\\s+|
\\s+|\\s+\\,|\\'s|\\'|\\;|\\(|\\)|\\-\\-\\s+|\\s+\\.', '', text)\n text = re.sub(r'\\.\\,', '. ,', text)\n text = re.sub(r'\\,', '', text)\n text = re.sub(r'\\$', '$ ', text)\n text = re.sub(r'\\%', ' %', text)\n text = re.sub(r'\\s\\\"\\s', ' ', text)\n text = re.sub(r'\\.\\s+', '. ', text)\n text = text.lower()\n return text","def result(target_text):\n\n display_text(target_text)\n readability(target_text)","def text(self, text: str, xo: int, yo: int, color: int):\n for offset, letter in enumerate(text):\n template = font.get(letter)\n for x, line in enumerate(template):\n line_str = '{:08b}'.format(line).replace('0b', '')\n if self.portrait:\n line_str = reversed(line_str)\n for y, pix in enumerate(line_str):\n if pix == '1':\n self.pixel(xo + x + (offset * 8), yo + y, color)","def parse_text(self, text):\r\n MAXLEN = 100\r\n sentences = []\r\n punct = [\",\",\":\",\";\",\".\",\"–\",\"?\",\"!\",\"(\",\")\"] # Interpunctuation marks\r\n text = text.replace(\"\\r\", \" \").replace(\"\\t\", \" \") # Remove CR and tabs\r\n words = text.split(\" \") if len(text) > MAXLEN else []\r\n sentence = \"\" if len(text) > MAXLEN else text\r\n\r\n # Preprocess list for silence markers\r\n if conf.SilenceMarker in text:\r\n words_new = []\r\n if not words and sentence: # Was too short to be cut initially\r\n words = text.split(\" \")\r\n sentence = \"\"\r\n for w in filter(None, words):\r\n if conf.SilenceMarker not in w.lower():\r\n words_new.append(w)\r\n else:\r\n text_chunks = w.lower().split(conf.SilenceMarker)\r\n for i, part in enumerate(text_chunks):\r\n if part:\r\n words_new.append(part)\r\n if i < len(text_chunks) - 1:\r\n words_new.append(conf.SilenceMarker)\r\n else:\r\n if words_new and conf.SilenceMarker in words_new[-1]:\r\n words_new[-1] += conf.SilenceMarker\r\n else:\r\n words_new.append(conf.SilenceMarker)\r\n words = words_new\r\n\r\n for w in words:\r\n if conf.SilenceMarker in w:\r\n if sentence:\r\n sentences.append(sentence.strip())\r\n sentences.append(w)\r\n sentence = \"\"\r\n elif w[-1] in punct or w[0] in punct: # Encountered punctuation\r\n if w[-1] in punct and (len(sentence) + len(w) + 1 < MAXLEN):\r\n # Word ends with punct and sentence can still be added to\r\n sentences.append(sentence.strip() + \" \" + w.strip())\r\n sentence = \"\" # Save sentence and word, start new sentence\r\n elif w[0] in punct and w[-1] not in punct:\r\n # Word starts with punctuation, like '('\r\n sentences.append(sentence.strip()) # Save current sentence\r\n sentence = w # Start a new sentence with punct and word\r\n else: # word ends with punct and sentence already long enough\r\n sentences.extend([sentence.strip(), w.strip()])\r\n sentence = \"\" \r\n else:\r\n if (len(sentence) + len(w) + 1 < MAXLEN): # Sentence still\r\n sentence += \" \" + w # short enough\r\n else: # Sentence too long\r\n sentences.append(sentence.strip())\r\n sentence = w # Start a new sentence with the word\r\n if sentence:\r\n sentences.append(sentence.strip())\r\n return sentences","def run_ocr_in_chart(chart, pad=0, psm=PSM.SINGLE_LINE):\n img = chart.image\n\n # add a padding to the initial figure\n fpad = 1\n img = cv2.copyMakeBorder(img.copy(), fpad, fpad, fpad, fpad, cv2.BORDER_CONSTANT, value=(255, 255, 255))\n fh, fw, _ = img.shape\n\n api = PyTessBaseAPI(psm=psm, lang='eng')\n clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))\n\n for tbox in chart.texts:\n # adding a pad to original image. Some case in quartz corpus, the text touch the border.\n x, y, w, h = ru.wrap_rect(u.ttoi(tbox.rect), (fh, fw), padx=pad, pady=pad)\n x, y = x + fpad, y + fpad\n\n if w * h == 0:\n tbox.text = ''\n continue\n\n # crop region of interest\n roi = img[y:y + h, x:x + w]\n # to gray scale\n roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n #\n roi_gray = cv2.resize(roi_gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)\n # binarization\n _, roi_bw = cv2.threshold(roi_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n # removing noise from borders\n roi_bw = 255 - clear_border(255-roi_bw)\n\n # roi_gray = cv2.copyMakeBorder(roi_gray, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=255)\n\n # when testing boxes from csv files\n if tbox.num_comp == 0:\n # Apply Contrast Limited Adaptive Histogram Equalization\n roi_gray2 = clahe.apply(roi_gray)\n _, roi_bw2 = cv2.threshold(roi_gray2, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n _, num_comp = morphology.label(roi_bw2, return_num=True, background=255)\n tbox.regions.extend(range(num_comp))\n\n pil_img = smp.toimage(roi_bw)\n if SHOW:\n pil_img.show()\n max_conf = -np.inf\n min_dist = np.inf\n correct_text = ''\n correct_angle = 0\n u.log('---------------')\n for angle in [0, -90, 90]:\n rot_img = pil_img.rotate(angle, expand=1)\n\n api.SetImage(rot_img)\n conf = api.MeanTextConf()\n text = api.GetUTF8Text().strip()\n dist = abs(len(text.replace(' ', '')) - tbox.num_comp)\n\n u.log('text: %s conf: %f dist: %d' % (text, conf, dist))\n if conf > max_conf and dist <= min_dist:\n max_conf = conf\n correct_text = text\n correct_angle = angle\n min_dist = dist\n\n tbox.text = post_process_text(lossy_unicode_to_ascii(correct_text))\n tbox.text_conf = max_conf\n tbox.text_dist = min_dist\n tbox.text_angle = correct_angle\n\n u.log('num comp %d' % tbox.num_comp)\n u.log(u'** text: {} conf: {} angle: {}'.format(correct_text, max_conf, correct_angle))\n\n api.End()\n\n # return boxes","def bert_preprocess(raw_text):\n nlp = English()\n nlp.add_pipe(nlp.create_pipe('sentencizer')) # updated\n doc = nlp(raw_text)\n sentences = [sent.string.strip() for sent in doc.sents][0:2] \n new_sentences = []\n for i, sentence in enumerate(sentences):\n if i==0:\n new_sentences.append(\"[CLS] \" + sentence + \" [SEP]\")\n else:\n new_sentences.append(sentence + \" [SEP]\")\n \n preprocessed_text = ' '.join(new_sentences)\n \n if \"[CLS]\" not in preprocessed_text:\n raise Exception(\"[CLS] not found in preprocessed text\")\n if \"[SEP]\" not in preprocessed_text:\n raise Exception(\"[SEP] not found in preprocessed text\")\n \n return","def sliptText(text):\n\treturn [char for char in text]","def edit_google_vision_text(self,text):\n s1=text\n try:\n log_info(\"Correcting google vision text to remove extra spacing\",MODULE_CONTEXT)\n i=0\n while(i\"\n if self.ents is None or label.upper() in self.ents:\n color = self.colors.get(label.upper(), self.default_color)\n ent_settings = {\n \"label\": label,\n \"text\": entity,\n \"bg\": color,\n \"kb_link\": kb_link,\n }\n ent_settings.update(additional_params)\n markup += self.ent_template.format(**ent_settings)\n else:\n markup += entity\n offset = end\n fragments = text[offset:].split(\"\\n\")\n for i, fragment in enumerate(fragments):\n markup += escape_html(fragment)\n if len(fragments) > 1 and i != len(fragments) - 1:\n markup += \"
\"\n markup = TPL_ENTS.format(content=markup, dir=self.direction)\n if title:\n markup = TPL_TITLE.format(title=title) + markup\n return markup","def display_text(target_text):\n\n print('Text to analyze:')\n print('')\n print('-------TEXT BELOW-------')\n print(target_text)\n print('-------TEXT ENDS-------')\n print('')","def process_text(self, text, language):","def process_text(text):\n text = text.strip()\n textList = text.split('\\n')\n newText = ''\n addNewline = True\n for line in textList:\n # Remove duplicate white space\n temp = ' '.join(line.split())\n # Trim any beginning non-alphabet letters\n temp = trim(temp)\n # Remove overly short lines, but keep ends of sentences\n # Add a newline if gap detected\n if len(temp) < 40 and not '.' in temp:\n if addNewline:\n newText += '\\n'\n addNewline = False\n continue\n # Add line to growing string\n newText += temp + ' '\n addNewline = True\n return newText","def sentences(summary, nlp):\n text = remove_spurious_words(text_of(summary))\n all_sentence = [sentence for sentence in re.split(\"[。，？！\\n]\", text) if sentence]\n all_sentence = [re.sub('[ ]+', ' ', sentence.encode('gb2312', 'ignore').decode('gb2312')).strip() for sentence in\n all_sentence]\n return [nlp.ner(sentence) for sentence in all_sentence if sentence]","async def ascii(self, ctx, *, text):\n text = text.replace(' ', '\\n')\n \n if not text:\n await ctx.send(f\"{ctx.tick(False)} You need to specify the text you want to convert!\")\n \n _fig = figlet_format(text.replace(' ', '\\n'))\n \n if len(_fig) > 1300:\n await ctx.send(f\"{ctx.tick(False)} That message is too long!\")\n await ctx.send(f\"{ctx.tick(True)} Done!\")\n await ctx.send(f\"```{_fig}```\")","def get_sentence(self):","def frontend(text):\n text = pyopenjtalk.g2p(text, kana=False)\n print(f\"Cleaned text: {text}\")\n charseq = text.split(\" \")\n idseq = []\n for c in charseq:\n if c.isspace():\n idseq += [char_to_id[\"
\\\\s+|
\\\\s+|\\\\s+\\\\,|\\\\'s|\\\\'|\\\\;|\\\\(|\\\\)|\\\\-\\\\-\\\\s+|\\\\s+\\\\.', '', text)\\n text = re.sub(r'\\\\.\\\\,', '. ,', text)\\n text = re.sub(r'\\\\,', '', text)\\n text = re.sub(r'\\\\$', '$ ', text)\\n text = re.sub(r'\\\\%', ' %', text)\\n text = re.sub(r'\\\\s\\\\\\\"\\\\s', ' ', text)\\n text = re.sub(r'\\\\.\\\\s+', '. ', text)\\n text = text.lower()\\n return text\",\n \"def result(target_text):\\n\\n display_text(target_text)\\n readability(target_text)\",\n \"def text(self, text: str, xo: int, yo: int, color: int):\\n for offset, letter in enumerate(text):\\n template = font.get(letter)\\n for x, line in enumerate(template):\\n line_str = '{:08b}'.format(line).replace('0b', '')\\n if self.portrait:\\n line_str = reversed(line_str)\\n for y, pix in enumerate(line_str):\\n if pix == '1':\\n self.pixel(xo + x + (offset * 8), yo + y, color)\",\n \"def parse_text(self, text):\\r\\n MAXLEN = 100\\r\\n sentences = []\\r\\n punct = [\\\",\\\",\\\":\\\",\\\";\\\",\\\".\\\",\\\"–\\\",\\\"?\\\",\\\"!\\\",\\\"(\\\",\\\")\\\"] # Interpunctuation marks\\r\\n text = text.replace(\\\"\\\\r\\\", \\\" \\\").replace(\\\"\\\\t\\\", \\\" \\\") # Remove CR and tabs\\r\\n words = text.split(\\\" \\\") if len(text) > MAXLEN else []\\r\\n sentence = \\\"\\\" if len(text) > MAXLEN else text\\r\\n\\r\\n # Preprocess list for silence markers\\r\\n if conf.SilenceMarker in text:\\r\\n words_new = []\\r\\n if not words and sentence: # Was too short to be cut initially\\r\\n words = text.split(\\\" \\\")\\r\\n sentence = \\\"\\\"\\r\\n for w in filter(None, words):\\r\\n if conf.SilenceMarker not in w.lower():\\r\\n words_new.append(w)\\r\\n else:\\r\\n text_chunks = w.lower().split(conf.SilenceMarker)\\r\\n for i, part in enumerate(text_chunks):\\r\\n if part:\\r\\n words_new.append(part)\\r\\n if i < len(text_chunks) - 1:\\r\\n words_new.append(conf.SilenceMarker)\\r\\n else:\\r\\n if words_new and conf.SilenceMarker in words_new[-1]:\\r\\n words_new[-1] += conf.SilenceMarker\\r\\n else:\\r\\n words_new.append(conf.SilenceMarker)\\r\\n words = words_new\\r\\n\\r\\n for w in words:\\r\\n if conf.SilenceMarker in w:\\r\\n if sentence:\\r\\n sentences.append(sentence.strip())\\r\\n sentences.append(w)\\r\\n sentence = \\\"\\\"\\r\\n elif w[-1] in punct or w[0] in punct: # Encountered punctuation\\r\\n if w[-1] in punct and (len(sentence) + len(w) + 1 < MAXLEN):\\r\\n # Word ends with punct and sentence can still be added to\\r\\n sentences.append(sentence.strip() + \\\" \\\" + w.strip())\\r\\n sentence = \\\"\\\" # Save sentence and word, start new sentence\\r\\n elif w[0] in punct and w[-1] not in punct:\\r\\n # Word starts with punctuation, like '('\\r\\n sentences.append(sentence.strip()) # Save current sentence\\r\\n sentence = w # Start a new sentence with punct and word\\r\\n else: # word ends with punct and sentence already long enough\\r\\n sentences.extend([sentence.strip(), w.strip()])\\r\\n sentence = \\\"\\\" \\r\\n else:\\r\\n if (len(sentence) + len(w) + 1 < MAXLEN): # Sentence still\\r\\n sentence += \\\" \\\" + w # short enough\\r\\n else: # Sentence too long\\r\\n sentences.append(sentence.strip())\\r\\n sentence = w # Start a new sentence with the word\\r\\n if sentence:\\r\\n sentences.append(sentence.strip())\\r\\n return sentences\",\n \"def run_ocr_in_chart(chart, pad=0, psm=PSM.SINGLE_LINE):\\n img = chart.image\\n\\n # add a padding to the initial figure\\n fpad = 1\\n img = cv2.copyMakeBorder(img.copy(), fpad, fpad, fpad, fpad, cv2.BORDER_CONSTANT, value=(255, 255, 255))\\n fh, fw, _ = img.shape\\n\\n api = PyTessBaseAPI(psm=psm, lang='eng')\\n clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))\\n\\n for tbox in chart.texts:\\n # adding a pad to original image. Some case in quartz corpus, the text touch the border.\\n x, y, w, h = ru.wrap_rect(u.ttoi(tbox.rect), (fh, fw), padx=pad, pady=pad)\\n x, y = x + fpad, y + fpad\\n\\n if w * h == 0:\\n tbox.text = ''\\n continue\\n\\n # crop region of interest\\n roi = img[y:y + h, x:x + w]\\n # to gray scale\\n roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\\n #\\n roi_gray = cv2.resize(roi_gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)\\n # binarization\\n _, roi_bw = cv2.threshold(roi_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\\n # removing noise from borders\\n roi_bw = 255 - clear_border(255-roi_bw)\\n\\n # roi_gray = cv2.copyMakeBorder(roi_gray, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=255)\\n\\n # when testing boxes from csv files\\n if tbox.num_comp == 0:\\n # Apply Contrast Limited Adaptive Histogram Equalization\\n roi_gray2 = clahe.apply(roi_gray)\\n _, roi_bw2 = cv2.threshold(roi_gray2, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\\n _, num_comp = morphology.label(roi_bw2, return_num=True, background=255)\\n tbox.regions.extend(range(num_comp))\\n\\n pil_img = smp.toimage(roi_bw)\\n if SHOW:\\n pil_img.show()\\n max_conf = -np.inf\\n min_dist = np.inf\\n correct_text = ''\\n correct_angle = 0\\n u.log('---------------')\\n for angle in [0, -90, 90]:\\n rot_img = pil_img.rotate(angle, expand=1)\\n\\n api.SetImage(rot_img)\\n conf = api.MeanTextConf()\\n text = api.GetUTF8Text().strip()\\n dist = abs(len(text.replace(' ', '')) - tbox.num_comp)\\n\\n u.log('text: %s conf: %f dist: %d' % (text, conf, dist))\\n if conf > max_conf and dist <= min_dist:\\n max_conf = conf\\n correct_text = text\\n correct_angle = angle\\n min_dist = dist\\n\\n tbox.text = post_process_text(lossy_unicode_to_ascii(correct_text))\\n tbox.text_conf = max_conf\\n tbox.text_dist = min_dist\\n tbox.text_angle = correct_angle\\n\\n u.log('num comp %d' % tbox.num_comp)\\n u.log(u'** text: {} conf: {} angle: {}'.format(correct_text, max_conf, correct_angle))\\n\\n api.End()\\n\\n # return boxes\",\n \"def bert_preprocess(raw_text):\\n nlp = English()\\n nlp.add_pipe(nlp.create_pipe('sentencizer')) # updated\\n doc = nlp(raw_text)\\n sentences = [sent.string.strip() for sent in doc.sents][0:2] \\n new_sentences = []\\n for i, sentence in enumerate(sentences):\\n if i==0:\\n new_sentences.append(\\\"[CLS] \\\" + sentence + \\\" [SEP]\\\")\\n else:\\n new_sentences.append(sentence + \\\" [SEP]\\\")\\n \\n preprocessed_text = ' '.join(new_sentences)\\n \\n if \\\"[CLS]\\\" not in preprocessed_text:\\n raise Exception(\\\"[CLS] not found in preprocessed text\\\")\\n if \\\"[SEP]\\\" not in preprocessed_text:\\n raise Exception(\\\"[SEP] not found in preprocessed text\\\")\\n \\n return\",\n \"def sliptText(text):\\n\\treturn [char for char in text]\",\n \"def edit_google_vision_text(self,text):\\n s1=text\\n try:\\n log_info(\\\"Correcting google vision text to remove extra spacing\\\",MODULE_CONTEXT)\\n i=0\\n while(i\\\"\\n if self.ents is None or label.upper() in self.ents:\\n color = self.colors.get(label.upper(), self.default_color)\\n ent_settings = {\\n \\\"label\\\": label,\\n \\\"text\\\": entity,\\n \\\"bg\\\": color,\\n \\\"kb_link\\\": kb_link,\\n }\\n ent_settings.update(additional_params)\\n markup += self.ent_template.format(**ent_settings)\\n else:\\n markup += entity\\n offset = end\\n fragments = text[offset:].split(\\\"\\\\n\\\")\\n for i, fragment in enumerate(fragments):\\n markup += escape_html(fragment)\\n if len(fragments) > 1 and i != len(fragments) - 1:\\n markup += \\\"
\\\"\\n markup = TPL_ENTS.format(content=markup, dir=self.direction)\\n if title:\\n markup = TPL_TITLE.format(title=title) + markup\\n return markup\",\n \"def display_text(target_text):\\n\\n print('Text to analyze:')\\n print('')\\n print('-------TEXT BELOW-------')\\n print(target_text)\\n print('-------TEXT ENDS-------')\\n print('')\",\n \"def process_text(self, text, language):\",\n \"def process_text(text):\\n text = text.strip()\\n textList = text.split('\\\\n')\\n newText = ''\\n addNewline = True\\n for line in textList:\\n # Remove duplicate white space\\n temp = ' '.join(line.split())\\n # Trim any beginning non-alphabet letters\\n temp = trim(temp)\\n # Remove overly short lines, but keep ends of sentences\\n # Add a newline if gap detected\\n if len(temp) < 40 and not '.' in temp:\\n if addNewline:\\n newText += '\\\\n'\\n addNewline = False\\n continue\\n # Add line to growing string\\n newText += temp + ' '\\n addNewline = True\\n return newText\",\n \"def sentences(summary, nlp):\\n text = remove_spurious_words(text_of(summary))\\n all_sentence = [sentence for sentence in re.split(\\\"[。，？！\\\\n]\\\", text) if sentence]\\n all_sentence = [re.sub('[ ]+', ' ', sentence.encode('gb2312', 'ignore').decode('gb2312')).strip() for sentence in\\n all_sentence]\\n return [nlp.ner(sentence) for sentence in all_sentence if sentence]\",\n \"async def ascii(self, ctx, *, text):\\n text = text.replace(' ', '\\\\n')\\n \\n if not text:\\n await ctx.send(f\\\"{ctx.tick(False)} You need to specify the text you want to convert!\\\")\\n \\n _fig = figlet_format(text.replace(' ', '\\\\n'))\\n \\n if len(_fig) > 1300:\\n await ctx.send(f\\\"{ctx.tick(False)} That message is too long!\\\")\\n await ctx.send(f\\\"{ctx.tick(True)} Done!\\\")\\n await ctx.send(f\\\"```{_fig}```\\\")\",\n \"def get_sentence(self):\",\n \"def frontend(text):\\n text = pyopenjtalk.g2p(text, kana=False)\\n print(f\\\"Cleaned text: {text}\\\")\\n charseq = text.split(\\\" \\\")\\n idseq = []\\n for c in charseq:\\n if c.isspace():\\n idseq += [char_to_id[\\\"
\\d*)'),\n re.compile(r'(?P\\d*)(?Pmass)(?P
\\d*)'),\n re.compile(r'(?P\\d*)(?Pet)(?P
\\d*)'),\n ]\n\n defaults = {\n 'et': ('101', 'inf'),\n 'mass': ('800', 'inf'),\n 'fbet': ('501', 'inf'),\n }\n\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n and\n (\n []\n simple\n (\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\n )\n combgen\n (\n [(10et, 0eta320)]\n dijet\n (\n [(%(masslo).0fdjmass, 26djdphi)]\n ) \n simple\n (\n [(10et, 0eta320)(20et, 0eta320)]\n )\n )\n )\"\"\" % argvals"},"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 chainDict2jetLabel(chain_dict):\n\n # suported scenarios \n router = {\n 'simple': _make_simple_label,\n 'HT': _make_ht_label,\n 'vbenf': _make_vbenf_label,\n 'dijet': _make_dijet_label,\n 'combinationsTest': _make_combinationsTest_label,\n 'partitionsTest': _make_partitionsTest_label,\n }\n\n # chain_part - scenario association\n cp_sorter = {}\n for k in router: cp_sorter[k] = []\n\n for cp in chain_dict['chainParts']:\n if cp['signature'] != 'Jet' and cp['signature'] != 'Bjet': \n continue\n for k in cp_sorter:\n if cp['hypoScenario'].startswith(k):\n cp_sorter[k].append(cp)\n break\n\n # obtain labels by scenario.\n labels = []\n for k, chain_parts in cp_sorter.items():\n if chain_parts: labels.append(router[k](chain_parts))\n\n assert labels\n nlabels = len(labels)\n if nlabels == 1: return labels[0]\n if nlabels == 2:\n alabel = \"\"\"\\\nand([]\n %s\n %s)\"\"\" % (tuple(labels))\n return alabel\n\n # more than 2 labels is not expected\n assert False","def _make_partitionsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'partitionsTest'\n\n \n\n return \"\"\"\n partgen(\n [(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\n )\"\"\"","def _make_simple_label(chain_parts):\n \n if not _select_simple_chainparts(chain_parts):\n msg = 'Jet Configuration error: '\\\n 'chain fails substring selection: not \"simple\" '\n\n raise NotImplementedError(msg)\n \n label = 'simple(['\n for cp in chain_parts:\n smcstr = str(cp['smc'])\n jvtstr = str(cp['jvt'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr: # Run 2 chains have \"INF\" in the SMC substring\n condition_str += ',%s)' % smcstr.replace('INF','')\n elif jvtstr:\n condition_str += ',%s)' % jvtstr\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def _make_simple_partition_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n label = 'simplepartition(['\n for cp in cps:\n smcstr = str(cp['smc'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr:\n condition_str += ',%s)'\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def _text_write_preprocess(self):\n self.check()\n\n max_name_len = np.max([len(name) for name in self.name])\n fieldtypes = [\"U\" + str(max_name_len), \"f8\", \"f8\"]\n comp_names = self._get_lon_lat_component_names()\n frame_obj = self._get_frame_obj()\n frame_desc_str = _get_frame_desc_str(frame_obj)\n\n component_fieldnames = []\n for comp_name in comp_names:\n # This will add e.g. ra_J2000 and dec_J2000 for FK5\n component_fieldnames.append(comp_name + \"_\" + frame_desc_str)\n fieldnames = [\"source_id\"] + component_fieldnames\n stokes_names = [\"I\", \"Q\", \"U\", \"V\"]\n fieldshapes = [()] * 3\n\n if self.stokes_error is not None:\n stokes_error_names = [(f\"{k}_error\") for k in [\"I\", \"Q\", \"U\", \"V\"]]\n\n n_stokes = 0\n stokes_keep = []\n for si, total in enumerate(np.nansum(self.stokes.to(\"Jy\"), axis=(1, 2))):\n if total > 0:\n fieldnames.append(stokes_names[si])\n fieldshapes.append((self.Nfreqs,))\n fieldtypes.append(\"f8\")\n if self.stokes_error is not None:\n fieldnames.append(stokes_error_names[si])\n fieldshapes.append((self.Nfreqs,))\n fieldtypes.append(\"f8\")\n n_stokes += 1\n stokes_keep.append(total > 0)\n\n assert n_stokes >= 1, \"No components with nonzero flux.\"\n\n if self.freq_array is not None:\n if self.spectral_type == \"subband\":\n fieldnames.append(\"subband_frequency\")\n else:\n fieldnames.append(\"frequency\")\n fieldtypes.append(\"f8\")\n fieldshapes.extend([(self.Nfreqs,)])\n elif self.reference_frequency is not None:\n fieldnames.extend([(\"reference_frequency\")])\n fieldtypes.extend([\"f8\"])\n fieldshapes.extend([()] * n_stokes + [()])\n if self.spectral_index is not None:\n fieldnames.append(\"spectral_index\")\n fieldtypes.append(\"f8\")\n fieldshapes.append(())\n\n if hasattr(self, \"_rise_lst\"):\n fieldnames.append(\"rise_lst\")\n fieldtypes.append(\"f8\")\n fieldshapes.append(())\n\n if hasattr(self, \"_set_lst\"):\n fieldnames.append(\"set_lst\")\n fieldtypes.append(\"f8\")\n fieldshapes.append(())\n\n dt = np.dtype(list(zip(fieldnames, fieldtypes, fieldshapes)))\n\n arr = np.empty(self.Ncomponents, dtype=dt)\n arr[\"source_id\"] = self.name\n\n for comp_ind, comp in enumerate(comp_names):\n arr[component_fieldnames[comp_ind]] = getattr(self.skycoord, comp).deg\n\n for ii in range(4):\n if stokes_keep[ii]:\n arr[stokes_names[ii]] = self.stokes[ii].T.to(\"Jy\").value\n if self.stokes_error is not None:\n arr[stokes_error_names[ii]] = self.stokes_error[ii].T.to(\"Jy\").value\n\n if self.freq_array is not None:\n if self.spectral_type == \"subband\":\n arr[\"subband_frequency\"] = self.freq_array.to(\"Hz\").value\n else:\n arr[\"frequency\"] = self.freq_array.to(\"Hz\").value\n elif self.reference_frequency is not None:\n arr[\"reference_frequency\"] = self.reference_frequency.to(\"Hz\").value\n if self.spectral_index is not None:\n arr[\"spectral_index\"] = self.spectral_index\n\n if hasattr(self, \"_rise_lst\"):\n arr[\"rise_lst\"] = self._rise_lst\n if hasattr(self, \"_set_lst\"):\n arr[\"set_lst\"] = self._set_lst\n\n return arr","def _body(self, x, ensembled_batch, non_ensembled_batch, idx):\n i, current_representations = x\n del x\n feats = self._slice_batch(i, ensembled_batch, non_ensembled_batch)\n representations_update = self.evoformer(*self.batch_expand(feats))\n new_representations = {}\n for k in current_representations:\n new_representations[k] = (\n current_representations[k] + representations_update[k])\n del representations_update\n return i+1, new_representations","def get_mapped_feature_name(self):\n\n # open a h5 file in case we need it\n f5 = h5py.File(self.train_database[0], 'r')\n mol_name = list(f5.keys())[0]\n mapped_data = f5.get(mol_name + '/mapped_features/')\n chain_tags = ['_chain1', '_chain2']\n\n # if we select all the features\n if self.select_feature == \"all\":\n\n # redefine dict\n self.select_feature = {}\n\n # loop over the feat types and add all the feat_names\n for feat_type, feat_names in mapped_data.items():\n self.select_feature[feat_type] = [\n name for name in feat_names]\n\n # if a selection was made\n else:\n\n # we loop over the input dict\n for feat_type, feat_names in self.select_feature.items():\n\n # if for a given type we need all the feature\n if feat_names == 'all':\n if feat_type in mapped_data:\n self.select_feature[feat_type] = list(\n mapped_data[feat_type].keys())\n else:\n self.print_possible_features()\n raise KeyError('Feature type %s not found')\n\n # if we have stored the individual\n # chainA chainB data we need to expand the feature list\n # however when we reload a pretrained model we already\n # come with _chainA, _chainB features.\n # So then we shouldn't add the tags\n else:\n # TODO to refactor this part\n if feat_type not in mapped_data:\n self.print_possible_features()\n raise KeyError('Feature type %s not found')\n\n self.select_feature[feat_type] = []\n\n # loop over all the specified feature names\n for name in feat_names:\n\n # check if there is not _chainA or _chainB in the name\n cond = [tag not in name for tag in chain_tags]\n\n # if there is no chain tag in the name\n if np.all(cond):\n\n # if we have a wild card e.g. PSSM_*\n # we check the matches and add them\n if '*' in name:\n match = name.split('*')[0]\n possible_names = list(\n mapped_data[feat_type].keys())\n match_names = [\n n for n in possible_names\n if n.startswith(match)]\n self.select_feature[feat_type] += match_names\n\n # if we don't have a wild card we append\n #_chainA and _chainB\n # to the list\n else:\n self.select_feature[feat_type] += [\n name + tag for tag in chain_tags]\n # if there is a chain tag in the name\n # (we probably relaod a pretrained model)\n # and we simply append the feaature name\n else:\n self.select_feature[feat_type].append(\n name)\n\n f5.close()","def DecodeStage():\n\n io = Io({\n 'if_id': Input(if_bundle),\n 'inst': Input(Bits(32)),\n 'stall': Input(Bits(1)),\n 'reg_write': Input(reg_write_bundle),\n 'ras_ctrl': Output(ras_ctrl_bundle),\n 'id_ex': Output(id_ex_bundle),\n 'rs1_data': Output(Bits(C['core-width'])),\n 'rs2_data': Output(Bits(C['core-width'])),\n })\n\n inst = Wire(Bits(32))\n\n with io.if_id.valid:\n inst <<= io.inst\n with otherwise:\n inst <<= 0\n\n regfile = Instance(RegisterFile())\n\n itype = Wire(Bits(ITypes.bitwidth))\n\n regfile.r0_addr <<= Rs1(inst)\n regfile.r0_en <<= ~io.stall\n regfile.r1_addr <<= Rs2(inst)\n regfile.r1_en <<= ~io.stall\n\n regfile.w0_addr <<= io.reg_write.w_addr\n regfile.w0_en <<= io.reg_write.w_en & ~io.stall\n regfile.w0_data <<= io.reg_write.w_data\n\n #\n # inst_data is metadata about the current instruction that is passed through\n # the pipeline unrelated to control signals. It's primary use is for hazard\n # detection and data forwarding.\n #\n\n io.id_ex.ctrl.valid <<= io.if_id.valid\n io.id_ex.ctrl.inst <<= inst\n io.id_ex.ctrl.pc <<= io.if_id.pc\n\n #\n # Hook up the register read outputs.\n #\n\n io.rs1_data <<= regfile.r0_data\n io.rs2_data <<= regfile.r1_data\n\n #\n # Control is a Python function that produces the primary decode logic. It\n # matches against a set of known instructions to produce control signals for\n # later stages in the pipeline. The known instructions are encoded in the\n # 'instructions' variable above.\n #\n\n Control(inst, itype, io.id_ex.ctrl)\n\n #\n # TODO: Documentation\n #\n\n HandleRasCtrl(io.ras_ctrl, inst, io.if_id.pc)\n\n #\n # GenerateImmediate produces logic that consume the itype (instruction\n # type, which is R, I, S, B, U, or J) and produces the immediate value for\n # this instruction.\n #\n\n io.id_ex.imm <<= GenerateImmediate(inst, itype)\n\n NameSignals(locals())","def _make_dijet_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('dijet')\n\n arg_res = [\n re.compile(r'^(?P\\d*)(?Pdjmass)(?P
\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1et)(?P
\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1eta)(?P
\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2et)(?P
\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2eta)(?P
\\d*)$'),\n ]\n\n defaults = {\n 'j1et': ('100', 'inf'),\n 'j2et': ('100', 'inf'),\n 'j1eta': ('0', '320'),\n 'j2eta': ('0', '320'),\n 'djmass': ('1000', 'inf'),\n }\n\n\n args = _args_from_scenario(scenario)\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n combgen(\n [(2)(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n (%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n ]\n \n dijet(\n [(%(djmasslo).0fdjmass)])\n simple([(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n (%(j2etlo).0fet, %(j2etalo).0feta%(j2etahi).0f)])\n )\"\"\" % argvals","def _indentity_block(self, X, filters, f, stage, block):\n\t\tconv_layer_name = 'res' + str(stage) + block + '_branch'\n\t\tbn_layer_name = 'bm' + str(stage) + block + '_branch'\n\n\t\tX_shortcut = X\n\n\t\tF1, F2, F3 = filters\n\n\t\t# First component of main path\n\t\tX = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1, 1), padding = 'valid',\n\t\t\tname = conv_layer_name + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n\t\tX = BatchNormalization(axis = 3, name = bn_layer_name + '2a')(X)\n\t\tX = Activation('relu')(X)\n\n\t\t# Second component of main path\n\t\tX = Conv2D(filters = F2, kernel_size = (1, 1), strides = (1, 1), padding = 'same',\n\t\t\tname = conv_layer_name + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n\t\tX = BatchNormalization(axis = 3, name = bn_layer_name + '2b')(X)\n\t\tX = Activation('relu')(X)\n\n\t\t# Third component of main path\n\t\tX = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1, 1), padding = 'valid',\n\t\t\tname = conv_layer_name + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n\t\tX = BatchNormalization(axis = 3, name = bn_layer_name + '2c')(X)\n\n\t\t# Final step : adding the shortcut componet to X and applying 'relu' activation on the combination \n\t\tX = Add()([X_shortcut,X])\n\t\tX = Activation('relu')(X)\n\n\t\treturn X","def output_fluent(fil,nodes,elems):\n print \"Nodal coordinates\"\n print nodes\n print \"Element connectivity\"\n print elems\n faces = array(Tet4.faces) # Turning faces into an array is important !\n print \"Tetraeder faces\"\n print faces\n elf = elems.take(faces,axis=1)\n # Remark: the shorter syntax elems[faces] takes its elements along the\n # axis 0. Then we would need to transpose() first (and probably\n # swap axes again later)\n print \"The faces of the elements:\"\n print elf\n # We need a copy to sort the nodes (sorting is done in-place)\n elfs = elf.copy()\n elfs.sort(axis=2) \n print \"The faces with sorted nodes:\"\n print elfs\n magic = elems.max()+1\n print \"Magic number = %d\" % magic\n code = encode(elfs[:,:,0],elfs[:,:,1],elfs[:,:,2],magic)\n # Remark how nice the encode function works on the whole array\n print \"Encoded faces:\"\n print code\n code = code.ravel()\n print code\n print \"Just A Check:\"\n print \"Element 5 face 2 is %s \" % elf[5,2]\n print \"Element 5 face 2 is %s \" % list(decode(code[4*5+2],magic))\n srt = code.argsort()\n print srt\n print code[srt]\n # Now shipout the faces in this order, removing the doubles\n j = -1 \n for i in srt:\n if j < 0: # no predecessor (or predecessor already shipped)\n j = i\n else:\n e1,f1 = j/4, j%4\n if code[i] == code[j]:\n e2,f2 = i/4, i%4\n j = -1\n else:\n e2 = -1\n j = i\n print \"Face %s belongs to el %s and el %s\" % ( elf[e1,f1], e2, e1 )","def pre_pipeline(self, results):\n results[\"img_prefix\"] = self.img_prefix\n results[\"seg_prefix\"] = self.seg_prefix\n results[\"proposal_file\"] = self.proposal_file\n results[\"bbox_fields\"] = []\n results[\"mask_fields\"] = []\n results[\"seg_fields\"] = []\n results[\"site_fields\"] = []\n results[\"label_fields\"] = []","def build_head(self):\n stages = [f'stage{i}' for i in range(1, 7)]\n for stage in stages:\n block = getattr(self.arch, stage)\n PAF, CFM = block.keys()\n PAF = build_blocks(block[PAF], 'head')\n CFM = build_blocks(block[CFM], 'head')\n setattr(self, f\"{stage}_PAF\", PAF)\n setattr(self, f\"{stage}_CFM\", CFM)","def _make_combinationsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'combinationsTest'\n\n \n\n return \"\"\"\n combgen(\n [(2)(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\n )\"\"\"","def _make_simple_comb_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n simple_strs = []\n\n for cp in cps:\n print(cp)\n simple_strs.append(_make_simple_label([cp]))\n\n label = 'combgen([(%d)]' % len(cps)\n for s in simple_strs:\n label += ' %s ' % s\n label += ')'\n return label","def generate_code(self, parts: list):\n for i in range(len(parts)):\n\n if not self._involves_this_party(parts[i][0]):\n # not our data, skip job\n continue\n\n if parts[i][1] == \"python\":\n cg = PythonCodeGen(\n self.config,\n parts[i][0],\n f\"{self.config.system_configs['CODEGEN'].workflow_name}-python-job-{i}\"\n )\n cg.generate()\n elif parts[i][1] == \"jiff\":\n cg = JiffCodeGen(\n self.config,\n parts[i][0],\n f\"{self.config.system_configs['CODEGEN'].workflow_name}-jiff-job-{i}\"\n )\n cg.generate()\n else:\n raise Exception(f\"Unrecognized backend from partition: {parts[i][1]}.\")","def __call__(self, blocks, with_cab=False):\n\n # for k, v in blocks.items():\n # print(k, v.shape)\n\n #down fpn\n f_down = self.FPN_Down_Fusion(blocks)\n # print(\"f_down shape: {}\".format(f_down.shape))\n #up fpn\n f_up = self.FPN_Up_Fusion(blocks)\n # print(\"f_up shape: {}\".format(f_up.shape))\n #fusion\n f_common = fluid.layers.elementwise_add(x=f_down, y=f_up)\n f_common = fluid.layers.relu(f_common)\n # print(\"f_common: {}\".format(f_common.shape))\n\n if self.with_cab:\n # print('enhence f_common with CAB.')\n f_common = self.cross_attention(f_common)\n\n f_score, f_border = self.SAST_Header1(f_common)\n f_tvo, f_tco = self.SAST_Header2(f_common)\n\n predicts = OrderedDict()\n predicts['f_score'] = f_score\n predicts['f_border'] = f_border\n predicts['f_tvo'] = f_tvo\n predicts['f_tco'] = f_tco\n return predicts","def __call__(self, node):\n\n # should throw an error\n if node.cfgInterface == None:\n return\n\n # //\n # // Extract LFN base from included WorkflowSpec parameters\n #//\n if self.unmerged:\n base = node.getParameter(\"UnmergedLFNBase\")[0]\n else:\n base = node.getParameter(\"MergedLFNBase\")[0]\n mergedBase = node.getParameter(\"MergedLFNBase\")[0]\n\n acqEra=None\n if node.hasParameter(\"AcquisitionEra\"):\n acqEra = node.getParameter(\"AcquisitionEra\")[0]\n\n\n # //\n # // iterate over outputmodules/data tiers\n #// Generate LFN, PFN and Catalog for each module\n\n for modName, outModule in node.cfgInterface.outputModules.items():\n\n if ( not outModule.has_key('fileName') ):\n msg = \"OutputModule %s does not contain a fileName entry\" % modName\n raise RuntimeError, msg\n\n preserveLfnGroup = str(self.lfnGroup)\n lastBit = outModule['processedDataset']\n # //\n # // Skip if the file does not stage out. (i.e.\n #// --stageout-intermediates=False)\n if lastBit is None:\n msg = \"OutputModule does not stage out. Skipping.\"\n logging.debug(msg)\n continue\n # //but this guy has the AcquisitionEra at the beginning... delimited\n # // by a dash... we don't need it twice... we try to safely\n #// remove it from the beginning, basically punting if its not\n #\\\\ disadvantage of getting this from the ds name is having to\n # \\\\ then strip off -unmerged\n if acqEra is not None:\n thingtoStrip=\"%s-\" % acqEra\n mypieces = lastBit.split(thingtoStrip, 1)\n if len(mypieces) > 1: \n lastBit = mypieces[1]\n remainingBits = lastBit.split(\"-unmerged\", 1)[0]\n \n outModule['LFNBase'] = os.path.join(base,\n outModule['primaryDataset'],\n outModule['dataTier'],\n remainingBits,\n preserveLfnGroup)\n outModule['MergedLFNBase'] = os.path.join(mergedBase,\n outModule['primaryDataset'],\n outModule['dataTier'],\n remainingBits,\n preserveLfnGroup)\n\n return","def build_stage2_6(self):\n paf, cfm = self.stage2_6.values()\n for i in range(2, 7):\n paf_ = OrderedDict([(k.replace('i', str(i)),paf[k]) for k in paf])\n cfm_ = OrderedDict([(k.replace('i', str(i)),cfm[k]) for k in cfm])\n stage_ = OrderedDict(PAF=paf_, CFM=cfm_)\n setattr(self, f'stage{i}', stage_)","def slice_graph_bwd( endea, reg ): \r\n\tgraph = vcg_Graph.vcgGraph({\"title\":'\"Slice for %s\"' % reg, \\\r\n\t\t\"manhattan_edges\":\"no\", \"layoutalgorithm\":\"maxdepth\"})\r\n\t#\r\n\t# Retrieve the name of the current basic block\r\n\t# \r\n\tworklist = []\r\n\tdata_bib = {}\r\n\t\r\n\tstartnode = slice_node( 0, endea, reg )\t\t# start at the end of the slice node\r\n\trootnode = graph.Add_Node( startnode.to_name() )\r\n\tdata_bib[ startnode.to_name() ] = startnode\r\n\tworklist.insert( 0, rootnode )\r\n\twhile len( worklist ) > 0:\r\n\t\tcurrnode = worklist.pop()\r\n\t\tcurrslice = data_bib[ currnode.get_name() ]\r\n\t\t[tgt_reg, split] = currslice.get_target_reg_bwd()\r\n\t\tprint tgt_reg\r\n\t\tprint split\r\n\t\tif tgt_reg == \"END\":\r\n\t\t\t# Do not process this node any further\r\n\t\t\tpass\r\n\t\telif tgt_reg == \"\" or (( len( currslice.get_lines()) > 0) and \\\r\n\t\t\tcurrslice.startea != currslice.get_lines()[0][0]):\r\n\t\t\t# Do process this node further, nothing really going on \r\n\t\t\tprint \"ZEZ\"\r\n\t\t\txrefs = get_crefs_to( currslice.startea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( 0,ref, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( newslice.to_name(), currnode.get_name() )\r\n\t\telse:\r\n\t\t\txrefs = get_crefs_to( currslice.startea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( 0,ref, tgt_reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( newslice.to_name(), currnode.get_name())\r\n\t\t\txrefs = get_crefs_to( currslice.startea )\r\n\t\t\tif split:\r\n\t\t\t\tfor ref in xrefs:\r\n\t\t\t\t\tnewslice = slice_node( 0,ref, currslice.reg )\r\n\t\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\t\tgraph.Add_Link( newslice.to_name(), currnode.get_name())\r\n\treturn [ graph, data_bib ]","def identity_block(self,X, f, filters, stage, block):\n # define name basis\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n # Retrieve filters\n f1,f2,f3 = filters\n\n # Save the input value. This needs to be added back to the main path later.\n X_shortcut = X\n\n # First component of the main path\n X = Conv2D(filters= f1, kernel_size=(1,1), strides=(1,1), padding='valid', name=conv_name_base + '2a', kernel_initializer=glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X)\n X = Activation('relu')(X)\n\n # Second component of the main path\n X = Conv2D(filters= f2, kernel_size=(f,f), strides=(1,1), padding='same', name=conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)\n X = Activation('relu')(X)\n\n # Third component of the main path\n X = Conv2D(filters= f3, kernel_size=(1,1), strides=(1,1), padding='valid', name=conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)\n\n # Add the shortcut value to the main path\n X = Add()([X_shortcut, X])\n X = Activation('relu')(X) \n\n return X","def onBuildModels(self):\n if self.refSeriesNumber != '-1':\n ref = self.refSeriesNumber\n refLongName = self.seriesMap[ref]['LongName']\n labelNodes = slicer.util.getNodes('*'+refLongName+'*-label*')\n\n numNodes = slicer.mrmlScene.GetNumberOfNodesByClass( \"vtkMRMLModelHierarchyNode\" )\n outHierarchy = None\n\n for n in xrange(numNodes):\n node = slicer.mrmlScene.GetNthNodeByClass( n, \"vtkMRMLModelHierarchyNode\" )\n if node.GetName() == 'mpReview-'+refLongName:\n outHierarchy = node\n break\n\n # Remove the previous models\n if outHierarchy:\n collection = vtk.vtkCollection()\n outHierarchy.GetChildrenModelNodes(collection)\n n = collection.GetNumberOfItems()\n if n != 0:\n for i in xrange(n):\n modelNode = collection.GetItemAsObject(i)\n slicer.mrmlScene.RemoveNode(modelNode)\n\n # if models hierarchy does not exist, create it.\n else:\n outHierarchy = slicer.vtkMRMLModelHierarchyNode()\n outHierarchy.SetScene( slicer.mrmlScene )\n outHierarchy.SetName( 'mpReview-'+refLongName )\n slicer.mrmlScene.AddNode( outHierarchy )\n\n progress = self.makeProgressIndicator(len(labelNodes))\n step = 0\n for label in labelNodes.values():\n labelName = label.GetName().split(':')[1]\n structureName = labelName[labelName[:-6].rfind(\"-\")+1:-6]\n # Only save labels with known structure names\n if any(structureName in s for s in self.structureNames):\n parameters = {}\n parameters[\"InputVolume\"] = label.GetID()\n parameters['FilterType'] = \"Sinc\"\n parameters['GenerateAll'] = True\n\n parameters[\"JointSmoothing\"] = False\n parameters[\"SplitNormals\"] = True\n parameters[\"PointNormals\"] = True\n parameters[\"SkipUnNamed\"] = True\n\n # create models for all labels\n parameters[\"StartLabel\"] = -1\n parameters[\"EndLabel\"] = -1\n\n parameters[\"Decimate\"] = 0\n parameters[\"Smooth\"] = 0\n\n parameters[\"ModelSceneFile\"] = outHierarchy\n\n progress.labelText = '\\nMaking Model for %s' % structureName\n progress.setValue(step)\n if progress.wasCanceled:\n break\n\n try:\n modelMaker = slicer.modules.modelmaker\n self.CLINode = slicer.cli.run(modelMaker, self.CLINode,\n parameters, wait_for_completion=True)\n except AttributeError:\n qt.QMessageBox.critical(slicer.util.mainWindow(),'Editor', 'The ModelMaker module is not available
Perhaps it was disabled in the application settings or did not load correctly.')\n step += 1\n progress.close()\n #\n\n if outHierarchy:\n collection = vtk.vtkCollection()\n outHierarchy.GetChildrenModelNodes(collection)\n n = collection.GetNumberOfItems()\n if n != 0:\n for i in xrange(n):\n modelNode = collection.GetItemAsObject(i)\n displayNode = modelNode.GetDisplayNode()\n displayNode.SetSliceIntersectionVisibility(1)\n displayNode.SetSliceIntersectionThickness(2)\n self.modelsVisibilityButton.checked = False\n self.updateViewRenderer()","def make_label_data(self):\n from xml.etree.ElementTree import Element, SubElement, dump, ElementTree, parse\n\n if not self.graphicsView.hasImage():\n self.showImageSelectionMessageBox()\n return\n\n app_doc_data = AppDocData.instance()\n project = app_doc_data.getCurrentProject()\n\n smalls = []\n bigs = []\n\n symbol_list = app_doc_data.getTargetSymbolList(all=True)\n for symbol in symbol_list:\n if symbol.width and symbol.height:\n if symbol.width > 300 or symbol.height > 300:\n bigs.append(symbol.getName())\n else:\n smalls.append(symbol.getName())\n\n symbols = [item for item in self.graphicsView.scene().items() if issubclass(type(item), SymbolSvgItem)]\n names = [smalls, bigs]\n\n img = app_doc_data.activeDrawing.image_origin\n\n small_size = 500\n big_size = 850\n\n save_path = project.getTrainingSymbolFilePath()\n\n index = 0\n for size in [small_size, big_size]:\n offsets = [0, int(size / 2)]\n\n width, height = img.shape[1], img.shape[0]\n width_count, height_count = width // size + 2, height // size + 2\n b_width, b_height = width_count * size, height_count * size\n b_img = np.zeros((b_height, b_width), np.uint8) + 255\n b_img[:height, :width] = img[:, :]\n\n for offset in offsets:\n for row in range(height_count):\n for col in range(width_count):\n x, y = col * size + offset, row * size + offset\n tile_rect = QRectF(x, y, size, size)\n tile_symbols = []\n for symbol in [symbol for symbol in symbols if symbol.name in names[index]]:\n if tile_rect.contains(symbol.sceneBoundingRect()):\n tile_symbols.append(symbol)\n symbols.remove(symbol)\n\n if tile_symbols:\n training_uid = str(uuid.uuid4())\n training_image_path = os.path.join(save_path, training_uid + '.png')\n training_xml_path = os.path.join(save_path, training_uid + '.xml')\n\n # save image\n #_img = b_img[round(tile_rect.top()):round(tile_rect.bottom()),\n # round(tile_rect.left()):round(tile_rect.right())]\n #cv2.imwrite(training_image_path, _img)\n _img = self.graphicsView.image().copy(round(tile_rect.left()), round(tile_rect.top()), round(tile_rect.width()), round(tile_rect.height()))\n _img.save(training_image_path)\n\n # save label\n xml = Element('annotation')\n SubElement(xml, 'folder').text = 'None'\n SubElement(xml, 'filename').text = os.path.basename(save_path)\n\n pathNode = Element('path')\n pathNode.text = save_path.replace('/', '\\\\')\n xml.append(pathNode)\n\n sourceNode = Element('source')\n databaseNode = Element('database')\n databaseNode.text = 'Unknown'\n sourceNode.append(databaseNode)\n xml.append(sourceNode)\n\n sizeNode = Element('size')\n widthNode = Element('width')\n widthNode.text = str(int(tile_rect.width()))\n sizeNode.append(widthNode)\n heightNode = Element('height')\n heightNode.text = str(int(tile_rect.height()))\n sizeNode.append(heightNode)\n depthNode = Element('depth')\n depthNode.text = '3'\n sizeNode.append(depthNode)\n xml.append(sizeNode)\n\n segmentedNode = Element('segmented')\n segmentedNode.text = '0'\n xml.append(segmentedNode)\n\n labelContent = []\n counts = {}\n for item in tile_symbols:\n rect = item.sceneBoundingRect()\n label, xMin, yMin, xMax, yMax = item.name, int(rect.x() - 5 - x), int(rect.y() - 5 - y), int(rect.x() + rect.width() + 5 - x), int(rect.y() + rect.height() + 5 - y)\n xMin = xMin if xMin > 0 else 0\n yMin = yMin if yMin > 0 else 0\n xMax = xMax if xMax < size else size\n yMax = yMax if yMax < size else size\n\n if label == 'None' or label == '':\n continue\n if label not in labelContent:\n labelContent.append(label)\n counts[label] = 1\n else:\n counts[label] = counts[label] + 1\n\n objectNode = Element('object')\n nameNode = Element('name')\n nameNode.text = label\n objectNode.append(nameNode)\n poseNode = Element('pose')\n poseNode.text = 'Unspecified'\n objectNode.append(poseNode)\n truncatedNode = Element('truncated')\n truncatedNode.text = '0'\n objectNode.append(truncatedNode)\n difficultNode = Element('difficult')\n difficultNode.text = '0'\n objectNode.append(difficultNode)\n\n bndboxNode = Element('bndbox')\n xminNode = Element('xmin')\n xminNode.text = str(xMin)\n bndboxNode.append(xminNode)\n yminNode = Element('ymin')\n yminNode.text = str(yMin)\n bndboxNode.append(yminNode)\n xmaxNode = Element('xmax')\n xmaxNode.text = str(xMax)\n bndboxNode.append(xmaxNode)\n ymaxNode = Element('ymax')\n ymaxNode.text = str(yMax)\n bndboxNode.append(ymaxNode)\n objectNode.append(bndboxNode)\n\n xml.append(objectNode)\n\n ElementTree(xml).write(training_xml_path)\n\n index += 1\n\n QMessageBox.about(self, self.tr(\"Notice\"), self.tr('Successfully applied. '))","def gexf_graph():\n # you must replace these lines and supply your own graph\n \n \n \n my_gexf = Gexf(\"JiajiaXie\", \"My awesome graph\")\n graph=my_gexf.addGraph(\"undirected\", \"static\", \"My awesome networks\")\n \n atr1=graph.addNodeAttribute('Type',type='string')\n\n\n for set in data_specific:\n if graph.nodeExists(set['set_num']) ==0:\n tm1=graph.addNode(set['set_num'], set['name'], r='0', g='0', b='0')\n tm1.addAttribute(atr1,\"set\")\n\n\n\n counter_test=1\n for set, part in data_parts.items():\n for key, part_list in part.items():\n interme =part_list['color']\n red=interme[0]+interme[1]\n green=interme[2]+interme[3]\n blue=interme[4]+interme[5]\n\n red_de=str(int(red,16))\n green_de=str(int(green,16))\n blue_de=str(int(blue,16))\n if graph.nodeExists(part_list['id'])==0:\n tm2=graph.addNode(part_list['id'], part_list['part_name'],r=red_de, g=green_de, b = blue_de)\n tm2.addAttribute(atr1,\"part\")\n\n\n counter_test+=1\n graph.addEdge(\"_\"+str(counter_test), set, part_list['id'], part_list['quantity'])\n\n\n\n f=open('bricks_graph.gexf','wb')\n my_gexf.write(f)\n\n\n return my_gexf.graphs[0]","def DontuseThis():\n BCM_outputs = ['phi','rho','theta',\n 'r_probabilityMaps','l_probabilityMaps',\n 'models']\n BCM_Models = pe.Node(interface=nio.DataGrabber(input_names=['structures'],\n outfields=BCM_outputs),\n name='10_BCM_Models')\n BCM_Models.inputs.base_directory = atlas_fname_wpath\n BCM_Models.inputs.template_args['phi'] = [['spatialImages','phi','nii.gz']]\n BCM_Models.inputs.template_args['rho'] = [['spatialImages','rho','nii.gz']]\n BCM_Models.inputs.template_args['theta'] = [['spatialImages','theta','nii.gz']]\n BCM_Models.inputs.template_args['r_probabilityMaps'] = [['structures']]\n BCM_Models.inputs.template_args['l_probabilityMaps'] = [['structures']]\n BCM_Models.inputs.template_args['models'] = [['structures']]\n\n BRAINSCut_structures = ['caudate','thalamus','putamen','hippocampus']\n #BRAINSCut_structures = ['caudate','thalamus']\n BCM_Models.iterables = ( 'structures', BRAINSCut_structures )\n BCM_Models.inputs.template = '%s/%s.%s'\n BCM_Models.inputs.field_template = dict(\n r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',\n l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',\n models='modelFiles/%sModel*',\n )\n\n \"\"\"\n The xml creation and BRAINSCut need to be their own mini-pipeline that gets\n executed once for each of the structures in BRAINSCut_structures. This can be\n accomplished with a map node and a new pipeline.\n \"\"\"\n \"\"\"\n Create xml file for BRAINSCut\n \"\"\"\n\n\n BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),name=\"BFitAtlasToSubject\")\n BFitAtlasToSubject.inputs.costMetric=\"MMI\"\n BFitAtlasToSubject.inputs.maskProcessingMode=\"ROI\"\n BFitAtlasToSubject.inputs.numberOfSamples=100000\n BFitAtlasToSubject.inputs.numberOfIterations=[1500,1500]\n BFitAtlasToSubject.inputs.numberOfHistogramBins=50\n BFitAtlasToSubject.inputs.maximumStepLength=0.2\n BFitAtlasToSubject.inputs.minimumStepLength=[0.005,0.005]\n BFitAtlasToSubject.inputs.transformType= [\"Affine\",\"BSpline\"]\n BFitAtlasToSubject.inputs.maxBSplineDisplacement= 7\n BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter=65\n BFitAtlasToSubject.inputs.splineGridSize=[28,20,24]\n BFitAtlasToSubject.inputs.outputVolume=\"Trial_Initializer_Output.nii.gz\"\n BFitAtlasToSubject.inputs.outputTransform=\"Trial_Initializer_Output.mat\"\n cutWF.connect(SplitAvgBABC,'avgBABCT1',BFitAtlasToSubject,'fixedVolume')\n cutWF.connect(BABC,'outputLabels',BFitAtlasToSubject,'fixedBinaryVolume')\n cutWF.connect(BAtlas,'template_t1',BFitAtlasToSubject,'movingVolume')\n cutWF.connect(BAtlas,'template_brain',BFitAtlasToSubject,'movingBinaryVolume')\n cutWF.connect(BLI,'outputTransformFilename',BFitAtlasToSubject,'initialTransform')\n\n CreateBRAINSCutXML = pe.Node(Function(input_names=['rho','phi','theta',\n 'model',\n 'r_probabilityMap',\n 'l_probabilityMap',\n 'atlasT1','atlasBrain',\n 'subjT1','subjT2',\n 'subjT1GAD','subjT2GAD',\n 'subjSGGAD','subjBrain',\n 'atlasToSubj','output_dir'],\n output_names=['xml_filename','rl_structure_filename_list'],\n function = create_BRAINSCut_XML),\n overwrite = True,\n name=\"CreateBRAINSCutXML\")\n\n ## HACK Makde better directory\n CreateBRAINSCutXML.inputs.output_dir = \".\" #os.path.join(cutWF.base_dir, \"BRAINSCut_output\")\n cutWF.connect(BCM_Models,'models',CreateBRAINSCutXML,'model')\n cutWF.connect(BCM_Models,'rho',CreateBRAINSCutXML,'rho')\n cutWF.connect(BCM_Models,'phi',CreateBRAINSCutXML,'phi')\n cutWF.connect(BCM_Models,'theta',CreateBRAINSCutXML,'theta')\n cutWF.connect(BCM_Models,'r_probabilityMaps',CreateBRAINSCutXML,'r_probabilityMap')\n cutWF.connect(BCM_Models,'l_probabilityMaps',CreateBRAINSCutXML,'l_probabilityMap')\n cutWF.connect(BAtlas,'template_t1',CreateBRAINSCutXML,'atlasT1')\n cutWF.connect(BAtlas,'template_brain',CreateBRAINSCutXML,'atlasBrain')\n cutWF.connect(SplitAvgBABC,'avgBABCT1',CreateBRAINSCutXML,'subjT1')\n cutWF.connect(SplitAvgBABC,'avgBABCT2',CreateBRAINSCutXML,'subjT2')\n cutWF.connect(GADT1,'outputVolume',CreateBRAINSCutXML,'subjT1GAD')\n cutWF.connect(GADT2,'outputVolume',CreateBRAINSCutXML,'subjT2GAD')\n cutWF.connect(SGI,'outputFileName',CreateBRAINSCutXML,'subjSGGAD')\n cutWF.connect(BABC,'outputLabels',CreateBRAINSCutXML,'subjBrain')\n cutWF.connect(BFitAtlasToSubject,'outputTransform',CreateBRAINSCutXML,'atlasToSubj')\n #CreateBRAINSCutXML.inputs.atlasToSubj = \"INTERNAL_REGISTER.mat\"\n #cutWF.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')\n\n \"\"\"\n ResampleNACLabels\n \"\"\"\n ResampleAtlasNACLabels=pe.Node(interface=BRAINSResample(),name=\"ResampleAtlasNACLabels\")\n ResampleAtlasNACLabels.inputs.interpolationMode = \"NearestNeighbor\"\n ResampleAtlasNACLabels.inputs.outputVolume = \"atlasToSubjectNACLabels.nii.gz\"\n\n cutWF.connect(cutWF,'OutputSpec.atlasToSubjectTransform',ResampleAtlasNACLabels,'warpTransform')\n cutWF.connect(cutWF,'OutputSpec.t1_corrected',ResampleAtlasNACLabels,'referenceVolume')\n cutWF.connect(BAtlas,'template_nac_lables',ResampleAtlasNACLabels,'inputVolume')\n\n \"\"\"\n BRAINSMush\n \"\"\"\n BMUSH=pe.Node(interface=BRAINSMush(),name=\"BMUSH\")\n BMUSH.inputs.outputVolume = \"MushImage.nii.gz\"\n BMUSH.inputs.outputMask = \"MushMask.nii.gz\"\n BMUSH.inputs.lowerThresholdFactor = 1.2\n BMUSH.inputs.upperThresholdFactor = 0.55\n\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BMUSH,'inputFirstVolume')\n cutWF.connect(myLocalTCWF,'OutputSpec.t2_corrected',BMUSH,'inputSecondVolume')\n cutWF.connect(myLocalTCWF,'OutputSpec.outputLabels',BMUSH,'inputMaskVolume')\n\n \"\"\"\n BRAINSROIAuto\n \"\"\"\n BROI = pe.Node(interface=BRAINSROIAuto(), name=\"BRAINSROIAuto\")\n BROI.inputs.closingSize=12\n BROI.inputs.otsuPercentileThreshold=0.01\n BROI.inputs.thresholdCorrectionFactor=1.0\n BROI.inputs.outputROIMaskVolume = \"temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz\"\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BROI,'inputVolume')\n\n \"\"\"\n Split the implicit outputs of BABCext\n \"\"\"\n SplitAvgBABC = pe.Node(Function(input_names=['in_files','T1_count'], output_names=['avgBABCT1','avgBABCT2'],\n function = get_first_T1_and_T2), run_without_submitting=True, name=\"99_SplitAvgBABC\")\n SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.\n\n cutWF.connect(myLocalTCWF,'OutputSpec.outputAverageImages',SplitAvgBABC,'in_files')\n\n\n\n def printFullPath(outFileFullPath):\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"{0}\".format(outFileFullPath))\n return outFileFullPath\n printOutImage = pe.Node( Function(function=printFullPath, input_names = ['outFileFullPath'], output_names = ['genoutFileFullPath']), run_without_submitting=True, name=\"99_printOutImage\")\n cutWF.connect( GADT2, 'outputVolume', printOutImage, 'outFileFullPath' )","def identity_block(X, f, filters, stage, block):\n \n # Defines name basis.\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n \n # Retrieves Filters.\n F1, F2, F3 = filters\n \n # Saves the input value. This is needed later to add back to the main path. \n X_shortcut = X\n \n ##### MAIN PATH #####\n # First component of main path.\n X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform())(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n X = Activation('relu')(X)\n \n \n # Second component of main path.\n X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform())(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n X = Activation('relu')(X)\n \n # Third component of main path.\n X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform())(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n\n\n # Final step: Adds shortcut value to main path, and pass it through a RELU activation.\n X = Add()([X, X_shortcut])\n X = Activation('relu')(X)\n\n return X","def identity_block(X, f, filters, stage, block):\n \n # defining name basis\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n \n # Retrieve Filters\n F1, F2, F3 = filters\n \n # Save the input value. You'll need this later to add back to the main path. \n X_shortcut = X\n \n # First component of main path\n X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n X = Activation('relu')(X)\n \n # Second component of main path\n X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n X = Activation('relu')(X)\n\n # Third component of main path\n X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n\n # Final step: Add shortcut value to main path, and pass it through a RELU activation\n X = Add()([X,X_shortcut])\n X = Activation('relu')(X)\n \n return X","def make_mixture_info(parts, operation='+'):\n # type: (List[ModelInfo], str) -> ModelInfo\n # Build new parameter list\n combined_pars = []\n\n # When creating a mixture model that is a sum of product models (ie (1*2)+(3*4))\n # the parameters for models 1 & 2 will be prefixed with A & B respectively,\n # but so will the parameters for models 3 & 4. We need to rename models 3 & 4\n # so that they are prefixed with C & D to avoid overlap of parameter names.\n used_prefixes = []\n for part in parts:\n if part.composition and part.composition[0] == 'mixture':\n i = 0\n for submodel in part.composition[1]:\n npars = len(submodel.parameters.kernel_parameters)\n # List of params of one of the constituent models of part\n submodel_pars = part.parameters.kernel_parameters[i:i+npars]\n # Prefix of the constituent model\n prefix = submodel_pars[0].name[0]\n if prefix not in used_prefixes: # Haven't seen this prefix so far\n used_prefixes.append(prefix)\n i += npars\n continue\n # TODO: don't modify submodel --- it may be used elsewhere\n # Existing code probably doesn't keep a handle on the model\n # parts so its probably okay, but it's possible that a mix\n # on user defined mixture models models will change the\n # parameters used for the parts in the GUI. Even worse if the\n # same plugin is used twice. For example, twosphere.py\n # contains sphere+sphere and you create twosphere+twosphere.\n while prefix in used_prefixes:\n # This prefix has been already used, so change it to the\n # next letter that hasn't been used\n prefix = chr(ord(prefix) + 1)\n used_prefixes.append(prefix)\n prefix += \"_\"\n # Update the parameters of this constituent model to use the\n # new prefix\n for par in submodel_pars:\n # Strip {prefix}_ using par.name[2:], etc.\n # TODO: fails for AB_scale\n par.id = prefix + par.id[2:]\n par.name = prefix + par.name[2:]\n if par.length_control is not None:\n par.length_control = prefix + par.length_control[2:]\n i += npars\n\n for part in parts:\n # Parameter prefix per model, A_, B_, ...\n # Note that prefix must also be applied to id and length_control\n # to support vector parameters\n prefix = ''\n if not part.composition or part.composition[0] == 'product':\n # Model isn't a composition model, so its parameters don't have a\n # a prefix. Add the next available prefix\n prefix = chr(ord('A')+len(used_prefixes))\n used_prefixes.append(prefix)\n prefix += '_'\n\n if operation == '+':\n # If model is a sum model, each constituent model gets its own scale parameter\n scale_prefix = prefix\n if prefix == '' and getattr(part, \"operation\", '') == '*':\n # `part` is a composition product model. Find the prefixes of\n # its parameters to form a new prefix for the scale.\n # For example, a model with A*B*C will have ABC_scale.\n sub_prefixes = []\n for param in part.parameters.kernel_parameters:\n # Prefix of constituent model\n sub_prefix = param.id.split('_')[0]\n if sub_prefix not in sub_prefixes:\n sub_prefixes.append(sub_prefix)\n # Concatenate sub_prefixes to form prefix for the scale\n scale_prefix = ''.join(sub_prefixes) + '_'\n scale = Parameter(scale_prefix + 'scale', default=1.0,\n description=\"model intensity for \" + part.name)\n combined_pars.append(scale)\n for p in part.parameters.kernel_parameters:\n p = copy(p)\n p.name = prefix + p.name\n p.id = prefix + p.id\n if p.length_control is not None:\n p.length_control = prefix + p.length_control\n combined_pars.append(p)\n parameters = ParameterTable(combined_pars)\n # Allow for the scenario in which each component has all its PD parameters\n # active simultaneously. details.make_details() will throw an error if\n # too many are used from any one component.\n parameters.max_pd = sum(part.parameters.max_pd for part in parts)\n\n def random():\n \"\"\"Random set of model parameters for mixture model\"\"\"\n combined_pars = {}\n for k, part in enumerate(parts):\n prefix = chr(ord('A')+k) + '_'\n pars = part.random()\n combined_pars.update((prefix+k, v) for k, v in pars.items())\n return combined_pars\n\n model_info = ModelInfo()\n model_info.id = operation.join(part.id for part in parts)\n model_info.operation = operation\n model_info.name = '(' + operation.join(part.name for part in parts) + ')'\n model_info.filename = None\n model_info.title = 'Mixture model with ' + model_info.name\n model_info.description = model_info.title\n model_info.docs = model_info.title\n model_info.category = \"custom\"\n model_info.parameters = parameters\n model_info.random = random\n #model_info.single = any(part['single'] for part in parts)\n model_info.structure_factor = False\n #model_info.tests = []\n #model_info.source = []\n # Remember the component info blocks so we can build the model\n model_info.composition = ('mixture', parts)\n return model_info","def tied_featurize(batch, device, chain_dict, fixed_position_dict=None, omit_AA_dict=None, tied_positions_dict=None, pssm_dict=None, bias_by_res_dict=None, ca_only=False):\n alphabet = 'ACDEFGHIKLMNPQRSTVWYX'\n B = len(batch)\n lengths = np.array([len(b['seq']) for b in batch], dtype=np.int32) #sum of chain seq lengths\n L_max = max([len(b['seq']) for b in batch])\n if ca_only:\n X = np.zeros([B, L_max, 1, 3])\n else:\n X = np.zeros([B, L_max, 4, 3])\n residue_idx = -100*np.ones([B, L_max], dtype=np.int32)\n chain_M = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted\n pssm_coef_all = np.zeros([B, L_max], dtype=np.float32) #1.0 for the bits that need to be predicted\n pssm_bias_all = np.zeros([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted\n pssm_log_odds_all = 10000.0*np.ones([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted\n chain_M_pos = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted\n bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32)\n chain_encoding_all = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted\n S = np.zeros([B, L_max], dtype=np.int32)\n omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=np.int32)\n # Build the batch\n letter_list_list = []\n visible_list_list = []\n masked_list_list = []\n masked_chain_length_list_list = []\n tied_pos_list_of_lists_list = []\n for i, b in enumerate(batch):\n if chain_dict != None:\n masked_chains, visible_chains = chain_dict[b['name']] #masked_chains a list of chain letters to predict [A, D, F]\n else:\n masked_chains = [item[-1:] for item in list(b) if item[:10]=='seq_chain_']\n visible_chains = []\n masked_chains.sort() #sort masked_chains \n visible_chains.sort() #sort visible_chains \n all_chains = masked_chains + visible_chains\n for i, b in enumerate(batch):\n mask_dict = {}\n a = 0\n x_chain_list = []\n chain_mask_list = []\n chain_seq_list = []\n chain_encoding_list = []\n c = 1\n letter_list = []\n global_idx_start_list = [0]\n visible_list = []\n masked_list = []\n masked_chain_length_list = []\n fixed_position_mask_list = []\n omit_AA_mask_list = []\n pssm_coef_list = []\n pssm_bias_list = []\n pssm_log_odds_list = []\n bias_by_res_list = []\n l0 = 0\n l1 = 0\n for step, letter in enumerate(all_chains):\n if letter in visible_chains:\n letter_list.append(letter)\n visible_list.append(letter)\n chain_seq = b[f'seq_chain_{letter}']\n chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])\n chain_length = len(chain_seq)\n global_idx_start_list.append(global_idx_start_list[-1]+chain_length)\n chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary\n chain_mask = np.zeros(chain_length) #0.0 for visible chains\n if ca_only:\n x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff\n if len(x_chain.shape) == 2:\n x_chain = x_chain[:,None,:]\n else:\n x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3]\n x_chain_list.append(x_chain)\n chain_mask_list.append(chain_mask)\n chain_seq_list.append(chain_seq)\n chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))\n l1 += chain_length\n residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)\n l0 += chain_length\n c+=1\n fixed_position_mask = np.ones(chain_length)\n fixed_position_mask_list.append(fixed_position_mask)\n omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)\n omit_AA_mask_list.append(omit_AA_mask_temp)\n pssm_coef = np.zeros(chain_length)\n pssm_bias = np.zeros([chain_length, 21])\n pssm_log_odds = 10000.0*np.ones([chain_length, 21])\n pssm_coef_list.append(pssm_coef)\n pssm_bias_list.append(pssm_bias)\n pssm_log_odds_list.append(pssm_log_odds)\n bias_by_res_list.append(np.zeros([chain_length, 21]))\n if letter in masked_chains:\n masked_list.append(letter)\n letter_list.append(letter)\n chain_seq = b[f'seq_chain_{letter}']\n chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])\n chain_length = len(chain_seq)\n global_idx_start_list.append(global_idx_start_list[-1]+chain_length)\n masked_chain_length_list.append(chain_length)\n chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary\n chain_mask = np.ones(chain_length) #1.0 for masked\n if ca_only:\n x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff\n if len(x_chain.shape) == 2:\n x_chain = x_chain[:,None,:]\n else:\n x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3] \n x_chain_list.append(x_chain)\n chain_mask_list.append(chain_mask)\n chain_seq_list.append(chain_seq)\n chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))\n l1 += chain_length\n residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)\n l0 += chain_length\n c+=1\n fixed_position_mask = np.ones(chain_length)\n if fixed_position_dict!=None:\n fixed_pos_list = fixed_position_dict[b['name']][letter]\n if fixed_pos_list:\n fixed_position_mask[np.array(fixed_pos_list)-1] = 0.0\n fixed_position_mask_list.append(fixed_position_mask)\n omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)\n if omit_AA_dict!=None:\n for item in omit_AA_dict[b['name']][letter]:\n idx_AA = np.array(item[0])-1\n AA_idx = np.array([np.argwhere(np.array(list(alphabet))== AA)[0][0] for AA in item[1]]).repeat(idx_AA.shape[0])\n idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx])\n omit_AA_mask_temp[idx_[:,0], idx_[:,1]] = 1\n omit_AA_mask_list.append(omit_AA_mask_temp)\n pssm_coef = np.zeros(chain_length)\n pssm_bias = np.zeros([chain_length, 21])\n pssm_log_odds = 10000.0*np.ones([chain_length, 21])\n if pssm_dict:\n if pssm_dict[b['name']][letter]:\n pssm_coef = pssm_dict[b['name']][letter]['pssm_coef']\n pssm_bias = pssm_dict[b['name']][letter]['pssm_bias']\n pssm_log_odds = pssm_dict[b['name']][letter]['pssm_log_odds']\n pssm_coef_list.append(pssm_coef)\n pssm_bias_list.append(pssm_bias)\n pssm_log_odds_list.append(pssm_log_odds)\n if bias_by_res_dict:\n bias_by_res_list.append(bias_by_res_dict[b['name']][letter])\n else:\n bias_by_res_list.append(np.zeros([chain_length, 21]))\n\n \n letter_list_np = np.array(letter_list)\n tied_pos_list_of_lists = []\n tied_beta = np.ones(L_max)\n if tied_positions_dict!=None:\n tied_pos_list = tied_positions_dict[b['name']]\n if tied_pos_list:\n set_chains_tied = set(list(itertools.chain(*[list(item) for item in tied_pos_list])))\n for tied_item in tied_pos_list:\n one_list = []\n for k, v in tied_item.items():\n start_idx = global_idx_start_list[np.argwhere(letter_list_np == k)[0][0]]\n if isinstance(v[0], list):\n for v_count in range(len(v[0])):\n one_list.append(start_idx+v[0][v_count]-1)#make 0 to be the first\n tied_beta[start_idx+v[0][v_count]-1] = v[1][v_count]\n else:\n for v_ in v:\n one_list.append(start_idx+v_-1)#make 0 to be the first\n tied_pos_list_of_lists.append(one_list)\n tied_pos_list_of_lists_list.append(tied_pos_list_of_lists)\n\n\n \n x = np.concatenate(x_chain_list,0) #[L, 4, 3]\n all_sequence = \"\".join(chain_seq_list)\n m = np.concatenate(chain_mask_list,0) #[L,], 1.0 for places that need to be predicted\n chain_encoding = np.concatenate(chain_encoding_list,0)\n m_pos = np.concatenate(fixed_position_mask_list,0) #[L,], 1.0 for places that need to be predicted\n\n pssm_coef_ = np.concatenate(pssm_coef_list,0) #[L,], 1.0 for places that need to be predicted\n pssm_bias_ = np.concatenate(pssm_bias_list,0) #[L,], 1.0 for places that need to be predicted\n pssm_log_odds_ = np.concatenate(pssm_log_odds_list,0) #[L,], 1.0 for places that need to be predicted\n\n bias_by_res_ = np.concatenate(bias_by_res_list, 0) #[L,21], 0.0 for places where AA frequencies don't need to be tweaked\n\n l = len(all_sequence)\n x_pad = np.pad(x, [[0,L_max-l], [0,0], [0,0]], 'constant', constant_values=(np.nan, ))\n X[i,:,:,:] = x_pad\n\n m_pad = np.pad(m, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\n m_pos_pad = np.pad(m_pos, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\n omit_AA_mask_pad = np.pad(np.concatenate(omit_AA_mask_list,0), [[0,L_max-l]], 'constant', constant_values=(0.0, ))\n chain_M[i,:] = m_pad\n chain_M_pos[i,:] = m_pos_pad\n omit_AA_mask[i,] = omit_AA_mask_pad\n\n chain_encoding_pad = np.pad(chain_encoding, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\n chain_encoding_all[i,:] = chain_encoding_pad\n\n pssm_coef_pad = np.pad(pssm_coef_, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\n pssm_bias_pad = np.pad(pssm_bias_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))\n pssm_log_odds_pad = np.pad(pssm_log_odds_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))\n\n pssm_coef_all[i,:] = pssm_coef_pad\n pssm_bias_all[i,:] = pssm_bias_pad\n pssm_log_odds_all[i,:] = pssm_log_odds_pad\n\n bias_by_res_pad = np.pad(bias_by_res_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))\n bias_by_res_all[i,:] = bias_by_res_pad\n\n # Convert to labels\n indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=np.int32)\n S[i, :l] = indices\n letter_list_list.append(letter_list)\n visible_list_list.append(visible_list)\n masked_list_list.append(masked_list)\n masked_chain_length_list_list.append(masked_chain_length_list)\n\n\n isnan = np.isnan(X)\n mask = np.isfinite(np.sum(X,(2,3))).astype(np.float32)\n X[isnan] = 0.\n\n # Conversion\n pssm_coef_all = torch.from_numpy(pssm_coef_all).to(dtype=torch.float32, device=device)\n pssm_bias_all = torch.from_numpy(pssm_bias_all).to(dtype=torch.float32, device=device)\n pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(dtype=torch.float32, device=device)\n\n tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device)\n\n jumps = ((residue_idx[:,1:]-residue_idx[:,:-1])==1).astype(np.float32)\n bias_by_res_all = torch.from_numpy(bias_by_res_all).to(dtype=torch.float32, device=device)\n phi_mask = np.pad(jumps, [[0,0],[1,0]])\n psi_mask = np.pad(jumps, [[0,0],[0,1]])\n omega_mask = np.pad(jumps, [[0,0],[0,1]])\n dihedral_mask = np.concatenate([phi_mask[:,:,None], psi_mask[:,:,None], omega_mask[:,:,None]], -1) #[B,L,3]\n dihedral_mask = torch.from_numpy(dihedral_mask).to(dtype=torch.float32, device=device)\n residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long,device=device)\n S = torch.from_numpy(S).to(dtype=torch.long,device=device)\n X = torch.from_numpy(X).to(dtype=torch.float32, device=device)\n mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device)\n chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device)\n chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device)\n omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device)\n chain_encoding_all = torch.from_numpy(chain_encoding_all).to(dtype=torch.long, device=device)\n if ca_only:\n X_out = X[:,:,0]\n else:\n X_out = X\n return X_out, S, mask, lengths, chain_M, chain_encoding_all, letter_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef_all, pssm_bias_all, pssm_log_odds_all, bias_by_res_all, tied_beta","def compiler_output(input_ckt, hier_graph_dict, design_name:str, result_dir:pathlib.Path, pdk_dir:pathlib.Path, uniform_height=False):\n layers_json = pdk_dir / 'layers.json'\n with open(layers_json,\"rt\") as fp:\n pdk_data=json.load(fp)\n design_config = pdk_data[\"design_info\"]\n\n if not result_dir.exists():\n result_dir.mkdir()\n logger.debug(f\"Writing results in dir: {result_dir} {hier_graph_dict}\")\n input_dir = input_ckt.parents[0]\n\n verilog_tbl = { 'modules': [], 'global_signals': []}\n\n design_setup = read_setup(input_dir / (design_name + '.setup'))\n try:\n POWER_PINS = [design_setup['GND'][0],design_setup['POWER'][0]]\n except (IndexError, ValueError):\n POWER_PINS = []\n logger.info(\"Power and ground nets not found. Power grid will not be constructed.\")\n\n #read lef to not write those modules as macros\n lef_path = pathlib.Path(__file__).resolve().parent.parent / 'config'\n all_lef = read_lef(lef_path)\n logger.debug(f\"Available library cells: {', '.join(all_lef)}\")\n\n primitives = {}\n for name,member in hier_graph_dict.items():\n logger.debug(f\"Found module: {name} {member['graph'].nodes()}\")\n graph = member[\"graph\"]\n constraints = member[\"constraints\"]\n\n for const in constraints:\n if isinstance(const, constraint.GuardRing):\n primitives['guard_ring'] = {'primitive':'guard_ring'}\n\n logger.debug(f\"Reading nodes from graph: {name}\")\n for node, attr in graph.nodes(data=True):\n if 'net' in attr['inst_type']: continue\n #Dropping floating ports\n lef_name = attr['inst_type']\n\n if \"values\" in attr and (lef_name in all_lef):\n block_name, block_args = generate_lef(lef_name, attr, primitives, design_config, uniform_height)\n #block_name_ext = block_name.replace(lef_name,'')\n logger.debug(f\"Created new lef for: {block_name} {lef_name}\")\n #Multiple instances of same module\n if 'inst_copy' in attr:\n for nm in list(hier_graph_dict.keys()):\n if nm == lef_name + attr['inst_copy']:\n if block_name not in hier_graph_dict.keys():\n logger.debug('Trying to modify a dictionary while iterating over it!')\n hier_graph_dict[block_name] = hier_graph_dict.pop(nm)\n else:\n #For cells with extra parameters than current primitive naming convention\n all_lef.append(nm)\n graph.nodes[node][\"inst_type\"] = block_name\n all_lef.append(block_name)\n\n # Only unit caps are generated\n if block_name.lower().startswith('cap'):\n graph.nodes[node]['inst_type'] = block_args['primitive']\n block_args['primitive'] = block_name\n else:\n graph.nodes[node]['inst_type'] = block_name\n\n if block_name in primitives:\n if block_args != primitives[block_name]:\n logging.warning(f\"two different primitve {block_name} of size {primitives[block_name]} {block_args}got approximated to same unit size\")\n else:\n primitives[block_name] = block_args\n elif \"values\" in attr and 'inst_copy' in attr:\n member[\"graph\"].nodes[node][\"inst_type\"]= lef_name + attr[\"inst_copy\"]\n all_lef.append(block_name)\n\n else:\n logger.debug(f\"No physical information found for: {name}\")\n logger.debug(f\"generated data for {name} : {pprint.pformat(primitives, indent=4)}\")\n logger.debug(f\"All available cell generator with updates: {all_lef}\")\n for name,member in hier_graph_dict.items():\n graph = member[\"graph\"]\n logger.debug(f\"Found module: {name} {graph.nodes()}\")\n inoutpin = []\n floating_ports=[]\n if \"ports_match\" in member and member[\"ports_match\"]:\n for key in member[\"ports_match\"].keys():\n if key not in POWER_PINS:\n inoutpin.append(key)\n if member[\"ports\"]:\n logger.debug(f'Found module ports: {member[\"ports\"]} {member[\"name\"]}')\n floating_ports = set(inoutpin) - set(member[\"ports\"]) - set(design_setup['POWER']) -set(design_setup['GND'])\n if len(list(floating_ports))> 0:\n logger.error(f\"floating ports found: {name} {floating_ports}\")\n raise SystemExit('Please remove floating ports')\n else:\n inoutpin = member[\"ports\"]\n if name not in all_lef:\n\n ## Removing constraints to fix cascoded cmc\n if name not in design_setup['DIGITAL']:\n logger.debug(f\"call constraint generator writer for block: {name}\")\n stop_points = design_setup['POWER'] + design_setup['GND'] + design_setup['CLOCK']\n constraints = member[\"constraints\"]\n if name not in design_setup['NO_CONST']:\n constraints = FindConst(graph, name, inoutpin, member[\"ports_weight\"], constraints, stop_points)\n constraints = CapConst(graph, name, design_config[\"unit_size_cap\"], constraints, design_setup['MERGE_SYMM_CAPS'])\n hier_graph_dict[name] = hier_graph_dict[name].copy(\n update={'constraints': constraints}\n )\n ## Write out modified netlist & constraints as JSON\n logger.debug(f\"call verilog writer for block: {name}\")\n wv = WriteVerilog(name, inoutpin, hier_graph_dict, POWER_PINS)\n verilog_tbl['modules'].append( wv.gen_dict())\n if len(POWER_PINS)>0:\n for i, nm in enumerate(POWER_PINS):\n verilog_tbl['global_signals'].append( { 'prefix' :'global_power', 'formal' : f'supply{i}', 'actual' : nm})\n\n with (result_dir / f'{design_name}.verilog.json').open( 'wt') as fp:\n json.dump( verilog_tbl, fp=fp, indent=2)\n\n with (result_dir / f'{design_name}.v').open( 'wt') as fp:\n write_verilog( verilog_tbl, fp)\n\n logger.info(\"Completed topology identification.\")\n logger.debug(f\"OUTPUT verilog json netlist at: {result_dir}/{design_name}.verilog.json\")\n logger.debug(f\"OUTPUT verilog netlist at: {result_dir}/{design_name}.v\")\n logger.debug(f\"OUTPUT const file at: {result_dir}/{design_name}.pnr.const.json\")\n return primitives","def convert2EbnerParam(joblib,list_prefix,directory):\n \n key=[p[0] for p in joblib]\n element=[p[1] for p in joblib]\n\n \n listSlice=element[key.index('listSlice')] #list of slice\n parameters=element[key.index('EvolutionParameters')][-1,:,:] #estimated paramters of the registration\n rejectedSlice=element[key.index('RejectedSlices')] #rejected slices\n\n \n images,mask = createVolumesFromAlist(listSlice.copy()) #list of images corresponding to differents original stacks\n \n for i_slice in range(len(listSlice)): \n slicei=listSlice[i_slice]\n slicei.set_parameters(parameters[i_slice,:]) #set parameters to the last estimated parameters\n \n mat = np.array([[-1,0,0,0],[0,-1,0,0],[0,0,1,0],[0,0,0,1]]) #matrix to convert affine matrix from nibabel to itk\n\n for n in range(len(images)): #for each stack\n \n imagen = images[n]\n \n for i_slice in range(len(images[n])): #for each slices (in each stacks)\n \n slicei=imagen[i_slice]\n s = (slicei.get_orientation(),slicei.get_index_slice())\n \n if s not in rejectedSlice:\n dimension=3\n X,Y,Z= slicei.get_slice().get_fdata().shape\n center= -slicei.get_center()\n centerMat = np.eye(4)\n centerMat[0:3,3] = center[0:3]\n invcenterMat = np.eye(4)\n invcenterMat[0:3,3] = -center[0:3] \n p = slicei.get_parameters()\n matrix = mat @ centerMat @ rigidMatrix([p[0],p[1],p[2],p[3],p[4],p[5]]) @ invcenterMat @ mat\n test = sitk.AffineTransform(dimension)\n test.SetMatrix(matrix[0:3,0:3].flatten())\n test.SetTranslation(matrix[0:3,3])\n images_index = slicei.get_index_image()\n sitk.WriteTransform(test,\"%s/%s_slice%d.tfm\" %(directory,list_prefix[images_index],slicei.get_index_slice())) #save rigid transformation, computed at the barycenter of the image, adatpted to itk\n #else:\n #print(s)","def build(width, height, depth, classes, stages, filters, include_top, pooling,\n reg=1e-3, bnEps=2e-5, bnMom=0.0):\n inputShape = (height, width, depth)\n chanDim = -1\n\n if K.image_data_format() == \"channels_first\":\n inputShape = (depth, height, width)\n chanDim = 1\n\n inputs = Input(shape=inputShape)\n\n\n # block 1 (initial conv block)\n x = ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(inputs)\n x = Conv2D(64, (7,7), use_bias=False, strides=(2,2),\n kernel_initializer=\"he_normal\", kernel_regularizer=l2(reg))(x)\n x = BatchNormalization(axis=chanDim, name=\"bn_conv1\")(x)\n x = Activation(\"relu\")(x)\n x = ZeroPadding2D(padding=((1,1), (1,1)), name=\"pool1_pad\")(x)\n x = MaxPooling2D(3, strides=2)(x)\n\n for i in range(0, len(stages)):\n stride = (1,1) if i == 0 else (2,2) # block 2 (projection block) w stride(1,1)\n\n print(\"Stage {}, Stride={}\".format(i, stride))\n x = SEResNet.residual_module(x, filters[i+1], stride,\n chanDim=chanDim, red=True, bnEps=bnEps, bnMom=bnMom)\n for j in range(0, stages[i] + 1): #stacking res block to each depth layer\n x = SEResNet.residual_module(x, filters[i+1], stride=(1,1),\n chanDim=chanDim, bnEps=bnEps,\n bnMom=bnMom)\n x = BatchNormalization(axis=chanDim, epsilon=bnEps,\n momentum=bnMom)(x)\n x = Activation(\"relu\")(x)\n\n if include_top:\n x = GlobalAveragePooling2D()(x)\n x = Dense(classes, use_bias=False, kernel_regularizer=l2(reg),\n activation='softmax')(x)\n else:\n if pooling == 'avg':\n print(\"Adding average pool\")\n x = GlobalAveragePooling2D()(x)\n elif pooling == 'max':\n x = GlobalMaxPooling2D()(x)\n\n model = Model(inputs=inputs, outputs=x, name=\"SEResNet\")\n return model","def _processLabel(self, kitti_label):\n label = {\n 'category': kitti_label['type'].lower(),\n 'box2D': kitti_label['bbox'].copy(),\n 'box3D': {\n 'location': {\n 'x': kitti_label['location']['x'],\n 'y': kitti_label['location']['y'] - kitti_label['dimensions']['height'] / 2.0, # move to center\n 'z': kitti_label['location']['z'],\n },\n 'dimensions': kitti_label['dimensions'].copy(),\n 'rotation_y': kitti_label['rotation_y'],\n },\n 'info': {\n 'truncated': kitti_label['truncated'],\n 'occluded': kitti_label['occluded'],\n },\n }\n if 'trackId' in kitti_label:\n # set trackId if given\n label['info']['trackId'] = kitti_label['trackId']\n return label","def generate_semisupervized_label(self, idx_known, idx_unknown):\n tmp_df = self.data_info.set_index(['patientID','body_part'])\n # associate semi-supervized settings\n if len(idx_known) > 0:\n df_known = tmp_df.loc[idx_known,:]\n df_known['semi_label'] = df_known.abnormal_XR.apply(lambda x: -1 if x==1 else 1)\n df_unknown = tmp_df.loc[idx_unknown,:]\n df_unknown['semi_label'] = 0\n return pd.concat([df_known, df_unknown], axis=0).reset_index()\n else:\n df_unknown = tmp_df.loc[idx_unknown,:]\n df_unknown['semi_label'] = 0\n return df_unknown.reset_index()","def serialize(mode):\r\n serialize_version(mode)\r\n vcb.serialize(mode) \r\n for x in xfrms:\r\n x.serialize(mode)","def label(self, cfg):\n rep = \"\"\n nl = \"\"\n for node in cfg.nodes:\n rep += nl + \"{}\\tgen={}\\tkill={}\\tout={}\".format(\n node, \n set(self.gen.get(node)),\n set(self.kill.get(node)),\n set(self.out.get(node)))\n nl = \"\\n\"\n return rep","def m_dump_tf():\n\n # Info about ENS and Resolver Smart Contracts\n #print(f\"ENS address: {ens.address()}\")\n print(f\"ENS address (contract address): {ENS.address}\")\n print(f\"Resolver address (contract address): {resolver.address()}\")\n print(f\"Resolver address from ENS(root): {ens.resolver('root')}\")\n print(\"\\n\")\n\n print(f\"Owner of ROOT record: {ens.owner('root')}\")\n ROOT_address, ROOT_key = wallet.account_from_name(\"ROOT\", \"ThePassword\")\n print(f\"ROOT address from wallet: {ROOT_address}\")\n\n n_subnodes = ens.numberSubnodes(0)\n print(f\"Number of subnodes of root: {n_subnodes}\")\n for i in range(n_subnodes):\n hash = ens.subnode(index=i).hex()\n print(f\" Subnode hash {i}: {ens.subnode(i).hex()}\")\n name = resolver.name(node_hash=hash)\n print(f\" Name: {name}\")\n resolver_address = ENS.functions.resolver(hash).call()\n print(f\" Resolver: {resolver_address}\")\n ala_address = ens.resolver(name)\n print(f\" Resolver by name: {ala_address}\")\n owner = owner = ENS.functions.owner(hash).call()\n print(f\" Owner: {owner}\")\n ROOT_address, ROOT_key = wallet.account_from_name(\"Alastria\", \"ThePassword\")\n print(f\" Address from wallet: {ROOT_address}\")\n\n n_subnodes = ens.numberSubnodes(\"ala\")\n print(f\"Number of subnodes of {name}: {n_subnodes}\")\n for i in range(n_subnodes):\n hash = ens.subnode(node_name=\"ala\", index=i).hex()\n print(f\" Subnode hash {i}: {hash}\")\n name = resolver.name(node_hash=hash)\n print(f\" Name: {name}\")\n resolver_address = ENS.functions.resolver(hash).call()\n print(f\" Resolver: {resolver_address}\")\n ala_address = ens.resolver(name)\n print(f\" Resolver by name: {ala_address}\")\n owner = owner = ENS.functions.owner(hash).call()\n print(f\" Owner: {owner}\")","def serialize(self):\n return pickle.dumps([block.serialize() for block in self.chain])","def identity_block(X, f, filters, stage, block):\n\n # defining name basis\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n # Retrieve Filters\n F1, F2, F3 = filters\n\n # Save the input value.\n X_shortcut = X\n\n # First component of main path\n X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n X = Activation('relu')(X)\n\n # Second component of main path\n X = Conv2D(filters = F2, kernel_size= (f,f),strides= (1,1), padding= 'same' , name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)\n X = Activation('relu')(X)\n\n # Third component of main path\n X = Conv2D(filters = F3, kernel_size= (1,1),strides= (1,1), padding= 'valid' , name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)\n\n # Final step: Add shortcut value to main path, and pass it through a RELU activation\n X = layers.add([X,X_shortcut])\n X = Activation('relu')(X)\n\n return X","def build_label_transform():\n\n return NALabelEncoder()","def _make_ht_label(chain_parts):\n\n assert len(chain_parts) == 1, '_make_ht_label, no. of chain parts != 1'\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('HT'), '_make_ht_label(): scenario does not start with HT'\n\n arg_res = [\n re.compile(r'^(?P Perhaps it was disabled in the application settings or did not load correctly.')\\n step += 1\\n progress.close()\\n #\\n\\n if outHierarchy:\\n collection = vtk.vtkCollection()\\n outHierarchy.GetChildrenModelNodes(collection)\\n n = collection.GetNumberOfItems()\\n if n != 0:\\n for i in xrange(n):\\n modelNode = collection.GetItemAsObject(i)\\n displayNode = modelNode.GetDisplayNode()\\n displayNode.SetSliceIntersectionVisibility(1)\\n displayNode.SetSliceIntersectionThickness(2)\\n self.modelsVisibilityButton.checked = False\\n self.updateViewRenderer()\",\n \"def make_label_data(self):\\n from xml.etree.ElementTree import Element, SubElement, dump, ElementTree, parse\\n\\n if not self.graphicsView.hasImage():\\n self.showImageSelectionMessageBox()\\n return\\n\\n app_doc_data = AppDocData.instance()\\n project = app_doc_data.getCurrentProject()\\n\\n smalls = []\\n bigs = []\\n\\n symbol_list = app_doc_data.getTargetSymbolList(all=True)\\n for symbol in symbol_list:\\n if symbol.width and symbol.height:\\n if symbol.width > 300 or symbol.height > 300:\\n bigs.append(symbol.getName())\\n else:\\n smalls.append(symbol.getName())\\n\\n symbols = [item for item in self.graphicsView.scene().items() if issubclass(type(item), SymbolSvgItem)]\\n names = [smalls, bigs]\\n\\n img = app_doc_data.activeDrawing.image_origin\\n\\n small_size = 500\\n big_size = 850\\n\\n save_path = project.getTrainingSymbolFilePath()\\n\\n index = 0\\n for size in [small_size, big_size]:\\n offsets = [0, int(size / 2)]\\n\\n width, height = img.shape[1], img.shape[0]\\n width_count, height_count = width // size + 2, height // size + 2\\n b_width, b_height = width_count * size, height_count * size\\n b_img = np.zeros((b_height, b_width), np.uint8) + 255\\n b_img[:height, :width] = img[:, :]\\n\\n for offset in offsets:\\n for row in range(height_count):\\n for col in range(width_count):\\n x, y = col * size + offset, row * size + offset\\n tile_rect = QRectF(x, y, size, size)\\n tile_symbols = []\\n for symbol in [symbol for symbol in symbols if symbol.name in names[index]]:\\n if tile_rect.contains(symbol.sceneBoundingRect()):\\n tile_symbols.append(symbol)\\n symbols.remove(symbol)\\n\\n if tile_symbols:\\n training_uid = str(uuid.uuid4())\\n training_image_path = os.path.join(save_path, training_uid + '.png')\\n training_xml_path = os.path.join(save_path, training_uid + '.xml')\\n\\n # save image\\n #_img = b_img[round(tile_rect.top()):round(tile_rect.bottom()),\\n # round(tile_rect.left()):round(tile_rect.right())]\\n #cv2.imwrite(training_image_path, _img)\\n _img = self.graphicsView.image().copy(round(tile_rect.left()), round(tile_rect.top()), round(tile_rect.width()), round(tile_rect.height()))\\n _img.save(training_image_path)\\n\\n # save label\\n xml = Element('annotation')\\n SubElement(xml, 'folder').text = 'None'\\n SubElement(xml, 'filename').text = os.path.basename(save_path)\\n\\n pathNode = Element('path')\\n pathNode.text = save_path.replace('/', '\\\\\\\\')\\n xml.append(pathNode)\\n\\n sourceNode = Element('source')\\n databaseNode = Element('database')\\n databaseNode.text = 'Unknown'\\n sourceNode.append(databaseNode)\\n xml.append(sourceNode)\\n\\n sizeNode = Element('size')\\n widthNode = Element('width')\\n widthNode.text = str(int(tile_rect.width()))\\n sizeNode.append(widthNode)\\n heightNode = Element('height')\\n heightNode.text = str(int(tile_rect.height()))\\n sizeNode.append(heightNode)\\n depthNode = Element('depth')\\n depthNode.text = '3'\\n sizeNode.append(depthNode)\\n xml.append(sizeNode)\\n\\n segmentedNode = Element('segmented')\\n segmentedNode.text = '0'\\n xml.append(segmentedNode)\\n\\n labelContent = []\\n counts = {}\\n for item in tile_symbols:\\n rect = item.sceneBoundingRect()\\n label, xMin, yMin, xMax, yMax = item.name, int(rect.x() - 5 - x), int(rect.y() - 5 - y), int(rect.x() + rect.width() + 5 - x), int(rect.y() + rect.height() + 5 - y)\\n xMin = xMin if xMin > 0 else 0\\n yMin = yMin if yMin > 0 else 0\\n xMax = xMax if xMax < size else size\\n yMax = yMax if yMax < size else size\\n\\n if label == 'None' or label == '':\\n continue\\n if label not in labelContent:\\n labelContent.append(label)\\n counts[label] = 1\\n else:\\n counts[label] = counts[label] + 1\\n\\n objectNode = Element('object')\\n nameNode = Element('name')\\n nameNode.text = label\\n objectNode.append(nameNode)\\n poseNode = Element('pose')\\n poseNode.text = 'Unspecified'\\n objectNode.append(poseNode)\\n truncatedNode = Element('truncated')\\n truncatedNode.text = '0'\\n objectNode.append(truncatedNode)\\n difficultNode = Element('difficult')\\n difficultNode.text = '0'\\n objectNode.append(difficultNode)\\n\\n bndboxNode = Element('bndbox')\\n xminNode = Element('xmin')\\n xminNode.text = str(xMin)\\n bndboxNode.append(xminNode)\\n yminNode = Element('ymin')\\n yminNode.text = str(yMin)\\n bndboxNode.append(yminNode)\\n xmaxNode = Element('xmax')\\n xmaxNode.text = str(xMax)\\n bndboxNode.append(xmaxNode)\\n ymaxNode = Element('ymax')\\n ymaxNode.text = str(yMax)\\n bndboxNode.append(ymaxNode)\\n objectNode.append(bndboxNode)\\n\\n xml.append(objectNode)\\n\\n ElementTree(xml).write(training_xml_path)\\n\\n index += 1\\n\\n QMessageBox.about(self, self.tr(\\\"Notice\\\"), self.tr('Successfully applied. '))\",\n \"def gexf_graph():\\n # you must replace these lines and supply your own graph\\n \\n \\n \\n my_gexf = Gexf(\\\"JiajiaXie\\\", \\\"My awesome graph\\\")\\n graph=my_gexf.addGraph(\\\"undirected\\\", \\\"static\\\", \\\"My awesome networks\\\")\\n \\n atr1=graph.addNodeAttribute('Type',type='string')\\n\\n\\n for set in data_specific:\\n if graph.nodeExists(set['set_num']) ==0:\\n tm1=graph.addNode(set['set_num'], set['name'], r='0', g='0', b='0')\\n tm1.addAttribute(atr1,\\\"set\\\")\\n\\n\\n\\n counter_test=1\\n for set, part in data_parts.items():\\n for key, part_list in part.items():\\n interme =part_list['color']\\n red=interme[0]+interme[1]\\n green=interme[2]+interme[3]\\n blue=interme[4]+interme[5]\\n\\n red_de=str(int(red,16))\\n green_de=str(int(green,16))\\n blue_de=str(int(blue,16))\\n if graph.nodeExists(part_list['id'])==0:\\n tm2=graph.addNode(part_list['id'], part_list['part_name'],r=red_de, g=green_de, b = blue_de)\\n tm2.addAttribute(atr1,\\\"part\\\")\\n\\n\\n counter_test+=1\\n graph.addEdge(\\\"_\\\"+str(counter_test), set, part_list['id'], part_list['quantity'])\\n\\n\\n\\n f=open('bricks_graph.gexf','wb')\\n my_gexf.write(f)\\n\\n\\n return my_gexf.graphs[0]\",\n \"def DontuseThis():\\n BCM_outputs = ['phi','rho','theta',\\n 'r_probabilityMaps','l_probabilityMaps',\\n 'models']\\n BCM_Models = pe.Node(interface=nio.DataGrabber(input_names=['structures'],\\n outfields=BCM_outputs),\\n name='10_BCM_Models')\\n BCM_Models.inputs.base_directory = atlas_fname_wpath\\n BCM_Models.inputs.template_args['phi'] = [['spatialImages','phi','nii.gz']]\\n BCM_Models.inputs.template_args['rho'] = [['spatialImages','rho','nii.gz']]\\n BCM_Models.inputs.template_args['theta'] = [['spatialImages','theta','nii.gz']]\\n BCM_Models.inputs.template_args['r_probabilityMaps'] = [['structures']]\\n BCM_Models.inputs.template_args['l_probabilityMaps'] = [['structures']]\\n BCM_Models.inputs.template_args['models'] = [['structures']]\\n\\n BRAINSCut_structures = ['caudate','thalamus','putamen','hippocampus']\\n #BRAINSCut_structures = ['caudate','thalamus']\\n BCM_Models.iterables = ( 'structures', BRAINSCut_structures )\\n BCM_Models.inputs.template = '%s/%s.%s'\\n BCM_Models.inputs.field_template = dict(\\n r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',\\n l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',\\n models='modelFiles/%sModel*',\\n )\\n\\n \\\"\\\"\\\"\\n The xml creation and BRAINSCut need to be their own mini-pipeline that gets\\n executed once for each of the structures in BRAINSCut_structures. This can be\\n accomplished with a map node and a new pipeline.\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n Create xml file for BRAINSCut\\n \\\"\\\"\\\"\\n\\n\\n BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),name=\\\"BFitAtlasToSubject\\\")\\n BFitAtlasToSubject.inputs.costMetric=\\\"MMI\\\"\\n BFitAtlasToSubject.inputs.maskProcessingMode=\\\"ROI\\\"\\n BFitAtlasToSubject.inputs.numberOfSamples=100000\\n BFitAtlasToSubject.inputs.numberOfIterations=[1500,1500]\\n BFitAtlasToSubject.inputs.numberOfHistogramBins=50\\n BFitAtlasToSubject.inputs.maximumStepLength=0.2\\n BFitAtlasToSubject.inputs.minimumStepLength=[0.005,0.005]\\n BFitAtlasToSubject.inputs.transformType= [\\\"Affine\\\",\\\"BSpline\\\"]\\n BFitAtlasToSubject.inputs.maxBSplineDisplacement= 7\\n BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter=65\\n BFitAtlasToSubject.inputs.splineGridSize=[28,20,24]\\n BFitAtlasToSubject.inputs.outputVolume=\\\"Trial_Initializer_Output.nii.gz\\\"\\n BFitAtlasToSubject.inputs.outputTransform=\\\"Trial_Initializer_Output.mat\\\"\\n cutWF.connect(SplitAvgBABC,'avgBABCT1',BFitAtlasToSubject,'fixedVolume')\\n cutWF.connect(BABC,'outputLabels',BFitAtlasToSubject,'fixedBinaryVolume')\\n cutWF.connect(BAtlas,'template_t1',BFitAtlasToSubject,'movingVolume')\\n cutWF.connect(BAtlas,'template_brain',BFitAtlasToSubject,'movingBinaryVolume')\\n cutWF.connect(BLI,'outputTransformFilename',BFitAtlasToSubject,'initialTransform')\\n\\n CreateBRAINSCutXML = pe.Node(Function(input_names=['rho','phi','theta',\\n 'model',\\n 'r_probabilityMap',\\n 'l_probabilityMap',\\n 'atlasT1','atlasBrain',\\n 'subjT1','subjT2',\\n 'subjT1GAD','subjT2GAD',\\n 'subjSGGAD','subjBrain',\\n 'atlasToSubj','output_dir'],\\n output_names=['xml_filename','rl_structure_filename_list'],\\n function = create_BRAINSCut_XML),\\n overwrite = True,\\n name=\\\"CreateBRAINSCutXML\\\")\\n\\n ## HACK Makde better directory\\n CreateBRAINSCutXML.inputs.output_dir = \\\".\\\" #os.path.join(cutWF.base_dir, \\\"BRAINSCut_output\\\")\\n cutWF.connect(BCM_Models,'models',CreateBRAINSCutXML,'model')\\n cutWF.connect(BCM_Models,'rho',CreateBRAINSCutXML,'rho')\\n cutWF.connect(BCM_Models,'phi',CreateBRAINSCutXML,'phi')\\n cutWF.connect(BCM_Models,'theta',CreateBRAINSCutXML,'theta')\\n cutWF.connect(BCM_Models,'r_probabilityMaps',CreateBRAINSCutXML,'r_probabilityMap')\\n cutWF.connect(BCM_Models,'l_probabilityMaps',CreateBRAINSCutXML,'l_probabilityMap')\\n cutWF.connect(BAtlas,'template_t1',CreateBRAINSCutXML,'atlasT1')\\n cutWF.connect(BAtlas,'template_brain',CreateBRAINSCutXML,'atlasBrain')\\n cutWF.connect(SplitAvgBABC,'avgBABCT1',CreateBRAINSCutXML,'subjT1')\\n cutWF.connect(SplitAvgBABC,'avgBABCT2',CreateBRAINSCutXML,'subjT2')\\n cutWF.connect(GADT1,'outputVolume',CreateBRAINSCutXML,'subjT1GAD')\\n cutWF.connect(GADT2,'outputVolume',CreateBRAINSCutXML,'subjT2GAD')\\n cutWF.connect(SGI,'outputFileName',CreateBRAINSCutXML,'subjSGGAD')\\n cutWF.connect(BABC,'outputLabels',CreateBRAINSCutXML,'subjBrain')\\n cutWF.connect(BFitAtlasToSubject,'outputTransform',CreateBRAINSCutXML,'atlasToSubj')\\n #CreateBRAINSCutXML.inputs.atlasToSubj = \\\"INTERNAL_REGISTER.mat\\\"\\n #cutWF.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')\\n\\n \\\"\\\"\\\"\\n ResampleNACLabels\\n \\\"\\\"\\\"\\n ResampleAtlasNACLabels=pe.Node(interface=BRAINSResample(),name=\\\"ResampleAtlasNACLabels\\\")\\n ResampleAtlasNACLabels.inputs.interpolationMode = \\\"NearestNeighbor\\\"\\n ResampleAtlasNACLabels.inputs.outputVolume = \\\"atlasToSubjectNACLabels.nii.gz\\\"\\n\\n cutWF.connect(cutWF,'OutputSpec.atlasToSubjectTransform',ResampleAtlasNACLabels,'warpTransform')\\n cutWF.connect(cutWF,'OutputSpec.t1_corrected',ResampleAtlasNACLabels,'referenceVolume')\\n cutWF.connect(BAtlas,'template_nac_lables',ResampleAtlasNACLabels,'inputVolume')\\n\\n \\\"\\\"\\\"\\n BRAINSMush\\n \\\"\\\"\\\"\\n BMUSH=pe.Node(interface=BRAINSMush(),name=\\\"BMUSH\\\")\\n BMUSH.inputs.outputVolume = \\\"MushImage.nii.gz\\\"\\n BMUSH.inputs.outputMask = \\\"MushMask.nii.gz\\\"\\n BMUSH.inputs.lowerThresholdFactor = 1.2\\n BMUSH.inputs.upperThresholdFactor = 0.55\\n\\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BMUSH,'inputFirstVolume')\\n cutWF.connect(myLocalTCWF,'OutputSpec.t2_corrected',BMUSH,'inputSecondVolume')\\n cutWF.connect(myLocalTCWF,'OutputSpec.outputLabels',BMUSH,'inputMaskVolume')\\n\\n \\\"\\\"\\\"\\n BRAINSROIAuto\\n \\\"\\\"\\\"\\n BROI = pe.Node(interface=BRAINSROIAuto(), name=\\\"BRAINSROIAuto\\\")\\n BROI.inputs.closingSize=12\\n BROI.inputs.otsuPercentileThreshold=0.01\\n BROI.inputs.thresholdCorrectionFactor=1.0\\n BROI.inputs.outputROIMaskVolume = \\\"temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz\\\"\\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BROI,'inputVolume')\\n\\n \\\"\\\"\\\"\\n Split the implicit outputs of BABCext\\n \\\"\\\"\\\"\\n SplitAvgBABC = pe.Node(Function(input_names=['in_files','T1_count'], output_names=['avgBABCT1','avgBABCT2'],\\n function = get_first_T1_and_T2), run_without_submitting=True, name=\\\"99_SplitAvgBABC\\\")\\n SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.\\n\\n cutWF.connect(myLocalTCWF,'OutputSpec.outputAverageImages',SplitAvgBABC,'in_files')\\n\\n\\n\\n def printFullPath(outFileFullPath):\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"{0}\\\".format(outFileFullPath))\\n return outFileFullPath\\n printOutImage = pe.Node( Function(function=printFullPath, input_names = ['outFileFullPath'], output_names = ['genoutFileFullPath']), run_without_submitting=True, name=\\\"99_printOutImage\\\")\\n cutWF.connect( GADT2, 'outputVolume', printOutImage, 'outFileFullPath' )\",\n \"def identity_block(X, f, filters, stage, block):\\n \\n # Defines name basis.\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n bn_name_base = 'bn' + str(stage) + block + '_branch'\\n \\n # Retrieves Filters.\\n F1, F2, F3 = filters\\n \\n # Saves the input value. This is needed later to add back to the main path. \\n X_shortcut = X\\n \\n ##### MAIN PATH #####\\n # First component of main path.\\n X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform())(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\\n X = Activation('relu')(X)\\n \\n \\n # Second component of main path.\\n X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform())(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\\n X = Activation('relu')(X)\\n \\n # Third component of main path.\\n X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform())(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\\n\\n\\n # Final step: Adds shortcut value to main path, and pass it through a RELU activation.\\n X = Add()([X, X_shortcut])\\n X = Activation('relu')(X)\\n\\n return X\",\n \"def identity_block(X, f, filters, stage, block):\\n \\n # defining name basis\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n bn_name_base = 'bn' + str(stage) + block + '_branch'\\n \\n # Retrieve Filters\\n F1, F2, F3 = filters\\n \\n # Save the input value. You'll need this later to add back to the main path. \\n X_shortcut = X\\n \\n # First component of main path\\n X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\\n X = Activation('relu')(X)\\n \\n # Second component of main path\\n X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\\n X = Activation('relu')(X)\\n\\n # Third component of main path\\n X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\\n\\n # Final step: Add shortcut value to main path, and pass it through a RELU activation\\n X = Add()([X,X_shortcut])\\n X = Activation('relu')(X)\\n \\n return X\",\n \"def make_mixture_info(parts, operation='+'):\\n # type: (List[ModelInfo], str) -> ModelInfo\\n # Build new parameter list\\n combined_pars = []\\n\\n # When creating a mixture model that is a sum of product models (ie (1*2)+(3*4))\\n # the parameters for models 1 & 2 will be prefixed with A & B respectively,\\n # but so will the parameters for models 3 & 4. We need to rename models 3 & 4\\n # so that they are prefixed with C & D to avoid overlap of parameter names.\\n used_prefixes = []\\n for part in parts:\\n if part.composition and part.composition[0] == 'mixture':\\n i = 0\\n for submodel in part.composition[1]:\\n npars = len(submodel.parameters.kernel_parameters)\\n # List of params of one of the constituent models of part\\n submodel_pars = part.parameters.kernel_parameters[i:i+npars]\\n # Prefix of the constituent model\\n prefix = submodel_pars[0].name[0]\\n if prefix not in used_prefixes: # Haven't seen this prefix so far\\n used_prefixes.append(prefix)\\n i += npars\\n continue\\n # TODO: don't modify submodel --- it may be used elsewhere\\n # Existing code probably doesn't keep a handle on the model\\n # parts so its probably okay, but it's possible that a mix\\n # on user defined mixture models models will change the\\n # parameters used for the parts in the GUI. Even worse if the\\n # same plugin is used twice. For example, twosphere.py\\n # contains sphere+sphere and you create twosphere+twosphere.\\n while prefix in used_prefixes:\\n # This prefix has been already used, so change it to the\\n # next letter that hasn't been used\\n prefix = chr(ord(prefix) + 1)\\n used_prefixes.append(prefix)\\n prefix += \\\"_\\\"\\n # Update the parameters of this constituent model to use the\\n # new prefix\\n for par in submodel_pars:\\n # Strip {prefix}_ using par.name[2:], etc.\\n # TODO: fails for AB_scale\\n par.id = prefix + par.id[2:]\\n par.name = prefix + par.name[2:]\\n if par.length_control is not None:\\n par.length_control = prefix + par.length_control[2:]\\n i += npars\\n\\n for part in parts:\\n # Parameter prefix per model, A_, B_, ...\\n # Note that prefix must also be applied to id and length_control\\n # to support vector parameters\\n prefix = ''\\n if not part.composition or part.composition[0] == 'product':\\n # Model isn't a composition model, so its parameters don't have a\\n # a prefix. Add the next available prefix\\n prefix = chr(ord('A')+len(used_prefixes))\\n used_prefixes.append(prefix)\\n prefix += '_'\\n\\n if operation == '+':\\n # If model is a sum model, each constituent model gets its own scale parameter\\n scale_prefix = prefix\\n if prefix == '' and getattr(part, \\\"operation\\\", '') == '*':\\n # `part` is a composition product model. Find the prefixes of\\n # its parameters to form a new prefix for the scale.\\n # For example, a model with A*B*C will have ABC_scale.\\n sub_prefixes = []\\n for param in part.parameters.kernel_parameters:\\n # Prefix of constituent model\\n sub_prefix = param.id.split('_')[0]\\n if sub_prefix not in sub_prefixes:\\n sub_prefixes.append(sub_prefix)\\n # Concatenate sub_prefixes to form prefix for the scale\\n scale_prefix = ''.join(sub_prefixes) + '_'\\n scale = Parameter(scale_prefix + 'scale', default=1.0,\\n description=\\\"model intensity for \\\" + part.name)\\n combined_pars.append(scale)\\n for p in part.parameters.kernel_parameters:\\n p = copy(p)\\n p.name = prefix + p.name\\n p.id = prefix + p.id\\n if p.length_control is not None:\\n p.length_control = prefix + p.length_control\\n combined_pars.append(p)\\n parameters = ParameterTable(combined_pars)\\n # Allow for the scenario in which each component has all its PD parameters\\n # active simultaneously. details.make_details() will throw an error if\\n # too many are used from any one component.\\n parameters.max_pd = sum(part.parameters.max_pd for part in parts)\\n\\n def random():\\n \\\"\\\"\\\"Random set of model parameters for mixture model\\\"\\\"\\\"\\n combined_pars = {}\\n for k, part in enumerate(parts):\\n prefix = chr(ord('A')+k) + '_'\\n pars = part.random()\\n combined_pars.update((prefix+k, v) for k, v in pars.items())\\n return combined_pars\\n\\n model_info = ModelInfo()\\n model_info.id = operation.join(part.id for part in parts)\\n model_info.operation = operation\\n model_info.name = '(' + operation.join(part.name for part in parts) + ')'\\n model_info.filename = None\\n model_info.title = 'Mixture model with ' + model_info.name\\n model_info.description = model_info.title\\n model_info.docs = model_info.title\\n model_info.category = \\\"custom\\\"\\n model_info.parameters = parameters\\n model_info.random = random\\n #model_info.single = any(part['single'] for part in parts)\\n model_info.structure_factor = False\\n #model_info.tests = []\\n #model_info.source = []\\n # Remember the component info blocks so we can build the model\\n model_info.composition = ('mixture', parts)\\n return model_info\",\n \"def tied_featurize(batch, device, chain_dict, fixed_position_dict=None, omit_AA_dict=None, tied_positions_dict=None, pssm_dict=None, bias_by_res_dict=None, ca_only=False):\\n alphabet = 'ACDEFGHIKLMNPQRSTVWYX'\\n B = len(batch)\\n lengths = np.array([len(b['seq']) for b in batch], dtype=np.int32) #sum of chain seq lengths\\n L_max = max([len(b['seq']) for b in batch])\\n if ca_only:\\n X = np.zeros([B, L_max, 1, 3])\\n else:\\n X = np.zeros([B, L_max, 4, 3])\\n residue_idx = -100*np.ones([B, L_max], dtype=np.int32)\\n chain_M = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted\\n pssm_coef_all = np.zeros([B, L_max], dtype=np.float32) #1.0 for the bits that need to be predicted\\n pssm_bias_all = np.zeros([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted\\n pssm_log_odds_all = 10000.0*np.ones([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted\\n chain_M_pos = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted\\n bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32)\\n chain_encoding_all = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted\\n S = np.zeros([B, L_max], dtype=np.int32)\\n omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=np.int32)\\n # Build the batch\\n letter_list_list = []\\n visible_list_list = []\\n masked_list_list = []\\n masked_chain_length_list_list = []\\n tied_pos_list_of_lists_list = []\\n for i, b in enumerate(batch):\\n if chain_dict != None:\\n masked_chains, visible_chains = chain_dict[b['name']] #masked_chains a list of chain letters to predict [A, D, F]\\n else:\\n masked_chains = [item[-1:] for item in list(b) if item[:10]=='seq_chain_']\\n visible_chains = []\\n masked_chains.sort() #sort masked_chains \\n visible_chains.sort() #sort visible_chains \\n all_chains = masked_chains + visible_chains\\n for i, b in enumerate(batch):\\n mask_dict = {}\\n a = 0\\n x_chain_list = []\\n chain_mask_list = []\\n chain_seq_list = []\\n chain_encoding_list = []\\n c = 1\\n letter_list = []\\n global_idx_start_list = [0]\\n visible_list = []\\n masked_list = []\\n masked_chain_length_list = []\\n fixed_position_mask_list = []\\n omit_AA_mask_list = []\\n pssm_coef_list = []\\n pssm_bias_list = []\\n pssm_log_odds_list = []\\n bias_by_res_list = []\\n l0 = 0\\n l1 = 0\\n for step, letter in enumerate(all_chains):\\n if letter in visible_chains:\\n letter_list.append(letter)\\n visible_list.append(letter)\\n chain_seq = b[f'seq_chain_{letter}']\\n chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])\\n chain_length = len(chain_seq)\\n global_idx_start_list.append(global_idx_start_list[-1]+chain_length)\\n chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary\\n chain_mask = np.zeros(chain_length) #0.0 for visible chains\\n if ca_only:\\n x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff\\n if len(x_chain.shape) == 2:\\n x_chain = x_chain[:,None,:]\\n else:\\n x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3]\\n x_chain_list.append(x_chain)\\n chain_mask_list.append(chain_mask)\\n chain_seq_list.append(chain_seq)\\n chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))\\n l1 += chain_length\\n residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)\\n l0 += chain_length\\n c+=1\\n fixed_position_mask = np.ones(chain_length)\\n fixed_position_mask_list.append(fixed_position_mask)\\n omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)\\n omit_AA_mask_list.append(omit_AA_mask_temp)\\n pssm_coef = np.zeros(chain_length)\\n pssm_bias = np.zeros([chain_length, 21])\\n pssm_log_odds = 10000.0*np.ones([chain_length, 21])\\n pssm_coef_list.append(pssm_coef)\\n pssm_bias_list.append(pssm_bias)\\n pssm_log_odds_list.append(pssm_log_odds)\\n bias_by_res_list.append(np.zeros([chain_length, 21]))\\n if letter in masked_chains:\\n masked_list.append(letter)\\n letter_list.append(letter)\\n chain_seq = b[f'seq_chain_{letter}']\\n chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])\\n chain_length = len(chain_seq)\\n global_idx_start_list.append(global_idx_start_list[-1]+chain_length)\\n masked_chain_length_list.append(chain_length)\\n chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary\\n chain_mask = np.ones(chain_length) #1.0 for masked\\n if ca_only:\\n x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff\\n if len(x_chain.shape) == 2:\\n x_chain = x_chain[:,None,:]\\n else:\\n x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3] \\n x_chain_list.append(x_chain)\\n chain_mask_list.append(chain_mask)\\n chain_seq_list.append(chain_seq)\\n chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))\\n l1 += chain_length\\n residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)\\n l0 += chain_length\\n c+=1\\n fixed_position_mask = np.ones(chain_length)\\n if fixed_position_dict!=None:\\n fixed_pos_list = fixed_position_dict[b['name']][letter]\\n if fixed_pos_list:\\n fixed_position_mask[np.array(fixed_pos_list)-1] = 0.0\\n fixed_position_mask_list.append(fixed_position_mask)\\n omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)\\n if omit_AA_dict!=None:\\n for item in omit_AA_dict[b['name']][letter]:\\n idx_AA = np.array(item[0])-1\\n AA_idx = np.array([np.argwhere(np.array(list(alphabet))== AA)[0][0] for AA in item[1]]).repeat(idx_AA.shape[0])\\n idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx])\\n omit_AA_mask_temp[idx_[:,0], idx_[:,1]] = 1\\n omit_AA_mask_list.append(omit_AA_mask_temp)\\n pssm_coef = np.zeros(chain_length)\\n pssm_bias = np.zeros([chain_length, 21])\\n pssm_log_odds = 10000.0*np.ones([chain_length, 21])\\n if pssm_dict:\\n if pssm_dict[b['name']][letter]:\\n pssm_coef = pssm_dict[b['name']][letter]['pssm_coef']\\n pssm_bias = pssm_dict[b['name']][letter]['pssm_bias']\\n pssm_log_odds = pssm_dict[b['name']][letter]['pssm_log_odds']\\n pssm_coef_list.append(pssm_coef)\\n pssm_bias_list.append(pssm_bias)\\n pssm_log_odds_list.append(pssm_log_odds)\\n if bias_by_res_dict:\\n bias_by_res_list.append(bias_by_res_dict[b['name']][letter])\\n else:\\n bias_by_res_list.append(np.zeros([chain_length, 21]))\\n\\n \\n letter_list_np = np.array(letter_list)\\n tied_pos_list_of_lists = []\\n tied_beta = np.ones(L_max)\\n if tied_positions_dict!=None:\\n tied_pos_list = tied_positions_dict[b['name']]\\n if tied_pos_list:\\n set_chains_tied = set(list(itertools.chain(*[list(item) for item in tied_pos_list])))\\n for tied_item in tied_pos_list:\\n one_list = []\\n for k, v in tied_item.items():\\n start_idx = global_idx_start_list[np.argwhere(letter_list_np == k)[0][0]]\\n if isinstance(v[0], list):\\n for v_count in range(len(v[0])):\\n one_list.append(start_idx+v[0][v_count]-1)#make 0 to be the first\\n tied_beta[start_idx+v[0][v_count]-1] = v[1][v_count]\\n else:\\n for v_ in v:\\n one_list.append(start_idx+v_-1)#make 0 to be the first\\n tied_pos_list_of_lists.append(one_list)\\n tied_pos_list_of_lists_list.append(tied_pos_list_of_lists)\\n\\n\\n \\n x = np.concatenate(x_chain_list,0) #[L, 4, 3]\\n all_sequence = \\\"\\\".join(chain_seq_list)\\n m = np.concatenate(chain_mask_list,0) #[L,], 1.0 for places that need to be predicted\\n chain_encoding = np.concatenate(chain_encoding_list,0)\\n m_pos = np.concatenate(fixed_position_mask_list,0) #[L,], 1.0 for places that need to be predicted\\n\\n pssm_coef_ = np.concatenate(pssm_coef_list,0) #[L,], 1.0 for places that need to be predicted\\n pssm_bias_ = np.concatenate(pssm_bias_list,0) #[L,], 1.0 for places that need to be predicted\\n pssm_log_odds_ = np.concatenate(pssm_log_odds_list,0) #[L,], 1.0 for places that need to be predicted\\n\\n bias_by_res_ = np.concatenate(bias_by_res_list, 0) #[L,21], 0.0 for places where AA frequencies don't need to be tweaked\\n\\n l = len(all_sequence)\\n x_pad = np.pad(x, [[0,L_max-l], [0,0], [0,0]], 'constant', constant_values=(np.nan, ))\\n X[i,:,:,:] = x_pad\\n\\n m_pad = np.pad(m, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\\n m_pos_pad = np.pad(m_pos, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\\n omit_AA_mask_pad = np.pad(np.concatenate(omit_AA_mask_list,0), [[0,L_max-l]], 'constant', constant_values=(0.0, ))\\n chain_M[i,:] = m_pad\\n chain_M_pos[i,:] = m_pos_pad\\n omit_AA_mask[i,] = omit_AA_mask_pad\\n\\n chain_encoding_pad = np.pad(chain_encoding, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\\n chain_encoding_all[i,:] = chain_encoding_pad\\n\\n pssm_coef_pad = np.pad(pssm_coef_, [[0,L_max-l]], 'constant', constant_values=(0.0, ))\\n pssm_bias_pad = np.pad(pssm_bias_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))\\n pssm_log_odds_pad = np.pad(pssm_log_odds_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))\\n\\n pssm_coef_all[i,:] = pssm_coef_pad\\n pssm_bias_all[i,:] = pssm_bias_pad\\n pssm_log_odds_all[i,:] = pssm_log_odds_pad\\n\\n bias_by_res_pad = np.pad(bias_by_res_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))\\n bias_by_res_all[i,:] = bias_by_res_pad\\n\\n # Convert to labels\\n indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=np.int32)\\n S[i, :l] = indices\\n letter_list_list.append(letter_list)\\n visible_list_list.append(visible_list)\\n masked_list_list.append(masked_list)\\n masked_chain_length_list_list.append(masked_chain_length_list)\\n\\n\\n isnan = np.isnan(X)\\n mask = np.isfinite(np.sum(X,(2,3))).astype(np.float32)\\n X[isnan] = 0.\\n\\n # Conversion\\n pssm_coef_all = torch.from_numpy(pssm_coef_all).to(dtype=torch.float32, device=device)\\n pssm_bias_all = torch.from_numpy(pssm_bias_all).to(dtype=torch.float32, device=device)\\n pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(dtype=torch.float32, device=device)\\n\\n tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device)\\n\\n jumps = ((residue_idx[:,1:]-residue_idx[:,:-1])==1).astype(np.float32)\\n bias_by_res_all = torch.from_numpy(bias_by_res_all).to(dtype=torch.float32, device=device)\\n phi_mask = np.pad(jumps, [[0,0],[1,0]])\\n psi_mask = np.pad(jumps, [[0,0],[0,1]])\\n omega_mask = np.pad(jumps, [[0,0],[0,1]])\\n dihedral_mask = np.concatenate([phi_mask[:,:,None], psi_mask[:,:,None], omega_mask[:,:,None]], -1) #[B,L,3]\\n dihedral_mask = torch.from_numpy(dihedral_mask).to(dtype=torch.float32, device=device)\\n residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long,device=device)\\n S = torch.from_numpy(S).to(dtype=torch.long,device=device)\\n X = torch.from_numpy(X).to(dtype=torch.float32, device=device)\\n mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device)\\n chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device)\\n chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device)\\n omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device)\\n chain_encoding_all = torch.from_numpy(chain_encoding_all).to(dtype=torch.long, device=device)\\n if ca_only:\\n X_out = X[:,:,0]\\n else:\\n X_out = X\\n return X_out, S, mask, lengths, chain_M, chain_encoding_all, letter_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef_all, pssm_bias_all, pssm_log_odds_all, bias_by_res_all, tied_beta\",\n \"def compiler_output(input_ckt, hier_graph_dict, design_name:str, result_dir:pathlib.Path, pdk_dir:pathlib.Path, uniform_height=False):\\n layers_json = pdk_dir / 'layers.json'\\n with open(layers_json,\\\"rt\\\") as fp:\\n pdk_data=json.load(fp)\\n design_config = pdk_data[\\\"design_info\\\"]\\n\\n if not result_dir.exists():\\n result_dir.mkdir()\\n logger.debug(f\\\"Writing results in dir: {result_dir} {hier_graph_dict}\\\")\\n input_dir = input_ckt.parents[0]\\n\\n verilog_tbl = { 'modules': [], 'global_signals': []}\\n\\n design_setup = read_setup(input_dir / (design_name + '.setup'))\\n try:\\n POWER_PINS = [design_setup['GND'][0],design_setup['POWER'][0]]\\n except (IndexError, ValueError):\\n POWER_PINS = []\\n logger.info(\\\"Power and ground nets not found. Power grid will not be constructed.\\\")\\n\\n #read lef to not write those modules as macros\\n lef_path = pathlib.Path(__file__).resolve().parent.parent / 'config'\\n all_lef = read_lef(lef_path)\\n logger.debug(f\\\"Available library cells: {', '.join(all_lef)}\\\")\\n\\n primitives = {}\\n for name,member in hier_graph_dict.items():\\n logger.debug(f\\\"Found module: {name} {member['graph'].nodes()}\\\")\\n graph = member[\\\"graph\\\"]\\n constraints = member[\\\"constraints\\\"]\\n\\n for const in constraints:\\n if isinstance(const, constraint.GuardRing):\\n primitives['guard_ring'] = {'primitive':'guard_ring'}\\n\\n logger.debug(f\\\"Reading nodes from graph: {name}\\\")\\n for node, attr in graph.nodes(data=True):\\n if 'net' in attr['inst_type']: continue\\n #Dropping floating ports\\n lef_name = attr['inst_type']\\n\\n if \\\"values\\\" in attr and (lef_name in all_lef):\\n block_name, block_args = generate_lef(lef_name, attr, primitives, design_config, uniform_height)\\n #block_name_ext = block_name.replace(lef_name,'')\\n logger.debug(f\\\"Created new lef for: {block_name} {lef_name}\\\")\\n #Multiple instances of same module\\n if 'inst_copy' in attr:\\n for nm in list(hier_graph_dict.keys()):\\n if nm == lef_name + attr['inst_copy']:\\n if block_name not in hier_graph_dict.keys():\\n logger.debug('Trying to modify a dictionary while iterating over it!')\\n hier_graph_dict[block_name] = hier_graph_dict.pop(nm)\\n else:\\n #For cells with extra parameters than current primitive naming convention\\n all_lef.append(nm)\\n graph.nodes[node][\\\"inst_type\\\"] = block_name\\n all_lef.append(block_name)\\n\\n # Only unit caps are generated\\n if block_name.lower().startswith('cap'):\\n graph.nodes[node]['inst_type'] = block_args['primitive']\\n block_args['primitive'] = block_name\\n else:\\n graph.nodes[node]['inst_type'] = block_name\\n\\n if block_name in primitives:\\n if block_args != primitives[block_name]:\\n logging.warning(f\\\"two different primitve {block_name} of size {primitives[block_name]} {block_args}got approximated to same unit size\\\")\\n else:\\n primitives[block_name] = block_args\\n elif \\\"values\\\" in attr and 'inst_copy' in attr:\\n member[\\\"graph\\\"].nodes[node][\\\"inst_type\\\"]= lef_name + attr[\\\"inst_copy\\\"]\\n all_lef.append(block_name)\\n\\n else:\\n logger.debug(f\\\"No physical information found for: {name}\\\")\\n logger.debug(f\\\"generated data for {name} : {pprint.pformat(primitives, indent=4)}\\\")\\n logger.debug(f\\\"All available cell generator with updates: {all_lef}\\\")\\n for name,member in hier_graph_dict.items():\\n graph = member[\\\"graph\\\"]\\n logger.debug(f\\\"Found module: {name} {graph.nodes()}\\\")\\n inoutpin = []\\n floating_ports=[]\\n if \\\"ports_match\\\" in member and member[\\\"ports_match\\\"]:\\n for key in member[\\\"ports_match\\\"].keys():\\n if key not in POWER_PINS:\\n inoutpin.append(key)\\n if member[\\\"ports\\\"]:\\n logger.debug(f'Found module ports: {member[\\\"ports\\\"]} {member[\\\"name\\\"]}')\\n floating_ports = set(inoutpin) - set(member[\\\"ports\\\"]) - set(design_setup['POWER']) -set(design_setup['GND'])\\n if len(list(floating_ports))> 0:\\n logger.error(f\\\"floating ports found: {name} {floating_ports}\\\")\\n raise SystemExit('Please remove floating ports')\\n else:\\n inoutpin = member[\\\"ports\\\"]\\n if name not in all_lef:\\n\\n ## Removing constraints to fix cascoded cmc\\n if name not in design_setup['DIGITAL']:\\n logger.debug(f\\\"call constraint generator writer for block: {name}\\\")\\n stop_points = design_setup['POWER'] + design_setup['GND'] + design_setup['CLOCK']\\n constraints = member[\\\"constraints\\\"]\\n if name not in design_setup['NO_CONST']:\\n constraints = FindConst(graph, name, inoutpin, member[\\\"ports_weight\\\"], constraints, stop_points)\\n constraints = CapConst(graph, name, design_config[\\\"unit_size_cap\\\"], constraints, design_setup['MERGE_SYMM_CAPS'])\\n hier_graph_dict[name] = hier_graph_dict[name].copy(\\n update={'constraints': constraints}\\n )\\n ## Write out modified netlist & constraints as JSON\\n logger.debug(f\\\"call verilog writer for block: {name}\\\")\\n wv = WriteVerilog(name, inoutpin, hier_graph_dict, POWER_PINS)\\n verilog_tbl['modules'].append( wv.gen_dict())\\n if len(POWER_PINS)>0:\\n for i, nm in enumerate(POWER_PINS):\\n verilog_tbl['global_signals'].append( { 'prefix' :'global_power', 'formal' : f'supply{i}', 'actual' : nm})\\n\\n with (result_dir / f'{design_name}.verilog.json').open( 'wt') as fp:\\n json.dump( verilog_tbl, fp=fp, indent=2)\\n\\n with (result_dir / f'{design_name}.v').open( 'wt') as fp:\\n write_verilog( verilog_tbl, fp)\\n\\n logger.info(\\\"Completed topology identification.\\\")\\n logger.debug(f\\\"OUTPUT verilog json netlist at: {result_dir}/{design_name}.verilog.json\\\")\\n logger.debug(f\\\"OUTPUT verilog netlist at: {result_dir}/{design_name}.v\\\")\\n logger.debug(f\\\"OUTPUT const file at: {result_dir}/{design_name}.pnr.const.json\\\")\\n return primitives\",\n \"def convert2EbnerParam(joblib,list_prefix,directory):\\n \\n key=[p[0] for p in joblib]\\n element=[p[1] for p in joblib]\\n\\n \\n listSlice=element[key.index('listSlice')] #list of slice\\n parameters=element[key.index('EvolutionParameters')][-1,:,:] #estimated paramters of the registration\\n rejectedSlice=element[key.index('RejectedSlices')] #rejected slices\\n\\n \\n images,mask = createVolumesFromAlist(listSlice.copy()) #list of images corresponding to differents original stacks\\n \\n for i_slice in range(len(listSlice)): \\n slicei=listSlice[i_slice]\\n slicei.set_parameters(parameters[i_slice,:]) #set parameters to the last estimated parameters\\n \\n mat = np.array([[-1,0,0,0],[0,-1,0,0],[0,0,1,0],[0,0,0,1]]) #matrix to convert affine matrix from nibabel to itk\\n\\n for n in range(len(images)): #for each stack\\n \\n imagen = images[n]\\n \\n for i_slice in range(len(images[n])): #for each slices (in each stacks)\\n \\n slicei=imagen[i_slice]\\n s = (slicei.get_orientation(),slicei.get_index_slice())\\n \\n if s not in rejectedSlice:\\n dimension=3\\n X,Y,Z= slicei.get_slice().get_fdata().shape\\n center= -slicei.get_center()\\n centerMat = np.eye(4)\\n centerMat[0:3,3] = center[0:3]\\n invcenterMat = np.eye(4)\\n invcenterMat[0:3,3] = -center[0:3] \\n p = slicei.get_parameters()\\n matrix = mat @ centerMat @ rigidMatrix([p[0],p[1],p[2],p[3],p[4],p[5]]) @ invcenterMat @ mat\\n test = sitk.AffineTransform(dimension)\\n test.SetMatrix(matrix[0:3,0:3].flatten())\\n test.SetTranslation(matrix[0:3,3])\\n images_index = slicei.get_index_image()\\n sitk.WriteTransform(test,\\\"%s/%s_slice%d.tfm\\\" %(directory,list_prefix[images_index],slicei.get_index_slice())) #save rigid transformation, computed at the barycenter of the image, adatpted to itk\\n #else:\\n #print(s)\",\n \"def build(width, height, depth, classes, stages, filters, include_top, pooling,\\n reg=1e-3, bnEps=2e-5, bnMom=0.0):\\n inputShape = (height, width, depth)\\n chanDim = -1\\n\\n if K.image_data_format() == \\\"channels_first\\\":\\n inputShape = (depth, height, width)\\n chanDim = 1\\n\\n inputs = Input(shape=inputShape)\\n\\n\\n # block 1 (initial conv block)\\n x = ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(inputs)\\n x = Conv2D(64, (7,7), use_bias=False, strides=(2,2),\\n kernel_initializer=\\\"he_normal\\\", kernel_regularizer=l2(reg))(x)\\n x = BatchNormalization(axis=chanDim, name=\\\"bn_conv1\\\")(x)\\n x = Activation(\\\"relu\\\")(x)\\n x = ZeroPadding2D(padding=((1,1), (1,1)), name=\\\"pool1_pad\\\")(x)\\n x = MaxPooling2D(3, strides=2)(x)\\n\\n for i in range(0, len(stages)):\\n stride = (1,1) if i == 0 else (2,2) # block 2 (projection block) w stride(1,1)\\n\\n print(\\\"Stage {}, Stride={}\\\".format(i, stride))\\n x = SEResNet.residual_module(x, filters[i+1], stride,\\n chanDim=chanDim, red=True, bnEps=bnEps, bnMom=bnMom)\\n for j in range(0, stages[i] + 1): #stacking res block to each depth layer\\n x = SEResNet.residual_module(x, filters[i+1], stride=(1,1),\\n chanDim=chanDim, bnEps=bnEps,\\n bnMom=bnMom)\\n x = BatchNormalization(axis=chanDim, epsilon=bnEps,\\n momentum=bnMom)(x)\\n x = Activation(\\\"relu\\\")(x)\\n\\n if include_top:\\n x = GlobalAveragePooling2D()(x)\\n x = Dense(classes, use_bias=False, kernel_regularizer=l2(reg),\\n activation='softmax')(x)\\n else:\\n if pooling == 'avg':\\n print(\\\"Adding average pool\\\")\\n x = GlobalAveragePooling2D()(x)\\n elif pooling == 'max':\\n x = GlobalMaxPooling2D()(x)\\n\\n model = Model(inputs=inputs, outputs=x, name=\\\"SEResNet\\\")\\n return model\",\n \"def _processLabel(self, kitti_label):\\n label = {\\n 'category': kitti_label['type'].lower(),\\n 'box2D': kitti_label['bbox'].copy(),\\n 'box3D': {\\n 'location': {\\n 'x': kitti_label['location']['x'],\\n 'y': kitti_label['location']['y'] - kitti_label['dimensions']['height'] / 2.0, # move to center\\n 'z': kitti_label['location']['z'],\\n },\\n 'dimensions': kitti_label['dimensions'].copy(),\\n 'rotation_y': kitti_label['rotation_y'],\\n },\\n 'info': {\\n 'truncated': kitti_label['truncated'],\\n 'occluded': kitti_label['occluded'],\\n },\\n }\\n if 'trackId' in kitti_label:\\n # set trackId if given\\n label['info']['trackId'] = kitti_label['trackId']\\n return label\",\n \"def generate_semisupervized_label(self, idx_known, idx_unknown):\\n tmp_df = self.data_info.set_index(['patientID','body_part'])\\n # associate semi-supervized settings\\n if len(idx_known) > 0:\\n df_known = tmp_df.loc[idx_known,:]\\n df_known['semi_label'] = df_known.abnormal_XR.apply(lambda x: -1 if x==1 else 1)\\n df_unknown = tmp_df.loc[idx_unknown,:]\\n df_unknown['semi_label'] = 0\\n return pd.concat([df_known, df_unknown], axis=0).reset_index()\\n else:\\n df_unknown = tmp_df.loc[idx_unknown,:]\\n df_unknown['semi_label'] = 0\\n return df_unknown.reset_index()\",\n \"def serialize(mode):\\r\\n serialize_version(mode)\\r\\n vcb.serialize(mode) \\r\\n for x in xfrms:\\r\\n x.serialize(mode)\",\n \"def label(self, cfg):\\n rep = \\\"\\\"\\n nl = \\\"\\\"\\n for node in cfg.nodes:\\n rep += nl + \\\"{}\\\\tgen={}\\\\tkill={}\\\\tout={}\\\".format(\\n node, \\n set(self.gen.get(node)),\\n set(self.kill.get(node)),\\n set(self.out.get(node)))\\n nl = \\\"\\\\n\\\"\\n return rep\",\n \"def m_dump_tf():\\n\\n # Info about ENS and Resolver Smart Contracts\\n #print(f\\\"ENS address: {ens.address()}\\\")\\n print(f\\\"ENS address (contract address): {ENS.address}\\\")\\n print(f\\\"Resolver address (contract address): {resolver.address()}\\\")\\n print(f\\\"Resolver address from ENS(root): {ens.resolver('root')}\\\")\\n print(\\\"\\\\n\\\")\\n\\n print(f\\\"Owner of ROOT record: {ens.owner('root')}\\\")\\n ROOT_address, ROOT_key = wallet.account_from_name(\\\"ROOT\\\", \\\"ThePassword\\\")\\n print(f\\\"ROOT address from wallet: {ROOT_address}\\\")\\n\\n n_subnodes = ens.numberSubnodes(0)\\n print(f\\\"Number of subnodes of root: {n_subnodes}\\\")\\n for i in range(n_subnodes):\\n hash = ens.subnode(index=i).hex()\\n print(f\\\" Subnode hash {i}: {ens.subnode(i).hex()}\\\")\\n name = resolver.name(node_hash=hash)\\n print(f\\\" Name: {name}\\\")\\n resolver_address = ENS.functions.resolver(hash).call()\\n print(f\\\" Resolver: {resolver_address}\\\")\\n ala_address = ens.resolver(name)\\n print(f\\\" Resolver by name: {ala_address}\\\")\\n owner = owner = ENS.functions.owner(hash).call()\\n print(f\\\" Owner: {owner}\\\")\\n ROOT_address, ROOT_key = wallet.account_from_name(\\\"Alastria\\\", \\\"ThePassword\\\")\\n print(f\\\" Address from wallet: {ROOT_address}\\\")\\n\\n n_subnodes = ens.numberSubnodes(\\\"ala\\\")\\n print(f\\\"Number of subnodes of {name}: {n_subnodes}\\\")\\n for i in range(n_subnodes):\\n hash = ens.subnode(node_name=\\\"ala\\\", index=i).hex()\\n print(f\\\" Subnode hash {i}: {hash}\\\")\\n name = resolver.name(node_hash=hash)\\n print(f\\\" Name: {name}\\\")\\n resolver_address = ENS.functions.resolver(hash).call()\\n print(f\\\" Resolver: {resolver_address}\\\")\\n ala_address = ens.resolver(name)\\n print(f\\\" Resolver by name: {ala_address}\\\")\\n owner = owner = ENS.functions.owner(hash).call()\\n print(f\\\" Owner: {owner}\\\")\",\n \"def serialize(self):\\n return pickle.dumps([block.serialize() for block in self.chain])\",\n \"def identity_block(X, f, filters, stage, block):\\n\\n # defining name basis\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n bn_name_base = 'bn' + str(stage) + block + '_branch'\\n\\n # Retrieve Filters\\n F1, F2, F3 = filters\\n\\n # Save the input value.\\n X_shortcut = X\\n\\n # First component of main path\\n X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\\n X = Activation('relu')(X)\\n\\n # Second component of main path\\n X = Conv2D(filters = F2, kernel_size= (f,f),strides= (1,1), padding= 'same' , name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)\\n X = Activation('relu')(X)\\n\\n # Third component of main path\\n X = Conv2D(filters = F3, kernel_size= (1,1),strides= (1,1), padding= 'valid' , name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)\\n\\n # Final step: Add shortcut value to main path, and pass it through a RELU activation\\n X = layers.add([X,X_shortcut])\\n X = Activation('relu')(X)\\n\\n return X\",\n \"def build_label_transform():\\n\\n return NALabelEncoder()\",\n \"def _make_ht_label(chain_parts):\\n\\n assert len(chain_parts) == 1, '_make_ht_label, no. of chain parts != 1'\\n scenario = chain_parts[0]['hypoScenario']\\n \\n assert scenario.startswith('HT'), '_make_ht_label(): scenario does not start with HT'\\n\\n arg_res = [\\n re.compile(r'^(?P\\\\d*)(?Pht)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pet)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Peta)(?P\\\\d*)$'),\\n ]\\n\\n defaults = {\\n 'ht': ('0', 'inf'),\\n 'et': ('0', 'inf'),\\n 'eta': ('0', 'inf'),\\n }\\n\\n\\n args = _args_from_scenario(scenario)\\n argvals = {}\\n nargs = len(args)\\n assert len(args) <= len(arg_res), 'bad num of args %d, expected < %d' % (len(args),\\n len(arg_res))\\n\\n # obtain argument values frrom scenario\\n while args:\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = float(defaults[key][0])\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = float(defaults[key][1])\\n argvals[key+'hi'] = hi\\n\\n print (argvals)\\n assert len(argvals) == 2*nargs, 'no of args: %d, expected %d' % (len(argvals), 2*nargs)\\n\\n print ('sent 100')\\n result = \\\"\\\"\\\"\\n ht([(%(htlo).0fht) \\n (%(etlo).0fet)\\n (%(etalo).0feta%(etahi).0f)\\n ])\\\"\\\"\\\" % argvals\\n print (result)\\n return result\",\n \"def makeBinaryChains():\\n\\t\\n\\t# retrieve the binding partner specifications\\n\\t(maxsize,types) = getTypes()\\n\\t\\n\\t# Do some basic argument checking for this model\\n\\tif (len(types) < 2):\\n\\t\\tprint \\\"Number of defined types must equal two for binary chain calculations.\\\"\\n\\t\\treturn\\n\\tif (maxsize == 0):\\n\\t\\tprint \\\"Must specify a valid maximum number for one or more components.\\\"\\n\\t\\treturn\\n\\n\\tallChains = []\\n\\tnewChainsA = [[]]\\n\\tnewChainsB = []\\n\\t\\n\\ttypeA = types[0]\\n\\ttypeB = types[1]\\n\\t\\n\\t# start the chain with a single type A component\\n\\taddComponent(newChainsA[0],typeA,0,0)\\n\\n\\tdepth = 0\\n\\tfor n in range(maxsize):\\n\\t\\tdepth+=1\\n\\t\\t\\n\\t\\t# go through all the chains created last iteration and append B components\\n\\t\\tnewChainsB = []\\n\\t\\tfor thisChain in newChainsA:\\n\\n\\t\\t\\t# get a list of new available sites in the provided chain\\n\\t\\t\\t# by setting depth -1, we will only add to components added last round\\n\\t\\t\\topenSites = makeSiteList(thisChain,typeB,depth-1)\\n\\t\\t\\t\\n\\t\\t\\t# make all the descendants from the current chain and append them to the pool\\n\\t\\t\\tif (n == 0) and (typeA['sym']): #if the starting binder is symmetric, no need to start chains at all its sites\\n\\t\\t\\t\\tnewChainsB = newChainsB + fillSites(openSites,thisChain,typeB,-1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnewChainsB = newChainsB + fillSites(openSites,thisChain,typeB,depth)\\n\\t\\t\\n\\t\\tprint('n:'+str(n)+', '+str(len(newChainsB))+ ' chains created at depth '+str(depth))\\n\\t\\t\\n\\t\\tallChains = allChains + newChainsB\\n\\t\\t\\n\\t\\tdepth+=1\\n\\t\\t\\n\\t\\t# add an additional component to all the previously modified chains\\n\\t\\tnewChainsA = []\\n\\t\\tfor thisChain in newChainsB:\\n\\n\\t\\t\\topenSites = makeSiteList(thisChain,typeA,depth-1)\\n\\t\\t\\tnewChainsA = newChainsA + fillSites(openSites,thisChain,typeA,depth)\\n\\t\\t\\t\\n\\t\\tprint('n:'+str(n)+', '+str(len(newChainsA))+ ' chains created at depth '+str(depth))\\n\\t\\t\\n\\t\\tallChains = allChains + newChainsA\\n\\n\\treturn allChains\",\n \"def __init__(self):\\n # Flag this instance as compiled now\\n self.is_compiled = True\\n \\n super(HState2CProcDef, self).__init__(name='HState2CProcDef', num_nodes=0, edges=[])\\n \\n \\n # Set the graph attributes\\n self[\\\"mm__\\\"] = ['HimesisMM']\\n \\n self[\\\"name\\\"] = \\\"\\\"\\\"State2CProcDef\\\"\\\"\\\"\\n self[\\\"GUID__\\\"] = uuid.uuid3(uuid.NAMESPACE_DNS,'State2CProcDef')\\n \\n # match model. We only support one match model\\n self.add_node()\\n self.vs[0][\\\"mm__\\\"] = \\\"\\\"\\\"MatchModel\\\"\\\"\\\"\\n \\n # apply model node\\n self.add_node()\\n self.vs[1][\\\"mm__\\\"] = \\\"\\\"\\\"ApplyModel\\\"\\\"\\\"\\n \\n # paired with relation between match and apply models\\n self.add_node()\\n self.vs[2][\\\"mm__\\\"] = \\\"\\\"\\\"paired_with\\\"\\\"\\\"\\n \\n \\n # match class State() node\\n self.add_node()\\n\\n self.vs[3][\\\"mm__\\\"] = \\\"\\\"\\\"State\\\"\\\"\\\" \\n self.vs[3][\\\"attr1\\\"] = \\\"\\\"\\\"+\\\"\\\"\\\" \\n # match_contains node for class State()\\n self.add_node()\\n self.vs[4][\\\"mm__\\\"] = \\\"\\\"\\\"match_contains\\\"\\\"\\\"\\n # match class Transition() node\\n self.add_node()\\n\\n self.vs[5][\\\"mm__\\\"] = \\\"\\\"\\\"Transition\\\"\\\"\\\" \\n self.vs[5][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\" \\n # match_contains node for class Transition()\\n self.add_node()\\n self.vs[6][\\\"mm__\\\"] = \\\"\\\"\\\"match_contains\\\"\\\"\\\"\\n # match class EntryPoint() node\\n self.add_node()\\n\\n self.vs[7][\\\"mm__\\\"] = \\\"\\\"\\\"EntryPoint\\\"\\\"\\\" \\n self.vs[7][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\" \\n # match_contains node for class EntryPoint()\\n self.add_node()\\n self.vs[8][\\\"mm__\\\"] = \\\"\\\"\\\"match_contains\\\"\\\"\\\"\\n # match class StateMachine() node\\n self.add_node()\\n\\n self.vs[9][\\\"mm__\\\"] = \\\"\\\"\\\"StateMachine\\\"\\\"\\\" \\n self.vs[9][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\" \\n # match_contains node for class StateMachine()\\n self.add_node()\\n self.vs[10][\\\"mm__\\\"] = \\\"\\\"\\\"match_contains\\\"\\\"\\\"\\n \\n \\n # apply class LocalDef() node\\n self.add_node()\\n\\n self.vs[11][\\\"mm__\\\"] = \\\"\\\"\\\"LocalDef\\\"\\\"\\\" \\n self.vs[11][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class LocalDef()\\n self.add_node()\\n self.vs[12][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class ProcDef() node\\n self.add_node()\\n\\n self.vs[13][\\\"mm__\\\"] = \\\"\\\"\\\"ProcDef\\\"\\\"\\\" \\n self.vs[13][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class ProcDef()\\n self.add_node()\\n self.vs[14][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[15][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[15][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[16][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[17][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[17][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[18][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[19][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[19][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[20][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[21][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[21][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[22][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class ConditionSet() node\\n self.add_node()\\n\\n self.vs[23][\\\"mm__\\\"] = \\\"\\\"\\\"ConditionSet\\\"\\\"\\\" \\n self.vs[23][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class ConditionSet()\\n self.add_node()\\n self.vs[24][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Inst() node\\n self.add_node()\\n\\n self.vs[25][\\\"mm__\\\"] = \\\"\\\"\\\"Inst\\\"\\\"\\\" \\n self.vs[25][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Inst()\\n self.add_node()\\n self.vs[26][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[27][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[27][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[28][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[29][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[29][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[30][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[31][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[31][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[32][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n # apply class Name() node\\n self.add_node()\\n\\n self.vs[33][\\\"mm__\\\"] = \\\"\\\"\\\"Name\\\"\\\"\\\" \\n self.vs[33][\\\"attr1\\\"] = \\\"\\\"\\\"1\\\"\\\"\\\"\\n # apply_contains node for class Name()\\n self.add_node()\\n self.vs[34][\\\"mm__\\\"] = \\\"\\\"\\\"apply_contains\\\"\\\"\\\"\\n \\n \\n # match association State--initialTransition-->Transition node\\n self.add_node()\\n self.vs[35][\\\"attr1\\\"] = \\\"\\\"\\\"initialTransition\\\"\\\"\\\"\\n self.vs[35][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_S\\\"\\\"\\\"\\n # match association Transition--dest-->EntryPoint node\\n self.add_node()\\n self.vs[36][\\\"attr1\\\"] = \\\"\\\"\\\"dest\\\"\\\"\\\"\\n self.vs[36][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_S\\\"\\\"\\\"\\n # match association EntryPoint--owningStateMachine-->StateMachine node\\n self.add_node()\\n self.vs[37][\\\"attr1\\\"] = \\\"\\\"\\\"owningStateMachine\\\"\\\"\\\"\\n self.vs[37][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_S\\\"\\\"\\\"\\n \\n # apply association LocalDef--def-->ProcDef node\\n self.add_node()\\n self.vs[38][\\\"attr1\\\"] = \\\"\\\"\\\"def\\\"\\\"\\\"\\n self.vs[38][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association ProcDef--channelNames-->Name node\\n self.add_node()\\n self.vs[39][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[39][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association ProcDef--channelNames-->Name node\\n self.add_node()\\n self.vs[40][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[40][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association ProcDef--channelNames-->Name node\\n self.add_node()\\n self.vs[41][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[41][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association ProcDef--channelNames-->Name node\\n self.add_node()\\n self.vs[42][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[42][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association ProcDef--p-->ConditionSet node\\n self.add_node()\\n self.vs[43][\\\"attr1\\\"] = \\\"\\\"\\\"p\\\"\\\"\\\"\\n self.vs[43][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association ConditionSet--alternative-->Inst node\\n self.add_node()\\n self.vs[44][\\\"attr1\\\"] = \\\"\\\"\\\"alternative\\\"\\\"\\\"\\n self.vs[44][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association Inst--channelNames-->Name node\\n self.add_node()\\n self.vs[45][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[45][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association Inst--channelNames-->Name node\\n self.add_node()\\n self.vs[46][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[46][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association Inst--channelNames-->Name node\\n self.add_node()\\n self.vs[47][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[47][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n # apply association Inst--channelNames-->Name node\\n self.add_node()\\n self.vs[48][\\\"attr1\\\"] = \\\"\\\"\\\"channelNames\\\"\\\"\\\"\\n self.vs[48][\\\"mm__\\\"] = \\\"\\\"\\\"directLink_T\\\"\\\"\\\"\\n \\n # backward association State---->LocalDef node\\n self.add_node()\\n\\n self.vs[49][\\\"mm__\\\"] = \\\"\\\"\\\"backward_link\\\"\\\"\\\"\\n \\n \\n \\n \\n \\n \\n # Add the edges\\n self.add_edges([\\n (0,4), # matchmodel -> match_contains\\n (4,3), # match_contains -> match_class State()\\n (0,6), # matchmodel -> match_contains\\n (6,5), # match_contains -> match_class Transition()\\n (0,8), # matchmodel -> match_contains\\n (8,7), # match_contains -> match_class EntryPoint()\\n (0,10), # matchmodel -> match_contains\\n (10,9), # match_contains -> match_class StateMachine()\\n (1,12), # applymodel -> apply_contains\\n (12,11), # apply_contains -> apply_class LocalDef()\\n (1,14), # applymodel -> apply_contains\\n (14,13), # apply_contains -> apply_class ProcDef()\\n (1,16), # applymodel -> apply_contains\\n (16,15), # apply_contains -> apply_class Name()\\n (1,18), # applymodel -> apply_contains\\n (18,17), # apply_contains -> apply_class Name()\\n (1,20), # applymodel -> apply_contains\\n (20,19), # apply_contains -> apply_class Name()\\n (1,22), # applymodel -> apply_contains\\n (22,21), # apply_contains -> apply_class Name()\\n (1,24), # applymodel -> apply_contains\\n (24,23), # apply_contains -> apply_class ConditionSet()\\n (1,26), # applymodel -> apply_contains\\n (26,25), # apply_contains -> apply_class Inst()\\n (1,28), # applymodel -> apply_contains\\n (28,27), # apply_contains -> apply_class Name()\\n (1,30), # applymodel -> apply_contains\\n (30,29), # apply_contains -> apply_class Name()\\n (1,32), # applymodel -> apply_contains\\n (32,31), # apply_contains -> apply_class Name()\\n (1,34), # applymodel -> apply_contains\\n (34,33), # apply_contains -> apply_class Name()\\n (3,35), # match_class State() -> association initialTransition\\n (35,5), # association initialTransition -> match_class Transition()\\n (5,36), # match_class Transition() -> association dest\\n (36,7), # association dest -> match_class EntryPoint()\\n (7,37), # match_class EntryPoint() -> association owningStateMachine\\n (37,9), # association owningStateMachine -> match_class StateMachine()\\n (11,38), # apply_class LocalDef() -> association def\\n (38,13), # association def -> apply_class ProcDef()\\n (13,39), # apply_class ProcDef() -> association channelNames\\n (39,15), # association channelNames -> apply_class Name()\\n (13,40), # apply_class ProcDef() -> association channelNames\\n (40,17), # association channelNames -> apply_class Name()\\n (13,41), # apply_class ProcDef() -> association channelNames\\n (41,19), # association channelNames -> apply_class Name()\\n (13,42), # apply_class ProcDef() -> association channelNames\\n (42,21), # association channelNames -> apply_class Name()\\n (13,43), # apply_class ProcDef() -> association p\\n (43,23), # association p -> apply_class ConditionSet()\\n (23,44), # apply_class ConditionSet() -> association alternative\\n (44,25), # association alternative -> apply_class Inst()\\n (25,45), # apply_class Inst() -> association channelNames\\n (45,27), # association channelNames -> apply_class Name()\\n (25,46), # apply_class Inst() -> association channelNames\\n (46,29), # association channelNames -> apply_class Name()\\n (25,47), # apply_class Inst() -> association channelNames\\n (47,31), # association channelNames -> apply_class Name()\\n (25,48), # apply_class Inst() -> association channelNames\\n (48,33), # association channelNames -> apply_class Name()\\n (11,49), # apply_class LocalDef() -> backward_association\\n (49,3), # backward_association -> apply_class State()\\n (0,2), # matchmodel -> pairedwith\\n (2,1) # pairedwith -> applyModel\\t\\t\\t\\t\\n\\t\\t])\\n\\n # Add the attribute equations\\n self[\\\"equations\\\"] = [((3,'isComposite'),('constant','true')), ((11,'ApplyAttribute'),('constant','solveRef')), ((13,'name'),('constant','C')), ((15,'literal'),('constant','enp')), ((17,'literal'),('constant','exit')), ((19,'literal'),('constant','exack')), ((21,'literal'),('constant','sh')), ((23,'ApplyAttribute'),('constant','solveRef')), ((25,'name'),('concat',(('constant','S'),(9,'name')))), ((27,'literal'),('concat',(('constant','A'),('concat',((7,'name'),('constant','A')))))), ((29,'literal'),('constant','exit_in')), ((31,'literal'),('constant','exack_in')), ((33,'literal'),('constant','sh_in')), ]\",\n \"def calculate_217f_part_count(**attributes):\\n _msg = ''\\n\\n # Dictionary containing MIL-HDBK-217FN2 parts count base hazard rates.\\n # First key is the subcategory_id, second key is the specification id. If\\n # the resistor subcategory is NOT specification dependent, then the second\\n # key will be zero. Current subcategory IDs are:\\n #\\n # 1. Fixed, Composition (RC, RCR)\\n # 2. Fixed, Film (RL, RLR, RN, RNC, RNN, RNR)\\n # 3. Fixed, Film, Power (RD)\\n # 4. Fixed, Film, Network (RZ)\\n # 5. Fixed, Wirewound, Power (RB, RBR)\\n # 6. Fixed, Wirewound, Power, Chassis Mounted (RE, RER)\\n # 7. Thermistor\\n # 8. Variable, Wirewound (RT, RTR)\\n # 9. Variable, Wirewound, Precision (RR)\\n # 10. Variable, Wirewound, Semiprecision (RA, RK)\\n # 11. Variable, Non-Wirewound (RJ, RJR)\\n # 12. Variable, Composition (RV)\\n # 13. Variable,Non-Wirewound, Film and Precision (RQ, RVC)\\n #\\n # These keys return a list of base hazard rates. The hazard rate to use is\\n # selected from the list depending on the active environment.\\n _dic_lambda_b = {\\n 1: [\\n 0.0005, 0.0022, 0.0071, 0.0037, 0.012, 0.0052, 0.0065, 0.016,\\n 0.025, 0.025, 0.00025, 0.0098, 0.035, 0.36\\n ],\\n 2: {\\n 1: [\\n 0.0012, 0.0027, 0.011, 0.0054, 0.020, 0.0063, 0.013, 0.018,\\n 0.033, 0.030, 0.00025, 0.014, 0.044, 0.69\\n ],\\n 2: [\\n 0.0012, 0.0027, 0.011, 0.0054, 0.020, 0.0063, 0.013, 0.018,\\n 0.033, 0.030, 0.00025, 0.014, 0.044, 0.69\\n ],\\n 3: [\\n 0.0014, 0.0031, 0.013, 0.0061, 0.023, 0.0072, 0.014, 0.021,\\n 0.038, 0.034, 0.00028, 0.016, 0.050, 0.78\\n ],\\n 4: [\\n 0.0014, 0.0031, 0.013, 0.0061, 0.023, 0.0072, 0.014, 0.021,\\n 0.038, 0.034, 0.00028, 0.016, 0.050, 0.78\\n ]\\n },\\n 3: [\\n 0.012, 0.025, 0.13, 0.062, 0.21, 0.078, 0.10, 0.19, 0.24, 0.32,\\n 0.0060, 0.18, 0.47, 8.2\\n ],\\n 4: [\\n 0.0023, 0.0066, 0.031, 0.013, 0.055, 0.022, 0.043, 0.077, 0.15,\\n 0.10, 0.0011, 0.055, 0.15, 1.7\\n ],\\n 5: [\\n 0.0085, 0.018, 0.10, 0.045, 0.16, 0.15, 0.17, 0.30, 0.38, 0.26,\\n 0.0068, 0.13, 0.37, 5.4\\n ],\\n 6: {\\n 1: [\\n 0.014, 0.031, 0.16, 0.077, 0.26, 0.073, 0.15, 0.19, 0.39, 0.42,\\n 0.0042, 0.21, 0.62, 9.4\\n ],\\n 2: [\\n 0.013, 0.028, 0.15, 0.070, 0.24, 0.065, 0.13, 0.18, 0.35, 0.38,\\n 0.0038, 0.19, 0.56, 8.6\\n ]\\n },\\n 7: [\\n 0.008, 0.18, 0.096, 0.045, 0.15, 0.044, 0.088, 0.12, 0.24, 0.25,\\n 0.004, 0.13, 0.37, 5.5\\n ],\\n 8: [\\n 0.065, 0.32, 1.4, 0.71, 1.6, 0.71, 1.9, 1.0, 2.7, 2.4, 0.032, 1.3,\\n 3.4, 62.0\\n ],\\n 9: [\\n 0.025, 0.055, 0.35, 0.15, 0.58, 0.16, 0.26, 0.35, 0.58, 1.1, 0.013,\\n 0.52, 1.6, 24.0\\n ],\\n 10: [\\n 0.33, 0.73, 7.0, 2.9, 12.0, 3.5, 5.3, 7.1, 9.8, 23.0, 0.16, 11.0,\\n 33.0, 510.0\\n ],\\n 11: [\\n 0.15, 0.35, 3.1, 1.2, 5.4, 1.9, 2.8, 0.0, 0.0, 9.0, 0.075, 0.0,\\n 0.0, 0.0\\n ],\\n 12: [\\n 0.15, 0.34, 2.9, 1.2, 5.0, 1.6, 2.4, 0.0, 0.0, 7.6, 0.076, 0.0,\\n 0.0, 0.0\\n ],\\n 13: [\\n 0.043, 0.15, 0.75, 0.35, 1.3, 0.39, 0.78, 1.8, 2.8, 2.5, 0.21, 1.2,\\n 3.7, 49.0\\n ],\\n 14: [\\n 0.05, 0.11, 1.1, 0.45, 1.7, 2.8, 4.6, 4.6, 7.5, 3.3, 0.025, 1.5,\\n 4.7, 67.0\\n ],\\n 15: [\\n 0.048, 0.16, 0.76, 0.36, 1.3, 0.36, 0.72, 1.4, 2.2, 2.3, 0.024,\\n 1.2, 3.4, 52.0\\n ]\\n }\\n\\n # List containing piQ values for parts count method. The list positions\\n # corrspond to the following quality levels:\\n #\\n # 0. Established reliability level S\\n # 1. Established reliability level R\\n # 2. Established reliability level P\\n # 3. Established reliability level M\\n # 4. Non-established reliability MIL-SPEC\\n # 5. Non-established reliability lower\\n #\\n # The quality_id attribute is used to select the proper value of piQ.\\n _lst_piQ = [0.030, 0.10, 0.30, 1.0, 3.0, 10.0]\\n\\n # Select the base hazard rate.\\n try:\\n if attributes['subcategory_id'] in [2, 6]:\\n _lst_base_hr = _dic_lambda_b[attributes['subcategory_id']][\\n attributes['specification_id']]\\n else:\\n _lst_base_hr = _dic_lambda_b[attributes['subcategory_id']]\\n except KeyError:\\n _lst_base_hr = [0.0]\\n\\n try:\\n attributes['lambda_b'] = _lst_base_hr[\\n attributes['environment_active_id'] - 1]\\n except IndexError:\\n attributes['lambda_b'] = 0.0\\n\\n # Select the piQ.\\n try:\\n attributes['piQ'] = _lst_piQ[attributes['quality_id'] - 1]\\n except IndexError:\\n attributes['piQ'] = 0.0\\n\\n # Confirm all inputs are within range. If not, set the message. The\\n # hazard rate will be calculated anyway, but will be zero.\\n if attributes['lambda_b'] <= 0.0:\\n _msg = _msg + 'RAMSTK WARNING: Base hazard rate is 0.0 when ' \\\\\\n 'calculating resistor, hardware ID: ' \\\\\\n '{0:d}, subcategory ID: {1:d}, specification ID: {2:d}, ' \\\\\\n 'active environment ID: {3:d}, and quality ID: ' \\\\\\n '{4:d}.\\\\n'.format(attributes['hardware_id'],\\n attributes['subcategory_id'],\\n attributes['specification_id'],\\n attributes['environment_active_id'],\\n attributes['quality_id'])\\n\\n if attributes['piQ'] <= 0.0:\\n _msg = _msg + 'RAMSTK WARNING: piQ is 0.0 when calculating ' \\\\\\n 'resistor, hardware ID: {0:d}, quality ID: ' \\\\\\n '{1:d}.'.format(attributes['hardware_id'],\\n attributes['quality_id'])\\n\\n # Calculate the hazard rate.\\n attributes['hazard_rate_active'] = (\\n attributes['lambda_b'] * attributes['piQ'])\\n\\n return attributes, _msg\",\n \"def identity_block(self,X,stage,block):\\n conv_name_base='res'+str(stage)+block+'_branch'\\n bn_name_base='bn'+str(stage)+block+'_branch'\\n \\n #retrieve the filters\\n #F1,F2,F3=filters\\n \\n X_shortcut=X\\n \\n #first component of main path\\n X=Conv3D(self.filter, (3,3,1),kernel_initializer=self.init,kernel_regularizer=self.regularizer,padding='same')(X)\\n X=BatchNormalization(axis=3)(X)\\n X=Activation('relu')(X)\\n \\n #second component of main path\\n X=Conv3D(self.filter, (3,3,1),kernel_initializer=self.init,kernel_regularizer=self.regularizer,padding='same')(X)\\n\\n \\n #final step, add the shortcut\\n X=Add()([X,X_shortcut])\\n X=Activation('relu')(X)\\n \\n \\n return X\",\n \"def run(self, voxels, entry='all', freq_raw=False):\\n\\n # Note. in this case we are processing all raw data into the data \\n # attribute, so despite having multiple raw FIDs, we are really \\n # only processing one voxel, so no for loop\\n\\n # local reference to input data\\n self.raw = self._dataset.get_source_data('prep')\\n\\n # Choose voxel - for saving result for current single voxel plot\\n self.voxel = voxels[0]\\n\\n # select the chain processing functor based on the entry point\\n if entry == 'all':\\n funct_fidsum_wbnaa.do_processing_all(self)\\n else:\\n print('oooops!')\\n\\n # save data and parameter results into the Block results arrays\\n self._block.data[0,0,0,:] = self.time_summed_offset.copy()\\n \\n # Return values specific to calling Tab that contains this Block.Chain\\n # Used to update its self.view (plot_panel_spectrum object).\\n\\n plot_results = { 'freq_current' : self.freq_current.copy(),\\n 'freq_summed' : self.freq_summed.copy(),\\n 'freq_summed_offset' : self.freq_summed_offset.copy() }\\n \\n return plot_results\",\n \"def dumpData(self,out,index):\\n #--SCVR\\n out.pack('4siBB2sB',\\n 'SCVR', 5+len(self.text), index+48, self.type, self.func, self.oper)\\n if self.text: out.write(self.text)\\n #--Value\\n if isinstance(self.value,int):\\n out.packSub('INTV','i', self.value)\\n else:\\n out.packSub('FLTV','f', self.value)\",\n \"def __init__(self,config = None):\\n \\n self.join_path = join_path\\n self.label_path = cfg['labels_path']\\n self.pick_path = (cfg['result_path'] + cfg['pickle_path'])\\n self.label_dir = os.path.join(CWD_PATH,self.join_path, self.label_path)\\n\\n #Variables inherent to the Fluent data: \\n self.num_ins = 4\\n\\n self.scale_var = cfg['scale_var']\\n # User set values are below. These can be adjusted in config.yml \\n self.MSE_thresh1 = (cfg['thresh1']*self.scale_var)**2\\n self.MSE_thresh2 = (cfg['thresh2']*self.scale_var)**2\\n self.MSE_thresh3 = (cfg['thresh3']*self.scale_var)**2\\n \\n self.rew_goal = cfg['reward'] * self.scale_var\\n\\n self.noise = cfg['noise']\\n self.minmaxbuffer = cfg['minmaxbuffer']\\n\\n # Get the function of input-output mapping, and max & min:\\n [self.O_CH4_flow_uniformity, mins,maxes] = self.get_funcs('O_CH4_flow_uniformity')\\n [self.O_CH4_mol_frac, mins,maxes] = self.get_funcs('O_CH4_mol_frac')\\n [self.O_t, mins, maxes] = self.get_funcs('O_t')\\n \\n self.mins = mins# * self.scale_var\\n self.maxes = maxes#* self.scale_var\\n #Action range is a percentage of the total range\\n self.action_range = cfg['action_range']*self.scale_var\\n\\n #Action space is the up & down range for the 4 actions \\n self.action_space = Box(-self.action_range, self.action_range, shape=(self.num_ins,), dtype=np.float32)\\n\\n # For ref, this is a 10d state space:\\n #in: 1 ch4 flow, 2 ch4 t, 3 o2 flow, 4 o2 t,\\n #out: 5 flow unif, 6 mol frac, 7 temp\\n #out - target: 8 flow unif, 9 mol frac, 10 temp\\n \\n self.observation_space = Tuple((Box(self.mins.values[0],self.maxes.values[0],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[1],self.maxes.values[1],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[2],self.maxes.values[2],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[3],self.maxes.values[3],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32)))\\n \\n # TODO this isn't really a proper gym spec\\n self._spec = lambda: None\\n self._spec.id = \\\"AllVar-v0\\\"\\n \\n # For rendering:\\n self.viewer = None\\n self.labels = cfg['labels']\\n \\n #initialize variables for tracking:\\n self.episode = 0\\n self.reward = 0\\n self.reset()\",\n \"def compile_xilinx_graph(self):\\n pass\",\n \"def levelsets_to_vector_field(levelsets, stepsize):\\r\\n vector_field_shape = levelsets[0][0].shape\\r\\n y_comp_combined = np.ndarray(vector_field_shape)\\r\\n x_comp_combined = np.ndarray(vector_field_shape)\\r\\n y_comp_combined.fill(np.nan)\\r\\n x_comp_combined.fill(np.nan)\\r\\n\\r\\n for source, target in levelsets:\\r\\n labels_present = set(np.array([source.flatten(),target.flatten()]).flatten())\\r\\n labels_present.remove(0)#relates to background\\r\\n\\r\\n #print(labels_present)\\r\\n for l in labels_present:\\r\\n\\r\\n source_cluster = source == l\\r\\n target_cluster = target == l\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"plt.imshow(source_cluster.astype(np.int32)+target_cluster.astype(np.int32))\\r\\n plt.show()\\r\\n print(\\\"-----------\\\")\\\"\\\"\\\"\\r\\n\\r\\n #plot_gradient_field(source_cluster.astype(np.int32), target_cluster.astype(np.int32))\\r\\n\\r\\n y_comp, x_comp = array_to_vector_field(source_cluster, target_cluster, stepsize=stepsize)\\r\\n y_comp_combined[~np.isnan(y_comp)] = y_comp[~np.isnan(y_comp)]\\r\\n x_comp_combined[~np.isnan(x_comp)] = x_comp[~np.isnan(x_comp)]\\r\\n return y_comp_combined, x_comp_combined\",\n \"def convert_propbank(detail=True):\\n\\n out_dir = \\\"../data/wsj_propbank/\\\"\\n os.system(\\\"rm -rf %s\\\" % (out_dir, ))\\n os.system(\\\"mkdir -p %s\\\" % (out_dir, ))\\n\\n pb_instances = propbank.instances()\\n # Count at first\\n verb2idx = {}\\n verb2frames = {}\\n for i in range(0, len(pb_instances)):\\n inst = pb_instances[i]\\n verb_lemma, frame = inst.roleset.split(\\\".\\\")\\n if verb_lemma not in verb2idx:\\n verb2idx[verb_lemma] = []\\n verb2idx[verb_lemma].append(i)\\n if verb_lemma not in verb2frames:\\n verb2frames[verb_lemma] = []\\n if frame not in verb2frames[verb_lemma]:\\n verb2frames[verb_lemma].append(frame)\\n verb_nums = len(verb2idx.keys())\\n verb_counter = 0\\n\\n pair_label = {'-LRB-':'(', '-RRB-':')', '-LCB-':'(', '-RCB-':')'}\\n for verb_lemma, idxs in verb2idx.items():\\n verb_counter += 1\\n if len(verb2frames[verb_lemma]) < 2:\\n continue\\n fh = open(\\\"%s/%s\\\" % (out_dir, verb_lemma), \\\"w\\\")\\n if detail:\\n print(\\\"processing %s(%s/%s)\\\"\\n % (verb_lemma, verb_counter, verb_nums))\\n for i in idxs:\\n inst = pb_instances[i]\\n fileid = inst.fileid\\n sent_num = inst.sentnum\\n verb_pos = inst.wordnum\\n verb_lemma, frame = inst.roleset.split(\\\".\\\")\\n section = [x for x in fileid if x.isdigit()][0:2]\\n section = \\\"\\\".join(section)\\n fileid_for_ptb = \\\"WSJ/%s/%s\\\" % (section, fileid.upper())\\n\\n tagged_sent = ptb.tagged_sents(fileid_for_ptb)[sent_num]\\n # Change tagged_sent from [tuples] to [list]\\n tagged_sent = [[x[0], x[1]]for x in tagged_sent]\\n verb_bak = tagged_sent[verb_pos][0]\\n verb_identifier = \\\"verb_identifier_xxxxx\\\"\\n tagged_sent[verb_pos][0] = verb_identifier\\n sent = []\\n for (token, tag)in tagged_sent:\\n if tag != '-NONE-':\\n if token in pair_label:\\n token = pair_label[token]\\n sent.append(token)\\n sent = \\\" \\\".join(sent)\\n sent_toks = nltk.sent_tokenize(sent)\\n candidate_sent = None\\n for sent_tok in sent_toks:\\n if sent_tok.find(verb_identifier) >= 0:\\n candidate_sent = sent_tok\\n left_sent, right_sent = candidate_sent.split(verb_identifier)\\n left_sent = left_sent.strip()\\n right_sent = right_sent.strip()\\n out_line = \\\"%s\\\\t%s\\\\t%s\\\\t%s\\\" % (frame, left_sent, verb_bak, right_sent)\\n out_line = remove_punctuations(out_line)\\n print(out_line, file=fh)\\n fh.close()\",\n \"def render(self): # pragma: no cover\\n from graphviz import Digraph\\n dot = Digraph(name=\\\"top\\\")\\n for block in self.blocks:\\n if isinstance(block, Branch):\\n label = \\\"if \\\" + astor.to_source(block.cond)\\n dot.node(str(id(block)), label.rstrip(), {\\\"shape\\\": \\\"invhouse\\\"})\\n elif isinstance(block, Yield):\\n label = astor.to_source(block.value)\\n # label += \\\"\\\\nLive Ins : \\\" + str(block.live_ins)\\n # label += \\\"\\\\nLive Outs : \\\" + str(block.live_outs)\\n # label += \\\"\\\\nGen : \\\" + str(block.gen)\\n # label += \\\"\\\\nKill : \\\" + str(block.kill)\\n dot.node(str(id(block)), label.rstrip(), {\\\"shape\\\": \\\"oval\\\"})\\n elif isinstance(block, BasicBlock):\\n label = \\\"\\\\n\\\".join(astor.to_source(stmt).rstrip() for stmt in block.statements)\\n # label += \\\"\\\\nLive Ins : \\\" + str(block.live_ins)\\n # label += \\\"\\\\nLive Outs : \\\" + str(block.live_outs)\\n # label += \\\"\\\\nGen : \\\" + str(block.gen)\\n # label += \\\"\\\\nKill : \\\" + str(block.kill)\\n dot.node(str(id(block)), label.rstrip(), {\\\"shape\\\": \\\"box\\\"})\\n elif isinstance(block, HeadBlock):\\n label = \\\"Initial\\\"\\n dot.node(str(id(block)) + \\\"_start\\\", label.rstrip(), {\\\"shape\\\": \\\"doublecircle\\\"})\\n label = \\\"\\\\n\\\".join(astor.to_source(stmt).rstrip() for stmt in block.initial_statements)\\n # label += \\\"\\\\nLive Ins : \\\" + str(block.live_ins)\\n # label += \\\"\\\\nLive Outs : \\\" + str(block.live_outs)\\n # label += \\\"\\\\nGen : \\\" + str(block.gen)\\n # label += \\\"\\\\nKill : \\\" + str(block.kill)\\n dot.node(str(id(block)), label.rstrip(), {\\\"shape\\\": \\\"box\\\"})\\n dot.edge(str(id(block)) + \\\"_start\\\", str(id(block)))\\n else:\\n raise NotImplementedError(type(block))\\n # for source, sink, label in self.edges:\\n for sink, label in block.outgoing_edges:\\n dot.edge(str(id(block)), str(id(sink)), label)\\n\\n\\n file_name = tempfile.mktemp(\\\"gv\\\")\\n dot.render(file_name, view=True)\\n # with open(\\\"cfg.dot\\\", \\\"w\\\") as file:\\n # file.write(dot.source)\\n # exit()\",\n \"def process_tree(tree):\\n c = circuit()\\n l = line()\\n names = {}\\n procedures = []\\n for lst in tree.children:\\n print(lst)\\n if type(lst[0]) is str:\\n names[lst[0]] = lst[1]\\n else:\\n procedures.append(lst)\\n print(names)\\n #print(procedures)\\n\\n for proc in procedures:\\n\\n proc_elements_names = proc[0]\\n proc_name = proc[1]\\n\\n #print(proc_elements_names)\\n #print(proc_name)\\n\\n if proc_name == \\\"set_mode\\\":\\n mode_name = proc_elements_names[0]\\n if mode_name != \\\"draw-mode\\\": \\n c.set_mode(mode_name)\\n elif mode_name == \\\"draw-mode\\\":\\n l1 = line()\\n # draw mode is different from other modes\\n for element in names:\\n e = CompleteElement(element)\\n e.set_other_attrs(names[element])\\n e.process_other_attrs()\\n l1.addElement(e)\\n c.connectInSeries(l1)\\n c.set_mode(\\\"draw-mode\\\")\\n \\n \\n if proc_name == \\\"series\\\":\\n l1 = line()\\n for element in proc_elements_names:\\n l1.addElement(names[element])\\n l = l1\\n c.connectInSeries(l)\\n #raise SyntaxError(\\\"Alias {0} referrenced before assignment\\\".format(item[0]))\\n\\n elif proc_name == \\\"parallel\\\":\\n l1 = line()\\n for element in proc_elements_names:\\n l1.addElement(names[element])\\n c.connectInParallel(l1)\\n l1 = line()\\n\\n\\n elif proc_name == \\\"add_parallel\\\":\\n new_element = proc_elements_names[1]\\n old_element = proc_elements_names[0]\\n l1 = line()\\n l1.addElement(names[new_element])\\n c.connection.append(l1)\\n\\n\\n elif proc_name == \\\"add_series\\\":\\n new_element = proc_elements_names[1]\\n old_element = proc_elements_names[0]\\n for ln in c.connection:\\n for e in ln.elements:\\n if names[old_element] == e:\\n ln.addElement(names[new_element])\\n\\n\\n c.evaluate(\\\"output.png\\\")\\n #print(c)\",\n \"def forward(self, Ca, mask, residue_idx, chain_labels):\\n if self.augment_eps > 0:\\n Ca = Ca + self.augment_eps * torch.randn_like(Ca)\\n\\n D_neighbors, E_idx, mask_neighbors = self._dist(Ca, mask)\\n\\n Ca_0 = torch.zeros(Ca.shape, device=Ca.device)\\n Ca_2 = torch.zeros(Ca.shape, device=Ca.device)\\n Ca_0[:,1:,:] = Ca[:,:-1,:]\\n Ca_1 = Ca\\n Ca_2[:,:-1,:] = Ca[:,1:,:]\\n\\n V, O_features = self._orientations_coarse(Ca, E_idx)\\n \\n RBF_all = []\\n RBF_all.append(self._rbf(D_neighbors)) #Ca_1-Ca_1\\n RBF_all.append(self._get_rbf(Ca_0, Ca_0, E_idx)) \\n RBF_all.append(self._get_rbf(Ca_2, Ca_2, E_idx))\\n\\n RBF_all.append(self._get_rbf(Ca_0, Ca_1, E_idx))\\n RBF_all.append(self._get_rbf(Ca_0, Ca_2, E_idx))\\n\\n RBF_all.append(self._get_rbf(Ca_1, Ca_0, E_idx))\\n RBF_all.append(self._get_rbf(Ca_1, Ca_2, E_idx))\\n\\n RBF_all.append(self._get_rbf(Ca_2, Ca_0, E_idx))\\n RBF_all.append(self._get_rbf(Ca_2, Ca_1, E_idx))\\n\\n\\n RBF_all = torch.cat(tuple(RBF_all), dim=-1)\\n\\n\\n offset = residue_idx[:,:,None]-residue_idx[:,None,:]\\n offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K]\\n\\n d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long()\\n E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0]\\n E_positional = self.embeddings(offset.long(), E_chains)\\n E = torch.cat((E_positional, RBF_all, O_features), -1)\\n \\n\\n E = self.edge_embedding(E)\\n E = self.norm_edges(E)\\n \\n return E, E_idx\",\n \"def trans_setup():\\n # slot7 slot6 slot5 slot4 slot3 slot2 slot1 <------ beam direction (slot 8 is currently B-fiber only)\\n # Be Be Be Be Be Be Be lens material\\n # 1.5 1.5 0.5 0.5 0.5 0.5 0.5 lens radius [mm]\\n # 1 1 5 8 4 2 1 number of lenses\\n lens_R=[0.5,0.5,0.5,0.5,0.5,1.5,1.5]\\n lens_mat=['Be','Be','Be','Be','Be','Be','Be']\\n lens_N=[1,2,4,8,5,1,1]\\n trans_pos=[35.2,35.8]\\n return {'lens_material':lens_mat,'lens_number':lens_N,'lens_radius':lens_R,'trans_position':trans_pos}\",\n \"def serialize(self):\\n p = []\\n p.append(self.particleCode)\\n if self.particleCode == 'manual':\\n p.append(str(self.particleMass))\\n p.append(str(self.particleCharge))\\n p.append(str(self.x0))\\n p.append(str(self.y0))\\n p.append(str(self.z0))\\n if self.useKineticEnergy:\\n p.append('Y')\\n p.append(str(self.kineticEnergy))\\n else:\\n p.append('n')\\n p.append(str(self.vx0))\\n p.append(str(self.vy0))\\n p.append(str(self.vz0))\\n p.append(self.fieldCode)\\n if self.fieldCode == 'Drift':\\n p.append(str(self.fieldBaseStrength[0]))\\n p.append(str(self.fieldBaseStrength[1]))\\n p.append(str(self.fieldBaseStrength[2]))\\n p.append(str(self.fieldGradient[0]))\\n elif self.fieldCode == 'Smooth':\\n p.append(str(self.fieldBaseStrength[0]))\\n p.append(str(self.fieldGradient[0]))\\n p.append(str(self.fieldGradient[1]))\\n elif self.fieldCode == 'Sharp':\\n p.append(str(self.fieldBaseStrength[0]))\\n p.append(str(self.fieldGradient[0]))\\n p.append(str(self.fieldGradient[1]))\\n p.append(str(self.fieldLength))\\n elif self.fieldCode == 'Sine':\\n p.append(str(self.fieldBaseStrength[0]))\\n p.append(str(self.fieldGradient[0]))\\n p.append(str(self.fieldGradient[1]))\\n p.append(str(self.fieldLength))\\n p.append(str(self.fieldFreq))\\n elif self.fieldCode == 'Helix':\\n p.append(str(self.fieldBaseStrength[0]))\\n p.append(str(self.fieldBaseStrength[1]))\\n p.append(str(self.fieldGradient[0]))\\n p.append(str(self.fieldGradient[1]))\\n p.append(str(self.fieldGradient[2]))\\n p.append(str(self.fieldLength))\\n p.append(str(self.fieldFreq))\\n elif self.fieldCode == 'Tokamak':\\n p.append(str(self.fieldBaseStrength[0]))\\n p.append(str(self.fieldBaseStrength[1]))\\n p.append(str(self.fieldGradient[0]))\\n p.append(str(self.fieldGradient[1]))\\n p.append(str(self.fieldGradient[2]))\\n p.append(str(self.fieldGradient[3]))\\n p.append(str(self.fieldGradient[4]))\\n p.append(str(self.fieldLength))\\n p.append(str(self.fieldFreq))\\n else:\\n for val in self.fieldBaseStrength:\\n p.append(str(val))\\n p.append(str(self.initialTime))\\n p.append(str(self.endTime))\\n p.append(str(self.timeStep))\\n p.append(str(self.tolerance))\\n return p\",\n \"def fst_tostring(self, fst_1, idx=False):\\n\\n fst_string = 'Transducer\\\\n'\\n for state in fst_1.states:\\n for arc in state.arcs:\\n olabel = self.wmap[arc.olabel].encode('utf-8') if not idx and arc.olabel in self.wmap else arc.olabel\\n\\n fst_string += '{} -> {} / {} : {} / {}\\\\n'.format(state.stateid, arc.nextstate, arc.ilabel, olabel,\\n float(arc.weight))\\n\\n if state.final:\\n fst_string += '%s / %s\\\\n' % (state.stateid, state.final)\\n\\n fst_string += '-------\\\\n'\\n\\n return fst_string\",\n \"def chainLabel(self,i):\\n assert(i >= 0 and i < self.nAtoms())\\n assert(self._c_structure is not NULL)\\n cdef char label[2]\\n label[0] = freesasa_structure_atom_chain(self._c_structure,i)\\n label[1] = '\\\\0'\\n return label\",\n \"def createAddOrSelectLabelMapNode(self, script=False):\\r\\n # productive\\r\\n profprint()\\r\\n print \\\"creating label map for working intensity volume\\\"\\r\\n # create, select label map\\r\\n volLogic = slicer.modules.volumes.logic()\\r\\n sliceLogic = slicer.app.layoutManager().sliceWidget(\\\"Red\\\").sliceLogic()\\r\\n vn = sliceLogic.GetBackgroundLayer().GetVolumeNode()\\r\\n self.labelMapNode = slicer.util.getNode(vn.GetName() + \\\"-label\\\")\\r\\n if not self.labelMapNode:\\r\\n self.labelMapNode = volLogic.CreateAndAddLabelVolume(slicer.mrmlScene, vn, vn.GetName() + \\\"-label\\\")\\r\\n # select label volume\\r\\n if not script: #TODO guess there is a bug here (at least while testing with parSearch): also changes the main volume!!\\r\\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\\r\\n selectionNode.SetReferenceActiveLabelVolumeID(self.labelMapNode.GetID())\\r\\n #slicer.app.applicationLogic().PropagateVolumeSelection() #<< in the group '{}'\\\"\\n .format(output_band.pk, self.inputs.group.value))\",\n \"def pack_firmware(self, work_dir, jobclient, ro, rw, version_string=\\\"\\\"):\\n dts_file_path = ro / \\\"zephyr\\\" / \\\"zephyr.dts\\\"\\n\\n # Copy the inputs into the work directory so that Binman can\\n # find them under a hard-coded name.\\n shutil.copy2(ro / \\\"zephyr\\\" / self.ro_file, work_dir / \\\"zephyr_ro.bin\\\")\\n shutil.copy2(rw / \\\"zephyr\\\" / self.rw_file, work_dir / \\\"zephyr_rw.bin\\\")\\n\\n # Version in FRID/FWID can be at most 31 bytes long (32, minus\\n # one for null character).\\n if len(version_string) > 31:\\n version_string = version_string[:31]\\n\\n proc = jobclient.popen(\\n [\\n \\\"binman\\\",\\n \\\"-v\\\",\\n \\\"5\\\",\\n \\\"build\\\",\\n \\\"-a\\\",\\n \\\"version={}\\\".format(version_string),\\n \\\"-d\\\",\\n dts_file_path,\\n \\\"-m\\\",\\n \\\"-O\\\",\\n work_dir,\\n ],\\n cwd=work_dir,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n encoding=\\\"utf-8\\\",\\n )\\n\\n zmake.multiproc.log_output(self.logger, logging.DEBUG, proc.stdout)\\n zmake.multiproc.log_output(self.logger, logging.ERROR, proc.stderr)\\n if proc.wait(timeout=60):\\n raise OSError(\\\"Failed to run binman\\\")\\n\\n yield work_dir / \\\"zephyr.bin\\\", \\\"zephyr.bin\\\"\\n yield ro / \\\"zephyr\\\" / \\\"zephyr.elf\\\", \\\"zephyr.ro.elf\\\"\\n yield rw / \\\"zephyr\\\" / \\\"zephyr.elf\\\", \\\"zephyr.rw.elf\\\"\",\n \"def _add_label_switching_node( self,\\n node_tree,\\n label_vec,\\n last_element,\\n label_ID_node=None,\\n node_index=0,\\n uv_map=None, \\n node_offset=[0,0]):\\n\\n # define local variables #######################################################################################\\n _step_node_width = 200 # x seperation of nodes\\n _step_node_height = 200 # y seperation of nodes\\n ################################################################################ end of define local variables #\\n\\n # create image ID handle #######################################################################################\\n if label_ID_node is None:\\n label_ID_node = node_tree.node_tree.nodes.new(\\\"ShaderNodeValue\\\")\\n label_ID_node.location = ((node_offset[0]-400,node_offset[1]-100))\\n label_ID_node.name = \\\"label_step_ID\\\"\\n label_ID_node.label = \\\"label_step_ID\\\"\\n label_ID_node.outputs[0].default_value = 1\\n ############################################################################### end of create image ID handle #\\n\\n # create image nodes ###########################################################################################\\n _x_offset = (node_index+1)*_step_node_width + node_offset[0]\\n _y_offset = (node_index+1)*_step_node_height + node_offset[1]\\n\\n _semantic_node_offset = [(node_index+1)*_step_node_width*2 + node_offset[0]-1000,(node_index+1)*\\\\\\n _step_node_height + node_offset[1]+200]\\n\\n _semantic_tree, self._semantic_pass_id = self.create_semantic_nodes(node_tree=self._world_node_tree,\\n label_ID_vec=label_vec,\\n num_label_per_channel=15, # TODO add in script\\n env_mode=True,\\n uv_map=uv_map,\\n node_offset=_semantic_node_offset)\\n\\n _semantic_tree.inputs[0].default_value = 1\\n\\n # create new mix node ######################################################################################\\n _current_mix_shader_node = node_tree.node_tree.nodes.new(\\\"ShaderNodeMixRGB\\\")\\n _current_mix_shader_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\\n (node_index+1)*_step_node_height + node_offset[1]))\\n ############################################################################### end of create new mix node #\\n\\n # create compare node ######################################################################################\\n _current_compare_node = node_tree.node_tree.nodes.new(\\\"ShaderNodeMath\\\")\\n _current_compare_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\\n node_offset[1]-_step_node_height))\\n _current_compare_node.operation = 'COMPARE'\\n _current_compare_node.inputs[0].default_value = node_index\\n _current_compare_node.inputs[2].default_value = 0 # delta value should be zero for equal comparison\\n ############################################################################### end of create compare node #\\n\\n\\n # link nodes togther #######################################################################################\\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[0], _current_compare_node.outputs[0])\\n if last_element is not None:\\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[1], last_element.outputs[0])\\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[2], _semantic_tree.outputs[0])\\n \\n node_tree.node_tree.links.new(_current_compare_node.inputs[1], label_ID_node.outputs[0])\\n ################################################################################ end of link nodes togther #\\n #################################################################################### end of create image nodes #\\n\\n # return last mix shader node\\n return _current_mix_shader_node, label_ID_node\",\n \"def main(self, case, profile):\\n case[\\\"branch\\\"][:, BR_STATUS] = ones(case[\\\"branch\\\"].shape[0])\\n mpc = ext2int(case)\\n baseMVA, bus, gen, branch, gencost = mpc[\\\"baseMVA\\\"], mpc[\\\"bus\\\"], mpc[\\\"gen\\\"], mpc[\\\"branch\\\"], mpc[\\\"gencost\\\"]\\n\\n nb = shape(mpc['bus'])[0] ## number of buses\\n nl = shape(mpc['branch'])[0] ## number of branches\\n ng = shape(mpc['gen'])[0] ## number of dispatchable injections\\n\\n f = branch[:, F_BUS] ## list of \\\"from\\\" buses\\n t = branch[:, T_BUS] ## list of \\\"to\\\" buses\\n i = range(nl) ## double set of row indices\\n # Connection matrix\\n Cf = sparse((ones(nl), (i, f)), (nl, nb))\\n Ct = sparse((ones(nl), (i, t)), (nl, nb))\\n Cg = sparse((ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))\\n Branch_R = branch[:, BR_R]\\n Branch_X = branch[:, BR_X]\\n Cf = Cf.T\\n Ct = Ct.T\\n # Obtain the boundary information\\n Slmax = branch[:, RATE_A] / baseMVA\\n\\n Pij_l = -Slmax\\n Qij_l = -Slmax\\n Iij_l = zeros(nl)\\n Vm_l = bus[:, VMIN] ** 2\\n Pg_l = gen[:, PMIN] / baseMVA\\n Qg_l = gen[:, QMIN] / baseMVA\\n Alpha_l = zeros(nl)\\n Beta_f_l = zeros(nl)\\n Beta_t_l = zeros(nl)\\n\\n Pij_u = Slmax\\n Qij_u = Slmax\\n Iij_u = Slmax\\n Vm_u = bus[:, VMAX] ** 2\\n Pg_u = 2 * gen[:, PMAX] / baseMVA\\n Qg_u = 2 * gen[:, QMAX] / baseMVA\\n Alpha_u = ones(nl)\\n Beta_f_u = ones(nl)\\n Beta_t_u = ones(nl)\\n bigM = max(Vm_u)\\n # For the spanning tree constraints\\n Root_node = find(bus[:, BUS_TYPE] == REF)\\n Root_line = find(branch[:, F_BUS] == Root_node)\\n\\n Span_f = zeros((nb, nl))\\n Span_t = zeros((nb, nl))\\n for i in range(nb):\\n Span_f[i, find(branch[:, F_BUS] == i)] = 1\\n Span_t[i, find(branch[:, T_BUS] == i)] = 1\\n\\n Alpha_l[Root_line] = 1\\n Alpha_u[Root_line] = 1\\n Beta_f_l[Root_line] = 0\\n Beta_f_l[Root_line] = 0\\n\\n T = len(profile)\\n nx = int(3 * nl + nb + 2 * ng)\\n lx = concatenate([Alpha_l, Beta_f_l, Beta_t_l, tile(concatenate([Pij_l, Qij_l, Iij_l, Vm_l, Pg_l, Qg_l]), T)])\\n ux = concatenate([Alpha_u, Beta_f_u, Beta_t_u, tile(concatenate([Pij_u, Qij_u, Iij_u, Vm_u, Pg_u, Qg_u]), T)])\\n vtypes = [\\\"b\\\"] * 2 * nl + [\\\"c\\\"] * nl + [\\\"c\\\"] * nx * T\\n\\n # Define the decision variables\\n NX = lx.shape[0]\\n\\n # Alpha = Beta_f + Beta_t\\n Aeq_f = zeros((nl, NX))\\n beq_f = zeros(nl)\\n Aeq_f[:, 0: nl] = -eye(nl)\\n Aeq_f[:, nl: 2 * nl] = eye(nl)\\n Aeq_f[:, 2 * nl:3 * nl] = eye(nl)\\n\\n # sum(alpha)=nb-1\\n Aeq_alpha = zeros((1, NX))\\n beq_alpha = zeros(1)\\n Aeq_alpha[0, 0: nl] = ones(nl)\\n beq_alpha[0] = nb - 1\\n\\n # Span_f*Beta_f+Span_t*Beta_t = Spanning_tree\\n Aeq_span = zeros((nb, NX))\\n beq_span = ones(nb)\\n beq_span[Root_node] = 0\\n Aeq_span[:, nl:2 * nl] = Span_f\\n Aeq_span[:, 2 * nl: 3 * nl] = Span_t\\n\\n # Add system level constraints\\n # 1) Active power balance\\n Aeq_p = zeros((nb * T, NX))\\n beq_p = zeros(nb * T)\\n for i in range(T):\\n Aeq_p[i * nb:(i + 1) * nb, 3 * nl + i * nx:3 * nl + (i + 1) * nx] = hstack([Ct - Cf, zeros((nb, nl)),\\n -diag(Ct * Branch_R) * Ct,\\n zeros((nb, nb)), Cg,\\n zeros((nb, ng))]).toarray()\\n beq_p[i * nb:(i + 1) * nb] = profile[i] * bus[:, PD] / baseMVA\\n\\n # 2) Reactive power balance\\n Aeq_q = zeros((nb * T, NX))\\n beq_q = zeros(nb * T)\\n for i in range(T):\\n Aeq_q[i * nb:(i + 1) * nb, 3 * nl + i * nx:3 * nl + (i + 1) * nx] = hstack([zeros((nb, nl)), Ct - Cf,\\n -diag(Ct * Branch_X) * Ct,\\n zeros((nb, nb)),\\n zeros((nb, ng)), Cg]).toarray()\\n beq_q[i * nb:(i + 1) * nb] = profile[i] * bus[:, QD] / baseMVA\\n\\n Aeq = vstack([Aeq_f, Aeq_alpha, Aeq_span, Aeq_p, Aeq_q]).toarray()\\n beq = concatenate([beq_f, beq_alpha, beq_span, beq_p, beq_q])\\n\\n # Inequality constraints\\n A = zeros((nl * T, NX))\\n b = zeros(nl * T)\\n for i in range(T):\\n A[i * nl:(i + 1) * nl, 3 * nl + i * nx + 2 * nl: 3 * nl + i * nx + 3 * nl] = eye(nl)\\n A[i * nl:(i + 1) * nl, 0: nl] = -diag(Iij_u)\\n\\n A_temp = zeros((nl * T, NX))\\n b_temp = zeros(nl * T)\\n for i in range(T):\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx: 3 * nl + i * nx + nl] = eye(nl)\\n A_temp[i * nl:(i + 1) * nl, 0: nl] = -diag(Pij_u)\\n A = concatenate([A, A_temp])\\n b = concatenate([b, b_temp])\\n #\\n A_temp = zeros((nl * T, NX))\\n b_temp = zeros(nl * T)\\n for i in range(T):\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + nl: 3 * nl + i * nx + 2 * nl] = eye(nl)\\n A_temp[i * nl:(i + 1) * nl, 0:nl] = -diag(Qij_u)\\n A = concatenate([A, A_temp])\\n b = concatenate([b, b_temp])\\n\\n A_temp = zeros((nl * T, NX))\\n for i in range(T):\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx:3 * nl + i * nx + nl] = -2 * diag(Branch_R)\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + nl:3 * nl + i * nx + 2 * nl] = -2 * diag(Branch_X)\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 2 * nl:3 * nl + i * nx + 3 * nl] = diag(Branch_R ** 2) + \\\\\\n diag(Branch_X ** 2)\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 3 * nl:3 * nl + i * nx + 3 * nl + nb] = \\\\\\n (Cf.T - Ct.T).toarray()\\n A_temp[i * nl:(i + 1) * nl, 0:nl] = eye(nl) * bigM\\n b_temp = ones(nl * T) * bigM\\n A = concatenate([A, A_temp])\\n b = concatenate([b, b_temp])\\n\\n A_temp = zeros((nl * T, NX))\\n for i in range(T):\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx:3 * nl + i * nx + nl] = 2 * diag(Branch_R)\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + nl:3 * nl + i * nx + 2 * nl] = 2 * diag(Branch_X)\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 2 * nl:3 * nl + i * nx + 3 * nl] = -diag(Branch_R ** 2) - \\\\\\n diag(Branch_X ** 2)\\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 3 * nl:3 * nl + i * nx + 3 * nl + nb] = \\\\\\n (-Cf.T + Ct.T).toarray()\\n A_temp[i * nl:(i + 1) * nl, 0: nl] = eye(nl) * bigM\\n b_temp = ones(nl * T) * bigM\\n A = concatenate([A, A_temp])\\n b = concatenate([b, b_temp])\\n\\n Qc = dict()\\n for t in range(T):\\n for i in range(nl):\\n Qc[t * nl + i] = [[int(3 * nl + t * nx + i), int(3 * nl + t * nx + i + nl),\\n int(3 * nl + t * nx + i + 2 * nl), int(3 * nl + t * nx + f[i] + 3 * nl)],\\n [int(3 * nl + t * nx + i), int(3 * nl + t * nx + i + nl),\\n int(3 * nl + t * nx + f[i] + 3 * nl), int(3 * nl + t * nx + i + 2 * nl)],\\n [1, 1, -1 / 2, -1 / 2]]\\n c = zeros(NX)\\n q = zeros(NX)\\n c0 = 0\\n for t in range(T):\\n for i in range(ng):\\n c[3 * nl + t * nx + i + 3 * nl + nb] = gencost[i, 5] * baseMVA\\n q[3 * nl + t * nx + i + 3 * nl + nb] = gencost[i, 4] * baseMVA * baseMVA\\n c0 += gencost[i, 6]\\n\\n sol = miqcp(c, q, Aeq=Aeq, beq=beq, A=A, b=b, xmin=lx, xmax=ux, vtypes=vtypes, Qc=Qc)\\n xx = sol[0]\\n Alpha = xx[0:nl]\\n Beta_f = xx[nl:2 * nl]\\n Beta_t = xx[2 * nl:3 * nl]\\n Pij = zeros((nl, T))\\n Qij = zeros((nl, T))\\n Iij = zeros((nl, T))\\n Vi = zeros((nb, T))\\n Pg = zeros((ng, T))\\n Qg = zeros((ng, T))\\n for i in range(T):\\n Pij[:, i] = xx[3 * nl + i * nx:3 * nl + i * nx + nl]\\n Qij[:, i] = xx[3 * nl + i * nx + nl:3 * nl + i * nx + 2 * nl]\\n Iij[:, i] = xx[3 * nl + i * nx + 2 * nl:3 * nl + i * nx + 3 * nl]\\n Vi[:, i] = xx[3 * nl + i * nx + 3 * nl:3 * nl + i * nx + 3 * nl + nb]\\n Pg[:, i] = xx[3 * nl + i * nx + 3 * nl + nb:3 * nl + i * nx + 3 * nl + nb + ng]\\n Qg[:, i] = xx[3 * nl + i * nx + 3 * nl + nb + ng:3 * nl + i * nx + 3 * nl + nb + 2 * ng]\\n\\n primal_residual = zeros((nl, T))\\n for t in range(T):\\n for i in range(nl):\\n primal_residual[i, t] = Pij[i, t] * Pij[i, t] + Qij[i, t] * Qij[i, t] - Iij[i, t] * Vi[int(f[i]), t]\\n\\n sol = {\\\"Pij\\\": Pij,\\n \\\"Qij\\\": Qij,\\n \\\"Iij\\\": Iij,\\n \\\"Vi\\\": Vi,\\n \\\"Pg\\\": Pg,\\n \\\"Qg\\\": Qg,\\n \\\"Alpha\\\": Alpha,\\n \\\"Beta_f\\\": Beta_f,\\n \\\"Beta_t\\\": Beta_t,\\n \\\"residual\\\": primal_residual,\\n \\\"obj\\\": sol[1] + c0}\\n\\n return sol\",\n \"def write_bc_vtk(self):\\n print \\\"Creating boundary condition arrays\\\"\\n obst_array = np.zeros(self.nnodes)\\n obst_array[list(self.obst_list)] = 100.\\n\\n #print type(self.inlet_list)\\n inlet_array = np.zeros(self.nnodes)\\n inlet_array[list(self.inlet_list)] = 200.\\n\\n outlet_array = np.zeros(self.nnodes)\\n outlet_array[list(self.outlet_list)] = 300.\\n\\n solid_array = np.zeros(self.nnodes)\\n solid_array[list(self.solid_list)] = 500.\\n \\n dims = [int(self.Nx), int(self.Ny), int(self.Nz)]\\n origin = [0., 0., 0.]\\n dx = self.x[1] - self.x[0]\\n spacing = [dx, dx, dx] #uniform lattice\\n \\n print \\\"Writing boundary conditions to VTK files\\\"\\n writeVTK(inlet_array,'inlet','inlet.vtk',dims,origin,spacing)\\n writeVTK(outlet_array,'outlet','outlet.vtk',dims,origin,spacing)\\n writeVTK(obst_array,'obst','obst.vtk',dims,origin,spacing)\\n writeVTK(solid_array,'solid','solid.vtk',dims,origin,spacing)\",\n \"def write_bc_vtk(self):\\n print \\\"Creating boundary condition arrays\\\"\\n obst_array = np.zeros(self.nnodes)\\n obst_array[list(self.obst_list)] = 100.\\n\\n #print type(self.inlet_list)\\n inlet_array = np.zeros(self.nnodes)\\n inlet_array[list(self.inlet_list)] = 200.\\n\\n outlet_array = np.zeros(self.nnodes)\\n outlet_array[list(self.outlet_list)] = 300.\\n\\n solid_array = np.zeros(self.nnodes)\\n solid_array[list(self.solid_list)] = 500.\\n \\n dims = [int(self.Nx), int(self.Ny), int(self.Nz)]\\n origin = [0., 0., 0.]\\n dx = self.x[1] - self.x[0]\\n spacing = [dx, dx, dx] #uniform lattice\\n \\n print \\\"Writing boundary conditions to VTK files\\\"\\n writeVTK(inlet_array,'inlet','inlet.vtk',dims,origin,spacing)\\n writeVTK(outlet_array,'outlet','outlet.vtk',dims,origin,spacing)\\n writeVTK(obst_array,'obst','obst.vtk',dims,origin,spacing)\\n writeVTK(solid_array,'solid','solid.vtk',dims,origin,spacing)\",\n \"def feature_extract(self, CT_pairs):\\n instances = []\\n for pair in CT_pairs:\\n config = pair[0]\\n label = pair[1]\\n data = []\\n featureset = {}\\n \\n # for nltk NaiveBayes feature selection stuff when doing MaxEnt decoding parser commit this\\n# featureset[\\\"topOfBuffer\\\"] = self.token_dict[config.beta.top()]\\n# featureset[\\\"topOfStack\\\"] = self.token_dict[config.sigma.top()]\\n# featureset[\\\"bufferStackPair\\\"] = (self.token_dict[config.sigma.top()], self.token_dict[config.beta.top()])\\n# featureset[\\\"topOfBuffer\\\"] = self.POS_dict[config.beta.top()]\\n# featureset[\\\"topOfStack\\\"] = self.POS_dict[config.sigma.top()]\\n# featureset[\\\"bufferStackPair\\\"] = tuple((self.POS_dict[config.sigma.top()], self.POS_dict[config.beta.top()]))\\n \\n # add the (StackTopPOS,BufferTopPOS,bufferchildren_POS) feature\\n #value_set = tuple([self.POS_dict[config.sigma.top()], self.POS_dict[config.beta.top()]] + [self.POS_dict[child] for child in self.getBufferChildren(config.beta.top())])\\n #featureset[\\\"bufferStackbufferChildrenPair\\\"] = value_set\\n \\n # for MaxEnt decoding stuff\\n # token variants\\n data.append((\\\"topOfBuffer\\\",self.token_dict[config.beta.top()]))\\n data.append((\\\"topOfStack\\\",self.token_dict[config.sigma.top()]))\\n data.append((\\\"bufferStackPair\\\",self.token_dict[config.sigma.top()],self.token_dict[config.beta.top()]))\\n #POS variants\\n data.append((\\\"topOfBuffer\\\",self.POS_dict[config.beta.top()]))\\n data.append((\\\"topOfStack\\\",self.POS_dict[config.sigma.top()]))\\n data.append((\\\"bufferStackPair\\\",self.POS_dict[config.sigma.top()],self.POS_dict[config.beta.top()]))\\n ins = Instance(label=label, data=data)\\n #ins = Instance(label=label, data=featureset)\\n instances.append(ins)\\n \\n return instances\",\n \"def update_heads(info,\\n heads):\\n\\n info[\\\"model_params\\\"][\\\"boltzmann_dict\\\"][\\\"num_heads\\\"] = heads\\n # Concatenate the fingerprints produced by the different heads\\n info[\\\"model_params\\\"][\\\"boltzmann_dict\\\"][\\\"head_pool\\\"] = \\\"concatenate\\\"\\n\\n readoutdict = info[\\\"model_params\\\"][\\\"readoutdict\\\"]\\n feat_dim = info[\\\"model_params\\\"][\\\"mol_basis\\\"]\\n\\n for key, lst in readoutdict.items():\\n for i, dic in enumerate(lst):\\n if \\\"param\\\" in dic and \\\"in_features\\\" in dic.get(\\\"param\\\", {}):\\n # make sure that the input dimension to the readout is equal to\\n # `heads * feat_dim`, where `feat_dim` is the feature dimension\\n # produced by each head\\n readoutdict[key][i][\\\"param\\\"][\\\"in_features\\\"] = feat_dim * heads\\n break\\n info[\\\"model_params\\\"][\\\"readoutdict\\\"] = readoutdict\",\n \"def detection_head_graph(self, feature_map, filters):\\n x = KL.Conv2D(filters, (1, 1), strides=(1, 1),\\n name=\\\"detection_head_\\\" + \\\"stage_1\\\", use_bias=True, padding=\\\"same\\\")(feature_map)\\n x = KL.Activation('relu', name='detection_head_stage_1_activation')(x)\\n x = KL.Conv2D(filters, (1, 1), strides=(1, 1),\\n name=\\\"detection_head_\\\" + \\\"stage_2\\\", use_bias=True, padding=\\\"same\\\")(x)\\n x = KL.Activation('relu', name='detection_head_stage_2_activation')(x)\\n x = KL.Conv2D(3, (1, 1), strides=(1, 1),\\n name=\\\"detection_head_\\\" + \\\"final_stage\\\", use_bias=True, padding=\\\"same\\\")(x)\\n return x\",\n \"def gexf_graph():\\n # you must replace these lines and supply your own graph\\n gexf = Gexf(\\\"author\\\", \\\"title\\\")\\n mygraph = gexf.addGraph(\\\"undirected\\\", \\\"static\\\", \\\"A web network\\\")\\n atr_type = mygraph.addNodeAttribute('Type', type='string')\\n atr_id = mygraph.addNodeAttribute('id', type='string')\\n atr_label = mygraph.addNodeAttribute('label', type='string')\\n atr_color_r = mygraph.addNodeAttribute('color_r', type='string', defaultValue='0')\\n atr_color_g = mygraph.addNodeAttribute('color_g', type='string', defaultValue='0')\\n atr_color_b = mygraph.addNodeAttribute('color_b', type='string', defaultValue='0')\\n k = 0\\n for i in range(min_parts()):\\n tmp = mygraph.addNode(set_num[i], name[i], r=\\\"0\\\", g=\\\"0\\\", b=\\\"0\\\")\\n tmp.addAttribute(atr_type, \\\"set\\\")\\n tmp.addAttribute(atr_id, set_num[i])\\n tmp.addAttribute(atr_label, name[i])\\n for j in range(len(Parts[i][\\\"Parts\\\"])):\\n if mygraph.nodeExists(Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"])==0:\\n temp = mygraph.addNode((Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"]), Parts[i][\\\"Parts\\\"][j][\\\"name\\\"], r=str(int(Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][0:2], 16)), g=str(int(Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][2:4], 16)), b=str(int(Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][4:6], 16)))\\n temp.addAttribute(atr_type, \\\"part\\\")\\n temp.addAttribute(atr_id, (Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"]))\\n temp.addAttribute(atr_label, Parts[i][\\\"Parts\\\"][j][\\\"name\\\"])\\n temp.addAttribute(atr_color_r, Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][0:2])\\n temp.addAttribute(atr_color_g, Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][2:4])\\n temp.addAttribute(atr_color_b, Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][4:6])\\n mygraph.addEdge(str(k), set_num[i], (Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"]), weight=Parts[i][\\\"Parts\\\"][j][\\\"quantity\\\"])\\n k = k+1\\n output_file = open(\\\"bricks_graph.gexf\\\", \\\"wb\\\")\\n gexf.write(output_file)\\n return -1\",\n \"def _viterbi_decode(self, feats):\\n backpointers = []\\n\\n init_vvars = torch.full((self.batch_size, 1, self.tagset_size), -10000.).to(self.device)\\n init_vvars[:, 0, self.tag2idx[START_TAG]] = 0.\\n\\n forward_var = init_vvars\\n for i in range(feats.shape[1]):\\n feat = feats[:,i,:]\\n\\n next_tag_var = forward_var + self.transitions + feat.view(self.batch_size, self.tagset_size, 1)\\n best_tag_var, best_tag_id = torch.max(next_tag_var, dim=-1)\\n backpointers.append(best_tag_id)\\n forward_var = best_tag_var.view(self.batch_size, 1, self.tagset_size)\\n\\n # Transition to STOP_TAG\\n terminal_var = forward_var + self.transitions[self.tag2idx[STOP_TAG]]\\n path_score, best_tag_id = torch.max(terminal_var, dim=-1)\\n\\n # Follow the back pointers to decode the best path.\\n best_path = [best_tag_id]\\n for bptrs_t in reversed(backpointers):\\n # gather the value in bptrs_t according to best_tag_id\\n best_tag_id = bptrs_t.gather(dim=-1,index=best_tag_id)\\n best_path.append(best_tag_id)\\n \\n # Pop off the start tag (we dont want to return that to the caller)\\n best_path.pop()\\n best_path.reverse()\\n best_path = torch.cat(best_path, dim=1)\\n # best_path = [[self.idx2tag[j] for j in i] for i in best_path.tolist()]\\n \\n return path_score, best_path\",\n \"def encodeToCartBoxesLabels(self, gt_instances):\\n raw_boxes_xywh = np.zeros((self.config_data[\\\"max_boxes_per_frame\\\"], 5))\\n ### initialize gronud truth labels as np.zeros ###\\n gt_labels = np.zeros(list(self.cart_shape[1:3]) + \\\\\\n [len(self.anchor_boxes_cart)] + \\\\\\n [len(self.config_data[\\\"all_classes\\\"]) + 5]) \\n\\n ### start transferring box to ground turth label format ###\\n for i in range(len(gt_instances[\\\"classes\\\"])):\\n if i > self.config_data[\\\"max_boxes_per_frame\\\"]:\\n continue\\n class_name = gt_instances[\\\"classes\\\"][i]\\n box_xywh = gt_instances[\\\"cart_boxes\\\"][i]\\n class_id = self.config_data[\\\"all_classes\\\"].index(class_name)\\n if i <= self.config_data[\\\"max_boxes_per_frame\\\"]:\\n raw_boxes_xywh[i, :4] = box_xywh\\n raw_boxes_xywh[i, 4] = class_id\\n class_onehot = helper.smoothOnehot(class_id, \\\\\\n len(self.config_data[\\\"all_classes\\\"]))\\n exist_positive = False\\n grid_strid = self.cart_grid_strides\\n anchors = self.anchor_boxes_cart\\n box_xywh_scaled = box_xywh[np.newaxis, :].astype(np.float32)\\n box_xywh_scaled[:, :2] /= grid_strid\\n anchors_xywh = np.zeros([len(anchors), 4])\\n anchors_xywh[:, :2] = np.floor(box_xywh_scaled[:, :2]) + 0.5\\n anchors_xywh[:, 2:] = anchors.astype(np.float32)\\n\\n iou_scaled = helper.iou2d(box_xywh_scaled, anchors_xywh)\\n ### NOTE: 0.3 is from YOLOv4, maybe this should be different here ###\\n ### it means, as long as iou is over 0.3 with an anchor, the anchor\\n ### should be taken into consideration as a ground truth label\\n iou_mask = iou_scaled > 0.3\\n\\n if np.any(iou_mask):\\n xind, yind = np.floor(np.squeeze(box_xywh_scaled)[:2]).astype(np.int32)\\n ### TODO: consider changing the box to raw yolohead output format ###\\n gt_labels[xind, yind, iou_mask, 0:4] = box_xywh\\n gt_labels[xind, yind, iou_mask, 4:5] = 1.\\n gt_labels[xind, yind, iou_mask, 5:] = class_onehot\\n exist_positive = True\\n\\n if not exist_positive:\\n ### NOTE: this is the normal one ###\\n ### it means take the anchor box with maximum iou to the raw\\n ### box as the ground truth label\\n iou_mask = iou_scaled == iou_scaled.max()\\n\\n if np.any(iou_mask):\\n xind, yind = np.floor(np.squeeze(box_xywh_scaled)[:2]).astype(np.int32)\\n ### TODO: consider changing the box to raw yolohead output format ###\\n gt_labels[xind, yind, iou_mask, 0:4] = box_xywh\\n gt_labels[xind, yind, iou_mask, 4:5] = 1.\\n gt_labels[xind, yind, iou_mask, 5:] = class_onehot\\n\\n has_label = False\\n if gt_labels.max() != 0:\\n has_label = True\\n gt_labels = np.where(gt_labels == 0, 1e-16, gt_labels)\\n return gt_labels, has_label, raw_boxes_xywh\",\n \"def _fusion(self, expert_outputs):\\n raise NotImplementedError\",\n \"def reagent_label_data(bcl_step):\\n\\n define_step = None\\n indexes = {}\\n flowcell_total_reads = 0\\n\\n for inp, outp in bcl_step.input_output_maps:\\n pre_bcl_steps = MASTER_STEPS_UDFS['reagent_labels']['steps']['pre_bcl']\\n if inp['parent-process'].type.name not in pre_bcl_steps:\\n continue\\n\\n if outp['output-generation-type'] != 'PerReagentLabel':\\n continue\\n\\n lane = inp['uri']\\n art = outp['uri']\\n\\n index_reads = art.udf.get(\\n MASTER_STEPS_UDFS['reagent_labels']['udfs']['reads'])\\n\\n if index_reads is None or art.qc_flag == 'FAILED':\\n continue\\n\\n flowcell_total_reads += index_reads\\n\\n sample = art.samples[0] # Will always be only one sample in the list\\n application_tag = sample.udf.get('Sequencing Analysis')\\n\\n if application_tag[0:2] in MASTER_STEPS_UDFS['reagent_labels'][\\n 'exclue_tags']:\\n continue\\n\\n if not define_step:\\n define_step_outputs, flowcell_target_reads, define_step = get_define_step_data(\\n lane)\\n\\n if not sample.id in define_step_outputs:\\n LOG.info('This sample whas put as a pool into the define step: ' +\\n sample.id + ' ' + application_tag)\\n continue\\n\\n index = art.reagent_labels[0]\\n\\n container, lane_nr = lane.location\\n if index not in indexes:\\n indexes[index] = {\\n '_id': '_'.join([index, container.name]),\\n 'url': index.replace(' ', ''),\\n 'index_total_reads': index_reads,\\n 'index_target_reads': define_step_outputs[sample.id],\\n 'flowcell_target_reads': flowcell_target_reads,\\n 'index': index,\\n 'sample': sample.id,\\n 'lanes': {\\n lane_nr: dict(art.udf.items())\\n },\\n 'flowcell_id': container.name,\\n 'flowcell_type': container.type.name,\\n 'define_step_udfs': dict(define_step.udf.items()),\\n 'define_step_id': define_step.id,\\n 'bcl_step_id': bcl_step.id\\n }\\n else:\\n indexes[index]['lanes'][lane_nr] = dict(art.udf.items())\\n indexes[index]['index_total_reads'] += index_reads\\n\\n for index, data in indexes.items():\\n data['flowcell_total_reads'] = flowcell_total_reads\\n indexes[index] = filter_none(data)\\n\\n return indexes\",\n \"def create_pipeline_flow(\\n self, cmp_deriv_subject_directory, nipype_deriv_subject_directory\\n ):\\n acquisition_model = self.stages[\\\"Diffusion\\\"].config.diffusion_imaging_model\\n recon_tool = self.stages[\\\"Diffusion\\\"].config.recon_processing_tool\\n\\n recon_model = \\\"DTI\\\"\\n\\n if acquisition_model == \\\"DSI\\\":\\n recon_model = \\\"SHORE\\\"\\n else:\\n if recon_tool == \\\"Dipy\\\" and self.stages[\\\"Diffusion\\\"].config.dipy_recon_config.local_model:\\n recon_model = \\\"CSD\\\"\\n elif recon_tool == \\\"MRtrix\\\" and self.stages[\\\"Diffusion\\\"].config.mrtrix_recon_config.local_model:\\n recon_model = \\\"CSD\\\"\\n\\n tracking_model = self.stages[\\\"Diffusion\\\"].config.diffusion_model\\n\\n if tracking_model == \\\"Deterministic\\\":\\n tracking_model = \\\"DET\\\"\\n elif tracking_model == \\\"Probabilistic\\\":\\n tracking_model = \\\"PROB\\\"\\n\\n if self.parcellation_scheme == \\\"Lausanne2018\\\":\\n bids_atlas_label = \\\"L2018\\\"\\n elif self.parcellation_scheme == \\\"NativeFreesurfer\\\":\\n bids_atlas_label = \\\"Desikan\\\"\\n elif self.parcellation_scheme == \\\"Custom\\\":\\n bids_atlas_label = self.custom_atlas_name\\n if self.custom_atlas_res is not None and self.custom_atlas_res != \\\"\\\":\\n bids_atlas_label += f'_res-{self.custom_atlas_res}'\\n\\n # Clear previous outputs\\n self.clear_stages_outputs()\\n\\n # Create diffusion workflow with input and output Identityinterface nodes\\n diffusion_flow = pe.Workflow(\\n name=\\\"diffusion_pipeline\\\",\\n base_dir=os.path.abspath(nipype_deriv_subject_directory),\\n )\\n\\n diffusion_inputnode = pe.Node(\\n interface=util.IdentityInterface(\\n fields=[\\n \\\"diffusion\\\",\\n \\\"bvecs\\\",\\n \\\"bvals\\\",\\n \\\"T1\\\",\\n \\\"aseg\\\",\\n \\\"aparc_aseg\\\",\\n \\\"brain\\\",\\n \\\"T2\\\",\\n \\\"brain_mask\\\",\\n \\\"wm_mask_file\\\",\\n \\\"roi_volumes\\\",\\n \\\"roi_graphMLs\\\",\\n \\\"subjects_dir\\\",\\n \\\"subject_id\\\",\\n \\\"parcellation_scheme\\\",\\n ]\\n ),\\n name=\\\"inputnode\\\",\\n )\\n diffusion_inputnode.inputs.parcellation_scheme = self.parcellation_scheme\\n diffusion_inputnode.inputs.atlas_info = self.atlas_info\\n\\n diffusion_outputnode = pe.Node(\\n interface=util.IdentityInterface(fields=[\\\"connectivity_matrices\\\"]),\\n name=\\\"outputnode\\\",\\n )\\n\\n diffusion_flow.add_nodes([diffusion_inputnode, diffusion_outputnode])\\n\\n # Data import\\n datasource = self.create_datagrabber_node(\\n base_directory=cmp_deriv_subject_directory,\\n bids_atlas_label=bids_atlas_label\\n )\\n\\n # Data sinker for output\\n sinker = self.create_datasinker_node(\\n base_directory=cmp_deriv_subject_directory,\\n bids_atlas_label=bids_atlas_label,\\n recon_model=recon_model,\\n tracking_model=tracking_model\\n )\\n\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n (datasource, diffusion_inputnode, [(\\\"diffusion\\\", \\\"diffusion\\\"),\\n (\\\"bvecs\\\", \\\"bvecs\\\"),\\n (\\\"bvals\\\", \\\"bvals\\\"),\\n (\\\"T1\\\", \\\"T1\\\"),\\n (\\\"aseg\\\", \\\"aseg\\\"),\\n (\\\"aparc_aseg\\\", \\\"aparc_aseg\\\"),\\n (\\\"brain\\\", \\\"brain\\\"),\\n (\\\"brain_mask\\\", \\\"brain_mask\\\"),\\n (\\\"wm_mask_file\\\", \\\"wm_mask_file\\\")]),\\n ]\\n )\\n # fmt:on\\n\\n merge_roi_volumes = pe.Node(interface=Merge(5), name=\\\"merge_roi_volumes\\\")\\n merge_roi_graphmls = pe.Node(interface=Merge(5), name=\\\"merge_roi_graphmls\\\")\\n\\n def remove_non_existing_scales(roi_volumes):\\n \\\"\\\"\\\"Returns a list which do not contained any empty element.\\n\\n Parameters\\n ----------\\n roi_volumes : list\\n A list of output parcellations that might contain empty element\\n in the case of the monoscale Desikan scheme for instance\\n\\n Returns\\n -------\\n out_roi_volumes : list\\n The list with no empty element\\n \\\"\\\"\\\"\\n out_roi_volumes = []\\n for vol in roi_volumes:\\n if vol is not None:\\n out_roi_volumes.append(vol)\\n return out_roi_volumes\\n\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n (datasource, merge_roi_volumes, [(\\\"roi_volume_s1\\\", \\\"in1\\\"),\\n (\\\"roi_volume_s2\\\", \\\"in2\\\"),\\n (\\\"roi_volume_s3\\\", \\\"in3\\\"),\\n (\\\"roi_volume_s4\\\", \\\"in4\\\"),\\n (\\\"roi_volume_s5\\\", \\\"in5\\\")]),\\n (datasource, merge_roi_graphmls, [(\\\"roi_graphml_s1\\\", \\\"in1\\\"),\\n (\\\"roi_graphml_s2\\\", \\\"in2\\\"),\\n (\\\"roi_graphml_s3\\\", \\\"in3\\\"),\\n (\\\"roi_graphml_s4\\\", \\\"in4\\\"),\\n (\\\"roi_graphml_s5\\\", \\\"in5\\\")]),\\n (merge_roi_volumes, diffusion_inputnode, [((\\\"out\\\", remove_non_existing_scales), \\\"roi_volumes\\\")],),\\n (merge_roi_graphmls, diffusion_inputnode, [((\\\"out\\\", remove_non_existing_scales), \\\"roi_graphMLs\\\")],),\\n ]\\n )\\n # fmt:on\\n\\n if self.stages[\\\"Preprocessing\\\"].enabled:\\n preproc_flow = self.create_stage_flow(\\\"Preprocessing\\\")\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n (diffusion_inputnode, preproc_flow, [(\\\"diffusion\\\", \\\"inputnode.diffusion\\\"),\\n (\\\"brain\\\", \\\"inputnode.brain\\\"),\\n (\\\"aseg\\\", \\\"inputnode.aseg\\\"),\\n (\\\"aparc_aseg\\\", \\\"inputnode.aparc_aseg\\\"),\\n (\\\"brain_mask\\\", \\\"inputnode.brain_mask\\\"),\\n (\\\"wm_mask_file\\\", \\\"inputnode.wm_mask_file\\\"),\\n (\\\"roi_volumes\\\", \\\"inputnode.roi_volumes\\\"),\\n (\\\"bvecs\\\", \\\"inputnode.bvecs\\\"),\\n (\\\"bvals\\\", \\\"inputnode.bvals\\\"),\\n (\\\"T1\\\", \\\"inputnode.T1\\\")]),\\n ]\\n )\\n # fmt:on\\n\\n if self.stages[\\\"Registration\\\"].enabled:\\n reg_flow = self.create_stage_flow(\\\"Registration\\\")\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n # (diffusion_inputnode,reg_flow,[('T2','inputnode.T2')]),\\n (preproc_flow, reg_flow, [(\\\"outputnode.T1\\\", \\\"inputnode.T1\\\"),\\n (\\\"outputnode.act_5TT\\\", \\\"inputnode.act_5TT\\\"),\\n (\\\"outputnode.gmwmi\\\", \\\"inputnode.gmwmi\\\"),\\n (\\\"outputnode.bvecs_rot\\\", \\\"inputnode.bvecs\\\"),\\n (\\\"outputnode.bvals\\\", \\\"inputnode.bvals\\\"),\\n (\\\"outputnode.wm_mask_file\\\", \\\"inputnode.wm_mask\\\"),\\n (\\\"outputnode.partial_volume_files\\\", \\\"inputnode.partial_volume_files\\\",),\\n (\\\"outputnode.roi_volumes\\\", \\\"inputnode.roi_volumes\\\"),\\n (\\\"outputnode.brain\\\", \\\"inputnode.brain\\\"),\\n (\\\"outputnode.brain_mask\\\", \\\"inputnode.brain_mask\\\"),\\n (\\\"outputnode.brain_mask_full\\\", \\\"inputnode.brain_mask_full\\\"),\\n (\\\"outputnode.diffusion_preproc\\\", \\\"inputnode.target\\\"),\\n (\\\"outputnode.dwi_brain_mask\\\", \\\"inputnode.target_mask\\\")]),\\n (preproc_flow, sinker, [(\\\"outputnode.bvecs_rot\\\", \\\"dwi.@bvecs_rot\\\"),\\n (\\\"outputnode.diffusion_preproc\\\", \\\"dwi.@diffusion_preproc\\\"),\\n (\\\"outputnode.dwi_brain_mask\\\", \\\"dwi.@diffusion_brainmask\\\")]),\\n ]\\n )\\n # fmt:on\\n if self.stages[\\\"Registration\\\"].config.registration_mode == \\\"BBregister (FS)\\\":\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n (diffusion_inputnode, reg_flow, [(\\\"subjects_dir\\\", \\\"inputnode.subjects_dir\\\"), (\\\"subject_id\\\", \\\"inputnode.subject_id\\\")]),\\n ]\\n )\\n # fmt:on\\n\\n if self.stages[\\\"Diffusion\\\"].enabled:\\n diff_flow = self.create_stage_flow(\\\"Diffusion\\\")\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n (preproc_flow, diff_flow, [(\\\"outputnode.diffusion_preproc\\\", \\\"inputnode.diffusion\\\")]),\\n (reg_flow, diff_flow, [(\\\"outputnode.wm_mask_registered_crop\\\", \\\"inputnode.wm_mask_registered\\\",),\\n (\\\"outputnode.brain_mask_registered_crop\\\", \\\"inputnode.brain_mask_registered\\\",),\\n (\\\"outputnode.partial_volumes_registered_crop\\\", \\\"inputnode.partial_volumes\\\",),\\n (\\\"outputnode.roi_volumes_registered_crop\\\", \\\"inputnode.roi_volumes\\\",),\\n (\\\"outputnode.act_5tt_registered_crop\\\", \\\"inputnode.act_5tt_registered\\\",),\\n (\\\"outputnode.gmwmi_registered_crop\\\", \\\"inputnode.gmwmi_registered\\\",),\\n (\\\"outputnode.grad\\\", \\\"inputnode.grad\\\"),\\n (\\\"outputnode.bvals\\\", \\\"inputnode.bvals\\\"),\\n (\\\"outputnode.bvecs\\\", \\\"inputnode.bvecs\\\")]),\\n (reg_flow, sinker, [(\\\"outputnode.target_epicorrected\\\", \\\"dwi.@bdiffusion_reg_crop\\\",),\\n (\\\"outputnode.grad\\\", \\\"dwi.@diffusion_grad\\\"),\\n (\\\"outputnode.affine_transform\\\", \\\"xfm.@affine_transform\\\"),\\n (\\\"outputnode.warp_field\\\", \\\"xfm.@warp_field\\\"),\\n (\\\"outputnode.T1_registered_crop\\\", \\\"anat.@T1_reg_crop\\\"),\\n (\\\"outputnode.act_5tt_registered_crop\\\", \\\"anat.@act_5tt_reg_crop\\\",),\\n (\\\"outputnode.gmwmi_registered_crop\\\", \\\"anat.@gmwmi_reg_crop\\\"),\\n (\\\"outputnode.brain_registered_crop\\\", \\\"anat.@brain_reg_crop\\\"),\\n (\\\"outputnode.brain_mask_registered_crop\\\", \\\"anat.@brain_mask_reg_crop\\\",),\\n (\\\"outputnode.wm_mask_registered_crop\\\", \\\"anat.@wm_mask_reg_crop\\\",),\\n (\\\"outputnode.roi_volumes_registered_crop\\\", \\\"anat.@roivs_reg_crop\\\",),\\n (\\\"outputnode.partial_volumes_registered_crop\\\", \\\"anat.@pves_reg_crop\\\",)],),\\n ]\\n )\\n # fmt:on\\n\\n if self.stages[\\\"Connectome\\\"].enabled:\\n self.stages[\\\"Connectome\\\"].config.probtrackx = False\\n self.stages[\\\"Connectome\\\"].config.subject = self.global_conf.subject\\n con_flow = self.create_stage_flow(\\\"Connectome\\\")\\n # fmt:off\\n diffusion_flow.connect(\\n [\\n (diffusion_inputnode, con_flow, [(\\\"parcellation_scheme\\\", \\\"inputnode.parcellation_scheme\\\"),\\n (\\\"atlas_info\\\", \\\"inputnode.atlas_info\\\"),\\n (\\\"roi_graphMLs\\\", \\\"inputnode.roi_graphMLs\\\")]),\\n (diff_flow, con_flow, [(\\\"outputnode.track_file\\\", \\\"inputnode.track_file\\\"),\\n (\\\"outputnode.FA\\\", \\\"inputnode.FA\\\"),\\n (\\\"outputnode.ADC\\\", \\\"inputnode.ADC\\\"),\\n (\\\"outputnode.AD\\\", \\\"inputnode.AD\\\"),\\n (\\\"outputnode.RD\\\", \\\"inputnode.RD\\\"),\\n (\\\"outputnode.roi_volumes\\\", \\\"inputnode.roi_volumes_registered\\\",),\\n (\\\"outputnode.skewness\\\", \\\"inputnode.skewness\\\"),\\n (\\\"outputnode.kurtosis\\\", \\\"inputnode.kurtosis\\\"),\\n (\\\"outputnode.P0\\\", \\\"inputnode.P0\\\"),\\n (\\\"outputnode.mapmri_maps\\\", \\\"inputnode.mapmri_maps\\\"),\\n (\\\"outputnode.shore_maps\\\", \\\"inputnode.shore_maps\\\")]),\\n (con_flow, diffusion_outputnode, [(\\\"outputnode.connectivity_matrices\\\", \\\"connectivity_matrices\\\")]),\\n (diff_flow, sinker, [(\\\"outputnode.fod_file\\\", \\\"dwi.@fod_file\\\"),\\n (\\\"outputnode.FA\\\", \\\"dwi.@FA\\\"),\\n (\\\"outputnode.ADC\\\", \\\"dwi.@ADC\\\"),\\n (\\\"outputnode.AD\\\", \\\"dwi.@AD\\\"),\\n (\\\"outputnode.RD\\\", \\\"dwi.@RD\\\"),\\n (\\\"outputnode.skewness\\\", \\\"dwi.@skewness\\\"),\\n (\\\"outputnode.kurtosis\\\", \\\"dwi.@kurtosis\\\"),\\n (\\\"outputnode.P0\\\", \\\"dwi.@P0\\\"),\\n (\\\"outputnode.mapmri_maps\\\", \\\"dwi.@mapmri_maps\\\"),\\n (\\\"outputnode.shore_maps\\\", \\\"dwi.@shore_maps\\\")]),\\n (con_flow, sinker, [(\\\"outputnode.streamline_final_file\\\", \\\"dwi.@streamline_final_file\\\"),\\n (\\\"outputnode.connectivity_matrices\\\", \\\"dwi.@connectivity_matrices\\\")]),\\n ]\\n )\\n # fmt:on\\n\\n return diffusion_flow\",\n \"def pack_firmware(self, work_dir, jobclient, version_string=\\\"\\\"):\\n raise NotImplementedError(\\\"Abstract method not implemented\\\")\",\n \"def __call__(self, node):\\n\\n # should throw an error\\n if node.cfgInterface == None:\\n return\\n\\n # //\\n # // Extract LFN base from included WorkflowSpec parameters\\n #//\\n base = node.getParameter(\\\"UnmergedLFNBase\\\")[0]\\n\\n # //\\n # // iterate over outputmodules/data tiers\\n #// Generate LFN, PFN and Catalog for each module\\n for modName, outModule in node.cfgInterface.outputModules.items():\\n if ( not outModule.has_key('fileName') ):\\n msg = \\\"OutputModule %s does not contain a fileName entry\\\" % modName\\n raise RuntimeError, msg\\n outModule['logicalFileName'] = os.path.join(base, outModule['dataTier'], str(self.lfnGroup))\\n outModule['logicalFileName'] += '/'\\n outModule['logicalFileName'] += outModule['fileName']\\n\\n return\",\n \"def encodeToLabels(self, gt_instances):\\n raw_boxes_xyzwhd = np.zeros((self.config_data[\\\"max_boxes_per_frame\\\"], 7))\\n ### initialize gronud truth labels as np.zeors ###\\n gt_labels = np.zeros(list(self.headoutput_shape[1:4]) + \\\\\\n [len(self.anchor_boxes)] + \\\\\\n [len(self.config_data[\\\"all_classes\\\"]) + 7])\\n\\n ### start transferring box to ground turth label format ###\\n for i in range(len(gt_instances[\\\"classes\\\"])):\\n if i > self.config_data[\\\"max_boxes_per_frame\\\"]:\\n continue\\n class_name = gt_instances[\\\"classes\\\"][i]\\n box_xyzwhd = gt_instances[\\\"boxes\\\"][i]\\n class_id = self.config_data[\\\"all_classes\\\"].index(class_name)\\n if i < self.config_data[\\\"max_boxes_per_frame\\\"]:\\n raw_boxes_xyzwhd[i, :6] = box_xyzwhd\\n raw_boxes_xyzwhd[i, 6] = class_id\\n class_onehot = helper.smoothOnehot(class_id, len(self.config_data[\\\"all_classes\\\"]))\\n \\n exist_positive = False\\n\\n grid_strid = self.grid_strides\\n anchor_stage = self.anchor_boxes\\n box_xyzwhd_scaled = box_xyzwhd[np.newaxis, :].astype(np.float32)\\n box_xyzwhd_scaled[:, :3] /= grid_strid\\n anchorstage_xyzwhd = np.zeros([len(anchor_stage), 6])\\n anchorstage_xyzwhd[:, :3] = np.floor(box_xyzwhd_scaled[:, :3]) + 0.5\\n anchorstage_xyzwhd[:, 3:] = anchor_stage.astype(np.float32)\\n\\n iou_scaled = helper.iou3d(box_xyzwhd_scaled, anchorstage_xyzwhd, \\\\\\n self.input_size)\\n ### NOTE: 0.3 is from YOLOv4, maybe this should be different here ###\\n ### it means, as long as iou is over 0.3 with an anchor, the anchor\\n ### should be taken into consideration as a ground truth label\\n iou_mask = iou_scaled > 0.3\\n\\n if np.any(iou_mask):\\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\\\\n astype(np.int32)\\n ### TODO: consider changing the box to raw yolohead output format ###\\n gt_labels[xind, yind, zind, iou_mask, 0:6] = box_xyzwhd\\n gt_labels[xind, yind, zind, iou_mask, 6:7] = 1.\\n gt_labels[xind, yind, zind, iou_mask, 7:] = class_onehot\\n exist_positive = True\\n\\n if not exist_positive:\\n ### NOTE: this is the normal one ###\\n ### it means take the anchor box with maximum iou to the raw\\n ### box as the ground truth label\\n anchor_ind = np.argmax(iou_scaled)\\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\\\\n astype(np.int32)\\n gt_labels[xind, yind, zind, anchor_ind, 0:6] = box_xyzwhd\\n gt_labels[xind, yind, zind, anchor_ind, 6:7] = 1.\\n gt_labels[xind, yind, zind, anchor_ind, 7:] = class_onehot\\n\\n has_label = False\\n for label_stage in gt_labels:\\n if label_stage.max() != 0:\\n has_label = True\\n gt_labels = [np.where(gt_i == 0, 1e-16, gt_i) for gt_i in gt_labels]\\n return gt_labels, has_label, raw_boxes_xyzwhd\",\n \"def worker(selection_idx, results_table):\\n randgen = np.random.RandomState()\\n \\n # Data-specific positive set partition (the real-world dataset consists of multiple motif classes, always exactly 3 instances of each class stored consequently).\\n # The partition assures that the training and test sets do not share instances of the same motif class\\n positive_n_train = round(0.8 * len(positive_set_) / 3) * 3\\n block_start_idx = randgen.randint(positive_n_train / 3 + 1) * 3 \\n block_end_idx = block_start_idx + len(positive_set_) - positive_n_train\\n positive_set_part_train, positive_set_part_test = (np.concatenate((positive_set_[: block_start_idx], positive_set_[block_end_idx: ])), positive_set_[block_start_idx: block_end_idx])\\n \\n # Negative set partition with random selection of elements to match the size of the positive set\\n negative_set = negative_set_[randgen.choice(len(negative_set_), size = positive_set_.shape[0], replace = False)]\\n negative_n = len(negative_set)\\n negative_n_train = round(negative_n * 0.8)\\n negative_set_part_train, negative_set_part_test = (negative_set[: negative_n_train], negative_set[negative_n_train: ])\\n \\n data_part_train = np.float64(np.concatenate((positive_set_part_train, negative_set_part_train)))\\n labels_part_train = np.concatenate((np.ones(len(positive_set_part_train), dtype = 'i1'), np.zeros(len(negative_set_part_train), dtype = 'i1')))\\n data_part_test = np.float64(np.concatenate((positive_set_part_test, negative_set_part_test)))\\n labels_part_test = np.concatenate((np.ones(len(positive_set_part_test), dtype = 'i1'), np.zeros(len(negative_set_part_test), dtype = 'i1')))\\n \\n # Specifying the pipeline and the CV structure\\n pruner = feature_selection.VarianceThreshold()\\n scaler = preprocessing.StandardScaler()\\n feature_selector = feature_selection.SelectKBest(feature_selection.f_classif)\\n classifier = svm.SVC(kernel = 'rbf', gamma = 0.01, class_weight = 'balanced')\\n pipeline0 = pipeline.Pipeline([\\n ('pruning', pruner),\\n ('scaling', scaler),\\n ('selection', feature_selector),\\n ('classification', classifier)\\n ])\\n cv_structure = model_selection.StratifiedShuffleSplit(n_splits = 10, test_size = 0.2)\\n scoring = 'recall_macro' #same as balanced accuracy\\n grid = model_selection.GridSearchCV(pipeline0, scoring = scoring, param_grid = param_grid, cv = cv_structure, n_jobs = 1)\\n \\n # Training the pipeline, saving the data\\n grid.fit(data_part_train, labels_part_train)\\n results_table[selection_idx][0] = np.log10(grid.best_params_['classification__C'])\\n results_table[selection_idx][1] = grid.best_params_['selection__k']\\n results_table[selection_idx][2] = grid.best_score_\\n \\n # Testing the pipeline, saving the data\\n results_table[selection_idx][3] = grid.score(data_part_test, labels_part_test)\",\n \"def _vVBEL_XXXX(self,vKNOT=None,OBJTYPE=None):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n \\r\\n try: \\r\\n \\r\\n #'KVR','PZON_NAME', 'FSTF_NAME', 'STOF_NAME', 'GMIX_NAME','UTMP_NAME'\\r\\n\\r\\n\\r\\n\\r\\n vXXXX=None\\r\\n\\r\\n vXXXX=pd.merge(self.dataFrames[OBJTYPE],vKNOT,left_on='fkKI',right_on='pk',suffixes=('','_i')) \\r\\n vXXXX=vXXXX[['fkKK','BESCHREIBUNG','IDREFERENZ','pk','tk','NAME','CONT','CONT_VKNO','pk_i','ZKOR']]\\r\\n vXXXX.rename(columns={'NAME':'NAME_i','CONT':'CONT_i','CONT_VKNO':'CONT_VKNO_i','ZKOR':'Z_i'},inplace=True)\\r\\n\\r\\n vXXXX=pd.merge(vXXXX,vKNOT,left_on='fkKK',right_on='pk',suffixes=('','_k'))\\r\\n vXXXX=vXXXX[['BESCHREIBUNG','IDREFERENZ','pk','tk','NAME_i','CONT_i','CONT_VKNO_i','Z_i','pk_i','NAME','CONT','CONT_VKNO','pk_k','ZKOR']]\\r\\n vXXXX.rename(columns={'NAME':'NAME_k','CONT':'CONT_k','CONT_VKNO':'CONT_VKNO_k','ZKOR':'Z_k'},inplace=True)\\r\\n \\r\\n vXXXX=vXXXX.assign(OBJTYPE=lambda x: OBJTYPE)\\r\\n vXXXX=vXXXX[['OBJTYPE','BESCHREIBUNG','IDREFERENZ','pk','tk','NAME_i','CONT_i','CONT_VKNO_i','Z_i','pk_i','NAME_k','CONT_k','CONT_VKNO_k','Z_k','pk_k']]\\r\\n\\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n logger.debug(logStrFinal) \\r\\n vXXXX=None\\r\\n \\r\\n finally:\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.'))\\r\\n return vXXXX\",\n \"def process_circuits(self, processor_fn, updated_aliases=None):\\n P = processor_fn # shorthand\\n cpy = LsGermsSerialStructure(self.Ls, list(map(P, self.germs)),\\n self.nMinorRows, self.nMinorCols,\\n updated_aliases, self.sequenceRules)\\n cpy.allstrs = list(map(P, self.allstrs))\\n cpy.allstrs_set = set(cpy.allstrs)\\n cpy.unindexed = list(map(P, self.unindexed))\\n cpy._plaquettes = {k: v.process_circuits(P, updated_aliases) for k, v in self._plaquettes.items()}\\n cpy._firsts = [(L, P(germ)) for (L, germ) in self._firsts]\\n cpy._baseStrToLGerm = {P(base): (L, P(germ)) for base, (L, germ) in self._baseStrToLGerm.items()}\\n return cpy\",\n \"def build(self):\\n super(VaporStateBlockData, self).build()\\n\\n # Object reference for molecular weight if needed by CV1D\\n # Molecular weights\\n self.mw_comp = Reference(self.params.mw_comp)\\n\\n self.flow_mol = Var(initialize=1.0,\\n domain=NonNegativeReals,\\n units=pyunits.mol / pyunits.s,\\n doc='Total molar flowrate')\\n\\n self.mole_frac_comp = Var(self.component_list,\\n domain=NonNegativeReals,\\n bounds=(0, 1),\\n units=None,\\n initialize=1 / len(self.component_list),\\n doc='Component mole fractions [-]')\\n\\n self.pressure = Var(initialize=101325,\\n domain=NonNegativeReals,\\n units=pyunits.Pa,\\n doc='Pressure [Pa]')\\n\\n self.temperature = Var(initialize=298.15,\\n domain=NonNegativeReals,\\n units=pyunits.K,\\n doc='Temperature [K]')\\n\\n # Sum mole fractions if not inlet block\\n if self.config.defined_state is False:\\n def sum_component_eqn(b):\\n return b.flow_mol == sum(b.flow_mol_comp[j]\\n for j in b._params.component_list)\\n self.sum_component_eqn = Constraint(rule=sum_component_eqn)\",\n \"def exec_attention(self,curr_step): \\n\\n assert(self.curr_step_idx > 0 and self.dlist is not None), \\\"Step Error: Must call init before combine\\\" \\n \\n detectType = curr_step[\\\"detectionNetwork\\\"]\\n paramsFile = curr_step[\\\"paramsFile\\\"]\\n funclist = curr_step[\\\"funclist\\\"]\\n\\n #verify raw data & dlist\\n # self.B_VER(self.sess_path, self.dlist)\\n raw_datadir = self.sess_path\\n dest_datadir = self.sess_path \\n\\n model_dict = {\\\"detectType\\\": detectType, \\\"paramsFile\\\" : paramsFile} \\n\\n for i, folder in enumerate(self.dlist):\\n flist = funclist[i]\\n self.data_utils.DETECT(raw_datadir, folder, dest_datadir, model_dict, flist=[], preview=False)\\n self.default_vis(curr_step)\",\n \"def encode(self):\\n payload = []\\n\\n # Generate Payload\\n if self.IsEnsembleData:\\n payload += self.EnsembleData.encode()\\n if self.IsAncillaryData:\\n payload += self.AncillaryData.encode()\\n if self.IsAmplitude:\\n payload += self.Amplitude.encode()\\n if self.IsCorrelation:\\n payload += self.Correlation.encode()\\n if self.IsBeamVelocity:\\n payload += self.BeamVelocity.encode()\\n if self.IsInstrumentVelocity:\\n payload += self.InstrumentVelocity.encode()\\n if self.IsEarthVelocity:\\n payload += self.EarthVelocity.encode()\\n if self.IsGoodBeam:\\n payload += self.GoodBeam.encode()\\n if self.IsGoodEarth:\\n payload += self.GoodEarth.encode()\\n if self.IsBottomTrack:\\n payload += self.BottomTrack.encode()\\n if self.IsRangeTracking:\\n payload += self.RangeTracking.encode()\\n if self.IsSystemSetup:\\n payload += self.SystemSetup.encode()\\n if self.IsNmeaData:\\n payload += self.NmeaData.encode()\\n\\n # Generate the header\\n # Get the ensemble number\\n ens_num = 0\\n if self.IsEnsembleData:\\n ens_num = self.EnsembleData.EnsembleNumber\\n\\n # Get the payload size\\n payload_size = len(payload)\\n\\n header = Ensemble.generate_ens_header(ens_num, payload_size)\\n\\n # Generate the Checksum CITT\\n # Parameters found at https: // pycrc.org / models.html\\n #crc = pycrc.algorithms.Crc(width=16, poly=0x1021,\\n # reflect_in=False, xor_in=0x1d0f,\\n # reflect_out=False, xor_out=0x0000)\\n #checksum = crc.bit_by_bit_fast(binascii.a2b_hex(bytes(payload)))\\n #checksum = Ensemble.int32_to_bytes(CRCCCITT().calculate(input_data=bytes(payload)))\\n checksum = crc16.crc16xmodem(payload)\\n\\n\\n result = []\\n result += header\\n result += payload\\n result += checksum\\n\\n return bytearray(result)\",\n \"def transform(self, inputs: list, stage: str) -> datapack.DataPack:\",\n \"def predict_structure(prefix, model_runner_1: alphafold.model.model.RunModel,\\n model_runner_3: alphafold.model.model.RunModel,\\n feature_dict, Ls: list[int], model_params: haiku.Params, use_model, do_relax=False,\\n random_seed=0):\\n\\n # Minkyung's code\\n # add big enough number to residue index to indicate chain breaks\\n idx_res = feature_dict['residue_index']\\n L_prev = 0\\n # Ls: number of residues in each chain\\n for L_i in Ls[:-1]:\\n idx_res[L_prev + L_i:] += 200\\n L_prev += L_i\\n chains = list(\\\"\\\".join([ascii_uppercase[n] * L for n, L in enumerate(Ls)]))\\n feature_dict['residue_index'] = idx_res\\n\\n # Run the models.\\n plddts, paes = [], []\\n unrelaxed_pdb_lines = []\\n relaxed_pdb_lines = []\\n\\n print(f\\\"Use_model {use_model}\\\")\\n\\n for model_name, params in model_params.items():\\n if model_name in use_model:\\n print(f\\\"running {model_name}\\\")\\n # swap params to avoid recompiling\\n # note: models 1,2 have diff number of params compared to models 3,4,5\\n if any(str(m) in model_name for m in [1, 2]): model_runner = model_runner_1\\n if any(str(m) in model_name for m in [3, 4, 5]): model_runner = model_runner_3\\n model_runner.params = params\\n\\n processed_feature_dict: affeatures.FeatureDict = model_runner.process_features(feature_dict,\\n random_seed=random_seed)\\n # prediction_result is a dictionary of NumPy feature arrays\\n prediction_result: dict = model_runner.predict(processed_feature_dict)\\n unrelaxed_protein: protein.Protein = protein.from_prediction(processed_feature_dict, prediction_result)\\n unrelaxed_pdb_lines.append(protein.to_pdb(unrelaxed_protein))\\n plddts.append(prediction_result['plddt'])\\n paes.append(prediction_result['predicted_aligned_error'])\\n\\n if do_relax:\\n # Relax the prediction.\\n amber_relaxer: relax.AmberRelaxation = relax.AmberRelaxation(max_iterations=0, tolerance=2.39,\\n stiffness=10.0, exclude_residues=[],\\n max_outer_iterations=20)\\n relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)\\n relaxed_pdb_lines.append(relaxed_pdb_str)\\n\\n # rerank models based on predicted lddt\\n lddt_rank = np.mean(plddts, -1).argsort()[::-1]\\n out = {}\\n print(\\\"reranking models based on avg. predicted lDDT\\\")\\n for n, r in enumerate(lddt_rank):\\n print(f\\\"model_{n + 1} {np.mean(plddts[r])}\\\")\\n\\n unrelaxed_pdb_path = f'{prefix}_unrelaxed_model_{n + 1}.pdb'\\n with open(unrelaxed_pdb_path, 'w') as f:\\n f.write(unrelaxed_pdb_lines[r])\\n set_bfactor(unrelaxed_pdb_path, plddts[r], idx_res, chains)\\n\\n if do_relax:\\n relaxed_pdb_path = f'{prefix}_relaxed_model_{n + 1}.pdb'\\n with open(relaxed_pdb_path, 'w') as f: f.write(relaxed_pdb_lines[r])\\n set_bfactor(relaxed_pdb_path, plddts[r], idx_res, chains)\\n\\n out[f\\\"model_{n + 1}\\\"] = {\\\"plddt\\\": plddts[r], \\\"pae\\\": paes[r]}\\n return out\",\n \"def __init__(self, objects=()):\\n\\n vtk.vtkPropAssembly.__init__(self)\\n\\n self.name = \\\"\\\"\\n self.created = \\\"\\\"\\n self.trail = None\\n self.trail_points = []\\n self.trail_segment_size = 0\\n self.trail_offset = None\\n self.shadows = []\\n self.info = {}\\n self.rendered_at = set()\\n self.transform = None\\n self.scalarbar = None\\n\\n for a in vedo.utils.flatten(objects):\\n if a:\\n self.AddPart(a)\\n\\n self.PickableOff()\",\n \"def dovisitcomb(allv) :\\n allvisits = allv[0]\\n load = allv[1]\\n field = allv[2][0]\\n apogee_id = allv[2][1]\\n clobber = allv[2][2]\\n pixelmask=bitmask.PixelBitMask()\\n\\n # already done?\\n outdir=os.path.dirname(load.filename('Field',field=field))\\n outdir=outdir.replace('/stars/','/rv/')\\n if os.path.exists(outdir+'/'+apogee_id+'.pkl') and not clobber:\\n print(apogee_id,' already done visitcomb')\\n fp=open(outdir+'/'+apogee_id+'.pkl','rb')\\n try: \\n out=pickle.load(fp)\\n fp.close()\\n return out\\n except: \\n print('error loading: ', apogee_id+'.pkl')\\n pass\\n\\n # do the combination\\n apstar=visitcomb(allvisits,load=load,plot=False)\\n\\n # dump\\n pickle.dump(apstar,open(outdir+'/'+apogee_id+'.pkl','wb'))\\n\\n return apstar\",\n \"def _form_computation_graph(self, idx):\\n _list, _set = list, set\\n if type(idx) is int:\\n node_layers = [np.array([idx], dtype=np.int64)]\\n elif type(idx) is list:\\n node_layers = [np.array(idx, dtype=np.int64)]\\n\\n for _ in range(self.n_layers):\\n prev = node_layers[-1]\\n arr = [node for node in prev]\\n arr.extend([e[0] for node in arr for e in self.nbrs_t[node]])\\n arr = np.array(_list(_set(arr)), dtype=np.int64)\\n node_layers.append(arr)\\n node_layers.reverse()\\n\\n mappings = [{j: i for (i, j) in enumerate(arr)} for arr in node_layers]\\n\\n return node_layers, mappings\",\n \"def genereate_echo_picklist(self):\\n sample_names = []\\n sample_wells = []\\n indices = {'i5 name': {}, 'i5 plate': {}, 'i5 sequence': {},\\n 'i5 well': {}, 'i7 name': {}, 'i7 plate': {},\\n 'i7 sequence': {}, 'i7 well': {}, 'index combo': {},\\n 'index combo seq': {}}\\n\\n for idx, well in enumerate(chain.from_iterable(self.plates[0].layout)):\\n # Add the sample well\\n sample_wells.append(well.well_id)\\n # Get the sample name - we need to go back to the SampleComposition\\n lib_comp = well.composition\\n sample_comp = lib_comp.normalized_gdna_composition\\\\\\n .gdna_composition.sample_composition\\n sample_names.append(sample_comp.content)\\n # Retrieve all the information about the indices\\n i5_comp = lib_comp.i5_composition.primer_set_composition\\n i5_well = i5_comp.container\\n indices['i5 name'][idx] = i5_comp.external_id\\n indices['i5 plate'][idx] = i5_well.plate.external_id\\n indices['i5 sequence'][idx] = i5_comp.barcode\\n indices['i5 well'][idx] = i5_well.well_id\\n\\n i7_comp = lib_comp.i7_composition.primer_set_composition\\n i7_well = i7_comp.container\\n indices['i7 name'][idx] = i7_comp.external_id\\n indices['i7 plate'][idx] = i7_well.plate.external_id\\n indices['i7 sequence'][idx] = i7_comp.barcode\\n indices['i7 well'][idx] = i7_well.well_id\\n\\n indices['index combo seq'][idx] = '%s%s' % (\\n indices['i5 sequence'][idx], indices['i7 sequence'][idx])\\n\\n sample_names = np.asarray(sample_names)\\n sample_wells = np.asarray(sample_wells)\\n indices = pd.DataFrame(indices)\\n\\n return LibraryPrepShotgunProcess._format_picklist(\\n sample_names, sample_wells, indices)\",\n \"def __rechaindict__(c):\\n from TriggerMenu.menu.DictFromChainName import DictFromChainName\\n dfcn = DictFromChainName()\\n\\n pl1 = []\\n for pch in c['chainParts']:\\n pl1.append(pch['L1item'])\\n\\n newname = c['chainName'].replace('dv_','').replace('TestChain','j')\\n nchlist = [ newname ,c['chainCounter'],c['L1item'],pl1,c['stream'],\\n c['groups'],c['EBstep'] ]\\n \\n return dfcn.getChainDict(nchlist)\",\n \"def hierarchical( x, output_prefix, labels_to_register=[2,3,4,5], is_test=False, verbose=True ):\\n if verbose:\\n print(\\\"Read\\\")\\n tfn = get_data('T_template0', target_extension='.nii.gz' )\\n tfnw = get_data('T_template0_WMP', target_extension='.nii.gz' )\\n tlrfn = get_data('T_template0_LR', target_extension='.nii.gz' )\\n bfn = antspynet.get_antsxnet_data( \\\"croppedMni152\\\" )\\n\\n ##### read images and do simple bxt ops\\n templatea = ants.image_read( tfn )\\n if verbose:\\n print(\\\"bxt\\\")\\n templatea = ( templatea * antspynet.brain_extraction( templatea, 't1' ) ).iMath( \\\"Normalize\\\" )\\n templateawmprior = ants.image_read( tfnw )\\n templatealr = ants.image_read( tlrfn )\\n templateb = ants.image_read( bfn )\\n templateb = ( templateb * antspynet.brain_extraction( templateb, 't1' ) ).iMath( \\\"Normalize\\\" )\\n imgbxt = brain_extraction( x )\\n img = x * imgbxt\\n\\n if verbose:\\n print(\\\"rbp\\\")\\n\\n # this is an unbiased method for identifying predictors that can be used to\\n # rank / sort data into clusters, some of which may be associated\\n # with outlierness or low-quality data\\n templatesmall = ants.resample_image( templateb, (91,109,91), use_voxels=True )\\n rbp = random_basis_projection( img, templatesmall )\\n\\n if verbose:\\n print(\\\"intensity\\\")\\n\\n ##### intensity modifications\\n img = ants.iMath( img, \\\"Normalize\\\" ) * 255.0\\n img = ants.denoise_image( img, imgbxt, noise_model='Gaussian')\\n img = ants.n4_bias_field_correction( img ).iMath(\\\"Normalize\\\")\\n\\n # optional - quick look at result\\n bxt_png = output_prefix + \\\"_brain_extraction_dnz_n4_view.png\\\"\\n ants.plot(img,axis=2,ncol=8,nslices=24, crop=True, black_bg=False,\\n filename = bxt_png )\\n\\n if verbose:\\n print(\\\"hemi\\\")\\n\\n # assuming data is reasonable quality, we should proceed with the rest ...\\n mylr = label_hemispheres( img, templatea, templatealr )\\n\\n if verbose:\\n print(\\\"parcellation\\\")\\n\\n ##### hierarchical labeling\\n myparc = deep_brain_parcellation( img, templateb,\\n do_cortical_propagation = not is_test, verbose=False )\\n\\n ##### accumulate data into data frames\\n hemi = map_segmentation_to_dataframe( \\\"hemisphere\\\", myparc['hemisphere_labels'] )\\n tissue = map_segmentation_to_dataframe( \\\"tissues\\\", myparc['tissue_segmentation'] )\\n dktl = map_segmentation_to_dataframe( \\\"lobes\\\", myparc['dkt_lobes'] )\\n dktp = map_segmentation_to_dataframe( \\\"dkt\\\", myparc['dkt_parcellation'] )\\n dktc = None\\n if not is_test:\\n dktc = map_segmentation_to_dataframe( \\\"dkt\\\", myparc['dkt_cortex'] )\\n\\n tissue_seg_png = output_prefix + \\\"_seg.png\\\"\\n ants.plot( img, myparc['tissue_segmentation'], axis=2, nslices=21, ncol=7,\\n alpha=0.6, filename=tissue_seg_png,\\n crop=True, black_bg=False )\\n\\n if verbose:\\n print(\\\"WMH\\\")\\n\\n ##### below here are more exploratory nice to have outputs\\n myhypo = t1_hypointensity(\\n img,\\n myparc['tissue_segmentation'], # segmentation\\n myparc['tissue_probabilities'][3], # wm posteriors\\n templatea,\\n templateawmprior )\\n\\n if verbose:\\n print(\\\"registration\\\")\\n\\n ##### traditional deformable registration as a high-resolution complement to above\\n wm_tractsL = None\\n wm_tractsR = None\\n wmtdfL = None\\n wmtdfR = None\\n reg = None\\n if labels_to_register is not None:\\n reg = hemi_reg(\\n input_image = img,\\n input_image_tissue_segmentation = myparc['tissue_segmentation'],\\n input_image_hemisphere_segmentation = mylr,\\n input_template=templatea,\\n input_template_hemisphere_labels=templatealr,\\n output_prefix = output_prefix + \\\"_SYN\\\",\\n labels_to_register = labels_to_register,\\n is_test=is_test )\\n if verbose:\\n print(\\\"wm tracts\\\")\\n ##### how to use the hemi-reg output to generate any roi value from a template roi\\n wm_tracts = ants.image_read( get_data( \\\"wm_major_tracts\\\", target_extension='.nii.gz' ) )\\n wm_tractsL = ants.apply_transforms( img, wm_tracts, reg['synL']['invtransforms'],\\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 1, 1 )\\n wm_tractsR = ants.apply_transforms( img, wm_tracts, reg['synR']['invtransforms'],\\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 2, 2 )\\n wmtdfL = map_segmentation_to_dataframe( \\\"wm_major_tracts\\\", wm_tractsL )\\n wmtdfR = map_segmentation_to_dataframe( \\\"wm_major_tracts\\\", wm_tractsR )\\n\\n if verbose:\\n print(\\\"hippocampus\\\")\\n\\n ##### specialized labeling for hippocampus\\n ntries = 10\\n if is_test:\\n ntries = 1\\n hippLR = deep_hippo( img, templateb, ntries )\\n\\n mydataframes = {\\n \\\"hemispheres\\\":hemi,\\n \\\"tissues\\\":tissue,\\n \\\"dktlobes\\\":dktl,\\n \\\"dktregions\\\":dktp,\\n \\\"dktcortex\\\":dktc,\\n \\\"wmtracts_left\\\":wmtdfL,\\n \\\"wmtracts_right\\\":wmtdfR,\\n \\\"wmh\\\":myhypo['wmh_summary']\\n }\\n\\n outputs = {\\n \\\"brain_n4_dnz\\\": img,\\n \\\"brain_n4_dnz_png\\\": bxt_png,\\n \\\"brain_extraction\\\": imgbxt,\\n \\\"tissue_seg_png\\\": tissue_seg_png,\\n \\\"rbp\\\": rbp,\\n \\\"left_right\\\": mylr,\\n \\\"dkt_parc\\\": myparc,\\n \\\"registration\\\":reg,\\n \\\"hippLR\\\":hippLR,\\n \\\"white_matter_hypointensity\\\":myhypo,\\n \\\"wm_tractsL\\\":wm_tractsL,\\n \\\"wm_tractsR\\\":wm_tractsR,\\n \\\"dataframes\\\": mydataframes\\n }\\n\\n return outputs\",\n \"def label(gt_dataset, volume_dim, voxel_dim, labeling_params):\\n labeled_volumes = dict()\\n labeled_cells = dict()\\n #Use global density and reduce the size of gt_dataset here\\n global_density = labeling_params[\\\"global_density\\\"]\\n gt_dataset = {k: v for k,v in gt_dataset.items() if random_sample() < global_density}\\n #Label in the order specified in the configuration\\n layers = sorted(labeling_params.keys())\\n #Remove global_density\\n layers.remove(\\\"global_density\\\")\\n for layer in layers:\\n print \\\"Labeling {}\\\".format(layer)\\n fluorophore = labeling_params[layer]['fluorophore']\\n volume, cells = brainbow(gt_dataset, volume_dim, voxel_dim, **labeling_params[layer])\\n if fluorophore in labeled_volumes:\\n labeled_volumes[fluorophore] += volume\\n labeled_cells[fluorophore] |= cells\\n else:\\n labeled_volumes[fluorophore] = volume\\n labeled_cells[fluorophore] = cells\\n return labeled_volumes, labeled_cells\",\n \"def mapping_image_to_label (self, labels_df, polygons, fpath_tiff): \\n \\n unread_tiff = rasterio.open(fpath_tiff)\\n\\n #Projecting the coordinates to that CRS \\n proj = Proj(init='epsg:32618')\\n data = []\\n labels = []\\n failed = []\\n \\n src = rasterio.open(fpath_tiff, 'r')\\n outfolder = '/train/batch'\\n \\n print (\\\"Hold on tight! Mapping each image to its respective label...\\\")\\n \\n \\n for num, row in labels_df.iterrows():\\n try:\\n \\n \\n roof_material_num = 0\\n polygon0 = polygons [num]\\n polygon0['coordinates'] = self.transforming_coordinates(polygon0['coordinates'], proj)\\n masked_image, out_transform = rasterio.mask.mask(src,[polygon0], filled = True, crop=True, nodata = 0)\\n img_image = reshape_as_image (masked_image)\\n \\n #Defining the name of the image file as \\\"buildingID+roofMaterial+png\\\" and its path \\n img_path = os.path.join (outfolder, str (row['id'])+'-'+ str (row['roof_material'])+'.png')\\n \\n #swapping the color channels from RGB2BGR\\n img_image = cv2.cvtColor (img_image, cv2.COLOR_RGB2BGR) #img_image is a numpy array\\n \\n #resizing the image dimensions to 128x128 to match ImageNet dimensions\\n img_image = cv2.resize(img_image, (128, 128))\\n \\n #writing the image in the file\\n #cv2.imwrite (img_path, img_image)\\n # update the data and labels lists, respectively\\n data.append(img_image) #data is a list\\n labels.append(row['roof_material'])\\n \\n except Exception as e:\\n print (e)\\n failed.append (num)\\n \\n \\n #print number of images we failed to crop and write \\n print (\\\"Bad News First: Failed to write\\\", len(failed), \\\"Images.\\\")\\n print (\\\"Good News: Successfully mapped\\\", len (data), \\\"Images.\\\")\\n data = np.array(data)\\n labels = np.array(labels)\\n #batch = data.sample(frac=0.5, replace=False, random_state=1)\\n #print(\\\"Size and shape of validY: {}\\\\n\\\".format(batch.shape))\\n return data, labels\",\n \"def case():\\r\\n #ppc = {\\\"version\\\": '2'}\\r\\n ppc = {}\\r\\n ##----- Power Flow Data -----##\\r\\n ## system MVA base\\r\\n ppc[\\\"baseMVA\\\"] = 100.0\\r\\n\\r\\n ## bus data\\r\\n # bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin\\r\\n ppc[\\\"bus\\\"] = array([\\r\\n [1, 3, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [2, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [3, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [4, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [5, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [6, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [7, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [8, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [9, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [10, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [11, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [12, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [13, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [14, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [15, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\\r\\n [16, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0]\\r\\n ])\\r\\n\\r\\n ## generator data\\r\\n # bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2,\\r\\n # Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf\\r\\n ppc[\\\"gen\\\"] = array([\\r\\n [1,\\t0,\\t0,\\t10,\\t-10,\\t1.0224,\\t100,\\t1,\\t10,\\t-10,\\t0,\\t0,\\t0,\\t0,\\t0,\\t0,\\t0,\\t0,\\t0,\\t0, 0, 0,0, 0, 0],\\r\\n [3 ,0, 0, 50e-3, -50e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\\r\\n [5 , 0, 0, 10e-3, -10e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\\r\\n [10 , 0, 0, 10e-3, -10e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\\r\\n [13 ,0, 0, 10e-3, -10e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\\r\\n [15 , 0, 0, 50e-3, -50e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0]\\r\\n ])\\r\\n load_b = array([2, 4, 9, 12, 14])\\r\\n ppc[\\\"bus\\\"][load_b, 2] = multiply(array([-2.1125, -0.2231, -0.1664, -0.0719, -1.4633]).T, 0.03)\\r\\n ppc[\\\"bus\\\"][load_b, 3] = multiply(array([1.6492, 0.4054, 0.8599, 0.8845, 0.6778]).T, 0.03)\\r\\n ## branch data\\r\\n # fbus, tbus, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax\\r\\n ppc[\\\"branch\\\"] = array([\\r\\n [1, 2, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [1, 8, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [1, 15, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [2, 3, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [2, 6, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [2, 7, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [3, 4, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [4, 5, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [8, 9, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [8, 12, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [8, 13, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [9, 10, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [9, 14, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [10, 11, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\\r\\n [15, 16, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0]\\r\\n ])\\r\\n R1 = 0.43\\r\\n L1 = 0.4e-3\\r\\n RS1 = 0.32\\r\\n LS1 = 0.39e-3\\r\\n Zbase = (0.4*0.4/100)\\r\\n branch_phase =array([\\r\\n [1, 1, 2, 188, R1, L1],\\r\\n [2, 1 ,8, 346, R1, L1],\\r\\n [3 ,1 ,15,501, R1 ,L1],\\r\\n [4, 2, 3, 130, RS1,LS1],\\r\\n [5, 2, 6, 145, RS1,LS1],\\r\\n [6, 2 ,7, 157, RS1,LS1],\\r\\n [7, 3, 4, 185, RS1,LS1],\\r\\n [8, 4, 5, 1000,RS1,LS1],\\r\\n [9, 8 ,9, 416, RS1,LS1],\\r\\n [10,8 ,12,130, RS1,LS1],\\r\\n [11,8 ,13,121, RS1,LS1],\\r\\n [12,9 ,10,130, RS1,LS1],\\r\\n [13,9 ,14,127, RS1,LS1],\\r\\n [14,10,11,251, RS1,LS1],\\r\\n [15,15,16,345, RS1,LS1]\\r\\n ])\\r\\n ppc[\\\"branch\\\"][:, [2,3]] = multiply(array([branch_phase[:, 4]*branch_phase[:, 3], branch_phase[:, 4]*branch_phase[:, 4]*100*pi]).T,0.001/Zbase)\\r\\n\\r\\n ##----- OPF Data -----##\\r\\n ## area data\\r\\n # area refbus\\r\\n\\r\\n\\r\\n ## generator cost data\\r\\n # 1 startup shutdown n x1 y1 ... xn yn\\r\\n # 2 startup shutdown n c(n-1) ... c0\\r\\n\\r\\n\\r\\n return ppc\",\n \"def _label_encoding(self):\\n for feat in self.cat_feats:\\n if self.train:\\n lbl = preprocessing.LabelEncoder()\\n lbl.fit(self.dataframe[feat].values)\\n self.dataframe_d_copy.loc[:,feat] = lbl.transform(self.dataframe[feat].values)\\n self.label_encoders[feat] = lbl\\n else:\\n lbl = self.encoders[feat]\\n self.dataframe_d_copy.loc[:,feat] = lbl.transform(self.dataframe[feat].values)\\n \\n if self.train:\\n encoder_path = f\\\"{self.output_path}/_label_encoder.pkl\\\"\\n self.cat_feats_cfg['encoder_path'] = encoder_path\\n joblib.dump(self.label_encoders, encoder_path)\\n \\n return self.dataframe_d_copy\",\n \"def convert2EbnerParamOriginalParam(listSlice,list_prefix,directory,paramAx,paramCor,paramSag):\\n paramAx=np.load(paramAx)\\n paramCor=np.load(paramCor)\\n paramSag=np.load(paramSag)\\n param=[]\\n param.append(paramAx)\\n param.append(paramCor)\\n param.append(paramSag)\\n \\n images,mask = createVolumesFromAlist(listSlice.copy()) #list of images corresponding to differents original stacks\\n \\n \\n mat = np.array([[-1,0,0,0],[0,-1,0,0],[0,0,1,0],[0,0,0,1]]) #matrix to convert affine matrix from nibabel to itk\\n\\n for n in range(len(images)): #for each stack\\n \\n imagen = images[n]\\n \\n for i_slice in range(len(images[n])): #for each slices (in each stacks)\\n \\n slicei=imagen[i_slice]\\n dimension=3\\n X,Y,Z= slicei.get_slice().get_fdata().shape\\n transfo = param[n][slicei.get_index_slice(),:,:]\\n #print()\\n matrix = mat @ transfo @ mat\\n #print(matrix)\\n test = sitk.AffineTransform(dimension)\\n test.SetMatrix(matrix[0:3,0:3].flatten())\\n test.SetTranslation(matrix[0:3,3])\\n images_index = slicei.get_index_image()\\n\\n sitk.WriteTransform(test,\\\"%s/%s_slice%d.tfm\\\" %(directory,list_prefix[images_index],slicei.get_index_slice())) #save rigid transformation, computed at the barycenter of the image, adatpted to itk\",\n \"def nodeSeparate(self,compInfo, ifSub, subname, subcktName,numNodesSub):\\n node = []\\n nodeTemp = []\\n nodeDic = {}\\n pinInit = 'Modelica.Electrical.Analog.Interfaces.Pin '\\n pinProtectedInit = 'Modelica.Electrical.Analog.Interfaces.Pin '\\n protectedNode = []\\n print \\\"CompInfo coming to nodeSeparate function: compInfo\\\",compInfo\\n \\n #Removing '[' and ']' from compInfo for Digital node\\n for i in range(0,len(compInfo),1):\\n compInfo[i] = compInfo[i].replace(\\\"[\\\",\\\"\\\").replace(\\\"]\\\",\\\"\\\")\\n \\n \\n for eachline in compInfo:\\n words = eachline.split()\\n if eachline[0] in ['m', 'e', 'g', 't','M','E','G','T']:\\n nodeTemp.append(words[1])\\n nodeTemp.append(words[2])\\n nodeTemp.append(words[3])\\n nodeTemp.append(words[4])\\n elif eachline[0] in ['q', 'j','J','Q']:\\n nodeTemp.append(words[1])\\n nodeTemp.append(words[2])\\n nodeTemp.append(words[3])\\n elif eachline[0]=='x' or eachline[0]=='X':\\n templine = eachline.split()\\n for i in range(0,len(templine),1):\\n if templine[i] in subcktName:\\n point = i \\n nodeTemp.extend(words[1:point])\\n else:\\n nodeTemp.append(words[1])\\n nodeTemp.append(words[2])\\n for i in nodeTemp:\\n if i not in node:\\n node.append(i)\\n \\n for i in range(0, len(node),1):\\n nodeDic[node[i]] = 'n' + node[i]\\n if ifSub == '0':\\n if i != len(node)-1:\\n pinInit = pinInit + nodeDic[node[i]] + ', '\\n else:\\n pinInit = pinInit + nodeDic[node[i]]\\n else:\\n nonprotectedNode = self.getSubInterface(subname, numNodesSub) \\n if node[i] in nonprotectedNode:\\n continue\\n else:\\n protectedNode.append(node[i])\\n if ifSub == '1':\\n if len(nonprotectedNode) > 0:\\n for i in range(0, len(nonprotectedNode),1):\\n if i != len(nonprotectedNode)-1:\\n pinProtectedInit = pinProtectedInit + nodeDic[nonprotectedNode[i]] + ','\\n else:\\n pinProtectedInit = pinProtectedInit + nodeDic[nonprotectedNode[i]]\\n if len(protectedNode) > 0:\\n for i in range(0, len(protectedNode),1):\\n if i != len(protectedNode)-1: \\n pinInit = pinInit + nodeDic[protectedNode[i]] + ','\\n else:\\n pinInit = pinInit + nodeDic[protectedNode[i]]\\n pinInit = pinInit + ';'\\n pinProtectedInit = pinProtectedInit + ';'\\n print \\\"Node---->\\\",node\\n print \\\"nodeDic----->\\\",nodeDic\\n print \\\"PinInit----->\\\",pinInit\\n print \\\"pinProtectedinit--->\\\",pinProtectedInit\\n return node, nodeDic, pinInit, pinProtectedInit\",\n \"def __repr__(self):\\n data = sorted(\\n (state, pos, tape_cache, outputs)\\n for pos, states in self.iteritems()\\n for state, (tape_cache, outputs) in states.iteritems())\\n branch = \\\"branch\\\" if len(data) == 1 else \\\"branches\\\"\\n result = \\\"process (%s %s)\\\" % (len(data), branch)\\n for s, sdata in itertools.groupby(data, lambda x: x[0]):\\n result += \\\"\\\\n+ at state %s\\\" % (s,)\\n for state, pos, tape_cache, outputs in sdata:\\n result += \\\"\\\\n+-- %s, %s\\\" % (tape_cache, outputs)\\n return result\",\n \"def _vVBEL(self,vKNOT=None,edges=vVBEL_edges,edgesD=vVBEL_edgesD,mColNames=['OBJTYPE','pk'],mIdxNames=['OBJTYPE','OBJID']):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n \\r\\n try: \\r\\n # construct \\r\\n vVBEL=None\\r\\n vVBEL_UnionList=[]\\r\\n\\r\\n for VBEL in edges:\\r\\n if VBEL in self.dataFrames:\\r\\n vXXXX=self._vVBEL_XXXX(vKNOT=vKNOT,OBJTYPE=VBEL)\\r\\n if vXXXX is None:\\r\\n pass\\r\\n else:\\r\\n vVBEL_UnionList.append(vXXXX)\\r\\n vVBEL=pd.concat(vVBEL_UnionList)\\r\\n\\r\\n # MIndices\\r\\n vVBEL=Xm.constructNewMultiindexFromCols(df=vVBEL,mColNames=mColNames,mIdxNames=mIdxNames)\\r\\n\\r\\n # Gruppenzugeh. ergaenzen\\r\\n vVBEL['LAYR']=[list() for dummy in vVBEL['tk']]\\r\\n dfLayr=self.dataFrames['vLAYR']\\r\\n if not dfLayr.empty:\\r\\n dfLayr=dfLayr.rename(columns={'OBJTYPE':'TYPE'}) \\r\\n dfLayr=pd.merge(\\r\\n vVBEL\\r\\n ,dfLayr\\r\\n ,how='inner' # nur die VBEL die eine Gruppenzugehoerigkeit haben\\r\\n ,left_index=True \\r\\n ,right_on=['TYPE','OBJID'] \\r\\n ,suffixes=('', '_y'))[['NAME','TYPE','OBJID','nrObjInGroup','nrObjtypeInGroup']]\\r\\n dfLayr=dfLayr[dfLayr.nrObjInGroup <= 1] # pro VBEL und Gruppe nur 1 Zeile\\r\\n\\r\\n for index, row in vVBEL.merge(dfLayr.sort_values(by=['NAME','OBJID']),how='left',left_index=True ,right_on=['TYPE','OBJID'],suffixes=('', '_y')).iterrows(): \\r\\n if pd.isnull(row.NAME):\\r\\n continue\\r\\n row.LAYR.append(row.NAME)\\r\\n\\r\\n # L ergaenzen\\r\\n Rohr=self.dataFrames['ROHR']\\r\\n VbelL=vVBEL.join(Rohr.set_index('pk').rename_axis('OBJID', axis='index'),rsuffix='_y')[['L']] \\r\\n vVBEL['L']=VbelL['L'].fillna(0) \\r\\n\\r\\n # D ergaenzen\\r\\n # Spalte erzeugen ... \\r\\n vRohr=self.dataFrames['vROHR']\\r\\n VbelD=vVBEL.join(vRohr.set_index('pk').rename_axis('OBJID', axis='index'),rsuffix='_y')[['DI']]\\r\\n vVBEL['D']=VbelD['DI'] # ... mit ROHR\\r\\n\\r\\n # ueber alle ausser ROHR\\r\\n for eIdx,edge in enumerate(edges):\\r\\n if edge == 'ROHR':\\r\\n continue\\r\\n edgeDCol=edgesD[eIdx]\\r\\n if edgeDCol=='':\\r\\n continue \\r\\n if edge not in self.dataFrames:\\r\\n continue\\r\\n Edge=self.dataFrames[edge] \\r\\n if edgeDCol not in Edge.columns.tolist():\\r\\n continue\\r\\n edgeD=vVBEL.join(Edge.set_index('pk').rename_axis('OBJID', axis='index'),rsuffix='_y',how='inner')[[edgeDCol]]\\r\\n vVBEL.loc[[edge],'D']=edgeD.loc[[edge],:].values\\r\\n\\r\\n\\r\\n # fehlende Spaltenwerte zuweisen\\r\\n #Vent=self.dataFrames['VENT']\\r\\n #VentD=vVBEL.join(Vent.set_index('pk'),rsuffix='_y',how='inner')[['DN']]\\r\\n #vVBEL.loc[['VENT'],'D']=VentD.loc[['VENT'],:].values\\r\\n\\r\\n # Finish\\r\\n vVBEL.sort_index(level=0,inplace=True)\\r\\n\\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n logger.debug(logStrFinal) \\r\\n finally:\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.'))\\r\\n return vVBEL\",\n \"def apply_grub_cmdline(self):\\n\\n for i in self._nodes.items():\\n node = i[1]\\n\\n # Get the isolated CPUs\\n other_workers = node[\\\"cpu\\\"][\\\"other_workers\\\"]\\n vpp_workers = node[\\\"cpu\\\"][\\\"vpp_workers\\\"]\\n if \\\"vpp_main_core\\\" in node[\\\"cpu\\\"]:\\n vpp_main_core = node[\\\"cpu\\\"][\\\"vpp_main_core\\\"]\\n else:\\n vpp_main_core = 0\\n all_workers = []\\n if other_workers is not None:\\n all_workers = [other_workers]\\n if vpp_main_core != 0:\\n all_workers += [(vpp_main_core, vpp_main_core)]\\n all_workers += vpp_workers\\n isolated_cpus = \\\"\\\"\\n for idx, worker in enumerate(all_workers):\\n if worker is None:\\n continue\\n if idx > 0:\\n isolated_cpus += \\\",\\\"\\n if worker[0] == worker[1]:\\n isolated_cpus += \\\"{}\\\".format(worker[0])\\n else:\\n isolated_cpus += \\\"{}-{}\\\".format(worker[0], worker[1])\\n\\n vppgrb = VppGrubUtil(node)\\n current_cmdline = vppgrb.get_current_cmdline()\\n if \\\"grub\\\" not in node:\\n node[\\\"grub\\\"] = {}\\n node[\\\"grub\\\"][\\\"current_cmdline\\\"] = current_cmdline\\n node[\\\"grub\\\"][\\\"default_cmdline\\\"] = vppgrb.apply_cmdline(node, isolated_cpus)\\n\\n self.updateconfig()\"\n]"},"negative_scores":{"kind":"list like","value":["0.53284836","0.52070326","0.50032526","0.4924041","0.48997957","0.48227805","0.4819547","0.4782699","0.47695082","0.4746857","0.4743185","0.47308764","0.4726845","0.46611047","0.46597615","0.463954","0.46242067","0.46234703","0.45942166","0.45858887","0.4571889","0.45699766","0.45352846","0.45307684","0.45059124","0.44992357","0.4491267","0.44876143","0.44860741","0.4449634","0.4436323","0.4420826","0.4415709","0.44151866","0.44132257","0.44119504","0.44111606","0.4409967","0.4408976","0.44019982","0.44017988","0.44003794","0.43899447","0.43819338","0.43688822","0.4354956","0.43437353","0.43380022","0.43350187","0.43290702","0.4328562","0.43279904","0.43196777","0.43035686","0.43007937","0.43005908","0.42981583","0.42862144","0.42832178","0.42798436","0.42769793","0.42730308","0.42673436","0.42671657","0.42671657","0.42641827","0.42627034","0.42608324","0.42561105","0.42515945","0.42507425","0.42477784","0.42443958","0.424284","0.4238558","0.42339242","0.42317352","0.42286026","0.4222909","0.42208576","0.4217499","0.4213684","0.42129332","0.42101938","0.4209228","0.42056566","0.42052203","0.42037198","0.41966754","0.4193763","0.41932732","0.41926092","0.41910368","0.419061","0.41901353","0.4189429","0.41860756","0.4182849","0.41811472","0.41706634"],"string":"[\n \"0.53284836\",\n \"0.52070326\",\n \"0.50032526\",\n \"0.4924041\",\n \"0.48997957\",\n \"0.48227805\",\n \"0.4819547\",\n \"0.4782699\",\n \"0.47695082\",\n \"0.4746857\",\n \"0.4743185\",\n \"0.47308764\",\n \"0.4726845\",\n \"0.46611047\",\n \"0.46597615\",\n \"0.463954\",\n \"0.46242067\",\n \"0.46234703\",\n \"0.45942166\",\n \"0.45858887\",\n \"0.4571889\",\n \"0.45699766\",\n \"0.45352846\",\n \"0.45307684\",\n \"0.45059124\",\n \"0.44992357\",\n \"0.4491267\",\n \"0.44876143\",\n \"0.44860741\",\n \"0.4449634\",\n \"0.4436323\",\n \"0.4420826\",\n \"0.4415709\",\n \"0.44151866\",\n \"0.44132257\",\n \"0.44119504\",\n \"0.44111606\",\n \"0.4409967\",\n \"0.4408976\",\n \"0.44019982\",\n \"0.44017988\",\n \"0.44003794\",\n \"0.43899447\",\n \"0.43819338\",\n \"0.43688822\",\n \"0.4354956\",\n \"0.43437353\",\n \"0.43380022\",\n \"0.43350187\",\n \"0.43290702\",\n \"0.4328562\",\n \"0.43279904\",\n \"0.43196777\",\n \"0.43035686\",\n \"0.43007937\",\n \"0.43005908\",\n 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particpating jets"},"document":{"kind":"string","value":"def _make_dijet_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('dijet')\n\n arg_res = [\n re.compile(r'^(?P\\d*)(?Pdjmass)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1et)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1eta)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2et)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2eta)(?P\\d*)$'),\n ]\n\n defaults = {\n 'j1et': ('100', 'inf'),\n 'j2et': ('100', 'inf'),\n 'j1eta': ('0', '320'),\n 'j2eta': ('0', '320'),\n 'djmass': ('1000', 'inf'),\n }\n\n\n args = _args_from_scenario(scenario)\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n combgen(\n [(2)(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n (%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n ]\n \n dijet(\n [(%(djmasslo).0fdjmass)])\n simple([(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n (%(j2etlo).0fet, %(j2etalo).0feta%(j2etahi).0f)])\n )\"\"\" % argvals"},"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_info(config, cut, label):\n cfg = filter(lambda c: c['name'] == cut, config['physics']['cuts'])[0]\n text = \"\"\n if 'max' not in cfg:\n text += \"#geq \"\n text += str(cfg['min'])\n if 'max' in cfg and cfg['max'] != cfg['min']:\n text += '-' + str(cfg['max']) + ' ' + label + 's'\n elif cfg['min'] != 1:\n text += ' ' + label + 's'\n else:\n text += ' ' + label\n return text","def makeDPartial( name\n , config\n , DecayDescriptor\n , inputSel\n ) :\n\n _Kcuts1 = \"~ISMUON & (PT > %(DaugPtLoose)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2Loose)s)\" % locals()['config']\n _KcutsPIDK = \" & (PIDK > %(HighPIDK)s)\" % locals()['config']\n _Kcuts2 = \" & (ISLONG) & (P > %(DaugPLoose)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2Loose)s)\" % locals()['config']\n _Kcuts = _Kcuts1 + _KcutsPIDK + _Kcuts2\n _Picuts1 = \"~ISMUON & (PT > %(DaugPtMin)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2)s)\" % locals()['config']\n _PicutsPIDK = \" & (PIDK < %(LowPIDK)s)\" % locals()['config']\n _Picuts2 = \" & (ISLONG) & (P > %(DaugP)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2)s)\" % locals()['config']\n _Picuts = _Picuts1 + _PicutsPIDK + _Picuts2\n _dauCuts = { 'K+': _Kcuts, 'pi+': _Picuts }\n #_Kcuts1 = \"~ISMUON & (PT > 500* MeV) & (MIPCHI2DV(PRIMARY) > 4)\"\n #_KcutsPIDK = \" & (PIDK > 5)\"\n #_Kcuts2 = \" & (ISLONG) & (P > 5000* MeV) & (TRCHI2DOF < 5)\"\n #_Kcuts = _Kcuts1 + _KcutsPIDK + _Kcuts2\n #_Picuts1 = \"~ISMUON & (PT > 500* MeV) & (MIPCHI2DV(PRIMARY) > 4)\"\n #_PicutsPIDK = \" & (PIDK < 0)\"\n #_Picuts2 = \" & (ISLONG) & (P > 5000* MeV) & (TRCHI2DOF < 5)\"\n #_Picuts = _Picuts1 + _PicutsPIDK + _Picuts2\n #_dauCuts = { 'K+': _Kcuts, 'pi+': _Picuts }\n\n _combCuts = \"(APT > %(D0PtLoose)s* MeV)\" \\\n \"& (AP > %(D0P)s* MeV)\" % locals()['config']\n\n _motherCuts = \"(VFASPF(VCHI2PDOF) < %(D0VtxChi2Ndof)s)\" \\\n \"& (BPVVDCHI2 > %(D0FDChi2)s)\" % locals()['config']\n\n\n _Dminus = CombineParticles( DecayDescriptor = DecayDescriptor\n , DaughtersCuts = _dauCuts\n , CombinationCut = _combCuts\n , MotherCut = _motherCuts\n )\n\n return Selection( name+'Sel',\n Algorithm = _Dminus,\n RequiredSelections = inputSel\n )","def x_group_label(\n x_gr: int, cut: int = 20, name_dict: Dict[AnyStr, AnyStr] = names_dict\n) -> AnyStr:\n name = name_dict[str(x_gr)]\n if len(name) > cut:\n return f\"{name[:cut-3]}...\"\n else:\n return name","def assignLabels(self):\n clusters = np.arange(0, len(self.V))[self.V < self.V1] #indexes self.V, volumes_sorted, and oldOrder\n self.clusterV = self.volumes_sorted[clusters]\n clusters = self.oldOrder[clusters] #indexes volumes\n self.clusters = self.nonBI[clusters] #indexes self.vor and self.data\n self.easyLabel = np.zeros(len(self.data))\n self.easyLabel[self.clusters] = 1\n print('Out of ' + str(len(self.data)) + ' particles, ' + str(len(self.clusters)) + ' (' + str(round(len(self.clusters)*100/len(self.data), 3)) +' %) are labelled as cluster particles.')","def __init__(self, data_cfg, pipeline_cfg, root_path, sel_index=0):\n\n super(DetRetailOneDataset, self).__init__(\n data_cfg, pipeline_cfg, root_path, sel_index\n )\n\n self.cat2label = {cat: i for i, cat in enumerate(self.class_names)}\n self.ORI_CLASSES = (\n \"asamu\",\n \"baishikele\",\n \"baokuangli\",\n \"aoliao\",\n \"bingqilinniunai\",\n \"chapai\",\n \"fenda\",\n \"guolicheng\",\n \"haoliyou\",\n \"heweidao\",\n \"hongniu\",\n \"hongniu2\",\n \"hongshaoniurou\",\n \"kafei\",\n \"kaomo_gali\",\n \"kaomo_jiaoyan\",\n \"kaomo_shaokao\",\n \"kaomo_xiangcon\",\n \"kele\",\n \"laotansuancai\",\n \"liaomian\",\n \"lingdukele\",\n \"maidong\",\n \"mangguoxiaolao\",\n \"moliqingcha\",\n \"niunai\",\n \"qinningshui\",\n \"quchenshixiangcao\",\n \"rousongbing\",\n \"suanlafen\",\n \"tangdaren\",\n \"wangzainiunai\",\n \"weic\",\n \"weitanai\",\n \"weitaningmeng\",\n \"wulongcha\",\n \"xuebi\",\n \"xuebi2\",\n \"yingyangkuaixian\",\n \"yuanqishui\",\n \"xuebi-b\",\n \"kebike\",\n \"tangdaren3\",\n \"chacui\",\n \"heweidao2\",\n \"youyanggudong\",\n \"baishikele-2\",\n \"heweidao3\",\n \"yibao\",\n \"kele-b\",\n \"AD\",\n \"jianjiao\",\n \"yezhi\",\n \"libaojian\",\n \"nongfushanquan\",\n \"weitanaiditang\",\n \"ufo\",\n \"zihaiguo\",\n \"nfc\",\n \"yitengyuan\",\n \"xianglaniurou\",\n \"gudasao\",\n \"buding\",\n \"ufo2\",\n \"damaicha\",\n \"chapai2\",\n \"tangdaren2\",\n \"suanlaniurou\",\n \"bingtangxueli\",\n \"weitaningmeng-bottle\",\n \"liziyuan\",\n \"yousuanru\",\n \"rancha-1\",\n \"rancha-2\",\n \"wanglaoji\",\n \"weitanai2\",\n \"qingdaowangzi-1\",\n \"qingdaowangzi-2\",\n \"binghongcha\",\n \"aerbeisi\",\n \"lujikafei\",\n \"kele-b-2\",\n \"anmuxi\",\n \"xianguolao\",\n \"haitai\",\n \"youlemei\",\n \"weiweidounai\",\n \"jindian\",\n \"3jia2\",\n \"meiniye\",\n \"rusuanjunqishui\",\n \"taipingshuda\",\n \"yida\",\n \"haochidian\",\n \"wuhounaicha\",\n \"baicha\",\n \"lingdukele-b\",\n \"jianlibao\",\n \"lujiaoxiang\",\n \"3+2-2\",\n \"luxiangniurou\",\n \"dongpeng\",\n \"dongpeng-b\",\n \"xianxiayuban\",\n \"niudufen\",\n \"zaocanmofang\",\n \"wanglaoji-c\",\n \"mengniu\",\n \"mengniuzaocan\",\n \"guolicheng2\",\n \"daofandian1\",\n \"daofandian2\",\n \"daofandian3\",\n \"daofandian4\",\n \"yingyingquqi\",\n \"lefuqiu\",\n )","def _make_vbenf_label(chain_parts):\n\n # toy label for development: run simple and dijet independently.\n # simple makes Et cuts on two jets. Independently (sharing possible)\n # of jets choosean by simple, the dijet\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n assert scenario.startswith('vbenf')\n args = _args_from_scenario(scenario)\n if not args:\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \n arg_res = [\n re.compile(r'(?P\\d*)(?Pfbet)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pmass)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pet)(?P\\d*)'),\n ]\n\n defaults = {\n 'et': ('101', 'inf'),\n 'mass': ('800', 'inf'),\n 'fbet': ('501', 'inf'),\n }\n\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n and\n (\n []\n simple\n (\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\n )\n combgen\n (\n [(10et, 0eta320)]\n dijet\n (\n [(%(masslo).0fdjmass, 26djdphi)]\n ) \n simple\n (\n [(10et, 0eta320)(20et, 0eta320)]\n )\n )\n )\"\"\" % argvals","def create_labelled_dataset(self):\n\n print(\"-------------------------------------------------------------------\")\n print(\" How to Use the Pole Hull Label Tool\")\n print(\"-------------------------------------------------------------------\")\n print(\"- If a hull is NOT associated to a pole: press the 1 button\")\n print(\"- If a hull IS associated to a pole: press the 2 button\")\n print(\"\\n- If any other key is pressed, the program EXITS\")\n print(\"-------------------------------------------------------------------\")\n\n detector = gate_detector.GateDetector(im_resize=3.0/4)\n\n imgs = []\n labels = []\n directory = os.path.dirname(os.getcwd())\n \n # Get absolute path of all images in the images folder\n for dirpath,_,filenames in os.walk(os.path.join(directory, 'images', 'gate')):\n for f in filenames:\n imgs.append(os.path.abspath(os.path.join(dirpath, f)))\n\n # Get the hulls from the segmented image and run the display and label program for each image\n for img in imgs:\n src = cv.imread(img, 1)\n pre = detector.preprocess(src)\n seg = detector.segment(pre)\n mor = detector.morphological(seg)\n hulls = detector.create_convex_hulls(seg)\n labels += self.display_and_label_hulls(hulls, pre)\n return labels","def setContourLabels(mode='none', ndigits=1):\n odict = {'none':'NONE', 'float':'FLOAT', 'string':'CONLAB'}\n dislin.labdig(ndigits, 'CONTUR')\n dislin.labels(odict[mode], 'CONTUR')","def _add_labels(self):\n coords = self['pore.coords']\n self['pore.front'] = coords[:,0]<(0.1*self._Lx)\n self['pore.back'] = coords[:,0]>(0.9*self._Lx)\n self['pore.left'] = coords[:,1]<(0.1*self._Ly)\n self['pore.right'] = coords[:,1]>(0.9*self._Ly)\n self['pore.bottom'] = coords[:,2]<(0.1*self._Lz)\n self['pore.top'] = coords[:,2]>(0.9*self._Lz)\n bnds = self.pores(labels=['front','back','left','right','bottom','top'])\n self['pore.boundary'] = False\n self['pore.boundary'] = bnds","def plot_data_assemble(self,kwargs_seg, add_mask ,img_name='data.pdf',cutout_text='lensed image',font_size=28):\n mask = self.data_mask\n image = self.raw_image\n picked_data = self.data\n selem = np.ones((add_mask, add_mask))\n img_mask = ndimage.binary_dilation(mask.astype(np.bool), selem)\n fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4, figsize=(19, 10))\n ax1.imshow(image, origin='lower', cmap=\"gist_heat\")\n ax1.set_title('Cutout Image',fontsize =font_size)\n ax1.text(image.shape[0] * 0.2, image.shape[0] * 0.05, cutout_text,size=20, color='white',weight=\"bold\")\n ax1.axis('off')\n segments_deblend_list, xcenter, ycenter, c_index=kwargs_seg\n ax2.imshow(segments_deblend_list, origin='lower')\n for i in range(len(xcenter)):\n ax2.text(xcenter[i] * 1.1, ycenter[i], 'Seg' + repr(i), size=20,color='w',weight=\"bold\")\n ax2.text(image.shape[0] * 0.2, image.shape[0] * 0.9, 'Seg' + repr(c_index) + ' ' + 'in center',\n size=20, color='white',weight=\"bold\")\n ax2.set_title('Segmentations',fontsize =font_size)\n ax2.axis('off')\n ax3.imshow(img_mask+mask, origin='lower',cmap=\"gist_heat\")\n ax3.set_title('Selected pixels',fontsize =font_size)\n ax3.text(image.shape[0] * 0.1, image.shape[0] * 0.05, 'pixels (S/N >' + repr(self.snr) + ')',size=20, color='white',weight=\"bold\")\n ax3.text(image.shape[0] * 0.1, image.shape[0] * 0.9, 'additional pixels', size=20, color='r',weight=\"bold\")\n ax3.axis('off')\n ax4.imshow(picked_data, origin='lower',cmap=\"gist_heat\")\n ax4.set_title('Processed Image',fontsize =font_size)\n ax4.axis('off')\n plt.show()\n fig.savefig(img_name)\n return 0","def BaseLabel(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_BaseLabel(self, *args)","def main():\n # Directory where the DICOM files are being stored (in this\n input_path = './Inputs/valve'\n\n # Original image from the filepath\n img_original = read_image(input_path)\n\n # Image with smoothing applied to reduce noise\n img_smooth = sitk.CurvatureFlow(image1=img_original, timeStep=0.125, numberOfIterations=10)\n\n # Create labels on our smoothed image for cardiac tissue and tissue with blood\n labels_tissue = sitk.BinaryThreshold(image1=img_smooth, lowerThreshold=325, upperThreshold=470, insideValue=1)\n labels_blood = sitk.BinaryThreshold(image1=img_smooth, lowerThreshold=450, upperThreshold=800, insideValue=1, outsideValue=0)\n\n # IMPORTANT STEP: essentially, this is the key to our algorithm. By finding the \"blood\" without cardiac tissue,\n # and then using binary hole filling with a fairly large radius, we are able to label a lot of the mitral valve\n # area without labeling too much of the other cardiac tissue. Thus, THIS is what lets us single out the mitral\n # valve tissue from the rest - all we need is the overlap of the two labels\n labels_tissue_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_tissue, radius=[2] * 3, majorityThreshold=1, backgroundValue=0, foregroundValue=1)\n labels_blood_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_blood, radius=[4] * 3, majorityThreshold=1, backgroundValue=0, foregroundValue=1)\n labels_valve = retrieve_overlap(labels_blood_no_holes, labels_tissue_no_holes)\n labels_valve_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_valve, radius=[2] * 3, majorityThreshold=1, backgroundValue=0, foregroundValue=1)\n labels_valve_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_valve_no_holes, radius=[1] * 3, majorityThreshold=0, backgroundValue=1, foregroundValue=0)\n\n # Fix intensity scaling on our original smoothed image for pretty diagram purposes\n img_smooth = sitk.Cast(sitk.RescaleIntensity(img_smooth), labels_tissue_no_holes.GetPixelID())\n\n # Use a density-based clustering algorithm to attempt to remove as much noise as possible\n labels_valve_filtered = dbscan_filter(labels_valve_no_holes, eps=2, use_z=False)\n labels_valve_filtered = dbscan_filter(labels_valve_filtered, eps=4)\n\n # Find likely start and end points of our image by setting a mininum number of labeled pixels\n start, end = filter_by_label_count(labels_valve_filtered, 10)\n img_smooth = img_smooth[:, :, start:end]\n labels_valve_filtered = labels_valve_filtered[:, :, start:end]\n\n # Remove all values distant from the center of our starting location by taking advantage of kmeans\n df = get_df_from_img(labels_valve_filtered[:, :, 0], dimensions=2)\n x_mid = df['x'].mean()\n y_mid = df['y'].mean()\n df = get_df_from_img(labels_valve_filtered)\n distance_df = df.drop('z', axis=1)\n distance_df['x_dist'] = abs(distance_df['x'] - x_mid)\n distance_df['y_dist'] = abs(distance_df['y'] - y_mid)\n fit = cluster.KMeans(n_clusters=2).fit(distance_df.drop(['x', 'y'], axis=1))\n labels = fit.labels_\n df['label'] = pd.Series(labels)\n counts = df['label'].value_counts().to_dict()\n largest_cluster = max(counts.iterkeys(), key=(lambda key: counts[key]))\n update_img_from_df(df, labels_valve_filtered, keep=largest_cluster)\n\n # Find likely start and end points of our image by setting a mininum number of labeled pixels\n start, end = filter_by_label_count(labels_valve_filtered, 10)\n img_smooth = img_smooth[:, :, start:end]\n labels_valve_filtered = labels_valve_filtered[:, :, start:end]\n\n # Use a segmentation-based clustering algorithm to attempt to find each valve\n label_segments, x_max = kmeans_segment(labels_valve_filtered, use_z=False)\n\n left, right = (label_segments[0], label_segments[1])\n if x_max[0] > x_max[1]:\n left, right = right, left\n\n # Finally, we can simply take the furthest point from the likely start/end points in order to get our annulus\n # this can be done by every z value\n left_points = {'x': [], 'y': [], 'z': []}\n right_points = {'x': [], 'y': [], 'z': []}\n zlen = len(sitk.GetArrayFromImage(left))\n for z in xrange(zlen):\n left_df = get_df_from_img(left[:, :, z], dimensions=2)\n if len(left_df['y']) > 0:\n index = left_df['y'].idxmin()\n row = left_df.iloc[index]\n left_points['x'].append(int(row['x']))\n left_points['y'].append(int(row['y']))\n left_points['z'].append(z)\n\n right_df = get_df_from_img(right[:, :, z], dimensions=2)\n if len(right_df['x']) > 0:\n index = right_df['x'].idxmax()\n row = right_df.iloc[index]\n right_points['x'].append(int(row['x']))\n right_points['y'].append(int(row['y']))\n right_points['z'].append(z)\n\n # These both represent the coordinates of our annulus ring. A simple spline can be used for interpolation between\n # points\n final_left = pd.DataFrame.from_dict(left_points)\n final_right = pd.DataFrame.from_dict(right_points)\n print('Coordinates for one side of the ring')\n print(final_left)\n print('\\n\\nCoordinates for the other side of the ring')\n print(final_right)\n\n final_image = make_empty_img_from_img(left)\n x = left_points['x'] + right_points['x']\n y = left_points['y'] + right_points['y']\n z = left_points['z'] + right_points['z']\n for x, y, z in zip(x, y, z):\n final_image.SetPixel(x, y, z, 1)\n\n show_all(img_smooth, final_image)","def label(self, cfg):\n rep = \"\"\n nl = \"\"\n for node in cfg.nodes:\n rep += nl + \"{}\\tgen={}\\tkill={}\\tout={}\".format(\n node, \n set(self.gen.get(node)),\n set(self.kill.get(node)),\n set(self.out.get(node)))\n nl = \"\\n\"\n return rep","def test_get_dim_label_with_label(self):\n\n dim = self.oecd_datasets['oecd']['dimension']['id'][0]\n dims_df = pyjstat.get_dim_label(self.oecd_datasets['oecd'], dim)\n self.assertTrue(dims_df.iloc[0]['id'] == 'UNR')\n self.assertTrue(dims_df.iloc[-1]['label'] == 'Unemployment rate')","def LabelDisks(self):\n pass","def visualize_detection(self, img, dets, seg, classes=[], thresh=0.6):\n from dataset.cs_labels import labels\n lut = np.zeros((256,3))\n for l in labels:\n if l.trainId<255 and l.trainId>=0:\n lut[l.trainId,:]=list(l.color)\n palette = lut\n # det2seg = {0:6,1:7,2:11,3:12,4:13,5:14,6:15,7:16,8:17,9:18,}\n det2seg = {0:11,1:12,2:13,3:14,4:15,5:16,6:17,7:18,}\n \n import cv2\n import random\n tic = time.time()\n color_white = (255, 255, 255)\n im = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # change to bgr\n yscale, xscale, ch = im.shape\n color = (0,0,128)\n fontFace = cv2.FONT_HERSHEY_PLAIN\n fontScale = .8*(yscale/float(320))\n thickness = 2 if yscale>320 else 1\n idx = np.argsort(dets[:,6],axis=0)[::-1] ## draw nearest first !!\n dets = dets[idx,:]\n for det in dets:\n cls_id = int(det[0])\n bbox = [det[2]*xscale,det[3]*yscale,det[4]*xscale,det[5]*yscale]\n score = det[1]\n distance = det[-1]\n if score > thresh:\n bbox = map(int, bbox)\n color = palette[det2seg[int(det[0])],(2,1,0)]\n cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=thickness)\n text = '%s %.0fm' % (short_class_name[classes[cls_id]], distance*255., )\n textSize, baseLine = cv2.getTextSize(text, fontFace, fontScale, thickness=1)\n cv2.rectangle(im, (bbox[0], bbox[1]-textSize[1]), (bbox[0]+textSize[0], bbox[1]), color=(128,0,0), thickness=-1)\n cv2.putText(im, text, (bbox[0], bbox[1]),\n color=color_white, fontFace=fontFace, fontScale=fontScale, thickness=1)\n disp = im.copy()\n if False: #disp.shape[1]>1000:\n hh, ww, ch = disp.shape\n resized = cv2.resize(disp, (int(round(ww*.92)),int(round(hh*.92))))\n else:\n resized = disp\n cv2.imshow(\"result\", resized)\n # cv2.imwrite(\"data/cityscapes/Results/stuttgart_%06d.png\" % (self.imgidx,), resized)\n # self.imgidx += 1","def kohonen():\n# plb.close('all')\n \n dim = 28*28\n data_range = 255.0\n \n # load in data and labels \n data = np.array(np.loadtxt('data.txt'))\n labels = np.loadtxt('labels.txt')\n\n # select 4 digits \n name = \"Stettler\"\n targetdigits = name2digits(name) # assign the four digits that should be used\n print(targetdigits) # output the digits that were selected\n\n # this selects all data vectors that corresponds to one of the four digits\n data = data[np.logical_or.reduce([labels==x for x in targetdigits]),:]\n \n dy, dx = data.shape\n \n #set the size of the Kohonen map. In this case it will be 6 X 6\n size_k = 6\n \n #set the width of the neighborhood via the width of the gaussian that\n #describes it\n sigma = 2.0\n \n #initialise the centers randomly\n centers = np.random.rand(size_k**2, dim) * data_range\n \n #build a neighborhood matrix\n neighbor = np.arange(size_k**2).reshape((size_k, size_k))\n\n #set the learning rate\n eta = 0.9 # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE\n \n #set the maximal iteration count\n tmax = 5000 # this might or might not work; use your own convergence criterion\n \n #set the random order in which the datapoints should be presented\n i_random = np.arange(tmax) % dy\n np.random.shuffle(i_random)\n \n for t, i in enumerate(i_random):\n som_step(centers, data[i,:],neighbor,eta,sigma)\n\n # for visualization, you can use this:\n for i in range(size_k**2):\n plb.subplot(size_k,size_k,i)\n \n plb.imshow(np.reshape(centers[i,:], [28, 28]),interpolation='bilinear')\n plb.axis('off')\n \n # leave the window open at the end of the loop\n plb.show()\n plb.draw()","def convert_kicad_coor(edif_pt):\n scale = 10\n return [edif_pt[0] * scale, +edif_pt[1] * scale]","def create_dimension_labels(gll, parameters: list):\n dimstr = '[ ' + ' | '.join(parameters) + ' ]'\n gll['MODEL/data'].dims[0].label = 'element'\n gll['MODEL/data'].dims[1].label = dimstr\n gll['MODEL/data'].dims[2].label = 'point'","def add_hdv(self, ROI_id, type_hdv='cum', checkbox_mode=False):\n\n appartenance_contourage = self.dicom_navigation.slice.get_appartenance_contourage(ROI_id)\n \n contourage = Contourage_from_matrice(appartenance_contourage, ROI_id) # On crée un objet 'Contourage_from_matrice' à partir du de la matrice booléenne\n\n dose_matrix = self.dicom_navigation.slice.get_dose_matrix()\n\n # Cas ou on ajoute pour la premiere fois un contourage\n if dose_matrix is None:\n return\n \n doses = Doses_from_matrice(dose_matrix) # On crée un objet 'Doses_from_matrice' à partir de la matrice de doses mise à jour\n\n var = tk.StringVar() # À VENIR... VARIABLE D'ÉTAT QUI INDIQUE SI ON EST EN MODE 'VOLUME RELATF' OU 'VOLUME ABSOLU'. CODÉ EN DUR POUR LE MOMENT\n var.set('r')\n\n self.ddc = Doses_dans_contourage(doses, contourage) # Triage des doses qui sont dans le contourage.\n\n if self.ddc.dose_max == 0: # Si la dose max est 0, on sait qu'on est à l'extérieur de la zone réduite. *** \n return\n\n if not ROI_id in self.dict_graph: \n self.dict_graph[ROI_id] = {} \n self.dict_plot[ROI_id] = {} \n self.dict_doses_max[ROI_id] = {} \n if self.dicom_navigation.var_etat_abs_rel.get() == 'a':\n self.dict_volumes_max[ROI_id] = {} \n\n self.dict_doses_max[ROI_id][type_hdv] = self.ddc.dose_max\n\n ###\n\n if self.dicom_navigation.var_etat_abs_rel.get() == 'r': # si on est en mode 'volume relatif', le range des axes sera définit différemment\n facteur = 100.0/self.ddc.nb_voxels # comme l'instance 'axe_volume' créée par les classes hdv_cumulatif et hdv_differentiel contient des données en NOMBRE DE VOXELS\n # (et non en pourcentage ou en volume réel), il faut multiplier ces données par le facteur de conversion approprié (il dépend\n # de si l'on est en mode 'relatf' ou 'absolu').\n\n if self.dicom_navigation.var_etat_abs_rel.get() == 'a': # si on est en mode 'volume absolu'.\n facteur = self.ddc.v_voxel\n self.dict_volumes_max[ROI_id][type_hdv] = self.ddc.v_voxel * self.ddc.nb_voxels \n self.y_lim = get_max_2D_dic(self.dict_volumes_max)\n\n ###\n\n if type_hdv == 'cum':\n hdv = HDV_cumulatif(self.ddc, 100)\n\n if type_hdv == 'diff':\n hdv = HDV_differentiel(self.ddc, 50)\n\n\n self.dict_graph[ROI_id][type_hdv] = hdv\n self.dict_plot[ROI_id][type_hdv], = self.fig.plot(hdv.axe_doses, facteur * hdv.axe_volume)\n\n ###\n\n self.x_lim = get_max_2D_dic(self.dict_doses_max) \n\n self.fig.set_xlim([0, 1.02*self.x_lim]) # dimension de l'axe des x\n self.fig.set_ylim([0, 1.02*self.y_lim]) # dimension de l'axe des y\n\n # Contraintes\n if self.got_contraintes and type_hdv == 'cum': # 'got_contraintes' SERA INITALISÉE À 'TRUE' LORSQUE L'ON AURA RÉCUPÉRÉ LE FICHIER DE CONTRAINTES\n self.dicom_navigation.get_dicom_contraintes().verifier_contraintes_sur_une_ROI(ROI_id)\n\n # Modifier\n if checkbox_mode:\n self.refresh_HDV()","def instance_label(task, pred, k=15, n_iters=1, dist_thresh=5, watershed=False):\n mask = pred\n\n # noise removal\n if k > 1 and n_iters > 0:\n kernel = np.ones((k, k), np.uint8)\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,\n iterations=n_iters)\n\n if watershed:\n from clab.live import filters\n mask = filters.watershed_filter(mask, dist_thresh=dist_thresh)\n\n mask = mask.astype(np.uint8)\n n_ccs, cc_labels = cv2.connectedComponents(mask, connectivity=4)\n return cc_labels","def old_ideal_label(I):\n a, c, d = ideal_HNF(I)\n return \"%s.%s.%s\" % (a * d, c, d)","def setContourLabelString(text=''):\n dislin.conlab(text)","def label(cmd):\r\n cmd = cmd.replace('make][.DP', 'make1][.NP')\r\n cmd = cmd.replace('make][.SC', 'make2][.SC')\r\n cmd = re.sub('(draw.*)one','\\\\1one1',cmd)\r\n cmd = re.sub('(make1.*)one','\\\\1one1',cmd)\r\n cmd = re.sub('(make2.*)one','\\\\1one2',cmd)\r\n cmd = re.sub('(move.*)one','\\\\1one2',cmd)\r\n cmd = re.sub('(hide.*)one','\\\\1one2',cmd)\r\n cmd = '[result ' + cmd + ']' #dummy function for plop\r\n return cmd","def label(gt_dataset, volume_dim, voxel_dim, labeling_params):\n labeled_volumes = dict()\n labeled_cells = dict()\n #Use global density and reduce the size of gt_dataset here\n global_density = labeling_params[\"global_density\"]\n gt_dataset = {k: v for k,v in gt_dataset.items() if random_sample() < global_density}\n #Label in the order specified in the configuration\n layers = sorted(labeling_params.keys())\n #Remove global_density\n layers.remove(\"global_density\")\n for layer in layers:\n print \"Labeling {}\".format(layer)\n fluorophore = labeling_params[layer]['fluorophore']\n volume, cells = brainbow(gt_dataset, volume_dim, voxel_dim, **labeling_params[layer])\n if fluorophore in labeled_volumes:\n labeled_volumes[fluorophore] += volume\n labeled_cells[fluorophore] |= cells\n else:\n labeled_volumes[fluorophore] = volume\n labeled_cells[fluorophore] = cells\n return labeled_volumes, labeled_cells","def make_data_label(self):\n data_label = \"\"\n if self.detector is not None:\n data_label += \"%s \"%self.detector\n if self.selection is not None:\n data_label += \"%s Event Selection\"%self.selection\n if data_label == \"\":\n data_label = \"IceCube\"\n return data_label","def write_label(self, contig_name, width, height, font, title_width, upper_left, vertical_label,\n strand, canvas, horizontal_centering=False, center_vertical=False, chop_text=True,\n label_color=(50, 50, 50, 255)):\n upper_left = list(upper_left) # to make it mutable\n shortened = contig_name[-title_width:] # max length 18. Last characters are most unique\n txt = Image.new('RGBA', (width, height))#, color=(0,0,0,50))\n txt_canvas = ImageDraw.Draw(txt)\n text_width = txt_canvas.textsize(shortened, font)[0]\n if not chop_text and text_width > width:\n txt = Image.new('RGBA', (text_width, height)) # TODO performance around txt_canvas\n txt_canvas = ImageDraw.Draw(txt)\n if center_vertical or vertical_label: # Large labels are centered in the column to look nice,\n # rotation indicates strand in big text\n vertically_centered = (height // 2) - multi_line_height(font, shortened, txt)//2\n else: # Place label at the beginning of gene based on strand\n vertically_centered = height - multi_line_height(font, shortened, txt) # bottom\n if strand == \"+\":\n vertically_centered = 0 # top of the box\n txt_canvas.multiline_text((0, max(0, vertically_centered)), shortened, font=font,\n fill=label_color)\n if vertical_label:\n rotation_direction = 90 if strand == '-' else -90\n txt = txt.rotate(rotation_direction, expand=True)\n upper_left[1] += -4 if strand == '-' else 4\n if horizontal_centering:\n margin = width - text_width\n upper_left[0] += margin // 2\n canvas.paste(txt, (upper_left[0], upper_left[1]), txt)","def convert_medical_decathlon_labels(mask, cohort, keep_all_label=False):\n label = 12*[0]\n if keep_all_label:\n label += [0,0]\n \n if cohort == 'liver':\n mask[mask == 2] = 6\n mask[mask == 1] = 6\n label[6] = 1\n \n elif cohort == 'pancreas':\n mask[mask == 2] = 11\n mask[mask == 1] = 11\n label[11] = 1\n\n elif cohort == 'spleen':\n mask[mask != 1] = 0\n label[1] = 1 \n\n elif cohort == 'hepatic':\n mask[mask == 2] = 0\n mask[mask == 1] = 0\n \n return mask, label","def get_labels(self):\n if self.option == \"term\":\n return ['platform characteristics', 'atmospheric winds', 'radio wave','weather events', 'geomagnetism', 'atmospheric electricity','microwave', 'atmospheric temperature', 'atmospheric water vapor','atmospheric pressure', 'aerosols', 'atmospheric radiation','atmospheric chemistry', 'precipitation', 'sensor characteristics','radar', 'infrared wavelengths', 'visible wavelengths','weather/climate advisories', 'clouds', 'lidar', 'ocean optics','ultraviolet wavelengths', 'cryospheric indicators','land use/land cover', 'topography', 'surface thermal properties','spectral/engineering', 'soils', 'snow/ice', 'geothermal dynamics','natural hazards', 'surface water', 'vegetation','land surface/agriculture indicators','gravity/gravitational field', 'marine advisories', 'altitude','water quality/water chemistry', 'ocean temperature','ocean winds', 'atmospheric/ocean indicators', 'coastal processes','erosion/sedimentation', 'marine sediments', 'ocean chemistry','salinity/density', 'ocean color', 'aquatic ecosystems','vegetation2', 'landscape', 'cloud properties','surface radiative properties', 'geodetics','agricultural plant science', 'forest science','ecological dynamics', 'environmental impacts', 'sustainability','boundaries', 'ecosystems', 'air quality', 'population','infrastructure', 'environmental governance/management','public health', 'economic resources', 'socioeconomics','environmental vulnerability index (evi)', 'human settlements','agricultural chemicals', 'animal science','habitat conversion/fragmentation', 'animals/vertebrates','earth gases/liquids', 'rocks/minerals/crystals','social behavior', 'ground water', 'frozen ground','terrestrial hydrosphere indicators', 'ocean heat budget','biospheric indicators', 'animal commodities', 'fungi', 'plants','carbon flux', 'geomorphic landforms/processes','paleoclimate indicators', 'ocean circulation', 'sea ice','geochemistry', 'visualization/image processing','subsetting/supersetting', 'transformation/conversion','ocean pressure', 'glaciers/ice sheets', 'protists','solar activity', 'sun-earth interactions','sea surface topography', 'solar energetic particle properties','solar energetic particle flux','ionosphere/magnetosphere dynamics']\n elif self.option == \"mostdepth\":\n return ['flight data logs', 'turbulence', 'radio wave flux', 'lightning', 'magnetic field', 'atmospheric conductivity', 'electric field', 'data synchronization time', 'brightness temperature', 'vertical profiles', 'water vapor profiles', 'air temperature', 'upper level winds', 'atmospheric pressure measurements', 'upper air temperature', 'humidity', 'dew point temperature', 'aerosol particle properties', 'emissivity', 'trace gases/trace species', 'liquid precipitation', 'cloud liquid water/ice', 'microwave radiance', 'sensor counts', 'total pressure', 'airspeed/ground speed', 'total temperature', 'static pressure', 'wind speed', 'wind direction', 'radar reflectivity', 'doppler velocity', 'infrared imagery', 'visible imagery', 'water vapor', 'vertical wind velocity/speed', 'aerosol backscatter', 'weather forecast', 'tropical cyclones', 'visible radiance', 'infrared radiance', 'total precipitable water', 'boundary layer temperature', 'atmospheric temperature indices', 'cloud height', 'flight level winds', 'cloud droplet distribution', 'cloud droplet concentration/size', 'cloud condensation nuclei', 'cloud microphysics', 'hydrometeors', 'ozone', 'wind profiles', 'cloud base temperature', 'cloud base height', 'liquid water equivalent', 'solar radiation', 'planetary boundary layer height', 'surface winds', 'precipitation amount', 'precipitation rate', 'surface pressure', 'rain', 'cloud optical depth/thickness', 'aerosol extinction', 'aerosol optical depth/thickness', 'cirrus cloud systems', 'lidar depolarization ratio', 'radar backscatter', 'radar cross-section', 'return power', 'mean radial velocity', 'radiance', 'air quality', 'climate advisories', 'atmospheric emitted radiation', 'optical depth/thickness', 'surface temperature', 'ultraviolet flux', 'spectrum width', 'microwave imagery', 'lidar backscatter', 'relative humidity', 'u/v wind components', 'wind speed/wind direction', 'radar imagery', 'snow depth', 'land use/land cover classification', 'digital elevation/terrain model (dem)', 'snow', 'droplet size', 'droplet concentration/size', 'drizzle', 'precipitation anomalies', 'snow water equivalent', 'solid precipitation', 'total surface precipitation rate', 'particle size distribution', 'skin temperature', 'attitude characteristics', 'land surface temperature', 'hail', 'reflectance', 'soil moisture/water content', 'soil temperature', 'soil bulk density', 'surface roughness', 'present weather', 'snow density', 'ambient temperature', 'aerosol forward scatter', 'floods', 'snow cover', 'sigma naught', 'precipitable water', 'stage height', 'rivers/streams', 'shortwave radiation', 'photosynthetically active radiation', 'longwave radiation', 'net radiation', 'hourly precipitation amount', '24 hour precipitation amount', 'soil moisture', 'satellite orbits/revolution', 'sea surface temperature', 'heat flux', 'latent heat flux', 'cloud fraction', '3 and 6 hour precipitation amount', 'geopotential height', 'particulate matter', 'particle images', 'water vapor indices', 'horizontal wind velocity/speed', 'electrical conductivity', 'dissolved carbon dioxide', 'hurricanes', 'tropical cyclone track', 'convective clouds/systems (observed/analyzed)', 'cloud top height', 'viewing geometry', 'temperature profiles', 'vertical wind shear', 'wind shear', 'carbon monoxide', 'sea level pressure', 'water vapor tendency', 'potential temperature', 'angstrom exponent', 'ultraviolet radiation', 'solar irradiance', 'scattering', 'absorption', 'water vapor mixing ratio profiles', 'sea surface temperature indices', 'extreme eastern tropical pacific sst', 'sedimentation', 'erosion', 'sediment transport', 'sediments', 'tropopause', 'ocean chemistry', 'ocean optics', 'ocean temperature', 'salinity/density', 'pigments', 'ocean color', 'attenuation/transmission', 'inorganic carbon', 'organic carbon', 'photosynthetically available radiation', 'chlorophyll', 'optical depth', 'fluorescence', 'vegetation index', 'gelbstoff', 'phytoplankton', 'vegetation index2', 'cloud precipitable water', 'landscape ecology', 'ultraviolet radiance', 'cloud ceiling', 'aerosol radiance', 'carbonaceous aerosols', 'dust/ash/smoke', 'nitrate particles', 'organic particles', 'sulfate particles', 'radiative flux', 'transmittance', 'atmospheric stability', 'cloud asymmetry', 'cloud frequency', 'cloud top pressure', 'cloud top temperature', 'cloud vertical distribution', 'cloud emissivity', 'cloud radiative forcing', 'cloud reflectance', 'rain storms', 'reflected infrared', 'thermal infrared', 'incoming solar radiation', 'clouds', 'cloud properties', 'cloud types', 'orbital characteristics', 'sensor characteristics', 'maximum/minimum temperature', 'condensation', 'platform characteristics', 'geolocation', 'geodetics', 'coordinate reference system', 'aerosols', 'topographical relief maps', 'terrain elevation', 'normalized difference vegetation index (ndvi)', 'infrared flux', 'visible flux', 'albedo', 'land use/land cover', 'topography', 'lidar', 'lidar waveform', 'plant phenology', 'vegetation cover', 'crop/plant yields', 'land use classes', 'landscape patterns', 'forest harvesting and engineering', 'forest management', 'total surface water', 'agricultural plant science', 'photosynthesis', 'primary production', 'leaf characteristics', 'evapotranspiration', 'fire occurrence', 'surface thermal properties', 'canopy characteristics', 'evergreen vegetation', 'crown', 'deciduous vegetation', 'anisotropy', 'fire ecology', 'biomass burning', 'wildfires', 'topographical relief', 'burned area', 'surface radiative properties', 'environmental sustainability', 'boundaries', 'anthropogenic/human influenced ecosystems', 'emissions', 'sulfur dioxide', 'population', 'infrastructure', 'environmental assessments', 'public health', 'conservation', 'agriculture production', 'administrative divisions', 'economic resources', 'socioeconomics', 'lake/pond', 'rivers/stream', 'political divisions', 'environmental vulnerability index (evi)', 'ecosystems', 'urban areas', 'sustainability', 'treaty agreements/results', 'human settlements', 'population estimates', 'nitrogen dioxide', 'cropland', 'pasture', 'particulates', 'cyclones', 'mortality', 'environmental impacts', 'droughts', 'earthquakes', 'population distribution', 'fertilizers', 'animal manure and waste', 'urbanization/urban sprawl', 'landslides', 'avalanche', 'urban lands', 'mangroves', 'volcanic eruptions', 'pesticides', 'population size', 'population density', 'lakes/reservoirs', 'surface water', 'rural areas', 'infant mortality rates', 'amphibians', 'mammals', 'carbon', 'sulfur oxides', 'methane', 'non-methane hydrocarbons/volatile organic compounds', 'nitrogen oxides', 'natural gas', 'coal', 'coastal elevation', 'biodiversity functions', 'nuclear radiation exposure', 'radiation exposure', 'poverty levels', 'malnutrition', 'wetlands', 'sea level rise', 'vulnerability levels/index', 'ground water', 'snow/ice', 'electricity', 'energy production/use', 'sustainable development', 'deforestation', 'household income', 'discharge/flow', 'hydropattern', 'nitrogen', 'phosphorus', 'carbon dioxide', 'alpine/tundra', 'forests', 'vegetation', 'permafrost', 'nutrients', 'plant characteristics', 'leaf area index (lai)', 'soil gas/air', 'ammonia', 'nitrous oxide', 'ecosystem functions', 'litter characteristics', 'soil chemistry', 'soil respiration', 'active layer', 'soil depth', 'cation exchange capacity', 'organic matter', 'soil porosity', 'soil texture', 'permafrost melt', 'land subsidence', 'freeze/thaw', 'surface water features', 'chlorinated hydrocarbons', 'methyl bromide', 'methyl chloride', 'molecular hydrogen', 'sulfur compounds', 'fire models', 'biomass', 'dominant species', 'vegetation species', 'sulfur', 'tree rings', 'soil classification', 'heat index', 'sea ice concentration', 'ocean heat budget', 'reforestation', 'even-toed ungulates', 'species recruitment', 'population dynamics', 'range changes', 'topographic effects', 'land resources', 'river ice depth/extent', 'snow melt', 'river ice', 'animal commodities', 'animal ecology and behavior', 'phenological changes', 'water depth', 'inundation', 'forest fire science', 'biogeochemical cycles', 'radiative forcing', 'soil heat budget', 'drainage', 'respiration rate', 'river/lake ice breakup', 'river/lake ice freeze', 'reclamation/revegetation/restoration', 'permafrost temperature', 'indigenous/native species', 'fire dynamics', 'lichens', 'plants', 'plant succession', 'carbon flux', 'coastal', 'salt marsh', 'degradation', 'altitude', 'carbon and hydrocarbon compounds', 'halocarbons and halogens', 'forest composition/vegetation structure', 'water vapor indicators', 'barometric altitude', 'atmospheric water vapor', 'terrestrial ecosystems', 'volatile organic compounds', 'boundary layer winds', 'forest fire danger index', 'periglacial processes', 'landscape processes', 'evaporation', 'soil horizons/profile', 'shrubland/scrub', 'soil ph', 'soils', 'soil water holding capacity', 'community structure', 'pingo', 'soil color', 'virtual temperature', 'formaldehyde', 'hydroxyl', 'photolysis rates', 'cloud dynamics', 'nitric oxide', 'molecular oxygen', 'smog', 'peroxyacyl nitrate', 'hydrogen compounds', 'nitrogen compounds', 'oxygen compounds', 'stable isotopes', 'chemical composition', 'actinic flux', 'tropospheric ozone', 'fossil fuel burning', 'industrial emissions', 'denitrification rate', 'sunshine', 'runoff', 'soil structure', 'mosses/hornworts/liverworts', 'peatlands', 'hydraulic conductivity', 'snow/ice temperature', 'vegetation water content', 'discharge', 'chlorophyll concentrations', 'outgoing longwave radiation', 'geomorphic landforms/processes', 'soil compaction', 'soil impedance', 'canopy transmittance', 'water table', 'decomposition', 'water temperature', 'dissolved gases', 'total dissolved solids', 'agricultural expansion', 'forest science', 'pressure tendency', 'visibility', 'biomass dynamics', 'agricultural lands', 'grasslands', 'savannas', 'grazing dynamics/plant herbivory', 'herbivory', 'paleoclimate reconstructions', 'drought indices', 'fire weather index', 'animal yields', 'multivariate enso index', 'dissolved solids', 'ocean currents', 'salinity', 'coastal processes', 'atmospheric pressure', 'afforestation/reforestation', 'fresh water river discharge', 'surface water chemistry', 'drainage basins', 'resource development site', 'dunes', 'flood plain', 'endangered species', 'precipitation indices', 'temperature indices', 'forest yields', 'stratigraphic sequence', 'freeze/frost', 'frost', 'hydrogen cyanide', 'land management', 'nutrient cycling', 'industrialization', 'suspended solids', 'deserts', 'weathering', 'gas flaring', 'atmospheric temperature', 'ice extent', 'fraction of absorbed photosynthetically active radiation (fapar)', 'marshes', 'swamps', 'lake ice', 'atmospheric winds', 'watershed characteristics', 'transportation', 'soil rooting depth', 'isotopes', 'cultural features', 'consumer behavior', 'boundary surveys', 'aquifers', 'land productivity', 'water quality/water chemistry', 'sediment composition', 'dissolved oxygen', 'surface water processes/measurements', 'turbidity', 'conductivity', 'ph', 'calcium', 'magnesium', 'potassium', 'micronutrients/trace elements', 'social behavior', 'sulfate', 'sediment chemistry', 'biogeochemical processes', 'water ion concentrations', 'cropping systems', 'percolation', 'groundwater chemistry', 'reforestation/revegetation', 'species/population interactions', 'soil infiltration', 'alkalinity', 'soil fertility', 'phosphorous compounds', 'radioisotopes', 'cooling degree days', 'angiosperms (flowering plants)', 'glacial landforms', 'glacial processes', 'contour maps', 'estuaries', 'methane production/use', 'natural gas production/use', 'petroleum production/use', 'visualization/image processing', 'subsetting/supersetting', 'transformation/conversion', 'forest mensuration', 'acid deposition', 'differential pressure', 'precipitation', 'marine ecosystems', 'consumption rates', 'radio wave', 'soil organic carbon (soc)', 'soil erosion', 'halocarbons', 'trace elements/trace metals', 'biomass energy production/use', 'riparian wetlands', 'soil consistence', 'snow stratigraphy', 'thermal conductivity', 'estuary', 'tidal height', 'plant diseases/disorders/pests', 'layered precipitable water', 'atmospheric chemistry', 'water vapor concentration profiles', 'specific humidity', 'total runoff', 'pressure thickness', 'wind stress', 'atmospheric heating', 'conduction', 'hydrogen chloride', 'nitric acid', 'radar', 'land surface/agriculture indicators', 'satellite soil moisture index', 'chlorine nitrate', 'chlorofluorocarbons', 'dinitrogen pentoxide', 'antenna temperature', 'glaciers', 'ice sheets', 'dimethyl sulfide', 'potential vorticity', 'ice fraction', 'atmospheric radiation', 'runoff rate', 'temperature tendency', 'wind dynamics', 'wind direction tendency', 'base flow', 'bromine monoxide', 'chlorine monoxide', 'methyl cyanide', 'hypochlorous acid', 'methanol', 'hydroperoxy', 'cloud base pressure', 'temperature anomalies', 'nitrate', 'ocean mixed layer', 'precipitation trends', 'temperature trends', 'convection', 'ground ice', 'oxygen', 'phosphate', 'solar induced fluorescence', 'chlorine dioxide', 'sun-earth interactions', 'uv aerosol index', 'volcanic activity', 'potential evapotranspiration', 'ultraviolet wavelengths', 'ice temperature', 'sea surface skin temperature', 'sea surface height', 'sublimation', 'convective surface precipitation rate', 'hydrogen fluoride', 'airglow', 'energy deposition', 'x-ray flux', 'electron flux', 'proton flux', 'magnetic fields/magnetic currents']\n else:\n return ['platform characteristics', 'atmospheric winds','radio wave', 'weather events', 'geomagnetism','atmospheric electricity', 'microwave', 'atmospheric temperature','atmospheric water vapor', 'atmospheric pressure', 'aerosols','atmospheric radiation', 'atmospheric chemistry', 'precipitation','sensor characteristics', 'radar', 'infrared wavelengths','visible wavelengths', 'weather/climate advisories', 'clouds','lidar', 'ocean optics', 'ultraviolet wavelengths','cryospheric indicators', 'land use/land cover', 'topography','surface thermal properties', 'spectral/engineering', 'soils','snow/ice', 'geothermal dynamics', 'natural hazards','surface water', 'vegetation','land surface/agriculture indicators','gravity/gravitational field', 'marine advisories', 'altitude','water quality/water chemistry', 'ocean temperature','ocean winds', 'atmospheric/ocean indicators', 'coastal processes','erosion/sedimentation', 'marine sediments', 'ocean chemistry','salinity/density', 'ocean color', 'aquatic ecosystems','vegetation2', 'landscape', 'cloud properties','surface radiative properties', 'geodetics','agricultural plant science', 'forest science','ecological dynamics', 'environmental impacts', 'sustainability','boundaries', 'ecosystems', 'air quality', 'population','infrastructure', 'environmental governance/management','public health', 'economic resources', 'socioeconomics','environmental vulnerability index (evi)', 'human settlements','agricultural chemicals', 'animal science','habitat conversion/fragmentation', 'animals/vertebrates','earth gases/liquids', 'rocks/minerals/crystals','social behavior', 'ground water', 'frozen ground','terrestrial hydrosphere indicators', 'ocean heat budget','biospheric indicators', 'animal commodities', 'fungi', 'plants','carbon flux', 'geomorphic landforms/processes','paleoclimate indicators', 'ocean circulation', 'sea ice','geochemistry', 'visualization/image processing','subsetting/supersetting', 'transformation/conversion','ocean pressure', 'glaciers/ice sheets', 'protists','solar activity', 'sun-earth interactions','sea surface topography', 'solar energetic particle properties','solar energetic particle flux','ionosphere/magnetosphere dynamics','flight data logs','wind dynamics', 'radio wave flux', 'lightning', 'magnetic field','atmospheric conductivity', 'electric field','data synchronization time', 'brightness temperature','upper air temperature', 'water vapor profiles','surface temperature', 'upper level winds','atmospheric pressure measurements', 'water vapor indicators','aerosol particle properties', 'emissivity','trace gases/trace species', 'liquid precipitation','cloud microphysics', 'microwave radiance', 'sensor counts','total pressure', 'airspeed/ground speed', 'total temperature','static pressure', 'humidity', 'radar reflectivity','doppler velocity', 'infrared imagery', 'visible imagery','aerosol backscatter', 'weather forecast', 'tropical cyclones','visible radiance', 'infrared radiance','atmospheric temperature indices', 'cloud droplet distribution','cloud condensation nuclei', 'hydrometeors', 'oxygen compounds','wind profiles', 'liquid water equivalent', 'solar radiation','planetary boundary layer height', 'surface winds','precipitation amount', 'precipitation rate', 'surface pressure','aerosol extinction', 'aerosol optical depth/thickness','tropospheric/high-level clouds (observed/analyzed)','lidar depolarization ratio', 'radar backscatter','radar cross-section', 'return power', 'radial velocity','radiance', 'climate advisories', 'atmospheric emitted radiation','optical depth/thickness', 'ultraviolet flux', 'spectrum width','microwave imagery', 'lidar backscatter', 'radar imagery','snow depth', 'land use/land cover classification','terrain elevation', 'solid precipitation', 'droplet size','droplet concentration/size', 'precipitation anomalies','snow water equivalent', 'total surface precipitation rate','skin temperature', 'water vapor', 'attitude characteristics','land surface temperature', 'reflectance','soil moisture/water content', 'soil temperature','soil bulk density', 'surface roughness', 'present weather','snow density', 'geothermal temperature','aerosol forward scatter', 'floods', 'snow cover', 'sigma naught','precipitable water', 'surface water processes/measurements','surface water features', 'shortwave radiation','photosynthetically active radiation', 'longwave radiation','net radiation', 'flight level winds', 'soil moisture','satellite orbits/revolution', 'heat flux','precipitation profiles', 'geopotential height','particulate matter', 'particle images', 'water vapor indices','electrical conductivity', 'gases', 'sea surface temperature','convective clouds/systems (observed/analyzed)','viewing geometry', 'wind shear','carbon and hydrocarbon compounds', 'sea level pressure','water vapor processes', 'ultraviolet radiation','solar irradiance', 'scattering', 'absorption','sea surface temperature indices', 'sedimentation', 'erosion','sediment transport', 'sediments', 'tropopause', 'nan', 'pigments','attenuation/transmission', 'inorganic carbon', 'organic carbon','photosynthetically available radiation', 'chlorophyll','optical depth', 'fluorescence', 'vegetation index', 'gelbstoff','plankton', 'vegetation index2', 'landscape ecology','ultraviolet radiance', 'aerosol radiance','carbonaceous aerosols', 'dust/ash/smoke', 'nitrate particles','organic particles', 'sulfate particles', 'radiative flux','transmittance', 'atmospheric stability','cloud radiative transfer', 'rain storms', 'reflected infrared','thermal infrared', 'incoming solar radiation', 'cloud types','orbital characteristics', 'geolocation','coordinate reference system', 'infrared flux', 'visible flux','albedo', 'lidar waveform', 'plant phenology', 'vegetation cover','crop/plant yields', 'land use classes', 'landscape patterns','forest harvesting and engineering', 'forest management','ecosystem functions', 'leaf characteristics', 'fire ecology','total surface water', 'primary production', 'photosynthesis','canopy characteristics', 'evergreen vegetation', 'crown','deciduous vegetation', 'anisotropy', 'biomass burning','wildfires', 'topographical relief','environmental sustainability','anthropogenic/human influenced ecosystems', 'emissions','sulfur compounds', 'environmental assessments', 'conservation','agriculture production', 'administrative divisions','freshwater ecosystems', 'political divisions', 'urban areas','treaty agreements/results', 'population estimates','nitrogen compounds', 'particulates', 'mortality', 'droughts','earthquakes', 'population distribution', 'fertilizers','animal manure and waste', 'urbanization/urban sprawl','landslides', 'avalanche', 'mangroves', 'volcanic eruptions','pesticides', 'population size', 'population density','rural areas', 'amphibians', 'mammals', 'carbon', 'sulfur oxides','land management', 'natural gas', 'sedimentary rocks','coastal elevation', 'community dynamics','nuclear radiation exposure', 'radiation exposure','poverty levels', 'malnutrition', 'sea level rise','vulnerability levels/index', 'electricity','energy production/use', 'sustainable development','deforestation', 'household income', 'nitrogen', 'phosphorus','terrestrial ecosystems', 'permafrost', 'nutrients','plant characteristics', 'soil gas/air', 'litter characteristics','soil chemistry', 'soil respiration', 'active layer', 'soil depth','cation exchange capacity', 'organic matter', 'soil porosity','soil texture', 'permafrost melt','ground water processes/measurements', 'freeze/thaw','halocarbons and halogens', 'hydrogen compounds', 'biomass','dominant species', 'vegetation species', 'sulfur', 'tree rings','soil classification', 'sea ice concentration', 'reforestation','species/population interactions', 'range changes','topographic effects', 'land resources', 'river ice depth/extent','snow melt', 'river ice', 'animal ecology and behavior','phenological changes', 'forest fire science', 'radiative forcing','soil heat budget', 'river/lake ice breakup','river/lake ice freeze', 'reclamation/revegetation/restoration','lichens', 'marine ecosystems', 'coastal landforms', 'degradation','forest composition/vegetation structure', 'barometric altitude','volatile organic compounds', 'forest fire danger index','periglacial processes', 'landscape processes','soil horizons/profile', 'soil ph', 'soil water holding capacity','fluvial landforms', 'soil color', 'glacial processes','photochemistry', 'cloud dynamics', 'nitrogen oxides', 'smog','chemical composition', 'actinic flux', 'tropospheric ozone','fossil fuel burning', 'industrial emissions','denitrification rate', 'sunshine', 'soil structure','mosses/hornworts/liverworts', 'hydraulic conductivity','snow/ice temperature', 'water characteristics','outgoing longwave radiation', 'soil compaction', 'soil impedance','canopy transmittance', 'ground water features', 'solids','agricultural expansion', 'pressure tendency', 'visibility','herbivory', 'paleoclimate reconstructions', 'drought indices','fire weather index', 'animal yields', 'teleconnections','carbon dioxide', 'dissolved solids', 'ocean currents', 'salinity','afforestation/reforestation', 'fresh water river discharge','surface water chemistry', 'aeolian landforms','precipitation indices', 'temperature indices', 'forest yields','stratigraphic sequence', 'freeze/frost', 'frost','industrialization', 'ice core records', 'suspended solids','weathering', 'gas flaring', 'ice extent', 'biogeochemical cycles','lake ice', 'isotopes', 'watershed characteristics','transportation', 'soil rooting depth', 'geochemical properties','carbon monoxide', 'cultural features', 'consumer behavior','boundary surveys', 'land productivity', 'sediment composition','calcium', 'magnesium', 'potassium','micronutrients/trace elements', 'sediment chemistry','biogeochemical processes', 'cropping systems','groundwater chemistry', 'reforestation/revegetation','soil infiltration', 'soil fertility','angiosperms (flowering plants)', 'glacial landforms','forest mensuration', 'acid deposition', 'differential pressure','soil erosion', 'trace elements/trace metals', 'soil consistence','snow stratigraphy', 'thermal conductivity', 'estuaries','tidal height', 'plant diseases/disorders/pests','pressure thickness', 'atmospheric heating', 'conduction','evaporation', 'turbulence', 'wind stress','satellite soil moisture index', 'antenna temperature', 'glaciers','ice sheets', 'nitrate', 'ocean mixed layer','precipitation indicators', 'temperature indicators', 'ground ice','alkalinity', 'dissolved gases', 'oxygen', 'ph', 'phosphate','solar induced fluorescence', 'volcanic activity','ice temperature', 'sea surface height', 'airglow','energy deposition', 'x-ray flux', 'electron flux', 'proton flux','magnetic fields/magnetic currents', 'vertical profiles','air temperature', 'dew point temperature','cloud liquid water/ice', 'wind speed', 'wind direction','vertical wind velocity/speed', 'total precipitable water','boundary layer temperature', 'cloud height','cloud droplet concentration/size', 'ozone','cloud base temperature', 'cloud base height', 'rain','cloud optical depth/thickness', 'cirrus/systems','mean radial velocity', 'relative humidity', 'u/v wind components','wind speed/wind direction','digital elevation/terrain model (dem)', 'snow', 'drizzle','particle size distribution', 'hail', 'ambient temperature','stage height', 'rivers/streams', 'hourly precipitation amount','24 hour precipitation amount', 'latent heat flux','cloud fraction', '3 and 6 hour precipitation amount','horizontal wind velocity/speed', 'dissolved carbon dioxide','hurricanes', 'tropical cyclone track', 'cloud top height','temperature profiles', 'vertical wind shear','water vapor tendency', 'potential temperature','angstrom exponent', 'water vapor mixing ratio profiles','extreme eastern tropical pacific sst', 'phytoplankton','cloud precipitable water', 'cloud asymmetry', 'cloud ceiling','cloud frequency', 'cloud top pressure', 'cloud top temperature','cloud vertical distribution', 'cloud emissivity','cloud radiative forcing', 'cloud reflectance','maximum/minimum temperature', 'condensation','topographical relief maps', 'evapotranspiration','fire occurrence', 'burned area', 'sulfur dioxide', 'lake/pond','rivers/stream', 'nitrogen dioxide', 'agricultural lands','cyclones', 'urban lands', 'lakes/reservoirs','infant mortality rates', 'methane','non-methane hydrocarbons/volatile organic compounds', 'coal','biodiversity functions', 'wetlands', 'discharge/flow','hydropattern', 'alpine/tundra', 'forests','leaf area index (lai)', 'ammonia', 'nitrous oxide','land subsidence', 'normalized difference vegetation index (ndvi)','chlorinated hydrocarbons', 'methyl bromide', 'methyl chloride','molecular hydrogen', 'fire models', 'heat index','even-toed ungulates', 'species recruitment','population dynamics', 'water depth', 'inundation', 'drainage','respiration rate', 'permafrost temperature','indigenous/native species', 'fire dynamics', 'plant succession','coastal', 'salt marsh', 'boundary layer winds', 'shrubland/scrub','community structure', 'pingo', 'virtual temperature','formaldehyde', 'hydroxyl', 'photolysis rates', 'nitric oxide','molecular oxygen', 'peroxyacyl nitrate', 'stable isotopes','runoff', 'vegetation water content', 'discharge','chlorophyll concentrations', 'water table', 'decomposition','water temperature', 'total dissolved solids', 'biomass dynamics','grasslands', 'savannas', 'grazing dynamics/plant herbivory','multivariate enso index', 'drainage basins','resource development site', 'dunes', 'flood plain','endangered species', 'hydrogen cyanide', 'nutrient cycling','deserts','fraction of absorbed photosynthetically active radiation (fapar)','aquifers', 'dissolved oxygen', 'turbidity', 'conductivity','sulfate', 'water ion concentrations', 'percolation','phosphorous compounds', 'radioisotopes', 'cooling degree days','contour maps', 'methane production/use','natural gas production/use', 'petroleum production/use','consumption rates', 'soil organic carbon (soc)', 'halocarbons','biomass energy production/use', 'estuary','layered precipitable water', 'water vapor concentration profiles','hydrogen chloride', 'nitric acid', 'chlorine nitrate','chlorofluorocarbons', 'dinitrogen pentoxide', 'dimethyl sulfide','vorticity', 'ice fraction', 'temperature tendency','wind direction tendency', 'bromine monoxide', 'chlorine monoxide','methyl cyanide', 'hypochlorous acid', 'methanol', 'hydroperoxy','cloud base pressure', 'temperature anomalies','precipitation trends', 'temperature trends', 'convection','chlorine dioxide', 'uv aerosol index','sea surface skin temperature', 'sublimation','convective surface precipitation rate', 'hydrogen fluoride']","def setContourLabelDistance(distance=500):\n dislin.labdis(distance, 'CONTUR')","def eval_final_label(args):\n cfg, lbl = util.get_label_cfg_by_args(args)\n uid = cfg['uniqueid']\n print('We are playing with %s' % uid)\n outdir='models/%s/gate_expert' % uid\n outname='gate_expert_model.pt'\n if KLLOSS:\n outname = 'gate_expert_kldiv_model.pt'\n if args.warm:\n outname = outname.replace('.pt', '_warm.pt')\n mdl_path = os.path.join(outdir, outname)\n gate_expert = GateExpertNet(mdl_path, False)\n eval_fun = gate_expert.get_p_y\n\n data = npload(cfg['file_path'], uid)\n datax = data[cfg['x_name']]\n p, v = eval_fun(datax)\n\n label = np.argmax(p, axis=1)\n\n if args.draw:\n fig, ax = plt.subplots()\n n_expert = np.amax(label) + 1\n for i in range(n_expert):\n mask = label == i\n ax.scatter(datax[mask, 0], datax[mask, 1])\n plt.show()\n\n label_name = 'data/pen/gate_expert_label.npy'\n if KLLOSS:\n label_name = label_name.replace('_label', '_kldiv_label')\n if args.warm:\n label_name = label_name.replace('.npy', '_warm.npy')\n np.save(label_name, label)","def donut_highD(N=1500, V=1500, binary=True, d=3):\n X = torch.randn(N + V, d)\n R = torch.sqrt(torch.sum(X ** 2, dim=1))\n ind_class0 = (R < 1.0).nonzero()[:, 0]\n ind_class1 = (R >= 1.0).nonzero()[:, 0]\n X = X * 1.2 # rescale data\n X.add_(0.2 * torch.randn_like(X)) # add some noise to the data\n \n return label(d, X, ind_class0, ind_class1, N, V, binary)","def label(self):\r\n raise NotImplementedError","def cell_description(self, gid):\n\n tree = arbor.segment_tree()\n\n tree.append(\n arbor.mnpos,\n arbor.mpoint(0, 0, 0, self.radius),\n arbor.mpoint(self.length, 0, 0, self.radius),\n tag=1,\n )\n\n labels = arbor.label_dict({\"cable\": \"(tag 1)\", \"start\": \"(location 0 0)\"})\n\n decor = (\n arbor.decor()\n .set_property(Vm=self.Vm, cm=self.cm, rL=self.rL)\n .paint('\"cable\"', arbor.density(f\"pas/e={self.Vm}\", g=self.g))\n .place(\n '\"start\"',\n arbor.iclamp(\n self.stimulus_start, self.stimulus_duration, self.stimulus_amplitude\n ),\n \"iclamp\",\n )\n )\n\n policy = arbor.cv_policy_max_extent(self.cv_policy_max_extent)\n decor.discretization(policy)\n\n return arbor.cable_cell(tree, decor, labels)","def label(cmd):\n cmd = cmd.replace('make][.DP', 'make1][.NP')\n cmd = cmd.replace('make][.SC', 'make2][.SC')\n cmd = re.sub('(draw.*)one','\\\\1one1',cmd)\n cmd = re.sub('(make1.*)one','\\\\1one1',cmd)\n cmd = re.sub('(make2.*)one','\\\\1one2',cmd)\n cmd = re.sub('(move.*)one','\\\\1one2',cmd)\n cmd = re.sub('(hide.*)one','\\\\1one2',cmd)\n cmd = '[result ' + cmd + ']' #dummy function for plop\n return cmd","def plot_countryperskill(data_df, **args):\n name = args.get('name', 'VARIABLE NAME')\n idx = args.get('idx', data_df.index.values)\n order = args.get('order', np.array([9, 0, 1, 2, 3, 4, 5, 6, 8, 7], int))\n dd = args.get('dd', .7) # 3.3\n wdth = args.get('wdth', 8) # 7\n hght = args.get('hght', 4)\n markersize = 60\n target_y = args.get('target_y', 1)\n label_y = args.get('label_y', r'$\\rho$')\n colors14 = args.get('colors14', ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', \\\n '#fb9a99', '#e31a1c', '#fdbf6f', '#ff7f00', \\\n '#cab2d6', '#6a3d9a', '#ffff99', '#b15928', \\\n '#dd1c77', '#8dd3c7'])\n plt.figure(facecolor='w', figsize=(wdth, hght))\n meth_labels = [r'$Lit$', r'$Lit^2$', r'$Lit^3$', r'$Lit^4$', r'$Lit^5$', \\\n r'$Pop$', r'$Pop^2$', r'$Lit^3Pop$', r'$Lit^2Pop$', r'$LitPop$']\n idx = idx[order]\n meth_labels = [meth_labels[i] for i in order]\n # empty plots for legend handlers:\n for i in np.arange(0, len(countries_sel)): # country\n plt.scatter([], [], marker='o', s=markersize, edgecolor='black', linewidth='.4',\\\n c=colors14[i], label=countries[countries_sel[i]])\n plt.legend()\n\n plt.scatter([0, len(idx)+dd], [0.7, 0.7], marker='.', lw=1, c='white') # legendspace\n\n # actual plotting:\n for i in np.arange(0, len(countries_sel)): # country\n for j in np.arange(0, len(idx)):\n # rp - pearson correlation:\n plt.scatter([j], data_df[countries[countries_sel[i]]][idx[j]], marker='o', \\\n s=markersize, edgecolor='black', linewidth='.4',\\\n alpha=1., c=colors14[i], zorder=j+10)\n if not target_y == 'none':\n plt.plot([0, j], [target_y, target_y], c='#d3d3d3', lw=5, ls='-', zorder=1)\n\n plt.xticks(np.arange(0, len(idx)), meth_labels, color='black', rotation=30)\n plt.grid(axis='y')\n # plt.xlabel('Method')\n plt.ylabel(label_y)\n plt.title(name)\n\n plt.savefig(os.path.join(output_path, experiment_name + '_' + 'allcountries_perScore_v4_' + name + '.pdf'),\\\n dpi=600, facecolor='w', edgecolor='w',\n orientation='portrait', papertype=None, format='pdf',\n transparent=False, bbox_inches=None, pad_inches=0.1,\n frameon=None, metadata=None)\n plt.show()","def showOneNeedle(self,i,visibility):\n #obsolete\n profbox()\n fidname = \"fid\"+self.option[i]\n pNode = self.parameterNode()\n needleID = pNode.GetParameter(self.option[i]+'.vtp')\n fidID = pNode.GetParameter(fidname) \n NeedleNode = slicer.mrmlScene.GetNodeByID(needleID)\n fiducialNode = slicer.mrmlScene.GetNodeByID(fidID) \n \n if NeedleNode !=None:\n displayNode =NeedleNode.GetModelDisplayNode()\n nVisibility=displayNode.GetVisibility() \n\n if fiducialNode == None:\n displayNode.SetVisibility(1) \n displayNode.SetOpacity(0.9)\n polyData = NeedleNode.GetPolyData()\n polyData.Update()\n nb = int(polyData.GetNumberOfPoints()-1)\n coord = [0,0,0]\n if nb>100:\n fiducialNode = slicer.vtkMRMLAnnotationFiducialNode()\n polyData.GetPoint(nb,coord) \n fiducialNode.SetName(self.option[i])\n fiducialNode.SetFiducialCoordinates(coord) \n fiducialNode.Initialize(slicer.mrmlScene)\n fiducialNode.SetLocked(1)\n fiducialNode.SetSelectable(0)\n fidDN = fiducialNode.GetDisplayNode()\n fidDN.SetColor(NeedleNode.GetDisplayNode().GetColor())\n fidDN.SetGlyphScale(0)\n fidTN = fiducialNode.GetAnnotationTextDisplayNode()\n fidTN.SetTextScale(3)\n fidTN.SetColor(NeedleNode.GetDisplayNode().GetColor())\n fiducialNode.SetDisplayVisibility(0)\n pNode.SetParameter(fidname,fiducialNode.GetID())\n fiducialNode.SetDisplayVisibility(1)\n\n if visibility ==0:\n\n displayNode.SetVisibility(0)\n displayNode.SetSliceIntersectionVisibility(0)\n if fiducialNode!=None:\n fiducialNode.SetDisplayVisibility(0)\n\n else:\n\n displayNode.SetVisibility(1)\n displayNode.SetSliceIntersectionVisibility(1)\n if fiducialNode!=None:\n fiducialNode.SetDisplayVisibility(1)\n\n else:\n vtkmat = vtk.vtkMatrix4x4()\n vtkmat.DeepCopy(self.m_vtkmat)\n vtkmat.SetElement(0,3,self.m_vtkmat.GetElement(0,3)+self.p[0][i])\n vtkmat.SetElement(1,3,self.m_vtkmat.GetElement(1,3)+self.p[1][i])\n vtkmat.SetElement(2,3,self.m_vtkmat.GetElement(2,3)+(30.0-150.0)/2.0)\n\n TransformPolyDataFilter=vtk.vtkTransformPolyDataFilter()\n Transform=vtk.vtkTransform() \n TransformPolyDataFilter.SetInput(self.m_polyCylinder)\n Transform.SetMatrix(vtkmat)\n TransformPolyDataFilter.SetTransform(Transform)\n TransformPolyDataFilter.Update()\n\n triangles=vtk.vtkTriangleFilter()\n triangles.SetInput(TransformPolyDataFilter.GetOutput()) \n self.AddModel(i,triangles.GetOutput())\n self.showOneNeedle(i,visibility)","def getLabel(*args):","def getLabel(*args):","def getLabel(*args):","def drawlabels(t, t1):\r\n t.fd(250)\r\n t.pd()\r\n t.write(\"Life\", font=(\"Arial\", 10, \"bold\"))\r\n t.pu()\r\n t.back(12)\r\n t.pd()\r\n t.write(\"Exp.\", font=(\"Arial\", 10, \"bold\"))\r\n t.pu()\r\n t.back(238)\r\n t.right(90)\r\n t.fd(80)\r\n t1.pu()\r\n t1.back(50)\r\n t1.rt(90)\r\n t1.fd(250)\r\n t1.pd()\r\n t1.write(\"Year\", font=(\"Arial\", 10, \"bold\"))\r\n t1.pu()\r\n t1.back(250)\r\n t1.left(90)\r\n t1.fd(50)","def ex_label(self,label,argl):\n if len(label) > 0 and label[0] != '_':\n return label\n comment = ''\n for i in argl:\n phrase = ''\n if i == 'l':\n phrase = label\n elif i in self._labels.keys():\n phrase = self._labels[i]\n comment += phrase\n return comment","def efficient_tagged_jets_hist(datalist,discriminant, discriminant_cut, CSV_cut, bins, Difference=False, mode=\"pT_jet\",Save=False):\n title = \"tagged_jets_vs_\"+mode\n AllJetsHistlist = []\n CSVHistlist = []\n DiscriminantHistlist = []\n if mode == \"pT_hadron\":\n feature = 2\n elif mode == \"pT_jet\":\n feature = 3\n elif mode == \"decay_vx\":\n feature = 4\n for n,data in enumerate(datalist):\n print \"working on\",data[1]\n ran = data[2]\n AllJetsHistlist.append(rt.TH1D(data[1]+\"_AllJets\",data[1]+\"_\"+title,bins,ran[0],ran[1]))\n AllJetsHistlist[n].SetLineColor(4)\n CSVHistlist.append(rt.TH1D(data[1]+\"_CSV\",data[1]+\"_\"+title,bins,ran[0],ran[1]))\n CSVHistlist[n].SetLineColor(3)\n DiscriminantHistlist.append(rt.TH1D(data[1]+\"_Discriminant\",data[1]+\"_\"+title,bins,ran[0],ran[1]))\n DiscriminantHistlist[n].SetLineColor(2)\n for particle in data[0]:\n AllJetsHistlist[n].Fill(particle[feature])\n if particle[1] >= CSV_cut: CSVHistlist[n].Fill(particle[feature])\n if Difference:\n L = particle[8]-particle[5]\n else:\n if particle[13] != 0:\n L = particle[16]/float(particle[13])\n else:\n continue\n if L >= discriminant_cut: DiscriminantHistlist[n].Fill(particle[feature])\n canvaslist = []\n legendlist = []\n Tfilelist = []\n for n,data in enumerate(datalist):\n canvaslist.append(rt.TCanvas(data[1]+\"_canvas\",\"canvas\",600,600))\n canvaslist[n].SetTitle(data[1]+\"_\"+title)\n rt.gStyle.SetOptStat(0)\n legendlist.append(rt.TLegend(0.9,0.9,0.65,0.75))\n legendlist[n].AddEntry(AllJetsHistlist[n], \"All jets\")\n legendlist[n].AddEntry(CSVHistlist[n], \"CSV\")\n legendlist[n].AddEntry(DiscriminantHistlist[n], discriminant)\n AllJetsHistlist[n].GetXaxis().SetTitle(mode)\n AllJetsHistlist[n].GetYaxis().SetTitle('# jets')\n AllJetsHistlist[n].GetYaxis().SetTitleOffset(1.5)\n #AllJetsHistlist[n].Draw()\n #CSVHistlist[n].Draw(\"SAME\")\n #DiscriminantHistlist[n].Draw(\"SAME\")\n #legendlist[n].Draw()\n if Save:\n #canvaslist[n].SaveAs(\"Thesis_Plots/\"+title+\"_\"+data[1]+discriminant+\".png\")\n Tfilelist.append(rt.TFile(\"Thesis_Plots/root_files/\"+title+\"_\"+data[1]+discriminant+\".root\",\"recreate\"))\n AllJetsHistlist[n].Write()\n CSVHistlist[n].Write()\n DiscriminantHistlist[n].Write()","def autolabel(rects):","def __init__(self, label_num, des_dir, des_dim=48):\n self.label_num = label_num\n self.des_dir = des_dir\n self.des_dim = 48","def _get_labels(self, ind):\n pass","def diet_label(self, diet_label):\n\n self._diet_label = diet_label","def dim_reduction_plot(data, label, block_flag):\n \n PCA_model = TruncatedSVD(n_components=3).fit(data)\n data_PCA = PCA_model.transform(data)\n idxc1 = np.where(label==0)\n idxc2 = np.where(label==1)\n plt.scatter(data_PCA[idxc1,0],data_PCA[idxc1,1],s=80,c='r', marker='^',linewidths = 0, label='healthy')\n plt.scatter(data_PCA[idxc2,0],data_PCA[idxc2,1],s=80,c='y', marker='o',linewidths = 0, label='infected')\n plt.gca().axes.get_xaxis().set_ticks([])\n plt.gca().axes.get_yaxis().set_ticks([])\n plt.title('PCA of the codes')\n plt.legend(scatterpoints=1,loc='best')\n plt.show(block=block_flag)","def plotrgcloud(self):\n print self.kpunten\n for i in range(len(self.kpunten[0])):\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\n if i == len(self.kpunten[0]) -1 :\n end = None\n else:\n end = self.kpunten[0][i+1][1] + 1\n print end\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)","def makeD2hhAsymm(name,\n config,\n KPIDK_string,\n PiPIDK_string,\n Mass_low_string,\n Mass_high_string,\n CombPIDK_string,\n DecayDescriptor,\n inputSel,\n useTOS,\n Hlt1TOS,\n Hlt2TOS\n ) :\n\n def makeTISTOS( name, _input, _hlttos ) :\n from Configurables import TisTosParticleTagger\n _tisTosFilter = TisTosParticleTagger( name + \"Tagger\" )\n _tisTosFilter.TisTosSpecs = _hlttos\n return Selection( name\n , Algorithm = _tisTosFilter\n , RequiredSelections = [ _input ]\n ) \n\n _Kcuts1 = \"~ISMUON & (PT > %(DaugPtMin)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2)s)\" % locals()['config']\n _KcutsPIDK = KPIDK_string % locals()['config']\n _Kcuts2 = \" & (ISLONG) & (P > %(DaugP)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2)s)\" % locals()['config']\n _Kcuts = _Kcuts1 + _KcutsPIDK + _Kcuts2\n _Picuts1 = \"~ISMUON & (PT > %(DaugPtMin)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2)s)\" % locals()['config']\n _PicutsPIDK = PiPIDK_string % locals()['config']\n _Picuts2 = \" & (ISLONG) & (P > %(DaugP)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2)s)\" % locals()['config']\n _Picuts = _Picuts1 + _PicutsPIDK + _Picuts2\n _dauCuts = { 'K+': _Kcuts, 'pi+': _Picuts }\n\n _massLow = Mass_low_string % locals()['config']\n _massHigh = Mass_high_string % locals()['config']\n _combCuts1 = \"(APT > %(D0Pt)s* MeV)\" \\\n \"& (AHASCHILD( PT > %(DaugPtMax)s* MeV ) )\" \\\n \"& (ADOCA(1,2)< %(D0DOCA)s* mm)\" \\\n \"& (AP > %(D0P)s* MeV)\" % locals()['config']\n _combCutsPIDK = CombPIDK_string % locals()['config']\n _combCuts = _combCuts1 + _combCutsPIDK + _massLow + _massHigh\n\n _motherCuts = \"(VFASPF(VCHI2PDOF) < %(D0VtxChi2Ndof)s)\" \\\n \"& (BPVVDCHI2 > %(D0FDChi2)s)\" \\\n \"& (BPVLTIME() > %(D0Tau)s)\" \\\n \"& (BPVDIRA > %(D0BPVDira)s)\" % locals()['config']\n\n _D0 = CombineParticles( DecayDescriptor = DecayDescriptor,\n MotherCut = _motherCuts,\n CombinationCut = _combCuts,\n DaughtersCuts = _dauCuts)\n\n _sel = Selection ( name+'Sel',\n Algorithm = _D0,\n RequiredSelections = inputSel )\n\n if not useTOS:\n return _sel\n\n _selD2hhHlt1TOS = makeTISTOS( name + \"D2hhHlt1TOS\"\n , _sel\n , Hlt1TOS\n )\n _selD2hhHlt2TOS = makeTISTOS( name + \"D2hhHlt2TOS\"\n , _selD2hhHlt1TOS\n , Hlt2TOS\n )\n \n return _selD2hhHlt2TOS","def efficient_binned_tagged_jets_hist(datalist,discriminant, discriminant_cuts, CSV_cuts, bins, nbins, Difference=False, mode=\"pT_jet\",Save=False):\n title = \"binned_tagged_jets_vs_\"+mode\n AllJetsHistlist = []\n CSVHistlist = []\n DiscriminantHistlist = []\n if mode == \"pT_hadron\":\n feature = 2\n elif mode == \"pT_jet\":\n feature = 3\n elif mode == \"decay_vx\":\n feature = 4\n for n,data in enumerate(datalist):\n print \"working on\",data[1]\n ran = data[2]\n AllJetsHistlist.append(rt.TH1D(data[1]+\"_AllJets\",data[1]+\"_\"+title,nbins,ran[0],ran[1]))\n AllJetsHistlist[n].SetLineColor(4)\n CSVHistlist.append(rt.TH1D(data[1]+\"_CSV\",data[1]+\"_\"+title,nbins,ran[0],ran[1]))\n CSVHistlist[n].SetLineColor(3)\n DiscriminantHistlist.append(rt.TH1D(data[1]+\"_Discriminant\",data[1]+\"_\"+title,nbins,ran[0],ran[1]))\n DiscriminantHistlist[n].SetLineColor(2)\n for particle in data[0]:\n bin_number = FCM.bin_selection(particle,bins)\n if bin_number == -100: continue\n AllJetsHistlist[n].Fill(particle[feature])\n if particle[1] >= CSV_cuts[bin_number]: CSVHistlist[n].Fill(particle[feature])\n if Difference:\n L = particle[8]-particle[5]\n else:\n if particle[17] != 0:\n L = particle[20]/float(particle[17])\n else:\n continue\n if L >= discriminant_cuts[bin_number]: DiscriminantHistlist[n].Fill(particle[feature])\n canvaslist = []\n legendlist = []\n Tfilelist = []\n for n,data in enumerate(datalist):\n canvaslist.append(rt.TCanvas(data[1]+\"_canvas\",\"canvas\",600,600))\n canvaslist[n].SetTitle(data[1]+\"_\"+title)\n rt.gStyle.SetOptStat(0)\n legendlist.append(rt.TLegend(0.9,0.9,0.65,0.75))\n legendlist[n].AddEntry(AllJetsHistlist[n], \"All jets\")\n legendlist[n].AddEntry(CSVHistlist[n], \"CSV\")\n legendlist[n].AddEntry(DiscriminantHistlist[n], discriminant)\n AllJetsHistlist[n].GetXaxis().SetTitle(\"jet p_{T} (GeV)\")\n AllJetsHistlist[n].GetYaxis().SetTitle('# jets')\n AllJetsHistlist[n].GetYaxis().SetTitleOffset(1.5)\n #AllJetsHistlist[n].Draw()\n #CSVHistlist[n].Draw(\"SAME\")\n #DiscriminantHistlist[n].Draw(\"SAME\")\n #legendlist[n].Draw()\n if Save:\n #canvaslist[n].SaveAs(title+\"_\"+data[1]+discriminant+\".png\")\n Tfilelist.append(rt.TFile(\"Thesis_Plots/root_files/\"+title+\"_\"+data[1]+discriminant+\".root\",\"recreate\"))\n print \"saved histogram as Thesis_Plots/root_files/\"+title+\"_\"+data[1]+discriminant+\".root\"\n AllJetsHistlist[n].Write()\n CSVHistlist[n].Write()\n DiscriminantHistlist[n].Write()","def fid_cuts(ptname, etaname):\n cuts = []\n cuts.append(combine_cuts([ptname + ' > 4.5',\n 'TMath::Abs(' + etaname + ') < 1.2']))\n cuts.append(combine_cuts([ptname + ' > 4.0',\n var_selection('TMath::Abs('+etaname+')', 1.2, 1.4)\n ]))\n cuts.append(combine_cuts([ptname + ' > 3.5',\n var_selection('TMath::Abs('+etaname+')', 1.4, 1.6)\n ]))\n return combine_cuts(cuts, ' || ')","def showComponents(self, mask):\n\n from skimage import measure\n\n thresh = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)[1]\n labels = measure.label(thresh, neighbors=8, background=0)\n for label in range(0,len(labels)):\n img = np.zeros(mask.shape)\n # if this is the background label, ignore it\n if label == 0:\n continue\n img[labels==label]=255\n numPixels = cv2.countNonZero(img)\n\n \t# if the number of pixels in the component is sufficiently\n \t# large, then add it to our mask of \"large blobs\"\n if numPixels > 500:\n showme(img, 'Contour '+str(label))","def getLabel2(*args):","def getLabel2(*args):","def DGTsLabel(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_DGTsLabel(*args)","def _get_component_label(self):\n labels = df_parser(self.workflow.builder.df_path, workflow=self.workflow).labels\n if self.pdc_component_df_label not in labels:\n raise PluginFailedException('No %s label in Dockerfile, can\\'t get PDC component',\n self.pdc_component_df_label)\n return labels[self.pdc_component_df_label]","def tagview(tab,label,x,y):\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n col=classifc[label]\r\n labnow=classif[label]\r\n# print (labnow, text)\r\n if label == 'back_ground':\r\n deltay=30\r\n else:\r\n# deltay=25*((labnow-1)%5)\r\n deltay=40+10*(labnow-1)\r\n\r\n viseg=cv2.putText(tab,label,(x, y+deltay), font,0.3,col,1)\r\n return viseg","def label(image,**kw):\n # default connectivity in OpenCV: 8 (which is equivalent to...)\n # default connectivity in scikit-image: 2\n n, labels = cv2.connectedComponents(image.astype(uint8), connectivity=4)\n #n, labels = cv2.connectedComponentsWithAlgorithm(image.astype(uint8), connectivity=4, ltype=2, ccltype=cv2.CCL_DEFAULT)\n return labels, n-1\n # try: return measurements.label(image,**kw)\n # except: pass\n # types = [\"int32\",\"uint32\",\"int64\",\"uint64\",\"int16\",\"uint16\"]\n # for t in types:\n # try: return measurements.label(array(image,dtype=t),**kw)\n # except: pass\n # # let it raise the same exception as before\n # return measurements.label(image,**kw)","def needleSegmentationCLIDEMO(self):\r\n # research\r\n profbox()\r\n widget = slicer.modules.NeedleFinderWidget\r\n scene = slicer.mrmlScene\r\n pNode = self.parameterNode()\r\n if slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\"baselineVolumeID\")) == None:\r\n inputVolume = self.__volumeSelector.currentNode()\r\n inputVolumeID = self.__volumeSelector.currentNode().GetID()\r\n else:\r\n inputVolume = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\"baselineVolumeID\"))\r\n inputVolumeID = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\"baselineVolumeID\")).GetID()\r\n inputLabelID = self.__needleLabelSelector.currentNode().GetID()\r\n\r\n datetime = time.strftime(\"%Y-%m-%d-%H_%M_%S\", time.localtime())\r\n\r\n inputVolume.SetAttribute(\"foldername\", datetime)\r\n self.outputVolumeNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelNode')\r\n self.outputVolumeNode.SetName(\"Output Needle Model\")\r\n outputVolumeStorageNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelStorageNode')\r\n slicer.mrmlScene.AddNode(self.outputVolumeNode)\r\n slicer.mrmlScene.AddNode(outputVolumeStorageNode)\r\n self.outputVolumeNode.AddAndObserveStorageNodeID(outputVolumeStorageNode.GetID())\r\n outputVolumeStorageNode.WriteData(self.outputVolumeNode)\r\n\r\n outputID = self.outputVolumeNode.GetID()\r\n\r\n self.foldername = '/NeedleModels/' + datetime\r\n\r\n # Set the parameters for the CLI module\r\n parameters = {}\r\n parameters['inputVolume'] = inputVolumeID\r\n parameters['inputLabel'] = inputLabelID\r\n parameters['outputVtk'] = outputID\r\n parameters['outputFolderName'] = self.foldername\r\n parameters['nbPointsPerLine'] = self.nbPointsPerLine.value\r\n parameters['nbRadiusIterations'] = self.nbRadiusIterations.value\r\n parameters['radiusMax'] = self.radiusMax.value\r\n parameters['numberOfPointsPerNeedle'] = self.numberOfPointsPerNeedle.value\r\n parameters['nbRotatingIterations'] = self.nbRotatingIterations.value\r\n\r\n module = slicer.modules.mainlabelneedletrackingcli\r\n self.__cliNode = None\r\n self.__cliNode = slicer.cli.run(module, None, parameters, wait_for_completion=True)\r\n\r\n ##### match the needles ######\r\n\r\n self.setNeedleCoordinates()\r\n self.computerPolydataAndMatrix()\r\n xmin = min(self.p[0])\r\n xmax = max(self.p[0])\r\n ymin = min(self.p[1])\r\n ymax = max(self.p[1])\r\n xdelta = xmax - xmin\r\n ydelta = ymax - ymin\r\n k = 0\r\n\r\n self.base = [[0 for j in range(3)] for j in range(63)]\r\n self.tip = [[0 for j in range(3)] for j in range(63)]\r\n self.needlenode = [[0 for j in range(2)] for j in range(63)]\r\n self.bentNeedleNode = [[0 for j in range(2)] for j in range(63)]\r\n self.displaynode = [0 for j in range(63)]\r\n self.displaynodeB = [0 for j in range(63)]\r\n self.fiducialnode = [0 for j in range(63)]\r\n\r\n for i in xrange(63):\r\n\r\n pathneedle = self.foldername + '/' + str(i) + '.vtp'\r\n pathBentNeedle = self.foldername + '/' + str(i) + '_bent.vtp'\r\n self.needlenode[i] = slicer.util.loadModel(pathneedle, True)\r\n self.bentNeedleNode[i] = slicer.util.loadModel(pathBentNeedle, True)\r\n\r\n if self.needlenode[i][0] == True and self.needlenode[i][1] != None:\r\n self.displaynode[i] = self.needlenode[i][1].GetDisplayNode()\r\n self.displaynodeB[i] = self.bentNeedleNode[i][1].GetDisplayNode()\r\n\r\n\r\n polydata = self.needlenode[i][1].GetPolyData()\r\n polydata.GetPoint(0, self.base[i])\r\n\r\n self.displaynode[i].SliceIntersectionVisibilityOn()\r\n self.displaynodeB[i].SliceIntersectionVisibilityOn()\r\n bestmatch = None\r\n mindist = None\r\n for j in xrange(63):\r\n delta = ((self.p[0][j] - (self.base[i][0])) ** 2 + (self.p[1][j] - self.base[i][1]) ** 2) ** (0.5)\r\n if delta < mindist or mindist == None:\r\n bestmatch = j\r\n mindist = delta\r\n\r\n bestmatch = k\r\n k += 1\r\n self.displaynode[i].SetColor(self.color[bestmatch])\r\n self.displaynodeB[i].SetColor(self.color[bestmatch])\r\n self.needlenode[i][1].SetName(self.option[bestmatch] + \"_segmented\")\r\n self.bentNeedleNode[i][1].SetName(self.option[bestmatch] + \"_optimized\")\r\n self.needlenode[i][1].SetAttribute(\"segmented\", \"1\")\r\n self.bentNeedleNode[i][1].SetAttribute(\"optimized\", \"1\")\r\n self.needlenode[i][1].SetAttribute(\"nth\", str(bestmatch))\r\n self.bentNeedleNode[i][1].SetAttribute(\"nth\", str(bestmatch))\r\n self.needlenode[i][1].SetAttribute(\"needleID\", self.needlenode[i][1].GetID())\r\n self.bentNeedleNode[i][1].SetAttribute(\"needleID\", self.bentNeedleNode[i][1].GetID())\r\n\r\n if widget.removeDuplicates.isChecked():\r\n self.positionFilteringNeedles()\r\n\r\n d = slicer.mrmlScene.GetNodeByID(outputID).GetDisplayNode()\r\n d.SetVisibility(0)\r\n\r\n self.__editorFrame.collapsed = 1\r\n\r\n self.addButtons()","def nifti2dicom(seg_nifti, bk_nifti, ref_dicom_dir, save_dir, description, mode_RGB=False, zoom_num=4, watermarks=True): \n #Load nifti, here is segmentation and background\n seg_image = sitk.ReadImage(seg_nifti)\n seg_image = sitk.GetArrayFromImage(seg_image)\n seg_image = seg_image.astype(np.uint8)\n \n # print(nifti_image.shape)\n bk_image = sitk.ReadImage(bk_nifti)\n bk_image = sitk.GetArrayFromImage(bk_image)\n\n #Get Volume report from the seg_image, cubic ml, and the 95% CI:\n v_nonenhancing = round(seg_image[seg_image==1].sum()/1000,1)\n ci_nonenhancing = round(v_nonenhancing*0.2,1)\n v_enhancing = round(seg_image[seg_image==4].sum()/1000,1)\n ci_enhancing = round(v_enhancing*0.3,1)\n v_edema = round(seg_image[seg_image==2].sum()/1000,1)\n ci_edema = round(v_edema*0.1,1)\n\n #Loading the reference dicom, in order to get the headers of each slice. \n series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(ref_dicom_dir)\n if not series_IDs:\n print(\"ERROR: given directory \\\"\"+data_directory+\"\\\" does not contain a DICOM series.\")\n sys.exit(1)\n\n series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(ref_dicom_dir, series_IDs[0])\n\n series_reader = sitk.ImageSeriesReader()\n series_reader.SetFileNames(series_file_names)\n\n # Configure the reader to load all of the DICOM tags (public+private):\n # By default tags are not loaded (saves time).\n # By default if tags are loaded, the private tags are not loaded.\n # We explicitly configure the reader to load tags, including the private ones.\n series_reader.MetaDataDictionaryArrayUpdateOn()\n series_reader.LoadPrivateTagsOn()\n ref_image = series_reader.Execute()\n \n #set reader for slice \n reader = sitk.ImageFileReader()\n reader.LoadPrivateTagsOn()\n \n writer = sitk.ImageFileWriter()\n # Use the study/series/frame of reference information given in the meta-data\n # dictionary and not the automatically generated information from the file IO\n writer.KeepOriginalImageUIDOn()\n\n # Copy some of the tags and add the relevant tags indicating the change.\n # For the series instance UID (0020|000e), each of the components is a number, cannot start\n # with zero, and separated by a '.' We create a unique series ID using the date and time. tags of interest:\n \n castFilter = sitk.CastImageFilter()\n castFilter.SetOutputPixelType(sitk.sitkInt16)\n ORG_ROOT=\"1.3.12.2\"\n #create SeriesInstanceUID and StudyInstanceUID\n SeriesInstanceUID = generateUID(org_root=ORG_ROOT)\n StudyInstanceUID = generateUID(org_root=ORG_ROOT)\n #create a prefix for the accession number\n acc='BTS'+series_reader.GetMetaData(0,\"0008|0050\")\n #changing spacing\n reader.SetFileName(series_file_names[0])\n reader.ReadImageInformation()\n\n if mode_RGB:\n customized_tag_values = [(\"0008|103e\", description),\n (\"0020|000e\", SeriesInstanceUID),\n (\"0008|0050\", acc),\n (\"0020|000d\", StudyInstanceUID), \n (\"0028|0004\", 'RGB'),\n (\"0028|0002\", \"3\")]\n else:\n customized_tag_values = [(\"0008|103e\", description),\n (\"0020|000e\", SeriesInstanceUID), \n (\"0008|0050\", acc),\n (\"0020|000d\", StudyInstanceUID)] \n\n os.makedirs(save_dir, exist_ok = True)\n\n #for nifti, the main axis is the first one, while for dicoms it is the last one\n for i in range(ref_image.GetDepth()):\n #zoom 2 times, todo need to figure out which axis to zoom, post is the 3rd\n #pre assume the first axis is the slice numbers\n bk_slice = ndimage.zoom(bk_image[i,:,:], zoom_num, order=0)\n seg_slice = ndimage.zoom(seg_image[i,:,:], zoom_num, order=0)\n \n #Due to the DICOM saving coordinate system is different with nifti,i.e mirrored, it is easier to flip array\n bk_slice = np.flip(bk_slice, (0, 1)) \n seg_slice = np.flip(seg_slice, (0, 1)) \n\n #get contours\n seg_idx = get_contours(seg_slice)\n \n #add watermarks\n if watermarks:\n canvas_tmp = np.zeros(list(bk_slice.shape), dtype=np.uint8)\n font = cv2.FONT_HERSHEY_PLAIN\n cv2.putText(canvas_tmp,'FOR RESEARCH ONLY;REFER TO OFFICIAL REPORT FOR DETAILS',(10,30), \n font,2,255,1)\n cv2.putText(canvas_tmp,'(This tool is intended for evaluation of gliomas, and results may be unreliable for other pathologies)',(90,50), \n font,1,255,1) \n #add Legend and volumes \n cv2.putText(canvas_tmp, 'Legend Volume(+/-95% CI)',(10,900), font,0.8,255,1)\n cv2.putText(canvas_tmp, f'Edema {v_edema}+/-{ci_edema} mL',(30,920), font,0.8,255,1)\n cv2.putText(canvas_tmp, f'Enhancing {v_enhancing}+/-{ci_enhancing} mL',(30,940), font,0.8,255,1)\n cv2.putText(canvas_tmp, f'Non- {v_nonenhancing}+/-{ci_nonenhancing} mL',(30,960), font,0.8,255,1)\n cv2.putText(canvas_tmp,'Enhancing', (30,975), font,0.8,255,1)\n cv2.putText(canvas_tmp,'(The error is based on testing of algorithm performance vs. manual segmentation)', (150,1000), font,1,255,1)\n\n \n \n #burning segmentation contour into slices\n cv2.line(seg_idx, (10,915), (20,915), 2, 2)\n cv2.line(seg_idx, (10,935), (20,935), 4, 2)\n cv2.line(seg_idx, (10,955), (20,955), 1, 2)\n \n if mode_RGB:\n #burning the watermarks\n bk_slice[canvas_tmp==255]=bk_slice.max()\n #convert dicom from nomogram to RGB\n bk_slice = toRGB(bk_slice)\n #colorize the bk_slice according to seg_idx\n bk_slice[0,:,:,0][seg_idx==1] = 255\n bk_slice[0,:,:,1][seg_idx==4] = 255\n bk_slice[0,:,:,2][seg_idx==2] = 255 \n else:\n #grey the ori_image_slice according to seg_idx\n bk_slice[canvas_tmp==255]=bk_slice.max()//2\n bk_slice[seg_idx==1] = bk_slice.max()*2//50\n bk_slice[seg_idx==2] = bk_slice.max()*1//50\n bk_slice[seg_idx==4] = bk_slice.max()*3//50\n\n converted_slice = sitk.GetImageFromArray(bk_slice)\n reader.SetFileName(series_file_names[i])\n reader.ReadImageInformation()\n spacing_new = [i/zoom_num for i in reader.GetSpacing()[:-1]] + [reader.GetSpacing()[-1]]\n \n #generate SOPInstanceUID\n SOPInstanceUID = generateUID(org_root=ORG_ROOT)\n series_tag_values = [(k, reader.GetMetaData(k)) for k in reader.GetMetaDataKeys()] + customized_tag_values + [(\"0008|0018\", SOPInstanceUID)]\n# print(series_tag_values)\n if '_seg_' in description:\n converted_slice = converted_slice \n \n # Tags shared by the series.\n for tag, value in series_tag_values:\n converted_slice.SetMetaData(tag, value)\n \n # especially set spacing tags\n # Image Position (Patient)\n converted_slice.SetMetaData(\"0020|0013\", str(i)) # Instance Number\n converted_slice.SetSpacing(spacing_new)\n \n # Write to the output directory and add the extension dcm, to force writing in DICOM format \n writer.SetFileName(os.path.join(save_dir, str(i)+'.dcm'))\n writer.Execute(converted_slice)","def needleSegmentationCLIDEMO(self):\n #research\n profbox()\n widget = slicer.modules.NeedleFinderWidget\n scene = slicer.mrmlScene\n pNode = self.parameterNode()\n if slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\"baselineVolumeID\")) == None:\n inputVolume = self.__volumeSelector.currentNode()\n inputVolumeID = self.__volumeSelector.currentNode().GetID()\n else:\n inputVolume = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\"baselineVolumeID\"))\n inputVolumeID = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\"baselineVolumeID\")).GetID()\n inputLabelID = self.__needleLabelSelector.currentNode().GetID()\n \n datetime = time.strftime(\"%Y-%m-%d-%H_%M_%S\", time.localtime())\n \n inputVolume.SetAttribute(\"foldername\",datetime)\n self.outputVolumeNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelNode')\n self.outputVolumeNode.SetName(\"Output Needle Model\")\n outputVolumeStorageNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelStorageNode')\n slicer.mrmlScene.AddNode(self.outputVolumeNode)\n slicer.mrmlScene.AddNode(outputVolumeStorageNode)\n self.outputVolumeNode.AddAndObserveStorageNodeID(outputVolumeStorageNode.GetID())\n outputVolumeStorageNode.WriteData(self.outputVolumeNode)\n \n outputID = self.outputVolumeNode.GetID()\n \n self.foldername = '/NeedleModels/' + datetime\n \n # Set the parameters for the CLI module \n parameters = {} \n parameters['inputVolume'] = inputVolumeID\n parameters['inputLabel'] = inputLabelID\n parameters['outputVtk'] = outputID\n parameters['outputFolderName'] = self.foldername\n parameters['nbPointsPerLine'] = self.nbPointsPerLine.value\n parameters['nbRadiusIterations'] = self.nbRadiusIterations.value\n parameters['distanceMax'] = self.distanceMax.value\n parameters['numberOfPointsPerNeedle'] = self.numberOfPointsPerNeedle.value\n parameters['nbRotatingIterations'] = self.nbRotatingIterations.value\n \n module = slicer.modules.mainlabelneedletrackingcli \n self.__cliNode = None\n self.__cliNode = slicer.cli.run(module, None, parameters, wait_for_completion=True)\n \n ##### match the needles ######\n\n self.setNeedleCoordinates()\n self.computerPolydataAndMatrix()\n xmin = min(self.p[0])\n xmax = max(self.p[0])\n ymin = min(self.p[1])\n ymax = max(self.p[1])\n xdelta = xmax - xmin\n ydelta = ymax - ymin\n k = 0\n\n self.base = [[0 for j in range(3)] for j in range(63)]\n self.tip = [[0 for j in range(3)] for j in range(63)]\n self.needlenode = [[0 for j in range(2)] for j in range(63)]\n self.bentNeedleNode = [[0 for j in range(2)] for j in range(63)]\n self.displaynode = [0 for j in range(63)]\n self.displaynodeB = [0 for j in range(63)]\n self.fiducialnode = [0 for j in range(63)]\n \n for i in xrange(63):\n\n pathneedle = self.foldername+'/'+str(i)+'.vtp'\n pathBentNeedle = self.foldername+'/'+str(i)+'_bent.vtp'\n self.needlenode[i] = slicer.util.loadModel(pathneedle, True)\n self.bentNeedleNode[i] = slicer.util.loadModel(pathBentNeedle, True)\n\n if self.needlenode[i][0] == True and self.needlenode[i][1] != None:\n self.displaynode[i] = self.needlenode[i][1].GetDisplayNode()\n self.displaynodeB[i] = self.bentNeedleNode[i][1].GetDisplayNode()\n\n \n polydata = self.needlenode[i][1].GetPolyData()\n polydata.GetPoint(0,self.base[i]) \n \n self.displaynode[i].SliceIntersectionVisibilityOn()\n self.displaynodeB[i].SliceIntersectionVisibilityOn()\n bestmatch = None\n mindist = None\n for j in xrange(63):\n delta = ((self.p[0][j]-(self.base[i][0]))**2+(self.p[1][j]-self.base[i][1])**2)**(0.5)\n if delta < mindist or mindist == None:\n bestmatch = j\n mindist = delta\n \n bestmatch = k\n k += 1\n self.displaynode[i].SetColor(self.color[bestmatch])\n self.displaynodeB[i].SetColor(self.color[bestmatch])\n self.needlenode[i][1].SetName(self.option[bestmatch]+\"_segmented\")\n self.bentNeedleNode[i][1].SetName(self.option[bestmatch]+\"_optimized\")\n self.needlenode[i][1].SetAttribute(\"segmented\",\"1\")\n self.bentNeedleNode[i][1].SetAttribute(\"optimized\",\"1\")\n self.needlenode[i][1].SetAttribute(\"nth\",str(bestmatch))\n self.bentNeedleNode[i][1].SetAttribute(\"nth\",str(bestmatch))\n self.needlenode[i][1].SetAttribute(\"needleID\",self.needlenode[i][1].GetID())\n self.bentNeedleNode[i][1].SetAttribute(\"needleID\",self.bentNeedleNode[i][1].GetID())\n \n if widget.removeDuplicates.isChecked():\n self.positionFilteringNeedles()\n\n d = slicer.mrmlScene.GetNodeByID(outputID).GetDisplayNode()\n d.SetVisibility(0)\n \n self.__editorFrame.collapsed = 1\n \n self.addButtons()","def create_slice_labels(dataset, base_task_name, slice_name, verbose=False):\n # TODO: break this out into more modular pieces oncee we have multiple slices\n slice_fn = globals()[slice_name]\n slice_indicators = torch.tensor(\n [slice_fn(dataset, idx) for idx in range(len(dataset))], dtype=torch.uint8\n ).view(-1, 1)\n\n Y_base = dataset.labels[f\"{base_task_name}_gold\"]\n Y_slice = Y_base.clone().masked_fill_(slice_indicators == 0, 0)\n\n if verbose:\n if not any(Y_slice):\n warnings.warn(f\"No examples were found to belong to slice {slice_name}\")\n else:\n print(f\"Found {sum(slice_indicators)} examples in slice {slice_name}.\")\n\n # NOTE: we assume here that all slice labels are for sentence-level tasks only\n return Y_slice","def __do_split_haghverdi16(self, Dseg, tips):\n # sort distance from first tip point\n # then the sequence of distances Dseg[tips[0]][idcs] increases\n idcs = np.argsort(Dseg[tips[0]])\n # consider now the sequence of distances from the other\n # two tip points, which only increase when being close to `tips[0]`\n # where they become correlated\n # at the point where this happens, we define a branching point\n if True:\n imax = self.kendall_tau_split(Dseg[tips[1]][idcs],\n Dseg[tips[2]][idcs])\n if False:\n # if we were in euclidian space, the following should work\n # as well, but here, it doesn't because the scales in Dseg are\n # highly different, one would need to write the following equation\n # in terms of an ordering, such as exploited by the kendall\n # correlation method above\n imax = np.argmin(Dseg[tips[0]][idcs]\n + Dseg[tips[1]][idcs]\n + Dseg[tips[2]][idcs])\n # init list to store new segments\n ssegs = []\n # first new segment: all points until, but excluding the branching point\n # increasing the following slightly from imax is a more conservative choice\n # as the criterion based on normalized distances, which follows below,\n # is less stable\n ibranch = imax + 2 # this used to be imax + 1!\n # ibranch = int(0.95 * imax)\n return idcs[:ibranch]\n # ssegs.append(idcs[:ibranch])\n # TODO get rid of the following heuristics\n # define nomalized distances to tip points for the rest of the data\n # dist1 = Dseg[tips[1], idcs[ibranch:]] / Dseg[tips[1], idcs[ibranch-1]]\n # dist2 = Dseg[tips[2], idcs[ibranch:]] / Dseg[tips[2], idcs[ibranch-1]]\n # assign points according to whether being closer to tip cell 1 or 2\n # ssegs.append(idcs[ibranch:][dist1 <= dist2])\n # ssegs.append(idcs[ibranch:][dist1 > dist2])\n # return ssegs","def Problem11():\n return 'Ductile Coulomb-Mohr'","def ANN_binned_tagged_jets_hist(datalist, model, discriminant_cuts, CSV_cuts, bins, nbins, mode=\"pT_jet\",Save=False,addFeature=False):\n title = \"binned_tagged_jets_vs_\"+mode\n\tdiscriminant = \"ANN\"\n AllJetsHistlist = []\n CSVHistlist = []\n DiscriminantHistlist = []\n if mode == \"pT_hadron\":\n feature = 2\n elif mode == \"pT_jet\":\n feature = 3\n elif mode == \"decay_vx\":\n feature = 4\n for n,data in enumerate(datalist):\n\t\tdatatitle = data[3]\n print \"working on\",datatitle\n ran = data[4]\n\t\tCSV = data[2]\n\t\tpT = data[1]\n\t\tx_data = data[0]\n AllJetsHistlist.append(rt.TH1D(datatitle+\"_AllJets\",datatitle+\"_\"+title,nbins,ran[0],ran[1]))\n AllJetsHistlist[n].SetLineColor(4)\n CSVHistlist.append(rt.TH1D(datatitle+\"_CSV\",datatitle+\"_\"+title,nbins,ran[0],ran[1]))\n CSVHistlist[n].SetLineColor(3)\n DiscriminantHistlist.append(rt.TH1D(datatitle+\"_Discriminant\",datatitle+\"_\"+title,nbins,ran[0],ran[1]))\n DiscriminantHistlist[n].SetLineColor(2)\n\t\n\t\tif addFeature == False:\n\t\t\tpred_y = model.predict(ANN_functional_shape(x_data))\n\t\telif addFeature == \"pT\":\n\t\t\tpred_y = model.predict(ANN_functional_shape(x_data)+[pT/200])\n\t\telif addFeature == \"PV\":\n\t\t\tassert x_data.shape[1] == 21, \"wrong x_data format: PV cannot be found\"\n\t\t\tpred_y = model.predict(ANN_functional_shape(x_data)+[x_data[:,-1]/10.])\n\t\telse:\n\t\t\tprint \"invalid feature input\"\n\t\t\treturn None\n\t\tbin_numbers = ANN_bin_selection(pT,bins)\n\n\t for i,pT_value in enumerate(pT):\n\t if bin_numbers[i] == -100: continue\n\t\t\tAllJetsHistlist[n].Fill(pT_value)\n\t if pred_y[i] >= discriminant_cuts[bin_numbers[i]]: DiscriminantHistlist[n].Fill(pT_value)\n\t if CSV[i] >= CSV_cuts[bin_numbers[i]]: CSVHistlist[n].Fill(pT_value)\n\n canvaslist = []\n legendlist = []\n Tfilelist = []\n for n,data in enumerate(datalist):\n\t\tdatatitle = data[3]\n canvaslist.append(rt.TCanvas(datatitle+\"_canvas\",\"canvas\",600,600))\n canvaslist[n].SetTitle(datatitle+\"_\"+title)\n rt.gStyle.SetOptStat(0)\n legendlist.append(rt.TLegend(0.9,0.9,0.65,0.75))\n legendlist[n].AddEntry(AllJetsHistlist[n], \"All jets\")\n legendlist[n].AddEntry(CSVHistlist[n], \"CSV\")\n legendlist[n].AddEntry(DiscriminantHistlist[n], discriminant)\n AllJetsHistlist[n].GetXaxis().SetTitle(mode)\n AllJetsHistlist[n].GetYaxis().SetTitle('# jets')\n AllJetsHistlist[n].GetYaxis().SetTitleOffset(1.5)\n #AllJetsHistlist[n].Draw()\n #CSVHistlist[n].Draw(\"SAME\")\n #DiscriminantHistlist[n].Draw(\"SAME\")\n #legendlist[n].Draw()\n if Save:\n #canvaslist[n].SaveAs(title+\"_\"+datatitle+discriminant+\".png\")\n Tfilelist.append(rt.TFile(\"Thesis_Plots/root_files/\"+title+\"_\"+datatitle+discriminant+\".root\",\"recreate\"))\n print \"saved histogram as Thesis_Plots/root_files/\"+title+\"_\"+datatitle+discriminant+\".root\"\n AllJetsHistlist[n].Write()\n CSVHistlist[n].Write()\n DiscriminantHistlist[n].Write()","def mask_label_contour(image, seg):\n return sitk.Mask(image, sitk.LabelContour(seg+1)==0)","def adjust_labels(data_y, label):\n\n if label == 'locomotion': # Labels for locomotion are adjusted\n data_y[data_y == 4] = 3\n data_y[data_y == 5] = 4\n elif label == 'gestures': # Labels for gestures are adjusted\n data_y[data_y == 406516] = 1\n data_y[data_y == 406517] = 2\n data_y[data_y == 404516] = 3\n data_y[data_y == 404517] = 4\n data_y[data_y == 406520] = 5\n data_y[data_y == 404520] = 6\n data_y[data_y == 406505] = 7\n data_y[data_y == 404505] = 8\n data_y[data_y == 406519] = 9\n data_y[data_y == 404519] = 10\n data_y[data_y == 406511] = 11\n data_y[data_y == 404511] = 12\n data_y[data_y == 406508] = 13\n data_y[data_y == 404508] = 14\n data_y[data_y == 408512] = 15\n data_y[data_y == 407521] = 16\n data_y[data_y == 405506] = 17\n return data_y","def GetDatumLabels(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_GetDatumLabels(self, *args)","def get_labels(self):\r\n return [\"X\", \"O\", \"B-a\", \"I-a\", \"B-b\", \"I-b\", \"B-c\", \"I-c\", \"S-a\", \"S-b\", \"S-c\", \"[CLS]\", \"[SEP]\"]","def _labels_of_sentence(self, sentence, split):\n labels = torch.ones(1)\n labels[0] = self.category_int_of_label_string(sentence[0][self.name_to_index_dict['label']]) #\n return labels","def _get_label(self):\n if self.model.name == '':\n return \"KPI\"\n return \"KPI: {} ({})\".format(self.model.name, self.model.objective)","def get_labels(self):\n return [\"A轮\", \"B轮\",\"C轮\",\"天使轮\",\"战略融资\"]","def GetDatumTolerLabels(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_GetDatumTolerLabels(self, *args)","def test_get_dim_label_with_index(self):\n\n dim = self.oecd_datasets['oecd']['dimension']['id'][2]\n dims_df = pyjstat.get_dim_label(self.oecd_datasets['oecd'], dim)\n self.assertTrue(dims_df.iloc[0]['id'] == '2003')\n self.assertTrue(dims_df.iloc[-1]['label'] == '2014')","def addFid(data, Dim=.5, nodName=\"N\", lableName=\"1\", color=\"red\", GlyphType=1):\n\t\txyz = tuple(data)\n\t\ttipFiducial = slicer.mrmlScene.AddNode(slicer.vtkMRMLMarkupsFiducialNode())\n\t\ttipFiducial.SetName(nodName)\n\t\ttipFiducial.AddFiducial(xyz[0], xyz[1], xyz[2])\n\t\ttipFiducial.SetNthFiducialLabel(0, lableName)\n\t\tslicer.mrmlScene.AddNode(tipFiducial)\n\t\ttipFiducial.SetDisplayVisibility(True)\n\t\ttipFiducial.GetDisplayNode().SetGlyphType(GlyphType) # Vertex2D\n\t\ttipFiducial.GetDisplayNode().SetGlyphScale(Dim * 10)\n\t\ttipFiducial.GetDisplayNode().SetTextScale(3)\n\t\ttipFiducial.GetDisplayNode().SetSelectedColor(Helper.myColor(color))\n\t\t'''\tGlyphShapes {\n GlyphTypeInvalid = 0, 1-StarBurst2D, 2-Cross2D, 3-CrossDot2D,\n 4-ThickCross2D, 5-Dash2D, 6-Sphere3D, 7-Vertex2D,\n 8-Circle2D,9-Triangle2D, 10-Square2D, Diamond2D,\n Arrow2D, ThickArrow2D, HookedArrow2D, GlyphType_Last\n }'''","def label_for(self, *pp, unit=True, description=True):\n if len(pp) > 1 and np.all([re.match(r\"k\\d+l\", p) for p in pp]):\n label = \"$k_nl$\"\n if unit:\n label += \" / $m^{-n}$\"\n return label\n return super().label_for(*pp, unit=unit, description=description)","def make_label(self, label, units):\n nice_label = self.tex_axis_label(label)\n if not (units == 'dimensionless') and \\\n (units is not None) and (not units == []):\n nice_label += ' (%s)'%self.tex_axis_label(units)\n return nice_label","def kaons ( self ) :\n from GaudiConfUtils.ConfigurableGenerators import FilterDesktop\n ## \n if self['NOPIDHADRONS'] :\n from StandardParticles import StdAllNoPIDsKaons as inpts\n kaoncut = self['KaonCut']\n else :\n from StandardParticles import StdAllLooseANNKaons as inpts \n kaoncut = \"(%s)&(%s)\" % ( self['KaonCut'] , self['KaonPIDCut'] ) \n #\n ##\n return self.make_selection (\n 'Kaon' ,\n FilterDesktop ,\n [ inpts ] ,\n Code = kaoncut ,\n )","def classify_defect_clusters_modifier(frame, data):\n\n if data.particles.count == 0:\n # No particles there to classify, create empty properties anyway\n data.particles_.create_property('Si_V', dtype=int, components=1)\n data.particles_.create_property('Si_I', dtype=int, components=1)\n data.particles_.create_property('Si_C', dtype=int, components=1)\n data.particles_.create_property('C_V', dtype=int, components=1)\n data.particles_.create_property('C_I', dtype=int, components=1)\n data.particles_.create_property('C_Si', dtype=int, components=1)\n return\n\n # TODO Create numpy arrays containing the number of Si vacancies,\n # interstitials, etc for each particle site in `data.particles`. These\n # next lines are just placeholders!\n si_vacancy = data.particles[\"vacancy_mask\"][...] * data.particles[\"Is Si Site\"][...]\n si_interstitial = (data.particles[\"Is Si Site\"][...] & (data.particles[\"Si Occupancy\"][...] > 1)) * (\n data.particles[\"Si Occupancy\"][...] - 1) + (\n (data.particles[\"Is C Site\"][...]) * data.particles[\"Si Occupancy\"][...]) - (\n data.particles[\"Is C Site\"][...] & data.particles[\"antisite_mask\"][...])\n si_antisite = data.particles[\"antisite_mask\"][...] * data.particles[\"Is Si Site\"][...]\n c_vacancy = data.particles[\"vacancy_mask\"][...] * data.particles[\"Is C Site\"][...]\n c_interstitial = (data.particles[\"Is C Site\"][...] & (data.particles[\"C Occupancy\"][...] > 1)) * (\n data.particles[\"C Occupancy\"][...] - 1) + (\n (data.particles[\"Is Si Site\"][...]) * data.particles[\"C Occupancy\"][...]) - (\n data.particles[\"Is Si Site\"][...] & data.particles[\"antisite_mask\"][...])\n c_antisite = data.particles[\"antisite_mask\"][...] * data.particles[\"Is C Site\"][...]\n\n\n data.particles_.create_property('Si_V', data=si_vacancy.astype(int))\n data.particles_.create_property('Si_I', data=si_interstitial.astype(int))\n data.particles_.create_property('Si_C', data=si_antisite.astype(int))\n data.particles_.create_property('C_V', data=c_vacancy.astype(int))\n data.particles_.create_property('C_I', data=c_interstitial.astype(int))\n data.particles_.create_property('C_Si', data=c_antisite.astype(int))","def _label_commuter_rail_rider(self, rider):\n if (rider['servicebrand_Commuter Rail'] > 0) and (rider['zonecr_1a'] == 0):\n label = 'CR except zone 1A'\n else:\n label = 'others'\n return label","def showOneNeedle(self, i, visibility):\r\n # obsolete\r\n profbox()\r\n fidname = \"fid\" + self.option[i]\r\n pNode = self.parameterNode()\r\n needleID = pNode.GetParameter(self.option[i] + '.vtp')\r\n fidID = pNode.GetParameter(fidname)\r\n NeedleNode = slicer.mrmlScene.GetNodeByID(needleID)\r\n fiducialNode = slicer.mrmlScene.GetNodeByID(fidID)\r\n\r\n if NeedleNode != None:\r\n displayNode = NeedleNode.GetModelDisplayNode()\r\n nVisibility = displayNode.GetVisibility()\r\n\r\n if fiducialNode == None:\r\n displayNode.SetVisibility(1)\r\n displayNode.SetOpacity(0.9)\r\n polyData = NeedleNode.GetPolyData()\r\n polyData.Update()\r\n nb = int(polyData.GetNumberOfPoints() - 1)\r\n coord = [0, 0, 0]\r\n if nb > 100:\r\n fiducialNode = slicer.vtkMRMLAnnotationFiducialNode()\r\n polyData.GetPoint(nb, coord)\r\n fiducialNode.SetName(self.option[i])\r\n fiducialNode.SetFiducialCoordinates(coord)\r\n fiducialNode.Initialize(slicer.mrmlScene)\r\n fiducialNode.SetLocked(1)\r\n fiducialNode.SetSelectable(0)\r\n fidDN = fiducialNode.GetDisplayNode()\r\n fidDN.SetColor(NeedleNode.GetDisplayNode().GetColor())\r\n fidDN.SetGlyphScale(0)\r\n fidTN = fiducialNode.GetAnnotationTextDisplayNode()\r\n fidTN.SetTextScale(3)\r\n fidTN.SetColor(NeedleNode.GetDisplayNode().GetColor())\r\n fiducialNode.SetDisplayVisibility(0)\r\n pNode.SetParameter(fidname, fiducialNode.GetID())\r\n fiducialNode.SetDisplayVisibility(1)\r\n\r\n if visibility == 0:\r\n\r\n displayNode.SetVisibility(0)\r\n displayNode.SetSliceIntersectionVisibility(0)\r\n if fiducialNode != None:\r\n fiducialNode.SetDisplayVisibility(0)\r\n\r\n else:\r\n\r\n displayNode.SetVisibility(1)\r\n displayNode.SetSliceIntersectionVisibility(1)\r\n if fiducialNode != None:\r\n fiducialNode.SetDisplayVisibility(1)\r\n\r\n else:\r\n vtkmat = vtk.vtkMatrix4x4()\r\n vtkmat.DeepCopy(self.m_vtkmat)\r\n vtkmat.SetElement(0, 3, self.m_vtkmat.GetElement(0, 3) + self.p[0][i])\r\n vtkmat.SetElement(1, 3, self.m_vtkmat.GetElement(1, 3) + self.p[1][i])\r\n vtkmat.SetElement(2, 3, self.m_vtkmat.GetElement(2, 3) + (30.0 - 150.0) / 2.0)\r\n\r\n TransformPolyDataFilter = vtk.vtkTransformPolyDataFilter()\r\n Transform = vtk.vtkTransform()\r\n TransformPolyDataFilter.SetInput(self.m_polyCylinder)\r\n Transform.SetMatrix(vtkmat)\r\n TransformPolyDataFilter.SetTransform(Transform)\r\n TransformPolyDataFilter.Update()\r\n\r\n triangles = vtk.vtkTriangleFilter()\r\n triangles.SetInput(TransformPolyDataFilter.GetOutput())\r\n self.AddModel(i, triangles.GetOutput())\r\n self.showOneNeedle(i, visibility)","def cvpr2018_labels():\n\n return {\n 0: 'others',\n 33: 'car',\n 34: 'motorcycle',\n 35: 'bicycle',\n 36: 'pedestrian',\n 38: 'truck',\n 39: 'bus',\n 40: 'tricycle'\n }","def syed_dilation(data, vessel):","def _compute_labels(self, element, data, mapping):\n lidx = element.nodes.get_dimension(self.label_index)\n if element.vdims:\n edges = Dataset(element)[element[element.vdims[0].name]>0]\n nodes = list(np.unique([edges.dimension_values(i) for i in range(2)]))\n nodes = element.nodes.select(**{element.nodes.kdims[2].name: nodes})\n else:\n nodes = element\n\n value_dim = element.vdims[0]\n labels = [lidx.pprint_value(v) for v in nodes.dimension_values(lidx)]\n if self.show_values:\n value_labels = []\n for i, node in enumerate(element._sankey['nodes']):\n value = value_dim.pprint_value(node['value'])\n label = '%s - %s' % (labels[i], value)\n if value_dim.unit:\n label += ' %s' % value_dim.unit\n value_labels.append(label)\n labels = value_labels\n\n ys = nodes.dimension_values(1)\n nodes = element._sankey['nodes']\n offset = (nodes[0]['x1']-nodes[0]['x0'])/4.\n if self.label_position == 'right':\n xs = np.array([node['x1'] for node in nodes])+offset\n else:\n xs = np.array([node['x0'] for node in nodes])-offset\n data['text_1'] = dict(x=xs, y=ys, text=[str(l) for l in labels])\n align = 'left' if self.label_position == 'right' else 'right'\n mapping['text_1'] = dict(text='text', x='x', y='y', text_baseline='middle', text_align=align)","def chainDict2jetLabel(chain_dict):\n\n # suported scenarios \n router = {\n 'simple': _make_simple_label,\n 'HT': _make_ht_label,\n 'vbenf': _make_vbenf_label,\n 'dijet': _make_dijet_label,\n 'combinationsTest': _make_combinationsTest_label,\n 'partitionsTest': _make_partitionsTest_label,\n }\n\n # chain_part - scenario association\n cp_sorter = {}\n for k in router: cp_sorter[k] = []\n\n for cp in chain_dict['chainParts']:\n if cp['signature'] != 'Jet' and cp['signature'] != 'Bjet': \n continue\n for k in cp_sorter:\n if cp['hypoScenario'].startswith(k):\n cp_sorter[k].append(cp)\n break\n\n # obtain labels by scenario.\n labels = []\n for k, chain_parts in cp_sorter.items():\n if chain_parts: labels.append(router[k](chain_parts))\n\n assert labels\n nlabels = len(labels)\n if nlabels == 1: return labels[0]\n if nlabels == 2:\n alabel = \"\"\"\\\nand([]\n %s\n %s)\"\"\" % (tuple(labels))\n return alabel\n\n # more than 2 labels is not expected\n assert False","def get_diar_target_labels(self, uniq_id, sample, fr_level_target):\n seg_target_list, base_clus_label = [], []\n self.scale_n = len(self.multiscale_timestamp_dict[uniq_id]['scale_dict'])\n subseg_time_stamp_list = self.multiscale_timestamp_dict[uniq_id][\"scale_dict\"][self.scale_n - 1][\"time_stamps\"]\n for (seg_stt, seg_end) in subseg_time_stamp_list:\n seg_stt_fr, seg_end_fr = int(seg_stt * self.frame_per_sec), int(seg_end * self.frame_per_sec)\n soft_label_vec_sess = torch.sum(fr_level_target[seg_stt_fr:seg_end_fr, :], axis=0) / (\n seg_end_fr - seg_stt_fr\n )\n label_int_sess = torch.argmax(soft_label_vec_sess)\n soft_label_vec = soft_label_vec_sess.unsqueeze(0)[:, sample.target_spks].squeeze()\n if label_int_sess in sample.target_spks and torch.sum(soft_label_vec_sess) > 0:\n label_int = sample.target_spks.index(label_int_sess)\n else:\n label_int = -1\n label_vec = (soft_label_vec > self.soft_label_thres).float()\n seg_target_list.append(label_vec.detach())\n base_clus_label.append(label_int)\n seg_target = torch.stack(seg_target_list)\n base_clus_label = torch.tensor(base_clus_label)\n return seg_target, base_clus_label","def onCut(self):\n pass","def autolabel(X_pos,values,height_lift):\r\n\theight= np.round(np.nan_to_num(values),2);y_pos = height_lift*height\r\n\tfor i in range(len(height)):\r\n\t\tax.text(X_pos[i],y_pos[i],'%4.2f' % height[i], ha='center', va='bottom',size=4)","def labels(self):\n\n param=self\n\n l=len(param)\n\n sweep_label=[]\n\n for index,name in enumerate(param.names):\n\n sweep_label.append((\\\n ''.join([c for c in name if c.isupper()]))\\\n .replace(\"IDT\",\"\")\\\n .replace(\"S\",\"\")\\\n .replace(\"M\",\"\"))\n\n stringout=[]\n\n unique={name:list(dict.fromkeys(values)) for name,values in zip(param.names,param.values)}\n\n for i in range(l):\n\n tmp_lab=''\n\n for lab,name in zip(sweep_label,self.names):\n\n tmp_lab=tmp_lab+lab+str(unique[name].index(param()[name][i]))\n\n stringout.append(tmp_lab)\n\n return stringout","def _change_name(self, suff, info_extra):\n if 'cable-ring' in self.path:\n i1 = info_extra['convex_hull_area']\n i2 = info_extra['best_possible_area']\n f = i1 / i2\n suff = suff.replace('.png',\n f'-area-{i1:0.3f}-best-{i2:0.3f}-FRAC-{f:0.3f}.png')\n elif 'cloth-flat' in self.path:\n i1 = info_extra['cloth_coverage']\n suff = suff.replace('.png', f'-coverage-{i1:0.3f}.png')\n elif 'bag-alone' in self.path:\n i1 = info_extra['convex_hull_area']\n i2 = info_extra['best_possible_area']\n suff = suff.replace('.png', f'-area-{i1:0.3f}-best-{i2:0.3f}.png')\n else:\n pass\n return suff","def Label(self) -> str:","def setPieLabels(label, position):\n pdict = {'left':'LEFT', 'right':'RIGHT'}\n dislin.pielab(label, pdict[position])","def test_reneaming_old_default_labels_to_new_fixed_labels():\n from dtscalibration import DataStore\n\n cable_len = 100.0\n nt = 3\n time = np.arange(nt)\n x = np.linspace(0.0, cable_len, 8)\n ts_cold = np.ones(nt) * 4.0 + np.cos(time) * 4\n ts_warm = np.ones(nt) * 20.0 + -np.sin(time) * 4\n\n C_p = 1324 # 1/2 * E0 * v * K_+/lam_+^4\n eta_pf = np.cos(time) / 10 + 1 # eta_+ (gain factor forward channel)\n eta_pb = np.sin(time) / 10 + 1 # eta_- (gain factor backward channel)\n C_m = 5000.0\n eta_mf = np.cos(time + np.pi / 8) / 10 + 1\n eta_mb = np.sin(time + np.pi / 8) / 10 + 1\n dalpha_r = 0.005284\n dalpha_m = 0.004961\n dalpha_p = 0.005607\n gamma = 482.6\n\n temp_real_kelvin = np.zeros((len(x), nt)) + 273.15\n temp_real_kelvin[x < 0.2 * cable_len] += ts_cold[None]\n temp_real_kelvin[x > 0.85 * cable_len] += ts_warm[None]\n temp_real_celsius = temp_real_kelvin - 273.15\n\n st = (\n eta_pf[None]\n * C_p\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_p * x[:, None])\n * np.exp(gamma / temp_real_kelvin)\n / (np.exp(gamma / temp_real_kelvin) - 1)\n )\n ast = (\n eta_mf[None]\n * C_m\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_m * x[:, None])\n / (np.exp(gamma / temp_real_kelvin) - 1)\n )\n rst = (\n eta_pb[None]\n * C_p\n * np.exp(-dalpha_r * (-x[:, None] + cable_len))\n * np.exp(-dalpha_p * (-x[:, None] + cable_len))\n * np.exp(gamma / temp_real_kelvin)\n / (np.exp(gamma / temp_real_kelvin) - 1)\n )\n rast = (\n eta_mb[None]\n * C_m\n * np.exp(-dalpha_r * (-x[:, None] + cable_len))\n * np.exp(-dalpha_m * (-x[:, None] + cable_len))\n / (np.exp(gamma / temp_real_kelvin) - 1)\n )\n\n c_f = np.log(eta_mf * C_m / (eta_pf * C_p))\n c_b = np.log(eta_mb * C_m / (eta_pb * C_p))\n\n dalpha = dalpha_p - dalpha_m # \\Delta\\alpha\n alpha_int = cable_len * dalpha\n\n df = c_f # reference section starts at first x-index\n db = c_b + alpha_int\n i_fw = np.log(st / ast)\n i_bw = np.log(rst / rast)\n\n E_real = (i_bw - i_fw) / 2 + (db - df) / 2\n\n ds = DataStore(\n {\n \"ST\": ([\"x\", \"time\"], st),\n \"AST\": ([\"x\", \"time\"], ast),\n \"REV-ST\": ([\"x\", \"time\"], rst),\n \"REV-AST\": ([\"x\", \"time\"], rast),\n \"userAcquisitionTimeFW\": ([\"time\"], np.ones(nt)),\n \"userAcquisitionTimeBW\": ([\"time\"], np.ones(nt)),\n \"cold\": ([\"time\"], ts_cold),\n \"warm\": ([\"time\"], ts_warm),\n },\n coords={\"x\": x, \"time\": time},\n attrs={\"isDoubleEnded\": \"1\"},\n )\n ds = ds.rename_labels()\n\n sections = {\n \"cold\": [slice(0.0, 0.09 * cable_len)],\n \"warm\": [slice(0.9 * cable_len, cable_len)],\n }\n\n real_ans2 = np.concatenate(([gamma], df, db, E_real[:, 0]))\n\n ds.calibration_double_ended(\n sections=sections,\n st_var=1.5,\n ast_var=1.5,\n rst_var=1.0,\n rast_var=1.0,\n method=\"wls\",\n solver=\"sparse\",\n fix_gamma=(gamma, 0.0),\n )\n\n assert_almost_equal_verbose(df, ds.df.values, decimal=14)\n assert_almost_equal_verbose(db, ds.db.values, decimal=13)\n assert_almost_equal_verbose(\n x * (dalpha_p - dalpha_m), ds.alpha.values - ds.alpha.values[0], decimal=13\n )\n assert np.all(np.abs(real_ans2 - ds.p_val.values) < 1e-10)\n assert_almost_equal_verbose(temp_real_celsius, ds.tmpf.values, decimal=10)\n assert_almost_equal_verbose(temp_real_celsius, ds.tmpb.values, decimal=10)\n assert_almost_equal_verbose(temp_real_celsius, ds.tmpw.values, decimal=10)\n pass","def autolabel(rects,array,axis,dist):\n ctr = 0\n label_array = [EM.truncate(v*100,1) for v in array]\n for entry in range(len(label_array)):\n if(label_array[entry]>=0) and (label_array[entry]<=1):\n label_array[entry] = EM.truncate(array[entry]*100,2)\n\n\n for rect in rects:\n height = rect.get_height()\n if(axis=='1'):\n ax1.text(rect.get_x() + rect.get_width()/2., height+dist,\n label_array[ctr],fontsize=fonts[3],\n #'%d' % int(height),\n ha='center', va='bottom',rotation=90)\n elif(axis=='2'):\n ax2.text(rect.get_x() + rect.get_width()/2., height+dist,\n label_array[ctr],fontsize=fonts[3],\n #'%d' % int(height),\n ha='center', va='bottom',rotation=90)\n elif(axis=='3'):\n ax3.text(rect.get_x() + rect.get_width()/2., height+dist,\n label_array[ctr],fontsize=fonts[3],\n #'%d' % int(height),\n ha='center', va='bottom',rotation=90)\n elif(axis=='4'):\n ax4.text(rect.get_x() + rect.get_width()/2., height+dist,\n label_array[ctr],fontsize=fonts[3],\n #'%d' % int(height),\n ha='center', va='bottom',rotation=90)\n ctr = ctr + 1","def label(d, X, ind_class0, ind_class1, N, V, binary):\n if binary == True:\n K = 1\n C = torch.zeros(N + V, K)\n C[ind_class0, :] = 0.0\n C[ind_class1, :] = 1.0\n else:\n K = 2\n C = torch.zeros(N + V, K)\n C[ind_class0, :] = torch.tensor([1.0, 0.0])\n C[ind_class1, :] = torch.tensor([0.0, 1.0])\n\n X_train = X[:N, :]\n X_val = X[N:, :]\n C_train = C[:N, :]\n C_val = C[N:, :]\n\n return [X_train, C_train, X_val, C_val, d, K]","def Write_GCode_Drag_Knife(self, PostPro):\n\n # initialisation of the string\n exstr = \"\"\n\n # Get the mill settings defined in the GUI\n safe_retract_depth = self.parentLayer.axis3_retract\n safe_margin = self.parentLayer.axis3_safe_margin\n\n workpiece_top_Z = self.axis3_start_mill_depth\n f_g1_plane = self.f_g1_plane\n f_g1_depth = self.f_g1_depth\n\n \"\"\"\n Cutting in slices is not supported for Swivel Knife tool. All is cut at once.\n \"\"\"\n mom_depth = self.axis3_mill_depth\n drag_depth = self.axis3_slice_depth\n\n # Move the tool to the start.\n exstr += self.stmove.geos.abs_el(0).Write_GCode(PostPro)\n\n # Add string to be added before the shape will be cut.\n exstr += PostPro.write_pre_shape_cut()\n\n # Move into workpiece and start cutting into Z\n exstr += PostPro.rap_pos_z(\n workpiece_top_Z + abs(safe_margin)) # Compute the safe margin from the initial mill depth\n exstr += PostPro.chg_feed_rate(f_g1_depth)\n\n # Write the geometries for the first cut\n if isinstance(self.stmove.geos.abs_el(1), ArcGeo):\n if self.stmove.geos.abs_el(1).drag:\n exstr += PostPro.lin_pol_z(drag_depth)\n drag = True\n else:\n exstr += PostPro.lin_pol_z(mom_depth)\n drag = False\n else:\n exstr += PostPro.lin_pol_z(mom_depth)\n drag = False\n exstr += PostPro.chg_feed_rate(f_g1_plane)\n\n exstr += self.stmove.geos.abs_el(1).Write_GCode(PostPro)\n\n for geo in Geos(self.stmove.geos[2:]).abs_iter():\n if isinstance(geo, ArcGeo):\n if geo.drag:\n exstr += PostPro.chg_feed_rate(f_g1_depth)\n exstr += PostPro.lin_pol_z(drag_depth)\n exstr += PostPro.chg_feed_rate(f_g1_plane)\n drag = True\n elif drag:\n exstr += PostPro.chg_feed_rate(f_g1_depth)\n exstr += PostPro.lin_pol_z(mom_depth)\n exstr += PostPro.chg_feed_rate(f_g1_plane)\n drag = False\n elif drag:\n exstr += PostPro.chg_feed_rate(f_g1_depth)\n exstr += PostPro.lin_pol_z(mom_depth)\n exstr += PostPro.chg_feed_rate(f_g1_plane)\n drag = False\n\n exstr += self.Write_GCode_for_geo(geo, PostPro)\n\n # Do the tool retraction\n exstr += PostPro.chg_feed_rate(f_g1_depth)\n exstr += PostPro.lin_pol_z(workpiece_top_Z + abs(safe_margin))\n exstr += PostPro.rap_pos_z(safe_retract_depth)\n\n # Add string to be added before the shape will be cut.\n exstr += PostPro.write_post_shape_cut()\n\n return exstr","def get_label(name):\n lower = name.lower()\n vals = lower.split('_')\n if 'ho' in vals:\n name = 'Independent Estimate'\n elif 'alldata' in vals:\n name = 'Extra-Data Estimate'\n elif 'ris' in vals[0]:\n name = 'RIS'\n if 'w' in vals[0]:\n name += ' WIS'\n if 'pd' in vals[0]:\n name += ' PDIS'\n elif 'is' in vals[0]:\n name = 'OIS'\n if 'w' in vals[0]:\n name += ' WIS'\n if 'pd' in vals[0]:\n name += ' PDIS'\n if 'dr' in vals:\n name += ' DR'\n if 'wdr' in vals:\n name += ' WDR'\n return name","def setLabelDistance(dist=24, axes='XYZ'):\n dislin.labdis(dist, axes)","def onAddCutToolClicked(self, event):\n i_cube = self.cube_choice.GetSelection()\n i_dimension = self.cut_dimension_choice.GetSelection()\n\n if i_dimension <= 0:\n dlg_func.openWarningBox(_(u'CUT'), _(u'Cut dimension not selected'))\n else:\n value = self.cut_value_textCtrl.GetValue()\n if not value.strip():\n dlg_func.openWarningBox(_(u'CUT'), _(u'Cut value not specified'))\n else:\n cube = self._OLAP_server.getCubes()[i_cube]\n dimension = cube.getDimensions()[i_dimension - 1]\n row = (dimension.getLabel(), dimension.getName(), value)\n self.appendListCtrlRow(listctrl=self.cut_listCtrl, row=row)\n\n # After adding, clear the controls\n self.cut_dimension_choice.SetSelection(0)\n self.cut_value_textCtrl.SetValue(u'')\n\n event.Skip()"],"string":"[\n \"def extract_info(config, cut, label):\\n cfg = filter(lambda c: c['name'] == cut, config['physics']['cuts'])[0]\\n text = \\\"\\\"\\n if 'max' not in cfg:\\n text += \\\"#geq \\\"\\n text += str(cfg['min'])\\n if 'max' in cfg and cfg['max'] != cfg['min']:\\n text += '-' + str(cfg['max']) + ' ' + label + 's'\\n elif cfg['min'] != 1:\\n text += ' ' + label + 's'\\n else:\\n text += ' ' + label\\n return text\",\n \"def makeDPartial( name\\n , config\\n , DecayDescriptor\\n , inputSel\\n ) :\\n\\n _Kcuts1 = \\\"~ISMUON & (PT > %(DaugPtLoose)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2Loose)s)\\\" % locals()['config']\\n _KcutsPIDK = \\\" & (PIDK > %(HighPIDK)s)\\\" % locals()['config']\\n _Kcuts2 = \\\" & (ISLONG) & (P > %(DaugPLoose)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2Loose)s)\\\" % locals()['config']\\n _Kcuts = _Kcuts1 + _KcutsPIDK + _Kcuts2\\n _Picuts1 = \\\"~ISMUON & (PT > %(DaugPtMin)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2)s)\\\" % locals()['config']\\n _PicutsPIDK = \\\" & (PIDK < %(LowPIDK)s)\\\" % locals()['config']\\n _Picuts2 = \\\" & (ISLONG) & (P > %(DaugP)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2)s)\\\" % locals()['config']\\n _Picuts = _Picuts1 + _PicutsPIDK + _Picuts2\\n _dauCuts = { 'K+': _Kcuts, 'pi+': _Picuts }\\n #_Kcuts1 = \\\"~ISMUON & (PT > 500* MeV) & (MIPCHI2DV(PRIMARY) > 4)\\\"\\n #_KcutsPIDK = \\\" & (PIDK > 5)\\\"\\n #_Kcuts2 = \\\" & (ISLONG) & (P > 5000* MeV) & (TRCHI2DOF < 5)\\\"\\n #_Kcuts = _Kcuts1 + _KcutsPIDK + _Kcuts2\\n #_Picuts1 = \\\"~ISMUON & (PT > 500* MeV) & (MIPCHI2DV(PRIMARY) > 4)\\\"\\n #_PicutsPIDK = \\\" & (PIDK < 0)\\\"\\n #_Picuts2 = \\\" & (ISLONG) & (P > 5000* MeV) & (TRCHI2DOF < 5)\\\"\\n #_Picuts = _Picuts1 + _PicutsPIDK + _Picuts2\\n #_dauCuts = { 'K+': _Kcuts, 'pi+': _Picuts }\\n\\n _combCuts = \\\"(APT > %(D0PtLoose)s* MeV)\\\" \\\\\\n \\\"& (AP > %(D0P)s* MeV)\\\" % locals()['config']\\n\\n _motherCuts = \\\"(VFASPF(VCHI2PDOF) < %(D0VtxChi2Ndof)s)\\\" \\\\\\n \\\"& (BPVVDCHI2 > %(D0FDChi2)s)\\\" % locals()['config']\\n\\n\\n _Dminus = CombineParticles( DecayDescriptor = DecayDescriptor\\n , DaughtersCuts = _dauCuts\\n , CombinationCut = _combCuts\\n , MotherCut = _motherCuts\\n )\\n\\n return Selection( name+'Sel',\\n Algorithm = _Dminus,\\n RequiredSelections = inputSel\\n )\",\n \"def x_group_label(\\n x_gr: int, cut: int = 20, name_dict: Dict[AnyStr, AnyStr] = names_dict\\n) -> AnyStr:\\n name = name_dict[str(x_gr)]\\n if len(name) > cut:\\n return f\\\"{name[:cut-3]}...\\\"\\n else:\\n return name\",\n \"def assignLabels(self):\\n clusters = np.arange(0, len(self.V))[self.V < self.V1] #indexes self.V, volumes_sorted, and oldOrder\\n self.clusterV = self.volumes_sorted[clusters]\\n clusters = self.oldOrder[clusters] #indexes volumes\\n self.clusters = self.nonBI[clusters] #indexes self.vor and self.data\\n self.easyLabel = np.zeros(len(self.data))\\n self.easyLabel[self.clusters] = 1\\n print('Out of ' + str(len(self.data)) + ' particles, ' + str(len(self.clusters)) + ' (' + str(round(len(self.clusters)*100/len(self.data), 3)) +' %) are labelled as cluster particles.')\",\n \"def __init__(self, data_cfg, pipeline_cfg, root_path, sel_index=0):\\n\\n super(DetRetailOneDataset, self).__init__(\\n data_cfg, pipeline_cfg, root_path, sel_index\\n )\\n\\n self.cat2label = {cat: i for i, cat in enumerate(self.class_names)}\\n self.ORI_CLASSES = (\\n \\\"asamu\\\",\\n \\\"baishikele\\\",\\n \\\"baokuangli\\\",\\n \\\"aoliao\\\",\\n \\\"bingqilinniunai\\\",\\n \\\"chapai\\\",\\n \\\"fenda\\\",\\n \\\"guolicheng\\\",\\n \\\"haoliyou\\\",\\n \\\"heweidao\\\",\\n \\\"hongniu\\\",\\n \\\"hongniu2\\\",\\n \\\"hongshaoniurou\\\",\\n \\\"kafei\\\",\\n \\\"kaomo_gali\\\",\\n \\\"kaomo_jiaoyan\\\",\\n \\\"kaomo_shaokao\\\",\\n \\\"kaomo_xiangcon\\\",\\n \\\"kele\\\",\\n \\\"laotansuancai\\\",\\n \\\"liaomian\\\",\\n \\\"lingdukele\\\",\\n \\\"maidong\\\",\\n \\\"mangguoxiaolao\\\",\\n \\\"moliqingcha\\\",\\n \\\"niunai\\\",\\n \\\"qinningshui\\\",\\n \\\"quchenshixiangcao\\\",\\n \\\"rousongbing\\\",\\n \\\"suanlafen\\\",\\n \\\"tangdaren\\\",\\n \\\"wangzainiunai\\\",\\n \\\"weic\\\",\\n \\\"weitanai\\\",\\n \\\"weitaningmeng\\\",\\n \\\"wulongcha\\\",\\n \\\"xuebi\\\",\\n \\\"xuebi2\\\",\\n \\\"yingyangkuaixian\\\",\\n \\\"yuanqishui\\\",\\n \\\"xuebi-b\\\",\\n \\\"kebike\\\",\\n \\\"tangdaren3\\\",\\n \\\"chacui\\\",\\n \\\"heweidao2\\\",\\n \\\"youyanggudong\\\",\\n \\\"baishikele-2\\\",\\n \\\"heweidao3\\\",\\n \\\"yibao\\\",\\n \\\"kele-b\\\",\\n \\\"AD\\\",\\n \\\"jianjiao\\\",\\n \\\"yezhi\\\",\\n \\\"libaojian\\\",\\n \\\"nongfushanquan\\\",\\n \\\"weitanaiditang\\\",\\n \\\"ufo\\\",\\n \\\"zihaiguo\\\",\\n \\\"nfc\\\",\\n \\\"yitengyuan\\\",\\n \\\"xianglaniurou\\\",\\n \\\"gudasao\\\",\\n \\\"buding\\\",\\n \\\"ufo2\\\",\\n \\\"damaicha\\\",\\n \\\"chapai2\\\",\\n \\\"tangdaren2\\\",\\n \\\"suanlaniurou\\\",\\n \\\"bingtangxueli\\\",\\n \\\"weitaningmeng-bottle\\\",\\n \\\"liziyuan\\\",\\n \\\"yousuanru\\\",\\n \\\"rancha-1\\\",\\n \\\"rancha-2\\\",\\n \\\"wanglaoji\\\",\\n \\\"weitanai2\\\",\\n \\\"qingdaowangzi-1\\\",\\n \\\"qingdaowangzi-2\\\",\\n \\\"binghongcha\\\",\\n \\\"aerbeisi\\\",\\n \\\"lujikafei\\\",\\n \\\"kele-b-2\\\",\\n \\\"anmuxi\\\",\\n \\\"xianguolao\\\",\\n \\\"haitai\\\",\\n \\\"youlemei\\\",\\n \\\"weiweidounai\\\",\\n \\\"jindian\\\",\\n \\\"3jia2\\\",\\n \\\"meiniye\\\",\\n \\\"rusuanjunqishui\\\",\\n \\\"taipingshuda\\\",\\n \\\"yida\\\",\\n \\\"haochidian\\\",\\n \\\"wuhounaicha\\\",\\n \\\"baicha\\\",\\n \\\"lingdukele-b\\\",\\n \\\"jianlibao\\\",\\n \\\"lujiaoxiang\\\",\\n \\\"3+2-2\\\",\\n \\\"luxiangniurou\\\",\\n \\\"dongpeng\\\",\\n \\\"dongpeng-b\\\",\\n \\\"xianxiayuban\\\",\\n \\\"niudufen\\\",\\n \\\"zaocanmofang\\\",\\n \\\"wanglaoji-c\\\",\\n \\\"mengniu\\\",\\n \\\"mengniuzaocan\\\",\\n \\\"guolicheng2\\\",\\n \\\"daofandian1\\\",\\n \\\"daofandian2\\\",\\n \\\"daofandian3\\\",\\n \\\"daofandian4\\\",\\n \\\"yingyingquqi\\\",\\n \\\"lefuqiu\\\",\\n )\",\n \"def _make_vbenf_label(chain_parts):\\n\\n # toy label for development: run simple and dijet independently.\\n # simple makes Et cuts on two jets. Independently (sharing possible)\\n # of jets choosean by simple, the dijet\\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n assert scenario.startswith('vbenf')\\n args = _args_from_scenario(scenario)\\n if not args:\\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \\n arg_res = [\\n re.compile(r'(?P\\\\d*)(?Pfbet)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pmass)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pet)(?P\\\\d*)'),\\n ]\\n\\n defaults = {\\n 'et': ('101', 'inf'),\\n 'mass': ('800', 'inf'),\\n 'fbet': ('501', 'inf'),\\n }\\n\\n argvals = {}\\n while args:\\n assert len(args) == len(arg_res)\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = defaults[key][0]\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = defaults[key][1]\\n argvals[key+'hi'] = hi\\n\\n assert len(args) == len(arg_res)\\n assert len(args) == 0\\n\\n return \\\"\\\"\\\"\\n and\\n (\\n []\\n simple\\n (\\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\\n )\\n combgen\\n (\\n [(10et, 0eta320)]\\n dijet\\n (\\n [(%(masslo).0fdjmass, 26djdphi)]\\n ) \\n simple\\n (\\n [(10et, 0eta320)(20et, 0eta320)]\\n )\\n )\\n )\\\"\\\"\\\" % argvals\",\n \"def create_labelled_dataset(self):\\n\\n print(\\\"-------------------------------------------------------------------\\\")\\n print(\\\" How to Use the Pole Hull Label Tool\\\")\\n print(\\\"-------------------------------------------------------------------\\\")\\n print(\\\"- If a hull is NOT associated to a pole: press the 1 button\\\")\\n print(\\\"- If a hull IS associated to a pole: press the 2 button\\\")\\n print(\\\"\\\\n- If any other key is pressed, the program EXITS\\\")\\n print(\\\"-------------------------------------------------------------------\\\")\\n\\n detector = gate_detector.GateDetector(im_resize=3.0/4)\\n\\n imgs = []\\n labels = []\\n directory = os.path.dirname(os.getcwd())\\n \\n # Get absolute path of all images in the images folder\\n for dirpath,_,filenames in os.walk(os.path.join(directory, 'images', 'gate')):\\n for f in filenames:\\n imgs.append(os.path.abspath(os.path.join(dirpath, f)))\\n\\n # Get the hulls from the segmented image and run the display and label program for each image\\n for img in imgs:\\n src = cv.imread(img, 1)\\n pre = detector.preprocess(src)\\n seg = detector.segment(pre)\\n mor = detector.morphological(seg)\\n hulls = detector.create_convex_hulls(seg)\\n labels += self.display_and_label_hulls(hulls, pre)\\n return labels\",\n \"def setContourLabels(mode='none', ndigits=1):\\n odict = {'none':'NONE', 'float':'FLOAT', 'string':'CONLAB'}\\n dislin.labdig(ndigits, 'CONTUR')\\n dislin.labels(odict[mode], 'CONTUR')\",\n \"def _add_labels(self):\\n coords = self['pore.coords']\\n self['pore.front'] = coords[:,0]<(0.1*self._Lx)\\n self['pore.back'] = coords[:,0]>(0.9*self._Lx)\\n self['pore.left'] = coords[:,1]<(0.1*self._Ly)\\n self['pore.right'] = coords[:,1]>(0.9*self._Ly)\\n self['pore.bottom'] = coords[:,2]<(0.1*self._Lz)\\n self['pore.top'] = coords[:,2]>(0.9*self._Lz)\\n bnds = self.pores(labels=['front','back','left','right','bottom','top'])\\n self['pore.boundary'] = False\\n self['pore.boundary'] = bnds\",\n \"def plot_data_assemble(self,kwargs_seg, add_mask ,img_name='data.pdf',cutout_text='lensed image',font_size=28):\\n mask = self.data_mask\\n image = self.raw_image\\n picked_data = self.data\\n selem = np.ones((add_mask, add_mask))\\n img_mask = ndimage.binary_dilation(mask.astype(np.bool), selem)\\n fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4, figsize=(19, 10))\\n ax1.imshow(image, origin='lower', cmap=\\\"gist_heat\\\")\\n ax1.set_title('Cutout Image',fontsize =font_size)\\n ax1.text(image.shape[0] * 0.2, image.shape[0] * 0.05, cutout_text,size=20, color='white',weight=\\\"bold\\\")\\n ax1.axis('off')\\n segments_deblend_list, xcenter, ycenter, c_index=kwargs_seg\\n ax2.imshow(segments_deblend_list, origin='lower')\\n for i in range(len(xcenter)):\\n ax2.text(xcenter[i] * 1.1, ycenter[i], 'Seg' + repr(i), size=20,color='w',weight=\\\"bold\\\")\\n ax2.text(image.shape[0] * 0.2, image.shape[0] * 0.9, 'Seg' + repr(c_index) + ' ' + 'in center',\\n size=20, color='white',weight=\\\"bold\\\")\\n ax2.set_title('Segmentations',fontsize =font_size)\\n ax2.axis('off')\\n ax3.imshow(img_mask+mask, origin='lower',cmap=\\\"gist_heat\\\")\\n ax3.set_title('Selected pixels',fontsize =font_size)\\n ax3.text(image.shape[0] * 0.1, image.shape[0] * 0.05, 'pixels (S/N >' + repr(self.snr) + ')',size=20, color='white',weight=\\\"bold\\\")\\n ax3.text(image.shape[0] * 0.1, image.shape[0] * 0.9, 'additional pixels', size=20, color='r',weight=\\\"bold\\\")\\n ax3.axis('off')\\n ax4.imshow(picked_data, origin='lower',cmap=\\\"gist_heat\\\")\\n ax4.set_title('Processed Image',fontsize =font_size)\\n ax4.axis('off')\\n plt.show()\\n fig.savefig(img_name)\\n return 0\",\n \"def BaseLabel(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_BaseLabel(self, *args)\",\n \"def main():\\n # Directory where the DICOM files are being stored (in this\\n input_path = './Inputs/valve'\\n\\n # Original image from the filepath\\n img_original = read_image(input_path)\\n\\n # Image with smoothing applied to reduce noise\\n img_smooth = sitk.CurvatureFlow(image1=img_original, timeStep=0.125, numberOfIterations=10)\\n\\n # Create labels on our smoothed image for cardiac tissue and tissue with blood\\n labels_tissue = sitk.BinaryThreshold(image1=img_smooth, lowerThreshold=325, upperThreshold=470, insideValue=1)\\n labels_blood = sitk.BinaryThreshold(image1=img_smooth, lowerThreshold=450, upperThreshold=800, insideValue=1, outsideValue=0)\\n\\n # IMPORTANT STEP: essentially, this is the key to our algorithm. By finding the \\\"blood\\\" without cardiac tissue,\\n # and then using binary hole filling with a fairly large radius, we are able to label a lot of the mitral valve\\n # area without labeling too much of the other cardiac tissue. Thus, THIS is what lets us single out the mitral\\n # valve tissue from the rest - all we need is the overlap of the two labels\\n labels_tissue_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_tissue, radius=[2] * 3, majorityThreshold=1, backgroundValue=0, foregroundValue=1)\\n labels_blood_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_blood, radius=[4] * 3, majorityThreshold=1, backgroundValue=0, foregroundValue=1)\\n labels_valve = retrieve_overlap(labels_blood_no_holes, labels_tissue_no_holes)\\n labels_valve_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_valve, radius=[2] * 3, majorityThreshold=1, backgroundValue=0, foregroundValue=1)\\n labels_valve_no_holes = sitk.VotingBinaryHoleFilling(image1=labels_valve_no_holes, radius=[1] * 3, majorityThreshold=0, backgroundValue=1, foregroundValue=0)\\n\\n # Fix intensity scaling on our original smoothed image for pretty diagram purposes\\n img_smooth = sitk.Cast(sitk.RescaleIntensity(img_smooth), labels_tissue_no_holes.GetPixelID())\\n\\n # Use a density-based clustering algorithm to attempt to remove as much noise as possible\\n labels_valve_filtered = dbscan_filter(labels_valve_no_holes, eps=2, use_z=False)\\n labels_valve_filtered = dbscan_filter(labels_valve_filtered, eps=4)\\n\\n # Find likely start and end points of our image by setting a mininum number of labeled pixels\\n start, end = filter_by_label_count(labels_valve_filtered, 10)\\n img_smooth = img_smooth[:, :, start:end]\\n labels_valve_filtered = labels_valve_filtered[:, :, start:end]\\n\\n # Remove all values distant from the center of our starting location by taking advantage of kmeans\\n df = get_df_from_img(labels_valve_filtered[:, :, 0], dimensions=2)\\n x_mid = df['x'].mean()\\n y_mid = df['y'].mean()\\n df = get_df_from_img(labels_valve_filtered)\\n distance_df = df.drop('z', axis=1)\\n distance_df['x_dist'] = abs(distance_df['x'] - x_mid)\\n distance_df['y_dist'] = abs(distance_df['y'] - y_mid)\\n fit = cluster.KMeans(n_clusters=2).fit(distance_df.drop(['x', 'y'], axis=1))\\n labels = fit.labels_\\n df['label'] = pd.Series(labels)\\n counts = df['label'].value_counts().to_dict()\\n largest_cluster = max(counts.iterkeys(), key=(lambda key: counts[key]))\\n update_img_from_df(df, labels_valve_filtered, keep=largest_cluster)\\n\\n # Find likely start and end points of our image by setting a mininum number of labeled pixels\\n start, end = filter_by_label_count(labels_valve_filtered, 10)\\n img_smooth = img_smooth[:, :, start:end]\\n labels_valve_filtered = labels_valve_filtered[:, :, start:end]\\n\\n # Use a segmentation-based clustering algorithm to attempt to find each valve\\n label_segments, x_max = kmeans_segment(labels_valve_filtered, use_z=False)\\n\\n left, right = (label_segments[0], label_segments[1])\\n if x_max[0] > x_max[1]:\\n left, right = right, left\\n\\n # Finally, we can simply take the furthest point from the likely start/end points in order to get our annulus\\n # this can be done by every z value\\n left_points = {'x': [], 'y': [], 'z': []}\\n right_points = {'x': [], 'y': [], 'z': []}\\n zlen = len(sitk.GetArrayFromImage(left))\\n for z in xrange(zlen):\\n left_df = get_df_from_img(left[:, :, z], dimensions=2)\\n if len(left_df['y']) > 0:\\n index = left_df['y'].idxmin()\\n row = left_df.iloc[index]\\n left_points['x'].append(int(row['x']))\\n left_points['y'].append(int(row['y']))\\n left_points['z'].append(z)\\n\\n right_df = get_df_from_img(right[:, :, z], dimensions=2)\\n if len(right_df['x']) > 0:\\n index = right_df['x'].idxmax()\\n row = right_df.iloc[index]\\n right_points['x'].append(int(row['x']))\\n right_points['y'].append(int(row['y']))\\n right_points['z'].append(z)\\n\\n # These both represent the coordinates of our annulus ring. A simple spline can be used for interpolation between\\n # points\\n final_left = pd.DataFrame.from_dict(left_points)\\n final_right = pd.DataFrame.from_dict(right_points)\\n print('Coordinates for one side of the ring')\\n print(final_left)\\n print('\\\\n\\\\nCoordinates for the other side of the ring')\\n print(final_right)\\n\\n final_image = make_empty_img_from_img(left)\\n x = left_points['x'] + right_points['x']\\n y = left_points['y'] + right_points['y']\\n z = left_points['z'] + right_points['z']\\n for x, y, z in zip(x, y, z):\\n final_image.SetPixel(x, y, z, 1)\\n\\n show_all(img_smooth, final_image)\",\n \"def label(self, cfg):\\n rep = \\\"\\\"\\n nl = \\\"\\\"\\n for node in cfg.nodes:\\n rep += nl + \\\"{}\\\\tgen={}\\\\tkill={}\\\\tout={}\\\".format(\\n node, \\n set(self.gen.get(node)),\\n set(self.kill.get(node)),\\n set(self.out.get(node)))\\n nl = \\\"\\\\n\\\"\\n return rep\",\n \"def test_get_dim_label_with_label(self):\\n\\n dim = self.oecd_datasets['oecd']['dimension']['id'][0]\\n dims_df = pyjstat.get_dim_label(self.oecd_datasets['oecd'], dim)\\n self.assertTrue(dims_df.iloc[0]['id'] == 'UNR')\\n self.assertTrue(dims_df.iloc[-1]['label'] == 'Unemployment rate')\",\n \"def LabelDisks(self):\\n pass\",\n \"def visualize_detection(self, img, dets, seg, classes=[], thresh=0.6):\\n from dataset.cs_labels import labels\\n lut = np.zeros((256,3))\\n for l in labels:\\n if l.trainId<255 and l.trainId>=0:\\n lut[l.trainId,:]=list(l.color)\\n palette = lut\\n # det2seg = {0:6,1:7,2:11,3:12,4:13,5:14,6:15,7:16,8:17,9:18,}\\n det2seg = {0:11,1:12,2:13,3:14,4:15,5:16,6:17,7:18,}\\n \\n import cv2\\n import random\\n tic = time.time()\\n color_white = (255, 255, 255)\\n im = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # change to bgr\\n yscale, xscale, ch = im.shape\\n color = (0,0,128)\\n fontFace = cv2.FONT_HERSHEY_PLAIN\\n fontScale = .8*(yscale/float(320))\\n thickness = 2 if yscale>320 else 1\\n idx = np.argsort(dets[:,6],axis=0)[::-1] ## draw nearest first !!\\n dets = dets[idx,:]\\n for det in dets:\\n cls_id = int(det[0])\\n bbox = [det[2]*xscale,det[3]*yscale,det[4]*xscale,det[5]*yscale]\\n score = det[1]\\n distance = det[-1]\\n if score > thresh:\\n bbox = map(int, bbox)\\n color = palette[det2seg[int(det[0])],(2,1,0)]\\n cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=thickness)\\n text = '%s %.0fm' % (short_class_name[classes[cls_id]], distance*255., )\\n textSize, baseLine = cv2.getTextSize(text, fontFace, fontScale, thickness=1)\\n cv2.rectangle(im, (bbox[0], bbox[1]-textSize[1]), (bbox[0]+textSize[0], bbox[1]), color=(128,0,0), thickness=-1)\\n cv2.putText(im, text, (bbox[0], bbox[1]),\\n color=color_white, fontFace=fontFace, fontScale=fontScale, thickness=1)\\n disp = im.copy()\\n if False: #disp.shape[1]>1000:\\n hh, ww, ch = disp.shape\\n resized = cv2.resize(disp, (int(round(ww*.92)),int(round(hh*.92))))\\n else:\\n resized = disp\\n cv2.imshow(\\\"result\\\", resized)\\n # cv2.imwrite(\\\"data/cityscapes/Results/stuttgart_%06d.png\\\" % (self.imgidx,), resized)\\n # self.imgidx += 1\",\n \"def kohonen():\\n# plb.close('all')\\n \\n dim = 28*28\\n data_range = 255.0\\n \\n # load in data and labels \\n data = np.array(np.loadtxt('data.txt'))\\n labels = np.loadtxt('labels.txt')\\n\\n # select 4 digits \\n name = \\\"Stettler\\\"\\n targetdigits = name2digits(name) # assign the four digits that should be used\\n print(targetdigits) # output the digits that were selected\\n\\n # this selects all data vectors that corresponds to one of the four digits\\n data = data[np.logical_or.reduce([labels==x for x in targetdigits]),:]\\n \\n dy, dx = data.shape\\n \\n #set the size of the Kohonen map. In this case it will be 6 X 6\\n size_k = 6\\n \\n #set the width of the neighborhood via the width of the gaussian that\\n #describes it\\n sigma = 2.0\\n \\n #initialise the centers randomly\\n centers = np.random.rand(size_k**2, dim) * data_range\\n \\n #build a neighborhood matrix\\n neighbor = np.arange(size_k**2).reshape((size_k, size_k))\\n\\n #set the learning rate\\n eta = 0.9 # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE\\n \\n #set the maximal iteration count\\n tmax = 5000 # this might or might not work; use your own convergence criterion\\n \\n #set the random order in which the datapoints should be presented\\n i_random = np.arange(tmax) % dy\\n np.random.shuffle(i_random)\\n \\n for t, i in enumerate(i_random):\\n som_step(centers, data[i,:],neighbor,eta,sigma)\\n\\n # for visualization, you can use this:\\n for i in range(size_k**2):\\n plb.subplot(size_k,size_k,i)\\n \\n plb.imshow(np.reshape(centers[i,:], [28, 28]),interpolation='bilinear')\\n plb.axis('off')\\n \\n # leave the window open at the end of the loop\\n plb.show()\\n plb.draw()\",\n \"def convert_kicad_coor(edif_pt):\\n scale = 10\\n return [edif_pt[0] * scale, +edif_pt[1] * scale]\",\n \"def create_dimension_labels(gll, parameters: list):\\n dimstr = '[ ' + ' | '.join(parameters) + ' ]'\\n gll['MODEL/data'].dims[0].label = 'element'\\n gll['MODEL/data'].dims[1].label = dimstr\\n gll['MODEL/data'].dims[2].label = 'point'\",\n \"def add_hdv(self, ROI_id, type_hdv='cum', checkbox_mode=False):\\n\\n appartenance_contourage = self.dicom_navigation.slice.get_appartenance_contourage(ROI_id)\\n \\n contourage = Contourage_from_matrice(appartenance_contourage, ROI_id) # On crée un objet 'Contourage_from_matrice' à partir du de la matrice booléenne\\n\\n dose_matrix = self.dicom_navigation.slice.get_dose_matrix()\\n\\n # Cas ou on ajoute pour la premiere fois un contourage\\n if dose_matrix is None:\\n return\\n \\n doses = Doses_from_matrice(dose_matrix) # On crée un objet 'Doses_from_matrice' à partir de la matrice de doses mise à jour\\n\\n var = tk.StringVar() # À VENIR... VARIABLE D'ÉTAT QUI INDIQUE SI ON EST EN MODE 'VOLUME RELATF' OU 'VOLUME ABSOLU'. CODÉ EN DUR POUR LE MOMENT\\n var.set('r')\\n\\n self.ddc = Doses_dans_contourage(doses, contourage) # Triage des doses qui sont dans le contourage.\\n\\n if self.ddc.dose_max == 0: # Si la dose max est 0, on sait qu'on est à l'extérieur de la zone réduite. *** \\n return\\n\\n if not ROI_id in self.dict_graph: \\n self.dict_graph[ROI_id] = {} \\n self.dict_plot[ROI_id] = {} \\n self.dict_doses_max[ROI_id] = {} \\n if self.dicom_navigation.var_etat_abs_rel.get() == 'a':\\n self.dict_volumes_max[ROI_id] = {} \\n\\n self.dict_doses_max[ROI_id][type_hdv] = self.ddc.dose_max\\n\\n ###\\n\\n if self.dicom_navigation.var_etat_abs_rel.get() == 'r': # si on est en mode 'volume relatif', le range des axes sera définit différemment\\n facteur = 100.0/self.ddc.nb_voxels # comme l'instance 'axe_volume' créée par les classes hdv_cumulatif et hdv_differentiel contient des données en NOMBRE DE VOXELS\\n # (et non en pourcentage ou en volume réel), il faut multiplier ces données par le facteur de conversion approprié (il dépend\\n # de si l'on est en mode 'relatf' ou 'absolu').\\n\\n if self.dicom_navigation.var_etat_abs_rel.get() == 'a': # si on est en mode 'volume absolu'.\\n facteur = self.ddc.v_voxel\\n self.dict_volumes_max[ROI_id][type_hdv] = self.ddc.v_voxel * self.ddc.nb_voxels \\n self.y_lim = get_max_2D_dic(self.dict_volumes_max)\\n\\n ###\\n\\n if type_hdv == 'cum':\\n hdv = HDV_cumulatif(self.ddc, 100)\\n\\n if type_hdv == 'diff':\\n hdv = HDV_differentiel(self.ddc, 50)\\n\\n\\n self.dict_graph[ROI_id][type_hdv] = hdv\\n self.dict_plot[ROI_id][type_hdv], = self.fig.plot(hdv.axe_doses, facteur * hdv.axe_volume)\\n\\n ###\\n\\n self.x_lim = get_max_2D_dic(self.dict_doses_max) \\n\\n self.fig.set_xlim([0, 1.02*self.x_lim]) # dimension de l'axe des x\\n self.fig.set_ylim([0, 1.02*self.y_lim]) # dimension de l'axe des y\\n\\n # Contraintes\\n if self.got_contraintes and type_hdv == 'cum': # 'got_contraintes' SERA INITALISÉE À 'TRUE' LORSQUE L'ON AURA RÉCUPÉRÉ LE FICHIER DE CONTRAINTES\\n self.dicom_navigation.get_dicom_contraintes().verifier_contraintes_sur_une_ROI(ROI_id)\\n\\n # Modifier\\n if checkbox_mode:\\n self.refresh_HDV()\",\n \"def instance_label(task, pred, k=15, n_iters=1, dist_thresh=5, watershed=False):\\n mask = pred\\n\\n # noise removal\\n if k > 1 and n_iters > 0:\\n kernel = np.ones((k, k), np.uint8)\\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,\\n iterations=n_iters)\\n\\n if watershed:\\n from clab.live import filters\\n mask = filters.watershed_filter(mask, dist_thresh=dist_thresh)\\n\\n mask = mask.astype(np.uint8)\\n n_ccs, cc_labels = cv2.connectedComponents(mask, connectivity=4)\\n return cc_labels\",\n \"def old_ideal_label(I):\\n a, c, d = ideal_HNF(I)\\n return \\\"%s.%s.%s\\\" % (a * d, c, d)\",\n \"def setContourLabelString(text=''):\\n dislin.conlab(text)\",\n \"def label(cmd):\\r\\n cmd = cmd.replace('make][.DP', 'make1][.NP')\\r\\n cmd = cmd.replace('make][.SC', 'make2][.SC')\\r\\n cmd = re.sub('(draw.*)one','\\\\\\\\1one1',cmd)\\r\\n cmd = re.sub('(make1.*)one','\\\\\\\\1one1',cmd)\\r\\n cmd = re.sub('(make2.*)one','\\\\\\\\1one2',cmd)\\r\\n cmd = re.sub('(move.*)one','\\\\\\\\1one2',cmd)\\r\\n cmd = re.sub('(hide.*)one','\\\\\\\\1one2',cmd)\\r\\n cmd = '[result ' + cmd + ']' #dummy function for plop\\r\\n return cmd\",\n \"def label(gt_dataset, volume_dim, voxel_dim, labeling_params):\\n labeled_volumes = dict()\\n labeled_cells = dict()\\n #Use global density and reduce the size of gt_dataset here\\n global_density = labeling_params[\\\"global_density\\\"]\\n gt_dataset = {k: v for k,v in gt_dataset.items() if random_sample() < global_density}\\n #Label in the order specified in the configuration\\n layers = sorted(labeling_params.keys())\\n #Remove global_density\\n layers.remove(\\\"global_density\\\")\\n for layer in layers:\\n print \\\"Labeling {}\\\".format(layer)\\n fluorophore = labeling_params[layer]['fluorophore']\\n volume, cells = brainbow(gt_dataset, volume_dim, voxel_dim, **labeling_params[layer])\\n if fluorophore in labeled_volumes:\\n labeled_volumes[fluorophore] += volume\\n labeled_cells[fluorophore] |= cells\\n else:\\n labeled_volumes[fluorophore] = volume\\n labeled_cells[fluorophore] = cells\\n return labeled_volumes, labeled_cells\",\n \"def make_data_label(self):\\n data_label = \\\"\\\"\\n if self.detector is not None:\\n data_label += \\\"%s \\\"%self.detector\\n if self.selection is not None:\\n data_label += \\\"%s Event Selection\\\"%self.selection\\n if data_label == \\\"\\\":\\n data_label = \\\"IceCube\\\"\\n return data_label\",\n \"def write_label(self, contig_name, width, height, font, title_width, upper_left, vertical_label,\\n strand, canvas, horizontal_centering=False, center_vertical=False, chop_text=True,\\n label_color=(50, 50, 50, 255)):\\n upper_left = list(upper_left) # to make it mutable\\n shortened = contig_name[-title_width:] # max length 18. Last characters are most unique\\n txt = Image.new('RGBA', (width, height))#, color=(0,0,0,50))\\n txt_canvas = ImageDraw.Draw(txt)\\n text_width = txt_canvas.textsize(shortened, font)[0]\\n if not chop_text and text_width > width:\\n txt = Image.new('RGBA', (text_width, height)) # TODO performance around txt_canvas\\n txt_canvas = ImageDraw.Draw(txt)\\n if center_vertical or vertical_label: # Large labels are centered in the column to look nice,\\n # rotation indicates strand in big text\\n vertically_centered = (height // 2) - multi_line_height(font, shortened, txt)//2\\n else: # Place label at the beginning of gene based on strand\\n vertically_centered = height - multi_line_height(font, shortened, txt) # bottom\\n if strand == \\\"+\\\":\\n vertically_centered = 0 # top of the box\\n txt_canvas.multiline_text((0, max(0, vertically_centered)), shortened, font=font,\\n fill=label_color)\\n if vertical_label:\\n rotation_direction = 90 if strand == '-' else -90\\n txt = txt.rotate(rotation_direction, expand=True)\\n upper_left[1] += -4 if strand == '-' else 4\\n if horizontal_centering:\\n margin = width - text_width\\n upper_left[0] += margin // 2\\n canvas.paste(txt, (upper_left[0], upper_left[1]), txt)\",\n \"def convert_medical_decathlon_labels(mask, cohort, keep_all_label=False):\\n label = 12*[0]\\n if keep_all_label:\\n label += [0,0]\\n \\n if cohort == 'liver':\\n mask[mask == 2] = 6\\n mask[mask == 1] = 6\\n label[6] = 1\\n \\n elif cohort == 'pancreas':\\n mask[mask == 2] = 11\\n mask[mask == 1] = 11\\n label[11] = 1\\n\\n elif cohort == 'spleen':\\n mask[mask != 1] = 0\\n label[1] = 1 \\n\\n elif cohort == 'hepatic':\\n mask[mask == 2] = 0\\n mask[mask == 1] = 0\\n \\n return mask, label\",\n \"def get_labels(self):\\n if self.option == \\\"term\\\":\\n return ['platform characteristics', 'atmospheric winds', 'radio wave','weather events', 'geomagnetism', 'atmospheric electricity','microwave', 'atmospheric temperature', 'atmospheric water vapor','atmospheric pressure', 'aerosols', 'atmospheric radiation','atmospheric chemistry', 'precipitation', 'sensor characteristics','radar', 'infrared wavelengths', 'visible wavelengths','weather/climate advisories', 'clouds', 'lidar', 'ocean optics','ultraviolet wavelengths', 'cryospheric indicators','land use/land cover', 'topography', 'surface thermal properties','spectral/engineering', 'soils', 'snow/ice', 'geothermal dynamics','natural hazards', 'surface water', 'vegetation','land surface/agriculture indicators','gravity/gravitational field', 'marine advisories', 'altitude','water quality/water chemistry', 'ocean temperature','ocean winds', 'atmospheric/ocean indicators', 'coastal processes','erosion/sedimentation', 'marine sediments', 'ocean chemistry','salinity/density', 'ocean color', 'aquatic ecosystems','vegetation2', 'landscape', 'cloud properties','surface radiative properties', 'geodetics','agricultural plant science', 'forest science','ecological dynamics', 'environmental impacts', 'sustainability','boundaries', 'ecosystems', 'air quality', 'population','infrastructure', 'environmental governance/management','public health', 'economic resources', 'socioeconomics','environmental vulnerability index (evi)', 'human settlements','agricultural chemicals', 'animal science','habitat conversion/fragmentation', 'animals/vertebrates','earth gases/liquids', 'rocks/minerals/crystals','social behavior', 'ground water', 'frozen ground','terrestrial hydrosphere indicators', 'ocean heat budget','biospheric indicators', 'animal commodities', 'fungi', 'plants','carbon flux', 'geomorphic landforms/processes','paleoclimate indicators', 'ocean circulation', 'sea ice','geochemistry', 'visualization/image processing','subsetting/supersetting', 'transformation/conversion','ocean pressure', 'glaciers/ice sheets', 'protists','solar activity', 'sun-earth interactions','sea surface topography', 'solar energetic particle properties','solar energetic particle flux','ionosphere/magnetosphere dynamics']\\n elif self.option == \\\"mostdepth\\\":\\n return ['flight data logs', 'turbulence', 'radio wave flux', 'lightning', 'magnetic field', 'atmospheric conductivity', 'electric field', 'data synchronization time', 'brightness temperature', 'vertical profiles', 'water vapor profiles', 'air temperature', 'upper level winds', 'atmospheric pressure measurements', 'upper air temperature', 'humidity', 'dew point temperature', 'aerosol particle properties', 'emissivity', 'trace gases/trace species', 'liquid precipitation', 'cloud liquid water/ice', 'microwave radiance', 'sensor counts', 'total pressure', 'airspeed/ground speed', 'total temperature', 'static pressure', 'wind speed', 'wind direction', 'radar reflectivity', 'doppler velocity', 'infrared imagery', 'visible imagery', 'water vapor', 'vertical wind velocity/speed', 'aerosol backscatter', 'weather forecast', 'tropical cyclones', 'visible radiance', 'infrared radiance', 'total precipitable water', 'boundary layer temperature', 'atmospheric temperature indices', 'cloud height', 'flight level winds', 'cloud droplet distribution', 'cloud droplet concentration/size', 'cloud condensation nuclei', 'cloud microphysics', 'hydrometeors', 'ozone', 'wind profiles', 'cloud base temperature', 'cloud base height', 'liquid water equivalent', 'solar radiation', 'planetary boundary layer height', 'surface winds', 'precipitation amount', 'precipitation rate', 'surface pressure', 'rain', 'cloud optical depth/thickness', 'aerosol extinction', 'aerosol optical depth/thickness', 'cirrus cloud systems', 'lidar depolarization ratio', 'radar backscatter', 'radar cross-section', 'return power', 'mean radial velocity', 'radiance', 'air quality', 'climate advisories', 'atmospheric emitted radiation', 'optical depth/thickness', 'surface temperature', 'ultraviolet flux', 'spectrum width', 'microwave imagery', 'lidar backscatter', 'relative humidity', 'u/v wind components', 'wind speed/wind direction', 'radar imagery', 'snow depth', 'land use/land cover classification', 'digital elevation/terrain model (dem)', 'snow', 'droplet size', 'droplet concentration/size', 'drizzle', 'precipitation anomalies', 'snow water equivalent', 'solid precipitation', 'total surface precipitation rate', 'particle size distribution', 'skin temperature', 'attitude characteristics', 'land surface temperature', 'hail', 'reflectance', 'soil moisture/water content', 'soil temperature', 'soil bulk density', 'surface roughness', 'present weather', 'snow density', 'ambient temperature', 'aerosol forward scatter', 'floods', 'snow cover', 'sigma naught', 'precipitable water', 'stage height', 'rivers/streams', 'shortwave radiation', 'photosynthetically active radiation', 'longwave radiation', 'net radiation', 'hourly precipitation amount', '24 hour precipitation amount', 'soil moisture', 'satellite orbits/revolution', 'sea surface temperature', 'heat flux', 'latent heat flux', 'cloud fraction', '3 and 6 hour precipitation amount', 'geopotential height', 'particulate matter', 'particle images', 'water vapor indices', 'horizontal wind velocity/speed', 'electrical conductivity', 'dissolved carbon dioxide', 'hurricanes', 'tropical cyclone track', 'convective clouds/systems (observed/analyzed)', 'cloud top height', 'viewing geometry', 'temperature profiles', 'vertical wind shear', 'wind shear', 'carbon monoxide', 'sea level pressure', 'water vapor tendency', 'potential temperature', 'angstrom exponent', 'ultraviolet radiation', 'solar irradiance', 'scattering', 'absorption', 'water vapor mixing ratio profiles', 'sea surface temperature indices', 'extreme eastern tropical pacific sst', 'sedimentation', 'erosion', 'sediment transport', 'sediments', 'tropopause', 'ocean chemistry', 'ocean optics', 'ocean temperature', 'salinity/density', 'pigments', 'ocean color', 'attenuation/transmission', 'inorganic carbon', 'organic carbon', 'photosynthetically available radiation', 'chlorophyll', 'optical depth', 'fluorescence', 'vegetation index', 'gelbstoff', 'phytoplankton', 'vegetation index2', 'cloud precipitable water', 'landscape ecology', 'ultraviolet radiance', 'cloud ceiling', 'aerosol radiance', 'carbonaceous aerosols', 'dust/ash/smoke', 'nitrate particles', 'organic particles', 'sulfate particles', 'radiative flux', 'transmittance', 'atmospheric stability', 'cloud asymmetry', 'cloud frequency', 'cloud top pressure', 'cloud top temperature', 'cloud vertical distribution', 'cloud emissivity', 'cloud radiative forcing', 'cloud reflectance', 'rain storms', 'reflected infrared', 'thermal infrared', 'incoming solar radiation', 'clouds', 'cloud properties', 'cloud types', 'orbital characteristics', 'sensor characteristics', 'maximum/minimum temperature', 'condensation', 'platform characteristics', 'geolocation', 'geodetics', 'coordinate reference system', 'aerosols', 'topographical relief maps', 'terrain elevation', 'normalized difference vegetation index (ndvi)', 'infrared flux', 'visible flux', 'albedo', 'land use/land cover', 'topography', 'lidar', 'lidar waveform', 'plant phenology', 'vegetation cover', 'crop/plant yields', 'land use classes', 'landscape patterns', 'forest harvesting and engineering', 'forest management', 'total surface water', 'agricultural plant science', 'photosynthesis', 'primary production', 'leaf characteristics', 'evapotranspiration', 'fire occurrence', 'surface thermal properties', 'canopy characteristics', 'evergreen vegetation', 'crown', 'deciduous vegetation', 'anisotropy', 'fire ecology', 'biomass burning', 'wildfires', 'topographical relief', 'burned area', 'surface radiative properties', 'environmental sustainability', 'boundaries', 'anthropogenic/human influenced ecosystems', 'emissions', 'sulfur dioxide', 'population', 'infrastructure', 'environmental assessments', 'public health', 'conservation', 'agriculture production', 'administrative divisions', 'economic resources', 'socioeconomics', 'lake/pond', 'rivers/stream', 'political divisions', 'environmental vulnerability index (evi)', 'ecosystems', 'urban areas', 'sustainability', 'treaty agreements/results', 'human settlements', 'population estimates', 'nitrogen dioxide', 'cropland', 'pasture', 'particulates', 'cyclones', 'mortality', 'environmental impacts', 'droughts', 'earthquakes', 'population distribution', 'fertilizers', 'animal manure and waste', 'urbanization/urban sprawl', 'landslides', 'avalanche', 'urban lands', 'mangroves', 'volcanic eruptions', 'pesticides', 'population size', 'population density', 'lakes/reservoirs', 'surface water', 'rural areas', 'infant mortality rates', 'amphibians', 'mammals', 'carbon', 'sulfur oxides', 'methane', 'non-methane hydrocarbons/volatile organic compounds', 'nitrogen oxides', 'natural gas', 'coal', 'coastal elevation', 'biodiversity functions', 'nuclear radiation exposure', 'radiation exposure', 'poverty levels', 'malnutrition', 'wetlands', 'sea level rise', 'vulnerability levels/index', 'ground water', 'snow/ice', 'electricity', 'energy production/use', 'sustainable development', 'deforestation', 'household income', 'discharge/flow', 'hydropattern', 'nitrogen', 'phosphorus', 'carbon dioxide', 'alpine/tundra', 'forests', 'vegetation', 'permafrost', 'nutrients', 'plant characteristics', 'leaf area index (lai)', 'soil gas/air', 'ammonia', 'nitrous oxide', 'ecosystem functions', 'litter characteristics', 'soil chemistry', 'soil respiration', 'active layer', 'soil depth', 'cation exchange capacity', 'organic matter', 'soil porosity', 'soil texture', 'permafrost melt', 'land subsidence', 'freeze/thaw', 'surface water features', 'chlorinated hydrocarbons', 'methyl bromide', 'methyl chloride', 'molecular hydrogen', 'sulfur compounds', 'fire models', 'biomass', 'dominant species', 'vegetation species', 'sulfur', 'tree rings', 'soil classification', 'heat index', 'sea ice concentration', 'ocean heat budget', 'reforestation', 'even-toed ungulates', 'species recruitment', 'population dynamics', 'range changes', 'topographic effects', 'land resources', 'river ice depth/extent', 'snow melt', 'river ice', 'animal commodities', 'animal ecology and behavior', 'phenological changes', 'water depth', 'inundation', 'forest fire science', 'biogeochemical cycles', 'radiative forcing', 'soil heat budget', 'drainage', 'respiration rate', 'river/lake ice breakup', 'river/lake ice freeze', 'reclamation/revegetation/restoration', 'permafrost temperature', 'indigenous/native species', 'fire dynamics', 'lichens', 'plants', 'plant succession', 'carbon flux', 'coastal', 'salt marsh', 'degradation', 'altitude', 'carbon and hydrocarbon compounds', 'halocarbons and halogens', 'forest composition/vegetation structure', 'water vapor indicators', 'barometric altitude', 'atmospheric water vapor', 'terrestrial ecosystems', 'volatile organic compounds', 'boundary layer winds', 'forest fire danger index', 'periglacial processes', 'landscape processes', 'evaporation', 'soil horizons/profile', 'shrubland/scrub', 'soil ph', 'soils', 'soil water holding capacity', 'community structure', 'pingo', 'soil color', 'virtual temperature', 'formaldehyde', 'hydroxyl', 'photolysis rates', 'cloud dynamics', 'nitric oxide', 'molecular oxygen', 'smog', 'peroxyacyl nitrate', 'hydrogen compounds', 'nitrogen compounds', 'oxygen compounds', 'stable isotopes', 'chemical composition', 'actinic flux', 'tropospheric ozone', 'fossil fuel burning', 'industrial emissions', 'denitrification rate', 'sunshine', 'runoff', 'soil structure', 'mosses/hornworts/liverworts', 'peatlands', 'hydraulic conductivity', 'snow/ice temperature', 'vegetation water content', 'discharge', 'chlorophyll concentrations', 'outgoing longwave radiation', 'geomorphic landforms/processes', 'soil compaction', 'soil impedance', 'canopy transmittance', 'water table', 'decomposition', 'water temperature', 'dissolved gases', 'total dissolved solids', 'agricultural expansion', 'forest science', 'pressure tendency', 'visibility', 'biomass dynamics', 'agricultural lands', 'grasslands', 'savannas', 'grazing dynamics/plant herbivory', 'herbivory', 'paleoclimate reconstructions', 'drought indices', 'fire weather index', 'animal yields', 'multivariate enso index', 'dissolved solids', 'ocean currents', 'salinity', 'coastal processes', 'atmospheric pressure', 'afforestation/reforestation', 'fresh water river discharge', 'surface water chemistry', 'drainage basins', 'resource development site', 'dunes', 'flood plain', 'endangered species', 'precipitation indices', 'temperature indices', 'forest yields', 'stratigraphic sequence', 'freeze/frost', 'frost', 'hydrogen cyanide', 'land management', 'nutrient cycling', 'industrialization', 'suspended solids', 'deserts', 'weathering', 'gas flaring', 'atmospheric temperature', 'ice extent', 'fraction of absorbed photosynthetically active radiation (fapar)', 'marshes', 'swamps', 'lake ice', 'atmospheric winds', 'watershed characteristics', 'transportation', 'soil rooting depth', 'isotopes', 'cultural features', 'consumer behavior', 'boundary surveys', 'aquifers', 'land productivity', 'water quality/water chemistry', 'sediment composition', 'dissolved oxygen', 'surface water processes/measurements', 'turbidity', 'conductivity', 'ph', 'calcium', 'magnesium', 'potassium', 'micronutrients/trace elements', 'social behavior', 'sulfate', 'sediment chemistry', 'biogeochemical processes', 'water ion concentrations', 'cropping systems', 'percolation', 'groundwater chemistry', 'reforestation/revegetation', 'species/population interactions', 'soil infiltration', 'alkalinity', 'soil fertility', 'phosphorous compounds', 'radioisotopes', 'cooling degree days', 'angiosperms (flowering plants)', 'glacial landforms', 'glacial processes', 'contour maps', 'estuaries', 'methane production/use', 'natural gas production/use', 'petroleum production/use', 'visualization/image processing', 'subsetting/supersetting', 'transformation/conversion', 'forest mensuration', 'acid deposition', 'differential pressure', 'precipitation', 'marine ecosystems', 'consumption rates', 'radio wave', 'soil organic carbon (soc)', 'soil erosion', 'halocarbons', 'trace elements/trace metals', 'biomass energy production/use', 'riparian wetlands', 'soil consistence', 'snow stratigraphy', 'thermal conductivity', 'estuary', 'tidal height', 'plant diseases/disorders/pests', 'layered precipitable water', 'atmospheric chemistry', 'water vapor concentration profiles', 'specific humidity', 'total runoff', 'pressure thickness', 'wind stress', 'atmospheric heating', 'conduction', 'hydrogen chloride', 'nitric acid', 'radar', 'land surface/agriculture indicators', 'satellite soil moisture index', 'chlorine nitrate', 'chlorofluorocarbons', 'dinitrogen pentoxide', 'antenna temperature', 'glaciers', 'ice sheets', 'dimethyl sulfide', 'potential vorticity', 'ice fraction', 'atmospheric radiation', 'runoff rate', 'temperature tendency', 'wind dynamics', 'wind direction tendency', 'base flow', 'bromine monoxide', 'chlorine monoxide', 'methyl cyanide', 'hypochlorous acid', 'methanol', 'hydroperoxy', 'cloud base pressure', 'temperature anomalies', 'nitrate', 'ocean mixed layer', 'precipitation trends', 'temperature trends', 'convection', 'ground ice', 'oxygen', 'phosphate', 'solar induced fluorescence', 'chlorine dioxide', 'sun-earth interactions', 'uv aerosol index', 'volcanic activity', 'potential evapotranspiration', 'ultraviolet wavelengths', 'ice temperature', 'sea surface skin temperature', 'sea surface height', 'sublimation', 'convective surface precipitation rate', 'hydrogen fluoride', 'airglow', 'energy deposition', 'x-ray flux', 'electron flux', 'proton flux', 'magnetic fields/magnetic currents']\\n else:\\n return ['platform characteristics', 'atmospheric winds','radio wave', 'weather events', 'geomagnetism','atmospheric electricity', 'microwave', 'atmospheric temperature','atmospheric water vapor', 'atmospheric pressure', 'aerosols','atmospheric radiation', 'atmospheric chemistry', 'precipitation','sensor characteristics', 'radar', 'infrared wavelengths','visible wavelengths', 'weather/climate advisories', 'clouds','lidar', 'ocean optics', 'ultraviolet wavelengths','cryospheric indicators', 'land use/land cover', 'topography','surface thermal properties', 'spectral/engineering', 'soils','snow/ice', 'geothermal dynamics', 'natural hazards','surface water', 'vegetation','land surface/agriculture indicators','gravity/gravitational field', 'marine advisories', 'altitude','water quality/water chemistry', 'ocean temperature','ocean winds', 'atmospheric/ocean indicators', 'coastal processes','erosion/sedimentation', 'marine sediments', 'ocean chemistry','salinity/density', 'ocean color', 'aquatic ecosystems','vegetation2', 'landscape', 'cloud properties','surface radiative properties', 'geodetics','agricultural plant science', 'forest science','ecological dynamics', 'environmental impacts', 'sustainability','boundaries', 'ecosystems', 'air quality', 'population','infrastructure', 'environmental governance/management','public health', 'economic resources', 'socioeconomics','environmental vulnerability index (evi)', 'human settlements','agricultural chemicals', 'animal science','habitat conversion/fragmentation', 'animals/vertebrates','earth gases/liquids', 'rocks/minerals/crystals','social behavior', 'ground water', 'frozen ground','terrestrial hydrosphere indicators', 'ocean heat budget','biospheric indicators', 'animal commodities', 'fungi', 'plants','carbon flux', 'geomorphic landforms/processes','paleoclimate indicators', 'ocean circulation', 'sea ice','geochemistry', 'visualization/image processing','subsetting/supersetting', 'transformation/conversion','ocean pressure', 'glaciers/ice sheets', 'protists','solar activity', 'sun-earth interactions','sea surface topography', 'solar energetic particle properties','solar energetic particle flux','ionosphere/magnetosphere dynamics','flight data logs','wind dynamics', 'radio wave flux', 'lightning', 'magnetic field','atmospheric conductivity', 'electric field','data synchronization time', 'brightness temperature','upper air temperature', 'water vapor profiles','surface temperature', 'upper level winds','atmospheric pressure measurements', 'water vapor indicators','aerosol particle properties', 'emissivity','trace gases/trace species', 'liquid precipitation','cloud microphysics', 'microwave radiance', 'sensor counts','total pressure', 'airspeed/ground speed', 'total temperature','static pressure', 'humidity', 'radar reflectivity','doppler velocity', 'infrared imagery', 'visible imagery','aerosol backscatter', 'weather forecast', 'tropical cyclones','visible radiance', 'infrared radiance','atmospheric temperature indices', 'cloud droplet distribution','cloud condensation nuclei', 'hydrometeors', 'oxygen compounds','wind profiles', 'liquid water equivalent', 'solar radiation','planetary boundary layer height', 'surface winds','precipitation amount', 'precipitation rate', 'surface pressure','aerosol extinction', 'aerosol optical depth/thickness','tropospheric/high-level clouds (observed/analyzed)','lidar depolarization ratio', 'radar backscatter','radar cross-section', 'return power', 'radial velocity','radiance', 'climate advisories', 'atmospheric emitted radiation','optical depth/thickness', 'ultraviolet flux', 'spectrum width','microwave imagery', 'lidar backscatter', 'radar imagery','snow depth', 'land use/land cover classification','terrain elevation', 'solid precipitation', 'droplet size','droplet concentration/size', 'precipitation anomalies','snow water equivalent', 'total surface precipitation rate','skin temperature', 'water vapor', 'attitude characteristics','land surface temperature', 'reflectance','soil moisture/water content', 'soil temperature','soil bulk density', 'surface roughness', 'present weather','snow density', 'geothermal temperature','aerosol forward scatter', 'floods', 'snow cover', 'sigma naught','precipitable water', 'surface water processes/measurements','surface water features', 'shortwave radiation','photosynthetically active radiation', 'longwave radiation','net radiation', 'flight level winds', 'soil moisture','satellite orbits/revolution', 'heat flux','precipitation profiles', 'geopotential height','particulate matter', 'particle images', 'water vapor indices','electrical conductivity', 'gases', 'sea surface temperature','convective clouds/systems (observed/analyzed)','viewing geometry', 'wind shear','carbon and hydrocarbon compounds', 'sea level pressure','water vapor processes', 'ultraviolet radiation','solar irradiance', 'scattering', 'absorption','sea surface temperature indices', 'sedimentation', 'erosion','sediment transport', 'sediments', 'tropopause', 'nan', 'pigments','attenuation/transmission', 'inorganic carbon', 'organic carbon','photosynthetically available radiation', 'chlorophyll','optical depth', 'fluorescence', 'vegetation index', 'gelbstoff','plankton', 'vegetation index2', 'landscape ecology','ultraviolet radiance', 'aerosol radiance','carbonaceous aerosols', 'dust/ash/smoke', 'nitrate particles','organic particles', 'sulfate particles', 'radiative flux','transmittance', 'atmospheric stability','cloud radiative transfer', 'rain storms', 'reflected infrared','thermal infrared', 'incoming solar radiation', 'cloud types','orbital characteristics', 'geolocation','coordinate reference system', 'infrared flux', 'visible flux','albedo', 'lidar waveform', 'plant phenology', 'vegetation cover','crop/plant yields', 'land use classes', 'landscape patterns','forest harvesting and engineering', 'forest management','ecosystem functions', 'leaf characteristics', 'fire ecology','total surface water', 'primary production', 'photosynthesis','canopy characteristics', 'evergreen vegetation', 'crown','deciduous vegetation', 'anisotropy', 'biomass burning','wildfires', 'topographical relief','environmental sustainability','anthropogenic/human influenced ecosystems', 'emissions','sulfur compounds', 'environmental assessments', 'conservation','agriculture production', 'administrative divisions','freshwater ecosystems', 'political divisions', 'urban areas','treaty agreements/results', 'population estimates','nitrogen compounds', 'particulates', 'mortality', 'droughts','earthquakes', 'population distribution', 'fertilizers','animal manure and waste', 'urbanization/urban sprawl','landslides', 'avalanche', 'mangroves', 'volcanic eruptions','pesticides', 'population size', 'population density','rural areas', 'amphibians', 'mammals', 'carbon', 'sulfur oxides','land management', 'natural gas', 'sedimentary rocks','coastal elevation', 'community dynamics','nuclear radiation exposure', 'radiation exposure','poverty levels', 'malnutrition', 'sea level rise','vulnerability levels/index', 'electricity','energy production/use', 'sustainable development','deforestation', 'household income', 'nitrogen', 'phosphorus','terrestrial ecosystems', 'permafrost', 'nutrients','plant characteristics', 'soil gas/air', 'litter characteristics','soil chemistry', 'soil respiration', 'active layer', 'soil depth','cation exchange capacity', 'organic matter', 'soil porosity','soil texture', 'permafrost melt','ground water processes/measurements', 'freeze/thaw','halocarbons and halogens', 'hydrogen compounds', 'biomass','dominant species', 'vegetation species', 'sulfur', 'tree rings','soil classification', 'sea ice concentration', 'reforestation','species/population interactions', 'range changes','topographic effects', 'land resources', 'river ice depth/extent','snow melt', 'river ice', 'animal ecology and behavior','phenological changes', 'forest fire science', 'radiative forcing','soil heat budget', 'river/lake ice breakup','river/lake ice freeze', 'reclamation/revegetation/restoration','lichens', 'marine ecosystems', 'coastal landforms', 'degradation','forest composition/vegetation structure', 'barometric altitude','volatile organic compounds', 'forest fire danger index','periglacial processes', 'landscape processes','soil horizons/profile', 'soil ph', 'soil water holding capacity','fluvial landforms', 'soil color', 'glacial processes','photochemistry', 'cloud dynamics', 'nitrogen oxides', 'smog','chemical composition', 'actinic flux', 'tropospheric ozone','fossil fuel burning', 'industrial emissions','denitrification rate', 'sunshine', 'soil structure','mosses/hornworts/liverworts', 'hydraulic conductivity','snow/ice temperature', 'water characteristics','outgoing longwave radiation', 'soil compaction', 'soil impedance','canopy transmittance', 'ground water features', 'solids','agricultural expansion', 'pressure tendency', 'visibility','herbivory', 'paleoclimate reconstructions', 'drought indices','fire weather index', 'animal yields', 'teleconnections','carbon dioxide', 'dissolved solids', 'ocean currents', 'salinity','afforestation/reforestation', 'fresh water river discharge','surface water chemistry', 'aeolian landforms','precipitation indices', 'temperature indices', 'forest yields','stratigraphic sequence', 'freeze/frost', 'frost','industrialization', 'ice core records', 'suspended solids','weathering', 'gas flaring', 'ice extent', 'biogeochemical cycles','lake ice', 'isotopes', 'watershed characteristics','transportation', 'soil rooting depth', 'geochemical properties','carbon monoxide', 'cultural features', 'consumer behavior','boundary surveys', 'land productivity', 'sediment composition','calcium', 'magnesium', 'potassium','micronutrients/trace elements', 'sediment chemistry','biogeochemical processes', 'cropping systems','groundwater chemistry', 'reforestation/revegetation','soil infiltration', 'soil fertility','angiosperms (flowering plants)', 'glacial landforms','forest mensuration', 'acid deposition', 'differential pressure','soil erosion', 'trace elements/trace metals', 'soil consistence','snow stratigraphy', 'thermal conductivity', 'estuaries','tidal height', 'plant diseases/disorders/pests','pressure thickness', 'atmospheric heating', 'conduction','evaporation', 'turbulence', 'wind stress','satellite soil moisture index', 'antenna temperature', 'glaciers','ice sheets', 'nitrate', 'ocean mixed layer','precipitation indicators', 'temperature indicators', 'ground ice','alkalinity', 'dissolved gases', 'oxygen', 'ph', 'phosphate','solar induced fluorescence', 'volcanic activity','ice temperature', 'sea surface height', 'airglow','energy deposition', 'x-ray flux', 'electron flux', 'proton flux','magnetic fields/magnetic currents', 'vertical profiles','air temperature', 'dew point temperature','cloud liquid water/ice', 'wind speed', 'wind direction','vertical wind velocity/speed', 'total precipitable water','boundary layer temperature', 'cloud height','cloud droplet concentration/size', 'ozone','cloud base temperature', 'cloud base height', 'rain','cloud optical depth/thickness', 'cirrus/systems','mean radial velocity', 'relative humidity', 'u/v wind components','wind speed/wind direction','digital elevation/terrain model (dem)', 'snow', 'drizzle','particle size distribution', 'hail', 'ambient temperature','stage height', 'rivers/streams', 'hourly precipitation amount','24 hour precipitation amount', 'latent heat flux','cloud fraction', '3 and 6 hour precipitation amount','horizontal wind velocity/speed', 'dissolved carbon dioxide','hurricanes', 'tropical cyclone track', 'cloud top height','temperature profiles', 'vertical wind shear','water vapor tendency', 'potential temperature','angstrom exponent', 'water vapor mixing ratio profiles','extreme eastern tropical pacific sst', 'phytoplankton','cloud precipitable water', 'cloud asymmetry', 'cloud ceiling','cloud frequency', 'cloud top pressure', 'cloud top temperature','cloud vertical distribution', 'cloud emissivity','cloud radiative forcing', 'cloud reflectance','maximum/minimum temperature', 'condensation','topographical relief maps', 'evapotranspiration','fire occurrence', 'burned area', 'sulfur dioxide', 'lake/pond','rivers/stream', 'nitrogen dioxide', 'agricultural lands','cyclones', 'urban lands', 'lakes/reservoirs','infant mortality rates', 'methane','non-methane hydrocarbons/volatile organic compounds', 'coal','biodiversity functions', 'wetlands', 'discharge/flow','hydropattern', 'alpine/tundra', 'forests','leaf area index (lai)', 'ammonia', 'nitrous oxide','land subsidence', 'normalized difference vegetation index (ndvi)','chlorinated hydrocarbons', 'methyl bromide', 'methyl chloride','molecular hydrogen', 'fire models', 'heat index','even-toed ungulates', 'species recruitment','population dynamics', 'water depth', 'inundation', 'drainage','respiration rate', 'permafrost temperature','indigenous/native species', 'fire dynamics', 'plant succession','coastal', 'salt marsh', 'boundary layer winds', 'shrubland/scrub','community structure', 'pingo', 'virtual temperature','formaldehyde', 'hydroxyl', 'photolysis rates', 'nitric oxide','molecular oxygen', 'peroxyacyl nitrate', 'stable isotopes','runoff', 'vegetation water content', 'discharge','chlorophyll concentrations', 'water table', 'decomposition','water temperature', 'total dissolved solids', 'biomass dynamics','grasslands', 'savannas', 'grazing dynamics/plant herbivory','multivariate enso index', 'drainage basins','resource development site', 'dunes', 'flood plain','endangered species', 'hydrogen cyanide', 'nutrient cycling','deserts','fraction of absorbed photosynthetically active radiation (fapar)','aquifers', 'dissolved oxygen', 'turbidity', 'conductivity','sulfate', 'water ion concentrations', 'percolation','phosphorous compounds', 'radioisotopes', 'cooling degree days','contour maps', 'methane production/use','natural gas production/use', 'petroleum production/use','consumption rates', 'soil organic carbon (soc)', 'halocarbons','biomass energy production/use', 'estuary','layered precipitable water', 'water vapor concentration profiles','hydrogen chloride', 'nitric acid', 'chlorine nitrate','chlorofluorocarbons', 'dinitrogen pentoxide', 'dimethyl sulfide','vorticity', 'ice fraction', 'temperature tendency','wind direction tendency', 'bromine monoxide', 'chlorine monoxide','methyl cyanide', 'hypochlorous acid', 'methanol', 'hydroperoxy','cloud base pressure', 'temperature anomalies','precipitation trends', 'temperature trends', 'convection','chlorine dioxide', 'uv aerosol index','sea surface skin temperature', 'sublimation','convective surface precipitation rate', 'hydrogen fluoride']\",\n \"def setContourLabelDistance(distance=500):\\n dislin.labdis(distance, 'CONTUR')\",\n \"def eval_final_label(args):\\n cfg, lbl = util.get_label_cfg_by_args(args)\\n uid = cfg['uniqueid']\\n print('We are playing with %s' % uid)\\n outdir='models/%s/gate_expert' % uid\\n outname='gate_expert_model.pt'\\n if KLLOSS:\\n outname = 'gate_expert_kldiv_model.pt'\\n if args.warm:\\n outname = outname.replace('.pt', '_warm.pt')\\n mdl_path = os.path.join(outdir, outname)\\n gate_expert = GateExpertNet(mdl_path, False)\\n eval_fun = gate_expert.get_p_y\\n\\n data = npload(cfg['file_path'], uid)\\n datax = data[cfg['x_name']]\\n p, v = eval_fun(datax)\\n\\n label = np.argmax(p, axis=1)\\n\\n if args.draw:\\n fig, ax = plt.subplots()\\n n_expert = np.amax(label) + 1\\n for i in range(n_expert):\\n mask = label == i\\n ax.scatter(datax[mask, 0], datax[mask, 1])\\n plt.show()\\n\\n label_name = 'data/pen/gate_expert_label.npy'\\n if KLLOSS:\\n label_name = label_name.replace('_label', '_kldiv_label')\\n if args.warm:\\n label_name = label_name.replace('.npy', '_warm.npy')\\n np.save(label_name, label)\",\n \"def donut_highD(N=1500, V=1500, binary=True, d=3):\\n X = torch.randn(N + V, d)\\n R = torch.sqrt(torch.sum(X ** 2, dim=1))\\n ind_class0 = (R < 1.0).nonzero()[:, 0]\\n ind_class1 = (R >= 1.0).nonzero()[:, 0]\\n X = X * 1.2 # rescale data\\n X.add_(0.2 * torch.randn_like(X)) # add some noise to the data\\n \\n return label(d, X, ind_class0, ind_class1, N, V, binary)\",\n \"def label(self):\\r\\n raise NotImplementedError\",\n \"def cell_description(self, gid):\\n\\n tree = arbor.segment_tree()\\n\\n tree.append(\\n arbor.mnpos,\\n arbor.mpoint(0, 0, 0, self.radius),\\n arbor.mpoint(self.length, 0, 0, self.radius),\\n tag=1,\\n )\\n\\n labels = arbor.label_dict({\\\"cable\\\": \\\"(tag 1)\\\", \\\"start\\\": \\\"(location 0 0)\\\"})\\n\\n decor = (\\n arbor.decor()\\n .set_property(Vm=self.Vm, cm=self.cm, rL=self.rL)\\n .paint('\\\"cable\\\"', arbor.density(f\\\"pas/e={self.Vm}\\\", g=self.g))\\n .place(\\n '\\\"start\\\"',\\n arbor.iclamp(\\n self.stimulus_start, self.stimulus_duration, self.stimulus_amplitude\\n ),\\n \\\"iclamp\\\",\\n )\\n )\\n\\n policy = arbor.cv_policy_max_extent(self.cv_policy_max_extent)\\n decor.discretization(policy)\\n\\n return arbor.cable_cell(tree, decor, labels)\",\n \"def label(cmd):\\n cmd = cmd.replace('make][.DP', 'make1][.NP')\\n cmd = cmd.replace('make][.SC', 'make2][.SC')\\n cmd = re.sub('(draw.*)one','\\\\\\\\1one1',cmd)\\n cmd = re.sub('(make1.*)one','\\\\\\\\1one1',cmd)\\n cmd = re.sub('(make2.*)one','\\\\\\\\1one2',cmd)\\n cmd = re.sub('(move.*)one','\\\\\\\\1one2',cmd)\\n cmd = re.sub('(hide.*)one','\\\\\\\\1one2',cmd)\\n cmd = '[result ' + cmd + ']' #dummy function for plop\\n return cmd\",\n \"def plot_countryperskill(data_df, **args):\\n name = args.get('name', 'VARIABLE NAME')\\n idx = args.get('idx', data_df.index.values)\\n order = args.get('order', np.array([9, 0, 1, 2, 3, 4, 5, 6, 8, 7], int))\\n dd = args.get('dd', .7) # 3.3\\n wdth = args.get('wdth', 8) # 7\\n hght = args.get('hght', 4)\\n markersize = 60\\n target_y = args.get('target_y', 1)\\n label_y = args.get('label_y', r'$\\\\rho$')\\n colors14 = args.get('colors14', ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', \\\\\\n '#fb9a99', '#e31a1c', '#fdbf6f', '#ff7f00', \\\\\\n '#cab2d6', '#6a3d9a', '#ffff99', '#b15928', \\\\\\n '#dd1c77', '#8dd3c7'])\\n plt.figure(facecolor='w', figsize=(wdth, hght))\\n meth_labels = [r'$Lit$', r'$Lit^2$', r'$Lit^3$', r'$Lit^4$', r'$Lit^5$', \\\\\\n r'$Pop$', r'$Pop^2$', r'$Lit^3Pop$', r'$Lit^2Pop$', r'$LitPop$']\\n idx = idx[order]\\n meth_labels = [meth_labels[i] for i in order]\\n # empty plots for legend handlers:\\n for i in np.arange(0, len(countries_sel)): # country\\n plt.scatter([], [], marker='o', s=markersize, edgecolor='black', linewidth='.4',\\\\\\n c=colors14[i], label=countries[countries_sel[i]])\\n plt.legend()\\n\\n plt.scatter([0, len(idx)+dd], [0.7, 0.7], marker='.', lw=1, c='white') # legendspace\\n\\n # actual plotting:\\n for i in np.arange(0, len(countries_sel)): # country\\n for j in np.arange(0, len(idx)):\\n # rp - pearson correlation:\\n plt.scatter([j], data_df[countries[countries_sel[i]]][idx[j]], marker='o', \\\\\\n s=markersize, edgecolor='black', linewidth='.4',\\\\\\n alpha=1., c=colors14[i], zorder=j+10)\\n if not target_y == 'none':\\n plt.plot([0, j], [target_y, target_y], c='#d3d3d3', lw=5, ls='-', zorder=1)\\n\\n plt.xticks(np.arange(0, len(idx)), meth_labels, color='black', rotation=30)\\n plt.grid(axis='y')\\n # plt.xlabel('Method')\\n plt.ylabel(label_y)\\n plt.title(name)\\n\\n plt.savefig(os.path.join(output_path, experiment_name + '_' + 'allcountries_perScore_v4_' + name + '.pdf'),\\\\\\n dpi=600, facecolor='w', edgecolor='w',\\n orientation='portrait', papertype=None, format='pdf',\\n transparent=False, bbox_inches=None, pad_inches=0.1,\\n frameon=None, metadata=None)\\n plt.show()\",\n \"def showOneNeedle(self,i,visibility):\\n #obsolete\\n profbox()\\n fidname = \\\"fid\\\"+self.option[i]\\n pNode = self.parameterNode()\\n needleID = pNode.GetParameter(self.option[i]+'.vtp')\\n fidID = pNode.GetParameter(fidname) \\n NeedleNode = slicer.mrmlScene.GetNodeByID(needleID)\\n fiducialNode = slicer.mrmlScene.GetNodeByID(fidID) \\n \\n if NeedleNode !=None:\\n displayNode =NeedleNode.GetModelDisplayNode()\\n nVisibility=displayNode.GetVisibility() \\n\\n if fiducialNode == None:\\n displayNode.SetVisibility(1) \\n displayNode.SetOpacity(0.9)\\n polyData = NeedleNode.GetPolyData()\\n polyData.Update()\\n nb = int(polyData.GetNumberOfPoints()-1)\\n coord = [0,0,0]\\n if nb>100:\\n fiducialNode = slicer.vtkMRMLAnnotationFiducialNode()\\n polyData.GetPoint(nb,coord) \\n fiducialNode.SetName(self.option[i])\\n fiducialNode.SetFiducialCoordinates(coord) \\n fiducialNode.Initialize(slicer.mrmlScene)\\n fiducialNode.SetLocked(1)\\n fiducialNode.SetSelectable(0)\\n fidDN = fiducialNode.GetDisplayNode()\\n fidDN.SetColor(NeedleNode.GetDisplayNode().GetColor())\\n fidDN.SetGlyphScale(0)\\n fidTN = fiducialNode.GetAnnotationTextDisplayNode()\\n fidTN.SetTextScale(3)\\n fidTN.SetColor(NeedleNode.GetDisplayNode().GetColor())\\n fiducialNode.SetDisplayVisibility(0)\\n pNode.SetParameter(fidname,fiducialNode.GetID())\\n fiducialNode.SetDisplayVisibility(1)\\n\\n if visibility ==0:\\n\\n displayNode.SetVisibility(0)\\n displayNode.SetSliceIntersectionVisibility(0)\\n if fiducialNode!=None:\\n fiducialNode.SetDisplayVisibility(0)\\n\\n else:\\n\\n displayNode.SetVisibility(1)\\n displayNode.SetSliceIntersectionVisibility(1)\\n if fiducialNode!=None:\\n fiducialNode.SetDisplayVisibility(1)\\n\\n else:\\n vtkmat = vtk.vtkMatrix4x4()\\n vtkmat.DeepCopy(self.m_vtkmat)\\n vtkmat.SetElement(0,3,self.m_vtkmat.GetElement(0,3)+self.p[0][i])\\n vtkmat.SetElement(1,3,self.m_vtkmat.GetElement(1,3)+self.p[1][i])\\n vtkmat.SetElement(2,3,self.m_vtkmat.GetElement(2,3)+(30.0-150.0)/2.0)\\n\\n TransformPolyDataFilter=vtk.vtkTransformPolyDataFilter()\\n Transform=vtk.vtkTransform() \\n TransformPolyDataFilter.SetInput(self.m_polyCylinder)\\n Transform.SetMatrix(vtkmat)\\n TransformPolyDataFilter.SetTransform(Transform)\\n TransformPolyDataFilter.Update()\\n\\n triangles=vtk.vtkTriangleFilter()\\n triangles.SetInput(TransformPolyDataFilter.GetOutput()) \\n self.AddModel(i,triangles.GetOutput())\\n self.showOneNeedle(i,visibility)\",\n \"def getLabel(*args):\",\n \"def getLabel(*args):\",\n \"def getLabel(*args):\",\n \"def drawlabels(t, t1):\\r\\n t.fd(250)\\r\\n t.pd()\\r\\n t.write(\\\"Life\\\", font=(\\\"Arial\\\", 10, \\\"bold\\\"))\\r\\n t.pu()\\r\\n t.back(12)\\r\\n t.pd()\\r\\n t.write(\\\"Exp.\\\", font=(\\\"Arial\\\", 10, \\\"bold\\\"))\\r\\n t.pu()\\r\\n t.back(238)\\r\\n t.right(90)\\r\\n t.fd(80)\\r\\n t1.pu()\\r\\n t1.back(50)\\r\\n t1.rt(90)\\r\\n t1.fd(250)\\r\\n t1.pd()\\r\\n t1.write(\\\"Year\\\", font=(\\\"Arial\\\", 10, \\\"bold\\\"))\\r\\n t1.pu()\\r\\n t1.back(250)\\r\\n t1.left(90)\\r\\n t1.fd(50)\",\n \"def ex_label(self,label,argl):\\n if len(label) > 0 and label[0] != '_':\\n return label\\n comment = ''\\n for i in argl:\\n phrase = ''\\n if i == 'l':\\n phrase = label\\n elif i in self._labels.keys():\\n phrase = self._labels[i]\\n comment += phrase\\n return comment\",\n \"def efficient_tagged_jets_hist(datalist,discriminant, discriminant_cut, CSV_cut, bins, Difference=False, mode=\\\"pT_jet\\\",Save=False):\\n title = \\\"tagged_jets_vs_\\\"+mode\\n AllJetsHistlist = []\\n CSVHistlist = []\\n DiscriminantHistlist = []\\n if mode == \\\"pT_hadron\\\":\\n feature = 2\\n elif mode == \\\"pT_jet\\\":\\n feature = 3\\n elif mode == \\\"decay_vx\\\":\\n feature = 4\\n for n,data in enumerate(datalist):\\n print \\\"working on\\\",data[1]\\n ran = data[2]\\n AllJetsHistlist.append(rt.TH1D(data[1]+\\\"_AllJets\\\",data[1]+\\\"_\\\"+title,bins,ran[0],ran[1]))\\n AllJetsHistlist[n].SetLineColor(4)\\n CSVHistlist.append(rt.TH1D(data[1]+\\\"_CSV\\\",data[1]+\\\"_\\\"+title,bins,ran[0],ran[1]))\\n CSVHistlist[n].SetLineColor(3)\\n DiscriminantHistlist.append(rt.TH1D(data[1]+\\\"_Discriminant\\\",data[1]+\\\"_\\\"+title,bins,ran[0],ran[1]))\\n DiscriminantHistlist[n].SetLineColor(2)\\n for particle in data[0]:\\n AllJetsHistlist[n].Fill(particle[feature])\\n if particle[1] >= CSV_cut: CSVHistlist[n].Fill(particle[feature])\\n if Difference:\\n L = particle[8]-particle[5]\\n else:\\n if particle[13] != 0:\\n L = particle[16]/float(particle[13])\\n else:\\n continue\\n if L >= discriminant_cut: DiscriminantHistlist[n].Fill(particle[feature])\\n canvaslist = []\\n legendlist = []\\n Tfilelist = []\\n for n,data in enumerate(datalist):\\n canvaslist.append(rt.TCanvas(data[1]+\\\"_canvas\\\",\\\"canvas\\\",600,600))\\n canvaslist[n].SetTitle(data[1]+\\\"_\\\"+title)\\n rt.gStyle.SetOptStat(0)\\n legendlist.append(rt.TLegend(0.9,0.9,0.65,0.75))\\n legendlist[n].AddEntry(AllJetsHistlist[n], \\\"All jets\\\")\\n legendlist[n].AddEntry(CSVHistlist[n], \\\"CSV\\\")\\n legendlist[n].AddEntry(DiscriminantHistlist[n], discriminant)\\n AllJetsHistlist[n].GetXaxis().SetTitle(mode)\\n AllJetsHistlist[n].GetYaxis().SetTitle('# jets')\\n AllJetsHistlist[n].GetYaxis().SetTitleOffset(1.5)\\n #AllJetsHistlist[n].Draw()\\n #CSVHistlist[n].Draw(\\\"SAME\\\")\\n #DiscriminantHistlist[n].Draw(\\\"SAME\\\")\\n #legendlist[n].Draw()\\n if Save:\\n #canvaslist[n].SaveAs(\\\"Thesis_Plots/\\\"+title+\\\"_\\\"+data[1]+discriminant+\\\".png\\\")\\n Tfilelist.append(rt.TFile(\\\"Thesis_Plots/root_files/\\\"+title+\\\"_\\\"+data[1]+discriminant+\\\".root\\\",\\\"recreate\\\"))\\n AllJetsHistlist[n].Write()\\n CSVHistlist[n].Write()\\n DiscriminantHistlist[n].Write()\",\n \"def autolabel(rects):\",\n \"def __init__(self, label_num, des_dir, des_dim=48):\\n self.label_num = label_num\\n self.des_dir = des_dir\\n self.des_dim = 48\",\n \"def _get_labels(self, ind):\\n pass\",\n \"def diet_label(self, diet_label):\\n\\n self._diet_label = diet_label\",\n \"def dim_reduction_plot(data, label, block_flag):\\n \\n PCA_model = TruncatedSVD(n_components=3).fit(data)\\n data_PCA = PCA_model.transform(data)\\n idxc1 = np.where(label==0)\\n idxc2 = np.where(label==1)\\n plt.scatter(data_PCA[idxc1,0],data_PCA[idxc1,1],s=80,c='r', marker='^',linewidths = 0, label='healthy')\\n plt.scatter(data_PCA[idxc2,0],data_PCA[idxc2,1],s=80,c='y', marker='o',linewidths = 0, label='infected')\\n plt.gca().axes.get_xaxis().set_ticks([])\\n plt.gca().axes.get_yaxis().set_ticks([])\\n plt.title('PCA of the codes')\\n plt.legend(scatterpoints=1,loc='best')\\n plt.show(block=block_flag)\",\n \"def plotrgcloud(self):\\n print self.kpunten\\n for i in range(len(self.kpunten[0])):\\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\\n if i == len(self.kpunten[0]) -1 :\\n end = None\\n else:\\n end = self.kpunten[0][i+1][1] + 1\\n print end\\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)\",\n \"def makeD2hhAsymm(name,\\n config,\\n KPIDK_string,\\n PiPIDK_string,\\n Mass_low_string,\\n Mass_high_string,\\n CombPIDK_string,\\n DecayDescriptor,\\n inputSel,\\n useTOS,\\n Hlt1TOS,\\n Hlt2TOS\\n ) :\\n\\n def makeTISTOS( name, _input, _hlttos ) :\\n from Configurables import TisTosParticleTagger\\n _tisTosFilter = TisTosParticleTagger( name + \\\"Tagger\\\" )\\n _tisTosFilter.TisTosSpecs = _hlttos\\n return Selection( name\\n , Algorithm = _tisTosFilter\\n , RequiredSelections = [ _input ]\\n ) \\n\\n _Kcuts1 = \\\"~ISMUON & (PT > %(DaugPtMin)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2)s)\\\" % locals()['config']\\n _KcutsPIDK = KPIDK_string % locals()['config']\\n _Kcuts2 = \\\" & (ISLONG) & (P > %(DaugP)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2)s)\\\" % locals()['config']\\n _Kcuts = _Kcuts1 + _KcutsPIDK + _Kcuts2\\n _Picuts1 = \\\"~ISMUON & (PT > %(DaugPtMin)s* MeV) & (MIPCHI2DV(PRIMARY) > %(DaugIPChi2)s)\\\" % locals()['config']\\n _PicutsPIDK = PiPIDK_string % locals()['config']\\n _Picuts2 = \\\" & (ISLONG) & (P > %(DaugP)s* MeV) & (TRCHI2DOF < %(DaugTrkChi2)s)\\\" % locals()['config']\\n _Picuts = _Picuts1 + _PicutsPIDK + _Picuts2\\n _dauCuts = { 'K+': _Kcuts, 'pi+': _Picuts }\\n\\n _massLow = Mass_low_string % locals()['config']\\n _massHigh = Mass_high_string % locals()['config']\\n _combCuts1 = \\\"(APT > %(D0Pt)s* MeV)\\\" \\\\\\n \\\"& (AHASCHILD( PT > %(DaugPtMax)s* MeV ) )\\\" \\\\\\n \\\"& (ADOCA(1,2)< %(D0DOCA)s* mm)\\\" \\\\\\n \\\"& (AP > %(D0P)s* MeV)\\\" % locals()['config']\\n _combCutsPIDK = CombPIDK_string % locals()['config']\\n _combCuts = _combCuts1 + _combCutsPIDK + _massLow + _massHigh\\n\\n _motherCuts = \\\"(VFASPF(VCHI2PDOF) < %(D0VtxChi2Ndof)s)\\\" \\\\\\n \\\"& (BPVVDCHI2 > %(D0FDChi2)s)\\\" \\\\\\n \\\"& (BPVLTIME() > %(D0Tau)s)\\\" \\\\\\n \\\"& (BPVDIRA > %(D0BPVDira)s)\\\" % locals()['config']\\n\\n _D0 = CombineParticles( DecayDescriptor = DecayDescriptor,\\n MotherCut = _motherCuts,\\n CombinationCut = _combCuts,\\n DaughtersCuts = _dauCuts)\\n\\n _sel = Selection ( name+'Sel',\\n Algorithm = _D0,\\n RequiredSelections = inputSel )\\n\\n if not useTOS:\\n return _sel\\n\\n _selD2hhHlt1TOS = makeTISTOS( name + \\\"D2hhHlt1TOS\\\"\\n , _sel\\n , Hlt1TOS\\n )\\n _selD2hhHlt2TOS = makeTISTOS( name + \\\"D2hhHlt2TOS\\\"\\n , _selD2hhHlt1TOS\\n , Hlt2TOS\\n )\\n \\n return _selD2hhHlt2TOS\",\n \"def efficient_binned_tagged_jets_hist(datalist,discriminant, discriminant_cuts, CSV_cuts, bins, nbins, Difference=False, mode=\\\"pT_jet\\\",Save=False):\\n title = \\\"binned_tagged_jets_vs_\\\"+mode\\n AllJetsHistlist = []\\n CSVHistlist = []\\n DiscriminantHistlist = []\\n if mode == \\\"pT_hadron\\\":\\n feature = 2\\n elif mode == \\\"pT_jet\\\":\\n feature = 3\\n elif mode == \\\"decay_vx\\\":\\n feature = 4\\n for n,data in enumerate(datalist):\\n print \\\"working on\\\",data[1]\\n ran = data[2]\\n AllJetsHistlist.append(rt.TH1D(data[1]+\\\"_AllJets\\\",data[1]+\\\"_\\\"+title,nbins,ran[0],ran[1]))\\n AllJetsHistlist[n].SetLineColor(4)\\n CSVHistlist.append(rt.TH1D(data[1]+\\\"_CSV\\\",data[1]+\\\"_\\\"+title,nbins,ran[0],ran[1]))\\n CSVHistlist[n].SetLineColor(3)\\n DiscriminantHistlist.append(rt.TH1D(data[1]+\\\"_Discriminant\\\",data[1]+\\\"_\\\"+title,nbins,ran[0],ran[1]))\\n DiscriminantHistlist[n].SetLineColor(2)\\n for particle in data[0]:\\n bin_number = FCM.bin_selection(particle,bins)\\n if bin_number == -100: continue\\n AllJetsHistlist[n].Fill(particle[feature])\\n if particle[1] >= CSV_cuts[bin_number]: CSVHistlist[n].Fill(particle[feature])\\n if Difference:\\n L = particle[8]-particle[5]\\n else:\\n if particle[17] != 0:\\n L = particle[20]/float(particle[17])\\n else:\\n continue\\n if L >= discriminant_cuts[bin_number]: DiscriminantHistlist[n].Fill(particle[feature])\\n canvaslist = []\\n legendlist = []\\n Tfilelist = []\\n for n,data in enumerate(datalist):\\n canvaslist.append(rt.TCanvas(data[1]+\\\"_canvas\\\",\\\"canvas\\\",600,600))\\n canvaslist[n].SetTitle(data[1]+\\\"_\\\"+title)\\n rt.gStyle.SetOptStat(0)\\n legendlist.append(rt.TLegend(0.9,0.9,0.65,0.75))\\n legendlist[n].AddEntry(AllJetsHistlist[n], \\\"All jets\\\")\\n legendlist[n].AddEntry(CSVHistlist[n], \\\"CSV\\\")\\n legendlist[n].AddEntry(DiscriminantHistlist[n], discriminant)\\n AllJetsHistlist[n].GetXaxis().SetTitle(\\\"jet p_{T} (GeV)\\\")\\n AllJetsHistlist[n].GetYaxis().SetTitle('# jets')\\n AllJetsHistlist[n].GetYaxis().SetTitleOffset(1.5)\\n #AllJetsHistlist[n].Draw()\\n #CSVHistlist[n].Draw(\\\"SAME\\\")\\n #DiscriminantHistlist[n].Draw(\\\"SAME\\\")\\n #legendlist[n].Draw()\\n if Save:\\n #canvaslist[n].SaveAs(title+\\\"_\\\"+data[1]+discriminant+\\\".png\\\")\\n Tfilelist.append(rt.TFile(\\\"Thesis_Plots/root_files/\\\"+title+\\\"_\\\"+data[1]+discriminant+\\\".root\\\",\\\"recreate\\\"))\\n print \\\"saved histogram as Thesis_Plots/root_files/\\\"+title+\\\"_\\\"+data[1]+discriminant+\\\".root\\\"\\n AllJetsHistlist[n].Write()\\n CSVHistlist[n].Write()\\n DiscriminantHistlist[n].Write()\",\n \"def fid_cuts(ptname, etaname):\\n cuts = []\\n cuts.append(combine_cuts([ptname + ' > 4.5',\\n 'TMath::Abs(' + etaname + ') < 1.2']))\\n cuts.append(combine_cuts([ptname + ' > 4.0',\\n var_selection('TMath::Abs('+etaname+')', 1.2, 1.4)\\n ]))\\n cuts.append(combine_cuts([ptname + ' > 3.5',\\n var_selection('TMath::Abs('+etaname+')', 1.4, 1.6)\\n ]))\\n return combine_cuts(cuts, ' || ')\",\n \"def showComponents(self, mask):\\n\\n from skimage import measure\\n\\n thresh = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)[1]\\n labels = measure.label(thresh, neighbors=8, background=0)\\n for label in range(0,len(labels)):\\n img = np.zeros(mask.shape)\\n # if this is the background label, ignore it\\n if label == 0:\\n continue\\n img[labels==label]=255\\n numPixels = cv2.countNonZero(img)\\n\\n \\t# if the number of pixels in the component is sufficiently\\n \\t# large, then add it to our mask of \\\"large blobs\\\"\\n if numPixels > 500:\\n showme(img, 'Contour '+str(label))\",\n \"def getLabel2(*args):\",\n \"def getLabel2(*args):\",\n \"def DGTsLabel(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_DGTsLabel(*args)\",\n \"def _get_component_label(self):\\n labels = df_parser(self.workflow.builder.df_path, workflow=self.workflow).labels\\n if self.pdc_component_df_label not in labels:\\n raise PluginFailedException('No %s label in Dockerfile, can\\\\'t get PDC component',\\n self.pdc_component_df_label)\\n return labels[self.pdc_component_df_label]\",\n \"def tagview(tab,label,x,y):\\r\\n font = cv2.FONT_HERSHEY_SIMPLEX\\r\\n col=classifc[label]\\r\\n labnow=classif[label]\\r\\n# print (labnow, text)\\r\\n if label == 'back_ground':\\r\\n deltay=30\\r\\n else:\\r\\n# deltay=25*((labnow-1)%5)\\r\\n deltay=40+10*(labnow-1)\\r\\n\\r\\n viseg=cv2.putText(tab,label,(x, y+deltay), font,0.3,col,1)\\r\\n return viseg\",\n \"def label(image,**kw):\\n # default connectivity in OpenCV: 8 (which is equivalent to...)\\n # default connectivity in scikit-image: 2\\n n, labels = cv2.connectedComponents(image.astype(uint8), connectivity=4)\\n #n, labels = cv2.connectedComponentsWithAlgorithm(image.astype(uint8), connectivity=4, ltype=2, ccltype=cv2.CCL_DEFAULT)\\n return labels, n-1\\n # try: return measurements.label(image,**kw)\\n # except: pass\\n # types = [\\\"int32\\\",\\\"uint32\\\",\\\"int64\\\",\\\"uint64\\\",\\\"int16\\\",\\\"uint16\\\"]\\n # for t in types:\\n # try: return measurements.label(array(image,dtype=t),**kw)\\n # except: pass\\n # # let it raise the same exception as before\\n # return measurements.label(image,**kw)\",\n \"def needleSegmentationCLIDEMO(self):\\r\\n # research\\r\\n profbox()\\r\\n widget = slicer.modules.NeedleFinderWidget\\r\\n scene = slicer.mrmlScene\\r\\n pNode = self.parameterNode()\\r\\n if slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\\\"baselineVolumeID\\\")) == None:\\r\\n inputVolume = self.__volumeSelector.currentNode()\\r\\n inputVolumeID = self.__volumeSelector.currentNode().GetID()\\r\\n else:\\r\\n inputVolume = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\\\"baselineVolumeID\\\"))\\r\\n inputVolumeID = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\\\"baselineVolumeID\\\")).GetID()\\r\\n inputLabelID = self.__needleLabelSelector.currentNode().GetID()\\r\\n\\r\\n datetime = time.strftime(\\\"%Y-%m-%d-%H_%M_%S\\\", time.localtime())\\r\\n\\r\\n inputVolume.SetAttribute(\\\"foldername\\\", datetime)\\r\\n self.outputVolumeNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelNode')\\r\\n self.outputVolumeNode.SetName(\\\"Output Needle Model\\\")\\r\\n outputVolumeStorageNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelStorageNode')\\r\\n slicer.mrmlScene.AddNode(self.outputVolumeNode)\\r\\n slicer.mrmlScene.AddNode(outputVolumeStorageNode)\\r\\n self.outputVolumeNode.AddAndObserveStorageNodeID(outputVolumeStorageNode.GetID())\\r\\n outputVolumeStorageNode.WriteData(self.outputVolumeNode)\\r\\n\\r\\n outputID = self.outputVolumeNode.GetID()\\r\\n\\r\\n self.foldername = '/NeedleModels/' + datetime\\r\\n\\r\\n # Set the parameters for the CLI module\\r\\n parameters = {}\\r\\n parameters['inputVolume'] = inputVolumeID\\r\\n parameters['inputLabel'] = inputLabelID\\r\\n parameters['outputVtk'] = outputID\\r\\n parameters['outputFolderName'] = self.foldername\\r\\n parameters['nbPointsPerLine'] = self.nbPointsPerLine.value\\r\\n parameters['nbRadiusIterations'] = self.nbRadiusIterations.value\\r\\n parameters['radiusMax'] = self.radiusMax.value\\r\\n parameters['numberOfPointsPerNeedle'] = self.numberOfPointsPerNeedle.value\\r\\n parameters['nbRotatingIterations'] = self.nbRotatingIterations.value\\r\\n\\r\\n module = slicer.modules.mainlabelneedletrackingcli\\r\\n self.__cliNode = None\\r\\n self.__cliNode = slicer.cli.run(module, None, parameters, wait_for_completion=True)\\r\\n\\r\\n ##### match the needles ######\\r\\n\\r\\n self.setNeedleCoordinates()\\r\\n self.computerPolydataAndMatrix()\\r\\n xmin = min(self.p[0])\\r\\n xmax = max(self.p[0])\\r\\n ymin = min(self.p[1])\\r\\n ymax = max(self.p[1])\\r\\n xdelta = xmax - xmin\\r\\n ydelta = ymax - ymin\\r\\n k = 0\\r\\n\\r\\n self.base = [[0 for j in range(3)] for j in range(63)]\\r\\n self.tip = [[0 for j in range(3)] for j in range(63)]\\r\\n self.needlenode = [[0 for j in range(2)] for j in range(63)]\\r\\n self.bentNeedleNode = [[0 for j in range(2)] for j in range(63)]\\r\\n self.displaynode = [0 for j in range(63)]\\r\\n self.displaynodeB = [0 for j in range(63)]\\r\\n self.fiducialnode = [0 for j in range(63)]\\r\\n\\r\\n for i in xrange(63):\\r\\n\\r\\n pathneedle = self.foldername + '/' + str(i) + '.vtp'\\r\\n pathBentNeedle = self.foldername + '/' + str(i) + '_bent.vtp'\\r\\n self.needlenode[i] = slicer.util.loadModel(pathneedle, True)\\r\\n self.bentNeedleNode[i] = slicer.util.loadModel(pathBentNeedle, True)\\r\\n\\r\\n if self.needlenode[i][0] == True and self.needlenode[i][1] != None:\\r\\n self.displaynode[i] = self.needlenode[i][1].GetDisplayNode()\\r\\n self.displaynodeB[i] = self.bentNeedleNode[i][1].GetDisplayNode()\\r\\n\\r\\n\\r\\n polydata = self.needlenode[i][1].GetPolyData()\\r\\n polydata.GetPoint(0, self.base[i])\\r\\n\\r\\n self.displaynode[i].SliceIntersectionVisibilityOn()\\r\\n self.displaynodeB[i].SliceIntersectionVisibilityOn()\\r\\n bestmatch = None\\r\\n mindist = None\\r\\n for j in xrange(63):\\r\\n delta = ((self.p[0][j] - (self.base[i][0])) ** 2 + (self.p[1][j] - self.base[i][1]) ** 2) ** (0.5)\\r\\n if delta < mindist or mindist == None:\\r\\n bestmatch = j\\r\\n mindist = delta\\r\\n\\r\\n bestmatch = k\\r\\n k += 1\\r\\n self.displaynode[i].SetColor(self.color[bestmatch])\\r\\n self.displaynodeB[i].SetColor(self.color[bestmatch])\\r\\n self.needlenode[i][1].SetName(self.option[bestmatch] + \\\"_segmented\\\")\\r\\n self.bentNeedleNode[i][1].SetName(self.option[bestmatch] + \\\"_optimized\\\")\\r\\n self.needlenode[i][1].SetAttribute(\\\"segmented\\\", \\\"1\\\")\\r\\n self.bentNeedleNode[i][1].SetAttribute(\\\"optimized\\\", \\\"1\\\")\\r\\n self.needlenode[i][1].SetAttribute(\\\"nth\\\", str(bestmatch))\\r\\n self.bentNeedleNode[i][1].SetAttribute(\\\"nth\\\", str(bestmatch))\\r\\n self.needlenode[i][1].SetAttribute(\\\"needleID\\\", self.needlenode[i][1].GetID())\\r\\n self.bentNeedleNode[i][1].SetAttribute(\\\"needleID\\\", self.bentNeedleNode[i][1].GetID())\\r\\n\\r\\n if widget.removeDuplicates.isChecked():\\r\\n self.positionFilteringNeedles()\\r\\n\\r\\n d = slicer.mrmlScene.GetNodeByID(outputID).GetDisplayNode()\\r\\n d.SetVisibility(0)\\r\\n\\r\\n self.__editorFrame.collapsed = 1\\r\\n\\r\\n self.addButtons()\",\n \"def nifti2dicom(seg_nifti, bk_nifti, ref_dicom_dir, save_dir, description, mode_RGB=False, zoom_num=4, watermarks=True): \\n #Load nifti, here is segmentation and background\\n seg_image = sitk.ReadImage(seg_nifti)\\n seg_image = sitk.GetArrayFromImage(seg_image)\\n seg_image = seg_image.astype(np.uint8)\\n \\n # print(nifti_image.shape)\\n bk_image = sitk.ReadImage(bk_nifti)\\n bk_image = sitk.GetArrayFromImage(bk_image)\\n\\n #Get Volume report from the seg_image, cubic ml, and the 95% CI:\\n v_nonenhancing = round(seg_image[seg_image==1].sum()/1000,1)\\n ci_nonenhancing = round(v_nonenhancing*0.2,1)\\n v_enhancing = round(seg_image[seg_image==4].sum()/1000,1)\\n ci_enhancing = round(v_enhancing*0.3,1)\\n v_edema = round(seg_image[seg_image==2].sum()/1000,1)\\n ci_edema = round(v_edema*0.1,1)\\n\\n #Loading the reference dicom, in order to get the headers of each slice. \\n series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(ref_dicom_dir)\\n if not series_IDs:\\n print(\\\"ERROR: given directory \\\\\\\"\\\"+data_directory+\\\"\\\\\\\" does not contain a DICOM series.\\\")\\n sys.exit(1)\\n\\n series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(ref_dicom_dir, series_IDs[0])\\n\\n series_reader = sitk.ImageSeriesReader()\\n series_reader.SetFileNames(series_file_names)\\n\\n # Configure the reader to load all of the DICOM tags (public+private):\\n # By default tags are not loaded (saves time).\\n # By default if tags are loaded, the private tags are not loaded.\\n # We explicitly configure the reader to load tags, including the private ones.\\n series_reader.MetaDataDictionaryArrayUpdateOn()\\n series_reader.LoadPrivateTagsOn()\\n ref_image = series_reader.Execute()\\n \\n #set reader for slice \\n reader = sitk.ImageFileReader()\\n reader.LoadPrivateTagsOn()\\n \\n writer = sitk.ImageFileWriter()\\n # Use the study/series/frame of reference information given in the meta-data\\n # dictionary and not the automatically generated information from the file IO\\n writer.KeepOriginalImageUIDOn()\\n\\n # Copy some of the tags and add the relevant tags indicating the change.\\n # For the series instance UID (0020|000e), each of the components is a number, cannot start\\n # with zero, and separated by a '.' We create a unique series ID using the date and time. tags of interest:\\n \\n castFilter = sitk.CastImageFilter()\\n castFilter.SetOutputPixelType(sitk.sitkInt16)\\n ORG_ROOT=\\\"1.3.12.2\\\"\\n #create SeriesInstanceUID and StudyInstanceUID\\n SeriesInstanceUID = generateUID(org_root=ORG_ROOT)\\n StudyInstanceUID = generateUID(org_root=ORG_ROOT)\\n #create a prefix for the accession number\\n acc='BTS'+series_reader.GetMetaData(0,\\\"0008|0050\\\")\\n #changing spacing\\n reader.SetFileName(series_file_names[0])\\n reader.ReadImageInformation()\\n\\n if mode_RGB:\\n customized_tag_values = [(\\\"0008|103e\\\", description),\\n (\\\"0020|000e\\\", SeriesInstanceUID),\\n (\\\"0008|0050\\\", acc),\\n (\\\"0020|000d\\\", StudyInstanceUID), \\n (\\\"0028|0004\\\", 'RGB'),\\n (\\\"0028|0002\\\", \\\"3\\\")]\\n else:\\n customized_tag_values = [(\\\"0008|103e\\\", description),\\n (\\\"0020|000e\\\", SeriesInstanceUID), \\n (\\\"0008|0050\\\", acc),\\n (\\\"0020|000d\\\", StudyInstanceUID)] \\n\\n os.makedirs(save_dir, exist_ok = True)\\n\\n #for nifti, the main axis is the first one, while for dicoms it is the last one\\n for i in range(ref_image.GetDepth()):\\n #zoom 2 times, todo need to figure out which axis to zoom, post is the 3rd\\n #pre assume the first axis is the slice numbers\\n bk_slice = ndimage.zoom(bk_image[i,:,:], zoom_num, order=0)\\n seg_slice = ndimage.zoom(seg_image[i,:,:], zoom_num, order=0)\\n \\n #Due to the DICOM saving coordinate system is different with nifti,i.e mirrored, it is easier to flip array\\n bk_slice = np.flip(bk_slice, (0, 1)) \\n seg_slice = np.flip(seg_slice, (0, 1)) \\n\\n #get contours\\n seg_idx = get_contours(seg_slice)\\n \\n #add watermarks\\n if watermarks:\\n canvas_tmp = np.zeros(list(bk_slice.shape), dtype=np.uint8)\\n font = cv2.FONT_HERSHEY_PLAIN\\n cv2.putText(canvas_tmp,'FOR RESEARCH ONLY;REFER TO OFFICIAL REPORT FOR DETAILS',(10,30), \\n font,2,255,1)\\n cv2.putText(canvas_tmp,'(This tool is intended for evaluation of gliomas, and results may be unreliable for other pathologies)',(90,50), \\n font,1,255,1) \\n #add Legend and volumes \\n cv2.putText(canvas_tmp, 'Legend Volume(+/-95% CI)',(10,900), font,0.8,255,1)\\n cv2.putText(canvas_tmp, f'Edema {v_edema}+/-{ci_edema} mL',(30,920), font,0.8,255,1)\\n cv2.putText(canvas_tmp, f'Enhancing {v_enhancing}+/-{ci_enhancing} mL',(30,940), font,0.8,255,1)\\n cv2.putText(canvas_tmp, f'Non- {v_nonenhancing}+/-{ci_nonenhancing} mL',(30,960), font,0.8,255,1)\\n cv2.putText(canvas_tmp,'Enhancing', (30,975), font,0.8,255,1)\\n cv2.putText(canvas_tmp,'(The error is based on testing of algorithm performance vs. manual segmentation)', (150,1000), font,1,255,1)\\n\\n \\n \\n #burning segmentation contour into slices\\n cv2.line(seg_idx, (10,915), (20,915), 2, 2)\\n cv2.line(seg_idx, (10,935), (20,935), 4, 2)\\n cv2.line(seg_idx, (10,955), (20,955), 1, 2)\\n \\n if mode_RGB:\\n #burning the watermarks\\n bk_slice[canvas_tmp==255]=bk_slice.max()\\n #convert dicom from nomogram to RGB\\n bk_slice = toRGB(bk_slice)\\n #colorize the bk_slice according to seg_idx\\n bk_slice[0,:,:,0][seg_idx==1] = 255\\n bk_slice[0,:,:,1][seg_idx==4] = 255\\n bk_slice[0,:,:,2][seg_idx==2] = 255 \\n else:\\n #grey the ori_image_slice according to seg_idx\\n bk_slice[canvas_tmp==255]=bk_slice.max()//2\\n bk_slice[seg_idx==1] = bk_slice.max()*2//50\\n bk_slice[seg_idx==2] = bk_slice.max()*1//50\\n bk_slice[seg_idx==4] = bk_slice.max()*3//50\\n\\n converted_slice = sitk.GetImageFromArray(bk_slice)\\n reader.SetFileName(series_file_names[i])\\n reader.ReadImageInformation()\\n spacing_new = [i/zoom_num for i in reader.GetSpacing()[:-1]] + [reader.GetSpacing()[-1]]\\n \\n #generate SOPInstanceUID\\n SOPInstanceUID = generateUID(org_root=ORG_ROOT)\\n series_tag_values = [(k, reader.GetMetaData(k)) for k in reader.GetMetaDataKeys()] + customized_tag_values + [(\\\"0008|0018\\\", SOPInstanceUID)]\\n# print(series_tag_values)\\n if '_seg_' in description:\\n converted_slice = converted_slice \\n \\n # Tags shared by the series.\\n for tag, value in series_tag_values:\\n converted_slice.SetMetaData(tag, value)\\n \\n # especially set spacing tags\\n # Image Position (Patient)\\n converted_slice.SetMetaData(\\\"0020|0013\\\", str(i)) # Instance Number\\n converted_slice.SetSpacing(spacing_new)\\n \\n # Write to the output directory and add the extension dcm, to force writing in DICOM format \\n writer.SetFileName(os.path.join(save_dir, str(i)+'.dcm'))\\n writer.Execute(converted_slice)\",\n \"def needleSegmentationCLIDEMO(self):\\n #research\\n profbox()\\n widget = slicer.modules.NeedleFinderWidget\\n scene = slicer.mrmlScene\\n pNode = self.parameterNode()\\n if slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\\\"baselineVolumeID\\\")) == None:\\n inputVolume = self.__volumeSelector.currentNode()\\n inputVolumeID = self.__volumeSelector.currentNode().GetID()\\n else:\\n inputVolume = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\\\"baselineVolumeID\\\"))\\n inputVolumeID = slicer.mrmlScene.GetNodeByID(pNode.GetParameter(\\\"baselineVolumeID\\\")).GetID()\\n inputLabelID = self.__needleLabelSelector.currentNode().GetID()\\n \\n datetime = time.strftime(\\\"%Y-%m-%d-%H_%M_%S\\\", time.localtime())\\n \\n inputVolume.SetAttribute(\\\"foldername\\\",datetime)\\n self.outputVolumeNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelNode')\\n self.outputVolumeNode.SetName(\\\"Output Needle Model\\\")\\n outputVolumeStorageNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLModelStorageNode')\\n slicer.mrmlScene.AddNode(self.outputVolumeNode)\\n slicer.mrmlScene.AddNode(outputVolumeStorageNode)\\n self.outputVolumeNode.AddAndObserveStorageNodeID(outputVolumeStorageNode.GetID())\\n outputVolumeStorageNode.WriteData(self.outputVolumeNode)\\n \\n outputID = self.outputVolumeNode.GetID()\\n \\n self.foldername = '/NeedleModels/' + datetime\\n \\n # Set the parameters for the CLI module \\n parameters = {} \\n parameters['inputVolume'] = inputVolumeID\\n parameters['inputLabel'] = inputLabelID\\n parameters['outputVtk'] = outputID\\n parameters['outputFolderName'] = self.foldername\\n parameters['nbPointsPerLine'] = self.nbPointsPerLine.value\\n parameters['nbRadiusIterations'] = self.nbRadiusIterations.value\\n parameters['distanceMax'] = self.distanceMax.value\\n parameters['numberOfPointsPerNeedle'] = self.numberOfPointsPerNeedle.value\\n parameters['nbRotatingIterations'] = self.nbRotatingIterations.value\\n \\n module = slicer.modules.mainlabelneedletrackingcli \\n self.__cliNode = None\\n self.__cliNode = slicer.cli.run(module, None, parameters, wait_for_completion=True)\\n \\n ##### match the needles ######\\n\\n self.setNeedleCoordinates()\\n self.computerPolydataAndMatrix()\\n xmin = min(self.p[0])\\n xmax = max(self.p[0])\\n ymin = min(self.p[1])\\n ymax = max(self.p[1])\\n xdelta = xmax - xmin\\n ydelta = ymax - ymin\\n k = 0\\n\\n self.base = [[0 for j in range(3)] for j in range(63)]\\n self.tip = [[0 for j in range(3)] for j in range(63)]\\n self.needlenode = [[0 for j in range(2)] for j in range(63)]\\n self.bentNeedleNode = [[0 for j in range(2)] for j in range(63)]\\n self.displaynode = [0 for j in range(63)]\\n self.displaynodeB = [0 for j in range(63)]\\n self.fiducialnode = [0 for j in range(63)]\\n \\n for i in xrange(63):\\n\\n pathneedle = self.foldername+'/'+str(i)+'.vtp'\\n pathBentNeedle = self.foldername+'/'+str(i)+'_bent.vtp'\\n self.needlenode[i] = slicer.util.loadModel(pathneedle, True)\\n self.bentNeedleNode[i] = slicer.util.loadModel(pathBentNeedle, True)\\n\\n if self.needlenode[i][0] == True and self.needlenode[i][1] != None:\\n self.displaynode[i] = self.needlenode[i][1].GetDisplayNode()\\n self.displaynodeB[i] = self.bentNeedleNode[i][1].GetDisplayNode()\\n\\n \\n polydata = self.needlenode[i][1].GetPolyData()\\n polydata.GetPoint(0,self.base[i]) \\n \\n self.displaynode[i].SliceIntersectionVisibilityOn()\\n self.displaynodeB[i].SliceIntersectionVisibilityOn()\\n bestmatch = None\\n mindist = None\\n for j in xrange(63):\\n delta = ((self.p[0][j]-(self.base[i][0]))**2+(self.p[1][j]-self.base[i][1])**2)**(0.5)\\n if delta < mindist or mindist == None:\\n bestmatch = j\\n mindist = delta\\n \\n bestmatch = k\\n k += 1\\n self.displaynode[i].SetColor(self.color[bestmatch])\\n self.displaynodeB[i].SetColor(self.color[bestmatch])\\n self.needlenode[i][1].SetName(self.option[bestmatch]+\\\"_segmented\\\")\\n self.bentNeedleNode[i][1].SetName(self.option[bestmatch]+\\\"_optimized\\\")\\n self.needlenode[i][1].SetAttribute(\\\"segmented\\\",\\\"1\\\")\\n self.bentNeedleNode[i][1].SetAttribute(\\\"optimized\\\",\\\"1\\\")\\n self.needlenode[i][1].SetAttribute(\\\"nth\\\",str(bestmatch))\\n self.bentNeedleNode[i][1].SetAttribute(\\\"nth\\\",str(bestmatch))\\n self.needlenode[i][1].SetAttribute(\\\"needleID\\\",self.needlenode[i][1].GetID())\\n self.bentNeedleNode[i][1].SetAttribute(\\\"needleID\\\",self.bentNeedleNode[i][1].GetID())\\n \\n if widget.removeDuplicates.isChecked():\\n self.positionFilteringNeedles()\\n\\n d = slicer.mrmlScene.GetNodeByID(outputID).GetDisplayNode()\\n d.SetVisibility(0)\\n \\n self.__editorFrame.collapsed = 1\\n \\n self.addButtons()\",\n \"def create_slice_labels(dataset, base_task_name, slice_name, verbose=False):\\n # TODO: break this out into more modular pieces oncee we have multiple slices\\n slice_fn = globals()[slice_name]\\n slice_indicators = torch.tensor(\\n [slice_fn(dataset, idx) for idx in range(len(dataset))], dtype=torch.uint8\\n ).view(-1, 1)\\n\\n Y_base = dataset.labels[f\\\"{base_task_name}_gold\\\"]\\n Y_slice = Y_base.clone().masked_fill_(slice_indicators == 0, 0)\\n\\n if verbose:\\n if not any(Y_slice):\\n warnings.warn(f\\\"No examples were found to belong to slice {slice_name}\\\")\\n else:\\n print(f\\\"Found {sum(slice_indicators)} examples in slice {slice_name}.\\\")\\n\\n # NOTE: we assume here that all slice labels are for sentence-level tasks only\\n return Y_slice\",\n \"def __do_split_haghverdi16(self, Dseg, tips):\\n # sort distance from first tip point\\n # then the sequence of distances Dseg[tips[0]][idcs] increases\\n idcs = np.argsort(Dseg[tips[0]])\\n # consider now the sequence of distances from the other\\n # two tip points, which only increase when being close to `tips[0]`\\n # where they become correlated\\n # at the point where this happens, we define a branching point\\n if True:\\n imax = self.kendall_tau_split(Dseg[tips[1]][idcs],\\n Dseg[tips[2]][idcs])\\n if False:\\n # if we were in euclidian space, the following should work\\n # as well, but here, it doesn't because the scales in Dseg are\\n # highly different, one would need to write the following equation\\n # in terms of an ordering, such as exploited by the kendall\\n # correlation method above\\n imax = np.argmin(Dseg[tips[0]][idcs]\\n + Dseg[tips[1]][idcs]\\n + Dseg[tips[2]][idcs])\\n # init list to store new segments\\n ssegs = []\\n # first new segment: all points until, but excluding the branching point\\n # increasing the following slightly from imax is a more conservative choice\\n # as the criterion based on normalized distances, which follows below,\\n # is less stable\\n ibranch = imax + 2 # this used to be imax + 1!\\n # ibranch = int(0.95 * imax)\\n return idcs[:ibranch]\\n # ssegs.append(idcs[:ibranch])\\n # TODO get rid of the following heuristics\\n # define nomalized distances to tip points for the rest of the data\\n # dist1 = Dseg[tips[1], idcs[ibranch:]] / Dseg[tips[1], idcs[ibranch-1]]\\n # dist2 = Dseg[tips[2], idcs[ibranch:]] / Dseg[tips[2], idcs[ibranch-1]]\\n # assign points according to whether being closer to tip cell 1 or 2\\n # ssegs.append(idcs[ibranch:][dist1 <= dist2])\\n # ssegs.append(idcs[ibranch:][dist1 > dist2])\\n # return ssegs\",\n \"def Problem11():\\n return 'Ductile Coulomb-Mohr'\",\n \"def ANN_binned_tagged_jets_hist(datalist, model, discriminant_cuts, CSV_cuts, bins, nbins, mode=\\\"pT_jet\\\",Save=False,addFeature=False):\\n title = \\\"binned_tagged_jets_vs_\\\"+mode\\n\\tdiscriminant = \\\"ANN\\\"\\n AllJetsHistlist = []\\n CSVHistlist = []\\n DiscriminantHistlist = []\\n if mode == \\\"pT_hadron\\\":\\n feature = 2\\n elif mode == \\\"pT_jet\\\":\\n feature = 3\\n elif mode == \\\"decay_vx\\\":\\n feature = 4\\n for n,data in enumerate(datalist):\\n\\t\\tdatatitle = data[3]\\n print \\\"working on\\\",datatitle\\n ran = data[4]\\n\\t\\tCSV = data[2]\\n\\t\\tpT = data[1]\\n\\t\\tx_data = data[0]\\n AllJetsHistlist.append(rt.TH1D(datatitle+\\\"_AllJets\\\",datatitle+\\\"_\\\"+title,nbins,ran[0],ran[1]))\\n AllJetsHistlist[n].SetLineColor(4)\\n CSVHistlist.append(rt.TH1D(datatitle+\\\"_CSV\\\",datatitle+\\\"_\\\"+title,nbins,ran[0],ran[1]))\\n CSVHistlist[n].SetLineColor(3)\\n DiscriminantHistlist.append(rt.TH1D(datatitle+\\\"_Discriminant\\\",datatitle+\\\"_\\\"+title,nbins,ran[0],ran[1]))\\n DiscriminantHistlist[n].SetLineColor(2)\\n\\t\\n\\t\\tif addFeature == False:\\n\\t\\t\\tpred_y = model.predict(ANN_functional_shape(x_data))\\n\\t\\telif addFeature == \\\"pT\\\":\\n\\t\\t\\tpred_y = model.predict(ANN_functional_shape(x_data)+[pT/200])\\n\\t\\telif addFeature == \\\"PV\\\":\\n\\t\\t\\tassert x_data.shape[1] == 21, \\\"wrong x_data format: PV cannot be found\\\"\\n\\t\\t\\tpred_y = model.predict(ANN_functional_shape(x_data)+[x_data[:,-1]/10.])\\n\\t\\telse:\\n\\t\\t\\tprint \\\"invalid feature input\\\"\\n\\t\\t\\treturn None\\n\\t\\tbin_numbers = ANN_bin_selection(pT,bins)\\n\\n\\t for i,pT_value in enumerate(pT):\\n\\t if bin_numbers[i] == -100: continue\\n\\t\\t\\tAllJetsHistlist[n].Fill(pT_value)\\n\\t if pred_y[i] >= discriminant_cuts[bin_numbers[i]]: DiscriminantHistlist[n].Fill(pT_value)\\n\\t if CSV[i] >= CSV_cuts[bin_numbers[i]]: CSVHistlist[n].Fill(pT_value)\\n\\n canvaslist = []\\n legendlist = []\\n Tfilelist = []\\n for n,data in enumerate(datalist):\\n\\t\\tdatatitle = data[3]\\n canvaslist.append(rt.TCanvas(datatitle+\\\"_canvas\\\",\\\"canvas\\\",600,600))\\n canvaslist[n].SetTitle(datatitle+\\\"_\\\"+title)\\n rt.gStyle.SetOptStat(0)\\n legendlist.append(rt.TLegend(0.9,0.9,0.65,0.75))\\n legendlist[n].AddEntry(AllJetsHistlist[n], \\\"All jets\\\")\\n legendlist[n].AddEntry(CSVHistlist[n], \\\"CSV\\\")\\n legendlist[n].AddEntry(DiscriminantHistlist[n], discriminant)\\n AllJetsHistlist[n].GetXaxis().SetTitle(mode)\\n AllJetsHistlist[n].GetYaxis().SetTitle('# jets')\\n AllJetsHistlist[n].GetYaxis().SetTitleOffset(1.5)\\n #AllJetsHistlist[n].Draw()\\n #CSVHistlist[n].Draw(\\\"SAME\\\")\\n #DiscriminantHistlist[n].Draw(\\\"SAME\\\")\\n #legendlist[n].Draw()\\n if Save:\\n #canvaslist[n].SaveAs(title+\\\"_\\\"+datatitle+discriminant+\\\".png\\\")\\n Tfilelist.append(rt.TFile(\\\"Thesis_Plots/root_files/\\\"+title+\\\"_\\\"+datatitle+discriminant+\\\".root\\\",\\\"recreate\\\"))\\n print \\\"saved histogram as Thesis_Plots/root_files/\\\"+title+\\\"_\\\"+datatitle+discriminant+\\\".root\\\"\\n AllJetsHistlist[n].Write()\\n CSVHistlist[n].Write()\\n DiscriminantHistlist[n].Write()\",\n \"def mask_label_contour(image, seg):\\n return sitk.Mask(image, sitk.LabelContour(seg+1)==0)\",\n \"def adjust_labels(data_y, label):\\n\\n if label == 'locomotion': # Labels for locomotion are adjusted\\n data_y[data_y == 4] = 3\\n data_y[data_y == 5] = 4\\n elif label == 'gestures': # Labels for gestures are adjusted\\n data_y[data_y == 406516] = 1\\n data_y[data_y == 406517] = 2\\n data_y[data_y == 404516] = 3\\n data_y[data_y == 404517] = 4\\n data_y[data_y == 406520] = 5\\n data_y[data_y == 404520] = 6\\n data_y[data_y == 406505] = 7\\n data_y[data_y == 404505] = 8\\n data_y[data_y == 406519] = 9\\n data_y[data_y == 404519] = 10\\n data_y[data_y == 406511] = 11\\n data_y[data_y == 404511] = 12\\n data_y[data_y == 406508] = 13\\n data_y[data_y == 404508] = 14\\n data_y[data_y == 408512] = 15\\n data_y[data_y == 407521] = 16\\n data_y[data_y == 405506] = 17\\n return data_y\",\n \"def GetDatumLabels(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_GetDatumLabels(self, *args)\",\n \"def get_labels(self):\\r\\n return [\\\"X\\\", \\\"O\\\", \\\"B-a\\\", \\\"I-a\\\", \\\"B-b\\\", \\\"I-b\\\", \\\"B-c\\\", \\\"I-c\\\", \\\"S-a\\\", \\\"S-b\\\", \\\"S-c\\\", \\\"[CLS]\\\", \\\"[SEP]\\\"]\",\n \"def _labels_of_sentence(self, sentence, split):\\n labels = torch.ones(1)\\n labels[0] = self.category_int_of_label_string(sentence[0][self.name_to_index_dict['label']]) #\\n return labels\",\n \"def _get_label(self):\\n if self.model.name == '':\\n return \\\"KPI\\\"\\n return \\\"KPI: {} ({})\\\".format(self.model.name, self.model.objective)\",\n \"def get_labels(self):\\n return [\\\"A轮\\\", \\\"B轮\\\",\\\"C轮\\\",\\\"天使轮\\\",\\\"战略融资\\\"]\",\n \"def GetDatumTolerLabels(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_GetDatumTolerLabels(self, *args)\",\n \"def test_get_dim_label_with_index(self):\\n\\n dim = self.oecd_datasets['oecd']['dimension']['id'][2]\\n dims_df = pyjstat.get_dim_label(self.oecd_datasets['oecd'], dim)\\n self.assertTrue(dims_df.iloc[0]['id'] == '2003')\\n self.assertTrue(dims_df.iloc[-1]['label'] == '2014')\",\n \"def addFid(data, Dim=.5, nodName=\\\"N\\\", lableName=\\\"1\\\", color=\\\"red\\\", GlyphType=1):\\n\\t\\txyz = tuple(data)\\n\\t\\ttipFiducial = slicer.mrmlScene.AddNode(slicer.vtkMRMLMarkupsFiducialNode())\\n\\t\\ttipFiducial.SetName(nodName)\\n\\t\\ttipFiducial.AddFiducial(xyz[0], xyz[1], xyz[2])\\n\\t\\ttipFiducial.SetNthFiducialLabel(0, lableName)\\n\\t\\tslicer.mrmlScene.AddNode(tipFiducial)\\n\\t\\ttipFiducial.SetDisplayVisibility(True)\\n\\t\\ttipFiducial.GetDisplayNode().SetGlyphType(GlyphType) # Vertex2D\\n\\t\\ttipFiducial.GetDisplayNode().SetGlyphScale(Dim * 10)\\n\\t\\ttipFiducial.GetDisplayNode().SetTextScale(3)\\n\\t\\ttipFiducial.GetDisplayNode().SetSelectedColor(Helper.myColor(color))\\n\\t\\t'''\\tGlyphShapes {\\n GlyphTypeInvalid = 0, 1-StarBurst2D, 2-Cross2D, 3-CrossDot2D,\\n 4-ThickCross2D, 5-Dash2D, 6-Sphere3D, 7-Vertex2D,\\n 8-Circle2D,9-Triangle2D, 10-Square2D, Diamond2D,\\n Arrow2D, ThickArrow2D, HookedArrow2D, GlyphType_Last\\n }'''\",\n \"def label_for(self, *pp, unit=True, description=True):\\n if len(pp) > 1 and np.all([re.match(r\\\"k\\\\d+l\\\", p) for p in pp]):\\n label = \\\"$k_nl$\\\"\\n if unit:\\n label += \\\" / $m^{-n}$\\\"\\n return label\\n return super().label_for(*pp, unit=unit, description=description)\",\n \"def make_label(self, label, units):\\n nice_label = self.tex_axis_label(label)\\n if not (units == 'dimensionless') and \\\\\\n (units is not None) and (not units == []):\\n nice_label += ' (%s)'%self.tex_axis_label(units)\\n return nice_label\",\n \"def kaons ( self ) :\\n from GaudiConfUtils.ConfigurableGenerators import FilterDesktop\\n ## \\n if self['NOPIDHADRONS'] :\\n from StandardParticles import StdAllNoPIDsKaons as inpts\\n kaoncut = self['KaonCut']\\n else :\\n from StandardParticles import StdAllLooseANNKaons as inpts \\n kaoncut = \\\"(%s)&(%s)\\\" % ( self['KaonCut'] , self['KaonPIDCut'] ) \\n #\\n ##\\n return self.make_selection (\\n 'Kaon' ,\\n FilterDesktop ,\\n [ inpts ] ,\\n Code = kaoncut ,\\n )\",\n \"def classify_defect_clusters_modifier(frame, data):\\n\\n if data.particles.count == 0:\\n # No particles there to classify, create empty properties anyway\\n data.particles_.create_property('Si_V', dtype=int, components=1)\\n data.particles_.create_property('Si_I', dtype=int, components=1)\\n data.particles_.create_property('Si_C', dtype=int, components=1)\\n data.particles_.create_property('C_V', dtype=int, components=1)\\n data.particles_.create_property('C_I', dtype=int, components=1)\\n data.particles_.create_property('C_Si', dtype=int, components=1)\\n return\\n\\n # TODO Create numpy arrays containing the number of Si vacancies,\\n # interstitials, etc for each particle site in `data.particles`. These\\n # next lines are just placeholders!\\n si_vacancy = data.particles[\\\"vacancy_mask\\\"][...] * data.particles[\\\"Is Si Site\\\"][...]\\n si_interstitial = (data.particles[\\\"Is Si Site\\\"][...] & (data.particles[\\\"Si Occupancy\\\"][...] > 1)) * (\\n data.particles[\\\"Si Occupancy\\\"][...] - 1) + (\\n (data.particles[\\\"Is C Site\\\"][...]) * data.particles[\\\"Si Occupancy\\\"][...]) - (\\n data.particles[\\\"Is C Site\\\"][...] & data.particles[\\\"antisite_mask\\\"][...])\\n si_antisite = data.particles[\\\"antisite_mask\\\"][...] * data.particles[\\\"Is Si Site\\\"][...]\\n c_vacancy = data.particles[\\\"vacancy_mask\\\"][...] * data.particles[\\\"Is C Site\\\"][...]\\n c_interstitial = (data.particles[\\\"Is C Site\\\"][...] & (data.particles[\\\"C Occupancy\\\"][...] > 1)) * (\\n data.particles[\\\"C Occupancy\\\"][...] - 1) + (\\n (data.particles[\\\"Is Si Site\\\"][...]) * data.particles[\\\"C Occupancy\\\"][...]) - (\\n data.particles[\\\"Is Si Site\\\"][...] & data.particles[\\\"antisite_mask\\\"][...])\\n c_antisite = data.particles[\\\"antisite_mask\\\"][...] * data.particles[\\\"Is C Site\\\"][...]\\n\\n\\n data.particles_.create_property('Si_V', data=si_vacancy.astype(int))\\n data.particles_.create_property('Si_I', data=si_interstitial.astype(int))\\n data.particles_.create_property('Si_C', data=si_antisite.astype(int))\\n data.particles_.create_property('C_V', data=c_vacancy.astype(int))\\n data.particles_.create_property('C_I', data=c_interstitial.astype(int))\\n data.particles_.create_property('C_Si', data=c_antisite.astype(int))\",\n \"def _label_commuter_rail_rider(self, rider):\\n if (rider['servicebrand_Commuter Rail'] > 0) and (rider['zonecr_1a'] == 0):\\n label = 'CR except zone 1A'\\n else:\\n label = 'others'\\n return label\",\n \"def showOneNeedle(self, i, visibility):\\r\\n # obsolete\\r\\n profbox()\\r\\n fidname = \\\"fid\\\" + self.option[i]\\r\\n pNode = self.parameterNode()\\r\\n needleID = pNode.GetParameter(self.option[i] + '.vtp')\\r\\n fidID = pNode.GetParameter(fidname)\\r\\n NeedleNode = slicer.mrmlScene.GetNodeByID(needleID)\\r\\n fiducialNode = slicer.mrmlScene.GetNodeByID(fidID)\\r\\n\\r\\n if NeedleNode != None:\\r\\n displayNode = NeedleNode.GetModelDisplayNode()\\r\\n nVisibility = displayNode.GetVisibility()\\r\\n\\r\\n if fiducialNode == None:\\r\\n displayNode.SetVisibility(1)\\r\\n displayNode.SetOpacity(0.9)\\r\\n polyData = NeedleNode.GetPolyData()\\r\\n polyData.Update()\\r\\n nb = int(polyData.GetNumberOfPoints() - 1)\\r\\n coord = [0, 0, 0]\\r\\n if nb > 100:\\r\\n fiducialNode = slicer.vtkMRMLAnnotationFiducialNode()\\r\\n polyData.GetPoint(nb, coord)\\r\\n fiducialNode.SetName(self.option[i])\\r\\n fiducialNode.SetFiducialCoordinates(coord)\\r\\n fiducialNode.Initialize(slicer.mrmlScene)\\r\\n fiducialNode.SetLocked(1)\\r\\n fiducialNode.SetSelectable(0)\\r\\n fidDN = fiducialNode.GetDisplayNode()\\r\\n fidDN.SetColor(NeedleNode.GetDisplayNode().GetColor())\\r\\n fidDN.SetGlyphScale(0)\\r\\n fidTN = fiducialNode.GetAnnotationTextDisplayNode()\\r\\n fidTN.SetTextScale(3)\\r\\n fidTN.SetColor(NeedleNode.GetDisplayNode().GetColor())\\r\\n fiducialNode.SetDisplayVisibility(0)\\r\\n pNode.SetParameter(fidname, fiducialNode.GetID())\\r\\n fiducialNode.SetDisplayVisibility(1)\\r\\n\\r\\n if visibility == 0:\\r\\n\\r\\n displayNode.SetVisibility(0)\\r\\n displayNode.SetSliceIntersectionVisibility(0)\\r\\n if fiducialNode != None:\\r\\n fiducialNode.SetDisplayVisibility(0)\\r\\n\\r\\n else:\\r\\n\\r\\n displayNode.SetVisibility(1)\\r\\n displayNode.SetSliceIntersectionVisibility(1)\\r\\n if fiducialNode != None:\\r\\n fiducialNode.SetDisplayVisibility(1)\\r\\n\\r\\n else:\\r\\n vtkmat = vtk.vtkMatrix4x4()\\r\\n vtkmat.DeepCopy(self.m_vtkmat)\\r\\n vtkmat.SetElement(0, 3, self.m_vtkmat.GetElement(0, 3) + self.p[0][i])\\r\\n vtkmat.SetElement(1, 3, self.m_vtkmat.GetElement(1, 3) + self.p[1][i])\\r\\n vtkmat.SetElement(2, 3, self.m_vtkmat.GetElement(2, 3) + (30.0 - 150.0) / 2.0)\\r\\n\\r\\n TransformPolyDataFilter = vtk.vtkTransformPolyDataFilter()\\r\\n Transform = vtk.vtkTransform()\\r\\n TransformPolyDataFilter.SetInput(self.m_polyCylinder)\\r\\n Transform.SetMatrix(vtkmat)\\r\\n TransformPolyDataFilter.SetTransform(Transform)\\r\\n TransformPolyDataFilter.Update()\\r\\n\\r\\n triangles = vtk.vtkTriangleFilter()\\r\\n triangles.SetInput(TransformPolyDataFilter.GetOutput())\\r\\n self.AddModel(i, triangles.GetOutput())\\r\\n self.showOneNeedle(i, visibility)\",\n \"def cvpr2018_labels():\\n\\n return {\\n 0: 'others',\\n 33: 'car',\\n 34: 'motorcycle',\\n 35: 'bicycle',\\n 36: 'pedestrian',\\n 38: 'truck',\\n 39: 'bus',\\n 40: 'tricycle'\\n }\",\n \"def syed_dilation(data, vessel):\",\n \"def _compute_labels(self, element, data, mapping):\\n lidx = element.nodes.get_dimension(self.label_index)\\n if element.vdims:\\n edges = Dataset(element)[element[element.vdims[0].name]>0]\\n nodes = list(np.unique([edges.dimension_values(i) for i in range(2)]))\\n nodes = element.nodes.select(**{element.nodes.kdims[2].name: nodes})\\n else:\\n nodes = element\\n\\n value_dim = element.vdims[0]\\n labels = [lidx.pprint_value(v) for v in nodes.dimension_values(lidx)]\\n if self.show_values:\\n value_labels = []\\n for i, node in enumerate(element._sankey['nodes']):\\n value = value_dim.pprint_value(node['value'])\\n label = '%s - %s' % (labels[i], value)\\n if value_dim.unit:\\n label += ' %s' % value_dim.unit\\n value_labels.append(label)\\n labels = value_labels\\n\\n ys = nodes.dimension_values(1)\\n nodes = element._sankey['nodes']\\n offset = (nodes[0]['x1']-nodes[0]['x0'])/4.\\n if self.label_position == 'right':\\n xs = np.array([node['x1'] for node in nodes])+offset\\n else:\\n xs = np.array([node['x0'] for node in nodes])-offset\\n data['text_1'] = dict(x=xs, y=ys, text=[str(l) for l in labels])\\n align = 'left' if self.label_position == 'right' else 'right'\\n mapping['text_1'] = dict(text='text', x='x', y='y', text_baseline='middle', text_align=align)\",\n \"def chainDict2jetLabel(chain_dict):\\n\\n # suported scenarios \\n router = {\\n 'simple': _make_simple_label,\\n 'HT': _make_ht_label,\\n 'vbenf': _make_vbenf_label,\\n 'dijet': _make_dijet_label,\\n 'combinationsTest': _make_combinationsTest_label,\\n 'partitionsTest': _make_partitionsTest_label,\\n }\\n\\n # chain_part - scenario association\\n cp_sorter = {}\\n for k in router: cp_sorter[k] = []\\n\\n for cp in chain_dict['chainParts']:\\n if cp['signature'] != 'Jet' and cp['signature'] != 'Bjet': \\n continue\\n for k in cp_sorter:\\n if cp['hypoScenario'].startswith(k):\\n cp_sorter[k].append(cp)\\n break\\n\\n # obtain labels by scenario.\\n labels = []\\n for k, chain_parts in cp_sorter.items():\\n if chain_parts: labels.append(router[k](chain_parts))\\n\\n assert labels\\n nlabels = len(labels)\\n if nlabels == 1: return labels[0]\\n if nlabels == 2:\\n alabel = \\\"\\\"\\\"\\\\\\nand([]\\n %s\\n %s)\\\"\\\"\\\" % (tuple(labels))\\n return alabel\\n\\n # more than 2 labels is not expected\\n assert False\",\n \"def get_diar_target_labels(self, uniq_id, sample, fr_level_target):\\n seg_target_list, base_clus_label = [], []\\n self.scale_n = len(self.multiscale_timestamp_dict[uniq_id]['scale_dict'])\\n subseg_time_stamp_list = self.multiscale_timestamp_dict[uniq_id][\\\"scale_dict\\\"][self.scale_n - 1][\\\"time_stamps\\\"]\\n for (seg_stt, seg_end) in subseg_time_stamp_list:\\n seg_stt_fr, seg_end_fr = int(seg_stt * self.frame_per_sec), int(seg_end * self.frame_per_sec)\\n soft_label_vec_sess = torch.sum(fr_level_target[seg_stt_fr:seg_end_fr, :], axis=0) / (\\n seg_end_fr - seg_stt_fr\\n )\\n label_int_sess = torch.argmax(soft_label_vec_sess)\\n soft_label_vec = soft_label_vec_sess.unsqueeze(0)[:, sample.target_spks].squeeze()\\n if label_int_sess in sample.target_spks and torch.sum(soft_label_vec_sess) > 0:\\n label_int = sample.target_spks.index(label_int_sess)\\n else:\\n label_int = -1\\n label_vec = (soft_label_vec > self.soft_label_thres).float()\\n seg_target_list.append(label_vec.detach())\\n base_clus_label.append(label_int)\\n seg_target = torch.stack(seg_target_list)\\n base_clus_label = torch.tensor(base_clus_label)\\n return seg_target, base_clus_label\",\n \"def onCut(self):\\n pass\",\n \"def autolabel(X_pos,values,height_lift):\\r\\n\\theight= np.round(np.nan_to_num(values),2);y_pos = height_lift*height\\r\\n\\tfor i in range(len(height)):\\r\\n\\t\\tax.text(X_pos[i],y_pos[i],'%4.2f' % height[i], ha='center', va='bottom',size=4)\",\n \"def labels(self):\\n\\n param=self\\n\\n l=len(param)\\n\\n sweep_label=[]\\n\\n for index,name in enumerate(param.names):\\n\\n sweep_label.append((\\\\\\n ''.join([c for c in name if c.isupper()]))\\\\\\n .replace(\\\"IDT\\\",\\\"\\\")\\\\\\n .replace(\\\"S\\\",\\\"\\\")\\\\\\n .replace(\\\"M\\\",\\\"\\\"))\\n\\n stringout=[]\\n\\n unique={name:list(dict.fromkeys(values)) for name,values in zip(param.names,param.values)}\\n\\n for i in range(l):\\n\\n tmp_lab=''\\n\\n for lab,name in zip(sweep_label,self.names):\\n\\n tmp_lab=tmp_lab+lab+str(unique[name].index(param()[name][i]))\\n\\n stringout.append(tmp_lab)\\n\\n return stringout\",\n \"def _change_name(self, suff, info_extra):\\n if 'cable-ring' in self.path:\\n i1 = info_extra['convex_hull_area']\\n i2 = info_extra['best_possible_area']\\n f = i1 / i2\\n suff = suff.replace('.png',\\n f'-area-{i1:0.3f}-best-{i2:0.3f}-FRAC-{f:0.3f}.png')\\n elif 'cloth-flat' in self.path:\\n i1 = info_extra['cloth_coverage']\\n suff = suff.replace('.png', f'-coverage-{i1:0.3f}.png')\\n elif 'bag-alone' in self.path:\\n i1 = info_extra['convex_hull_area']\\n i2 = info_extra['best_possible_area']\\n suff = suff.replace('.png', f'-area-{i1:0.3f}-best-{i2:0.3f}.png')\\n else:\\n pass\\n return suff\",\n \"def Label(self) -> str:\",\n \"def setPieLabels(label, position):\\n pdict = {'left':'LEFT', 'right':'RIGHT'}\\n dislin.pielab(label, pdict[position])\",\n \"def test_reneaming_old_default_labels_to_new_fixed_labels():\\n from dtscalibration import DataStore\\n\\n cable_len = 100.0\\n nt = 3\\n time = np.arange(nt)\\n x = np.linspace(0.0, cable_len, 8)\\n ts_cold = np.ones(nt) * 4.0 + np.cos(time) * 4\\n ts_warm = np.ones(nt) * 20.0 + -np.sin(time) * 4\\n\\n C_p = 1324 # 1/2 * E0 * v * K_+/lam_+^4\\n eta_pf = np.cos(time) / 10 + 1 # eta_+ (gain factor forward channel)\\n eta_pb = np.sin(time) / 10 + 1 # eta_- (gain factor backward channel)\\n C_m = 5000.0\\n eta_mf = np.cos(time + np.pi / 8) / 10 + 1\\n eta_mb = np.sin(time + np.pi / 8) / 10 + 1\\n dalpha_r = 0.005284\\n dalpha_m = 0.004961\\n dalpha_p = 0.005607\\n gamma = 482.6\\n\\n temp_real_kelvin = np.zeros((len(x), nt)) + 273.15\\n temp_real_kelvin[x < 0.2 * cable_len] += ts_cold[None]\\n temp_real_kelvin[x > 0.85 * cable_len] += ts_warm[None]\\n temp_real_celsius = temp_real_kelvin - 273.15\\n\\n st = (\\n eta_pf[None]\\n * C_p\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_p * x[:, None])\\n * np.exp(gamma / temp_real_kelvin)\\n / (np.exp(gamma / temp_real_kelvin) - 1)\\n )\\n ast = (\\n eta_mf[None]\\n * C_m\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_m * x[:, None])\\n / (np.exp(gamma / temp_real_kelvin) - 1)\\n )\\n rst = (\\n eta_pb[None]\\n * C_p\\n * np.exp(-dalpha_r * (-x[:, None] + cable_len))\\n * np.exp(-dalpha_p * (-x[:, None] + cable_len))\\n * np.exp(gamma / temp_real_kelvin)\\n / (np.exp(gamma / temp_real_kelvin) - 1)\\n )\\n rast = (\\n eta_mb[None]\\n * C_m\\n * np.exp(-dalpha_r * (-x[:, None] + cable_len))\\n * np.exp(-dalpha_m * (-x[:, None] + cable_len))\\n / (np.exp(gamma / temp_real_kelvin) - 1)\\n )\\n\\n c_f = np.log(eta_mf * C_m / (eta_pf * C_p))\\n c_b = np.log(eta_mb * C_m / (eta_pb * C_p))\\n\\n dalpha = dalpha_p - dalpha_m # \\\\Delta\\\\alpha\\n alpha_int = cable_len * dalpha\\n\\n df = c_f # reference section starts at first x-index\\n db = c_b + alpha_int\\n i_fw = np.log(st / ast)\\n i_bw = np.log(rst / rast)\\n\\n E_real = (i_bw - i_fw) / 2 + (db - df) / 2\\n\\n ds = DataStore(\\n {\\n \\\"ST\\\": ([\\\"x\\\", \\\"time\\\"], st),\\n \\\"AST\\\": ([\\\"x\\\", \\\"time\\\"], ast),\\n \\\"REV-ST\\\": ([\\\"x\\\", \\\"time\\\"], rst),\\n \\\"REV-AST\\\": ([\\\"x\\\", \\\"time\\\"], rast),\\n \\\"userAcquisitionTimeFW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"userAcquisitionTimeBW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"cold\\\": ([\\\"time\\\"], ts_cold),\\n \\\"warm\\\": ([\\\"time\\\"], ts_warm),\\n },\\n coords={\\\"x\\\": x, \\\"time\\\": time},\\n attrs={\\\"isDoubleEnded\\\": \\\"1\\\"},\\n )\\n ds = ds.rename_labels()\\n\\n sections = {\\n \\\"cold\\\": [slice(0.0, 0.09 * cable_len)],\\n \\\"warm\\\": [slice(0.9 * cable_len, cable_len)],\\n }\\n\\n real_ans2 = np.concatenate(([gamma], df, db, E_real[:, 0]))\\n\\n ds.calibration_double_ended(\\n sections=sections,\\n st_var=1.5,\\n ast_var=1.5,\\n rst_var=1.0,\\n rast_var=1.0,\\n method=\\\"wls\\\",\\n solver=\\\"sparse\\\",\\n fix_gamma=(gamma, 0.0),\\n )\\n\\n assert_almost_equal_verbose(df, ds.df.values, decimal=14)\\n assert_almost_equal_verbose(db, ds.db.values, decimal=13)\\n assert_almost_equal_verbose(\\n x * (dalpha_p - dalpha_m), ds.alpha.values - ds.alpha.values[0], decimal=13\\n )\\n assert np.all(np.abs(real_ans2 - ds.p_val.values) < 1e-10)\\n assert_almost_equal_verbose(temp_real_celsius, ds.tmpf.values, decimal=10)\\n assert_almost_equal_verbose(temp_real_celsius, ds.tmpb.values, decimal=10)\\n assert_almost_equal_verbose(temp_real_celsius, ds.tmpw.values, decimal=10)\\n pass\",\n \"def autolabel(rects,array,axis,dist):\\n ctr = 0\\n label_array = [EM.truncate(v*100,1) for v in array]\\n for entry in range(len(label_array)):\\n if(label_array[entry]>=0) and (label_array[entry]<=1):\\n label_array[entry] = EM.truncate(array[entry]*100,2)\\n\\n\\n for rect in rects:\\n height = rect.get_height()\\n if(axis=='1'):\\n ax1.text(rect.get_x() + rect.get_width()/2., height+dist,\\n label_array[ctr],fontsize=fonts[3],\\n #'%d' % int(height),\\n ha='center', va='bottom',rotation=90)\\n elif(axis=='2'):\\n ax2.text(rect.get_x() + rect.get_width()/2., height+dist,\\n label_array[ctr],fontsize=fonts[3],\\n #'%d' % int(height),\\n ha='center', va='bottom',rotation=90)\\n elif(axis=='3'):\\n ax3.text(rect.get_x() + rect.get_width()/2., height+dist,\\n label_array[ctr],fontsize=fonts[3],\\n #'%d' % int(height),\\n ha='center', va='bottom',rotation=90)\\n elif(axis=='4'):\\n ax4.text(rect.get_x() + rect.get_width()/2., height+dist,\\n label_array[ctr],fontsize=fonts[3],\\n #'%d' % int(height),\\n ha='center', va='bottom',rotation=90)\\n ctr = ctr + 1\",\n \"def label(d, X, ind_class0, ind_class1, N, V, binary):\\n if binary == True:\\n K = 1\\n C = torch.zeros(N + V, K)\\n C[ind_class0, :] = 0.0\\n C[ind_class1, :] = 1.0\\n else:\\n K = 2\\n C = torch.zeros(N + V, K)\\n C[ind_class0, :] = torch.tensor([1.0, 0.0])\\n C[ind_class1, :] = torch.tensor([0.0, 1.0])\\n\\n X_train = X[:N, :]\\n X_val = X[N:, :]\\n C_train = C[:N, :]\\n C_val = C[N:, :]\\n\\n return [X_train, C_train, X_val, C_val, d, K]\",\n \"def Write_GCode_Drag_Knife(self, PostPro):\\n\\n # initialisation of the string\\n exstr = \\\"\\\"\\n\\n # Get the mill settings defined in the GUI\\n safe_retract_depth = self.parentLayer.axis3_retract\\n safe_margin = self.parentLayer.axis3_safe_margin\\n\\n workpiece_top_Z = self.axis3_start_mill_depth\\n f_g1_plane = self.f_g1_plane\\n f_g1_depth = self.f_g1_depth\\n\\n \\\"\\\"\\\"\\n Cutting in slices is not supported for Swivel Knife tool. All is cut at once.\\n \\\"\\\"\\\"\\n mom_depth = self.axis3_mill_depth\\n drag_depth = self.axis3_slice_depth\\n\\n # Move the tool to the start.\\n exstr += self.stmove.geos.abs_el(0).Write_GCode(PostPro)\\n\\n # Add string to be added before the shape will be cut.\\n exstr += PostPro.write_pre_shape_cut()\\n\\n # Move into workpiece and start cutting into Z\\n exstr += PostPro.rap_pos_z(\\n workpiece_top_Z + abs(safe_margin)) # Compute the safe margin from the initial mill depth\\n exstr += PostPro.chg_feed_rate(f_g1_depth)\\n\\n # Write the geometries for the first cut\\n if isinstance(self.stmove.geos.abs_el(1), ArcGeo):\\n if self.stmove.geos.abs_el(1).drag:\\n exstr += PostPro.lin_pol_z(drag_depth)\\n drag = True\\n else:\\n exstr += PostPro.lin_pol_z(mom_depth)\\n drag = False\\n else:\\n exstr += PostPro.lin_pol_z(mom_depth)\\n drag = False\\n exstr += PostPro.chg_feed_rate(f_g1_plane)\\n\\n exstr += self.stmove.geos.abs_el(1).Write_GCode(PostPro)\\n\\n for geo in Geos(self.stmove.geos[2:]).abs_iter():\\n if isinstance(geo, ArcGeo):\\n if geo.drag:\\n exstr += PostPro.chg_feed_rate(f_g1_depth)\\n exstr += PostPro.lin_pol_z(drag_depth)\\n exstr += PostPro.chg_feed_rate(f_g1_plane)\\n drag = True\\n elif drag:\\n exstr += PostPro.chg_feed_rate(f_g1_depth)\\n exstr += PostPro.lin_pol_z(mom_depth)\\n exstr += PostPro.chg_feed_rate(f_g1_plane)\\n drag = False\\n elif drag:\\n exstr += PostPro.chg_feed_rate(f_g1_depth)\\n exstr += PostPro.lin_pol_z(mom_depth)\\n exstr += PostPro.chg_feed_rate(f_g1_plane)\\n drag = False\\n\\n exstr += self.Write_GCode_for_geo(geo, PostPro)\\n\\n # Do the tool retraction\\n exstr += PostPro.chg_feed_rate(f_g1_depth)\\n exstr += PostPro.lin_pol_z(workpiece_top_Z + abs(safe_margin))\\n exstr += PostPro.rap_pos_z(safe_retract_depth)\\n\\n # Add string to be added before the shape will be cut.\\n exstr += PostPro.write_post_shape_cut()\\n\\n return exstr\",\n \"def get_label(name):\\n lower = name.lower()\\n vals = lower.split('_')\\n if 'ho' in vals:\\n name = 'Independent Estimate'\\n elif 'alldata' in vals:\\n name = 'Extra-Data Estimate'\\n elif 'ris' in vals[0]:\\n name = 'RIS'\\n if 'w' in vals[0]:\\n name += ' WIS'\\n if 'pd' in vals[0]:\\n name += ' PDIS'\\n elif 'is' in vals[0]:\\n name = 'OIS'\\n if 'w' in vals[0]:\\n name += ' WIS'\\n if 'pd' in vals[0]:\\n name += ' PDIS'\\n if 'dr' in vals:\\n name += ' DR'\\n if 'wdr' in vals:\\n name += ' WDR'\\n return name\",\n \"def setLabelDistance(dist=24, axes='XYZ'):\\n dislin.labdis(dist, axes)\",\n \"def onAddCutToolClicked(self, event):\\n i_cube = self.cube_choice.GetSelection()\\n i_dimension = self.cut_dimension_choice.GetSelection()\\n\\n if i_dimension <= 0:\\n dlg_func.openWarningBox(_(u'CUT'), _(u'Cut dimension not selected'))\\n else:\\n value = self.cut_value_textCtrl.GetValue()\\n if not value.strip():\\n dlg_func.openWarningBox(_(u'CUT'), _(u'Cut value not specified'))\\n else:\\n cube = self._OLAP_server.getCubes()[i_cube]\\n dimension = cube.getDimensions()[i_dimension - 1]\\n row = (dimension.getLabel(), dimension.getName(), value)\\n self.appendListCtrlRow(listctrl=self.cut_listCtrl, row=row)\\n\\n # After adding, clear the controls\\n self.cut_dimension_choice.SetSelection(0)\\n self.cut_value_textCtrl.SetValue(u'')\\n\\n event.Skip()\"\n]"},"negative_scores":{"kind":"list like","value":["0.5650385","0.54846936","0.54270923","0.5375306","0.53342646","0.5226105","0.5209129","0.51919734","0.51784825","0.5173263","0.5163355","0.51560795","0.5154937","0.51164854","0.5109441","0.5093374","0.50911057","0.5076122","0.50540954","0.50480634","0.50421095","0.5034301","0.5032512","0.5028899","0.50236046","0.50234383","0.50142574","0.50132155","0.500397","0.49963433","0.49924707","0.49883384","0.49860042","0.49729776","0.49658248","0.49618146","0.49567258","0.49540654","0.49540654","0.49540654","0.49483338","0.4944248","0.49440426","0.49428022","0.49410334","0.4940744","0.49205828","0.49146846","0.49107683","0.49019703","0.49014068","0.4897067","0.4896715","0.4887378","0.4887378","0.48800677","0.48689413","0.48567855","0.485127","0.48479623","0.48441973","0.48358923","0.48252445","0.4818517","0.4812086","0.4808856","0.48074234","0.48025966","0.47962397","0.47950944","0.47950405","0.47939527","0.47912276","0.47896543","0.47838947","0.47807014","0.47798583","0.4776863","0.47678357","0.47665718","0.475749","0.47557047","0.47531223","0.47501165","0.4749837","0.474615","0.4737223","0.47342587","0.47282302","0.47193056","0.4719002","0.47179413","0.47126937","0.47052646","0.47046396","0.4704533","0.47022855","0.4702027","0.47002992","0.4694844"],"string":"[\n \"0.5650385\",\n \"0.54846936\",\n \"0.54270923\",\n \"0.5375306\",\n \"0.53342646\",\n \"0.5226105\",\n \"0.5209129\",\n \"0.51919734\",\n \"0.51784825\",\n \"0.5173263\",\n \"0.5163355\",\n \"0.51560795\",\n \"0.5154937\",\n \"0.51164854\",\n \"0.5109441\",\n \"0.5093374\",\n \"0.50911057\",\n \"0.5076122\",\n \"0.50540954\",\n \"0.50480634\",\n \"0.50421095\",\n \"0.5034301\",\n \"0.5032512\",\n \"0.5028899\",\n \"0.50236046\",\n \"0.50234383\",\n \"0.50142574\",\n \"0.50132155\",\n \"0.500397\",\n \"0.49963433\",\n \"0.49924707\",\n \"0.49883384\",\n \"0.49860042\",\n \"0.49729776\",\n \"0.49658248\",\n \"0.49618146\",\n \"0.49567258\",\n \"0.49540654\",\n \"0.49540654\",\n \"0.49540654\",\n \"0.49483338\",\n \"0.4944248\",\n \"0.49440426\",\n \"0.49428022\",\n \"0.49410334\",\n \"0.4940744\",\n \"0.49205828\",\n \"0.49146846\",\n \"0.49107683\",\n \"0.49019703\",\n \"0.49014068\",\n \"0.4897067\",\n \"0.4896715\",\n \"0.4887378\",\n \"0.4887378\",\n \"0.48800677\",\n \"0.48689413\",\n \"0.48567855\",\n \"0.485127\",\n \"0.48479623\",\n \"0.48441973\",\n \"0.48358923\",\n \"0.48252445\",\n \"0.4818517\",\n \"0.4812086\",\n \"0.4808856\",\n \"0.48074234\",\n \"0.48025966\",\n \"0.47962397\",\n \"0.47950944\",\n \"0.47950405\",\n \"0.47939527\",\n \"0.47912276\",\n \"0.47896543\",\n \"0.47838947\",\n \"0.47807014\",\n \"0.47798583\",\n \"0.4776863\",\n \"0.47678357\",\n \"0.47665718\",\n \"0.475749\",\n \"0.47557047\",\n \"0.47531223\",\n \"0.47501165\",\n \"0.4749837\",\n \"0.474615\",\n \"0.4737223\",\n \"0.47342587\",\n \"0.47282302\",\n \"0.47193056\",\n \"0.4719002\",\n \"0.47179413\",\n \"0.47126937\",\n \"0.47052646\",\n \"0.47046396\",\n \"0.4704533\",\n \"0.47022855\",\n \"0.4702027\",\n \"0.47002992\",\n \"0.4694844\"\n]"},"document_score":{"kind":"string","value":"0.6397082"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8789,"cells":{"query":{"kind":"string","value":"ht label. ht cuts, and cuts on particpating jets"},"document":{"kind":"string","value":"def _make_ht_label(chain_parts):\n\n assert len(chain_parts) == 1, '_make_ht_label, no. of chain parts != 1'\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('HT'), '_make_ht_label(): scenario does not start with HT'\n\n arg_res = [\n re.compile(r'^(?P\\d*)(?Pht)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pet)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Peta)(?P\\d*)$'),\n ]\n\n defaults = {\n 'ht': ('0', 'inf'),\n 'et': ('0', 'inf'),\n 'eta': ('0', 'inf'),\n }\n\n\n args = _args_from_scenario(scenario)\n argvals = {}\n nargs = len(args)\n assert len(args) <= len(arg_res), 'bad num of args %d, expected < %d' % (len(args),\n len(arg_res))\n\n # obtain argument values frrom scenario\n while args:\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = float(defaults[key][0])\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = float(defaults[key][1])\n argvals[key+'hi'] = hi\n\n print (argvals)\n assert len(argvals) == 2*nargs, 'no of args: %d, expected %d' % (len(argvals), 2*nargs)\n\n print ('sent 100')\n result = \"\"\"\n ht([(%(htlo).0fht) \n (%(etlo).0fet)\n (%(etalo).0feta%(etahi).0f)\n ])\"\"\" % argvals\n print (result)\n return result"},"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 label_thin(self, orig_label):\n pil_thin = thin(orig_label)\n # Keep the original label and set non-thinning label as 0\n orig_label[~pil_thin] = 0\n\n return orig_label","def kohonen():\n# plb.close('all')\n \n dim = 28*28\n data_range = 255.0\n \n # load in data and labels \n data = np.array(np.loadtxt('data.txt'))\n labels = np.loadtxt('labels.txt')\n\n # select 4 digits \n name = \"Stettler\"\n targetdigits = name2digits(name) # assign the four digits that should be used\n print(targetdigits) # output the digits that were selected\n\n # this selects all data vectors that corresponds to one of the four digits\n data = data[np.logical_or.reduce([labels==x for x in targetdigits]),:]\n \n dy, dx = data.shape\n \n #set the size of the Kohonen map. In this case it will be 6 X 6\n size_k = 6\n \n #set the width of the neighborhood via the width of the gaussian that\n #describes it\n sigma = 2.0\n \n #initialise the centers randomly\n centers = np.random.rand(size_k**2, dim) * data_range\n \n #build a neighborhood matrix\n neighbor = np.arange(size_k**2).reshape((size_k, size_k))\n\n #set the learning rate\n eta = 0.9 # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE\n \n #set the maximal iteration count\n tmax = 5000 # this might or might not work; use your own convergence criterion\n \n #set the random order in which the datapoints should be presented\n i_random = np.arange(tmax) % dy\n np.random.shuffle(i_random)\n \n for t, i in enumerate(i_random):\n som_step(centers, data[i,:],neighbor,eta,sigma)\n\n # for visualization, you can use this:\n for i in range(size_k**2):\n plb.subplot(size_k,size_k,i)\n \n plb.imshow(np.reshape(centers[i,:], [28, 28]),interpolation='bilinear')\n plb.axis('off')\n \n # leave the window open at the end of the loop\n plb.show()\n plb.draw()","def onCut(self):\n pass","def odemis_to_hyperspy(filename='sampledata/cltest.h5',specbin=1) :\r\n\r\n f=h5.File(filename,'r')\r\n shome = 'Acquisition2//ImageData/'\r\n x = f[shome + 'Image']\r\n cdesc =f['Acquisition2/PhysicalData/ChannelDescription'].value[0].decode('utf-8')\r\n #print(cdesc)\r\n\r\n cltype = None\r\n if 'Spectrum' in cdesc :\r\n cltype = 'spectrum'\r\n elif 'CL intensity' in cdesc:\r\n cltype = 'panchrom'\r\n\r\n print('<' + filename + '> original shape :' ,x.shape, cltype)\r\n\r\n # strip unused dimensions and transpose/ reverse index order\r\n if cltype == 'panchrom' :\r\n xx=x[0,0,0,:,:].transpose((1,0))\r\n # just an image..\r\n else :\r\n xx=x[:,0,0,:,:].transpose((2,1,0))\r\n\r\n if cltype == 'spectrum' :\r\n #interpolate data to linearize the wavelength scale\r\n w = f[shome + 'DimensionScaleC'].value *1e9\r\n wx = np.linspace(w.min(),w.max(),w.size)\r\n for i in np.arange(xx.shape[0]) :\r\n for k in np.arange(xx.shape[1]) :\r\n xx[i,k,:] = np.interp(wx,w,xx[i,k,:])\r\n\r\n wslope = wx[1]-wx[0]\r\n woffset = wx.min()\r\n #wx = np.arange(w.size)\r\n #wslope,woffset=np.polyfit(wx,w,1)\r\n s = hs.signals.Signal1D(xx)\r\n\r\n elif cltype == 'panchrom' :\r\n s = hs.signals.Signal2D(xx)\r\n else :\r\n print('unknown type')\r\n\r\n print('hyperspy shape :' ,s.data.shape)\r\n\r\n\r\n s.metadata.General.title = 'Odemis: ' + cdesc\r\n s.metadata.General.original_filename = filename\r\n s.metadata.General.notes = cltype\r\n s.axes_manager[0].name = 'pos x'\r\n s.axes_manager[0].scale = f[shome + 'DimensionScaleX'].value * 1e6\r\n s.axes_manager[0].offset = f[shome + 'XOffset'].value * 1e6\r\n s.axes_manager[0].units = 'um'\r\n\r\n\r\n s.axes_manager[1].name = 'pos y'\r\n s.axes_manager[1].scale = f[shome + 'DimensionScaleX'].value * 1e6\r\n s.axes_manager[1].offset = f[shome + 'YOffset'].value * 1e6\r\n s.axes_manager[1].units = 'um'\r\n\r\n if cltype == 'spectrum' :\r\n s.axes_manager[2].name = 'wavelength'\r\n s.axes_manager[2].units = 'nm'\r\n s.axes_manager[2].offset = woffset\r\n s.axes_manager[2].scale = wslope\r\n s.metadata.signal_type = 'CL'\r\n\r\n f.close()\r\n if (specbin > 1) and (cltype == 'spectrum'):\r\n return( s.rebin(scale=[1,1,specbin]) )\r\n else :\r\n return( s )\r\n #end odemis_to_hyperspy\r\n #######################\r","def cut(S, T, graph):\n ###TODO\n pass","def __init__(self, label=None):\n super().__init__(\"h\", 1, [], label=label)","def extract_info(config, cut, label):\n cfg = filter(lambda c: c['name'] == cut, config['physics']['cuts'])[0]\n text = \"\"\n if 'max' not in cfg:\n text += \"#geq \"\n text += str(cfg['min'])\n if 'max' in cfg and cfg['max'] != cfg['min']:\n text += '-' + str(cfg['max']) + ' ' + label + 's'\n elif cfg['min'] != 1:\n text += ' ' + label + 's'\n else:\n text += ' ' + label\n return text","def ch(h1):\n return -(pic_height / float(h)) * h1","def drawlabels(t, t1):\r\n t.fd(250)\r\n t.pd()\r\n t.write(\"Life\", font=(\"Arial\", 10, \"bold\"))\r\n t.pu()\r\n t.back(12)\r\n t.pd()\r\n t.write(\"Exp.\", font=(\"Arial\", 10, \"bold\"))\r\n t.pu()\r\n t.back(238)\r\n t.right(90)\r\n t.fd(80)\r\n t1.pu()\r\n t1.back(50)\r\n t1.rt(90)\r\n t1.fd(250)\r\n t1.pd()\r\n t1.write(\"Year\", font=(\"Arial\", 10, \"bold\"))\r\n t1.pu()\r\n t1.back(250)\r\n t1.left(90)\r\n t1.fd(50)","def hxlcut():\n run_script(hxlcut_main)","def write_label_ps(header_lines, base_lines, tail_lines, shape_list, title, outFn, cutofflist=[0.3,0.5,0.7], mode='fill'):\n OUT = open(outFn, \"w\")\n for header_line in header_lines:\n if r'{title}' in header_line:\n header_line = header_line.format(title=title)\n OUT.writelines(header_line)\n #print(len(shape_list), len())\n for shape,base_line in zip(shape_list,base_lines):\n if mode=='label':\n OUT.writelines( _color_command_segmented(shape, cutofflist)+\"\\n\" )\n elif mode=='heatmap':\n OUT.writelines( _color_command_heatmap(shape, Gradient_Colors, 0, 1)+\"\\n\" )\n else:\n raise RuntimeError(\"Sorry: mode='fill' Not applicant now\")\n OUT.writelines(base_line)\n for tail_line in tail_lines:\n OUT.writelines(tail_line)\n OUT.close()","def __get_ohe_label__(self, label_idx) -> List[int]:\n\n label = [0] * self.n_classes\n label[label_idx] = 1\n\n return label","def agglo_from_labelmask(\n h5path_in,\n h5path_lv='',\n ratio_threshold=0,\n h5path_out='',\n save_steps=False,\n protective=False,\n ):\n\n # check output paths\n outpaths = {'out': h5path_out}\n status = utils.output_check(outpaths, save_steps, protective)\n if status == \"CANCELLED\":\n return\n\n # open data for reading\n h5file_in, ds_in, elsize, axlab = utils.h5_load(h5path_in)\n h5file_lv, ds_lv, _, _ = utils.h5_load(h5path_lv)\n\n # open data for writing\n h5file_out, ds_out = utils.h5_write(None, ds_in.shape, ds_in.dtype,\n h5path_out,\n element_size_um=elsize,\n axislabels=axlab)\n\n ulabels = np.unique(ds_in)\n maxlabel = np.amax(ulabels)\n print(\"number of labels in watershed: {:d}\".format(maxlabel))\n\n fwmap = np.zeros(maxlabel + 1, dtype='i')\n\n areas_ws = np.bincount(ds_in.ravel())\n\n labelsets = {}\n rp_lw = regionprops(ds_lv, ds_in)\n for prop in rp_lw:\n\n maskedregion = prop.intensity_image[prop.image]\n counts = np.bincount(maskedregion)\n svoxs_in_label = [l for sl in np.argwhere(counts) for l in sl]\n\n ratios_svox_in_label = [float(counts[svox]) / float(areas_ws[svox])\n for svox in svoxs_in_label]\n fwmask = np.greater(ratios_svox_in_label, ratio_threshold)\n labelset = np.array(svoxs_in_label)[fwmask]\n labelsets[prop.label] = set(labelset) - set([0])\n\n basepath = h5path_in.split('.h5/')[0]\n utils.write_labelsets(labelsets, basepath + \"_svoxsets\",\n filetypes=['pickle'])\n\n ds_out[:] = utils.forward_map(np.array(fwmap), ds_in, labelsets)\n\n # close and return\n h5file_in.close()\n h5file_lv.close()\n try:\n h5file_out.close()\n except (ValueError, AttributeError):\n return ds_out","def cut(self,cell):\r\n self.grid[cell[0]][cell[1]] = 1","def o_wo_per_head(self):\n assert self.ff % self.heads == 0\n # fuse ff->e and projection layer of self-attention\n return (self.ff // (self.heads-self.padded_heads)) + self.qkv","def ksh(i,t,htanses):\n for (zs,ys,zx,yx) in htanses[i]:\n alex.penup()\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\n alex.pendown()\n alex.goto((ys%m+1)*20-10*m,(ys//m)*20-10*n)\n alex.goto((yx%m+1)*20-10*m,(yx//m+1)*20-10*n)\n alex.goto((zx%m)*20-10*m,(zx//m+1)*20-10*n)\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\n alex.hideturtle()","def create_teacher(self):\n\n #words = [\"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\"]\n\n #print(\"self.length: \", self.length)\n for i, image in enumerate(self.images):\n if False:#i % 2 == 0:\n img = list(chunks(image, 10))\n plt.imshow(img, interpolation=\"nearest\", origin=\"upper\")\n plt.colorbar()\n plt.title(self.labels[i])\n plt.show()\n label = np.argmax(self.labels[i]) + 1\n label_vector = self.labels[i]\n timesteps = label + 1\n\n for chain in list(self.traces[i]):\n count_tensor = list()\n input_tensor = list()\n target_tensor = list()\n \n #count_tensor.append([0] * z_size)\n #input_tensor.append(image)\n #target_tensor.append([None, None])\n\n #count_padding = [0 for x in range(z_size)]\n \n for count, link in enumerate(chain):\n count_vector = [0 if x is not count else 1 for x in range(z_size)]\n count_tensor.append(count_vector)\n x, y = link\n image[y*200+x] = 255\n input_tensor.append(image)\n target_tensor.append(list(link))\n\n # Fill in the rest of the list with the same (current one doe\n #for cont in range(count + 1, z_size):\n for cont in range(count + 1, z_size + 1):\n #count_tensor.append(count_padding)\n count_tensor.append(count_vector)\n input_tensor.append(image)\n target_tensor.append(list(link))\n\n \n\n self.explode_lbls.append(label)\n self.explode_labels.append(label_vector)\n self.explode_counts.append(count_tensor)\n self.explode_images.append(input_tensor)\n self.explode_traces.append(target_tensor)\n #print(target_tensor)\n\n \n \n self.explode_length = len(self.explode_images)","def get_hbls_hbbl(self):\n [Ly,N] = self.b.shape\n z_u_w = self.grid_dict['z_u_w']\n z_u_r = self.grid_dict['z_u_r']\n u = self.u\n v = self.v\n \n v_upts = TTTW_func.v2u(v)\n Hz = z_u_w[:,1:] - z_u_w[:,:-1]\n\n\n\n # CALCULATE swr_frac\n self.swr_frac = TTTW_func.lmd_swr_frac(self.grid_dict)\n\n\n # WHOLE THING HAPPENS IN j loop through y-indices\n \n # INITIALIZE ARRAYS\n self.kmo = np.zeros([Ly])\n self.Cr = np.zeros([Ly])\n self.kbl = np.empty([Ly],dtype='int')\n self.C_h_MO = np.zeros([Ly])\n self.Cr = np.zeros([Ly,N+1]) # sum term\n self.FC = np.zeros([Ly,N+1])\n self.swdk_r = np.zeros([Ly,N+1])\n \n self.zscale = np.zeros([Ly,N])\n self.Kern = np.zeros([Ly,N])\n\n \n # --> LOOP THROUGH Y-INDICES\n for j in range(Ly):\n if self.LIMIT_MO_DEPTH:\n self.kmo[j] = 0\n self.C_h_MO[j] = self.C_MO *self.ustar[j]**3/self.vonKar\n \n self.kbl[j] = 0\n self.Cr[j,-1] = 0 # set top Cr\n self.Cr[j,0] = 0 # set bottom Cr\n \n # SEARCH FOR MIXED LAYER DEPTH\n self.FC[j,-1] = 0.\n\n\n # ---> LOOP TOP TO BOTTOM (FORTRAN ==> k=N-1,1,-1)\n for k in range(N-1,0,-1):\n # INDEX MAP\n k_r = k-1\n k_w = k\n\n \n zscale = z_u_w[j,N] - z_u_r[j,k_r]\n self.zscale[j,k_w] = zscale\n if self.LMD_KPP:\n if self.LMD_BKPP:\n zscaleb = z_u_r[j,k_r] - z_u_w[j,0]\n Kern = zscale * zscaleb**2 / ( (zscale + self.epssfcs*self.hbls_old[j]) * (zscaleb**2+(self.epssfcb**2*self.hbbl_old[j]**2)))\n else:\n Kern = zscale / (zscale + (self.epssfcs*self.hbls_old[j]))\n else:\n Kern = 1.\n \n\n\n self.Kern[j,k_w] = Kern\n self.FC[j,k_w] = self.FC[j,k_w+1] + Kern * (\\\n ( ( u[j,k_r+1] - u[j,k_r] )**2 + ( v_upts[j,k_r+1] - v_upts[j,k_r])**2 ) \\\n / (Hz[j,k_r] + Hz[j,k_r+1]) \\\n - 0.5 * ( Hz[j,k_r] + Hz[j,k_r+1]) * (self.Ri_inv * self.bvf[j,k_w] + self.C_Ek*self.f[j]*self.f[j]))\n\n\n #\t\tLOOP THAT FINDS BL DEPTH ##\n #----> LOOP TOP TO BOTTOM (start at free surface, w-level surface) \n \n if self.LMD_KPP:\n #swdk_r only used in this function so don't need to be class attribute\n # but for testing make it an attribute to see what it is\n \n # fortran equivlanet ===> k=N,1,-1 \n for k in range(N,0,-1):\n # INDEX MAP\n k_r = k-1\n k_w = k\n\n ###################################################################### \n self.swdk_r[j,k_w] = np.sqrt( self.swr_frac[j,k_w] * self.swr_frac[j,k_w-1])\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\n Bfsfc = self.Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])\n \n self.bvf_max = np.sqrt(np.max([0,self.bvf[j,k_w-1]]))\n \n # CALCULATE TURBULENT VELOCITY SCALE FOR TRACERS\n \t\t\t self.ws = self.lmd_wscale_ws_only(Bfsfc, zscale,self.hbls_old[j],self.ustar[j])\n \n self.Vtsq = self.Vtc * self.ws* self.bvf_max + self.V0\n \n\n self.Cr[j,k_w] = self.FC[j,k_w] + self.Vtsq\n \n\n #######################################################################\n \n # SEARCH FOR hbls vertical level #\n '''\n kbl is specified at vertical w-level (via Cr which is at\n vertical w-levels)\n '''\n if self.kbl[j] == 0 and self.Cr[j,k_w] < 0:\n self.kbl[j] = k_w\n if self.LIMIT_MO_DEPTH:\n if self.kmo[j] == 0 and Bfsfc*(z_u_w[j,N] - z_u_r[j,k_r]) > self.C_h_MO[j]:\n self.kmo[j] = k_w\n\n \n #--> still in j-loop\n #######################################################\n \n # \t\tGET SURFACE BOUNDARY LAYER DEPTH # \n self.hbls[j] = z_u_w[j,N] - z_u_w[j,0] + self.eps # set hbls as depth of entire water column\n if self.kbl[j] > 0:\n k_w = self.kbl[j]\n k_r = k_w - 1 \n if k_w == N: # set hbls at the surface btwn w- and rho-levels at surface\n self.hbls[j] = z_u_w[j,N] - z_u_r[j,N-1]\n \n else:\n self.hbls[j] = z_u_w[j,N] - ( z_u_r[j,k_r] * self.Cr[j,k_w+1] - z_u_r[j,k_r+1] * self.Cr[j,k_w]) / \\\n (self.Cr[j,k_w+1] - self.Cr[j,k_w])\n \n if self.LIMIT_MO_DEPTH:\n if self.kmo[j] > 0:\n k_w = self.kmo[j]\n k_r = k_w-1\n if k_w == N:\n z_up = z_u_w[j,N]\n cff_up = np.max([0,Bo[j]])\n else:\n z_up = z_r[j,k_w+1]\n cff_up = np.max([0, Bo[j] + self.Bosol[j]*(1-self.swdk_r[j,(k_w-1)+1])])\n \n cff_dn = np.max([0,Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])]) \n h_MO = z_u_w[j,N] + self.C_h_MO[j] * ( cff_up*z_up - cff_dn * z_u_r[j,k_r] ) \\\n / ( cff_up * cff_dn * (z_up - z_u_r[j,k_r]) ) \\\n + self.C_h_MO[j] * (cff_dn - cff_up)\n\n self.hbls[j] = np.min([self.hbls[j],np.max([h_MO,0])])\n\n\n\n #### GET BOTTOM BOUNDARY LAYER DEPTH #######\n if self.LMD_BKPP:\n self.kbl[j] = 0 # reset Cr at bottom and kbl for BKPP\n self.Cr[j,0] = 0.\n self.FC[j,0] = 1.5 * self.FC[j,1] - 0.5 * self.FC[j,2] # linear extrapolation\n \n #---> LOOP BOTTOM TO TOP\n # FIND kbl for BBL\n for k in range(1,N+1):\n k_r = k-1\n k_w = k \n self.Cr[j,k_w] = self.FC[j,k_w] - self.FC[j,0]\n \n # LOOK FOR FIRST ZERO CROSSING FROM BOTTOM UP\n if self.kbl[j] == 0 and self.Cr[j,k_w] > 0:\n self.kbl[j] = k_w \n \n\n self.hbbl[j] = z_u_w[j,N] - z_u_w[j,0] # total depth\n if self.kbl[j] > 0 :\n k_w = self.kbl[j] \n k_r = k_w -1\n if k_w == 1: # NO BBL CASE\n self.hbbl[j] = z_u_r[j,0] - z_u_w[j,0] #in between bottom rho and w-level\n else:\n self.hbbl[j] = ( z_u_r[j,k_r-1] * self.Cr[j,k_w] - z_u_r[j,k_r] * self.Cr[j,k_w-1]) / \\\n (self.Cr[j,k_w] - self.Cr[j,k_w-1]) - z_u_w[j,0]","def process_labels(ctx, tex, chapter):\n headings = ['chapter'] + ['sub'*i + 'section' for i in range(4)]\n reh = r'(' + '|'.join(headings) + r'){(.+?)}'\n environments = ['thm', 'lem', 'exc', 'figure', 'equation']\n ree = r'begin{(' + '|'.join(environments) + r')}'\n rel = r'(\\w+)label{(.+?)}'\n rel2 = r'label{(.+?)}'\n bigone = r'\\\\({})|\\\\({})|\\\\({})|\\\\(caption)|\\\\({})'.format(reh, ree, rel, rel2)\n rx = re.compile(bigone)\n\n sec_ctr = [chapter] + [0]*(len(headings))\n env_ctr = [0]*len(environments)\n blocks = catlist()\n lastlabel = None\n lastidx = 0\n m = rx.search(tex, lastidx)\n while m:\n blocks.append(tex[lastidx:m.start()])\n lastidx = m.start()\n cmd = next_command(tex, lastidx)\n lastidx = cmd.end\n if m.group(2):\n # This is a sectioning command (chapter, subsection,...)\n name = m.group(2)\n i = headings.index(name)\n if i == 0:\n env_ctr = [0]*len(env_ctr)\n sec_ctr[i:] = [sec_ctr[i]+1]+[0]*(len(headings)-i-1)\n number = \".\".join([str(x) for x in sec_ctr[:i+1]])\n idd = \"{}:{}\".format(name, number)\n lastlabel = idd\n blocks.append(\"\".format(idd))\n\n title = '{} {}'.format(number, cmd.args[0])\n blocks.append(r'\\{}{{{}}}'.format(name, title))\n\n elif m.group(5):\n # This is an environment (thm, lem, ...)\n name = m.group(5)\n lastenv = name # save this for a caption command coming later...\n i = environments.index(name)\n env_ctr[i] += 1\n number = \"{}.{}\".format(sec_ctr[0], env_ctr[i])\n idd = \"{}:{}\".format(name, number)\n lastlabel = idd\n blocks.append(\"\".format(idd))\n\n if name in ctx.theoremlike_environments:\n nicename = ctx.named_entities[name]\n title = '{} {}'.format(nicename, number)\n blocks.append(r'\\begin{{{}}}[{}]'.format(name, title))\n else:\n blocks.append(r'\\begin{{{}}}'.format(name))\n\n elif m.group(6):\n # This is a labelling command (\\thmlabel, \\seclabel,...)\n label = \"{}:{}\".format(m.group(7), m.group(8))\n ctx.label_map[label] = (ctx.outputfile, lastlabel)\n\n elif m.group(9):\n # This is a caption command\n name = lastenv\n i = environments.index(name)\n number = \"{}.{}\".format(sec_ctr[0], env_ctr[i])\n idd = \"{}:{}\".format(name, number)\n lastlabel = idd\n nicename = ctx.named_entities[name]\n title = '{} {}'.format(nicename, number)\n text = '{} {}'.format(title, cmd.args[0])\n blocks.append(r'\\caption{{{}}}'.format(text))\n\n elif m.group(10):\n # This is a \\label command, probably the target of a pageref\n idd = gen_unique_id()\n blocks.append(\"\".format(idd))\n ctx.label_map[m.group(11)] = (ctx.outputfile, idd)\n\n m = rx.search(tex, lastidx)\n blocks.append(tex[lastidx:])\n return \"\".join(blocks)","def simple_core(block,cut,laser):\r\n\r\n\tlayers = int(block[\"thickness\"]/laser[\"z_spacing\"])\r\n\r\n\t#Since all cuts are square, the offsets are more obvious than in the general linear case.\r\n\ttaper = math.tan(math.radians(laser[\"kerf_angle\"]/2)) * laser[\"z_spacing\"]\r\n\tmax_delta = math.tan(math.radians(laser[\"kerf_angle\"]/2)) * (block[\"thickness\"] + laser[\"z_final_overshoot\"]) * 2\r\n\t\r\n\tcutlist = []\r\n\tcutlist.append([\"a_abs\", \"0\"])\r\n\tcutlist.append([\"c_abs\", str(block[\"physical_rotation\"])])\r\n\tcutlist.append([\"z_abs\", str(block[\"thickness\"])])\r\n\r\n\tfor a in range(layers):\r\n\t\tx1, y1 = cut[\"final_dimension_x\"]/2 + a*taper, cut[\"final_dimension_y\"]/2 + a*taper\r\n\t\twhile abs(x1-cut[\"final_dimension_x\"]/2) < abs(max_delta):\r\n\t\t\tcutlist.append([\"jump\", str(x1 + block[\"origin_x\"]), str(y1 + block[\"origin_y\"])])\r\n\t\t\tcutlist.append([\"mark\", str(x1 + block[\"origin_x\"]), str(-y1 + block[\"origin_y\"])])\r\n\t\t\tcutlist.append([\"mark\", str(-x1 + block[\"origin_x\"]), str(-y1 + block[\"origin_y\"])])\r\n\t\t\tcutlist.append([\"mark\", str(-x1 + block[\"origin_x\"]), str(y1 + block[\"origin_y\"])])\r\n\t\t\tcutlist.append([\"mark\", str(x1 + block[\"origin_x\"]), str(y1 + block[\"origin_y\"])])\r\n\t\t\tx1, y1 = x1 + laser[\"xy_spacing\"], y1 + laser[\"xy_spacing\"]\r\n\t\tcutlist.append([\"z_step\", str(-laser[\"z_spacing\"])])\r\n\t\tmax_delta = max_delta - taper \r\n\treturn json.dumps(cutlist)","def LabelDisks(self):\n pass","def split2(self, eccMap, patchName='patch00', cutStep=1, borderWidth=2, isplot=False):\r\n minMarker = localMin(eccMap, cutStep)\r\n\r\n connectivity = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])\r\n\r\n newLabel = sm.watershed(eccMap, minMarker, connectivity=connectivity, mask=self.array)\r\n\r\n border = ni.binary_dilation(self.array).astype(np.int8) - self.array\r\n\r\n for i in range(1, np.amax(newLabel) + 1):\r\n currArray = np.zeros(self.array.shape, dtype=np.int8)\r\n currArray[newLabel == i] = 1\r\n currBorder = ni.binary_dilation(currArray).astype(np.int8) - currArray\r\n border = border + currBorder\r\n\r\n border[border > 1] = 1\r\n border = sm.skeletonize(border)\r\n\r\n if borderWidth > 1:\r\n border = ni.binary_dilation(border, iterations=borderWidth - 1).astype(np.int8)\r\n\r\n newPatchMap = ni.binary_dilation(self.array).astype(np.int8) * (-1 * (border - 1))\r\n\r\n labeledNewPatchMap, patchNum = ni.label(newPatchMap)\r\n\r\n # if patchNum != np.amax(newLabel):\r\n # print 'number of patches: ', patchNum, '; number of local minimum:', np.amax(newLabel)\r\n # raise ValueError, \"Number of patches after splitting does not equal to number of local minimum!\"\r\n\r\n newPatchDict = {}\r\n\r\n for j in range(1, patchNum + 1):\r\n\r\n currPatchName = patchName + '.' + str(j)\r\n currArray = np.zeros(self.array.shape, dtype=np.int8)\r\n currArray[labeledNewPatchMap == j] = 1\r\n currArray = currArray * self.array\r\n\r\n if np.sum(currArray[:]) > 0:\r\n newPatchDict.update({currPatchName: Patch(currArray, self.sign)})\r\n\r\n if isplot:\r\n plt.figure()\r\n plt.subplot(121)\r\n plt.imshow(self.array, interpolation='nearest')\r\n plt.title(patchName + ': before split')\r\n plt.subplot(122)\r\n plt.imshow(labeledNewPatchMap, interpolation='nearest')\r\n plt.title(patchName + ': after split')\r\n\r\n return newPatchDict","def __init__(self, smoothing=0.1):\n super(LabelSmoothing, self).__init__()\n self.confidence = 1.0 - smoothing\n self.smoothing = smoothing","def hilfe(self):\n toto_hilfe(3)","def identify_leaflets(u, time_ts):\n z = u.select_atoms(\"all\").center_of_geometry()[2]\n COM_z= np.array([0,0,z]) #defines the global midplane position along z\n x, y, z = u.trajectory.ts.triclinic_dimensions[0][0], u.trajectory.ts.triclinic_dimensions[1][1], u.trajectory.ts.triclinic_dimensions[2][2]\n box = np.array([x, y, z, 90, 90, 90]) \n ### Determining side of the bilayer CHOL belongs to in this frame\n lipid1 = 'CHL'\n lipid2 = 'DLIP'\n lipid3 = 'SSM'\n lipid4 = 'DSPC'\n \n lpd1_atoms = u.select_atoms('resname %s and name O2'%lipid1) \n lpd2_atoms = u.select_atoms('resname %s and name P '%lipid2) \n lpd3_atoms = u.select_atoms('resname %s and name P '%lipid3) \n lpd4_atoms = u.select_atoms('resname %s and name P '%lipid4)\n \n num_lpd2 = lpd2_atoms.n_atoms\n num_lpd3 = lpd3_atoms.n_atoms\n num_lpd4 = lpd4_atoms.n_atoms \n # atoms in the upper leaflet as defined by insane.py or the CHARMM-GUI membrane builders\n # select cholesterol headgroups within 1.5 nm of lipid headgroups in the selected leaflet\n # this must be done because CHOL rapidly flip-flops between leaflets\n # so we must assign CHOL to each leaflet at every time step, and in large systems\n # with substantial membrane undulations, a simple cut-off in the z-axis just will not cut it\n if side == 'up':\n lpd2i = lpd2_atoms[:int((num_lpd2)/2)]\n lpd3i = lpd3_atoms[:int((num_lpd3)/2)]\n lpd4i = lpd4_atoms[:int((num_lpd4)/2)]\n \n\n lipids = lpd2i + lpd3i + lpd4i \n\n ns_lipids = NS.AtomNeighborSearch(lpd1_atoms, box=box) \n lpd1i = ns_lipids.search(lipids,15.0) #1.5 nm\n leaflet = lpd1i + lpd2i + lpd3i + lpd4i \n\n elif side == 'down':\n lpd2i = lpd2_atoms[int((num_lpd2)/2):]\n lpd3i = lpd3_atoms[int((num_lpd3)/2):]\n lpd4i = lpd4_atoms[int((num_lpd4)/2):]\n\n lipids = lpd2i + lpd3i + lpd4i #+ lpd3i\n \n ns_lipids = NS.AtomNeighborSearch(lpd1_atoms, box=box)\n lpd1i = ns_lipids.search(lipids,15.0) # 1.5nm\n leaflet = lpd1i + lpd2i + lpd3i+ lpd4i \n return lpd1i, lpd2i, lpd3i, lpd4i, COM_z, box, leaflet","def cmd_label_merged_boundaries(self,sun_dir,output_dir=None):\n sun=sunreader.SunReader(sun_dir)\n if output_dir is None:\n # defaults location of dense output\n output_dir=os.path.join(sun.datadir,'dwaq',\"global-dense\")\n \n hyd_fn=glob.glob(os.path.join(output_dir,'*.hyd'))[0]\n \n hydro=SunHydro(sun=sun,hyd_path=hyd_fn,flow_shps=[self.flows_shp])\n\n class SpliceScenario(waq_scenario.Scenario):\n base_path=output_dir\n name=\"spliced\"\n\n scen=SpliceScenario(hydro=hydro)\n\n self.log.info(\"Writing labels\")\n scen.hydro.write_boundary_links()","def __init__(self, smoothing=0.0):\n super(LabelSmoothing, self).__init__()\n self.confidence = 1.0 - smoothing\n self.smoothing = smoothing","def __init__(self, smoothing=0.0):\n super(LabelSmoothing, self).__init__()\n self.confidence = 1.0 - smoothing\n self.smoothing = smoothing","def make_stehle(self):\n\n temp_k = self.temp * e / k # temperature in K\n dens_cm = self.e_dens * 1.e-6 # electronic density in cm-3\n prefix = 'n_' + str(self.n_upper) + '_' + str(self.n_lower) + '_'\n\n # extract raw tabulated tabulated_data\n tab_temp_k = np.array(pystark.nc.variables[prefix + 'tempe'].data) # tabulated electron temperatures (K)\n olam0 = pystark.nc.variables[prefix + 'olam0'].data # line centre wavelength (A)\n num_tab_dens = pystark.nc.variables[prefix + 'id_max'].data\n fainom = pystark.nc.variables[prefix + 'fainom'].data\n tab_dens_cm = np.array(pystark.nc.variables[prefix + 'dense'].data) # tabulated electron densities (cm ** -3)\n f00 = np.array(pystark.nc.variables[prefix + 'f00'].data) # normal Holtsmark field strength (30 kV / m)\n dl12 = np.array(pystark.nc.variables[prefix + 'dl12'].data)\n dl12s = np.array(pystark.nc.variables[prefix + 'dl12s'].data)\n fainu = pystark.nc.variables[\n prefix + 'fainu'].data # Asymptotic value of iStark * (alpha ** 2.5) (\"wings factor in alfa units\")\n pr0 = np.array(pystark.nc.variables[\n prefix + 'pr0'].data) # Ratio of the mean interelectronic distance to the electronic Debye length\n jtot = np.array(pystark.nc.variables[prefix + 'jtot'].data,\n dtype=np.int) # \"number of wave lengths for the couple (T,Ne)\"\n dom = np.array(pystark.nc.variables[prefix + 'dom'].data) # frequency detunings in units (rad / (s*ues)\n d1om = np.array(pystark.nc.variables[prefix + 'd1om'].data)\n o1line = np.array(pystark.nc.variables[prefix + 'o1line'].data)\n o1lines = np.array(pystark.nc.variables[prefix + 'o1lines'].data)\n\n # ensure given temperature + density falls within tabulated values\n # change sligtly the value of the input density\n # dens_cm in order to remain , as far as possible, inside the tabulation\n # JSA: this first step seems bogus!\n\n if np.abs(dens_cm - tab_dens_cm[0]) / dens_cm <= 1.0E-3:\n dens_cm = tab_dens_cm[0] * 1.001\n\n for id in np.arange(1, num_tab_dens + 1):\n if np.abs(dens_cm - tab_dens_cm[id]) / dens_cm <= 1.0E-3:\n dens_cm = tab_dens_cm[id] * 0.999\n\n if dens_cm >= 2.0 * tab_dens_cm[num_tab_dens]:\n raise Exception(\n 'Your input density is higher than the largest tabulated value %f' % tab_dens_cm[num_tab_dens])\n\n if dens_cm <= tab_dens_cm[0]:\n raise Exception('Your input density is smaller than the smallest tabulated value %f' % tab_dens_cm[0])\n\n if temp_k >= tab_temp_k[9]:\n raise Exception('Your input temperature is higher than the largest tabulated value %f' % tab_temp_k[9])\n\n if temp_k <= tab_temp_k[0]:\n raise Exception('Your input temperature is lower than the smallest tabulated value %f' % tab_temp_k[0])\n\n normal_holtsmark_field = 1.25e-9 * (dens_cm ** (2. / 3.)) # normal field value in ues\n\n # calculate line centre wavelength and frequency using Rydberg formula\n # JSA: I have made this step clearer and corrected for deuteron mass in the Rydberg constant (though the effect is small)\n # TODO make sure this matches olam0 parameter above -- why were there two variables in the first place?!\n # rydberg_m = Rydberg / (1. + (electron_mass / physical_constants['deuteron mass'][0]))\n # wl_0_angst = 1e10 * (rydberg_m * (1 / n_lower ** 2 - 1 / n_upper ** 2)) ** -1\n\n wl_centre_angst = self.wl_centre * 1e10\n\n c_angst = c * 1e10 # velocity of light in Ansgtroms / s\n angular_freq_0 = 2 * np.pi * c_angst / wl_centre_angst # rad / s\n\n otrans = -2 * np.pi * c_angst / wl_centre_angst ** 2\n\n olines = o1lines / np.abs(otrans)\n oline = o1line / np.abs(otrans)\n\n # Limit analysis_tools to uncorrelated plasmas.\n # check that mean interelectronic distance is smaller than the electronic Debye length (equ. 10)\n PR0_exp = 0.0898 * (dens_cm ** (1. / 6.)) / np.sqrt(temp_k) # = (r0 / debye)\n if PR0_exp > 1.:\n raise Exception('The plasma is too strongly correlated\\ni.e. r0/debye=0.1\\nthe line cannot be computed.')\n\n # fainom_exp=fainom*(F00_exp**1.5)\n # fainum_exp=fainom_exp/( (OPI*2.)**1.5)\n\n # ========================\n # TABULATION Format CDS\n # si on veut ecrire\n # n -np lambda0 kalpha Ne E0 T R0/Debye Dalpha iDoppler iStark\n\n # IN_cds= N+0.01\n # INP_cds = NP+0.01\n\n # ***********************************************************\n # Don't edit the CDS format...\n # ***********************************************************\n\n # Skipped the code in the IF statement starting at line 470, since it\n # isn't used, if (.FALSE.) ...\n\n # ==============================================\n # define an unique detunings grid - domm - for the tabulated\n # profiles ( various temperatures , densities)\n # calculate all the line shapes for this common grid\n # units used at this points are Domega_new= Delta(omega)/F00\n # in rd/(s-1 ues)\n\n max_num_dens = 30 # Maximum number of densities\n max_num_tab_temp = 10\n max_num_detunings = 60 # Maximum number of detunings\n jtot = jtot.astype(np.int)\n domm = np.zeros(100000)\n dom0 = np.zeros(10000)\n tprof = np.zeros([max_num_dens, max_num_tab_temp, 10000])\n tprofs = np.zeros([max_num_dens, max_num_tab_temp, 10000])\n uprof = np.zeros([max_num_dens, 10000])\n uprofs = np.zeros([max_num_dens, 10000])\n\n inc = 0\n domm[inc] = 0.0\n # ---- Look to replace this loop\n for id in np.arange(num_tab_dens + 1): # loop over tab densities\n for j in np.arange(max_num_tab_temp): # loop over tab temperatures (?)\n for i in np.arange(1, jtot[id, j]):\n inc += 1\n dom0[inc] = dom[id, j, i]\n\n inc = np.count_nonzero(dom)\n npik = inc + 1\n # nut=10000\n\n # Calling numpy sort instead of piksrt\n tmp = np.sort(dom0[0:npik])\n dom0[0:npik] = tmp[0:npik]\n # dom0 seems to agree with the FORTRAN version\n\n inc = 0\n domm[0] = 0.0\n # print 'npik',npik\n # ---- Look to replace this loop\n for i in np.arange(1, npik):\n dif = (dom0[i] - dom0[i - 1])\n if dif <= 1.0E-6:\n continue\n if dif / np.abs(dom0[i]) <= 0.1:\n continue\n inc = inc + 1\n domm[inc] = dom0[i]\n\n jdom = inc + 1 # One line after marker 35\n\n for id in np.arange(num_tab_dens):\n for j in np.arange(10):\n if pr0[id, j] > 1.0:\n continue\n\n tprof[id, j, 0] = oline[id, j, 0]\n tprofs[id, j, 0] = olines[id, j, 0]\n\n if jtot[id, j] == 0:\n continue\n\n for i in np.arange(1, jdom + 1):\n skip1 = False\n skip2 = False\n # print 'i',i\n domeg = domm[i]\n ij_max = jtot[id, j]\n # print 'domeg,ij_max',domeg,ij_max\n for ij in np.arange(1, ij_max - 1):\n # print 'ij',ij\n test = (domeg - dom[id, j, ij]) * (domeg - dom[id, j, ij - 1])\n # print 'test1:',test\n if test <= 0.0:\n # print 'triggered test1'\n x1 = dom[id, j, ij - 1]\n x2 = dom[id, j, ij]\n x3 = dom[id, j, ij + 1]\n y1 = oline[id, j, ij - 1]\n y2 = oline[id, j, ij]\n y3 = oline[id, j, ij + 1]\n # print 'x1,x2,x3',x1,x2,x3\n # print 'y1,y2,y3',y1,y2,y3\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n y1 = olines[id, j, ij - 1]\n y2 = olines[id, j, ij]\n y3 = olines[id, j, ij + 1]\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n # print 'tprof[id,j,i]',tprof[id,j,i]\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\n skip1 = True\n skip2 = True\n break\n\n if skip1 is False:\n test = (domeg - dom[id, j, ij_max - 2]) * (domeg - dom[id, j, ij_max - 1])\n # print 'test2:',test\n # print 'domeg',domeg\n # print 'dom[id,j,ij_max-1]',dom[id,j,ij_max-2]\n # print 'dom[id,j,ij_max]',dom[id,j,ij_max-1]\n if test <= 0.0:\n # print 'triggered test2'\n x1 = dom[id, j, ij_max - 3]\n x2 = dom[id, j, ij_max - 2]\n x3 = dom[id, j, ij_max - 1]\n y1 = oline[id, j, ij_max - 3]\n y2 = oline[id, j, ij_max - 2]\n y3 = oline[id, j, ij_max - 1]\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n y1 = olines[id, j, ij_max - 3]\n y2 = olines[id, j, ij_max - 2]\n y3 = olines[id, j, ij_max - 1]\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n skip2 = True\n # print 'x1,x2,x3',x1,x2,x3\n # print 'y1,y2,y3',y1,y2,y3\n # print 'tprof[id,j,i]',tprof[id,j,i]\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\n continue\n\n if skip2 is False:\n if domeg > dom[id, j, ij_max]:\n # print 'triggered test3'\n tprof[id, j, i] = fainom / (domeg ** 2.5)\n tprofs[id, j, i] = tprof[id, j, i]\n continue\n\n # We can skip writing the intermediate file\n\n\n for id in np.arange(num_tab_dens):\n otest_dens = (dens_cm - tab_dens_cm[id]) * (dens_cm - tab_dens_cm[id + 1])\n if otest_dens <= 0.0:\n dense1 = tab_dens_cm[id]\n dense2 = tab_dens_cm[id + 1]\n id1 = id\n id2 = id + 1\n break\n\n if dens_cm >= tab_dens_cm[num_tab_dens]:\n dense1 = tab_dens_cm[num_tab_dens - 1]\n dense2 = tab_dens_cm[num_tab_dens]\n id1 = num_tab_dens - 1\n id2 = num_tab_dens\n\n for it in np.arange(10):\n otest = (temp_k - tab_temp_k[it]) * (temp_k - tab_temp_k[it + 1])\n if otest <= 0.0:\n it1 = it\n it2 = it + 1\n # pr01 = pr0[id2,it1] # max value of pr0 for T1,T2,dense1,dense2\n tempe1 = tab_temp_k[it]\n tempe2 = tab_temp_k[it + 1]\n break\n\n # interpolation in temperature\n for id in np.arange(id1, id2 + 1):\n for i in np.arange(jdom):\n uprof[id, i] = tprof[id, it1, i] + (temp_k - tempe1) * (tprof[id, it2, i] - tprof[id, it1, i]) / (\n tempe2 - tempe1)\n uprofs[id, i] = tprofs[id, it1, i] + (temp_k - tempe1) * (tprofs[id, it2, i] - tprofs[id, it1, i]) / (\n tempe2 - tempe1)\n\n delta_lambda = np.zeros(jdom)\n delta_nu = np.zeros(jdom)\n wprof_nu = np.zeros(jdom)\n wprofs_nu = np.zeros(jdom)\n\n for i in np.arange(jdom):\n wprof = uprof[id1, i] + (dens_cm - dense1) * (uprof[id2, i] - uprof[id1, i]) / (dense2 - dense1)\n wprofs = uprofs[id1, i] + (dens_cm - dense1) * (uprofs[id2, i] - uprofs[id1, i]) / (dense2 - dense1)\n delta_omega = domm[i] * normal_holtsmark_field\n delta_nu[i] = delta_omega / (2 * np.pi)\n delta_lambda[i] = wl_centre_angst * delta_omega / (angular_freq_0 + delta_omega)\n # print(delta_lambda[i])\n wprof_nu[i] = (wprof / normal_holtsmark_field) * (2. * np.pi)\n wprofs_nu[i] = (wprofs / normal_holtsmark_field) * (2. * np.pi)\n # print '%e %e %e %e' %(delta_lambda[i],delta_nu[i],wprof_nu[i],wprofs_nu[i])\n\n delta_lambda2 = np.concatenate((-delta_lambda[::-1], delta_lambda)) + wl_centre_angst # + olam0\n delta_nu2 = np.concatenate((-delta_nu[::-1], delta_nu))\n wprof_nu2 = np.concatenate((wprof_nu[::-1], wprof_nu))\n wprofs_nu2 = np.concatenate((wprofs_nu[::-1], wprofs_nu))\n\n # for some reason, i only get a good agreement with the other models if i take the pure Stark broadened Stehle\n # output and manually convolve it with the Doppler profile -- not sure why...\n ls_sd = wprofs_nu2\n\n # interpolate onto frequency axis\n ls_sd = np.interp(self.freq_axis, delta_nu2 + self.freq_centre, ls_sd)\n\n return ls_sd","def _add_labels(self):\n coords = self['pore.coords']\n self['pore.front'] = coords[:,0]<(0.1*self._Lx)\n self['pore.back'] = coords[:,0]>(0.9*self._Lx)\n self['pore.left'] = coords[:,1]<(0.1*self._Ly)\n self['pore.right'] = coords[:,1]>(0.9*self._Ly)\n self['pore.bottom'] = coords[:,2]<(0.1*self._Lz)\n self['pore.top'] = coords[:,2]>(0.9*self._Lz)\n bnds = self.pores(labels=['front','back','left','right','bottom','top'])\n self['pore.boundary'] = False\n self['pore.boundary'] = bnds","def __call__(self, src, label):\n\n h, w, _ = src.shape\n # interp = np.random.randint(0, 5)\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\n img, flips = timage.random_flip(img, px=0.5)\n img = img.astype(np.float32)\n target_list_1 = []\n target_list_2 = []\n\n for k in range(self.teacher_num):\n if self.teacher_aug:\n target_image_1 = self.random_color_aug(img)\n else:\n target_image_1 = img\n\n target_image_2 = self.random_color_aug(img)\n\n target_image_1 = mx.nd.image.to_tensor(target_image_1)\n target_image_1 = mx.nd.image.normalize(target_image_1, mean=self._mean, std=self._std)\n\n target_image_2 = mx.nd.image.to_tensor(target_image_2)\n target_image_2 = mx.nd.image.normalize(target_image_2, mean=self._mean, std=self._std)\n target_list_1.append(target_image_1)\n target_list_2.append(target_image_2)\n target_list_1 = mx.nd.concat(*target_list_1, dim=0)\n target_list_2 = mx.nd.concat(*target_list_2, dim=0)\n return target_list_1, target_list_2","def _build_ham(self):\n path = self._solverpath.long_tail()\n print(path)\n current, k = self.snake.head(), 0\n for direc in path:\n self.information[current.x][current.y].idx = k\n self.information[current.x][current.y].direc = direc\n current = current.adj(direc)\n k += 1\n # Process snake bodies\n current = self.snake.tail()\n for _ in range(self.snake.len() - 1):\n self.information[current.x][current.y].idx = k\n self.information[current.x][current.y].direc = self.snake.direc\n current = current.adj(self.snake.direc)\n k += 1","def tagview(tab,label,x,y):\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n col=classifc[label]\r\n labnow=classif[label]\r\n# print (labnow, text)\r\n if label == 'back_ground':\r\n deltay=30\r\n else:\r\n# deltay=25*((labnow-1)%5)\r\n deltay=40+10*(labnow-1)\r\n\r\n viseg=cv2.putText(tab,label,(x, y+deltay), font,0.3,col,1)\r\n return viseg","def split(self, eccMap, patchName='patch00', cutStep=1, borderWidth=2, isplot=False):\r\n minMarker = localMin(eccMap, cutStep)\r\n\r\n plt.figure()\r\n plt.imshow(minMarker, vmin=0, interpolation='nearest')\r\n plt.colorbar()\r\n plt.title('markers 1')\r\n plt.show()\r\n\r\n minMarker = minMarker.astype(np.int32)\r\n selfArray = self.array.astype(np.int32)\r\n minMarker = minMarker + 1\r\n minMarker[minMarker == 1] = 0\r\n minMarker = minMarker + (-1 * (selfArray - 1))\r\n # minMarker: marker type for opencv watershed,\r\n # sure background = 1\r\n # unknow = 0\r\n # sure forgrand = 2,3,4... etc\r\n\r\n plt.figure()\r\n plt.imshow(minMarker, vmin=0, interpolation='nearest')\r\n plt.colorbar()\r\n plt.title('markers 2')\r\n plt.show()\r\n\r\n eccMapNor = (np.round(ia.array_nor(eccMap) * 255)).astype(np.uint8)\r\n eccMapRGB = cv2.cvtColor(eccMapNor, cv2.COLOR_GRAY2RGB)\r\n # eccMapRGB: image type for opencv watershed, RGB, [uint8, uint8, uint8]\r\n\r\n newLabel = cv2.watershed(eccMapRGB, minMarker)\r\n\r\n plt.figure()\r\n plt.imshow(newLabel, vmin=0, interpolation='nearest')\r\n plt.colorbar()\r\n plt.title('markers 3')\r\n plt.show()\r\n\r\n newBorder = np.zeros(newLabel.shape).astype(np.int)\r\n\r\n newBorder[newLabel == -1] = 1\r\n\r\n border = ni.binary_dilation(self.array).astype(np.int) - self.array\r\n\r\n border = newBorder + border\r\n\r\n border[border > 1] = 1\r\n\r\n border = sm.skeletonize(border)\r\n\r\n if borderWidth > 1:\r\n border = ni.binary_dilation(border, iterations=borderWidth - 1).astype(np.int8)\r\n\r\n newPatchMap = ni.binary_dilation(self.array).astype(np.int8) * (-1 * (border - 1))\r\n\r\n labeledNewPatchMap, patchNum = ni.label(newPatchMap)\r\n\r\n # if patchNum != np.amax(newLabel):\r\n # print 'number of patches: ', patchNum, '; number of local minimum:', np.amax(newLabel)\r\n # raise ValueError, \"Number of patches after splitting does not equal to number of local minimum!\"\r\n\r\n newPatchDict = {}\r\n\r\n for j in range(1, patchNum + 1):\r\n\r\n currPatchName = patchName + '.' + str(j)\r\n currArray = np.zeros(self.array.shape, dtype=np.int8)\r\n currArray[labeledNewPatchMap == j] = 1\r\n currArray = currArray * self.array\r\n\r\n if np.sum(currArray[:]) > 0:\r\n newPatchDict.update({currPatchName: Patch(currArray, self.sign)})\r\n\r\n if isplot:\r\n plt.figure()\r\n plt.subplot(121)\r\n plt.imshow(self.array, interpolation='nearest')\r\n plt.title(patchName + ': before split')\r\n plt.subplot(122)\r\n plt.imshow(labeledNewPatchMap, interpolation='nearest')\r\n plt.title(patchName + ': after split')\r\n\r\n return newPatchDict","def do_htt_plots(tree, output_dir, cut=''):\n for logz in [True, False]:\n make_2d_plot(tree, 'httRef', HTT_REF_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1', HTT_L1_STR, NB_HTT, HTT_MIN, HTT_MAX,\n os.path.join(output_dir, 'httRef_httL1.pdf'), logz=logz, normx=False,\n cut=cut, title=TITLE, diagonal_line=True)\n for normx in [True, False]:\n make_2d_plot(tree, 'httL1', HTT_L1_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1/httRef', HTT_RATIO_STR, NB_HTT_RATIO, HTT_RATIO_MIN, HTT_RATIO_MAX,\n os.path.join(output_dir, 'httRatio_httL1.pdf'), logz=logz, normx=normx,\n cut=cut, title=TITLE, horizontal_line=True)\n make_2d_plot(tree, 'httRef', HTT_REF_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1/httRef', HTT_RATIO_STR, NB_HTT_RATIO, HTT_RATIO_MIN, HTT_RATIO_MAX,\n os.path.join(output_dir, 'httRatio_httRef.pdf'), logz=logz, normx=normx,\n cut=cut, title=TITLE, horizontal_line=True)\n\n make_2d_plot(tree, 'httL1', HTT_L1_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1-httRef', HTT_DIFF_STR, NB_HTT_DIFF, HTT_DIFF_MIN, HTT_DIFF_MAX,\n os.path.join(output_dir, 'httDiff_httL1.pdf'), logz=logz, normx=normx,\n cut=cut, title=TITLE, horizontal_line=True)\n make_2d_plot(tree, 'httRef', HTT_REF_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1-httRef', HTT_DIFF_STR, NB_HTT_DIFF, HTT_DIFF_MIN, HTT_DIFF_MAX,\n os.path.join(output_dir, 'httDiff_httRef.pdf'), logz=logz, normx=normx,\n cut=cut, title=TITLE, horizontal_line=True)\n make_2d_plot(tree, 'httL1/httRef', HTT_RATIO_STR, NB_HTT_RATIO, HTT_RATIO_MIN, HTT_RATIO_MAX,\n 'httL1-httRef', HTT_DIFF_STR, NB_HTT_DIFF, HTT_DIFF_MIN, HTT_DIFF_MAX,\n os.path.join(output_dir, 'httDiff_httRatio.pdf'), logz=logz, normx=normx,\n cut=cut, title=TITLE, horizontal_line=True)","def __init__(self, trg_vocab_size, label_smoothing=0.1, reduction='mean', with_logits=True, ignore_index=0):\n super(LabelSmoothingLoss, self).__init__()\n self.with_logits = with_logits\n self.ignore_index = ignore_index\n self.kl_divergence = nn.KLDivLoss(reduction=reduction)\n\n self._create_one_hot(label_smoothing, trg_vocab_size)\n self.confidence = 1.0 - label_smoothing","def nodules_connection(label_data, label_header):\n\n\n las_labels = measure.label(label_data,\n neighbors=8,\n background=0,\n return_num=True)\n\n las_labels_nzero = np.nonzero(las_labels[0])\n [xdif, ydif, zdif] = [np.amax(las_labels_nzero[0])-np.amin(las_labels_nzero[0]),\n np.amax(las_labels_nzero[1])-np.amin(las_labels_nzero[1]),\n np.amax(las_labels_nzero[2])-np.amin(las_labels_nzero[2])]\n\n # conversion pixels to mm\n dims = label_header['pixdim']\n if label_header['xyzt_units'] == 10:\n #dimensions in mm\n print('xyzt_units=10')\n xdif=dims[1]*xdif\n ydif=dims[2]*ydif\n zdif=dims[3]*zdif\n\n\n return las_labels,[xdif,ydif,zdif]","def at_s2ncut(self):\n\n\t # Notch out the transit and recompute\n\t fmcut = self.fm.copy()\n\t fmcut.fill_value=0\n\t # Widen by twice the transit duration\n\t tmask = self.rLbl['tRegLbl'] >= 0\n\t tmask = np.convolve(\n\t tmask.astype(float),\n\t np.ones(self.header['tdurcad'] * 2),\n\t mode='same'\n\t )\n\t tmask = tmask.astype(bool)\n\t fmcut.mask = fmcut.mask | tmask\n\t grid = tfind.Grid(self.t,fmcut)\n\n\n\t pgram_params = [\n\t dict(Pcad1=self.Pcad - 1, Pcad2=self.Pcad + 1, twdG = [self.header['tdurcad']])\n\t ]\n\t pgram = grid.periodogram(pgram_params,mode='max')\n\t idxmax = pgram.s2n.idxmax()\n\n\t dkeys = 's2ncut s2ncut_t0 s2ncut_mean'.split()\n\t pkeys = 's2n t0 mean'.split()\n\n\t for dkey,pkey in zip(dkeys,pkeys):\n\t self.add_attr(dkey,pgram.ix[idxmax,pkey])","def at_s2ncut(self):\n\n\t # Notch out the transit and recompute\n\t fmcut = self.fm.copy()\n\t fmcut.fill_value=0\n\t # Widen by twice the transit duration\n\t tmask = self.rLbl['tRegLbl'] >= 0\n\t tmask = np.convolve(\n\t tmask.astype(float),\n\t np.ones(self.header['tdurcad'] * 2),\n\t mode='same'\n\t )\n\t tmask = tmask.astype(bool)\n\t fmcut.mask = fmcut.mask | tmask\n\t grid = tfind.Grid(self.t,fmcut)\n\n\n\t pgram_params = [\n\t dict(Pcad1=self.Pcad - 1, Pcad2=self.Pcad + 1, twdG = [self.header['tdurcad']])\n\t ]\n\t pgram = grid.periodogram(pgram_params,mode='max')\n\t idxmax = pgram.s2n.idxmax()\n\n\t dkeys = 's2ncut s2ncut_t0 s2ncut_mean'.split()\n\t pkeys = 's2n t0 mean'.split()\n\n\t for dkey,pkey in zip(dkeys,pkeys):\n\t self.add_attr(dkey,pgram.ix[idxmax,pkey])","def housing_labels_(strat_train_set):\n logging.info(\"copy of dataset\")\n housing_labels = strat_train_set[\"median_house_value\"].copy()\n return housing_labels","def addLabels(t):\n if not t.label:\n t.label = \"\".join([choice(\"abcdefghijklmnopqrstuvwxyz\") for i in range(4)])\n for r,w in t.children:\n addLabels(r)","def draw_heading(self, heading): \n\n heading_label = ttk.Label(self.frame, text=heading, background=\"blue\",\n foreground=\"white\", anchor=CENTER)\n \n heading_label.configure(font=('Times', 15, \"bold\"))\n heading_label.configure(wraplength=self.width) \n heading_label.pack(side=TOP, fill=X, ipady=10)","def h_t(self, x, t):\n ret = 0\n strong_classifier = self.classifiers[0:t+1]\n for wc in strong_classifier:\n ret += wc.classify(x)\n return ret","def h_t(self, x, t):\n ret = 0\n strong_classifier = self.classifiers[0:t+1]\n for wc in strong_classifier:\n ret += wc.classify(x)\n return ret","def encodeToLabels(self, gt_instances):\n raw_boxes_xyzwhd = np.zeros((self.config_data[\"max_boxes_per_frame\"], 7))\n ### initialize gronud truth labels as np.zeors ###\n gt_labels = np.zeros(list(self.headoutput_shape[1:4]) + \\\n [len(self.anchor_boxes)] + \\\n [len(self.config_data[\"all_classes\"]) + 7])\n\n ### start transferring box to ground turth label format ###\n for i in range(len(gt_instances[\"classes\"])):\n if i > self.config_data[\"max_boxes_per_frame\"]:\n continue\n class_name = gt_instances[\"classes\"][i]\n box_xyzwhd = gt_instances[\"boxes\"][i]\n class_id = self.config_data[\"all_classes\"].index(class_name)\n if i < self.config_data[\"max_boxes_per_frame\"]:\n raw_boxes_xyzwhd[i, :6] = box_xyzwhd\n raw_boxes_xyzwhd[i, 6] = class_id\n class_onehot = helper.smoothOnehot(class_id, len(self.config_data[\"all_classes\"]))\n \n exist_positive = False\n\n grid_strid = self.grid_strides\n anchor_stage = self.anchor_boxes\n box_xyzwhd_scaled = box_xyzwhd[np.newaxis, :].astype(np.float32)\n box_xyzwhd_scaled[:, :3] /= grid_strid\n anchorstage_xyzwhd = np.zeros([len(anchor_stage), 6])\n anchorstage_xyzwhd[:, :3] = np.floor(box_xyzwhd_scaled[:, :3]) + 0.5\n anchorstage_xyzwhd[:, 3:] = anchor_stage.astype(np.float32)\n\n iou_scaled = helper.iou3d(box_xyzwhd_scaled, anchorstage_xyzwhd, \\\n self.input_size)\n ### NOTE: 0.3 is from YOLOv4, maybe this should be different here ###\n ### it means, as long as iou is over 0.3 with an anchor, the anchor\n ### should be taken into consideration as a ground truth label\n iou_mask = iou_scaled > 0.3\n\n if np.any(iou_mask):\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\n astype(np.int32)\n ### TODO: consider changing the box to raw yolohead output format ###\n gt_labels[xind, yind, zind, iou_mask, 0:6] = box_xyzwhd\n gt_labels[xind, yind, zind, iou_mask, 6:7] = 1.\n gt_labels[xind, yind, zind, iou_mask, 7:] = class_onehot\n exist_positive = True\n\n if not exist_positive:\n ### NOTE: this is the normal one ###\n ### it means take the anchor box with maximum iou to the raw\n ### box as the ground truth label\n anchor_ind = np.argmax(iou_scaled)\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\n astype(np.int32)\n gt_labels[xind, yind, zind, anchor_ind, 0:6] = box_xyzwhd\n gt_labels[xind, yind, zind, anchor_ind, 6:7] = 1.\n gt_labels[xind, yind, zind, anchor_ind, 7:] = class_onehot\n\n has_label = False\n for label_stage in gt_labels:\n if label_stage.max() != 0:\n has_label = True\n gt_labels = [np.where(gt_i == 0, 1e-16, gt_i) for gt_i in gt_labels]\n return gt_labels, has_label, raw_boxes_xyzwhd","def __init__(self, iht_size=4096, num_tilings=8, num_tiles=8):\n self.iht = tc.IHT(iht_size)\n self.num_tilings = num_tilings\n self.num_tiles = num_tiles","def horde_step(self, observation):","def _make_vbenf_label(chain_parts):\n\n # toy label for development: run simple and dijet independently.\n # simple makes Et cuts on two jets. Independently (sharing possible)\n # of jets choosean by simple, the dijet\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n assert scenario.startswith('vbenf')\n args = _args_from_scenario(scenario)\n if not args:\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \n arg_res = [\n re.compile(r'(?P\\d*)(?Pfbet)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pmass)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pet)(?P\\d*)'),\n ]\n\n defaults = {\n 'et': ('101', 'inf'),\n 'mass': ('800', 'inf'),\n 'fbet': ('501', 'inf'),\n }\n\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n and\n (\n []\n simple\n (\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\n )\n combgen\n (\n [(10et, 0eta320)]\n dijet\n (\n [(%(masslo).0fdjmass, 26djdphi)]\n ) \n simple\n (\n [(10et, 0eta320)(20et, 0eta320)]\n )\n )\n )\"\"\" % argvals","def removeLabel(edge):\n return edge[:-2]","def removeLabel(edge):\n return edge[:-2]","def cutPaper(self, cut='partial', feed=True):\n if cut not in ['partial', 'full']:\n raise ValueError('cut must be \\'partial\\' or \\'full\\'')\n elif type(feed) is not bool:\n raise ValueError('feed must be True or False')\n else:\n value = 0 if cut == 'full' else 1\n value += 65 if feed else 0\n self._write(self.__class__.__GS + 'V' + chr(value))","def resize_label(label):\n # If current ratio greater than given ratio\n if label[HEIGHT_INDEX] / label[WIDTH_INDEX] > 1.5:\n previous = label[WIDTH_INDEX]\n label[WIDTH_INDEX] = label[HEIGHT_INDEX] / 1.5\n label[J_INDEX] -= (label[WIDTH_INDEX] - previous) / 2\n # If current ratio smaller than given ratio\n else:\n previous = label[HEIGHT_INDEX]\n label[HEIGHT_INDEX] = label[WIDTH_INDEX] * 1.5\n label[I_INDEX] -= (label[HEIGHT_INDEX] - previous) / 2\n return label","def label(gt_dataset, volume_dim, voxel_dim, labeling_params):\n labeled_volumes = dict()\n labeled_cells = dict()\n #Use global density and reduce the size of gt_dataset here\n global_density = labeling_params[\"global_density\"]\n gt_dataset = {k: v for k,v in gt_dataset.items() if random_sample() < global_density}\n #Label in the order specified in the configuration\n layers = sorted(labeling_params.keys())\n #Remove global_density\n layers.remove(\"global_density\")\n for layer in layers:\n print \"Labeling {}\".format(layer)\n fluorophore = labeling_params[layer]['fluorophore']\n volume, cells = brainbow(gt_dataset, volume_dim, voxel_dim, **labeling_params[layer])\n if fluorophore in labeled_volumes:\n labeled_volumes[fluorophore] += volume\n labeled_cells[fluorophore] |= cells\n else:\n labeled_volumes[fluorophore] = volume\n labeled_cells[fluorophore] = cells\n return labeled_volumes, labeled_cells","def analyze(self, event):\n jets = Collection(event, \"Jet\")\n\n BTagWeightN = 1.0\n BTagWeightN_up = 1.0\n BTagWeightN_down = 1.0\n BTagWeightN_FS = 1.0\n BTagWeightN_up_FS = 1.0\n BTagWeightN_down_FS = 1.0\n BTagWeightD = 1.0\n BTagWeightNHeavy = 1.0\n BTagWeightNHeavy_up = 1.0\n BTagWeightNHeavy_down = 1.0\n BTagWeightNHeavy_FS = 1.0\n BTagWeightNHeavy_up_FS = 1.0\n BTagWeightNHeavy_down_FS = 1.0\n BTagWeightDHeavy = 1.0\n BTagWeightNLight = 1.0\n BTagWeightNLight_FS = 1.0\n BTagWeightNLight_up = 1.0\n BTagWeightNLight_up_FS= 1.0\n BTagWeightNLight_down = 1.0\n BTagWeightNLight_down_FS = 1.0\n BTagWeightDLight = 1.0\n\n for jet in jets:\n pt = jet.pt\n eta = abs(jet.eta)\n flavor = jet.hadronFlavour\n\n if not ( pt > self.jetPtMin and eta < self.jetEtaMax): continue\n\n if flavor == 5:\n pt_bin = self.h_eff_b.GetXaxis().FindBin(pt); \n if pt_bin > self.h_eff_b.GetXaxis().GetNbins():\n pt_bin = self.h_eff_b.GetXaxis().GetNbins(); \n eta_bin = self.h_eff_b.GetYaxis().FindBin(eta); \n if eta_bin > self.h_eff_b.GetYaxis().GetNbins():\n eta_bin = self.h_eff_b.GetYaxis().GetNbins();\n\n eff = self.h_eff_b.GetBinContent(pt_bin, eta_bin);\n\n elif flavor == 4:\n pt_bin = self.h_eff_c.GetXaxis().FindBin(pt); \n if pt_bin > self.h_eff_c.GetXaxis().GetNbins():\n pt_bin = self.h_eff_c.GetXaxis().GetNbins(); \n eta_bin = self.h_eff_c.GetYaxis().FindBin(eta); \n if eta_bin > self.h_eff_c.GetYaxis().GetNbins():\n eta_bin = self.h_eff_c.GetYaxis().GetNbins();\n\n eff = self.h_eff_c.GetBinContent(pt_bin, eta_bin);\n\n else:\n pt_bin = self.h_eff_udsg.GetXaxis().FindBin(pt); \n if pt_bin > self.h_eff_udsg.GetXaxis().GetNbins():\n pt_bin = self.h_eff_udsg.GetXaxis().GetNbins(); \n eta_bin = self.h_eff_udsg.GetYaxis().FindBin(eta); \n if eta_bin > self.h_eff_udsg.GetYaxis().GetNbins():\n eta_bin = self.h_eff_udsg.GetYaxis().GetNbins();\n\n eff = self.h_eff_udsg.GetBinContent(pt_bin, eta_bin);\n \n if self.FastSim:\n btagSF = jet.btagSF\n btagSF_FS=jet.btagSF_FS\n btagSF_up_FS = jet.btagSF_FS_up\n btagSF_down_FS = jet.btagSF_FS_down\n btagSF_down = jet.btagSF_down\n btagSF_up = jet.btagSF_up\n else:\n btagSF = jet.btagSF\n btagSF_FS= 1.0\n btagSF_up = jet.btagSF_up\n btagSF_down = jet.btagSF_down\n btagSF_up_FS = 1.0\n btagSF_down_FS = 1.0\n \n if jet.btagDeepB > self.bDiscCut:\n #check if eff is zero\n if eff < 0.001:\n eff = 0.001\n \n BTagWeightN *= btagSF * eff\n BTagWeightN_FS *= btagSF_FS * eff\n BTagWeightN_up *= btagSF_up * eff\n BTagWeightN_down *= btagSF_down * eff\n BTagWeightN_up_FS *= btagSF_up_FS * eff\n BTagWeightN_down_FS *= btagSF_down_FS * eff\n\n if abs(flavor) == 5:\n BTagWeightNHeavy *= btagSF * eff\n BTagWeightNHeavy_FS *= btagSF_FS * eff\n BTagWeightNHeavy_up *= btagSF_up * eff\n BTagWeightNHeavy_down *= btagSF_down * eff\n BTagWeightNHeavy_up_FS *= btagSF_up_FS * eff\n BTagWeightNHeavy_down_FS *= btagSF_down_FS * eff\n BTagWeightDHeavy *= eff\n else:\n BTagWeightNLight *= btagSF * eff\n BTagWeightNLight_FS *= btagSF_FS * eff\n BTagWeightNLight_up *= btagSF_up * eff\n BTagWeightNLight_down *= btagSF_down * eff\n BTagWeightNLight_up_FS *= btagSF_up_FS * eff\n BTagWeightNLight_down_FS *= btagSF_down_FS * eff\n BTagWeightDLight *= eff\n\n BTagWeightD *= eff\n else:\n #check if eff is 1.0\n if eff > 0.999:\n eff = 0.999\n\n BTagWeightN *= 1 - btagSF * eff\n BTagWeightN_FS *= 1 - btagSF_FS * eff\n BTagWeightN_up *= 1 - btagSF_up * eff\n BTagWeightN_down *= 1 - btagSF_down * eff\n BTagWeightN_up_FS *= 1 - btagSF_up_FS * eff\n BTagWeightN_down_FS *= 1 - btagSF_down_FS * eff\n\n if abs(flavor) == 5:\n BTagWeightNHeavy *= 1 - btagSF * eff\n BTagWeightNHeavy_FS *= 1 - btagSF_FS * eff\n BTagWeightNHeavy_up *= 1 - btagSF_up * eff\n BTagWeightNHeavy_down *= 1 - btagSF_down * eff\n BTagWeightNHeavy_up_FS *= 1 - btagSF_up_FS * eff\n BTagWeightNHeavy_down_FS *= 1 - btagSF_down_FS * eff\n BTagWeightDHeavy *= 1 - eff\n else:\n BTagWeightNLight *= 1 - btagSF * eff\n BTagWeightNLight_FS *= 1 - btagSF_FS * eff\n BTagWeightNLight_up *= 1 - btagSF_up * eff\n BTagWeightNLight_up_FS *= 1 - btagSF_up_FS * eff\n BTagWeightNLight_down *= 1 - btagSF_down * eff\n BTagWeightNLight_down_FS *= 1 - btagSF_down_FS * eff\n BTagWeightDLight *= 1 - eff\n\n BTagWeightD *= 1 - eff\n \n if self.FastSim:\n self.out.fillBranch(\"BTagWeight_FS\", BTagWeightN_FS / BTagWeightD)\n self.out.fillBranch(\"BTagWeight_Up_FS\", BTagWeightN_up_FS / BTagWeightD)\n self.out.fillBranch(\"BTagWeight_Down_FS\", BTagWeightN_down_FS / BTagWeightD)\n self.out.fillBranch(\"BTagWeightHeavy_FS\", BTagWeightNHeavy_FS / BTagWeightDHeavy)\n self.out.fillBranch(\"BTagWeightHeavy_Up_FS\", BTagWeightNHeavy_up_FS / BTagWeightDHeavy)\n self.out.fillBranch(\"BTagWeightHeavy_Down_FS\", BTagWeightNHeavy_down_FS / BTagWeightDHeavy)\n self.out.fillBranch(\"BTagWeightLight_FS\", BTagWeightNLight_FS / BTagWeightDLight)\n self.out.fillBranch(\"BTagWeightLight_Up_FS\", BTagWeightNLight_up_FS / BTagWeightDLight)\n self.out.fillBranch(\"BTagWeightLight_Down_FS\", BTagWeightNLight_down_FS / BTagWeightDLight)\n self.out.fillBranch(\"BTagWeight\", BTagWeightN / BTagWeightD)\n self.out.fillBranch(\"BTagWeight_Up\", BTagWeightN_up / BTagWeightD)\n self.out.fillBranch(\"BTagWeight_Down\", BTagWeightN_down / BTagWeightD)\n self.out.fillBranch(\"BTagWeightHeavy\", BTagWeightNHeavy / BTagWeightDHeavy)\n self.out.fillBranch(\"BTagWeightHeavy_Up\", BTagWeightNHeavy_up / BTagWeightDHeavy)\n self.out.fillBranch(\"BTagWeightHeavy_Down\", BTagWeightNHeavy_down / BTagWeightDHeavy)\n self.out.fillBranch(\"BTagWeightLight\", BTagWeightNLight / BTagWeightDLight)\n self.out.fillBranch(\"BTagWeightLight_Up\", BTagWeightNLight_up / BTagWeightDLight)\n self.out.fillBranch(\"BTagWeightLight_Down\", BTagWeightNLight_down / BTagWeightDLight)\n return True","def torsion_helix_strain(x, dof):\n base = np.zeros([6, dof])\n base[0, 0] = 1 # torsion\n base[1, 1] = 1 # y-bending\n base[2, 2] = 1 # z-bending\n return base","def make_cutout_table(ra_in, dec_in, other=None, othername=None, table=(2,7), compare=False, scale_unit='pixscale', scale=0.25, layer='decals-dr7', layer2=None, savefile=None): \n de_img = []\n wi_img = []\n N = table[0]*table[1]\n \n for i in range(N):\n de_cutout_url = 'http://legacysurvey.org/viewer-dev/jpeg-cutout/?ra=%g&dec=%g&%s=%g&layer=%s&size=180' % (ra_in[i],dec_in[i], scale_unit, scale, layer)\n img = plt.imread(download_file(de_cutout_url,cache=True,show_progress=False,timeout=120))\n de_img.append(img)\n \n if compare:\n wi_cutout_url = 'http://legacysurvey.org/viewer-dev/jpeg-cutout/?ra=%g&dec=%g&%s=%g&layer=%s&size=180' % (ra_in[i],dec_in[i], scale_unit, scale, layer2)\n img = plt.imread(download_file(wi_cutout_url,cache=True,show_progress=False,timeout=120))\n wi_img.append(img)\n \n fig = plt.figure(figsize=(4*table[1],4*table[0]))\n\n for i in range(len(de_img)):\n ax = fig.add_subplot(table[0],table[1],i+1)\n ax.imshow(de_img[i])\n ax.xaxis.set_major_formatter(NullFormatter())\n ax.yaxis.set_major_formatter(NullFormatter())\n if other[i] != None:\n ax.text(0.1,0.9,'%s=%.1f'%(othername,other[i]),transform=ax.transAxes,fontsize=14,color='white')\n\n plt.subplots_adjust(wspace=0.07, hspace=0.07)\n \n if compare:\n fig = plt.figure(figsize=(4*table[1],4*table[0]))\n for i in range(len(wi_img)):\n ax = fig.add_subplot(table[0],table[1],i+1)\n ax.imshow(wi_img[i])\n ax.xaxis.set_major_formatter(NullFormatter())\n ax.yaxis.set_major_formatter(NullFormatter())\n ax.text(0.1,0.9,'r=%.1f'%(mag[i]),transform=ax.transAxes,fontsize=14,color='white')\n\n plt.subplots_adjust(wspace=0.07, hspace=0.07)\n \n if savefile != None:\n fig.savefig(savefile +'.png')\n fig.savefig(savefile +'.pdf')","def assignLabels(self):\n clusters = np.arange(0, len(self.V))[self.V < self.V1] #indexes self.V, volumes_sorted, and oldOrder\n self.clusterV = self.volumes_sorted[clusters]\n clusters = self.oldOrder[clusters] #indexes volumes\n self.clusters = self.nonBI[clusters] #indexes self.vor and self.data\n self.easyLabel = np.zeros(len(self.data))\n self.easyLabel[self.clusters] = 1\n print('Out of ' + str(len(self.data)) + ' particles, ' + str(len(self.clusters)) + ' (' + str(round(len(self.clusters)*100/len(self.data), 3)) +' %) are labelled as cluster particles.')","def __init__(self, smoothing=0.1):\n super(LabelSmoothingCrossEntropy, self).__init__()\n assert smoothing < 1.0\n self.smoothing = smoothing\n self.confidence = 1. - smoothing","def __init__(self):\r\n self.label = \"Longest Flow Path\"\r\n self.description = \"this is my Longest Flow Path python tool\"","def adjust_labels(data_y, label):\n\n if label == 'locomotion': # Labels for locomotion are adjusted\n data_y[data_y == 4] = 3\n data_y[data_y == 5] = 4\n elif label == 'gestures': # Labels for gestures are adjusted\n data_y[data_y == 406516] = 1\n data_y[data_y == 406517] = 2\n data_y[data_y == 404516] = 3\n data_y[data_y == 404517] = 4\n data_y[data_y == 406520] = 5\n data_y[data_y == 404520] = 6\n data_y[data_y == 406505] = 7\n data_y[data_y == 404505] = 8\n data_y[data_y == 406519] = 9\n data_y[data_y == 404519] = 10\n data_y[data_y == 406511] = 11\n data_y[data_y == 404511] = 12\n data_y[data_y == 406508] = 13\n data_y[data_y == 404508] = 14\n data_y[data_y == 408512] = 15\n data_y[data_y == 407521] = 16\n data_y[data_y == 405506] = 17\n return data_y","def cut_bkg(self):\n c = TCut(self.cut_both)\n c += TCut(self._return_if('_cut_bkg'))\n return c","def __init__(self):\n Algorithm.__init__(self)\n self.name = \"Otsus Threshold\"\n self.parent = \"Segmentation\"","def old_ideal_label(I):\n a, c, d = ideal_HNF(I)\n return \"%s.%s.%s\" % (a * d, c, d)","def calc_Hcp_ij(self):\n\t\n\thp0_delayed = self.hp_wavelet.get_Psi(self.xi[0] + self.Orbit.L/l.Clight)\n\thp0 = self.hp_wavelet.get_Psi(self.xi[0])\n\thc0_delayed = self.hc_wavelet.get_Psi(self.xi[0] + self.Orbit.L/l.Clight)\n\thc0 = self.hc_wavelet.get_Psi(self.xi[0])\n\t\n\thp1_delayed = self.hp_wavelet.get_Psi(self.xi[1] + self.Orbit.L/l.Clight)\n\thp1 = self.hp_wavelet.get_Psi(self.xi[1])\n\thc1_delayed = self.hc_wavelet.get_Psi(self.xi[1] + self.Orbit.L/l.Clight)\n\thc1 = self.hc_wavelet.get_Psi(self.xi[1])\n\t\n\thp2_delayed = self.hp_wavelet.get_Psi(self.xi[2] + self.Orbit.L/l.Clight)\n\thp2 = self.hp_wavelet.get_Psi(self.xi[2])\n\thc2_delayed = self.hc_wavelet.get_Psi(self.xi[2] + self.Orbit.L/l.Clight)\n\thc2 = self.hc_wavelet.get_Psi(self.xi[2])\n\t\n\tself.Hpij[0,1] = hp1_delayed - hp0\n\tself.Hpij[1,0] = hp0_delayed - hp1\n\n\tself.Hpij[0,2] = hp2_delayed - hp0\n\tself.Hpij[2,0] = hp0_delayed - hp2\n\n\tself.Hpij[1,2] = hp2_delayed - hp1\n\tself.Hpij[2,1] = hp1_delayed - hp2\n\t\n\t# cross-polarization\n\tself.Hcij[0,1] = hc1_delayed - hc0\n\tself.Hcij[1,0] = hc0_delayed - hc1\n\n\tself.Hcij[0,2] = hc2_delayed - hc0\n\tself.Hcij[2,0] = hc0_delayed - hc2\n\n\tself.Hcij[1,2] = hc2_delayed - hc1\n\tself.Hcij[2,1] = hc1_delayed - hc2\n\t\n\treturn","def __init__(self, training_data, smoothing_factor=1.0):\n\t\tsuper(HostnameFeature, self).__init__('Hostname', 4, smoothing_factor)\n\t\tself.category_bag_of_hostname = {}\n\t\tfor record in training_data:\n\t\t\tcategory = record[6]\n\t\t\tif self.category_bag_of_hostname.get(category) is None:\n\t\t\t\tself.category_bag_of_hostname[category] = {}\n\t\t\tpublishers = self.category_bag_of_hostname[category]\n\t\t\tpublisher_name = record[4].strip().lower()\n\t\t\tif publisher_name not in publishers:\n\t\t\t\tpublishers[publisher_name] = 0\n\t\t\tpublishers[publisher_name] += 1\n\t\t# for k, bw in self.category_bag_of_hostname.items():\n\t\t# \tprint 'category ', k, ' with number of different hostname', len(bw)","def instance_label(task, pred, k=15, n_iters=1, dist_thresh=5, watershed=False):\n mask = pred\n\n # noise removal\n if k > 1 and n_iters > 0:\n kernel = np.ones((k, k), np.uint8)\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,\n iterations=n_iters)\n\n if watershed:\n from clab.live import filters\n mask = filters.watershed_filter(mask, dist_thresh=dist_thresh)\n\n mask = mask.astype(np.uint8)\n n_ccs, cc_labels = cv2.connectedComponents(mask, connectivity=4)\n return cc_labels","def fredkin(ha, hb, hc):\n\n return controlled_U(ha, swap(hb, hc))","def labels(self, threshold, segment=True, exclude_border=0):\n data = self.unmasked_data\n isfin = numpy.isfinite(data)\n data[~isfin] = numpy.amin(data[isfin])\n regions = (data > threshold)\n if segment:\n local_max = peak_local_max(data, indices=False,\n exclude_border=0,\n footprint=numpy.ones((3, 3)),\n labels=regions)\n markers = measurements.label(local_max)[0]\n labels = watershed(-data, markers, mask=regions)\n if exclude_border > 0:\n # Remove basins originating from edge peaks\n diff = numpy.zeros_like(local_max)\n for i in range(local_max.ndim):\n local_max = local_max.swapaxes(0, i)\n diff = diff.swapaxes(0, i)\n diff[:exclude_border] = local_max[:exclude_border]\n diff[-exclude_border:] = local_max[-exclude_border:]\n diff = diff.swapaxes(0, i)\n local_max = local_max.swapaxes(0, i)\n \n for l in numpy.sort(labels[diff])[::-1]:\n labels[labels == l] = 0\n labels[labels > l] -= 1\n ulabels = numpy.unique(labels)\n n = ulabels[ulabels != 0].size\n else:\n data_thres = numpy.zeros_like(data)\n data_thres[regions] = data[regions]\n labels, n = measurements.label(data_thres)\n return labels, n","def graphCut(img, center, radius, temp, edge, count, editPoints, padList, theta_width, phi_width):\r\n\r\n\r\n \"\"\"Important note. The labeled image is referred to as temp, or self.temp in the interface.\r\n This stands for template. The previously labled image is fed back into the graphcut\"\"\"\r\n \r\n \"\"\"create polar images and cost arrays\"\"\"\r\n \r\n print \"RUNNING GRAPHCUT!\"\r\n img= padImage(img, padList)\r\n temp= padImage(temp, padList)\r\n edge= padImage(edge, padList)\r\n center= padCenter(center, padList)\r\n \r\n polar_img= img2polar(img, center, radius, theta_width=theta_width, phi_width=phi_width)\r\n\r\n \r\n \r\n polar_grad, y, x = np.gradient(np.array(polar_img, dtype='float'))\r\n \"\"\"Lockett 100416 replacement line below to not use gradient when the image has a surface label\"\"\"\r\n \"\"\"polar_grad = -1 * np.array(polar_img, dtype='float')\"\"\"\r\n \r\n \r\n polar_cost = -1 * np.ones(polar_img.shape)\r\n for r in range(1,radius):\r\n polar_cost[r]= polar_grad[r]-polar_grad[r-1]\r\n\r\n \r\n \r\n \"\"\"\r\n flip the cost image upside down. This is so that the base set is at the bottom of the array\r\n since the graphcut cuts from top to bottom, this inversion is necessary.\r\n \"\"\"\r\n polar_cost_inv=polar_cost[::-1,:,:]\r\n\r\n print \"CONSTRUCTING GRAPH EDGES... \"\r\n \r\n \"\"\"construct the graph using PyMaxFlow\"\"\"\r\n g=maxflow.GraphFloat()\r\n nodeids=g.add_grid_nodes(polar_img.shape)\r\n structure=np.zeros((3,3,3))\r\n structure[2]= np.array([[0,10000,0],[10000, 10000, 10000],[0, 10000, 0]])\r\n g.add_grid_edges(nodeids, structure=structure, symmetric=False)\r\n\r\n \r\n \"\"\"convert the previously labeled image (temp) into a polar transform image. Take the labels and\r\n give them high cost edge weights so the segmentation avoids previously labeled objects\"\"\"\r\n polar_lbl_img= img2polar(temp, center, radius, theta_width=theta_width, phi_width=phi_width)\r\n polar_lbl_img_inv= polar_lbl_img[::-1,:]\r\n \r\n lbl_caps= polar_lbl_img_inv>0\r\n self_caps= (polar_lbl_img_inv==count)\r\n lbl_caps-=self_caps\r\n lbl_source_caps= np.zeros(lbl_caps.shape)\r\n lbl_sink_caps= lbl_caps*10000\r\n g.add_grid_tedges(nodeids, lbl_source_caps, lbl_sink_caps)\r\n \r\n structure2= 10000*np.array([[0,0,0],[0,0,1],[0,1,0]])\r\n g.add_grid_edges(nodeids[radius-1], structure=structure2, symmetric=True)\r\n\r\n \"\"\"add terminal edges using two arrays whose elemnts are the costs of the edges from the source and to the\r\n sink\"\"\"\r\n print \"CONSTRUCTING GRAPH TEDGES...\"\r\n sinkcaps= polar_cost_inv * (polar_cost_inv>=0)\r\n sourcecaps = -1 * polar_cost_inv * (polar_cost_inv<0)\r\n g.add_grid_tedges(nodeids, sourcecaps, sinkcaps)\r\n\r\n \r\n\r\n \r\n \"\"\"accounts for edit points. Takes every point in the edit point list, converts it to its spherical coordinate, and adds high cost\r\n edges in the column of that edit point inverts the x and y coordinates of the center\"\"\"\r\n center= np.array((center[0], center[2], center[1]))\r\n if len(editPoints)!=0:\r\n for coords in editPoints:\r\n\r\n \r\n rad= math.sqrt((center[0]-coords[0])**2+ (center[1]-coords[2])**2 + (center[2]-coords[1])**2) \r\n theta= math.atan2(center[2]-coords[1], coords[2]-center[1])\r\n print str((coords[0]-center[0])/(rad+1))\r\n phi=math.acos(float(coords[0]-center[0])/(rad+1))\r\n if theta<0:\r\n theta=2*math.pi+ theta\r\n theta= theta_width- theta_width*theta/(2*math.pi)-1\r\n phi= phi_width*phi/(math.pi)-1\r\n rad= radius- rad\r\n print \"POLAR COORDS: \" + str((rad, theta, phi))\r\n\r\n for r in range(0, radius):\r\n if r<=rad:\r\n g.add_tedge(nodeids[r, theta, phi], 0, 10000)\r\n \r\n else:\r\n g.add_tedge(nodeids[r, theta, phi], 10000, 0) \r\n\r\n\r\n\r\n\r\n print \"CUTTING GRAPH...\"\r\n g.maxflow()\r\n\r\n \"\"\"s-t mincut of graph. This is converted to cartesian coordinates with the function img2cart. The\r\n images are also closed to eliminate spotty areas\"\"\"\r\n \r\n print \"STARTING CARTESIAN TRANSFORM...\"\r\n polar_img_seg= np.invert(g.get_grid_segments(nodeids)[::-1,:,:])\r\n\r\n \r\n edge_img= np.zeros(img.shape)\r\n seg_img= ndimage.binary_closing(img2cart(img, polar_img_seg, center, radius, theta_width, phi_width))\r\n \r\n \r\n \"\"\"create an edge image of the segmented object\"\"\"\r\n strel=np.ones((3,3,3))\r\n erode_img=ndimage.binary_erosion(seg_img, strel)\r\n edge_img=np.logical_xor(seg_img, erode_img)\r\n \r\n\r\n \"\"\"shears the segmentation image and edge if padding was applied\"\"\"\r\n \r\n\r\n \"\"\"add the object back on to the template image (and the edge image back on the template edge)\r\n If there was an editpoint involved, remove the previous segmentation of that object and add back\r\n on the edited object\"\"\"\r\n if len(editPoints)!=0:\r\n del_img= (temp==count)*count\r\n temp-=del_img\r\n\r\n del_edge_img= (edge==count)*count\r\n edge-= del_edge_img\r\n\r\n\r\n temp+=seg_img*count\r\n edge+=edge_img*count\r\n\r\n temp= shearImage(temp, padList)\r\n edge= shearImage(edge, padList)\r\n \r\n \r\n\r\n print \"FINISHED!\"\r\n \r\n return temp, edge","def __init__(self, *args):\n _BRepAlgo.BRepAlgo_Cut_swiginit(self,_BRepAlgo.new_BRepAlgo_Cut(*args))","def labeling(self, tab, i, j, element):\n label = element\n label.grid(row=i, column=j) # this specifies where in the grid\n tab.grid_columnconfigure(j, weight=1) \n # this last line makes the width of the column responsive to change in width of the window","def label(self, cfg):\n rep = \"\"\n nl = \"\"\n for node in cfg.nodes:\n rep += nl + \"{}\\tgen={}\\tkill={}\\tout={}\".format(\n node, \n set(self.gen.get(node)),\n set(self.kill.get(node)),\n set(self.out.get(node)))\n nl = \"\\n\"\n return rep","def _make_dijet_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('dijet')\n\n arg_res = [\n re.compile(r'^(?P\\d*)(?Pdjmass)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1et)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1eta)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2et)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2eta)(?P\\d*)$'),\n ]\n\n defaults = {\n 'j1et': ('100', 'inf'),\n 'j2et': ('100', 'inf'),\n 'j1eta': ('0', '320'),\n 'j2eta': ('0', '320'),\n 'djmass': ('1000', 'inf'),\n }\n\n\n args = _args_from_scenario(scenario)\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n combgen(\n [(2)(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n (%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n ]\n \n dijet(\n [(%(djmasslo).0fdjmass)])\n simple([(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\n (%(j2etlo).0fet, %(j2etalo).0feta%(j2etahi).0f)])\n )\"\"\" % argvals","def hilfe(self):\n sZweieck_hilfe(3)","def gen_hts_lab_full(self):\n self.gen_hts_lab_mono()\n\n hts_lab_time_prn = tuple(' '.join(l[0:2]) for l in self._hts_lab_mono_ttpl)\n\n self.hts_lab_full_prn = tuple(' '.join((l_time, l_gen)) for (l_time, l_gen)\n in zip(hts_lab_time_prn, self.hts_lab_gen_prn))","def x_group_label(\n x_gr: int, cut: int = 20, name_dict: Dict[AnyStr, AnyStr] = names_dict\n) -> AnyStr:\n name = name_dict[str(x_gr)]\n if len(name) > cut:\n return f\"{name[:cut-3]}...\"\n else:\n return name","def _labels_of_sentence(self, sentence, split):\n labels = torch.ones(1)\n labels[0] = self.category_int_of_label_string(sentence[0][self.name_to_index_dict['label']]) #\n return labels","def vertical_core(block,cut,laser):\r\n\r\n\tlayers = int(block[\"thickness\"]/laser[\"z_spacing\"])\r\n\tangle = math.radians(laser[\"kerf_angle\"]/2)\r\n\ttaper = math.tan(angle) * laser[\"z_spacing\"]\r\n\r\n\tu = math.tan(2 * angle) * (block[\"thickness\"] + laser[\"z_final_overshoot\"])\r\n\tz_0 = block[\"thickness\"]*math.cos(angle) + math.sin(angle)*((cut[\"final_dimension_y\"])/2 - block[\"origin_y\"] + u)\r\n\tz_1 = block[\"thickness\"]*math.cos(angle) + math.sin(angle)*((cut[\"final_dimension_x\"])/2 + block[\"origin_x\"] + u)\r\n\tz_2 = block[\"thickness\"]*math.cos(angle) + math.sin(angle)*((cut[\"final_dimension_y\"])/2 + block[\"origin_y\"] + u)\r\n\tz_3 = block[\"thickness\"]*math.cos(angle) + math.sin(angle)*((cut[\"final_dimension_x\"])/2 - block[\"origin_x\"] + u)\r\n\t\r\n\tcutlist = []\r\n\tcutlist.append([\"a_abs\", f\"{math.degrees(angle):.6f}\"])\r\n\tcutlist.append([\"c_abs\", str(block[\"physical_rotation\"])])\r\n\tcutlist.append([\"z_abs\", f\"{z_0:.6f}\"])\r\n\r\n\ty_start_wide = ((u + cut[\"final_dimension_x\"]/2)* math.cos(angle) \r\n\t\t\t\t - block[\"thickness\"]*math.sin(angle) \r\n\t\t\t\t - u/math.cos(angle))\r\n\ty_start_length = ((u + cut[\"final_dimension_y\"]/2)* math.cos(angle) \r\n\t\t\t\t - block[\"thickness\"]*math.sin(angle) \r\n\t\t\t\t - u/math.cos(angle))\r\n\r\n\tdepth_cut = (block[\"thickness\"] + laser[\"z_final_overshoot\"]) * math.cos(angle)/math.cos(2*angle)\r\n\r\n\tcut1 = json.loads(line(block[\"width\"]/2 - block[\"origin_x\"],y_start_length - block[\"origin_y\"],-block[\"width\"]/2 - block[\"origin_x\"],y_start_length - block[\"origin_y\"],depth_cut,laser))\r\n\r\n\tcut2 = json.loads(line(block[\"length\"]/2 + block[\"origin_y\"],y_start_wide - block[\"origin_x\"],-block[\"length\"]/2 + block[\"origin_y\"],y_start_wide - block[\"origin_x\"],depth_cut,laser))\r\n\r\n\tcut3 = json.loads(line(block[\"width\"]/2 + block[\"origin_x\"],y_start_length + block[\"origin_y\"],-block[\"width\"]/2 + block[\"origin_x\"],y_start_length + block[\"origin_y\"],depth_cut,laser))\r\n\r\n\tcut4 = json.loads(line(block[\"length\"]/2 - block[\"origin_y\"],y_start_wide + block[\"origin_x\"],-block[\"length\"]/2 - block[\"origin_y\"],y_start_wide + block[\"origin_x\"],depth_cut,laser))\r\n\r\n\t#cut1 = json.loads(line(block[\"width\"]/2,y_start_length,-block[\"width\"]/2,y_start_length,depth_cut,laser))\r\n\r\n\t#cut2 = json.loads(line(block[\"length\"]/2,y_start_wide,-cut[\"final_dimension_y\"]/2,y_start_wide,depth_cut,laser))\r\n\r\n\t#cut3 = json.loads(line(block[\"width\"]/2,y_start_length,-cut[\"final_dimension_x\"]/2,y_start_length,depth_cut,laser))\r\n\r\n\t#cut4 = json.loads(line(cut[\"final_dimension_y\"]/2,y_start_wide,-cut[\"final_dimension_y\"]/2,y_start_wide,depth_cut,laser))\r\n\r\n\tcutlist = (cutlist + cut1\r\n\t + [[\"c_rel\", \"90\"],[\"z_abs\", f\"{z_1:.6f}\"],] \r\n\t + cut2\r\n\t + [[\"c_rel\", \"90\"],[\"z_abs\", f\"{z_2:.6f}\"]] \r\n\t\t\t\t\t + cut3 \r\n\t\t\t\t\t + [[\"z_abs\", f\"{z_3:.6f}\"],[\"c_rel\", \"90\"]] \r\n\t\t\t\t\t + cut4)\r\n\r\n\tcutlist.insert(0, [\"set_trigger4\", \"1\", \"0\", \"7\", \"8\", \"45\"])\r\n\tcutlist.append([\"stop_trigger\"])\r\n\r\n\treturn json.dumps(cutlist)","def SCEC_LOH_1():\n\n #Initialize CrustModel\n model = CrustModel(2)\n\n #Slow layer\n vp=4.000\n vs=2.000\n rho=2.600\n Qa=10000.\n Qb=10000.\n thickness = 1.0\n\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\n\n #Halfspace\n vp=6.000\n vs=3.464\n rho=2.700\n Qa=10000.\n Qb=10000.\n thickness = 0 #Infinite thickness!\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\n\n return model","def sils_cut(T,f,c,d,h, conshdlr):\n Ts = range(1,T+1)\n\n model = sils(T,f,c,d,h)\n y,x,I = model.data\n\n # relax integer variables\n for t in Ts:\n model.chgVarType(y[t], \"C\")\n model.addVar(vtype=\"B\", name=\"fake\") # for making the problem MIP\n\n # compute D[i,j] = sum_{t=i}^j d[t]\n D = {}\n for t in Ts:\n s = 0\n for j in range(t,T+1):\n s += d[j]\n D[t,j] = s\n\n #include the lot sizing constraint handler\n model.includeConshdlr(conshdlr, \"SILS\", \"Constraint handler for single item lot sizing\",\n sepapriority = 0, enfopriority = -1, chckpriority = -1, sepafreq = -1, propfreq = -1,\n eagerfreq = -1, maxprerounds = 0, delaysepa = False, delayprop = False, needscons = False,\n presoltiming = SCIP_PRESOLTIMING.FAST, proptiming = SCIP_PROPTIMING.BEFORELP)\n conshdlr.data = D,Ts\n\n model.data = y,x,I\n return model","def _subconstituent_name(h):\n if h == 1:\n o = \"1st\"\n elif h == 2:\n o = \"2nd\"\n elif h == 3:\n o = \"3rd\"\n else:\n o = \"%dth\" % h\n return \"%s subconstituent\" % o","def display_and_label_hulls(self, hulls, src):\n \n labels = []\n\n for hull in hulls:\n\n angle = 0\n MA = 1\n ma = 1\n try:\n _,(MA,ma),angle = cv.fitEllipse(hull)\n except:\n pass\n cosAngle = np.abs(np.cos(angle*np.pi/180))\n\n # Only human-classify hulls if it is reasonably a vertically oriented rectangle\n # This is a hueristic to not have to waste time clasifying hulls clearly not poles\n if (cosAngle < 1.75) and (cosAngle > 0.85) and (MA/ma < 0.28):\n cpy = src.copy()\n hull_img = cv.polylines(cpy, [hull], True, (0,0,255), 3)\n cv.imshow(\"Hull\", hull_img)\n keycode = cv.waitKey(0)\n if keycode == 49:\n labels.append((hull, 0))\n print(\"Not a Pole\")\n elif keycode == 50:\n labels.append((hull, 1))\n print(\"A Pole!\")\n else:\n raise Exception(\"Unexpected Key Pressed\")\n else:\n labels.append((hull, 0))\n cv.destroyAllWindows()\n return labels","def __init__(self, word_embed_size, vocab):\n super(ModelEmbeddings, self).__init__()\n\n ### YOUR CODE HERE for part 1h\n dropout_rate = 0.3\n n_chars = len(vocab.char2id)\n self.char_embed_size = 50\n self.word_embed_size = word_embed_size\n self.vocab = vocab\n self.char_embed = nn.Embedding(n_chars, self.char_embed_size)\n self.conv = CNN(self.char_embed_size, word_embed_size)\n self.highway = Highway(word_embed_size)\n self.dropout = nn.Dropout(dropout_rate)\n ### END YOUR CODE","def plot_mass_flow(self,\n watershed, \n output, \n title = 'Subbasin Reach Mass Flow Diagram',\n fontsize = 6, \n theight = 0.2, \n l = 8.5, \n w = 11, \n verbose = True, \n overwrite = True,\n ):\n\n if os.path.exists(output) and not overwrite:\n if verbose: print('file %s exists' % output)\n return\n elif verbose: print('generating a mass linkage plot\\n')\n\n fontheight = fontsize / 72.\n rheight = 3 * fontheight\n rwidth = 12 * fontheight\n xgap = fontheight\n ygap = rheight\n awidth = rheight / 4\n aheight = rheight / 3\n\n # set up a sheet to write the image\n\n fig = pyplot.figure(figsize = (w, l))\n\n ax = fig.add_subplot(111, aspect = 'equal')\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n t = ax.set_title(title)\n\n # divide the subbasins into rows and put them on the chart\n # start at the bottom to organize the linkages better\n\n rows = [watershed.outlets, ['outlet']]\n\n top = False\n while not top:\n row = []\n for next in rows[0]:\n for subbasin in watershed.updown:\n if watershed.updown[subbasin] == next: row.append(subbasin)\n if len(row) > 0: \n rows.insert(0, row)\n else: \n top = True\n\n # add an inlet box in the row above each inlet\n\n for inlet in watershed.inlets: \n\n i = 0\n while i < len(rows) - 1:\n\n for subbasin in rows[i]:\n\n if subbasin == inlet:\n \n # find the position of the subbasin in the chart\n\n j = rows[i].index(inlet)\n\n if i > 0:\n\n # figure out where the subbasins point\n \n updowns = [watershed.updown[s] for s in rows[i-1]]\n \n # if first or last, add it there in the row above\n\n if j == 0: \n rows[i-1].insert(0, 'inlet')\n elif j == len(rows[i]) - 1: \n rows[i-1].append('inlet')\n else:\n\n # find the place to add in the preceeding row \n\n n = updowns.index(rows[i][j-1]) + 1\n rows[i-1].insert(n, 'inlet')\n\n i += 1\n\n # write the subbasin boxes to the chart\n\n middle = math.ceil(w // (rwidth + xgap)) // 2\n last = 0\n\n # keep track of the bounding box of the plot\n\n xmin, ymin, xmax, ymax = middle, 0, middle, 0\n\n for i in range(len(rows)):\n\n row = rows[i]\n \n y = (ygap + rheight) * i + theight\n\n # figure out which cell to put in the main column\n\n if i == 0:\n main = row[(len(row) - 1) // 2]\n elif i < len(rows) - 1:\n main = watershed.updown[rows[i-1][last]]\n else: main = 'outlet'\n\n start = middle - row.index(main)\n\n if i < len(rows) - 1: next_row = rows[i + 1]\n\n for subbasin in row:\n x = (rwidth + xgap) * (start + row.index(subbasin))\n r = patches.Rectangle((x, y), rwidth, rheight, fill = False)\n\n # adjust the bounding box\n\n if x < xmin: xmin = x\n if x + rwidth > xmax: xmax = x + rwidth\n if y < ymin: ymin = y\n if y + rheight > ymax: ymax = y + rheight\n\n if subbasin != 'outlet': ax.add_patch(r)\n\n b = ax.text(x + rwidth / 2, y + rheight / 2, subbasin,\n horizontalalignment = 'center',\n verticalalignment = 'center')\n\n # draw the arrow\n\n if i < len(rows) - 1:\n\n x1 = x + rwidth / 2\n\n if i < len(rows) - 2 and subbasin != 'inlet':\n next = watershed.updown[subbasin]\n next_start = (middle - \n next_row.index(watershed.updown[main]))\n x2 = ((rwidth + xgap) * \n (next_start + next_row.index(next))\n + rwidth / 2)\n\n elif subbasin == 'inlet':\n next = watershed.inlets[0]\n next_start = (middle - \n next_row.index(watershed.updown[main]))\n\n x2 = ((rwidth + xgap) * \n (next_start + next_row.index(next))\n + rwidth / 2)\n\n else:\n next_start = middle\n x2 = ((rwidth + xgap) * (middle) + rwidth / 2)\n\n a = pyplot.arrow(x1, y + rheight, x2 - x1, ygap, \n head_width = awidth, head_length = aheight,\n fc = 'k', ec = 'k', \n length_includes_head = True)\n ax.add_patch(a)\n\n last = row.index(main)\n i += 1\n \n pad = 0.02\n\n xmin = xmin - (xmax - xmin) * pad\n xmax = xmax + (xmax - xmin) * pad\n ymin = ymin - (ymax - ymin) * pad\n ymax = ymax + (ymax - ymin) * pad\n\n ax.set_xlim(xmin, xmax)\n ax.set_ylim(ymax, ymin)\n pyplot.axis('off')\n pyplot.savefig(output, dpi = 200)\n\n pyplot.clf()\n pyplot.close()","def _get_labels(self, ind):\n pass","def plot_labels_bundle_pth(device, model, dataset, dataloader):\n ## Get labels and preds\n labels, preds = get_labels_and_preds(device, model, dataloader)\n \n ## Plot the figure\n plot_labels(labels, preds, dataset.id_to_class_dict)","def write_label(self, contig_name, width, height, font, title_width, upper_left, vertical_label,\n strand, canvas, horizontal_centering=False, center_vertical=False, chop_text=True,\n label_color=(50, 50, 50, 255)):\n upper_left = list(upper_left) # to make it mutable\n shortened = contig_name[-title_width:] # max length 18. Last characters are most unique\n txt = Image.new('RGBA', (width, height))#, color=(0,0,0,50))\n txt_canvas = ImageDraw.Draw(txt)\n text_width = txt_canvas.textsize(shortened, font)[0]\n if not chop_text and text_width > width:\n txt = Image.new('RGBA', (text_width, height)) # TODO performance around txt_canvas\n txt_canvas = ImageDraw.Draw(txt)\n if center_vertical or vertical_label: # Large labels are centered in the column to look nice,\n # rotation indicates strand in big text\n vertically_centered = (height // 2) - multi_line_height(font, shortened, txt)//2\n else: # Place label at the beginning of gene based on strand\n vertically_centered = height - multi_line_height(font, shortened, txt) # bottom\n if strand == \"+\":\n vertically_centered = 0 # top of the box\n txt_canvas.multiline_text((0, max(0, vertically_centered)), shortened, font=font,\n fill=label_color)\n if vertical_label:\n rotation_direction = 90 if strand == '-' else -90\n txt = txt.rotate(rotation_direction, expand=True)\n upper_left[1] += -4 if strand == '-' else 4\n if horizontal_centering:\n margin = width - text_width\n upper_left[0] += margin // 2\n canvas.paste(txt, (upper_left[0], upper_left[1]), txt)","def hbnb():\n return 'HBNB'","def TH(self, full=False):\n\t\treturn (arange(self.thbins + 3*self.thbins*(full==True)) + 0.5) * (pi / 2) / self.thbins","def displayNeedle(self,i):\n #obsolete\n profbox()\n modelNodes = slicer.util.getNodes('vtkMRMLModelNode*')\n for modelNode in modelNodes.values():\n if modelNode.GetAttribute(\"nth\")==str(i) and modelNode.GetAttribute(\"segmented\")=='1' :\n displayNode = modelNode.GetModelDisplayNode()\n nVisibility = displayNode.GetVisibility()\n \n if nVisibility:\n displayNode.SliceIntersectionVisibilityOff()\n displayNode.SetVisibility(0)\n else:\n displayNode.SliceIntersectionVisibilityOn()\n displayNode.SetVisibility(1)","def label_joints():\n side_dict = {'C': 0,\n 'L': 1,\n 'R': 2}\n for jnt in mc.ls(type='joint'):\n mc.setAttr('{}.side'.format(jnt), side_dict[jnt.split('_')[0]])\n mc.setAttr('{}.type'.format(jnt), 18)\n mc.setAttr('{}.otherType'.format(jnt), jnt.split('_')[1], type=\"string\")","def __init__(self, h, d_model, dropout=0.1):\n super(MultiHeadedAttention, self).__init__()\n assert d_model % h == 0\n # We assume d_v always equals d_k\n self.d_k = d_model // h\n self.h = h\n self.linears = clones(nn.Linear(d_model, d_model), 4)\n self.attn = None\n self.dropout = nn.Dropout(p=dropout)","def selection_correction_method1(tree, scale, h_in, h_out):\n #h_in = ROOT.TH1D(\"h_in\", \"neutron spectrum with all cuts: inside onset window; Energy [keV]; counts\", 50, 0, 25)\n #h_out = ROOT.TH1D(\"h_out\", \"neutron spectrum with all cuts: outside onset window; Energy [keV]; counts\", 50, 0, 25)\n for event in tree:\n cut = [0, 0]\n S15_ch = i_channel(0, event)\n bpm_ch = i_channel(4, event)\n RT = event.DD_Rise[S15_ch]\n S15_w2 = event.DD_AmplADU[S15_ch]\n onset = event.DD_Rise10pct[S15_ch]\n if cut[0]==0:\n # first cut: for inside onset window\n # if event passes the first cuts\n if S15_w2>1000 and RT>1.1 and RT<1.51 and onset>39 and onset<47:\n # loop over the pmt channel numbers to calculate the time of flight: time bd - time bpm\n for n_channel in range(5, 16):\n pmt_i = i_channel(n_channel, event)\n cfd_pmt = event.cfdPulse_CFDNS[pmt_i]\n cfd_bpm = event.cfdPulse_CFDNS[bpm_ch]\n # calculation of the time of flight\n tof = (cfd_pmt-cfd_bpm)%400\n #cut on tof: time of flight of the neutron\n if tof<335 and tof>295:\n energy2 = S15_w2*scale\n # fill histogram inside onset window\n h_in.Fill(energy2)\n cut[0]=1\n break\n if cut[1]==0:\n if S15_w2>1000 and RT<1.51 and RT>1.1 and ((onset<36 and onset>15) or (onset>50 and onset<=110)):\n for n_channel in range(5, 16):\n pmt_i = i_channel(n_channel, event)\n cfd_pmt = event.cfdPulse_CFDNS[pmt_i]\n cfd_bpm = event.cfdPulse_CFDNS[bpm_ch]\n tof = (cfd_pmt-cfd_bpm)%400\n if tof<335 and tof>295:\n energy2 = S15_w2*scale\n h_out.Fill(energy2)\n cut[1]=1\n break\n return h_in, h_out","def split(self, frame, y):\n return super(H2OStratifiedKFold, self).split(frame, y)","def hbnb():\n return \"HBNB\"","def hbnb():\n return \"HBNB\"","def label(self):\r\n raise NotImplementedError","def width_h_invis(self):\n if m_higgs > 2.0 * self.mx:\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\n\n val = (\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\n / (8.0 * m_higgs**2 * np.pi)\n ).real\n\n assert val >= 0\n\n return val\n else:\n return 0.0","def J_J(h):\n\n h = MTS(h)\n hdot = h.dot\n J_𝒥 = 0.5j * 𝔇inverseLaplacianinverse(\n 0.125 * (3 * h * hdot.bar.ethbar - 3 * hdot * h.bar.ethbar + hdot.bar * h.ethbar - h.bar * hdot.ethbar).eth.im\n ).ethbar.ethbar\n\n return J_𝒥","def _extract_vanHove(g,j,count_cut,wind):\n count = g[fd('step',j)]['y']['disp_count'][:]\n # get better statistics\n count += g[fd('step',j)]['x']['disp_count'][:]\n # count += count[::-1]\n\n edges = g[fd('step',j)]['y']['disp_edges'][:]\n \n edges = np.array([np.mean(edges[j:j+wind]) for j in range(0,len(count)-wind,wind)])\n count = np.array([np.sum(count[j:j+wind]) for j in range(0,len(count)-wind,wind)])\n\n x_lim = (np.min(edges),np.max(edges))\n\n edges = edges[count>count_cut]\n count = count[count>count_cut]\n\n return edges,count,x_lim"],"string":"[\n \"def label_thin(self, orig_label):\\n pil_thin = thin(orig_label)\\n # Keep the original label and set non-thinning label as 0\\n orig_label[~pil_thin] = 0\\n\\n return orig_label\",\n \"def kohonen():\\n# plb.close('all')\\n \\n dim = 28*28\\n data_range = 255.0\\n \\n # load in data and labels \\n data = np.array(np.loadtxt('data.txt'))\\n labels = np.loadtxt('labels.txt')\\n\\n # select 4 digits \\n name = \\\"Stettler\\\"\\n targetdigits = name2digits(name) # assign the four digits that should be used\\n print(targetdigits) # output the digits that were selected\\n\\n # this selects all data vectors that corresponds to one of the four digits\\n data = data[np.logical_or.reduce([labels==x for x in targetdigits]),:]\\n \\n dy, dx = data.shape\\n \\n #set the size of the Kohonen map. In this case it will be 6 X 6\\n size_k = 6\\n \\n #set the width of the neighborhood via the width of the gaussian that\\n #describes it\\n sigma = 2.0\\n \\n #initialise the centers randomly\\n centers = np.random.rand(size_k**2, dim) * data_range\\n \\n #build a neighborhood matrix\\n neighbor = np.arange(size_k**2).reshape((size_k, size_k))\\n\\n #set the learning rate\\n eta = 0.9 # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE\\n \\n #set the maximal iteration count\\n tmax = 5000 # this might or might not work; use your own convergence criterion\\n \\n #set the random order in which the datapoints should be presented\\n i_random = np.arange(tmax) % dy\\n np.random.shuffle(i_random)\\n \\n for t, i in enumerate(i_random):\\n som_step(centers, data[i,:],neighbor,eta,sigma)\\n\\n # for visualization, you can use this:\\n for i in range(size_k**2):\\n plb.subplot(size_k,size_k,i)\\n \\n plb.imshow(np.reshape(centers[i,:], [28, 28]),interpolation='bilinear')\\n plb.axis('off')\\n \\n # leave the window open at the end of the loop\\n plb.show()\\n plb.draw()\",\n \"def onCut(self):\\n pass\",\n \"def odemis_to_hyperspy(filename='sampledata/cltest.h5',specbin=1) :\\r\\n\\r\\n f=h5.File(filename,'r')\\r\\n shome = 'Acquisition2//ImageData/'\\r\\n x = f[shome + 'Image']\\r\\n cdesc =f['Acquisition2/PhysicalData/ChannelDescription'].value[0].decode('utf-8')\\r\\n #print(cdesc)\\r\\n\\r\\n cltype = None\\r\\n if 'Spectrum' in cdesc :\\r\\n cltype = 'spectrum'\\r\\n elif 'CL intensity' in cdesc:\\r\\n cltype = 'panchrom'\\r\\n\\r\\n print('<' + filename + '> original shape :' ,x.shape, cltype)\\r\\n\\r\\n # strip unused dimensions and transpose/ reverse index order\\r\\n if cltype == 'panchrom' :\\r\\n xx=x[0,0,0,:,:].transpose((1,0))\\r\\n # just an image..\\r\\n else :\\r\\n xx=x[:,0,0,:,:].transpose((2,1,0))\\r\\n\\r\\n if cltype == 'spectrum' :\\r\\n #interpolate data to linearize the wavelength scale\\r\\n w = f[shome + 'DimensionScaleC'].value *1e9\\r\\n wx = np.linspace(w.min(),w.max(),w.size)\\r\\n for i in np.arange(xx.shape[0]) :\\r\\n for k in np.arange(xx.shape[1]) :\\r\\n xx[i,k,:] = np.interp(wx,w,xx[i,k,:])\\r\\n\\r\\n wslope = wx[1]-wx[0]\\r\\n woffset = wx.min()\\r\\n #wx = np.arange(w.size)\\r\\n #wslope,woffset=np.polyfit(wx,w,1)\\r\\n s = hs.signals.Signal1D(xx)\\r\\n\\r\\n elif cltype == 'panchrom' :\\r\\n s = hs.signals.Signal2D(xx)\\r\\n else :\\r\\n print('unknown type')\\r\\n\\r\\n print('hyperspy shape :' ,s.data.shape)\\r\\n\\r\\n\\r\\n s.metadata.General.title = 'Odemis: ' + cdesc\\r\\n s.metadata.General.original_filename = filename\\r\\n s.metadata.General.notes = cltype\\r\\n s.axes_manager[0].name = 'pos x'\\r\\n s.axes_manager[0].scale = f[shome + 'DimensionScaleX'].value * 1e6\\r\\n s.axes_manager[0].offset = f[shome + 'XOffset'].value * 1e6\\r\\n s.axes_manager[0].units = 'um'\\r\\n\\r\\n\\r\\n s.axes_manager[1].name = 'pos y'\\r\\n s.axes_manager[1].scale = f[shome + 'DimensionScaleX'].value * 1e6\\r\\n s.axes_manager[1].offset = f[shome + 'YOffset'].value * 1e6\\r\\n s.axes_manager[1].units = 'um'\\r\\n\\r\\n if cltype == 'spectrum' :\\r\\n s.axes_manager[2].name = 'wavelength'\\r\\n s.axes_manager[2].units = 'nm'\\r\\n s.axes_manager[2].offset = woffset\\r\\n s.axes_manager[2].scale = wslope\\r\\n s.metadata.signal_type = 'CL'\\r\\n\\r\\n f.close()\\r\\n if (specbin > 1) and (cltype == 'spectrum'):\\r\\n return( s.rebin(scale=[1,1,specbin]) )\\r\\n else :\\r\\n return( s )\\r\\n #end odemis_to_hyperspy\\r\\n #######################\\r\",\n \"def cut(S, T, graph):\\n ###TODO\\n pass\",\n \"def __init__(self, label=None):\\n super().__init__(\\\"h\\\", 1, [], label=label)\",\n \"def extract_info(config, cut, label):\\n cfg = filter(lambda c: c['name'] == cut, config['physics']['cuts'])[0]\\n text = \\\"\\\"\\n if 'max' not in cfg:\\n text += \\\"#geq \\\"\\n text += str(cfg['min'])\\n if 'max' in cfg and cfg['max'] != cfg['min']:\\n text += '-' + str(cfg['max']) + ' ' + label + 's'\\n elif cfg['min'] != 1:\\n text += ' ' + label + 's'\\n else:\\n text += ' ' + label\\n return text\",\n \"def ch(h1):\\n return -(pic_height / float(h)) * h1\",\n \"def drawlabels(t, t1):\\r\\n t.fd(250)\\r\\n t.pd()\\r\\n t.write(\\\"Life\\\", font=(\\\"Arial\\\", 10, \\\"bold\\\"))\\r\\n t.pu()\\r\\n t.back(12)\\r\\n t.pd()\\r\\n t.write(\\\"Exp.\\\", font=(\\\"Arial\\\", 10, \\\"bold\\\"))\\r\\n t.pu()\\r\\n t.back(238)\\r\\n t.right(90)\\r\\n t.fd(80)\\r\\n t1.pu()\\r\\n t1.back(50)\\r\\n t1.rt(90)\\r\\n t1.fd(250)\\r\\n t1.pd()\\r\\n t1.write(\\\"Year\\\", font=(\\\"Arial\\\", 10, \\\"bold\\\"))\\r\\n t1.pu()\\r\\n t1.back(250)\\r\\n t1.left(90)\\r\\n t1.fd(50)\",\n \"def hxlcut():\\n run_script(hxlcut_main)\",\n \"def write_label_ps(header_lines, base_lines, tail_lines, shape_list, title, outFn, cutofflist=[0.3,0.5,0.7], mode='fill'):\\n OUT = open(outFn, \\\"w\\\")\\n for header_line in header_lines:\\n if r'{title}' in header_line:\\n header_line = header_line.format(title=title)\\n OUT.writelines(header_line)\\n #print(len(shape_list), len())\\n for shape,base_line in zip(shape_list,base_lines):\\n if mode=='label':\\n OUT.writelines( _color_command_segmented(shape, cutofflist)+\\\"\\\\n\\\" )\\n elif mode=='heatmap':\\n OUT.writelines( _color_command_heatmap(shape, Gradient_Colors, 0, 1)+\\\"\\\\n\\\" )\\n else:\\n raise RuntimeError(\\\"Sorry: mode='fill' Not applicant now\\\")\\n OUT.writelines(base_line)\\n for tail_line in tail_lines:\\n OUT.writelines(tail_line)\\n OUT.close()\",\n \"def __get_ohe_label__(self, label_idx) -> List[int]:\\n\\n label = [0] * self.n_classes\\n label[label_idx] = 1\\n\\n return label\",\n \"def agglo_from_labelmask(\\n h5path_in,\\n h5path_lv='',\\n ratio_threshold=0,\\n h5path_out='',\\n save_steps=False,\\n protective=False,\\n ):\\n\\n # check output paths\\n outpaths = {'out': h5path_out}\\n status = utils.output_check(outpaths, save_steps, protective)\\n if status == \\\"CANCELLED\\\":\\n return\\n\\n # open data for reading\\n h5file_in, ds_in, elsize, axlab = utils.h5_load(h5path_in)\\n h5file_lv, ds_lv, _, _ = utils.h5_load(h5path_lv)\\n\\n # open data for writing\\n h5file_out, ds_out = utils.h5_write(None, ds_in.shape, ds_in.dtype,\\n h5path_out,\\n element_size_um=elsize,\\n axislabels=axlab)\\n\\n ulabels = np.unique(ds_in)\\n maxlabel = np.amax(ulabels)\\n print(\\\"number of labels in watershed: {:d}\\\".format(maxlabel))\\n\\n fwmap = np.zeros(maxlabel + 1, dtype='i')\\n\\n areas_ws = np.bincount(ds_in.ravel())\\n\\n labelsets = {}\\n rp_lw = regionprops(ds_lv, ds_in)\\n for prop in rp_lw:\\n\\n maskedregion = prop.intensity_image[prop.image]\\n counts = np.bincount(maskedregion)\\n svoxs_in_label = [l for sl in np.argwhere(counts) for l in sl]\\n\\n ratios_svox_in_label = [float(counts[svox]) / float(areas_ws[svox])\\n for svox in svoxs_in_label]\\n fwmask = np.greater(ratios_svox_in_label, ratio_threshold)\\n labelset = np.array(svoxs_in_label)[fwmask]\\n labelsets[prop.label] = set(labelset) - set([0])\\n\\n basepath = h5path_in.split('.h5/')[0]\\n utils.write_labelsets(labelsets, basepath + \\\"_svoxsets\\\",\\n filetypes=['pickle'])\\n\\n ds_out[:] = utils.forward_map(np.array(fwmap), ds_in, labelsets)\\n\\n # close and return\\n h5file_in.close()\\n h5file_lv.close()\\n try:\\n h5file_out.close()\\n except (ValueError, AttributeError):\\n return ds_out\",\n \"def cut(self,cell):\\r\\n self.grid[cell[0]][cell[1]] = 1\",\n \"def o_wo_per_head(self):\\n assert self.ff % self.heads == 0\\n # fuse ff->e and projection layer of self-attention\\n return (self.ff // (self.heads-self.padded_heads)) + self.qkv\",\n \"def ksh(i,t,htanses):\\n for (zs,ys,zx,yx) in htanses[i]:\\n alex.penup()\\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\\n alex.pendown()\\n alex.goto((ys%m+1)*20-10*m,(ys//m)*20-10*n)\\n alex.goto((yx%m+1)*20-10*m,(yx//m+1)*20-10*n)\\n alex.goto((zx%m)*20-10*m,(zx//m+1)*20-10*n)\\n alex.goto((zs%m)*20-10*m,(zs//m)*20-10*n)\\n alex.hideturtle()\",\n \"def create_teacher(self):\\n\\n #words = [\\\"one\\\", \\\"two\\\", \\\"three\\\", \\\"four\\\", \\\"five\\\", \\\"six\\\", \\\"seven\\\", \\\"eight\\\", \\\"nine\\\"]\\n\\n #print(\\\"self.length: \\\", self.length)\\n for i, image in enumerate(self.images):\\n if False:#i % 2 == 0:\\n img = list(chunks(image, 10))\\n plt.imshow(img, interpolation=\\\"nearest\\\", origin=\\\"upper\\\")\\n plt.colorbar()\\n plt.title(self.labels[i])\\n plt.show()\\n label = np.argmax(self.labels[i]) + 1\\n label_vector = self.labels[i]\\n timesteps = label + 1\\n\\n for chain in list(self.traces[i]):\\n count_tensor = list()\\n input_tensor = list()\\n target_tensor = list()\\n \\n #count_tensor.append([0] * z_size)\\n #input_tensor.append(image)\\n #target_tensor.append([None, None])\\n\\n #count_padding = [0 for x in range(z_size)]\\n \\n for count, link in enumerate(chain):\\n count_vector = [0 if x is not count else 1 for x in range(z_size)]\\n count_tensor.append(count_vector)\\n x, y = link\\n image[y*200+x] = 255\\n input_tensor.append(image)\\n target_tensor.append(list(link))\\n\\n # Fill in the rest of the list with the same (current one doe\\n #for cont in range(count + 1, z_size):\\n for cont in range(count + 1, z_size + 1):\\n #count_tensor.append(count_padding)\\n count_tensor.append(count_vector)\\n input_tensor.append(image)\\n target_tensor.append(list(link))\\n\\n \\n\\n self.explode_lbls.append(label)\\n self.explode_labels.append(label_vector)\\n self.explode_counts.append(count_tensor)\\n self.explode_images.append(input_tensor)\\n self.explode_traces.append(target_tensor)\\n #print(target_tensor)\\n\\n \\n \\n self.explode_length = len(self.explode_images)\",\n \"def get_hbls_hbbl(self):\\n [Ly,N] = self.b.shape\\n z_u_w = self.grid_dict['z_u_w']\\n z_u_r = self.grid_dict['z_u_r']\\n u = self.u\\n v = self.v\\n \\n v_upts = TTTW_func.v2u(v)\\n Hz = z_u_w[:,1:] - z_u_w[:,:-1]\\n\\n\\n\\n # CALCULATE swr_frac\\n self.swr_frac = TTTW_func.lmd_swr_frac(self.grid_dict)\\n\\n\\n # WHOLE THING HAPPENS IN j loop through y-indices\\n \\n # INITIALIZE ARRAYS\\n self.kmo = np.zeros([Ly])\\n self.Cr = np.zeros([Ly])\\n self.kbl = np.empty([Ly],dtype='int')\\n self.C_h_MO = np.zeros([Ly])\\n self.Cr = np.zeros([Ly,N+1]) # sum term\\n self.FC = np.zeros([Ly,N+1])\\n self.swdk_r = np.zeros([Ly,N+1])\\n \\n self.zscale = np.zeros([Ly,N])\\n self.Kern = np.zeros([Ly,N])\\n\\n \\n # --> LOOP THROUGH Y-INDICES\\n for j in range(Ly):\\n if self.LIMIT_MO_DEPTH:\\n self.kmo[j] = 0\\n self.C_h_MO[j] = self.C_MO *self.ustar[j]**3/self.vonKar\\n \\n self.kbl[j] = 0\\n self.Cr[j,-1] = 0 # set top Cr\\n self.Cr[j,0] = 0 # set bottom Cr\\n \\n # SEARCH FOR MIXED LAYER DEPTH\\n self.FC[j,-1] = 0.\\n\\n\\n # ---> LOOP TOP TO BOTTOM (FORTRAN ==> k=N-1,1,-1)\\n for k in range(N-1,0,-1):\\n # INDEX MAP\\n k_r = k-1\\n k_w = k\\n\\n \\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\\n self.zscale[j,k_w] = zscale\\n if self.LMD_KPP:\\n if self.LMD_BKPP:\\n zscaleb = z_u_r[j,k_r] - z_u_w[j,0]\\n Kern = zscale * zscaleb**2 / ( (zscale + self.epssfcs*self.hbls_old[j]) * (zscaleb**2+(self.epssfcb**2*self.hbbl_old[j]**2)))\\n else:\\n Kern = zscale / (zscale + (self.epssfcs*self.hbls_old[j]))\\n else:\\n Kern = 1.\\n \\n\\n\\n self.Kern[j,k_w] = Kern\\n self.FC[j,k_w] = self.FC[j,k_w+1] + Kern * (\\\\\\n ( ( u[j,k_r+1] - u[j,k_r] )**2 + ( v_upts[j,k_r+1] - v_upts[j,k_r])**2 ) \\\\\\n / (Hz[j,k_r] + Hz[j,k_r+1]) \\\\\\n - 0.5 * ( Hz[j,k_r] + Hz[j,k_r+1]) * (self.Ri_inv * self.bvf[j,k_w] + self.C_Ek*self.f[j]*self.f[j]))\\n\\n\\n #\\t\\tLOOP THAT FINDS BL DEPTH ##\\n #----> LOOP TOP TO BOTTOM (start at free surface, w-level surface) \\n \\n if self.LMD_KPP:\\n #swdk_r only used in this function so don't need to be class attribute\\n # but for testing make it an attribute to see what it is\\n \\n # fortran equivlanet ===> k=N,1,-1 \\n for k in range(N,0,-1):\\n # INDEX MAP\\n k_r = k-1\\n k_w = k\\n\\n ###################################################################### \\n self.swdk_r[j,k_w] = np.sqrt( self.swr_frac[j,k_w] * self.swr_frac[j,k_w-1])\\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\\n Bfsfc = self.Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])\\n \\n self.bvf_max = np.sqrt(np.max([0,self.bvf[j,k_w-1]]))\\n \\n # CALCULATE TURBULENT VELOCITY SCALE FOR TRACERS\\n \\t\\t\\t self.ws = self.lmd_wscale_ws_only(Bfsfc, zscale,self.hbls_old[j],self.ustar[j])\\n \\n self.Vtsq = self.Vtc * self.ws* self.bvf_max + self.V0\\n \\n\\n self.Cr[j,k_w] = self.FC[j,k_w] + self.Vtsq\\n \\n\\n #######################################################################\\n \\n # SEARCH FOR hbls vertical level #\\n '''\\n kbl is specified at vertical w-level (via Cr which is at\\n vertical w-levels)\\n '''\\n if self.kbl[j] == 0 and self.Cr[j,k_w] < 0:\\n self.kbl[j] = k_w\\n if self.LIMIT_MO_DEPTH:\\n if self.kmo[j] == 0 and Bfsfc*(z_u_w[j,N] - z_u_r[j,k_r]) > self.C_h_MO[j]:\\n self.kmo[j] = k_w\\n\\n \\n #--> still in j-loop\\n #######################################################\\n \\n # \\t\\tGET SURFACE BOUNDARY LAYER DEPTH # \\n self.hbls[j] = z_u_w[j,N] - z_u_w[j,0] + self.eps # set hbls as depth of entire water column\\n if self.kbl[j] > 0:\\n k_w = self.kbl[j]\\n k_r = k_w - 1 \\n if k_w == N: # set hbls at the surface btwn w- and rho-levels at surface\\n self.hbls[j] = z_u_w[j,N] - z_u_r[j,N-1]\\n \\n else:\\n self.hbls[j] = z_u_w[j,N] - ( z_u_r[j,k_r] * self.Cr[j,k_w+1] - z_u_r[j,k_r+1] * self.Cr[j,k_w]) / \\\\\\n (self.Cr[j,k_w+1] - self.Cr[j,k_w])\\n \\n if self.LIMIT_MO_DEPTH:\\n if self.kmo[j] > 0:\\n k_w = self.kmo[j]\\n k_r = k_w-1\\n if k_w == N:\\n z_up = z_u_w[j,N]\\n cff_up = np.max([0,Bo[j]])\\n else:\\n z_up = z_r[j,k_w+1]\\n cff_up = np.max([0, Bo[j] + self.Bosol[j]*(1-self.swdk_r[j,(k_w-1)+1])])\\n \\n cff_dn = np.max([0,Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])]) \\n h_MO = z_u_w[j,N] + self.C_h_MO[j] * ( cff_up*z_up - cff_dn * z_u_r[j,k_r] ) \\\\\\n / ( cff_up * cff_dn * (z_up - z_u_r[j,k_r]) ) \\\\\\n + self.C_h_MO[j] * (cff_dn - cff_up)\\n\\n self.hbls[j] = np.min([self.hbls[j],np.max([h_MO,0])])\\n\\n\\n\\n #### GET BOTTOM BOUNDARY LAYER DEPTH #######\\n if self.LMD_BKPP:\\n self.kbl[j] = 0 # reset Cr at bottom and kbl for BKPP\\n self.Cr[j,0] = 0.\\n self.FC[j,0] = 1.5 * self.FC[j,1] - 0.5 * self.FC[j,2] # linear extrapolation\\n \\n #---> LOOP BOTTOM TO TOP\\n # FIND kbl for BBL\\n for k in range(1,N+1):\\n k_r = k-1\\n k_w = k \\n self.Cr[j,k_w] = self.FC[j,k_w] - self.FC[j,0]\\n \\n # LOOK FOR FIRST ZERO CROSSING FROM BOTTOM UP\\n if self.kbl[j] == 0 and self.Cr[j,k_w] > 0:\\n self.kbl[j] = k_w \\n \\n\\n self.hbbl[j] = z_u_w[j,N] - z_u_w[j,0] # total depth\\n if self.kbl[j] > 0 :\\n k_w = self.kbl[j] \\n k_r = k_w -1\\n if k_w == 1: # NO BBL CASE\\n self.hbbl[j] = z_u_r[j,0] - z_u_w[j,0] #in between bottom rho and w-level\\n else:\\n self.hbbl[j] = ( z_u_r[j,k_r-1] * self.Cr[j,k_w] - z_u_r[j,k_r] * self.Cr[j,k_w-1]) / \\\\\\n (self.Cr[j,k_w] - self.Cr[j,k_w-1]) - z_u_w[j,0]\",\n \"def process_labels(ctx, tex, chapter):\\n headings = ['chapter'] + ['sub'*i + 'section' for i in range(4)]\\n reh = r'(' + '|'.join(headings) + r'){(.+?)}'\\n environments = ['thm', 'lem', 'exc', 'figure', 'equation']\\n ree = r'begin{(' + '|'.join(environments) + r')}'\\n rel = r'(\\\\w+)label{(.+?)}'\\n rel2 = r'label{(.+?)}'\\n bigone = r'\\\\\\\\({})|\\\\\\\\({})|\\\\\\\\({})|\\\\\\\\(caption)|\\\\\\\\({})'.format(reh, ree, rel, rel2)\\n rx = re.compile(bigone)\\n\\n sec_ctr = [chapter] + [0]*(len(headings))\\n env_ctr = [0]*len(environments)\\n blocks = catlist()\\n lastlabel = None\\n lastidx = 0\\n m = rx.search(tex, lastidx)\\n while m:\\n blocks.append(tex[lastidx:m.start()])\\n lastidx = m.start()\\n cmd = next_command(tex, lastidx)\\n lastidx = cmd.end\\n if m.group(2):\\n # This is a sectioning command (chapter, subsection,...)\\n name = m.group(2)\\n i = headings.index(name)\\n if i == 0:\\n env_ctr = [0]*len(env_ctr)\\n sec_ctr[i:] = [sec_ctr[i]+1]+[0]*(len(headings)-i-1)\\n number = \\\".\\\".join([str(x) for x in sec_ctr[:i+1]])\\n idd = \\\"{}:{}\\\".format(name, number)\\n lastlabel = idd\\n blocks.append(\\\"\\\".format(idd))\\n\\n title = '{} {}'.format(number, cmd.args[0])\\n blocks.append(r'\\\\{}{{{}}}'.format(name, title))\\n\\n elif m.group(5):\\n # This is an environment (thm, lem, ...)\\n name = m.group(5)\\n lastenv = name # save this for a caption command coming later...\\n i = environments.index(name)\\n env_ctr[i] += 1\\n number = \\\"{}.{}\\\".format(sec_ctr[0], env_ctr[i])\\n idd = \\\"{}:{}\\\".format(name, number)\\n lastlabel = idd\\n blocks.append(\\\"\\\".format(idd))\\n\\n if name in ctx.theoremlike_environments:\\n nicename = ctx.named_entities[name]\\n title = '{} {}'.format(nicename, number)\\n blocks.append(r'\\\\begin{{{}}}[{}]'.format(name, title))\\n else:\\n blocks.append(r'\\\\begin{{{}}}'.format(name))\\n\\n elif m.group(6):\\n # This is a labelling command (\\\\thmlabel, \\\\seclabel,...)\\n label = \\\"{}:{}\\\".format(m.group(7), m.group(8))\\n ctx.label_map[label] = (ctx.outputfile, lastlabel)\\n\\n elif m.group(9):\\n # This is a caption command\\n name = lastenv\\n i = environments.index(name)\\n number = \\\"{}.{}\\\".format(sec_ctr[0], env_ctr[i])\\n idd = \\\"{}:{}\\\".format(name, number)\\n lastlabel = idd\\n nicename = ctx.named_entities[name]\\n title = '{} {}'.format(nicename, number)\\n text = '{} {}'.format(title, cmd.args[0])\\n blocks.append(r'\\\\caption{{{}}}'.format(text))\\n\\n elif m.group(10):\\n # This is a \\\\label command, probably the target of a pageref\\n idd = gen_unique_id()\\n blocks.append(\\\"\\\".format(idd))\\n ctx.label_map[m.group(11)] = (ctx.outputfile, idd)\\n\\n m = rx.search(tex, lastidx)\\n blocks.append(tex[lastidx:])\\n return \\\"\\\".join(blocks)\",\n \"def simple_core(block,cut,laser):\\r\\n\\r\\n\\tlayers = int(block[\\\"thickness\\\"]/laser[\\\"z_spacing\\\"])\\r\\n\\r\\n\\t#Since all cuts are square, the offsets are more obvious than in the general linear case.\\r\\n\\ttaper = math.tan(math.radians(laser[\\\"kerf_angle\\\"]/2)) * laser[\\\"z_spacing\\\"]\\r\\n\\tmax_delta = math.tan(math.radians(laser[\\\"kerf_angle\\\"]/2)) * (block[\\\"thickness\\\"] + laser[\\\"z_final_overshoot\\\"]) * 2\\r\\n\\t\\r\\n\\tcutlist = []\\r\\n\\tcutlist.append([\\\"a_abs\\\", \\\"0\\\"])\\r\\n\\tcutlist.append([\\\"c_abs\\\", str(block[\\\"physical_rotation\\\"])])\\r\\n\\tcutlist.append([\\\"z_abs\\\", str(block[\\\"thickness\\\"])])\\r\\n\\r\\n\\tfor a in range(layers):\\r\\n\\t\\tx1, y1 = cut[\\\"final_dimension_x\\\"]/2 + a*taper, cut[\\\"final_dimension_y\\\"]/2 + a*taper\\r\\n\\t\\twhile abs(x1-cut[\\\"final_dimension_x\\\"]/2) < abs(max_delta):\\r\\n\\t\\t\\tcutlist.append([\\\"jump\\\", str(x1 + block[\\\"origin_x\\\"]), str(y1 + block[\\\"origin_y\\\"])])\\r\\n\\t\\t\\tcutlist.append([\\\"mark\\\", str(x1 + block[\\\"origin_x\\\"]), str(-y1 + block[\\\"origin_y\\\"])])\\r\\n\\t\\t\\tcutlist.append([\\\"mark\\\", str(-x1 + block[\\\"origin_x\\\"]), str(-y1 + block[\\\"origin_y\\\"])])\\r\\n\\t\\t\\tcutlist.append([\\\"mark\\\", str(-x1 + block[\\\"origin_x\\\"]), str(y1 + block[\\\"origin_y\\\"])])\\r\\n\\t\\t\\tcutlist.append([\\\"mark\\\", str(x1 + block[\\\"origin_x\\\"]), str(y1 + block[\\\"origin_y\\\"])])\\r\\n\\t\\t\\tx1, y1 = x1 + laser[\\\"xy_spacing\\\"], y1 + laser[\\\"xy_spacing\\\"]\\r\\n\\t\\tcutlist.append([\\\"z_step\\\", str(-laser[\\\"z_spacing\\\"])])\\r\\n\\t\\tmax_delta = max_delta - taper \\r\\n\\treturn json.dumps(cutlist)\",\n \"def LabelDisks(self):\\n pass\",\n \"def split2(self, eccMap, patchName='patch00', cutStep=1, borderWidth=2, isplot=False):\\r\\n minMarker = localMin(eccMap, cutStep)\\r\\n\\r\\n connectivity = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])\\r\\n\\r\\n newLabel = sm.watershed(eccMap, minMarker, connectivity=connectivity, mask=self.array)\\r\\n\\r\\n border = ni.binary_dilation(self.array).astype(np.int8) - self.array\\r\\n\\r\\n for i in range(1, np.amax(newLabel) + 1):\\r\\n currArray = np.zeros(self.array.shape, dtype=np.int8)\\r\\n currArray[newLabel == i] = 1\\r\\n currBorder = ni.binary_dilation(currArray).astype(np.int8) - currArray\\r\\n border = border + currBorder\\r\\n\\r\\n border[border > 1] = 1\\r\\n border = sm.skeletonize(border)\\r\\n\\r\\n if borderWidth > 1:\\r\\n border = ni.binary_dilation(border, iterations=borderWidth - 1).astype(np.int8)\\r\\n\\r\\n newPatchMap = ni.binary_dilation(self.array).astype(np.int8) * (-1 * (border - 1))\\r\\n\\r\\n labeledNewPatchMap, patchNum = ni.label(newPatchMap)\\r\\n\\r\\n # if patchNum != np.amax(newLabel):\\r\\n # print 'number of patches: ', patchNum, '; number of local minimum:', np.amax(newLabel)\\r\\n # raise ValueError, \\\"Number of patches after splitting does not equal to number of local minimum!\\\"\\r\\n\\r\\n newPatchDict = {}\\r\\n\\r\\n for j in range(1, patchNum + 1):\\r\\n\\r\\n currPatchName = patchName + '.' + str(j)\\r\\n currArray = np.zeros(self.array.shape, dtype=np.int8)\\r\\n currArray[labeledNewPatchMap == j] = 1\\r\\n currArray = currArray * self.array\\r\\n\\r\\n if np.sum(currArray[:]) > 0:\\r\\n newPatchDict.update({currPatchName: Patch(currArray, self.sign)})\\r\\n\\r\\n if isplot:\\r\\n plt.figure()\\r\\n plt.subplot(121)\\r\\n plt.imshow(self.array, interpolation='nearest')\\r\\n plt.title(patchName + ': before split')\\r\\n plt.subplot(122)\\r\\n plt.imshow(labeledNewPatchMap, interpolation='nearest')\\r\\n plt.title(patchName + ': after split')\\r\\n\\r\\n return newPatchDict\",\n \"def __init__(self, smoothing=0.1):\\n super(LabelSmoothing, self).__init__()\\n self.confidence = 1.0 - smoothing\\n self.smoothing = smoothing\",\n \"def hilfe(self):\\n toto_hilfe(3)\",\n \"def identify_leaflets(u, time_ts):\\n z = u.select_atoms(\\\"all\\\").center_of_geometry()[2]\\n COM_z= np.array([0,0,z]) #defines the global midplane position along z\\n x, y, z = u.trajectory.ts.triclinic_dimensions[0][0], u.trajectory.ts.triclinic_dimensions[1][1], u.trajectory.ts.triclinic_dimensions[2][2]\\n box = np.array([x, y, z, 90, 90, 90]) \\n ### Determining side of the bilayer CHOL belongs to in this frame\\n lipid1 = 'CHL'\\n lipid2 = 'DLIP'\\n lipid3 = 'SSM'\\n lipid4 = 'DSPC'\\n \\n lpd1_atoms = u.select_atoms('resname %s and name O2'%lipid1) \\n lpd2_atoms = u.select_atoms('resname %s and name P '%lipid2) \\n lpd3_atoms = u.select_atoms('resname %s and name P '%lipid3) \\n lpd4_atoms = u.select_atoms('resname %s and name P '%lipid4)\\n \\n num_lpd2 = lpd2_atoms.n_atoms\\n num_lpd3 = lpd3_atoms.n_atoms\\n num_lpd4 = lpd4_atoms.n_atoms \\n # atoms in the upper leaflet as defined by insane.py or the CHARMM-GUI membrane builders\\n # select cholesterol headgroups within 1.5 nm of lipid headgroups in the selected leaflet\\n # this must be done because CHOL rapidly flip-flops between leaflets\\n # so we must assign CHOL to each leaflet at every time step, and in large systems\\n # with substantial membrane undulations, a simple cut-off in the z-axis just will not cut it\\n if side == 'up':\\n lpd2i = lpd2_atoms[:int((num_lpd2)/2)]\\n lpd3i = lpd3_atoms[:int((num_lpd3)/2)]\\n lpd4i = lpd4_atoms[:int((num_lpd4)/2)]\\n \\n\\n lipids = lpd2i + lpd3i + lpd4i \\n\\n ns_lipids = NS.AtomNeighborSearch(lpd1_atoms, box=box) \\n lpd1i = ns_lipids.search(lipids,15.0) #1.5 nm\\n leaflet = lpd1i + lpd2i + lpd3i + lpd4i \\n\\n elif side == 'down':\\n lpd2i = lpd2_atoms[int((num_lpd2)/2):]\\n lpd3i = lpd3_atoms[int((num_lpd3)/2):]\\n lpd4i = lpd4_atoms[int((num_lpd4)/2):]\\n\\n lipids = lpd2i + lpd3i + lpd4i #+ lpd3i\\n \\n ns_lipids = NS.AtomNeighborSearch(lpd1_atoms, box=box)\\n lpd1i = ns_lipids.search(lipids,15.0) # 1.5nm\\n leaflet = lpd1i + lpd2i + lpd3i+ lpd4i \\n return lpd1i, lpd2i, lpd3i, lpd4i, COM_z, box, leaflet\",\n \"def cmd_label_merged_boundaries(self,sun_dir,output_dir=None):\\n sun=sunreader.SunReader(sun_dir)\\n if output_dir is None:\\n # defaults location of dense output\\n output_dir=os.path.join(sun.datadir,'dwaq',\\\"global-dense\\\")\\n \\n hyd_fn=glob.glob(os.path.join(output_dir,'*.hyd'))[0]\\n \\n hydro=SunHydro(sun=sun,hyd_path=hyd_fn,flow_shps=[self.flows_shp])\\n\\n class SpliceScenario(waq_scenario.Scenario):\\n base_path=output_dir\\n name=\\\"spliced\\\"\\n\\n scen=SpliceScenario(hydro=hydro)\\n\\n self.log.info(\\\"Writing labels\\\")\\n scen.hydro.write_boundary_links()\",\n \"def __init__(self, smoothing=0.0):\\n super(LabelSmoothing, self).__init__()\\n self.confidence = 1.0 - smoothing\\n self.smoothing = smoothing\",\n \"def __init__(self, smoothing=0.0):\\n super(LabelSmoothing, self).__init__()\\n self.confidence = 1.0 - smoothing\\n self.smoothing = smoothing\",\n \"def make_stehle(self):\\n\\n temp_k = self.temp * e / k # temperature in K\\n dens_cm = self.e_dens * 1.e-6 # electronic density in cm-3\\n prefix = 'n_' + str(self.n_upper) + '_' + str(self.n_lower) + '_'\\n\\n # extract raw tabulated tabulated_data\\n tab_temp_k = np.array(pystark.nc.variables[prefix + 'tempe'].data) # tabulated electron temperatures (K)\\n olam0 = pystark.nc.variables[prefix + 'olam0'].data # line centre wavelength (A)\\n num_tab_dens = pystark.nc.variables[prefix + 'id_max'].data\\n fainom = pystark.nc.variables[prefix + 'fainom'].data\\n tab_dens_cm = np.array(pystark.nc.variables[prefix + 'dense'].data) # tabulated electron densities (cm ** -3)\\n f00 = np.array(pystark.nc.variables[prefix + 'f00'].data) # normal Holtsmark field strength (30 kV / m)\\n dl12 = np.array(pystark.nc.variables[prefix + 'dl12'].data)\\n dl12s = np.array(pystark.nc.variables[prefix + 'dl12s'].data)\\n fainu = pystark.nc.variables[\\n prefix + 'fainu'].data # Asymptotic value of iStark * (alpha ** 2.5) (\\\"wings factor in alfa units\\\")\\n pr0 = np.array(pystark.nc.variables[\\n prefix + 'pr0'].data) # Ratio of the mean interelectronic distance to the electronic Debye length\\n jtot = np.array(pystark.nc.variables[prefix + 'jtot'].data,\\n dtype=np.int) # \\\"number of wave lengths for the couple (T,Ne)\\\"\\n dom = np.array(pystark.nc.variables[prefix + 'dom'].data) # frequency detunings in units (rad / (s*ues)\\n d1om = np.array(pystark.nc.variables[prefix + 'd1om'].data)\\n o1line = np.array(pystark.nc.variables[prefix + 'o1line'].data)\\n o1lines = np.array(pystark.nc.variables[prefix + 'o1lines'].data)\\n\\n # ensure given temperature + density falls within tabulated values\\n # change sligtly the value of the input density\\n # dens_cm in order to remain , as far as possible, inside the tabulation\\n # JSA: this first step seems bogus!\\n\\n if np.abs(dens_cm - tab_dens_cm[0]) / dens_cm <= 1.0E-3:\\n dens_cm = tab_dens_cm[0] * 1.001\\n\\n for id in np.arange(1, num_tab_dens + 1):\\n if np.abs(dens_cm - tab_dens_cm[id]) / dens_cm <= 1.0E-3:\\n dens_cm = tab_dens_cm[id] * 0.999\\n\\n if dens_cm >= 2.0 * tab_dens_cm[num_tab_dens]:\\n raise Exception(\\n 'Your input density is higher than the largest tabulated value %f' % tab_dens_cm[num_tab_dens])\\n\\n if dens_cm <= tab_dens_cm[0]:\\n raise Exception('Your input density is smaller than the smallest tabulated value %f' % tab_dens_cm[0])\\n\\n if temp_k >= tab_temp_k[9]:\\n raise Exception('Your input temperature is higher than the largest tabulated value %f' % tab_temp_k[9])\\n\\n if temp_k <= tab_temp_k[0]:\\n raise Exception('Your input temperature is lower than the smallest tabulated value %f' % tab_temp_k[0])\\n\\n normal_holtsmark_field = 1.25e-9 * (dens_cm ** (2. / 3.)) # normal field value in ues\\n\\n # calculate line centre wavelength and frequency using Rydberg formula\\n # JSA: I have made this step clearer and corrected for deuteron mass in the Rydberg constant (though the effect is small)\\n # TODO make sure this matches olam0 parameter above -- why were there two variables in the first place?!\\n # rydberg_m = Rydberg / (1. + (electron_mass / physical_constants['deuteron mass'][0]))\\n # wl_0_angst = 1e10 * (rydberg_m * (1 / n_lower ** 2 - 1 / n_upper ** 2)) ** -1\\n\\n wl_centre_angst = self.wl_centre * 1e10\\n\\n c_angst = c * 1e10 # velocity of light in Ansgtroms / s\\n angular_freq_0 = 2 * np.pi * c_angst / wl_centre_angst # rad / s\\n\\n otrans = -2 * np.pi * c_angst / wl_centre_angst ** 2\\n\\n olines = o1lines / np.abs(otrans)\\n oline = o1line / np.abs(otrans)\\n\\n # Limit analysis_tools to uncorrelated plasmas.\\n # check that mean interelectronic distance is smaller than the electronic Debye length (equ. 10)\\n PR0_exp = 0.0898 * (dens_cm ** (1. / 6.)) / np.sqrt(temp_k) # = (r0 / debye)\\n if PR0_exp > 1.:\\n raise Exception('The plasma is too strongly correlated\\\\ni.e. r0/debye=0.1\\\\nthe line cannot be computed.')\\n\\n # fainom_exp=fainom*(F00_exp**1.5)\\n # fainum_exp=fainom_exp/( (OPI*2.)**1.5)\\n\\n # ========================\\n # TABULATION Format CDS\\n # si on veut ecrire\\n # n -np lambda0 kalpha Ne E0 T R0/Debye Dalpha iDoppler iStark\\n\\n # IN_cds= N+0.01\\n # INP_cds = NP+0.01\\n\\n # ***********************************************************\\n # Don't edit the CDS format...\\n # ***********************************************************\\n\\n # Skipped the code in the IF statement starting at line 470, since it\\n # isn't used, if (.FALSE.) ...\\n\\n # ==============================================\\n # define an unique detunings grid - domm - for the tabulated\\n # profiles ( various temperatures , densities)\\n # calculate all the line shapes for this common grid\\n # units used at this points are Domega_new= Delta(omega)/F00\\n # in rd/(s-1 ues)\\n\\n max_num_dens = 30 # Maximum number of densities\\n max_num_tab_temp = 10\\n max_num_detunings = 60 # Maximum number of detunings\\n jtot = jtot.astype(np.int)\\n domm = np.zeros(100000)\\n dom0 = np.zeros(10000)\\n tprof = np.zeros([max_num_dens, max_num_tab_temp, 10000])\\n tprofs = np.zeros([max_num_dens, max_num_tab_temp, 10000])\\n uprof = np.zeros([max_num_dens, 10000])\\n uprofs = np.zeros([max_num_dens, 10000])\\n\\n inc = 0\\n domm[inc] = 0.0\\n # ---- Look to replace this loop\\n for id in np.arange(num_tab_dens + 1): # loop over tab densities\\n for j in np.arange(max_num_tab_temp): # loop over tab temperatures (?)\\n for i in np.arange(1, jtot[id, j]):\\n inc += 1\\n dom0[inc] = dom[id, j, i]\\n\\n inc = np.count_nonzero(dom)\\n npik = inc + 1\\n # nut=10000\\n\\n # Calling numpy sort instead of piksrt\\n tmp = np.sort(dom0[0:npik])\\n dom0[0:npik] = tmp[0:npik]\\n # dom0 seems to agree with the FORTRAN version\\n\\n inc = 0\\n domm[0] = 0.0\\n # print 'npik',npik\\n # ---- Look to replace this loop\\n for i in np.arange(1, npik):\\n dif = (dom0[i] - dom0[i - 1])\\n if dif <= 1.0E-6:\\n continue\\n if dif / np.abs(dom0[i]) <= 0.1:\\n continue\\n inc = inc + 1\\n domm[inc] = dom0[i]\\n\\n jdom = inc + 1 # One line after marker 35\\n\\n for id in np.arange(num_tab_dens):\\n for j in np.arange(10):\\n if pr0[id, j] > 1.0:\\n continue\\n\\n tprof[id, j, 0] = oline[id, j, 0]\\n tprofs[id, j, 0] = olines[id, j, 0]\\n\\n if jtot[id, j] == 0:\\n continue\\n\\n for i in np.arange(1, jdom + 1):\\n skip1 = False\\n skip2 = False\\n # print 'i',i\\n domeg = domm[i]\\n ij_max = jtot[id, j]\\n # print 'domeg,ij_max',domeg,ij_max\\n for ij in np.arange(1, ij_max - 1):\\n # print 'ij',ij\\n test = (domeg - dom[id, j, ij]) * (domeg - dom[id, j, ij - 1])\\n # print 'test1:',test\\n if test <= 0.0:\\n # print 'triggered test1'\\n x1 = dom[id, j, ij - 1]\\n x2 = dom[id, j, ij]\\n x3 = dom[id, j, ij + 1]\\n y1 = oline[id, j, ij - 1]\\n y2 = oline[id, j, ij]\\n y3 = oline[id, j, ij + 1]\\n # print 'x1,x2,x3',x1,x2,x3\\n # print 'y1,y2,y3',y1,y2,y3\\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n y1 = olines[id, j, ij - 1]\\n y2 = olines[id, j, ij]\\n y3 = olines[id, j, ij + 1]\\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n # print 'tprof[id,j,i]',tprof[id,j,i]\\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\\n skip1 = True\\n skip2 = True\\n break\\n\\n if skip1 is False:\\n test = (domeg - dom[id, j, ij_max - 2]) * (domeg - dom[id, j, ij_max - 1])\\n # print 'test2:',test\\n # print 'domeg',domeg\\n # print 'dom[id,j,ij_max-1]',dom[id,j,ij_max-2]\\n # print 'dom[id,j,ij_max]',dom[id,j,ij_max-1]\\n if test <= 0.0:\\n # print 'triggered test2'\\n x1 = dom[id, j, ij_max - 3]\\n x2 = dom[id, j, ij_max - 2]\\n x3 = dom[id, j, ij_max - 1]\\n y1 = oline[id, j, ij_max - 3]\\n y2 = oline[id, j, ij_max - 2]\\n y3 = oline[id, j, ij_max - 1]\\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n y1 = olines[id, j, ij_max - 3]\\n y2 = olines[id, j, ij_max - 2]\\n y3 = olines[id, j, ij_max - 1]\\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n skip2 = True\\n # print 'x1,x2,x3',x1,x2,x3\\n # print 'y1,y2,y3',y1,y2,y3\\n # print 'tprof[id,j,i]',tprof[id,j,i]\\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\\n continue\\n\\n if skip2 is False:\\n if domeg > dom[id, j, ij_max]:\\n # print 'triggered test3'\\n tprof[id, j, i] = fainom / (domeg ** 2.5)\\n tprofs[id, j, i] = tprof[id, j, i]\\n continue\\n\\n # We can skip writing the intermediate file\\n\\n\\n for id in np.arange(num_tab_dens):\\n otest_dens = (dens_cm - tab_dens_cm[id]) * (dens_cm - tab_dens_cm[id + 1])\\n if otest_dens <= 0.0:\\n dense1 = tab_dens_cm[id]\\n dense2 = tab_dens_cm[id + 1]\\n id1 = id\\n id2 = id + 1\\n break\\n\\n if dens_cm >= tab_dens_cm[num_tab_dens]:\\n dense1 = tab_dens_cm[num_tab_dens - 1]\\n dense2 = tab_dens_cm[num_tab_dens]\\n id1 = num_tab_dens - 1\\n id2 = num_tab_dens\\n\\n for it in np.arange(10):\\n otest = (temp_k - tab_temp_k[it]) * (temp_k - tab_temp_k[it + 1])\\n if otest <= 0.0:\\n it1 = it\\n it2 = it + 1\\n # pr01 = pr0[id2,it1] # max value of pr0 for T1,T2,dense1,dense2\\n tempe1 = tab_temp_k[it]\\n tempe2 = tab_temp_k[it + 1]\\n break\\n\\n # interpolation in temperature\\n for id in np.arange(id1, id2 + 1):\\n for i in np.arange(jdom):\\n uprof[id, i] = tprof[id, it1, i] + (temp_k - tempe1) * (tprof[id, it2, i] - tprof[id, it1, i]) / (\\n tempe2 - tempe1)\\n uprofs[id, i] = tprofs[id, it1, i] + (temp_k - tempe1) * (tprofs[id, it2, i] - tprofs[id, it1, i]) / (\\n tempe2 - tempe1)\\n\\n delta_lambda = np.zeros(jdom)\\n delta_nu = np.zeros(jdom)\\n wprof_nu = np.zeros(jdom)\\n wprofs_nu = np.zeros(jdom)\\n\\n for i in np.arange(jdom):\\n wprof = uprof[id1, i] + (dens_cm - dense1) * (uprof[id2, i] - uprof[id1, i]) / (dense2 - dense1)\\n wprofs = uprofs[id1, i] + (dens_cm - dense1) * (uprofs[id2, i] - uprofs[id1, i]) / (dense2 - dense1)\\n delta_omega = domm[i] * normal_holtsmark_field\\n delta_nu[i] = delta_omega / (2 * np.pi)\\n delta_lambda[i] = wl_centre_angst * delta_omega / (angular_freq_0 + delta_omega)\\n # print(delta_lambda[i])\\n wprof_nu[i] = (wprof / normal_holtsmark_field) * (2. * np.pi)\\n wprofs_nu[i] = (wprofs / normal_holtsmark_field) * (2. * np.pi)\\n # print '%e %e %e %e' %(delta_lambda[i],delta_nu[i],wprof_nu[i],wprofs_nu[i])\\n\\n delta_lambda2 = np.concatenate((-delta_lambda[::-1], delta_lambda)) + wl_centre_angst # + olam0\\n delta_nu2 = np.concatenate((-delta_nu[::-1], delta_nu))\\n wprof_nu2 = np.concatenate((wprof_nu[::-1], wprof_nu))\\n wprofs_nu2 = np.concatenate((wprofs_nu[::-1], wprofs_nu))\\n\\n # for some reason, i only get a good agreement with the other models if i take the pure Stark broadened Stehle\\n # output and manually convolve it with the Doppler profile -- not sure why...\\n ls_sd = wprofs_nu2\\n\\n # interpolate onto frequency axis\\n ls_sd = np.interp(self.freq_axis, delta_nu2 + self.freq_centre, ls_sd)\\n\\n return ls_sd\",\n \"def _add_labels(self):\\n coords = self['pore.coords']\\n self['pore.front'] = coords[:,0]<(0.1*self._Lx)\\n self['pore.back'] = coords[:,0]>(0.9*self._Lx)\\n self['pore.left'] = coords[:,1]<(0.1*self._Ly)\\n self['pore.right'] = coords[:,1]>(0.9*self._Ly)\\n self['pore.bottom'] = coords[:,2]<(0.1*self._Lz)\\n self['pore.top'] = coords[:,2]>(0.9*self._Lz)\\n bnds = self.pores(labels=['front','back','left','right','bottom','top'])\\n self['pore.boundary'] = False\\n self['pore.boundary'] = bnds\",\n \"def __call__(self, src, label):\\n\\n h, w, _ = src.shape\\n # interp = np.random.randint(0, 5)\\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\\n img, flips = timage.random_flip(img, px=0.5)\\n img = img.astype(np.float32)\\n target_list_1 = []\\n target_list_2 = []\\n\\n for k in range(self.teacher_num):\\n if self.teacher_aug:\\n target_image_1 = self.random_color_aug(img)\\n else:\\n target_image_1 = img\\n\\n target_image_2 = self.random_color_aug(img)\\n\\n target_image_1 = mx.nd.image.to_tensor(target_image_1)\\n target_image_1 = mx.nd.image.normalize(target_image_1, mean=self._mean, std=self._std)\\n\\n target_image_2 = mx.nd.image.to_tensor(target_image_2)\\n target_image_2 = mx.nd.image.normalize(target_image_2, mean=self._mean, std=self._std)\\n target_list_1.append(target_image_1)\\n target_list_2.append(target_image_2)\\n target_list_1 = mx.nd.concat(*target_list_1, dim=0)\\n target_list_2 = mx.nd.concat(*target_list_2, dim=0)\\n return target_list_1, target_list_2\",\n \"def _build_ham(self):\\n path = self._solverpath.long_tail()\\n print(path)\\n current, k = self.snake.head(), 0\\n for direc in path:\\n self.information[current.x][current.y].idx = k\\n self.information[current.x][current.y].direc = direc\\n current = current.adj(direc)\\n k += 1\\n # Process snake bodies\\n current = self.snake.tail()\\n for _ in range(self.snake.len() - 1):\\n self.information[current.x][current.y].idx = k\\n self.information[current.x][current.y].direc = self.snake.direc\\n current = current.adj(self.snake.direc)\\n k += 1\",\n \"def tagview(tab,label,x,y):\\r\\n font = cv2.FONT_HERSHEY_SIMPLEX\\r\\n col=classifc[label]\\r\\n labnow=classif[label]\\r\\n# print (labnow, text)\\r\\n if label == 'back_ground':\\r\\n deltay=30\\r\\n else:\\r\\n# deltay=25*((labnow-1)%5)\\r\\n deltay=40+10*(labnow-1)\\r\\n\\r\\n viseg=cv2.putText(tab,label,(x, y+deltay), font,0.3,col,1)\\r\\n return viseg\",\n \"def split(self, eccMap, patchName='patch00', cutStep=1, borderWidth=2, isplot=False):\\r\\n minMarker = localMin(eccMap, cutStep)\\r\\n\\r\\n plt.figure()\\r\\n plt.imshow(minMarker, vmin=0, interpolation='nearest')\\r\\n plt.colorbar()\\r\\n plt.title('markers 1')\\r\\n plt.show()\\r\\n\\r\\n minMarker = minMarker.astype(np.int32)\\r\\n selfArray = self.array.astype(np.int32)\\r\\n minMarker = minMarker + 1\\r\\n minMarker[minMarker == 1] = 0\\r\\n minMarker = minMarker + (-1 * (selfArray - 1))\\r\\n # minMarker: marker type for opencv watershed,\\r\\n # sure background = 1\\r\\n # unknow = 0\\r\\n # sure forgrand = 2,3,4... etc\\r\\n\\r\\n plt.figure()\\r\\n plt.imshow(minMarker, vmin=0, interpolation='nearest')\\r\\n plt.colorbar()\\r\\n plt.title('markers 2')\\r\\n plt.show()\\r\\n\\r\\n eccMapNor = (np.round(ia.array_nor(eccMap) * 255)).astype(np.uint8)\\r\\n eccMapRGB = cv2.cvtColor(eccMapNor, cv2.COLOR_GRAY2RGB)\\r\\n # eccMapRGB: image type for opencv watershed, RGB, [uint8, uint8, uint8]\\r\\n\\r\\n newLabel = cv2.watershed(eccMapRGB, minMarker)\\r\\n\\r\\n plt.figure()\\r\\n plt.imshow(newLabel, vmin=0, interpolation='nearest')\\r\\n plt.colorbar()\\r\\n plt.title('markers 3')\\r\\n plt.show()\\r\\n\\r\\n newBorder = np.zeros(newLabel.shape).astype(np.int)\\r\\n\\r\\n newBorder[newLabel == -1] = 1\\r\\n\\r\\n border = ni.binary_dilation(self.array).astype(np.int) - self.array\\r\\n\\r\\n border = newBorder + border\\r\\n\\r\\n border[border > 1] = 1\\r\\n\\r\\n border = sm.skeletonize(border)\\r\\n\\r\\n if borderWidth > 1:\\r\\n border = ni.binary_dilation(border, iterations=borderWidth - 1).astype(np.int8)\\r\\n\\r\\n newPatchMap = ni.binary_dilation(self.array).astype(np.int8) * (-1 * (border - 1))\\r\\n\\r\\n labeledNewPatchMap, patchNum = ni.label(newPatchMap)\\r\\n\\r\\n # if patchNum != np.amax(newLabel):\\r\\n # print 'number of patches: ', patchNum, '; number of local minimum:', np.amax(newLabel)\\r\\n # raise ValueError, \\\"Number of patches after splitting does not equal to number of local minimum!\\\"\\r\\n\\r\\n newPatchDict = {}\\r\\n\\r\\n for j in range(1, patchNum + 1):\\r\\n\\r\\n currPatchName = patchName + '.' + str(j)\\r\\n currArray = np.zeros(self.array.shape, dtype=np.int8)\\r\\n currArray[labeledNewPatchMap == j] = 1\\r\\n currArray = currArray * self.array\\r\\n\\r\\n if np.sum(currArray[:]) > 0:\\r\\n newPatchDict.update({currPatchName: Patch(currArray, self.sign)})\\r\\n\\r\\n if isplot:\\r\\n plt.figure()\\r\\n plt.subplot(121)\\r\\n plt.imshow(self.array, interpolation='nearest')\\r\\n plt.title(patchName + ': before split')\\r\\n plt.subplot(122)\\r\\n plt.imshow(labeledNewPatchMap, interpolation='nearest')\\r\\n plt.title(patchName + ': after split')\\r\\n\\r\\n return newPatchDict\",\n \"def do_htt_plots(tree, output_dir, cut=''):\\n for logz in [True, False]:\\n make_2d_plot(tree, 'httRef', HTT_REF_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1', HTT_L1_STR, NB_HTT, HTT_MIN, HTT_MAX,\\n os.path.join(output_dir, 'httRef_httL1.pdf'), logz=logz, normx=False,\\n cut=cut, title=TITLE, diagonal_line=True)\\n for normx in [True, False]:\\n make_2d_plot(tree, 'httL1', HTT_L1_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1/httRef', HTT_RATIO_STR, NB_HTT_RATIO, HTT_RATIO_MIN, HTT_RATIO_MAX,\\n os.path.join(output_dir, 'httRatio_httL1.pdf'), logz=logz, normx=normx,\\n cut=cut, title=TITLE, horizontal_line=True)\\n make_2d_plot(tree, 'httRef', HTT_REF_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1/httRef', HTT_RATIO_STR, NB_HTT_RATIO, HTT_RATIO_MIN, HTT_RATIO_MAX,\\n os.path.join(output_dir, 'httRatio_httRef.pdf'), logz=logz, normx=normx,\\n cut=cut, title=TITLE, horizontal_line=True)\\n\\n make_2d_plot(tree, 'httL1', HTT_L1_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1-httRef', HTT_DIFF_STR, NB_HTT_DIFF, HTT_DIFF_MIN, HTT_DIFF_MAX,\\n os.path.join(output_dir, 'httDiff_httL1.pdf'), logz=logz, normx=normx,\\n cut=cut, title=TITLE, horizontal_line=True)\\n make_2d_plot(tree, 'httRef', HTT_REF_STR, NB_HTT, HTT_MIN, HTT_MAX, 'httL1-httRef', HTT_DIFF_STR, NB_HTT_DIFF, HTT_DIFF_MIN, HTT_DIFF_MAX,\\n os.path.join(output_dir, 'httDiff_httRef.pdf'), logz=logz, normx=normx,\\n cut=cut, title=TITLE, horizontal_line=True)\\n make_2d_plot(tree, 'httL1/httRef', HTT_RATIO_STR, NB_HTT_RATIO, HTT_RATIO_MIN, HTT_RATIO_MAX,\\n 'httL1-httRef', HTT_DIFF_STR, NB_HTT_DIFF, HTT_DIFF_MIN, HTT_DIFF_MAX,\\n os.path.join(output_dir, 'httDiff_httRatio.pdf'), logz=logz, normx=normx,\\n cut=cut, title=TITLE, horizontal_line=True)\",\n \"def __init__(self, trg_vocab_size, label_smoothing=0.1, reduction='mean', with_logits=True, ignore_index=0):\\n super(LabelSmoothingLoss, self).__init__()\\n self.with_logits = with_logits\\n self.ignore_index = ignore_index\\n self.kl_divergence = nn.KLDivLoss(reduction=reduction)\\n\\n self._create_one_hot(label_smoothing, trg_vocab_size)\\n self.confidence = 1.0 - label_smoothing\",\n \"def nodules_connection(label_data, label_header):\\n\\n\\n las_labels = measure.label(label_data,\\n neighbors=8,\\n background=0,\\n return_num=True)\\n\\n las_labels_nzero = np.nonzero(las_labels[0])\\n [xdif, ydif, zdif] = [np.amax(las_labels_nzero[0])-np.amin(las_labels_nzero[0]),\\n np.amax(las_labels_nzero[1])-np.amin(las_labels_nzero[1]),\\n np.amax(las_labels_nzero[2])-np.amin(las_labels_nzero[2])]\\n\\n # conversion pixels to mm\\n dims = label_header['pixdim']\\n if label_header['xyzt_units'] == 10:\\n #dimensions in mm\\n print('xyzt_units=10')\\n xdif=dims[1]*xdif\\n ydif=dims[2]*ydif\\n zdif=dims[3]*zdif\\n\\n\\n return las_labels,[xdif,ydif,zdif]\",\n \"def at_s2ncut(self):\\n\\n\\t # Notch out the transit and recompute\\n\\t fmcut = self.fm.copy()\\n\\t fmcut.fill_value=0\\n\\t # Widen by twice the transit duration\\n\\t tmask = self.rLbl['tRegLbl'] >= 0\\n\\t tmask = np.convolve(\\n\\t tmask.astype(float),\\n\\t np.ones(self.header['tdurcad'] * 2),\\n\\t mode='same'\\n\\t )\\n\\t tmask = tmask.astype(bool)\\n\\t fmcut.mask = fmcut.mask | tmask\\n\\t grid = tfind.Grid(self.t,fmcut)\\n\\n\\n\\t pgram_params = [\\n\\t dict(Pcad1=self.Pcad - 1, Pcad2=self.Pcad + 1, twdG = [self.header['tdurcad']])\\n\\t ]\\n\\t pgram = grid.periodogram(pgram_params,mode='max')\\n\\t idxmax = pgram.s2n.idxmax()\\n\\n\\t dkeys = 's2ncut s2ncut_t0 s2ncut_mean'.split()\\n\\t pkeys = 's2n t0 mean'.split()\\n\\n\\t for dkey,pkey in zip(dkeys,pkeys):\\n\\t self.add_attr(dkey,pgram.ix[idxmax,pkey])\",\n \"def at_s2ncut(self):\\n\\n\\t # Notch out the transit and recompute\\n\\t fmcut = self.fm.copy()\\n\\t fmcut.fill_value=0\\n\\t # Widen by twice the transit duration\\n\\t tmask = self.rLbl['tRegLbl'] >= 0\\n\\t tmask = np.convolve(\\n\\t tmask.astype(float),\\n\\t np.ones(self.header['tdurcad'] * 2),\\n\\t mode='same'\\n\\t )\\n\\t tmask = tmask.astype(bool)\\n\\t fmcut.mask = fmcut.mask | tmask\\n\\t grid = tfind.Grid(self.t,fmcut)\\n\\n\\n\\t pgram_params = [\\n\\t dict(Pcad1=self.Pcad - 1, Pcad2=self.Pcad + 1, twdG = [self.header['tdurcad']])\\n\\t ]\\n\\t pgram = grid.periodogram(pgram_params,mode='max')\\n\\t idxmax = pgram.s2n.idxmax()\\n\\n\\t dkeys = 's2ncut s2ncut_t0 s2ncut_mean'.split()\\n\\t pkeys = 's2n t0 mean'.split()\\n\\n\\t for dkey,pkey in zip(dkeys,pkeys):\\n\\t self.add_attr(dkey,pgram.ix[idxmax,pkey])\",\n \"def housing_labels_(strat_train_set):\\n logging.info(\\\"copy of dataset\\\")\\n housing_labels = strat_train_set[\\\"median_house_value\\\"].copy()\\n return housing_labels\",\n \"def addLabels(t):\\n if not t.label:\\n t.label = \\\"\\\".join([choice(\\\"abcdefghijklmnopqrstuvwxyz\\\") for i in range(4)])\\n for r,w in t.children:\\n addLabels(r)\",\n \"def draw_heading(self, heading): \\n\\n heading_label = ttk.Label(self.frame, text=heading, background=\\\"blue\\\",\\n foreground=\\\"white\\\", anchor=CENTER)\\n \\n heading_label.configure(font=('Times', 15, \\\"bold\\\"))\\n heading_label.configure(wraplength=self.width) \\n heading_label.pack(side=TOP, fill=X, ipady=10)\",\n \"def h_t(self, x, t):\\n ret = 0\\n strong_classifier = self.classifiers[0:t+1]\\n for wc in strong_classifier:\\n ret += wc.classify(x)\\n return ret\",\n \"def h_t(self, x, t):\\n ret = 0\\n strong_classifier = self.classifiers[0:t+1]\\n for wc in strong_classifier:\\n ret += wc.classify(x)\\n return ret\",\n \"def encodeToLabels(self, gt_instances):\\n raw_boxes_xyzwhd = np.zeros((self.config_data[\\\"max_boxes_per_frame\\\"], 7))\\n ### initialize gronud truth labels as np.zeors ###\\n gt_labels = np.zeros(list(self.headoutput_shape[1:4]) + \\\\\\n [len(self.anchor_boxes)] + \\\\\\n [len(self.config_data[\\\"all_classes\\\"]) + 7])\\n\\n ### start transferring box to ground turth label format ###\\n for i in range(len(gt_instances[\\\"classes\\\"])):\\n if i > self.config_data[\\\"max_boxes_per_frame\\\"]:\\n continue\\n class_name = gt_instances[\\\"classes\\\"][i]\\n box_xyzwhd = gt_instances[\\\"boxes\\\"][i]\\n class_id = self.config_data[\\\"all_classes\\\"].index(class_name)\\n if i < self.config_data[\\\"max_boxes_per_frame\\\"]:\\n raw_boxes_xyzwhd[i, :6] = box_xyzwhd\\n raw_boxes_xyzwhd[i, 6] = class_id\\n class_onehot = helper.smoothOnehot(class_id, len(self.config_data[\\\"all_classes\\\"]))\\n \\n exist_positive = False\\n\\n grid_strid = self.grid_strides\\n anchor_stage = self.anchor_boxes\\n box_xyzwhd_scaled = box_xyzwhd[np.newaxis, :].astype(np.float32)\\n box_xyzwhd_scaled[:, :3] /= grid_strid\\n anchorstage_xyzwhd = np.zeros([len(anchor_stage), 6])\\n anchorstage_xyzwhd[:, :3] = np.floor(box_xyzwhd_scaled[:, :3]) + 0.5\\n anchorstage_xyzwhd[:, 3:] = anchor_stage.astype(np.float32)\\n\\n iou_scaled = helper.iou3d(box_xyzwhd_scaled, anchorstage_xyzwhd, \\\\\\n self.input_size)\\n ### NOTE: 0.3 is from YOLOv4, maybe this should be different here ###\\n ### it means, as long as iou is over 0.3 with an anchor, the anchor\\n ### should be taken into consideration as a ground truth label\\n iou_mask = iou_scaled > 0.3\\n\\n if np.any(iou_mask):\\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\\\\n astype(np.int32)\\n ### TODO: consider changing the box to raw yolohead output format ###\\n gt_labels[xind, yind, zind, iou_mask, 0:6] = box_xyzwhd\\n gt_labels[xind, yind, zind, iou_mask, 6:7] = 1.\\n gt_labels[xind, yind, zind, iou_mask, 7:] = class_onehot\\n exist_positive = True\\n\\n if not exist_positive:\\n ### NOTE: this is the normal one ###\\n ### it means take the anchor box with maximum iou to the raw\\n ### box as the ground truth label\\n anchor_ind = np.argmax(iou_scaled)\\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\\\\n astype(np.int32)\\n gt_labels[xind, yind, zind, anchor_ind, 0:6] = box_xyzwhd\\n gt_labels[xind, yind, zind, anchor_ind, 6:7] = 1.\\n gt_labels[xind, yind, zind, anchor_ind, 7:] = class_onehot\\n\\n has_label = False\\n for label_stage in gt_labels:\\n if label_stage.max() != 0:\\n has_label = True\\n gt_labels = [np.where(gt_i == 0, 1e-16, gt_i) for gt_i in gt_labels]\\n return gt_labels, has_label, raw_boxes_xyzwhd\",\n \"def __init__(self, iht_size=4096, num_tilings=8, num_tiles=8):\\n self.iht = tc.IHT(iht_size)\\n self.num_tilings = num_tilings\\n self.num_tiles = num_tiles\",\n \"def horde_step(self, observation):\",\n \"def _make_vbenf_label(chain_parts):\\n\\n # toy label for development: run simple and dijet independently.\\n # simple makes Et cuts on two jets. Independently (sharing possible)\\n # of jets choosean by simple, the dijet\\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n assert scenario.startswith('vbenf')\\n args = _args_from_scenario(scenario)\\n if not args:\\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \\n arg_res = [\\n re.compile(r'(?P\\\\d*)(?Pfbet)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pmass)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pet)(?P\\\\d*)'),\\n ]\\n\\n defaults = {\\n 'et': ('101', 'inf'),\\n 'mass': ('800', 'inf'),\\n 'fbet': ('501', 'inf'),\\n }\\n\\n argvals = {}\\n while args:\\n assert len(args) == len(arg_res)\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = defaults[key][0]\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = defaults[key][1]\\n argvals[key+'hi'] = hi\\n\\n assert len(args) == len(arg_res)\\n assert len(args) == 0\\n\\n return \\\"\\\"\\\"\\n and\\n (\\n []\\n simple\\n (\\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\\n )\\n combgen\\n (\\n [(10et, 0eta320)]\\n dijet\\n (\\n [(%(masslo).0fdjmass, 26djdphi)]\\n ) \\n simple\\n (\\n [(10et, 0eta320)(20et, 0eta320)]\\n )\\n )\\n )\\\"\\\"\\\" % argvals\",\n \"def removeLabel(edge):\\n return edge[:-2]\",\n \"def removeLabel(edge):\\n return edge[:-2]\",\n \"def cutPaper(self, cut='partial', feed=True):\\n if cut not in ['partial', 'full']:\\n raise ValueError('cut must be \\\\'partial\\\\' or \\\\'full\\\\'')\\n elif type(feed) is not bool:\\n raise ValueError('feed must be True or False')\\n else:\\n value = 0 if cut == 'full' else 1\\n value += 65 if feed else 0\\n self._write(self.__class__.__GS + 'V' + chr(value))\",\n \"def resize_label(label):\\n # If current ratio greater than given ratio\\n if label[HEIGHT_INDEX] / label[WIDTH_INDEX] > 1.5:\\n previous = label[WIDTH_INDEX]\\n label[WIDTH_INDEX] = label[HEIGHT_INDEX] / 1.5\\n label[J_INDEX] -= (label[WIDTH_INDEX] - previous) / 2\\n # If current ratio smaller than given ratio\\n else:\\n previous = label[HEIGHT_INDEX]\\n label[HEIGHT_INDEX] = label[WIDTH_INDEX] * 1.5\\n label[I_INDEX] -= (label[HEIGHT_INDEX] - previous) / 2\\n return label\",\n \"def label(gt_dataset, volume_dim, voxel_dim, labeling_params):\\n labeled_volumes = dict()\\n labeled_cells = dict()\\n #Use global density and reduce the size of gt_dataset here\\n global_density = labeling_params[\\\"global_density\\\"]\\n gt_dataset = {k: v for k,v in gt_dataset.items() if random_sample() < global_density}\\n #Label in the order specified in the configuration\\n layers = sorted(labeling_params.keys())\\n #Remove global_density\\n layers.remove(\\\"global_density\\\")\\n for layer in layers:\\n print \\\"Labeling {}\\\".format(layer)\\n fluorophore = labeling_params[layer]['fluorophore']\\n volume, cells = brainbow(gt_dataset, volume_dim, voxel_dim, **labeling_params[layer])\\n if fluorophore in labeled_volumes:\\n labeled_volumes[fluorophore] += volume\\n labeled_cells[fluorophore] |= cells\\n else:\\n labeled_volumes[fluorophore] = volume\\n labeled_cells[fluorophore] = cells\\n return labeled_volumes, labeled_cells\",\n \"def analyze(self, event):\\n jets = Collection(event, \\\"Jet\\\")\\n\\n BTagWeightN = 1.0\\n BTagWeightN_up = 1.0\\n BTagWeightN_down = 1.0\\n BTagWeightN_FS = 1.0\\n BTagWeightN_up_FS = 1.0\\n BTagWeightN_down_FS = 1.0\\n BTagWeightD = 1.0\\n BTagWeightNHeavy = 1.0\\n BTagWeightNHeavy_up = 1.0\\n BTagWeightNHeavy_down = 1.0\\n BTagWeightNHeavy_FS = 1.0\\n BTagWeightNHeavy_up_FS = 1.0\\n BTagWeightNHeavy_down_FS = 1.0\\n BTagWeightDHeavy = 1.0\\n BTagWeightNLight = 1.0\\n BTagWeightNLight_FS = 1.0\\n BTagWeightNLight_up = 1.0\\n BTagWeightNLight_up_FS= 1.0\\n BTagWeightNLight_down = 1.0\\n BTagWeightNLight_down_FS = 1.0\\n BTagWeightDLight = 1.0\\n\\n for jet in jets:\\n pt = jet.pt\\n eta = abs(jet.eta)\\n flavor = jet.hadronFlavour\\n\\n if not ( pt > self.jetPtMin and eta < self.jetEtaMax): continue\\n\\n if flavor == 5:\\n pt_bin = self.h_eff_b.GetXaxis().FindBin(pt); \\n if pt_bin > self.h_eff_b.GetXaxis().GetNbins():\\n pt_bin = self.h_eff_b.GetXaxis().GetNbins(); \\n eta_bin = self.h_eff_b.GetYaxis().FindBin(eta); \\n if eta_bin > self.h_eff_b.GetYaxis().GetNbins():\\n eta_bin = self.h_eff_b.GetYaxis().GetNbins();\\n\\n eff = self.h_eff_b.GetBinContent(pt_bin, eta_bin);\\n\\n elif flavor == 4:\\n pt_bin = self.h_eff_c.GetXaxis().FindBin(pt); \\n if pt_bin > self.h_eff_c.GetXaxis().GetNbins():\\n pt_bin = self.h_eff_c.GetXaxis().GetNbins(); \\n eta_bin = self.h_eff_c.GetYaxis().FindBin(eta); \\n if eta_bin > self.h_eff_c.GetYaxis().GetNbins():\\n eta_bin = self.h_eff_c.GetYaxis().GetNbins();\\n\\n eff = self.h_eff_c.GetBinContent(pt_bin, eta_bin);\\n\\n else:\\n pt_bin = self.h_eff_udsg.GetXaxis().FindBin(pt); \\n if pt_bin > self.h_eff_udsg.GetXaxis().GetNbins():\\n pt_bin = self.h_eff_udsg.GetXaxis().GetNbins(); \\n eta_bin = self.h_eff_udsg.GetYaxis().FindBin(eta); \\n if eta_bin > self.h_eff_udsg.GetYaxis().GetNbins():\\n eta_bin = self.h_eff_udsg.GetYaxis().GetNbins();\\n\\n eff = self.h_eff_udsg.GetBinContent(pt_bin, eta_bin);\\n \\n if self.FastSim:\\n btagSF = jet.btagSF\\n btagSF_FS=jet.btagSF_FS\\n btagSF_up_FS = jet.btagSF_FS_up\\n btagSF_down_FS = jet.btagSF_FS_down\\n btagSF_down = jet.btagSF_down\\n btagSF_up = jet.btagSF_up\\n else:\\n btagSF = jet.btagSF\\n btagSF_FS= 1.0\\n btagSF_up = jet.btagSF_up\\n btagSF_down = jet.btagSF_down\\n btagSF_up_FS = 1.0\\n btagSF_down_FS = 1.0\\n \\n if jet.btagDeepB > self.bDiscCut:\\n #check if eff is zero\\n if eff < 0.001:\\n eff = 0.001\\n \\n BTagWeightN *= btagSF * eff\\n BTagWeightN_FS *= btagSF_FS * eff\\n BTagWeightN_up *= btagSF_up * eff\\n BTagWeightN_down *= btagSF_down * eff\\n BTagWeightN_up_FS *= btagSF_up_FS * eff\\n BTagWeightN_down_FS *= btagSF_down_FS * eff\\n\\n if abs(flavor) == 5:\\n BTagWeightNHeavy *= btagSF * eff\\n BTagWeightNHeavy_FS *= btagSF_FS * eff\\n BTagWeightNHeavy_up *= btagSF_up * eff\\n BTagWeightNHeavy_down *= btagSF_down * eff\\n BTagWeightNHeavy_up_FS *= btagSF_up_FS * eff\\n BTagWeightNHeavy_down_FS *= btagSF_down_FS * eff\\n BTagWeightDHeavy *= eff\\n else:\\n BTagWeightNLight *= btagSF * eff\\n BTagWeightNLight_FS *= btagSF_FS * eff\\n BTagWeightNLight_up *= btagSF_up * eff\\n BTagWeightNLight_down *= btagSF_down * eff\\n BTagWeightNLight_up_FS *= btagSF_up_FS * eff\\n BTagWeightNLight_down_FS *= btagSF_down_FS * eff\\n BTagWeightDLight *= eff\\n\\n BTagWeightD *= eff\\n else:\\n #check if eff is 1.0\\n if eff > 0.999:\\n eff = 0.999\\n\\n BTagWeightN *= 1 - btagSF * eff\\n BTagWeightN_FS *= 1 - btagSF_FS * eff\\n BTagWeightN_up *= 1 - btagSF_up * eff\\n BTagWeightN_down *= 1 - btagSF_down * eff\\n BTagWeightN_up_FS *= 1 - btagSF_up_FS * eff\\n BTagWeightN_down_FS *= 1 - btagSF_down_FS * eff\\n\\n if abs(flavor) == 5:\\n BTagWeightNHeavy *= 1 - btagSF * eff\\n BTagWeightNHeavy_FS *= 1 - btagSF_FS * eff\\n BTagWeightNHeavy_up *= 1 - btagSF_up * eff\\n BTagWeightNHeavy_down *= 1 - btagSF_down * eff\\n BTagWeightNHeavy_up_FS *= 1 - btagSF_up_FS * eff\\n BTagWeightNHeavy_down_FS *= 1 - btagSF_down_FS * eff\\n BTagWeightDHeavy *= 1 - eff\\n else:\\n BTagWeightNLight *= 1 - btagSF * eff\\n BTagWeightNLight_FS *= 1 - btagSF_FS * eff\\n BTagWeightNLight_up *= 1 - btagSF_up * eff\\n BTagWeightNLight_up_FS *= 1 - btagSF_up_FS * eff\\n BTagWeightNLight_down *= 1 - btagSF_down * eff\\n BTagWeightNLight_down_FS *= 1 - btagSF_down_FS * eff\\n BTagWeightDLight *= 1 - eff\\n\\n BTagWeightD *= 1 - eff\\n \\n if self.FastSim:\\n self.out.fillBranch(\\\"BTagWeight_FS\\\", BTagWeightN_FS / BTagWeightD)\\n self.out.fillBranch(\\\"BTagWeight_Up_FS\\\", BTagWeightN_up_FS / BTagWeightD)\\n self.out.fillBranch(\\\"BTagWeight_Down_FS\\\", BTagWeightN_down_FS / BTagWeightD)\\n self.out.fillBranch(\\\"BTagWeightHeavy_FS\\\", BTagWeightNHeavy_FS / BTagWeightDHeavy)\\n self.out.fillBranch(\\\"BTagWeightHeavy_Up_FS\\\", BTagWeightNHeavy_up_FS / BTagWeightDHeavy)\\n self.out.fillBranch(\\\"BTagWeightHeavy_Down_FS\\\", BTagWeightNHeavy_down_FS / BTagWeightDHeavy)\\n self.out.fillBranch(\\\"BTagWeightLight_FS\\\", BTagWeightNLight_FS / BTagWeightDLight)\\n self.out.fillBranch(\\\"BTagWeightLight_Up_FS\\\", BTagWeightNLight_up_FS / BTagWeightDLight)\\n self.out.fillBranch(\\\"BTagWeightLight_Down_FS\\\", BTagWeightNLight_down_FS / BTagWeightDLight)\\n self.out.fillBranch(\\\"BTagWeight\\\", BTagWeightN / BTagWeightD)\\n self.out.fillBranch(\\\"BTagWeight_Up\\\", BTagWeightN_up / BTagWeightD)\\n self.out.fillBranch(\\\"BTagWeight_Down\\\", BTagWeightN_down / BTagWeightD)\\n self.out.fillBranch(\\\"BTagWeightHeavy\\\", BTagWeightNHeavy / BTagWeightDHeavy)\\n self.out.fillBranch(\\\"BTagWeightHeavy_Up\\\", BTagWeightNHeavy_up / BTagWeightDHeavy)\\n self.out.fillBranch(\\\"BTagWeightHeavy_Down\\\", BTagWeightNHeavy_down / BTagWeightDHeavy)\\n self.out.fillBranch(\\\"BTagWeightLight\\\", BTagWeightNLight / BTagWeightDLight)\\n self.out.fillBranch(\\\"BTagWeightLight_Up\\\", BTagWeightNLight_up / BTagWeightDLight)\\n self.out.fillBranch(\\\"BTagWeightLight_Down\\\", BTagWeightNLight_down / BTagWeightDLight)\\n return True\",\n \"def torsion_helix_strain(x, dof):\\n base = np.zeros([6, dof])\\n base[0, 0] = 1 # torsion\\n base[1, 1] = 1 # y-bending\\n base[2, 2] = 1 # z-bending\\n return base\",\n \"def make_cutout_table(ra_in, dec_in, other=None, othername=None, table=(2,7), compare=False, scale_unit='pixscale', scale=0.25, layer='decals-dr7', layer2=None, savefile=None): \\n de_img = []\\n wi_img = []\\n N = table[0]*table[1]\\n \\n for i in range(N):\\n de_cutout_url = 'http://legacysurvey.org/viewer-dev/jpeg-cutout/?ra=%g&dec=%g&%s=%g&layer=%s&size=180' % (ra_in[i],dec_in[i], scale_unit, scale, layer)\\n img = plt.imread(download_file(de_cutout_url,cache=True,show_progress=False,timeout=120))\\n de_img.append(img)\\n \\n if compare:\\n wi_cutout_url = 'http://legacysurvey.org/viewer-dev/jpeg-cutout/?ra=%g&dec=%g&%s=%g&layer=%s&size=180' % (ra_in[i],dec_in[i], scale_unit, scale, layer2)\\n img = plt.imread(download_file(wi_cutout_url,cache=True,show_progress=False,timeout=120))\\n wi_img.append(img)\\n \\n fig = plt.figure(figsize=(4*table[1],4*table[0]))\\n\\n for i in range(len(de_img)):\\n ax = fig.add_subplot(table[0],table[1],i+1)\\n ax.imshow(de_img[i])\\n ax.xaxis.set_major_formatter(NullFormatter())\\n ax.yaxis.set_major_formatter(NullFormatter())\\n if other[i] != None:\\n ax.text(0.1,0.9,'%s=%.1f'%(othername,other[i]),transform=ax.transAxes,fontsize=14,color='white')\\n\\n plt.subplots_adjust(wspace=0.07, hspace=0.07)\\n \\n if compare:\\n fig = plt.figure(figsize=(4*table[1],4*table[0]))\\n for i in range(len(wi_img)):\\n ax = fig.add_subplot(table[0],table[1],i+1)\\n ax.imshow(wi_img[i])\\n ax.xaxis.set_major_formatter(NullFormatter())\\n ax.yaxis.set_major_formatter(NullFormatter())\\n ax.text(0.1,0.9,'r=%.1f'%(mag[i]),transform=ax.transAxes,fontsize=14,color='white')\\n\\n plt.subplots_adjust(wspace=0.07, hspace=0.07)\\n \\n if savefile != None:\\n fig.savefig(savefile +'.png')\\n fig.savefig(savefile +'.pdf')\",\n \"def assignLabels(self):\\n clusters = np.arange(0, len(self.V))[self.V < self.V1] #indexes self.V, volumes_sorted, and oldOrder\\n self.clusterV = self.volumes_sorted[clusters]\\n clusters = self.oldOrder[clusters] #indexes volumes\\n self.clusters = self.nonBI[clusters] #indexes self.vor and self.data\\n self.easyLabel = np.zeros(len(self.data))\\n self.easyLabel[self.clusters] = 1\\n print('Out of ' + str(len(self.data)) + ' particles, ' + str(len(self.clusters)) + ' (' + str(round(len(self.clusters)*100/len(self.data), 3)) +' %) are labelled as cluster particles.')\",\n \"def __init__(self, smoothing=0.1):\\n super(LabelSmoothingCrossEntropy, self).__init__()\\n assert smoothing < 1.0\\n self.smoothing = smoothing\\n self.confidence = 1. - smoothing\",\n \"def __init__(self):\\r\\n self.label = \\\"Longest Flow Path\\\"\\r\\n self.description = \\\"this is my Longest Flow Path python tool\\\"\",\n \"def adjust_labels(data_y, label):\\n\\n if label == 'locomotion': # Labels for locomotion are adjusted\\n data_y[data_y == 4] = 3\\n data_y[data_y == 5] = 4\\n elif label == 'gestures': # Labels for gestures are adjusted\\n data_y[data_y == 406516] = 1\\n data_y[data_y == 406517] = 2\\n data_y[data_y == 404516] = 3\\n data_y[data_y == 404517] = 4\\n data_y[data_y == 406520] = 5\\n data_y[data_y == 404520] = 6\\n data_y[data_y == 406505] = 7\\n data_y[data_y == 404505] = 8\\n data_y[data_y == 406519] = 9\\n data_y[data_y == 404519] = 10\\n data_y[data_y == 406511] = 11\\n data_y[data_y == 404511] = 12\\n data_y[data_y == 406508] = 13\\n data_y[data_y == 404508] = 14\\n data_y[data_y == 408512] = 15\\n data_y[data_y == 407521] = 16\\n data_y[data_y == 405506] = 17\\n return data_y\",\n \"def cut_bkg(self):\\n c = TCut(self.cut_both)\\n c += TCut(self._return_if('_cut_bkg'))\\n return c\",\n \"def __init__(self):\\n Algorithm.__init__(self)\\n self.name = \\\"Otsus Threshold\\\"\\n self.parent = \\\"Segmentation\\\"\",\n \"def old_ideal_label(I):\\n a, c, d = ideal_HNF(I)\\n return \\\"%s.%s.%s\\\" % (a * d, c, d)\",\n \"def calc_Hcp_ij(self):\\n\\t\\n\\thp0_delayed = self.hp_wavelet.get_Psi(self.xi[0] + self.Orbit.L/l.Clight)\\n\\thp0 = self.hp_wavelet.get_Psi(self.xi[0])\\n\\thc0_delayed = self.hc_wavelet.get_Psi(self.xi[0] + self.Orbit.L/l.Clight)\\n\\thc0 = self.hc_wavelet.get_Psi(self.xi[0])\\n\\t\\n\\thp1_delayed = self.hp_wavelet.get_Psi(self.xi[1] + self.Orbit.L/l.Clight)\\n\\thp1 = self.hp_wavelet.get_Psi(self.xi[1])\\n\\thc1_delayed = self.hc_wavelet.get_Psi(self.xi[1] + self.Orbit.L/l.Clight)\\n\\thc1 = self.hc_wavelet.get_Psi(self.xi[1])\\n\\t\\n\\thp2_delayed = self.hp_wavelet.get_Psi(self.xi[2] + self.Orbit.L/l.Clight)\\n\\thp2 = self.hp_wavelet.get_Psi(self.xi[2])\\n\\thc2_delayed = self.hc_wavelet.get_Psi(self.xi[2] + self.Orbit.L/l.Clight)\\n\\thc2 = self.hc_wavelet.get_Psi(self.xi[2])\\n\\t\\n\\tself.Hpij[0,1] = hp1_delayed - hp0\\n\\tself.Hpij[1,0] = hp0_delayed - hp1\\n\\n\\tself.Hpij[0,2] = hp2_delayed - hp0\\n\\tself.Hpij[2,0] = hp0_delayed - hp2\\n\\n\\tself.Hpij[1,2] = hp2_delayed - hp1\\n\\tself.Hpij[2,1] = hp1_delayed - hp2\\n\\t\\n\\t# cross-polarization\\n\\tself.Hcij[0,1] = hc1_delayed - hc0\\n\\tself.Hcij[1,0] = hc0_delayed - hc1\\n\\n\\tself.Hcij[0,2] = hc2_delayed - hc0\\n\\tself.Hcij[2,0] = hc0_delayed - hc2\\n\\n\\tself.Hcij[1,2] = hc2_delayed - hc1\\n\\tself.Hcij[2,1] = hc1_delayed - hc2\\n\\t\\n\\treturn\",\n \"def __init__(self, training_data, smoothing_factor=1.0):\\n\\t\\tsuper(HostnameFeature, self).__init__('Hostname', 4, smoothing_factor)\\n\\t\\tself.category_bag_of_hostname = {}\\n\\t\\tfor record in training_data:\\n\\t\\t\\tcategory = record[6]\\n\\t\\t\\tif self.category_bag_of_hostname.get(category) is None:\\n\\t\\t\\t\\tself.category_bag_of_hostname[category] = {}\\n\\t\\t\\tpublishers = self.category_bag_of_hostname[category]\\n\\t\\t\\tpublisher_name = record[4].strip().lower()\\n\\t\\t\\tif publisher_name not in publishers:\\n\\t\\t\\t\\tpublishers[publisher_name] = 0\\n\\t\\t\\tpublishers[publisher_name] += 1\\n\\t\\t# for k, bw in self.category_bag_of_hostname.items():\\n\\t\\t# \\tprint 'category ', k, ' with number of different hostname', len(bw)\",\n \"def instance_label(task, pred, k=15, n_iters=1, dist_thresh=5, watershed=False):\\n mask = pred\\n\\n # noise removal\\n if k > 1 and n_iters > 0:\\n kernel = np.ones((k, k), np.uint8)\\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,\\n iterations=n_iters)\\n\\n if watershed:\\n from clab.live import filters\\n mask = filters.watershed_filter(mask, dist_thresh=dist_thresh)\\n\\n mask = mask.astype(np.uint8)\\n n_ccs, cc_labels = cv2.connectedComponents(mask, connectivity=4)\\n return cc_labels\",\n \"def fredkin(ha, hb, hc):\\n\\n return controlled_U(ha, swap(hb, hc))\",\n \"def labels(self, threshold, segment=True, exclude_border=0):\\n data = self.unmasked_data\\n isfin = numpy.isfinite(data)\\n data[~isfin] = numpy.amin(data[isfin])\\n regions = (data > threshold)\\n if segment:\\n local_max = peak_local_max(data, indices=False,\\n exclude_border=0,\\n footprint=numpy.ones((3, 3)),\\n labels=regions)\\n markers = measurements.label(local_max)[0]\\n labels = watershed(-data, markers, mask=regions)\\n if exclude_border > 0:\\n # Remove basins originating from edge peaks\\n diff = numpy.zeros_like(local_max)\\n for i in range(local_max.ndim):\\n local_max = local_max.swapaxes(0, i)\\n diff = diff.swapaxes(0, i)\\n diff[:exclude_border] = local_max[:exclude_border]\\n diff[-exclude_border:] = local_max[-exclude_border:]\\n diff = diff.swapaxes(0, i)\\n local_max = local_max.swapaxes(0, i)\\n \\n for l in numpy.sort(labels[diff])[::-1]:\\n labels[labels == l] = 0\\n labels[labels > l] -= 1\\n ulabels = numpy.unique(labels)\\n n = ulabels[ulabels != 0].size\\n else:\\n data_thres = numpy.zeros_like(data)\\n data_thres[regions] = data[regions]\\n labels, n = measurements.label(data_thres)\\n return labels, n\",\n \"def graphCut(img, center, radius, temp, edge, count, editPoints, padList, theta_width, phi_width):\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"Important note. The labeled image is referred to as temp, or self.temp in the interface.\\r\\n This stands for template. The previously labled image is fed back into the graphcut\\\"\\\"\\\"\\r\\n \\r\\n \\\"\\\"\\\"create polar images and cost arrays\\\"\\\"\\\"\\r\\n \\r\\n print \\\"RUNNING GRAPHCUT!\\\"\\r\\n img= padImage(img, padList)\\r\\n temp= padImage(temp, padList)\\r\\n edge= padImage(edge, padList)\\r\\n center= padCenter(center, padList)\\r\\n \\r\\n polar_img= img2polar(img, center, radius, theta_width=theta_width, phi_width=phi_width)\\r\\n\\r\\n \\r\\n \\r\\n polar_grad, y, x = np.gradient(np.array(polar_img, dtype='float'))\\r\\n \\\"\\\"\\\"Lockett 100416 replacement line below to not use gradient when the image has a surface label\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"polar_grad = -1 * np.array(polar_img, dtype='float')\\\"\\\"\\\"\\r\\n \\r\\n \\r\\n polar_cost = -1 * np.ones(polar_img.shape)\\r\\n for r in range(1,radius):\\r\\n polar_cost[r]= polar_grad[r]-polar_grad[r-1]\\r\\n\\r\\n \\r\\n \\r\\n \\\"\\\"\\\"\\r\\n flip the cost image upside down. This is so that the base set is at the bottom of the array\\r\\n since the graphcut cuts from top to bottom, this inversion is necessary.\\r\\n \\\"\\\"\\\"\\r\\n polar_cost_inv=polar_cost[::-1,:,:]\\r\\n\\r\\n print \\\"CONSTRUCTING GRAPH EDGES... \\\"\\r\\n \\r\\n \\\"\\\"\\\"construct the graph using PyMaxFlow\\\"\\\"\\\"\\r\\n g=maxflow.GraphFloat()\\r\\n nodeids=g.add_grid_nodes(polar_img.shape)\\r\\n structure=np.zeros((3,3,3))\\r\\n structure[2]= np.array([[0,10000,0],[10000, 10000, 10000],[0, 10000, 0]])\\r\\n g.add_grid_edges(nodeids, structure=structure, symmetric=False)\\r\\n\\r\\n \\r\\n \\\"\\\"\\\"convert the previously labeled image (temp) into a polar transform image. Take the labels and\\r\\n give them high cost edge weights so the segmentation avoids previously labeled objects\\\"\\\"\\\"\\r\\n polar_lbl_img= img2polar(temp, center, radius, theta_width=theta_width, phi_width=phi_width)\\r\\n polar_lbl_img_inv= polar_lbl_img[::-1,:]\\r\\n \\r\\n lbl_caps= polar_lbl_img_inv>0\\r\\n self_caps= (polar_lbl_img_inv==count)\\r\\n lbl_caps-=self_caps\\r\\n lbl_source_caps= np.zeros(lbl_caps.shape)\\r\\n lbl_sink_caps= lbl_caps*10000\\r\\n g.add_grid_tedges(nodeids, lbl_source_caps, lbl_sink_caps)\\r\\n \\r\\n structure2= 10000*np.array([[0,0,0],[0,0,1],[0,1,0]])\\r\\n g.add_grid_edges(nodeids[radius-1], structure=structure2, symmetric=True)\\r\\n\\r\\n \\\"\\\"\\\"add terminal edges using two arrays whose elemnts are the costs of the edges from the source and to the\\r\\n sink\\\"\\\"\\\"\\r\\n print \\\"CONSTRUCTING GRAPH TEDGES...\\\"\\r\\n sinkcaps= polar_cost_inv * (polar_cost_inv>=0)\\r\\n sourcecaps = -1 * polar_cost_inv * (polar_cost_inv<0)\\r\\n g.add_grid_tedges(nodeids, sourcecaps, sinkcaps)\\r\\n\\r\\n \\r\\n\\r\\n \\r\\n \\\"\\\"\\\"accounts for edit points. Takes every point in the edit point list, converts it to its spherical coordinate, and adds high cost\\r\\n edges in the column of that edit point inverts the x and y coordinates of the center\\\"\\\"\\\"\\r\\n center= np.array((center[0], center[2], center[1]))\\r\\n if len(editPoints)!=0:\\r\\n for coords in editPoints:\\r\\n\\r\\n \\r\\n rad= math.sqrt((center[0]-coords[0])**2+ (center[1]-coords[2])**2 + (center[2]-coords[1])**2) \\r\\n theta= math.atan2(center[2]-coords[1], coords[2]-center[1])\\r\\n print str((coords[0]-center[0])/(rad+1))\\r\\n phi=math.acos(float(coords[0]-center[0])/(rad+1))\\r\\n if theta<0:\\r\\n theta=2*math.pi+ theta\\r\\n theta= theta_width- theta_width*theta/(2*math.pi)-1\\r\\n phi= phi_width*phi/(math.pi)-1\\r\\n rad= radius- rad\\r\\n print \\\"POLAR COORDS: \\\" + str((rad, theta, phi))\\r\\n\\r\\n for r in range(0, radius):\\r\\n if r<=rad:\\r\\n g.add_tedge(nodeids[r, theta, phi], 0, 10000)\\r\\n \\r\\n else:\\r\\n g.add_tedge(nodeids[r, theta, phi], 10000, 0) \\r\\n\\r\\n\\r\\n\\r\\n\\r\\n print \\\"CUTTING GRAPH...\\\"\\r\\n g.maxflow()\\r\\n\\r\\n \\\"\\\"\\\"s-t mincut of graph. This is converted to cartesian coordinates with the function img2cart. The\\r\\n images are also closed to eliminate spotty areas\\\"\\\"\\\"\\r\\n \\r\\n print \\\"STARTING CARTESIAN TRANSFORM...\\\"\\r\\n polar_img_seg= np.invert(g.get_grid_segments(nodeids)[::-1,:,:])\\r\\n\\r\\n \\r\\n edge_img= np.zeros(img.shape)\\r\\n seg_img= ndimage.binary_closing(img2cart(img, polar_img_seg, center, radius, theta_width, phi_width))\\r\\n \\r\\n \\r\\n \\\"\\\"\\\"create an edge image of the segmented object\\\"\\\"\\\"\\r\\n strel=np.ones((3,3,3))\\r\\n erode_img=ndimage.binary_erosion(seg_img, strel)\\r\\n edge_img=np.logical_xor(seg_img, erode_img)\\r\\n \\r\\n\\r\\n \\\"\\\"\\\"shears the segmentation image and edge if padding was applied\\\"\\\"\\\"\\r\\n \\r\\n\\r\\n \\\"\\\"\\\"add the object back on to the template image (and the edge image back on the template edge)\\r\\n If there was an editpoint involved, remove the previous segmentation of that object and add back\\r\\n on the edited object\\\"\\\"\\\"\\r\\n if len(editPoints)!=0:\\r\\n del_img= (temp==count)*count\\r\\n temp-=del_img\\r\\n\\r\\n del_edge_img= (edge==count)*count\\r\\n edge-= del_edge_img\\r\\n\\r\\n\\r\\n temp+=seg_img*count\\r\\n edge+=edge_img*count\\r\\n\\r\\n temp= shearImage(temp, padList)\\r\\n edge= shearImage(edge, padList)\\r\\n \\r\\n \\r\\n\\r\\n print \\\"FINISHED!\\\"\\r\\n \\r\\n return temp, edge\",\n \"def __init__(self, *args):\\n _BRepAlgo.BRepAlgo_Cut_swiginit(self,_BRepAlgo.new_BRepAlgo_Cut(*args))\",\n \"def labeling(self, tab, i, j, element):\\n label = element\\n label.grid(row=i, column=j) # this specifies where in the grid\\n tab.grid_columnconfigure(j, weight=1) \\n # this last line makes the width of the column responsive to change in width of the window\",\n \"def label(self, cfg):\\n rep = \\\"\\\"\\n nl = \\\"\\\"\\n for node in cfg.nodes:\\n rep += nl + \\\"{}\\\\tgen={}\\\\tkill={}\\\\tout={}\\\".format(\\n node, \\n set(self.gen.get(node)),\\n set(self.kill.get(node)),\\n set(self.out.get(node)))\\n nl = \\\"\\\\n\\\"\\n return rep\",\n \"def _make_dijet_label(chain_parts):\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n \\n assert scenario.startswith('dijet')\\n\\n arg_res = [\\n re.compile(r'^(?P\\\\d*)(?Pdjmass)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj1et)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj1eta)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj2et)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj2eta)(?P\\\\d*)$'),\\n ]\\n\\n defaults = {\\n 'j1et': ('100', 'inf'),\\n 'j2et': ('100', 'inf'),\\n 'j1eta': ('0', '320'),\\n 'j2eta': ('0', '320'),\\n 'djmass': ('1000', 'inf'),\\n }\\n\\n\\n args = _args_from_scenario(scenario)\\n argvals = {}\\n while args:\\n assert len(args) == len(arg_res)\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = defaults[key][0]\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = defaults[key][1]\\n argvals[key+'hi'] = hi\\n\\n assert len(args) == len(arg_res)\\n assert len(args) == 0\\n\\n return \\\"\\\"\\\"\\n combgen(\\n [(2)(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\\n (%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\\n ]\\n \\n dijet(\\n [(%(djmasslo).0fdjmass)])\\n simple([(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\\n (%(j2etlo).0fet, %(j2etalo).0feta%(j2etahi).0f)])\\n )\\\"\\\"\\\" % argvals\",\n \"def hilfe(self):\\n sZweieck_hilfe(3)\",\n \"def gen_hts_lab_full(self):\\n self.gen_hts_lab_mono()\\n\\n hts_lab_time_prn = tuple(' '.join(l[0:2]) for l in self._hts_lab_mono_ttpl)\\n\\n self.hts_lab_full_prn = tuple(' '.join((l_time, l_gen)) for (l_time, l_gen)\\n in zip(hts_lab_time_prn, self.hts_lab_gen_prn))\",\n \"def x_group_label(\\n x_gr: int, cut: int = 20, name_dict: Dict[AnyStr, AnyStr] = names_dict\\n) -> AnyStr:\\n name = name_dict[str(x_gr)]\\n if len(name) > cut:\\n return f\\\"{name[:cut-3]}...\\\"\\n else:\\n return name\",\n \"def _labels_of_sentence(self, sentence, split):\\n labels = torch.ones(1)\\n labels[0] = self.category_int_of_label_string(sentence[0][self.name_to_index_dict['label']]) #\\n return labels\",\n \"def vertical_core(block,cut,laser):\\r\\n\\r\\n\\tlayers = int(block[\\\"thickness\\\"]/laser[\\\"z_spacing\\\"])\\r\\n\\tangle = math.radians(laser[\\\"kerf_angle\\\"]/2)\\r\\n\\ttaper = math.tan(angle) * laser[\\\"z_spacing\\\"]\\r\\n\\r\\n\\tu = math.tan(2 * angle) * (block[\\\"thickness\\\"] + laser[\\\"z_final_overshoot\\\"])\\r\\n\\tz_0 = block[\\\"thickness\\\"]*math.cos(angle) + math.sin(angle)*((cut[\\\"final_dimension_y\\\"])/2 - block[\\\"origin_y\\\"] + u)\\r\\n\\tz_1 = block[\\\"thickness\\\"]*math.cos(angle) + math.sin(angle)*((cut[\\\"final_dimension_x\\\"])/2 + block[\\\"origin_x\\\"] + u)\\r\\n\\tz_2 = block[\\\"thickness\\\"]*math.cos(angle) + math.sin(angle)*((cut[\\\"final_dimension_y\\\"])/2 + block[\\\"origin_y\\\"] + u)\\r\\n\\tz_3 = block[\\\"thickness\\\"]*math.cos(angle) + math.sin(angle)*((cut[\\\"final_dimension_x\\\"])/2 - block[\\\"origin_x\\\"] + u)\\r\\n\\t\\r\\n\\tcutlist = []\\r\\n\\tcutlist.append([\\\"a_abs\\\", f\\\"{math.degrees(angle):.6f}\\\"])\\r\\n\\tcutlist.append([\\\"c_abs\\\", str(block[\\\"physical_rotation\\\"])])\\r\\n\\tcutlist.append([\\\"z_abs\\\", f\\\"{z_0:.6f}\\\"])\\r\\n\\r\\n\\ty_start_wide = ((u + cut[\\\"final_dimension_x\\\"]/2)* math.cos(angle) \\r\\n\\t\\t\\t\\t - block[\\\"thickness\\\"]*math.sin(angle) \\r\\n\\t\\t\\t\\t - u/math.cos(angle))\\r\\n\\ty_start_length = ((u + cut[\\\"final_dimension_y\\\"]/2)* math.cos(angle) \\r\\n\\t\\t\\t\\t - block[\\\"thickness\\\"]*math.sin(angle) \\r\\n\\t\\t\\t\\t - u/math.cos(angle))\\r\\n\\r\\n\\tdepth_cut = (block[\\\"thickness\\\"] + laser[\\\"z_final_overshoot\\\"]) * math.cos(angle)/math.cos(2*angle)\\r\\n\\r\\n\\tcut1 = json.loads(line(block[\\\"width\\\"]/2 - block[\\\"origin_x\\\"],y_start_length - block[\\\"origin_y\\\"],-block[\\\"width\\\"]/2 - block[\\\"origin_x\\\"],y_start_length - block[\\\"origin_y\\\"],depth_cut,laser))\\r\\n\\r\\n\\tcut2 = json.loads(line(block[\\\"length\\\"]/2 + block[\\\"origin_y\\\"],y_start_wide - block[\\\"origin_x\\\"],-block[\\\"length\\\"]/2 + block[\\\"origin_y\\\"],y_start_wide - block[\\\"origin_x\\\"],depth_cut,laser))\\r\\n\\r\\n\\tcut3 = json.loads(line(block[\\\"width\\\"]/2 + block[\\\"origin_x\\\"],y_start_length + block[\\\"origin_y\\\"],-block[\\\"width\\\"]/2 + block[\\\"origin_x\\\"],y_start_length + block[\\\"origin_y\\\"],depth_cut,laser))\\r\\n\\r\\n\\tcut4 = json.loads(line(block[\\\"length\\\"]/2 - block[\\\"origin_y\\\"],y_start_wide + block[\\\"origin_x\\\"],-block[\\\"length\\\"]/2 - block[\\\"origin_y\\\"],y_start_wide + block[\\\"origin_x\\\"],depth_cut,laser))\\r\\n\\r\\n\\t#cut1 = json.loads(line(block[\\\"width\\\"]/2,y_start_length,-block[\\\"width\\\"]/2,y_start_length,depth_cut,laser))\\r\\n\\r\\n\\t#cut2 = json.loads(line(block[\\\"length\\\"]/2,y_start_wide,-cut[\\\"final_dimension_y\\\"]/2,y_start_wide,depth_cut,laser))\\r\\n\\r\\n\\t#cut3 = json.loads(line(block[\\\"width\\\"]/2,y_start_length,-cut[\\\"final_dimension_x\\\"]/2,y_start_length,depth_cut,laser))\\r\\n\\r\\n\\t#cut4 = json.loads(line(cut[\\\"final_dimension_y\\\"]/2,y_start_wide,-cut[\\\"final_dimension_y\\\"]/2,y_start_wide,depth_cut,laser))\\r\\n\\r\\n\\tcutlist = (cutlist + cut1\\r\\n\\t + [[\\\"c_rel\\\", \\\"90\\\"],[\\\"z_abs\\\", f\\\"{z_1:.6f}\\\"],] \\r\\n\\t + cut2\\r\\n\\t + [[\\\"c_rel\\\", \\\"90\\\"],[\\\"z_abs\\\", f\\\"{z_2:.6f}\\\"]] \\r\\n\\t\\t\\t\\t\\t + cut3 \\r\\n\\t\\t\\t\\t\\t + [[\\\"z_abs\\\", f\\\"{z_3:.6f}\\\"],[\\\"c_rel\\\", \\\"90\\\"]] \\r\\n\\t\\t\\t\\t\\t + cut4)\\r\\n\\r\\n\\tcutlist.insert(0, [\\\"set_trigger4\\\", \\\"1\\\", \\\"0\\\", \\\"7\\\", \\\"8\\\", \\\"45\\\"])\\r\\n\\tcutlist.append([\\\"stop_trigger\\\"])\\r\\n\\r\\n\\treturn json.dumps(cutlist)\",\n \"def SCEC_LOH_1():\\n\\n #Initialize CrustModel\\n model = CrustModel(2)\\n\\n #Slow layer\\n vp=4.000\\n vs=2.000\\n rho=2.600\\n Qa=10000.\\n Qb=10000.\\n thickness = 1.0\\n\\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\\n\\n #Halfspace\\n vp=6.000\\n vs=3.464\\n rho=2.700\\n Qa=10000.\\n Qb=10000.\\n thickness = 0 #Infinite thickness!\\n model.add_layer(thickness, vp, vs, rho, Qa, Qb)\\n\\n return model\",\n \"def sils_cut(T,f,c,d,h, conshdlr):\\n Ts = range(1,T+1)\\n\\n model = sils(T,f,c,d,h)\\n y,x,I = model.data\\n\\n # relax integer variables\\n for t in Ts:\\n model.chgVarType(y[t], \\\"C\\\")\\n model.addVar(vtype=\\\"B\\\", name=\\\"fake\\\") # for making the problem MIP\\n\\n # compute D[i,j] = sum_{t=i}^j d[t]\\n D = {}\\n for t in Ts:\\n s = 0\\n for j in range(t,T+1):\\n s += d[j]\\n D[t,j] = s\\n\\n #include the lot sizing constraint handler\\n model.includeConshdlr(conshdlr, \\\"SILS\\\", \\\"Constraint handler for single item lot sizing\\\",\\n sepapriority = 0, enfopriority = -1, chckpriority = -1, sepafreq = -1, propfreq = -1,\\n eagerfreq = -1, maxprerounds = 0, delaysepa = False, delayprop = False, needscons = False,\\n presoltiming = SCIP_PRESOLTIMING.FAST, proptiming = SCIP_PROPTIMING.BEFORELP)\\n conshdlr.data = D,Ts\\n\\n model.data = y,x,I\\n return model\",\n \"def _subconstituent_name(h):\\n if h == 1:\\n o = \\\"1st\\\"\\n elif h == 2:\\n o = \\\"2nd\\\"\\n elif h == 3:\\n o = \\\"3rd\\\"\\n else:\\n o = \\\"%dth\\\" % h\\n return \\\"%s subconstituent\\\" % o\",\n \"def display_and_label_hulls(self, hulls, src):\\n \\n labels = []\\n\\n for hull in hulls:\\n\\n angle = 0\\n MA = 1\\n ma = 1\\n try:\\n _,(MA,ma),angle = cv.fitEllipse(hull)\\n except:\\n pass\\n cosAngle = np.abs(np.cos(angle*np.pi/180))\\n\\n # Only human-classify hulls if it is reasonably a vertically oriented rectangle\\n # This is a hueristic to not have to waste time clasifying hulls clearly not poles\\n if (cosAngle < 1.75) and (cosAngle > 0.85) and (MA/ma < 0.28):\\n cpy = src.copy()\\n hull_img = cv.polylines(cpy, [hull], True, (0,0,255), 3)\\n cv.imshow(\\\"Hull\\\", hull_img)\\n keycode = cv.waitKey(0)\\n if keycode == 49:\\n labels.append((hull, 0))\\n print(\\\"Not a Pole\\\")\\n elif keycode == 50:\\n labels.append((hull, 1))\\n print(\\\"A Pole!\\\")\\n else:\\n raise Exception(\\\"Unexpected Key Pressed\\\")\\n else:\\n labels.append((hull, 0))\\n cv.destroyAllWindows()\\n return labels\",\n \"def __init__(self, word_embed_size, vocab):\\n super(ModelEmbeddings, self).__init__()\\n\\n ### YOUR CODE HERE for part 1h\\n dropout_rate = 0.3\\n n_chars = len(vocab.char2id)\\n self.char_embed_size = 50\\n self.word_embed_size = word_embed_size\\n self.vocab = vocab\\n self.char_embed = nn.Embedding(n_chars, self.char_embed_size)\\n self.conv = CNN(self.char_embed_size, word_embed_size)\\n self.highway = Highway(word_embed_size)\\n self.dropout = nn.Dropout(dropout_rate)\\n ### END YOUR CODE\",\n \"def plot_mass_flow(self,\\n watershed, \\n output, \\n title = 'Subbasin Reach Mass Flow Diagram',\\n fontsize = 6, \\n theight = 0.2, \\n l = 8.5, \\n w = 11, \\n verbose = True, \\n overwrite = True,\\n ):\\n\\n if os.path.exists(output) and not overwrite:\\n if verbose: print('file %s exists' % output)\\n return\\n elif verbose: print('generating a mass linkage plot\\\\n')\\n\\n fontheight = fontsize / 72.\\n rheight = 3 * fontheight\\n rwidth = 12 * fontheight\\n xgap = fontheight\\n ygap = rheight\\n awidth = rheight / 4\\n aheight = rheight / 3\\n\\n # set up a sheet to write the image\\n\\n fig = pyplot.figure(figsize = (w, l))\\n\\n ax = fig.add_subplot(111, aspect = 'equal')\\n ax.get_xaxis().set_visible(False)\\n ax.get_yaxis().set_visible(False)\\n t = ax.set_title(title)\\n\\n # divide the subbasins into rows and put them on the chart\\n # start at the bottom to organize the linkages better\\n\\n rows = [watershed.outlets, ['outlet']]\\n\\n top = False\\n while not top:\\n row = []\\n for next in rows[0]:\\n for subbasin in watershed.updown:\\n if watershed.updown[subbasin] == next: row.append(subbasin)\\n if len(row) > 0: \\n rows.insert(0, row)\\n else: \\n top = True\\n\\n # add an inlet box in the row above each inlet\\n\\n for inlet in watershed.inlets: \\n\\n i = 0\\n while i < len(rows) - 1:\\n\\n for subbasin in rows[i]:\\n\\n if subbasin == inlet:\\n \\n # find the position of the subbasin in the chart\\n\\n j = rows[i].index(inlet)\\n\\n if i > 0:\\n\\n # figure out where the subbasins point\\n \\n updowns = [watershed.updown[s] for s in rows[i-1]]\\n \\n # if first or last, add it there in the row above\\n\\n if j == 0: \\n rows[i-1].insert(0, 'inlet')\\n elif j == len(rows[i]) - 1: \\n rows[i-1].append('inlet')\\n else:\\n\\n # find the place to add in the preceeding row \\n\\n n = updowns.index(rows[i][j-1]) + 1\\n rows[i-1].insert(n, 'inlet')\\n\\n i += 1\\n\\n # write the subbasin boxes to the chart\\n\\n middle = math.ceil(w // (rwidth + xgap)) // 2\\n last = 0\\n\\n # keep track of the bounding box of the plot\\n\\n xmin, ymin, xmax, ymax = middle, 0, middle, 0\\n\\n for i in range(len(rows)):\\n\\n row = rows[i]\\n \\n y = (ygap + rheight) * i + theight\\n\\n # figure out which cell to put in the main column\\n\\n if i == 0:\\n main = row[(len(row) - 1) // 2]\\n elif i < len(rows) - 1:\\n main = watershed.updown[rows[i-1][last]]\\n else: main = 'outlet'\\n\\n start = middle - row.index(main)\\n\\n if i < len(rows) - 1: next_row = rows[i + 1]\\n\\n for subbasin in row:\\n x = (rwidth + xgap) * (start + row.index(subbasin))\\n r = patches.Rectangle((x, y), rwidth, rheight, fill = False)\\n\\n # adjust the bounding box\\n\\n if x < xmin: xmin = x\\n if x + rwidth > xmax: xmax = x + rwidth\\n if y < ymin: ymin = y\\n if y + rheight > ymax: ymax = y + rheight\\n\\n if subbasin != 'outlet': ax.add_patch(r)\\n\\n b = ax.text(x + rwidth / 2, y + rheight / 2, subbasin,\\n horizontalalignment = 'center',\\n verticalalignment = 'center')\\n\\n # draw the arrow\\n\\n if i < len(rows) - 1:\\n\\n x1 = x + rwidth / 2\\n\\n if i < len(rows) - 2 and subbasin != 'inlet':\\n next = watershed.updown[subbasin]\\n next_start = (middle - \\n next_row.index(watershed.updown[main]))\\n x2 = ((rwidth + xgap) * \\n (next_start + next_row.index(next))\\n + rwidth / 2)\\n\\n elif subbasin == 'inlet':\\n next = watershed.inlets[0]\\n next_start = (middle - \\n next_row.index(watershed.updown[main]))\\n\\n x2 = ((rwidth + xgap) * \\n (next_start + next_row.index(next))\\n + rwidth / 2)\\n\\n else:\\n next_start = middle\\n x2 = ((rwidth + xgap) * (middle) + rwidth / 2)\\n\\n a = pyplot.arrow(x1, y + rheight, x2 - x1, ygap, \\n head_width = awidth, head_length = aheight,\\n fc = 'k', ec = 'k', \\n length_includes_head = True)\\n ax.add_patch(a)\\n\\n last = row.index(main)\\n i += 1\\n \\n pad = 0.02\\n\\n xmin = xmin - (xmax - xmin) * pad\\n xmax = xmax + (xmax - xmin) * pad\\n ymin = ymin - (ymax - ymin) * pad\\n ymax = ymax + (ymax - ymin) * pad\\n\\n ax.set_xlim(xmin, xmax)\\n ax.set_ylim(ymax, ymin)\\n pyplot.axis('off')\\n pyplot.savefig(output, dpi = 200)\\n\\n pyplot.clf()\\n pyplot.close()\",\n \"def _get_labels(self, ind):\\n pass\",\n \"def plot_labels_bundle_pth(device, model, dataset, dataloader):\\n ## Get labels and preds\\n labels, preds = get_labels_and_preds(device, model, dataloader)\\n \\n ## Plot the figure\\n plot_labels(labels, preds, dataset.id_to_class_dict)\",\n \"def write_label(self, contig_name, width, height, font, title_width, upper_left, vertical_label,\\n strand, canvas, horizontal_centering=False, center_vertical=False, chop_text=True,\\n label_color=(50, 50, 50, 255)):\\n upper_left = list(upper_left) # to make it mutable\\n shortened = contig_name[-title_width:] # max length 18. Last characters are most unique\\n txt = Image.new('RGBA', (width, height))#, color=(0,0,0,50))\\n txt_canvas = ImageDraw.Draw(txt)\\n text_width = txt_canvas.textsize(shortened, font)[0]\\n if not chop_text and text_width > width:\\n txt = Image.new('RGBA', (text_width, height)) # TODO performance around txt_canvas\\n txt_canvas = ImageDraw.Draw(txt)\\n if center_vertical or vertical_label: # Large labels are centered in the column to look nice,\\n # rotation indicates strand in big text\\n vertically_centered = (height // 2) - multi_line_height(font, shortened, txt)//2\\n else: # Place label at the beginning of gene based on strand\\n vertically_centered = height - multi_line_height(font, shortened, txt) # bottom\\n if strand == \\\"+\\\":\\n vertically_centered = 0 # top of the box\\n txt_canvas.multiline_text((0, max(0, vertically_centered)), shortened, font=font,\\n fill=label_color)\\n if vertical_label:\\n rotation_direction = 90 if strand == '-' else -90\\n txt = txt.rotate(rotation_direction, expand=True)\\n upper_left[1] += -4 if strand == '-' else 4\\n if horizontal_centering:\\n margin = width - text_width\\n upper_left[0] += margin // 2\\n canvas.paste(txt, (upper_left[0], upper_left[1]), txt)\",\n \"def hbnb():\\n return 'HBNB'\",\n \"def TH(self, full=False):\\n\\t\\treturn (arange(self.thbins + 3*self.thbins*(full==True)) + 0.5) * (pi / 2) / self.thbins\",\n \"def displayNeedle(self,i):\\n #obsolete\\n profbox()\\n modelNodes = slicer.util.getNodes('vtkMRMLModelNode*')\\n for modelNode in modelNodes.values():\\n if modelNode.GetAttribute(\\\"nth\\\")==str(i) and modelNode.GetAttribute(\\\"segmented\\\")=='1' :\\n displayNode = modelNode.GetModelDisplayNode()\\n nVisibility = displayNode.GetVisibility()\\n \\n if nVisibility:\\n displayNode.SliceIntersectionVisibilityOff()\\n displayNode.SetVisibility(0)\\n else:\\n displayNode.SliceIntersectionVisibilityOn()\\n displayNode.SetVisibility(1)\",\n \"def label_joints():\\n side_dict = {'C': 0,\\n 'L': 1,\\n 'R': 2}\\n for jnt in mc.ls(type='joint'):\\n mc.setAttr('{}.side'.format(jnt), side_dict[jnt.split('_')[0]])\\n mc.setAttr('{}.type'.format(jnt), 18)\\n mc.setAttr('{}.otherType'.format(jnt), jnt.split('_')[1], type=\\\"string\\\")\",\n \"def __init__(self, h, d_model, dropout=0.1):\\n super(MultiHeadedAttention, self).__init__()\\n assert d_model % h == 0\\n # We assume d_v always equals d_k\\n self.d_k = d_model // h\\n self.h = h\\n self.linears = clones(nn.Linear(d_model, d_model), 4)\\n self.attn = None\\n self.dropout = nn.Dropout(p=dropout)\",\n \"def selection_correction_method1(tree, scale, h_in, h_out):\\n #h_in = ROOT.TH1D(\\\"h_in\\\", \\\"neutron spectrum with all cuts: inside onset window; Energy [keV]; counts\\\", 50, 0, 25)\\n #h_out = ROOT.TH1D(\\\"h_out\\\", \\\"neutron spectrum with all cuts: outside onset window; Energy [keV]; counts\\\", 50, 0, 25)\\n for event in tree:\\n cut = [0, 0]\\n S15_ch = i_channel(0, event)\\n bpm_ch = i_channel(4, event)\\n RT = event.DD_Rise[S15_ch]\\n S15_w2 = event.DD_AmplADU[S15_ch]\\n onset = event.DD_Rise10pct[S15_ch]\\n if cut[0]==0:\\n # first cut: for inside onset window\\n # if event passes the first cuts\\n if S15_w2>1000 and RT>1.1 and RT<1.51 and onset>39 and onset<47:\\n # loop over the pmt channel numbers to calculate the time of flight: time bd - time bpm\\n for n_channel in range(5, 16):\\n pmt_i = i_channel(n_channel, event)\\n cfd_pmt = event.cfdPulse_CFDNS[pmt_i]\\n cfd_bpm = event.cfdPulse_CFDNS[bpm_ch]\\n # calculation of the time of flight\\n tof = (cfd_pmt-cfd_bpm)%400\\n #cut on tof: time of flight of the neutron\\n if tof<335 and tof>295:\\n energy2 = S15_w2*scale\\n # fill histogram inside onset window\\n h_in.Fill(energy2)\\n cut[0]=1\\n break\\n if cut[1]==0:\\n if S15_w2>1000 and RT<1.51 and RT>1.1 and ((onset<36 and onset>15) or (onset>50 and onset<=110)):\\n for n_channel in range(5, 16):\\n pmt_i = i_channel(n_channel, event)\\n cfd_pmt = event.cfdPulse_CFDNS[pmt_i]\\n cfd_bpm = event.cfdPulse_CFDNS[bpm_ch]\\n tof = (cfd_pmt-cfd_bpm)%400\\n if tof<335 and tof>295:\\n energy2 = S15_w2*scale\\n h_out.Fill(energy2)\\n cut[1]=1\\n break\\n return h_in, h_out\",\n \"def split(self, frame, y):\\n return super(H2OStratifiedKFold, self).split(frame, y)\",\n \"def hbnb():\\n return \\\"HBNB\\\"\",\n \"def hbnb():\\n return \\\"HBNB\\\"\",\n \"def label(self):\\r\\n raise NotImplementedError\",\n \"def width_h_invis(self):\\n if m_higgs > 2.0 * self.mx:\\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\\n\\n val = (\\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\\n / (8.0 * m_higgs**2 * np.pi)\\n ).real\\n\\n assert val >= 0\\n\\n return val\\n else:\\n return 0.0\",\n \"def J_J(h):\\n\\n h = MTS(h)\\n hdot = h.dot\\n J_𝒥 = 0.5j * 𝔇inverseLaplacianinverse(\\n 0.125 * (3 * h * hdot.bar.ethbar - 3 * hdot * h.bar.ethbar + hdot.bar * h.ethbar - h.bar * hdot.ethbar).eth.im\\n ).ethbar.ethbar\\n\\n return J_𝒥\",\n \"def _extract_vanHove(g,j,count_cut,wind):\\n count = g[fd('step',j)]['y']['disp_count'][:]\\n # get better statistics\\n count += g[fd('step',j)]['x']['disp_count'][:]\\n # count += count[::-1]\\n\\n edges = g[fd('step',j)]['y']['disp_edges'][:]\\n \\n edges = np.array([np.mean(edges[j:j+wind]) for j in range(0,len(count)-wind,wind)])\\n count = np.array([np.sum(count[j:j+wind]) for j in range(0,len(count)-wind,wind)])\\n\\n x_lim = (np.min(edges),np.max(edges))\\n\\n edges = edges[count>count_cut]\\n count = count[count>count_cut]\\n\\n return edges,count,x_lim\"\n]"},"negative_scores":{"kind":"list like","value":["0.5253033","0.5244755","0.5173087","0.51096773","0.5103869","0.50852627","0.5053167","0.4967331","0.49329308","0.4927268","0.4886447","0.4883699","0.48704627","0.48665786","0.48657504","0.48568624","0.484965","0.48423848","0.48153552","0.4808797","0.47966635","0.47905484","0.47897264","0.4789602","0.47883236","0.4778537","0.47747788","0.47747788","0.47688013","0.47625986","0.47623906","0.47590986","0.47565174","0.47555858","0.47543168","0.47531363","0.47462907","0.47368893","0.47368893","0.4735266","0.47266668","0.47257203","0.47236973","0.47236973","0.4721428","0.47145838","0.47108462","0.46985096","0.46970034","0.46970034","0.46903425","0.4678745","0.46785632","0.46737352","0.46721482","0.46716332","0.4668553","0.46664682","0.46631366","0.4650076","0.4649602","0.46484134","0.46478298","0.46409085","0.46390003","0.4636259","0.46301612","0.46296418","0.46274868","0.46253723","0.46242732","0.4620448","0.4620207","0.46172926","0.4612796","0.46107337","0.46043545","0.45970452","0.45920217","0.45911533","0.45852852","0.45762974","0.45722124","0.45691916","0.4559683","0.4556273","0.455437","0.45497307","0.45415783","0.45405617","0.45398763","0.45394397","0.45376632","0.45373675","0.45241743","0.45241743","0.45232397","0.4519799","0.45189357","0.45187494"],"string":"[\n \"0.5253033\",\n \"0.5244755\",\n \"0.5173087\",\n \"0.51096773\",\n \"0.5103869\",\n \"0.50852627\",\n \"0.5053167\",\n \"0.4967331\",\n \"0.49329308\",\n \"0.4927268\",\n \"0.4886447\",\n \"0.4883699\",\n \"0.48704627\",\n \"0.48665786\",\n \"0.48657504\",\n \"0.48568624\",\n \"0.484965\",\n \"0.48423848\",\n \"0.48153552\",\n \"0.4808797\",\n \"0.47966635\",\n \"0.47905484\",\n \"0.47897264\",\n \"0.4789602\",\n \"0.47883236\",\n \"0.4778537\",\n \"0.47747788\",\n \"0.47747788\",\n \"0.47688013\",\n \"0.47625986\",\n \"0.47623906\",\n \"0.47590986\",\n \"0.47565174\",\n \"0.47555858\",\n \"0.47543168\",\n \"0.47531363\",\n \"0.47462907\",\n \"0.47368893\",\n \"0.47368893\",\n \"0.4735266\",\n \"0.47266668\",\n \"0.47257203\",\n \"0.47236973\",\n \"0.47236973\",\n \"0.4721428\",\n \"0.47145838\",\n \"0.47108462\",\n \"0.46985096\",\n \"0.46970034\",\n \"0.46970034\",\n \"0.46903425\",\n \"0.4678745\",\n \"0.46785632\",\n \"0.46737352\",\n \"0.46721482\",\n \"0.46716332\",\n \"0.4668553\",\n \"0.46664682\",\n \"0.46631366\",\n \"0.4650076\",\n \"0.4649602\",\n \"0.46484134\",\n \"0.46478298\",\n \"0.46409085\",\n \"0.46390003\",\n \"0.4636259\",\n \"0.46301612\",\n \"0.46296418\",\n \"0.46274868\",\n \"0.46253723\",\n \"0.46242732\",\n \"0.4620448\",\n \"0.4620207\",\n \"0.46172926\",\n \"0.4612796\",\n \"0.46107337\",\n \"0.46043545\",\n \"0.45970452\",\n \"0.45920217\",\n \"0.45911533\",\n \"0.45852852\",\n \"0.45762974\",\n \"0.45722124\",\n \"0.45691916\",\n \"0.4559683\",\n \"0.4556273\",\n \"0.455437\",\n \"0.45497307\",\n \"0.45415783\",\n \"0.45405617\",\n \"0.45398763\",\n \"0.45394397\",\n \"0.45376632\",\n \"0.45373675\",\n \"0.45241743\",\n \"0.45241743\",\n \"0.45232397\",\n \"0.4519799\",\n \"0.45189357\",\n \"0.45187494\"\n]"},"document_score":{"kind":"string","value":"0.5767312"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8790,"cells":{"query":{"kind":"string","value":"make test label for combinations helper with two simple children."},"document":{"kind":"string","value":"def _make_combinationsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'combinationsTest'\n\n \n\n return \"\"\"\n combgen(\n [(2)(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\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 test_sums():\n assert label_parent(1, 2) == 3\n assert label_parent (1, 4) == 8\n # Should ignore arg order\n assert label_parent(4, 1) == 8","def _make_partitionsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'partitionsTest'\n\n \n\n return \"\"\"\n partgen(\n [(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\n )\"\"\"","def test_recipe_nutrition_label_widget(self):\n pass","def test_select_label(self):\n title = ('Transportation Challenges Limit Education Choices for '\n 'Denver Parents')\n summary = \"\"\"\n Many families in the Denver metro area use public\n transportation instead of a school bus because for them, a\n quality education is worth hours of daily commuting. Colorado's\n school choice program is meant to foster educational equity,\n but the families who benefit most are those who have time and\n money to travel. Low-income families are often left in a lurch.\n \"\"\"\n byline = \"Mile High Connects\"\n story = create_story(title=title, summary=summary, byline=byline)\n layout = SectionLayout.objects.get(sectionlayouttranslation__name=\"Side by Side\")\n section1 = create_section(title=\"Test Section 1\", story=story, layout=layout)\n section2 = create_section(title=\"Test Section 2\", story=story, layout=layout)\n form = SectionRelationAdminForm()\n choices_list = list(form.fields['parent'].widget.choices)\n self.assertIn(story.title, choices_list[1][1])\n self.assertIn(story.title, choices_list[2][1])","def setUp(self):\n\n singleLabels = linkoCreate.Linkograph(\n [({'A'}, set(), {1,2,3}),\n ({'D'}, {0}, {3,4}),\n ({'A'}, {0}, {4}),\n ({'C'}, {0,1}, {4}),\n ({'A'}, {1,2,3}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_2 = linkoCreate.Linkograph(\n [({'A'}, set(), {1,2}),\n ({'D'}, {0}, set()),\n ({'A'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_1 = linkoCreate.Linkograph(\n [({'A'}, set(), {1}),\n ({'D'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_0 = linkoCreate.Linkograph(\n [({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko1_2 = linkoCreate.Linkograph(\n [({'D'}, set(), set()),\n ({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko1_1 = linkoCreate.Linkograph(\n [({'D'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n trivialLinkograph = linkoCreate.Linkograph(\n [], ['A', 'B', 'C', 'D'])\n\n\n singleSubLinko1_4 = linkoCreate.Linkograph(\n [({'D'}, set(), {2,3}),\n ({'A'}, set(), {3}),\n ({'C'}, {0}, {3}),\n ({'A'}, {0,1,2}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko2_4 = linkoCreate.Linkograph(\n [({'A'}, set(), {2}),\n ({'C'}, set(), {2}),\n ({'A'}, {0,1}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko3_4 = linkoCreate.Linkograph(\n [({'C'}, set(), {1}),\n ({'A'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko4_4 = linkoCreate.Linkograph(\n [({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n simpleLinko = linkoCreate.Linkograph(\n [({'A', 'B', 'C'}, set(), {1,2,3}),\n ({'D'}, {0}, {3,4}),\n ({'A'}, {0}, {4}),\n ({'B', 'C'}, {0,1}, {4}),\n ({'A'}, {1,2,3}, set())],\n ['A', 'B', 'C', 'D'])\n\n if self.id().split('.')[-1] == 'test_createSubLinkographWithoutCommands':\n self.testParams = [\n {'linko': singleLabels,\n 'lowerBound': None,\n 'upperBound': None,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 5,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': -1,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': None,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 1,\n 'ExpectedLinkograph': singleSubLinko0_1},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 0,\n 'ExpectedLinkograph': singleSubLinko0_0},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': -1,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko1_2},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 1,\n 'ExpectedLinkograph': singleSubLinko1_1},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 0,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': -1,\n 'upperBound': -1,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko1_4},\n\n {'linko': singleLabels,\n 'lowerBound': 2,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko2_4},\n\n {'linko': singleLabels,\n 'lowerBound': 3,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko3_4},\n\n {'linko': singleLabels,\n 'lowerBound': 4,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko4_4},\n\n ]","def test_labels(self):\n self.compliance_tester.test_labels(self.oi)","def test_parent_label(self):\n l = self.d.label(1)\n l2 = self.d.label(31405)\n\n self.assertTrue(l.parent_label is None)\n self.assertTrue(l2 in l.sublabels)\n self.assertEqual(l2.parent_label, l)","def test_simple(self):\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n content = '((h,p)hp:1,g)hpg;'\n self._do_test(content, exp)\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP,\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n self._do_test(content, exp)","def test_label(self):\n xs = t.Label(t.Exactly(\"x\"), 'CustomLabel')\n self.assertEqual(writePython(xs),\n dd(\"\"\"\n def _G_label_1():\n _G_exactly_2, lastError = self.exactly('x')\n self.considerError(lastError, None)\n return (_G_exactly_2, self.currentError)\n _G_label_3, lastError = self.label(_G_label_1, \"CustomLabel\")\n self.considerError(lastError, None)\n _G_label_3\n \"\"\"))","def addLabels(t):\n if not t.label:\n t.label = \"\".join([choice(\"abcdefghijklmnopqrstuvwxyz\") for i in range(4)])\n for r,w in t.children:\n addLabels(r)","def test_general_subset_level():\n pass","def _make_simple_comb_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n simple_strs = []\n\n for cp in cps:\n print(cp)\n simple_strs.append(_make_simple_label([cp]))\n\n label = 'combgen([(%d)]' % len(cps)\n for s in simple_strs:\n label += ' %s ' % s\n label += ')'\n return label","def _generateLabelAndName(self, obj, **args):\n result = []\n label = self._generateLabel(obj, **args)\n name = self._generateName(obj, **args)\n result.extend(label)\n if not len(label):\n result.extend(name)\n elif len(name) and name[0].strip() != label[0].strip():\n result.extend(name)\n return result","def tests_ti_document_add_label(self):\n super().group_add_label()","def getLabel2(*args):","def getLabel2(*args):","def test_checkboxtextgroup(self):\r\n self.check_group('checkboxtextgroup', 'choice', 'checkbox')","def _generateTableCell2ChildLabel(self, obj, **args):\n result = []\n\n # If this table cell has 2 children and one of them has a\n # 'toggle' action and the other does not, then present this\n # as a checkbox where:\n # 1) we get the checked state from the cell with the 'toggle' action\n # 2) we get the label from the other cell.\n # See Orca bug #376015 for more details.\n #\n if obj.childCount == 2:\n cellOrder = []\n hasToggle = [False, False]\n for i, child in enumerate(obj):\n if self._script.utilities.hasMeaningfulToggleAction(child):\n hasToggle[i] = True\n break\n if hasToggle[0] and not hasToggle[1]:\n cellOrder = [ 1, 0 ]\n elif not hasToggle[0] and hasToggle[1]:\n cellOrder = [ 0, 1 ]\n if cellOrder:\n for i in cellOrder:\n if not hasToggle[i]:\n result.extend(self.generate(obj[i], **args))\n return result","def test_process_label_in_node(self):\n tree = Node(children=[\n Node(\"Defining secret phrase.\", label=['AB', 'a']),\n Node(\"Has secret phrase. Then some other content\", \n label=['AB', 'b'])\n ], label=['AB'])\n t = Terms(tree)\n t.scoped_terms = {\n ('AB',): [Ref(\"secret phrase\", \"AB-a\", (9,22))]\n }\n # Term is defined in the first child\n self.assertEqual([], t.process(tree.children[0]))\n self.assertEqual(1, len(t.process(tree.children[1])))","def get_extra_label(self, label_name: str, hierarchy: List[str]) -> Any:","def test_get_scenarios_expanded(self):\n pass","def test_nested():\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.root.metadata.choose(lambda m: (m[\"name\"], m.uid))))\n assert \"type\" in res # from conditions\n assert \"reason\" in res # from conditions\n assert \"name\" in res # from metadata\n assert \"uid\" in res # from metadata","def _make_simple_label(chain_parts):\n \n if not _select_simple_chainparts(chain_parts):\n msg = 'Jet Configuration error: '\\\n 'chain fails substring selection: not \"simple\" '\n\n raise NotImplementedError(msg)\n \n label = 'simple(['\n for cp in chain_parts:\n smcstr = str(cp['smc'])\n jvtstr = str(cp['jvt'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr: # Run 2 chains have \"INF\" in the SMC substring\n condition_str += ',%s)' % smcstr.replace('INF','')\n elif jvtstr:\n condition_str += ',%s)' % jvtstr\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def test_nested_sequence(self):\n\n self.taxon_tester('Apis mellifera')\n self.taxon_tester('Apis')\n self.taxon_tester('Apini')\n self.taxon_tester('Apinae')\n # Apidae at 5680 species is a struggle\n self.taxon_tester('Apidae')\n if False:\n # Apoidea: 19566 takes 223 seconds\n self.taxon_tester('Apoidea')\n # Aculeata fails after 339 seconds\n self.taxon_tester('Aculeata')\n self.taxon_tester('Apocrita')\n self.taxon_tester('Hymenoptera')\n self.taxon_tester('Endopterygota')\n self.taxon_tester('Neoptera')\n self.taxon_tester('Pterygota')\n self.taxon_tester('Dicondylia')\n self.taxon_tester('Insecta')\n self.taxon_tester('Hexapoda')\n self.taxon_tester('Pancrustacea')\n self.taxon_tester('Mandibulata')\n self.taxon_tester('Arthropoda')\n self.taxon_tester('Panarthropoda')\n self.taxon_tester('Ecdysozoa')\n self.taxon_tester('Protostomia')\n self.taxon_tester('Bilateria')\n self.taxon_tester('Eumetazoa')\n self.taxon_tester('Metazoa')\n self.taxon_tester('Holozoa')\n self.taxon_tester('Opisthokonta')\n self.taxon_tester('Eukaryota')","def test_bootstrap_support_labeled(self):\r\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\r\n \"\"\"\r\n /-------.5 /-a\r\n ---1| \\-b\r\n \\------.5 /-c\r\n \\-d\r\n \"\"\"\r\n t1 = parse_newick('((a:6,b:8.2)hi:2,(c:1,d:2):7);') # same structure\r\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\r\n new_master, bootstraps = tc.bootstrap_support(master_tree, [t1, t2])\r\n expected = dict([('ab', .5), ('cd', .5), ('rt', 1.0)])\r\n self.assertDictEqual(bootstraps, expected)","def test_title(names):","def test_verbose_name_group(self): \n field_verboses = {\n \"title\": \"Название группы\",\n \"slug\": \"Слаг\",\n \"description\": \"Описание группы\",\n }\n for value, expected in field_verboses.items():\n with self.subTest(value=value):\n self.assertEqual(self.group._meta.get_field(value).verbose_name, expected)","def plugin_second_label():\n return \"second\"","def test_product_labels(self):\n\n prd = Product.objects.get(id=1)\n # label name\n label_name = prd._meta.get_field('name').verbose_name\n self.assertEqual(label_name, 'name')\n # label description\n label_name = prd._meta.get_field('description').verbose_name\n self.assertEqual(label_name, 'description')\n # label nutrition_grade\n label_name = prd._meta.get_field('nutrition_grade').name\n self.assertEqual(label_name, 'nutrition_grade')\n # label barcode\n label_name = prd._meta.get_field('barcode').verbose_name\n self.assertEqual(label_name, 'barcode')\n # label url\n label_name = prd._meta.get_field('url').verbose_name\n self.assertEqual(label_name, 'url')\n # label url_pic\n label_name = prd._meta.get_field('url_pic').name\n self.assertEqual(label_name, 'url_pic')\n # label store\n label_name = prd._meta.get_field('store').verbose_name\n self.assertEqual(label_name, 'store')\n # label prd_cat\n label_name = prd._meta.get_field('prd_cat').name\n self.assertEqual(label_name, 'prd_cat')\n # label fat\n label_name = prd._meta.get_field('fat').verbose_name\n self.assertEqual(label_name, 'fat')\n # label saturated_fat\n label_name = prd._meta.get_field('saturated_fat').name\n self.assertEqual(label_name, 'saturated_fat')\n # label sugar\n label_name = prd._meta.get_field('sugar').verbose_name\n self.assertEqual(label_name, 'sugar')\n # label salt\n label_name = prd._meta.get_field('salt').verbose_name\n self.assertEqual(label_name, 'salt')","def is_test(self):\r\n return self.has_label('tests')","def setUp(self):\n\n\n # InverseLabeling\n invLabeling0 = {'L0': [0, 1, 2]}\n\n invLabeling1 = {'L0' : [0, 2],\n 'L1' : [1]}\n\n invLabeling2 = {\n 'L0' : [0],\n 'L1' : [1],\n 'L2' : [2]\n }\n\n invLabeling3 = {\n 'L1' : [0, 1],\n 'L2' : [2]\n }\n\n invLabeling4 = {\n 'L0' : [0,1],\n 'L1' : [0],\n 'L2' : [2]\n }\n\n invLabeling5 = {\n 'L0': [0, 1, 2],\n 'L1': []\n }\n \n # Create some ontologies\n ontology0 = {'L0': ['L0']}\n\n ontology1 = {}\n\n ontology2 = {'L0': ['L1']}\n\n ontology3 = {'L0': ['L1', 'L2'],\n 'L1': ['L2'],\n 'L2': ['L0']}\n\n if self.id().split('.')[-1] == 'test_createLinkograph':\n self.testParams = [\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology0,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1, 2}),\n ({'L0'}, {0}, {2}),\n ({'L0'}, {0,1}, set())] \n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology1,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology2,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology0,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {2}),\n ({'L1'}, set(), set()),\n ({'L0'}, {0}, set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology1,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L1'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology2,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1}),\n ({'L1'}, {0}, set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1}),\n ({'L1'}, {0}, set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling2,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1,2}),\n ({'L1'}, {0}, {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling3,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L1'}, set(), {2}),\n ({'L1'}, set(), {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling4,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0', 'L1'}, set(), {2}),\n ({'L0'}, set(), {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling5,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n ]","def test_mutual_exclusivity_of_labels(self, tmpdir, docker_tasker, labels, expected_fail):\n runner = mock_env(tmpdir, docker_tasker, labels=labels)\n if expected_fail:\n with pytest.raises(PluginFailedException) as e:\n runner.run()\n assert 'only one of labels' in str(e.value)\n else:\n runner.run()","def helper_label(helper):\n return helper.info","def test_Tree():","def test_col_data_label_with_attrs(self):\n help_tag = 'span'\n help_text_br = False\n names = ('first', 'billing_address_1')\n attrs = {'style': 'width: 10rem; display: inline-block'}\n label_attrs = {name: attrs for name in names}\n txt = '{}=\"{}\"'.format(*list(attrs.items())[0])\n expected = [''.format(txt)]\n expected.append(''.format(txt))\n actual = []\n for name in names:\n field = self.form.fields[name]\n response = self.form.collect_col_data(name, field, help_tag, help_text_br, label_attrs)\n actual.append(response.get('label'))\n\n for expect, got in zip(expected, actual):\n self.assertEqual(expect, got)","def test_help_text_group(self): \n field_help_text = {\n \"title\": \"Дайте назание группе\",\n \"slug\": ('Укажите адрес для группы. Используйте '\n 'только латиницу, цифры, дефисы и знаки '\n 'подчёркивания'),\n } \n for value, expected in field_help_text.items():\n with self.subTest(value=value):\n self.assertEqual(self.group._meta.get_field(value).help_text, expected)","def test_label_choices(self):\n test_classes = (\n (0, 'No'),\n (1, 'Yes')\n )\n\n form = SingleLabelClassifierForm(classes=test_classes)\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)\n\n form = MultiLabelClassifierForm(classes=test_classes)\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)","def test_filter_tree(self):\r\n actual = [e.Name for e in filter_tree(\r\n self.tree1, ['bbb', 'ccc']).tips()]\r\n #(a_a:10,(b_b:2,c_c:4):5);\r\n expected = [\r\n e.Name for e in DndParser(\r\n \"((bbb:2,ccc:4));\",\r\n PhyloNode).tips(\r\n )]\r\n self.assertEqual(actual, expected)\r\n\r\n actual = [e.Name for e in filter_tree(\r\n self.tree2, ['bbb', 'ccc']).tips()]\r\n #(a_a:10,(b_b:2,c_c:4):5);\r\n expected = [\r\n e.Name for e in DndParser(\r\n \"(('bbb':2,ccc:4));\",\r\n PhyloNode).tips(\r\n )]\r\n self.assertEqual(actual, expected)","def test_radiotextgroup(self):\r\n self.check_group('radiotextgroup', 'choice', 'radio')","def gen_test_case_combination(self):\n\n cases = '\\n'\n\n binary_ops = list(self.BINARY_OPS)\n binary_ops.reverse()\n for op1 in self.BINARY_OPS:\n for op2 in binary_ops:\n\n result = []\n ret = IntOp.binary_op(op2, '0', '1', self.lane_width)\n ret = IntOp.binary_op(op1, ret, '2', self.lane_width)\n result.append(ret)\n\n cases += '\\n' + str(AssertReturn('{lane_type}.{op1}-{lane_type}.{op2}'.format(lane_type=self.LANE_TYPE, op1=op1, op2=op2),\n [SIMD.v128_const('0', self.LANE_TYPE),\n SIMD.v128_const('1', self.LANE_TYPE),\n SIMD.v128_const('2', self.LANE_TYPE)],\n SIMD.v128_const(result, self.LANE_TYPE)))\n cases += '\\n'\n return cases","def test_multiple_task_groups_dag(\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\n ):\n (\n dag,\n group1,\n group2,\n group3,\n (\n group1_emp1,\n group1_emp2,\n group1_emp3,\n group2_emp1,\n group2_emp2,\n group2_emp3,\n group2_op1,\n group2_op2,\n group3_emp1,\n group3_emp2,\n group3_emp3,\n emp_in1,\n emp_in2,\n emp_in3,\n emp_in4,\n emp_out1,\n emp_out2,\n emp_out3,\n emp_out4,\n op_in1,\n op_out1,\n ),\n ) = test_multiple_taskgroups_dag\n\n group1_emp1 >> Label(\"label group1.group1_emp1 <=> group1.group1_emp2\") >> group1_emp3\n\n emp_in1 >> group1\n emp_in2 >> Label(\"label emp_in2 <=> group1\") >> group1\n [emp_in3, emp_in4] >> Label(\"label emp_in3/emp_in4 <=> group1\") >> group1\n XComArg(op_in1, \"test_key\") >> Label(\"label op_in1 <=> group1\") >> group1\n\n (\n [group2_emp1, group2_emp2]\n >> Label(\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\")\n >> group2_emp3\n )\n (\n group2_emp1\n >> Label(\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\")\n >> [group2_emp2, group2_emp3]\n )\n group2_emp3 >> Label(\"label group2.group2_emp3 <=> group3\") >> group3\n\n (\n XComArg(group2_op1, \"test_key\")\n >> Label(\"label group2.group2_op1 <=> group2.group2_op2\")\n >> XComArg(group2_op2, \"test_key\")\n )\n XComArg(group2_op2, \"test_key\") >> Label(\"label group2.group2_op2 <=> group3\") >> group3\n\n group3 >> emp_out1\n group3 >> Label(\"label group3 <=> emp_out2\") >> emp_out2\n group3 >> Label(\"label group3 <=> emp_out3/emp_out4\") >> [emp_out3, emp_out4]\n group3 >> Label(\"label group3 <=> op_out1\") >> XComArg(op_out1, \"test_key\")\n\n group1 >> Label(\"label group1 <=> group2\") >> group2\n\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)","def test_issue_create_label(self):\n pass","def test__create_label_w_no_ent_id(ruler: SpaczzRuler) -> None:\n assert ruler._create_label(\"TEST\", None) == \"TEST\"","def test_grouping(self):\n s = self.create(ComponentItem, UML.Component)\n uc1 = self.create(UseCaseItem, UML.UseCase)\n uc2 = self.create(UseCaseItem, UML.UseCase)\n\n self.group(s, uc1)\n assert 1 == len(uc1.subject.subject)\n self.group(s, uc2)\n assert 1 == len(uc2.subject.subject)\n\n # Classifier.useCase is not navigable to UseCase\n # self.assertEqual(2, len(s.subject.useCase))","def setLabel2(*args):","def tests_ti_document_get_label(self):\n super().group_get_label()","def test_render_label(self):\n label = self.block.meta.label\n self.assertEqual(label, 'Google Calendar', 'The labels are not the same')","def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\n\n oid = obj.attrib.get('id')\n klass = obj.attrib.get('class')\n\n # \"Don't care\" special case\n if klass in widgets_ignored:\n return\n for suffix in widgets_suffixignored:\n if klass[-len(suffix):] == suffix:\n return\n\n # Widgets usual do not strictly require a label, i.e. a labelled parent\n # is enough for context, but some do always need one.\n requires_label = klass in widgets_needlabel\n\n if oid is not None:\n # Check that ids are unique\n if oid in ids_dup:\n if ids[oid] == obj:\n # We are the first, warn\n duplicates = tree.findall(\".//object[@id='\" + oid + \"']\")\n err(filename, tree, obj, \"duplicate-id\", \"has the same id as other elements \" + elms_names_lines(duplicates))\n\n # Check label-for and their dual labelled-by\n label_for = check_rels(filename, tree, root, obj, \"label-for\", \"labelled-by\")\n\n # Check labelled-by and its dual label-for\n labelled_by = check_rels(filename, tree, root, obj, \"labelled-by\", \"label-for\")\n\n # Check label-for and their dual labelled-by\n description_for = check_rels(filename, tree, root, obj, \"description-for\", \"described-by\")\n\n # Check labelled-by and its dual label-for\n described_by = check_rels(filename, tree, root, obj, \"described-by\", \"description-for\")\n\n # Should have only one label\n if len(labelled_by) >= 1:\n if oid in mnemonic_for_elm:\n warn(filename, tree, obj, \"labelled-by-and-mnemonic\",\n \"has both a mnemonic \" + elm_name_line(mnemonic_for_elm[oid][0]) + \"and labelled-by relation\")\n if len(labelled_by) > 1:\n warn(filename, tree, obj, \"multiple-labelled-by\", \"has multiple labelled-by relations\")\n if oid in label_for_elm:\n if len(label_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-label-for\", \"is referenced by multiple label-for \" + elms_names_lines(label_for_elm[oid]))\n if oid in mnemonic_for_elm:\n if len(mnemonic_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-mnemonic\", \"is referenced by multiple mnemonic_widget \" + elms_names_lines(mnemonic_for_elm[oid]))\n\n # Check member-of\n member_of = check_rels(filename, tree, root, obj, \"member-of\")\n\n # Labels special case\n if klass in widgets_labels:\n properties = check_props(filename, tree, root, obj, \"mnemonic_widget\") + \\\n check_props(filename, tree, root, obj, \"mnemonic-widget\")\n if len(properties) > 1:\n err(filename, tree, obj, \"multiple-mnemonic\", \"has multiple mnemonic_widgets properties\"\n \"%s\" % elms_lines(properties))\n\n # Emit orphaning warnings\n if warn_orphan_labels or orphan_widgets:\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\n\n # We are done with the label\n return\n\n # Not a label, will perhaps need one\n\n # Emit orphaning warnings\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)","def get_label(self, hierarchy: List[str]) -> Any:","def test_first_level_from_bids_no_duplicate_sub_labels(bids_dataset):\n models, *_ = first_level_from_bids(\n dataset_path=bids_dataset,\n task_label='main',\n sub_labels=[\"01\", \"01\"],\n space_label='MNI',\n img_filters=[('desc', 'preproc')],\n slice_time_ref=None,\n )\n\n assert len(models) == 1","def test_add_empty_nodes_with_label_when_splitting(self):\n print \"----- test_add_empty_nodes_with_label_when_splitting -----\"\n sel_axis = (lambda axis: axis)\n \n #create tree, first node splits in x direction\n tree = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\n kdtree.visualize(tree)\n \n point_left = [0.4, 0.5]\n tree.split2(point_left, axis = 0)\n kdtree.visualize(tree)\n \n point1 = [0.3, 0.5]\n found_node = tree.get_path_to_leaf(point1)[-1]\n correct_node1 = 3\n self.assertEqual(found_node.label, correct_node1, \"Not correct node found\")\n \n point_right = [0.6, 0.5]\n tree.split2(point_right, axis = 1)\n kdtree.visualize(tree)\n \n point2 = [0.6, 0.7]\n found_node = tree.get_path_to_leaf(point2)[-1]\n correct_node2 = 6\n self.assertEqual(found_node.label, correct_node2, \"Not correct node found\")\n \n print \"----- end: test_add_empty_nodes_with_label_when_splitting -----\"","def test_render_value_label(self):\n self.check_html(\n self.widget(choices=self.beatles),\n \"beatles\",\n [\"John\"],\n html=(\n \"\"\"\"\"\"\n ),\n )","def test_issue_add_label(self):\n pass","def test_create_metering_label(self):\r\n resource = 'metering_label'\r\n cmd = metering.CreateMeteringLabel(\r\n test_cli20.MyApp(sys.stdout), None)\r\n name = 'my label'\r\n myid = 'myid'\r\n description = 'my description'\r\n args = [name, '--description', description, '--shared']\r\n position_names = ['name', 'description', 'shared']\r\n position_values = [name, description, True]\r\n self._test_create_resource(resource, cmd, name, myid, args,\r\n position_names, position_values)","def getLabel(*args):","def getLabel(*args):","def getLabel(*args):","def _make_vbenf_label(chain_parts):\n\n # toy label for development: run simple and dijet independently.\n # simple makes Et cuts on two jets. Independently (sharing possible)\n # of jets choosean by simple, the dijet\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n assert scenario.startswith('vbenf')\n args = _args_from_scenario(scenario)\n if not args:\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \n arg_res = [\n re.compile(r'(?P\\d*)(?Pfbet)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pmass)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pet)(?P\\d*)'),\n ]\n\n defaults = {\n 'et': ('101', 'inf'),\n 'mass': ('800', 'inf'),\n 'fbet': ('501', 'inf'),\n }\n\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n and\n (\n []\n simple\n (\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\n )\n combgen\n (\n [(10et, 0eta320)]\n dijet\n (\n [(%(masslo).0fdjmass, 26djdphi)]\n ) \n simple\n (\n [(10et, 0eta320)(20et, 0eta320)]\n )\n )\n )\"\"\" % argvals","def testFormatLabelAndValue(self):\n\n self.assertEqual('Abc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 1))\n self.assertEqual('ABc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 2))\n self.assertEqual('ABC: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 4))","def test_recipe_nutrition_label_image(self):\n pass","def test_label_10_targets_with_a_b_c_(self):\r\n user_input = '[{\"a\":\"target1\"}, \\\r\n {\"b\":\"target2\"},{\"c\":\"target3\"},{\"a\":\"target4\"},{\"b\":\"target5\"}, \\\r\n {\"c\":\"target6\"}, {\"a\":\"target7\"},{\"b\":\"target8\"},{\"c\":\"target9\"}, \\\r\n {\"a\":\"target10\"}]'\r\n correct_answer = [\r\n {\r\n 'draggables': ['a'],\r\n 'targets': ['target1', 'target4', 'target7', 'target10'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['b'],\r\n 'targets': ['target2', 'target5', 'target8'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['c'],\r\n 'targets': ['target3', 'target6', 'target9'],\r\n 'rule': 'unordered_equal'\r\n }\r\n ]\r\n self.assertTrue(draganddrop.grade(user_input, correct_answer))","def test_label_10_targets_with_a_b_c_multiple(self):\r\n user_input = '[{\"a\":\"target1\"}, \\\r\n {\"b\":\"target2\"},{\"c\":\"target3\"},{\"b\":\"target5\"}, \\\r\n {\"c\":\"target6\"}, {\"a\":\"target7\"},{\"b\":\"target8\"},{\"c\":\"target9\"}, \\\r\n {\"a\":\"target1\"}]'\r\n correct_answer = [\r\n {\r\n 'draggables': ['a', 'a', 'a'],\r\n 'targets': ['target1', 'target4', 'target7', 'target10'],\r\n 'rule': 'anyof+number'\r\n },\r\n {\r\n 'draggables': ['b', 'b', 'b'],\r\n 'targets': ['target2', 'target5', 'target8'],\r\n 'rule': 'anyof+number'\r\n },\r\n {\r\n 'draggables': ['c', 'c', 'c'],\r\n 'targets': ['target3', 'target6', 'target9'],\r\n 'rule': 'anyof+number'\r\n }\r\n ]\r\n self.assertTrue(draganddrop.grade(user_input, correct_answer))","def test_subsystems(self):\n pass","def test_multiply_some_type_links():","def test_build_match_tree_with_pairs():\n abbreviation_list = [[\"ELIF\", \"ELI.\"], [\"ELSE\", \"E.\"]]\n expected_tree = {\"E\": {\"L\": {\"I\": {\"F\": \"ELI.\"}, \"S\": {\"E\": \"E.\"}}}}\n tree = build_match_tree(abbreviation_list)\n assert repr(tree) == repr(expected_tree)","def check_a11y_relation(filename, tree):\n global widgets_ignored, ids, label_for_elm, labelled_by_elm, description_for_elm, described_by_elm, mnemonic_for_elm\n\n def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\n \"\"\"\n Check one element, knowing that orphan_labels/widgets tell whether\n there are orphan labels and widgets within orphan_root\n \"\"\"\n\n oid = obj.attrib.get('id')\n klass = obj.attrib.get('class')\n\n # \"Don't care\" special case\n if klass in widgets_ignored:\n return\n for suffix in widgets_suffixignored:\n if klass[-len(suffix):] == suffix:\n return\n\n # Widgets usual do not strictly require a label, i.e. a labelled parent\n # is enough for context, but some do always need one.\n requires_label = klass in widgets_needlabel\n\n if oid is not None:\n # Check that ids are unique\n if oid in ids_dup:\n if ids[oid] == obj:\n # We are the first, warn\n duplicates = tree.findall(\".//object[@id='\" + oid + \"']\")\n err(filename, tree, obj, \"duplicate-id\", \"has the same id as other elements \" + elms_names_lines(duplicates))\n\n # Check label-for and their dual labelled-by\n label_for = check_rels(filename, tree, root, obj, \"label-for\", \"labelled-by\")\n\n # Check labelled-by and its dual label-for\n labelled_by = check_rels(filename, tree, root, obj, \"labelled-by\", \"label-for\")\n\n # Check label-for and their dual labelled-by\n description_for = check_rels(filename, tree, root, obj, \"description-for\", \"described-by\")\n\n # Check labelled-by and its dual label-for\n described_by = check_rels(filename, tree, root, obj, \"described-by\", \"description-for\")\n\n # Should have only one label\n if len(labelled_by) >= 1:\n if oid in mnemonic_for_elm:\n warn(filename, tree, obj, \"labelled-by-and-mnemonic\",\n \"has both a mnemonic \" + elm_name_line(mnemonic_for_elm[oid][0]) + \"and labelled-by relation\")\n if len(labelled_by) > 1:\n warn(filename, tree, obj, \"multiple-labelled-by\", \"has multiple labelled-by relations\")\n if oid in label_for_elm:\n if len(label_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-label-for\", \"is referenced by multiple label-for \" + elms_names_lines(label_for_elm[oid]))\n if oid in mnemonic_for_elm:\n if len(mnemonic_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-mnemonic\", \"is referenced by multiple mnemonic_widget \" + elms_names_lines(mnemonic_for_elm[oid]))\n\n # Check member-of\n member_of = check_rels(filename, tree, root, obj, \"member-of\")\n\n # Labels special case\n if klass in widgets_labels:\n properties = check_props(filename, tree, root, obj, \"mnemonic_widget\") + \\\n check_props(filename, tree, root, obj, \"mnemonic-widget\")\n if len(properties) > 1:\n err(filename, tree, obj, \"multiple-mnemonic\", \"has multiple mnemonic_widgets properties\"\n \"%s\" % elms_lines(properties))\n\n # Emit orphaning warnings\n if warn_orphan_labels or orphan_widgets:\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\n\n # We are done with the label\n return\n\n # Not a label, will perhaps need one\n\n # Emit orphaning warnings\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\n\n root = tree.getroot()\n\n # Flush ids and relations from previous files\n ids = {}\n ids_dup = {}\n labelled_by_elm = {}\n label_for_elm = {}\n described_by_elm = {}\n description_for_elm = {}\n mnemonic_for_elm = {}\n\n # First pass to get links into hash tables, no warning, just record duplicates\n for obj in root.iter('object'):\n oid = obj.attrib.get('id')\n if oid is not None:\n if oid not in ids:\n ids[oid] = obj\n else:\n ids_dup[oid] = True\n\n labelled_by = obj.findall(\"accessibility/relation[@type='labelled-by']\")\n labelled_by += obj.findall(\"accessibility/relation[@name='labelled-by']\")\n for rel in labelled_by:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in labelled_by_elm:\n labelled_by_elm[target] = [ obj ]\n else:\n labelled_by_elm[target].append(obj)\n\n label_for = obj.findall(\"accessibility/relation[@type='label-for']\")\n label_for += obj.findall(\"accessibility/relation[@name='label-for']\")\n for rel in label_for:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in label_for_elm:\n label_for_elm[target] = [ obj ]\n else:\n label_for_elm[target].append(obj)\n\n described_by = obj.findall(\"accessibility/relation[@type='described-by']\")\n described_by += obj.findall(\"accessibility/relation[@name='described-by']\")\n for rel in described_by:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in described_by_elm:\n described_by_elm[target] = [ obj ]\n else:\n described_by_elm[target].append(obj)\n\n description_for = obj.findall(\"accessibility/relation[@type='description-for']\")\n description_for += obj.findall(\"accessibility/relation[@name='description-for']\")\n for rel in description_for:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in description_for_elm:\n description_for_elm[target] = [ obj ]\n else:\n description_for_elm[target].append(obj)\n\n mnemonic_for = obj.findall(\"property[@name='mnemonic_widget']\") + \\\n obj.findall(\"property[@name='mnemonic-widget']\")\n for rel in mnemonic_for:\n target = rel.text\n if target is not None:\n if target not in mnemonic_for_elm:\n mnemonic_for_elm[target] = [ obj ]\n else:\n mnemonic_for_elm[target].append(obj)\n\n # Second pass, recursive depth-first, to be able to efficiently know whether\n # there are orphan labels within a part of the tree.\n def recurse(orphan_root, obj, orphan_labels, orphan_widgets):\n if obj == root or is_labelled_parent(obj):\n orphan_root = obj\n orphan_labels, orphan_widgets = orphan_items(filename, tree, root, obj)\n\n if obj.tag == 'object':\n check_elm(orphan_root, obj, orphan_labels, orphan_widgets)\n\n for o in obj:\n recurse(orphan_root, o, orphan_labels, orphan_widgets)\n\n recurse(root, root, False, False)","def test_stratis_list_default(self):\n for subcommand in [[\"pool\"], [\"filesystem\"], [\"blockdev\"]]:\n for prefix in [[], [\"--propagate\"]]:\n self.assertEqual(RUNNER(prefix + subcommand), 0)","def pytest_can_run_together(item1, item2):","def test_series_in_features(self):\n assert parse_command({'test{{A,B}}': {'depends_on': 'name{{A,B}}'}}) == [\n ('testA', {'depends_on': 'nameA'}), ('testB', {'depends_on': 'nameB'})]","def test_first_level_from_bids_with_subject_labels(bids_dataset):\n warning_message = ('Subject label foo is not present in'\n ' the dataset and cannot be processed')\n with pytest.warns(UserWarning, match=warning_message):\n models, *_ = first_level_from_bids(\n dataset_path=bids_dataset,\n task_label='main',\n sub_labels=[\"foo\", \"01\"],\n space_label='MNI',\n img_filters=[('desc', 'preproc')],\n slice_time_ref=None,\n )\n\n assert models[0].subject_label == '01'","def test_pathop12(self):\n xpb = XPathBuilder()\n # braces not needed\n xp = xpb.foo & (xpb.bar.foo).parenthesize() | xpb.foobar\n exp = '/foo and (/bar/foo) or /foobar'\n self.assertEqual(xp.tostring(), exp)","def test_dummy6(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n xp = xpb.bar | xp\n exp = '/bar'\n self.assertEqual(xp.tostring(), exp)","def test_create_labels(self):\n test_labels = {\"app\": \"my_app\", \"host\": \"examplehost\"}\n labels = pmp.utils.create_labels(test_labels)\n self.assertIsInstance(labels, list)\n self.assertTrue(all([isinstance(x, pmp.LabelPair) for x in labels]))","def test_xdist_and_select_test_by_bdd_label(xdist_runner: AllurePytestRunner):\n\n output = xdist_runner.run_docstring(\"-v\", \"--allure-features=boo\", \"-n1\")\n\n assert_that(output, has_only_testcases(\n has_entry(\n \"fullName\",\n ends_with(\"test_with_feature_boo\")\n )\n ))","def test_nested_condition() -> None:\n\n @argcomb(Or(And(\"a\", \"b\"), And(\"c\", \"d\")))\n def f(a: Any = None, b: Any = None, c: Any = None, d: Any = None) -> None:\n ...\n\n # valid\n f(a=1, b=1)\n f(c=1, d=1)\n f(a=1, b=1, c=1, d=1)\n\n # invalid\n with pytest.raises(InvalidArgumentCombination):\n f(a=1)\n with pytest.raises(InvalidArgumentCombination):\n f(a=1, c=1)\n with pytest.raises(InvalidArgumentCombination):\n f()","def test_abbreviation(self):\n self.assertEqual(self.compound.abbreviation, \"Cool\")","def test_general_subset_all():\n pass","def test_check_tree_exact_match(self):\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_3tips_fp)\r\n\r\n # Should find all and give True, True result\r\n\r\n self.assertEqual(actual_subset_results, [True, True])\r\n\r\n # Should get tips not found in fasta labels with 5 tip tree\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find all and give True result\r\n\r\n self.assertEqual(actual_subset_results, [True, ['seq5', 'seq4']])\r\n\r\n # Change two of the fasta labels to not match tree tips\r\n\r\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find seqX and seqY as not being a subset\r\n\r\n self.assertEqual(actual_subset_results, [['seqX', 'seqY'],\r\n ['seq3', 'seq5', 'seq4']])","def test_03_visit_special(self):","def test_labels(ruler: SpaczzRuler) -> None:\n assert all(\n [label in ruler.labels for label in [\"GPE\", \"STREET\", \"DRUG\", \"NAME\", \"BAND\"]]\n )\n assert len(ruler.labels) == 5","def test_createSubLinkographWithoutCommands(self):\n self.performTestForParams()","def test_first_level_from_bids_several_labels_per_entity(tmp_path, entity):\n n_sub = 1\n n_ses = 1\n tasks = [\"main\"]\n n_runs = [1]\n\n bids_path = create_fake_bids_dataset(\n base_dir=tmp_path,\n n_sub=n_sub,\n n_ses=n_ses,\n tasks=tasks,\n n_runs=n_runs,\n entities={entity: [\"A\", \"B\"]},\n )\n\n models, m_imgs, m_events, m_confounds = first_level_from_bids(\n dataset_path=bids_path,\n task_label=\"main\",\n space_label=\"MNI\",\n img_filters=[(\"desc\", \"preproc\"), (entity, \"A\")],\n slice_time_ref=None,\n )\n\n _check_output_first_level_from_bids(n_sub,\n models,\n m_imgs,\n m_events,\n m_confounds)\n n_imgs_expected = n_ses * n_runs[0]\n assert len(m_imgs[0]) == n_imgs_expected","def test_setter_child_list_tuple(self):\n root = netapp_api.NaElement('root')\n root['l'] = ['l1', 'l2']\n root['t'] = ('t1', 't2')\n l = root.get_child_by_name('l')\n self.assertIsInstance(l, netapp_api.NaElement)\n t = root.get_child_by_name('t')\n self.assertIsInstance(t, netapp_api.NaElement)\n for le in l.get_children():\n self.assertIn(le.get_name(), ['l1', 'l2'])\n for te in t.get_children():\n self.assertIn(te.get_name(), ['t1', 't2'])","def test_tree_support(self):\r\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\r\n \"\"\"\r\n /-------.5 /-a\r\n ---1| \\-b\r\n \\------.5 /-c\r\n \\-d\r\n \"\"\"\r\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\r\n\r\n tc.tree_support(master_tree, t2)\r\n assert_almost_equal(\r\n master_tree.getNodeMatchingName('rt').bootstrap_support, 1.0)","def test_labels(self):\n return self._test_labels","def test_issue_edit_label(self):\n pass","def test_dbpa002_radio_items(dash_duo):\n app = Dash()\n\n options = {\n \"OptionA\": \"Option 1\",\n \"OptionB\": \"Option 2\",\n \"OptionC\": \"Option 3\",\n }\n\n value = \"OptionB\"\n\n with_keywords = RadioItems(\n options=options,\n value=value,\n id=\"with-keywords\",\n )\n without_keywords = RadioItems(options, value, id=\"without-keywords\")\n\n app.layout = html.Div([with_keywords, without_keywords])\n\n dash_duo.start_server(app)\n\n # Check values\n assert [\n a.get_attribute(\"value\")\n for a in dash_duo.wait_for_element(\n \"#with-keywords\"\n ).find_elements_by_tag_name(\"input\")\n ] == [\n a.get_attribute(\"value\")\n for a in dash_duo.wait_for_element(\n \"#without-keywords\"\n ).find_elements_by_tag_name(\"input\")\n ]\n\n # Check labels\n assert [\n a.text\n for a in dash_duo.wait_for_element(\n \"#with-keywords\"\n ).find_elements_by_tag_name(\"label\")\n ] == [\n a.text\n for a in dash_duo.wait_for_element(\n \"#without-keywords\"\n ).find_elements_by_tag_name(\"label\")\n ]","def test(self):\n for doc, label in zip(self.test_docs(), self.test_labels()):\n yield doc, label","def test_get_parent_type_name(self):\n pass","def test_pathop8(self):\n xpb = XPathBuilder()\n xp = (xpb.foo.bar | xpb.foobar).parenthesize() & xpb.action.source\n exp = '(/foo/bar or /foobar) and /action/source'\n self.assertEqual(xp.tostring(), exp)","def test_setter_child_list_tuple(self):\n root = netapp_api.NaElement('root')\n root['l'] = ['l1', 'l2']\n root['t'] = ('t1', 't2')\n l_element = root.get_child_by_name('l')\n self.assertIsInstance(l_element, netapp_api.NaElement)\n t = root.get_child_by_name('t')\n self.assertIsInstance(t, netapp_api.NaElement)\n for le in l_element.get_children():\n self.assertIn(le.get_name(), ['l1', 'l2'])\n for te in t.get_children():\n self.assertIn(te.get_name(), ['t1', 't2'])","def __init__(self, root_node, label1, label2):\n super().__init__(self.PROBLEM_NAME)\n self.root_node = root_node\n self.label1 = label1\n self.label2 = label2","def test_radioselect_field():","def test_label_callback():\n release_numbers = dict(a='123')\n data = dict(revision='a', attributes=dict(b='c'))\n data2 = dict(revision='b', attributes=dict(d='e'))\n\n assert _label_callback(data, release_numbers) == u'a\\n- Release: 123\\n- b: c'\n assert _label_callback(data2) == u'b\\n- Release: Unknown\\n- d: e'","def _make_simple_partition_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n label = 'simplepartition(['\n for cp in cps:\n smcstr = str(cp['smc'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr:\n condition_str += ',%s)'\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def test_case1(self):\n\n graph = BipartiteGraph()\n\n graph.addEdge(\"supervisor1\",\"student1\")\n\n val1 = graph.getStudents(\"supervisor1\")\n val2 = graph.getSupervisors(\"student1\")\n\n expected1 = [\"student1\"]\n expected2 = [\"supervisor1\"]\n\n self.assertEqual((val1,val2),(expected1,expected2))","def test_with_multiple_descriptions():\n soup = generate_case(\"with_descriptions\")\n\n tests.html_schema_doc_asserts.assert_descriptions(\n soup,\n [\n \"Exact address\",\n \"Exact address\",\n \"Delivery info depending on the delivery type\",\n \"The delivery is a gift, no prices displayed\",\n ],\n )","def test_BuildModel2(self):\n print(\"\\nTest 6: Building a Model with Concat\")\n builder = StaticBuilder(\"Concat\")\n in1 = builder.addInput(10)\n in2 = builder.addInput(20)\n enc1 = builder.addInner(3, num_islots=2)\n out1 = builder.addOutput()\n\n builder.addDirectedLink(in1, enc1, islot=0)\n builder.addDirectedLink(in2, enc1, islot=1)\n builder.addDirectedLink(enc1, out1)\n \n builder.build()","def test_multiple_label_traversals(self):\r\n TestEdge.create(self.v1, self.v2)\r\n OtherTestEdge.create(self.v1, self.v3)\r\n YetAnotherTestEdge.create(self.v1, self.v4)\r\n\r\n assert len(self.v1.outV()) == 3\r\n\r\n assert len(self.v1.outV(TestEdge)) == 1\r\n assert len(self.v1.outV(OtherTestEdge)) == 1\r\n assert len(self.v1.outV(YetAnotherTestEdge)) == 1\r\n\r\n out = self.v1.outV(TestEdge, OtherTestEdge)\r\n assert len(out) == 2\r\n assert self.v2.vid in [v.vid for v in out]\r\n assert self.v3.vid in [v.vid for v in out]\r\n\r\n out = self.v1.outV(OtherTestEdge, YetAnotherTestEdge)\r\n assert len(out) == 2\r\n assert self.v3.vid in [v.vid for v in out]\r\n assert self.v4.vid in [v.vid for v in out]","def test_label_10_targets_with_a_b_c_false(self):\r\n user_input = '[{\"a\":\"target1\"}, \\\r\n {\"b\":\"target2\"},{\"c\":\"target3\"},{\"a\":\"target4\"},{\"b\":\"target5\"}, \\\r\n {\"c\":\"target6\"}, {\"a\":\"target7\"},{\"b\":\"target8\"},{\"c\":\"target9\"}, \\\r\n {\"a\":\"target1\"}]'\r\n correct_answer = [\r\n {\r\n 'draggables': ['a'],\r\n 'targets': ['target1', 'target4', 'target7', 'target10'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['b'],\r\n 'targets': ['target2', 'target5', 'target8'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['c'],\r\n 'targets': ['target3', 'target6', 'target9'],\r\n 'rule': 'unordered_equal'\r\n }\r\n ]\r\n self.assertFalse(draganddrop.grade(user_input, correct_answer))"],"string":"[\n \"def test_sums():\\n assert label_parent(1, 2) == 3\\n assert label_parent (1, 4) == 8\\n # Should ignore arg order\\n assert label_parent(4, 1) == 8\",\n \"def _make_partitionsTest_label(chain_parts):\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n \\n assert scenario == 'partitionsTest'\\n\\n \\n\\n return \\\"\\\"\\\"\\n partgen(\\n [(20et, 0eta320)]\\n \\n simple([(40et, 0eta320) (50et, 0eta320)])\\n simple([(35et, 0eta240) (55et, 0eta240)])\\n )\\\"\\\"\\\"\",\n \"def test_recipe_nutrition_label_widget(self):\\n pass\",\n \"def test_select_label(self):\\n title = ('Transportation Challenges Limit Education Choices for '\\n 'Denver Parents')\\n summary = \\\"\\\"\\\"\\n Many families in the Denver metro area use public\\n transportation instead of a school bus because for them, a\\n quality education is worth hours of daily commuting. Colorado's\\n school choice program is meant to foster educational equity,\\n but the families who benefit most are those who have time and\\n money to travel. Low-income families are often left in a lurch.\\n \\\"\\\"\\\"\\n byline = \\\"Mile High Connects\\\"\\n story = create_story(title=title, summary=summary, byline=byline)\\n layout = SectionLayout.objects.get(sectionlayouttranslation__name=\\\"Side by Side\\\")\\n section1 = create_section(title=\\\"Test Section 1\\\", story=story, layout=layout)\\n section2 = create_section(title=\\\"Test Section 2\\\", story=story, layout=layout)\\n form = SectionRelationAdminForm()\\n choices_list = list(form.fields['parent'].widget.choices)\\n self.assertIn(story.title, choices_list[1][1])\\n self.assertIn(story.title, choices_list[2][1])\",\n \"def setUp(self):\\n\\n singleLabels = linkoCreate.Linkograph(\\n [({'A'}, set(), {1,2,3}),\\n ({'D'}, {0}, {3,4}),\\n ({'A'}, {0}, {4}),\\n ({'C'}, {0,1}, {4}),\\n ({'A'}, {1,2,3}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_2 = linkoCreate.Linkograph(\\n [({'A'}, set(), {1,2}),\\n ({'D'}, {0}, set()),\\n ({'A'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_1 = linkoCreate.Linkograph(\\n [({'A'}, set(), {1}),\\n ({'D'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_0 = linkoCreate.Linkograph(\\n [({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko1_2 = linkoCreate.Linkograph(\\n [({'D'}, set(), set()),\\n ({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko1_1 = linkoCreate.Linkograph(\\n [({'D'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n trivialLinkograph = linkoCreate.Linkograph(\\n [], ['A', 'B', 'C', 'D'])\\n\\n\\n singleSubLinko1_4 = linkoCreate.Linkograph(\\n [({'D'}, set(), {2,3}),\\n ({'A'}, set(), {3}),\\n ({'C'}, {0}, {3}),\\n ({'A'}, {0,1,2}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko2_4 = linkoCreate.Linkograph(\\n [({'A'}, set(), {2}),\\n ({'C'}, set(), {2}),\\n ({'A'}, {0,1}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko3_4 = linkoCreate.Linkograph(\\n [({'C'}, set(), {1}),\\n ({'A'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko4_4 = linkoCreate.Linkograph(\\n [({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n simpleLinko = linkoCreate.Linkograph(\\n [({'A', 'B', 'C'}, set(), {1,2,3}),\\n ({'D'}, {0}, {3,4}),\\n ({'A'}, {0}, {4}),\\n ({'B', 'C'}, {0,1}, {4}),\\n ({'A'}, {1,2,3}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n if self.id().split('.')[-1] == 'test_createSubLinkographWithoutCommands':\\n self.testParams = [\\n {'linko': singleLabels,\\n 'lowerBound': None,\\n 'upperBound': None,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 5,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': -1,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': None,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 1,\\n 'ExpectedLinkograph': singleSubLinko0_1},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 0,\\n 'ExpectedLinkograph': singleSubLinko0_0},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': -1,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko1_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 1,\\n 'ExpectedLinkograph': singleSubLinko1_1},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 0,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': -1,\\n 'upperBound': -1,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko1_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 2,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko2_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 3,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko3_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 4,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko4_4},\\n\\n ]\",\n \"def test_labels(self):\\n self.compliance_tester.test_labels(self.oi)\",\n \"def test_parent_label(self):\\n l = self.d.label(1)\\n l2 = self.d.label(31405)\\n\\n self.assertTrue(l.parent_label is None)\\n self.assertTrue(l2 in l.sublabels)\\n self.assertEqual(l2.parent_label, l)\",\n \"def test_simple(self):\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n content = '((h,p)hp:1,g)hpg;'\\n self._do_test(content, exp)\\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP,\\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n self._do_test(content, exp)\",\n \"def test_label(self):\\n xs = t.Label(t.Exactly(\\\"x\\\"), 'CustomLabel')\\n self.assertEqual(writePython(xs),\\n dd(\\\"\\\"\\\"\\n def _G_label_1():\\n _G_exactly_2, lastError = self.exactly('x')\\n self.considerError(lastError, None)\\n return (_G_exactly_2, self.currentError)\\n _G_label_3, lastError = self.label(_G_label_1, \\\"CustomLabel\\\")\\n self.considerError(lastError, None)\\n _G_label_3\\n \\\"\\\"\\\"))\",\n \"def addLabels(t):\\n if not t.label:\\n t.label = \\\"\\\".join([choice(\\\"abcdefghijklmnopqrstuvwxyz\\\") for i in range(4)])\\n for r,w in t.children:\\n addLabels(r)\",\n \"def test_general_subset_level():\\n pass\",\n \"def _make_simple_comb_label(chain_dict):\\n\\n cps = chain_dict['chainParts']\\n if not (_select_simple_chainparts(cps)):\\n raise NotImplementedError(\\n 'chain fails substring selection: not \\\"simple\\\": %s' % (\\n chain_dict['chainName']))\\n \\n simple_strs = []\\n\\n for cp in cps:\\n print(cp)\\n simple_strs.append(_make_simple_label([cp]))\\n\\n label = 'combgen([(%d)]' % len(cps)\\n for s in simple_strs:\\n label += ' %s ' % s\\n label += ')'\\n return label\",\n \"def _generateLabelAndName(self, obj, **args):\\n result = []\\n label = self._generateLabel(obj, **args)\\n name = self._generateName(obj, **args)\\n result.extend(label)\\n if not len(label):\\n result.extend(name)\\n elif len(name) and name[0].strip() != label[0].strip():\\n result.extend(name)\\n return result\",\n \"def tests_ti_document_add_label(self):\\n super().group_add_label()\",\n \"def getLabel2(*args):\",\n \"def getLabel2(*args):\",\n \"def test_checkboxtextgroup(self):\\r\\n self.check_group('checkboxtextgroup', 'choice', 'checkbox')\",\n \"def _generateTableCell2ChildLabel(self, obj, **args):\\n result = []\\n\\n # If this table cell has 2 children and one of them has a\\n # 'toggle' action and the other does not, then present this\\n # as a checkbox where:\\n # 1) we get the checked state from the cell with the 'toggle' action\\n # 2) we get the label from the other cell.\\n # See Orca bug #376015 for more details.\\n #\\n if obj.childCount == 2:\\n cellOrder = []\\n hasToggle = [False, False]\\n for i, child in enumerate(obj):\\n if self._script.utilities.hasMeaningfulToggleAction(child):\\n hasToggle[i] = True\\n break\\n if hasToggle[0] and not hasToggle[1]:\\n cellOrder = [ 1, 0 ]\\n elif not hasToggle[0] and hasToggle[1]:\\n cellOrder = [ 0, 1 ]\\n if cellOrder:\\n for i in cellOrder:\\n if not hasToggle[i]:\\n result.extend(self.generate(obj[i], **args))\\n return result\",\n \"def test_process_label_in_node(self):\\n tree = Node(children=[\\n Node(\\\"Defining secret phrase.\\\", label=['AB', 'a']),\\n Node(\\\"Has secret phrase. Then some other content\\\", \\n label=['AB', 'b'])\\n ], label=['AB'])\\n t = Terms(tree)\\n t.scoped_terms = {\\n ('AB',): [Ref(\\\"secret phrase\\\", \\\"AB-a\\\", (9,22))]\\n }\\n # Term is defined in the first child\\n self.assertEqual([], t.process(tree.children[0]))\\n self.assertEqual(1, len(t.process(tree.children[1])))\",\n \"def get_extra_label(self, label_name: str, hierarchy: List[str]) -> Any:\",\n \"def test_get_scenarios_expanded(self):\\n pass\",\n \"def test_nested():\\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.root.metadata.choose(lambda m: (m[\\\"name\\\"], m.uid))))\\n assert \\\"type\\\" in res # from conditions\\n assert \\\"reason\\\" in res # from conditions\\n assert \\\"name\\\" in res # from metadata\\n assert \\\"uid\\\" in res # from metadata\",\n \"def _make_simple_label(chain_parts):\\n \\n if not _select_simple_chainparts(chain_parts):\\n msg = 'Jet Configuration error: '\\\\\\n 'chain fails substring selection: not \\\"simple\\\" '\\n\\n raise NotImplementedError(msg)\\n \\n label = 'simple(['\\n for cp in chain_parts:\\n smcstr = str(cp['smc'])\\n jvtstr = str(cp['jvt'])\\n if smcstr == 'nosmc':\\n smcstr = ''\\n for i in range(int(cp['multiplicity'])):\\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\\n # str(cp['etaRange']),\\n # smcstr,)\\n condition_str = '(%set,%s' % (str(cp['threshold']),\\n str(cp['etaRange']),)\\n if smcstr: # Run 2 chains have \\\"INF\\\" in the SMC substring\\n condition_str += ',%s)' % smcstr.replace('INF','')\\n elif jvtstr:\\n condition_str += ',%s)' % jvtstr\\n else:\\n condition_str += ')'\\n label += condition_str\\n label += '])'\\n return label\",\n \"def test_nested_sequence(self):\\n\\n self.taxon_tester('Apis mellifera')\\n self.taxon_tester('Apis')\\n self.taxon_tester('Apini')\\n self.taxon_tester('Apinae')\\n # Apidae at 5680 species is a struggle\\n self.taxon_tester('Apidae')\\n if False:\\n # Apoidea: 19566 takes 223 seconds\\n self.taxon_tester('Apoidea')\\n # Aculeata fails after 339 seconds\\n self.taxon_tester('Aculeata')\\n self.taxon_tester('Apocrita')\\n self.taxon_tester('Hymenoptera')\\n self.taxon_tester('Endopterygota')\\n self.taxon_tester('Neoptera')\\n self.taxon_tester('Pterygota')\\n self.taxon_tester('Dicondylia')\\n self.taxon_tester('Insecta')\\n self.taxon_tester('Hexapoda')\\n self.taxon_tester('Pancrustacea')\\n self.taxon_tester('Mandibulata')\\n self.taxon_tester('Arthropoda')\\n self.taxon_tester('Panarthropoda')\\n self.taxon_tester('Ecdysozoa')\\n self.taxon_tester('Protostomia')\\n self.taxon_tester('Bilateria')\\n self.taxon_tester('Eumetazoa')\\n self.taxon_tester('Metazoa')\\n self.taxon_tester('Holozoa')\\n self.taxon_tester('Opisthokonta')\\n self.taxon_tester('Eukaryota')\",\n \"def test_bootstrap_support_labeled(self):\\r\\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\\r\\n \\\"\\\"\\\"\\r\\n /-------.5 /-a\\r\\n ---1| \\\\-b\\r\\n \\\\------.5 /-c\\r\\n \\\\-d\\r\\n \\\"\\\"\\\"\\r\\n t1 = parse_newick('((a:6,b:8.2)hi:2,(c:1,d:2):7);') # same structure\\r\\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\\r\\n new_master, bootstraps = tc.bootstrap_support(master_tree, [t1, t2])\\r\\n expected = dict([('ab', .5), ('cd', .5), ('rt', 1.0)])\\r\\n self.assertDictEqual(bootstraps, expected)\",\n \"def test_title(names):\",\n \"def test_verbose_name_group(self): \\n field_verboses = {\\n \\\"title\\\": \\\"Название группы\\\",\\n \\\"slug\\\": \\\"Слаг\\\",\\n \\\"description\\\": \\\"Описание группы\\\",\\n }\\n for value, expected in field_verboses.items():\\n with self.subTest(value=value):\\n self.assertEqual(self.group._meta.get_field(value).verbose_name, expected)\",\n \"def plugin_second_label():\\n return \\\"second\\\"\",\n \"def test_product_labels(self):\\n\\n prd = Product.objects.get(id=1)\\n # label name\\n label_name = prd._meta.get_field('name').verbose_name\\n self.assertEqual(label_name, 'name')\\n # label description\\n label_name = prd._meta.get_field('description').verbose_name\\n self.assertEqual(label_name, 'description')\\n # label nutrition_grade\\n label_name = prd._meta.get_field('nutrition_grade').name\\n self.assertEqual(label_name, 'nutrition_grade')\\n # label barcode\\n label_name = prd._meta.get_field('barcode').verbose_name\\n self.assertEqual(label_name, 'barcode')\\n # label url\\n label_name = prd._meta.get_field('url').verbose_name\\n self.assertEqual(label_name, 'url')\\n # label url_pic\\n label_name = prd._meta.get_field('url_pic').name\\n self.assertEqual(label_name, 'url_pic')\\n # label store\\n label_name = prd._meta.get_field('store').verbose_name\\n self.assertEqual(label_name, 'store')\\n # label prd_cat\\n label_name = prd._meta.get_field('prd_cat').name\\n self.assertEqual(label_name, 'prd_cat')\\n # label fat\\n label_name = prd._meta.get_field('fat').verbose_name\\n self.assertEqual(label_name, 'fat')\\n # label saturated_fat\\n label_name = prd._meta.get_field('saturated_fat').name\\n self.assertEqual(label_name, 'saturated_fat')\\n # label sugar\\n label_name = prd._meta.get_field('sugar').verbose_name\\n self.assertEqual(label_name, 'sugar')\\n # label salt\\n label_name = prd._meta.get_field('salt').verbose_name\\n self.assertEqual(label_name, 'salt')\",\n \"def is_test(self):\\r\\n return self.has_label('tests')\",\n \"def setUp(self):\\n\\n\\n # InverseLabeling\\n invLabeling0 = {'L0': [0, 1, 2]}\\n\\n invLabeling1 = {'L0' : [0, 2],\\n 'L1' : [1]}\\n\\n invLabeling2 = {\\n 'L0' : [0],\\n 'L1' : [1],\\n 'L2' : [2]\\n }\\n\\n invLabeling3 = {\\n 'L1' : [0, 1],\\n 'L2' : [2]\\n }\\n\\n invLabeling4 = {\\n 'L0' : [0,1],\\n 'L1' : [0],\\n 'L2' : [2]\\n }\\n\\n invLabeling5 = {\\n 'L0': [0, 1, 2],\\n 'L1': []\\n }\\n \\n # Create some ontologies\\n ontology0 = {'L0': ['L0']}\\n\\n ontology1 = {}\\n\\n ontology2 = {'L0': ['L1']}\\n\\n ontology3 = {'L0': ['L1', 'L2'],\\n 'L1': ['L2'],\\n 'L2': ['L0']}\\n\\n if self.id().split('.')[-1] == 'test_createLinkograph':\\n self.testParams = [\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology0,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1, 2}),\\n ({'L0'}, {0}, {2}),\\n ({'L0'}, {0,1}, set())] \\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology1,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology2,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology0,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {2}),\\n ({'L1'}, set(), set()),\\n ({'L0'}, {0}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology1,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L1'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology2,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1}),\\n ({'L1'}, {0}, set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1}),\\n ({'L1'}, {0}, set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling2,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1,2}),\\n ({'L1'}, {0}, {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling3,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L1'}, set(), {2}),\\n ({'L1'}, set(), {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling4,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0', 'L1'}, set(), {2}),\\n ({'L0'}, set(), {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling5,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n ]\",\n \"def test_mutual_exclusivity_of_labels(self, tmpdir, docker_tasker, labels, expected_fail):\\n runner = mock_env(tmpdir, docker_tasker, labels=labels)\\n if expected_fail:\\n with pytest.raises(PluginFailedException) as e:\\n runner.run()\\n assert 'only one of labels' in str(e.value)\\n else:\\n runner.run()\",\n \"def helper_label(helper):\\n return helper.info\",\n \"def test_Tree():\",\n \"def test_col_data_label_with_attrs(self):\\n help_tag = 'span'\\n help_text_br = False\\n names = ('first', 'billing_address_1')\\n attrs = {'style': 'width: 10rem; display: inline-block'}\\n label_attrs = {name: attrs for name in names}\\n txt = '{}=\\\"{}\\\"'.format(*list(attrs.items())[0])\\n expected = [''.format(txt)]\\n expected.append(''.format(txt))\\n actual = []\\n for name in names:\\n field = self.form.fields[name]\\n response = self.form.collect_col_data(name, field, help_tag, help_text_br, label_attrs)\\n actual.append(response.get('label'))\\n\\n for expect, got in zip(expected, actual):\\n self.assertEqual(expect, got)\",\n \"def test_help_text_group(self): \\n field_help_text = {\\n \\\"title\\\": \\\"Дайте назание группе\\\",\\n \\\"slug\\\": ('Укажите адрес для группы. Используйте '\\n 'только латиницу, цифры, дефисы и знаки '\\n 'подчёркивания'),\\n } \\n for value, expected in field_help_text.items():\\n with self.subTest(value=value):\\n self.assertEqual(self.group._meta.get_field(value).help_text, expected)\",\n \"def test_label_choices(self):\\n test_classes = (\\n (0, 'No'),\\n (1, 'Yes')\\n )\\n\\n form = SingleLabelClassifierForm(classes=test_classes)\\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)\\n\\n form = MultiLabelClassifierForm(classes=test_classes)\\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)\",\n \"def test_filter_tree(self):\\r\\n actual = [e.Name for e in filter_tree(\\r\\n self.tree1, ['bbb', 'ccc']).tips()]\\r\\n #(a_a:10,(b_b:2,c_c:4):5);\\r\\n expected = [\\r\\n e.Name for e in DndParser(\\r\\n \\\"((bbb:2,ccc:4));\\\",\\r\\n PhyloNode).tips(\\r\\n )]\\r\\n self.assertEqual(actual, expected)\\r\\n\\r\\n actual = [e.Name for e in filter_tree(\\r\\n self.tree2, ['bbb', 'ccc']).tips()]\\r\\n #(a_a:10,(b_b:2,c_c:4):5);\\r\\n expected = [\\r\\n e.Name for e in DndParser(\\r\\n \\\"(('bbb':2,ccc:4));\\\",\\r\\n PhyloNode).tips(\\r\\n )]\\r\\n self.assertEqual(actual, expected)\",\n \"def test_radiotextgroup(self):\\r\\n self.check_group('radiotextgroup', 'choice', 'radio')\",\n \"def gen_test_case_combination(self):\\n\\n cases = '\\\\n'\\n\\n binary_ops = list(self.BINARY_OPS)\\n binary_ops.reverse()\\n for op1 in self.BINARY_OPS:\\n for op2 in binary_ops:\\n\\n result = []\\n ret = IntOp.binary_op(op2, '0', '1', self.lane_width)\\n ret = IntOp.binary_op(op1, ret, '2', self.lane_width)\\n result.append(ret)\\n\\n cases += '\\\\n' + str(AssertReturn('{lane_type}.{op1}-{lane_type}.{op2}'.format(lane_type=self.LANE_TYPE, op1=op1, op2=op2),\\n [SIMD.v128_const('0', self.LANE_TYPE),\\n SIMD.v128_const('1', self.LANE_TYPE),\\n SIMD.v128_const('2', self.LANE_TYPE)],\\n SIMD.v128_const(result, self.LANE_TYPE)))\\n cases += '\\\\n'\\n return cases\",\n \"def test_multiple_task_groups_dag(\\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\\n ):\\n (\\n dag,\\n group1,\\n group2,\\n group3,\\n (\\n group1_emp1,\\n group1_emp2,\\n group1_emp3,\\n group2_emp1,\\n group2_emp2,\\n group2_emp3,\\n group2_op1,\\n group2_op2,\\n group3_emp1,\\n group3_emp2,\\n group3_emp3,\\n emp_in1,\\n emp_in2,\\n emp_in3,\\n emp_in4,\\n emp_out1,\\n emp_out2,\\n emp_out3,\\n emp_out4,\\n op_in1,\\n op_out1,\\n ),\\n ) = test_multiple_taskgroups_dag\\n\\n group1_emp1 >> Label(\\\"label group1.group1_emp1 <=> group1.group1_emp2\\\") >> group1_emp3\\n\\n emp_in1 >> group1\\n emp_in2 >> Label(\\\"label emp_in2 <=> group1\\\") >> group1\\n [emp_in3, emp_in4] >> Label(\\\"label emp_in3/emp_in4 <=> group1\\\") >> group1\\n XComArg(op_in1, \\\"test_key\\\") >> Label(\\\"label op_in1 <=> group1\\\") >> group1\\n\\n (\\n [group2_emp1, group2_emp2]\\n >> Label(\\\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\\\")\\n >> group2_emp3\\n )\\n (\\n group2_emp1\\n >> Label(\\\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\\\")\\n >> [group2_emp2, group2_emp3]\\n )\\n group2_emp3 >> Label(\\\"label group2.group2_emp3 <=> group3\\\") >> group3\\n\\n (\\n XComArg(group2_op1, \\\"test_key\\\")\\n >> Label(\\\"label group2.group2_op1 <=> group2.group2_op2\\\")\\n >> XComArg(group2_op2, \\\"test_key\\\")\\n )\\n XComArg(group2_op2, \\\"test_key\\\") >> Label(\\\"label group2.group2_op2 <=> group3\\\") >> group3\\n\\n group3 >> emp_out1\\n group3 >> Label(\\\"label group3 <=> emp_out2\\\") >> emp_out2\\n group3 >> Label(\\\"label group3 <=> emp_out3/emp_out4\\\") >> [emp_out3, emp_out4]\\n group3 >> Label(\\\"label group3 <=> op_out1\\\") >> XComArg(op_out1, \\\"test_key\\\")\\n\\n group1 >> Label(\\\"label group1 <=> group2\\\") >> group2\\n\\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)\",\n \"def test_issue_create_label(self):\\n pass\",\n \"def test__create_label_w_no_ent_id(ruler: SpaczzRuler) -> None:\\n assert ruler._create_label(\\\"TEST\\\", None) == \\\"TEST\\\"\",\n \"def test_grouping(self):\\n s = self.create(ComponentItem, UML.Component)\\n uc1 = self.create(UseCaseItem, UML.UseCase)\\n uc2 = self.create(UseCaseItem, UML.UseCase)\\n\\n self.group(s, uc1)\\n assert 1 == len(uc1.subject.subject)\\n self.group(s, uc2)\\n assert 1 == len(uc2.subject.subject)\\n\\n # Classifier.useCase is not navigable to UseCase\\n # self.assertEqual(2, len(s.subject.useCase))\",\n \"def setLabel2(*args):\",\n \"def tests_ti_document_get_label(self):\\n super().group_get_label()\",\n \"def test_render_label(self):\\n label = self.block.meta.label\\n self.assertEqual(label, 'Google Calendar', 'The labels are not the same')\",\n \"def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\\n\\n oid = obj.attrib.get('id')\\n klass = obj.attrib.get('class')\\n\\n # \\\"Don't care\\\" special case\\n if klass in widgets_ignored:\\n return\\n for suffix in widgets_suffixignored:\\n if klass[-len(suffix):] == suffix:\\n return\\n\\n # Widgets usual do not strictly require a label, i.e. a labelled parent\\n # is enough for context, but some do always need one.\\n requires_label = klass in widgets_needlabel\\n\\n if oid is not None:\\n # Check that ids are unique\\n if oid in ids_dup:\\n if ids[oid] == obj:\\n # We are the first, warn\\n duplicates = tree.findall(\\\".//object[@id='\\\" + oid + \\\"']\\\")\\n err(filename, tree, obj, \\\"duplicate-id\\\", \\\"has the same id as other elements \\\" + elms_names_lines(duplicates))\\n\\n # Check label-for and their dual labelled-by\\n label_for = check_rels(filename, tree, root, obj, \\\"label-for\\\", \\\"labelled-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n labelled_by = check_rels(filename, tree, root, obj, \\\"labelled-by\\\", \\\"label-for\\\")\\n\\n # Check label-for and their dual labelled-by\\n description_for = check_rels(filename, tree, root, obj, \\\"description-for\\\", \\\"described-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n described_by = check_rels(filename, tree, root, obj, \\\"described-by\\\", \\\"description-for\\\")\\n\\n # Should have only one label\\n if len(labelled_by) >= 1:\\n if oid in mnemonic_for_elm:\\n warn(filename, tree, obj, \\\"labelled-by-and-mnemonic\\\",\\n \\\"has both a mnemonic \\\" + elm_name_line(mnemonic_for_elm[oid][0]) + \\\"and labelled-by relation\\\")\\n if len(labelled_by) > 1:\\n warn(filename, tree, obj, \\\"multiple-labelled-by\\\", \\\"has multiple labelled-by relations\\\")\\n if oid in label_for_elm:\\n if len(label_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-label-for\\\", \\\"is referenced by multiple label-for \\\" + elms_names_lines(label_for_elm[oid]))\\n if oid in mnemonic_for_elm:\\n if len(mnemonic_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-mnemonic\\\", \\\"is referenced by multiple mnemonic_widget \\\" + elms_names_lines(mnemonic_for_elm[oid]))\\n\\n # Check member-of\\n member_of = check_rels(filename, tree, root, obj, \\\"member-of\\\")\\n\\n # Labels special case\\n if klass in widgets_labels:\\n properties = check_props(filename, tree, root, obj, \\\"mnemonic_widget\\\") + \\\\\\n check_props(filename, tree, root, obj, \\\"mnemonic-widget\\\")\\n if len(properties) > 1:\\n err(filename, tree, obj, \\\"multiple-mnemonic\\\", \\\"has multiple mnemonic_widgets properties\\\"\\n \\\"%s\\\" % elms_lines(properties))\\n\\n # Emit orphaning warnings\\n if warn_orphan_labels or orphan_widgets:\\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\\n\\n # We are done with the label\\n return\\n\\n # Not a label, will perhaps need one\\n\\n # Emit orphaning warnings\\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\",\n \"def get_label(self, hierarchy: List[str]) -> Any:\",\n \"def test_first_level_from_bids_no_duplicate_sub_labels(bids_dataset):\\n models, *_ = first_level_from_bids(\\n dataset_path=bids_dataset,\\n task_label='main',\\n sub_labels=[\\\"01\\\", \\\"01\\\"],\\n space_label='MNI',\\n img_filters=[('desc', 'preproc')],\\n slice_time_ref=None,\\n )\\n\\n assert len(models) == 1\",\n \"def test_add_empty_nodes_with_label_when_splitting(self):\\n print \\\"----- test_add_empty_nodes_with_label_when_splitting -----\\\"\\n sel_axis = (lambda axis: axis)\\n \\n #create tree, first node splits in x direction\\n tree = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\\n kdtree.visualize(tree)\\n \\n point_left = [0.4, 0.5]\\n tree.split2(point_left, axis = 0)\\n kdtree.visualize(tree)\\n \\n point1 = [0.3, 0.5]\\n found_node = tree.get_path_to_leaf(point1)[-1]\\n correct_node1 = 3\\n self.assertEqual(found_node.label, correct_node1, \\\"Not correct node found\\\")\\n \\n point_right = [0.6, 0.5]\\n tree.split2(point_right, axis = 1)\\n kdtree.visualize(tree)\\n \\n point2 = [0.6, 0.7]\\n found_node = tree.get_path_to_leaf(point2)[-1]\\n correct_node2 = 6\\n self.assertEqual(found_node.label, correct_node2, \\\"Not correct node found\\\")\\n \\n print \\\"----- end: test_add_empty_nodes_with_label_when_splitting -----\\\"\",\n \"def test_render_value_label(self):\\n self.check_html(\\n self.widget(choices=self.beatles),\\n \\\"beatles\\\",\\n [\\\"John\\\"],\\n html=(\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n ),\\n )\",\n \"def test_issue_add_label(self):\\n pass\",\n \"def test_create_metering_label(self):\\r\\n resource = 'metering_label'\\r\\n cmd = metering.CreateMeteringLabel(\\r\\n test_cli20.MyApp(sys.stdout), None)\\r\\n name = 'my label'\\r\\n myid = 'myid'\\r\\n description = 'my description'\\r\\n args = [name, '--description', description, '--shared']\\r\\n position_names = ['name', 'description', 'shared']\\r\\n position_values = [name, description, True]\\r\\n self._test_create_resource(resource, cmd, name, myid, args,\\r\\n position_names, position_values)\",\n \"def getLabel(*args):\",\n \"def getLabel(*args):\",\n \"def getLabel(*args):\",\n \"def _make_vbenf_label(chain_parts):\\n\\n # toy label for development: run simple and dijet independently.\\n # simple makes Et cuts on two jets. Independently (sharing possible)\\n # of jets choosean by simple, the dijet\\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n assert scenario.startswith('vbenf')\\n args = _args_from_scenario(scenario)\\n if not args:\\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \\n arg_res = [\\n re.compile(r'(?P\\\\d*)(?Pfbet)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pmass)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pet)(?P\\\\d*)'),\\n ]\\n\\n defaults = {\\n 'et': ('101', 'inf'),\\n 'mass': ('800', 'inf'),\\n 'fbet': ('501', 'inf'),\\n }\\n\\n argvals = {}\\n while args:\\n assert len(args) == len(arg_res)\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = defaults[key][0]\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = defaults[key][1]\\n argvals[key+'hi'] = hi\\n\\n assert len(args) == len(arg_res)\\n assert len(args) == 0\\n\\n return \\\"\\\"\\\"\\n and\\n (\\n []\\n simple\\n (\\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\\n )\\n combgen\\n (\\n [(10et, 0eta320)]\\n dijet\\n (\\n [(%(masslo).0fdjmass, 26djdphi)]\\n ) \\n simple\\n (\\n [(10et, 0eta320)(20et, 0eta320)]\\n )\\n )\\n )\\\"\\\"\\\" % argvals\",\n \"def testFormatLabelAndValue(self):\\n\\n self.assertEqual('Abc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 1))\\n self.assertEqual('ABc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 2))\\n self.assertEqual('ABC: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 4))\",\n \"def test_recipe_nutrition_label_image(self):\\n pass\",\n \"def test_label_10_targets_with_a_b_c_(self):\\r\\n user_input = '[{\\\"a\\\":\\\"target1\\\"}, \\\\\\r\\n {\\\"b\\\":\\\"target2\\\"},{\\\"c\\\":\\\"target3\\\"},{\\\"a\\\":\\\"target4\\\"},{\\\"b\\\":\\\"target5\\\"}, \\\\\\r\\n {\\\"c\\\":\\\"target6\\\"}, {\\\"a\\\":\\\"target7\\\"},{\\\"b\\\":\\\"target8\\\"},{\\\"c\\\":\\\"target9\\\"}, \\\\\\r\\n {\\\"a\\\":\\\"target10\\\"}]'\\r\\n correct_answer = [\\r\\n {\\r\\n 'draggables': ['a'],\\r\\n 'targets': ['target1', 'target4', 'target7', 'target10'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['b'],\\r\\n 'targets': ['target2', 'target5', 'target8'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['c'],\\r\\n 'targets': ['target3', 'target6', 'target9'],\\r\\n 'rule': 'unordered_equal'\\r\\n }\\r\\n ]\\r\\n self.assertTrue(draganddrop.grade(user_input, correct_answer))\",\n \"def test_label_10_targets_with_a_b_c_multiple(self):\\r\\n user_input = '[{\\\"a\\\":\\\"target1\\\"}, \\\\\\r\\n {\\\"b\\\":\\\"target2\\\"},{\\\"c\\\":\\\"target3\\\"},{\\\"b\\\":\\\"target5\\\"}, \\\\\\r\\n {\\\"c\\\":\\\"target6\\\"}, {\\\"a\\\":\\\"target7\\\"},{\\\"b\\\":\\\"target8\\\"},{\\\"c\\\":\\\"target9\\\"}, \\\\\\r\\n {\\\"a\\\":\\\"target1\\\"}]'\\r\\n correct_answer = [\\r\\n {\\r\\n 'draggables': ['a', 'a', 'a'],\\r\\n 'targets': ['target1', 'target4', 'target7', 'target10'],\\r\\n 'rule': 'anyof+number'\\r\\n },\\r\\n {\\r\\n 'draggables': ['b', 'b', 'b'],\\r\\n 'targets': ['target2', 'target5', 'target8'],\\r\\n 'rule': 'anyof+number'\\r\\n },\\r\\n {\\r\\n 'draggables': ['c', 'c', 'c'],\\r\\n 'targets': ['target3', 'target6', 'target9'],\\r\\n 'rule': 'anyof+number'\\r\\n }\\r\\n ]\\r\\n self.assertTrue(draganddrop.grade(user_input, correct_answer))\",\n \"def test_subsystems(self):\\n pass\",\n \"def test_multiply_some_type_links():\",\n \"def test_build_match_tree_with_pairs():\\n abbreviation_list = [[\\\"ELIF\\\", \\\"ELI.\\\"], [\\\"ELSE\\\", \\\"E.\\\"]]\\n expected_tree = {\\\"E\\\": {\\\"L\\\": {\\\"I\\\": {\\\"F\\\": \\\"ELI.\\\"}, \\\"S\\\": {\\\"E\\\": \\\"E.\\\"}}}}\\n tree = build_match_tree(abbreviation_list)\\n assert repr(tree) == repr(expected_tree)\",\n \"def check_a11y_relation(filename, tree):\\n global widgets_ignored, ids, label_for_elm, labelled_by_elm, description_for_elm, described_by_elm, mnemonic_for_elm\\n\\n def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\\n \\\"\\\"\\\"\\n Check one element, knowing that orphan_labels/widgets tell whether\\n there are orphan labels and widgets within orphan_root\\n \\\"\\\"\\\"\\n\\n oid = obj.attrib.get('id')\\n klass = obj.attrib.get('class')\\n\\n # \\\"Don't care\\\" special case\\n if klass in widgets_ignored:\\n return\\n for suffix in widgets_suffixignored:\\n if klass[-len(suffix):] == suffix:\\n return\\n\\n # Widgets usual do not strictly require a label, i.e. a labelled parent\\n # is enough for context, but some do always need one.\\n requires_label = klass in widgets_needlabel\\n\\n if oid is not None:\\n # Check that ids are unique\\n if oid in ids_dup:\\n if ids[oid] == obj:\\n # We are the first, warn\\n duplicates = tree.findall(\\\".//object[@id='\\\" + oid + \\\"']\\\")\\n err(filename, tree, obj, \\\"duplicate-id\\\", \\\"has the same id as other elements \\\" + elms_names_lines(duplicates))\\n\\n # Check label-for and their dual labelled-by\\n label_for = check_rels(filename, tree, root, obj, \\\"label-for\\\", \\\"labelled-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n labelled_by = check_rels(filename, tree, root, obj, \\\"labelled-by\\\", \\\"label-for\\\")\\n\\n # Check label-for and their dual labelled-by\\n description_for = check_rels(filename, tree, root, obj, \\\"description-for\\\", \\\"described-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n described_by = check_rels(filename, tree, root, obj, \\\"described-by\\\", \\\"description-for\\\")\\n\\n # Should have only one label\\n if len(labelled_by) >= 1:\\n if oid in mnemonic_for_elm:\\n warn(filename, tree, obj, \\\"labelled-by-and-mnemonic\\\",\\n \\\"has both a mnemonic \\\" + elm_name_line(mnemonic_for_elm[oid][0]) + \\\"and labelled-by relation\\\")\\n if len(labelled_by) > 1:\\n warn(filename, tree, obj, \\\"multiple-labelled-by\\\", \\\"has multiple labelled-by relations\\\")\\n if oid in label_for_elm:\\n if len(label_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-label-for\\\", \\\"is referenced by multiple label-for \\\" + elms_names_lines(label_for_elm[oid]))\\n if oid in mnemonic_for_elm:\\n if len(mnemonic_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-mnemonic\\\", \\\"is referenced by multiple mnemonic_widget \\\" + elms_names_lines(mnemonic_for_elm[oid]))\\n\\n # Check member-of\\n member_of = check_rels(filename, tree, root, obj, \\\"member-of\\\")\\n\\n # Labels special case\\n if klass in widgets_labels:\\n properties = check_props(filename, tree, root, obj, \\\"mnemonic_widget\\\") + \\\\\\n check_props(filename, tree, root, obj, \\\"mnemonic-widget\\\")\\n if len(properties) > 1:\\n err(filename, tree, obj, \\\"multiple-mnemonic\\\", \\\"has multiple mnemonic_widgets properties\\\"\\n \\\"%s\\\" % elms_lines(properties))\\n\\n # Emit orphaning warnings\\n if warn_orphan_labels or orphan_widgets:\\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\\n\\n # We are done with the label\\n return\\n\\n # Not a label, will perhaps need one\\n\\n # Emit orphaning warnings\\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\\n\\n root = tree.getroot()\\n\\n # Flush ids and relations from previous files\\n ids = {}\\n ids_dup = {}\\n labelled_by_elm = {}\\n label_for_elm = {}\\n described_by_elm = {}\\n description_for_elm = {}\\n mnemonic_for_elm = {}\\n\\n # First pass to get links into hash tables, no warning, just record duplicates\\n for obj in root.iter('object'):\\n oid = obj.attrib.get('id')\\n if oid is not None:\\n if oid not in ids:\\n ids[oid] = obj\\n else:\\n ids_dup[oid] = True\\n\\n labelled_by = obj.findall(\\\"accessibility/relation[@type='labelled-by']\\\")\\n labelled_by += obj.findall(\\\"accessibility/relation[@name='labelled-by']\\\")\\n for rel in labelled_by:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in labelled_by_elm:\\n labelled_by_elm[target] = [ obj ]\\n else:\\n labelled_by_elm[target].append(obj)\\n\\n label_for = obj.findall(\\\"accessibility/relation[@type='label-for']\\\")\\n label_for += obj.findall(\\\"accessibility/relation[@name='label-for']\\\")\\n for rel in label_for:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in label_for_elm:\\n label_for_elm[target] = [ obj ]\\n else:\\n label_for_elm[target].append(obj)\\n\\n described_by = obj.findall(\\\"accessibility/relation[@type='described-by']\\\")\\n described_by += obj.findall(\\\"accessibility/relation[@name='described-by']\\\")\\n for rel in described_by:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in described_by_elm:\\n described_by_elm[target] = [ obj ]\\n else:\\n described_by_elm[target].append(obj)\\n\\n description_for = obj.findall(\\\"accessibility/relation[@type='description-for']\\\")\\n description_for += obj.findall(\\\"accessibility/relation[@name='description-for']\\\")\\n for rel in description_for:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in description_for_elm:\\n description_for_elm[target] = [ obj ]\\n else:\\n description_for_elm[target].append(obj)\\n\\n mnemonic_for = obj.findall(\\\"property[@name='mnemonic_widget']\\\") + \\\\\\n obj.findall(\\\"property[@name='mnemonic-widget']\\\")\\n for rel in mnemonic_for:\\n target = rel.text\\n if target is not None:\\n if target not in mnemonic_for_elm:\\n mnemonic_for_elm[target] = [ obj ]\\n else:\\n mnemonic_for_elm[target].append(obj)\\n\\n # Second pass, recursive depth-first, to be able to efficiently know whether\\n # there are orphan labels within a part of the tree.\\n def recurse(orphan_root, obj, orphan_labels, orphan_widgets):\\n if obj == root or is_labelled_parent(obj):\\n orphan_root = obj\\n orphan_labels, orphan_widgets = orphan_items(filename, tree, root, obj)\\n\\n if obj.tag == 'object':\\n check_elm(orphan_root, obj, orphan_labels, orphan_widgets)\\n\\n for o in obj:\\n recurse(orphan_root, o, orphan_labels, orphan_widgets)\\n\\n recurse(root, root, False, False)\",\n \"def test_stratis_list_default(self):\\n for subcommand in [[\\\"pool\\\"], [\\\"filesystem\\\"], [\\\"blockdev\\\"]]:\\n for prefix in [[], [\\\"--propagate\\\"]]:\\n self.assertEqual(RUNNER(prefix + subcommand), 0)\",\n \"def pytest_can_run_together(item1, item2):\",\n \"def test_series_in_features(self):\\n assert parse_command({'test{{A,B}}': {'depends_on': 'name{{A,B}}'}}) == [\\n ('testA', {'depends_on': 'nameA'}), ('testB', {'depends_on': 'nameB'})]\",\n \"def test_first_level_from_bids_with_subject_labels(bids_dataset):\\n warning_message = ('Subject label foo is not present in'\\n ' the dataset and cannot be processed')\\n with pytest.warns(UserWarning, match=warning_message):\\n models, *_ = first_level_from_bids(\\n dataset_path=bids_dataset,\\n task_label='main',\\n sub_labels=[\\\"foo\\\", \\\"01\\\"],\\n space_label='MNI',\\n img_filters=[('desc', 'preproc')],\\n slice_time_ref=None,\\n )\\n\\n assert models[0].subject_label == '01'\",\n \"def test_pathop12(self):\\n xpb = XPathBuilder()\\n # braces not needed\\n xp = xpb.foo & (xpb.bar.foo).parenthesize() | xpb.foobar\\n exp = '/foo and (/bar/foo) or /foobar'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_dummy6(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n xp = xpb.bar | xp\\n exp = '/bar'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_create_labels(self):\\n test_labels = {\\\"app\\\": \\\"my_app\\\", \\\"host\\\": \\\"examplehost\\\"}\\n labels = pmp.utils.create_labels(test_labels)\\n self.assertIsInstance(labels, list)\\n self.assertTrue(all([isinstance(x, pmp.LabelPair) for x in labels]))\",\n \"def test_xdist_and_select_test_by_bdd_label(xdist_runner: AllurePytestRunner):\\n\\n output = xdist_runner.run_docstring(\\\"-v\\\", \\\"--allure-features=boo\\\", \\\"-n1\\\")\\n\\n assert_that(output, has_only_testcases(\\n has_entry(\\n \\\"fullName\\\",\\n ends_with(\\\"test_with_feature_boo\\\")\\n )\\n ))\",\n \"def test_nested_condition() -> None:\\n\\n @argcomb(Or(And(\\\"a\\\", \\\"b\\\"), And(\\\"c\\\", \\\"d\\\")))\\n def f(a: Any = None, b: Any = None, c: Any = None, d: Any = None) -> None:\\n ...\\n\\n # valid\\n f(a=1, b=1)\\n f(c=1, d=1)\\n f(a=1, b=1, c=1, d=1)\\n\\n # invalid\\n with pytest.raises(InvalidArgumentCombination):\\n f(a=1)\\n with pytest.raises(InvalidArgumentCombination):\\n f(a=1, c=1)\\n with pytest.raises(InvalidArgumentCombination):\\n f()\",\n \"def test_abbreviation(self):\\n self.assertEqual(self.compound.abbreviation, \\\"Cool\\\")\",\n \"def test_general_subset_all():\\n pass\",\n \"def test_check_tree_exact_match(self):\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_3tips_fp)\\r\\n\\r\\n # Should find all and give True, True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, [True, True])\\r\\n\\r\\n # Should get tips not found in fasta labels with 5 tip tree\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find all and give True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, [True, ['seq5', 'seq4']])\\r\\n\\r\\n # Change two of the fasta labels to not match tree tips\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find seqX and seqY as not being a subset\\r\\n\\r\\n self.assertEqual(actual_subset_results, [['seqX', 'seqY'],\\r\\n ['seq3', 'seq5', 'seq4']])\",\n \"def test_03_visit_special(self):\",\n \"def test_labels(ruler: SpaczzRuler) -> None:\\n assert all(\\n [label in ruler.labels for label in [\\\"GPE\\\", \\\"STREET\\\", \\\"DRUG\\\", \\\"NAME\\\", \\\"BAND\\\"]]\\n )\\n assert len(ruler.labels) == 5\",\n \"def test_createSubLinkographWithoutCommands(self):\\n self.performTestForParams()\",\n \"def test_first_level_from_bids_several_labels_per_entity(tmp_path, entity):\\n n_sub = 1\\n n_ses = 1\\n tasks = [\\\"main\\\"]\\n n_runs = [1]\\n\\n bids_path = create_fake_bids_dataset(\\n base_dir=tmp_path,\\n n_sub=n_sub,\\n n_ses=n_ses,\\n tasks=tasks,\\n n_runs=n_runs,\\n entities={entity: [\\\"A\\\", \\\"B\\\"]},\\n )\\n\\n models, m_imgs, m_events, m_confounds = first_level_from_bids(\\n dataset_path=bids_path,\\n task_label=\\\"main\\\",\\n space_label=\\\"MNI\\\",\\n img_filters=[(\\\"desc\\\", \\\"preproc\\\"), (entity, \\\"A\\\")],\\n slice_time_ref=None,\\n )\\n\\n _check_output_first_level_from_bids(n_sub,\\n models,\\n m_imgs,\\n m_events,\\n m_confounds)\\n n_imgs_expected = n_ses * n_runs[0]\\n assert len(m_imgs[0]) == n_imgs_expected\",\n \"def test_setter_child_list_tuple(self):\\n root = netapp_api.NaElement('root')\\n root['l'] = ['l1', 'l2']\\n root['t'] = ('t1', 't2')\\n l = root.get_child_by_name('l')\\n self.assertIsInstance(l, netapp_api.NaElement)\\n t = root.get_child_by_name('t')\\n self.assertIsInstance(t, netapp_api.NaElement)\\n for le in l.get_children():\\n self.assertIn(le.get_name(), ['l1', 'l2'])\\n for te in t.get_children():\\n self.assertIn(te.get_name(), ['t1', 't2'])\",\n \"def test_tree_support(self):\\r\\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\\r\\n \\\"\\\"\\\"\\r\\n /-------.5 /-a\\r\\n ---1| \\\\-b\\r\\n \\\\------.5 /-c\\r\\n \\\\-d\\r\\n \\\"\\\"\\\"\\r\\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\\r\\n\\r\\n tc.tree_support(master_tree, t2)\\r\\n assert_almost_equal(\\r\\n master_tree.getNodeMatchingName('rt').bootstrap_support, 1.0)\",\n \"def test_labels(self):\\n return self._test_labels\",\n \"def test_issue_edit_label(self):\\n pass\",\n \"def test_dbpa002_radio_items(dash_duo):\\n app = Dash()\\n\\n options = {\\n \\\"OptionA\\\": \\\"Option 1\\\",\\n \\\"OptionB\\\": \\\"Option 2\\\",\\n \\\"OptionC\\\": \\\"Option 3\\\",\\n }\\n\\n value = \\\"OptionB\\\"\\n\\n with_keywords = RadioItems(\\n options=options,\\n value=value,\\n id=\\\"with-keywords\\\",\\n )\\n without_keywords = RadioItems(options, value, id=\\\"without-keywords\\\")\\n\\n app.layout = html.Div([with_keywords, without_keywords])\\n\\n dash_duo.start_server(app)\\n\\n # Check values\\n assert [\\n a.get_attribute(\\\"value\\\")\\n for a in dash_duo.wait_for_element(\\n \\\"#with-keywords\\\"\\n ).find_elements_by_tag_name(\\\"input\\\")\\n ] == [\\n a.get_attribute(\\\"value\\\")\\n for a in dash_duo.wait_for_element(\\n \\\"#without-keywords\\\"\\n ).find_elements_by_tag_name(\\\"input\\\")\\n ]\\n\\n # Check labels\\n assert [\\n a.text\\n for a in dash_duo.wait_for_element(\\n \\\"#with-keywords\\\"\\n ).find_elements_by_tag_name(\\\"label\\\")\\n ] == [\\n a.text\\n for a in dash_duo.wait_for_element(\\n \\\"#without-keywords\\\"\\n ).find_elements_by_tag_name(\\\"label\\\")\\n ]\",\n \"def test(self):\\n for doc, label in zip(self.test_docs(), self.test_labels()):\\n yield doc, label\",\n \"def test_get_parent_type_name(self):\\n pass\",\n \"def test_pathop8(self):\\n xpb = XPathBuilder()\\n xp = (xpb.foo.bar | xpb.foobar).parenthesize() & xpb.action.source\\n exp = '(/foo/bar or /foobar) and /action/source'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_setter_child_list_tuple(self):\\n root = netapp_api.NaElement('root')\\n root['l'] = ['l1', 'l2']\\n root['t'] = ('t1', 't2')\\n l_element = root.get_child_by_name('l')\\n self.assertIsInstance(l_element, netapp_api.NaElement)\\n t = root.get_child_by_name('t')\\n self.assertIsInstance(t, netapp_api.NaElement)\\n for le in l_element.get_children():\\n self.assertIn(le.get_name(), ['l1', 'l2'])\\n for te in t.get_children():\\n self.assertIn(te.get_name(), ['t1', 't2'])\",\n \"def __init__(self, root_node, label1, label2):\\n super().__init__(self.PROBLEM_NAME)\\n self.root_node = root_node\\n self.label1 = label1\\n self.label2 = label2\",\n \"def test_radioselect_field():\",\n \"def test_label_callback():\\n release_numbers = dict(a='123')\\n data = dict(revision='a', attributes=dict(b='c'))\\n data2 = dict(revision='b', attributes=dict(d='e'))\\n\\n assert _label_callback(data, release_numbers) == u'a\\\\n- Release: 123\\\\n- b: c'\\n assert _label_callback(data2) == u'b\\\\n- Release: Unknown\\\\n- d: e'\",\n \"def _make_simple_partition_label(chain_dict):\\n\\n cps = chain_dict['chainParts']\\n if not (_select_simple_chainparts(cps)):\\n raise NotImplementedError(\\n 'chain fails substring selection: not \\\"simple\\\": %s' % (\\n chain_dict['chainName']))\\n \\n label = 'simplepartition(['\\n for cp in cps:\\n smcstr = str(cp['smc'])\\n if smcstr == 'nosmc':\\n smcstr = ''\\n for i in range(int(cp['multiplicity'])):\\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\\n # str(cp['etaRange']),\\n # smcstr,)\\n condition_str = '(%set,%s' % (str(cp['threshold']),\\n str(cp['etaRange']),)\\n if smcstr:\\n condition_str += ',%s)'\\n else:\\n condition_str += ')'\\n label += condition_str\\n label += '])'\\n return label\",\n \"def test_case1(self):\\n\\n graph = BipartiteGraph()\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student1\\\")\\n\\n val1 = graph.getStudents(\\\"supervisor1\\\")\\n val2 = graph.getSupervisors(\\\"student1\\\")\\n\\n expected1 = [\\\"student1\\\"]\\n expected2 = [\\\"supervisor1\\\"]\\n\\n self.assertEqual((val1,val2),(expected1,expected2))\",\n \"def test_with_multiple_descriptions():\\n soup = generate_case(\\\"with_descriptions\\\")\\n\\n tests.html_schema_doc_asserts.assert_descriptions(\\n soup,\\n [\\n \\\"Exact address\\\",\\n \\\"Exact address\\\",\\n \\\"Delivery info depending on the delivery type\\\",\\n \\\"The delivery is a gift, no prices displayed\\\",\\n ],\\n )\",\n \"def test_BuildModel2(self):\\n print(\\\"\\\\nTest 6: Building a Model with Concat\\\")\\n builder = StaticBuilder(\\\"Concat\\\")\\n in1 = builder.addInput(10)\\n in2 = builder.addInput(20)\\n enc1 = builder.addInner(3, num_islots=2)\\n out1 = builder.addOutput()\\n\\n builder.addDirectedLink(in1, enc1, islot=0)\\n builder.addDirectedLink(in2, enc1, islot=1)\\n builder.addDirectedLink(enc1, out1)\\n \\n builder.build()\",\n \"def test_multiple_label_traversals(self):\\r\\n TestEdge.create(self.v1, self.v2)\\r\\n OtherTestEdge.create(self.v1, self.v3)\\r\\n YetAnotherTestEdge.create(self.v1, self.v4)\\r\\n\\r\\n assert len(self.v1.outV()) == 3\\r\\n\\r\\n assert len(self.v1.outV(TestEdge)) == 1\\r\\n assert len(self.v1.outV(OtherTestEdge)) == 1\\r\\n assert len(self.v1.outV(YetAnotherTestEdge)) == 1\\r\\n\\r\\n out = self.v1.outV(TestEdge, OtherTestEdge)\\r\\n assert len(out) == 2\\r\\n assert self.v2.vid in [v.vid for v in out]\\r\\n assert self.v3.vid in [v.vid for v in out]\\r\\n\\r\\n out = self.v1.outV(OtherTestEdge, YetAnotherTestEdge)\\r\\n assert len(out) == 2\\r\\n assert self.v3.vid in [v.vid for v in out]\\r\\n assert self.v4.vid in [v.vid for v in out]\",\n \"def test_label_10_targets_with_a_b_c_false(self):\\r\\n user_input = '[{\\\"a\\\":\\\"target1\\\"}, \\\\\\r\\n {\\\"b\\\":\\\"target2\\\"},{\\\"c\\\":\\\"target3\\\"},{\\\"a\\\":\\\"target4\\\"},{\\\"b\\\":\\\"target5\\\"}, \\\\\\r\\n {\\\"c\\\":\\\"target6\\\"}, {\\\"a\\\":\\\"target7\\\"},{\\\"b\\\":\\\"target8\\\"},{\\\"c\\\":\\\"target9\\\"}, \\\\\\r\\n {\\\"a\\\":\\\"target1\\\"}]'\\r\\n correct_answer = [\\r\\n {\\r\\n 'draggables': ['a'],\\r\\n 'targets': ['target1', 'target4', 'target7', 'target10'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['b'],\\r\\n 'targets': ['target2', 'target5', 'target8'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['c'],\\r\\n 'targets': ['target3', 'target6', 'target9'],\\r\\n 'rule': 'unordered_equal'\\r\\n }\\r\\n ]\\r\\n self.assertFalse(draganddrop.grade(user_input, correct_answer))\"\n]"},"negative_scores":{"kind":"list like","value":["0.6137433","0.6108757","0.60195917","0.5986544","0.5936767","0.58589","0.5759686","0.56950617","0.5624841","0.55902916","0.55750483","0.5560212","0.5554626","0.55542696","0.5553175","0.5553175","0.55467266","0.5503819","0.5457172","0.5451371","0.54313546","0.53999746","0.5376586","0.5375577","0.5364844","0.5359766","0.535864","0.5338235","0.53258616","0.53186446","0.5317656","0.5315998","0.5315065","0.528178","0.5272056","0.5271867","0.5262255","0.5256516","0.5254785","0.5251119","0.5245671","0.52348095","0.5232146","0.5209617","0.520278","0.52023655","0.51976585","0.51973414","0.5193984","0.5191381","0.51910853","0.5168662","0.51612574","0.5156716","0.5155904","0.5155904","0.5155904","0.515586","0.51550406","0.51405126","0.51366323","0.5126447","0.51249856","0.5119241","0.5116798","0.5115688","0.5107617","0.510387","0.5094173","0.5092521","0.5089139","0.50838256","0.5078707","0.5075308","0.50716203","0.5061532","0.5061452","0.50607544","0.50605214","0.50554216","0.50476885","0.5047116","0.50363714","0.5028754","0.5026291","0.5024467","0.50243545","0.5023076","0.50198036","0.5014891","0.5011064","0.5007032","0.50008494","0.49937844","0.49911672","0.49879867","0.49861643","0.49833554","0.49800858","0.49767923"],"string":"[\n \"0.6137433\",\n \"0.6108757\",\n \"0.60195917\",\n \"0.5986544\",\n \"0.5936767\",\n \"0.58589\",\n \"0.5759686\",\n \"0.56950617\",\n \"0.5624841\",\n \"0.55902916\",\n \"0.55750483\",\n \"0.5560212\",\n \"0.5554626\",\n \"0.55542696\",\n \"0.5553175\",\n \"0.5553175\",\n \"0.55467266\",\n \"0.5503819\",\n \"0.5457172\",\n \"0.5451371\",\n \"0.54313546\",\n \"0.53999746\",\n \"0.5376586\",\n \"0.5375577\",\n \"0.5364844\",\n \"0.5359766\",\n \"0.535864\",\n \"0.5338235\",\n \"0.53258616\",\n \"0.53186446\",\n \"0.5317656\",\n \"0.5315998\",\n \"0.5315065\",\n \"0.528178\",\n \"0.5272056\",\n \"0.5271867\",\n \"0.5262255\",\n \"0.5256516\",\n \"0.5254785\",\n \"0.5251119\",\n \"0.5245671\",\n \"0.52348095\",\n \"0.5232146\",\n \"0.5209617\",\n \"0.520278\",\n \"0.52023655\",\n \"0.51976585\",\n \"0.51973414\",\n \"0.5193984\",\n \"0.5191381\",\n \"0.51910853\",\n \"0.5168662\",\n \"0.51612574\",\n \"0.5156716\",\n \"0.5155904\",\n \"0.5155904\",\n \"0.5155904\",\n \"0.515586\",\n \"0.51550406\",\n \"0.51405126\",\n \"0.51366323\",\n \"0.5126447\",\n \"0.51249856\",\n \"0.5119241\",\n \"0.5116798\",\n \"0.5115688\",\n \"0.5107617\",\n \"0.510387\",\n \"0.5094173\",\n \"0.5092521\",\n \"0.5089139\",\n \"0.50838256\",\n \"0.5078707\",\n \"0.5075308\",\n \"0.50716203\",\n \"0.5061532\",\n \"0.5061452\",\n \"0.50607544\",\n \"0.50605214\",\n \"0.50554216\",\n \"0.50476885\",\n \"0.5047116\",\n \"0.50363714\",\n \"0.5028754\",\n \"0.5026291\",\n \"0.5024467\",\n \"0.50243545\",\n \"0.5023076\",\n \"0.50198036\",\n \"0.5014891\",\n \"0.5011064\",\n \"0.5007032\",\n \"0.50008494\",\n \"0.49937844\",\n \"0.49911672\",\n \"0.49879867\",\n \"0.49861643\",\n \"0.49833554\",\n \"0.49800858\",\n \"0.49767923\"\n]"},"document_score":{"kind":"string","value":"0.6972838"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8791,"cells":{"query":{"kind":"string","value":"make test label for combinations helper with two simple children."},"document":{"kind":"string","value":"def _make_partitionsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'partitionsTest'\n\n \n\n return \"\"\"\n partgen(\n [(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\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 _make_combinationsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'combinationsTest'\n\n \n\n return \"\"\"\n combgen(\n [(2)(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\n )\"\"\"","def test_sums():\n assert label_parent(1, 2) == 3\n assert label_parent (1, 4) == 8\n # Should ignore arg order\n assert label_parent(4, 1) == 8","def test_recipe_nutrition_label_widget(self):\n pass","def test_select_label(self):\n title = ('Transportation Challenges Limit Education Choices for '\n 'Denver Parents')\n summary = \"\"\"\n Many families in the Denver metro area use public\n transportation instead of a school bus because for them, a\n quality education is worth hours of daily commuting. Colorado's\n school choice program is meant to foster educational equity,\n but the families who benefit most are those who have time and\n money to travel. Low-income families are often left in a lurch.\n \"\"\"\n byline = \"Mile High Connects\"\n story = create_story(title=title, summary=summary, byline=byline)\n layout = SectionLayout.objects.get(sectionlayouttranslation__name=\"Side by Side\")\n section1 = create_section(title=\"Test Section 1\", story=story, layout=layout)\n section2 = create_section(title=\"Test Section 2\", story=story, layout=layout)\n form = SectionRelationAdminForm()\n choices_list = list(form.fields['parent'].widget.choices)\n self.assertIn(story.title, choices_list[1][1])\n self.assertIn(story.title, choices_list[2][1])","def setUp(self):\n\n singleLabels = linkoCreate.Linkograph(\n [({'A'}, set(), {1,2,3}),\n ({'D'}, {0}, {3,4}),\n ({'A'}, {0}, {4}),\n ({'C'}, {0,1}, {4}),\n ({'A'}, {1,2,3}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_2 = linkoCreate.Linkograph(\n [({'A'}, set(), {1,2}),\n ({'D'}, {0}, set()),\n ({'A'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_1 = linkoCreate.Linkograph(\n [({'A'}, set(), {1}),\n ({'D'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_0 = linkoCreate.Linkograph(\n [({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko1_2 = linkoCreate.Linkograph(\n [({'D'}, set(), set()),\n ({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko1_1 = linkoCreate.Linkograph(\n [({'D'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n trivialLinkograph = linkoCreate.Linkograph(\n [], ['A', 'B', 'C', 'D'])\n\n\n singleSubLinko1_4 = linkoCreate.Linkograph(\n [({'D'}, set(), {2,3}),\n ({'A'}, set(), {3}),\n ({'C'}, {0}, {3}),\n ({'A'}, {0,1,2}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko2_4 = linkoCreate.Linkograph(\n [({'A'}, set(), {2}),\n ({'C'}, set(), {2}),\n ({'A'}, {0,1}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko3_4 = linkoCreate.Linkograph(\n [({'C'}, set(), {1}),\n ({'A'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko4_4 = linkoCreate.Linkograph(\n [({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n simpleLinko = linkoCreate.Linkograph(\n [({'A', 'B', 'C'}, set(), {1,2,3}),\n ({'D'}, {0}, {3,4}),\n ({'A'}, {0}, {4}),\n ({'B', 'C'}, {0,1}, {4}),\n ({'A'}, {1,2,3}, set())],\n ['A', 'B', 'C', 'D'])\n\n if self.id().split('.')[-1] == 'test_createSubLinkographWithoutCommands':\n self.testParams = [\n {'linko': singleLabels,\n 'lowerBound': None,\n 'upperBound': None,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 5,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': -1,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': None,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 1,\n 'ExpectedLinkograph': singleSubLinko0_1},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 0,\n 'ExpectedLinkograph': singleSubLinko0_0},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': -1,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko1_2},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 1,\n 'ExpectedLinkograph': singleSubLinko1_1},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 0,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': -1,\n 'upperBound': -1,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko1_4},\n\n {'linko': singleLabels,\n 'lowerBound': 2,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko2_4},\n\n {'linko': singleLabels,\n 'lowerBound': 3,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko3_4},\n\n {'linko': singleLabels,\n 'lowerBound': 4,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko4_4},\n\n ]","def test_labels(self):\n self.compliance_tester.test_labels(self.oi)","def test_parent_label(self):\n l = self.d.label(1)\n l2 = self.d.label(31405)\n\n self.assertTrue(l.parent_label is None)\n self.assertTrue(l2 in l.sublabels)\n self.assertEqual(l2.parent_label, l)","def test_simple(self):\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n content = '((h,p)hp:1,g)hpg;'\n self._do_test(content, exp)\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP,\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n self._do_test(content, exp)","def test_label(self):\n xs = t.Label(t.Exactly(\"x\"), 'CustomLabel')\n self.assertEqual(writePython(xs),\n dd(\"\"\"\n def _G_label_1():\n _G_exactly_2, lastError = self.exactly('x')\n self.considerError(lastError, None)\n return (_G_exactly_2, self.currentError)\n _G_label_3, lastError = self.label(_G_label_1, \"CustomLabel\")\n self.considerError(lastError, None)\n _G_label_3\n \"\"\"))","def addLabels(t):\n if not t.label:\n t.label = \"\".join([choice(\"abcdefghijklmnopqrstuvwxyz\") for i in range(4)])\n for r,w in t.children:\n addLabels(r)","def test_general_subset_level():\n pass","def _make_simple_comb_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n simple_strs = []\n\n for cp in cps:\n print(cp)\n simple_strs.append(_make_simple_label([cp]))\n\n label = 'combgen([(%d)]' % len(cps)\n for s in simple_strs:\n label += ' %s ' % s\n label += ')'\n return label","def _generateLabelAndName(self, obj, **args):\n result = []\n label = self._generateLabel(obj, **args)\n name = self._generateName(obj, **args)\n result.extend(label)\n if not len(label):\n result.extend(name)\n elif len(name) and name[0].strip() != label[0].strip():\n result.extend(name)\n return result","def tests_ti_document_add_label(self):\n super().group_add_label()","def getLabel2(*args):","def getLabel2(*args):","def test_checkboxtextgroup(self):\r\n self.check_group('checkboxtextgroup', 'choice', 'checkbox')","def _generateTableCell2ChildLabel(self, obj, **args):\n result = []\n\n # If this table cell has 2 children and one of them has a\n # 'toggle' action and the other does not, then present this\n # as a checkbox where:\n # 1) we get the checked state from the cell with the 'toggle' action\n # 2) we get the label from the other cell.\n # See Orca bug #376015 for more details.\n #\n if obj.childCount == 2:\n cellOrder = []\n hasToggle = [False, False]\n for i, child in enumerate(obj):\n if self._script.utilities.hasMeaningfulToggleAction(child):\n hasToggle[i] = True\n break\n if hasToggle[0] and not hasToggle[1]:\n cellOrder = [ 1, 0 ]\n elif not hasToggle[0] and hasToggle[1]:\n cellOrder = [ 0, 1 ]\n if cellOrder:\n for i in cellOrder:\n if not hasToggle[i]:\n result.extend(self.generate(obj[i], **args))\n return result","def test_process_label_in_node(self):\n tree = Node(children=[\n Node(\"Defining secret phrase.\", label=['AB', 'a']),\n Node(\"Has secret phrase. Then some other content\", \n label=['AB', 'b'])\n ], label=['AB'])\n t = Terms(tree)\n t.scoped_terms = {\n ('AB',): [Ref(\"secret phrase\", \"AB-a\", (9,22))]\n }\n # Term is defined in the first child\n self.assertEqual([], t.process(tree.children[0]))\n self.assertEqual(1, len(t.process(tree.children[1])))","def get_extra_label(self, label_name: str, hierarchy: List[str]) -> Any:","def test_get_scenarios_expanded(self):\n pass","def test_nested():\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.root.metadata.choose(lambda m: (m[\"name\"], m.uid))))\n assert \"type\" in res # from conditions\n assert \"reason\" in res # from conditions\n assert \"name\" in res # from metadata\n assert \"uid\" in res # from metadata","def _make_simple_label(chain_parts):\n \n if not _select_simple_chainparts(chain_parts):\n msg = 'Jet Configuration error: '\\\n 'chain fails substring selection: not \"simple\" '\n\n raise NotImplementedError(msg)\n \n label = 'simple(['\n for cp in chain_parts:\n smcstr = str(cp['smc'])\n jvtstr = str(cp['jvt'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr: # Run 2 chains have \"INF\" in the SMC substring\n condition_str += ',%s)' % smcstr.replace('INF','')\n elif jvtstr:\n condition_str += ',%s)' % jvtstr\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def test_nested_sequence(self):\n\n self.taxon_tester('Apis mellifera')\n self.taxon_tester('Apis')\n self.taxon_tester('Apini')\n self.taxon_tester('Apinae')\n # Apidae at 5680 species is a struggle\n self.taxon_tester('Apidae')\n if False:\n # Apoidea: 19566 takes 223 seconds\n self.taxon_tester('Apoidea')\n # Aculeata fails after 339 seconds\n self.taxon_tester('Aculeata')\n self.taxon_tester('Apocrita')\n self.taxon_tester('Hymenoptera')\n self.taxon_tester('Endopterygota')\n self.taxon_tester('Neoptera')\n self.taxon_tester('Pterygota')\n self.taxon_tester('Dicondylia')\n self.taxon_tester('Insecta')\n self.taxon_tester('Hexapoda')\n self.taxon_tester('Pancrustacea')\n self.taxon_tester('Mandibulata')\n self.taxon_tester('Arthropoda')\n self.taxon_tester('Panarthropoda')\n self.taxon_tester('Ecdysozoa')\n self.taxon_tester('Protostomia')\n self.taxon_tester('Bilateria')\n self.taxon_tester('Eumetazoa')\n self.taxon_tester('Metazoa')\n self.taxon_tester('Holozoa')\n self.taxon_tester('Opisthokonta')\n self.taxon_tester('Eukaryota')","def test_bootstrap_support_labeled(self):\r\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\r\n \"\"\"\r\n /-------.5 /-a\r\n ---1| \\-b\r\n \\------.5 /-c\r\n \\-d\r\n \"\"\"\r\n t1 = parse_newick('((a:6,b:8.2)hi:2,(c:1,d:2):7);') # same structure\r\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\r\n new_master, bootstraps = tc.bootstrap_support(master_tree, [t1, t2])\r\n expected = dict([('ab', .5), ('cd', .5), ('rt', 1.0)])\r\n self.assertDictEqual(bootstraps, expected)","def test_title(names):","def test_verbose_name_group(self): \n field_verboses = {\n \"title\": \"Название группы\",\n \"slug\": \"Слаг\",\n \"description\": \"Описание группы\",\n }\n for value, expected in field_verboses.items():\n with self.subTest(value=value):\n self.assertEqual(self.group._meta.get_field(value).verbose_name, expected)","def plugin_second_label():\n return \"second\"","def test_product_labels(self):\n\n prd = Product.objects.get(id=1)\n # label name\n label_name = prd._meta.get_field('name').verbose_name\n self.assertEqual(label_name, 'name')\n # label description\n label_name = prd._meta.get_field('description').verbose_name\n self.assertEqual(label_name, 'description')\n # label nutrition_grade\n label_name = prd._meta.get_field('nutrition_grade').name\n self.assertEqual(label_name, 'nutrition_grade')\n # label barcode\n label_name = prd._meta.get_field('barcode').verbose_name\n self.assertEqual(label_name, 'barcode')\n # label url\n label_name = prd._meta.get_field('url').verbose_name\n self.assertEqual(label_name, 'url')\n # label url_pic\n label_name = prd._meta.get_field('url_pic').name\n self.assertEqual(label_name, 'url_pic')\n # label store\n label_name = prd._meta.get_field('store').verbose_name\n self.assertEqual(label_name, 'store')\n # label prd_cat\n label_name = prd._meta.get_field('prd_cat').name\n self.assertEqual(label_name, 'prd_cat')\n # label fat\n label_name = prd._meta.get_field('fat').verbose_name\n self.assertEqual(label_name, 'fat')\n # label saturated_fat\n label_name = prd._meta.get_field('saturated_fat').name\n self.assertEqual(label_name, 'saturated_fat')\n # label sugar\n label_name = prd._meta.get_field('sugar').verbose_name\n self.assertEqual(label_name, 'sugar')\n # label salt\n label_name = prd._meta.get_field('salt').verbose_name\n self.assertEqual(label_name, 'salt')","def is_test(self):\r\n return self.has_label('tests')","def setUp(self):\n\n\n # InverseLabeling\n invLabeling0 = {'L0': [0, 1, 2]}\n\n invLabeling1 = {'L0' : [0, 2],\n 'L1' : [1]}\n\n invLabeling2 = {\n 'L0' : [0],\n 'L1' : [1],\n 'L2' : [2]\n }\n\n invLabeling3 = {\n 'L1' : [0, 1],\n 'L2' : [2]\n }\n\n invLabeling4 = {\n 'L0' : [0,1],\n 'L1' : [0],\n 'L2' : [2]\n }\n\n invLabeling5 = {\n 'L0': [0, 1, 2],\n 'L1': []\n }\n \n # Create some ontologies\n ontology0 = {'L0': ['L0']}\n\n ontology1 = {}\n\n ontology2 = {'L0': ['L1']}\n\n ontology3 = {'L0': ['L1', 'L2'],\n 'L1': ['L2'],\n 'L2': ['L0']}\n\n if self.id().split('.')[-1] == 'test_createLinkograph':\n self.testParams = [\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology0,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1, 2}),\n ({'L0'}, {0}, {2}),\n ({'L0'}, {0,1}, set())] \n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology1,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology2,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology0,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {2}),\n ({'L1'}, set(), set()),\n ({'L0'}, {0}, set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology1,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L1'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology2,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1}),\n ({'L1'}, {0}, set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1}),\n ({'L1'}, {0}, set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling2,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1,2}),\n ({'L1'}, {0}, {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling3,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L1'}, set(), {2}),\n ({'L1'}, set(), {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling4,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0', 'L1'}, set(), {2}),\n ({'L0'}, set(), {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling5,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n ]","def test_mutual_exclusivity_of_labels(self, tmpdir, docker_tasker, labels, expected_fail):\n runner = mock_env(tmpdir, docker_tasker, labels=labels)\n if expected_fail:\n with pytest.raises(PluginFailedException) as e:\n runner.run()\n assert 'only one of labels' in str(e.value)\n else:\n runner.run()","def helper_label(helper):\n return helper.info","def test_Tree():","def test_col_data_label_with_attrs(self):\n help_tag = 'span'\n help_text_br = False\n names = ('first', 'billing_address_1')\n attrs = {'style': 'width: 10rem; display: inline-block'}\n label_attrs = {name: attrs for name in names}\n txt = '{}=\"{}\"'.format(*list(attrs.items())[0])\n expected = [''.format(txt)]\n expected.append(''.format(txt))\n actual = []\n for name in names:\n field = self.form.fields[name]\n response = self.form.collect_col_data(name, field, help_tag, help_text_br, label_attrs)\n actual.append(response.get('label'))\n\n for expect, got in zip(expected, actual):\n self.assertEqual(expect, got)","def test_help_text_group(self): \n field_help_text = {\n \"title\": \"Дайте назание группе\",\n \"slug\": ('Укажите адрес для группы. Используйте '\n 'только латиницу, цифры, дефисы и знаки '\n 'подчёркивания'),\n } \n for value, expected in field_help_text.items():\n with self.subTest(value=value):\n self.assertEqual(self.group._meta.get_field(value).help_text, expected)","def test_label_choices(self):\n test_classes = (\n (0, 'No'),\n (1, 'Yes')\n )\n\n form = SingleLabelClassifierForm(classes=test_classes)\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)\n\n form = MultiLabelClassifierForm(classes=test_classes)\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)","def test_filter_tree(self):\r\n actual = [e.Name for e in filter_tree(\r\n self.tree1, ['bbb', 'ccc']).tips()]\r\n #(a_a:10,(b_b:2,c_c:4):5);\r\n expected = [\r\n e.Name for e in DndParser(\r\n \"((bbb:2,ccc:4));\",\r\n PhyloNode).tips(\r\n )]\r\n self.assertEqual(actual, expected)\r\n\r\n actual = [e.Name for e in filter_tree(\r\n self.tree2, ['bbb', 'ccc']).tips()]\r\n #(a_a:10,(b_b:2,c_c:4):5);\r\n expected = [\r\n e.Name for e in DndParser(\r\n \"(('bbb':2,ccc:4));\",\r\n PhyloNode).tips(\r\n )]\r\n self.assertEqual(actual, expected)","def test_radiotextgroup(self):\r\n self.check_group('radiotextgroup', 'choice', 'radio')","def gen_test_case_combination(self):\n\n cases = '\\n'\n\n binary_ops = list(self.BINARY_OPS)\n binary_ops.reverse()\n for op1 in self.BINARY_OPS:\n for op2 in binary_ops:\n\n result = []\n ret = IntOp.binary_op(op2, '0', '1', self.lane_width)\n ret = IntOp.binary_op(op1, ret, '2', self.lane_width)\n result.append(ret)\n\n cases += '\\n' + str(AssertReturn('{lane_type}.{op1}-{lane_type}.{op2}'.format(lane_type=self.LANE_TYPE, op1=op1, op2=op2),\n [SIMD.v128_const('0', self.LANE_TYPE),\n SIMD.v128_const('1', self.LANE_TYPE),\n SIMD.v128_const('2', self.LANE_TYPE)],\n SIMD.v128_const(result, self.LANE_TYPE)))\n cases += '\\n'\n return cases","def test_multiple_task_groups_dag(\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\n ):\n (\n dag,\n group1,\n group2,\n group3,\n (\n group1_emp1,\n group1_emp2,\n group1_emp3,\n group2_emp1,\n group2_emp2,\n group2_emp3,\n group2_op1,\n group2_op2,\n group3_emp1,\n group3_emp2,\n group3_emp3,\n emp_in1,\n emp_in2,\n emp_in3,\n emp_in4,\n emp_out1,\n emp_out2,\n emp_out3,\n emp_out4,\n op_in1,\n op_out1,\n ),\n ) = test_multiple_taskgroups_dag\n\n group1_emp1 >> Label(\"label group1.group1_emp1 <=> group1.group1_emp2\") >> group1_emp3\n\n emp_in1 >> group1\n emp_in2 >> Label(\"label emp_in2 <=> group1\") >> group1\n [emp_in3, emp_in4] >> Label(\"label emp_in3/emp_in4 <=> group1\") >> group1\n XComArg(op_in1, \"test_key\") >> Label(\"label op_in1 <=> group1\") >> group1\n\n (\n [group2_emp1, group2_emp2]\n >> Label(\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\")\n >> group2_emp3\n )\n (\n group2_emp1\n >> Label(\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\")\n >> [group2_emp2, group2_emp3]\n )\n group2_emp3 >> Label(\"label group2.group2_emp3 <=> group3\") >> group3\n\n (\n XComArg(group2_op1, \"test_key\")\n >> Label(\"label group2.group2_op1 <=> group2.group2_op2\")\n >> XComArg(group2_op2, \"test_key\")\n )\n XComArg(group2_op2, \"test_key\") >> Label(\"label group2.group2_op2 <=> group3\") >> group3\n\n group3 >> emp_out1\n group3 >> Label(\"label group3 <=> emp_out2\") >> emp_out2\n group3 >> Label(\"label group3 <=> emp_out3/emp_out4\") >> [emp_out3, emp_out4]\n group3 >> Label(\"label group3 <=> op_out1\") >> XComArg(op_out1, \"test_key\")\n\n group1 >> Label(\"label group1 <=> group2\") >> group2\n\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)","def test_issue_create_label(self):\n pass","def test__create_label_w_no_ent_id(ruler: SpaczzRuler) -> None:\n assert ruler._create_label(\"TEST\", None) == \"TEST\"","def test_grouping(self):\n s = self.create(ComponentItem, UML.Component)\n uc1 = self.create(UseCaseItem, UML.UseCase)\n uc2 = self.create(UseCaseItem, UML.UseCase)\n\n self.group(s, uc1)\n assert 1 == len(uc1.subject.subject)\n self.group(s, uc2)\n assert 1 == len(uc2.subject.subject)\n\n # Classifier.useCase is not navigable to UseCase\n # self.assertEqual(2, len(s.subject.useCase))","def setLabel2(*args):","def tests_ti_document_get_label(self):\n super().group_get_label()","def test_render_label(self):\n label = self.block.meta.label\n self.assertEqual(label, 'Google Calendar', 'The labels are not the same')","def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\n\n oid = obj.attrib.get('id')\n klass = obj.attrib.get('class')\n\n # \"Don't care\" special case\n if klass in widgets_ignored:\n return\n for suffix in widgets_suffixignored:\n if klass[-len(suffix):] == suffix:\n return\n\n # Widgets usual do not strictly require a label, i.e. a labelled parent\n # is enough for context, but some do always need one.\n requires_label = klass in widgets_needlabel\n\n if oid is not None:\n # Check that ids are unique\n if oid in ids_dup:\n if ids[oid] == obj:\n # We are the first, warn\n duplicates = tree.findall(\".//object[@id='\" + oid + \"']\")\n err(filename, tree, obj, \"duplicate-id\", \"has the same id as other elements \" + elms_names_lines(duplicates))\n\n # Check label-for and their dual labelled-by\n label_for = check_rels(filename, tree, root, obj, \"label-for\", \"labelled-by\")\n\n # Check labelled-by and its dual label-for\n labelled_by = check_rels(filename, tree, root, obj, \"labelled-by\", \"label-for\")\n\n # Check label-for and their dual labelled-by\n description_for = check_rels(filename, tree, root, obj, \"description-for\", \"described-by\")\n\n # Check labelled-by and its dual label-for\n described_by = check_rels(filename, tree, root, obj, \"described-by\", \"description-for\")\n\n # Should have only one label\n if len(labelled_by) >= 1:\n if oid in mnemonic_for_elm:\n warn(filename, tree, obj, \"labelled-by-and-mnemonic\",\n \"has both a mnemonic \" + elm_name_line(mnemonic_for_elm[oid][0]) + \"and labelled-by relation\")\n if len(labelled_by) > 1:\n warn(filename, tree, obj, \"multiple-labelled-by\", \"has multiple labelled-by relations\")\n if oid in label_for_elm:\n if len(label_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-label-for\", \"is referenced by multiple label-for \" + elms_names_lines(label_for_elm[oid]))\n if oid in mnemonic_for_elm:\n if len(mnemonic_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-mnemonic\", \"is referenced by multiple mnemonic_widget \" + elms_names_lines(mnemonic_for_elm[oid]))\n\n # Check member-of\n member_of = check_rels(filename, tree, root, obj, \"member-of\")\n\n # Labels special case\n if klass in widgets_labels:\n properties = check_props(filename, tree, root, obj, \"mnemonic_widget\") + \\\n check_props(filename, tree, root, obj, \"mnemonic-widget\")\n if len(properties) > 1:\n err(filename, tree, obj, \"multiple-mnemonic\", \"has multiple mnemonic_widgets properties\"\n \"%s\" % elms_lines(properties))\n\n # Emit orphaning warnings\n if warn_orphan_labels or orphan_widgets:\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\n\n # We are done with the label\n return\n\n # Not a label, will perhaps need one\n\n # Emit orphaning warnings\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)","def get_label(self, hierarchy: List[str]) -> Any:","def test_first_level_from_bids_no_duplicate_sub_labels(bids_dataset):\n models, *_ = first_level_from_bids(\n dataset_path=bids_dataset,\n task_label='main',\n sub_labels=[\"01\", \"01\"],\n space_label='MNI',\n img_filters=[('desc', 'preproc')],\n slice_time_ref=None,\n )\n\n assert len(models) == 1","def test_add_empty_nodes_with_label_when_splitting(self):\n print \"----- test_add_empty_nodes_with_label_when_splitting -----\"\n sel_axis = (lambda axis: axis)\n \n #create tree, first node splits in x direction\n tree = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\n kdtree.visualize(tree)\n \n point_left = [0.4, 0.5]\n tree.split2(point_left, axis = 0)\n kdtree.visualize(tree)\n \n point1 = [0.3, 0.5]\n found_node = tree.get_path_to_leaf(point1)[-1]\n correct_node1 = 3\n self.assertEqual(found_node.label, correct_node1, \"Not correct node found\")\n \n point_right = [0.6, 0.5]\n tree.split2(point_right, axis = 1)\n kdtree.visualize(tree)\n \n point2 = [0.6, 0.7]\n found_node = tree.get_path_to_leaf(point2)[-1]\n correct_node2 = 6\n self.assertEqual(found_node.label, correct_node2, \"Not correct node found\")\n \n print \"----- end: test_add_empty_nodes_with_label_when_splitting -----\"","def test_render_value_label(self):\n self.check_html(\n self.widget(choices=self.beatles),\n \"beatles\",\n [\"John\"],\n html=(\n \"\"\"\"\"\"\n ),\n )","def test_issue_add_label(self):\n pass","def test_create_metering_label(self):\r\n resource = 'metering_label'\r\n cmd = metering.CreateMeteringLabel(\r\n test_cli20.MyApp(sys.stdout), None)\r\n name = 'my label'\r\n myid = 'myid'\r\n description = 'my description'\r\n args = [name, '--description', description, '--shared']\r\n position_names = ['name', 'description', 'shared']\r\n position_values = [name, description, True]\r\n self._test_create_resource(resource, cmd, name, myid, args,\r\n position_names, position_values)","def getLabel(*args):","def getLabel(*args):","def getLabel(*args):","def _make_vbenf_label(chain_parts):\n\n # toy label for development: run simple and dijet independently.\n # simple makes Et cuts on two jets. Independently (sharing possible)\n # of jets choosean by simple, the dijet\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n assert scenario.startswith('vbenf')\n args = _args_from_scenario(scenario)\n if not args:\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \n arg_res = [\n re.compile(r'(?P\\d*)(?Pfbet)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pmass)(?P\\d*)'),\n re.compile(r'(?P\\d*)(?Pet)(?P\\d*)'),\n ]\n\n defaults = {\n 'et': ('101', 'inf'),\n 'mass': ('800', 'inf'),\n 'fbet': ('501', 'inf'),\n }\n\n argvals = {}\n while args:\n assert len(args) == len(arg_res)\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = defaults[key][0]\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = defaults[key][1]\n argvals[key+'hi'] = hi\n\n assert len(args) == len(arg_res)\n assert len(args) == 0\n\n return \"\"\"\n and\n (\n []\n simple\n (\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\n )\n combgen\n (\n [(10et, 0eta320)]\n dijet\n (\n [(%(masslo).0fdjmass, 26djdphi)]\n ) \n simple\n (\n [(10et, 0eta320)(20et, 0eta320)]\n )\n )\n )\"\"\" % argvals","def testFormatLabelAndValue(self):\n\n self.assertEqual('Abc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 1))\n self.assertEqual('ABc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 2))\n self.assertEqual('ABC: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 4))","def test_recipe_nutrition_label_image(self):\n pass","def test_label_10_targets_with_a_b_c_(self):\r\n user_input = '[{\"a\":\"target1\"}, \\\r\n {\"b\":\"target2\"},{\"c\":\"target3\"},{\"a\":\"target4\"},{\"b\":\"target5\"}, \\\r\n {\"c\":\"target6\"}, {\"a\":\"target7\"},{\"b\":\"target8\"},{\"c\":\"target9\"}, \\\r\n {\"a\":\"target10\"}]'\r\n correct_answer = [\r\n {\r\n 'draggables': ['a'],\r\n 'targets': ['target1', 'target4', 'target7', 'target10'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['b'],\r\n 'targets': ['target2', 'target5', 'target8'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['c'],\r\n 'targets': ['target3', 'target6', 'target9'],\r\n 'rule': 'unordered_equal'\r\n }\r\n ]\r\n self.assertTrue(draganddrop.grade(user_input, correct_answer))","def test_label_10_targets_with_a_b_c_multiple(self):\r\n user_input = '[{\"a\":\"target1\"}, \\\r\n {\"b\":\"target2\"},{\"c\":\"target3\"},{\"b\":\"target5\"}, \\\r\n {\"c\":\"target6\"}, {\"a\":\"target7\"},{\"b\":\"target8\"},{\"c\":\"target9\"}, \\\r\n {\"a\":\"target1\"}]'\r\n correct_answer = [\r\n {\r\n 'draggables': ['a', 'a', 'a'],\r\n 'targets': ['target1', 'target4', 'target7', 'target10'],\r\n 'rule': 'anyof+number'\r\n },\r\n {\r\n 'draggables': ['b', 'b', 'b'],\r\n 'targets': ['target2', 'target5', 'target8'],\r\n 'rule': 'anyof+number'\r\n },\r\n {\r\n 'draggables': ['c', 'c', 'c'],\r\n 'targets': ['target3', 'target6', 'target9'],\r\n 'rule': 'anyof+number'\r\n }\r\n ]\r\n self.assertTrue(draganddrop.grade(user_input, correct_answer))","def test_subsystems(self):\n pass","def test_multiply_some_type_links():","def test_build_match_tree_with_pairs():\n abbreviation_list = [[\"ELIF\", \"ELI.\"], [\"ELSE\", \"E.\"]]\n expected_tree = {\"E\": {\"L\": {\"I\": {\"F\": \"ELI.\"}, \"S\": {\"E\": \"E.\"}}}}\n tree = build_match_tree(abbreviation_list)\n assert repr(tree) == repr(expected_tree)","def check_a11y_relation(filename, tree):\n global widgets_ignored, ids, label_for_elm, labelled_by_elm, description_for_elm, described_by_elm, mnemonic_for_elm\n\n def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\n \"\"\"\n Check one element, knowing that orphan_labels/widgets tell whether\n there are orphan labels and widgets within orphan_root\n \"\"\"\n\n oid = obj.attrib.get('id')\n klass = obj.attrib.get('class')\n\n # \"Don't care\" special case\n if klass in widgets_ignored:\n return\n for suffix in widgets_suffixignored:\n if klass[-len(suffix):] == suffix:\n return\n\n # Widgets usual do not strictly require a label, i.e. a labelled parent\n # is enough for context, but some do always need one.\n requires_label = klass in widgets_needlabel\n\n if oid is not None:\n # Check that ids are unique\n if oid in ids_dup:\n if ids[oid] == obj:\n # We are the first, warn\n duplicates = tree.findall(\".//object[@id='\" + oid + \"']\")\n err(filename, tree, obj, \"duplicate-id\", \"has the same id as other elements \" + elms_names_lines(duplicates))\n\n # Check label-for and their dual labelled-by\n label_for = check_rels(filename, tree, root, obj, \"label-for\", \"labelled-by\")\n\n # Check labelled-by and its dual label-for\n labelled_by = check_rels(filename, tree, root, obj, \"labelled-by\", \"label-for\")\n\n # Check label-for and their dual labelled-by\n description_for = check_rels(filename, tree, root, obj, \"description-for\", \"described-by\")\n\n # Check labelled-by and its dual label-for\n described_by = check_rels(filename, tree, root, obj, \"described-by\", \"description-for\")\n\n # Should have only one label\n if len(labelled_by) >= 1:\n if oid in mnemonic_for_elm:\n warn(filename, tree, obj, \"labelled-by-and-mnemonic\",\n \"has both a mnemonic \" + elm_name_line(mnemonic_for_elm[oid][0]) + \"and labelled-by relation\")\n if len(labelled_by) > 1:\n warn(filename, tree, obj, \"multiple-labelled-by\", \"has multiple labelled-by relations\")\n if oid in label_for_elm:\n if len(label_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-label-for\", \"is referenced by multiple label-for \" + elms_names_lines(label_for_elm[oid]))\n if oid in mnemonic_for_elm:\n if len(mnemonic_for_elm[oid]) > 1:\n warn(filename, tree, obj, \"duplicate-mnemonic\", \"is referenced by multiple mnemonic_widget \" + elms_names_lines(mnemonic_for_elm[oid]))\n\n # Check member-of\n member_of = check_rels(filename, tree, root, obj, \"member-of\")\n\n # Labels special case\n if klass in widgets_labels:\n properties = check_props(filename, tree, root, obj, \"mnemonic_widget\") + \\\n check_props(filename, tree, root, obj, \"mnemonic-widget\")\n if len(properties) > 1:\n err(filename, tree, obj, \"multiple-mnemonic\", \"has multiple mnemonic_widgets properties\"\n \"%s\" % elms_lines(properties))\n\n # Emit orphaning warnings\n if warn_orphan_labels or orphan_widgets:\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\n\n # We are done with the label\n return\n\n # Not a label, will perhaps need one\n\n # Emit orphaning warnings\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\n\n root = tree.getroot()\n\n # Flush ids and relations from previous files\n ids = {}\n ids_dup = {}\n labelled_by_elm = {}\n label_for_elm = {}\n described_by_elm = {}\n description_for_elm = {}\n mnemonic_for_elm = {}\n\n # First pass to get links into hash tables, no warning, just record duplicates\n for obj in root.iter('object'):\n oid = obj.attrib.get('id')\n if oid is not None:\n if oid not in ids:\n ids[oid] = obj\n else:\n ids_dup[oid] = True\n\n labelled_by = obj.findall(\"accessibility/relation[@type='labelled-by']\")\n labelled_by += obj.findall(\"accessibility/relation[@name='labelled-by']\")\n for rel in labelled_by:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in labelled_by_elm:\n labelled_by_elm[target] = [ obj ]\n else:\n labelled_by_elm[target].append(obj)\n\n label_for = obj.findall(\"accessibility/relation[@type='label-for']\")\n label_for += obj.findall(\"accessibility/relation[@name='label-for']\")\n for rel in label_for:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in label_for_elm:\n label_for_elm[target] = [ obj ]\n else:\n label_for_elm[target].append(obj)\n\n described_by = obj.findall(\"accessibility/relation[@type='described-by']\")\n described_by += obj.findall(\"accessibility/relation[@name='described-by']\")\n for rel in described_by:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in described_by_elm:\n described_by_elm[target] = [ obj ]\n else:\n described_by_elm[target].append(obj)\n\n description_for = obj.findall(\"accessibility/relation[@type='description-for']\")\n description_for += obj.findall(\"accessibility/relation[@name='description-for']\")\n for rel in description_for:\n target = rel.attrib.get('target')\n if target is not None:\n if target not in description_for_elm:\n description_for_elm[target] = [ obj ]\n else:\n description_for_elm[target].append(obj)\n\n mnemonic_for = obj.findall(\"property[@name='mnemonic_widget']\") + \\\n obj.findall(\"property[@name='mnemonic-widget']\")\n for rel in mnemonic_for:\n target = rel.text\n if target is not None:\n if target not in mnemonic_for_elm:\n mnemonic_for_elm[target] = [ obj ]\n else:\n mnemonic_for_elm[target].append(obj)\n\n # Second pass, recursive depth-first, to be able to efficiently know whether\n # there are orphan labels within a part of the tree.\n def recurse(orphan_root, obj, orphan_labels, orphan_widgets):\n if obj == root or is_labelled_parent(obj):\n orphan_root = obj\n orphan_labels, orphan_widgets = orphan_items(filename, tree, root, obj)\n\n if obj.tag == 'object':\n check_elm(orphan_root, obj, orphan_labels, orphan_widgets)\n\n for o in obj:\n recurse(orphan_root, o, orphan_labels, orphan_widgets)\n\n recurse(root, root, False, False)","def test_stratis_list_default(self):\n for subcommand in [[\"pool\"], [\"filesystem\"], [\"blockdev\"]]:\n for prefix in [[], [\"--propagate\"]]:\n self.assertEqual(RUNNER(prefix + subcommand), 0)","def pytest_can_run_together(item1, item2):","def test_series_in_features(self):\n assert parse_command({'test{{A,B}}': {'depends_on': 'name{{A,B}}'}}) == [\n ('testA', {'depends_on': 'nameA'}), ('testB', {'depends_on': 'nameB'})]","def test_first_level_from_bids_with_subject_labels(bids_dataset):\n warning_message = ('Subject label foo is not present in'\n ' the dataset and cannot be processed')\n with pytest.warns(UserWarning, match=warning_message):\n models, *_ = first_level_from_bids(\n dataset_path=bids_dataset,\n task_label='main',\n sub_labels=[\"foo\", \"01\"],\n space_label='MNI',\n img_filters=[('desc', 'preproc')],\n slice_time_ref=None,\n )\n\n assert models[0].subject_label == '01'","def test_pathop12(self):\n xpb = XPathBuilder()\n # braces not needed\n xp = xpb.foo & (xpb.bar.foo).parenthesize() | xpb.foobar\n exp = '/foo and (/bar/foo) or /foobar'\n self.assertEqual(xp.tostring(), exp)","def test_dummy6(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n xp = xpb.bar | xp\n exp = '/bar'\n self.assertEqual(xp.tostring(), exp)","def test_create_labels(self):\n test_labels = {\"app\": \"my_app\", \"host\": \"examplehost\"}\n labels = pmp.utils.create_labels(test_labels)\n self.assertIsInstance(labels, list)\n self.assertTrue(all([isinstance(x, pmp.LabelPair) for x in labels]))","def test_xdist_and_select_test_by_bdd_label(xdist_runner: AllurePytestRunner):\n\n output = xdist_runner.run_docstring(\"-v\", \"--allure-features=boo\", \"-n1\")\n\n assert_that(output, has_only_testcases(\n has_entry(\n \"fullName\",\n ends_with(\"test_with_feature_boo\")\n )\n ))","def test_nested_condition() -> None:\n\n @argcomb(Or(And(\"a\", \"b\"), And(\"c\", \"d\")))\n def f(a: Any = None, b: Any = None, c: Any = None, d: Any = None) -> None:\n ...\n\n # valid\n f(a=1, b=1)\n f(c=1, d=1)\n f(a=1, b=1, c=1, d=1)\n\n # invalid\n with pytest.raises(InvalidArgumentCombination):\n f(a=1)\n with pytest.raises(InvalidArgumentCombination):\n f(a=1, c=1)\n with pytest.raises(InvalidArgumentCombination):\n f()","def test_abbreviation(self):\n self.assertEqual(self.compound.abbreviation, \"Cool\")","def test_general_subset_all():\n pass","def test_check_tree_exact_match(self):\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_3tips_fp)\r\n\r\n # Should find all and give True, True result\r\n\r\n self.assertEqual(actual_subset_results, [True, True])\r\n\r\n # Should get tips not found in fasta labels with 5 tip tree\r\n\r\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find all and give True result\r\n\r\n self.assertEqual(actual_subset_results, [True, ['seq5', 'seq4']])\r\n\r\n # Change two of the fasta labels to not match tree tips\r\n\r\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\r\n\r\n actual_subset_results = check_tree_exact_match(fasta_labels,\r\n self.sample_tree_5tips_fp)\r\n\r\n # Should find seqX and seqY as not being a subset\r\n\r\n self.assertEqual(actual_subset_results, [['seqX', 'seqY'],\r\n ['seq3', 'seq5', 'seq4']])","def test_03_visit_special(self):","def test_labels(ruler: SpaczzRuler) -> None:\n assert all(\n [label in ruler.labels for label in [\"GPE\", \"STREET\", \"DRUG\", \"NAME\", \"BAND\"]]\n )\n assert len(ruler.labels) == 5","def test_createSubLinkographWithoutCommands(self):\n self.performTestForParams()","def test_first_level_from_bids_several_labels_per_entity(tmp_path, entity):\n n_sub = 1\n n_ses = 1\n tasks = [\"main\"]\n n_runs = [1]\n\n bids_path = create_fake_bids_dataset(\n base_dir=tmp_path,\n n_sub=n_sub,\n n_ses=n_ses,\n tasks=tasks,\n n_runs=n_runs,\n entities={entity: [\"A\", \"B\"]},\n )\n\n models, m_imgs, m_events, m_confounds = first_level_from_bids(\n dataset_path=bids_path,\n task_label=\"main\",\n space_label=\"MNI\",\n img_filters=[(\"desc\", \"preproc\"), (entity, \"A\")],\n slice_time_ref=None,\n )\n\n _check_output_first_level_from_bids(n_sub,\n models,\n m_imgs,\n m_events,\n m_confounds)\n n_imgs_expected = n_ses * n_runs[0]\n assert len(m_imgs[0]) == n_imgs_expected","def test_setter_child_list_tuple(self):\n root = netapp_api.NaElement('root')\n root['l'] = ['l1', 'l2']\n root['t'] = ('t1', 't2')\n l = root.get_child_by_name('l')\n self.assertIsInstance(l, netapp_api.NaElement)\n t = root.get_child_by_name('t')\n self.assertIsInstance(t, netapp_api.NaElement)\n for le in l.get_children():\n self.assertIn(le.get_name(), ['l1', 'l2'])\n for te in t.get_children():\n self.assertIn(te.get_name(), ['t1', 't2'])","def test_tree_support(self):\r\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\r\n \"\"\"\r\n /-------.5 /-a\r\n ---1| \\-b\r\n \\------.5 /-c\r\n \\-d\r\n \"\"\"\r\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\r\n\r\n tc.tree_support(master_tree, t2)\r\n assert_almost_equal(\r\n master_tree.getNodeMatchingName('rt').bootstrap_support, 1.0)","def test_labels(self):\n return self._test_labels","def test_issue_edit_label(self):\n pass","def test_dbpa002_radio_items(dash_duo):\n app = Dash()\n\n options = {\n \"OptionA\": \"Option 1\",\n \"OptionB\": \"Option 2\",\n \"OptionC\": \"Option 3\",\n }\n\n value = \"OptionB\"\n\n with_keywords = RadioItems(\n options=options,\n value=value,\n id=\"with-keywords\",\n )\n without_keywords = RadioItems(options, value, id=\"without-keywords\")\n\n app.layout = html.Div([with_keywords, without_keywords])\n\n dash_duo.start_server(app)\n\n # Check values\n assert [\n a.get_attribute(\"value\")\n for a in dash_duo.wait_for_element(\n \"#with-keywords\"\n ).find_elements_by_tag_name(\"input\")\n ] == [\n a.get_attribute(\"value\")\n for a in dash_duo.wait_for_element(\n \"#without-keywords\"\n ).find_elements_by_tag_name(\"input\")\n ]\n\n # Check labels\n assert [\n a.text\n for a in dash_duo.wait_for_element(\n \"#with-keywords\"\n ).find_elements_by_tag_name(\"label\")\n ] == [\n a.text\n for a in dash_duo.wait_for_element(\n \"#without-keywords\"\n ).find_elements_by_tag_name(\"label\")\n ]","def test(self):\n for doc, label in zip(self.test_docs(), self.test_labels()):\n yield doc, label","def test_get_parent_type_name(self):\n pass","def test_pathop8(self):\n xpb = XPathBuilder()\n xp = (xpb.foo.bar | xpb.foobar).parenthesize() & xpb.action.source\n exp = '(/foo/bar or /foobar) and /action/source'\n self.assertEqual(xp.tostring(), exp)","def test_setter_child_list_tuple(self):\n root = netapp_api.NaElement('root')\n root['l'] = ['l1', 'l2']\n root['t'] = ('t1', 't2')\n l_element = root.get_child_by_name('l')\n self.assertIsInstance(l_element, netapp_api.NaElement)\n t = root.get_child_by_name('t')\n self.assertIsInstance(t, netapp_api.NaElement)\n for le in l_element.get_children():\n self.assertIn(le.get_name(), ['l1', 'l2'])\n for te in t.get_children():\n self.assertIn(te.get_name(), ['t1', 't2'])","def __init__(self, root_node, label1, label2):\n super().__init__(self.PROBLEM_NAME)\n self.root_node = root_node\n self.label1 = label1\n self.label2 = label2","def test_radioselect_field():","def test_label_callback():\n release_numbers = dict(a='123')\n data = dict(revision='a', attributes=dict(b='c'))\n data2 = dict(revision='b', attributes=dict(d='e'))\n\n assert _label_callback(data, release_numbers) == u'a\\n- Release: 123\\n- b: c'\n assert _label_callback(data2) == u'b\\n- Release: Unknown\\n- d: e'","def _make_simple_partition_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n label = 'simplepartition(['\n for cp in cps:\n smcstr = str(cp['smc'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr:\n condition_str += ',%s)'\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def test_case1(self):\n\n graph = BipartiteGraph()\n\n graph.addEdge(\"supervisor1\",\"student1\")\n\n val1 = graph.getStudents(\"supervisor1\")\n val2 = graph.getSupervisors(\"student1\")\n\n expected1 = [\"student1\"]\n expected2 = [\"supervisor1\"]\n\n self.assertEqual((val1,val2),(expected1,expected2))","def test_with_multiple_descriptions():\n soup = generate_case(\"with_descriptions\")\n\n tests.html_schema_doc_asserts.assert_descriptions(\n soup,\n [\n \"Exact address\",\n \"Exact address\",\n \"Delivery info depending on the delivery type\",\n \"The delivery is a gift, no prices displayed\",\n ],\n )","def test_BuildModel2(self):\n print(\"\\nTest 6: Building a Model with Concat\")\n builder = StaticBuilder(\"Concat\")\n in1 = builder.addInput(10)\n in2 = builder.addInput(20)\n enc1 = builder.addInner(3, num_islots=2)\n out1 = builder.addOutput()\n\n builder.addDirectedLink(in1, enc1, islot=0)\n builder.addDirectedLink(in2, enc1, islot=1)\n builder.addDirectedLink(enc1, out1)\n \n builder.build()","def test_multiple_label_traversals(self):\r\n TestEdge.create(self.v1, self.v2)\r\n OtherTestEdge.create(self.v1, self.v3)\r\n YetAnotherTestEdge.create(self.v1, self.v4)\r\n\r\n assert len(self.v1.outV()) == 3\r\n\r\n assert len(self.v1.outV(TestEdge)) == 1\r\n assert len(self.v1.outV(OtherTestEdge)) == 1\r\n assert len(self.v1.outV(YetAnotherTestEdge)) == 1\r\n\r\n out = self.v1.outV(TestEdge, OtherTestEdge)\r\n assert len(out) == 2\r\n assert self.v2.vid in [v.vid for v in out]\r\n assert self.v3.vid in [v.vid for v in out]\r\n\r\n out = self.v1.outV(OtherTestEdge, YetAnotherTestEdge)\r\n assert len(out) == 2\r\n assert self.v3.vid in [v.vid for v in out]\r\n assert self.v4.vid in [v.vid for v in out]","def test_label_10_targets_with_a_b_c_false(self):\r\n user_input = '[{\"a\":\"target1\"}, \\\r\n {\"b\":\"target2\"},{\"c\":\"target3\"},{\"a\":\"target4\"},{\"b\":\"target5\"}, \\\r\n {\"c\":\"target6\"}, {\"a\":\"target7\"},{\"b\":\"target8\"},{\"c\":\"target9\"}, \\\r\n {\"a\":\"target1\"}]'\r\n correct_answer = [\r\n {\r\n 'draggables': ['a'],\r\n 'targets': ['target1', 'target4', 'target7', 'target10'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['b'],\r\n 'targets': ['target2', 'target5', 'target8'],\r\n 'rule': 'unordered_equal'\r\n },\r\n {\r\n 'draggables': ['c'],\r\n 'targets': ['target3', 'target6', 'target9'],\r\n 'rule': 'unordered_equal'\r\n }\r\n ]\r\n self.assertFalse(draganddrop.grade(user_input, correct_answer))"],"string":"[\n \"def _make_combinationsTest_label(chain_parts):\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n \\n assert scenario == 'combinationsTest'\\n\\n \\n\\n return \\\"\\\"\\\"\\n combgen(\\n [(2)(20et, 0eta320)]\\n \\n simple([(40et, 0eta320) (50et, 0eta320)])\\n simple([(35et, 0eta240) (55et, 0eta240)])\\n )\\\"\\\"\\\"\",\n \"def test_sums():\\n assert label_parent(1, 2) == 3\\n assert label_parent (1, 4) == 8\\n # Should ignore arg order\\n assert label_parent(4, 1) == 8\",\n \"def test_recipe_nutrition_label_widget(self):\\n pass\",\n \"def test_select_label(self):\\n title = ('Transportation Challenges Limit Education Choices for '\\n 'Denver Parents')\\n summary = \\\"\\\"\\\"\\n Many families in the Denver metro area use public\\n transportation instead of a school bus because for them, a\\n quality education is worth hours of daily commuting. Colorado's\\n school choice program is meant to foster educational equity,\\n but the families who benefit most are those who have time and\\n money to travel. Low-income families are often left in a lurch.\\n \\\"\\\"\\\"\\n byline = \\\"Mile High Connects\\\"\\n story = create_story(title=title, summary=summary, byline=byline)\\n layout = SectionLayout.objects.get(sectionlayouttranslation__name=\\\"Side by Side\\\")\\n section1 = create_section(title=\\\"Test Section 1\\\", story=story, layout=layout)\\n section2 = create_section(title=\\\"Test Section 2\\\", story=story, layout=layout)\\n form = SectionRelationAdminForm()\\n choices_list = list(form.fields['parent'].widget.choices)\\n self.assertIn(story.title, choices_list[1][1])\\n self.assertIn(story.title, choices_list[2][1])\",\n \"def setUp(self):\\n\\n singleLabels = linkoCreate.Linkograph(\\n [({'A'}, set(), {1,2,3}),\\n ({'D'}, {0}, {3,4}),\\n ({'A'}, {0}, {4}),\\n ({'C'}, {0,1}, {4}),\\n ({'A'}, {1,2,3}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_2 = linkoCreate.Linkograph(\\n [({'A'}, set(), {1,2}),\\n ({'D'}, {0}, set()),\\n ({'A'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_1 = linkoCreate.Linkograph(\\n [({'A'}, set(), {1}),\\n ({'D'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_0 = linkoCreate.Linkograph(\\n [({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko1_2 = linkoCreate.Linkograph(\\n [({'D'}, set(), set()),\\n ({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko1_1 = linkoCreate.Linkograph(\\n [({'D'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n trivialLinkograph = linkoCreate.Linkograph(\\n [], ['A', 'B', 'C', 'D'])\\n\\n\\n singleSubLinko1_4 = linkoCreate.Linkograph(\\n [({'D'}, set(), {2,3}),\\n ({'A'}, set(), {3}),\\n ({'C'}, {0}, {3}),\\n ({'A'}, {0,1,2}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko2_4 = linkoCreate.Linkograph(\\n [({'A'}, set(), {2}),\\n ({'C'}, set(), {2}),\\n ({'A'}, {0,1}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko3_4 = linkoCreate.Linkograph(\\n [({'C'}, set(), {1}),\\n ({'A'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko4_4 = linkoCreate.Linkograph(\\n [({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n simpleLinko = linkoCreate.Linkograph(\\n [({'A', 'B', 'C'}, set(), {1,2,3}),\\n ({'D'}, {0}, {3,4}),\\n ({'A'}, {0}, {4}),\\n ({'B', 'C'}, {0,1}, {4}),\\n ({'A'}, {1,2,3}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n if self.id().split('.')[-1] == 'test_createSubLinkographWithoutCommands':\\n self.testParams = [\\n {'linko': singleLabels,\\n 'lowerBound': None,\\n 'upperBound': None,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 5,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': -1,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': None,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 1,\\n 'ExpectedLinkograph': singleSubLinko0_1},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 0,\\n 'ExpectedLinkograph': singleSubLinko0_0},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': -1,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko1_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 1,\\n 'ExpectedLinkograph': singleSubLinko1_1},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 0,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': -1,\\n 'upperBound': -1,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko1_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 2,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko2_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 3,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko3_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 4,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko4_4},\\n\\n ]\",\n \"def test_labels(self):\\n self.compliance_tester.test_labels(self.oi)\",\n \"def test_parent_label(self):\\n l = self.d.label(1)\\n l2 = self.d.label(31405)\\n\\n self.assertTrue(l.parent_label is None)\\n self.assertTrue(l2 in l.sublabels)\\n self.assertEqual(l2.parent_label, l)\",\n \"def test_simple(self):\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n content = '((h,p)hp:1,g)hpg;'\\n self._do_test(content, exp)\\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP,\\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n self._do_test(content, exp)\",\n \"def test_label(self):\\n xs = t.Label(t.Exactly(\\\"x\\\"), 'CustomLabel')\\n self.assertEqual(writePython(xs),\\n dd(\\\"\\\"\\\"\\n def _G_label_1():\\n _G_exactly_2, lastError = self.exactly('x')\\n self.considerError(lastError, None)\\n return (_G_exactly_2, self.currentError)\\n _G_label_3, lastError = self.label(_G_label_1, \\\"CustomLabel\\\")\\n self.considerError(lastError, None)\\n _G_label_3\\n \\\"\\\"\\\"))\",\n \"def addLabels(t):\\n if not t.label:\\n t.label = \\\"\\\".join([choice(\\\"abcdefghijklmnopqrstuvwxyz\\\") for i in range(4)])\\n for r,w in t.children:\\n addLabels(r)\",\n \"def test_general_subset_level():\\n pass\",\n \"def _make_simple_comb_label(chain_dict):\\n\\n cps = chain_dict['chainParts']\\n if not (_select_simple_chainparts(cps)):\\n raise NotImplementedError(\\n 'chain fails substring selection: not \\\"simple\\\": %s' % (\\n chain_dict['chainName']))\\n \\n simple_strs = []\\n\\n for cp in cps:\\n print(cp)\\n simple_strs.append(_make_simple_label([cp]))\\n\\n label = 'combgen([(%d)]' % len(cps)\\n for s in simple_strs:\\n label += ' %s ' % s\\n label += ')'\\n return label\",\n \"def _generateLabelAndName(self, obj, **args):\\n result = []\\n label = self._generateLabel(obj, **args)\\n name = self._generateName(obj, **args)\\n result.extend(label)\\n if not len(label):\\n result.extend(name)\\n elif len(name) and name[0].strip() != label[0].strip():\\n result.extend(name)\\n return result\",\n \"def tests_ti_document_add_label(self):\\n super().group_add_label()\",\n \"def getLabel2(*args):\",\n \"def getLabel2(*args):\",\n \"def test_checkboxtextgroup(self):\\r\\n self.check_group('checkboxtextgroup', 'choice', 'checkbox')\",\n \"def _generateTableCell2ChildLabel(self, obj, **args):\\n result = []\\n\\n # If this table cell has 2 children and one of them has a\\n # 'toggle' action and the other does not, then present this\\n # as a checkbox where:\\n # 1) we get the checked state from the cell with the 'toggle' action\\n # 2) we get the label from the other cell.\\n # See Orca bug #376015 for more details.\\n #\\n if obj.childCount == 2:\\n cellOrder = []\\n hasToggle = [False, False]\\n for i, child in enumerate(obj):\\n if self._script.utilities.hasMeaningfulToggleAction(child):\\n hasToggle[i] = True\\n break\\n if hasToggle[0] and not hasToggle[1]:\\n cellOrder = [ 1, 0 ]\\n elif not hasToggle[0] and hasToggle[1]:\\n cellOrder = [ 0, 1 ]\\n if cellOrder:\\n for i in cellOrder:\\n if not hasToggle[i]:\\n result.extend(self.generate(obj[i], **args))\\n return result\",\n \"def test_process_label_in_node(self):\\n tree = Node(children=[\\n Node(\\\"Defining secret phrase.\\\", label=['AB', 'a']),\\n Node(\\\"Has secret phrase. Then some other content\\\", \\n label=['AB', 'b'])\\n ], label=['AB'])\\n t = Terms(tree)\\n t.scoped_terms = {\\n ('AB',): [Ref(\\\"secret phrase\\\", \\\"AB-a\\\", (9,22))]\\n }\\n # Term is defined in the first child\\n self.assertEqual([], t.process(tree.children[0]))\\n self.assertEqual(1, len(t.process(tree.children[1])))\",\n \"def get_extra_label(self, label_name: str, hierarchy: List[str]) -> Any:\",\n \"def test_get_scenarios_expanded(self):\\n pass\",\n \"def test_nested():\\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.root.metadata.choose(lambda m: (m[\\\"name\\\"], m.uid))))\\n assert \\\"type\\\" in res # from conditions\\n assert \\\"reason\\\" in res # from conditions\\n assert \\\"name\\\" in res # from metadata\\n assert \\\"uid\\\" in res # from metadata\",\n \"def _make_simple_label(chain_parts):\\n \\n if not _select_simple_chainparts(chain_parts):\\n msg = 'Jet Configuration error: '\\\\\\n 'chain fails substring selection: not \\\"simple\\\" '\\n\\n raise NotImplementedError(msg)\\n \\n label = 'simple(['\\n for cp in chain_parts:\\n smcstr = str(cp['smc'])\\n jvtstr = str(cp['jvt'])\\n if smcstr == 'nosmc':\\n smcstr = ''\\n for i in range(int(cp['multiplicity'])):\\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\\n # str(cp['etaRange']),\\n # smcstr,)\\n condition_str = '(%set,%s' % (str(cp['threshold']),\\n str(cp['etaRange']),)\\n if smcstr: # Run 2 chains have \\\"INF\\\" in the SMC substring\\n condition_str += ',%s)' % smcstr.replace('INF','')\\n elif jvtstr:\\n condition_str += ',%s)' % jvtstr\\n else:\\n condition_str += ')'\\n label += condition_str\\n label += '])'\\n return label\",\n \"def test_nested_sequence(self):\\n\\n self.taxon_tester('Apis mellifera')\\n self.taxon_tester('Apis')\\n self.taxon_tester('Apini')\\n self.taxon_tester('Apinae')\\n # Apidae at 5680 species is a struggle\\n self.taxon_tester('Apidae')\\n if False:\\n # Apoidea: 19566 takes 223 seconds\\n self.taxon_tester('Apoidea')\\n # Aculeata fails after 339 seconds\\n self.taxon_tester('Aculeata')\\n self.taxon_tester('Apocrita')\\n self.taxon_tester('Hymenoptera')\\n self.taxon_tester('Endopterygota')\\n self.taxon_tester('Neoptera')\\n self.taxon_tester('Pterygota')\\n self.taxon_tester('Dicondylia')\\n self.taxon_tester('Insecta')\\n self.taxon_tester('Hexapoda')\\n self.taxon_tester('Pancrustacea')\\n self.taxon_tester('Mandibulata')\\n self.taxon_tester('Arthropoda')\\n self.taxon_tester('Panarthropoda')\\n self.taxon_tester('Ecdysozoa')\\n self.taxon_tester('Protostomia')\\n self.taxon_tester('Bilateria')\\n self.taxon_tester('Eumetazoa')\\n self.taxon_tester('Metazoa')\\n self.taxon_tester('Holozoa')\\n self.taxon_tester('Opisthokonta')\\n self.taxon_tester('Eukaryota')\",\n \"def test_bootstrap_support_labeled(self):\\r\\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\\r\\n \\\"\\\"\\\"\\r\\n /-------.5 /-a\\r\\n ---1| \\\\-b\\r\\n \\\\------.5 /-c\\r\\n \\\\-d\\r\\n \\\"\\\"\\\"\\r\\n t1 = parse_newick('((a:6,b:8.2)hi:2,(c:1,d:2):7);') # same structure\\r\\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\\r\\n new_master, bootstraps = tc.bootstrap_support(master_tree, [t1, t2])\\r\\n expected = dict([('ab', .5), ('cd', .5), ('rt', 1.0)])\\r\\n self.assertDictEqual(bootstraps, expected)\",\n \"def test_title(names):\",\n \"def test_verbose_name_group(self): \\n field_verboses = {\\n \\\"title\\\": \\\"Название группы\\\",\\n \\\"slug\\\": \\\"Слаг\\\",\\n \\\"description\\\": \\\"Описание группы\\\",\\n }\\n for value, expected in field_verboses.items():\\n with self.subTest(value=value):\\n self.assertEqual(self.group._meta.get_field(value).verbose_name, expected)\",\n \"def plugin_second_label():\\n return \\\"second\\\"\",\n \"def test_product_labels(self):\\n\\n prd = Product.objects.get(id=1)\\n # label name\\n label_name = prd._meta.get_field('name').verbose_name\\n self.assertEqual(label_name, 'name')\\n # label description\\n label_name = prd._meta.get_field('description').verbose_name\\n self.assertEqual(label_name, 'description')\\n # label nutrition_grade\\n label_name = prd._meta.get_field('nutrition_grade').name\\n self.assertEqual(label_name, 'nutrition_grade')\\n # label barcode\\n label_name = prd._meta.get_field('barcode').verbose_name\\n self.assertEqual(label_name, 'barcode')\\n # label url\\n label_name = prd._meta.get_field('url').verbose_name\\n self.assertEqual(label_name, 'url')\\n # label url_pic\\n label_name = prd._meta.get_field('url_pic').name\\n self.assertEqual(label_name, 'url_pic')\\n # label store\\n label_name = prd._meta.get_field('store').verbose_name\\n self.assertEqual(label_name, 'store')\\n # label prd_cat\\n label_name = prd._meta.get_field('prd_cat').name\\n self.assertEqual(label_name, 'prd_cat')\\n # label fat\\n label_name = prd._meta.get_field('fat').verbose_name\\n self.assertEqual(label_name, 'fat')\\n # label saturated_fat\\n label_name = prd._meta.get_field('saturated_fat').name\\n self.assertEqual(label_name, 'saturated_fat')\\n # label sugar\\n label_name = prd._meta.get_field('sugar').verbose_name\\n self.assertEqual(label_name, 'sugar')\\n # label salt\\n label_name = prd._meta.get_field('salt').verbose_name\\n self.assertEqual(label_name, 'salt')\",\n \"def is_test(self):\\r\\n return self.has_label('tests')\",\n \"def setUp(self):\\n\\n\\n # InverseLabeling\\n invLabeling0 = {'L0': [0, 1, 2]}\\n\\n invLabeling1 = {'L0' : [0, 2],\\n 'L1' : [1]}\\n\\n invLabeling2 = {\\n 'L0' : [0],\\n 'L1' : [1],\\n 'L2' : [2]\\n }\\n\\n invLabeling3 = {\\n 'L1' : [0, 1],\\n 'L2' : [2]\\n }\\n\\n invLabeling4 = {\\n 'L0' : [0,1],\\n 'L1' : [0],\\n 'L2' : [2]\\n }\\n\\n invLabeling5 = {\\n 'L0': [0, 1, 2],\\n 'L1': []\\n }\\n \\n # Create some ontologies\\n ontology0 = {'L0': ['L0']}\\n\\n ontology1 = {}\\n\\n ontology2 = {'L0': ['L1']}\\n\\n ontology3 = {'L0': ['L1', 'L2'],\\n 'L1': ['L2'],\\n 'L2': ['L0']}\\n\\n if self.id().split('.')[-1] == 'test_createLinkograph':\\n self.testParams = [\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology0,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1, 2}),\\n ({'L0'}, {0}, {2}),\\n ({'L0'}, {0,1}, set())] \\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology1,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology2,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology0,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {2}),\\n ({'L1'}, set(), set()),\\n ({'L0'}, {0}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology1,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L1'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology2,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1}),\\n ({'L1'}, {0}, set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1}),\\n ({'L1'}, {0}, set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling2,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1,2}),\\n ({'L1'}, {0}, {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling3,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L1'}, set(), {2}),\\n ({'L1'}, set(), {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling4,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0', 'L1'}, set(), {2}),\\n ({'L0'}, set(), {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling5,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n ]\",\n \"def test_mutual_exclusivity_of_labels(self, tmpdir, docker_tasker, labels, expected_fail):\\n runner = mock_env(tmpdir, docker_tasker, labels=labels)\\n if expected_fail:\\n with pytest.raises(PluginFailedException) as e:\\n runner.run()\\n assert 'only one of labels' in str(e.value)\\n else:\\n runner.run()\",\n \"def helper_label(helper):\\n return helper.info\",\n \"def test_Tree():\",\n \"def test_col_data_label_with_attrs(self):\\n help_tag = 'span'\\n help_text_br = False\\n names = ('first', 'billing_address_1')\\n attrs = {'style': 'width: 10rem; display: inline-block'}\\n label_attrs = {name: attrs for name in names}\\n txt = '{}=\\\"{}\\\"'.format(*list(attrs.items())[0])\\n expected = [''.format(txt)]\\n expected.append(''.format(txt))\\n actual = []\\n for name in names:\\n field = self.form.fields[name]\\n response = self.form.collect_col_data(name, field, help_tag, help_text_br, label_attrs)\\n actual.append(response.get('label'))\\n\\n for expect, got in zip(expected, actual):\\n self.assertEqual(expect, got)\",\n \"def test_help_text_group(self): \\n field_help_text = {\\n \\\"title\\\": \\\"Дайте назание группе\\\",\\n \\\"slug\\\": ('Укажите адрес для группы. Используйте '\\n 'только латиницу, цифры, дефисы и знаки '\\n 'подчёркивания'),\\n } \\n for value, expected in field_help_text.items():\\n with self.subTest(value=value):\\n self.assertEqual(self.group._meta.get_field(value).help_text, expected)\",\n \"def test_label_choices(self):\\n test_classes = (\\n (0, 'No'),\\n (1, 'Yes')\\n )\\n\\n form = SingleLabelClassifierForm(classes=test_classes)\\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)\\n\\n form = MultiLabelClassifierForm(classes=test_classes)\\n self.assertEqual(tuple(form.fields['label'].choices), test_classes)\",\n \"def test_filter_tree(self):\\r\\n actual = [e.Name for e in filter_tree(\\r\\n self.tree1, ['bbb', 'ccc']).tips()]\\r\\n #(a_a:10,(b_b:2,c_c:4):5);\\r\\n expected = [\\r\\n e.Name for e in DndParser(\\r\\n \\\"((bbb:2,ccc:4));\\\",\\r\\n PhyloNode).tips(\\r\\n )]\\r\\n self.assertEqual(actual, expected)\\r\\n\\r\\n actual = [e.Name for e in filter_tree(\\r\\n self.tree2, ['bbb', 'ccc']).tips()]\\r\\n #(a_a:10,(b_b:2,c_c:4):5);\\r\\n expected = [\\r\\n e.Name for e in DndParser(\\r\\n \\\"(('bbb':2,ccc:4));\\\",\\r\\n PhyloNode).tips(\\r\\n )]\\r\\n self.assertEqual(actual, expected)\",\n \"def test_radiotextgroup(self):\\r\\n self.check_group('radiotextgroup', 'choice', 'radio')\",\n \"def gen_test_case_combination(self):\\n\\n cases = '\\\\n'\\n\\n binary_ops = list(self.BINARY_OPS)\\n binary_ops.reverse()\\n for op1 in self.BINARY_OPS:\\n for op2 in binary_ops:\\n\\n result = []\\n ret = IntOp.binary_op(op2, '0', '1', self.lane_width)\\n ret = IntOp.binary_op(op1, ret, '2', self.lane_width)\\n result.append(ret)\\n\\n cases += '\\\\n' + str(AssertReturn('{lane_type}.{op1}-{lane_type}.{op2}'.format(lane_type=self.LANE_TYPE, op1=op1, op2=op2),\\n [SIMD.v128_const('0', self.LANE_TYPE),\\n SIMD.v128_const('1', self.LANE_TYPE),\\n SIMD.v128_const('2', self.LANE_TYPE)],\\n SIMD.v128_const(result, self.LANE_TYPE)))\\n cases += '\\\\n'\\n return cases\",\n \"def test_multiple_task_groups_dag(\\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\\n ):\\n (\\n dag,\\n group1,\\n group2,\\n group3,\\n (\\n group1_emp1,\\n group1_emp2,\\n group1_emp3,\\n group2_emp1,\\n group2_emp2,\\n group2_emp3,\\n group2_op1,\\n group2_op2,\\n group3_emp1,\\n group3_emp2,\\n group3_emp3,\\n emp_in1,\\n emp_in2,\\n emp_in3,\\n emp_in4,\\n emp_out1,\\n emp_out2,\\n emp_out3,\\n emp_out4,\\n op_in1,\\n op_out1,\\n ),\\n ) = test_multiple_taskgroups_dag\\n\\n group1_emp1 >> Label(\\\"label group1.group1_emp1 <=> group1.group1_emp2\\\") >> group1_emp3\\n\\n emp_in1 >> group1\\n emp_in2 >> Label(\\\"label emp_in2 <=> group1\\\") >> group1\\n [emp_in3, emp_in4] >> Label(\\\"label emp_in3/emp_in4 <=> group1\\\") >> group1\\n XComArg(op_in1, \\\"test_key\\\") >> Label(\\\"label op_in1 <=> group1\\\") >> group1\\n\\n (\\n [group2_emp1, group2_emp2]\\n >> Label(\\\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\\\")\\n >> group2_emp3\\n )\\n (\\n group2_emp1\\n >> Label(\\\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\\\")\\n >> [group2_emp2, group2_emp3]\\n )\\n group2_emp3 >> Label(\\\"label group2.group2_emp3 <=> group3\\\") >> group3\\n\\n (\\n XComArg(group2_op1, \\\"test_key\\\")\\n >> Label(\\\"label group2.group2_op1 <=> group2.group2_op2\\\")\\n >> XComArg(group2_op2, \\\"test_key\\\")\\n )\\n XComArg(group2_op2, \\\"test_key\\\") >> Label(\\\"label group2.group2_op2 <=> group3\\\") >> group3\\n\\n group3 >> emp_out1\\n group3 >> Label(\\\"label group3 <=> emp_out2\\\") >> emp_out2\\n group3 >> Label(\\\"label group3 <=> emp_out3/emp_out4\\\") >> [emp_out3, emp_out4]\\n group3 >> Label(\\\"label group3 <=> op_out1\\\") >> XComArg(op_out1, \\\"test_key\\\")\\n\\n group1 >> Label(\\\"label group1 <=> group2\\\") >> group2\\n\\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)\",\n \"def test_issue_create_label(self):\\n pass\",\n \"def test__create_label_w_no_ent_id(ruler: SpaczzRuler) -> None:\\n assert ruler._create_label(\\\"TEST\\\", None) == \\\"TEST\\\"\",\n \"def test_grouping(self):\\n s = self.create(ComponentItem, UML.Component)\\n uc1 = self.create(UseCaseItem, UML.UseCase)\\n uc2 = self.create(UseCaseItem, UML.UseCase)\\n\\n self.group(s, uc1)\\n assert 1 == len(uc1.subject.subject)\\n self.group(s, uc2)\\n assert 1 == len(uc2.subject.subject)\\n\\n # Classifier.useCase is not navigable to UseCase\\n # self.assertEqual(2, len(s.subject.useCase))\",\n \"def setLabel2(*args):\",\n \"def tests_ti_document_get_label(self):\\n super().group_get_label()\",\n \"def test_render_label(self):\\n label = self.block.meta.label\\n self.assertEqual(label, 'Google Calendar', 'The labels are not the same')\",\n \"def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\\n\\n oid = obj.attrib.get('id')\\n klass = obj.attrib.get('class')\\n\\n # \\\"Don't care\\\" special case\\n if klass in widgets_ignored:\\n return\\n for suffix in widgets_suffixignored:\\n if klass[-len(suffix):] == suffix:\\n return\\n\\n # Widgets usual do not strictly require a label, i.e. a labelled parent\\n # is enough for context, but some do always need one.\\n requires_label = klass in widgets_needlabel\\n\\n if oid is not None:\\n # Check that ids are unique\\n if oid in ids_dup:\\n if ids[oid] == obj:\\n # We are the first, warn\\n duplicates = tree.findall(\\\".//object[@id='\\\" + oid + \\\"']\\\")\\n err(filename, tree, obj, \\\"duplicate-id\\\", \\\"has the same id as other elements \\\" + elms_names_lines(duplicates))\\n\\n # Check label-for and their dual labelled-by\\n label_for = check_rels(filename, tree, root, obj, \\\"label-for\\\", \\\"labelled-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n labelled_by = check_rels(filename, tree, root, obj, \\\"labelled-by\\\", \\\"label-for\\\")\\n\\n # Check label-for and their dual labelled-by\\n description_for = check_rels(filename, tree, root, obj, \\\"description-for\\\", \\\"described-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n described_by = check_rels(filename, tree, root, obj, \\\"described-by\\\", \\\"description-for\\\")\\n\\n # Should have only one label\\n if len(labelled_by) >= 1:\\n if oid in mnemonic_for_elm:\\n warn(filename, tree, obj, \\\"labelled-by-and-mnemonic\\\",\\n \\\"has both a mnemonic \\\" + elm_name_line(mnemonic_for_elm[oid][0]) + \\\"and labelled-by relation\\\")\\n if len(labelled_by) > 1:\\n warn(filename, tree, obj, \\\"multiple-labelled-by\\\", \\\"has multiple labelled-by relations\\\")\\n if oid in label_for_elm:\\n if len(label_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-label-for\\\", \\\"is referenced by multiple label-for \\\" + elms_names_lines(label_for_elm[oid]))\\n if oid in mnemonic_for_elm:\\n if len(mnemonic_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-mnemonic\\\", \\\"is referenced by multiple mnemonic_widget \\\" + elms_names_lines(mnemonic_for_elm[oid]))\\n\\n # Check member-of\\n member_of = check_rels(filename, tree, root, obj, \\\"member-of\\\")\\n\\n # Labels special case\\n if klass in widgets_labels:\\n properties = check_props(filename, tree, root, obj, \\\"mnemonic_widget\\\") + \\\\\\n check_props(filename, tree, root, obj, \\\"mnemonic-widget\\\")\\n if len(properties) > 1:\\n err(filename, tree, obj, \\\"multiple-mnemonic\\\", \\\"has multiple mnemonic_widgets properties\\\"\\n \\\"%s\\\" % elms_lines(properties))\\n\\n # Emit orphaning warnings\\n if warn_orphan_labels or orphan_widgets:\\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\\n\\n # We are done with the label\\n return\\n\\n # Not a label, will perhaps need one\\n\\n # Emit orphaning warnings\\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\",\n \"def get_label(self, hierarchy: List[str]) -> Any:\",\n \"def test_first_level_from_bids_no_duplicate_sub_labels(bids_dataset):\\n models, *_ = first_level_from_bids(\\n dataset_path=bids_dataset,\\n task_label='main',\\n sub_labels=[\\\"01\\\", \\\"01\\\"],\\n space_label='MNI',\\n img_filters=[('desc', 'preproc')],\\n slice_time_ref=None,\\n )\\n\\n assert len(models) == 1\",\n \"def test_add_empty_nodes_with_label_when_splitting(self):\\n print \\\"----- test_add_empty_nodes_with_label_when_splitting -----\\\"\\n sel_axis = (lambda axis: axis)\\n \\n #create tree, first node splits in x direction\\n tree = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\\n kdtree.visualize(tree)\\n \\n point_left = [0.4, 0.5]\\n tree.split2(point_left, axis = 0)\\n kdtree.visualize(tree)\\n \\n point1 = [0.3, 0.5]\\n found_node = tree.get_path_to_leaf(point1)[-1]\\n correct_node1 = 3\\n self.assertEqual(found_node.label, correct_node1, \\\"Not correct node found\\\")\\n \\n point_right = [0.6, 0.5]\\n tree.split2(point_right, axis = 1)\\n kdtree.visualize(tree)\\n \\n point2 = [0.6, 0.7]\\n found_node = tree.get_path_to_leaf(point2)[-1]\\n correct_node2 = 6\\n self.assertEqual(found_node.label, correct_node2, \\\"Not correct node found\\\")\\n \\n print \\\"----- end: test_add_empty_nodes_with_label_when_splitting -----\\\"\",\n \"def test_render_value_label(self):\\n self.check_html(\\n self.widget(choices=self.beatles),\\n \\\"beatles\\\",\\n [\\\"John\\\"],\\n html=(\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n ),\\n )\",\n \"def test_issue_add_label(self):\\n pass\",\n \"def test_create_metering_label(self):\\r\\n resource = 'metering_label'\\r\\n cmd = metering.CreateMeteringLabel(\\r\\n test_cli20.MyApp(sys.stdout), None)\\r\\n name = 'my label'\\r\\n myid = 'myid'\\r\\n description = 'my description'\\r\\n args = [name, '--description', description, '--shared']\\r\\n position_names = ['name', 'description', 'shared']\\r\\n position_values = [name, description, True]\\r\\n self._test_create_resource(resource, cmd, name, myid, args,\\r\\n position_names, position_values)\",\n \"def getLabel(*args):\",\n \"def getLabel(*args):\",\n \"def getLabel(*args):\",\n \"def _make_vbenf_label(chain_parts):\\n\\n # toy label for development: run simple and dijet independently.\\n # simple makes Et cuts on two jets. Independently (sharing possible)\\n # of jets choosean by simple, the dijet\\n # scenario requires a dijet of mass > 900, and opening angle in phi > 2.6\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n assert scenario.startswith('vbenf')\\n args = _args_from_scenario(scenario)\\n if not args:\\n return 'and([]simple([(50et)(70et)])combgen([(2)] dijet([(900djmass, 26djdphi)])))' \\n arg_res = [\\n re.compile(r'(?P\\\\d*)(?Pfbet)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pmass)(?P\\\\d*)'),\\n re.compile(r'(?P\\\\d*)(?Pet)(?P\\\\d*)'),\\n ]\\n\\n defaults = {\\n 'et': ('101', 'inf'),\\n 'mass': ('800', 'inf'),\\n 'fbet': ('501', 'inf'),\\n }\\n\\n argvals = {}\\n while args:\\n assert len(args) == len(arg_res)\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = defaults[key][0]\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = defaults[key][1]\\n argvals[key+'hi'] = hi\\n\\n assert len(args) == len(arg_res)\\n assert len(args) == 0\\n\\n return \\\"\\\"\\\"\\n and\\n (\\n []\\n simple\\n (\\n [(%(etlo).0fet, 500neta)(%(etlo).0fet, peta500)]\\n )\\n combgen\\n (\\n [(10et, 0eta320)]\\n dijet\\n (\\n [(%(masslo).0fdjmass, 26djdphi)]\\n ) \\n simple\\n (\\n [(10et, 0eta320)(20et, 0eta320)]\\n )\\n )\\n )\\\"\\\"\\\" % argvals\",\n \"def testFormatLabelAndValue(self):\\n\\n self.assertEqual('Abc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 1))\\n self.assertEqual('ABc: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 2))\\n self.assertEqual('ABC: xyz', self.inv._FormatLabelAndValue('abc', 'xyz', 4))\",\n \"def test_recipe_nutrition_label_image(self):\\n pass\",\n \"def test_label_10_targets_with_a_b_c_(self):\\r\\n user_input = '[{\\\"a\\\":\\\"target1\\\"}, \\\\\\r\\n {\\\"b\\\":\\\"target2\\\"},{\\\"c\\\":\\\"target3\\\"},{\\\"a\\\":\\\"target4\\\"},{\\\"b\\\":\\\"target5\\\"}, \\\\\\r\\n {\\\"c\\\":\\\"target6\\\"}, {\\\"a\\\":\\\"target7\\\"},{\\\"b\\\":\\\"target8\\\"},{\\\"c\\\":\\\"target9\\\"}, \\\\\\r\\n {\\\"a\\\":\\\"target10\\\"}]'\\r\\n correct_answer = [\\r\\n {\\r\\n 'draggables': ['a'],\\r\\n 'targets': ['target1', 'target4', 'target7', 'target10'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['b'],\\r\\n 'targets': ['target2', 'target5', 'target8'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['c'],\\r\\n 'targets': ['target3', 'target6', 'target9'],\\r\\n 'rule': 'unordered_equal'\\r\\n }\\r\\n ]\\r\\n self.assertTrue(draganddrop.grade(user_input, correct_answer))\",\n \"def test_label_10_targets_with_a_b_c_multiple(self):\\r\\n user_input = '[{\\\"a\\\":\\\"target1\\\"}, \\\\\\r\\n {\\\"b\\\":\\\"target2\\\"},{\\\"c\\\":\\\"target3\\\"},{\\\"b\\\":\\\"target5\\\"}, \\\\\\r\\n {\\\"c\\\":\\\"target6\\\"}, {\\\"a\\\":\\\"target7\\\"},{\\\"b\\\":\\\"target8\\\"},{\\\"c\\\":\\\"target9\\\"}, \\\\\\r\\n {\\\"a\\\":\\\"target1\\\"}]'\\r\\n correct_answer = [\\r\\n {\\r\\n 'draggables': ['a', 'a', 'a'],\\r\\n 'targets': ['target1', 'target4', 'target7', 'target10'],\\r\\n 'rule': 'anyof+number'\\r\\n },\\r\\n {\\r\\n 'draggables': ['b', 'b', 'b'],\\r\\n 'targets': ['target2', 'target5', 'target8'],\\r\\n 'rule': 'anyof+number'\\r\\n },\\r\\n {\\r\\n 'draggables': ['c', 'c', 'c'],\\r\\n 'targets': ['target3', 'target6', 'target9'],\\r\\n 'rule': 'anyof+number'\\r\\n }\\r\\n ]\\r\\n self.assertTrue(draganddrop.grade(user_input, correct_answer))\",\n \"def test_subsystems(self):\\n pass\",\n \"def test_multiply_some_type_links():\",\n \"def test_build_match_tree_with_pairs():\\n abbreviation_list = [[\\\"ELIF\\\", \\\"ELI.\\\"], [\\\"ELSE\\\", \\\"E.\\\"]]\\n expected_tree = {\\\"E\\\": {\\\"L\\\": {\\\"I\\\": {\\\"F\\\": \\\"ELI.\\\"}, \\\"S\\\": {\\\"E\\\": \\\"E.\\\"}}}}\\n tree = build_match_tree(abbreviation_list)\\n assert repr(tree) == repr(expected_tree)\",\n \"def check_a11y_relation(filename, tree):\\n global widgets_ignored, ids, label_for_elm, labelled_by_elm, description_for_elm, described_by_elm, mnemonic_for_elm\\n\\n def check_elm(orphan_root, obj, orphan_labels, orphan_widgets):\\n \\\"\\\"\\\"\\n Check one element, knowing that orphan_labels/widgets tell whether\\n there are orphan labels and widgets within orphan_root\\n \\\"\\\"\\\"\\n\\n oid = obj.attrib.get('id')\\n klass = obj.attrib.get('class')\\n\\n # \\\"Don't care\\\" special case\\n if klass in widgets_ignored:\\n return\\n for suffix in widgets_suffixignored:\\n if klass[-len(suffix):] == suffix:\\n return\\n\\n # Widgets usual do not strictly require a label, i.e. a labelled parent\\n # is enough for context, but some do always need one.\\n requires_label = klass in widgets_needlabel\\n\\n if oid is not None:\\n # Check that ids are unique\\n if oid in ids_dup:\\n if ids[oid] == obj:\\n # We are the first, warn\\n duplicates = tree.findall(\\\".//object[@id='\\\" + oid + \\\"']\\\")\\n err(filename, tree, obj, \\\"duplicate-id\\\", \\\"has the same id as other elements \\\" + elms_names_lines(duplicates))\\n\\n # Check label-for and their dual labelled-by\\n label_for = check_rels(filename, tree, root, obj, \\\"label-for\\\", \\\"labelled-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n labelled_by = check_rels(filename, tree, root, obj, \\\"labelled-by\\\", \\\"label-for\\\")\\n\\n # Check label-for and their dual labelled-by\\n description_for = check_rels(filename, tree, root, obj, \\\"description-for\\\", \\\"described-by\\\")\\n\\n # Check labelled-by and its dual label-for\\n described_by = check_rels(filename, tree, root, obj, \\\"described-by\\\", \\\"description-for\\\")\\n\\n # Should have only one label\\n if len(labelled_by) >= 1:\\n if oid in mnemonic_for_elm:\\n warn(filename, tree, obj, \\\"labelled-by-and-mnemonic\\\",\\n \\\"has both a mnemonic \\\" + elm_name_line(mnemonic_for_elm[oid][0]) + \\\"and labelled-by relation\\\")\\n if len(labelled_by) > 1:\\n warn(filename, tree, obj, \\\"multiple-labelled-by\\\", \\\"has multiple labelled-by relations\\\")\\n if oid in label_for_elm:\\n if len(label_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-label-for\\\", \\\"is referenced by multiple label-for \\\" + elms_names_lines(label_for_elm[oid]))\\n if oid in mnemonic_for_elm:\\n if len(mnemonic_for_elm[oid]) > 1:\\n warn(filename, tree, obj, \\\"duplicate-mnemonic\\\", \\\"is referenced by multiple mnemonic_widget \\\" + elms_names_lines(mnemonic_for_elm[oid]))\\n\\n # Check member-of\\n member_of = check_rels(filename, tree, root, obj, \\\"member-of\\\")\\n\\n # Labels special case\\n if klass in widgets_labels:\\n properties = check_props(filename, tree, root, obj, \\\"mnemonic_widget\\\") + \\\\\\n check_props(filename, tree, root, obj, \\\"mnemonic-widget\\\")\\n if len(properties) > 1:\\n err(filename, tree, obj, \\\"multiple-mnemonic\\\", \\\"has multiple mnemonic_widgets properties\\\"\\n \\\"%s\\\" % elms_lines(properties))\\n\\n # Emit orphaning warnings\\n if warn_orphan_labels or orphan_widgets:\\n is_orphan_label(filename, tree, root, obj, orphan_root, True)\\n\\n # We are done with the label\\n return\\n\\n # Not a label, will perhaps need one\\n\\n # Emit orphaning warnings\\n is_orphan_widget(filename, tree, root, obj, orphan_labels, orphan_root, True)\\n\\n root = tree.getroot()\\n\\n # Flush ids and relations from previous files\\n ids = {}\\n ids_dup = {}\\n labelled_by_elm = {}\\n label_for_elm = {}\\n described_by_elm = {}\\n description_for_elm = {}\\n mnemonic_for_elm = {}\\n\\n # First pass to get links into hash tables, no warning, just record duplicates\\n for obj in root.iter('object'):\\n oid = obj.attrib.get('id')\\n if oid is not None:\\n if oid not in ids:\\n ids[oid] = obj\\n else:\\n ids_dup[oid] = True\\n\\n labelled_by = obj.findall(\\\"accessibility/relation[@type='labelled-by']\\\")\\n labelled_by += obj.findall(\\\"accessibility/relation[@name='labelled-by']\\\")\\n for rel in labelled_by:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in labelled_by_elm:\\n labelled_by_elm[target] = [ obj ]\\n else:\\n labelled_by_elm[target].append(obj)\\n\\n label_for = obj.findall(\\\"accessibility/relation[@type='label-for']\\\")\\n label_for += obj.findall(\\\"accessibility/relation[@name='label-for']\\\")\\n for rel in label_for:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in label_for_elm:\\n label_for_elm[target] = [ obj ]\\n else:\\n label_for_elm[target].append(obj)\\n\\n described_by = obj.findall(\\\"accessibility/relation[@type='described-by']\\\")\\n described_by += obj.findall(\\\"accessibility/relation[@name='described-by']\\\")\\n for rel in described_by:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in described_by_elm:\\n described_by_elm[target] = [ obj ]\\n else:\\n described_by_elm[target].append(obj)\\n\\n description_for = obj.findall(\\\"accessibility/relation[@type='description-for']\\\")\\n description_for += obj.findall(\\\"accessibility/relation[@name='description-for']\\\")\\n for rel in description_for:\\n target = rel.attrib.get('target')\\n if target is not None:\\n if target not in description_for_elm:\\n description_for_elm[target] = [ obj ]\\n else:\\n description_for_elm[target].append(obj)\\n\\n mnemonic_for = obj.findall(\\\"property[@name='mnemonic_widget']\\\") + \\\\\\n obj.findall(\\\"property[@name='mnemonic-widget']\\\")\\n for rel in mnemonic_for:\\n target = rel.text\\n if target is not None:\\n if target not in mnemonic_for_elm:\\n mnemonic_for_elm[target] = [ obj ]\\n else:\\n mnemonic_for_elm[target].append(obj)\\n\\n # Second pass, recursive depth-first, to be able to efficiently know whether\\n # there are orphan labels within a part of the tree.\\n def recurse(orphan_root, obj, orphan_labels, orphan_widgets):\\n if obj == root or is_labelled_parent(obj):\\n orphan_root = obj\\n orphan_labels, orphan_widgets = orphan_items(filename, tree, root, obj)\\n\\n if obj.tag == 'object':\\n check_elm(orphan_root, obj, orphan_labels, orphan_widgets)\\n\\n for o in obj:\\n recurse(orphan_root, o, orphan_labels, orphan_widgets)\\n\\n recurse(root, root, False, False)\",\n \"def test_stratis_list_default(self):\\n for subcommand in [[\\\"pool\\\"], [\\\"filesystem\\\"], [\\\"blockdev\\\"]]:\\n for prefix in [[], [\\\"--propagate\\\"]]:\\n self.assertEqual(RUNNER(prefix + subcommand), 0)\",\n \"def pytest_can_run_together(item1, item2):\",\n \"def test_series_in_features(self):\\n assert parse_command({'test{{A,B}}': {'depends_on': 'name{{A,B}}'}}) == [\\n ('testA', {'depends_on': 'nameA'}), ('testB', {'depends_on': 'nameB'})]\",\n \"def test_first_level_from_bids_with_subject_labels(bids_dataset):\\n warning_message = ('Subject label foo is not present in'\\n ' the dataset and cannot be processed')\\n with pytest.warns(UserWarning, match=warning_message):\\n models, *_ = first_level_from_bids(\\n dataset_path=bids_dataset,\\n task_label='main',\\n sub_labels=[\\\"foo\\\", \\\"01\\\"],\\n space_label='MNI',\\n img_filters=[('desc', 'preproc')],\\n slice_time_ref=None,\\n )\\n\\n assert models[0].subject_label == '01'\",\n \"def test_pathop12(self):\\n xpb = XPathBuilder()\\n # braces not needed\\n xp = xpb.foo & (xpb.bar.foo).parenthesize() | xpb.foobar\\n exp = '/foo and (/bar/foo) or /foobar'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_dummy6(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n xp = xpb.bar | xp\\n exp = '/bar'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_create_labels(self):\\n test_labels = {\\\"app\\\": \\\"my_app\\\", \\\"host\\\": \\\"examplehost\\\"}\\n labels = pmp.utils.create_labels(test_labels)\\n self.assertIsInstance(labels, list)\\n self.assertTrue(all([isinstance(x, pmp.LabelPair) for x in labels]))\",\n \"def test_xdist_and_select_test_by_bdd_label(xdist_runner: AllurePytestRunner):\\n\\n output = xdist_runner.run_docstring(\\\"-v\\\", \\\"--allure-features=boo\\\", \\\"-n1\\\")\\n\\n assert_that(output, has_only_testcases(\\n has_entry(\\n \\\"fullName\\\",\\n ends_with(\\\"test_with_feature_boo\\\")\\n )\\n ))\",\n \"def test_nested_condition() -> None:\\n\\n @argcomb(Or(And(\\\"a\\\", \\\"b\\\"), And(\\\"c\\\", \\\"d\\\")))\\n def f(a: Any = None, b: Any = None, c: Any = None, d: Any = None) -> None:\\n ...\\n\\n # valid\\n f(a=1, b=1)\\n f(c=1, d=1)\\n f(a=1, b=1, c=1, d=1)\\n\\n # invalid\\n with pytest.raises(InvalidArgumentCombination):\\n f(a=1)\\n with pytest.raises(InvalidArgumentCombination):\\n f(a=1, c=1)\\n with pytest.raises(InvalidArgumentCombination):\\n f()\",\n \"def test_abbreviation(self):\\n self.assertEqual(self.compound.abbreviation, \\\"Cool\\\")\",\n \"def test_general_subset_all():\\n pass\",\n \"def test_check_tree_exact_match(self):\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_3tips_fp)\\r\\n\\r\\n # Should find all and give True, True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, [True, True])\\r\\n\\r\\n # Should get tips not found in fasta labels with 5 tip tree\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seq1_2', 'seq2_3', 'seq3_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find all and give True result\\r\\n\\r\\n self.assertEqual(actual_subset_results, [True, ['seq5', 'seq4']])\\r\\n\\r\\n # Change two of the fasta labels to not match tree tips\\r\\n\\r\\n fasta_labels = ['seq1_1', 'seqX_2', 'seq2_3', 'seqY_4']\\r\\n\\r\\n actual_subset_results = check_tree_exact_match(fasta_labels,\\r\\n self.sample_tree_5tips_fp)\\r\\n\\r\\n # Should find seqX and seqY as not being a subset\\r\\n\\r\\n self.assertEqual(actual_subset_results, [['seqX', 'seqY'],\\r\\n ['seq3', 'seq5', 'seq4']])\",\n \"def test_03_visit_special(self):\",\n \"def test_labels(ruler: SpaczzRuler) -> None:\\n assert all(\\n [label in ruler.labels for label in [\\\"GPE\\\", \\\"STREET\\\", \\\"DRUG\\\", \\\"NAME\\\", \\\"BAND\\\"]]\\n )\\n assert len(ruler.labels) == 5\",\n \"def test_createSubLinkographWithoutCommands(self):\\n self.performTestForParams()\",\n \"def test_first_level_from_bids_several_labels_per_entity(tmp_path, entity):\\n n_sub = 1\\n n_ses = 1\\n tasks = [\\\"main\\\"]\\n n_runs = [1]\\n\\n bids_path = create_fake_bids_dataset(\\n base_dir=tmp_path,\\n n_sub=n_sub,\\n n_ses=n_ses,\\n tasks=tasks,\\n n_runs=n_runs,\\n entities={entity: [\\\"A\\\", \\\"B\\\"]},\\n )\\n\\n models, m_imgs, m_events, m_confounds = first_level_from_bids(\\n dataset_path=bids_path,\\n task_label=\\\"main\\\",\\n space_label=\\\"MNI\\\",\\n img_filters=[(\\\"desc\\\", \\\"preproc\\\"), (entity, \\\"A\\\")],\\n slice_time_ref=None,\\n )\\n\\n _check_output_first_level_from_bids(n_sub,\\n models,\\n m_imgs,\\n m_events,\\n m_confounds)\\n n_imgs_expected = n_ses * n_runs[0]\\n assert len(m_imgs[0]) == n_imgs_expected\",\n \"def test_setter_child_list_tuple(self):\\n root = netapp_api.NaElement('root')\\n root['l'] = ['l1', 'l2']\\n root['t'] = ('t1', 't2')\\n l = root.get_child_by_name('l')\\n self.assertIsInstance(l, netapp_api.NaElement)\\n t = root.get_child_by_name('t')\\n self.assertIsInstance(t, netapp_api.NaElement)\\n for le in l.get_children():\\n self.assertIn(le.get_name(), ['l1', 'l2'])\\n for te in t.get_children():\\n self.assertIn(te.get_name(), ['t1', 't2'])\",\n \"def test_tree_support(self):\\r\\n master_tree = parse_newick('((a:2,b:3)ab:2,(c:1,d:2)cd:7)rt;')\\r\\n \\\"\\\"\\\"\\r\\n /-------.5 /-a\\r\\n ---1| \\\\-b\\r\\n \\\\------.5 /-c\\r\\n \\\\-d\\r\\n \\\"\\\"\\\"\\r\\n t2 = parse_newick('((a:2,b:3,c:33)ho:2,d:7);') # abc are siblings\\r\\n\\r\\n tc.tree_support(master_tree, t2)\\r\\n assert_almost_equal(\\r\\n master_tree.getNodeMatchingName('rt').bootstrap_support, 1.0)\",\n \"def test_labels(self):\\n return self._test_labels\",\n \"def test_issue_edit_label(self):\\n pass\",\n \"def test_dbpa002_radio_items(dash_duo):\\n app = Dash()\\n\\n options = {\\n \\\"OptionA\\\": \\\"Option 1\\\",\\n \\\"OptionB\\\": \\\"Option 2\\\",\\n \\\"OptionC\\\": \\\"Option 3\\\",\\n }\\n\\n value = \\\"OptionB\\\"\\n\\n with_keywords = RadioItems(\\n options=options,\\n value=value,\\n id=\\\"with-keywords\\\",\\n )\\n without_keywords = RadioItems(options, value, id=\\\"without-keywords\\\")\\n\\n app.layout = html.Div([with_keywords, without_keywords])\\n\\n dash_duo.start_server(app)\\n\\n # Check values\\n assert [\\n a.get_attribute(\\\"value\\\")\\n for a in dash_duo.wait_for_element(\\n \\\"#with-keywords\\\"\\n ).find_elements_by_tag_name(\\\"input\\\")\\n ] == [\\n a.get_attribute(\\\"value\\\")\\n for a in dash_duo.wait_for_element(\\n \\\"#without-keywords\\\"\\n ).find_elements_by_tag_name(\\\"input\\\")\\n ]\\n\\n # Check labels\\n assert [\\n a.text\\n for a in dash_duo.wait_for_element(\\n \\\"#with-keywords\\\"\\n ).find_elements_by_tag_name(\\\"label\\\")\\n ] == [\\n a.text\\n for a in dash_duo.wait_for_element(\\n \\\"#without-keywords\\\"\\n ).find_elements_by_tag_name(\\\"label\\\")\\n ]\",\n \"def test(self):\\n for doc, label in zip(self.test_docs(), self.test_labels()):\\n yield doc, label\",\n \"def test_get_parent_type_name(self):\\n pass\",\n \"def test_pathop8(self):\\n xpb = XPathBuilder()\\n xp = (xpb.foo.bar | xpb.foobar).parenthesize() & xpb.action.source\\n exp = '(/foo/bar or /foobar) and /action/source'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_setter_child_list_tuple(self):\\n root = netapp_api.NaElement('root')\\n root['l'] = ['l1', 'l2']\\n root['t'] = ('t1', 't2')\\n l_element = root.get_child_by_name('l')\\n self.assertIsInstance(l_element, netapp_api.NaElement)\\n t = root.get_child_by_name('t')\\n self.assertIsInstance(t, netapp_api.NaElement)\\n for le in l_element.get_children():\\n self.assertIn(le.get_name(), ['l1', 'l2'])\\n for te in t.get_children():\\n self.assertIn(te.get_name(), ['t1', 't2'])\",\n \"def __init__(self, root_node, label1, label2):\\n super().__init__(self.PROBLEM_NAME)\\n self.root_node = root_node\\n self.label1 = label1\\n self.label2 = label2\",\n \"def test_radioselect_field():\",\n \"def test_label_callback():\\n release_numbers = dict(a='123')\\n data = dict(revision='a', attributes=dict(b='c'))\\n data2 = dict(revision='b', attributes=dict(d='e'))\\n\\n assert _label_callback(data, release_numbers) == u'a\\\\n- Release: 123\\\\n- b: c'\\n assert _label_callback(data2) == u'b\\\\n- Release: Unknown\\\\n- d: e'\",\n \"def _make_simple_partition_label(chain_dict):\\n\\n cps = chain_dict['chainParts']\\n if not (_select_simple_chainparts(cps)):\\n raise NotImplementedError(\\n 'chain fails substring selection: not \\\"simple\\\": %s' % (\\n chain_dict['chainName']))\\n \\n label = 'simplepartition(['\\n for cp in cps:\\n smcstr = str(cp['smc'])\\n if smcstr == 'nosmc':\\n smcstr = ''\\n for i in range(int(cp['multiplicity'])):\\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\\n # str(cp['etaRange']),\\n # smcstr,)\\n condition_str = '(%set,%s' % (str(cp['threshold']),\\n str(cp['etaRange']),)\\n if smcstr:\\n condition_str += ',%s)'\\n else:\\n condition_str += ')'\\n label += condition_str\\n label += '])'\\n return label\",\n \"def test_case1(self):\\n\\n graph = BipartiteGraph()\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student1\\\")\\n\\n val1 = graph.getStudents(\\\"supervisor1\\\")\\n val2 = graph.getSupervisors(\\\"student1\\\")\\n\\n expected1 = [\\\"student1\\\"]\\n expected2 = [\\\"supervisor1\\\"]\\n\\n self.assertEqual((val1,val2),(expected1,expected2))\",\n \"def test_with_multiple_descriptions():\\n soup = generate_case(\\\"with_descriptions\\\")\\n\\n tests.html_schema_doc_asserts.assert_descriptions(\\n soup,\\n [\\n \\\"Exact address\\\",\\n \\\"Exact address\\\",\\n \\\"Delivery info depending on the delivery type\\\",\\n \\\"The delivery is a gift, no prices displayed\\\",\\n ],\\n )\",\n \"def test_BuildModel2(self):\\n print(\\\"\\\\nTest 6: Building a Model with Concat\\\")\\n builder = StaticBuilder(\\\"Concat\\\")\\n in1 = builder.addInput(10)\\n in2 = builder.addInput(20)\\n enc1 = builder.addInner(3, num_islots=2)\\n out1 = builder.addOutput()\\n\\n builder.addDirectedLink(in1, enc1, islot=0)\\n builder.addDirectedLink(in2, enc1, islot=1)\\n builder.addDirectedLink(enc1, out1)\\n \\n builder.build()\",\n \"def test_multiple_label_traversals(self):\\r\\n TestEdge.create(self.v1, self.v2)\\r\\n OtherTestEdge.create(self.v1, self.v3)\\r\\n YetAnotherTestEdge.create(self.v1, self.v4)\\r\\n\\r\\n assert len(self.v1.outV()) == 3\\r\\n\\r\\n assert len(self.v1.outV(TestEdge)) == 1\\r\\n assert len(self.v1.outV(OtherTestEdge)) == 1\\r\\n assert len(self.v1.outV(YetAnotherTestEdge)) == 1\\r\\n\\r\\n out = self.v1.outV(TestEdge, OtherTestEdge)\\r\\n assert len(out) == 2\\r\\n assert self.v2.vid in [v.vid for v in out]\\r\\n assert self.v3.vid in [v.vid for v in out]\\r\\n\\r\\n out = self.v1.outV(OtherTestEdge, YetAnotherTestEdge)\\r\\n assert len(out) == 2\\r\\n assert self.v3.vid in [v.vid for v in out]\\r\\n assert self.v4.vid in [v.vid for v in out]\",\n \"def test_label_10_targets_with_a_b_c_false(self):\\r\\n user_input = '[{\\\"a\\\":\\\"target1\\\"}, \\\\\\r\\n {\\\"b\\\":\\\"target2\\\"},{\\\"c\\\":\\\"target3\\\"},{\\\"a\\\":\\\"target4\\\"},{\\\"b\\\":\\\"target5\\\"}, \\\\\\r\\n {\\\"c\\\":\\\"target6\\\"}, {\\\"a\\\":\\\"target7\\\"},{\\\"b\\\":\\\"target8\\\"},{\\\"c\\\":\\\"target9\\\"}, \\\\\\r\\n {\\\"a\\\":\\\"target1\\\"}]'\\r\\n correct_answer = [\\r\\n {\\r\\n 'draggables': ['a'],\\r\\n 'targets': ['target1', 'target4', 'target7', 'target10'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['b'],\\r\\n 'targets': ['target2', 'target5', 'target8'],\\r\\n 'rule': 'unordered_equal'\\r\\n },\\r\\n {\\r\\n 'draggables': ['c'],\\r\\n 'targets': ['target3', 'target6', 'target9'],\\r\\n 'rule': 'unordered_equal'\\r\\n }\\r\\n ]\\r\\n self.assertFalse(draganddrop.grade(user_input, correct_answer))\"\n]"},"negative_scores":{"kind":"list like","value":["0.6972838","0.6137433","0.60195917","0.5986544","0.5936767","0.58589","0.5759686","0.56950617","0.5624841","0.55902916","0.55750483","0.5560212","0.5554626","0.55542696","0.5553175","0.5553175","0.55467266","0.5503819","0.5457172","0.5451371","0.54313546","0.53999746","0.5376586","0.5375577","0.5364844","0.5359766","0.535864","0.5338235","0.53258616","0.53186446","0.5317656","0.5315998","0.5315065","0.528178","0.5272056","0.5271867","0.5262255","0.5256516","0.5254785","0.5251119","0.5245671","0.52348095","0.5232146","0.5209617","0.520278","0.52023655","0.51976585","0.51973414","0.5193984","0.5191381","0.51910853","0.5168662","0.51612574","0.5156716","0.5155904","0.5155904","0.5155904","0.515586","0.51550406","0.51405126","0.51366323","0.5126447","0.51249856","0.5119241","0.5116798","0.5115688","0.5107617","0.510387","0.5094173","0.5092521","0.5089139","0.50838256","0.5078707","0.5075308","0.50716203","0.5061532","0.5061452","0.50607544","0.50605214","0.50554216","0.50476885","0.5047116","0.50363714","0.5028754","0.5026291","0.5024467","0.50243545","0.5023076","0.50198036","0.5014891","0.5011064","0.5007032","0.50008494","0.49937844","0.49911672","0.49879867","0.49861643","0.49833554","0.49800858","0.49767923"],"string":"[\n \"0.6972838\",\n \"0.6137433\",\n \"0.60195917\",\n \"0.5986544\",\n \"0.5936767\",\n \"0.58589\",\n \"0.5759686\",\n \"0.56950617\",\n \"0.5624841\",\n \"0.55902916\",\n \"0.55750483\",\n \"0.5560212\",\n \"0.5554626\",\n \"0.55542696\",\n \"0.5553175\",\n \"0.5553175\",\n \"0.55467266\",\n \"0.5503819\",\n \"0.5457172\",\n \"0.5451371\",\n \"0.54313546\",\n \"0.53999746\",\n \"0.5376586\",\n \"0.5375577\",\n \"0.5364844\",\n \"0.5359766\",\n \"0.535864\",\n \"0.5338235\",\n \"0.53258616\",\n \"0.53186446\",\n \"0.5317656\",\n \"0.5315998\",\n \"0.5315065\",\n \"0.528178\",\n \"0.5272056\",\n \"0.5271867\",\n \"0.5262255\",\n \"0.5256516\",\n \"0.5254785\",\n \"0.5251119\",\n \"0.5245671\",\n \"0.52348095\",\n \"0.5232146\",\n \"0.5209617\",\n \"0.520278\",\n \"0.52023655\",\n \"0.51976585\",\n \"0.51973414\",\n \"0.5193984\",\n \"0.5191381\",\n \"0.51910853\",\n \"0.5168662\",\n \"0.51612574\",\n \"0.5156716\",\n \"0.5155904\",\n \"0.5155904\",\n \"0.5155904\",\n \"0.515586\",\n \"0.51550406\",\n \"0.51405126\",\n \"0.51366323\",\n \"0.5126447\",\n \"0.51249856\",\n \"0.5119241\",\n \"0.5116798\",\n \"0.5115688\",\n \"0.5107617\",\n \"0.510387\",\n \"0.5094173\",\n \"0.5092521\",\n \"0.5089139\",\n \"0.50838256\",\n \"0.5078707\",\n \"0.5075308\",\n \"0.50716203\",\n \"0.5061532\",\n \"0.5061452\",\n \"0.50607544\",\n \"0.50605214\",\n \"0.50554216\",\n \"0.50476885\",\n \"0.5047116\",\n \"0.50363714\",\n \"0.5028754\",\n \"0.5026291\",\n \"0.5024467\",\n \"0.50243545\",\n \"0.5023076\",\n \"0.50198036\",\n \"0.5014891\",\n \"0.5011064\",\n \"0.5007032\",\n \"0.50008494\",\n \"0.49937844\",\n \"0.49911672\",\n \"0.49879867\",\n \"0.49861643\",\n \"0.49833554\",\n \"0.49800858\",\n \"0.49767923\"\n]"},"document_score":{"kind":"string","value":"0.6108757"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":8792,"cells":{"query":{"kind":"string","value":"Entry point to this Module. Return a chain label according to the value of cp['hypoScenario'], where cp is an element of list/ chainDict['chainPart']"},"document":{"kind":"string","value":"def chainDict2jetLabel(chain_dict):\n\n # suported scenarios \n router = {\n 'simple': _make_simple_label,\n 'HT': _make_ht_label,\n 'vbenf': _make_vbenf_label,\n 'dijet': _make_dijet_label,\n 'combinationsTest': _make_combinationsTest_label,\n 'partitionsTest': _make_partitionsTest_label,\n }\n\n # chain_part - scenario association\n cp_sorter = {}\n for k in router: cp_sorter[k] = []\n\n for cp in chain_dict['chainParts']:\n if cp['signature'] != 'Jet' and cp['signature'] != 'Bjet': \n continue\n for k in cp_sorter:\n if cp['hypoScenario'].startswith(k):\n cp_sorter[k].append(cp)\n break\n\n # obtain labels by scenario.\n labels = []\n for k, chain_parts in cp_sorter.items():\n if chain_parts: labels.append(router[k](chain_parts))\n\n assert labels\n nlabels = len(labels)\n if nlabels == 1: return labels[0]\n if nlabels == 2:\n alabel = \"\"\"\\\nand([]\n %s\n %s)\"\"\" % (tuple(labels))\n return alabel\n\n # more than 2 labels is not expected\n assert 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 _make_ht_label(chain_parts):\n\n assert len(chain_parts) == 1, '_make_ht_label, no. of chain parts != 1'\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('HT'), '_make_ht_label(): scenario does not start with HT'\n\n arg_res = [\n re.compile(r'^(?P\\d*)(?Pht)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pet)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Peta)(?P\\d*)$'),\n ]\n\n defaults = {\n 'ht': ('0', 'inf'),\n 'et': ('0', 'inf'),\n 'eta': ('0', 'inf'),\n }\n\n\n args = _args_from_scenario(scenario)\n argvals = {}\n nargs = len(args)\n assert len(args) <= len(arg_res), 'bad num of args %d, expected < %d' % (len(args),\n len(arg_res))\n\n # obtain argument values frrom scenario\n while args:\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = float(defaults[key][0])\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = float(defaults[key][1])\n argvals[key+'hi'] = hi\n\n print (argvals)\n assert len(argvals) == 2*nargs, 'no of args: %d, expected %d' % (len(argvals), 2*nargs)\n\n print ('sent 100')\n result = \"\"\"\n ht([(%(htlo).0fht) \n (%(etlo).0fet)\n (%(etalo).0feta%(etahi).0f)\n ])\"\"\" % argvals\n print (result)\n return result","def _make_combinationsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'combinationsTest'\n\n \n\n return \"\"\"\n combgen(\n [(2)(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\n )\"\"\"","def _make_simple_comb_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n simple_strs = []\n\n for cp in cps:\n print(cp)\n simple_strs.append(_make_simple_label([cp]))\n\n label = 'combgen([(%d)]' % len(cps)\n for s in simple_strs:\n label += ' %s ' % s\n label += ')'\n return label","def _make_simple_label(chain_parts):\n \n if not _select_simple_chainparts(chain_parts):\n msg = 'Jet Configuration error: '\\\n 'chain fails substring selection: not \"simple\" '\n\n raise NotImplementedError(msg)\n \n label = 'simple(['\n for cp in chain_parts:\n smcstr = str(cp['smc'])\n jvtstr = str(cp['jvt'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr: # Run 2 chains have \"INF\" in the SMC substring\n condition_str += ',%s)' % smcstr.replace('INF','')\n elif jvtstr:\n condition_str += ',%s)' % jvtstr\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def _make_simple_partition_label(chain_dict):\n\n cps = chain_dict['chainParts']\n if not (_select_simple_chainparts(cps)):\n raise NotImplementedError(\n 'chain fails substring selection: not \"simple\": %s' % (\n chain_dict['chainName']))\n \n label = 'simplepartition(['\n for cp in cps:\n smcstr = str(cp['smc'])\n if smcstr == 'nosmc':\n smcstr = ''\n for i in range(int(cp['multiplicity'])):\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\n # str(cp['etaRange']),\n # smcstr,)\n condition_str = '(%set,%s' % (str(cp['threshold']),\n str(cp['etaRange']),)\n if smcstr:\n condition_str += ',%s)'\n else:\n condition_str += ')'\n label += condition_str\n label += '])'\n return label","def _make_partitionsTest_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario == 'partitionsTest'\n\n \n\n return \"\"\"\n partgen(\n [(20et, 0eta320)]\n \n simple([(40et, 0eta320) (50et, 0eta320)])\n simple([(35et, 0eta240) (55et, 0eta240)])\n )\"\"\"","def _make_dijet_label(chain_parts):\n\n assert len(chain_parts) == 1\n scenario = chain_parts[0]['hypoScenario']\n \n assert scenario.startswith('dijet')\n\n arg_res = [\n re.compile(r'^(?P\\d*)(?Pdjmass)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1et)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj1eta)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?Pj2et)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?P\\d*)$'),\n re.compile(r'^(?P \\d*)(?P\\d*)$'),\n re.compile(r'^(?P\\d*)(?P eta)(?P\\d*)$'),\n ]\n\n defaults = {\n 'ht': ('0', 'inf'),\n 'et': ('0', 'inf'),\n 'eta': ('0', 'inf'),\n }\n\n\n args = _args_from_scenario(scenario)\n argvals = {}\n nargs = len(args)\n assert len(args) <= len(arg_res), 'bad num of args %d, expected < %d' % (len(args),\n len(arg_res))\n\n # obtain argument values frrom scenario\n while args:\n arg = args.pop()\n for r in arg_res:\n m = r.match(arg)\n if m is not None:\n arg_res.remove(r)\n gd = m.groupdict()\n key = gd['key']\n\n try:\n lo = float(gd['lo'])\n except ValueError:\n lo = float(defaults[key][0])\n argvals[key+'lo'] = lo \n try:\n hi = float(gd['hi'])\n except ValueError:\n hi = float(defaults[key][1])\n argvals[key+'hi'] = hi\n\n print (argvals)\n assert len(argvals) == 2*nargs, 'no of args: %d, expected %d' % (len(argvals), 2*nargs)\n\n print ('sent 100')\n result = \"\"\"\n ht([(%(htlo).0fht) \n (%(etlo).0fet)\n (%(etalo).0feta%(etahi).0f)\n ])\"\"\" % argvals\n print (result)\n return result","def makeBinaryChains():\n\t\n\t# retrieve the binding partner specifications\n\t(maxsize,types) = getTypes()\n\t\n\t# Do some basic argument checking for this model\n\tif (len(types) < 2):\n\t\tprint \"Number of defined types must equal two for binary chain calculations.\"\n\t\treturn\n\tif (maxsize == 0):\n\t\tprint \"Must specify a valid maximum number for one or more components.\"\n\t\treturn\n\n\tallChains = []\n\tnewChainsA = [[]]\n\tnewChainsB = []\n\t\n\ttypeA = types[0]\n\ttypeB = types[1]\n\t\n\t# start the chain with a single type A component\n\taddComponent(newChainsA[0],typeA,0,0)\n\n\tdepth = 0\n\tfor n in range(maxsize):\n\t\tdepth+=1\n\t\t\n\t\t# go through all the chains created last iteration and append B components\n\t\tnewChainsB = []\n\t\tfor thisChain in newChainsA:\n\n\t\t\t# get a list of new available sites in the provided chain\n\t\t\t# by setting depth -1, we will only add to components added last round\n\t\t\topenSites = makeSiteList(thisChain,typeB,depth-1)\n\t\t\t\n\t\t\t# make all the descendants from the current chain and append them to the pool\n\t\t\tif (n == 0) and (typeA['sym']): #if the starting binder is symmetric, no need to start chains at all its sites\n\t\t\t\tnewChainsB = newChainsB + fillSites(openSites,thisChain,typeB,-1)\n\t\t\telse:\n\t\t\t\tnewChainsB = newChainsB + fillSites(openSites,thisChain,typeB,depth)\n\t\t\n\t\tprint('n:'+str(n)+', '+str(len(newChainsB))+ ' chains created at depth '+str(depth))\n\t\t\n\t\tallChains = allChains + newChainsB\n\t\t\n\t\tdepth+=1\n\t\t\n\t\t# add an additional component to all the previously modified chains\n\t\tnewChainsA = []\n\t\tfor thisChain in newChainsB:\n\n\t\t\topenSites = makeSiteList(thisChain,typeA,depth-1)\n\t\t\tnewChainsA = newChainsA + fillSites(openSites,thisChain,typeA,depth)\n\t\t\t\n\t\tprint('n:'+str(n)+', '+str(len(newChainsA))+ ' chains created at depth '+str(depth))\n\t\t\n\t\tallChains = allChains + newChainsA\n\n\treturn allChains","def __init__(self):\n # Flag this instance as compiled now\n self.is_compiled = True\n \n super(HState2CProcDef, self).__init__(name='HState2CProcDef', num_nodes=0, edges=[])\n \n \n # Set the graph attributes\n self[\"mm__\"] = ['HimesisMM']\n \n self[\"name\"] = \"\"\"State2CProcDef\"\"\"\n self[\"GUID__\"] = uuid.uuid3(uuid.NAMESPACE_DNS,'State2CProcDef')\n \n # match model. We only support one match model\n self.add_node()\n self.vs[0][\"mm__\"] = \"\"\"MatchModel\"\"\"\n \n # apply model node\n self.add_node()\n self.vs[1][\"mm__\"] = \"\"\"ApplyModel\"\"\"\n \n # paired with relation between match and apply models\n self.add_node()\n self.vs[2][\"mm__\"] = \"\"\"paired_with\"\"\"\n \n \n # match class State() node\n self.add_node()\n\n self.vs[3][\"mm__\"] = \"\"\"State\"\"\" \n self.vs[3][\"attr1\"] = \"\"\"+\"\"\" \n # match_contains node for class State()\n self.add_node()\n self.vs[4][\"mm__\"] = \"\"\"match_contains\"\"\"\n # match class Transition() node\n self.add_node()\n\n self.vs[5][\"mm__\"] = \"\"\"Transition\"\"\" \n self.vs[5][\"attr1\"] = \"\"\"1\"\"\" \n # match_contains node for class Transition()\n self.add_node()\n self.vs[6][\"mm__\"] = \"\"\"match_contains\"\"\"\n # match class EntryPoint() node\n self.add_node()\n\n self.vs[7][\"mm__\"] = \"\"\"EntryPoint\"\"\" \n self.vs[7][\"attr1\"] = \"\"\"1\"\"\" \n # match_contains node for class EntryPoint()\n self.add_node()\n self.vs[8][\"mm__\"] = \"\"\"match_contains\"\"\"\n # match class StateMachine() node\n self.add_node()\n\n self.vs[9][\"mm__\"] = \"\"\"StateMachine\"\"\" \n self.vs[9][\"attr1\"] = \"\"\"1\"\"\" \n # match_contains node for class StateMachine()\n self.add_node()\n self.vs[10][\"mm__\"] = \"\"\"match_contains\"\"\"\n \n \n # apply class LocalDef() node\n self.add_node()\n\n self.vs[11][\"mm__\"] = \"\"\"LocalDef\"\"\" \n self.vs[11][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class LocalDef()\n self.add_node()\n self.vs[12][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class ProcDef() node\n self.add_node()\n\n self.vs[13][\"mm__\"] = \"\"\"ProcDef\"\"\" \n self.vs[13][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class ProcDef()\n self.add_node()\n self.vs[14][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[15][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[15][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[16][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[17][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[17][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[18][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[19][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[19][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[20][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[21][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[21][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[22][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class ConditionSet() node\n self.add_node()\n\n self.vs[23][\"mm__\"] = \"\"\"ConditionSet\"\"\" \n self.vs[23][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class ConditionSet()\n self.add_node()\n self.vs[24][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Inst() node\n self.add_node()\n\n self.vs[25][\"mm__\"] = \"\"\"Inst\"\"\" \n self.vs[25][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Inst()\n self.add_node()\n self.vs[26][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[27][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[27][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[28][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[29][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[29][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[30][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[31][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[31][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[32][\"mm__\"] = \"\"\"apply_contains\"\"\"\n # apply class Name() node\n self.add_node()\n\n self.vs[33][\"mm__\"] = \"\"\"Name\"\"\" \n self.vs[33][\"attr1\"] = \"\"\"1\"\"\"\n # apply_contains node for class Name()\n self.add_node()\n self.vs[34][\"mm__\"] = \"\"\"apply_contains\"\"\"\n \n \n # match association State--initialTransition-->Transition node\n self.add_node()\n self.vs[35][\"attr1\"] = \"\"\"initialTransition\"\"\"\n self.vs[35][\"mm__\"] = \"\"\"directLink_S\"\"\"\n # match association Transition--dest-->EntryPoint node\n self.add_node()\n self.vs[36][\"attr1\"] = \"\"\"dest\"\"\"\n self.vs[36][\"mm__\"] = \"\"\"directLink_S\"\"\"\n # match association EntryPoint--owningStateMachine-->StateMachine node\n self.add_node()\n self.vs[37][\"attr1\"] = \"\"\"owningStateMachine\"\"\"\n self.vs[37][\"mm__\"] = \"\"\"directLink_S\"\"\"\n \n # apply association LocalDef--def-->ProcDef node\n self.add_node()\n self.vs[38][\"attr1\"] = \"\"\"def\"\"\"\n self.vs[38][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association ProcDef--channelNames-->Name node\n self.add_node()\n self.vs[39][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[39][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association ProcDef--channelNames-->Name node\n self.add_node()\n self.vs[40][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[40][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association ProcDef--channelNames-->Name node\n self.add_node()\n self.vs[41][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[41][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association ProcDef--channelNames-->Name node\n self.add_node()\n self.vs[42][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[42][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association ProcDef--p-->ConditionSet node\n self.add_node()\n self.vs[43][\"attr1\"] = \"\"\"p\"\"\"\n self.vs[43][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association ConditionSet--alternative-->Inst node\n self.add_node()\n self.vs[44][\"attr1\"] = \"\"\"alternative\"\"\"\n self.vs[44][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association Inst--channelNames-->Name node\n self.add_node()\n self.vs[45][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[45][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association Inst--channelNames-->Name node\n self.add_node()\n self.vs[46][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[46][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association Inst--channelNames-->Name node\n self.add_node()\n self.vs[47][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[47][\"mm__\"] = \"\"\"directLink_T\"\"\"\n # apply association Inst--channelNames-->Name node\n self.add_node()\n self.vs[48][\"attr1\"] = \"\"\"channelNames\"\"\"\n self.vs[48][\"mm__\"] = \"\"\"directLink_T\"\"\"\n \n # backward association State---->LocalDef node\n self.add_node()\n\n self.vs[49][\"mm__\"] = \"\"\"backward_link\"\"\"\n \n \n \n \n \n \n # Add the edges\n self.add_edges([\n (0,4), # matchmodel -> match_contains\n (4,3), # match_contains -> match_class State()\n (0,6), # matchmodel -> match_contains\n (6,5), # match_contains -> match_class Transition()\n (0,8), # matchmodel -> match_contains\n (8,7), # match_contains -> match_class EntryPoint()\n (0,10), # matchmodel -> match_contains\n (10,9), # match_contains -> match_class StateMachine()\n (1,12), # applymodel -> apply_contains\n (12,11), # apply_contains -> apply_class LocalDef()\n (1,14), # applymodel -> apply_contains\n (14,13), # apply_contains -> apply_class ProcDef()\n (1,16), # applymodel -> apply_contains\n (16,15), # apply_contains -> apply_class Name()\n (1,18), # applymodel -> apply_contains\n (18,17), # apply_contains -> apply_class Name()\n (1,20), # applymodel -> apply_contains\n (20,19), # apply_contains -> apply_class Name()\n (1,22), # applymodel -> apply_contains\n (22,21), # apply_contains -> apply_class Name()\n (1,24), # applymodel -> apply_contains\n (24,23), # apply_contains -> apply_class ConditionSet()\n (1,26), # applymodel -> apply_contains\n (26,25), # apply_contains -> apply_class Inst()\n (1,28), # applymodel -> apply_contains\n (28,27), # apply_contains -> apply_class Name()\n (1,30), # applymodel -> apply_contains\n (30,29), # apply_contains -> apply_class Name()\n (1,32), # applymodel -> apply_contains\n (32,31), # apply_contains -> apply_class Name()\n (1,34), # applymodel -> apply_contains\n (34,33), # apply_contains -> apply_class Name()\n (3,35), # match_class State() -> association initialTransition\n (35,5), # association initialTransition -> match_class Transition()\n (5,36), # match_class Transition() -> association dest\n (36,7), # association dest -> match_class EntryPoint()\n (7,37), # match_class EntryPoint() -> association owningStateMachine\n (37,9), # association owningStateMachine -> match_class StateMachine()\n (11,38), # apply_class LocalDef() -> association def\n (38,13), # association def -> apply_class ProcDef()\n (13,39), # apply_class ProcDef() -> association channelNames\n (39,15), # association channelNames -> apply_class Name()\n (13,40), # apply_class ProcDef() -> association channelNames\n (40,17), # association channelNames -> apply_class Name()\n (13,41), # apply_class ProcDef() -> association channelNames\n (41,19), # association channelNames -> apply_class Name()\n (13,42), # apply_class ProcDef() -> association channelNames\n (42,21), # association channelNames -> apply_class Name()\n (13,43), # apply_class ProcDef() -> association p\n (43,23), # association p -> apply_class ConditionSet()\n (23,44), # apply_class ConditionSet() -> association alternative\n (44,25), # association alternative -> apply_class Inst()\n (25,45), # apply_class Inst() -> association channelNames\n (45,27), # association channelNames -> apply_class Name()\n (25,46), # apply_class Inst() -> association channelNames\n (46,29), # association channelNames -> apply_class Name()\n (25,47), # apply_class Inst() -> association channelNames\n (47,31), # association channelNames -> apply_class Name()\n (25,48), # apply_class Inst() -> association channelNames\n (48,33), # association channelNames -> apply_class Name()\n (11,49), # apply_class LocalDef() -> backward_association\n (49,3), # backward_association -> apply_class State()\n (0,2), # matchmodel -> pairedwith\n (2,1) # pairedwith -> applyModel\t\t\t\t\n\t\t])\n\n # Add the attribute equations\n self[\"equations\"] = [((3,'isComposite'),('constant','true')), ((11,'ApplyAttribute'),('constant','solveRef')), ((13,'name'),('constant','C')), ((15,'literal'),('constant','enp')), ((17,'literal'),('constant','exit')), ((19,'literal'),('constant','exack')), ((21,'literal'),('constant','sh')), ((23,'ApplyAttribute'),('constant','solveRef')), ((25,'name'),('concat',(('constant','S'),(9,'name')))), ((27,'literal'),('concat',(('constant','A'),('concat',((7,'name'),('constant','A')))))), ((29,'literal'),('constant','exit_in')), ((31,'literal'),('constant','exack_in')), ((33,'literal'),('constant','sh_in')), ]","def calculate_217f_part_count(**attributes):\n _msg = ''\n\n # Dictionary containing MIL-HDBK-217FN2 parts count base hazard rates.\n # First key is the subcategory_id, second key is the specification id. If\n # the resistor subcategory is NOT specification dependent, then the second\n # key will be zero. Current subcategory IDs are:\n #\n # 1. Fixed, Composition (RC, RCR)\n # 2. Fixed, Film (RL, RLR, RN, RNC, RNN, RNR)\n # 3. Fixed, Film, Power (RD)\n # 4. Fixed, Film, Network (RZ)\n # 5. Fixed, Wirewound, Power (RB, RBR)\n # 6. Fixed, Wirewound, Power, Chassis Mounted (RE, RER)\n # 7. Thermistor\n # 8. Variable, Wirewound (RT, RTR)\n # 9. Variable, Wirewound, Precision (RR)\n # 10. Variable, Wirewound, Semiprecision (RA, RK)\n # 11. Variable, Non-Wirewound (RJ, RJR)\n # 12. Variable, Composition (RV)\n # 13. Variable,Non-Wirewound, Film and Precision (RQ, RVC)\n #\n # These keys return a list of base hazard rates. The hazard rate to use is\n # selected from the list depending on the active environment.\n _dic_lambda_b = {\n 1: [\n 0.0005, 0.0022, 0.0071, 0.0037, 0.012, 0.0052, 0.0065, 0.016,\n 0.025, 0.025, 0.00025, 0.0098, 0.035, 0.36\n ],\n 2: {\n 1: [\n 0.0012, 0.0027, 0.011, 0.0054, 0.020, 0.0063, 0.013, 0.018,\n 0.033, 0.030, 0.00025, 0.014, 0.044, 0.69\n ],\n 2: [\n 0.0012, 0.0027, 0.011, 0.0054, 0.020, 0.0063, 0.013, 0.018,\n 0.033, 0.030, 0.00025, 0.014, 0.044, 0.69\n ],\n 3: [\n 0.0014, 0.0031, 0.013, 0.0061, 0.023, 0.0072, 0.014, 0.021,\n 0.038, 0.034, 0.00028, 0.016, 0.050, 0.78\n ],\n 4: [\n 0.0014, 0.0031, 0.013, 0.0061, 0.023, 0.0072, 0.014, 0.021,\n 0.038, 0.034, 0.00028, 0.016, 0.050, 0.78\n ]\n },\n 3: [\n 0.012, 0.025, 0.13, 0.062, 0.21, 0.078, 0.10, 0.19, 0.24, 0.32,\n 0.0060, 0.18, 0.47, 8.2\n ],\n 4: [\n 0.0023, 0.0066, 0.031, 0.013, 0.055, 0.022, 0.043, 0.077, 0.15,\n 0.10, 0.0011, 0.055, 0.15, 1.7\n ],\n 5: [\n 0.0085, 0.018, 0.10, 0.045, 0.16, 0.15, 0.17, 0.30, 0.38, 0.26,\n 0.0068, 0.13, 0.37, 5.4\n ],\n 6: {\n 1: [\n 0.014, 0.031, 0.16, 0.077, 0.26, 0.073, 0.15, 0.19, 0.39, 0.42,\n 0.0042, 0.21, 0.62, 9.4\n ],\n 2: [\n 0.013, 0.028, 0.15, 0.070, 0.24, 0.065, 0.13, 0.18, 0.35, 0.38,\n 0.0038, 0.19, 0.56, 8.6\n ]\n },\n 7: [\n 0.008, 0.18, 0.096, 0.045, 0.15, 0.044, 0.088, 0.12, 0.24, 0.25,\n 0.004, 0.13, 0.37, 5.5\n ],\n 8: [\n 0.065, 0.32, 1.4, 0.71, 1.6, 0.71, 1.9, 1.0, 2.7, 2.4, 0.032, 1.3,\n 3.4, 62.0\n ],\n 9: [\n 0.025, 0.055, 0.35, 0.15, 0.58, 0.16, 0.26, 0.35, 0.58, 1.1, 0.013,\n 0.52, 1.6, 24.0\n ],\n 10: [\n 0.33, 0.73, 7.0, 2.9, 12.0, 3.5, 5.3, 7.1, 9.8, 23.0, 0.16, 11.0,\n 33.0, 510.0\n ],\n 11: [\n 0.15, 0.35, 3.1, 1.2, 5.4, 1.9, 2.8, 0.0, 0.0, 9.0, 0.075, 0.0,\n 0.0, 0.0\n ],\n 12: [\n 0.15, 0.34, 2.9, 1.2, 5.0, 1.6, 2.4, 0.0, 0.0, 7.6, 0.076, 0.0,\n 0.0, 0.0\n ],\n 13: [\n 0.043, 0.15, 0.75, 0.35, 1.3, 0.39, 0.78, 1.8, 2.8, 2.5, 0.21, 1.2,\n 3.7, 49.0\n ],\n 14: [\n 0.05, 0.11, 1.1, 0.45, 1.7, 2.8, 4.6, 4.6, 7.5, 3.3, 0.025, 1.5,\n 4.7, 67.0\n ],\n 15: [\n 0.048, 0.16, 0.76, 0.36, 1.3, 0.36, 0.72, 1.4, 2.2, 2.3, 0.024,\n 1.2, 3.4, 52.0\n ]\n }\n\n # List containing piQ values for parts count method. The list positions\n # corrspond to the following quality levels:\n #\n # 0. Established reliability level S\n # 1. Established reliability level R\n # 2. Established reliability level P\n # 3. Established reliability level M\n # 4. Non-established reliability MIL-SPEC\n # 5. Non-established reliability lower\n #\n # The quality_id attribute is used to select the proper value of piQ.\n _lst_piQ = [0.030, 0.10, 0.30, 1.0, 3.0, 10.0]\n\n # Select the base hazard rate.\n try:\n if attributes['subcategory_id'] in [2, 6]:\n _lst_base_hr = _dic_lambda_b[attributes['subcategory_id']][\n attributes['specification_id']]\n else:\n _lst_base_hr = _dic_lambda_b[attributes['subcategory_id']]\n except KeyError:\n _lst_base_hr = [0.0]\n\n try:\n attributes['lambda_b'] = _lst_base_hr[\n attributes['environment_active_id'] - 1]\n except IndexError:\n attributes['lambda_b'] = 0.0\n\n # Select the piQ.\n try:\n attributes['piQ'] = _lst_piQ[attributes['quality_id'] - 1]\n except IndexError:\n attributes['piQ'] = 0.0\n\n # Confirm all inputs are within range. If not, set the message. The\n # hazard rate will be calculated anyway, but will be zero.\n if attributes['lambda_b'] <= 0.0:\n _msg = _msg + 'RAMSTK WARNING: Base hazard rate is 0.0 when ' \\\n 'calculating resistor, hardware ID: ' \\\n '{0:d}, subcategory ID: {1:d}, specification ID: {2:d}, ' \\\n 'active environment ID: {3:d}, and quality ID: ' \\\n '{4:d}.\\n'.format(attributes['hardware_id'],\n attributes['subcategory_id'],\n attributes['specification_id'],\n attributes['environment_active_id'],\n attributes['quality_id'])\n\n if attributes['piQ'] <= 0.0:\n _msg = _msg + 'RAMSTK WARNING: piQ is 0.0 when calculating ' \\\n 'resistor, hardware ID: {0:d}, quality ID: ' \\\n '{1:d}.'.format(attributes['hardware_id'],\n attributes['quality_id'])\n\n # Calculate the hazard rate.\n attributes['hazard_rate_active'] = (\n attributes['lambda_b'] * attributes['piQ'])\n\n return attributes, _msg","def identity_block(self,X,stage,block):\n conv_name_base='res'+str(stage)+block+'_branch'\n bn_name_base='bn'+str(stage)+block+'_branch'\n \n #retrieve the filters\n #F1,F2,F3=filters\n \n X_shortcut=X\n \n #first component of main path\n X=Conv3D(self.filter, (3,3,1),kernel_initializer=self.init,kernel_regularizer=self.regularizer,padding='same')(X)\n X=BatchNormalization(axis=3)(X)\n X=Activation('relu')(X)\n \n #second component of main path\n X=Conv3D(self.filter, (3,3,1),kernel_initializer=self.init,kernel_regularizer=self.regularizer,padding='same')(X)\n\n \n #final step, add the shortcut\n X=Add()([X,X_shortcut])\n X=Activation('relu')(X)\n \n \n return X","def run(self, voxels, entry='all', freq_raw=False):\n\n # Note. in this case we are processing all raw data into the data \n # attribute, so despite having multiple raw FIDs, we are really \n # only processing one voxel, so no for loop\n\n # local reference to input data\n self.raw = self._dataset.get_source_data('prep')\n\n # Choose voxel - for saving result for current single voxel plot\n self.voxel = voxels[0]\n\n # select the chain processing functor based on the entry point\n if entry == 'all':\n funct_fidsum_wbnaa.do_processing_all(self)\n else:\n print('oooops!')\n\n # save data and parameter results into the Block results arrays\n self._block.data[0,0,0,:] = self.time_summed_offset.copy()\n \n # Return values specific to calling Tab that contains this Block.Chain\n # Used to update its self.view (plot_panel_spectrum object).\n\n plot_results = { 'freq_current' : self.freq_current.copy(),\n 'freq_summed' : self.freq_summed.copy(),\n 'freq_summed_offset' : self.freq_summed_offset.copy() }\n \n return plot_results","def dumpData(self,out,index):\n #--SCVR\n out.pack('4siBB2sB',\n 'SCVR', 5+len(self.text), index+48, self.type, self.func, self.oper)\n if self.text: out.write(self.text)\n #--Value\n if isinstance(self.value,int):\n out.packSub('INTV','i', self.value)\n else:\n out.packSub('FLTV','f', self.value)","def __init__(self,config = None):\n \n self.join_path = join_path\n self.label_path = cfg['labels_path']\n self.pick_path = (cfg['result_path'] + cfg['pickle_path'])\n self.label_dir = os.path.join(CWD_PATH,self.join_path, self.label_path)\n\n #Variables inherent to the Fluent data: \n self.num_ins = 4\n\n self.scale_var = cfg['scale_var']\n # User set values are below. These can be adjusted in config.yml \n self.MSE_thresh1 = (cfg['thresh1']*self.scale_var)**2\n self.MSE_thresh2 = (cfg['thresh2']*self.scale_var)**2\n self.MSE_thresh3 = (cfg['thresh3']*self.scale_var)**2\n \n self.rew_goal = cfg['reward'] * self.scale_var\n\n self.noise = cfg['noise']\n self.minmaxbuffer = cfg['minmaxbuffer']\n\n # Get the function of input-output mapping, and max & min:\n [self.O_CH4_flow_uniformity, mins,maxes] = self.get_funcs('O_CH4_flow_uniformity')\n [self.O_CH4_mol_frac, mins,maxes] = self.get_funcs('O_CH4_mol_frac')\n [self.O_t, mins, maxes] = self.get_funcs('O_t')\n \n self.mins = mins# * self.scale_var\n self.maxes = maxes#* self.scale_var\n #Action range is a percentage of the total range\n self.action_range = cfg['action_range']*self.scale_var\n\n #Action space is the up & down range for the 4 actions \n self.action_space = Box(-self.action_range, self.action_range, shape=(self.num_ins,), dtype=np.float32)\n\n # For ref, this is a 10d state space:\n #in: 1 ch4 flow, 2 ch4 t, 3 o2 flow, 4 o2 t,\n #out: 5 flow unif, 6 mol frac, 7 temp\n #out - target: 8 flow unif, 9 mol frac, 10 temp\n \n self.observation_space = Tuple((Box(self.mins.values[0],self.maxes.values[0],shape=(1,), dtype=np.float32),\n Box(self.mins.values[1],self.maxes.values[1],shape=(1,), dtype=np.float32),\n Box(self.mins.values[2],self.maxes.values[2],shape=(1,), dtype=np.float32),\n Box(self.mins.values[3],self.maxes.values[3],shape=(1,), dtype=np.float32),\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32),\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32)))\n \n # TODO this isn't really a proper gym spec\n self._spec = lambda: None\n self._spec.id = \"AllVar-v0\"\n \n # For rendering:\n self.viewer = None\n self.labels = cfg['labels']\n \n #initialize variables for tracking:\n self.episode = 0\n self.reward = 0\n self.reset()","def compile_xilinx_graph(self):\n pass","def levelsets_to_vector_field(levelsets, stepsize):\r\n vector_field_shape = levelsets[0][0].shape\r\n y_comp_combined = np.ndarray(vector_field_shape)\r\n x_comp_combined = np.ndarray(vector_field_shape)\r\n y_comp_combined.fill(np.nan)\r\n x_comp_combined.fill(np.nan)\r\n\r\n for source, target in levelsets:\r\n labels_present = set(np.array([source.flatten(),target.flatten()]).flatten())\r\n labels_present.remove(0)#relates to background\r\n\r\n #print(labels_present)\r\n for l in labels_present:\r\n\r\n source_cluster = source == l\r\n target_cluster = target == l\r\n\r\n\r\n \"\"\"plt.imshow(source_cluster.astype(np.int32)+target_cluster.astype(np.int32))\r\n plt.show()\r\n print(\"-----------\")\"\"\"\r\n\r\n #plot_gradient_field(source_cluster.astype(np.int32), target_cluster.astype(np.int32))\r\n\r\n y_comp, x_comp = array_to_vector_field(source_cluster, target_cluster, stepsize=stepsize)\r\n y_comp_combined[~np.isnan(y_comp)] = y_comp[~np.isnan(y_comp)]\r\n x_comp_combined[~np.isnan(x_comp)] = x_comp[~np.isnan(x_comp)]\r\n return y_comp_combined, x_comp_combined","def convert_propbank(detail=True):\n\n out_dir = \"../data/wsj_propbank/\"\n os.system(\"rm -rf %s\" % (out_dir, ))\n os.system(\"mkdir -p %s\" % (out_dir, ))\n\n pb_instances = propbank.instances()\n # Count at first\n verb2idx = {}\n verb2frames = {}\n for i in range(0, len(pb_instances)):\n inst = pb_instances[i]\n verb_lemma, frame = inst.roleset.split(\".\")\n if verb_lemma not in verb2idx:\n verb2idx[verb_lemma] = []\n verb2idx[verb_lemma].append(i)\n if verb_lemma not in verb2frames:\n verb2frames[verb_lemma] = []\n if frame not in verb2frames[verb_lemma]:\n verb2frames[verb_lemma].append(frame)\n verb_nums = len(verb2idx.keys())\n verb_counter = 0\n\n pair_label = {'-LRB-':'(', '-RRB-':')', '-LCB-':'(', '-RCB-':')'}\n for verb_lemma, idxs in verb2idx.items():\n verb_counter += 1\n if len(verb2frames[verb_lemma]) < 2:\n continue\n fh = open(\"%s/%s\" % (out_dir, verb_lemma), \"w\")\n if detail:\n print(\"processing %s(%s/%s)\"\n % (verb_lemma, verb_counter, verb_nums))\n for i in idxs:\n inst = pb_instances[i]\n fileid = inst.fileid\n sent_num = inst.sentnum\n verb_pos = inst.wordnum\n verb_lemma, frame = inst.roleset.split(\".\")\n section = [x for x in fileid if x.isdigit()][0:2]\n section = \"\".join(section)\n fileid_for_ptb = \"WSJ/%s/%s\" % (section, fileid.upper())\n\n tagged_sent = ptb.tagged_sents(fileid_for_ptb)[sent_num]\n # Change tagged_sent from [tuples] to [list]\n tagged_sent = [[x[0], x[1]]for x in tagged_sent]\n verb_bak = tagged_sent[verb_pos][0]\n verb_identifier = \"verb_identifier_xxxxx\"\n tagged_sent[verb_pos][0] = verb_identifier\n sent = []\n for (token, tag)in tagged_sent:\n if tag != '-NONE-':\n if token in pair_label:\n token = pair_label[token]\n sent.append(token)\n sent = \" \".join(sent)\n sent_toks = nltk.sent_tokenize(sent)\n candidate_sent = None\n for sent_tok in sent_toks:\n if sent_tok.find(verb_identifier) >= 0:\n candidate_sent = sent_tok\n left_sent, right_sent = candidate_sent.split(verb_identifier)\n left_sent = left_sent.strip()\n right_sent = right_sent.strip()\n out_line = \"%s\\t%s\\t%s\\t%s\" % (frame, left_sent, verb_bak, right_sent)\n out_line = remove_punctuations(out_line)\n print(out_line, file=fh)\n fh.close()","def render(self): # pragma: no cover\n from graphviz import Digraph\n dot = Digraph(name=\"top\")\n for block in self.blocks:\n if isinstance(block, Branch):\n label = \"if \" + astor.to_source(block.cond)\n dot.node(str(id(block)), label.rstrip(), {\"shape\": \"invhouse\"})\n elif isinstance(block, Yield):\n label = astor.to_source(block.value)\n # label += \"\\nLive Ins : \" + str(block.live_ins)\n # label += \"\\nLive Outs : \" + str(block.live_outs)\n # label += \"\\nGen : \" + str(block.gen)\n # label += \"\\nKill : \" + str(block.kill)\n dot.node(str(id(block)), label.rstrip(), {\"shape\": \"oval\"})\n elif isinstance(block, BasicBlock):\n label = \"\\n\".join(astor.to_source(stmt).rstrip() for stmt in block.statements)\n # label += \"\\nLive Ins : \" + str(block.live_ins)\n # label += \"\\nLive Outs : \" + str(block.live_outs)\n # label += \"\\nGen : \" + str(block.gen)\n # label += \"\\nKill : \" + str(block.kill)\n dot.node(str(id(block)), label.rstrip(), {\"shape\": \"box\"})\n elif isinstance(block, HeadBlock):\n label = \"Initial\"\n dot.node(str(id(block)) + \"_start\", label.rstrip(), {\"shape\": \"doublecircle\"})\n label = \"\\n\".join(astor.to_source(stmt).rstrip() for stmt in block.initial_statements)\n # label += \"\\nLive Ins : \" + str(block.live_ins)\n # label += \"\\nLive Outs : \" + str(block.live_outs)\n # label += \"\\nGen : \" + str(block.gen)\n # label += \"\\nKill : \" + str(block.kill)\n dot.node(str(id(block)), label.rstrip(), {\"shape\": \"box\"})\n dot.edge(str(id(block)) + \"_start\", str(id(block)))\n else:\n raise NotImplementedError(type(block))\n # for source, sink, label in self.edges:\n for sink, label in block.outgoing_edges:\n dot.edge(str(id(block)), str(id(sink)), label)\n\n\n file_name = tempfile.mktemp(\"gv\")\n dot.render(file_name, view=True)\n # with open(\"cfg.dot\", \"w\") as file:\n # file.write(dot.source)\n # exit()","def process_tree(tree):\n c = circuit()\n l = line()\n names = {}\n procedures = []\n for lst in tree.children:\n print(lst)\n if type(lst[0]) is str:\n names[lst[0]] = lst[1]\n else:\n procedures.append(lst)\n print(names)\n #print(procedures)\n\n for proc in procedures:\n\n proc_elements_names = proc[0]\n proc_name = proc[1]\n\n #print(proc_elements_names)\n #print(proc_name)\n\n if proc_name == \"set_mode\":\n mode_name = proc_elements_names[0]\n if mode_name != \"draw-mode\": \n c.set_mode(mode_name)\n elif mode_name == \"draw-mode\":\n l1 = line()\n # draw mode is different from other modes\n for element in names:\n e = CompleteElement(element)\n e.set_other_attrs(names[element])\n e.process_other_attrs()\n l1.addElement(e)\n c.connectInSeries(l1)\n c.set_mode(\"draw-mode\")\n \n \n if proc_name == \"series\":\n l1 = line()\n for element in proc_elements_names:\n l1.addElement(names[element])\n l = l1\n c.connectInSeries(l)\n #raise SyntaxError(\"Alias {0} referrenced before assignment\".format(item[0]))\n\n elif proc_name == \"parallel\":\n l1 = line()\n for element in proc_elements_names:\n l1.addElement(names[element])\n c.connectInParallel(l1)\n l1 = line()\n\n\n elif proc_name == \"add_parallel\":\n new_element = proc_elements_names[1]\n old_element = proc_elements_names[0]\n l1 = line()\n l1.addElement(names[new_element])\n c.connection.append(l1)\n\n\n elif proc_name == \"add_series\":\n new_element = proc_elements_names[1]\n old_element = proc_elements_names[0]\n for ln in c.connection:\n for e in ln.elements:\n if names[old_element] == e:\n ln.addElement(names[new_element])\n\n\n c.evaluate(\"output.png\")\n #print(c)","def forward(self, Ca, mask, residue_idx, chain_labels):\n if self.augment_eps > 0:\n Ca = Ca + self.augment_eps * torch.randn_like(Ca)\n\n D_neighbors, E_idx, mask_neighbors = self._dist(Ca, mask)\n\n Ca_0 = torch.zeros(Ca.shape, device=Ca.device)\n Ca_2 = torch.zeros(Ca.shape, device=Ca.device)\n Ca_0[:,1:,:] = Ca[:,:-1,:]\n Ca_1 = Ca\n Ca_2[:,:-1,:] = Ca[:,1:,:]\n\n V, O_features = self._orientations_coarse(Ca, E_idx)\n \n RBF_all = []\n RBF_all.append(self._rbf(D_neighbors)) #Ca_1-Ca_1\n RBF_all.append(self._get_rbf(Ca_0, Ca_0, E_idx)) \n RBF_all.append(self._get_rbf(Ca_2, Ca_2, E_idx))\n\n RBF_all.append(self._get_rbf(Ca_0, Ca_1, E_idx))\n RBF_all.append(self._get_rbf(Ca_0, Ca_2, E_idx))\n\n RBF_all.append(self._get_rbf(Ca_1, Ca_0, E_idx))\n RBF_all.append(self._get_rbf(Ca_1, Ca_2, E_idx))\n\n RBF_all.append(self._get_rbf(Ca_2, Ca_0, E_idx))\n RBF_all.append(self._get_rbf(Ca_2, Ca_1, E_idx))\n\n\n RBF_all = torch.cat(tuple(RBF_all), dim=-1)\n\n\n offset = residue_idx[:,:,None]-residue_idx[:,None,:]\n offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K]\n\n d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long()\n E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0]\n E_positional = self.embeddings(offset.long(), E_chains)\n E = torch.cat((E_positional, RBF_all, O_features), -1)\n \n\n E = self.edge_embedding(E)\n E = self.norm_edges(E)\n \n return E, E_idx","def trans_setup():\n # slot7 slot6 slot5 slot4 slot3 slot2 slot1 <------ beam direction (slot 8 is currently B-fiber only)\n # Be Be Be Be Be Be Be lens material\n # 1.5 1.5 0.5 0.5 0.5 0.5 0.5 lens radius [mm]\n # 1 1 5 8 4 2 1 number of lenses\n lens_R=[0.5,0.5,0.5,0.5,0.5,1.5,1.5]\n lens_mat=['Be','Be','Be','Be','Be','Be','Be']\n lens_N=[1,2,4,8,5,1,1]\n trans_pos=[35.2,35.8]\n return {'lens_material':lens_mat,'lens_number':lens_N,'lens_radius':lens_R,'trans_position':trans_pos}","def serialize(self):\n p = []\n p.append(self.particleCode)\n if self.particleCode == 'manual':\n p.append(str(self.particleMass))\n p.append(str(self.particleCharge))\n p.append(str(self.x0))\n p.append(str(self.y0))\n p.append(str(self.z0))\n if self.useKineticEnergy:\n p.append('Y')\n p.append(str(self.kineticEnergy))\n else:\n p.append('n')\n p.append(str(self.vx0))\n p.append(str(self.vy0))\n p.append(str(self.vz0))\n p.append(self.fieldCode)\n if self.fieldCode == 'Drift':\n p.append(str(self.fieldBaseStrength[0]))\n p.append(str(self.fieldBaseStrength[1]))\n p.append(str(self.fieldBaseStrength[2]))\n p.append(str(self.fieldGradient[0]))\n elif self.fieldCode == 'Smooth':\n p.append(str(self.fieldBaseStrength[0]))\n p.append(str(self.fieldGradient[0]))\n p.append(str(self.fieldGradient[1]))\n elif self.fieldCode == 'Sharp':\n p.append(str(self.fieldBaseStrength[0]))\n p.append(str(self.fieldGradient[0]))\n p.append(str(self.fieldGradient[1]))\n p.append(str(self.fieldLength))\n elif self.fieldCode == 'Sine':\n p.append(str(self.fieldBaseStrength[0]))\n p.append(str(self.fieldGradient[0]))\n p.append(str(self.fieldGradient[1]))\n p.append(str(self.fieldLength))\n p.append(str(self.fieldFreq))\n elif self.fieldCode == 'Helix':\n p.append(str(self.fieldBaseStrength[0]))\n p.append(str(self.fieldBaseStrength[1]))\n p.append(str(self.fieldGradient[0]))\n p.append(str(self.fieldGradient[1]))\n p.append(str(self.fieldGradient[2]))\n p.append(str(self.fieldLength))\n p.append(str(self.fieldFreq))\n elif self.fieldCode == 'Tokamak':\n p.append(str(self.fieldBaseStrength[0]))\n p.append(str(self.fieldBaseStrength[1]))\n p.append(str(self.fieldGradient[0]))\n p.append(str(self.fieldGradient[1]))\n p.append(str(self.fieldGradient[2]))\n p.append(str(self.fieldGradient[3]))\n p.append(str(self.fieldGradient[4]))\n p.append(str(self.fieldLength))\n p.append(str(self.fieldFreq))\n else:\n for val in self.fieldBaseStrength:\n p.append(str(val))\n p.append(str(self.initialTime))\n p.append(str(self.endTime))\n p.append(str(self.timeStep))\n p.append(str(self.tolerance))\n return p","def fst_tostring(self, fst_1, idx=False):\n\n fst_string = 'Transducer\\n'\n for state in fst_1.states:\n for arc in state.arcs:\n olabel = self.wmap[arc.olabel].encode('utf-8') if not idx and arc.olabel in self.wmap else arc.olabel\n\n fst_string += '{} -> {} / {} : {} / {}\\n'.format(state.stateid, arc.nextstate, arc.ilabel, olabel,\n float(arc.weight))\n\n if state.final:\n fst_string += '%s / %s\\n' % (state.stateid, state.final)\n\n fst_string += '-------\\n'\n\n return fst_string","def chainLabel(self,i):\n assert(i >= 0 and i < self.nAtoms())\n assert(self._c_structure is not NULL)\n cdef char label[2]\n label[0] = freesasa_structure_atom_chain(self._c_structure,i)\n label[1] = '\\0'\n return label","def createAddOrSelectLabelMapNode(self, script=False):\r\n # productive\r\n profprint()\r\n print \"creating label map for working intensity volume\"\r\n # create, select label map\r\n volLogic = slicer.modules.volumes.logic()\r\n sliceLogic = slicer.app.layoutManager().sliceWidget(\"Red\").sliceLogic()\r\n vn = sliceLogic.GetBackgroundLayer().GetVolumeNode()\r\n self.labelMapNode = slicer.util.getNode(vn.GetName() + \"-label\")\r\n if not self.labelMapNode:\r\n self.labelMapNode = volLogic.CreateAndAddLabelVolume(slicer.mrmlScene, vn, vn.GetName() + \"-label\")\r\n # select label volume\r\n if not script: #TODO guess there is a bug here (at least while testing with parSearch): also changes the main volume!!\r\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\r\n selectionNode.SetReferenceActiveLabelVolumeID(self.labelMapNode.GetID())\r\n #slicer.app.applicationLogic().PropagateVolumeSelection() #<< in the group '{}'\"\n .format(output_band.pk, self.inputs.group.value))","def pack_firmware(self, work_dir, jobclient, ro, rw, version_string=\"\"):\n dts_file_path = ro / \"zephyr\" / \"zephyr.dts\"\n\n # Copy the inputs into the work directory so that Binman can\n # find them under a hard-coded name.\n shutil.copy2(ro / \"zephyr\" / self.ro_file, work_dir / \"zephyr_ro.bin\")\n shutil.copy2(rw / \"zephyr\" / self.rw_file, work_dir / \"zephyr_rw.bin\")\n\n # Version in FRID/FWID can be at most 31 bytes long (32, minus\n # one for null character).\n if len(version_string) > 31:\n version_string = version_string[:31]\n\n proc = jobclient.popen(\n [\n \"binman\",\n \"-v\",\n \"5\",\n \"build\",\n \"-a\",\n \"version={}\".format(version_string),\n \"-d\",\n dts_file_path,\n \"-m\",\n \"-O\",\n work_dir,\n ],\n cwd=work_dir,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n encoding=\"utf-8\",\n )\n\n zmake.multiproc.log_output(self.logger, logging.DEBUG, proc.stdout)\n zmake.multiproc.log_output(self.logger, logging.ERROR, proc.stderr)\n if proc.wait(timeout=60):\n raise OSError(\"Failed to run binman\")\n\n yield work_dir / \"zephyr.bin\", \"zephyr.bin\"\n yield ro / \"zephyr\" / \"zephyr.elf\", \"zephyr.ro.elf\"\n yield rw / \"zephyr\" / \"zephyr.elf\", \"zephyr.rw.elf\"","def _add_label_switching_node( self,\n node_tree,\n label_vec,\n last_element,\n label_ID_node=None,\n node_index=0,\n uv_map=None, \n node_offset=[0,0]):\n\n # define local variables #######################################################################################\n _step_node_width = 200 # x seperation of nodes\n _step_node_height = 200 # y seperation of nodes\n ################################################################################ end of define local variables #\n\n # create image ID handle #######################################################################################\n if label_ID_node is None:\n label_ID_node = node_tree.node_tree.nodes.new(\"ShaderNodeValue\")\n label_ID_node.location = ((node_offset[0]-400,node_offset[1]-100))\n label_ID_node.name = \"label_step_ID\"\n label_ID_node.label = \"label_step_ID\"\n label_ID_node.outputs[0].default_value = 1\n ############################################################################### end of create image ID handle #\n\n # create image nodes ###########################################################################################\n _x_offset = (node_index+1)*_step_node_width + node_offset[0]\n _y_offset = (node_index+1)*_step_node_height + node_offset[1]\n\n _semantic_node_offset = [(node_index+1)*_step_node_width*2 + node_offset[0]-1000,(node_index+1)*\\\n _step_node_height + node_offset[1]+200]\n\n _semantic_tree, self._semantic_pass_id = self.create_semantic_nodes(node_tree=self._world_node_tree,\n label_ID_vec=label_vec,\n num_label_per_channel=15, # TODO add in script\n env_mode=True,\n uv_map=uv_map,\n node_offset=_semantic_node_offset)\n\n _semantic_tree.inputs[0].default_value = 1\n\n # create new mix node ######################################################################################\n _current_mix_shader_node = node_tree.node_tree.nodes.new(\"ShaderNodeMixRGB\")\n _current_mix_shader_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\n (node_index+1)*_step_node_height + node_offset[1]))\n ############################################################################### end of create new mix node #\n\n # create compare node ######################################################################################\n _current_compare_node = node_tree.node_tree.nodes.new(\"ShaderNodeMath\")\n _current_compare_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\n node_offset[1]-_step_node_height))\n _current_compare_node.operation = 'COMPARE'\n _current_compare_node.inputs[0].default_value = node_index\n _current_compare_node.inputs[2].default_value = 0 # delta value should be zero for equal comparison\n ############################################################################### end of create compare node #\n\n\n # link nodes togther #######################################################################################\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[0], _current_compare_node.outputs[0])\n if last_element is not None:\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[1], last_element.outputs[0])\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[2], _semantic_tree.outputs[0])\n \n node_tree.node_tree.links.new(_current_compare_node.inputs[1], label_ID_node.outputs[0])\n ################################################################################ end of link nodes togther #\n #################################################################################### end of create image nodes #\n\n # return last mix shader node\n return _current_mix_shader_node, label_ID_node","def main(self, case, profile):\n case[\"branch\"][:, BR_STATUS] = ones(case[\"branch\"].shape[0])\n mpc = ext2int(case)\n baseMVA, bus, gen, branch, gencost = mpc[\"baseMVA\"], mpc[\"bus\"], mpc[\"gen\"], mpc[\"branch\"], mpc[\"gencost\"]\n\n nb = shape(mpc['bus'])[0] ## number of buses\n nl = shape(mpc['branch'])[0] ## number of branches\n ng = shape(mpc['gen'])[0] ## number of dispatchable injections\n\n f = branch[:, F_BUS] ## list of \"from\" buses\n t = branch[:, T_BUS] ## list of \"to\" buses\n i = range(nl) ## double set of row indices\n # Connection matrix\n Cf = sparse((ones(nl), (i, f)), (nl, nb))\n Ct = sparse((ones(nl), (i, t)), (nl, nb))\n Cg = sparse((ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))\n Branch_R = branch[:, BR_R]\n Branch_X = branch[:, BR_X]\n Cf = Cf.T\n Ct = Ct.T\n # Obtain the boundary information\n Slmax = branch[:, RATE_A] / baseMVA\n\n Pij_l = -Slmax\n Qij_l = -Slmax\n Iij_l = zeros(nl)\n Vm_l = bus[:, VMIN] ** 2\n Pg_l = gen[:, PMIN] / baseMVA\n Qg_l = gen[:, QMIN] / baseMVA\n Alpha_l = zeros(nl)\n Beta_f_l = zeros(nl)\n Beta_t_l = zeros(nl)\n\n Pij_u = Slmax\n Qij_u = Slmax\n Iij_u = Slmax\n Vm_u = bus[:, VMAX] ** 2\n Pg_u = 2 * gen[:, PMAX] / baseMVA\n Qg_u = 2 * gen[:, QMAX] / baseMVA\n Alpha_u = ones(nl)\n Beta_f_u = ones(nl)\n Beta_t_u = ones(nl)\n bigM = max(Vm_u)\n # For the spanning tree constraints\n Root_node = find(bus[:, BUS_TYPE] == REF)\n Root_line = find(branch[:, F_BUS] == Root_node)\n\n Span_f = zeros((nb, nl))\n Span_t = zeros((nb, nl))\n for i in range(nb):\n Span_f[i, find(branch[:, F_BUS] == i)] = 1\n Span_t[i, find(branch[:, T_BUS] == i)] = 1\n\n Alpha_l[Root_line] = 1\n Alpha_u[Root_line] = 1\n Beta_f_l[Root_line] = 0\n Beta_f_l[Root_line] = 0\n\n T = len(profile)\n nx = int(3 * nl + nb + 2 * ng)\n lx = concatenate([Alpha_l, Beta_f_l, Beta_t_l, tile(concatenate([Pij_l, Qij_l, Iij_l, Vm_l, Pg_l, Qg_l]), T)])\n ux = concatenate([Alpha_u, Beta_f_u, Beta_t_u, tile(concatenate([Pij_u, Qij_u, Iij_u, Vm_u, Pg_u, Qg_u]), T)])\n vtypes = [\"b\"] * 2 * nl + [\"c\"] * nl + [\"c\"] * nx * T\n\n # Define the decision variables\n NX = lx.shape[0]\n\n # Alpha = Beta_f + Beta_t\n Aeq_f = zeros((nl, NX))\n beq_f = zeros(nl)\n Aeq_f[:, 0: nl] = -eye(nl)\n Aeq_f[:, nl: 2 * nl] = eye(nl)\n Aeq_f[:, 2 * nl:3 * nl] = eye(nl)\n\n # sum(alpha)=nb-1\n Aeq_alpha = zeros((1, NX))\n beq_alpha = zeros(1)\n Aeq_alpha[0, 0: nl] = ones(nl)\n beq_alpha[0] = nb - 1\n\n # Span_f*Beta_f+Span_t*Beta_t = Spanning_tree\n Aeq_span = zeros((nb, NX))\n beq_span = ones(nb)\n beq_span[Root_node] = 0\n Aeq_span[:, nl:2 * nl] = Span_f\n Aeq_span[:, 2 * nl: 3 * nl] = Span_t\n\n # Add system level constraints\n # 1) Active power balance\n Aeq_p = zeros((nb * T, NX))\n beq_p = zeros(nb * T)\n for i in range(T):\n Aeq_p[i * nb:(i + 1) * nb, 3 * nl + i * nx:3 * nl + (i + 1) * nx] = hstack([Ct - Cf, zeros((nb, nl)),\n -diag(Ct * Branch_R) * Ct,\n zeros((nb, nb)), Cg,\n zeros((nb, ng))]).toarray()\n beq_p[i * nb:(i + 1) * nb] = profile[i] * bus[:, PD] / baseMVA\n\n # 2) Reactive power balance\n Aeq_q = zeros((nb * T, NX))\n beq_q = zeros(nb * T)\n for i in range(T):\n Aeq_q[i * nb:(i + 1) * nb, 3 * nl + i * nx:3 * nl + (i + 1) * nx] = hstack([zeros((nb, nl)), Ct - Cf,\n -diag(Ct * Branch_X) * Ct,\n zeros((nb, nb)),\n zeros((nb, ng)), Cg]).toarray()\n beq_q[i * nb:(i + 1) * nb] = profile[i] * bus[:, QD] / baseMVA\n\n Aeq = vstack([Aeq_f, Aeq_alpha, Aeq_span, Aeq_p, Aeq_q]).toarray()\n beq = concatenate([beq_f, beq_alpha, beq_span, beq_p, beq_q])\n\n # Inequality constraints\n A = zeros((nl * T, NX))\n b = zeros(nl * T)\n for i in range(T):\n A[i * nl:(i + 1) * nl, 3 * nl + i * nx + 2 * nl: 3 * nl + i * nx + 3 * nl] = eye(nl)\n A[i * nl:(i + 1) * nl, 0: nl] = -diag(Iij_u)\n\n A_temp = zeros((nl * T, NX))\n b_temp = zeros(nl * T)\n for i in range(T):\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx: 3 * nl + i * nx + nl] = eye(nl)\n A_temp[i * nl:(i + 1) * nl, 0: nl] = -diag(Pij_u)\n A = concatenate([A, A_temp])\n b = concatenate([b, b_temp])\n #\n A_temp = zeros((nl * T, NX))\n b_temp = zeros(nl * T)\n for i in range(T):\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + nl: 3 * nl + i * nx + 2 * nl] = eye(nl)\n A_temp[i * nl:(i + 1) * nl, 0:nl] = -diag(Qij_u)\n A = concatenate([A, A_temp])\n b = concatenate([b, b_temp])\n\n A_temp = zeros((nl * T, NX))\n for i in range(T):\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx:3 * nl + i * nx + nl] = -2 * diag(Branch_R)\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + nl:3 * nl + i * nx + 2 * nl] = -2 * diag(Branch_X)\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 2 * nl:3 * nl + i * nx + 3 * nl] = diag(Branch_R ** 2) + \\\n diag(Branch_X ** 2)\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 3 * nl:3 * nl + i * nx + 3 * nl + nb] = \\\n (Cf.T - Ct.T).toarray()\n A_temp[i * nl:(i + 1) * nl, 0:nl] = eye(nl) * bigM\n b_temp = ones(nl * T) * bigM\n A = concatenate([A, A_temp])\n b = concatenate([b, b_temp])\n\n A_temp = zeros((nl * T, NX))\n for i in range(T):\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx:3 * nl + i * nx + nl] = 2 * diag(Branch_R)\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + nl:3 * nl + i * nx + 2 * nl] = 2 * diag(Branch_X)\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 2 * nl:3 * nl + i * nx + 3 * nl] = -diag(Branch_R ** 2) - \\\n diag(Branch_X ** 2)\n A_temp[i * nl:(i + 1) * nl, 3 * nl + i * nx + 3 * nl:3 * nl + i * nx + 3 * nl + nb] = \\\n (-Cf.T + Ct.T).toarray()\n A_temp[i * nl:(i + 1) * nl, 0: nl] = eye(nl) * bigM\n b_temp = ones(nl * T) * bigM\n A = concatenate([A, A_temp])\n b = concatenate([b, b_temp])\n\n Qc = dict()\n for t in range(T):\n for i in range(nl):\n Qc[t * nl + i] = [[int(3 * nl + t * nx + i), int(3 * nl + t * nx + i + nl),\n int(3 * nl + t * nx + i + 2 * nl), int(3 * nl + t * nx + f[i] + 3 * nl)],\n [int(3 * nl + t * nx + i), int(3 * nl + t * nx + i + nl),\n int(3 * nl + t * nx + f[i] + 3 * nl), int(3 * nl + t * nx + i + 2 * nl)],\n [1, 1, -1 / 2, -1 / 2]]\n c = zeros(NX)\n q = zeros(NX)\n c0 = 0\n for t in range(T):\n for i in range(ng):\n c[3 * nl + t * nx + i + 3 * nl + nb] = gencost[i, 5] * baseMVA\n q[3 * nl + t * nx + i + 3 * nl + nb] = gencost[i, 4] * baseMVA * baseMVA\n c0 += gencost[i, 6]\n\n sol = miqcp(c, q, Aeq=Aeq, beq=beq, A=A, b=b, xmin=lx, xmax=ux, vtypes=vtypes, Qc=Qc)\n xx = sol[0]\n Alpha = xx[0:nl]\n Beta_f = xx[nl:2 * nl]\n Beta_t = xx[2 * nl:3 * nl]\n Pij = zeros((nl, T))\n Qij = zeros((nl, T))\n Iij = zeros((nl, T))\n Vi = zeros((nb, T))\n Pg = zeros((ng, T))\n Qg = zeros((ng, T))\n for i in range(T):\n Pij[:, i] = xx[3 * nl + i * nx:3 * nl + i * nx + nl]\n Qij[:, i] = xx[3 * nl + i * nx + nl:3 * nl + i * nx + 2 * nl]\n Iij[:, i] = xx[3 * nl + i * nx + 2 * nl:3 * nl + i * nx + 3 * nl]\n Vi[:, i] = xx[3 * nl + i * nx + 3 * nl:3 * nl + i * nx + 3 * nl + nb]\n Pg[:, i] = xx[3 * nl + i * nx + 3 * nl + nb:3 * nl + i * nx + 3 * nl + nb + ng]\n Qg[:, i] = xx[3 * nl + i * nx + 3 * nl + nb + ng:3 * nl + i * nx + 3 * nl + nb + 2 * ng]\n\n primal_residual = zeros((nl, T))\n for t in range(T):\n for i in range(nl):\n primal_residual[i, t] = Pij[i, t] * Pij[i, t] + Qij[i, t] * Qij[i, t] - Iij[i, t] * Vi[int(f[i]), t]\n\n sol = {\"Pij\": Pij,\n \"Qij\": Qij,\n \"Iij\": Iij,\n \"Vi\": Vi,\n \"Pg\": Pg,\n \"Qg\": Qg,\n \"Alpha\": Alpha,\n \"Beta_f\": Beta_f,\n \"Beta_t\": Beta_t,\n \"residual\": primal_residual,\n \"obj\": sol[1] + c0}\n\n return sol","def write_bc_vtk(self):\n print \"Creating boundary condition arrays\"\n obst_array = np.zeros(self.nnodes)\n obst_array[list(self.obst_list)] = 100.\n\n #print type(self.inlet_list)\n inlet_array = np.zeros(self.nnodes)\n inlet_array[list(self.inlet_list)] = 200.\n\n outlet_array = np.zeros(self.nnodes)\n outlet_array[list(self.outlet_list)] = 300.\n\n solid_array = np.zeros(self.nnodes)\n solid_array[list(self.solid_list)] = 500.\n \n dims = [int(self.Nx), int(self.Ny), int(self.Nz)]\n origin = [0., 0., 0.]\n dx = self.x[1] - self.x[0]\n spacing = [dx, dx, dx] #uniform lattice\n \n print \"Writing boundary conditions to VTK files\"\n writeVTK(inlet_array,'inlet','inlet.vtk',dims,origin,spacing)\n writeVTK(outlet_array,'outlet','outlet.vtk',dims,origin,spacing)\n writeVTK(obst_array,'obst','obst.vtk',dims,origin,spacing)\n writeVTK(solid_array,'solid','solid.vtk',dims,origin,spacing)","def write_bc_vtk(self):\n print \"Creating boundary condition arrays\"\n obst_array = np.zeros(self.nnodes)\n obst_array[list(self.obst_list)] = 100.\n\n #print type(self.inlet_list)\n inlet_array = np.zeros(self.nnodes)\n inlet_array[list(self.inlet_list)] = 200.\n\n outlet_array = np.zeros(self.nnodes)\n outlet_array[list(self.outlet_list)] = 300.\n\n solid_array = np.zeros(self.nnodes)\n solid_array[list(self.solid_list)] = 500.\n \n dims = [int(self.Nx), int(self.Ny), int(self.Nz)]\n origin = [0., 0., 0.]\n dx = self.x[1] - self.x[0]\n spacing = [dx, dx, dx] #uniform lattice\n \n print \"Writing boundary conditions to VTK files\"\n writeVTK(inlet_array,'inlet','inlet.vtk',dims,origin,spacing)\n writeVTK(outlet_array,'outlet','outlet.vtk',dims,origin,spacing)\n writeVTK(obst_array,'obst','obst.vtk',dims,origin,spacing)\n writeVTK(solid_array,'solid','solid.vtk',dims,origin,spacing)","def feature_extract(self, CT_pairs):\n instances = []\n for pair in CT_pairs:\n config = pair[0]\n label = pair[1]\n data = []\n featureset = {}\n \n # for nltk NaiveBayes feature selection stuff when doing MaxEnt decoding parser commit this\n# featureset[\"topOfBuffer\"] = self.token_dict[config.beta.top()]\n# featureset[\"topOfStack\"] = self.token_dict[config.sigma.top()]\n# featureset[\"bufferStackPair\"] = (self.token_dict[config.sigma.top()], self.token_dict[config.beta.top()])\n# featureset[\"topOfBuffer\"] = self.POS_dict[config.beta.top()]\n# featureset[\"topOfStack\"] = self.POS_dict[config.sigma.top()]\n# featureset[\"bufferStackPair\"] = tuple((self.POS_dict[config.sigma.top()], self.POS_dict[config.beta.top()]))\n \n # add the (StackTopPOS,BufferTopPOS,bufferchildren_POS) feature\n #value_set = tuple([self.POS_dict[config.sigma.top()], self.POS_dict[config.beta.top()]] + [self.POS_dict[child] for child in self.getBufferChildren(config.beta.top())])\n #featureset[\"bufferStackbufferChildrenPair\"] = value_set\n \n # for MaxEnt decoding stuff\n # token variants\n data.append((\"topOfBuffer\",self.token_dict[config.beta.top()]))\n data.append((\"topOfStack\",self.token_dict[config.sigma.top()]))\n data.append((\"bufferStackPair\",self.token_dict[config.sigma.top()],self.token_dict[config.beta.top()]))\n #POS variants\n data.append((\"topOfBuffer\",self.POS_dict[config.beta.top()]))\n data.append((\"topOfStack\",self.POS_dict[config.sigma.top()]))\n data.append((\"bufferStackPair\",self.POS_dict[config.sigma.top()],self.POS_dict[config.beta.top()]))\n ins = Instance(label=label, data=data)\n #ins = Instance(label=label, data=featureset)\n instances.append(ins)\n \n return instances","def update_heads(info,\n heads):\n\n info[\"model_params\"][\"boltzmann_dict\"][\"num_heads\"] = heads\n # Concatenate the fingerprints produced by the different heads\n info[\"model_params\"][\"boltzmann_dict\"][\"head_pool\"] = \"concatenate\"\n\n readoutdict = info[\"model_params\"][\"readoutdict\"]\n feat_dim = info[\"model_params\"][\"mol_basis\"]\n\n for key, lst in readoutdict.items():\n for i, dic in enumerate(lst):\n if \"param\" in dic and \"in_features\" in dic.get(\"param\", {}):\n # make sure that the input dimension to the readout is equal to\n # `heads * feat_dim`, where `feat_dim` is the feature dimension\n # produced by each head\n readoutdict[key][i][\"param\"][\"in_features\"] = feat_dim * heads\n break\n info[\"model_params\"][\"readoutdict\"] = readoutdict","def detection_head_graph(self, feature_map, filters):\n x = KL.Conv2D(filters, (1, 1), strides=(1, 1),\n name=\"detection_head_\" + \"stage_1\", use_bias=True, padding=\"same\")(feature_map)\n x = KL.Activation('relu', name='detection_head_stage_1_activation')(x)\n x = KL.Conv2D(filters, (1, 1), strides=(1, 1),\n name=\"detection_head_\" + \"stage_2\", use_bias=True, padding=\"same\")(x)\n x = KL.Activation('relu', name='detection_head_stage_2_activation')(x)\n x = KL.Conv2D(3, (1, 1), strides=(1, 1),\n name=\"detection_head_\" + \"final_stage\", use_bias=True, padding=\"same\")(x)\n return x","def gexf_graph():\n # you must replace these lines and supply your own graph\n gexf = Gexf(\"author\", \"title\")\n mygraph = gexf.addGraph(\"undirected\", \"static\", \"A web network\")\n atr_type = mygraph.addNodeAttribute('Type', type='string')\n atr_id = mygraph.addNodeAttribute('id', type='string')\n atr_label = mygraph.addNodeAttribute('label', type='string')\n atr_color_r = mygraph.addNodeAttribute('color_r', type='string', defaultValue='0')\n atr_color_g = mygraph.addNodeAttribute('color_g', type='string', defaultValue='0')\n atr_color_b = mygraph.addNodeAttribute('color_b', type='string', defaultValue='0')\n k = 0\n for i in range(min_parts()):\n tmp = mygraph.addNode(set_num[i], name[i], r=\"0\", g=\"0\", b=\"0\")\n tmp.addAttribute(atr_type, \"set\")\n tmp.addAttribute(atr_id, set_num[i])\n tmp.addAttribute(atr_label, name[i])\n for j in range(len(Parts[i][\"Parts\"])):\n if mygraph.nodeExists(Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"])==0:\n temp = mygraph.addNode((Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"]), Parts[i][\"Parts\"][j][\"name\"], r=str(int(Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][0:2], 16)), g=str(int(Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][2:4], 16)), b=str(int(Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][4:6], 16)))\n temp.addAttribute(atr_type, \"part\")\n temp.addAttribute(atr_id, (Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"]))\n temp.addAttribute(atr_label, Parts[i][\"Parts\"][j][\"name\"])\n temp.addAttribute(atr_color_r, Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][0:2])\n temp.addAttribute(atr_color_g, Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][2:4])\n temp.addAttribute(atr_color_b, Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][4:6])\n mygraph.addEdge(str(k), set_num[i], (Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"]), weight=Parts[i][\"Parts\"][j][\"quantity\"])\n k = k+1\n output_file = open(\"bricks_graph.gexf\", \"wb\")\n gexf.write(output_file)\n return -1","def _viterbi_decode(self, feats):\n backpointers = []\n\n init_vvars = torch.full((self.batch_size, 1, self.tagset_size), -10000.).to(self.device)\n init_vvars[:, 0, self.tag2idx[START_TAG]] = 0.\n\n forward_var = init_vvars\n for i in range(feats.shape[1]):\n feat = feats[:,i,:]\n\n next_tag_var = forward_var + self.transitions + feat.view(self.batch_size, self.tagset_size, 1)\n best_tag_var, best_tag_id = torch.max(next_tag_var, dim=-1)\n backpointers.append(best_tag_id)\n forward_var = best_tag_var.view(self.batch_size, 1, self.tagset_size)\n\n # Transition to STOP_TAG\n terminal_var = forward_var + self.transitions[self.tag2idx[STOP_TAG]]\n path_score, best_tag_id = torch.max(terminal_var, dim=-1)\n\n # Follow the back pointers to decode the best path.\n best_path = [best_tag_id]\n for bptrs_t in reversed(backpointers):\n # gather the value in bptrs_t according to best_tag_id\n best_tag_id = bptrs_t.gather(dim=-1,index=best_tag_id)\n best_path.append(best_tag_id)\n \n # Pop off the start tag (we dont want to return that to the caller)\n best_path.pop()\n best_path.reverse()\n best_path = torch.cat(best_path, dim=1)\n # best_path = [[self.idx2tag[j] for j in i] for i in best_path.tolist()]\n \n return path_score, best_path","def encodeToCartBoxesLabels(self, gt_instances):\n raw_boxes_xywh = np.zeros((self.config_data[\"max_boxes_per_frame\"], 5))\n ### initialize gronud truth labels as np.zeros ###\n gt_labels = np.zeros(list(self.cart_shape[1:3]) + \\\n [len(self.anchor_boxes_cart)] + \\\n [len(self.config_data[\"all_classes\"]) + 5]) \n\n ### start transferring box to ground turth label format ###\n for i in range(len(gt_instances[\"classes\"])):\n if i > self.config_data[\"max_boxes_per_frame\"]:\n continue\n class_name = gt_instances[\"classes\"][i]\n box_xywh = gt_instances[\"cart_boxes\"][i]\n class_id = self.config_data[\"all_classes\"].index(class_name)\n if i <= self.config_data[\"max_boxes_per_frame\"]:\n raw_boxes_xywh[i, :4] = box_xywh\n raw_boxes_xywh[i, 4] = class_id\n class_onehot = helper.smoothOnehot(class_id, \\\n len(self.config_data[\"all_classes\"]))\n exist_positive = False\n grid_strid = self.cart_grid_strides\n anchors = self.anchor_boxes_cart\n box_xywh_scaled = box_xywh[np.newaxis, :].astype(np.float32)\n box_xywh_scaled[:, :2] /= grid_strid\n anchors_xywh = np.zeros([len(anchors), 4])\n anchors_xywh[:, :2] = np.floor(box_xywh_scaled[:, :2]) + 0.5\n anchors_xywh[:, 2:] = anchors.astype(np.float32)\n\n iou_scaled = helper.iou2d(box_xywh_scaled, anchors_xywh)\n ### NOTE: 0.3 is from YOLOv4, maybe this should be different here ###\n ### it means, as long as iou is over 0.3 with an anchor, the anchor\n ### should be taken into consideration as a ground truth label\n iou_mask = iou_scaled > 0.3\n\n if np.any(iou_mask):\n xind, yind = np.floor(np.squeeze(box_xywh_scaled)[:2]).astype(np.int32)\n ### TODO: consider changing the box to raw yolohead output format ###\n gt_labels[xind, yind, iou_mask, 0:4] = box_xywh\n gt_labels[xind, yind, iou_mask, 4:5] = 1.\n gt_labels[xind, yind, iou_mask, 5:] = class_onehot\n exist_positive = True\n\n if not exist_positive:\n ### NOTE: this is the normal one ###\n ### it means take the anchor box with maximum iou to the raw\n ### box as the ground truth label\n iou_mask = iou_scaled == iou_scaled.max()\n\n if np.any(iou_mask):\n xind, yind = np.floor(np.squeeze(box_xywh_scaled)[:2]).astype(np.int32)\n ### TODO: consider changing the box to raw yolohead output format ###\n gt_labels[xind, yind, iou_mask, 0:4] = box_xywh\n gt_labels[xind, yind, iou_mask, 4:5] = 1.\n gt_labels[xind, yind, iou_mask, 5:] = class_onehot\n\n has_label = False\n if gt_labels.max() != 0:\n has_label = True\n gt_labels = np.where(gt_labels == 0, 1e-16, gt_labels)\n return gt_labels, has_label, raw_boxes_xywh","def _fusion(self, expert_outputs):\n raise NotImplementedError","def reagent_label_data(bcl_step):\n\n define_step = None\n indexes = {}\n flowcell_total_reads = 0\n\n for inp, outp in bcl_step.input_output_maps:\n pre_bcl_steps = MASTER_STEPS_UDFS['reagent_labels']['steps']['pre_bcl']\n if inp['parent-process'].type.name not in pre_bcl_steps:\n continue\n\n if outp['output-generation-type'] != 'PerReagentLabel':\n continue\n\n lane = inp['uri']\n art = outp['uri']\n\n index_reads = art.udf.get(\n MASTER_STEPS_UDFS['reagent_labels']['udfs']['reads'])\n\n if index_reads is None or art.qc_flag == 'FAILED':\n continue\n\n flowcell_total_reads += index_reads\n\n sample = art.samples[0] # Will always be only one sample in the list\n application_tag = sample.udf.get('Sequencing Analysis')\n\n if application_tag[0:2] in MASTER_STEPS_UDFS['reagent_labels'][\n 'exclue_tags']:\n continue\n\n if not define_step:\n define_step_outputs, flowcell_target_reads, define_step = get_define_step_data(\n lane)\n\n if not sample.id in define_step_outputs:\n LOG.info('This sample whas put as a pool into the define step: ' +\n sample.id + ' ' + application_tag)\n continue\n\n index = art.reagent_labels[0]\n\n container, lane_nr = lane.location\n if index not in indexes:\n indexes[index] = {\n '_id': '_'.join([index, container.name]),\n 'url': index.replace(' ', ''),\n 'index_total_reads': index_reads,\n 'index_target_reads': define_step_outputs[sample.id],\n 'flowcell_target_reads': flowcell_target_reads,\n 'index': index,\n 'sample': sample.id,\n 'lanes': {\n lane_nr: dict(art.udf.items())\n },\n 'flowcell_id': container.name,\n 'flowcell_type': container.type.name,\n 'define_step_udfs': dict(define_step.udf.items()),\n 'define_step_id': define_step.id,\n 'bcl_step_id': bcl_step.id\n }\n else:\n indexes[index]['lanes'][lane_nr] = dict(art.udf.items())\n indexes[index]['index_total_reads'] += index_reads\n\n for index, data in indexes.items():\n data['flowcell_total_reads'] = flowcell_total_reads\n indexes[index] = filter_none(data)\n\n return indexes","def create_pipeline_flow(\n self, cmp_deriv_subject_directory, nipype_deriv_subject_directory\n ):\n acquisition_model = self.stages[\"Diffusion\"].config.diffusion_imaging_model\n recon_tool = self.stages[\"Diffusion\"].config.recon_processing_tool\n\n recon_model = \"DTI\"\n\n if acquisition_model == \"DSI\":\n recon_model = \"SHORE\"\n else:\n if recon_tool == \"Dipy\" and self.stages[\"Diffusion\"].config.dipy_recon_config.local_model:\n recon_model = \"CSD\"\n elif recon_tool == \"MRtrix\" and self.stages[\"Diffusion\"].config.mrtrix_recon_config.local_model:\n recon_model = \"CSD\"\n\n tracking_model = self.stages[\"Diffusion\"].config.diffusion_model\n\n if tracking_model == \"Deterministic\":\n tracking_model = \"DET\"\n elif tracking_model == \"Probabilistic\":\n tracking_model = \"PROB\"\n\n if self.parcellation_scheme == \"Lausanne2018\":\n bids_atlas_label = \"L2018\"\n elif self.parcellation_scheme == \"NativeFreesurfer\":\n bids_atlas_label = \"Desikan\"\n elif self.parcellation_scheme == \"Custom\":\n bids_atlas_label = self.custom_atlas_name\n if self.custom_atlas_res is not None and self.custom_atlas_res != \"\":\n bids_atlas_label += f'_res-{self.custom_atlas_res}'\n\n # Clear previous outputs\n self.clear_stages_outputs()\n\n # Create diffusion workflow with input and output Identityinterface nodes\n diffusion_flow = pe.Workflow(\n name=\"diffusion_pipeline\",\n base_dir=os.path.abspath(nipype_deriv_subject_directory),\n )\n\n diffusion_inputnode = pe.Node(\n interface=util.IdentityInterface(\n fields=[\n \"diffusion\",\n \"bvecs\",\n \"bvals\",\n \"T1\",\n \"aseg\",\n \"aparc_aseg\",\n \"brain\",\n \"T2\",\n \"brain_mask\",\n \"wm_mask_file\",\n \"roi_volumes\",\n \"roi_graphMLs\",\n \"subjects_dir\",\n \"subject_id\",\n \"parcellation_scheme\",\n ]\n ),\n name=\"inputnode\",\n )\n diffusion_inputnode.inputs.parcellation_scheme = self.parcellation_scheme\n diffusion_inputnode.inputs.atlas_info = self.atlas_info\n\n diffusion_outputnode = pe.Node(\n interface=util.IdentityInterface(fields=[\"connectivity_matrices\"]),\n name=\"outputnode\",\n )\n\n diffusion_flow.add_nodes([diffusion_inputnode, diffusion_outputnode])\n\n # Data import\n datasource = self.create_datagrabber_node(\n base_directory=cmp_deriv_subject_directory,\n bids_atlas_label=bids_atlas_label\n )\n\n # Data sinker for output\n sinker = self.create_datasinker_node(\n base_directory=cmp_deriv_subject_directory,\n bids_atlas_label=bids_atlas_label,\n recon_model=recon_model,\n tracking_model=tracking_model\n )\n\n # fmt:off\n diffusion_flow.connect(\n [\n (datasource, diffusion_inputnode, [(\"diffusion\", \"diffusion\"),\n (\"bvecs\", \"bvecs\"),\n (\"bvals\", \"bvals\"),\n (\"T1\", \"T1\"),\n (\"aseg\", \"aseg\"),\n (\"aparc_aseg\", \"aparc_aseg\"),\n (\"brain\", \"brain\"),\n (\"brain_mask\", \"brain_mask\"),\n (\"wm_mask_file\", \"wm_mask_file\")]),\n ]\n )\n # fmt:on\n\n merge_roi_volumes = pe.Node(interface=Merge(5), name=\"merge_roi_volumes\")\n merge_roi_graphmls = pe.Node(interface=Merge(5), name=\"merge_roi_graphmls\")\n\n def remove_non_existing_scales(roi_volumes):\n \"\"\"Returns a list which do not contained any empty element.\n\n Parameters\n ----------\n roi_volumes : list\n A list of output parcellations that might contain empty element\n in the case of the monoscale Desikan scheme for instance\n\n Returns\n -------\n out_roi_volumes : list\n The list with no empty element\n \"\"\"\n out_roi_volumes = []\n for vol in roi_volumes:\n if vol is not None:\n out_roi_volumes.append(vol)\n return out_roi_volumes\n\n # fmt:off\n diffusion_flow.connect(\n [\n (datasource, merge_roi_volumes, [(\"roi_volume_s1\", \"in1\"),\n (\"roi_volume_s2\", \"in2\"),\n (\"roi_volume_s3\", \"in3\"),\n (\"roi_volume_s4\", \"in4\"),\n (\"roi_volume_s5\", \"in5\")]),\n (datasource, merge_roi_graphmls, [(\"roi_graphml_s1\", \"in1\"),\n (\"roi_graphml_s2\", \"in2\"),\n (\"roi_graphml_s3\", \"in3\"),\n (\"roi_graphml_s4\", \"in4\"),\n (\"roi_graphml_s5\", \"in5\")]),\n (merge_roi_volumes, diffusion_inputnode, [((\"out\", remove_non_existing_scales), \"roi_volumes\")],),\n (merge_roi_graphmls, diffusion_inputnode, [((\"out\", remove_non_existing_scales), \"roi_graphMLs\")],),\n ]\n )\n # fmt:on\n\n if self.stages[\"Preprocessing\"].enabled:\n preproc_flow = self.create_stage_flow(\"Preprocessing\")\n # fmt:off\n diffusion_flow.connect(\n [\n (diffusion_inputnode, preproc_flow, [(\"diffusion\", \"inputnode.diffusion\"),\n (\"brain\", \"inputnode.brain\"),\n (\"aseg\", \"inputnode.aseg\"),\n (\"aparc_aseg\", \"inputnode.aparc_aseg\"),\n (\"brain_mask\", \"inputnode.brain_mask\"),\n (\"wm_mask_file\", \"inputnode.wm_mask_file\"),\n (\"roi_volumes\", \"inputnode.roi_volumes\"),\n (\"bvecs\", \"inputnode.bvecs\"),\n (\"bvals\", \"inputnode.bvals\"),\n (\"T1\", \"inputnode.T1\")]),\n ]\n )\n # fmt:on\n\n if self.stages[\"Registration\"].enabled:\n reg_flow = self.create_stage_flow(\"Registration\")\n # fmt:off\n diffusion_flow.connect(\n [\n # (diffusion_inputnode,reg_flow,[('T2','inputnode.T2')]),\n (preproc_flow, reg_flow, [(\"outputnode.T1\", \"inputnode.T1\"),\n (\"outputnode.act_5TT\", \"inputnode.act_5TT\"),\n (\"outputnode.gmwmi\", \"inputnode.gmwmi\"),\n (\"outputnode.bvecs_rot\", \"inputnode.bvecs\"),\n (\"outputnode.bvals\", \"inputnode.bvals\"),\n (\"outputnode.wm_mask_file\", \"inputnode.wm_mask\"),\n (\"outputnode.partial_volume_files\", \"inputnode.partial_volume_files\",),\n (\"outputnode.roi_volumes\", \"inputnode.roi_volumes\"),\n (\"outputnode.brain\", \"inputnode.brain\"),\n (\"outputnode.brain_mask\", \"inputnode.brain_mask\"),\n (\"outputnode.brain_mask_full\", \"inputnode.brain_mask_full\"),\n (\"outputnode.diffusion_preproc\", \"inputnode.target\"),\n (\"outputnode.dwi_brain_mask\", \"inputnode.target_mask\")]),\n (preproc_flow, sinker, [(\"outputnode.bvecs_rot\", \"dwi.@bvecs_rot\"),\n (\"outputnode.diffusion_preproc\", \"dwi.@diffusion_preproc\"),\n (\"outputnode.dwi_brain_mask\", \"dwi.@diffusion_brainmask\")]),\n ]\n )\n # fmt:on\n if self.stages[\"Registration\"].config.registration_mode == \"BBregister (FS)\":\n # fmt:off\n diffusion_flow.connect(\n [\n (diffusion_inputnode, reg_flow, [(\"subjects_dir\", \"inputnode.subjects_dir\"), (\"subject_id\", \"inputnode.subject_id\")]),\n ]\n )\n # fmt:on\n\n if self.stages[\"Diffusion\"].enabled:\n diff_flow = self.create_stage_flow(\"Diffusion\")\n # fmt:off\n diffusion_flow.connect(\n [\n (preproc_flow, diff_flow, [(\"outputnode.diffusion_preproc\", \"inputnode.diffusion\")]),\n (reg_flow, diff_flow, [(\"outputnode.wm_mask_registered_crop\", \"inputnode.wm_mask_registered\",),\n (\"outputnode.brain_mask_registered_crop\", \"inputnode.brain_mask_registered\",),\n (\"outputnode.partial_volumes_registered_crop\", \"inputnode.partial_volumes\",),\n (\"outputnode.roi_volumes_registered_crop\", \"inputnode.roi_volumes\",),\n (\"outputnode.act_5tt_registered_crop\", \"inputnode.act_5tt_registered\",),\n (\"outputnode.gmwmi_registered_crop\", \"inputnode.gmwmi_registered\",),\n (\"outputnode.grad\", \"inputnode.grad\"),\n (\"outputnode.bvals\", \"inputnode.bvals\"),\n (\"outputnode.bvecs\", \"inputnode.bvecs\")]),\n (reg_flow, sinker, [(\"outputnode.target_epicorrected\", \"dwi.@bdiffusion_reg_crop\",),\n (\"outputnode.grad\", \"dwi.@diffusion_grad\"),\n (\"outputnode.affine_transform\", \"xfm.@affine_transform\"),\n (\"outputnode.warp_field\", \"xfm.@warp_field\"),\n (\"outputnode.T1_registered_crop\", \"anat.@T1_reg_crop\"),\n (\"outputnode.act_5tt_registered_crop\", \"anat.@act_5tt_reg_crop\",),\n (\"outputnode.gmwmi_registered_crop\", \"anat.@gmwmi_reg_crop\"),\n (\"outputnode.brain_registered_crop\", \"anat.@brain_reg_crop\"),\n (\"outputnode.brain_mask_registered_crop\", \"anat.@brain_mask_reg_crop\",),\n (\"outputnode.wm_mask_registered_crop\", \"anat.@wm_mask_reg_crop\",),\n (\"outputnode.roi_volumes_registered_crop\", \"anat.@roivs_reg_crop\",),\n (\"outputnode.partial_volumes_registered_crop\", \"anat.@pves_reg_crop\",)],),\n ]\n )\n # fmt:on\n\n if self.stages[\"Connectome\"].enabled:\n self.stages[\"Connectome\"].config.probtrackx = False\n self.stages[\"Connectome\"].config.subject = self.global_conf.subject\n con_flow = self.create_stage_flow(\"Connectome\")\n # fmt:off\n diffusion_flow.connect(\n [\n (diffusion_inputnode, con_flow, [(\"parcellation_scheme\", \"inputnode.parcellation_scheme\"),\n (\"atlas_info\", \"inputnode.atlas_info\"),\n (\"roi_graphMLs\", \"inputnode.roi_graphMLs\")]),\n (diff_flow, con_flow, [(\"outputnode.track_file\", \"inputnode.track_file\"),\n (\"outputnode.FA\", \"inputnode.FA\"),\n (\"outputnode.ADC\", \"inputnode.ADC\"),\n (\"outputnode.AD\", \"inputnode.AD\"),\n (\"outputnode.RD\", \"inputnode.RD\"),\n (\"outputnode.roi_volumes\", \"inputnode.roi_volumes_registered\",),\n (\"outputnode.skewness\", \"inputnode.skewness\"),\n (\"outputnode.kurtosis\", \"inputnode.kurtosis\"),\n (\"outputnode.P0\", \"inputnode.P0\"),\n (\"outputnode.mapmri_maps\", \"inputnode.mapmri_maps\"),\n (\"outputnode.shore_maps\", \"inputnode.shore_maps\")]),\n (con_flow, diffusion_outputnode, [(\"outputnode.connectivity_matrices\", \"connectivity_matrices\")]),\n (diff_flow, sinker, [(\"outputnode.fod_file\", \"dwi.@fod_file\"),\n (\"outputnode.FA\", \"dwi.@FA\"),\n (\"outputnode.ADC\", \"dwi.@ADC\"),\n (\"outputnode.AD\", \"dwi.@AD\"),\n (\"outputnode.RD\", \"dwi.@RD\"),\n (\"outputnode.skewness\", \"dwi.@skewness\"),\n (\"outputnode.kurtosis\", \"dwi.@kurtosis\"),\n (\"outputnode.P0\", \"dwi.@P0\"),\n (\"outputnode.mapmri_maps\", \"dwi.@mapmri_maps\"),\n (\"outputnode.shore_maps\", \"dwi.@shore_maps\")]),\n (con_flow, sinker, [(\"outputnode.streamline_final_file\", \"dwi.@streamline_final_file\"),\n (\"outputnode.connectivity_matrices\", \"dwi.@connectivity_matrices\")]),\n ]\n )\n # fmt:on\n\n return diffusion_flow","def pack_firmware(self, work_dir, jobclient, version_string=\"\"):\n raise NotImplementedError(\"Abstract method not implemented\")","def __call__(self, node):\n\n # should throw an error\n if node.cfgInterface == None:\n return\n\n # //\n # // Extract LFN base from included WorkflowSpec parameters\n #//\n base = node.getParameter(\"UnmergedLFNBase\")[0]\n\n # //\n # // iterate over outputmodules/data tiers\n #// Generate LFN, PFN and Catalog for each module\n for modName, outModule in node.cfgInterface.outputModules.items():\n if ( not outModule.has_key('fileName') ):\n msg = \"OutputModule %s does not contain a fileName entry\" % modName\n raise RuntimeError, msg\n outModule['logicalFileName'] = os.path.join(base, outModule['dataTier'], str(self.lfnGroup))\n outModule['logicalFileName'] += '/'\n outModule['logicalFileName'] += outModule['fileName']\n\n return","def encodeToLabels(self, gt_instances):\n raw_boxes_xyzwhd = np.zeros((self.config_data[\"max_boxes_per_frame\"], 7))\n ### initialize gronud truth labels as np.zeors ###\n gt_labels = np.zeros(list(self.headoutput_shape[1:4]) + \\\n [len(self.anchor_boxes)] + \\\n [len(self.config_data[\"all_classes\"]) + 7])\n\n ### start transferring box to ground turth label format ###\n for i in range(len(gt_instances[\"classes\"])):\n if i > self.config_data[\"max_boxes_per_frame\"]:\n continue\n class_name = gt_instances[\"classes\"][i]\n box_xyzwhd = gt_instances[\"boxes\"][i]\n class_id = self.config_data[\"all_classes\"].index(class_name)\n if i < self.config_data[\"max_boxes_per_frame\"]:\n raw_boxes_xyzwhd[i, :6] = box_xyzwhd\n raw_boxes_xyzwhd[i, 6] = class_id\n class_onehot = helper.smoothOnehot(class_id, len(self.config_data[\"all_classes\"]))\n \n exist_positive = False\n\n grid_strid = self.grid_strides\n anchor_stage = self.anchor_boxes\n box_xyzwhd_scaled = box_xyzwhd[np.newaxis, :].astype(np.float32)\n box_xyzwhd_scaled[:, :3] /= grid_strid\n anchorstage_xyzwhd = np.zeros([len(anchor_stage), 6])\n anchorstage_xyzwhd[:, :3] = np.floor(box_xyzwhd_scaled[:, :3]) + 0.5\n anchorstage_xyzwhd[:, 3:] = anchor_stage.astype(np.float32)\n\n iou_scaled = helper.iou3d(box_xyzwhd_scaled, anchorstage_xyzwhd, \\\n self.input_size)\n ### NOTE: 0.3 is from YOLOv4, maybe this should be different here ###\n ### it means, as long as iou is over 0.3 with an anchor, the anchor\n ### should be taken into consideration as a ground truth label\n iou_mask = iou_scaled > 0.3\n\n if np.any(iou_mask):\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\n astype(np.int32)\n ### TODO: consider changing the box to raw yolohead output format ###\n gt_labels[xind, yind, zind, iou_mask, 0:6] = box_xyzwhd\n gt_labels[xind, yind, zind, iou_mask, 6:7] = 1.\n gt_labels[xind, yind, zind, iou_mask, 7:] = class_onehot\n exist_positive = True\n\n if not exist_positive:\n ### NOTE: this is the normal one ###\n ### it means take the anchor box with maximum iou to the raw\n ### box as the ground truth label\n anchor_ind = np.argmax(iou_scaled)\n xind, yind, zind = np.floor(np.squeeze(box_xyzwhd_scaled)[:3]).\\\n astype(np.int32)\n gt_labels[xind, yind, zind, anchor_ind, 0:6] = box_xyzwhd\n gt_labels[xind, yind, zind, anchor_ind, 6:7] = 1.\n gt_labels[xind, yind, zind, anchor_ind, 7:] = class_onehot\n\n has_label = False\n for label_stage in gt_labels:\n if label_stage.max() != 0:\n has_label = True\n gt_labels = [np.where(gt_i == 0, 1e-16, gt_i) for gt_i in gt_labels]\n return gt_labels, has_label, raw_boxes_xyzwhd","def worker(selection_idx, results_table):\n randgen = np.random.RandomState()\n \n # Data-specific positive set partition (the real-world dataset consists of multiple motif classes, always exactly 3 instances of each class stored consequently).\n # The partition assures that the training and test sets do not share instances of the same motif class\n positive_n_train = round(0.8 * len(positive_set_) / 3) * 3\n block_start_idx = randgen.randint(positive_n_train / 3 + 1) * 3 \n block_end_idx = block_start_idx + len(positive_set_) - positive_n_train\n positive_set_part_train, positive_set_part_test = (np.concatenate((positive_set_[: block_start_idx], positive_set_[block_end_idx: ])), positive_set_[block_start_idx: block_end_idx])\n \n # Negative set partition with random selection of elements to match the size of the positive set\n negative_set = negative_set_[randgen.choice(len(negative_set_), size = positive_set_.shape[0], replace = False)]\n negative_n = len(negative_set)\n negative_n_train = round(negative_n * 0.8)\n negative_set_part_train, negative_set_part_test = (negative_set[: negative_n_train], negative_set[negative_n_train: ])\n \n data_part_train = np.float64(np.concatenate((positive_set_part_train, negative_set_part_train)))\n labels_part_train = np.concatenate((np.ones(len(positive_set_part_train), dtype = 'i1'), np.zeros(len(negative_set_part_train), dtype = 'i1')))\n data_part_test = np.float64(np.concatenate((positive_set_part_test, negative_set_part_test)))\n labels_part_test = np.concatenate((np.ones(len(positive_set_part_test), dtype = 'i1'), np.zeros(len(negative_set_part_test), dtype = 'i1')))\n \n # Specifying the pipeline and the CV structure\n pruner = feature_selection.VarianceThreshold()\n scaler = preprocessing.StandardScaler()\n feature_selector = feature_selection.SelectKBest(feature_selection.f_classif)\n classifier = svm.SVC(kernel = 'rbf', gamma = 0.01, class_weight = 'balanced')\n pipeline0 = pipeline.Pipeline([\n ('pruning', pruner),\n ('scaling', scaler),\n ('selection', feature_selector),\n ('classification', classifier)\n ])\n cv_structure = model_selection.StratifiedShuffleSplit(n_splits = 10, test_size = 0.2)\n scoring = 'recall_macro' #same as balanced accuracy\n grid = model_selection.GridSearchCV(pipeline0, scoring = scoring, param_grid = param_grid, cv = cv_structure, n_jobs = 1)\n \n # Training the pipeline, saving the data\n grid.fit(data_part_train, labels_part_train)\n results_table[selection_idx][0] = np.log10(grid.best_params_['classification__C'])\n results_table[selection_idx][1] = grid.best_params_['selection__k']\n results_table[selection_idx][2] = grid.best_score_\n \n # Testing the pipeline, saving the data\n results_table[selection_idx][3] = grid.score(data_part_test, labels_part_test)","def _vVBEL_XXXX(self,vKNOT=None,OBJTYPE=None):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n \r\n try: \r\n \r\n #'KVR','PZON_NAME', 'FSTF_NAME', 'STOF_NAME', 'GMIX_NAME','UTMP_NAME'\r\n\r\n\r\n\r\n vXXXX=None\r\n\r\n vXXXX=pd.merge(self.dataFrames[OBJTYPE],vKNOT,left_on='fkKI',right_on='pk',suffixes=('','_i')) \r\n vXXXX=vXXXX[['fkKK','BESCHREIBUNG','IDREFERENZ','pk','tk','NAME','CONT','CONT_VKNO','pk_i','ZKOR']]\r\n vXXXX.rename(columns={'NAME':'NAME_i','CONT':'CONT_i','CONT_VKNO':'CONT_VKNO_i','ZKOR':'Z_i'},inplace=True)\r\n\r\n vXXXX=pd.merge(vXXXX,vKNOT,left_on='fkKK',right_on='pk',suffixes=('','_k'))\r\n vXXXX=vXXXX[['BESCHREIBUNG','IDREFERENZ','pk','tk','NAME_i','CONT_i','CONT_VKNO_i','Z_i','pk_i','NAME','CONT','CONT_VKNO','pk_k','ZKOR']]\r\n vXXXX.rename(columns={'NAME':'NAME_k','CONT':'CONT_k','CONT_VKNO':'CONT_VKNO_k','ZKOR':'Z_k'},inplace=True)\r\n \r\n vXXXX=vXXXX.assign(OBJTYPE=lambda x: OBJTYPE)\r\n vXXXX=vXXXX[['OBJTYPE','BESCHREIBUNG','IDREFERENZ','pk','tk','NAME_i','CONT_i','CONT_VKNO_i','Z_i','pk_i','NAME_k','CONT_k','CONT_VKNO_k','Z_k','pk_k']]\r\n\r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n logger.debug(logStrFinal) \r\n vXXXX=None\r\n \r\n finally:\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.'))\r\n return vXXXX","def process_circuits(self, processor_fn, updated_aliases=None):\n P = processor_fn # shorthand\n cpy = LsGermsSerialStructure(self.Ls, list(map(P, self.germs)),\n self.nMinorRows, self.nMinorCols,\n updated_aliases, self.sequenceRules)\n cpy.allstrs = list(map(P, self.allstrs))\n cpy.allstrs_set = set(cpy.allstrs)\n cpy.unindexed = list(map(P, self.unindexed))\n cpy._plaquettes = {k: v.process_circuits(P, updated_aliases) for k, v in self._plaquettes.items()}\n cpy._firsts = [(L, P(germ)) for (L, germ) in self._firsts]\n cpy._baseStrToLGerm = {P(base): (L, P(germ)) for base, (L, germ) in self._baseStrToLGerm.items()}\n return cpy","def build(self):\n super(VaporStateBlockData, self).build()\n\n # Object reference for molecular weight if needed by CV1D\n # Molecular weights\n self.mw_comp = Reference(self.params.mw_comp)\n\n self.flow_mol = Var(initialize=1.0,\n domain=NonNegativeReals,\n units=pyunits.mol / pyunits.s,\n doc='Total molar flowrate')\n\n self.mole_frac_comp = Var(self.component_list,\n domain=NonNegativeReals,\n bounds=(0, 1),\n units=None,\n initialize=1 / len(self.component_list),\n doc='Component mole fractions [-]')\n\n self.pressure = Var(initialize=101325,\n domain=NonNegativeReals,\n units=pyunits.Pa,\n doc='Pressure [Pa]')\n\n self.temperature = Var(initialize=298.15,\n domain=NonNegativeReals,\n units=pyunits.K,\n doc='Temperature [K]')\n\n # Sum mole fractions if not inlet block\n if self.config.defined_state is False:\n def sum_component_eqn(b):\n return b.flow_mol == sum(b.flow_mol_comp[j]\n for j in b._params.component_list)\n self.sum_component_eqn = Constraint(rule=sum_component_eqn)","def exec_attention(self,curr_step): \n\n assert(self.curr_step_idx > 0 and self.dlist is not None), \"Step Error: Must call init before combine\" \n \n detectType = curr_step[\"detectionNetwork\"]\n paramsFile = curr_step[\"paramsFile\"]\n funclist = curr_step[\"funclist\"]\n\n #verify raw data & dlist\n # self.B_VER(self.sess_path, self.dlist)\n raw_datadir = self.sess_path\n dest_datadir = self.sess_path \n\n model_dict = {\"detectType\": detectType, \"paramsFile\" : paramsFile} \n\n for i, folder in enumerate(self.dlist):\n flist = funclist[i]\n self.data_utils.DETECT(raw_datadir, folder, dest_datadir, model_dict, flist=[], preview=False)\n self.default_vis(curr_step)","def encode(self):\n payload = []\n\n # Generate Payload\n if self.IsEnsembleData:\n payload += self.EnsembleData.encode()\n if self.IsAncillaryData:\n payload += self.AncillaryData.encode()\n if self.IsAmplitude:\n payload += self.Amplitude.encode()\n if self.IsCorrelation:\n payload += self.Correlation.encode()\n if self.IsBeamVelocity:\n payload += self.BeamVelocity.encode()\n if self.IsInstrumentVelocity:\n payload += self.InstrumentVelocity.encode()\n if self.IsEarthVelocity:\n payload += self.EarthVelocity.encode()\n if self.IsGoodBeam:\n payload += self.GoodBeam.encode()\n if self.IsGoodEarth:\n payload += self.GoodEarth.encode()\n if self.IsBottomTrack:\n payload += self.BottomTrack.encode()\n if self.IsRangeTracking:\n payload += self.RangeTracking.encode()\n if self.IsSystemSetup:\n payload += self.SystemSetup.encode()\n if self.IsNmeaData:\n payload += self.NmeaData.encode()\n\n # Generate the header\n # Get the ensemble number\n ens_num = 0\n if self.IsEnsembleData:\n ens_num = self.EnsembleData.EnsembleNumber\n\n # Get the payload size\n payload_size = len(payload)\n\n header = Ensemble.generate_ens_header(ens_num, payload_size)\n\n # Generate the Checksum CITT\n # Parameters found at https: // pycrc.org / models.html\n #crc = pycrc.algorithms.Crc(width=16, poly=0x1021,\n # reflect_in=False, xor_in=0x1d0f,\n # reflect_out=False, xor_out=0x0000)\n #checksum = crc.bit_by_bit_fast(binascii.a2b_hex(bytes(payload)))\n #checksum = Ensemble.int32_to_bytes(CRCCCITT().calculate(input_data=bytes(payload)))\n checksum = crc16.crc16xmodem(payload)\n\n\n result = []\n result += header\n result += payload\n result += checksum\n\n return bytearray(result)","def transform(self, inputs: list, stage: str) -> datapack.DataPack:","def predict_structure(prefix, model_runner_1: alphafold.model.model.RunModel,\n model_runner_3: alphafold.model.model.RunModel,\n feature_dict, Ls: list[int], model_params: haiku.Params, use_model, do_relax=False,\n random_seed=0):\n\n # Minkyung's code\n # add big enough number to residue index to indicate chain breaks\n idx_res = feature_dict['residue_index']\n L_prev = 0\n # Ls: number of residues in each chain\n for L_i in Ls[:-1]:\n idx_res[L_prev + L_i:] += 200\n L_prev += L_i\n chains = list(\"\".join([ascii_uppercase[n] * L for n, L in enumerate(Ls)]))\n feature_dict['residue_index'] = idx_res\n\n # Run the models.\n plddts, paes = [], []\n unrelaxed_pdb_lines = []\n relaxed_pdb_lines = []\n\n print(f\"Use_model {use_model}\")\n\n for model_name, params in model_params.items():\n if model_name in use_model:\n print(f\"running {model_name}\")\n # swap params to avoid recompiling\n # note: models 1,2 have diff number of params compared to models 3,4,5\n if any(str(m) in model_name for m in [1, 2]): model_runner = model_runner_1\n if any(str(m) in model_name for m in [3, 4, 5]): model_runner = model_runner_3\n model_runner.params = params\n\n processed_feature_dict: affeatures.FeatureDict = model_runner.process_features(feature_dict,\n random_seed=random_seed)\n # prediction_result is a dictionary of NumPy feature arrays\n prediction_result: dict = model_runner.predict(processed_feature_dict)\n unrelaxed_protein: protein.Protein = protein.from_prediction(processed_feature_dict, prediction_result)\n unrelaxed_pdb_lines.append(protein.to_pdb(unrelaxed_protein))\n plddts.append(prediction_result['plddt'])\n paes.append(prediction_result['predicted_aligned_error'])\n\n if do_relax:\n # Relax the prediction.\n amber_relaxer: relax.AmberRelaxation = relax.AmberRelaxation(max_iterations=0, tolerance=2.39,\n stiffness=10.0, exclude_residues=[],\n max_outer_iterations=20)\n relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)\n relaxed_pdb_lines.append(relaxed_pdb_str)\n\n # rerank models based on predicted lddt\n lddt_rank = np.mean(plddts, -1).argsort()[::-1]\n out = {}\n print(\"reranking models based on avg. predicted lDDT\")\n for n, r in enumerate(lddt_rank):\n print(f\"model_{n + 1} {np.mean(plddts[r])}\")\n\n unrelaxed_pdb_path = f'{prefix}_unrelaxed_model_{n + 1}.pdb'\n with open(unrelaxed_pdb_path, 'w') as f:\n f.write(unrelaxed_pdb_lines[r])\n set_bfactor(unrelaxed_pdb_path, plddts[r], idx_res, chains)\n\n if do_relax:\n relaxed_pdb_path = f'{prefix}_relaxed_model_{n + 1}.pdb'\n with open(relaxed_pdb_path, 'w') as f: f.write(relaxed_pdb_lines[r])\n set_bfactor(relaxed_pdb_path, plddts[r], idx_res, chains)\n\n out[f\"model_{n + 1}\"] = {\"plddt\": plddts[r], \"pae\": paes[r]}\n return out","def __init__(self, objects=()):\n\n vtk.vtkPropAssembly.__init__(self)\n\n self.name = \"\"\n self.created = \"\"\n self.trail = None\n self.trail_points = []\n self.trail_segment_size = 0\n self.trail_offset = None\n self.shadows = []\n self.info = {}\n self.rendered_at = set()\n self.transform = None\n self.scalarbar = None\n\n for a in vedo.utils.flatten(objects):\n if a:\n self.AddPart(a)\n\n self.PickableOff()","def dovisitcomb(allv) :\n allvisits = allv[0]\n load = allv[1]\n field = allv[2][0]\n apogee_id = allv[2][1]\n clobber = allv[2][2]\n pixelmask=bitmask.PixelBitMask()\n\n # already done?\n outdir=os.path.dirname(load.filename('Field',field=field))\n outdir=outdir.replace('/stars/','/rv/')\n if os.path.exists(outdir+'/'+apogee_id+'.pkl') and not clobber:\n print(apogee_id,' already done visitcomb')\n fp=open(outdir+'/'+apogee_id+'.pkl','rb')\n try: \n out=pickle.load(fp)\n fp.close()\n return out\n except: \n print('error loading: ', apogee_id+'.pkl')\n pass\n\n # do the combination\n apstar=visitcomb(allvisits,load=load,plot=False)\n\n # dump\n pickle.dump(apstar,open(outdir+'/'+apogee_id+'.pkl','wb'))\n\n return apstar","def _form_computation_graph(self, idx):\n _list, _set = list, set\n if type(idx) is int:\n node_layers = [np.array([idx], dtype=np.int64)]\n elif type(idx) is list:\n node_layers = [np.array(idx, dtype=np.int64)]\n\n for _ in range(self.n_layers):\n prev = node_layers[-1]\n arr = [node for node in prev]\n arr.extend([e[0] for node in arr for e in self.nbrs_t[node]])\n arr = np.array(_list(_set(arr)), dtype=np.int64)\n node_layers.append(arr)\n node_layers.reverse()\n\n mappings = [{j: i for (i, j) in enumerate(arr)} for arr in node_layers]\n\n return node_layers, mappings","def genereate_echo_picklist(self):\n sample_names = []\n sample_wells = []\n indices = {'i5 name': {}, 'i5 plate': {}, 'i5 sequence': {},\n 'i5 well': {}, 'i7 name': {}, 'i7 plate': {},\n 'i7 sequence': {}, 'i7 well': {}, 'index combo': {},\n 'index combo seq': {}}\n\n for idx, well in enumerate(chain.from_iterable(self.plates[0].layout)):\n # Add the sample well\n sample_wells.append(well.well_id)\n # Get the sample name - we need to go back to the SampleComposition\n lib_comp = well.composition\n sample_comp = lib_comp.normalized_gdna_composition\\\n .gdna_composition.sample_composition\n sample_names.append(sample_comp.content)\n # Retrieve all the information about the indices\n i5_comp = lib_comp.i5_composition.primer_set_composition\n i5_well = i5_comp.container\n indices['i5 name'][idx] = i5_comp.external_id\n indices['i5 plate'][idx] = i5_well.plate.external_id\n indices['i5 sequence'][idx] = i5_comp.barcode\n indices['i5 well'][idx] = i5_well.well_id\n\n i7_comp = lib_comp.i7_composition.primer_set_composition\n i7_well = i7_comp.container\n indices['i7 name'][idx] = i7_comp.external_id\n indices['i7 plate'][idx] = i7_well.plate.external_id\n indices['i7 sequence'][idx] = i7_comp.barcode\n indices['i7 well'][idx] = i7_well.well_id\n\n indices['index combo seq'][idx] = '%s%s' % (\n indices['i5 sequence'][idx], indices['i7 sequence'][idx])\n\n sample_names = np.asarray(sample_names)\n sample_wells = np.asarray(sample_wells)\n indices = pd.DataFrame(indices)\n\n return LibraryPrepShotgunProcess._format_picklist(\n sample_names, sample_wells, indices)","def __rechaindict__(c):\n from TriggerMenu.menu.DictFromChainName import DictFromChainName\n dfcn = DictFromChainName()\n\n pl1 = []\n for pch in c['chainParts']:\n pl1.append(pch['L1item'])\n\n newname = c['chainName'].replace('dv_','').replace('TestChain','j')\n nchlist = [ newname ,c['chainCounter'],c['L1item'],pl1,c['stream'],\n c['groups'],c['EBstep'] ]\n \n return dfcn.getChainDict(nchlist)","def hierarchical( x, output_prefix, labels_to_register=[2,3,4,5], is_test=False, verbose=True ):\n if verbose:\n print(\"Read\")\n tfn = get_data('T_template0', target_extension='.nii.gz' )\n tfnw = get_data('T_template0_WMP', target_extension='.nii.gz' )\n tlrfn = get_data('T_template0_LR', target_extension='.nii.gz' )\n bfn = antspynet.get_antsxnet_data( \"croppedMni152\" )\n\n ##### read images and do simple bxt ops\n templatea = ants.image_read( tfn )\n if verbose:\n print(\"bxt\")\n templatea = ( templatea * antspynet.brain_extraction( templatea, 't1' ) ).iMath( \"Normalize\" )\n templateawmprior = ants.image_read( tfnw )\n templatealr = ants.image_read( tlrfn )\n templateb = ants.image_read( bfn )\n templateb = ( templateb * antspynet.brain_extraction( templateb, 't1' ) ).iMath( \"Normalize\" )\n imgbxt = brain_extraction( x )\n img = x * imgbxt\n\n if verbose:\n print(\"rbp\")\n\n # this is an unbiased method for identifying predictors that can be used to\n # rank / sort data into clusters, some of which may be associated\n # with outlierness or low-quality data\n templatesmall = ants.resample_image( templateb, (91,109,91), use_voxels=True )\n rbp = random_basis_projection( img, templatesmall )\n\n if verbose:\n print(\"intensity\")\n\n ##### intensity modifications\n img = ants.iMath( img, \"Normalize\" ) * 255.0\n img = ants.denoise_image( img, imgbxt, noise_model='Gaussian')\n img = ants.n4_bias_field_correction( img ).iMath(\"Normalize\")\n\n # optional - quick look at result\n bxt_png = output_prefix + \"_brain_extraction_dnz_n4_view.png\"\n ants.plot(img,axis=2,ncol=8,nslices=24, crop=True, black_bg=False,\n filename = bxt_png )\n\n if verbose:\n print(\"hemi\")\n\n # assuming data is reasonable quality, we should proceed with the rest ...\n mylr = label_hemispheres( img, templatea, templatealr )\n\n if verbose:\n print(\"parcellation\")\n\n ##### hierarchical labeling\n myparc = deep_brain_parcellation( img, templateb,\n do_cortical_propagation = not is_test, verbose=False )\n\n ##### accumulate data into data frames\n hemi = map_segmentation_to_dataframe( \"hemisphere\", myparc['hemisphere_labels'] )\n tissue = map_segmentation_to_dataframe( \"tissues\", myparc['tissue_segmentation'] )\n dktl = map_segmentation_to_dataframe( \"lobes\", myparc['dkt_lobes'] )\n dktp = map_segmentation_to_dataframe( \"dkt\", myparc['dkt_parcellation'] )\n dktc = None\n if not is_test:\n dktc = map_segmentation_to_dataframe( \"dkt\", myparc['dkt_cortex'] )\n\n tissue_seg_png = output_prefix + \"_seg.png\"\n ants.plot( img, myparc['tissue_segmentation'], axis=2, nslices=21, ncol=7,\n alpha=0.6, filename=tissue_seg_png,\n crop=True, black_bg=False )\n\n if verbose:\n print(\"WMH\")\n\n ##### below here are more exploratory nice to have outputs\n myhypo = t1_hypointensity(\n img,\n myparc['tissue_segmentation'], # segmentation\n myparc['tissue_probabilities'][3], # wm posteriors\n templatea,\n templateawmprior )\n\n if verbose:\n print(\"registration\")\n\n ##### traditional deformable registration as a high-resolution complement to above\n wm_tractsL = None\n wm_tractsR = None\n wmtdfL = None\n wmtdfR = None\n reg = None\n if labels_to_register is not None:\n reg = hemi_reg(\n input_image = img,\n input_image_tissue_segmentation = myparc['tissue_segmentation'],\n input_image_hemisphere_segmentation = mylr,\n input_template=templatea,\n input_template_hemisphere_labels=templatealr,\n output_prefix = output_prefix + \"_SYN\",\n labels_to_register = labels_to_register,\n is_test=is_test )\n if verbose:\n print(\"wm tracts\")\n ##### how to use the hemi-reg output to generate any roi value from a template roi\n wm_tracts = ants.image_read( get_data( \"wm_major_tracts\", target_extension='.nii.gz' ) )\n wm_tractsL = ants.apply_transforms( img, wm_tracts, reg['synL']['invtransforms'],\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 1, 1 )\n wm_tractsR = ants.apply_transforms( img, wm_tracts, reg['synR']['invtransforms'],\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 2, 2 )\n wmtdfL = map_segmentation_to_dataframe( \"wm_major_tracts\", wm_tractsL )\n wmtdfR = map_segmentation_to_dataframe( \"wm_major_tracts\", wm_tractsR )\n\n if verbose:\n print(\"hippocampus\")\n\n ##### specialized labeling for hippocampus\n ntries = 10\n if is_test:\n ntries = 1\n hippLR = deep_hippo( img, templateb, ntries )\n\n mydataframes = {\n \"hemispheres\":hemi,\n \"tissues\":tissue,\n \"dktlobes\":dktl,\n \"dktregions\":dktp,\n \"dktcortex\":dktc,\n \"wmtracts_left\":wmtdfL,\n \"wmtracts_right\":wmtdfR,\n \"wmh\":myhypo['wmh_summary']\n }\n\n outputs = {\n \"brain_n4_dnz\": img,\n \"brain_n4_dnz_png\": bxt_png,\n \"brain_extraction\": imgbxt,\n \"tissue_seg_png\": tissue_seg_png,\n \"rbp\": rbp,\n \"left_right\": mylr,\n \"dkt_parc\": myparc,\n \"registration\":reg,\n \"hippLR\":hippLR,\n \"white_matter_hypointensity\":myhypo,\n \"wm_tractsL\":wm_tractsL,\n \"wm_tractsR\":wm_tractsR,\n \"dataframes\": mydataframes\n }\n\n return outputs","def label(gt_dataset, volume_dim, voxel_dim, labeling_params):\n labeled_volumes = dict()\n labeled_cells = dict()\n #Use global density and reduce the size of gt_dataset here\n global_density = labeling_params[\"global_density\"]\n gt_dataset = {k: v for k,v in gt_dataset.items() if random_sample() < global_density}\n #Label in the order specified in the configuration\n layers = sorted(labeling_params.keys())\n #Remove global_density\n layers.remove(\"global_density\")\n for layer in layers:\n print \"Labeling {}\".format(layer)\n fluorophore = labeling_params[layer]['fluorophore']\n volume, cells = brainbow(gt_dataset, volume_dim, voxel_dim, **labeling_params[layer])\n if fluorophore in labeled_volumes:\n labeled_volumes[fluorophore] += volume\n labeled_cells[fluorophore] |= cells\n else:\n labeled_volumes[fluorophore] = volume\n labeled_cells[fluorophore] = cells\n return labeled_volumes, labeled_cells","def mapping_image_to_label (self, labels_df, polygons, fpath_tiff): \n \n unread_tiff = rasterio.open(fpath_tiff)\n\n #Projecting the coordinates to that CRS \n proj = Proj(init='epsg:32618')\n data = []\n labels = []\n failed = []\n \n src = rasterio.open(fpath_tiff, 'r')\n outfolder = '/train/batch'\n \n print (\"Hold on tight! Mapping each image to its respective label...\")\n \n \n for num, row in labels_df.iterrows():\n try:\n \n \n roof_material_num = 0\n polygon0 = polygons [num]\n polygon0['coordinates'] = self.transforming_coordinates(polygon0['coordinates'], proj)\n masked_image, out_transform = rasterio.mask.mask(src,[polygon0], filled = True, crop=True, nodata = 0)\n img_image = reshape_as_image (masked_image)\n \n #Defining the name of the image file as \"buildingID+roofMaterial+png\" and its path \n img_path = os.path.join (outfolder, str (row['id'])+'-'+ str (row['roof_material'])+'.png')\n \n #swapping the color channels from RGB2BGR\n img_image = cv2.cvtColor (img_image, cv2.COLOR_RGB2BGR) #img_image is a numpy array\n \n #resizing the image dimensions to 128x128 to match ImageNet dimensions\n img_image = cv2.resize(img_image, (128, 128))\n \n #writing the image in the file\n #cv2.imwrite (img_path, img_image)\n # update the data and labels lists, respectively\n data.append(img_image) #data is a list\n labels.append(row['roof_material'])\n \n except Exception as e:\n print (e)\n failed.append (num)\n \n \n #print number of images we failed to crop and write \n print (\"Bad News First: Failed to write\", len(failed), \"Images.\")\n print (\"Good News: Successfully mapped\", len (data), \"Images.\")\n data = np.array(data)\n labels = np.array(labels)\n #batch = data.sample(frac=0.5, replace=False, random_state=1)\n #print(\"Size and shape of validY: {}\\n\".format(batch.shape))\n return data, labels","def case():\r\n #ppc = {\"version\": '2'}\r\n ppc = {}\r\n ##----- Power Flow Data -----##\r\n ## system MVA base\r\n ppc[\"baseMVA\"] = 100.0\r\n\r\n ## bus data\r\n # bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin\r\n ppc[\"bus\"] = array([\r\n [1, 3, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [2, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [3, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [4, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [5, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [6, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [7, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [8, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [9, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [10, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [11, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [12, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [13, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [14, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [15, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0],\r\n [16, 1, 0, 0, 0, 0, 1, 1, 0, 0.4, 1, 1.1, 0.9, 0,0, 0, 0]\r\n ])\r\n\r\n ## generator data\r\n # bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2,\r\n # Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf\r\n ppc[\"gen\"] = array([\r\n [1,\t0,\t0,\t10,\t-10,\t1.0224,\t100,\t1,\t10,\t-10,\t0,\t0,\t0,\t0,\t0,\t0,\t0,\t0,\t0,\t0, 0, 0,0, 0, 0],\r\n [3 ,0, 0, 50e-3, -50e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\r\n [5 , 0, 0, 10e-3, -10e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\r\n [10 , 0, 0, 10e-3, -10e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\r\n [13 ,0, 0, 10e-3, -10e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0],\r\n [15 , 0, 0, 50e-3, -50e-3, 1, 100, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0]\r\n ])\r\n load_b = array([2, 4, 9, 12, 14])\r\n ppc[\"bus\"][load_b, 2] = multiply(array([-2.1125, -0.2231, -0.1664, -0.0719, -1.4633]).T, 0.03)\r\n ppc[\"bus\"][load_b, 3] = multiply(array([1.6492, 0.4054, 0.8599, 0.8845, 0.6778]).T, 0.03)\r\n ## branch data\r\n # fbus, tbus, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax\r\n ppc[\"branch\"] = array([\r\n [1, 2, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [1, 8, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [1, 15, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [2, 3, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [2, 6, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [2, 7, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [3, 4, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [4, 5, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [8, 9, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [8, 12, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [8, 13, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [9, 10, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [9, 14, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [10, 11, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0],\r\n [15, 16, 0.0, 0.0, 0.0, 250, 250, 250, 0, 0, 1, -360, 360, 0,0, 0, 0, 0,0, 0, 0]\r\n ])\r\n R1 = 0.43\r\n L1 = 0.4e-3\r\n RS1 = 0.32\r\n LS1 = 0.39e-3\r\n Zbase = (0.4*0.4/100)\r\n branch_phase =array([\r\n [1, 1, 2, 188, R1, L1],\r\n [2, 1 ,8, 346, R1, L1],\r\n [3 ,1 ,15,501, R1 ,L1],\r\n [4, 2, 3, 130, RS1,LS1],\r\n [5, 2, 6, 145, RS1,LS1],\r\n [6, 2 ,7, 157, RS1,LS1],\r\n [7, 3, 4, 185, RS1,LS1],\r\n [8, 4, 5, 1000,RS1,LS1],\r\n [9, 8 ,9, 416, RS1,LS1],\r\n [10,8 ,12,130, RS1,LS1],\r\n [11,8 ,13,121, RS1,LS1],\r\n [12,9 ,10,130, RS1,LS1],\r\n [13,9 ,14,127, RS1,LS1],\r\n [14,10,11,251, RS1,LS1],\r\n [15,15,16,345, RS1,LS1]\r\n ])\r\n ppc[\"branch\"][:, [2,3]] = multiply(array([branch_phase[:, 4]*branch_phase[:, 3], branch_phase[:, 4]*branch_phase[:, 4]*100*pi]).T,0.001/Zbase)\r\n\r\n ##----- OPF Data -----##\r\n ## area data\r\n # area refbus\r\n\r\n\r\n ## generator cost data\r\n # 1 startup shutdown n x1 y1 ... xn yn\r\n # 2 startup shutdown n c(n-1) ... c0\r\n\r\n\r\n return ppc","def _label_encoding(self):\n for feat in self.cat_feats:\n if self.train:\n lbl = preprocessing.LabelEncoder()\n lbl.fit(self.dataframe[feat].values)\n self.dataframe_d_copy.loc[:,feat] = lbl.transform(self.dataframe[feat].values)\n self.label_encoders[feat] = lbl\n else:\n lbl = self.encoders[feat]\n self.dataframe_d_copy.loc[:,feat] = lbl.transform(self.dataframe[feat].values)\n \n if self.train:\n encoder_path = f\"{self.output_path}/_label_encoder.pkl\"\n self.cat_feats_cfg['encoder_path'] = encoder_path\n joblib.dump(self.label_encoders, encoder_path)\n \n return self.dataframe_d_copy","def convert2EbnerParamOriginalParam(listSlice,list_prefix,directory,paramAx,paramCor,paramSag):\n paramAx=np.load(paramAx)\n paramCor=np.load(paramCor)\n paramSag=np.load(paramSag)\n param=[]\n param.append(paramAx)\n param.append(paramCor)\n param.append(paramSag)\n \n images,mask = createVolumesFromAlist(listSlice.copy()) #list of images corresponding to differents original stacks\n \n \n mat = np.array([[-1,0,0,0],[0,-1,0,0],[0,0,1,0],[0,0,0,1]]) #matrix to convert affine matrix from nibabel to itk\n\n for n in range(len(images)): #for each stack\n \n imagen = images[n]\n \n for i_slice in range(len(images[n])): #for each slices (in each stacks)\n \n slicei=imagen[i_slice]\n dimension=3\n X,Y,Z= slicei.get_slice().get_fdata().shape\n transfo = param[n][slicei.get_index_slice(),:,:]\n #print()\n matrix = mat @ transfo @ mat\n #print(matrix)\n test = sitk.AffineTransform(dimension)\n test.SetMatrix(matrix[0:3,0:3].flatten())\n test.SetTranslation(matrix[0:3,3])\n images_index = slicei.get_index_image()\n\n sitk.WriteTransform(test,\"%s/%s_slice%d.tfm\" %(directory,list_prefix[images_index],slicei.get_index_slice())) #save rigid transformation, computed at the barycenter of the image, adatpted to itk","def nodeSeparate(self,compInfo, ifSub, subname, subcktName,numNodesSub):\n node = []\n nodeTemp = []\n nodeDic = {}\n pinInit = 'Modelica.Electrical.Analog.Interfaces.Pin '\n pinProtectedInit = 'Modelica.Electrical.Analog.Interfaces.Pin '\n protectedNode = []\n print \"CompInfo coming to nodeSeparate function: compInfo\",compInfo\n \n #Removing '[' and ']' from compInfo for Digital node\n for i in range(0,len(compInfo),1):\n compInfo[i] = compInfo[i].replace(\"[\",\"\").replace(\"]\",\"\")\n \n \n for eachline in compInfo:\n words = eachline.split()\n if eachline[0] in ['m', 'e', 'g', 't','M','E','G','T']:\n nodeTemp.append(words[1])\n nodeTemp.append(words[2])\n nodeTemp.append(words[3])\n nodeTemp.append(words[4])\n elif eachline[0] in ['q', 'j','J','Q']:\n nodeTemp.append(words[1])\n nodeTemp.append(words[2])\n nodeTemp.append(words[3])\n elif eachline[0]=='x' or eachline[0]=='X':\n templine = eachline.split()\n for i in range(0,len(templine),1):\n if templine[i] in subcktName:\n point = i \n nodeTemp.extend(words[1:point])\n else:\n nodeTemp.append(words[1])\n nodeTemp.append(words[2])\n for i in nodeTemp:\n if i not in node:\n node.append(i)\n \n for i in range(0, len(node),1):\n nodeDic[node[i]] = 'n' + node[i]\n if ifSub == '0':\n if i != len(node)-1:\n pinInit = pinInit + nodeDic[node[i]] + ', '\n else:\n pinInit = pinInit + nodeDic[node[i]]\n else:\n nonprotectedNode = self.getSubInterface(subname, numNodesSub) \n if node[i] in nonprotectedNode:\n continue\n else:\n protectedNode.append(node[i])\n if ifSub == '1':\n if len(nonprotectedNode) > 0:\n for i in range(0, len(nonprotectedNode),1):\n if i != len(nonprotectedNode)-1:\n pinProtectedInit = pinProtectedInit + nodeDic[nonprotectedNode[i]] + ','\n else:\n pinProtectedInit = pinProtectedInit + nodeDic[nonprotectedNode[i]]\n if len(protectedNode) > 0:\n for i in range(0, len(protectedNode),1):\n if i != len(protectedNode)-1: \n pinInit = pinInit + nodeDic[protectedNode[i]] + ','\n else:\n pinInit = pinInit + nodeDic[protectedNode[i]]\n pinInit = pinInit + ';'\n pinProtectedInit = pinProtectedInit + ';'\n print \"Node---->\",node\n print \"nodeDic----->\",nodeDic\n print \"PinInit----->\",pinInit\n print \"pinProtectedinit--->\",pinProtectedInit\n return node, nodeDic, pinInit, pinProtectedInit","def __repr__(self):\n data = sorted(\n (state, pos, tape_cache, outputs)\n for pos, states in self.iteritems()\n for state, (tape_cache, outputs) in states.iteritems())\n branch = \"branch\" if len(data) == 1 else \"branches\"\n result = \"process (%s %s)\" % (len(data), branch)\n for s, sdata in itertools.groupby(data, lambda x: x[0]):\n result += \"\\n+ at state %s\" % (s,)\n for state, pos, tape_cache, outputs in sdata:\n result += \"\\n+-- %s, %s\" % (tape_cache, outputs)\n return result","def _vVBEL(self,vKNOT=None,edges=vVBEL_edges,edgesD=vVBEL_edgesD,mColNames=['OBJTYPE','pk'],mIdxNames=['OBJTYPE','OBJID']):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n \r\n try: \r\n # construct \r\n vVBEL=None\r\n vVBEL_UnionList=[]\r\n\r\n for VBEL in edges:\r\n if VBEL in self.dataFrames:\r\n vXXXX=self._vVBEL_XXXX(vKNOT=vKNOT,OBJTYPE=VBEL)\r\n if vXXXX is None:\r\n pass\r\n else:\r\n vVBEL_UnionList.append(vXXXX)\r\n vVBEL=pd.concat(vVBEL_UnionList)\r\n\r\n # MIndices\r\n vVBEL=Xm.constructNewMultiindexFromCols(df=vVBEL,mColNames=mColNames,mIdxNames=mIdxNames)\r\n\r\n # Gruppenzugeh. ergaenzen\r\n vVBEL['LAYR']=[list() for dummy in vVBEL['tk']]\r\n dfLayr=self.dataFrames['vLAYR']\r\n if not dfLayr.empty:\r\n dfLayr=dfLayr.rename(columns={'OBJTYPE':'TYPE'}) \r\n dfLayr=pd.merge(\r\n vVBEL\r\n ,dfLayr\r\n ,how='inner' # nur die VBEL die eine Gruppenzugehoerigkeit haben\r\n ,left_index=True \r\n ,right_on=['TYPE','OBJID'] \r\n ,suffixes=('', '_y'))[['NAME','TYPE','OBJID','nrObjInGroup','nrObjtypeInGroup']]\r\n dfLayr=dfLayr[dfLayr.nrObjInGroup <= 1] # pro VBEL und Gruppe nur 1 Zeile\r\n\r\n for index, row in vVBEL.merge(dfLayr.sort_values(by=['NAME','OBJID']),how='left',left_index=True ,right_on=['TYPE','OBJID'],suffixes=('', '_y')).iterrows(): \r\n if pd.isnull(row.NAME):\r\n continue\r\n row.LAYR.append(row.NAME)\r\n\r\n # L ergaenzen\r\n Rohr=self.dataFrames['ROHR']\r\n VbelL=vVBEL.join(Rohr.set_index('pk').rename_axis('OBJID', axis='index'),rsuffix='_y')[['L']] \r\n vVBEL['L']=VbelL['L'].fillna(0) \r\n\r\n # D ergaenzen\r\n # Spalte erzeugen ... \r\n vRohr=self.dataFrames['vROHR']\r\n VbelD=vVBEL.join(vRohr.set_index('pk').rename_axis('OBJID', axis='index'),rsuffix='_y')[['DI']]\r\n vVBEL['D']=VbelD['DI'] # ... mit ROHR\r\n\r\n # ueber alle ausser ROHR\r\n for eIdx,edge in enumerate(edges):\r\n if edge == 'ROHR':\r\n continue\r\n edgeDCol=edgesD[eIdx]\r\n if edgeDCol=='':\r\n continue \r\n if edge not in self.dataFrames:\r\n continue\r\n Edge=self.dataFrames[edge] \r\n if edgeDCol not in Edge.columns.tolist():\r\n continue\r\n edgeD=vVBEL.join(Edge.set_index('pk').rename_axis('OBJID', axis='index'),rsuffix='_y',how='inner')[[edgeDCol]]\r\n vVBEL.loc[[edge],'D']=edgeD.loc[[edge],:].values\r\n\r\n\r\n # fehlende Spaltenwerte zuweisen\r\n #Vent=self.dataFrames['VENT']\r\n #VentD=vVBEL.join(Vent.set_index('pk'),rsuffix='_y',how='inner')[['DN']]\r\n #vVBEL.loc[['VENT'],'D']=VentD.loc[['VENT'],:].values\r\n\r\n # Finish\r\n vVBEL.sort_index(level=0,inplace=True)\r\n\r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n logger.debug(logStrFinal) \r\n finally:\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.'))\r\n return vVBEL","def apply_grub_cmdline(self):\n\n for i in self._nodes.items():\n node = i[1]\n\n # Get the isolated CPUs\n other_workers = node[\"cpu\"][\"other_workers\"]\n vpp_workers = node[\"cpu\"][\"vpp_workers\"]\n if \"vpp_main_core\" in node[\"cpu\"]:\n vpp_main_core = node[\"cpu\"][\"vpp_main_core\"]\n else:\n vpp_main_core = 0\n all_workers = []\n if other_workers is not None:\n all_workers = [other_workers]\n if vpp_main_core != 0:\n all_workers += [(vpp_main_core, vpp_main_core)]\n all_workers += vpp_workers\n isolated_cpus = \"\"\n for idx, worker in enumerate(all_workers):\n if worker is None:\n continue\n if idx > 0:\n isolated_cpus += \",\"\n if worker[0] == worker[1]:\n isolated_cpus += \"{}\".format(worker[0])\n else:\n isolated_cpus += \"{}-{}\".format(worker[0], worker[1])\n\n vppgrb = VppGrubUtil(node)\n current_cmdline = vppgrb.get_current_cmdline()\n if \"grub\" not in node:\n node[\"grub\"] = {}\n node[\"grub\"][\"current_cmdline\"] = current_cmdline\n node[\"grub\"][\"default_cmdline\"] = vppgrb.apply_cmdline(node, isolated_cpus)\n\n self.updateconfig()"],"string":"[\n \"def chainDict2jetLabel(chain_dict):\\n\\n # suported scenarios \\n router = {\\n 'simple': _make_simple_label,\\n 'HT': _make_ht_label,\\n 'vbenf': _make_vbenf_label,\\n 'dijet': _make_dijet_label,\\n 'combinationsTest': _make_combinationsTest_label,\\n 'partitionsTest': _make_partitionsTest_label,\\n }\\n\\n # chain_part - scenario association\\n cp_sorter = {}\\n for k in router: cp_sorter[k] = []\\n\\n for cp in chain_dict['chainParts']:\\n if cp['signature'] != 'Jet' and cp['signature'] != 'Bjet': \\n continue\\n for k in cp_sorter:\\n if cp['hypoScenario'].startswith(k):\\n cp_sorter[k].append(cp)\\n break\\n\\n # obtain labels by scenario.\\n labels = []\\n for k, chain_parts in cp_sorter.items():\\n if chain_parts: labels.append(router[k](chain_parts))\\n\\n assert labels\\n nlabels = len(labels)\\n if nlabels == 1: return labels[0]\\n if nlabels == 2:\\n alabel = \\\"\\\"\\\"\\\\\\nand([]\\n %s\\n %s)\\\"\\\"\\\" % (tuple(labels))\\n return alabel\\n\\n # more than 2 labels is not expected\\n assert False\",\n \"def _make_partitionsTest_label(chain_parts):\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n \\n assert scenario == 'partitionsTest'\\n\\n \\n\\n return \\\"\\\"\\\"\\n partgen(\\n [(20et, 0eta320)]\\n \\n simple([(40et, 0eta320) (50et, 0eta320)])\\n simple([(35et, 0eta240) (55et, 0eta240)])\\n )\\\"\\\"\\\"\",\n \"def _make_simple_label(chain_parts):\\n \\n if not _select_simple_chainparts(chain_parts):\\n msg = 'Jet Configuration error: '\\\\\\n 'chain fails substring selection: not \\\"simple\\\" '\\n\\n raise NotImplementedError(msg)\\n \\n label = 'simple(['\\n for cp in chain_parts:\\n smcstr = str(cp['smc'])\\n jvtstr = str(cp['jvt'])\\n if smcstr == 'nosmc':\\n smcstr = ''\\n for i in range(int(cp['multiplicity'])):\\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\\n # str(cp['etaRange']),\\n # smcstr,)\\n condition_str = '(%set,%s' % (str(cp['threshold']),\\n str(cp['etaRange']),)\\n if smcstr: # Run 2 chains have \\\"INF\\\" in the SMC substring\\n condition_str += ',%s)' % smcstr.replace('INF','')\\n elif jvtstr:\\n condition_str += ',%s)' % jvtstr\\n else:\\n condition_str += ')'\\n label += condition_str\\n label += '])'\\n return label\",\n \"def _make_simple_partition_label(chain_dict):\\n\\n cps = chain_dict['chainParts']\\n if not (_select_simple_chainparts(cps)):\\n raise NotImplementedError(\\n 'chain fails substring selection: not \\\"simple\\\": %s' % (\\n chain_dict['chainName']))\\n \\n label = 'simplepartition(['\\n for cp in cps:\\n smcstr = str(cp['smc'])\\n if smcstr == 'nosmc':\\n smcstr = ''\\n for i in range(int(cp['multiplicity'])):\\n # condition_str = '(%set,%s,%s)' % (str(cp['threshold']),\\n # str(cp['etaRange']),\\n # smcstr,)\\n condition_str = '(%set,%s' % (str(cp['threshold']),\\n str(cp['etaRange']),)\\n if smcstr:\\n condition_str += ',%s)'\\n else:\\n condition_str += ')'\\n label += condition_str\\n label += '])'\\n return label\",\n \"def _text_write_preprocess(self):\\n self.check()\\n\\n max_name_len = np.max([len(name) for name in self.name])\\n fieldtypes = [\\\"U\\\" + str(max_name_len), \\\"f8\\\", \\\"f8\\\"]\\n comp_names = self._get_lon_lat_component_names()\\n frame_obj = self._get_frame_obj()\\n frame_desc_str = _get_frame_desc_str(frame_obj)\\n\\n component_fieldnames = []\\n for comp_name in comp_names:\\n # This will add e.g. ra_J2000 and dec_J2000 for FK5\\n component_fieldnames.append(comp_name + \\\"_\\\" + frame_desc_str)\\n fieldnames = [\\\"source_id\\\"] + component_fieldnames\\n stokes_names = [\\\"I\\\", \\\"Q\\\", \\\"U\\\", \\\"V\\\"]\\n fieldshapes = [()] * 3\\n\\n if self.stokes_error is not None:\\n stokes_error_names = [(f\\\"{k}_error\\\") for k in [\\\"I\\\", \\\"Q\\\", \\\"U\\\", \\\"V\\\"]]\\n\\n n_stokes = 0\\n stokes_keep = []\\n for si, total in enumerate(np.nansum(self.stokes.to(\\\"Jy\\\"), axis=(1, 2))):\\n if total > 0:\\n fieldnames.append(stokes_names[si])\\n fieldshapes.append((self.Nfreqs,))\\n fieldtypes.append(\\\"f8\\\")\\n if self.stokes_error is not None:\\n fieldnames.append(stokes_error_names[si])\\n fieldshapes.append((self.Nfreqs,))\\n fieldtypes.append(\\\"f8\\\")\\n n_stokes += 1\\n stokes_keep.append(total > 0)\\n\\n assert n_stokes >= 1, \\\"No components with nonzero flux.\\\"\\n\\n if self.freq_array is not None:\\n if self.spectral_type == \\\"subband\\\":\\n fieldnames.append(\\\"subband_frequency\\\")\\n else:\\n fieldnames.append(\\\"frequency\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.extend([(self.Nfreqs,)])\\n elif self.reference_frequency is not None:\\n fieldnames.extend([(\\\"reference_frequency\\\")])\\n fieldtypes.extend([\\\"f8\\\"])\\n fieldshapes.extend([()] * n_stokes + [()])\\n if self.spectral_index is not None:\\n fieldnames.append(\\\"spectral_index\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.append(())\\n\\n if hasattr(self, \\\"_rise_lst\\\"):\\n fieldnames.append(\\\"rise_lst\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.append(())\\n\\n if hasattr(self, \\\"_set_lst\\\"):\\n fieldnames.append(\\\"set_lst\\\")\\n fieldtypes.append(\\\"f8\\\")\\n fieldshapes.append(())\\n\\n dt = np.dtype(list(zip(fieldnames, fieldtypes, fieldshapes)))\\n\\n arr = np.empty(self.Ncomponents, dtype=dt)\\n arr[\\\"source_id\\\"] = self.name\\n\\n for comp_ind, comp in enumerate(comp_names):\\n arr[component_fieldnames[comp_ind]] = getattr(self.skycoord, comp).deg\\n\\n for ii in range(4):\\n if stokes_keep[ii]:\\n arr[stokes_names[ii]] = self.stokes[ii].T.to(\\\"Jy\\\").value\\n if self.stokes_error is not None:\\n arr[stokes_error_names[ii]] = self.stokes_error[ii].T.to(\\\"Jy\\\").value\\n\\n if self.freq_array is not None:\\n if self.spectral_type == \\\"subband\\\":\\n arr[\\\"subband_frequency\\\"] = self.freq_array.to(\\\"Hz\\\").value\\n else:\\n arr[\\\"frequency\\\"] = self.freq_array.to(\\\"Hz\\\").value\\n elif self.reference_frequency is not None:\\n arr[\\\"reference_frequency\\\"] = self.reference_frequency.to(\\\"Hz\\\").value\\n if self.spectral_index is not None:\\n arr[\\\"spectral_index\\\"] = self.spectral_index\\n\\n if hasattr(self, \\\"_rise_lst\\\"):\\n arr[\\\"rise_lst\\\"] = self._rise_lst\\n if hasattr(self, \\\"_set_lst\\\"):\\n arr[\\\"set_lst\\\"] = self._set_lst\\n\\n return arr\",\n \"def _body(self, x, ensembled_batch, non_ensembled_batch, idx):\\n i, current_representations = x\\n del x\\n feats = self._slice_batch(i, ensembled_batch, non_ensembled_batch)\\n representations_update = self.evoformer(*self.batch_expand(feats))\\n new_representations = {}\\n for k in current_representations:\\n new_representations[k] = (\\n current_representations[k] + representations_update[k])\\n del representations_update\\n return i+1, new_representations\",\n \"def get_mapped_feature_name(self):\\n\\n # open a h5 file in case we need it\\n f5 = h5py.File(self.train_database[0], 'r')\\n mol_name = list(f5.keys())[0]\\n mapped_data = f5.get(mol_name + '/mapped_features/')\\n chain_tags = ['_chain1', '_chain2']\\n\\n # if we select all the features\\n if self.select_feature == \\\"all\\\":\\n\\n # redefine dict\\n self.select_feature = {}\\n\\n # loop over the feat types and add all the feat_names\\n for feat_type, feat_names in mapped_data.items():\\n self.select_feature[feat_type] = [\\n name for name in feat_names]\\n\\n # if a selection was made\\n else:\\n\\n # we loop over the input dict\\n for feat_type, feat_names in self.select_feature.items():\\n\\n # if for a given type we need all the feature\\n if feat_names == 'all':\\n if feat_type in mapped_data:\\n self.select_feature[feat_type] = list(\\n mapped_data[feat_type].keys())\\n else:\\n self.print_possible_features()\\n raise KeyError('Feature type %s not found')\\n\\n # if we have stored the individual\\n # chainA chainB data we need to expand the feature list\\n # however when we reload a pretrained model we already\\n # come with _chainA, _chainB features.\\n # So then we shouldn't add the tags\\n else:\\n # TODO to refactor this part\\n if feat_type not in mapped_data:\\n self.print_possible_features()\\n raise KeyError('Feature type %s not found')\\n\\n self.select_feature[feat_type] = []\\n\\n # loop over all the specified feature names\\n for name in feat_names:\\n\\n # check if there is not _chainA or _chainB in the name\\n cond = [tag not in name for tag in chain_tags]\\n\\n # if there is no chain tag in the name\\n if np.all(cond):\\n\\n # if we have a wild card e.g. PSSM_*\\n # we check the matches and add them\\n if '*' in name:\\n match = name.split('*')[0]\\n possible_names = list(\\n mapped_data[feat_type].keys())\\n match_names = [\\n n for n in possible_names\\n if n.startswith(match)]\\n self.select_feature[feat_type] += match_names\\n\\n # if we don't have a wild card we append\\n # \\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj1et)(?P \\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj1eta)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj2et)(?P\\\\d*)$'),\\n re.compile(r'^(?P\\\\d*)(?Pj2eta)(?P\\\\d*)$'),\\n ]\\n\\n defaults = {\\n 'j1et': ('100', 'inf'),\\n 'j2et': ('100', 'inf'),\\n 'j1eta': ('0', '320'),\\n 'j2eta': ('0', '320'),\\n 'djmass': ('1000', 'inf'),\\n }\\n\\n\\n args = _args_from_scenario(scenario)\\n argvals = {}\\n while args:\\n assert len(args) == len(arg_res)\\n arg = args.pop()\\n for r in arg_res:\\n m = r.match(arg)\\n if m is not None:\\n arg_res.remove(r)\\n gd = m.groupdict()\\n key = gd['key']\\n\\n try:\\n lo = float(gd['lo'])\\n except ValueError:\\n lo = defaults[key][0]\\n argvals[key+'lo'] = lo \\n try:\\n hi = float(gd['hi'])\\n except ValueError:\\n hi = defaults[key][1]\\n argvals[key+'hi'] = hi\\n\\n assert len(args) == len(arg_res)\\n assert len(args) == 0\\n\\n return \\\"\\\"\\\"\\n combgen(\\n [(2)(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\\n (%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\\n ]\\n \\n dijet(\\n [(%(djmasslo).0fdjmass)])\\n simple([(%(j1etlo).0fet, %(j1etalo).0feta%(j1etahi).0f)\\n (%(j2etlo).0fet, %(j2etalo).0feta%(j2etahi).0f)])\\n )\\\"\\\"\\\" % argvals\",\n \"def _indentity_block(self, X, filters, f, stage, block):\\n\\t\\tconv_layer_name = 'res' + str(stage) + block + '_branch'\\n\\t\\tbn_layer_name = 'bm' + str(stage) + block + '_branch'\\n\\n\\t\\tX_shortcut = X\\n\\n\\t\\tF1, F2, F3 = filters\\n\\n\\t\\t# First component of main path\\n\\t\\tX = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1, 1), padding = 'valid',\\n\\t\\t\\tname = conv_layer_name + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\\n\\t\\tX = BatchNormalization(axis = 3, name = bn_layer_name + '2a')(X)\\n\\t\\tX = Activation('relu')(X)\\n\\n\\t\\t# Second component of main path\\n\\t\\tX = Conv2D(filters = F2, kernel_size = (1, 1), strides = (1, 1), padding = 'same',\\n\\t\\t\\tname = conv_layer_name + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\\n\\t\\tX = BatchNormalization(axis = 3, name = bn_layer_name + '2b')(X)\\n\\t\\tX = Activation('relu')(X)\\n\\n\\t\\t# Third component of main path\\n\\t\\tX = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1, 1), padding = 'valid',\\n\\t\\t\\tname = conv_layer_name + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\\n\\t\\tX = BatchNormalization(axis = 3, name = bn_layer_name + '2c')(X)\\n\\n\\t\\t# Final step : adding the shortcut componet to X and applying 'relu' activation on the combination \\n\\t\\tX = Add()([X_shortcut,X])\\n\\t\\tX = Activation('relu')(X)\\n\\n\\t\\treturn X\",\n \"def output_fluent(fil,nodes,elems):\\n print \\\"Nodal coordinates\\\"\\n print nodes\\n print \\\"Element connectivity\\\"\\n print elems\\n faces = array(Tet4.faces) # Turning faces into an array is important !\\n print \\\"Tetraeder faces\\\"\\n print faces\\n elf = elems.take(faces,axis=1)\\n # Remark: the shorter syntax elems[faces] takes its elements along the\\n # axis 0. Then we would need to transpose() first (and probably\\n # swap axes again later)\\n print \\\"The faces of the elements:\\\"\\n print elf\\n # We need a copy to sort the nodes (sorting is done in-place)\\n elfs = elf.copy()\\n elfs.sort(axis=2) \\n print \\\"The faces with sorted nodes:\\\"\\n print elfs\\n magic = elems.max()+1\\n print \\\"Magic number = %d\\\" % magic\\n code = encode(elfs[:,:,0],elfs[:,:,1],elfs[:,:,2],magic)\\n # Remark how nice the encode function works on the whole array\\n print \\\"Encoded faces:\\\"\\n print code\\n code = code.ravel()\\n print code\\n print \\\"Just A Check:\\\"\\n print \\\"Element 5 face 2 is %s \\\" % elf[5,2]\\n print \\\"Element 5 face 2 is %s \\\" % list(decode(code[4*5+2],magic))\\n srt = code.argsort()\\n print srt\\n print code[srt]\\n # Now shipout the faces in this order, removing the doubles\\n j = -1 \\n for i in srt:\\n if j < 0: # no predecessor (or predecessor already shipped)\\n j = i\\n else:\\n e1,f1 = j/4, j%4\\n if code[i] == code[j]:\\n e2,f2 = i/4, i%4\\n j = -1\\n else:\\n e2 = -1\\n j = i\\n print \\\"Face %s belongs to el %s and el %s\\\" % ( elf[e1,f1], e2, e1 )\",\n \"def pre_pipeline(self, results):\\n results[\\\"img_prefix\\\"] = self.img_prefix\\n results[\\\"seg_prefix\\\"] = self.seg_prefix\\n results[\\\"proposal_file\\\"] = self.proposal_file\\n results[\\\"bbox_fields\\\"] = []\\n results[\\\"mask_fields\\\"] = []\\n results[\\\"seg_fields\\\"] = []\\n results[\\\"site_fields\\\"] = []\\n results[\\\"label_fields\\\"] = []\",\n \"def build_head(self):\\n stages = [f'stage{i}' for i in range(1, 7)]\\n for stage in stages:\\n block = getattr(self.arch, stage)\\n PAF, CFM = block.keys()\\n PAF = build_blocks(block[PAF], 'head')\\n CFM = build_blocks(block[CFM], 'head')\\n setattr(self, f\\\"{stage}_PAF\\\", PAF)\\n setattr(self, f\\\"{stage}_CFM\\\", CFM)\",\n \"def _make_combinationsTest_label(chain_parts):\\n\\n assert len(chain_parts) == 1\\n scenario = chain_parts[0]['hypoScenario']\\n \\n assert scenario == 'combinationsTest'\\n\\n \\n\\n return \\\"\\\"\\\"\\n combgen(\\n [(2)(20et, 0eta320)]\\n \\n simple([(40et, 0eta320) (50et, 0eta320)])\\n simple([(35et, 0eta240) (55et, 0eta240)])\\n )\\\"\\\"\\\"\",\n \"def _make_simple_comb_label(chain_dict):\\n\\n cps = chain_dict['chainParts']\\n if not (_select_simple_chainparts(cps)):\\n raise NotImplementedError(\\n 'chain fails substring selection: not \\\"simple\\\": %s' % (\\n chain_dict['chainName']))\\n \\n simple_strs = []\\n\\n for cp in cps:\\n print(cp)\\n simple_strs.append(_make_simple_label([cp]))\\n\\n label = 'combgen([(%d)]' % len(cps)\\n for s in simple_strs:\\n label += ' %s ' % s\\n label += ')'\\n return label\",\n \"def generate_code(self, parts: list):\\n for i in range(len(parts)):\\n\\n if not self._involves_this_party(parts[i][0]):\\n # not our data, skip job\\n continue\\n\\n if parts[i][1] == \\\"python\\\":\\n cg = PythonCodeGen(\\n self.config,\\n parts[i][0],\\n f\\\"{self.config.system_configs['CODEGEN'].workflow_name}-python-job-{i}\\\"\\n )\\n cg.generate()\\n elif parts[i][1] == \\\"jiff\\\":\\n cg = JiffCodeGen(\\n self.config,\\n parts[i][0],\\n f\\\"{self.config.system_configs['CODEGEN'].workflow_name}-jiff-job-{i}\\\"\\n )\\n cg.generate()\\n else:\\n raise Exception(f\\\"Unrecognized backend from partition: {parts[i][1]}.\\\")\",\n \"def __call__(self, blocks, with_cab=False):\\n\\n # for k, v in blocks.items():\\n # print(k, v.shape)\\n\\n #down fpn\\n f_down = self.FPN_Down_Fusion(blocks)\\n # print(\\\"f_down shape: {}\\\".format(f_down.shape))\\n #up fpn\\n f_up = self.FPN_Up_Fusion(blocks)\\n # print(\\\"f_up shape: {}\\\".format(f_up.shape))\\n #fusion\\n f_common = fluid.layers.elementwise_add(x=f_down, y=f_up)\\n f_common = fluid.layers.relu(f_common)\\n # print(\\\"f_common: {}\\\".format(f_common.shape))\\n\\n if self.with_cab:\\n # print('enhence f_common with CAB.')\\n f_common = self.cross_attention(f_common)\\n\\n f_score, f_border = self.SAST_Header1(f_common)\\n f_tvo, f_tco = self.SAST_Header2(f_common)\\n\\n predicts = OrderedDict()\\n predicts['f_score'] = f_score\\n predicts['f_border'] = f_border\\n predicts['f_tvo'] = f_tvo\\n predicts['f_tco'] = f_tco\\n return predicts\",\n \"def __call__(self, node):\\n\\n # should throw an error\\n if node.cfgInterface == None:\\n return\\n\\n # //\\n # // Extract LFN base from included WorkflowSpec parameters\\n #//\\n if self.unmerged:\\n base = node.getParameter(\\\"UnmergedLFNBase\\\")[0]\\n else:\\n base = node.getParameter(\\\"MergedLFNBase\\\")[0]\\n mergedBase = node.getParameter(\\\"MergedLFNBase\\\")[0]\\n\\n acqEra=None\\n if node.hasParameter(\\\"AcquisitionEra\\\"):\\n acqEra = node.getParameter(\\\"AcquisitionEra\\\")[0]\\n\\n\\n # //\\n # // iterate over outputmodules/data tiers\\n #// Generate LFN, PFN and Catalog for each module\\n\\n for modName, outModule in node.cfgInterface.outputModules.items():\\n\\n if ( not outModule.has_key('fileName') ):\\n msg = \\\"OutputModule %s does not contain a fileName entry\\\" % modName\\n raise RuntimeError, msg\\n\\n preserveLfnGroup = str(self.lfnGroup)\\n lastBit = outModule['processedDataset']\\n # //\\n # // Skip if the file does not stage out. (i.e.\\n #// --stageout-intermediates=False)\\n if lastBit is None:\\n msg = \\\"OutputModule does not stage out. Skipping.\\\"\\n logging.debug(msg)\\n continue\\n # //but this guy has the AcquisitionEra at the beginning... delimited\\n # // by a dash... we don't need it twice... we try to safely\\n #// remove it from the beginning, basically punting if its not\\n #\\\\\\\\ disadvantage of getting this from the ds name is having to\\n # \\\\\\\\ then strip off -unmerged\\n if acqEra is not None:\\n thingtoStrip=\\\"%s-\\\" % acqEra\\n mypieces = lastBit.split(thingtoStrip, 1)\\n if len(mypieces) > 1: \\n lastBit = mypieces[1]\\n remainingBits = lastBit.split(\\\"-unmerged\\\", 1)[0]\\n \\n outModule['LFNBase'] = os.path.join(base,\\n outModule['primaryDataset'],\\n outModule['dataTier'],\\n remainingBits,\\n preserveLfnGroup)\\n outModule['MergedLFNBase'] = os.path.join(mergedBase,\\n outModule['primaryDataset'],\\n outModule['dataTier'],\\n remainingBits,\\n preserveLfnGroup)\\n\\n return\",\n \"def build_stage2_6(self):\\n paf, cfm = self.stage2_6.values()\\n for i in range(2, 7):\\n paf_ = OrderedDict([(k.replace('i', str(i)),paf[k]) for k in paf])\\n cfm_ = OrderedDict([(k.replace('i', str(i)),cfm[k]) for k in cfm])\\n stage_ = OrderedDict(PAF=paf_, CFM=cfm_)\\n setattr(self, f'stage{i}', stage_)\",\n \"def slice_graph_bwd( endea, reg ): \\r\\n\\tgraph = vcg_Graph.vcgGraph({\\\"title\\\":'\\\"Slice for %s\\\"' % reg, \\\\\\r\\n\\t\\t\\\"manhattan_edges\\\":\\\"no\\\", \\\"layoutalgorithm\\\":\\\"maxdepth\\\"})\\r\\n\\t#\\r\\n\\t# Retrieve the name of the current basic block\\r\\n\\t# \\r\\n\\tworklist = []\\r\\n\\tdata_bib = {}\\r\\n\\t\\r\\n\\tstartnode = slice_node( 0, endea, reg )\\t\\t# start at the end of the slice node\\r\\n\\trootnode = graph.Add_Node( startnode.to_name() )\\r\\n\\tdata_bib[ startnode.to_name() ] = startnode\\r\\n\\tworklist.insert( 0, rootnode )\\r\\n\\twhile len( worklist ) > 0:\\r\\n\\t\\tcurrnode = worklist.pop()\\r\\n\\t\\tcurrslice = data_bib[ currnode.get_name() ]\\r\\n\\t\\t[tgt_reg, split] = currslice.get_target_reg_bwd()\\r\\n\\t\\tprint tgt_reg\\r\\n\\t\\tprint split\\r\\n\\t\\tif tgt_reg == \\\"END\\\":\\r\\n\\t\\t\\t# Do not process this node any further\\r\\n\\t\\t\\tpass\\r\\n\\t\\telif tgt_reg == \\\"\\\" or (( len( currslice.get_lines()) > 0) and \\\\\\r\\n\\t\\t\\tcurrslice.startea != currslice.get_lines()[0][0]):\\r\\n\\t\\t\\t# Do process this node further, nothing really going on \\r\\n\\t\\t\\tprint \\\"ZEZ\\\"\\r\\n\\t\\t\\txrefs = get_crefs_to( currslice.startea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( 0,ref, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( newslice.to_name(), currnode.get_name() )\\r\\n\\t\\telse:\\r\\n\\t\\t\\txrefs = get_crefs_to( currslice.startea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( 0,ref, tgt_reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( newslice.to_name(), currnode.get_name())\\r\\n\\t\\t\\txrefs = get_crefs_to( currslice.startea )\\r\\n\\t\\t\\tif split:\\r\\n\\t\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\t\\tnewslice = slice_node( 0,ref, currslice.reg )\\r\\n\\t\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\t\\tgraph.Add_Link( newslice.to_name(), currnode.get_name())\\r\\n\\treturn [ graph, data_bib ]\",\n \"def identity_block(self,X, f, filters, stage, block):\\n # define name basis\\n conv_name_base = 'res' + str(stage) + block + '_branch'\\n bn_name_base = 'bn' + str(stage) + block + '_branch'\\n\\n # Retrieve filters\\n f1,f2,f3 = filters\\n\\n # Save the input value. This needs to be added back to the main path later.\\n X_shortcut = X\\n\\n # First component of the main path\\n X = Conv2D(filters= f1, kernel_size=(1,1), strides=(1,1), padding='valid', name=conv_name_base + '2a', kernel_initializer=glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X)\\n X = Activation('relu')(X)\\n\\n # Second component of the main path\\n X = Conv2D(filters= f2, kernel_size=(f,f), strides=(1,1), padding='same', name=conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)\\n X = Activation('relu')(X)\\n\\n # Third component of the main path\\n X = Conv2D(filters= f3, kernel_size=(1,1), strides=(1,1), padding='valid', name=conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X)\\n X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)\\n\\n # Add the shortcut value to the main path\\n X = Add()([X_shortcut, X])\\n X = Activation('relu')(X) \\n\\n return X\",\n \"def onBuildModels(self):\\n if self.refSeriesNumber != '-1':\\n ref = self.refSeriesNumber\\n refLongName = self.seriesMap[ref]['LongName']\\n labelNodes = slicer.util.getNodes('*'+refLongName+'*-label*')\\n\\n numNodes = slicer.mrmlScene.GetNumberOfNodesByClass( \\\"vtkMRMLModelHierarchyNode\\\" )\\n outHierarchy = None\\n\\n for n in xrange(numNodes):\\n node = slicer.mrmlScene.GetNthNodeByClass( n, \\\"vtkMRMLModelHierarchyNode\\\" )\\n if node.GetName() == 'mpReview-'+refLongName:\\n outHierarchy = node\\n break\\n\\n # Remove the previous models\\n if outHierarchy:\\n collection = vtk.vtkCollection()\\n outHierarchy.GetChildrenModelNodes(collection)\\n n = collection.GetNumberOfItems()\\n if n != 0:\\n for i in xrange(n):\\n modelNode = collection.GetItemAsObject(i)\\n slicer.mrmlScene.RemoveNode(modelNode)\\n\\n # if models hierarchy does not exist, create it.\\n else:\\n outHierarchy = slicer.vtkMRMLModelHierarchyNode()\\n outHierarchy.SetScene( slicer.mrmlScene )\\n outHierarchy.SetName( 'mpReview-'+refLongName )\\n slicer.mrmlScene.AddNode( outHierarchy )\\n\\n progress = self.makeProgressIndicator(len(labelNodes))\\n step = 0\\n for label in labelNodes.values():\\n labelName = label.GetName().split(':')[1]\\n structureName = labelName[labelName[:-6].rfind(\\\"-\\\")+1:-6]\\n # Only save labels with known structure names\\n if any(structureName in s for s in self.structureNames):\\n parameters = {}\\n parameters[\\\"InputVolume\\\"] = label.GetID()\\n parameters['FilterType'] = \\\"Sinc\\\"\\n parameters['GenerateAll'] = True\\n\\n parameters[\\\"JointSmoothing\\\"] = False\\n parameters[\\\"SplitNormals\\\"] = True\\n parameters[\\\"PointNormals\\\"] = True\\n parameters[\\\"SkipUnNamed\\\"] = True\\n\\n # create models for all labels\\n parameters[\\\"StartLabel\\\"] = -1\\n parameters[\\\"EndLabel\\\"] = -1\\n\\n parameters[\\\"Decimate\\\"] = 0\\n parameters[\\\"Smooth\\\"] = 0\\n\\n parameters[\\\"ModelSceneFile\\\"] = outHierarchy\\n\\n progress.labelText = '\\\\nMaking Model for %s' % structureName\\n progress.setValue(step)\\n if progress.wasCanceled:\\n break\\n\\n try:\\n modelMaker = slicer.modules.modelmaker\\n self.CLINode = slicer.cli.run(modelMaker, self.CLINode,\\n parameters, wait_for_completion=True)\\n except AttributeError:\\n qt.QMessageBox.critical(slicer.util.mainWindow(),'Editor', 'The ModelMaker module is not available