\\1+\\\", ' multiExclamation ', text)\\n return text\",\n \"def variables_referenced(text):\\n return set(substitution_pattern.findall(text))\",\n \"def process_and_dispatch(self):\\n references = []\\n for raw_block_references in self.raw_references:\\n bibcode = raw_block_references['bibcode']\\n block_references = raw_block_references['block_references']\\n item_nums = raw_block_references.get('item_nums', [])\\n\\n parsed_references = []\\n for i, reference in enumerate(block_references):\\n reference = self.latex_reference.cleanup(reference)\\n logger.debug(\\\"confTEX: parsing %s\\\" % reference)\\n parsed_references.append(self.merge({'refstr': reference, 'refraw': reference}, self.any_item_num(item_nums, i)))\\n\\n references.append({'bibcode': bibcode, 'references': parsed_references})\\n logger.debug(\\\"%s: parsed %d references\\\" % (bibcode, len(references)))\\n\\n return references\",\n \"def replace_delicious_tags(generator):\\n from jinja2 import Template\\n template = Template(generator.context.get('DELICIOUS_TEMPLATE'))\\n\\n for page in generator.pages:\\n for match in delicious_regex.findall(page._content):\\n bookmarks = sorted([bookmark for bookmark\\n in generator.context.get('DELICIOUS_BOOKMARKS')\\n if set(match.split()).issubset(bookmark.tags)])\\n\\n # Create a context to render with\\n context = generator.context.copy()\\n context.update({'bookmarks': bookmarks})\\n\\n replacement = template.render(context)\\n\\n page._content = page._content.replace(\\n \\\"[delicious \\\" + match + \\\"]\\\", replacement)\",\n \"def _replace_links(input_string,link_dict={}):\\n\\n # Dictionary to hold link string/target pairs\\n target_dict = {}\\n\\n # Strip white space from string\\n this_string = input_string.strip()\\n\\n # If we have a string stub, return it\\n if len(input_string) < 2:\\n return input_string, {}\\n\\n # Look for patterns: \\\";_\\\" OR \\\",_\\\" OR \\\" _\\\" OR \\\":_\\\"\\n search_pattern = re.compile(\\\"[\\\\,\\\\s\\\\;\\\\:]\\\\_|\\\\A\\\\_\\\")\\n\\n # Look for place to break link: \\\";\\\" OR \\\",\\\" OR \\\":\\\" OR \\\" \\\" OR \\\".\\\"\\n end_pattern = re.compile(\\\"[\\\\,\\\\s\\\\;\\\\:\\\\.]\\\")\\n\\n # Look for a match\\n match = search_pattern.search(this_string)\\n while match:\\n\\n # If started with \\\"_\\\", link_alias starts at match.span()[0]\\n if this_string[match.span()[0]] == \\\"_\\\":\\n start = match.span()[0]\\n\\n # If started with \\\",_\\\" or the like, link_alias starts at match.span()[0] + 1\\n else:\\n start = match.span()[0] + 1\\n\\n # Chop string into before and after _ in link (front and back)\\n front = this_string[:start]\\n back = this_string[start:]\\n\\n # Look for end of the link\\n link_end = end_pattern.search(back)\\n\\n # If we find the end, split back into link_alias and trailing\\n if link_end:\\n link_alias = back[:link_end.span()[0]]\\n trailing = back[link_end.span()[0]:]\\n\\n # If we do not find the end, the whole back is link_alias ... no trailing\\n else:\\n link_alias = back[:]\\n trailing = \\\"\\\"\\n\\n # Extract url and text for constructing the link text\\n try:\\n url, text = link_dict[link_alias]\\n\\n if url == \\\"\\\":\\n if text == \\\"\\\":\\n raise KeyError\\n else:\\n # Replace the link_alias with the text, no url\\n link_string = text\\n else:\\n if text == \\\"\\\":\\n # Replace the link_alias with link_alias[1:] -> url\\n label = link_alias[1:]\\n else:\\n # Replace the link_alias with the text -> url\\n label = text\\n\\n link_string = \\\"`{}`_\\\".format(label)\\n url_string = \\\".. _`{}`: {}\\\".format(label,url)\\n try:\\n already_seen = target_dict[label]\\n if already_seen != url_string:\\n err = \\\"The same link_text '{}' corresponds to more than one url\\\\n\\\".format(text)\\n raise ValueError(err)\\n except KeyError:\\n target_dict[label] = url_string\\n\\n except KeyError:\\n\\n # Replace the link_alias with link_alias[1:]\\n link_string = link_alias[1:]\\n\\n # Rebuild this_string with front + new link + trailing\\n this_string = \\\"{}{}{}\\\".format(front,link_string,trailing)\\n\\n # Look for another link\\n match = search_pattern.search(this_string)\\n\\n return this_string, target_dict\",\n \"def display_abstract( id, highlights=[]):\\n a = Abstracts[ id ]\\n for h in highlights:\\n a = re.sub(r'\\\\b(%s)\\\\b'%h,'<mark>\\\\\\\\1</mark>',a, flags=re.IGNORECASE)\\n print ('<blockquote>%s</blockquote>' % a)\",\n \"def filter_substitution_image_links(links):\\n return [link for link in links if '{' not in link]\",\n \"def find_replace_all(self):\\n old_term = self.text_find.get()\\n new_term = self.text_replace.get()\\n while True:\\n idx = '1.0'\\n idx = self.text.search(old_term, idx, nocase=1,\\n stopindex=tk.END)\\n lastidx = '% s+% dc' % (idx, len(old_term))\\n if not idx:\\n break\\n self.text.delete(idx, lastidx)\\n self.text.insert(idx, new_term)\",\n \"def journal_booktitle_unify(entries: t.List[t.Dict]) -> t.List[t.Dict[str, str]]:\\n\\n def _process(\\n entries: t.List[t.Dict], etype: str, val: str, replacing_guide_file: str\\n ) -> t.List[t.Dict[str, str]]:\\n rep_rules = {}\\n with codecs.open(replacing_guide_file, \\\"r\\\", \\\"utf-8\\\") as gf:\\n for line in gf:\\n line = line.strip()\\n raw, sep, rep = line.partition(\\\"=\\\")\\n logging.debug(f'{line}')\\n if len(sep) != 0:\\n rep_rules[raw] = rep\\n logging.debug(f'{rep_rules}')\\n for e in entries:\\n if e[\\\"ENTRYTYPE\\\"].lower() == etype.lower():\\n value = e.get(val, \\\"\\\")\\n if value in rep_rules:\\n logging.debug(\\\"Before: %s\\\", e[val])\\n e[val] = rep_rules[value]\\n logging.debug(\\\"After: %s\\\", e[val])\\n return entries\\n\\n entries = _process(entries, \\\"InProceedings\\\", \\\"booktitle\\\", \\\"conferences.txt\\\")\\n return _process(entries, \\\"Article\\\", \\\"journal\\\", \\\"journals.txt\\\")\",\n \"def find_references(doi):\\n if doi is None:\\n return None\\n\\n references = []\\n if doi:\\n response = requests.get(f\\\"https://opencitations.net/index/api/v1/references/{doi}\\\").json()\\n if response:\\n references = [{\\\"doi\\\": r['cited'].replace(\\\"coci =>\\\", \\\"\\\")} for r in response]\\n\\n if references:\\n return references\\n else:\\n return None\",\n \"def convert_text(s):\\n for d in config.repl: # loaded from config.py\\n if \\\"flags\\\" in d:\\n s = re.sub(d[\\\"ptrn\\\"], d[\\\"repl\\\"], s, flags=d[\\\"flags\\\"])\\n else:\\n s = re.sub(d[\\\"ptrn\\\"], d[\\\"repl\\\"], s)\\n return s\",\n \"def placeNoteLists(self, text):\\n if self.notes:\\n o = OrderedDict()\\n for label, info in self.notes.items():\\n if 'seq' in info:\\n i = info['seq']\\n info['seq'] = label\\n o[i] = info\\n else:\\n self.unreferencedNotes[label] = info\\n\\n if o:\\n # sort o by key\\n o = OrderedDict(sorted(o.items(), key=lambda t: t[0]))\\n self.notes = o\\n text_re = re.compile('<p>notelist(%s)(?:\\\\:([\\\\w|%s]))?([\\\\^!]?)(\\\\+?)\\\\.?[\\\\s]*</p>'\\n % (self.c, self.syms), re.U)\\n text = text_re.sub(self.fNoteLists, text)\\n return text\",\n \"def update_time_references_with_impact_zones(self, docFeatList, timexList):\\n \\n if not self.timexImpactZones:\\n return self.timeReferences\\n \\n impactZoneStart = self.timexImpactZones[0][0]\\n features = [feat for feat in docFeatList if feat.getStartPos() > impactZoneStart \\n and self.sentence_tags[feat.getSentNum()]=='NORMAL' and not feat.inClause() \\n and feat.getType() in ['VACCINE', 'DRUG']] \\n for feature in features:\\n sentnumber = feature.getSentNum()\\n if not feature.getTlink() or not feature.getTlink().getDateTime():\\n if 'concomitant' in [tg[0] for tg in self.sentence_full_tags[sentnumber]]:\\n continue\\n feature = self.assign_feature_time_with_impact_zones(feature, self.timexImpactZones)\\n tlink = feature.getTlink()\\n if tlink:\\n if feature.getType()=='VACCINE':\\n refType = 'Vaccine'\\n confidence = 0.6\\n else:\\n refType = 'Drug'\\n confidence = 0.6\\n \\n self.timeReferences[(refType, feature.getString(), 0, sentnumber, \\n feature.getStartPos(), feature.getEndPos(), confidence)] = tlink.getDateTime()\\n \\n ##: Too many false alarms. Disable this part for now.\\n ##: E.g, in 3603565(4th). .... Before treatment, .... \\n# exposureSet = ['Vaccination', 'Injection']\\n# for sentnum, sentence in enumerate(self.sentences):\\n# timexes = [t for t in timexList if t.getDateTime() and t.getSentNum()==sentnum]\\n# if timexes: ##: has been processed for time references\\n# continue\\n# \\n# tags = set([tg[1] for tg in self.taggedSentences[sentnum]])\\n# intersect = tags.intersection(exposureSet)\\n# if intersect:\\n# tgs = [tg for tg in self.taggedSentences[sentnum] if tg[1] in exposureSet]\\n# word = tgs[0][0]\\n# sentStartPos = self.sentence_startPos[sentnum]\\n# dtimes = [r[2] for r in self.timexImpactZones if sentStartPos >=r[0] and sentStartPos<=r[1]]\\n# if dtimes:\\n# self.timeReferences[(tgs[0][1], word, None, sentnum, sentStartPos, sentStartPos, 0.6)] = dtimes[0]\\n# \\n return self.timeReferences\",\n \"def multiwordReplace(text, wordDic):\\r\\n rc = re.compile('|'.join(map(re.escape, wordDic)))\\r\\n def translate(match):\\r\\n return wordDic[match.group(0)]\\r\\n return rc.sub(translate, text)\",\n \"def process_bug_links(text):\\n text = LP_RE.sub(r\\\"[\\\\1](https://bugs.launchpad.net/bugs/\\\\2)\\\", text)\\n text = BDO_RE.sub(r\\\"[\\\\1](https://bugs.debian.org/\\\\2)\\\", text)\\n text = ISSUE_RE.sub(r\\\"[\\\\1](https://github.com/gammu/gammu/\\\\2)\\\", text)\\n return BUG_RE.sub(r\\\"[\\\\1](https://bugs.cihar.com/\\\\2)\\\", text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5983218","0.5867871","0.5652614","0.558464","0.55107594","0.5485288","0.5324073","0.5307263","0.5304254","0.5300193","0.52907175","0.52869886","0.5280883","0.52644163","0.52494705","0.52478","0.5241941","0.5224906","0.5224906","0.52240586","0.52064675","0.5191581","0.51661456","0.51567537","0.5129909","0.51004004","0.5094927","0.50858456","0.50130254","0.50098175","0.50016874","0.4983427","0.4982535","0.4947556","0.49467102","0.4906238","0.48962227","0.48879653","0.4886749","0.48797446","0.4875933","0.48725143","0.48715377","0.48669708","0.48433575","0.4807192","0.48033246","0.4797879","0.47888568","0.47820684","0.47720668","0.4771131","0.4769469","0.47677967","0.47446743","0.47435746","0.47432318","0.47365984","0.47363648","0.47232693","0.47186247","0.47005403","0.4686104","0.46787935","0.46764314","0.46662328","0.46606392","0.46560833","0.4655929","0.46523482","0.4651872","0.4650074","0.46425202","0.4640487","0.46335405","0.46280956","0.46214846","0.46178758","0.46107712","0.46018282","0.45901513","0.45869747","0.45852152","0.4578986","0.45746323","0.45670578","0.45602226","0.45598677","0.45581415","0.4557924","0.45552266","0.45490974","0.454343","0.45413768","0.45382848","0.45370856","0.4530786","0.45282418","0.45237574","0.45234266"],"string":"[\n \"0.5983218\",\n \"0.5867871\",\n \"0.5652614\",\n \"0.558464\",\n \"0.55107594\",\n \"0.5485288\",\n \"0.5324073\",\n \"0.5307263\",\n \"0.5304254\",\n \"0.5300193\",\n \"0.52907175\",\n \"0.52869886\",\n \"0.5280883\",\n \"0.52644163\",\n \"0.52494705\",\n \"0.52478\",\n \"0.5241941\",\n \"0.5224906\",\n \"0.5224906\",\n \"0.52240586\",\n \"0.52064675\",\n \"0.5191581\",\n \"0.51661456\",\n \"0.51567537\",\n \"0.5129909\",\n \"0.51004004\",\n \"0.5094927\",\n \"0.50858456\",\n \"0.50130254\",\n \"0.50098175\",\n \"0.50016874\",\n \"0.4983427\",\n \"0.4982535\",\n \"0.4947556\",\n \"0.49467102\",\n \"0.4906238\",\n \"0.48962227\",\n \"0.48879653\",\n \"0.4886749\",\n \"0.48797446\",\n \"0.4875933\",\n \"0.48725143\",\n \"0.48715377\",\n \"0.48669708\",\n \"0.48433575\",\n \"0.4807192\",\n \"0.48033246\",\n \"0.4797879\",\n \"0.47888568\",\n \"0.47820684\",\n \"0.47720668\",\n \"0.4771131\",\n \"0.4769469\",\n \"0.47677967\",\n \"0.47446743\",\n \"0.47435746\",\n \"0.47432318\",\n \"0.47365984\",\n \"0.47363648\",\n \"0.47232693\",\n \"0.47186247\",\n \"0.47005403\",\n \"0.4686104\",\n \"0.46787935\",\n \"0.46764314\",\n \"0.46662328\",\n \"0.46606392\",\n \"0.46560833\",\n \"0.4655929\",\n \"0.46523482\",\n \"0.4651872\",\n \"0.4650074\",\n \"0.46425202\",\n \"0.4640487\",\n \"0.46335405\",\n \"0.46280956\",\n \"0.46214846\",\n \"0.46178758\",\n \"0.46107712\",\n \"0.46018282\",\n \"0.45901513\",\n \"0.45869747\",\n \"0.45852152\",\n \"0.4578986\",\n \"0.45746323\",\n \"0.45670578\",\n \"0.45602226\",\n \"0.45598677\",\n \"0.45581415\",\n \"0.4557924\",\n \"0.45552266\",\n \"0.45490974\",\n \"0.454343\",\n \"0.45413768\",\n \"0.45382848\",\n \"0.45370856\",\n \"0.4530786\",\n \"0.45282418\",\n \"0.45237574\",\n \"0.45234266\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6253816"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85692,"cells":{"query":{"kind":"string","value":"Patch django.request logger to ignore warnings."},"document":{"kind":"string","value":"def no_request_warning():\n return patch_logger('django.request', 'warning', log_kwargs=False)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add_logger(log, request):\n request.cls.log = log","def unpatch():\n _u(sanic.Sanic, \"handle_request\")\n if not SANIC_PRE_21:\n _u(sanic.Sanic, \"_run_request_middleware\")\n _u(sanic.request.Request, \"respond\")\n if not getattr(sanic, \"__datadog_patch\", False):\n return\n setattr(sanic, \"__datadog_patch\", False)","def log_request(task_request, request):\n msg = \"{0.method} {0.url}: {0.body}\".format(request)\n log_info(task_request, msg)","def _log_request(self):\n log = self.server.log\n if log:\n if hasattr(log, \"info\"):\n log.info(self.format_request() + '\\n')\n else:\n log.write(self.format_request() + '\\n')","def warningglobal(self, *args, **kwargs):\n return self.logger.log(logging.WARNING+1, *args, **kwargs)","def setup_logger(self):\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n request_log = logging.getLogger(\"requests.packages.urllib3\")\n request_log.setLevel(logging.DEBUG)\n request_log.propagate = True\n return logger, request_log","def isolated_logger(request, monkeypatch):\n if \"original_logger\" in request.keywords:\n yield\n return\n\n # Get a fresh new logger, not used anywhere\n raw_logger = logging.getLogger(uniqstr())\n raw_logger.setLevel(logging.NOTSET)\n new_handler = logging.StreamHandler()\n\n patches = {\n \"propagate\": True, # <- needed for caplog\n \"nesting\": 0,\n \"wrapped\": raw_logger,\n \"handler\": new_handler,\n \"formatter\": ReportFormatter(),\n }\n for key, value in patches.items():\n monkeypatch.setattr(f\"pyscaffold.log.logger.{key}\", value)\n\n try:\n yield\n finally:\n new_handler.close()","def debug_requests_on():\n HTTPConnection.debuglevel = 2\n\n logging.basicConfig(filename='example1.log', filemode='w', level=logging.INFO, format='%(asctime)s %(message)s',\n datefmt='%m/%d/%Y %I:%M:%S %p')\n logging.getLogger().setLevel(logging.DEBUG)\n\n requests_log = logging.getLogger(\"requests.packages.urllib3\")\n requests_log.setLevel(logging.DEBUG)\n requests_log.propagate = True","def before():\n app.logger.info(\"Local Timestamp: {}\".format(str(datetime.now())))\n app.logger.info(\"Request Method: {}\".format(request.method))\n app.logger.info(\"Request URL: {}\".format(request.url))\n app.logger.info(\"Request Access Route: {}\".format(request.access_route[0]))\n headers = \"\"\n for (key, value) in request.headers:\n # hide authorization header from logs\n if key == \"Authorization\":\n value = \"[provided]\" \n headers += \"{}: {}\\n\".format(key, value)\n app.logger.info(\"Request Headers:{}\\n{}\\n{}\".format(\"-\"*45,str(headers)[:-1], \"-\"*60))\n body = copy.deepcopy(request.json)\n if type(body) is dict and \"password\" in body:\n body['password'] = \"[provided]\"\n app.logger.info(\"Request Body: {}\".format(body))","def silence_requests_warnings():\n requests.packages.urllib3.disable_warnings()","def log(self):\n\n\t\theader_dict = dict(request.headers)\n\n\t\ttry:\n\t\t\ttracker_id = header_dict[\"tracker_id\"]\n\t\texcept Exception:\n\t\t\ttracker_id = None\n\t\t\n\t\ttry:\n\t\t\tuser_agent = header_dict[\"User-Agent\"]\n\t\texcept Exception:\n\t\t\tuser_agent = None\n\n\t\ttry:\n\t\t\tlanguage = header_dict[\"Accept-Language\"]\n\t\texcept Exception:\n\t\t\tlanguage = None\n\n\t\ttry:\n\t\t\treferer = header_dict[\"Referer\"]\n\t\texcept Exception:\n\t\t\treferer = None\n\n\t\ttry:\n\t\t\torigin = header_dict[\"Origin\"]\n\t\texcept Exception:\n\t\t\torigin = None\n\n\t\ttry:\n\t\t\tjson_data = request.json\n\t\texcept Exception:\n\t\t\tjson_data = None\n\n\t\ttry:\n\t\t\tplatform = request.user_agent.platform.title()\n\t\texcept Exception:\n\t\t\tplatform = None\n\n\t\ttry:\n\t\t\tbrowser = request.user_agent.browser.title()\n\t\texcept Exception:\n\t\t\tbrowser = None\n\n\t\ttry:\n\t\t\tauth_header_token = header_dict[\"Authorization\"].split(\" \")[1]\n\t\texcept Exception:\n\t\t\tauth_header_token = None\n\t\t\n\t\t## If set to run before a request: This is the default setting\n\t\tif self.pre_request:\n\t\t\t@self.app.before_request()\n\t\t\tdef run():\n\t\t\t\t## If the path accessed is in the do_not_log list, it is skipped\n\t\t\t\tif request.path in self.do_not_log:\n\t\t\t\t\treturn\n\t\t\t\t## If the path accessed is not in the do_not_log list, it is posted\n\t\t\t\telse:\n\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\"error\": None,\n\t\t\t\t\t\t\"stack_trace\": None,\n\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\"status_code\": 200, ## Assumed to be 200 due to the nature of the function\n\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t}\n\n\t\t\t\t\tself.startPost(post_data)\n\n\t\t\t\t\treturn\n\n\t\t\treturn run\n\t\t\n\t\t## If set to as a wrapper to a function\n\t\telse:\n\t\t\tdef log_decorator(func):\n\n\t\t\t\t@wraps(func)\n\t\t\t\tdef execute(*args, **kwargs):\n\t\t\t\t\ttry:\n\t\t\t\t\t\tresult = func(*args, **kwargs)\n\n\t\t\t\t\t\tresult_response = make_response(result)\n\n\t\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\t\"error\": None,\n\t\t\t\t\t\t\t\"stack_trace\": None,\n\t\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\t\"status_code\": result_response.status_code,\n\t\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tself.startPost(post_data)\n\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tresult = func(*args, **kwargs)\n\t\t\t\t\t\t\n\t\t\t\t\t\ttrace = traceback.format_exc()\n\n\t\t\t\t\t\tkwargs = {\n\t\t\t\t\t\t\t\"trace\": trace,\n\t\t\t\t\t\t\t\"exception\": str(e)\n\t\t\t\t\t\t}\n\t\t\t\t\t\t\n\t\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\t\"error\": str(e),\n\t\t\t\t\t\t\t\"stack_trace\": trace,\n\t\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\t\"status_code\": 500,\n\t\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tself.startPost(post_data)\n\t\t\t\t\t\n\t\t\t\t\treturn result\n\t\t\t\t\n\t\t\t\treturn execute\n\t\t\t\n\t\t\treturn log_decorator","def log(msg):\n\tfrom http_request import req\n\tif not req: return\n\t\t\n\tif not req.out.get('_log'):\n\t\treq.out['_log'] = []\n\treq.out['_log'].append(msg)","def warning(self, tag, message, exc_info=False):\n \n self.log(logging.warning,tag, message, exc_info)","def getLogging(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def patch():\n global SANIC_PRE_21\n\n if getattr(sanic, \"__datadog_patch\", False):\n return\n setattr(sanic, \"__datadog_patch\", True)\n\n SANIC_PRE_21 = sanic.__version__[:2] < \"21\"\n\n _w(\"sanic\", \"Sanic.handle_request\", patch_handle_request)\n if not SANIC_PRE_21:\n _w(\"sanic\", \"Sanic._run_request_middleware\", patch_run_request_middleware)\n _w(sanic.request, \"Request.respond\", patch_request_respond)","def warning(self, msg):\r\n self.logger.warning(msg)","def postLogging(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def log_bad_request_details(r):\n logger.info(\"Response status code: \" + str(r.status_code))\n logger.info(\"Response Details: \" + json.dumps(r.json()))\n logger.info(\"History: \" + str(r.history))\n logger.info(\"Cookies: \" + str(requests.utils.dict_from_cookiejar(r.cookies)))\n logger.info(\"URL: \" + str(r.url))\n logger.info(\"Links: \" + str(r.links))","def warning ( self , message , *args , **kwargs ) :\n return self.logger.warning ( message , *args , **kwargs )","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def log_check_warnings(self):\n pass","def sanitize_http_request(request_info: HttpRequestInfo):\n\n print(\"*\" * 50)\n print(\"[sanitize_http_request] Implement me!\")\n print(\"*\" * 50)\n return request_info","def _log_request(res: SpamResult) -> None:\n _log.info(f\"requestId=[{request.id}] result=[{res.label}] reason=[{res.reason}]\")","def setLogger():\n logger = logging.getLogger()\n logger.setLevel(logging.CRITICAL)","def __init__(self, logger):\n super(UserRequestLogger, self).__init__(logger, {})","def SetLogLevel(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def attach_request_log(response):\n allure.attach(\n dump.dump_all(response).decode(\"utf-8\"),\n name=\"Full request log\",\n extension=\"txt\",\n )","def clear_request_log():\n if not var_cache['local'].clear_request_list():\n raise ValueError('Clear request log failed.')","def __init__(self, context, request, logger):","def log_request(self, r):\n\n token = r.headers.get(self.header, None)\n r.token = token\n self.requests.append(r)\n if r.token:\n self.log.debug('[%s] %s', token or '/', r.url)","def warning(self, msg):\n self.__logger.warning(msg)","def warning(self, msg):\n\n self.logger.warning(msg)","def _alter_logger(*args, **kwargs):\n\n # TODO: dress up root logger here under Celery\n # configure_logging()\n pass","def logging(self):\r\n return None","def log_request(self, code='-', size='-'):\n pass","def logwarning(self, msg):\n self.logger.warning(msg)","def after_request(response):\n # This avoids the duplication of registry in the log,\n # since that 500 is already logged via @app.errorhandler.\n if response.status_code != 500:\n ts = strftime('[%Y-%b-%d %H:%M]')\n logger.error('%s %s %s %s %s %s',\n ts,\n request.remote_addr,\n request.method,\n request.scheme,\n request.full_path,\n response.status)\n return response","def _disable_pywikibot_logging():\n # We need to wake up Pywikibot's logging interface so that its logger level\n # won't get overridden by a later logging call:\n import pywikibot\n pywikibot.debug(\"Disabling routine logging\", \"logging\")\n getLogger(\"pywiki\").setLevel(\"WARNING\")","def isLogEnabled(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def log_request(self, code='-', size='-'):\n if self.server.log_requests:\n BaseHTTPServer.BaseHTTPRequestHandler.log_request(self, code, size)","def rest_api_log(self):\n with self.resource_lock:\n pass","def __init__(self, request, log=\"\"):\n self.log = log\n self.request = request","def disableCLangLogger(self):\n pass","def disablePyLangLogger(self):\n pass","def warning(msg):\n log('WARNING', msg)","def before_request():\n pass","def setup_logger():\n now = datetime.now()\n logging.basicConfig(level=logging.DEBUG)\n logging.getLogger(\"requests\").setLevel(logging.WARNING)\n logging.getLogger(\"urllib3\").setLevel(logging.WARNING)\n logging.info(f\"Script run on: {now}\")","def after_request(response):\n # This avoids the duplication of registry in the log,\n # since that 500 is already logged via @app.errorhandler.\n if response.status_code != 500:\n ts = strftime('[%Y-%b-%d %H:%M]')\n message = '{0} {1} {2} {3} {4} {5}'.format(\n ts,\n request.remote_addr,\n request.method,\n request.scheme,\n request.full_path,\n response.status)\n print(message)\n return response","def no_additional_complaints() -> None:\n logging.getLogger(\"asyncio\").setLevel(\"CRITICAL\")\n warnings.simplefilter(\"ignore\")","def SetDebugLevel(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def dont_apply_response_fixes(original_function, self, request, response):\n return response","def warning(self, msg: str):\n self._logger.warning(msg)","def warning(self, *args, **kwargs):","def go_quiet():\n my_logger = logging.getLogger(__name__)\n my_logger.setLevel(LEVELS['WARNING'])","def _get_logger(self):","def print_quiet_logs():\n print(request_logger.get_quiet_logs())","def w(msg):\n logging.warning('##### %s' % repr(msg))","def warn():\n pass","def process_request(self, request):\n # This middleware is only needed for regular browser pages.\n # It is incompatible with the mobile apps and APIs in general.\n if is_feature_enabled() and not is_api_request(request):\n self.patch_request(request)","def warning(self, msg, *args, **kwargs):\n self._logger.warning(msg, *args, **kwargs)","def _silent_request(self, *args):\n raise NotImplementedError","def web_logger(response):\n LOG.info('%s %s %s %s', flask.request.remote_addr, flask.request.method,\n flask.request.full_path, response.status)\n return response","def _filter_in_request(self):\n pass","def setup_requests_debugging(self):\n\n # These two lines enable debugging at httplib level (requests->urllib3->http.client)\n # You will see the REQUEST, including HEADERS and DATA, and RESPONSE with HEADERS but without DATA.\n # The only thing missing will be the response.body which is not logged.\n try:\n import http.client as http_client\n except ImportError:\n # Python 2\n import httplib as http_client\n http_client.HTTPConnection.debuglevel = 1\n\n # You must initialize logging, otherwise you'll not see debug output.\n self.logger.setLevel(logging.DEBUG)\n requests_log = logging.getLogger(\"requests.packages.urllib3\")\n requests_log.setLevel(logging.DEBUG)\n requests_log.propagate = True","def warning(self, message, *args, **kwargs):\n\n self.logger.warning(message, *args, **kwargs)","def ReportWarning(self, warning_message, status=200):\n logging.warning('Reporting warning: %r', warning_message)\n self.response.set_status(status)\n self.response.out.write('%s\\nrequest_id:%s\\n' %\n (warning_message, utils.GetRequestId()))","def debug_only(func):\n @wraps(func)\n def decorated_view(*args, **kwargs):\n if not current_app.config['DEBUG']:\n abort(404)\n current_app.logger.warn('This endpoint disabled in live builds: {}'.format(request.url))\n return func(*args, **kwargs)\n return decorated_view","def _logRequestArgs(self, request):\n\t\ttry:\n\t\t\tif request.args:\n\t\t\t\t# even if there are args, don't log them if only boring ones\n\t\t\t\t# were given\n\t\t\t\tfmtArgs = _formatRequestArgs(request.args)\n\t\t\t\tif fmtArgs!='{}':\n\t\t\t\t\tlog.msg(\"# Processing starts: %s %s\"%(request.path, \n\t\t\t\t\t\tfmtArgs))\n\t\texcept: # don't fail because of logging problems\n\t\t\tbase.ui.notifyError(\"Formatting of request args failed.\")","def _log_throttled_access(self, request):\n request_method = request.method.lower()\n self._meta.throttle.accessed(self._meta.authentication.get_identifier(request), url=request.get_full_path(), request_method=request_method)","def handle_error(self, request, error):\n self.log.error(\"An error occurred at request \" + repr(request) + \": \" + repr(error))","def my_log_traceback(severity=logging.CRITICAL):\n from cherrypy import _cperror\n h = [\" %s: %s\" % (k, v) for k, v in cherrypy.request.header_list]\n cherrypy.log('\\nRequest Headers:\\n' + '\\n'.join(h) + '\\n\\n' + _cperror.format_exc(), \"HTTP\", severity=severity)","def warnings():\n return THE_LOGGER.warnings","def log_warning(self, msg, *args, **kwargs):\n if self.action_logging_enabled and self._log is not None:\n self._log.warning(msg, *args, **kwargs)\n return","def warning(self, msg, *args):\n if self.lvl<=logging.WARNING: return self._log(msg, *args)","def warning(module, message):\n if loggingLevel >= loggingLevelWarning:\n ModuLog.log(\"W\", module, message)","def nologger(*args, **kwargs):\n return","def warning(warning_message: str):\n logger.warning(warning_message)","def disable_logging():\n logging.disable(logging.INFO)","def _suppressGoogleLogWarning():\n\n try:\n # Tensorflow uses Google's abseil-py library, which uses a Google-specific\n # wrapper for logging. That wrapper will write a warning to sys.stderr if\n # the Google command-line flags library has not been initialized.\n #\n # https://github.com/abseil/abseil-py/blob/pypi-v0.7.1/absl/logging/__init__.py#L819-L825\n #\n # We don't want this here because we have our own logging setup.\n import absl.logging\n\n # https://github.com/abseil/abseil-py/issues/99\n logging.root.removeHandler(absl.logging._absl_handler)\n # https://github.com/abseil/abseil-py/issues/102\n absl.logging._warn_preinit_stderr = False\n except Exception:\n pass","def _log_some_info(self):\n logging.info('info')","def ignore_warnings(my_func):\n\n def wrapper(self, *args, **kwargs):\n \"\"\"\n This is where the warning suppression occurs.\n \"\"\"\n if sys.version_info >= (3, 2):\n warnings.simplefilter(\"ignore\", ResourceWarning)\n with warnings.catch_warnings():\n my_func(self, *args, **kwargs)\n\n return wrapper","def after_request(response):\n logger = logging.getLogger(\"app.access\")\n logger.info(\n \"%s [%s] %s %s %s %s %s %s %s\",\n request.remote_addr,\n dt.utcnow().strftime(\"%d/%b/%Y:%H:%M:%S.%f\")[:-3],\n request.method,\n request.path,\n request.scheme,\n response.status,\n response.content_length,\n request.referrer,\n request.user_agent,\n )\n return response","def log_extra(self, msg, *args):\n if self.settings.EXTRA_LOGGING.value:\n self.log.info(\"(Extra logging) \" + msg, *args)","def _log(self, message):\n pass","def log_request_response(task_request, response):\n log_request(task_request, response.request)\n log_response(task_request, response)","def filter_request():\n if request.method not in ALLOWED_METHODS:\n return \"Method Not Allowed\", 405\n ua = str(request.user_agent)\n if \"Mozilla\" not in ua or \"Gecko\" not in ua:\n return \"No Scrappers!\", 403","def after_request(response):\n # TODO: Send log reports to a monitor service such as DataDog?\n return response","def disable_traceback_filtering():\n global _ENABLE_TRACEBACK_FILTERING\n _ENABLE_TRACEBACK_FILTERING.value = False","def WARNING_LOG(msg, *args, **kwargs):\n logger.warning(msg, *args, **kwargs)","def log_requests(response):\n ts = strftime('[%Y-%b-%d %H:%M-%S]')\n\n logger.info('Flask: {0} {1} {2} {3} {4} {5}'.\n format(ts, request.remote_addr, request.method, request.scheme, request.full_path, response.status))\n\n return response","def onRequestTemporaryError(self, api, request, error):\n logging.info('Request temporary error ({}); Error: {}'\n .format(request, error))","def setLogging(self, val: bool):\n self._logRequests = val","def enableCLangLogger(self):","def log_debug(task_request, message):\n _log(logger.debug, task_request, message)"],"string":"[\n \"def add_logger(log, request):\\n request.cls.log = log\",\n \"def unpatch():\\n _u(sanic.Sanic, \\\"handle_request\\\")\\n if not SANIC_PRE_21:\\n _u(sanic.Sanic, \\\"_run_request_middleware\\\")\\n _u(sanic.request.Request, \\\"respond\\\")\\n if not getattr(sanic, \\\"__datadog_patch\\\", False):\\n return\\n setattr(sanic, \\\"__datadog_patch\\\", False)\",\n \"def log_request(task_request, request):\\n msg = \\\"{0.method} {0.url}: {0.body}\\\".format(request)\\n log_info(task_request, msg)\",\n \"def _log_request(self):\\n log = self.server.log\\n if log:\\n if hasattr(log, \\\"info\\\"):\\n log.info(self.format_request() + '\\\\n')\\n else:\\n log.write(self.format_request() + '\\\\n')\",\n \"def warningglobal(self, *args, **kwargs):\\n return self.logger.log(logging.WARNING+1, *args, **kwargs)\",\n \"def setup_logger(self):\\n logger = logging.getLogger()\\n logger.setLevel(logging.DEBUG)\\n request_log = logging.getLogger(\\\"requests.packages.urllib3\\\")\\n request_log.setLevel(logging.DEBUG)\\n request_log.propagate = True\\n return logger, request_log\",\n \"def isolated_logger(request, monkeypatch):\\n if \\\"original_logger\\\" in request.keywords:\\n yield\\n return\\n\\n # Get a fresh new logger, not used anywhere\\n raw_logger = logging.getLogger(uniqstr())\\n raw_logger.setLevel(logging.NOTSET)\\n new_handler = logging.StreamHandler()\\n\\n patches = {\\n \\\"propagate\\\": True, # <- needed for caplog\\n \\\"nesting\\\": 0,\\n \\\"wrapped\\\": raw_logger,\\n \\\"handler\\\": new_handler,\\n \\\"formatter\\\": ReportFormatter(),\\n }\\n for key, value in patches.items():\\n monkeypatch.setattr(f\\\"pyscaffold.log.logger.{key}\\\", value)\\n\\n try:\\n yield\\n finally:\\n new_handler.close()\",\n \"def debug_requests_on():\\n HTTPConnection.debuglevel = 2\\n\\n logging.basicConfig(filename='example1.log', filemode='w', level=logging.INFO, format='%(asctime)s %(message)s',\\n datefmt='%m/%d/%Y %I:%M:%S %p')\\n logging.getLogger().setLevel(logging.DEBUG)\\n\\n requests_log = logging.getLogger(\\\"requests.packages.urllib3\\\")\\n requests_log.setLevel(logging.DEBUG)\\n requests_log.propagate = True\",\n \"def before():\\n app.logger.info(\\\"Local Timestamp: {}\\\".format(str(datetime.now())))\\n app.logger.info(\\\"Request Method: {}\\\".format(request.method))\\n app.logger.info(\\\"Request URL: {}\\\".format(request.url))\\n app.logger.info(\\\"Request Access Route: {}\\\".format(request.access_route[0]))\\n headers = \\\"\\\"\\n for (key, value) in request.headers:\\n # hide authorization header from logs\\n if key == \\\"Authorization\\\":\\n value = \\\"[provided]\\\" \\n headers += \\\"{}: {}\\\\n\\\".format(key, value)\\n app.logger.info(\\\"Request Headers:{}\\\\n{}\\\\n{}\\\".format(\\\"-\\\"*45,str(headers)[:-1], \\\"-\\\"*60))\\n body = copy.deepcopy(request.json)\\n if type(body) is dict and \\\"password\\\" in body:\\n body['password'] = \\\"[provided]\\\"\\n app.logger.info(\\\"Request Body: {}\\\".format(body))\",\n \"def silence_requests_warnings():\\n requests.packages.urllib3.disable_warnings()\",\n \"def log(self):\\n\\n\\t\\theader_dict = dict(request.headers)\\n\\n\\t\\ttry:\\n\\t\\t\\ttracker_id = header_dict[\\\"tracker_id\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\ttracker_id = None\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tuser_agent = header_dict[\\\"User-Agent\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\tuser_agent = None\\n\\n\\t\\ttry:\\n\\t\\t\\tlanguage = header_dict[\\\"Accept-Language\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\tlanguage = None\\n\\n\\t\\ttry:\\n\\t\\t\\treferer = header_dict[\\\"Referer\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\treferer = None\\n\\n\\t\\ttry:\\n\\t\\t\\torigin = header_dict[\\\"Origin\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\torigin = None\\n\\n\\t\\ttry:\\n\\t\\t\\tjson_data = request.json\\n\\t\\texcept Exception:\\n\\t\\t\\tjson_data = None\\n\\n\\t\\ttry:\\n\\t\\t\\tplatform = request.user_agent.platform.title()\\n\\t\\texcept Exception:\\n\\t\\t\\tplatform = None\\n\\n\\t\\ttry:\\n\\t\\t\\tbrowser = request.user_agent.browser.title()\\n\\t\\texcept Exception:\\n\\t\\t\\tbrowser = None\\n\\n\\t\\ttry:\\n\\t\\t\\tauth_header_token = header_dict[\\\"Authorization\\\"].split(\\\" \\\")[1]\\n\\t\\texcept Exception:\\n\\t\\t\\tauth_header_token = None\\n\\t\\t\\n\\t\\t## If set to run before a request: This is the default setting\\n\\t\\tif self.pre_request:\\n\\t\\t\\t@self.app.before_request()\\n\\t\\t\\tdef run():\\n\\t\\t\\t\\t## If the path accessed is in the do_not_log list, it is skipped\\n\\t\\t\\t\\tif request.path in self.do_not_log:\\n\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\t## If the path accessed is not in the do_not_log list, it is posted\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\\"error\\\": None,\\n\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": None,\\n\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\\"status_code\\\": 200, ## Assumed to be 200 due to the nature of the function\\n\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\n\\t\\t\\t\\t\\treturn\\n\\n\\t\\t\\treturn run\\n\\t\\t\\n\\t\\t## If set to as a wrapper to a function\\n\\t\\telse:\\n\\t\\t\\tdef log_decorator(func):\\n\\n\\t\\t\\t\\t@wraps(func)\\n\\t\\t\\t\\tdef execute(*args, **kwargs):\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tresult = func(*args, **kwargs)\\n\\n\\t\\t\\t\\t\\t\\tresult_response = make_response(result)\\n\\n\\t\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"error\\\": None,\\n\\t\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": None,\\n\\t\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\t\\\"status_code\\\": result_response.status_code,\\n\\t\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\n\\t\\t\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\t\\t\\tresult = func(*args, **kwargs)\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\ttrace = traceback.format_exc()\\n\\n\\t\\t\\t\\t\\t\\tkwargs = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"trace\\\": trace,\\n\\t\\t\\t\\t\\t\\t\\t\\\"exception\\\": str(e)\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"error\\\": str(e),\\n\\t\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": trace,\\n\\t\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\t\\\"status_code\\\": 500,\\n\\t\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\treturn result\\n\\t\\t\\t\\t\\n\\t\\t\\t\\treturn execute\\n\\t\\t\\t\\n\\t\\t\\treturn log_decorator\",\n \"def log(msg):\\n\\tfrom http_request import req\\n\\tif not req: return\\n\\t\\t\\n\\tif not req.out.get('_log'):\\n\\t\\treq.out['_log'] = []\\n\\treq.out['_log'].append(msg)\",\n \"def warning(self, tag, message, exc_info=False):\\n \\n self.log(logging.warning,tag, message, exc_info)\",\n \"def getLogging(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def patch():\\n global SANIC_PRE_21\\n\\n if getattr(sanic, \\\"__datadog_patch\\\", False):\\n return\\n setattr(sanic, \\\"__datadog_patch\\\", True)\\n\\n SANIC_PRE_21 = sanic.__version__[:2] < \\\"21\\\"\\n\\n _w(\\\"sanic\\\", \\\"Sanic.handle_request\\\", patch_handle_request)\\n if not SANIC_PRE_21:\\n _w(\\\"sanic\\\", \\\"Sanic._run_request_middleware\\\", patch_run_request_middleware)\\n _w(sanic.request, \\\"Request.respond\\\", patch_request_respond)\",\n \"def warning(self, msg):\\r\\n self.logger.warning(msg)\",\n \"def postLogging(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def log_bad_request_details(r):\\n logger.info(\\\"Response status code: \\\" + str(r.status_code))\\n logger.info(\\\"Response Details: \\\" + json.dumps(r.json()))\\n logger.info(\\\"History: \\\" + str(r.history))\\n logger.info(\\\"Cookies: \\\" + str(requests.utils.dict_from_cookiejar(r.cookies)))\\n logger.info(\\\"URL: \\\" + str(r.url))\\n logger.info(\\\"Links: \\\" + str(r.links))\",\n \"def warning ( self , message , *args , **kwargs ) :\\n return self.logger.warning ( message , *args , **kwargs )\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def log_check_warnings(self):\\n pass\",\n \"def sanitize_http_request(request_info: HttpRequestInfo):\\n\\n print(\\\"*\\\" * 50)\\n print(\\\"[sanitize_http_request] Implement me!\\\")\\n print(\\\"*\\\" * 50)\\n return request_info\",\n \"def _log_request(res: SpamResult) -> None:\\n _log.info(f\\\"requestId=[{request.id}] result=[{res.label}] reason=[{res.reason}]\\\")\",\n \"def setLogger():\\n logger = logging.getLogger()\\n logger.setLevel(logging.CRITICAL)\",\n \"def __init__(self, logger):\\n super(UserRequestLogger, self).__init__(logger, {})\",\n \"def SetLogLevel(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def attach_request_log(response):\\n allure.attach(\\n dump.dump_all(response).decode(\\\"utf-8\\\"),\\n name=\\\"Full request log\\\",\\n extension=\\\"txt\\\",\\n )\",\n \"def clear_request_log():\\n if not var_cache['local'].clear_request_list():\\n raise ValueError('Clear request log failed.')\",\n \"def __init__(self, context, request, logger):\",\n \"def log_request(self, r):\\n\\n token = r.headers.get(self.header, None)\\n r.token = token\\n self.requests.append(r)\\n if r.token:\\n self.log.debug('[%s] %s', token or '/', r.url)\",\n \"def warning(self, msg):\\n self.__logger.warning(msg)\",\n \"def warning(self, msg):\\n\\n self.logger.warning(msg)\",\n \"def _alter_logger(*args, **kwargs):\\n\\n # TODO: dress up root logger here under Celery\\n # configure_logging()\\n pass\",\n \"def logging(self):\\r\\n return None\",\n \"def log_request(self, code='-', size='-'):\\n pass\",\n \"def logwarning(self, msg):\\n self.logger.warning(msg)\",\n \"def after_request(response):\\n # This avoids the duplication of registry in the log,\\n # since that 500 is already logged via @app.errorhandler.\\n if response.status_code != 500:\\n ts = strftime('[%Y-%b-%d %H:%M]')\\n logger.error('%s %s %s %s %s %s',\\n ts,\\n request.remote_addr,\\n request.method,\\n request.scheme,\\n request.full_path,\\n response.status)\\n return response\",\n \"def _disable_pywikibot_logging():\\n # We need to wake up Pywikibot's logging interface so that its logger level\\n # won't get overridden by a later logging call:\\n import pywikibot\\n pywikibot.debug(\\\"Disabling routine logging\\\", \\\"logging\\\")\\n getLogger(\\\"pywiki\\\").setLevel(\\\"WARNING\\\")\",\n \"def isLogEnabled(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def log_request(self, code='-', size='-'):\\n if self.server.log_requests:\\n BaseHTTPServer.BaseHTTPRequestHandler.log_request(self, code, size)\",\n \"def rest_api_log(self):\\n with self.resource_lock:\\n pass\",\n \"def __init__(self, request, log=\\\"\\\"):\\n self.log = log\\n self.request = request\",\n \"def disableCLangLogger(self):\\n pass\",\n \"def disablePyLangLogger(self):\\n pass\",\n \"def warning(msg):\\n log('WARNING', msg)\",\n \"def before_request():\\n pass\",\n \"def setup_logger():\\n now = datetime.now()\\n logging.basicConfig(level=logging.DEBUG)\\n logging.getLogger(\\\"requests\\\").setLevel(logging.WARNING)\\n logging.getLogger(\\\"urllib3\\\").setLevel(logging.WARNING)\\n logging.info(f\\\"Script run on: {now}\\\")\",\n \"def after_request(response):\\n # This avoids the duplication of registry in the log,\\n # since that 500 is already logged via @app.errorhandler.\\n if response.status_code != 500:\\n ts = strftime('[%Y-%b-%d %H:%M]')\\n message = '{0} {1} {2} {3} {4} {5}'.format(\\n ts,\\n request.remote_addr,\\n request.method,\\n request.scheme,\\n request.full_path,\\n response.status)\\n print(message)\\n return response\",\n \"def no_additional_complaints() -> None:\\n logging.getLogger(\\\"asyncio\\\").setLevel(\\\"CRITICAL\\\")\\n warnings.simplefilter(\\\"ignore\\\")\",\n \"def SetDebugLevel(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def dont_apply_response_fixes(original_function, self, request, response):\\n return response\",\n \"def warning(self, msg: str):\\n self._logger.warning(msg)\",\n \"def warning(self, *args, **kwargs):\",\n \"def go_quiet():\\n my_logger = logging.getLogger(__name__)\\n my_logger.setLevel(LEVELS['WARNING'])\",\n \"def _get_logger(self):\",\n \"def print_quiet_logs():\\n print(request_logger.get_quiet_logs())\",\n \"def w(msg):\\n logging.warning('##### %s' % repr(msg))\",\n \"def warn():\\n pass\",\n \"def process_request(self, request):\\n # This middleware is only needed for regular browser pages.\\n # It is incompatible with the mobile apps and APIs in general.\\n if is_feature_enabled() and not is_api_request(request):\\n self.patch_request(request)\",\n \"def warning(self, msg, *args, **kwargs):\\n self._logger.warning(msg, *args, **kwargs)\",\n \"def _silent_request(self, *args):\\n raise NotImplementedError\",\n \"def web_logger(response):\\n LOG.info('%s %s %s %s', flask.request.remote_addr, flask.request.method,\\n flask.request.full_path, response.status)\\n return response\",\n \"def _filter_in_request(self):\\n pass\",\n \"def setup_requests_debugging(self):\\n\\n # These two lines enable debugging at httplib level (requests->urllib3->http.client)\\n # You will see the REQUEST, including HEADERS and DATA, and RESPONSE with HEADERS but without DATA.\\n # The only thing missing will be the response.body which is not logged.\\n try:\\n import http.client as http_client\\n except ImportError:\\n # Python 2\\n import httplib as http_client\\n http_client.HTTPConnection.debuglevel = 1\\n\\n # You must initialize logging, otherwise you'll not see debug output.\\n self.logger.setLevel(logging.DEBUG)\\n requests_log = logging.getLogger(\\\"requests.packages.urllib3\\\")\\n requests_log.setLevel(logging.DEBUG)\\n requests_log.propagate = True\",\n \"def warning(self, message, *args, **kwargs):\\n\\n self.logger.warning(message, *args, **kwargs)\",\n \"def ReportWarning(self, warning_message, status=200):\\n logging.warning('Reporting warning: %r', warning_message)\\n self.response.set_status(status)\\n self.response.out.write('%s\\\\nrequest_id:%s\\\\n' %\\n (warning_message, utils.GetRequestId()))\",\n \"def debug_only(func):\\n @wraps(func)\\n def decorated_view(*args, **kwargs):\\n if not current_app.config['DEBUG']:\\n abort(404)\\n current_app.logger.warn('This endpoint disabled in live builds: {}'.format(request.url))\\n return func(*args, **kwargs)\\n return decorated_view\",\n \"def _logRequestArgs(self, request):\\n\\t\\ttry:\\n\\t\\t\\tif request.args:\\n\\t\\t\\t\\t# even if there are args, don't log them if only boring ones\\n\\t\\t\\t\\t# were given\\n\\t\\t\\t\\tfmtArgs = _formatRequestArgs(request.args)\\n\\t\\t\\t\\tif fmtArgs!='{}':\\n\\t\\t\\t\\t\\tlog.msg(\\\"# Processing starts: %s %s\\\"%(request.path, \\n\\t\\t\\t\\t\\t\\tfmtArgs))\\n\\t\\texcept: # don't fail because of logging problems\\n\\t\\t\\tbase.ui.notifyError(\\\"Formatting of request args failed.\\\")\",\n \"def _log_throttled_access(self, request):\\n request_method = request.method.lower()\\n self._meta.throttle.accessed(self._meta.authentication.get_identifier(request), url=request.get_full_path(), request_method=request_method)\",\n \"def handle_error(self, request, error):\\n self.log.error(\\\"An error occurred at request \\\" + repr(request) + \\\": \\\" + repr(error))\",\n \"def my_log_traceback(severity=logging.CRITICAL):\\n from cherrypy import _cperror\\n h = [\\\" %s: %s\\\" % (k, v) for k, v in cherrypy.request.header_list]\\n cherrypy.log('\\\\nRequest Headers:\\\\n' + '\\\\n'.join(h) + '\\\\n\\\\n' + _cperror.format_exc(), \\\"HTTP\\\", severity=severity)\",\n \"def warnings():\\n return THE_LOGGER.warnings\",\n \"def log_warning(self, msg, *args, **kwargs):\\n if self.action_logging_enabled and self._log is not None:\\n self._log.warning(msg, *args, **kwargs)\\n return\",\n \"def warning(self, msg, *args):\\n if self.lvl<=logging.WARNING: return self._log(msg, *args)\",\n \"def warning(module, message):\\n if loggingLevel >= loggingLevelWarning:\\n ModuLog.log(\\\"W\\\", module, message)\",\n \"def nologger(*args, **kwargs):\\n return\",\n \"def warning(warning_message: str):\\n logger.warning(warning_message)\",\n \"def disable_logging():\\n logging.disable(logging.INFO)\",\n \"def _suppressGoogleLogWarning():\\n\\n try:\\n # Tensorflow uses Google's abseil-py library, which uses a Google-specific\\n # wrapper for logging. That wrapper will write a warning to sys.stderr if\\n # the Google command-line flags library has not been initialized.\\n #\\n # https://github.com/abseil/abseil-py/blob/pypi-v0.7.1/absl/logging/__init__.py#L819-L825\\n #\\n # We don't want this here because we have our own logging setup.\\n import absl.logging\\n\\n # https://github.com/abseil/abseil-py/issues/99\\n logging.root.removeHandler(absl.logging._absl_handler)\\n # https://github.com/abseil/abseil-py/issues/102\\n absl.logging._warn_preinit_stderr = False\\n except Exception:\\n pass\",\n \"def _log_some_info(self):\\n logging.info('info')\",\n \"def ignore_warnings(my_func):\\n\\n def wrapper(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n This is where the warning suppression occurs.\\n \\\"\\\"\\\"\\n if sys.version_info >= (3, 2):\\n warnings.simplefilter(\\\"ignore\\\", ResourceWarning)\\n with warnings.catch_warnings():\\n my_func(self, *args, **kwargs)\\n\\n return wrapper\",\n \"def after_request(response):\\n logger = logging.getLogger(\\\"app.access\\\")\\n logger.info(\\n \\\"%s [%s] %s %s %s %s %s %s %s\\\",\\n request.remote_addr,\\n dt.utcnow().strftime(\\\"%d/%b/%Y:%H:%M:%S.%f\\\")[:-3],\\n request.method,\\n request.path,\\n request.scheme,\\n response.status,\\n response.content_length,\\n request.referrer,\\n request.user_agent,\\n )\\n return response\",\n \"def log_extra(self, msg, *args):\\n if self.settings.EXTRA_LOGGING.value:\\n self.log.info(\\\"(Extra logging) \\\" + msg, *args)\",\n \"def _log(self, message):\\n pass\",\n \"def log_request_response(task_request, response):\\n log_request(task_request, response.request)\\n log_response(task_request, response)\",\n \"def filter_request():\\n if request.method not in ALLOWED_METHODS:\\n return \\\"Method Not Allowed\\\", 405\\n ua = str(request.user_agent)\\n if \\\"Mozilla\\\" not in ua or \\\"Gecko\\\" not in ua:\\n return \\\"No Scrappers!\\\", 403\",\n \"def after_request(response):\\n # TODO: Send log reports to a monitor service such as DataDog?\\n return response\",\n \"def disable_traceback_filtering():\\n global _ENABLE_TRACEBACK_FILTERING\\n _ENABLE_TRACEBACK_FILTERING.value = False\",\n \"def WARNING_LOG(msg, *args, **kwargs):\\n logger.warning(msg, *args, **kwargs)\",\n \"def log_requests(response):\\n ts = strftime('[%Y-%b-%d %H:%M-%S]')\\n\\n logger.info('Flask: {0} {1} {2} {3} {4} {5}'.\\n format(ts, request.remote_addr, request.method, request.scheme, request.full_path, response.status))\\n\\n return response\",\n \"def onRequestTemporaryError(self, api, request, error):\\n logging.info('Request temporary error ({}); Error: {}'\\n .format(request, error))\",\n \"def setLogging(self, val: bool):\\n self._logRequests = val\",\n \"def enableCLangLogger(self):\",\n \"def log_debug(task_request, message):\\n _log(logger.debug, task_request, message)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6512308","0.6345203","0.6293786","0.628367","0.6275584","0.62672555","0.623322","0.6210527","0.61037356","0.6073067","0.6044601","0.60088855","0.5969443","0.59051746","0.5904183","0.5851811","0.58263904","0.58186126","0.5768054","0.5733492","0.5733492","0.5733492","0.5733492","0.5733492","0.5733492","0.5733492","0.5733492","0.5725408","0.57230186","0.5722221","0.57137597","0.5703703","0.5665302","0.566392","0.5657078","0.56307155","0.56209165","0.56131774","0.56128263","0.5603678","0.55979383","0.5581705","0.5579619","0.55632776","0.5556167","0.55519444","0.5527851","0.5527251","0.55232656","0.5510995","0.551002","0.5509293","0.55085784","0.55049974","0.5502584","0.5496376","0.5476459","0.54713315","0.54704237","0.54549813","0.5448302","0.54482913","0.54240036","0.5411067","0.5408855","0.54074377","0.5401894","0.5386532","0.53830373","0.5380586","0.5376165","0.537195","0.5371793","0.5367509","0.53672725","0.5363819","0.5362956","0.5362065","0.53489196","0.53372574","0.5318952","0.5318772","0.5312999","0.5312882","0.5312478","0.53098434","0.530692","0.5295434","0.5287795","0.5286024","0.52787083","0.52707857","0.5269349","0.5265096","0.52620584","0.5261821","0.525797","0.52550983","0.52529126","0.5237635"],"string":"[\n \"0.6512308\",\n \"0.6345203\",\n \"0.6293786\",\n \"0.628367\",\n \"0.6275584\",\n \"0.62672555\",\n \"0.623322\",\n \"0.6210527\",\n \"0.61037356\",\n \"0.6073067\",\n \"0.6044601\",\n \"0.60088855\",\n \"0.5969443\",\n \"0.59051746\",\n \"0.5904183\",\n \"0.5851811\",\n \"0.58263904\",\n \"0.58186126\",\n \"0.5768054\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5733492\",\n \"0.5725408\",\n \"0.57230186\",\n \"0.5722221\",\n \"0.57137597\",\n \"0.5703703\",\n \"0.5665302\",\n \"0.566392\",\n \"0.5657078\",\n \"0.56307155\",\n \"0.56209165\",\n \"0.56131774\",\n \"0.56128263\",\n \"0.5603678\",\n \"0.55979383\",\n \"0.5581705\",\n \"0.5579619\",\n \"0.55632776\",\n \"0.5556167\",\n \"0.55519444\",\n \"0.5527851\",\n \"0.5527251\",\n \"0.55232656\",\n \"0.5510995\",\n \"0.551002\",\n \"0.5509293\",\n \"0.55085784\",\n \"0.55049974\",\n \"0.5502584\",\n \"0.5496376\",\n \"0.5476459\",\n \"0.54713315\",\n \"0.54704237\",\n \"0.54549813\",\n \"0.5448302\",\n \"0.54482913\",\n \"0.54240036\",\n \"0.5411067\",\n \"0.5408855\",\n \"0.54074377\",\n \"0.5401894\",\n \"0.5386532\",\n \"0.53830373\",\n \"0.5380586\",\n \"0.5376165\",\n \"0.537195\",\n \"0.5371793\",\n \"0.5367509\",\n \"0.53672725\",\n \"0.5363819\",\n \"0.5362956\",\n \"0.5362065\",\n \"0.53489196\",\n \"0.53372574\",\n \"0.5318952\",\n \"0.5318772\",\n \"0.5312999\",\n \"0.5312882\",\n \"0.5312478\",\n \"0.53098434\",\n \"0.530692\",\n \"0.5295434\",\n \"0.5287795\",\n \"0.5286024\",\n \"0.52787083\",\n \"0.52707857\",\n \"0.5269349\",\n \"0.5265096\",\n \"0.52620584\",\n \"0.5261821\",\n \"0.525797\",\n \"0.52550983\",\n \"0.52529126\",\n \"0.5237635\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85700905"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85693,"cells":{"query":{"kind":"string","value":"Create transform from (lon, lat) to (column, row) coordinates"},"document":{"kind":"string","value":"def lonlat2cr_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(new_cs, old_cs)\n\n def composite(lon, lat):\n \"\"\"xform from (lon, lat) to (c, r)\"\"\"\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return (~gta * transform.TransformPoint(lat, lon)[:2])\n \n return composite"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def transform_from_latlon(lat, lon):\n from affine import Affine\n lat = np.asarray(lat)\n lon = np.asarray(lon)\n trans = Affine.translation(lon[0], lat[0])\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n return trans * scale","def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\n\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\n return (-1,-1)\n\n lat_step = abs(max_lat-min_lat)/rows\n lon_step = abs(max_lon-min_lon)/cols\n\n lat_spot = int((max_lat-lat)/lat_step)\n lon_spot = int((lon-min_lon)/lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def transform(self, lens):\n coords_t = np.zeros(self.coords.shape)\n coords_t[:,0] = (\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\n coords_t[:,1] = (\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\n return coords_t","def translation(coord, lat, lon):\n ty = merc_y(lat) - 73.43 # Y translation\n tx = merc_x(lon) - 57.55 # X translation\n for i in range(len(coord)):\n for j in range(len(coord[i])):\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\n return coord","def createCoordTuples(data):\n data['xy'] = None\n for i, row in data.iterrows():\n data['xy'][i] = [np.round(row['geometry'].x, decimals=5), np.round(row['geometry'].y, decimals=5)]\n return data","def map_coord_transformer(df, proj_string, lat_column_name, long_column_name):\n logging.info('Generating coordinate reference systems... ')\n #generate coordinate reference system objects for details of how this works \n from_crs = pyproj.CRS.from_string(proj_string)\n from_proj = pyproj.Proj(from_crs)\n gps_proj = pyproj.Proj('epsg:4326')\n original_coordinates_to_latlong_obj = pyproj.Transformer.from_proj(from_proj, gps_proj)\n logging.info('Defining transformation functions...')\n def original_coordinates_to_latlong(adf):\n (lat,long) = original_coordinates_to_latlong_obj.transform(adf[lat_column_name], adf[long_column_name])\n return lat, long\n \n #apply converter to generate series\n logging.info('Converting coordinates...')\n latlong_series = df.apply(original_coordinates_to_latlong, axis=1)\n \n #get calculated values and put back into df.\n logging.info('Splitting series...')\n lat_series = latlong_series.copy().apply(lambda x: x[0])\n long_series = latlong_series.copy().apply(lambda x: x[1])\n \n #return the values as \n logging.info('Preparing to return calc_lat and calc_long...')\n df.loc[:,'calc_lat'] = lat_series.copy()\n df.loc[:,'calc_long'] = long_series.copy()\n \n return df","def map_coordinates(self,geometry):\n\t\tg = self.geomatrix\n\t\tdef project_coord(x,y,z=None):\n\t\t\tx = g[0] + g[1] * x + g[2] * y\n\t\t\ty = g[3] + g[4] * x + g[5] * y\n\t\t\tif z is None:\n\t\t\t\treturn x,y\n\t\t\telse:\n\t\t\t\treturn x,y,z\n\t\treturn transform(project_coord, geometry)","def _build_geotransform(self, i, j):\n assert isinstance(i, int), (\"i is not an integer\")\n assert isinstance(j, int), (\"j is not an integer\")\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\n self.image_metadata.geotransform)\n # integer conversion to reduce floating point error\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \n y_ignore, y_res) \n return geotransform","def convertView2Geo(self, x, y):\n\n # x_pix is from left map edge, y_pix from top map edge\n x_pix = x + self.view_offset_x\n y_pix = y + self.view_offset_y\n\n lon = self.map_llon + x_pix/self.ppd_x\n lat = self.map_tlat - y_pix/self.ppd_y\n\n return (lon, lat)","def transform_coordinates(coords):\n # WGS 84 reference coordinate system parameters\n A = 6378.137 # major axis [km]\n E2 = 6.69437999014e-3 # eccentricity squared\n\n coords = prepare_coords(coords)\n\n # convert to radiants\n lat_rad = np.radians(coords[:, 0])\n lon_rad = np.radians(coords[:, 1])\n\n # convert to cartesian coordinates\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\n z = r_n * (1 - E2) * np.sin(lat_rad)\n\n return np.column_stack((x, y, z))","def convertCoord(lon, lat, inEPSG, outEPSG):\n from pyproj import Proj, transform\n inProj = Proj(init='epsg:'+str(inEPSG))\n outProj = Proj(init='epsg:'+str(outEPSG))\n x, y = transform(inProj, outProj, lon, lat)\n return x, y\n # epsg:4326 WGS84\n # epsg:2950 MTM8\n # epsg:6622 Quebec Lambert","def transform_coords(x, y, w, h, nw, nh):\r\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)","def _getXYZ ( lon, lat ):\n d2r = pi / 180.\n rlon, rlat = ( d2r * lon, d2r * lat )\n x = cos(rlat) * cos(rlon)\n y = cos(rlat) * sin(rlon)\n z = sin(rlat)\n return (x,y,z)","def convertFrom1Dto2D(coord, num_cols):\n y = int(np.floor(coord/num_cols))\n x = coord % num_cols\n return (y,x)","def transform_point(transform, x_in, y_in):\n # create point geometry from coordinates\n point = ogr.Geometry(ogr.wkbPoint)\n point.AddPoint(x_in, y_in)\n point.Transform(transform)\n\n x_out = point.GetX()\n y_out = point.GetY()\n return x_out, y_out","def ll_to_xy(lon, lat):\n lllon = lon.min()\n lllat = lat.min()\n urlon = lon.max()\n urlat = lat.max()\n\n dlon = urlon - lllon\n dlat = urlat - lllat\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\n\n x = dx_*(lon - lllon)/dlon\n y = dy_*(lat - lllat)/dlat\n\n return x, y","def rotated_grid_transform(lon_arr, lat_arr, np_lon, np_lat,\n direction='rot2geo'):\n dimensions = lon_arr.shape\n lon_arr_new = np.zeros(dimensions)\n lat_arr_new = np.zeros(dimensions)\n for i in range(dimensions[1]): # x-direction\n for j in range(dimensions[0]): # y-direction\n lon = lon_arr[j, i]\n lat = lat_arr[j, i]\n lon_new, lat_new = rotated_coord_transform(\n lon, lat, np_lon, np_lat, direction=direction)\n lon_arr_new[j, i] = lon_new\n lat_arr_new[j, i] = lat_new\n\n return (lon_arr_new, lat_arr_new)","def get_latlon_point(self, row, col):\n p1 = Proj(self.src.crs)\n window = rasterio.windows.Window(col, row, 1, 1)\n trnsfrm = self.src.window_transform(window)\n T1 = trnsfrm * Affine.translation(0.5, 0.5)\n p2 = Proj(proj='latlong', datum='WGS84')\n x, y = self.src.xy(row, col)\n lon, lat = transform(p1, p2, x, y)\n return lat, lon","def row_to_pixel(row, image_shape, columns=None):\n start_y, start_x = latlon_to_pixel(row[\"s_lat\"],\n row[\"s_lng\"], image_shape)\n end_y, end_x = latlon_to_pixel(row[\"e_lat\"],\n row[\"e_lng\"], image_shape)\n xy = (start_x, start_y, end_x, end_y)\n return xy","def pixel2coord(tf, x, y):\n lat = tf[0] + x*tf[1] + y*tf[2]\n lon = tf[3] + x*tf[4] + y*tf[5]\n\n return lat, lon","def transform(self, pol_lon=None, pol_lat=None):\n if self.rotated:\n direction = 'rot2geo'\n pol_lon = self.pol_lon\n pol_lat = self.pol_lat\n else:\n if pol_lon is None or pol_lat is None:\n pol_lon = self.pol_lon\n pol_lat = self.pol_lat\n #raise Exception('grid is not rotated, transform requires pol_lon and pol_lat')\n direction = 'geo2rot'\n lon_arr_trans, lat_arr_trans = rotated_grid_transform(\n self.lon_arr, self.lat_arr, pol_lon, pol_lat,\n direction=direction)\n if self.rotated:\n return Grid(lon_arr_trans, lat_arr_trans)\n else:\n return Grid(lon_arr_trans, lat_arr_trans, pol_lon, pol_lat)","def latlon_to_pixels(lon, lat):\n radius = 489.5\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\n x_pos = x_pos_from_center + 512.5\n \n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\n y_pos = y_pos_from_center + 512.5\n \n return x_pos, y_pos","def latlon_2_grid(x, y, z, origin):\n new_y = (y - origin[1]) * 111111\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\n return new_x, new_y, z","def convertLatLon(latCell, lonCell):\n cell_lats = np.array([])\n cell_lons = np.array([])\n for lat in latCell:\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \n for lon in lonCell:\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \n\n return cell_lats, cell_lons","def convertGeo2View(self, lon, lat):\n\n x_pix = (lon - self.view_llon) * self.ppd_x\n y_pix = (self.view_tlat - lat) * self.ppd_y\n\n return (x_pix, y_pix)","def map(self, lat, long):\r\n rxy = self._r*np.sqrt(1-np.cos(lat))\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def convert(self, lat, lon):\r\n a = self.a\r\n b = self.b\r\n long0 = self.long0\r\n k0 = self.k0\r\n dx = self.dx\r\n\r\n e = (1 - b ** 2 / a ** 2) ** 0.5\r\n e2 = e ** 2 / (1 - e ** 2)\r\n n = (a - b) / (a + b)\r\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\r\n p = lon - long0\r\n\r\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\r\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\r\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\r\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\r\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\r\n\r\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\r\n\r\n K1 = S * k0\r\n K2 = k0 * nu * sin(2 * lat)/4.0\r\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\r\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\r\n\r\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\r\n\r\n K4 = k0 * nu * cos(lat)\r\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\r\n\r\n x = K4 * p + K5 * (p ** 3) + dx\r\n return x, y","def ll2xyz(lon_pt,lat_pt):\n\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\n zPt = np.sin(lat_pt)\n return [xPt,yPt,zPt]","def ll2fxy(lon,lat):\n return xyz2fxy(*ll2xyz(lon,lat))","def pixel2coords(self, x, y):\n xoff, a, b, yoff, d, e = self.geotransform()\n\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n return (xp, yp)","def map(self, lat, long):\r\n rxy = self._r*lat/(np.pi/2)\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def coord2pixel(tf, lat, lon):\n x = int(round((lon-tf[0])/tf[1]))\n y = int(round((lat-tf[3])/tf[5]))\n\n return x, y","def _pixel_to_map(coordinates, geotransform):\n coordinates_map = np.empty(coordinates.shape)\n coordinates_map[..., 0] = (\n geotransform[0]\n + geotransform[1] * coordinates[..., 0]\n + geotransform[2] * coordinates[..., 1]\n )\n coordinates_map[..., 1] = (\n geotransform[3]\n + geotransform[4] * coordinates[..., 0]\n + geotransform[5] * coordinates[..., 1]\n )\n return coordinates_map","def coords_to_structure(self) -> None:\n ...","def make_simple_coords():\n \n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\n coords = np.vstack((x,y)).T\n return coords","def Pixel2World(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xdist = geoMatrix[1]\r\n ydist = geoMatrix[5]\r\n coorX = (ulX + (x * xdist))\r\n coorY = (ulY + (y * ydist))\r\n return (coorX, coorY)","def rotated_coord_transform(lon, lat, np_lon, np_lat,\n direction='rot2geo'):\n\n # Convert degrees to radians\n lon = (lon * math.pi) / 180.\n lat = (lat * math.pi) / 180.\n\n# SP_lon = SP_coor(1)\n# SP_lat = SP_coor(2)\n\n theta = 90. - np_lat # Rotation around y-axis\n phi = np_lon + 180. # Rotation around z-axis\n\n # Convert degrees to radians\n phi = (phi * math.pi) / 180.\n theta = (theta * math.pi) / 180.\n\n # Convert from spherical to cartesian coordinates\n x = math.cos(lon) * math.cos(lat)\n y = math.sin(lon) * math.cos(lat)\n z = math.sin(lat)\n\n # Regular -> Rotated\n if direction == 'geo2rot':\n\n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.cos(theta) * math.sin(phi) * y +\n math.sin(theta) * z)\n y_new = (- math.sin(phi) * x +\n math.cos(phi) * y)\n z_new = (- math.sin(theta) * math.cos(phi) * x -\n math.sin(theta) * math.sin(phi) * y +\n math.cos(theta) * z)\n\n # Rotated -> Regular\n elif direction == 'rot2geo':\n \n phi = - phi\n theta = - theta\n \n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.sin(phi) * y +\n math.sin(theta) * math.cos(phi) * z)\n y_new = (- math.cos(theta) * math.sin(phi) * x +\n math.cos(phi) * y -\n math.sin(theta) * math.sin(phi) * z)\n z_new = (- math.sin(theta) * x +\n math.cos(theta) * z)\n\n # Convert cartesian back to spherical coordinates\n lon_new = math.atan2(y_new, x_new)\n lat_new = math.asin(z_new)\n\n # Convert radians back to degrees\n lon_new = (lon_new * 180.) / math.pi\n lat_new = (lat_new * 180.) / math.pi;\n\n return (lon_new, lat_new)","def ll_coordinates(self):\n\n x, y = self.xy_coordinates\n proj_out = check_crs('EPSG:4326')\n\n return transform_proj(self.proj, proj_out, x, y)","def pixel2coord(self, x, y):\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\n # partly taken from Sean Gillies \"affine.py\"\n a,b,c,d,e,f = self.coordspace_transform\n det = a*e - b*d\n idet = 1 / float(det)\n ra = e * idet\n rb = -b * idet\n rd = -d * idet\n re = a * idet\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\n newy = (x*rd + y*re + (-c*rd - f*re) )\n return newx,newy","def pixelloc_to_geoloc(row, col, loff, coff):\n row = np.atleast_1d(row)\n col = np.atleast_1d(col)\n\n assert row.shape == col.shape\n\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\n\n if lat.size == lon.size == 1:\n # Return scalars\n return lat[0], lon[0]\n else:\n return lat, lon","def coords_to_node(self,row,col):\n return row*self.cols + col + 1","def _indices_to_coords(c,r):\n\n column = _index_to_column(c)\n row = r + 1\n\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}","def ind2coord(self, index):\n\n # assert (index >= 0)\n # assert(index < self.n - 1)\n\n col = index // self.rows\n row = index % self.rows\n\n return [row, col]","def geo2cell(geofile, posfile):","def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\r\n # convert decimals to seconds...\r\n s_lon = dec2sec(s_lon)\r\n s_lat = dec2sec(s_lat)\r\n\r\n ## Auxiliary values \r\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\r\n s_lng_aux = (s_lon - 26782.5)/10000.\r\n s_lat_aux = (s_lat - 169028.66)/10000.\r\n \r\n # easting\r\n s_x = (600072.37 \r\n + 211455.93*s_lng_aux \r\n - 10938.51*s_lng_aux*s_lat_aux \r\n - 0.36*s_lng_aux*(s_lat_aux**2) \r\n - 44.54*(s_lng_aux**3))\r\n \r\n # northing\r\n s_y = (200147.07 \r\n + 308807.95*s_lat_aux \r\n + 3745.25*(s_lng_aux**2) \r\n + 76.63*(s_lat_aux**2) \r\n - 194.56*(s_lng_aux**2)*s_lat_aux \r\n + 119.79*(s_lat_aux**3))\r\n\r\n return s_x, s_y","def geo_transform(self):\n pass","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def lon_lat_to_cartesian(lon, lat, R = 1):\n lon_r = np.radians(lon)\n lat_r = np.radians(lat)\n\n x = R * np.cos(lat_r) * np.cos(lon_r)\n y = R * np.cos(lat_r) * np.sin(lon_r)\n z = R * np.sin(lat_r)\n return x,y,z","def _transform_coordinates(rectangle, Q=np.matrix(((1, 1), (-1, 1)))):\n return tuple((rectangle[0]*Q).A1), tuple((rectangle[1]*Q).A1)","def trans_to_coordinates(T, pts):\n p = []\n for i in range(len(pts)):\n \n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\n p_a = np.matmul(T, p_b).tolist()\n p.append(p_a[0:3])\n\n return p","def trans_to_coordinates(T, pts):\n p = []\n for i in range(len(pts)):\n \n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\n p_a = np.matmul(T, p_b).tolist()\n p.append(p_a[0:3])\n\n return p","def gps_to_node(self,lat,lon):\n row,col = self.gps_to_coords(lat,lon)\n if row == -1:\n return -1\n return self.coords_to_node(row,col)","def calcPosition (lat, lon):\n nauticalMilePerLat = 60.00721\n nauticalMilePerLongitude = 60.10793\n rad = math.pi / 180.0\n milesPerNauticalMile = 1.15078\n \n y = lat * nauticalMilePerLat\n x = math.cos(lat * rad) * lon * nauticalMilePerLongitude\n\n return x * milesPerNauticalMile * 1609.344, y * milesPerNauticalMile * 1609.344","def _generate_coords(self):\n coords = np.dstack([self.X.ravel(), self.Y.ravel()])[0]\n return coords, spatial.cKDTree(coords)","def makeToCoordinates(fromCoords, Uframe, Vframe, scale):\n\n out = []\n\n for e in fromCoords:\n x = e[0]\n y = e[1]\n toX = Uframe[x][y]\n toY = Vframe[x][y]\n out.append((int(round(x+toX*scale)),int(round(y+toY*scale))))\n\n return out","def read_affine(file):\n data = open(file, 'r').read()\n data = data.split('\\n')\n for i in range(1, 5):\n data[i] = data[i].split(':')\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\n Nlon = len(int_lon) - 1\n Nlat = len(int_lat) - 1\n data[3][1] = data[3][1].split(',')\n data[4][1] = data[4][1].split(',')\n lon_transform = np.zeros((Nlon, 2))\n lat_transform = np.zeros((Nlat, 2))\n for i in range(Nlon):\n data[3][1][i] = data[3][1][i].split(' ')\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\n for i in range(Nlat):\n data[4][1][i] = data[4][1][i].split(' ')\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\n lon_transform = np.array(lon_transform).astype('float')\n lat_transform = np.array(lat_transform).astype('float')\n return int_lon, int_lat, lon_transform, lat_transform","def lat_lng(row):\r\n lat = row[\"latitude\"]\r\n lng = row[\"longitude\"]\r\n n = int(lat/GRANULARITY)\r\n nlat_start = n * GRANULARITY\r\n nlat_end = nlat_start + GRANULARITY\r\n nlg=int(lng/GRANULARITY)\r\n nlng_start = nlg * GRANULARITY\r\n nlng_end = nlng_start + GRANULARITY\r\n latlng=[(nlat_start,nlng_start), (nlat_start,nlng_end), (nlat_end,nlng_end), (nlat_end,nlng_start)]\r\n return latlng","def create_pseudo_epsg4326_coordinates(self):\n self.create_3d_coord_on_sphere(on_sphere=True)\n self.df_attributes['lat'] = 180*(pi/2 - np.arccos(self.df_attributes['coord_z']))/pi\n self.df_attributes['lon'] = 180*np.arctan2(self.df_attributes['coord_y'], self.df_attributes['coord_x'])/pi","def _transform_point(self, x, y):\n return (x, y)","def wgs84_to_mercator(df, lon, lat):\n k = 6378137\n df[\"x\"] = df[lon] * (k * np.pi/180.0)\n df[\"y\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\n return df","def rowcol2XY(row,col,CCD):\n pixscale = 0.015 #mm/pix\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\n return X,Y","def get_coordinates_geo(self):\n if not self.rotated:\n lon_arr_geo = self.lon_arr\n lat_arr_geo = self.lat_arr\n else:\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \n\n return lon_arr_geo, lat_arr_geo","def process_coords():\n split_coords = row[\"map_coord\"].split(',')\n map_x, map_y = [int(i) for i in split_coords]\n map_x_normed = ((map_x*2) / self.MINIMAP_DIM) - 1\n map_y_normed = -(((map_y*2) / self.MINIMAP_DIM) - 1)\n return map_x_normed, map_y_normed","def rowcol_to_latlon(rowcol_str):\n\n # Vectors for code in file name\n latspace = np.linspace(-90,90,46)\n lonspace = np.linspace(-180,180,73)\n lat = latspace[int(rowcol_str[0:2])-1]\n lon = lonspace[int(rowcol_str[2:])-1]\n return (lat,lon)","def to_position(self, x, y, i, j):\n return (x * self.SIZE + i, y * self.SIZE + j)","def node_to_coords(self,node_num):\n row = (node_num - 1) / self.cols\n col = (node_num - 1) % self.cols\n return (row,col)","def transform_pnt(pnt, inEPSG, outEPSG):\r\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\r\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\r\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\r\n\r\n return pyproj.transform(inProj, outProj, x1, y1)","def flowline_latlon(coords, fromproj=pyproj.Proj(\"+init=epsg:3413\"), toproj=pyproj.Proj(\"+init=EPSG:4326\")):\n xs = coords[:,0]\n ys = coords[:,1]\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\n latlon_coords = np.asarray(zip(x_lon, y_lat))\n return latlon_coords","def spher2cart(lon: np.ndarray,\n lat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n rlon = np.radians(lon)\n rlat = np.radians(lat)\n x = np.cos(rlon) * np.cos(rlat)\n y = np.sin(rlon) * np.cos(rlat)\n z = np.sin(rlat)\n return x, y, z","def proj(self,lon,lat):\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\n x[x > 1e29] = None\n y[y > 1e29] = None\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\n return x, y","def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False):\n\n x, y = np.ma.array(x), np.ma.array(y)\n\n # First to local proj\n _crs = check_crs(crs, raise_on_error=True)\n if isinstance(_crs, pyproj.Proj):\n x, y = transform_proj(_crs, self.proj, x, y)\n elif isinstance(_crs, Grid):\n x, y = _crs.ij_to_crs(x, y, crs=self.proj)\n\n # Then to local grid\n x = (x - self.x0) / self.dx\n y = (y - self.y0) / self.dy\n\n # See if we need to round\n if nearest:\n f = np.rint if self.pixel_ref == 'center' else np.floor\n x = f(x).astype(int)\n y = f(y).astype(int)\n\n # Mask?\n if maskout:\n if self.pixel_ref == 'center':\n mask = ~((x >= -0.5) & (x < self.nx-0.5) &\n (y >= -0.5) & (y < self.ny-0.5))\n else:\n mask = ~((x >= 0) & (x < self.nx) &\n (y >= 0) & (y < self.ny))\n x = np.ma.array(x, mask=mask)\n y = np.ma.array(y, mask=mask)\n\n return x, y","def row_to_lat(self, row):\n return self.max_absolute_lat - row * self.lat_step","def transform_coordinates(self, p):\n if type(p) == PhysicalObject:\n return self.transform_coordinates(p.position)\n elif type(p) == Vector:\n return tuple(map(\n lambda x: int(x),\n (p / SCALE_FACTOR - self.pos_shift))\n )","def CreateTargetGeoMap(latS, latN, lonW, lonE, latlen, lonlen):\n\n lat_grid = np.linspace(latS, latN, latlen)\n lon_grid = np.linspace(lonW, lonE, lonlen)\n\n return lat_grid,lon_grid","def composite(c, r):\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat","def coords_to_gps(self,coords):\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))","def ll2px(lat, lng, zoom):\n # assert isinstance(lat, (float, int, long)), \\\n # ValueError(\"lat must be a float\")\n lat = float(lat)\n # assert isinstance(lng, (float, int, long)), \\\n # ValueError(\"lng must be a float\")\n lng = float(lng)\n # assert isinstance(zoom, int), TypeError(\"zoom must be an int from 0 to 30\")\n # assert 0 <= zoom <= 30, ValueError(\"zoom must be an int from 0 to 30\")\n\n cbk = CBK[zoom]\n\n x = int(round(cbk + (lng * CEK[zoom])))\n\n foo = math.sin(lat * math.pi / 180)\n if foo < -0.9999:\n foo = -0.9999\n elif foo > 0.9999:\n foo = 0.9999\n\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\n\n return (x, y)","def tile_coordinates(text):\n UL = (text[1]), (text[2]) # Upper Left\n UR = (text[3]), (text[2]) # Upper Right\n LR = (text[3]), (text[4]) # Lower Right\n LL = (text[1]), (text[4]) # Lower Left\n coordinates = (UL, UR, LR, LL)\n return text[0], [tuple(float(x) for x in xs) for xs in coordinates]","def convertGeoToPixel(self, lon, lat):\n x = (lon - self.mapLonLeft) * (self.mapWidth / self.mapLonDelta)\n\n lat = lat * math.pi / 180.0\n self.worldMapWidth = ((self.mapWidth / self.mapLonDelta) * 360) / (2 * math.pi)\n self.mapOffsetY = (self.worldMapWidth / 2 * math.log((1 + math.sin(self.mapLatBottomDegree)) / (1 - math.sin(self.mapLatBottomDegree))))\n y = self.mapHeight - ((self.worldMapWidth / 2 * math.log((1 + math.sin(lat)) / (1 - math.sin(lat)))) - self.mapOffsetY)\n\n return (x, y)","def merc(lat, lon):\n\tr_major = 6378137.000\n\tx = r_major * math.radians(lon)\n\tscale = x/lon\n\ty = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale\n\treturn (x, y)","def index_to_xy(index):\n x = index % columns\n y = index // columns\n return x, y","def cr2lonlat_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(old_cs, new_cs)\n\n def composite(c, r):\n \"\"\"xform from (c, r) to (lon, lat)\"\"\"\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat\n \n return composite","def proj_coord(coord, proj_in, proj_out):\n x, y = coord\n return pyproj.transform(proj_in, proj_out, x, y)","def gps_to_coords(self,lat,lon):\n\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\n return (-1,-1)\n\n lat_spot = int((self.max_lat-lat)/self.lat_step)\n lon_spot = int((lon-self.min_lon)/self.lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def _wrap_coord(tensors):\n cell = tf.gather_nd(tensors['cell'], tensors['ind_1'])\n coord = tf.expand_dims(tensors['coord'], -1)\n frac_coord = tf.linalg.solve(tf.transpose(cell, perm=[0, 2, 1]), coord)\n frac_coord %= 1\n coord = tf.matmul(tf.transpose(cell, perm=[0, 2, 1]), frac_coord)\n return tf.squeeze(coord, -1)","def chunk_coords(self) -> Generator[Tuple[int, int], None, None]:\n cx, cz = int(self.camera_location[0]) >> 4, int(self.camera_location[2]) >> 4\n\n sign = 1\n length = 1\n for _ in range(self.render_distance*2+1):\n for _ in range(length):\n yield cx, cz\n cx += sign\n for _ in range(length):\n yield cx, cz\n cz += sign\n sign *= -1\n length += 1","def coord(self, x, y):\n origin_x = self._raster_meta['transform'][3]\n origin_y = self._raster_meta['transform'][0]\n pixel_x = self._raster_meta['transform'][5]\n pixel_y = self._raster_meta['transform'][1]\n\n x = int((x - origin_x) / pixel_x)\n y = int((y - origin_y) / pixel_y)\n return self[x, y]","def coords_from_cell(cell, lon = [-8.73, -8.50], lat = [41.10, 41.25], N = 100, M = 75):\n lon_step = (lon[1] - lon[0]) / N \n lat_step = (lat[1] - lat[0]) / M\n \n middle_lon = lon[0] + cell[0] * lon_step + lon_step / 2\n middle_lat = lat[0] + cell[1] * lat_step + lat_step / 2\n \n return [middle_lon, middle_lat]","def transform_from(self, coordinates) -> np.ndarray:\n return (coordinates @ self.T) + self._o","def convert_coords(self):\n if self.coordsys in ['image', 'physical']:\n coords = self._convert_pix_coords()\n else:\n coords = self._convert_sky_coords()\n\n if self.region_type == 'line':\n coords = [coords[0][0], coords[0][1]]\n\n if self.region_type == 'text':\n coords.append(self.meta['text'])\n\n return coords","def coordinates(self):\n return np.array([[f.x, f.y] for f in self])","def transform_to(self, coordinates) -> np.ndarray:\n return (coordinates - self._o) @ self.T.T","def world2Pixel(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xDist = geoMatrix[1]\r\n yDist = geoMatrix[5]\r\n rtnX = geoMatrix[2]\r\n rtnY = geoMatrix[4]\r\n pixel = int((x - ulX) / xDist)\r\n line = int((ulY - y) / xDist)\r\n return (pixel, line)","def make_grid(dataset):\n top_left_lat = dataset[\"a\"][0]\n top_left_lng = dataset[\"a\"][1]\n top_right_lng = dataset[\"c\"][1]\n bot_left_lat = dataset[\"b\"][0]\n\n lng_row = []\n lat_col = []\n i = top_left_lng\n while i < top_right_lng:\n lng_row.append(round(i, 5))\n i += step\n j = bot_left_lat\n while j < top_left_lat:\n lat_col.append(round(j, 5))\n j += step\n out_grid = []\n for i in lat_col:\n row = []\n for j in lng_row:\n row.append(\"{0}:{1}:0\".format(i, j))\n out_grid.append(row)\n return out_grid","def cart_coords2d(self):\r\n\r\n mgx, mgy = self.meshup2d()\r\n coords = np.column_stack((mgx.flatten(), mgy.flatten()))\r\n\r\n return coords","def coords2D(self):\n return (self.x, self.y)","def xy_coordinates(self):\n\n return np.meshgrid(self.x_coord, self.y_coord)","def chunk_coords_to_region_coords(cx: int, cz: int) -> ChunkCoordinates:\n return cx >> 5, cz >> 5","def coords(self):\n return np.column_stack((self.x_coord_list, self.y_coord_list, self.z_coord_list))","def to_coords(self, px, py):\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\n raise IndexError\n return (px // self.SIZE, py // self.SIZE,\n px % self.SIZE, py % self.SIZE)","def xmymwh2xyxy(coords: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\n new_coords = coords.clone() if isinstance(coords, torch.Tensor) else coords.copy()\n new_coords[:, 0] = coords[:, 0] - coords[:, 2] / 2 # top left x\n new_coords[:, 1] = coords[:, 1] - coords[:, 3] / 2 # top left y\n new_coords[:, 2] = coords[:, 0] + coords[:, 2] / 2 # bottom right x\n new_coords[:, 3] = coords[:, 1] + coords[:, 3] / 2 # bottom right y\n return new_coords"],"string":"[\n \"def transform_from_latlon(lat, lon):\\n from affine import Affine\\n lat = np.asarray(lat)\\n lon = np.asarray(lon)\\n trans = Affine.translation(lon[0], lat[0])\\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\\n return trans * scale\",\n \"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\\n\\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\\n return (-1,-1)\\n\\n lat_step = abs(max_lat-min_lat)/rows\\n lon_step = abs(max_lon-min_lon)/cols\\n\\n lat_spot = int((max_lat-lat)/lat_step)\\n lon_spot = int((lon-min_lon)/lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def transform(self, lens):\\n coords_t = np.zeros(self.coords.shape)\\n coords_t[:,0] = (\\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\\n coords_t[:,1] = (\\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\\n return coords_t\",\n \"def translation(coord, lat, lon):\\n ty = merc_y(lat) - 73.43 # Y translation\\n tx = merc_x(lon) - 57.55 # X translation\\n for i in range(len(coord)):\\n for j in range(len(coord[i])):\\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\\n return coord\",\n \"def createCoordTuples(data):\\n data['xy'] = None\\n for i, row in data.iterrows():\\n data['xy'][i] = [np.round(row['geometry'].x, decimals=5), np.round(row['geometry'].y, decimals=5)]\\n return data\",\n \"def map_coord_transformer(df, proj_string, lat_column_name, long_column_name):\\n logging.info('Generating coordinate reference systems... ')\\n #generate coordinate reference system objects for details of how this works \\n from_crs = pyproj.CRS.from_string(proj_string)\\n from_proj = pyproj.Proj(from_crs)\\n gps_proj = pyproj.Proj('epsg:4326')\\n original_coordinates_to_latlong_obj = pyproj.Transformer.from_proj(from_proj, gps_proj)\\n logging.info('Defining transformation functions...')\\n def original_coordinates_to_latlong(adf):\\n (lat,long) = original_coordinates_to_latlong_obj.transform(adf[lat_column_name], adf[long_column_name])\\n return lat, long\\n \\n #apply converter to generate series\\n logging.info('Converting coordinates...')\\n latlong_series = df.apply(original_coordinates_to_latlong, axis=1)\\n \\n #get calculated values and put back into df.\\n logging.info('Splitting series...')\\n lat_series = latlong_series.copy().apply(lambda x: x[0])\\n long_series = latlong_series.copy().apply(lambda x: x[1])\\n \\n #return the values as \\n logging.info('Preparing to return calc_lat and calc_long...')\\n df.loc[:,'calc_lat'] = lat_series.copy()\\n df.loc[:,'calc_long'] = long_series.copy()\\n \\n return df\",\n \"def map_coordinates(self,geometry):\\n\\t\\tg = self.geomatrix\\n\\t\\tdef project_coord(x,y,z=None):\\n\\t\\t\\tx = g[0] + g[1] * x + g[2] * y\\n\\t\\t\\ty = g[3] + g[4] * x + g[5] * y\\n\\t\\t\\tif z is None:\\n\\t\\t\\t\\treturn x,y\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn x,y,z\\n\\t\\treturn transform(project_coord, geometry)\",\n \"def _build_geotransform(self, i, j):\\n assert isinstance(i, int), (\\\"i is not an integer\\\")\\n assert isinstance(j, int), (\\\"j is not an integer\\\")\\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\\n self.image_metadata.geotransform)\\n # integer conversion to reduce floating point error\\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \\n y_ignore, y_res) \\n return geotransform\",\n \"def convertView2Geo(self, x, y):\\n\\n # x_pix is from left map edge, y_pix from top map edge\\n x_pix = x + self.view_offset_x\\n y_pix = y + self.view_offset_y\\n\\n lon = self.map_llon + x_pix/self.ppd_x\\n lat = self.map_tlat - y_pix/self.ppd_y\\n\\n return (lon, lat)\",\n \"def transform_coordinates(coords):\\n # WGS 84 reference coordinate system parameters\\n A = 6378.137 # major axis [km]\\n E2 = 6.69437999014e-3 # eccentricity squared\\n\\n coords = prepare_coords(coords)\\n\\n # convert to radiants\\n lat_rad = np.radians(coords[:, 0])\\n lon_rad = np.radians(coords[:, 1])\\n\\n # convert to cartesian coordinates\\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\\n z = r_n * (1 - E2) * np.sin(lat_rad)\\n\\n return np.column_stack((x, y, z))\",\n \"def convertCoord(lon, lat, inEPSG, outEPSG):\\n from pyproj import Proj, transform\\n inProj = Proj(init='epsg:'+str(inEPSG))\\n outProj = Proj(init='epsg:'+str(outEPSG))\\n x, y = transform(inProj, outProj, lon, lat)\\n return x, y\\n # epsg:4326 WGS84\\n # epsg:2950 MTM8\\n # epsg:6622 Quebec Lambert\",\n \"def transform_coords(x, y, w, h, nw, nh):\\r\\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)\",\n \"def _getXYZ ( lon, lat ):\\n d2r = pi / 180.\\n rlon, rlat = ( d2r * lon, d2r * lat )\\n x = cos(rlat) * cos(rlon)\\n y = cos(rlat) * sin(rlon)\\n z = sin(rlat)\\n return (x,y,z)\",\n \"def convertFrom1Dto2D(coord, num_cols):\\n y = int(np.floor(coord/num_cols))\\n x = coord % num_cols\\n return (y,x)\",\n \"def transform_point(transform, x_in, y_in):\\n # create point geometry from coordinates\\n point = ogr.Geometry(ogr.wkbPoint)\\n point.AddPoint(x_in, y_in)\\n point.Transform(transform)\\n\\n x_out = point.GetX()\\n y_out = point.GetY()\\n return x_out, y_out\",\n \"def ll_to_xy(lon, lat):\\n lllon = lon.min()\\n lllat = lat.min()\\n urlon = lon.max()\\n urlat = lat.max()\\n\\n dlon = urlon - lllon\\n dlat = urlat - lllat\\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\\n\\n x = dx_*(lon - lllon)/dlon\\n y = dy_*(lat - lllat)/dlat\\n\\n return x, y\",\n \"def rotated_grid_transform(lon_arr, lat_arr, np_lon, np_lat,\\n direction='rot2geo'):\\n dimensions = lon_arr.shape\\n lon_arr_new = np.zeros(dimensions)\\n lat_arr_new = np.zeros(dimensions)\\n for i in range(dimensions[1]): # x-direction\\n for j in range(dimensions[0]): # y-direction\\n lon = lon_arr[j, i]\\n lat = lat_arr[j, i]\\n lon_new, lat_new = rotated_coord_transform(\\n lon, lat, np_lon, np_lat, direction=direction)\\n lon_arr_new[j, i] = lon_new\\n lat_arr_new[j, i] = lat_new\\n\\n return (lon_arr_new, lat_arr_new)\",\n \"def get_latlon_point(self, row, col):\\n p1 = Proj(self.src.crs)\\n window = rasterio.windows.Window(col, row, 1, 1)\\n trnsfrm = self.src.window_transform(window)\\n T1 = trnsfrm * Affine.translation(0.5, 0.5)\\n p2 = Proj(proj='latlong', datum='WGS84')\\n x, y = self.src.xy(row, col)\\n lon, lat = transform(p1, p2, x, y)\\n return lat, lon\",\n \"def row_to_pixel(row, image_shape, columns=None):\\n start_y, start_x = latlon_to_pixel(row[\\\"s_lat\\\"],\\n row[\\\"s_lng\\\"], image_shape)\\n end_y, end_x = latlon_to_pixel(row[\\\"e_lat\\\"],\\n row[\\\"e_lng\\\"], image_shape)\\n xy = (start_x, start_y, end_x, end_y)\\n return xy\",\n \"def pixel2coord(tf, x, y):\\n lat = tf[0] + x*tf[1] + y*tf[2]\\n lon = tf[3] + x*tf[4] + y*tf[5]\\n\\n return lat, lon\",\n \"def transform(self, pol_lon=None, pol_lat=None):\\n if self.rotated:\\n direction = 'rot2geo'\\n pol_lon = self.pol_lon\\n pol_lat = self.pol_lat\\n else:\\n if pol_lon is None or pol_lat is None:\\n pol_lon = self.pol_lon\\n pol_lat = self.pol_lat\\n #raise Exception('grid is not rotated, transform requires pol_lon and pol_lat')\\n direction = 'geo2rot'\\n lon_arr_trans, lat_arr_trans = rotated_grid_transform(\\n self.lon_arr, self.lat_arr, pol_lon, pol_lat,\\n direction=direction)\\n if self.rotated:\\n return Grid(lon_arr_trans, lat_arr_trans)\\n else:\\n return Grid(lon_arr_trans, lat_arr_trans, pol_lon, pol_lat)\",\n \"def latlon_to_pixels(lon, lat):\\n radius = 489.5\\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\\n x_pos = x_pos_from_center + 512.5\\n \\n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\\n y_pos = y_pos_from_center + 512.5\\n \\n return x_pos, y_pos\",\n \"def latlon_2_grid(x, y, z, origin):\\n new_y = (y - origin[1]) * 111111\\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\\n return new_x, new_y, z\",\n \"def convertLatLon(latCell, lonCell):\\n cell_lats = np.array([])\\n cell_lons = np.array([])\\n for lat in latCell:\\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \\n for lon in lonCell:\\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \\n\\n return cell_lats, cell_lons\",\n \"def convertGeo2View(self, lon, lat):\\n\\n x_pix = (lon - self.view_llon) * self.ppd_x\\n y_pix = (self.view_tlat - lat) * self.ppd_y\\n\\n return (x_pix, y_pix)\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*np.sqrt(1-np.cos(lat))\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def convert(self, lat, lon):\\r\\n a = self.a\\r\\n b = self.b\\r\\n long0 = self.long0\\r\\n k0 = self.k0\\r\\n dx = self.dx\\r\\n\\r\\n e = (1 - b ** 2 / a ** 2) ** 0.5\\r\\n e2 = e ** 2 / (1 - e ** 2)\\r\\n n = (a - b) / (a + b)\\r\\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\\r\\n p = lon - long0\\r\\n\\r\\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\\r\\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\\r\\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\\r\\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\\r\\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\\r\\n\\r\\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\\r\\n\\r\\n K1 = S * k0\\r\\n K2 = k0 * nu * sin(2 * lat)/4.0\\r\\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\\\\r\\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\\r\\n\\r\\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\\r\\n\\r\\n K4 = k0 * nu * cos(lat)\\r\\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\\r\\n\\r\\n x = K4 * p + K5 * (p ** 3) + dx\\r\\n return x, y\",\n \"def ll2xyz(lon_pt,lat_pt):\\n\\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\\n zPt = np.sin(lat_pt)\\n return [xPt,yPt,zPt]\",\n \"def ll2fxy(lon,lat):\\n return xyz2fxy(*ll2xyz(lon,lat))\",\n \"def pixel2coords(self, x, y):\\n xoff, a, b, yoff, d, e = self.geotransform()\\n\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n return (xp, yp)\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*lat/(np.pi/2)\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def coord2pixel(tf, lat, lon):\\n x = int(round((lon-tf[0])/tf[1]))\\n y = int(round((lat-tf[3])/tf[5]))\\n\\n return x, y\",\n \"def _pixel_to_map(coordinates, geotransform):\\n coordinates_map = np.empty(coordinates.shape)\\n coordinates_map[..., 0] = (\\n geotransform[0]\\n + geotransform[1] * coordinates[..., 0]\\n + geotransform[2] * coordinates[..., 1]\\n )\\n coordinates_map[..., 1] = (\\n geotransform[3]\\n + geotransform[4] * coordinates[..., 0]\\n + geotransform[5] * coordinates[..., 1]\\n )\\n return coordinates_map\",\n \"def coords_to_structure(self) -> None:\\n ...\",\n \"def make_simple_coords():\\n \\n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\\n coords = np.vstack((x,y)).T\\n return coords\",\n \"def Pixel2World(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xdist = geoMatrix[1]\\r\\n ydist = geoMatrix[5]\\r\\n coorX = (ulX + (x * xdist))\\r\\n coorY = (ulY + (y * ydist))\\r\\n return (coorX, coorY)\",\n \"def rotated_coord_transform(lon, lat, np_lon, np_lat,\\n direction='rot2geo'):\\n\\n # Convert degrees to radians\\n lon = (lon * math.pi) / 180.\\n lat = (lat * math.pi) / 180.\\n\\n# SP_lon = SP_coor(1)\\n# SP_lat = SP_coor(2)\\n\\n theta = 90. - np_lat # Rotation around y-axis\\n phi = np_lon + 180. # Rotation around z-axis\\n\\n # Convert degrees to radians\\n phi = (phi * math.pi) / 180.\\n theta = (theta * math.pi) / 180.\\n\\n # Convert from spherical to cartesian coordinates\\n x = math.cos(lon) * math.cos(lat)\\n y = math.sin(lon) * math.cos(lat)\\n z = math.sin(lat)\\n\\n # Regular -> Rotated\\n if direction == 'geo2rot':\\n\\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.cos(theta) * math.sin(phi) * y +\\n math.sin(theta) * z)\\n y_new = (- math.sin(phi) * x +\\n math.cos(phi) * y)\\n z_new = (- math.sin(theta) * math.cos(phi) * x -\\n math.sin(theta) * math.sin(phi) * y +\\n math.cos(theta) * z)\\n\\n # Rotated -> Regular\\n elif direction == 'rot2geo':\\n \\n phi = - phi\\n theta = - theta\\n \\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.sin(phi) * y +\\n math.sin(theta) * math.cos(phi) * z)\\n y_new = (- math.cos(theta) * math.sin(phi) * x +\\n math.cos(phi) * y -\\n math.sin(theta) * math.sin(phi) * z)\\n z_new = (- math.sin(theta) * x +\\n math.cos(theta) * z)\\n\\n # Convert cartesian back to spherical coordinates\\n lon_new = math.atan2(y_new, x_new)\\n lat_new = math.asin(z_new)\\n\\n # Convert radians back to degrees\\n lon_new = (lon_new * 180.) / math.pi\\n lat_new = (lat_new * 180.) / math.pi;\\n\\n return (lon_new, lat_new)\",\n \"def ll_coordinates(self):\\n\\n x, y = self.xy_coordinates\\n proj_out = check_crs('EPSG:4326')\\n\\n return transform_proj(self.proj, proj_out, x, y)\",\n \"def pixel2coord(self, x, y):\\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\\n # partly taken from Sean Gillies \\\"affine.py\\\"\\n a,b,c,d,e,f = self.coordspace_transform\\n det = a*e - b*d\\n idet = 1 / float(det)\\n ra = e * idet\\n rb = -b * idet\\n rd = -d * idet\\n re = a * idet\\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\\n newy = (x*rd + y*re + (-c*rd - f*re) )\\n return newx,newy\",\n \"def pixelloc_to_geoloc(row, col, loff, coff):\\n row = np.atleast_1d(row)\\n col = np.atleast_1d(col)\\n\\n assert row.shape == col.shape\\n\\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\\n\\n if lat.size == lon.size == 1:\\n # Return scalars\\n return lat[0], lon[0]\\n else:\\n return lat, lon\",\n \"def coords_to_node(self,row,col):\\n return row*self.cols + col + 1\",\n \"def _indices_to_coords(c,r):\\n\\n column = _index_to_column(c)\\n row = r + 1\\n\\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}\",\n \"def ind2coord(self, index):\\n\\n # assert (index >= 0)\\n # assert(index < self.n - 1)\\n\\n col = index // self.rows\\n row = index % self.rows\\n\\n return [row, col]\",\n \"def geo2cell(geofile, posfile):\",\n \"def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\\r\\n # convert decimals to seconds...\\r\\n s_lon = dec2sec(s_lon)\\r\\n s_lat = dec2sec(s_lat)\\r\\n\\r\\n ## Auxiliary values \\r\\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\\r\\n s_lng_aux = (s_lon - 26782.5)/10000.\\r\\n s_lat_aux = (s_lat - 169028.66)/10000.\\r\\n \\r\\n # easting\\r\\n s_x = (600072.37 \\r\\n + 211455.93*s_lng_aux \\r\\n - 10938.51*s_lng_aux*s_lat_aux \\r\\n - 0.36*s_lng_aux*(s_lat_aux**2) \\r\\n - 44.54*(s_lng_aux**3))\\r\\n \\r\\n # northing\\r\\n s_y = (200147.07 \\r\\n + 308807.95*s_lat_aux \\r\\n + 3745.25*(s_lng_aux**2) \\r\\n + 76.63*(s_lat_aux**2) \\r\\n - 194.56*(s_lng_aux**2)*s_lat_aux \\r\\n + 119.79*(s_lat_aux**3))\\r\\n\\r\\n return s_x, s_y\",\n \"def geo_transform(self):\\n pass\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def lon_lat_to_cartesian(lon, lat, R = 1):\\n lon_r = np.radians(lon)\\n lat_r = np.radians(lat)\\n\\n x = R * np.cos(lat_r) * np.cos(lon_r)\\n y = R * np.cos(lat_r) * np.sin(lon_r)\\n z = R * np.sin(lat_r)\\n return x,y,z\",\n \"def _transform_coordinates(rectangle, Q=np.matrix(((1, 1), (-1, 1)))):\\n return tuple((rectangle[0]*Q).A1), tuple((rectangle[1]*Q).A1)\",\n \"def trans_to_coordinates(T, pts):\\n p = []\\n for i in range(len(pts)):\\n \\n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\\n p_a = np.matmul(T, p_b).tolist()\\n p.append(p_a[0:3])\\n\\n return p\",\n \"def trans_to_coordinates(T, pts):\\n p = []\\n for i in range(len(pts)):\\n \\n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\\n p_a = np.matmul(T, p_b).tolist()\\n p.append(p_a[0:3])\\n\\n return p\",\n \"def gps_to_node(self,lat,lon):\\n row,col = self.gps_to_coords(lat,lon)\\n if row == -1:\\n return -1\\n return self.coords_to_node(row,col)\",\n \"def calcPosition (lat, lon):\\n nauticalMilePerLat = 60.00721\\n nauticalMilePerLongitude = 60.10793\\n rad = math.pi / 180.0\\n milesPerNauticalMile = 1.15078\\n \\n y = lat * nauticalMilePerLat\\n x = math.cos(lat * rad) * lon * nauticalMilePerLongitude\\n\\n return x * milesPerNauticalMile * 1609.344, y * milesPerNauticalMile * 1609.344\",\n \"def _generate_coords(self):\\n coords = np.dstack([self.X.ravel(), self.Y.ravel()])[0]\\n return coords, spatial.cKDTree(coords)\",\n \"def makeToCoordinates(fromCoords, Uframe, Vframe, scale):\\n\\n out = []\\n\\n for e in fromCoords:\\n x = e[0]\\n y = e[1]\\n toX = Uframe[x][y]\\n toY = Vframe[x][y]\\n out.append((int(round(x+toX*scale)),int(round(y+toY*scale))))\\n\\n return out\",\n \"def read_affine(file):\\n data = open(file, 'r').read()\\n data = data.split('\\\\n')\\n for i in range(1, 5):\\n data[i] = data[i].split(':')\\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\\n Nlon = len(int_lon) - 1\\n Nlat = len(int_lat) - 1\\n data[3][1] = data[3][1].split(',')\\n data[4][1] = data[4][1].split(',')\\n lon_transform = np.zeros((Nlon, 2))\\n lat_transform = np.zeros((Nlat, 2))\\n for i in range(Nlon):\\n data[3][1][i] = data[3][1][i].split(' ')\\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\\n for i in range(Nlat):\\n data[4][1][i] = data[4][1][i].split(' ')\\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\\n lon_transform = np.array(lon_transform).astype('float')\\n lat_transform = np.array(lat_transform).astype('float')\\n return int_lon, int_lat, lon_transform, lat_transform\",\n \"def lat_lng(row):\\r\\n lat = row[\\\"latitude\\\"]\\r\\n lng = row[\\\"longitude\\\"]\\r\\n n = int(lat/GRANULARITY)\\r\\n nlat_start = n * GRANULARITY\\r\\n nlat_end = nlat_start + GRANULARITY\\r\\n nlg=int(lng/GRANULARITY)\\r\\n nlng_start = nlg * GRANULARITY\\r\\n nlng_end = nlng_start + GRANULARITY\\r\\n latlng=[(nlat_start,nlng_start), (nlat_start,nlng_end), (nlat_end,nlng_end), (nlat_end,nlng_start)]\\r\\n return latlng\",\n \"def create_pseudo_epsg4326_coordinates(self):\\n self.create_3d_coord_on_sphere(on_sphere=True)\\n self.df_attributes['lat'] = 180*(pi/2 - np.arccos(self.df_attributes['coord_z']))/pi\\n self.df_attributes['lon'] = 180*np.arctan2(self.df_attributes['coord_y'], self.df_attributes['coord_x'])/pi\",\n \"def _transform_point(self, x, y):\\n return (x, y)\",\n \"def wgs84_to_mercator(df, lon, lat):\\n k = 6378137\\n df[\\\"x\\\"] = df[lon] * (k * np.pi/180.0)\\n df[\\\"y\\\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\\n return df\",\n \"def rowcol2XY(row,col,CCD):\\n pixscale = 0.015 #mm/pix\\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\\n return X,Y\",\n \"def get_coordinates_geo(self):\\n if not self.rotated:\\n lon_arr_geo = self.lon_arr\\n lat_arr_geo = self.lat_arr\\n else:\\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \\n\\n return lon_arr_geo, lat_arr_geo\",\n \"def process_coords():\\n split_coords = row[\\\"map_coord\\\"].split(',')\\n map_x, map_y = [int(i) for i in split_coords]\\n map_x_normed = ((map_x*2) / self.MINIMAP_DIM) - 1\\n map_y_normed = -(((map_y*2) / self.MINIMAP_DIM) - 1)\\n return map_x_normed, map_y_normed\",\n \"def rowcol_to_latlon(rowcol_str):\\n\\n # Vectors for code in file name\\n latspace = np.linspace(-90,90,46)\\n lonspace = np.linspace(-180,180,73)\\n lat = latspace[int(rowcol_str[0:2])-1]\\n lon = lonspace[int(rowcol_str[2:])-1]\\n return (lat,lon)\",\n \"def to_position(self, x, y, i, j):\\n return (x * self.SIZE + i, y * self.SIZE + j)\",\n \"def node_to_coords(self,node_num):\\n row = (node_num - 1) / self.cols\\n col = (node_num - 1) % self.cols\\n return (row,col)\",\n \"def transform_pnt(pnt, inEPSG, outEPSG):\\r\\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\\r\\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\\r\\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\\r\\n\\r\\n return pyproj.transform(inProj, outProj, x1, y1)\",\n \"def flowline_latlon(coords, fromproj=pyproj.Proj(\\\"+init=epsg:3413\\\"), toproj=pyproj.Proj(\\\"+init=EPSG:4326\\\")):\\n xs = coords[:,0]\\n ys = coords[:,1]\\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\\n latlon_coords = np.asarray(zip(x_lon, y_lat))\\n return latlon_coords\",\n \"def spher2cart(lon: np.ndarray,\\n lat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n rlon = np.radians(lon)\\n rlat = np.radians(lat)\\n x = np.cos(rlon) * np.cos(rlat)\\n y = np.sin(rlon) * np.cos(rlat)\\n z = np.sin(rlat)\\n return x, y, z\",\n \"def proj(self,lon,lat):\\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\\n x[x > 1e29] = None\\n y[y > 1e29] = None\\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\\n return x, y\",\n \"def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False):\\n\\n x, y = np.ma.array(x), np.ma.array(y)\\n\\n # First to local proj\\n _crs = check_crs(crs, raise_on_error=True)\\n if isinstance(_crs, pyproj.Proj):\\n x, y = transform_proj(_crs, self.proj, x, y)\\n elif isinstance(_crs, Grid):\\n x, y = _crs.ij_to_crs(x, y, crs=self.proj)\\n\\n # Then to local grid\\n x = (x - self.x0) / self.dx\\n y = (y - self.y0) / self.dy\\n\\n # See if we need to round\\n if nearest:\\n f = np.rint if self.pixel_ref == 'center' else np.floor\\n x = f(x).astype(int)\\n y = f(y).astype(int)\\n\\n # Mask?\\n if maskout:\\n if self.pixel_ref == 'center':\\n mask = ~((x >= -0.5) & (x < self.nx-0.5) &\\n (y >= -0.5) & (y < self.ny-0.5))\\n else:\\n mask = ~((x >= 0) & (x < self.nx) &\\n (y >= 0) & (y < self.ny))\\n x = np.ma.array(x, mask=mask)\\n y = np.ma.array(y, mask=mask)\\n\\n return x, y\",\n \"def row_to_lat(self, row):\\n return self.max_absolute_lat - row * self.lat_step\",\n \"def transform_coordinates(self, p):\\n if type(p) == PhysicalObject:\\n return self.transform_coordinates(p.position)\\n elif type(p) == Vector:\\n return tuple(map(\\n lambda x: int(x),\\n (p / SCALE_FACTOR - self.pos_shift))\\n )\",\n \"def CreateTargetGeoMap(latS, latN, lonW, lonE, latlen, lonlen):\\n\\n lat_grid = np.linspace(latS, latN, latlen)\\n lon_grid = np.linspace(lonW, lonE, lonlen)\\n\\n return lat_grid,lon_grid\",\n \"def composite(c, r):\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\",\n \"def coords_to_gps(self,coords):\\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))\",\n \"def ll2px(lat, lng, zoom):\\n # assert isinstance(lat, (float, int, long)), \\\\\\n # ValueError(\\\"lat must be a float\\\")\\n lat = float(lat)\\n # assert isinstance(lng, (float, int, long)), \\\\\\n # ValueError(\\\"lng must be a float\\\")\\n lng = float(lng)\\n # assert isinstance(zoom, int), TypeError(\\\"zoom must be an int from 0 to 30\\\")\\n # assert 0 <= zoom <= 30, ValueError(\\\"zoom must be an int from 0 to 30\\\")\\n\\n cbk = CBK[zoom]\\n\\n x = int(round(cbk + (lng * CEK[zoom])))\\n\\n foo = math.sin(lat * math.pi / 180)\\n if foo < -0.9999:\\n foo = -0.9999\\n elif foo > 0.9999:\\n foo = 0.9999\\n\\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\\n\\n return (x, y)\",\n \"def tile_coordinates(text):\\n UL = (text[1]), (text[2]) # Upper Left\\n UR = (text[3]), (text[2]) # Upper Right\\n LR = (text[3]), (text[4]) # Lower Right\\n LL = (text[1]), (text[4]) # Lower Left\\n coordinates = (UL, UR, LR, LL)\\n return text[0], [tuple(float(x) for x in xs) for xs in coordinates]\",\n \"def convertGeoToPixel(self, lon, lat):\\n x = (lon - self.mapLonLeft) * (self.mapWidth / self.mapLonDelta)\\n\\n lat = lat * math.pi / 180.0\\n self.worldMapWidth = ((self.mapWidth / self.mapLonDelta) * 360) / (2 * math.pi)\\n self.mapOffsetY = (self.worldMapWidth / 2 * math.log((1 + math.sin(self.mapLatBottomDegree)) / (1 - math.sin(self.mapLatBottomDegree))))\\n y = self.mapHeight - ((self.worldMapWidth / 2 * math.log((1 + math.sin(lat)) / (1 - math.sin(lat)))) - self.mapOffsetY)\\n\\n return (x, y)\",\n \"def merc(lat, lon):\\n\\tr_major = 6378137.000\\n\\tx = r_major * math.radians(lon)\\n\\tscale = x/lon\\n\\ty = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale\\n\\treturn (x, y)\",\n \"def index_to_xy(index):\\n x = index % columns\\n y = index // columns\\n return x, y\",\n \"def cr2lonlat_for_geotif(path):\\n old_cs, new_cs, gta, local_vars = _create_xform(path)\\n transform = osr.CoordinateTransformation(old_cs, new_cs)\\n\\n def composite(c, r):\\n \\\"\\\"\\\"xform from (c, r) to (lon, lat)\\\"\\\"\\\"\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\\n \\n return composite\",\n \"def proj_coord(coord, proj_in, proj_out):\\n x, y = coord\\n return pyproj.transform(proj_in, proj_out, x, y)\",\n \"def gps_to_coords(self,lat,lon):\\n\\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\\n return (-1,-1)\\n\\n lat_spot = int((self.max_lat-lat)/self.lat_step)\\n lon_spot = int((lon-self.min_lon)/self.lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def _wrap_coord(tensors):\\n cell = tf.gather_nd(tensors['cell'], tensors['ind_1'])\\n coord = tf.expand_dims(tensors['coord'], -1)\\n frac_coord = tf.linalg.solve(tf.transpose(cell, perm=[0, 2, 1]), coord)\\n frac_coord %= 1\\n coord = tf.matmul(tf.transpose(cell, perm=[0, 2, 1]), frac_coord)\\n return tf.squeeze(coord, -1)\",\n \"def chunk_coords(self) -> Generator[Tuple[int, int], None, None]:\\n cx, cz = int(self.camera_location[0]) >> 4, int(self.camera_location[2]) >> 4\\n\\n sign = 1\\n length = 1\\n for _ in range(self.render_distance*2+1):\\n for _ in range(length):\\n yield cx, cz\\n cx += sign\\n for _ in range(length):\\n yield cx, cz\\n cz += sign\\n sign *= -1\\n length += 1\",\n \"def coord(self, x, y):\\n origin_x = self._raster_meta['transform'][3]\\n origin_y = self._raster_meta['transform'][0]\\n pixel_x = self._raster_meta['transform'][5]\\n pixel_y = self._raster_meta['transform'][1]\\n\\n x = int((x - origin_x) / pixel_x)\\n y = int((y - origin_y) / pixel_y)\\n return self[x, y]\",\n \"def coords_from_cell(cell, lon = [-8.73, -8.50], lat = [41.10, 41.25], N = 100, M = 75):\\n lon_step = (lon[1] - lon[0]) / N \\n lat_step = (lat[1] - lat[0]) / M\\n \\n middle_lon = lon[0] + cell[0] * lon_step + lon_step / 2\\n middle_lat = lat[0] + cell[1] * lat_step + lat_step / 2\\n \\n return [middle_lon, middle_lat]\",\n \"def transform_from(self, coordinates) -> np.ndarray:\\n return (coordinates @ self.T) + self._o\",\n \"def convert_coords(self):\\n if self.coordsys in ['image', 'physical']:\\n coords = self._convert_pix_coords()\\n else:\\n coords = self._convert_sky_coords()\\n\\n if self.region_type == 'line':\\n coords = [coords[0][0], coords[0][1]]\\n\\n if self.region_type == 'text':\\n coords.append(self.meta['text'])\\n\\n return coords\",\n \"def coordinates(self):\\n return np.array([[f.x, f.y] for f in self])\",\n \"def transform_to(self, coordinates) -> np.ndarray:\\n return (coordinates - self._o) @ self.T.T\",\n \"def world2Pixel(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xDist = geoMatrix[1]\\r\\n yDist = geoMatrix[5]\\r\\n rtnX = geoMatrix[2]\\r\\n rtnY = geoMatrix[4]\\r\\n pixel = int((x - ulX) / xDist)\\r\\n line = int((ulY - y) / xDist)\\r\\n return (pixel, line)\",\n \"def make_grid(dataset):\\n top_left_lat = dataset[\\\"a\\\"][0]\\n top_left_lng = dataset[\\\"a\\\"][1]\\n top_right_lng = dataset[\\\"c\\\"][1]\\n bot_left_lat = dataset[\\\"b\\\"][0]\\n\\n lng_row = []\\n lat_col = []\\n i = top_left_lng\\n while i < top_right_lng:\\n lng_row.append(round(i, 5))\\n i += step\\n j = bot_left_lat\\n while j < top_left_lat:\\n lat_col.append(round(j, 5))\\n j += step\\n out_grid = []\\n for i in lat_col:\\n row = []\\n for j in lng_row:\\n row.append(\\\"{0}:{1}:0\\\".format(i, j))\\n out_grid.append(row)\\n return out_grid\",\n \"def cart_coords2d(self):\\r\\n\\r\\n mgx, mgy = self.meshup2d()\\r\\n coords = np.column_stack((mgx.flatten(), mgy.flatten()))\\r\\n\\r\\n return coords\",\n \"def coords2D(self):\\n return (self.x, self.y)\",\n \"def xy_coordinates(self):\\n\\n return np.meshgrid(self.x_coord, self.y_coord)\",\n \"def chunk_coords_to_region_coords(cx: int, cz: int) -> ChunkCoordinates:\\n return cx >> 5, cz >> 5\",\n \"def coords(self):\\n return np.column_stack((self.x_coord_list, self.y_coord_list, self.z_coord_list))\",\n \"def to_coords(self, px, py):\\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\\n raise IndexError\\n return (px // self.SIZE, py // self.SIZE,\\n px % self.SIZE, py % self.SIZE)\",\n \"def xmymwh2xyxy(coords: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\\n new_coords = coords.clone() if isinstance(coords, torch.Tensor) else coords.copy()\\n new_coords[:, 0] = coords[:, 0] - coords[:, 2] / 2 # top left x\\n new_coords[:, 1] = coords[:, 1] - coords[:, 3] / 2 # top left y\\n new_coords[:, 2] = coords[:, 0] + coords[:, 2] / 2 # bottom right x\\n new_coords[:, 3] = coords[:, 1] + coords[:, 3] / 2 # bottom right y\\n return new_coords\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68157905","0.66313314","0.661171","0.6593962","0.6544747","0.6531103","0.6518472","0.6451298","0.6443582","0.6433205","0.63895917","0.6381785","0.63414675","0.63401574","0.6333194","0.63325125","0.6327625","0.6324136","0.6297547","0.6277964","0.6277665","0.6276004","0.6258406","0.62417674","0.623816","0.62362885","0.6228485","0.6178982","0.617396","0.61729395","0.6159952","0.6140463","0.6136282","0.61293423","0.6090665","0.60815835","0.6063063","0.60526216","0.6043208","0.6032001","0.6031672","0.6031121","0.6029248","0.60169786","0.6000241","0.59699005","0.5965952","0.5959996","0.5943583","0.59356356","0.59356356","0.5929435","0.5929105","0.5919291","0.59181726","0.5917917","0.5913972","0.59102625","0.58964884","0.58950865","0.58905655","0.58890396","0.5884812","0.58450097","0.5835442","0.5831858","0.58305997","0.5828817","0.5827072","0.5826842","0.5819427","0.5817181","0.5808054","0.5802085","0.5777629","0.57755715","0.5770862","0.5768481","0.5759841","0.575915","0.57522446","0.5748363","0.5744657","0.57426745","0.57412255","0.5737493","0.5733368","0.57333523","0.57323164","0.5728604","0.5722705","0.5719953","0.5719934","0.5712969","0.5712695","0.57113004","0.570069","0.56991273","0.5695329","0.5687645","0.56828785"],"string":"[\n \"0.68157905\",\n \"0.66313314\",\n \"0.661171\",\n \"0.6593962\",\n \"0.6544747\",\n \"0.6531103\",\n \"0.6518472\",\n \"0.6451298\",\n \"0.6443582\",\n \"0.6433205\",\n \"0.63895917\",\n \"0.6381785\",\n \"0.63414675\",\n \"0.63401574\",\n \"0.6333194\",\n \"0.63325125\",\n \"0.6327625\",\n \"0.6324136\",\n \"0.6297547\",\n \"0.6277964\",\n \"0.6277665\",\n \"0.6276004\",\n \"0.6258406\",\n \"0.62417674\",\n \"0.623816\",\n \"0.62362885\",\n \"0.6228485\",\n \"0.6178982\",\n \"0.617396\",\n \"0.61729395\",\n \"0.6159952\",\n \"0.6140463\",\n \"0.6136282\",\n \"0.61293423\",\n \"0.6090665\",\n \"0.60815835\",\n \"0.6063063\",\n \"0.60526216\",\n \"0.6043208\",\n \"0.6032001\",\n \"0.6031672\",\n \"0.6031121\",\n \"0.6029248\",\n \"0.60169786\",\n \"0.6000241\",\n \"0.59699005\",\n \"0.5965952\",\n \"0.5959996\",\n \"0.5943583\",\n \"0.59356356\",\n \"0.59356356\",\n \"0.5929435\",\n \"0.5929105\",\n \"0.5919291\",\n \"0.59181726\",\n \"0.5917917\",\n \"0.5913972\",\n \"0.59102625\",\n \"0.58964884\",\n \"0.58950865\",\n \"0.58905655\",\n \"0.58890396\",\n \"0.5884812\",\n \"0.58450097\",\n \"0.5835442\",\n \"0.5831858\",\n \"0.58305997\",\n \"0.5828817\",\n \"0.5827072\",\n \"0.5826842\",\n \"0.5819427\",\n \"0.5817181\",\n \"0.5808054\",\n \"0.5802085\",\n \"0.5777629\",\n \"0.57755715\",\n \"0.5770862\",\n \"0.5768481\",\n \"0.5759841\",\n \"0.575915\",\n \"0.57522446\",\n \"0.5748363\",\n \"0.5744657\",\n \"0.57426745\",\n \"0.57412255\",\n \"0.5737493\",\n \"0.5733368\",\n \"0.57333523\",\n \"0.57323164\",\n \"0.5728604\",\n \"0.5722705\",\n \"0.5719953\",\n \"0.5719934\",\n \"0.5712969\",\n \"0.5712695\",\n \"0.57113004\",\n \"0.570069\",\n \"0.56991273\",\n \"0.5695329\",\n \"0.5687645\",\n \"0.56828785\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":85694,"cells":{"query":{"kind":"string","value":"xform from (lon, lat) to (c, r)"},"document":{"kind":"string","value":"def composite(lon, lat):\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return (~gta * transform.TransformPoint(lat, lon)[:2])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def composite(c, r):\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat","def merc(lat, lon):\n\tr_major = 6378137.000\n\tx = r_major * math.radians(lon)\n\tscale = x/lon\n\ty = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale\n\treturn (x, y)","def lonlat2cr_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(new_cs, old_cs)\n\n def composite(lon, lat):\n \"\"\"xform from (lon, lat) to (c, r)\"\"\"\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return (~gta * transform.TransformPoint(lat, lon)[:2])\n \n return composite","def lon_lat_to_cartesian(lon, lat, R = 1):\n lon_r = np.radians(lon)\n lat_r = np.radians(lat)\n\n x = R * np.cos(lat_r) * np.cos(lon_r)\n y = R * np.cos(lat_r) * np.sin(lon_r)\n z = R * np.sin(lat_r)\n return x,y,z","def cr2lonlat_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(old_cs, new_cs)\n\n def composite(c, r):\n \"\"\"xform from (c, r) to (lon, lat)\"\"\"\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat\n \n return composite","def convert(self, lat, lon):\r\n a = self.a\r\n b = self.b\r\n long0 = self.long0\r\n k0 = self.k0\r\n dx = self.dx\r\n\r\n e = (1 - b ** 2 / a ** 2) ** 0.5\r\n e2 = e ** 2 / (1 - e ** 2)\r\n n = (a - b) / (a + b)\r\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\r\n p = lon - long0\r\n\r\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\r\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\r\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\r\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\r\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\r\n\r\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\r\n\r\n K1 = S * k0\r\n K2 = k0 * nu * sin(2 * lat)/4.0\r\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\r\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\r\n\r\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\r\n\r\n K4 = k0 * nu * cos(lat)\r\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\r\n\r\n x = K4 * p + K5 * (p ** 3) + dx\r\n return x, y","def _getXYZ ( lon, lat ):\n d2r = pi / 180.\n rlon, rlat = ( d2r * lon, d2r * lat )\n x = cos(rlat) * cos(rlon)\n y = cos(rlat) * sin(rlon)\n z = sin(rlat)\n return (x,y,z)","def cartesian_to_lon_lat(x, y, z, R = 1):\n lon = np.degrees(np.arctan2(y,x))\n lat = np.degrees(np.pi/2-np.arctan2((x**2+y**2)**0.5,z))\n\n return lon,lat","def transform_coordinates(coords):\n # WGS 84 reference coordinate system parameters\n A = 6378.137 # major axis [km]\n E2 = 6.69437999014e-3 # eccentricity squared\n\n coords = prepare_coords(coords)\n\n # convert to radiants\n lat_rad = np.radians(coords[:, 0])\n lon_rad = np.radians(coords[:, 1])\n\n # convert to cartesian coordinates\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\n z = r_n * (1 - E2) * np.sin(lat_rad)\n\n return np.column_stack((x, y, z))","def convertCoord(lon, lat, inEPSG, outEPSG):\n from pyproj import Proj, transform\n inProj = Proj(init='epsg:'+str(inEPSG))\n outProj = Proj(init='epsg:'+str(outEPSG))\n x, y = transform(inProj, outProj, lon, lat)\n return x, y\n # epsg:4326 WGS84\n # epsg:2950 MTM8\n # epsg:6622 Quebec Lambert","def ll2csflat(lon,lat,ncs):\n j,f,i = ll2jfi(lon,lat,ncs)\n return (j*6+f)*ncs+i","def spherical_to_cartesian(r, lat, lon):\n import math\n\n if np.isscalar(r) and np.isscalar(lat) and np.isscalar(lon):\n x = r * math.cos(lat) * math.cos(lon)\n y = r * math.cos(lat) * math.sin(lon)\n z = r * math.sin(lat)\n else:\n x = r * np.cos(lat) * np.cos(lon)\n y = r * np.cos(lat) * np.sin(lon)\n z = r * np.sin(lat)\n\n return x, y, z","def transform_from_latlon(lat, lon):\n from affine import Affine\n lat = np.asarray(lat)\n lon = np.asarray(lon)\n trans = Affine.translation(lon[0], lat[0])\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n return trans * scale","def _indices_to_coords(c,r):\n\n column = _index_to_column(c)\n row = r + 1\n\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}","def ll2xyz(lon_pt,lat_pt):\n\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\n zPt = np.sin(lat_pt)\n return [xPt,yPt,zPt]","def project(self, (lng, lat)):\n x = lng * DEG_TO_RAD\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\n y = lat * DEG_TO_RAD\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)","def scalar_g2r(al, be, ga, lon, lat):\n\n rad = mt.pi / 180\n al = al * rad\n be = be * rad\n ga = ga * rad\n\n rotate_matrix = np.zeros(shape=(3, 3))\n\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\n al\n )\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\n al\n )\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\n rotate_matrix[2, 2] = np.cos(be)\n \n #rotate_matrix = np.linalg.pinv(rotate_matrix)\n \n lat = lat * rad\n lon = lon * rad\n\n # geographical Cartesian coordinates:\n xr = np.cos(lat) * np.cos(lon)\n yr = np.cos(lat) * np.sin(lon)\n zr = np.sin(lat)\n\n # rotated Cartesian coordinates:\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\n zg = rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\n\n # rotated coordinates:\n rlat = np.arcsin(zg)\n rlon = np.arctan2(yg, xg)\n\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\n if a:\n lon[a] = 0\n\n rlat = rlat / rad\n rlon = rlon / rad\n\n return (rlon, rlat)","def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\r\n # convert decimals to seconds...\r\n s_lon = dec2sec(s_lon)\r\n s_lat = dec2sec(s_lat)\r\n\r\n ## Auxiliary values \r\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\r\n s_lng_aux = (s_lon - 26782.5)/10000.\r\n s_lat_aux = (s_lat - 169028.66)/10000.\r\n \r\n # easting\r\n s_x = (600072.37 \r\n + 211455.93*s_lng_aux \r\n - 10938.51*s_lng_aux*s_lat_aux \r\n - 0.36*s_lng_aux*(s_lat_aux**2) \r\n - 44.54*(s_lng_aux**3))\r\n \r\n # northing\r\n s_y = (200147.07 \r\n + 308807.95*s_lat_aux \r\n + 3745.25*(s_lng_aux**2) \r\n + 76.63*(s_lat_aux**2) \r\n - 194.56*(s_lng_aux**2)*s_lat_aux \r\n + 119.79*(s_lat_aux**3))\r\n\r\n return s_x, s_y","def scalar_r2g(al, be, ga, rlon, rlat):\n\n rad = mt.pi / 180\n al = al * rad\n be = be * rad\n ga = ga * rad\n rotate_matrix = np.zeros(shape=(3, 3))\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\n al\n )\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\n al\n )\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\n rotate_matrix[2, 2] = np.cos(be)\n\n rotate_matrix = np.linalg.pinv(rotate_matrix)\n\n rlat = rlat * rad\n rlon = rlon * rad\n\n # Rotated Cartesian coordinates:\n xr = np.cos(rlat) * np.cos(rlon)\n yr = np.cos(rlat) * np.sin(rlon)\n zr = np.sin(rlat)\n\n # Geographical Cartesian coordinates:\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\n zg = (\n rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\n )\n\n # Geographical coordinates:\n lat = np.arcsin(zg)\n lon = np.arctan2(yg, xg)\n\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\n if a:\n lon[a] = 0\n\n lat = lat / rad\n lon = lon / rad\n\n return (lon, lat)","def latlon_2_grid(x, y, z, origin):\n new_y = (y - origin[1]) * 111111\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\n return new_x, new_y, z","def ll2fxy(lon,lat):\n return xyz2fxy(*ll2xyz(lon,lat))","def convert(coords):\n lat = coords[:4]\n lon = coords[4:]\n\n lat = lat[:2] + \".\" + lat[2:]\n\n if int(lon[0]) > 5:\n lon = \"-\" + lon[:2] + \".\" + lon[2:]\n else:\n lon = \"-1\" + lon[:2] + \".\" + lon[2:]\n\n return (float(lat), float(lon))","def xyz2longlat(x,y,z):\n r=[]\n long=[]\n d=[]\n lat=[]\n longnew=[]\n latnew=[]\n i=0\n for i in range(len(x)):\n r.append(sp.sqrt(x[i]*x[i] + y[i]*y[i] + z[i]*z[i]))\n \t long.append(sp.arctan2(y[i],x[i]))\n\t d.append(sp.sqrt(x[i]*x[i] + y[i]*y[i]))\n\t lat.append(sp.arcsin(z[i]/r[i]))\n\n i=0\n for i in range(len(x)):\n longnew.append(long[i]*deg)\n latnew.append(lat[i]*deg)\n\n return longnew, latnew, r","def spher2cart(lon: np.ndarray,\n lat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n rlon = np.radians(lon)\n rlat = np.radians(lat)\n x = np.cos(rlon) * np.cos(rlat)\n y = np.sin(rlon) * np.cos(rlat)\n z = np.sin(rlat)\n return x, y, z","def map(self, lat, long):\r\n rxy = self._r*lat/(np.pi/2)\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def coords_to_gps(self,coords):\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))","def proj(self,lon,lat):\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\n x[x > 1e29] = None\n y[y > 1e29] = None\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\n return x, y","def merc_x(lon):\n r_major = 6378137.000\n x = r_major * math.radians(lon)\n return x","def coord2pixel(tf, lat, lon):\n x = int(round((lon-tf[0])/tf[1]))\n y = int(round((lat-tf[3])/tf[5]))\n\n return x, y","def to_lnglat(r):\n \n x, y = r['x_coordinate'], r['y_coordinate']\n if np.isnan(x) or np.isnan(y):\n return None, None\n return projection(x, y, inverse = True)","def convertView2Geo(self, x, y):\n\n # x_pix is from left map edge, y_pix from top map edge\n x_pix = x + self.view_offset_x\n y_pix = y + self.view_offset_y\n\n lon = self.map_llon + x_pix/self.ppd_x\n lat = self.map_tlat - y_pix/self.ppd_y\n\n return (lon, lat)","def map(self, lat, long):\r\n rxy = self._r*np.sqrt(1-np.cos(lat))\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def ecef_coords(lats: ndarray, lons: ndarray) -> Tuple[ndarray, ndarray, ndarray]:\n # Cartopy Geodetic and Geocentric both default to the WGS84 datum\n spherical_latlon_crs = Geodetic()\n ecef_crs = Geocentric()\n xyz = ecef_crs.transform_points(\n spherical_latlon_crs, np.array(lons), np.array(lats)\n )\n return xyz[..., 0], xyz[..., 1], xyz[..., 2]","def mapr(r):\n return np.rad2deg(np.arctan(r)*2)","def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\n\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\n return (-1,-1)\n\n lat_step = abs(max_lat-min_lat)/rows\n lon_step = abs(max_lon-min_lon)/cols\n\n lat_spot = int((max_lat-lat)/lat_step)\n lon_spot = int((lon-min_lon)/lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def p2c(r, t):\n if np.isnan(r) or np.isnan(t):\n return 0, 0 # return 0, 0\n else:\n return r * np.sin(t), r * np.cos(t) # x, y","def reverse(self, lon, lat):","def xyz_to_lonlat(x,y,z):\n\n XsqPlusYsq = x**2 + y**2\n r = np.sqrt(XsqPlusYsq + z**2) # r\n #elev = np.arccos(z/np.sqrt(XsqPlusYsq)) # theta\n elev = np.arccos(z/r)\n az = np.arctan2(y,x) # phi\n return np.rad2deg(az), 90 - np.rad2deg(elev) # want ","def cartesian_coordinates(self):\n # extract RA items\n ra_hours, ra_minutes, ra_seconds = RA_RE.match(str(self.ra)).groups()\n # then cast\n ra_hours = int(ra_hours)\n ra_minutes = int(ra_minutes)\n ra_seconds = float(ra_seconds)\n\n # extract DEC items\n dec_sign, dec_degrees, dec_minutes, dec_seconds = DEC_RE.match(str(self.dec)).groups()\n # then cast\n dec_sign = -1 if dec_sign == '-' else 1\n dec_degrees = int(dec_degrees)\n dec_minutes = int(dec_minutes)\n dec_seconds = float(dec_seconds)\n\n # to degrees\n a = (ra_hours*15) + (ra_minutes*0.25) + (ra_seconds*0.004166)\n b = abs(dec_degrees + dec_minutes/60 + dec_seconds/3600) * dec_sign\n\n # to radians\n a = math.radians(a)\n b = math.radians(b)\n\n distance = float(self.distance)\n\n x = (distance * math.cos(b)) * math.cos(a)\n y = (distance * math.cos(b)) * math.sin(a)\n z = distance * math.sin(b)\n\n return x, y, z","def translation(coord, lat, lon):\n ty = merc_y(lat) - 73.43 # Y translation\n tx = merc_x(lon) - 57.55 # X translation\n for i in range(len(coord)):\n for j in range(len(coord[i])):\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\n return coord","def convertLatLon(latCell, lonCell):\n cell_lats = np.array([])\n cell_lons = np.array([])\n for lat in latCell:\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \n for lon in lonCell:\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \n\n return cell_lats, cell_lons","def latlon_to_pixels(lon, lat):\n radius = 489.5\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\n x_pos = x_pos_from_center + 512.5\n \n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\n y_pos = y_pos_from_center + 512.5\n \n return x_pos, y_pos","def convertGeo2View(self, lon, lat):\n\n x_pix = (lon - self.view_llon) * self.ppd_x\n y_pix = (self.view_tlat - lat) * self.ppd_y\n\n return (x_pix, y_pix)","def coords_to_X(c, shape):\n a, b = c\n assert 0 <= a <= shape[0]\n assert 0 <= b <= shape[1]\n a = float(a)\n b = float(b)\n a = a + 0.5 # center cells\n b = b + 0.5\n u = a / (shape[0])\n v = b / (shape[1])\n x = 2 * (u - 0.5)\n y = 2 * (v - 0.5)\n assert -1 <= x <= +1\n assert -1 <= y <= +1\n return (x, y)","def ReprojectCoords(coords,src_srs,tgt_srs):\n trans_coords = []\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\n for x,y in coords:\n x,y,z = transform.TransformPoint(x,y)\n trans_coords.append([x,y])\n return trans_coords","def get_ecmwf_lat_lon(nc_file):\n from netCDF4 import Dataset\n \n fh = Dataset(nc_file, mode='r')\n\n latitude_ecmwf = fh.variables['latitude_ecmwf'][:]\n longitude_ecmwf = fh.variables['longitude_ecmwf'][:]\n\n lonmesh_ecmwf,latmesh_ecmwf = np.meshgrid(longitude_ecmwf,latitude_ecmwf)\n\n print('latitude_ecmwf: ', latitude_ecmwf.shape)\n print('longitude_ecmwf: ', longitude_ecmwf.shape)\n \n return latitude_ecmwf, longitude_ecmwf, latmesh_ecmwf, lonmesh_ecmwf;","def project_p2c_points(R, C, H): #---- project to cylindrical\r\n Rc= H[0]/2; Cc= H[1]/2; # center coordinate\r\n phi = H[3]; S= H[4]; # rotation angle and sizing\r\n Tv = H[5]; Tu= H[6]; # displacement\r\n COSF= np.cos(phi); SINF= np.sin(phi); # \r\n U = Tu + S*( COSF*(C- Cc)- SINF*(R- Rc) ); \r\n V = Tv + S*( SINF*(C- Cc)+ COSF*(R- Rc) );\r\n return V, U","def rotated_coord_transform(lon, lat, np_lon, np_lat,\n direction='rot2geo'):\n\n # Convert degrees to radians\n lon = (lon * math.pi) / 180.\n lat = (lat * math.pi) / 180.\n\n# SP_lon = SP_coor(1)\n# SP_lat = SP_coor(2)\n\n theta = 90. - np_lat # Rotation around y-axis\n phi = np_lon + 180. # Rotation around z-axis\n\n # Convert degrees to radians\n phi = (phi * math.pi) / 180.\n theta = (theta * math.pi) / 180.\n\n # Convert from spherical to cartesian coordinates\n x = math.cos(lon) * math.cos(lat)\n y = math.sin(lon) * math.cos(lat)\n z = math.sin(lat)\n\n # Regular -> Rotated\n if direction == 'geo2rot':\n\n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.cos(theta) * math.sin(phi) * y +\n math.sin(theta) * z)\n y_new = (- math.sin(phi) * x +\n math.cos(phi) * y)\n z_new = (- math.sin(theta) * math.cos(phi) * x -\n math.sin(theta) * math.sin(phi) * y +\n math.cos(theta) * z)\n\n # Rotated -> Regular\n elif direction == 'rot2geo':\n \n phi = - phi\n theta = - theta\n \n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.sin(phi) * y +\n math.sin(theta) * math.cos(phi) * z)\n y_new = (- math.cos(theta) * math.sin(phi) * x +\n math.cos(phi) * y -\n math.sin(theta) * math.sin(phi) * z)\n z_new = (- math.sin(theta) * x +\n math.cos(theta) * z)\n\n # Convert cartesian back to spherical coordinates\n lon_new = math.atan2(y_new, x_new)\n lat_new = math.asin(z_new)\n\n # Convert radians back to degrees\n lon_new = (lon_new * 180.) / math.pi\n lat_new = (lat_new * 180.) / math.pi;\n\n return (lon_new, lat_new)","def LongLat(x, y, z):\n r = (x**2 + y**2 + z**2)**0.5\n long = numpy.arctan2(y, x)\n lat = numpy.arcsin(z / r)\n return long, lat, r","def pixel2coord(tf, x, y):\n lat = tf[0] + x*tf[1] + y*tf[2]\n lon = tf[3] + x*tf[4] + y*tf[5]\n\n return lat, lon","def unproject(self, (x, y)):\n lng = x/EARTH_RADIUS * RAD_TO_DEG\n lat = 2 * math.atan(math.exp(y/EARTH_RADIUS)) - math.pi/2 * RAD_TO_DEG\n return (lng, lat)","def proj_coords(coords, proj_in, proj_out): \n return [proj_coord(coord, proj_in, proj_out) for coord in coords]","def ra2xy(self, ra):\n return -math.sin(ra), math.cos(ra)","def vec2geo(r_hat):\n ra = degrees(atan2(r_hat[1], r_hat[0]))\n dec = degrees(atan2(r_hat[2], sqrt(r_hat[0] ** 2 + r_hat[1] ** 2)))\n return ra, dec","def undo_mercator_project(x,y):\n lon = y*np.pi\n ex = np.exp(4*np.pi*x)\n lat = np.arcsin((ex - 1)/(ex +1 ))\n lon = lon*360/2/np.pi\n lat = lat*360 /2/np.pi\n return lon, lat","def rr_at_coords(*args, **kws):\n return raw2rr(value_at_coords(*args, **kws))","def geocentric2geodetic(latitude):\n\n return np.rad2deg(np.arctan(1.0067395 * np.tan(np.deg2rad(latitude))))","def pixelloc_to_geoloc(row, col, loff, coff):\n row = np.atleast_1d(row)\n col = np.atleast_1d(col)\n\n assert row.shape == col.shape\n\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\n\n if lat.size == lon.size == 1:\n # Return scalars\n return lat[0], lon[0]\n else:\n return lat, lon","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def get_cartesian_coord(lat, lon, h):\n a = 6378137.0\n rf = 298.257223563\n lat_rad = radians(lat)\n lon_rad = radians(lon)\n N = sqrt(a / (1 - (1 - (1 - 1 / rf) ** 2) * (sin(lat_rad)) ** 2))\n X = (N + h) * cos(lat_rad) * cos(lon_rad)\n Y = (N + h) * cos(lat_rad) * sin(lon_rad)\n Z = ((1 - 1 / rf) ** 2 * N + h) * sin(lat_rad)\n return X, Y, Z","def __convert(args):\n a, b, zone, ellipsoid, datum, inverse = args\n projection = Proj(\"+proj=utm +zone={}, +ellps={} +datum={} +units=m +no_defs\".format(zone, ellipsoid, datum))\n c, d = projection(a, b, inverse=inverse)\n\n return c, d","def ll_to_xy(lon, lat):\n lllon = lon.min()\n lllat = lat.min()\n urlon = lon.max()\n urlat = lat.max()\n\n dlon = urlon - lllon\n dlat = urlat - lllat\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\n\n x = dx_*(lon - lllon)/dlon\n y = dy_*(lat - lllat)/dlat\n\n return x, y","def update_coords(self, l, b):\n self.l = l\n self.b = b\n self.ra, self.dec = astLib.astCoords.convertCoords(\n \"GALACTIC\", \"J2000\", self.l, self.b, epoch=2000.)","def gps_to_coords(self,lat,lon):\n\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\n return (-1,-1)\n\n lat_spot = int((self.max_lat-lat)/self.lat_step)\n lon_spot = int((lon-self.min_lon)/self.lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def _getNewCoords(self, coords, offset):\n\n oldlat = math.radians(float(coords[0]))\n oldlon = math.radians(float(coords[1]))\n magnitude = float(offset[0]) / 6370.\n direction = math.radians(360.-float(offset[1]))\n\n # Calculate lat/lon given radial and distnace (http://www.edwilliams.org/avform.htm#LL)\n lat = math.asin(math.sin(oldlat) * math.cos(magnitude) + math.cos(oldlat) \\\n * math.sin(magnitude) * math.cos(direction))\n lon = (oldlon - math.asin(math.sin(direction) * math.sin(magnitude) / math.cos(lat)) \\\n + math.pi) % (2 * math.pi) - math.pi\n\n # print coords, offset, oldlat, oldlon, magnitude, direction, math.degrees(lat), math.degrees(lon)\n return (math.degrees(lat), math.degrees(lon))","def read_affine(file):\n data = open(file, 'r').read()\n data = data.split('\\n')\n for i in range(1, 5):\n data[i] = data[i].split(':')\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\n Nlon = len(int_lon) - 1\n Nlat = len(int_lat) - 1\n data[3][1] = data[3][1].split(',')\n data[4][1] = data[4][1].split(',')\n lon_transform = np.zeros((Nlon, 2))\n lat_transform = np.zeros((Nlat, 2))\n for i in range(Nlon):\n data[3][1][i] = data[3][1][i].split(' ')\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\n for i in range(Nlat):\n data[4][1][i] = data[4][1][i].split(' ')\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\n lon_transform = np.array(lon_transform).astype('float')\n lat_transform = np.array(lat_transform).astype('float')\n return int_lon, int_lat, lon_transform, lat_transform","def proj_coord(coord, proj_in, proj_out):\n x, y = coord\n return pyproj.transform(proj_in, proj_out, x, y)","def mercX(lon):\r\n zoom = 1.0\r\n lon = math.radians(lon)\r\n a = (256.0 / math.pi) * pow(2.0, zoom)\r\n b = lon + math.pi\r\n return int(a * b)","def transform_point(p,R,t):\r\n x = R[0][0]*p[0]+R[0][1]*p[1]+t[0]\r\n y = R[1][0]*p[0]+R[1][1]*p[1]+t[1]\r\n return [x,y]","def transform_pnt(pnt, inEPSG, outEPSG):\r\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\r\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\r\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\r\n\r\n return pyproj.transform(inProj, outProj, x1, y1)","def pixels_to_latlon(x_pos, y_pos):\n radius = 489.5\n x_pos_from_center = -1*(512.5 - x_pos)\n angle_1 = np.rad2deg(np.arccos(x_pos_from_center/radius))\n\n y_pos_from_center = -1*(512.5 - y_pos)\n angle_2 = np.rad2deg(np.arccos(y_pos_from_center/radius)) \n\n return -1*angle_1, angle_2","def gnomonic_project_toxy(RA1, Dec1, RAcen, Deccen):\n # also used in Global Telescope Network website\n cosc = np.sin(Deccen) * np.sin(Dec1) + np.cos(Deccen) * np.cos(Dec1) * np.cos(RA1-RAcen)\n x = np.cos(Dec1) * np.sin(RA1-RAcen) / cosc\n y = (np.cos(Deccen)*np.sin(Dec1) - np.sin(Deccen)*np.cos(Dec1)*np.cos(RA1-RAcen)) / cosc\n return x, y","def cartesian_decoder(coord, r_E=6371):\n def _to_deg(rad):\n return rad * 180. / np.pi\n\n x, y, z = coord[:, 0], coord[:, 1], coord[:, 2]\n\n theta = np.arcsin(z / r_E)\n phi = np.arctan(y / x)\n\n # Convert to degrees. Longitudes, are bound between -90;90 in decode step, so correct in 3 and 4th quadrant of x-y plane (Asia)\n lat = _to_deg(theta)\n lon = _to_deg(phi) - 180 * ((x < 0) * (y < 0)) + 180 * ((x < 0) * (y > 0))\n\n return np.concatenate([lat.reshape(-1, 1), lon.reshape(-1, 1)], axis=1)","def transform_coords(x, y, w, h, nw, nh):\r\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)","def coord_to_pointing (coordinates):\n\n clat = np.cos (coordinates[0])\n return np.array ([clat * np.cos (coordinates[1]),\n clat * np.sin (coordinates[1]),\n np.sin (coordinates[0])])","def map_coordinates(self,geometry):\n\t\tg = self.geomatrix\n\t\tdef project_coord(x,y,z=None):\n\t\t\tx = g[0] + g[1] * x + g[2] * y\n\t\t\ty = g[3] + g[4] * x + g[5] * y\n\t\t\tif z is None:\n\t\t\t\treturn x,y\n\t\t\telse:\n\t\t\t\treturn x,y,z\n\t\treturn transform(project_coord, geometry)","def geo2Cartesian(lat, lon, h, julian_date):\n\n lat_rad = np.radians(lat)\n lon_rad = np.radians(lon)\n\n # Calculate ECEF coordinates\n ecef_x, ecef_y, ecef_z = latLonAlt2ECEF(lat_rad, lon_rad, h)\n\n\n # Get Local Sidreal Time\n LST_rad = math.radians(JD2LST(julian_date, np.degrees(lon_rad))[0])\n\n\n # Calculate the Earth radius at given latitude\n Rh = math.sqrt(ecef_x**2 + ecef_y**2 + ecef_z**2)\n\n # Calculate the geocentric latitude (latitude which considers the Earth as an elipsoid)\n lat_geocentric = math.atan2(ecef_z, math.sqrt(ecef_x**2 + ecef_y**2))\n\n # Calculate Cartesian ECI coordinates (in meters), in the epoch of date\n x = Rh*np.cos(lat_geocentric)*np.cos(LST_rad)\n y = Rh*np.cos(lat_geocentric)*np.sin(LST_rad)\n z = Rh*np.sin(lat_geocentric)\n\n return x, y, z","def getCoords(self):\n if self._ra == \"\" or self._dec == \"\":\n raise ValueError('Object named ' + self._name +' has no coordinates in database.')\n ra = self._ra.split(\":\")\n dec = self._dec.split(\":\")\n raTuple = (int(ra[0]), int(ra[1]), float(ra[2]))\n decTuple = (dec[0][0], int(dec[0][1:]), int(dec[1]), float(dec[2]))\n return raTuple, decTuple","def cartesian2Geo(julian_date, x, y, z):\n\n\n # Calculate LLA\n lat, r_LST, ele = ecef2LatLonAlt(x, y, z)\n\n # Calculate proper longitude from the given JD\n lon, _ = LST2LongitudeEast(julian_date, np.degrees(r_LST))\n\n # Convert longitude to radians\n lon = np.radians(lon)\n\n\n return np.degrees(lat), np.degrees(lon), ele","def wgs84_to_mercator(df, lon, lat):\n k = 6378137\n df[\"x\"] = df[lon] * (k * np.pi/180.0)\n df[\"y\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\n return df","def get_corners(self):\n lon, lat = self.coordinates\n \n ll = (lon[0][0],lat[0][0])\n ul = (lon[-1][0],lat[-1][0])\n ur = (lon[-1][-1],lat[-1][-1])\n lr = (lon[0][-1],lat[0][-1])\n\n return (ll, ul, ur, lr)","def coords_to_min_max_lat_lon(self,coords):\n row = coords[0]\n col = coords[1]\n max_lat = self.max_lat - row*(self.lat_step)\n min_lat = max_lat - self.lat_step\n min_lon = self.min_lon + col*(self.lon_step)\n max_lon = min_lon + self.lon_step\n return min_lat,max_lat,min_lon,max_lon","def flowline_latlon(coords, fromproj=pyproj.Proj(\"+init=epsg:3413\"), toproj=pyproj.Proj(\"+init=EPSG:4326\")):\n xs = coords[:,0]\n ys = coords[:,1]\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\n latlon_coords = np.asarray(zip(x_lon, y_lat))\n return latlon_coords","def coords_to_structure(self) -> None:\n ...","def mercator(lat, lon, zoom):\n # convert to radians\n x1, y1 = lon * pi / 180, lat * pi / 180\n\n # project to mercator\n x2, y2 = x1, log(tan(0.25 * pi + 0.5 * y1))\n\n # transform to tile space\n tiles, diameter = 2 ** zoom, 2 * pi\n x3, y3 = int(tiles * (x2 + pi) / diameter), int(tiles * (pi - y2) / diameter)\n\n return zoom, x3, y3","def fromECEFtoLatLongDegrees(x, y, z):\n ret = fromECEFtoLatLong(x, y, z)\n return math.degrees(ret[0]), math.degrees(ret[1]), ret[2]","def plane2sphere(x,y,latc,lonc):\n if np.abs(y) > 1:\n lat = np.nan\n lon = np.nan\n return lat,lon\n else:\n pass\n\n coslatc = np.cos(latc)\n sinlatc = np.sin(latc)\n\n cosphi = np.sqrt(1.0 - y**2)\n lat = np.arcsin((y*coslatc) + (cosphi*np.cos(x)*sinlatc))\n\n if np.cos(lat) == 0:\n test = 0.0\n else:\n test = cosphi*np.sin(x)/np.cos(lat)\n\n lon = np.arcsin(test) + lonc\n\n x0 = x\n if np.abs(x0) > np.pi/2.0:\n while x0 > np.pi/2.0:\n lon = np.pi - lon\n x0 = x0 - np.pi\n\n while x0 < -np.pi/2.0:\n lon = -np.pi - lon\n x0 = x0 + np.pi\n\n return lat,lon","def mapc2p(xc,yc):\n from numpy import where, cos, pi\n import math\n\n # Assumes 0 <= xc <= 30 and -1 <= yc <= 1\n\n # Nozzle geometry\n rad1 = 0.75-0.25*cos(pi/2 - pi*(xc-10.)/10.)\n radius = where(abs(xc-15)<10, rad1, 1.)\n xp = xc\n yp = yc * radius\n return xp,yp","def reproject(self, lon, lat):\n if self.xform is None:\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\n return self.xform.transform(QgsPointXY(lon, lat))","def ll2px(lat, lng, zoom):\n # assert isinstance(lat, (float, int, long)), \\\n # ValueError(\"lat must be a float\")\n lat = float(lat)\n # assert isinstance(lng, (float, int, long)), \\\n # ValueError(\"lng must be a float\")\n lng = float(lng)\n # assert isinstance(zoom, int), TypeError(\"zoom must be an int from 0 to 30\")\n # assert 0 <= zoom <= 30, ValueError(\"zoom must be an int from 0 to 30\")\n\n cbk = CBK[zoom]\n\n x = int(round(cbk + (lng * CEK[zoom])))\n\n foo = math.sin(lat * math.pi / 180)\n if foo < -0.9999:\n foo = -0.9999\n elif foo > 0.9999:\n foo = 0.9999\n\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\n\n return (x, y)","def chunk_coords_to_region_coords(cx: int, cz: int) -> ChunkCoordinates:\n return cx >> 5, cz >> 5","def createCoordTuples(data):\n data['xy'] = None\n for i, row in data.iterrows():\n data['xy'][i] = [np.round(row['geometry'].x, decimals=5), np.round(row['geometry'].y, decimals=5)]\n return data","def pixel2coords(self, x, y):\n xoff, a, b, yoff, d, e = self.geotransform()\n\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n return (xp, yp)","def point2wgs84(self, crsop):\n latr = math.radians(self.lat)\n lngr = math.radians(self.lng)\n \n # a is the semi-major axis of given datum.\n a = crsop.SemimajorAxisInM\n \n # f is the flattening of given datum\n f = 1.0/crsop.InverseFlattening\n dx = crsop.dx\n dy = crsop.dy\n dz = crsop.dz\n \n # da is the difference between the WGS84 and source ellipsoid semi-major axes.\n da = 6378137.0 - a\n \n # df is the difference between the WGS84 and source CRS flattenings.\n df = 1.0/298.257223563 - f\n \n e_squared = f*(2-f)\n rho = a*(1-e_squared)/math.pow((1-e_squared*sqr(math.sin(latr))),1.5)\n nu = a/math.pow((1-e_squared*sqr(math.sin(latr))),0.5)\n dlat = (1/rho)*(-dx*math.sin(latr)*math.cos(lngr) - \\\n dy*math.sin(latr)*math.sin(lngr) + \\\n dz*math.cos(latr) + (f*da + a*df)*math.sin(2*latr))\n dlng = (-dx*math.sin(lngr) + dy*math.cos(lngr))/(nu*math.cos(latr))\n newlng = lng180(math.degrees(lngr + dlng))\n newlat = math.degrees(latr + dlat)\n return Point(float(truncate(newlng,DEGREE_DIGITS)), \\\n float(truncate(newlat,DEGREE_DIGITS)))","def reconstructXY(self, inputs):\n return (self.reconstructX(inputs),\n self.reconstructY(inputs))","def get_coordinates_geo(self):\n if not self.rotated:\n lon_arr_geo = self.lon_arr\n lat_arr_geo = self.lat_arr\n else:\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \n\n return lon_arr_geo, lat_arr_geo","def cartesian_to_geographical(coordinate_triples):\n if len(coordinate_triples.shape) == 1:\n x = coordinate_triples[0]\n y = coordinate_triples[1]\n z = coordinate_triples[2]\n elif len(coordinate_triples.shape) == 2:\n assert coordinate_triples.shape[1] == 3\n x = coordinate_triples[:, 0]\n y = coordinate_triples[:, 1]\n z = coordinate_triples[:, 2]\n radius = np.sqrt(x**2 + y**2 + z**2)\n longitudes = np.arctan2(y, x)\n latitudes = np.arcsin(z/radius)\n return (latitudes, longitudes)","def fromECEFtoLatLong(x, y, z):\n r = math.sqrt(x*x+y*y)\n F = 54*b*b*z*z\n G = r*r +(1.0-e2)*z*z-e2*E2\n C = (e2*e2*F*r*r)/(G*G*G)\n s = pow(1.0+C+math.sqrt(C*C+2*C),1.0/3.0)\n P = F/(3.0*pow((s+(1.0/s)+1.0),2.0)*G*G)\n Q = math.sqrt(1.0+2.0*e2*e2*P)\n r0 = (-(P*e2*r)/(1.0+Q))+math.sqrt((1.0/2.0)*a*a*(1.0+1.0/Q)-((P*(1-e2)*z*z)/(Q*(1.0+Q)))-(1.0/2.0)*P*r*r)\n U = math.sqrt(pow(r-e2*r0,2.0)+z*z)\n V = math.sqrt(pow(r-e2*r0,2.0)+(1.0-e2)*z*z)\n Z0 = b*b*z/(a*V)\n return math.atan((z+ep2*Z0)/r), math.atan2(y, x), U*(1.0-(b*b)/(a*V))","def transform(self, lens):\n coords_t = np.zeros(self.coords.shape)\n coords_t[:,0] = (\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\n coords_t[:,1] = (\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\n return coords_t","def invmap(self, x, y):\r\n r = self._r\r\n rxy = np.sqrt(x*x + y*y)\r\n\r\n lat = np.arccos(1-(rxy/r)**2)\r\n long = np.arctan2(y, x)\r\n\r\n try:\r\n long[np.isnan(lat)] = np.nan\r\n except TypeError: # Thrown if long is scalar\r\n if np.isnan(lat): long = np.nan\r\n return (lat, long)"],"string":"[\n \"def composite(c, r):\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\",\n \"def merc(lat, lon):\\n\\tr_major = 6378137.000\\n\\tx = r_major * math.radians(lon)\\n\\tscale = x/lon\\n\\ty = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale\\n\\treturn (x, y)\",\n \"def lonlat2cr_for_geotif(path):\\n old_cs, new_cs, gta, local_vars = _create_xform(path)\\n transform = osr.CoordinateTransformation(new_cs, old_cs)\\n\\n def composite(lon, lat):\\n \\\"\\\"\\\"xform from (lon, lat) to (c, r)\\\"\\\"\\\"\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return (~gta * transform.TransformPoint(lat, lon)[:2])\\n \\n return composite\",\n \"def lon_lat_to_cartesian(lon, lat, R = 1):\\n lon_r = np.radians(lon)\\n lat_r = np.radians(lat)\\n\\n x = R * np.cos(lat_r) * np.cos(lon_r)\\n y = R * np.cos(lat_r) * np.sin(lon_r)\\n z = R * np.sin(lat_r)\\n return x,y,z\",\n \"def cr2lonlat_for_geotif(path):\\n old_cs, new_cs, gta, local_vars = _create_xform(path)\\n transform = osr.CoordinateTransformation(old_cs, new_cs)\\n\\n def composite(c, r):\\n \\\"\\\"\\\"xform from (c, r) to (lon, lat)\\\"\\\"\\\"\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\\n \\n return composite\",\n \"def convert(self, lat, lon):\\r\\n a = self.a\\r\\n b = self.b\\r\\n long0 = self.long0\\r\\n k0 = self.k0\\r\\n dx = self.dx\\r\\n\\r\\n e = (1 - b ** 2 / a ** 2) ** 0.5\\r\\n e2 = e ** 2 / (1 - e ** 2)\\r\\n n = (a - b) / (a + b)\\r\\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\\r\\n p = lon - long0\\r\\n\\r\\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\\r\\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\\r\\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\\r\\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\\r\\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\\r\\n\\r\\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\\r\\n\\r\\n K1 = S * k0\\r\\n K2 = k0 * nu * sin(2 * lat)/4.0\\r\\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\\\\r\\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\\r\\n\\r\\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\\r\\n\\r\\n K4 = k0 * nu * cos(lat)\\r\\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\\r\\n\\r\\n x = K4 * p + K5 * (p ** 3) + dx\\r\\n return x, y\",\n \"def _getXYZ ( lon, lat ):\\n d2r = pi / 180.\\n rlon, rlat = ( d2r * lon, d2r * lat )\\n x = cos(rlat) * cos(rlon)\\n y = cos(rlat) * sin(rlon)\\n z = sin(rlat)\\n return (x,y,z)\",\n \"def cartesian_to_lon_lat(x, y, z, R = 1):\\n lon = np.degrees(np.arctan2(y,x))\\n lat = np.degrees(np.pi/2-np.arctan2((x**2+y**2)**0.5,z))\\n\\n return lon,lat\",\n \"def transform_coordinates(coords):\\n # WGS 84 reference coordinate system parameters\\n A = 6378.137 # major axis [km]\\n E2 = 6.69437999014e-3 # eccentricity squared\\n\\n coords = prepare_coords(coords)\\n\\n # convert to radiants\\n lat_rad = np.radians(coords[:, 0])\\n lon_rad = np.radians(coords[:, 1])\\n\\n # convert to cartesian coordinates\\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\\n z = r_n * (1 - E2) * np.sin(lat_rad)\\n\\n return np.column_stack((x, y, z))\",\n \"def convertCoord(lon, lat, inEPSG, outEPSG):\\n from pyproj import Proj, transform\\n inProj = Proj(init='epsg:'+str(inEPSG))\\n outProj = Proj(init='epsg:'+str(outEPSG))\\n x, y = transform(inProj, outProj, lon, lat)\\n return x, y\\n # epsg:4326 WGS84\\n # epsg:2950 MTM8\\n # epsg:6622 Quebec Lambert\",\n \"def ll2csflat(lon,lat,ncs):\\n j,f,i = ll2jfi(lon,lat,ncs)\\n return (j*6+f)*ncs+i\",\n \"def spherical_to_cartesian(r, lat, lon):\\n import math\\n\\n if np.isscalar(r) and np.isscalar(lat) and np.isscalar(lon):\\n x = r * math.cos(lat) * math.cos(lon)\\n y = r * math.cos(lat) * math.sin(lon)\\n z = r * math.sin(lat)\\n else:\\n x = r * np.cos(lat) * np.cos(lon)\\n y = r * np.cos(lat) * np.sin(lon)\\n z = r * np.sin(lat)\\n\\n return x, y, z\",\n \"def transform_from_latlon(lat, lon):\\n from affine import Affine\\n lat = np.asarray(lat)\\n lon = np.asarray(lon)\\n trans = Affine.translation(lon[0], lat[0])\\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\\n return trans * scale\",\n \"def _indices_to_coords(c,r):\\n\\n column = _index_to_column(c)\\n row = r + 1\\n\\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}\",\n \"def ll2xyz(lon_pt,lat_pt):\\n\\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\\n zPt = np.sin(lat_pt)\\n return [xPt,yPt,zPt]\",\n \"def project(self, (lng, lat)):\\n x = lng * DEG_TO_RAD\\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\\n y = lat * DEG_TO_RAD\\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)\",\n \"def scalar_g2r(al, be, ga, lon, lat):\\n\\n rad = mt.pi / 180\\n al = al * rad\\n be = be * rad\\n ga = ga * rad\\n\\n rotate_matrix = np.zeros(shape=(3, 3))\\n\\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\\n al\\n )\\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\\n al\\n )\\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\\n rotate_matrix[2, 2] = np.cos(be)\\n \\n #rotate_matrix = np.linalg.pinv(rotate_matrix)\\n \\n lat = lat * rad\\n lon = lon * rad\\n\\n # geographical Cartesian coordinates:\\n xr = np.cos(lat) * np.cos(lon)\\n yr = np.cos(lat) * np.sin(lon)\\n zr = np.sin(lat)\\n\\n # rotated Cartesian coordinates:\\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\\n zg = rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\\n\\n # rotated coordinates:\\n rlat = np.arcsin(zg)\\n rlon = np.arctan2(yg, xg)\\n\\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\\n if a:\\n lon[a] = 0\\n\\n rlat = rlat / rad\\n rlon = rlon / rad\\n\\n return (rlon, rlat)\",\n \"def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\\r\\n # convert decimals to seconds...\\r\\n s_lon = dec2sec(s_lon)\\r\\n s_lat = dec2sec(s_lat)\\r\\n\\r\\n ## Auxiliary values \\r\\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\\r\\n s_lng_aux = (s_lon - 26782.5)/10000.\\r\\n s_lat_aux = (s_lat - 169028.66)/10000.\\r\\n \\r\\n # easting\\r\\n s_x = (600072.37 \\r\\n + 211455.93*s_lng_aux \\r\\n - 10938.51*s_lng_aux*s_lat_aux \\r\\n - 0.36*s_lng_aux*(s_lat_aux**2) \\r\\n - 44.54*(s_lng_aux**3))\\r\\n \\r\\n # northing\\r\\n s_y = (200147.07 \\r\\n + 308807.95*s_lat_aux \\r\\n + 3745.25*(s_lng_aux**2) \\r\\n + 76.63*(s_lat_aux**2) \\r\\n - 194.56*(s_lng_aux**2)*s_lat_aux \\r\\n + 119.79*(s_lat_aux**3))\\r\\n\\r\\n return s_x, s_y\",\n \"def scalar_r2g(al, be, ga, rlon, rlat):\\n\\n rad = mt.pi / 180\\n al = al * rad\\n be = be * rad\\n ga = ga * rad\\n rotate_matrix = np.zeros(shape=(3, 3))\\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\\n al\\n )\\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\\n al\\n )\\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\\n rotate_matrix[2, 2] = np.cos(be)\\n\\n rotate_matrix = np.linalg.pinv(rotate_matrix)\\n\\n rlat = rlat * rad\\n rlon = rlon * rad\\n\\n # Rotated Cartesian coordinates:\\n xr = np.cos(rlat) * np.cos(rlon)\\n yr = np.cos(rlat) * np.sin(rlon)\\n zr = np.sin(rlat)\\n\\n # Geographical Cartesian coordinates:\\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\\n zg = (\\n rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\\n )\\n\\n # Geographical coordinates:\\n lat = np.arcsin(zg)\\n lon = np.arctan2(yg, xg)\\n\\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\\n if a:\\n lon[a] = 0\\n\\n lat = lat / rad\\n lon = lon / rad\\n\\n return (lon, lat)\",\n \"def latlon_2_grid(x, y, z, origin):\\n new_y = (y - origin[1]) * 111111\\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\\n return new_x, new_y, z\",\n \"def ll2fxy(lon,lat):\\n return xyz2fxy(*ll2xyz(lon,lat))\",\n \"def convert(coords):\\n lat = coords[:4]\\n lon = coords[4:]\\n\\n lat = lat[:2] + \\\".\\\" + lat[2:]\\n\\n if int(lon[0]) > 5:\\n lon = \\\"-\\\" + lon[:2] + \\\".\\\" + lon[2:]\\n else:\\n lon = \\\"-1\\\" + lon[:2] + \\\".\\\" + lon[2:]\\n\\n return (float(lat), float(lon))\",\n \"def xyz2longlat(x,y,z):\\n r=[]\\n long=[]\\n d=[]\\n lat=[]\\n longnew=[]\\n latnew=[]\\n i=0\\n for i in range(len(x)):\\n r.append(sp.sqrt(x[i]*x[i] + y[i]*y[i] + z[i]*z[i]))\\n \\t long.append(sp.arctan2(y[i],x[i]))\\n\\t d.append(sp.sqrt(x[i]*x[i] + y[i]*y[i]))\\n\\t lat.append(sp.arcsin(z[i]/r[i]))\\n\\n i=0\\n for i in range(len(x)):\\n longnew.append(long[i]*deg)\\n latnew.append(lat[i]*deg)\\n\\n return longnew, latnew, r\",\n \"def spher2cart(lon: np.ndarray,\\n lat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n rlon = np.radians(lon)\\n rlat = np.radians(lat)\\n x = np.cos(rlon) * np.cos(rlat)\\n y = np.sin(rlon) * np.cos(rlat)\\n z = np.sin(rlat)\\n return x, y, z\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*lat/(np.pi/2)\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def coords_to_gps(self,coords):\\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))\",\n \"def proj(self,lon,lat):\\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\\n x[x > 1e29] = None\\n y[y > 1e29] = None\\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\\n return x, y\",\n \"def merc_x(lon):\\n r_major = 6378137.000\\n x = r_major * math.radians(lon)\\n return x\",\n \"def coord2pixel(tf, lat, lon):\\n x = int(round((lon-tf[0])/tf[1]))\\n y = int(round((lat-tf[3])/tf[5]))\\n\\n return x, y\",\n \"def to_lnglat(r):\\n \\n x, y = r['x_coordinate'], r['y_coordinate']\\n if np.isnan(x) or np.isnan(y):\\n return None, None\\n return projection(x, y, inverse = True)\",\n \"def convertView2Geo(self, x, y):\\n\\n # x_pix is from left map edge, y_pix from top map edge\\n x_pix = x + self.view_offset_x\\n y_pix = y + self.view_offset_y\\n\\n lon = self.map_llon + x_pix/self.ppd_x\\n lat = self.map_tlat - y_pix/self.ppd_y\\n\\n return (lon, lat)\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*np.sqrt(1-np.cos(lat))\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def ecef_coords(lats: ndarray, lons: ndarray) -> Tuple[ndarray, ndarray, ndarray]:\\n # Cartopy Geodetic and Geocentric both default to the WGS84 datum\\n spherical_latlon_crs = Geodetic()\\n ecef_crs = Geocentric()\\n xyz = ecef_crs.transform_points(\\n spherical_latlon_crs, np.array(lons), np.array(lats)\\n )\\n return xyz[..., 0], xyz[..., 1], xyz[..., 2]\",\n \"def mapr(r):\\n return np.rad2deg(np.arctan(r)*2)\",\n \"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\\n\\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\\n return (-1,-1)\\n\\n lat_step = abs(max_lat-min_lat)/rows\\n lon_step = abs(max_lon-min_lon)/cols\\n\\n lat_spot = int((max_lat-lat)/lat_step)\\n lon_spot = int((lon-min_lon)/lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def p2c(r, t):\\n if np.isnan(r) or np.isnan(t):\\n return 0, 0 # return 0, 0\\n else:\\n return r * np.sin(t), r * np.cos(t) # x, y\",\n \"def reverse(self, lon, lat):\",\n \"def xyz_to_lonlat(x,y,z):\\n\\n XsqPlusYsq = x**2 + y**2\\n r = np.sqrt(XsqPlusYsq + z**2) # r\\n #elev = np.arccos(z/np.sqrt(XsqPlusYsq)) # theta\\n elev = np.arccos(z/r)\\n az = np.arctan2(y,x) # phi\\n return np.rad2deg(az), 90 - np.rad2deg(elev) # want \",\n \"def cartesian_coordinates(self):\\n # extract RA items\\n ra_hours, ra_minutes, ra_seconds = RA_RE.match(str(self.ra)).groups()\\n # then cast\\n ra_hours = int(ra_hours)\\n ra_minutes = int(ra_minutes)\\n ra_seconds = float(ra_seconds)\\n\\n # extract DEC items\\n dec_sign, dec_degrees, dec_minutes, dec_seconds = DEC_RE.match(str(self.dec)).groups()\\n # then cast\\n dec_sign = -1 if dec_sign == '-' else 1\\n dec_degrees = int(dec_degrees)\\n dec_minutes = int(dec_minutes)\\n dec_seconds = float(dec_seconds)\\n\\n # to degrees\\n a = (ra_hours*15) + (ra_minutes*0.25) + (ra_seconds*0.004166)\\n b = abs(dec_degrees + dec_minutes/60 + dec_seconds/3600) * dec_sign\\n\\n # to radians\\n a = math.radians(a)\\n b = math.radians(b)\\n\\n distance = float(self.distance)\\n\\n x = (distance * math.cos(b)) * math.cos(a)\\n y = (distance * math.cos(b)) * math.sin(a)\\n z = distance * math.sin(b)\\n\\n return x, y, z\",\n \"def translation(coord, lat, lon):\\n ty = merc_y(lat) - 73.43 # Y translation\\n tx = merc_x(lon) - 57.55 # X translation\\n for i in range(len(coord)):\\n for j in range(len(coord[i])):\\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\\n return coord\",\n \"def convertLatLon(latCell, lonCell):\\n cell_lats = np.array([])\\n cell_lons = np.array([])\\n for lat in latCell:\\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \\n for lon in lonCell:\\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \\n\\n return cell_lats, cell_lons\",\n \"def latlon_to_pixels(lon, lat):\\n radius = 489.5\\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\\n x_pos = x_pos_from_center + 512.5\\n \\n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\\n y_pos = y_pos_from_center + 512.5\\n \\n return x_pos, y_pos\",\n \"def convertGeo2View(self, lon, lat):\\n\\n x_pix = (lon - self.view_llon) * self.ppd_x\\n y_pix = (self.view_tlat - lat) * self.ppd_y\\n\\n return (x_pix, y_pix)\",\n \"def coords_to_X(c, shape):\\n a, b = c\\n assert 0 <= a <= shape[0]\\n assert 0 <= b <= shape[1]\\n a = float(a)\\n b = float(b)\\n a = a + 0.5 # center cells\\n b = b + 0.5\\n u = a / (shape[0])\\n v = b / (shape[1])\\n x = 2 * (u - 0.5)\\n y = 2 * (v - 0.5)\\n assert -1 <= x <= +1\\n assert -1 <= y <= +1\\n return (x, y)\",\n \"def ReprojectCoords(coords,src_srs,tgt_srs):\\n trans_coords = []\\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\\n for x,y in coords:\\n x,y,z = transform.TransformPoint(x,y)\\n trans_coords.append([x,y])\\n return trans_coords\",\n \"def get_ecmwf_lat_lon(nc_file):\\n from netCDF4 import Dataset\\n \\n fh = Dataset(nc_file, mode='r')\\n\\n latitude_ecmwf = fh.variables['latitude_ecmwf'][:]\\n longitude_ecmwf = fh.variables['longitude_ecmwf'][:]\\n\\n lonmesh_ecmwf,latmesh_ecmwf = np.meshgrid(longitude_ecmwf,latitude_ecmwf)\\n\\n print('latitude_ecmwf: ', latitude_ecmwf.shape)\\n print('longitude_ecmwf: ', longitude_ecmwf.shape)\\n \\n return latitude_ecmwf, longitude_ecmwf, latmesh_ecmwf, lonmesh_ecmwf;\",\n \"def project_p2c_points(R, C, H): #---- project to cylindrical\\r\\n Rc= H[0]/2; Cc= H[1]/2; # center coordinate\\r\\n phi = H[3]; S= H[4]; # rotation angle and sizing\\r\\n Tv = H[5]; Tu= H[6]; # displacement\\r\\n COSF= np.cos(phi); SINF= np.sin(phi); # \\r\\n U = Tu + S*( COSF*(C- Cc)- SINF*(R- Rc) ); \\r\\n V = Tv + S*( SINF*(C- Cc)+ COSF*(R- Rc) );\\r\\n return V, U\",\n \"def rotated_coord_transform(lon, lat, np_lon, np_lat,\\n direction='rot2geo'):\\n\\n # Convert degrees to radians\\n lon = (lon * math.pi) / 180.\\n lat = (lat * math.pi) / 180.\\n\\n# SP_lon = SP_coor(1)\\n# SP_lat = SP_coor(2)\\n\\n theta = 90. - np_lat # Rotation around y-axis\\n phi = np_lon + 180. # Rotation around z-axis\\n\\n # Convert degrees to radians\\n phi = (phi * math.pi) / 180.\\n theta = (theta * math.pi) / 180.\\n\\n # Convert from spherical to cartesian coordinates\\n x = math.cos(lon) * math.cos(lat)\\n y = math.sin(lon) * math.cos(lat)\\n z = math.sin(lat)\\n\\n # Regular -> Rotated\\n if direction == 'geo2rot':\\n\\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.cos(theta) * math.sin(phi) * y +\\n math.sin(theta) * z)\\n y_new = (- math.sin(phi) * x +\\n math.cos(phi) * y)\\n z_new = (- math.sin(theta) * math.cos(phi) * x -\\n math.sin(theta) * math.sin(phi) * y +\\n math.cos(theta) * z)\\n\\n # Rotated -> Regular\\n elif direction == 'rot2geo':\\n \\n phi = - phi\\n theta = - theta\\n \\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.sin(phi) * y +\\n math.sin(theta) * math.cos(phi) * z)\\n y_new = (- math.cos(theta) * math.sin(phi) * x +\\n math.cos(phi) * y -\\n math.sin(theta) * math.sin(phi) * z)\\n z_new = (- math.sin(theta) * x +\\n math.cos(theta) * z)\\n\\n # Convert cartesian back to spherical coordinates\\n lon_new = math.atan2(y_new, x_new)\\n lat_new = math.asin(z_new)\\n\\n # Convert radians back to degrees\\n lon_new = (lon_new * 180.) / math.pi\\n lat_new = (lat_new * 180.) / math.pi;\\n\\n return (lon_new, lat_new)\",\n \"def LongLat(x, y, z):\\n r = (x**2 + y**2 + z**2)**0.5\\n long = numpy.arctan2(y, x)\\n lat = numpy.arcsin(z / r)\\n return long, lat, r\",\n \"def pixel2coord(tf, x, y):\\n lat = tf[0] + x*tf[1] + y*tf[2]\\n lon = tf[3] + x*tf[4] + y*tf[5]\\n\\n return lat, lon\",\n \"def unproject(self, (x, y)):\\n lng = x/EARTH_RADIUS * RAD_TO_DEG\\n lat = 2 * math.atan(math.exp(y/EARTH_RADIUS)) - math.pi/2 * RAD_TO_DEG\\n return (lng, lat)\",\n \"def proj_coords(coords, proj_in, proj_out): \\n return [proj_coord(coord, proj_in, proj_out) for coord in coords]\",\n \"def ra2xy(self, ra):\\n return -math.sin(ra), math.cos(ra)\",\n \"def vec2geo(r_hat):\\n ra = degrees(atan2(r_hat[1], r_hat[0]))\\n dec = degrees(atan2(r_hat[2], sqrt(r_hat[0] ** 2 + r_hat[1] ** 2)))\\n return ra, dec\",\n \"def undo_mercator_project(x,y):\\n lon = y*np.pi\\n ex = np.exp(4*np.pi*x)\\n lat = np.arcsin((ex - 1)/(ex +1 ))\\n lon = lon*360/2/np.pi\\n lat = lat*360 /2/np.pi\\n return lon, lat\",\n \"def rr_at_coords(*args, **kws):\\n return raw2rr(value_at_coords(*args, **kws))\",\n \"def geocentric2geodetic(latitude):\\n\\n return np.rad2deg(np.arctan(1.0067395 * np.tan(np.deg2rad(latitude))))\",\n \"def pixelloc_to_geoloc(row, col, loff, coff):\\n row = np.atleast_1d(row)\\n col = np.atleast_1d(col)\\n\\n assert row.shape == col.shape\\n\\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\\n\\n if lat.size == lon.size == 1:\\n # Return scalars\\n return lat[0], lon[0]\\n else:\\n return lat, lon\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def get_cartesian_coord(lat, lon, h):\\n a = 6378137.0\\n rf = 298.257223563\\n lat_rad = radians(lat)\\n lon_rad = radians(lon)\\n N = sqrt(a / (1 - (1 - (1 - 1 / rf) ** 2) * (sin(lat_rad)) ** 2))\\n X = (N + h) * cos(lat_rad) * cos(lon_rad)\\n Y = (N + h) * cos(lat_rad) * sin(lon_rad)\\n Z = ((1 - 1 / rf) ** 2 * N + h) * sin(lat_rad)\\n return X, Y, Z\",\n \"def __convert(args):\\n a, b, zone, ellipsoid, datum, inverse = args\\n projection = Proj(\\\"+proj=utm +zone={}, +ellps={} +datum={} +units=m +no_defs\\\".format(zone, ellipsoid, datum))\\n c, d = projection(a, b, inverse=inverse)\\n\\n return c, d\",\n \"def ll_to_xy(lon, lat):\\n lllon = lon.min()\\n lllat = lat.min()\\n urlon = lon.max()\\n urlat = lat.max()\\n\\n dlon = urlon - lllon\\n dlat = urlat - lllat\\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\\n\\n x = dx_*(lon - lllon)/dlon\\n y = dy_*(lat - lllat)/dlat\\n\\n return x, y\",\n \"def update_coords(self, l, b):\\n self.l = l\\n self.b = b\\n self.ra, self.dec = astLib.astCoords.convertCoords(\\n \\\"GALACTIC\\\", \\\"J2000\\\", self.l, self.b, epoch=2000.)\",\n \"def gps_to_coords(self,lat,lon):\\n\\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\\n return (-1,-1)\\n\\n lat_spot = int((self.max_lat-lat)/self.lat_step)\\n lon_spot = int((lon-self.min_lon)/self.lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def _getNewCoords(self, coords, offset):\\n\\n oldlat = math.radians(float(coords[0]))\\n oldlon = math.radians(float(coords[1]))\\n magnitude = float(offset[0]) / 6370.\\n direction = math.radians(360.-float(offset[1]))\\n\\n # Calculate lat/lon given radial and distnace (http://www.edwilliams.org/avform.htm#LL)\\n lat = math.asin(math.sin(oldlat) * math.cos(magnitude) + math.cos(oldlat) \\\\\\n * math.sin(magnitude) * math.cos(direction))\\n lon = (oldlon - math.asin(math.sin(direction) * math.sin(magnitude) / math.cos(lat)) \\\\\\n + math.pi) % (2 * math.pi) - math.pi\\n\\n # print coords, offset, oldlat, oldlon, magnitude, direction, math.degrees(lat), math.degrees(lon)\\n return (math.degrees(lat), math.degrees(lon))\",\n \"def read_affine(file):\\n data = open(file, 'r').read()\\n data = data.split('\\\\n')\\n for i in range(1, 5):\\n data[i] = data[i].split(':')\\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\\n Nlon = len(int_lon) - 1\\n Nlat = len(int_lat) - 1\\n data[3][1] = data[3][1].split(',')\\n data[4][1] = data[4][1].split(',')\\n lon_transform = np.zeros((Nlon, 2))\\n lat_transform = np.zeros((Nlat, 2))\\n for i in range(Nlon):\\n data[3][1][i] = data[3][1][i].split(' ')\\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\\n for i in range(Nlat):\\n data[4][1][i] = data[4][1][i].split(' ')\\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\\n lon_transform = np.array(lon_transform).astype('float')\\n lat_transform = np.array(lat_transform).astype('float')\\n return int_lon, int_lat, lon_transform, lat_transform\",\n \"def proj_coord(coord, proj_in, proj_out):\\n x, y = coord\\n return pyproj.transform(proj_in, proj_out, x, y)\",\n \"def mercX(lon):\\r\\n zoom = 1.0\\r\\n lon = math.radians(lon)\\r\\n a = (256.0 / math.pi) * pow(2.0, zoom)\\r\\n b = lon + math.pi\\r\\n return int(a * b)\",\n \"def transform_point(p,R,t):\\r\\n x = R[0][0]*p[0]+R[0][1]*p[1]+t[0]\\r\\n y = R[1][0]*p[0]+R[1][1]*p[1]+t[1]\\r\\n return [x,y]\",\n \"def transform_pnt(pnt, inEPSG, outEPSG):\\r\\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\\r\\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\\r\\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\\r\\n\\r\\n return pyproj.transform(inProj, outProj, x1, y1)\",\n \"def pixels_to_latlon(x_pos, y_pos):\\n radius = 489.5\\n x_pos_from_center = -1*(512.5 - x_pos)\\n angle_1 = np.rad2deg(np.arccos(x_pos_from_center/radius))\\n\\n y_pos_from_center = -1*(512.5 - y_pos)\\n angle_2 = np.rad2deg(np.arccos(y_pos_from_center/radius)) \\n\\n return -1*angle_1, angle_2\",\n \"def gnomonic_project_toxy(RA1, Dec1, RAcen, Deccen):\\n # also used in Global Telescope Network website\\n cosc = np.sin(Deccen) * np.sin(Dec1) + np.cos(Deccen) * np.cos(Dec1) * np.cos(RA1-RAcen)\\n x = np.cos(Dec1) * np.sin(RA1-RAcen) / cosc\\n y = (np.cos(Deccen)*np.sin(Dec1) - np.sin(Deccen)*np.cos(Dec1)*np.cos(RA1-RAcen)) / cosc\\n return x, y\",\n \"def cartesian_decoder(coord, r_E=6371):\\n def _to_deg(rad):\\n return rad * 180. / np.pi\\n\\n x, y, z = coord[:, 0], coord[:, 1], coord[:, 2]\\n\\n theta = np.arcsin(z / r_E)\\n phi = np.arctan(y / x)\\n\\n # Convert to degrees. Longitudes, are bound between -90;90 in decode step, so correct in 3 and 4th quadrant of x-y plane (Asia)\\n lat = _to_deg(theta)\\n lon = _to_deg(phi) - 180 * ((x < 0) * (y < 0)) + 180 * ((x < 0) * (y > 0))\\n\\n return np.concatenate([lat.reshape(-1, 1), lon.reshape(-1, 1)], axis=1)\",\n \"def transform_coords(x, y, w, h, nw, nh):\\r\\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)\",\n \"def coord_to_pointing (coordinates):\\n\\n clat = np.cos (coordinates[0])\\n return np.array ([clat * np.cos (coordinates[1]),\\n clat * np.sin (coordinates[1]),\\n np.sin (coordinates[0])])\",\n \"def map_coordinates(self,geometry):\\n\\t\\tg = self.geomatrix\\n\\t\\tdef project_coord(x,y,z=None):\\n\\t\\t\\tx = g[0] + g[1] * x + g[2] * y\\n\\t\\t\\ty = g[3] + g[4] * x + g[5] * y\\n\\t\\t\\tif z is None:\\n\\t\\t\\t\\treturn x,y\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn x,y,z\\n\\t\\treturn transform(project_coord, geometry)\",\n \"def geo2Cartesian(lat, lon, h, julian_date):\\n\\n lat_rad = np.radians(lat)\\n lon_rad = np.radians(lon)\\n\\n # Calculate ECEF coordinates\\n ecef_x, ecef_y, ecef_z = latLonAlt2ECEF(lat_rad, lon_rad, h)\\n\\n\\n # Get Local Sidreal Time\\n LST_rad = math.radians(JD2LST(julian_date, np.degrees(lon_rad))[0])\\n\\n\\n # Calculate the Earth radius at given latitude\\n Rh = math.sqrt(ecef_x**2 + ecef_y**2 + ecef_z**2)\\n\\n # Calculate the geocentric latitude (latitude which considers the Earth as an elipsoid)\\n lat_geocentric = math.atan2(ecef_z, math.sqrt(ecef_x**2 + ecef_y**2))\\n\\n # Calculate Cartesian ECI coordinates (in meters), in the epoch of date\\n x = Rh*np.cos(lat_geocentric)*np.cos(LST_rad)\\n y = Rh*np.cos(lat_geocentric)*np.sin(LST_rad)\\n z = Rh*np.sin(lat_geocentric)\\n\\n return x, y, z\",\n \"def getCoords(self):\\n if self._ra == \\\"\\\" or self._dec == \\\"\\\":\\n raise ValueError('Object named ' + self._name +' has no coordinates in database.')\\n ra = self._ra.split(\\\":\\\")\\n dec = self._dec.split(\\\":\\\")\\n raTuple = (int(ra[0]), int(ra[1]), float(ra[2]))\\n decTuple = (dec[0][0], int(dec[0][1:]), int(dec[1]), float(dec[2]))\\n return raTuple, decTuple\",\n \"def cartesian2Geo(julian_date, x, y, z):\\n\\n\\n # Calculate LLA\\n lat, r_LST, ele = ecef2LatLonAlt(x, y, z)\\n\\n # Calculate proper longitude from the given JD\\n lon, _ = LST2LongitudeEast(julian_date, np.degrees(r_LST))\\n\\n # Convert longitude to radians\\n lon = np.radians(lon)\\n\\n\\n return np.degrees(lat), np.degrees(lon), ele\",\n \"def wgs84_to_mercator(df, lon, lat):\\n k = 6378137\\n df[\\\"x\\\"] = df[lon] * (k * np.pi/180.0)\\n df[\\\"y\\\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\\n return df\",\n \"def get_corners(self):\\n lon, lat = self.coordinates\\n \\n ll = (lon[0][0],lat[0][0])\\n ul = (lon[-1][0],lat[-1][0])\\n ur = (lon[-1][-1],lat[-1][-1])\\n lr = (lon[0][-1],lat[0][-1])\\n\\n return (ll, ul, ur, lr)\",\n \"def coords_to_min_max_lat_lon(self,coords):\\n row = coords[0]\\n col = coords[1]\\n max_lat = self.max_lat - row*(self.lat_step)\\n min_lat = max_lat - self.lat_step\\n min_lon = self.min_lon + col*(self.lon_step)\\n max_lon = min_lon + self.lon_step\\n return min_lat,max_lat,min_lon,max_lon\",\n \"def flowline_latlon(coords, fromproj=pyproj.Proj(\\\"+init=epsg:3413\\\"), toproj=pyproj.Proj(\\\"+init=EPSG:4326\\\")):\\n xs = coords[:,0]\\n ys = coords[:,1]\\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\\n latlon_coords = np.asarray(zip(x_lon, y_lat))\\n return latlon_coords\",\n \"def coords_to_structure(self) -> None:\\n ...\",\n \"def mercator(lat, lon, zoom):\\n # convert to radians\\n x1, y1 = lon * pi / 180, lat * pi / 180\\n\\n # project to mercator\\n x2, y2 = x1, log(tan(0.25 * pi + 0.5 * y1))\\n\\n # transform to tile space\\n tiles, diameter = 2 ** zoom, 2 * pi\\n x3, y3 = int(tiles * (x2 + pi) / diameter), int(tiles * (pi - y2) / diameter)\\n\\n return zoom, x3, y3\",\n \"def fromECEFtoLatLongDegrees(x, y, z):\\n ret = fromECEFtoLatLong(x, y, z)\\n return math.degrees(ret[0]), math.degrees(ret[1]), ret[2]\",\n \"def plane2sphere(x,y,latc,lonc):\\n if np.abs(y) > 1:\\n lat = np.nan\\n lon = np.nan\\n return lat,lon\\n else:\\n pass\\n\\n coslatc = np.cos(latc)\\n sinlatc = np.sin(latc)\\n\\n cosphi = np.sqrt(1.0 - y**2)\\n lat = np.arcsin((y*coslatc) + (cosphi*np.cos(x)*sinlatc))\\n\\n if np.cos(lat) == 0:\\n test = 0.0\\n else:\\n test = cosphi*np.sin(x)/np.cos(lat)\\n\\n lon = np.arcsin(test) + lonc\\n\\n x0 = x\\n if np.abs(x0) > np.pi/2.0:\\n while x0 > np.pi/2.0:\\n lon = np.pi - lon\\n x0 = x0 - np.pi\\n\\n while x0 < -np.pi/2.0:\\n lon = -np.pi - lon\\n x0 = x0 + np.pi\\n\\n return lat,lon\",\n \"def mapc2p(xc,yc):\\n from numpy import where, cos, pi\\n import math\\n\\n # Assumes 0 <= xc <= 30 and -1 <= yc <= 1\\n\\n # Nozzle geometry\\n rad1 = 0.75-0.25*cos(pi/2 - pi*(xc-10.)/10.)\\n radius = where(abs(xc-15)<10, rad1, 1.)\\n xp = xc\\n yp = yc * radius\\n return xp,yp\",\n \"def reproject(self, lon, lat):\\n if self.xform is None:\\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\\n return self.xform.transform(QgsPointXY(lon, lat))\",\n \"def ll2px(lat, lng, zoom):\\n # assert isinstance(lat, (float, int, long)), \\\\\\n # ValueError(\\\"lat must be a float\\\")\\n lat = float(lat)\\n # assert isinstance(lng, (float, int, long)), \\\\\\n # ValueError(\\\"lng must be a float\\\")\\n lng = float(lng)\\n # assert isinstance(zoom, int), TypeError(\\\"zoom must be an int from 0 to 30\\\")\\n # assert 0 <= zoom <= 30, ValueError(\\\"zoom must be an int from 0 to 30\\\")\\n\\n cbk = CBK[zoom]\\n\\n x = int(round(cbk + (lng * CEK[zoom])))\\n\\n foo = math.sin(lat * math.pi / 180)\\n if foo < -0.9999:\\n foo = -0.9999\\n elif foo > 0.9999:\\n foo = 0.9999\\n\\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\\n\\n return (x, y)\",\n \"def chunk_coords_to_region_coords(cx: int, cz: int) -> ChunkCoordinates:\\n return cx >> 5, cz >> 5\",\n \"def createCoordTuples(data):\\n data['xy'] = None\\n for i, row in data.iterrows():\\n data['xy'][i] = [np.round(row['geometry'].x, decimals=5), np.round(row['geometry'].y, decimals=5)]\\n return data\",\n \"def pixel2coords(self, x, y):\\n xoff, a, b, yoff, d, e = self.geotransform()\\n\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n return (xp, yp)\",\n \"def point2wgs84(self, crsop):\\n latr = math.radians(self.lat)\\n lngr = math.radians(self.lng)\\n \\n # a is the semi-major axis of given datum.\\n a = crsop.SemimajorAxisInM\\n \\n # f is the flattening of given datum\\n f = 1.0/crsop.InverseFlattening\\n dx = crsop.dx\\n dy = crsop.dy\\n dz = crsop.dz\\n \\n # da is the difference between the WGS84 and source ellipsoid semi-major axes.\\n da = 6378137.0 - a\\n \\n # df is the difference between the WGS84 and source CRS flattenings.\\n df = 1.0/298.257223563 - f\\n \\n e_squared = f*(2-f)\\n rho = a*(1-e_squared)/math.pow((1-e_squared*sqr(math.sin(latr))),1.5)\\n nu = a/math.pow((1-e_squared*sqr(math.sin(latr))),0.5)\\n dlat = (1/rho)*(-dx*math.sin(latr)*math.cos(lngr) - \\\\\\n dy*math.sin(latr)*math.sin(lngr) + \\\\\\n dz*math.cos(latr) + (f*da + a*df)*math.sin(2*latr))\\n dlng = (-dx*math.sin(lngr) + dy*math.cos(lngr))/(nu*math.cos(latr))\\n newlng = lng180(math.degrees(lngr + dlng))\\n newlat = math.degrees(latr + dlat)\\n return Point(float(truncate(newlng,DEGREE_DIGITS)), \\\\\\n float(truncate(newlat,DEGREE_DIGITS)))\",\n \"def reconstructXY(self, inputs):\\n return (self.reconstructX(inputs),\\n self.reconstructY(inputs))\",\n \"def get_coordinates_geo(self):\\n if not self.rotated:\\n lon_arr_geo = self.lon_arr\\n lat_arr_geo = self.lat_arr\\n else:\\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \\n\\n return lon_arr_geo, lat_arr_geo\",\n \"def cartesian_to_geographical(coordinate_triples):\\n if len(coordinate_triples.shape) == 1:\\n x = coordinate_triples[0]\\n y = coordinate_triples[1]\\n z = coordinate_triples[2]\\n elif len(coordinate_triples.shape) == 2:\\n assert coordinate_triples.shape[1] == 3\\n x = coordinate_triples[:, 0]\\n y = coordinate_triples[:, 1]\\n z = coordinate_triples[:, 2]\\n radius = np.sqrt(x**2 + y**2 + z**2)\\n longitudes = np.arctan2(y, x)\\n latitudes = np.arcsin(z/radius)\\n return (latitudes, longitudes)\",\n \"def fromECEFtoLatLong(x, y, z):\\n r = math.sqrt(x*x+y*y)\\n F = 54*b*b*z*z\\n G = r*r +(1.0-e2)*z*z-e2*E2\\n C = (e2*e2*F*r*r)/(G*G*G)\\n s = pow(1.0+C+math.sqrt(C*C+2*C),1.0/3.0)\\n P = F/(3.0*pow((s+(1.0/s)+1.0),2.0)*G*G)\\n Q = math.sqrt(1.0+2.0*e2*e2*P)\\n r0 = (-(P*e2*r)/(1.0+Q))+math.sqrt((1.0/2.0)*a*a*(1.0+1.0/Q)-((P*(1-e2)*z*z)/(Q*(1.0+Q)))-(1.0/2.0)*P*r*r)\\n U = math.sqrt(pow(r-e2*r0,2.0)+z*z)\\n V = math.sqrt(pow(r-e2*r0,2.0)+(1.0-e2)*z*z)\\n Z0 = b*b*z/(a*V)\\n return math.atan((z+ep2*Z0)/r), math.atan2(y, x), U*(1.0-(b*b)/(a*V))\",\n \"def transform(self, lens):\\n coords_t = np.zeros(self.coords.shape)\\n coords_t[:,0] = (\\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\\n coords_t[:,1] = (\\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\\n return coords_t\",\n \"def invmap(self, x, y):\\r\\n r = self._r\\r\\n rxy = np.sqrt(x*x + y*y)\\r\\n\\r\\n lat = np.arccos(1-(rxy/r)**2)\\r\\n long = np.arctan2(y, x)\\r\\n\\r\\n try:\\r\\n long[np.isnan(lat)] = np.nan\\r\\n except TypeError: # Thrown if long is scalar\\r\\n if np.isnan(lat): long = np.nan\\r\\n return (lat, long)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76137656","0.69122946","0.6881515","0.68766624","0.6610878","0.65857524","0.6521325","0.64422315","0.6299409","0.62912303","0.62782985","0.62745625","0.62368286","0.6209349","0.6204365","0.6120023","0.607636","0.60706896","0.6065761","0.6049449","0.60393035","0.60276866","0.6016231","0.6012965","0.59992146","0.599234","0.59853786","0.5978804","0.5967068","0.59623575","0.5956715","0.5947226","0.5935876","0.59253085","0.5922793","0.589168","0.5878029","0.58367026","0.58354723","0.5833373","0.5833261","0.5823124","0.58161795","0.5795403","0.57829607","0.5767805","0.57580614","0.57523674","0.5724045","0.572341","0.57232755","0.57111347","0.5702928","0.5684078","0.56840605","0.56667054","0.56529427","0.56521195","0.5646196","0.5635965","0.5632857","0.5627246","0.5617187","0.5599278","0.55962783","0.5590006","0.5588363","0.55802053","0.55773187","0.55668193","0.5566248","0.556341","0.5545999","0.5537882","0.5535966","0.55346763","0.5534514","0.5531852","0.55314714","0.5530832","0.5525876","0.5504816","0.5501384","0.55002314","0.54992354","0.54868346","0.5480766","0.5477828","0.54763955","0.54725575","0.5461763","0.54602456","0.54571384","0.54540515","0.54498875","0.5447575","0.544709","0.54416454","0.5439734","0.54342514"],"string":"[\n \"0.76137656\",\n \"0.69122946\",\n \"0.6881515\",\n \"0.68766624\",\n \"0.6610878\",\n \"0.65857524\",\n \"0.6521325\",\n \"0.64422315\",\n \"0.6299409\",\n \"0.62912303\",\n \"0.62782985\",\n \"0.62745625\",\n \"0.62368286\",\n \"0.6209349\",\n \"0.6204365\",\n \"0.6120023\",\n \"0.607636\",\n \"0.60706896\",\n \"0.6065761\",\n \"0.6049449\",\n \"0.60393035\",\n \"0.60276866\",\n \"0.6016231\",\n \"0.6012965\",\n \"0.59992146\",\n \"0.599234\",\n \"0.59853786\",\n \"0.5978804\",\n \"0.5967068\",\n \"0.59623575\",\n \"0.5956715\",\n \"0.5947226\",\n \"0.5935876\",\n \"0.59253085\",\n \"0.5922793\",\n \"0.589168\",\n \"0.5878029\",\n \"0.58367026\",\n \"0.58354723\",\n \"0.5833373\",\n \"0.5833261\",\n \"0.5823124\",\n \"0.58161795\",\n \"0.5795403\",\n \"0.57829607\",\n \"0.5767805\",\n \"0.57580614\",\n \"0.57523674\",\n \"0.5724045\",\n \"0.572341\",\n \"0.57232755\",\n \"0.57111347\",\n \"0.5702928\",\n \"0.5684078\",\n \"0.56840605\",\n \"0.56667054\",\n \"0.56529427\",\n \"0.56521195\",\n \"0.5646196\",\n \"0.5635965\",\n \"0.5632857\",\n \"0.5627246\",\n \"0.5617187\",\n \"0.5599278\",\n \"0.55962783\",\n \"0.5590006\",\n \"0.5588363\",\n \"0.55802053\",\n \"0.55773187\",\n \"0.55668193\",\n \"0.5566248\",\n \"0.556341\",\n \"0.5545999\",\n \"0.5537882\",\n \"0.5535966\",\n \"0.55346763\",\n \"0.5534514\",\n \"0.5531852\",\n \"0.55314714\",\n \"0.5530832\",\n \"0.5525876\",\n \"0.5504816\",\n \"0.5501384\",\n \"0.55002314\",\n \"0.54992354\",\n \"0.54868346\",\n \"0.5480766\",\n \"0.5477828\",\n \"0.54763955\",\n \"0.54725575\",\n \"0.5461763\",\n \"0.54602456\",\n \"0.54571384\",\n \"0.54540515\",\n \"0.54498875\",\n \"0.5447575\",\n \"0.544709\",\n \"0.54416454\",\n \"0.5439734\",\n \"0.54342514\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5487496"},"document_rank":{"kind":"string","value":"85"}}},{"rowIdx":85695,"cells":{"query":{"kind":"string","value":"Create transform from (column, row) to (lon, lat) coordinates"},"document":{"kind":"string","value":"def cr2lonlat_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(old_cs, new_cs)\n\n def composite(c, r):\n \"\"\"xform from (c, r) to (lon, lat)\"\"\"\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat\n \n return composite"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def transform_from_latlon(lat, lon):\n from affine import Affine\n lat = np.asarray(lat)\n lon = np.asarray(lon)\n trans = Affine.translation(lon[0], lat[0])\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n return trans * scale","def _build_geotransform(self, i, j):\n assert isinstance(i, int), (\"i is not an integer\")\n assert isinstance(j, int), (\"j is not an integer\")\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\n self.image_metadata.geotransform)\n # integer conversion to reduce floating point error\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \n y_ignore, y_res) \n return geotransform","def map_coord_transformer(df, proj_string, lat_column_name, long_column_name):\n logging.info('Generating coordinate reference systems... ')\n #generate coordinate reference system objects for details of how this works \n from_crs = pyproj.CRS.from_string(proj_string)\n from_proj = pyproj.Proj(from_crs)\n gps_proj = pyproj.Proj('epsg:4326')\n original_coordinates_to_latlong_obj = pyproj.Transformer.from_proj(from_proj, gps_proj)\n logging.info('Defining transformation functions...')\n def original_coordinates_to_latlong(adf):\n (lat,long) = original_coordinates_to_latlong_obj.transform(adf[lat_column_name], adf[long_column_name])\n return lat, long\n \n #apply converter to generate series\n logging.info('Converting coordinates...')\n latlong_series = df.apply(original_coordinates_to_latlong, axis=1)\n \n #get calculated values and put back into df.\n logging.info('Splitting series...')\n lat_series = latlong_series.copy().apply(lambda x: x[0])\n long_series = latlong_series.copy().apply(lambda x: x[1])\n \n #return the values as \n logging.info('Preparing to return calc_lat and calc_long...')\n df.loc[:,'calc_lat'] = lat_series.copy()\n df.loc[:,'calc_long'] = long_series.copy()\n \n return df","def map_coordinates(self,geometry):\n\t\tg = self.geomatrix\n\t\tdef project_coord(x,y,z=None):\n\t\t\tx = g[0] + g[1] * x + g[2] * y\n\t\t\ty = g[3] + g[4] * x + g[5] * y\n\t\t\tif z is None:\n\t\t\t\treturn x,y\n\t\t\telse:\n\t\t\t\treturn x,y,z\n\t\treturn transform(project_coord, geometry)","def createCoordTuples(data):\n data['xy'] = None\n for i, row in data.iterrows():\n data['xy'][i] = [np.round(row['geometry'].x, decimals=5), np.round(row['geometry'].y, decimals=5)]\n return data","def transform_point(transform, x_in, y_in):\n # create point geometry from coordinates\n point = ogr.Geometry(ogr.wkbPoint)\n point.AddPoint(x_in, y_in)\n point.Transform(transform)\n\n x_out = point.GetX()\n y_out = point.GetY()\n return x_out, y_out","def transform(self, lens):\n coords_t = np.zeros(self.coords.shape)\n coords_t[:,0] = (\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\n coords_t[:,1] = (\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\n return coords_t","def translation(coord, lat, lon):\n ty = merc_y(lat) - 73.43 # Y translation\n tx = merc_x(lon) - 57.55 # X translation\n for i in range(len(coord)):\n for j in range(len(coord[i])):\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\n return coord","def get_latlon_point(self, row, col):\n p1 = Proj(self.src.crs)\n window = rasterio.windows.Window(col, row, 1, 1)\n trnsfrm = self.src.window_transform(window)\n T1 = trnsfrm * Affine.translation(0.5, 0.5)\n p2 = Proj(proj='latlong', datum='WGS84')\n x, y = self.src.xy(row, col)\n lon, lat = transform(p1, p2, x, y)\n return lat, lon","def convertView2Geo(self, x, y):\n\n # x_pix is from left map edge, y_pix from top map edge\n x_pix = x + self.view_offset_x\n y_pix = y + self.view_offset_y\n\n lon = self.map_llon + x_pix/self.ppd_x\n lat = self.map_tlat - y_pix/self.ppd_y\n\n return (lon, lat)","def row_to_pixel(row, image_shape, columns=None):\n start_y, start_x = latlon_to_pixel(row[\"s_lat\"],\n row[\"s_lng\"], image_shape)\n end_y, end_x = latlon_to_pixel(row[\"e_lat\"],\n row[\"e_lng\"], image_shape)\n xy = (start_x, start_y, end_x, end_y)\n return xy","def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\n\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\n return (-1,-1)\n\n lat_step = abs(max_lat-min_lat)/rows\n lon_step = abs(max_lon-min_lon)/cols\n\n lat_spot = int((max_lat-lat)/lat_step)\n lon_spot = int((lon-min_lon)/lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def geo_transform(self):\n pass","def pixel2coord(tf, x, y):\n lat = tf[0] + x*tf[1] + y*tf[2]\n lon = tf[3] + x*tf[4] + y*tf[5]\n\n return lat, lon","def transform(self, pol_lon=None, pol_lat=None):\n if self.rotated:\n direction = 'rot2geo'\n pol_lon = self.pol_lon\n pol_lat = self.pol_lat\n else:\n if pol_lon is None or pol_lat is None:\n pol_lon = self.pol_lon\n pol_lat = self.pol_lat\n #raise Exception('grid is not rotated, transform requires pol_lon and pol_lat')\n direction = 'geo2rot'\n lon_arr_trans, lat_arr_trans = rotated_grid_transform(\n self.lon_arr, self.lat_arr, pol_lon, pol_lat,\n direction=direction)\n if self.rotated:\n return Grid(lon_arr_trans, lat_arr_trans)\n else:\n return Grid(lon_arr_trans, lat_arr_trans, pol_lon, pol_lat)","def transform_coords(x, y, w, h, nw, nh):\r\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)","def row_to_lat(self, row):\n return self.max_absolute_lat - row * self.lat_step","def pixel2coord(self, x, y):\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\n # partly taken from Sean Gillies \"affine.py\"\n a,b,c,d,e,f = self.coordspace_transform\n det = a*e - b*d\n idet = 1 / float(det)\n ra = e * idet\n rb = -b * idet\n rd = -d * idet\n re = a * idet\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\n newy = (x*rd + y*re + (-c*rd - f*re) )\n return newx,newy","def transform_coordinates(coords):\n # WGS 84 reference coordinate system parameters\n A = 6378.137 # major axis [km]\n E2 = 6.69437999014e-3 # eccentricity squared\n\n coords = prepare_coords(coords)\n\n # convert to radiants\n lat_rad = np.radians(coords[:, 0])\n lon_rad = np.radians(coords[:, 1])\n\n # convert to cartesian coordinates\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\n z = r_n * (1 - E2) * np.sin(lat_rad)\n\n return np.column_stack((x, y, z))","def pixel2coords(self, x, y):\n xoff, a, b, yoff, d, e = self.geotransform()\n\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n return (xp, yp)","def convertCoord(lon, lat, inEPSG, outEPSG):\n from pyproj import Proj, transform\n inProj = Proj(init='epsg:'+str(inEPSG))\n outProj = Proj(init='epsg:'+str(outEPSG))\n x, y = transform(inProj, outProj, lon, lat)\n return x, y\n # epsg:4326 WGS84\n # epsg:2950 MTM8\n # epsg:6622 Quebec Lambert","def convertFrom1Dto2D(coord, num_cols):\n y = int(np.floor(coord/num_cols))\n x = coord % num_cols\n return (y,x)","def pixelloc_to_geoloc(row, col, loff, coff):\n row = np.atleast_1d(row)\n col = np.atleast_1d(col)\n\n assert row.shape == col.shape\n\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\n\n if lat.size == lon.size == 1:\n # Return scalars\n return lat[0], lon[0]\n else:\n return lat, lon","def _transform_point(self, x, y):\n return (x, y)","def coords_to_node(self,row,col):\n return row*self.cols + col + 1","def rotated_grid_transform(lon_arr, lat_arr, np_lon, np_lat,\n direction='rot2geo'):\n dimensions = lon_arr.shape\n lon_arr_new = np.zeros(dimensions)\n lat_arr_new = np.zeros(dimensions)\n for i in range(dimensions[1]): # x-direction\n for j in range(dimensions[0]): # y-direction\n lon = lon_arr[j, i]\n lat = lat_arr[j, i]\n lon_new, lat_new = rotated_coord_transform(\n lon, lat, np_lon, np_lat, direction=direction)\n lon_arr_new[j, i] = lon_new\n lat_arr_new[j, i] = lat_new\n\n return (lon_arr_new, lat_arr_new)","def convert(self, lat, lon):\r\n a = self.a\r\n b = self.b\r\n long0 = self.long0\r\n k0 = self.k0\r\n dx = self.dx\r\n\r\n e = (1 - b ** 2 / a ** 2) ** 0.5\r\n e2 = e ** 2 / (1 - e ** 2)\r\n n = (a - b) / (a + b)\r\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\r\n p = lon - long0\r\n\r\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\r\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\r\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\r\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\r\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\r\n\r\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\r\n\r\n K1 = S * k0\r\n K2 = k0 * nu * sin(2 * lat)/4.0\r\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\r\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\r\n\r\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\r\n\r\n K4 = k0 * nu * cos(lat)\r\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\r\n\r\n x = K4 * p + K5 * (p ** 3) + dx\r\n return x, y","def _pixel_to_map(coordinates, geotransform):\n coordinates_map = np.empty(coordinates.shape)\n coordinates_map[..., 0] = (\n geotransform[0]\n + geotransform[1] * coordinates[..., 0]\n + geotransform[2] * coordinates[..., 1]\n )\n coordinates_map[..., 1] = (\n geotransform[3]\n + geotransform[4] * coordinates[..., 0]\n + geotransform[5] * coordinates[..., 1]\n )\n return coordinates_map","def create_pseudo_epsg4326_coordinates(self):\n self.create_3d_coord_on_sphere(on_sphere=True)\n self.df_attributes['lat'] = 180*(pi/2 - np.arccos(self.df_attributes['coord_z']))/pi\n self.df_attributes['lon'] = 180*np.arctan2(self.df_attributes['coord_y'], self.df_attributes['coord_x'])/pi","def _getXYZ ( lon, lat ):\n d2r = pi / 180.\n rlon, rlat = ( d2r * lon, d2r * lat )\n x = cos(rlat) * cos(rlon)\n y = cos(rlat) * sin(rlon)\n z = sin(rlat)\n return (x,y,z)","def Pixel2World(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xdist = geoMatrix[1]\r\n ydist = geoMatrix[5]\r\n coorX = (ulX + (x * xdist))\r\n coorY = (ulY + (y * ydist))\r\n return (coorX, coorY)","def latlon_2_grid(x, y, z, origin):\n new_y = (y - origin[1]) * 111111\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\n return new_x, new_y, z","def _transform_coordinates(rectangle, Q=np.matrix(((1, 1), (-1, 1)))):\n return tuple((rectangle[0]*Q).A1), tuple((rectangle[1]*Q).A1)","def geo2cell(geofile, posfile):","def map(self, lat, long):\r\n rxy = self._r*np.sqrt(1-np.cos(lat))\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def convertLatLon(latCell, lonCell):\n cell_lats = np.array([])\n cell_lons = np.array([])\n for lat in latCell:\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \n for lon in lonCell:\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \n\n return cell_lats, cell_lons","def convertGeo2View(self, lon, lat):\n\n x_pix = (lon - self.view_llon) * self.ppd_x\n y_pix = (self.view_tlat - lat) * self.ppd_y\n\n return (x_pix, y_pix)","def ll_to_xy(lon, lat):\n lllon = lon.min()\n lllat = lat.min()\n urlon = lon.max()\n urlat = lat.max()\n\n dlon = urlon - lllon\n dlat = urlat - lllat\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\n\n x = dx_*(lon - lllon)/dlon\n y = dy_*(lat - lllat)/dlat\n\n return x, y","def read_affine(file):\n data = open(file, 'r').read()\n data = data.split('\\n')\n for i in range(1, 5):\n data[i] = data[i].split(':')\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\n Nlon = len(int_lon) - 1\n Nlat = len(int_lat) - 1\n data[3][1] = data[3][1].split(',')\n data[4][1] = data[4][1].split(',')\n lon_transform = np.zeros((Nlon, 2))\n lat_transform = np.zeros((Nlat, 2))\n for i in range(Nlon):\n data[3][1][i] = data[3][1][i].split(' ')\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\n for i in range(Nlat):\n data[4][1][i] = data[4][1][i].split(' ')\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\n lon_transform = np.array(lon_transform).astype('float')\n lat_transform = np.array(lat_transform).astype('float')\n return int_lon, int_lat, lon_transform, lat_transform","def ll_coordinates(self):\n\n x, y = self.xy_coordinates\n proj_out = check_crs('EPSG:4326')\n\n return transform_proj(self.proj, proj_out, x, y)","def ll2fxy(lon,lat):\n return xyz2fxy(*ll2xyz(lon,lat))","def coords_to_structure(self) -> None:\n ...","def __affine_geo_transformation(x, y, gtr):\n\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\n # Affine transformation rewritten for rasterio:\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\n\n return gtr_x, gtr_y","def wgs84_to_mercator(df, lon, lat):\n k = 6378137\n df[\"x\"] = df[lon] * (k * np.pi/180.0)\n df[\"y\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\n return df","def rotated_coord_transform(lon, lat, np_lon, np_lat,\n direction='rot2geo'):\n\n # Convert degrees to radians\n lon = (lon * math.pi) / 180.\n lat = (lat * math.pi) / 180.\n\n# SP_lon = SP_coor(1)\n# SP_lat = SP_coor(2)\n\n theta = 90. - np_lat # Rotation around y-axis\n phi = np_lon + 180. # Rotation around z-axis\n\n # Convert degrees to radians\n phi = (phi * math.pi) / 180.\n theta = (theta * math.pi) / 180.\n\n # Convert from spherical to cartesian coordinates\n x = math.cos(lon) * math.cos(lat)\n y = math.sin(lon) * math.cos(lat)\n z = math.sin(lat)\n\n # Regular -> Rotated\n if direction == 'geo2rot':\n\n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.cos(theta) * math.sin(phi) * y +\n math.sin(theta) * z)\n y_new = (- math.sin(phi) * x +\n math.cos(phi) * y)\n z_new = (- math.sin(theta) * math.cos(phi) * x -\n math.sin(theta) * math.sin(phi) * y +\n math.cos(theta) * z)\n\n # Rotated -> Regular\n elif direction == 'rot2geo':\n \n phi = - phi\n theta = - theta\n \n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.sin(phi) * y +\n math.sin(theta) * math.cos(phi) * z)\n y_new = (- math.cos(theta) * math.sin(phi) * x +\n math.cos(phi) * y -\n math.sin(theta) * math.sin(phi) * z)\n z_new = (- math.sin(theta) * x +\n math.cos(theta) * z)\n\n # Convert cartesian back to spherical coordinates\n lon_new = math.atan2(y_new, x_new)\n lat_new = math.asin(z_new)\n\n # Convert radians back to degrees\n lon_new = (lon_new * 180.) / math.pi\n lat_new = (lat_new * 180.) / math.pi;\n\n return (lon_new, lat_new)","def map(self, lat, long):\r\n rxy = self._r*lat/(np.pi/2)\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def lat_lng(row):\r\n lat = row[\"latitude\"]\r\n lng = row[\"longitude\"]\r\n n = int(lat/GRANULARITY)\r\n nlat_start = n * GRANULARITY\r\n nlat_end = nlat_start + GRANULARITY\r\n nlg=int(lng/GRANULARITY)\r\n nlng_start = nlg * GRANULARITY\r\n nlng_end = nlng_start + GRANULARITY\r\n latlng=[(nlat_start,nlng_start), (nlat_start,nlng_end), (nlat_end,nlng_end), (nlat_end,nlng_start)]\r\n return latlng","def coord2pixel(tf, lat, lon):\n x = int(round((lon-tf[0])/tf[1]))\n y = int(round((lat-tf[3])/tf[5]))\n\n return x, y","def ind2coord(self, index):\n\n # assert (index >= 0)\n # assert(index < self.n - 1)\n\n col = index // self.rows\n row = index % self.rows\n\n return [row, col]","def make_simple_coords():\n \n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\n coords = np.vstack((x,y)).T\n return coords","def rowcol_to_latlon(rowcol_str):\n\n # Vectors for code in file name\n latspace = np.linspace(-90,90,46)\n lonspace = np.linspace(-180,180,73)\n lat = latspace[int(rowcol_str[0:2])-1]\n lon = lonspace[int(rowcol_str[2:])-1]\n return (lat,lon)","def _indices_to_coords(c,r):\n\n column = _index_to_column(c)\n row = r + 1\n\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}","def ll2xyz(lon_pt,lat_pt):\n\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\n zPt = np.sin(lat_pt)\n return [xPt,yPt,zPt]","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def transform_coordinates(self, p):\n if type(p) == PhysicalObject:\n return self.transform_coordinates(p.position)\n elif type(p) == Vector:\n return tuple(map(\n lambda x: int(x),\n (p / SCALE_FACTOR - self.pos_shift))\n )","def _generate_coords(self):\n coords = np.dstack([self.X.ravel(), self.Y.ravel()])[0]\n return coords, spatial.cKDTree(coords)","def coord(self, x, y):\n origin_x = self._raster_meta['transform'][3]\n origin_y = self._raster_meta['transform'][0]\n pixel_x = self._raster_meta['transform'][5]\n pixel_y = self._raster_meta['transform'][1]\n\n x = int((x - origin_x) / pixel_x)\n y = int((y - origin_y) / pixel_y)\n return self[x, y]","def latlon_to_pixels(lon, lat):\n radius = 489.5\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\n x_pos = x_pos_from_center + 512.5\n \n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\n y_pos = y_pos_from_center + 512.5\n \n return x_pos, y_pos","def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False):\n\n x, y = np.ma.array(x), np.ma.array(y)\n\n # First to local proj\n _crs = check_crs(crs, raise_on_error=True)\n if isinstance(_crs, pyproj.Proj):\n x, y = transform_proj(_crs, self.proj, x, y)\n elif isinstance(_crs, Grid):\n x, y = _crs.ij_to_crs(x, y, crs=self.proj)\n\n # Then to local grid\n x = (x - self.x0) / self.dx\n y = (y - self.y0) / self.dy\n\n # See if we need to round\n if nearest:\n f = np.rint if self.pixel_ref == 'center' else np.floor\n x = f(x).astype(int)\n y = f(y).astype(int)\n\n # Mask?\n if maskout:\n if self.pixel_ref == 'center':\n mask = ~((x >= -0.5) & (x < self.nx-0.5) &\n (y >= -0.5) & (y < self.ny-0.5))\n else:\n mask = ~((x >= 0) & (x < self.nx) &\n (y >= 0) & (y < self.ny))\n x = np.ma.array(x, mask=mask)\n y = np.ma.array(y, mask=mask)\n\n return x, y","def get_coordinates_geo(self):\n if not self.rotated:\n lon_arr_geo = self.lon_arr\n lat_arr_geo = self.lat_arr\n else:\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \n\n return lon_arr_geo, lat_arr_geo","def transform_pnt(pnt, inEPSG, outEPSG):\r\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\r\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\r\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\r\n\r\n return pyproj.transform(inProj, outProj, x1, y1)","def rowcol2XY(row,col,CCD):\n pixscale = 0.015 #mm/pix\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\n return X,Y","def trans_to_coordinates(T, pts):\n p = []\n for i in range(len(pts)):\n \n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\n p_a = np.matmul(T, p_b).tolist()\n p.append(p_a[0:3])\n\n return p","def trans_to_coordinates(T, pts):\n p = []\n for i in range(len(pts)):\n \n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\n p_a = np.matmul(T, p_b).tolist()\n p.append(p_a[0:3])\n\n return p","def gps_to_node(self,lat,lon):\n row,col = self.gps_to_coords(lat,lon)\n if row == -1:\n return -1\n return self.coords_to_node(row,col)","def to_position(self, x, y, i, j):\n return (x * self.SIZE + i, y * self.SIZE + j)","def map(self, obj):\n if isinstance(obj, np.ndarray) and obj.ndim >= 2 and obj.shape[0] in (2,3):\n return fn.transformCoordinates(self, obj)\n else:\n return QtGui.QMatrix4x4.map(self, obj)","def transform(self, srid: ir.IntegerValue) -> GeoSpatialValue:\n return ops.GeoTransform(self, srid).to_expr()","def transform_from(self, coordinates) -> np.ndarray:\n return (coordinates @ self.T) + self._o","def transform(self, x: Array2D) -> Array2D:","def reproject(self, lon, lat):\n if self.xform is None:\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\n return self.xform.transform(QgsPointXY(lon, lat))","def composite(c, r):\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat","def node_to_coords(self,node_num):\n row = (node_num - 1) / self.cols\n col = (node_num - 1) % self.cols\n return (row,col)","def proj(self,lon,lat):\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\n x[x > 1e29] = None\n y[y > 1e29] = None\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\n return x, y","def transform(o):\n\t\t\tp = o.pos() - self.pos()\n\t\t\tx = cosa * p.x + sina * p.y\n\t\t\ty = -sina * p.x + cosa * p.y\n\t\t\treturn tuple(px_to_mm(x,y))","def transform_to(self, coordinates) -> np.ndarray:\n return (coordinates - self._o) @ self.T.T","def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\r\n # convert decimals to seconds...\r\n s_lon = dec2sec(s_lon)\r\n s_lat = dec2sec(s_lat)\r\n\r\n ## Auxiliary values \r\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\r\n s_lng_aux = (s_lon - 26782.5)/10000.\r\n s_lat_aux = (s_lat - 169028.66)/10000.\r\n \r\n # easting\r\n s_x = (600072.37 \r\n + 211455.93*s_lng_aux \r\n - 10938.51*s_lng_aux*s_lat_aux \r\n - 0.36*s_lng_aux*(s_lat_aux**2) \r\n - 44.54*(s_lng_aux**3))\r\n \r\n # northing\r\n s_y = (200147.07 \r\n + 308807.95*s_lat_aux \r\n + 3745.25*(s_lng_aux**2) \r\n + 76.63*(s_lat_aux**2) \r\n - 194.56*(s_lng_aux**2)*s_lat_aux \r\n + 119.79*(s_lat_aux**3))\r\n\r\n return s_x, s_y","def coordinates(self):","def makeToCoordinates(fromCoords, Uframe, Vframe, scale):\n\n out = []\n\n for e in fromCoords:\n x = e[0]\n y = e[1]\n toX = Uframe[x][y]\n toY = Vframe[x][y]\n out.append((int(round(x+toX*scale)),int(round(y+toY*scale))))\n\n return out","def proj_coord(coord, proj_in, proj_out):\n x, y = coord\n return pyproj.transform(proj_in, proj_out, x, y)","def transform_data(features):\n\n def cart2pol(my_row):\n x = my_row[0]\n y = my_row[1]\n rho = np.sqrt(x**2 + y**2)\n phi = np.arctan2(y, x)\n return [rho, phi]\n\n #vfunc = np.vectorize(cart2pol)\n #transformed = vfunc(features)\n out = []\n #print(features, \"\\n\\n\")\n for row in features:\n out.append(cart2pol(row))\n out = np.array(out)\n #print(\"out is\\n\", out)\n \"\"\"\n x = out[:,0]\n y = out[:,1]\n \n plt.scatter(x,y)\n plt.show()\n \"\"\"\n return out","def coords_to_gps(self,coords):\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))","def process_coords():\n split_coords = row[\"map_coord\"].split(',')\n map_x, map_y = [int(i) for i in split_coords]\n map_x_normed = ((map_x*2) / self.MINIMAP_DIM) - 1\n map_y_normed = -(((map_y*2) / self.MINIMAP_DIM) - 1)\n return map_x_normed, map_y_normed","def _wrap_coord(tensors):\n cell = tf.gather_nd(tensors['cell'], tensors['ind_1'])\n coord = tf.expand_dims(tensors['coord'], -1)\n frac_coord = tf.linalg.solve(tf.transpose(cell, perm=[0, 2, 1]), coord)\n frac_coord %= 1\n coord = tf.matmul(tf.transpose(cell, perm=[0, 2, 1]), frac_coord)\n return tf.squeeze(coord, -1)","def flowline_latlon(coords, fromproj=pyproj.Proj(\"+init=epsg:3413\"), toproj=pyproj.Proj(\"+init=EPSG:4326\")):\n xs = coords[:,0]\n ys = coords[:,1]\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\n latlon_coords = np.asarray(zip(x_lon, y_lat))\n return latlon_coords","def coordinates(self):\n return np.array([[f.x, f.y] for f in self])","def rowcol_to_xy(rows, cols, affine):\n\n # make it a 3x3 matrix\n aff_array = numpy.array(affine).reshape((3, 3))\n\n # check that rows amd cols are indeed 1-D vectors\n rows = validate.is_vector(rows)\n cols = validate.is_vector(cols)\n\n # filler\n layers = numpy.ones_like(rows)\n vector = numpy.array([cols, rows, layers])\n\n # compute xy\n xy = numpy.dot(aff_array, vector)[:2, :]\n return xy","def calcPosition (lat, lon):\n nauticalMilePerLat = 60.00721\n nauticalMilePerLongitude = 60.10793\n rad = math.pi / 180.0\n milesPerNauticalMile = 1.15078\n \n y = lat * nauticalMilePerLat\n x = math.cos(lat * rad) * lon * nauticalMilePerLongitude\n\n return x * milesPerNauticalMile * 1609.344, y * milesPerNauticalMile * 1609.344","def index_to_xy(index):\n x = index % columns\n y = index // columns\n return x, y","def add_latlon(df):\n LLs = [num2deg(x,y,z) for x,y,z in zip(df['x'],df['y'],df['z'])]\n LLdf = pd.DataFrame.from_records(LLs,columns = ['latitude','longitude'])\n return pd.concat([df.reset_index(drop=True),LLdf],axis = 1)","def extractCoord(df):\n dfcol = df['Coord']\n for i in range(len(dfcol)):\n dfcol[i] = dfcol[i][6:-1]\n return df","def world2Pixel(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xDist = geoMatrix[1]\r\n yDist = geoMatrix[5]\r\n rtnX = geoMatrix[2]\r\n rtnY = geoMatrix[4]\r\n pixel = int((x - ulX) / xDist)\r\n line = int((ulY - y) / xDist)\r\n return (pixel, line)","def convert_coords(self):\n if self.coordsys in ['image', 'physical']:\n coords = self._convert_pix_coords()\n else:\n coords = self._convert_sky_coords()\n\n if self.region_type == 'line':\n coords = [coords[0][0], coords[0][1]]\n\n if self.region_type == 'text':\n coords.append(self.meta['text'])\n\n return coords","def lonlat2cr_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(new_cs, old_cs)\n\n def composite(lon, lat):\n \"\"\"xform from (lon, lat) to (c, r)\"\"\"\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return (~gta * transform.TransformPoint(lat, lon)[:2])\n \n return composite","def project(self, (lng, lat)):\n x = lng * DEG_TO_RAD\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\n y = lat * DEG_TO_RAD\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)","def lon_lat_to_cartesian(lon, lat, R = 1):\n lon_r = np.radians(lon)\n lat_r = np.radians(lat)\n\n x = R * np.cos(lat_r) * np.cos(lon_r)\n y = R * np.cos(lat_r) * np.sin(lon_r)\n z = R * np.sin(lat_r)\n return x,y,z","def transform_points(transf_matrix, points):\n if(type(points)==list):\n temp_pts = [np.array([x[0],x[1],x[2],1]) for x in points]\n newpts = []\n for pt in temp_pts:\n newpts.append((transf_matrix@pt)[:3])\n else:\n temp_pts = np.array([points[0],points[1],points[2],1])\n newpts=(transf_matrix@temp_pts)[:3]\n return newpts","def ll2px(lat, lng, zoom):\n # assert isinstance(lat, (float, int, long)), \\\n # ValueError(\"lat must be a float\")\n lat = float(lat)\n # assert isinstance(lng, (float, int, long)), \\\n # ValueError(\"lng must be a float\")\n lng = float(lng)\n # assert isinstance(zoom, int), TypeError(\"zoom must be an int from 0 to 30\")\n # assert 0 <= zoom <= 30, ValueError(\"zoom must be an int from 0 to 30\")\n\n cbk = CBK[zoom]\n\n x = int(round(cbk + (lng * CEK[zoom])))\n\n foo = math.sin(lat * math.pi / 180)\n if foo < -0.9999:\n foo = -0.9999\n elif foo > 0.9999:\n foo = 0.9999\n\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\n\n return (x, y)","def coords_from_cell(cell, lon = [-8.73, -8.50], lat = [41.10, 41.25], N = 100, M = 75):\n lon_step = (lon[1] - lon[0]) / N \n lat_step = (lat[1] - lat[0]) / M\n \n middle_lon = lon[0] + cell[0] * lon_step + lon_step / 2\n middle_lat = lat[0] + cell[1] * lat_step + lat_step / 2\n \n return [middle_lon, middle_lat]","def coords2D(self):\n return (self.x, self.y)"],"string":"[\n \"def transform_from_latlon(lat, lon):\\n from affine import Affine\\n lat = np.asarray(lat)\\n lon = np.asarray(lon)\\n trans = Affine.translation(lon[0], lat[0])\\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\\n return trans * scale\",\n \"def _build_geotransform(self, i, j):\\n assert isinstance(i, int), (\\\"i is not an integer\\\")\\n assert isinstance(j, int), (\\\"j is not an integer\\\")\\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\\n self.image_metadata.geotransform)\\n # integer conversion to reduce floating point error\\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \\n y_ignore, y_res) \\n return geotransform\",\n \"def map_coord_transformer(df, proj_string, lat_column_name, long_column_name):\\n logging.info('Generating coordinate reference systems... ')\\n #generate coordinate reference system objects for details of how this works \\n from_crs = pyproj.CRS.from_string(proj_string)\\n from_proj = pyproj.Proj(from_crs)\\n gps_proj = pyproj.Proj('epsg:4326')\\n original_coordinates_to_latlong_obj = pyproj.Transformer.from_proj(from_proj, gps_proj)\\n logging.info('Defining transformation functions...')\\n def original_coordinates_to_latlong(adf):\\n (lat,long) = original_coordinates_to_latlong_obj.transform(adf[lat_column_name], adf[long_column_name])\\n return lat, long\\n \\n #apply converter to generate series\\n logging.info('Converting coordinates...')\\n latlong_series = df.apply(original_coordinates_to_latlong, axis=1)\\n \\n #get calculated values and put back into df.\\n logging.info('Splitting series...')\\n lat_series = latlong_series.copy().apply(lambda x: x[0])\\n long_series = latlong_series.copy().apply(lambda x: x[1])\\n \\n #return the values as \\n logging.info('Preparing to return calc_lat and calc_long...')\\n df.loc[:,'calc_lat'] = lat_series.copy()\\n df.loc[:,'calc_long'] = long_series.copy()\\n \\n return df\",\n \"def map_coordinates(self,geometry):\\n\\t\\tg = self.geomatrix\\n\\t\\tdef project_coord(x,y,z=None):\\n\\t\\t\\tx = g[0] + g[1] * x + g[2] * y\\n\\t\\t\\ty = g[3] + g[4] * x + g[5] * y\\n\\t\\t\\tif z is None:\\n\\t\\t\\t\\treturn x,y\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn x,y,z\\n\\t\\treturn transform(project_coord, geometry)\",\n \"def createCoordTuples(data):\\n data['xy'] = None\\n for i, row in data.iterrows():\\n data['xy'][i] = [np.round(row['geometry'].x, decimals=5), np.round(row['geometry'].y, decimals=5)]\\n return data\",\n \"def transform_point(transform, x_in, y_in):\\n # create point geometry from coordinates\\n point = ogr.Geometry(ogr.wkbPoint)\\n point.AddPoint(x_in, y_in)\\n point.Transform(transform)\\n\\n x_out = point.GetX()\\n y_out = point.GetY()\\n return x_out, y_out\",\n \"def transform(self, lens):\\n coords_t = np.zeros(self.coords.shape)\\n coords_t[:,0] = (\\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\\n coords_t[:,1] = (\\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\\n return coords_t\",\n \"def translation(coord, lat, lon):\\n ty = merc_y(lat) - 73.43 # Y translation\\n tx = merc_x(lon) - 57.55 # X translation\\n for i in range(len(coord)):\\n for j in range(len(coord[i])):\\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\\n return coord\",\n \"def get_latlon_point(self, row, col):\\n p1 = Proj(self.src.crs)\\n window = rasterio.windows.Window(col, row, 1, 1)\\n trnsfrm = self.src.window_transform(window)\\n T1 = trnsfrm * Affine.translation(0.5, 0.5)\\n p2 = Proj(proj='latlong', datum='WGS84')\\n x, y = self.src.xy(row, col)\\n lon, lat = transform(p1, p2, x, y)\\n return lat, lon\",\n \"def convertView2Geo(self, x, y):\\n\\n # x_pix is from left map edge, y_pix from top map edge\\n x_pix = x + self.view_offset_x\\n y_pix = y + self.view_offset_y\\n\\n lon = self.map_llon + x_pix/self.ppd_x\\n lat = self.map_tlat - y_pix/self.ppd_y\\n\\n return (lon, lat)\",\n \"def row_to_pixel(row, image_shape, columns=None):\\n start_y, start_x = latlon_to_pixel(row[\\\"s_lat\\\"],\\n row[\\\"s_lng\\\"], image_shape)\\n end_y, end_x = latlon_to_pixel(row[\\\"e_lat\\\"],\\n row[\\\"e_lng\\\"], image_shape)\\n xy = (start_x, start_y, end_x, end_y)\\n return xy\",\n \"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\\n\\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\\n return (-1,-1)\\n\\n lat_step = abs(max_lat-min_lat)/rows\\n lon_step = abs(max_lon-min_lon)/cols\\n\\n lat_spot = int((max_lat-lat)/lat_step)\\n lon_spot = int((lon-min_lon)/lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def geo_transform(self):\\n pass\",\n \"def pixel2coord(tf, x, y):\\n lat = tf[0] + x*tf[1] + y*tf[2]\\n lon = tf[3] + x*tf[4] + y*tf[5]\\n\\n return lat, lon\",\n \"def transform(self, pol_lon=None, pol_lat=None):\\n if self.rotated:\\n direction = 'rot2geo'\\n pol_lon = self.pol_lon\\n pol_lat = self.pol_lat\\n else:\\n if pol_lon is None or pol_lat is None:\\n pol_lon = self.pol_lon\\n pol_lat = self.pol_lat\\n #raise Exception('grid is not rotated, transform requires pol_lon and pol_lat')\\n direction = 'geo2rot'\\n lon_arr_trans, lat_arr_trans = rotated_grid_transform(\\n self.lon_arr, self.lat_arr, pol_lon, pol_lat,\\n direction=direction)\\n if self.rotated:\\n return Grid(lon_arr_trans, lat_arr_trans)\\n else:\\n return Grid(lon_arr_trans, lat_arr_trans, pol_lon, pol_lat)\",\n \"def transform_coords(x, y, w, h, nw, nh):\\r\\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)\",\n \"def row_to_lat(self, row):\\n return self.max_absolute_lat - row * self.lat_step\",\n \"def pixel2coord(self, x, y):\\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\\n # partly taken from Sean Gillies \\\"affine.py\\\"\\n a,b,c,d,e,f = self.coordspace_transform\\n det = a*e - b*d\\n idet = 1 / float(det)\\n ra = e * idet\\n rb = -b * idet\\n rd = -d * idet\\n re = a * idet\\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\\n newy = (x*rd + y*re + (-c*rd - f*re) )\\n return newx,newy\",\n \"def transform_coordinates(coords):\\n # WGS 84 reference coordinate system parameters\\n A = 6378.137 # major axis [km]\\n E2 = 6.69437999014e-3 # eccentricity squared\\n\\n coords = prepare_coords(coords)\\n\\n # convert to radiants\\n lat_rad = np.radians(coords[:, 0])\\n lon_rad = np.radians(coords[:, 1])\\n\\n # convert to cartesian coordinates\\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\\n z = r_n * (1 - E2) * np.sin(lat_rad)\\n\\n return np.column_stack((x, y, z))\",\n \"def pixel2coords(self, x, y):\\n xoff, a, b, yoff, d, e = self.geotransform()\\n\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n return (xp, yp)\",\n \"def convertCoord(lon, lat, inEPSG, outEPSG):\\n from pyproj import Proj, transform\\n inProj = Proj(init='epsg:'+str(inEPSG))\\n outProj = Proj(init='epsg:'+str(outEPSG))\\n x, y = transform(inProj, outProj, lon, lat)\\n return x, y\\n # epsg:4326 WGS84\\n # epsg:2950 MTM8\\n # epsg:6622 Quebec Lambert\",\n \"def convertFrom1Dto2D(coord, num_cols):\\n y = int(np.floor(coord/num_cols))\\n x = coord % num_cols\\n return (y,x)\",\n \"def pixelloc_to_geoloc(row, col, loff, coff):\\n row = np.atleast_1d(row)\\n col = np.atleast_1d(col)\\n\\n assert row.shape == col.shape\\n\\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\\n\\n if lat.size == lon.size == 1:\\n # Return scalars\\n return lat[0], lon[0]\\n else:\\n return lat, lon\",\n \"def _transform_point(self, x, y):\\n return (x, y)\",\n \"def coords_to_node(self,row,col):\\n return row*self.cols + col + 1\",\n \"def rotated_grid_transform(lon_arr, lat_arr, np_lon, np_lat,\\n direction='rot2geo'):\\n dimensions = lon_arr.shape\\n lon_arr_new = np.zeros(dimensions)\\n lat_arr_new = np.zeros(dimensions)\\n for i in range(dimensions[1]): # x-direction\\n for j in range(dimensions[0]): # y-direction\\n lon = lon_arr[j, i]\\n lat = lat_arr[j, i]\\n lon_new, lat_new = rotated_coord_transform(\\n lon, lat, np_lon, np_lat, direction=direction)\\n lon_arr_new[j, i] = lon_new\\n lat_arr_new[j, i] = lat_new\\n\\n return (lon_arr_new, lat_arr_new)\",\n \"def convert(self, lat, lon):\\r\\n a = self.a\\r\\n b = self.b\\r\\n long0 = self.long0\\r\\n k0 = self.k0\\r\\n dx = self.dx\\r\\n\\r\\n e = (1 - b ** 2 / a ** 2) ** 0.5\\r\\n e2 = e ** 2 / (1 - e ** 2)\\r\\n n = (a - b) / (a + b)\\r\\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\\r\\n p = lon - long0\\r\\n\\r\\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\\r\\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\\r\\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\\r\\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\\r\\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\\r\\n\\r\\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\\r\\n\\r\\n K1 = S * k0\\r\\n K2 = k0 * nu * sin(2 * lat)/4.0\\r\\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\\\\r\\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\\r\\n\\r\\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\\r\\n\\r\\n K4 = k0 * nu * cos(lat)\\r\\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\\r\\n\\r\\n x = K4 * p + K5 * (p ** 3) + dx\\r\\n return x, y\",\n \"def _pixel_to_map(coordinates, geotransform):\\n coordinates_map = np.empty(coordinates.shape)\\n coordinates_map[..., 0] = (\\n geotransform[0]\\n + geotransform[1] * coordinates[..., 0]\\n + geotransform[2] * coordinates[..., 1]\\n )\\n coordinates_map[..., 1] = (\\n geotransform[3]\\n + geotransform[4] * coordinates[..., 0]\\n + geotransform[5] * coordinates[..., 1]\\n )\\n return coordinates_map\",\n \"def create_pseudo_epsg4326_coordinates(self):\\n self.create_3d_coord_on_sphere(on_sphere=True)\\n self.df_attributes['lat'] = 180*(pi/2 - np.arccos(self.df_attributes['coord_z']))/pi\\n self.df_attributes['lon'] = 180*np.arctan2(self.df_attributes['coord_y'], self.df_attributes['coord_x'])/pi\",\n \"def _getXYZ ( lon, lat ):\\n d2r = pi / 180.\\n rlon, rlat = ( d2r * lon, d2r * lat )\\n x = cos(rlat) * cos(rlon)\\n y = cos(rlat) * sin(rlon)\\n z = sin(rlat)\\n return (x,y,z)\",\n \"def Pixel2World(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xdist = geoMatrix[1]\\r\\n ydist = geoMatrix[5]\\r\\n coorX = (ulX + (x * xdist))\\r\\n coorY = (ulY + (y * ydist))\\r\\n return (coorX, coorY)\",\n \"def latlon_2_grid(x, y, z, origin):\\n new_y = (y - origin[1]) * 111111\\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\\n return new_x, new_y, z\",\n \"def _transform_coordinates(rectangle, Q=np.matrix(((1, 1), (-1, 1)))):\\n return tuple((rectangle[0]*Q).A1), tuple((rectangle[1]*Q).A1)\",\n \"def geo2cell(geofile, posfile):\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*np.sqrt(1-np.cos(lat))\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def convertLatLon(latCell, lonCell):\\n cell_lats = np.array([])\\n cell_lons = np.array([])\\n for lat in latCell:\\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \\n for lon in lonCell:\\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \\n\\n return cell_lats, cell_lons\",\n \"def convertGeo2View(self, lon, lat):\\n\\n x_pix = (lon - self.view_llon) * self.ppd_x\\n y_pix = (self.view_tlat - lat) * self.ppd_y\\n\\n return (x_pix, y_pix)\",\n \"def ll_to_xy(lon, lat):\\n lllon = lon.min()\\n lllat = lat.min()\\n urlon = lon.max()\\n urlat = lat.max()\\n\\n dlon = urlon - lllon\\n dlat = urlat - lllat\\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\\n\\n x = dx_*(lon - lllon)/dlon\\n y = dy_*(lat - lllat)/dlat\\n\\n return x, y\",\n \"def read_affine(file):\\n data = open(file, 'r').read()\\n data = data.split('\\\\n')\\n for i in range(1, 5):\\n data[i] = data[i].split(':')\\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\\n Nlon = len(int_lon) - 1\\n Nlat = len(int_lat) - 1\\n data[3][1] = data[3][1].split(',')\\n data[4][1] = data[4][1].split(',')\\n lon_transform = np.zeros((Nlon, 2))\\n lat_transform = np.zeros((Nlat, 2))\\n for i in range(Nlon):\\n data[3][1][i] = data[3][1][i].split(' ')\\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\\n for i in range(Nlat):\\n data[4][1][i] = data[4][1][i].split(' ')\\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\\n lon_transform = np.array(lon_transform).astype('float')\\n lat_transform = np.array(lat_transform).astype('float')\\n return int_lon, int_lat, lon_transform, lat_transform\",\n \"def ll_coordinates(self):\\n\\n x, y = self.xy_coordinates\\n proj_out = check_crs('EPSG:4326')\\n\\n return transform_proj(self.proj, proj_out, x, y)\",\n \"def ll2fxy(lon,lat):\\n return xyz2fxy(*ll2xyz(lon,lat))\",\n \"def coords_to_structure(self) -> None:\\n ...\",\n \"def __affine_geo_transformation(x, y, gtr):\\n\\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\\n # Affine transformation rewritten for rasterio:\\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\\n\\n return gtr_x, gtr_y\",\n \"def wgs84_to_mercator(df, lon, lat):\\n k = 6378137\\n df[\\\"x\\\"] = df[lon] * (k * np.pi/180.0)\\n df[\\\"y\\\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\\n return df\",\n \"def rotated_coord_transform(lon, lat, np_lon, np_lat,\\n direction='rot2geo'):\\n\\n # Convert degrees to radians\\n lon = (lon * math.pi) / 180.\\n lat = (lat * math.pi) / 180.\\n\\n# SP_lon = SP_coor(1)\\n# SP_lat = SP_coor(2)\\n\\n theta = 90. - np_lat # Rotation around y-axis\\n phi = np_lon + 180. # Rotation around z-axis\\n\\n # Convert degrees to radians\\n phi = (phi * math.pi) / 180.\\n theta = (theta * math.pi) / 180.\\n\\n # Convert from spherical to cartesian coordinates\\n x = math.cos(lon) * math.cos(lat)\\n y = math.sin(lon) * math.cos(lat)\\n z = math.sin(lat)\\n\\n # Regular -> Rotated\\n if direction == 'geo2rot':\\n\\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.cos(theta) * math.sin(phi) * y +\\n math.sin(theta) * z)\\n y_new = (- math.sin(phi) * x +\\n math.cos(phi) * y)\\n z_new = (- math.sin(theta) * math.cos(phi) * x -\\n math.sin(theta) * math.sin(phi) * y +\\n math.cos(theta) * z)\\n\\n # Rotated -> Regular\\n elif direction == 'rot2geo':\\n \\n phi = - phi\\n theta = - theta\\n \\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.sin(phi) * y +\\n math.sin(theta) * math.cos(phi) * z)\\n y_new = (- math.cos(theta) * math.sin(phi) * x +\\n math.cos(phi) * y -\\n math.sin(theta) * math.sin(phi) * z)\\n z_new = (- math.sin(theta) * x +\\n math.cos(theta) * z)\\n\\n # Convert cartesian back to spherical coordinates\\n lon_new = math.atan2(y_new, x_new)\\n lat_new = math.asin(z_new)\\n\\n # Convert radians back to degrees\\n lon_new = (lon_new * 180.) / math.pi\\n lat_new = (lat_new * 180.) / math.pi;\\n\\n return (lon_new, lat_new)\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*lat/(np.pi/2)\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def lat_lng(row):\\r\\n lat = row[\\\"latitude\\\"]\\r\\n lng = row[\\\"longitude\\\"]\\r\\n n = int(lat/GRANULARITY)\\r\\n nlat_start = n * GRANULARITY\\r\\n nlat_end = nlat_start + GRANULARITY\\r\\n nlg=int(lng/GRANULARITY)\\r\\n nlng_start = nlg * GRANULARITY\\r\\n nlng_end = nlng_start + GRANULARITY\\r\\n latlng=[(nlat_start,nlng_start), (nlat_start,nlng_end), (nlat_end,nlng_end), (nlat_end,nlng_start)]\\r\\n return latlng\",\n \"def coord2pixel(tf, lat, lon):\\n x = int(round((lon-tf[0])/tf[1]))\\n y = int(round((lat-tf[3])/tf[5]))\\n\\n return x, y\",\n \"def ind2coord(self, index):\\n\\n # assert (index >= 0)\\n # assert(index < self.n - 1)\\n\\n col = index // self.rows\\n row = index % self.rows\\n\\n return [row, col]\",\n \"def make_simple_coords():\\n \\n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\\n coords = np.vstack((x,y)).T\\n return coords\",\n \"def rowcol_to_latlon(rowcol_str):\\n\\n # Vectors for code in file name\\n latspace = np.linspace(-90,90,46)\\n lonspace = np.linspace(-180,180,73)\\n lat = latspace[int(rowcol_str[0:2])-1]\\n lon = lonspace[int(rowcol_str[2:])-1]\\n return (lat,lon)\",\n \"def _indices_to_coords(c,r):\\n\\n column = _index_to_column(c)\\n row = r + 1\\n\\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}\",\n \"def ll2xyz(lon_pt,lat_pt):\\n\\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\\n zPt = np.sin(lat_pt)\\n return [xPt,yPt,zPt]\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def transform_coordinates(self, p):\\n if type(p) == PhysicalObject:\\n return self.transform_coordinates(p.position)\\n elif type(p) == Vector:\\n return tuple(map(\\n lambda x: int(x),\\n (p / SCALE_FACTOR - self.pos_shift))\\n )\",\n \"def _generate_coords(self):\\n coords = np.dstack([self.X.ravel(), self.Y.ravel()])[0]\\n return coords, spatial.cKDTree(coords)\",\n \"def coord(self, x, y):\\n origin_x = self._raster_meta['transform'][3]\\n origin_y = self._raster_meta['transform'][0]\\n pixel_x = self._raster_meta['transform'][5]\\n pixel_y = self._raster_meta['transform'][1]\\n\\n x = int((x - origin_x) / pixel_x)\\n y = int((y - origin_y) / pixel_y)\\n return self[x, y]\",\n \"def latlon_to_pixels(lon, lat):\\n radius = 489.5\\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\\n x_pos = x_pos_from_center + 512.5\\n \\n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\\n y_pos = y_pos_from_center + 512.5\\n \\n return x_pos, y_pos\",\n \"def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False):\\n\\n x, y = np.ma.array(x), np.ma.array(y)\\n\\n # First to local proj\\n _crs = check_crs(crs, raise_on_error=True)\\n if isinstance(_crs, pyproj.Proj):\\n x, y = transform_proj(_crs, self.proj, x, y)\\n elif isinstance(_crs, Grid):\\n x, y = _crs.ij_to_crs(x, y, crs=self.proj)\\n\\n # Then to local grid\\n x = (x - self.x0) / self.dx\\n y = (y - self.y0) / self.dy\\n\\n # See if we need to round\\n if nearest:\\n f = np.rint if self.pixel_ref == 'center' else np.floor\\n x = f(x).astype(int)\\n y = f(y).astype(int)\\n\\n # Mask?\\n if maskout:\\n if self.pixel_ref == 'center':\\n mask = ~((x >= -0.5) & (x < self.nx-0.5) &\\n (y >= -0.5) & (y < self.ny-0.5))\\n else:\\n mask = ~((x >= 0) & (x < self.nx) &\\n (y >= 0) & (y < self.ny))\\n x = np.ma.array(x, mask=mask)\\n y = np.ma.array(y, mask=mask)\\n\\n return x, y\",\n \"def get_coordinates_geo(self):\\n if not self.rotated:\\n lon_arr_geo = self.lon_arr\\n lat_arr_geo = self.lat_arr\\n else:\\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \\n\\n return lon_arr_geo, lat_arr_geo\",\n \"def transform_pnt(pnt, inEPSG, outEPSG):\\r\\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\\r\\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\\r\\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\\r\\n\\r\\n return pyproj.transform(inProj, outProj, x1, y1)\",\n \"def rowcol2XY(row,col,CCD):\\n pixscale = 0.015 #mm/pix\\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\\n return X,Y\",\n \"def trans_to_coordinates(T, pts):\\n p = []\\n for i in range(len(pts)):\\n \\n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\\n p_a = np.matmul(T, p_b).tolist()\\n p.append(p_a[0:3])\\n\\n return p\",\n \"def trans_to_coordinates(T, pts):\\n p = []\\n for i in range(len(pts)):\\n \\n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\\n p_a = np.matmul(T, p_b).tolist()\\n p.append(p_a[0:3])\\n\\n return p\",\n \"def gps_to_node(self,lat,lon):\\n row,col = self.gps_to_coords(lat,lon)\\n if row == -1:\\n return -1\\n return self.coords_to_node(row,col)\",\n \"def to_position(self, x, y, i, j):\\n return (x * self.SIZE + i, y * self.SIZE + j)\",\n \"def map(self, obj):\\n if isinstance(obj, np.ndarray) and obj.ndim >= 2 and obj.shape[0] in (2,3):\\n return fn.transformCoordinates(self, obj)\\n else:\\n return QtGui.QMatrix4x4.map(self, obj)\",\n \"def transform(self, srid: ir.IntegerValue) -> GeoSpatialValue:\\n return ops.GeoTransform(self, srid).to_expr()\",\n \"def transform_from(self, coordinates) -> np.ndarray:\\n return (coordinates @ self.T) + self._o\",\n \"def transform(self, x: Array2D) -> Array2D:\",\n \"def reproject(self, lon, lat):\\n if self.xform is None:\\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\\n return self.xform.transform(QgsPointXY(lon, lat))\",\n \"def composite(c, r):\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\",\n \"def node_to_coords(self,node_num):\\n row = (node_num - 1) / self.cols\\n col = (node_num - 1) % self.cols\\n return (row,col)\",\n \"def proj(self,lon,lat):\\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\\n x[x > 1e29] = None\\n y[y > 1e29] = None\\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\\n return x, y\",\n \"def transform(o):\\n\\t\\t\\tp = o.pos() - self.pos()\\n\\t\\t\\tx = cosa * p.x + sina * p.y\\n\\t\\t\\ty = -sina * p.x + cosa * p.y\\n\\t\\t\\treturn tuple(px_to_mm(x,y))\",\n \"def transform_to(self, coordinates) -> np.ndarray:\\n return (coordinates - self._o) @ self.T.T\",\n \"def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\\r\\n # convert decimals to seconds...\\r\\n s_lon = dec2sec(s_lon)\\r\\n s_lat = dec2sec(s_lat)\\r\\n\\r\\n ## Auxiliary values \\r\\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\\r\\n s_lng_aux = (s_lon - 26782.5)/10000.\\r\\n s_lat_aux = (s_lat - 169028.66)/10000.\\r\\n \\r\\n # easting\\r\\n s_x = (600072.37 \\r\\n + 211455.93*s_lng_aux \\r\\n - 10938.51*s_lng_aux*s_lat_aux \\r\\n - 0.36*s_lng_aux*(s_lat_aux**2) \\r\\n - 44.54*(s_lng_aux**3))\\r\\n \\r\\n # northing\\r\\n s_y = (200147.07 \\r\\n + 308807.95*s_lat_aux \\r\\n + 3745.25*(s_lng_aux**2) \\r\\n + 76.63*(s_lat_aux**2) \\r\\n - 194.56*(s_lng_aux**2)*s_lat_aux \\r\\n + 119.79*(s_lat_aux**3))\\r\\n\\r\\n return s_x, s_y\",\n \"def coordinates(self):\",\n \"def makeToCoordinates(fromCoords, Uframe, Vframe, scale):\\n\\n out = []\\n\\n for e in fromCoords:\\n x = e[0]\\n y = e[1]\\n toX = Uframe[x][y]\\n toY = Vframe[x][y]\\n out.append((int(round(x+toX*scale)),int(round(y+toY*scale))))\\n\\n return out\",\n \"def proj_coord(coord, proj_in, proj_out):\\n x, y = coord\\n return pyproj.transform(proj_in, proj_out, x, y)\",\n \"def transform_data(features):\\n\\n def cart2pol(my_row):\\n x = my_row[0]\\n y = my_row[1]\\n rho = np.sqrt(x**2 + y**2)\\n phi = np.arctan2(y, x)\\n return [rho, phi]\\n\\n #vfunc = np.vectorize(cart2pol)\\n #transformed = vfunc(features)\\n out = []\\n #print(features, \\\"\\\\n\\\\n\\\")\\n for row in features:\\n out.append(cart2pol(row))\\n out = np.array(out)\\n #print(\\\"out is\\\\n\\\", out)\\n \\\"\\\"\\\"\\n x = out[:,0]\\n y = out[:,1]\\n \\n plt.scatter(x,y)\\n plt.show()\\n \\\"\\\"\\\"\\n return out\",\n \"def coords_to_gps(self,coords):\\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))\",\n \"def process_coords():\\n split_coords = row[\\\"map_coord\\\"].split(',')\\n map_x, map_y = [int(i) for i in split_coords]\\n map_x_normed = ((map_x*2) / self.MINIMAP_DIM) - 1\\n map_y_normed = -(((map_y*2) / self.MINIMAP_DIM) - 1)\\n return map_x_normed, map_y_normed\",\n \"def _wrap_coord(tensors):\\n cell = tf.gather_nd(tensors['cell'], tensors['ind_1'])\\n coord = tf.expand_dims(tensors['coord'], -1)\\n frac_coord = tf.linalg.solve(tf.transpose(cell, perm=[0, 2, 1]), coord)\\n frac_coord %= 1\\n coord = tf.matmul(tf.transpose(cell, perm=[0, 2, 1]), frac_coord)\\n return tf.squeeze(coord, -1)\",\n \"def flowline_latlon(coords, fromproj=pyproj.Proj(\\\"+init=epsg:3413\\\"), toproj=pyproj.Proj(\\\"+init=EPSG:4326\\\")):\\n xs = coords[:,0]\\n ys = coords[:,1]\\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\\n latlon_coords = np.asarray(zip(x_lon, y_lat))\\n return latlon_coords\",\n \"def coordinates(self):\\n return np.array([[f.x, f.y] for f in self])\",\n \"def rowcol_to_xy(rows, cols, affine):\\n\\n # make it a 3x3 matrix\\n aff_array = numpy.array(affine).reshape((3, 3))\\n\\n # check that rows amd cols are indeed 1-D vectors\\n rows = validate.is_vector(rows)\\n cols = validate.is_vector(cols)\\n\\n # filler\\n layers = numpy.ones_like(rows)\\n vector = numpy.array([cols, rows, layers])\\n\\n # compute xy\\n xy = numpy.dot(aff_array, vector)[:2, :]\\n return xy\",\n \"def calcPosition (lat, lon):\\n nauticalMilePerLat = 60.00721\\n nauticalMilePerLongitude = 60.10793\\n rad = math.pi / 180.0\\n milesPerNauticalMile = 1.15078\\n \\n y = lat * nauticalMilePerLat\\n x = math.cos(lat * rad) * lon * nauticalMilePerLongitude\\n\\n return x * milesPerNauticalMile * 1609.344, y * milesPerNauticalMile * 1609.344\",\n \"def index_to_xy(index):\\n x = index % columns\\n y = index // columns\\n return x, y\",\n \"def add_latlon(df):\\n LLs = [num2deg(x,y,z) for x,y,z in zip(df['x'],df['y'],df['z'])]\\n LLdf = pd.DataFrame.from_records(LLs,columns = ['latitude','longitude'])\\n return pd.concat([df.reset_index(drop=True),LLdf],axis = 1)\",\n \"def extractCoord(df):\\n dfcol = df['Coord']\\n for i in range(len(dfcol)):\\n dfcol[i] = dfcol[i][6:-1]\\n return df\",\n \"def world2Pixel(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xDist = geoMatrix[1]\\r\\n yDist = geoMatrix[5]\\r\\n rtnX = geoMatrix[2]\\r\\n rtnY = geoMatrix[4]\\r\\n pixel = int((x - ulX) / xDist)\\r\\n line = int((ulY - y) / xDist)\\r\\n return (pixel, line)\",\n \"def convert_coords(self):\\n if self.coordsys in ['image', 'physical']:\\n coords = self._convert_pix_coords()\\n else:\\n coords = self._convert_sky_coords()\\n\\n if self.region_type == 'line':\\n coords = [coords[0][0], coords[0][1]]\\n\\n if self.region_type == 'text':\\n coords.append(self.meta['text'])\\n\\n return coords\",\n \"def lonlat2cr_for_geotif(path):\\n old_cs, new_cs, gta, local_vars = _create_xform(path)\\n transform = osr.CoordinateTransformation(new_cs, old_cs)\\n\\n def composite(lon, lat):\\n \\\"\\\"\\\"xform from (lon, lat) to (c, r)\\\"\\\"\\\"\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return (~gta * transform.TransformPoint(lat, lon)[:2])\\n \\n return composite\",\n \"def project(self, (lng, lat)):\\n x = lng * DEG_TO_RAD\\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\\n y = lat * DEG_TO_RAD\\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)\",\n \"def lon_lat_to_cartesian(lon, lat, R = 1):\\n lon_r = np.radians(lon)\\n lat_r = np.radians(lat)\\n\\n x = R * np.cos(lat_r) * np.cos(lon_r)\\n y = R * np.cos(lat_r) * np.sin(lon_r)\\n z = R * np.sin(lat_r)\\n return x,y,z\",\n \"def transform_points(transf_matrix, points):\\n if(type(points)==list):\\n temp_pts = [np.array([x[0],x[1],x[2],1]) for x in points]\\n newpts = []\\n for pt in temp_pts:\\n newpts.append((transf_matrix@pt)[:3])\\n else:\\n temp_pts = np.array([points[0],points[1],points[2],1])\\n newpts=(transf_matrix@temp_pts)[:3]\\n return newpts\",\n \"def ll2px(lat, lng, zoom):\\n # assert isinstance(lat, (float, int, long)), \\\\\\n # ValueError(\\\"lat must be a float\\\")\\n lat = float(lat)\\n # assert isinstance(lng, (float, int, long)), \\\\\\n # ValueError(\\\"lng must be a float\\\")\\n lng = float(lng)\\n # assert isinstance(zoom, int), TypeError(\\\"zoom must be an int from 0 to 30\\\")\\n # assert 0 <= zoom <= 30, ValueError(\\\"zoom must be an int from 0 to 30\\\")\\n\\n cbk = CBK[zoom]\\n\\n x = int(round(cbk + (lng * CEK[zoom])))\\n\\n foo = math.sin(lat * math.pi / 180)\\n if foo < -0.9999:\\n foo = -0.9999\\n elif foo > 0.9999:\\n foo = 0.9999\\n\\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\\n\\n return (x, y)\",\n \"def coords_from_cell(cell, lon = [-8.73, -8.50], lat = [41.10, 41.25], N = 100, M = 75):\\n lon_step = (lon[1] - lon[0]) / N \\n lat_step = (lat[1] - lat[0]) / M\\n \\n middle_lon = lon[0] + cell[0] * lon_step + lon_step / 2\\n middle_lat = lat[0] + cell[1] * lat_step + lat_step / 2\\n \\n return [middle_lon, middle_lat]\",\n \"def coords2D(self):\\n return (self.x, self.y)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66467303","0.6624821","0.66023815","0.65764207","0.6559197","0.6513407","0.6504504","0.6468915","0.6444075","0.63868356","0.63452953","0.63281846","0.6316108","0.6261537","0.62128407","0.61810315","0.61589795","0.6146225","0.61366695","0.61320674","0.61221755","0.6119672","0.61184245","0.6110834","0.61034334","0.60973513","0.6093197","0.6078339","0.6071216","0.6037053","0.60218614","0.6019969","0.60084957","0.5984568","0.59712565","0.5970025","0.59673214","0.59549135","0.5949858","0.5942927","0.5923335","0.5919791","0.5919431","0.5912139","0.5910367","0.5901057","0.5898693","0.5896501","0.5875813","0.58654743","0.58652204","0.58649313","0.58616966","0.5860355","0.58470786","0.5843196","0.58263576","0.58107483","0.5803726","0.57901657","0.57880795","0.57878816","0.5784932","0.5784932","0.57787365","0.57689434","0.575777","0.5740741","0.57320386","0.57281774","0.57270473","0.5720092","0.57067806","0.570512","0.5696017","0.56822795","0.56818867","0.5677532","0.5677358","0.5663823","0.5663246","0.5662721","0.565919","0.5656542","0.5650695","0.5647151","0.56427693","0.56182647","0.561261","0.56083333","0.55982876","0.55894357","0.5585194","0.558227","0.5578931","0.5578763","0.5577356","0.557305","0.5570153","0.5559854"],"string":"[\n \"0.66467303\",\n \"0.6624821\",\n \"0.66023815\",\n \"0.65764207\",\n \"0.6559197\",\n \"0.6513407\",\n \"0.6504504\",\n \"0.6468915\",\n \"0.6444075\",\n \"0.63868356\",\n \"0.63452953\",\n \"0.63281846\",\n \"0.6316108\",\n \"0.6261537\",\n \"0.62128407\",\n \"0.61810315\",\n \"0.61589795\",\n \"0.6146225\",\n \"0.61366695\",\n \"0.61320674\",\n \"0.61221755\",\n \"0.6119672\",\n \"0.61184245\",\n \"0.6110834\",\n \"0.61034334\",\n \"0.60973513\",\n \"0.6093197\",\n \"0.6078339\",\n \"0.6071216\",\n \"0.6037053\",\n \"0.60218614\",\n \"0.6019969\",\n \"0.60084957\",\n \"0.5984568\",\n \"0.59712565\",\n \"0.5970025\",\n \"0.59673214\",\n \"0.59549135\",\n \"0.5949858\",\n \"0.5942927\",\n \"0.5923335\",\n \"0.5919791\",\n \"0.5919431\",\n \"0.5912139\",\n \"0.5910367\",\n \"0.5901057\",\n \"0.5898693\",\n \"0.5896501\",\n \"0.5875813\",\n \"0.58654743\",\n \"0.58652204\",\n \"0.58649313\",\n \"0.58616966\",\n \"0.5860355\",\n \"0.58470786\",\n \"0.5843196\",\n \"0.58263576\",\n \"0.58107483\",\n \"0.5803726\",\n \"0.57901657\",\n \"0.57880795\",\n \"0.57878816\",\n \"0.5784932\",\n \"0.5784932\",\n \"0.57787365\",\n \"0.57689434\",\n \"0.575777\",\n \"0.5740741\",\n \"0.57320386\",\n \"0.57281774\",\n \"0.57270473\",\n \"0.5720092\",\n \"0.57067806\",\n \"0.570512\",\n \"0.5696017\",\n \"0.56822795\",\n \"0.56818867\",\n \"0.5677532\",\n \"0.5677358\",\n \"0.5663823\",\n \"0.5663246\",\n \"0.5662721\",\n \"0.565919\",\n \"0.5656542\",\n \"0.5650695\",\n \"0.5647151\",\n \"0.56427693\",\n \"0.56182647\",\n \"0.561261\",\n \"0.56083333\",\n \"0.55982876\",\n \"0.55894357\",\n \"0.5585194\",\n \"0.558227\",\n \"0.5578931\",\n \"0.5578763\",\n \"0.5577356\",\n \"0.557305\",\n \"0.5570153\",\n \"0.5559854\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5831346"},"document_rank":{"kind":"string","value":"56"}}},{"rowIdx":85696,"cells":{"query":{"kind":"string","value":"xform from (c, r) to (lon, lat)"},"document":{"kind":"string","value":"def composite(c, r):\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cr2lonlat_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(old_cs, new_cs)\n\n def composite(c, r):\n \"\"\"xform from (c, r) to (lon, lat)\"\"\"\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat\n \n return composite","def lonlat2cr_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(new_cs, old_cs)\n\n def composite(lon, lat):\n \"\"\"xform from (lon, lat) to (c, r)\"\"\"\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return (~gta * transform.TransformPoint(lat, lon)[:2])\n \n return composite","def merc(lat, lon):\n\tr_major = 6378137.000\n\tx = r_major * math.radians(lon)\n\tscale = x/lon\n\ty = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale\n\treturn (x, y)","def convert(self, lat, lon):\r\n a = self.a\r\n b = self.b\r\n long0 = self.long0\r\n k0 = self.k0\r\n dx = self.dx\r\n\r\n e = (1 - b ** 2 / a ** 2) ** 0.5\r\n e2 = e ** 2 / (1 - e ** 2)\r\n n = (a - b) / (a + b)\r\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\r\n p = lon - long0\r\n\r\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\r\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\r\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\r\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\r\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\r\n\r\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\r\n\r\n K1 = S * k0\r\n K2 = k0 * nu * sin(2 * lat)/4.0\r\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\r\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\r\n\r\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\r\n\r\n K4 = k0 * nu * cos(lat)\r\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\r\n\r\n x = K4 * p + K5 * (p ** 3) + dx\r\n return x, y","def lon_lat_to_cartesian(lon, lat, R = 1):\n lon_r = np.radians(lon)\n lat_r = np.radians(lat)\n\n x = R * np.cos(lat_r) * np.cos(lon_r)\n y = R * np.cos(lat_r) * np.sin(lon_r)\n z = R * np.sin(lat_r)\n return x,y,z","def cartesian_to_lon_lat(x, y, z, R = 1):\n lon = np.degrees(np.arctan2(y,x))\n lat = np.degrees(np.pi/2-np.arctan2((x**2+y**2)**0.5,z))\n\n return lon,lat","def _getXYZ ( lon, lat ):\n d2r = pi / 180.\n rlon, rlat = ( d2r * lon, d2r * lat )\n x = cos(rlat) * cos(rlon)\n y = cos(rlat) * sin(rlon)\n z = sin(rlat)\n return (x,y,z)","def convertCoord(lon, lat, inEPSG, outEPSG):\n from pyproj import Proj, transform\n inProj = Proj(init='epsg:'+str(inEPSG))\n outProj = Proj(init='epsg:'+str(outEPSG))\n x, y = transform(inProj, outProj, lon, lat)\n return x, y\n # epsg:4326 WGS84\n # epsg:2950 MTM8\n # epsg:6622 Quebec Lambert","def ll2xyz(lon_pt,lat_pt):\n\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\n zPt = np.sin(lat_pt)\n return [xPt,yPt,zPt]","def transform_from_latlon(lat, lon):\n from affine import Affine\n lat = np.asarray(lat)\n lon = np.asarray(lon)\n trans = Affine.translation(lon[0], lat[0])\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n return trans * scale","def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\r\n # convert decimals to seconds...\r\n s_lon = dec2sec(s_lon)\r\n s_lat = dec2sec(s_lat)\r\n\r\n ## Auxiliary values \r\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\r\n s_lng_aux = (s_lon - 26782.5)/10000.\r\n s_lat_aux = (s_lat - 169028.66)/10000.\r\n \r\n # easting\r\n s_x = (600072.37 \r\n + 211455.93*s_lng_aux \r\n - 10938.51*s_lng_aux*s_lat_aux \r\n - 0.36*s_lng_aux*(s_lat_aux**2) \r\n - 44.54*(s_lng_aux**3))\r\n \r\n # northing\r\n s_y = (200147.07 \r\n + 308807.95*s_lat_aux \r\n + 3745.25*(s_lng_aux**2) \r\n + 76.63*(s_lat_aux**2) \r\n - 194.56*(s_lng_aux**2)*s_lat_aux \r\n + 119.79*(s_lat_aux**3))\r\n\r\n return s_x, s_y","def to_lnglat(r):\n \n x, y = r['x_coordinate'], r['y_coordinate']\n if np.isnan(x) or np.isnan(y):\n return None, None\n return projection(x, y, inverse = True)","def convertView2Geo(self, x, y):\n\n # x_pix is from left map edge, y_pix from top map edge\n x_pix = x + self.view_offset_x\n y_pix = y + self.view_offset_y\n\n lon = self.map_llon + x_pix/self.ppd_x\n lat = self.map_tlat - y_pix/self.ppd_y\n\n return (lon, lat)","def transform_coordinates(coords):\n # WGS 84 reference coordinate system parameters\n A = 6378.137 # major axis [km]\n E2 = 6.69437999014e-3 # eccentricity squared\n\n coords = prepare_coords(coords)\n\n # convert to radiants\n lat_rad = np.radians(coords[:, 0])\n lon_rad = np.radians(coords[:, 1])\n\n # convert to cartesian coordinates\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\n z = r_n * (1 - E2) * np.sin(lat_rad)\n\n return np.column_stack((x, y, z))","def _indices_to_coords(c,r):\n\n column = _index_to_column(c)\n row = r + 1\n\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}","def ll2fxy(lon,lat):\n return xyz2fxy(*ll2xyz(lon,lat))","def coords_to_gps(self,coords):\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))","def spherical_to_cartesian(r, lat, lon):\n import math\n\n if np.isscalar(r) and np.isscalar(lat) and np.isscalar(lon):\n x = r * math.cos(lat) * math.cos(lon)\n y = r * math.cos(lat) * math.sin(lon)\n z = r * math.sin(lat)\n else:\n x = r * np.cos(lat) * np.cos(lon)\n y = r * np.cos(lat) * np.sin(lon)\n z = r * np.sin(lat)\n\n return x, y, z","def xyz2longlat(x,y,z):\n r=[]\n long=[]\n d=[]\n lat=[]\n longnew=[]\n latnew=[]\n i=0\n for i in range(len(x)):\n r.append(sp.sqrt(x[i]*x[i] + y[i]*y[i] + z[i]*z[i]))\n \t long.append(sp.arctan2(y[i],x[i]))\n\t d.append(sp.sqrt(x[i]*x[i] + y[i]*y[i]))\n\t lat.append(sp.arcsin(z[i]/r[i]))\n\n i=0\n for i in range(len(x)):\n longnew.append(long[i]*deg)\n latnew.append(lat[i]*deg)\n\n return longnew, latnew, r","def project(self, (lng, lat)):\n x = lng * DEG_TO_RAD\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\n y = lat * DEG_TO_RAD\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)","def xyz_to_lonlat(x,y,z):\n\n XsqPlusYsq = x**2 + y**2\n r = np.sqrt(XsqPlusYsq + z**2) # r\n #elev = np.arccos(z/np.sqrt(XsqPlusYsq)) # theta\n elev = np.arccos(z/r)\n az = np.arctan2(y,x) # phi\n return np.rad2deg(az), 90 - np.rad2deg(elev) # want ","def convert(coords):\n lat = coords[:4]\n lon = coords[4:]\n\n lat = lat[:2] + \".\" + lat[2:]\n\n if int(lon[0]) > 5:\n lon = \"-\" + lon[:2] + \".\" + lon[2:]\n else:\n lon = \"-1\" + lon[:2] + \".\" + lon[2:]\n\n return (float(lat), float(lon))","def map(self, lat, long):\r\n rxy = self._r*lat/(np.pi/2)\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def merc_x(lon):\n r_major = 6378137.000\n x = r_major * math.radians(lon)\n return x","def latlon_2_grid(x, y, z, origin):\n new_y = (y - origin[1]) * 111111\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\n return new_x, new_y, z","def coords_to_X(c, shape):\n a, b = c\n assert 0 <= a <= shape[0]\n assert 0 <= b <= shape[1]\n a = float(a)\n b = float(b)\n a = a + 0.5 # center cells\n b = b + 0.5\n u = a / (shape[0])\n v = b / (shape[1])\n x = 2 * (u - 0.5)\n y = 2 * (v - 0.5)\n assert -1 <= x <= +1\n assert -1 <= y <= +1\n return (x, y)","def map(self, lat, long):\r\n rxy = self._r*np.sqrt(1-np.cos(lat))\r\n x = rxy*np.cos(long)\r\n y = rxy*np.sin(long)\r\n return (x, y)","def proj(self,lon,lat):\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\n x[x > 1e29] = None\n y[y > 1e29] = None\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\n return x, y","def scalar_g2r(al, be, ga, lon, lat):\n\n rad = mt.pi / 180\n al = al * rad\n be = be * rad\n ga = ga * rad\n\n rotate_matrix = np.zeros(shape=(3, 3))\n\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\n al\n )\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\n al\n )\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\n rotate_matrix[2, 2] = np.cos(be)\n \n #rotate_matrix = np.linalg.pinv(rotate_matrix)\n \n lat = lat * rad\n lon = lon * rad\n\n # geographical Cartesian coordinates:\n xr = np.cos(lat) * np.cos(lon)\n yr = np.cos(lat) * np.sin(lon)\n zr = np.sin(lat)\n\n # rotated Cartesian coordinates:\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\n zg = rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\n\n # rotated coordinates:\n rlat = np.arcsin(zg)\n rlon = np.arctan2(yg, xg)\n\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\n if a:\n lon[a] = 0\n\n rlat = rlat / rad\n rlon = rlon / rad\n\n return (rlon, rlat)","def get_ecmwf_lat_lon(nc_file):\n from netCDF4 import Dataset\n \n fh = Dataset(nc_file, mode='r')\n\n latitude_ecmwf = fh.variables['latitude_ecmwf'][:]\n longitude_ecmwf = fh.variables['longitude_ecmwf'][:]\n\n lonmesh_ecmwf,latmesh_ecmwf = np.meshgrid(longitude_ecmwf,latitude_ecmwf)\n\n print('latitude_ecmwf: ', latitude_ecmwf.shape)\n print('longitude_ecmwf: ', longitude_ecmwf.shape)\n \n return latitude_ecmwf, longitude_ecmwf, latmesh_ecmwf, lonmesh_ecmwf;","def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\n\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\n return (-1,-1)\n\n lat_step = abs(max_lat-min_lat)/rows\n lon_step = abs(max_lon-min_lon)/cols\n\n lat_spot = int((max_lat-lat)/lat_step)\n lon_spot = int((lon-min_lon)/lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def translation(coord, lat, lon):\n ty = merc_y(lat) - 73.43 # Y translation\n tx = merc_x(lon) - 57.55 # X translation\n for i in range(len(coord)):\n for j in range(len(coord[i])):\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\n return coord","def scalar_r2g(al, be, ga, rlon, rlat):\n\n rad = mt.pi / 180\n al = al * rad\n be = be * rad\n ga = ga * rad\n rotate_matrix = np.zeros(shape=(3, 3))\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\n al\n )\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\n al\n )\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\n rotate_matrix[2, 2] = np.cos(be)\n\n rotate_matrix = np.linalg.pinv(rotate_matrix)\n\n rlat = rlat * rad\n rlon = rlon * rad\n\n # Rotated Cartesian coordinates:\n xr = np.cos(rlat) * np.cos(rlon)\n yr = np.cos(rlat) * np.sin(rlon)\n zr = np.sin(rlat)\n\n # Geographical Cartesian coordinates:\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\n zg = (\n rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\n )\n\n # Geographical coordinates:\n lat = np.arcsin(zg)\n lon = np.arctan2(yg, xg)\n\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\n if a:\n lon[a] = 0\n\n lat = lat / rad\n lon = lon / rad\n\n return (lon, lat)","def ll2csflat(lon,lat,ncs):\n j,f,i = ll2jfi(lon,lat,ncs)\n return (j*6+f)*ncs+i","def pixel2coord(tf, x, y):\n lat = tf[0] + x*tf[1] + y*tf[2]\n lon = tf[3] + x*tf[4] + y*tf[5]\n\n return lat, lon","def coord2pixel(tf, lat, lon):\n x = int(round((lon-tf[0])/tf[1]))\n y = int(round((lat-tf[3])/tf[5]))\n\n return x, y","def ecef_coords(lats: ndarray, lons: ndarray) -> Tuple[ndarray, ndarray, ndarray]:\n # Cartopy Geodetic and Geocentric both default to the WGS84 datum\n spherical_latlon_crs = Geodetic()\n ecef_crs = Geocentric()\n xyz = ecef_crs.transform_points(\n spherical_latlon_crs, np.array(lons), np.array(lats)\n )\n return xyz[..., 0], xyz[..., 1], xyz[..., 2]","def unproject(self, (x, y)):\n lng = x/EARTH_RADIUS * RAD_TO_DEG\n lat = 2 * math.atan(math.exp(y/EARTH_RADIUS)) - math.pi/2 * RAD_TO_DEG\n return (lng, lat)","def reverse(self, lon, lat):","def undo_mercator_project(x,y):\n lon = y*np.pi\n ex = np.exp(4*np.pi*x)\n lat = np.arcsin((ex - 1)/(ex +1 ))\n lon = lon*360/2/np.pi\n lat = lat*360 /2/np.pi\n return lon, lat","def ll_to_xy(lon, lat):\n lllon = lon.min()\n lllat = lat.min()\n urlon = lon.max()\n urlat = lat.max()\n\n dlon = urlon - lllon\n dlat = urlat - lllat\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\n\n x = dx_*(lon - lllon)/dlon\n y = dy_*(lat - lllat)/dlat\n\n return x, y","def convertGeo2View(self, lon, lat):\n\n x_pix = (lon - self.view_llon) * self.ppd_x\n y_pix = (self.view_tlat - lat) * self.ppd_y\n\n return (x_pix, y_pix)","def spher2cart(lon: np.ndarray,\n lat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n rlon = np.radians(lon)\n rlat = np.radians(lat)\n x = np.cos(rlon) * np.cos(rlat)\n y = np.sin(rlon) * np.cos(rlat)\n z = np.sin(rlat)\n return x, y, z","def LongLat(x, y, z):\n r = (x**2 + y**2 + z**2)**0.5\n long = numpy.arctan2(y, x)\n lat = numpy.arcsin(z / r)\n return long, lat, r","def ra2xy(self, ra):\n return -math.sin(ra), math.cos(ra)","def rotated_coord_transform(lon, lat, np_lon, np_lat,\n direction='rot2geo'):\n\n # Convert degrees to radians\n lon = (lon * math.pi) / 180.\n lat = (lat * math.pi) / 180.\n\n# SP_lon = SP_coor(1)\n# SP_lat = SP_coor(2)\n\n theta = 90. - np_lat # Rotation around y-axis\n phi = np_lon + 180. # Rotation around z-axis\n\n # Convert degrees to radians\n phi = (phi * math.pi) / 180.\n theta = (theta * math.pi) / 180.\n\n # Convert from spherical to cartesian coordinates\n x = math.cos(lon) * math.cos(lat)\n y = math.sin(lon) * math.cos(lat)\n z = math.sin(lat)\n\n # Regular -> Rotated\n if direction == 'geo2rot':\n\n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.cos(theta) * math.sin(phi) * y +\n math.sin(theta) * z)\n y_new = (- math.sin(phi) * x +\n math.cos(phi) * y)\n z_new = (- math.sin(theta) * math.cos(phi) * x -\n math.sin(theta) * math.sin(phi) * y +\n math.cos(theta) * z)\n\n # Rotated -> Regular\n elif direction == 'rot2geo':\n \n phi = - phi\n theta = - theta\n \n x_new = (math.cos(theta) * math.cos(phi) * x +\n math.sin(phi) * y +\n math.sin(theta) * math.cos(phi) * z)\n y_new = (- math.cos(theta) * math.sin(phi) * x +\n math.cos(phi) * y -\n math.sin(theta) * math.sin(phi) * z)\n z_new = (- math.sin(theta) * x +\n math.cos(theta) * z)\n\n # Convert cartesian back to spherical coordinates\n lon_new = math.atan2(y_new, x_new)\n lat_new = math.asin(z_new)\n\n # Convert radians back to degrees\n lon_new = (lon_new * 180.) / math.pi\n lat_new = (lat_new * 180.) / math.pi;\n\n return (lon_new, lat_new)","def pixelloc_to_geoloc(row, col, loff, coff):\n row = np.atleast_1d(row)\n col = np.atleast_1d(col)\n\n assert row.shape == col.shape\n\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\n\n if lat.size == lon.size == 1:\n # Return scalars\n return lat[0], lon[0]\n else:\n return lat, lon","def mapr(r):\n return np.rad2deg(np.arctan(r)*2)","def cartesian2Geo(julian_date, x, y, z):\n\n\n # Calculate LLA\n lat, r_LST, ele = ecef2LatLonAlt(x, y, z)\n\n # Calculate proper longitude from the given JD\n lon, _ = LST2LongitudeEast(julian_date, np.degrees(r_LST))\n\n # Convert longitude to radians\n lon = np.radians(lon)\n\n\n return np.degrees(lat), np.degrees(lon), ele","def convertLatLon(latCell, lonCell):\n cell_lats = np.array([])\n cell_lons = np.array([])\n for lat in latCell:\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \n for lon in lonCell:\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \n\n return cell_lats, cell_lons","def ReprojectCoords(coords,src_srs,tgt_srs):\n trans_coords = []\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\n for x,y in coords:\n x,y,z = transform.TransformPoint(x,y)\n trans_coords.append([x,y])\n return trans_coords","def transform_pnt(pnt, inEPSG, outEPSG):\r\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\r\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\r\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\r\n\r\n return pyproj.transform(inProj, outProj, x1, y1)","def cartesian_coordinates(self):\n # extract RA items\n ra_hours, ra_minutes, ra_seconds = RA_RE.match(str(self.ra)).groups()\n # then cast\n ra_hours = int(ra_hours)\n ra_minutes = int(ra_minutes)\n ra_seconds = float(ra_seconds)\n\n # extract DEC items\n dec_sign, dec_degrees, dec_minutes, dec_seconds = DEC_RE.match(str(self.dec)).groups()\n # then cast\n dec_sign = -1 if dec_sign == '-' else 1\n dec_degrees = int(dec_degrees)\n dec_minutes = int(dec_minutes)\n dec_seconds = float(dec_seconds)\n\n # to degrees\n a = (ra_hours*15) + (ra_minutes*0.25) + (ra_seconds*0.004166)\n b = abs(dec_degrees + dec_minutes/60 + dec_seconds/3600) * dec_sign\n\n # to radians\n a = math.radians(a)\n b = math.radians(b)\n\n distance = float(self.distance)\n\n x = (distance * math.cos(b)) * math.cos(a)\n y = (distance * math.cos(b)) * math.sin(a)\n z = distance * math.sin(b)\n\n return x, y, z","def latlon_to_pixels(lon, lat):\n radius = 489.5\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\n x_pos = x_pos_from_center + 512.5\n \n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\n y_pos = y_pos_from_center + 512.5\n \n return x_pos, y_pos","def map_coordinates(self,geometry):\n\t\tg = self.geomatrix\n\t\tdef project_coord(x,y,z=None):\n\t\t\tx = g[0] + g[1] * x + g[2] * y\n\t\t\ty = g[3] + g[4] * x + g[5] * y\n\t\t\tif z is None:\n\t\t\t\treturn x,y\n\t\t\telse:\n\t\t\t\treturn x,y,z\n\t\treturn transform(project_coord, geometry)","def read_affine(file):\n data = open(file, 'r').read()\n data = data.split('\\n')\n for i in range(1, 5):\n data[i] = data[i].split(':')\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\n Nlon = len(int_lon) - 1\n Nlat = len(int_lat) - 1\n data[3][1] = data[3][1].split(',')\n data[4][1] = data[4][1].split(',')\n lon_transform = np.zeros((Nlon, 2))\n lat_transform = np.zeros((Nlat, 2))\n for i in range(Nlon):\n data[3][1][i] = data[3][1][i].split(' ')\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\n for i in range(Nlat):\n data[4][1][i] = data[4][1][i].split(' ')\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\n lon_transform = np.array(lon_transform).astype('float')\n lat_transform = np.array(lat_transform).astype('float')\n return int_lon, int_lat, lon_transform, lat_transform","def gnomonic_project_toxy(RA1, Dec1, RAcen, Deccen):\n # also used in Global Telescope Network website\n cosc = np.sin(Deccen) * np.sin(Dec1) + np.cos(Deccen) * np.cos(Dec1) * np.cos(RA1-RAcen)\n x = np.cos(Dec1) * np.sin(RA1-RAcen) / cosc\n y = (np.cos(Deccen)*np.sin(Dec1) - np.sin(Deccen)*np.cos(Dec1)*np.cos(RA1-RAcen)) / cosc\n return x, y","def gps_to_coords(self,lat,lon):\n\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\n return (-1,-1)\n\n lat_spot = int((self.max_lat-lat)/self.lat_step)\n lon_spot = int((lon-self.min_lon)/self.lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def update_coords(self, l, b):\n self.l = l\n self.b = b\n self.ra, self.dec = astLib.astCoords.convertCoords(\n \"GALACTIC\", \"J2000\", self.l, self.b, epoch=2000.)","def flowline_latlon(coords, fromproj=pyproj.Proj(\"+init=epsg:3413\"), toproj=pyproj.Proj(\"+init=EPSG:4326\")):\n xs = coords[:,0]\n ys = coords[:,1]\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\n latlon_coords = np.asarray(zip(x_lon, y_lat))\n return latlon_coords","def ll2px(lat, lng, zoom):\n # assert isinstance(lat, (float, int, long)), \\\n # ValueError(\"lat must be a float\")\n lat = float(lat)\n # assert isinstance(lng, (float, int, long)), \\\n # ValueError(\"lng must be a float\")\n lng = float(lng)\n # assert isinstance(zoom, int), TypeError(\"zoom must be an int from 0 to 30\")\n # assert 0 <= zoom <= 30, ValueError(\"zoom must be an int from 0 to 30\")\n\n cbk = CBK[zoom]\n\n x = int(round(cbk + (lng * CEK[zoom])))\n\n foo = math.sin(lat * math.pi / 180)\n if foo < -0.9999:\n foo = -0.9999\n elif foo > 0.9999:\n foo = 0.9999\n\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\n\n return (x, y)","def fn2lonlat(filename):\n tokens = filename.split(\"/\")[-1].rsplit(\".\", 1)[0].split(\"x\")\n return [0 - float(tokens[0]), float(tokens[1])]","def geocentric2geodetic(latitude):\n\n return np.rad2deg(np.arctan(1.0067395 * np.tan(np.deg2rad(latitude))))","def vec2geo(r_hat):\n ra = degrees(atan2(r_hat[1], r_hat[0]))\n dec = degrees(atan2(r_hat[2], sqrt(r_hat[0] ** 2 + r_hat[1] ** 2)))\n return ra, dec","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def coord_to_pointing (coordinates):\n\n clat = np.cos (coordinates[0])\n return np.array ([clat * np.cos (coordinates[1]),\n clat * np.sin (coordinates[1]),\n np.sin (coordinates[0])])","def transform_point(p,R,t):\r\n x = R[0][0]*p[0]+R[0][1]*p[1]+t[0]\r\n y = R[1][0]*p[0]+R[1][1]*p[1]+t[1]\r\n return [x,y]","def plane2sphere(x,y,latc,lonc):\n if np.abs(y) > 1:\n lat = np.nan\n lon = np.nan\n return lat,lon\n else:\n pass\n\n coslatc = np.cos(latc)\n sinlatc = np.sin(latc)\n\n cosphi = np.sqrt(1.0 - y**2)\n lat = np.arcsin((y*coslatc) + (cosphi*np.cos(x)*sinlatc))\n\n if np.cos(lat) == 0:\n test = 0.0\n else:\n test = cosphi*np.sin(x)/np.cos(lat)\n\n lon = np.arcsin(test) + lonc\n\n x0 = x\n if np.abs(x0) > np.pi/2.0:\n while x0 > np.pi/2.0:\n lon = np.pi - lon\n x0 = x0 - np.pi\n\n while x0 < -np.pi/2.0:\n lon = -np.pi - lon\n x0 = x0 + np.pi\n\n return lat,lon","def get_latlon_point(self, row, col):\n p1 = Proj(self.src.crs)\n window = rasterio.windows.Window(col, row, 1, 1)\n trnsfrm = self.src.window_transform(window)\n T1 = trnsfrm * Affine.translation(0.5, 0.5)\n p2 = Proj(proj='latlong', datum='WGS84')\n x, y = self.src.xy(row, col)\n lon, lat = transform(p1, p2, x, y)\n return lat, lon","def wgs84_to_mercator(df, lon, lat):\n k = 6378137\n df[\"x\"] = df[lon] * (k * np.pi/180.0)\n df[\"y\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\n return df","def pixels_to_latlon(x_pos, y_pos):\n radius = 489.5\n x_pos_from_center = -1*(512.5 - x_pos)\n angle_1 = np.rad2deg(np.arccos(x_pos_from_center/radius))\n\n y_pos_from_center = -1*(512.5 - y_pos)\n angle_2 = np.rad2deg(np.arccos(y_pos_from_center/radius)) \n\n return -1*angle_1, angle_2","def proj_coords(coords, proj_in, proj_out): \n return [proj_coord(coord, proj_in, proj_out) for coord in coords]","def desiredENU2geo(self, x_L, y_L, z):\n\t\tx = cos(self.local_rot)*x_L - sin(self.local_rot)*y_L\n\t\ty = sin(self.local_rot)*x_L + cos(self.local_rot)*y_L\n\n\t\tlat0 = self.origin[0]\n\t\tlon0 = self.origin[1]\n\n\t\tlat, lon, alt = pm.enu2geodetic(x, y, z, lat0, lon0, self.h0)\n\t\treturn lat, lon, alt","def pixel2coords(self, x, y):\n xoff, a, b, yoff, d, e = self.geotransform()\n\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n return (xp, yp)","def proj_coord(coord, proj_in, proj_out):\n x, y = coord\n return pyproj.transform(proj_in, proj_out, x, y)","def __convert(args):\n a, b, zone, ellipsoid, datum, inverse = args\n projection = Proj(\"+proj=utm +zone={}, +ellps={} +datum={} +units=m +no_defs\".format(zone, ellipsoid, datum))\n c, d = projection(a, b, inverse=inverse)\n\n return c, d","def project_p2c_points(R, C, H): #---- project to cylindrical\r\n Rc= H[0]/2; Cc= H[1]/2; # center coordinate\r\n phi = H[3]; S= H[4]; # rotation angle and sizing\r\n Tv = H[5]; Tu= H[6]; # displacement\r\n COSF= np.cos(phi); SINF= np.sin(phi); # \r\n U = Tu + S*( COSF*(C- Cc)- SINF*(R- Rc) ); \r\n V = Tv + S*( SINF*(C- Cc)+ COSF*(R- Rc) );\r\n return V, U","def lat_lons(self):","def coords_trans(map, coords, up_lift=True):\n if len(coords) < 3:\n coords = np.append(coords, [0])\n pass\n\n exact_location = carla.Location(x=coords[0], y=coords[1], z=coords[2])\n waypoint = map.get_waypoint(exact_location) # carla.waypoint\n road_center_location = waypoint.transform.location\n\n if up_lift == True:\n exact_location.z += 1.0\n road_center_location.z += 1.0\n\n return exact_location, waypoint, road_center_location","def coords_to_structure(self) -> None:\n ...","def mercX(lon):\r\n zoom = 1.0\r\n lon = math.radians(lon)\r\n a = (256.0 / math.pi) * pow(2.0, zoom)\r\n b = lon + math.pi\r\n return int(a * b)","def ll_coordinates(self):\n\n x, y = self.xy_coordinates\n proj_out = check_crs('EPSG:4326')\n\n return transform_proj(self.proj, proj_out, x, y)","def invmap(self, x, y):\r\n r = self._r\r\n rxy = np.sqrt(x*x + y*y)\r\n\r\n lat = np.arccos(1-(rxy/r)**2)\r\n long = np.arctan2(y, x)\r\n\r\n try:\r\n long[np.isnan(lat)] = np.nan\r\n except TypeError: # Thrown if long is scalar\r\n if np.isnan(lat): long = np.nan\r\n return (lat, long)","def transform_coords(x, y, w, h, nw, nh):\r\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)","def reproject(self, lon, lat):\n if self.xform is None:\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\n return self.xform.transform(QgsPointXY(lon, lat))","def coords_to_min_max_lat_lon(self,coords):\n row = coords[0]\n col = coords[1]\n max_lat = self.max_lat - row*(self.lat_step)\n min_lat = max_lat - self.lat_step\n min_lon = self.min_lon + col*(self.lon_step)\n max_lon = min_lon + self.lon_step\n return min_lat,max_lat,min_lon,max_lon","def geo_transform(self):\n pass","def point2wgs84(self, crsop):\n latr = math.radians(self.lat)\n lngr = math.radians(self.lng)\n \n # a is the semi-major axis of given datum.\n a = crsop.SemimajorAxisInM\n \n # f is the flattening of given datum\n f = 1.0/crsop.InverseFlattening\n dx = crsop.dx\n dy = crsop.dy\n dz = crsop.dz\n \n # da is the difference between the WGS84 and source ellipsoid semi-major axes.\n da = 6378137.0 - a\n \n # df is the difference between the WGS84 and source CRS flattenings.\n df = 1.0/298.257223563 - f\n \n e_squared = f*(2-f)\n rho = a*(1-e_squared)/math.pow((1-e_squared*sqr(math.sin(latr))),1.5)\n nu = a/math.pow((1-e_squared*sqr(math.sin(latr))),0.5)\n dlat = (1/rho)*(-dx*math.sin(latr)*math.cos(lngr) - \\\n dy*math.sin(latr)*math.sin(lngr) + \\\n dz*math.cos(latr) + (f*da + a*df)*math.sin(2*latr))\n dlng = (-dx*math.sin(lngr) + dy*math.cos(lngr))/(nu*math.cos(latr))\n newlng = lng180(math.degrees(lngr + dlng))\n newlat = math.degrees(latr + dlat)\n return Point(float(truncate(newlng,DEGREE_DIGITS)), \\\n float(truncate(newlat,DEGREE_DIGITS)))","def fromECEFtoLatLongDegrees(x, y, z):\n ret = fromECEFtoLatLong(x, y, z)\n return math.degrees(ret[0]), math.degrees(ret[1]), ret[2]","def cartesian_to_geographical(coordinate_triples):\n if len(coordinate_triples.shape) == 1:\n x = coordinate_triples[0]\n y = coordinate_triples[1]\n z = coordinate_triples[2]\n elif len(coordinate_triples.shape) == 2:\n assert coordinate_triples.shape[1] == 3\n x = coordinate_triples[:, 0]\n y = coordinate_triples[:, 1]\n z = coordinate_triples[:, 2]\n radius = np.sqrt(x**2 + y**2 + z**2)\n longitudes = np.arctan2(y, x)\n latitudes = np.arcsin(z/radius)\n return (latitudes, longitudes)","def get_coordinates_geo(self):\n if not self.rotated:\n lon_arr_geo = self.lon_arr\n lat_arr_geo = self.lat_arr\n else:\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \n\n return lon_arr_geo, lat_arr_geo","def xy2ll(x, y):\n lon = math.degrees(x / EARTH_RADIUS)\n lat = math.degrees(2 * math.atan(math.exp(y / EARTH_RADIUS)) - math.pi / 2)\n return lon, lat + 51","def invmap(self, x, y):\r\n r = self._r\r\n rxy = np.sqrt(x*x + y*y)\r\n\r\n lat = (rxy/r)*(np.pi/2)\r\n long = np.arctan2(y, x)\r\n\r\n return (lat, long)","def fromECEFtoLatLong(x, y, z):\n r = math.sqrt(x*x+y*y)\n F = 54*b*b*z*z\n G = r*r +(1.0-e2)*z*z-e2*E2\n C = (e2*e2*F*r*r)/(G*G*G)\n s = pow(1.0+C+math.sqrt(C*C+2*C),1.0/3.0)\n P = F/(3.0*pow((s+(1.0/s)+1.0),2.0)*G*G)\n Q = math.sqrt(1.0+2.0*e2*e2*P)\n r0 = (-(P*e2*r)/(1.0+Q))+math.sqrt((1.0/2.0)*a*a*(1.0+1.0/Q)-((P*(1-e2)*z*z)/(Q*(1.0+Q)))-(1.0/2.0)*P*r*r)\n U = math.sqrt(pow(r-e2*r0,2.0)+z*z)\n V = math.sqrt(pow(r-e2*r0,2.0)+(1.0-e2)*z*z)\n Z0 = b*b*z/(a*V)\n return math.atan((z+ep2*Z0)/r), math.atan2(y, x), U*(1.0-(b*b)/(a*V))","def pixel2coord(self, x, y):\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\n # partly taken from Sean Gillies \"affine.py\"\n a,b,c,d,e,f = self.coordspace_transform\n det = a*e - b*d\n idet = 1 / float(det)\n ra = e * idet\n rb = -b * idet\n rd = -d * idet\n re = a * idet\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\n newy = (x*rd + y*re + (-c*rd - f*re) )\n return newx,newy","def _getNewCoords(self, coords, offset):\n\n oldlat = math.radians(float(coords[0]))\n oldlon = math.radians(float(coords[1]))\n magnitude = float(offset[0]) / 6370.\n direction = math.radians(360.-float(offset[1]))\n\n # Calculate lat/lon given radial and distnace (http://www.edwilliams.org/avform.htm#LL)\n lat = math.asin(math.sin(oldlat) * math.cos(magnitude) + math.cos(oldlat) \\\n * math.sin(magnitude) * math.cos(direction))\n lon = (oldlon - math.asin(math.sin(direction) * math.sin(magnitude) / math.cos(lat)) \\\n + math.pi) % (2 * math.pi) - math.pi\n\n # print coords, offset, oldlat, oldlon, magnitude, direction, math.degrees(lat), math.degrees(lon)\n return (math.degrees(lat), math.degrees(lon))","def _build_geotransform(self, i, j):\n assert isinstance(i, int), (\"i is not an integer\")\n assert isinstance(j, int), (\"j is not an integer\")\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\n self.image_metadata.geotransform)\n # integer conversion to reduce floating point error\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \n y_ignore, y_res) \n return geotransform","def transform(self, lens):\n coords_t = np.zeros(self.coords.shape)\n coords_t[:,0] = (\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\n coords_t[:,1] = (\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\n return coords_t","def map_aux(lat, long, direction, r1, r2):\r\n\r\n dlat, dlong = direction\r\n\r\n # Transform from spherical to Cartesian coordinates\r\n # lat = 0 on z axis\r\n x1 = r1*np.sin(lat)*np.cos(long)\r\n y1 = r1*np.sin(lat)*np.sin(long)\r\n z1 = r1*np.cos(lat)\r\n\r\n dx1 = np.sin(dlat)*np.cos(dlong)\r\n dy1 = np.sin(dlat)*np.sin(dlong)\r\n dz1 = np.cos(dlat)\r\n \r\n res_shape = x1.shape\r\n\r\n xdx = x1*dx1 + y1*dy1 + z1*dz1\r\n\r\n # D may be <0 in this case nan will be returned\r\n D = xdx*xdx - r1*r1 + r2*r2\r\n # select the minimum of the 2 roots\r\n l12 = np.array([-xdx + np.sqrt(D), -xdx - np.sqrt(D)]).reshape(2, -1)\r\n indxl = np.argmin(abs(l12), axis=0)\r\n indyl = np.indices(indxl.shape)[0]\r\n\r\n # l is how far to move in direction dx\r\n l = l12[indxl, indyl].reshape(res_shape)\r\n\r\n x2 = x1 + l*dx1\r\n y2 = y1 + l*dy1\r\n z2 = z1 + l*dz1\r\n\r\n map_lat = np.arccos(z2/r2)\r\n map_long = np.arctan2(y2, x2)\r\n\r\n try:\r\n return (map_lat.reshape(res_shape), map_long.reshape(res_shape))\r\n except AttributeError: # if lat and long scalars\r\n return (np.asscalar(map_lat), np.asscalar(map_long))","def p2c(r, t):\n if np.isnan(r) or np.isnan(t):\n return 0, 0 # return 0, 0\n else:\n return r * np.sin(t), r * np.cos(t) # x, y"],"string":"[\n \"def cr2lonlat_for_geotif(path):\\n old_cs, new_cs, gta, local_vars = _create_xform(path)\\n transform = osr.CoordinateTransformation(old_cs, new_cs)\\n\\n def composite(c, r):\\n \\\"\\\"\\\"xform from (c, r) to (lon, lat)\\\"\\\"\\\"\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\\n \\n return composite\",\n \"def lonlat2cr_for_geotif(path):\\n old_cs, new_cs, gta, local_vars = _create_xform(path)\\n transform = osr.CoordinateTransformation(new_cs, old_cs)\\n\\n def composite(lon, lat):\\n \\\"\\\"\\\"xform from (lon, lat) to (c, r)\\\"\\\"\\\"\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return (~gta * transform.TransformPoint(lat, lon)[:2])\\n \\n return composite\",\n \"def merc(lat, lon):\\n\\tr_major = 6378137.000\\n\\tx = r_major * math.radians(lon)\\n\\tscale = x/lon\\n\\ty = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale\\n\\treturn (x, y)\",\n \"def convert(self, lat, lon):\\r\\n a = self.a\\r\\n b = self.b\\r\\n long0 = self.long0\\r\\n k0 = self.k0\\r\\n dx = self.dx\\r\\n\\r\\n e = (1 - b ** 2 / a ** 2) ** 0.5\\r\\n e2 = e ** 2 / (1 - e ** 2)\\r\\n n = (a - b) / (a + b)\\r\\n nu = a / (1 - (e ** 2) * (sin(lat) ** 2)) ** 0.5\\r\\n p = lon - long0\\r\\n\\r\\n A = a * (1 - n + (5 / 4.0) * (n ** 2 - n ** 3) + (81 / 64.0)*(n ** 4 - n ** 5))\\r\\n B = (3 * a * n / 2.0) * (1 - n + (7 / 8.0) * (n ** 2 - n ** 3) + (55 / 64.0) * (n ** 4 - n ** 5))\\r\\n C = (15 * a * (n ** 2) / 16.0) * (1 - n + (3 / 4.0) * (n ** 2 - n ** 3))\\r\\n D = (35 * a * (n ** 3) / 48.0) * (1 - n + (11 / 16.0) * (n ** 2 - n ** 3))\\r\\n E = (315 * a * (n ** 4) / 51.0) * (1 - n)\\r\\n\\r\\n S = A * lat - B * sin(2 * lat) + C * sin(4 * lat) - D * sin(6 * lat) + E * sin(8 * lat)\\r\\n\\r\\n K1 = S * k0\\r\\n K2 = k0 * nu * sin(2 * lat)/4.0\\r\\n K3 = (k0 * nu * sin(lat) * (cos(lat) ** 3) / 24.0) * \\\\\\r\\n (5 - tan(lat) ** 2 + 9 * e2 * (cos(lat) ** 2) + 4 * (e2 ** 2) * (cos(lat) ** 4))\\r\\n\\r\\n y = K1 + K2 * (p ** 2) + K3 * (p ** 4)\\r\\n\\r\\n K4 = k0 * nu * cos(lat)\\r\\n K5 = (k0 * nu * (cos(lat) ** 3) / 6.0) * (1 - tan(lat) ** 2 + e2 * (cos(lat) ** 2))\\r\\n\\r\\n x = K4 * p + K5 * (p ** 3) + dx\\r\\n return x, y\",\n \"def lon_lat_to_cartesian(lon, lat, R = 1):\\n lon_r = np.radians(lon)\\n lat_r = np.radians(lat)\\n\\n x = R * np.cos(lat_r) * np.cos(lon_r)\\n y = R * np.cos(lat_r) * np.sin(lon_r)\\n z = R * np.sin(lat_r)\\n return x,y,z\",\n \"def cartesian_to_lon_lat(x, y, z, R = 1):\\n lon = np.degrees(np.arctan2(y,x))\\n lat = np.degrees(np.pi/2-np.arctan2((x**2+y**2)**0.5,z))\\n\\n return lon,lat\",\n \"def _getXYZ ( lon, lat ):\\n d2r = pi / 180.\\n rlon, rlat = ( d2r * lon, d2r * lat )\\n x = cos(rlat) * cos(rlon)\\n y = cos(rlat) * sin(rlon)\\n z = sin(rlat)\\n return (x,y,z)\",\n \"def convertCoord(lon, lat, inEPSG, outEPSG):\\n from pyproj import Proj, transform\\n inProj = Proj(init='epsg:'+str(inEPSG))\\n outProj = Proj(init='epsg:'+str(outEPSG))\\n x, y = transform(inProj, outProj, lon, lat)\\n return x, y\\n # epsg:4326 WGS84\\n # epsg:2950 MTM8\\n # epsg:6622 Quebec Lambert\",\n \"def ll2xyz(lon_pt,lat_pt):\\n\\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\\n zPt = np.sin(lat_pt)\\n return [xPt,yPt,zPt]\",\n \"def transform_from_latlon(lat, lon):\\n from affine import Affine\\n lat = np.asarray(lat)\\n lon = np.asarray(lon)\\n trans = Affine.translation(lon[0], lat[0])\\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\\n return trans * scale\",\n \"def lonlat2xy(s_lon, s_lat): # x: easting, y: northing\\r\\n # convert decimals to seconds...\\r\\n s_lon = dec2sec(s_lon)\\r\\n s_lat = dec2sec(s_lat)\\r\\n\\r\\n ## Auxiliary values \\r\\n # i.e. differences of latitude and longitude relative to Bern in the unit [10000'']\\r\\n s_lng_aux = (s_lon - 26782.5)/10000.\\r\\n s_lat_aux = (s_lat - 169028.66)/10000.\\r\\n \\r\\n # easting\\r\\n s_x = (600072.37 \\r\\n + 211455.93*s_lng_aux \\r\\n - 10938.51*s_lng_aux*s_lat_aux \\r\\n - 0.36*s_lng_aux*(s_lat_aux**2) \\r\\n - 44.54*(s_lng_aux**3))\\r\\n \\r\\n # northing\\r\\n s_y = (200147.07 \\r\\n + 308807.95*s_lat_aux \\r\\n + 3745.25*(s_lng_aux**2) \\r\\n + 76.63*(s_lat_aux**2) \\r\\n - 194.56*(s_lng_aux**2)*s_lat_aux \\r\\n + 119.79*(s_lat_aux**3))\\r\\n\\r\\n return s_x, s_y\",\n \"def to_lnglat(r):\\n \\n x, y = r['x_coordinate'], r['y_coordinate']\\n if np.isnan(x) or np.isnan(y):\\n return None, None\\n return projection(x, y, inverse = True)\",\n \"def convertView2Geo(self, x, y):\\n\\n # x_pix is from left map edge, y_pix from top map edge\\n x_pix = x + self.view_offset_x\\n y_pix = y + self.view_offset_y\\n\\n lon = self.map_llon + x_pix/self.ppd_x\\n lat = self.map_tlat - y_pix/self.ppd_y\\n\\n return (lon, lat)\",\n \"def transform_coordinates(coords):\\n # WGS 84 reference coordinate system parameters\\n A = 6378.137 # major axis [km]\\n E2 = 6.69437999014e-3 # eccentricity squared\\n\\n coords = prepare_coords(coords)\\n\\n # convert to radiants\\n lat_rad = np.radians(coords[:, 0])\\n lon_rad = np.radians(coords[:, 1])\\n\\n # convert to cartesian coordinates\\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\\n z = r_n * (1 - E2) * np.sin(lat_rad)\\n\\n return np.column_stack((x, y, z))\",\n \"def _indices_to_coords(c,r):\\n\\n column = _index_to_column(c)\\n row = r + 1\\n\\n return {'c': column, 'r': row, 'coord': f'{column}{row}'}\",\n \"def ll2fxy(lon,lat):\\n return xyz2fxy(*ll2xyz(lon,lat))\",\n \"def coords_to_gps(self,coords):\\n return ((self.max_lat - (self.lat_step * (0.5+coords[0]))),(self.min_lon + (self.lon_step * (0.5+coords[1]))))\",\n \"def spherical_to_cartesian(r, lat, lon):\\n import math\\n\\n if np.isscalar(r) and np.isscalar(lat) and np.isscalar(lon):\\n x = r * math.cos(lat) * math.cos(lon)\\n y = r * math.cos(lat) * math.sin(lon)\\n z = r * math.sin(lat)\\n else:\\n x = r * np.cos(lat) * np.cos(lon)\\n y = r * np.cos(lat) * np.sin(lon)\\n z = r * np.sin(lat)\\n\\n return x, y, z\",\n \"def xyz2longlat(x,y,z):\\n r=[]\\n long=[]\\n d=[]\\n lat=[]\\n longnew=[]\\n latnew=[]\\n i=0\\n for i in range(len(x)):\\n r.append(sp.sqrt(x[i]*x[i] + y[i]*y[i] + z[i]*z[i]))\\n \\t long.append(sp.arctan2(y[i],x[i]))\\n\\t d.append(sp.sqrt(x[i]*x[i] + y[i]*y[i]))\\n\\t lat.append(sp.arcsin(z[i]/r[i]))\\n\\n i=0\\n for i in range(len(x)):\\n longnew.append(long[i]*deg)\\n latnew.append(lat[i]*deg)\\n\\n return longnew, latnew, r\",\n \"def project(self, (lng, lat)):\\n x = lng * DEG_TO_RAD\\n lat = max(min(MAX_LATITUDE, lat), -MAX_LATITUDE)\\n y = lat * DEG_TO_RAD\\n y = math.log(math.tan((math.pi / 4) + (y / 2)))\\n return (x*EARTH_RADIUS, y*EARTH_RADIUS)\",\n \"def xyz_to_lonlat(x,y,z):\\n\\n XsqPlusYsq = x**2 + y**2\\n r = np.sqrt(XsqPlusYsq + z**2) # r\\n #elev = np.arccos(z/np.sqrt(XsqPlusYsq)) # theta\\n elev = np.arccos(z/r)\\n az = np.arctan2(y,x) # phi\\n return np.rad2deg(az), 90 - np.rad2deg(elev) # want \",\n \"def convert(coords):\\n lat = coords[:4]\\n lon = coords[4:]\\n\\n lat = lat[:2] + \\\".\\\" + lat[2:]\\n\\n if int(lon[0]) > 5:\\n lon = \\\"-\\\" + lon[:2] + \\\".\\\" + lon[2:]\\n else:\\n lon = \\\"-1\\\" + lon[:2] + \\\".\\\" + lon[2:]\\n\\n return (float(lat), float(lon))\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*lat/(np.pi/2)\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def merc_x(lon):\\n r_major = 6378137.000\\n x = r_major * math.radians(lon)\\n return x\",\n \"def latlon_2_grid(x, y, z, origin):\\n new_y = (y - origin[1]) * 111111\\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\\n return new_x, new_y, z\",\n \"def coords_to_X(c, shape):\\n a, b = c\\n assert 0 <= a <= shape[0]\\n assert 0 <= b <= shape[1]\\n a = float(a)\\n b = float(b)\\n a = a + 0.5 # center cells\\n b = b + 0.5\\n u = a / (shape[0])\\n v = b / (shape[1])\\n x = 2 * (u - 0.5)\\n y = 2 * (v - 0.5)\\n assert -1 <= x <= +1\\n assert -1 <= y <= +1\\n return (x, y)\",\n \"def map(self, lat, long):\\r\\n rxy = self._r*np.sqrt(1-np.cos(lat))\\r\\n x = rxy*np.cos(long)\\r\\n y = rxy*np.sin(long)\\r\\n return (x, y)\",\n \"def proj(self,lon,lat):\\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\\n x[x > 1e29] = None\\n y[y > 1e29] = None\\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\\n return x, y\",\n \"def scalar_g2r(al, be, ga, lon, lat):\\n\\n rad = mt.pi / 180\\n al = al * rad\\n be = be * rad\\n ga = ga * rad\\n\\n rotate_matrix = np.zeros(shape=(3, 3))\\n\\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\\n al\\n )\\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\\n al\\n )\\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\\n rotate_matrix[2, 2] = np.cos(be)\\n \\n #rotate_matrix = np.linalg.pinv(rotate_matrix)\\n \\n lat = lat * rad\\n lon = lon * rad\\n\\n # geographical Cartesian coordinates:\\n xr = np.cos(lat) * np.cos(lon)\\n yr = np.cos(lat) * np.sin(lon)\\n zr = np.sin(lat)\\n\\n # rotated Cartesian coordinates:\\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\\n zg = rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\\n\\n # rotated coordinates:\\n rlat = np.arcsin(zg)\\n rlon = np.arctan2(yg, xg)\\n\\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\\n if a:\\n lon[a] = 0\\n\\n rlat = rlat / rad\\n rlon = rlon / rad\\n\\n return (rlon, rlat)\",\n \"def get_ecmwf_lat_lon(nc_file):\\n from netCDF4 import Dataset\\n \\n fh = Dataset(nc_file, mode='r')\\n\\n latitude_ecmwf = fh.variables['latitude_ecmwf'][:]\\n longitude_ecmwf = fh.variables['longitude_ecmwf'][:]\\n\\n lonmesh_ecmwf,latmesh_ecmwf = np.meshgrid(longitude_ecmwf,latitude_ecmwf)\\n\\n print('latitude_ecmwf: ', latitude_ecmwf.shape)\\n print('longitude_ecmwf: ', longitude_ecmwf.shape)\\n \\n return latitude_ecmwf, longitude_ecmwf, latmesh_ecmwf, lonmesh_ecmwf;\",\n \"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\\n\\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\\n return (-1,-1)\\n\\n lat_step = abs(max_lat-min_lat)/rows\\n lon_step = abs(max_lon-min_lon)/cols\\n\\n lat_spot = int((max_lat-lat)/lat_step)\\n lon_spot = int((lon-min_lon)/lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def translation(coord, lat, lon):\\n ty = merc_y(lat) - 73.43 # Y translation\\n tx = merc_x(lon) - 57.55 # X translation\\n for i in range(len(coord)):\\n for j in range(len(coord[i])):\\n coord[i][j][0], coord[i][j][1] = coord[i][j][0] + tx, coord[i][j][1] + ty\\n return coord\",\n \"def scalar_r2g(al, be, ga, rlon, rlat):\\n\\n rad = mt.pi / 180\\n al = al * rad\\n be = be * rad\\n ga = ga * rad\\n rotate_matrix = np.zeros(shape=(3, 3))\\n rotate_matrix[0, 0] = np.cos(ga) * np.cos(al) - np.sin(ga) * np.cos(be) * np.sin(al)\\n rotate_matrix[0, 1] = np.cos(ga) * np.sin(al) + np.sin(ga) * np.cos(be) * np.cos(al)\\n rotate_matrix[0, 2] = np.sin(ga) * np.sin(be)\\n rotate_matrix[1, 0] = -np.sin(ga) * np.cos(al) - np.cos(ga) * np.cos(be) * np.sin(\\n al\\n )\\n rotate_matrix[1, 1] = -np.sin(ga) * np.sin(al) + np.cos(ga) * np.cos(be) * np.cos(\\n al\\n )\\n rotate_matrix[1, 2] = np.cos(ga) * np.sin(be)\\n rotate_matrix[2, 0] = np.sin(be) * np.sin(al)\\n rotate_matrix[2, 1] = -np.sin(be) * np.cos(al)\\n rotate_matrix[2, 2] = np.cos(be)\\n\\n rotate_matrix = np.linalg.pinv(rotate_matrix)\\n\\n rlat = rlat * rad\\n rlon = rlon * rad\\n\\n # Rotated Cartesian coordinates:\\n xr = np.cos(rlat) * np.cos(rlon)\\n yr = np.cos(rlat) * np.sin(rlon)\\n zr = np.sin(rlat)\\n\\n # Geographical Cartesian coordinates:\\n xg = rotate_matrix[0, 0] * xr + rotate_matrix[0, 1] * yr + rotate_matrix[0, 2] * zr\\n yg = rotate_matrix[1, 0] * xr + rotate_matrix[1, 1] * yr + rotate_matrix[1, 2] * zr\\n zg = (\\n rotate_matrix[2, 0] * xr + rotate_matrix[2, 1] * yr + rotate_matrix[2, 2] * zr\\n )\\n\\n # Geographical coordinates:\\n lat = np.arcsin(zg)\\n lon = np.arctan2(yg, xg)\\n\\n a = np.where((np.abs(xg) + np.abs(yg)) == 0)\\n if a:\\n lon[a] = 0\\n\\n lat = lat / rad\\n lon = lon / rad\\n\\n return (lon, lat)\",\n \"def ll2csflat(lon,lat,ncs):\\n j,f,i = ll2jfi(lon,lat,ncs)\\n return (j*6+f)*ncs+i\",\n \"def pixel2coord(tf, x, y):\\n lat = tf[0] + x*tf[1] + y*tf[2]\\n lon = tf[3] + x*tf[4] + y*tf[5]\\n\\n return lat, lon\",\n \"def coord2pixel(tf, lat, lon):\\n x = int(round((lon-tf[0])/tf[1]))\\n y = int(round((lat-tf[3])/tf[5]))\\n\\n return x, y\",\n \"def ecef_coords(lats: ndarray, lons: ndarray) -> Tuple[ndarray, ndarray, ndarray]:\\n # Cartopy Geodetic and Geocentric both default to the WGS84 datum\\n spherical_latlon_crs = Geodetic()\\n ecef_crs = Geocentric()\\n xyz = ecef_crs.transform_points(\\n spherical_latlon_crs, np.array(lons), np.array(lats)\\n )\\n return xyz[..., 0], xyz[..., 1], xyz[..., 2]\",\n \"def unproject(self, (x, y)):\\n lng = x/EARTH_RADIUS * RAD_TO_DEG\\n lat = 2 * math.atan(math.exp(y/EARTH_RADIUS)) - math.pi/2 * RAD_TO_DEG\\n return (lng, lat)\",\n \"def reverse(self, lon, lat):\",\n \"def undo_mercator_project(x,y):\\n lon = y*np.pi\\n ex = np.exp(4*np.pi*x)\\n lat = np.arcsin((ex - 1)/(ex +1 ))\\n lon = lon*360/2/np.pi\\n lat = lat*360 /2/np.pi\\n return lon, lat\",\n \"def ll_to_xy(lon, lat):\\n lllon = lon.min()\\n lllat = lat.min()\\n urlon = lon.max()\\n urlat = lat.max()\\n\\n dlon = urlon - lllon\\n dlat = urlat - lllat\\n dx_ = np.squeeze(gsw.distance([lllon, urlon], [lllat, lllat])) # dx of box\\n dy_ = np.squeeze(gsw.distance([lllon, lllon], [lllat, urlat])) # dy of box\\n\\n x = dx_*(lon - lllon)/dlon\\n y = dy_*(lat - lllat)/dlat\\n\\n return x, y\",\n \"def convertGeo2View(self, lon, lat):\\n\\n x_pix = (lon - self.view_llon) * self.ppd_x\\n y_pix = (self.view_tlat - lat) * self.ppd_y\\n\\n return (x_pix, y_pix)\",\n \"def spher2cart(lon: np.ndarray,\\n lat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n rlon = np.radians(lon)\\n rlat = np.radians(lat)\\n x = np.cos(rlon) * np.cos(rlat)\\n y = np.sin(rlon) * np.cos(rlat)\\n z = np.sin(rlat)\\n return x, y, z\",\n \"def LongLat(x, y, z):\\n r = (x**2 + y**2 + z**2)**0.5\\n long = numpy.arctan2(y, x)\\n lat = numpy.arcsin(z / r)\\n return long, lat, r\",\n \"def ra2xy(self, ra):\\n return -math.sin(ra), math.cos(ra)\",\n \"def rotated_coord_transform(lon, lat, np_lon, np_lat,\\n direction='rot2geo'):\\n\\n # Convert degrees to radians\\n lon = (lon * math.pi) / 180.\\n lat = (lat * math.pi) / 180.\\n\\n# SP_lon = SP_coor(1)\\n# SP_lat = SP_coor(2)\\n\\n theta = 90. - np_lat # Rotation around y-axis\\n phi = np_lon + 180. # Rotation around z-axis\\n\\n # Convert degrees to radians\\n phi = (phi * math.pi) / 180.\\n theta = (theta * math.pi) / 180.\\n\\n # Convert from spherical to cartesian coordinates\\n x = math.cos(lon) * math.cos(lat)\\n y = math.sin(lon) * math.cos(lat)\\n z = math.sin(lat)\\n\\n # Regular -> Rotated\\n if direction == 'geo2rot':\\n\\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.cos(theta) * math.sin(phi) * y +\\n math.sin(theta) * z)\\n y_new = (- math.sin(phi) * x +\\n math.cos(phi) * y)\\n z_new = (- math.sin(theta) * math.cos(phi) * x -\\n math.sin(theta) * math.sin(phi) * y +\\n math.cos(theta) * z)\\n\\n # Rotated -> Regular\\n elif direction == 'rot2geo':\\n \\n phi = - phi\\n theta = - theta\\n \\n x_new = (math.cos(theta) * math.cos(phi) * x +\\n math.sin(phi) * y +\\n math.sin(theta) * math.cos(phi) * z)\\n y_new = (- math.cos(theta) * math.sin(phi) * x +\\n math.cos(phi) * y -\\n math.sin(theta) * math.sin(phi) * z)\\n z_new = (- math.sin(theta) * x +\\n math.cos(theta) * z)\\n\\n # Convert cartesian back to spherical coordinates\\n lon_new = math.atan2(y_new, x_new)\\n lat_new = math.asin(z_new)\\n\\n # Convert radians back to degrees\\n lon_new = (lon_new * 180.) / math.pi\\n lat_new = (lat_new * 180.) / math.pi;\\n\\n return (lon_new, lat_new)\",\n \"def pixelloc_to_geoloc(row, col, loff, coff):\\n row = np.atleast_1d(row)\\n col = np.atleast_1d(col)\\n\\n assert row.shape == col.shape\\n\\n lat, lon = _nav.pixelloc_to_geoloc(row, col, loff, coff)\\n\\n if lat.size == lon.size == 1:\\n # Return scalars\\n return lat[0], lon[0]\\n else:\\n return lat, lon\",\n \"def mapr(r):\\n return np.rad2deg(np.arctan(r)*2)\",\n \"def cartesian2Geo(julian_date, x, y, z):\\n\\n\\n # Calculate LLA\\n lat, r_LST, ele = ecef2LatLonAlt(x, y, z)\\n\\n # Calculate proper longitude from the given JD\\n lon, _ = LST2LongitudeEast(julian_date, np.degrees(r_LST))\\n\\n # Convert longitude to radians\\n lon = np.radians(lon)\\n\\n\\n return np.degrees(lat), np.degrees(lon), ele\",\n \"def convertLatLon(latCell, lonCell):\\n cell_lats = np.array([])\\n cell_lons = np.array([])\\n for lat in latCell:\\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \\n for lon in lonCell:\\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \\n\\n return cell_lats, cell_lons\",\n \"def ReprojectCoords(coords,src_srs,tgt_srs):\\n trans_coords = []\\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\\n for x,y in coords:\\n x,y,z = transform.TransformPoint(x,y)\\n trans_coords.append([x,y])\\n return trans_coords\",\n \"def transform_pnt(pnt, inEPSG, outEPSG):\\r\\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\\r\\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\\r\\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\\r\\n\\r\\n return pyproj.transform(inProj, outProj, x1, y1)\",\n \"def cartesian_coordinates(self):\\n # extract RA items\\n ra_hours, ra_minutes, ra_seconds = RA_RE.match(str(self.ra)).groups()\\n # then cast\\n ra_hours = int(ra_hours)\\n ra_minutes = int(ra_minutes)\\n ra_seconds = float(ra_seconds)\\n\\n # extract DEC items\\n dec_sign, dec_degrees, dec_minutes, dec_seconds = DEC_RE.match(str(self.dec)).groups()\\n # then cast\\n dec_sign = -1 if dec_sign == '-' else 1\\n dec_degrees = int(dec_degrees)\\n dec_minutes = int(dec_minutes)\\n dec_seconds = float(dec_seconds)\\n\\n # to degrees\\n a = (ra_hours*15) + (ra_minutes*0.25) + (ra_seconds*0.004166)\\n b = abs(dec_degrees + dec_minutes/60 + dec_seconds/3600) * dec_sign\\n\\n # to radians\\n a = math.radians(a)\\n b = math.radians(b)\\n\\n distance = float(self.distance)\\n\\n x = (distance * math.cos(b)) * math.cos(a)\\n y = (distance * math.cos(b)) * math.sin(a)\\n z = distance * math.sin(b)\\n\\n return x, y, z\",\n \"def latlon_to_pixels(lon, lat):\\n radius = 489.5\\n x_pos_from_center = radius * np.cos(np.deg2rad(lon))\\n x_pos = x_pos_from_center + 512.5\\n \\n y_pos_from_center = radius * np.cos(np.deg2rad(lat))\\n y_pos = y_pos_from_center + 512.5\\n \\n return x_pos, y_pos\",\n \"def map_coordinates(self,geometry):\\n\\t\\tg = self.geomatrix\\n\\t\\tdef project_coord(x,y,z=None):\\n\\t\\t\\tx = g[0] + g[1] * x + g[2] * y\\n\\t\\t\\ty = g[3] + g[4] * x + g[5] * y\\n\\t\\t\\tif z is None:\\n\\t\\t\\t\\treturn x,y\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn x,y,z\\n\\t\\treturn transform(project_coord, geometry)\",\n \"def read_affine(file):\\n data = open(file, 'r').read()\\n data = data.split('\\\\n')\\n for i in range(1, 5):\\n data[i] = data[i].split(':')\\n int_lon = np.fromstring(data[1][1], dtype='float', sep=',')\\n int_lat = np.fromstring(data[2][1], dtype='float', sep=',')\\n Nlon = len(int_lon) - 1\\n Nlat = len(int_lat) - 1\\n data[3][1] = data[3][1].split(',')\\n data[4][1] = data[4][1].split(',')\\n lon_transform = np.zeros((Nlon, 2))\\n lat_transform = np.zeros((Nlat, 2))\\n for i in range(Nlon):\\n data[3][1][i] = data[3][1][i].split(' ')\\n lon_transform[i] = [data[3][1][i][0], data[3][1][i][1]]\\n for i in range(Nlat):\\n data[4][1][i] = data[4][1][i].split(' ')\\n lat_transform[i] = [data[4][1][i][0], data[4][1][i][1]]\\n lon_transform = np.array(lon_transform).astype('float')\\n lat_transform = np.array(lat_transform).astype('float')\\n return int_lon, int_lat, lon_transform, lat_transform\",\n \"def gnomonic_project_toxy(RA1, Dec1, RAcen, Deccen):\\n # also used in Global Telescope Network website\\n cosc = np.sin(Deccen) * np.sin(Dec1) + np.cos(Deccen) * np.cos(Dec1) * np.cos(RA1-RAcen)\\n x = np.cos(Dec1) * np.sin(RA1-RAcen) / cosc\\n y = (np.cos(Deccen)*np.sin(Dec1) - np.sin(Deccen)*np.cos(Dec1)*np.cos(RA1-RAcen)) / cosc\\n return x, y\",\n \"def gps_to_coords(self,lat,lon):\\n\\n if (lat <= self.min_lat or lat >= self.max_lat or lon <= self.min_lon or lon >= self.max_lon):\\n return (-1,-1)\\n\\n lat_spot = int((self.max_lat-lat)/self.lat_step)\\n lon_spot = int((lon-self.min_lon)/self.lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def update_coords(self, l, b):\\n self.l = l\\n self.b = b\\n self.ra, self.dec = astLib.astCoords.convertCoords(\\n \\\"GALACTIC\\\", \\\"J2000\\\", self.l, self.b, epoch=2000.)\",\n \"def flowline_latlon(coords, fromproj=pyproj.Proj(\\\"+init=epsg:3413\\\"), toproj=pyproj.Proj(\\\"+init=EPSG:4326\\\")):\\n xs = coords[:,0]\\n ys = coords[:,1]\\n x_lon, y_lat = pyproj.transform(fromproj, toproj, xs, ys)\\n latlon_coords = np.asarray(zip(x_lon, y_lat))\\n return latlon_coords\",\n \"def ll2px(lat, lng, zoom):\\n # assert isinstance(lat, (float, int, long)), \\\\\\n # ValueError(\\\"lat must be a float\\\")\\n lat = float(lat)\\n # assert isinstance(lng, (float, int, long)), \\\\\\n # ValueError(\\\"lng must be a float\\\")\\n lng = float(lng)\\n # assert isinstance(zoom, int), TypeError(\\\"zoom must be an int from 0 to 30\\\")\\n # assert 0 <= zoom <= 30, ValueError(\\\"zoom must be an int from 0 to 30\\\")\\n\\n cbk = CBK[zoom]\\n\\n x = int(round(cbk + (lng * CEK[zoom])))\\n\\n foo = math.sin(lat * math.pi / 180)\\n if foo < -0.9999:\\n foo = -0.9999\\n elif foo > 0.9999:\\n foo = 0.9999\\n\\n y = int(round(cbk + (0.5 * math.log((1+foo)/(1-foo)) * (-CFK[zoom]))))\\n\\n return (x, y)\",\n \"def fn2lonlat(filename):\\n tokens = filename.split(\\\"/\\\")[-1].rsplit(\\\".\\\", 1)[0].split(\\\"x\\\")\\n return [0 - float(tokens[0]), float(tokens[1])]\",\n \"def geocentric2geodetic(latitude):\\n\\n return np.rad2deg(np.arctan(1.0067395 * np.tan(np.deg2rad(latitude))))\",\n \"def vec2geo(r_hat):\\n ra = degrees(atan2(r_hat[1], r_hat[0]))\\n dec = degrees(atan2(r_hat[2], sqrt(r_hat[0] ** 2 + r_hat[1] ** 2)))\\n return ra, dec\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def coord_to_pointing (coordinates):\\n\\n clat = np.cos (coordinates[0])\\n return np.array ([clat * np.cos (coordinates[1]),\\n clat * np.sin (coordinates[1]),\\n np.sin (coordinates[0])])\",\n \"def transform_point(p,R,t):\\r\\n x = R[0][0]*p[0]+R[0][1]*p[1]+t[0]\\r\\n y = R[1][0]*p[0]+R[1][1]*p[1]+t[1]\\r\\n return [x,y]\",\n \"def plane2sphere(x,y,latc,lonc):\\n if np.abs(y) > 1:\\n lat = np.nan\\n lon = np.nan\\n return lat,lon\\n else:\\n pass\\n\\n coslatc = np.cos(latc)\\n sinlatc = np.sin(latc)\\n\\n cosphi = np.sqrt(1.0 - y**2)\\n lat = np.arcsin((y*coslatc) + (cosphi*np.cos(x)*sinlatc))\\n\\n if np.cos(lat) == 0:\\n test = 0.0\\n else:\\n test = cosphi*np.sin(x)/np.cos(lat)\\n\\n lon = np.arcsin(test) + lonc\\n\\n x0 = x\\n if np.abs(x0) > np.pi/2.0:\\n while x0 > np.pi/2.0:\\n lon = np.pi - lon\\n x0 = x0 - np.pi\\n\\n while x0 < -np.pi/2.0:\\n lon = -np.pi - lon\\n x0 = x0 + np.pi\\n\\n return lat,lon\",\n \"def get_latlon_point(self, row, col):\\n p1 = Proj(self.src.crs)\\n window = rasterio.windows.Window(col, row, 1, 1)\\n trnsfrm = self.src.window_transform(window)\\n T1 = trnsfrm * Affine.translation(0.5, 0.5)\\n p2 = Proj(proj='latlong', datum='WGS84')\\n x, y = self.src.xy(row, col)\\n lon, lat = transform(p1, p2, x, y)\\n return lat, lon\",\n \"def wgs84_to_mercator(df, lon, lat):\\n k = 6378137\\n df[\\\"x\\\"] = df[lon] * (k * np.pi/180.0)\\n df[\\\"y\\\"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k\\n return df\",\n \"def pixels_to_latlon(x_pos, y_pos):\\n radius = 489.5\\n x_pos_from_center = -1*(512.5 - x_pos)\\n angle_1 = np.rad2deg(np.arccos(x_pos_from_center/radius))\\n\\n y_pos_from_center = -1*(512.5 - y_pos)\\n angle_2 = np.rad2deg(np.arccos(y_pos_from_center/radius)) \\n\\n return -1*angle_1, angle_2\",\n \"def proj_coords(coords, proj_in, proj_out): \\n return [proj_coord(coord, proj_in, proj_out) for coord in coords]\",\n \"def desiredENU2geo(self, x_L, y_L, z):\\n\\t\\tx = cos(self.local_rot)*x_L - sin(self.local_rot)*y_L\\n\\t\\ty = sin(self.local_rot)*x_L + cos(self.local_rot)*y_L\\n\\n\\t\\tlat0 = self.origin[0]\\n\\t\\tlon0 = self.origin[1]\\n\\n\\t\\tlat, lon, alt = pm.enu2geodetic(x, y, z, lat0, lon0, self.h0)\\n\\t\\treturn lat, lon, alt\",\n \"def pixel2coords(self, x, y):\\n xoff, a, b, yoff, d, e = self.geotransform()\\n\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n return (xp, yp)\",\n \"def proj_coord(coord, proj_in, proj_out):\\n x, y = coord\\n return pyproj.transform(proj_in, proj_out, x, y)\",\n \"def __convert(args):\\n a, b, zone, ellipsoid, datum, inverse = args\\n projection = Proj(\\\"+proj=utm +zone={}, +ellps={} +datum={} +units=m +no_defs\\\".format(zone, ellipsoid, datum))\\n c, d = projection(a, b, inverse=inverse)\\n\\n return c, d\",\n \"def project_p2c_points(R, C, H): #---- project to cylindrical\\r\\n Rc= H[0]/2; Cc= H[1]/2; # center coordinate\\r\\n phi = H[3]; S= H[4]; # rotation angle and sizing\\r\\n Tv = H[5]; Tu= H[6]; # displacement\\r\\n COSF= np.cos(phi); SINF= np.sin(phi); # \\r\\n U = Tu + S*( COSF*(C- Cc)- SINF*(R- Rc) ); \\r\\n V = Tv + S*( SINF*(C- Cc)+ COSF*(R- Rc) );\\r\\n return V, U\",\n \"def lat_lons(self):\",\n \"def coords_trans(map, coords, up_lift=True):\\n if len(coords) < 3:\\n coords = np.append(coords, [0])\\n pass\\n\\n exact_location = carla.Location(x=coords[0], y=coords[1], z=coords[2])\\n waypoint = map.get_waypoint(exact_location) # carla.waypoint\\n road_center_location = waypoint.transform.location\\n\\n if up_lift == True:\\n exact_location.z += 1.0\\n road_center_location.z += 1.0\\n\\n return exact_location, waypoint, road_center_location\",\n \"def coords_to_structure(self) -> None:\\n ...\",\n \"def mercX(lon):\\r\\n zoom = 1.0\\r\\n lon = math.radians(lon)\\r\\n a = (256.0 / math.pi) * pow(2.0, zoom)\\r\\n b = lon + math.pi\\r\\n return int(a * b)\",\n \"def ll_coordinates(self):\\n\\n x, y = self.xy_coordinates\\n proj_out = check_crs('EPSG:4326')\\n\\n return transform_proj(self.proj, proj_out, x, y)\",\n \"def invmap(self, x, y):\\r\\n r = self._r\\r\\n rxy = np.sqrt(x*x + y*y)\\r\\n\\r\\n lat = np.arccos(1-(rxy/r)**2)\\r\\n long = np.arctan2(y, x)\\r\\n\\r\\n try:\\r\\n long[np.isnan(lat)] = np.nan\\r\\n except TypeError: # Thrown if long is scalar\\r\\n if np.isnan(lat): long = np.nan\\r\\n return (lat, long)\",\n \"def transform_coords(x, y, w, h, nw, nh):\\r\\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)\",\n \"def reproject(self, lon, lat):\\n if self.xform is None:\\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\\n return self.xform.transform(QgsPointXY(lon, lat))\",\n \"def coords_to_min_max_lat_lon(self,coords):\\n row = coords[0]\\n col = coords[1]\\n max_lat = self.max_lat - row*(self.lat_step)\\n min_lat = max_lat - self.lat_step\\n min_lon = self.min_lon + col*(self.lon_step)\\n max_lon = min_lon + self.lon_step\\n return min_lat,max_lat,min_lon,max_lon\",\n \"def geo_transform(self):\\n pass\",\n \"def point2wgs84(self, crsop):\\n latr = math.radians(self.lat)\\n lngr = math.radians(self.lng)\\n \\n # a is the semi-major axis of given datum.\\n a = crsop.SemimajorAxisInM\\n \\n # f is the flattening of given datum\\n f = 1.0/crsop.InverseFlattening\\n dx = crsop.dx\\n dy = crsop.dy\\n dz = crsop.dz\\n \\n # da is the difference between the WGS84 and source ellipsoid semi-major axes.\\n da = 6378137.0 - a\\n \\n # df is the difference between the WGS84 and source CRS flattenings.\\n df = 1.0/298.257223563 - f\\n \\n e_squared = f*(2-f)\\n rho = a*(1-e_squared)/math.pow((1-e_squared*sqr(math.sin(latr))),1.5)\\n nu = a/math.pow((1-e_squared*sqr(math.sin(latr))),0.5)\\n dlat = (1/rho)*(-dx*math.sin(latr)*math.cos(lngr) - \\\\\\n dy*math.sin(latr)*math.sin(lngr) + \\\\\\n dz*math.cos(latr) + (f*da + a*df)*math.sin(2*latr))\\n dlng = (-dx*math.sin(lngr) + dy*math.cos(lngr))/(nu*math.cos(latr))\\n newlng = lng180(math.degrees(lngr + dlng))\\n newlat = math.degrees(latr + dlat)\\n return Point(float(truncate(newlng,DEGREE_DIGITS)), \\\\\\n float(truncate(newlat,DEGREE_DIGITS)))\",\n \"def fromECEFtoLatLongDegrees(x, y, z):\\n ret = fromECEFtoLatLong(x, y, z)\\n return math.degrees(ret[0]), math.degrees(ret[1]), ret[2]\",\n \"def cartesian_to_geographical(coordinate_triples):\\n if len(coordinate_triples.shape) == 1:\\n x = coordinate_triples[0]\\n y = coordinate_triples[1]\\n z = coordinate_triples[2]\\n elif len(coordinate_triples.shape) == 2:\\n assert coordinate_triples.shape[1] == 3\\n x = coordinate_triples[:, 0]\\n y = coordinate_triples[:, 1]\\n z = coordinate_triples[:, 2]\\n radius = np.sqrt(x**2 + y**2 + z**2)\\n longitudes = np.arctan2(y, x)\\n latitudes = np.arcsin(z/radius)\\n return (latitudes, longitudes)\",\n \"def get_coordinates_geo(self):\\n if not self.rotated:\\n lon_arr_geo = self.lon_arr\\n lat_arr_geo = self.lat_arr\\n else:\\n lon_arr_geo, lat_arr_geo = self.transform().get_coordinates() \\n\\n return lon_arr_geo, lat_arr_geo\",\n \"def xy2ll(x, y):\\n lon = math.degrees(x / EARTH_RADIUS)\\n lat = math.degrees(2 * math.atan(math.exp(y / EARTH_RADIUS)) - math.pi / 2)\\n return lon, lat + 51\",\n \"def invmap(self, x, y):\\r\\n r = self._r\\r\\n rxy = np.sqrt(x*x + y*y)\\r\\n\\r\\n lat = (rxy/r)*(np.pi/2)\\r\\n long = np.arctan2(y, x)\\r\\n\\r\\n return (lat, long)\",\n \"def fromECEFtoLatLong(x, y, z):\\n r = math.sqrt(x*x+y*y)\\n F = 54*b*b*z*z\\n G = r*r +(1.0-e2)*z*z-e2*E2\\n C = (e2*e2*F*r*r)/(G*G*G)\\n s = pow(1.0+C+math.sqrt(C*C+2*C),1.0/3.0)\\n P = F/(3.0*pow((s+(1.0/s)+1.0),2.0)*G*G)\\n Q = math.sqrt(1.0+2.0*e2*e2*P)\\n r0 = (-(P*e2*r)/(1.0+Q))+math.sqrt((1.0/2.0)*a*a*(1.0+1.0/Q)-((P*(1-e2)*z*z)/(Q*(1.0+Q)))-(1.0/2.0)*P*r*r)\\n U = math.sqrt(pow(r-e2*r0,2.0)+z*z)\\n V = math.sqrt(pow(r-e2*r0,2.0)+(1.0-e2)*z*z)\\n Z0 = b*b*z/(a*V)\\n return math.atan((z+ep2*Z0)/r), math.atan2(y, x), U*(1.0-(b*b)/(a*V))\",\n \"def pixel2coord(self, x, y):\\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\\n # partly taken from Sean Gillies \\\"affine.py\\\"\\n a,b,c,d,e,f = self.coordspace_transform\\n det = a*e - b*d\\n idet = 1 / float(det)\\n ra = e * idet\\n rb = -b * idet\\n rd = -d * idet\\n re = a * idet\\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\\n newy = (x*rd + y*re + (-c*rd - f*re) )\\n return newx,newy\",\n \"def _getNewCoords(self, coords, offset):\\n\\n oldlat = math.radians(float(coords[0]))\\n oldlon = math.radians(float(coords[1]))\\n magnitude = float(offset[0]) / 6370.\\n direction = math.radians(360.-float(offset[1]))\\n\\n # Calculate lat/lon given radial and distnace (http://www.edwilliams.org/avform.htm#LL)\\n lat = math.asin(math.sin(oldlat) * math.cos(magnitude) + math.cos(oldlat) \\\\\\n * math.sin(magnitude) * math.cos(direction))\\n lon = (oldlon - math.asin(math.sin(direction) * math.sin(magnitude) / math.cos(lat)) \\\\\\n + math.pi) % (2 * math.pi) - math.pi\\n\\n # print coords, offset, oldlat, oldlon, magnitude, direction, math.degrees(lat), math.degrees(lon)\\n return (math.degrees(lat), math.degrees(lon))\",\n \"def _build_geotransform(self, i, j):\\n assert isinstance(i, int), (\\\"i is not an integer\\\")\\n assert isinstance(j, int), (\\\"j is not an integer\\\")\\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\\n self.image_metadata.geotransform)\\n # integer conversion to reduce floating point error\\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \\n y_ignore, y_res) \\n return geotransform\",\n \"def transform(self, lens):\\n coords_t = np.zeros(self.coords.shape)\\n coords_t[:,0] = (\\n self.coords[:,0] * lens.nx / (lens.xmax - lens.xmin))\\n coords_t[:,1] = (\\n self.coords[:,1] * lens.ny / (lens.ymax - lens.ymin))\\n return coords_t\",\n \"def map_aux(lat, long, direction, r1, r2):\\r\\n\\r\\n dlat, dlong = direction\\r\\n\\r\\n # Transform from spherical to Cartesian coordinates\\r\\n # lat = 0 on z axis\\r\\n x1 = r1*np.sin(lat)*np.cos(long)\\r\\n y1 = r1*np.sin(lat)*np.sin(long)\\r\\n z1 = r1*np.cos(lat)\\r\\n\\r\\n dx1 = np.sin(dlat)*np.cos(dlong)\\r\\n dy1 = np.sin(dlat)*np.sin(dlong)\\r\\n dz1 = np.cos(dlat)\\r\\n \\r\\n res_shape = x1.shape\\r\\n\\r\\n xdx = x1*dx1 + y1*dy1 + z1*dz1\\r\\n\\r\\n # D may be <0 in this case nan will be returned\\r\\n D = xdx*xdx - r1*r1 + r2*r2\\r\\n # select the minimum of the 2 roots\\r\\n l12 = np.array([-xdx + np.sqrt(D), -xdx - np.sqrt(D)]).reshape(2, -1)\\r\\n indxl = np.argmin(abs(l12), axis=0)\\r\\n indyl = np.indices(indxl.shape)[0]\\r\\n\\r\\n # l is how far to move in direction dx\\r\\n l = l12[indxl, indyl].reshape(res_shape)\\r\\n\\r\\n x2 = x1 + l*dx1\\r\\n y2 = y1 + l*dy1\\r\\n z2 = z1 + l*dz1\\r\\n\\r\\n map_lat = np.arccos(z2/r2)\\r\\n map_long = np.arctan2(y2, x2)\\r\\n\\r\\n try:\\r\\n return (map_lat.reshape(res_shape), map_long.reshape(res_shape))\\r\\n except AttributeError: # if lat and long scalars\\r\\n return (np.asscalar(map_lat), np.asscalar(map_long))\",\n \"def p2c(r, t):\\n if np.isnan(r) or np.isnan(t):\\n return 0, 0 # return 0, 0\\n else:\\n return r * np.sin(t), r * np.cos(t) # x, y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6946017","0.6875609","0.6777287","0.67524993","0.6716408","0.66537076","0.6602633","0.64604294","0.6418981","0.6406474","0.6382838","0.6360858","0.63093865","0.6304608","0.6299599","0.6263775","0.6230886","0.6198476","0.6191175","0.61556995","0.6152255","0.61249316","0.6118711","0.6094595","0.60879076","0.608356","0.6072247","0.60550004","0.6051795","0.6048452","0.60369694","0.6036503","0.6035315","0.60326356","0.60101897","0.599683","0.5996011","0.59680265","0.59607023","0.5948106","0.59386915","0.5930006","0.59111774","0.5903709","0.58962375","0.5885998","0.5876392","0.5872551","0.587119","0.58597064","0.5846699","0.5839273","0.5830909","0.5829342","0.5826805","0.5824798","0.58217233","0.58083254","0.57991487","0.5798967","0.5776642","0.5774388","0.5772396","0.57658076","0.5762746","0.5759354","0.5752829","0.5748937","0.57451576","0.5744562","0.5742115","0.57323813","0.57284445","0.57193047","0.56990755","0.56987387","0.5697691","0.56875896","0.5686963","0.5686885","0.56708443","0.5670241","0.5667696","0.5665444","0.5662888","0.5647672","0.5639905","0.5639862","0.563429","0.5626234","0.56259","0.56221","0.5618856","0.56122756","0.56068236","0.5580452","0.5573186","0.5571921","0.5565189","0.556507"],"string":"[\n \"0.6946017\",\n \"0.6875609\",\n \"0.6777287\",\n \"0.67524993\",\n \"0.6716408\",\n \"0.66537076\",\n \"0.6602633\",\n \"0.64604294\",\n \"0.6418981\",\n \"0.6406474\",\n \"0.6382838\",\n \"0.6360858\",\n \"0.63093865\",\n \"0.6304608\",\n \"0.6299599\",\n \"0.6263775\",\n \"0.6230886\",\n \"0.6198476\",\n \"0.6191175\",\n \"0.61556995\",\n \"0.6152255\",\n \"0.61249316\",\n \"0.6118711\",\n \"0.6094595\",\n \"0.60879076\",\n \"0.608356\",\n \"0.6072247\",\n \"0.60550004\",\n \"0.6051795\",\n \"0.6048452\",\n \"0.60369694\",\n \"0.6036503\",\n \"0.6035315\",\n \"0.60326356\",\n \"0.60101897\",\n \"0.599683\",\n \"0.5996011\",\n \"0.59680265\",\n \"0.59607023\",\n \"0.5948106\",\n \"0.59386915\",\n \"0.5930006\",\n \"0.59111774\",\n \"0.5903709\",\n \"0.58962375\",\n \"0.5885998\",\n \"0.5876392\",\n \"0.5872551\",\n \"0.587119\",\n \"0.58597064\",\n \"0.5846699\",\n \"0.5839273\",\n \"0.5830909\",\n \"0.5829342\",\n \"0.5826805\",\n \"0.5824798\",\n \"0.58217233\",\n \"0.58083254\",\n \"0.57991487\",\n \"0.5798967\",\n \"0.5776642\",\n \"0.5774388\",\n \"0.5772396\",\n \"0.57658076\",\n \"0.5762746\",\n \"0.5759354\",\n \"0.5752829\",\n \"0.5748937\",\n \"0.57451576\",\n \"0.5744562\",\n \"0.5742115\",\n \"0.57323813\",\n \"0.57284445\",\n \"0.57193047\",\n \"0.56990755\",\n \"0.56987387\",\n \"0.5697691\",\n \"0.56875896\",\n \"0.5686963\",\n \"0.5686885\",\n \"0.56708443\",\n \"0.5670241\",\n \"0.5667696\",\n \"0.5665444\",\n \"0.5662888\",\n \"0.5647672\",\n \"0.5639905\",\n \"0.5639862\",\n \"0.563429\",\n \"0.5626234\",\n \"0.56259\",\n \"0.56221\",\n \"0.5618856\",\n \"0.56122756\",\n \"0.56068236\",\n \"0.5580452\",\n \"0.5573186\",\n \"0.5571921\",\n \"0.5565189\",\n \"0.556507\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7614783"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85697,"cells":{"query":{"kind":"string","value":"Straighten an image that has been rotated and has blank (alpha == 0) corners"},"document":{"kind":"string","value":"def straighten_image(img, scale=1):\n img = img[::scale, ::scale]\n blank = (img[:, :, 3] == 0)\n degrees = _find_angle(blank)\n if degrees > 45:\n degrees = degrees - 90\n nr, nc = img.shape[:2]\n radians = np.radians(degrees)\n # Rotate image\n img = img.astype(float) / img.max()\n new_img = transform.rotate(img, degrees)\n new_blank = transform.rotate(blank, degrees, order=0, mode='constant', cval=1) > 0.5\n # Find clip boundaries\n valid = ~new_blank\n for i0 in range(nr):\n if valid[i0].sum():\n break\n for i1 in range(nr - 1, -1, -1):\n if valid[i1].sum():\n break\n for j0 in range(nc):\n if valid[:, j0].sum():\n break\n for j1 in range(nc - 1, -1, -1):\n if valid[:, j1].sum():\n break\n # Create transform\n center = np.array((scale * nc, scale * nr)) / 2. - 0.5\n trans = affine.Affine.translation\n xform = trans(*center) * affine.Affine.rotation(degrees) * trans(*-center) * trans(scale * j0, scale * i0)\n\n return new_img[i0:i1, j0:j1], xform, degrees"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def straighten(image):\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n gray = cv2.bitwise_not(gray)\n thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n coords = np.column_stack(np.where(thresh > 0))\n angle = cv2.minAreaRect(coords)[-1]\n if angle < -45:\n angle = -(90 + angle)\n else:\n angle = -angle\n (h, w) = image.shape[:2]\n center = (w // 2, h // 2)\n M = cv2.getRotationMatrix2D(center=center, angle=angle, scale=1.0)\n rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)\n # print(\"Straightening angle : \", angle)\n return rotated","def straighten_thresh(image):\n coords = np.column_stack(np.where(image > 0))\n angle = cv2.minAreaRect(coords)[-1]\n if angle < -45:\n angle = -(90 + angle)\n else:\n angle = -angle\n (h, w) = image.shape[:2]\n center = (w // 2, h // 2)\n M = cv2.getRotationMatrix2D(center=center, angle=angle, scale=1.0)\n rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)\n # print(\"Straightening angle : \", angle)\n return rotated","def rotatedView(img, angle, enlarge=True, extend=Views.extendBorder):\n cx = img.dimension(0) / 2.0\n cy = img.dimension(1) / 2.0\n toCenter = AffineTransform2D()\n toCenter.translate(-cx, -cy)\n rotation = AffineTransform2D()\n # Step 1: place origin of rotation at the center of the image\n rotation.preConcatenate(toCenter)\n # Step 2: rotate around the Z axis\n rotation.rotate(radians(angle))\n # Step 3: undo translation to the center\n rotation.preConcatenate(toCenter.inverse())\n rotated = RV.transform(Views.interpolate(extend(img),\n NLinearInterpolatorFactory()), rotation)\n if enlarge:\n # Bounds:\n bounds = repeat((sys.maxint, 0)) # initial upper- and lower-bound values \n # for min, max to compare against \n transformed = zeros(2, 'f')\n for corner in product(*zip(repeat(0), Intervals.maxAsLongArray(img))):\n rotation.apply(corner, transformed)\n bounds = [(min(vmin, int(floor(v))), max(vmax, int(ceil(v))))\n for (vmin, vmax), v in zip(bounds, transformed)]\n minC, maxC = map(list, zip(*bounds)) # transpose list of 2 pairs\n # into 2 lists of 2 values\n imgRot = Views.zeroMin(Views.interval(rotated, minC, maxC))\n else:\n imgRot = Views.interval(rotated, img)\n return imgRot","def rotate_and_wrap_image(self, image, degree_of_rotation):\n\n image_center = tuple(np.array(image.shape[1::-1]) / 2)\n rot_mat = cv2.getRotationMatrix2D(image_center, degree_of_rotation, 1.0)\n # borderMode (constant) and borderValue are important for maintaiing consistency \n ri = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR, borderMode = cv2.BORDER_CONSTANT,borderValue = (255,255,255))\n return ri","def img_rotate(img, angle, center, fillval=0):\n rows, cols = img.shape[:2]\n M = cv2.getRotationMatrix2D(center, angle, 1)\n return cv2.warpAffine(img, M, (cols, rows), borderValue=fillval)","def _correct_rotation(img: np.ndarray) -> np.ndarray:\n edges = cv2.Canny(img, 50, 150, apertureSize=3)\n lines = cv2.HoughLinesP(\n edges, 1, np.pi / 180, 100, minLineLength=100, maxLineGap=10\n )\n avg_slope = 0\n cnt = 0\n try:\n for line in lines:\n rise = line[0][3] - line[0][1]\n run = line[0][2] - line[0][0]\n if run != 0:\n if rise / run < 0.5:\n avg_slope += rise / run\n cnt += 1\n avg_slope = avg_slope / cnt\n\n image_center = tuple(np.array(img.shape[1::-1]) / 2)\n rot_mat = cv2.getRotationMatrix2D(\n image_center, np.arctan(avg_slope) * 180 / np.pi, 1.0\n )\n result = cv2.warpAffine(img, rot_mat, img.shape[1::-1], flags=cv2.INTER_LINEAR)\n return result\n except TypeError:\n return img","def rotate_no_clip(self, angle):\n # Calculate the size the expanded image needs to be to contain rotated image\n x, y = self.width, self.height\n w = abs(x*math.cos(angle)) + abs(y*math.sin(angle))\n h = abs(x*math.sin(angle)) + abs(y*math.cos(angle))\n\n # Paste the image into a larger frame and rotate\n img = Image.blank(w, h, 4, 0)\n img.paste(self, w/2-x/2, h/2-y/2)\n rotated = img.rotate(angle, (w/2, h/2))\n\n return rotated","def rotate_with_extension(image, alpha):\n # Determine if alpha is larger than 90 degrees and rotate accordingly\n # Number of 90 degree turns\n quarter = int(alpha / 90)\n # turn the image 90 deg quarter times\n image = image.rotate(quarter * 90)\n # Get the angle of the rest of the rotation\n alpha = alpha % 90\n\n # Size of image\n size = image.size[0]\n # Get radians\n alpha_rad = alpha/180 * np.pi\n\n # Compute the size of the extended image needed to keep an image of the\n # right size after cropping\n cos = np.cos(alpha_rad)\n sin = np.sin(alpha_rad)\n L = int(size * (sin + cos))\n\n # Extend the image then rotate it\n extend = mirror_extend(int((L-size)/2), image)\n rotate = extend.rotate(alpha, expand=1)\n # Get the current side length of the rotated image and compute the\n # difference with the original image to obtain the number of pixels to remove\n # from each border\n side = rotate.size[0]\n pixel_to_remove = (side - size)/2\n # Crop the image\n return rotate.crop((pixel_to_remove, pixel_to_remove, pixel_to_remove + size, pixel_to_remove+size))","def _augment(img):\r\n return flip(img, axis=2)","def image_augmentation(img):\n return np.fliplr(img)","def get_opt_rotate(obj_img, back_img,\n back_center_x, back_center_y,\n obj_center_x, obj_center_y,\n prev_rot_angle=0.,\n is_erosion=False):\n width = obj_img.shape[0]\n rot_img = ndimage.rotate(obj_img, prev_rot_angle, reshape=False)\n induce_x, induce_y = int(back_center_x - obj_center_x), int(back_center_y - obj_center_y)\n combine_img = back_img.copy()\n combine_img[induce_y:induce_y + width, induce_x:induce_x + width] -= rot_img\n neg_count = len(np.argwhere(combine_img < 0))\n if is_erosion:\n angle_amount = 4.\n else:\n angle_amount = 16.\n # check combine_img.dtype; rot_img.dtype; back_img\n curr_angle = prev_rot_angle\n while angle_amount > 0.5:\n angle_amount /= 2.\n\n rotate_1 = ndimage.rotate(obj_img, curr_angle + angle_amount, reshape=False)\n combine_img = back_img.copy()\n combine_img[induce_y:induce_y+width, induce_x:induce_x+width] -= rotate_1\n neg_count_1 = len(np.argwhere(combine_img < 0))\n\n rotate_2 = ndimage.rotate(obj_img, curr_angle - angle_amount, reshape=False)\n combine_img = back_img.copy()\n combine_img[induce_y:induce_y + width, induce_x:induce_x + width] -= rotate_2\n neg_count_2 = len(np.argwhere(combine_img < 0))\n\n if neg_count_1 < neg_count_2:\n if neg_count_1 < neg_count:\n neg_count = neg_count_1\n curr_angle = curr_angle + angle_amount\n else:\n if neg_count_2 < neg_count:\n neg_count = neg_count_2\n curr_angle = curr_angle - angle_amount\n # print(curr_angle)\n # print(neg_count, neg_count_1, neg_count_2)\n # print('Negative Pix Count Rotation: %d.' % neg_count)\n # print('Optimal Rotation: ', curr_angle)\n return curr_angle, neg_count","def _augment(img):\n return flip(img, axis=2)","def rotate_image(image, angle):\n\n # Get the image size\n # No that's not an error - NumPy stores image matricies backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n ''' Calculate Rotation Matrix '''\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\n (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n ''' Calculate Translation Matrix '''\n # We require a translation matrix to keep the image centred\n trans_mat = np.matrix([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the transform for the combined rotation and translation\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv2.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv2.INTER_LINEAR\n )\n\n return result","def preprocess(self, image):\n if self.rotate == 0:\n return image\n\n angle = self.rotate * -90\n return image.rotate(angle, expand=True).crop((0, 0, self._w, self._h))","def rotate_image(image, angle):\n\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(array(image_size) / 2)\n\n rot_mat = vstack([cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]])\n trans_mat = identity(3)\n\n w2 = image_size[0] * 0.5\n h2 = image_size[1] * 0.5\n\n rot_mat_notranslate = matrix(rot_mat[0:2, 0:2])\n\n tl = (array([-w2, h2]) * rot_mat_notranslate).A[0]\n tr = (array([w2, h2]) * rot_mat_notranslate).A[0]\n bl = (array([-w2, -h2]) * rot_mat_notranslate).A[0]\n br = (array([w2, -h2]) * rot_mat_notranslate).A[0]\n\n x_coords = [pt[0] for pt in [tl, tr, bl, br]]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in [tl, tr, bl, br]]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n new_image_size = (new_w, new_h)\n\n new_midx = new_w * 0.5\n new_midy = new_h * 0.5\n\n dx = int(new_midx - w2)\n dy = int(new_midy - h2)\n\n trans_mat = getTranslationMatrix2d(dx, dy)\n affine_mat = (matrix(trans_mat) * matrix(rot_mat))[0:2, :]\n result = cv2.warpAffine(image, affine_mat, new_image_size, flags=cv2.INTER_LINEAR)\n\n return result","def deskew(image, gray):\n\n # Flip the foreground\n gray = cv2.bitwise_not(gray)\n\n # Treshold, set foreground px to 255 and background to 0\n tresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n\n # Get the (x,y) coord\n coords = np.column_stack(np.where(tresh > 0))\n\n # Get the rotated bouding box of these coords\n # https://stackoverflow.com/questions/15956124/minarearect-angles-unsure-about-the-angle-returned\n angle = cv2.minAreaRect(coords)[-1] # Return val btwn [-90,0]\n\n # As the rect rotate clockwise, angle --> 0\n if angle < -45:\n angle = -(90 + angle)\n else:\n # Just take the inverse\n angle = -angle\n\n rotated = None\n\n if(np.abs(angle) != 0):\n # Rotate the image\n (height, width) = gray.shape[:2]\n center = (width // 2, height // 2)\n matrix = cv2.getRotationMatrix2D(center, angle, 1.0)\n rotated = cv2.warpAffine(\n image,\n matrix,\n (width, height),\n flags=cv2.INTER_CUBIC,\n borderMode=cv2.BORDER_REPLICATE,\n )\n\n return [rotated, angle]","def rotate_image(image, angle):\n\n # Get the image size\n # No that's not an error - NumPy stores image matricies backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n # We require a translation matrix to keep the image centred\n trans_mat = np.matrix([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the tranform for the combined rotation and translation\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv2.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv2.INTER_LINEAR\n )\n\n return result","def sym_left_img(img):\n cropped = img.crop(box=(0, 0, img.width / 2, img.height))\n mirror = cropped.transpose(Image.FLIP_LEFT_RIGHT)\n combine = Image.new('RGB', (cropped.width * 2, cropped.height), 'white')\n combine.paste(cropped, (0, 0, cropped.width, cropped.height))\n combine.paste(mirror, (cropped.width, 0, cropped.width * 2, cropped.height))\n return combine","def del_alpha(img): \n img = img[:,:,0:3].copy()\n return img","def rotated_image(image):\n orientation = parameter('Orientation',90) # in degrees counter-clockwise\n if orientation == None: orienation = 0\n return image.rotate(orientation)","def deskew_image(image, delta = 0.5, limit = 5):\n try:\n print(\"TESTED\")\n img_input_array = cv2.imread(image,cv2.IMREAD_GRAYSCALE)\n image_original=img_input_array.copy()\n image_skew=cv2.resize(img_input_array,(int(img_input_array.shape[1]*0.5),int(img_input_array.shape[0]*0.5)))\n image_bin=image_binary(image_skew)\n angles = np.arange(-limit, limit + delta, delta)\n scores = [find_score(image_bin,angle) for angle in angles]\n best_score = max(scores)\n best_angle = angles[scores.index(best_score)]\n image_inv=image_binary(image_original)\n data = inter.rotate(image_inv, best_angle, reshape=False, order=0)\n image_skewed=((~data.astype(bool)).astype(int))*255\n print(image_skewed, best_angle)\n return image_skewed, best_angle\n except AttributeError:\n print(\"OOPS\")\n return img_input_array, 0","def rotate_image(image, angle):\n\n # Get the image size\n # No that's not an error - NumPy stores image matrices backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\n (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n # We require a translation matrix to keep the image centred\n trans_mat = np.matrix([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the tranform for the combined rotation and translation\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv2.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv2.INTER_LINEAR\n )\n\n return result","def rotate(self, image, angle):\n tmpImage = pygame.transform.rotate(image ,angle)\n imageCentreX = tmpImage.get_rect()[0] + tmpImage.get_rect()[2]/2\n imageCentreY = tmpImage.get_rect()[1] + tmpImage.get_rect()[3]/2\n\n targetWidth = tmpImage.get_rect()[2]\n targetHeight = tmpImage.get_rect()[3]\n\n imageOut = pygame.Surface((targetWidth, targetHeight))\n imageOut.fill((255,255,0))\n imageOut.set_colorkey((255,255,0))\n imageOut.blit(tmpImage,(0,0), pygame.Rect( imageCentreX-targetWidth/2,imageCentreY-targetHeight/2, targetWidth, targetHeight ) )\n return imageOut","def rotated_e():\n x = np.zeros((5, 5))\n x[:, 0] = 1.\n y = np.zeros((5, 5))\n y[:, 2] = 1.\n z = np.zeros((5, 5))\n z[:, 4] = 1.\n a = np.zeros((5, 5))\n a[0, :] = 1.\n b = np.zeros((5, 5))\n b[2, :] = 1.\n c = np.zeros((5, 5))\n c[4, :] = 1.\n\n img = np.zeros((4, 5, 5))\n img[0] = x + y + z + a\n img[1] = x + y + z + c\n img[2] = a + b + c + x\n img[3] = a + b + c + z\n img[img > 0] = 1.\n\n return img.astype('float32')","def flattener(image, pts, w, h):\n temp_rect = np.zeros((4,2), dtype = \"float32\")\n \n s = np.sum(pts, axis = 2)\n\n tl = pts[np.argmin(s)]\n br = pts[np.argmax(s)]\n\n diff = np.diff(pts, axis = -1)\n tr = pts[np.argmin(diff)]\n bl = pts[np.argmax(diff)]\n\n if w <= 0.8 * h: # If card is vertically oriented\n temp_rect[0] = tl\n temp_rect[1] = tr\n temp_rect[2] = br\n temp_rect[3] = bl\n\n if w >= 1.2 * h: # If card is horizontally oriented\n temp_rect[0] = bl\n temp_rect[1] = tl\n temp_rect[2] = tr\n temp_rect[3] = br\n\n if w > 0.8 * h and w < 1.2 * h: #If card is diamond oriented\n # If furthest left point is higher than furthest right point,\n # card is tilted to the left.\n if pts[1][0][1] <= pts[3][0][1]:\n # If card is titled to the left, approxPolyDP returns points\n # in this order: top right, top left, bottom left, bottom right\n temp_rect[0] = pts[1][0] # Top left\n temp_rect[1] = pts[0][0] # Top right\n temp_rect[2] = pts[3][0] # Bottom right\n temp_rect[3] = pts[2][0] # Bottom left\n\n # If furthest left point is lower than furthest right point,\n # card is tilted to the right\n if pts[1][0][1] > pts[3][0][1]:\n # If card is titled to the right, approxPolyDP returns points\n # in this order: top left, bottom left, bottom right, top right\n temp_rect[0] = pts[0][0] # Top left\n temp_rect[1] = pts[3][0] # Top right\n temp_rect[2] = pts[2][0] # Bottom right\n temp_rect[3] = pts[1][0] # Bottom left\n \n # Create destination array, calculate perspective transform matrix,\n # and warp card image\n dst = np.array([[0, 0], [MAXWIDTH - 1, 0], [MAXWIDTH - 1, MAXHEIGHT - 1], [0, MAXHEIGHT - 1]], np.float32)\n M = cv2.getPerspectiveTransform(temp_rect, dst)\n warp = cv2.warpPerspective(image, M, (MAXWIDTH, MAXHEIGHT))\n\n return warp","def rotate(img, angle, resample=False, expand=False, center=None):\r\n \r\n return img.rotate(angle, resample, expand, center)","def rotate_image(image, angle, crop=True, keepSize=True):\n\n # do we need to convert the image?\n prevShape = image.shape\n image, reshaped = reshape_to_cv_format(image, False)\n\n # this is 10-times faster than: return ndimage.rotate(image, angle)\n # --\n # After http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders\n # Get the image size\n # No that's not an error - NumPy stores image matrices backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n # We require a translation matrix to keep the image centered\n trans_mat = np.matrix([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the transform for the combined rotation and translation\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv.INTER_LINEAR\n )\n if not crop:\n # if we do not need to crop the image we can stop here, but the image might contain some black \"corners\"\n if reshaped: \n result = result.reshape(prevShape[0], result.shape[0], result.shape[1])\n return result\n\n image = crop_around_center(result, *rotated_rect_with_max_area(image_size[0], image_size[1], math.radians(angle)))\n\n # if keepSize the same, the image is square and the size has changed than change size to fit input again\n if keepSize and image_size[0] == image_size[1] and (image_size[0] != image.shape[1] or image_size[1] != image.shape[0]):\n image = equalize_image_size(image, (image_size[0]*image_size[0]))\n\n if reshaped: \n image = image.reshape(prevShape[0], image.shape[0], image.shape[1])\n return image","def _rotate(self):\n \r\n if self.clr == 1: # (default rotation) \r\n # o o o o \r\n # o x x o x o o x\r\n # o o o o\r\n _colOffsets = [[-1,-1, 0, 0], [-1, 0, 0, 1], [ 1, 1, 0, 0], [ 1, 0, 0,-1]] #\r\n _rowOffsets = [[ 1, 0, 0,-1], [-1,-1, 0, 0], [-1, 0, 0, 1], [ 1, 1, 0, 0]] #\r\n elif self.clr == 2:\r\n # o o o o \r\n # o x o x x o x o\r\n # o o o o\r\n _colOffsets = [[-1,-1, 0, 0], [ 1, 0, 0,-1], [ 1, 1, 0, 0], [-1, 0, 0, 1]] #\r\n _rowOffsets = [[-1, 0, 0, 1], [-1,-1, 0, 0], [ 1, 0, 0,-1], [ 1, 1, 0, 0]] #\n \r\n elif self.clr == 3: # \r\n # o o o o \r\n # x o x o x o x o\r\n # o o o o\n \r\n _colOffsets = [[-1, 0, 0, 0], [-1,-1, 0, 1], [ 1, 0, 0, 0], [ 1, 1, 0,-1]] #\r\n _rowOffsets = [[ 1, 1, 0,-1], [-1, 0, 0, 0], [-1,-1, 0, 1], [ 1, 0, 0, 0]] #\n \r\n elif self.clr == 4:\r\n # o o o o \r\n # x o x o x o x o\r\n # o o o o\r\n _colOffsets = [[-1, 0, 0, 0], [1, 1, 0, -1], [1, 0, 0,0], [-1, -1, 0,1]]\n _rowOffsets = [[-1,-1, 0, 1], [-1,0, 0, 0], [1,1, 0,-1], [1,0, 0, 0]]\n \r\n elif self.clr == 5: # o o\r\n # o x \r\n # x o x o o o o o x o\r\n # o o \r\n _colOffsets = [[ 0, 0, 0, 0], [ 2, 1, 0,-1], [ 0, 0, 0, 0], [-2,-1, 0, 1]] #\r\n _rowOffsets = [[-2,-1, 0, 1], [ 0, 0, 0, 0], [ 2, 1, 0,-1], [ 0, 0, 0, 0]] #\r\n elif self.clr == 6: #\r\n # o o o \r\n # o x o x o x o o x o\r\n # o o o \r\n _colOffsets = [[ 0,-1, 0, 0], [-1, 0, 0, 1], [ 0, 1, 0, 0], [ 1, 0, 0,-1]] #\r\n _rowOffsets = [[ 1, 0, 0,-1], [ 0,-1, 0, 0], [-1, 0, 0, 1], [ 0, 1, 0, 0]] #\r\n elif self.clr == 7: # \r\n # o o o o o o o o\r\n # o x o x o x o x\r\n # \r\n _colOffsets = [[-1,-1, 0, 0], [-1,-1, 0, 0], [-1,-1, 0, 0], [-1,-1, 0, 0]] #@@\r\n _rowOffsets = [[ 0,-1, 0,-1], [ 0,-1, 0,-1], [ 0,-1, 0,-1], [ 0,-1, 0,-1]] #@@\n \r\n self._colOffsets = _colOffsets[self._rot] #@@\r\n self._rowOffsets = _rowOffsets[self._rot] #@@\r\n self._update() #@@\r","def flattener(image, pts, w, h):\n temp_rect = np.zeros((4,2), dtype = \"float32\")\n\n s = np.sum(pts, axis = 2)\n\n tl = pts[np.argmin(s)]\n br = pts[np.argmax(s)]\n\n diff = np.diff(pts, axis = -1)\n tr = pts[np.argmin(diff)]\n bl = pts[np.argmax(diff)]\n\n # Need to create an array listing points in order of\n # [top left, top right, bottom right, bottom left]\n # before doing the perspective transform\n\n if w <= 0.8*h: # If card is vertically oriented\n temp_rect[0] = tl\n temp_rect[1] = tr\n temp_rect[2] = br\n temp_rect[3] = bl\n\n if w >= 1.2*h: # If card is horizontally oriented\n temp_rect[0] = bl\n temp_rect[1] = tl\n temp_rect[2] = tr\n temp_rect[3] = br\n\n # If the card is 'diamond' oriented, a different algorithm\n # has to be used to identify which point is top left, top right\n # bottom left, and bottom right.\n\n if w > 0.8*h and w < 1.2*h: #If card is diamond oriented\n # If furthest left point is higher than furthest right point,\n # card is tilted to the left.\n if pts[1][0][1] <= pts[3][0][1]:\n # If card is titled to the left, approxPolyDP returns points\n # in this order: top right, top left, bottom left, bottom right\n temp_rect[0] = pts[1][0] # Top left\n temp_rect[1] = pts[0][0] # Top right\n temp_rect[2] = pts[3][0] # Bottom right\n temp_rect[3] = pts[2][0] # Bottom left\n\n # If furthest left point is lower than furthest right point,\n # card is tilted to the right\n if pts[1][0][1] > pts[3][0][1]:\n # If card is titled to the right, approxPolyDP returns points\n # in this order: top left, bottom left, bottom right, top right\n temp_rect[0] = pts[0][0] # Top left\n temp_rect[1] = pts[3][0] # Top right\n temp_rect[2] = pts[2][0] # Bottom right\n temp_rect[3] = pts[1][0] # Bottom left\n\n maxWidth = 200\n maxHeight = 300\n\n # Create destination array, calculate perspective transform matrix,\n # and warp card image\n dst = np.array([[0,0],[maxWidth-1,0],[maxWidth-1,maxHeight-1],[0, maxHeight-1]], np.float32)\n M = cv2.getPerspectiveTransform(temp_rect,dst)\n warp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n return cv2.cvtColor(warp, cv2.COLOR_BGR2RGB)","def draw_final_image(self, image, warped, undist, ploty, left_fitx, right_fitx, Minv, left_rad, right_rad):\n gray = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)\n # Create an image to draw the lines on\n warp_zero = np.zeros_like(gray).astype(np.uint8)\n color_warp = np.dstack((warp_zero, warp_zero, warp_zero))\n\n # Recast the x and y points into usable format for cv2.fillPoly()\n pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])\n pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])\n pts = np.hstack((pts_left, pts_right))\n\n # Draw the lane onto the warped blank image\n cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))\n\n # Warp the blank back to original image space using inverse perspective matrix (Minv)\n newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))\n # Combine the result with the original image\n result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)\n\n font = cv2.FONT_HERSHEY_SIMPLEX\n off_center = calculate_center(left_fitx, right_fitx, image.shape)\n direction_str = 'left' if off_center < 0 else 'right'\n center_str = '{:.2f} m of center {}'.format(abs(off_center), direction_str)\n cv2.putText(result, center_str, (430, 630), font, 1, (0, 0, 255), 2, cv2.LINE_AA)\n if left_rad and right_rad:\n curvature = 0.5 * (round(right_rad / 1000, 1) + round(left_rad / 1000, 1))\n else:\n curvature = 0\n str2 = 'Radius of curvature: {} km'.format(curvature)\n cv2.putText(result, str2, (430, 670), font, 1, (0, 0, 255), 2, cv2.LINE_AA)\n\n if self.args.is_test:\n plt.imshow(result)\n plt.show()\n\n return result","def augment_image(image,max_angle):\n angles = [-max_angle,max_angle]\n axes = [(0,1),(0,2),(1,2)]\n images_aug = [image,image[::-1]]\n for angle in angles:\n for axis in axes:\n images_aug.append(aug.rotate(image, angle, axes=axis, reshape=False, order=0))\n images_aug.append(aug.rotate(image[::-1], angle, axes=axis, reshape=False, order=0))\n return images_aug","def augment_img(img):\n img = random_hflip_img(img)\n img = cutout_img(img, size=12)\n img = zero_pad_and_crop_img(img)\n return img","def get_rotation_angle(prev_image, curr_image, size_of_cropped_image):\n max_value = np.amax(prev_image)\n\n if prev_image.dtype == 'float' and max_value <= 1:\n prev_image = np.uint8(prev_image * 255)\n curr_image = np.uint8(curr_image * 255)\n\n if prev_image.dtype == 'float' and max_value > 1:\n prev_image = np.uint8(prev_image)\n curr_image = np.uint8(curr_image)\n\n prev_image = cv.equalizeHist(prev_image)\n curr_image = cv.equalizeHist(curr_image)\n\n # Initiate ORB detector\n orb = cv.ORB_create(nfeatures=200)\n\n # find the keypoints and descriptors with ORB\n kp1, des1 = orb.detectAndCompute(prev_image, None)\n kp2, des2 = orb.detectAndCompute(curr_image, None)\n\n # do feature matching\n bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)\n matches = bf.match(des1, des2)\n matches = sorted(matches, key=lambda x: x.distance)\n\n # calculate perspective transform matrix\n src_pts = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)\n dst_pts = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)\n transform_matrix, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)\n\n if transform_matrix is None:\n transform_matrix, mask = cv.findHomography(src_pts, dst_pts, 0)\n\n if transform_matrix is None:\n transform_matrix, mask = cv.findHomography(src_pts, dst_pts, 0)\n\n vector_along_x_axis_from_center = \\\n np.float32([[size_of_cropped_image / 2, size_of_cropped_image / 2],\n [size_of_cropped_image, size_of_cropped_image / 2]]).reshape(-1, 1, 2)\n vector_transformed = cv.perspectiveTransform(vector_along_x_axis_from_center, transform_matrix)\n\n theta = - np.arctan2(vector_transformed[1, 0, 1] - vector_transformed[0, 0, 1],\n vector_transformed[1, 0, 0] - vector_transformed[0, 0, 0]) * 180 / np.pi\n # negative sign is to make the sign of the angle to correspond to one in a right-handed coordinate system\n return theta","def azizen(self):\n # x0,y0 array pixel coordinates relative to cx,cy\n# ndy0,ndx0=img.shape\n ndy0=self.ndy0\n ndx0=self.ndx0\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.linspace(0,ndy0-1,ndy0)-self.cy)\n r0=np.sqrt(x0**2+y0**2)/self.pr0 # fractional radial distance from 0,0\n# self.roi=np.s_[ystart:ystart+self.ny0,xstart:xstart+self.nx0]\n # why not model the zenith angle dependence with polynomial directly\n # rather than linear interpolation between roots.\n roots=np.zeros(51)\n rr=np.arange(51)/100.0\n for i,ref in enumerate(rr):\n roots[i]=np.real(np.roots([self.c3,0,self.c2,0,self.c1,-ref])[-1])\n theta0 = np.interp(r0/2,rr,roots)\n \n phi0 = np.arctan2(x0,y0) - self.rot ####phi (i.e., azimuth) is reckoned with -pi corresponding to north, increasing clockwise, NOTE: pysolar use sub-standard definition\n phi0 = phi0%(2*np.pi)\n\n #####correction for the tilt of the camera\n k=np.array((np.sin(self.azm),np.cos(self.azm),0))\n a=np.array([np.sin(theta0)*np.cos(phi0),np.sin(theta0)*np.sin(phi0),np.cos(theta0)]); \n a = np.transpose(a,[1,2,0])\n b=np.cos(self.beta)*a + np.sin(self.beta)*np.cross(k,a,axisb=2) \\\n + np.reshape(np.outer(np.dot(a,k),k),(self.ndy0,self.ndx0,3))*(1-np.cos(self.beta))\n theta0=np.arctan(np.sqrt(b[:,:,0]**2+b[:,:,1]**2)/b[:,:,2])\n phi0=np.arctan2(b[:,:,1],b[:,:,0])%(2*np.pi)\n# max_theta *= deg2rad \n# valid0 = (theta0<max_theta) & (theta0>0); \n# theta0[valid0]=np.nan;\n self.theta0,self.phi0=theta0,phi0","def draw_image(self):\n dir_ship = self.direction\n dir_cannon=self.cannon_dir\n temp_image = self.base_image.copy()\n pygame.draw.polygon(temp_image, (0,0,0), [(2,2),(2,3),(3,3),(3,2)])\n if dir_cannon == dir_ship:\n pygame.draw.polygon(temp_image, (60,60,60), [(4,3),(4,2), (5,3),(5,2)])\n if (dir_ship - dir_cannon)%4 ==1:#-90° angle\n pygame.draw.polygon(temp_image, (60,60,60), [(2,4),(3,4), (2,5),(3,5)])\n if (dir_ship - dir_cannon)%4 ==3:#+90° angle\n pygame.draw.polygon(temp_image, (60,60,60), [(2,1),(3,1), (2,0),(3,0)])\n if (dir_ship - dir_cannon)%4 ==2:#180° angle\n pygame.draw.polygon(temp_image, (60,60,60), [(1,2),(1,3), (0,2),(0,3)])\n temp_image=pygame.transform.rotate(temp_image,dir_ship*90)\n return temp_image","def rotate(self, image, angle):\n # Get the image size\n # No that's not an error - NumPy stores image matricies backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\n (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n # We require a translation matrix to keep the image centred\n trans_mat = np.matrix([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the tranform for the combined rotation and translation\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv2.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv2.INTER_LINEAR\n )\n\n return result","def rotate(img_path):\r\n\timg = cv2.imread(img_path)\r\n\timgcorrect = ImgCorrect(img)\r\n\tlines_img = imgcorrect.img_lines()\r\n\tif lines_img is None:\r\n\t rotated = imgcorrect.rotate_image(0)\r\n\telse:\r\n\t degree = imgcorrect.search_lines()\r\n\t rotated = imgcorrect.rotate_image(degree)\r\n\tcv2.imwrite(img_path, rotated)","def rotate(\n img,\n angle,\n interpolation='nearest',\n expand=False,\n center=None,\n fill=None,\n data_format='CHW',\n):\n\n angle = -angle % 360\n img = img.unsqueeze(0)\n\n # n, c, h, w = img.shape\n w, h = _get_image_size(img, data_format=data_format)\n\n img = img if data_format.lower() == 'chw' else img.transpose((0, 3, 1, 2))\n\n post_trans = [0, 0]\n\n if center is None:\n rotn_center = [0, 0]\n else:\n rotn_center = [(p - s * 0.5) for p, s in zip(center, [w, h])]\n\n if paddle.in_dynamic_mode():\n angle = math.radians(angle)\n matrix = [\n math.cos(angle),\n math.sin(angle),\n 0.0,\n -math.sin(angle),\n math.cos(angle),\n 0.0,\n ]\n matrix = paddle.to_tensor(matrix, place=img.place)\n\n matrix[2] += (\n matrix[0] * (-rotn_center[0] - post_trans[0])\n + matrix[1] * (-rotn_center[1] - post_trans[1])\n + rotn_center[0]\n )\n matrix[5] += (\n matrix[3] * (-rotn_center[0] - post_trans[0])\n + matrix[4] * (-rotn_center[1] - post_trans[1])\n + rotn_center[1]\n )\n else:\n angle = angle / 180 * math.pi\n matrix = paddle.concat(\n [\n paddle.cos(angle),\n paddle.sin(angle),\n paddle.zeros([1]),\n -paddle.sin(angle),\n paddle.cos(angle),\n paddle.zeros([1]),\n ]\n )\n matrix = paddle.static.setitem(\n matrix,\n 2,\n matrix[2]\n + matrix[0] * (-rotn_center[0] - post_trans[0])\n + matrix[1] * (-rotn_center[1] - post_trans[1])\n + rotn_center[0],\n )\n matrix = paddle.static.setitem(\n matrix,\n 5,\n matrix[5]\n + matrix[3] * (-rotn_center[0] - post_trans[0])\n + matrix[4] * (-rotn_center[1] - post_trans[1])\n + rotn_center[1],\n )\n\n matrix = matrix.reshape((1, 2, 3))\n\n if expand:\n # calculate output size\n if paddle.in_dynamic_mode():\n corners = paddle.to_tensor(\n [\n [-0.5 * w, -0.5 * h, 1.0],\n [-0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, -0.5 * h, 1.0],\n ],\n place=matrix.place,\n ).astype(matrix.dtype)\n else:\n corners = paddle.assign(\n [\n [-0.5 * w, -0.5 * h, 1.0],\n [-0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, -0.5 * h, 1.0],\n ],\n ).astype(matrix.dtype)\n\n _pos = (\n corners.reshape((1, -1, 3))\n .bmm(matrix.transpose((0, 2, 1)))\n .reshape((1, -1, 2))\n )\n _min = _pos.min(axis=-2).floor()\n _max = _pos.max(axis=-2).ceil()\n\n npos = _max - _min\n nw = npos[0][0]\n nh = npos[0][1]\n\n if paddle.in_dynamic_mode():\n ow, oh = int(nw), int(nh)\n else:\n ow, oh = nw.astype(\"int32\"), nh.astype(\"int32\")\n\n else:\n ow, oh = w, h\n\n grid = _affine_grid(matrix, w, h, ow, oh)\n\n out = _grid_transform(img, grid, mode=interpolation, fill=fill)\n\n out = out if data_format.lower() == 'chw' else out.transpose((0, 2, 3, 1))\n\n return out.squeeze(0)","def rotate_image(image, angle):\r\n\r\n # Get the image size\r\n # No that's not an error - NumPy stores image matricies backwards\r\n image_size = (image.shape[1], image.shape[0])\r\n image_center = tuple(np.array(image_size) / 2)\r\n\r\n # Convert the OpenCV 3x2 rotation matrix to 3x3\r\n rot_mat = np.vstack(\r\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\r\n )\r\n\r\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\r\n\r\n # Shorthand for below calcs\r\n image_w2 = image_size[0] * 0.5\r\n image_h2 = image_size[1] * 0.5\r\n\r\n # Obtain the rotated coordinates of the image corners\r\n rotated_coords = [\r\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\r\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\r\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\r\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\r\n ]\r\n\r\n # Find the size of the new image\r\n x_coords = [pt[0] for pt in rotated_coords]\r\n x_pos = [x for x in x_coords if x > 0]\r\n x_neg = [x for x in x_coords if x < 0]\r\n\r\n y_coords = [pt[1] for pt in rotated_coords]\r\n y_pos = [y for y in y_coords if y > 0]\r\n y_neg = [y for y in y_coords if y < 0]\r\n\r\n right_bound = max(x_pos)\r\n left_bound = min(x_neg)\r\n top_bound = max(y_pos)\r\n bot_bound = min(y_neg)\r\n\r\n new_w = int(abs(right_bound - left_bound))\r\n new_h = int(abs(top_bound - bot_bound))\r\n\r\n # We require a translation matrix to keep the image centred\r\n trans_mat = np.matrix([\r\n [1, 0, int(new_w * 0.5 - image_w2)],\r\n [0, 1, int(new_h * 0.5 - image_h2)],\r\n [0, 0, 1]\r\n ])\r\n\r\n # Compute the tranform for the combined rotation and translation\r\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\r\n\r\n # Apply the transform\r\n result = cv2.warpAffine(\r\n image,\r\n affine_mat,\r\n (new_w, new_h),\r\n flags=cv2.INTER_LINEAR\r\n )\r\n\r\n return result","def tile_image(im):\n r1 = np.concatenate((im[::-1,::-1], im[::-1], im[::-1, ::-1]), 1)\n r2 = np.concatenate((im[:,::-1], im, im[:, ::-1]), 1)\n r3 = np.concatenate((im[::-1,::-1], im[::-1], im[::-1, ::-1]), 1)\n return(np.concatenate((r1, r2,r3), 0))","def flip_image_horizontal(image):\n return cv.flip(image, 0)","def augment_image(im):\n # First crop out the face to save reduce computation load\n bb = im.landmarks['bb'].lms\n bb_vec = bb.as_vector()\n bb_ul = (np.array([bb_vec[0], bb_vec[1]]) - bb.centre()) * 2\n bb_lr = (np.array([bb_vec[4], bb_vec[5]]) - bb.centre()) * 2\n ul = bb_ul + bb.centre()\n lr = bb_lr + bb.centre()\n im = im.crop(ul, lr, constrain_to_boundary=True)\n if im.pixels.shape[0] == 1:\n pix = np.zeros((3, im.pixels.shape[1], im.pixels.shape[2]))\n pix[:,] = im.pixels\n im.pixels = pix\n\n beta = 0.3\n cx = np.random.uniform(-beta, beta)\n cy = np.random.uniform(-beta, beta)\n fx = 1.0\n fy = np.random.uniform(0.6, 1.4)\n max_rotation = 30\n theta = np.random.uniform(-max_rotation, max_rotation)\n\n rotation = menpo.transform.Rotation.init_from_2d_ccw_angle(theta)\n shear = menpo.transform.Affine(np.array([[1, cx, 0],[cy, 1, 0], [0,0,1]]))\n scale = menpo.transform.Affine(np.array([[fx, 0, 0],[0, fy, 0], [0,0,1]]))\n T = scale.compose_after(shear).compose_after(rotation)\n\n t_im = im.transform_about_centre(T)\n\n t_im = add_color_jetting(t_im)\n t_im = add_occlusion(t_im)\n\n\n new_bb = t_im.landmarks['PTS'].lms.bounding_box()\n\n #new_bb contains the gt bounding box\n augmented_bb = add_bb_noise(new_bb)\n augmented_bb = augmented_bb.reshape((4,2))\n augmented_bb = menpo.shape.PointCloud(augmented_bb)\n t_im.landmarks['bb'] = menpo.landmark.LandmarkGroup.init_with_all_label(augmented_bb)\n\n return t_im","def deskew_grayscale(image_skew, delta = 0.5, limit = 5):\n try:\n print(\"TESTED1\")\n image_original=image_skew.copy()\n image_skew=cv2.resize(image_skew,(int(image_skew.shape[1]*0.5),int(image_skew.shape[0]*0.5)))\n image_bin=image_binary(image_skew)\n angles = np.arange(-limit, limit + delta, delta)\n scores = [find_score(image_bin,angle) for angle in angles]\n best_score = max(scores)\n best_angle = angles[scores.index(best_score)]\n image_inv=image_binary(image_original)\n data = inter.rotate(image_inv, best_angle, reshape=False, order=0)\n image_skewed=((~data.astype(bool)).astype(int))*255\n print(image_skewed, best_angle)\n return image_skewed, best_angle\n except AttributeError:\n print(\"OOPS1\")\n return image_skew, 0","def inverse_warping(img_initial, img_final, pts_initial, pts_final): \n \n # YOU SHOULDN'T NEED TO CHANGE THIS\n pts_final = pts_final.astype(int)\n \n projected_img = img_initial.copy()\n for i in range(3):\n sub_img_i = img_initial[:,:,i][pts_initial[:,1], pts_initial[:,0]]\n sub_img_f = img_final[:,:,i][pts_final[:,1], pts_final[:,0]]\n \n sub_img = sub_img_i*0.5 + sub_img_f*0.5\n projected_img[:,:,i][pts_initial[:,1], pts_initial[:,0]] = sub_img\n \n return projected_img","def concat_images_corner(imga, imgb, xoffset=0, yoffset=0, direction='horizontal',\n ontop=True, adjust_z=False):\n if direction == 'horizontal':\n max_dim = np.maximum.reduce([imga.shape, imgb.shape])\n\n offset = (abs(yoffset), abs(xoffset))\n tmp_offset = np.array(offset)\n\n # if (max_dim == imgb.shape).all():\n # tmp = np.copy(imgb)\n # imgb = np.copy(imga)\n # imga = np.copy(tmp)\n # ontop = toggle(ontop)\n # xoffset *= -1\n # yoffset *= -1\n\n # center_new = np.array(np.divide(max_dim, 2), dtype=int)\n new_img = np.full(np.add(max_dim, np.abs(offset)), np.nan)\n\n Sa0 = slice(0, imga.shape[0])\n Sa1 = slice(0, imga.shape[1])\n Sb0 = slice(abs(yoffset), abs(yoffset) + imgb.shape[0])\n Sb1 = slice(abs(xoffset), abs(xoffset) + imgb.shape[1])\n\n xdir = np.sign(xoffset)\n ydir = np.sign(yoffset)\n\n if ydir == 0:\n ydir = 1\n if xdir == 0:\n xdir = 1\n\n imga = imga[::ydir, ::xdir]\n imgb = imgb[::ydir, ::xdir]\n\n if adjust_z:\n top_img = 1 * new_img\n top_img[Sa0, Sa1] = imga\n top_img[Sb0, Sb1] = imgb\n low_img = 1 * new_img\n low_img[Sb0, Sb1] = imgb\n low_img[Sa0, Sa1] = imga\n\n diff = top_img - low_img\n m = np.nanmean(diff)\n s = np.nanstd(diff)\n mask = np.abs(diff) < m + s\n diff[mask] = np.nan\n add = np.nanmean(diff)\n\n print(add)\n\n imgb -= add\n\n if ontop:\n new_img[Sa0, Sa1] = imga\n new_img[Sb0, Sb1] = imgb\n else:\n new_img[Sb0, Sb1] = imgb\n new_img[Sa0, Sa1] = imga\n\n return new_img[::ydir, ::xdir]","def triangle_warping(img1, img2, t1, t2, method):\n # Find bounding rectangle for each triangle\n x1, y1, w1, h1 = cv2.boundingRect(np.float32([t1]))\n x2, y2, w2, h2 = cv2.boundingRect(np.float32([t2]))\n\n # Offset points by left top corner of the respective rectangles\n t1Rect = []\n t2Rect = []\n\n for i in range(0, 3):\n t1Rect.append(((t1[i][0] - x1), (t1[i][1] - y1)))\n t2Rect.append(((t2[i][0] - x2), (t2[i][1] - y2)))\n\n # Get mask by filling triangle\n mask = np.zeros((h2, w2, 3), dtype=np.float32)\n\n cv2.fillConvexPoly(mask, np.int32(t2Rect), (1.0, 1.0, 1.0), 16, 0)\n\n # Apply warpImage to small rectangular patches\n img1Rect = img1[y1:y1 + h1, x1:x1 + w1]\n img2Rect = np.zeros((h2, w2), dtype=img1Rect.dtype)\n\n size = (w2, h2)\n\n if method == \"affine\":\n img2Rect = affine_warping(img1Rect, t1Rect, t2Rect, size)\n else:\n img2Rect = triangulationWarping(img1Rect, t1Rect, t2Rect, size)\n\n img2Rect = img2Rect * mask\n\n a = (1.0, 1.0, 1.0) - mask\n\n # Copy triangular region of the rectangular patch to the output image\n img2[y2:y2 + h2, x2:x2 + w2] = img2[y2:y2 + h2, x2:x2 + w2] * ((1.0, 1.0, 1.0) - mask)\n img2[y2:y2 + h2, x2:x2 + w2] = img2[y2:y2 + h2, x2:x2 + w2] + img2Rect","def sharpen(im):\n kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])\n im = cv2.filter2D(im, -1, kernel)\n return im","def create_straightened(src_path, tgt_path):\n img = skio.imread(src_path)\n straight_img, xform, angle = straighten_image(img)\n save_geotiff(tgt_path, (straight_img * 255).astype('uint8'), xform)","def rotate_right_90(img):\r\n #reading image\r\n im = Image.open(\"filename\")\r\n \r\n #flipping image 90 degrees\r\n newimg = im.transpose(PIL.Image.ROTATE_90)\r\n \r\n return img","def _rotate_image(self, src, theta, ox, oy, fill=255):\n # Images have origin at the top left, so negate the angle.\n theta = -theta\n\n # Dimensions of source image. Note that scipy.misc.imread loads\n # images in row-major order, so src.shape gives (height, width).\n sh, sw = src.shape\n\n # Rotated positions of the corners of the source image.\n cx, cy = self._rotate_coords([0, sw, sw, 0], [0, 0, sh, sh], theta, ox, oy)\n\n # Determine dimensions of destination image.\n dw, dh = (int(self._pkgs['numpy'].ceil(c.max() - c.min())) for c in (cx, cy))\n\n # Coordinates of pixels in destination image.\n dx, dy = self._pkgs['numpy'].meshgrid(self._pkgs['numpy'].arange(dw), self._pkgs['numpy'].arange(dh))\n\n # Corresponding coordinates in source image. Since we are\n # transforming dest-to-src here, the rotation is negated.\n sx, sy = self._rotate_coords(dx + cx.min(), dy + cy.min(), -theta, ox, oy)\n\n # Select nearest neighbour.\n sx, sy = sx.round().astype(int), sy.round().astype(int)\n\n # Mask for valid coordinates.\n mask = (0 <= sx) & (sx < sw) & (0 <= sy) & (sy < sh)\n\n # Create destination image.\n dest = self._pkgs['numpy'].empty(shape=(dh, dw), dtype=src.dtype)\n\n # Copy valid coordinates from source image.\n dest[dy[mask], dx[mask]] = src[sy[mask], sx[mask]]\n\n # Fill invalid coordinates.\n dest[dy[~mask], dx[~mask]] = fill\n\n return dest","def set_transparent(img):\n assert img.shape[-1] == 4\n white_pix = np.all(img == [255, 255, 255, 255], axis=-1)\n # print(white_pix)\n img[white_pix, -1] = 0\n # return img","def concat_3dimages_corners(imga, imgb, xoffset=0, yoffset=0, zoffset=0,\n transpose=True, ontop=True, center_offset=True,\n adjust_z=(0, 1)):\n print(\"Concating images with reference point being the lower left corner\")\n if transpose:\n print(\"Transpose images\")\n imga = np.transpose(imga, axes=(0, 2, 1))\n imgb = np.transpose(imgb, axes=(0, 2, 1))\n\n offset = (abs(zoffset), abs(yoffset), abs(xoffset))\n max_dim = np.maximum.reduce([imga.shape, np.add(imgb.shape, offset)])\n\n # center_a = np.array(np.divide(imga.shape, 2), dtype=int)\n # center_b = np.array(np.divide(imgb.shape, 2), dtype=int)\n\n # if (max_dim == imgb.shape).all():\n # tmp = np.copy(imgb)\n # imgb = np.copy(imga)\n # imga = np.copy(tmp)\n # ontop = toggle(ontop)\n # xoffset *= -1\n # yoffset *= -1\n # zoffset *= -1\n\n # tmp_offset = np.array(offset)\n # tmp_offset[tmp_offset > 0] = 0\n # new_img = np.full(np.add(max_dim, np.abs(offset)), np.nan)\n new_img = np.full(max_dim, np.nan)\n\n Sa0 = slice(0, imga.shape[0])\n Sa1 = slice(0, imga.shape[1])\n Sa2 = slice(0, imga.shape[2])\n Sb0 = slice(abs(zoffset), abs(zoffset) + imgb.shape[0])\n Sb1 = slice(abs(yoffset), abs(yoffset) + imgb.shape[1])\n Sb2 = slice(abs(xoffset), abs(xoffset) + imgb.shape[2])\n\n xdir = np.sign(xoffset)\n ydir = np.sign(yoffset)\n zdir = np.sign(zoffset)\n\n if ydir == 0:\n ydir = 1\n if xdir == 0:\n xdir = 1\n if zdir == 0:\n zdir = 1\n\n imga = imga[::zdir, ::ydir, ::xdir]\n imgb = imgb[::zdir, ::ydir, ::xdir]\n\n if adjust_z:\n for ix in adjust_z:\n top_img = 1 * new_img[ix]\n top_img[Sa1, Sa2] = imga[ix]\n top_img[Sb1, Sb2] = imgb[ix]\n low_img = 1 * new_img[ix]\n low_img[Sb1, Sb2] = imgb[ix]\n low_img[Sa1, Sa2] = imga[ix]\n\n diff = top_img - low_img\n m = np.nanmean(diff)\n s = np.nanstd(diff)\n mask = np.abs(diff) < m + s\n diff[mask] = np.nan\n add = np.nanmean(diff)\n\n print(add)\n\n imgb[ix] -= add\n\n print(\"new_img shape: \", new_img.shape)\n\n if ontop:\n new_img[Sa0, Sa1, Sa2] = imga\n new_img[Sb0, Sb1, Sb2] = imgb\n else:\n new_img[Sb0, Sb1, Sb2] = imgb\n new_img[Sa0, Sa1, Sa2] = imga\n\n new_img\n\n if transpose:\n print(\"Transpose back\")\n return np.transpose(new_img[::zdir, ::ydir, ::xdir], axes=(0, 2, 1))\n else:\n return new_img[::zdir, ::ydir, ::xdir]","def steerright(self):\n self.direction = self.direction-self.steering\n if self.direction < 0:\n self.direction = 360-90\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)","def normalise(image):","def rotateImage(image, angle):\n image_center = tuple(np.array(image.shape[1::-1]) / 2)\n rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)\n result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_NEAREST)\n return result","def Rotate(angle):\n def rotate_img(img, angle=angle):\n img = Ft.rotate(img, angle, resample=BILINEAR)\n return img\n return rotate_img","def align(image):\n angle = find_angle(image)\n image = rotate(image, angle)\n return image","def rot_center(image, angle):\r\n orig_rect = image.get_rect()\r\n rot_image = pygame.transform.rotate(image, angle)\r\n rot_rect = orig_rect.copy()\r\n rot_rect.center = rot_image.get_rect().center\r\n rot_image = rot_image.subsurface(rot_rect).copy()\r\n return rot_image","def transform_image(image, transform, mapping, alpha = 1, incr_x = 10, incr_y = 10):\r\n background = [255, 255, 255, 0]\r\n width, height = image.size\r\n image_in = np.array(image.convert(\"RGBA\"))\r\n image_out = [[background[:] for j in range(width)] for i in range(height)]\r\n transform_row = []\r\n for i in range(0, width + incr_x, incr_x):\r\n transform_row.append(transform(vec2(i, 0), mapping, alpha))\r\n for i in range(incr_y, height + incr_y, incr_y):\r\n p_ur = transform_row[0]\r\n p_lr = transform_row[0] = transform(vec2(0, i), mapping, alpha)\r\n for j in range(incr_x, width + incr_x, incr_x):\r\n p_ul = p_ur\r\n p_ll = p_lr\r\n p_ur = transform_row[j//incr_x]\r\n p_lr = transform_row[j//incr_x] = transform(vec2(j, i), mapping, alpha)\r\n a = p_ur - p_ul\r\n b = p_ll - p_ul\r\n det = a.x*b.y - a.y*b.x\r\n if (det != 0.0):\r\n for p in triangle(p_ul, p_ur, p_ll, width, height):\r\n c = p - p_ul\r\n rx = (b.y*c.x - b.x*c.y)/det\r\n ry = (a.x*c.y - a.y*c.x)/det\r\n image_out[p.y][p.x] = image_in[min(height - 1, max(0, round(i + (ry - 1)*incr_y)))][min(width - 1, max(0, round(j + (rx - 1)*incr_x)))]\r\n a = p_lr - p_ll\r\n b = p_lr - p_ur\r\n det = a.x*b.y - a.y*b.x\r\n if (det != 0.0):\r\n p_ulr = p_ur + p_ll - p_lr\r\n for p in triangle(p_ur, p_ll, p_lr, width, height):\r\n c = p - p_ulr\r\n rx = (b.y*c.x - b.x*c.y)/det\r\n ry = (a.x*c.y - a.y*c.x)/det\r\n image_out[p.y][p.x] = image_in[min(height - 1, max(0, round(i + (ry - 1)*incr_y)))][min(width - 1, max(0, round(j + (rx - 1)*incr_x)))]\r\n image_out = Image.fromarray(np.uint8(image_out))\r\n return image_out","def detect_and_rotate(image):\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n # hsv value for the various mask\n # computing of the blue mask to isolate the contours of the map\n mask_blue = cv2.inRange(hsv, low_blue, up_blue)\n print(\"Results of the blue mask\")\n plt.figure()\n plt.imshow(mask_blue)\n plt.show()\n # find the outside blue contours of the map on the whole world\n contours, _ = cv2.findContours(mask_blue, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n\n # find the rectangle which includes the contours\n maxArea = 0\n best = None\n for contour in contours:\n area = cv2.contourArea(contour)\n\n if area > maxArea:\n maxArea = area\n best = contour\n\n # If no map found\n if maxArea < 10:\n return None\n\n # Find the outside rectangle\n rect = cv2.minAreaRect(best)\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n\n # crop image inside bounding box\n scale = 1\n W = rect[1][0]\n H = rect[1][1]\n\n # finding the box to rotate\n Xs = [i[0] for i in box]\n Ys = [i[1] for i in box]\n x1 = min(Xs)\n x2 = max(Xs)\n y1 = min(Ys)\n y2 = max(Ys)\n\n # finding the rotation angle between vertical and longest size of rectangle\n angle = rect[2]\n rotated = False\n if angle < -45:\n angle += 90\n rotated = True\n\n # rotation center and rotation matrix\n center = (int((x1 + x2) / 2), int((y1 + y2) / 2))\n size = (int(scale * (x2 - x1)), int(scale * (y2 - y1)))\n M = cv2.getRotationMatrix2D((size[0] / 2, size[1] / 2), angle, 1.0)\n\n # cropping the image and rotating it\n cropped = cv2.getRectSubPix(image, size, center)\n cropped = cv2.warpAffine(cropped, M, size)\n croppedW = W if not rotated else H\n croppedH = H if not rotated else W\n\n corrected = cv2.getRectSubPix(cropped, (int(croppedW * scale), int(croppedH * scale)),\n (size[0] / 2, size[1] / 2))\n final_grid = np.array(corrected)\n return final_grid","def get_image_skew(frame: imageType) -> float:\n \"\"\"find contours in an edged image, and calculate angle from horizontal\"\"\"\n largest_contour = get_largest_contour(frame)\n if largest_contour is not None:\n point: pointType\n # array of array of arrays of int, int -> list of tuple of int,int\n pxs_coords: List[pointType] = [cast(pointType, tuple(point_array[0])) for point_array in largest_contour if largest_contour is not None]\n x_coords: List[int] = [point[0] for point in pxs_coords]\n # y_coords: List[int] = [point[1] for point in pxs_coords]\n x_min: int = min(x_coords)\n x_max: int = max(x_coords)\n\n # get point with x_min\n x_min_points: List[Tuple[int, int]] = [point for point in pxs_coords if point[0] == x_min]\n x_max_points: List[Tuple[int, int]] = [point for point in pxs_coords if point[0] == x_max]\n y_of_max: float = sum(point[1] for point in x_max_points) / len(x_max_points)\n y_of_min: float = sum(point[1] for point in x_min_points) / len(x_min_points)\n\n # x_max_point = (x_max, y_of_max)\n # print(x_min_points, x_max_points)\n\n x_diff = x_max - x_min\n y_diff = y_of_max - y_of_min\n # skew is positive -> is clockwise\n skew = math.degrees(math.atan(y_diff / x_diff))\n # print(skew)\n # if y_of _max > y_of_min:\n # else: return -skew\n else:\n skew = 0.0\n return skew","def rot_center(image, angle):\n orig_rect = image.get_rect()\n rot_image = pygame.transform.rotate(image, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def rot_center(image, angle):\n orig_rect = image.get_rect()\n rot_image = pygame.transform.rotate(image, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def rot_center(image, angle):\n orig_rect = image.get_rect()\n rot_image = pygame.transform.rotate(image, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def _rotate_image_90(self, img: ndarray, k: int) -> ndarray:\n if img.shape[0] < img.shape[1]:\n self.y = np.rot90(img, k)\n return self.y\n else:\n return img","def fixBorder(frame):\n s = frame.shape\n # Scale the image 2% without moving the center\n T = cv2.getRotationMatrix2D((s[1]/2, s[0]/2), 0, 1.02)\n frame = cv2.warpAffine(frame, T, (s[1], s[0]))\n return frame","def slanted_rect(self,image,i,j):\r\n s1 = 0.7\r\n s2 = -1.42814\r\n x1 = np.arctan(s1)\r\n x2 = np.arctan(s2)\r\n d1 = np.cos(np.pi - x1)\r\n d2 = np.cos(np.pi - x2)\r\n a = -(self.padding/d1)\r\n b = -(self.padding/d2)\r\n if (-0.7*j+1*i)>=(73.4 - a) and (i+1.42814*j)>=(172.55 - b) and (-0.7*j+1*i)<=(99.81 + a) and (i+1.42814*j)<=(429.07 + b):\r\n image[self.maximum_size-i,j,:]=0,0,0\r\n self.image_p[i,j,:]=2","def blitRotateCenter(surf, image, topleft, angle):\n new_rect = image.get_rect(center=image.get_rect(topleft=topleft).center)\n\n surf.blit(image, new_rect.topleft)","def reverse_black_and_white(img):\n img = 255 - img\n\n return img","def rot_center(image, angle):\r\n orig_rect = image.get_rect()\r\n rot_image = transform.rotate(image, angle)\r\n rot_rect = orig_rect.copy()\r\n rot_rect.center = rot_image.get_rect().center\r\n rot_image = rot_image.subsurface(rot_rect).copy()\r\n return rot_image","def rotate_image(rgb, dt, dl, angle):\n width = rgb.shape[1]\n height = rgb.shape[0]\n pivot = (width / 2, height / 2)\n rotationMatrix = cv2.getRotationMatrix2D(pivot, angle, 1.0)\n bgr_rotated = cv2.warpAffine(rgb, rotationMatrix, (100, 100), borderMode=cv2.BORDER_REPLICATE)\n # no interpolation for depth values because that doesn't make a lot of sense and leads to values which are not\n # seen in the non-upscaled data\n dt_rotated = cv2.warpAffine(dt, rotationMatrix, (100, 100), flags=cv2.INTER_NEAREST + cv2.WARP_FILL_OUTLIERS,\n borderMode=cv2.BORDER_REPLICATE)\n dl_rotated = cv2.warpAffine(dl, rotationMatrix, (100, 100), flags=cv2.INTER_NEAREST + cv2.WARP_FILL_OUTLIERS,\n borderMode=cv2.BORDER_REPLICATE)\n return bgr_rotated, dt_rotated, dl_rotated","def rotate(img, bbox, landmark, alpha):\r\n center = (bbox.x+bbox.w/2, bbox.y+bbox.h/2)\r\n rot_mat = cv2.getRotationMatrix2D(center, alpha, 1)\r\n img_rotated_by_alpha = cv2.warpAffine(img, rot_mat, img.shape)\r\n landmark_ = np.asarray([(rot_mat[0][0]*x+rot_mat[0][1]*y+rot_mat[0][2],\r\n rot_mat[1][0]*x+rot_mat[1][1]*y+rot_mat[1][2]) for (x, y) in landmark])\r\n face = img_rotated_by_alpha[bbox.y:bbox.y+bbox.h,bbox.x:bbox.x+bbox.w]\r\n return (face, landmark_)","def visualize_augmentation(image, angle):\n\n # Create a copy of the image to prevent changing the original\n img = np.copy(image)\n\n cols = 2\n rows = 6\n fig_size = (7 * cols, 4 * rows) # Figure width and height, in inches\n\n fig, ax = plt.subplots(rows, cols, figsize=fig_size)\n # Plot original images in the left column\n for idx in range(rows):\n ax[idx, 0].imshow(img)\n ax[idx, 0].set_title(\"Original, Angle = \" + str(round(angle, 3)))\n # Horizontal Flip\n tmp_img, tmp_angle = random_horizontal_flip(img, angle, 1.0)\n ax[0, 1].imshow(tmp_img)\n ax[0, 1].set_title(\"Horizontal Flip, Angle = \" + str(round(tmp_angle, 3)))\n # Translation\n tmp_img, tmp_angle = random_translation(img, angle)\n ax[1, 1].imshow(tmp_img)\n ax[1, 1].set_title(\"Translation, Angle = \" + str(round(tmp_angle, 3)))\n # Gaussian Noise\n tmp_img = random_gaussian(img)\n ax[2, 1].imshow(tmp_img)\n ax[2, 1].set_title(\"Gaussian Noise, Angle = \" + str(round(angle, 3)))\n # Shadows\n tmp_img = random_shadows(img, 1.0, 0.9)\n ax[3, 1].imshow(tmp_img)\n ax[3, 1].set_title(\"Shadows, Angle = \" + str(round(angle, 3)))\n # Brightness\n tmp_img = random_brightness(img)\n ax[4, 1].imshow(tmp_img)\n ax[4, 1].set_title(\"Brightness, Angle = \" + str(round(angle, 3)))\n # All Augmentation\n tmp_img, tmp_angle = random_all(img, angle)\n ax[5, 1].imshow(tmp_img)\n ax[5, 1].set_title(\"All Randomization, Angle = \" +\n str(round(tmp_angle, 3)))\n\n return fig","def getTiltedRotateShift(img1, img2, tiltdiff, angle=0, bin=1, msg=True):\n\n\t### untilt images by stretching and compressing\n\t# choose angle s/t compressFactor = 1/stretchFactor\n\t# this only works if one image is untilted (RCT) of both images are opposite tilt (OTR)\n\t#halftilt = abs(tiltdiff)/2.0\n\thalftiltrad = math.acos(math.sqrt(math.cos(abs(tiltdiff)/180.0*math.pi)))\n\t# go from zero tilt to half tilt\n\tcompressFactor = math.cos(halftiltrad)\n\t# go from max tilt to half tilt\n\tstretchFactor = math.cos(halftiltrad) / math.cos(abs(tiltdiff)/180.0*math.pi)\n\tif tiltdiff > 0:\n\t\tif msg is True:\n\t\t\tapDisplay.printMsg(\"compress image 1\")\n\t\tuntilt1 = transformImage(img1, compressFactor, angle)\n\t\tuntilt2 = transformImage(img2, stretchFactor, angle)\n\t\txfactor = compressFactor\n\telse:\n\t\tif msg is True:\n\t\t\tapDisplay.printMsg(\"stretch image 1\")\n\t\tuntilt1 = transformImage(img1, stretchFactor, angle)\n\t\tuntilt2 = transformImage(img2, compressFactor, angle)\n\t\txfactor = stretchFactor\n\n\t### filtering was done earlier\n\tfilt1 = untilt1\n\tfilt2 = untilt2\n\n\tif filt1.shape != filt2.shape:\n\t\tnewshape = ( max(filt1.shape[0],filt2.shape[0]), max(filt1.shape[1],filt2.shape[1]) )\n\t\tapDisplay.printMsg(\"Resizing images to: \"+str(newshape))\n\t\tfilt1 = apImage.frame_constant(filt1, newshape, filt1.mean())\n\t\tfilt2 = apImage.frame_constant(filt2, newshape, filt2.mean())\n\n\t### cross-correlate\n\tcc = correlator.cross_correlate(filt1, filt2, pad=True)\n\trad = min(cc.shape)/20.0\n\tcc = apImage.highPassFilter(cc, radius=rad)\n\tcc = apImage.normRange(cc)\n\tcc = blackEdges(cc)\n\tcc = apImage.normRange(cc)\n\tcc = blackEdges(cc)\n\tcc = apImage.normRange(cc)\n\tcc = apImage.lowPassFilter(cc, radius=10.0)\n\n\t#find peak\n\tpeakdict = peakfinder.findSubpixelPeak(cc, lpf=0)\n\t#import pprint\n\t#pprint.pprint(peak)\n\tpixpeak = peakdict['subpixel peak']\n\tif msg is True:\n\t\tapDisplay.printMsg(\"Pixel peak: \"+str(pixpeak))\n\t\tapImage.arrayToJpegPlusPeak(cc, \"guess-cross-ang\"+str(abs(angle))+\".jpg\", pixpeak)\n\n\trawpeak = numpy.array([pixpeak[1], pixpeak[0]]) #swap coord\n\tshift = numpy.asarray(correlator.wrap_coord(rawpeak, cc.shape))*bin\n\n\tif msg is True:\n\t\tapDisplay.printMsg(\"Found xy-shift btw two images\"\n\t\t\t+\";\\n\\t SNR= \"+str(round(peakdict['snr'],2))\n\t\t\t+\";\\n\\t halftilt= \"+str(round(halftiltrad*180/math.pi, 3))\n\t\t\t+\";\\n\\t compressFactor= \"+str(round(compressFactor, 3))\n\t\t\t+\";\\n\\t stretchFactor= \"+str(round(stretchFactor, 3))\n\t\t\t+\";\\n\\t xFactor= \"+str(round(xfactor, 3))\n\t\t\t+\";\\n\\t rawpeak= \"+str(numpy.around(rawpeak*bin, 1))\n\t\t\t+\";\\n\\t shift= \"+str(numpy.around(shift, 1))\n\t\t)\n\n\treturn shift, xfactor, peakdict['snr']","def transparency(img):\n \n black_mask_undistorted_file = open(os.path.join(settings.BASE_DIR, 'static', 'mask_undistorted.npy'), 'r')\n black_mask_undistorted = np.load(black_mask_undistorted_file)\n alpha = 200 * black_mask_undistorted\n img = scipy.misc.imresize(img, (np.shape(alpha)[0], np.shape(alpha)[1]))\n\n result = np.empty([img.shape[0], img.shape[1], 4], dtype=np.uint8)\n result[:,:,0] = img[:,:,0]\n result[:,:,1] = img[:,:,1]\n result[:,:,2] = img[:,:,2]\n result[:,:,3] = alpha\n\n return result","def transform(self, previousimage):","def rot_image(original_image, angle):\n # Tämä copypastettu jostain netistä. Vaatii että kuva on neliö.\n assert original_image.get_width() == original_image.get_height(), \\\n \"Can't rotate image - not square. %r\" % original_image\n orig_rect = original_image.get_rect()\n rot_image = pygame.transform.rotate(original_image, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def rotate(self):\n\n last_center = self.rect.center\n self.image = pg.transform.rotate(self.image_copy,self.angle)\n self.rect = self.image.get_rect()\n self.rect.center = last_center\n self.angle +=self.rotate_by","def sub_image(image, center, theta, width, height):\n if 45 < theta <= 90:\n theta = theta - 90\n width, height = height, width\n\n theta *= math.pi / 180 # convert to rad\n v_x = (math.cos(theta), math.sin(theta))\n v_y = (-math.sin(theta), math.cos(theta))\n s_x = center[0] - v_x[0] * (width / 2) - v_y[0] * (height / 2)\n s_y = center[1] - v_x[1] * (width / 2) - v_y[1] * (height / 2)\n mapping = np.array([[v_x[0], v_y[0], s_x], [v_x[1], v_y[1], s_y]])\n\n return cv2.warpAffine(image, mapping, (width, height), flags=cv2.WARP_INVERSE_MAP, borderMode=cv2.BORDER_REPLICATE)","def rotate_img(img, random):\n degrees = 45 * (random - 0.5)\n h, w = (int(2 * i / 3) for i in img.shape[:2])\n img = imutils.rotate_bound(img, degrees)\n h2, w2 = img.shape[:2]\n x = int((w2 - w) / 2)\n y = int((h2 - h) / 2)\n img = img[y:y + h, x:x + w]\n return img","def rotate_bound(image, angle, bordervalue):\n\n # grab the dimensions of the image and then determine the\n # center\n (h, w) = image.shape[:2]\n (cX, cY) = (w // 2, h // 2)\n\n # grab the rotation matrix (applying the negative of the\n # angle to rotate clockwise), then grab the sine and cosine\n # (i.e., the rotation components of the matrix)\n M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)\n cos = np.abs(M[0, 0])\n sin = np.abs(M[0, 1])\n\n # compute the new bounding dimensions of the image\n nW = int((h * sin) + (w * cos))\n nH = int((h * cos) + (w * sin))\n\n # adjust the rotation matrix to take into account translation\n M[0, 2] += (nW / 2) - cX\n M[1, 2] += (nH / 2) - cY\n\n # perform the actual rotation and return the image\n return cv2.warpAffine(image, M, (nW, nH), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT,\n borderValue=bordervalue), M","def _rotate_img(self, results):\n angle = self.angle\n height, width = results['img_shape'][:2]\n center = ((width - 1) * 0.5, (height - 1) * 0.5)\n\n # Compute rotation matrix\n mat_rotation = cv2.getRotationMatrix2D(center, -angle, 1)\n\n # Compute height and width of new image after rotation\n cos = np.abs(mat_rotation[0, 0])\n sin = np.abs(mat_rotation[0, 1])\n width_new = height * sin + width * cos\n height_new = height * cos + width * sin\n mat_rotation[0, 2] += (width_new - width) * 0.5\n mat_rotation[1, 2] += (height_new - height) * 0.5\n width_new = int(np.round(width_new))\n height_new = int(np.round(height_new))\n \n # Rotate image\n img_rotation = cv2.warpAffine(results['img'], mat_rotation, (width_new, height_new),\n borderValue=self.borderValue)\n results['img'] = img_rotation\n results['img_shape'] = results['img'].shape\n\n # Rotate corresponding annotations: all boxes in bbox_fields\n for key in results.get('bbox_fields', []):\n gt_boxes_ret = []\n for poly in results[key]:\n rot_array = []\n poly_length = len(poly)\n\n if poly_length == 4:\n poly = [poly[0], poly[1], poly[2], poly[1], poly[2], poly[3], poly[0], poly[3]]\n\n # Convert to np.array of shape (:, 2)\n for i in range(0, len(poly), 2):\n rot_array.append(np.array([poly[i], poly[i + 1]]))\n\n # Rotate corresponding annotations\n rot_array = np.array([rot_array])\n rot_array = cv2.transform(rot_array, mat_rotation).squeeze().reshape(len(poly))\n\n if poly_length == 4:\n x_coords = rot_array[0::2]\n y_coords = rot_array[1::2]\n rot_array = np.array([\n np.min(x_coords),\n np.min(y_coords),\n np.max(x_coords),\n np.max(y_coords)\n ])\n\n gt_boxes_ret.append(rot_array)\n if len(results[key]) > 0:\n results[key] = gt_boxes_ret\n\n # Rotate gt_bboxes according to gt_poly_bboxes\n if 'gt_bboxes' in results and 'gt_poly_bboxes' in results:\n gt_bboxes = []\n gt_bboxes_ignore = []\n\n for poly in results['gt_poly_bboxes']:\n poly = np.array(poly, dtype=np.double)\n x_coords = poly[0::2]\n y_coords = poly[1::2]\n aligned_poly = [\n np.min(x_coords),\n np.min(y_coords),\n np.max(x_coords),\n np.max(y_coords)\n ]\n gt_bboxes.append(aligned_poly)\n\n for poly in results['gt_poly_bboxes_ignore']:\n poly = np.array(poly, dtype=np.double)\n x_coords = poly[0::2]\n y_coords = poly[1::2]\n aligned_poly = [\n np.min(x_coords),\n np.min(y_coords),\n np.max(x_coords),\n np.max(y_coords)\n ]\n gt_bboxes_ignore.append(aligned_poly)\n\n if len(gt_bboxes) == 0:\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\n else:\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\n\n if len(gt_bboxes_ignore) == 0:\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\n else:\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\n\n results['gt_bboxes'] = gt_bboxes\n results['gt_bboxes_ignore'] = gt_bboxes_ignore\n\n # Rotate corresponding annotations: all boxes in cbbox_fields\n for key in results.get('cbbox_fields', []):\n gt_cboxes_ret = []\n for instance in results[key]:\n tmp_cboxes = []\n for poly in instance:\n rot_array = []\n # Convert to np.array of shape (:, 2)\n for i in range(0, len(poly), 2):\n rot_array.append(np.array([poly[i], poly[i + 1]]))\n rot_array = np.array([rot_array])\n # Rotate corresponding annotations\n rot_array = cv2.transform(rot_array, mat_rotation).squeeze().reshape(len(poly))\n tmp_cboxes.append(rot_array)\n gt_cboxes_ret.append(tmp_cboxes)\n results[key] = gt_cboxes_ret\n\n # Rotate corresponding annotations: all masks in mask_fields\n for key in results.get('mask_fields', []):\n mask = results[key].masks.transpose((1, 2, 0))\n if len(results[key].masks) == 0:\n results[key] = results[key].resize((height_new, width_new))\n else:\n # Rotate mask\n mask_rotation = cv2.warpAffine(mask, mat_rotation, (width_new, height_new),\n borderValue=self.borderValue)\n if mask_rotation.ndim == 2:\n # case when only one mask, (h, w)\n mask_rotation = mask_rotation[:, :, None] # (h, w, 1)\n mask_rotation = mask_rotation.transpose((2, 0, 1))\n results[key].masks = mask_rotation\n results[key].height = height_new\n results[key].width = width_new","def removeplane(img, slce=0.4):\n img[img == 0] = np.nan\n\n xr, yr = np.arange(slce*img.shape[0],(1-slce)*img.shape[0],dtype=int),\\\n np.arange(slce*img.shape[1],(1-slce)*img.shape[1],dtype=int)\n x, y = np.meshgrid(xr,yr)\n\n \n subimg = img[xr[0]:xr[-1]+1,yr[0]:yr[-1]+1]\n imgf = subimg[np.isfinite(subimg)].flatten()\n\n vecs = np.ones((5,imgf.size))\n vecs[0,:] = x[np.isfinite(subimg)].flatten()\n vecs[1,:] = y[np.isfinite(subimg)].flatten()\n vecs[2,:] = x[np.isfinite(subimg)].flatten()**2\n vecs[3,:] = y[np.isfinite(subimg)].flatten()**2\n\n C = vecs.dot(vecs.T)\n xv = la.inv(C).dot(vecs.dot(imgf[:,np.newaxis]))\n x, y = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))\n\n img -= (xv[0]*x + xv[1]*y + \\\n xv[2]*x**2 + xv[3]*y**2 + \\\n xv[4])\n return img","def rot_center(self, image, angle):\n orig_rect = image.get_rect()\n rot_image = pygame.transform.rotate(image, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def rot_center(self, image, angle):\n orig_rect = image.get_rect()\n rot_image = pygame.transform.rotate(image, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def rotate_image(image, theta, row_axis, col_axis, channel_axis, fill_mode='constant'):\n image = apply_affine_transform(image,\n theta=theta,\n row_axis=row_axis,\n col_axis=col_axis,\n channel_axis=channel_axis,\n fill_mode=fill_mode,\n cval=0.0,\n order=1)\n return image","def perspective_transform(img, chars):\n # img_expanded, (x1,y1,x2,y2) = simple_expand(img)\n x1,y1,x2,y2 = get_xy_minmax(chars)\n trans = get_random_transform(img, (x1,y1,x2,y2))\n warped_img = cv2.warpPerspective(img, trans, (img.shape[1], img.shape[0]))\n for char in chars:\n for corner in char['corners']:\n # print(corner)\n a = [corner[0], corner[1], 1]\n transformed_corner = np.matmul(trans, np.asarray(a).reshape(3,1))\n corner[0] = transformed_corner[0] / transformed_corner[2]\n corner[1] = transformed_corner[1] / transformed_corner[2]\n # print(corner)\n return warped_img, chars","def fill_blind_pores(im):\n holes = find_disconnected_voxels(im)\n im[holes] = False\n return im","def crop_image(image: np.ndarray) -> np.ndarray:\n # convert image to grayscale and apply blur to reduce noise\n image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n blurred = cv2.GaussianBlur(image_gray, (3, 3), 0)\n\n # global threshold using Otsu\n # Note: Although unpacking like this results in one of the variables to be unused and makes\n # PyTA heavily depressed, this is standard OpenCV notation.\n # For reference, you may check docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html\n\n ret1, thresh1 = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n # invert image color and find contours\n ret2, thresh2 = cv2.threshold(thresh1, 150, 255, cv2.THRESH_BINARY_INV)\n contours, hierarchy = cv2.findContours(thresh2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n # create list of tuples with the contour itself and its arc length\n # then sort by arc length and take the two longest\n cont_len = [(cont, cv2.arcLength(cont, True)) for cont in contours]\n cont_len.sort(key=lambda x: -x[1])\n longest_2 = cont_len[0:2]\n rects = [cv2.boundingRect(tup[0]) for tup in longest_2]\n\n # take the smallest coordinates for the top left corner of rect\n # and largest for the bottom right corner\n min_x0, min_y0, max_x0, max_y0 = rects[0][0], rects[0][1], \\\n rects[0][0] + rects[0][2], \\\n rects[0][1] + rects[0][3]\n min_x1, min_y1, max_x1, max_y1 = rects[1][0], rects[1][1], \\\n rects[1][0] + rects[1][2], \\\n rects[1][1] + rects[1][3]\n min_x = min(min_x0, min_x1) + 1\n min_y = min(min_y0, min_y1) + 1\n max_x = max(max_x0, max_x1) - 1\n max_y = max(max_y0, max_y1) - 1\n\n cropped_img = image[min_y: max_y, min_x: max_x]\n return cropped_img","def process(self):\n self.output_image = cv.cornerHarris(self.input_image, 2, 3, 0.04)\n self.output_image = cv.dilate(self.output_image, kernel=None, iterations=2)\n return self.output_image","def crop_image(img):\n gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n _, threshed = cv.threshold(gray, 240, 255, cv.THRESH_BINARY_INV)\n\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (11, 11))\n morphed = cv.morphologyEx(threshed, cv.MORPH_CLOSE, kernel)\n\n cnts = cv.findContours(morphed, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[-2]\n cnt = sorted(cnts, key=cv.contourArea)[-1]\n\n x, y, w, h = cv.boundingRect(cnt)\n dst = img[y:y+h, x:x+w]\n cv.imwrite(\"001.png\", dst)\n return dst","def cv2_skeletonize(img):\n img = img.copy() # don't clobber original\n skel = img.copy()\n\n skel[:,:] = 0\n kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))\n while True:\n eroded = cv2.morphologyEx(img, cv2.MORPH_ERODE, kernel)\n temp = cv2.morphologyEx(eroded, cv2.MORPH_DILATE, kernel)\n temp = cv2.subtract(img, temp)\n skel = cv2.bitwise_or(skel, temp)\n img[:,:] = eroded[:,:]\n if cv2.countNonZero(img) == 0:\n break\n return skel","def rotate(image, rect, angle):\n # Rotate the original image without modifying it.\n new_image = pg.transform.rotate(image, angle)\n # Get a new rect with the center of the old rect.\n rect = new_image.get_rect(center=rect.center)\n return new_image, rect","def rotate_image(image):\n return tf.image.rot90(image)","def rotate(image, rect, angle):\n new_image = pygame.transform.rotate(image, angle) # Rotate the original image without modifying it.\n rect = new_image.get_rect(center=rect.center) # Get a new rect with the center of the old rect.\n return new_image, rect","def test_rotating_phantom(self):\n cheese = TomoCheese.from_demo_images()\n cheese.analyze()\n assert math.isclose(cheese.catphan_roll, -0.25, abs_tol=0.05)\n for img in cheese.dicom_stack:\n img.array = rotate(img.array, angle=3, mode=\"edge\")\n cheese.analyze()\n assert math.isclose(cheese.catphan_roll, -3.25, abs_tol=0.05)","def rotateImage(self, img, angle=90):\n if (angle == 90) :\n return(cv2.flip(cv2.transpose(img),flipCode=0))\n elif (angle == -90) :\n return(cv2.flip(cv2.transpose(img),flipCode=1))\n else :\n center = (img.shape[1]/2.0,img.shape[0]/2.0)\n rotate = cv2.getRotationMatrix2D(center, angle, 1.0)\n return cv2.warpAffine(img, rotate, (img.shape[1], img.shape[0]))","def cw_rotate(self):\n self.grid = [list(x) for x in zip(*self.grid[::-1])]\n self.find_edges()","def rotate90(self):","def edge_virtualv2(src, point_list=None, degree=3):\n src = cv2.cvtColor(np.asarray(src), cv2.COLOR_RGBA2BGRA)\n xmax = np.amax(point_list, axis=0)[0]\n ymax = np.amax(point_list, axis=0)[1]\n xmin = np.amin(point_list, axis=0)[0]\n ymin = np.amin(point_list, axis=0)[1]\n widthTrans = xmax - xmin\n heightTrans = ymax - ymin\n side = min(widthTrans, heightTrans)\n\n # create initial mask covering the actual image.\n alpha = src[:, :, 3]\n alpha[0, :], alpha[-1, :], alpha[:, 0], alpha[:, -1] = 0, 0, 0, 0\n idx = np.where(src[:, :, 3] > 15)\n mask = np.zeros(src.shape[:2], dtype=np.uint8)\n mask[idx] = 255\n mask[0, :], mask[-1, :], mask[:, 0], mask[:, -1] = 0, 0, 0, 0\n # iteratively erode the mask and assigned the removed region to division factor in descend.\n if side < 100:\n factors = np.linspace(1.5, 1, 5)\n else:\n factors = np.linspace(2, 1, 10)\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\n for factor in factors:\n dil = cv2.erode(mask, kernel)\n diff1 = mask - dil\n mask = dil\n removed_loc = np.where(diff1 > 0)\n alpha[removed_loc] = alpha[removed_loc] // factor\n\n src[:, :, 3] = alpha\n img = Image.fromarray(cv2.cvtColor(src, cv2.COLOR_BGRA2RGBA))\n return img, point_list"],"string":"[\n \"def straighten(image):\\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n gray = cv2.bitwise_not(gray)\\n thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\\n coords = np.column_stack(np.where(thresh > 0))\\n angle = cv2.minAreaRect(coords)[-1]\\n if angle < -45:\\n angle = -(90 + angle)\\n else:\\n angle = -angle\\n (h, w) = image.shape[:2]\\n center = (w // 2, h // 2)\\n M = cv2.getRotationMatrix2D(center=center, angle=angle, scale=1.0)\\n rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)\\n # print(\\\"Straightening angle : \\\", angle)\\n return rotated\",\n \"def straighten_thresh(image):\\n coords = np.column_stack(np.where(image > 0))\\n angle = cv2.minAreaRect(coords)[-1]\\n if angle < -45:\\n angle = -(90 + angle)\\n else:\\n angle = -angle\\n (h, w) = image.shape[:2]\\n center = (w // 2, h // 2)\\n M = cv2.getRotationMatrix2D(center=center, angle=angle, scale=1.0)\\n rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)\\n # print(\\\"Straightening angle : \\\", angle)\\n return rotated\",\n \"def rotatedView(img, angle, enlarge=True, extend=Views.extendBorder):\\n cx = img.dimension(0) / 2.0\\n cy = img.dimension(1) / 2.0\\n toCenter = AffineTransform2D()\\n toCenter.translate(-cx, -cy)\\n rotation = AffineTransform2D()\\n # Step 1: place origin of rotation at the center of the image\\n rotation.preConcatenate(toCenter)\\n # Step 2: rotate around the Z axis\\n rotation.rotate(radians(angle))\\n # Step 3: undo translation to the center\\n rotation.preConcatenate(toCenter.inverse())\\n rotated = RV.transform(Views.interpolate(extend(img),\\n NLinearInterpolatorFactory()), rotation)\\n if enlarge:\\n # Bounds:\\n bounds = repeat((sys.maxint, 0)) # initial upper- and lower-bound values \\n # for min, max to compare against \\n transformed = zeros(2, 'f')\\n for corner in product(*zip(repeat(0), Intervals.maxAsLongArray(img))):\\n rotation.apply(corner, transformed)\\n bounds = [(min(vmin, int(floor(v))), max(vmax, int(ceil(v))))\\n for (vmin, vmax), v in zip(bounds, transformed)]\\n minC, maxC = map(list, zip(*bounds)) # transpose list of 2 pairs\\n # into 2 lists of 2 values\\n imgRot = Views.zeroMin(Views.interval(rotated, minC, maxC))\\n else:\\n imgRot = Views.interval(rotated, img)\\n return imgRot\",\n \"def rotate_and_wrap_image(self, image, degree_of_rotation):\\n\\n image_center = tuple(np.array(image.shape[1::-1]) / 2)\\n rot_mat = cv2.getRotationMatrix2D(image_center, degree_of_rotation, 1.0)\\n # borderMode (constant) and borderValue are important for maintaiing consistency \\n ri = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR, borderMode = cv2.BORDER_CONSTANT,borderValue = (255,255,255))\\n return ri\",\n \"def img_rotate(img, angle, center, fillval=0):\\n rows, cols = img.shape[:2]\\n M = cv2.getRotationMatrix2D(center, angle, 1)\\n return cv2.warpAffine(img, M, (cols, rows), borderValue=fillval)\",\n \"def _correct_rotation(img: np.ndarray) -> np.ndarray:\\n edges = cv2.Canny(img, 50, 150, apertureSize=3)\\n lines = cv2.HoughLinesP(\\n edges, 1, np.pi / 180, 100, minLineLength=100, maxLineGap=10\\n )\\n avg_slope = 0\\n cnt = 0\\n try:\\n for line in lines:\\n rise = line[0][3] - line[0][1]\\n run = line[0][2] - line[0][0]\\n if run != 0:\\n if rise / run < 0.5:\\n avg_slope += rise / run\\n cnt += 1\\n avg_slope = avg_slope / cnt\\n\\n image_center = tuple(np.array(img.shape[1::-1]) / 2)\\n rot_mat = cv2.getRotationMatrix2D(\\n image_center, np.arctan(avg_slope) * 180 / np.pi, 1.0\\n )\\n result = cv2.warpAffine(img, rot_mat, img.shape[1::-1], flags=cv2.INTER_LINEAR)\\n return result\\n except TypeError:\\n return img\",\n \"def rotate_no_clip(self, angle):\\n # Calculate the size the expanded image needs to be to contain rotated image\\n x, y = self.width, self.height\\n w = abs(x*math.cos(angle)) + abs(y*math.sin(angle))\\n h = abs(x*math.sin(angle)) + abs(y*math.cos(angle))\\n\\n # Paste the image into a larger frame and rotate\\n img = Image.blank(w, h, 4, 0)\\n img.paste(self, w/2-x/2, h/2-y/2)\\n rotated = img.rotate(angle, (w/2, h/2))\\n\\n return rotated\",\n \"def rotate_with_extension(image, alpha):\\n # Determine if alpha is larger than 90 degrees and rotate accordingly\\n # Number of 90 degree turns\\n quarter = int(alpha / 90)\\n # turn the image 90 deg quarter times\\n image = image.rotate(quarter * 90)\\n # Get the angle of the rest of the rotation\\n alpha = alpha % 90\\n\\n # Size of image\\n size = image.size[0]\\n # Get radians\\n alpha_rad = alpha/180 * np.pi\\n\\n # Compute the size of the extended image needed to keep an image of the\\n # right size after cropping\\n cos = np.cos(alpha_rad)\\n sin = np.sin(alpha_rad)\\n L = int(size * (sin + cos))\\n\\n # Extend the image then rotate it\\n extend = mirror_extend(int((L-size)/2), image)\\n rotate = extend.rotate(alpha, expand=1)\\n # Get the current side length of the rotated image and compute the\\n # difference with the original image to obtain the number of pixels to remove\\n # from each border\\n side = rotate.size[0]\\n pixel_to_remove = (side - size)/2\\n # Crop the image\\n return rotate.crop((pixel_to_remove, pixel_to_remove, pixel_to_remove + size, pixel_to_remove+size))\",\n \"def _augment(img):\\r\\n return flip(img, axis=2)\",\n \"def image_augmentation(img):\\n return np.fliplr(img)\",\n \"def get_opt_rotate(obj_img, back_img,\\n back_center_x, back_center_y,\\n obj_center_x, obj_center_y,\\n prev_rot_angle=0.,\\n is_erosion=False):\\n width = obj_img.shape[0]\\n rot_img = ndimage.rotate(obj_img, prev_rot_angle, reshape=False)\\n induce_x, induce_y = int(back_center_x - obj_center_x), int(back_center_y - obj_center_y)\\n combine_img = back_img.copy()\\n combine_img[induce_y:induce_y + width, induce_x:induce_x + width] -= rot_img\\n neg_count = len(np.argwhere(combine_img < 0))\\n if is_erosion:\\n angle_amount = 4.\\n else:\\n angle_amount = 16.\\n # check combine_img.dtype; rot_img.dtype; back_img\\n curr_angle = prev_rot_angle\\n while angle_amount > 0.5:\\n angle_amount /= 2.\\n\\n rotate_1 = ndimage.rotate(obj_img, curr_angle + angle_amount, reshape=False)\\n combine_img = back_img.copy()\\n combine_img[induce_y:induce_y+width, induce_x:induce_x+width] -= rotate_1\\n neg_count_1 = len(np.argwhere(combine_img < 0))\\n\\n rotate_2 = ndimage.rotate(obj_img, curr_angle - angle_amount, reshape=False)\\n combine_img = back_img.copy()\\n combine_img[induce_y:induce_y + width, induce_x:induce_x + width] -= rotate_2\\n neg_count_2 = len(np.argwhere(combine_img < 0))\\n\\n if neg_count_1 < neg_count_2:\\n if neg_count_1 < neg_count:\\n neg_count = neg_count_1\\n curr_angle = curr_angle + angle_amount\\n else:\\n if neg_count_2 < neg_count:\\n neg_count = neg_count_2\\n curr_angle = curr_angle - angle_amount\\n # print(curr_angle)\\n # print(neg_count, neg_count_1, neg_count_2)\\n # print('Negative Pix Count Rotation: %d.' % neg_count)\\n # print('Optimal Rotation: ', curr_angle)\\n return curr_angle, neg_count\",\n \"def _augment(img):\\n return flip(img, axis=2)\",\n \"def rotate_image(image, angle):\\n\\n # Get the image size\\n # No that's not an error - NumPy stores image matricies backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n ''' Calculate Rotation Matrix '''\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n ''' Calculate Translation Matrix '''\\n # We require a translation matrix to keep the image centred\\n trans_mat = np.matrix([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the transform for the combined rotation and translation\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv2.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv2.INTER_LINEAR\\n )\\n\\n return result\",\n \"def preprocess(self, image):\\n if self.rotate == 0:\\n return image\\n\\n angle = self.rotate * -90\\n return image.rotate(angle, expand=True).crop((0, 0, self._w, self._h))\",\n \"def rotate_image(image, angle):\\n\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(array(image_size) / 2)\\n\\n rot_mat = vstack([cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]])\\n trans_mat = identity(3)\\n\\n w2 = image_size[0] * 0.5\\n h2 = image_size[1] * 0.5\\n\\n rot_mat_notranslate = matrix(rot_mat[0:2, 0:2])\\n\\n tl = (array([-w2, h2]) * rot_mat_notranslate).A[0]\\n tr = (array([w2, h2]) * rot_mat_notranslate).A[0]\\n bl = (array([-w2, -h2]) * rot_mat_notranslate).A[0]\\n br = (array([w2, -h2]) * rot_mat_notranslate).A[0]\\n\\n x_coords = [pt[0] for pt in [tl, tr, bl, br]]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in [tl, tr, bl, br]]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n new_image_size = (new_w, new_h)\\n\\n new_midx = new_w * 0.5\\n new_midy = new_h * 0.5\\n\\n dx = int(new_midx - w2)\\n dy = int(new_midy - h2)\\n\\n trans_mat = getTranslationMatrix2d(dx, dy)\\n affine_mat = (matrix(trans_mat) * matrix(rot_mat))[0:2, :]\\n result = cv2.warpAffine(image, affine_mat, new_image_size, flags=cv2.INTER_LINEAR)\\n\\n return result\",\n \"def deskew(image, gray):\\n\\n # Flip the foreground\\n gray = cv2.bitwise_not(gray)\\n\\n # Treshold, set foreground px to 255 and background to 0\\n tresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\\n\\n # Get the (x,y) coord\\n coords = np.column_stack(np.where(tresh > 0))\\n\\n # Get the rotated bouding box of these coords\\n # https://stackoverflow.com/questions/15956124/minarearect-angles-unsure-about-the-angle-returned\\n angle = cv2.minAreaRect(coords)[-1] # Return val btwn [-90,0]\\n\\n # As the rect rotate clockwise, angle --> 0\\n if angle < -45:\\n angle = -(90 + angle)\\n else:\\n # Just take the inverse\\n angle = -angle\\n\\n rotated = None\\n\\n if(np.abs(angle) != 0):\\n # Rotate the image\\n (height, width) = gray.shape[:2]\\n center = (width // 2, height // 2)\\n matrix = cv2.getRotationMatrix2D(center, angle, 1.0)\\n rotated = cv2.warpAffine(\\n image,\\n matrix,\\n (width, height),\\n flags=cv2.INTER_CUBIC,\\n borderMode=cv2.BORDER_REPLICATE,\\n )\\n\\n return [rotated, angle]\",\n \"def rotate_image(image, angle):\\n\\n # Get the image size\\n # No that's not an error - NumPy stores image matricies backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n # We require a translation matrix to keep the image centred\\n trans_mat = np.matrix([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the tranform for the combined rotation and translation\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv2.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv2.INTER_LINEAR\\n )\\n\\n return result\",\n \"def sym_left_img(img):\\n cropped = img.crop(box=(0, 0, img.width / 2, img.height))\\n mirror = cropped.transpose(Image.FLIP_LEFT_RIGHT)\\n combine = Image.new('RGB', (cropped.width * 2, cropped.height), 'white')\\n combine.paste(cropped, (0, 0, cropped.width, cropped.height))\\n combine.paste(mirror, (cropped.width, 0, cropped.width * 2, cropped.height))\\n return combine\",\n \"def del_alpha(img): \\n img = img[:,:,0:3].copy()\\n return img\",\n \"def rotated_image(image):\\n orientation = parameter('Orientation',90) # in degrees counter-clockwise\\n if orientation == None: orienation = 0\\n return image.rotate(orientation)\",\n \"def deskew_image(image, delta = 0.5, limit = 5):\\n try:\\n print(\\\"TESTED\\\")\\n img_input_array = cv2.imread(image,cv2.IMREAD_GRAYSCALE)\\n image_original=img_input_array.copy()\\n image_skew=cv2.resize(img_input_array,(int(img_input_array.shape[1]*0.5),int(img_input_array.shape[0]*0.5)))\\n image_bin=image_binary(image_skew)\\n angles = np.arange(-limit, limit + delta, delta)\\n scores = [find_score(image_bin,angle) for angle in angles]\\n best_score = max(scores)\\n best_angle = angles[scores.index(best_score)]\\n image_inv=image_binary(image_original)\\n data = inter.rotate(image_inv, best_angle, reshape=False, order=0)\\n image_skewed=((~data.astype(bool)).astype(int))*255\\n print(image_skewed, best_angle)\\n return image_skewed, best_angle\\n except AttributeError:\\n print(\\\"OOPS\\\")\\n return img_input_array, 0\",\n \"def rotate_image(image, angle):\\n\\n # Get the image size\\n # No that's not an error - NumPy stores image matrices backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n # We require a translation matrix to keep the image centred\\n trans_mat = np.matrix([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the tranform for the combined rotation and translation\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv2.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv2.INTER_LINEAR\\n )\\n\\n return result\",\n \"def rotate(self, image, angle):\\n tmpImage = pygame.transform.rotate(image ,angle)\\n imageCentreX = tmpImage.get_rect()[0] + tmpImage.get_rect()[2]/2\\n imageCentreY = tmpImage.get_rect()[1] + tmpImage.get_rect()[3]/2\\n\\n targetWidth = tmpImage.get_rect()[2]\\n targetHeight = tmpImage.get_rect()[3]\\n\\n imageOut = pygame.Surface((targetWidth, targetHeight))\\n imageOut.fill((255,255,0))\\n imageOut.set_colorkey((255,255,0))\\n imageOut.blit(tmpImage,(0,0), pygame.Rect( imageCentreX-targetWidth/2,imageCentreY-targetHeight/2, targetWidth, targetHeight ) )\\n return imageOut\",\n \"def rotated_e():\\n x = np.zeros((5, 5))\\n x[:, 0] = 1.\\n y = np.zeros((5, 5))\\n y[:, 2] = 1.\\n z = np.zeros((5, 5))\\n z[:, 4] = 1.\\n a = np.zeros((5, 5))\\n a[0, :] = 1.\\n b = np.zeros((5, 5))\\n b[2, :] = 1.\\n c = np.zeros((5, 5))\\n c[4, :] = 1.\\n\\n img = np.zeros((4, 5, 5))\\n img[0] = x + y + z + a\\n img[1] = x + y + z + c\\n img[2] = a + b + c + x\\n img[3] = a + b + c + z\\n img[img > 0] = 1.\\n\\n return img.astype('float32')\",\n \"def flattener(image, pts, w, h):\\n temp_rect = np.zeros((4,2), dtype = \\\"float32\\\")\\n \\n s = np.sum(pts, axis = 2)\\n\\n tl = pts[np.argmin(s)]\\n br = pts[np.argmax(s)]\\n\\n diff = np.diff(pts, axis = -1)\\n tr = pts[np.argmin(diff)]\\n bl = pts[np.argmax(diff)]\\n\\n if w <= 0.8 * h: # If card is vertically oriented\\n temp_rect[0] = tl\\n temp_rect[1] = tr\\n temp_rect[2] = br\\n temp_rect[3] = bl\\n\\n if w >= 1.2 * h: # If card is horizontally oriented\\n temp_rect[0] = bl\\n temp_rect[1] = tl\\n temp_rect[2] = tr\\n temp_rect[3] = br\\n\\n if w > 0.8 * h and w < 1.2 * h: #If card is diamond oriented\\n # If furthest left point is higher than furthest right point,\\n # card is tilted to the left.\\n if pts[1][0][1] <= pts[3][0][1]:\\n # If card is titled to the left, approxPolyDP returns points\\n # in this order: top right, top left, bottom left, bottom right\\n temp_rect[0] = pts[1][0] # Top left\\n temp_rect[1] = pts[0][0] # Top right\\n temp_rect[2] = pts[3][0] # Bottom right\\n temp_rect[3] = pts[2][0] # Bottom left\\n\\n # If furthest left point is lower than furthest right point,\\n # card is tilted to the right\\n if pts[1][0][1] > pts[3][0][1]:\\n # If card is titled to the right, approxPolyDP returns points\\n # in this order: top left, bottom left, bottom right, top right\\n temp_rect[0] = pts[0][0] # Top left\\n temp_rect[1] = pts[3][0] # Top right\\n temp_rect[2] = pts[2][0] # Bottom right\\n temp_rect[3] = pts[1][0] # Bottom left\\n \\n # Create destination array, calculate perspective transform matrix,\\n # and warp card image\\n dst = np.array([[0, 0], [MAXWIDTH - 1, 0], [MAXWIDTH - 1, MAXHEIGHT - 1], [0, MAXHEIGHT - 1]], np.float32)\\n M = cv2.getPerspectiveTransform(temp_rect, dst)\\n warp = cv2.warpPerspective(image, M, (MAXWIDTH, MAXHEIGHT))\\n\\n return warp\",\n \"def rotate(img, angle, resample=False, expand=False, center=None):\\r\\n \\r\\n return img.rotate(angle, resample, expand, center)\",\n \"def rotate_image(image, angle, crop=True, keepSize=True):\\n\\n # do we need to convert the image?\\n prevShape = image.shape\\n image, reshaped = reshape_to_cv_format(image, False)\\n\\n # this is 10-times faster than: return ndimage.rotate(image, angle)\\n # --\\n # After http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders\\n # Get the image size\\n # No that's not an error - NumPy stores image matrices backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n # We require a translation matrix to keep the image centered\\n trans_mat = np.matrix([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the transform for the combined rotation and translation\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv.INTER_LINEAR\\n )\\n if not crop:\\n # if we do not need to crop the image we can stop here, but the image might contain some black \\\"corners\\\"\\n if reshaped: \\n result = result.reshape(prevShape[0], result.shape[0], result.shape[1])\\n return result\\n\\n image = crop_around_center(result, *rotated_rect_with_max_area(image_size[0], image_size[1], math.radians(angle)))\\n\\n # if keepSize the same, the image is square and the size has changed than change size to fit input again\\n if keepSize and image_size[0] == image_size[1] and (image_size[0] != image.shape[1] or image_size[1] != image.shape[0]):\\n image = equalize_image_size(image, (image_size[0]*image_size[0]))\\n\\n if reshaped: \\n image = image.reshape(prevShape[0], image.shape[0], image.shape[1])\\n return image\",\n \"def _rotate(self):\\n \\r\\n if self.clr == 1: # (default rotation) \\r\\n # o o o o \\r\\n # o x x o x o o x\\r\\n # o o o o\\r\\n _colOffsets = [[-1,-1, 0, 0], [-1, 0, 0, 1], [ 1, 1, 0, 0], [ 1, 0, 0,-1]] #\\r\\n _rowOffsets = [[ 1, 0, 0,-1], [-1,-1, 0, 0], [-1, 0, 0, 1], [ 1, 1, 0, 0]] #\\r\\n elif self.clr == 2:\\r\\n # o o o o \\r\\n # o x o x x o x o\\r\\n # o o o o\\r\\n _colOffsets = [[-1,-1, 0, 0], [ 1, 0, 0,-1], [ 1, 1, 0, 0], [-1, 0, 0, 1]] #\\r\\n _rowOffsets = [[-1, 0, 0, 1], [-1,-1, 0, 0], [ 1, 0, 0,-1], [ 1, 1, 0, 0]] #\\n \\r\\n elif self.clr == 3: # \\r\\n # o o o o \\r\\n # x o x o x o x o\\r\\n # o o o o\\n \\r\\n _colOffsets = [[-1, 0, 0, 0], [-1,-1, 0, 1], [ 1, 0, 0, 0], [ 1, 1, 0,-1]] #\\r\\n _rowOffsets = [[ 1, 1, 0,-1], [-1, 0, 0, 0], [-1,-1, 0, 1], [ 1, 0, 0, 0]] #\\n \\r\\n elif self.clr == 4:\\r\\n # o o o o \\r\\n # x o x o x o x o\\r\\n # o o o o\\r\\n _colOffsets = [[-1, 0, 0, 0], [1, 1, 0, -1], [1, 0, 0,0], [-1, -1, 0,1]]\\n _rowOffsets = [[-1,-1, 0, 1], [-1,0, 0, 0], [1,1, 0,-1], [1,0, 0, 0]]\\n \\r\\n elif self.clr == 5: # o o\\r\\n # o x \\r\\n # x o x o o o o o x o\\r\\n # o o \\r\\n _colOffsets = [[ 0, 0, 0, 0], [ 2, 1, 0,-1], [ 0, 0, 0, 0], [-2,-1, 0, 1]] #\\r\\n _rowOffsets = [[-2,-1, 0, 1], [ 0, 0, 0, 0], [ 2, 1, 0,-1], [ 0, 0, 0, 0]] #\\r\\n elif self.clr == 6: #\\r\\n # o o o \\r\\n # o x o x o x o o x o\\r\\n # o o o \\r\\n _colOffsets = [[ 0,-1, 0, 0], [-1, 0, 0, 1], [ 0, 1, 0, 0], [ 1, 0, 0,-1]] #\\r\\n _rowOffsets = [[ 1, 0, 0,-1], [ 0,-1, 0, 0], [-1, 0, 0, 1], [ 0, 1, 0, 0]] #\\r\\n elif self.clr == 7: # \\r\\n # o o o o o o o o\\r\\n # o x o x o x o x\\r\\n # \\r\\n _colOffsets = [[-1,-1, 0, 0], [-1,-1, 0, 0], [-1,-1, 0, 0], [-1,-1, 0, 0]] #@@\\r\\n _rowOffsets = [[ 0,-1, 0,-1], [ 0,-1, 0,-1], [ 0,-1, 0,-1], [ 0,-1, 0,-1]] #@@\\n \\r\\n self._colOffsets = _colOffsets[self._rot] #@@\\r\\n self._rowOffsets = _rowOffsets[self._rot] #@@\\r\\n self._update() #@@\\r\",\n \"def flattener(image, pts, w, h):\\n temp_rect = np.zeros((4,2), dtype = \\\"float32\\\")\\n\\n s = np.sum(pts, axis = 2)\\n\\n tl = pts[np.argmin(s)]\\n br = pts[np.argmax(s)]\\n\\n diff = np.diff(pts, axis = -1)\\n tr = pts[np.argmin(diff)]\\n bl = pts[np.argmax(diff)]\\n\\n # Need to create an array listing points in order of\\n # [top left, top right, bottom right, bottom left]\\n # before doing the perspective transform\\n\\n if w <= 0.8*h: # If card is vertically oriented\\n temp_rect[0] = tl\\n temp_rect[1] = tr\\n temp_rect[2] = br\\n temp_rect[3] = bl\\n\\n if w >= 1.2*h: # If card is horizontally oriented\\n temp_rect[0] = bl\\n temp_rect[1] = tl\\n temp_rect[2] = tr\\n temp_rect[3] = br\\n\\n # If the card is 'diamond' oriented, a different algorithm\\n # has to be used to identify which point is top left, top right\\n # bottom left, and bottom right.\\n\\n if w > 0.8*h and w < 1.2*h: #If card is diamond oriented\\n # If furthest left point is higher than furthest right point,\\n # card is tilted to the left.\\n if pts[1][0][1] <= pts[3][0][1]:\\n # If card is titled to the left, approxPolyDP returns points\\n # in this order: top right, top left, bottom left, bottom right\\n temp_rect[0] = pts[1][0] # Top left\\n temp_rect[1] = pts[0][0] # Top right\\n temp_rect[2] = pts[3][0] # Bottom right\\n temp_rect[3] = pts[2][0] # Bottom left\\n\\n # If furthest left point is lower than furthest right point,\\n # card is tilted to the right\\n if pts[1][0][1] > pts[3][0][1]:\\n # If card is titled to the right, approxPolyDP returns points\\n # in this order: top left, bottom left, bottom right, top right\\n temp_rect[0] = pts[0][0] # Top left\\n temp_rect[1] = pts[3][0] # Top right\\n temp_rect[2] = pts[2][0] # Bottom right\\n temp_rect[3] = pts[1][0] # Bottom left\\n\\n maxWidth = 200\\n maxHeight = 300\\n\\n # Create destination array, calculate perspective transform matrix,\\n # and warp card image\\n dst = np.array([[0,0],[maxWidth-1,0],[maxWidth-1,maxHeight-1],[0, maxHeight-1]], np.float32)\\n M = cv2.getPerspectiveTransform(temp_rect,dst)\\n warp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\\n return cv2.cvtColor(warp, cv2.COLOR_BGR2RGB)\",\n \"def draw_final_image(self, image, warped, undist, ploty, left_fitx, right_fitx, Minv, left_rad, right_rad):\\n gray = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)\\n # Create an image to draw the lines on\\n warp_zero = np.zeros_like(gray).astype(np.uint8)\\n color_warp = np.dstack((warp_zero, warp_zero, warp_zero))\\n\\n # Recast the x and y points into usable format for cv2.fillPoly()\\n pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])\\n pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])\\n pts = np.hstack((pts_left, pts_right))\\n\\n # Draw the lane onto the warped blank image\\n cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))\\n\\n # Warp the blank back to original image space using inverse perspective matrix (Minv)\\n newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))\\n # Combine the result with the original image\\n result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)\\n\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n off_center = calculate_center(left_fitx, right_fitx, image.shape)\\n direction_str = 'left' if off_center < 0 else 'right'\\n center_str = '{:.2f} m of center {}'.format(abs(off_center), direction_str)\\n cv2.putText(result, center_str, (430, 630), font, 1, (0, 0, 255), 2, cv2.LINE_AA)\\n if left_rad and right_rad:\\n curvature = 0.5 * (round(right_rad / 1000, 1) + round(left_rad / 1000, 1))\\n else:\\n curvature = 0\\n str2 = 'Radius of curvature: {} km'.format(curvature)\\n cv2.putText(result, str2, (430, 670), font, 1, (0, 0, 255), 2, cv2.LINE_AA)\\n\\n if self.args.is_test:\\n plt.imshow(result)\\n plt.show()\\n\\n return result\",\n \"def augment_image(image,max_angle):\\n angles = [-max_angle,max_angle]\\n axes = [(0,1),(0,2),(1,2)]\\n images_aug = [image,image[::-1]]\\n for angle in angles:\\n for axis in axes:\\n images_aug.append(aug.rotate(image, angle, axes=axis, reshape=False, order=0))\\n images_aug.append(aug.rotate(image[::-1], angle, axes=axis, reshape=False, order=0))\\n return images_aug\",\n \"def augment_img(img):\\n img = random_hflip_img(img)\\n img = cutout_img(img, size=12)\\n img = zero_pad_and_crop_img(img)\\n return img\",\n \"def get_rotation_angle(prev_image, curr_image, size_of_cropped_image):\\n max_value = np.amax(prev_image)\\n\\n if prev_image.dtype == 'float' and max_value <= 1:\\n prev_image = np.uint8(prev_image * 255)\\n curr_image = np.uint8(curr_image * 255)\\n\\n if prev_image.dtype == 'float' and max_value > 1:\\n prev_image = np.uint8(prev_image)\\n curr_image = np.uint8(curr_image)\\n\\n prev_image = cv.equalizeHist(prev_image)\\n curr_image = cv.equalizeHist(curr_image)\\n\\n # Initiate ORB detector\\n orb = cv.ORB_create(nfeatures=200)\\n\\n # find the keypoints and descriptors with ORB\\n kp1, des1 = orb.detectAndCompute(prev_image, None)\\n kp2, des2 = orb.detectAndCompute(curr_image, None)\\n\\n # do feature matching\\n bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)\\n matches = bf.match(des1, des2)\\n matches = sorted(matches, key=lambda x: x.distance)\\n\\n # calculate perspective transform matrix\\n src_pts = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)\\n dst_pts = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)\\n transform_matrix, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)\\n\\n if transform_matrix is None:\\n transform_matrix, mask = cv.findHomography(src_pts, dst_pts, 0)\\n\\n if transform_matrix is None:\\n transform_matrix, mask = cv.findHomography(src_pts, dst_pts, 0)\\n\\n vector_along_x_axis_from_center = \\\\\\n np.float32([[size_of_cropped_image / 2, size_of_cropped_image / 2],\\n [size_of_cropped_image, size_of_cropped_image / 2]]).reshape(-1, 1, 2)\\n vector_transformed = cv.perspectiveTransform(vector_along_x_axis_from_center, transform_matrix)\\n\\n theta = - np.arctan2(vector_transformed[1, 0, 1] - vector_transformed[0, 0, 1],\\n vector_transformed[1, 0, 0] - vector_transformed[0, 0, 0]) * 180 / np.pi\\n # negative sign is to make the sign of the angle to correspond to one in a right-handed coordinate system\\n return theta\",\n \"def azizen(self):\\n # x0,y0 array pixel coordinates relative to cx,cy\\n# ndy0,ndx0=img.shape\\n ndy0=self.ndy0\\n ndx0=self.ndx0\\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.linspace(0,ndy0-1,ndy0)-self.cy)\\n r0=np.sqrt(x0**2+y0**2)/self.pr0 # fractional radial distance from 0,0\\n# self.roi=np.s_[ystart:ystart+self.ny0,xstart:xstart+self.nx0]\\n # why not model the zenith angle dependence with polynomial directly\\n # rather than linear interpolation between roots.\\n roots=np.zeros(51)\\n rr=np.arange(51)/100.0\\n for i,ref in enumerate(rr):\\n roots[i]=np.real(np.roots([self.c3,0,self.c2,0,self.c1,-ref])[-1])\\n theta0 = np.interp(r0/2,rr,roots)\\n \\n phi0 = np.arctan2(x0,y0) - self.rot ####phi (i.e., azimuth) is reckoned with -pi corresponding to north, increasing clockwise, NOTE: pysolar use sub-standard definition\\n phi0 = phi0%(2*np.pi)\\n\\n #####correction for the tilt of the camera\\n k=np.array((np.sin(self.azm),np.cos(self.azm),0))\\n a=np.array([np.sin(theta0)*np.cos(phi0),np.sin(theta0)*np.sin(phi0),np.cos(theta0)]); \\n a = np.transpose(a,[1,2,0])\\n b=np.cos(self.beta)*a + np.sin(self.beta)*np.cross(k,a,axisb=2) \\\\\\n + np.reshape(np.outer(np.dot(a,k),k),(self.ndy0,self.ndx0,3))*(1-np.cos(self.beta))\\n theta0=np.arctan(np.sqrt(b[:,:,0]**2+b[:,:,1]**2)/b[:,:,2])\\n phi0=np.arctan2(b[:,:,1],b[:,:,0])%(2*np.pi)\\n# max_theta *= deg2rad \\n# valid0 = (theta0<max_theta) & (theta0>0); \\n# theta0[valid0]=np.nan;\\n self.theta0,self.phi0=theta0,phi0\",\n \"def draw_image(self):\\n dir_ship = self.direction\\n dir_cannon=self.cannon_dir\\n temp_image = self.base_image.copy()\\n pygame.draw.polygon(temp_image, (0,0,0), [(2,2),(2,3),(3,3),(3,2)])\\n if dir_cannon == dir_ship:\\n pygame.draw.polygon(temp_image, (60,60,60), [(4,3),(4,2), (5,3),(5,2)])\\n if (dir_ship - dir_cannon)%4 ==1:#-90° angle\\n pygame.draw.polygon(temp_image, (60,60,60), [(2,4),(3,4), (2,5),(3,5)])\\n if (dir_ship - dir_cannon)%4 ==3:#+90° angle\\n pygame.draw.polygon(temp_image, (60,60,60), [(2,1),(3,1), (2,0),(3,0)])\\n if (dir_ship - dir_cannon)%4 ==2:#180° angle\\n pygame.draw.polygon(temp_image, (60,60,60), [(1,2),(1,3), (0,2),(0,3)])\\n temp_image=pygame.transform.rotate(temp_image,dir_ship*90)\\n return temp_image\",\n \"def rotate(self, image, angle):\\n # Get the image size\\n # No that's not an error - NumPy stores image matricies backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n # We require a translation matrix to keep the image centred\\n trans_mat = np.matrix([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the tranform for the combined rotation and translation\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv2.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv2.INTER_LINEAR\\n )\\n\\n return result\",\n \"def rotate(img_path):\\r\\n\\timg = cv2.imread(img_path)\\r\\n\\timgcorrect = ImgCorrect(img)\\r\\n\\tlines_img = imgcorrect.img_lines()\\r\\n\\tif lines_img is None:\\r\\n\\t rotated = imgcorrect.rotate_image(0)\\r\\n\\telse:\\r\\n\\t degree = imgcorrect.search_lines()\\r\\n\\t rotated = imgcorrect.rotate_image(degree)\\r\\n\\tcv2.imwrite(img_path, rotated)\",\n \"def rotate(\\n img,\\n angle,\\n interpolation='nearest',\\n expand=False,\\n center=None,\\n fill=None,\\n data_format='CHW',\\n):\\n\\n angle = -angle % 360\\n img = img.unsqueeze(0)\\n\\n # n, c, h, w = img.shape\\n w, h = _get_image_size(img, data_format=data_format)\\n\\n img = img if data_format.lower() == 'chw' else img.transpose((0, 3, 1, 2))\\n\\n post_trans = [0, 0]\\n\\n if center is None:\\n rotn_center = [0, 0]\\n else:\\n rotn_center = [(p - s * 0.5) for p, s in zip(center, [w, h])]\\n\\n if paddle.in_dynamic_mode():\\n angle = math.radians(angle)\\n matrix = [\\n math.cos(angle),\\n math.sin(angle),\\n 0.0,\\n -math.sin(angle),\\n math.cos(angle),\\n 0.0,\\n ]\\n matrix = paddle.to_tensor(matrix, place=img.place)\\n\\n matrix[2] += (\\n matrix[0] * (-rotn_center[0] - post_trans[0])\\n + matrix[1] * (-rotn_center[1] - post_trans[1])\\n + rotn_center[0]\\n )\\n matrix[5] += (\\n matrix[3] * (-rotn_center[0] - post_trans[0])\\n + matrix[4] * (-rotn_center[1] - post_trans[1])\\n + rotn_center[1]\\n )\\n else:\\n angle = angle / 180 * math.pi\\n matrix = paddle.concat(\\n [\\n paddle.cos(angle),\\n paddle.sin(angle),\\n paddle.zeros([1]),\\n -paddle.sin(angle),\\n paddle.cos(angle),\\n paddle.zeros([1]),\\n ]\\n )\\n matrix = paddle.static.setitem(\\n matrix,\\n 2,\\n matrix[2]\\n + matrix[0] * (-rotn_center[0] - post_trans[0])\\n + matrix[1] * (-rotn_center[1] - post_trans[1])\\n + rotn_center[0],\\n )\\n matrix = paddle.static.setitem(\\n matrix,\\n 5,\\n matrix[5]\\n + matrix[3] * (-rotn_center[0] - post_trans[0])\\n + matrix[4] * (-rotn_center[1] - post_trans[1])\\n + rotn_center[1],\\n )\\n\\n matrix = matrix.reshape((1, 2, 3))\\n\\n if expand:\\n # calculate output size\\n if paddle.in_dynamic_mode():\\n corners = paddle.to_tensor(\\n [\\n [-0.5 * w, -0.5 * h, 1.0],\\n [-0.5 * w, 0.5 * h, 1.0],\\n [0.5 * w, 0.5 * h, 1.0],\\n [0.5 * w, -0.5 * h, 1.0],\\n ],\\n place=matrix.place,\\n ).astype(matrix.dtype)\\n else:\\n corners = paddle.assign(\\n [\\n [-0.5 * w, -0.5 * h, 1.0],\\n [-0.5 * w, 0.5 * h, 1.0],\\n [0.5 * w, 0.5 * h, 1.0],\\n [0.5 * w, -0.5 * h, 1.0],\\n ],\\n ).astype(matrix.dtype)\\n\\n _pos = (\\n corners.reshape((1, -1, 3))\\n .bmm(matrix.transpose((0, 2, 1)))\\n .reshape((1, -1, 2))\\n )\\n _min = _pos.min(axis=-2).floor()\\n _max = _pos.max(axis=-2).ceil()\\n\\n npos = _max - _min\\n nw = npos[0][0]\\n nh = npos[0][1]\\n\\n if paddle.in_dynamic_mode():\\n ow, oh = int(nw), int(nh)\\n else:\\n ow, oh = nw.astype(\\\"int32\\\"), nh.astype(\\\"int32\\\")\\n\\n else:\\n ow, oh = w, h\\n\\n grid = _affine_grid(matrix, w, h, ow, oh)\\n\\n out = _grid_transform(img, grid, mode=interpolation, fill=fill)\\n\\n out = out if data_format.lower() == 'chw' else out.transpose((0, 2, 3, 1))\\n\\n return out.squeeze(0)\",\n \"def rotate_image(image, angle):\\r\\n\\r\\n # Get the image size\\r\\n # No that's not an error - NumPy stores image matricies backwards\\r\\n image_size = (image.shape[1], image.shape[0])\\r\\n image_center = tuple(np.array(image_size) / 2)\\r\\n\\r\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\r\\n rot_mat = np.vstack(\\r\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\r\\n )\\r\\n\\r\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\r\\n\\r\\n # Shorthand for below calcs\\r\\n image_w2 = image_size[0] * 0.5\\r\\n image_h2 = image_size[1] * 0.5\\r\\n\\r\\n # Obtain the rotated coordinates of the image corners\\r\\n rotated_coords = [\\r\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\r\\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\\r\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\r\\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\r\\n ]\\r\\n\\r\\n # Find the size of the new image\\r\\n x_coords = [pt[0] for pt in rotated_coords]\\r\\n x_pos = [x for x in x_coords if x > 0]\\r\\n x_neg = [x for x in x_coords if x < 0]\\r\\n\\r\\n y_coords = [pt[1] for pt in rotated_coords]\\r\\n y_pos = [y for y in y_coords if y > 0]\\r\\n y_neg = [y for y in y_coords if y < 0]\\r\\n\\r\\n right_bound = max(x_pos)\\r\\n left_bound = min(x_neg)\\r\\n top_bound = max(y_pos)\\r\\n bot_bound = min(y_neg)\\r\\n\\r\\n new_w = int(abs(right_bound - left_bound))\\r\\n new_h = int(abs(top_bound - bot_bound))\\r\\n\\r\\n # We require a translation matrix to keep the image centred\\r\\n trans_mat = np.matrix([\\r\\n [1, 0, int(new_w * 0.5 - image_w2)],\\r\\n [0, 1, int(new_h * 0.5 - image_h2)],\\r\\n [0, 0, 1]\\r\\n ])\\r\\n\\r\\n # Compute the tranform for the combined rotation and translation\\r\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\r\\n\\r\\n # Apply the transform\\r\\n result = cv2.warpAffine(\\r\\n image,\\r\\n affine_mat,\\r\\n (new_w, new_h),\\r\\n flags=cv2.INTER_LINEAR\\r\\n )\\r\\n\\r\\n return result\",\n \"def tile_image(im):\\n r1 = np.concatenate((im[::-1,::-1], im[::-1], im[::-1, ::-1]), 1)\\n r2 = np.concatenate((im[:,::-1], im, im[:, ::-1]), 1)\\n r3 = np.concatenate((im[::-1,::-1], im[::-1], im[::-1, ::-1]), 1)\\n return(np.concatenate((r1, r2,r3), 0))\",\n \"def flip_image_horizontal(image):\\n return cv.flip(image, 0)\",\n \"def augment_image(im):\\n # First crop out the face to save reduce computation load\\n bb = im.landmarks['bb'].lms\\n bb_vec = bb.as_vector()\\n bb_ul = (np.array([bb_vec[0], bb_vec[1]]) - bb.centre()) * 2\\n bb_lr = (np.array([bb_vec[4], bb_vec[5]]) - bb.centre()) * 2\\n ul = bb_ul + bb.centre()\\n lr = bb_lr + bb.centre()\\n im = im.crop(ul, lr, constrain_to_boundary=True)\\n if im.pixels.shape[0] == 1:\\n pix = np.zeros((3, im.pixels.shape[1], im.pixels.shape[2]))\\n pix[:,] = im.pixels\\n im.pixels = pix\\n\\n beta = 0.3\\n cx = np.random.uniform(-beta, beta)\\n cy = np.random.uniform(-beta, beta)\\n fx = 1.0\\n fy = np.random.uniform(0.6, 1.4)\\n max_rotation = 30\\n theta = np.random.uniform(-max_rotation, max_rotation)\\n\\n rotation = menpo.transform.Rotation.init_from_2d_ccw_angle(theta)\\n shear = menpo.transform.Affine(np.array([[1, cx, 0],[cy, 1, 0], [0,0,1]]))\\n scale = menpo.transform.Affine(np.array([[fx, 0, 0],[0, fy, 0], [0,0,1]]))\\n T = scale.compose_after(shear).compose_after(rotation)\\n\\n t_im = im.transform_about_centre(T)\\n\\n t_im = add_color_jetting(t_im)\\n t_im = add_occlusion(t_im)\\n\\n\\n new_bb = t_im.landmarks['PTS'].lms.bounding_box()\\n\\n #new_bb contains the gt bounding box\\n augmented_bb = add_bb_noise(new_bb)\\n augmented_bb = augmented_bb.reshape((4,2))\\n augmented_bb = menpo.shape.PointCloud(augmented_bb)\\n t_im.landmarks['bb'] = menpo.landmark.LandmarkGroup.init_with_all_label(augmented_bb)\\n\\n return t_im\",\n \"def deskew_grayscale(image_skew, delta = 0.5, limit = 5):\\n try:\\n print(\\\"TESTED1\\\")\\n image_original=image_skew.copy()\\n image_skew=cv2.resize(image_skew,(int(image_skew.shape[1]*0.5),int(image_skew.shape[0]*0.5)))\\n image_bin=image_binary(image_skew)\\n angles = np.arange(-limit, limit + delta, delta)\\n scores = [find_score(image_bin,angle) for angle in angles]\\n best_score = max(scores)\\n best_angle = angles[scores.index(best_score)]\\n image_inv=image_binary(image_original)\\n data = inter.rotate(image_inv, best_angle, reshape=False, order=0)\\n image_skewed=((~data.astype(bool)).astype(int))*255\\n print(image_skewed, best_angle)\\n return image_skewed, best_angle\\n except AttributeError:\\n print(\\\"OOPS1\\\")\\n return image_skew, 0\",\n \"def inverse_warping(img_initial, img_final, pts_initial, pts_final): \\n \\n # YOU SHOULDN'T NEED TO CHANGE THIS\\n pts_final = pts_final.astype(int)\\n \\n projected_img = img_initial.copy()\\n for i in range(3):\\n sub_img_i = img_initial[:,:,i][pts_initial[:,1], pts_initial[:,0]]\\n sub_img_f = img_final[:,:,i][pts_final[:,1], pts_final[:,0]]\\n \\n sub_img = sub_img_i*0.5 + sub_img_f*0.5\\n projected_img[:,:,i][pts_initial[:,1], pts_initial[:,0]] = sub_img\\n \\n return projected_img\",\n \"def concat_images_corner(imga, imgb, xoffset=0, yoffset=0, direction='horizontal',\\n ontop=True, adjust_z=False):\\n if direction == 'horizontal':\\n max_dim = np.maximum.reduce([imga.shape, imgb.shape])\\n\\n offset = (abs(yoffset), abs(xoffset))\\n tmp_offset = np.array(offset)\\n\\n # if (max_dim == imgb.shape).all():\\n # tmp = np.copy(imgb)\\n # imgb = np.copy(imga)\\n # imga = np.copy(tmp)\\n # ontop = toggle(ontop)\\n # xoffset *= -1\\n # yoffset *= -1\\n\\n # center_new = np.array(np.divide(max_dim, 2), dtype=int)\\n new_img = np.full(np.add(max_dim, np.abs(offset)), np.nan)\\n\\n Sa0 = slice(0, imga.shape[0])\\n Sa1 = slice(0, imga.shape[1])\\n Sb0 = slice(abs(yoffset), abs(yoffset) + imgb.shape[0])\\n Sb1 = slice(abs(xoffset), abs(xoffset) + imgb.shape[1])\\n\\n xdir = np.sign(xoffset)\\n ydir = np.sign(yoffset)\\n\\n if ydir == 0:\\n ydir = 1\\n if xdir == 0:\\n xdir = 1\\n\\n imga = imga[::ydir, ::xdir]\\n imgb = imgb[::ydir, ::xdir]\\n\\n if adjust_z:\\n top_img = 1 * new_img\\n top_img[Sa0, Sa1] = imga\\n top_img[Sb0, Sb1] = imgb\\n low_img = 1 * new_img\\n low_img[Sb0, Sb1] = imgb\\n low_img[Sa0, Sa1] = imga\\n\\n diff = top_img - low_img\\n m = np.nanmean(diff)\\n s = np.nanstd(diff)\\n mask = np.abs(diff) < m + s\\n diff[mask] = np.nan\\n add = np.nanmean(diff)\\n\\n print(add)\\n\\n imgb -= add\\n\\n if ontop:\\n new_img[Sa0, Sa1] = imga\\n new_img[Sb0, Sb1] = imgb\\n else:\\n new_img[Sb0, Sb1] = imgb\\n new_img[Sa0, Sa1] = imga\\n\\n return new_img[::ydir, ::xdir]\",\n \"def triangle_warping(img1, img2, t1, t2, method):\\n # Find bounding rectangle for each triangle\\n x1, y1, w1, h1 = cv2.boundingRect(np.float32([t1]))\\n x2, y2, w2, h2 = cv2.boundingRect(np.float32([t2]))\\n\\n # Offset points by left top corner of the respective rectangles\\n t1Rect = []\\n t2Rect = []\\n\\n for i in range(0, 3):\\n t1Rect.append(((t1[i][0] - x1), (t1[i][1] - y1)))\\n t2Rect.append(((t2[i][0] - x2), (t2[i][1] - y2)))\\n\\n # Get mask by filling triangle\\n mask = np.zeros((h2, w2, 3), dtype=np.float32)\\n\\n cv2.fillConvexPoly(mask, np.int32(t2Rect), (1.0, 1.0, 1.0), 16, 0)\\n\\n # Apply warpImage to small rectangular patches\\n img1Rect = img1[y1:y1 + h1, x1:x1 + w1]\\n img2Rect = np.zeros((h2, w2), dtype=img1Rect.dtype)\\n\\n size = (w2, h2)\\n\\n if method == \\\"affine\\\":\\n img2Rect = affine_warping(img1Rect, t1Rect, t2Rect, size)\\n else:\\n img2Rect = triangulationWarping(img1Rect, t1Rect, t2Rect, size)\\n\\n img2Rect = img2Rect * mask\\n\\n a = (1.0, 1.0, 1.0) - mask\\n\\n # Copy triangular region of the rectangular patch to the output image\\n img2[y2:y2 + h2, x2:x2 + w2] = img2[y2:y2 + h2, x2:x2 + w2] * ((1.0, 1.0, 1.0) - mask)\\n img2[y2:y2 + h2, x2:x2 + w2] = img2[y2:y2 + h2, x2:x2 + w2] + img2Rect\",\n \"def sharpen(im):\\n kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])\\n im = cv2.filter2D(im, -1, kernel)\\n return im\",\n \"def create_straightened(src_path, tgt_path):\\n img = skio.imread(src_path)\\n straight_img, xform, angle = straighten_image(img)\\n save_geotiff(tgt_path, (straight_img * 255).astype('uint8'), xform)\",\n \"def rotate_right_90(img):\\r\\n #reading image\\r\\n im = Image.open(\\\"filename\\\")\\r\\n \\r\\n #flipping image 90 degrees\\r\\n newimg = im.transpose(PIL.Image.ROTATE_90)\\r\\n \\r\\n return img\",\n \"def _rotate_image(self, src, theta, ox, oy, fill=255):\\n # Images have origin at the top left, so negate the angle.\\n theta = -theta\\n\\n # Dimensions of source image. Note that scipy.misc.imread loads\\n # images in row-major order, so src.shape gives (height, width).\\n sh, sw = src.shape\\n\\n # Rotated positions of the corners of the source image.\\n cx, cy = self._rotate_coords([0, sw, sw, 0], [0, 0, sh, sh], theta, ox, oy)\\n\\n # Determine dimensions of destination image.\\n dw, dh = (int(self._pkgs['numpy'].ceil(c.max() - c.min())) for c in (cx, cy))\\n\\n # Coordinates of pixels in destination image.\\n dx, dy = self._pkgs['numpy'].meshgrid(self._pkgs['numpy'].arange(dw), self._pkgs['numpy'].arange(dh))\\n\\n # Corresponding coordinates in source image. Since we are\\n # transforming dest-to-src here, the rotation is negated.\\n sx, sy = self._rotate_coords(dx + cx.min(), dy + cy.min(), -theta, ox, oy)\\n\\n # Select nearest neighbour.\\n sx, sy = sx.round().astype(int), sy.round().astype(int)\\n\\n # Mask for valid coordinates.\\n mask = (0 <= sx) & (sx < sw) & (0 <= sy) & (sy < sh)\\n\\n # Create destination image.\\n dest = self._pkgs['numpy'].empty(shape=(dh, dw), dtype=src.dtype)\\n\\n # Copy valid coordinates from source image.\\n dest[dy[mask], dx[mask]] = src[sy[mask], sx[mask]]\\n\\n # Fill invalid coordinates.\\n dest[dy[~mask], dx[~mask]] = fill\\n\\n return dest\",\n \"def set_transparent(img):\\n assert img.shape[-1] == 4\\n white_pix = np.all(img == [255, 255, 255, 255], axis=-1)\\n # print(white_pix)\\n img[white_pix, -1] = 0\\n # return img\",\n \"def concat_3dimages_corners(imga, imgb, xoffset=0, yoffset=0, zoffset=0,\\n transpose=True, ontop=True, center_offset=True,\\n adjust_z=(0, 1)):\\n print(\\\"Concating images with reference point being the lower left corner\\\")\\n if transpose:\\n print(\\\"Transpose images\\\")\\n imga = np.transpose(imga, axes=(0, 2, 1))\\n imgb = np.transpose(imgb, axes=(0, 2, 1))\\n\\n offset = (abs(zoffset), abs(yoffset), abs(xoffset))\\n max_dim = np.maximum.reduce([imga.shape, np.add(imgb.shape, offset)])\\n\\n # center_a = np.array(np.divide(imga.shape, 2), dtype=int)\\n # center_b = np.array(np.divide(imgb.shape, 2), dtype=int)\\n\\n # if (max_dim == imgb.shape).all():\\n # tmp = np.copy(imgb)\\n # imgb = np.copy(imga)\\n # imga = np.copy(tmp)\\n # ontop = toggle(ontop)\\n # xoffset *= -1\\n # yoffset *= -1\\n # zoffset *= -1\\n\\n # tmp_offset = np.array(offset)\\n # tmp_offset[tmp_offset > 0] = 0\\n # new_img = np.full(np.add(max_dim, np.abs(offset)), np.nan)\\n new_img = np.full(max_dim, np.nan)\\n\\n Sa0 = slice(0, imga.shape[0])\\n Sa1 = slice(0, imga.shape[1])\\n Sa2 = slice(0, imga.shape[2])\\n Sb0 = slice(abs(zoffset), abs(zoffset) + imgb.shape[0])\\n Sb1 = slice(abs(yoffset), abs(yoffset) + imgb.shape[1])\\n Sb2 = slice(abs(xoffset), abs(xoffset) + imgb.shape[2])\\n\\n xdir = np.sign(xoffset)\\n ydir = np.sign(yoffset)\\n zdir = np.sign(zoffset)\\n\\n if ydir == 0:\\n ydir = 1\\n if xdir == 0:\\n xdir = 1\\n if zdir == 0:\\n zdir = 1\\n\\n imga = imga[::zdir, ::ydir, ::xdir]\\n imgb = imgb[::zdir, ::ydir, ::xdir]\\n\\n if adjust_z:\\n for ix in adjust_z:\\n top_img = 1 * new_img[ix]\\n top_img[Sa1, Sa2] = imga[ix]\\n top_img[Sb1, Sb2] = imgb[ix]\\n low_img = 1 * new_img[ix]\\n low_img[Sb1, Sb2] = imgb[ix]\\n low_img[Sa1, Sa2] = imga[ix]\\n\\n diff = top_img - low_img\\n m = np.nanmean(diff)\\n s = np.nanstd(diff)\\n mask = np.abs(diff) < m + s\\n diff[mask] = np.nan\\n add = np.nanmean(diff)\\n\\n print(add)\\n\\n imgb[ix] -= add\\n\\n print(\\\"new_img shape: \\\", new_img.shape)\\n\\n if ontop:\\n new_img[Sa0, Sa1, Sa2] = imga\\n new_img[Sb0, Sb1, Sb2] = imgb\\n else:\\n new_img[Sb0, Sb1, Sb2] = imgb\\n new_img[Sa0, Sa1, Sa2] = imga\\n\\n new_img\\n\\n if transpose:\\n print(\\\"Transpose back\\\")\\n return np.transpose(new_img[::zdir, ::ydir, ::xdir], axes=(0, 2, 1))\\n else:\\n return new_img[::zdir, ::ydir, ::xdir]\",\n \"def steerright(self):\\n self.direction = self.direction-self.steering\\n if self.direction < 0:\\n self.direction = 360-90\\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)\",\n \"def normalise(image):\",\n \"def rotateImage(image, angle):\\n image_center = tuple(np.array(image.shape[1::-1]) / 2)\\n rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)\\n result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_NEAREST)\\n return result\",\n \"def Rotate(angle):\\n def rotate_img(img, angle=angle):\\n img = Ft.rotate(img, angle, resample=BILINEAR)\\n return img\\n return rotate_img\",\n \"def align(image):\\n angle = find_angle(image)\\n image = rotate(image, angle)\\n return image\",\n \"def rot_center(image, angle):\\r\\n orig_rect = image.get_rect()\\r\\n rot_image = pygame.transform.rotate(image, angle)\\r\\n rot_rect = orig_rect.copy()\\r\\n rot_rect.center = rot_image.get_rect().center\\r\\n rot_image = rot_image.subsurface(rot_rect).copy()\\r\\n return rot_image\",\n \"def transform_image(image, transform, mapping, alpha = 1, incr_x = 10, incr_y = 10):\\r\\n background = [255, 255, 255, 0]\\r\\n width, height = image.size\\r\\n image_in = np.array(image.convert(\\\"RGBA\\\"))\\r\\n image_out = [[background[:] for j in range(width)] for i in range(height)]\\r\\n transform_row = []\\r\\n for i in range(0, width + incr_x, incr_x):\\r\\n transform_row.append(transform(vec2(i, 0), mapping, alpha))\\r\\n for i in range(incr_y, height + incr_y, incr_y):\\r\\n p_ur = transform_row[0]\\r\\n p_lr = transform_row[0] = transform(vec2(0, i), mapping, alpha)\\r\\n for j in range(incr_x, width + incr_x, incr_x):\\r\\n p_ul = p_ur\\r\\n p_ll = p_lr\\r\\n p_ur = transform_row[j//incr_x]\\r\\n p_lr = transform_row[j//incr_x] = transform(vec2(j, i), mapping, alpha)\\r\\n a = p_ur - p_ul\\r\\n b = p_ll - p_ul\\r\\n det = a.x*b.y - a.y*b.x\\r\\n if (det != 0.0):\\r\\n for p in triangle(p_ul, p_ur, p_ll, width, height):\\r\\n c = p - p_ul\\r\\n rx = (b.y*c.x - b.x*c.y)/det\\r\\n ry = (a.x*c.y - a.y*c.x)/det\\r\\n image_out[p.y][p.x] = image_in[min(height - 1, max(0, round(i + (ry - 1)*incr_y)))][min(width - 1, max(0, round(j + (rx - 1)*incr_x)))]\\r\\n a = p_lr - p_ll\\r\\n b = p_lr - p_ur\\r\\n det = a.x*b.y - a.y*b.x\\r\\n if (det != 0.0):\\r\\n p_ulr = p_ur + p_ll - p_lr\\r\\n for p in triangle(p_ur, p_ll, p_lr, width, height):\\r\\n c = p - p_ulr\\r\\n rx = (b.y*c.x - b.x*c.y)/det\\r\\n ry = (a.x*c.y - a.y*c.x)/det\\r\\n image_out[p.y][p.x] = image_in[min(height - 1, max(0, round(i + (ry - 1)*incr_y)))][min(width - 1, max(0, round(j + (rx - 1)*incr_x)))]\\r\\n image_out = Image.fromarray(np.uint8(image_out))\\r\\n return image_out\",\n \"def detect_and_rotate(image):\\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\\n # hsv value for the various mask\\n # computing of the blue mask to isolate the contours of the map\\n mask_blue = cv2.inRange(hsv, low_blue, up_blue)\\n print(\\\"Results of the blue mask\\\")\\n plt.figure()\\n plt.imshow(mask_blue)\\n plt.show()\\n # find the outside blue contours of the map on the whole world\\n contours, _ = cv2.findContours(mask_blue, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\\n\\n # find the rectangle which includes the contours\\n maxArea = 0\\n best = None\\n for contour in contours:\\n area = cv2.contourArea(contour)\\n\\n if area > maxArea:\\n maxArea = area\\n best = contour\\n\\n # If no map found\\n if maxArea < 10:\\n return None\\n\\n # Find the outside rectangle\\n rect = cv2.minAreaRect(best)\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n\\n # crop image inside bounding box\\n scale = 1\\n W = rect[1][0]\\n H = rect[1][1]\\n\\n # finding the box to rotate\\n Xs = [i[0] for i in box]\\n Ys = [i[1] for i in box]\\n x1 = min(Xs)\\n x2 = max(Xs)\\n y1 = min(Ys)\\n y2 = max(Ys)\\n\\n # finding the rotation angle between vertical and longest size of rectangle\\n angle = rect[2]\\n rotated = False\\n if angle < -45:\\n angle += 90\\n rotated = True\\n\\n # rotation center and rotation matrix\\n center = (int((x1 + x2) / 2), int((y1 + y2) / 2))\\n size = (int(scale * (x2 - x1)), int(scale * (y2 - y1)))\\n M = cv2.getRotationMatrix2D((size[0] / 2, size[1] / 2), angle, 1.0)\\n\\n # cropping the image and rotating it\\n cropped = cv2.getRectSubPix(image, size, center)\\n cropped = cv2.warpAffine(cropped, M, size)\\n croppedW = W if not rotated else H\\n croppedH = H if not rotated else W\\n\\n corrected = cv2.getRectSubPix(cropped, (int(croppedW * scale), int(croppedH * scale)),\\n (size[0] / 2, size[1] / 2))\\n final_grid = np.array(corrected)\\n return final_grid\",\n \"def get_image_skew(frame: imageType) -> float:\\n \\\"\\\"\\\"find contours in an edged image, and calculate angle from horizontal\\\"\\\"\\\"\\n largest_contour = get_largest_contour(frame)\\n if largest_contour is not None:\\n point: pointType\\n # array of array of arrays of int, int -> list of tuple of int,int\\n pxs_coords: List[pointType] = [cast(pointType, tuple(point_array[0])) for point_array in largest_contour if largest_contour is not None]\\n x_coords: List[int] = [point[0] for point in pxs_coords]\\n # y_coords: List[int] = [point[1] for point in pxs_coords]\\n x_min: int = min(x_coords)\\n x_max: int = max(x_coords)\\n\\n # get point with x_min\\n x_min_points: List[Tuple[int, int]] = [point for point in pxs_coords if point[0] == x_min]\\n x_max_points: List[Tuple[int, int]] = [point for point in pxs_coords if point[0] == x_max]\\n y_of_max: float = sum(point[1] for point in x_max_points) / len(x_max_points)\\n y_of_min: float = sum(point[1] for point in x_min_points) / len(x_min_points)\\n\\n # x_max_point = (x_max, y_of_max)\\n # print(x_min_points, x_max_points)\\n\\n x_diff = x_max - x_min\\n y_diff = y_of_max - y_of_min\\n # skew is positive -> is clockwise\\n skew = math.degrees(math.atan(y_diff / x_diff))\\n # print(skew)\\n # if y_of _max > y_of_min:\\n # else: return -skew\\n else:\\n skew = 0.0\\n return skew\",\n \"def rot_center(image, angle):\\n orig_rect = image.get_rect()\\n rot_image = pygame.transform.rotate(image, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def rot_center(image, angle):\\n orig_rect = image.get_rect()\\n rot_image = pygame.transform.rotate(image, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def rot_center(image, angle):\\n orig_rect = image.get_rect()\\n rot_image = pygame.transform.rotate(image, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def _rotate_image_90(self, img: ndarray, k: int) -> ndarray:\\n if img.shape[0] < img.shape[1]:\\n self.y = np.rot90(img, k)\\n return self.y\\n else:\\n return img\",\n \"def fixBorder(frame):\\n s = frame.shape\\n # Scale the image 2% without moving the center\\n T = cv2.getRotationMatrix2D((s[1]/2, s[0]/2), 0, 1.02)\\n frame = cv2.warpAffine(frame, T, (s[1], s[0]))\\n return frame\",\n \"def slanted_rect(self,image,i,j):\\r\\n s1 = 0.7\\r\\n s2 = -1.42814\\r\\n x1 = np.arctan(s1)\\r\\n x2 = np.arctan(s2)\\r\\n d1 = np.cos(np.pi - x1)\\r\\n d2 = np.cos(np.pi - x2)\\r\\n a = -(self.padding/d1)\\r\\n b = -(self.padding/d2)\\r\\n if (-0.7*j+1*i)>=(73.4 - a) and (i+1.42814*j)>=(172.55 - b) and (-0.7*j+1*i)<=(99.81 + a) and (i+1.42814*j)<=(429.07 + b):\\r\\n image[self.maximum_size-i,j,:]=0,0,0\\r\\n self.image_p[i,j,:]=2\",\n \"def blitRotateCenter(surf, image, topleft, angle):\\n new_rect = image.get_rect(center=image.get_rect(topleft=topleft).center)\\n\\n surf.blit(image, new_rect.topleft)\",\n \"def reverse_black_and_white(img):\\n img = 255 - img\\n\\n return img\",\n \"def rot_center(image, angle):\\r\\n orig_rect = image.get_rect()\\r\\n rot_image = transform.rotate(image, angle)\\r\\n rot_rect = orig_rect.copy()\\r\\n rot_rect.center = rot_image.get_rect().center\\r\\n rot_image = rot_image.subsurface(rot_rect).copy()\\r\\n return rot_image\",\n \"def rotate_image(rgb, dt, dl, angle):\\n width = rgb.shape[1]\\n height = rgb.shape[0]\\n pivot = (width / 2, height / 2)\\n rotationMatrix = cv2.getRotationMatrix2D(pivot, angle, 1.0)\\n bgr_rotated = cv2.warpAffine(rgb, rotationMatrix, (100, 100), borderMode=cv2.BORDER_REPLICATE)\\n # no interpolation for depth values because that doesn't make a lot of sense and leads to values which are not\\n # seen in the non-upscaled data\\n dt_rotated = cv2.warpAffine(dt, rotationMatrix, (100, 100), flags=cv2.INTER_NEAREST + cv2.WARP_FILL_OUTLIERS,\\n borderMode=cv2.BORDER_REPLICATE)\\n dl_rotated = cv2.warpAffine(dl, rotationMatrix, (100, 100), flags=cv2.INTER_NEAREST + cv2.WARP_FILL_OUTLIERS,\\n borderMode=cv2.BORDER_REPLICATE)\\n return bgr_rotated, dt_rotated, dl_rotated\",\n \"def rotate(img, bbox, landmark, alpha):\\r\\n center = (bbox.x+bbox.w/2, bbox.y+bbox.h/2)\\r\\n rot_mat = cv2.getRotationMatrix2D(center, alpha, 1)\\r\\n img_rotated_by_alpha = cv2.warpAffine(img, rot_mat, img.shape)\\r\\n landmark_ = np.asarray([(rot_mat[0][0]*x+rot_mat[0][1]*y+rot_mat[0][2],\\r\\n rot_mat[1][0]*x+rot_mat[1][1]*y+rot_mat[1][2]) for (x, y) in landmark])\\r\\n face = img_rotated_by_alpha[bbox.y:bbox.y+bbox.h,bbox.x:bbox.x+bbox.w]\\r\\n return (face, landmark_)\",\n \"def visualize_augmentation(image, angle):\\n\\n # Create a copy of the image to prevent changing the original\\n img = np.copy(image)\\n\\n cols = 2\\n rows = 6\\n fig_size = (7 * cols, 4 * rows) # Figure width and height, in inches\\n\\n fig, ax = plt.subplots(rows, cols, figsize=fig_size)\\n # Plot original images in the left column\\n for idx in range(rows):\\n ax[idx, 0].imshow(img)\\n ax[idx, 0].set_title(\\\"Original, Angle = \\\" + str(round(angle, 3)))\\n # Horizontal Flip\\n tmp_img, tmp_angle = random_horizontal_flip(img, angle, 1.0)\\n ax[0, 1].imshow(tmp_img)\\n ax[0, 1].set_title(\\\"Horizontal Flip, Angle = \\\" + str(round(tmp_angle, 3)))\\n # Translation\\n tmp_img, tmp_angle = random_translation(img, angle)\\n ax[1, 1].imshow(tmp_img)\\n ax[1, 1].set_title(\\\"Translation, Angle = \\\" + str(round(tmp_angle, 3)))\\n # Gaussian Noise\\n tmp_img = random_gaussian(img)\\n ax[2, 1].imshow(tmp_img)\\n ax[2, 1].set_title(\\\"Gaussian Noise, Angle = \\\" + str(round(angle, 3)))\\n # Shadows\\n tmp_img = random_shadows(img, 1.0, 0.9)\\n ax[3, 1].imshow(tmp_img)\\n ax[3, 1].set_title(\\\"Shadows, Angle = \\\" + str(round(angle, 3)))\\n # Brightness\\n tmp_img = random_brightness(img)\\n ax[4, 1].imshow(tmp_img)\\n ax[4, 1].set_title(\\\"Brightness, Angle = \\\" + str(round(angle, 3)))\\n # All Augmentation\\n tmp_img, tmp_angle = random_all(img, angle)\\n ax[5, 1].imshow(tmp_img)\\n ax[5, 1].set_title(\\\"All Randomization, Angle = \\\" +\\n str(round(tmp_angle, 3)))\\n\\n return fig\",\n \"def getTiltedRotateShift(img1, img2, tiltdiff, angle=0, bin=1, msg=True):\\n\\n\\t### untilt images by stretching and compressing\\n\\t# choose angle s/t compressFactor = 1/stretchFactor\\n\\t# this only works if one image is untilted (RCT) of both images are opposite tilt (OTR)\\n\\t#halftilt = abs(tiltdiff)/2.0\\n\\thalftiltrad = math.acos(math.sqrt(math.cos(abs(tiltdiff)/180.0*math.pi)))\\n\\t# go from zero tilt to half tilt\\n\\tcompressFactor = math.cos(halftiltrad)\\n\\t# go from max tilt to half tilt\\n\\tstretchFactor = math.cos(halftiltrad) / math.cos(abs(tiltdiff)/180.0*math.pi)\\n\\tif tiltdiff > 0:\\n\\t\\tif msg is True:\\n\\t\\t\\tapDisplay.printMsg(\\\"compress image 1\\\")\\n\\t\\tuntilt1 = transformImage(img1, compressFactor, angle)\\n\\t\\tuntilt2 = transformImage(img2, stretchFactor, angle)\\n\\t\\txfactor = compressFactor\\n\\telse:\\n\\t\\tif msg is True:\\n\\t\\t\\tapDisplay.printMsg(\\\"stretch image 1\\\")\\n\\t\\tuntilt1 = transformImage(img1, stretchFactor, angle)\\n\\t\\tuntilt2 = transformImage(img2, compressFactor, angle)\\n\\t\\txfactor = stretchFactor\\n\\n\\t### filtering was done earlier\\n\\tfilt1 = untilt1\\n\\tfilt2 = untilt2\\n\\n\\tif filt1.shape != filt2.shape:\\n\\t\\tnewshape = ( max(filt1.shape[0],filt2.shape[0]), max(filt1.shape[1],filt2.shape[1]) )\\n\\t\\tapDisplay.printMsg(\\\"Resizing images to: \\\"+str(newshape))\\n\\t\\tfilt1 = apImage.frame_constant(filt1, newshape, filt1.mean())\\n\\t\\tfilt2 = apImage.frame_constant(filt2, newshape, filt2.mean())\\n\\n\\t### cross-correlate\\n\\tcc = correlator.cross_correlate(filt1, filt2, pad=True)\\n\\trad = min(cc.shape)/20.0\\n\\tcc = apImage.highPassFilter(cc, radius=rad)\\n\\tcc = apImage.normRange(cc)\\n\\tcc = blackEdges(cc)\\n\\tcc = apImage.normRange(cc)\\n\\tcc = blackEdges(cc)\\n\\tcc = apImage.normRange(cc)\\n\\tcc = apImage.lowPassFilter(cc, radius=10.0)\\n\\n\\t#find peak\\n\\tpeakdict = peakfinder.findSubpixelPeak(cc, lpf=0)\\n\\t#import pprint\\n\\t#pprint.pprint(peak)\\n\\tpixpeak = peakdict['subpixel peak']\\n\\tif msg is True:\\n\\t\\tapDisplay.printMsg(\\\"Pixel peak: \\\"+str(pixpeak))\\n\\t\\tapImage.arrayToJpegPlusPeak(cc, \\\"guess-cross-ang\\\"+str(abs(angle))+\\\".jpg\\\", pixpeak)\\n\\n\\trawpeak = numpy.array([pixpeak[1], pixpeak[0]]) #swap coord\\n\\tshift = numpy.asarray(correlator.wrap_coord(rawpeak, cc.shape))*bin\\n\\n\\tif msg is True:\\n\\t\\tapDisplay.printMsg(\\\"Found xy-shift btw two images\\\"\\n\\t\\t\\t+\\\";\\\\n\\\\t SNR= \\\"+str(round(peakdict['snr'],2))\\n\\t\\t\\t+\\\";\\\\n\\\\t halftilt= \\\"+str(round(halftiltrad*180/math.pi, 3))\\n\\t\\t\\t+\\\";\\\\n\\\\t compressFactor= \\\"+str(round(compressFactor, 3))\\n\\t\\t\\t+\\\";\\\\n\\\\t stretchFactor= \\\"+str(round(stretchFactor, 3))\\n\\t\\t\\t+\\\";\\\\n\\\\t xFactor= \\\"+str(round(xfactor, 3))\\n\\t\\t\\t+\\\";\\\\n\\\\t rawpeak= \\\"+str(numpy.around(rawpeak*bin, 1))\\n\\t\\t\\t+\\\";\\\\n\\\\t shift= \\\"+str(numpy.around(shift, 1))\\n\\t\\t)\\n\\n\\treturn shift, xfactor, peakdict['snr']\",\n \"def transparency(img):\\n \\n black_mask_undistorted_file = open(os.path.join(settings.BASE_DIR, 'static', 'mask_undistorted.npy'), 'r')\\n black_mask_undistorted = np.load(black_mask_undistorted_file)\\n alpha = 200 * black_mask_undistorted\\n img = scipy.misc.imresize(img, (np.shape(alpha)[0], np.shape(alpha)[1]))\\n\\n result = np.empty([img.shape[0], img.shape[1], 4], dtype=np.uint8)\\n result[:,:,0] = img[:,:,0]\\n result[:,:,1] = img[:,:,1]\\n result[:,:,2] = img[:,:,2]\\n result[:,:,3] = alpha\\n\\n return result\",\n \"def transform(self, previousimage):\",\n \"def rot_image(original_image, angle):\\n # Tämä copypastettu jostain netistä. Vaatii että kuva on neliö.\\n assert original_image.get_width() == original_image.get_height(), \\\\\\n \\\"Can't rotate image - not square. %r\\\" % original_image\\n orig_rect = original_image.get_rect()\\n rot_image = pygame.transform.rotate(original_image, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def rotate(self):\\n\\n last_center = self.rect.center\\n self.image = pg.transform.rotate(self.image_copy,self.angle)\\n self.rect = self.image.get_rect()\\n self.rect.center = last_center\\n self.angle +=self.rotate_by\",\n \"def sub_image(image, center, theta, width, height):\\n if 45 < theta <= 90:\\n theta = theta - 90\\n width, height = height, width\\n\\n theta *= math.pi / 180 # convert to rad\\n v_x = (math.cos(theta), math.sin(theta))\\n v_y = (-math.sin(theta), math.cos(theta))\\n s_x = center[0] - v_x[0] * (width / 2) - v_y[0] * (height / 2)\\n s_y = center[1] - v_x[1] * (width / 2) - v_y[1] * (height / 2)\\n mapping = np.array([[v_x[0], v_y[0], s_x], [v_x[1], v_y[1], s_y]])\\n\\n return cv2.warpAffine(image, mapping, (width, height), flags=cv2.WARP_INVERSE_MAP, borderMode=cv2.BORDER_REPLICATE)\",\n \"def rotate_img(img, random):\\n degrees = 45 * (random - 0.5)\\n h, w = (int(2 * i / 3) for i in img.shape[:2])\\n img = imutils.rotate_bound(img, degrees)\\n h2, w2 = img.shape[:2]\\n x = int((w2 - w) / 2)\\n y = int((h2 - h) / 2)\\n img = img[y:y + h, x:x + w]\\n return img\",\n \"def rotate_bound(image, angle, bordervalue):\\n\\n # grab the dimensions of the image and then determine the\\n # center\\n (h, w) = image.shape[:2]\\n (cX, cY) = (w // 2, h // 2)\\n\\n # grab the rotation matrix (applying the negative of the\\n # angle to rotate clockwise), then grab the sine and cosine\\n # (i.e., the rotation components of the matrix)\\n M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)\\n cos = np.abs(M[0, 0])\\n sin = np.abs(M[0, 1])\\n\\n # compute the new bounding dimensions of the image\\n nW = int((h * sin) + (w * cos))\\n nH = int((h * cos) + (w * sin))\\n\\n # adjust the rotation matrix to take into account translation\\n M[0, 2] += (nW / 2) - cX\\n M[1, 2] += (nH / 2) - cY\\n\\n # perform the actual rotation and return the image\\n return cv2.warpAffine(image, M, (nW, nH), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT,\\n borderValue=bordervalue), M\",\n \"def _rotate_img(self, results):\\n angle = self.angle\\n height, width = results['img_shape'][:2]\\n center = ((width - 1) * 0.5, (height - 1) * 0.5)\\n\\n # Compute rotation matrix\\n mat_rotation = cv2.getRotationMatrix2D(center, -angle, 1)\\n\\n # Compute height and width of new image after rotation\\n cos = np.abs(mat_rotation[0, 0])\\n sin = np.abs(mat_rotation[0, 1])\\n width_new = height * sin + width * cos\\n height_new = height * cos + width * sin\\n mat_rotation[0, 2] += (width_new - width) * 0.5\\n mat_rotation[1, 2] += (height_new - height) * 0.5\\n width_new = int(np.round(width_new))\\n height_new = int(np.round(height_new))\\n \\n # Rotate image\\n img_rotation = cv2.warpAffine(results['img'], mat_rotation, (width_new, height_new),\\n borderValue=self.borderValue)\\n results['img'] = img_rotation\\n results['img_shape'] = results['img'].shape\\n\\n # Rotate corresponding annotations: all boxes in bbox_fields\\n for key in results.get('bbox_fields', []):\\n gt_boxes_ret = []\\n for poly in results[key]:\\n rot_array = []\\n poly_length = len(poly)\\n\\n if poly_length == 4:\\n poly = [poly[0], poly[1], poly[2], poly[1], poly[2], poly[3], poly[0], poly[3]]\\n\\n # Convert to np.array of shape (:, 2)\\n for i in range(0, len(poly), 2):\\n rot_array.append(np.array([poly[i], poly[i + 1]]))\\n\\n # Rotate corresponding annotations\\n rot_array = np.array([rot_array])\\n rot_array = cv2.transform(rot_array, mat_rotation).squeeze().reshape(len(poly))\\n\\n if poly_length == 4:\\n x_coords = rot_array[0::2]\\n y_coords = rot_array[1::2]\\n rot_array = np.array([\\n np.min(x_coords),\\n np.min(y_coords),\\n np.max(x_coords),\\n np.max(y_coords)\\n ])\\n\\n gt_boxes_ret.append(rot_array)\\n if len(results[key]) > 0:\\n results[key] = gt_boxes_ret\\n\\n # Rotate gt_bboxes according to gt_poly_bboxes\\n if 'gt_bboxes' in results and 'gt_poly_bboxes' in results:\\n gt_bboxes = []\\n gt_bboxes_ignore = []\\n\\n for poly in results['gt_poly_bboxes']:\\n poly = np.array(poly, dtype=np.double)\\n x_coords = poly[0::2]\\n y_coords = poly[1::2]\\n aligned_poly = [\\n np.min(x_coords),\\n np.min(y_coords),\\n np.max(x_coords),\\n np.max(y_coords)\\n ]\\n gt_bboxes.append(aligned_poly)\\n\\n for poly in results['gt_poly_bboxes_ignore']:\\n poly = np.array(poly, dtype=np.double)\\n x_coords = poly[0::2]\\n y_coords = poly[1::2]\\n aligned_poly = [\\n np.min(x_coords),\\n np.min(y_coords),\\n np.max(x_coords),\\n np.max(y_coords)\\n ]\\n gt_bboxes_ignore.append(aligned_poly)\\n\\n if len(gt_bboxes) == 0:\\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\\n else:\\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\\n\\n if len(gt_bboxes_ignore) == 0:\\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\\n else:\\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\\n\\n results['gt_bboxes'] = gt_bboxes\\n results['gt_bboxes_ignore'] = gt_bboxes_ignore\\n\\n # Rotate corresponding annotations: all boxes in cbbox_fields\\n for key in results.get('cbbox_fields', []):\\n gt_cboxes_ret = []\\n for instance in results[key]:\\n tmp_cboxes = []\\n for poly in instance:\\n rot_array = []\\n # Convert to np.array of shape (:, 2)\\n for i in range(0, len(poly), 2):\\n rot_array.append(np.array([poly[i], poly[i + 1]]))\\n rot_array = np.array([rot_array])\\n # Rotate corresponding annotations\\n rot_array = cv2.transform(rot_array, mat_rotation).squeeze().reshape(len(poly))\\n tmp_cboxes.append(rot_array)\\n gt_cboxes_ret.append(tmp_cboxes)\\n results[key] = gt_cboxes_ret\\n\\n # Rotate corresponding annotations: all masks in mask_fields\\n for key in results.get('mask_fields', []):\\n mask = results[key].masks.transpose((1, 2, 0))\\n if len(results[key].masks) == 0:\\n results[key] = results[key].resize((height_new, width_new))\\n else:\\n # Rotate mask\\n mask_rotation = cv2.warpAffine(mask, mat_rotation, (width_new, height_new),\\n borderValue=self.borderValue)\\n if mask_rotation.ndim == 2:\\n # case when only one mask, (h, w)\\n mask_rotation = mask_rotation[:, :, None] # (h, w, 1)\\n mask_rotation = mask_rotation.transpose((2, 0, 1))\\n results[key].masks = mask_rotation\\n results[key].height = height_new\\n results[key].width = width_new\",\n \"def removeplane(img, slce=0.4):\\n img[img == 0] = np.nan\\n\\n xr, yr = np.arange(slce*img.shape[0],(1-slce)*img.shape[0],dtype=int),\\\\\\n np.arange(slce*img.shape[1],(1-slce)*img.shape[1],dtype=int)\\n x, y = np.meshgrid(xr,yr)\\n\\n \\n subimg = img[xr[0]:xr[-1]+1,yr[0]:yr[-1]+1]\\n imgf = subimg[np.isfinite(subimg)].flatten()\\n\\n vecs = np.ones((5,imgf.size))\\n vecs[0,:] = x[np.isfinite(subimg)].flatten()\\n vecs[1,:] = y[np.isfinite(subimg)].flatten()\\n vecs[2,:] = x[np.isfinite(subimg)].flatten()**2\\n vecs[3,:] = y[np.isfinite(subimg)].flatten()**2\\n\\n C = vecs.dot(vecs.T)\\n xv = la.inv(C).dot(vecs.dot(imgf[:,np.newaxis]))\\n x, y = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))\\n\\n img -= (xv[0]*x + xv[1]*y + \\\\\\n xv[2]*x**2 + xv[3]*y**2 + \\\\\\n xv[4])\\n return img\",\n \"def rot_center(self, image, angle):\\n orig_rect = image.get_rect()\\n rot_image = pygame.transform.rotate(image, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def rot_center(self, image, angle):\\n orig_rect = image.get_rect()\\n rot_image = pygame.transform.rotate(image, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def rotate_image(image, theta, row_axis, col_axis, channel_axis, fill_mode='constant'):\\n image = apply_affine_transform(image,\\n theta=theta,\\n row_axis=row_axis,\\n col_axis=col_axis,\\n channel_axis=channel_axis,\\n fill_mode=fill_mode,\\n cval=0.0,\\n order=1)\\n return image\",\n \"def perspective_transform(img, chars):\\n # img_expanded, (x1,y1,x2,y2) = simple_expand(img)\\n x1,y1,x2,y2 = get_xy_minmax(chars)\\n trans = get_random_transform(img, (x1,y1,x2,y2))\\n warped_img = cv2.warpPerspective(img, trans, (img.shape[1], img.shape[0]))\\n for char in chars:\\n for corner in char['corners']:\\n # print(corner)\\n a = [corner[0], corner[1], 1]\\n transformed_corner = np.matmul(trans, np.asarray(a).reshape(3,1))\\n corner[0] = transformed_corner[0] / transformed_corner[2]\\n corner[1] = transformed_corner[1] / transformed_corner[2]\\n # print(corner)\\n return warped_img, chars\",\n \"def fill_blind_pores(im):\\n holes = find_disconnected_voxels(im)\\n im[holes] = False\\n return im\",\n \"def crop_image(image: np.ndarray) -> np.ndarray:\\n # convert image to grayscale and apply blur to reduce noise\\n image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\\n blurred = cv2.GaussianBlur(image_gray, (3, 3), 0)\\n\\n # global threshold using Otsu\\n # Note: Although unpacking like this results in one of the variables to be unused and makes\\n # PyTA heavily depressed, this is standard OpenCV notation.\\n # For reference, you may check docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html\\n\\n ret1, thresh1 = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\\n\\n # invert image color and find contours\\n ret2, thresh2 = cv2.threshold(thresh1, 150, 255, cv2.THRESH_BINARY_INV)\\n contours, hierarchy = cv2.findContours(thresh2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\\n\\n # create list of tuples with the contour itself and its arc length\\n # then sort by arc length and take the two longest\\n cont_len = [(cont, cv2.arcLength(cont, True)) for cont in contours]\\n cont_len.sort(key=lambda x: -x[1])\\n longest_2 = cont_len[0:2]\\n rects = [cv2.boundingRect(tup[0]) for tup in longest_2]\\n\\n # take the smallest coordinates for the top left corner of rect\\n # and largest for the bottom right corner\\n min_x0, min_y0, max_x0, max_y0 = rects[0][0], rects[0][1], \\\\\\n rects[0][0] + rects[0][2], \\\\\\n rects[0][1] + rects[0][3]\\n min_x1, min_y1, max_x1, max_y1 = rects[1][0], rects[1][1], \\\\\\n rects[1][0] + rects[1][2], \\\\\\n rects[1][1] + rects[1][3]\\n min_x = min(min_x0, min_x1) + 1\\n min_y = min(min_y0, min_y1) + 1\\n max_x = max(max_x0, max_x1) - 1\\n max_y = max(max_y0, max_y1) - 1\\n\\n cropped_img = image[min_y: max_y, min_x: max_x]\\n return cropped_img\",\n \"def process(self):\\n self.output_image = cv.cornerHarris(self.input_image, 2, 3, 0.04)\\n self.output_image = cv.dilate(self.output_image, kernel=None, iterations=2)\\n return self.output_image\",\n \"def crop_image(img):\\n gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\\n _, threshed = cv.threshold(gray, 240, 255, cv.THRESH_BINARY_INV)\\n\\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (11, 11))\\n morphed = cv.morphologyEx(threshed, cv.MORPH_CLOSE, kernel)\\n\\n cnts = cv.findContours(morphed, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[-2]\\n cnt = sorted(cnts, key=cv.contourArea)[-1]\\n\\n x, y, w, h = cv.boundingRect(cnt)\\n dst = img[y:y+h, x:x+w]\\n cv.imwrite(\\\"001.png\\\", dst)\\n return dst\",\n \"def cv2_skeletonize(img):\\n img = img.copy() # don't clobber original\\n skel = img.copy()\\n\\n skel[:,:] = 0\\n kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))\\n while True:\\n eroded = cv2.morphologyEx(img, cv2.MORPH_ERODE, kernel)\\n temp = cv2.morphologyEx(eroded, cv2.MORPH_DILATE, kernel)\\n temp = cv2.subtract(img, temp)\\n skel = cv2.bitwise_or(skel, temp)\\n img[:,:] = eroded[:,:]\\n if cv2.countNonZero(img) == 0:\\n break\\n return skel\",\n \"def rotate(image, rect, angle):\\n # Rotate the original image without modifying it.\\n new_image = pg.transform.rotate(image, angle)\\n # Get a new rect with the center of the old rect.\\n rect = new_image.get_rect(center=rect.center)\\n return new_image, rect\",\n \"def rotate_image(image):\\n return tf.image.rot90(image)\",\n \"def rotate(image, rect, angle):\\n new_image = pygame.transform.rotate(image, angle) # Rotate the original image without modifying it.\\n rect = new_image.get_rect(center=rect.center) # Get a new rect with the center of the old rect.\\n return new_image, rect\",\n \"def test_rotating_phantom(self):\\n cheese = TomoCheese.from_demo_images()\\n cheese.analyze()\\n assert math.isclose(cheese.catphan_roll, -0.25, abs_tol=0.05)\\n for img in cheese.dicom_stack:\\n img.array = rotate(img.array, angle=3, mode=\\\"edge\\\")\\n cheese.analyze()\\n assert math.isclose(cheese.catphan_roll, -3.25, abs_tol=0.05)\",\n \"def rotateImage(self, img, angle=90):\\n if (angle == 90) :\\n return(cv2.flip(cv2.transpose(img),flipCode=0))\\n elif (angle == -90) :\\n return(cv2.flip(cv2.transpose(img),flipCode=1))\\n else :\\n center = (img.shape[1]/2.0,img.shape[0]/2.0)\\n rotate = cv2.getRotationMatrix2D(center, angle, 1.0)\\n return cv2.warpAffine(img, rotate, (img.shape[1], img.shape[0]))\",\n \"def cw_rotate(self):\\n self.grid = [list(x) for x in zip(*self.grid[::-1])]\\n self.find_edges()\",\n \"def rotate90(self):\",\n \"def edge_virtualv2(src, point_list=None, degree=3):\\n src = cv2.cvtColor(np.asarray(src), cv2.COLOR_RGBA2BGRA)\\n xmax = np.amax(point_list, axis=0)[0]\\n ymax = np.amax(point_list, axis=0)[1]\\n xmin = np.amin(point_list, axis=0)[0]\\n ymin = np.amin(point_list, axis=0)[1]\\n widthTrans = xmax - xmin\\n heightTrans = ymax - ymin\\n side = min(widthTrans, heightTrans)\\n\\n # create initial mask covering the actual image.\\n alpha = src[:, :, 3]\\n alpha[0, :], alpha[-1, :], alpha[:, 0], alpha[:, -1] = 0, 0, 0, 0\\n idx = np.where(src[:, :, 3] > 15)\\n mask = np.zeros(src.shape[:2], dtype=np.uint8)\\n mask[idx] = 255\\n mask[0, :], mask[-1, :], mask[:, 0], mask[:, -1] = 0, 0, 0, 0\\n # iteratively erode the mask and assigned the removed region to division factor in descend.\\n if side < 100:\\n factors = np.linspace(1.5, 1, 5)\\n else:\\n factors = np.linspace(2, 1, 10)\\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\\n for factor in factors:\\n dil = cv2.erode(mask, kernel)\\n diff1 = mask - dil\\n mask = dil\\n removed_loc = np.where(diff1 > 0)\\n alpha[removed_loc] = alpha[removed_loc] // factor\\n\\n src[:, :, 3] = alpha\\n img = Image.fromarray(cv2.cvtColor(src, cv2.COLOR_BGRA2RGBA))\\n return img, point_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7247929","0.7087627","0.6532112","0.63538","0.62858284","0.6238983","0.6210797","0.62102747","0.61778975","0.61557895","0.6126098","0.61174065","0.60648906","0.6063642","0.60582924","0.6030168","0.60160047","0.60000926","0.5985503","0.59584457","0.593563","0.5932475","0.5880149","0.5873213","0.5815486","0.58019567","0.5786685","0.5781794","0.5771551","0.57596314","0.5753069","0.57522136","0.5735395","0.5731859","0.57230204","0.5714635","0.56958646","0.56891865","0.56874543","0.56512517","0.56462514","0.5644062","0.5640546","0.5622587","0.5607172","0.56061","0.56031966","0.5601658","0.56008613","0.55994725","0.5592522","0.55869585","0.5579372","0.5570479","0.55660415","0.5563963","0.55628294","0.5555986","0.55548596","0.5541469","0.5526525","0.55162305","0.55162305","0.55162305","0.550318","0.5492985","0.54913807","0.548871","0.5475014","0.54746187","0.5472881","0.5467335","0.54638034","0.54636693","0.54586583","0.5451637","0.5445961","0.5444481","0.5443738","0.5435171","0.54208136","0.54184633","0.54154325","0.53964674","0.53964674","0.5395892","0.5391748","0.5389184","0.5383701","0.5380151","0.53627956","0.53512126","0.53496146","0.53488135","0.5346378","0.53430665","0.53395575","0.53381705","0.5326488","0.53256285"],"string":"[\n \"0.7247929\",\n \"0.7087627\",\n \"0.6532112\",\n \"0.63538\",\n \"0.62858284\",\n \"0.6238983\",\n \"0.6210797\",\n \"0.62102747\",\n \"0.61778975\",\n \"0.61557895\",\n \"0.6126098\",\n \"0.61174065\",\n \"0.60648906\",\n \"0.6063642\",\n \"0.60582924\",\n \"0.6030168\",\n \"0.60160047\",\n \"0.60000926\",\n \"0.5985503\",\n \"0.59584457\",\n \"0.593563\",\n \"0.5932475\",\n \"0.5880149\",\n \"0.5873213\",\n \"0.5815486\",\n \"0.58019567\",\n \"0.5786685\",\n \"0.5781794\",\n \"0.5771551\",\n \"0.57596314\",\n \"0.5753069\",\n \"0.57522136\",\n \"0.5735395\",\n \"0.5731859\",\n \"0.57230204\",\n \"0.5714635\",\n \"0.56958646\",\n \"0.56891865\",\n \"0.56874543\",\n \"0.56512517\",\n \"0.56462514\",\n \"0.5644062\",\n \"0.5640546\",\n \"0.5622587\",\n \"0.5607172\",\n \"0.56061\",\n \"0.56031966\",\n \"0.5601658\",\n \"0.56008613\",\n \"0.55994725\",\n \"0.5592522\",\n \"0.55869585\",\n \"0.5579372\",\n \"0.5570479\",\n \"0.55660415\",\n \"0.5563963\",\n \"0.55628294\",\n \"0.5555986\",\n \"0.55548596\",\n \"0.5541469\",\n \"0.5526525\",\n \"0.55162305\",\n \"0.55162305\",\n \"0.55162305\",\n \"0.550318\",\n \"0.5492985\",\n \"0.54913807\",\n \"0.548871\",\n \"0.5475014\",\n \"0.54746187\",\n \"0.5472881\",\n \"0.5467335\",\n \"0.54638034\",\n \"0.54636693\",\n \"0.54586583\",\n \"0.5451637\",\n \"0.5445961\",\n \"0.5444481\",\n \"0.5443738\",\n \"0.5435171\",\n \"0.54208136\",\n \"0.54184633\",\n \"0.54154325\",\n \"0.53964674\",\n \"0.53964674\",\n \"0.5395892\",\n \"0.5391748\",\n \"0.5389184\",\n \"0.5383701\",\n \"0.5380151\",\n \"0.53627956\",\n \"0.53512126\",\n \"0.53496146\",\n \"0.53488135\",\n \"0.5346378\",\n \"0.53430665\",\n \"0.53395575\",\n \"0.53381705\",\n \"0.5326488\",\n \"0.53256285\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6804736"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":85698,"cells":{"query":{"kind":"string","value":"Save straightened version of geotiff at `src_path` to `tgt_path`."},"document":{"kind":"string","value":"def create_straightened(src_path, tgt_path):\n img = skio.imread(src_path)\n straight_img, xform, angle = straighten_image(img)\n save_geotiff(tgt_path, (straight_img * 255).astype('uint8'), xform)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def saveAsGeotiff(self, geotiff_fname, overwrite=True, verbose=False):\n if not overwrite and os.path.exists(geotiff_fname):\n raise ValueError('output geotiff already exists:\\n {}'.format(\n geotiff_fname))\n\n if overwrite and os.path.exists(geotiff_fname):\n os.remove(geotiff_fname)\n\n gggdal = geogrid_as_gdalInMem(self)\n\n dst = gdal.GetDriverByName('GTiff').CreateCopy(\n geotiff_fname, gggdal)\n dst = None","def save_geotiff(path, raster, xform):\n # Import rasterio here, so that we don't need it in the cloud.\n import rasterio\n import affine\n n_lats, n_lons, depth = raster.shape\n\n assert raster.max() <= 255\n assert raster.min() >= 0\n \n profile = {\n 'crs': 'EPSG:4326',\n 'nodata': 0,\n 'dtype': rasterio.uint8,\n 'height': n_lats,\n 'width': n_lons,\n 'count': depth,\n 'driver':\"GTiff\",\n 'transform': xform}\n\n with rasterio.open(path, 'w', **profile) as dst:\n for i in range(depth):\n dst.write(raster[:, :, i], indexes=i+1)","def reprojectAndSaveNewRaster(inFilepath,outFilepath,to_EPSG):\r\n from osgeo import gdal\r\n input_raster = gdal.Open(inFilepath)\r\n EPSG_string = \"EPSG:\"+str(to_EPSG)\r\n ras = gdal.Warp(outFilepath,input_raster,dstSRS=EPSG_string)\r\n del ras","def reprojectAndSaveNewShapefile(inFilepath,outFilepath,to_EPSG):\r\n import geopandas as gpd\r\n from fiona.crs import from_epsg\r\n\r\n inFile = gpd.read_file(inFilepath)\r\n inFile_proj = inFile.copy()\r\n inFile_proj['geometry'] = inFile_proj['geometry'].to_crs(epsg=to_EPSG)\r\n inFile_proj.crs = from_epsg(to_EPSG)\r\n inFile_proj.to_file(outFilepath)","def UpdateGT(out_file, data, src_file, epsg = 4326, drv = 'GTiff', datatype = gdal.GDT_Float32, NoData = -999):\n \n #assign which driver to use for the file format - default is .tif\n driver = gdal.GetDriverByName(drv)\n \n #source raster from which to use the geospatial metadata\n src_gt = gdal.Open(src_file)\n \n #data shape - detects whether to produce multiple bands or not\n if len(data.shape) == 2:\n [cols, rows] = data.shape\n n = 1\n else:\n [n, cols, rows] = data.shape\n\n #create the destination file\n dst_gt = driver.Create(out_file, rows, cols, n, datatype)\n \n #get the geotransform from the source file\n gt = src_gt.GetGeoTransform()\n \n #coordinate system in which to create the raster, in wkt form\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(epsg)\n dest_wkt = srs.ExportToWkt()\n \n #set the projection in the desired reference system\n dst_gt.SetGeoTransform(gt)\n dst_gt.SetProjection(dest_wkt)\n \n #set the data\n if n==1:\n dst_gt.GetRasterBand(1).WriteArray(data)\n dst_gt.GetRasterBand(1).SetNoDataValue(NoData)\n #close and write to file\n dst_gt.FlushCache()\n #used if there are multiple bands\n else:\n for i in range(n):\n dst_gt.GetRasterBand(i+1).WriteArray(data[i])\n dst_gt.GetRasterBand(i+1).SetNoDataValue(NoData)\n dst_gt.FlushCache()","def copy(self, src_path: str, tgt_path: str) -> None:","def _export_geotiff(self, filepath: str, z_positive_up: bool = True):\n\n nodatavalue = 1000000.0\n data, geo_transform, bandnames = self._gdal_preprocessing(nodatavalue=nodatavalue, z_positive_up=z_positive_up)\n gdal_raster_create(filepath, data, geo_transform, self.crs, nodatavalue=nodatavalue, bandnames=bandnames,\n driver='GTiff')","def save_geotiff(df, data_col, crs, x_col='x', y_col='y', time_col=None, nfiles='many', export_path='geotiff.tif', grid_res=None):\n\n ### create the xy coordinates\n if time_col is None:\n xy1 = df[[x_col, y_col]]\n else:\n time = df[time_col].sort_values().unique()\n xy1 = df.loc[df[time_col] == time[0], [x_col, y_col]]\n if any(xy1.duplicated()):\n raise ValueError('x and y coordinates are not unique!')\n\n ### Determine grid res\n if grid_res is None:\n res_df1 = (xy1.loc[0] - xy1).abs()\n res_df2 = res_df1.replace(0, nan).min()\n x_res = res_df2[x_col]\n y_res = res_df2[y_col]\n elif isinstance(grid_res, int):\n x_res = y_res = grid_res\n else:\n raise ValueError('grid_res must either be None or an integer.')\n\n ### Make the affline transformation for Rasterio\n trans2 = transform.from_origin(xy1[x_col].min() - x_res/2, xy1[y_col].max() + y_res/2, x_res, y_res)\n\n ### Make the rasters\n if time_col is None:\n z = df.set_index([y_col, x_col])[data_col].unstack().values[::-1]\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs, pass_str=True), transform=trans2)\n new_dataset.write(z, 1)\n new_dataset.close()\n else:\n if nfiles == 'one':\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=len(time), dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\n for i in range(1, len(time)+1):\n z = df.loc[df[time_col] == time[i - 1]].set_index([y_col, x_col])[data_col].unstack().values[::-1]\n new_dataset.write(z, i)\n new_dataset.close()\n elif nfiles == 'many':\n file1 = path.splitext(export_path)[0]\n for i in time:\n str_date = to_datetime(i).strftime('%Y-%m-%d_%H')\n file2 = file1 + '_' + str_date + '.tif'\n z = df.loc[df[time_col] == i].set_index([y_col, x_col])[data_col].unstack().values[::-1]\n new_dataset = ras_open(file2, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\n new_dataset.write(z, 1)\n new_dataset.close()","def create_geotiff(pixels, gps_bounds, out_path, nodata=-99, asfloat=False, extractor_info=None, system_md=None, extra_metadata=None, compress=False):\n dimensions = numpy.shape(pixels)\n if len(dimensions) == 2:\n nrows, ncols = dimensions\n channels = 1\n else:\n nrows, ncols, channels = dimensions\n\n geotransform = (\n gps_bounds[2], # upper-left x\n (gps_bounds[3] - gps_bounds[2])/float(ncols), # W-E pixel resolution\n 0, # rotation (0 = North is up)\n gps_bounds[1], # upper-left y\n 0, # rotation (0 = North is up)\n -((gps_bounds[1] - gps_bounds[0])/float(nrows)) # N-S pixel resolution\n )\n\n # Create output GeoTIFF and set coordinates & projection\n if asfloat:\n dtype = gdal.GDT_Float32\n else:\n dtype = gdal.GDT_Byte\n\n if compress:\n output_raster = gdal.GetDriverByName('GTiff') \\\n .Create(out_path, ncols, nrows, channels, dtype, ['COMPRESS=LZW'])\n else:\n output_raster = gdal.GetDriverByName('GTiff') \\\n .Create(out_path, ncols, nrows, channels, dtype)\n\n output_raster.SetGeoTransform(geotransform)\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326) # google mercator\n output_raster.SetProjection( srs.ExportToWkt() )\n\n if not extra_metadata:\n extra_metadata = prepare_metadata_for_geotiff(extractor_info, system_md)\n\n output_raster.SetMetadata(extra_metadata)\n\n if channels == 3:\n # typically 3 channels = RGB channels\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\n output_raster.GetRasterBand(1).WriteArray(pixels[:,:,0].astype('uint8'))\n output_raster.GetRasterBand(1).SetColorInterpretation(gdal.GCI_RedBand)\n output_raster.GetRasterBand(1).FlushCache()\n if nodata:\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\n\n output_raster.GetRasterBand(2).WriteArray(pixels[:,:,1].astype('uint8'))\n output_raster.GetRasterBand(2).SetColorInterpretation(gdal.GCI_GreenBand)\n output_raster.GetRasterBand(2).FlushCache()\n if nodata:\n output_raster.GetRasterBand(2).SetNoDataValue(nodata)\n\n output_raster.GetRasterBand(3).WriteArray(pixels[:,:,2].astype('uint8'))\n output_raster.GetRasterBand(3).SetColorInterpretation(gdal.GCI_BlueBand)\n output_raster.GetRasterBand(3).FlushCache()\n if nodata:\n output_raster.GetRasterBand(3).SetNoDataValue(nodata)\n\n elif channels > 1:\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\n for chan in range(channels):\n band = chan + 1\n output_raster.GetRasterBand(band).WriteArray(pixels[:,:,chan].astype('uint8'))\n output_raster.GetRasterBand(band).FlushCache()\n if nodata:\n output_raster.GetRasterBand(band).SetNoDataValue(nodata)\n else:\n # single channel image, e.g. temperature\n output_raster.GetRasterBand(1).WriteArray(pixels)\n output_raster.GetRasterBand(1).FlushCache()\n if nodata:\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\n\n output_raster = None","def reproject(src_path, out_path, target_crs='EPSG:4326'):\n\n # load satdata\n satdata = load(src_path)\n\n # calculate a transform and new dimensions using our dataset's current CRS and dimensions\n transform, width, height = calculate_default_transform(satdata.crs,\n target_crs,\n satdata.width,\n satdata.height,\n *satdata.bounds)\n\n # Using a copy of the metadata from the clipped raster dataset and the transform we defined above,\n # we can write a new geoTIFF containing the reprojected and clipped raster data:\n metadata = satdata.meta.copy()\n\n # Change the CRS, transform, and dimensions in metadata to match our desired output dataset\n metadata.update({'crs':target_crs,\n 'transform':transform,\n 'width':width,\n 'height':height})\n\n # apply the transform & metadata to perform the reprojection\n with rasterio.open(out_path, 'w', **metadata) as reprojected:\n for band in range(1, satdata.count + 1):\n rasterio_reproject(\n source=rasterio.band(satdata, band),\n destination=rasterio.band(reprojected, band),\n src_transform=satdata.transform,\n src_crs=satdata.crs,\n dst_transform=transform,\n dst_crs=target_crs\n )","def _gtTSmap(self):\n if os.path.isfile(self.outtsmap):\n # Already exists\n return\n\n if self.csys == 'GAL':\n center_icrs = SkyCoord(ra=self.ra*u.degree, dec=self.dec*u.degree, frame='icrs')\n self.ra = center_icrs.galactic.l.deg\n self.dec = center_icrs.galactic.b.deg\n\n model = os.path.join(self.workpath, 'TSmapModel.xml') \n rfil = open(self.outmodel, 'r')\n wfil = open(model, 'w')\n isSrc = False\n isDif = False\n for line in rfil:\n if (isSrc) and ('<source name' in line):\n # Arrived to a new source, restart copying\n isSrc = False\n if (isDif) and ('<source name' in line) and ('PointSource' in line):\n isDif = False\n if 'TARGET' in line:\n isSrc = True\n if ('<source name=\"gll_iem_v06\"' in line) or ('<source name=\"iso_source_v06\"' in line): \n isDif = True\n \n if isSrc:\n # Do not copy the Target model to make it appear in the TS map\n pass\n else:\n if isDif:\n # Leave Diffuse model normalizations free\n wfil.write(line)\n else:\n # Make sur the gtlike output source model has all source parameters fixed\n wfil.write(line.replace('free=\"1\"', 'free=\"0\"'))\n rfil.close()\n wfil.close()\n\n # Launch the gttsmap tool \n if self.mode == 'binned':\n os.popen(\"gttsmap evfile={} scfile={} bexpmap={} expcube={} cmap={} srcmdl={}\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=BINNED ftol=1e-2\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\".format(self.outmktime,\n self.ft2, self.outbinexp, self.outltcube, self.outbincub, model, self.outtsmap, self.evtype,\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\n elif self.mode == 'unbinned':\n os.popen(\"gttsmap evfile={} scfile={} expmap={} expcube={} srcmdl={}\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=UNBINNED ftol=1e-2\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\".format(self.outmktime,\n self.ft2, self.outexpmap, self.outltcube, model, self.outtsmap, self.evtype,\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\n else:\n return\n\n if self.csys == 'GAL':\n self.ra = center_icrs.ra.deg\n self.dec = center_icrs.dec.deg\n return","def test_convert_to_geotiff(self):\n fnames = modis.convert_to_geotiff(self.fnames[0], outdir=os.path.dirname(__file__))\n for f in fnames:\n ext = os.path.splitext(f)[1]\n suffix = os.path.splitext(f)[0].split('_')[1]\n self.assertTrue(os.path.exists(f))\n if ext != '.ovr':\n self.assertTrue(suffix in products['MCD43A4.006']['bandnames'])","def tiffwrite(filename, im):\n tf.imwrite(filename, im)","def store_image(self):\n cv2.imwrite(self.__diff_filename(), self.__diff_image())","def save_converted_paths(\n src_trace_tuples: Sequence[TraceTuple],\n dest_trace_tuples: Sequence[TraceTuple],\n driver: str,\n destination: Path,\n):\n for src_trace_tuple, dest_trace_tuple in zip(src_trace_tuples, dest_trace_tuples):\n for original_path, convert_path in zip(\n (src_trace_tuple.traces_path, src_trace_tuple.area_path),\n (dest_trace_tuple.traces_path, dest_trace_tuple.area_path),\n ):\n convert_filetype(original_path, destination / convert_path, driver=driver)","def gtiff(location):\n\n for src_asc in os.listdir(location):\n if \".asc\" in src_asc:\n dest_gtif = src_asc[:-4] + '.gtiff'\n command = \"gdal_translate -of GTiff -ot Float32 \" + location +'\\\\' \\\n + src_asc + ' ' + location +'\\\\' + dest_gtif\n os.system(command)","def save_array_as_geotif(array, source_tif_path, out_path):\n if len(array.shape) > 2:\n height, width, depth = array.shape\n else:\n height, width = array.shape\n depth = 1\n source_tif = gdal.Open(source_tif_path)\n driver = gdal.GetDriverByName(\"GTiff\")\n dataset = driver.Create(out_path, width, height, depth, gdal.GDT_Float32)\n if depth != 1:\n for i in range(depth):\n dataset.GetRasterBand(i + 1).WriteArray(array[:, :, i])\n else:\n dataset.GetRasterBand(1).WriteArray(array)\n geotrans = source_tif.GetGeoTransform()\n proj = source_tif.GetProjection()\n dataset.SetGeoTransform(geotrans)\n dataset.SetProjection(proj)\n dataset.FlushCache()\n dataset = None","def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\n args = [\n \"gdal_calc.py\",\n '-A', infile,\n '--outfile={}'.format(tmpfile_1),\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\n '--type=Byte', '--NoDataValue=0',\n '--co=SPARSE_OK=YES',\n '--co=NBITS=1',\n '--quiet'\n # Could enable compression\n # --co=\"COMPRESS=LZW\"\n ]\n subprocess.run(args)\n\n subprocess.run([\n \"gdal_polygonize.py\",\n tmpfile_1,\n '-q',\n '-f', 'ESRI Shapefile',\n tmpfile_2\n ])\n\n subprocess.run([\n \"ogr2ogr\",\n '-a_srs', 'EPSG:4326',\n outfile,\n tmpfile_2\n ])\n\n subprocess.run([\"rm\", tmpfile_1])\n subprocess.run([\"rm\", tmpfile_2])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'shx')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'dbf')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'prj')])","def geotiff_to_netcdf(path, epsg, download=False, output_path='', name='', non_negative=True, savefig=False,\n fig_path=''):\n\n ds = gdal.Open(path)\n\n # upper left, resolution and skew of x and y\n ulx, xres, xskew, uly, yskew, yres = ds.GetGeoTransform()\n\n # lower right x and y\n lrx = ulx + (ds.RasterXSize * xres)\n lry = uly + (ds.RasterYSize * yres)\n\n # setup the source projection - you can also import from epsg, proj4...\n source = osr.SpatialReference()\n source.ImportFromWkt(ds.GetProjection())\n\n # the target projection\n target = osr.SpatialReference()\n target.ImportFromEPSG(4326)\n\n # create the transform - this can be used repeatedly\n transform = osr.CoordinateTransformation(source, target)\n\n # transform the point. You can also create an ogr geometry and use the more generic `point.Transform()`\n uly, ulx, _ = transform.TransformPoint(ulx, uly)\n lry, lrx, _ = transform.TransformPoint(lrx, lry)\n\n lons = np.linspace(ulx, lrx, ds.RasterXSize)\n lats = np.linspace(lry, uly, ds.RasterYSize)\n\n data = ds.ReadAsArray()\n\n if non_negative: data[data < 0] = np.nan\n\n ds = xr.DataArray(np.flipud(data), coords={'lat': lats, 'lon': lons, }, dims=['lat', 'lon'])\n\n ds.name = name\n\n if download and len(output_path) > 0:\n ds.to_netcdf(output_path)\n\n if savefig and len(fig_path) > 0:\n fig, ax = plt.subplots(figsize=(15, 10))\n ds.plot(ax=ax)\n plt.savefig(fig_path)\n plt.close()\n\n return ds","def generate(path, cam_orig, cam_new, fnames_left, fnames_right,\n fnames_target_left, fnames_target_right):\n with tf.io.TFRecordWriter(path) as tfrecord_writer:\n with tf.Graph().as_default():\n im0 = tf.compat.v1.placeholder(dtype=tf.uint8)\n im1 = tf.compat.v1.placeholder(dtype=tf.uint8)\n encoded0 = tf.image.encode_png(im0)\n encoded1 = tf.image.encode_png(im1)\n\n with tf.compat.v1.Session() as sess:\n for fleft, fright, ftleft, ftright in zip(fnames_left, fnames_right,\n fnames_target_left,\n fnames_target_right):\n assert (os.path.basename(\n os.path.splitext(fleft)[0]) == os.path.basename(\n os.path.splitext(ftleft)[0]))\n assert (os.path.basename(\n os.path.splitext(fright)[0]) == os.path.basename(\n os.path.splitext(ftright)[0]))\n print(fleft)\n image_left = utils.read_image(fleft)\n image_right = utils.read_image(fright)\n targs_left = utils.read_target_pb(ftleft)\n targs_right = utils.read_target_pb(ftright)\n image_left = utils.resize_image(image_left, cam_new, cam_orig,\n targs_left)\n image_right = utils.resize_image(image_right, cam_new, cam_orig,\n targs_right)\n st0, st1 = sess.run([encoded0, encoded1],\n feed_dict={\n im0: image_left,\n im1: image_right\n })\n feats = {'img_L': bytes_feature(st0), 'img_R': bytes_feature(st1)}\n feats.update(make_features(targs_left, 'L'))\n feats.update(make_features(targs_right, 'R'))\n example = tf.train.Example(features=tf.train.Features(feature=feats))\n tfrecord_writer.write(example.SerializeToString())","def copy_vrt(in_fname, out_fname=None, bbox=None, verbose=True):\n from gdal import Translate\n\n if out_fname is None:\n out_fname = in_fname + \".vrt\"\n\n # Using Translate... but would use Warp if every reprojecting\n if bbox:\n left, bottom, right, top = bbox\n projwin = (left, top, right, bottom) # unclear why Translate does UL LR\n else:\n projwin = None\n if verbose:\n logger.info(f\"Creating {out_fname}, subset bbox: {bbox}\")\n Translate(out_fname, in_fname, projWin=projwin)","def test_make_mask_w_output_file(self):\n output_mask = footprint_mask(\n os.path.join(data_dir, 'sample.csv'),\n geom_col=\"PolygonWKT_Pix\",\n reference_im=os.path.join(data_dir, \"sample_geotiff.tif\"),\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(os.path.join(data_dir,\n 'sample_fp_mask.tif'))\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(output_mask, truth_mask)\n assert np.array_equal(saved_output_mask, truth_mask)\n # clean up\n os.remove(os.path.join(data_dir, 'test_out.tif'))","def test_make_mask_w_file_and_transform(self):\n output_mask = footprint_mask(\n os.path.join(data_dir, 'geotiff_labels.geojson'),\n reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\n do_transform=True,\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(\n os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif')\n )\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(output_mask, truth_mask)\n assert np.array_equal(saved_output_mask, truth_mask)\n # clean up\n os.remove(os.path.join(data_dir, 'test_out.tif'))","def subset_raster_to_extent_of_other(src_filename, match_filename, dst_filename):\n\n\t# Source\n\tsrc = gdal.Open(src_filename, gdalconst.GA_ReadOnly)\n\tsrc_proj = src.GetProjection()\n\tsrc_geotrans = src.GetGeoTransform()\n\n\t# We want a section of source that matches this:\n\tmatch_ds = gdal.Open(match_filename, gdalconst.GA_ReadOnly)\n\tmatch_proj = match_ds.GetProjection()\n\tmatch_geotrans = match_ds.GetGeoTransform()\n\twide = match_ds.RasterXSize\n\thigh = match_ds.RasterYSize\n\n\t# Output / destination\n\tdst = gdal.GetDriverByName('GTiff').Create(dst_filename, wide, high, 1, gdalconst.GDT_Float32)\n\tdst.SetGeoTransform( match_geotrans )\n\tdst.SetProjection( match_proj)\n\n\t# Do the work\n\tgdal.ReprojectImage(src, dst, src_proj, match_proj, gdalconst.GRA_Bilinear)\n\n\tdel dst # Flush","def savecopy(self, filename, lineendings=\"default\", encoding=\"latin-1\"):\n super(IDF, self).save(filename, lineendings, encoding)\n return Path(filename)","def _write_image(self):\r\n # Create an output raster with the correct number of rows and columns.\r\n gtiff_driver = gdal.GetDriverByName('GTiff')\r\n out_ds = gtiff_driver.Create(os.path.join(self.out_folder, self.out_file_name), self.column, self.row, 1)\r\n out_ds.SetProjection(self.in_ds.GetProjection())\r\n\r\n # Convert the offsets to real-world coordinates for the georeferencing info.\r\n # We can't use the coordinates above because they don't correspond to the pixel edges.\r\n subset_ulx, subset_uly = gdal.ApplyGeoTransform(self.in_gt, self.off_ulx, self.off_uly)\r\n out_gt = list(self.in_gt)\r\n out_gt[0] = subset_ulx\r\n out_gt[3] = subset_uly\r\n out_ds.SetGeoTransform(out_gt)\r\n\r\n data = self.read_image()\r\n out_band = out_ds.GetRasterBand(1)\r\n out_band.WriteArray(data)\r\n\r\n del out_ds","def run_merge(*src, argv=None):\n tmpdir = tempfile.mkdtemp()\n inputs = []\n for i, drv in enumerate(src):\n if type(drv) != str:\n tmppath = os.path.join(tmpdir, \"input_%s.tif\" % i)\n drv.write(tmppath)\n inputs.append(tmppath)\n else:\n inputs.append(src)\n\n if \"-o\" in argv:\n outpath = argv[argv.index(\"-o\") + 1]\n else:\n outpath = os.path.join(tempfile.gettempdir(), \"%s.tif\" % next(tempfile._get_candidate_names()))\n logger.debug(\"Writing to file %s\" % outpath)\n argv += [\"-o\", outpath]\n argv = gdal.GeneralCmdLineProcessor(argv)\n options = argv + inputs\n assert run_external_app(\"gdal_merge.py\", options) == 0, \"Error running gdal_merge\"\n remove_directory(tmpdir)\n return gdal.Open(outpath)","def ascii_to_tiff(infile, outfile, refIm):","def make_diff(file_before, file_after, file_output_name):\n if os.path.exists(file_output_name):\n shutil.rmtree(file_output_name)\n os.mkdir(file_output_name)\n psd_diff = diff(file_before, file_after)\n diff_content = {}\n for attr in [\"header\", \"layer\"]:\n diff_content[attr] = getattr(psd_diff, attr)\n with open(os.path.join(file_output_name, \"diff.json\"), \"w\") as diff_file:\n json.dump(diff_content, diff_file, indent=4)\n saved_files = []\n for layer_id in psd_diff.layer.keys():\n if len(psd_diff.layer_image[layer_id]) > 1:\n output_image = os.path.join(file_output_name, layer_id)\n psd_diff.layer_image[layer_id][\"before\"].save(output_image + \".before.png\")\n psd_diff.layer_image[layer_id][\"after\"].save(output_image + \".after.png\")\n diff_image_before = Image.new(\"RGBA\", psd_diff.layer_image[layer_id][\"before\"].size)\n diff_image_before_data = diff_image_before.load()\n diff_image_after = Image.new(\"RGBA\", psd_diff.layer_image[layer_id][\"after\"].size)\n diff_image_after_data = diff_image_after.load()\n width, height = diff_image_before.size\n pixel_index = 1\n for y in xrange(height):\n for x in xrange(width):\n if str(pixel_index) in diff_content[\"layer\"][layer_id][\"pixel\"]:\n diff_image_before_data[x, y] = tuple(diff_content[\"layer\"][layer_id][\"pixel\"][str(pixel_index)][\"before\"])\n diff_image_after_data[x, y] = tuple(diff_content[\"layer\"][layer_id][\"pixel\"][str(pixel_index)][\"after\"])\n else:\n diff_image_before_data[x, y] = (0, 0, 0, 0)\n diff_image_after_data[x, y] = (0, 0, 0, 0)\n pixel_index += 1\n diff_image_before.save(output_image + \".before.diff.png\", \"PNG\")\n diff_image_after.save(output_image + \".after.diff.png\", \"PNG\")\n saved_files.append(output_image + \".before.png\")\n saved_files.append(output_image + \".before.diff.png\")\n saved_files.append(output_image + \".after.diff.png\")\n saved_files.append(output_image + \".after.png\")\n saved_files.append(file_output_name + \"/diff.json\")\n return saved_files","def copy_model_file(src_dir, dst_dir, file_name, file_ext=None):\n file_ext = 'osm' if file_ext is None else file_ext\n\n src_file = src_dir.joinpath('in.{}'.format(file_ext))\n dst_file = dst_dir.joinpath('{}.{}'.format(file_name, file_ext))\n try:\n shutil.copyfile(src_file, dst_file)\n except FileNotFoundError:\n print('''in.{} doesn't exist.'''.format(file_ext))","def save(self, fname):\n lh_data = self.data[:len(self.lh_vertno)]\n rh_data = self.data[-len(self.rh_vertno):]\n\n print 'Writing STC to disk...',\n write_stc(fname + '-lh.stc', tmin=self.tmin, tstep=self.tstep,\n vertices=self.lh_vertno, data=lh_data)\n write_stc(fname + '-rh.stc', tmin=self.tmin, tstep=self.tstep,\n vertices=self.rh_vertno, data=rh_data)\n print '[done]'","def tiff(location):\n\n for src_gtiff in os.listdir(location):\n if \".gtiff\" in src_gtiff:\n dest_tif = src_gtiff[:-6] + '.tiff'\n command = \"gdal_translate -co profile=baseline \" + src_gtiff + ' ' \\\n + dest_tif\n os.system(command)","def update_sensor_file(src, dst):\n\t\tcon = mdb.connect('localhost', 'root', 'sensepass', 'sensecambrowser')\n\t\twith con:\n\t\t\tsrc = \"'\"+\tsrc+\"'\"\n\t\t\tdst = \"'\"+\tdst+\"'\"\n\t\t\tquery = \"UPDATE fileuploader_sensorfile SET file=%s WHERE file=%s\" % (src,dst)\n\t\t\tcur = con.cursor()\n\t\t\tcur.execute(query)","def nc2gtiff(input_name, epsg_in=3413, epsg_out=3857):\n sub_name=[\"dX\",\"dY\"]\n \n for sub in sub_name:\n result = os.system(\"echo ''\")\n #netCDF to geotiff (EPSG code is not changed) \n out_name = \"./data/tiff_raw/{}_{}_tmp.tiff\".format(os.path.basename(input_name)[:-3],sub)\n #c is shell command\n #options\n #-a_srs:EPSGcode of input files\n #-of:output format\n #\n print(\"nc2gtiff\")\n c = \"gdal_translate -a_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' NETCDF:'{in_name}':{sub} \\\n -of 'Gtiff' \\\n '{out_name}'\".format(epsg=epsg_in,sub=sub, \\\n in_name=input_name,out_name=out_name) \n result = os.system(c) #run shell command\n #print(c) #if you want to see the filled c, remove '#' in the head of this line\n if result!=0: # if it raises error, return filename \n print(input_name, result, \"translate\")\n \n result = os.system(\"echo ''\")\n\n #geotiff EPSG_in to EPCG_out\n print(\"geotiff2geotiff\")\n target_name=\"./data/tiff_target/{}_{}.tiff\".format(os.path.basename(input_name)[:-3],sub)\n c = \"gdalwarp -overwrite -s_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' {out_name} -r cubic\\\n {target_name} -t_srs EPSG:{epsg_out} -of \\\n 'GTIFF'\".format(out_name=out_name, target_name=target_name, \n epsg_in=epsg_in, epsg_out=epsg_out)\n result = os.system(c) # run shell command\n if result!=0:\n print(input_name, result, \"warp\")","def test_RawImage_write_out():\n i.write_out()\n # now compare the output with reference\n print i.outpath\n print t.processed_path\n assert_image_equal(i.outpath, t.processed_path)","def correct(img_src, shift, destfile, crop=None):\n (y0, y1, x0, x1) = crop\n (shift_y, shift_x) = shift\n tf_shift = transform.SimilarityTransform(translation=[shift_x, shift_y])\n img = transform.warp(img_src, tf_shift)\n res = img[-y0:-y1, -x0:-x1, :]\n io.imsave(destfile, res)\n return destfile","def outputGeoTiff(data, filename, geo_t, proj, output_dir = os.getcwd(), dtype = 6, nodata = None):\n \n from osgeo import osr, gdal\n \n # Get full output path\n output_path = '%s/%s.tif'%(os.path.abspath(os.path.expanduser(output_dir)), filename.rstrip('.tif'))\n \n # Save image with georeference info\n driver = gdal.GetDriverByName('GTiff')\n ds = driver.Create(output_path, data.shape[1], data.shape[0], 1, dtype, options = ['COMPRESS=LZW'])\n ds.SetGeoTransform(geo_t)\n ds.SetProjection(proj)\n \n # Set nodata\n if nodata != None:\n ds.GetRasterBand(1).SetNoDataValue(nodata)\n \n # Write data for masked and unmasked arrays\n if np.ma.isMaskedArray(data):\n ds.GetRasterBand(1).WriteArray(data.filled(nodata))\n else:\n ds.GetRasterBand(1).WriteArray(data)\n ds = None","def save_tif(self, name='', data='', geo='', projection=''):\n # save as a geotiff\n driver = gdal.GetDriverByName(\"GTiff\")\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1,\n gdal.GDT_Float32, ['COMPRESS=LZW'])\n srse = osr.SpatialReference()\n if projection == '':\n srse.SetWellKnownGeogCS(\"WGS84\")\n\n else:\n try:\n if not srse.SetWellKnownGeogCS(projection) == 6:\n srse.SetWellKnownGeogCS(projection)\n else:\n try:\n srse.ImportFromEPSG(int(projection))\n except BaseException as err:\n print(err)\n else:\n srse.ImportFromWkt(projection)\n except BaseException:\n try:\n srse.ImportFromEPSG(int(projection))\n except BaseException as err:\n print(err)\n else:\n srse.ImportFromWkt(projection)\n\n dst_ds.SetProjection(srse.ExportToWkt())\n dst_ds.SetGeoTransform(geo)\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\n dst_ds.GetRasterBand(1).WriteArray(data)\n dst_ds = None\n\n return","def transcode(self, src: Path, dest: Path) -> None:\n pass","def transfer_roi(im_src, im_dst, dst_img):\n \n # get the pixel value and the location of the roi\n roi_pos = np.where(im_src[:,:,2] == 255)\n \n #get the list of the x and y coordinates of the location of roi\n x = list(roi_pos[0])\n y = list(roi_pos[1])\n \n dst_with_roi = copy.deepcopy(im_dst)\n dst_with_roi[x, y] = [0,0,255]\n \n marked_folder_roi = 'E:\\\\transfer_roi\\\\ir\\\\marked'\n marked_path = os.path.join(marked_folder_roi, dst_img)\n \n if not os.path.exists(marked_path):\n cv2.imwrite(marked_path, dst_with_roi)\n \n return dst_with_roi","def save_img(self):\r\n self.extract_info_from_file()\r\n path_0 = os.path.join(self.output_path, self.field_id, self.patient_id + self.ext)\r\n path_1 = os.path.join(self.output_path, self.field_id + '_' + self.instance, self.patient_id + self.ext)\r\n if self.shot == '0': # first shot\r\n if os.path.exists(path_0) or os.path.exists(path_1):\r\n print(self.patient_id, 'already done')\r\n pass\r\n else:\r\n if not self.img_computed:\r\n self.compute_img()\r\n if self.instance == '0':\r\n self.img.save(path_0)\r\n else:\r\n self.img.save(path_1)\r\n else: # newer shot\r\n if not self.img_computed:\r\n self.compute_img()\r\n if self.instance == '0':\r\n self.img.save(path_0)\r\n else:\r\n self.img.save(path_1)","def _translate(src_path, dst_path, profile=\"lzw\", profile_options={}, **options):\n # Format creation option (see gdalwarp `-co` option)\n output_profile = cog_profiles.get(profile)\n output_profile.update(dict(BIGTIFF=\"IF_SAFER\"))\n output_profile.update(profile_options)\n\n # Dataset Open option (see gdalwarp `-oo` option)\n config = dict(\n GDAL_NUM_THREADS=\"ALL_CPUS\",\n GDAL_TIFF_INTERNAL_MASK=True,\n GDAL_TIFF_OVR_BLOCKSIZE=\"128\",\n )\n\n cog_translate(\n src_path,\n dst_path,\n output_profile,\n config=config,\n in_memory=False,\n quiet=True,\n **options,\n )\n return True","def copy_local(dwn_url, copy_dir):\n # Path to local repository of DTMs\n local_dir = \"l:\\\\Slovenija\\\\DEM_D96\"\n # Prepare paths\n _, name = split(dwn_url[:-4])\n fil_nam = name + \".tif\"\n src_pth = join(local_dir, fil_nam)\n dst_pth = join(copy_dir, fil_nam)\n\n # Open TIF file\n tif = rasterio.open(src_pth)\n\n # Check CRS\n if tif.crs is None:\n meta = tif.profile.copy()\n meta.update({\"crs\": CRS.from_epsg(3794)}) # D96/TM\n array = tif.read()\n with rasterio.open(dst_pth, \"w\", **meta) as dest:\n dest.write(array)\n tif.close()\n else:\n tif.close()\n copyfile(src_pth, dst_pth)\n\n # Output\n status_msg = f\"{fil_nam} succsesfully copied\"\n\n return status_msg, fil_nam","def save_file(self, input_path, output_path):\n try:\n im = Image.open(input_path)\n im.save(output_path)\n return output_path\n except Exception as e:\n return '!ERROR' + str(e)","def cli(source_f, raster_f, output, verbose):\n with fiona.open(source_f, 'r') as source:\n source_driver = source.driver\n source_crs = source.crs\n sink_schema = source.schema.copy()\n\n source_geom = source.schema['geometry']\n if source_geom == 'Point':\n sink_schema['geometry'] = '3D Point'\n elif source_geom == 'LineString':\n sink_schema['geometry'] = '3D LineString'\n elif source_geom == '3D Point' or source_geom == '3D LineString':\n pass\n else:\n click.BadParameter(\"Source geometry type {} not implemented\".format(source_geom))\n\n with rasterio.open(raster_f) as raster:\n if source_crs != raster.crs:\n click.BadParameter(\"Features and raster have different CRS.\")\n if raster.count > 1:\n warnings.warn(\"Found {0} bands in {1}, expected a single band raster\".format(raster.bands, raster_f))\n supported = ['int16', 'int32', 'float32', 'float64']\n if raster.dtypes[0] not in supported:\n warnings.warn(\"Found {0} type in {1}, expected one of {2}\".format(raster.dtypes[0], raster_f, supported))\n with fiona.open(\n output, 'w',\n driver=source_driver,\n crs=source_crs,\n schema=sink_schema) as sink:\n\n for feature in source:\n try:\n feature_z = drapery.drape(raster, feature)\n sink.write({\n 'geometry': mapping(feature_z),\n 'properties': feature['properties'],\n })\n except Exception:\n logging.exception(\"Error processing feature %s:\", feature['id'])\n #print(sink.closed)\n #print(raster.closed)\n #print(source.closed)","def copy(self, newPath):\n\t\tif self.hasUdim:\n\t\t\tfor a in self.udimPaths:\n\t\t\t\tfi = a.copy( newPath )\n\t\t\treturn fi\n\t\telse:\n\t\t\treturn textureFile( super( textureFile, self ).copy( newPath ).path )","def test_save_tif():\n img = Image.new('RGB', (10, 20))\n\n parameters = {'path': 'green-dot.tif', 'data': [img]}\n\n assert images.save(parameters)","def reproject_vector( path, epsg_from=None, epsg_to=None):\n\n if not epsg_to: raise Exception(\"please, specify the output EPSG codes\")\n\n inDataSet = None\n outDataSet = None\n inFeature = None\n outFeature = None\n outLayer = None\n\n try:\n\n driver = ogr.GetDriverByName('ESRI Shapefile')\n inDataSet = driver.Open(path, 0) # 0 means read-only\n\n # define input SpatialReference\n if not epsg_from:\n layer = inDataSet.GetLayer()\n inSpatialRef = layer.GetSpatialRef()\n else:\n inSpatialRef = osr.SpatialReference()\n inSpatialRef.ImportFromEPSG(epsg_from)\n\n # define output SpatialReference\n outSpatialRef = osr.SpatialReference()\n outSpatialRef.ImportFromEPSG(epsg_to)\n\n # create the CoordinateTransformation\n coordTrans = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)\n\n # get the first input layer and the geometry type\n inLayer = inDataSet.GetLayer()\n geotype = inLayer.GetGeomType()\n lname = inLayer.GetName()\n\n drv = ogr.GetDriverByName(\"ESRI Shapefile\")\n outDataSet = drv.CreateDataSource(\"/vsimem/memory.shp\")\n\n outLayer = outDataSet.CreateLayer(lname, srs=outSpatialRef, geom_type=geotype)\n\n # add fields\n inLayerDefn = inLayer.GetLayerDefn()\n\n for i in range(0, inLayerDefn.GetFieldCount()):\n fieldDefn = inLayerDefn.GetFieldDefn(i)\n outLayer.CreateField(fieldDefn)\n\n # get the output layer\"s feature definition\n outLayerDefn = outLayer.GetLayerDefn()\n\n counter = 1\n\n # loop through the input features\n inFeature = inLayer.GetNextFeature()\n while inFeature:\n # get the input geometry\n geom = inFeature.GetGeometryRef()\n # reproject the geometry\n geom.Transform(coordTrans)\n # create a new feature\n outFeature = ogr.Feature(outLayerDefn)\n # set the geometry and attribute\n outFeature.SetGeometry(geom)\n for i in range(0, outLayerDefn.GetFieldCount()):\n outFeature.SetField(outLayerDefn.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n # add the feature to the shapefile\n outLayer.CreateFeature(outFeature)\n\n # destroy the features and get the next input feature\n if outFeature: outFeature = None\n inFeature = inLayer.GetNextFeature()\n\n counter += 1\n #print(counter)\n\n return outDataSet\n\n except RuntimeError as err:\n raise err\n except Exception as e:\n raise e\n\n finally:\n if inDataSet: outDataSet == None # give back control to C++\n if outDataSet: outDataSet == None\n if outLayer: outLayer == None\n if inFeature: inFeature == None\n if outFeature: outFeature = None","def copy_file(src, dest):\n with open_local_or_gcs(src, 'r') as h_src:\n with open_local_or_gcs(dest, 'w') as h_dest:\n shutil.copyfileobj(h_src, h_dest)","def test_make_mask_w_file_and_transform(self):\n output_mask = road_mask(\n os.path.join(data_dir, 'sample_roads_for_masking.geojson'),\n reference_im=os.path.join(data_dir, 'road_mask_input.tif'),\n width=4, meters=True, do_transform=True,\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(\n os.path.join(data_dir, 'sample_road_raster_mask.tif')\n )\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(output_mask, truth_mask)\n assert np.array_equal(saved_output_mask, truth_mask)\n # clean up\n os.remove(os.path.join(data_dir, 'test_out.tif'))","def set_src_dst_tensor(self, tik_instance):\n src_element_number = functools_reduce(lambda x1, x2: x1 * x2,\n self.src_shape_ori[:])\n dst_element_number = functools_reduce(lambda x1, x2: x1 * x2,\n self.dst_shape[:])\n self.src_gm = tik_instance.Tensor(self.dtype,\n (src_element_number,),\n name=\"src_gm\",\n scope=tik.scope_gm)\n self.dst_gm = tik_instance.Tensor(self.dtype,\n (dst_element_number,),\n name=\"dst_gm\",\n scope=tik.scope_gm)","def set_src_dst_tensor(self, tik_instance):\n src_element_number = functools_reduce(lambda x1, x2: x1 * x2,\n self.src_shape_ori[:])\n dst_element_number = functools_reduce(lambda x1, x2: x1 * x2,\n self.dst_shape[:])\n self.src_gm = tik_instance.Tensor(self.dtype,\n (src_element_number,),\n name=\"src_gm\",\n scope=tik.scope_gm)\n self.dst_gm = tik_instance.Tensor(self.dtype,\n (dst_element_number,),\n name=\"dst_gm\",\n scope=tik.scope_gm)","def tif_to_png(source, remove_old_files):\n os.chdir(source)\n subprocess.Popen(\"i_view32.exe *.tif /tifc=5 /convert=*.png\", shell=True,\n stdout=subprocess.PIPE, env={'PATH': os.getenv('PATH')}).stdout.read()\n if remove_old_files == 'yes':\n list_of_files_in_the_folder = os.listdir(source)\n for files in list_of_files_in_the_folder:\n name, ext = os.path.splitext(files)\n if ext.lower() == \".tif\":\n os.remove(os.path.join(source, files))","def single_to_rgb(R_file,G_file,B_file): \n R=gdal_array.LoadFile(R_file)\n G=gdal_array.LoadFile(G_file)\n B=gdal_array.LoadFile(B_file)\n \n \n basename=os.path.basename(R_file)\n basename=basename[:3]+basename[4:]\n basename=basename[:-4]+\"_rgb_.tif\" \n \n\n file_path=os.path.dirname(os.path.abspath(R_file))+\"/\"+basename\n\n \n driver=osgeo.gdal.GetDriverByName(\"GTiff\")\n options = ['PHOTOMETRIC=RGB', 'PROFILE=GeoTIFF']\n print(file_path)\n print(np.max(np.array([R.shape[1],B.shape[1],G.shape[1]])), np.max(np.array([R.shape[0],B.shape[0],G.shape[0]])))\n Xlen=np.max(np.array([R.shape[1],B.shape[1],G.shape[1]]))\n Ylen= np.max(np.array([R.shape[0],B.shape[0],G.shape[0]]))\n dataset=driver.Create(file_path, int(Xlen),int(Ylen), 3, osgeo.gdal.GDT_UInt16, options) \n \n dataset.GetRasterBand(1).WriteArray(R)\n dataset.GetRasterBand(2).WriteArray(G)\n dataset.GetRasterBand(3).WriteArray(B)\n \n return file_path","def set_src_dst_tensor(self, tik_instance):\n src_element_number = functools_reduce(lambda x, y: x * y,\n self.src_shape[:])\n dst_element_number = functools_reduce(lambda x, y: x * y,\n self.dst_shape[:])\n self.src_gm = tik_instance.Tensor(self.dtype,\n (src_element_number,),\n name=\"src_gm\",\n scope=tik.scope_gm)\n self.dst_gm = tik_instance.Tensor(self.dtype,\n (dst_element_number,),\n name=\"dst_gm\",\n scope=tik.scope_gm)","def imgOutputPath(self, newpath):\n newimg = self.imagePath().replace(self.home, newpath)\n return newimg","def exporting_cropped_images (fpath_tiff):\n src = rasterio.open(fpath_tiff, 'r')\n outfolder_irregular = '/train/irregular'\n outfolder_healthy = '/train/healthy'\n outfolder_concrete = '/train/concrete'\n outfolder_incomplete = '/train/incomplete'\n outfolder_other = '/train/other'\n outfolder = '/train/batch'\n #os.makedirs (outfolder, exist_ok = True)","def copy_image(source, dest):\n\n # Cast to abs path \n abs_src = abspath(source)\n abs_dst = abspath(dest)\n\n # OpenCV to open and save image\n img = cv2.imread(abs_src)\n cv2.imwrite(abs_dst, img)","def Save_as_tiff(name='', data='', geo='', projection=''):\n \n dir_name = os.path.dirname(name)\n \n if not os.path.exists(dir_name):\n os.makedirs(dir_name)\n \n # save as a geotiff\n driver = gdal.GetDriverByName(\"GTiff\")\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1, gdal.GDT_Float32, ['COMPRESS=LZW'])\n srse = osr.SpatialReference()\n if projection == '':\n srse.SetWellKnownGeogCS(\"WGS84\")\n\n else:\n try:\n if not srse.SetWellKnownGeogCS(projection) == 6:\n srse.SetWellKnownGeogCS(projection)\n else:\n try:\n srse.ImportFromEPSG(int(projection))\n except:\n srse.ImportFromWkt(projection)\n except:\n try:\n srse.ImportFromEPSG(int(projection))\n except:\n srse.ImportFromWkt(projection)\n\n dst_ds.SetProjection(srse.ExportToWkt())\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\n dst_ds.SetGeoTransform(geo)\n dst_ds.GetRasterBand(1).WriteArray(data)\n dst_ds = None\n return()","def save(annotation, new_filename, original_path):\n \n destination = \"../../standardized-data/\"\n if os.path.isdir(destination + \"/\" + annotation) == False:\n os.mkdir(destination + \"/\" + annotation)\n print(annotation, \"FOLDER CREATED\")\n if os.path.exists(destination + \"/\" + annotation + \"/\" + new_filename):\n print('FILE EXISTS: DOUBLE CHECK FOR DUPLICATION :', new_filename)\n else:\n shutil.copyfile(original_path, destination + \"/\" + annotation + \"/\" + new_filename)\n return","def save(self, path=''):\n if not self.__isBuilt:\n self._rebuild()\n if not path:\n self.w.save(self.path)\n else:\n if not path.endswith('.shp'):\n path = os.path.splitext(path)[0] + '.shp'\n self.w.save(path)","def copy_one_image( class_id, orig_path, train_dir, test_dir, train_prop ) :\n\n img = skimage.io.imread(orig_path)\n\n img_st = { 'class_id' : class_id,\n 'orig_path' : orig_path,\n 'shape0' : img.shape[0],\n 'shape1' : img.shape[1],\n 'shape2' : img.shape[2] }\n\n dest_dir = train_dir if random.random() < train_prop else test_dir\n fname = os.path.basename( orig_path )\n\n dest_path = \"%s/%s_%02d.ppm\" % (dest_dir, fname[:-4], class_id )\n\n shutil.copy( orig_path, dest_path )\n return img_st, dest_path","def savePhoto(self, path):\n if self.original_image and path:\n cv2.imwrite(path, self.original_image)","def __make_geo(self):\n # gmsh freecad_part.iges -o out_iges.geo -0\n fname_list = self.__fname.split('.')\n geo_file = fname_list[0]+'.geo'\n runstr = \"%s %s -o %s -0\" % (environment.GMSH, self.__fname, geo_file)\n print(runstr)\n subprocess.call(runstr, shell=True)\n print('Wrote file: %s' % geo_file)","def merge_layers(self, l_src, l_tgt, revert=False):\n if l_src == l_tgt:\n return\n u_src = self.gs[l_src]\n u_tgt = self.gs[l_tgt]\n s_src = self.states[l_src + 1]\n s_tgt = self.states[l_tgt + 1]\n\n if self.overlap:\n u_src_base = self.__get_base_u(u_src)[0]\n u_tgt_base = self.__get_base_u(u_tgt)[0]\n else:\n u_src_base = u_src\n u_tgt_base = u_tgt\n\n intersection = u_src_base.new_vertex_property(\"int64_t\", -1)\n\n u_tgt_vmap = u_tgt_base.vp[\"vmap\"]\n vmap = {}\n for v in u_tgt_base.vertices():\n vmap[u_tgt_vmap[v]] = v\n\n u_src_vmap = u_src_base.vp[\"vmap\"]\n for v in u_src_base.vertices():\n w = u_src_vmap[v]\n if w in vmap:\n intersection[v] = int(vmap[w])\n\n if self.overlap:\n u_tgt_base.ep[\"b\"] = self.states[l_tgt + 1].get_edge_blocks()\n u_src_base.ep[\"b\"] = self.states[l_src + 1].get_edge_blocks()\n else:\n u_tgt_base.vp[\"b\"] = self.states[l_tgt + 1].b\n u_src_base.vp[\"b\"] = self.states[l_src + 1].b\n\n tgt_bmap = {}\n src_rbmap = {}\n r_max = 0\n for r in range(self.B):\n if self.bmap.has(l_tgt + 1, r):\n tgt_bmap[r] = self.bmap.get(l_tgt + 1, r)\n r_max = max(r_max, tgt_bmap[r])\n if self.bmap.has(l_src + 1, r):\n src_rbmap[self.bmap.get(l_src + 1, r)] = r\n\n r_missing = list(set(range(r_max)) - set(tgt_bmap.values()))\n r_max += 1\n\n if self.overlap:\n b = u_src_base.ep[\"b\"].copy()\n for e in u_src_base.edges():\n nb = []\n for r in b[e]:\n nb.append(src_rbmap[r])\n for i, r in enumerate(nb):\n if r in tgt_bmap:\n nb[i] = tgt_bmap[r]\n else:\n if len(r_missing) > 0:\n rr = r_missing[0]\n del r_missing[0]\n else:\n rr = r_max\n r_max += 1\n self.bmap.set(l_tgt + 1, r, rr)\n nb[i] = rr\n tgt_bmap[r] = rr\n b[e] = nb\n b_src = b\n b_tgt = u_tgt_base.ep[\"b\"]\n u_tgt_base.ep[\"weight\"] = u_tgt_base.new_edge_property(\"int\", 1)\n u_tgt_base.vp[\"weight\"] = u_tgt_base.new_vertex_property(\"int\", 1)\n u_src_base.ep[\"weight\"] = u_src_base.new_edge_property(\"int\", 1)\n u_src_base.vp[\"weight\"] = u_src_base.new_vertex_property(\"int\", 1)\n else:\n b = u_src_base.vp[\"b\"].copy()\n for v in u_src_base.vertices():\n r = src_rbmap[b[v]]\n if r in tgt_bmap:\n b[v] = tgt_bmap[r]\n else:\n if len(r_missing) > 0:\n rr = r_missing[0]\n del r_missing[0]\n else:\n rr = r_max\n r_max += 1\n self.bmap.set(l_tgt + 1, r, rr)\n b[v] = rr\n tgt_bmap[r] = rr\n b_src = b\n b_tgt = u_tgt_base.vp[\"b\"]\n\n props = [(b_tgt, b_src),\n (u_tgt_base.vp[\"vmap\"], u_src_base.vp[\"vmap\"]),\n (u_tgt_base.vp[\"weight\"], u_src_base.vp[\"weight\"]),\n (u_tgt_base.ep[\"weight\"], u_src_base.ep[\"weight\"])]\n\n if not self.overlap:\n props.append((u_tgt_base.vp[\"brmap\"],\n u_src_base.vp[\"brmap\"]))\n\n u, props = graph_union(u_tgt_base, u_src_base,\n intersection=intersection,\n props=props,\n include=False)\n\n if self.overlap:\n u.ep[\"b\"] = props[0]\n else:\n u.vp[\"b\"] = props[0]\n u.vp[\"brmap\"] = props[4]\n\n u.vp[\"vmap\"] = props[1]\n u.vp[\"weight\"] = props[2]\n u.ep[\"weight\"] = props[3]\n\n if self.overlap:\n u, b, node_index, half_edges, eindex = half_edge_graph(u, u.ep[\"b\"],\n self.B)\n u.vp[\"vmap\"] = node_index\n u.vp[\"weight\"] = u.new_vertex_property(\"int\", 1)\n u.vp[\"b\"] = b\n self.gs[l_tgt] = u\n self.states[l_tgt + 1] = self.__gen_state(self.gs[l_tgt])\n else:\n self.gs[l_tgt] = u\n self.states[l_tgt + 1] = self.__gen_state(self.gs[l_tgt])\n\n del self.states[l_src + 1]\n del self.gs[l_src]\n\n old_ec = self.ec.copy()\n self.ec.a[self.ec.a == l_src] = l_tgt\n self.ec.a[self.ec.a > l_src] -= 1\n if self.overlap:\n old_base_ec = self.base_ec.copy()\n self.base_ec.a[self.base_ec.a == l_src] = l_tgt\n self.base_ec.a[self.base_ec.a > l_src] -= 1\n self.C -= 1\n old_bmap = self.bmap.copy()\n self.bmap.del_c(l_src + 1)\n self.__bg = None\n old_layer_entropy = self.__layer_entropy\n self.__layer_entropy = None\n\n yield\n\n if revert:\n self.gs.insert(l_src, u_src)\n self.gs[l_tgt] = u_tgt\n self.states.insert(l_src + 1, s_src)\n self.states[l_tgt + 1] = s_tgt\n self.ec.a[:] = old_ec.a\n if self.overlap:\n self.base_ec.a[:] = old_base_ec.a\n self.C += 1\n self.bmap = old_bmap\n self.__layer_entropy = old_layer_entropy","def upgrade_file(path, multipoint):\n node0 = nrml.read(path, chatty=False)[0]\n shutil.copy(path, path + '.bak') # make a backup of the original file\n tag = striptag(node0.tag)\n gml = True\n if tag == 'vulnerabilityModel':\n vf_dict, cat_dict = get_vulnerability_functions_04(path)\n # below I am converting into a NRML 0.5 vulnerabilityModel\n node0 = Node(\n 'vulnerabilityModel', cat_dict,\n nodes=[obj_to_node(val) for val in vf_dict.values()])\n gml = False\n elif tag == 'fragilityModel':\n node0 = read_nrml.convert_fragility_model_04(\n nrml.read(path)[0], path)\n gml = False\n elif tag == 'sourceModel':\n node0 = nrml.read(path)[0]\n dic = groupby(node0.nodes, operator.itemgetter('tectonicRegion'))\n node0.nodes = [Node('sourceGroup',\n dict(tectonicRegion=trt, name=\"group %s\" % i),\n nodes=srcs)\n for i, (trt, srcs) in enumerate(dic.items(), 1)]\n if multipoint:\n sourceconverter.update_source_model(node0)\n with open(path, 'wb') as f:\n nrml.write([node0], f, gml=gml)","def lightenSvgFile(inFname, outFname=\"\"):\n if not outFname:\n # create outFname based on inFname\n if \"-screen\" in inFname:\n outFname = inFname.replace(\"-screen\", \"-print\")\n else:\n outFname = inFname.replace(\".svg\", \"\")+\"-print.svg\"\n if \"-dark\" in outFname:\n outFname = outFname.replace(\"-dark\", \"\")\n with open(inFname) as infile, open(outFname,\"w\") as outfile:\n l=infile.readline()\n while l:\n outfile.write(ligthenOneLine(l))\n l=infile.readline()\n return outFname","def test_GFD_import_equals_export(self):\n filepath = '2.txt'\n original_gfd = flow_processing_input.GroundFlowData()\n original_gfd.detector_flow_data = createGFDDataset(10).dataset\n original_gfd.export_to_file(filepath)\n new_gfd = flow_processing_input.GroundFlowData(filepath)\n os.remove(filepath)\n # Check if new_gfd contains the same attributes as the original_gfd\n self.assertTrue(new_gfd == original_gfd)\n # Check if new_gfd is not equal to a different gfd of same size\n random_gfd = flow_processing_input.GroundFlowData()\n random_gfd.detector_flow_data = createGFDDataset(10).dataset\n self.assertFalse(new_gfd == random_gfd)","def reprojectShapefile(in_shapefile, model_raster, out_dir, name_override=None) -> str:\n\tshapefile_path = in_shapefile # old variable name; standardized to match function parameters\n\t# get out_path from out_dir\n\tif name_override:\n\t\tout_path = os.path.join(out_dir,name_override)\n\telse:\n\t\tin_base,in_ext = os.path.splitext(os.path.basename(in_shapefile))\n\t\tout_path = os.path.join(out_dir,in_base+\"_REPROJ\"+in_ext)\n\t# get raster projection as wkt\n\twith rasterio.open(model_raster,'r') as img:\n\t\traster_wkt = img.profile['crs'].to_wkt()\n\t# get shapefile projection as wkt\n\twith open(shapefile_path.replace(\".shp\",\".prj\")) as rf:\n\t\tshapefile_wkt = rf.read()\n\n\t# if it's a match, nothing needs to be done\n\tif raster_wkt == shapefile_wkt:\n\t\tlog.warning(\"CRS already match\")\n\t\t# get input directory and filename\n\t\tin_dir = os.path.dirname(shapefile_path)\n\t\tin_name = os.path.splitext(os.path.basename(shapefile_path))[0]\n\t\t# list all elements of shapefile\n\t\tall_shape_files = glob.glob(os.path.join(in_dir,f\"{in_name}.*\"))\n\t\t# get output directory and filenames\n\t\tout_dir = os.path.dirname(out_path)\n\t\tout_name = os.path.splitext(os.path.basename(out_path))[0]\n\t\tfor f in all_shape_files:\n\t\t\tname, ext = os.path.splitext(os.path.basename(f))\n\t\t\tout_f = os.path.join(out_dir,f\"{out_name}{ext}\")\n\t\t\twith open(f,'rb') as rf:\n\t\t\t\twith open(out_f,'wb') as wf:\n\t\t\t\t\tshutil.copyfileobj(rf,wf)\n\telse:\n\t\t# get CRS objects\n\t\traster_crs = CRS.from_wkt(raster_wkt)\n\t\tshapefile_crs = CRS.from_wkt(shapefile_wkt)\n\t\t#transformer = Transformer.from_crs(raster_crs,shapefile_crs)\n\n\t\t# convert geometry and crs\n\t\tout_shapefile_path = out_path # os.path.join(temp_dir,os.path.basename(shapefile_path))\n\t\tdata = gpd.read_file(shapefile_path)\n\t\tdata_proj = data.copy()\n\t\tdata_proj['geometry'] = data_proj['geometry'].to_crs(raster_crs)\n\t\tdata_proj.crs = raster_crs\n\n\t\t# save output\n\t\tdata_proj.to_file(out_shapefile_path)\n\n\n\treturn out_shapefile_path","def copy_model_to_local(gcs_path, dest_path):\n copy_start_time = time.time()\n logging.debug(\"Starting to copy files from %s to %s\", gcs_path, dest_path)\n if not os.path.exists(dest_path):\n os.makedirs(dest_path)\n gcs_path = os.path.join(gcs_path, \"*\")\n try:\n # Removed parallel downloads (\"-m\") because it was not working well in\n # gVisor (b/37269226).\n subprocess.check_call([\n \"gsutil\", \"cp\", \"-R\", gcs_path, dest_path], stdin=subprocess.PIPE)\n except subprocess.CalledProcessError:\n logging.exception(\"Could not copy model using gsutil.\")\n raise\n logging.debug(\"Files copied from %s to %s: took %f seconds\", gcs_path,\n dest_path, time.time() - copy_start_time)","def convert(\n path_in, path_out, pathway_iri, wp_id, pathway_version, scale=100, theme=\"plain\"\n):\n if not path.exists(path_in):\n raise Exception(f\"Missing file '{path_in}'\")\n\n if path.exists(path_out):\n print(f\"File {path_out} already exists. Skipping.\")\n return True\n\n dir_in = path.dirname(path_in)\n base_in = path.basename(path_in)\n # example base_in: 'WP4542.gpml'\n [stub_in, ext_in_with_dot] = path.splitext(base_in)\n # gettting rid of the leading dot, e.g., '.gpml' to 'gpml'\n ext_in = LEADING_DOT_RE.sub(\"\", ext_in_with_dot)\n\n if ext_in != \"gpml\":\n # TODO: how about *.gpml.xml?\n raise Exception(f\"Currently only accepting *.gpml for path_in\")\n gpml_f = path_in\n\n dir_out = path.dirname(path_out)\n # example base_out: 'WP4542.svg'\n base_out = path.basename(path_out)\n [stub_out, ext_out_with_dot] = path.splitext(base_out)\n # getting rid of the leading dot, e.g., '.svg' to 'svg'\n ext_out = LEADING_DOT_RE.sub(\"\", ext_out_with_dot)\n\n tree = ET.parse(gpml_f, parser=parser)\n root = tree.getroot()\n\n if root is None:\n raise Exception(\"no root element\")\n if root.tag is None:\n raise Exception(\"no root tag\")\n\n gpml_version = re.sub(r\"{http://pathvisio.org/GPML/(\\w+)}Pathway\", r\"\\1\", root.tag)\n if ext_out != \"gpml\" and gpml_version != LATEST_GPML_VERSION:\n old_f = f\"{dir_in}/{stub_in}.{gpml_version}.gpml\"\n rename(gpml_f, old_f)\n convert(old_f, gpml_f, pathway_iri, wp_id, pathway_version, scale)\n\n # trying to get wd ids via sparql via pywikibot\n site = pywikibot.Site(\"wikidata\", \"wikidata\")\n repo = site.data_repository() # this is a DataSite object\n wd_sparql = sparql.SparqlQuery(\n endpoint=\"https://query.wikidata.org/sparql\", repo=repo\n )\n # (self, endpoint=None, entity_url=None, repo=None, 2 max_retries=None, retry_wait=None)\n\n if ext_out in [\"gpml\", \"owl\", \"pdf\", \"pwf\", \"txt\"]:\n subprocess.run(shlex.split(f\"pathvisio convert {path_in} {path_out}\"))\n elif ext_out == \"png\":\n # TODO: look at using --scale as an option (instead of an argument),\n # for both pathvisio and gpmlconverter.\n # TODO: move the setting of a default value for scale into\n # pathvisio instead of here.\n subprocess.run(shlex.split(f\"pathvisio convert {path_in} {path_out} {scale}\"))\n # Use interlacing? See https://github.com/PathVisio/pathvisio/issues/78\n # It's probably not worthwhile. If we did it, we would need to install\n # imagemagick and then run this:\n # mv \"$path_out\" \"$path_out.noninterlaced.png\"\n # convert -interlace PNG \"$path_out.noninterlaced.png\" \"$path_out\"\n elif ext_out in [\"json\", \"jsonld\"]:\n gpml2json(path_in, path_out, pathway_iri, wp_id, pathway_version, wd_sparql)\n elif ext_out in [\"svg\", \"pvjssvg\"]:\n #############################\n # SVG\n #############################\n\n json_f = f\"{dir_out}/{stub_in}.json\"\n if not path.isfile(json_f):\n gpml2json(path_in, json_f, pathway_iri, wp_id, pathway_version, wd_sparql)\n\n json2svg(json_f, path_out, pathway_iri, wp_id, pathway_version, theme)\n else:\n raise Exception(f\"Invalid output extension: '{ext_out}'\")","def convertCSVToGeoTIFF(geoproperties, datafile, outputdir, outfile):\n \n data = numpy.genfromtxt(datafile, delimiter=',')\n \n Ny, Nx = data.shape\n \n #print data.size\n #print data.shape\n #print data\n \n # slice out the last column of null values\n if str(data[Ny-1][Nx-1]) == 'nan':\n data = data[:,:-1]\n \n Ny, Nx = data.shape\n \n #print data.size\n #print data.shape\n #print data\n #print Ny, Nx\n \n startPos = [geoproperties['tl']['long'],geoproperties['tl']['lat']]\n d_lat = (geoproperties['br']['lat'] - geoproperties['tl']['lat']) / (Ny - 1)\n d_long = (geoproperties['br']['long'] - geoproperties['tl']['long']) / (Nx - 1)\n \n #print startPos, d_lat, d_long\n \n driver = gdal.GetDriverByName(\"GTiff\")\n ds = driver.Create(os.path.join(outputdir,outfile),Nx,Ny,1,gdal.GDT_Float32)\n #ds = driver.Create('output/output.tif',Nx,Ny,1,gdal.GDT_Byte)\n #ds.SetGeoTransform( [ -158.584, .008, 0, 21.108, 0, .008 ] )\n ds.SetGeoTransform( [ startPos[0], d_long, 0, startPos[1], 0, d_lat ] )\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326)\n ds.SetProjection( srs.ExportToWkt() )\n ds.GetRasterBand(1).WriteArray(data)\n ds = None","def test_07_source_save(self):\n for activity, gpx_file in zip(self.manager_stravagpx, TEST_GPX_FILES_2):\n self.assert_files_equal(activity.metadata.source_file, gpx_file)\n manager_fit = get_manager(CONFIG_FIT, TEST_FIT_FILES)\n for activity, fit_file in zip(manager_fit, TEST_FIT_FILES):\n self.assert_files_equal(activity.metadata.source_file, fit_file)","def save_graph(self, path):\n if path.split('.')[-1]=='gexf':\n nx.write_gexf(self.graph, path)\n else:\n nx.write_gpickle(self.graph, path)","def copyGISFileWithName(pathToLocation, givenFile):\n givenFileCopy = os.path.join(pathToLocation, os.path.basename(givenFile))\n arcpy.Copy_management(givenFile, givenFileCopy)","def unify(src, dst):\n\n # NOTE: at this point it is assumed files are unifiable\n\n # get a temp file name\n dir = os.path.split(src)[0]\n tmp_handle, tmp_path = tempfile.mkstemp(dir=dir)\n os.close(tmp_handle)\n\n # rename the destination, in case we need to back out\n os.rename(dst, tmp_path)\n\n # link source to destination\n try:\n os.link(src, dst)\n except:\n # back out\n print 'Could not link %s -> %s, backing out' % (src, dst)\n try:\n if os.path.exists(dst):\n os.unlink(dst)\n os.rename(tmp_path, dst)\n except:\n print 'Could not back out!!! the destination file is still there as', tmp_file\n raise exceptions.OSError\n\n # done, remove the temp file\n os.unlink(tmp_path)","def save_samples(iteration, fixed_Y, fixed_X, G_YtoX, G_XtoY, opts):\n fake_X = G_YtoX(fixed_Y, False)\n fake_Y = G_XtoY(fixed_X, False)\n\n X, fake_X = fixed_X.cpu().data.numpy(), fake_X.cpu().data.numpy()\n Y, fake_Y = fixed_Y.cpu().data.numpy(), fake_Y.cpu().data.numpy()\n\n merged = merge_images(X, fake_Y, opts)\n path = os.path.join(opts.sample_dir, 'sample-{:06d}-X-Y.png'.format(iteration))\n scipy.misc.imsave(path, merged)\n print('Saved {}'.format(path))\n\n merged = merge_images(Y, fake_X, opts)\n path = os.path.join(opts.sample_dir, 'sample-{:06d}-Y-X.png'.format(iteration))\n scipy.misc.imsave(path, merged)\n print('Saved {}'.format(path))","def stot(self, sfp):\n\n return self.target_directory / sfp.relative_to(self.source_directory).with_suffix('.txt')","def test_make_contact_mask_w_save(self):\n output_mask = contact_mask(\n os.path.join(data_dir, 'sample.csv'), geom_col=\"PolygonWKT_Pix\",\n contact_spacing=10,\n reference_im=os.path.join(data_dir, \"sample_geotiff.tif\"),\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(os.path.join(data_dir,\n 'sample_c_mask.tif'))\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(output_mask, truth_mask)\n assert np.array_equal(saved_output_mask, truth_mask)\n os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after","def copy_path(src_path: str, dst_path: str, append=True) -> None:\n with io.open(src_path, \"r\", encoding=\"utf-8\") as src:\n with io.open(dst_path, \"a\" if append else \"w\", encoding=\"utf-8\") as dst:\n for line in src:\n dst.write(line)","def write_src_multfile(src_coords, args, file_name = 'source.mul',):\n print 'write_src_multfile'\n file_in = open(file_name, 'w')\n file_in.write('#REFERENCE 3 0.0 0.0\\n')\n\n for i in range(len(src_coords)):\n image_id = str(i+1) + ' '\n data = str(src_coords[i][0]) + ' ' + str(src_coords[i][1]) \\\n + str(' 0.2 0.2 0 ') + str(args.source_real_z) + ' 0'\n final = image_id + data + '\\n'\n file_in.write(final)\n file_in.close()","def write_and_filter_paths(self, source, target, relation, label, paths):\n file_dir = os.path.join(self.save_dir, relation + \"_\" + str(self.maximum_length) + \"_\" + str(self.remaining_percentage) + \"_\" + str(self.random_seed) + \".txt\")\n with open(file_dir, \"a\") as fh:\n fh.write(str(label) + \"\\t\" + str(source) + \"\\t\" + str(target) + \"\\t\")\n for pdx, path in enumerate(paths):\n if not self.include_entity:\n if len(path) == 1:\n continue\n for rdx, rel_idx in enumerate(path):\n fh.write(self.idx_to_relation[rel_idx])\n if rdx != len(path)-1:\n fh.write(\"|\")\n if pdx != len(paths)-1:\n fh.write(\"###\")\n else:\n if len(path) == 3:\n continue\n fh.write(self.idx_to_node[path[0]].get_name())\n fh.write(\"|\")\n for rdx in range(0, (len(path)-1)/2):\n fh.write(self.idx_to_relation[path[rdx*2+1]])\n fh.write(\"|\")\n fh.write(self.idx_to_node[path[rdx*2+2]].get_name())\n if rdx*2+2 != len(path)-1:\n fh.write(\"|\")\n if pdx != len(paths)-1:\n fh.write(\"###\")\n fh.write(\"\\n\")","def _export_file(self, src_path, ext_dst):\n if not self.mount():\n return False\n _log(\"AnnexGvfsBackend._export_file(%r -> %r)\" % (src_path, ext_dst))\n src_uri = self.path_to_uri(src_path)\n ext_tmp = ext_dst + \".part\"\n try:\n if not self.gvfs.copy_file(src_uri, ext_tmp):\n raise IOError()\n if not self.gvfs.rename_file(ext_tmp, ext_dst):\n raise IOError()\n except IOError:\n self.gvfs.delete_file(ext_tmp)\n return False\n else:\n return True","def _opt_path(self, src: int, dst: int) -> Optional[np.ndarray]:\n if self.arc_fun == 'exp':\n path = _pyift.livewire_path(self.image, self.costs, self.preds, self.labels,\n self.arc_fun, self.sigma, src, dst)\n elif self.arc_fun == 'exp-saliency':\n path = _pyift.livewire_path(self.image, self.saliency, self.costs, self.preds, self.labels,\n self.arc_fun, self.sigma, self.gamma, src, dst)\n else:\n raise NotImplementedError\n\n return path","def save_step_4(imgs, output_path=\"./output/step4\"):\n # ... your code here ...\n cv2.imwrite(output_path+\"/output.jpg\", imgs)","def save_raster ( output_name, raster_data, dataset, driver=\"GTiff\" ):\n\n # Open the reference dataset\n g_input = gdal.Open ( dataset )\n # Get the Geotransform vector\n geo_transform = g_input.GetGeoTransform ()\n x_size = g_input.RasterXSize # Raster xsize\n y_size = g_input.RasterYSize # Raster ysize\n srs = g_input.GetProjectionRef () # Projection\n # Need a driver object. By default, we use GeoTIFF\n if driver == \"GTiff\":\n driver = gdal.GetDriverByName ( driver )\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\n gdal.GDT_Float32, ['TFW=YES', \\\n 'COMPRESS=LZW', 'TILED=YES'] )\n else:\n driver = gdal.GetDriverByName ( driver )\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\n gdal.GDT_Float32 )\n\n dataset_out.SetGeoTransform ( geo_transform )\n dataset_out.SetProjection ( srs )\n dataset_out.GetRasterBand ( 1 ).WriteArray ( \\\n raster_data.astype(np.float32) )\n dataset_out.GetRasterBand ( 1 ).SetNoDataValue ( float(-999) )\n dataset_out = None","def register_landmarks(fname_src, fname_dest, dof, fname_affine='affine.txt', verbose=1, path_qc='./'):\n from msct_image import Image\n # open src label\n im_src = Image(fname_src)\n # coord_src = im_src.getNonZeroCoordinates(sorting='value') # landmarks are sorted by value\n coord_src = im_src.getCoordinatesAveragedByValue() # landmarks are sorted by value\n # open dest labels\n im_dest = Image(fname_dest)\n # coord_dest = im_dest.getNonZeroCoordinates(sorting='value')\n coord_dest = im_dest.getCoordinatesAveragedByValue()\n # Reorganize landmarks\n\n points_src, points_dest = [], []\n for coord in coord_src:\n point_src = im_src.transfo_pix2phys([[coord.x, coord.y, coord.z]])\n # convert NIFTI to ITK world coordinate\n # points_src.append([point_src[0][0], point_src[0][1], point_src[0][2]])\n points_src.append([-point_src[0][0], -point_src[0][1], point_src[0][2]])\n for coord in coord_dest:\n point_dest = im_dest.transfo_pix2phys([[coord.x, coord.y, coord.z]])\n # convert NIFTI to ITK world coordinate\n # points_dest.append([point_dest[0][0], point_dest[0][1], point_dest[0][2]])\n points_dest.append([-point_dest[0][0], -point_dest[0][1], point_dest[0][2]])\n\n # display\n sct.printv('Labels src: ' + str(points_src), verbose)\n sct.printv('Labels dest: ' + str(points_dest), verbose)\n sct.printv('Degrees of freedom (dof): ' + dof, verbose)\n\n if len(coord_src) != len(coord_dest):\n raise Exception('Error: number of source and destination landmarks are not the same, so landmarks cannot be paired.')\n\n # estimate transformation\n # N.B. points_src and points_dest are inverted below, because ITK uses inverted transformation matrices, i.e., src->dest is defined in dest instead of src.\n # (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_dest, points_src, constraints=dof, verbose=verbose, path_qc=path_qc)\n (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_src, points_dest, constraints=dof, verbose=verbose, path_qc=path_qc)\n # writing rigid transformation file\n # N.B. x and y dimensions have a negative sign to ensure compatibility between Python and ITK transfo\n text_file = open(fname_affine, 'w')\n text_file.write(\"#Insight Transform File V1.0\\n\")\n text_file.write(\"#Transform 0\\n\")\n text_file.write(\"Transform: AffineTransform_double_3_3\\n\")\n text_file.write(\"Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\\n\" % (\n rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],\n rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],\n rotation_matrix[2, 0], rotation_matrix[2, 1], rotation_matrix[2, 2],\n translation_array[0, 0], translation_array[0, 1], translation_array[0, 2]))\n text_file.write(\"FixedParameters: %.9f %.9f %.9f\\n\" % (points_moving_barycenter[0],\n points_moving_barycenter[1],\n points_moving_barycenter[2]))\n text_file.close()","def Reproject(cls,inRaster, outRaster,EPSG):\r\n try:\r\n import gdal\r\n except:\r\n raise ImportError(\"Can not import module GDAL\")\r\n try:\r\n dataset = gdal.Open(infile)\r\n out = dataset.GetRasterBand(1)\r\n print dataset.GetMetadata()\r\n return out\r\n except:\r\n raise ImportError(\"Can not read band\")\r\n #if not tmp:\r\n # raise Exception(\"Could not orthorectify the image \")\r\n\r\n com=\" \".join([\"gdalwarp -t_srs EPSG:\"+str(EPSG),InRaster,OutRaster])\r\n tmp=os.system(com)\r\n print tmp\r\n #if not tmp:\r\n # raise Exception(\"Could not reproject the image \")\r\n #os.remove(OutRaster)\r","def gdal_read_geotiff_file(sFilename_in):\n \n if os.path.exists(sFilename_in):\n pass\n else:\n print('The file does not exist!')\n return\n\n sDriverName='GTiff'\n pDriver = gdal.GetDriverByName(sDriverName) \n\n if pDriver is None:\n print (\"%s pDriver not available.\\n\" % sDriverName)\n else:\n print (\"%s pDriver IS available.\\n\" % sDriverName) \n\n pDataset = gdal.Open(sFilename_in, gdal.GA_ReadOnly)\n\n if pDataset is None:\n print(\"Couldn't open this file: \" + sFilename_in)\n sys.exit(\"Try again!\")\n else: \n pProjection = pDataset.GetProjection()\n\n pDataset.GetMetadata()\n \n ncolumn = pDataset.RasterXSize\n nrow = pDataset.RasterYSize\n nband = pDataset.RasterCount\n\n pGeotransform = pDataset.GetGeoTransform()\n dOriginX = pGeotransform[0]\n dOriginY = pGeotransform[3]\n dPixelWidth = pGeotransform[1]\n pPixelHeight = pGeotransform[5]\n\n pBand = pDataset.GetRasterBand(1)\n\n # Data type of the values\n gdal.GetDataTypeName(pBand.DataType)\n # Compute statistics if needed\n if pBand.GetMinimum() is None or pBand.GetMaximum() is None:\n pBand.ComputeStatistics(0)\n\n dMissing_value = pBand.GetNoDataValue()\n \n aData_out = pBand.ReadAsArray(0, 0, ncolumn, nrow)\n \n #we will use one of them to keep the consistency\n pSpatial_reference = osr.SpatialReference(wkt=pProjection)\n \n\n pDataset = None\n pBand = None \n pBand = None\n\n return aData_out, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection, pSpatial_reference","def copy_file(fs, inpath, outpath):\n fs.copy(inpath, outpath)","def transform(infile, output, insrs, format_name):\n\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \n in_srs = osr.SpatialReference()\n in_srs.ImportFromEPSG(insrs)\n out_srs = osr.SpatialReference()\n out_srs.ImportFromEPSG(4324)\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\n\n in_dsn = ogr.Open(infile)\n in_layer = in_dsn.GetLayer()\n in_feature_definition = in_layer.GetLayerDefn()\n\n out_driver = ogr.GetDriverByName(format_name)\n out_dsn = out_driver.CreateDataSource(output)\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\n geom_type=in_layer.GetGeomType())\n\n # add fields\n for i in range(0, in_feature_definition.GetFieldCount()):\n fieldDefn = in_feature_definition.GetFieldDefn(i)\n out_layer.CreateField(fieldDefn)\n\n # get the output layer's feature definition\n out_feature_definition = out_layer.GetLayerDefn()\n\n # loop through the input features\n inFeature = in_layer.GetNextFeature()\n while inFeature:\n # get the input geometry\n geom = inFeature.GetGeometryRef().Clone()\n # reproject the geometry\n geom.Transform(coordTrans)\n # create a new feature\n outFeature = ogr.Feature(out_feature_definition)\n # set the geometry and attribute\n outFeature.SetGeometry(geom)\n for i in range(0, out_feature_definition.GetFieldCount()):\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n # add the feature to the shapefile\n out_layer.CreateFeature(outFeature)\n # destroy the features and get the next input feature\n outFeature.Destroy()\n inFeature.Destroy()\n inFeature = in_layer.GetNextFeature()\n\n # close the shapefiles\n in_dsn.Destroy()\n out_dsn.Destroy()","def save_groudtruth(im, coords, filename):\n print 'Saving ground truth ......{0}'.format(filename)\n img_draw = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\n draw = ImageDraw.Draw(img_draw)\n for coord in coords:\n draw.polygon([(float(coord[0]), float(coord[1])), (float(coord[2]), float(coord[3])),\n (float(coord[4]), float(coord[5])), (float(coord[6]), float(coord[7]))],\n outline=\"red\", fill=\"blue\")\n img_draw = np.array(img_draw)\n img_draw = cv2.cvtColor(img_draw, cv2.COLOR_RGB2BGR)\n bname_excludepoint = filename.split('/')[-1].split('.')[0]\n image_path = '/home/yuquanjie/Documents/deep-direct-regression/result/' + bname_excludepoint + '_gt.jpg'\n cv2.imwrite(image_path, img_draw[0: img_draw.shape[0], 0: img_draw.shape[1]])","def test_make_mask_w_ref_image(self):\n output_mask = instance_mask(\n os.path.join(data_dir, 'geotiff_labels.geojson'),\n reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\n do_transform=True,\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(os.path.join(data_dir,\n 'sample_inst_mask.tif'))\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(saved_output_mask, truth_mask)\n # clean up\n os.remove(os.path.join(data_dir, 'test_out.tif'))\n assert np.array_equal(output_mask, truth_mask)","def output_diff_local_files(gold_session, image_name):\n given_file = gold_session.GetGivenImageLink(image_name)\n closest_file = gold_session.GetClosestImageLink(image_name)\n diff_file = gold_session.GetDiffImageLink(image_name)\n failure_message = 'Unable to retrieve link'\n logging.error('Generated image: %s', given_file or failure_message)\n logging.error('Closest image: %s', closest_file or failure_message)\n logging.error('Diff image: %s', diff_file or failure_message)","def find_path(orig, dest):\n\t#x,y=orig.pos\n\t#orig = surface.tile(x,y)\n\t#x,y=dest.pos\n\t#dest = surface.tile(x,y)\n\treturn pathfinder.astar(orig, dest)","def copyFile( src, dest ):\n\tinFile = open( src, 'r' )\n\toutFile = open( dest, 'w' )\n\tfor line in inFile:\n\t\toutFile.write( line )\n\toutFile.close()\n\tinFile.close()","def reprojectRaster(in_raster,model_raster,out_dir,name_override = None):\n\tif name_override:\n\t\tout_name = os.path.join(out_dir,name_override)\n\telse:\n\t\tin_base,in_ext = os.path.splitext(os.path.basename(in_raster))\n\t\tout_name = os.path.join(out_dir,in_base+\"_REPROJ\"+in_ext)\n\t\t#print(out_name)\n\tt_ds = gdal.Open(model_raster,0)\n\tt_wkt = t_ds.GetProjection()\n\ts_ds = gdal.Open(in_raster,0)\n\ts_wkt = s_ds.GetProjection()\n\tt_ds = s_ds = None\n\tif s_wkt != t_wkt:\n\t\treproj_args = [r\"C:\\OSGeo4W64\\bin\\gdalwarp.exe\",\"-t_srs\",t_wkt,in_raster,out_name]\n\t\tsubprocess.call(reproj_args)\n\t\treturn out_name\n\telse:\n\t\tlog.warning(\"Projections already match, doing nothing.\")\n\t\treturn in_raster","def cpr(src, dst):\n shutil.copytree(src, dst)","def save_img_as_tiff(seg_img: np.ndarray, time: int, track_save_dir: str):\n img = sitk.GetImageFromArray(seg_img.astype(\"uint16\"))\n filename = 'mask'+'%0*d' % (3, time) + '.tif'\n sitk.WriteImage(img, os.path.join(track_save_dir, filename))","def save_image(nom, image, _geo_trans):\n cols = image.shape[2]\n rows = image.shape[1]\n bands = image.shape[0]\n print(bands, cols, rows)\n driver = gdal.GetDriverByName(\"GTiff\")\n out_raster = driver.Create(nom, cols, rows, bands, gdal.GDT_Byte)\n # if (geo_trans):\n # outRaster.SetGeoTransform(geo_trans)\n band = out_raster.GetRasterBand(1)\n band.WriteArray(np.round(image[0]))\n band = out_raster.GetRasterBand(2)\n band.WriteArray(np.round(image[1]))\n band = out_raster.GetRasterBand(3)\n band.WriteArray(np.round(image[2]))"],"string":"[\n \"def saveAsGeotiff(self, geotiff_fname, overwrite=True, verbose=False):\\n if not overwrite and os.path.exists(geotiff_fname):\\n raise ValueError('output geotiff already exists:\\\\n {}'.format(\\n geotiff_fname))\\n\\n if overwrite and os.path.exists(geotiff_fname):\\n os.remove(geotiff_fname)\\n\\n gggdal = geogrid_as_gdalInMem(self)\\n\\n dst = gdal.GetDriverByName('GTiff').CreateCopy(\\n geotiff_fname, gggdal)\\n dst = None\",\n \"def save_geotiff(path, raster, xform):\\n # Import rasterio here, so that we don't need it in the cloud.\\n import rasterio\\n import affine\\n n_lats, n_lons, depth = raster.shape\\n\\n assert raster.max() <= 255\\n assert raster.min() >= 0\\n \\n profile = {\\n 'crs': 'EPSG:4326',\\n 'nodata': 0,\\n 'dtype': rasterio.uint8,\\n 'height': n_lats,\\n 'width': n_lons,\\n 'count': depth,\\n 'driver':\\\"GTiff\\\",\\n 'transform': xform}\\n\\n with rasterio.open(path, 'w', **profile) as dst:\\n for i in range(depth):\\n dst.write(raster[:, :, i], indexes=i+1)\",\n \"def reprojectAndSaveNewRaster(inFilepath,outFilepath,to_EPSG):\\r\\n from osgeo import gdal\\r\\n input_raster = gdal.Open(inFilepath)\\r\\n EPSG_string = \\\"EPSG:\\\"+str(to_EPSG)\\r\\n ras = gdal.Warp(outFilepath,input_raster,dstSRS=EPSG_string)\\r\\n del ras\",\n \"def reprojectAndSaveNewShapefile(inFilepath,outFilepath,to_EPSG):\\r\\n import geopandas as gpd\\r\\n from fiona.crs import from_epsg\\r\\n\\r\\n inFile = gpd.read_file(inFilepath)\\r\\n inFile_proj = inFile.copy()\\r\\n inFile_proj['geometry'] = inFile_proj['geometry'].to_crs(epsg=to_EPSG)\\r\\n inFile_proj.crs = from_epsg(to_EPSG)\\r\\n inFile_proj.to_file(outFilepath)\",\n \"def UpdateGT(out_file, data, src_file, epsg = 4326, drv = 'GTiff', datatype = gdal.GDT_Float32, NoData = -999):\\n \\n #assign which driver to use for the file format - default is .tif\\n driver = gdal.GetDriverByName(drv)\\n \\n #source raster from which to use the geospatial metadata\\n src_gt = gdal.Open(src_file)\\n \\n #data shape - detects whether to produce multiple bands or not\\n if len(data.shape) == 2:\\n [cols, rows] = data.shape\\n n = 1\\n else:\\n [n, cols, rows] = data.shape\\n\\n #create the destination file\\n dst_gt = driver.Create(out_file, rows, cols, n, datatype)\\n \\n #get the geotransform from the source file\\n gt = src_gt.GetGeoTransform()\\n \\n #coordinate system in which to create the raster, in wkt form\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(epsg)\\n dest_wkt = srs.ExportToWkt()\\n \\n #set the projection in the desired reference system\\n dst_gt.SetGeoTransform(gt)\\n dst_gt.SetProjection(dest_wkt)\\n \\n #set the data\\n if n==1:\\n dst_gt.GetRasterBand(1).WriteArray(data)\\n dst_gt.GetRasterBand(1).SetNoDataValue(NoData)\\n #close and write to file\\n dst_gt.FlushCache()\\n #used if there are multiple bands\\n else:\\n for i in range(n):\\n dst_gt.GetRasterBand(i+1).WriteArray(data[i])\\n dst_gt.GetRasterBand(i+1).SetNoDataValue(NoData)\\n dst_gt.FlushCache()\",\n \"def copy(self, src_path: str, tgt_path: str) -> None:\",\n \"def _export_geotiff(self, filepath: str, z_positive_up: bool = True):\\n\\n nodatavalue = 1000000.0\\n data, geo_transform, bandnames = self._gdal_preprocessing(nodatavalue=nodatavalue, z_positive_up=z_positive_up)\\n gdal_raster_create(filepath, data, geo_transform, self.crs, nodatavalue=nodatavalue, bandnames=bandnames,\\n driver='GTiff')\",\n \"def save_geotiff(df, data_col, crs, x_col='x', y_col='y', time_col=None, nfiles='many', export_path='geotiff.tif', grid_res=None):\\n\\n ### create the xy coordinates\\n if time_col is None:\\n xy1 = df[[x_col, y_col]]\\n else:\\n time = df[time_col].sort_values().unique()\\n xy1 = df.loc[df[time_col] == time[0], [x_col, y_col]]\\n if any(xy1.duplicated()):\\n raise ValueError('x and y coordinates are not unique!')\\n\\n ### Determine grid res\\n if grid_res is None:\\n res_df1 = (xy1.loc[0] - xy1).abs()\\n res_df2 = res_df1.replace(0, nan).min()\\n x_res = res_df2[x_col]\\n y_res = res_df2[y_col]\\n elif isinstance(grid_res, int):\\n x_res = y_res = grid_res\\n else:\\n raise ValueError('grid_res must either be None or an integer.')\\n\\n ### Make the affline transformation for Rasterio\\n trans2 = transform.from_origin(xy1[x_col].min() - x_res/2, xy1[y_col].max() + y_res/2, x_res, y_res)\\n\\n ### Make the rasters\\n if time_col is None:\\n z = df.set_index([y_col, x_col])[data_col].unstack().values[::-1]\\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs, pass_str=True), transform=trans2)\\n new_dataset.write(z, 1)\\n new_dataset.close()\\n else:\\n if nfiles == 'one':\\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=len(time), dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\\n for i in range(1, len(time)+1):\\n z = df.loc[df[time_col] == time[i - 1]].set_index([y_col, x_col])[data_col].unstack().values[::-1]\\n new_dataset.write(z, i)\\n new_dataset.close()\\n elif nfiles == 'many':\\n file1 = path.splitext(export_path)[0]\\n for i in time:\\n str_date = to_datetime(i).strftime('%Y-%m-%d_%H')\\n file2 = file1 + '_' + str_date + '.tif'\\n z = df.loc[df[time_col] == i].set_index([y_col, x_col])[data_col].unstack().values[::-1]\\n new_dataset = ras_open(file2, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\\n new_dataset.write(z, 1)\\n new_dataset.close()\",\n \"def create_geotiff(pixels, gps_bounds, out_path, nodata=-99, asfloat=False, extractor_info=None, system_md=None, extra_metadata=None, compress=False):\\n dimensions = numpy.shape(pixels)\\n if len(dimensions) == 2:\\n nrows, ncols = dimensions\\n channels = 1\\n else:\\n nrows, ncols, channels = dimensions\\n\\n geotransform = (\\n gps_bounds[2], # upper-left x\\n (gps_bounds[3] - gps_bounds[2])/float(ncols), # W-E pixel resolution\\n 0, # rotation (0 = North is up)\\n gps_bounds[1], # upper-left y\\n 0, # rotation (0 = North is up)\\n -((gps_bounds[1] - gps_bounds[0])/float(nrows)) # N-S pixel resolution\\n )\\n\\n # Create output GeoTIFF and set coordinates & projection\\n if asfloat:\\n dtype = gdal.GDT_Float32\\n else:\\n dtype = gdal.GDT_Byte\\n\\n if compress:\\n output_raster = gdal.GetDriverByName('GTiff') \\\\\\n .Create(out_path, ncols, nrows, channels, dtype, ['COMPRESS=LZW'])\\n else:\\n output_raster = gdal.GetDriverByName('GTiff') \\\\\\n .Create(out_path, ncols, nrows, channels, dtype)\\n\\n output_raster.SetGeoTransform(geotransform)\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326) # google mercator\\n output_raster.SetProjection( srs.ExportToWkt() )\\n\\n if not extra_metadata:\\n extra_metadata = prepare_metadata_for_geotiff(extractor_info, system_md)\\n\\n output_raster.SetMetadata(extra_metadata)\\n\\n if channels == 3:\\n # typically 3 channels = RGB channels\\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\\n output_raster.GetRasterBand(1).WriteArray(pixels[:,:,0].astype('uint8'))\\n output_raster.GetRasterBand(1).SetColorInterpretation(gdal.GCI_RedBand)\\n output_raster.GetRasterBand(1).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\\n\\n output_raster.GetRasterBand(2).WriteArray(pixels[:,:,1].astype('uint8'))\\n output_raster.GetRasterBand(2).SetColorInterpretation(gdal.GCI_GreenBand)\\n output_raster.GetRasterBand(2).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(2).SetNoDataValue(nodata)\\n\\n output_raster.GetRasterBand(3).WriteArray(pixels[:,:,2].astype('uint8'))\\n output_raster.GetRasterBand(3).SetColorInterpretation(gdal.GCI_BlueBand)\\n output_raster.GetRasterBand(3).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(3).SetNoDataValue(nodata)\\n\\n elif channels > 1:\\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\\n for chan in range(channels):\\n band = chan + 1\\n output_raster.GetRasterBand(band).WriteArray(pixels[:,:,chan].astype('uint8'))\\n output_raster.GetRasterBand(band).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(band).SetNoDataValue(nodata)\\n else:\\n # single channel image, e.g. temperature\\n output_raster.GetRasterBand(1).WriteArray(pixels)\\n output_raster.GetRasterBand(1).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\\n\\n output_raster = None\",\n \"def reproject(src_path, out_path, target_crs='EPSG:4326'):\\n\\n # load satdata\\n satdata = load(src_path)\\n\\n # calculate a transform and new dimensions using our dataset's current CRS and dimensions\\n transform, width, height = calculate_default_transform(satdata.crs,\\n target_crs,\\n satdata.width,\\n satdata.height,\\n *satdata.bounds)\\n\\n # Using a copy of the metadata from the clipped raster dataset and the transform we defined above,\\n # we can write a new geoTIFF containing the reprojected and clipped raster data:\\n metadata = satdata.meta.copy()\\n\\n # Change the CRS, transform, and dimensions in metadata to match our desired output dataset\\n metadata.update({'crs':target_crs,\\n 'transform':transform,\\n 'width':width,\\n 'height':height})\\n\\n # apply the transform & metadata to perform the reprojection\\n with rasterio.open(out_path, 'w', **metadata) as reprojected:\\n for band in range(1, satdata.count + 1):\\n rasterio_reproject(\\n source=rasterio.band(satdata, band),\\n destination=rasterio.band(reprojected, band),\\n src_transform=satdata.transform,\\n src_crs=satdata.crs,\\n dst_transform=transform,\\n dst_crs=target_crs\\n )\",\n \"def _gtTSmap(self):\\n if os.path.isfile(self.outtsmap):\\n # Already exists\\n return\\n\\n if self.csys == 'GAL':\\n center_icrs = SkyCoord(ra=self.ra*u.degree, dec=self.dec*u.degree, frame='icrs')\\n self.ra = center_icrs.galactic.l.deg\\n self.dec = center_icrs.galactic.b.deg\\n\\n model = os.path.join(self.workpath, 'TSmapModel.xml') \\n rfil = open(self.outmodel, 'r')\\n wfil = open(model, 'w')\\n isSrc = False\\n isDif = False\\n for line in rfil:\\n if (isSrc) and ('<source name' in line):\\n # Arrived to a new source, restart copying\\n isSrc = False\\n if (isDif) and ('<source name' in line) and ('PointSource' in line):\\n isDif = False\\n if 'TARGET' in line:\\n isSrc = True\\n if ('<source name=\\\"gll_iem_v06\\\"' in line) or ('<source name=\\\"iso_source_v06\\\"' in line): \\n isDif = True\\n \\n if isSrc:\\n # Do not copy the Target model to make it appear in the TS map\\n pass\\n else:\\n if isDif:\\n # Leave Diffuse model normalizations free\\n wfil.write(line)\\n else:\\n # Make sur the gtlike output source model has all source parameters fixed\\n wfil.write(line.replace('free=\\\"1\\\"', 'free=\\\"0\\\"'))\\n rfil.close()\\n wfil.close()\\n\\n # Launch the gttsmap tool \\n if self.mode == 'binned':\\n os.popen(\\\"gttsmap evfile={} scfile={} bexpmap={} expcube={} cmap={} srcmdl={}\\\\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=BINNED ftol=1e-2\\\\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\\\".format(self.outmktime,\\n self.ft2, self.outbinexp, self.outltcube, self.outbincub, model, self.outtsmap, self.evtype,\\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\\n elif self.mode == 'unbinned':\\n os.popen(\\\"gttsmap evfile={} scfile={} expmap={} expcube={} srcmdl={}\\\\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=UNBINNED ftol=1e-2\\\\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\\\".format(self.outmktime,\\n self.ft2, self.outexpmap, self.outltcube, model, self.outtsmap, self.evtype,\\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\\n else:\\n return\\n\\n if self.csys == 'GAL':\\n self.ra = center_icrs.ra.deg\\n self.dec = center_icrs.dec.deg\\n return\",\n \"def test_convert_to_geotiff(self):\\n fnames = modis.convert_to_geotiff(self.fnames[0], outdir=os.path.dirname(__file__))\\n for f in fnames:\\n ext = os.path.splitext(f)[1]\\n suffix = os.path.splitext(f)[0].split('_')[1]\\n self.assertTrue(os.path.exists(f))\\n if ext != '.ovr':\\n self.assertTrue(suffix in products['MCD43A4.006']['bandnames'])\",\n \"def tiffwrite(filename, im):\\n tf.imwrite(filename, im)\",\n \"def store_image(self):\\n cv2.imwrite(self.__diff_filename(), self.__diff_image())\",\n \"def save_converted_paths(\\n src_trace_tuples: Sequence[TraceTuple],\\n dest_trace_tuples: Sequence[TraceTuple],\\n driver: str,\\n destination: Path,\\n):\\n for src_trace_tuple, dest_trace_tuple in zip(src_trace_tuples, dest_trace_tuples):\\n for original_path, convert_path in zip(\\n (src_trace_tuple.traces_path, src_trace_tuple.area_path),\\n (dest_trace_tuple.traces_path, dest_trace_tuple.area_path),\\n ):\\n convert_filetype(original_path, destination / convert_path, driver=driver)\",\n \"def gtiff(location):\\n\\n for src_asc in os.listdir(location):\\n if \\\".asc\\\" in src_asc:\\n dest_gtif = src_asc[:-4] + '.gtiff'\\n command = \\\"gdal_translate -of GTiff -ot Float32 \\\" + location +'\\\\\\\\' \\\\\\n + src_asc + ' ' + location +'\\\\\\\\' + dest_gtif\\n os.system(command)\",\n \"def save_array_as_geotif(array, source_tif_path, out_path):\\n if len(array.shape) > 2:\\n height, width, depth = array.shape\\n else:\\n height, width = array.shape\\n depth = 1\\n source_tif = gdal.Open(source_tif_path)\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n dataset = driver.Create(out_path, width, height, depth, gdal.GDT_Float32)\\n if depth != 1:\\n for i in range(depth):\\n dataset.GetRasterBand(i + 1).WriteArray(array[:, :, i])\\n else:\\n dataset.GetRasterBand(1).WriteArray(array)\\n geotrans = source_tif.GetGeoTransform()\\n proj = source_tif.GetProjection()\\n dataset.SetGeoTransform(geotrans)\\n dataset.SetProjection(proj)\\n dataset.FlushCache()\\n dataset = None\",\n \"def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\\n args = [\\n \\\"gdal_calc.py\\\",\\n '-A', infile,\\n '--outfile={}'.format(tmpfile_1),\\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\\n '--type=Byte', '--NoDataValue=0',\\n '--co=SPARSE_OK=YES',\\n '--co=NBITS=1',\\n '--quiet'\\n # Could enable compression\\n # --co=\\\"COMPRESS=LZW\\\"\\n ]\\n subprocess.run(args)\\n\\n subprocess.run([\\n \\\"gdal_polygonize.py\\\",\\n tmpfile_1,\\n '-q',\\n '-f', 'ESRI Shapefile',\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\n \\\"ogr2ogr\\\",\\n '-a_srs', 'EPSG:4326',\\n outfile,\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\\"rm\\\", tmpfile_1])\\n subprocess.run([\\\"rm\\\", tmpfile_2])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'shx')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'dbf')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'prj')])\",\n \"def geotiff_to_netcdf(path, epsg, download=False, output_path='', name='', non_negative=True, savefig=False,\\n fig_path=''):\\n\\n ds = gdal.Open(path)\\n\\n # upper left, resolution and skew of x and y\\n ulx, xres, xskew, uly, yskew, yres = ds.GetGeoTransform()\\n\\n # lower right x and y\\n lrx = ulx + (ds.RasterXSize * xres)\\n lry = uly + (ds.RasterYSize * yres)\\n\\n # setup the source projection - you can also import from epsg, proj4...\\n source = osr.SpatialReference()\\n source.ImportFromWkt(ds.GetProjection())\\n\\n # the target projection\\n target = osr.SpatialReference()\\n target.ImportFromEPSG(4326)\\n\\n # create the transform - this can be used repeatedly\\n transform = osr.CoordinateTransformation(source, target)\\n\\n # transform the point. You can also create an ogr geometry and use the more generic `point.Transform()`\\n uly, ulx, _ = transform.TransformPoint(ulx, uly)\\n lry, lrx, _ = transform.TransformPoint(lrx, lry)\\n\\n lons = np.linspace(ulx, lrx, ds.RasterXSize)\\n lats = np.linspace(lry, uly, ds.RasterYSize)\\n\\n data = ds.ReadAsArray()\\n\\n if non_negative: data[data < 0] = np.nan\\n\\n ds = xr.DataArray(np.flipud(data), coords={'lat': lats, 'lon': lons, }, dims=['lat', 'lon'])\\n\\n ds.name = name\\n\\n if download and len(output_path) > 0:\\n ds.to_netcdf(output_path)\\n\\n if savefig and len(fig_path) > 0:\\n fig, ax = plt.subplots(figsize=(15, 10))\\n ds.plot(ax=ax)\\n plt.savefig(fig_path)\\n plt.close()\\n\\n return ds\",\n \"def generate(path, cam_orig, cam_new, fnames_left, fnames_right,\\n fnames_target_left, fnames_target_right):\\n with tf.io.TFRecordWriter(path) as tfrecord_writer:\\n with tf.Graph().as_default():\\n im0 = tf.compat.v1.placeholder(dtype=tf.uint8)\\n im1 = tf.compat.v1.placeholder(dtype=tf.uint8)\\n encoded0 = tf.image.encode_png(im0)\\n encoded1 = tf.image.encode_png(im1)\\n\\n with tf.compat.v1.Session() as sess:\\n for fleft, fright, ftleft, ftright in zip(fnames_left, fnames_right,\\n fnames_target_left,\\n fnames_target_right):\\n assert (os.path.basename(\\n os.path.splitext(fleft)[0]) == os.path.basename(\\n os.path.splitext(ftleft)[0]))\\n assert (os.path.basename(\\n os.path.splitext(fright)[0]) == os.path.basename(\\n os.path.splitext(ftright)[0]))\\n print(fleft)\\n image_left = utils.read_image(fleft)\\n image_right = utils.read_image(fright)\\n targs_left = utils.read_target_pb(ftleft)\\n targs_right = utils.read_target_pb(ftright)\\n image_left = utils.resize_image(image_left, cam_new, cam_orig,\\n targs_left)\\n image_right = utils.resize_image(image_right, cam_new, cam_orig,\\n targs_right)\\n st0, st1 = sess.run([encoded0, encoded1],\\n feed_dict={\\n im0: image_left,\\n im1: image_right\\n })\\n feats = {'img_L': bytes_feature(st0), 'img_R': bytes_feature(st1)}\\n feats.update(make_features(targs_left, 'L'))\\n feats.update(make_features(targs_right, 'R'))\\n example = tf.train.Example(features=tf.train.Features(feature=feats))\\n tfrecord_writer.write(example.SerializeToString())\",\n \"def copy_vrt(in_fname, out_fname=None, bbox=None, verbose=True):\\n from gdal import Translate\\n\\n if out_fname is None:\\n out_fname = in_fname + \\\".vrt\\\"\\n\\n # Using Translate... but would use Warp if every reprojecting\\n if bbox:\\n left, bottom, right, top = bbox\\n projwin = (left, top, right, bottom) # unclear why Translate does UL LR\\n else:\\n projwin = None\\n if verbose:\\n logger.info(f\\\"Creating {out_fname}, subset bbox: {bbox}\\\")\\n Translate(out_fname, in_fname, projWin=projwin)\",\n \"def test_make_mask_w_output_file(self):\\n output_mask = footprint_mask(\\n os.path.join(data_dir, 'sample.csv'),\\n geom_col=\\\"PolygonWKT_Pix\\\",\\n reference_im=os.path.join(data_dir, \\\"sample_geotiff.tif\\\"),\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(os.path.join(data_dir,\\n 'sample_fp_mask.tif'))\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(output_mask, truth_mask)\\n assert np.array_equal(saved_output_mask, truth_mask)\\n # clean up\\n os.remove(os.path.join(data_dir, 'test_out.tif'))\",\n \"def test_make_mask_w_file_and_transform(self):\\n output_mask = footprint_mask(\\n os.path.join(data_dir, 'geotiff_labels.geojson'),\\n reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\\n do_transform=True,\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(\\n os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif')\\n )\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(output_mask, truth_mask)\\n assert np.array_equal(saved_output_mask, truth_mask)\\n # clean up\\n os.remove(os.path.join(data_dir, 'test_out.tif'))\",\n \"def subset_raster_to_extent_of_other(src_filename, match_filename, dst_filename):\\n\\n\\t# Source\\n\\tsrc = gdal.Open(src_filename, gdalconst.GA_ReadOnly)\\n\\tsrc_proj = src.GetProjection()\\n\\tsrc_geotrans = src.GetGeoTransform()\\n\\n\\t# We want a section of source that matches this:\\n\\tmatch_ds = gdal.Open(match_filename, gdalconst.GA_ReadOnly)\\n\\tmatch_proj = match_ds.GetProjection()\\n\\tmatch_geotrans = match_ds.GetGeoTransform()\\n\\twide = match_ds.RasterXSize\\n\\thigh = match_ds.RasterYSize\\n\\n\\t# Output / destination\\n\\tdst = gdal.GetDriverByName('GTiff').Create(dst_filename, wide, high, 1, gdalconst.GDT_Float32)\\n\\tdst.SetGeoTransform( match_geotrans )\\n\\tdst.SetProjection( match_proj)\\n\\n\\t# Do the work\\n\\tgdal.ReprojectImage(src, dst, src_proj, match_proj, gdalconst.GRA_Bilinear)\\n\\n\\tdel dst # Flush\",\n \"def savecopy(self, filename, lineendings=\\\"default\\\", encoding=\\\"latin-1\\\"):\\n super(IDF, self).save(filename, lineendings, encoding)\\n return Path(filename)\",\n \"def _write_image(self):\\r\\n # Create an output raster with the correct number of rows and columns.\\r\\n gtiff_driver = gdal.GetDriverByName('GTiff')\\r\\n out_ds = gtiff_driver.Create(os.path.join(self.out_folder, self.out_file_name), self.column, self.row, 1)\\r\\n out_ds.SetProjection(self.in_ds.GetProjection())\\r\\n\\r\\n # Convert the offsets to real-world coordinates for the georeferencing info.\\r\\n # We can't use the coordinates above because they don't correspond to the pixel edges.\\r\\n subset_ulx, subset_uly = gdal.ApplyGeoTransform(self.in_gt, self.off_ulx, self.off_uly)\\r\\n out_gt = list(self.in_gt)\\r\\n out_gt[0] = subset_ulx\\r\\n out_gt[3] = subset_uly\\r\\n out_ds.SetGeoTransform(out_gt)\\r\\n\\r\\n data = self.read_image()\\r\\n out_band = out_ds.GetRasterBand(1)\\r\\n out_band.WriteArray(data)\\r\\n\\r\\n del out_ds\",\n \"def run_merge(*src, argv=None):\\n tmpdir = tempfile.mkdtemp()\\n inputs = []\\n for i, drv in enumerate(src):\\n if type(drv) != str:\\n tmppath = os.path.join(tmpdir, \\\"input_%s.tif\\\" % i)\\n drv.write(tmppath)\\n inputs.append(tmppath)\\n else:\\n inputs.append(src)\\n\\n if \\\"-o\\\" in argv:\\n outpath = argv[argv.index(\\\"-o\\\") + 1]\\n else:\\n outpath = os.path.join(tempfile.gettempdir(), \\\"%s.tif\\\" % next(tempfile._get_candidate_names()))\\n logger.debug(\\\"Writing to file %s\\\" % outpath)\\n argv += [\\\"-o\\\", outpath]\\n argv = gdal.GeneralCmdLineProcessor(argv)\\n options = argv + inputs\\n assert run_external_app(\\\"gdal_merge.py\\\", options) == 0, \\\"Error running gdal_merge\\\"\\n remove_directory(tmpdir)\\n return gdal.Open(outpath)\",\n \"def ascii_to_tiff(infile, outfile, refIm):\",\n \"def make_diff(file_before, file_after, file_output_name):\\n if os.path.exists(file_output_name):\\n shutil.rmtree(file_output_name)\\n os.mkdir(file_output_name)\\n psd_diff = diff(file_before, file_after)\\n diff_content = {}\\n for attr in [\\\"header\\\", \\\"layer\\\"]:\\n diff_content[attr] = getattr(psd_diff, attr)\\n with open(os.path.join(file_output_name, \\\"diff.json\\\"), \\\"w\\\") as diff_file:\\n json.dump(diff_content, diff_file, indent=4)\\n saved_files = []\\n for layer_id in psd_diff.layer.keys():\\n if len(psd_diff.layer_image[layer_id]) > 1:\\n output_image = os.path.join(file_output_name, layer_id)\\n psd_diff.layer_image[layer_id][\\\"before\\\"].save(output_image + \\\".before.png\\\")\\n psd_diff.layer_image[layer_id][\\\"after\\\"].save(output_image + \\\".after.png\\\")\\n diff_image_before = Image.new(\\\"RGBA\\\", psd_diff.layer_image[layer_id][\\\"before\\\"].size)\\n diff_image_before_data = diff_image_before.load()\\n diff_image_after = Image.new(\\\"RGBA\\\", psd_diff.layer_image[layer_id][\\\"after\\\"].size)\\n diff_image_after_data = diff_image_after.load()\\n width, height = diff_image_before.size\\n pixel_index = 1\\n for y in xrange(height):\\n for x in xrange(width):\\n if str(pixel_index) in diff_content[\\\"layer\\\"][layer_id][\\\"pixel\\\"]:\\n diff_image_before_data[x, y] = tuple(diff_content[\\\"layer\\\"][layer_id][\\\"pixel\\\"][str(pixel_index)][\\\"before\\\"])\\n diff_image_after_data[x, y] = tuple(diff_content[\\\"layer\\\"][layer_id][\\\"pixel\\\"][str(pixel_index)][\\\"after\\\"])\\n else:\\n diff_image_before_data[x, y] = (0, 0, 0, 0)\\n diff_image_after_data[x, y] = (0, 0, 0, 0)\\n pixel_index += 1\\n diff_image_before.save(output_image + \\\".before.diff.png\\\", \\\"PNG\\\")\\n diff_image_after.save(output_image + \\\".after.diff.png\\\", \\\"PNG\\\")\\n saved_files.append(output_image + \\\".before.png\\\")\\n saved_files.append(output_image + \\\".before.diff.png\\\")\\n saved_files.append(output_image + \\\".after.diff.png\\\")\\n saved_files.append(output_image + \\\".after.png\\\")\\n saved_files.append(file_output_name + \\\"/diff.json\\\")\\n return saved_files\",\n \"def copy_model_file(src_dir, dst_dir, file_name, file_ext=None):\\n file_ext = 'osm' if file_ext is None else file_ext\\n\\n src_file = src_dir.joinpath('in.{}'.format(file_ext))\\n dst_file = dst_dir.joinpath('{}.{}'.format(file_name, file_ext))\\n try:\\n shutil.copyfile(src_file, dst_file)\\n except FileNotFoundError:\\n print('''in.{} doesn't exist.'''.format(file_ext))\",\n \"def save(self, fname):\\n lh_data = self.data[:len(self.lh_vertno)]\\n rh_data = self.data[-len(self.rh_vertno):]\\n\\n print 'Writing STC to disk...',\\n write_stc(fname + '-lh.stc', tmin=self.tmin, tstep=self.tstep,\\n vertices=self.lh_vertno, data=lh_data)\\n write_stc(fname + '-rh.stc', tmin=self.tmin, tstep=self.tstep,\\n vertices=self.rh_vertno, data=rh_data)\\n print '[done]'\",\n \"def tiff(location):\\n\\n for src_gtiff in os.listdir(location):\\n if \\\".gtiff\\\" in src_gtiff:\\n dest_tif = src_gtiff[:-6] + '.tiff'\\n command = \\\"gdal_translate -co profile=baseline \\\" + src_gtiff + ' ' \\\\\\n + dest_tif\\n os.system(command)\",\n \"def update_sensor_file(src, dst):\\n\\t\\tcon = mdb.connect('localhost', 'root', 'sensepass', 'sensecambrowser')\\n\\t\\twith con:\\n\\t\\t\\tsrc = \\\"'\\\"+\\tsrc+\\\"'\\\"\\n\\t\\t\\tdst = \\\"'\\\"+\\tdst+\\\"'\\\"\\n\\t\\t\\tquery = \\\"UPDATE fileuploader_sensorfile SET file=%s WHERE file=%s\\\" % (src,dst)\\n\\t\\t\\tcur = con.cursor()\\n\\t\\t\\tcur.execute(query)\",\n \"def nc2gtiff(input_name, epsg_in=3413, epsg_out=3857):\\n sub_name=[\\\"dX\\\",\\\"dY\\\"]\\n \\n for sub in sub_name:\\n result = os.system(\\\"echo ''\\\")\\n #netCDF to geotiff (EPSG code is not changed) \\n out_name = \\\"./data/tiff_raw/{}_{}_tmp.tiff\\\".format(os.path.basename(input_name)[:-3],sub)\\n #c is shell command\\n #options\\n #-a_srs:EPSGcode of input files\\n #-of:output format\\n #\\n print(\\\"nc2gtiff\\\")\\n c = \\\"gdal_translate -a_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' NETCDF:'{in_name}':{sub} \\\\\\n -of 'Gtiff' \\\\\\n '{out_name}'\\\".format(epsg=epsg_in,sub=sub, \\\\\\n in_name=input_name,out_name=out_name) \\n result = os.system(c) #run shell command\\n #print(c) #if you want to see the filled c, remove '#' in the head of this line\\n if result!=0: # if it raises error, return filename \\n print(input_name, result, \\\"translate\\\")\\n \\n result = os.system(\\\"echo ''\\\")\\n\\n #geotiff EPSG_in to EPCG_out\\n print(\\\"geotiff2geotiff\\\")\\n target_name=\\\"./data/tiff_target/{}_{}.tiff\\\".format(os.path.basename(input_name)[:-3],sub)\\n c = \\\"gdalwarp -overwrite -s_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' {out_name} -r cubic\\\\\\n {target_name} -t_srs EPSG:{epsg_out} -of \\\\\\n 'GTIFF'\\\".format(out_name=out_name, target_name=target_name, \\n epsg_in=epsg_in, epsg_out=epsg_out)\\n result = os.system(c) # run shell command\\n if result!=0:\\n print(input_name, result, \\\"warp\\\")\",\n \"def test_RawImage_write_out():\\n i.write_out()\\n # now compare the output with reference\\n print i.outpath\\n print t.processed_path\\n assert_image_equal(i.outpath, t.processed_path)\",\n \"def correct(img_src, shift, destfile, crop=None):\\n (y0, y1, x0, x1) = crop\\n (shift_y, shift_x) = shift\\n tf_shift = transform.SimilarityTransform(translation=[shift_x, shift_y])\\n img = transform.warp(img_src, tf_shift)\\n res = img[-y0:-y1, -x0:-x1, :]\\n io.imsave(destfile, res)\\n return destfile\",\n \"def outputGeoTiff(data, filename, geo_t, proj, output_dir = os.getcwd(), dtype = 6, nodata = None):\\n \\n from osgeo import osr, gdal\\n \\n # Get full output path\\n output_path = '%s/%s.tif'%(os.path.abspath(os.path.expanduser(output_dir)), filename.rstrip('.tif'))\\n \\n # Save image with georeference info\\n driver = gdal.GetDriverByName('GTiff')\\n ds = driver.Create(output_path, data.shape[1], data.shape[0], 1, dtype, options = ['COMPRESS=LZW'])\\n ds.SetGeoTransform(geo_t)\\n ds.SetProjection(proj)\\n \\n # Set nodata\\n if nodata != None:\\n ds.GetRasterBand(1).SetNoDataValue(nodata)\\n \\n # Write data for masked and unmasked arrays\\n if np.ma.isMaskedArray(data):\\n ds.GetRasterBand(1).WriteArray(data.filled(nodata))\\n else:\\n ds.GetRasterBand(1).WriteArray(data)\\n ds = None\",\n \"def save_tif(self, name='', data='', geo='', projection=''):\\n # save as a geotiff\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1,\\n gdal.GDT_Float32, ['COMPRESS=LZW'])\\n srse = osr.SpatialReference()\\n if projection == '':\\n srse.SetWellKnownGeogCS(\\\"WGS84\\\")\\n\\n else:\\n try:\\n if not srse.SetWellKnownGeogCS(projection) == 6:\\n srse.SetWellKnownGeogCS(projection)\\n else:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except BaseException as err:\\n print(err)\\n else:\\n srse.ImportFromWkt(projection)\\n except BaseException:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except BaseException as err:\\n print(err)\\n else:\\n srse.ImportFromWkt(projection)\\n\\n dst_ds.SetProjection(srse.ExportToWkt())\\n dst_ds.SetGeoTransform(geo)\\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\\n dst_ds.GetRasterBand(1).WriteArray(data)\\n dst_ds = None\\n\\n return\",\n \"def transcode(self, src: Path, dest: Path) -> None:\\n pass\",\n \"def transfer_roi(im_src, im_dst, dst_img):\\n \\n # get the pixel value and the location of the roi\\n roi_pos = np.where(im_src[:,:,2] == 255)\\n \\n #get the list of the x and y coordinates of the location of roi\\n x = list(roi_pos[0])\\n y = list(roi_pos[1])\\n \\n dst_with_roi = copy.deepcopy(im_dst)\\n dst_with_roi[x, y] = [0,0,255]\\n \\n marked_folder_roi = 'E:\\\\\\\\transfer_roi\\\\\\\\ir\\\\\\\\marked'\\n marked_path = os.path.join(marked_folder_roi, dst_img)\\n \\n if not os.path.exists(marked_path):\\n cv2.imwrite(marked_path, dst_with_roi)\\n \\n return dst_with_roi\",\n \"def save_img(self):\\r\\n self.extract_info_from_file()\\r\\n path_0 = os.path.join(self.output_path, self.field_id, self.patient_id + self.ext)\\r\\n path_1 = os.path.join(self.output_path, self.field_id + '_' + self.instance, self.patient_id + self.ext)\\r\\n if self.shot == '0': # first shot\\r\\n if os.path.exists(path_0) or os.path.exists(path_1):\\r\\n print(self.patient_id, 'already done')\\r\\n pass\\r\\n else:\\r\\n if not self.img_computed:\\r\\n self.compute_img()\\r\\n if self.instance == '0':\\r\\n self.img.save(path_0)\\r\\n else:\\r\\n self.img.save(path_1)\\r\\n else: # newer shot\\r\\n if not self.img_computed:\\r\\n self.compute_img()\\r\\n if self.instance == '0':\\r\\n self.img.save(path_0)\\r\\n else:\\r\\n self.img.save(path_1)\",\n \"def _translate(src_path, dst_path, profile=\\\"lzw\\\", profile_options={}, **options):\\n # Format creation option (see gdalwarp `-co` option)\\n output_profile = cog_profiles.get(profile)\\n output_profile.update(dict(BIGTIFF=\\\"IF_SAFER\\\"))\\n output_profile.update(profile_options)\\n\\n # Dataset Open option (see gdalwarp `-oo` option)\\n config = dict(\\n GDAL_NUM_THREADS=\\\"ALL_CPUS\\\",\\n GDAL_TIFF_INTERNAL_MASK=True,\\n GDAL_TIFF_OVR_BLOCKSIZE=\\\"128\\\",\\n )\\n\\n cog_translate(\\n src_path,\\n dst_path,\\n output_profile,\\n config=config,\\n in_memory=False,\\n quiet=True,\\n **options,\\n )\\n return True\",\n \"def copy_local(dwn_url, copy_dir):\\n # Path to local repository of DTMs\\n local_dir = \\\"l:\\\\\\\\Slovenija\\\\\\\\DEM_D96\\\"\\n # Prepare paths\\n _, name = split(dwn_url[:-4])\\n fil_nam = name + \\\".tif\\\"\\n src_pth = join(local_dir, fil_nam)\\n dst_pth = join(copy_dir, fil_nam)\\n\\n # Open TIF file\\n tif = rasterio.open(src_pth)\\n\\n # Check CRS\\n if tif.crs is None:\\n meta = tif.profile.copy()\\n meta.update({\\\"crs\\\": CRS.from_epsg(3794)}) # D96/TM\\n array = tif.read()\\n with rasterio.open(dst_pth, \\\"w\\\", **meta) as dest:\\n dest.write(array)\\n tif.close()\\n else:\\n tif.close()\\n copyfile(src_pth, dst_pth)\\n\\n # Output\\n status_msg = f\\\"{fil_nam} succsesfully copied\\\"\\n\\n return status_msg, fil_nam\",\n \"def save_file(self, input_path, output_path):\\n try:\\n im = Image.open(input_path)\\n im.save(output_path)\\n return output_path\\n except Exception as e:\\n return '!ERROR' + str(e)\",\n \"def cli(source_f, raster_f, output, verbose):\\n with fiona.open(source_f, 'r') as source:\\n source_driver = source.driver\\n source_crs = source.crs\\n sink_schema = source.schema.copy()\\n\\n source_geom = source.schema['geometry']\\n if source_geom == 'Point':\\n sink_schema['geometry'] = '3D Point'\\n elif source_geom == 'LineString':\\n sink_schema['geometry'] = '3D LineString'\\n elif source_geom == '3D Point' or source_geom == '3D LineString':\\n pass\\n else:\\n click.BadParameter(\\\"Source geometry type {} not implemented\\\".format(source_geom))\\n\\n with rasterio.open(raster_f) as raster:\\n if source_crs != raster.crs:\\n click.BadParameter(\\\"Features and raster have different CRS.\\\")\\n if raster.count > 1:\\n warnings.warn(\\\"Found {0} bands in {1}, expected a single band raster\\\".format(raster.bands, raster_f))\\n supported = ['int16', 'int32', 'float32', 'float64']\\n if raster.dtypes[0] not in supported:\\n warnings.warn(\\\"Found {0} type in {1}, expected one of {2}\\\".format(raster.dtypes[0], raster_f, supported))\\n with fiona.open(\\n output, 'w',\\n driver=source_driver,\\n crs=source_crs,\\n schema=sink_schema) as sink:\\n\\n for feature in source:\\n try:\\n feature_z = drapery.drape(raster, feature)\\n sink.write({\\n 'geometry': mapping(feature_z),\\n 'properties': feature['properties'],\\n })\\n except Exception:\\n logging.exception(\\\"Error processing feature %s:\\\", feature['id'])\\n #print(sink.closed)\\n #print(raster.closed)\\n #print(source.closed)\",\n \"def copy(self, newPath):\\n\\t\\tif self.hasUdim:\\n\\t\\t\\tfor a in self.udimPaths:\\n\\t\\t\\t\\tfi = a.copy( newPath )\\n\\t\\t\\treturn fi\\n\\t\\telse:\\n\\t\\t\\treturn textureFile( super( textureFile, self ).copy( newPath ).path )\",\n \"def test_save_tif():\\n img = Image.new('RGB', (10, 20))\\n\\n parameters = {'path': 'green-dot.tif', 'data': [img]}\\n\\n assert images.save(parameters)\",\n \"def reproject_vector( path, epsg_from=None, epsg_to=None):\\n\\n if not epsg_to: raise Exception(\\\"please, specify the output EPSG codes\\\")\\n\\n inDataSet = None\\n outDataSet = None\\n inFeature = None\\n outFeature = None\\n outLayer = None\\n\\n try:\\n\\n driver = ogr.GetDriverByName('ESRI Shapefile')\\n inDataSet = driver.Open(path, 0) # 0 means read-only\\n\\n # define input SpatialReference\\n if not epsg_from:\\n layer = inDataSet.GetLayer()\\n inSpatialRef = layer.GetSpatialRef()\\n else:\\n inSpatialRef = osr.SpatialReference()\\n inSpatialRef.ImportFromEPSG(epsg_from)\\n\\n # define output SpatialReference\\n outSpatialRef = osr.SpatialReference()\\n outSpatialRef.ImportFromEPSG(epsg_to)\\n\\n # create the CoordinateTransformation\\n coordTrans = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)\\n\\n # get the first input layer and the geometry type\\n inLayer = inDataSet.GetLayer()\\n geotype = inLayer.GetGeomType()\\n lname = inLayer.GetName()\\n\\n drv = ogr.GetDriverByName(\\\"ESRI Shapefile\\\")\\n outDataSet = drv.CreateDataSource(\\\"/vsimem/memory.shp\\\")\\n\\n outLayer = outDataSet.CreateLayer(lname, srs=outSpatialRef, geom_type=geotype)\\n\\n # add fields\\n inLayerDefn = inLayer.GetLayerDefn()\\n\\n for i in range(0, inLayerDefn.GetFieldCount()):\\n fieldDefn = inLayerDefn.GetFieldDefn(i)\\n outLayer.CreateField(fieldDefn)\\n\\n # get the output layer\\\"s feature definition\\n outLayerDefn = outLayer.GetLayerDefn()\\n\\n counter = 1\\n\\n # loop through the input features\\n inFeature = inLayer.GetNextFeature()\\n while inFeature:\\n # get the input geometry\\n geom = inFeature.GetGeometryRef()\\n # reproject the geometry\\n geom.Transform(coordTrans)\\n # create a new feature\\n outFeature = ogr.Feature(outLayerDefn)\\n # set the geometry and attribute\\n outFeature.SetGeometry(geom)\\n for i in range(0, outLayerDefn.GetFieldCount()):\\n outFeature.SetField(outLayerDefn.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\\n # add the feature to the shapefile\\n outLayer.CreateFeature(outFeature)\\n\\n # destroy the features and get the next input feature\\n if outFeature: outFeature = None\\n inFeature = inLayer.GetNextFeature()\\n\\n counter += 1\\n #print(counter)\\n\\n return outDataSet\\n\\n except RuntimeError as err:\\n raise err\\n except Exception as e:\\n raise e\\n\\n finally:\\n if inDataSet: outDataSet == None # give back control to C++\\n if outDataSet: outDataSet == None\\n if outLayer: outLayer == None\\n if inFeature: inFeature == None\\n if outFeature: outFeature = None\",\n \"def copy_file(src, dest):\\n with open_local_or_gcs(src, 'r') as h_src:\\n with open_local_or_gcs(dest, 'w') as h_dest:\\n shutil.copyfileobj(h_src, h_dest)\",\n \"def test_make_mask_w_file_and_transform(self):\\n output_mask = road_mask(\\n os.path.join(data_dir, 'sample_roads_for_masking.geojson'),\\n reference_im=os.path.join(data_dir, 'road_mask_input.tif'),\\n width=4, meters=True, do_transform=True,\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(\\n os.path.join(data_dir, 'sample_road_raster_mask.tif')\\n )\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(output_mask, truth_mask)\\n assert np.array_equal(saved_output_mask, truth_mask)\\n # clean up\\n os.remove(os.path.join(data_dir, 'test_out.tif'))\",\n \"def set_src_dst_tensor(self, tik_instance):\\n src_element_number = functools_reduce(lambda x1, x2: x1 * x2,\\n self.src_shape_ori[:])\\n dst_element_number = functools_reduce(lambda x1, x2: x1 * x2,\\n self.dst_shape[:])\\n self.src_gm = tik_instance.Tensor(self.dtype,\\n (src_element_number,),\\n name=\\\"src_gm\\\",\\n scope=tik.scope_gm)\\n self.dst_gm = tik_instance.Tensor(self.dtype,\\n (dst_element_number,),\\n name=\\\"dst_gm\\\",\\n scope=tik.scope_gm)\",\n \"def set_src_dst_tensor(self, tik_instance):\\n src_element_number = functools_reduce(lambda x1, x2: x1 * x2,\\n self.src_shape_ori[:])\\n dst_element_number = functools_reduce(lambda x1, x2: x1 * x2,\\n self.dst_shape[:])\\n self.src_gm = tik_instance.Tensor(self.dtype,\\n (src_element_number,),\\n name=\\\"src_gm\\\",\\n scope=tik.scope_gm)\\n self.dst_gm = tik_instance.Tensor(self.dtype,\\n (dst_element_number,),\\n name=\\\"dst_gm\\\",\\n scope=tik.scope_gm)\",\n \"def tif_to_png(source, remove_old_files):\\n os.chdir(source)\\n subprocess.Popen(\\\"i_view32.exe *.tif /tifc=5 /convert=*.png\\\", shell=True,\\n stdout=subprocess.PIPE, env={'PATH': os.getenv('PATH')}).stdout.read()\\n if remove_old_files == 'yes':\\n list_of_files_in_the_folder = os.listdir(source)\\n for files in list_of_files_in_the_folder:\\n name, ext = os.path.splitext(files)\\n if ext.lower() == \\\".tif\\\":\\n os.remove(os.path.join(source, files))\",\n \"def single_to_rgb(R_file,G_file,B_file): \\n R=gdal_array.LoadFile(R_file)\\n G=gdal_array.LoadFile(G_file)\\n B=gdal_array.LoadFile(B_file)\\n \\n \\n basename=os.path.basename(R_file)\\n basename=basename[:3]+basename[4:]\\n basename=basename[:-4]+\\\"_rgb_.tif\\\" \\n \\n\\n file_path=os.path.dirname(os.path.abspath(R_file))+\\\"/\\\"+basename\\n\\n \\n driver=osgeo.gdal.GetDriverByName(\\\"GTiff\\\")\\n options = ['PHOTOMETRIC=RGB', 'PROFILE=GeoTIFF']\\n print(file_path)\\n print(np.max(np.array([R.shape[1],B.shape[1],G.shape[1]])), np.max(np.array([R.shape[0],B.shape[0],G.shape[0]])))\\n Xlen=np.max(np.array([R.shape[1],B.shape[1],G.shape[1]]))\\n Ylen= np.max(np.array([R.shape[0],B.shape[0],G.shape[0]]))\\n dataset=driver.Create(file_path, int(Xlen),int(Ylen), 3, osgeo.gdal.GDT_UInt16, options) \\n \\n dataset.GetRasterBand(1).WriteArray(R)\\n dataset.GetRasterBand(2).WriteArray(G)\\n dataset.GetRasterBand(3).WriteArray(B)\\n \\n return file_path\",\n \"def set_src_dst_tensor(self, tik_instance):\\n src_element_number = functools_reduce(lambda x, y: x * y,\\n self.src_shape[:])\\n dst_element_number = functools_reduce(lambda x, y: x * y,\\n self.dst_shape[:])\\n self.src_gm = tik_instance.Tensor(self.dtype,\\n (src_element_number,),\\n name=\\\"src_gm\\\",\\n scope=tik.scope_gm)\\n self.dst_gm = tik_instance.Tensor(self.dtype,\\n (dst_element_number,),\\n name=\\\"dst_gm\\\",\\n scope=tik.scope_gm)\",\n \"def imgOutputPath(self, newpath):\\n newimg = self.imagePath().replace(self.home, newpath)\\n return newimg\",\n \"def exporting_cropped_images (fpath_tiff):\\n src = rasterio.open(fpath_tiff, 'r')\\n outfolder_irregular = '/train/irregular'\\n outfolder_healthy = '/train/healthy'\\n outfolder_concrete = '/train/concrete'\\n outfolder_incomplete = '/train/incomplete'\\n outfolder_other = '/train/other'\\n outfolder = '/train/batch'\\n #os.makedirs (outfolder, exist_ok = True)\",\n \"def copy_image(source, dest):\\n\\n # Cast to abs path \\n abs_src = abspath(source)\\n abs_dst = abspath(dest)\\n\\n # OpenCV to open and save image\\n img = cv2.imread(abs_src)\\n cv2.imwrite(abs_dst, img)\",\n \"def Save_as_tiff(name='', data='', geo='', projection=''):\\n \\n dir_name = os.path.dirname(name)\\n \\n if not os.path.exists(dir_name):\\n os.makedirs(dir_name)\\n \\n # save as a geotiff\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1, gdal.GDT_Float32, ['COMPRESS=LZW'])\\n srse = osr.SpatialReference()\\n if projection == '':\\n srse.SetWellKnownGeogCS(\\\"WGS84\\\")\\n\\n else:\\n try:\\n if not srse.SetWellKnownGeogCS(projection) == 6:\\n srse.SetWellKnownGeogCS(projection)\\n else:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except:\\n srse.ImportFromWkt(projection)\\n except:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except:\\n srse.ImportFromWkt(projection)\\n\\n dst_ds.SetProjection(srse.ExportToWkt())\\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\\n dst_ds.SetGeoTransform(geo)\\n dst_ds.GetRasterBand(1).WriteArray(data)\\n dst_ds = None\\n return()\",\n \"def save(annotation, new_filename, original_path):\\n \\n destination = \\\"../../standardized-data/\\\"\\n if os.path.isdir(destination + \\\"/\\\" + annotation) == False:\\n os.mkdir(destination + \\\"/\\\" + annotation)\\n print(annotation, \\\"FOLDER CREATED\\\")\\n if os.path.exists(destination + \\\"/\\\" + annotation + \\\"/\\\" + new_filename):\\n print('FILE EXISTS: DOUBLE CHECK FOR DUPLICATION :', new_filename)\\n else:\\n shutil.copyfile(original_path, destination + \\\"/\\\" + annotation + \\\"/\\\" + new_filename)\\n return\",\n \"def save(self, path=''):\\n if not self.__isBuilt:\\n self._rebuild()\\n if not path:\\n self.w.save(self.path)\\n else:\\n if not path.endswith('.shp'):\\n path = os.path.splitext(path)[0] + '.shp'\\n self.w.save(path)\",\n \"def copy_one_image( class_id, orig_path, train_dir, test_dir, train_prop ) :\\n\\n img = skimage.io.imread(orig_path)\\n\\n img_st = { 'class_id' : class_id,\\n 'orig_path' : orig_path,\\n 'shape0' : img.shape[0],\\n 'shape1' : img.shape[1],\\n 'shape2' : img.shape[2] }\\n\\n dest_dir = train_dir if random.random() < train_prop else test_dir\\n fname = os.path.basename( orig_path )\\n\\n dest_path = \\\"%s/%s_%02d.ppm\\\" % (dest_dir, fname[:-4], class_id )\\n\\n shutil.copy( orig_path, dest_path )\\n return img_st, dest_path\",\n \"def savePhoto(self, path):\\n if self.original_image and path:\\n cv2.imwrite(path, self.original_image)\",\n \"def __make_geo(self):\\n # gmsh freecad_part.iges -o out_iges.geo -0\\n fname_list = self.__fname.split('.')\\n geo_file = fname_list[0]+'.geo'\\n runstr = \\\"%s %s -o %s -0\\\" % (environment.GMSH, self.__fname, geo_file)\\n print(runstr)\\n subprocess.call(runstr, shell=True)\\n print('Wrote file: %s' % geo_file)\",\n \"def merge_layers(self, l_src, l_tgt, revert=False):\\n if l_src == l_tgt:\\n return\\n u_src = self.gs[l_src]\\n u_tgt = self.gs[l_tgt]\\n s_src = self.states[l_src + 1]\\n s_tgt = self.states[l_tgt + 1]\\n\\n if self.overlap:\\n u_src_base = self.__get_base_u(u_src)[0]\\n u_tgt_base = self.__get_base_u(u_tgt)[0]\\n else:\\n u_src_base = u_src\\n u_tgt_base = u_tgt\\n\\n intersection = u_src_base.new_vertex_property(\\\"int64_t\\\", -1)\\n\\n u_tgt_vmap = u_tgt_base.vp[\\\"vmap\\\"]\\n vmap = {}\\n for v in u_tgt_base.vertices():\\n vmap[u_tgt_vmap[v]] = v\\n\\n u_src_vmap = u_src_base.vp[\\\"vmap\\\"]\\n for v in u_src_base.vertices():\\n w = u_src_vmap[v]\\n if w in vmap:\\n intersection[v] = int(vmap[w])\\n\\n if self.overlap:\\n u_tgt_base.ep[\\\"b\\\"] = self.states[l_tgt + 1].get_edge_blocks()\\n u_src_base.ep[\\\"b\\\"] = self.states[l_src + 1].get_edge_blocks()\\n else:\\n u_tgt_base.vp[\\\"b\\\"] = self.states[l_tgt + 1].b\\n u_src_base.vp[\\\"b\\\"] = self.states[l_src + 1].b\\n\\n tgt_bmap = {}\\n src_rbmap = {}\\n r_max = 0\\n for r in range(self.B):\\n if self.bmap.has(l_tgt + 1, r):\\n tgt_bmap[r] = self.bmap.get(l_tgt + 1, r)\\n r_max = max(r_max, tgt_bmap[r])\\n if self.bmap.has(l_src + 1, r):\\n src_rbmap[self.bmap.get(l_src + 1, r)] = r\\n\\n r_missing = list(set(range(r_max)) - set(tgt_bmap.values()))\\n r_max += 1\\n\\n if self.overlap:\\n b = u_src_base.ep[\\\"b\\\"].copy()\\n for e in u_src_base.edges():\\n nb = []\\n for r in b[e]:\\n nb.append(src_rbmap[r])\\n for i, r in enumerate(nb):\\n if r in tgt_bmap:\\n nb[i] = tgt_bmap[r]\\n else:\\n if len(r_missing) > 0:\\n rr = r_missing[0]\\n del r_missing[0]\\n else:\\n rr = r_max\\n r_max += 1\\n self.bmap.set(l_tgt + 1, r, rr)\\n nb[i] = rr\\n tgt_bmap[r] = rr\\n b[e] = nb\\n b_src = b\\n b_tgt = u_tgt_base.ep[\\\"b\\\"]\\n u_tgt_base.ep[\\\"weight\\\"] = u_tgt_base.new_edge_property(\\\"int\\\", 1)\\n u_tgt_base.vp[\\\"weight\\\"] = u_tgt_base.new_vertex_property(\\\"int\\\", 1)\\n u_src_base.ep[\\\"weight\\\"] = u_src_base.new_edge_property(\\\"int\\\", 1)\\n u_src_base.vp[\\\"weight\\\"] = u_src_base.new_vertex_property(\\\"int\\\", 1)\\n else:\\n b = u_src_base.vp[\\\"b\\\"].copy()\\n for v in u_src_base.vertices():\\n r = src_rbmap[b[v]]\\n if r in tgt_bmap:\\n b[v] = tgt_bmap[r]\\n else:\\n if len(r_missing) > 0:\\n rr = r_missing[0]\\n del r_missing[0]\\n else:\\n rr = r_max\\n r_max += 1\\n self.bmap.set(l_tgt + 1, r, rr)\\n b[v] = rr\\n tgt_bmap[r] = rr\\n b_src = b\\n b_tgt = u_tgt_base.vp[\\\"b\\\"]\\n\\n props = [(b_tgt, b_src),\\n (u_tgt_base.vp[\\\"vmap\\\"], u_src_base.vp[\\\"vmap\\\"]),\\n (u_tgt_base.vp[\\\"weight\\\"], u_src_base.vp[\\\"weight\\\"]),\\n (u_tgt_base.ep[\\\"weight\\\"], u_src_base.ep[\\\"weight\\\"])]\\n\\n if not self.overlap:\\n props.append((u_tgt_base.vp[\\\"brmap\\\"],\\n u_src_base.vp[\\\"brmap\\\"]))\\n\\n u, props = graph_union(u_tgt_base, u_src_base,\\n intersection=intersection,\\n props=props,\\n include=False)\\n\\n if self.overlap:\\n u.ep[\\\"b\\\"] = props[0]\\n else:\\n u.vp[\\\"b\\\"] = props[0]\\n u.vp[\\\"brmap\\\"] = props[4]\\n\\n u.vp[\\\"vmap\\\"] = props[1]\\n u.vp[\\\"weight\\\"] = props[2]\\n u.ep[\\\"weight\\\"] = props[3]\\n\\n if self.overlap:\\n u, b, node_index, half_edges, eindex = half_edge_graph(u, u.ep[\\\"b\\\"],\\n self.B)\\n u.vp[\\\"vmap\\\"] = node_index\\n u.vp[\\\"weight\\\"] = u.new_vertex_property(\\\"int\\\", 1)\\n u.vp[\\\"b\\\"] = b\\n self.gs[l_tgt] = u\\n self.states[l_tgt + 1] = self.__gen_state(self.gs[l_tgt])\\n else:\\n self.gs[l_tgt] = u\\n self.states[l_tgt + 1] = self.__gen_state(self.gs[l_tgt])\\n\\n del self.states[l_src + 1]\\n del self.gs[l_src]\\n\\n old_ec = self.ec.copy()\\n self.ec.a[self.ec.a == l_src] = l_tgt\\n self.ec.a[self.ec.a > l_src] -= 1\\n if self.overlap:\\n old_base_ec = self.base_ec.copy()\\n self.base_ec.a[self.base_ec.a == l_src] = l_tgt\\n self.base_ec.a[self.base_ec.a > l_src] -= 1\\n self.C -= 1\\n old_bmap = self.bmap.copy()\\n self.bmap.del_c(l_src + 1)\\n self.__bg = None\\n old_layer_entropy = self.__layer_entropy\\n self.__layer_entropy = None\\n\\n yield\\n\\n if revert:\\n self.gs.insert(l_src, u_src)\\n self.gs[l_tgt] = u_tgt\\n self.states.insert(l_src + 1, s_src)\\n self.states[l_tgt + 1] = s_tgt\\n self.ec.a[:] = old_ec.a\\n if self.overlap:\\n self.base_ec.a[:] = old_base_ec.a\\n self.C += 1\\n self.bmap = old_bmap\\n self.__layer_entropy = old_layer_entropy\",\n \"def upgrade_file(path, multipoint):\\n node0 = nrml.read(path, chatty=False)[0]\\n shutil.copy(path, path + '.bak') # make a backup of the original file\\n tag = striptag(node0.tag)\\n gml = True\\n if tag == 'vulnerabilityModel':\\n vf_dict, cat_dict = get_vulnerability_functions_04(path)\\n # below I am converting into a NRML 0.5 vulnerabilityModel\\n node0 = Node(\\n 'vulnerabilityModel', cat_dict,\\n nodes=[obj_to_node(val) for val in vf_dict.values()])\\n gml = False\\n elif tag == 'fragilityModel':\\n node0 = read_nrml.convert_fragility_model_04(\\n nrml.read(path)[0], path)\\n gml = False\\n elif tag == 'sourceModel':\\n node0 = nrml.read(path)[0]\\n dic = groupby(node0.nodes, operator.itemgetter('tectonicRegion'))\\n node0.nodes = [Node('sourceGroup',\\n dict(tectonicRegion=trt, name=\\\"group %s\\\" % i),\\n nodes=srcs)\\n for i, (trt, srcs) in enumerate(dic.items(), 1)]\\n if multipoint:\\n sourceconverter.update_source_model(node0)\\n with open(path, 'wb') as f:\\n nrml.write([node0], f, gml=gml)\",\n \"def lightenSvgFile(inFname, outFname=\\\"\\\"):\\n if not outFname:\\n # create outFname based on inFname\\n if \\\"-screen\\\" in inFname:\\n outFname = inFname.replace(\\\"-screen\\\", \\\"-print\\\")\\n else:\\n outFname = inFname.replace(\\\".svg\\\", \\\"\\\")+\\\"-print.svg\\\"\\n if \\\"-dark\\\" in outFname:\\n outFname = outFname.replace(\\\"-dark\\\", \\\"\\\")\\n with open(inFname) as infile, open(outFname,\\\"w\\\") as outfile:\\n l=infile.readline()\\n while l:\\n outfile.write(ligthenOneLine(l))\\n l=infile.readline()\\n return outFname\",\n \"def test_GFD_import_equals_export(self):\\n filepath = '2.txt'\\n original_gfd = flow_processing_input.GroundFlowData()\\n original_gfd.detector_flow_data = createGFDDataset(10).dataset\\n original_gfd.export_to_file(filepath)\\n new_gfd = flow_processing_input.GroundFlowData(filepath)\\n os.remove(filepath)\\n # Check if new_gfd contains the same attributes as the original_gfd\\n self.assertTrue(new_gfd == original_gfd)\\n # Check if new_gfd is not equal to a different gfd of same size\\n random_gfd = flow_processing_input.GroundFlowData()\\n random_gfd.detector_flow_data = createGFDDataset(10).dataset\\n self.assertFalse(new_gfd == random_gfd)\",\n \"def reprojectShapefile(in_shapefile, model_raster, out_dir, name_override=None) -> str:\\n\\tshapefile_path = in_shapefile # old variable name; standardized to match function parameters\\n\\t# get out_path from out_dir\\n\\tif name_override:\\n\\t\\tout_path = os.path.join(out_dir,name_override)\\n\\telse:\\n\\t\\tin_base,in_ext = os.path.splitext(os.path.basename(in_shapefile))\\n\\t\\tout_path = os.path.join(out_dir,in_base+\\\"_REPROJ\\\"+in_ext)\\n\\t# get raster projection as wkt\\n\\twith rasterio.open(model_raster,'r') as img:\\n\\t\\traster_wkt = img.profile['crs'].to_wkt()\\n\\t# get shapefile projection as wkt\\n\\twith open(shapefile_path.replace(\\\".shp\\\",\\\".prj\\\")) as rf:\\n\\t\\tshapefile_wkt = rf.read()\\n\\n\\t# if it's a match, nothing needs to be done\\n\\tif raster_wkt == shapefile_wkt:\\n\\t\\tlog.warning(\\\"CRS already match\\\")\\n\\t\\t# get input directory and filename\\n\\t\\tin_dir = os.path.dirname(shapefile_path)\\n\\t\\tin_name = os.path.splitext(os.path.basename(shapefile_path))[0]\\n\\t\\t# list all elements of shapefile\\n\\t\\tall_shape_files = glob.glob(os.path.join(in_dir,f\\\"{in_name}.*\\\"))\\n\\t\\t# get output directory and filenames\\n\\t\\tout_dir = os.path.dirname(out_path)\\n\\t\\tout_name = os.path.splitext(os.path.basename(out_path))[0]\\n\\t\\tfor f in all_shape_files:\\n\\t\\t\\tname, ext = os.path.splitext(os.path.basename(f))\\n\\t\\t\\tout_f = os.path.join(out_dir,f\\\"{out_name}{ext}\\\")\\n\\t\\t\\twith open(f,'rb') as rf:\\n\\t\\t\\t\\twith open(out_f,'wb') as wf:\\n\\t\\t\\t\\t\\tshutil.copyfileobj(rf,wf)\\n\\telse:\\n\\t\\t# get CRS objects\\n\\t\\traster_crs = CRS.from_wkt(raster_wkt)\\n\\t\\tshapefile_crs = CRS.from_wkt(shapefile_wkt)\\n\\t\\t#transformer = Transformer.from_crs(raster_crs,shapefile_crs)\\n\\n\\t\\t# convert geometry and crs\\n\\t\\tout_shapefile_path = out_path # os.path.join(temp_dir,os.path.basename(shapefile_path))\\n\\t\\tdata = gpd.read_file(shapefile_path)\\n\\t\\tdata_proj = data.copy()\\n\\t\\tdata_proj['geometry'] = data_proj['geometry'].to_crs(raster_crs)\\n\\t\\tdata_proj.crs = raster_crs\\n\\n\\t\\t# save output\\n\\t\\tdata_proj.to_file(out_shapefile_path)\\n\\n\\n\\treturn out_shapefile_path\",\n \"def copy_model_to_local(gcs_path, dest_path):\\n copy_start_time = time.time()\\n logging.debug(\\\"Starting to copy files from %s to %s\\\", gcs_path, dest_path)\\n if not os.path.exists(dest_path):\\n os.makedirs(dest_path)\\n gcs_path = os.path.join(gcs_path, \\\"*\\\")\\n try:\\n # Removed parallel downloads (\\\"-m\\\") because it was not working well in\\n # gVisor (b/37269226).\\n subprocess.check_call([\\n \\\"gsutil\\\", \\\"cp\\\", \\\"-R\\\", gcs_path, dest_path], stdin=subprocess.PIPE)\\n except subprocess.CalledProcessError:\\n logging.exception(\\\"Could not copy model using gsutil.\\\")\\n raise\\n logging.debug(\\\"Files copied from %s to %s: took %f seconds\\\", gcs_path,\\n dest_path, time.time() - copy_start_time)\",\n \"def convert(\\n path_in, path_out, pathway_iri, wp_id, pathway_version, scale=100, theme=\\\"plain\\\"\\n):\\n if not path.exists(path_in):\\n raise Exception(f\\\"Missing file '{path_in}'\\\")\\n\\n if path.exists(path_out):\\n print(f\\\"File {path_out} already exists. Skipping.\\\")\\n return True\\n\\n dir_in = path.dirname(path_in)\\n base_in = path.basename(path_in)\\n # example base_in: 'WP4542.gpml'\\n [stub_in, ext_in_with_dot] = path.splitext(base_in)\\n # gettting rid of the leading dot, e.g., '.gpml' to 'gpml'\\n ext_in = LEADING_DOT_RE.sub(\\\"\\\", ext_in_with_dot)\\n\\n if ext_in != \\\"gpml\\\":\\n # TODO: how about *.gpml.xml?\\n raise Exception(f\\\"Currently only accepting *.gpml for path_in\\\")\\n gpml_f = path_in\\n\\n dir_out = path.dirname(path_out)\\n # example base_out: 'WP4542.svg'\\n base_out = path.basename(path_out)\\n [stub_out, ext_out_with_dot] = path.splitext(base_out)\\n # getting rid of the leading dot, e.g., '.svg' to 'svg'\\n ext_out = LEADING_DOT_RE.sub(\\\"\\\", ext_out_with_dot)\\n\\n tree = ET.parse(gpml_f, parser=parser)\\n root = tree.getroot()\\n\\n if root is None:\\n raise Exception(\\\"no root element\\\")\\n if root.tag is None:\\n raise Exception(\\\"no root tag\\\")\\n\\n gpml_version = re.sub(r\\\"{http://pathvisio.org/GPML/(\\\\w+)}Pathway\\\", r\\\"\\\\1\\\", root.tag)\\n if ext_out != \\\"gpml\\\" and gpml_version != LATEST_GPML_VERSION:\\n old_f = f\\\"{dir_in}/{stub_in}.{gpml_version}.gpml\\\"\\n rename(gpml_f, old_f)\\n convert(old_f, gpml_f, pathway_iri, wp_id, pathway_version, scale)\\n\\n # trying to get wd ids via sparql via pywikibot\\n site = pywikibot.Site(\\\"wikidata\\\", \\\"wikidata\\\")\\n repo = site.data_repository() # this is a DataSite object\\n wd_sparql = sparql.SparqlQuery(\\n endpoint=\\\"https://query.wikidata.org/sparql\\\", repo=repo\\n )\\n # (self, endpoint=None, entity_url=None, repo=None, 2 max_retries=None, retry_wait=None)\\n\\n if ext_out in [\\\"gpml\\\", \\\"owl\\\", \\\"pdf\\\", \\\"pwf\\\", \\\"txt\\\"]:\\n subprocess.run(shlex.split(f\\\"pathvisio convert {path_in} {path_out}\\\"))\\n elif ext_out == \\\"png\\\":\\n # TODO: look at using --scale as an option (instead of an argument),\\n # for both pathvisio and gpmlconverter.\\n # TODO: move the setting of a default value for scale into\\n # pathvisio instead of here.\\n subprocess.run(shlex.split(f\\\"pathvisio convert {path_in} {path_out} {scale}\\\"))\\n # Use interlacing? See https://github.com/PathVisio/pathvisio/issues/78\\n # It's probably not worthwhile. If we did it, we would need to install\\n # imagemagick and then run this:\\n # mv \\\"$path_out\\\" \\\"$path_out.noninterlaced.png\\\"\\n # convert -interlace PNG \\\"$path_out.noninterlaced.png\\\" \\\"$path_out\\\"\\n elif ext_out in [\\\"json\\\", \\\"jsonld\\\"]:\\n gpml2json(path_in, path_out, pathway_iri, wp_id, pathway_version, wd_sparql)\\n elif ext_out in [\\\"svg\\\", \\\"pvjssvg\\\"]:\\n #############################\\n # SVG\\n #############################\\n\\n json_f = f\\\"{dir_out}/{stub_in}.json\\\"\\n if not path.isfile(json_f):\\n gpml2json(path_in, json_f, pathway_iri, wp_id, pathway_version, wd_sparql)\\n\\n json2svg(json_f, path_out, pathway_iri, wp_id, pathway_version, theme)\\n else:\\n raise Exception(f\\\"Invalid output extension: '{ext_out}'\\\")\",\n \"def convertCSVToGeoTIFF(geoproperties, datafile, outputdir, outfile):\\n \\n data = numpy.genfromtxt(datafile, delimiter=',')\\n \\n Ny, Nx = data.shape\\n \\n #print data.size\\n #print data.shape\\n #print data\\n \\n # slice out the last column of null values\\n if str(data[Ny-1][Nx-1]) == 'nan':\\n data = data[:,:-1]\\n \\n Ny, Nx = data.shape\\n \\n #print data.size\\n #print data.shape\\n #print data\\n #print Ny, Nx\\n \\n startPos = [geoproperties['tl']['long'],geoproperties['tl']['lat']]\\n d_lat = (geoproperties['br']['lat'] - geoproperties['tl']['lat']) / (Ny - 1)\\n d_long = (geoproperties['br']['long'] - geoproperties['tl']['long']) / (Nx - 1)\\n \\n #print startPos, d_lat, d_long\\n \\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n ds = driver.Create(os.path.join(outputdir,outfile),Nx,Ny,1,gdal.GDT_Float32)\\n #ds = driver.Create('output/output.tif',Nx,Ny,1,gdal.GDT_Byte)\\n #ds.SetGeoTransform( [ -158.584, .008, 0, 21.108, 0, .008 ] )\\n ds.SetGeoTransform( [ startPos[0], d_long, 0, startPos[1], 0, d_lat ] )\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326)\\n ds.SetProjection( srs.ExportToWkt() )\\n ds.GetRasterBand(1).WriteArray(data)\\n ds = None\",\n \"def test_07_source_save(self):\\n for activity, gpx_file in zip(self.manager_stravagpx, TEST_GPX_FILES_2):\\n self.assert_files_equal(activity.metadata.source_file, gpx_file)\\n manager_fit = get_manager(CONFIG_FIT, TEST_FIT_FILES)\\n for activity, fit_file in zip(manager_fit, TEST_FIT_FILES):\\n self.assert_files_equal(activity.metadata.source_file, fit_file)\",\n \"def save_graph(self, path):\\n if path.split('.')[-1]=='gexf':\\n nx.write_gexf(self.graph, path)\\n else:\\n nx.write_gpickle(self.graph, path)\",\n \"def copyGISFileWithName(pathToLocation, givenFile):\\n givenFileCopy = os.path.join(pathToLocation, os.path.basename(givenFile))\\n arcpy.Copy_management(givenFile, givenFileCopy)\",\n \"def unify(src, dst):\\n\\n # NOTE: at this point it is assumed files are unifiable\\n\\n # get a temp file name\\n dir = os.path.split(src)[0]\\n tmp_handle, tmp_path = tempfile.mkstemp(dir=dir)\\n os.close(tmp_handle)\\n\\n # rename the destination, in case we need to back out\\n os.rename(dst, tmp_path)\\n\\n # link source to destination\\n try:\\n os.link(src, dst)\\n except:\\n # back out\\n print 'Could not link %s -> %s, backing out' % (src, dst)\\n try:\\n if os.path.exists(dst):\\n os.unlink(dst)\\n os.rename(tmp_path, dst)\\n except:\\n print 'Could not back out!!! the destination file is still there as', tmp_file\\n raise exceptions.OSError\\n\\n # done, remove the temp file\\n os.unlink(tmp_path)\",\n \"def save_samples(iteration, fixed_Y, fixed_X, G_YtoX, G_XtoY, opts):\\n fake_X = G_YtoX(fixed_Y, False)\\n fake_Y = G_XtoY(fixed_X, False)\\n\\n X, fake_X = fixed_X.cpu().data.numpy(), fake_X.cpu().data.numpy()\\n Y, fake_Y = fixed_Y.cpu().data.numpy(), fake_Y.cpu().data.numpy()\\n\\n merged = merge_images(X, fake_Y, opts)\\n path = os.path.join(opts.sample_dir, 'sample-{:06d}-X-Y.png'.format(iteration))\\n scipy.misc.imsave(path, merged)\\n print('Saved {}'.format(path))\\n\\n merged = merge_images(Y, fake_X, opts)\\n path = os.path.join(opts.sample_dir, 'sample-{:06d}-Y-X.png'.format(iteration))\\n scipy.misc.imsave(path, merged)\\n print('Saved {}'.format(path))\",\n \"def stot(self, sfp):\\n\\n return self.target_directory / sfp.relative_to(self.source_directory).with_suffix('.txt')\",\n \"def test_make_contact_mask_w_save(self):\\n output_mask = contact_mask(\\n os.path.join(data_dir, 'sample.csv'), geom_col=\\\"PolygonWKT_Pix\\\",\\n contact_spacing=10,\\n reference_im=os.path.join(data_dir, \\\"sample_geotiff.tif\\\"),\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(os.path.join(data_dir,\\n 'sample_c_mask.tif'))\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(output_mask, truth_mask)\\n assert np.array_equal(saved_output_mask, truth_mask)\\n os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after\",\n \"def copy_path(src_path: str, dst_path: str, append=True) -> None:\\n with io.open(src_path, \\\"r\\\", encoding=\\\"utf-8\\\") as src:\\n with io.open(dst_path, \\\"a\\\" if append else \\\"w\\\", encoding=\\\"utf-8\\\") as dst:\\n for line in src:\\n dst.write(line)\",\n \"def write_src_multfile(src_coords, args, file_name = 'source.mul',):\\n print 'write_src_multfile'\\n file_in = open(file_name, 'w')\\n file_in.write('#REFERENCE 3 0.0 0.0\\\\n')\\n\\n for i in range(len(src_coords)):\\n image_id = str(i+1) + ' '\\n data = str(src_coords[i][0]) + ' ' + str(src_coords[i][1]) \\\\\\n + str(' 0.2 0.2 0 ') + str(args.source_real_z) + ' 0'\\n final = image_id + data + '\\\\n'\\n file_in.write(final)\\n file_in.close()\",\n \"def write_and_filter_paths(self, source, target, relation, label, paths):\\n file_dir = os.path.join(self.save_dir, relation + \\\"_\\\" + str(self.maximum_length) + \\\"_\\\" + str(self.remaining_percentage) + \\\"_\\\" + str(self.random_seed) + \\\".txt\\\")\\n with open(file_dir, \\\"a\\\") as fh:\\n fh.write(str(label) + \\\"\\\\t\\\" + str(source) + \\\"\\\\t\\\" + str(target) + \\\"\\\\t\\\")\\n for pdx, path in enumerate(paths):\\n if not self.include_entity:\\n if len(path) == 1:\\n continue\\n for rdx, rel_idx in enumerate(path):\\n fh.write(self.idx_to_relation[rel_idx])\\n if rdx != len(path)-1:\\n fh.write(\\\"|\\\")\\n if pdx != len(paths)-1:\\n fh.write(\\\"###\\\")\\n else:\\n if len(path) == 3:\\n continue\\n fh.write(self.idx_to_node[path[0]].get_name())\\n fh.write(\\\"|\\\")\\n for rdx in range(0, (len(path)-1)/2):\\n fh.write(self.idx_to_relation[path[rdx*2+1]])\\n fh.write(\\\"|\\\")\\n fh.write(self.idx_to_node[path[rdx*2+2]].get_name())\\n if rdx*2+2 != len(path)-1:\\n fh.write(\\\"|\\\")\\n if pdx != len(paths)-1:\\n fh.write(\\\"###\\\")\\n fh.write(\\\"\\\\n\\\")\",\n \"def _export_file(self, src_path, ext_dst):\\n if not self.mount():\\n return False\\n _log(\\\"AnnexGvfsBackend._export_file(%r -> %r)\\\" % (src_path, ext_dst))\\n src_uri = self.path_to_uri(src_path)\\n ext_tmp = ext_dst + \\\".part\\\"\\n try:\\n if not self.gvfs.copy_file(src_uri, ext_tmp):\\n raise IOError()\\n if not self.gvfs.rename_file(ext_tmp, ext_dst):\\n raise IOError()\\n except IOError:\\n self.gvfs.delete_file(ext_tmp)\\n return False\\n else:\\n return True\",\n \"def _opt_path(self, src: int, dst: int) -> Optional[np.ndarray]:\\n if self.arc_fun == 'exp':\\n path = _pyift.livewire_path(self.image, self.costs, self.preds, self.labels,\\n self.arc_fun, self.sigma, src, dst)\\n elif self.arc_fun == 'exp-saliency':\\n path = _pyift.livewire_path(self.image, self.saliency, self.costs, self.preds, self.labels,\\n self.arc_fun, self.sigma, self.gamma, src, dst)\\n else:\\n raise NotImplementedError\\n\\n return path\",\n \"def save_step_4(imgs, output_path=\\\"./output/step4\\\"):\\n # ... your code here ...\\n cv2.imwrite(output_path+\\\"/output.jpg\\\", imgs)\",\n \"def save_raster ( output_name, raster_data, dataset, driver=\\\"GTiff\\\" ):\\n\\n # Open the reference dataset\\n g_input = gdal.Open ( dataset )\\n # Get the Geotransform vector\\n geo_transform = g_input.GetGeoTransform ()\\n x_size = g_input.RasterXSize # Raster xsize\\n y_size = g_input.RasterYSize # Raster ysize\\n srs = g_input.GetProjectionRef () # Projection\\n # Need a driver object. By default, we use GeoTIFF\\n if driver == \\\"GTiff\\\":\\n driver = gdal.GetDriverByName ( driver )\\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\\\\n gdal.GDT_Float32, ['TFW=YES', \\\\\\n 'COMPRESS=LZW', 'TILED=YES'] )\\n else:\\n driver = gdal.GetDriverByName ( driver )\\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\\\\n gdal.GDT_Float32 )\\n\\n dataset_out.SetGeoTransform ( geo_transform )\\n dataset_out.SetProjection ( srs )\\n dataset_out.GetRasterBand ( 1 ).WriteArray ( \\\\\\n raster_data.astype(np.float32) )\\n dataset_out.GetRasterBand ( 1 ).SetNoDataValue ( float(-999) )\\n dataset_out = None\",\n \"def register_landmarks(fname_src, fname_dest, dof, fname_affine='affine.txt', verbose=1, path_qc='./'):\\n from msct_image import Image\\n # open src label\\n im_src = Image(fname_src)\\n # coord_src = im_src.getNonZeroCoordinates(sorting='value') # landmarks are sorted by value\\n coord_src = im_src.getCoordinatesAveragedByValue() # landmarks are sorted by value\\n # open dest labels\\n im_dest = Image(fname_dest)\\n # coord_dest = im_dest.getNonZeroCoordinates(sorting='value')\\n coord_dest = im_dest.getCoordinatesAveragedByValue()\\n # Reorganize landmarks\\n\\n points_src, points_dest = [], []\\n for coord in coord_src:\\n point_src = im_src.transfo_pix2phys([[coord.x, coord.y, coord.z]])\\n # convert NIFTI to ITK world coordinate\\n # points_src.append([point_src[0][0], point_src[0][1], point_src[0][2]])\\n points_src.append([-point_src[0][0], -point_src[0][1], point_src[0][2]])\\n for coord in coord_dest:\\n point_dest = im_dest.transfo_pix2phys([[coord.x, coord.y, coord.z]])\\n # convert NIFTI to ITK world coordinate\\n # points_dest.append([point_dest[0][0], point_dest[0][1], point_dest[0][2]])\\n points_dest.append([-point_dest[0][0], -point_dest[0][1], point_dest[0][2]])\\n\\n # display\\n sct.printv('Labels src: ' + str(points_src), verbose)\\n sct.printv('Labels dest: ' + str(points_dest), verbose)\\n sct.printv('Degrees of freedom (dof): ' + dof, verbose)\\n\\n if len(coord_src) != len(coord_dest):\\n raise Exception('Error: number of source and destination landmarks are not the same, so landmarks cannot be paired.')\\n\\n # estimate transformation\\n # N.B. points_src and points_dest are inverted below, because ITK uses inverted transformation matrices, i.e., src->dest is defined in dest instead of src.\\n # (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_dest, points_src, constraints=dof, verbose=verbose, path_qc=path_qc)\\n (rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = getRigidTransformFromLandmarks(points_src, points_dest, constraints=dof, verbose=verbose, path_qc=path_qc)\\n # writing rigid transformation file\\n # N.B. x and y dimensions have a negative sign to ensure compatibility between Python and ITK transfo\\n text_file = open(fname_affine, 'w')\\n text_file.write(\\\"#Insight Transform File V1.0\\\\n\\\")\\n text_file.write(\\\"#Transform 0\\\\n\\\")\\n text_file.write(\\\"Transform: AffineTransform_double_3_3\\\\n\\\")\\n text_file.write(\\\"Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\\\\n\\\" % (\\n rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],\\n rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],\\n rotation_matrix[2, 0], rotation_matrix[2, 1], rotation_matrix[2, 2],\\n translation_array[0, 0], translation_array[0, 1], translation_array[0, 2]))\\n text_file.write(\\\"FixedParameters: %.9f %.9f %.9f\\\\n\\\" % (points_moving_barycenter[0],\\n points_moving_barycenter[1],\\n points_moving_barycenter[2]))\\n text_file.close()\",\n \"def Reproject(cls,inRaster, outRaster,EPSG):\\r\\n try:\\r\\n import gdal\\r\\n except:\\r\\n raise ImportError(\\\"Can not import module GDAL\\\")\\r\\n try:\\r\\n dataset = gdal.Open(infile)\\r\\n out = dataset.GetRasterBand(1)\\r\\n print dataset.GetMetadata()\\r\\n return out\\r\\n except:\\r\\n raise ImportError(\\\"Can not read band\\\")\\r\\n #if not tmp:\\r\\n # raise Exception(\\\"Could not orthorectify the image \\\")\\r\\n\\r\\n com=\\\" \\\".join([\\\"gdalwarp -t_srs EPSG:\\\"+str(EPSG),InRaster,OutRaster])\\r\\n tmp=os.system(com)\\r\\n print tmp\\r\\n #if not tmp:\\r\\n # raise Exception(\\\"Could not reproject the image \\\")\\r\\n #os.remove(OutRaster)\\r\",\n \"def gdal_read_geotiff_file(sFilename_in):\\n \\n if os.path.exists(sFilename_in):\\n pass\\n else:\\n print('The file does not exist!')\\n return\\n\\n sDriverName='GTiff'\\n pDriver = gdal.GetDriverByName(sDriverName) \\n\\n if pDriver is None:\\n print (\\\"%s pDriver not available.\\\\n\\\" % sDriverName)\\n else:\\n print (\\\"%s pDriver IS available.\\\\n\\\" % sDriverName) \\n\\n pDataset = gdal.Open(sFilename_in, gdal.GA_ReadOnly)\\n\\n if pDataset is None:\\n print(\\\"Couldn't open this file: \\\" + sFilename_in)\\n sys.exit(\\\"Try again!\\\")\\n else: \\n pProjection = pDataset.GetProjection()\\n\\n pDataset.GetMetadata()\\n \\n ncolumn = pDataset.RasterXSize\\n nrow = pDataset.RasterYSize\\n nband = pDataset.RasterCount\\n\\n pGeotransform = pDataset.GetGeoTransform()\\n dOriginX = pGeotransform[0]\\n dOriginY = pGeotransform[3]\\n dPixelWidth = pGeotransform[1]\\n pPixelHeight = pGeotransform[5]\\n\\n pBand = pDataset.GetRasterBand(1)\\n\\n # Data type of the values\\n gdal.GetDataTypeName(pBand.DataType)\\n # Compute statistics if needed\\n if pBand.GetMinimum() is None or pBand.GetMaximum() is None:\\n pBand.ComputeStatistics(0)\\n\\n dMissing_value = pBand.GetNoDataValue()\\n \\n aData_out = pBand.ReadAsArray(0, 0, ncolumn, nrow)\\n \\n #we will use one of them to keep the consistency\\n pSpatial_reference = osr.SpatialReference(wkt=pProjection)\\n \\n\\n pDataset = None\\n pBand = None \\n pBand = None\\n\\n return aData_out, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection, pSpatial_reference\",\n \"def copy_file(fs, inpath, outpath):\\n fs.copy(inpath, outpath)\",\n \"def transform(infile, output, insrs, format_name):\\n\\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \\n in_srs = osr.SpatialReference()\\n in_srs.ImportFromEPSG(insrs)\\n out_srs = osr.SpatialReference()\\n out_srs.ImportFromEPSG(4324)\\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\\n\\n in_dsn = ogr.Open(infile)\\n in_layer = in_dsn.GetLayer()\\n in_feature_definition = in_layer.GetLayerDefn()\\n\\n out_driver = ogr.GetDriverByName(format_name)\\n out_dsn = out_driver.CreateDataSource(output)\\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\\n geom_type=in_layer.GetGeomType())\\n\\n # add fields\\n for i in range(0, in_feature_definition.GetFieldCount()):\\n fieldDefn = in_feature_definition.GetFieldDefn(i)\\n out_layer.CreateField(fieldDefn)\\n\\n # get the output layer's feature definition\\n out_feature_definition = out_layer.GetLayerDefn()\\n\\n # loop through the input features\\n inFeature = in_layer.GetNextFeature()\\n while inFeature:\\n # get the input geometry\\n geom = inFeature.GetGeometryRef().Clone()\\n # reproject the geometry\\n geom.Transform(coordTrans)\\n # create a new feature\\n outFeature = ogr.Feature(out_feature_definition)\\n # set the geometry and attribute\\n outFeature.SetGeometry(geom)\\n for i in range(0, out_feature_definition.GetFieldCount()):\\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\\n # add the feature to the shapefile\\n out_layer.CreateFeature(outFeature)\\n # destroy the features and get the next input feature\\n outFeature.Destroy()\\n inFeature.Destroy()\\n inFeature = in_layer.GetNextFeature()\\n\\n # close the shapefiles\\n in_dsn.Destroy()\\n out_dsn.Destroy()\",\n \"def save_groudtruth(im, coords, filename):\\n print 'Saving ground truth ......{0}'.format(filename)\\n img_draw = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\\n draw = ImageDraw.Draw(img_draw)\\n for coord in coords:\\n draw.polygon([(float(coord[0]), float(coord[1])), (float(coord[2]), float(coord[3])),\\n (float(coord[4]), float(coord[5])), (float(coord[6]), float(coord[7]))],\\n outline=\\\"red\\\", fill=\\\"blue\\\")\\n img_draw = np.array(img_draw)\\n img_draw = cv2.cvtColor(img_draw, cv2.COLOR_RGB2BGR)\\n bname_excludepoint = filename.split('/')[-1].split('.')[0]\\n image_path = '/home/yuquanjie/Documents/deep-direct-regression/result/' + bname_excludepoint + '_gt.jpg'\\n cv2.imwrite(image_path, img_draw[0: img_draw.shape[0], 0: img_draw.shape[1]])\",\n \"def test_make_mask_w_ref_image(self):\\n output_mask = instance_mask(\\n os.path.join(data_dir, 'geotiff_labels.geojson'),\\n reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\\n do_transform=True,\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(os.path.join(data_dir,\\n 'sample_inst_mask.tif'))\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(saved_output_mask, truth_mask)\\n # clean up\\n os.remove(os.path.join(data_dir, 'test_out.tif'))\\n assert np.array_equal(output_mask, truth_mask)\",\n \"def output_diff_local_files(gold_session, image_name):\\n given_file = gold_session.GetGivenImageLink(image_name)\\n closest_file = gold_session.GetClosestImageLink(image_name)\\n diff_file = gold_session.GetDiffImageLink(image_name)\\n failure_message = 'Unable to retrieve link'\\n logging.error('Generated image: %s', given_file or failure_message)\\n logging.error('Closest image: %s', closest_file or failure_message)\\n logging.error('Diff image: %s', diff_file or failure_message)\",\n \"def find_path(orig, dest):\\n\\t#x,y=orig.pos\\n\\t#orig = surface.tile(x,y)\\n\\t#x,y=dest.pos\\n\\t#dest = surface.tile(x,y)\\n\\treturn pathfinder.astar(orig, dest)\",\n \"def copyFile( src, dest ):\\n\\tinFile = open( src, 'r' )\\n\\toutFile = open( dest, 'w' )\\n\\tfor line in inFile:\\n\\t\\toutFile.write( line )\\n\\toutFile.close()\\n\\tinFile.close()\",\n \"def reprojectRaster(in_raster,model_raster,out_dir,name_override = None):\\n\\tif name_override:\\n\\t\\tout_name = os.path.join(out_dir,name_override)\\n\\telse:\\n\\t\\tin_base,in_ext = os.path.splitext(os.path.basename(in_raster))\\n\\t\\tout_name = os.path.join(out_dir,in_base+\\\"_REPROJ\\\"+in_ext)\\n\\t\\t#print(out_name)\\n\\tt_ds = gdal.Open(model_raster,0)\\n\\tt_wkt = t_ds.GetProjection()\\n\\ts_ds = gdal.Open(in_raster,0)\\n\\ts_wkt = s_ds.GetProjection()\\n\\tt_ds = s_ds = None\\n\\tif s_wkt != t_wkt:\\n\\t\\treproj_args = [r\\\"C:\\\\OSGeo4W64\\\\bin\\\\gdalwarp.exe\\\",\\\"-t_srs\\\",t_wkt,in_raster,out_name]\\n\\t\\tsubprocess.call(reproj_args)\\n\\t\\treturn out_name\\n\\telse:\\n\\t\\tlog.warning(\\\"Projections already match, doing nothing.\\\")\\n\\t\\treturn in_raster\",\n \"def cpr(src, dst):\\n shutil.copytree(src, dst)\",\n \"def save_img_as_tiff(seg_img: np.ndarray, time: int, track_save_dir: str):\\n img = sitk.GetImageFromArray(seg_img.astype(\\\"uint16\\\"))\\n filename = 'mask'+'%0*d' % (3, time) + '.tif'\\n sitk.WriteImage(img, os.path.join(track_save_dir, filename))\",\n \"def save_image(nom, image, _geo_trans):\\n cols = image.shape[2]\\n rows = image.shape[1]\\n bands = image.shape[0]\\n print(bands, cols, rows)\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n out_raster = driver.Create(nom, cols, rows, bands, gdal.GDT_Byte)\\n # if (geo_trans):\\n # outRaster.SetGeoTransform(geo_trans)\\n band = out_raster.GetRasterBand(1)\\n band.WriteArray(np.round(image[0]))\\n band = out_raster.GetRasterBand(2)\\n band.WriteArray(np.round(image[1]))\\n band = out_raster.GetRasterBand(3)\\n band.WriteArray(np.round(image[2]))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6665498","0.6444286","0.59382063","0.5935782","0.5847079","0.5664643","0.56095916","0.54957837","0.5420205","0.5419931","0.53277814","0.5223335","0.51851106","0.5165319","0.5149921","0.51454896","0.51343054","0.5089744","0.50778294","0.50766367","0.5061234","0.5031714","0.5009797","0.5006587","0.49889082","0.49589285","0.4949932","0.4927654","0.4901593","0.48994282","0.48954764","0.48800275","0.4867443","0.4865931","0.48497206","0.4842774","0.48405978","0.4837367","0.48370227","0.48366317","0.48352918","0.4826244","0.4812796","0.4803379","0.4795015","0.4777979","0.47760585","0.47757506","0.47736788","0.4762363","0.47563657","0.47563657","0.4754947","0.47504383","0.47503942","0.47059527","0.46991548","0.46973568","0.46864167","0.46787852","0.46778476","0.4675147","0.4674627","0.46719843","0.46657157","0.46613133","0.46497104","0.46394613","0.46310014","0.4614195","0.4595026","0.45856208","0.45850256","0.45794672","0.4575359","0.4575212","0.45750517","0.4574857","0.4564682","0.4564394","0.45635912","0.4560553","0.45464134","0.45456564","0.45423704","0.45422885","0.45350572","0.45313072","0.45311686","0.4531031","0.45294186","0.45267284","0.45262176","0.4517909","0.45149714","0.45139742","0.45101333","0.45099908","0.45093727","0.45047912"],"string":"[\n \"0.6665498\",\n \"0.6444286\",\n \"0.59382063\",\n \"0.5935782\",\n \"0.5847079\",\n \"0.5664643\",\n \"0.56095916\",\n \"0.54957837\",\n \"0.5420205\",\n \"0.5419931\",\n \"0.53277814\",\n \"0.5223335\",\n \"0.51851106\",\n \"0.5165319\",\n \"0.5149921\",\n \"0.51454896\",\n \"0.51343054\",\n \"0.5089744\",\n \"0.50778294\",\n \"0.50766367\",\n \"0.5061234\",\n \"0.5031714\",\n \"0.5009797\",\n \"0.5006587\",\n \"0.49889082\",\n \"0.49589285\",\n \"0.4949932\",\n \"0.4927654\",\n \"0.4901593\",\n \"0.48994282\",\n \"0.48954764\",\n \"0.48800275\",\n \"0.4867443\",\n \"0.4865931\",\n \"0.48497206\",\n \"0.4842774\",\n \"0.48405978\",\n \"0.4837367\",\n \"0.48370227\",\n \"0.48366317\",\n \"0.48352918\",\n \"0.4826244\",\n \"0.4812796\",\n \"0.4803379\",\n \"0.4795015\",\n \"0.4777979\",\n \"0.47760585\",\n \"0.47757506\",\n \"0.47736788\",\n \"0.4762363\",\n \"0.47563657\",\n \"0.47563657\",\n \"0.4754947\",\n \"0.47504383\",\n \"0.47503942\",\n \"0.47059527\",\n \"0.46991548\",\n \"0.46973568\",\n \"0.46864167\",\n \"0.46787852\",\n \"0.46778476\",\n \"0.4675147\",\n \"0.4674627\",\n \"0.46719843\",\n \"0.46657157\",\n \"0.46613133\",\n \"0.46497104\",\n \"0.46394613\",\n \"0.46310014\",\n \"0.4614195\",\n \"0.4595026\",\n \"0.45856208\",\n \"0.45850256\",\n \"0.45794672\",\n \"0.4575359\",\n \"0.4575212\",\n \"0.45750517\",\n \"0.4574857\",\n \"0.4564682\",\n \"0.4564394\",\n \"0.45635912\",\n \"0.4560553\",\n \"0.45464134\",\n \"0.45456564\",\n \"0.45423704\",\n \"0.45422885\",\n \"0.45350572\",\n \"0.45313072\",\n \"0.45311686\",\n \"0.4531031\",\n \"0.45294186\",\n \"0.45267284\",\n \"0.45262176\",\n \"0.4517909\",\n \"0.45149714\",\n \"0.45139742\",\n \"0.45101333\",\n \"0.45099908\",\n \"0.45093727\",\n \"0.45047912\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7338728"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85699,"cells":{"query":{"kind":"string","value":"Save a raster image as a geotiff with associated transform"},"document":{"kind":"string","value":"def save_geotiff(path, raster, xform):\n # Import rasterio here, so that we don't need it in the cloud.\n import rasterio\n import affine\n n_lats, n_lons, depth = raster.shape\n\n assert raster.max() <= 255\n assert raster.min() >= 0\n \n profile = {\n 'crs': 'EPSG:4326',\n 'nodata': 0,\n 'dtype': rasterio.uint8,\n 'height': n_lats,\n 'width': n_lons,\n 'count': depth,\n 'driver':\"GTiff\",\n 'transform': xform}\n\n with rasterio.open(path, 'w', **profile) as dst:\n for i in range(depth):\n dst.write(raster[:, :, i], indexes=i+1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save_image(nom, image, _geo_trans):\n cols = image.shape[2]\n rows = image.shape[1]\n bands = image.shape[0]\n print(bands, cols, rows)\n driver = gdal.GetDriverByName(\"GTiff\")\n out_raster = driver.Create(nom, cols, rows, bands, gdal.GDT_Byte)\n # if (geo_trans):\n # outRaster.SetGeoTransform(geo_trans)\n band = out_raster.GetRasterBand(1)\n band.WriteArray(np.round(image[0]))\n band = out_raster.GetRasterBand(2)\n band.WriteArray(np.round(image[1]))\n band = out_raster.GetRasterBand(3)\n band.WriteArray(np.round(image[2]))","def reprojectAndSaveNewRaster(inFilepath,outFilepath,to_EPSG):\r\n from osgeo import gdal\r\n input_raster = gdal.Open(inFilepath)\r\n EPSG_string = \"EPSG:\"+str(to_EPSG)\r\n ras = gdal.Warp(outFilepath,input_raster,dstSRS=EPSG_string)\r\n del ras","def _export_geotiff(self, filepath: str, z_positive_up: bool = True):\n\n nodatavalue = 1000000.0\n data, geo_transform, bandnames = self._gdal_preprocessing(nodatavalue=nodatavalue, z_positive_up=z_positive_up)\n gdal_raster_create(filepath, data, geo_transform, self.crs, nodatavalue=nodatavalue, bandnames=bandnames,\n driver='GTiff')","def _write_image(self):\r\n # Create an output raster with the correct number of rows and columns.\r\n gtiff_driver = gdal.GetDriverByName('GTiff')\r\n out_ds = gtiff_driver.Create(os.path.join(self.out_folder, self.out_file_name), self.column, self.row, 1)\r\n out_ds.SetProjection(self.in_ds.GetProjection())\r\n\r\n # Convert the offsets to real-world coordinates for the georeferencing info.\r\n # We can't use the coordinates above because they don't correspond to the pixel edges.\r\n subset_ulx, subset_uly = gdal.ApplyGeoTransform(self.in_gt, self.off_ulx, self.off_uly)\r\n out_gt = list(self.in_gt)\r\n out_gt[0] = subset_ulx\r\n out_gt[3] = subset_uly\r\n out_ds.SetGeoTransform(out_gt)\r\n\r\n data = self.read_image()\r\n out_band = out_ds.GetRasterBand(1)\r\n out_band.WriteArray(data)\r\n\r\n del out_ds","def save_raster(outfile, data, info=None):\n\n proj4txt = (info and info['grid'].srs())\n geoxf = (info and info['grid'].geoxf())\n meta = None # XXX fixme\n\n dirname,fname = os.path.split(os.path.abspath(outfile))\n if not os.path.isdir(dirname):\n os.makedirs(dirname)\n\n if len(data.shape) == 3:\n ysize,xsize,nbands = data.shape\n else:\n ysize,xsize = data.shape\n nbands = 1\n\n # get the gdal datatype from the numpy datatype\n gdtype = gdal_array.flip_code(data.dtype.type)\n\n # do not add COMPRESS=DEFLATE here\n # generate all TIFFs in blocks for faster random access\n blocksize = 128\n opts = [\n 'TILED=YES',\n 'BLOCKXSIZE=%d' % blocksize,\n 'BLOCKYSIZE=%d' % blocksize\n ]\n\n #print 'SAVE %r %r' % (outfile, (data.shape, blocksize, opts))\n\n gtiff = gdal.GetDriverByName('GTiff')\n\n #print 'GDTY %r %r %r' % (data.dtype, gdtype, gdalconst.GDT_Byte)\n\n ds = gtiff.Create(str(outfile), xsize, ysize, nbands,\n gdtype, opts)\n\n if proj4txt is not None:\n srs = osr.SpatialReference()\n srs.ImportFromProj4(proj4txt)\n ds.SetProjection(srs.ExportToWkt())\n\n if geoxf is not None:\n ds.SetGeoTransform(geoxf)\n\n if meta is not None:\n ds.SetMetadata(meta)\n\n for i in range(nbands):\n rband = ds.GetRasterBand(i+1)\n\n if nbands == 1:\n rband.SetRasterColorInterpretation(gdalconst.GCI_GrayIndex)\n elif i == 0:\n rband.SetRasterColorInterpretation(gdalconst.GCI_RedBand)\n elif i == 1:\n rband.SetRasterColorInterpretation(gdalconst.GCI_GreenBand)\n elif i == 2:\n rband.SetRasterColorInterpretation(gdalconst.GCI_BlueBand)\n elif i == 3:\n rband.SetRasterColorInterpretation(gdalconst.GCI_AlphaBand)\n\n #rband.SetNoDataValue(xxx):\n\n bdata = data\n if nbands > 1:\n bdata = data[:,:,i]\n\n # transform data to unsigned byte?\n print 'DATA %r: %f < x < %f' % (outfile, N.min(bdata), N.max(bdata))\n\n rband.WriteArray(bdata)","def save_raster ( output_name, raster_data, dataset, driver=\"GTiff\" ):\n\n # Open the reference dataset\n g_input = gdal.Open ( dataset )\n # Get the Geotransform vector\n geo_transform = g_input.GetGeoTransform ()\n x_size = g_input.RasterXSize # Raster xsize\n y_size = g_input.RasterYSize # Raster ysize\n srs = g_input.GetProjectionRef () # Projection\n # Need a driver object. By default, we use GeoTIFF\n if driver == \"GTiff\":\n driver = gdal.GetDriverByName ( driver )\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\n gdal.GDT_Float32, ['TFW=YES', \\\n 'COMPRESS=LZW', 'TILED=YES'] )\n else:\n driver = gdal.GetDriverByName ( driver )\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\n gdal.GDT_Float32 )\n\n dataset_out.SetGeoTransform ( geo_transform )\n dataset_out.SetProjection ( srs )\n dataset_out.GetRasterBand ( 1 ).WriteArray ( \\\n raster_data.astype(np.float32) )\n dataset_out.GetRasterBand ( 1 ).SetNoDataValue ( float(-999) )\n dataset_out = None","def create_geotiff(pixels, gps_bounds, out_path, nodata=-99, asfloat=False, extractor_info=None, system_md=None, extra_metadata=None, compress=False):\n dimensions = numpy.shape(pixels)\n if len(dimensions) == 2:\n nrows, ncols = dimensions\n channels = 1\n else:\n nrows, ncols, channels = dimensions\n\n geotransform = (\n gps_bounds[2], # upper-left x\n (gps_bounds[3] - gps_bounds[2])/float(ncols), # W-E pixel resolution\n 0, # rotation (0 = North is up)\n gps_bounds[1], # upper-left y\n 0, # rotation (0 = North is up)\n -((gps_bounds[1] - gps_bounds[0])/float(nrows)) # N-S pixel resolution\n )\n\n # Create output GeoTIFF and set coordinates & projection\n if asfloat:\n dtype = gdal.GDT_Float32\n else:\n dtype = gdal.GDT_Byte\n\n if compress:\n output_raster = gdal.GetDriverByName('GTiff') \\\n .Create(out_path, ncols, nrows, channels, dtype, ['COMPRESS=LZW'])\n else:\n output_raster = gdal.GetDriverByName('GTiff') \\\n .Create(out_path, ncols, nrows, channels, dtype)\n\n output_raster.SetGeoTransform(geotransform)\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326) # google mercator\n output_raster.SetProjection( srs.ExportToWkt() )\n\n if not extra_metadata:\n extra_metadata = prepare_metadata_for_geotiff(extractor_info, system_md)\n\n output_raster.SetMetadata(extra_metadata)\n\n if channels == 3:\n # typically 3 channels = RGB channels\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\n output_raster.GetRasterBand(1).WriteArray(pixels[:,:,0].astype('uint8'))\n output_raster.GetRasterBand(1).SetColorInterpretation(gdal.GCI_RedBand)\n output_raster.GetRasterBand(1).FlushCache()\n if nodata:\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\n\n output_raster.GetRasterBand(2).WriteArray(pixels[:,:,1].astype('uint8'))\n output_raster.GetRasterBand(2).SetColorInterpretation(gdal.GCI_GreenBand)\n output_raster.GetRasterBand(2).FlushCache()\n if nodata:\n output_raster.GetRasterBand(2).SetNoDataValue(nodata)\n\n output_raster.GetRasterBand(3).WriteArray(pixels[:,:,2].astype('uint8'))\n output_raster.GetRasterBand(3).SetColorInterpretation(gdal.GCI_BlueBand)\n output_raster.GetRasterBand(3).FlushCache()\n if nodata:\n output_raster.GetRasterBand(3).SetNoDataValue(nodata)\n\n elif channels > 1:\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\n for chan in range(channels):\n band = chan + 1\n output_raster.GetRasterBand(band).WriteArray(pixels[:,:,chan].astype('uint8'))\n output_raster.GetRasterBand(band).FlushCache()\n if nodata:\n output_raster.GetRasterBand(band).SetNoDataValue(nodata)\n else:\n # single channel image, e.g. temperature\n output_raster.GetRasterBand(1).WriteArray(pixels)\n output_raster.GetRasterBand(1).FlushCache()\n if nodata:\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\n\n output_raster = None","def save_geotiff(df, data_col, crs, x_col='x', y_col='y', time_col=None, nfiles='many', export_path='geotiff.tif', grid_res=None):\n\n ### create the xy coordinates\n if time_col is None:\n xy1 = df[[x_col, y_col]]\n else:\n time = df[time_col].sort_values().unique()\n xy1 = df.loc[df[time_col] == time[0], [x_col, y_col]]\n if any(xy1.duplicated()):\n raise ValueError('x and y coordinates are not unique!')\n\n ### Determine grid res\n if grid_res is None:\n res_df1 = (xy1.loc[0] - xy1).abs()\n res_df2 = res_df1.replace(0, nan).min()\n x_res = res_df2[x_col]\n y_res = res_df2[y_col]\n elif isinstance(grid_res, int):\n x_res = y_res = grid_res\n else:\n raise ValueError('grid_res must either be None or an integer.')\n\n ### Make the affline transformation for Rasterio\n trans2 = transform.from_origin(xy1[x_col].min() - x_res/2, xy1[y_col].max() + y_res/2, x_res, y_res)\n\n ### Make the rasters\n if time_col is None:\n z = df.set_index([y_col, x_col])[data_col].unstack().values[::-1]\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs, pass_str=True), transform=trans2)\n new_dataset.write(z, 1)\n new_dataset.close()\n else:\n if nfiles == 'one':\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=len(time), dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\n for i in range(1, len(time)+1):\n z = df.loc[df[time_col] == time[i - 1]].set_index([y_col, x_col])[data_col].unstack().values[::-1]\n new_dataset.write(z, i)\n new_dataset.close()\n elif nfiles == 'many':\n file1 = path.splitext(export_path)[0]\n for i in time:\n str_date = to_datetime(i).strftime('%Y-%m-%d_%H')\n file2 = file1 + '_' + str_date + '.tif'\n z = df.loc[df[time_col] == i].set_index([y_col, x_col])[data_col].unstack().values[::-1]\n new_dataset = ras_open(file2, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\n new_dataset.write(z, 1)\n new_dataset.close()","def save_tif(self, name='', data='', geo='', projection=''):\n # save as a geotiff\n driver = gdal.GetDriverByName(\"GTiff\")\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1,\n gdal.GDT_Float32, ['COMPRESS=LZW'])\n srse = osr.SpatialReference()\n if projection == '':\n srse.SetWellKnownGeogCS(\"WGS84\")\n\n else:\n try:\n if not srse.SetWellKnownGeogCS(projection) == 6:\n srse.SetWellKnownGeogCS(projection)\n else:\n try:\n srse.ImportFromEPSG(int(projection))\n except BaseException as err:\n print(err)\n else:\n srse.ImportFromWkt(projection)\n except BaseException:\n try:\n srse.ImportFromEPSG(int(projection))\n except BaseException as err:\n print(err)\n else:\n srse.ImportFromWkt(projection)\n\n dst_ds.SetProjection(srse.ExportToWkt())\n dst_ds.SetGeoTransform(geo)\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\n dst_ds.GetRasterBand(1).WriteArray(data)\n dst_ds = None\n\n return","def _export_raster(self, raster_name,\n format=\"GTiff\",\n additional_options=[],\n use_raster_region=False):\n # Export the layer\n prefix = \".tiff\"\n if format == \"GTiff\":\n prefix = \".tiff\"\n\n # Remove a potential mapset\n file_name = raster_name.split(\"@\")[0] + prefix\n\n if use_raster_region is True:\n\n p = Process(exec_type=\"grass\",\n executable=\"g.region\",\n executable_params=[\"raster=%s\"%raster_name, \"-g\"],\n stdin_source=None)\n\n self._update_num_of_steps(1)\n self._run_module(p)\n\n # Save the file in the temporary directory of the temporary gisdb\n output_path = os.path.join(self.temp_file_path, file_name)\n\n module_name = \"r.out.gdal\"\n args = [\"-fm\", \"input=%s\"%raster_name, \"format=%s\"%format,\n \"createopt=COMPRESS=LZW\", \"output=%s\"%output_path]\n\n if additional_options:\n args.extend(additional_options)\n\n p = Process(exec_type=\"grass\",\n executable=module_name,\n executable_params=args,\n stdin_source=None)\n\n self._update_num_of_steps(1)\n self._run_module(p)\n\n return file_name, output_path","def saveAsGeotiff(self, geotiff_fname, overwrite=True, verbose=False):\n if not overwrite and os.path.exists(geotiff_fname):\n raise ValueError('output geotiff already exists:\\n {}'.format(\n geotiff_fname))\n\n if overwrite and os.path.exists(geotiff_fname):\n os.remove(geotiff_fname)\n\n gggdal = geogrid_as_gdalInMem(self)\n\n dst = gdal.GetDriverByName('GTiff').CreateCopy(\n geotiff_fname, gggdal)\n dst = None","def write_tiff (self, filename, prototype_filename = None, proj_string = None):\n if prototype_filename is None:\n if self.prototype_filename is None:\n use_prototype = False\n else:\n prototype_filename = self.prototype_filename # use the pre-defined one \n use_prototype = True\n \n if use_prototype:\n raster = gdal.Open (prototype_filename)\n geotransform = raster.GetGeoTransform()\n originX = geotransform[0]\n originY = geotransform[3]\n cell_Width = geotransform[1]\n cell_Height = geotransform[5]\n ncols = self.ras.shape[1]\n nrows = self.ras.shape[0]\n else:\n originX = self.originX\n originY = self.originY\n cell_Width = self.cell_Width\n cell_Height = self.cell_Height\n ncols = self.ncols\n nrows = self.nrows\n \n # create driver\n driver = gdal.GetDriverByName('GTiff')\n outRaster = driver.Create (filename, ncols, nrows, 1, gdal.GDT_Float32)\n outRaster.SetGeoTransform((originX, cell_Width, 0, originY, 0, cell_Height))\n outband = outRaster.GetRasterBand(1)\n \n # make a copy to write out\n x = self.ras.copy()\n \n # set the nodata flag properly\n if use_prototype:\n nodata_flag = raster.GetRasterBand(1).GetNoDataValue() # get the original value\n else:\n nodata_flag = -9999.0 # use hardcoded nan value\n x [np.isnan(x)] = nodata_flag # assign it to the array\n outband.SetNoDataValue (nodata_flag) # set it in the output band\n \n # write array and set projection (if projection supplied)\n outband.WriteArray (x)\n outRasterSRS = osr.SpatialReference ()\n if use_prototype:\n outRasterSRS.ImportFromWkt (raster.GetProjectionRef())\n outRaster.SetProjection (outRasterSRS.ExportToWkt())\n else:\n if not proj_string is None:\n outRasterSRS.ImportFromWkt (proj_string)\n outRaster.SetProjection (outRasterSRS.ExportToWkt())\n \n outband.FlushCache ()\n return","def Reproject(cls,inRaster, outRaster,EPSG):\r\n try:\r\n import gdal\r\n except:\r\n raise ImportError(\"Can not import module GDAL\")\r\n try:\r\n dataset = gdal.Open(infile)\r\n out = dataset.GetRasterBand(1)\r\n print dataset.GetMetadata()\r\n return out\r\n except:\r\n raise ImportError(\"Can not read band\")\r\n #if not tmp:\r\n # raise Exception(\"Could not orthorectify the image \")\r\n\r\n com=\" \".join([\"gdalwarp -t_srs EPSG:\"+str(EPSG),InRaster,OutRaster])\r\n tmp=os.system(com)\r\n print tmp\r\n #if not tmp:\r\n # raise Exception(\"Could not reproject the image \")\r\n #os.remove(OutRaster)\r","def GetGeoTransform(raster_path):\n \n #open a GDAL object containig the raster\n gdal_img = gdal.Open(raster_path)\n \n #extract basic geospatial data\n ulx, xres, xskew, uly, yskew, yres = gdal_img.GetGeoTransform()\n \n #calculate lower right coordinates from upper left coordinates and raster size\n lrx = ulx + (gdal_img.RasterXSize * xres)\n lry = uly + (gdal_img.RasterYSize * yres)\n \n geoinfo = {'ulx': ulx,\n 'lrx': lrx,\n 'uly': uly,\n 'lry': lry,\n 'xres': xres,\n 'xskew': xskew,\n 'yres': yres,\n 'yskew': yskew\n }\n \n return geoinfo","def array_to_raster(array, x, y):\n\n # Files info\n dst_filename = 'atiff.tiff'\n \n # Load matlab file\n front_dict = loadmat(infile,squeeze_me=True, struct_as_record=False)\n #print front_dict\n \n # You need to get those values like you did.\n x_pixels = len(x) # number of pixels in x\n y_pixels = len(y) # number of pixels in y\n PIXEL_SIZE = 1000 # size of the pixel...(in m?) \n x_min = np.min(x)\n y_max = np.min(y) # x_min & y_max are like the \"top left\" corner.\n wkt_projection = 'a projection in wkt that you got from other file'\n\n driver = gdal.GetDriverByName('GTiff')\n\n dataset = driver.Create(\n dst_filename,\n x_pixels,\n y_pixels,\n 1,\n gdal.GDT_Float32, )\n\n dataset.SetGeoTransform((\n x_min, # 0\n PIXEL_SIZE, # 1\n 0, # 2\n y_max, # 3\n 0, # 4\n -PIXEL_SIZE)) \n\n dataset.SetProjection(wkt_projection)\n dataset.GetRasterBand(1).WriteArray(array)\n dataset.FlushCache() # Write to disk.\n return dataset, dataset.GetRasterBand(1) #If you need to ret","def tiffwrite(filename, im):\n tf.imwrite(filename, im)","def outputGeoTiff(data, filename, geo_t, proj, output_dir = os.getcwd(), dtype = 6, nodata = None):\n \n from osgeo import osr, gdal\n \n # Get full output path\n output_path = '%s/%s.tif'%(os.path.abspath(os.path.expanduser(output_dir)), filename.rstrip('.tif'))\n \n # Save image with georeference info\n driver = gdal.GetDriverByName('GTiff')\n ds = driver.Create(output_path, data.shape[1], data.shape[0], 1, dtype, options = ['COMPRESS=LZW'])\n ds.SetGeoTransform(geo_t)\n ds.SetProjection(proj)\n \n # Set nodata\n if nodata != None:\n ds.GetRasterBand(1).SetNoDataValue(nodata)\n \n # Write data for masked and unmasked arrays\n if np.ma.isMaskedArray(data):\n ds.GetRasterBand(1).WriteArray(data.filled(nodata))\n else:\n ds.GetRasterBand(1).WriteArray(data)\n ds = None","def Save_as_tiff(name='', data='', geo='', projection=''):\n \n dir_name = os.path.dirname(name)\n \n if not os.path.exists(dir_name):\n os.makedirs(dir_name)\n \n # save as a geotiff\n driver = gdal.GetDriverByName(\"GTiff\")\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1, gdal.GDT_Float32, ['COMPRESS=LZW'])\n srse = osr.SpatialReference()\n if projection == '':\n srse.SetWellKnownGeogCS(\"WGS84\")\n\n else:\n try:\n if not srse.SetWellKnownGeogCS(projection) == 6:\n srse.SetWellKnownGeogCS(projection)\n else:\n try:\n srse.ImportFromEPSG(int(projection))\n except:\n srse.ImportFromWkt(projection)\n except:\n try:\n srse.ImportFromEPSG(int(projection))\n except:\n srse.ImportFromWkt(projection)\n\n dst_ds.SetProjection(srse.ExportToWkt())\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\n dst_ds.SetGeoTransform(geo)\n dst_ds.GetRasterBand(1).WriteArray(data)\n dst_ds = None\n return()","def test_save_tif():\n img = Image.new('RGB', (10, 20))\n\n parameters = {'path': 'green-dot.tif', 'data': [img]}\n\n assert images.save(parameters)","def save_grtrans_image(grt_obj):\n I_im = grt_obj.ivals[:,0,0].reshape(npix,npix).flatten()\n Q_im = grt_obj.ivals[:,1,0].reshape(npix,npix).flatten()\n U_im = grt_obj.ivals[:,2,0].reshape(npix,npix).flatten()\n V_im = grt_obj.ivals[:,3,0].reshape(npix,npix).flatten()\n\n # convert to Tb\n factor = 3.254e13/(RF**2 * psize_rad**2)\n I_im *= factor\n Q_im *= factor\n U_im *= factor\n V_im *= factor\n\n x = np.array([[i for i in range(npix)] for j in range(npix)]).flatten()\n y = np.array([[j for i in range(npix)] for j in range(npix)]).flatten()\n\n x -= npix/2\n y -= npix/2\n x = x*psize_uas\n y = y*psize_uas\n\n outdat = np.vstack((x.T,y.T,I_im.T,Q_im.T,U_im.T,V_im.T)).T\n np.savetxt('../rrjet_and_riaf/'+FNAME,outdat)\n #np.savetxt('../rrjet_and_riaf/grtrans_jet_compare_positron_noconv.txt',outdat)\n return","def array2tif(raster_file, new_raster_file, array):\n # Invoke the GDAL Geotiff driver\n raster = gdal.Open(raster_file)\n\n driver = gdal.GetDriverByName('GTiff')\n out_raster = driver.Create(new_raster_file,\n raster.RasterXSize,\n raster.RasterYSize,\n 1,\n gdal.GDT_Float32)\n out_raster.SetProjection(raster.GetProjection())\n # Set transformation - same logic as above.\n out_raster.SetGeoTransform(raster.GetGeoTransform())\n # Set up a new band.\n out_band = out_raster.GetRasterBand(1)\n # Set NoData Value\n out_band.SetNoDataValue(-1)\n # Write our Numpy array to the new band!\n out_band.WriteArray(array)","def array_to_raster(inTiff,array,outFile,dataType=gdal.GDT_Float32):\n \n inDataset = gdal.Open(inTiff, GA_ReadOnly)\n\n # You need to get those values like you did.\n x_pixels = inDataset.RasterXSize # number of pixels in x\n y_pixels = inDataset.RasterYSize # number of pixels in y\n PIXEL_SIZE = inDataset.GetGeoTransform()[1] # size of the pixel... \n x_min = inDataset.GetGeoTransform()[0] \n y_max = inDataset.GetGeoTransform()[3] # x_min & y_max are like the \"top left\" corner.\n wkt_projection = inDataset.GetProjectionRef()\n\n driver = gdal.GetDriverByName('GTiff')\n\n outDataset = driver.Create(\n outFile,\n x_pixels,\n y_pixels,\n 1,\n dataType, )\n\n outDataset.SetGeoTransform((\n x_min, # 0\n PIXEL_SIZE, # 1\n 0, # 2\n y_max, # 3\n 0, # 4\n -PIXEL_SIZE))\n\n outDataset.SetProjection(wkt_projection)\n outDataset.GetRasterBand(1).WriteArray(array)\n outDataset.FlushCache() # Write to disk.\n return outDataset, outDataset.GetRasterBand(1) #If you need to return, remenber to return also the dataset because the band don`t live without dataset.","def reprojectRaster(in_raster,model_raster,out_dir,name_override = None):\n\tif name_override:\n\t\tout_name = os.path.join(out_dir,name_override)\n\telse:\n\t\tin_base,in_ext = os.path.splitext(os.path.basename(in_raster))\n\t\tout_name = os.path.join(out_dir,in_base+\"_REPROJ\"+in_ext)\n\t\t#print(out_name)\n\tt_ds = gdal.Open(model_raster,0)\n\tt_wkt = t_ds.GetProjection()\n\ts_ds = gdal.Open(in_raster,0)\n\ts_wkt = s_ds.GetProjection()\n\tt_ds = s_ds = None\n\tif s_wkt != t_wkt:\n\t\treproj_args = [r\"C:\\OSGeo4W64\\bin\\gdalwarp.exe\",\"-t_srs\",t_wkt,in_raster,out_name]\n\t\tsubprocess.call(reproj_args)\n\t\treturn out_name\n\telse:\n\t\tlog.warning(\"Projections already match, doing nothing.\")\n\t\treturn in_raster","def reprojectAndSaveNewShapefile(inFilepath,outFilepath,to_EPSG):\r\n import geopandas as gpd\r\n from fiona.crs import from_epsg\r\n\r\n inFile = gpd.read_file(inFilepath)\r\n inFile_proj = inFile.copy()\r\n inFile_proj['geometry'] = inFile_proj['geometry'].to_crs(epsg=to_EPSG)\r\n inFile_proj.crs = from_epsg(to_EPSG)\r\n inFile_proj.to_file(outFilepath)","def convertCSVToGeoTIFF(geoproperties, datafile, outputdir, outfile):\n \n data = numpy.genfromtxt(datafile, delimiter=',')\n \n Ny, Nx = data.shape\n \n #print data.size\n #print data.shape\n #print data\n \n # slice out the last column of null values\n if str(data[Ny-1][Nx-1]) == 'nan':\n data = data[:,:-1]\n \n Ny, Nx = data.shape\n \n #print data.size\n #print data.shape\n #print data\n #print Ny, Nx\n \n startPos = [geoproperties['tl']['long'],geoproperties['tl']['lat']]\n d_lat = (geoproperties['br']['lat'] - geoproperties['tl']['lat']) / (Ny - 1)\n d_long = (geoproperties['br']['long'] - geoproperties['tl']['long']) / (Nx - 1)\n \n #print startPos, d_lat, d_long\n \n driver = gdal.GetDriverByName(\"GTiff\")\n ds = driver.Create(os.path.join(outputdir,outfile),Nx,Ny,1,gdal.GDT_Float32)\n #ds = driver.Create('output/output.tif',Nx,Ny,1,gdal.GDT_Byte)\n #ds.SetGeoTransform( [ -158.584, .008, 0, 21.108, 0, .008 ] )\n ds.SetGeoTransform( [ startPos[0], d_long, 0, startPos[1], 0, d_lat ] )\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326)\n ds.SetProjection( srs.ExportToWkt() )\n ds.GetRasterBand(1).WriteArray(data)\n ds = None","def UpdateGT(out_file, data, src_file, epsg = 4326, drv = 'GTiff', datatype = gdal.GDT_Float32, NoData = -999):\n \n #assign which driver to use for the file format - default is .tif\n driver = gdal.GetDriverByName(drv)\n \n #source raster from which to use the geospatial metadata\n src_gt = gdal.Open(src_file)\n \n #data shape - detects whether to produce multiple bands or not\n if len(data.shape) == 2:\n [cols, rows] = data.shape\n n = 1\n else:\n [n, cols, rows] = data.shape\n\n #create the destination file\n dst_gt = driver.Create(out_file, rows, cols, n, datatype)\n \n #get the geotransform from the source file\n gt = src_gt.GetGeoTransform()\n \n #coordinate system in which to create the raster, in wkt form\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(epsg)\n dest_wkt = srs.ExportToWkt()\n \n #set the projection in the desired reference system\n dst_gt.SetGeoTransform(gt)\n dst_gt.SetProjection(dest_wkt)\n \n #set the data\n if n==1:\n dst_gt.GetRasterBand(1).WriteArray(data)\n dst_gt.GetRasterBand(1).SetNoDataValue(NoData)\n #close and write to file\n dst_gt.FlushCache()\n #used if there are multiple bands\n else:\n for i in range(n):\n dst_gt.GetRasterBand(i+1).WriteArray(data[i])\n dst_gt.GetRasterBand(i+1).SetNoDataValue(NoData)\n dst_gt.FlushCache()","def save_image(self, filename):\n raster.save_image(filename, self.image, self.metadata)","def single_to_rgb(R_file,G_file,B_file): \n R=gdal_array.LoadFile(R_file)\n G=gdal_array.LoadFile(G_file)\n B=gdal_array.LoadFile(B_file)\n \n \n basename=os.path.basename(R_file)\n basename=basename[:3]+basename[4:]\n basename=basename[:-4]+\"_rgb_.tif\" \n \n\n file_path=os.path.dirname(os.path.abspath(R_file))+\"/\"+basename\n\n \n driver=osgeo.gdal.GetDriverByName(\"GTiff\")\n options = ['PHOTOMETRIC=RGB', 'PROFILE=GeoTIFF']\n print(file_path)\n print(np.max(np.array([R.shape[1],B.shape[1],G.shape[1]])), np.max(np.array([R.shape[0],B.shape[0],G.shape[0]])))\n Xlen=np.max(np.array([R.shape[1],B.shape[1],G.shape[1]]))\n Ylen= np.max(np.array([R.shape[0],B.shape[0],G.shape[0]]))\n dataset=driver.Create(file_path, int(Xlen),int(Ylen), 3, osgeo.gdal.GDT_UInt16, options) \n \n dataset.GetRasterBand(1).WriteArray(R)\n dataset.GetRasterBand(2).WriteArray(G)\n dataset.GetRasterBand(3).WriteArray(B)\n \n return file_path","def __init__(self, raster_path):\n self.raster_path = raster_path\n dataset = gdal.Open(raster_path)\n self.width = dataset.RasterXSize\n self.height = dataset.RasterYSize\n # Gets the gdal geo transformation tuples\n # gdal_version = gdal.__version__\n self._txf = dataset.GetGeoTransform()\n # self._inv_txf = gdal.InvGeoTransform(self._txf)[1]\n self._inv_txf = gdal.InvGeoTransform(self._txf)\n # Gets the transformation from lat/lon to coordinates\n wgs84_ref = osr.SpatialReference()\n wgs84_ref.ImportFromEPSG(4326) # WGS84\n sref = osr.SpatialReference()\n sref.ImportFromWkt(dataset.GetProjection())\n if int(osgeo.__version__[0]) >= 3:\n # Output order has changed in osgeo v3\n wgs84_ref.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\n sref.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\n\n self._transform = osr.CoordinateTransformation(wgs84_ref, sref)\n inv_transform = osr.CoordinateTransformation(sref, wgs84_ref)\n # Find a loose lat/lon bounding box for quick check without\n # having to do full coordinates transformation\n corners = []\n for x in [0, self.width]:\n for y in [0, self.height]:\n corners.append([self._txf[0] + self._txf[1] * x + self._txf[2] * y,\n self._txf[3] + self._txf[4] * x + self._txf[5] * y])\n self.max_lat = -100\n self.min_lat = 100\n self.max_lon = -500\n self.min_lon = 500\n for c in corners:\n p = inv_transform.TransformPoint(c[0], c[1])\n if p[0] > self.max_lon:\n self.max_lon = p[0]\n if p[0] < self.min_lon:\n self.min_lon = p[0]\n if p[1] > self.max_lat:\n self.max_lat = p[1]\n if p[1] < self.min_lat:\n self.min_lat = p[1]\n dataset = None","def rasterToBinary(input_raster,output_dir,name_override=None):\n\tif name_override:\n\t\tout_path = os.path.join(output_dir,name_override)\n\telse:\n\t\tin_base,in_ext = os.path.splitext(os.path.basename(input_raster))\n\t\tout_path = os.path.join(output_dir,in_base+\"_BINARY\"+in_ext)\n\n\tds = gdal.Open(input_raster,0)\n\tband = ds.GetRasterBand(1)\n\tnoData = band.GetNoDataValue()\n\tsrs = ds.GetProjection()\n\tgt = ds.GetGeoTransform()\n\tarr = BandReadAsArray(band)\n\tds = band = None # close dataset and band\n\tarr[arr != noData] = 1\n\tarr[arr == noData] = noData\n\trasterYSize, rasterXSize = arr.shape\n\tdriver = gdal.GetDriverByName('GTiff')\n\tdataset = driver.Create(out_path,rasterXSize,rasterYSize,1,gdal.GDT_Byte,['COMPRESS=DEFLATE'])\n\tdataset.GetRasterBand(1).WriteArray(arr)\n\tdataset.GetRasterBand(1).SetNoDataValue(noData)\n\tdataset.SetGeoTransform(gt)\n\tdataset.SetProjection(srs)\n\tdataset.FlushCache() # Write to disk\n\tdel dataset\n\n\treturn out_path","def save_array_as_geotif(array, source_tif_path, out_path):\n if len(array.shape) > 2:\n height, width, depth = array.shape\n else:\n height, width = array.shape\n depth = 1\n source_tif = gdal.Open(source_tif_path)\n driver = gdal.GetDriverByName(\"GTiff\")\n dataset = driver.Create(out_path, width, height, depth, gdal.GDT_Float32)\n if depth != 1:\n for i in range(depth):\n dataset.GetRasterBand(i + 1).WriteArray(array[:, :, i])\n else:\n dataset.GetRasterBand(1).WriteArray(array)\n geotrans = source_tif.GetGeoTransform()\n proj = source_tif.GetProjection()\n dataset.SetGeoTransform(geotrans)\n dataset.SetProjection(proj)\n dataset.FlushCache()\n dataset = None","def reproject_raster(res: int, src_epsg: int, dst_epsg: int) -> None:\n src_name = f\"res_{res}_epsg_{src_epsg}\"\n src_path = os.path.join(src_name, f\"{src_name}.tif\")\n with rio.open(src_path) as src:\n dst_crs = f\"epsg:{dst_epsg}\"\n transform, width, height = calculate_default_transform(\n src.crs, dst_crs, src.width, src.height, *src.bounds\n )\n profile = src.profile.copy()\n profile.update(\n {\"crs\": dst_crs, \"transform\": transform, \"width\": width, \"height\": height}\n )\n dst_name = f\"res_{res}_epsg_{dst_epsg}\"\n os.makedirs(dst_name, exist_ok=True)\n dst_path = os.path.join(dst_name, f\"{dst_name}.tif\")\n with rio.open(dst_path, \"w\", **profile) as dst:\n reproject(\n source=rio.band(src, 1),\n destination=rio.band(dst, 1),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=dst.transform,\n dst_crs=dst.crs,\n )","def save_mask2file(mask, filename, crs=None, transform=None):\n height, width = mask.shape\n if mask.dtype == np.bool:\n mask = mask.astype(np.uint8)\n with rasterio.open(\n filename,\n 'w',\n driver='GTiff',\n dtype=mask.dtype,\n count=1,\n width=width,\n height=height,\n crs=crs,\n transform=transform,\n ) as dst:\n dst.write(mask, indexes=1)","def geotiff_to_netcdf(path, epsg, download=False, output_path='', name='', non_negative=True, savefig=False,\n fig_path=''):\n\n ds = gdal.Open(path)\n\n # upper left, resolution and skew of x and y\n ulx, xres, xskew, uly, yskew, yres = ds.GetGeoTransform()\n\n # lower right x and y\n lrx = ulx + (ds.RasterXSize * xres)\n lry = uly + (ds.RasterYSize * yres)\n\n # setup the source projection - you can also import from epsg, proj4...\n source = osr.SpatialReference()\n source.ImportFromWkt(ds.GetProjection())\n\n # the target projection\n target = osr.SpatialReference()\n target.ImportFromEPSG(4326)\n\n # create the transform - this can be used repeatedly\n transform = osr.CoordinateTransformation(source, target)\n\n # transform the point. You can also create an ogr geometry and use the more generic `point.Transform()`\n uly, ulx, _ = transform.TransformPoint(ulx, uly)\n lry, lrx, _ = transform.TransformPoint(lrx, lry)\n\n lons = np.linspace(ulx, lrx, ds.RasterXSize)\n lats = np.linspace(lry, uly, ds.RasterYSize)\n\n data = ds.ReadAsArray()\n\n if non_negative: data[data < 0] = np.nan\n\n ds = xr.DataArray(np.flipud(data), coords={'lat': lats, 'lon': lons, }, dims=['lat', 'lon'])\n\n ds.name = name\n\n if download and len(output_path) > 0:\n ds.to_netcdf(output_path)\n\n if savefig and len(fig_path) > 0:\n fig, ax = plt.subplots(figsize=(15, 10))\n ds.plot(ax=ax)\n plt.savefig(fig_path)\n plt.close()\n\n return ds","def nc2gtiff(input_name, epsg_in=3413, epsg_out=3857):\n sub_name=[\"dX\",\"dY\"]\n \n for sub in sub_name:\n result = os.system(\"echo ''\")\n #netCDF to geotiff (EPSG code is not changed) \n out_name = \"./data/tiff_raw/{}_{}_tmp.tiff\".format(os.path.basename(input_name)[:-3],sub)\n #c is shell command\n #options\n #-a_srs:EPSGcode of input files\n #-of:output format\n #\n print(\"nc2gtiff\")\n c = \"gdal_translate -a_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' NETCDF:'{in_name}':{sub} \\\n -of 'Gtiff' \\\n '{out_name}'\".format(epsg=epsg_in,sub=sub, \\\n in_name=input_name,out_name=out_name) \n result = os.system(c) #run shell command\n #print(c) #if you want to see the filled c, remove '#' in the head of this line\n if result!=0: # if it raises error, return filename \n print(input_name, result, \"translate\")\n \n result = os.system(\"echo ''\")\n\n #geotiff EPSG_in to EPCG_out\n print(\"geotiff2geotiff\")\n target_name=\"./data/tiff_target/{}_{}.tiff\".format(os.path.basename(input_name)[:-3],sub)\n c = \"gdalwarp -overwrite -s_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' {out_name} -r cubic\\\n {target_name} -t_srs EPSG:{epsg_out} -of \\\n 'GTIFF'\".format(out_name=out_name, target_name=target_name, \n epsg_in=epsg_in, epsg_out=epsg_out)\n result = os.system(c) # run shell command\n if result!=0:\n print(input_name, result, \"warp\")","def geotif_image(self, tile_bounds, image_bounds, imagepath,image_gdal):\n i_srid=3857\n s_srid=\"WGS 84 / Pseudo-Mercator\"\n # i_srid=3395\n # s_srid=\"WGS 84 / World Mercator\"\n # 4326 Wsg84\n # Upper Left ( -8.4375000, 77.1571625) ( 8d26'15.00\"W, 77d 9'25.79\"N)\n # Lower Left ( -8.4375000, 35.4606700) ( 8d26'15.00\"W, 35d27'38.41\"N)\n # Upper Right ( 80.1562500, 77.1571625) ( 80d 9'22.50\"E, 77d 9'25.79\"N)\n # Lower Right ( 80.1562500, 35.4606700) ( 80d 9'22.50\"E, 35d27'38.41\"N)\n # Center ( 35.8593750, 56.3089162) ( 35d51'33.75\"E, 56d18'32.10\"N)\n # 3857 'WGS 84 / Pseudo-Mercator'\n # Upper Left ( -939258.204,13932330.020) ( 8d26'15.00\"W, 77d 9'25.79\"N)\n # Lower Left ( -939258.204, 4226661.916) ( 8d26'15.00\"W, 35d27'38.41\"N)\n # Upper Right ( 8922952.934,13932330.020) ( 80d 9'22.50\"E, 77d 9'25.79\"N)\n # Lower Right ( 8922952.934, 4226661.916) ( 80d 9'22.50\"E, 35d27'38.41\"N)\n # Center ( 3991847.365, 9079495.968) ( 35d51'33.75\"E, 62d54'54.84\"N)\n # 3395 'WGS 84 / World Mercator'\n # Upper Left ( -939258.204,13932330.020) ( 8d26'15.00\"W, 77d14'24.81\"N)\n # Lower Left ( -939258.204, 4226661.916) ( 8d26'15.00\"W, 35d38'33.56\"N)\n # Upper Right ( 8922952.934,13932330.020) ( 80d 9'22.50\"E, 77d14'24.81\"N)\n # Lower Right ( 8922952.934, 4226661.916) ( 80d 9'22.50\"E, 35d38'33.56\"N)\n # Center ( 3991847.365, 9079495.968) ( 35d51'33.75\"E, 63d 4'14.87\"N)\n bounds_west,bounds_south,bounds_east,bounds_north=tile_bounds\n bounds_wsg84=\"bounds_wsg84: %f,%f,%f,%f\"% (bounds_west,bounds_south,bounds_east,bounds_north)\n mercator = GlobalMercator()\n tile_bounds=mercator.BoundsToMeters(tile_bounds)\n mbtiles_name=\"\";\n mbtiles_description=\"\"\n s_TIFFTAG_DOCUMENTNAME=\"\"\n s_TIFFTAG_IMAGEDESCRIPTION=\"\"\n s_TIFFTAG_SOFTWARE=\"\"\n s_TIFFTAG_DATETIME=\"\"\n s_TIFFTAG_ARTIST=\"\"\n s_TIFFTAG_HOSTCOMPUTER=\"\"\n s_TIFFTAG_COPYRIGHT=\"\"\n if self.metadata_input:\n metadata=dict(self.metadata_input)\n mbtiles_name=metadata.get('name','')\n mbtiles_description=metadata.get('description','')\n if self._metadata:\n for metadata_list in self._metadata:\n metadata=dict(metadata_list[0])\n mbtiles_name=metadata.get('name',mbtiles_name)\n mbtiles_description=metadata.get('description',mbtiles_description)\n s_TIFFTAG_DOCUMENTNAME=metadata.get('TIFFTAG_DOCUMENTNAME',mbtiles_name)\n s_TIFFTAG_IMAGEDESCRIPTION=metadata.get('TIFFTAG_IMAGEDESCRIPTION',mbtiles_description)\n s_TIFFTAG_SOFTWARE=metadata.get('TIFFTAG_SOFTWARE','')\n s_TIFFTAG_DATETIME=metadata.get('TIFFTAG_DATETIME','')\n s_TIFFTAG_ARTIST=metadata.get('TIFFTAG_ARTIST','')\n s_TIFFTAG_HOSTCOMPUTER=metadata.get('TIFFTAG_HOSTCOMPUTER','')\n s_TIFFTAG_COPYRIGHT=metadata.get('TIFFTAG_COPYRIGHT','')\n if s_TIFFTAG_DOCUMENTNAME == \"\":\n s_TIFFTAG_DOCUMENTNAME=mbtiles_name\n if s_TIFFTAG_IMAGEDESCRIPTION == \"\":\n s_TIFFTAG_IMAGEDESCRIPTION=mbtiles_description\n tiff_metadata=[]\n if s_TIFFTAG_DOCUMENTNAME != \"\":\n tiff_metadata.append(('TIFFTAG_DOCUMENTNAME',s_TIFFTAG_DOCUMENTNAME))\n if s_TIFFTAG_IMAGEDESCRIPTION != \"\":\n tiff_metadata.append(('TIFFTAG_IMAGEDESCRIPTION',s_TIFFTAG_IMAGEDESCRIPTION))\n if s_TIFFTAG_SOFTWARE != \"\":\n tiff_metadata.append(('TIFFTAG_SOFTWARE',s_TIFFTAG_SOFTWARE))\n else:\n tiff_metadata.append(('TIFFTAG_SOFTWARE',bounds_wsg84))\n if s_TIFFTAG_DATETIME != \"\":\n tiff_metadata.append(('TIFFTAG_DATETIME',s_TIFFTAG_DATETIME))\n if s_TIFFTAG_ARTIST != \"\":\n tiff_metadata.append(('TIFFTAG_ARTIST',s_TIFFTAG_ARTIST))\n if s_TIFFTAG_HOSTCOMPUTER != \"\":\n tiff_metadata.append(('TIFFTAG_HOSTCOMPUTER',s_TIFFTAG_HOSTCOMPUTER))\n if s_TIFFTAG_COPYRIGHT != \"\":\n tiff_metadata.append(('TIFFTAG_COPYRIGHT',s_TIFFTAG_COPYRIGHT))\n # this assumes the projection is Geographic lat/lon WGS 84\n xmin,ymin,xmax,ymax=tile_bounds\n image_width,image_height=image_bounds\n # Upper Left ( 20800.000, 22000.000)\n # Lower Right ( 24000.000, 19600.000)\n # Size is 15118, 11339\n # (24000-20800)/15118 = 3200 = 0,21166821 [xres]\n # (19600-22000)/11339 = 2400 = −0,211658876 [yres]\n # geo_transform = (20800.0, 0.2116682100807, 0.0, 22000.0, 0.0, -0.21165887644413)\n geo_transform = [xmin, (xmax-xmin)/image_width, 0, ymax, 0, (ymin-ymax)/image_height ]\n spatial_projection = osr.SpatialReference()\n spatial_projection.ImportFromEPSG(i_srid)\n logger.info(_(\"-I-> geotif_image: Saving as GeoTiff - image[%s] compression[%s]\") % (imagepath,self.tiff_compression))\n image_dataset = gdal.Open(image_gdal, gdal.GA_Update )\n image_dataset.SetProjection(spatial_projection.ExportToWkt())\n image_dataset.SetGeoTransform(geo_transform)\n driver = gdal.GetDriverByName(\"GTiff\")\n output_dataset = driver.CreateCopy(imagepath,image_dataset, 0, self.tiff_compression )\n if tiff_metadata:\n logger.info(_(\"-I-> geotif_image: tiff_metadata[%s]\") % tiff_metadata)\n output_dataset.SetMetadata(dict(tiff_metadata))\n # Once we're done, close properly the dataset\n output_dataset = None\n image_dataset = None\n os.remove(image_gdal)\n logger.info(_(\"-I-> geotif_image: Saved resulting image to '%s' as GeoTiff- bounds[%s]\") % (imagepath,tile_bounds))","def export_image(self, bbox, zoomlevel, imagepath):\n assert has_pil, _(\"Cannot export image without python PIL\")\n grid,tile_bounds = self.grid_tiles(bbox, zoomlevel)\n width = len(grid[0])\n height = len(grid)\n widthpix = width * self.tile_size\n heightpix = height * self.tile_size\n result = Image.new(\"RGBA\", (widthpix, heightpix))\n offset = (0, 0)\n for i, row in enumerate(grid):\n for j, (x, y) in enumerate(row):\n offset = (j * self.tile_size, i * self.tile_size)\n img = self._tile_image(self.tile((zoomlevel, x, y)))\n result.paste(img, offset)\n if imagepath.endswith(\".tif\") or imagepath.endswith(\".tiff\"):\n # http://effbot.org/imagingbook/pil-index.htm#appendixes\n # Packbits, LZW, or JPEG\n # In the current version, PIL always writes uncompressed TIFF files.\n image_gdal=\"gdal_input.tif\"\n result.save(image_gdal, format=\"TIFF\", compression=\"JPEG\")\n self.geotif_image(tile_bounds,(widthpix,heightpix),imagepath,image_gdal)\n else:\n if imagepath.endswith(\".jpg\") or imagepath.endswith(\".jpeg\"):\n # IOError: encoder error -2 when writing image file\n result.save(imagepath, format=\"JPEG\", quality=int(self.jpg_quality), optimize=True, progressive=False)\n elif imagepath.endswith(\".png\") :\n result.save(imagepath, format=\"PNG\",optimize=True)\n else:\n result.save(imagepath)\n logger.info(_(\"-I-> export_image: Save resulting image to '%s' - bounds[%s]\") % (imagepath,tile_bounds))","def extent_to_gdal_transform(extent):\n\n # extent = SpatialExtent()\n\n pixel_height = math.copysign(extent.height, extent.bottom - extent.top)\n pixel_width = math.copysign(extent.width, extent.right - extent.left)\n\n transform = [extent.top, pixel_width, 0, extent.left, 0, pixel_height]\n return transform","def export_array(in_array, output_path):\n global proj, geotrans, row, col\n proj = band.GetProjection()\n geotrans = band.GetGeoTransform()\n row = band.RasterYSize\n col = band.RasterXSize\n driver = gdal.GetDriverByName(\"GTiff\")\n outdata = driver.Create(output_path, col, row, 1)\n outband = outdata.GetRasterBand(1)\n outband.SetNoDataValue(-9999)\n outband.WriteArray(in_array)\n # Georeference the image\n outdata.SetGeoTransform(geotrans)\n # Write projection information\n outdata.SetProjection(proj)\n outdata.FlushCache()\n outdata = None","def align_rasters(ref_raster, tar_raster, output_suffix):\n command = [\"gdalbuildvrt\", \"-te\"]\n hDataset = gdal.Open(ref_raster, gdal.GA_ReadOnly)\n if hDataset is None:\n return False\n adfGeoTransform = hDataset.GetGeoTransform(can_return_null=True)\n\n tif_file=tar_raster\n vrt_file = tif_file.replace('.tif', '.vrt')\n\n if adfGeoTransform is not None:\n dfGeoXUL = adfGeoTransform[0]\n dfGeoYUL = adfGeoTransform[3]\n dfGeoXLR = adfGeoTransform[0] + adfGeoTransform[1] * hDataset.RasterXSize + \\\n adfGeoTransform[2] * hDataset.RasterYSize\n dfGeoYLR = adfGeoTransform[3] + adfGeoTransform[4] * hDataset.RasterXSize + \\\n adfGeoTransform[5] * hDataset.RasterYSize\n xres = str(abs(adfGeoTransform[1]))\n yres = str(abs(adfGeoTransform[5]))\n\n subprocess.call(command + [str(dfGeoXUL), str(dfGeoYLR), str(dfGeoXLR),\n str(dfGeoYUL), \"-q\", \"-tr\", xres, yres,\n vrt_file, tif_file])\n\n output_file = tif_file.replace('.tif', output_suffix)\n\n print('gdal_translate -q {} {}'.format(vrt_file, output_file))\n\n cmd = 'gdal_translate -q {} {}'.format(vrt_file, output_file)\n\n #print(dfGeoXUL, dfGeoYLR, dfGeoXLR, dfGeoYUL, xres, yres)\n\n subprocess.call(cmd, shell=True)\n os.remove(vrt_file)\n\n return True\n\n else:\n\n return False","def reproject_dataset(filename):\n\n # We want to project the GeoTIFF coordinate reference system (crs)\n # to WGS84 (e.g. into the familiar Lat/Lon pairs). WGS84 is analogous\n # to EPSG:4326\n dst_crs = 'EPSG:4326'\n\n with rasterio.open(filename) as src:\n transform, width, height = rasterio.warp.calculate_default_transform(\n src.crs, dst_crs, src.width, src.height, *src.bounds)\n kwargs = src.meta.copy()\n kwargs.update({\n 'crs': dst_crs,\n 'transform': transform,\n 'width': width,\n 'height': height,\n 'compress': 'lzw'\n })\n\n out_path = filename.split('.tif')[0] + '.wgs84.tif'\n with rasterio.open(out_path, 'w', **kwargs) as dst:\n for i in range(1, src.count + 1):\n rasterio.warp.reproject(\n source=rasterio.band(src, i),\n destination=rasterio.band(dst, i),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=transform,\n dst_crs=dst_crs,\n resampling=rasterio.warp.Resampling.nearest)\n\n return out_path","def write_raster(\n res: int = RES[0],\n epsg: int = EPSG[0],\n dtype: str = \"uint8\",\n path: Optional[str] = None,\n) -> None:\n size = SIZE // res\n profile = {\n \"driver\": \"GTiff\",\n \"dtype\": dtype,\n \"count\": 1,\n \"crs\": f\"epsg:{epsg}\",\n \"transform\": from_bounds(0, 0, SIZE, SIZE, size, size),\n \"height\": size,\n \"width\": size,\n \"nodata\": 0,\n }\n\n if path is None:\n name = f\"res_{res}_epsg_{epsg}\"\n path = os.path.join(name, f\"{name}.tif\")\n\n directory = os.path.dirname(path)\n os.makedirs(directory, exist_ok=True)\n\n with rio.open(path, \"w\", **profile) as f:\n x = np.ones((1, size, size))\n f.write(x)","def save_tile_img(tif, xyz, dataset, tile_size, region, zone, save_path, display=False):\n \n prefix = f'{region}{zone}{dataset}_'\n x,y,z = xyz\n tile, mask = rt_main.tile(tif, x,y,z, tilesize=tile_size)\n if display: \n plt.imshow(np.moveaxis(tile,0,2))\n plt.show()\n \n skimage.io.imsave(f'{save_path}/{prefix}{z}_{x}_{y}.png',np.moveaxis(tile,0,2), check_contrast=False)","def make_raster(in_ds, fn, data, data_type, nodata=None):\n driver = gdal.GetDriverByName('GTiff')\n out_ds = driver.Create(\n fn, in_ds.RasterXSize, in_ds.RasterYSize, 1, data_type)\n out_ds.SetProjection(in_ds.GetProjection())\n out_ds.SetGeoTransform(in_ds.GetGeoTransform())\n out_band = out_ds.GetRasterBand(1)\n if nodata is not None:\n out_band.SetNoDataValue(nodata)\n out_band.WriteArray(data)\n out_band.FlushCache()\n out_band.ComputeStatistics(False)\n return out_ds","def create_raster_from_matrix(matrix,outfile,extent=None,cellsize=CELL_SIZE,format='GTiff',srid=settings.GEOMETRY_DB_SRID):\n if extent is None:\n extent = StudyRegion.objects.current().geometry.extent\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(srid)\n ydist = extent[3] - extent[1]\n xdist = extent[2] - extent[0]\n xcount = int((xdist/cellsize)+1)\n ycount = int((ydist/cellsize)+1)\n\n # Create output raster \n driver = gdal.GetDriverByName( format )\n dst_ds = driver.Create( outfile, xcount, ycount, 1, gdal.GDT_UInt16 )\n\n # This is bizzarly complicated\n # the GT(2) and GT(4) coefficients are zero, \n # and the GT(1) is pixel width, and GT(5) is pixel height. \n # The (GT(0),GT(3)) position is the top left corner of the top left pixel\n gt = (extent[0],cellsize,0,extent[3],0,(cellsize*-1.))\n dst_ds.SetGeoTransform(gt)\n dst_ds.SetProjection( srs.ExportToWkt() )\n\n dst_band = dst_ds.GetRasterBand(1)\n dst_band.WriteArray(matrix,0,0)\n dst_band.SetNoDataValue(0.0)\n min_val, max_val = dst_band.ComputeRasterMinMax()\n start_color = (255,255,0,160)\n end_color = (255,0,0,160)\n ct = gdal.ColorTable()\n ct.CreateColorRamp(int(min_val),start_color,int(max_val),end_color)\n dst_band.SetColorTable(ct)\n dst_ds = None\n return outfile","def output_rasters(self, arr, outdir, outname):\n\n outpath = os.path.join(outdir, outname)\n print('the outpath for file {} is {}'.format(outname, outpath))\n\n # get the geoinfo from sample tiff to output intermediate files\n ds = rasterio.open(self.geoproperties_file)\n band1 = arr\n with rasterio.open(outpath, 'w', driver='GTiff', height=self.rows, width=self.cols,\n count=1, dtype='float64', crs=self.crs, transform=self.transform) as wrast:\n wrast.write(band1, indexes=1)\n\n # TODO - Set an AWS Cloud flag in the config_dict file to activate this function or not...\n # delete files created locally and put in bucket\n # PathManager.s3_delete_local(from_file, bucket, prefix_no_slash)","def gdal_read_geotiff_file(sFilename_in):\n \n if os.path.exists(sFilename_in):\n pass\n else:\n print('The file does not exist!')\n return\n\n sDriverName='GTiff'\n pDriver = gdal.GetDriverByName(sDriverName) \n\n if pDriver is None:\n print (\"%s pDriver not available.\\n\" % sDriverName)\n else:\n print (\"%s pDriver IS available.\\n\" % sDriverName) \n\n pDataset = gdal.Open(sFilename_in, gdal.GA_ReadOnly)\n\n if pDataset is None:\n print(\"Couldn't open this file: \" + sFilename_in)\n sys.exit(\"Try again!\")\n else: \n pProjection = pDataset.GetProjection()\n\n pDataset.GetMetadata()\n \n ncolumn = pDataset.RasterXSize\n nrow = pDataset.RasterYSize\n nband = pDataset.RasterCount\n\n pGeotransform = pDataset.GetGeoTransform()\n dOriginX = pGeotransform[0]\n dOriginY = pGeotransform[3]\n dPixelWidth = pGeotransform[1]\n pPixelHeight = pGeotransform[5]\n\n pBand = pDataset.GetRasterBand(1)\n\n # Data type of the values\n gdal.GetDataTypeName(pBand.DataType)\n # Compute statistics if needed\n if pBand.GetMinimum() is None or pBand.GetMaximum() is None:\n pBand.ComputeStatistics(0)\n\n dMissing_value = pBand.GetNoDataValue()\n \n aData_out = pBand.ReadAsArray(0, 0, ncolumn, nrow)\n \n #we will use one of them to keep the consistency\n pSpatial_reference = osr.SpatialReference(wkt=pProjection)\n \n\n pDataset = None\n pBand = None \n pBand = None\n\n return aData_out, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection, pSpatial_reference","def process_image(overviews, db_graph, input_filename, color, out_raster_srs):\n if verbose > 0:\n print(\"~~~process_image\")\n input_image = gdal.Open(input_filename)\n stem = Path(input_filename).stem\n if not(\"dataSet\" in overviews):\n overviews['dataSet'] = {}\n overviews['dataSet']['boundingBox'] = {}\n overviews['dataSet']['limits'] = {}\n\n tile_limits = get_tile_limits(input_filename)\n\n if not(\"LowerCorner\" in overviews['dataSet']['boundingBox']):\n overviews['dataSet']['boundingBox'] = tile_limits\n else:\n if tile_limits['LowerCorner'][0] < overviews['dataSet']['boundingBox']['LowerCorner'][0]:\n overviews['dataSet']['boundingBox']['LowerCorner'][0] = tile_limits['LowerCorner'][0]\n if tile_limits['LowerCorner'][1] < overviews['dataSet']['boundingBox']['LowerCorner'][1]:\n overviews['dataSet']['boundingBox']['LowerCorner'][1] = tile_limits['LowerCorner'][1]\n if tile_limits['UpperCorner'][0] > overviews['dataSet']['boundingBox']['UpperCorner'][0]:\n overviews['dataSet']['boundingBox']['UpperCorner'][0] = tile_limits['UpperCorner'][0]\n if tile_limits['UpperCorner'][1] > overviews['dataSet']['boundingBox']['UpperCorner'][1]:\n overviews['dataSet']['boundingBox']['UpperCorner'][1] = tile_limits['UpperCorner'][1]\n\n # for z in tiles:\n for tile_z in range(overviews['level']['min'], overviews['level']['max'] + 1):\n print('Niveau de zoom : ', tile_z)\n\n resolution = overviews['resolution'] * 2 ** (overviews['level']['max'] - tile_z)\n\n MinTileCol = \\\n math.floor(round((tile_limits['LowerCorner'][0] - overviews['crs']['boundingBox']['xmin'])/(resolution*overviews['tileSize']['width']),8))\n MinTileRow = \\\n math.floor(round((overviews['crs']['boundingBox']['ymax']-tile_limits['UpperCorner'][1])/(resolution*overviews['tileSize']['height']),8))\n MaxTileCol = \\\n math.ceil(round((tile_limits['UpperCorner'][0] - overviews['crs']['boundingBox']['xmin'])/(resolution*overviews['tileSize']['width']),8)) - 1\n MaxTileRow = \\\n math.ceil(round((overviews['crs']['boundingBox']['ymax']-tile_limits['LowerCorner'][1])/(resolution*overviews['tileSize']['height']),8)) - 1\n\n if not( str(tile_z) in overviews['dataSet']['limits'] ):\n overviews['dataSet']['limits'][str(tile_z)] = {\n 'MinTileCol': MinTileCol,\n 'MinTileRow': MinTileRow,\n 'MaxTileCol': MaxTileCol,\n 'MaxTileRow': MaxTileRow,\n }\n\n else:\n if MinTileCol < overviews['dataSet']['limits'][str(tile_z)]['MinTileCol']:\n overviews['dataSet']['limits'][str(tile_z)]['MinTileCol'] = MinTileCol\n if MinTileRow < overviews['dataSet']['limits'][str(tile_z)]['MinTileRow']:\n overviews['dataSet']['limits'][str(tile_z)]['MinTileRow'] = MinTileRow\n if MaxTileCol > overviews['dataSet']['limits'][str(tile_z)]['MaxTileCol']:\n overviews['dataSet']['limits'][str(tile_z)]['MaxTileCol'] = MaxTileCol\n if MaxTileRow > overviews['dataSet']['limits'][str(tile_z)]['MaxTileRow']:\n overviews['dataSet']['limits'][str(tile_z)]['MaxTileRow'] = MaxTileRow\n\n for tile_x in range(MinTileCol, MaxTileCol + 1): \n for tile_y in range(MinTileRow, MaxTileRow + 1):\n # on cree une image 3 canaux pour la tuile\n opi = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\n # on reech l'OPI dans cette image\n gdal.Warp(opi, input_image)\n # si necessaire on cree le dossier de la tuile\n tile_dir = args.cache+'/'+str(tile_z)+'/'+str(tile_y)+'/'+str(tile_x)\n Path(tile_dir).mkdir(parents=True, exist_ok=True)\n # on export en jpeg (todo: gerer le niveau de Q)\n PNG_DRIVER.CreateCopy(tile_dir+\"/\"+stem+\".png\", opi)\n # on cree une image mono canal pour la tuile\n mask = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\n # on rasterise la partie du graphe qui concerne ce cliche\n gdal.Rasterize(mask, db_graph,\n SQLStatement='select geom from ' + args.table + ' where cliche = \\''+stem+'\\' ')\n img_mask = mask.GetRasterBand(1).ReadAsArray()\n # si le mask est vide, on a termine\n val_max = np.amax(img_mask)\n if val_max > 0:\n # on cree le graphe et l'ortho\n ortho = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\n graph = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\n if Path(tile_dir+\"/ortho.png\").is_file():\n existing_ortho = gdal.Open(tile_dir+\"/ortho.png\")\n existing_graph = gdal.Open(tile_dir+\"/graph.png\")\n else:\n existing_ortho = False\n existing_graph = False\n for i in range(3):\n opi_i = opi.GetRasterBand(i+1).ReadAsArray()\n if existing_ortho:\n ortho_i = existing_ortho.GetRasterBand(i+1).ReadAsArray()\n else:\n ortho_i = ortho.GetRasterBand(i+1).ReadAsArray()\n opi_i[(img_mask == 0)] = 0\n ortho_i[(img_mask != 0)] = 0\n ortho.GetRasterBand(i+1).WriteArray(np.add(opi_i, ortho_i))\n if existing_graph:\n graph_i = existing_graph.GetRasterBand(i+1).ReadAsArray()\n else:\n graph_i = graph.GetRasterBand(i+1).ReadAsArray()\n graph_i[(img_mask != 0)] = color[i]\n graph.GetRasterBand(i+1).WriteArray(graph_i)\n existing_ortho = None\n existing_graph = None\n PNG_DRIVER.CreateCopy(tile_dir+\"/ortho.png\", ortho)\n PNG_DRIVER.CreateCopy(tile_dir+\"/graph.png\", graph)","def Projected(InputFilePath,OutputFilePath): # perform a reproject raster onto a DEM and return\r\n try:\r\n print(\"\"\"\r\nReprojecting Raster...\r\n \"\"\") \r\n \r\n arcpy.ProjectRaster_management(in_raster=InputFilePath,out_raster=OutputFilePath,out_coor_system=\"PROJCS['NAD_1983_StatePlane_California_III_FIPS_0403',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',2000000.0],PARAMETER['False_Northing',500000.0],PARAMETER['Central_Meridian',-120.5],PARAMETER['Standard_Parallel_1',37.06666666666667],PARAMETER['Standard_Parallel_2',38.43333333333333],PARAMETER['Latitude_Of_Origin',36.5],UNIT['Meter',1.0]]\",resampling_type=\"NEAREST\",cell_size=\"27.8165597364916 27.8165597364914\",geographic_transform=\"WGS_1984_(ITRF00)_To_NAD_1983\",Registration_Point=\"#\",in_coor_system=\"GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.257223563]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]]\")\r\n print(\"Complete\")\r\n \r\n except Exception, err: # an error occurred (probably in arcGIS)\r\n raise RuntimeError(\"** Error: Project Raster Failed (\"+str(err)+\")\")","def write(self, p_out, **kwargs):\n nodata = kwargs.pop(\"nodata\", self.nodata_value)\n driver = kwargs.pop(\"driver\", \"GTiff\")\n return ImageIO.gdal_write(driver, self.array, p_out, self.projection, self.geotransform,\n nodata=nodata, **kwargs)","def ascii_to_tiff(infile, outfile, refIm):","def array_to_raster_noTi(x_pix,y_pix,pixSize,x_min,y_max,proj,array,outFile):\n \n\n driver = gdal.GetDriverByName('GTiff')\n\n outDataset = driver.Create(\n outFile,\n x_pix,\n y_pix,\n 1,\n gdal.GDT_Float32, )\n\n outDataset.SetGeoTransform((\n x_min, # 0 * top left border of pixel\n pixSize, # 1\n 0, # 2\n y_max, # 3 top left border of pixel\n 0, # 4\n -pixSize)) \n\n projx = osr.SpatialReference()\n projx.SetWellKnownGeogCS( proj ) #Get the long coordinate system name\n wkt_projection = projx.ExportToWkt()\n\n outDataset.SetProjection(wkt_projection)\n outDataset.GetRasterBand(1).WriteArray(array)\n outDataset.FlushCache() # Write to disk.\n return outDataset, outDataset.GetRasterBand(1) #If you need to return, remenber to return also the dataset because the band don`t live without dataset.","def geotiff_read(ifile,metaData):\r\n\r\n file = gdal.Open(ifile, GA_ReadOnly)\r\n\r\n projection = file.GetProjection()\r\n src = osr.SpatialReference()\r\n src.ImportFromWkt(projection)\r\n proj = src.ExportToWkt()\r\n\r\n Nx = file.RasterXSize\r\n Ny = file.RasterYSize\r\n\r\n trans = file.GetGeoTransform()\r\n\r\n dx = trans[1]\r\n dy = trans[5]\r\n\r\n if metaData == \"A\":\r\n\r\n xp = np.arange(Nx)\r\n yp = np.arange(Ny)\r\n\r\n (Xp, Yp) = np.meshgrid(xp,yp)\r\n\r\n X = trans[0] + (Xp+0.5)*trans[1] + (Yp+0.5)*trans[2] #FIXME: bottleneck!\r\n Y = trans[3] + (Xp+0.5)*trans[4] + (Yp+0.5)*trans[5]\r\n\r\n if metaData == \"P\":\r\n\r\n xp = np.arange(Nx)\r\n yp = np.arange(Ny)\r\n\r\n (Xp, Yp) = np.meshgrid(xp,yp)\r\n\r\n X = trans[0] + Xp*trans[1] + Yp*trans[2] #FIXME: bottleneck!\r\n Y = trans[3] + Xp*trans[4] + Yp*trans[5]\r\n\r\n band = file.GetRasterBand(1)\r\n\r\n Z = band.ReadAsArray()\r\n\r\n dx = np.abs(dx)\r\n dy = np.abs(dy)\r\n\r\n return X, Y, Z, dx, dy, proj","def save_tile_mask(label_poly_series, tile_poly, xyz, tile_size, dataset, region, zone, save_path, channels = 3, display=False):\n \n \n\n prefix = f'{region}{zone}{dataset}_'\n x,y,z = xyz\n tfm = from_bounds(*tile_poly.bounds, tile_size, tile_size) \n \n cropped_polys = [poly for poly in label_poly_series if poly.intersects(tile_poly)]\n cropped_polys_gdf = gpd.GeoDataFrame(geometry=cropped_polys, crs={'init': 'epsg:4326'})\n \n fbc_mask = burn_mask(cropped_polys_gdf, tfm, tile_size, channels)\n # fbc_mask = sol.vector.mask.df_to_px_mask(df=cropped_polys_gdf,\n # channels=['footprint', 'boundary', 'contact'],\n # affine_obj=tfm, shape=(tile_size,tile_size),\n # boundary_width=5, boundary_type='inner', contact_spacing=5, meters=True)\n \n if display: \n plt.imshow(fbc_mask); plt.show()\n \n skimage.io.imsave(f'{save_path}/{prefix}{z}_{x}_{y}_mask.png',fbc_mask, check_contrast=False)","def test_make_mask_w_file_and_transform(self):\n output_mask = footprint_mask(\n os.path.join(data_dir, 'geotiff_labels.geojson'),\n reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\n do_transform=True,\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(\n os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif')\n )\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(output_mask, truth_mask)\n assert np.array_equal(saved_output_mask, truth_mask)\n # clean up\n os.remove(os.path.join(data_dir, 'test_out.tif'))","def save_grd(filename, meta, map):\n if os.path.exists(filename):\n raise ValueError(\"File already exists: {}\".format(filename))\n if map.shape != (meta['NX'], meta['NY'], meta['NCOMP']):\n raise ValueError(\"The map shape does not match the metadata dictionary.\")\n points = meta['NX'] * meta['NY']\n components = meta['NCOMP']\n data = np.empty((points, 2 * components))\n for component in range(components):\n data[:, 2 * component] = map[:, :, component].reshape(points, order='F').real\n data[:, 2 * component + 1] = map[:, :, component].reshape(points, order='F').imag\n with open(filename, 'w') as f:\n for line in meta['header']:\n f.write('{}\\n'.format(line))\n f.write('{:2d}\\n'.format(meta['KTYPE']))\n f.write('{:12d}{:12d}{:12d}{:12d}\\n'.format(meta['NSET'], meta['ICOMP'], meta['NCOMP'], meta['IGRID']))\n f.write('{:12d}{:12d}\\n'.format(meta['IX'], meta['IY']))\n f.write(' {: 0.10E} {: 0.10E} {: 0.10E} {: 0.10E}\\n'.format(meta['XS'], meta['YS'], meta['XE'], meta['YE']))\n f.write('{:12d}{:12d}{:12d}\\n'.format(meta['NX'], meta['NY'], meta['KLIMIT']))\n for p in range(points):\n f.write(''.join([float_to_string(number) for number in data[p, :]]) + '\\n')","def test_rasters_created_with_projected_srs(self):\n\n # Create test data\n x_ul = 220534 # x value of upper left corner\n y_ul = 827790 # y_value of upper left corner\n numx = 8 # Number of xs\n numy = 5 # Number of ys\n dx = 200\n dy = -200\n\n # Define array where ys are rows and xs columns\n A1 = numpy.zeros((numy, numx))\n\n # Establish coordinates for lower left corner\n y_ll = y_ul - numy * dy\n x_ll = x_ul\n\n # Define pixel centers along each direction\n x = numpy.linspace(x_ll + 0.5, x_ll + numx - 0.5, numx)\n y = numpy.linspace(y_ll + 0.5, y_ll + numy - 0.5, numy)\n\n # Define raster with latitudes going bottom-up (south to north).\n # Longitudes go left-right (west to east)\n for i in range(numy):\n for j in range(numx):\n A1[numy - 1 - i, j] = linear_function(x[j], y[i])\n\n # Throw in a nodata element\n A1[2, 6] = numpy.nan\n\n # Upper left corner\n assert A1[0, 0] == linear_function(x[0], y[4])\n\n # Lower left corner\n assert A1[4, 0] == linear_function(x[0], y[0])\n\n # Upper right corner\n assert A1[0, 7] == linear_function(x[7], y[4])\n\n # Lower right corner\n assert A1[4, 7] == linear_function(x[7], y[0])\n\n # Generate raster object and write\n projection = \"\"\"PROJCS[\"DGN95 / Indonesia TM-3 zone 48.2\",\n GEOGCS[\"DGN95\",\n DATUM[\"Datum_Geodesi_Nasional_1995\",\n SPHEROID[\"WGS 84\",6378137,298.257223563,\n AUTHORITY[\"EPSG\",\"7030\"]],\n TOWGS84[0,0,0,0,0,0,0],\n AUTHORITY[\"EPSG\",\"6755\"]],\n PRIMEM[\"Greenwich\",0,\n AUTHORITY[\"EPSG\",\"8901\"]],\n UNIT[\"degree\",0.01745329251994328,\n AUTHORITY[\"EPSG\",\"9122\"]],\n AUTHORITY[\"EPSG\",\"4755\"]],\n UNIT[\"metre\",1,\n AUTHORITY[\"EPSG\",\"9001\"]],\n PROJECTION[\"Transverse_Mercator\"],\n PARAMETER[\"latitude_of_origin\",0],\n PARAMETER[\"central_meridian\",106.5],\n PARAMETER[\"scale_factor\",0.9999],\n PARAMETER[\"false_easting\",200000],\n PARAMETER[\"false_northing\",1500000],\n AUTHORITY[\"EPSG\",\"23834\"],\n AXIS[\"X\",EAST],\n AXIS[\"Y\",NORTH]]\"\"\"\n\n geotransform = (x_ul, dx, 0, y_ul, 0, dy)\n R1 = Raster(A1, projection, geotransform,\n keywords={'testkwd': 'testval', 'size': 'small'})\n\n # Check string representation of raster class\n assert str(R1).startswith('Raster data')\n assert str(R1.rows) in str(R1)\n assert str(R1.columns) in str(R1)\n\n assert nanallclose(R1.get_data(), A1, rtol=1.0e-12)\n assert nanallclose(R1.get_geotransform(), geotransform,\n rtol=1.0e-12)\n assert 'DGN95' in R1.get_projection()","def get_zone_pixels(feat, input_zone_polygon, input_value_raster, band, coords=[]): #, raster_band\n \n \n \n # Open data\n raster = gdal.Open(input_value_raster)\n shp = ogr.Open(input_zone_polygon)\n lyr = shp.GetLayer()\n \n # Get raster georeference info\n transform = raster.GetGeoTransform()\n xOrigin = transform[0]\n yOrigin = transform[3]\n pixelWidth = transform[1]\n pixelHeight = transform[5]\n \n sizeX = raster.RasterXSize\n sizeY = raster.RasterYSize\n lrx = xOrigin + (sizeX * pixelWidth)\n lry = yOrigin + (sizeY * pixelHeight)\n \n \n \n # Reproject vector geometry to same projection as raster\n #sourceSR = lyr.GetSpatialRef()\n #targetSR = osr.SpatialReference()\n #targetSR.ImportFromWkt(raster.GetProjectionRef())\n #coordTrans = osr.CoordinateTransformation(sourceSR,targetSR)\n #feat = lyr.GetNextFeature()\n #geom = feat.GetGeometryRef()\n #geom.Transform(coordTrans)\n \n # Get extent of feat\n geom = feat.GetGeometryRef()\n if (geom.GetGeometryName() == 'MULTIPOLYGON'):\n count = 0\n pointsX = []; pointsY = []\n for polygon in geom:\n geomInner = geom.GetGeometryRef(count)\n ring = geomInner.GetGeometryRef(0)\n numpoints = ring.GetPointCount()\n for p in range(numpoints):\n lon, lat, z = ring.GetPoint(p)\n pointsX.append(lon)\n pointsY.append(lat)\n count += 1\n elif (geom.GetGeometryName() == 'POLYGON'):\n ring = geom.GetGeometryRef(0)\n numpoints = ring.GetPointCount()\n pointsX = []; pointsY = []\n for p in range(numpoints):\n lon, lat, z = ring.GetPoint(p)\n pointsX.append(lon)\n pointsY.append(lat)\n\n else:\n sys.exit(\"ERROR: Geometry needs to be either Polygon or Multipolygon\")\n\n #xmin = min(pointsX) \n #xmax = max(pointsX)\n #ymin = min(pointsY)\n #ymax = max(pointsY)\n \n \n if len(coords) == 0: \n xmin = xOrigin if (min(pointsX) < xOrigin) else min(pointsX)\n xmax = lrx if (max(pointsX) > lrx) else max(pointsX)\n ymin = lry if (min(pointsY) < lry) else min(pointsY)\n ymax = yOrigin if (max(pointsY) > yOrigin) else max(pointsY)\n else:\n xmin = coords[0] if (min(pointsX) < coords[0]) else min(pointsX)\n xmax = coords[1] if (max(pointsX) > coords[1]) else max(pointsX)\n ymin = coords[2] if (min(pointsY) < coords[2]) else min(pointsY)\n ymax = coords[3] if (max(pointsY) > coords[3]) else max(pointsY)\n \n # Specify offset and rows and columns to read\n xoff = int((xmin - xOrigin)/pixelWidth)\n yoff = int((yOrigin - ymax)/pixelWidth)\n xcount = int((xmax - xmin)/pixelWidth) #+1 !!!!!!!!!!!!!!!!!!!!! This adds a pixel to the right side\n ycount = int((ymax - ymin)/pixelWidth) #+1 !!!!!!!!!!!!!!!!!!!!! This adds a pixel to the bottom side\n \n #print(xoff, yoff, xcount, ycount)\n \n # Create memory target raster\n target_ds = gdal.GetDriverByName('MEM').Create('', xcount, ycount, 1, gdal.GDT_Byte)\n target_ds.SetGeoTransform((\n xmin, pixelWidth, 0,\n ymax, 0, pixelHeight,\n ))\n\n # Create for target raster the same projection as for the value raster\n raster_srs = osr.SpatialReference()\n raster_srs.ImportFromWkt(raster.GetProjectionRef())\n target_ds.SetProjection(raster_srs.ExportToWkt())\n\n # Rasterize zone polygon to raster\n gdal.RasterizeLayer(target_ds, [1], lyr, burn_values=[1])\n\n # Read raster as arrays\n dataBandRaster = raster.GetRasterBand(band)\n data = dataBandRaster.ReadAsArray(xoff, yoff, xcount, ycount).astype(np.float)\n bandmask = target_ds.GetRasterBand(1)\n datamask = bandmask.ReadAsArray(0, 0, xcount, ycount).astype(np.float)\n\n # data zone of raster\n dataZone = np.ma.masked_array(data, np.logical_not(datamask))\n\n raster_srs = None\n raster = None\n shp = None\n lyr = None\n return [dataZone, [xmin,xmax,ymin,ymax]]","def save_groudtruth(im, coords, filename):\n print 'Saving ground truth ......{0}'.format(filename)\n img_draw = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\n draw = ImageDraw.Draw(img_draw)\n for coord in coords:\n draw.polygon([(float(coord[0]), float(coord[1])), (float(coord[2]), float(coord[3])),\n (float(coord[4]), float(coord[5])), (float(coord[6]), float(coord[7]))],\n outline=\"red\", fill=\"blue\")\n img_draw = np.array(img_draw)\n img_draw = cv2.cvtColor(img_draw, cv2.COLOR_RGB2BGR)\n bname_excludepoint = filename.split('/')[-1].split('.')[0]\n image_path = '/home/yuquanjie/Documents/deep-direct-regression/result/' + bname_excludepoint + '_gt.jpg'\n cv2.imwrite(image_path, img_draw[0: img_draw.shape[0], 0: img_draw.shape[1]])","def raster_to_geodataframe(*a, **kw) -> gpd.GeoDataFrame:\n kw[\"geo\"] = True\n return raster_to_dataframe(*a, **kw)","def tif_to_png(source, remove_old_files):\n os.chdir(source)\n subprocess.Popen(\"i_view32.exe *.tif /tifc=5 /convert=*.png\", shell=True,\n stdout=subprocess.PIPE, env={'PATH': os.getenv('PATH')}).stdout.read()\n if remove_old_files == 'yes':\n list_of_files_in_the_folder = os.listdir(source)\n for files in list_of_files_in_the_folder:\n name, ext = os.path.splitext(files)\n if ext.lower() == \".tif\":\n os.remove(os.path.join(source, files))","def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\n args = [\n \"gdal_calc.py\",\n '-A', infile,\n '--outfile={}'.format(tmpfile_1),\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\n '--type=Byte', '--NoDataValue=0',\n '--co=SPARSE_OK=YES',\n '--co=NBITS=1',\n '--quiet'\n # Could enable compression\n # --co=\"COMPRESS=LZW\"\n ]\n subprocess.run(args)\n\n subprocess.run([\n \"gdal_polygonize.py\",\n tmpfile_1,\n '-q',\n '-f', 'ESRI Shapefile',\n tmpfile_2\n ])\n\n subprocess.run([\n \"ogr2ogr\",\n '-a_srs', 'EPSG:4326',\n outfile,\n tmpfile_2\n ])\n\n subprocess.run([\"rm\", tmpfile_1])\n subprocess.run([\"rm\", tmpfile_2])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'shx')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'dbf')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'prj')])","def rasterize_vector(shp, rows, cols, geoTrans=None, saveto=None, method='within', transform=None):\r\n # Open the shapefile\r\n shp = ogr.Open(shp)\r\n\r\n # Get the layer from the shape\r\n layer = shp.GetLayer()\r\n\r\n # Get the layer's information\r\n lyrSrs = layer.GetSpatialRef().ExportToWkt()\r\n\r\n # Optionally transform to specified transformation\r\n if transform and transform.ExportToWkt() != lyrSrs:\r\n # Get the layer geometry\r\n poly = layer.GetNextFeature()\r\n geom = poly.GetGeometryRef()\r\n\r\n # Transform the geometry.\r\n geom.TransformTo(transform)\r\n\r\n # Create a new layer.\r\n lyr_driver = ogr.GetDriverByName('ESRI Shapefile')\r\n\r\n lyr_driver_name = tempfile.NamedTemporaryFile(suffix='.shp').name\r\n lyr_source = lyr_driver.CreateDataSource(lyr_driver_name)\r\n new_lyr = lyr_source.CreateLayer(lyr_driver_name, transform, geom_type=ogr.wkbPolygon)\r\n\r\n # Add an ID field to tie the geometry to\r\n id_field = ogr.FieldDefn('id', ogr.OFTInteger)\r\n new_lyr.CreateField(id_field)\r\n\r\n # Set the transformed geometry\r\n feature_defn = new_lyr.GetLayerDefn()\r\n feature = ogr.Feature(feature_defn)\r\n feature.SetGeometry(geom)\r\n feature.SetField('id',1)\r\n new_lyr.CreateFeature(feature)\r\n\r\n # Set the existing layer to be the new layer\r\n layer = new_lyr\r\n lyrSrs = transform.ExportToWkt()\r\n\r\n # Create the raster's name\r\n if not saveto:\r\n remove = True\r\n saveto = tempfile.NamedTemporaryFile(suffix='.tif')\r\n saveto = saveto.name\r\n else:\r\n remove = False\r\n\r\n # Create the new raster\r\n driver = gdal.GetDriverByName('GTiff')\r\n outRas = driver.Create(saveto, cols, rows, 1)\r\n outRas.SetProjection(lyrSrs)\r\n outRas.SetGeoTransform(geoTrans)\r\n outRas.GetRasterBand(1).Fill(1)\r\n\r\n # Rasterize the layer\r\n if method.lower() == 'touches':\r\n gdal.RasterizeLayer(outRas,[1],layer,None, None, [0], ['ALL_TOUCHED=TRUE'])\r\n else: # Just default to this.\r\n gdal.RasterizeLayer(outRas,[1],layer,None, None, [0])\r\n arr = outRas.ReadAsArray()\r\n if remove:\r\n os.remove(saveto)\r\n\r\n # Return the numpy array\r\n return arr","def Save_as_MEM(data='', geo='', projection=''):\n # save as a geotiff\n driver = gdal.GetDriverByName(\"MEM\")\n dst_ds = driver.Create('', int(data.shape[1]), int(data.shape[0]), 1,\n gdal.GDT_Float32)\n srse = osr.SpatialReference()\n if projection == '':\n srse.SetWellKnownGeogCS(\"WGS84\")\n\n else:\n try:\n if not srse.SetWellKnownGeogCS(projection) == 6:\n srse.SetWellKnownGeogCS(projection)\n else:\n try:\n srse.ImportFromEPSG(int(projection))\n except:\n srse.ImportFromWkt(projection)\n except:\n try:\n srse.ImportFromEPSG(int(projection))\n except:\n srse.ImportFromWkt(projection)\n dst_ds.SetProjection(srse.ExportToWkt())\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\n dst_ds.SetGeoTransform(geo)\n dst_ds.GetRasterBand(1).WriteArray(data)\n return(dst_ds)","def retrieve_raster_boundary(infile,\n gdal_path=None,\n nodata=None,\n resize_factor=None):\n # create overview\n cur_time = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n infile_bname = os.path.splitext(os.path.basename(infile))[0]\n qlook = os.path.join(os.path.dirname(infile),\n \"temp_{}_{}.tif\".format(infile_bname, cur_time))\n\n img_ds = open_image(infile)\n width, height = img_ds.XSize(), img_ds.YSize()\n img_geot = img_ds.geotransform()\n img_extent = img_ds.get_extent()\n img_ds = None\n qlook_size = 400.0 if resize_factor is None else resize_factor\n factor = int(np.round(max(width / qlook_size, height / qlook_size)))\n x_res = img_geot[1] * factor\n y_res = img_geot[5] * factor\n\n options = {\n '-of': 'GTiff',\n '-r': 'near',\n '-te': ' '.join(map(str, img_extent)),\n '-tr': '{} -{}'.format(x_res, np.abs(y_res)),\n '-co': 'COMPRESS=LZW',\n '-srcnodata': nodata,\n '-dstnodata': nodata\n }\n\n succeed, _ = call_gdal_util('gdalwarp',\n src_files=infile,\n dst_file=qlook,\n gdal_path=gdal_path,\n options=options)\n if not succeed:\n return None\n\n # make binary image\n qlook_dst = open_image(qlook)\n src_arr = qlook_dst.read_band(1)\n mask = (src_arr != nodata)\n src_arr = None\n\n # morphologic dilation\n pixels = 3\n struct = ndimage.generate_binary_structure(2, 2)\n struct = ndimage.morphology.iterate_structure(struct, pixels)\n new_mask = ndimage.binary_dilation(mask, structure=struct)\n src_arr = np.zeros_like(mask, dtype=np.uint8)\n src_arr[new_mask] = 1\n\n # make a memory dataset\n mem_drv = gdal.GetDriverByName('MEM')\n binary_dst = mem_drv.Create('', src_arr.shape[1], src_arr.shape[0], 1,\n gdal.GDT_Byte)\n binary_dst.SetGeoTransform(qlook_dst.geotransform())\n binary_dst.SetProjection(qlook_dst.projection())\n binary_band = binary_dst.GetRasterBand(1)\n binary_band.WriteArray(src_arr)\n binary_band.SetNoDataValue(0)\n maskband = binary_band.GetMaskBand()\n\n # Polygonize\n driver = ogr.GetDriverByName('Memory')\n dst_ds = driver.CreateDataSource(\"\")\n\n srs = None\n if qlook_dst.projection():\n srs = osr.SpatialReference()\n srs.ImportFromWkt(qlook_dst.projection())\n\n dst_layer = dst_ds.CreateLayer(\"out\", srs=srs)\n fd = ogr.FieldDefn('DN', ogr.OFTInteger)\n dst_layer.CreateField(fd)\n dst_field = 0\n\n geom = None\n if gdal.CE_None == gdal.Polygonize(binary_band,\n maskband,\n dst_layer,\n dst_field,\n callback=None):\n # get polygons from dataset\n multipolygon = ogr.Geometry(ogr.wkbMultiPolygon)\n for feature in dst_ds.GetLayerByIndex():\n polygon = feature.GetGeometryRef()\n\n # When the polygon has hole inside, only the out line ring will\n # be used as extent polygon\n if polygon.GetGeometryCount() > 1:\n ring = polygon.GetGeometryRef(0)\n poly = ogr.Geometry(ogr.wkbPolygon)\n poly.AddGeometry(ring)\n polygon = poly\n\n multipolygon.AddGeometry(polygon)\n multipolygon.AssignSpatialReference(dst_ds.GetLayer(0).GetSpatialRef())\n\n geom = multipolygon\n\n # clean tmp file\n dst_ds.Destroy()\n binary_dst = None\n qlook_dst = None\n # remove temp qlook\n os.remove(qlook)\n\n return geom","def tiled_writing(red, nir, output):\n \n #open datasets\n src_red = rio.open(red)\n src_nir = rio.open(nir)\n \n #define raster properies and update datatype\n meta = src_red.meta.copy()\n meta.update({'dtype':'float32'}) # meta is a dictionary\n outfile = output\n #open outfile in writing mode with the properties of defined raster band\n with rio.open(outfile, 'w', **meta) as dst:\n #iterate over blocks of the bands, calculate ndvi for each block \n # and put the blocks back together\n for window in calc_tiles(src_red, tile_size_x, tile_size_y):\n red_block = src_red.read(window=window, masked=True)\n nir_block = src_nir.read(window=window, masked=True)\n #cast ndarrays to Float32 type\n red = red_block.astype('f4')\n nir = nir_block.astype('f4')\n #allow division by zero\n np.seterr(divide='ignore', invalid='ignore')\n #calculate ndvi and write raster\n ndvi = (nir - red) / (nir + red)\n dst.write(ndvi, window=window)\n\n #close dataset\n src_red.close()\n src_nir.close()\n return outfile","def _encodeTiledImageFromVips(self, vimg, iterInfo, image, **kwargs):\n convertParams = large_image.tilesource.base._vipsParameters(\n defaultCompression='lzw', **kwargs)\n convertParams.pop('pyramid', None)\n vimg = large_image.tilesource.base._vipsCast(\n vimg, convertParams['compression'] in {'webp', 'jpeg'})\n gdalParams = large_image.tilesource.base._gdalParameters(\n defaultCompression='lzw', **kwargs)\n for ch in range(image['channels']):\n gdalParams += [\n '-b' if ch not in (1, 3) or ch + 1 != image['channels'] else '-mask', str(ch + 1)]\n tl = self.pixelToProjection(\n iterInfo['region']['left'], iterInfo['region']['top'], iterInfo['level'])\n br = self.pixelToProjection(\n iterInfo['region']['right'], iterInfo['region']['bottom'], iterInfo['level'])\n gdalParams += [\n '-a_srs',\n iterInfo['metadata']['bounds']['srs'],\n '-a_ullr',\n str(tl[0]),\n str(tl[1]),\n str(br[0]),\n str(br[1]),\n ]\n fd, tempPath = tempfile.mkstemp('.tiff', 'tiledRegion_')\n os.close(fd)\n fd, outputPath = tempfile.mkstemp('.tiff', 'tiledGeoRegion_')\n os.close(fd)\n try:\n vimg.write_to_file(tempPath, **convertParams)\n ds = gdal.Open(tempPath, gdalconst.GA_ReadOnly)\n gdal.Translate(outputPath, ds, options=gdalParams)\n os.unlink(tempPath)\n except Exception as exc:\n try:\n os.unlink(tempPath)\n except Exception:\n pass\n try:\n os.unlink(outputPath)\n except Exception:\n pass\n raise exc\n return pathlib.Path(outputPath), TileOutputMimeTypes['TILED']","def create_test_file(filename, array):\n array = np.ma.asanyarray(array)\n crs = rasterio.crs.CRS(init='epsg:4326')\n transform = from_origin(52, 4, 10, 10)\n with rasterio.open(\n filename,\n mode='w',\n driver='GTiff',\n width=array.shape[1],\n height=array.shape[2],\n count=array.shape[0],\n dtype=array.dtype,\n crs=crs,\n transform=transform) as dataset:\n for band, data in enumerate(array, start=1):\n dataset.write(data, band)","def test_make_mask_w_file_and_transform(self):\n output_mask = road_mask(\n os.path.join(data_dir, 'sample_roads_for_masking.geojson'),\n reference_im=os.path.join(data_dir, 'road_mask_input.tif'),\n width=4, meters=True, do_transform=True,\n out_file=os.path.join(data_dir, 'test_out.tif')\n )\n truth_mask = skimage.io.imread(\n os.path.join(data_dir, 'sample_road_raster_mask.tif')\n )\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\n 'test_out.tif'))\n\n assert np.array_equal(output_mask, truth_mask)\n assert np.array_equal(saved_output_mask, truth_mask)\n # clean up\n os.remove(os.path.join(data_dir, 'test_out.tif'))","def save_img_as_tiff(seg_img: np.ndarray, time: int, track_save_dir: str):\n img = sitk.GetImageFromArray(seg_img.astype(\"uint16\"))\n filename = 'mask'+'%0*d' % (3, time) + '.tif'\n sitk.WriteImage(img, os.path.join(track_save_dir, filename))","def test_grdimage_file():\n fig = Figure()\n fig.grdimage(\n \"@earth_relief_01d_g\",\n cmap=\"ocean\",\n region=[-180, 180, -70, 70],\n projection=\"W0/10i\",\n shading=True,\n )\n return fig","def tiff(location):\n\n for src_gtiff in os.listdir(location):\n if \".gtiff\" in src_gtiff:\n dest_tif = src_gtiff[:-6] + '.tiff'\n command = \"gdal_translate -co profile=baseline \" + src_gtiff + ' ' \\\n + dest_tif\n os.system(command)","def as_tiff(imagename):\n return change_extension(imagename,'tif')","def to_file_map(self, file_map=None):\n if file_map is None:\n file_map = self.file_map\n f = file_map['image'].get_prepare_fileobj('wb')\n f.write(self.to_xml())","def gdal_compliant(\n self, rename_dims=True, force_sn=False\n ) -> xr.DataArray | xr.Dataset:\n obj_out = self._obj\n crs = obj_out.raster.crs\n if (\n obj_out.raster.res[1] < 0 and force_sn\n ): # write data with South -> North orientation\n obj_out = obj_out.raster.flipud()\n x_dim, y_dim, x_attrs, y_attrs = gis_utils.axes_attrs(crs)\n if rename_dims:\n obj_out = obj_out.rename(\n {obj_out.raster.x_dim: x_dim, obj_out.raster.y_dim: y_dim}\n )\n else:\n x_dim = obj_out.raster.x_dim\n y_dim = obj_out.raster.y_dim\n obj_out[x_dim].attrs.update(x_attrs)\n obj_out[y_dim].attrs.update(y_attrs)\n obj_out = obj_out.drop_vars([\"spatial_ref\"], errors=\"ignore\")\n obj_out.rio.write_crs(crs, inplace=True)\n obj_out.rio.write_transform(obj_out.raster.transform, inplace=True)\n obj_out.raster.set_spatial_dims()\n\n return obj_out","def retrieve_geotiff_metadata(sFilename_geotiff_in):\n pDriver = gdal.GetDriverByName('GTiff')\n \n pDataset = gdal.Open(sFilename_geotiff_in, gdal.GA_ReadOnly)\n\n if pDataset is None:\n print(\"Couldn't open this file: \" + sFilename_geotiff_in)\n sys.exit(\"Try again!\")\n else: \n pProjection = pDataset.GetProjection()\n pSpatial_reference = osr.SpatialReference(wkt=pProjection) \n ncolumn = pDataset.RasterXSize\n nrow = pDataset.RasterYSize \n pGeotransform = pDataset.GetGeoTransform()\n dOriginX = pGeotransform[0]\n dOriginY = pGeotransform[3]\n dPixelWidth = pGeotransform[1]\n pPixelHeight = pGeotransform[5] \n return dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, pSpatial_reference, pProjection, pGeotransform","def save_tiff(self, to_file=None):\n self.tif_file.clear() # Empty the array first\n\n # Header\n byteo = 'II'\n if self.byteOrder != 'little':\n byteo = 'MM'\n self.tif_file.insert_bytes(list(byteo.encode())) # byte order\n self.tif_file.insert_int(42, 2) # Magic number\n self.tif_file.insert_int(8, 4) # first IFD always at 0x08\n\n for ifd in self.ifds:\n # self.calculateIFDSpace(ifd) # Readjusts counts because of changes to image data\n endpos = self.save_ifd(ifd)\n self.save_image(ifd, endpos)\n\n self.tif_file.write(to_file) # lastly, write to file","def loadGeoTransform(filepath):\n \n from osgeo import gdal\n \n ds = gdal.Open(filepath, 0)\n \n return ds.GetGeoTransform()","def open_raster_file(self):\n self.log('Opening raster file as GDALRaster.')\n\n # Open raster file\n self.dataset = GDALRaster(os.path.join(self.tmpdir, self.rastername), write=True)\n\n # Make sure nodata value is set from input\n self.hist_values = []\n self.hist_bins = []\n for i, band in enumerate(self.dataset.bands):\n if self.rasterlayer.nodata is not None:\n band.nodata_value = float(self.rasterlayer.nodata)\n\n # Create band metatdata object\n bandmeta = RasterLayerBandMetadata.objects.create(\n rasterlayer=self.rasterlayer,\n band=i,\n nodata_value=band.nodata_value,\n min=band.min,\n max=band.max\n )\n\n # Prepare numpy hist values and bins\n self.hist_values.append(numpy.array(bandmeta.hist_values))\n self.hist_bins.append(numpy.array(bandmeta.hist_bins))\n\n # Store original metadata for this raster\n meta = self.rasterlayer.metadata\n\n meta.uperleftx = self.dataset.origin.x\n meta.uperlefty = self.dataset.origin.y\n meta.width = self.dataset.width\n meta.height = self.dataset.height\n meta.scalex = self.dataset.scale.x\n meta.scaley = self.dataset.scale.y\n meta.skewx = self.dataset.skew.x\n meta.skewy = self.dataset.skew.y\n meta.numbands = len(self.dataset.bands)\n meta.srs_wkt = self.dataset.srs.wkt\n meta.srid = self.dataset.srs.srid\n\n meta.save()","def __make_geo(self):\n # gmsh freecad_part.iges -o out_iges.geo -0\n fname_list = self.__fname.split('.')\n geo_file = fname_list[0]+'.geo'\n runstr = \"%s %s -o %s -0\" % (environment.GMSH, self.__fname, geo_file)\n print(runstr)\n subprocess.call(runstr, shell=True)\n print('Wrote file: %s' % geo_file)","def transform(infile, output, insrs, format_name):\n\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \n in_srs = osr.SpatialReference()\n in_srs.ImportFromEPSG(insrs)\n out_srs = osr.SpatialReference()\n out_srs.ImportFromEPSG(4324)\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\n\n in_dsn = ogr.Open(infile)\n in_layer = in_dsn.GetLayer()\n in_feature_definition = in_layer.GetLayerDefn()\n\n out_driver = ogr.GetDriverByName(format_name)\n out_dsn = out_driver.CreateDataSource(output)\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\n geom_type=in_layer.GetGeomType())\n\n # add fields\n for i in range(0, in_feature_definition.GetFieldCount()):\n fieldDefn = in_feature_definition.GetFieldDefn(i)\n out_layer.CreateField(fieldDefn)\n\n # get the output layer's feature definition\n out_feature_definition = out_layer.GetLayerDefn()\n\n # loop through the input features\n inFeature = in_layer.GetNextFeature()\n while inFeature:\n # get the input geometry\n geom = inFeature.GetGeometryRef().Clone()\n # reproject the geometry\n geom.Transform(coordTrans)\n # create a new feature\n outFeature = ogr.Feature(out_feature_definition)\n # set the geometry and attribute\n outFeature.SetGeometry(geom)\n for i in range(0, out_feature_definition.GetFieldCount()):\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n # add the feature to the shapefile\n out_layer.CreateFeature(outFeature)\n # destroy the features and get the next input feature\n outFeature.Destroy()\n inFeature.Destroy()\n inFeature = in_layer.GetNextFeature()\n\n # close the shapefiles\n in_dsn.Destroy()\n out_dsn.Destroy()","def process_tile(tile):\n global base_kwds, resampling, src\n # Get the bounds of the tile.\n ulx, uly = mercantile.xy(\n *mercantile.ul(tile.x, tile.y, tile.z))\n lrx, lry = mercantile.xy(\n *mercantile.ul(tile.x + 1, tile.y + 1, tile.z))\n\n kwds = base_kwds.copy()\n kwds['transform'] = from_bounds(ulx, lry, lrx, uly, 256, 256)\n src_nodata = kwds.pop('src_nodata', None)\n dst_nodata = kwds.pop('dst_nodata', None)\n\n with rasterio.open('/vsimem/tileimg', 'w', **kwds) as tmp:\n reproject(rasterio.band(src, src.indexes),\n rasterio.band(tmp, tmp.indexes),\n src_nodata=src_nodata,\n dst_nodata=dst_nodata,\n num_threads=1,\n resampling=resampling)\n\n data = bytearray(virtual_file_to_buffer('/vsimem/tileimg'))\n\n # Workaround for https://bugs.python.org/issue23349.\n if sys.version_info[0] == 2 and sys.version_info[2] < 10:\n # Check for backported bug fix before re-ordering\n\tif kwds['driver'] == 'PNG' and data[0:8] == png_header:\n # Properly constructed PNG, no need to re-order bytes\n pass\n\telif kwds['driver'] == 'JPEG' and data[0:4] == jpeg_header:\n # Properly constructed JPEG, no need to re-order bytes\n pass\n\telse:\n data[:] = data[-1:] + data[:-1]\n\n return tile, data","def store_image(self):\n cv2.imwrite(self.__diff_filename(), self.__diff_image())","def single_image_band_match(tif):\n tif_raster=gdal_array.LoadFile(tif)\n \n file_path=tif[:-4]+\"matched.tif\"\n \n R=tif_raster[0]\n G=tif_raster[1]\n B=tif_raster[2]\n \n print(R.shape)\n result=ird.similarity(G,R , numiter=1, order=1)\n R= ird.transform_img(R, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=1)\n # print(result)\n print(R.shape)\n \n print(B.shape)\n result=ird.similarity(G,B , numiter=1, order=1)\n B= ird.transform_img(B, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=1)\n # print(result)\n print(B.shape)\n driver=osgeo.gdal.GetDriverByName(\"GTiff\")\n options = ['PHOTOMETRIC=RGB', 'PROFILE=GeoTIFF']\n \n dataset=driver.Create(file_path, R.shape[1],R.shape[0], 3, osgeo.gdal.GDT_UInt16, options) \n dataset.GetRasterBand(1).WriteArray(R)\n dataset.GetRasterBand(2).WriteArray(G)\n dataset.GetRasterBand(3).WriteArray(B)","def save_gray_tiff(array: np.ndarray, filename: str):\n img_gray = array\n imwrite(filename, img_gray, shape=img_gray.shape)\n\n return filename","def save_exr_from_numpy(filename, image_as_array):\n width = image_as_array.shape[1]\n height = image_as_array.shape[0]\n data = image_as_array.tostring()\n exr = OpenEXR.OutputFile(filename, OpenEXR.Header(width, height))\n exr.writePixels({'R': data, 'G': data, 'B': data})\n exr.close()","def pan_corr(file):\n\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\flatfield\\\\NHDBflat_1D'\n # # infile = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Jure_naloga_banje_raw_pyt\\\\NHDRGoreMorje_3D'\n #\n # # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\n # in_ref = in_path + in_pan_ref_file\n #\n # inreffil = gdal.Open(in_ref)\n # image_ref = inreffil.ReadAsArray()\n # # size_ref = image_ref.shape\n # # pix_count = size_ref[0]*size_ref[1]\n #\n # image_ref = image_ref[800:930, 1420:1640]\n # size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n #\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # raz_g1 = 1\n # raz_g2 = g1_avg/g2_avg\n # raz_r1 = g1_avg/r1_avg\n # raz_b1 = g1_avg/b1_avg\n #\n # avg = (g1+g2+r1+b1)/pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n raz_g1 = 1\n raz_g2 = 1.0245196396115988\n raz_r1 = 1.0131841989689434\n raz_b1 = 1.0517113199247086\n\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Slo_PAN\\_26_30\\\\'\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\"nhd\") and filename.lower().endswith(\"tif\")]\n\n \n\n \n\n # print('image', i)\n in_ref=file\n inreffil = gdal.Open(in_ref)\n image_ref = inreffil.ReadAsArray()\n size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n # pix_count = np.count_nonzero(image_ref)\n # pix_count = 3664*650\n\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # avg = (g1 + g2 + r1 + b1) / pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n # popravek\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\n\n\n for i in range(size_ref[0]):\n for j in range(size_ref[1]):\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\n \n _,_,_,_,P=return_flatfield_set_path(2)\n P_flat=gdal_array.LoadFile(P)\n \n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \n \n # outout\n \n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\n \n out=os.path.abspath(file)+\"/corr/\"+os.path.basename(file)[:-4] + \"_pop_flat_corr.tif\"\n\n \n # out = in_ref[:-4] + \"_pop_flat_corr.tif\"\n\n driver = gdal.GetDriverByName('GTiff')\n\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\n\n outband = outRaster.GetRasterBand(1)\n outband.WriteArray(im_p_pop)\n outband.FlushCache()","def load_image(nom):\n print(\"load_image : [\", nom, \"]\")\n fic = gdal.Open(nom)\n print(fic)\n return fic.ReadAsArray(), fic.GetGeoTransform()","def aggregate_to_degree(raster_path, aggregate_func, target_path):\r\n base_raster = gdal.OpenEx(raster_path, gdal.OF_RASTER)\r\n base_gt = base_raster.GetGeoTransform()\r\n base_band = base_raster.GetRasterBand(1)\r\n base_nodata = base_band.GetNoDataValue()\r\n\r\n wgs84sr = osr.SpatialReference()\r\n wgs84sr.ImportFromEPSG(4326)\r\n\r\n driver = gdal.GetDriverByName('GTiff')\r\n n_rows = int(\r\n abs((base_gt[5] * base_band.YSize) / 1.0))\r\n n_cols = int(\r\n abs((base_gt[1] * base_band.XSize) / -1.0))\r\n target_raster = driver.Create(\r\n target_path, n_cols, n_rows, 1, gdal.GDT_Float32)\r\n target_raster.SetProjection(wgs84sr.ExportToWkt())\r\n degree_geotransform = [base_gt[0], 1., 0., base_gt[3], 0., -1.]\r\n target_raster.SetGeoTransform(degree_geotransform)\r\n target_band = target_raster.GetRasterBand(1)\r\n target_band.SetNoDataValue(base_nodata)\r\n target_band.Fill(base_nodata)\r\n\r\n base_y_winsize = int(round(abs(1. / base_gt[5])))\r\n base_x_winsize = int(round(abs(1. / base_gt[1])))\r\n\r\n last_time = time.time()\r\n for row_index in range(n_rows):\r\n lat_coord = (\r\n degree_geotransform[3] + degree_geotransform[5] * row_index)\r\n base_y_coord = int((lat_coord - base_gt[3]) / base_gt[5])\r\n target_y_coord = int(\r\n (lat_coord - degree_geotransform[3]) / degree_geotransform[5])\r\n for col_index in range(n_cols):\r\n long_coord = (\r\n degree_geotransform[0] + degree_geotransform[1] * col_index)\r\n base_x_coord = int((long_coord - base_gt[0]) / base_gt[1])\r\n target_x_coord = int(\r\n (long_coord - degree_geotransform[0]) / degree_geotransform[1])\r\n\r\n base_array = base_band.ReadAsArray(\r\n xoff=base_x_coord, yoff=base_y_coord,\r\n win_xsize=base_x_winsize, win_ysize=base_y_winsize)\r\n valid_array = ~numpy.isclose(base_array, base_nodata)\r\n if valid_array.any():\r\n target_band.WriteArray(\r\n numpy.array([[aggregate_func(base_array[valid_array])]]),\r\n xoff=target_x_coord, yoff=target_y_coord)\r\n\r\n current_time = time.time()\r\n if (current_time - last_time) > 5.0:\r\n LOGGER.info(\r\n \"%.2f%% complete\", 100.0 * float(row_index+1) / n_rows)\r\n last_time = current_time\r\n target_band.FlushCache()\r\n target_band = None\r\n target_raster = None\r\n base_band = None\r\n base_raster = None\r\n LOGGER.info(\"100%% complete\")","def merge_rasters(self):\n for index, i in enumerate(self.months):\n month = str(index + 1)\n if len(month) < 2:\n month = '0' + month\n rasters = [str(x) for x in i.joinpath('subnational').iterdir() if not x.name.endswith('txt') if x.name.endswith('norm.tif')]\n outfile = i.joinpath(f'{self.country}_{month}_normalised.tif')\n tiffs = \" \".join(rasters)\n gdal_cmd = f\"gdal_merge.py -o {outfile} -a_nodata -99999.0 -of gtiff {tiffs}\"\n subprocess.call(gdal_cmd, shell=True)","def fixImageCoordinates( filename, projection):\n printcount = 0\n\n inname = filename\n nchar = len(inname)\n # strip off the end of the previous image and add the new projection name\n outname = inname[0:nchar-8]\n outname = outname + projection + \".fit\"\n\n# get the input image coordinate transform, Usually Cartesian\n win = wcs.WCS(filename)\n\n hdu = fits.open(filename)[0]\n imageData = fits.getdata( filename)\n imageCopy = copy.deepcopy( imageData)\n#\n header = hdu.header\n nx = header['NAXIS1']\n ny = header['NAXIS2']\n\n crval1 = header['CRVAL1']\n crval2 = header['CRVAL2']\n crpix1 = header['CRPIX1']\n crpix2 = header['CRPIX2']\n cdelt1 = header['CDELT1']\n cdelt2 = header['CDELT2']\n ctype1 = header['CTYPE1']\n ctype2 = header['CTYPE2']\n\n xmin = crval1 + (1. - crpix1)*cdelt1\n xmax = crval1 + (nx - crpix1)*cdelt1\n ymin = crval2 + (1. - crpix2)*cdelt2\n ymax = crval2 + (nx - crpix2)*cdelt2\n\n print( \"fixImage: %.2f,%2f %.1f,%.1f %.3f,%.3f\" % (crval1,crval2,crpix1,crpix2,cdelt1,cdelt2))\n print( \"fixImage: %s,%s\" % (ctype1,ctype2))\n # redefine for new projection desired\n ctype1 = ctype1[0:4]+projection\n ctype2 = ctype2[0:4]+projection\n print( \"=> %s, %s\" % (ctype1, ctype2))\n\n header['CTYPE1'] = ctype1\n header['CTYPE2'] = ctype2\n\n# for output image the reference coordinate x pixel can be anywhere\n# move the center to zero \n header['CRVAL1'] = 0.\n\n header['LONPOLE'] = 0.0\n header['LATPOLE'] = 90.0\n header.update()\n\n tempname = \"GridSave.fits\"\n hdu = fits.PrimaryHDU(header=header, data=imageCopy)\n print(\"Outname: %s\" % (tempname))\n if os.path.exists(tempname):\n os.remove(tempname)\n hdu.writeto(tempname)\n\n wout = wcs.WCS(tempname)\n # now that coordinates are defined, remove temporary file\n if os.path.exists(tempname):\n os.remove(tempname)\n\n pixin = np.array([[0, 0], [nx-1, ny-1]], dtype=np.float64)\n pixout = np.array([[0, 0], [nx-1, ny-1]], dtype=np.float64)\n\n print(\"NX, NY: %d,%d\" % (nx, ny))\n\n nan = float(\"NAN\")\n# print(\"Nan = %f\" % (nan))\n# assume no data until found\n for jjj in range (ny):\n for iii in range (nx):\n imageCopy[jjj][iii] = nan\n\n# now for output image check all pixel values\n for jjj in range (ny):\n for iii in range (nx):\n # if this image pixal has no value\n pixout[0] = (iii,jjj)\n oworld = wout.wcs_pix2world(pixout, 0)\n xy = oworld[0]\n if np.isnan(xy[0]):\n continue\n# print(\"pixout: %d,%d : world %.f,%.2f\" % (iii,jjj,xy[0],xy[1]))\n pixin[0] = oworld[0]\n ipixels = win.wcs_world2pix(pixin, 0)\n# get input pixels for coordinate\n ixy = ipixels[0]\n# if outside of current image skip this pixel\n if np.isnan( ixy[0]):\n continue\n ix = int(ixy[0])\n iy = int(ixy[1])\n ix = max( min( nx-1, ix), 0)\n iy = max( min( ny-1, iy), 0)\n ix = int(ix)\n iy = int(iy)\n# print(\"pixin : %d,%d : world %.f,%.2f\" % (ix,iy,xy[0],xy[1]))\n# print(\"OX,OY:%d,%d <= IX,IY:%d,%d\" %( ox,oy, ix,iy))\n imageCopy[jjj][iii] = imageData[iy][ix]\n\n print(\"Preparing to write new coordiante transform: %s\" % (outname))\n if os.path.exists(outname):\n os.remove(outname)\n newhdu = fits.PrimaryHDU(header=header, data=imageCopy)\n newhdu.writeto(outname)\n print(\"Wrote new\")\n\n return","def geo_transform(self):\n pass","def __affine_geo_transformation(x, y, gtr):\n\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\n # Affine transformation rewritten for rasterio:\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\n\n return gtr_x, gtr_y","def shapefileToRaster(in_shapefile, model_raster, out_dir, name_override=None, zone_field:str = None, dtype = None, *args, **kwargs) -> str:\n\t# correct variable names\n\tshapefile_path = in_shapefile\n\t# get out_path\n\tif name_override:\n\t\tout_path = os.path.join(out_dir,name_override)\n\telse:\n\t\tin_base = os.path.splitext(os.path.basename(in_shapefile))[0]\n\t\tmodel_ext = os.path.splitext(model_raster)[1]\n\t\tout_path= os.path.join(out_dir,in_base+\"_RASTER\"+model_ext)\n\t# read file\n\tshp = gpd.read_file(shapefile_path)\n\twith rasterio.open(model_raster,'r') as rst:\n\t\tmeta = rst.meta.copy()\n\n\t# this is where we create a generator of geom, value pairs to use in rasterizing\n\tif zone_field is not None:\n\t\tzone_vals = []\n\t\tfor i in range(len(shp)):\n\t\t\tzone_vals.append(shp.at[i,zone_field])\n\t\tzone_codes = [i for i, val in enumerate(zone_vals)]\n\t\tshapes = ((geom,val) for geom, val in zip(shp.geometry,zone_codes))\n\telse:\n\t\tshapes = ((geom,1) for geom in shp.geometry)\n\n\t# set data type\n\tif dtype:\n\t\tmeta.update(dtype=dtype)\n\telif zone_field:\n\t\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype(zone_codes))\n\telse:\n\t\tmeta.update(dtype=\"int16\")\n\n\ttry:\n\t\tout = rasterio.open(out_path, 'w+', **meta)\n\t# merra-2 files have a very high nodata value, beyond the range of int32.\n\t# This block catches the resulting ValueError and swaps in the minimum\n\t# allowable data type. Nice of rasterio to have such a function.\n\texcept ValueError:\n\t\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype([meta['nodata']]))\n\t\tout = rasterio.open(out_path, 'w+', **meta)\n\t\tout_arr = out.read(1)\n\t\tburned = features.rasterize(shapes=shapes, fill=0, out=out_arr, transform=out.transform)\n\t\tout.write_band(1, burned)\n\tout.close()\n\n\treturn out_path","def saveTiles(z, x, y, ntiles, mapname, image, suffix = 'png', imgtype = None):\n for dx in range(0, ntiles):\n tilex = x*ntiles + dx\n ensureDirExists(getTileDir(mapname, z, tilex))\n for dy in range(0, ntiles): \n tiley = y*ntiles + dy\n offsetx = BORDER_WIDTH + dx*TILE_SIZE\n offsety = BORDER_WIDTH + dy*TILE_SIZE\n view = image.view(offsetx, offsety, TILE_SIZE, TILE_SIZE)\n if imgtype:\n view.save(getTilePath(mapname, z, tilex, tiley, suffix), imgtype)\n else:\n view.save(getTilePath(mapname, z, tilex, tiley, suffix))","def test_convert_to_geotiff(self):\n fnames = modis.convert_to_geotiff(self.fnames[0], outdir=os.path.dirname(__file__))\n for f in fnames:\n ext = os.path.splitext(f)[1]\n suffix = os.path.splitext(f)[0].split('_')[1]\n self.assertTrue(os.path.exists(f))\n if ext != '.ovr':\n self.assertTrue(suffix in products['MCD43A4.006']['bandnames'])","def save_regions_to_file(\n label_layer,\n destination_directory,\n ignore_empty=True,\n image_extension=\".tiff\",\n):\n data = label_layer.data\n if ignore_empty:\n if data.sum() == 0:\n return\n\n name = label_layer.name\n\n filename = destination_directory / (name + image_extension)\n imio.to_tiff(data.astype(np.int16), filename)","def to_raster(\n self,\n raster_path,\n driver=\"GTiff\",\n dtype=None,\n tags=None,\n windowed=False,\n mask=False,\n logger=logger,\n **profile_kwargs,\n ):\n for k in [\"height\", \"width\", \"count\", \"transform\"]:\n if k in profile_kwargs:\n msg = f\"{k} will be set based on the DataArray, remove the argument\"\n raise ValueError(msg)\n da_out = self._obj\n # set nodata, mask, crs and dtype\n if \"nodata\" in profile_kwargs:\n da_out.raster.set_nodata(profile_kwargs.pop(\"nodata\"))\n nodata = da_out.raster.nodata\n if nodata is not None and not np.isnan(nodata):\n da_out = da_out.fillna(nodata)\n elif nodata is None:\n logger.warning(f\"nodata value missing for {raster_path}\")\n if mask and \"mask\" in da_out.coords and nodata is not None:\n da_out = da_out.where(da_out.coords[\"mask\"] != 0, nodata)\n if dtype is not None:\n da_out = da_out.astype(dtype)\n if \"crs\" in profile_kwargs:\n da_out.raster.set_crs(profile_kwargs.pop(\"crs\"))\n # check dimensionality\n dim0 = da_out.raster.dim0\n count = 1\n if dim0 is not None:\n count = da_out[dim0].size\n da_out = da_out.sortby(dim0)\n # write\n profile = dict(\n driver=driver,\n height=da_out.raster.height,\n width=da_out.raster.width,\n count=count,\n dtype=str(da_out.dtype),\n crs=da_out.raster.crs,\n transform=da_out.raster.transform,\n nodata=nodata,\n **profile_kwargs,\n )\n with rasterio.open(raster_path, \"w\", **profile) as dst:\n if windowed:\n window_iter = dst.block_windows(1)\n else:\n window_iter = [(None, None)]\n for _, window in window_iter:\n if window is not None:\n row_slice, col_slice = window.toslices()\n sel = {self.x_dim: col_slice, self.y_dim: row_slice}\n data = da_out.isel(sel).load().values\n else:\n data = da_out.load().values\n if data.ndim == 2:\n dst.write(data, 1, window=window)\n else:\n dst.write(data, window=window)\n if tags is not None:\n dst.update_tags(**tags)","def render(self, outputfile, cmd=GDALTRANSLATE, working_memory=1024,\n compress=None, tempdir=None):\n tmpfile = NamedTemporaryFile(\n suffix='.tif', prefix='gdalrender',\n dir=os.path.dirname(outputfile), delete=False\n )\n\n try:\n with self.get_tempfile(dir=tempdir) as inputfile:\n warp_cmd = [\n cmd,\n '-q', # Quiet - FIXME: Use logging\n '-of', 'GTiff', # Output to GeoTIFF\n '-co', 'BIGTIFF=IF_SAFER', # Use BigTIFF if >2GB\n '-co', 'NUM_THREADS=ALL_CPUS', # multithreaded compression for GeoTiff\n # gdal_translate does not support the following\n # '-multi', # Use multiple processes\n # '-overwrite', # Overwrite outputfile\n ]\n\n # Set the working memory so that gdalwarp doesn't stall of disk\n # I/O\n warp_cmd.extend([\n # gdal_translate does not support -wm\n # '-wm', working_memory,\n '--config', 'GDAL_CACHEMAX', working_memory\n ])\n\n # Use compression\n compress = str(compress).upper()\n if compress and compress != 'NONE':\n warp_cmd.extend(['-co', 'COMPRESS=%s' % compress])\n if compress in ('LZW', 'DEFLATE'):\n warp_cmd.extend(['-co', 'PREDICTOR=2'])\n\n # Run gdalwarp and output to tmpfile.name\n warp_cmd.extend([inputfile.name, tmpfile.name])\n check_output_gdal([str(e) for e in warp_cmd])\n\n # If it succeeds, then we move it to overwrite the actual\n # output\n os.rename(tmpfile.name, outputfile)\n return outputfile\n finally:\n rmfile(tmpfile.name, ignore_missing=True)\n rmfile(tmpfile.name + '.aux.xml', ignore_missing=True)","def warp_to_raster(\r\n base_raster_path, canonical_raster_path, resample_method,\r\n target_raster_path):\r\n canonical_raster_info = pygeoprocessing.get_raster_info(\r\n canonical_raster_path)\r\n pygeoprocessing.warp_raster(\r\n base_raster_path, canonical_raster_info['pixel_size'],\r\n target_raster_path,\r\n resample_method, target_bb=canonical_raster_info['bounding_box'],\r\n n_threads=2)"],"string":"[\n \"def save_image(nom, image, _geo_trans):\\n cols = image.shape[2]\\n rows = image.shape[1]\\n bands = image.shape[0]\\n print(bands, cols, rows)\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n out_raster = driver.Create(nom, cols, rows, bands, gdal.GDT_Byte)\\n # if (geo_trans):\\n # outRaster.SetGeoTransform(geo_trans)\\n band = out_raster.GetRasterBand(1)\\n band.WriteArray(np.round(image[0]))\\n band = out_raster.GetRasterBand(2)\\n band.WriteArray(np.round(image[1]))\\n band = out_raster.GetRasterBand(3)\\n band.WriteArray(np.round(image[2]))\",\n \"def reprojectAndSaveNewRaster(inFilepath,outFilepath,to_EPSG):\\r\\n from osgeo import gdal\\r\\n input_raster = gdal.Open(inFilepath)\\r\\n EPSG_string = \\\"EPSG:\\\"+str(to_EPSG)\\r\\n ras = gdal.Warp(outFilepath,input_raster,dstSRS=EPSG_string)\\r\\n del ras\",\n \"def _export_geotiff(self, filepath: str, z_positive_up: bool = True):\\n\\n nodatavalue = 1000000.0\\n data, geo_transform, bandnames = self._gdal_preprocessing(nodatavalue=nodatavalue, z_positive_up=z_positive_up)\\n gdal_raster_create(filepath, data, geo_transform, self.crs, nodatavalue=nodatavalue, bandnames=bandnames,\\n driver='GTiff')\",\n \"def _write_image(self):\\r\\n # Create an output raster with the correct number of rows and columns.\\r\\n gtiff_driver = gdal.GetDriverByName('GTiff')\\r\\n out_ds = gtiff_driver.Create(os.path.join(self.out_folder, self.out_file_name), self.column, self.row, 1)\\r\\n out_ds.SetProjection(self.in_ds.GetProjection())\\r\\n\\r\\n # Convert the offsets to real-world coordinates for the georeferencing info.\\r\\n # We can't use the coordinates above because they don't correspond to the pixel edges.\\r\\n subset_ulx, subset_uly = gdal.ApplyGeoTransform(self.in_gt, self.off_ulx, self.off_uly)\\r\\n out_gt = list(self.in_gt)\\r\\n out_gt[0] = subset_ulx\\r\\n out_gt[3] = subset_uly\\r\\n out_ds.SetGeoTransform(out_gt)\\r\\n\\r\\n data = self.read_image()\\r\\n out_band = out_ds.GetRasterBand(1)\\r\\n out_band.WriteArray(data)\\r\\n\\r\\n del out_ds\",\n \"def save_raster(outfile, data, info=None):\\n\\n proj4txt = (info and info['grid'].srs())\\n geoxf = (info and info['grid'].geoxf())\\n meta = None # XXX fixme\\n\\n dirname,fname = os.path.split(os.path.abspath(outfile))\\n if not os.path.isdir(dirname):\\n os.makedirs(dirname)\\n\\n if len(data.shape) == 3:\\n ysize,xsize,nbands = data.shape\\n else:\\n ysize,xsize = data.shape\\n nbands = 1\\n\\n # get the gdal datatype from the numpy datatype\\n gdtype = gdal_array.flip_code(data.dtype.type)\\n\\n # do not add COMPRESS=DEFLATE here\\n # generate all TIFFs in blocks for faster random access\\n blocksize = 128\\n opts = [\\n 'TILED=YES',\\n 'BLOCKXSIZE=%d' % blocksize,\\n 'BLOCKYSIZE=%d' % blocksize\\n ]\\n\\n #print 'SAVE %r %r' % (outfile, (data.shape, blocksize, opts))\\n\\n gtiff = gdal.GetDriverByName('GTiff')\\n\\n #print 'GDTY %r %r %r' % (data.dtype, gdtype, gdalconst.GDT_Byte)\\n\\n ds = gtiff.Create(str(outfile), xsize, ysize, nbands,\\n gdtype, opts)\\n\\n if proj4txt is not None:\\n srs = osr.SpatialReference()\\n srs.ImportFromProj4(proj4txt)\\n ds.SetProjection(srs.ExportToWkt())\\n\\n if geoxf is not None:\\n ds.SetGeoTransform(geoxf)\\n\\n if meta is not None:\\n ds.SetMetadata(meta)\\n\\n for i in range(nbands):\\n rband = ds.GetRasterBand(i+1)\\n\\n if nbands == 1:\\n rband.SetRasterColorInterpretation(gdalconst.GCI_GrayIndex)\\n elif i == 0:\\n rband.SetRasterColorInterpretation(gdalconst.GCI_RedBand)\\n elif i == 1:\\n rband.SetRasterColorInterpretation(gdalconst.GCI_GreenBand)\\n elif i == 2:\\n rband.SetRasterColorInterpretation(gdalconst.GCI_BlueBand)\\n elif i == 3:\\n rband.SetRasterColorInterpretation(gdalconst.GCI_AlphaBand)\\n\\n #rband.SetNoDataValue(xxx):\\n\\n bdata = data\\n if nbands > 1:\\n bdata = data[:,:,i]\\n\\n # transform data to unsigned byte?\\n print 'DATA %r: %f < x < %f' % (outfile, N.min(bdata), N.max(bdata))\\n\\n rband.WriteArray(bdata)\",\n \"def save_raster ( output_name, raster_data, dataset, driver=\\\"GTiff\\\" ):\\n\\n # Open the reference dataset\\n g_input = gdal.Open ( dataset )\\n # Get the Geotransform vector\\n geo_transform = g_input.GetGeoTransform ()\\n x_size = g_input.RasterXSize # Raster xsize\\n y_size = g_input.RasterYSize # Raster ysize\\n srs = g_input.GetProjectionRef () # Projection\\n # Need a driver object. By default, we use GeoTIFF\\n if driver == \\\"GTiff\\\":\\n driver = gdal.GetDriverByName ( driver )\\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\\\\n gdal.GDT_Float32, ['TFW=YES', \\\\\\n 'COMPRESS=LZW', 'TILED=YES'] )\\n else:\\n driver = gdal.GetDriverByName ( driver )\\n dataset_out = driver.Create ( output_name, x_size, y_size, 1, \\\\\\n gdal.GDT_Float32 )\\n\\n dataset_out.SetGeoTransform ( geo_transform )\\n dataset_out.SetProjection ( srs )\\n dataset_out.GetRasterBand ( 1 ).WriteArray ( \\\\\\n raster_data.astype(np.float32) )\\n dataset_out.GetRasterBand ( 1 ).SetNoDataValue ( float(-999) )\\n dataset_out = None\",\n \"def create_geotiff(pixels, gps_bounds, out_path, nodata=-99, asfloat=False, extractor_info=None, system_md=None, extra_metadata=None, compress=False):\\n dimensions = numpy.shape(pixels)\\n if len(dimensions) == 2:\\n nrows, ncols = dimensions\\n channels = 1\\n else:\\n nrows, ncols, channels = dimensions\\n\\n geotransform = (\\n gps_bounds[2], # upper-left x\\n (gps_bounds[3] - gps_bounds[2])/float(ncols), # W-E pixel resolution\\n 0, # rotation (0 = North is up)\\n gps_bounds[1], # upper-left y\\n 0, # rotation (0 = North is up)\\n -((gps_bounds[1] - gps_bounds[0])/float(nrows)) # N-S pixel resolution\\n )\\n\\n # Create output GeoTIFF and set coordinates & projection\\n if asfloat:\\n dtype = gdal.GDT_Float32\\n else:\\n dtype = gdal.GDT_Byte\\n\\n if compress:\\n output_raster = gdal.GetDriverByName('GTiff') \\\\\\n .Create(out_path, ncols, nrows, channels, dtype, ['COMPRESS=LZW'])\\n else:\\n output_raster = gdal.GetDriverByName('GTiff') \\\\\\n .Create(out_path, ncols, nrows, channels, dtype)\\n\\n output_raster.SetGeoTransform(geotransform)\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326) # google mercator\\n output_raster.SetProjection( srs.ExportToWkt() )\\n\\n if not extra_metadata:\\n extra_metadata = prepare_metadata_for_geotiff(extractor_info, system_md)\\n\\n output_raster.SetMetadata(extra_metadata)\\n\\n if channels == 3:\\n # typically 3 channels = RGB channels\\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\\n output_raster.GetRasterBand(1).WriteArray(pixels[:,:,0].astype('uint8'))\\n output_raster.GetRasterBand(1).SetColorInterpretation(gdal.GCI_RedBand)\\n output_raster.GetRasterBand(1).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\\n\\n output_raster.GetRasterBand(2).WriteArray(pixels[:,:,1].astype('uint8'))\\n output_raster.GetRasterBand(2).SetColorInterpretation(gdal.GCI_GreenBand)\\n output_raster.GetRasterBand(2).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(2).SetNoDataValue(nodata)\\n\\n output_raster.GetRasterBand(3).WriteArray(pixels[:,:,2].astype('uint8'))\\n output_raster.GetRasterBand(3).SetColorInterpretation(gdal.GCI_BlueBand)\\n output_raster.GetRasterBand(3).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(3).SetNoDataValue(nodata)\\n\\n elif channels > 1:\\n # TODO: Something wonky w/ uint8s --> ending up w/ lots of gaps in data (white pixels)\\n for chan in range(channels):\\n band = chan + 1\\n output_raster.GetRasterBand(band).WriteArray(pixels[:,:,chan].astype('uint8'))\\n output_raster.GetRasterBand(band).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(band).SetNoDataValue(nodata)\\n else:\\n # single channel image, e.g. temperature\\n output_raster.GetRasterBand(1).WriteArray(pixels)\\n output_raster.GetRasterBand(1).FlushCache()\\n if nodata:\\n output_raster.GetRasterBand(1).SetNoDataValue(nodata)\\n\\n output_raster = None\",\n \"def save_geotiff(df, data_col, crs, x_col='x', y_col='y', time_col=None, nfiles='many', export_path='geotiff.tif', grid_res=None):\\n\\n ### create the xy coordinates\\n if time_col is None:\\n xy1 = df[[x_col, y_col]]\\n else:\\n time = df[time_col].sort_values().unique()\\n xy1 = df.loc[df[time_col] == time[0], [x_col, y_col]]\\n if any(xy1.duplicated()):\\n raise ValueError('x and y coordinates are not unique!')\\n\\n ### Determine grid res\\n if grid_res is None:\\n res_df1 = (xy1.loc[0] - xy1).abs()\\n res_df2 = res_df1.replace(0, nan).min()\\n x_res = res_df2[x_col]\\n y_res = res_df2[y_col]\\n elif isinstance(grid_res, int):\\n x_res = y_res = grid_res\\n else:\\n raise ValueError('grid_res must either be None or an integer.')\\n\\n ### Make the affline transformation for Rasterio\\n trans2 = transform.from_origin(xy1[x_col].min() - x_res/2, xy1[y_col].max() + y_res/2, x_res, y_res)\\n\\n ### Make the rasters\\n if time_col is None:\\n z = df.set_index([y_col, x_col])[data_col].unstack().values[::-1]\\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs, pass_str=True), transform=trans2)\\n new_dataset.write(z, 1)\\n new_dataset.close()\\n else:\\n if nfiles == 'one':\\n new_dataset = ras_open(export_path, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=len(time), dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\\n for i in range(1, len(time)+1):\\n z = df.loc[df[time_col] == time[i - 1]].set_index([y_col, x_col])[data_col].unstack().values[::-1]\\n new_dataset.write(z, i)\\n new_dataset.close()\\n elif nfiles == 'many':\\n file1 = path.splitext(export_path)[0]\\n for i in time:\\n str_date = to_datetime(i).strftime('%Y-%m-%d_%H')\\n file2 = file1 + '_' + str_date + '.tif'\\n z = df.loc[df[time_col] == i].set_index([y_col, x_col])[data_col].unstack().values[::-1]\\n new_dataset = ras_open(file2, 'w', driver='GTiff', height=len(xy1[y_col].unique()), width=len(xy1[x_col].unique()), count=1, dtype=df[data_col].dtype, crs=convert_crs(crs), transform=trans2)\\n new_dataset.write(z, 1)\\n new_dataset.close()\",\n \"def save_tif(self, name='', data='', geo='', projection=''):\\n # save as a geotiff\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1,\\n gdal.GDT_Float32, ['COMPRESS=LZW'])\\n srse = osr.SpatialReference()\\n if projection == '':\\n srse.SetWellKnownGeogCS(\\\"WGS84\\\")\\n\\n else:\\n try:\\n if not srse.SetWellKnownGeogCS(projection) == 6:\\n srse.SetWellKnownGeogCS(projection)\\n else:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except BaseException as err:\\n print(err)\\n else:\\n srse.ImportFromWkt(projection)\\n except BaseException:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except BaseException as err:\\n print(err)\\n else:\\n srse.ImportFromWkt(projection)\\n\\n dst_ds.SetProjection(srse.ExportToWkt())\\n dst_ds.SetGeoTransform(geo)\\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\\n dst_ds.GetRasterBand(1).WriteArray(data)\\n dst_ds = None\\n\\n return\",\n \"def _export_raster(self, raster_name,\\n format=\\\"GTiff\\\",\\n additional_options=[],\\n use_raster_region=False):\\n # Export the layer\\n prefix = \\\".tiff\\\"\\n if format == \\\"GTiff\\\":\\n prefix = \\\".tiff\\\"\\n\\n # Remove a potential mapset\\n file_name = raster_name.split(\\\"@\\\")[0] + prefix\\n\\n if use_raster_region is True:\\n\\n p = Process(exec_type=\\\"grass\\\",\\n executable=\\\"g.region\\\",\\n executable_params=[\\\"raster=%s\\\"%raster_name, \\\"-g\\\"],\\n stdin_source=None)\\n\\n self._update_num_of_steps(1)\\n self._run_module(p)\\n\\n # Save the file in the temporary directory of the temporary gisdb\\n output_path = os.path.join(self.temp_file_path, file_name)\\n\\n module_name = \\\"r.out.gdal\\\"\\n args = [\\\"-fm\\\", \\\"input=%s\\\"%raster_name, \\\"format=%s\\\"%format,\\n \\\"createopt=COMPRESS=LZW\\\", \\\"output=%s\\\"%output_path]\\n\\n if additional_options:\\n args.extend(additional_options)\\n\\n p = Process(exec_type=\\\"grass\\\",\\n executable=module_name,\\n executable_params=args,\\n stdin_source=None)\\n\\n self._update_num_of_steps(1)\\n self._run_module(p)\\n\\n return file_name, output_path\",\n \"def saveAsGeotiff(self, geotiff_fname, overwrite=True, verbose=False):\\n if not overwrite and os.path.exists(geotiff_fname):\\n raise ValueError('output geotiff already exists:\\\\n {}'.format(\\n geotiff_fname))\\n\\n if overwrite and os.path.exists(geotiff_fname):\\n os.remove(geotiff_fname)\\n\\n gggdal = geogrid_as_gdalInMem(self)\\n\\n dst = gdal.GetDriverByName('GTiff').CreateCopy(\\n geotiff_fname, gggdal)\\n dst = None\",\n \"def write_tiff (self, filename, prototype_filename = None, proj_string = None):\\n if prototype_filename is None:\\n if self.prototype_filename is None:\\n use_prototype = False\\n else:\\n prototype_filename = self.prototype_filename # use the pre-defined one \\n use_prototype = True\\n \\n if use_prototype:\\n raster = gdal.Open (prototype_filename)\\n geotransform = raster.GetGeoTransform()\\n originX = geotransform[0]\\n originY = geotransform[3]\\n cell_Width = geotransform[1]\\n cell_Height = geotransform[5]\\n ncols = self.ras.shape[1]\\n nrows = self.ras.shape[0]\\n else:\\n originX = self.originX\\n originY = self.originY\\n cell_Width = self.cell_Width\\n cell_Height = self.cell_Height\\n ncols = self.ncols\\n nrows = self.nrows\\n \\n # create driver\\n driver = gdal.GetDriverByName('GTiff')\\n outRaster = driver.Create (filename, ncols, nrows, 1, gdal.GDT_Float32)\\n outRaster.SetGeoTransform((originX, cell_Width, 0, originY, 0, cell_Height))\\n outband = outRaster.GetRasterBand(1)\\n \\n # make a copy to write out\\n x = self.ras.copy()\\n \\n # set the nodata flag properly\\n if use_prototype:\\n nodata_flag = raster.GetRasterBand(1).GetNoDataValue() # get the original value\\n else:\\n nodata_flag = -9999.0 # use hardcoded nan value\\n x [np.isnan(x)] = nodata_flag # assign it to the array\\n outband.SetNoDataValue (nodata_flag) # set it in the output band\\n \\n # write array and set projection (if projection supplied)\\n outband.WriteArray (x)\\n outRasterSRS = osr.SpatialReference ()\\n if use_prototype:\\n outRasterSRS.ImportFromWkt (raster.GetProjectionRef())\\n outRaster.SetProjection (outRasterSRS.ExportToWkt())\\n else:\\n if not proj_string is None:\\n outRasterSRS.ImportFromWkt (proj_string)\\n outRaster.SetProjection (outRasterSRS.ExportToWkt())\\n \\n outband.FlushCache ()\\n return\",\n \"def Reproject(cls,inRaster, outRaster,EPSG):\\r\\n try:\\r\\n import gdal\\r\\n except:\\r\\n raise ImportError(\\\"Can not import module GDAL\\\")\\r\\n try:\\r\\n dataset = gdal.Open(infile)\\r\\n out = dataset.GetRasterBand(1)\\r\\n print dataset.GetMetadata()\\r\\n return out\\r\\n except:\\r\\n raise ImportError(\\\"Can not read band\\\")\\r\\n #if not tmp:\\r\\n # raise Exception(\\\"Could not orthorectify the image \\\")\\r\\n\\r\\n com=\\\" \\\".join([\\\"gdalwarp -t_srs EPSG:\\\"+str(EPSG),InRaster,OutRaster])\\r\\n tmp=os.system(com)\\r\\n print tmp\\r\\n #if not tmp:\\r\\n # raise Exception(\\\"Could not reproject the image \\\")\\r\\n #os.remove(OutRaster)\\r\",\n \"def GetGeoTransform(raster_path):\\n \\n #open a GDAL object containig the raster\\n gdal_img = gdal.Open(raster_path)\\n \\n #extract basic geospatial data\\n ulx, xres, xskew, uly, yskew, yres = gdal_img.GetGeoTransform()\\n \\n #calculate lower right coordinates from upper left coordinates and raster size\\n lrx = ulx + (gdal_img.RasterXSize * xres)\\n lry = uly + (gdal_img.RasterYSize * yres)\\n \\n geoinfo = {'ulx': ulx,\\n 'lrx': lrx,\\n 'uly': uly,\\n 'lry': lry,\\n 'xres': xres,\\n 'xskew': xskew,\\n 'yres': yres,\\n 'yskew': yskew\\n }\\n \\n return geoinfo\",\n \"def array_to_raster(array, x, y):\\n\\n # Files info\\n dst_filename = 'atiff.tiff'\\n \\n # Load matlab file\\n front_dict = loadmat(infile,squeeze_me=True, struct_as_record=False)\\n #print front_dict\\n \\n # You need to get those values like you did.\\n x_pixels = len(x) # number of pixels in x\\n y_pixels = len(y) # number of pixels in y\\n PIXEL_SIZE = 1000 # size of the pixel...(in m?) \\n x_min = np.min(x)\\n y_max = np.min(y) # x_min & y_max are like the \\\"top left\\\" corner.\\n wkt_projection = 'a projection in wkt that you got from other file'\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n dataset = driver.Create(\\n dst_filename,\\n x_pixels,\\n y_pixels,\\n 1,\\n gdal.GDT_Float32, )\\n\\n dataset.SetGeoTransform((\\n x_min, # 0\\n PIXEL_SIZE, # 1\\n 0, # 2\\n y_max, # 3\\n 0, # 4\\n -PIXEL_SIZE)) \\n\\n dataset.SetProjection(wkt_projection)\\n dataset.GetRasterBand(1).WriteArray(array)\\n dataset.FlushCache() # Write to disk.\\n return dataset, dataset.GetRasterBand(1) #If you need to ret\",\n \"def tiffwrite(filename, im):\\n tf.imwrite(filename, im)\",\n \"def outputGeoTiff(data, filename, geo_t, proj, output_dir = os.getcwd(), dtype = 6, nodata = None):\\n \\n from osgeo import osr, gdal\\n \\n # Get full output path\\n output_path = '%s/%s.tif'%(os.path.abspath(os.path.expanduser(output_dir)), filename.rstrip('.tif'))\\n \\n # Save image with georeference info\\n driver = gdal.GetDriverByName('GTiff')\\n ds = driver.Create(output_path, data.shape[1], data.shape[0], 1, dtype, options = ['COMPRESS=LZW'])\\n ds.SetGeoTransform(geo_t)\\n ds.SetProjection(proj)\\n \\n # Set nodata\\n if nodata != None:\\n ds.GetRasterBand(1).SetNoDataValue(nodata)\\n \\n # Write data for masked and unmasked arrays\\n if np.ma.isMaskedArray(data):\\n ds.GetRasterBand(1).WriteArray(data.filled(nodata))\\n else:\\n ds.GetRasterBand(1).WriteArray(data)\\n ds = None\",\n \"def Save_as_tiff(name='', data='', geo='', projection=''):\\n \\n dir_name = os.path.dirname(name)\\n \\n if not os.path.exists(dir_name):\\n os.makedirs(dir_name)\\n \\n # save as a geotiff\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n dst_ds = driver.Create(name, int(data.shape[1]), int(data.shape[0]), 1, gdal.GDT_Float32, ['COMPRESS=LZW'])\\n srse = osr.SpatialReference()\\n if projection == '':\\n srse.SetWellKnownGeogCS(\\\"WGS84\\\")\\n\\n else:\\n try:\\n if not srse.SetWellKnownGeogCS(projection) == 6:\\n srse.SetWellKnownGeogCS(projection)\\n else:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except:\\n srse.ImportFromWkt(projection)\\n except:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except:\\n srse.ImportFromWkt(projection)\\n\\n dst_ds.SetProjection(srse.ExportToWkt())\\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\\n dst_ds.SetGeoTransform(geo)\\n dst_ds.GetRasterBand(1).WriteArray(data)\\n dst_ds = None\\n return()\",\n \"def test_save_tif():\\n img = Image.new('RGB', (10, 20))\\n\\n parameters = {'path': 'green-dot.tif', 'data': [img]}\\n\\n assert images.save(parameters)\",\n \"def save_grtrans_image(grt_obj):\\n I_im = grt_obj.ivals[:,0,0].reshape(npix,npix).flatten()\\n Q_im = grt_obj.ivals[:,1,0].reshape(npix,npix).flatten()\\n U_im = grt_obj.ivals[:,2,0].reshape(npix,npix).flatten()\\n V_im = grt_obj.ivals[:,3,0].reshape(npix,npix).flatten()\\n\\n # convert to Tb\\n factor = 3.254e13/(RF**2 * psize_rad**2)\\n I_im *= factor\\n Q_im *= factor\\n U_im *= factor\\n V_im *= factor\\n\\n x = np.array([[i for i in range(npix)] for j in range(npix)]).flatten()\\n y = np.array([[j for i in range(npix)] for j in range(npix)]).flatten()\\n\\n x -= npix/2\\n y -= npix/2\\n x = x*psize_uas\\n y = y*psize_uas\\n\\n outdat = np.vstack((x.T,y.T,I_im.T,Q_im.T,U_im.T,V_im.T)).T\\n np.savetxt('../rrjet_and_riaf/'+FNAME,outdat)\\n #np.savetxt('../rrjet_and_riaf/grtrans_jet_compare_positron_noconv.txt',outdat)\\n return\",\n \"def array2tif(raster_file, new_raster_file, array):\\n # Invoke the GDAL Geotiff driver\\n raster = gdal.Open(raster_file)\\n\\n driver = gdal.GetDriverByName('GTiff')\\n out_raster = driver.Create(new_raster_file,\\n raster.RasterXSize,\\n raster.RasterYSize,\\n 1,\\n gdal.GDT_Float32)\\n out_raster.SetProjection(raster.GetProjection())\\n # Set transformation - same logic as above.\\n out_raster.SetGeoTransform(raster.GetGeoTransform())\\n # Set up a new band.\\n out_band = out_raster.GetRasterBand(1)\\n # Set NoData Value\\n out_band.SetNoDataValue(-1)\\n # Write our Numpy array to the new band!\\n out_band.WriteArray(array)\",\n \"def array_to_raster(inTiff,array,outFile,dataType=gdal.GDT_Float32):\\n \\n inDataset = gdal.Open(inTiff, GA_ReadOnly)\\n\\n # You need to get those values like you did.\\n x_pixels = inDataset.RasterXSize # number of pixels in x\\n y_pixels = inDataset.RasterYSize # number of pixels in y\\n PIXEL_SIZE = inDataset.GetGeoTransform()[1] # size of the pixel... \\n x_min = inDataset.GetGeoTransform()[0] \\n y_max = inDataset.GetGeoTransform()[3] # x_min & y_max are like the \\\"top left\\\" corner.\\n wkt_projection = inDataset.GetProjectionRef()\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n outDataset = driver.Create(\\n outFile,\\n x_pixels,\\n y_pixels,\\n 1,\\n dataType, )\\n\\n outDataset.SetGeoTransform((\\n x_min, # 0\\n PIXEL_SIZE, # 1\\n 0, # 2\\n y_max, # 3\\n 0, # 4\\n -PIXEL_SIZE))\\n\\n outDataset.SetProjection(wkt_projection)\\n outDataset.GetRasterBand(1).WriteArray(array)\\n outDataset.FlushCache() # Write to disk.\\n return outDataset, outDataset.GetRasterBand(1) #If you need to return, remenber to return also the dataset because the band don`t live without dataset.\",\n \"def reprojectRaster(in_raster,model_raster,out_dir,name_override = None):\\n\\tif name_override:\\n\\t\\tout_name = os.path.join(out_dir,name_override)\\n\\telse:\\n\\t\\tin_base,in_ext = os.path.splitext(os.path.basename(in_raster))\\n\\t\\tout_name = os.path.join(out_dir,in_base+\\\"_REPROJ\\\"+in_ext)\\n\\t\\t#print(out_name)\\n\\tt_ds = gdal.Open(model_raster,0)\\n\\tt_wkt = t_ds.GetProjection()\\n\\ts_ds = gdal.Open(in_raster,0)\\n\\ts_wkt = s_ds.GetProjection()\\n\\tt_ds = s_ds = None\\n\\tif s_wkt != t_wkt:\\n\\t\\treproj_args = [r\\\"C:\\\\OSGeo4W64\\\\bin\\\\gdalwarp.exe\\\",\\\"-t_srs\\\",t_wkt,in_raster,out_name]\\n\\t\\tsubprocess.call(reproj_args)\\n\\t\\treturn out_name\\n\\telse:\\n\\t\\tlog.warning(\\\"Projections already match, doing nothing.\\\")\\n\\t\\treturn in_raster\",\n \"def reprojectAndSaveNewShapefile(inFilepath,outFilepath,to_EPSG):\\r\\n import geopandas as gpd\\r\\n from fiona.crs import from_epsg\\r\\n\\r\\n inFile = gpd.read_file(inFilepath)\\r\\n inFile_proj = inFile.copy()\\r\\n inFile_proj['geometry'] = inFile_proj['geometry'].to_crs(epsg=to_EPSG)\\r\\n inFile_proj.crs = from_epsg(to_EPSG)\\r\\n inFile_proj.to_file(outFilepath)\",\n \"def convertCSVToGeoTIFF(geoproperties, datafile, outputdir, outfile):\\n \\n data = numpy.genfromtxt(datafile, delimiter=',')\\n \\n Ny, Nx = data.shape\\n \\n #print data.size\\n #print data.shape\\n #print data\\n \\n # slice out the last column of null values\\n if str(data[Ny-1][Nx-1]) == 'nan':\\n data = data[:,:-1]\\n \\n Ny, Nx = data.shape\\n \\n #print data.size\\n #print data.shape\\n #print data\\n #print Ny, Nx\\n \\n startPos = [geoproperties['tl']['long'],geoproperties['tl']['lat']]\\n d_lat = (geoproperties['br']['lat'] - geoproperties['tl']['lat']) / (Ny - 1)\\n d_long = (geoproperties['br']['long'] - geoproperties['tl']['long']) / (Nx - 1)\\n \\n #print startPos, d_lat, d_long\\n \\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n ds = driver.Create(os.path.join(outputdir,outfile),Nx,Ny,1,gdal.GDT_Float32)\\n #ds = driver.Create('output/output.tif',Nx,Ny,1,gdal.GDT_Byte)\\n #ds.SetGeoTransform( [ -158.584, .008, 0, 21.108, 0, .008 ] )\\n ds.SetGeoTransform( [ startPos[0], d_long, 0, startPos[1], 0, d_lat ] )\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326)\\n ds.SetProjection( srs.ExportToWkt() )\\n ds.GetRasterBand(1).WriteArray(data)\\n ds = None\",\n \"def UpdateGT(out_file, data, src_file, epsg = 4326, drv = 'GTiff', datatype = gdal.GDT_Float32, NoData = -999):\\n \\n #assign which driver to use for the file format - default is .tif\\n driver = gdal.GetDriverByName(drv)\\n \\n #source raster from which to use the geospatial metadata\\n src_gt = gdal.Open(src_file)\\n \\n #data shape - detects whether to produce multiple bands or not\\n if len(data.shape) == 2:\\n [cols, rows] = data.shape\\n n = 1\\n else:\\n [n, cols, rows] = data.shape\\n\\n #create the destination file\\n dst_gt = driver.Create(out_file, rows, cols, n, datatype)\\n \\n #get the geotransform from the source file\\n gt = src_gt.GetGeoTransform()\\n \\n #coordinate system in which to create the raster, in wkt form\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(epsg)\\n dest_wkt = srs.ExportToWkt()\\n \\n #set the projection in the desired reference system\\n dst_gt.SetGeoTransform(gt)\\n dst_gt.SetProjection(dest_wkt)\\n \\n #set the data\\n if n==1:\\n dst_gt.GetRasterBand(1).WriteArray(data)\\n dst_gt.GetRasterBand(1).SetNoDataValue(NoData)\\n #close and write to file\\n dst_gt.FlushCache()\\n #used if there are multiple bands\\n else:\\n for i in range(n):\\n dst_gt.GetRasterBand(i+1).WriteArray(data[i])\\n dst_gt.GetRasterBand(i+1).SetNoDataValue(NoData)\\n dst_gt.FlushCache()\",\n \"def save_image(self, filename):\\n raster.save_image(filename, self.image, self.metadata)\",\n \"def single_to_rgb(R_file,G_file,B_file): \\n R=gdal_array.LoadFile(R_file)\\n G=gdal_array.LoadFile(G_file)\\n B=gdal_array.LoadFile(B_file)\\n \\n \\n basename=os.path.basename(R_file)\\n basename=basename[:3]+basename[4:]\\n basename=basename[:-4]+\\\"_rgb_.tif\\\" \\n \\n\\n file_path=os.path.dirname(os.path.abspath(R_file))+\\\"/\\\"+basename\\n\\n \\n driver=osgeo.gdal.GetDriverByName(\\\"GTiff\\\")\\n options = ['PHOTOMETRIC=RGB', 'PROFILE=GeoTIFF']\\n print(file_path)\\n print(np.max(np.array([R.shape[1],B.shape[1],G.shape[1]])), np.max(np.array([R.shape[0],B.shape[0],G.shape[0]])))\\n Xlen=np.max(np.array([R.shape[1],B.shape[1],G.shape[1]]))\\n Ylen= np.max(np.array([R.shape[0],B.shape[0],G.shape[0]]))\\n dataset=driver.Create(file_path, int(Xlen),int(Ylen), 3, osgeo.gdal.GDT_UInt16, options) \\n \\n dataset.GetRasterBand(1).WriteArray(R)\\n dataset.GetRasterBand(2).WriteArray(G)\\n dataset.GetRasterBand(3).WriteArray(B)\\n \\n return file_path\",\n \"def __init__(self, raster_path):\\n self.raster_path = raster_path\\n dataset = gdal.Open(raster_path)\\n self.width = dataset.RasterXSize\\n self.height = dataset.RasterYSize\\n # Gets the gdal geo transformation tuples\\n # gdal_version = gdal.__version__\\n self._txf = dataset.GetGeoTransform()\\n # self._inv_txf = gdal.InvGeoTransform(self._txf)[1]\\n self._inv_txf = gdal.InvGeoTransform(self._txf)\\n # Gets the transformation from lat/lon to coordinates\\n wgs84_ref = osr.SpatialReference()\\n wgs84_ref.ImportFromEPSG(4326) # WGS84\\n sref = osr.SpatialReference()\\n sref.ImportFromWkt(dataset.GetProjection())\\n if int(osgeo.__version__[0]) >= 3:\\n # Output order has changed in osgeo v3\\n wgs84_ref.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\\n sref.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)\\n\\n self._transform = osr.CoordinateTransformation(wgs84_ref, sref)\\n inv_transform = osr.CoordinateTransformation(sref, wgs84_ref)\\n # Find a loose lat/lon bounding box for quick check without\\n # having to do full coordinates transformation\\n corners = []\\n for x in [0, self.width]:\\n for y in [0, self.height]:\\n corners.append([self._txf[0] + self._txf[1] * x + self._txf[2] * y,\\n self._txf[3] + self._txf[4] * x + self._txf[5] * y])\\n self.max_lat = -100\\n self.min_lat = 100\\n self.max_lon = -500\\n self.min_lon = 500\\n for c in corners:\\n p = inv_transform.TransformPoint(c[0], c[1])\\n if p[0] > self.max_lon:\\n self.max_lon = p[0]\\n if p[0] < self.min_lon:\\n self.min_lon = p[0]\\n if p[1] > self.max_lat:\\n self.max_lat = p[1]\\n if p[1] < self.min_lat:\\n self.min_lat = p[1]\\n dataset = None\",\n \"def rasterToBinary(input_raster,output_dir,name_override=None):\\n\\tif name_override:\\n\\t\\tout_path = os.path.join(output_dir,name_override)\\n\\telse:\\n\\t\\tin_base,in_ext = os.path.splitext(os.path.basename(input_raster))\\n\\t\\tout_path = os.path.join(output_dir,in_base+\\\"_BINARY\\\"+in_ext)\\n\\n\\tds = gdal.Open(input_raster,0)\\n\\tband = ds.GetRasterBand(1)\\n\\tnoData = band.GetNoDataValue()\\n\\tsrs = ds.GetProjection()\\n\\tgt = ds.GetGeoTransform()\\n\\tarr = BandReadAsArray(band)\\n\\tds = band = None # close dataset and band\\n\\tarr[arr != noData] = 1\\n\\tarr[arr == noData] = noData\\n\\trasterYSize, rasterXSize = arr.shape\\n\\tdriver = gdal.GetDriverByName('GTiff')\\n\\tdataset = driver.Create(out_path,rasterXSize,rasterYSize,1,gdal.GDT_Byte,['COMPRESS=DEFLATE'])\\n\\tdataset.GetRasterBand(1).WriteArray(arr)\\n\\tdataset.GetRasterBand(1).SetNoDataValue(noData)\\n\\tdataset.SetGeoTransform(gt)\\n\\tdataset.SetProjection(srs)\\n\\tdataset.FlushCache() # Write to disk\\n\\tdel dataset\\n\\n\\treturn out_path\",\n \"def save_array_as_geotif(array, source_tif_path, out_path):\\n if len(array.shape) > 2:\\n height, width, depth = array.shape\\n else:\\n height, width = array.shape\\n depth = 1\\n source_tif = gdal.Open(source_tif_path)\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n dataset = driver.Create(out_path, width, height, depth, gdal.GDT_Float32)\\n if depth != 1:\\n for i in range(depth):\\n dataset.GetRasterBand(i + 1).WriteArray(array[:, :, i])\\n else:\\n dataset.GetRasterBand(1).WriteArray(array)\\n geotrans = source_tif.GetGeoTransform()\\n proj = source_tif.GetProjection()\\n dataset.SetGeoTransform(geotrans)\\n dataset.SetProjection(proj)\\n dataset.FlushCache()\\n dataset = None\",\n \"def reproject_raster(res: int, src_epsg: int, dst_epsg: int) -> None:\\n src_name = f\\\"res_{res}_epsg_{src_epsg}\\\"\\n src_path = os.path.join(src_name, f\\\"{src_name}.tif\\\")\\n with rio.open(src_path) as src:\\n dst_crs = f\\\"epsg:{dst_epsg}\\\"\\n transform, width, height = calculate_default_transform(\\n src.crs, dst_crs, src.width, src.height, *src.bounds\\n )\\n profile = src.profile.copy()\\n profile.update(\\n {\\\"crs\\\": dst_crs, \\\"transform\\\": transform, \\\"width\\\": width, \\\"height\\\": height}\\n )\\n dst_name = f\\\"res_{res}_epsg_{dst_epsg}\\\"\\n os.makedirs(dst_name, exist_ok=True)\\n dst_path = os.path.join(dst_name, f\\\"{dst_name}.tif\\\")\\n with rio.open(dst_path, \\\"w\\\", **profile) as dst:\\n reproject(\\n source=rio.band(src, 1),\\n destination=rio.band(dst, 1),\\n src_transform=src.transform,\\n src_crs=src.crs,\\n dst_transform=dst.transform,\\n dst_crs=dst.crs,\\n )\",\n \"def save_mask2file(mask, filename, crs=None, transform=None):\\n height, width = mask.shape\\n if mask.dtype == np.bool:\\n mask = mask.astype(np.uint8)\\n with rasterio.open(\\n filename,\\n 'w',\\n driver='GTiff',\\n dtype=mask.dtype,\\n count=1,\\n width=width,\\n height=height,\\n crs=crs,\\n transform=transform,\\n ) as dst:\\n dst.write(mask, indexes=1)\",\n \"def geotiff_to_netcdf(path, epsg, download=False, output_path='', name='', non_negative=True, savefig=False,\\n fig_path=''):\\n\\n ds = gdal.Open(path)\\n\\n # upper left, resolution and skew of x and y\\n ulx, xres, xskew, uly, yskew, yres = ds.GetGeoTransform()\\n\\n # lower right x and y\\n lrx = ulx + (ds.RasterXSize * xres)\\n lry = uly + (ds.RasterYSize * yres)\\n\\n # setup the source projection - you can also import from epsg, proj4...\\n source = osr.SpatialReference()\\n source.ImportFromWkt(ds.GetProjection())\\n\\n # the target projection\\n target = osr.SpatialReference()\\n target.ImportFromEPSG(4326)\\n\\n # create the transform - this can be used repeatedly\\n transform = osr.CoordinateTransformation(source, target)\\n\\n # transform the point. You can also create an ogr geometry and use the more generic `point.Transform()`\\n uly, ulx, _ = transform.TransformPoint(ulx, uly)\\n lry, lrx, _ = transform.TransformPoint(lrx, lry)\\n\\n lons = np.linspace(ulx, lrx, ds.RasterXSize)\\n lats = np.linspace(lry, uly, ds.RasterYSize)\\n\\n data = ds.ReadAsArray()\\n\\n if non_negative: data[data < 0] = np.nan\\n\\n ds = xr.DataArray(np.flipud(data), coords={'lat': lats, 'lon': lons, }, dims=['lat', 'lon'])\\n\\n ds.name = name\\n\\n if download and len(output_path) > 0:\\n ds.to_netcdf(output_path)\\n\\n if savefig and len(fig_path) > 0:\\n fig, ax = plt.subplots(figsize=(15, 10))\\n ds.plot(ax=ax)\\n plt.savefig(fig_path)\\n plt.close()\\n\\n return ds\",\n \"def nc2gtiff(input_name, epsg_in=3413, epsg_out=3857):\\n sub_name=[\\\"dX\\\",\\\"dY\\\"]\\n \\n for sub in sub_name:\\n result = os.system(\\\"echo ''\\\")\\n #netCDF to geotiff (EPSG code is not changed) \\n out_name = \\\"./data/tiff_raw/{}_{}_tmp.tiff\\\".format(os.path.basename(input_name)[:-3],sub)\\n #c is shell command\\n #options\\n #-a_srs:EPSGcode of input files\\n #-of:output format\\n #\\n print(\\\"nc2gtiff\\\")\\n c = \\\"gdal_translate -a_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' NETCDF:'{in_name}':{sub} \\\\\\n -of 'Gtiff' \\\\\\n '{out_name}'\\\".format(epsg=epsg_in,sub=sub, \\\\\\n in_name=input_name,out_name=out_name) \\n result = os.system(c) #run shell command\\n #print(c) #if you want to see the filled c, remove '#' in the head of this line\\n if result!=0: # if it raises error, return filename \\n print(input_name, result, \\\"translate\\\")\\n \\n result = os.system(\\\"echo ''\\\")\\n\\n #geotiff EPSG_in to EPCG_out\\n print(\\\"geotiff2geotiff\\\")\\n target_name=\\\"./data/tiff_target/{}_{}.tiff\\\".format(os.path.basename(input_name)[:-3],sub)\\n c = \\\"gdalwarp -overwrite -s_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' {out_name} -r cubic\\\\\\n {target_name} -t_srs EPSG:{epsg_out} -of \\\\\\n 'GTIFF'\\\".format(out_name=out_name, target_name=target_name, \\n epsg_in=epsg_in, epsg_out=epsg_out)\\n result = os.system(c) # run shell command\\n if result!=0:\\n print(input_name, result, \\\"warp\\\")\",\n \"def geotif_image(self, tile_bounds, image_bounds, imagepath,image_gdal):\\n i_srid=3857\\n s_srid=\\\"WGS 84 / Pseudo-Mercator\\\"\\n # i_srid=3395\\n # s_srid=\\\"WGS 84 / World Mercator\\\"\\n # 4326 Wsg84\\n # Upper Left ( -8.4375000, 77.1571625) ( 8d26'15.00\\\"W, 77d 9'25.79\\\"N)\\n # Lower Left ( -8.4375000, 35.4606700) ( 8d26'15.00\\\"W, 35d27'38.41\\\"N)\\n # Upper Right ( 80.1562500, 77.1571625) ( 80d 9'22.50\\\"E, 77d 9'25.79\\\"N)\\n # Lower Right ( 80.1562500, 35.4606700) ( 80d 9'22.50\\\"E, 35d27'38.41\\\"N)\\n # Center ( 35.8593750, 56.3089162) ( 35d51'33.75\\\"E, 56d18'32.10\\\"N)\\n # 3857 'WGS 84 / Pseudo-Mercator'\\n # Upper Left ( -939258.204,13932330.020) ( 8d26'15.00\\\"W, 77d 9'25.79\\\"N)\\n # Lower Left ( -939258.204, 4226661.916) ( 8d26'15.00\\\"W, 35d27'38.41\\\"N)\\n # Upper Right ( 8922952.934,13932330.020) ( 80d 9'22.50\\\"E, 77d 9'25.79\\\"N)\\n # Lower Right ( 8922952.934, 4226661.916) ( 80d 9'22.50\\\"E, 35d27'38.41\\\"N)\\n # Center ( 3991847.365, 9079495.968) ( 35d51'33.75\\\"E, 62d54'54.84\\\"N)\\n # 3395 'WGS 84 / World Mercator'\\n # Upper Left ( -939258.204,13932330.020) ( 8d26'15.00\\\"W, 77d14'24.81\\\"N)\\n # Lower Left ( -939258.204, 4226661.916) ( 8d26'15.00\\\"W, 35d38'33.56\\\"N)\\n # Upper Right ( 8922952.934,13932330.020) ( 80d 9'22.50\\\"E, 77d14'24.81\\\"N)\\n # Lower Right ( 8922952.934, 4226661.916) ( 80d 9'22.50\\\"E, 35d38'33.56\\\"N)\\n # Center ( 3991847.365, 9079495.968) ( 35d51'33.75\\\"E, 63d 4'14.87\\\"N)\\n bounds_west,bounds_south,bounds_east,bounds_north=tile_bounds\\n bounds_wsg84=\\\"bounds_wsg84: %f,%f,%f,%f\\\"% (bounds_west,bounds_south,bounds_east,bounds_north)\\n mercator = GlobalMercator()\\n tile_bounds=mercator.BoundsToMeters(tile_bounds)\\n mbtiles_name=\\\"\\\";\\n mbtiles_description=\\\"\\\"\\n s_TIFFTAG_DOCUMENTNAME=\\\"\\\"\\n s_TIFFTAG_IMAGEDESCRIPTION=\\\"\\\"\\n s_TIFFTAG_SOFTWARE=\\\"\\\"\\n s_TIFFTAG_DATETIME=\\\"\\\"\\n s_TIFFTAG_ARTIST=\\\"\\\"\\n s_TIFFTAG_HOSTCOMPUTER=\\\"\\\"\\n s_TIFFTAG_COPYRIGHT=\\\"\\\"\\n if self.metadata_input:\\n metadata=dict(self.metadata_input)\\n mbtiles_name=metadata.get('name','')\\n mbtiles_description=metadata.get('description','')\\n if self._metadata:\\n for metadata_list in self._metadata:\\n metadata=dict(metadata_list[0])\\n mbtiles_name=metadata.get('name',mbtiles_name)\\n mbtiles_description=metadata.get('description',mbtiles_description)\\n s_TIFFTAG_DOCUMENTNAME=metadata.get('TIFFTAG_DOCUMENTNAME',mbtiles_name)\\n s_TIFFTAG_IMAGEDESCRIPTION=metadata.get('TIFFTAG_IMAGEDESCRIPTION',mbtiles_description)\\n s_TIFFTAG_SOFTWARE=metadata.get('TIFFTAG_SOFTWARE','')\\n s_TIFFTAG_DATETIME=metadata.get('TIFFTAG_DATETIME','')\\n s_TIFFTAG_ARTIST=metadata.get('TIFFTAG_ARTIST','')\\n s_TIFFTAG_HOSTCOMPUTER=metadata.get('TIFFTAG_HOSTCOMPUTER','')\\n s_TIFFTAG_COPYRIGHT=metadata.get('TIFFTAG_COPYRIGHT','')\\n if s_TIFFTAG_DOCUMENTNAME == \\\"\\\":\\n s_TIFFTAG_DOCUMENTNAME=mbtiles_name\\n if s_TIFFTAG_IMAGEDESCRIPTION == \\\"\\\":\\n s_TIFFTAG_IMAGEDESCRIPTION=mbtiles_description\\n tiff_metadata=[]\\n if s_TIFFTAG_DOCUMENTNAME != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_DOCUMENTNAME',s_TIFFTAG_DOCUMENTNAME))\\n if s_TIFFTAG_IMAGEDESCRIPTION != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_IMAGEDESCRIPTION',s_TIFFTAG_IMAGEDESCRIPTION))\\n if s_TIFFTAG_SOFTWARE != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_SOFTWARE',s_TIFFTAG_SOFTWARE))\\n else:\\n tiff_metadata.append(('TIFFTAG_SOFTWARE',bounds_wsg84))\\n if s_TIFFTAG_DATETIME != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_DATETIME',s_TIFFTAG_DATETIME))\\n if s_TIFFTAG_ARTIST != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_ARTIST',s_TIFFTAG_ARTIST))\\n if s_TIFFTAG_HOSTCOMPUTER != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_HOSTCOMPUTER',s_TIFFTAG_HOSTCOMPUTER))\\n if s_TIFFTAG_COPYRIGHT != \\\"\\\":\\n tiff_metadata.append(('TIFFTAG_COPYRIGHT',s_TIFFTAG_COPYRIGHT))\\n # this assumes the projection is Geographic lat/lon WGS 84\\n xmin,ymin,xmax,ymax=tile_bounds\\n image_width,image_height=image_bounds\\n # Upper Left ( 20800.000, 22000.000)\\n # Lower Right ( 24000.000, 19600.000)\\n # Size is 15118, 11339\\n # (24000-20800)/15118 = 3200 = 0,21166821 [xres]\\n # (19600-22000)/11339 = 2400 = −0,211658876 [yres]\\n # geo_transform = (20800.0, 0.2116682100807, 0.0, 22000.0, 0.0, -0.21165887644413)\\n geo_transform = [xmin, (xmax-xmin)/image_width, 0, ymax, 0, (ymin-ymax)/image_height ]\\n spatial_projection = osr.SpatialReference()\\n spatial_projection.ImportFromEPSG(i_srid)\\n logger.info(_(\\\"-I-> geotif_image: Saving as GeoTiff - image[%s] compression[%s]\\\") % (imagepath,self.tiff_compression))\\n image_dataset = gdal.Open(image_gdal, gdal.GA_Update )\\n image_dataset.SetProjection(spatial_projection.ExportToWkt())\\n image_dataset.SetGeoTransform(geo_transform)\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n output_dataset = driver.CreateCopy(imagepath,image_dataset, 0, self.tiff_compression )\\n if tiff_metadata:\\n logger.info(_(\\\"-I-> geotif_image: tiff_metadata[%s]\\\") % tiff_metadata)\\n output_dataset.SetMetadata(dict(tiff_metadata))\\n # Once we're done, close properly the dataset\\n output_dataset = None\\n image_dataset = None\\n os.remove(image_gdal)\\n logger.info(_(\\\"-I-> geotif_image: Saved resulting image to '%s' as GeoTiff- bounds[%s]\\\") % (imagepath,tile_bounds))\",\n \"def export_image(self, bbox, zoomlevel, imagepath):\\n assert has_pil, _(\\\"Cannot export image without python PIL\\\")\\n grid,tile_bounds = self.grid_tiles(bbox, zoomlevel)\\n width = len(grid[0])\\n height = len(grid)\\n widthpix = width * self.tile_size\\n heightpix = height * self.tile_size\\n result = Image.new(\\\"RGBA\\\", (widthpix, heightpix))\\n offset = (0, 0)\\n for i, row in enumerate(grid):\\n for j, (x, y) in enumerate(row):\\n offset = (j * self.tile_size, i * self.tile_size)\\n img = self._tile_image(self.tile((zoomlevel, x, y)))\\n result.paste(img, offset)\\n if imagepath.endswith(\\\".tif\\\") or imagepath.endswith(\\\".tiff\\\"):\\n # http://effbot.org/imagingbook/pil-index.htm#appendixes\\n # Packbits, LZW, or JPEG\\n # In the current version, PIL always writes uncompressed TIFF files.\\n image_gdal=\\\"gdal_input.tif\\\"\\n result.save(image_gdal, format=\\\"TIFF\\\", compression=\\\"JPEG\\\")\\n self.geotif_image(tile_bounds,(widthpix,heightpix),imagepath,image_gdal)\\n else:\\n if imagepath.endswith(\\\".jpg\\\") or imagepath.endswith(\\\".jpeg\\\"):\\n # IOError: encoder error -2 when writing image file\\n result.save(imagepath, format=\\\"JPEG\\\", quality=int(self.jpg_quality), optimize=True, progressive=False)\\n elif imagepath.endswith(\\\".png\\\") :\\n result.save(imagepath, format=\\\"PNG\\\",optimize=True)\\n else:\\n result.save(imagepath)\\n logger.info(_(\\\"-I-> export_image: Save resulting image to '%s' - bounds[%s]\\\") % (imagepath,tile_bounds))\",\n \"def extent_to_gdal_transform(extent):\\n\\n # extent = SpatialExtent()\\n\\n pixel_height = math.copysign(extent.height, extent.bottom - extent.top)\\n pixel_width = math.copysign(extent.width, extent.right - extent.left)\\n\\n transform = [extent.top, pixel_width, 0, extent.left, 0, pixel_height]\\n return transform\",\n \"def export_array(in_array, output_path):\\n global proj, geotrans, row, col\\n proj = band.GetProjection()\\n geotrans = band.GetGeoTransform()\\n row = band.RasterYSize\\n col = band.RasterXSize\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n outdata = driver.Create(output_path, col, row, 1)\\n outband = outdata.GetRasterBand(1)\\n outband.SetNoDataValue(-9999)\\n outband.WriteArray(in_array)\\n # Georeference the image\\n outdata.SetGeoTransform(geotrans)\\n # Write projection information\\n outdata.SetProjection(proj)\\n outdata.FlushCache()\\n outdata = None\",\n \"def align_rasters(ref_raster, tar_raster, output_suffix):\\n command = [\\\"gdalbuildvrt\\\", \\\"-te\\\"]\\n hDataset = gdal.Open(ref_raster, gdal.GA_ReadOnly)\\n if hDataset is None:\\n return False\\n adfGeoTransform = hDataset.GetGeoTransform(can_return_null=True)\\n\\n tif_file=tar_raster\\n vrt_file = tif_file.replace('.tif', '.vrt')\\n\\n if adfGeoTransform is not None:\\n dfGeoXUL = adfGeoTransform[0]\\n dfGeoYUL = adfGeoTransform[3]\\n dfGeoXLR = adfGeoTransform[0] + adfGeoTransform[1] * hDataset.RasterXSize + \\\\\\n adfGeoTransform[2] * hDataset.RasterYSize\\n dfGeoYLR = adfGeoTransform[3] + adfGeoTransform[4] * hDataset.RasterXSize + \\\\\\n adfGeoTransform[5] * hDataset.RasterYSize\\n xres = str(abs(adfGeoTransform[1]))\\n yres = str(abs(adfGeoTransform[5]))\\n\\n subprocess.call(command + [str(dfGeoXUL), str(dfGeoYLR), str(dfGeoXLR),\\n str(dfGeoYUL), \\\"-q\\\", \\\"-tr\\\", xres, yres,\\n vrt_file, tif_file])\\n\\n output_file = tif_file.replace('.tif', output_suffix)\\n\\n print('gdal_translate -q {} {}'.format(vrt_file, output_file))\\n\\n cmd = 'gdal_translate -q {} {}'.format(vrt_file, output_file)\\n\\n #print(dfGeoXUL, dfGeoYLR, dfGeoXLR, dfGeoYUL, xres, yres)\\n\\n subprocess.call(cmd, shell=True)\\n os.remove(vrt_file)\\n\\n return True\\n\\n else:\\n\\n return False\",\n \"def reproject_dataset(filename):\\n\\n # We want to project the GeoTIFF coordinate reference system (crs)\\n # to WGS84 (e.g. into the familiar Lat/Lon pairs). WGS84 is analogous\\n # to EPSG:4326\\n dst_crs = 'EPSG:4326'\\n\\n with rasterio.open(filename) as src:\\n transform, width, height = rasterio.warp.calculate_default_transform(\\n src.crs, dst_crs, src.width, src.height, *src.bounds)\\n kwargs = src.meta.copy()\\n kwargs.update({\\n 'crs': dst_crs,\\n 'transform': transform,\\n 'width': width,\\n 'height': height,\\n 'compress': 'lzw'\\n })\\n\\n out_path = filename.split('.tif')[0] + '.wgs84.tif'\\n with rasterio.open(out_path, 'w', **kwargs) as dst:\\n for i in range(1, src.count + 1):\\n rasterio.warp.reproject(\\n source=rasterio.band(src, i),\\n destination=rasterio.band(dst, i),\\n src_transform=src.transform,\\n src_crs=src.crs,\\n dst_transform=transform,\\n dst_crs=dst_crs,\\n resampling=rasterio.warp.Resampling.nearest)\\n\\n return out_path\",\n \"def write_raster(\\n res: int = RES[0],\\n epsg: int = EPSG[0],\\n dtype: str = \\\"uint8\\\",\\n path: Optional[str] = None,\\n) -> None:\\n size = SIZE // res\\n profile = {\\n \\\"driver\\\": \\\"GTiff\\\",\\n \\\"dtype\\\": dtype,\\n \\\"count\\\": 1,\\n \\\"crs\\\": f\\\"epsg:{epsg}\\\",\\n \\\"transform\\\": from_bounds(0, 0, SIZE, SIZE, size, size),\\n \\\"height\\\": size,\\n \\\"width\\\": size,\\n \\\"nodata\\\": 0,\\n }\\n\\n if path is None:\\n name = f\\\"res_{res}_epsg_{epsg}\\\"\\n path = os.path.join(name, f\\\"{name}.tif\\\")\\n\\n directory = os.path.dirname(path)\\n os.makedirs(directory, exist_ok=True)\\n\\n with rio.open(path, \\\"w\\\", **profile) as f:\\n x = np.ones((1, size, size))\\n f.write(x)\",\n \"def save_tile_img(tif, xyz, dataset, tile_size, region, zone, save_path, display=False):\\n \\n prefix = f'{region}{zone}{dataset}_'\\n x,y,z = xyz\\n tile, mask = rt_main.tile(tif, x,y,z, tilesize=tile_size)\\n if display: \\n plt.imshow(np.moveaxis(tile,0,2))\\n plt.show()\\n \\n skimage.io.imsave(f'{save_path}/{prefix}{z}_{x}_{y}.png',np.moveaxis(tile,0,2), check_contrast=False)\",\n \"def make_raster(in_ds, fn, data, data_type, nodata=None):\\n driver = gdal.GetDriverByName('GTiff')\\n out_ds = driver.Create(\\n fn, in_ds.RasterXSize, in_ds.RasterYSize, 1, data_type)\\n out_ds.SetProjection(in_ds.GetProjection())\\n out_ds.SetGeoTransform(in_ds.GetGeoTransform())\\n out_band = out_ds.GetRasterBand(1)\\n if nodata is not None:\\n out_band.SetNoDataValue(nodata)\\n out_band.WriteArray(data)\\n out_band.FlushCache()\\n out_band.ComputeStatistics(False)\\n return out_ds\",\n \"def create_raster_from_matrix(matrix,outfile,extent=None,cellsize=CELL_SIZE,format='GTiff',srid=settings.GEOMETRY_DB_SRID):\\n if extent is None:\\n extent = StudyRegion.objects.current().geometry.extent\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(srid)\\n ydist = extent[3] - extent[1]\\n xdist = extent[2] - extent[0]\\n xcount = int((xdist/cellsize)+1)\\n ycount = int((ydist/cellsize)+1)\\n\\n # Create output raster \\n driver = gdal.GetDriverByName( format )\\n dst_ds = driver.Create( outfile, xcount, ycount, 1, gdal.GDT_UInt16 )\\n\\n # This is bizzarly complicated\\n # the GT(2) and GT(4) coefficients are zero, \\n # and the GT(1) is pixel width, and GT(5) is pixel height. \\n # The (GT(0),GT(3)) position is the top left corner of the top left pixel\\n gt = (extent[0],cellsize,0,extent[3],0,(cellsize*-1.))\\n dst_ds.SetGeoTransform(gt)\\n dst_ds.SetProjection( srs.ExportToWkt() )\\n\\n dst_band = dst_ds.GetRasterBand(1)\\n dst_band.WriteArray(matrix,0,0)\\n dst_band.SetNoDataValue(0.0)\\n min_val, max_val = dst_band.ComputeRasterMinMax()\\n start_color = (255,255,0,160)\\n end_color = (255,0,0,160)\\n ct = gdal.ColorTable()\\n ct.CreateColorRamp(int(min_val),start_color,int(max_val),end_color)\\n dst_band.SetColorTable(ct)\\n dst_ds = None\\n return outfile\",\n \"def output_rasters(self, arr, outdir, outname):\\n\\n outpath = os.path.join(outdir, outname)\\n print('the outpath for file {} is {}'.format(outname, outpath))\\n\\n # get the geoinfo from sample tiff to output intermediate files\\n ds = rasterio.open(self.geoproperties_file)\\n band1 = arr\\n with rasterio.open(outpath, 'w', driver='GTiff', height=self.rows, width=self.cols,\\n count=1, dtype='float64', crs=self.crs, transform=self.transform) as wrast:\\n wrast.write(band1, indexes=1)\\n\\n # TODO - Set an AWS Cloud flag in the config_dict file to activate this function or not...\\n # delete files created locally and put in bucket\\n # PathManager.s3_delete_local(from_file, bucket, prefix_no_slash)\",\n \"def gdal_read_geotiff_file(sFilename_in):\\n \\n if os.path.exists(sFilename_in):\\n pass\\n else:\\n print('The file does not exist!')\\n return\\n\\n sDriverName='GTiff'\\n pDriver = gdal.GetDriverByName(sDriverName) \\n\\n if pDriver is None:\\n print (\\\"%s pDriver not available.\\\\n\\\" % sDriverName)\\n else:\\n print (\\\"%s pDriver IS available.\\\\n\\\" % sDriverName) \\n\\n pDataset = gdal.Open(sFilename_in, gdal.GA_ReadOnly)\\n\\n if pDataset is None:\\n print(\\\"Couldn't open this file: \\\" + sFilename_in)\\n sys.exit(\\\"Try again!\\\")\\n else: \\n pProjection = pDataset.GetProjection()\\n\\n pDataset.GetMetadata()\\n \\n ncolumn = pDataset.RasterXSize\\n nrow = pDataset.RasterYSize\\n nband = pDataset.RasterCount\\n\\n pGeotransform = pDataset.GetGeoTransform()\\n dOriginX = pGeotransform[0]\\n dOriginY = pGeotransform[3]\\n dPixelWidth = pGeotransform[1]\\n pPixelHeight = pGeotransform[5]\\n\\n pBand = pDataset.GetRasterBand(1)\\n\\n # Data type of the values\\n gdal.GetDataTypeName(pBand.DataType)\\n # Compute statistics if needed\\n if pBand.GetMinimum() is None or pBand.GetMaximum() is None:\\n pBand.ComputeStatistics(0)\\n\\n dMissing_value = pBand.GetNoDataValue()\\n \\n aData_out = pBand.ReadAsArray(0, 0, ncolumn, nrow)\\n \\n #we will use one of them to keep the consistency\\n pSpatial_reference = osr.SpatialReference(wkt=pProjection)\\n \\n\\n pDataset = None\\n pBand = None \\n pBand = None\\n\\n return aData_out, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection, pSpatial_reference\",\n \"def process_image(overviews, db_graph, input_filename, color, out_raster_srs):\\n if verbose > 0:\\n print(\\\"~~~process_image\\\")\\n input_image = gdal.Open(input_filename)\\n stem = Path(input_filename).stem\\n if not(\\\"dataSet\\\" in overviews):\\n overviews['dataSet'] = {}\\n overviews['dataSet']['boundingBox'] = {}\\n overviews['dataSet']['limits'] = {}\\n\\n tile_limits = get_tile_limits(input_filename)\\n\\n if not(\\\"LowerCorner\\\" in overviews['dataSet']['boundingBox']):\\n overviews['dataSet']['boundingBox'] = tile_limits\\n else:\\n if tile_limits['LowerCorner'][0] < overviews['dataSet']['boundingBox']['LowerCorner'][0]:\\n overviews['dataSet']['boundingBox']['LowerCorner'][0] = tile_limits['LowerCorner'][0]\\n if tile_limits['LowerCorner'][1] < overviews['dataSet']['boundingBox']['LowerCorner'][1]:\\n overviews['dataSet']['boundingBox']['LowerCorner'][1] = tile_limits['LowerCorner'][1]\\n if tile_limits['UpperCorner'][0] > overviews['dataSet']['boundingBox']['UpperCorner'][0]:\\n overviews['dataSet']['boundingBox']['UpperCorner'][0] = tile_limits['UpperCorner'][0]\\n if tile_limits['UpperCorner'][1] > overviews['dataSet']['boundingBox']['UpperCorner'][1]:\\n overviews['dataSet']['boundingBox']['UpperCorner'][1] = tile_limits['UpperCorner'][1]\\n\\n # for z in tiles:\\n for tile_z in range(overviews['level']['min'], overviews['level']['max'] + 1):\\n print('Niveau de zoom : ', tile_z)\\n\\n resolution = overviews['resolution'] * 2 ** (overviews['level']['max'] - tile_z)\\n\\n MinTileCol = \\\\\\n math.floor(round((tile_limits['LowerCorner'][0] - overviews['crs']['boundingBox']['xmin'])/(resolution*overviews['tileSize']['width']),8))\\n MinTileRow = \\\\\\n math.floor(round((overviews['crs']['boundingBox']['ymax']-tile_limits['UpperCorner'][1])/(resolution*overviews['tileSize']['height']),8))\\n MaxTileCol = \\\\\\n math.ceil(round((tile_limits['UpperCorner'][0] - overviews['crs']['boundingBox']['xmin'])/(resolution*overviews['tileSize']['width']),8)) - 1\\n MaxTileRow = \\\\\\n math.ceil(round((overviews['crs']['boundingBox']['ymax']-tile_limits['LowerCorner'][1])/(resolution*overviews['tileSize']['height']),8)) - 1\\n\\n if not( str(tile_z) in overviews['dataSet']['limits'] ):\\n overviews['dataSet']['limits'][str(tile_z)] = {\\n 'MinTileCol': MinTileCol,\\n 'MinTileRow': MinTileRow,\\n 'MaxTileCol': MaxTileCol,\\n 'MaxTileRow': MaxTileRow,\\n }\\n\\n else:\\n if MinTileCol < overviews['dataSet']['limits'][str(tile_z)]['MinTileCol']:\\n overviews['dataSet']['limits'][str(tile_z)]['MinTileCol'] = MinTileCol\\n if MinTileRow < overviews['dataSet']['limits'][str(tile_z)]['MinTileRow']:\\n overviews['dataSet']['limits'][str(tile_z)]['MinTileRow'] = MinTileRow\\n if MaxTileCol > overviews['dataSet']['limits'][str(tile_z)]['MaxTileCol']:\\n overviews['dataSet']['limits'][str(tile_z)]['MaxTileCol'] = MaxTileCol\\n if MaxTileRow > overviews['dataSet']['limits'][str(tile_z)]['MaxTileRow']:\\n overviews['dataSet']['limits'][str(tile_z)]['MaxTileRow'] = MaxTileRow\\n\\n for tile_x in range(MinTileCol, MaxTileCol + 1): \\n for tile_y in range(MinTileRow, MaxTileRow + 1):\\n # on cree une image 3 canaux pour la tuile\\n opi = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\\n # on reech l'OPI dans cette image\\n gdal.Warp(opi, input_image)\\n # si necessaire on cree le dossier de la tuile\\n tile_dir = args.cache+'/'+str(tile_z)+'/'+str(tile_y)+'/'+str(tile_x)\\n Path(tile_dir).mkdir(parents=True, exist_ok=True)\\n # on export en jpeg (todo: gerer le niveau de Q)\\n PNG_DRIVER.CreateCopy(tile_dir+\\\"/\\\"+stem+\\\".png\\\", opi)\\n # on cree une image mono canal pour la tuile\\n mask = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\\n # on rasterise la partie du graphe qui concerne ce cliche\\n gdal.Rasterize(mask, db_graph,\\n SQLStatement='select geom from ' + args.table + ' where cliche = \\\\''+stem+'\\\\' ')\\n img_mask = mask.GetRasterBand(1).ReadAsArray()\\n # si le mask est vide, on a termine\\n val_max = np.amax(img_mask)\\n if val_max > 0:\\n # on cree le graphe et l'ortho\\n ortho = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\\n graph = create_blank_tile(overviews, {'x': tile_x, 'y': tile_y, 'resolution': resolution}, 3, out_raster_srs)\\n if Path(tile_dir+\\\"/ortho.png\\\").is_file():\\n existing_ortho = gdal.Open(tile_dir+\\\"/ortho.png\\\")\\n existing_graph = gdal.Open(tile_dir+\\\"/graph.png\\\")\\n else:\\n existing_ortho = False\\n existing_graph = False\\n for i in range(3):\\n opi_i = opi.GetRasterBand(i+1).ReadAsArray()\\n if existing_ortho:\\n ortho_i = existing_ortho.GetRasterBand(i+1).ReadAsArray()\\n else:\\n ortho_i = ortho.GetRasterBand(i+1).ReadAsArray()\\n opi_i[(img_mask == 0)] = 0\\n ortho_i[(img_mask != 0)] = 0\\n ortho.GetRasterBand(i+1).WriteArray(np.add(opi_i, ortho_i))\\n if existing_graph:\\n graph_i = existing_graph.GetRasterBand(i+1).ReadAsArray()\\n else:\\n graph_i = graph.GetRasterBand(i+1).ReadAsArray()\\n graph_i[(img_mask != 0)] = color[i]\\n graph.GetRasterBand(i+1).WriteArray(graph_i)\\n existing_ortho = None\\n existing_graph = None\\n PNG_DRIVER.CreateCopy(tile_dir+\\\"/ortho.png\\\", ortho)\\n PNG_DRIVER.CreateCopy(tile_dir+\\\"/graph.png\\\", graph)\",\n \"def Projected(InputFilePath,OutputFilePath): # perform a reproject raster onto a DEM and return\\r\\n try:\\r\\n print(\\\"\\\"\\\"\\r\\nReprojecting Raster...\\r\\n \\\"\\\"\\\") \\r\\n \\r\\n arcpy.ProjectRaster_management(in_raster=InputFilePath,out_raster=OutputFilePath,out_coor_system=\\\"PROJCS['NAD_1983_StatePlane_California_III_FIPS_0403',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',2000000.0],PARAMETER['False_Northing',500000.0],PARAMETER['Central_Meridian',-120.5],PARAMETER['Standard_Parallel_1',37.06666666666667],PARAMETER['Standard_Parallel_2',38.43333333333333],PARAMETER['Latitude_Of_Origin',36.5],UNIT['Meter',1.0]]\\\",resampling_type=\\\"NEAREST\\\",cell_size=\\\"27.8165597364916 27.8165597364914\\\",geographic_transform=\\\"WGS_1984_(ITRF00)_To_NAD_1983\\\",Registration_Point=\\\"#\\\",in_coor_system=\\\"GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.257223563]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]]\\\")\\r\\n print(\\\"Complete\\\")\\r\\n \\r\\n except Exception, err: # an error occurred (probably in arcGIS)\\r\\n raise RuntimeError(\\\"** Error: Project Raster Failed (\\\"+str(err)+\\\")\\\")\",\n \"def write(self, p_out, **kwargs):\\n nodata = kwargs.pop(\\\"nodata\\\", self.nodata_value)\\n driver = kwargs.pop(\\\"driver\\\", \\\"GTiff\\\")\\n return ImageIO.gdal_write(driver, self.array, p_out, self.projection, self.geotransform,\\n nodata=nodata, **kwargs)\",\n \"def ascii_to_tiff(infile, outfile, refIm):\",\n \"def array_to_raster_noTi(x_pix,y_pix,pixSize,x_min,y_max,proj,array,outFile):\\n \\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n outDataset = driver.Create(\\n outFile,\\n x_pix,\\n y_pix,\\n 1,\\n gdal.GDT_Float32, )\\n\\n outDataset.SetGeoTransform((\\n x_min, # 0 * top left border of pixel\\n pixSize, # 1\\n 0, # 2\\n y_max, # 3 top left border of pixel\\n 0, # 4\\n -pixSize)) \\n\\n projx = osr.SpatialReference()\\n projx.SetWellKnownGeogCS( proj ) #Get the long coordinate system name\\n wkt_projection = projx.ExportToWkt()\\n\\n outDataset.SetProjection(wkt_projection)\\n outDataset.GetRasterBand(1).WriteArray(array)\\n outDataset.FlushCache() # Write to disk.\\n return outDataset, outDataset.GetRasterBand(1) #If you need to return, remenber to return also the dataset because the band don`t live without dataset.\",\n \"def geotiff_read(ifile,metaData):\\r\\n\\r\\n file = gdal.Open(ifile, GA_ReadOnly)\\r\\n\\r\\n projection = file.GetProjection()\\r\\n src = osr.SpatialReference()\\r\\n src.ImportFromWkt(projection)\\r\\n proj = src.ExportToWkt()\\r\\n\\r\\n Nx = file.RasterXSize\\r\\n Ny = file.RasterYSize\\r\\n\\r\\n trans = file.GetGeoTransform()\\r\\n\\r\\n dx = trans[1]\\r\\n dy = trans[5]\\r\\n\\r\\n if metaData == \\\"A\\\":\\r\\n\\r\\n xp = np.arange(Nx)\\r\\n yp = np.arange(Ny)\\r\\n\\r\\n (Xp, Yp) = np.meshgrid(xp,yp)\\r\\n\\r\\n X = trans[0] + (Xp+0.5)*trans[1] + (Yp+0.5)*trans[2] #FIXME: bottleneck!\\r\\n Y = trans[3] + (Xp+0.5)*trans[4] + (Yp+0.5)*trans[5]\\r\\n\\r\\n if metaData == \\\"P\\\":\\r\\n\\r\\n xp = np.arange(Nx)\\r\\n yp = np.arange(Ny)\\r\\n\\r\\n (Xp, Yp) = np.meshgrid(xp,yp)\\r\\n\\r\\n X = trans[0] + Xp*trans[1] + Yp*trans[2] #FIXME: bottleneck!\\r\\n Y = trans[3] + Xp*trans[4] + Yp*trans[5]\\r\\n\\r\\n band = file.GetRasterBand(1)\\r\\n\\r\\n Z = band.ReadAsArray()\\r\\n\\r\\n dx = np.abs(dx)\\r\\n dy = np.abs(dy)\\r\\n\\r\\n return X, Y, Z, dx, dy, proj\",\n \"def save_tile_mask(label_poly_series, tile_poly, xyz, tile_size, dataset, region, zone, save_path, channels = 3, display=False):\\n \\n \\n\\n prefix = f'{region}{zone}{dataset}_'\\n x,y,z = xyz\\n tfm = from_bounds(*tile_poly.bounds, tile_size, tile_size) \\n \\n cropped_polys = [poly for poly in label_poly_series if poly.intersects(tile_poly)]\\n cropped_polys_gdf = gpd.GeoDataFrame(geometry=cropped_polys, crs={'init': 'epsg:4326'})\\n \\n fbc_mask = burn_mask(cropped_polys_gdf, tfm, tile_size, channels)\\n # fbc_mask = sol.vector.mask.df_to_px_mask(df=cropped_polys_gdf,\\n # channels=['footprint', 'boundary', 'contact'],\\n # affine_obj=tfm, shape=(tile_size,tile_size),\\n # boundary_width=5, boundary_type='inner', contact_spacing=5, meters=True)\\n \\n if display: \\n plt.imshow(fbc_mask); plt.show()\\n \\n skimage.io.imsave(f'{save_path}/{prefix}{z}_{x}_{y}_mask.png',fbc_mask, check_contrast=False)\",\n \"def test_make_mask_w_file_and_transform(self):\\n output_mask = footprint_mask(\\n os.path.join(data_dir, 'geotiff_labels.geojson'),\\n reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\\n do_transform=True,\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(\\n os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif')\\n )\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(output_mask, truth_mask)\\n assert np.array_equal(saved_output_mask, truth_mask)\\n # clean up\\n os.remove(os.path.join(data_dir, 'test_out.tif'))\",\n \"def save_grd(filename, meta, map):\\n if os.path.exists(filename):\\n raise ValueError(\\\"File already exists: {}\\\".format(filename))\\n if map.shape != (meta['NX'], meta['NY'], meta['NCOMP']):\\n raise ValueError(\\\"The map shape does not match the metadata dictionary.\\\")\\n points = meta['NX'] * meta['NY']\\n components = meta['NCOMP']\\n data = np.empty((points, 2 * components))\\n for component in range(components):\\n data[:, 2 * component] = map[:, :, component].reshape(points, order='F').real\\n data[:, 2 * component + 1] = map[:, :, component].reshape(points, order='F').imag\\n with open(filename, 'w') as f:\\n for line in meta['header']:\\n f.write('{}\\\\n'.format(line))\\n f.write('{:2d}\\\\n'.format(meta['KTYPE']))\\n f.write('{:12d}{:12d}{:12d}{:12d}\\\\n'.format(meta['NSET'], meta['ICOMP'], meta['NCOMP'], meta['IGRID']))\\n f.write('{:12d}{:12d}\\\\n'.format(meta['IX'], meta['IY']))\\n f.write(' {: 0.10E} {: 0.10E} {: 0.10E} {: 0.10E}\\\\n'.format(meta['XS'], meta['YS'], meta['XE'], meta['YE']))\\n f.write('{:12d}{:12d}{:12d}\\\\n'.format(meta['NX'], meta['NY'], meta['KLIMIT']))\\n for p in range(points):\\n f.write(''.join([float_to_string(number) for number in data[p, :]]) + '\\\\n')\",\n \"def test_rasters_created_with_projected_srs(self):\\n\\n # Create test data\\n x_ul = 220534 # x value of upper left corner\\n y_ul = 827790 # y_value of upper left corner\\n numx = 8 # Number of xs\\n numy = 5 # Number of ys\\n dx = 200\\n dy = -200\\n\\n # Define array where ys are rows and xs columns\\n A1 = numpy.zeros((numy, numx))\\n\\n # Establish coordinates for lower left corner\\n y_ll = y_ul - numy * dy\\n x_ll = x_ul\\n\\n # Define pixel centers along each direction\\n x = numpy.linspace(x_ll + 0.5, x_ll + numx - 0.5, numx)\\n y = numpy.linspace(y_ll + 0.5, y_ll + numy - 0.5, numy)\\n\\n # Define raster with latitudes going bottom-up (south to north).\\n # Longitudes go left-right (west to east)\\n for i in range(numy):\\n for j in range(numx):\\n A1[numy - 1 - i, j] = linear_function(x[j], y[i])\\n\\n # Throw in a nodata element\\n A1[2, 6] = numpy.nan\\n\\n # Upper left corner\\n assert A1[0, 0] == linear_function(x[0], y[4])\\n\\n # Lower left corner\\n assert A1[4, 0] == linear_function(x[0], y[0])\\n\\n # Upper right corner\\n assert A1[0, 7] == linear_function(x[7], y[4])\\n\\n # Lower right corner\\n assert A1[4, 7] == linear_function(x[7], y[0])\\n\\n # Generate raster object and write\\n projection = \\\"\\\"\\\"PROJCS[\\\"DGN95 / Indonesia TM-3 zone 48.2\\\",\\n GEOGCS[\\\"DGN95\\\",\\n DATUM[\\\"Datum_Geodesi_Nasional_1995\\\",\\n SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,\\n AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],\\n TOWGS84[0,0,0,0,0,0,0],\\n AUTHORITY[\\\"EPSG\\\",\\\"6755\\\"]],\\n PRIMEM[\\\"Greenwich\\\",0,\\n AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],\\n UNIT[\\\"degree\\\",0.01745329251994328,\\n AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],\\n AUTHORITY[\\\"EPSG\\\",\\\"4755\\\"]],\\n UNIT[\\\"metre\\\",1,\\n AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],\\n PROJECTION[\\\"Transverse_Mercator\\\"],\\n PARAMETER[\\\"latitude_of_origin\\\",0],\\n PARAMETER[\\\"central_meridian\\\",106.5],\\n PARAMETER[\\\"scale_factor\\\",0.9999],\\n PARAMETER[\\\"false_easting\\\",200000],\\n PARAMETER[\\\"false_northing\\\",1500000],\\n AUTHORITY[\\\"EPSG\\\",\\\"23834\\\"],\\n AXIS[\\\"X\\\",EAST],\\n AXIS[\\\"Y\\\",NORTH]]\\\"\\\"\\\"\\n\\n geotransform = (x_ul, dx, 0, y_ul, 0, dy)\\n R1 = Raster(A1, projection, geotransform,\\n keywords={'testkwd': 'testval', 'size': 'small'})\\n\\n # Check string representation of raster class\\n assert str(R1).startswith('Raster data')\\n assert str(R1.rows) in str(R1)\\n assert str(R1.columns) in str(R1)\\n\\n assert nanallclose(R1.get_data(), A1, rtol=1.0e-12)\\n assert nanallclose(R1.get_geotransform(), geotransform,\\n rtol=1.0e-12)\\n assert 'DGN95' in R1.get_projection()\",\n \"def get_zone_pixels(feat, input_zone_polygon, input_value_raster, band, coords=[]): #, raster_band\\n \\n \\n \\n # Open data\\n raster = gdal.Open(input_value_raster)\\n shp = ogr.Open(input_zone_polygon)\\n lyr = shp.GetLayer()\\n \\n # Get raster georeference info\\n transform = raster.GetGeoTransform()\\n xOrigin = transform[0]\\n yOrigin = transform[3]\\n pixelWidth = transform[1]\\n pixelHeight = transform[5]\\n \\n sizeX = raster.RasterXSize\\n sizeY = raster.RasterYSize\\n lrx = xOrigin + (sizeX * pixelWidth)\\n lry = yOrigin + (sizeY * pixelHeight)\\n \\n \\n \\n # Reproject vector geometry to same projection as raster\\n #sourceSR = lyr.GetSpatialRef()\\n #targetSR = osr.SpatialReference()\\n #targetSR.ImportFromWkt(raster.GetProjectionRef())\\n #coordTrans = osr.CoordinateTransformation(sourceSR,targetSR)\\n #feat = lyr.GetNextFeature()\\n #geom = feat.GetGeometryRef()\\n #geom.Transform(coordTrans)\\n \\n # Get extent of feat\\n geom = feat.GetGeometryRef()\\n if (geom.GetGeometryName() == 'MULTIPOLYGON'):\\n count = 0\\n pointsX = []; pointsY = []\\n for polygon in geom:\\n geomInner = geom.GetGeometryRef(count)\\n ring = geomInner.GetGeometryRef(0)\\n numpoints = ring.GetPointCount()\\n for p in range(numpoints):\\n lon, lat, z = ring.GetPoint(p)\\n pointsX.append(lon)\\n pointsY.append(lat)\\n count += 1\\n elif (geom.GetGeometryName() == 'POLYGON'):\\n ring = geom.GetGeometryRef(0)\\n numpoints = ring.GetPointCount()\\n pointsX = []; pointsY = []\\n for p in range(numpoints):\\n lon, lat, z = ring.GetPoint(p)\\n pointsX.append(lon)\\n pointsY.append(lat)\\n\\n else:\\n sys.exit(\\\"ERROR: Geometry needs to be either Polygon or Multipolygon\\\")\\n\\n #xmin = min(pointsX) \\n #xmax = max(pointsX)\\n #ymin = min(pointsY)\\n #ymax = max(pointsY)\\n \\n \\n if len(coords) == 0: \\n xmin = xOrigin if (min(pointsX) < xOrigin) else min(pointsX)\\n xmax = lrx if (max(pointsX) > lrx) else max(pointsX)\\n ymin = lry if (min(pointsY) < lry) else min(pointsY)\\n ymax = yOrigin if (max(pointsY) > yOrigin) else max(pointsY)\\n else:\\n xmin = coords[0] if (min(pointsX) < coords[0]) else min(pointsX)\\n xmax = coords[1] if (max(pointsX) > coords[1]) else max(pointsX)\\n ymin = coords[2] if (min(pointsY) < coords[2]) else min(pointsY)\\n ymax = coords[3] if (max(pointsY) > coords[3]) else max(pointsY)\\n \\n # Specify offset and rows and columns to read\\n xoff = int((xmin - xOrigin)/pixelWidth)\\n yoff = int((yOrigin - ymax)/pixelWidth)\\n xcount = int((xmax - xmin)/pixelWidth) #+1 !!!!!!!!!!!!!!!!!!!!! This adds a pixel to the right side\\n ycount = int((ymax - ymin)/pixelWidth) #+1 !!!!!!!!!!!!!!!!!!!!! This adds a pixel to the bottom side\\n \\n #print(xoff, yoff, xcount, ycount)\\n \\n # Create memory target raster\\n target_ds = gdal.GetDriverByName('MEM').Create('', xcount, ycount, 1, gdal.GDT_Byte)\\n target_ds.SetGeoTransform((\\n xmin, pixelWidth, 0,\\n ymax, 0, pixelHeight,\\n ))\\n\\n # Create for target raster the same projection as for the value raster\\n raster_srs = osr.SpatialReference()\\n raster_srs.ImportFromWkt(raster.GetProjectionRef())\\n target_ds.SetProjection(raster_srs.ExportToWkt())\\n\\n # Rasterize zone polygon to raster\\n gdal.RasterizeLayer(target_ds, [1], lyr, burn_values=[1])\\n\\n # Read raster as arrays\\n dataBandRaster = raster.GetRasterBand(band)\\n data = dataBandRaster.ReadAsArray(xoff, yoff, xcount, ycount).astype(np.float)\\n bandmask = target_ds.GetRasterBand(1)\\n datamask = bandmask.ReadAsArray(0, 0, xcount, ycount).astype(np.float)\\n\\n # data zone of raster\\n dataZone = np.ma.masked_array(data, np.logical_not(datamask))\\n\\n raster_srs = None\\n raster = None\\n shp = None\\n lyr = None\\n return [dataZone, [xmin,xmax,ymin,ymax]]\",\n \"def save_groudtruth(im, coords, filename):\\n print 'Saving ground truth ......{0}'.format(filename)\\n img_draw = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\\n draw = ImageDraw.Draw(img_draw)\\n for coord in coords:\\n draw.polygon([(float(coord[0]), float(coord[1])), (float(coord[2]), float(coord[3])),\\n (float(coord[4]), float(coord[5])), (float(coord[6]), float(coord[7]))],\\n outline=\\\"red\\\", fill=\\\"blue\\\")\\n img_draw = np.array(img_draw)\\n img_draw = cv2.cvtColor(img_draw, cv2.COLOR_RGB2BGR)\\n bname_excludepoint = filename.split('/')[-1].split('.')[0]\\n image_path = '/home/yuquanjie/Documents/deep-direct-regression/result/' + bname_excludepoint + '_gt.jpg'\\n cv2.imwrite(image_path, img_draw[0: img_draw.shape[0], 0: img_draw.shape[1]])\",\n \"def raster_to_geodataframe(*a, **kw) -> gpd.GeoDataFrame:\\n kw[\\\"geo\\\"] = True\\n return raster_to_dataframe(*a, **kw)\",\n \"def tif_to_png(source, remove_old_files):\\n os.chdir(source)\\n subprocess.Popen(\\\"i_view32.exe *.tif /tifc=5 /convert=*.png\\\", shell=True,\\n stdout=subprocess.PIPE, env={'PATH': os.getenv('PATH')}).stdout.read()\\n if remove_old_files == 'yes':\\n list_of_files_in_the_folder = os.listdir(source)\\n for files in list_of_files_in_the_folder:\\n name, ext = os.path.splitext(files)\\n if ext.lower() == \\\".tif\\\":\\n os.remove(os.path.join(source, files))\",\n \"def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\\n args = [\\n \\\"gdal_calc.py\\\",\\n '-A', infile,\\n '--outfile={}'.format(tmpfile_1),\\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\\n '--type=Byte', '--NoDataValue=0',\\n '--co=SPARSE_OK=YES',\\n '--co=NBITS=1',\\n '--quiet'\\n # Could enable compression\\n # --co=\\\"COMPRESS=LZW\\\"\\n ]\\n subprocess.run(args)\\n\\n subprocess.run([\\n \\\"gdal_polygonize.py\\\",\\n tmpfile_1,\\n '-q',\\n '-f', 'ESRI Shapefile',\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\n \\\"ogr2ogr\\\",\\n '-a_srs', 'EPSG:4326',\\n outfile,\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\\"rm\\\", tmpfile_1])\\n subprocess.run([\\\"rm\\\", tmpfile_2])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'shx')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'dbf')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'prj')])\",\n \"def rasterize_vector(shp, rows, cols, geoTrans=None, saveto=None, method='within', transform=None):\\r\\n # Open the shapefile\\r\\n shp = ogr.Open(shp)\\r\\n\\r\\n # Get the layer from the shape\\r\\n layer = shp.GetLayer()\\r\\n\\r\\n # Get the layer's information\\r\\n lyrSrs = layer.GetSpatialRef().ExportToWkt()\\r\\n\\r\\n # Optionally transform to specified transformation\\r\\n if transform and transform.ExportToWkt() != lyrSrs:\\r\\n # Get the layer geometry\\r\\n poly = layer.GetNextFeature()\\r\\n geom = poly.GetGeometryRef()\\r\\n\\r\\n # Transform the geometry.\\r\\n geom.TransformTo(transform)\\r\\n\\r\\n # Create a new layer.\\r\\n lyr_driver = ogr.GetDriverByName('ESRI Shapefile')\\r\\n\\r\\n lyr_driver_name = tempfile.NamedTemporaryFile(suffix='.shp').name\\r\\n lyr_source = lyr_driver.CreateDataSource(lyr_driver_name)\\r\\n new_lyr = lyr_source.CreateLayer(lyr_driver_name, transform, geom_type=ogr.wkbPolygon)\\r\\n\\r\\n # Add an ID field to tie the geometry to\\r\\n id_field = ogr.FieldDefn('id', ogr.OFTInteger)\\r\\n new_lyr.CreateField(id_field)\\r\\n\\r\\n # Set the transformed geometry\\r\\n feature_defn = new_lyr.GetLayerDefn()\\r\\n feature = ogr.Feature(feature_defn)\\r\\n feature.SetGeometry(geom)\\r\\n feature.SetField('id',1)\\r\\n new_lyr.CreateFeature(feature)\\r\\n\\r\\n # Set the existing layer to be the new layer\\r\\n layer = new_lyr\\r\\n lyrSrs = transform.ExportToWkt()\\r\\n\\r\\n # Create the raster's name\\r\\n if not saveto:\\r\\n remove = True\\r\\n saveto = tempfile.NamedTemporaryFile(suffix='.tif')\\r\\n saveto = saveto.name\\r\\n else:\\r\\n remove = False\\r\\n\\r\\n # Create the new raster\\r\\n driver = gdal.GetDriverByName('GTiff')\\r\\n outRas = driver.Create(saveto, cols, rows, 1)\\r\\n outRas.SetProjection(lyrSrs)\\r\\n outRas.SetGeoTransform(geoTrans)\\r\\n outRas.GetRasterBand(1).Fill(1)\\r\\n\\r\\n # Rasterize the layer\\r\\n if method.lower() == 'touches':\\r\\n gdal.RasterizeLayer(outRas,[1],layer,None, None, [0], ['ALL_TOUCHED=TRUE'])\\r\\n else: # Just default to this.\\r\\n gdal.RasterizeLayer(outRas,[1],layer,None, None, [0])\\r\\n arr = outRas.ReadAsArray()\\r\\n if remove:\\r\\n os.remove(saveto)\\r\\n\\r\\n # Return the numpy array\\r\\n return arr\",\n \"def Save_as_MEM(data='', geo='', projection=''):\\n # save as a geotiff\\n driver = gdal.GetDriverByName(\\\"MEM\\\")\\n dst_ds = driver.Create('', int(data.shape[1]), int(data.shape[0]), 1,\\n gdal.GDT_Float32)\\n srse = osr.SpatialReference()\\n if projection == '':\\n srse.SetWellKnownGeogCS(\\\"WGS84\\\")\\n\\n else:\\n try:\\n if not srse.SetWellKnownGeogCS(projection) == 6:\\n srse.SetWellKnownGeogCS(projection)\\n else:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except:\\n srse.ImportFromWkt(projection)\\n except:\\n try:\\n srse.ImportFromEPSG(int(projection))\\n except:\\n srse.ImportFromWkt(projection)\\n dst_ds.SetProjection(srse.ExportToWkt())\\n dst_ds.GetRasterBand(1).SetNoDataValue(-9999)\\n dst_ds.SetGeoTransform(geo)\\n dst_ds.GetRasterBand(1).WriteArray(data)\\n return(dst_ds)\",\n \"def retrieve_raster_boundary(infile,\\n gdal_path=None,\\n nodata=None,\\n resize_factor=None):\\n # create overview\\n cur_time = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n infile_bname = os.path.splitext(os.path.basename(infile))[0]\\n qlook = os.path.join(os.path.dirname(infile),\\n \\\"temp_{}_{}.tif\\\".format(infile_bname, cur_time))\\n\\n img_ds = open_image(infile)\\n width, height = img_ds.XSize(), img_ds.YSize()\\n img_geot = img_ds.geotransform()\\n img_extent = img_ds.get_extent()\\n img_ds = None\\n qlook_size = 400.0 if resize_factor is None else resize_factor\\n factor = int(np.round(max(width / qlook_size, height / qlook_size)))\\n x_res = img_geot[1] * factor\\n y_res = img_geot[5] * factor\\n\\n options = {\\n '-of': 'GTiff',\\n '-r': 'near',\\n '-te': ' '.join(map(str, img_extent)),\\n '-tr': '{} -{}'.format(x_res, np.abs(y_res)),\\n '-co': 'COMPRESS=LZW',\\n '-srcnodata': nodata,\\n '-dstnodata': nodata\\n }\\n\\n succeed, _ = call_gdal_util('gdalwarp',\\n src_files=infile,\\n dst_file=qlook,\\n gdal_path=gdal_path,\\n options=options)\\n if not succeed:\\n return None\\n\\n # make binary image\\n qlook_dst = open_image(qlook)\\n src_arr = qlook_dst.read_band(1)\\n mask = (src_arr != nodata)\\n src_arr = None\\n\\n # morphologic dilation\\n pixels = 3\\n struct = ndimage.generate_binary_structure(2, 2)\\n struct = ndimage.morphology.iterate_structure(struct, pixels)\\n new_mask = ndimage.binary_dilation(mask, structure=struct)\\n src_arr = np.zeros_like(mask, dtype=np.uint8)\\n src_arr[new_mask] = 1\\n\\n # make a memory dataset\\n mem_drv = gdal.GetDriverByName('MEM')\\n binary_dst = mem_drv.Create('', src_arr.shape[1], src_arr.shape[0], 1,\\n gdal.GDT_Byte)\\n binary_dst.SetGeoTransform(qlook_dst.geotransform())\\n binary_dst.SetProjection(qlook_dst.projection())\\n binary_band = binary_dst.GetRasterBand(1)\\n binary_band.WriteArray(src_arr)\\n binary_band.SetNoDataValue(0)\\n maskband = binary_band.GetMaskBand()\\n\\n # Polygonize\\n driver = ogr.GetDriverByName('Memory')\\n dst_ds = driver.CreateDataSource(\\\"\\\")\\n\\n srs = None\\n if qlook_dst.projection():\\n srs = osr.SpatialReference()\\n srs.ImportFromWkt(qlook_dst.projection())\\n\\n dst_layer = dst_ds.CreateLayer(\\\"out\\\", srs=srs)\\n fd = ogr.FieldDefn('DN', ogr.OFTInteger)\\n dst_layer.CreateField(fd)\\n dst_field = 0\\n\\n geom = None\\n if gdal.CE_None == gdal.Polygonize(binary_band,\\n maskband,\\n dst_layer,\\n dst_field,\\n callback=None):\\n # get polygons from dataset\\n multipolygon = ogr.Geometry(ogr.wkbMultiPolygon)\\n for feature in dst_ds.GetLayerByIndex():\\n polygon = feature.GetGeometryRef()\\n\\n # When the polygon has hole inside, only the out line ring will\\n # be used as extent polygon\\n if polygon.GetGeometryCount() > 1:\\n ring = polygon.GetGeometryRef(0)\\n poly = ogr.Geometry(ogr.wkbPolygon)\\n poly.AddGeometry(ring)\\n polygon = poly\\n\\n multipolygon.AddGeometry(polygon)\\n multipolygon.AssignSpatialReference(dst_ds.GetLayer(0).GetSpatialRef())\\n\\n geom = multipolygon\\n\\n # clean tmp file\\n dst_ds.Destroy()\\n binary_dst = None\\n qlook_dst = None\\n # remove temp qlook\\n os.remove(qlook)\\n\\n return geom\",\n \"def tiled_writing(red, nir, output):\\n \\n #open datasets\\n src_red = rio.open(red)\\n src_nir = rio.open(nir)\\n \\n #define raster properies and update datatype\\n meta = src_red.meta.copy()\\n meta.update({'dtype':'float32'}) # meta is a dictionary\\n outfile = output\\n #open outfile in writing mode with the properties of defined raster band\\n with rio.open(outfile, 'w', **meta) as dst:\\n #iterate over blocks of the bands, calculate ndvi for each block \\n # and put the blocks back together\\n for window in calc_tiles(src_red, tile_size_x, tile_size_y):\\n red_block = src_red.read(window=window, masked=True)\\n nir_block = src_nir.read(window=window, masked=True)\\n #cast ndarrays to Float32 type\\n red = red_block.astype('f4')\\n nir = nir_block.astype('f4')\\n #allow division by zero\\n np.seterr(divide='ignore', invalid='ignore')\\n #calculate ndvi and write raster\\n ndvi = (nir - red) / (nir + red)\\n dst.write(ndvi, window=window)\\n\\n #close dataset\\n src_red.close()\\n src_nir.close()\\n return outfile\",\n \"def _encodeTiledImageFromVips(self, vimg, iterInfo, image, **kwargs):\\n convertParams = large_image.tilesource.base._vipsParameters(\\n defaultCompression='lzw', **kwargs)\\n convertParams.pop('pyramid', None)\\n vimg = large_image.tilesource.base._vipsCast(\\n vimg, convertParams['compression'] in {'webp', 'jpeg'})\\n gdalParams = large_image.tilesource.base._gdalParameters(\\n defaultCompression='lzw', **kwargs)\\n for ch in range(image['channels']):\\n gdalParams += [\\n '-b' if ch not in (1, 3) or ch + 1 != image['channels'] else '-mask', str(ch + 1)]\\n tl = self.pixelToProjection(\\n iterInfo['region']['left'], iterInfo['region']['top'], iterInfo['level'])\\n br = self.pixelToProjection(\\n iterInfo['region']['right'], iterInfo['region']['bottom'], iterInfo['level'])\\n gdalParams += [\\n '-a_srs',\\n iterInfo['metadata']['bounds']['srs'],\\n '-a_ullr',\\n str(tl[0]),\\n str(tl[1]),\\n str(br[0]),\\n str(br[1]),\\n ]\\n fd, tempPath = tempfile.mkstemp('.tiff', 'tiledRegion_')\\n os.close(fd)\\n fd, outputPath = tempfile.mkstemp('.tiff', 'tiledGeoRegion_')\\n os.close(fd)\\n try:\\n vimg.write_to_file(tempPath, **convertParams)\\n ds = gdal.Open(tempPath, gdalconst.GA_ReadOnly)\\n gdal.Translate(outputPath, ds, options=gdalParams)\\n os.unlink(tempPath)\\n except Exception as exc:\\n try:\\n os.unlink(tempPath)\\n except Exception:\\n pass\\n try:\\n os.unlink(outputPath)\\n except Exception:\\n pass\\n raise exc\\n return pathlib.Path(outputPath), TileOutputMimeTypes['TILED']\",\n \"def create_test_file(filename, array):\\n array = np.ma.asanyarray(array)\\n crs = rasterio.crs.CRS(init='epsg:4326')\\n transform = from_origin(52, 4, 10, 10)\\n with rasterio.open(\\n filename,\\n mode='w',\\n driver='GTiff',\\n width=array.shape[1],\\n height=array.shape[2],\\n count=array.shape[0],\\n dtype=array.dtype,\\n crs=crs,\\n transform=transform) as dataset:\\n for band, data in enumerate(array, start=1):\\n dataset.write(data, band)\",\n \"def test_make_mask_w_file_and_transform(self):\\n output_mask = road_mask(\\n os.path.join(data_dir, 'sample_roads_for_masking.geojson'),\\n reference_im=os.path.join(data_dir, 'road_mask_input.tif'),\\n width=4, meters=True, do_transform=True,\\n out_file=os.path.join(data_dir, 'test_out.tif')\\n )\\n truth_mask = skimage.io.imread(\\n os.path.join(data_dir, 'sample_road_raster_mask.tif')\\n )\\n saved_output_mask = skimage.io.imread(os.path.join(data_dir,\\n 'test_out.tif'))\\n\\n assert np.array_equal(output_mask, truth_mask)\\n assert np.array_equal(saved_output_mask, truth_mask)\\n # clean up\\n os.remove(os.path.join(data_dir, 'test_out.tif'))\",\n \"def save_img_as_tiff(seg_img: np.ndarray, time: int, track_save_dir: str):\\n img = sitk.GetImageFromArray(seg_img.astype(\\\"uint16\\\"))\\n filename = 'mask'+'%0*d' % (3, time) + '.tif'\\n sitk.WriteImage(img, os.path.join(track_save_dir, filename))\",\n \"def test_grdimage_file():\\n fig = Figure()\\n fig.grdimage(\\n \\\"@earth_relief_01d_g\\\",\\n cmap=\\\"ocean\\\",\\n region=[-180, 180, -70, 70],\\n projection=\\\"W0/10i\\\",\\n shading=True,\\n )\\n return fig\",\n \"def tiff(location):\\n\\n for src_gtiff in os.listdir(location):\\n if \\\".gtiff\\\" in src_gtiff:\\n dest_tif = src_gtiff[:-6] + '.tiff'\\n command = \\\"gdal_translate -co profile=baseline \\\" + src_gtiff + ' ' \\\\\\n + dest_tif\\n os.system(command)\",\n \"def as_tiff(imagename):\\n return change_extension(imagename,'tif')\",\n \"def to_file_map(self, file_map=None):\\n if file_map is None:\\n file_map = self.file_map\\n f = file_map['image'].get_prepare_fileobj('wb')\\n f.write(self.to_xml())\",\n \"def gdal_compliant(\\n self, rename_dims=True, force_sn=False\\n ) -> xr.DataArray | xr.Dataset:\\n obj_out = self._obj\\n crs = obj_out.raster.crs\\n if (\\n obj_out.raster.res[1] < 0 and force_sn\\n ): # write data with South -> North orientation\\n obj_out = obj_out.raster.flipud()\\n x_dim, y_dim, x_attrs, y_attrs = gis_utils.axes_attrs(crs)\\n if rename_dims:\\n obj_out = obj_out.rename(\\n {obj_out.raster.x_dim: x_dim, obj_out.raster.y_dim: y_dim}\\n )\\n else:\\n x_dim = obj_out.raster.x_dim\\n y_dim = obj_out.raster.y_dim\\n obj_out[x_dim].attrs.update(x_attrs)\\n obj_out[y_dim].attrs.update(y_attrs)\\n obj_out = obj_out.drop_vars([\\\"spatial_ref\\\"], errors=\\\"ignore\\\")\\n obj_out.rio.write_crs(crs, inplace=True)\\n obj_out.rio.write_transform(obj_out.raster.transform, inplace=True)\\n obj_out.raster.set_spatial_dims()\\n\\n return obj_out\",\n \"def retrieve_geotiff_metadata(sFilename_geotiff_in):\\n pDriver = gdal.GetDriverByName('GTiff')\\n \\n pDataset = gdal.Open(sFilename_geotiff_in, gdal.GA_ReadOnly)\\n\\n if pDataset is None:\\n print(\\\"Couldn't open this file: \\\" + sFilename_geotiff_in)\\n sys.exit(\\\"Try again!\\\")\\n else: \\n pProjection = pDataset.GetProjection()\\n pSpatial_reference = osr.SpatialReference(wkt=pProjection) \\n ncolumn = pDataset.RasterXSize\\n nrow = pDataset.RasterYSize \\n pGeotransform = pDataset.GetGeoTransform()\\n dOriginX = pGeotransform[0]\\n dOriginY = pGeotransform[3]\\n dPixelWidth = pGeotransform[1]\\n pPixelHeight = pGeotransform[5] \\n return dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, pSpatial_reference, pProjection, pGeotransform\",\n \"def save_tiff(self, to_file=None):\\n self.tif_file.clear() # Empty the array first\\n\\n # Header\\n byteo = 'II'\\n if self.byteOrder != 'little':\\n byteo = 'MM'\\n self.tif_file.insert_bytes(list(byteo.encode())) # byte order\\n self.tif_file.insert_int(42, 2) # Magic number\\n self.tif_file.insert_int(8, 4) # first IFD always at 0x08\\n\\n for ifd in self.ifds:\\n # self.calculateIFDSpace(ifd) # Readjusts counts because of changes to image data\\n endpos = self.save_ifd(ifd)\\n self.save_image(ifd, endpos)\\n\\n self.tif_file.write(to_file) # lastly, write to file\",\n \"def loadGeoTransform(filepath):\\n \\n from osgeo import gdal\\n \\n ds = gdal.Open(filepath, 0)\\n \\n return ds.GetGeoTransform()\",\n \"def open_raster_file(self):\\n self.log('Opening raster file as GDALRaster.')\\n\\n # Open raster file\\n self.dataset = GDALRaster(os.path.join(self.tmpdir, self.rastername), write=True)\\n\\n # Make sure nodata value is set from input\\n self.hist_values = []\\n self.hist_bins = []\\n for i, band in enumerate(self.dataset.bands):\\n if self.rasterlayer.nodata is not None:\\n band.nodata_value = float(self.rasterlayer.nodata)\\n\\n # Create band metatdata object\\n bandmeta = RasterLayerBandMetadata.objects.create(\\n rasterlayer=self.rasterlayer,\\n band=i,\\n nodata_value=band.nodata_value,\\n min=band.min,\\n max=band.max\\n )\\n\\n # Prepare numpy hist values and bins\\n self.hist_values.append(numpy.array(bandmeta.hist_values))\\n self.hist_bins.append(numpy.array(bandmeta.hist_bins))\\n\\n # Store original metadata for this raster\\n meta = self.rasterlayer.metadata\\n\\n meta.uperleftx = self.dataset.origin.x\\n meta.uperlefty = self.dataset.origin.y\\n meta.width = self.dataset.width\\n meta.height = self.dataset.height\\n meta.scalex = self.dataset.scale.x\\n meta.scaley = self.dataset.scale.y\\n meta.skewx = self.dataset.skew.x\\n meta.skewy = self.dataset.skew.y\\n meta.numbands = len(self.dataset.bands)\\n meta.srs_wkt = self.dataset.srs.wkt\\n meta.srid = self.dataset.srs.srid\\n\\n meta.save()\",\n \"def __make_geo(self):\\n # gmsh freecad_part.iges -o out_iges.geo -0\\n fname_list = self.__fname.split('.')\\n geo_file = fname_list[0]+'.geo'\\n runstr = \\\"%s %s -o %s -0\\\" % (environment.GMSH, self.__fname, geo_file)\\n print(runstr)\\n subprocess.call(runstr, shell=True)\\n print('Wrote file: %s' % geo_file)\",\n \"def transform(infile, output, insrs, format_name):\\n\\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \\n in_srs = osr.SpatialReference()\\n in_srs.ImportFromEPSG(insrs)\\n out_srs = osr.SpatialReference()\\n out_srs.ImportFromEPSG(4324)\\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\\n\\n in_dsn = ogr.Open(infile)\\n in_layer = in_dsn.GetLayer()\\n in_feature_definition = in_layer.GetLayerDefn()\\n\\n out_driver = ogr.GetDriverByName(format_name)\\n out_dsn = out_driver.CreateDataSource(output)\\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\\n geom_type=in_layer.GetGeomType())\\n\\n # add fields\\n for i in range(0, in_feature_definition.GetFieldCount()):\\n fieldDefn = in_feature_definition.GetFieldDefn(i)\\n out_layer.CreateField(fieldDefn)\\n\\n # get the output layer's feature definition\\n out_feature_definition = out_layer.GetLayerDefn()\\n\\n # loop through the input features\\n inFeature = in_layer.GetNextFeature()\\n while inFeature:\\n # get the input geometry\\n geom = inFeature.GetGeometryRef().Clone()\\n # reproject the geometry\\n geom.Transform(coordTrans)\\n # create a new feature\\n outFeature = ogr.Feature(out_feature_definition)\\n # set the geometry and attribute\\n outFeature.SetGeometry(geom)\\n for i in range(0, out_feature_definition.GetFieldCount()):\\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\\n # add the feature to the shapefile\\n out_layer.CreateFeature(outFeature)\\n # destroy the features and get the next input feature\\n outFeature.Destroy()\\n inFeature.Destroy()\\n inFeature = in_layer.GetNextFeature()\\n\\n # close the shapefiles\\n in_dsn.Destroy()\\n out_dsn.Destroy()\",\n \"def process_tile(tile):\\n global base_kwds, resampling, src\\n # Get the bounds of the tile.\\n ulx, uly = mercantile.xy(\\n *mercantile.ul(tile.x, tile.y, tile.z))\\n lrx, lry = mercantile.xy(\\n *mercantile.ul(tile.x + 1, tile.y + 1, tile.z))\\n\\n kwds = base_kwds.copy()\\n kwds['transform'] = from_bounds(ulx, lry, lrx, uly, 256, 256)\\n src_nodata = kwds.pop('src_nodata', None)\\n dst_nodata = kwds.pop('dst_nodata', None)\\n\\n with rasterio.open('/vsimem/tileimg', 'w', **kwds) as tmp:\\n reproject(rasterio.band(src, src.indexes),\\n rasterio.band(tmp, tmp.indexes),\\n src_nodata=src_nodata,\\n dst_nodata=dst_nodata,\\n num_threads=1,\\n resampling=resampling)\\n\\n data = bytearray(virtual_file_to_buffer('/vsimem/tileimg'))\\n\\n # Workaround for https://bugs.python.org/issue23349.\\n if sys.version_info[0] == 2 and sys.version_info[2] < 10:\\n # Check for backported bug fix before re-ordering\\n\\tif kwds['driver'] == 'PNG' and data[0:8] == png_header:\\n # Properly constructed PNG, no need to re-order bytes\\n pass\\n\\telif kwds['driver'] == 'JPEG' and data[0:4] == jpeg_header:\\n # Properly constructed JPEG, no need to re-order bytes\\n pass\\n\\telse:\\n data[:] = data[-1:] + data[:-1]\\n\\n return tile, data\",\n \"def store_image(self):\\n cv2.imwrite(self.__diff_filename(), self.__diff_image())\",\n \"def single_image_band_match(tif):\\n tif_raster=gdal_array.LoadFile(tif)\\n \\n file_path=tif[:-4]+\\\"matched.tif\\\"\\n \\n R=tif_raster[0]\\n G=tif_raster[1]\\n B=tif_raster[2]\\n \\n print(R.shape)\\n result=ird.similarity(G,R , numiter=1, order=1)\\n R= ird.transform_img(R, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=1)\\n # print(result)\\n print(R.shape)\\n \\n print(B.shape)\\n result=ird.similarity(G,B , numiter=1, order=1)\\n B= ird.transform_img(B, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=1)\\n # print(result)\\n print(B.shape)\\n driver=osgeo.gdal.GetDriverByName(\\\"GTiff\\\")\\n options = ['PHOTOMETRIC=RGB', 'PROFILE=GeoTIFF']\\n \\n dataset=driver.Create(file_path, R.shape[1],R.shape[0], 3, osgeo.gdal.GDT_UInt16, options) \\n dataset.GetRasterBand(1).WriteArray(R)\\n dataset.GetRasterBand(2).WriteArray(G)\\n dataset.GetRasterBand(3).WriteArray(B)\",\n \"def save_gray_tiff(array: np.ndarray, filename: str):\\n img_gray = array\\n imwrite(filename, img_gray, shape=img_gray.shape)\\n\\n return filename\",\n \"def save_exr_from_numpy(filename, image_as_array):\\n width = image_as_array.shape[1]\\n height = image_as_array.shape[0]\\n data = image_as_array.tostring()\\n exr = OpenEXR.OutputFile(filename, OpenEXR.Header(width, height))\\n exr.writePixels({'R': data, 'G': data, 'B': data})\\n exr.close()\",\n \"def pan_corr(file):\\n\\n # # infile = 'd:\\\\\\\\Projekti\\\\\\\\Satelit\\\\\\\\CO\\\\\\\\Razpis\\\\\\\\Flat field images_new2020\\\\\\\\flatfield\\\\\\\\NHDBflat_1D'\\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Jure_naloga_banje_raw_pyt\\\\\\\\NHDRGoreMorje_3D'\\n #\\n # # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\\n # in_ref = in_path + in_pan_ref_file\\n #\\n # inreffil = gdal.Open(in_ref)\\n # image_ref = inreffil.ReadAsArray()\\n # # size_ref = image_ref.shape\\n # # pix_count = size_ref[0]*size_ref[1]\\n #\\n # image_ref = image_ref[800:930, 1420:1640]\\n # size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n #\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # raz_g1 = 1\\n # raz_g2 = g1_avg/g2_avg\\n # raz_r1 = g1_avg/r1_avg\\n # raz_b1 = g1_avg/b1_avg\\n #\\n # avg = (g1+g2+r1+b1)/pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n raz_g1 = 1\\n raz_g2 = 1.0245196396115988\\n raz_r1 = 1.0131841989689434\\n raz_b1 = 1.0517113199247086\\n\\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Slo_PAN\\\\_26_30\\\\\\\\'\\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\\\"nhd\\\") and filename.lower().endswith(\\\"tif\\\")]\\n\\n \\n\\n \\n\\n # print('image', i)\\n in_ref=file\\n inreffil = gdal.Open(in_ref)\\n image_ref = inreffil.ReadAsArray()\\n size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n # pix_count = np.count_nonzero(image_ref)\\n # pix_count = 3664*650\\n\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # avg = (g1 + g2 + r1 + b1) / pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n # popravek\\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\\n\\n\\n for i in range(size_ref[0]):\\n for j in range(size_ref[1]):\\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\\n \\n _,_,_,_,P=return_flatfield_set_path(2)\\n P_flat=gdal_array.LoadFile(P)\\n \\n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \\n \\n # outout\\n \\n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\\n \\n out=os.path.abspath(file)+\\\"/corr/\\\"+os.path.basename(file)[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n \\n # out = in_ref[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\\n\\n outband = outRaster.GetRasterBand(1)\\n outband.WriteArray(im_p_pop)\\n outband.FlushCache()\",\n \"def load_image(nom):\\n print(\\\"load_image : [\\\", nom, \\\"]\\\")\\n fic = gdal.Open(nom)\\n print(fic)\\n return fic.ReadAsArray(), fic.GetGeoTransform()\",\n \"def aggregate_to_degree(raster_path, aggregate_func, target_path):\\r\\n base_raster = gdal.OpenEx(raster_path, gdal.OF_RASTER)\\r\\n base_gt = base_raster.GetGeoTransform()\\r\\n base_band = base_raster.GetRasterBand(1)\\r\\n base_nodata = base_band.GetNoDataValue()\\r\\n\\r\\n wgs84sr = osr.SpatialReference()\\r\\n wgs84sr.ImportFromEPSG(4326)\\r\\n\\r\\n driver = gdal.GetDriverByName('GTiff')\\r\\n n_rows = int(\\r\\n abs((base_gt[5] * base_band.YSize) / 1.0))\\r\\n n_cols = int(\\r\\n abs((base_gt[1] * base_band.XSize) / -1.0))\\r\\n target_raster = driver.Create(\\r\\n target_path, n_cols, n_rows, 1, gdal.GDT_Float32)\\r\\n target_raster.SetProjection(wgs84sr.ExportToWkt())\\r\\n degree_geotransform = [base_gt[0], 1., 0., base_gt[3], 0., -1.]\\r\\n target_raster.SetGeoTransform(degree_geotransform)\\r\\n target_band = target_raster.GetRasterBand(1)\\r\\n target_band.SetNoDataValue(base_nodata)\\r\\n target_band.Fill(base_nodata)\\r\\n\\r\\n base_y_winsize = int(round(abs(1. / base_gt[5])))\\r\\n base_x_winsize = int(round(abs(1. / base_gt[1])))\\r\\n\\r\\n last_time = time.time()\\r\\n for row_index in range(n_rows):\\r\\n lat_coord = (\\r\\n degree_geotransform[3] + degree_geotransform[5] * row_index)\\r\\n base_y_coord = int((lat_coord - base_gt[3]) / base_gt[5])\\r\\n target_y_coord = int(\\r\\n (lat_coord - degree_geotransform[3]) / degree_geotransform[5])\\r\\n for col_index in range(n_cols):\\r\\n long_coord = (\\r\\n degree_geotransform[0] + degree_geotransform[1] * col_index)\\r\\n base_x_coord = int((long_coord - base_gt[0]) / base_gt[1])\\r\\n target_x_coord = int(\\r\\n (long_coord - degree_geotransform[0]) / degree_geotransform[1])\\r\\n\\r\\n base_array = base_band.ReadAsArray(\\r\\n xoff=base_x_coord, yoff=base_y_coord,\\r\\n win_xsize=base_x_winsize, win_ysize=base_y_winsize)\\r\\n valid_array = ~numpy.isclose(base_array, base_nodata)\\r\\n if valid_array.any():\\r\\n target_band.WriteArray(\\r\\n numpy.array([[aggregate_func(base_array[valid_array])]]),\\r\\n xoff=target_x_coord, yoff=target_y_coord)\\r\\n\\r\\n current_time = time.time()\\r\\n if (current_time - last_time) > 5.0:\\r\\n LOGGER.info(\\r\\n \\\"%.2f%% complete\\\", 100.0 * float(row_index+1) / n_rows)\\r\\n last_time = current_time\\r\\n target_band.FlushCache()\\r\\n target_band = None\\r\\n target_raster = None\\r\\n base_band = None\\r\\n base_raster = None\\r\\n LOGGER.info(\\\"100%% complete\\\")\",\n \"def merge_rasters(self):\\n for index, i in enumerate(self.months):\\n month = str(index + 1)\\n if len(month) < 2:\\n month = '0' + month\\n rasters = [str(x) for x in i.joinpath('subnational').iterdir() if not x.name.endswith('txt') if x.name.endswith('norm.tif')]\\n outfile = i.joinpath(f'{self.country}_{month}_normalised.tif')\\n tiffs = \\\" \\\".join(rasters)\\n gdal_cmd = f\\\"gdal_merge.py -o {outfile} -a_nodata -99999.0 -of gtiff {tiffs}\\\"\\n subprocess.call(gdal_cmd, shell=True)\",\n \"def fixImageCoordinates( filename, projection):\\n printcount = 0\\n\\n inname = filename\\n nchar = len(inname)\\n # strip off the end of the previous image and add the new projection name\\n outname = inname[0:nchar-8]\\n outname = outname + projection + \\\".fit\\\"\\n\\n# get the input image coordinate transform, Usually Cartesian\\n win = wcs.WCS(filename)\\n\\n hdu = fits.open(filename)[0]\\n imageData = fits.getdata( filename)\\n imageCopy = copy.deepcopy( imageData)\\n#\\n header = hdu.header\\n nx = header['NAXIS1']\\n ny = header['NAXIS2']\\n\\n crval1 = header['CRVAL1']\\n crval2 = header['CRVAL2']\\n crpix1 = header['CRPIX1']\\n crpix2 = header['CRPIX2']\\n cdelt1 = header['CDELT1']\\n cdelt2 = header['CDELT2']\\n ctype1 = header['CTYPE1']\\n ctype2 = header['CTYPE2']\\n\\n xmin = crval1 + (1. - crpix1)*cdelt1\\n xmax = crval1 + (nx - crpix1)*cdelt1\\n ymin = crval2 + (1. - crpix2)*cdelt2\\n ymax = crval2 + (nx - crpix2)*cdelt2\\n\\n print( \\\"fixImage: %.2f,%2f %.1f,%.1f %.3f,%.3f\\\" % (crval1,crval2,crpix1,crpix2,cdelt1,cdelt2))\\n print( \\\"fixImage: %s,%s\\\" % (ctype1,ctype2))\\n # redefine for new projection desired\\n ctype1 = ctype1[0:4]+projection\\n ctype2 = ctype2[0:4]+projection\\n print( \\\"=> %s, %s\\\" % (ctype1, ctype2))\\n\\n header['CTYPE1'] = ctype1\\n header['CTYPE2'] = ctype2\\n\\n# for output image the reference coordinate x pixel can be anywhere\\n# move the center to zero \\n header['CRVAL1'] = 0.\\n\\n header['LONPOLE'] = 0.0\\n header['LATPOLE'] = 90.0\\n header.update()\\n\\n tempname = \\\"GridSave.fits\\\"\\n hdu = fits.PrimaryHDU(header=header, data=imageCopy)\\n print(\\\"Outname: %s\\\" % (tempname))\\n if os.path.exists(tempname):\\n os.remove(tempname)\\n hdu.writeto(tempname)\\n\\n wout = wcs.WCS(tempname)\\n # now that coordinates are defined, remove temporary file\\n if os.path.exists(tempname):\\n os.remove(tempname)\\n\\n pixin = np.array([[0, 0], [nx-1, ny-1]], dtype=np.float64)\\n pixout = np.array([[0, 0], [nx-1, ny-1]], dtype=np.float64)\\n\\n print(\\\"NX, NY: %d,%d\\\" % (nx, ny))\\n\\n nan = float(\\\"NAN\\\")\\n# print(\\\"Nan = %f\\\" % (nan))\\n# assume no data until found\\n for jjj in range (ny):\\n for iii in range (nx):\\n imageCopy[jjj][iii] = nan\\n\\n# now for output image check all pixel values\\n for jjj in range (ny):\\n for iii in range (nx):\\n # if this image pixal has no value\\n pixout[0] = (iii,jjj)\\n oworld = wout.wcs_pix2world(pixout, 0)\\n xy = oworld[0]\\n if np.isnan(xy[0]):\\n continue\\n# print(\\\"pixout: %d,%d : world %.f,%.2f\\\" % (iii,jjj,xy[0],xy[1]))\\n pixin[0] = oworld[0]\\n ipixels = win.wcs_world2pix(pixin, 0)\\n# get input pixels for coordinate\\n ixy = ipixels[0]\\n# if outside of current image skip this pixel\\n if np.isnan( ixy[0]):\\n continue\\n ix = int(ixy[0])\\n iy = int(ixy[1])\\n ix = max( min( nx-1, ix), 0)\\n iy = max( min( ny-1, iy), 0)\\n ix = int(ix)\\n iy = int(iy)\\n# print(\\\"pixin : %d,%d : world %.f,%.2f\\\" % (ix,iy,xy[0],xy[1]))\\n# print(\\\"OX,OY:%d,%d <= IX,IY:%d,%d\\\" %( ox,oy, ix,iy))\\n imageCopy[jjj][iii] = imageData[iy][ix]\\n\\n print(\\\"Preparing to write new coordiante transform: %s\\\" % (outname))\\n if os.path.exists(outname):\\n os.remove(outname)\\n newhdu = fits.PrimaryHDU(header=header, data=imageCopy)\\n newhdu.writeto(outname)\\n print(\\\"Wrote new\\\")\\n\\n return\",\n \"def geo_transform(self):\\n pass\",\n \"def __affine_geo_transformation(x, y, gtr):\\n\\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\\n # Affine transformation rewritten for rasterio:\\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\\n\\n return gtr_x, gtr_y\",\n \"def shapefileToRaster(in_shapefile, model_raster, out_dir, name_override=None, zone_field:str = None, dtype = None, *args, **kwargs) -> str:\\n\\t# correct variable names\\n\\tshapefile_path = in_shapefile\\n\\t# get out_path\\n\\tif name_override:\\n\\t\\tout_path = os.path.join(out_dir,name_override)\\n\\telse:\\n\\t\\tin_base = os.path.splitext(os.path.basename(in_shapefile))[0]\\n\\t\\tmodel_ext = os.path.splitext(model_raster)[1]\\n\\t\\tout_path= os.path.join(out_dir,in_base+\\\"_RASTER\\\"+model_ext)\\n\\t# read file\\n\\tshp = gpd.read_file(shapefile_path)\\n\\twith rasterio.open(model_raster,'r') as rst:\\n\\t\\tmeta = rst.meta.copy()\\n\\n\\t# this is where we create a generator of geom, value pairs to use in rasterizing\\n\\tif zone_field is not None:\\n\\t\\tzone_vals = []\\n\\t\\tfor i in range(len(shp)):\\n\\t\\t\\tzone_vals.append(shp.at[i,zone_field])\\n\\t\\tzone_codes = [i for i, val in enumerate(zone_vals)]\\n\\t\\tshapes = ((geom,val) for geom, val in zip(shp.geometry,zone_codes))\\n\\telse:\\n\\t\\tshapes = ((geom,1) for geom in shp.geometry)\\n\\n\\t# set data type\\n\\tif dtype:\\n\\t\\tmeta.update(dtype=dtype)\\n\\telif zone_field:\\n\\t\\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype(zone_codes))\\n\\telse:\\n\\t\\tmeta.update(dtype=\\\"int16\\\")\\n\\n\\ttry:\\n\\t\\tout = rasterio.open(out_path, 'w+', **meta)\\n\\t# merra-2 files have a very high nodata value, beyond the range of int32.\\n\\t# This block catches the resulting ValueError and swaps in the minimum\\n\\t# allowable data type. Nice of rasterio to have such a function.\\n\\texcept ValueError:\\n\\t\\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype([meta['nodata']]))\\n\\t\\tout = rasterio.open(out_path, 'w+', **meta)\\n\\t\\tout_arr = out.read(1)\\n\\t\\tburned = features.rasterize(shapes=shapes, fill=0, out=out_arr, transform=out.transform)\\n\\t\\tout.write_band(1, burned)\\n\\tout.close()\\n\\n\\treturn out_path\",\n \"def saveTiles(z, x, y, ntiles, mapname, image, suffix = 'png', imgtype = None):\\n for dx in range(0, ntiles):\\n tilex = x*ntiles + dx\\n ensureDirExists(getTileDir(mapname, z, tilex))\\n for dy in range(0, ntiles): \\n tiley = y*ntiles + dy\\n offsetx = BORDER_WIDTH + dx*TILE_SIZE\\n offsety = BORDER_WIDTH + dy*TILE_SIZE\\n view = image.view(offsetx, offsety, TILE_SIZE, TILE_SIZE)\\n if imgtype:\\n view.save(getTilePath(mapname, z, tilex, tiley, suffix), imgtype)\\n else:\\n view.save(getTilePath(mapname, z, tilex, tiley, suffix))\",\n \"def test_convert_to_geotiff(self):\\n fnames = modis.convert_to_geotiff(self.fnames[0], outdir=os.path.dirname(__file__))\\n for f in fnames:\\n ext = os.path.splitext(f)[1]\\n suffix = os.path.splitext(f)[0].split('_')[1]\\n self.assertTrue(os.path.exists(f))\\n if ext != '.ovr':\\n self.assertTrue(suffix in products['MCD43A4.006']['bandnames'])\",\n \"def save_regions_to_file(\\n label_layer,\\n destination_directory,\\n ignore_empty=True,\\n image_extension=\\\".tiff\\\",\\n):\\n data = label_layer.data\\n if ignore_empty:\\n if data.sum() == 0:\\n return\\n\\n name = label_layer.name\\n\\n filename = destination_directory / (name + image_extension)\\n imio.to_tiff(data.astype(np.int16), filename)\",\n \"def to_raster(\\n self,\\n raster_path,\\n driver=\\\"GTiff\\\",\\n dtype=None,\\n tags=None,\\n windowed=False,\\n mask=False,\\n logger=logger,\\n **profile_kwargs,\\n ):\\n for k in [\\\"height\\\", \\\"width\\\", \\\"count\\\", \\\"transform\\\"]:\\n if k in profile_kwargs:\\n msg = f\\\"{k} will be set based on the DataArray, remove the argument\\\"\\n raise ValueError(msg)\\n da_out = self._obj\\n # set nodata, mask, crs and dtype\\n if \\\"nodata\\\" in profile_kwargs:\\n da_out.raster.set_nodata(profile_kwargs.pop(\\\"nodata\\\"))\\n nodata = da_out.raster.nodata\\n if nodata is not None and not np.isnan(nodata):\\n da_out = da_out.fillna(nodata)\\n elif nodata is None:\\n logger.warning(f\\\"nodata value missing for {raster_path}\\\")\\n if mask and \\\"mask\\\" in da_out.coords and nodata is not None:\\n da_out = da_out.where(da_out.coords[\\\"mask\\\"] != 0, nodata)\\n if dtype is not None:\\n da_out = da_out.astype(dtype)\\n if \\\"crs\\\" in profile_kwargs:\\n da_out.raster.set_crs(profile_kwargs.pop(\\\"crs\\\"))\\n # check dimensionality\\n dim0 = da_out.raster.dim0\\n count = 1\\n if dim0 is not None:\\n count = da_out[dim0].size\\n da_out = da_out.sortby(dim0)\\n # write\\n profile = dict(\\n driver=driver,\\n height=da_out.raster.height,\\n width=da_out.raster.width,\\n count=count,\\n dtype=str(da_out.dtype),\\n crs=da_out.raster.crs,\\n transform=da_out.raster.transform,\\n nodata=nodata,\\n **profile_kwargs,\\n )\\n with rasterio.open(raster_path, \\\"w\\\", **profile) as dst:\\n if windowed:\\n window_iter = dst.block_windows(1)\\n else:\\n window_iter = [(None, None)]\\n for _, window in window_iter:\\n if window is not None:\\n row_slice, col_slice = window.toslices()\\n sel = {self.x_dim: col_slice, self.y_dim: row_slice}\\n data = da_out.isel(sel).load().values\\n else:\\n data = da_out.load().values\\n if data.ndim == 2:\\n dst.write(data, 1, window=window)\\n else:\\n dst.write(data, window=window)\\n if tags is not None:\\n dst.update_tags(**tags)\",\n \"def render(self, outputfile, cmd=GDALTRANSLATE, working_memory=1024,\\n compress=None, tempdir=None):\\n tmpfile = NamedTemporaryFile(\\n suffix='.tif', prefix='gdalrender',\\n dir=os.path.dirname(outputfile), delete=False\\n )\\n\\n try:\\n with self.get_tempfile(dir=tempdir) as inputfile:\\n warp_cmd = [\\n cmd,\\n '-q', # Quiet - FIXME: Use logging\\n '-of', 'GTiff', # Output to GeoTIFF\\n '-co', 'BIGTIFF=IF_SAFER', # Use BigTIFF if >2GB\\n '-co', 'NUM_THREADS=ALL_CPUS', # multithreaded compression for GeoTiff\\n # gdal_translate does not support the following\\n # '-multi', # Use multiple processes\\n # '-overwrite', # Overwrite outputfile\\n ]\\n\\n # Set the working memory so that gdalwarp doesn't stall of disk\\n # I/O\\n warp_cmd.extend([\\n # gdal_translate does not support -wm\\n # '-wm', working_memory,\\n '--config', 'GDAL_CACHEMAX', working_memory\\n ])\\n\\n # Use compression\\n compress = str(compress).upper()\\n if compress and compress != 'NONE':\\n warp_cmd.extend(['-co', 'COMPRESS=%s' % compress])\\n if compress in ('LZW', 'DEFLATE'):\\n warp_cmd.extend(['-co', 'PREDICTOR=2'])\\n\\n # Run gdalwarp and output to tmpfile.name\\n warp_cmd.extend([inputfile.name, tmpfile.name])\\n check_output_gdal([str(e) for e in warp_cmd])\\n\\n # If it succeeds, then we move it to overwrite the actual\\n # output\\n os.rename(tmpfile.name, outputfile)\\n return outputfile\\n finally:\\n rmfile(tmpfile.name, ignore_missing=True)\\n rmfile(tmpfile.name + '.aux.xml', ignore_missing=True)\",\n \"def warp_to_raster(\\r\\n base_raster_path, canonical_raster_path, resample_method,\\r\\n target_raster_path):\\r\\n canonical_raster_info = pygeoprocessing.get_raster_info(\\r\\n canonical_raster_path)\\r\\n pygeoprocessing.warp_raster(\\r\\n base_raster_path, canonical_raster_info['pixel_size'],\\r\\n target_raster_path,\\r\\n resample_method, target_bb=canonical_raster_info['bounding_box'],\\r\\n n_threads=2)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.748401","0.70260197","0.69436365","0.69406134","0.69158727","0.6690425","0.66588354","0.6629013","0.6620407","0.6539317","0.65314543","0.6381361","0.63328516","0.63243276","0.62995696","0.628616","0.6275659","0.61972815","0.6176187","0.61506325","0.6034297","0.5958071","0.5951712","0.59337837","0.593172","0.59062433","0.5903323","0.5897532","0.5896494","0.58810616","0.580647","0.578247","0.5773507","0.57340986","0.5721054","0.5688099","0.568254","0.56819934","0.56737745","0.56723976","0.56586355","0.56300443","0.5613378","0.5600082","0.5582612","0.5559556","0.5555341","0.5525765","0.5507645","0.5499038","0.5485534","0.54812706","0.54779756","0.547102","0.5466781","0.5464857","0.54516643","0.5442309","0.54395247","0.5403598","0.53890425","0.5376858","0.5373116","0.53425294","0.53411746","0.5338628","0.53299034","0.53288186","0.52725816","0.5260149","0.5243789","0.5239398","0.52223116","0.5212763","0.5211683","0.5210677","0.5208758","0.5205907","0.5202552","0.51956946","0.5189166","0.5186378","0.51848495","0.5183239","0.51774925","0.5176843","0.51758754","0.517375","0.51599073","0.5157089","0.515569","0.5149434","0.5147002","0.5141059","0.51394","0.5133526","0.5125505","0.5120119","0.5120009","0.5113294"],"string":"[\n \"0.748401\",\n \"0.70260197\",\n \"0.69436365\",\n \"0.69406134\",\n \"0.69158727\",\n \"0.6690425\",\n \"0.66588354\",\n \"0.6629013\",\n \"0.6620407\",\n \"0.6539317\",\n \"0.65314543\",\n \"0.6381361\",\n \"0.63328516\",\n \"0.63243276\",\n \"0.62995696\",\n \"0.628616\",\n \"0.6275659\",\n \"0.61972815\",\n \"0.6176187\",\n \"0.61506325\",\n \"0.6034297\",\n \"0.5958071\",\n \"0.5951712\",\n \"0.59337837\",\n \"0.593172\",\n \"0.59062433\",\n \"0.5903323\",\n \"0.5897532\",\n \"0.5896494\",\n \"0.58810616\",\n \"0.580647\",\n \"0.578247\",\n \"0.5773507\",\n \"0.57340986\",\n \"0.5721054\",\n \"0.5688099\",\n \"0.568254\",\n \"0.56819934\",\n \"0.56737745\",\n \"0.56723976\",\n \"0.56586355\",\n \"0.56300443\",\n \"0.5613378\",\n \"0.5600082\",\n \"0.5582612\",\n \"0.5559556\",\n \"0.5555341\",\n \"0.5525765\",\n \"0.5507645\",\n \"0.5499038\",\n \"0.5485534\",\n \"0.54812706\",\n \"0.54779756\",\n \"0.547102\",\n \"0.5466781\",\n \"0.5464857\",\n \"0.54516643\",\n \"0.5442309\",\n \"0.54395247\",\n \"0.5403598\",\n \"0.53890425\",\n \"0.5376858\",\n \"0.5373116\",\n \"0.53425294\",\n \"0.53411746\",\n \"0.5338628\",\n \"0.53299034\",\n \"0.53288186\",\n \"0.52725816\",\n \"0.5260149\",\n \"0.5243789\",\n \"0.5239398\",\n \"0.52223116\",\n \"0.5212763\",\n \"0.5211683\",\n \"0.5210677\",\n \"0.5208758\",\n \"0.5205907\",\n \"0.5202552\",\n \"0.51956946\",\n \"0.5189166\",\n \"0.5186378\",\n \"0.51848495\",\n \"0.5183239\",\n \"0.51774925\",\n \"0.5176843\",\n \"0.51758754\",\n \"0.517375\",\n \"0.51599073\",\n \"0.5157089\",\n \"0.515569\",\n \"0.5149434\",\n \"0.5147002\",\n \"0.5141059\",\n \"0.51394\",\n \"0.5133526\",\n \"0.5125505\",\n \"0.5120119\",\n \"0.5120009\",\n \"0.5113294\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7922152"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":856,"numItemsPerPage":100,"numTotalItems":95772,"offset":85600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTk2MDQ5MywianRpIjoiZWNiODgzZmMtOTJiZi00NTkwLWI0ZDEtYzdmNDllYjc3ZGZmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5NjQwOTMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.IZKGNTFt1hqBXmiJTQxYEgeY9lT5o1sb8puGbke9RZN1UCnP8tGrUGaFd9E3BvgQyUiNbeNVFYia0s1RkHWMAQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Represent an intersection using the Cantor pairing function. | def intersection(st, ave):
return (st+ave)*(st+ave+1)//2 + ave | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_union_intersection():\n X = np.random.randn(d, 100)\n assert np.array_equal(lincon.indicator_intersection(X), 1-lincon.indicator_union(X))",
"def intersection(x, y, f, p):",
"def intersection(self, other):\n return self._geomgen(capi.geom_intersection, other)",
"def intersect(self, *arg... | [
"0.6529781",
"0.64454335",
"0.643646",
"0.6377812",
"0.6290983",
"0.6158032",
"0.61379325",
"0.6099429",
"0.60879844",
"0.60427094",
"0.6039375",
"0.6011817",
"0.5966033",
"0.5949825",
"0.5942497",
"0.5928957",
"0.59143835",
"0.59129083",
"0.5910618",
"0.5907354",
"0.5888551"... | 0.0 | -1 |
Return the taxicab distance between two intersections. >>> times_square = intersection(46, 7) >>> ess_a_bagel = intersection(51, 3) >>> taxicab(times_square, ess_a_bagel) 9 >>> taxicab(ess_a_bagel, times_square) 9 | def taxicab(a, b):
"*** YOUR CODE HERE ***"
return abs(street(a) - street(b)) + abs(avenue(a) - avenue(b)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def taxicab(a, b):\n street_1, street_2 = street(a), street(b)\n avenue_1, avenue_2 = avenue(a), avenue(b)\n return abs(street_1 - street_2) + abs(avenue_1 - avenue_2)",
"def taxicab(a, b):\n\t\"*** YOUR CODE HERE ***\"\n\treturn abs(street(a)-street(b)) + abs(avenue(a)-avenue(b))",
"def taxicab(a, b)... | [
"0.7120027",
"0.6839901",
"0.65796494",
"0.65705454",
"0.5223681",
"0.50767803",
"0.5042108",
"0.49975678",
"0.48670506",
"0.48530948",
"0.4835566",
"0.4795915",
"0.47938675",
"0.47879708",
"0.47753534",
"0.47272784",
"0.46994123",
"0.46631414",
"0.465091",
"0.46485525",
"0.4... | 0.67126614 | 3 |
Returns a new list containing square roots of the elements of the original list that are perfect squares. >>> seq = [8, 49, 8, 9, 2, 1, 100, 102] >>> squares(seq) [7, 3, 1, 10] >>> seq = [500, 30] >>> squares(seq) [] | def squares(s):
"*** YOUR CODE HERE ***"
result = []
for num in s:
sr = round(math.sqrt(num))
if sr * sr == num:
result.append(sr)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_squares(self):\n\t\tself.squares = [x for x in range(self.low, self.high + 1) if sqrt(x) == int(sqrt(x))]",
"def squares(s):\n\n \"*** YOUR CODE HERE ***\"\n return [int(x**(1/2)) for x in s if x**(1/2) == round(x**(1/2))]",
"def list_squared(start, stop):\n result = []\n\n for num in rang... | [
"0.7532929",
"0.70103526",
"0.69574976",
"0.68897986",
"0.6871598",
"0.68224853",
"0.6734844",
"0.6657192",
"0.6642378",
"0.66411835",
"0.6640479",
"0.66332394",
"0.66236895",
"0.6610896",
"0.6526553",
"0.65028816",
"0.6489638",
"0.648101",
"0.64674306",
"0.631349",
"0.630965... | 0.7138826 | 1 |
Return the value of G(n), computed recursively. >>> g(1) 1 >>> g(2) 2 >>> g(3) 3 >>> g(4) 10 >>> g(5) 22 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'g', ['While', 'For']) True | def g(n):
"*** YOUR CODE HERE ***"
if n < 4:
return n
else:
return g(n-1) + 2*g(n-2) + 3*g(n-3) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def g(n):\n\t\"*** YOUR CODE HERE ***\"\n\tif n <= 3:\n\t\treturn n\n\telse:\n\t\treturn g(n-1) + 2*g(n-2) + 3*g(n-3)",
"def g(n):\n \"*** YOUR CODE HERE ***\"\n if n <=3:\n return n\n else:\n return g(n-1)+2*g(n-2)+3*g(n-3)",
"def g(n):\n \"*** YOUR CODE HERE ***\"\n if n <= 3:\n ... | [
"0.65347075",
"0.64384305",
"0.6329685",
"0.6329685",
"0.61571145",
"0.60239685",
"0.5937985",
"0.55639344",
"0.55369043",
"0.5523096",
"0.5420632",
"0.5410982",
"0.5398192",
"0.53921515",
"0.5342789",
"0.5293935",
"0.52893794",
"0.5279808",
"0.52543837",
"0.5252531",
"0.5185... | 0.6510758 | 1 |
Return the value of G(n), computed iteratively. >>> g_iter(1) 1 >>> g_iter(2) 2 >>> g_iter(3) 3 >>> g_iter(4) 10 >>> g_iter(5) 22 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'g_iter', ['Recursion']) True | def g_iter(n):
"*** YOUR CODE HERE ***"
if n < 4:
return n
else:
g1 = 1
g2 = 2
g3 = 3
i = 3
while(i < n):
i += 1
t = g3 + 2*g2 + 3*g1
g1 = g2
g2 = g3
g3 = t
return g3 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def g_iter(n):\n\tif n <= 3:\n\t\treturn n\n\telse:\n\t\tg_n_1, g_n_2, g_n_3 = 3, 2, 1\n\t\t# always update the g_i until reach the final n\n\t\tfor i in range(4,n+1):\n\t\t\tg_i = g_n_1 + 2*g_n_2 + 3*g_n_3\n# \t\t\tupdate the g(n-1), g(n-2), g(n-3)\n\t\t\tg_n_1, g_n_2, g_n_3 = g_i, g_n_1, g_n_2\n\t\treturn g_i\n\... | [
"0.73015696",
"0.68882436",
"0.62829113",
"0.6223856",
"0.6131208",
"0.60937464",
"0.6027959",
"0.597907",
"0.5950993",
"0.59052163",
"0.58999294",
"0.58999294",
"0.584625",
"0.5844759",
"0.58446234",
"0.5835706",
"0.5794958",
"0.5790122",
"0.57023543",
"0.5638049",
"0.563491... | 0.7122139 | 1 |
Return the number of ways to make change for amount. >>> count_change(7) 6 >>> count_change(10) 14 >>> count_change(20) 60 >>> count_change(100) 9828 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'count_change', ['While', 'For']) True | def count_change(amount):
"*** YOUR CODE HERE ***"
if amount < 1:
return 0
elif amount == 1:
return 1
elif amount == 2:
return 2
else:
return count_change(amount - 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_change(amount):\n\toptions = [2**i for i in range(amount+1) if 2**i <= amount]\n\toptions = sorted(options, reverse = True)\n\tlength = len(options)\n\n\t# print(length)\n\tdef helper(remains, i, options, length):\n\t\t# loop until reaching the smallest coin\n\t\tif i >= length :\n\t\t\treturn 0\n\t\t# c... | [
"0.750737",
"0.7155193",
"0.6912649",
"0.69010884",
"0.67702913",
"0.6648249",
"0.65593016",
"0.65591896",
"0.6208104",
"0.5816895",
"0.56851083",
"0.5558492",
"0.5312004",
"0.52741164",
"0.52447283",
"0.52056",
"0.518745",
"0.5175362",
"0.5113141",
"0.5073923",
"0.5057803",
... | 0.72280043 | 1 |
Print instructions to move a disk. | def print_move(origin, destination):
print("Move the top disk from rod", origin, "to rod", destination) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self, num_tower, position):\n #Keep track of the location of the disk\n self.peg = num_tower\n self.position_on_peg = position\n \n #Change coordenates in graphical representation of the disk\n #Calling base clase move method\n super().move( Disk.get_center... | [
"0.6039234",
"0.6002185",
"0.5936001",
"0.58213556",
"0.57730645",
"0.5628979",
"0.56272626",
"0.5441529",
"0.54332095",
"0.5373397",
"0.5345443",
"0.53429705",
"0.53327715",
"0.5329446",
"0.5281114",
"0.52739465",
"0.5241653",
"0.5238077",
"0.5231009",
"0.5230801",
"0.523073... | 0.7575055 | 3 |
Print the moves required to move n disks on the start pole to the end pole without violating the rules of Towers of Hanoi. n number of disks start a pole position, either 1, 2, or 3 end a pole position, either 1, 2, or 3 There are exactly three poles, and start and end must be different. Assume that the start pole has ... | def move_stack(n, start, end):
assert 1 <= start <= 3 and 1 <= end <= 3 and start != end, "Bad start/end"
"*** YOUR CODE HERE ***" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_stack(n, start, end):\n assert 1 <= start <= 3 and 1 <= end <= 3 and start != end, \"Bad start/end\"\n \n if n == 1:\n print_move(start, end) \n else:\n extra_pole = 6 - start - end\n move_stack(n-1, start, extra_pole)\n move_stack(1, start, end)\n move_sta... | [
"0.7980914",
"0.7740771",
"0.6126784",
"0.59013176",
"0.5819141",
"0.55706495",
"0.5505688",
"0.5504053",
"0.5500867",
"0.54933286",
"0.5467977",
"0.5244985",
"0.5197591",
"0.5105983",
"0.50870454",
"0.50858366",
"0.508075",
"0.49935585",
"0.49684587",
"0.49679634",
"0.496256... | 0.71134025 | 3 |
Return the value of an expression that computes factorial. >>> make_anonymous_factorial()(5) 120 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'make_anonymous_factorial', ['Assign', 'AugAssign', 'FunctionDef', 'Recursion']) True | def make_anonymous_factorial():
return 'YOUR_EXPRESSION_HERE' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_anonymous_factorial():\n return lambda n: 1 if n == 1 else mul(n, make_anonymous_factorial()(sub(n, 1)))",
"def make_anonymous_factorial():\n\t# u need to use a helper function if your lambda statement does\n\t# not have a name\n\t# fact = lambda n: 1 if n == 1 else mul(n, fact(sub(n, 1)))\n\tdef rec... | [
"0.795322",
"0.76479423",
"0.7496367",
"0.6421492",
"0.6419824",
"0.64167154",
"0.63810945",
"0.63466245",
"0.6338506",
"0.63343805",
"0.62276936",
"0.61999434",
"0.6188147",
"0.61714816",
"0.6145468",
"0.61416566",
"0.6131425",
"0.6131287",
"0.6107738",
"0.60987043",
"0.6065... | 0.8405621 | 3 |
Create the webhook if it doesn't already exist. The Webhook is triggered when new messages are posted to the room. | def create_new_message_webhook(room, webhook_url):
# when a message
resource = "messages"
# is created
event = "created"
filter_ = ""
# insecure example secret used to generate the payload signature
secret = config.SPARK_WEBHOOK_SECRET
# Connect to SparkAPI with SPARK_TOKEN
spark_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_webhooks():\n client = RequestManager()\n client.set_method(\"POST\")\n client.set_endpoint(\"/projects/{0}/webhooks\".format(STORED_ID['project_id']))\n name = \"\".join(choices(string.ascii_letters, k=6))\n body = {\"webhook_url\": 'https://' + name, \"webhook_versio... | [
"0.7510101",
"0.71174085",
"0.6854709",
"0.66574806",
"0.6462748",
"0.6379622",
"0.6142991",
"0.61359745",
"0.6063905",
"0.604515",
"0.60279125",
"0.60028166",
"0.59783626",
"0.5958843",
"0.58807003",
"0.58486533",
"0.57544047",
"0.57061857",
"0.5696835",
"0.56884986",
"0.566... | 0.7444837 | 1 |
Find an active agent on the team and assign them to the room | def assign_team_member_to_customer_room(room, **room_args):
logging.log(logging.INFO, 'assign_team_member_to_customer_room')
# setup Spark API connection using SPARK_TOKEN
spark_api = ciscosparkapi.CiscoSparkAPI(access_token=config.SPARK_TOKEN)
person_ids_in_room = []
logging.log(logging.INFO, ro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_team(self, agents_on_team):\n self.agents_on_team = agents_on_team",
"def registerTeam(self, agentsOnTeam):\n\n self.agentsOnTeam = agentsOnTeam",
"def assign_agents(particle,self):\n\n self.models[particle].state2agents(self.states[particle])\n\n return self.models[particle]",... | [
"0.5883716",
"0.582684",
"0.57222563",
"0.5710909",
"0.56679535",
"0.5631196",
"0.55353266",
"0.54998595",
"0.5459364",
"0.54566145",
"0.54290885",
"0.5423177",
"0.53831804",
"0.53805006",
"0.5376918",
"0.5325809",
"0.5302468",
"0.5296057",
"0.5238835",
"0.5231388",
"0.521540... | 0.64783984 | 0 |
Posts message to customer's Spark Room for customer_id using room_args. Room/Webhook will be created if necessary. | def customer_room_message_send(customer_id, **room_args):
# Basic sanity checking
if 'text' not in room_args and 'markup' not in room_args and 'files' not in room_args:
raise PassedParametersError("Must specify at least one of text/markup/files")
# setup Spark API connection using SPARK_TOKEN
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def customer_new_signup(spark_api, customer_id, team_id, message_from_customer, webhook_url):\n\n # Send message via tropo\n message = \"Thanks for signing up! To get in touch, reply to this message or call this number during business hours.\"\n tropo.send_sms(customer_id[-10:], message)\n\n # Fetch al... | [
"0.59380233",
"0.56253624",
"0.5252518",
"0.5224164",
"0.51958823",
"0.5193579",
"0.50124633",
"0.50122374",
"0.49953628",
"0.49653572",
"0.49550867",
"0.49480957",
"0.4946862",
"0.49219984",
"0.48729196",
"0.48683706",
"0.48597386",
"0.48506933",
"0.48418263",
"0.48284423",
... | 0.7371444 | 0 |
Check if given room belongs to customer_id. Looks at last 10 characters of each | def is_customers_room(room, customer_id):
room_last_10 = room.title[-10:]
customer_id_last10 = customer_id[-10:]
if room_last_10 == customer_id_last10:
return True
logging.log(logging.INFO, "NO MATCH for %s %s " % (room_last_10, customer_id_last10))
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_room(r_l, out, char_bud):\n if r_l == \"R\":\n if len(out) + len(sentence[\"tokens\"][counter_r][0]) < char_bud:\n return True\n else:\n return False\n if r_l == \"L\":\n if len(out) + len(sentence[\"tokens\"][counter_l][0]) < cha... | [
"0.5715806",
"0.56134945",
"0.5574621",
"0.5250753",
"0.5223137",
"0.5128236",
"0.5072222",
"0.506887",
"0.505743",
"0.4931521",
"0.4929062",
"0.4925988",
"0.4905181",
"0.48851773",
"0.47877455",
"0.47873718",
"0.4782352",
"0.47482577",
"0.47185382",
"0.4704556",
"0.46382764"... | 0.8444396 | 0 |
Create room/webhook and post message room then send customer SMS | def customer_new_signup(spark_api, customer_id, team_id, message_from_customer, webhook_url):
# Send message via tropo
message = "Thanks for signing up! To get in touch, reply to this message or call this number during business hours."
tropo.send_sms(customer_id[-10:], message)
# Fetch all rooms and l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def POST(self):\n\t\t\n\t\tjson_data = web.data()\t\t# Get the POST data sent from Webex Teams\n\t\t#print(\"\\nWEBHOOK POST RECEIVED:\")\n\t\t#print(json_data, \"\\n\")\n\n\t\twebhook_obj = Webhook(json_data)\t\t\t\t\t# Create a Webhook object from the JSON data\n\t\troom = api.rooms.get(webhook_obj.data.roomId)\... | [
"0.7098938",
"0.6646875",
"0.6630591",
"0.6442523",
"0.64140123",
"0.63342357",
"0.63222736",
"0.6215194",
"0.6198079",
"0.6152976",
"0.6151927",
"0.61304444",
"0.60952866",
"0.60899824",
"0.6030755",
"0.60275304",
"0.60214525",
"0.601809",
"0.60107744",
"0.6006407",
"0.59974... | 0.6217545 | 7 |
Send any room messages to customer by SMS, making sure not to echo customer's SMS or messages. | def webhook_process(request):
# Basic sanity checking
if not request.json or not ('event' in request.json and 'data' in request.json):
logging.log(logging.ERROR, 'No json or data in json')
abort(400)
if request.json['event'] != 'created':
logging.log(logging.ERROR, 'Event is not cr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_sms(self, sms):\n pass",
"def send_sms(self,msg,to=None,long=True):\n if long:\n return self.send_msg(msg,to,\"SendCatSMS\")\n else:\n return self.send_msg(msg,to,\"SendSMS\")",
"def send_sms(self, body):\n message = self.twilio_client.sms.messages.cre... | [
"0.72153",
"0.69181114",
"0.6639897",
"0.66254675",
"0.6553501",
"0.65526444",
"0.6525755",
"0.64059794",
"0.63927513",
"0.635507",
"0.63540757",
"0.627183",
"0.62477255",
"0.62430453",
"0.6224343",
"0.62242794",
"0.618873",
"0.6184785",
"0.61725867",
"0.614549",
"0.612009",
... | 0.0 | -1 |
It should take nothing And return json array of all user's and their information | def list_users():
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
reqdata = request.json
if not check_token(reqdata["token"]):
return jsonify(status=TOKEN_ERROR)
users = db.session.query(User).all()
resdata = []
for user in users:
resdata.append({"i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_user():\n user = UserModel.objects()\n return jsonify(user), 200",
"def users_no_id_get():\n all_users = []\n for user in storage.all(\"User\").values():\n all_users.append(user.to_dict())\n return jsonify(all_users)",
"def get_users():\n selection = []\n try:\n s... | [
"0.7767868",
"0.75064695",
"0.7486039",
"0.74693805",
"0.7443739",
"0.741982",
"0.74109954",
"0.7366981",
"0.7364997",
"0.73534054",
"0.73403007",
"0.73249465",
"0.72949857",
"0.7291494",
"0.72630936",
"0.726186",
"0.7206385",
"0.7199901",
"0.7198208",
"0.7172977",
"0.7171010... | 0.648453 | 85 |
It should be used for user creation so it takes all data from user model described earlier except id and relationships And return user's temporary jwt token ? | def create_user():
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
data = request.json
#TODO check if request body contain required keys
#if ["login", "password", "user", "email", "first_name", "second_name", "phone"].sort() != (data.keys()).sort():
# return jsonif... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def UserToken(self) -> object:",
"def for_user(cls, user):\n\n token = super().for_user(user)\n\n TokenMeta.objects.get_or_create(\n jti=token['jti'],\n token=str(token),\n )\n\n return token",
"def create(self, data):\n token, cr... | [
"0.7068197",
"0.6890675",
"0.68891424",
"0.68891424",
"0.68891424",
"0.68891424",
"0.68891424",
"0.68891424",
"0.6834859",
"0.67731804",
"0.6667489",
"0.6624938",
"0.6584035",
"0.6555688",
"0.6482245",
"0.64695615",
"0.64479065",
"0.6439664",
"0.64168483",
"0.6408788",
"0.640... | 0.0 | -1 |
It should take user token and update database section And return well.... nothing | def update_user():
#TODO user update
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_user():",
"def _update_token(token):\n session.token = token",
"def temp_getToken():\n # First check if query is okay or not\n data = json.loads(request.data)\n username = data.get(\"username\",None)\n password = data.get(\"password\",None)\n \n if request.headers.get('X-VITASK-... | [
"0.6980469",
"0.6788381",
"0.66331285",
"0.6625706",
"0.6619976",
"0.6572579",
"0.6507547",
"0.6471451",
"0.6423794",
"0.64029384",
"0.63858753",
"0.6384523",
"0.63422924",
"0.6332013",
"0.6330532",
"0.6312202",
"0.62036765",
"0.6197012",
"0.6196945",
"0.6188228",
"0.6155419"... | 0.66499823 | 2 |
It should take user token and password And return nothing. just delete this user from database | def delete_user():
#TODO user delete
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_user():",
"def delete(cls):\n user = user_schema.load(request.get_json(), partial=(\"email\",))\n\n current_identity = get_jwt_identity()\n db_user = UserModel.find_by_id(current_identity)\n logging.info(\n f\"Delete called by {db_user.id}: {db_user.username} wit... | [
"0.8245988",
"0.8094278",
"0.79578537",
"0.77676976",
"0.75711685",
"0.748723",
"0.74508",
"0.74470454",
"0.73795223",
"0.72883624",
"0.7269549",
"0.71676147",
"0.71491003",
"0.70365703",
"0.7034477",
"0.70184124",
"0.70036274",
"0.6996407",
"0.699598",
"0.69952154",
"0.69941... | 0.7995271 | 2 |
It should take limiter (for example 10 random path's) And return array of paths with all data provided except coordinates (but when dev build it's okay) | def list_paths():
paths = db.session.query(Path).all()
data = []
for path in paths:
data.append({"id" : path.id,
"title":path.title,"rating":path.rating,
"description":path.description,"date":path.date,
"start_coordinate":path.start_coordinate,
"end_co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def optimized_path(coords, startid, mask):\n coords = np.column_stack((coords, mask))\n pass_by = np.asarray(coords)\n path = [coords[startid]]\n pass_by = np.delete(pass_by, startid, axis=0)\n while pass_by.any():\n nearest_id, nearest = min(\n enumerate(pass_by), key=lambda x: di... | [
"0.62314683",
"0.6198397",
"0.58058447",
"0.5761351",
"0.57430595",
"0.57112736",
"0.5702389",
"0.56382763",
"0.5606894",
"0.55856305",
"0.55711824",
"0.55240107",
"0.54835707",
"0.54586005",
"0.54335684",
"0.5425482",
"0.54141396",
"0.54081756",
"0.5407028",
"0.53954357",
"0... | 0.0 | -1 |
It should take all data needed for creation of path And no return | def create_path():
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
data = request.json
if not check_token(data["token"]):
return jsonify(status=TOKEN_ERROR)
#TODO check that we have required keys in request body to Path creation
title = data["title"]
rating... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def path(self):\n ...",
"def prepare_path(self,path):\n debug.info(4,\"Set path: \" + str(path))\n\n # This is marked for debug\n path.set_path()\n\n # For debugging... if the path failed to route.\n if False or path==None:\n self.write_debug_gds()\n\n ... | [
"0.6511428",
"0.64666474",
"0.6107135",
"0.61054385",
"0.6089046",
"0.6060304",
"0.6022867",
"0.599679",
"0.59728765",
"0.59154546",
"0.58834463",
"0.58768857",
"0.58740234",
"0.58728284",
"0.5834883",
"0.5831352",
"0.5823931",
"0.58224046",
"0.58064187",
"0.58030295",
"0.579... | 0.5744296 | 27 |
It should update all user information and we need to check user token | def update_path():
#TODO update path information
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_user():",
"def update_user():\n #TODO user update \n pass",
"async def update(self):\n self.data = await self.api.user.get()",
"def test_update_user(self):\n token = self.authenticate_user(self.auth_user_data).data[\"token\"]\n response = self.client.put(self.user_url,\n... | [
"0.8475117",
"0.80762476",
"0.7211855",
"0.71284574",
"0.70691365",
"0.7039774",
"0.700582",
"0.69647455",
"0.68810004",
"0.682694",
"0.6816446",
"0.6799031",
"0.670049",
"0.6663624",
"0.6663624",
"0.6656589",
"0.66043735",
"0.6600797",
"0.65735185",
"0.6551525",
"0.65486246"... | 0.0 | -1 |
it should take user token (it should be user with role admin or so) and password and no return. but delete this path from database | def delete_path():
#TODO delete path from database
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_user():",
"def delete_user():\n #TODO user delete\n pass",
"def delete_user():\n token = request.args.get('token')\n data = jwt.decode(token, app.config['SECRET_KEY'])\n\n permit = functions.delete_user(data)\n if permit:\n return make_response(jsonify({'Delete': 'User Delet... | [
"0.6882728",
"0.6600979",
"0.6442104",
"0.6431626",
"0.6199002",
"0.6198386",
"0.6184373",
"0.6175392",
"0.61739355",
"0.6154988",
"0.6083398",
"0.6067171",
"0.6040718",
"0.6025336",
"0.5950242",
"0.59005874",
"0.58870757",
"0.58870757",
"0.58725816",
"0.5852299",
"0.58464605... | 0.55862695 | 57 |
we should take user token and path id and create assoc betweeb user and his password | def create_user_path_assoc():
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
reqdata = request.json
if not check_token(reqdata["token"]):
return jsonify(status=TOKEN_ERROR)
#TODO check that request body contain needed data
#if ["user_id", "path_id", "ready", "... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def activate():\n try:\n body = request.get_json()\n\n activate_token = body[\"activate_token\"]\n password = body[\"password\"]\n\n if len(password) < 3 or len(password) > 50:\n return bad_request()\n\n if not models.token_exists(activate_token):\n\n ret... | [
"0.62621117",
"0.62346333",
"0.61671156",
"0.61413085",
"0.61245376",
"0.6083655",
"0.6033242",
"0.6020575",
"0.59848195",
"0.5967562",
"0.59649783",
"0.59640026",
"0.59355044",
"0.5934715",
"0.5880868",
"0.5853217",
"0.58479744",
"0.584258",
"0.5823585",
"0.58147603",
"0.581... | 0.6367729 | 0 |
here we should take user token and return all his paths | def return_array_of_user_paths():
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
reqdata = request.json
if not check_token(reqdata["token"]):
return jsonify(status=TOKEN_ERROR)
user = db.session.query(User).get_or_404(reqdata["id"])
print(user.paths[0].path.ti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def UserToken(self) -> object:",
"def getAllSocialPaths(self, userID):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n pass",
"def get_all_access():\n\t# Get the email from the user making the request\n\temail = get_jwt_identity()\n\treturn get_all_acces... | [
"0.58251524",
"0.5674125",
"0.5542355",
"0.5537274",
"0.54388803",
"0.54378325",
"0.5408396",
"0.54047626",
"0.5392191",
"0.5388384",
"0.533989",
"0.5315466",
"0.53092307",
"0.5298692",
"0.52615076",
"0.5257217",
"0.52303827",
"0.52263474",
"0.5226098",
"0.522373",
"0.5207918... | 0.60975367 | 0 |
here we should take user token and delete his path | def delete_user_path_assoc():
#TODO delete user path assoc
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_user():",
"def delete_user():\n #TODO user delete\n pass",
"def delete_user():\n del globalopts.appdata[request.user]\n del globalopts.users[request.user]\n return \"\", 200",
"def deauth(request):\n\n if(request.token):\n request.token.delete()\n return JsonResponse({'messag... | [
"0.6992621",
"0.66495234",
"0.6613394",
"0.65799147",
"0.6460222",
"0.64533365",
"0.6449197",
"0.64322025",
"0.64186275",
"0.6408798",
"0.64074534",
"0.6357774",
"0.63450295",
"0.63366646",
"0.63124985",
"0.629666",
"0.6272013",
"0.622002",
"0.6215148",
"0.61695504",
"0.61331... | 0.69666207 | 1 |
return all users registered at excursion | def assoc_list():
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
reqdata = request.json
if not check_token(reqdata["token"]):
return jsonify(status=TOKEN_ERROR)
users_paths = db.session.query(UserPathAssociation).all()
resdata = []
for e in users_paths:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_users():",
"def get_users(self):\r\n\t\tlogger.debug(\"Fetch users\")\r\n\t\t\r\n\t\treturn login.get_users()",
"def get_users():\n return db.fetch_users()",
"def getInterestedUsers():",
"def get_users(self):\n users = []\n page = 1\n while not len(users) % 100:\n ... | [
"0.8572794",
"0.7658152",
"0.7489579",
"0.7470524",
"0.7346282",
"0.73064995",
"0.73031485",
"0.7269989",
"0.72584635",
"0.72584635",
"0.72584635",
"0.72584635",
"0.7252011",
"0.72429305",
"0.7241409",
"0.7196677",
"0.71711403",
"0.71708596",
"0.71601003",
"0.7156562",
"0.715... | 0.0 | -1 |
development demo for frontend work check | def test_route():
coordinates = "54.971288,82.875554|54.988211,82.906425|55.038288,82.937137|55.060150,82.984610"
return jsonify(data=coordinates, status=OK_STATUS) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def demo():\n ...",
"def development_function(self): \n return None",
"def test_Demo(self):\n self._run(self._example_scenarios, \"Demo\")",
"def should_show():",
"def test_quick_build(self):\n pass",
"def test(self):\n pass",
"def testing(self):\n print('test s... | [
"0.63391596",
"0.6087888",
"0.60736024",
"0.60091513",
"0.59981024",
"0.5935011",
"0.5920325",
"0.58774805",
"0.58619386",
"0.5839741",
"0.5838705",
"0.58170086",
"0.57993233",
"0.57925045",
"0.5769986",
"0.57453775",
"0.57439023",
"0.57287276",
"0.57160985",
"0.5709616",
"0.... | 0.0 | -1 |
takes path_id and returns all waypoints | def generate_coordinate():
#TODO debug dat fucking code
if not check_content_type():
return jsonify(status=CONTENT_TYPE_ERROR)
reqdata = request.json
try:
if not check_token(reqdata["token"]):
return jsonify(status=TOKEN_ERROR)
except:
return jsonify(status=REQ_ER... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_path(self, starting_waypoint, destination_id):\n # Run dijkstra's algorithm\n dijkstra_output = dijkstra.DijkstraSPF(self.graph, starting_waypoint)\n \n # Get waypoint_id of destination\n cur = armaps.model.get_db()\n cur.execute(\n \"SELECT waypoint_id ... | [
"0.64917606",
"0.62769824",
"0.62649757",
"0.6230449",
"0.621781",
"0.6089363",
"0.60655874",
"0.601833",
"0.6014707",
"0.5981585",
"0.5973532",
"0.59211046",
"0.587732",
"0.5869312",
"0.58577955",
"0.58385986",
"0.5831729",
"0.5825229",
"0.5812677",
"0.57631683",
"0.57394826... | 0.0 | -1 |
actually that's just pass hashing lol | def raw_password_to_string(raw_string):
return hashlib.sha256(str(raw_string).encode('utf-8')).hexdigest() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def HashAlgorithm(self) -> _n_7_t_0:",
"def hash(self) -> str:\r\n ...",
"def hash(self) -> bytes:",
"def hash_key(self):",
"def hashing(word) :\r\n ans = hashlib.sha256(word.encode())\r\n return ans.hexdigest()",
"def current_hash(self):",
"def hashcode(o):",
"def __hash__(self):\n ... | [
"0.78062844",
"0.769477",
"0.75788176",
"0.7432525",
"0.7197758",
"0.7175085",
"0.71015054",
"0.7054006",
"0.7051356",
"0.70512056",
"0.7031017",
"0.70059437",
"0.6994937",
"0.6960184",
"0.6944826",
"0.6941813",
"0.6928904",
"0.6898604",
"0.68907386",
"0.6880028",
"0.6879239"... | 0.0 | -1 |
magic generation! very top secret | def generate_token(login, password):
time = datetime.datetime.now().timestamp()
raw_string = str(login) + str(password) + str(time)
return hashlib.sha256(str(raw_string).encode('utf-8')).hexdigest() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate():",
"def generate(self):",
"def passwordGen() :\n\treturn __randomString(12)",
"def gensalt():\n return hexlify(os.urandom(24)).decode()",
"def generate_secret_number(self):\n self.secret_number = randint(0, self.difficulty)\n # print(f\"secret_number = {self.secret_number}\"... | [
"0.77251697",
"0.6991",
"0.6777404",
"0.6668828",
"0.6621261",
"0.6607946",
"0.65829366",
"0.6549961",
"0.6549961",
"0.6549961",
"0.65182585",
"0.64814043",
"0.6452127",
"0.64483225",
"0.6382743",
"0.6376844",
"0.6344381",
"0.6260516",
"0.62596536",
"0.6241607",
"0.62277144",... | 0.0 | -1 |
return true in case if token valid | def check_token(token):
token = db.session.query(Token).filter(Token.token==token).first()
if token == None:
return False
#TODO token lifetime
#if (datetime.datetime.now() - token.date >= datetime.timedelta(day=2)):
# return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def validate_token(self, token):",
"def check_for_token(token):\n try:\n decode_token(token)\n return True\n except:\n return False",
"def verify_token(self, token):\n return False",
"def test_unused_token_is_valid(self):\n assert self.token.is_valid()",
"def ... | [
"0.82097185",
"0.8140379",
"0.81355923",
"0.7914643",
"0.78615093",
"0.78422207",
"0.7573815",
"0.74966556",
"0.7471199",
"0.7436773",
"0.7294061",
"0.72937906",
"0.7229699",
"0.71921486",
"0.7146299",
"0.70567137",
"0.69968945",
"0.69913256",
"0.68749946",
"0.68700546",
"0.6... | 0.6887068 | 18 |
return true in case if http contenttype header application/json | def check_content_type():
return request.content_type == "application/json" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_json(self):\n mt = self.mimetype or self.headers.get('Accept')\n if not mt:\n return False\n if 'application/json' in mt:\n return True\n if mt.startswith('application/') and mt.endswith('+json'):\n return True\n return False",
"def is_json(request: HttpRequest):\n c... | [
"0.8645014",
"0.82041943",
"0.779997",
"0.7696868",
"0.76209074",
"0.6923115",
"0.6919368",
"0.6727148",
"0.6703706",
"0.6674128",
"0.66583306",
"0.6607934",
"0.65860164",
"0.6428396",
"0.6419199",
"0.6374357",
"0.632561",
"0.63124114",
"0.6275513",
"0.6273118",
"0.6266922",
... | 0.8678554 | 0 |
Ensure we can create a new building. | def test_post_building(self):
url = '/building/'
data = {"address": "дом 50 улица Ленина", "total_bricks_required": 300}
today = date.today()
date_output = today.strftime("%Y-%m-%d")
data_output = {
"id": 1,
"address": "дом 50 улица... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def creates_valid_building(self, cell):\n def wont_cut_off_other_commercial_buildings(pos):\n \"\"\"Helper function that ensures other commercial buildings won't be cutoff if a commercial building is\n placed at pos\"\"\"\n for adj_cell in self.environment.grid.get_neighborh... | [
"0.6149066",
"0.60781157",
"0.5911046",
"0.5865075",
"0.5821171",
"0.5813078",
"0.5784675",
"0.5746099",
"0.57275873",
"0.57275873",
"0.569468",
"0.5693626",
"0.5692525",
"0.566429",
"0.56606543",
"0.56389713",
"0.56243294",
"0.558071",
"0.55754536",
"0.5574062",
"0.55678195"... | 0.0 | -1 |
Ensure that without a building we can not create a new brick_task. | def test_post_brick_task(self):
task_url = '/building/id/3/add-bricks'
task_data = {
"bricks": 200,
"date_load": "2030-03-10"
}
response = self.client.post(task_url, task_data, format='json')
self.assertEqual(response.status_code, status.HTTP_400_BAD_R... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dumb_task():\n return True",
"def _check_can_submit(self):\n if not self.parallel and self.last_submitted_i != self.highest_continuous_done_i:\n raise CannotSubmitNewTask(\n f\"Attempt to get task for {self} \"\n f\"out of order: last submitted {self.last_su... | [
"0.58794796",
"0.58149904",
"0.57713705",
"0.57603836",
"0.56704164",
"0.5647729",
"0.55785745",
"0.5520681",
"0.546715",
"0.5453792",
"0.5427059",
"0.54269284",
"0.5421115",
"0.540672",
"0.53743273",
"0.5352993",
"0.53447807",
"0.5338095",
"0.53360236",
"0.53190583",
"0.5313... | 0.51699674 | 38 |
Dispatch some bricks and check the stats | def test_post_brick_task(self):
task_url = '/building/id/1/add-bricks'
task_data = {
"bricks": 100,
"date_load": "2030-03-10"
}
self.client.post(task_url, task_data, format='json')
task_url = '/building/id/1/add-bricks'
task_data = {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _monitor(self):\n # while CONF.weight == 'bw':\n while True:\n self._send_echo_request()\n self.create_link_delay()\n # self.get_loss()\n self.stats['flow'] = {}\n self.stats['port'] = {}\n for dp in self.datapaths.values():\n ... | [
"0.57859915",
"0.55887634",
"0.5506207",
"0.5475481",
"0.5435428",
"0.537355",
"0.5356436",
"0.53383297",
"0.5325674",
"0.53071356",
"0.5215758",
"0.52093613",
"0.5204521",
"0.51719254",
"0.5168573",
"0.51683146",
"0.51554996",
"0.5150338",
"0.51472735",
"0.51448953",
"0.5141... | 0.5560453 | 2 |
Query of minmum Tier and CGPA on job id | def University_calculation(jobid):
min_cgpa=90
"""~~~~~~~~~"""
dbconnect= connect_to_db()
Candidate_qualifications=pd.read_sql("select candidate_id,university_name,institute_name,aggregate from candidate_qualification where candidate_id in(select candidate_id from master_id where job_id="+str(jobid)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_job_query(self):\n context = aq_inner(self.context)\n catalog = getToolByName(context, 'portal_catalog')\n mt = getToolByName(self, 'portal_membership') \n currentUser = mt.getAuthenticatedMember() \n \n if \"Site Administrators\" not in currentUser.getGroups():\n\treturn catalog.searchRe... | [
"0.5880039",
"0.5506129",
"0.5467628",
"0.5228536",
"0.5168022",
"0.5120933",
"0.51059866",
"0.50934154",
"0.5050494",
"0.5010462",
"0.49944985",
"0.49767104",
"0.49651697",
"0.494382",
"0.48621768",
"0.48598516",
"0.48432046",
"0.4807202",
"0.48071113",
"0.47951928",
"0.4794... | 0.4691528 | 34 |
insert a new reservation into the reservation table | def insert_reservation(house, id, check_in_date, check_in_time, check_out_date, guest_name,
guest_cell, guest_telegram, num_guest, comment, confirm):
sql = """INSERT INTO %s VALUES(%s, '%s', '%s', '%s', '%s', '%s', '%s', %s, '%s', %s) RETURNING reservation_id;"""
conn = None
reservati... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SaveToPeopleSQL(self, reservationid, humanid):\n cursor = self.cursor\n\n selector = '''\n insert into dbo.[people] ([reservationid], [humanid])\n values (?,?)\n '''\n\n values = (reservationid, humanid)\n cursor.execute(selector, values)",
"def create_n... | [
"0.6577621",
"0.6466516",
"0.6437117",
"0.638199",
"0.63248324",
"0.6276918",
"0.62141246",
"0.6211627",
"0.61856735",
"0.604802",
"0.6041068",
"0.60130626",
"0.59944504",
"0.59232014",
"0.5874383",
"0.58662474",
"0.5826088",
"0.5755627",
"0.5755627",
"0.5755627",
"0.5724426"... | 0.6811607 | 0 |
Return dict of file types and its extensions. | def TYPES():
if config.types_cache:
return config.types_cache
types = {
'actionscript': '.as .mxml',
'asm': '.asm .s',
'batch': '.bat .cmd',
#'binary': 'Binary files, as defined by Perl's -B op (default: off)',
'cc': '.c .h .xs',
'cfmx': '.cfc .cfm .cfml... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_extension_to_type_map(file_types):\n extension_to_type = dict()\n for file_type in file_types:\n for file_ext in file_type['extensions']:\n if file_ext not in extension_to_type:\n extension_to_type[file_ext] = file_type\n return extension_to_type",
"def ComputeF... | [
"0.763715",
"0.7182069",
"0.6990045",
"0.68455327",
"0.67907184",
"0.6781619",
"0.6718573",
"0.65699273",
"0.65577275",
"0.6477392",
"0.638216",
"0.6374214",
"0.6350945",
"0.62902635",
"0.62902635",
"0.624474",
"0.61979735",
"0.61897975",
"0.6184642",
"0.6162244",
"0.61284614... | 0.63220716 | 13 |
Build options for parsing language types. | def build_lang_options(parser):
langs = OptionGroup(parser, "File type options",
("Use this options to select file types you'd like to process.\n"
"Use --help-types option to output all available file types."))
for ftype in TYPES():
langs.add_option('--%s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_options(self, options):\n pass",
"def ParseOptions(cls, options, config_object):",
"def options(self, parser, env):\n pass",
"def add_parse_options(cls, parser):\n # Decoder params\n parser.add_argument(\"-beam_size\", default=1, type=int, help=\"Beam size\")\n pa... | [
"0.56313854",
"0.55033135",
"0.549861",
"0.5472193",
"0.5451628",
"0.544645",
"0.543898",
"0.543763",
"0.54214746",
"0.5410095",
"0.5380468",
"0.5370253",
"0.5363826",
"0.5360438",
"0.53587586",
"0.5352318",
"0.5351205",
"0.5351205",
"0.5342864",
"0.5329086",
"0.53279406",
... | 0.75984526 | 0 |
Search pattern in each line of file. | def search(path, f):
started = False
for count, line in enumerate(f):
number = count + 1
if search_line(line):
if not started:
print config.term.highlight(relpath(path), 'GREEN')
if config.filenames:
break
started ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grep(pattern, filename):\n rx = re.compile(pattern)\n with open(filename, 'r') as file:\n for line in file:\n if rx.search(line):\n yield line",
"def grep_me(pattern, fname):\n for line in stream_reader(fname):\n if re.search(pattern, line, re.I):\n ... | [
"0.7754115",
"0.772458",
"0.7566962",
"0.7401021",
"0.73548084",
"0.70622396",
"0.7045306",
"0.6969334",
"0.69634515",
"0.69435984",
"0.6909578",
"0.68896705",
"0.68886346",
"0.6710138",
"0.66423684",
"0.6598283",
"0.65414864",
"0.65358496",
"0.64989007",
"0.6468703",
"0.6442... | 0.65670025 | 16 |
Search and replace patterns in file. If backup setting is on then for each modified file create unique backup file with .FILENAME.XXX~ there XXX is numeral suffix | def search_replace(path, f):
base, name = os.path.split(path)
while True:
tmp_path = '%s.%s.sr~' % (path, str(time.time()).replace('.', ''))
if not os.path.exists(tmp_path):
break
data = f.read()
if search_line(data):
print path
if config.explain:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_file_replace(path, args):\n try:\n fisier = open(path, 'r')\n except IOError:\n print(\"Nu am putut deschide fisierul :\", path)\n return\n full_data = fisier.read()\n fisier.close()\n\n try:\n fisier = open(path, \"w+\")\n except IOError:\n print(\"Nu... | [
"0.6075093",
"0.58167344",
"0.57572097",
"0.57559025",
"0.57524633",
"0.5682571",
"0.56750935",
"0.5581424",
"0.5558917",
"0.54431474",
"0.5407974",
"0.53983253",
"0.5390096",
"0.53896135",
"0.53821135",
"0.53563595",
"0.5337024",
"0.53308517",
"0.5323081",
"0.53059065",
"0.5... | 0.614383 | 0 |
Iterator over files which names satisfies the search parameters. | def walk_files():
# TODO: not check twice the same dir or file
for path in config.targets:
abs_path = os.path.join(cwd, path)
if not os.path.islink(abs_path) and os.path.isfile(abs_path):
walked.append(abs_path)
yield abs_path
#process_file(abs_path)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_files(pathname: str) -> Iterator[dict]:\n for filename in glob.iglob(f'{pathname}/*'):\n r = match_filename(filename=filename)\n if r:\n yield r",
"def filenames(self, glob='*', limit=0, recurse=False):\n for a in self.ls_names(glob, limit=limit, recurse=recurse):\n ... | [
"0.7143449",
"0.6806924",
"0.6717614",
"0.66894305",
"0.66321737",
"0.65333736",
"0.6480589",
"0.6405443",
"0.64030373",
"0.6400605",
"0.6353649",
"0.6306214",
"0.630192",
"0.62971354",
"0.62838227",
"0.62457496",
"0.62377614",
"0.62249476",
"0.6202404",
"0.61908376",
"0.6190... | 0.0 | -1 |
Determine if fname is backup file name. | def isbackup(fname):
return bool(BACKUP_RE.search(fname)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_backuped_file(file_name):\n return str(file_name).endswith(BACKUPED_FILES)",
"def check_base_filename(self, record):\n time_tuple = time.localtime()\n\n if self.file_name_format:\n pass\n\n if self.suffix_time != time.strftime(self.suffix, time_tuple) or not os.path.exis... | [
"0.7815696",
"0.6688547",
"0.64778984",
"0.6264358",
"0.62476087",
"0.61512923",
"0.60459733",
"0.5963137",
"0.59307975",
"0.5864477",
"0.57959646",
"0.5760985",
"0.57010245",
"0.5660149",
"0.5625667",
"0.5619978",
"0.56119597",
"0.5611512",
"0.55985445",
"0.55823183",
"0.558... | 0.83070534 | 0 |
Create a `TerminalController` and initialize its attributes with appropriate values for the current terminal. `term_stream` is the stream that will be used for terminal output; if this stream is not a tty, then the terminal is assumed to be a dumb terminal (i.e., have no capabilities). | def __init__(self, term_stream=sys.stdout):
try:
import curses
curses.setupterm()
except:
return
if not term_stream.isatty():
return
# Look up string capabilities.
for capability in self._STRING_CAPABILITIES:
(attrib,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def terminal_init(self):\n pass",
"def __init__(self, stream):\n if stream.isatty():\n pass\n else:\n raise TypeError(\"Stream must be TTY (sys.stdout, sys.stderr)\")\n \n self._BS = \"\\x08\"\n self._SP = \" \"\n self._last_width = 0\n ... | [
"0.59652",
"0.5615144",
"0.54912895",
"0.52887625",
"0.52851677",
"0.5223518",
"0.5165743",
"0.5147226",
"0.5071659",
"0.505962",
"0.5025898",
"0.48908564",
"0.48805162",
"0.48645216",
"0.48614362",
"0.4857036",
"0.48461643",
"0.4845205",
"0.48380554",
"0.48092356",
"0.480592... | 0.64451367 | 0 |
Display available file types | def display_file_types():
print 'Available file types. Each line contains the file type and the list of extensions by those the file type is determined. To include FOOBAR file type to search use --FOOBAR, to exlude use --noFOOBAR. You can include and exclude a number of file types.'
for ftype, extensions in TY... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file_type_options():\n curr_type = global_settings.settings[menu_option]\n msg = \"\"\n for i in range(len(file_types)):\n if curr_type == file_types[i]:\n msg += str(i) + \" \" + file_types[i] + \" SELECTED\\n\"\n else:\n msg += str(i) + \" \" + file_types[i] +... | [
"0.7317718",
"0.71191174",
"0.70702636",
"0.70702636",
"0.67736834",
"0.6644009",
"0.6632094",
"0.6614344",
"0.65085596",
"0.64952815",
"0.6491149",
"0.6471447",
"0.6457224",
"0.63667023",
"0.6285033",
"0.6212446",
"0.6169332",
"0.61309534",
"0.61190706",
"0.6087771",
"0.6035... | 0.88249856 | 0 |
Compute the pointer to the word element index index from the base base. A word element has a size of 32bit. ``prt = base + 4index`` | def word_ptr(base, index):
return base + 4*index | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def idx2off(i):\n return i * 32 - (24 * (i//4))",
"def bytes_to_index(lead, tail):\n lead_offset = 0x81 if lead < 0xA0 else 0xC1\n tail_offset = 0x40 if tail < 0x7F else 0x41\n return (lead - lead_offset) * 188 + tail - tail_offset",
"def get_base_index(self, base):\n num_bases = len(self.to... | [
"0.62375116",
"0.60986",
"0.6095896",
"0.5959151",
"0.58488744",
"0.5837316",
"0.57247674",
"0.57137364",
"0.5572218",
"0.5571219",
"0.55461186",
"0.55414087",
"0.55056304",
"0.5488403",
"0.5464307",
"0.54163104",
"0.54005605",
"0.53965604",
"0.5387477",
"0.5376323",
"0.53682... | 0.8458481 | 0 |
Seek to the table table. | def _seek_to_table(self, table):
self.stream.seek(self.table_pointers[table]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next(self):\n self.curr_table += 1\n\n if self.curr_table == self.numtables:\n raise StopIteration()\n else:\n return self.getTableByIndex(self.curr_table)",
"def _get_table(self, cursor):\n raise NotImplementedError",
"def getTableByIndex(self, index):\n ... | [
"0.6716625",
"0.6608818",
"0.6537215",
"0.6471446",
"0.6471446",
"0.61668456",
"0.6163812",
"0.6123178",
"0.60995656",
"0.6055506",
"0.5928246",
"0.59138536",
"0.5907426",
"0.58347434",
"0.5818251",
"0.58148175",
"0.57715636",
"0.5771209",
"0.57641464",
"0.5751659",
"0.573318... | 0.8310612 | 0 |
Return a pointer to the word element at index in the table table. | def _position_in_table(self, table, index):
return self.word_ptr(self.table_pointers[table], index) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def word_ptr(base, index):\n\n return base + 4*index",
"def get_word_with_index(index):\n return reverse_word_index[index-3]",
"def word_from_id(self, idx: int) -> str:\n try:\n word = self.id2word[idx]\n except:\n raise KeyError(\"invalid index to get correspondin... | [
"0.71713465",
"0.6710033",
"0.647052",
"0.64275444",
"0.64000016",
"0.627637",
"0.6230234",
"0.620498",
"0.5997333",
"0.5994419",
"0.5897665",
"0.5840249",
"0.58399916",
"0.5783078",
"0.575517",
"0.5751453",
"0.5734819",
"0.5734819",
"0.5702223",
"0.5684829",
"0.5657467",
"... | 0.75631076 | 0 |
Return the fix word in table table at index index. | def _read_fix_word_in_table(self, table, index):
return self.stream.read_fix_word(self._position_in_table(table, index)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_word_with_index(index):\n return reverse_word_index[index-3]",
"def word_at(self, index, signed = False):\n return 0",
"def _position_in_table(self, table, index):\n\n return self.word_ptr(self.table_pointers[table], index)",
"def get_next_word(self, index, orignal=False):\n try:\... | [
"0.7023475",
"0.6607199",
"0.63332784",
"0.62407106",
"0.61667347",
"0.5965174",
"0.59546214",
"0.5891202",
"0.58116204",
"0.56669796",
"0.5634454",
"0.5632258",
"0.5579958",
"0.5555492",
"0.55403674",
"0.5468816",
"0.5456604",
"0.5415371",
"0.5396345",
"0.5366563",
"0.536528... | 0.8199197 | 0 |
Return the four numbers in table table at index index. | def _read_four_byte_numbers_in_table(self, table, index):
return self.stream.read_four_byte_numbers(self._position_in_table(table, index)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_info(index, n):\n return index/n, index%n",
"def __get_row(self, index: int) -> int:\n return index // self.columns",
"def __get_column(self, index: int) -> int:\n return index % self.columns",
"def index(self) -> int:",
"def index2qindexb(self, index):\n r = index // 0x10\n... | [
"0.6256296",
"0.61769336",
"0.5994595",
"0.5719449",
"0.56975514",
"0.56891197",
"0.563756",
"0.5593905",
"0.558259",
"0.55777436",
"0.55777436",
"0.5575794",
"0.55332804",
"0.55188483",
"0.54888606",
"0.5472156",
"0.54072744",
"0.5406689",
"0.53736943",
"0.53688806",
"0.5360... | 0.75347275 | 0 |
Return the extensible recipe, four numbers, at index index. Extensible characters are specified by an extensible recipe, which consists of four bytes called top, mid, bot, and rep (in this order). These bytes are the character codes of individual pieces used to build up a large symbol. If top, mid, or bot are zero, the... | def _read_extensible_recipe(self, index):
return self._read_four_byte_numbers_in_table(tables.extensible_character, index) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_extension_info(self, data, counter):\n try:\n if data[counter+47] == 11 or data[counter+50:counter+53] == b\"\\x0e\\xac\\x0b\" or data[82:85] == b\"\\x0f\\xf0\\x0b\":\n return \"|||\"\n\n count = 49 + counter\n length = int.from_bytes(data[counter... | [
"0.5214011",
"0.49292856",
"0.47483248",
"0.46619806",
"0.4635413",
"0.44699928",
"0.44410896",
"0.4437927",
"0.44217527",
"0.43964815",
"0.43601838",
"0.43417162",
"0.4337935",
"0.43295974",
"0.43120626",
"0.43041757",
"0.4291322",
"0.42824116",
"0.42597744",
"0.42349783",
"... | 0.76511264 | 0 |
The fist 24 bytes (6 words) of a TFM file contain twelve 16bit integers that give the | def _read_lengths(self):
stream = self.stream
stream.seek(0)
###########
# Read and set table lengths
self.table_lengths = [None]*len(tables)
(self.entire_file_length,
header_length,
self.smallest_character_code,
self.largest_character_code) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def m16i():\n\n global offset\n\n x = 0L\n for i in range(2):\n try:\n byte = midifile[offset]\n offset += 1\n except:\n error(\"Invalid MIDI file include (i16->int, offset=%s)\" % offset)\n x = (x << 8) + ord(byte)\n\n return int(x)",
"def read_i... | [
"0.6806584",
"0.63461256",
"0.62110627",
"0.6183381",
"0.6170893",
"0.6077263",
"0.6055383",
"0.59995687",
"0.59053993",
"0.5870395",
"0.5807494",
"0.5766607",
"0.57580394",
"0.5752466",
"0.5727707",
"0.5727707",
"0.57185113",
"0.57169336",
"0.5705531",
"0.5690018",
"0.566762... | 0.0 | -1 |
The first data array is a block of header information, which contains general facts about the font. The header must contain at least two words, and for TFM files to be used with Xerox printing software it must contain at least 18 words, allocated as described below. ``header[0]`` is a 32bit check sum that TEX will copy... | def _read_header(self):
stream = self.stream
self._seek_to_table(tables.header)
# Read header[0 ... 1]
checksum = stream.read_unsigned_byte4()
design_font_size = stream.read_fix_word()
# Read header[2 ... 11] if there
character_info_table_position = self.table... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_font_parameters(self):\n\n stream = self.stream\n\n self._seek_to_table(tables.font_parameter)\n\n if self.tfm.character_coding_scheme == 'TeX math italic':\n # undocumented in tftopl web\n pass\n else:\n # Read the seven fix word parameters\n ... | [
"0.5547137",
"0.5544906",
"0.5527031",
"0.54338384",
"0.54278547",
"0.5304448",
"0.5289456",
"0.52327514",
"0.5182959",
"0.51704085",
"0.5146259",
"0.51352066",
"0.5132421",
"0.51025385",
"0.5036685",
"0.50115645",
"0.5003028",
"0.5002731",
"0.49602506",
"0.49543583",
"0.4951... | 0.5401551 | 5 |
The final portion of a TFM fie is the param array, which is another sequence of fix word values. param[1] = ``slant`` is the amount of italic slant, which is used to help position accents. For example, slant = .25 means that when you go up one unit, you also go .25 units to the right. The slant is a pure number; it's t... | def _read_font_parameters(self):
stream = self.stream
self._seek_to_table(tables.font_parameter)
if self.tfm.character_coding_scheme == 'TeX math italic':
# undocumented in tftopl web
pass
else:
# Read the seven fix word parameters
self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doParametersOfInterest(self):\r\n if self.fg4fixed:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0]\")\r\n self.modelBuilder.doVar(\"r[1,0,4]\")\r\n print \"Fixing CMS_zz4l_fg4\"\r\n poi = \"r\"\r\n else:\r\n if self.modelBuilder.out.var(\"CMS_... | [
"0.5400104",
"0.5254214",
"0.5206212",
"0.520376",
"0.5155425",
"0.5094093",
"0.5069205",
"0.5046871",
"0.50354075",
"0.5022196",
"0.49847862",
"0.49655205",
"0.49636424",
"0.4954346",
"0.4911089",
"0.48956797",
"0.48862684",
"0.48778197",
"0.48648793",
"0.4850119",
"0.484206... | 0.5571183 | 0 |
The lig kern array contains instructions in a simple programming language that explains what to do for special letter pairs. Each word is a lig kern command of four bytes. | def _read_lig_kern_programs(self):
# print 'Lig/Kern Table'
# Fixme: complete special cases
# Read very first instruction of the table
(first_skip_byte,
next_char,
op_byte,
remainder) = self._read_four_byte_numbers_in_table(tables.lig_kern, 0)
if fir... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def singleglyph(x):\n return [glyph(x)]",
"def init_kern(num_pitches, lengthscale, energy, frequency):\n\n kern_act = init_kern_act(num_pitches)\n kern_com = init_kern_com(num_pitches, lengthscale, energy, frequency)\n kern = [kern_act, kern_com]\n return kern",
"def on_hot_lower(self,splitted_w... | [
"0.52664185",
"0.52324265",
"0.5190644",
"0.49902183",
"0.49524304",
"0.48243922",
"0.4799325",
"0.4796385",
"0.4771531",
"0.47566155",
"0.47562125",
"0.47500035",
"0.47483367",
"0.47292683",
"0.47266775",
"0.47224197",
"0.4721936",
"0.4693159",
"0.46886936",
"0.46700308",
"0... | 0.6828259 | 0 |
Next comes the char info array, which contains one char info word per character. Each char info word contains six fields packed into four bytes as follows. | def _read_characters(self):
# Read the character information table
for c in range(self.smallest_character_code, self.largest_character_code + 1):
self._process_char(c) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_char_info(self, c):\n\n index = c - self.smallest_character_code\n bytes = self._read_four_byte_numbers_in_table(\n tables.character_info, index)\n\n width_index = bytes[0]\n height_index = bytes[1] >> 4\n depth_index = bytes[1] & 0xF\n italic_index = ... | [
"0.66203916",
"0.5909764",
"0.5874805",
"0.5723141",
"0.5625157",
"0.5544843",
"0.55069035",
"0.5447513",
"0.5435269",
"0.537435",
"0.52129316",
"0.5208179",
"0.5158724",
"0.51543826",
"0.5144566",
"0.5135801",
"0.5110744",
"0.5065201",
"0.5037014",
"0.5036735",
"0.5024731",
... | 0.5025694 | 20 |
Process the character code c in the character information table. | def _process_char(self, c):
width_index, height_index, depth_index, italic_index, tag, remainder = self._read_char_info(
c)
# Get the parameters in the corresponding tables
if width_index != 0:
width = self._read_fix_word_in_table(tables.width, width_index)
else... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map_char(self, char):\n for key, pattern in self.char_map.items():\n if char in pattern:\n return key\n return 'U'",
"def _charToIndex(self,ch): \n return self.char_dict[ch]",
"def getChar(self,code):\r\n return chr(code)",
"def process_chara... | [
"0.60199857",
"0.5995649",
"0.5895552",
"0.5874629",
"0.5780173",
"0.57260835",
"0.57081205",
"0.5705879",
"0.5598203",
"0.5580874",
"0.554651",
"0.5473547",
"0.54209983",
"0.5418408",
"0.5327699",
"0.5319574",
"0.53130203",
"0.53003746",
"0.5280291",
"0.5277612",
"0.5248423"... | 0.7244657 | 0 |
Read the character code c data in the character information table. | def _read_char_info(self, c):
index = c - self.smallest_character_code
bytes = self._read_four_byte_numbers_in_table(
tables.character_info, index)
width_index = bytes[0]
height_index = bytes[1] >> 4
depth_index = bytes[1] & 0xF
italic_index = bytes[2] >> 6
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_characters(self):\n\n # Read the character information table\n for c in range(self.smallest_character_code, self.largest_character_code + 1):\n self._process_char(c)",
"def test_read_c(self):\n self._test_read(self.encoding_c, self.hashing_algorithm_c,\n ... | [
"0.6755259",
"0.63797873",
"0.6293932",
"0.62920505",
"0.6089438",
"0.6071494",
"0.602174",
"0.6018201",
"0.59013844",
"0.5839739",
"0.5755356",
"0.57003623",
"0.549275",
"0.54288435",
"0.54024756",
"0.5398537",
"0.53903955",
"0.537822",
"0.5324148",
"0.5303587",
"0.5300114",... | 0.7199772 | 0 |
Tests moving a file | def test_move_file_new_workspace(self, mock_message, mock_delete, mock_upload, mock_download, mock_paths):
volume_path = os.path.join('the', 'volume', 'path')
file_path_1 = os.path.join('my_dir', 'my_file.txt')
file_path_2 = os.path.join('my_dir', 'my_file.json')
full_path_file_1 = os.p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moveFile(source, dest):\n try:\n shutil.move(source, dest) \n except IOError as e:\n print (\"Unable to move file. %s\" %(e))",
"def move_file(path):\n new_path = os.path.join(TEST_DIR, TEST_FILE)\n command = ['mv', TEST_FILE, new_path]\n file_operation(path, command)",
"def mo... | [
"0.7711519",
"0.7582299",
"0.7474023",
"0.73866624",
"0.7370703",
"0.73142",
"0.7272098",
"0.7219259",
"0.7162279",
"0.7143103",
"0.712096",
"0.7079723",
"0.69801605",
"0.6915869",
"0.6910514",
"0.6820217",
"0.6798866",
"0.66927844",
"0.6688219",
"0.6658179",
"0.66407305",
... | 0.6973797 | 13 |
Tests moving a file | def test_move_file_new_workspace_without_download(self, mock_message, mock_delete, mock_upload, mock_download, mock_paths):
volume_path = os.path.join('the', 'volume', 'path')
file_path_1 = os.path.join('my_dir', 'my_file.txt')
file_path_2 = os.path.join('my_dir', 'my_file.json')
full_p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moveFile(source, dest):\n try:\n shutil.move(source, dest) \n except IOError as e:\n print (\"Unable to move file. %s\" %(e))",
"def move_file(path):\n new_path = os.path.join(TEST_DIR, TEST_FILE)\n command = ['mv', TEST_FILE, new_path]\n file_operation(path, command)",
"def mo... | [
"0.7711519",
"0.7582299",
"0.7474023",
"0.73866624",
"0.7370703",
"0.73142",
"0.7272098",
"0.7219259",
"0.7162279",
"0.7143103",
"0.712096",
"0.7079723",
"0.69801605",
"0.6973797",
"0.6915869",
"0.6910514",
"0.6820217",
"0.6798866",
"0.66927844",
"0.6658179",
"0.66407305",
... | 0.6688219 | 19 |
Tests moving a file | def test_move_file_new_path(self, mock_message, mock_move):
volume_path = os.path.join('the', 'volume', 'path')
file_path_1 = os.path.join('my_dir', 'my_file.txt')
file_path_2 = os.path.join('my_dir', 'my_file.json')
full_path_file_1 = os.path.join(volume_path, file_path_1)
full... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moveFile(source, dest):\n try:\n shutil.move(source, dest) \n except IOError as e:\n print (\"Unable to move file. %s\" %(e))",
"def move_file(path):\n new_path = os.path.join(TEST_DIR, TEST_FILE)\n command = ['mv', TEST_FILE, new_path]\n file_operation(path, command)",
"def mo... | [
"0.7711519",
"0.7582299",
"0.7474023",
"0.73866624",
"0.7370703",
"0.73142",
"0.7219259",
"0.7162279",
"0.7143103",
"0.712096",
"0.7079723",
"0.69801605",
"0.6973797",
"0.6915869",
"0.6910514",
"0.6820217",
"0.6798866",
"0.66927844",
"0.6688219",
"0.6658179",
"0.66407305",
... | 0.7272098 | 6 |
Returns a generator for delimiterseparated combinations of the strings from `strings`. The combination sizes range from `min_parts` to the smaller of `max_parts` (if it is not `None`) and the number of items in `strings`. | def combine_strings(
strings: Iterable[str],
delimiter: str = " ",
min_parts: int = 0,
max_parts: int = None,
) -> Generator[str, None, None]:
min_parts = 0 if min_parts is None else min_parts
if min_parts < 0:
raise ValueError("min_parts cannot be negative.")
if max_parts is not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bruteforce(strip, min_length, max_length):\n return (''.join(char) for char in chain.from_iterable(product(strip, repeat=x)\n for x in range(min_length, max_length+1)))",
"def _tokens_partitions(tokens, min_number_of_tokens, number_of_partitions):\n... | [
"0.5976369",
"0.5884152",
"0.561761",
"0.5489877",
"0.5373369",
"0.5336096",
"0.5319506",
"0.53185827",
"0.5291077",
"0.52784747",
"0.523727",
"0.5198446",
"0.5193889",
"0.5150338",
"0.514983",
"0.5130469",
"0.51253945",
"0.509443",
"0.50677395",
"0.50625944",
"0.50609386",
... | 0.8301148 | 0 |
Returns a generator for the lines in a file. All trailing spaces and newlines are removed from each read line. The parameter `file_path` is the path to a text file for which the lines are to be fetched from. | def read_lines(file_path: str) -> Generator[str, None, None]:
with open(file_path) as f:
for line in f:
yield line.rstrip() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_text_file(file_path):\n with open(file_path, 'r') as f:\n for line in f:\n line = line.rstrip()\n if not line:\n continue\n yield line",
"def readfile(path: str):\n with open(path) as file:\n for line in file.readlines():\n ... | [
"0.7864164",
"0.7831481",
"0.7813181",
"0.7415857",
"0.73461574",
"0.73056495",
"0.70490074",
"0.70286083",
"0.70038885",
"0.6995738",
"0.69923013",
"0.6933258",
"0.68956476",
"0.6863389",
"0.6857514",
"0.6850605",
"0.67782885",
"0.6752518",
"0.67178637",
"0.6717156",
"0.6702... | 0.8636409 | 0 |
Prints the `combination` string on its own line. This string is delimited by `delimiter`, preceded by the `prefix` string, and followed by the `postfix` string. | def print_combination(
combination: str, delimiter: str = " ", prefix: str = None, postfix: str = None
) -> None:
if prefix is not None:
print(prefix, end=delimiter)
print(combination, end="")
if postfix is not None:
print(delimiter if len(combination) > 0 else "", end=postfix)
pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(\n file_path: str,\n delimiter: str = \" \",\n prefix: str = None,\n postfix: str = None,\n min_parts: int = 0,\n max_parts: int = None,\n) -> None:\n lines = read_lines(file_path)\n lines_with_text = (line for line in lines if len(line) > 0)\n line_combinations = combine_string... | [
"0.6388247",
"0.5657468",
"0.48115373",
"0.46865362",
"0.46651104",
"0.46057737",
"0.46045065",
"0.45726445",
"0.4551462",
"0.451783",
"0.45060995",
"0.44773766",
"0.44678497",
"0.4443884",
"0.44389498",
"0.44321448",
"0.44154876",
"0.44086614",
"0.44011688",
"0.43997845",
"0... | 0.84563273 | 0 |
Runs the program. The parameter `file_path` is the path to the file which is to be read. The `delimiter` argument specifies which delimiter to use to separate the output strings. The parameters `prefix` and `postfix` are optional strings appended and prepended to the output lines. The `min_parts` parameter specifies th... | def main(
file_path: str,
delimiter: str = " ",
prefix: str = None,
postfix: str = None,
min_parts: int = 0,
max_parts: int = None,
) -> None:
lines = read_lines(file_path)
lines_with_text = (line for line in lines if len(line) > 0)
line_combinations = combine_strings(
lines_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def console_start(\n file_path: str,\n delimiter: str,\n prefix: str,\n postfix: str,\n min_parts: int,\n max_parts: int,\n) -> None:\n import sys\n\n error_message = None\n try:\n main(\n file_path,\n delimiter=delimiter,\n prefix=prefix,\n ... | [
"0.66171956",
"0.5419413",
"0.50911546",
"0.5056001",
"0.48763207",
"0.48553953",
"0.46703777",
"0.46060747",
"0.45071557",
"0.44918415",
"0.44612107",
"0.4441966",
"0.44117573",
"0.44062215",
"0.43964303",
"0.43940163",
"0.43858075",
"0.4366964",
"0.43627298",
"0.43624535",
... | 0.7501073 | 0 |
This program takes in a file with strings, and produces delimiterseparated combinations of these strings. | def console_start(
file_path: str,
delimiter: str,
prefix: str,
postfix: str,
min_parts: int,
max_parts: int,
) -> None:
import sys
error_message = None
try:
main(
file_path,
delimiter=delimiter,
prefix=prefix,
postfix=postfix,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(\n file_path: str,\n delimiter: str = \" \",\n prefix: str = None,\n postfix: str = None,\n min_parts: int = 0,\n max_parts: int = None,\n) -> None:\n lines = read_lines(file_path)\n lines_with_text = (line for line in lines if len(line) > 0)\n line_combinations = combine_string... | [
"0.6874418",
"0.54696983",
"0.5427321",
"0.5409542",
"0.5376775",
"0.5370502",
"0.5347375",
"0.5314152",
"0.53054637",
"0.5301588",
"0.5301126",
"0.5252907",
"0.5232522",
"0.52278763",
"0.5190499",
"0.5180537",
"0.5164951",
"0.51628846",
"0.51388574",
"0.51068133",
"0.5078360... | 0.0 | -1 |
check if exchange rates exist for all Price List currencies (to company's currency) | def validate_exchange_rates_exist(self):
company_currency = frappe.db.get_value("Company", self.company, "default_currency")
if not company_currency:
msgprint(_("Please specify currency in Company") + ": " + self.company,
raise_exception=ShoppingCartSetupError)
price_list_currency_map = frappe.db.get_valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getActiveCurrencies():",
"def getCurrencies():",
"def update_all_currencies():\n rates_to_update = []\n rates_to_create = []\n for currency in settings.ALLOWED_CURRENCIES:\n try:\n rate = update_currency_data_from_rss(currency, commit=False)\n if rate.id is None:\n ... | [
"0.71018636",
"0.66748184",
"0.6629021",
"0.6588203",
"0.6566872",
"0.6488751",
"0.6484487",
"0.64375925",
"0.6365472",
"0.63330144",
"0.62951094",
"0.629225",
"0.6266622",
"0.6142166",
"0.6140089",
"0.6060332",
"0.60507655",
"0.60332155",
"0.6025823",
"0.6020183",
"0.5985447... | 0.84533095 | 0 |
Checks that the script checker works | def test_otter_check_script(self):
# run for each individual test
for file in glob(TEST_FILES_PATH + "tests/*.py"):
# capture stdout
output = StringIO()
with contextlib.redirect_stdout(output):
# mock block_print otherwise it interferes with capture o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scriptChecker(filename):\n if not os.path.exists(filename):\n print 'ERROR: %s does not exist' % filename\n import errno\n return errno.ENOENT\n\n # The script-checker program is called directly. If we call the code\n # from this python interpreter, any changes to an observing scr... | [
"0.70963734",
"0.7057622",
"0.682522",
"0.6817014",
"0.66594744",
"0.64477414",
"0.642847",
"0.63979995",
"0.63742423",
"0.63742423",
"0.63742423",
"0.63742423",
"0.63665223",
"0.6331992",
"0.62861043",
"0.6219874",
"0.6213596",
"0.6207089",
"0.6204746",
"0.62004364",
"0.6199... | 0.0 | -1 |
Checks that the script checker works | def test_otter_check_notebook(self):
# run for each individual test
for file in glob(TEST_FILES_PATH + "tests/*.py"):
# capture stdout
output = StringIO()
with contextlib.redirect_stdout(output):
# mock block_print otherwise it interferes with capture... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scriptChecker(filename):\n if not os.path.exists(filename):\n print 'ERROR: %s does not exist' % filename\n import errno\n return errno.ENOENT\n\n # The script-checker program is called directly. If we call the code\n # from this python interpreter, any changes to an observing scr... | [
"0.70963734",
"0.7057622",
"0.682522",
"0.6817014",
"0.66594744",
"0.64477414",
"0.642847",
"0.63979995",
"0.63742423",
"0.63742423",
"0.63742423",
"0.63742423",
"0.63665223",
"0.6331992",
"0.62861043",
"0.6219874",
"0.6213596",
"0.6207089",
"0.6204746",
"0.62004364",
"0.6199... | 0.0 | -1 |
Function to model the price of a European Option, under the BlackScholes pricing model heuristic, using an antithetic variates method variancereduced MonteCarlo simulation. This function simulates two perfectly negatively correlated simple Geometric Brownian Motion (GBM) processes of the underlying asset price, before ... | def blackScholes(current: float, volatility: float, ttm: float, strike: float,
rf: float, dividend: float, sim_count: int, eval_count: int,
opt_type: str='C', **kwargs) -> dict:
# Verify option choice
if opt_type not in ['C', 'P']:
raise ValueError('Incorrect option ty... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare(numberOfSimulations, initialValue, sigma, T, strike, r = 0):\n \n numberOfTests = 100\n \n \n #the two lists that will contain our average errors for the different tests\n errorsStandardMonteCarlo = []\n errorsMonteCarloWithAV = []\n \n #our benchmark: the analytic price ... | [
"0.66519797",
"0.60911804",
"0.60525775",
"0.5962542",
"0.58621377",
"0.58234996",
"0.5763114",
"0.5741226",
"0.567553",
"0.56474507",
"0.5644827",
"0.5632423",
"0.5624563",
"0.5607738",
"0.55745095",
"0.5509977",
"0.55045414",
"0.5465388",
"0.5444405",
"0.54173887",
"0.53988... | 0.6501072 | 1 |
Mock copy file to. | def mock_copy_file_to(iterator, metadata):
chunks = [chunk.data for chunk in iterator]
self.assertEqual(3, len(chunks))
self.assertEqual([('path-bin', b'/file')], metadata)
data = b''.join(chunks)
self.assertEqual(data, contents)
return untrusted_runner_pb2.CopyFileToResponse(resu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_15_copyto(self):\n with mock.patch(BUILTINS + '.open', mock.mock_open()):\n status = udocker.FileUtil(\"source\").copyto(\"dest\")\n self.assertTrue(status)\n status = udocker.FileUtil(\"source\").copyto(\"dest\", \"w\")\n self.assertTrue(status)\n ... | [
"0.8122548",
"0.74830496",
"0.7332181",
"0.72871107",
"0.72668934",
"0.6915809",
"0.6846528",
"0.67997044",
"0.67707396",
"0.67559165",
"0.67559165",
"0.67559165",
"0.67180955",
"0.66859967",
"0.6684247",
"0.6660035",
"0.66151756",
"0.6505115",
"0.6496338",
"0.6477941",
"0.64... | 0.72541636 | 5 |
Test get worker path. | def test_get_cf_worker_path(self):
os.environ['WORKER_ROOT_DIR'] = '/worker'
local_path = os.path.join(os.environ['ROOT_DIR'], 'a', 'b', 'c')
self.assertEqual(
file_host.rebase_to_worker_root(local_path), '/worker/a/b/c')
local_path = os.environ['ROOT_DIR']
self.assertEqual(file_host.rebas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_worker(self):\n worker_helper = WorkerHelper()\n worker_d = worker_helper.get_worker(ToyWorker, {'foo': 'bar'})\n worker = success_result_of(worker_d)\n self.assertIsInstance(worker, ToyWorker)\n self.assertIsInstance(worker._amqp_client, FakeAMQClient)\n self... | [
"0.65934354",
"0.6360944",
"0.62332517",
"0.59729254",
"0.5945865",
"0.5931231",
"0.58893883",
"0.58634233",
"0.58264047",
"0.5816103",
"0.57897335",
"0.57879317",
"0.5751385",
"0.57352287",
"0.56841",
"0.56825376",
"0.56722623",
"0.56704295",
"0.5656956",
"0.5656956",
"0.565... | 0.6835389 | 0 |
Test get host path. | def test_get_cf_host_path(self):
os.environ['ROOT_DIR'] = '/host'
os.environ['WORKER_ROOT_DIR'] = '/worker'
worker_path = os.path.join(os.environ['WORKER_ROOT_DIR'], 'a', 'b', 'c')
self.assertEqual(file_host.rebase_to_host_root(worker_path), '/host/a/b/c')
worker_path = os.environ['WORKER_ROOT_DIR... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_host(self):\n pass",
"def test_get_host_access(self):\n pass",
"def test_host_path(self):\n url = create_url(\n host=\"www.example.com\", path=\"path/to/resource\", scheme_no_ssl=\"http\"\n )\n self.assertEqual(url, \"http://www.example.com/path/to/res... | [
"0.8315634",
"0.77355766",
"0.7611911",
"0.72548294",
"0.72548294",
"0.70257354",
"0.6795541",
"0.6527053",
"0.65192544",
"0.6490157",
"0.6485603",
"0.64630127",
"0.64582074",
"0.64465535",
"0.6401503",
"0.63968176",
"0.63678986",
"0.63454664",
"0.6296492",
"0.624655",
"0.624... | 0.67514616 | 7 |
Remove html tags from a string | def remove_html_tags(text: str) -> str:
clean = re.compile('<.*?>')
return re.sub(clean, '', str(text)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_html_tags(text):\n print('VOU REMOVER AS TAGS DA STRING')\n clean = re.compile('<.*?>')\n print('',re.sub(clean, '', text))\n return re.sub(clean, '', text)",
"def remove_html_tags(self,text):\n #https://medium.com/@jorlugaqui/how-to-strip-html-tags-from-a-string-in-python-7cb81a2bb... | [
"0.8635796",
"0.8504325",
"0.8412075",
"0.8405605",
"0.83518916",
"0.83518916",
"0.83284837",
"0.83064634",
"0.8298507",
"0.8230413",
"0.8216462",
"0.8161706",
"0.8151882",
"0.81427675",
"0.8129797",
"0.80991477",
"0.8070709",
"0.80528504",
"0.80186284",
"0.80122066",
"0.7980... | 0.8376141 | 4 |
Scrape links of WBUR articles for a specified year | def scrape_article_links(year: int) -> List[str]:
# Take into considerations leap years and days when no articles are published
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_year_with_links():\n response = get_response(MAIN_PAGE)\n if response.ok:\n soup = BeautifulSoup(response.text, 'html.parser')\n years_li = soup.find_all(\n 'md-card-footer'\n )\n years_dict = {}\n # Not including the last <a> tag because that is not rele... | [
"0.71862036",
"0.71593505",
"0.7155051",
"0.6790815",
"0.6759994",
"0.67148083",
"0.66857797",
"0.65773827",
"0.65297294",
"0.65275764",
"0.650633",
"0.6365005",
"0.63623565",
"0.63112026",
"0.62049234",
"0.61433667",
"0.6139933",
"0.6039888",
"0.599428",
"0.59832263",
"0.598... | 0.8203429 | 0 |
Scrape contents of articles given the URLs | def scrape_articles(urls: List[str]) -> List[str]:
base_url = "https://www.wbur.org"
articles = []
for url in urls:
url = f"{base_url}{url}"
req = Request(url)
html_page = urlopen(req)
soup = BeautifulSoup(html_page, 'lxml')
paragraphs = soup.find_all('p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_content(soup: bs4.BeautifulSoup, keyword: str=\"\") -> dict:\n articles = {}\n urls = _filter_duplicate_urls(_get_urls(soup, keyword))\n for url in urls:\n try:\n response = requests.get(url)\n if response.status_code == 200:\n # Comment/Uncomment below... | [
"0.7043554",
"0.7042964",
"0.70412135",
"0.7004407",
"0.69894034",
"0.6858116",
"0.68372273",
"0.68079615",
"0.67389524",
"0.67154986",
"0.6683458",
"0.6680562",
"0.6663507",
"0.66628796",
"0.66282916",
"0.6562888",
"0.6555466",
"0.65241516",
"0.6505032",
"0.65037906",
"0.642... | 0.7597384 | 0 |
Creates and saves a csv file containing a single column for the scraped articles | def create_csv(articles: List[str], year: int) -> None:
df = pd.DataFrame({
'text': articles
})
df.to_csv(f"wbur{year}.csv") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_csv(companies):\n print(\"Saving companies.csv...\")\n\n Path(\"output\").mkdir(parents=True, exist_ok=True)\n file_name = 'output/companies.csv'\n\n with open(file_name, 'w', newline='') as csvfile:\n writer = csv.writer(csvfile, delimiter=',')\n i = 0\n while i < 500:\n ... | [
"0.6890485",
"0.6742668",
"0.65642285",
"0.65591663",
"0.6520742",
"0.6491834",
"0.6458035",
"0.64558274",
"0.64135855",
"0.6413078",
"0.6404125",
"0.63978297",
"0.6386491",
"0.6349512",
"0.63473815",
"0.63460076",
"0.633349",
"0.6327605",
"0.6324338",
"0.6314723",
"0.626726"... | 0.74493724 | 0 |
Return a tuple of 'footnotes' and collection of footnotes. Footnotes are "collected" from the node and its children. | def apply_layer(self, text_index):
footnotes = []
for label in self.layer.keys():
if is_contained_in(label, text_index):
footnotes += [x['footnote_data']
for x in self.layer[label]
if 'footnote_data' in x]
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeFootnotesDiv(self, root):\n\n if not list(self.footnotes.keys()):\n return None\n\n div = etree.Element(\"div\")\n div.set('class', 'footnote')\n etree.SubElement(div, \"hr\")\n ol = etree.SubElement(div, \"ol\")\n\n for id in self.footnotes.keys():\n ... | [
"0.6317781",
"0.61900926",
"0.6149558",
"0.6146513",
"0.5762061",
"0.56409013",
"0.5610856",
"0.54278535",
"0.5426516",
"0.53579605",
"0.52695143",
"0.51903385",
"0.51892966",
"0.51519656",
"0.5131307",
"0.5113108",
"0.5091132",
"0.5089666",
"0.5081637",
"0.50717264",
"0.5071... | 0.5484363 | 7 |
Print status each time. | def on_status(self, status):
if self._counter < self._limit:
if re.match(r'^en(-gb)?$', status.lang): # english tweets only
with open(self._file, "a+") as f:
f.write(
json.dumps(self._map_status_fields(status)) + ',\n')
pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _print_status(self):",
"def status(s):\n print('\\033[1m{0}\\033[0m'.format(s))\n time.sleep(2)",
"def _PrintStatus(self):\n comment = 'iter %i' % (self.n_iter)\n self.gfile.write(fileio.GetPrintCoordsXyzString(\n self.mol.atoms, comment, 14, 8))\n self.PrintEnergy(self.n_iter)\n s... | [
"0.8277072",
"0.75176513",
"0.7487483",
"0.7366643",
"0.7340062",
"0.7316466",
"0.73164415",
"0.73164415",
"0.73164415",
"0.73164415",
"0.7304564",
"0.7295404",
"0.7295404",
"0.7295404",
"0.7295404",
"0.7240836",
"0.72358745",
"0.72280544",
"0.7167335",
"0.71368235",
"0.71220... | 0.0 | -1 |
Extract fields from status to save to json. | def _map_status_fields(self, tweet):
data = {
# status
"date": tweet.created_at.strftime('%Y-%m-%d %H:%M:%S'),
"id": tweet.id_str,
"text": tweet.text,
"truncated": tweet.truncated,
"lang": tweet.lang,
# user
"user_id... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_status(self, status) -> None:\r\n if \"VehicleInfo\" in status:\r\n if \"RemoteHvacInfo\" in status[\"VehicleInfo\"]:\r\n self.hvac = status[\"VehicleInfo\"][\"RemoteHvacInfo\"]\r\n\r\n if \"ChargeInfo\" in status[\"VehicleInfo\"]:\r\n self.bat... | [
"0.6647689",
"0.6349667",
"0.60837454",
"0.6059531",
"0.6050074",
"0.5994271",
"0.59692353",
"0.59652483",
"0.59057873",
"0.59021086",
"0.58989656",
"0.5840351",
"0.5840351",
"0.5793476",
"0.57565075",
"0.567152",
"0.5638876",
"0.5635663",
"0.55974156",
"0.556527",
"0.5554442... | 0.6915547 | 0 |
Cleanup the test byproducts. | def classCleanup(cls):
cls.RemoveTempFile(SettingsCommandTestCase.output_file_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n self.tmp.cleanup()",
"def teardown(self):\n\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\n\n return",
"def cleanup(self):\n super(Test200SmartSanityDownload004, self).cleanup()",
"def teardown(self):\n del self.testInst, self... | [
"0.7640776",
"0.74029046",
"0.7368922",
"0.7343883",
"0.7318036",
"0.7304285",
"0.7304285",
"0.7304285",
"0.7271605",
"0.7271605",
"0.7271605",
"0.7263641",
"0.7263641",
"0.7263641",
"0.7233525",
"0.72201914",
"0.72102296",
"0.72102296",
"0.7205624",
"0.7205624",
"0.71853197"... | 0.0 | -1 |
Test argument parsing. Run the program with args_in. The program dumps its arguments to stdout. Compare the stdout with args_out. | def expect_args(self, args_in, args_out):
filename = SettingsCommandTestCase.output_file_name
outfile = self.getBuildArtifact(filename)
if lldb.remote_platform:
outfile_arg = os.path.join(
lldb.remote_platform.GetWorkingDirectory(), filename
)
el... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main_parse_args(args):\n # Must return so that check command return value is passed back to calling routine\n # otherwise py.test will fail\n return main(parse_args(args))",
"def main_parse_args(args):\n # Must return so that check command return value is passed back to calling routine\n # oth... | [
"0.70857084",
"0.70857084",
"0.6719469",
"0.6544911",
"0.64907324",
"0.6395866",
"0.6362561",
"0.632354",
"0.63156325",
"0.62827814",
"0.6258688",
"0.6254855",
"0.6252494",
"0.62392056",
"0.6207981",
"0.61993474",
"0.61984104",
"0.61981714",
"0.6184711",
"0.61758745",
"0.6159... | 0.7031648 | 2 |
Responsible for verifying techniques directory and generating techniques index markdown | def generate():
# Verify if directory exists
if not os.path.isdir(config.techniques_markdown_path):
os.mkdir(config.techniques_markdown_path)
#Write the technique index.html page
with open(os.path.join(config.techniques_markdown_path, "overview.md"), "w", encoding='utf8') as md_file:
m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def walkthrough(software_map):\n\n for i in software_map:\n\n if not i[\"is_file\"]:\n\n # for each directory: make a index.md\n dname = \"./docs/\" + i[\"name\"]\n index = \"./docs/\" + i[\"name\"] + \"/index.md\"\n print(index)\n os.mkdir(dname)\n\... | [
"0.58886623",
"0.58457476",
"0.57502025",
"0.5703051",
"0.56759125",
"0.56134313",
"0.5500864",
"0.5470556",
"0.5454886",
"0.5448571",
"0.5448571",
"0.54260826",
"0.54148185",
"0.54075974",
"0.53453696",
"0.53054494",
"0.5277186",
"0.52735656",
"0.5244364",
"0.5243433",
"0.52... | 0.7166579 | 0 |
Generate technique index markdown for each domain and generates shared data for techniques | def generate_domain_markdown(domain):
#Reads the STIX and creates a list of the ATT&CK Techniques
full_techniques = stixhelpers.get_techniques(config.ms[domain])
tactic_list = stixhelpers.get_tactic_list(config.ms[domain])
data = {}
technique_list = get_techniques_list(full_techniques)
d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate():\n\n # Verify if directory exists\n if not os.path.isdir(config.techniques_markdown_path):\n os.mkdir(config.techniques_markdown_path)\n\n #Write the technique index.html page\n with open(os.path.join(config.techniques_markdown_path, \"overview.md\"), \"w\", encoding='utf8') as md... | [
"0.7529587",
"0.6753163",
"0.5811377",
"0.5526393",
"0.5446928",
"0.5422765",
"0.5422765",
"0.5392124",
"0.5317624",
"0.52876973",
"0.5264621",
"0.5209",
"0.52000964",
"0.5198834",
"0.5198507",
"0.5195234",
"0.51844853",
"0.5183828",
"0.51717633",
"0.5149823",
"0.5149334",
... | 0.69349456 | 1 |
Generetes markdown data for given technique | def generate_technique_md(technique, domain, side_menu_data, tactic_list):
attack_id = util.get_attack_id(technique)
# Only add technique if the attack id was found
if attack_id:
technique_dict = {}
technique_dict['attack_id'] = attack_id
technique_dict['domain'] = domain.split("... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate():\n\n # Verify if directory exists\n if not os.path.isdir(config.techniques_markdown_path):\n os.mkdir(config.techniques_markdown_path)\n\n #Write the technique index.html page\n with open(os.path.join(config.techniques_markdown_path, \"overview.md\"), \"w\", encoding='utf8') as md... | [
"0.73341054",
"0.7133402",
"0.6663783",
"0.659057",
"0.6576931",
"0.63710845",
"0.62863004",
"0.6106517",
"0.60687923",
"0.60239404",
"0.5961844",
"0.5960654",
"0.5940064",
"0.5900538",
"0.5900436",
"0.5838119",
"0.5826031",
"0.58112127",
"0.57876456",
"0.5779548",
"0.5773856... | 0.73468506 | 0 |
Given a technique and a tactic list, return data of related techniques. Data includes technique id/name and tactic name/id for each related technique | def get_related_techniques_data(technique, tactic_list):
technique_data = []
if config.related_techniques.get(technique['id']):
for rel_tech in config.related_techniques[technique['id']]:
attack_id = util.get_attack_id(rel_tech['object'])
if attack_id:
row... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_examples_table_data(technique, reference_list, next_reference_number):\n\n # Creating map to avoid repeating the code 3 times\n examples_map = [\n { 'example_type': config.tools_using_technique }, \n { 'example_type': config.malware_using_technique },\n { 'example_type': config.g... | [
"0.6661438",
"0.6644859",
"0.66175026",
"0.6393515",
"0.6246491",
"0.61199707",
"0.60754085",
"0.5725204",
"0.5500558",
"0.5417878",
"0.533964",
"0.52860373",
"0.5199164",
"0.516859",
"0.51545167",
"0.49810523",
"0.4943632",
"0.48681778",
"0.48513177",
"0.48226207",
"0.478531... | 0.85353535 | 0 |
Given a technique a reference list, find mitigations that mitigate technique and return list with mitigation data. Also modifies the reference list if it finds a reference that is not on the list | def get_mitigations_table_data(technique, reference_list, next_reference_number):
mitigation_data = []
# Check if technique has mitigations
if config.technique_mitigated.get(technique['id']):
# Iterate through technique mitigations
for mitigation in config.technique_mitigated[technique['id... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_techniques_used_by_malware():\n global techniques_used_by_malware\n \n if not techniques_used_by_malware:\n techniques_used_by_malware = rsh.techniques_used_by_malware(get_srcs())\n \n return techniques_used_by_malware",
"def get_mitigation_mitigates_techniques():\n global mitiga... | [
"0.6026351",
"0.59703493",
"0.58191",
"0.5710842",
"0.56594396",
"0.55248374",
"0.5523887",
"0.54103875",
"0.5382938",
"0.5350205",
"0.52979887",
"0.52793556",
"0.51606363",
"0.5085763",
"0.49043208",
"0.47701126",
"0.47418934",
"0.47043297",
"0.47007185",
"0.46617642",
"0.46... | 0.60934097 | 0 |
Given a technique object, find examples in malware using technique, tools using technique and groups using technique. Return list with example data | def get_examples_table_data(technique, reference_list, next_reference_number):
# Creating map to avoid repeating the code 3 times
examples_map = [
{ 'example_type': config.tools_using_technique },
{ 'example_type': config.malware_using_technique },
{ 'example_type': config.groups_using... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)",
"def get_techniques_used_by_groups():\n global techniqu... | [
"0.628506",
"0.62599236",
"0.6136026",
"0.60789555",
"0.5992689",
"0.5951035",
"0.5889614",
"0.5713279",
"0.563185",
"0.5599353",
"0.5587775",
"0.55412316",
"0.5538815",
"0.5419111",
"0.5401357",
"0.5392721",
"0.53859246",
"0.5369623",
"0.5243388",
"0.52172226",
"0.521398",
... | 0.659891 | 0 |
Responsible for generating the links that are located on the left side of individual technique domain pages | def get_technique_side_menu_data(domain, technique_list, tactic_list):
# Get tactics techniques side menu data to fill out with jinja
tactics_techniques_menu_data = []
for tactic in tactic_list:
tactic_row = {}
tactic_row['name'] = tactic['name']
tactic_row['id'] = util.ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getExpandedLinks():",
"def make_navbar_for_homepage(self):\n links = [\n \"home\", [\"Result Pages\", self._result_page_links()], \"Version\"\n ]\n if len(self.samples) > 1:\n links[1][1] += [\"Comparison\"]\n if self.publication:\n links.insert(2,... | [
"0.603881",
"0.59611195",
"0.5923129",
"0.5906821",
"0.5832609",
"0.58210886",
"0.5725273",
"0.5673449",
"0.56281406",
"0.558823",
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"0.5566538",
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"0.55155784",
"0.5482084",
"0.547729",
"0.5472593",
"0.54505503",
"0.5410811",
"0.5399908",
"0.5361225",
... | 0.0 | -1 |
This method is used to generate a list of techniques | def get_techniques_list(techniques):
technique_list = {}
for technique in techniques:
if 'revoked' not in technique or technique['revoked'] == False:
attack_id = util.get_attack_id(technique)
if attack_id:
technique_dict = {}
technique_dic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)",
"def techniques(self):\n return self._get_child_pag... | [
"0.6610979",
"0.6524354",
"0.62929714",
"0.61466",
"0.61216724",
"0.61157763",
"0.59929365",
"0.5992363",
"0.5967745",
"0.59375405",
"0.59261817",
"0.5862121",
"0.5525391",
"0.5521534",
"0.5519102",
"0.5513661",
"0.5487172",
"0.5474245",
"0.54732275",
"0.54732275",
"0.5459485... | 0.6863604 | 0 |
Given the technique's detection string, replace the citations and add them to the reference_list is they are not found. Return modified detection string | def get_detection_string(detection, reference_list, next_reference_number):
citations_from_descr = util.get_citations_from_descr(detection)
filtered_detection = util.replace_html_chars(markdown.markdown(detection))
filtered_detection = util.filter_urls(filtered_detection)
filtered_detection = util.get... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ref_tag_preprocess(inp_tag_text):\n\n # some words in references\n inp_tag_text = inp_tag_text.replace(\"до римлян\", \"Римл\")\n inp_tag_text = inp_tag_text.replace(\" и \", \"; \")\n inp_tag_text = inp_tag_text.replace(\" і \", \"; \")\n inp_tag_text = inp_tag_text.replace(\"–\", \"-\")\n #... | [
"0.5983218",
"0.5867871",
"0.5652614",
"0.558464",
"0.55107594",
"0.5485288",
"0.5324073",
"0.5307263",
"0.5304254",
"0.5300193",
"0.52907175",
"0.52869886",
"0.5280883",
"0.52644163",
"0.52494705",
"0.52478",
"0.5241941",
"0.5224906",
"0.5224906",
"0.52240586",
"0.52064675",... | 0.6253816 | 0 |
Patch django.request logger to ignore warnings. | def no_request_warning():
return patch_logger('django.request', 'warning', log_kwargs=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_logger(log, request):\n request.cls.log = log",
"def unpatch():\n _u(sanic.Sanic, \"handle_request\")\n if not SANIC_PRE_21:\n _u(sanic.Sanic, \"_run_request_middleware\")\n _u(sanic.request.Request, \"respond\")\n if not getattr(sanic, \"__datadog_patch\", False):\n retu... | [
"0.6512308",
"0.6345203",
"0.6293786",
"0.628367",
"0.6275584",
"0.62672555",
"0.623322",
"0.6210527",
"0.61037356",
"0.6073067",
"0.6044601",
"0.60088855",
"0.5969443",
"0.59051746",
"0.5904183",
"0.5851811",
"0.58263904",
"0.58186126",
"0.5768054",
"0.5733492",
"0.5733492",... | 0.85700905 | 0 |
Create transform from (lon, lat) to (column, row) coordinates | def lonlat2cr_for_geotif(path):
old_cs, new_cs, gta, local_vars = _create_xform(path)
transform = osr.CoordinateTransformation(new_cs, old_cs)
def composite(lon, lat):
"""xform from (lon, lat) to (c, r)"""
if not -90 <= lat <= 90:
raise ValueError('illegal lat value, did you swi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform_from_latlon(lat, lon):\n from affine import Affine\n lat = np.asarray(lat)\n lon = np.asarray(lon)\n trans = Affine.translation(lon[0], lat[0])\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n return trans * scale",
"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_la... | [
"0.68157905",
"0.66313314",
"0.661171",
"0.6593962",
"0.6544747",
"0.6531103",
"0.6518472",
"0.6451298",
"0.6443582",
"0.6433205",
"0.63895917",
"0.6381785",
"0.63414675",
"0.63401574",
"0.6333194",
"0.63325125",
"0.6327625",
"0.6324136",
"0.6297547",
"0.6277964",
"0.6277665"... | 0.0 | -1 |
xform from (lon, lat) to (c, r) | def composite(lon, lat):
if not -90 <= lat <= 90:
raise ValueError('illegal lat value, did you switch coordinates')
return (~gta * transform.TransformPoint(lat, lon)[:2]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def composite(c, r):\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat",
"def merc(lat, lon):\n\tr_major = 6378137.000\n\tx = r_major * math.... | [
"0.76137656",
"0.69122946",
"0.6881515",
"0.68766624",
"0.6610878",
"0.65857524",
"0.6521325",
"0.64422315",
"0.6299409",
"0.62912303",
"0.62782985",
"0.62745625",
"0.62368286",
"0.6209349",
"0.6204365",
"0.6120023",
"0.607636",
"0.60706896",
"0.6065761",
"0.6049449",
"0.6039... | 0.5487496 | 85 |
Create transform from (column, row) to (lon, lat) coordinates | def cr2lonlat_for_geotif(path):
old_cs, new_cs, gta, local_vars = _create_xform(path)
transform = osr.CoordinateTransformation(old_cs, new_cs)
def composite(c, r):
"""xform from (c, r) to (lon, lat)"""
x, y = gta * (c, r)
lat, lon = transform.TransformPoint(x, y)[:2]
if not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform_from_latlon(lat, lon):\n from affine import Affine\n lat = np.asarray(lat)\n lon = np.asarray(lon)\n trans = Affine.translation(lon[0], lat[0])\n scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n return trans * scale",
"def _build_geotransform(self, i, j):\n assert i... | [
"0.66467303",
"0.6624821",
"0.66023815",
"0.65764207",
"0.6559197",
"0.6513407",
"0.6504504",
"0.6468915",
"0.6444075",
"0.63868356",
"0.63452953",
"0.63281846",
"0.6316108",
"0.6261537",
"0.62128407",
"0.61810315",
"0.61589795",
"0.6146225",
"0.61366695",
"0.61320674",
"0.61... | 0.5831346 | 56 |
xform from (c, r) to (lon, lat) | def composite(c, r):
x, y = gta * (c, r)
lat, lon = transform.TransformPoint(x, y)[:2]
if not -90 <= lat <= 90:
raise ValueError('illegal lat value, did you switch coordinates')
return lon, lat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cr2lonlat_for_geotif(path):\n old_cs, new_cs, gta, local_vars = _create_xform(path)\n transform = osr.CoordinateTransformation(old_cs, new_cs)\n\n def composite(c, r):\n \"\"\"xform from (c, r) to (lon, lat)\"\"\"\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:... | [
"0.6946017",
"0.6875609",
"0.6777287",
"0.67524993",
"0.6716408",
"0.66537076",
"0.6602633",
"0.64604294",
"0.6418981",
"0.6406474",
"0.6382838",
"0.6360858",
"0.63093865",
"0.6304608",
"0.6299599",
"0.6263775",
"0.6230886",
"0.6198476",
"0.6191175",
"0.61556995",
"0.6152255"... | 0.7614783 | 0 |
Straighten an image that has been rotated and has blank (alpha == 0) corners | def straighten_image(img, scale=1):
img = img[::scale, ::scale]
blank = (img[:, :, 3] == 0)
degrees = _find_angle(blank)
if degrees > 45:
degrees = degrees - 90
nr, nc = img.shape[:2]
radians = np.radians(degrees)
# Rotate image
img = img.astype(float) / img.max()
new_img = t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def straighten(image):\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n gray = cv2.bitwise_not(gray)\n thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n coords = np.column_stack(np.where(thresh > 0))\n angle = cv2.minAreaRect(coords)[-1]\n if angle < -45:\n a... | [
"0.7247929",
"0.7087627",
"0.6532112",
"0.63538",
"0.62858284",
"0.6238983",
"0.6210797",
"0.62102747",
"0.61778975",
"0.61557895",
"0.6126098",
"0.61174065",
"0.60648906",
"0.6063642",
"0.60582924",
"0.6030168",
"0.60160047",
"0.60000926",
"0.5985503",
"0.59584457",
"0.59356... | 0.6804736 | 2 |
Save straightened version of geotiff at `src_path` to `tgt_path`. | def create_straightened(src_path, tgt_path):
img = skio.imread(src_path)
straight_img, xform, angle = straighten_image(img)
save_geotiff(tgt_path, (straight_img * 255).astype('uint8'), xform) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def saveAsGeotiff(self, geotiff_fname, overwrite=True, verbose=False):\n if not overwrite and os.path.exists(geotiff_fname):\n raise ValueError('output geotiff already exists:\\n {}'.format(\n geotiff_fname))\n\n if overwrite and os.path.exists(geotiff_fname):\n ... | [
"0.6665498",
"0.6444286",
"0.59382063",
"0.5935782",
"0.5847079",
"0.5664643",
"0.56095916",
"0.54957837",
"0.5420205",
"0.5419931",
"0.53277814",
"0.5223335",
"0.51851106",
"0.5165319",
"0.5149921",
"0.51454896",
"0.51343054",
"0.5089744",
"0.50778294",
"0.50766367",
"0.5061... | 0.7338728 | 0 |
Save a raster image as a geotiff with associated transform | def save_geotiff(path, raster, xform):
# Import rasterio here, so that we don't need it in the cloud.
import rasterio
import affine
n_lats, n_lons, depth = raster.shape
assert raster.max() <= 255
assert raster.min() >= 0
profile = {
'crs': 'EPSG:4326',
'nodata': 0,
'dtype':... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_image(nom, image, _geo_trans):\n cols = image.shape[2]\n rows = image.shape[1]\n bands = image.shape[0]\n print(bands, cols, rows)\n driver = gdal.GetDriverByName(\"GTiff\")\n out_raster = driver.Create(nom, cols, rows, bands, gdal.GDT_Byte)\n # if (geo_trans):\n # outRaster.Se... | [
"0.748401",
"0.70260197",
"0.69436365",
"0.69406134",
"0.69158727",
"0.6690425",
"0.66588354",
"0.6629013",
"0.6620407",
"0.6539317",
"0.65314543",
"0.6381361",
"0.63328516",
"0.63243276",
"0.62995696",
"0.628616",
"0.6275659",
"0.61972815",
"0.6176187",
"0.61506325",
"0.6034... | 0.7922152 | 0 |
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