\\n' % (sen.senid_, sen.senid_)\n for n in sen.named_entities_:\n nes += get_ne(sen.named_entities_[n])\n nes += '
'\n out.write('\\n%s\\n%s\\n%s\\n%s\\n%s\\n\\n' % (senid, sentence,\n amr, nes, graph))\n\ndef html(amres, filename, outdir, curt=False):\n out = open('%s/%s.html' % (outdir, filename), 'w')\n import visualizer\n\n graphdir = '%s/%s' % (outdir, 'graphs')\n try:\n os.mkdir(graphdir)\n except OSError:\n pass\n\n # head = '\\n'\n head = os.path.dirname(os.path.abspath(__file__)) + '/../docs/html_head'\n out.write(open(head, 'r').read())\n\n for snt in amres:\n if curt:\n visualizer.visualizer_curt(snt, graphdir)\n else:\n visualizer.visualizer(snt, graphdir)\n get_sentence(snt, out)\n\n'''\n Visualized AMR graphs\n Input: 'Sentence' object\n'''\ndef graph(amres, outdir, curt=False):\n import visualizer\n\n graphdir = '%s/%s' % (outdir, 'graphs')\n try:\n os.mkdir(graphdir)\n except OSError:\n pass\n\n for snt in amres:\n if curt:\n visualizer.visualizer_curt(snt, graphdir)\n else:\n visualizer.visualizer(snt, graphdir)\n\n'''\n AMR nodes\n if you would like to modify the output format of AMR node, modify\n node.py: def __str__(self):\n'''\ndef node(amres, outdir):\n out = open('%s/amr_nodes' % outdir, 'w')\n for snt in amres:\n for acr in snt.amr_nodes_:\n node = snt.amr_nodes_[acr]\n if node.ful_name_ == 'name': # Ingore 'n / name' node\n pass\n else:\n out.write('%s\\n%s\\n' % (snt.senid_, node))\n\n'''\n Named entities\n'''\ndef namedentity(amres, outdir):\n out = open('%s/amr_nes' % outdir, 'w')\n for snt in amres:\n for acr in snt.amr_nodes_:\n node = snt.amr_nodes_[acr]\n if node.is_entity_:\n out.write('%s\\t%s / %s\\t%s\\t%s\\n' % (snt.senid_, node.name_,\n node.ful_name_,\n node.entity_name_,\n node.wiki_))\n\n'''\n AMR paths\n'''\ndef path(amres, outdir):\n out = open('%s/amr_paths' % outdir, 'w')\n for snt in amres:\n for path_type in snt.amr_paths_:\n paths = snt.amr_paths_[path_type]\n for p in paths:\n path = ''\n for i in p:\n path += '(\\'%s\\', \\'%s\\'), ' % (i[0], i[1])\n out.write('%s\\t%s\\t[%s]\\n' % (snt.senid_,\n path_type,\n path.strip(', ')))\n\n'''\n AMR named entity queries\n'''\ndef query(amres, outdir):\n out = open('%s/amr_queries' % outdir, 'w')\n for snt in amres:\n for i in snt.named_entities_:\n ne = snt.named_entities_[i]\n query = '%s(%s|%s)' % (ne.name(), ne.subtype_, ne.maintype_)\n for i in ne.neighbors_:\n query += '%s;' % i[1]\n query += '|'\n for i in ne.coherence_:\n query += '%s;' % i[2].name()\n\n out.write('%s\\t%s\\t%s\\n' % (snt.senid_,\n ne.entity_name_,\n query.strip(';')))\n\n'''\n Named entity wiki titile\n'''\ndef newiki(amr_table, outdir):\n wiki = set()\n output = open(outdir + 'amr_nes_wiki', 'w')\n for docid in sorted(amr_table):\n for senid in sorted(amr_table[docid]):\n sen = amr_table[docid][senid]\n assert sen.senid_ == senid\n amr_nodes = sen.amr_nodes_\n for n in amr_nodes:\n node = amr_nodes[n]\n if node.is_entity_:\n wiki.add(node.wiki_)\n for i in sorted(wiki):\n output.write('%s\\n' % i)\n","sub_path":"amr-reader/src/output.py","file_name":"output.py","file_ext":"py","file_size_in_byte":5114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"621334925","text":"from collections import OrderedDict\n\nfrom mapping import Mapping\nfrom recommenders.generic_recommender import GenericRecommender, Stats\n\n\nclass CooccurRank(GenericRecommender):\n def __init__(self, BASEDIR, session_only = False, cycle_time=1):\n super().__init__(BASEDIR, session_only, cycle_time)\n self.name = 'coocrank'\n\n mapper = Mapping()\n self.rec_mapping = mapper.get_header_rec()\n self.event_mapping = mapper.get_header_event()\n self.item_id_idx = self.rec_mapping.index('ITEM_SOURCE')\n self.publisher_id_idx = self.rec_mapping.index('PUBLISHER')\n self.recs_idx = self.event_mapping.index('recs')\n self.user_id_idx = self.event_mapping.index('USER_COOKIE')\n\n self.user_item_dict = {}\n self.cooccur_dict = {}\n\n self.correct = 0\n self.total_events = 0\n self.nrrows =0\n self.i = 0\n\n def init_new_day(self):\n self.evaluation = Stats()\n self.user_item_dict = {}\n self.cooccur_dict = {}\n self.session_length = {}\n\n def store(self, item_id, user_id ):\n if not user_id in self.user_item_dict.keys():\n self.user_item_dict[user_id] = []\n if item_id == 0 or item_id =='0' or user_id == '0' or user_id == 0:\n return\n if item_id in self.user_item_dict[user_id]:\n self.i += 1\n return\n\n if item_id not in self.cooccur_dict.keys():\n self.cooccur_dict[item_id] = {}\n for coitem in self.user_item_dict[user_id]:\n if coitem not in self.cooccur_dict.keys():\n self.cooccur_dict[coitem] = {}\n if coitem in self.cooccur_dict[item_id].keys():\n self.cooccur_dict[item_id][coitem] += 1\n else:\n self.cooccur_dict[item_id][coitem] = 1\n if item_id in self.cooccur_dict[coitem].keys():\n self.cooccur_dict[coitem][item_id] += 1\n else:\n self.cooccur_dict[coitem][item_id] = 1\n if not item_id in self.user_item_dict[user_id]:\n self.user_item_dict[user_id].append(item_id)\n\n def store_view(self, nextrec):\n try:\n item_id = nextrec[self.item_id_idx]\n user_id = nextrec[self.user_id_idx]\n\n self.store(item_id, user_id)\n except:\n print(\"exception\")\n print(nextrec)\n print(self.nrrows)\n\n\n def store_event(self, nextevent):\n user_id = nextevent[self.user_id_idx]\n rec_clicked = self.true_rec(nextevent)\n # self.store_view(nextevent)\n\n if user_id != '0':\n self.store(item_id=rec_clicked, user_id=user_id)\n else:\n item_id = nextevent[self.item_id_idx]\n if item_id not in self.cooccur_dict.keys():\n self.cooccur_dict[item_id] = {}\n if rec_clicked not in self.cooccur_dict.keys():\n self.cooccur_dict[rec_clicked] = {}\n if rec_clicked in self.cooccur_dict[item_id].keys():\n self.cooccur_dict[item_id][rec_clicked] += 1\n else:\n self.cooccur_dict[item_id][rec_clicked] = 1\n if item_id in self.cooccur_dict[rec_clicked].keys():\n self.cooccur_dict[rec_clicked][item_id] += 1\n else:\n self.cooccur_dict[rec_clicked][item_id] = 1\n\n def get_recommendation(self, nextevent):\n item_id = nextevent[self.item_id_idx]\n user_id = nextevent[self.user_id_idx]\n sorted_item_list = []\n try:\n item_dict = self.cooccur_dict[item_id]\n ordered = OrderedDict(sorted(item_dict.items(),key=lambda t: t[1], reverse=True))\n sorted_item_list = list(ordered.keys())\n except:\n pass\n if 0 in sorted_item_list:\n sorted_item_list.remove(0)\n if item_id in sorted_item_list:\n sorted_item_list.remove(item_id)\n try:\n user_items = self.user_item_dict[user_id]\n result = [x for x in sorted_item_list if x not in user_items]\n return result[0:6]\n except:\n return sorted_item_list[0:6]\n\n\n\n def run_ranker(self):\n for line in self.bridge_table:\n command = line.split('\\t')[0]\n if(command == 'rec'):\n nextrec = self.rec_csv.readline().split('\\t')\n self.store_view(nextrec)\n self.add_session(nextrec)\n if(command == 'event'):\n self.total_events += 1\n nextevent = self.event_csv.readline().split('\\t')\n self.add_score(nextevent)\n self.store_event(nextevent)\n\n self.nrrows += 1\n if (self.nrrows % 100000 == 0):\n print(self.i)\n self.logging()","sub_path":"recommenders/cooccur_based_ranker.py","file_name":"cooccur_based_ranker.py","file_ext":"py","file_size_in_byte":4812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"306730856","text":"import struct\n\na = \"hello this is my [redacted]\"\nb = bytearray()\n\nb.extend(map(ord,a))\n\nl = len(a)\n\np = struct.pack('>I',l) + b\n\nprint(l)\nprint(p)\n\nsiz = struct.calcsize('>I')\nprint(siz)\n\nv = struct.unpack('>I',p[:siz])\nprint(v)\nprint(p[siz:])\n\n\nu = \"\\x00\\x00\\x00\\x2C{\\\"Type\\\":6,\\\"Info\\\":\\\"info\\\",\\\"PayLoad\\\":\\\"payload\\\"}\"\n\nub = bytearray()\nub.extend(map(ord,u))\n\ng = struct.unpack('>I',ub[:siz])\n\nprint(g)\n","sub_path":"PythonTests/BInaryTest.py","file_name":"BInaryTest.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"273821540","text":"import flask\nimport random\nimport pandas as pd\nimport logging\nimport os\nimport sqlite3\nfrom flask_json import FlaskJSON, JsonError, as_json\n# logging\nlogger = logging.getLogger(__name__)\nlogging.basicConfig(level='DEBUG')\n\n\n# ----| IO functions\n\ndef get_customer_prediction(customer_id=None):\n \"\"\"Get predictions for a customer\n\n customer_id: returns random customer if None\n \"\"\"\n logger.info('reading predictions from db')\n file=flask.current_app.config.predictions_file\n conn=sqlite3.connect(file)\n if customer_id:\n query='select CLV from predictions where customer_id = \"{}\"'\n results=conn.execute(query.format(customer_id)).fetchall()\n if results:\n prediction=results[0][0]\n else:\n raise JsonError(description='no data for this customer_id',\n status_=404)\n else:\n query='select * from predictions order by random() limit 1'\n customer_id, prediction=conn.execute(query).fetchall()[0]\n return customer_id, prediction\n\n\n# CLV service\nCLV_server = flask.Blueprint('CLV_server', 'CLV_server')\n\n\n@CLV_server.route('/customers//predicted_CLV')\n@as_json\ndef CLV_prediction(customer_id):\n \"\"\"Return CLV prediction for the given customer_id\n\n if customer_id is 'random', the customer id is chosen at random from the available data.\n\n ex:\n >>GET /customers/2504175708a53b19fe067b06472e4cec/predicted_CLV\n {\n \"customer_id\": \"2504175708a53b19fe067b06472e4cec\", \n \"predicted_CLV\": 116.92\n }\n \"\"\"\n if customer_id == 'random':\n customer_id = None\n customer_id, predicted_CLV = get_customer_prediction(customer_id)\n payload = {\n 'customer_id': customer_id,\n 'predicted_CLV': predicted_CLV\n }\n return payload\n\n@CLV_server.route('/test')\ndef test():\n return 'OK'\n\n# ----| app startup\n\n\ndef create_app(config='prod'):\n app = flask.Flask('CLV_predict')\n app.register_blueprint(CLV_server)\n FlaskJSON(app)\n\n if config == 'debug':\n app.config.predictions_file = 'data/predictions_test.db'\n app.debug = True\n os.environ['WERKZEUG_DEBUG_PIN'] = 'off'\n elif config == 'test':\n app.config.predictions_file = 'data/predictions_test.db'\n elif config == 'prod':\n app.config.predictions_file = 'data/predictions.db'\n return app\n\n\nif __name__ == '__main__':\n app = create_app()\n app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"292921971","text":"# -*- coding: utf-8 -*-\n# __author__ = 'qinjincheng'\n\nimport socket\nimport random\nimport time\n\ndef main():\n host = '127.0.0.1'\n port = 8080\n for i in range(random.randrange(10, 20)):\n time.sleep(1)\n s = socket.socket()\n s.connect((host, port))\n print('client send ({}) to server'.format(i))\n s.send(str(i).encode())\n data = s.recv(1024)\n print('client recv raw data ({}) from server'.format(data))\n print('client recv dec data ({}) from server'.format(data.decode()))\n s.close()\n\ndef main(sk):\n {'tcp': tcp_client, 'udp': udp_client}[sk]()\n\ndef tcp_client():\n host = socket.gethostname()\n port = 12345\n print('Connect -> {}:{}'.format(host, port))\n s = socket.socket()\n s.connect((host, port))\n print('Connection already established')\n info = str()\n while info != 'byebye':\n data = input('Content send: ')\n s.send(data.encode())\n if data == 'byebye':\n break\n info = s.recv(1024).decode()\n print('Content received:\\n{}'.format(info))\n s.close()\n\ndef udp_client():\n host = socket.gethostname()\n port = 54321\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n data = input('Please input a Centigrade degree: ')\n s.sendto(data.encode(), (host, port))\n print(s.recv(1024).decode())\n s.close()\n\nif __name__ == '__main__':\n main(sk='udp')\n","sub_path":"sckt/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"561456820","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport urllib\nimport mechanize\n\nurl = \"http://127.0.0.1:8000/cgi-bin/index.py\"\n\nbr = mechanize.Browser()\npage = br.open(url)\n\nbr.select_form(nr=0)\nbr[\"name\"]=\"MOTOUCHI\"\nbr[\"comments\"]=\"hello world\"\nbr.submit()\n","sub_path":"network/seccamp/client3.py","file_name":"client3.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"501064558","text":"\"\"\"BioDNA : a module of DNA processing functions \"\"\"\nimport re\n# ----------------------------------------\n\ndef complementDNA_to_DNA( DNA ):\n \"\"\"Returns the complement strand of DNA\"\"\"\n\n # you need to do the work here\n changeOriginal = \"ACTG\"\n toComplement = \"TGAC\"\n transTable = str.maketrans(changeOriginal, toComplement)\n complementaryStrand = DNA.translate(transTable)\n \n return complementaryStrand\n# ----------------------------------------\n\n# ----------------------------------------\ndef complementDNA_to_RNA( DNA ):\n \"\"\"Returns the complement strand of RNA\"\"\"\n\n # you need to do the work here\n changeOriginal = \"T\"\n toComplement = \"U\"\n transTable = str.maketrans(changeOriginal, toComplement)\n complementaryStrand = DNA.translate(transTable) \n\n return complementaryStrand\n# ----------------------------------------\n\n# ---------------------------------------- \ndef transcribe( DNA ):\n \"\"\"Simulates transcription by building template strand and then complementing that to RNA\"\"\"\n \n # gene is on the DNA strand\n # but RNA copy is built off the template (or anti-sense) strand\n\n mRNA = \"\" \n\n # (a) get complement of the DNA strand (that is, make the template strand)\n compDNA = complementDNA_to_DNA(DNA)\n \n # (b) get mRNA complement of the template strand\n # (c) return the mRNA\n \n # you need to do the work here \n \n complementDNA = complementDNA_to_DNA(DNA)\n \n print (\" DNA: 5'\", DNA, \"3'\")\n print (\" DNA: 3'\", complementDNA, \"5'\")\n \n Bars = \"\"\n DNALen = len(DNA)\n while (DNALen > 0):\n Bars = Bars + \"|\"\n DNALen = DNALen - 1\n \n print (\" \", Bars) \n \n mRNA = complementDNA_to_RNA(DNA)\n \n\n \n \n return mRNA\n# ----------------------------------------\ndef getDNA(filename):\n \"\"\" Takes a .fna file of a DNA sequence and reads in the lines\"\"\"\n \n INFILE = open(filename, 'r')\n \n for filelines in INFILE:\n sequence = filelines\n \n return sequence\n\n\n#-----------------------------------------\ndef findDirectRepeats(upstream):\n \"\"\"Uses regex to search through the file and find direct repeats\"\"\"\n DRregex = re.compile(r\"(.)(.)(.?)\\1\\2\\3\")\n \n DRiterator = DRregex.finditer(upstream)\n DRtotal = 0\n for nextDR in DRiterator: \n DR = nextDR.group() \n DRstart = nextDR.start() \n DRend = nextDR.end() \n DRtotal = DRtotal + len(DR)\n \n print (\"Found DR:\", DR, \"[\", DRstart+1, \"to\", DRend, \"bp]\") \n return DRtotal\n \ndef findMirrorRepeats(upstream):\n \"\"\"Uses regex to search through the file and find mirror repeats\"\"\"\n \n MRregex = re.compile( r\"(.)(.)(.)\\3\\2\\1\" ) # build regex for MR of len=4bp\n \n MRiterator = MRregex.finditer(upstream) # find all matches\n MRtotal = 0 #going to be used to find percentile\n for nextMR in MRiterator: # loop thru list of matches\n MR = nextMR.group() # get the actual regex match \n MRstart = nextMR.start() # where did it begin\n MRend = nextMR.end() # location just after the end\n MRtotal = MRtotal + len(MR)\n \n print (\"Found MR:\", MR, \"[\", MRstart+1, \"to\", MRend, \"bp]\") \n return MRtotal\n#-----------------------------------------\ndef findATRepeats(upstream):\n \"\"\"Uses regex to search through the file and find ATAT repeats\"\"\"\n \n ATregex = re.compile(r\"(A)(T)(A)(T)\\1\\2\\3\\4\")\n \n ATiterator = ATregex.finditer(upstream)\n ATtotal = 0\n for nextAT in ATiterator: \n AT = nextAT.group() \n ATstart = nextAT.start() \n ATend = nextAT.end() \n ATtotal = ATtotal + len(AT)\n \n print (\"Found AT:\", AT, \"[\", ATstart+1, \"to\", ATend, \"bp]\") \n return ATtotal\n#-----------------------------------------\ndef makeAminoAcidTable( ):\n \"\"\"Returns a Python Dictionary (hash table) that maps RNA nucleotides to Amino Acid symbols;\n both one letter and three letter symbols are returned; the user will need to parse which of\n these symbols they want to use\"\"\"\n \n AAtable = { \"UUU\":\"F|Phe\",\"UUC\":\"F|Phe\",\"UUA\":\"L|Leu\",\"UUG\":\"L|Leu\",\"UCU\":\"S|Ser\",\"UCC\":\"S|Ser\",\n \"UCA\":\"S|Ser\",\"UCG\":\"S|Ser\",\"UAU\":\"Y|Tyr\",\"UAC\":\"Y|Tyr\",\"UAA\":\"*|***\",\"UAG\":\"*|***\",\n \"UGU\":\"C|Cys\",\"UGC\":\"C|Cys\",\"UGA\":\"*|***\",\"UGG\":\"W|Trp\",\"CUU\":\"L|Leu\",\"CUC\":\"L|Leu\",\n \"CUA\":\"L|Leu\",\"CUG\":\"L|Leu\",\"CCU\":\"P|Pro\",\"CCC\":\"P|Pro\",\"CCA\":\"P|Pro\",\"CCG\":\"P|Pro\",\n \"CAU\":\"H|His\",\"CAC\":\"H|His\",\"CAA\":\"Q|Gln\",\"CAG\":\"Q|Gln\",\"CGU\":\"R|Arg\",\"CGC\":\"R|Arg\",\n \"CGA\":\"R|Arg\",\"CGG\":\"R|Arg\",\"AUU\":\"I|Ile\",\"AUC\":\"I|Ile\",\"AUA\":\"I|Ile\",\"AUG\":\"M|Met\",\n \"ACU\":\"T|Thr\",\"ACC\":\"T|Thr\",\"ACA\":\"T|Thr\",\"ACG\":\"T|Thr\",\"AAU\":\"N|Asn\",\"AAC\":\"N|Asn\",\n \"AAA\":\"K|Lys\",\"AAG\":\"K|Lys\",\"AGU\":\"S|Ser\",\"AGC\":\"S|Ser\",\"AGA\":\"R|Arg\",\"AGG\":\"R|Arg\",\n \"GUU\":\"V|Val\",\"GUC\":\"V|Val\",\"GUA\":\"V|Val\",\"GUG\":\"V|Val\",\"GCU\":\"A|Ala\",\"GCC\":\"A|Ala\",\n \"GCA\":\"A|Ala\",\"GCG\":\"A|Ala\",\"GAU\":\"D|Asp\",\"GAC\":\"D|Asp\",\"GAA\":\"E|Glu\",\n \"GAG\":\"E|Glu\",\"GGU\":\"G|Gly\",\"GGC\":\"G|Gly\",\"GGA\":\"G|Gly\",\"GGG\":\"G|Gly\"}\n \n return AAtable\n# ------------------------\n \n\ndef translate( mRNA, AAtable ): \n \"\"\"Returns the 3-letter amino acid string of symbols for the corresponding mRNA\"\"\"\n \n protein = \"\"\n \n startBP = 0\n endBP = len(mRNA)\n \n nextCodonStart = startBP\n # while we don't run off the end of the sequence of mRNA, snag nucleotide codons\n # and build a sequence of amino acid symbols (uses the 3-letter symbols)\n while ( nextCodonStart+3 <= endBP):\n \n # slice out the new RNA nucleotide triple\n nextCodon = mRNA[nextCodonStart:nextCodonStart+3]\n \n # play RIBOSOME: convert an RNA nucleotide-triple to an amino acid symbol\n nextAA = AAtable[ nextCodon ]\n \n # amino acid symbols come as two types of symbols (3 letter and 1 letter);\n # in general: triple:|\n # e.g., \"GAG\":\"E|Glu\" ... thus we need to parse out which type of symbol we want\n \n # we want the 3-letter 'Glu' version here, so slice out from location 2 to the end\n AAsymbol = nextAA[2:]\n \n # add this AA onto the end of the growing amino-acid (protein) chain\n protein = protein + AAsymbol\n \n # move down to next triple\n nextCodonStart = nextCodonStart + 3\n \n # end while more triples to check\n \n return protein\n\n# ---- end translate() ----------------------------------------","sub_path":"Upstream_Sequences/BioDNA.py","file_name":"BioDNA.py","file_ext":"py","file_size_in_byte":6651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"33586782","text":"import feedparser\nimport sys\n\nfrom twisted.python import log\n\nimport plugin\nfrom utils import str_utils, command_saver\n\nSAVE_FILE = \"feedretriver_settings.save\"\nFAIL_MESSAGE = (\"Unable to download or parse feed. Remove unused feeds using \"\n \"the !listfeed and !removefeed commands.\")\n\nHELP_MESSAGE = (\"!addfeed url [fetch time [custom title]] where:\\n\"\n \"url - is the url of the atom or rss feed\\n\"\n \"fetch time - is the number of minutes between each request\\n\"\n \"custom title - is the title used for this feed.\\n\"\n \"If no title is given, the default title parsed from the \"\n \"feed will be used instead.\")\n\nREMOVING_FEED_MESSAGE = u\"Removing: #{} - {}\"\nLIST_FEED_ITEM_MESSAGE = u\"#{}: {}\"\nNO_FEED_MESSAGE = u\"No feeds\"\n\nDEFAULT_FETCH_TIME = 10*60\n\ndef FeedItemToString(title, link, feed_title = \"\"):\n return str_utils.sanitize_string(u\"{}: {} <{}>\".format(feed_title, title, link))\n\n# The Feed class handles printing out new entries\nclass Feed():\n # Note that data could both be a url, or an already parsed feed\n def __init__(self, data, title):\n if isinstance(data, basestring):\n data = feedparser.parse(data)\n self.last_entry = 0\n self._set_last(data.entries)\n self.title = title\n self._update_title(data)\n\n def update(self, data, say):\n if isinstance(data, basestring):\n data = feedparser.parse(data)\n if data.bozo != 0:\n log.msg(\"Error updating feed \" + self.title)\n return\n self._update_title(data)\n log.msg(\"Updating feed: \" + self.title)\n for entry in data.entries:\n # TODO: Check id, title and link, etc\n # Maybe save the entire data.entries and remove all duplicate when\n # a new update happens?\n if entry.published_parsed <= self.last_entry:\n break\n say(FeedItemToString(entry.title, entry.link, self.title))\n self._set_last(data.entries)\n\n def _update_title(self, parsed):\n if parsed.bozo == 0 and self.title == \"\":\n self.title = parsed.feed.title\n self.title = str_utils.sanitize_string(self.title)\n\n def _set_last(self, entries):\n if len(entries) > 0:\n self.last_entry = entries[0].published_parsed\n\n\n# Simple polling class, fetches the feed in a regular intervall and passes\n# the information on to the Feed object\nclass Feedpoller():\n def __init__(self, say, url, update_freq=DEFAULT_FETCH_TIME, title=\"\"):\n parsed = feedparser.parse(url)\n self.feed = Feed(parsed, title)\n if parsed.bozo == 0:\n self.modified = parsed.get(\"modified\", None)\n self.etag = parsed.get(\"etag\", None)\n say(\"Added feed: \" + self.feed.title)\n else:\n self.modified = \"\"\n say(FAIL_MESSAGE)\n self.say = say\n self.url = url\n self.consecutive_fails = 0\n self.update_freq = update_freq\n self.update_count = 0\n\n def update(self):\n self.update_count += 1\n if self.update_count >= self.update_freq:\n self.update_count = 0\n parsed = feedparser.parse(self.url, modified=self.modified, etag=self.etag)\n if parsed.bozo == 1:\n self.consecutive_fails += 1\n if self.consecutive_fails % 10 == 0:\n self.say(FAIL_MESSAGE)\n else:\n self.modified = parsed.get(\"modified\", None)\n self.etag = parsed.get(\"etag\", None)\n self.feed.update(parsed, self.say)\n self.consecutive_fails = 0\n\n# Aggregator class for adding and handling feeds\nclass Feedretriever(plugin.Plugin):\n def __init__(self):\n plugin.Plugin.__init__(self, \"Feedretriever\")\n self.feeds = []\n self.saver = command_saver.CommandSaver(SAVE_FILE)\n\n def started(self, settings):\n log.msg(\"Feedretriever.started\", settings)\n self.saver.read(lambda server, channel, message: self.privmsg(str(server), None, str(channel), message))\n\n def privmsg(self, server, user, channel, message):\n say = lambda msg: self.say(server, channel, msg)\n if message.startswith(\"!feed\") or message.startswith(\"!addfeed\"):\n _, url, time, title = str_utils.split(message, \" \", 4)\n try:\n time = int(time) * 60\n except:\n time = DEFAULT_FETCH_TIME\n if url == \"\":\n say(HELP_MESSAGE)\n return\n self.feeds.append(Feedpoller(say, url, time, title))\n self.saver.save(server, channel, message)\n elif message.startswith(\"!removefeed\"):\n feeds = []\n for i in message.split(\" \"):\n i = int(i) if unicode(i).isdecimal() else -1\n if i >= 0 and i < len(self.feeds):\n feeds.append(i);\n for i in sorted(feeds, reverse=True):\n say(REMOVING_FEED_MESSAGE.format(i, self.feeds[i].feed.title))\n del self.feeds[i]\n self.saver.remove(i)\n log.msg(\"Removed feed: \" + str(i))\n elif message.startswith(\"!listfeed\"):\n if len(self.feeds) == 0:\n say(NO_FEED_MESSAGE)\n for i, feed in enumerate(self.feeds):\n say(LIST_FEED_ITEM_MESSAGE.format(i, feed.feed.title))\n\n def update(self):\n for feed in self.feeds:\n feed.update()\n\nif __name__ == \"__main__\":\n sys.exit(Feedretriever.run())\n","sub_path":"plugins/feedretriever/feedretriever.py","file_name":"feedretriever.py","file_ext":"py","file_size_in_byte":5600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"15825505","text":"from core.decorators import instance, command\nfrom core.command_param_types import Any\nimport re\n\n\n@instance()\nclass CalculatorController:\n def __init__(self):\n self.allow_chars_regex = re.compile(\"^[0123456789.,+\\-*%()/ &|^~<>]+$\")\n\n @command(command=\"calc\", params=[Any(\"formula\")], access_level=\"all\",\n description=\"Perform a calculation\")\n def calc_cmd(self, channel, sender, reply, args):\n forumla = args[0]\n if self.allow_chars_regex.match(forumla):\n try:\n reply(\"%s = %s\" % (forumla, round(eval(forumla), 4)))\n except SyntaxError:\n reply(\"Invalid formula supplied.\")\n else:\n reply(\"Invalid character detected.\")\n","sub_path":"modules/standard/helpbot/calculator_controller.py","file_name":"calculator_controller.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"185198006","text":"import tkinter as tk\nfrom tkinter import *\n\nlaunch_angle = None\nlaunch_speed = None\n\nheightm = 1\nlaunch_height = 500 - heightm * 50\n\ndistance = None\nmax_height = None\nflight_time = None\n\n\ndef runsim():\n print('its working')\n\n\nclass canvas:\n def __init__(self, master=None):\n self.master = master\n\n self.x = 0\n self.y = 0\n\n self.canvas = Canvas(self.master, bg=\"blue\", width=960, height=500)\n self.canvas.pack()\n\n self.ball = self.canvas.create_oval(10, launch_height - 70, 80, launch_height, outline='black', fill='yellow',\n width=2)\n\n self.movement()\n\n run_sim = tk.Button(self.master, text=\"Run Simulation\", padx=5, pady=10, fg=\"white\", bg=\"green\", command=runsim)\n run_sim.pack()\n\n def movement(self):\n self.canvas.move(self.ball, self.x, self.y)\n\n self.canvas.after(100, self.movement)\n\n def launch(self, angle, speed, height):\n height = launch_height\n angle = launch_angle\n speed = launch_speed\n\n\n# enable tkinter modules that create a new window with its name and dimensions and initiates the main loop\nif __name__ == '__main__':\n window = Tk()\n canvas = canvas(window)\n\n window.title(\"Launch Simulator\")\n window.geometry('960x540')\n window.mainloop()\n","sub_path":"Sim.py","file_name":"Sim.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"413903324","text":"import os, sys, re, time\nimport json\nimport requests\nimport hashlib\nimport pandas\nimport random\nfrom bs4 import BeautifulSoup\nfrom urllib import parse\nfrom PIL import Image\nfrom threading import Thread\nimport inspect\nimport ctypes\nimport win32gui\nimport win32con\nimport datetime, time\nfrom selenium import webdriver\nfrom PostgreSQL import PostgreSQL\nfrom function import *\nfrom setting import *\n\nsearch = '/v2/movie/search?q='\n\nUSER_AGENTS = [\n 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:52.0) Gecko/20100101 Firefox/52.0',\n 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:60.0) Gecko/20100101 Firefox/60.0',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.90 Safari/537.36 2345Explorer/9.3.2.17331',\n 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Win64; x64; Trident/4.0; .NET CLR 2.0.50727; SLCC2; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET4.0C; .NET4.0E)',\n 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)',\n 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Acoo Browser; SLCC1; .NET CLR 2.0.50727; Media Center PC 5.0; .NET CLR 3.0.04506)',\n 'Mozilla/4.0 (compatible; MSIE 7.0; AOL 9.5; AOLBuild 4337.35; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)',\n 'Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)',\n 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)',\n 'Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)',\n 'Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)',\n 'Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)',\n 'Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6',\n 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1',\n 'Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0',\n 'Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5',\n 'Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.8) Gecko Fedora/1.9.0.8-1.fc10 Kazehakase/0.5.6',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11',\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.20 (KHTML, like Gecko) Chrome/19.0.1036.7 Safari/535.20',\n 'Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; fr) Presto/2.9.168 Version/11.52',\n]\n\nclass DouBan(object):\n \"\"\"docstring for DouBan\"\"\"\n name = 'DouBan'\n domain = 'https://api.douban.com'\n\n def __init__(self):\n super(DouBan, self).__init__()\n headers = {\n 'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Encoding':'gzip, deflate, br',\n 'Accept-Language':'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3',\n 'Connection':'keep-alive',\n 'Upgrade-Insecure-Requests':'1',\n }\n self.s = requests.session()\n self.s.headers.update(headers)\n self.path = path\n self.pg = PostgreSQL(db)\n # self.action()\n\n def __getResponse(self, url, data={}, method=''):\n self.s.headers['User-Agent'] = random.choice(USER_AGENTS)\n if method == 'post':\n if len(data) == 0: return\n try: resp = self.s.post(url, data=data)\n except Exception as e: pass\n else: return resp\n else:\n try: resp = self.s.get(url, data=data) if len(data) > 0 else self.s.get(url)\n except Exception as e: pass\n else: return resp\n\n def webSearch(self, mid, title, year, imdb):\n url = 'https://movie.douban.com/subject_search?search_text=%s&cat=1002' % parse.quote(title)\n browser = webdriver.Firefox()\n browser.get(url)\n html = BeautifulSoup(browser.page_source, 'html.parser')\n divs = html.find_all('div',{'class':'item-root'})\n divs = [i.find('a',{'class':'cover-link'}) for i in divs if i.find('a',{'class':'cover-link'}) is not None]\n divs = [{'href':i.get('href'),'title':i.find('img').get('alt'),'src':i.find('img').get('src')} for i in divs]\n reg = r\"([0-9]{4})\"\n num = 1\n string = ''\n for i in divs:\n string += ' %d-%s' % (num,i['title'])\n num += 1\n res = re.findall(reg, i['title'])\n if len(res) > 0 and res[0] == str(year):\n browser.quit()\n return i['href'].split('/')[-2]\n else:\n url = None\n if ':' in title:\n for key in title.split(':'):\n url = self.webSearch(mid, key, year, imdb)\n if url is not None: break\n if url is None:\n panel = win32gui.FindWindow(None, '管理员: 命令提示符 - DouBan')\n if string != '':\n img = self.pg.select('res_img',['filename'],[\"mid = '%s'\" % mid])\n thread = Thread(target=self.openPic, name=\"Open.__process\", args=(img[0][0],))\n thread.setDaemon(True)\n thread.start()\n time.sleep(1)\n win32gui.SetForegroundWindow(panel)\n while True:\n print()\n print('名称:',title)\n print('年代:',year)\n print('imdb:',imdb)\n print(string)\n print()\n number = input(\"请输入确定的序号: \") if string != '' else '0'\n if number.isdigit() and int(number) <= len(divs): break\n if string != '':\n imgHwnd = win32gui.FindWindow('RCImageViewerFrame', None)\n win32gui.PostMessage(imgHwnd, win32con.WM_CLOSE, 0, 0)\n browser.quit()\n if int(number) > 0: return divs[int(number)-1]['href'].split('/')[-2]\n else:\n browser.quit()\n return url\n\n def openPic(self,file):\n img = Image.open(path+'Picture/'+file)\n img.show()\n\n def apiSearch(self, keyword, year=None):\n res = self.__getResponse('%s%s%s' % (self.domain, search, keyword))\n try: res = json.loads(res)\n except Exception as e: pass\n else:\n if res.get('subjects') is None: return\n for i in res.get('subjects'):\n if year is not None and i.get('year') == str(year): return i['id']\n else:\n if ':' in keyword:\n for key in keyword.split(':'):\n con = self.apiSearch(key.strip(), year)\n if con is not None: return con\n\n def getContent(self, id, imdb=None, type='record'):\n url = 'https://movie.douban.com/subject/%s/' % id\n html = self.__getResponse(url)\n if html is None: return\n html = BeautifulSoup(html.text, 'html.parser')\n\n ims = html.findAll('a',{'rel':'nofollow','target':'_blank'})\n im = None\n for i in ims:\n if 'imdb' in i.get('href'): im = i.get('href')\n if im is not None and im != imdb and type == 'record': return\n\n data = {'url':url}\n\n imdb_html = self.__getResponse(im)\n if imdb_html is None: return\n imdb_html = BeautifulSoup(imdb_html.text,'html.parser')\n imdb_html = imdb_html.find('span',{'itemprop':'ratingValue'})\n if imdb_html is not None: data['imdb_score'] = imdb_html.getText()\n\n info = html.find('span', {'property': 'v:itemreviewed'})\n data['title'] = info.text if info is not None else ''\n\n info = html.find('a', {'rel': 'v:directedBy'})\n data['directors'] = modifyName(info.text if info is not None else '')\n\n info = html.find_all('a', {'rel': 'v:starring'})\n if info is not None: data['actors'] = modifyName(','.join([i.text for i in info]))\n\n info = html.find_all('span', {'property': 'v:genre'})\n if info is not None: data['types'] = ','.join([i.text for i in info])\n\n info = html.find_all('span', {'property': 'v:initialReleaseDate'})\n if info is not None: data['playdate'] = ','.join([i.text for i in info])\n\n info = html.find('span', {'property': 'v:runtime'})\n data['duration'] = info.text if info is not None else ''\n\n data['description'] = ''\n data['thestory'] = ''\n info = html.find('span',{'class':'all hidden'})\n if info is None: info = html.find('span', {'property': 'v:summary'})\n if info is not None:\n info = info.text.split('\\n')\n info = [i.lstrip().rstrip() for i in info]\n while '' in info: info.remove('')\n data['description'] = ''.join(info)[:200]\n for i in info: data['thestory'] += '
%s
' % i\n\n info = html.find('div', id='info')\n if info is not None:\n info = info.contents\n for i in range(0, len(info)):\n\n if len(str(info[i])) < 10: continue\n if str(info[i]).find('语言:') != -1: data['language'] = ','.join(modifyLanguage(info[i+1].replace(' / ', ',')))\n if str(info[i]).find('制片国家') != -1: data['district'] = ','.join(modifyDistrict(info[i+1].replace(' / ', ',')))\n if str(info[i]).find('又名:') != -1: data['alias'] = info[i + 1].replace(' / ', ',')\n if str(info[i]).find('编剧:') != -1:\n bj = info[i].findAll('a')\n b = [j.getText() for j in bj]\n data['scriptwriters'] = modifyName(','.join(b))\n\n info = html.find('div', {'class': 'tags-body'})\n if info is not None: data['label'] = ','.join([i.getText() for i in info.findAll('a')])\n info = html.find('strong', {'class': 'll rating_num'})\n if info is not None: data['douban_score'] = info.getText()\n # print(data)\n return data\n \n def __getPoster(self,id,mid):\n url = 'https://movie.douban.com/subject/%s/photos?type=R' % id\n html = self.__getResponse(url)\n if html is None: return False\n if '异常请求' in html.text: return False\n if 'window.location.href' in html.text and 'sec.douban.com' in html.text: return False\n if 'Your IP is restricted' in html.text: return False\n \n # if html is None: return\n html = BeautifulSoup(html.text,'html.parser')\n divs = html.find_all('div',{'class':'cover'})\n divs = [i.find('a').get('href') for i in divs]\n\n if len(divs) < 1:\n print('片子:%s 没有海报' % mid)\n data = dict(\n mid = mid,\n type = 'poster',\n site = self.name\n )\n self.pg.insert('res_defect', data)\n for i in divs:\n self.s.headers['Referer'] = i\n picid = i.split('/')[-2]\n picurl = 'https://img3.doubanio.com/view/photo/raw/public/p%s.jpg' % picid\n where = [\"resource LIKE '%\"+picid+\"%'\"]\n where.append(\"type = 'poster'\")\n where.append(\"site = '%s'\" % self.name)\n where.append(\"mid = '%s'\" % mid)\n rec = self.pg.select('res_img',where=where)\n if len(rec) > 0: continue\n # # rec = self.pg.select('res_img',where=[\"resource = '%s'\" % src])\n # # if len(rec) > 0: continue\n file = self.__downloads(picurl)\n data = dict(\n mid = mid,\n filename = '%s/%s.jpg' % (self.name, file),\n resource = i,\n type = 'poster',\n site = self.name,\n classes = 'resource'\n )\n self.pg.insert('res_img',data)\n \n def __downloads(self, url, type='img'):\n path = '%s/Picture/%s' % (self.path,self.name) if type == 'img' else '%s/Torrent/%s' % (self.path,self.name)\n if not os.path.exists(path): os.makedirs(path)\n file = hashlib.md5(url.encode('utf8')).hexdigest()\n path = '%s/%s.jpg' % (path,file) if type == 'img' else '%s/%s.torrent' % (path,file)\n data = self.__getResponse(url)\n with open(path, 'wb') as f: f.write(data.content)\n return file\n\n def getPoster(self):\n defect = self.pg.select('res_defect',['mid'],[\"site = '%s'\" % self.name,\"type = 'poster'\"])\n # defect = [str(i[0]) for i in defect]\n maxid = self.pg.custom(\"SELECT mid FROM res_img WHERE site = 'DouBan' and type = 'poster' ORDER BY mid DESC LIMIT 1 OFFSET 0\")\n pid = self.pg.custom(\"SELECT mid FROM res_img WHERE site = 'DouBan' and type = 'poster' GROUP BY mid\")\n pid = [str(i[0]) for i in pid+defect if i[0] != maxid[0][0]]\n ids = self.pg.select('res_info', ['id','mid','url'], ['\"mid\" NOT IN (%s)' % ','.join(pid)], order=['mid ASC'])\n for i in ids:\n doubanid = i[2].split('/')[-2]\n print('信息ID:%s 资源ID:%s ' % (i[0],i[1]))\n # break\n if self.__getPoster(doubanid,i[1]) is False: break\n \n\n\n def action(self):\n ids = self.pg.select('res_info', ['mid'])\n ids = [str(i[0]) for i in ids]\n res = self.pg.select('res_site',['id','title','year','imdb'],['\"id\" NOT IN (%s)' % ','.join(ids)],['id DESC'])\n today = datetime.date.today()\n for i in res:\n # id = self.apiSearch(i[1],i[2])\n id = self.webSearch(i[0],i[1],i[2],i[3])\n if id is None: continue\n self.__getPoster(id,i[0])\n content = self.getContent(id,i[3])\n if content is not None:\n content['mid'] = i[0]\n content['site'] = self.name\n content['record'] = today\n content['update'] = today\n self.pg.insert('res_info',content)\n print(i[0],i[1],'数据下载完成')\n\n def update(self):\n today = datetime.date.today()\n where = [\"update = '%s'\" % '2018-05-25']\n # where = [\"id = 1012\"]\n column = self.pg.getColumns('res_info')\n data = self.pg.select('res_info', column, where)\n data = pandas.DataFrame(list(data), columns=column)\n \n for key, val in data.iterrows():\n print(val.id)\n content = self.getContent(val.url.split('/')[-2],type='update')\n content['update'] = '2018-05-22'\n self.pg.update('res_info', ['id = %s' % val.id], content)\n # print(content[])\n # break\n\n\nif __name__ == '__main__':\n db = DouBan()\n db.action()\n # db.getPoster()\n # db.getPoster('3450654','157')\n # print(db.search('Paws P.I.','2018'))\n # res = db.webSearch(13664,'Paws P.I.','2018','http://www.imdb.com/title/tt8064262')\n # print(res)\n # db.getContent('ddd','ddd')\n # content = db.search('Messengers 2: The Scarecrow',2009)\n # content = db.search('xxxxxxxxxxxxxxxxxxxxx:xxx',2017)\n # print(content)\n # for k,v in content.items():\n # print(k, ' : ', v)\n # print()\n","sub_path":"DouBan.py","file_name":"DouBan.py","file_ext":"py","file_size_in_byte":15511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"461261339","text":"from sklearn.model_selection import train_test_split\nimport pandas as pd\nimport xgboost as xgb\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(\"../data/preprocessed/precrocessed2.csv\")\nres = pd.read_csv(\"../data/yancheng_testA_20171225.csv\")\nres_df = res.copy()\nres_df = pd.merge(res_df[['predict_date','class_id']],df,how='left',on=['class_id'])\nres_df = res_df.drop('sale_date',axis=1)\nres_df.rename(columns={'predict_date':'sale_date'},inplace = True)\n\n\n#print(df.sale_quantity.skew())\n\ndf_y = df['sale_quantity']\ndf_x = df.drop('sale_quantity',axis=1)\n\n#train_x,test_x,train_y,test_y = train_test_split(df_x,df_y,test_size=0.3)\n\nxgb_params = {\n 'eta' : 0.05,\n 'booster': 'gbtree',\n 'max_depth' : 8,\n 'subsample' : 0.7,\n 'colsample_bytree' : 0.7,\n 'objective' : 'reg:linear',\n 'eval_metric' : \"rmse\",\n 'silent' : 0,\n}\n\ntrain_y_log = np.log(df_y)\n#test_y_log = np.log(test_y)\n\ndtrain_log = xgb.DMatrix(df_x,train_y_log)\n#dvalid_log = xgb.DMatrix(test_x,test_y_log)\n#watchlist = [(dtrain_log,'train'),(dvalid_log,'eval')]\n\n#print(train_y_log.skew())\n\n#cv_output_log = xgb.cv(xgb_params,dtrain_log,num_boost_round=10000,early_stopping_rounds=5000,verbose_eval=50,show_stdv=False)\n#num_boost_rounds = len(cv_output_log)\n\n\nmodel = xgb.train(xgb_params,dtrain_log,num_boost_round=100000)\n\npredict = res_df.drop('sale_quantity',axis=1)\ndpredict = xgb.DMatrix(predict)\npred_quantity_log = model.predict(dpredict)\npred_quantity = np.exp(pred_quantity_log)\nsub_ = pd.DataFrame({u'sale_quantity':pred_quantity})\nres_df['sale_quantity'] = sub_['sale_quantity']\n\nresult = res_df.groupby(['class_id']).sale_quantity.sum().round()\npredict = result.reset_index()\nresult = pd.merge(res[['predict_date','class_id']],predict,how='left',on=['class_id'])\nresult = result.fillna(0)\nresult.columns = ['predict_date','class_id','predict_quantity']\nresult.to_csv(\"../result/xgb0125_without_watchlist.csv\",index=False,header=True)\n\n\n#fig,ax = plt.subplot(1,1,figsize=(8,13))\n#xgb.plot_importance(model,max_num_features=50,height=0.5,ax=ax)\n#plt.show()","sub_path":"code/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"394581762","text":"# -*-coding=utf-8 -*-\nfrom sklearn.neighbors import KNeighborsClassifier\nimport numpy as np\nimport scipy.io as sio\nimport random\nimport matplotlib.pyplot as plt\nimport os\n\npath = os.path.join(\"C:\\JY\\Dataset\\Hyperspectral-Image\")# change this path for your dataset\nPaviaU = os.path.join(path,'PaviaU.mat')\nPaviaU_gt = os.path.join(path,'PaviaU_gt.mat')\nmethod_path = 'KNN'\n\n# 加载数据\ndata = sio.loadmat(PaviaU)\ndata_gt = sio.loadmat(PaviaU_gt)\nim = data['paviaU'] # pair with your dataset\nimGIS = data_gt['paviaU_gt']# pair with your dataset\n# 归一化\nim = (im - float(np.min(im)))\nim = im/np.max(im)\n\nsample_num = 200\ndeepth = im.shape[2]\nclasses = np.max(imGIS)\ntest_bg = False\n\n# 模型参数,neighbor k of knn\nneighuour_num = 5\n\ndata_pos = {}\ntrain_pos = {}\ntest_pos = {}\n\nfor i in range(1,classes+1):\n data_pos[i]=[]\n train_pos[i]=[]\n test_pos[i] = []\n\nfor i in range(imGIS.shape[0]):\n for j in range(imGIS.shape[1]):\n for k in range(1,classes+1):\n if imGIS[i,j]==k:\n data_pos[k].append([i,j])\n continue\n\nfor i in range(1,classes+1): \n indexies = random.sample(range(len(data_pos[i])),sample_num)\n for k in range(len(data_pos[i])):\n if k not in indexies:\n test_pos[i].append(data_pos[i][k])\n else:\n train_pos[i].append(data_pos[i][k])\n\ntrain = []\ntrain_label = []\ntest = []\ntest_label = []\n\nfor i in range(1,len(train_pos)+1):\n for j in range(len(train_pos[i])):\n row,col = train_pos[i][j]\n train.append(im[row,col])\n train_label.append(i)\n\nfor i in range(1,len(test_pos)+1):\n for j in range(len(test_pos[i])):\n row,col = test_pos[i][j]\n test.append(im[row,col])\n test_label.append(i)\nif not os.path.exists(os.path.join(method_path,'result')):\n os.makedirs(os.path.join(method_path,'result'))\n\nclf = KNeighborsClassifier(n_neighbors=neighuour_num)\ntrain = np.asarray(train)\ntrain_label = np.asarray(train_label)\nclf.fit(train,train_label)\nC = np.max(imGIS)\n\nmatrix = np.zeros((C,C))\nfor i in range(len(test)):\n r = clf.predict(test[i].reshape(-1,len(test[i])))\n matrix[r-1,test_label[i]-1] += 1\n\nac_list = []\nfor i in range(len(matrix)):\n ac = matrix[i, i] / sum(matrix[:, i])\n ac_list.append(ac)\n print(i+1,'class:','(', matrix[i, i], '/', sum(matrix[:, i]), ')', ac)\nprint('confusion matrix:')\nprint(np.int_(matrix))\nprint('total right num:', np.sum(np.trace(matrix)))\nprint('total test num:',np.sum(matrix))\naccuracy = np.sum(np.trace(matrix)) / np.sum(matrix)\nprint('Overall accuracy:', accuracy)\n# kappa\nkk = 0\nfor i in range(matrix.shape[0]):\n kk += np.sum(matrix[i]) * np.sum(matrix[:, i])\npe = kk / (np.sum(matrix) * np.sum(matrix))\npa = np.trace(matrix) / np.sum(matrix)\nkappa = (pa - pe) / (1 - pe)\nac_list = np.asarray(ac_list)\naa = np.mean(ac_list)\nprint('Average accuracy:',aa)\nprint('Kappa:', kappa)\nsio.savemat(os.path.join('result', 'result.mat'), {'oa': accuracy,'aa':aa,'kappa':kappa,'ac_list':ac_list,'matrix':matrix})\niG = np.zeros((imGIS.shape[0],imGIS.shape[1]))\nfor i in range(imGIS.shape[0]):\n for j in range(imGIS.shape[1]):\n if imGIS[i,j] == 0:\n if test_bg:\n iG[i,j] = (clf.predict(im[i,j].reshape(-1,len(im[i,j]))))\n else:\n iG[i,j]=0\n else:\n iG[i,j] = (clf.predict(im[i,j].reshape(-1,len(im[i,j]))))\nif test_bg:\n iG[0,0] = 0\nde_map = iG[::-1]\nfig, _ = plt.subplots()\nheight, width = de_map.shape\nfig.set_size_inches(width/100.0, height/100.0)\nplt.gca().xaxis.set_major_locator(plt.NullLocator())\nplt.gca().yaxis.set_major_locator(plt.NullLocator())\nplt.subplots_adjust(top=1,bottom=0,left=0,right=1,hspace=0,wspace=0)\nplt.axis('off')\nplt.axis('equal')\nplt.pcolor(de_map, cmap='jet')\nplt.savefig(os.path.join('result', 'decode_map.png'),format='png',dpi=600)#bbox_inches='tight',pad_inches=0)\nplt.close()\nprint('decode map get finished')\n","sub_path":"KNN/knn.py","file_name":"knn.py","file_ext":"py","file_size_in_byte":3929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"21109856","text":"import os\nimport sys\nimport collections\nimport json\nimport shutil\nimport subprocess\nimport glob\n\nfrom bs4 import BeautifulSoup as bs4\nimport requests\n\nfile = sys.argv[0]\n\npathname = os.path.dirname(file)\n#print(pathname)\n#print('running from %s' % os.path.abspath(pathname))\n#print(file)\n\nrunning_from_path = os.path.abspath(pathname) + '/'\n#print(running_from_path)\n\n\ndef copy_to_remote(copied_file, remote_username, remote_pass, remote_hostname, remote_port, escaped_remote):\n #remote_username = 'u0_a97'\n #remote_hostname = '192.168.1.36'\n #escaped_remote = '/storage/1527-15E5/Android/data/com.termux/files/youtube/ashin-zaw-ti-ka-nyaungdone/'\n copied_file = running_from_path + copied_file\n \n print(glob.glob(copied_file))\n \n if os.path.isfile(copied_file):\n cmd = \"sshpass -p %s /usr/bin/rsync -P --partial -avzzz %s -e 'ssh -p %s' %s@%s:'%s'\" % (remote_pass, copied_file, remote_port, remote_username, remote_hostname, escaped_remote)\n\n result = 1 \n while result != 0:\n result = subprocess.Popen(cmd,shell=True).wait()\n #text = result.communicate()[0]\n #returncode = result.returncode\n \n print(result)\n\n if os.path.isfile(copied_file):\n print('Moving file...', copied_file)\n shutil.move(copied_file, '%sfinished/' % (running_from_path)) \n\n\ndef get_line_from_file(file_in):\n file_in = running_from_path + file_in\n for f in open(file_in, 'r'):\n yield f.split('|')[1]\n\n\ndef check_link(url):\n r = requests.get(url)\n return r.status_code\n \n\n\ndef splited_lines_generator(lines, n):\n for i in range(0, len(lines), n):\n yield lines[i: i + n]\n\ndef get_splited_lines(file_in, line_num):\n \n file_in = running_from_path + file_in\n files = [f.strip('\\n') for f in open(file_in, 'r') if len(f) > 2]\n \n \n for index, lines in enumerate(splited_lines_generator(files, line_num)):\n with open('{}{}.txt'.format(running_from_path, str(index)), 'w') as f:\n f.write('\\n'.join(lines))\n \n\ndef change(num):\n mm = ['၀','၁','၂','၃', '၄', '၅', '၆','၇','၈','၉']\n strmm = ''\n mmlist = list(map(int, str(num)))\n \n for i in mmlist:\n strmm += mm[i]\n\n return strmm\n \ndef get_json(file_in_1, file_in_2, file_out, playlist, description_title, source=''):\n\n file_in_1 = running_from_path+file_in_1\n file_in_2 = running_from_path+file_in_2\n \n file_out = running_from_path+file_out\n \n titles = [title.strip('\\n') for title in open(file_in_1)]\n #get_count = len(titles)\n #print(get_count)\n\n\n descriptions = [title.strip('\\n') for title in open(file_in_2)]\n #get_count_descriptions = len(descriptions)\n #print(get_count_descriptions)\n\n description_title = description_title\n source = source\n\n\n results = [] \n playlist = playlist\n\n for title, description in zip(titles, descriptions):\n \n #print(title)\n if len(title.split('|')[2]) > 100:\n dict_title = {\n \"playlist\" : playlist,\n \"title\": \"{}\".format(title.split('|')[2]),\n \"description\": \"{}\\n{}{}\".format(description_title, description, source), \n \"id\": \"{}\".format( title.split('|')[0].split('.')[0] )\n } \n\n #print('{}။{} greater than 100'.format(change(counter), title.split('။')[1]))\n print('{} greater than 100'.format(title.split('|')[2]))\n results.append(dict_title)\n else:\n if len(description.split('|')[1]) > 0:\n \n dict_title = {\n \"playlist\" : playlist,\n \"title\": \"{}\".format(title.split('|')[2]),\n \"description\": \"{}\\n{} ({}){}\".format(description_title, description.split('|')[0], description.split('|')[1], source), \n \"id\": \"{}\".format(title.split('|')[0].split('.')[0])\n } \n\n results.append(dict_title)\n else:\n dict_title = {\n \"playlist\" : playlist,\n \"title\": \"{}\".format(title.split('|')[2]),\n \"description\": \"{}\\n{} {}\".format(description_title, description.split('|')[0], source), \n \"id\": \"{}\".format(title.split('|')[0].split('.')[0])\n } \n\n results.append(dict_title) \n \n \n data = json.dumps(results, indent=4) \n with open(file_out, encoding='utf-8', mode='w') as f:\n json.dump(data, f) \n \n# convert result to json \ndef get_json_fb(file_in_1, file_in_2, file_out, playlist, description_title, source=''):\n\n file_in_1 = running_from_path+file_in_1\n file_in_2 = running_from_path+file_in_2\n \n file_out = running_from_path+file_out\n \n titles = [title.strip('\\n') for title in open(file_in_1)]\n #get_count = len(titles)\n #print(get_count)\n\n\n descriptions = [title.strip('\\n') for title in open(file_in_2)]\n #get_count_descriptions = len(descriptions)\n #print(get_count_descriptions)\n\n description_title = description_title\n source = source\n\n\n results = [] \n playlist = playlist\n\n for title, description in zip(titles, descriptions):\n \n #print(title)\n \n if len(\"{}\\n{} {}\".format(description_title, description.split('|')[2], source)) > 5000:\n print(len(\"{}\\n{} {}\".format(description_title, description.split('|')[2], source)))\n print(\"{}\\n{}\\n{} {}\".format(description.split('|')[0].split('.')[0],description_title, description.split('|')[2], source))\n \n if len(title) > 100:\n dict_title = {\n \"playlist\" : playlist,\n \"title\": \"{}\".format(title),\n \"description\": \"{}\\n{}{}\".format(description_title, description, source), \n \"id\": \"{}\".format( description.split('|')[0].split('.')[0] )\n } \n\n #print('{}။{} greater than 100'.format(change(counter), title.split('။')[1]))\n print('{} greater than 100'.format(title))\n results.append(dict_title)\n else:\n \n dict_title = {\n \"playlist\" : playlist,\n \"title\": \"{}\".format(title),\n \"description\": \"{}\\n{} {}\".format(description_title, description.split('|')[2], source), \n \"id\": \"{}\".format(description.split('|')[0].split('.')[0])\n } \n\n results.append(dict_title) \n \n \n data = json.dumps(results, indent=4) \n with open(file_out, encoding='utf-8', mode='w') as f:\n json.dump(data, f) \n \n \ndef convert_myanmar_number(file_in, file_out, count=1, desc=None):\n\n file_in = running_from_path+file_in\n \n file_out = running_from_path+file_out\n \n titles = [title.strip('\\n\\n\\n') for title in open(file_in)]\n \n \n if desc is None:\n \n with open(file_out, 'w') as f:\n counter = count \n for title in titles: \n \n if title.find('။') > 0: # found\n f.write( '{}။{}|\\n'.format(change(counter), title.split('|')[2].split('။')[1]) )\n else:\n f.write( '{}။{}|\\n'.format(change(counter), title.split('|')[2]) ) \n \n counter += 1 \n else:\n \n with open(file_out, 'w') as f:\n counter = count\n for dd in desc.items():\n \n for title in titles:\n \n number = int(title.split('|')[0].split('.')[0])\n #print(type(number))\n if dd[1][1] <= number <= dd[1][2]:\n print(dd[1][0])\n print(title.split('|')[2]) \n \n try:\n \n f.write('{}။{}|{}\\n'.format(change(counter), title.split('|')[2].split('။')[1], dd[1][0]))\n #print('{}။{}|\\n'.format(change(counter), title.split('|')[2].split('။')[1]))\n #print(title.split('|')[2])\n \n except IndexError as err:\n f.write('{}။{}|\\n'.format(change(counter), title.split('|')[2]))\n #print('{}။{}|\\n'.format(change(counter), title.split('|')[2]))\n \n \n #print('{}။{}'.format(change(counter), title))\n \n counter += 1 \n \n\ndef check_duplicate(file_in):\n\n file_in = running_from_path+file_in\n \n \n urls = [url.split('|')[1] for url in open(file_in)]\n \n for key, val in collections.Counter(urls).items():\n if val > 1:\n print(key, val)\n \ndef update_raw_titles_links(file_in, file_out, count=1):\n\n file_in = running_from_path+file_in\n #print(file_in)\n file_out = running_from_path+file_out\n \n lines = [f.strip('\\n') for f in open(file_in)]\n \n with open(file_out, 'w') as f:\n counter = count\n for line in lines:\n #print(line.split('|')[1])\n #media = line.split('|')[0]\n url = line.split('|')[1]\n ext = url.split('.')[-1]\n media = '{:03d}'.format(counter)\n title = line.split('|')[2]\n if title.find('။') > 0: # found\n f.write( '{}.{}|{}|{}။{}\\n'.format(media, ext, url, change(counter), title.split('။')[1]) )\n else:\n f.write( '{}.{}|{}|{}။{}\\n'.format(media, ext, url, change(counter), title) ) \n \n counter += 1\n \ndef update_raw_reversed_titles_links(file_in, file_out, count=1):\n\n file_in = running_from_path+file_in\n #print(file_in)\n file_out = running_from_path+file_out\n \n lines = [f.strip('\\n') for f in open(file_in)]\n \n with open(file_out, 'w') as f:\n counter = count\n for line in reversed(lines):\n #media = line.split('|')[0]\n url = line.split('|')[1]\n ext = url.split('.')[-1]\n media = '{:03d}'.format(counter)\n title = line.split('|')[2]\n if title.find('။') > 0: # found\n f.write( '{}.{}|{}|{}။{}\\n'.format(media, ext, url, change(counter), title.split('။')[1]) )\n else:\n f.write( '{}.{}|{}|{}။{}\\n'.format(media, ext, url, change(counter), title) ) \n \n counter += 1 \n \n \n\n'''\nparam -> get_url.txt\n'''\ndef get_html_mp4(file_in, file_out, count=1):\n \n file_in = running_from_path+file_in\n #print(file_in)\n file_out = running_from_path+file_out\n \n text = open(file_in, 'r').read()\n\n soup = bs4(text, 'html.parser')\n \n #files = param_folder + 'titles_links.txt'\n #files = 'titles_links.txt'\n\n count = count\n with open(file_out, 'w') as fd:\n\n for key in soup.find_all('a'):\n ext = key.get('href').split('.')[-1]\n #print(ext)\n if ext in ['mp4', 'wmv']:\n #print(ext)\n #print(key.get('href'))\n counter = '{:03d}'.format(count)\n fd.write('{}.{}|{}|{}\\n'.format(counter, ext, ''.join(key.get('href').split()), ' '.join(key.get_text().split())))\n\n count += 1\n \n'''\nparam -> get_url.txt\n'''\ndef get_html_mp3(file_in, file_out, count=1):\n\n file_in = running_from_path+file_in\n \n file_out = running_from_path+file_out\n\n text = open(file_in, 'r').read()\n\n soup = bs4(text, 'html.parser')\n \n \n count = count\n \n with open(file_out, 'w') as fd:\n\n for key in soup.find_all('a'):\n if '.mp3' in key.get('href'):\n counter = '{:03d}'.format(count)\n fd.write('{}.mp3|{}|{}\\n'.format(counter, ''.join(key.get('href').split()), ' '.join(key.get_text().split())))\n\n count += 1 \n'''\nif __name__ == '__main__':\n get_html_mp4('get_url.txt', 3)\n'''\n \n\n\n\n","sub_path":"future/crawler/crawler/stack.py","file_name":"stack.py","file_ext":"py","file_size_in_byte":12598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"286958145","text":"# -*- coding: utf-8 -*-\n# @Author: Guillaume Viejo\n# @Date: 2022-07-07 11:11:16\n# @Last Modified by: Guillaume Viejo\n# @Last Modified time: 2023-03-03 16:57:10\nimport numpy as np\nimport pandas as pd\nimport pynapple as nap\nfrom pylab import *\nfrom functions import *\nimport sys\nfrom pycircstat.descriptive import mean as circmean\nimport _pickle as cPickle\nfrom matplotlib.gridspec import GridSpec\nfrom itertools import combinations\nfrom scipy.stats import zscore\nfrom sklearn.linear_model import LogisticRegression, LogisticRegressionCV\nfrom sklearn.preprocessing import StandardScaler\nfrom xgboost import XGBClassifier\nfrom sklearn.decomposition import PCA, FastICA, KernelPCA\nfrom sklearn.manifold import Isomap\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.metrics import f1_score\n\n\n############################################################################################### \n# GENERAL infos\n###############################################################################################\n# data_directory = '/mnt/DataRAID2/'\ndata_directory = '/mnt/ceph/users/gviejo'\ndatasets = np.genfromtxt(os.path.join(data_directory,'datasets_LMN_ADN.list'), delimiter = '\\n', dtype = str, comments = '#')\n\nsta_r = {'adn':[], 'lmn':[]} # trigger average of proba from the other structure spikes\ncc_down = {'adn':[], 'lmn':[]} # cross corr of adn and lmn / adn down states\nsta_r_down = {'adn':[], 'lmn':[]} # proba trigger on down states\n\n\n\nfor s in datasets:\n print(s)\n ############################################################################################### \n # LOADING DATA\n ###############################################################################################\n path = os.path.join(data_directory, s)\n data = nap.load_session(path, 'neurosuite')\n spikes = data.spikes\n position = data.position\n wake_ep = data.epochs['wake']\n sws_ep = data.read_neuroscope_intervals('sws')\n rem_ep = data.read_neuroscope_intervals('rem')\n down_ep = data.read_neuroscope_intervals('down')\n idx = spikes._metadata[spikes._metadata[\"location\"].str.contains(\"adn|lmn\")].index.values\n spikes = spikes[idx]\n \n ############################################################################################### \n # COMPUTING TUNING CURVES\n ###############################################################################################\n tuning_curves = nap.compute_1d_tuning_curves(spikes, position['ry'], 120, minmax=(0, 2*np.pi), ep = position.time_support.loc[[0]])\n tuning_curves = smoothAngularTuningCurves(tuning_curves) \n tcurves = tuning_curves\n SI = nap.compute_1d_mutual_info(tcurves, position['ry'], position.time_support.loc[[0]], (0, 2*np.pi))\n peaks = pd.Series(index=tcurves.columns,data = np.array([circmean(tcurves.index.values, tcurves[i].values) for i in tcurves.columns]))\n\n spikes.set_info(SI, peaks=peaks)\n\n adn = list(spikes.getby_category(\"location\")[\"adn\"].getby_threshold(\"SI\", 0.3).index)\n lmn = list(spikes.getby_category(\"location\")[\"lmn\"].getby_threshold(\"SI\", 0.1).index)\n\n # figure()\n # for i, n in enumerate(spikes.index):\n # subplot(10,10,i+1, projection='polar') \n # if n in adn:\n # plot(tcurves[n], color = 'red')\n # elif n in lmn:\n # plot(tcurves[n], color = 'green')\n # else:\n # plot(tcurves[n], color = 'grey')\n # xticks([])\n # yticks([])\n\n # sys.exit()\n\n tokeep = adn+lmn\n tokeep = np.array(tokeep)\n spikes = spikes[tokeep] \n\n velocity = computeLinearVelocity(position[['x', 'z']], position.time_support.loc[[0]], 0.2)\n newwake_ep = velocity.threshold(0.001).time_support \n\n ############################################################################################### \n # LOGIT\n ###############################################################################################\n groups = spikes.getby_category(\"location\")\n\n if len(groups['adn'])>6 and len(groups['lmn'])>8:\n\n ## MUA ########\n mua = {\n 0:nap.Ts(t=np.sort(np.hstack([groups['adn'][j].index.values for j in groups['adn'].index]))),\n 1:nap.Ts(t=np.sort(np.hstack([groups['lmn'][j].index.values for j in groups['lmn'].index])))}\n\n mua = nap.TsGroup(mua, time_support = spikes.time_support)\n\n ## DOWN CENTER ######\n down_center = (down_ep[\"start\"] + (down_ep['end'] - down_ep['start'])/2).values\n down_center = nap.TsGroup({\n 0:nap.Ts(t=down_center, time_support = sws_ep)\n })\n\n ## SHUFFLING #####\n bin_size_wake = 0.1\n bin_size_sws = 0.01\n\n gmap = {'adn':'lmn', 'lmn':'adn'}\n\n for i, g in enumerate(gmap.keys()):\n\n # WAKE \n count = groups[g].count(bin_size_wake, newwake_ep)\n rate = count/bin_size_wake\n rate = rate.rolling(window=100,win_type='gaussian',center=True,min_periods=1, axis = 0).mean(std=2)\n rate_wak = StandardScaler().fit_transform(rate)\n\n # WAKE SHUFFLE\n count = nap.randomize.shuffle_ts_intervals(groups[g]).count(bin_size_wake, newwake_ep)\n rate = count/bin_size_wake\n rate = rate.rolling(window=100,win_type='gaussian',center=True,min_periods=1, axis = 0).mean(std=2)\n rate_shu = StandardScaler().fit_transform(rate)\n\n # # adding a zero vector\n # rate_shu = np.vstack((rate_shu, np.zeros((1,rate_shu.shape[1]))))\n \n # SWS\n count = groups[g].count(bin_size_sws, sws_ep)\n time_index = count.index.values\n rate = count/bin_size_sws\n rate = rate.rolling(window=100,win_type='gaussian',center=True,min_periods=1, axis = 0).mean(std=1)\n rate_sws = StandardScaler().fit_transform(rate)\n\n X = np.vstack((rate_wak, rate_shu))\n y = np.hstack((np.zeros(len(rate_wak)), np.ones(len(rate_shu)))).astype(np.int32)\n Xt = rate_sws\n\n #####################\n bst = XGBClassifier(\n n_estimators=100, max_depth=20, \n learning_rate=0.0001, objective='binary:logistic', \n random_state = 0,# booster='dart',\n #eval_metric=f1_score)\n )\n bst.fit(X, y) \n # tmp = bst.predict_proba(Xt)[:,0]\n tmp = 1.0-bst.predict(Xt)\n ######################\n\n p = nap.Tsd(t = time_index, d = tmp, time_support = sws_ep)\n\n # figure()\n # subplot(121)\n # scatter(a[:, 0], a[:, 1], marker=\".\", c=y, alpha=0.4)\n # title(\"Truth\")\n # subplot(122)\n # scatter(a[:, 0], a[:, 1], marker=\".\", c=tmp, alpha=0.4)\n # title(\"Predicted\")\n\n # STA / neurons \n sta_neurons = nap.compute_event_trigger_average(groups[gmap[g]], p, bin_size_sws, (-0.4, 0.4), sws_ep)\n #sta_neurons = nap.compute_event_trigger_average(mua[[i]], p, bin_size_sws, (-0.4, 0.4), sws_ep) \n #sta_neurons = sta_neurons.as_dataframe().apply(zscore) \n \n # STA / down\n sta_down = nap.compute_event_trigger_average(down_center, p, bin_size_sws, (-0.4, 0.4), sws_ep)\n #sta_down = sta_down.as_dataframe().apply(zscore) \n\n # CC / down\n cc_d = nap.compute_eventcorrelogram(groups[g], down_center[0], bin_size_sws, 0.4, ep=sws_ep)\n\n ### SAVING ####\n sta_r[g].append(sta_neurons) # trigger average of reactivtion from the other structure spikes\n cc_down[g].append(cc_d) # cross corr of adn and lmn / adn down states\n sta_r_down[g].append(sta_down) # reactivation trigger on down states\n\nfor i, g in enumerate(['adn', 'lmn']):\n sta_r[g] = pd.concat(sta_r[g], 1)\n sta_r_down[g] = pd.concat(sta_r_down[g], 1)\n cc_down[g] = pd.concat(cc_down[g], 1)\n\n# sys.exit()\n\n\nfigure()\nsubplot(1,3,1)\nplot(sta_r['adn'].mean(1), label = 'adn r')\nplot(sta_r['lmn'].mean(1), label = 'lmn r')\nlegend()\nxlabel(\"Time from the other\")\ntitle(\"STA proba attractorness\")\n\nsubplot(1,3,2)\nplot(sta_r_down['adn'].mean(1), label = 'adn r')\nplot(sta_r_down['lmn'].mean(1), label = 'lmn r')\nxlabel(\"Time from ADN down center\")\ntitle(\"STA proba attractorness\")\nlegend()\n\nsubplot(1,3,3)\nplot(cc_down['adn'].mean(1), label = 'adn')\nplot(cc_down['lmn'].mean(1), label = 'lmn')\nlegend()\nxlabel(\"Time from ADN down center\")\ntitle(\"CC\")\n\n\nfigure()\nsubplot(2,2,1)\nplot(sta_r['adn'], label = 'adn r')\ntitle(\"STA proba attractorness\")\nxlabel(\"Time from LMN spikes\")\nlegend()\n\nsubplot(2,2,3)\nplot(sta_r['lmn'], label = 'lmn r')\nlegend()\nxlabel(\"Time from ADN spikes\")\n\n\nsubplot(2,2,2)\nplot(sta_r_down['adn'].mean(1), label = 'adn r')\ntitle(\"STA proba attractorness\")\n\nsubplot(2,2,4)\nplot(sta_r_down['lmn'].mean(1), label = 'lmn r')\nxlabel(\"Time from ADN down center\")\n\nshow()\n","sub_path":"pyna/main_pyna_LMN_ADN_LOGIT.py","file_name":"main_pyna_LMN_ADN_LOGIT.py","file_ext":"py","file_size_in_byte":9018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"562052598","text":"# -*- coding: utf-8 -*-\nimport pprint, re, requests, scrapy\n\nfrom forum_duanzibar_com.items import ForumDuanzibarComItem\nfrom forum_duanzibar_com.settings import API_SETTINGS\n\n\nclass CosplayTorikoComSpider(scrapy.Spider):\n\tname = \"cosplay_toriko_com\"\n\tallowed_domains = [\"cosplay-toriko.com\"]\n\tstart_urls = ['http://cosplay-toriko.com']\n\n\tcategories = []\n\tfid = 61\n\tpages = 20\n\t\n\n\tdef image_handle_redirect(self, image_url, headers={}):\n\t\tresponse = requests.head(image_url, headers=headers)\n\t\tif response.status_code in range(300, 400):\n\t\t\timage_url = response.headers[\"Location\"]\n\t\treturn image_url\n\n\n\tdef parse(self, response):\n\t\t# for page in range(self.pages - 10, self.pages + 1)[::-1]:\n\t\tfor page in range(self.pages + 1)[::-1]:\n\t\t\tif page > 0:\n\t\t\t\turl = \"{}/category/101/{}/\".format(self.start_urls[0], page)\n\t\t\t\tyield scrapy.Request(url, callback=self.parsePage)\n\n\n\tdef parsePage(self, response):\n\t\tposts = response.xpath(\"\"\"//div[@id=\"postlist\"]/div[@class=\"item_box\"]\"\"\")\n\t\tfor post in posts:\n\t\t\titem = ForumDuanzibarComItem()\n\t\t\ttitle = post.xpath(\"\"\"./div[@class=\"clearfix\"]/div/h3/a/text()\"\"\").extract_first()\n\t\t\tarticle_url = \"{}{}\".format(self.start_urls[0], post.xpath(\"\"\"./div[@class=\"clearfix\"]/div/h3/a/@href\"\"\").extract_first())\n\t\t\tthumbnail_src = \"{}{}\".format(self.start_urls[0], post.xpath(\"\"\"./div[@class=\"clearfix\"]/p/a/img/@src\"\"\").extract_first())\n\t\t\titem['fid'] = self.fid\n\t\t\titem['response_url'] = response.url\n\t\t\titem['title'] = title\n\t\t\titem['categories'] = self.categories\n\t\t\titem['image_urls'] = []\n\t\t\titem['thumbnail_url'] = self.image_handle_redirect(thumbnail_src, {'Referer': article_url})\n\t\t\titem['image_urls'].append(item['thumbnail_url'])\n\t\t\titem['content_url'] = article_url\n\t\t\titem['referer'] = article_url\n\t\t\titem['contents'] = []\n\t\t\tyield scrapy.Request(item['content_url'], meta={'item': item}, callback=self.parseContent)\n\n\n\tdef parseContent(self, response):\n\t\titem = response.meta['item']\n\t\timages = response.xpath(\"\"\"//div[@id=\"post_pict_box\"]/ul/li\"\"\")\n\t\tfor img in images:\n\t\t\timage_src = self.image_handle_redirect(\"{}{}\".format(self.start_urls[0], img.xpath(\"\"\"./p[@class=\"post_img\"]/a/@href\"\"\").extract_first()))\n\t\t\tif image_src:\n\t\t\t\titem['contents'].append(image_src)\n\t\t\t\tif image_src not in item['image_urls']:\n\t\t\t\t\titem['image_urls'].append(image_src)\n\t\tyield item","sub_path":"forum_duanzibar_com/forum_duanzibar_com/spiders/cosplay_toriko_com.py","file_name":"cosplay_toriko_com.py","file_ext":"py","file_size_in_byte":2320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"70731045","text":"from django import forms\nfrom django.contrib.auth.forms import UserCreationForm, UserChangeForm\n\nfrom .models import User\n\n\nclass CustomUserCreationForm(UserCreationForm):\n class Meta(UserCreationForm.Meta):\n model = User\n fields = ('email', )\n\n\nclass CustomUserChangeForm(UserChangeForm):\n class Meta(UserChangeForm.Meta):\n model = User\n fields = ('email', )\n\n\nclass UserRegistrationForm(CustomUserCreationForm):\n first_name = forms.CharField(max_length=60)\n last_name = forms.CharField(max_length=60)\n\n class Meta(CustomUserCreationForm.Meta):\n fields = CustomUserCreationForm.Meta.fields + ('first_name', 'last_name')\n\n\nclass ProfileUpdateForm(forms.ModelForm):\n class Meta:\n model = User\n fields = ('first_name', 'last_name')\n","sub_path":"mailauth/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"357371682","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\n# useful for handling different item types with a single interface\nfrom itemadapter import ItemAdapter\nfrom pymongo import MongoClient\n\n\nclass Lesson8ScrapyPipeline:\n def __init__(self):\n client = MongoClient('localhost', 27017)\n self.mongo_base = client.instagram\n\n def process_item(self, item, spider):\n\n if item['being_scraped'] == item['profile_owner']:\n # получаем название коллекции по типу randomuser1_followers/following\n collection = self.mongo_base[item['being_scraped']+'_followers']\n del item['being_scraped']\n\n elif item['being_scraped'] == item['follower']:\n collection = self.mongo_base[item['being_scraped'] + '_following']\n del item['being_scraped']\n\n collection.insert_one(item)\n return item\n","sub_path":"lesson8_scrapy/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"97807511","text":"#coding=utf8\r\nu''' 实现记录日志功能 \r\n默认情况下，logging将日志打印到屏幕，日志级别为WARNING；\r\n日志级别大小关系为：CRITICAL > ERROR > WARNING > INFO > DEBUG > NOTSET\r\n''' \r\n\r\nimport logging,os\r\nimport config\r\nfrom logging.handlers import RotatingFileHandler\r\n\r\nBASE_DIR = config.readConfig(\"config.conf\", \"pro_dir\", \"pro_dir\")\r\n\r\nclass Log():\r\n log_name = config.readConfig(\"config.conf\", \"log\", \"log_name\")\r\n \r\n def __init__(self,file_name=log_name):\r\n self.log_dir = os.path.join(BASE_DIR,config.readConfig(\"config.conf\", \"log\", \"log_dir\"))\r\n self.log_size = int(config.readConfig(\"config.conf\", \"log\", \"log_size\")) * 1024 * 1024\r\n self.log_backup_count = int(config.readConfig(\"config.conf\", \"log\", \"log_backup_count\"))\r\n self.log_name = file_name\r\n self.log_level = config.readConfig(\"config.conf\", \"log\", \"log_level\")\r\n self.log_level = self.log_level.lower() #防止大小写不一致的问题，大写转小写\r\n \r\n if self.log_level == \"notest\":\r\n self.log_level = logging.NOTSET\r\n \r\n elif self.log_level == \"debug\":\r\n self.log_level = logging.DEBUG\r\n \r\n elif self.log_level == \"info\":\r\n self.log_level = logging.INFO\r\n \r\n elif self.log_level == \"warning\":\r\n self.log_level = logging.WARNING\r\n \r\n elif self.log_level == \"error\":\r\n self.log_level = logging.ERROR\r\n \r\n elif self.log_level == \"critical\":\r\n self.log_level = logging.CRITICAL\r\n \r\n else:\r\n self.log_level = logging.INFO\r\n \r\n '''\r\n logging.basicConfig(level=self.log_level,\r\n format='%(asctime)s %(filename)s ---> method:%(funcName)s[line:%(lineno)d] %(levelname)s %(message)s',\r\n datefmt='%Y-%m-%d %H:%M:%S',\r\n filename = os.path.join(self.log_dir,self.log_name),\r\n filemod='w')\r\n '''\r\n logger = logging.getLogger()\r\n logger.setLevel(self.log_level)\r\n handler = RotatingFileHandler(os.path.join(self.log_dir,self.log_name),\r\n maxBytes=self.log_size,\r\n backupCount=self.log_backup_count)\r\n formatter = logging.Formatter('%(asctime)s %(filename)s ---> method:%(funcName)s[line:%(lineno)d] %(levelname)s %(message)s')\r\n handler.setFormatter(formatter)\r\n logger.addHandler(handler)\r\n self.logger = logger\r\n'''\r\nif __name__ == \"__main__\":\r\n log = Log()\r\n log.info(\"this is a test info message,wa ha ha O(∩_∩)O~\")\r\n'''","sub_path":"selenium_learning/common_modules/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"33061124","text":"from common import *\nfrom authentication import *\nimport authentication\nfrom sync import sync_methods\nfrom handlers import tagHandler, syncHandler\n\n@accepted_roles(admin_roles())\ndef get():\n tags = [\n {\"name\": \"YEAR\", \"address\": \"%MW0\", \"type\": \"value\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"MONTH\", \"address\": \"%MW1\", \"type\": \"value\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"DAY\", \"address\": \"%MW2\", \"type\": \"value\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"HOUR\", \"address\": \"%MW3\", \"type\": \"value\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"MINUTE\", \"address\": \"%MW4\", \"type\": \"value\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"SECOND\", \"address\": \"%MW5\", \"type\": \"value\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"ALARM_MINUTE\", \"address\": \"%MW4:X0\", \"type\": \"alarm\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"},\n {\"name\": \"WARNING_SECOND\", \"address\": \"%MW5:X1\", \"type\": \"warning\", \"controller_ip\": \"192.168.0.1\", \"installation\":\"66002174487292\"}\n ]\n res = []\n for tag in tags:\n res.append(tagHandler.insert(tag))\n for i in range(0,255):\n addr_num = i % 12\n name = \"gen_tag_%s\"%(i,)\n address = \"%%MW%s\" % (addr_num,)\n type = \"value\"\n controller_ip = \"192.168.0.1\"\n installation = \"66002174487292\"\n gen_tag = {\"name\": name, \"address\": address, \"type\": type, \"controller_ip\": controller_ip, \"installation\": installation}\n res.append(tagHandler.insert(gen_tag))\n if not (False in res or None in res):\n return OkResponse(\"Successfully added the new tag\")\n else:\n return ConflictResponse(\"Could not insert the tag for that controller/installation/customer\")\n\n","sub_path":"portal/backend/api/populate_demo_data.py","file_name":"populate_demo_data.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"1353359","text":"import unittest, socket\nfrom raygun4py import raygunmsgs\n\nclass TestRaygunMessageBuilder(unittest.TestCase):\n\n def setUp(self):\n self.builder = raygunmsgs.RaygunMessageBuilder().new()\n self.request = {\n \"headers\": {\n \"referer\": \"localhost\",\n \"user-Agent\": \"Mozilla\"\n },\n \"hostName\": \"localhost\",\n \"url\": \"/resource\",\n \"httpMethod\": \"GET\",\n \"ipAddress\": \"127.0.0.1\",\n \"queryString\": None,\n \"form\": None,\n \"rawData\": None\n }\n\n def test_machinename(self):\n self.builder.set_machine_name(socket.gethostname())\n self.assertIsNotNone(self.builder.raygunMessage.details['machineName'])\n\n def test_customdata(self):\n self.builder.set_customdata({1: \"one\"})\n self.assertIsInstance(self.builder.raygunMessage.details['userCustomData'], dict)\n\n def test_tags(self):\n self.builder.set_tags([1, 2, 3])\n self.assertIsInstance(self.builder.raygunMessage.details['tags'], list)\n\n def test_request_ip(self):\n self.builder.set_request_details(self.request)\n self.assertEqual(self.builder.raygunMessage.details['request']['iPAddress'], '127.0.0.1')\n\n def test_user(self):\n self.builder.set_user('user1')\n self.assertEqual(self.builder.raygunMessage.details['user'], { 'identifier': 'user1' })\n\n\ndef main():\n unittest.main()\n\nif __name__ == '__main__':\n main()","sub_path":"tests/test_raygunmsgs.py","file_name":"test_raygunmsgs.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"307723878","text":"from persistent.mapping import PersistentMapping\nfrom zope.interface import implementer\n\nfrom substanced.content import content\nfrom substanced.folder import Folder\n\nfrom ..interfaces import (\n ITournaments,\n ITournament\n )\n\nfrom ..utils import (\n BaseContent,\n ATTENDING\n )\n\n@content(\n ITournaments,\n name='Tournament',\n icon='icon-tags',\n)\n@implementer(ITournaments)\nclass Tournaments(Folder):\n def __init__(self):\n Folder.__init__(self)\n self.title = 'Tournaments'\n\n\n@content(\n ITournament,\n name='Tournament',\n icon='icon-tags',\n)\n@implementer(ITournament)\nclass Tournament(BaseContent):\n props = None\n\n def __init__(self, external_id, title, position, props=None):\n BaseContent.__init__(self)\n self.external_id = external_id\n self.title = title\n self.position = position\n\n if props:\n self.props = PersistentMapping()\n for k, v in props.items():\n self.props[k] = v\n\n def players(self):\n \"\"\" Sorted list of players attending tournament \"\"\"\n players = list(self.get_sources(ATTENDING))\n return sorted(players, key=lambda p: p.title)\n\n\n","sub_path":"src/fastbreak/fastbreak/tournament/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"300205753","text":"import random\n\n\nclass Hand(object):\n def __init__(self, n):\n '''\n Initialize a Hand.\n\n n: integer, the size of the hand.\n '''\n assert type(n) == int\n self.HAND_SIZE = n\n self.VOWELS = 'aeiou'\n self.CONSONANTS = 'bcdfghjklmnpqrstvwxyz'\n\n # Deal a new hand\n self.dealNewHand()\n\n def dealNewHand(self):\n '''\n Deals a new hand, and sets the hand attribute to the new hand.\n '''\n # Set self.hand to a new, empty dictionary\n self.hand = {}\n\n # Build the hand\n numVowels = self.HAND_SIZE // 3\n\n for i in range(numVowels):\n x = self.VOWELS[random.randrange(0, len(self.VOWELS))]\n self.hand[x] = self.hand.get(x, 0) + 1\n\n for i in range(numVowels, self.HAND_SIZE):\n x = self.CONSONANTS[random.randrange(0, len(self.CONSONANTS))]\n self.hand[x] = self.hand.get(x, 0) + 1\n\n def setDummyHand(self, handString):\n '''\n Allows you to set a dummy hand. Useful for testing your implementation.\n\n handString: A string of letters you wish to be in the hand. Length of\n this string must be equal to self.HAND_SIZE.\n\n This method converts sets the hand attribute to a dictionary\n containing the letters of handString.\n '''\n assert len(handString) == self.HAND_SIZE, \\\n \"Length of handString ({0}) must equal length of \\\n HAND_SIZE ({1})\".format(len(handString), self.HAND_SIZE)\n self.hand = {}\n for char in handString:\n self.hand[char] = self.hand.get(char, 0) + 1\n\n def calculateLen(self):\n '''\n Calculate the length of the hand.\n '''\n ans = 0\n for k in self.hand:\n ans += self.hand[k]\n return ans\n\n def __str__(self):\n '''\n Display a string representation of the hand.\n '''\n output = ''\n hand_keys = sorted(self.hand.keys())\n for letter in hand_keys:\n for j in range(self.hand[letter]):\n output += letter\n return output\n\n def update(self, word):\n \"\"\"\n Does not assume that self.hand has all the letters in word.\n\n Updates the hand: if self.hand does have all the letters to make\n the word, modifies self.hand by using up the letters in the given word.\n\n Returns True if the word was able to be made with the letter in\n the hand; False otherwise.\n\n word: string\n returns: Boolean (if the word was or was not made)\n \"\"\"\n # Your code here\n # Pseudocode\n # 0. Get letter frequency of the word\n word_freq = {}\n for letter in word:\n word_freq[letter] = word_freq.get(letter, 0) + 1\n # 1. Check self.hand has all the letters to make the word\n invalid_letters = {key: value for key, value in word_freq.items()\n if key not in [*self.hand] or\n value > self.hand.get(letter, 0)}\n # 2. If yes, modify self.hand, then return True\n if not len(invalid_letters):\n self.hand = {key: (value - word_freq.get(key, 0))\n for key, value in self.hand.items()}\n return True\n # 3. If no, return False\n else:\n return False\n\n\nmyHand = Hand(14)\nprint(myHand)\nprint(myHand.calculateLen())\n\nmyHand.setDummyHand('cccllaapppttrr')\nprint(myHand)\nprint(myHand.calculateLen())\n\nmyHand.update('zaclaptrap')\nprint(myHand)\n","sub_path":"mit6001/week5/hand.py","file_name":"hand.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"258899853","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('tracks/', views.tracks, name='tracks-all'),\n path('tracks/top', views.tracks_top, name='tracks-top'),\n path('tracks/', views.track, name='track-single'),\n\n path('albums/', views.albums, name='albums-all'),\n path('albums/', views.album, name='album-single'),\n\n path('purchases//send', views.send_songs, name='send_songs'),\n\n path('customers/new', views.customer_new, name='customer-new'),\n path('customers//purchases', views.customer_email_purchases, name=\"customer-email-purchases\"),\n path('customers//purchases/new', views.customer_purchase_new, name=\"customer-purchase-new\")\n]\n","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"516565619","text":"import wx\nfrom patternpad import ID_QUIT\nfrom patternpad.MenuBar import MenuBar\n\nclass Frame(wx.Frame):\n\tdef __init__(self, *args, **kwargs):\n\t\twx.Frame.__init__(self, title='PatternPad', *args, **kwargs)\n\n\t\tself.Bind(wx.EVT_CLOSE, self.OnClose)\n\t\tself.Bind(wx.EVT_MENU, self.OnClose, id=ID_QUIT)\n\t\t\n\t\tself.SetMenuBar(MenuBar())\n\t\tself.Show()\n\n\tdef OnClose(self, event):\n\t\t#check isSaved and prompt if not\n\t\tself.Destroy()\n","sub_path":"patternpad/Frame.py","file_name":"Frame.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"193011771","text":"import unittest\r\nfrom unittest.mock import MagicMock\r\nfrom unittest import mock\r\n\r\nimport salary\r\n\r\nclass TestSalary(unittest.TestCase):\r\n def test_calculation_salary(self):\r\n s = salary.Salary(year=2017)\r\n s.bonus_api.bonus_price = MagicMock(return_value=1)\r\n self.assertEqual(s.calculation_salary(), 101)\r\n s.bonus_api.bonus_price.assert_called_once()\r\n s.bonus_api.bonus_price.assert_called_with(year=2017)\r\n\r\n def test_calculation_salary_no_salary(self):\r\n s = salary.Salary(year=2050)\r\n s.bonus_api.bonus_price = MagicMock(return_value=0)\r\n self.assertEqual(s.calculation_salary(), 100)\r\n s.bonus_api.bonus_price.assert_not_called()\r\n\r\n @mock.patch('salary.ThirdPartyBonusRestApi.bonus_price',\r\n return_value=1)\r\n def test_calculation_salary_no_salary_patch(self, mock_bonus):\r\n s = salary.Salary(year=2017)\r\n self.assertEqual(s.calculation_salary(), 101)\r\n mock_bonus.assert_called()\r\n\r\n def test_calculation_salary_no_salary_patch_with(self):\r\n with mock.patch(\r\n 'salary.ThirdPartyBonusRestApi.bonus_price') as mock_bonus:\r\n mock_bonus.return_value = 1\r\n\r\n s = salary.Salary(year=2017)\r\n salary_price = s.calculation_salary()\r\n\r\n self.assertEqual(salary_price, 101)\r\n mock_bonus.assert_called()\r\n\r\n def setUp(self):\r\n self.patcher = mock.patch('salary.ThirdPartyBonusRestApi.bonus_price')\r\n self.mock_bonus = self.patcher.start()\r\n\r\n def tearDown():\r\n self.patcher.stop()\r\n\r\n def test_calculation_salary_no_salary_patch_patcher(self):\r\n self.mock_bonus.return_value = 1\r\n\r\n s = salary.Salary(year=2017)\r\n salary_price = s.calculation_salary()\r\n\r\n self.assertEqual(salary_price, 101)\r\n mock_bonus.assert_called()\r\n\r\n def test_calculation_salary_no_salary_patch_side_effect(self):\r\n def f(year):\r\n return 1\r\n self.mock_bonus.side_effect = ConnectionRefusedError\r\n\r\n s = salary.Salary(year=2017)\r\n salary_price = s.calculation_salary()\r\n\r\n self.assertEqual(salary_price, 100)\r\n mock_bonus.assert_called()\r\n","sub_path":"test_salary.py","file_name":"test_salary.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"380080477","text":"import json\nfrom django.conf import settings\nfrom notifier.event_handler.event import Event\n\n\nclass RunFinishedEvent(Event):\n def __init__(\n self,\n job_notifier,\n request_id,\n run_id,\n pipeline,\n pipeline_version,\n pipeline_link,\n output_directory,\n run_status,\n tags,\n running,\n completed,\n failed,\n total,\n operator_run_id,\n lsf_log_location,\n input_json_location,\n job_group_id,\n ):\n self.job_notifier = job_notifier\n self.request_id = request_id\n self.pipeline = pipeline\n self.pipeline_version = pipeline_version\n self.pipeline_link = pipeline_link\n self.output_directory = output_directory\n self.run_id = run_id\n self.run_status = run_status\n self.tags = tags\n self.running = running\n self.completed = completed\n self.failed = failed\n self.total = total\n self.operator_run_id = operator_run_id\n self.lsf_log_location = lsf_log_location\n self.input_json_location = input_json_location\n self.job_group_id = job_group_id\n\n @classmethod\n def get_type(cls):\n return \"RunFinishedEvent\"\n\n @classmethod\n def get_method(cls):\n return \"process_run_completed\"\n\n def __str__(self):\n RUN_TEMPLATE = \"\"\"\n\n Run Id: {run_id}\n Pipeline: {pipeline_name}\n Pipeline Link: {pipeline_link}\n Output Directory: {output_directory}\n {tags}\n Status: {status}\n Link: {link}\n LSF Log Location: {lsf_log_location}\n inputs.json Location: {inputs_json_location}\n \n _____________________________________________\n \n {run_status}\n \n Running: {running}\n Completed: {completed}\n Failed: {failed}\n \n TOTAL: {total}\n \\n\n {rerun_info}\n \"\"\"\n link = \"%s%s%s\\n\" % (settings.BEAGLE_URL, \"/v0/run/api/\", self.run_id)\n if self.operator_run_id:\n status = \"OperatorRun {operator_run} status\".format(operator_run=self.operator_run_id)\n else:\n status = \"Run status\"\n tags = \"\"\n run_ids = None\n for k, v in self.tags.items():\n tags += f\"{k}: {json.dumps(v) if isinstance(v, list) or isinstance(v, dict) else str(v)}\\n\"\n if k == \"argos_run_ids\":\n run_ids = v\n\n rerun_json = {}\n rerun_json[\"pipelines\"] = [self.pipeline]\n rerun_json[\"pipeline_versions\"] = [self.pipeline_version]\n rerun_json[\"job_group_id\"] = self.job_group_id\n if run_ids:\n rerun_json[\"run_ids\"] = run_ids\n else:\n rerun_json[\"request_ids\"] = self.request_id\n\n if self.run_status == \"FAILED\":\n rerun_str = f\"\"\"\n API Body for re-run:\n \n {json.dumps(rerun_json)}\n \"\"\"\n else:\n rerun_str = \"\"\n\n return RUN_TEMPLATE.format(\n run_id=self.run_id,\n pipeline_name=self.pipeline,\n pipeline_link=self.pipeline_link,\n status=self.run_status,\n link=link,\n running=str(self.running),\n completed=str(self.completed),\n failed=str(self.failed),\n total=str(self.total),\n tags=tags,\n run_status=status,\n output_directory=self.output_directory,\n lsf_log_location=self.lsf_log_location,\n inputs_json_location=self.input_json_location,\n rerun_info=rerun_str,\n )\n","sub_path":"notifier/events/run_finished_event.py","file_name":"run_finished_event.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"132501426","text":"#首先定义字典，用于应相应的二级选项,主键对应页面选项，值对应数据库字段名\nusr_basic_info_dict ={\n \"ID\":\"ID\",\n \"手机号码\":\"MSISDN\",\n \"手机号注册城市\":\"CITY\",\n \"手机号注册区县\":\"AREA\",\n \"手机号注册乡镇\":\"AREA1\",\n \"农村城市属性\":\"OVERLAY_AREA\",\n \"用户星级\":\"STAR_TYPE\",\n \"入网时间\":\"START_DATE\",\n \"在网时长\":\"ONLINE_TIME\",\n \"号码归属地\":\"ATTRIB_ADDR\",\n}\nusr_expenses_dict={\n \"ID\":\"ID\",\n \"手机号码\":\"MSISDN\",\n \"主套餐名称\":\"PACKAGES_NAME\",\n \"是否500M以上流量或套餐订购用户\":\"UP_500M_USER\",\n \"主套餐加附加套餐包含流量\":\"FLOWS\",\n \"是否校园客户\":\"COLLEGE_USER\",\n \"是否超套餐流量用户\":\"OVER_PACKGES_USER\",\n \"近三个月月均ARPU\":\"LAST3M_AVG_ARPU\",\n \"近三个月月均DOU\":\"LAST3M_AVG_DOU\"\n}\nusr_location={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"用户经度\":\"LONGITUDE\",\n \"用户纬度\":\"LATITUDE\",\n \"用户常驻小区\":\"CELL\",\n}\ntac_imei_relation={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"终端品牌\":\"BRAND\",\n \"终端型号\":\"TYPE\",\n}\nusr_app_pecpt={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"TOP1APP名称\":\"TOP1_APP_NAME\",\n \"TOP2APP名称\":\"TOP2_APP_NAME\",\n \"TOP3APP名称\":\"TOP3_APP_NAME\",\n \"TOP4APP名称\":\"TOP4_APP_NAME\",\n \"TOP5APP名称\":\"TOP5_APP_NAME\"\n}\nusr_app_rank={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"TOP1APP名称\": \"TOP1_APP_NAME\",\n \"TOP2APP名称\": \"TOP2_APP_NAME\",\n \"TOP3APP名称\": \"TOP3_APP_NAME\",\n \"TOP4APP名称\": \"TOP4_APP_NAME\",\n \"TOP5APP名称\": \"TOP5_APP_NAME\",\n \"TOP1APP流量\":\"TOP1_DATA\",\n \"TOP2APP流量\":\"TOP2_DATA\",\n \"TOP3APP流量\":\"TOP3_DATA\",\n \"TOP4APP流量\":\"TOP4_DATA\",\n \"TOP5APP流量\":\"TOP5_DATA\",\n}\nusr_cover_pecpt={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"MR采样点\":\"SUM_POINT\",\n \"MR弱采样点\":\"POOR_POINT\",\n \"低于-100的采样点\":\"WEAK_POINT\",\n \"平均RSRP\":\"AVG_RSRP\",\n \"大于-100的覆盖率\":\"MR_RATE_100\",\n \"MR覆盖率\":\"MR_RATE\",\n \"脱网率\":\"OFF_RATE\",\n \"差PHR率\":\"BAD_PHR_RATE\",\n}\nusr_complt_record={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"是否离网预警用户\":\"IF_RISK_USER\",\n \"离网预警原因\":\"POLICY_PRODUCT\",\n \"是否投诉用户\":\"IF_COMPLAINT_USER\",\n \"投诉原因1\":\"REASON1\",\n \"投诉原因2\":\"REASON2\",\n \"投诉业务\":\"BUSINESS\",\n \"是否不满意用户\":\"IF_DISCONTENT_USER\",\n \"不满意原因\":\"RFD\",\n \"维系措施\":\"DFW\",\n \"是否校园客户\":\"COLLEGE_USR\",\n \"超套餐流量客户\":\"OVER_PACKGES_USER\"\n}\nusr_call_pecpt={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"寻呼成功率\":\"PAGING_RATE\",\n \"掉话率\":\"DC_RATE\",\n}\nusr_speed_pecpt={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"全业务下行速率\":\"DL_SPEED\",\n \"大包下行速率\":\"BP_DL_SPEED\",\n \"大包下行流量\":\"BP_DL_DATA\",\n \"大包下行时长\":\"BP_DL_DURA\",\n \"全业务上行速率\":\"UL_SPEED\",\n \"大包上行速率\":\"BP_UL_SPEED\",\n \"小包下行速率\":\"SP_DL_DELAY\",\n \"小包上行速率\":\"SP_UL_DELAY\",\n}\nusr_ete_pecpt={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"HTTP响应成功率\":\"HTTP_RES_RATE\",\n \"HTTP请求次数\":\"HTTP_ATT\",\n \"HTTP响应时延\":\"HTTP_RES_DELAY\",\n \"TCP核心网成功率\":\"TCP_CORE_RATE\",\n \"TCP核心网时延\":\"TCP_CORE_DELAY \",\n \"TCP无线成功率\":\"TCP_RADIO_RATE\",\n \"TCP无线时延\":\"TCP_RADIO_DELAY\",\n \"TCP下行重传率\":\"TCP_DL_RE_RATE\",\n \"TCP上行重传率\":\"TCP_UL_RE_RATE\",\n \"TCP重传率\":\"TCP_RE_RATE\",\n \"页面响应时延\":\"PAGE_RES_DELAY\",\n \"页面响应次数\":\"PAGE_RES_SESSION\",\n}\nusr_core_pecpt={\n \"ID\":\"ID\",\n \"电话号码\":\"MSISDN\",\n \"差RIP率\":\"BAD_RIP_RATE\",\n \"切换成功率\":\"HO_RATE\",\n \"服务请求成功率\":\"SR_RATE\",\n \"附着成功率\":\"ATTACH_RATE\",\n \"PDN连接成功率\":\"PDN_RATE\",\n \"INITIAL成功率\":\"INITIAL_RATE\",\n \"TAU成功率\":\"TAU_RATE\",\n}\n\ntable_dict={\n \"用户基本信息表\":usr_basic_info_dict,\n \"用户消费情况表\":usr_expenses_dict,\n \"用户位置情况表\":usr_location,\n \"用户终端情况表\":tac_imei_relation,\n \"用户APP感知表\":usr_app_pecpt,\n \"用户APP偏好表\":usr_app_rank,\n \"用户覆盖感知表\":usr_cover_pecpt,\n \"用户投诉记录表\":usr_complt_record,\n \"用户通话情况表\":usr_call_pecpt,\n \"用户速率感知表\":usr_speed_pecpt,\n \"用户端到端情况表\":usr_ete_pecpt,\n \"用户核心网感知表\":usr_core_pecpt,\n};\n\n#表的字典，对应不同的不同的表名\ntable_name_dict={\n \"用户基本信息表\":\"usr_basic_info\",\n \"用户消费情况表\":\"usr_expenses\",\n \"用户位置情况表\":\"usr_location\",\n \"用户终端情况表\":\"tac_imei_relation\",\n \"用户APP感知表\":\"usr_app_pecpt\",\n \"用户APP偏好表\":\"usr_app_rank\",\n \"用户覆盖感知表\":\"usr_cover_pecpt\",\n \"用户投诉记录表\":\"usr_complt_record\",\n \"用户通话情况表\":\"usr_call_pecpt\",\n \"用户速率感知表\":\"usr_speed_pecpt\",\n \"用户端到端情况表\":\"usr_ete_pecpt\",\n \"用户核心网感知表\":\"usr_core_pecpt\",\n};\n#以下将使用各种规则\nbijiao={\n \"等于\":\"=\",\n \"大于\":\">\",\n \"小于\":\"<\",\n}\nget_value={\n \"计数\":\"\",\n \"求和\":\"\",\n \"求平均值\":\"\",\n \"求方差\":\"\",\n}\n\ndef a(r):\n if r:\n print(\"good\");\n return\n print(\"bad\");\n return;\n\na(False);\n\n","sub_path":"user/db_data.py","file_name":"db_data.py","file_ext":"py","file_size_in_byte":5482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"292980089","text":"from collections import deque\n\nN, M = map(int, input().split())\nMOD = 10**9 + 7\nINF = float(\"inf\")\n\ng = [[] for _ in range(N)]\nalready = [False for _ in range(N)]\nnum = [0 for _ in range(N)]\nnum[0] += 1\ncosts = [INF for _ in range(N)]\n\nqueue = deque()\n\nfor _ in range(M):\n tmp = list(map(int, input().split()))\n g[tmp[0]-1].append(tmp[1]-1)\n g[tmp[1]-1].append(tmp[0]-1)\n\nqueue.append([0, 0])\n\nwhile queue:\n now, cost = queue.popleft()\n \n if N-1 == now:\n break\n if already[now]:\n continue\n already[now] = True\n \n for i in g[now]:\n if costs[i] > cost:\n costs[i] = cost\n num[i] = num[now]\n num[i] %= MOD\n queue.append([i, cost+1])\n elif costs[i] == cost:\n num[i] += num[now]\n num[i] %= MOD\n\nprint(num[N-1])\n ","sub_path":"contest/abc211/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"232007449","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n Script to remove specific labels from a atlas volumes.\n\n >>> scil_remove_labels.py DKT_labels.nii out_labels.nii.gz -i 5001 5002\n\"\"\"\n\n\nimport argparse\nimport logging\n\nimport nibabel as nib\nimport numpy as np\n\nfrom scilpy.io.image import get_data_as_label\nfrom scilpy.io.utils import (add_overwrite_arg, assert_inputs_exist,\n assert_outputs_exist)\nEPILOG = \"\"\"\n References:\n [1] Al-Sharif N.B., St-Onge E., Vogel J.W., Theaud G.,\n Evans A.C. and Descoteaux M. OHBM 2019.\n Surface integration for connectome analysis in age prediction.\n \"\"\"\n\n\ndef _build_arg_parser():\n p = argparse.ArgumentParser(description=__doc__, epilog=EPILOG,\n formatter_class=argparse.RawTextHelpFormatter)\n\n p.add_argument('in_labels',\n help='Input labels volume.')\n\n p.add_argument('out_labels',\n help='Output labels volume.')\n\n p.add_argument('-i', '--indices', type=int, nargs='+', required=True,\n help='List of labels indices to remove.')\n\n p.add_argument('--background', type=int, default=0,\n help='Integer used for removed labels [%(default)s].')\n add_overwrite_arg(p)\n return p\n\n\ndef main():\n parser = _build_arg_parser()\n args = parser.parse_args()\n\n assert_inputs_exist(parser, args.in_labels)\n assert_outputs_exist(parser, args, args.out_labels)\n\n # Load volume\n label_img = nib.load(args.in_labels)\n labels_volume = get_data_as_label(label_img)\n\n # Remove given labels from the volume\n for index in np.unique(args.indices):\n mask = labels_volume == index\n labels_volume[mask] = args.background\n if np.count_nonzero(mask) == 0:\n logging.warning(\"Label {} was not in the volume\".format(index))\n\n # Save final volume\n nii = nib.Nifti1Image(labels_volume, volume_img.affine, volume_img.header)\n nib.save(nii, args.out_labels)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts/scil_remove_labels.py","file_name":"scil_remove_labels.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"138268458","text":"\"\"\"Objects dealing with timeseries and ensemble statistics.\"\"\"\nimport numpy as np\nimport numpy.polynomial.polynomial as poly\nimport scipy.stats as ss\nimport xarray as xr\nfrom scipy.signal import periodogram\nfrom scipy.stats import norm\n\nfrom xskillscore import pearson_r, pearson_r_p_value\n\n\n# --------------------------------------------#\n# HELPER FUNCTIONS\n# Should only be used internally by esmtools.\n# --------------------------------------------#\ndef _check_xarray(x):\n \"\"\"Check if the object being submitted is either a Dataset or DataArray.\"\"\"\n if not (isinstance(x, xr.DataArray) or isinstance(x, xr.Dataset)):\n typecheck = type(x)\n raise IOError(f\"\"\"The input data is not an xarray object (an xarray\n DataArray or Dataset). esmtools is built to wrap xarray to make\n use of its awesome features. Please input an xarray object and\n retry the function.\n\n Your input was of type: {typecheck}\"\"\")\n\n\ndef _get_coords(da):\n return list(da.coords)\n\n\ndef _get_dims(da):\n return list(da.dims)\n\n\ndef _get_vars(ds):\n return list(ds.data_vars)\n\n\n# ----------------------------------#\n# TIME SERIES\n# Functions related to time series.\n# ----------------------------------#\ndef xr_corr(x, y, dim='time', lag=0, return_p=False):\n \"\"\"Computes the Pearson product-moment coefficient of linear correlation.\n\n This version calculates the effective degrees of freedom, accounting\n for autocorrelation within each time series that could fluff the\n significance of the correlation.\n\n References:\n * Wilks, Daniel S. Statistical methods in the atmospheric sciences.\n Vol. 100. Academic press, 2011.\n * Lovenduski, Nicole S., and Nicolas Gruber. \"Impact of the Southern\n Annular Mode on Southern Ocean circulation and biology.\" Geophysical\n Research Letters 32.11 (2005).\n\n Todo:\n * Test and adapt for xr.Datasets\n\n Args:\n x (xarray object): Independent variable time series or grid of time\n series.\n y (xarray object): Dependent variable time series or grid of time\n series\n dim (optional str): Correlation dimension\n lag (optional int): Lag to apply to correlaton, with x predicting y.\n return_p (optional bool): If True, return correlation coefficients\n as well as p values.\n Returns:\n Pearson correlation coefficients\n\n If return_p True, associated p values.\n\n \"\"\"\n _check_xarray(x)\n _check_xarray(y)\n if lag != 0:\n N = x[dim].size\n normal = x.isel({dim: slice(0, N-lag)})\n shifted = y.isel({dim: slice(0 + lag, N)})\n if dim not in list(x.coords):\n normal[dim] = np.arange(1, N)\n shifted[dim] = normal[dim]\n r = pearson_r(normal, shifted, dim)\n else:\n r = pearson_r(x, y, dim)\n if return_p:\n p = _xr_eff_p_value(x, y, r, dim)\n # return with proper dimension labeling. would be easier with\n # apply_ufunc, but having trouble getting it to work here. issue\n # probably has to do with core dims.\n dimlist = _get_dims(r)\n for i in range(len(dimlist)):\n p = p.rename({'dim_' + str(i): dimlist[i]})\n return r, p\n else:\n return r\n\n\ndef _xr_eff_p_value(x, y, r, dim):\n \"\"\"Computes p values accounting for autocorrelation in time series.\n\n Args:\n x (xarray object): Independent time series.\n y (xarray object): Dependent time series.\n r (xarray object): Pearson correlations between x and y.\n dim (str): Dimension to compute compute p values over.\n\n Returns:\n p values accounting for autocorrelation in input time series.\n\n References:\n * Wilks, Daniel S. Statistical methods in the atmospheric sciences.\n Vol. 100. Academic press, 2011.\n \"\"\"\n def _compute_autocorr(v, dim, n):\n \"\"\"\n Return normal and shifted time series\n with equal dimensions so as not to\n throw an error.\n \"\"\"\n shifted = v.isel({dim: slice(1, n)})\n normal = v.isel({dim: slice(0, n-1)})\n # see explanation in xr_autocorr for this\n if dim not in list(v.coords):\n normal[dim] = np.arange(1, n)\n shifted[dim] = normal[dim]\n return pearson_r(shifted, normal, dim)\n\n n = x[dim].size\n # find autocorrelation\n xa, ya = x - x.mean(dim), y - y.mean(dim)\n xauto = _compute_autocorr(xa, dim, n)\n yauto = _compute_autocorr(ya, dim, n)\n # compute effective sample size\n n_eff = n * (1 - xauto * yauto) / (1 + xauto * yauto)\n n_eff = np.floor(n_eff)\n # constrain n_eff to be at maximum the total number of samples\n n_eff = n_eff.where(n_eff <= n, n)\n # compute t-statistic\n t = r * np.sqrt((n_eff - 2) / (1 - r**2))\n p = xr.DataArray(ss.t.sf(np.abs(t), n_eff - 2) * 2)\n return p\n\n\ndef xr_rm_poly(ds, order, dim='time'):\n \"\"\"Returns xarray object with nth-order fit removed.\n\n Args:\n ds (xarray object): Time series to be detrended.\n order (int): Order of polynomial fit to be removed.\n dim (optional str): Dimension over which to remove the polynomial fit.\n\n Returns:\n xarray object with polynomial fit removed.\n \"\"\"\n _check_xarray(ds) # this could be a decorator I think?\n\n if dim not in ds.dims:\n raise KeyError(\n f\"Input dim, '{dim}', was not found in the ds; \"\n f\"found only the following dims: {list(ds.dims)}.\"\n )\n\n # handle both datasets and dataarray\n if isinstance(ds, xr.Dataset):\n da = ds.to_array()\n return_ds = True\n else:\n da = ds.copy()\n return_ds = False\n\n da_dims_orig = list(da.dims) # orig -> original\n if len(da_dims_orig) > 1:\n # want independent axis to be the leading dimension\n da_dims_swap = da_dims_orig.copy() # copy to prevent contamination\n\n # https://stackoverflow.com/questions/1014523/\n # simple-syntax-for-bringing-a-list-element-to-the-front-in-python\n da_dims_swap.insert(0, da_dims_swap.pop(da_dims_swap.index(dim)))\n da = da.transpose(*da_dims_swap)\n\n # hide other dims into a single dim\n da = da.stack({'other_dims': da_dims_swap[1:]})\n dims_swapped = True\n else:\n dims_swapped = False\n\n # NaNs will make the polyfit fail--interpolate any NaNs in\n # the provided dim to prevent poor fit, while other dims' NaNs\n # will be filled with 0s; however, all NaNs will be replaced\n # in the final output\n nan_locs = np.isnan(da.values)\n\n # any(nan_locs.sum(axis=0)) fails if not 2D\n if nan_locs.ndim == 1:\n nan_locs = nan_locs.reshape(len(nan_locs), 1)\n nan_reshaped = True\n else:\n nan_reshaped = False\n\n # check if there's any NaNs in the provided dim because\n # interpolate_na is computationally expensive to run regardless of NaNs\n if any(nan_locs.sum(axis=0)) > 0:\n if any(nan_locs[0, :]):\n # [np.nan, 1, 2], no first value to interpolate from; back fill\n da = da.bfill(dim)\n elif any(nan_locs[-1, :]):\n # [0, 1, np.nan], no last value to interpolate from; forward fill\n da = da.ffill(dim)\n else: # [0, np.nan, 2], can interpolate\n da = da.interpolate_na(dim)\n\n # this handles the other axes; doesn't matter since it won't affect the fit\n da = da.fillna(0)\n\n # the actual operation of detrending\n y = da.values\n x = np.arange(0, len(y), 1)\n coefs = poly.polyfit(x, y, order)\n fit = poly.polyval(x, coefs)\n y_dt = y - fit.transpose() # dt -> detrended\n da.data[:] = y_dt\n\n # replace back the filled NaNs (keep values where not NaN)\n if nan_reshaped:\n nan_locs = nan_locs[:, 0]\n da = da.where(~nan_locs)\n\n if dims_swapped:\n # revert the other dimensions to its original form and ordering\n da = da.unstack('other_dims').transpose(*da_dims_orig)\n\n if return_ds:\n # revert back into a dataset\n return xr.merge(da.sel(variable=var).rename(var).drop('variable')\n for var in da['variable'].values)\n else:\n return da\n\n\ndef xr_rm_trend(da, dim='time'):\n \"\"\"Calls ``xr_rm_poly`` with an order 1 argument.\"\"\"\n return xr_rm_poly(da, 1, dim=dim)\n\n\n# # TODO: coords lon, lat get lost for curvilinear ds\ndef xr_varweighted_mean_period(ds, time_dim='time'):\n \"\"\"Calculate the variance weighted mean period of time series.\n\n ..math:\n P_x = \\sum_k V(f_k,x) / \\sum_k f_k V(f_k,x)\n\n Reference:\n * Branstator, Grant, and Haiyan Teng. “Two Limits of Initial-Value\n Decadal Predictability in a CGCM.\" Journal of Climate 23, no. 23\n (August 27, 2010): 6292-6311. https://doi.org/10/bwq92h.\n\n Args:\n ds (xarray object): Time series.\n time_dim (optional str): Name of time dimension.\n\n \"\"\"\n _check_xarray(ds)\n\n def _create_dataset(ds, f, Pxx, time_dim):\n \"\"\"\n Organize results of periodogram into clean dataset.\n \"\"\"\n dimlist = [i for i in _get_dims(ds) if i not in [time_dim]]\n PSD = xr.DataArray(Pxx, dims=['freq'] + dimlist)\n PSD.coords['freq'] = f\n return PSD\n\n f, Pxx = periodogram(ds, axis=0, scaling='spectrum')\n PSD = _create_dataset(ds, f, Pxx, time_dim)\n T = PSD.sum('freq') / ((PSD * PSD.freq).sum('freq'))\n return T\n\n\ndef xr_autocorr(ds, lag=1, dim='time', return_p=False):\n \"\"\"Calculate the lagged correlation of time series.\n\n Args:\n ds (xarray object): Time series or grid of time series.\n lag (optional int): Number of time steps to lag correlate to.\n dim (optional str): Name of dimension to autocorrelate over.\n return_p (optional bool): If True, return correlation coefficients\n and p values.\n\n Returns:\n Pearson correlation coefficients.\n\n If return_p, also returns their associated p values.\n \"\"\"\n _check_xarray(ds)\n N = ds[dim].size\n normal = ds.isel({dim: slice(0, N - lag)})\n shifted = ds.isel({dim: slice(0 + lag, N)})\n \"\"\"\n xskillscore pearson_r looks for the dimensions to be matching, but we\n shifted them so they probably won't be. This solution doesn't work\n if the user provides a dataset without a coordinate for the main\n dimension, so we need to create a dummy dimension in that case.\n \"\"\"\n if dim not in list(ds.coords):\n normal[dim] = np.arange(1, N)\n shifted[dim] = normal[dim]\n r = pearson_r(normal, shifted, dim)\n if return_p:\n # NOTE: This assumes 2-tailed. Need to update xr_eff_pearsonr\n # to utilize xskillscore's metrics but then compute own effective\n # p-value with option for one-tailed.\n p = pearson_r_p_value(normal, shifted, dim)\n return r, p\n else:\n return r\n\n\ndef xr_decorrelation_time(da, r=20, dim='time'):\n \"\"\"Calculate the decorrelaton time of a time series.\n\n .. math::\n tau_{d} = 1 + 2 * \\sum_{k=1}^{\\inf}(alpha_{k})^{k}\n\n Reference:\n * Storch, H. v, and Francis W. Zwiers. Statistical Analysis in Climate\n Research. Cambridge ; New York: Cambridge University Press, 1999.,\n p.373\n\n Args:\n da (xarray object): Time series.\n r (optional int): Number of iterations to run the above formula.\n dim (optional str): Time dimension for xarray object.\n\n Returns:\n Decorrelation time of time series.\n\n \"\"\"\n _check_xarray(da)\n one = da.mean(dim) / da.mean(dim)\n return one + 2 * xr.concat([xr_autocorr(da, dim=dim, lag=i) ** i for i in\n range(1, r)], 'it').sum('it')\n\n\n# --------------------------------------------#\n# Diagnostic Potential Predictability (DPP)\n# Functions related to DPP from Boer et al.\n# --------------------------------------------#\n# # TODO: coords lon, lat get lost for curvilinear ds\ndef DPP(ds, m=10, chunk=True):\n \"\"\"\n Calculate Diagnostic Potential Predictability (DPP) as potentially\n predictable variance fraction (ppvf) in Boer 2004.\n\n Note: Resplandy et al. 2015 and Seferian et al. 2018 calculate unbiased DPP\n in a slightly different way. chunk=False\n\n .. math::\n\n DPP_{\\text{unbiased}}(m)=\\frac{\\sigma^2_m - 1/m \\cdot \\sigma^2}{\\sigma^2}\n\n References:\n * Boer, G. J. “Long Time-Scale Potential Predictability in an Ensemble of\n Coupled Climate Models.” Climate Dynamics 23, no. 1 (August 1, 2004):\n 29–44. https://doi.org/10/csjjbh.\n * Resplandy, L., R. Séférian, and L. Bopp. “Natural Variability of CO2 and\n O2 Fluxes: What Can We Learn from Centuries-Long Climate Models\n Simulations?” Journal of Geophysical Research: Oceans 120, no. 1\n (January 2015): 384–404. https://doi.org/10/f63c3h.\n * Séférian, Roland, Sarah Berthet, and Matthieu Chevallier. “Assessing the\n Decadal Predictability of Land and Ocean Carbon Uptake.” Geophysical\n Research Letters, March 15, 2018. https://doi.org/10/gdb424.\n\n Args:\n ds (xr.DataArray): control simulation with time dimension as years.\n m (optional int): separation time scale in years between predictable\n low-freq component and high-freq noise.\n chunk (optional boolean): Whether chunking is applied. Default: True.\n If False, then uses Resplandy 2015 / Seferian 2018 method.\n\n Returns:\n dpp (xr.DataArray): ds without time dimension.\n\n \"\"\"\n # TODO: rename or find xr equiv\n def _chunking(ds, number_chunks=False, chunk_length=False):\n \"\"\"\n Separate data into chunks and reshapes chunks in a c dimension.\n\n Specify either the number chunks or the length of chunks.\n Needed for DPP.\n\n Args:\n ds (xr.DataArray): control simulation with time dimension as years.\n chunk_length (int): see DPP(m)\n number_chunks (int): number of chunks in the return data.\n\n Returns:\n c (xr.DataArray): chunked ds, but with additional dimension c.\n\n \"\"\"\n if number_chunks and not chunk_length:\n chunk_length = np.floor(ds['time'].size / number_chunks)\n cmin = int(ds['time'].min())\n elif not number_chunks and chunk_length:\n cmin = int(ds['time'].min())\n number_chunks = int(np.floor(ds['time'].size / chunk_length))\n else:\n raise ValueError('set number_chunks or chunk_length to True')\n c = ds.sel(time=slice(cmin, cmin + chunk_length - 1))\n c = c.expand_dims('c')\n c['c'] = [0]\n for i in range(1, number_chunks):\n c2 = ds.sel(time=slice(cmin + chunk_length * i,\n cmin + (i + 1) * chunk_length - 1))\n c2 = c2.expand_dims('c')\n c2['c'] = [i]\n c2['time'] = c['time']\n c = xr.concat([c, c2], 'c')\n return c\n\n if not chunk: # Resplandy 2015, Seferian 2018\n s2v = ds.rolling(time=m).mean().var('time')\n s2 = ds.var('time')\n\n if chunk: # Boer 2004 ppvf\n # first chunk\n chunked_means = _chunking(\n ds, chunk_length=m).mean('time')\n # sub means in chunks\n chunked_deviations = _chunking(\n ds, chunk_length=m) - chunked_means\n s2v = chunked_means.var('c')\n s2e = chunked_deviations.var(['time', 'c'])\n s2 = s2v + s2e\n dpp = (s2v - s2 / (m)) / s2\n return dpp\n\n\n# -------\n# Z SCORE\n# -------\ndef _z_score(ci):\n \"\"\"Returns critical z score given a confidence interval\n\n Source: https://stackoverflow.com/questions/20864847/\n probability-to-z-score-and-vice-versa-in-python\n \"\"\"\n diff = (100 - ci) / 2\n return norm.ppf((100 - diff) / 100)\n\n\ndef z_significance(r1, r2, N, ci=90):\n \"\"\"Computes the z test statistic for two ACC time series, e.g. an\n initialized ensemble ACC and persistence forecast ACC.\n\n Inputs:\n r1, r2: (xarray objects) time series, grids, etc. of pearson\n correlation coefficients between the two prediction systems\n of interest.\n N: (int) length of original time series being correlated.\n ci: (optional int) confidence level for z-statistic test\n\n Returns:\n Boolean array of same dimensions as input where True means r1 is\n significantly different from r2 at ci.\n\n Reference:\n https://www.statisticssolutions.com/comparing-correlation-coefficients/\n \"\"\"\n def _r_to_z(r):\n \"\"\"Fisher's r to z transformation\"\"\"\n return 0.5 * (np.log(1 + r) - np.log(1 - r))\n\n z1, z2 = _r_to_z(r1), _r_to_z(r2)\n difference = np.abs(z1 - z2)\n zo = difference / (np.sqrt(2*(1 / (N - 3))))\n confidence = np.zeros_like(zo)\n confidence[:] = _z_score(ci)\n sig = xr.DataArray(zo > confidence)\n return sig\n","sub_path":"climpred/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":16950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"51375711","text":"# imports \nimport argparse\nimport re\nfrom copy import deepcopy\n\noutput = open(\"output.txt\", \"w\")\n\nclass Predicate:\n \n def __init__(self, name, nargs):\n self.name = name\n self.nargs = nargs\n self.negation = False\n self.vargs = []\n \n def isNegation(self):\n return self.negation\n\nclass Clause:\n \n def __init__(self, fact=False):\n self.lhs = []\n self.rhs = None\n self.fact = fact\n \n def addLhs(self, predicate):\n self.lhs.append(predicate)\n return\n \n def addRhs(self, predicate):\n self.rhs = predicate\n return\n\n def isFact(self):\n return self.fact\n\ndef printToOutput(qoa, query):\n ostr = query.name + \"(\"\n if query.vargs[0][0].islower():\n ostr = ostr + \"_\"\n else:\n ostr = ostr + query.vargs[0]\n for i in range(1, query.nargs):\n if query.vargs[i][0].islower():\n ostr = ostr + \", \" + \"_\"\n else:\n ostr = ostr + \", \" + query.vargs[i]\n ostr = ostr + \")\\n\" \n output.write(qoa + \": \" + ostr)\n\n\ndef fol_ask_query(clauses, query):\n for resQuery in fol_or(clauses, query):\n if resQuery == None:\n printToOutput(str(False), query)\n return False\n else:\n printToOutput(str(True), resQuery)\n return True\n printToOutput(str(False), query)\n return False\n \ndef fol_ask(clauses, queries):\n \n for query in queries:\n if fol_ask_query(clauses, query) == False:\n return False\n \n return True\n\n\ndef fol_or_reverted(matchedClauses, index, query):\n \n return\n\ndef fol_or(origClauses, query):\n clauses = deepcopy(origClauses)\n matchedClauses = match_clauses(clauses, query)\n origQuery = deepcopy(query)\n if len(matchedClauses) == 0:\n printToOutput(\"Ask\", query)\n yield None\n for clause in matchedClauses:\n printToOutput(\"Ask\", query)\n # clause is fact\n if clause.isFact() == True:\n #isSubstituted = False\n subMap = {}\n ret = True\n newQuery = deepcopy(query)\n if clause.rhs.nargs == query.nargs and clause.rhs.vargs != query.vargs:\n for i in range(clause.rhs.nargs):\n if clause.rhs.vargs[i] != query.vargs[i] and query.vargs[i][0].islower():\n subMap[query.vargs[i]] = clause.rhs.vargs[i]\n #isSubstituted = True\n elif clause.rhs.vargs[i] != query.vargs[i] and query.vargs[i][0].isupper():\n ret = False\n if ret == False:\n continue\n \n for i in range(newQuery.nargs):\n if newQuery.vargs[i] in subMap.keys():\n newQuery.vargs[i] = subMap[query.vargs[i]]\n #return ret, isSubstituted, query\n yield newQuery\n # clause is => : have to perform and search\n else:\n copyClause = deepcopy(clause)\n copyQuery = deepcopy(query)\n unify(copyQuery, copyClause)\n for subMapA in fol_and(copyClause.lhs, origClauses):\n# if subMapA == None:\n# query = deepcopy(origQuery)\n# break\n# else:\n# substituteRHS(copyClause.rhs, subMapA)\n# if equals(query, copyClause.rhs):\n# substituteQuery(copyClause.rhs, query)\n# #return True, True, query\n# yield query\n if subMapA != None:\n newCopyClause = deepcopy(copyClause)\n substituteRHS(newCopyClause.rhs, subMapA)\n if equals(query, newCopyClause.rhs):\n newQuery = deepcopy(query)\n substituteQuery(newCopyClause.rhs, newQuery)\n #return True, True, query\n yield newQuery\n else:\n query = deepcopy(origQuery)\n break\n #if resGot == False:\n # query = deepcopy(origQuery)\n \n #yield None\n return \n #return False, False, query\n\ndef substituteQuery(rhs, query):\n for i in range(query.nargs):\n if query.vargs[i][0].islower() and rhs.vargs[i][0].isupper():\n query.vargs[i] = rhs.vargs[i]\n return\n\ndef equals(query, rhs):\n for j in range(rhs.nargs):\n if rhs.vargs[j] != query.vargs[j]:\n if rhs.vargs[j].isupper() and query.vargs[j].isupper():\n return False\n return True\n \n\ndef fol_and(lhsQueriesOrig, origClauses):\n subMapForAnd = {}\n if len(lhsQueriesOrig) == 0:\n yield subMapForAnd\n else:\n lhsQueries = deepcopy(lhsQueriesOrig)\n firstQuery = lhsQueries[0]\n restAll = lhsQueries[1:]\n oldQuery = deepcopy(firstQuery)\n for resQuery in fol_or(origClauses, firstQuery):\n if resQuery == None:\n printToOutput(\"False\", firstQuery)\n yield None\n return\n else:\n printToOutput(\"True\", resQuery)\n restAllCopy = deepcopy(restAll)\n subMap = substituteLHS(restAllCopy, oldQuery, resQuery)\n subMapForAnd.update(subMap)\n for subMapO in fol_and(restAllCopy, origClauses):\n if subMapO != None and len(subMapO) > 0:\n subMapForAnd.update(subMapO)\n if subMapO == None:\n firstQuery = deepcopy(oldQuery)\n break\n yield subMapForAnd\n \n #yield subMapForAnd\n yield None\n return \n\ndef substituteRHS(rhs, subMapA):\n for j in range(rhs.nargs):\n if rhs.vargs[j] in subMapA.keys():\n rhs.vargs[j] = subMapA[rhs.vargs[j]]\n\ndef substituteLHS(lhsQueries, oldQuery, query):\n \n subMap = {}\n for i in range(oldQuery.nargs):\n if oldQuery.vargs[i][0].islower() and query.vargs[i][0].isupper():\n subMap[oldQuery.vargs[i]] = query.vargs[i]\n \n for pred in lhsQueries:\n for j in range(pred.nargs):\n if pred.vargs[j] in subMap.keys():\n pred.vargs[j] = subMap[pred.vargs[j]]\n \n return subMap\n\ndef unify(query, clause):\n varMap = {}\n for i in range(query.nargs):\n if query.vargs[i][0].isupper() and clause.rhs.vargs[i][0].islower():\n varMap[clause.rhs.vargs[i]] = query.vargs[i]\n clause.rhs.vargs[i] = query.vargs[i]\n \n for pred in clause.lhs:\n for j in range(pred.nargs):\n if pred.vargs[j] in varMap.keys():\n pred.vargs[j] = varMap[pred.vargs[j]]\n \n return query, clause\n\ndef match_clauses(clauses, query):\n matchedClauses = []\n for clause in clauses:\n rhsClause = clause.rhs\n if rhsClause.name == query.name:\n add = True\n for i in range(rhsClause.nargs):\n if rhsClause.vargs[i][0].isupper() and query.vargs[i][0].isupper() and rhsClause.vargs[i] != query.vargs[i]:\n add = False\n if add:\n matchedClauses.append(clause)\n return matchedClauses\n\ndef extractQueries(queryStr):\n \n queries = queryStr.split('&&')\n queryPreds = []\n for i in range(len(queries)):\n qStr = queries[i].strip(' \\t\\n\\r')\n qReg = re.compile(r'(.*)\\((.*)\\)')\n qu = qReg.search(qStr)\n predName = qu.group(1).strip(' \\t\\n\\r')\n predVar = qu.group(2).strip(' \\t\\n\\r').split(',')\n pred = Predicate(predName, len(predVar))\n for j in range(len(predVar)):\n pred.vargs.append(predVar[j].strip(' \\t\\n\\r'))\n queryPreds.append(pred)\n \n return queryPreds\n\ndef parseLHS(lhsStr, clause):\n lhsClauses = lhsStr.split('&&')\n for cStr in lhsClauses:\n cStr = cStr.strip(' \\t\\n\\r')\n cReg = re.compile(r'(.*)\\((.*)\\)')\n factStr = cReg.search(cStr)\n predName = factStr.group(1).strip(' \\t\\n\\r')\n predVar = factStr.group(2).strip(' \\t\\n\\r').split(',')\n if predName[0] == '~':\n pred = Predicate(predName[1:], len(predVar))\n pred.negation = True\n else:\n pred = Predicate(predName, len(predVar))\n for j in range(len(predVar)):\n pred.vargs.append(predVar[j].strip(' \\t\\n\\r'))\n clause.addLhs(pred)\n return\n\ndef parseRHS(rhsStr, clause):\n cReg = re.compile(r'(.*)\\((.*)\\)')\n factStr = cReg.search(rhsStr)\n predName = factStr.group(1).strip(' \\t\\n\\r')\n predVar = factStr.group(2).strip(' \\t\\n\\r').split(',')\n if predName[0] == '~':\n pred = Predicate(predName[1:], len(predVar))\n pred.negation = True\n else:\n pred = Predicate(predName, len(predVar))\n for j in range(len(predVar)):\n pred.vargs.append(predVar[j].strip(' \\t\\n\\r'))\n clause.addRhs(pred)\n return\n\n# main\nif __name__ == \"__main__\":\n clauses = []\n queries = []\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', required=True)\n args = parser.parse_args()\n inFile = args.i\n inF = open(inFile, 'r')\n dataLines = inF.readlines()\n queries = extractQueries(dataLines[0])\n numClauses = int(dataLines[1])\n for i in range(0, numClauses):\n clauseStr = dataLines[2+i].strip(' \\t\\n\\r').split('=>')\n if len(clauseStr) == 2:\n clause = Clause()\n parseLHS(clauseStr[0].strip(' \\t\\n\\r'), clause)\n parseRHS(clauseStr[1].strip(' \\t\\n\\r'), clause)\n clauses.append(clause)\n else:\n clause = Clause(fact=True)\n parseRHS(clauseStr[0], clause)\n clauses.append(clause)\n \n ret = fol_ask(clauses, queries)\n output.write(str(ret))\n ","sub_path":"Assignment_2/hw2cs561s16.py","file_name":"hw2cs561s16.py","file_ext":"py","file_size_in_byte":9948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"74145524","text":"import sys\n# .ntps to .obj\ndef ntps2obj(ntps_file, obj_file):\n with open(ntps_file) as f_in:\n f_out = open(obj_file, 'w')\n for line in f_in:\n xyz_list = line.split(' ')\n f_out.write('v '+line)\n\nif len(sys.argv) < 3:\n print(\"npts to obj:\\nformat_scripts xxx.npts xxx.obj\")\n exit\nntps2obj(sys.argv[1], sys.argv[2])","sub_path":"PythonScript/format_scripts.py","file_name":"format_scripts.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"536868856","text":"# Periscope Quadcopter WAMP Endpoint\n# Evan Widloski - 2017-07-21\n\n#FIXME - script cant handle a pixhawk reset\n#FIXME - script only tries to connect to pixhawk once at startup\nimport sys\n\nfrom twisted.python import log as twisted_log\nfrom twisted.logger import Logger, textFileLogObserver\nfrom twisted.internet.defer import inlineCallbacks\nfrom twisted.internet import task\nfrom zope.interface import provider\nimport logging\nimport sys\n\nfrom autobahn.twisted.util import sleep\nfrom autobahn.twisted.wamp import ApplicationSession\nfrom autobahn.wamp.exception import ApplicationError\nfrom socket import error as SocketError\nfrom autobahn import wamp\nfrom dronekit import connect, VehicleMode\nfrom dronekit.lib import APIException\nfrom pymavlink import mavutil\n\nfrom movements import goto_position_target_body_offset_ned, goto_position_target_local_ned\n\n\nlog = Logger()\n# observer = textFileLogObserver(sys.stdout)\n# log.observer.addObserver(observer)\n\n\n# try to connect to pixhawk different ways\nconnections = []\nconnections.append('tcp:localhost:14550')\n\nserial_connections = mavutil.auto_detect_serial(preferred_list=['*FTDI*',\n \"*Arduino_Mega_2560*\",\n \"*3D_Robotics*\",\n \"*USB_to_UART*\",\n '*PX4*',\n '*FMU*'])\nif len(serial_connections) > 0:\n connections.append(str(serial_connections[0]))\n\nfor connection in connections:\n log.info(\"Trying to connect to {}\".format(connection))\n try:\n vehicle = connect(connection, heartbeat_timeout=600)\n log.info(\"Connection to {} succeeded\".format(connection))\n print('connected')\n break\n except Exception as e:\n log.info(\"Connection to {} failed\".format(str(connection)))\nelse:\n log.error(\"Could not connect!\")\n sys.exit(1)\n\nlog.info(\"connected to pixhawk\")\n\nclass QuadcopterSession(ApplicationSession):\n\n\n def __init__(self, *args, **kwargs):\n super(QuadcopterSession, self).__init__(*args, **kwargs)\n self.last_alt = None\n\n\n # ------------------- PUB/SUB ----------------------\n def publish_messages(self, event):\n #FIXME event dicts coming from twisted's logging system are formatted\n # differently depending on whether the message comes from mavlink or\n # this code\n # with open('/tmp/test', 'a') as f:\n # f.write(str(event))\n # f.write('\\n\\n')\n\n print(event)\n # tracebacks\n if 'traceback' in event:\n message = event['traceback']\n # system messages\n if 'message' in event:\n message = event['message']\n # mavlink messages\n elif 'log_io' in event:\n message = event['log_io']\n # log messages\n elif 'log_format' in event:\n message = event['log_format']\n\n self.publish(u'com.quadcopter.messages', message)\n\n # publish changes of flight mode\n def publish_mode(self, vehicle, attr_name, mode):\n self.log.info(\"published mode {}\".format(mode.name))\n self.publish(u'com.quadcopter.mode', mode.name)\n\n # publish changes of num satellites\n def publish_satellites(self, vehicle, attr_name, gps_0):\n self.log.info(\"published satellites {}\".format(gps_0.satellites_visible))\n self.publish(u'com.quadcopter.satellites', gps_0.satellites_visible)\n\n # publish changes of alt in global_relative_frame\n def publish_altitude(self, vehicle, attr_name, global_relative_frame):\n if not global_relative_frame.alt == self.last_alt:\n self.log.info(\"published altitude {}\".format(global_relative_frame.alt))\n self.publish(u'com.quadcopter.altitude', global_relative_frame.alt)\n self.last_alt = global_relative_frame.alt\n\n # publish changes of armed status\n def publish_armed(self, vehicle, attr_name, armed):\n self.log.info(\"published armed {}\".format(armed))\n self.publish(u'com.quadcopter.armed', armed)\n\n\n # ------------------- RPC ----------------------\n\n # translate vehicle in cardinal directions\n def set_translate(self, direction):\n self.log.info(\"moving {}\".format(direction))\n # set vehicle mode\n def set_mode(self, mode):\n self.log.info(\"Changing mode to {}\".format(mode))\n vehicle.mode = VehicleMode(mode)\n # arm or disarm vehicle\n def set_armed(self, armed):\n self.log.info(\"setting armed to {}\".format(armed))\n vehicle.armed = armed\n\n # takeoff to `altitude` meters\n def set_takeoff(self, altitude):\n self.log.info(\"taking off\")\n vehicle.simple_takeoff(altitude)\n\n # go to altitude\n def set_altitude(self, altitude):\n self.log.info(\"going to altitude {}\".format(altitude))\n goto_position_target_local_ned(vehicle, 0, 0, -float(altitude))\n\n # go to relative altitude\n def set_altitude_relative(self, altitude):\n self.log.info(\"adjusting altitude by {}\".format(altitude))\n goto_position_target_body_offset_ned(vehicle, 0, 0, -float(altitude))\n\n\n @inlineCallbacks\n def onJoin(self, details):\n\n # start sending log messages\n log.observer.addObserver(self.publish_messages)\n\n # publish session id so clients know which session is the quadcopter\n yield self.log.info(\"published session id\")\n yield self.publish(u'com.quadcopter.session_id', details.session)\n\n # try connecting to pixhawk, set up publications\n try:\n self.log.info(\"published pixhawk_connection True\")\n self.publish(u'com.quadcopter.pixhawk_connection', True)\n\n # set up publications to trigger on attribute change, immediately publish an event\n vehicle.add_attribute_listener('mode', self.publish_mode)\n self.publish_mode(None, None, vehicle.mode)\n\n vehicle.add_attribute_listener('gps_0.satellites_visible', self.publish_satellites)\n self.publish_satellites(None, None, vehicle.gps_0)\n\n vehicle.add_attribute_listener('location.global_relative_frame', self.publish_altitude)\n self.publish_altitude(None, None, vehicle.location.global_relative_frame)\n\n vehicle.add_attribute_listener('armed', self.publish_armed)\n self.publish_armed(None, None, vehicle.armed)\n\n # if connection fails, try again in 5 seconds\n except socket_error as e:\n self.log.info(\"could not connect to pixhawk (errno {}), trying again\".format(e.errno))\n # register RPCs\n yield self.register(self.set_translate, u'com.quadcopter.set_translate')\n yield self.register(self.set_mode, u'com.quadcopter.set_mode')\n yield self.register(self.set_armed, u'com.quadcopter.set_armed')\n yield self.register(self.set_takeoff, u'com.quadcopter.set_takeoff')\n yield self.register(self.set_altitude, u'com.quadcopter.set_altitude')\n yield self.register(self.set_altitude_relative, u'com.quadcopter.set_altitude_relative')\n\n def onClose(self, *args):\n # remove attribute listeners\n vehicle.remove_attribute_listener('mode', self.publish_mode)\n vehicle.remove_attribute_listener('gps_0.satellites_visible', self.publish_satellites)\n vehicle.remove_attribute_listener('location.global_relative_frame', self.publish_altitude)\n vehicle.remove_attribute_listener('armed', self.publish_armed)\n log.observer.removeObserver(self.publish_messages)\n\nfrom autobahn.twisted.wamp import ApplicationRunner\n\n# runner = ApplicationRunner(url=u\"ws://localhost:8080/ws\", realm=u\"realm1\", retry_delay_growth=10, initial_retry_delay=5, max_retries=-1, retry_delay_jitter=2)\nrunner = ApplicationRunner(url=u\"ws://localhost:8080/ws\", realm=u\"realm1\")\nrunner.run(QuadcopterSession, auto_reconnect=True)\n","sub_path":"controls/vehicle/wamp.py","file_name":"wamp.py","file_ext":"py","file_size_in_byte":7990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"492520819","text":"##!/bin/python2.7\n#########################################\n##Import needed libraries\n#########################################\nimport time #To be able to sleep\nimport requests\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nimport codecs #To be able to write the file\nfrom local_paths import output\nfrom local_paths import output_error\nfrom local_paths import input\n\n\n#########################################\n##Static paths and variables\n#########################################\nstatic1 = \"https://secust.msse.se/se/nordnetny/funds/overview.aspx?cid=\"\nstatic2 = \"&cntry=SE\"\nvDate = datetime.now().strftime(\"%Y-%m-%d\")\n\n\n#########################################\n## Create empty file so we can write to it later\n#########################################\nNordnet_Nav = codecs.open(output + '/' + 'Nav' + vDate + '.txt', 'w', 'utf-8')\ntexttowrite = 'ID@NAV@NAVDATE'\nNordnet_Nav.write(texttowrite + '\\r\\n')\n\n#########################################\n# Funds with errors\n#########################################\nErrorID = codecs.open(output_error + '/' + 'Error' + vDate + '.txt', 'w', 'utf-8')\ntexttowrite = 'ID@ERROR'\nErrorID.write(texttowrite + '\\r\\n')\n\n\n#########################################\n## Get IDs to read\n#########################################\nwith codecs.open(input + '/' + 'ID.txt', 'r', encoding='utf-8') as Funds:\n\tfor Fund in Funds:\n\t\tFund2 = Fund.strip()\n\t\tres = requests.get(static1 + Fund2 + static2)\n\t\ttry:\n\t\t\tsoup = BeautifulSoup(res.content, 'lxml')\n\t\t\ttable = soup.find('table', {\"id\" : \"Table1\"})\n\t\t\tNav = table.find_all('td')[3].text\n\t\t\tPreNavDate = table.find_all('td')[5].text\n\t\t\tNavDate = PreNavDate.strip()\n################################################\n## Write to file\n################################################\n\t\t\tNordnet_Nav.write(\"%s\" %Fund2 + \"@\" + \"%s\" %Nav + \"@\" \"%s\" %NavDate + '\\r\\n')\n\t\texcept Exception as e:\n\t\t\tErrorID.write(\"%s\" % x_stripped + \"@\" + \"%s\" % str(e) + '\\r\\n')\n","sub_path":"Nordnet_GetNav_BS.py","file_name":"Nordnet_GetNav_BS.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"61093540","text":"import numpy as np\r\nimport sys\r\nimport matplotlib.pyplot as plt\r\n\r\n#Plot curve\r\nx = np.linspace(0,4*np.pi,100)\r\ny = np.sin(x)\r\nplt.plot(x,y)\r\nplt.show()\r\n\r\n#Marker\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# plt.plot(x,y)\r\n# plt.plot(x,y,'-^') # adding triangle markers\r\n# plt.plot(x,y,'-o') # adding circle markers\r\n# plt.show()\r\n\r\n\r\n#Line Style\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# plt.plot(x,y,'--')\r\n# plt.show()\r\n\r\n#color\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# plt.plot(x,y,'g')\r\n# plt.show()\r\n\r\n#grid line\r\nx = np.linspace(0,4*np.pi,100)\r\ny = np.sin(x)\r\nplt.plot(x,y,'g')\r\nplt.grid()\r\nplt.show()\r\n\r\n#label\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# plt.plot(x,y,'g')\r\n# plt.xlabel('x')\r\n# plt.ylabel('y')\r\n# plt.title('A sine curve')\r\n# plt.axis([0,4*np.pi,-2,2])\r\n# plt.show()\r\n\r\n\r\n#Overlay Plots and Legend\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# y1= np.cos(x)\r\n# plt.plot(x,y,'c',label='y=sin(x)')\r\n# plt.plot(x,y1,'y',label='y=cos(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.show()\r\n\r\n\r\n\r\n#subplot\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# y1= np.cos(x)\r\n# plt.subplot(2,1,1)\r\n# plt.plot(x,y,'c',label='y=sin(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.subplot(2,1,2)\r\n# plt.plot(x,y1,'y',label='y=cos(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.show()\r\n\r\n#-------------------------------\r\n#chanllenge\r\n\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# y2 = np.cos(x)\r\n# y3 = np.sin(x) * np.cos(x)\r\n# y4 = np.sin(x)- np.cos(x)\r\n\r\n# plt.subplot(2,2,1)\r\n# plt.plot(x,y,'r',label='y=sin(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.subplot(2,2,2)\r\n# plt.plot(x,y2,'y',label='y=cos(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.subplot(2,2,3)\r\n# plt.plot(x,y3,'g',label='y=sin(x) * cos(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.subplot(2,2,4)\r\n# plt.plot(x,y4,'b',label='y=cos(x) - sin(x)')\r\n# plt.legend(loc='upper left')\r\n# plt.subplots_adjust(wspace=0.6, hspace=0.6, left=0.1, bottom=0.22, right=0.96, top=0.96)\r\n# plt.show()\r\n\r\n#---------------------------------\r\n# x = np.linspace(0,4*np.pi,100)\r\n# y = np.sin(x)\r\n# y2 = np.cos(x)\r\n# y3 = np.sin(x) * np.cos(x)\r\n# y4 = np.sin(x)- np.cos(x)\r\n\r\n# plt.subplot(2,2,1)\r\n# plt.plot(x,y,'r')\r\n# plt.subplot(2,2,2)\r\n# plt.plot(x,y2,'y')\r\n# plt.subplot(2,2,3)\r\n# plt.plot(x,y3,'g')\r\n# plt.subplot(2,2,4)\r\n# plt.plot(x,y4,'b')\r\n\r\n# plt.legend(loc='upper left')\r\n# plt.show()\r\n\r\n\r\n\r\n#-----------------------------------------------------\r\n#Examples\r\n# def plot5():\r\n# data = [ (1, 0), (2, 0.1 ), (3, 1.1), (4, 1.2), (5, 2.3), \r\n# (6, 3.5), (7, 5.8) ]\r\n# X = [ x for (x,y) in data ]\r\n# Y = [ y for (x,y) in data ]\r\n# #print X\r\n# #print Y\r\n# plt.plot( X, Y, ':rs' )\r\n# plt.axis( [0, 8, 0, 6])\r\n# plt.xlabel( \"X values\" )\r\n# plt.ylabel( \"Y values\" )\r\n# plt.show()\r\n\r\n# plot5()\r\n\r\n\r\n#------------------------------------------------------\r\n# #Scatter Plot\r\n# x = np.arange(1, 51)\r\n# print(x)\r\n# y = np.random.rand(50) \r\n# print(y)\r\n\r\n# plt.scatter(x,y)\r\n# plt.show()\r\n#--------------------------------------------------\r\n#Bar Plot\r\ndata = [ (\"data1\", 34), (\"data2\", 22),\r\n (\"data3\", 11), ( \"data4\", 28),\r\n (\"data5\", 57), ( \"data6\", 39),\r\n (\"data7\", 23), ( \"data8\", 98)]\r\nN = len( data )\r\nx = np.arange(1, N+1)\r\ny = [ num for (s, num) in data ]\r\nlabels = [ s for (s, num) in data ]\r\nwidth = 1\r\nbar1 = plt.bar( x, y, width, color=\"y\" )\r\nplt.ylabel( 'Intensity' )\r\nplt.xticks(x + width/2.0, labels )\r\nplt.show()\r\n\r\n#------------------------------------------\r\n#Contour Plot\r\n# x = np.linspace(-1,1,255)\r\n# y = np.linspace(-2,2,300)\r\n# X, Y = np.meshgrid(x, y)\r\n# z = np.sin(X)*np.cos(Y)\r\n# plt.contour(X,Y,z,255)\r\n# plt.show()\r\n#-------------------------------------\r\n\r\n#Hisotgram\r\n# import matplotlib.pyplot as plt\r\n# import numpy as np\r\n# gaussian_numbers = np.random.normal(size=10000)\r\n# print(gaussian_numbers)\r\n# plt.hist(gaussian_numbers)\r\n# plt.title(\"Gaussian Histogram\")\r\n# plt.xlabel(\"Value\")\r\n# plt.ylabel(\"Frequency\")\r\n# plt.show()\r\n\r\n\r\n#----------------------------------------------\r\n# #Pie chart\r\n# print (sys.version)\r\n# x = np.linspace(0,4*np.pi,10)\r\n# plt.pie(x)\r\n# plt.show()","sub_path":"Examples -Students/Module12-Numpy/matplotlibEx.py","file_name":"matplotlibEx.py","file_ext":"py","file_size_in_byte":4198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"277728623","text":"import numpy as np\nfrom ploos import checkableComboBox\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\n\n\nclass Ui_Dialog(object):\n def setupUi(self, Dialog, canvas):\n self.canvas = canvas\n self.doscar = canvas.doscar[0]\n self.axn = 0\n self.linecounter = 0\n if not Dialog.objectName():\n Dialog.setObjectName(u\"Projector\")\n Dialog.resize(402, 368)\n self.horizontalLayout_3 = QHBoxLayout(Dialog)\n self.horizontalLayout_3.setObjectName(u\"horizontalLayout_3\")\n self.verticalLayout_2 = QVBoxLayout()\n self.verticalLayout_2.setObjectName(u\"verticalLayout_2\")\n self.verticalLayout_2.setSizeConstraint(QLayout.SetMaximumSize)\n self.label_ax = QLabel(Dialog)\n self.label_ax.setObjectName(u\"label_ax\")\n self.verticalLayout_2.addWidget(self.label_ax)\n self.horizontalLayout = QHBoxLayout()\n self.horizontalLayout.setObjectName(u\"horizontalLayout\")\n self.label = QLabel(Dialog)\n self.label.setObjectName(u\"label\")\n\n self.horizontalLayout.addWidget(self.label)\n\n# self.spinBox = QSpinBox(Dialog)\n# self.spinBox.setObjectName(u\"spinBox\")\n# self.spinBox.setRange(1, doscar.natoms)\n#\n# self.horizontalLayout.addWidget(self.spinBox)\n#\n# self.horizontalSpacer = QSpacerItem(60, 40, QSizePolicy.Preferred, QSizePolicy.Minimum)\n#\n# self.horizontalLayout.addItem(self.horizontalSpacer)\n#\n# self.label_2 = QLabel(Dialog)\n# self.label_2.setObjectName(u\"label_2\")\n#\n# self.horizontalLayout.addWidget(self.label_2)\n#\n# self.spinBox_2 = QSpinBox(Dialog)\n# self.spinBox_2.setObjectName(u\"spinBox_2\")\n# self.spinBox_2.setRange(1, doscar.natoms)\n#\n# self.horizontalLayout.addWidget(self.spinBox_2)\n\n\n# self.verticalLayout_2.addLayout(self.horizontalLayout)\n self.axis = checkableComboBox.CheckableComboBox(Dialog) \n if canvas.compare:\n self.axis.addItems( [ str(i) for i,x in enumerate(canvas.axes) ] )\n self.verticalLayout_2.addWidget(self.axis)\n self.loadDoscar = QPushButton(Dialog)\n self.loadDoscar.setObjectName(u\"loadDoscar\")\n self.loadDoscar.clicked.connect(self.load)\n self.verticalLayout_2.addWidget(self.loadDoscar)\n\n self.label_3 = QLabel(Dialog)\n self.label_3.setObjectName(u\"label_3\")\n self.verticalLayout_2.addWidget(self.label_3)\n self.atoms = checkableComboBox.CheckableComboBox(Dialog) \n self.atoms.addItems( self.doscar.atoms )\n\n self.verticalLayout_2.addWidget(self.atoms)\n \n self.label_4 = QLabel(Dialog)\n self.label_4.setObjectName(u\"label_4\")\n\n self.verticalLayout_2.addWidget(self.label_4)\n\n self.orbitals = checkableComboBox.CheckableComboBox(Dialog) \n self.orbitals.addItems( self.doscar.guiLabel )\n\n self.verticalLayout_2.addWidget(self.orbitals)\n \n self.label_5 = QLabel(Dialog)\n self.label_5.setObjectName(u\"label_5\")\n\n self.verticalLayout_2.addWidget(self.label_5)\n\n self.lineEdit = QLineEdit(Dialog)\n self.lineEdit.setObjectName(u\"lineEdit\")\n\n self.verticalLayout_2.addWidget(self.lineEdit)\n\n self.verticalSpacer = QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding)\n\n self.verticalLayout_2.addItem(self.verticalSpacer)\n\n self.horizontalLayout_2 = QHBoxLayout()\n self.horizontalLayout_2.setObjectName(u\"horizontalLayout_2\")\n self.pushButton = QPushButton(Dialog)\n self.pushButton.setObjectName(u\"pushButton\")\n self.pushButton.clicked.connect(self.clickAddLine)\n\n self.horizontalLayout_2.addWidget(self.pushButton)\n\n# self.pushButton_2 = QPushButton(Dialog)\n# self.pushButton_2.setObjectName(u\"pushButton_2\")\n# self.pushButton_2.clicked.connect(self.clickRemoveLine)\n#\n# self.horizontalLayout_2.addWidget(self.pushButton_2)\n\n self.pushButton_3 = QPushButton(Dialog)\n self.pushButton_3.setObjectName(u\"pushButton_3\")\n self.pushButton_3.clicked.connect(self.close)\n\n self.horizontalLayout_2.addWidget(self.pushButton_3)\n\n\n self.verticalLayout_2.addLayout(self.horizontalLayout_2)\n\n\n self.horizontalLayout_3.addLayout(self.verticalLayout_2)\n\n\n self.retranslateUi(Dialog)\n\n QMetaObject.connectSlotsByName(Dialog)\n # setupUi\n\n def retranslateUi(self, Dialog):\n Dialog.setWindowTitle(QCoreApplication.translate(\"Projector\", u\"Projector\", None))\n self.label_ax.setText(QCoreApplication.translate(\"Dialog\", u\"Choose axis:\", None))\n self.label_3.setText(QCoreApplication.translate(\"Dialog\", u\"Project onto atoms:\", None))\n# self.label.setText(QCoreApplication.translate(\"Dialog\", u\"from:\", None))\n# self.label_2.setText(QCoreApplication.translate(\"Dialog\", u\"to:\", None))\n self.label_4.setText(QCoreApplication.translate(\"Dialog\", u\"Project onto orbitals\", None))\n self.label_5.setText(QCoreApplication.translate(\"Dialog\", u\"Line label:\", None))\n self.loadDoscar.setText(QCoreApplication.translate(\"Dialog\", \n u\"Load DOSCAR\", None))\n self.pushButton.setText(QCoreApplication.translate(\"Dialog\", u\"Add line\", None))\n# self.pushButton_2.setText(QCoreApplication.translate(\"Dialog\", u\"Remove line\", None))\n self.pushButton_3.setText(QCoreApplication.translate(\"Dialog\", u\"Close\", None))\n # retranslateUi\n\n\n def clickAddLine(self):\n self.linecounter += 1\n# atomlist = np.arange( self.spinBox.value()-1, self.spinBox_2.value() )\n atomlist = self.atoms.currentData()\n orblist = self.orbitals.currentData()\n mylabel = self.lineEdit.text()\n color = self.canvas.colors[self.linecounter]\n \n lines = []\n for axis in self.canvas.axes.flatten():\n for line in axis.get_lines():\n lines.append( line )\n for line in lines:\n if line.get_label() == mylabel:\n color = line.get_color()\n break\n\n self.canvas.axes[self.axn].plot(self.doscar.energy, self.doscar.projector(atomlist, orblist), \n color=color, label=mylabel)\n# self.canvas.axes[self.axn].legend(loc='best', prop={'size' : 20})\n# self.canvas.legend = self.canvas.fig.legend(loc='upper right',\n# prop={'size':20})\n self.canvas.draw()\n self.lineEdit.clear()\n\n\n def load(self):\n self.axn = int(self.axis.currentData()[0])\n self.doscar = self.canvas.doscar[ self.axn ]\n# def clickRemoveLine(self):\n# if ( self.linecounter > 0 ):\n# self.canvas.axes.get_legend().remove()\n# self.canvas.axes.lines.pop(self.linecounter)\n# self.canvas.axes.legend(loc='best')\n# self.canvas.draw()\n# self.linecounter -= 1\n\n\nclass AppProj(QDialog, Ui_Dialog):\n def __init__(self, canvas, parent=None):\n super(AppProj, self).__init__(parent)\n for dd in canvas.doscar: \n if dd.enableProjector: self.setupUi(self, canvas)\n break\n\n\n","sub_path":"ploos/ProjectorGUI.py","file_name":"ProjectorGUI.py","file_ext":"py","file_size_in_byte":7184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"274305277","text":"import re\nimport json\n\nfrom database.lockstatus import LockStatus\n\n\nclass Update:\n\n def strip_text(self,text):\n return re.sub(' +', ' ', text.strip())\n\n def update_row(self, username,dbname,query,logger,fname):\n check_lock = LockStatus().checklock(username)\n # create db copy\n src_fname = dbname + \"_Tables.txt\"\n dest_dname = dbname + \"_Tables_copy.txt\"\n if fname is None:\n filename = src_fname\n status = False\n else:\n filename = dest_dname\n status = True\n file1 = open(filename, \"r\")\n f1 = file1.read()\n file1.close()\n update_set_dict = {}\n dict_obj = json.loads(f1)\n is_update_query = False\n # query = \"UPDATE Player SET player_name = 'ABC', position = 'forward' WHERE team_id = '1';\"\n if re.split(\" \", query)[0].lower() == \"update\":\n is_update_query = True\n\n if is_update_query:\n # do operation\n update_query_set_values = re.split(\",\",\n re.sub(\"[^A-Za-z0-9=,_]\", \" \",\n re.findall(r'set(.*?)where', query.lower())[0]))\n update_query_condition = re.split(\",\", re.sub(\"[^A-Za-z0-9=,_]\", \" \", re.split('where', query.lower())[1]))\n table_name = self.strip_text(re.findall(r'update(.*?)set', query.lower())[0].strip())\n\n for y in update_query_condition:\n list_condition_update = re.split(\"= |< |> |<= |>= \", y)\n key_to_search_for_update_condition = self.strip_text(list_condition_update[0])\n value_update_condition = self.strip_text(list_condition_update[1])\n\n for x in update_query_set_values:\n list_key_value_update = re.split(\"=\", x)\n key_to_search = self.strip_text(list_key_value_update[0])\n value_to_update = self.strip_text(list_key_value_update[1])\n update_set_dict[key_to_search] = value_to_update\n # search in dict for condition and perform updates\n\n print(\"Update Query Operation\")\n #print(update_set_dict)\n tables_info = dict_obj['Tables']\n #print(tables_info)\n for values in tables_info:\n if values.get(\"Table_name\") == table_name:\n print(\"found\")\n values_info = values['Table_columns']\n for inside_list_value in values_info:\n if inside_list_value.get(key_to_search_for_update_condition) == value_update_condition:\n #print(inside_list_value)\n inside_list_value.update(update_set_dict)\n\n #print(dict_obj)\n #print(values)\n #print('check')\n file1 = open(filename, \"w+\")\n f1 = file1.write(json.dumps(dict_obj))\n file1.close()\n return status","sub_path":"database/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"454002212","text":"# -*- coding: utf-8 -*-\nfrom datetime import datetime, timedelta\nfrom odoo import api, fields, models\nimport datetime\nclass ScheduleReport(models.AbstractModel):\n _name = 'report.jptip_core.class_list.xlsx'\n _inherit = 'report.report_xlsx.abstract'\n\n def generate_xlsx_report(self,workbook, data, env):\n time_interval = self.env['class_schedule.print'].search([('company_id', '=', self.env.user.company_id.id)],limit=1)\n start_date = time_interval.start_date\n end_date = time_interval.end_date\n class_data = self.env['class.timetable'].search([('date', '>=', start_date), ('date', '<=', end_date), ('company_id', '=', self.env.user.company_id.id)], order=\"class_name asc\")\n class_room = self.env['jptip.class.room'].search([('company_id','=',self.env.user.company_id.id)])\n format21 = workbook.add_format({'font_size': 10, 'align': 'center', 'text_wrap': 1, 'bold': True, 'top':1, 'left':1, 'right':1,'bottom':1})\n format22 = workbook.add_format({'font_size': 8, 'align': 'center', 'text_wrap': 1})\n format23 = workbook.add_format({'font_size': 12, 'align': 'vcenter', 'text_wrap': 1})\n format24 = workbook.add_format({'font_size': 10, 'align': 'center', 'text_wrap': 1, 'bold': True, 'top':2, 'left':2, 'right':2,'bottom':2})\n format23.set_align('center')\n format24.set_bg_color('gray')\n sheet = workbook.add_worksheet(u'%s 課表' % self.env.user.company_id.name[0:2])\n start_day = None\n end_day = None\n for line in class_data.sorted(key=lambda r: r.date, reverse=False):\n start_day = line.date\n break\n for line in class_data.sorted(key=lambda r: r.date, reverse=True):\n end_day = line.date\n break\n days = datetime.datetime.strptime(end_day, '%Y-%m-%d') - datetime.datetime.strptime(start_day, '%Y-%m-%d')\n delta = datetime.timedelta(days=1)\n j = 0\n for line in range(0, days.days + 1):\n class_data_set = self.env['class.timetable'].search([('date', '=', start_day), ('company_id', '=', self.env.user.company_id.id)])\n if not class_data_set:\n continue\n i = 2\n sheet.merge_range(j, 0, j, len(class_room) + 1, '%s 教室班課表' % self.env.user.company_id.class_header, format21)\n j += 1\n sheet.write(j, i - 2, '日期', format24)\n sheet.write(j, i - 1, '時間', format24)\n for row in class_room.sorted(key=lambda r: r.name, reverse=False):\n sheet.write(j, i, row.name, format24)\n row.excel_location = i\n i += 1\n j += 1\n temp = j\n if datetime.datetime.strptime(start_day, '%Y-%m-%d').weekday() != 5 and datetime.datetime.strptime(start_day, '%Y-%m-%d').weekday() != 6:\n time_session = self.env['jt.time.session'].search([('SA_day','!=','1'),('SU_day','!=','1'),('company_id','=',self.env.user.company_id.id)])\n for row in time_session:\n for x in row.time_duration_ids:\n sheet.write(j,1, x.begin_time + '\\n' + ' ~ \\n' + x.end_time, format21)\n for y in class_data_set:\n if y.class_time_begin.begin_time == x.begin_time and y.class_time_begin.end_time == x.end_time:\n sheet.write(j, y.class_room.excel_location, y.class_name.course_id.name + '\\n' + y.course_seq + '\\n' + y.teacher.name if y.teacher else y.class_name.course_id.name + '\\n' + y.course_seq + '\\n', format22)\n j += 1\n sheet.merge_range(temp, 0, j - 1, 0, str(datetime.datetime.strptime(start_day, '%Y-%m-%d').month) + '/' + str(datetime.datetime.strptime(start_day, '%Y-%m-%d').day), format23)\n start_day = str(datetime.datetime.strptime(start_day, '%Y-%m-%d') + delta).replace(' 00:00:00', '')\n if datetime.datetime.strptime(start_day, '%Y-%m-%d').weekday() == 5 or datetime.datetime.strptime(start_day, '%Y-%m-%d').weekday() == 6:\n time_session = self.env['jt.time.session'].search([('SA_day','=','1'),('SU_day','=','1'),('company_id','=',self.env.user.company_id.id)])\n for row in time_session:\n for x in row.time_duration_ids:\n sheet.write(j,1, x.begin_time + '\\n' + ' ~ \\n' + x.end_time, format21)\n for y in class_data_set:\n if y.class_time_begin.begin_time == x.begin_time and y.class_time_begin.end_time == x.end_time:\n sheet.write(j, y.class_room.excel_location, y.class_name.course_id.name + '\\n' + y.course_seq + '\\n' + y.teacher.name if y.teacher else y.class_name.course_id.name + '\\n' + y.course_seq + '\\n', format22)\n j += 1\n sheet.merge_range(temp, 0, j - 1, 0, str(datetime.datetime.strptime(start_day, '%Y-%m-%d').month) + '/' + str(datetime.datetime.strptime(start_day, '%Y-%m-%d').day), format23)\n start_day = str(datetime.datetime.strptime(start_day, '%Y-%m-%d') + delta).replace(' 00:00:00', '')\n time_interval.unlink()","sub_path":"addons_jptip/jptip_core/report/class_schedule_report.py","file_name":"class_schedule_report.py","file_ext":"py","file_size_in_byte":5209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"427887932","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('track', '0002_auto_20140910_1824'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='track',\n name='file',\n ),\n migrations.AddField(\n model_name='track',\n name='album',\n field=models.ForeignKey(default=1, to='track.Album'),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='album',\n name='artist',\n field=models.ForeignKey(to='users.UserProxy'),\n ),\n ]\n","sub_path":"musicsite/track/migrations/0003_auto_20140913_1302.py","file_name":"0003_auto_20140913_1302.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"419444864","text":"\nfrom dbt.logger import GLOBAL_LOGGER as logger\n\nimport psycopg2\nimport logging\nimport time\nimport re\n\nSCHEMA_PERMISSION_DENIED_MESSAGE = \"\"\"\nThe user '{user}' does not have sufficient permissions to create the schema\n'{schema}'. Either create the schema manually, or adjust the permissions of\nthe '{user}' user.\"\"\"\n\nRELATION_PERMISSION_DENIED_MESSAGE = \"\"\"\nThe user '{user}' does not have sufficient permissions to create the model\n'{model}' in the schema '{schema}'. Please adjust the permissions of the\n'{user}' user on the '{schema}' schema. With a superuser account, execute the\nfollowing commands, then re-run dbt.\n\ngrant usage, create on schema \"{schema}\" to \"{user}\";\ngrant select, insert, delete on all tables in schema \"{schema}\" to \"{user}\";\"\"\"\n\nRELATION_NOT_OWNER_MESSAGE = \"\"\"\nThe user '{user}' does not have sufficient permissions to drop the model\n'{model}' in the schema '{schema}'. This is likely because the relation was\ncreated by a different user. Either delete the model \"{schema}\".\"{model}\"\nmanually, or adjust the permissions of the '{user}' user in the '{schema}'\nschema.\"\"\"\n\nREAD_PERMISSION_DENIED_ERROR = \"\"\"\nEncountered an error while executing model '{model}'.\n> {error}\nCheck that the user '{user}' has sufficient permissions to read from all\nnecessary source tables\"\"\"\n\n\nclass Column(object):\n def __init__(self, column, dtype, char_size):\n self.column = column\n self.dtype = dtype\n self.char_size = char_size\n\n @property\n def name(self):\n return self.column\n\n @property\n def quoted(self):\n return '\"{}\"'.format(self.column)\n\n @property\n def data_type(self):\n if self.is_string():\n return Column.string_type(self.string_size())\n else:\n return self.dtype\n\n def is_string(self):\n return self.dtype in ['text', 'character varying']\n\n def string_size(self):\n if not self.is_string():\n raise RuntimeError(\"Called string_size() on non-string field!\")\n\n if self.dtype == 'text' or self.char_size is None:\n # char_size should never be None. Handle it reasonably just in case\n return 255\n else:\n return int(self.char_size)\n\n def can_expand_to(self, other_column):\n \"\"\"returns True if this column can be expanded to the size of the\n other column\"\"\"\n if not self.is_string() or not other_column.is_string():\n return False\n\n return other_column.string_size() > self.string_size()\n\n @classmethod\n def string_type(cls, size):\n return \"character varying({})\".format(size)\n\n def __repr__(self):\n return \"\".format(self.name, self.data_type)\n\n\nclass Schema(object):\n def __init__(self, project, target):\n self.project = project\n self.target = target\n\n self.schema_cache = {}\n self.runtime_existing = self.query_for_existing(self.target.schema)\n\n def cache_table_columns(self, schema, table, columns):\n tid = (schema, table)\n\n if tid not in self.schema_cache:\n self.schema_cache[tid] = columns\n\n return tid\n\n def get_table_columns_if_cached(self, schema, table):\n tid = (schema, table)\n return self.schema_cache.get(tid, None)\n\n def get_schemas(self):\n existing = []\n results = self.execute_and_fetch(\n 'select nspname from pg_catalog.pg_namespace')\n return [name for (name,) in results]\n\n def create_schema(self, schema_name):\n target_cfg = self.project.run_environment()\n user = target_cfg['user']\n\n try:\n self.execute(\n 'create schema if not exists \"{}\"'.format(schema_name))\n except psycopg2.ProgrammingError as e:\n if \"permission denied for\" in e.diag.message_primary:\n raise RuntimeError(\n SCHEMA_PERMISSION_DENIED_MESSAGE.format(\n schema=schema_name, user=user))\n else:\n raise e\n\n def query_for_existing(self, schema):\n sql = \"\"\"\n select tablename as name, 'table' as type from pg_tables where schemaname = '{schema}'\n union all\n select viewname as name, 'view' as type from pg_views where schemaname = '{schema}' \"\"\".format(schema=schema) # noqa\n\n results = self.execute_and_fetch(sql)\n existing = [(name, relation_type) for (name, relation_type) in results]\n\n return dict(existing)\n\n def execute(self, sql):\n with self.target.get_handle() as handle:\n with handle.cursor() as cursor:\n try:\n logger.debug(\"SQL: %s\", sql)\n pre = time.time()\n cursor.execute(sql)\n post = time.time()\n logger.debug(\n \"SQL status: %s in %0.2f seconds\",\n cursor.statusmessage, post-pre)\n return cursor.statusmessage\n except Exception as e:\n self.target.rollback()\n logger.debug(\"Error running SQL: %s\", sql)\n logger.debug(\"rolling back connection\")\n raise e\n\n def execute_and_fetch(self, sql):\n with self.target.get_handle() as handle:\n with handle.cursor() as cursor:\n try:\n logger.debug(\"SQL: %s\", sql)\n pre = time.time()\n cursor.execute(sql)\n post = time.time()\n logger.debug(\n \"SQL status: %s in %0.2f seconds\",\n cursor.statusmessage, post-pre)\n data = cursor.fetchall()\n logger.debug(\"SQL response: %s\", data)\n return data\n except Exception as e:\n self.target.rollback()\n logger.debug(\"Error running SQL: %s\", sql)\n logger.debug(\"rolling back connection\")\n raise e\n\n def execute_and_handle_permissions(self, query, model_name):\n try:\n return self.execute(query)\n except psycopg2.ProgrammingError as e:\n error_data = {\"model\": model_name,\n \"schema\": self.target.schema,\n \"user\": self.target.user}\n if 'must be owner of relation' in e.diag.message_primary:\n raise RuntimeError(\n RELATION_NOT_OWNER_MESSAGE.format(**error_data))\n elif \"permission denied for\" in e.diag.message_primary:\n raise RuntimeError(\n RELATION_PERMISSION_DENIED_MESSAGE.format(**error_data))\n else:\n raise e\n\n def execute_without_auto_commit(self, sql, handle=None):\n if handle is None:\n handle = self.target.get_handle()\n\n cursor = handle.cursor()\n\n try:\n logger.debug(\"SQL: %s\", sql)\n pre = time.time()\n cursor.execute(sql)\n post = time.time()\n logger.debug(\n \"SQL status: %s in %0.2f seconds\",\n cursor.statusmessage, post-pre)\n return handle, cursor.statusmessage\n except Exception as e:\n self.target.rollback()\n logger.debug(\"Error running SQL: %s\", sql)\n logger.debug(\"rolling back connection\")\n raise e\n finally:\n cursor.close()\n\n def truncate(self, schema, relation):\n sql = ('truncate table \"{schema}\".\"{relation}\"'\n .format(schema=schema, relation=relation))\n logger.debug(\"dropping table %s.%s\", schema, relation)\n self.execute_and_handle_permissions(sql, relation)\n logger.debug(\"dropped %s.%s\", schema, relation)\n\n def drop(self, schema, relation_type, relation):\n sql = ('drop {relation_type} if exists \"{schema}\".\"{relation}\" cascade'\n .format(\n schema=schema,\n relation_type=relation_type,\n relation=relation))\n logger.debug(\"dropping %s %s.%s\", relation_type, schema, relation)\n self.execute_and_handle_permissions(sql, relation)\n logger.debug(\"dropped %s %s.%s\", relation_type, schema, relation)\n\n def sql_columns_in_table(self, schema_name, table_name):\n sql = (\"\"\"\n select column_name, data_type, character_maximum_length\n from information_schema.columns\n where table_name = '{table_name}'\"\"\"\n .format(table_name=table_name).strip())\n\n if schema_name is not None:\n sql += (\" AND table_schema = '{schema_name}'\"\n .format(schema_name=schema_name))\n\n return sql\n\n def get_columns_in_table(self, schema_name, table_name, use_cached=True):\n logger.debug(\"getting columns in table %s.%s\", schema_name, table_name)\n\n columns = self.get_table_columns_if_cached(schema_name, table_name)\n if columns is not None and use_cached:\n logger.debug(\"Found columns (in cache): %s\", columns)\n return columns\n\n sql = self.sql_columns_in_table(schema_name, table_name)\n results = self.execute_and_fetch(sql)\n\n columns = []\n for result in results:\n column, data_type, char_size = result\n col = Column(column, data_type, char_size)\n columns.append(col)\n\n self.cache_table_columns(schema_name, table_name, columns)\n\n logger.debug(\"Found columns: %s\", columns)\n return columns\n\n def rename(self, schema, from_name, to_name):\n rename_query = 'alter table \"{schema}\".\"{from_name}\" rename to \"{to_name}\"'.format(schema=schema, from_name=from_name, to_name=to_name) # noqa\n logger.debug(\n \"renaming model %s.%s --> %s.%s\",\n schema, from_name, schema, to_name)\n self.execute_and_handle_permissions(rename_query, from_name)\n logger.debug(\n \"renamed model %s.%s --> %s.%s\",\n schema, from_name, schema, to_name)\n\n def get_missing_columns(self, from_schema, from_table, to_schema,\n to_table):\n \"\"\"Returns dict of {column:type} for columns in from_table that are\n missing from to_table\"\"\"\n from_columns = {col.name: col for col in\n self.get_columns_in_table(from_schema, from_table)}\n to_columns = {col.name: col for col in\n self.get_columns_in_table(to_schema, to_table)}\n\n missing_columns = set(from_columns.keys()) - set(to_columns.keys())\n\n return [col for (col_name, col) in from_columns.items()\n if col_name in missing_columns]\n\n def create_table(self, schema, table, columns, sort, dist):\n fields = ['\"{field}\" {data_type}'.format(\n field=column.name, data_type=column.data_type\n ) for column in columns]\n fields_csv = \",\\n \".join(fields)\n dist = self.target.dist_qualifier(dist)\n sort = self.target.sort_qualifier('compound', sort)\n sql = 'create table if not exists \"{schema}\".\"{table}\" (\\n {fields}\\n) {dist} {sort};'.format(schema=schema, table=table, fields=fields_csv, sort=sort, dist=dist) # noqa\n logger.debug('creating table \"%s\".\"%s\"'.format(schema, table))\n self.execute_and_handle_permissions(sql, table)\n\n def create_schema_if_not_exists(self, schema_name):\n schemas = self.get_schemas()\n\n if schema_name not in schemas:\n self.create_schema(schema_name)\n\n def alter_column_type(self, schema, table, column_name, new_column_type):\n \"\"\"\n 1. Create a new column (w/ temp name and correct type)\n 2. Copy data over to it\n 3. Drop the existing column (cascade!)\n 4. Rename the new column to existing column\n \"\"\"\n\n opts = {\n \"schema\": schema,\n \"table\": table,\n \"old_column\": column_name,\n \"tmp_column\": \"{}__dbt_alter\".format(column_name),\n \"dtype\": new_column_type\n }\n\n sql = \"\"\"\n alter table \"{schema}\".\"{table}\" add column \"{tmp_column}\" {dtype};\n update \"{schema}\".\"{table}\" set \"{tmp_column}\" = \"{old_column}\";\n alter table \"{schema}\".\"{table}\" drop column \"{old_column}\" cascade;\n alter table \"{schema}\".\"{table}\" rename column \"{tmp_column}\" to \"{old_column}\";\n \"\"\".format(**opts) # noqa\n\n status = self.execute(sql)\n return status\n\n def expand_column_types_if_needed(self, temp_table, to_schema, to_table):\n source_columns = {col.name: col for col in\n self.get_columns_in_table(None, temp_table)}\n dest_columns = {col.name: col for col in\n self.get_columns_in_table(to_schema, to_table)}\n\n for column_name, source_column in source_columns.items():\n dest_column = dest_columns.get(column_name)\n\n if dest_column is not None and \\\n dest_column.can_expand_to(source_column):\n new_type = Column.string_type(source_column.string_size())\n logger.debug(\"Changing col type from %s to %s in table %s.%s\",\n dest_column.data_type,\n new_type,\n to_schema,\n to_table)\n self.alter_column_type(\n to_schema, to_table, column_name, new_type)\n\n # update these cols in the cache! This is a hack to fix broken\n # incremental models for type expansion. TODO\n self.cache_table_columns(to_schema, to_table, source_columns)\n\n def table_exists(self, schema, table):\n if schema == self.target.schema:\n exists = self.runtime_existing.get(table) is not None\n return exists\n else:\n tables = self.query_for_existing(schema)\n exists = tables.get(table) is not None\n return exists\n","sub_path":"dbt/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":14070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"403360928","text":"\"\"\"\n0.10 Cs2O • 0.90 SiO2 MAS-ETA\n=============================\n\"\"\"\n\n# %%\n# The following example is an application of the statistical learning method in\n# determining the distribution of the Si-29 echo train decay constants in glasses.\n#\n# Import all relevant packages.\nimport csdmpy as cp\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom mrinversion.kernel import relaxation\nfrom mrinversion.linear_model import LassoFistaCV, TSVDCompression\nfrom csdmpy import statistics as stats\n\nplt.rcParams[\"pdf.fonttype\"] = 42 # For using plots in Illustrator\nplt.rc(\"font\", size=9)\n\n\ndef plot2D(csdm_object, **kwargs):\n plt.figure(figsize=(4, 3))\n csdm_object.plot(cmap=\"gist_ncar_r\", **kwargs)\n plt.tight_layout()\n plt.show()\n\n\n# sphinx_gallery_thumbnail_number = 4\n\n# %%\n# Dataset setup\n# -------------\n# Import the dataset\n# ''''''''''''''''''\n# Load the dataset as a CSDM data-object.\n\n# The 2D SE-PIETA MAS dataset in csdm format\ndomain = \"https://www.ssnmr.org/sites/default/files/mrsimulator\"\nfilename = f\"{domain}/MAS_SE_PIETA_10Cs_90Si_FT.csdf\"\ndata_object = cp.load(filename)\n\n# Inversion only requires the real part of the complex dataset.\ndata_object = data_object.real\nsigma = 1270.825 # data standard deviation\n\n# Convert the MAS dimension from Hz to ppm.\ndata_object.dimensions[0].to(\"ppm\", \"nmr_frequency_ratio\")\nplot2D(data_object)\n\n# %%\n# Prepping the data for inversion\n# '''''''''''''''''''''''''''''''\ndata_object = data_object.T\ndata_object_truncated = data_object[:, 1220:-1220]\nplot2D(data_object_truncated)\n\n# %%\n# Linear Inversion setup\n# ----------------------\n# Dimension setup\n# '''''''''''''''\ndata_object_truncated.dimensions[0].to(\"s\") # set coordinates to 's'\nkernel_dimension = data_object_truncated.dimensions[0]\n\n# %%\n# Generating the kernel\n# '''''''''''''''''''''\nrelaxT2 = relaxation.T2(\n kernel_dimension=kernel_dimension,\n inverse_dimension=dict(\n count=32,\n minimum=\"1e-3 s\",\n maximum=\"1e4 s\",\n scale=\"log\",\n label=r\"log ($\\lambda^{-1}$ / s)\",\n ),\n)\ninverse_dimension = relaxT2.inverse_dimension\nK = relaxT2.kernel(supersampling=20)\n\n# %%\n# Data Compression\n# ''''''''''''''''\nnew_system = TSVDCompression(K, data_object_truncated)\ncompressed_K = new_system.compressed_K\ncompressed_s = new_system.compressed_s\n\nprint(f\"truncation_index = {new_system.truncation_index}\")\n\n# %%\n# Solving the inverse problem\n# ---------------------------\n# FISTA LASSO cross-validation\n# '''''''''''''''''''''''''''''\n\n# setup the pre-defined range of alpha and lambda values\nlambdas = 10 ** (-4 + 5 * (np.arange(32) / 31))\n\n# setup the smooth lasso cross-validation class\ns_lasso = LassoFistaCV(\n lambdas=lambdas, # A numpy array of lambda values.\n sigma=sigma, # data standard deviation\n folds=5, # The number of folds in n-folds cross-validation.\n inverse_dimension=inverse_dimension, # previously defined inverse dimensions.\n)\n\n# run the fit method on the compressed kernel and compressed data.\ns_lasso.fit(K=compressed_K, s=compressed_s)\n\n# %%\n# The optimum hyper-parameters\n# ''''''''''''''''''''''''''''\nprint(s_lasso.hyperparameters)\n\n# %%\n# The cross-validation curve\n# ''''''''''''''''''''''''''\nplt.figure(figsize=(4, 3))\ns_lasso.cv_plot()\nplt.tight_layout()\nplt.show()\n\n# %%\n# The optimum solution\n# ''''''''''''''''''''\nf_sol = s_lasso.f\n\nlevels = np.arange(15) / 15 + 0.1\nplt.figure(figsize=(3.85, 2.75)) # set the figure size\nax = plt.subplot(projection=\"csdm\")\ncb = ax.contourf(f_sol / f_sol.max(), levels=levels, cmap=\"jet_r\")\nax.set_ylim(-70, -130)\nax.set_xlim(-3, 2.5)\nplt.title(\"10Cs:90Si\")\nax.set_xlabel(r\"$\\log(\\lambda^{-1}\\,/\\,$s)\")\nax.set_ylabel(\"Frequency / ppm\")\nplt.grid(linestyle=\"--\", alpha=0.75)\nplt.colorbar(cb, ticks=np.arange(11) / 10)\nplt.tight_layout()\nplt.show()\n\n\n# %%\n# The fit residuals\n# '''''''''''''''''\nresiduals = s_lasso.residuals(K=K, s=data_object_truncated)\nplot2D(residuals)\n\n# %%\n# The standard deviation of the residuals is\nresiduals.std()\n\n# %%\n# Saving the solution\n# '''''''''''''''''''\nf_sol.save(\"10Cs-90Si_inverse.csdf\") # save the solution\nresiduals.save(\"10Cs-90Si_residue.csdf\") # save the residuals\n\n# %%\n# Analysis\n# --------\n\n# Normalize the distribution to 1.\nf_sol /= f_sol.max()\n\n# Get the Q4 and Q3 cross-sections.\nQ4_coordinate = -108.9e-6 # ppm\nQ3_coordinate = -98.6e-6 # ppm\nQ4_index = np.where(f_sol.dimensions[1].coordinates >= Q4_coordinate)[0][0]\nQ3_index = np.where(f_sol.dimensions[1].coordinates >= Q3_coordinate)[0][0]\n\nQ4_region = f_sol[:, Q4_index]\nQ3_region = f_sol[:, Q3_index]\n\n# %%\n# Plot of the Q4 and Q3 cross-sections\nfig, ax = plt.subplots(1, 2, figsize=(7, 2.75), subplot_kw={\"projection\": \"csdm\"})\ncb = ax[0].contourf(f_sol, levels=levels, cmap=\"jet_r\")\nax[0].arrow(1, Q4_coordinate * 1e6, -0.5, 0, color=\"blue\")\nax[0].arrow(1, Q3_coordinate * 1e6, -0.5, 0, color=\"orange\")\nax[0].set_ylim(-70, -130)\nax[0].set_xlim(-3, 2.5)\nax[0].set_xlabel(r\"$\\log(\\lambda^{-1}\\,/\\,$s)\")\nax[0].set_ylabel(\"Frequency / ppm\")\nax[0].grid(linestyle=\"--\", alpha=0.75)\n\nax[1].plot(Q4_region, label=\"Q4\")\nax[1].plot(Q3_region, label=\"Q3\")\nax[1].set_xlim(-3, 2.5)\nax[1].set_xlabel(r\"$\\log(\\lambda^{-1}\\,/\\,$s)\")\nax[1].grid(linestyle=\"--\", alpha=0.75)\n\nplt.colorbar(cb, ax=ax[0], ticks=np.arange(11) / 10)\nplt.tight_layout()\nplt.legend()\nplt.savefig(\"10Cs-90Si.pdf\")\nplt.show()\n\n# %%\n# Mean and mode analysis\n# ''''''''''''''''''''''\n# The T2 distribution is sampled over a log-linear scale. The statistical mean of\n# the `Q4_region` and `Q3_region` in log10(T2). The mean T2 is 10**(log10(T2)),\n# in units of seconds.\n\nQ4_mean = 10 ** stats.mean(Q4_region)[0] * 1e3 # ms\nQ3_mean = 10 ** stats.mean(Q3_region)[0] * 1e3 # ms\n\n# %%\n# Mode the argument corresponding to the max distribution.\n\n# index corresponding to the max distribution.\narg_index_Q4 = int(np.argmax(Q4_region))\narg_index_Q3 = int(np.argmax(Q3_region))\n\n# log10(T2) coordinates corresponding to the max distribution.\narg_coord_Q4 = Q4_region.dimensions[0].coordinates[arg_index_Q4]\narg_coord_Q3 = Q3_region.dimensions[0].coordinates[arg_index_Q3]\n\n# T2 coordinates corresponding to the max distribution.\nQ4_mode = 10**arg_coord_Q4 * 1e3 # ms\nQ3_mode = 10**arg_coord_Q3 * 1e3 # ms\n\n# %%\n# Results\n# '''''''\nprint(f\"Q4 statistics:\\n\\tmean = {Q4_mean} ms,\\n\\tmode = {Q4_mode} ms\\n\")\nprint(f\"Q3 statistics:\\n\\tmean = {Q3_mean} ms,\\n\\tmode = {Q3_mode} ms\\n\")\nprint(f\"r_λ (mean) = {Q4_mean/Q3_mean}\")\nprint(f\"r_λ (mode) = {Q4_mode/Q3_mode}\")\n","sub_path":"examples/relaxation/plot-10Cs-90Si.py","file_name":"plot-10Cs-90Si.py","file_ext":"py","file_size_in_byte":6478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"18075398","text":"from mindsdb.integrations.clickhouse.clickhouse import Clickhouse\nfrom mindsdb.integrations.postgres.postgres import PostgreSQL\nfrom mindsdb.integrations.mariadb.mariadb import Mariadb\nfrom mindsdb.integrations.mysql.mysql import MySQL\nfrom mindsdb.integrations.mssql.mssql import MSSQL\nfrom mindsdb.integrations.mongodb.mongodb import MongoDB\nfrom mindsdb.integrations.redis.redisdb import Redis\nfrom mindsdb.integrations.kafka.kafkadb import Kafka\nfrom mindsdb.utilities.log import log as logger\nfrom mindsdb.utilities.config import Config\nfrom mindsdb.interfaces.database.integrations import DatasourceController\nfrom mindsdb.utilities.with_kwargs_wrapper import WithKWArgsWrapper\n\n\nclass DatabaseWrapper():\n known_dbs = {'clickhouse': Clickhouse,\n 'mariadb': Mariadb,\n 'mysql': MySQL,\n 'postgres': PostgreSQL,\n 'mssql': MSSQL,\n 'mongodb': MongoDB,\n 'redis': Redis,\n 'kafka': Kafka}\n\n def __init__(self, company_id):\n self.config = Config()\n self.company_id = company_id\n self.datasource_interface = WithKWArgsWrapper(\n DatasourceController(), company_id=company_id\n )\n\n def setup_integration(self, db_alias):\n try:\n # If this is the name of an integration\n integration = self._get_integration(db_alias)\n if integration is False:\n raise Exception(f'Unkonw database integration type for: {db_alias}')\n if integration is not True:\n integration.setup()\n except Exception as e:\n logger.warning('Failed to integrate with database ' + db_alias + f', error: {e}')\n\n def _get_integration(self, db_alias):\n integration = self.datasource_interface.get_db_integration(db_alias)\n if integration:\n db_type = integration['type']\n if db_type in self.known_dbs:\n return self.known_dbs[db_type](self.config, db_alias, integration)\n logger.warning(f'Uknown integration type: {db_type} for database called: {db_alias}')\n return False\n return True\n\n def _get_integrations(self, publish=False):\n all_integrations = self.datasource_interface.get_db_integrations()\n if publish is True:\n all_integrations = [\n x for x, y in self.datasource_interface.get_db_integrations().items()\n if y.get('publish') is True\n ]\n else:\n all_integrations = [x for x in self.datasource_interface.get_db_integrations()]\n integrations = [self._get_integration(x) for x in all_integrations]\n integrations = [x for x in integrations if x is not True and x is not False]\n return integrations\n\n def register_predictors(self, model_data_arr, integration_name=None):\n if integration_name is None:\n integrations = self._get_integrations(publish=True)\n else:\n integration = self._get_integration(integration_name)\n integrations = [] if isinstance(integration, bool) else [integration]\n\n for integration in integrations:\n if integration.check_connection():\n try:\n integration.register_predictors(model_data_arr)\n except Exception as e:\n logger.warning(f\"Error {e} when trying to register predictor to {integration.name}. Predictor wouldn't be registred.\")\n else:\n logger.warning(f\"There is no connection to {integration.name}. Predictor wouldn't be registred.\")\n\n def unregister_predictor(self, name):\n for integration in self._get_integrations(publish=True):\n # FIXME\n # !!! Integrations from config.json add to db on each start!!!!\n if '@@@@@' in name:\n sn = name.split('@@@@@')\n assert len(sn) < 3 # security\n name = sn[1]\n if integration.check_connection():\n integration.unregister_predictor(name)\n else:\n logger.warning(f\"There is no connection to {integration.name}. predictor wouldn't be unregistred\")\n\n def check_connections(self):\n connections = {}\n for integration in self._get_integrations():\n connections[integration.name] = integration.check_connection()\n\n return connections\n","sub_path":"mindsdb/interfaces/database/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":4426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"636229557","text":"#!/usr/bin/env python3\n\"\"\"\nTrain de-novo classifier for berlin som data.\n\"\"\"\nimport numpy as np\nfrom sklearn import metrics\nfrom sklearn.preprocessing import LabelBinarizer\nfrom tensorflow import keras\nfrom tensorflow.keras import layers, regularizers, models # pylint: disable=import-error\n# from keras import layers, regularizers, models\nfrom argmagic import argmagic\n\nfrom flowcat import utils, io_functions, mappings\nfrom flowcat.som_dataset import SOMDataset, SOMSequence\n\n\ndef create_model_early_merge(input_shapes, yshape, global_decay=5e-6):\n inputs = []\n for xshape in input_shapes:\n ix = layers.Input(shape=xshape)\n inputs.append(ix)\n\n x = layers.concatenate(inputs)\n x = layers.Conv2D(\n filters=64, kernel_size=4, activation=\"relu\", strides=3,\n kernel_regularizer=regularizers.l2(global_decay))(x)\n x = layers.Conv2D(\n filters=96, kernel_size=3, activation=\"relu\", strides=2,\n kernel_regularizer=regularizers.l2(global_decay))(x)\n x = layers.Conv2D(\n filters=128, kernel_size=1, activation=\"relu\", strides=1,\n kernel_regularizer=regularizers.l2(global_decay))(x)\n x = layers.GlobalAveragePooling2D()(x)\n # x = layers.MaxPooling2D(pool_size=2, strides=2)(x)\n # x = layers.Dropout(0.2)(x)\n\n # x = layers.Dense(\n # units=128, activation=\"relu\", kernel_initializer=\"uniform\",\n # kernel_regularizer=regularizers.l2(global_decay)\n # )(x)\n # x = layers.BatchNormalization()(x)\n # x = layers.Dropout(0.2)(x)\n x = layers.Dense(\n units=128, activation=\"relu\",\n # kernel_initializer=\"uniform\",\n kernel_regularizer=regularizers.l2(global_decay)\n )(x)\n # x = layers.BatchNormalization()(x)\n x = layers.Dense(\n units=64, activation=\"relu\",\n # kernel_initializer=\"uniform\",\n kernel_regularizer=regularizers.l2(global_decay)\n )(x)\n # x = layers.BatchNormalization()(x)\n # x = layers.BatchNormalization()(x)\n # x = layers.Dropout(0.2)(x)\n\n x = layers.Dense(\n units=yshape, activation=\"softmax\"\n )(x)\n\n model = models.Model(inputs=inputs, outputs=x)\n for layer in model.layers:\n print(layer.output_shape)\n return model\n\n\ndef create_model_multi_input(input_shapes, yshape, global_decay=5e-6):\n segments = []\n inputs = []\n print(input_shapes)\n for xshape in input_shapes:\n ix = layers.Input(shape=xshape)\n inputs.append(ix)\n x = layers.Conv2D(\n filters=32, kernel_size=4, activation=\"relu\", strides=1,\n kernel_regularizer=regularizers.l2(global_decay),\n )(ix)\n x = layers.Conv2D(\n filters=48, kernel_size=3, activation=\"relu\", strides=1,\n kernel_regularizer=regularizers.l2(global_decay),\n )(x)\n # x = layers.Conv2D(\n # filters=32, kernel_size=2, activation=\"relu\", strides=1,\n # kernel_regularizer=regularizers.l2(global_decay),\n # )(x)\n # x = layers.Conv2D(\n # filters=64, kernel_size=2, activation=\"relu\", strides=1,\n # # kernel_regularizer=regularizers.l2(global_decay),\n # )(x)\n # x = layers.MaxPooling2D(pool_size=2, strides=2)(x)\n x = layers.Conv2D(\n filters=48, kernel_size=2, activation=\"relu\", strides=1,\n kernel_regularizer=regularizers.l2(global_decay),\n )(x)\n x = layers.Conv2D(\n filters=64, kernel_size=2, activation=\"relu\", strides=1,\n kernel_regularizer=regularizers.l2(global_decay),\n )(x)\n # x = layers.MaxPooling2D(pool_size=2, strides=2)(x)\n\n # x = layers.GlobalAveragePooling2D()(x)\n x = layers.GlobalMaxPooling2D()(x)\n segments.append(x)\n\n x = layers.concatenate(segments)\n # x = layers.Conv2D(\n # filters=32, kernel_size=2, activation=\"relu\", strides=1,\n # kernel_regularizer=regularizers.l2(global_decay))(x)\n # x = layers.MaxPooling2D(pool_size=2, strides=2)(x)\n # x = layers.Dropout(0.2)(x)\n\n # x = layers.Flatten()(ix)\n\n # x = layers.Dense(\n # units=128, activation=\"relu\", kernel_initializer=\"uniform\",\n # kernel_regularizer=regularizers.l2(global_decay)\n # )(x)\n # x = layers.BatchNormalization()(x)\n # x = layers.Dropout(0.2)(x)\n x = layers.Dense(\n units=128, activation=\"relu\",\n # kernel_initializer=\"uniform\",\n kernel_regularizer=regularizers.l2(global_decay)\n )(x)\n # x = layers.BatchNormalization()(x)\n x = layers.Dense(\n units=64, activation=\"relu\",\n # kernel_initializer=\"uniform\",\n kernel_regularizer=regularizers.l2(global_decay)\n )(x)\n # x = layers.BatchNormalization()(x)\n # x = layers.BatchNormalization()(x)\n # x = layers.Dropout(0.2)(x)\n\n x = layers.Dense(\n units=yshape, activation=\"softmax\"\n )(x)\n\n model = models.Model(inputs=inputs, outputs=x)\n for layer in model.layers:\n print(layer.output_shape)\n return model\n\n\ndef get_model(channel_config, groups, **kwargs):\n inputs = tuple([*d[\"dims\"][:-1], len(d[\"channels\"])] for d in channel_config.values())\n output = len(groups)\n\n # model = create_model_multi_input(inputs, output, **kwargs)\n model = create_model_multi_input(inputs, output, **kwargs)\n model.compile(\n loss=\"categorical_crossentropy\",\n # loss=\"binary_crossentropy\",\n optimizer=\"adam\",\n # optimizer=optimizers.Adam(lr=0.0, decay=0.0, epsilon=epsilon),\n metrics=[\n \"acc\",\n ]\n )\n\n binarizer = LabelBinarizer()\n binarizer.fit(groups)\n return binarizer, model\n\n\ndef main(data: utils.URLPath, meta: utils.URLPath, output: utils.URLPath):\n \"\"\"\n Args:\n data: Path to som dataset\n output: Output path\n \"\"\"\n tubes = (\"2\", \"3\", \"4\")\n pad_width = 1\n\n group_mapping = mappings.GROUP_MAPS[\"8class\"]\n mapping = group_mapping[\"map\"]\n groups = group_mapping[\"groups\"]\n\n # dataset = io_functions.load_case_collection(data, meta)\n dataset = SOMDataset.from_path(data)\n if mapping:\n dataset = dataset.map_groups(mapping)\n\n dataset = dataset.filter(groups=[g for g in groups if g not in (\"LPL\", \"MZL\")])\n\n dataset_groups = {d.group for d in dataset}\n\n # if set(groups) != dataset_groups:\n # raise RuntimeError(f\"Group mismatch: {groups}, but got {dataset_groups}\")\n\n validate, train = dataset.create_split(10, stratify=True)\n\n group_count = train.group_count\n num_cases = sum(group_count.values())\n balanced_nums = num_cases / len(dataset_groups)\n balanced_loss_weights = [balanced_nums / group_count.get(g, balanced_nums) for g in groups]\n min_ratio = min(balanced_loss_weights)\n balanced_loss_weights = {i: v / min_ratio for i, v in enumerate(balanced_loss_weights)}\n print(balanced_loss_weights)\n\n # train = train.balance(2000)\n # train = train.balance_per_group({\n # \"CM\": 6000,\n # # \"CLL\": 4000,\n # # \"MBL\": 2000,\n # \"MCL\": 1000,\n # \"PL\": 1000,\n # \"LPL\": 1000,\n # \"MZL\": 1000,\n # \"FL\": 1000,\n # \"HCL\": 1000,\n # \"normal\": 6000,\n # })\n\n io_functions.save_json(train.labels, output / \"ids_train.json\")\n io_functions.save_json(validate.labels, output / \"ids_validate.json\")\n\n som_config = io_functions.load_json(data + \"_config.json\")\n selected_tubes = {tube: som_config[tube] for tube in tubes}\n\n config = {\n \"tubes\": selected_tubes,\n \"groups\": groups,\n \"pad_width\": pad_width,\n \"mapping\": group_mapping,\n }\n io_functions.save_json(config, output / \"config.json\")\n\n for tube in tubes:\n x, y, z = selected_tubes[tube][\"dims\"]\n selected_tubes[tube][\"dims\"] = (x + 2 * pad_width, y + 2 * pad_width, z)\n\n binarizer, model = get_model(selected_tubes, groups=groups, global_decay=5e-7)\n\n def getter_fun(sample, tube):\n return sample.get_tube(tube)\n\n trainseq = SOMSequence(\n train, binarizer,\n tube=tubes,\n get_array_fun=getter_fun,\n batch_size=32,\n pad_width=pad_width)\n validseq = SOMSequence(\n validate, binarizer,\n tube=tubes,\n get_array_fun=getter_fun,\n batch_size=128,\n pad_width=pad_width)\n\n tensorboard_dir = str(output / \"tensorboard\")\n tensorboard_callback = keras.callbacks.TensorBoard(\n log_dir=str(tensorboard_dir),\n histogram_freq=5,\n write_grads=True,\n write_images=True,\n )\n nan_callback = keras.callbacks.TerminateOnNaN()\n\n model.fit_generator(\n epochs=15, shuffle=True,\n callbacks=[tensorboard_callback, nan_callback],\n class_weight=balanced_loss_weights,\n generator=trainseq, validation_data=validseq)\n\n model.save(str(output / \"model.h5\"))\n io_functions.save_joblib(binarizer, output / \"binarizer.joblib\")\n\n preds = []\n for pred in model.predict_generator(validseq):\n preds.append(pred)\n pred_arr = np.array(preds)\n pred_labels = binarizer.inverse_transform(pred_arr)\n true_labels = validseq.true_labels\n\n confusion = metrics.confusion_matrix(true_labels, pred_labels, labels=groups)\n print(groups)\n print(confusion)\n balanced = metrics.balanced_accuracy_score(true_labels, pred_labels)\n print(balanced)\n\n # preds = []\n # for pred in model.predict_generator(validseq):\n # preds.append(pred)\n\n # args.output.local.mkdir(parents=True, exist_ok=True)\n\n\nif __name__ == \"__main__\":\n argmagic(main)\n","sub_path":"scripts/53_denovo.py","file_name":"53_denovo.py","file_ext":"py","file_size_in_byte":9511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"134006239","text":"import sqlite3\nimport os\nfrom telethon.tl import types\nfrom . import states\n\n\nclass Database:\n def __init__(self, dbpath):\n conn = sqlite3.connect(dbpath)\n cursor = conn.cursor()\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS channels (\n channel_id INTEGER PRIMARY KEY NOT NULL,\n username TEXT,\n title TEXT NOT NULL\n );\n \"\"\")\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS messages (\n rowid INTEGER PRIMARY KEY AUTOINCREMENT,\n message_id INTEGER NOT NULL,\n channel_id INTEGER NOT NULL,\n content TEXT NOT NULL\n );\n \"\"\")\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS users (\n user_id INTEGER PRIMARY KEY,\n username TEXT,\n state INTEGER NOT NULL,\n selected_id INTEGER,\n lang TEXT DEFAULT follow NOT NULL\n );\n \"\"\")\n # A junction table to handle many-to-many relationship between channels and admins\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS channels_admins (\n channel_id INTEGER NOT NULL,\n user_id INTEGER NOT NULL\n );\n \"\"\")\n try:\n cursor.execute('SELECT lang FROM users')\n except sqlite3.OperationalError:\n cursor.execute('ALTER TABLE users ADD lang TEXT DEFAULT follow NOT NULL')\n self.conn = conn\n self.cursor = cursor\n\n def save_user(self, user: types.User):\n self.cursor.execute(\n 'INSERT OR IGNORE INTO users (user_id, username, state) VALUES (?, ?, ?)',\n (user.id, user.username, states.Empty))\n self.clear_user_state(user)\n\n def get_user_state(self, user: types.User):\n self.cursor.execute('SELECT state FROM users WHERE user_id = ?',\n (user.id, ))\n fetched = self.cursor.fetchone()\n return None if fetched is None else states.State(fetched[0])\n\n def set_user_state(self, user: types.User, state: states.State):\n self.cursor.execute('UPDATE users SET state = ? WHERE user_id = ?',\n (state.numerator, user.id))\n self.conn.commit()\n\n def clear_user_state(self, user: types.User):\n self.set_user_state(user, state=states.Empty)\n\n def check_channel_saved(self, channel: types.Channel):\n self.cursor.execute('SELECT * FROM channels WHERE channel_id = ?',\n (channel.id, ))\n return self.cursor.fetchone() is not None\n\n def save_channel(self, channel: types.Channel):\n self.cursor.execute(\n 'INSERT INTO channels (channel_id, username, title) VALUES (?, ?, ?)',\n (channel.id, channel.username, channel.title))\n\n def save_channel_admin_relation(self, channel_id: int, admin: types.User):\n if admin.bot:\n return\n self.cursor.execute(\n 'INSERT INTO channels_admins (channel_id, user_id) VALUES (?, ?)',\n (channel_id, admin.id))\n\n def get_user_owned_channels(self, user: types.User):\n self.cursor.execute(\n 'SELECT channel_id FROM channels_admins WHERE user_id = ?',\n (user.id, ))\n sqlresult = self.cursor.fetchall()\n return [x[0] for x in sqlresult]\n\n def get_channel_admins(self, channel_id: types.Channel):\n self.cursor.execute(\n 'SELECT user_id FROM channels_admins WHERE channel_id = ?',\n (channel_id, ))\n sqlresult = self.cursor.fetchall()\n # Return ids only\n return map(lambda x: x[0], sqlresult)\n\n def get_channel_title(self, channel_id: int):\n self.cursor.execute('SELECT title FROM channels WHERE channel_id = ?',\n (channel_id, ))\n return self.cursor.fetchone()[0]\n\n def save_message(self, message: types.Message):\n if not isinstance(message, types.Message):\n return\n if (message.message is None) or (len(message.message) == 0):\n return\n self.cursor.execute(\n 'INSERT INTO messages (message_id, channel_id, content) VALUES (?, ?, ?)',\n (message.id, message.to_id.channel_id, message.message))\n\n def update_message(self, message: types.Message):\n self.cursor.execute(\n 'SELECT rowid FROM messages WHERE message_id=? AND channel_id=?',\n (message.id, message.to_id.channel_id))\n sqlresult = self.cursor.fetchone()\n if sqlresult is None:\n self.cursor.execute(\n 'INSERT INTO messages (message_id, channel_id, content) VALUES (?, ?, ?)',\n (message.id, message.to_id.channel_id, message.message))\n else:\n self.cursor.execute('UPDATE messages SET content=? WHERE rowid=?',\n (message.message, sqlresult[0]))\n\n def find_in_messages(self, channel_id: int, pattern: str):\n self.cursor.execute(\n 'SELECT message_id, content FROM messages WHERE channel_id = ?',\n (channel_id, ))\n messages = self.cursor.fetchall()\n lower_pattern = pattern.lower()\n\n def filter_matched(m):\n if m[1] is None:\n return False\n return m[1].lower().find(lower_pattern) != -1\n\n matched_messages = filter(filter_matched, messages)\n message_ids = [m[0] for m in matched_messages]\n return message_ids\n\n def set_user_selected(self, user_id: int, channel_id: int):\n self.cursor.execute('UPDATE users SET selected_id=? WHERE user_id=?',\n (channel_id, user_id))\n self.conn.commit()\n\n def get_user_selected(self, user_id: int):\n self.cursor.execute('SELECT selected_id FROM users WHERE user_id=?',\n (user_id, ))\n return self.cursor.fetchone()[0]\n\n def set_user_lang(self, user_id: int, langcode: str):\n self.cursor.execute('UPDATE users SET lang=? WHERE user_id=?',\n (langcode, user_id))\n\n def get_user_lang(self, user_id: int):\n self.cursor.execute('SELECT lang FROM users WHERE user_id=?',\n (user_id, ))\n return self.cursor.fetchone()[0]\n","sub_path":"tgficbot/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":6330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"317766797","text":"from .portfolio_base import PortfolioBase\nfrom ...signals import pyti_macd_signal\n\n\n# custom param, default\n# 'short_window', 12\n# 'long_window', 26\n# 'signal_window', 9\nclass Macd(PortfolioBase):\n\n def __init__(self, default, limits, custom, portfolio, portfolio_id=None, strategy_id=None):\n super().__init__(default, limits, portfolio, portfolio_id, strategy_id)\n self.signal = pyti_macd_signal.PytiMacdSignal(\n self.market,\n self.interval,\n custom,\n self\n )\n\n def on_data(self, candle):\n action = self.signal.check_condition(candle)\n if self.get_open_position_count() >= self.position_limit and action == 'buy':\n action = 'hold'\n self.execute(\n self.order_quantity,\n self.fixed_stoploss_percentage,\n self.trailing_stoploss_percentage,\n self.profit_target_percentage,\n action\n )\n","sub_path":"kryptobot/strategies/t2/macd.py","file_name":"macd.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"320400711","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/ztfy/sendit/skin/packet/widget.py\n# Compiled at: 2013-05-31 13:02:11\nfrom ztfy.security.browser.widget.interfaces import IPrincipalListWidget\nfrom ztfy.sendit.app.interfaces import ISenditApplication\nfrom ztfy.sendit.profile.interfaces import IProfile\nfrom z3c.form.widget import FieldWidget\nfrom ztfy.security.browser.widget.principal import PrincipalListWidget\nfrom ztfy.utils.traversing import getParent\n\nclass IPacketRecipientsWidget(IPrincipalListWidget):\n \"\"\"Packet recipients widget interface\"\"\"\n\n def canRegisterUser(self):\n \"\"\"Check if user can register new external users\"\"\"\n pass\n\n\nclass PacketRecipientsWidget(PrincipalListWidget):\n \"\"\"Packet recipients widget\"\"\"\n query_name = 'findFilteredPrincipals'\n registration_view_name = 'register_user.html'\n\n def canRegisterUser(self):\n profile = IProfile(self.request.principal)\n name, _plugin, _info = profile.getAuthenticatorPlugin()\n if name is None:\n return False\n else:\n app = getParent(self.context, ISenditApplication)\n return app is not None and name in app.internal_auth_plugins\n\n\ndef PacketRecipientsWidgetFactory(field, request):\n return FieldWidget(field, PacketRecipientsWidget(request))","sub_path":"pycfiles/ztfy.sendit-0.1.20-py2.7/widget.py","file_name":"widget.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"107984279","text":"# coding=UTF-8\nfrom datetime import datetime\nfrom flask import render_template, flash, redirect, url_for, request\nfrom flask_login import login_user, logout_user, current_user\nfrom app import app, db, lm\n\nfrom app.models.tables import User, Comentario, Texto\nfrom app.models.forms import LoginForm, RegisterComentario, RegisterPost\n\n@lm.user_loader\ndef load_user(id):\n\treturn User.query.filter_by(id=id).first()\n\n@app.route(\"/index\")\n@app.route(\"/\")\ndef index():\n\treturn render_template('index.html')\n\n@app.route(\"/login\", methods=[\"GET\",\"POST\"])\ndef login():\n\tform = LoginForm()\n\tif form.validate_on_submit():\n\t\tuser = User.query.filter_by(username=form.username.data).first()\n\t\tif user and user.password == form.password.data:\n\t\t\tlogin_user(user)\n\t\t\tflash(\"Logged in\")\n\t\t\treturn redirect(url_for(\"timeline\"))\n\t\telse:\n\t\t\tflash(\"Invalid login\")\n\treturn render_template('login.html',\n\t\t form=form)\n\n@app.route(\"/logout\")\ndef logout():\n\tlogout_user()\n\tflash(\"Logged out\")\n\treturn redirect(url_for(\"index\"))\n\n@app.route(\"/cadastrar\")\ndef cadastrar():\n\treturn render_template(\"cadastro.html\")\n\n@app.route(\"/cadastro\", methods=['GET', 'POST'])\ndef cadastro():\n\tif request.method == \"POST\":\n\t\tusername = request.form.get(\"username\")\n\t\tpassword = request.form.get(\"password\")\n\t\tname = request.form.get(\"name\")\n\t\temail = request.form.get(\"email\")\n\n\t\tif username and password and name and email:\n\t\t\tp = User(username, password, name, email)\n\t\t\tdb.session.add(p)\n\t\t\tdb.session.commit()\n\n\treturn redirect(url_for(\"cadastrado\"))\n\n@app.route(\"/cadastrado\")\ndef cadastrado():\n\treturn render_template(\"cadastrado.html\")\n\n@app.route(\"/publicar\")\ndef publicar():\n\tform = RegisterPost()\n\treturn render_template(\"publicarPost.html\", form=form)\n\n@app.route(\"/publicacoes\", methods=['GET', 'POST'])\ndef publicacoes():\n\tform = RegisterPost()\n\taul = current_user.username\n\tif request.method == \"POST\":\n\t\ttitulo = request.form.get(\"titulo\")\n\t\ttexto = request.form.get(\"texto\")\n\t\tcoautores = request.form.get(\"coautores\")\n\t\tdescricao = request.form.get(\"descricao\")\n\t\tautor = aul\n\n\t\tif titulo and texto and coautores and descricao and autor:\n\t\t\ti = Texto(titulo, texto, coautores, descricao, autor)\n\t\t\tdb.session.add(i)\n\t\t\tdb.session.commit()\n\t\t#return \"
'''.format(\n search_hint, search_input_hint, search_loading_hint, search_download_err_hint, search_other_docs_result_hint, search_curr_doc_result_hint)\n items = [search_btn]\n return items\n \n def on_copy_files(self):\n return self.files_to_copy\n\n def on_htmls(self, htmls_files, htmls_pages):\n '''\n update htmls, may not all html, just partially\n htmls_files: {\n \"/get_started/zh\":{\n \"url\":{\n \"title\": \"\",\n \"desc\": \"\",\n \"keywords\": [],\n \"body\": html,\n \"url\": \"\",\n \"raw\": \"\"\n }\n }\n }\n '''\n # for file, html in htmls_files.items():\n # self.content[\"articles\"][html[\"url\"]] = html[\"raw\"]\n # for file, html in htmls_pages.items():\n # self.content[\"pages\"][html[\"url\"]] = html[\"raw\"]\n docs_url = htmls_files.keys()\n pages_url = htmls_pages.keys()\n index_content = {}\n sub_index_path = []\n generated_index_json = {}\n for i, url in enumerate(docs_url):\n index_content[url] = \"{}static/search_index/index_{}.json\".format(self.site_config[\"site_root_url\"], i)\n path = os.path.join(self.temp_dir, \"index_{}.json\".format(i))\n sub_index_path.append(path)\n # write content to sub index file\n with open(path, \"w\", encoding=\"utf-8\") as f:\n htmls_files[url][\"body\"] = \"\" # remove body, only use raw\n json.dump(htmls_files[url], f, ensure_ascii=False)\n for i, url in enumerate(pages_url, len(docs_url)):\n index_content[url] = \"{}static/search_index/index_{}.json\".format(self.site_config[\"site_root_url\"], i)\n path = os.path.join(self.temp_dir, \"index_{}.json\".format(i))\n sub_index_path.append(path)\n # write content to sub index file\n with open(path, \"w\", encoding=\"utf-8\") as f:\n htmls_pages[url][\"body\"] = \"\" # remove body, only use raw\n json.dump(htmls_pages[url], f, ensure_ascii=False)\n # write content to files\n # index file\n index_path = os.path.join(self.temp_dir, \"index.json\")\n with open(index_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(index_content, f, ensure_ascii=False)\n\n # add to copy file list\n generated_index_json[\"/static/search_index/index.json\"] = index_path\n for i, path in enumerate(sub_index_path):\n generated_index_json[\"/static/search_index/index_{}.json\".format(i)] = path\n self.files_to_copy.update(generated_index_json)\n \n\n\n\nif __name__ == \"__main__\":\n config = {\n }\n plug = Plugin(config=config)\n\n","sub_path":"plugins/teedoc-plugin-search/teedoc_plugin_search/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":9454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"635099873","text":"import tensorflow as tf \nimport numpy as np \nimport matplotlib.pyplot as plt\nimport os\nimport sys\nimport math\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets('/tmp/data',one_hot=True);\n\ntf.set_random_seed(0);\n\n#tuning_knobs\nlearning_rate = 0.001;\nbatch_size = 128;\nn_epochs = 500; \n\n#model params\nz_dim = 32;\n\ntfd = tf.contrib.distributions #we will use this to calculate kl-divergence\n\nX = tf.placeholder(tf.float32,[None,784]);\nepoch_number = tf.placeholder(tf.float32,[]);\n\ndef get_activations(name,input_image,session):\n\tactvtns = session.run(name,feed_dict={X:input_image});\n\treturn actvtns;\n\ndef encoder_dist(X,isTrainable=True,reuse=False,name='encoder'):\n\twith tf.variable_scope(name,reuse=tf.AUTO_REUSE) as scope:\n\t\tX = tf.reshape(X,[-1,28,28,1]);\n\t\toutputs={};\n\t\tconv1 = tf.layers.conv2d(X,filters=16,kernel_size=[3,3],strides=(1,1),padding='SAME',activation=tf.nn.leaky_relu,trainable=isTrainable,reuse=reuse,name='conv1_layer');\n\t\tconv1 = tf.layers.batch_normalization(conv1,name='conv1_layer_batchnorm',trainable=isTrainable,reuse=reuse);\n\t\tconv1 = tf.nn.max_pool(conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='conv1_layer_maxpool');\n\t\t\n\t\toutputs['conv1'] = conv1;\n\t\t\n\t\tconv2 = tf.layers.conv2d(conv1,filters=32,kernel_size=[3,3],strides=(1,1),padding='SAME',activation=tf.nn.leaky_relu,trainable=isTrainable,reuse=reuse,name='conv2_layer');\n\t\tconv2 = tf.layers.batch_normalization(conv2,name='conv2_layer_batchnorm',trainable=isTrainable,reuse=reuse);\n\t\tconv2 = tf.nn.max_pool(conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='conv2_layer_maxpool');\n\t\t\n\t\toutputs['conv2'] = conv2;\n\n\t\tconv3 = tf.layers.conv2d(conv2,filters=32,kernel_size=[3,3],strides=(1,1),padding='SAME',activation=tf.nn.leaky_relu,trainable=isTrainable,reuse=reuse,name='conv3_layer');\n\t\tconv3 = tf.layers.batch_normalization(conv3,name='conv3_layer_batchnorm',trainable=isTrainable,reuse=reuse);\n\t\tconv3 = tf.nn.max_pool(conv3,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='conv3_layer_maxpool');\n\t\t\n\t\toutputs['conv3'] = conv3;\n\t\tconv3_flt = tf.layers.flatten(conv3,name='flattened_conv3');\n\t\n\twith tf.variable_scope('VAE_mean_and_var_'+name) as scope:\n\n\t\tmean_fc = tf.layers.dense(conv3_flt,z_dim,trainable=True,reuse=False,activation=tf.nn.tanh,name='mean_fully_connected');\n\t\tvar_fc = tf.layers.dense(conv3_flt,z_dim,activation=tf.nn.softplus,trainable=True,reuse=False,name='var_fully_connected');\n\t\t\n\t\tdist = tfd.MultivariateNormalDiag(mean_fc,var_fc);\n\t\treturn dist,outputs;\n\ndef decoder(Z,isTrainable=True,reuse=False,name='decoder'):\n\twith tf.variable_scope(name) as scope:\n\t\tZ = tf.layers.dense(Z,4*4*32,activation=tf.nn.tanh,trainable=isTrainable,reuse=reuse,name='fully_connected_decoder_from_z_dim');\n\t\toutputs={};\n\t\tZ = tf.reshape(Z,[-1,4,4,32]);\n\t\t\n\t\tdeconv1 = tf.image.resize_images(Z,size=[7,7],align_corners=False);\n\t\tdeconv1 = tf.layers.conv2d_transpose(deconv1,filters=32,kernel_size=[3,3],strides=(1,1),padding='SAME',activation=tf.nn.leaky_relu,trainable=isTrainable,reuse=reuse,name='deconv1_layer');\n\t\tdeconv1 = tf.layers.batch_normalization(deconv1,name='deconv1_layer_batchnorm',trainable=isTrainable,reuse=reuse);\n\t\toutputs['deconv1'] = deconv1;\n\t\t\n\t\tdeconv2 = tf.image.resize_images(deconv1,size=[14,14],align_corners=False);\n\t\tdeconv2 = tf.layers.conv2d_transpose(deconv2,filters=16,kernel_size=[3,3],strides=(1,1),padding='SAME',activation=tf.nn.leaky_relu,trainable=isTrainable,reuse=reuse,name='deconv2_layer');\n\t\tdeconv2 = tf.layers.batch_normalization(deconv2,name='deconv2_layer_batchnorm',trainable=isTrainable,reuse=reuse);\n\t\toutputs['deconv2'] = deconv2;\n\t\t\n\t\tdeconv3 = tf.image.resize_images(deconv2,size=[28,28],align_corners=False);\n\t\tdeconv3 = tf.layers.conv2d_transpose(deconv3,filters=1,kernel_size=[3,3],strides=(1,1),padding='SAME',activation=tf.nn.leaky_relu,trainable=isTrainable,reuse=reuse,name='deconv3_layer');\n\t\toutputs['deconv3'] = deconv3;\n\t\tdeconv3_reshaped = tf.reshape(deconv3,[-1,784]);\n\t\treturn deconv3_reshaped,outputs;\n\nprior_dist = tfd.MultivariateNormalDiag(tf.zeros(z_dim),tf.ones(z_dim));\nposterior_dist,encoder_outputs = encoder_dist(X,isTrainable=False);\n\n## z_sample = posterior_dist.sample();\nepsilon_value = tfd.MultivariateNormalDiag(tf.zeros(z_dim),tf.ones(z_dim)).sample(tf.shape(X)[0]);\nz_sample = tf.add(posterior_dist.mean(),tf.multiply(posterior_dist.stddev(),epsilon_value));\n#z_sample = tf.placeholder(tf.float32,[None,z_dim]);\nreconstruction,decoder_outputs = decoder(z_sample);\nreconstruction_loss = tf.reduce_mean(tf.pow(X - reconstruction,2));\nKL_loss = tf.reduce_mean(tfd.kl_divergence(posterior_dist,prior_dist));\n\nkl_weight = 1.0 / (1.0 + tf.exp(-epoch_number/3+4));\n\nkl_weight *= 0.001;\nloss = kl_weight*KL_loss + reconstruction_loss;\n\n#####changes#####\n\n\n#optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss);\n\n\n## FOR TENSORBOARD ##\n\nenc_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='encoder');\nVAE_mean_and_var_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='VAE_mean_and_var_'+'encoder');\ndec_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='decoder');\n\noptimizer = tf.train.AdamOptimizer(learning_rate = learning_rate);\n\nupdate_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS);\nwith tf.control_dependencies(update_ops):\n\tgradsVars = optimizer.compute_gradients(loss, tf.trainable_variables());\n\ttrain_optimizer = optimizer.apply_gradients(gradsVars);\n\ntf.summary.scalar(\"reconstruction_loss\",reconstruction_loss);\ntf.summary.scalar(\"KL_loss\",KL_loss);\n\nmerged_all = tf.summary.merge_all();\nlog_directory = 'Modified-VAE-dir';\nmodel_directory='Modified-VAE-model_dir';\n\nsave_model_directory = 'Reconstruction-VAE-model_dir';\n\nif not os.path.exists(log_directory):\n os.makedirs(log_directory);\nif not os.path.exists(model_directory):\n os.makedirs(model_directory);\n####################################\n\n\n\n#code = encoder(X);\n\ndef train_model():\n\twith tf.Session() as sess:\n\t\tsess.run(tf.global_variables_initializer());\n\n\t\tn_batches = mnist.train.num_examples/batch_size;\n\t\tn_batches = int(n_batches);\n\n\t\tsaver = tf.train.Saver();\n\n\t\tparams = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder');\n\t\tsaver = tf.train.Saver(var_list=params);\n\n\t\tprint('----------------PARAMS-----------------');\n\t\tfor var in params:\n\t\t print (var.name+\"\\t\");\n\t\tprint('---------------------------------');\n\n\t\tstring = save_model_directory+'/model_'+str(98); \n\n\t\ttry:\n\t\t saver.restore(sess, string);\n\t\texcept:\n\t\t print(\"Previous weights not found of encoder\"); \n\t\t sys.exit(0);\n\n\t\tprint('---------------------------------');\n\t\tprint (\"Model loaded\");\n\t\tprint('---------------------------------');\n\t\tsaver = tf.train.Saver();\n\t\t\n\t\twriter = tf.summary.FileWriter(log_directory,sess.graph);\n\t\t\n\t\ttrain_list = tf.trainable_variables();\n\n\t\tprint('----------------TRAINABLE_VARIABLES----------------');\n\t\tfor it in train_list:\n\t\t\tprint(it.name+\"\\t\");\n\n\t\tprint('---------------------------------------------------');\n\t\t\n\t\tfor epoch in range(n_epochs):\n\t\t\tepoch_loss = 0;\n\t\t\tepoch_KL_loss = 0;\n\t\t\tepoch_reconstruction_loss = 0;\n\t\t\tfor batch in range(n_batches):\n\t\t\t\tX_batch,_ = mnist.train.next_batch(batch_size);\n\t\t\t\t_,batch_cost,merged,batch_KL_loss,batch_reconstruction_loss = sess.run([train_optimizer,loss,merged_all,KL_loss,reconstruction_loss],feed_dict={X:X_batch,epoch_number:epoch});\n\t\t\t\tepoch_loss += batch_cost;\n\t\t\t\tepoch_KL_loss += batch_KL_loss;\n\t\t\t\tepoch_reconstruction_loss += batch_reconstruction_loss;\n\t\t\t\twriter.add_summary(merged,epoch*n_batches+batch);\n\t\t\tprint('At epoch #',epoch,' loss is ',epoch_loss ,' where recons loss : ',epoch_reconstruction_loss,' and KL_loss : ',epoch_KL_loss);\n\t\t\tif(epoch % 2) == 0:\n\t\t\t\tsave_path = saver.save(sess, model_directory+'/model_'+str(epoch));\n\t\t\t\tprint(\"At epoch #\",epoch,\" Model is saved at path: \",save_path);\n\t\tprint('Optimization Done !!');\n\t\tn = 5;\n\t\t\n\t\treconstructed = np.empty((28*n,28*n));\n\t\toriginal = np.empty((28*n,28*n));\n\n\t\tfor i in range(n):\n\t\t\t\n\t\t\tbatch_X,_ = mnist.test.next_batch(n);\n\t\t\trecons = sess.run(reconstruction,feed_dict={X:batch_X});\n\t\t\tprint ('recons : ',recons.shape);\n\t\t\trecons = np.reshape(recons,[-1,784]);\n\t\t\tprint ('recons : ',recons.shape);\n\n\t\t\tfor j in range(n):\n\t\t \toriginal[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_X[j].reshape([28, 28]);\n\n\t\t\tfor j in range(n):\n\t\t\t\treconstructed[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = recons[j].reshape([28, 28]);\n\n\t\tprint(\"Original Images\");\n\t\tplt.figure(figsize=(n, n));\n\t\tplt.imshow(original, origin=\"upper\", cmap=\"gray\");\n\t\tplt.savefig('original_new_vae.png');\n\n\t\tprint(\"Reconstructed Images\");\n\t\tplt.figure(figsize=(n, n));\n\t\tplt.imshow(reconstructed, origin=\"upper\", cmap=\"gray\");\n\t\tplt.savefig('reconstructed_new_vae.png');\n\t\t\n\t\t\n\ndef generateSample():\n\twith tf.Session() as sess:\n\t\tsess.run(tf.global_variables_initializer())\n\t\tsaver = tf.train.Saver();\n\n\t\tparams = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='decoder');\n\t\tsaver = tf.train.Saver(var_list=params);\n\n\t\tfor var in params:\n\t\t print (var.name+\"\\t\");\n\n\t\tstring = model_directory+'/model_'+str(n_epochs-2); \n\n\t\ttry:\n\t\t saver.restore(sess, string);\n\t\texcept:\n\t\t print(\"Previous weights not found of decoder\"); \n\t\t sys.exit(0);\n\n\t\tprint (\"Model loaded\");\n\t\tsaver = tf.train.Saver();\n\n\t\tsample = tf.random_normal([1,z_dim]);\n\t\trecons = sess.run(reconstruction,feed_dict={z_sample:sample.eval()});\n\t\tplt.imshow(np.reshape(recons,[28,28]), interpolation=\"nearest\", cmap=\"gray\");\n\t\tplt.title('Generated Image');\n\t\tplt.savefig('gen-img.png');\n\ndef plot_conv_layers():\n\twith tf.Session() as sess:\n\t\tsess.run(tf.global_variables_initializer())\n\t\tsaver = tf.train.Saver();\n\n\t\tparams = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder');\n\t\tsaver = tf.train.Saver(var_list=params);\n\n\t\tfor var in params:\n\t\t print (var.name+\"\\t\");\n\n\t\tstring = model_directory+'/model_'+str(n_epochs-2); \n\n\t\ttry:\n\t\t saver.restore(sess, string);\n\t\texcept:\n\t\t print(\"Previous weights not found of decoder\"); \n\t\t sys.exit(0);\n\n\t\tprint (\"Model loaded\");\n\t\tsaver = tf.train.Saver();\n\n\t\tif not os.path.exists('out'):\n\t\t\tos.makedirs('out');\n\n\t\tx_img,y_img = mnist.train.next_batch(1);\n\t\tplt.imshow(np.reshape(x_img,[28,28]), interpolation=\"nearest\", cmap=\"gray\");\n\t\tplt.title('Original Image');\n\t\tplt.savefig('out/real-img');\n\n\t\tencoder_output = sess.run(encoder_outputs,feed_dict={X:x_img});\n\t\tprint ('conv1 shape--> ',encoder_output['conv1'].shape);\n\t\tprint ('conv2 shape--> ',encoder_output['conv2'].shape);\n\n\t\t#plt.subplots_adjust(left=0.125, bottom=0.01, right=0.9, top=0.9, wspace=0.5, hspace=0.001)\n\n\t\top = encoder_output['conv1'];\n\t\tfilters = op.shape[3];\n\t\tplt.figure(1, figsize=(20,20));\n\t\tn_columns = 8;\n\t\tn_rows = math.ceil(filters / n_columns) + 1;\n\t\t\n\t\tfor i in range(filters):\n\t\t\tplt.subplot(n_rows, n_columns, i+1);\n\t\t\tplt.title('Filter ' + str(i));\n\t\t\tplt.imshow(op[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\");\n\t\tplt.tight_layout();\n\t\tplt.savefig('out/conv1-op');\n\n\t\top = encoder_output['conv2'];\n\t\tfilters = op.shape[3];\n\t\tplt.figure(2, figsize=(30,30));\n\t\tn_columns = 8;\n\t\tn_rows = math.ceil(filters / n_columns) + 1;\n\t\tfor i in range(filters):\n\t\t\tplt.subplot(n_rows, n_columns, i+1);\n\t\t\tplt.title('Filter ' + str(i));\n\t\t\tplt.imshow(op[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\");\n\t\tplt.tight_layout();\n\t\tplt.savefig('out/conv2-op');\n\n\t\top = encoder_output['conv3'];\n\t\tfilters = op.shape[3];\n\t\tplt.figure(3, figsize=(30,30));\n\t\tn_columns = 8;\n\t\tn_rows = math.ceil(filters / n_columns) + 1;\n\t\tfor i in range(filters):\n\t\t\tplt.subplot(n_rows, n_columns, i+1);\n\t\t\tplt.title('Filter ' + str(i));\n\t\t\tplt.imshow(op[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\");\n\t\tplt.tight_layout();\n\t\tplt.savefig('out/conv3-op');\n\n\n#train_model();\ngenerateSample();\t\nplot_conv_layers();\n","sub_path":"_site/alpha.py","file_name":"alpha.py","file_ext":"py","file_size_in_byte":11872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"154701117","text":"from collections import namedtuple\r\nimport tensorflow as tf\r\nfrom aggregators import MaxPoolingAggregator\r\n\r\n\r\nclass BilstmModel(tf.keras.Model):\r\n def __init__(self, dims0, dims1,batch_size, **kwargs):\r\n\r\n super(BilstmModel, self).__init__(**kwargs)\r\n \r\n self.output_dim_bottom = 200\r\n self.output_dim_top = 200\r\n self.number_of_words = dims0\r\n self.dim_embed = dims1\r\n self.batch_size= batch_size\r\n self.masker = tf.keras.layers.Masking(mask_value = 0.,input_shape = (self.number_of_words,self.dim_embed))\r\n\r\n # Word Embedding Layer\r\n def build(self, inputs):\r\n print(self.number_of_words)\r\n self.w = tf.Variable(tf.random.uniform([1,2*self.output_dim_top]), name = \"attention\")\r\n # self.embedding =tf.keras.layers.Embedding(input_dim=self.number_of_words, output_dim=16, mask_zero=True)\r\n \r\n self.forward_layer = tf.keras.layers.LSTM(self.output_dim_bottom,dropout = 0.5, return_sequences=True,recurrent_activation = 'sigmoid', input_shape=(self.number_of_words, self.dim_embed))\r\n # self.backward_layer = tf.keras.layers.LSTM(self.output_dim_top,dropout = 0.2, return_sequences=True, go_backwards=True, input_shape = (self.number_of_words, self.dim_embed))\r\n self.bilstm = tf.keras.layers.Bidirectional(self.forward_layer, merge_mode = \"concat\")\r\n\r\n\r\n def call(self, inputs, training=None, mask=None):\r\n w1 = tf.tile(tf.expand_dims(self.w, axis = 0), [inputs.shape[0], 1,1 ])\r\n #rajouter le batch size a la place de 100 \r\n #masque = self.embedding.compute_mask(inputs)\r\n #print(\"masque dim\", masque.shape)\r\n #print(\"inputs dim\", inputs.shape)\r\n # print(self.masker(inputs))\r\n inputs2 = self.masker(inputs)\r\n sortie1 = self.bilstm(inputs2)\r\n #print(sortie1.shape, \"sum shape\")\r\n \r\n #print(sortie1.shape)\r\n \r\n b = tf.matmul(w1,tf.transpose(sortie1,[0,2,1]))\r\n #print(\"b shape\", b.shape)\r\n alpha = tf.nn.softmax(b, axis =2)\r\n #print(\"alpha shape\", alpha.shape)\r\n sortie2 = tf.matmul(alpha, sortie1)\r\n sortie3 = tf.squeeze(sortie2, axis = 1)\r\n #print(sortie3.shape)\r\n return sortie3\r\n\r\n\r\n\r\n\r\n# SAGEInfo is a namedtuple that specifies the parameters\r\n# of the recursive GraphSAGE layers\r\nSAGEInfo = namedtuple(\"SAGEInfo\",\r\n ['layer_name', # name of the layer (to get feature embedding etc.)\r\n 'neigh_sampler', # callable neigh_sampler constructor\r\n 'num_samples',\r\n 'output_dim' # the output (i.e., hidden) dimension\r\n ])\r\n\r\nclass SampleAndAggregate(tf.keras.Model):\r\n \"\"\"\r\n Base implementation of unsupervised GraphSAGE\r\n \"\"\"\r\n\r\n def __init__(self, features, adj, degrees,\r\n layer_infos,\r\n **kwargs):\r\n '''\r\n Args:\r\n - placeholders: Stanford TensorFlow placeholder object.\r\n - features: Numpy array with node features. \r\n NOTE: Pass a None object to train in featureless mode (identity features for nodes)!\r\n - adj: Numpy array with adjacency lists (padded with random re-samples)\r\n - degrees: Numpy array with node degrees. \r\n - layer_infos: List of SAGEInfo namedtuples that describe the parameters of all \r\n the recursive layers. See SAGEInfo definition above.\r\n - concat: whether to concatenate during recursive iterations\r\n - aggregator_type: how to aggregate neighbor information\r\n - model_size: one of \"small\" and \"big\"\r\n - identity_dim: Set to positive int to use identity features (slow and cannot generalize, but better accuracy)\r\n '''\r\n super(SampleAndAggregate, self).__init__(**kwargs)\r\n\r\n self.aggregator_cls = MaxPoolingAggregator\r\n self.model_size = 512\r\n\r\n #INUTILE???????????\r\n #self.adj_info = adj\r\n\r\n\r\n\r\n '''\r\n if identity_dim > 0:\r\n self.embeds = tf.get_variable(\"node_embeddings\", [adj.get_shape().as_list()[0], identity_dim])\r\n else:\r\n self.embeds = None\r\n if features is None: \r\n if identity_dim == 0:\r\n raise Exception(\"Must have a positive value for identity feature dimension if no input features given.\")\r\n self.features = self.embeds\r\n else:\r\n self.features = tf.Variable(tf.constant(features, dtype=tf.float32), trainable=False)\r\n if not self.embeds is None:\r\n self.features = tf.concat([self.embeds, self.features], axis=1)\r\n '''\r\n\r\n self.adj = adj\r\n self.features = features\r\n self.degrees = degrees\r\n self.dims = [len(self.features[0])]\r\n self.dims.extend([layer_infos[i].output_dim for i in range(len(layer_infos))])\r\n \r\n self.batch_size = 100\r\n self.layer_infos = layer_infos\r\n #self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\r\n self.aggregators = None\r\n\r\n def sample(self, inputs, layer_infos):\r\n \"\"\" Sample neighbors to be the supportive fields for multi-layer convolutions.\r\n\r\n Args:\r\n inputs: batch inputs\r\n batch_size: the number of inputs (different for batch inputs and negative samples).\r\n \"\"\"\r\n \r\n\r\n batch_size = inputs.shape[0]\r\n samples = [inputs]\r\n # size of convolution support at each layer per node\r\n support_size = 1\r\n support_sizes = [support_size]\r\n for k in range(len(layer_infos)):\r\n \r\n t = len(layer_infos) - k - 1\r\n support_size *= layer_infos[t].num_samples\r\n sampler = layer_infos[t].neigh_sampler\r\n\r\n node = sampler((samples[k], layer_infos[t].num_samples))\r\n \r\n samples.append(tf.reshape(node, [support_size * batch_size,]))\r\n support_sizes.append(support_size)\r\n return samples, support_sizes\r\n\r\n\r\n def aggregate(self, samples, input_features, dims, num_samples, support_sizes,\r\n name=None, concat=False, model_size=\"small\"):\r\n \"\"\" At each layer, aggregate hidden representations of neighbors to compute the hidden representations \r\n at next layer.\r\n Args:\r\n samples: a list of samples of variable hops away for convolving at each layer of the\r\n network. Length is the number of layers + 1. Each is a vector of node indices.\r\n input_features: the input features for each sample of various hops away.\r\n dims: a list of dimensions of the hidden representations from the input layer to the\r\n final layer. Length is the number of layers + 1.\r\n num_samples: list of number of samples for each layer.\r\n support_sizes: the number of nodes to gather information from for each layer.\r\n batch_size: the number of inputs (different for batch inputs and negative samples).\r\n Returns:\r\n The hidden representation at the final layer for all nodes in batch\r\n \"\"\"\r\n\r\n\r\n batch_size = self.batch_size\r\n\r\n # length: number of layers + 1\r\n\r\n hidden = [tf.nn.embedding_lookup(input_features, node_samples) for node_samples in samples]\r\n #hidden = features de tous les nodes qui sont à une certaine distance\r\n #en pratique on prendra input_features = [features] donc tous les titres des noeuds.\r\n new_agg = self.aggregators is None\r\n if new_agg:\r\n self.aggregators = []\r\n #len(num_samples) est le nombre de layers K\r\n \r\n for layer in range(len(num_samples)):\r\n if new_agg:\r\n dim_mult = 2 if (layer!=0) else 1\r\n # aggregator at current layer\r\n if layer == len(num_samples) - 1:\r\n #si on est à la fin, l'activation est l'identité\r\n aggregator = self.aggregator_cls(dim_mult*dims[layer], dims[layer+1], act=lambda x : x,\r\n dropout=0.2)\r\n else:\r\n #sinon l'activation est le relu (de base)\r\n aggregator = self.aggregator_cls(dim_mult*dims[layer], dims[layer+1], act = tf.keras.activations.relu,\r\n dropout=0.2)\r\n self.aggregators.append(aggregator)\r\n else:\r\n aggregator = self.aggregators[layer]\r\n # hidden representation at current layer for all support nodes that are various hops away\r\n next_hidden = []\r\n # as layer increases, the number of support nodes needed decreases\r\n for hop in range(len(num_samples) - layer):\r\n dim_mult = 2 if (layer!=0) else 1\r\n neigh_dims = [batch_size * support_sizes[hop], \r\n num_samples[len(num_samples) - hop - 1], \r\n dim_mult*dims[layer]]\r\n\r\n h = aggregator((hidden[hop],tf.reshape(hidden[hop + 1], neigh_dims)))\r\n next_hidden.append(h)\r\n hidden = next_hidden\r\n\r\n\r\n return hidden[0]\r\n #on prend 0 car on s'intéresse juste aux embeddings des noeuds de base\r\n\r\n def call(self, inputs, training=None, mask=None):\r\n\r\n \r\n inputs1 = inputs[\"batch1\"]\r\n self.batch_size = inputs1.shape[0]\r\n #inputs1: noeud 1\r\n inputs2 = inputs[\"batch2\"]\r\n\r\n #inputs2: noeud 2\r\n \r\n # perform \"convolution\"\r\n samples1, support_sizes1 = self.sample(inputs1, self.layer_infos)\r\n\r\n samples2, support_sizes2 = self.sample(inputs2, self.layer_infos)\r\n\r\n num_samples = [layer_info.num_samples for layer_info in self.layer_infos]\r\n outputs1 = self.aggregate(samples1, [self.features], self.dims, num_samples, support_sizes1)\r\n outputs2 = self.aggregate(samples2, [self.features], self.dims, num_samples, support_sizes2)\r\n\r\n\r\n outputs1 = tf.nn.l2_normalize(outputs1, 1)\r\n outputs2 = tf.nn.l2_normalize(outputs2, 1)\r\n return outputs1, outputs2\r\n\r\n\"\"\"\r\n def build(self):\r\n self._build()\r\n\r\n # TF graph management\r\n self._loss()\r\n self._accuracy()\r\n self.loss = self.loss / tf.cast(self.batch_size, tf.float32)\r\n grads_and_vars = self.optimizer.compute_gradients(self.loss)\r\n clipped_grads_and_vars = [(tf.clip_by_value(grad, -5.0, 5.0) if grad is not None else None, var) \r\n for grad, var in grads_and_vars]\r\n self.grad, _ = clipped_grads_and_vars[0]\r\n self.opt_op = self.optimizer.apply_gradients(clipped_grads_and_vars)\r\n\r\n def _loss(self):\r\n for aggregator in self.aggregators:\r\n for var in aggregator.vars.values():\r\n self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)\r\n\r\n self.loss += self.link_pred_layer.loss(self.outputs1, self.outputs2, self.neg_outputs) \r\n tf.summary.scalar('loss', self.loss)\r\n\r\n def _accuracy(self):\r\n # shape: [batch_size]\r\n aff = self.link_pred_layer.affinity(self.outputs1, self.outputs2)\r\n # shape : [batch_size x num_neg_samples]\r\n\r\n _aff = tf.expand_dims(aff, axis=1)\r\n self.aff_all = tf.concat(axis=1, values=[self.neg_aff, _aff])\r\n size = tf.shape(self.aff_all)[1]\r\n _, indices_of_ranks = tf.nn.top_k(self.aff_all, k=size)\r\n _, self.ranks = tf.nn.top_k(-indices_of_ranks, k=size)\r\n self.mrr = tf.reduce_mean(tf.div(1.0, tf.cast(self.ranks[:, -1] + 1, tf.float32)))\r\n tf.summary.scalar('mrr', self.mrr)\r\n\r\n\"\"\"\r\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"298848082","text":"import datetime\n\ndef dayAdd(s,d):\n\tdate = datetime.datetime.strptime(s, \"%Y-%m-%d %H:%M:%S\")\n\tdate += datetime.timedelta(days=d)\n\treturn (date.strftime('%Y-%m-%d %H:%M:%S'))\n\ndef main():\n\ts = \"2017-10-06 13:19:37\"\n\tprint(s)\n\tprint(dayAdd(s,3))\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"scripts/dateadd.py","file_name":"dateadd.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"449173969","text":"# Onufriev-Bashford-Case Generalized Born term\n# can be added to any other force field\n\nfrom MMTK import ParticleScalar\nfrom MMTK.ForceFields.ForceField import ForceField\nfrom MMTK_OBC import OBCTerm\n\nclass OBCForceField(ForceField):\n\n \"\"\"\n Onufriev-Bashford-Case Generalized Born\n \"\"\"\n\n def __init__(self, prmtopFN,\n prmtop_atom_order, inv_prmtop_atom_order):\n \"\"\"\n @param prmtopFN: an AMBER parameter and topology file\n @type strength: C{str}\n \"\"\"\n # Initialize the ForceField class, giving a name to this one.\n ForceField.__init__(self, 'OBC')\n\n # Store arguments that recreate the force field from a pickled\n # universe or from a trajectory.\n self.arguments = (prmtopFN, prmtop_atom_order, inv_prmtop_atom_order)\n \n self.prmtopFN = prmtopFN\n self.prmtop_atom_order = prmtop_atom_order\n self.inv_prmtop_atom_order = inv_prmtop_atom_order\n\n # The following method is called by the energy evaluation engine\n # to inquire if this force field term has all the parameters it\n # requires. This is necessary for interdependent force field\n # terms. In our case, we just say \"yes\" immediately.\n def ready(self, global_data):\n return True\n\n # The following method is called by the energy evaluation engine\n # to obtain a list of the low-level evaluator objects (the C routines)\n # that handle the calculations.\n def evaluatorTerms(self, universe, subset1, subset2, global_data):\n # The energy for subsets is defined as consisting only\n # of interactions within that subset, so the contribution\n # of an external field is zero. Therefore we just return\n # an empty list of energy terms.\n if subset1 is not None or subset2 is not None:\n return []\n\n # Get charges, radii, and scale factors from OpenMM\n import simtk.openmm\n import simtk.openmm.app as OpenMM_app\n \n prmtop = OpenMM_app.AmberPrmtopFile(self.prmtopFN)\n OMM_system = prmtop.createSystem(\\\n \tnonbondedMethod=OpenMM_app.CutoffNonPeriodic, \\\n nonbondedCutoff=1.5, \\\n constraints=None, \\\n implicitSolvent=OpenMM_app.OBC2)\n f = OMM_system.getForces()[-2]\n\n import numpy as np\n numParticles = f.getNumParticles()\n charges = np.zeros(numParticles)\n atomicRadii = np.zeros(numParticles)\n scaleFactors = np.zeros(numParticles)\n for n in range(numParticles):\n (charge, radius, scaleFactor) = f.getParticleParameters(n)\n charges[n] = charge/simtk.unit.elementary_charge\n atomicRadii[n] = radius/simtk.unit.nanometer\n scaleFactors[n] = scaleFactor\n\n charges = charges[self.inv_prmtop_atom_order]\n atomicRadii = atomicRadii[self.inv_prmtop_atom_order]\n scaleFactors = scaleFactors[self.inv_prmtop_atom_order]\n\n# import time\n# import os.path\n# import MMTK_OBC\n# OBCpath = MMTK_OBC.__file__\n# print \"\"\"\n# in {0}\n# last modified {1}\n# \"\"\".format(OBCpath, time.ctime(os.path.getmtime(OBCpath)))\n\n # Here we pass all the parameters as \"simple\" data types to\n # the C code that handles energy calculations.\n return [OBCTerm(universe._spec, charges, atomicRadii, scaleFactors, 'OBC')]\n","sub_path":"AlGDock/ForceFields/OBC/OBC.py","file_name":"OBC.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"268910388","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nimport sys\n\nfrom time import sleep\nimport random\nimport rospy\nimport actionlib\n\nfrom actionlib_msgs.msg import GoalStatus\nfrom geometry_msgs.msg import Vector3\nfrom bitbots_msgs.msg import DynUpGoal, DynUpAction, DynUpFeedback\nfrom visualization_msgs.msg import Marker\n\nshowing_feedback = False\n\nif __name__ == \"__main__\":\n print(\"[..] Initializing node\", end='')\n rospy.init_node('dynup_dummy_client', anonymous=True)\n print(\"\\r[OK] Initializing node\")\n\n\n def done_cb(state, result):\n print('Action completed: ', end='')\n if state == GoalStatus.PENDING:\n print('Pending')\n elif state == GoalStatus.ACTIVE:\n print('Active')\n elif state == GoalStatus.PREEMPTED:\n print('Preempted')\n elif state == GoalStatus.SUCCEEDED:\n print('Succeeded')\n elif state == GoalStatus.ABORTED:\n print('Aborted')\n elif state == GoalStatus.REJECTED:\n print('Rejected')\n elif state == GoalStatus.PREEMPTING:\n print('Preempting')\n elif state == GoalStatus.RECALLING:\n print('Recalling')\n elif state == GoalStatus.RECALLED:\n print('Recalled')\n elif state == GoalStatus.LOST:\n print('Lost')\n else:\n print('Unknown state', state)\n print(str(result))\n\n\n def active_cb():\n print(\"Server accepted action\")\n\n\n def feedback_cb(feedback):\n if len(sys.argv) > 1 and sys.argv[1] == '--feedback':\n print('Feedback')\n print(feedback)\n print()\n\n\n print('[..] Connecting to action server \\'dynup\\'', end='')\n sys.stdout.flush()\n client = actionlib.SimpleActionClient('dynup', DynUpAction)\n if not client.wait_for_server():\n exit(1)\n print('\\r[OK] Connecting to action server \\'dynup\\'')\n print()\n\n goal = DynUpGoal()\n goal.front = True\n\n client.send_goal(goal)\n client.done_cb = done_cb\n client.feedback_cb = feedback_cb\n client.active_cb = active_cb\n print(\"Sent new goal. Waiting for result\")\n client.wait_for_result()\n","sub_path":"bitbots_dynup/scripts/dummy_client.py","file_name":"dummy_client.py","file_ext":"py","file_size_in_byte":2168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"412176232","text":"#!/usr/bin/python3\n\"\"\"\n View Cities\n\"\"\"\n\nfrom api.v1.views import app_views\nfrom flask import Flask, jsonify, abort, make_response, request\nfrom models.city import City\nfrom models import storage\nimport json\n\n\n@app_views.route('/states//cities', strict_slashes=False)\ndef cities(state_id):\n \"\"\"\n Return all cities of a state\n \"\"\"\n state = storage.get('State', state_id)\n if state is None:\n abort(404)\n cities = [city.to_dict() for city in state.cities]\n\n return jsonify(cities)\n\n\n@app_views.route('/cities/', strict_slashes=False)\ndef list_cities(city_id):\n \"\"\"\n Retrieves a city object\n \"\"\"\n city = storage.get('City', city_id)\n if city is None:\n abort(404)\n city = city.to_dict()\n\n return jsonify(city)\n\n\n@app_views.route('/cities/', methods=['DELETE'],\n strict_slashes=False)\ndef delete_city(city_id):\n \"\"\"\n deletes a city object\n \"\"\"\n city = storage.get('City', city_id)\n if city is None:\n abort(404)\n\n storage.delete(city)\n storage.save()\n\n return make_response(jsonify({}), 200)\n\n\n@app_views.route('/states//cities', methods=['POST'],\n strict_slashes=False)\ndef post_city(state_id):\n \"\"\"\n post a city object\n \"\"\"\n if not request.json:\n abort(400, \"Not a JSON\")\n data = request.json\n if 'name' not in data.keys():\n abort(400, \"Missing name\")\n\n state = storage.get(\"State\", state_id)\n if state is None:\n abort(404)\n data['state_id'] = state_id\n instance = City(**data)\n storage.new(instance)\n storage.save()\n\n return make_response(jsonify(instance.to_dict()), 201)\n\n\n@app_views.route('/cities/', methods=['PUT'], strict_slashes=False)\ndef update_city(city_id):\n \"\"\"\n update a city object\n \"\"\"\n city = storage.get('City', city_id)\n if city is None:\n abort(404)\n\n if not request.json:\n abort(400, \"Not a JSON\")\n\n data = request.json\n for key, value in data.items():\n setattr(city, key, value)\n\n storage.save()\n\n return make_response(jsonify(city.to_dict()), 200)\n","sub_path":"api/v1/views/cities.py","file_name":"cities.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"427971696","text":"import gtk\nimport pandas as pd\nfrom pandas import Series as s\nfrom pandas import DataFrame as df\nimport numpy as np\nfrom matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas\nfrom matplotlib.backends.backend_gtkagg import NavigationToolbar2GTKAgg as NavigationToolbar\nfrom matplotlib.backend_bases import key_press_handler\nfrom matplotlib.figure import Figure\n\nclass PyApp(gtk.Window):\n\n\tdata = pd.read_csv(\"Crimes_commited_ipc2.csv\")\n\tcombobox_district = gtk.ComboBox()\t\n\tcombobox_year = gtk.ComboBox()\n\tcombobox_state = gtk.ComboBox()\n\tcombobox_crime = gtk.ComboBox()\n\tcombobox_state1 = gtk.ComboBox()\n\tcombobox_state2 = gtk.ComboBox()\n\tcombobox_yearc = gtk.ComboBox()\n\tbutton_show_graph = gtk.Button(\"Show Graph\")\n\tcompare_button = gtk.Button(\"Compare\")\n\tfiltered_data = df()\n\n\tstore_dist = gtk.ListStore(str)\n\n\tdef __init__(self):\n\t\tsuper(PyApp, self).__init__()\n\n\t\tself.set_title(\"Crimes in India\")\n\t\tself.set_size_request(700, 500)\n\t\t#self.set_icon_from_file(\"/home/safwan/Downloads/icon.png\")\n\n\t\t# Set window background image\n\t\tpixbuf = gtk.gdk.pixbuf_new_from_file(\"vector-grunge-texture-background.jpg\")\n\t\tpixmap, mask = pixbuf.render_pixmap_and_mask()\n\t\twidth, height = pixmap.get_size()\n\t\t#del pixbuf\n\t\tself.set_app_paintable(gtk.TRUE)\n\t\tself.resize(width, height)\n\t\tself.realize()\n\t\tself.window.set_back_pixmap(pixmap, gtk.FALSE)\n\t\t#del pixmap\n\n\t\tnb = gtk.Notebook()\n\t\tnb.set_tab_pos(gtk.POS_TOP)\n\t\tvbox = gtk.VBox(False, 5)\n\n\t\tvb = gtk.VBox()\n\t\thbox = gtk.HBox(True, 3)\n\t\thlabelsbox = gtk.HBox(True, 120)\n\t\thbuttonbox = gtk.HBox(True, 10)\n\n\t\t# Horizontal box for labels\n\t\tlabel_state = gtk.Label(\"State\")\n\t\tlabel_district = gtk.Label(\"District\")\n\t\tlabel_year = gtk.Label(\"Year\")\n\t\tlabel_crime = gtk.Label(\"Crime\")\n\n\t\thlabelsbox.pack_start(label_state, True, True, 10)\n\t\thlabelsbox.pack_start(label_district, True, True, 10)\n\t\thlabelsbox.pack_start(label_year, True, True, 10)\n\t\thlabelsbox.pack_start(label_crime, True, True, 10)\n\n\t\tvalign = gtk.Alignment(0.25, 0.10, 0, 0)\n\t\tvalign1 = gtk.Alignment(0, 1, 0, 0)\n\t\tvalign2 = gtk.Alignment(0, 0, 1, 0)\n\t\t\n\t\tstore = gtk.ListStore(str)\n\t\tcell = gtk.CellRendererText()\n\t\tself.combobox_state.pack_start(cell)\n\t\tself.combobox_state.set_title(\"States\")\n\t\tself.combobox_state.add_attribute(cell, 'text', 0)\n\n\t\thbox.pack_start(self.combobox_state, True, True, 10)\n\n\t\tstore.append ([\"ANDHRA PRADESH\"])\n\t\tstore.append ([\"ARUNACHAL PRADESH\"])\n\t\tstore.append ([\"ASSAM\"])\n\t\tstore.append ([\"BIHAR\"])\n\t\tstore.append ([\"GOA\"])\n\t\tstore.append ([\"GUJARAT\"])\n\t\tstore.append ([\"HARYANA\"])\n\t\tstore.append ([\"HIMACHAL PRADESH\"])\n\t\tstore.append ([\"JAMMU & KASHMIR\"])\n\t\tstore.append ([\"JHARKHAND\"])\n\t\tstore.append ([\"KARNATAKA\"])\n\t\tstore.append ([\"KERALA\"])\n\t\tstore.append ([\"MADHYA PRADESH\"])\n\t\tstore.append ([\"MAHARASHTRA\"])\n\t\tstore.append ([\"MANIPUR\"])\n\t\tstore.append ([\"MEGHALAYA\"])\n\t\tstore.append ([\"MIZORAM\"])\n\t\tstore.append ([\"NAGALAND\"])\n\t\tstore.append ([\"ODISHA\"])\n\t\tstore.append ([\"MIZORAM\"])\n\t\tstore.append ([\"PUNJAB\"])\n\t\tstore.append ([\"RAJASTHAN\"])\n\t\tstore.append ([\"TAMIL NADU\"])\n\t\tstore.append ([\"TRIPURA\"])\n\t\tstore.append ([\"UTTAR PRADESH\"])\n\t\tstore.append ([\"UTTARAKHAND\"])\n\t\tstore.append ([\"WEST BENGAL\"])\n\t\tstore.append ([\"A & N ISLANDS\"])\n\t\tstore.append ([\"CHANDIGARH\"])\n\t\tstore.append ([\"D & N HAVELI\"])\n\t\tstore.append ([\"DAMAN & DIU\"])\n\t\tstore.append ([\"DELHI UT\"])\n\t\tstore.append ([\"LAKSHADWEEP\"])\n\t\tstore.append ([\"PUDUCHERRY\"])\n\t\t\n\t\tself.combobox_state.set_model(store)\n\t\tself.combobox_state.connect('changed', self.on_changed)\n\t\tself.combobox_state.set_active(0)\n\n\t\tself.store_dist = gtk.ListStore(str)\n\t\tcell_dist = gtk.CellRendererText()\n\t\tself.combobox_district.pack_start(cell_dist)\n\t\tself.combobox_district.add_attribute(cell_dist, 'text', 0)\n\t\tself.combobox_district.connect('changed', self.on_changed_district)\n\t\thbox.pack_start(self.combobox_district, True, True, 10)\n\n\t\t\n\t\t#Year dropbox\n\t\t\n\t\tstore_year = gtk.ListStore(str)\n\t\tcell_year = gtk.CellRendererText()\n\t\tself.combobox_year.pack_start(cell_year)\n\t\tself.combobox_year.add_attribute(cell_year, 'text', 0)\n\n\t\thbox.pack_start(self.combobox_year, True, True, 10)\n\n\t\tdistrict = self.data[(self.data['STATEorUT']=='ANDHRA PRADESH') | (self.data['STATEorUT']=='Andhra Pradesh')].filter(items=['STATE/UT','DISTRICT','YEAR'])\n\t\tyear=s(district['YEAR']).unique().tolist()\n\t\tstore_year.append([\"All\"])\n\t\tfor i in year:\n\t\t\tstore_year.append([i])\n\t\t\n\t\tself.combobox_year.set_model(store_year)\n\t\t#combobox.connect('changed', self.on_changed)\n\t\tself.combobox_year.set_active(0)\n\t\tself.combobox_year.connect('changed', self.on_changed_year)\n\n\t\t# Crime dropbox\n\t\t\n\t\tstore_crime = gtk.ListStore(str)\n\t\tcell_crime = gtk.CellRendererText()\n\t\tself.combobox_crime.pack_start(cell_crime)\n\t\tself.combobox_crime.add_attribute(cell_crime, 'text', 0)\n\t\tself.combobox_crime.connect('changed', self.on_changed_crime)\n\n\t\thbox.pack_start(self.combobox_crime, True, True, 10)\n\n\t\tstore_crime.append ([\"MURDER\"])\n\t\tstore_crime.append ([\"RAPE\"])\n\t\tstore_crime.append ([\"KIDNAPPING\"])\n\t\tstore_crime.append ([\"RIOTS\"])\n\t\tstore_crime.append ([\"ROBBERY\"])\n\t\tstore_crime.append ([\"BURGLARY\"])\n\t\tstore_crime.append ([\"DOWRY DEATHS\"])\n\t\tstore_crime.append ([\"TOTAL IPC CRIMES\"])\n\t\tself.combobox_crime.set_model(store_crime)\n\t\t#combobox.connect('changed', self.on_changed)\n\t\tself.combobox_crime.set_active(0)\t\t\n\n\t\tvalign.add(hbox)\n\t\tvalign1.add(hlabelsbox)\n\t\t\n\t\tbutton_disp_data = gtk.Button(\"Display Data\")\n\n\t\tbutton_disp_data.connect(\"clicked\", self.display_data)\n\t\tself.button_show_graph.connect(\"clicked\", self.display_graph)\n\n\t\tself.button_show_graph.set_sensitive(False)\n\n\t\thbuttonbox.pack_start(button_disp_data, True, True, 10)\n\t\thbuttonbox.pack_start(self.button_show_graph, True, True, 10)\n\t\tvalign2.add(hbuttonbox)\n\t\tvbox.pack_start(valign1)\n\t\tvbox.pack_start(valign)\n\t\tvbox.pack_start(valign2)\n\t\t\n\t\tnb.append_page(vbox)\n\t\tnb.set_tab_label_text(vbox, \"District wise crimes\")\n\n\t\t# Code for 2nd TAB\n\n\t\tstate1lbl = gtk.Label(\"State 1\")\n\t\tstate2lbl = gtk.Label(\"State 2\")\n\t\t\n\t\t\n\t\t\n\t\tself.compare_button.connect(\"clicked\", self.compare_states)\n\t\tself.compare_button.set_sensitive(False)\n\n\t\ttable = gtk.Table(8,4,True)\n\t\ttable.set_col_spacings(7)\n\n\t\tstates = [\"ANDHRA PRADESH\", \"ARUNACHAL PRADESH\", \"ASSAM\", \"BIHAR\",\"CHHATTISGARH\", \"GOA\",\"GUJARAT\",\"HARYANA\",\"HIMACHAL PRADESH\", \"JAMMU & KASHMIR\", \"JHARKHAND\", \"KARNATAKA\", \"KERALA\", \"MADHYA PRADESH\", \"MAHARASHTRA\", \"MANIPUR\", \"MEGHALAYA\", \"MIZORAM\", \"NAGALAND\", \"ODISHA\", \"PUNJAB\", \"RAJASTHAN\", \"SIKKIM\", \"TAMIL NADU\", \"TRIPURA\", \"UTTAR PRADESH\", \"UTTARAKHAND\", \"WEST BENGAL\",\"A & N ISLANDS\", \"CHANDIGARH\", \"D & N HAVELI\", \"DAMAN & DIU\", \"DELHI UT\", \"LAKSHADWEEP\", \"PUDUCHERRY\"]\n\t\tstorestate1 = gtk.ListStore(str)\n\t\tcellstate1 = gtk.CellRendererText()\n\t\tself.combobox_state1.pack_start(cellstate1)\n\t\tself.combobox_state1.add_attribute(cellstate1, 'text', 0)\n\t\tself.combobox_state1.set_active(0)\n\t\tfor i in states:\n\t\t\tstorestate1.append([i])\n\t\tself.combobox_state1.set_model(storestate1)\n\t\t\n\n\t\tstorestate2 = gtk.ListStore(str)\n\t\tcellstate2 = gtk.CellRendererText()\n\t\tself.combobox_state2.pack_start(cellstate2)\n\t\tself.combobox_state2.add_attribute(cellstate2, 'text', 0)\n\t\tself.combobox_state2.set_active(0)\n\t\tfor i in states:\n\t\t\tstorestate2.append([i])\n\t\tself.combobox_state2.set_model(storestate2)\n\n\t\tstore_yearc = gtk.ListStore(str)\n\t\tcell_yearc = gtk.CellRendererText()\t\t\n\t\tself.combobox_yearc.pack_start(cell_yearc)\n\t\tself.combobox_yearc.add_attribute(cell_yearc, 'text', 0)\n\t\tself.combobox_yearc.set_active(0)\n\n\t\tfor i in year:\n\t\t\tstore_yearc.append([i])\n\t\tself.combobox_yearc.set_model(store_yearc)\n\n\t\tself.combobox_state1.connect('changed', self.on_changed_state1)\n\t\tself.combobox_state2.connect('changed', self.on_changed_state2)\n\t\tself.combobox_yearc.connect('changed', self.on_changed_yearc)\n\n\n\t\ttable.attach(state1lbl, 0, 1, 3, 4)\n\t\ttable.attach(state2lbl, 2, 3, 3, 4)\n\t\ttable.attach(self.combobox_state1, 0, 1, 4, 5,xpadding = 8, ypadding=9)\n\t\ttable.attach(self.combobox_state2, 2, 3, 4, 5,xpadding = 8, ypadding=9)\n\t\ttable.attach(self.combobox_yearc, 1, 2, 4, 5,xpadding = 8, ypadding=9)\n\t\ttable.attach(self.compare_button, 1, 2, 6, 7, xpadding = 8, ypadding=9)\n\t\t\n\t\tnb.append_page(table)\n\t\tnb.set_tab_label_text(table, \"Comparison of states\")\n\n\t\ttv = gtk.TextView()\n\t\tnb.append_page(tv)\n\t\tnb.set_tab_label_text(tv, \"About\")\n\t\t\n\t\tself.add(nb)\n\t\t\n\t\tself.connect(\"destroy\", gtk.main_quit)\t\t\n\t\tself.show_all()\n\n\tdef on_changed_state1(self, widget):\n\t\tif(widget.get_active_text() == self.combobox_state2.get_active_text()):\n\t\t\tself.compare_button.set_sensitive(False)\n\t\telse:\n\t\t\tself.compare_button.set_sensitive(True)\n\t\treturn\n\n\tdef on_changed_state2(self, widget):\n\t\tif(widget.get_active_text() == self.combobox_state1.get_active_text()):\n\t\t\tself.compare_button.set_sensitive(False)\n\t\telse:\n\t\t\tself.compare_button.set_sensitive(True)\n\t\treturn\n\n\tdef on_changed_yearc(self, widget):\n\t\treturn\n\n\tdef on_changed(self, widget):\n\t\t#data = pd.read_csv(\"/home/safwan/crime-in-india/crime/01_District_wise_crimes_committed_IPC_2013.csv\"\t\n\t\tdistrict = s(self.data[self.data['STATEorUT']==widget.get_active_text()].filter(items=['DISTRICT'])['DISTRICT']).unique().tolist()\n\t\tself.store_dist.clear()\n\t\tself.store_dist.append([\"All\"])\n\t\tfor i in district:\n\t\t\tself.store_dist.append([i])\n\n\t\tself.combobox_district.set_model(self.store_dist)\n\t\t#combobox.connect('changed', self.on_changed)\n\t\tself.combobox_district.set_active(0)\n\n\n\tdef display_data(self, widget):\n\t\t#print self.combobox_state.get_active_text(), self.combobox_year.get_active_text(), self.combobox_district.get_active_text(), self.combobox_crime.get_active_text()\n\t\t#filtered_data = self.data[((self.data['STATE.UT']==self.combobox_state.get_active_text()) & (self.data['DISTRICT']==self.combobox_district.get_active_text()) & (self.data['YEAR']==self.combobox_year.get_active_text()))].filter(items=['STATE/UT','DISTRICT','YEAR', self.combobox_crime.get_active_text()])\n\n\t\tdialog = gtk.Dialog(\"My dialog\",\n\t\tself,\n\t\tgtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT,\n\t\t(gtk.STOCK_OK, gtk.RESPONSE_ACCEPT))\n\t\tdialog.set_size_request(400,700)\n\t\t\n\t\tliststore = gtk.ListStore(str, str, str, str)\n\n\t\ttreeview = gtk.TreeView(liststore)\n\n\t\ttvcolumn = gtk.TreeViewColumn(\"STATE/UT\")\n\t\ttvcolumn1 = gtk.TreeViewColumn(\"DISTRICT\")\n\t\ttvcolumn2 = gtk.TreeViewColumn(\"YEAR\")\n\t\ttvcolumn3 = gtk.TreeViewColumn(self.combobox_crime.get_active_text())\n\t\t\n\t\tfor i in self.filtered_data.values.tolist():\n\t\t\tliststore.append(i)\t\n\t\t#liststore.append(['Open', gtk.STOCK_OPEN, 'Open a File', True])\n\t\n\t\ttreeview.append_column(tvcolumn)\n\t\ttreeview.append_column(tvcolumn1)\n\t\ttreeview.append_column(tvcolumn2)\n\t\ttreeview.append_column(tvcolumn3)\n\n\t\tcell = gtk.CellRendererText()\n\t\tcell1 = gtk.CellRendererText()\n\t\tcell2 = gtk.CellRendererText()\n\t\tcell3 = gtk.CellRendererText()\n\t\t\n\t\tcell.set_property('cell-background', 'yellow')\n\t\tcell1.set_property('cell-background', 'cyan')\n\t\tcell2.set_property('cell-background', 'pink')\n\t\tcell3.set_property('cell-background', 'red')\n\n\t\ttvcolumn.pack_start(cell, False)\n\t\ttvcolumn1.pack_start(cell1, True)\n\t\ttvcolumn2.pack_start(cell2, True)\n\t\ttvcolumn3.pack_start(cell3, True)\n\n\t\ttvcolumn.set_attributes(cell, text = 0)\n\t\ttvcolumn1.set_attributes(cell1, text = 1)\n\t\ttvcolumn2.set_attributes(cell2, text = 2)\n\t\ttvcolumn3.set_attributes(cell3, text = 3)\n\n\t\tscrolled_window = gtk.ScrolledWindow()\n\t\tscrolled_window.set_policy(gtk.POLICY_NEVER, gtk.POLICY_AUTOMATIC)\n\t\tscrolled_window.add(treeview)\n\t\tscrolled_window.set_border_width(10)\n\t\t#scrolled_window.set_min_content_height(200)\n\n\t\tscrolled_window.add(treeview)\n\n\t\t\n\t\ttreeview.set_search_column(0)\n\t\tdialog.vbox.add(scrolled_window)\n\t\ttreeview.show()\n\t\tscrolled_window.show()\n\t\tres = dialog.run()\n\t\tdialog.destroy()\n\t\t\n\t\treturn\n\n\tdef display_graph(self, widget):\n\t\tdialog = gtk.Dialog(\"My dialog\",\n\t\tself,\n\t\tgtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT,\n\t\t(gtk.STOCK_OK, gtk.RESPONSE_ACCEPT))\n\t\tdialog.set_size_request(1300,500)\n\n\t\t# Crime name filter\n\t\tif \" \" not in self.combobox_crime.get_active_text():\n\t\t\tcrime = self.combobox_crime.get_active_text()\n\t\telse:\n\t\t\tcrime = \".\".join(self.combobox_crime.get_active_text().split(\" \"))\n\n\t\t\n\n\t\tfig = Figure(figsize=(12, 10), dpi=100)\n\n\t\tsales = [{'Groups':'0-9', 'Counts':38},\n\t\t\t\t{'Groups':'10-19', 'Counts':41},\n\t\t\t\t{'Groups':'20-29', 'Counts':77},\n\t\t\t\t{'Groups':'30-39', 'Counts':73},\n\t\t\t\t{'Groups':'40-49', 'Counts':77}]\n\t\tdf = pd.DataFrame(sales)\n\n\t\tax = fig.add_subplot(111)\n\n\t\tif (self.combobox_year.get_active_text() == \"All\" and self.combobox_district.get_active_text() != \"All\"):\n\t\t\t\n\t\t\tself.filtered_data = self.filtered_data.reset_index(drop=True)\n\t\t\typos = np.arange(len(self.filtered_data['YEAR'].tolist()))\n\t\t\tp1 = ax.bar(ypos, self.filtered_data[crime], width=0.6, color='r')\n\n\t\t\tax.set_title(crime.lower() +'s in ' + self.combobox_district.get_active_text() +' - Yearwise')\n\t\t\tax.set_xticks(ypos+0.3)\n\t\t\tax.set_xticklabels(self.filtered_data.YEAR)\n\t\telif (self.combobox_district.get_active_text() == \"All\" and self.combobox_year.get_active_text() != \"All\"):\n\t\t\tfd_total_removed = self.filtered_data[self.filtered_data.DISTRICT != 'TOTAL']\n\t\t\typos = np.arange(len(fd_total_removed['DISTRICT'].tolist()))\n\t\t\t\n\t\t\tp1 = ax.bar(ypos, fd_total_removed[crime], width=0.3, color='r')\n\t\t\tfontx = {'fontsize': 7,\n \t\t\t\t\t'fontweight': 2,\n \t\t\t\t\t'verticalalignment': 'center',\n \t\t\t\t\t'horizontalalignment': 'center'}\n\n\t\t\tax.set_title(crime + 's in ' + self.combobox_state.get_active_text() + '(' +self.combobox_state.get_active_text()+' )' + ' - Districtwise')\n\t\t\tax.set_xticks(ypos+0.15)\n\t\t\tax.set_xticklabels(fd_total_removed.DISTRICT, fontdict=fontx)\n\t\telse:\n\t\t\tprint(df.index)\n\t\t\tp1 = ax.bar(df.index, df.Counts, width=0.8, color='r')\n\n\n\t\t\tax.set_title('Scores by group and gender')\n\t\t\tax.set_xticks(df.index+0.4)\n\t\t\tax.set_xticklabels(df.Groups)\n\n\t\tcanvas = FigureCanvas(fig) # a gtk.DrawingArea\n\t\tcanvas.set_size_request(800, 600)\n\t\tdialog.vbox.pack_start(canvas)\n\t\ttoolbar = NavigationToolbar(canvas, dialog)\n\t\tdialog.vbox.pack_start(toolbar, False, False)\n\t\tcanvas.show()\n\t\tdialog.run()\n\t\tdialog.destroy()\n\t\treturn\n\n\tdef on_changed_district(self, widget):\n\t\tif(self.combobox_year.get_active_text() != 'All'):\t\t\n\t\t\tintyear = int(self.combobox_year.get_active_text())\n\n\t\t# Check crime field\n\t\tif \" \" not in self.combobox_crime.get_active_text():\n\t\t\tcrime = self.combobox_crime.get_active_text()\n\t\telse:\n\t\t\tcrime = \".\".join(self.combobox_crime.get_active_text().split(\" \"))\n\n\t\tif(self.combobox_district.get_active_text() == 'All' and self.combobox_year.get_active_text() == 'All'):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text()').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\tself.button_show_graph.set_sensitive(False)\n\t\telif(self.combobox_district.get_active_text() == 'All'):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\tself.button_show_graph.set_sensitive(True)\n\t\telif (self.combobox_year.get_active_text() == \"All\"):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and DISTRICT == @self.combobox_district.get_active_text()').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\tself.button_show_graph.set_sensitive(True)\t\n\t\telse:\n\t\t\tself.button_show_graph.set_sensitive(False)\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and DISTRICT == @self.combobox_district.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\t\t\n\t\treturn\n\n\tdef on_changed_year(self, widget):\n\t\tif(self.combobox_year.get_active_text() != 'All'):\t\t\n\t\t\tintyear = int(self.combobox_year.get_active_text())\n\n\t\t# Check crime field\n\t\tif \" \" not in self.combobox_crime.get_active_text():\n\t\t\tcrime = self.combobox_crime.get_active_text()\n\t\telse:\n\t\t\tcrime = \".\".join(self.combobox_crime.get_active_text().split(\" \"))\n\n\t\tif(self.combobox_district.get_active_text() == 'All' and self.combobox_year.get_active_text() == 'All'):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text()').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\tself.button_show_graph.set_sensitive(False)\n\t\telif(self.combobox_district.get_active_text() == 'All'):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\tself.button_show_graph.set_sensitive(True)\n\t\telif (self.combobox_year.get_active_text() == \"All\"):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and DISTRICT == @self.combobox_district.get_active_text()').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\tself.button_show_graph.set_sensitive(True)\t\n\t\telse:\n\t\t\tself.button_show_graph.set_sensitive(False)\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and DISTRICT == @self.combobox_district.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\t\t\t\n\t\treturn\n\n\tdef on_changed_crime(self, widget):\n\t\tif(self.combobox_year.get_active_text() != 'All'):\t\t\n\t\t\tintyear = int(self.combobox_year.get_active_text())\n\n\t\t# Check crime field\n\t\tif \" \" not in self.combobox_crime.get_active_text():\n\t\t\tcrime = self.combobox_crime.get_active_text()\n\t\telse:\n\t\t\tcrime = \".\".join(self.combobox_crime.get_active_text().split(\" \"))\n\n\t\tif(self.combobox_district.get_active_text() == 'All' and self.combobox_year.get_active_text() == 'All'):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text()').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\telif(self.combobox_district.get_active_text() == 'All'):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\telif (self.combobox_year.get_active_text() == \"All\"):\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and DISTRICT == @self.combobox_district.get_active_text()').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\t\n\t\telse:\n\t\t\tself.filtered_data = self.data.query('STATEorUT == @self.combobox_state.get_active_text() and DISTRICT == @self.combobox_district.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT','DISTRICT','YEAR', crime])\n\t\treturn\n\n\tdef compare_states(self, widget):\n\t\tcrimes = [\"MURDER\", \"RAPE\", \"KIDNAPPING.ABDUCTION\", \"RIOTS\", \"ROBBERY\", \"BURGLARY\", \"DOWRY.DEATHS\"]\n\t\tintyear = int(self.combobox_yearc.get_active_text()) \n\t\tstate1_data = self.data.query('STATEorUT == @self.combobox_state1.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT', 'DISTRICT', 'YEAR', crimes[0], crimes[1],crimes[2],crimes[3],crimes[4],crimes[5],crimes[6]])[self.data.DISTRICT == 'TOTAL']\n\t\tstate2_data = self.data.query('STATEorUT == @self.combobox_state2.get_active_text() and YEAR == @intyear').filter(items=['STATEorUT', 'DISTRICT', 'YEAR', crimes[0], crimes[1],crimes[2],crimes[3],crimes[4],crimes[5],crimes[6]])[self.data.DISTRICT == 'TOTAL']\n\t\tprint(state1_data.iloc[0]['MURDER'])\n\n\t\tstate1_total = [state1_data.iloc[0][crimes[0]],state1_data.iloc[0][crimes[1]],state1_data.iloc[0][crimes[2]],state1_data.iloc[0][crimes[3]],state1_data.iloc[0][crimes[4]],state1_data.iloc[0][crimes[5]],state1_data.iloc[0][crimes[6]]]\n\t\tstate2_total = [state2_data.iloc[0][crimes[0]],state2_data.iloc[0][crimes[1]],state2_data.iloc[0][crimes[2]],state2_data.iloc[0][crimes[3]],state2_data.iloc[0][crimes[4]],state2_data.iloc[0][crimes[5]],state2_data.iloc[0][crimes[6]]]\n\t\tprint(state1_total)\n\n\t\tdialog = gtk.Dialog(\"My dialog\",\n\t\tself,\n\t\tgtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT,\n\t\t(gtk.STOCK_OK, gtk.RESPONSE_ACCEPT))\n\t\tfig = Figure(figsize=(5, 4), dpi=100)\n\t\tdialog.set_size_request(1300,500)\n\t\tax = fig.add_subplot(111)\n\t\typos = np.arange(len(crimes))\n\t\tprint(ypos)\n\t\tp1 = ax.bar(ypos-0.4, state1_total, width=0.4, color='r', align='center')\n\t\tp2 = ax.bar(ypos, state2_total, width=0.4, color='b', align='center')\n\n\t\tax.set_title(\"Comparison of \" + self.combobox_state1.get_active_text() + \" and \" + self.combobox_state2.get_active_text())\n\t\tax.set_xticks(ypos-0.2)\n\t\tax.set_xticklabels(crimes)\n\t\tax.set_ylabel('Total Crimes')\n\t\tax.legend((p1[0], p2[0]), (self.combobox_state1.get_active_text(), self.combobox_state2.get_active_text()))\n\n\t\tcanvas = FigureCanvas(fig) # a gtk.DrawingArea\n\t\tcanvas.set_size_request(800, 600)\n\t\tdialog.vbox.pack_start(canvas)\n\t\ttoolbar = NavigationToolbar(canvas, dialog)\n\t\tdialog.vbox.pack_start(toolbar, False, False)\n\t\tcanvas.show()\n\t\tdialog.run()\n\t\tdialog.destroy()\n\t\t\n\t\treturn\t\t\n\nPyApp()\ngtk.main()\n\n","sub_path":"ics3.py","file_name":"ics3.py","file_ext":"py","file_size_in_byte":20791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"548653523","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\nimport re\nimport sys\n\ndef login(driver):\n print('Logging in')\n driver.get('https://clims4.genewiz.com/RegisterAccount/Login')\n fill_box = driver.find_element_by_xpath('//*[@id=\"LoginName\"]')\n fill_box.clear()\n fill_box.send_keys('******')\n fill_box = driver.find_element_by_xpath('//*[@id=\"Password\"]')\n fill_box.send_keys('*****')\n driver.find_element_by_xpath('//*[@id=\"btnSubmit\"]').click()\n\ndef sample_search(driver):\n global sample_status\n table = driver.find_element_by_xpath('//*[@id=\"myOrdersTable\"]/tbody')\n try:\n for i,td in enumerate(table.find_elements_by_xpath('//*[@id=\"myOrdersTable\"]/tbody/tr/td[4]'),1):\n if td.text == '':\n continue\n number_search=re.match('^\\d+-',td.text)\n if number_search == None:\n number_value=''\n else:\n number_value=number_search.group(0)\n if td.text == (number_value+user_input):\n number_of_samples=driver.find_element_by_xpath(f'//*[@id=\"myOrdersTable\"]/tbody/tr[{i}]/td[9]')\n sample_status=driver.find_element_by_xpath(f'//*[@id=\"myOrdersTable\"]/tbody/tr[{i}]/td[11]/button')\n driver.find_element_by_xpath(f'//*[@id=\"myOrdersTable\"]/tbody/tr[{i}]/td[11]/button').click()\n break\n print(f'\\n{number_of_samples.text} samples detected\\n')\n print(f'\\nEstimated time of completion {int(number_of_samples.text)*2} seconds')\n flag=True\n except:\n flag=False\n WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"myOrdersTable_paginate\"]/ul/li[8]/a'))).click()\n return flag\n\n\nsample_status=()\nuser_input=sys.argv[1]\ndef get_sequence():\n global sample_status\n start_time = time.time()\n print('Starting Program')\n options = Options()\n options.add_argument(\"--window-size=1920,1080\")\n options.add_argument(\"--start-maximized\")\n options.add_argument(\"--disable-gpu\")\n options.add_argument('--disable-extensions')\n options.add_argument('--no-sandbox')\n options.add_argument(\"--headless\")\n options.add_argument('log-level=3')\n options.add_argument('--proxy-server=\"direct://\"')\n options.add_argument('--proxy-bypass-list=*')\n driver = webdriver.Chrome(options=options)\n login(driver)\n WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"hs-eu-confirmation-button\"]'))).click()\n print('\\nCompiling Sequences\\n')\n page=0\n while sample_search(driver) is False:\n page+=1\n print(f'Checking Page {page}')\n if page>50:\n print(f'Sequence ID does not exist')\n print('Ending Program')\n sample_status='Error'\n return\n if sample_status.text != 'View Results':\n print('\\nSample is not finished sequencing yet\\n')\n return\n WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"gwzSngrOrderResultPanelRoot\"]/table/tbody')))\n seq_list=[]\n table=driver.find_element_by_xpath('//*[@id=\"gwzSngrOrderResultPanelRoot\"]/table/tbody')\n for x,sequence in enumerate(table.find_elements_by_xpath('//*[@id=\"gwzSngrOrderResultPanelRoot\"]/table/tbody/tr/td[9]'),1):\n WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.XPATH, f'//*[@id=\"gwzSngrOrderResultPanelRoot\"]/table/tbody//tr[{x}]/td[9]/span[2]'))).click()\n seq_list.append([(WebDriverWait(driver, 20).until(EC.visibility_of_element_located((By.XPATH, \"//*[@id='gwzViewResultsModalDialog']/div/div/div[2]/div\"))).text)])\n WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.XPATH, '//*[@id=\"gwzViewResultsModalDialog\"]/div/div/div[3]/button'))).click()\n #print(time.time() - start_time)\n driver.close()\n return seq_list\n\n\ndna_codon_dict={'TTT':'F','TTC':'F',\n 'TTA':'L','TTG':'L',\n 'CTT':'L','CTC':'L',\n 'CTA':'L','CTG':'L',\n 'ATT':'I','ATC':'I',\n 'ATA':'I','ATG':'M',\n 'GTT':'V','GTC':'V',\n 'GTA':'V','GTG':'V',\n 'TCT':'S','TCC':'S',\n 'TCA':'S','TCG':'S',\n 'CCT':'P','CCC':'P',\n 'CCA':'P','CCG':'P',\n 'ACT':'T','ACC':'T',\n 'ACA':'T','ACG':'T',\n 'GCT':'A','GCC':'A',\n 'GCA':'A','GCG':'A',\n 'TAT':'Y','TAC':'Y',\n 'CAT':'H','CAC':'H',\n 'CAA':'Q','CAG':'Q',\n 'AAT':'N','AAC':'N',\n 'AAA':'K','AAG':'K',\n 'GAT':'D','GAC':'D',\n 'GAA':'E','GAG':'E',\n 'TGT':'C','TGC':'C',\n 'TGG':'W','CGT':'R',\n 'CGC':'R','CGA':'R',\n 'CGG':'R','AGT':'S',\n 'AGC':'S','AGA':'R',\n 'AGG':'R','GGT':'G',\n 'GGC':'G','GGA':'G',\n 'GGG':'G','TAA':'X',\n 'TAG':'X','TGA':'X'}\n\nDNA_complement_dict={'A':'T',\n 'T':'A',\n 'G':'C',\n 'C':'G',\n 'N':'N'}\n\ndef rev(global_codon_list):\n return [DNA_complement_dict[codons] for codons in reversed(global_codon_list)]\n\n\ndef codon_translation(global_codon_list):\n codon_triple_list=[]\n open_reading_frame_lists=[[],[],[],]\n for i,codons in enumerate(open_reading_frame_lists,1):\n codon_triple_list.clear()\n open_reading_frame_count=1\n for codons in global_codon_list:\n if open_reading_frame_count20:\n sequences_to_search.append(''.join(sequence_to_add_to_search_list))\n sequence_to_add_to_search_list.clear()\n else:\n sequence_to_add_to_search_list.clear()\n return sequences_to_search\n\n\n\n\nglobal_codon_list=[]\nsequence_list1=[]\ndef manual_mode():\n global global_codon_list\n global sequence_list1\n sample_statues_check=get_sequence()\n if sample_status == 'Error':\n return\n if sample_statues_check == None:\n print('\\nEnding program\\n')\n return\n for i,sequence in enumerate(sample_statues_check):\n title=re.search(r'^>.*\\n',sequence[0])\n remove_title=re.sub(r'^>.*\\n','',sequence[0])\n global_codon_list=re.sub(r'\\n','',remove_title)\n sequences_to_search=find_open_reading_frames(global_codon_list)\n sequence_to_search=[]\n print(f'\\nsample {title.group(0)}')\n for number,sequences in enumerate(sequences_to_search,1):\n print(f'row {number} sequence: {sequences}')\n sequence_to_search.append(sequences)\n pick_sequence_to_search=input('indicate which row # sequence to search, if no match, type \"n\": ')\n if pick_sequence_to_search != 'n':\n sequence_list1.append(sequence_to_search[int(pick_sequence_to_search)-1])\n questionairre_question=input('would you like to search BLAST and or save your sequence and translated protein? (y/n): ')\n if questionairre_question != 'n':\n questionairre(title)\n else:\n reverse_loop(title)\n if sequence_list1 != []:\n questionairre_question=input('would you like to search BLAST and or save your sequence and translated protein? (y/n): ')\n if questionairre_question != 'n':\n questionairre(title)\n\ndef questionairre(title):\n BLAST_question=input('Would you like to run sequence against BLAST? (y/n): ')\n if BLAST_question != 'n':\n BLAST()\n dna_save_file_question=input('Would you like to save your DNA file?\\n If yes, please type in the desired filename, else type \"n\": ')\n if dna_save_file_question != 'n':\n with open(dna_save_file_question,'w') as dna_file:\n dna_file.write(title.group(0)+global_codon_list)\n protein_save_file_question=input('Would you like to save your translated protein sequence?\\nIf yes, please type in the desired filename, else type \"n\": ')\n if protein_save_file_question !='n':\n with open(protein_save_file_question,'w') as protein_file:\n protein_file.write(title.group(0)+'\\n'+(''.join(sequence_list1)))\n expasy_question=input('Would you like to generate an expasy file for your protein? (y/n): ')\n if expasy_question != 'n':\n expasy()\n\n\ndef reverse_loop(title):\n global global_codon_list\n global sequence_list1\n\n global_codon_list=rev(global_codon_list)\n sequences_to_search=find_open_reading_frames(global_codon_list)\n sequence_to_search=[]\n print(f'sample {title.group(0)}')\n for number,sequence in enumerate(sequences_to_search,1):\n print(f'row {number} sequence: {sequence}')\n sequence_to_search.append(sequence)\n pick_sequence_to_search=input('indicate which row # sequence to search, if no match, type \"n\": ')\n if pick_sequence_to_search != 'n':\n sequence_list1.append(sequence_to_search[int(pick_sequence_to_search)-1])\n else:\n print('\\nYour protein was not found in Sequencing\\n')\n sequence_list1.clear()\n\n\ndef BLAST():\n driver = webdriver.Chrome()\n driver.get('https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome')\n fill_box = driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/form/div[3]/fieldset/div[1]/div[1]/textarea')\n fill_box.clear()\n fill_box.send_keys(''.join(sequence_list1))\n sumbit_button=driver.find_element_by_xpath('/html/body/div[2]/div/div[2]/form/div[6]/div/div[1]/div[1]/input')\n sumbit_button.click()\n while True:\n try:\n tmp = driver.title\n except:\n break\ndef expasy():\n driver = webdriver.Chrome()\n driver.get('https://web.expasy.org/protparam/')\n fill_box = driver.find_element_by_xpath('/html/body/div[2]/div[2]/form/textarea')\n fill_box.clear()\n fill_box.send_keys(''.join(sequence_list1))\n sumbit_button=driver.find_element_by_xpath('/html/body/div[2]/div[2]/form/p[1]/input[2]')\n sumbit_button.click()\n while True:\n try:\n tmp = driver.title\n except:\n print('program done')\n break\n\nprotein_size=100\ndef find_open_reading_frames_auto_mode(global_codon_list):\n sequences_to_search=[]\n sequence_to_add_to_search_list=[]\n add_to_string=False\n for open_reading_frames in codon_translation(global_codon_list):\n for amino_acids in open_reading_frames:\n if amino_acids == 'M':\n add_to_string=True\n if add_to_string is True:\n sequence_to_add_to_search_list.append(amino_acids)\n if amino_acids == 'X':\n add_to_string=False\n if len(sequence_to_add_to_search_list)>protein_size:\n sequences_to_search.append(''.join(sequence_to_add_to_search_list))\n sequence_to_add_to_search_list.clear()\n else:\n sequence_to_add_to_search_list.clear()\n return sequences_to_search\n\ndef reverse_loop_auto(title):\n global global_codon_list\n global sequence_list1\n\n global_codon_list=rev(global_codon_list)\n sequences_to_search=find_open_reading_frames_auto_mode(global_codon_list)\n sequence_to_search=[]\n print(f'sample {title.group(0)}')\n for number,sequence in enumerate(sequences_to_search,1):\n print(f'row {number} sequence: {sequence}')\n sequence_to_search.append(sequence)\n if sequence_to_search == []:\n print(f'{title.group(0)} not found')\n\ndef auto_mode():\n global global_codon_list\n global sequence_list1\n sample_statues_check=get_sequence()\n if sample_status == 'Error':\n return\n if sample_statues_check == None:\n print('\\nEnding program\\n')\n return\n for i,sequence in enumerate(sample_statues_check):\n title=re.search(r'^>.*\\n',sequence[0])\n remove_title=re.sub(r'^>.*\\n','',sequence[0])\n global_codon_list=re.sub(r'\\n','',remove_title)\n sequences_to_search=find_open_reading_frames_auto_mode(global_codon_list)\n sequence_to_search=[]\n print(f'\\nsample {title.group(0)}')\n for number,sequences in enumerate(sequences_to_search,1):\n print(f'row {number} sequence: {sequences}')\n sequence_to_search.append(sequences)\n if sequences_to_search == []:\n print('\\nno forward sequence found, checking reverse sequence\\n')\n reverse_loop_auto(title)\n\ndef main_loop():\n auto_or_manual_question=input('would you like auto-mode, or manual mode? (type auto for auto-mode, manual for maual mode): ')\n if auto_or_manual_question == 'auto':\n auto_mode()\n else:\n manual_mode()\n\n\nmain_loop()\n","sub_path":"DTB_V2.py","file_name":"DTB_V2.py","file_ext":"py","file_size_in_byte":14127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"626485891","text":"from catan_core.building.settlement import Settlement\nfrom catan_core.edge import Edge\nfrom catan_core.player.player import Player\nfrom catan_core.road import Road\nfrom catan_core.vertex import Vertex\n\n\nclass TestEdge:\n def test_init_no_vertices(self):\n edge = Edge(id=0)\n assert edge.id == 0\n assert edge.vertices == []\n\n def test_repr(self):\n assert Edge(id=0).__repr__() == \"edge-0\"\n\n def test_set_vertices(self):\n edge = Edge(id=0)\n vertex = Vertex(id=0)\n\n edge.vertices = [vertex]\n\n assert edge.vertices == [vertex]\n\n def test_assign_road(self):\n player = Player()\n edge = Edge(id=0)\n assert not edge.road\n road = Road(player=player)\n edge.assign_road(road=road)\n assert edge.road == road\n\n def test_can_place_road_returns_false_if_road_already_exists(self):\n edge = Edge(id=0)\n player = Player()\n edge.road = Road(player=player)\n assert not edge.can_place_road(player=player)\n\n def test_can_place_road_returns_false_if_no_adjacent_roads_for_player(\n self,\n ):\n player1 = Player()\n player2 = Player()\n\n edge = Edge(id=0)\n edge_no_road = Edge(id=1)\n edge_with_road_player2 = Edge(id=2)\n edge_with_road_player2.road = Road(player=player2)\n\n v1 = Vertex(id=0)\n v2 = Vertex(id=1)\n\n # Connect vertex to edges\n edge.vertices = [v1, v2]\n edge_no_road.vertices = [v1]\n edge_with_road_player2.vertices = [v2]\n v1.edges = [edge, edge_no_road]\n v2.edges = [edge, edge_with_road_player2]\n\n assert not edge.can_place_road(player=player1)\n\n def test_can_place_road_retruns_true_if_adjacent_building_for_player(self):\n player = Player()\n\n edge = Edge(id=0)\n vertex = Vertex(id=0)\n\n edge.vertices = [vertex]\n vertex.edges = [edge]\n vertex.assign_building(building=Settlement(player=player))\n\n assert edge.can_place_road(player=player)\n\n def test_can_place_road_returns_true_if_adjacent_road_for_player(self):\n player = Player()\n\n edge = Edge(id=0)\n edge_with_road = Edge(id=1)\n edge_with_road.road = Road(player=player)\n\n vertex = Vertex(id=0)\n\n # Connect vertex to edges\n edge.vertices = [vertex]\n edge_with_road.vertices = [vertex]\n vertex.edges = [edge, edge_with_road]\n\n assert edge.can_place_road(player=player)\n","sub_path":"catan_core/test/test_edge.py","file_name":"test_edge.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"277561532","text":"import numpy as np\nimport chainer\nfrom chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils\nfrom chainer import Link, Chain, ChainList\nimport chainer.functions as F\nimport chainer.links as L\n\nfrom impls import rnn, helpers\n\nname = helpers.get_script_name(__file__)\ndata_path = '.data/ptb.train.min.txt'\ndest_root = '.dest'\n\nvocab = {}\ntrain_data = helpers.load_data(data_path, vocab)\n\neos_id = vocab['']\ndemb = 100\nmodel = rnn.Rnn(len(vocab), eos_id, demb)\noptimizer = optimizers.Adam()\noptimizer.setup(model)\n\nfor epoch in range(5):\n s = []\n for pos in range(len(train_data)):\n id = train_data[pos]\n s.append(id)\n if id == eos_id:\n model.zerograds()\n loss = model(s)\n loss.backward()\n optimizer.update()\n s = []\n outfile = \"{0}/rnn-{1}.model\".format(dest_root, epoch)\n serializers.save_npz(outfile, model)\n","sub_path":"try-chainer/chapter07/rnn.py","file_name":"rnn.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"30608012","text":"# ====== Legal notices\r\n#\r\n# Copyright (C) 2013 GEATEC engineering\r\n#\r\n# This program is free software.\r\n# You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicence.\r\n#\r\n# This program is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY, without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\r\n# See the QQuickLicence for details.\r\n#\r\n# The QQuickLicense can be accessed at: http://www.geatec.com/qqLicence.html\r\n#\r\n# __________________________________________________________________________\r\n#\r\n#\r\n# THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!\r\n#\r\n# __________________________________________________________________________\r\n#\r\n# It is meant for training purposes only.\r\n#\r\n# Removing this header ends your licence.\r\n#\r\n\r\nimport simpylc as sp\r\n\r\nclass Control (sp.Module):\r\n def __init__ (self):\r\n sp.Module.__init__ (self)\r\n \r\n self.page ('train control')\r\n\r\n self.group ('control buttons', True)\r\n self.brakeLiftButton = sp.Marker ()\r\n self.driveEnableButton = sp.Marker ()\r\n\r\n self.group ('warning lamps')\r\n self.brakeWarnLamp = sp.Marker ()\r\n self.speedWarnLamp = sp.Marker ()\r\n\r\n self.group ('state')\r\n self.accel = sp.Register ()\r\n self.speed = sp.Register ()\r\n self.position = sp.Register ()\r\n\r\n self.group ('auxiliary', True)\r\n self.maxSpeed = sp.Register ()\r\n self.speedWarnFraction = sp.Register (0.8)\r\n self.oldSpeed = sp.Register ()\r\n self.blinkTime = sp.Register (0.5)\r\n self.blinkTimer = sp.Timer ()\r\n self.blinkEdge = sp.Oneshot ()\r\n self.blinkOn = sp.Marker ()\r\n\r\n self.group ('sweep time measurement')\r\n self.sweepMin = sp.Register (sp.finity)\r\n self.sweepMax = sp.Register ()\r\n self.watchTime = sp.Register (2)\r\n self.watchTimer = sp.Timer ()\r\n self.run = sp.Runner ()\r\n\r\n def input (self):\r\n self.part ('speed')\r\n self.speed.set (sp.world.physics.speed)\r\n self.maxSpeed.set (sp.world.physics.maxSpeed)\r\n\r\n self.part ('position')\r\n self.position.set (sp.world.physics.position)\r\n\r\n def sweep (self):\r\n self.part ('dynamics')\r\n self.accel.set ((self.speed - self.oldSpeed) / sp.world.period)\r\n self.oldSpeed.set (self.speed)\r\n\r\n self.part ('warnings')\r\n self.blinkTimer.reset (self.blinkTimer > self.blinkTime)\r\n self.blinkEdge.trigger (not self.blinkTimer)\r\n self.blinkOn.mark (not self.blinkOn, self.blinkEdge)\r\n self.brakeWarnLamp.mark (not self.brakeLiftButton and self.driveEnableButton and self.blinkOn)\r\n self.speedWarnLamp.mark (self.speed > self.speedWarnFraction * self.maxSpeed and self.blinkOn)\r\n \r\n self.part ('sweep time masurement')\r\n self.sweepMin.set (sp.world.period, sp.world.period < self.sweepMin)\r\n self.sweepMax.set (sp.world.period, sp.world.period > self.sweepMax)\r\n self.watchTimer.reset (self.watchTimer > self.watchTime)\r\n self.sweepMin.set (sp.finity, not self.watchTimer)\r\n self.sweepMax.set (0, not self.watchTimer)\r\n \r\n def output (self):\r\n self.part ('control signals')\r\n sp.world.physics.brakeLift.mark (self.brakeLiftButton)\r\n sp.world.physics.driveEnable.mark (self.driveEnableButton)\r\n \r\n","sub_path":"module_plc_besturingen/les_2_van_simulatie_naar_on_site_debugging/progs/train/control.py","file_name":"control.py","file_ext":"py","file_size_in_byte":3462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"598320110","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom palettable.colorbrewer.qualitative import Paired_9 as mycorder\r\nfrom shapely.geometry import LineString, Point, MultiPoint\r\nimport warnings\r\nfrom viroconcom.contours import Contour\r\nfrom viroconcom.read_write import read_ecbenchmark_dataset, read_contour\r\nfrom viroconcom.plot import plot_contour, plot_confidence_interval\r\n\r\ndataset_char = 'D'\r\nreturn_period = 50\r\n\r\nlastname_firstname = [\r\n 'Wei_Bernt',\r\n 'GC_CGS',\r\n 'hannesdottir_asta',\r\n 'haselsteiner_andreas',\r\n 'BV',\r\n 'mackay_ed',\r\n 'qiao_chi',\r\n 'rode_anna',\r\n 'vanem_DirectSampling',\r\n]\r\nn_contours_to_analyze = len(lastname_firstname)\r\n\r\nclass Object(object):\r\n pass\r\n\r\n\r\n# Code from https://github.com/ahaselsteiner/2020-paper-omae-hierarchical-models/blob/master/contour_intersection.py\r\ndef contour_intersection(contour_x, contour_y, line_x, line_y, do_plot_in_case_of_failure=False):\r\n \"\"\"\r\n Computes the intersection point between two lines.\r\n The first line should be the environmental contour and the second line a\r\n straight line. If there are more multiple intersections the point that has\r\n the longest distance to the origin is returned (assuming the origin is at\r\n the first point of the straight line).\r\n Parameters\r\n ----------\r\n contour_x : ndarray of floats,\r\n Coordinates in the first dimension of the contour.\r\n contour_y : ndarray of floats\r\n Coordinates in the second dimension of the contour.\r\n line_x : ndarray of floats\r\n Coordinates in the first dimension of the straight line.\r\n line_y : ndarray of floats\r\n Coordaintes in the second dimension of the straight line.\r\n do_plot_in_case_of_failure : Boolean,\r\n if True a plot of the two lines for which no intersection could be\r\n found is shown.\r\n Returns\r\n -------\r\n x : float\r\n Intersection coordinate in the first dimension.\r\n y : float\r\n Intersection coordinate in the second dimension.\r\n \"\"\"\r\n\r\n point_list_l1 = list()\r\n for x,y in zip(contour_x, contour_y):\r\n point = Point(x, y)\r\n point_list_l1.append(point)\r\n contour = LineString(point_list_l1)\r\n\r\n point_list_l2 = list()\r\n for x,y in zip(line_x, line_y):\r\n point = Point(x, y)\r\n point_list_l2.append(point)\r\n line2 = LineString(point_list_l2)\r\n\r\n intersection = contour.intersection(line2)\r\n if type(intersection) is Point:\r\n return intersection.x, intersection.y\r\n if type(intersection) is MultiPoint:\r\n if len(intersection.geoms) > 0:\r\n print(str(len(intersection.geoms)) + ' intersections were found.'\r\n + ' Using the intersection that is the farthest'\r\n + ' away from the origin.')\r\n origin = Point(line_x[0], line_y[0])\r\n for i, p in enumerate(intersection.geoms):\r\n if i == 0:\r\n inter_x = p.x\r\n inter_y = p.y\r\n longest_distance = origin.distance(p)\r\n else:\r\n if origin.distance(p) > longest_distance:\r\n inter_x = p.x\r\n inter_y = p.y\r\n return inter_x, inter_y\r\n else:\r\n print('The result is: ' + str(intersection))\r\n warnings.warn('No point of intersection could be found. '\r\n 'Returning (nan, nan).', UserWarning)\r\n if do_plot_in_case_of_failure:\r\n fig = plt.figure(figsize=(5, 5), dpi=150)\r\n fig.add_subplot(111)\r\n plt.plot(contour_x, contour_y, 'b-')\r\n plt.plot(line_x, line_y, 'r-')\r\n plt.show()\r\n return np.nan, np.nan\r\n\r\n# Code from: https://github.com/ahaselsteiner/2020-paper-omae-hierarchical-models/blob/master/contour_statistics.py#L44\r\ndef thetastar_to_theta(thetastar, xspread, yspread):\r\n \"\"\"\r\n Parameters\r\n ----------\r\n thetastar : ndarray of floats\r\n Angle in the normalized coordinate system.\r\n xspread : float\r\n Spread of x (xmax - ymin).\r\n yspread : float\r\n Spread of y (ymax - amin).\r\n Returns\r\n -------\r\n theta : float,\r\n The angle theta in the original coordinate system. The angle is\r\n defined counter clockwise, 0 at (x=1, y=0) and is converted to be\r\n inside the interval [0 360).\r\n \"\"\"\r\n theta = np.arctan2(np.sin(thetastar) * yspread, np.cos(thetastar) * xspread)\r\n for i, t in enumerate(theta):\r\n if t < 0:\r\n theta[i] = t + 2 * np.pi\r\n return theta\r\n\r\ncolors_for_contribution = mycorder.mpl_colors\r\nfor idx in range(2):\r\n colors_for_contribution.append(colors_for_contribution[8])\r\ncolors_for_contribution.append('blue')\r\n\r\n\r\nfig, axs = plt.subplots(1, 2, sharey=True, figsize=(8, 4))\r\nmax_hs_of_sample = 0\r\n\r\n# Load the environmental data.\r\nfile_name_provided = 'datasets/' + dataset_char + '.txt'\r\nv_p, hs_p, label_v, label_hs = read_ecbenchmark_dataset(file_name_provided)\r\nmax_hs_of_sample = max([max_hs_of_sample, max(hs_p)])\r\n\r\ncontours = []\r\ncontours_v = []\r\ncontours_hs = []\r\nmax_hs_on_contours = np.empty(n_contours_to_analyze)\r\nfor i in range(n_contours_to_analyze):\r\n contribution_nr = i + 1\r\n if 11 >= contribution_nr >= 9:\r\n contribution_nr = 9\r\n elif contribution_nr > 11:\r\n # Because contribution 9 holds 3 sets of contours.\r\n contribution_nr = contribution_nr - 2\r\n folder_name = 'results/exercise-1/contribution-' + str(contribution_nr)\r\n file_name = folder_name + '/' + lastname_firstname[i] + '_dataset_' + \\\r\n dataset_char + '_' + str(return_period) + '.txt'\r\n if contribution_nr in (1, 2, 3, 5, 6, 8, 10):\r\n (hs, v) = read_contour(file_name)\r\n else:\r\n (v, hs) = read_contour(file_name)\r\n contour = Object()\r\n contour.c = (np.append(v, v[0]), np.append(hs, hs[0]))\r\n contours.append(contour)\r\n contours_v.append(v)\r\n contours_hs.append(hs)\r\n max_hs_on_contours[i] = max(hs[~np.isnan(hs)])\r\n\r\n# Compute the min, max and median contour.\r\n# First, define the origin and angles based on normalization.\r\nv0 = np.mean(v_p)\r\nhs0 = np.mean(hs_p)\r\nangle_step_for_ci = 2\r\ntheta_stars = np.arange(0, 360, angle_step_for_ci) / 180 * np.pi\r\nt1 = max(v_p) - min(v_p)\r\nt2 = max(hs_p) - min(hs_p)\r\nthetas = thetastar_to_theta(theta_stars, t1, t2)\r\nnr_of_datapoints_on_angled_line = 10\r\nline_tot_length = 50.0\r\nline_length = np.linspace(0.0, line_tot_length, nr_of_datapoints_on_angled_line)\r\n\r\n# Then, compute lines that have an angle theta to the x-axis.\r\nfor i, contour in enumerate(contours):\r\n theta_line_v = list()\r\n theta_line_hs = list()\r\n contour.theta_v = list()\r\n contour.theta_hs = list()\r\n for j, theta in enumerate(thetas):\r\n theta_line_v.append(np.multiply(np.cos(theta), line_length) + v0)\r\n theta_line_hs.append(np.multiply(np.sin(theta), line_length) + hs0)\r\n theta_v_j, theta_hs_j = contour_intersection(\r\n theta_line_v[j], theta_line_hs[j], contour.c[0], contour.c[1], True)\r\n\r\n contour.theta_v.append(theta_v_j)\r\n contour.theta_hs.append(theta_hs_j)\r\n\r\n\r\ntheta_v_ij = np.zeros(shape=(len(contours), thetas.size))\r\ntheta_hs_ij = np.zeros(shape=(len(contours), thetas.size))\r\ndistance_to_origin_ij = np.zeros(shape=(len(contours), thetas.size))\r\nfor i, contour in enumerate(contours):\r\n for j, (v_j, hs_j) in enumerate(zip(contour.theta_v, contour.theta_hs)):\r\n theta_v_ij[i, j] = v_j\r\n theta_hs_ij[i, j] = hs_j\r\n o = np.array([v0, hs0])\r\n p = np.array([v_j, hs_j]).flatten()\r\n op = p - o\r\n distance_to_origin_ij[i, j] = np.sqrt(op[0]*op[0] + op[1]*op[1])\r\nsorted_v = np.zeros(shape=(len(contours), thetas.size))\r\nsorted_hs = np.zeros(shape=(len(contours), thetas.size))\r\nfor j in range(thetas.size):\r\n sorted_indices = np.argsort(distance_to_origin_ij[:, j])\r\n sorted_v[:, j] = theta_v_ij[sorted_indices, j]\r\n sorted_hs[:, j] = theta_hs_ij[sorted_indices, j]\r\nlower_index = 0\r\nmedian_index = 4\r\nupper_index = 8\r\n\r\ncl = Object()\r\ncm = Object()\r\ncu = Object()\r\n\r\ncl.v = sorted_v[lower_index, :]\r\ncl.hs = sorted_hs[lower_index, :]\r\ncm.v = sorted_v[median_index, :]\r\ncm.hs = sorted_hs[median_index, :]\r\ncu.v = sorted_v[upper_index, :]\r\ncu.hs = sorted_hs[upper_index, :]\r\n\r\n\r\n# Create the overlay plot.\r\naxs[0].scatter(v_p, hs_p, c='black', alpha=0.5, zorder=-2, rasterized=True, label='Dataset D (provided)')\r\nfor i in range(n_contours_to_analyze):\r\n if i == 0:\r\n clabel = 'Submitted 50-yr contours'\r\n else:\r\n clabel = None\r\n plot_contour(contours_v[i], contours_hs[i],\r\n alpha=0.5, ax=axs[0], contour_label=clabel)\r\n\r\naxs[0].set_rasterization_zorder(-1)\r\naxs[0].set_xlabel(label_v.capitalize())\r\naxs[0].set_ylabel(label_hs.capitalize())\r\n\r\n# Create the confidence bound plot.\r\naxs[1].scatter(v_p, hs_p, c='black', alpha=0.5, zorder=-2, rasterized=True)\r\nplot_confidence_interval(x_median=cm.v, y_median=cm.hs, \r\n x_bottom=cl.v, y_bottom=cl.hs, x_upper=cu.v, y_upper=cu.hs, ax=axs[1], \r\n x_label=label_v.capitalize(), y_label=label_hs.capitalize(), \r\n contour_labels=['Median contour', 'Min. contour', 'Max. contour'])\r\n \r\nfor ax in axs.flat:\r\n ax.label_outer()\r\n ax.set_xlim((0, 35))\r\n ax.set_ylim((0, 18))\r\nfig.tight_layout()\r\nfig.savefig('results/discussion/gfx/e1_confidence_bounds.pdf', bbox_inches='tight')\r\n","sub_path":"results/discussion/e1_confidence_bound.py","file_name":"e1_confidence_bound.py","file_ext":"py","file_size_in_byte":9392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"151890070","text":"import sys\nimport os\n\nfrom sqlalchemy import Column, ForeignKey, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy import create_engine\n\nBase = declarative_base()\n\n#-----------------------------------------------------\n# Class representing the restaurant table in the\n# database. \n#-----------------------------------------------------\nclass Restaurant(Base):\n\t__tablename__ = 'restaurant'\n\tid = Column(Integer, primary_key = True)\n\tname = Column(String(80), nullable = False)\n\tdescription = Column(String(250), nullable = True)\n\taddress = Column(String(300), nullable = True)\n\tphone = Column(String(25), nullable = True)\n\twebsite = Column(String(100), nullable = True)\n\n\t@property\n\tdef serialize(self):\n\t\treturn { 'id': self.id,\n\t\t'name': self.name, \n\t\t'description': self.description, \n\t\t'adddress' : self.address, \n\t\t'phone' : self.phone, \n\t\t'website' : self.website }\n\n\t\n#-----------------------------------------------------\n# Class representing the menu item table in the\n# database. \n#----------------------------------------------------\t\nclass MenuItem(Base):\n\t__tablename__ = 'menu_item'\n\tid = Column(Integer, primary_key = True)\n\tname = Column(String(80), nullable = False)\n\tcourse = Column(String(250))\n\tdescription = Column(String(250))\n\tprice = Column(String(8))\n\trestaurant_id = Column(Integer, ForeignKey('restaurant.id'))\n\trestaurant = relationship(Restaurant)\n\t\n\t@property\n\tdef serialize(self):\n\t\treturn { \n\t\t'id': self.id,\n\t\t'name': self.name, \n\t\t'course' :self.course,\n\t\t'description': self.description, \n\t\t'price' : self.price}\n\t\t\n#-----------------------------------------------------\nengine = create_engine('sqlite:///restaurantmenu.db')\nBase.metadata.create_all(engine)\n","sub_path":"database/restaurant_database_setup.py","file_name":"restaurant_database_setup.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"609504201","text":"import pandas as pd\nimport numpy as np\nfrom os import listdir\n\ndef data_process(input_data):\n # extract features from raw data\n # mean, variance, median, mean absolute deviation, max, min\n X = []\n for i in range(len(input_data[0])):\n data = []\n for j in range(len(input_data)):\n data.append(input_data[j][i])\n # mean\n X.append(np.mean(data))\n # variance\n X.append(np.var(data))\n # median\n X.append(np.median(data))\n # mean absolute deviation\n X.append(np.mean(np.absolute(data - np.mean(data))))\n # max\n X.append(max(data))\n # min\n X.append(min(data))\n return np.array(X)\n\nwindow_size = 50\ndance_data = np.array([[0.0]*91])\nfor f in listdir(\"../final_data/\"):\n if \".txt\" in f:\n dataframe = pd.read_csv(\"../final_data/\"+f, sep=\",\", header=None)\n dataset = dataframe.values\n print(f)\n i = 0\n while i+window_size <= len(dataset): \n X = dataset[i:i+window_size,0:15]\n X = data_process(X)\n if \"chicken\" in f:\n X = np.append(X, 0)\n if \"cowboy_front\" in f:\n X = np.append(X, 1)\n if \"cowboy_back\" in f:\n X = np.append(X, 2)\n if \"crab\" in f:\n X = np.append(X, 3)\n if \"hunchback\" in f:\n X = np.append(X, 4)\n if \"raffles_left\" in f:\n X = np.append(X, 5)\n if \"raffles_right\" in f:\n X = np.append(X, 6)\n if \"running\" in f:\n X = np.append(X, 7)\n if \"james\" in f:\n X = np.append(X, 8)\n if \"snake\" in f:\n X = np.append(X, 9)\n if \"double\" in f:\n X = np.append(X, 10)\n if \"mermaid\" in f:\n X = np.append(X, 11)\n if \"final\" in f:\n X = np.append(X, 12)\n dance_data = np.append(dance_data, [X], axis=0)\n i += 10\ndance_data = dance_data[1:]\nnp.savetxt(\"../final_data/processed_data/window50.csv\", dance_data, delimiter=\",\")\n","sub_path":"software/classifiers/process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"491177528","text":"import pulp\nimport numpy as np\n\n#\n# ShiftSolver solves the linear problem of give team shift table\n# The number of shifts solved by ShiftSolver is at maximum `max_shift`\n#\n# Each request is a model.Request which has\n# (id, member, team, start_time, end_time, availability)\n#\n\nclass ShiftSolver:\n HOURS_IN_DAY = 24\n MINIMUM_MEMBERS = 3\n def __init__(self, team, member_list, days_in_shift=7):\n self.team = team\n self.member_list = member_list\n self.num_members = len(self.member_list)\n self.days_in_shift = days_in_shift\n self.problem = pulp.LpProblem('shift_table', pulp.LpMaximize)\n self.lp_variables = pulp.LpVariable.dicts(\n 'VAR',\n (range(self.num_members), range(self.days_in_shift), range(ShiftSolver.HOURS_IN_DAY)),\n 0, 1, 'Binary'\n )\n self.availability = np.zeros([\n self.num_members,\n self.days_in_shift,\n ShiftSolver.HOURS_IN_DAY\n ])\n\n def _init_availability(self, shift):\n '''\n @params shift: which includes (days, requests) in tuple format\n '''\n (days, requests, start_day) = shift\n start_in_shift = days[0]\n assert len(days) == self.days_in_shift\n for r in requests:\n if r.team == self.team:\n d = (r.start_time.date() - start_in_shift).days\n for t in xrange(r.start_time.hour, r.end_time.hour):\n self.availability[r.member][d][t] = r.availability\n\n\n\n def solve(self, shift):\n self._init_availability(shift)\n # Create the objective function\n obj = None\n for m in xrange(self.num_members):\n for d in xrange(self.days_in_shift):\n for t in xrange(ShiftSolver.HOURS_IN_DAY):\n obj += self.availability[m][d][t] * self.lp_variables[m][d][t]\n\n # Set objective function of this linear problem\n self.problem += obj\n\n # Set the contraint of variables of this problem\n for d in xrange(self.days_in_shift):\n for t in xrange(ShiftSolver.HOURS_IN_DAY):\n c = None\n for m in xrange(self.num_members):\n c += self.lp_variables[m][d][t]\n self.problem += c >= ShiftSolver.MINIMUM_MEMBERS\n\n status = self.problem.solve()\n\n result = np.zeros([self.num_members, self.days_in_shift, ShiftSolver.HOURS_IN_DAY])\n for m in xrange(self.num_members):\n for d in xrange(self.days_in_shift):\n for t in xrange(ShiftSolver.HOURS_IN_DAY):\n result[m][d][t] = self.lp_variables[m][d][t].value()\n\n return (pulp.LpStatus[status], result)\n","sub_path":"src/ShiftSolver.py","file_name":"ShiftSolver.py","file_ext":"py","file_size_in_byte":2713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"491083241","text":"import typing\nfrom enum import Enum\nfrom fastapi import Body\nfrom fastapi.routing import APIRouter\nfrom pathlib import Path\nfrom pydantic import BaseModel, Field\n\nfrom invokeai.backend.image_util.patchmatch import PatchMatch\nfrom invokeai.backend.image_util.safety_checker import SafetyChecker\nfrom invokeai.backend.image_util.invisible_watermark import InvisibleWatermark\nfrom invokeai.app.invocations.upscale import ESRGAN_MODELS\n\nfrom invokeai.version import __version__\n\nfrom ..dependencies import ApiDependencies\nfrom invokeai.backend.util.logging import logging\n\n\nclass LogLevel(int, Enum):\n NotSet = logging.NOTSET\n Debug = logging.DEBUG\n Info = logging.INFO\n Warning = logging.WARNING\n Error = logging.ERROR\n Critical = logging.CRITICAL\n\n\nclass Upscaler(BaseModel):\n upscaling_method: str = Field(description=\"Name of upscaling method\")\n upscaling_models: list[str] = Field(description=\"List of upscaling models for this method\")\n\n\napp_router = APIRouter(prefix=\"/v1/app\", tags=[\"app\"])\n\n\nclass AppVersion(BaseModel):\n \"\"\"App Version Response\"\"\"\n\n version: str = Field(description=\"App version\")\n\n\nclass AppConfig(BaseModel):\n \"\"\"App Config Response\"\"\"\n\n infill_methods: list[str] = Field(description=\"List of available infill methods\")\n upscaling_methods: list[Upscaler] = Field(description=\"List of upscaling methods\")\n nsfw_methods: list[str] = Field(description=\"List of NSFW checking methods\")\n watermarking_methods: list[str] = Field(description=\"List of invisible watermark methods\")\n\n\n@app_router.get(\"/version\", operation_id=\"app_version\", status_code=200, response_model=AppVersion)\nasync def get_version() -> AppVersion:\n return AppVersion(version=__version__)\n\n\n@app_router.get(\"/config\", operation_id=\"get_config\", status_code=200, response_model=AppConfig)\nasync def get_config() -> AppConfig:\n infill_methods = [\"tile\", \"lama\"]\n if PatchMatch.patchmatch_available():\n infill_methods.append(\"patchmatch\")\n\n upscaling_models = []\n for model in typing.get_args(ESRGAN_MODELS):\n upscaling_models.append(str(Path(model).stem))\n upscaler = Upscaler(upscaling_method=\"esrgan\", upscaling_models=upscaling_models)\n\n nsfw_methods = []\n if SafetyChecker.safety_checker_available():\n nsfw_methods.append(\"nsfw_checker\")\n\n watermarking_methods = []\n if InvisibleWatermark.invisible_watermark_available():\n watermarking_methods.append(\"invisible_watermark\")\n\n return AppConfig(\n infill_methods=infill_methods,\n upscaling_methods=[upscaler],\n nsfw_methods=nsfw_methods,\n watermarking_methods=watermarking_methods,\n )\n\n\n@app_router.get(\n \"/logging\",\n operation_id=\"get_log_level\",\n responses={200: {\"description\": \"The operation was successful\"}},\n response_model=LogLevel,\n)\nasync def get_log_level() -> LogLevel:\n \"\"\"Returns the log level\"\"\"\n return LogLevel(ApiDependencies.invoker.services.logger.level)\n\n\n@app_router.post(\n \"/logging\",\n operation_id=\"set_log_level\",\n responses={200: {\"description\": \"The operation was successful\"}},\n response_model=LogLevel,\n)\nasync def set_log_level(\n level: LogLevel = Body(description=\"New log verbosity level\"),\n) -> LogLevel:\n \"\"\"Sets the log verbosity level\"\"\"\n ApiDependencies.invoker.services.logger.setLevel(level)\n return LogLevel(ApiDependencies.invoker.services.logger.level)\n","sub_path":"invokeai/app/api/routers/app_info.py","file_name":"app_info.py","file_ext":"py","file_size_in_byte":3414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"517578487","text":"#!/usr/bin/env python3\n\nimport numpy as np\nfrom additional.settingsWindow import *\nimport socket\n\n#class for intitializing socket and socket functions\n#__init__ decided whether socket should act as server or client\n#creates integer userInput: 1 for server, 2 for client\n#can be hard coded or user inputted ip and port\n\nclass sockFunc:\n def __init__(self,dialog):\n if dialog != None:\n self.userInput = dialog[0]\n elif not dialog or dialog == None:\n self.userInput = None\n if self.userInput == 1:\n self.sockHost = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.servPort = dialog[1]\n self.sockHost.bind((\"\",int(self.servPort)))\n self.sockHost.listen(1)\n self.sockHost.settimeout(0.3)\n q = 0\n while True:\n if q%3 == 0:\n statusBar.config(text=\"Waiting on opponent. \")\n elif q%3 == 1:\n statusBar.config(text=\"Waiting on opponent.. \")\n elif q%3 == 2:\n statusBar.config(text=\"Waiting on opponent...\")\n root.update()\n try:\n self.sock, self.addr = self.sockHost.accept()\n except:\n q += 1\n continue\n if self.sock and self.addr:\n statusBar.config(text=\"\")\n break\n print(\"Connection received from \",self.addr)\n self.startOrder = self.userInput\n elif self.userInput == 2:\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.servIP = dialog[1]\n self.servPort = dialog[2]\n self.sock.connect((self.servIP,int(self.servPort)))\n print(\"Connected to server at \",self.servIP,\":\",self.servPort)\n if self.servIP == '192.232.224.13' or self.servIP == '192.232.224.14': #looks for the dedicated server\n self.startOrder = self.rcvData()\n else:\n self.startOrder = self.userInput\n else:\n print(\"Aborted.\")\n def sendData(self, dataOut):\n self.dataOut = str(dataOut).encode()\n self.sock.sendall(self.dataOut)\n def rcvData(self):\n self.sock.settimeout(0.3)\n q = 0\n while True:\n if q%3 == 0:\n statusBar.config(text=\"Waiting on opponent. \")\n elif q%3 == 1:\n statusBar.config(text=\"Waiting on opponent.. \")\n elif q%3 == 2:\n statusBar.config(text=\"Waiting on opponent...\")\n root.update()\n try:\n self.dataIn = self.sock.recv(4096)\n except:\n q += 1\n continue\n if self.dataIn:\n statusBar.config(text=\"\")\n break\n if self.dataIn.decode() != '86':\n return int(self.dataIn.decode())\n elif self.dataIn.decode() == '86':\n statusBar.config(text=\"Disconnected.\")\n popup = Tk()\n popup.protocol(\"WM_DELETE_WINDOW\", lambda: popupButton(popup,767))\n drawButton = Button(popup,text=\"Opponent disconnected\",command=lambda: popupButton(popup,767))\n drawButton.pack()\n print(\"Opponent Disconnected\")\n return 767\n def __del__(self):\n self.sock.close()\n print(\"Closed\")\n\n#takes a player color, row, col\n#player 1 is blue\n#player 2 is red\n#can change this with global pColor\n\ndef puckDraw(pColor,row,col):\n if pColor == 1:\n tempLbl = imgCanv.create_image(42.5+(80*col),42.5, image=bluepuck)\n imgCanv.tag_lower(tempLbl)\n imgCanv.update()\n if pColor == 2:\n tempLbl = imgCanv.create_image(42.5+(80*col),42.5, image=redpuck)\n imgCanv.tag_lower(tempLbl)\n imgCanv.update()\n for x in range(row*40):\n imgCanv.move(tempLbl, 0, 2)\n imgCanv.update()\n\n#winfound takes a player identifier\n#creates a popup message dependent on player number\n\ndef winfound(player):\n popup = Tk()\n if player == 1:\n popup.protocol(\"WM_DELETE_WINDOW\", lambda: popupButton(popup,1))\n winButton = Button(popup,text=\"You Won\",command=lambda: popupButton(popup,1))\n else:\n popup.protocol(\"WM_DELETE_WINDOW\", lambda: popupButton(popup,2))\n winButton = Button(popup,text=\"You Lost\",command=lambda: popupButton(popup,2))\n winButton.pack()\n\n#takes the last played row and column and checks for a win\n#returns true or false\n#does not check who won\n\ndef wincheck(row,column,xtemp,ytemp):\n while (xtemp <= 1) and (ytemp <= 1):\n if (row+xtemp < 6 and column+ytemp < 7) and (row+xtemp > -1 and column+ytemp > -1):\n if matrix[row,column] == matrix[row+xtemp,column+ytemp]:\n if (row+(xtemp*-1) < 6 and column+(ytemp*-1) < 7) and (row+(xtemp*-1) > -1 and column+(ytemp*-1) > -1):\n if matrix[row,column] == matrix[row+(xtemp*-1),column+(ytemp*-1)]:\n if (row+(xtemp*2) < 6 and column+(ytemp*2) < 7) and (row+(xtemp*2) > -1 and column+(ytemp*2) > -1):\n if matrix[row,column] == matrix[row+(xtemp*2),column+(ytemp*2)]:\n #print(\"win found at:\",row+xtemp*2,column+ytemp*2)\n return TRUE\n if (row+(xtemp*2)*-1 < 6 and column+(ytemp*2)*-1 < 7) and (row+(xtemp*2)*-1 > -1 and column+(ytemp*2)*-1 > -1):\n if matrix[row,column] == matrix[row+(xtemp*2)*-1,column+(ytemp*2)*-1]: \n #print(\"alt win found at:\",row+(xtemp*2)*-1,column+(ytemp*2)*-1)\n return TRUE\n if (row+(xtemp*2) < 6 and column+(ytemp*2) < 7) and (row+(xtemp*2) > -1 and column+(ytemp*2) > -1):\n if matrix[row,column] == matrix[row+(xtemp*2),column+(ytemp*2)]:\n if (row+(xtemp*3) < 6 and column+(ytemp*3) < 7) and (row+(xtemp*3) > -1 and column+(ytemp*3) > -1):\n if matrix[row,column] == matrix[row+(xtemp*3),column+(ytemp*3)]:\n #print(\"four in a row:\",row+xtemp*3,column+ytemp*3)\n return TRUE\n xtemp += 1\n if (xtemp == 0) and (ytemp == 0):\n xtemp += 1\n if xtemp == 2:\n ytemp += 1\n xtemp = -1\n return FALSE\n\n#function to check for a draw condition\n#creates a popup window if true\n\ndef drawCheck():\n for x in range(7):\n if matrix[0,x] == 0:\n break\n if x == 6 and matrix[0,6] != 0:\n popup = Tk()\n popup.protocol(\"WM_DELETE_WINDOW\", lambda: popupButton(popup,3))\n drawButton = Button(popup,text=\"Draw\",command=lambda: popupButton(popup,3))\n drawButton.pack()\n return 1\n return 0\n\n#returns an alternating value 1/2 from argument\n\ndef turnchange(turn):\n if turn == 1:\n turn = 2\n elif turn == 2:\n turn = 1\n return turn\n\n#first wait for being client\ndef firstWait():\n column = userSock.rcvData()\n if column != 767:\n dropPuck(column,turnchange(pNumb),turnchange(pColor))\n\n#find row\n#set matrix value to pNumb\n#draw the puck\n#check for win -> if true winfound\n#check for top row -> if true check for draw\n\ndef dropPuck(column,pNumb,pColor):\n statusBar.config(text=\"\")\n global matrix\n global buttStatus\n x = 5\n while matrix[x][column] != 0:\n x -= 1\n matrix[x][column] = pNumb\n puckDraw(pColor,x,column)\n winStatus = wincheck(x,column,-1,-1)\n if winStatus == TRUE:\n winfound(pNumb)\n return 1\n if x == 0:\n buttFunc[column].config(state=DISABLED)\n buttStatus[column] = 1\n return drawCheck()\n return 0\n\ndef dropMain(column, pOrder, pNumb, pColor):\n winBreak = dropPuck(column, pNumb, pColor)\n userSock.sendData(column)\n for q in range(7):\n buttFunc[q].config(state=DISABLED)\n root.update()\n if winBreak == 0: #prevents hanging for winning player side\n column = userSock.rcvData()\n if column != 767:\n winBreak = dropPuck(column,turnchange(pNumb),turnchange(pColor)) #calls drop with alternate pNumb from 1(player 1) to 2(opponent)\n if winBreak == 0: #prevents hanging for losing player side\n for q in range(7):\n if buttStatus[q] == 0:\n buttFunc[q].config(state=NORMAL)\n \n\n#resets the matrix and image canvas\n#conditionally change the start orders\n#increments game score\n\ndef reset(gameEnd):\n global matrix\n global startOrder\n global buttStatus\n global pScore\n global oScore\n for q in range(7):\n buttStatus[q] = 0\n matrix = np.zeros((6,7))\n imgCanv.delete(ALL)\n gridLbl = imgCanv.create_image(282.5, 242.5, image=grid)\n #startOrder is the global start of round variable\n if gameEnd == 1: #player won - start second\n startOrder = 2\n pScore += 1\n pScoreLbl.config(text=\"Player: \"+str(pScore))\n elif gameEnd == 2: #player lost - start first\n startOrder = 1\n oScore += 1\n oScoreLbl.config(text=\"Opponent: \"+str(oScore))\n elif gameEnd == 3: #draw - alternate start orders\n startOrder = turnchange(startOrder)\n if startOrder == 2 and userSock.userInput == 2:\n if userSock.servIP == '192.232.224.13' or userSock.servIP == '192.232.224.14':\n userSock.sendData(69) #sends a flag to dedicated server on draw\n else: #first round decided by serv/client\n startOrder = userSock.startOrder\n if startOrder == 1:\n for q in range(7):\n buttFunc[q].config(state=NORMAL)\n elif startOrder == 2:\n for q in range(7):\n buttFunc[q].config(state=DISABLED)\n firstWait()\n for q in range(7):\n buttFunc[q].config(state=NORMAL)\n\ndef callback():\n userSock.sendData(86)\n root.destroy()\n\n#function to be called on button press after win/draw\n#destroys window\n#calls for reset\n\ndef popupButton(window,gameEnd):\n if gameEnd == 767:\n window.destroy()\n root.destroy()\n else:\n window.destroy()\n reset(gameEnd)\n\ndialogBox = settingsWindow()\n\nroot = Tk()\nroot.title(\"Four in a row\")\nbuttFrame = Frame(root)\nbuttFrame.pack()\n\n#puck image size = 150, 150\n#grid image size = 1130, 970\nimgCanv = Canvas(root, width=565, height=485)\nimgCanv.pack_propagate(0)\nimgCanv.pack()\n\nimgFile1 = Image.open(\"resources/grid.png\")\nimgWidth,imgHeight = imgFile1.size\nimgFile1.thumbnail((imgWidth*0.5,imgHeight*0.5))\ngrid = ImageTk.PhotoImage(imgFile1)\n\nimgFile2 = Image.open(\"resources/redpuck.png\")\nimgWidth,imgHeight = imgFile2.size\nimgFile2.thumbnail((imgWidth*0.5,imgHeight*0.5))\nredpuck = ImageTk.PhotoImage(imgFile2)\n\nimgFile3 = Image.open(\"resources/bluepuck.png\")\nimgWidth,imgHeight = imgFile3.size\nimgFile3.thumbnail((imgWidth*0.5,imgHeight*0.5))\nbluepuck = ImageTk.PhotoImage(imgFile3)\n\nbut1 = Button(text=\"1\",command=lambda: dropMain(0,startOrder,pNumb,pColor),state=DISABLED)\nbut1.grid(in_=buttFrame,ipadx=22,row=0,column=0,sticky=W+E)\nbut2 = Button(text=\"2\",command=lambda: dropMain(1,startOrder,pNumb,pColor),state=DISABLED)\nbut2.grid(in_=buttFrame,ipadx=22,row=0,column=1,sticky=W+E)\nbut3 = Button(text=\"3\",command=lambda: dropMain(2,startOrder,pNumb,pColor),state=DISABLED)\nbut3.grid(in_=buttFrame,ipadx=22,row=0,column=2,sticky=W+E)\nbut4 = Button(text=\"4\",command=lambda: dropMain(3,startOrder,pNumb,pColor),state=DISABLED)\nbut4.grid(in_=buttFrame,ipadx=22,row=0,column=3,sticky=W+E)\nbut5 = Button(text=\"5\",command=lambda: dropMain(4,startOrder,pNumb,pColor),state=DISABLED)\nbut5.grid(in_=buttFrame,ipadx=22,row=0,column=4,sticky=W+E)\nbut6 = Button(text=\"6\",command=lambda: dropMain(5,startOrder,pNumb,pColor),state=DISABLED)\nbut6.grid(in_=buttFrame,ipadx=22,row=0,column=5,sticky=W+E)\nbut7 = Button(text=\"7\",command=lambda: dropMain(6,startOrder,pNumb,pColor),state=DISABLED)\nbut7.grid(in_=buttFrame,ipadx=22,row=0,column=6,sticky=W+E)\n\npScore = 0 #player score tracker\noScore = 0 #opponent score tracker\npColor = dialogBox.colorInput #color decided by dialog box\npNumb = 1 #constant, player is always set to 1 for matrix functions\n\nstatusFrame = Frame(root)\nstatusFrame.grid_columnconfigure(1,minsize=150)\npScoreLbl = Label(root,text=\"Player: \"+str(pScore))\npScoreLbl.grid(in_=statusFrame,padx=50,row=0,column=0)\noScoreLbl = Label(root,text=\"Opponent: \"+str(oScore))\noScoreLbl.grid(in_=statusFrame,padx=50,row=0,column=2)\nstatusBar = Label(root,text=\"\")\nstatusBar.grid(in_=statusFrame,row=0,column=1)\nstatusFrame.pack()\n\nbuttFunc = {0:but1,1:but2,2:but3,3:but4,4:but5,5:but6,6:but7}\nbuttStatus = [0,0,0,0,0,0,0]\n#buttStatus will be used to track full column button disables\n#buttons should be disabled/enabled depending on sending/receiving data\n\nuserSock = sockFunc(dialogBox.networkInputs)\n\nroot.protocol(\"WM_DELETE_WINDOW\", callback)\n\nreset(0)\n\nroot.mainloop()\n","sub_path":"c4_net_cli.py","file_name":"c4_net_cli.py","file_ext":"py","file_size_in_byte":12997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"420531792","text":"import csv\nimport datetime\nfrom datetime import date, timedelta\nimport pandas as pd\nfrom lxml import html\nimport time\nimport mechanicalsoup\nfrom bs4 import BeautifulSoup\nfrom keboola import docker\n\n# this is the way how to store data in config files:\n# cfg = docker.Config('data')\ncfg = docker.Config('/data/')\nparameters = cfg.get_parameters()\n# load category ids to scrape\n# df = pd.read_csv('in/tables/categories_to_scrape.csv')\n# category_ids = df.category_id\n\n# date format checker\ndef validate(date_text):\n try:\n datetime.datetime.strptime(date_text, '%Y-%m-%d')\n except ValueError:\n raise ValueError(\"Incorrect data format, should be YYYY-MM-DD\")\n\n# initialize scrape_dates dict.\nscrape_dates = {}\n\n# date preset from input parameters. Bud date_preset='Yesteday'/'last_week' nebo vsechny datumy ve stanovenem intervalu\n# ! parametr 'date_preset' ma prednost.\nif parameters.get('Date_preset') == 'Yesterday':\n yesterday = date.today() - timedelta(1)\n d1 = yesterday\n d2 = d1\nelif parameters.get('Date_preset') == 'last_week':\n d1 = date.today() - timedelta(7)\n d2 = date.today() - timedelta(1)\nelif parameters.get('Date_preset') == 'last_3_days':\n d1 = date.today() - timedelta(3)\n d2 = date.today() - timedelta(1) \nelif parameters.get('Date_preset') == 'last_31_days':\n d1 = date.today() - timedelta(31)\n d2 = date.today() - timedelta(1)\t\nelif parameters.get('Date_preset') == 'last_year':\n d1 = date.today() - timedelta(365)\n d2 = date.today() - timedelta(1)\n# customdate\tif not preseted\nelse:\n validate(parameters.get('Date_from'))\n validate(parameters.get('Date_to'))\n d1 = datetime.datetime.strptime(parameters.get('Date_from'), '%Y-%m-%d')\n d2 = datetime.datetime.strptime(parameters.get('Date_to'), '%Y-%m-%d')\n# vypocet timedelty, ktera urcuje delku tahanych dni zpet\t\ndelta = d2 - d1\nfor i in range(delta.days+1):\n scrape_dates[i] = (d1+timedelta(i)).strftime('%Y-%m-%d')\n\n# devide scraped results into two columns: Value and currency;\n# So I can easy work with Eur or another currency in future.\n\ndef sanitizeStrings(text):\n textSplitted = text.string.rsplit(None, 1)\n firstResultTemp = textSplitted[0].replace('\\xa0', '') # if value > 999 and it result would be X XXX\n firstResult = firstResultTemp.replace(',', '.')\n secondResult = textSplitted[1]\n return firstResult, secondResult\n\n\n# yesterday = date.today() - timedelta(1)\n# desired_date = date.today() - timedelta(DATE_PERIOD)\n\n\n# print('Scraping from ' + desired_date.strftime('%Y-%m-%d') + ' to ' + yesterday.strftime('%Y-%m-%d'))\n\n# count days between dates\n# delta = yesterday - desired_date\n\n# for i in range(delta.days+1):\n# scrape_dates[i]=(desired_date+timedelta(i)).strftime('%Y-%m-%d')\n\nfor i in range(len(scrape_dates)):\n scrape_date = scrape_dates[i]\n print(\"Started scraping for \" + scrape_date)\n for entity in parameters.get('Entity').keys():\n for login in parameters.get('Entity').get(entity).keys():\n # Create a browser object\n browser = mechanicalsoup.Browser()\n\n if entity == 'Heureka.cz':\n Url_login = 'https://ucet.heureka.cz/prihlaseni'\n if entity == 'Heureka.sk':\n Url_login = 'https://ucet.heureka.sk/prihlasenie'\n\n login_page = browser.get(Url_login)\n\n # grab the login form\n login_form = login_page.soup.find(\"form\", {\"class\": \"c-form c-form--login js-form\"})\n\n login_form.find(\"input\", {\"name\": \"email\"})[\"value\"] = parameters.get('Entity').get(entity).get(login).get('Login')\n login_form.find(\"input\", {\"name\": \"password\"})[\"value\"] = parameters.get('Entity').get(entity).get(login).get('Password')\n\n # submit form\n browser.submit(login_form, login_page.url)\n\n # this is way how to load config from config JSON.\n NO_OF_SHOPS = len(parameters.get('Entity').get(entity).get(login).get('Shop_name'))\n\n for index in range(0, NO_OF_SHOPS):\n if entity == 'Heureka.cz':\n report_url = browser.get('http://sluzby.heureka.cz/obchody/statistiky/?shop=' + parameters.get('Entity').get(entity).get(login).get('Shop_id')[index] + '&from=' + scrape_date + '&to=' + scrape_date)\n if entity == 'Heureka.sk':\n report_url = browser.get('http://sluzby.heureka.sk/obchody/statistiky/?shop=' + parameters.get('Entity').get(entity).get(login).get('Shop_id')[index] + '&from=' + scrape_date + '&to=' + scrape_date)\n\n # placeholder for SK heureka or sometihing simillar\n shop = parameters.get('Entity').get(entity).get(login).get('Shop_name')[index]\n # create BeautifulSoup object\n report_object = report_url.soup\n # create HTML of content table\n tabulka = report_object.find_all('table', {'class': 'shop-list roi'})\n # create empty list so it will be easy to append results there.\n L = []\n rows = BeautifulSoup(str(tabulka), features=\"lxml\").findChildren(['tr'])\n for row in rows:\n cells = row.findChildren('td') # define table\n cells = cells[0:4] # Take just first 4 values of table\n # replace HTML chars\n if len(cells) >= 4:\n temp = sanitizeStrings(cells[3])\n costs = temp[0]\n currency = temp[1]\n if currency == u'Kč':\n currency = 'CZK'\n if currency == u'€':\n currency = 'EUR'\n\n temp = sanitizeStrings(cells[2])\n cpc = temp[0]\n visits_temp = cells[1].string.replace('\\xa0', '') # if value > 999 and it result would be 'X XXX'\n visits = float(visits_temp)\n # name cleaning...\n name = cells[0].string\n if name is None:\n name = entity\n\n prvekL = {'shop': shop, 'date': scrape_date, 'name': name, 'visits': visits, 'cpc': cpc,\n 'costs': costs, 'currency': currency}\n\n L.append(prvekL)\n\n keys = ['name', 'visits', 'cpc', 'costs', 'currency', 'shop', 'date']\n\n with open('/data/out/tables/' + parameters.get('Entity').get(entity).get(login).get('Shop_name')[index] + '.csv', mode='a+', encoding='utf-8') as output_file:\n dict_writer = csv.DictWriter(output_file, keys, lineterminator='\\n', delimiter=',', quotechar='\"')\n dict_writer.writeheader()\n dict_writer.writerows(L)\n","sub_path":"main_new.py","file_name":"main_new.py","file_ext":"py","file_size_in_byte":6856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"613030330","text":"#!/usr/bin/env python3\n\nfrom parsers import read_smartctl\nfrom parsers import read_decode_dimms\nfrom parsers import read_dmidecode\nfrom parsers import read_lspci_and_glxinfo\nfrom parsers import read_lscpu\nfrom tests.parsers.read_file import read_file\n\nfiledir = \"tests/source_files/polveroso/\"\n\n\ndef test_lspci():\n expect = [\n {\n \"type\": \"graphics-card\",\n \"working\": \"yes\",\n \"brand\": \"ASUSTeK Computer Inc.\",\n \"model\": \"GeForce 9400 GT\",\n \"internal-name\": \"G96\", # Missing glxinfo...\n \"brand-manufacturer\": \"Nvidia\",\n }\n ]\n # False to ignore missing glxinfo\n output = read_lspci_and_glxinfo.parse_lspci_and_glxinfo(False, read_file(filedir, \"lspci.txt\"), read_file(filedir, \"glxinfo.txt\"))\n\n assert output == expect\n\n\ndef test_lscpu():\n expect = [\n {\n \"type\": \"cpu\",\n \"working\": \"yes\",\n \"isa\": \"x86-64\",\n \"model\": \"Core 2 Duo E7300\",\n \"brand\": \"Intel\",\n \"core-n\": 2,\n \"thread-n\": 2,\n \"frequency-hertz\": 2660000000,\n }\n ]\n output = read_lscpu.parse_lscpu(read_file(filedir, \"lscpu.txt\"))\n\n assert output == expect\n\n\ndef test_ram():\n output = read_decode_dimms.parse_decode_dimms(read_file(filedir, \"dimms.txt\"))\n\n assert len(output) == 0\n\n\ndef test_baseboard():\n expect = {\n \"type\": \"motherboard\",\n \"working\": \"yes\",\n \"brand\": \"ASUSTeK Computer INC.\",\n \"model\": \"P5QL-E\",\n \"sn\": \"MS666999ABCDEF123\",\n }\n output = read_dmidecode._get_baseboard(read_file(filedir, \"baseboard.txt\"))\n\n assert output == expect\n\n\ndef test_connector():\n baseboard = read_dmidecode._get_baseboard(read_file(filedir, \"baseboard.txt\"))\n\n expect = {\n \"type\": \"motherboard\",\n \"working\": \"yes\",\n \"brand\": \"ASUSTeK Computer INC.\",\n \"model\": \"P5QL-E\",\n \"sn\": \"MS666999ABCDEF123\",\n \"ps2-ports-n\": 2,\n \"usb-ports-n\": 6,\n \"serial-ports-n\": 1,\n \"mini-jack-ports-n\": 1,\n \"ethernet-ports-n\": 1,\n \"ide-ports-n\": 1,\n \"sata-ports-n\": 6,\n \"esata-ports-n\": 1,\n \"firewire-ports-n\": 2,\n \"notes\": \"Unknown connector: None / Other (AUDIO / AUDIO)\",\n }\n output = read_dmidecode._get_connectors(read_file(filedir, \"connector.txt\"), baseboard)\n\n assert output == expect\n\n\ndef test_chassis():\n expect = [\n {\n \"type\": \"case\",\n \"brand\": \"Chassis Manufacture\",\n \"sn\": \"Chassis Serial Number\",\n }\n ]\n output = read_dmidecode.parse_case(read_file(filedir, \"chassis.txt\"))\n\n assert output == expect\n","sub_path":"tests/parsers/test_polveroso.py","file_name":"test_polveroso.py","file_ext":"py","file_size_in_byte":2684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"329323811","text":"class Solution:\n def maxSum(self, nums1: List[int], nums2: List[int]) -> int:\n res, a, b, i, j, m, n=0, 0, 0, 0, 0, len(nums1), len(nums2)\n mod=10**9+7\n while inums2[j]):\n b+=nums2[j]\n j+=1\n else:\n res+=max(a, b)+nums1[i]\n a, b=0, 0\n i+=1\n j+=1\n return (res+max(a, b))%mod\n\n","sub_path":"python/get-the-maximum-score.py","file_name":"get-the-maximum-score.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"248063819","text":"from ant_environments.create_maze_env import create_maze_env\n\n\"\"\"Environments taken from HIRO paper github repo: https://github.com/tensorflow/models/tree/master/research/efficient-hrl\"\"\"\n\nclass Ant_Navigation_Environments():\n\n def __init__(self, environment_name):\n self.env = create_maze_env(env_name=environment_name).gym # names [\"AntGather\" ,\"AntMaze\", \"AntPush\", \"AntFall\", \"AntBlock\"]\n self.unwrapped = self.env.unwrapped\n self.spec = self.env.spec\n self.action_space = self.env.action_space\n\n def reset(self):\n self.steps_taken = 0\n return self.env.reset()\n\n def step(self, action):\n self.steps_taken += 1\n next_state, reward, _, _ = self.env.step(action)\n if self.steps_taken >= 500: done = True\n else: done = False\n return next_state, reward, done, _\n\n\n\n","sub_path":"Environments/Ant_Navigation_Environments.py","file_name":"Ant_Navigation_Environments.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"133244850","text":"import matplotlib.pyplot as plot\nimport math\nimport numpy as np\nfrom scipy import signal\n\n\ndef _EXIT_():\n print('Выход..')\n exit(0)\n\n\ndef INPUT():\n global str1\n str1 = input('ВВОД P: ')\n\n\ndef add_value_in_register(summator, value):\n for i in range(len(summator) - 1):\n summator[i] = summator[i + 1]\n summator[len(summator) - 1] = value\n\ndef get_summ(summator):\n result = 0\n for value in summator:\n result += value\n\n return result\n\ndef getResult(N, P):\n k = P / N\n # пустые массивы значений\n X = np.array(0)\n S = np.array(0)\n # частота пропускания и частота заграждения\n wp, ws = 0.2, 0.3\n # усиление в полосе пропускания и подавление вне её\n gpass, gstop = 0.1, 50.0\n\n b, a = signal.iirdesign(wp, ws, gpass, gstop, analog=False, ftype='butter', output='ba')\n w, h = signal.freqz(b, a)\n\n x_values = w/np.pi\n y_values = 20*np.log10(abs(h))\n return {'x_values': x_values, 'y_values': y_values}\n\n\ndef plotGraf(data, x_label=\"x_values\", y_label='y_values'):\n data_x = data[x_label]\n data_y = data[y_label]\n plot.ylabel('рис 1')\n plot.plot(data_x, data_y)\n\n\n\n\nif __name__ == \"__main__\":\n N = 100\n print('run task4')\n #INPUT()\n #P = int(str1)\n P = 15\n data = getResult(N, P)\n plot.subplot(311)\n plotGraf(data)\n plot.show()","sub_path":"task4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"246272942","text":"import sys\nfrom scipy.io import wavfile\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport peakutils\nfrom peakutils.plot import plot as pplot\nfrom matplotlib import pyplot\nfrom scipy.signal import butter, lfilter, freqz\n\n#class Event:\n#\tfloat timeStamp\n#\tfloat intensity\n#\tfloat duration\n\n##GLOBALS\norder = 6\nfs = 30.0\ncutoff = 3.667 \nSTEP = 1.0\n\ndef butter_lowpass(cutoff, fs, order=5):\n nyq = 0.5 * fs\n normal_cutoff = cutoff / nyq\n b, a = butter(order, normal_cutoff, btype='low', analog=False)\n return b, a\n\ndef butter_lowpass_filter(data, cutoff, fs, order=5):\n b, a = butter_lowpass(cutoff, fs, order=order)\n y = lfilter(b, a, data)\n return y\n\ndef readAndExtract(filename):\n\t# Load the data and calculate the time of each sample\n\tsamplerate, y = wavfile.read(filename)\n\tx = np.arange(len(y))\n\t#Handle both mono and stereo audi\t\n\tif type(y[0]) == np.ndarray:\n\t\ty = y[0:len(y)-1:STEP, 1]\n\telse:\n\t\ty = y[0:len(y)-1:STEP]\n\ty = normalize(y)[0]\n\ty = butter_lowpass_filter(y, cutoff, fs, order)\n\tx = x[0:len(x)-1:STEP]\n\treturn x, y\n\ndef findPeaks(y):\n\treturn peakutils.indexes(y, thres=0.1, min_dist=44100)\n\ndef plot(x, y, indexes, breaths):\n\tpyplot.figure(figsize=(10,6))\n\tpplot(x, y, indexes)\n\tpyplot.title('Find peaks')\n\t#show breaths\n\tfor breath in breaths:\n\t\tpyplot.axvspan(breath[2], breath[3], color='y', alpha=0.5, lw=0)\n\t\n\tpyplot.show()\n\ndef normalize(a, axis=-1, order=2):\n l2 = np.atleast_1d(np.linalg.norm(a, order, axis))\n l2[l2==0] = 1\n return a / np.expand_dims(l2, axis)\n\ndef findBreaths(x, y, peaks):\n\tbreaths = []\n\tMAX_BREATH = 44100\n\tBYTE_SIZE = 5000\n\tTHRESH = .03\n\tbmin = None\n\tbmax = None\n\tfor peak in peaks:\n\t\tindex = peak - BYTE_SIZE\n\t\t#find begining of breath\n\t\twhile (index > peak-MAX_BREATH):\n\t\t\tsublist = y[(index)*STEP:(index + BYTE_SIZE)*STEP]\n\t\t\tif not len(sublist): break\n\t\t\tavg = sum([abs(x) for x in sublist])/len(sublist)\n\t\t\tif (abs(avg) < THRESH):\n\t\t\t\tbmin = index\n\t\t\t\tbreak\n\t\t\tindex = index - BYTE_SIZE\n\t\tif not bmin: bmin = index\n\t\tindex = peak\n\t\t\n\t\t#find end of breath\n\t\twhile (index < peak+MAX_BREATH):\n\t\t\tsublist = y[(index)*STEP:(index + BYTE_SIZE)*STEP]\n\t\t\tif not len(sublist): break\n\t\t\tavg = sum([abs(x) for x in sublist])/len(sublist)\n\t\t\tif (abs(avg) < THRESH):\n\t\t\t\tbmax = index\n\t\t\t\tbreak\n\t\t\tindex = index + BYTE_SIZE\n\t\tif not bmax: bmax = index\n\t\tbreaths.append([peak, y[peak], bmin, bmax])\n\t\tbmin = None\n\t\tbmax= None\n\treturn aggregate(breaths)\n\ndef aggregate(breaths):\n\ti = 0\n\twhile i < len(breaths)-1:\n\t\tif (breaths[i][3] > breaths[i+1][2]):\n\t\t\tbreaths[i][3] = breaths[i+1][3]\n\t\t\tbreaths[i][1] = max(breaths[i][1], breaths[i+1][1])\n\t\t\tdel breaths[i+1]\n\t\ti = i + 1\n\treturn breaths\n\ndef writeToFile(file, breaths):\n\tfile = open(file, 'w')\n\tfor breath in breaths:\n\t\tfor val in breath:\n\t\t\tfile.write(str(val)+\", \")\n\t\t\tprint(val)\n\t\tfile.write(\"\\n\")\n\t\tprint()\n\tfile.close()\n\nif (len(sys.argv)):\n\tfile = sys.argv[1]\nelse:\n\tfile = \"30sleep.wav\"\n\nx,y = readAndExtract(file)\npeaks = findPeaks(y)\nbreaths = findBreaths(x, y, peaks)\nwriteToFile('out.txt', breaths)\n#plot(x, y, peaks, breaths)\nprint(breaths)\n","sub_path":"api/pd2.py","file_name":"pd2.py","file_ext":"py","file_size_in_byte":3085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"163855836","text":"\"\"\"A class defining an airfoil.\"\"\"\n\nimport math\nimport json\nimport copy\nimport os\nimport operator\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport scipy.interpolate as interp\nimport subprocess as sp\nimport numpy as np\n\nfrom .poly_fits import multivariablePolynomialFit, multivariablePolynomialFunction, autoPolyFit, multivariableRMS, multivariableR2\nfrom .exceptions import DatabaseBoundsError, CamberSolverNotConvergedError, PolyFitBoundsError\n\n\nclass Airfoil:\n \"\"\"A class defining an airfoil. If the airfoil geometry is defined using outline\n points, then when this class is initialized, a solver will be automatically\n run to determine the camber line and thickness distribution of the airfoil.\n The parameters \"camber_relaxation\", \"le_loc\", and \"camber_termination_tol\" then\n have bearing on this solver.\n\n When using the member methods get_CL, get_CD, get_Cm, get_CLa, get_CLM, get_CLRe,\n and get_aL0, the default parameters are dependent upon the type of airfoil.\n\n For all airfoil types except 'functional', 'alpha', 'trailing_flap_deflection',\n and 'trailing_flap_fraction' default to 0.0.\n\n For 'functional' airfoils, the defaults are specified by the user.\n\n For 'linear' airfoils, 'Mach' and 'Rey' have no effect on computations.\n\n For 'database' airfoils, 'Mach' and 'Rey' default to the average value in the database,\n if the database is dependent on that variable. Otherwise, they default to the value\n specified as constant when the database was generated.\n\n For 'poly_fit' airfoils, 'Mach' and 'Rey' default to the values given in the fit file.\n If export_polynomial_fits() is used, these values are the same as those for the \n 'database' type.\n\n Parameters\n ----------\n name : str\n Name of the airfoil.\n\n airfoil_input : dict or str\n Dictionary or path to JSON object describing the airfoil.\n\n verbose : bool, optional\n Whether to display information on the progress of parameterizing the geometry\n for an airfoil defined by a set of outline points. Defaults to False.\n\n camber_relaxation : float, optional\n A value between 0.0 and 1.0 that defines how much of the update at each\n iteration of the camber line solver should be accepted. Helpful for some\n poorly behaved cases. Defaults to 1.0 (full update).\n \n le_loc : list, optional\n Gives the location of the leading edge relative to the given points for an airfoil\n defined by a set of outline points. If this is given, the camber line will be forced\n to intersect this point. THIS POINT SHOULD LIE ON THE AIRFOIL OUTLINE. If not given,\n the camber line solver will try to iteratively find the leading edge (the point where\n the camber line intersects the front of the profile).\n\n camber_termination_tol : float, optional\n The tolerance below which the maximum approximate error in the camber line estimate\n must fall in order for the camber line solver to terminate. Defaults to 1e-10.\n\n max_iterations : int, optional\n Maximum number of iterations for the camber line solver. Defaults to 100.\n \"\"\"\n\n def __init__(self, name, airfoil_input, **kwargs):\n \n self.name = name\n self._load_params(airfoil_input)\n if \"camber_solver_kwargs\" in list(self._input_dict.keys()):\n kwarg_dict = self._input_dict[\"camber_solver_kwargs\"]\n self._verbose = kwarg_dict.get(\"verbose\", False)\n self._camber_relaxation = kwarg_dict.get(\"camber_relaxation\", 1.0)\n self._le_loc = kwarg_dict.get(\"le_loc\", None)\n self._camber_termination_tol = kwarg_dict.get(\"camber_termination_tol\", 1e-10)\n self._max_iterations = kwarg_dict.get(\"max_iterations\", 100)\n else:\n self._verbose = kwargs.get(\"verbose\", False)\n self._camber_relaxation = kwargs.get(\"camber_relaxation\", 1.0)\n self._le_loc = kwargs.get(\"le_loc\", None)\n self._camber_termination_tol = kwargs.get(\"camber_termination_tol\", 1e-10)\n self._max_iterations = kwargs.get(\"max_iterations\", 100)\n\n # Load flaps\n self._load_flaps()\n\n # Store undeformed outlines\n self._initialize_geometry()\n\n # Specify database DOF parameters\n self._allowable_dofs = [\"alpha\", \"Rey\", \"Mach\", \"trailing_flap_deflection\", \"trailing_flap_fraction\"]\n self._dof_defaults = {\n \"alpha\" : 0.0,\n \"Rey\" : 1e6,\n \"Mach\" : 0.0,\n \"trailing_flap_deflection\" : 0.0,\n \"trailing_flap_fraction\" : 0.0\n }\n\n # Load input file\n input_file = self._input_dict.get(\"input_file\", None)\n if input_file is not None:\n if self._type == \"database\":\n self.import_database(filename=input_file)\n elif self._type == \"poly_fit\":\n self.import_polynomial_fits(filename=input_file)\n\n\n self._raise_poly_bounds_error = True\n\n\n def set_err_state(self, **kwargs):\n \"\"\"Sets the error state for the airfoil. Each may be specified as 'raise' or 'ignore'.\n\n Parameters\n ----------\n poly_fit_bounds : str, optional\n How to handle PolyFitBoundsError. Defaults to 'raise'.\n \"\"\"\n\n # Polynomial fit bounds\n if kwargs.get(\"poly_fit_bounds\", \"raise\") == \"raise\":\n self._raise_poly_bounds_error = True\n else:\n self._raise_poly_bounds_error = False\n\n\n def set_verbosity(self, verbosity):\n \"\"\"Sets the verbosity of the airfoil.\"\"\"\n self._verbose = verbosity\n\n\n def set_type(self, database_type):\n \"\"\"Determines how the aerodynamic coefficients will be calculated.\n\n Parameters\n ----------\n database_type: str\n \"linear\", \"functional\", \"database\", or \"poly_fit\". Airfoil\n will automatically check if it has the necessary to perform\n the given type of computation and throw a warning if it does not.\n\n \"\"\"\n\n # Check for proper type spec\n if database_type not in [\"linear\", \"database\", \"poly_fit\", \"functional\"]:\n raise IOError(\"{0} is not a valid type specification.\".format(database_type))\n\n # Check for linear data\n if database_type == \"linear\":\n if not hasattr(self, \"_CLa\"):\n raise RuntimeWarning(\"Airfoil {0} does not have linear coefficients specified. Reverting to type '{1}' for computations.\".format(self.name, self._type))\n else:\n self._type = database_type\n\n # Check for database\n if database_type == \"database\":\n if not hasattr(self, \"_data\"):\n raise RuntimeWarning(\"Airfoil {0} does not have a database of coefficients. Reverting to type '{1}' for computations.\".format(self.name, self._type))\n else:\n self._type = database_type\n\n # Set up data normalization\n self._data_norms = np.zeros((1,self._num_dofs))\n for i in range(self._num_dofs):\n self._data_norms[0,i] = np.max(np.abs(self._data[:,i]))\n\n # Make sure we don't divide by zero\n self._data_norms[np.where(self._data_norms==0.0)] = 1.0\n\n # Normalize independent vars\n self._normed_ind_vars = self._data[:,:self._num_dofs]/self._data_norms\n\n # Determine default Mach and Reynolds number\n if \"Rey\" in list(self._dof_db_cols.keys()):\n i = self._dof_db_cols[\"Rey\"]\n Re_min = np.min(self._data[:,i])\n Re_max = np.max(self._data[:,i])\n self._dof_defaults[\"Rey\"] = 0.5*(Re_max+Re_min)\n\n if \"Mach\" in list(self._dof_db_cols.keys()):\n i = self._dof_db_cols[\"Mach\"]\n M_min = np.min(self._data[:,i])\n M_max = np.max(self._data[:,i])\n self._dof_defaults[\"Mach\"] = 0.5*(M_max+M_min)\n\n # Check for polynomial fits\n if database_type == \"poly_fit\":\n if not hasattr(self, \"_CL_poly_coefs\"):\n raise RuntimeWarning(\"Airfoil {0} does not have a set of polynomial fits. Reverting to type '{1}' for computations.\".format(self.name, self._type))\n else:\n self._type = database_type\n\n # Check for functional definition\n if database_type == \"functional\":\n if not hasattr(self, \"_CL\"):\n raise RuntimeWarning(\"Airfoil {0} does not have functional definitions of coefficients. Reverting to type '{1}' for computations.\".format(self.name, self._type))\n else:\n self._type = database_type\n\n\n def _load_params(self, airfoil_input):\n\n # Load input dict\n if isinstance(airfoil_input, str):\n # Load JSON object\n with open(airfoil_input, 'r') as json_handle:\n self._input_dict = json.load(json_handle)\n elif isinstance(airfoil_input, dict):\n self._input_dict = airfoil_input\n else:\n raise IOError(\"{0} is not an allowed airfoil definition. Must be path or dictionary.\".format(airfoil_input))\n\n # Store type\n self._type = self._input_dict.get(\"type\", \"linear\")\n\n # If linear, store coefficients\n if self._type == \"linear\":\n self._aL0 = self._input_dict.get(\"aL0\", 0.0)\n self._CLa = self._input_dict.get(\"CLa\", 2*np.pi)\n self._Cma = self._input_dict.get(\"Cma\", 0.0)\n self._CD0 = self._input_dict.get(\"CD0\", 0.0)\n self._CD1 = self._input_dict.get(\"CD1\", 0.0)\n self._CD2 = self._input_dict.get(\"CD2\", 0.0)\n self._CL_max = self._input_dict.get(\"CL_max\", np.inf)\n if abs(self._CL_max) < 1e-10:\n warnings.warn(\"You have specified a maximum lift coefficient of 0. Are you sure you want to do this?...\")\n self._CmL0 = self._input_dict.get(\"CmL0\", 0.0)\n\n # For functional, store functions\n elif self._type == \"functional\":\n self._CL = self._input_dict[\"CL\"]\n self._CD = self._input_dict[\"CD\"]\n self._Cm = self._input_dict[\"Cm\"]\n\n\n def _load_flaps(self):\n # Loads flaps based on the input dict\n self._trailing_flap_type = self._input_dict.get(\"trailing_flap_type\", None)\n self._trailing_flap_hinge_height = self._input_dict.get(\"trailing_flap_hinge_height\", 0.0)\n\n\n def _initialize_geometry(self):\n # Initialize geometry based on whether points or a NACA designation were given\n\n # Check that there's only one geometry definition\n geom_dict = self._input_dict.get(\"geometry\", {})\n outline_points = geom_dict.get(\"outline_points\", None)\n NACA = geom_dict.get(\"NACA\", None)\n if outline_points is not None and NACA is not None:\n raise IOError(\"Outline points and a NACA designation may not be both specified for airfoil {0}.\".format(self.name))\n\n # Check for user-given points\n if outline_points is not None:\n self.geom_specification = \"points\"\n\n # Array given\n if isinstance(outline_points, np.ndarray):\n self._raw_outline = outline_points\n\n # 2D list given\n elif isinstance(outline_points, list):\n self._raw_outline = np.array(outline_points)\n \n # File\n else:\n\n # Import\n with open(outline_points, 'r') as input_handle:\n self._raw_outline = np.genfromtxt(input_handle)\n\n # Check for comma-delimited\n if np.isnan(self._raw_outline).any():\n with open(outline_points, 'r') as input_handle:\n self._raw_outline = np.genfromtxt(input_handle, delimiter=',')\n\n # Get number of points\n self._N_orig = self._raw_outline.shape[0]\n\n # Rearrange the coordinates if necessary to be top first then bottom\n top_ind = np.argmax(self._raw_outline[:,1])\n bot_ind = np.argmin(self._raw_outline[:,1])\n if bot_ind < top_ind: # Bottom came first\n self._raw_outline = self._raw_outline[::-1]\n\n # Calculate camber line and thickness\n self._calc_geometry_from_points()\n\n # NACA definition\n elif NACA is not None:\n self.geom_specification = \"NACA\"\n self._naca_closed_te = geom_dict.get(\"NACA_closed_te\", False)\n self._naca_des = NACA\n\n self._calc_geometry_from_NACA()\n\n # No geometry given\n else:\n self.geom_specification = \"none\"\n self._max_camber = geom_dict.get(\"max_camber\", 0.0)\n self._max_thickness = geom_dict.get(\"max_thickness\", 0.0)\n return\n\n \n def _calc_geometry_from_NACA(self):\n # Creates thickness, camber, camber derivative, and outline splines base on the NACA equations\n\n # 4-digit series\n if len(self._naca_des) == 4:\n\n # Stores the camber and thickness getters based on the NACA designation of the airfoil\n self._m = float(self._naca_des[0])/100\n self._p = float(self._naca_des[1])/10\n self._t = float(self._naca_des[2:])/100\n self._max_camber = self._m\n self._max_thickness = self._t\n\n # Camber line\n def camber(x):\n if self._p != 0.0:\n return np.where(x self._camber_termination_tol:\n iteration += 1\n\n # Determine camber line slope\n dyc_dx = np.gradient(y_c, x_c, edge_order=camber_deriv_edge_order)\n \n # Determine slope of lines perpendicular to the camber\n with np.errstate(divide=\"ignore\"):\n b = -1.0/dyc_dx\n\n # Loop through points on the camber line to find where their normal intersects the outline\n for i in range(num_camber_points):\n if self._le_loc is not None and i == 0:\n continue\n\n # Get point information\n xc = x_c[i]\n yc = y_c[i]\n bi = b[i]\n\n # Estimate the intersection points\n x_t[i], y_t[i], _ = self._get_intersection_point(xc, yc, bi, \"top\")\n x_b[i], y_b[i], _ = self._get_intersection_point(xc, yc, bi, \"bottom\")\n\n # Calculate new camber line points\n x_c_new = 0.5*(x_t+x_b)\n y_c_new = 0.5*(y_t+y_b)\n if self._le_loc is not None:\n x_c_new[0] = self._le_loc[0]\n y_c_new[0] = self._le_loc[1]\n\n # Plot new and old estimate\n if False:\n plt.figure()\n plt.plot(self._raw_outline[:,0], self._raw_outline[:,1], 'b-', label='Outline Data')\n plt.plot(x_c, y_c, 'r--', label='Old Camber Line Estimate')\n plt.plot(x_c_new, y_c_new, 'g--', label='New Camber Line Estimate')\n plt.plot(x_t, y_t, 'rx')\n plt.plot(x_b, y_b, 'ro')\n plt.legend()\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n\n # Approximate error\n x_diff = x_c_new-x_c\n y_diff = y_c_new-y_c\n camber_error = np.max(np.sqrt(x_diff*x_diff+y_diff*y_diff))\n if self._verbose:\n print(\"{0:<20}{1:<20}\".format(iteration, camber_error))\n\n # Sort, just in case things got messed up\n sorted_ind = np.argsort(x_c_new)\n x_c_new = x_c_new[sorted_ind]\n y_c_new = y_c_new[sorted_ind]\n\n # Update for next iteration\n x_c = self._camber_relaxation*x_c_new+(1.0-self._camber_relaxation)*x_c\n y_c = self._camber_relaxation*y_c_new+(1.0-self._camber_relaxation)*y_c\n\n # Check iteration limit\n if iteration == self._max_iterations:\n raise CamberSolverNotConvergedError(self.name, camber_error)\n\n if self._verbose: print(\"Camber line solver converged.\")\n\n # Calculate where the camber line intersects the outline to find the leading edge\n if self._le_loc is None:\n dyc_dx = np.gradient(y_c, x_c, edge_order=2)\n b = dyc_dx[0]\n x_le, y_le, self._s_le = self._get_intersection_point(x_c[0], y_c[0], b, \"leading_edge\")\n le = np.array([x_le, y_le])\n x_c = np.insert(x_c, 0, le[0])\n y_c = np.insert(y_c, 0, le[1])\n else:\n le = self._le_loc\n\n if self._verbose: print(\"Leading edge: {0}\".format(le))\n\n if self._verbose:\n # Plot\n plt.figure()\n plt.plot(self._raw_outline[:,0], self._raw_outline[:,1], 'b-', label='Outline Data')\n plt.plot(x_c, y_c, 'g--', label='Final Camber Line Estimate')\n plt.legend()\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n\n # Get trailing edge\n te = (self._raw_outline[0]+self._raw_outline[-1])*0.5\n if self._verbose: print(\"Trailing edge: {0}\".format(te))\n\n # Renormalize using new leading and trailing edge\n self._normalize_points(self._raw_outline, le, te)\n self._x_outline, self._y_outline = self._create_splines_of_s(self._raw_outline)\n\n # Normalize camber line points\n camber_points = np.concatenate([x_c[:,np.newaxis], y_c[:,np.newaxis]], axis=1)\n self._normalize_points(camber_points, le, te)\n\n # Store camber and max camber\n self._camber_line = interp.UnivariateSpline(camber_points[:,0], camber_points[:,1], k=5, s=1e-10)\n self._max_camber = np.max(camber_points[:,1])\n\n # Store camber line derivative\n y_c = self._camber_line(x_space)\n dyc_dx= np.gradient(y_c, x_space)\n self._camber_deriv = interp.UnivariateSpline(x_space, dyc_dx, k=5, s=1e-10)\n with np.errstate(divide=\"ignore\"):\n b = -1.0/dyc_dx\n\n # Find points on the surface to determine the thickness\n if self._verbose: print(\"Calculating thickness distribution...\", end=\"\")\n x_t_t = np.zeros_like(x_space)\n x_b_t = np.zeros_like(x_space)\n y_t_t = np.zeros_like(x_space)\n y_b_t = np.zeros_like(x_space)\n for i, xc in enumerate(x_space):\n if i == 0: continue # This point intersects the outline by definition and so will have zero thickness\n yc = y_c[i]\n bi = b[i]\n\n x_t_t[i], y_t_t[i],_ = self._get_intersection_point(xc, yc, bi, \"top\")\n x_b_t[i], y_b_t[i],_ = self._get_intersection_point(xc, yc, bi, \"bottom\")\n\n # Store thickness distribution\n t = 0.5*np.sqrt((x_t_t-x_b_t)*(x_t_t-x_b_t)+(y_t_t-y_b_t)*(y_t_t-y_b_t))\n self._thickness = interp.UnivariateSpline(x_space, t, k=5, s=1e-10)\n self._max_thickness = np.max(t)\n if self._verbose: print(\"Done\")\n\n # Calculate estimated top and bottom points\n y_c_pred = self._camber_line(x_space)\n dyc_dx = np.gradient(y_c_pred, x_space)\n t_pred = self._thickness(x_space)\n theta = np.arctan(dyc_dx)\n S_theta = np.sin(theta)\n C_theta = np.cos(theta)\n\n x_b = x_space+t_pred*S_theta\n y_b = y_c_pred-t_pred*C_theta\n x_t = x_space-t_pred*S_theta\n y_t = y_c_pred+t_pred*C_theta\n \n # Plot\n if self._verbose:\n plt.plot(self._raw_outline[:,0], self._raw_outline[:,1], 'b-', label='Original Data')\n plt.plot(x_t, y_t, 'r--', label='Top Fit')\n plt.plot(x_b, y_b, 'r--', label='Bottom Fit')\n plt.legend()\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n\n\n def _normalize_points(self, points, le_coords, te_coords):\n # Takes the given points, translates them to the origin, rotates them to be at zero angle of attack, and scales so the chord length is unity\n\n # Translate\n points[:,0] -= le_coords[0]\n points[:,1] -= le_coords[1]\n\n # Rotate\n x_diff = te_coords[0]-le_coords[0]\n y_diff = te_coords[1]-le_coords[1]\n theta = -np.arctan2(y_diff, x_diff)\n R = np.array([[np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)]])\n points = np.matmul(R, points.T)\n\n #Scale\n c_unscaled = np.sqrt(x_diff*x_diff+y_diff*y_diff)\n points = points/c_unscaled\n\n\n def _get_intersection_point(self, xc, yc, b, surface, plot=False):\n # Calculates the point on the surface where the line extending from (xc, yc) with slope of b intersects\n # Uses the secant method to converge in s\n\n # Start s in the middle of the respective surface\n if surface == \"top\":\n s0 = 0.24\n s1 = 0.26\n elif surface == \"bottom\":\n s0 = 0.74\n s1 = 0.76\n elif surface == \"leading_edge\":\n s0 = 0.49\n s1 = 0.51\n\n # Initial points on outline\n x0 = self._x_outline(s0)\n y0 = self._y_outline(s0)\n x1 = self._x_outline(s1)\n y1 = self._y_outline(s1)\n\n # Initial distances\n d0 = self._distance(x0, y0, xc, yc, b)\n d1 = self._distance(x1, y1, xc, yc, b)\n\n # Secant method\n while abs(d1) > 1e-10:\n \n # Get new estimate in s\n if d1 > 0.2 or (s1 > 0.35 and s1 < 0.65): # Apply some relaxation when we're far away or near the leading edge (to keep from shooting to the other surface)\n s2 = s1-0.2*d1*(s0-s1)/(d0-d1)\n else:\n s2 = s1-d1*(s0-s1)/(d0-d1)\n\n # Make sure we're in bounds\n if s2 > 1.1:\n s2 = 1-0.01*s2\n if s2 < -0.1:\n s2 = -0.01*s2\n\n # Get new point\n x2 = self._x_outline(s2)\n y2 = self._y_outline(s2)\n\n # Get new distance\n d2 = self._distance(x2, y2, xc, yc, b)\n\n # Plot\n if plot:\n plt.figure()\n s_space = np.linspace(0.0, 1.0, 10000)\n plt.plot(self._x_outline(s_space), self._y_outline(s_space), 'b')\n plt.plot(xc, yc, 'or')\n plt.plot(x0, y0, 'bx')\n plt.plot(x1, y1, 'gx')\n plt.plot(x2, y2, 'rx')\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n\n # Update for next iteration\n s0 = s1\n d0 = d1\n x0 = x1\n y0 = y1\n\n s1 = s2\n d1 = d2\n x1 = x2\n y1 = y2\n\n return x1, y1, s1\n\n\n def _distance(self, x0, y0, x, y, b):\n if not np.isnan(b) and not np.isinf(b):\n return (-b*x0+y0+b*x-y)/np.sqrt(b*b+1)\n else:\n return abs(x0-x)\n\n\n def check_against_NACA(self, naca_des):\n \"\"\"Checks the error in the camber and thickness against that predicted by the NACA equations. This is recommended as a check for the user\n if unusual geometries are being imported. Checks against the open trailing edge formulation of the NACA equations.\n\n Parameters\n ----------\n naca_des : str\n NACA designation of the airfoil to compare against as a string. May only be 4-digit series.\n \"\"\"\n\n print(\"Checking estimated thickness and camber against NACA equations for NACA {0}...\".format(naca_des))\n\n # 4-digit series\n if len(naca_des) == 4:\n\n # Generate true coordinates\n x_space = np.linspace(0.0, 1.0, 10000)\n m = float(naca_des[0])/100\n p = float(naca_des[1])/10\n if p != 0.0:\n y_c_true = np.where(x_space0.19198621771937624, -0.4995016675499485*np.abs(d_f)+1.09589743589744, 1.0)\n return hinge_eff*flap_eff*eps_flap_ideal*d_f\n\n\n def _get_database_data(self, data_index, **kwargs):\n # Returns an interpolated data point from the database.\n # data_index: 0 = CL, 1 = CD, 2 = Cm\n\n # Determine size of query\n max_size = 1\n for dof in self._dof_db_order:\n param = kwargs.get(dof, self._dof_defaults[dof])\n if isinstance(param, np.ndarray):\n max_size = max(max_size, param.shape[0])\n\n # Get params\n param_vals = np.zeros((max_size,self._num_dofs))\n for i, dof in enumerate(self._dof_db_order):\n param_vals[:,i] = kwargs.get(dof, self._dof_defaults[dof])\n\n # Interpolate\n return_val = interp.griddata(self._normed_ind_vars, self._data[:,self._num_dofs+data_index].flatten(), param_vals/self._data_norms, method='linear').flatten()\n #return_val = interp.griddata(self._data[:,:self._num_dofs], self._data[:,self._num_dofs+data_index].flatten(), param_vals, method='linear').flatten()\n\n # Check for going out of bounds\n if np.isnan(return_val).any():\n raise DatabaseBoundsError(self.name, np.argwhere(np.isnan(return_val)).flatten(), kwargs)\n\n # Return\n if max_size == 1:\n return return_val.item()\n else:\n return return_val\n\n\n def get_CL(self, **kwargs):\n \"\"\"Returns the coefficient of lift. Note: all parameters can be given as numpy arrays, in which case a numpy array of the coefficient will be returned.\n To do this, all parameter arrays must have only one dimension and must have the same length.\n\n Parameters\n ----------\n alpha : float, optional\n Angle of attack in radians. Defaults to 0.0.\n\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n Returns\n -------\n float or ndarray\n Lift coefficient\n \"\"\"\n \n # Linearized model\n if self._type == \"linear\":\n\n # Get params\n alpha = kwargs.get(\"alpha\", 0.0)\n d_f = kwargs.get(\"trailing_flap_deflection\", 0.0)\n c_f = kwargs.get(\"trailing_flap_fraction\", 0.0)\n\n # Calculate lift coefficient\n if np.array(d_f == 0.0).all() or np.array(c_f == 0.0).all():\n CL = self._CLa*(alpha-self._aL0)\n else:\n delta_a_d_f = self._get_flap_influence(c_f, d_f)\n CL = self._CLa*(alpha-self._aL0+delta_a_d_f)\n \n # Saturate\n with np.errstate(invalid='ignore'):\n if isinstance(CL, np.ndarray):\n CL = np.where((CL > self._CL_max) | (CL < -self._CL_max), np.sign(CL)*self._CL_max, CL)\n elif CL > self._CL_max or CL < -self._CL_max:\n CL = np.sign(CL)*self._CL_max\n\n # Functional model\n elif self._type == \"functional\":\n CL = self._CL(**kwargs)\n\n # Generated/imported database\n elif self._type == \"database\":\n CL = self._get_database_data(0, **kwargs)\n\n # Fits\n elif self._type == \"poly_fit\":\n CL = self._get_polynomial_data(0, **kwargs)\n\n return CL\n\n\n def get_CD(self, **kwargs):\n \"\"\"Returns the coefficient of drag. note: all parameters can be given as numpy arrays, in which case a numpy array of the coefficient will be returned.\n to do this, all parameter arrays must have only one dimension and must have the same length.\n\n Parameters\n ----------\n alpha : float, optional\n Angle of attack in radians. Defaults to 0.0.\n\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n Returns\n -------\n float or ndarray\n Drag coefficient\n \"\"\"\n\n # Linear type\n if self._type == \"linear\":\n d_f = kwargs.pop(\"trailing_flap_deflection\", 0.0)\n CL = self.get_CL(**kwargs)\n CD_flap = 0.002*np.abs(np.degrees(d_f)) # A *very* rough estimate for flaps\n CD = self._CD0+self._CD1*CL+self._CD2*CL**2+CD_flap\n\n # Functional model\n elif self._type == \"functional\":\n CD = self._CD(**kwargs)\n\n # Generated/imported database\n elif self._type == \"database\":\n CD = self._get_database_data(1, **kwargs)\n\n # Fits\n elif self._type == \"poly_fit\":\n CD = self._get_polynomial_data(1, **kwargs)\n\n return CD\n\n\n def get_Cm(self, **kwargs):\n \"\"\"Returns the moment coefficient. note: all parameters can be given as numpy arrays, in which case a numpy array of the coefficient will be returned.\n to do this, all parameter arrays must have only one dimension and must have the same length.\n\n Parameters\n ----------\n alpha : float, optional\n Angle of attack in radians. Defaults to 0.0.\n\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_moment_deriv : float or ndarray, optional\n Change in section moment with respect to trailing flap deflection. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n Returns\n -------\n float or ndarray\n Moment coefficient\n \"\"\"\n\n # Linear type\n if self._type == \"linear\":\n\n # Get parameters\n alpha = kwargs.get(\"alpha\", 0.0)\n d_f = kwargs.get(\"trailing_flap_deflection\", 0.0)\n c_f = kwargs.get(\"trailing_flap_fraction\", 0.0)\n\n # No control deflection\n if np.array(d_f == 0.0).all() or np.array(c_f == 0.0).all():\n Cm = self._CmL0+self._Cma*(alpha-self._aL0)\n else:\n theta_f = np.arccos(2.0*c_f-1.0)\n Cm_df = 0.25*(np.sin(2.0*theta_f)-2.0*np.sin(theta_f))\n Cm = self._CmL0+self._Cma*(alpha-self._aL0)+Cm_df*d_f\n\n # Functional model\n elif self._type == \"functional\":\n Cm = self._Cm(**kwargs)\n\n # Generated/imported database\n elif self._type == \"database\":\n Cm = self._get_database_data(2, **kwargs)\n\n # Fits\n elif self._type == \"poly_fit\":\n Cm = self._get_polynomial_data(2, **kwargs)\n\n return Cm\n\n\n def get_aL0(self, **kwargs):\n \"\"\"Returns the zero-lift angle of attack, taking flap deflection into account.\n\n Parameters\n ----------\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n Returns\n -------\n float\n Zero-lift angle of attack\n \"\"\"\n\n # Linear airfoil model\n if self._type == \"linear\":\n\n # Get params\n d_f = kwargs.get(\"trailing_flap_deflection\", 0.0)\n c_f = kwargs.get(\"trailing_flap_fraction\", 0.0)\n\n # Calculate lift coefficient\n if np.array(d_f == 0.0).all() or np.array(c_f == 0.0).all():\n aL0 = self._aL0\n else:\n delta_a_d_f = self._get_flap_influence(c_f, d_f)\n aL0 = self._aL0-delta_a_d_f\n \n return aL0\n\n # Database/poly fit/functional\n # Use secant method in alpha to find a_L0\n elif self._type == \"database\" or self._type == \"poly_fit\" or self._type == \"functional\":\n\n # Remove alpha from kwargs\n kwargs.pop('alpha', None)\n\n # Initialize secant method\n a0 = 0.0\n CL0 = self.get_CL(alpha=a0, **kwargs)\n a0 = np.zeros_like(CL0)\n a1 = np.zeros_like(CL0)+0.01\n CL1 = self.get_CL(alpha=a1, **kwargs)\n a2 = np.zeros_like(CL1)\n\n # If we're outside the domain of the database, aL0 should be nan\n if a2.size == 1:\n if np.isnan(CL1):\n a2 = np.nan\n else:\n a2[np.where(np.isnan(CL1))] = np.nan\n \n # Iterate\n np.seterr(invalid='ignore')\n not_converged = np.where(np.array(np.abs(CL1)>1e-10))[0]\n while not_converged.size>0:\n \n # Update estimate\n if a2.size == 1:\n a2 = (a1-CL1*(a0-a1)/(CL0-CL1))\n else:\n a2[not_converged] = (a1-CL1*(a0-a1)/(CL0-CL1))[not_converged]\n CL2 = self.get_CL(alpha=a2, **kwargs)\n\n # Update for next iteration\n a0 = np.copy(a1)\n CL0 = np.copy(CL1)\n a1 = np.copy(a2)\n CL1 = np.copy(CL2)\n\n # Check convergence\n not_converged = np.where(np.array(np.abs(CL1)>1e-10))[0]\n\n np.seterr()\n return a2\n\n\n def get_CLM(self, **kwargs):\n \"\"\"Returns the lift slope with respect to Mach number using a forward-difference approximation.\n Simply returns 0 for a type 'linear' airfoil.\n\n Parameters\n ----------\n alpha : float, optional\n Angle of attack in radians. Defaults to 0.0.\n\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n dx : float\n Step size for finite-difference equation. Defaults to 0.05.\n\n Returns\n -------\n float or ndarray\n Lift slope with respect to Mach number\n \"\"\"\n\n # Linear model\n if self._type == \"linear\":\n return 0.0\n\n # Database\n elif self._type == \"database\" or self._type == \"poly_fit\" or self._type == \"functional\":\n\n # Check the database is dependent on Mach\n if (self._type == \"database\" or self._type == \"poly_fit\") and \"Mach\" not in self._dof_db_order:\n return 0.0\n\n # Get center Mach value\n dx = kwargs.get(\"dx\", 0.05)\n Mach = kwargs.pop(\"Mach\", 0.0)\n \n # Calculate forward and center points (since we'll often be at M=0 and negative M doesn't work)\n CL1 = self.get_CL(Mach=Mach+dx, **kwargs)\n CL0 = self.get_CL(Mach=Mach, **kwargs)\n\n return (CL1-CL0)/dx\n\n\n def get_CLRe(self, **kwargs):\n \"\"\"Returns the lift slope with respect to Reynolds number using a central-difference approximation.\n Simply returns 0 for a type 'linear' airfoil.\n\n Parameters\n ----------\n alpha : float, optional\n Angle of attack in radians. Defaults to 0.0.\n\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n dx : float\n Step size for finite-difference equation. Defaults to 1000.\n\n Returns\n -------\n float or ndarray\n Lift slope with respect to Reynolds number\n \"\"\"\n\n # Linear model\n if self._type == \"linear\":\n return 0.0\n\n # Database\n elif self._type == \"database\" or self._type == \"poly_fit\" or self._type == \"functional\":\n\n # Check the database is dependent on Re\n if (self._type == \"database\" or self._type == \"poly_fit\") and \"Rey\" not in self._dof_db_order:\n return 0.0\n\n # Get center Re value\n dx = kwargs.get(\"dx\", 1000)\n Rey = kwargs.pop(\"Rey\", 1000000)\n \n # Calculate forward and backward points\n CL1 = self.get_CL(Rey=Rey+dx, **kwargs)\n CL0 = self.get_CL(Rey=Rey-dx, **kwargs)\n\n return (CL1-CL0)/(2*dx)\n\n\n def get_CLa(self, **kwargs):\n \"\"\"Returns the lift slope using a central-difference approximation.\n\n Parameters\n ----------\n alpha : float, optional\n Angle of attack in radians. Defaults to 0.0.\n\n Rey : float, optional\n Reynolds number.\n\n Mach : float, optional\n Mach number.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians. Defaults to 0.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord length. Defaults to 0.\n\n dx : float, optional\n Step size for finite-difference equation. Defaults to 0.001 radians.\n\n Returns\n -------\n float or ndarray\n Lift slope\n \"\"\"\n\n # Linear model\n if self._type == \"linear\":\n return self._CLa\n\n # Database\n elif self._type == \"database\" or self._type == \"poly_fit\" or self._type == \"functional\":\n\n # Check the database is dependent on alpha\n if (self._type == \"database\" or self._type == \"poly_fit\") and \"alpha\" not in self._dof_db_order:\n return 0.0\n\n # Get center alpha value\n dx = kwargs.get(\"dx\", 0.001)\n alpha = kwargs.pop(\"alpha\", 0.0)\n \n # Calculate forward and backward points\n CL1 = self.get_CL(alpha=alpha+dx, **kwargs)\n CL0 = self.get_CL(alpha=alpha-dx, **kwargs)\n\n return (CL1-CL0)/(2*dx)\n\n\n def get_thickness(self, x):\n \"\"\"Returns the thickness normal to the camber line at the specified x location(s).\n\n Parameters\n ----------\n x : float or ndarray\n x location(s) at which to get the thickness.\n\n Returns\n -------\n float or ndarray\n Thickness as a percentage of the chord.\n \"\"\"\n if isinstance(x, float):\n return self._thickness(x).item()\n else:\n return self._thickness(x)\n\n\n def get_max_thickness(self):\n \"\"\"Returns the maximum thickness of the airfoil, divided by the chord length.\n \"\"\"\n return self._max_thickness\n\n\n def get_camber(self, x):\n \"\"\"Returns the y coordinate(s) of the camber line at the specified x location(s).\n\n Parameters\n ----------\n x : float or ndarray\n x location(s) at which to get the thickness.\n\n Returns\n -------\n float or ndarray\n Camber line y coordinate(s) as a percentage of the chord.\n \"\"\"\n if isinstance(x, float):\n return self._camber_line(x).item()\n else:\n return self._camber_line(x)\n\n\n def get_max_camber(self):\n \"\"\"Returns the maximum camber of the airfoil, divided by the chord length.\n \"\"\"\n return self._max_camber\n\n\n def get_outline_points(self, **kwargs):\n \"\"\"Returns an array of outline points showing the geometry of the airfoil.\n\n Parameters\n ----------\n N : int, optional\n The number of outline points to return. This function will not always return exactly this many\n but never more. Defaults to 200.\n\n cluster : bool, optional\n Whether to cluster points about the leading and trailing edges. Defaults to True.\n\n trailing_flap_deflection : float, optional\n Trailing flap deflection in radians (positive down). Defaults to zero.\n\n trailing_flap_fraction : float, optional\n Trailing flap fraction of the chord. Defaults to zero.\n\n export : str, optional\n If specified, the outline points will be saved to a file. Defaults to no file.\n\n top_first : bool, optional\n The order of the coordinates when exported. Defaults to going from the trailing edge along the top and\n the around to the bottom.\n\n close_te : bool, optional\n Whether the top and bottom trailing edge points should be forced to be equal. Defaults to False\n\n plot : bool, optional\n Whether a plot of the outline points should be displayed once computed. Defaults to False.\n\n original_points : bool, optional\n Whether this should simply return the original inputted points. If set to True, all other kwargs relating\n to the geometry will be ignored. Defaults to False.\n\n Returns\n -------\n ndarray\n Outline points in airfoil coordinates.\n \"\"\"\n\n # Check the geometry has been defined\n if self.geom_specification != \"none\":\n\n # Get kwargs\n N = kwargs.get(\"N\", 200)\n cluster = kwargs.get(\"cluster\", True)\n trailing_flap_deflection = kwargs.get(\"trailing_flap_deflection\", 0.0)\n trailing_flap_fraction = kwargs.get(\"trailing_flap_fraction\", 0.0)\n close_te = kwargs.get(\"close_te\", False)\n\n if not kwargs.get(\"original_points\", False):\n \n # Zach's method of determining NACA airfoils with deflected parabolic flaps (actually works really well for all of them...)\n\n # Get flap parameters\n d_f = trailing_flap_deflection\n x_f = 1.0-trailing_flap_fraction\n y_f = self._input_dict.get(\"trailing_flap_hinge_height\", self._camber_line(x_f))\n \n # Calculate distributions\n theta_c = np.linspace(np.pi, -np.pi, N) # Loop over entire airfoil from TE->top->LE->bottom->TE\n if cluster:\n x_c = 0.5*(1.0-np.cos(theta_c))\n else:\n x_c = abs(np.linspace(-1.0, 1.0, N))\n \n # Calculate undeformed camber line and thickness\n y_c = self._camber_line(x_c)\n t = self._thickness(x_c)\n dyc_dx = self._camber_deriv(x_c)\n \n if d_f != 0.0 and x_f < 1.0:\n # Determine which camber points belong to the flap\n flap_ind = np.where(x_c>x_f)\n\n # Linear flap\n if self._trailing_flap_type == \"linear\":\n\n # Calculate deflected camber line Eqs. (8-10) in \"Geometry and Aerodynamic Performance of Parabolic...\" by Hunsaker, et al. 2018\n r = np.sqrt((y_c-y_f)*(y_c-y_f)+(x_c-x_f)*(x_c-x_f))\n psi = np.arctan((y_c-y_f)/(x_c-x_f))\n x_c[flap_ind] = x_f+(r*np.cos(d_f-psi))[flap_ind]\n y_c[flap_ind] = y_f-(r*np.sin(d_f-psi))[flap_ind]\n dyc_dx[flap_ind] = np.gradient(y_c[flap_ind], x_c[flap_ind], edge_order=2)\n\n else:\n # Parabolic flap from \"Geometry and Aerodynamic Performance of Parabolic...\" by Hunsaker, et al. 2018\n\n # Calculate the neutral line parameters\n l_n = np.sqrt(y_f*y_f+(1-x_f)*(1-x_f))\n phi_n = -np.arctan2(y_f, 1-x_f)\n\n # Calculate the location of the deflected trailing edge\n tan_df = np.tan(d_f)\n R = np.sqrt(4*tan_df*tan_df+1)+np.arcsinh(2*tan_df)/(2*tan_df)\n E_te = 2.0*l_n/R\n\n # Find E_p using secant method\n\n # Constants\n R_tan_df = R*tan_df\n R_tan_df2 = R_tan_df*R_tan_df\n E_0 = ((x_c-x_f)/(1-x_f)*l_n)[flap_ind]\n\n # Initial guesses\n E_p0 = E_te*E_0/l_n\n R0 = E_p0/2*np.sqrt(E_p0**2/l_n**2*R_tan_df2+1)+l_n/(2*R_tan_df)*np.arcsinh(E_p0/l_n*R_tan_df)-E_0\n E_p1 = E_te*E_0/l_n+0.001\n R1 = E_p1/2*np.sqrt(E_p1**2/l_n**2*R_tan_df2+1)+l_n/(2*R_tan_df)*np.arcsinh(E_p1/l_n*R_tan_df)-E_0\n\n # Suppress warnings because an error will often occur within the np.where that has no effect on computation\n with np.errstate(invalid='ignore'):\n \n # Iterate\n while (abs(R1)>1e-10).any():\n\n # Update value\n E_p2 = np.where(np.abs(R0-R1) != 0.0, E_p1-R1*(E_p0-E_p1)/(R0-R1), E_p1)\n\n # Get residual\n R2 = E_p2/2*np.sqrt(E_p2**2/l_n**2*R_tan_df2+1)+l_n/(2*R_tan_df)*np.arcsinh(E_p2/l_n*R_tan_df)-E_0\n\n # Update for next iteration\n E_p0 = E_p1\n R0 = R1\n E_p1 = E_p2\n R1 = R2\n\n # Store final result\n E_p = E_p1\n n_p = -E_p*E_p/E_te*tan_df\n\n # Calculate deflected neutral line\n x_p = x_f+E_p*np.cos(phi_n)-n_p*np.sin(phi_n)\n y_p = y_f+E_p*np.sin(phi_n)+n_p*np.cos(phi_n)\n y_nl = y_f*(1-(x_c-x_f)/(1.0-x_f))\n dy_c = (y_c-y_nl)[flap_ind]\n\n # Calculate deflected camber line\n C = np.arctan(2*E_p/E_te*tan_df)\n x_c[flap_ind] = x_p+dy_c*np.sin(C)\n y_c[flap_ind] = y_p+dy_c*np.cos(C)\n\n dyc_dx[flap_ind] = (dyc_dx[flap_ind] - 2*E_p*tan_df/E_te) / (1 + 2*E_p*tan_df/E_te*dyc_dx[flap_ind])\n\n # Outline points\n X = x_c-t*np.sin(np.arctan(dyc_dx))*np.sign(theta_c)\n Y = y_c+t*np.cos(np.arctan(dyc_dx))*np.sign(theta_c)\n\n # Trim overlapping points from linear flap deflection\n if self._trailing_flap_type == \"linear\" and d_f != 0.0 and x_f < 1.0:\n\n # Get indices of top and bottom points\n top_ind = theta_c > 0.0\n bot_ind = theta_c <= 0.0\n\n # Split points into top and bottom\n X_t = X[top_ind]\n Y_t = Y[top_ind]\n X_b = X[bot_ind]\n Y_b = Y[bot_ind]\n\n # Find the point on the surface where the hinge breaks\n # These equations determine the slope of the line bisecting the angle between the camber line before the flap\n # and the camber line of the flap\n psi_prime = np.pi+np.arctan(self._camber_deriv(x_f)) # Angle to the camber line before the flap\n d_f_prime = -d_f+np.arctan(self._camber_deriv(x_f)) # Angle to the camber line after the flap\n phi = 0.5*(psi_prime+d_f_prime)\n b = np.tan(phi)\n x_t_break, y_t_break, _ = self._get_intersection_point(x_f, y_f, b, \"top\")\n x_b_break, y_b_break, _ = self._get_intersection_point(x_f, y_f, b, \"bottom\")\n\n # Trim overlapping points off of both surfaces\n X_b, Y_b, num_trimmed_from_bot = self._trim_surface(X_b, Y_b, \"forward\")\n X_t, Y_t, num_trimmed_from_top = self._trim_surface(X_t, Y_t, \"backward\")\n\n # Check if we need to fill anything in so the number of outline points remains the same\n if num_trimmed_from_bot>0:\n r_t = self._get_cart_dist(x_t_break, y_t_break, x_f, y_f)\n X_t, Y_t = self._fill_surface(X_t, Y_t, x_f, y_f, r_t, x_t_break, num_trimmed_from_bot, \"backward\")\n\n if num_trimmed_from_top>0:\n r_b = self._get_cart_dist(x_b_break, y_b_break, x_f, y_f)\n X_b, Y_b = self._fill_surface(X_b, Y_b, x_f, y_f, r_b, x_b_break, num_trimmed_from_top, \"forward\")\n\n # Concatenate top and bottom points\n X = np.concatenate([X_t, X_b])\n Y = np.concatenate([Y_t, Y_b])\n\n # Make sure the trailing edge is sealed\n if close_te:\n x_te = 0.5*(X[0]+X[-1])\n y_te = 0.5*(Y[0]+Y[-1])\n X[0] = x_te\n Y[0] = y_te\n X[-1] = x_te\n Y[-1] = y_te\n\n # Just use the original points\n else:\n X = self._raw_outline[:,0]\n Y = self._raw_outline[:,1]\n\n # Plot result\n if kwargs.get(\"plot\", False):\n plt.figure()\n plt.plot(X, Y)\n #plt.plot(x_t_break, y_t_break, 'rx')\n #plt.plot(x_b_break, y_b_break, 'rx')\n #plt.plot(x_f, y_f, 'rx')\n plt.plot(x_c, y_c, 'r--')\n plt.xlabel('x')\n plt.ylabel('y')\n plt.title(self.name)\n plt.gca().set_aspect('equal', adjustable='box')\n plt.show()\n\n # Concatenate x and y\n outline_points = np.concatenate([X[:,np.newaxis], Y[:,np.newaxis]], axis=1)\n if not kwargs.get(\"top_first\", True):\n outline_points = outline_points[::-1,:]\n \n # Save to file\n export = kwargs.get(\"export\", None)\n if export is not None:\n np.savetxt(export, outline_points, fmt='%10.8f')\n\n return outline_points\n\n else:\n raise RuntimeError(\"The geometry has not been defined for airfoil {0}. Outline points cannot be generated.\".format(self.name))\n\n\n def _trim_surface(self, X, Y, direction):\n # Trims any points within a region of doubling back in x\n\n # Loop through points\n trim_indices = []\n rev = False\n fwd = False\n\n # Determine direction\n indices = range(X.shape[0])\n if direction == \"backward\":\n indices = indices[::-1]\n \n # Loop through points\n trim_indices = []\n i_prev = 0\n for i in indices:\n \n # Skip first third of the section so we don't end up trimming off the nose\n if X[i] < 0.3:\n continue\n\n # Check if we've started going backwards\n if not rev and X[i] < X[i_prev]:\n rev = i_prev\n\n # Check if we've started going forward again\n if rev and not fwd and X[i] > X[i_prev]:\n fwd = i_prev\n break\n\n i_prev = i\n \n # Trim and insert\n if rev and fwd:\n\n # Determine where to trim\n if direction == \"forward\":\n trim_indices = list(range(rev, fwd+1))\n else:\n trim_indices = list(range(fwd, rev+1))\n\n # Trim\n X = np.delete(X, trim_indices)\n Y = np.delete(Y, trim_indices)\n\n return X, Y, len(trim_indices)\n \n else:\n return X, Y, 0\n\n\n\n def _fill_surface(self, X, Y, x_h, y_h, r, x_break, num_points, direction):\n # Fills in num_points along the arc defined by x_h, y_h, and r\n\n # Determine direction\n indices = range(X.shape[0])\n if direction == \"backward\":\n indices = indices[::-1]\n\n # Find the two points we need to fill in between\n for i in indices:\n \n # Skip first third of the section\n if X[i] < 0.3:\n continue\n\n if X[i] > x_break:\n fill_start = i-1\n fill_stop = i\n break\n\n # No filling needed, apparently...\n else:\n return X, Y\n\n # Get angles from the hinge point to the start and stop points\n theta0 = math.atan2(Y[fill_start]-y_h, X[fill_start]-x_h)\n theta1 = math.atan2(Y[fill_stop]-y_h, X[fill_stop]-x_h)\n\n # Find fill in points\n theta_fill = np.linspace(theta0, theta1, num_points+2)[1:-1]\n x_fill = x_h+r*np.cos(theta_fill)\n y_fill = y_h+r*np.sin(theta_fill)\n\n return np.insert(X, fill_stop, x_fill), np.insert(Y, fill_stop, y_fill)\n\n\n def _get_closest_point_on_surface(self, x, y, surface):\n # Finds the point on the surface of the airfoil which is closest to the given point using Golden section search\n # Returns the coordinates and the radius from the given point\n\n # Decide where to start in s\n if surface == \"top\":\n s0 = 0.0\n s3 = 0.5\n else:\n s0 = 0.5\n s3 = 1.0\n\n # Get Golden ratio\n R = 2.0/(1.0+math.sqrt(5.0))\n\n # Loop until interval converges\n while abs(s0-s3)>1e-10:\n \n # Get interior points\n diff = s3-s0\n s1 = s3-diff*R\n s2 = s0+diff*R\n\n # Calculate points\n x1 = self._x_outline(s1)\n y1 = self._y_outline(s1)\n x2 = self._x_outline(s2)\n y2 = self._y_outline(s2)\n \n # Calculate distances\n d1 = self._get_cart_dist(x, y, x1, y1)\n d2 = self._get_cart_dist(x, y, x2, y2)\n\n # Check\n if d1 < d2:\n s3 = s2\n else:\n s0 = s1\n\n return x1, y1, d1\n\n\n def _get_cart_dist(self, x0, y0, x1, y1):\n x_diff = x0-x1\n y_diff = y0-y1\n return math.sqrt(x_diff*x_diff+y_diff*y_diff)\n\n\n def generate_database(self, **kwargs):\n \"\"\"Makes calls to Xfoil to calculate CL, CD, and Cm as a function of each given degree of freedom.\n\n Parameters\n ----------\n degrees_of_freedom : dict\n A dict specifying which degrees of freedom the database should perturb. Allowable degrees of \n freedom are \"alpha\", \"Rey\", \"Mach\", \"trailing_flap_deflection\", and \"trailing_flap_fraction\".\n\n Each key should be one of these degrees of freedom. To specify a range for the degree of freedom,\n a dictionary with the following keys should be given:\n\n \"range\" : list\n The lower and upper limits for this DOF.\n\n \"steps\" : int\n The number of points in the range to interrogate.\n\n \"index\" : int\n Index of the column for this degree of freedom in the database.\n\n \"log_step\" : bool, optional\n Whether the steps in this dof should be spaced linearly (False) or logarithmically\n (True). Defaults to False.\n\n If instead of perturbing a variable, you want the database to be evaluated at a constant value of\n that variable, a float should be given instead of a dictionary. That variable will then not be considered\n a degree of freedom of the database and will not appear in the database.\n \n An example is shown below:\n\n dofs = {\n \"alpha\" : {\n \"range\" : [math.radians(-15.0), math.radians(15.0)],\n \"steps\" : 21,\n \"index\" : 1\n },\n \"Rey\" : 2900000.0,\n \"trailing_flap_deflection\" : {\n \"range\" : [math.radians(-20.0), math.radians(20.0)],\n \"steps\" : 1,\n \"index\" : 0\n },\n \"trailing_flap_fraction\" : 0.25\n }\n\n The above input will run the airfoil through angles of attack from -15 to 15 degrees at a Reynolds number\n of 2900000.0 and through flap deflections from -20 to 20 degrees with a chord fraction of 25%.\n\n If a float, the degree of freedom is assumed to be constant at that value.\n \n If not specified, each degree of freedom defaults to the following:\n\n \"alpha\" : 0.0\n\n \"Rey\" : 1000000.0\n\n \"Mach\" : 0.0\n\n \"trailing_flap_deflection\" : 0.0\n\n \"trailing_flap_fraction\" : 0.0\n\n Please note that all angular degreees of freedom are in radians, rather than degrees.\n\n Currently, the number of steps in Reynolds number multiplied by the number of steps in Mach number\n may not exceed 12. This is due to internal limitations in Xfoil. However, due to the weak dependence\n of airfoil properties on Reynolds number, we do not expect this to be a great hinderance.\n\n N : int, optional\n Number of panel nodes for Xfoil to use. Defaults to 200.\n\n max_iter : int, optional\n Maximum iterations for Xfoil. Defaults to 100.\n\n x_trip : float or list, optional\n x location, non-dimensionalized by the chord length, of the boundary layer trip position. This is \n specified for the top and bottom of the airfoil. If a float, the value is the same for the top\n and the bottom. If a list, the first list element is the top trip location and the second list element\n is the bottom trip location. Defaults to 1.0 for both.\n\n visc : bool, optional\n Whether to include viscosity. Defaults to True.\n\n N_crit : float, optional\n Critical amplification exponent for the boundary layer in Xfoil. Defaults to 9.0.\n\n update_type : bool, optional\n Whether to update the airfoil to use the newly computed database for calculations. Defaults to True.\n\n show_xfoil_output : bool, optional\n Display whatever Xfoil prints out. Defaults to False.\n\n show_xfoil_plots : bool, optional\n Display Xfoil plots. Defaults to True.\n\n resize_xfoil_window : float, optional\n resizes the xfoil window to screen size fraction. Xfoil defaults to 0.8 window/screen size.\n This variable defaults to None. Has no effect if show_xfoil_plots is False.\n\n CD_type : str, optional\n Which drag coefficient to read in. May be 'total', 'friction', or 'pressure'.\n Defaults to 'total'.\n\n verbose : bool, optional\n Defaults to True\n \"\"\"\n\n # Set up lists of independent vars\n xfoil_args = {}\n self._dof_db_cols = {}\n num_total_runs = 1\n for dof, params in kwargs.pop(\"degrees_of_freedom\", {}).items():\n if dof not in self._allowable_dofs:\n raise IOError(\"{0} is not an allowable DOF.\".format(dof))\n vals, column_index = self._setup_ind_var(params)\n xfoil_args[dof] = vals\n num_total_runs *= len(vals)\n if column_index is not None:\n self._dof_db_cols[dof] = column_index\n\n # Get coefficients\n CL, CD, Cm = self.run_xfoil(**xfoil_args, **kwargs)\n\n # Determine the rows and cols in the database; each independent var and coefficient is a column to be iterpolated using scipy.interpolate.griddata\n self._num_dofs = len(list(self._dof_db_cols.keys()))\n num_cols = 3+self._num_dofs\n num_rows = CL.size-np.count_nonzero(np.isnan(CL))\n dof_sorted = sorted(self._dof_db_cols.items(), key=operator.itemgetter(1))\n self._dof_db_order = [x[0] for x in dof_sorted]\n\n # Arrange into 2D database\n self._data = np.zeros((num_rows, num_cols))\n database_row = 0\n coef_shape = CL.shape\n\n for i in range(coef_shape[0]):\n for j in range(coef_shape[1]):\n for k in range(coef_shape[2]):\n for l in range(coef_shape[3]):\n for m in range(coef_shape[4]):\n\n # Check for nan\n if np.isnan(CL[i,j,k,l,m]):\n continue\n\n # Append independent vars to database\n for n, dof in enumerate(self._dof_db_order):\n if dof == \"alpha\":\n ind = i\n elif dof == \"Rey\":\n ind = j\n elif dof == \"Mach\":\n ind = k\n elif dof == \"trailing_flap_deflection\":\n ind = l\n else:\n ind = m\n self._data[database_row,n] = xfoil_args[dof][ind]\n \n # Append coefficients\n self._data[database_row,self._num_dofs] = CL[i,j,k,l,m]\n self._data[database_row,self._num_dofs+1] = CD[i,j,k,l,m]\n self._data[database_row,self._num_dofs+2] = Cm[i,j,k,l,m]\n\n database_row += 1\n\n # Sort by columns so the first column is perfectly in order\n dtype = \",\".join(['i8' for i in range(num_cols)])\n for i in range(self._num_dofs,-1,-1):\n self._data = self._data[self._data[:,i].argsort(axis=0, kind='stable')] # 'stable' option is necessary to maintain ordering of columns not being actively sorted\n\n # Let the user know how much of the design space we actually got results for\n if kwargs.get(\"verbose\", True):\n percent_success = round(self._data.shape[0]/num_total_runs*100, 2)\n print(\"\\nDatabase generation complete.\")\n print(\"Convergent results obtained from Xfoil for {0}% of the requested points.\".format(percent_success))\n\n # Update type\n if kwargs.get(\"update_type\", True):\n self.set_type(\"database\")\n\n\n def _setup_ind_var(self, input_dict):\n # Sets up a range of independent variables\n\n # Constant value\n if isinstance(input_dict, float):\n lower = input_dict\n upper = input_dict\n N = 1\n index = None\n log_step = False\n\n # Range\n else:\n limits = input_dict.get(\"range\")\n lower = limits[0]\n upper = limits[1]\n N = input_dict.get(\"steps\")\n index = input_dict.get(\"index\", None)\n log_step = input_dict.get(\"log_step\", False)\n\n # Check that the range will actually have multiple points\n if N == 1:\n raise IOError(\"A range with only one step may not be specified for a degree of freedom. Please give a single float instead.\")\n \n if log_step:\n return list(np.logspace(np.log10(lower), np.log10(upper), N)), index\n else:\n return list(np.linspace(lower, upper, N)), index\n\n\n def export_database(self, **kwargs):\n \"\"\"Exports the database generated by generate_database().\n\n Parameters\n ----------\n filename : str\n File to export the database to.\n \"\"\"\n\n filename = kwargs.get(\"filename\")\n\n # Check the database is there\n if hasattr(self, \"_data\"):\n\n # Create header\n header = []\n\n # Add degrees of freedom\n for dof in sorted(self._dof_db_cols.items(), key=operator.itemgetter(1)):\n header.append(\"{:<25s}\".format(dof[0]))\n\n # Add coefficients\n header.append(\"{:<25s}\".format('CL'))\n header.append(\"{:<25s}\".format('CD'))\n header.append(\"{:<25s}\".format('Cm'))\n header = \" \".join(header)\n\n # Export\n with open(filename, 'w') as db_file:\n np.savetxt(db_file, self._data, '%25.10E', header=header)\n else:\n raise RuntimeError(\"No database has been generated for airfoil {0}. Please create a database before exporting.\".format(self.name))\n\n\n def import_database(self, **kwargs):\n \"\"\"Imports the specified database. Please note that if you have generated your own database not\n using AirfoilDatabase, angle of attack should be stored in radians, rather than degrees.\n\n Parameters\n ----------\n filename : str\n File to import the database from\n\n update_type : bool, optional\n Whether to update the airfoil to use the newly imported database for calculations. Defaults to True.\n \"\"\"\n\n filename = kwargs.get(\"filename\")\n\n # Load data from file\n with open(filename, 'r') as db_file:\n self._data = np.loadtxt(db_file)\n\n # Determine the column indices\n with open(filename, 'r') as db_file:\n header = db_file.readline().strip('#')\n self._dof_db_cols = {}\n self._num_dofs = 0\n for i, col_name in enumerate(header.split()):\n\n # Stop once we get to coefficient columns\n if col_name == \"CL\":\n break\n\n # Add\n self._dof_db_cols[col_name] = i\n self._num_dofs += 1\n\n # Figure out the order of the columns in the database\n dof_sorted = sorted(self._dof_db_cols.items(), key=operator.itemgetter(1))\n self._dof_db_order = [x[0] for x in dof_sorted]\n\n # Update type\n if kwargs.get(\"update_type\", True):\n self.set_type(\"database\")\n\n\n def run_xfoil(self, **kwargs):\n \"\"\"Calls Xfoil and extracts the aerodynamic coefficients at the given state.\n\n Parameters\n ----------\n alpha : float or list of float\n Angle(s) of attack to calculate the coefficients at in radians. Defaults to 0.0.\n\n Rey : float or list of float\n Reynolds number(s) to calculate the coefficients at. Defaults to 1000000.\n\n Mach : float or list of float\n Mach number(s) to calculate the coefficients at. Defaults to 0.0.\n\n trailing_flap_deflection : float or list of float\n Flap deflection(s) to calculate the coefficients at in radians. Defaults to 0.0.\n\n trailing_flap_fraction : float or list of float\n Flap fraction(s) to calculate the coefficients at in radians. Defaults to 0.0.\n\n N : int, optional\n Number of panels for Xfoil to use. Defaults to 200.\n\n max_iter : int, optional\n Maximum iterations for Xfoil. Defaults to 100.\n\n visc : bool, optional\n Whether to include viscosity. Defaults to True.\n\n x_trip : float or list, optional\n x location, non-dimensionalized by the chord length, of the boundary layer trip position. This is \n specified for the top and bottom of the airfoil. If a float, the value is the same for the top\n and the bottom. If a list, the first list element is the top trip location and the second list element\n is the bottom trip location. Defaults to 1.0 for both.\n\n xycm : list, optional\n x-y coordinates, non-dimensionalized by the chord length, of the reference point for determining the\n moment coefficient. Defaults to the quater-chord.\n\n N_crit : float, optional\n Critical amplification exponent for the boundary layer in Xfoil. Defaults to 9.0.\n\n show_xfoil_output : bool, optional\n Display whatever Xfoil outputs from the command line interface. Defaults to False.\n\n show_xfoil_plots : bool, optional\n Display Xfoil plots. Defaults to True.\n \n resize_xfoil_window : float, optional\n resizes the xfoil window to screen size fraction. Xfoil defaults to 0.8 window/screen size.\n This variable defaults to None. Has no effect if show_xfoil_plots is False.\n\n CD_type : str, optional\n Which drag coefficient to read in. May be 'total', 'friction', or 'pressure'.\n Defaults to 'total'.\n\n verbose : bool, optional\n\n Returns\n -------\n CL : ndarray\n Coefficient of lift. First dimension will match the length of alpha, second will match Rey, etc.\n\n CD : ndarray\n Coefficient of drag. Dimensions same as CL.\n\n Cm : ndarray\n Moment coefficient. Dimensions same as CL.\n \"\"\"\n N = kwargs.get(\"N\", 200)\n max_iter = kwargs.get(\"max_iter\", 100)\n verbose = kwargs.get(\"verbose\", True)\n show_xfoil_output = kwargs.get(\"show_xfoil_output\", False)\n x_trip = kwargs.get(\"x_trip\", [1.0, 1.0])\n xycm = kwargs.get(\"xycm\", [0.25, 0.0])\n if isinstance(x_trip, float):\n x_trip = [x_trip, x_trip]\n N_crit = kwargs.get(\"N_crit\", 9.0)\n\n # Get states\n # Angle of attack\n alphas = kwargs.get(\"alpha\", [0.0])\n if isinstance(alphas, float):\n alphas = [alphas]\n first_dim = len(alphas)\n \n # Reynolds number\n Reys = kwargs.get(\"Rey\", [1000000.0])\n if isinstance(Reys, float):\n Reys = [Reys]\n second_dim = len(Reys)\n\n # Mach number\n Machs = kwargs.get(\"Mach\", [0.0])\n if isinstance(Machs, float):\n Machs = [Machs]\n third_dim = len(Machs)\n\n # Flap deflections\n delta_fts = kwargs.get(\"trailing_flap_deflection\", [0.0])\n if isinstance(delta_fts, float):\n delta_fts = [delta_fts]\n fourth_dim = len(delta_fts)\n\n # Flap fractions\n c_fts = kwargs.get(\"trailing_flap_fraction\", [0.0])\n if isinstance(c_fts, float):\n c_fts = [c_fts]\n fifth_dim = len(c_fts)\n\n # xfoil window resizing\n resize_xfoil_window = kwargs.get('resize_xfoil_window', None)\n\n # Initialize coefficient arrays\n CL = np.empty((first_dim, second_dim, third_dim, fourth_dim, fifth_dim))\n CD = np.empty((first_dim, second_dim, third_dim, fourth_dim, fifth_dim))\n Cm = np.empty((first_dim, second_dim, third_dim, fourth_dim, fifth_dim))\n CL[:] = np.nan\n CD[:] = np.nan\n Cm[:] = np.nan\n\n # Clean up from previous iterations\n dir_list = os.listdir()\n for item in dir_list:\n if os.path.isfile(item) and \".pacc\" in item or \".geom\" in item:\n os.remove(item)\n\n # Loop through flap deflections and fractions\n if verbose:\n print(\"Running Xfoil...\")\n print(\"{0:>25}{1:>25}{2:>25}\".format(\"Percent Complete\", \"Flap Deflection [deg]\", \"Flap Fraction\"))\n print(''.join(['-']*75))\n num_xfoil_runs = fourth_dim*fifth_dim\n for l, delta_ft in enumerate(delta_fts):\n for m, c_ft in enumerate(c_fts):\n \n # Clear pacc file list\n pacc_files = []\n file_id = 0\n \n # Export geometry\n outline_points = \"a_{0:1.6f}_{1:1.6f}.geom\".format(delta_ft, c_ft)\n #outline_points = os.path.abspath(\"xfoil_geom_{0:1.6f}.geom\".format(delta_ft))\n self.get_outline_points(N=N, trailing_flap_deflection=delta_ft, trailing_flap_fraction=c_ft, export=outline_points, close_te=False)\n\n # Display update\n if verbose:\n percent_complete = round((l*fifth_dim+m)/(num_xfoil_runs)*100)\n print(\"{0:>24}%{1:>25}{2:>25}\".format(percent_complete, math.degrees(delta_ft), c_ft))\n\n # Loop through Reynolds number\n for Re in Reys:\n\n # Initialize xfoil execution\n with sp.Popen(['xfoil'], stdin=sp.PIPE, stdout=sp.PIPE) as xfoil_process:\n\n commands = []\n\n # Turn off plots\n if not kwargs.get(\"show_xfoil_plots\", True):\n commands += ['PLOP',\n 'G',\n '']\n elif resize_xfoil_window != None:\n commands += ['PLOP',\n 'W {}'.format(resize_xfoil_window),\n '']\n\n # Read in geometry\n commands += ['LOAD {0}'.format(outline_points),\n '{0}'.format(self.name)]\n\n # Set panelling ratio and let Xfoil make its own panels\n # This throws a fortran error if the plots are turned off with Xfoil 6.99\n commands += ['PPAR',\n 'N',\n '{0}'.format(N),\n 'T',\n '1',\n '',\n '']\n\n # Set moment reference point\n commands += ['XYCM',\n str(xycm[0]),\n str(xycm[1])]\n\n # Set viscous mode (if desired)\n if kwargs.get(\"visc\", True):\n commands += ['OPER',\n 'VISC',\n '']\n\n # Set boundary layer parameters\n commands += ['VPAR',\n 'Xtr',\n str(x_trip[0]),\n str(x_trip[1]),\n 'N',\n str(N_crit),\n '',\n '']\n\n # Initialize PACC index\n pacc_index = 0\n\n # Loop through Mach numbers\n for M in Machs:\n\n # Polar accumulation file\n file_id += 1\n pacc_file = \"xfoil_results_{0}.pacc\".format(file_id)\n pacc_files.append(pacc_file)\n\n # Set Mach, Reynolds number, iteration limit, and polar accumulation\n if kwargs.get(\"visc\", True):\n commands += ['OPER',\n 'RE',\n str(Re),\n 'MACH',\n str(M),\n 'ITER {0}'.format(max_iter),\n 'PACC',\n pacc_file,\n '']\n else:\n commands += ['OPER',\n 'MACH',\n str(M),\n 'ITER {0}'.format(max_iter),\n 'PACC',\n pacc_file,\n '']\n\n # Sweep angle of attack\n if len(alphas) == 1:\n commands.append('ALFA {0:1.6f}'.format(math.degrees(alphas[0])))\n\n else:\n # Sweep from 0 aoa up\n zero_ind = np.argmin(np.abs(alphas))\n if zero_ind != len(alphas)-1:\n for a in alphas[zero_ind:]:\n commands.append('ALFA {0:1.6f}'.format(math.degrees(a)))\n \n # Reset solver\n commands.append('INIT')\n\n # Sweep from 0 aoa down\n if zero_ind != 0:\n for a in alphas[zero_ind-1::-1]:\n commands.append('ALFA {0:1.6f}'.format(math.degrees(a)))\n \n # Reset solver\n commands.append('INIT')\n\n # End polar accumulation\n commands += ['PACC {0}'.format(pacc_index),\n '']\n pacc_index += 1\n\n # Finish commands\n commands += ['',\n 'QUIT']\n\n # Run Xfoil\n xfoil_input = '\\r'.join(commands).encode('utf-8')\n response = xfoil_process.communicate(xfoil_input)\n\n # Show output\n if show_xfoil_output:\n print(response[0].decode('utf-8'))\n if response[1] is not None:\n print(response[1].decode('utf-8'))\n\n # Clean up geometry\n os.remove(outline_points)\n\n # Read in files and store arrays\n for filename in pacc_files:\n\n # Read in file\n try:\n alpha_i, CL_i, CD_i, Cm_i, Re_i, M_i = self.read_pacc_file(filename, CD_type=kwargs.get('CD_type', 'total'))\n except FileNotFoundError:\n warnings.warn(\"Couldn't find results file {0}. Usually an indication of Xfoil crashing.\".format(filename))\n continue\n\n # Determine the Reynolds and Mach indices\n j = min(range(len(Reys)), key=lambda i: abs(Reys[i]-Re_i))\n\n k = min(range(len(Machs)), key=lambda i: abs(Machs[i]-M_i))\n\n # Loop through alphas\n i_true = 0\n for i_iter, alpha in enumerate(alpha_i):\n\n # Line up with our original independent alpha, as Xfoil does not output a non-converged result\n i_true = min(range(len(alphas)), key=lambda i: abs(alphas[i]-alpha))\n\n CL[i_true,j,k,l,m] = CL_i[i_iter]\n CD[i_true,j,k,l,m] = CD_i[i_iter]\n Cm[i_true,j,k,l,m] = Cm_i[i_iter]\n\n # Interpolate missing values\n for i, alpha in enumerate(alpha_i):\n if np.isnan(CL[i,j,k,l,m]): # Result did not converge\n\n # Mid-value\n if i != 0 and i != len(alpha_i)-1:\n weight = (alpha_i[i+1]-alpha)/(alpha_i[i+1]-alpha_i[i-1])\n CL[i,j,k,l,m] = CL[i-1,j,k,l,m]*(1-weight)+CL[i+1,j,k,l,m]*weight\n CD[i,j,k,l,m] = CD[i-1,j,k,l,m]*(1-weight)+CD[i+1,j,k,l,m]*weight\n Cm[i,j,k,l,m] = Cm[i-1,j,k,l,m]*(1-weight)+Cm[i+1,j,k,l,m]*weight\n\n # Clean up polar files\n os.remove(filename)\n\n return CL, CD, Cm\n\n\n def read_pacc_file(self, filename, CD_type='total'):\n \"\"\"Reads in and formats an Xfoil polar accumulation file.\n\n Parameters\n ----------\n filename : str\n File to read in.\n\n CD_type : str, optional\n Which drag coefficient to read in. May be 'total', 'friction', or 'pressure'.\n Defaults to 'total'.\n\n Returns\n -------\n alpha : list\n List of angles of attack from the accumulation polar.\n\n CL : list\n Coefficient of lift at each alpha.\n\n CD : list\n Coefficient of drag at each alpha.\n \n Cm : list\n Moment coefficient at each alpha.\n\n Re : float\n Reynolds number for the polar.\n\n M : float\n Mach number for the polar.\n \"\"\"\n\n # Open file\n with open(filename, 'r') as file_handle:\n\n # Read in line by line\n lines = []\n line = file_handle.readline()\n while line:\n lines.append(line)\n line = file_handle.readline()\n\n # Find Mach and Reynolds number\n mr_line = lines[8].split()\n M = float(mr_line[2])\n Re = float(''.join(mr_line[5:8]))\n\n # Collect alpha and coefficients\n alpha = []\n CL = []\n CD = []\n Cm = []\n for line in lines[12:]:\n split_line = line.split()\n alpha.append(math.radians(float(split_line[0])))\n CL.append(float(split_line[1]))\n if CD_type=='total':\n CD.append(float(split_line[2]))\n elif CD_type=='pressure':\n CD.append(float(split_line[3]))\n elif CD_type=='friction':\n CD.append(float(split_line[2])-float(split_line[3]))\n Cm.append(float(split_line[4]))\n\n # Sort in alpha\n sorted_indices = np.argsort(alpha)\n alpha = np.array(alpha)[sorted_indices]\n CL = np.array(CL)[sorted_indices]\n CD = np.array(CD)[sorted_indices]\n Cm = np.array(Cm)[sorted_indices]\n\n return alpha, CL, CD, Cm, Re, M\n\n\n def _create_filled_database(self):\n # Fills in missing values in the database to make it a consistent d-dimensional array.\n\n # Initialize storage for independent vars\n self._dof_filled = []\n for i in range(self._num_dofs):\n self._dof_filled.append([])\n\n # Gather independent vars\n for i in range(self._data.shape[0]):\n for j in range(self._num_dofs):\n\n # Check if the value is already there\n if not self._data[i,j] in self._dof_filled[j]:\n self._dof_filled[j].append(self._data[i,j])\n\n # Sort independent vars and initialize filled array\n shape = []\n for dof in self._dof_filled:\n dof.sort()\n shape.append(len(dof))\n filled_CL = np.zeros(tuple(shape))\n filled_CD = np.zeros(tuple(shape))\n filled_Cm = np.zeros(tuple(shape))\n N = filled_CL.size\n\n # Create grid of independent vars\n raw_grid = np.meshgrid(*self._dof_filled)\n\n # Parse independent vars for passing to getters\n params = {}\n for i, dof in enumerate(self._dof_db_order):\n params[dof] = raw_grid[i].flatten()\n\n # Fill in values\n filled_CL_view = filled_CL.reshape(N)\n filled_CL_view[:] = self.get_CL(**params)\n filled_CD_view = filled_CD.reshape(N)\n filled_CD_view[:] = self.get_CD(**params)\n filled_Cm_view = filled_Cm.reshape(N)\n filled_Cm_view[:] = self.get_Cm(**params)\n\n # Huh. Turns out I don't actually need this, so I'm not going to bother developing it further. But I'll keep it here in case it becomes useful\n\n\n def generate_polynomial_fit(self, **kwargs):\n \"\"\"Generates a set of multivariable polynomials using least-squares regression to approximate the database.\n Note: This airfoil must have a database already for fits to be created.\n\n Parameters\n ----------\n CL_degrees : dict or str, optional\n If dict, order of fit polynomial for the coefficient of lift for each degree of freedom,\n formatted as\n\n {\n \"\" : ,\n \"\" : ,\n ...\n }\n\n Orders must be integers. Defaults to 1 for any not specified.\n\n Can also be specified as \"auto\". In this case, the fit degrees will be determined automatically\n using the method described in Morelli, \"Global Nonlinear Aerodynamic Modeling Using\n Multivariate Orthogonal Functions,\" Journal of Aircraft, 1995. The algorithm tried to minimize\n the RMS error between the data and the prediction while also minimizing the degree of the fit. \n This will automatically determine the fit order for each degree of freedom.\n\n CD_degrees : dict, optional\n Same as CL_degrees.\n\n Cm_degrees : dict, optional\n Same as CL_degrees.\n\n CL_kwargs : dict, optional\n keyword arguments sent to the CL polynomial fit function\n \n When CL_degrees is specified as \"auto\" then CL_kwargs can be\n \n \"max_order\" : int, optional\n gives the max order of polynomial for any one of the independent varialbes\n to try. Defaults to 6.\n \"tol\" : float, optional\n Gives the cut-off value for any polynomial coefficient to not be included\n in the final results. If a coefficient has an absolute value below tol,\n it won't be included. Defaults to 1e-12.\n \"sigma\" : float, optional\n value used to determine the trade off between how good of a fit to perform\n and how many terms to keep. Defaults to None, which causes the function to\n calculate sigma automatically using the mean squared of the difference of\n the independent variable values with respect to the mean independent\n variable value of the dataset\n \"sigma_multiplier\" : float, optional\n term multiplied onto sigma to change it's value. Allows using a multiple\n of the automatically determined sigma value. Defaults to 1.\n \n Otherwise CL_kwargs could be\n \n \"interaction\" : boolean, optional\n value with default set to True. This variable determines whether or not\n interaction terms are included in the fit function. If set to True,\n interaction terms up the max order for each independent variable are\n included, i.e. if Nvec = [3,2] then the highest interaction term included\n is x_1^3*x_2^2. Specific interaction terms can be omitted using the\n constraints input\n \"sym\" : list, optional\n Defaults to an empty list. If used, the length should be V and each element\n should contain a boolean, True or False. The ith element determines if the\n ith independent variable is symmetric either even or odd, which is\n determined by the order given in Nvec. This will also remove the\n cooresponding interaction terms if they are enabled.\n \"sym_same\" : list, optional\n Defaults as an empty list. If used, the entries in the list should be\n tuples with two integers. The integers represent the independent variables\n that the \"same\" symmetry condition will be applied. The \"same\" symmetry\n ensures all interaction terms with exponents of the two independent\n variables that are either odd-odd or even-even to be forced to zero\n \"sym_diff\" : = list, optional\n Defaults as an empty list. Similar to \"sym_same\" except it enforces the\n \"diff\" symmetry condition which ensures all interaction terms with exponents\n of the two independent variables that are either odd-even or even-odd to be\n forced to zero\n \"zeroConstraints\" : list, optional\n Defaults as an empty list. Entries in the list contain integer tuples of\n length V. The integer values represent the powers of the independent variables\n whose coefficient will be forced to 0 before the best fit calculations are\n performed, allowing the user to omit specific interaction terms or regular\n polynomial terms\n \"constraints\" : list, optional\n Defaults to an empty list. Entries in the list contain tuples of length 2.\n The first entry is a list of integers that represent the powers of the\n independent variables whose coefficient will then be forced to be equal to the\n second entry in the tuple, which should be a float.\n \"percent\" : boolean, optional\n Default set to False. When set to True the least squares is performed on the\n percent error squared. This option should not be used if y contains any zero\n or near zero values, as this might cause a divide by zero error.\n \"weighting\" : function, optional\n Defaults to None. If given, weighting should be a function that takes as\n arguments x, y, and p where x and y are the independent and dependent\n variables defined above and p is the index representing a certain data point.\n weighting should return a 'weighting factor' that determines how important\n that datapoint is. Returning a '1' weights the datapoint normally.\n \n CD_kwargs : dict, optional\n Same as CL_kwargs\n\n Cm_kwargs : dict, optional\n Same as CL_kwargs\n\n update_type : bool, optional\n Whether to update the airfoil to use the newly computed polynomial fits for calculations. Defaults to True.\n\n verbose : bool, optional\n \"\"\"\n\n # Check for database\n if not hasattr(self, \"_data\"):\n raise RuntimeError(\"No database found! Please generate or import a database before trying to create polynomial fits.\")\n\n # Determine what the maximum fit order is for autoPolyFit\n CL_degrees = kwargs.pop(\"CL_degrees\", {})\n CD_degrees = kwargs.pop(\"CD_degrees\", {})\n Cm_degrees = kwargs.pop(\"Cm_degrees\", {})\n CL_kwargs = kwargs.pop(\"CL_kwargs\", {})\n CD_kwargs = kwargs.pop(\"CD_kwargs\", {})\n Cm_kwargs = kwargs.pop(\"Cm_kwargs\", {})\n verbose = kwargs.pop('verbose', True)\n if verbose: print('Generating Polynomial Fits for airfoil {}'.format(self.name))\n CL_kwargs['verbose'] = verbose\n CD_kwargs['verbose'] = verbose\n Cm_kwargs['verbose'] = verbose\n\n # CL\n if verbose: print('Performing CL curve fit')\n if CL_degrees==\"auto\":\n self._CL_poly_coefs, self._CL_degrees, self._CLfit_R2 = autoPolyFit(self._data[:,:self._num_dofs], self._data[:, self._num_dofs], **CL_kwargs)\n\n elif isinstance(CL_degrees, dict):\n\n # Sort fit degrees\n self._CL_degrees = []\n for dof in self._dof_db_order:\n self._CL_degrees.append(CL_degrees.get(dof, 1))\n\n # Generate\n self._CL_poly_coefs, self._CLfit_R2 = multivariablePolynomialFit(self._CL_degrees, self._data[:,:self._num_dofs], self._data[:, self._num_dofs], **CL_kwargs)\n\n else:\n raise IOError(\"Fit degree specification must be 'auto' or type(dict). Got {0} type {1}.\".format(CL_degrees, type(CL_degrees)))\n \n # CD\n if verbose: print('Performing CD curve fit')\n if CD_degrees==\"auto\":\n self._CD_poly_coefs, self._CD_degrees, self._CDfit_R2 = autoPolyFit(self._data[:,:self._num_dofs], self._data[:,self._num_dofs+1], **CD_kwargs)\n\n elif isinstance(CL_degrees, dict):\n\n # Sort fit degrees\n self._CD_degrees = []\n for dof in self._dof_db_order:\n self._CD_degrees.append(CD_degrees.get(dof, 1))\n\n # Generate\n self._CD_poly_coefs, self._CDfit_R2 = multivariablePolynomialFit(self._CD_degrees, self._data[:,:self._num_dofs], self._data[:,self._num_dofs+1], **CD_kwargs)\n\n else:\n raise IOError(\"Fit degree specification must be 'auto' or type(dict). Got {0} type {1}.\".format(CL_degrees, type(CL_degrees)))\n \n # Cm\n if verbose: print('Performing Cm curve fit')\n if Cm_degrees==\"auto\":\n self._Cm_poly_coefs, self._Cm_degrees, self._Cmfit_R2 = autoPolyFit(self._data[:,:self._num_dofs], self._data[:,self._num_dofs+2], **Cm_kwargs)\n\n elif isinstance(Cm_degrees, dict):\n\n # Sort fit degrees\n self._Cm_degrees = []\n for dof in self._dof_db_order:\n self._Cm_degrees.append(Cm_degrees.get(dof, 1))\n\n # Generate polynomial fit\n self._Cm_poly_coefs, self._Cmfit_R2 = multivariablePolynomialFit(self._Cm_degrees, self._data[:,:self._num_dofs], self._data[:, self._num_dofs+2], **Cm_kwargs)\n\n # Store limits\n self._dof_limits = []\n for i in range(self._num_dofs):\n self._dof_limits.append([np.min(self._data[:,i]), np.max(self._data[:,i])])\n\n # Update type\n if kwargs.get(\"update_type\", True):\n self.set_type(\"poly_fit\")\n \n self._CLfit_RMS, self._CLfit_RMSN = multivariableRMS(self._data[:,:self._num_dofs], self._data[:,self._num_dofs], self._CL_poly_coefs, self._CL_degrees, verbose=verbose)\n self._CDfit_RMS, self._CDfit_RMSN = multivariableRMS(self._data[:,:self._num_dofs], self._data[:,self._num_dofs+1], self._CD_poly_coefs, self._CD_degrees, verbose=verbose)\n self._Cmfit_RMS, self._Cmfit_RMSN = multivariableRMS(self._data[:,:self._num_dofs], self._data[:,self._num_dofs+2], self._Cm_poly_coefs, self._Cm_degrees, verbose=verbose)\n \n if verbose:\n print('\\nCL fits\\n'+'='*20)\n print('R^2 : {}'.format(self._CLfit_R2))\n print('RMS : {}'.format(self._CLfit_RMS))\n print('RMSN: {}\\n'.format(self._CLfit_RMSN))\n print('CD fits\\n'+'='*20)\n print('R^2 : {}'.format(self._CDfit_R2))\n print('RMS : {}'.format(self._CDfit_RMS))\n print('RMSN: {}\\n'.format(self._CDfit_RMSN))\n print('Cm fits\\n'+'='*20)\n print('R^2 : {}'.format(self._Cmfit_R2))\n print('RMS : {}'.format(self._Cmfit_RMS))\n print('RMSN: {}\\n'.format(self._Cmfit_RMSN))\n\n\n def export_polynomial_fits(self, **kwargs):\n \"\"\"Save the polynomial fit to a JSON object.\n\n Parameters\n ----------\n filename : str\n JSON object to write polynomial fit data to.\n\n write_limits : bool, optional\n Whether to limit the polynomial fits based on the original range of data.\n Defaults to True.\n\n \"\"\"\n\n # Get filename\n filename = kwargs.get(\"filename\")\n\n # Create export dictionary\n export = {}\n export[\"tag\"] = \"Polynomial fit to database for {0} airfoil.\".format(self.name)\n export[\"degrees_of_freedom\"] = self._dof_db_order\n if kwargs.get(\"write_limits\"):\n export[\"limits\"] = self._dof_limits\n export[\"defaults\"] = self._dof_defaults\n export[\"fit_degrees\"] = {}\n export[\"fit_degrees\"][\"CL\"] = self._CL_degrees\n export[\"fit_degrees\"][\"CD\"] = self._CD_degrees\n export[\"fit_degrees\"][\"Cm\"] = self._Cm_degrees\n export['fit_error'] = {}\n export['fit_error']['CL'] = {}\n export['fit_error']['CL']['R^2'] = self._CLfit_R2\n export['fit_error']['CL']['RMS'] = self._CLfit_RMS\n export['fit_error']['CL']['RMSN'] = self._CLfit_RMSN\n export['fit_error']['CD'] = {}\n export['fit_error']['CD']['R^2'] = self._CDfit_R2\n export['fit_error']['CD']['RMS'] = self._CDfit_RMS\n export['fit_error']['CD']['RMSN'] = self._CDfit_RMSN\n export['fit_error']['Cm'] = {}\n export['fit_error']['Cm']['R^2'] = self._Cmfit_R2\n export['fit_error']['Cm']['RMS'] = self._Cmfit_RMS\n export['fit_error']['Cm']['RMSN'] = self._Cmfit_RMSN\n export[\"fit_coefs\"] = {}\n export[\"fit_coefs\"][\"CL\"] = list(self._CL_poly_coefs)\n export[\"fit_coefs\"][\"CD\"] = list(self._CD_poly_coefs)\n export[\"fit_coefs\"][\"Cm\"] = list(self._Cm_poly_coefs)\n\n # Export data\n with open(filename, 'w') as export_file_handle:\n json.dump(export, export_file_handle, indent=4)\n\n\n def import_polynomial_fits(self, **kwargs):\n \"\"\"Read in polynomial fit data from a JSON object.\n\n Parameters\n ----------\n filename : str\n JSON object to read polynomial fit data from.\n\n update_type : bool, optional\n Whether to update the airfoil to use the newly imported polynomial fits for calculations. Defaults to True.\n\n \"\"\"\n\n # Get filename\n filename = kwargs.get(\"filename\")\n\n # Read in data\n with open(filename, 'r') as import_file_handle:\n input_dict = json.load(import_file_handle)\n\n # Parse input dict\n self._dof_db_order = input_dict[\"degrees_of_freedom\"]\n self._num_dofs = len(self._dof_db_order)\n self._dof_limits = input_dict.get(\"limits\", [[-np.inf, np.inf]]*self._num_dofs)\n self._CL_degrees = input_dict[\"fit_degrees\"][\"CL\"]\n self._CD_degrees = input_dict[\"fit_degrees\"][\"CD\"]\n self._Cm_degrees = input_dict[\"fit_degrees\"][\"Cm\"]\n self._CL_poly_coefs = np.array(input_dict[\"fit_coefs\"][\"CL\"])\n self._CD_poly_coefs = np.array(input_dict[\"fit_coefs\"][\"CD\"])\n self._Cm_poly_coefs = np.array(input_dict[\"fit_coefs\"][\"Cm\"])\n if isinstance(input_dict.get('fit_error', None), dict):\n if isinstance(input_dict['fit_error'].get('CL', None), dict):\n self._CLfit_R2 = input_dict['fit_error']['CL'].get('R^2', None)\n self._CLfit_RMS = input_dict['fit_error']['CL'].get('RMS', None)\n self._CLfit_RMSN = input_dict['fit_error']['CL'].get('RMSN', None)\n if isinstance(input_dict['fit_error'].get('CD', None), dict):\n self._CDfit_R2 = input_dict['fit_error']['CD'].get('R^2', None)\n self._CDfit_RMS = input_dict['fit_error']['CD'].get('RMS', None)\n self._CDfit_RMSN = input_dict['fit_error']['CD'].get('RMSN', None)\n if isinstance(input_dict['fit_error'].get('Cm', None), dict):\n self._Cmfit_R2 = input_dict['fit_error']['Cm'].get('R^2', None)\n self._Cmfit_RMS = input_dict['fit_error']['Cm'].get('RMS', None)\n self._Cmfit_RMSN = input_dict['fit_error']['Cm'].get('RMSN', None)\n\n # Update type\n if kwargs.get(\"update_type\", True):\n self.set_type(\"poly_fit\")\n\n\n def _get_polynomial_data(self, coef_index, **kwargs):\n # Determines the value of the given coefficient from the polynomial fit\n # coef_index | coef\n # 0 | CL\n # 1 | CD\n # 2 | Cm\n\n # Stack up independent vars\n ind_vars = []\n N = 1\n for dof in self._dof_db_order:\n\n # Get independent variable values\n ind_var = kwargs.get(dof, self._dof_defaults[dof])\n \n # Check for array of size 1\n if not np.isscalar(ind_var) and len(ind_var) == 1:\n ind_vars.append(np.asscalar(ind_var))\n else:\n ind_vars.append(ind_var)\n\n # Update max size\n if not np.isscalar(ind_var):\n N = max(N, len(ind_var))\n\n # Fill any values\n if N > 1:\n for i, ind_var in enumerate(ind_vars):\n if np.isscalar(ind_var):\n ind_vars[i] = np.full(N, ind_var)\n\n # Setup input matrix\n if N == 1:\n x = np.array(ind_vars)[:,np.newaxis]\n else:\n x = np.array(ind_vars)\n\n # Get data\n if coef_index == 0:\n coef = multivariablePolynomialFunction(self._CL_poly_coefs, self._CL_degrees, x)\n elif coef_index == 1:\n coef = multivariablePolynomialFunction(self._CD_poly_coefs, self._CD_degrees, x)\n elif coef_index == 2:\n coef = multivariablePolynomialFunction(self._Cm_poly_coefs, self._Cm_degrees, x)\n\n # Check limits\n if self._raise_poly_bounds_error:\n for i in range(N):\n for j in range(self._num_dofs):\n if x[j,i] > self._dof_limits[j][1] or x[j,i] < self._dof_limits[j][0]:\n coef[i] = np.nan\n\n # Check for going out of bounds\n if np.isnan(coef).any():\n raise PolyFitBoundsError(self.name, np.argwhere(np.isnan(coef)).flatten(), kwargs)\n\n return coef\n\n\n def generate_linear_model(self, **kwargs):\n \"\"\"Creates a linearized model of the airfoil coefficients in alpha. Pulls from the\n database or poly_fit information to generate this model. Cannot be used on a type\n \"linear\" airfoil.\n \n linear_limits : list, optional\n Limits in alpha for which the behavior of the airfoil can be considered linear. If not\n given, the user will be prompted to graphically select this region. Given in degrees.\n\n update_type : bool, optional\n Whether to change the type of the airfoil to \"linear\" once the model is determined.\n Defaults to True.\n\n plot_model : bool, optional\n Whether to display a polar of the linear region determined. Defaults to False.\n\n Rey : float, optional\n Reynolds number at which to evaluate the model.\n\n Mach : float, optional\n Mach number at which to evaluate the model.\n \"\"\"\n\n # Check for correct type\n if self._type == \"linear\":\n raise RuntimeError(\"generate_linear_model() cannot be called on a 'linear' type airfoil.\")\n\n # Determine range of alpha\n linear_limits = kwargs.get(\"linear_limits\", None)\n if linear_limits is None:\n alpha = np.linspace(-20.0, 20.0, 30)\n else:\n alpha = np.linspace(linear_limits[0], linear_limits[1], 30)\n\n # Generate dataset\n CL = np.zeros(30)\n Cm = np.zeros(30)\n CD = np.zeros(30)\n for i, a in enumerate(alpha):\n try:\n CL[i] = self.get_CL(alpha=np.radians(a), **kwargs)\n Cm[i] = self.get_Cm(alpha=np.radians(a), **kwargs)\n CD[i] = self.get_CD(alpha=np.radians(a), **kwargs)\n except DatabaseBoundsError:\n continue\n\n # Plot dataset for user to select linear region\n if linear_limits is None:\n\n # Define picker\n self._selected_ind = []\n def on_pick(event):\n\n # Get index\n i = int(event.ind[0])\n self._selected_ind.append(i)\n\n # Add x\n fig.axes[0].plot(alpha[i], CL[i], 'rx')\n\n # Check if we have enough\n if len(self._selected_ind) >= 2:\n plt.close(fig)\n\n # Display\n plt.ion()\n fig, ax = plt.subplots()\n ax.plot(alpha, CL, 'bo', picker=3)\n plt.xlabel(\"Alpha [deg]\")\n plt.ylabel(\"Lift Coefficient\")\n plt.title(\"To generate a linear model, select the lower and upper limits of the linear region.\")\n fig.canvas.mpl_connect('pick_event', on_pick)\n plt.show(block=True)\n plt.ioff()\n\n # Trim arrays\n self._selected_ind = sorted(self._selected_ind)\n if len(self._selected_ind) != 2:\n raise RuntimeError(\"No points were selected to determine the linear region.\")\n alpha = np.radians(alpha[self._selected_ind[0]:self._selected_ind[1]+1])\n CL = CL[self._selected_ind[0]:self._selected_ind[1]+1]\n Cm = Cm[self._selected_ind[0]:self._selected_ind[1]+1]\n CD = CD[self._selected_ind[0]:self._selected_ind[1]+1]\n\n else:\n alpha = np.radians(alpha)\n\n # Get CL model\n coef_array = np.polyfit(alpha, CL, 1)\n self._aL0 = coef_array[1]\n self._CLa = coef_array[0]\n self._CL_max = np.max(CL)\n\n # Get Cm model\n coef_array = np.polyfit(alpha, Cm, 1)\n self._Cma = coef_array[0]\n self._CmL0 = self._Cma*self._aL0+coef_array[1]\n\n # Get CD model\n coef_array = np.polyfit(CL, CD, 2)\n self._CD0 = coef_array[2]\n self._CD1 = coef_array[1]\n self._CD2 = coef_array[0]\n\n # Plot model within linear region\n if kwargs.get(\"plot_model\", False):\n\n # CL\n plt.close('all')\n fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3)\n fig.suptitle(\"Linear Model for {0}\".format(self.name))\n ax0.set_title(\"CL\")\n ax0.plot(alpha, CL, \"gx\", label=\"Data\")\n ax0.plot(alpha, alpha*self._CLa+self._aL0, \"g-\", label=\"Model\")\n ax0.legend()\n ax0.set_xlabel(\"Angle of Attack [rad]\")\n ax0.set_ylabel(\"Lift Coefficient\")\n\n # Cm\n ax1.set_title(\"Cm\")\n ax1.plot(alpha, Cm, 'bx', label=\"Data\")\n ax1.plot(alpha, (alpha-self._aL0)*self._Cma+self._CmL0, \"b-\", label=\"Model\")\n ax1.legend()\n ax1.set_xlabel(\"Angle of Attack [rad]\")\n ax1.set_ylabel(\"Moment Coefficient\")\n\n # CD\n ax2.set_title(\"CD\")\n ax2.plot(CL, CD, 'rx', label=\"Data\")\n ax2.plot(CL, self._CD0+self._CD1*CL+self._CD2*CL*CL, 'r-', label=\"Model\")\n ax2.legend()\n ax2.set_xlabel(\"Lift Coefficient\")\n ax2.set_ylabel(\"Drag Coefficient\")\n plt.show()\n\n # Update type\n if kwargs.get(\"update_type\", True):\n self._type = \"linear\"\n\n\n def export_linear_model(self, **kwargs):\n \"\"\"Exports the linear coefficients used to predict the behavior of the airfoil.\n\n Parameters\n ----------\n filename : str\n JSON file to export the model data to.\n \"\"\"\n\n # Check there is a model to export\n if not hasattr(self, \"_aL0\"):\n raise RuntimeError(\"A linear model for {0} could not be exported because it has none.\".format(self.name))\n\n # Parse coefficients\n model_dict = {\n \"aL0\" : self._aL0,\n \"CLa\" : self._CLa,\n \"CmL0\" : self._CmL0,\n \"Cma\" : self._Cma,\n \"CD0\" : self._CD0,\n \"CD1\" : self._CD1,\n \"CD2\" : self._CD2,\n \"CL_max\" : self._CL_max\n }\n\n # Export\n filename = kwargs.get(\"filename\")\n with open(filename, 'w') as export_file_handle:\n json.dump(model_dict, export_file_handle, indent=4)\n","sub_path":"airfoil_db/airfoil.py","file_name":"airfoil.py","file_ext":"py","file_size_in_byte":115083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"566765188","text":"# coding=utf-8\nimport atexit\nimport time\n\nimport os\nimport random\nimport persistent\nimport transaction\nfrom BTrees.OOBTree import OOBTree\nfrom zentropi import (\n Agent,\n on_message,\n)\nfrom ZODB import DB, FileStorage\n\n\nclass Todo(persistent.Persistent):\n def __init__(self, items=None):\n self.items = items or []\n\n def add_item(self, item: str):\n self.items.append(item)\n self._p_changed = True\n transaction.commit()\n return len(self.items) - 1\n\n def mark_done(self, index: int):\n del self.items[index]\n self._p_changed = True\n transaction.commit()\n\n\nclass Activity(persistent.Persistent):\n def __init__(self):\n self.stack = []\n self.log = {} # type: dict\n\n def track_activity(self, activity):\n self.log.update({time.time(): activity})\n if self.stack[-1] == activity:\n # track only changes in the stack\n return\n self.stack.append(activity)\n\n\nDB_CLASSES = {\n 'activity': Activity,\n 'location': OOBTree,\n 'notification': OOBTree,\n 'todo': Todo,\n}\n\nFAQS = \"\"\"\\\nwhat can you do\nwhat functions do you have\n---\nI can track your TODOs, bookmark & search your favorite webpages \nor you can teach me how to operate other agents in your home.\n\"\"\"\nFAQS = FAQS.strip().split('===')\nFAQS = [faq.strip().split('---') for faq in FAQS]\nFAQS = [[q.strip().split('\\n'), a.strip().replace('\\n', ' ').replace(' ', ' ')] for (q, a) in FAQS]\n\n\ndef format_list(lst):\n return '\\n'.join(['{}: {}'.format(i, v) for i, v in enumerate(lst)])\n\n\nclass Maya(Agent):\n def __init__(self, name=None):\n super().__init__(name=name)\n self.zodb_connection = None\n self.zodb_db = None\n self.zodb_storage = None\n self.states.activity = ''\n self.setup_faqs()\n self.db = self.load_db('~/.mayaisabot/mayaisabot.fs')\n\n def load_db(self, file_name):\n file_name = os.path.expanduser(file_name)\n print('*** Loading db {!r}'.format(file_name))\n storage = FileStorage.FileStorage(file_name)\n db = DB(storage)\n conn = db.open()\n root = conn.root()\n for table_name, table_class in DB_CLASSES.items():\n if not hasattr(root, table_name):\n setattr(root, table_name, table_class())\n print('*** Created table {!r}'.format(table_name))\n self.zodb_connection = conn\n self.zodb_db = db\n self.zodb_storage = storage\n atexit.register(self.save_db)\n return root\n\n def save_db(self):\n print('*** Saving db...')\n self.zodb_connection.close()\n self.zodb_db.close()\n self.zodb_storage.close()\n\n def setup_faqs(self):\n for questions, answer in FAQS:\n for q in questions:\n print('***', q, answer)\n self.on_message(q, fuzzy=True)(lambda _: answer)\n\n @on_message('hey')\n @on_message('hi')\n @on_message('hello')\n @on_message('hello {}', parse=True)\n async def greeting(self, message):\n if message.source == self.name:\n return\n return '{}! What can I do for you?'.format(\n random.choice(\"Hey, Hi, Hello, Hey there\"\n \"\".split(',')).strip())\n\n @on_message('todo {item}', parse=True)\n def todo_add(self, message):\n item = message.data.item\n index = self.db.todo.add_item(item)\n return 'Saved {}: {}'.format(index, item)\n\n @on_message('todos')\n def todo_list(self, message):\n items = self.db.todo.items\n return format_list(items) or 'No todo items.'\n\n @on_message('done {index}', parse=True)\n def todo_done(self, message):\n try:\n index = int(message.data.index)\n except ValueError:\n return 'Expected an integer, got: {!r}'.format(message.data.index)\n if 0 <= index < len(self.db.todo.items):\n item = self.db.todo.items[index]\n self.db.todo.mark_done(index)\n return '{}{!r} is done!'.format(\n random.choice(['Whoohoo! ', 'Super! ', '', '', '']),\n item,\n )\n return 'Expected 0 <= index < {}, got: {}'.format(\n len(self.db.todo.items), index)\n\n @on_message('tg {chat_id} {text}', parse=True)\n def send_tg_message(self, message):\n text = message.data.text\n chat_id = message.data.chat_id\n if not text:\n return\n self.emit('send_telegram_message', data={'text': text, 'chat_id': chat_id})\n","sub_path":"src/mayaisabot/maya.py","file_name":"maya.py","file_ext":"py","file_size_in_byte":4529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"36109877","text":"from rw_reg_lpgbt import *\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.colors as mcolors\r\nfrom matplotlib import cm\r\nimport numpy as np\r\nimport os, sys, glob\r\nimport argparse\r\n\r\nplt.rcParams.update({\"font.size\": 22}) # Increase font size\r\n\r\ndef getCalData(calib_path):\r\n slope_adc = {}\r\n intercept_adc = {}\r\n\r\n if os.path.isfile(calib_path):\r\n calib_file = open(calib_path)\r\n for line in calib_file.readlines():\r\n if \"vfat\" in line:\r\n continue\r\n vfat = int(line.split(\";\")[0])\r\n slope_adc[vfat] = float(line.split(\";\")[2])\r\n intercept_adc[vfat] = float(line.split(\";\")[3])\r\n calib_file.close()\r\n\r\n return slope_adc, intercept_adc\r\n\r\ndef DACToCharge(dac, slope_adc, intercept_adc, current_pulse_sf, vfat, mode):\r\n \"\"\"\r\n Slope and intercept for all VFATs from the CAL_DAC cal file.\r\n If cal file not present, use default values here are a rough average of cal data.\r\n \"\"\"\r\n\r\n slope = -9999\r\n intercept = -9999\r\n\r\n if vfat in slope_adc:\r\n if slope_adc[vfat]!=-9999 and intercept_adc[vfat]!=-9999:\r\n if mode==\"voltage\":\r\n slope = slope_adc[vfat]\r\n intercept = intercept_adc[vfat]\r\n elif mode==\"current\":\r\n slope = abs(slope_adc[vfat])\r\n intercept = 0\r\n if slope==-9999 or intercept==-9999: # use average values\r\n print (Colors.YELLOW + \"ADC Cal data not present for VFAT%d, using avergae values\"%vfat + Colors.ENDC)\r\n if mode==\"voltage\":\r\n slope = -0.22 # fC/DAC\r\n intercept = 56.1 # fC\r\n elif mode==\"current\":\r\n slope = 0.22 # fC/DAC\r\n intercept = 0\r\n charge = (dac * slope) + intercept\r\n if mode == \"current\":\r\n charge = charge * current_pulse_sf\r\n return charge\r\n\r\nif __name__ == \"__main__\":\r\n\r\n # Parsing arguments\r\n parser = argparse.ArgumentParser(description=\"Plotting VFAT SCurve\")\r\n parser.add_argument(\"-f\", \"--filename\", action=\"store\", dest=\"filename\", help=\"SCurve result filename\")\r\n #parser.add_argument(\"-t\", \"--type\", action=\"store\", dest=\"type\", help=\"type = daq or sbit\")\r\n parser.add_argument(\"-m\", \"--mode\", action=\"store\", dest=\"mode\", help=\"mode = voltage or current\")\r\n parser.add_argument(\"-c\", \"--channels\", action=\"store\", nargs=\"+\", dest=\"channels\", help=\"Channels to plot for each VFAT\")\r\n parser.add_argument(\"-fs\", \"--cal_fs\", action=\"store\", dest=\"cal_fs\", help=\"cal_fs = value of CAL_FS used (0-3), default = taken from VFAT config text file\")\r\n args = parser.parse_args()\r\n\r\n if args.channels is None:\r\n print(Colors.YELLOW + \"Enter channel list to plot SCurves\" + Colors.ENDC)\r\n sys.exit()\r\n\r\n if args.mode not in [\"voltage\", \"current\"]:\r\n print(Colors.YELLOW + \"Mode can only be voltage or current\" + Colors.ENDC)\r\n sys.exit()\r\n\r\n #if args.type not in [\"daq\", \"sbit\"]:\r\n # print(Colors.YELLOW + \"Type can only be daq or sbit\" + Colors.ENDC)\r\n # sys.exit()\r\n\r\n directoryName = args.filename.split(\".txt\")[0]\r\n plot_filename_prefix = (directoryName.split(\"/\"))[2]\r\n oh = plot_filename_prefix.split(\"_vfat\")[0]\r\n file = open(args.filename)\r\n\r\n try:\r\n os.makedirs(directoryName) # create directory for scurve analysis results\r\n except FileExistsError: # skip if directory already exists\r\n pass\r\n\r\n cal_fs = -9999\r\n if args.cal_fs is None:\r\n vfat_config_path = \"vfat_data/\"+oh+\"_vfatConfig.txt\"\r\n if not os.path.isfile(vfat_config_path):\r\n print(Colors.YELLOW + \"VFAT config file not present, provide CAL_FS used\" + Colors.ENDC)\r\n sys.exit()\r\n file_config = open(vfat_config_path)\r\n for line in file_config.readlines():\r\n if \"CFG_CAL_FS\" in line:\r\n cal_fs = int(line.split()[1])\r\n break\r\n file_config.close()\r\n else:\r\n cal_fs = int(args.cal_fs)\r\n if cal_fs > 3:\r\n print(Colors.YELLOW + \"CAL_FS can be only 0-3\" + Colors.ENDC)\r\n sys.exit()\r\n current_pulse_sf = -9999\r\n if cal_fs == 0:\r\n current_pulse_sf = 0.25\r\n elif cal_fs == 1:\r\n current_pulse_sf = 0.50\r\n elif cal_fs == 2:\r\n current_pulse_sf = 0.75\r\n elif cal_fs == 3:\r\n current_pulse_sf = 1.00\r\n if current_pulse_sf == -9999:\r\n print(Colors.YELLOW + \"invalid Current Pulse SF\" + Colors.ENDC)\r\n sys.exit()\r\n\r\n calib_path = \"vfat_data/vfat_calib_data/\"+oh+\"_vfat_calib_info_calDac.txt\"\r\n slope_adc, intercept_adc = getCalData(calib_path)\r\n\r\n scurve_result = {}\r\n for line in file.readlines():\r\n if \"vfat\" in line:\r\n continue\r\n\r\n vfat = int(line.split()[0])\r\n channel = int(line.split()[1])\r\n charge = int(line.split()[2])\r\n fired = int(line.split()[3])\r\n events = int(line.split()[4])\r\n\r\n #if args.mode == \"voltage\":\r\n # charge = 255 - charge\r\n charge = DACToCharge(charge, slope_adc, intercept_adc, current_pulse_sf, vfat, args.mode) # convert to fC\r\n\r\n if vfat not in scurve_result:\r\n scurve_result[vfat] = {}\r\n if channel not in scurve_result[vfat]:\r\n scurve_result[vfat][channel] = {}\r\n if fired == -9999 or events == -9999 or events == 0:\r\n scurve_result[vfat][channel][charge] = 0\r\n else:\r\n scurve_result[vfat][channel][charge] = float(fired)/float(events)\r\n file.close()\r\n\r\n channelNum = np.arange(0, 128, 1)\r\n chargeVals = np.arange(0, 256, 1)\r\n\r\n numVfats = len(scurve_result.keys())\r\n if numVfats == 1:\r\n fig1, ax1 = plt.subplots(1, numVfats, figsize=(numVfats*10,10))\r\n cf1 = 0\r\n cbar1 = 0\r\n elif numVfats <= 3:\r\n fig1, ax1 = plt.subplots(1, numVfats, figsize=(numVfats*10,10))\r\n cf1 ={}\r\n cbar1 ={}\r\n elif numVfats <= 6:\r\n fig1, ax1 = plt.subplots(2, 3, figsize=(30,20))\r\n cf1 ={}\r\n cbar1 ={}\r\n elif numVfats <= 12:\r\n fig1, ax1 = plt.subplots(2, 6, figsize=(60,20))\r\n cf1 ={}\r\n cbar1 ={}\r\n elif numVfats <= 18:\r\n fig1, ax1 = plt.subplots(3, 6, figsize=(60,30))\r\n cf1 ={}\r\n cbar1 ={}\r\n elif numVfats <= 24:\r\n fig1, ax1 = plt.subplots(4, 6, figsize=(60,40))\r\n cf1 ={}\r\n cbar1 ={}\r\n\r\n vfatCnt0 = 0\r\n for vfat in scurve_result:\r\n fig, axs = plt.subplots(figsize=(10,10))\r\n axs.set_xlabel(\"Channel number\", loc = 'right')\r\n axs.set_ylabel(\"Injected charge (fC)\", loc = 'top')\r\n #axs.xlim(0,128)\r\n #axs.ylim(0,256)\r\n\r\n plot_data = []\r\n plot_data_x = []\r\n plot_data_y = []\r\n for dac in range(0,256):\r\n charge = DACToCharge(dac, slope_adc, intercept_adc, current_pulse_sf, vfat, args.mode)\r\n plot_data_y.append(charge)\r\n data = []\r\n for channel in range(0,128):\r\n if channel not in scurve_result[vfat]:\r\n data.append(0)\r\n elif charge not in scurve_result[vfat][channel]:\r\n data.append(0)\r\n else:\r\n data.append(scurve_result[vfat][channel][charge])\r\n plot_data.append(data)\r\n for channel in range(0,128):\r\n plot_data_x.append(channel)\r\n\r\n cf = plt.pcolormesh(plot_data_x, plot_data_y, plot_data, cmap=cm.ocean_r, shading=\"nearest\")\r\n #chargeVals_mod = chargeVals\r\n #for i in range(0,len(chargeVals_mod)):\r\n # chargeVals_mod[i] = DACToCharge(chargeVals_mod[i], slope_adc, intercept_adc, current_pulse_sf, vfat, args.mode)\r\n #plot = axs.imshow(plot_data, extent=[min(channelNum), max(channelNum), min(chargeVals_mod), max(chargeVals_mod)], origin=\"lower\", cmap=cm.ocean_r,interpolation=\"nearest\", aspect=\"auto\")\r\n cbar = fig.colorbar(cf, ax=axs, pad=0.01)\r\n cbar.set_label(\"Fired events / total events\", loc = 'top')\r\n axs.set_title(\"VFAT%02d\"%vfat)\r\n axs.set_xticks(np.arange(min(channelNum), max(channelNum)+1, 20))\r\n axs.text(-0.12, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=axs.transAxes)\r\n axs.text(0.02, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=axs.transAxes)\r\n fig.tight_layout()\r\n fig.savefig((directoryName+\"/scurve2Dhist_\"+oh+\"_VFAT%02d.pdf\")%vfat)\r\n plt.close(fig)\r\n\r\n if numVfats == 1:\r\n ax1.set_xlabel(\"Channel number\", loc = 'right')\r\n ax1.set_ylabel(\"Injected charge (fC)\", loc = 'top')\r\n ax1.set_title(\"VFAT%02d\"%vfat)\r\n cf1 = ax1.pcolormesh(plot_data_x, plot_data_y, plot_data, cmap=cm.ocean_r, shading=\"nearest\")\r\n cbar1 = fig1.colorbar(cf1, ax=ax1, pad=0.01)\r\n cbar1.set_label(\"Fired events / total events\", loc = 'top')\r\n ax1.set_xticks(np.arange(min(channelNum), max(channelNum)+1, 20))\r\n ax1.text(-0.12, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax1.transAxes)\r\n ax1.text(0.02, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax1.transAxes)\r\n elif numVfats <= 3:\r\n ax1[vfatCnt0].set_xlabel(\"Channel number\", loc = 'right')\r\n ax1[vfatCnt0].set_ylabel(\"Injected charge (fC)\", loc = 'top')\r\n ax1[vfatCnt0].set_title(\"VFAT%02d\"%vfat)\r\n cf1[vfatCnt0] = ax1[vfatCnt0].pcolormesh(plot_data_x, plot_data_y, plot_data, cmap=cm.ocean_r, shading=\"nearest\")\r\n cbar1[vfatCnt0] = fig1.colorbar(cf1[vfatCnt0], ax=ax1[vfatCnt0], pad=0.01)\r\n cbar1[vfatCnt0].set_label(\"Fired events / total events\", loc = 'top')\r\n ax1[vfatCnt0].set_xticks(np.arange(min(channelNum), max(channelNum)+1, 20))\r\n ax1[vfatCnt0].text(-0.12, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax1[vfatCnt0].transAxes)\r\n ax1[vfatCnt0].text(0.02, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax1[vfatCnt0].transAxes)\r\n elif numVfats <= 6:\r\n ax1[int(vfatCnt0/3), vfatCnt0%3].set_xlabel(\"Channel number\", loc = 'right')\r\n ax1[int(vfatCnt0/3), vfatCnt0%3].set_ylabel(\"Injected charge (fC)\", loc = 'top')\r\n ax1[int(vfatCnt0/3), vfatCnt0%3].set_title(\"VFAT%02d\"%vfat)\r\n cf1[int(vfatCnt0/3), vfatCnt0%3] = ax1[int(vfatCnt0/3), vfatCnt0%3].pcolormesh(plot_data_x, plot_data_y, plot_data, cmap=cm.ocean_r, shading=\"nearest\")\r\n cbar1[int(vfatCnt0/3), vfatCnt0%3] = fig1.colorbar(cf1[int(vfatCnt0/3), vfatCnt0%3], ax=ax1[int(vfatCnt0/3), vfatCnt0%3], pad=0.01)\r\n cbar1[int(vfatCnt0/3), vfatCnt0%3].set_label(\"Fired events / total events\", loc = 'top')\r\n ax1[int(vfatCnt0/3), vfatCnt0%3].set_xticks(np.arange(min(channelNum), max(channelNum)+1, 20))\r\n ax1[int(vfatCnt0/3), vfatCnt0%3].text(-0.12, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax1[int(vfatCnt0/3), vfatCnt0%3].transAxes)\r\n ax1[int(vfatCnt0/3), vfatCnt0%3].text(0.015, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax1[int(vfatCnt0/3), vfatCnt0%3].transAxes)\r\n else:\r\n ax1[int(vfatCnt0/6), vfatCnt0%6].set_xlabel(\"Channel number\", loc = 'right')\r\n ax1[int(vfatCnt0/6), vfatCnt0%6].set_ylabel(\"Injected charge (fC)\", loc = 'top')\r\n ax1[int(vfatCnt0/6), vfatCnt0%6].set_title(\"VFAT%02d\"%vfat)\r\n cf1[int(vfatCnt0/6), vfatCnt0%6] = ax1[int(vfatCnt0/6), vfatCnt0%6].pcolormesh(plot_data_x, plot_data_y, plot_data, cmap=cm.ocean_r, shading=\"nearest\")\r\n cbar1[int(vfatCnt0/6), vfatCnt0%6] = fig1.colorbar(cf1[int(vfatCnt0/6), vfatCnt0%6], ax=ax1[int(vfatCnt0/6), vfatCnt0%6], pad=0.01)\r\n cbar1[int(vfatCnt0/6), vfatCnt0%6].set_label(\"Fired events / total events\", loc = 'top')\r\n ax1[int(vfatCnt0/6), vfatCnt0%6].set_xticks(np.arange(min(channelNum), max(channelNum)+1, 20))\r\n ax1[int(vfatCnt0/6), vfatCnt0%6].text(-0.1, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax1[int(vfatCnt0/6), vfatCnt0%6].transAxes)\r\n ax1[int(vfatCnt0/6), vfatCnt0%6].text(0.02, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax1[int(vfatCnt0/6), vfatCnt0%6].transAxes)\r\n\r\n vfatCnt0+=1\r\n\r\n fig1.tight_layout()\r\n fig1.savefig((directoryName+\"/scurve2Dhist_\"+oh+\".pdf\"))\r\n plt.close(fig1)\r\n\r\n\r\n if numVfats <= 3:\r\n fig2, ax2 = plt.subplots(1, numVfats, figsize=(numVfats*10,10))\r\n leg2 = 0\r\n elif numVfats <= 6:\r\n fig2, ax2 = plt.subplots(2, 3, figsize=(30,20))\r\n leg2 ={}\r\n elif numVfats <= 12:\r\n fig2, ax2 = plt.subplots(2, 6, figsize=(60,20))\r\n leg2 ={}\r\n elif numVfats <= 18:\r\n fig2, ax2 = plt.subplots(3, 6, figsize=(60,30))\r\n leg2 ={}\r\n elif numVfats <= 24:\r\n fig2, ax2 = plt.subplots(4, 6, figsize=(60,40))\r\n leg2 ={}\r\n\r\n vfatCnt0 = 0\r\n for vfat in scurve_result:\r\n fig, ax = plt.subplots(figsize=(12,10))\r\n ax.set_xlabel(\"Injected charge (fC)\", loc = 'right')\r\n ax.set_ylabel(\"Fired events / total events\", loc = 'top')\r\n #if args.type == \"daq\":\r\n # plt.ylim(-0.1,1.1)\r\n #else:\r\n # plt.ylim(-0.1,2.1)\r\n\r\n for channel in args.channels:\r\n channel = int(channel)\r\n if channel not in scurve_result[vfat]:\r\n print (Colors.YELLOW + \"Channel %d not in SCurve scan\"%channel + Colors.ENDC)\r\n continue\r\n dac = range(0,256)\r\n charge_plot = []\r\n frac = []\r\n for d in dac:\r\n c = DACToCharge(d, slope_adc, intercept_adc, current_pulse_sf, vfat, args.mode)\r\n if c in scurve_result[vfat][channel]:\r\n charge_plot.append(c)\r\n frac.append(scurve_result[vfat][channel][c])\r\n ax.grid()\r\n\r\n ax.plot(charge_plot, frac, \"o\",markersize = 6, label=\"Channel %d\"%channel)\r\n ax.text(-0.1, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax.transAxes)\r\n ax.text(0.01, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax.transAxes)\r\n if numVfats == 1:\r\n ax2.grid()\r\n ax2.plot(charge_plot, frac, \"o\", markersize = 6, label=\"Channel %d\"%channel)\r\n elif numVfats <= 3:\r\n ax2[vfatCnt0].grid()\r\n ax2[vfatCnt0].plot(charge_plot, frac, \"o\", markersize = 6, label=\"Channel %d\"%channel)\r\n elif numVfats <= 6:\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].grid()\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].plot(charge_plot, frac, \"o\", markersize = 6, label=\"Channel %d\"%channel)\r\n else:\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].grid()\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].plot(charge_plot, frac, \"o\", markersize = 6, label=\"Channel %d\"%channel)\r\n leg = ax.legend(loc=\"center right\", ncol=2)\r\n ax.set_title(\"VFAT%02d\"%vfat)\r\n fig.savefig((directoryName+\"/scurve_\"+oh+\"_VFAT%02d.pdf\")%vfat)\r\n plt.close(fig)\r\n\r\n if numVfats == 1:\r\n ax2.set_xlabel(\"Injected charge (fC)\", loc = 'right')\r\n ax2.set_ylabel(\"Fired events / total events\", loc = 'top')\r\n ax2.set_title(\"VFAT%02d\"%vfat)\r\n leg2 = ax2.legend(loc=\"center right\", ncol=2)\r\n ax2.text(-0.09, 1.01, 'CMS', fontweight='bold', fontsize=26, transform=ax2.transAxes)\r\n ax2.text(0.01, 1.01, 'Muon R&D',fontstyle='italic', fontsize=24, transform=ax2.transAxes)\r\n elif numVfats <= 3:\r\n ax2[vfatCnt0].set_xlabel(\"Injected charge (fC)\", loc = 'right')\r\n ax2[vfatCnt0].set_ylabel(\"Fired events / total events\", loc = 'top')\r\n ax2[vfatCnt0].set_title(\"VFAT%02d\"%vfat)\r\n leg2[vfatCnt0] = ax2[vfatCnt0].legend(loc=\"center right\", ncol=2)\r\n ax2[vfatCnt0].text(-0.09, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax2[vfatCnt0].transAxes)\r\n ax2[vfatCnt0].text(0.01, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax2[vfatCnt0].transAxes)\r\n elif numVfats <= 6:\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].set_xlabel(\"Injected charge (fC)\", loc = 'right')\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].set_ylabel(\"Fired Events / Total Events\", loc = 'top')\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].set_title(\"VFAT%02d\"%vfat)\r\n leg2[int(vfatCnt0/3), vfatCnt0%3] = ax2[int(vfatCnt0/3), vfatCnt0%3].legend(loc=\"center right\", ncol=2)\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].text(-0.11, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax2[int(vfatCnt0/3), vfatCnt0%3].transAxes)\r\n ax2[int(vfatCnt0/3), vfatCnt0%3].text(0.02, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax2[int(vfatCnt0/3), vfatCnt0%3].transAxes)\r\n else:\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].set_xlabel(\"Injected charge (fC)\", loc = 'right')\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].set_ylabel(\"Fired Events / Total Events\", loc = 'top')\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].set_title(\"VFAT%02d\"%vfat)\r\n leg2[int(vfatCnt0/6), vfatCnt0%6] = ax2[int(vfatCnt0/6), vfatCnt0%6].legend(loc=\"center right\", ncol=2)\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].text(-0.12, 1.01, 'CMS', fontweight='bold', fontsize=28, transform=ax2[int(vfatCnt0/6), vfatCnt0%6].transAxes)\r\n ax2[int(vfatCnt0/6), vfatCnt0%6].text(0.01, 1.01, 'Muon R&D',fontstyle='italic', fontsize=26, transform=ax2[int(vfatCnt0/6), vfatCnt0%6].transAxes)\r\n\r\n\r\n vfatCnt0+=1\r\n\r\n fig2.tight_layout()\r\n fig2.savefig((directoryName+\"/scurve_\"+oh+\".pdf\"))\r\n plt.close(fig2)\r\n\r\n print(Colors.GREEN + 'Plots saved at %s' % directoryName + Colors.ENDC)\r\n\r\n\r\n\r\n\r\n","sub_path":"lpgbt_vfat_plot_scurve.py","file_name":"lpgbt_vfat_plot_scurve.py","file_ext":"py","file_size_in_byte":17846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"67427358","text":"\"\"\"\nCommon narrative logging functions.\n\nOther biokbase modules can use the logging like this:\n\n from biokbase.narrative.common.kblogging import get_logger\n _log = get_logger(__name__)\n\nLog messages are free-form, *but* the MongoDB handler will break any\nparts of the message in the form '=' into their own fields\nin the MongoDB record.\n\nLogging to MongoDB will be enabled, via proxy, if the proxy is\nrunning on the pre-configured host/port\n\"\"\"\n__author__ = 'Dan Gunter '\n__date__ = '2014-07-31'\n\nimport logging\nfrom logging.handlers import SocketHandler\nimport os\nimport re\nimport socket\n# IPython\nimport IPython\n# Local\nfrom .util import kbase_env\nfrom . import log_proxy\n\n## Constants\n\nKBASE_TMP_DIR = \"/tmp\"\nKBASE_TMP_LOGFILE = os.path.join(KBASE_TMP_DIR, \"kbase-narrative.log\")\n\n# env var with location of proxy config file\nKBASE_PROXY_ENV = 'KB_PROXY_CONFIG'\n\n## Functions\n\ndef get_logger(name=\"\"):\n \"\"\"Get a given KBase log obj.\n\n :param name: name (a.b.c) of the logging namespace, which may be\n relative or absolute (starting with 'biokbase.'), or\n empty in which case the root logger is returned\n :return: Log object\n :rtype: logging.Logger\n \"\"\"\n # no name => root\n if not name:\n log = logging.getLogger(\"biokbase\")\n # absolute name\n elif name.startswith(\"biokbase.\"):\n log = logging.getLogger(name)\n # relative name\n else:\n log = logging.getLogger(\"biokbase.\" + name)\n\n adapter = LogAdapter(log, _get_meta())\n\n return adapter\n\ndef log_event(log, event, mapping):\n \"\"\"Log an event and a mapping.\n \"\"\"\n kvp = \" \".join([\"{}={}\".format(k, v) for k, v in mapping.iteritems()])\n log.info(\"{}{}{}\".format(event, log_proxy.EVENT_MSG_SEP, kvp))\n\nclass LogAdapter(logging.LoggerAdapter):\n \"\"\"\n Add some extra methods to the stock LoggerAdapter\n \"\"\"\n def __init__(self, log, extra):\n logging.LoggerAdapter.__init__(self, log, extra)\n self.handlers = log.handlers\n self.addHandler = log.addHandler\n self.removeHandler = log.removeHandler\n self.setLevel = log.setLevel\n self.isEnabledFor = log.isEnabledFor\n\ndef _get_meta():\n meta = {}\n\n\n # Auth values\n token = kbase_env.auth_token\n\n if token:\n # User\n m = re.search('un=([^|]+)', token)\n if m is not None:\n meta['user'] = m.group(1)\n\n # Session id\n sess = kbase_env.session\n if sess:\n meta['session_id'] = sess\n\n # Notebook name\n if kbase_env.narrative:\n meta['narr'] = kbase_env.narrative\n\n return meta\n\n\nclass MetaFormatter(logging.Formatter):\n def __init__(self):\n \"\"\"\n Format with metadata in the mix.\n \"\"\"\n logging.Formatter.__init__(\n self,\n \"%(levelname)s %(asctime)s %(name)s %(message)s\")\n\n def format(self, record):\n \"\"\"\n Add KB_* environment values at format time.\n \"\"\"\n s = logging.Formatter.format(self, record)\n return \"{} [{}]\".format(s, ' '.join([\"{}={}\".format(k, v)\n for k, v in os.environ.items()\n if k.startswith('KB_')]))\n\n\ndef init_handlers():\n \"\"\"\n Initialize and add the log handlers.\n \"\"\"\n # Turn on debugging by setting environment variable KBASE_DEBUG.\n if os.environ.get(\"KBASE_DEBUG\", None):\n g_log.setLevel(logging.DEBUG)\n else:\n g_log.setLevel(logging.INFO)\n\n # Add log handler and assoc. formatter for metadata\n hndlr = logging.FileHandler(KBASE_TMP_LOGFILE)\n hndlr.setFormatter(MetaFormatter())\n g_log.addHandler(hndlr)\n\n # If local forwarder is available, add that one too\n has_local_forwarder = True\n config_file = os.environ.get(KBASE_PROXY_ENV, None)\n proxy_config = log_proxy.ProxyConfiguration(config_file)\n # Attempt a connection\n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect((proxy_config.host, proxy_config.port))\n except socket.error:\n has_local_forwarder = False\n g_log.debug(\"init_handlers local_forwarder=false\")\n # If connection succeeds, add a logging.handler\n if has_local_forwarder:\n g_log.debug(\"init_handlers local_forwarder=true\")\n sock_handler = SocketHandler(proxy_config.host,\n proxy_config.port)\n g_log.addHandler(sock_handler)\n else:\n g_log.debug(\"init_handlers local_forwarder=false\")\n\ndef reset_handlers():\n \"\"\"Remove & re-add all handlers.\n \"\"\"\n while g_log.handlers:\n g_log.removeHandler(g_log.handlers.pop())\n init_handlers()\n\n## Run the rest of this on import\n\n# Get root log obj.\ng_log = get_logger()\n\n# If no handlers, initialize them\nif not g_log.handlers:\n init_handlers()\n","sub_path":"src/biokbase/narrative/common/kblogging.py","file_name":"kblogging.py","file_ext":"py","file_size_in_byte":4856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"163624038","text":"# -*- coding: utf-8 -*-\n\"\"\"\nLocation - app to save location aware objects into the database.\nThis is created for ASI (Aalto Social Interface).\n\nPostGIS data type 'geography' is used instead of traditional geometry. For further details, see:\nhttp://postgis.refractions.net/documentation/manual-1.5/ch04.html#PostGIS_GeographyVSGeometry\n\"\"\"\n\nimport string\nimport random\n\nfrom django.conf import settings\nfrom django.contrib.gis.db import models\nfrom django.contrib.auth.models import User\n\ndef get_guid(length=12):\n \"\"\"\n Generate and return a random string which can be considered unique.\n Default length is 12 characters from set [a-zA-Z0-9].\n \"\"\"\n alphanum = string.letters + string.digits\n return ''.join([alphanum[random.randint(0,len(alphanum)-1)] for i in xrange(length)])\n\nclass Entity(models.Model):\n \"\"\"\n Entity is an object with geographical coordinates.\n Fields:\n * guid, globally unique id, usually a random string\n * user, foreign key to an Auth.User object\n * name, optional\n * description, optional\n * created and updated, auto-timestamps\n * geography, GIS field for location (see PostGIS 1.5) or\n http://workshops.opengeo.org/postgis-intro/geography.html\n \"\"\"\n guid = models.CharField(max_length=40, default=get_guid, unique=True, db_index=True)\n user = models.ForeignKey(User, blank=True, null=True)\n name = models.CharField(max_length=150, blank=True, default='', editable=True)\n description = models.TextField(blank=True, default='', editable=True)\n created = models.DateTimeField(auto_now_add=True, editable=False)\n updated = models.DateTimeField(auto_now=True, editable=False)\n objects = models.GeoManager()\n geography = models.PointField(geography=True, editable=True)\n\n def __unicode__(self):\n return u\"%s (%s)\" % (self.name, str(self.geography)[:50])\n","sub_path":"MestaDB/mestadb/location/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"555869084","text":"import torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..functions.ctc_loss import CTCLossFunction\n\n\nclass CTCLoss(nn.Module):\n \"\"\"\n Criterion to compute CTC Loss as described in ``_\n\n :param size_average: if compute average loss (only if reduce is True)\n :param reduce: if compute mean or average loss (if None, returns full tensor of shape ``(batch_size,)``)\n :param after_logsoftmax: if logsoftmax is used before passing neural network outputs \\n\n (else takes pure network outputs)\n :param time_major: if logits are time major (or batch major), default ``True``\n :param blank_idx: id of blank label, default ``0``\n \"\"\"\n\n def __init__(self, size_average=None, reduce=None, after_logsoftmax=False, time_major=False, blank_idx=0):\n super(CTCLoss, self).__init__()\n self._blank_index = blank_idx\n self._reduce = reduce\n self._size_average = size_average\n self._after_logsoftmax = after_logsoftmax\n self._time_major = time_major\n\n def forward(self, logits, targets, logits_lengths, targets_lengths):\n \"\"\"\n Computes CTC Loss\n\n :param logits: Float or Double Tensor (network output)\n of shape ``(sequence_length, batch_size, alphabet_size)`` if ``time_major`` is True,\n else of shape ``(batch_size, sequence_length, alphabet_size)``\n :param targets: Tensor with targets of shape ``(batch_size, targets_sequence_length)``\n :param logits_lengths: Tensor of shape ``(batch_size,)`` with lengths of sequences\n :param targets_lengths: Tensor of shape ``(batch_size,)`` with lengths of target sequences\n :return: tensor with CTC loss of shape ``(batch_size,)`` if ``reduce is None`` else of shape ``(1,)``\n \"\"\"\n if self._after_logsoftmax:\n logits_logsoftmax = logits\n else:\n logits_logsoftmax = F.log_softmax(logits, dim=2)\n\n if self._time_major:\n logits_logsoftmax = logits_logsoftmax.permute(1, 0, 2)\n # shape of logits_logsoftmax now: batch_size, sequence_length, alphabet_size\n\n loss = CTCLossFunction().apply(logits_logsoftmax, targets, logits_lengths, targets_lengths, self._blank_index)\n\n if self._reduce:\n if self._size_average:\n return loss.mean()\n else:\n return loss.sum()\n return loss\n","sub_path":"pytorch_end2end/modules/ctc_loss.py","file_name":"ctc_loss.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"240880021","text":"from distutils.spawn import find_executable\nfrom distutils.command.build import build\nfrom distutils.command.clean import clean\nfrom distutils.cmd import Command\nfrom distutils import log\nimport sys\nimport os\n\nclass clean_proto(clean):\n def run(self):\n clean.run(self)\n for package in self.distribution.packages:\n for root, dirs, files in os.walk(package):\n for filename in files:\n path = os.path.join(root, filename)\n if self.is_pb2_file(filename):\n log.info(\"removing generated file '%s'\" % path)\n os.remove(path)\n\n def is_pb2_file(self, filename):\n base, u, rest = filename.rpartition('_')\n return rest == 'pb2.py'\n\nclass build_proto(Command):\n description = \"build .proto files into Python modules\"\n user_options = [\n ('protoc=', None, 'location of the protoc compiler'),\n ('indir=', 'i', 'where to find the *.proto files to compile'),\n ('outdir=', 'o', 'where to output the generated Python code'),\n ('force', 'f', 'forcibly build everything (ignore file timestamps)')]\n\n boolean_options = ['force']\n\n def initialize_options(self):\n self.indir = None\n self.outdir = None\n self.protoc = None\n self.force = None\n\n def finalize_options(self):\n self.set_undefined_options('build', ('force', 'force'))\n\n if self.indir is None:\n self.indir = os.path.join('.', 'src')\n if self.outdir is None:\n self.outdir = os.path.join('.', self.distribution.packages[0])\n\n if self.protoc is None:\n self.protoc = find_executable('protoc')\n if self.protoc is None:\n raise RuntimeError(\"No protoc compiler was found!\")\n\n def run(self):\n for protofile in self.get_proto_files():\n outputfile = self.pb2_filename(protofile)\n outputdir = os.path.dirname(outputfile)\n self.make_file(protofile, outputfile, self.generate_proto, [protofile, outputdir])\n self.reinitialize_command('build_py')\n\n def get_proto_files(self):\n protos = []\n for root, dirs, files in os.walk(self.indir):\n for filename in files:\n if self.is_proto_file(filename):\n self.debug_print(\"Found protobuffs definition: %s\" % filename)\n protos.append(os.path.join(root, filename))\n return protos\n\n def is_proto_file(self, filename):\n base, dot, ext = filename.rpartition(\".\")\n return (ext == \"proto\")\n\n def pb2_filename(self, source):\n return source.replace(\".proto\", \"_pb2.py\").replace(self.indir, self.outdir)\n\n # Lifted and modified from the google project\n def generate_proto(self, source, outputdir):\n \"\"\"Invokes the Protocol Compiler to generate a _pb2.py from the given\n .proto file..\"\"\"\n\n protoc_command = [ self.protoc,\n \"-I%s\" % self.indir,\n \"--python_out=%s\" % outputdir,\n source ]\n result = self.spawn(protoc_command, 0)\n if result is not None and result[1] is not 0:\n raise SystemError(\"protoc command failed: '%s'\" % protoc_command.join(' '))\n\n# Inject our .proto compiler into the front of the build commands\nbuild.sub_commands.insert(0, ('build_proto', None))\n","sub_path":"proto_cmd.py","file_name":"proto_cmd.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"389305055","text":"__author__ = 'TzAnAnY'\n\nSAVED = {}\n\n\ndef patch_item(module, attr, new_item):\n obj = object()\n old_item = getattr(module, attr, obj)\n if old_item is not obj:\n SAVED.setdefault(module.__name__, {}).setdefault(attr, old_item)\n setattr(module, attr, new_item)\n\n\ndef patch_socks():\n from .httpclient import HTTPRequest\n import tornado.httpclient\n patch_item(tornado.httpclient, 'HTTPRequest', HTTPRequest)\n\n from .curl_httpclient import CurlAsyncHTTPClient\n import tornado.curl_httpclient\n patch_item(tornado.curl_httpclient, 'CurlAsyncHTTPClient', CurlAsyncHTTPClient)\n\n","sub_path":"monkey/patch.py","file_name":"patch.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"395346409","text":"from day5 import compile\ndef init():\n input = ''\n with open('./day7-input.txt', 'r', encoding='utf-8') as file:\n input = file.read()\n instructions = [int(piece) for piece in input.split(',')]\n return instructions\nprogram = init()\nprint(program)\n\ncompile(program)","sub_path":"day7.py","file_name":"day7.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"531290446","text":"import rospy\nfrom robot import Robot\n\n\ndef main():\n\n rospy.init_node('tr_control')\n\n rate = rospy.Rate(10)\n r = Robot()\n\n while not rospy.is_shutdown():\n r.publish_motors()\n rate.sleep()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"tr_control/src/tr_control/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"274096927","text":"import json\n\nfrom kafka import KafkaProducer\n\nfrom code.servers import bootstrap_servers\nfrom code.topics import TOPIC_1\n\n\ndef on_send_success(record_metadata):\n print(f'PRODUCER: Send to {record_metadata.topic}:{record_metadata.partition}:{record_metadata.offset}')\n\n\ndef on_send_error(ex):\n print(f'PRODUCER: {ex}')\n # handle exception\n\n\ndef send_json_message(key: str, value: str):\n\n print('Start Producing')\n\n producer = KafkaProducer(bootstrap_servers=bootstrap_servers,\n value_serializer=lambda m: json.dumps(m).encode('ascii'))\n producer.send(TOPIC_1, {key: value})\\\n .add_callback(on_send_success)\\\n .add_errback(on_send_error)\n\n # block until all async messages are sent\n producer.flush()\n\n print('Finish Producing')\n return True\n\n\nif __name__ == '__main__':\n\n send_json_message('Key', 'Value')\n","sub_path":"code/messaging_async/producer.py","file_name":"producer.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"28713275","text":"from PySide2 import QtWidgets, QtCore, QtGui\nfrom maya import OpenMayaUI as omui\nimport maya.cmds as m\nfrom shiboken2 import wrapInstance\nfrom peel_solve import time_util, roots, solve\nimport math\n\n\n\n\n\n\nclass TimeRangeWidget(QtWidgets.QWidget):\n\n def __init__(self, parent=None):\n \"\"\" The initialization includes setting up the UI framework for the tool window, which asks the user\n for the c3d files, as well as the start and end frames.\"\"\"\n\n super(TimeRangeWidget, self).__init__(parent)\n\n layout = QtWidgets.QVBoxLayout()\n\n self.ranges = QtWidgets.QTableWidget()\n self.ranges.setColumnCount(3)\n self.ranges.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\n self.ranges.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)\n self.ranges.cellDoubleClicked.connect(self.select_event)\n\n layout.addWidget(self.ranges)\n\n low_bar = QtWidgets.QHBoxLayout()\n\n low_bar.addWidget(QtWidgets.QLabel(\"Start TC\"))\n self.tc_start = QtWidgets.QLineEdit()\n low_bar.addWidget(self.tc_start)\n\n low_bar.addWidget(QtWidgets.QLabel(\"TC Rate\"))\n self.tc_rate = QtWidgets.QLineEdit()\n low_bar.addWidget(self.tc_rate)\n\n low_bar.addWidget(QtWidgets.QLabel(\"Offset (sec)\"))\n self.tc_offset = QtWidgets.QLineEdit()\n low_bar.addWidget(self.tc_offset)\n\n low_bar.addWidget(QtWidgets.QLabel(\"Start#\"))\n self.frame_start = QtWidgets.QLineEdit()\n low_bar.addWidget(self.frame_start)\n\n self.solve_button = QtWidgets.QPushButton(\"Solve Selected\")\n self.solve_button.pressed.connect(self.do_solve)\n low_bar.addWidget(self.solve_button)\n\n self.solve_all_button = QtWidgets.QPushButton(\"Solve All\")\n self.solve_all_button.pressed.connect(self.do_solve_all)\n low_bar.addWidget(self.solve_all_button)\n\n low_bar.addStretch(1)\n\n layout.addItem(low_bar)\n\n self.setLayout(layout)\n\n self.resize(500, 250)\n\n self.populate()\n\n def clear(self):\n self.populate()\n\n def populate(self):\n self.ranges.setRowCount(0)\n self.ranges.clear()\n\n optical_root = roots.optical()\n if optical_root is None:\n self.tc_offset.setText(\"\")\n self.tc_rate.setText(\"\")\n self.tc_start.setText(\"\")\n return\n \n self.frame_start.setText(str(time_util.c3d_start(optical_root)))\n\n tc_standard = m.getAttr(optical_root + \".C3dTimecodeStandard\")\n offset = m.getAttr(optical_root + \".C3dFirstField\")\n rate = m.getAttr(optical_root + \".C3dRate\")\n self.tc_offset.setText(\"%.2f\" % (offset / rate))\n self.tc_rate.setText(str(tc_standard))\n\n hh = m.getAttr(optical_root + \".C3dTimecodeH\")\n mm = m.getAttr(optical_root + \".C3dTimecodeM\")\n ss = m.getAttr(optical_root + \".C3dTimecodeS\")\n ff = m.getAttr(optical_root + \".C3dTimecodeF\")\n\n self.tc_start.setText(\"%02d:%02d:%02d:%02d\" % (hh, mm, ss, ff))\n\n def add_range(self, name, start, end):\n row = self.ranges.rowCount()\n self.ranges.setRowCount(row+1)\n self.ranges.setItem(row, 0, QtWidgets.QTableWidgetItem(name))\n self.ranges.setItem(row, 1, QtWidgets.QTableWidgetItem(start))\n self.ranges.setItem(row, 2, QtWidgets.QTableWidgetItem(end))\n\n def get_range(self, row):\n tc_rate = float(self.tc_rate.text())\n\n c3d_start = time_util.c3d_start(roots.optical())\n\n start_tc = time_util.Timecode(str(self.ranges.item(row, 1).text()), tc_rate)\n end_tc = time_util.Timecode(str(self.ranges.item(row, 2).text()), tc_rate)\n\n print(\"Start: \" + start_tc.info())\n print(\"End: \" + end_tc.info())\n print(\"Offset: \" + c3d_start.info())\n\n a = start_tc - c3d_start\n b = end_tc - c3d_start\n\n a.set_rate(time_util.fps())\n b.set_rate(time_util.fps())\n\n print(\"Start: \" + a.info())\n print(\"End: \" + b.info())\n\n return a.frame(), b.frame()\n\n def select_event(self, row):\n start, end = self.get_range(row)\n\n m.playbackOptions(min=math.floor(start), max=math.ceil(end))\n\n def do_solve(self, all=False):\n for i in range(self.ranges.rowCount()):\n row = self.ranges.item(i, 0)\n if all is True or row.isSelected():\n start, end = self.get_range(i)\n solve.run(start=start, end=end)\n\n def do_solve_all(self):\n self.do_solve(all=True)\n\n\n\nclass TimeRanges(QtWidgets.QDialog):\n\n def __init__(self, parent=None):\n\n if parent is None:\n pointer = omui.MQtUtil.mainWindow()\n parent = wrapInstance(long(pointer), QtWidgets.QWidget)\n\n super(TimeRanges, self).__init__(parent)\n\n layout = QtWidgets.QVBoxLayout()\n self.table = TimeRangeWidget()\n layout.addWidget(self.table)\n self.setLayout(layout)\n self.resize(500, 250)\n\nINSTANCE = None\n\ndef show():\n \"\"\" Create the gui if it doesn't exist, or show if it does \"\"\"\n global INSTANCE\n if not INSTANCE:\n INSTANCE = TimeRanges()\n INSTANCE.show()\n return INSTANCE\n","sub_path":"python/peel_solve/time_range.py","file_name":"time_range.py","file_ext":"py","file_size_in_byte":5190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"579209489","text":"# Copyright 2021 Curtin University\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Author: James Diprose\n\nfrom __future__ import annotations\n\nimport json\nimport os\n\nfrom oaebu_workflows.config import elastic_mappings_folder\nfrom oaebu_workflows.dags.elastic_import_workflow import load_elastic_mappings_oaebu\nfrom observatory.platform.utils.config_utils import module_file_path\nfrom observatory.platform.utils.jinja2_utils import render_template\nfrom observatory.platform.utils.test_utils import ObservatoryEnvironment, ObservatoryTestCase\nfrom observatory.platform.utils.workflow_utils import make_dag_id\n\n\nclass TestElasticImportWorkflow(ObservatoryTestCase):\n \"\"\"Tests for the Elastic Import Workflow\"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.project_id = os.getenv(\"TEST_GCP_PROJECT_ID\")\n self.data_location = os.getenv(\"TEST_GCP_DATA_LOCATION\")\n\n def test_load_elastic_mappings_oaebu(self):\n \"\"\"Test load_elastic_mappings_oaebu\"\"\"\n\n aggregate_level = \"product\"\n path = elastic_mappings_folder()\n expected = [\n (\n \"oaebu_anu_press_book_product_author_metrics\",\n render_template(\n os.path.join(path, \"oaebu-author-metrics-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_list\",\n render_template(\n os.path.join(path, \"oaebu-list-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_metrics\",\n render_template(\n os.path.join(path, \"oaebu-metrics-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_metrics_city\",\n render_template(\n os.path.join(path, \"oaebu-metrics-city-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_metrics_country\",\n render_template(\n os.path.join(path, \"oaebu-metrics-country-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_metrics_events\",\n render_template(\n os.path.join(path, \"oaebu-metrics-events-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_metrics_institution\",\n render_template(\n os.path.join(path, \"oaebu-metrics-institution-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_book_product_metrics_referrer\",\n render_template(\n os.path.join(path, \"oaebu-metrics-referrer-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_book_product_publisher_metrics\",\n render_template(\n os.path.join(path, \"oaebu-publisher-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_book_product_subject_bic_metrics\",\n render_template(\n os.path.join(path, \"oaebu-subject-bic-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_book_product_subject_bisac_metrics\",\n render_template(\n os.path.join(path, \"oaebu-subject-bisac-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_book_product_subject_thema_metrics\",\n render_template(\n os.path.join(path, \"oaebu-subject-thema-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_book_product_subject_year_metrics\",\n render_template(\n os.path.join(path, \"oaebu-subject-year-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_book_product_year_metrics\",\n render_template(\n os.path.join(path, \"oaebu-year-metrics-mappings.json.jinja2\"), aggregation_level=aggregate_level\n ),\n ),\n (\n \"oaebu_anu_press_unmatched_book_metrics\",\n render_template(\n os.path.join(path, \"oaebu-unmatched-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_unmatched_book_metrics\",\n render_template(\n os.path.join(path, \"oaebu-unmatched-metrics-mappings.json.jinja2\"),\n aggregation_level=aggregate_level,\n ),\n ),\n (\n \"oaebu_anu_press_institution_list\",\n render_template(os.path.join(path, \"oaebu-institution-list-mappings.json.jinja2\")),\n ),\n ]\n\n for table_id, expected_mappings_str in expected:\n print(table_id)\n expected_mappings = json.loads(expected_mappings_str)\n actual_mappings = load_elastic_mappings_oaebu(path, table_id)\n self.assertEqual(expected_mappings, actual_mappings)\n\n def test_dag_load(self):\n \"\"\"Test that the DAG can be loaded from a DAG bag.\n\n :return: None\n \"\"\"\n\n env = ObservatoryEnvironment(self.project_id, self.data_location, enable_api=False)\n with env.create():\n expected_dag_ids = [\n make_dag_id(\"elastic_import\", suffix)\n for suffix in [\"anu_press\", \"ucl_press\", \"wits_university_press\", \"university_of_michigan_press\"]\n ]\n\n dag_file = os.path.join(module_file_path(\"oaebu_workflows.dags\"), \"elastic_import_workflow.py\")\n for dag_id in expected_dag_ids:\n self.assert_dag_load(dag_id, dag_file)\n","sub_path":"oaebu_workflows/workflows/tests/test_elastic_workflow.py","file_name":"test_elastic_workflow.py","file_ext":"py","file_size_in_byte":7163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"568935389","text":"import torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model import common\n\n\ndef make_model(args):\n return EPISTEMIC(args)\n\n\nclass EPISTEMIC(nn.Module):\n def __init__(self, config):\n super(EPISTEMIC, self).__init__()\n self.drop_rate = config.drop_rate\n in_channels = config.in_channels\n filter_config = (64, 128)\n\n self.encoders = nn.ModuleList()\n self.decoders = nn.ModuleList()\n\n # setup number of conv-bn-relu blocks per module and number of filters\n encoder_n_layers = (2, 2, 3, 3, 3)\n encoder_filter_config = (in_channels,) + filter_config\n decoder_n_layers = (3, 3, 3, 2, 1)\n decoder_filter_config = filter_config[::-1] + (filter_config[0],)\n\n for i in range(0, 2):\n # encoder architecture\n self.encoders.append(_Encoder(encoder_filter_config[i],\n encoder_filter_config[i + 1],\n encoder_n_layers[i]))\n\n # decoder architecture\n self.decoders.append(_Decoder(decoder_filter_config[i],\n decoder_filter_config[i + 1],\n decoder_n_layers[i]))\n\n # final classifier (equivalent to a fully connected layer)\n self.classifier = nn.Conv2d(filter_config[0], in_channels, 3, 1, 1)\n\n def forward(self, x):\n indices = []\n unpool_sizes = []\n feat = x\n\n # encoder path, keep track of pooling indices and features size\n for i in range(0, 2):\n (feat, ind), size = self.encoders[i](feat)\n if i == 1:\n feat = F.dropout(feat, p=self.drop_rate, training=True)\n indices.append(ind)\n unpool_sizes.append(size)\n\n # decoder path, upsampling with corresponding indices and size\n for i in range(0, 2):\n feat = self.decoders[i](feat, indices[1 - i], unpool_sizes[1 - i])\n if i == 0:\n feat = F.dropout(feat, p=self.drop_rate, training=True)\n\n output = self.classifier(feat)\n results = {'mean': output}\n\n return results\n\n\nclass _Encoder(nn.Module):\n def __init__(self, n_in_feat, n_out_feat, n_blocks=2):\n \"\"\"Encoder layer follows VGG rules + keeps pooling indices\n Args:\n n_in_feat (int): number of input features\n n_out_feat (int): number of output features\n n_blocks (int): number of conv-batch-relu block inside the encoder\n drop_rate (float): dropout rate to use\n \"\"\"\n super(_Encoder, self).__init__()\n\n layers = [nn.Conv2d(n_in_feat, n_out_feat, 3, 1, 1),\n nn.BatchNorm2d(n_out_feat),\n nn.ReLU()]\n\n if n_blocks > 1:\n layers += [nn.Conv2d(n_out_feat, n_out_feat, 3, 1, 1),\n nn.BatchNorm2d(n_out_feat),\n nn.ReLU()]\n\n self.features = nn.Sequential(*layers)\n\n def forward(self, x):\n output = self.features(x)\n return F.max_pool2d(output, 2, 2, return_indices=True), output.size()\n\n\nclass _Decoder(nn.Module):\n \"\"\"Decoder layer decodes the features by unpooling with respect to\n the pooling indices of the corresponding decoder part.\n Args:\n n_in_feat (int): number of input features\n n_out_feat (int): number of output features\n n_blocks (int): number of conv-batch-relu block inside the decoder\n drop_rate (float): dropout rate to use\n \"\"\"\n\n def __init__(self, n_in_feat, n_out_feat, n_blocks=2):\n super(_Decoder, self).__init__()\n\n layers = [nn.Conv2d(n_in_feat, n_in_feat, 3, 1, 1),\n nn.BatchNorm2d(n_in_feat),\n nn.ReLU()]\n\n if n_blocks > 1:\n layers += [nn.Conv2d(n_in_feat, n_out_feat, 3, 1, 1),\n nn.BatchNorm2d(n_out_feat),\n nn.ReLU()]\n\n self.features = nn.Sequential(*layers)\n\n def forward(self, x, indices, size):\n unpooled = F.max_unpool2d(x, indices, 2, 2, 0, size)\n return self.features(unpooled)\n","sub_path":"MCBN_src/model/epistemic.py","file_name":"epistemic.py","file_ext":"py","file_size_in_byte":4147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"50149439","text":"import matplotlib.pyplot as plt\nfrom collections import Counter\nimport seaborn as sns\nimport requests\nfrom time import sleep\nimport pandas as pd\nfrom pandas.io.json import json_normalize\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n\ndef split_by_whitespace(data):\n \"\"\"\n this function split the data by whitespace\n \"\"\"\n return data.split()\n\n\ndef count_word_number(text):\n \"\"\"\n this function return the number of words in the data\n \"\"\"\n return len(split_by_whitespace(text))\n\n\ndef plot_word_number_histogram(data):\n \"\"\"\n this function plot a histogram of the number of words in the data\n \"\"\"\n hist = dict()\n for idx, row in data.iterrows():\n cur_len = str(count_word_number(row['text']))\n if cur_len in hist.keys():\n hist[cur_len] += 1\n else:\n hist[cur_len] = 1\n new_hist = dict(sorted(hist.items(), key = lambda x:x[0]))\n plt.bar(new_hist.keys(), new_hist.values())\n plt.title(\"Word Number Histogram\")\n plt.xlabel(\"words\")\n plt.ylabel(\"frequancy\")\n plt.show()\n\n\ndef plot_top_20_common_words(data, file_name):\n \"\"\"\n this function plot the top 20 common words in the data\n \"\"\"\n general_counter = Counter()\n for idx, row in data.iterrows():\n general_counter += Counter(split_by_whitespace(row['text']))\n most = general_counter.most_common(20)\n x, y = [], []\n for word, count in most:\n x.append(word)\n y.append(count)\n fig = sns.barplot(x=y, y=invert_words(x))\n plt.title(\"Top commom words\")\n plt.ylabel(\"words\")\n plt.xlabel(\"frequancy\")\n plt.show()\n plt.savefig(\"statistic/\" + file_name + \".png\")\n\n\ndef plot_top_20_bigrams_words(data, file_name):\n \"\"\"\n This function plot the 20 common bigrams in the data\n \"\"\"\n vec = CountVectorizer(ngram_range = (2,2))\n bow = vec.fit_transform(list(data['text']))\n sum_words = bow.sum(axis=0)\n words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)[:20]\n x, y = map(list, zip(*words_freq))\n fig = sns.barplot(x=y, y=invert_words(x))\n plt.title(\"Top Bigrams Barchart\")\n plt.ylabel(\"words\")\n plt.xlabel(\"frequancy\")\n plt.show()\n plt.savefig(\"statistic/\" + file_name + \".png\")\n\n\ndef invert_words(words):\n \"\"\"\n this function invert every word from the given list\n \"\"\"\n return [w[::-1] for w in words]\n\n\ndef plot_top_non_stop_words_barchart(data, file_name):\n \"\"\"\n this function plot toe non stop words barchart\n \"\"\"\n stop = get_hebrew_stop_words()\n general_counter = Counter()\n for idx, row in data.iterrows():\n non_stop_words_text = [word for word in row['text'].split() if word not in stop]\n general_counter += Counter(non_stop_words_text)\n most = general_counter.most_common(20)\n x, y = [], []\n for word, count in most:\n x.append(word)\n y.append(count)\n fig = sns.barplot(x=y, y=invert_words(x))\n plt.title(\"Top Non-Stopwords Barchart\")\n plt.ylabel(\"words\")\n plt.xlabel(\"frequancy\")\n plt.show()\n plt.savefig(\"statistic/\" + file_name + \".png\")\n\n\ndef get_hebrew_stop_words():\n \"\"\"\n this function return the hebrew stop words\n \"\"\"\n stop_words = \"heb_stopwords.txt\"\n with open(stop_words, encoding=\"utf-8\") as in_file:\n lines = in_file.readlines()\n res = [l.strip() for l in lines]\n return res\n\ndef plot_top_bigrams_non_stop_words_barchart(data, file_name):\n \"\"\"\n This function plot the 20 common bigrams non stop words\n \"\"\"\n vec = CountVectorizer(ngram_range = (2,2))\n bow = vec.fit_transform(list(data['text']))\n sum_words = bow.sum(axis=0)\n stop = get_hebrew_stop_words()\n words_freq = []\n for word, idx in vec.vocabulary_.items():\n flag = False\n for w in word.split():\n if w in stop:\n flag = True\n if not flag:\n words_freq.append((word, sum_words[0, idx]))\n words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)[:20]\n x, y = map(list, zip(*words_freq))\n fig = sns.barplot(x=y, y=invert_words(x))\n plt.title(\"Top bigrams non stop barchart\")\n plt.ylabel(\"words\")\n plt.xlabel(\"frequancy\")\n plt.show()\n plt.savefig(\"statistic/\" + file_name + \".png\")\n\n\ndef get_yap_analysis(text):\n \"\"\"\n this function return yap analysis\n \"\"\"\n text = text.replace(r'\"', r'\\\"')\n url = f'https://www.langndata.com/api/heb_parser?token=a70b54d01ef5e9c055ab9051b9deafee'\n _json = '{\"data\":\"' + text.strip() + '\"}'\n sleep(3)\n r = requests.post(url, data=_json.encode('utf-8'), headers={'Content-type': 'application/json; charset=utf-8'},\n verify=False)\n json_obj = r.json()\n md_lattice = json_obj[\"md_lattice\"]\n res_df = pd.io.json.json_normalize([md_lattice[i] for i in md_lattice.keys()])\n print(res_df)\n return res_df\n\n\ndef get_text_statistics(text, text_name):\n \"\"\"\n this function return the text statistics\n \"\"\"\n plot_top_20_common_words(text, \"commonWords_\" + text_name)\n plot_top_non_stop_words_barchart(text, \"commonNonStopwords_\" + text_name)\n plot_top_20_bigrams_words(text, \"commonBigrams_\" + text_name)\n plot_top_bigrams_non_stop_words_barchart(text, \"commonBigramsNonStopwords_\" + text_name)\n\n","sub_path":"text_statistics.py","file_name":"text_statistics.py","file_ext":"py","file_size_in_byte":5371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"480083347","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 28 14:24:33 2019\r\n\r\n@author: mxc18bsu\r\n\"\"\"\r\nimport os\r\nimport pandas as pd\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.feature_extraction import text \r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn.dummy import DummyClassifier\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.metrics import roc_auc_score, roc_curve\r\nimport matplotlib.pyplot as plt\r\nimport scipy.stats as stats\r\nfrom sklearn import metrics\r\nfrom sklearn.linear_model import Perceptron\r\nfrom sklearn.neural_network import MLPClassifier\r\n\r\nmy_additional_stop_words = [\"allow\",\"allowed\",\"dismiss\",\"dismissed\"]\r\nstop_words = text.ENGLISH_STOP_WORDS.union(my_additional_stop_words)\r\n\r\n\r\n\r\n\r\n####################### load in data\r\nos.chdir('U:\\Documents\\PyProjects\\dissertation\\\\texts')\r\ntotal1 = pd.read_pickle(\"Text_Documents_unbalanced_PP2\")\r\ntotal2 = total1.loc[total1['Label'].isin(['Against','For'])]\r\nX = total2['modText1'].values\r\ny = total2['Label'].values\r\nEncoder = LabelEncoder()\r\ny = Encoder.fit_transform(y)\r\n#list(Encoder.inverse_transform([0,1,2]))\r\n#remove 20% for validation set to be used for parameter tuning\r\n#remaining 80% to be used in CV\r\nX, X_val, y, y_val = train_test_split(X, y,stratify=y, test_size=0.2)\r\n\r\n###scoring function\r\n\r\ndef scorer(classifier,features,y_input):\r\n kf = KFold(n_splits=5)\r\n cv_scores = cross_val_score(classifier, features, y_input, cv=kf,scoring='accuracy')\r\n print(sum(cv_scores)/len(cv_scores))\r\n cv_scores = cross_val_score(classifier, features, y_input, cv=kf,scoring='f1_macro')\r\n print(\"f1_macro: \"+str(sum(cv_scores)/len(cv_scores)))\r\n cv_scores = cross_val_score(classifier, features, y_input, cv=kf,scoring='precision_macro')\r\n print(\"precision_macro: \"+str(sum(cv_scores)/len(cv_scores)))\r\n cv_scores = cross_val_score(classifier, features, y_input, cv=kf,scoring='recall_macro')\r\n print(\"recall_macro: \"+str(sum(cv_scores)/len(cv_scores)))\r\n\r\n\r\n### Dummy classifier\r\n\r\ncv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,6),\r\n min_df=1)\r\nfeatures_count = cv.fit_transform(X)\r\ndummy = DummyClassifier(strategy=\"uniform\")\r\nscorer(dummy,features_count,y)\r\n\r\n\r\n\r\n### classifiers\r\n\r\n#count features\r\ndef tfidfvector1():\r\n for i in [1,2,3,4]:\r\n cv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=i)\r\n features_count = cv.fit_transform(X)\r\n \r\n svclassifier = SVC(kernel='linear') \r\n \r\n scorer(svclassifier,features_count,y)\r\n \r\n\r\ndef tfidfvector2():\r\n for i in [1,2,3,4]:\r\n cv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=i)\r\n features_count = cv.fit_transform(X)\r\n \r\n rf = RandomForestClassifier(n_estimators=100, max_depth=20,random_state=0)\r\n \r\n scorer(rf,features_count,y)\r\n\r\n \r\ndef tfidfvector3():\r\n for i in [1,2,3,4]:\r\n cv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=i)\r\n features_count = cv.fit_transform(X)\r\n \r\n lr = LogisticRegression()\r\n \r\n scorer(lr,features_count,y)\r\n \r\n \r\ndef tfidfvector4():\r\n for i in [1,2,3,4]:\r\n cv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=i)\r\n features_count = cv.fit_transform(X)\r\n \r\n knc = KNeighborsClassifier(n_neighbors=5)\r\n \r\n scorer(knc,features_count,y) \r\n\r\ndef tfidfvector5():\r\n #for i in [200,500,800,1000,2000,3000,4000,5000,6000,7000,8000,10000]:\r\n cv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,4),\r\n min_df=3)\r\n features_count = cv.fit_transform(X)\r\n \r\n slp = Perceptron(tol=1e-3, random_state=0)\r\n \r\n scorer(slp,features_count,y) \r\n\r\ndef tfidfvector6():\r\n #for i in [1,2,3,4,5,6]:\r\n cv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,2),\r\n min_df=1)\r\n features_count = cv.fit_transform(X)\r\n \r\n mlp = MLPClassifier(solver='lbfgs',activation='relu', alpha=1e-5, hidden_layer_sizes=(2,2), random_state=1)\r\n \r\n scorer(mlp,features_count,y) \r\n\r\nprint(\"SVM\")\r\ntfidfvector1()\r\nprint(\"RF\")\r\ntfidfvector2()\r\nprint(\"LR\")\r\ntfidfvector3()\r\nprint(\"k-NN\")\r\ntfidfvector4()\r\nprint(\"slp\")\r\ntfidfvector5()\r\nprint(\"mlp\")\r\ntfidfvector6()\r\n\r\n\r\n\r\n\r\n############## model confidence plot\r\n\r\nX, X_test, y, y_test = train_test_split(X, y,stratify=y, test_size=0.2)\r\n\r\ncv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,4),\r\n min_df=1)\r\nfeatures_count = cv.fit_transform(X)\r\n\r\nrf = RandomForestClassifier(n_estimators=100, max_depth=20,random_state=0)\r\nclassifier = rf.fit(features_count,y)\r\n\r\nX_predictions = cv.transform(X_test)\r\npredictions = classifier.predict_proba(X_predictions) \r\n\r\n#labels = ['Against', 'For', 'Split']\r\n\r\ny_test_l = []\r\ny_test_l = list(y_test)\r\n\r\ndataset = pd.DataFrame({'Label':y_test_l,'Label 0 (Against)':predictions[:,0],'Label 1 (for)':predictions[:,1]})\r\ndataset.sort_values(by=['Label'])\r\n\r\n\r\n\r\nfig = plt.figure(figsize=(10, 5))\r\nplt.scatter(dataset['Label'],dataset['Label 0 (Against)'], color='g',label='Label 0 (against)',alpha=0.3)\r\nplt.scatter(dataset['Label'],dataset['Label 1 (for)'], color='r',label='label 1 (for)',alpha=0.3)\r\nplt.ylabel('Confidence')\r\nplt.xlabel('Accuracy')\r\nplt.title('Scatterplot of Confidence by Category')\r\nplt.legend(loc='lower right')\r\nplt.show()\r\nfig.savefig('model_confidence',dpi=300)\r\n\r\n\r\nplt.xticks(4, labels, size='small')\r\n\r\n\r\n\r\n\r\n#################################\r\n#statistical test for model confidence\r\ndataset0 = dataset[dataset['Label']==0]\r\ndataset1 = dataset[dataset['Label']==1]\r\ndataset2 = dataset[dataset['Label']==2]\r\n\r\n\r\nt_stat, p_val = stats.ttest_ind(dataset0.iloc[:,1], dataset0.iloc[:,2], equal_var=False)\r\nprint(t_stat,p_val)\r\nprint(dataset0.iloc[:,1].mean()-dataset0.iloc[:,2].mean())\r\n\r\nt_stat, p_val = stats.ttest_ind(dataset1.iloc[:,1], dataset1.iloc[:,2], equal_var=False)\r\nprint(t_stat,p_val)\r\nprint(dataset1.iloc[:,1].mean()-dataset1.iloc[:,2].mean())\r\n\r\nt_stat, p_val = stats.ttest_ind(dataset2.iloc[:,1], dataset2.iloc[:,2], equal_var=False)\r\nprint(t_stat,p_val)\r\nprint(dataset2.iloc[:,1].mean()-dataset2.iloc[:,2].mean())\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n##################plot max_feature size accuracy by model\r\n#number of features results\r\nsvmlist = [0.5745954962468722,0.5618682235195998,0.5683069224353628,0.5719266055045872,0.5554545454545454,0.5628023352793995,0.558256880733945,0.5646455379482902,0.5692160133444537,0.5737781484570474,0.5765221017514596,0.5755879899916596]\r\nrflist = [0.6121100917431193,0.6039199332777315,0.6532944120100084,0.6450458715596331,0.6568890742285237,0.6569224353628023,0.6606005004170142,0.6742201834862386,0.6642201834862386,0.6861801501251042,0.688023352793995,0.709933277731443]\r\nlrlist = [0.5883319432860717,0.5682902418682236,0.5700834028356965,0.5792243536280234,0.5746955796497082,0.5802001668056713,0.5756380316930776,0.5765721434528774,0.5820266889074228,0.588465387823186,0.5929941618015013,0.5865721434528774]\r\nknclist = [0.5252210175145955,0.5262218515429524,0.5160550458715597,0.5315763135946623,0.5480900750625521,0.5480316930775647,0.5435029190992494,0.5480567139282736,0.5407673060884071,0.5444286905754796,0.5288490408673895,0.5562885738115096]\r\nslp = [0.5361861982299938,0.5647629742520254,0.5339696799550815,0.5200903719151895,0.534001764658699,0.54870190636614,0.5325472580947033,0.5325472580947034,0.547159166867196,0.5610866019625144,0.5398385069917917,0.5574235982995107]\r\nmlp = [0.45572310900778057,0.45572310900778057,0.5435202267319056,0.5340178070105077,0.5376647683217026,0.45572310900778057,0.5596214004973129,0.5325285420175931,0.45572310900778057,0.5479131574022085,0.45572310900778057,0.5632630143579049]\r\n#ngram results\r\nsvmlist = [0.5562135112593828,0.5644370308590492,0.5854795663052543,0.5918849040867389,0.5909591326105088,0.5918598832360301]\r\nrflist = [0.6019849874895746,0.6330859049207673,0.6458632193494578,0.6787739783152628,0.6604503753127606,0.665095913261051]\r\nlrlist = [0.5653294412010009,0.5699165971643035,0.5808924103419516,0.5790575479566304,0.5827272727272728,0.5809090909090908]\r\nknclist = [0.5204503753127606,0.5323603002502085,0.5205838198498749,0.5314595496246872,0.5360967472894079,0.532418682235196]\r\nslp = [0.5479211785781128,0.5164701478570092,0.5178818748161814,0.5413277719847063,0.5501216545012165,0.5493409267131895]\r\nmlp = [0.542060372717307,0.5728001925082217,0.46667201411726955,0.5640089837170128,0.45572310900778057,0.55633347897441]\r\n\r\n#min occurences results\r\nsvmlist = [0.5854795663052543,0.5790408673894912,0.5699082568807341,0.5717347789824854]\r\nrflist = [0.6111592994161802,0.6486321934945789,0.6458632193494578,0.6687823185988323]\r\nlrlist = [0.5808924103419516,0.5781484570475396,0.577256046705588,0.5735863219349457]\r\nknclist = [0.5205838198498749,0.5342118432026689,0.5387989991659716,0.535187656380317]\r\nslp = [0.5178818748161814,0.5201010668163952,0.5537846581642202,0.5427902997246064]\r\nmlp = [0.5640089837170128,0.45572310900778057,0.5559610705596107,0.45572310900778057]\r\n\r\n#### \r\nx=[200,500,800,1000,2000,3000,4000,5000,6000,7000,8000,10000]\r\nfig = plt.figure(figsize=(10, 5))\r\nplt.plot(x,rflist, color='plum',label='Random Forest',linewidth=3)\r\nplt.plot(x,svmlist, color='aqua',label='SVM',linewidth=3)\r\nplt.plot(x,lrlist, color='royalblue',label='Logistic Regression',linewidth=3)\r\nplt.plot(x,knclist, color='darkturquoise',label='k-NN',linewidth=3)\r\nplt.plot(x,slp, color='limegreen',label='SLP',linewidth=3)\r\nplt.plot(x,mlp, color='darkviolet',label='MLP',linewidth=3)\r\nplt.ylabel('Accuracy')\r\nplt.xlabel('Maximum Model Features')\r\nplt.title('Plot of model accuracy against maximum number of features')\r\nplt.legend(loc='upper right')\r\nplt.show()\r\nfig.savefig('accuracy_features',dpi=300)\r\n\r\nx=[1,2,3,4,5,6]\r\nfig = plt.figure(figsize=(10, 5))\r\nplt.plot(x,rflist, color='plum',label='Random Forest',linewidth=3)\r\nplt.plot(x,svmlist, color='aqua',label='SVM',linewidth=3)\r\nplt.plot(x,lrlist, color='royalblue',label='Logistic Regression',linewidth=3)\r\nplt.plot(x,knclist, color='darkturquoise',label='k-NN',linewidth=3)\r\nplt.plot(x,slp, color='limegreen',label='SLP',linewidth=3)\r\nplt.plot(x,mlp, color='darkviolet',label='MLP',linewidth=3)\r\nplt.ylabel('Accuracy %')\r\nplt.xlabel('Maximum n')\r\nplt.title('Plot of model accuracy against maximum n-gram value')\r\nplt.legend(loc='upper left')\r\nplt.show()\r\nfig.savefig('accuracy_n',dpi=300)\r\n\r\nx=[1,2,3,4]\r\nfig = plt.figure(figsize=(10, 5))\r\nplt.plot(x,rflist, color='plum',label='Random Forest',linewidth=3)\r\nplt.plot(x,svmlist, color='aqua',label='SVM',linewidth=3)\r\nplt.plot(x,lrlist, color='royalblue',label='Logistic Regression',linewidth=3)\r\nplt.plot(x,knclist, color='darkturquoise',label='k-NN',linewidth=3)\r\nplt.plot(x,slp, color='limegreen',label='SLP',linewidth=3)\r\nplt.plot(x,mlp, color='darkviolet',label='MLP',linewidth=3)\r\nplt.ylabel('Accuracy %')\r\nplt.xlabel('Minimum Occurence')\r\naxes= plt.axes()\r\naxes.set_xticks([1,2,3,4])\r\nplt.title('Plot of model accuracy against minimum number of feature occurrences')\r\nplt.legend(loc='upper left')\r\nplt.show()\r\nfig.savefig('accuracy_occurences',dpi=300)\r\n\r\n\r\n##########roc curves\r\n# roc curve and auc\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom itertools import cycle\r\nimport sklearn.metrics as metrics\r\nfrom sklearn import svm, datasets\r\nfrom sklearn.metrics import roc_curve, auc\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import label_binarize\r\nfrom sklearn.multiclass import OneVsRestClassifier\r\nfrom scipy import interp\r\nfrom sklearn.decomposition import LatentDirichletAllocation\r\n\r\n\r\nX, X_test, y, y_test = train_test_split(X, y,stratify=y, test_size=0.2)\r\n\r\n####\r\ncv = TfidfVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=3)\r\nfeatures_count = cv.fit_transform(X)\r\nfeatures_test = cv.transform(X_test)\r\nclf = RandomForestClassifier(n_estimators=100, max_depth=20,random_state=0)\r\n\r\nclassifier = clf.fit(features_count, y)\r\npredictions = classifier.predict_proba(features_test) \r\n\r\npreds = predictions[:,1]\r\nfpr_TFIDF, tpr_TFIDF, threshold = metrics.roc_curve(y_test, preds)\r\nroc_auc_TFIDF = metrics.auc(fpr_TFIDF, tpr_TFIDF)\r\n####\r\ncv = CountVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=3)\r\nfeatures_count = cv.fit_transform(X)\r\nfeatures_test = cv.transform(X_test)\r\nclf = RandomForestClassifier(n_estimators=100, max_depth=20,random_state=0)\r\n\r\nclassifier = clf.fit(features_count, y)\r\npredictions = classifier.predict_proba(features_test) \r\n\r\npreds = predictions[:,1]\r\nfpr_COUNT, tpr_COUNT, threshold = metrics.roc_curve(y_test, preds)\r\nroc_auc_COUNT = metrics.auc(fpr_COUNT, tpr_COUNT)\r\n####\r\ncv = CountVectorizer(\r\n analyzer = \"word\", \r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = stop_words, \r\n lowercase=True,\r\n ngram_range=(1,3),\r\n min_df=3)\r\nfeatures_count = cv.fit_transform(X)\r\nfeatures_test = cv.transform(X_test)\r\nno_topics = 35\r\nlda_count = LatentDirichletAllocation(n_components=no_topics, max_iter=5, \r\n learning_method='online', learning_offset=50.,\r\n random_state=0).fit_transform(features_count)\r\nlda_test = LatentDirichletAllocation(n_components=no_topics, max_iter=5, \r\n learning_method='online', learning_offset=50.,\r\n random_state=0).fit_transform(features_test)\r\n\r\nclf = RandomForestClassifier(n_estimators=100, max_depth=20,random_state=0)\r\n\r\nclassifier = clf.fit(lda_count, y)\r\npredictions = classifier.predict_proba(lda_test) \r\n\r\npreds = predictions[:,1]\r\nfpr_LDA, tpr_LDA, threshold = metrics.roc_curve(y_test, preds)\r\nroc_auc_LDA = metrics.auc(fpr_LDA, tpr_LDA)\r\n######\r\n\r\n\r\n\r\n#######\r\n\r\nfig = plt.figure()\r\n\r\nplt.subplot(2, 2, 1)\r\nplt.title('Count Vectors - RF')\r\nplt.plot(fpr_COUNT, tpr_COUNT, color='darkturquoise', label = 'AUC = %0.2f' % roc_auc_COUNT,linewidth=3)\r\nplt.legend(loc = 'lower right')\r\nplt.plot([0, 1], [0, 1],'r--',color='black')\r\nplt.xlim([0, 1])\r\nplt.ylim([0, 1])\r\nplt.ylabel('True Positive Rate')\r\nplt.xlabel('False Positive Rate')\r\nplt.tight_layout()\r\n\r\nplt.subplot(2, 2, 2)\r\nplt.title('TFIDF Vectors - RF')\r\nplt.plot(fpr_TFIDF, tpr_TFIDF, color='darkturquoise', label = 'AUC = %0.2f' % roc_auc_TFIDF,linewidth=3)\r\nplt.legend(loc = 'lower right')\r\nplt.plot([0, 1], [0, 1],'r--',color='black')\r\nplt.xlim([0, 1])\r\nplt.ylim([0, 1])\r\nplt.ylabel('True Positive Rate')\r\nplt.xlabel('False Positive Rate')\r\nplt.tight_layout()\r\n\r\nplt.subplot(2, 2, 3)\r\nplt.title('Clusters - RF')\r\nplt.plot(fpr_LDA, tpr_LDA, color='darkturquoise', label = 'AUC = %0.2f' % roc_auc_LDA,linewidth=3)\r\nplt.legend(loc = 'lower right')\r\nplt.plot([0, 1], [0, 1],'r--',color='black')\r\nplt.xlim([0, 1])\r\nplt.ylim([0, 1])\r\nplt.ylabel('True Positive Rate')\r\nplt.xlabel('False Positive Rate')\r\nplt.tight_layout()\r\n\r\nplt.subplot(2, 2, 4)\r\nplt.title('Doc2Vec - LR')\r\nplt.plot(fpr_D2V, tpr_D2V, color='darkturquoise', label = 'AUC = %0.2f' % roc_auc_D2V,linewidth=3)\r\nplt.legend(loc = 'lower right')\r\nplt.plot([0, 1], [0, 1],'r--',color='black')\r\nplt.xlim([0, 1])\r\nplt.ylim([0, 1])\r\nplt.ylabel('True Positive Rate')\r\nplt.xlabel('False Positive Rate')\r\nplt.tight_layout()\r\n\r\n\r\nplt.show()\r\nfig.savefig('roc_1',dpi=300)\r\n\r\n\r\n\r\n\r\n####stats test\r\n\r\nfrom scipy.stats import spearmanr\r\nx = list(range(1,7))\r\nspearmanr(x, slp)\r\nspearmanr(x, mlp)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"models_ml_ngrams.py","file_name":"models_ml_ngrams.py","file_ext":"py","file_size_in_byte":18814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"344969296","text":"import logging\nlog = logging.getLogger(__name__)\n\nimport lmfit\nimport numpy as np\nfrom scipy import optimize\nfrom collections import OrderedDict\nfrom pycqed.analysis import fitting_models as fit_mods\nfrom pycqed.analysis_v3 import fitting as fit_mod\nfrom pycqed.analysis_v3 import plotting as plot_mod\nfrom pycqed.analysis_v3 import helper_functions as hlp_mod\nfrom pycqed.analysis_v3 import processing_pipeline as pp_mod\nfrom pycqed.measurement import sweep_points as sp_mod\nfrom pycqed.measurement.calibration import calibration_points as cp_mod\nfrom copy import deepcopy\n\nimport sys\npp_mod.search_modules.add(sys.modules[__name__])\n\n# Create pipelines\n\n\ndef pipeline_single_qubit_rb_ssro(meas_obj_names, mospm, sweep_points,\n n_shots, dim_hilbert, cal_points=None,\n ro_thresholds=None, nreps=1,\n plot_all_shots=False, sweep_type=None,\n processing_pipeline=None):\n\n \"\"\"\n Wrapper to create the standard processing pipeline for an single qubit RB\n measurement, measured in SSRO.\n WARNING: if you use plot_all_shots=True, disable data saving. It will try\n to save a huge string of the large numpy array this node will generate.\n :param meas_obj_names: list of measured object names\n :param mospm: meas_obj_sweep_points_map\n :param sweep_points: SweepPoints object (of one file if the measurement\n was split into several files)\n :param n_shots: number of shots\n :param dim_hilbert: dimension of Hilebert space. 4 for 2QB RB, 2 for 1QB RB\n :param cal_points: CalibrationPoints object\n :param ro_thresholds: optional (the threshold_data node can also extract\n them from the data_dict. See docstring there).\n Dict with meas_obj_names as keys and their readout thresholds as values.\n :param nreps: int specifying the number of files to combine into one\n measurement. IMPORTANT! This feature only works if the measurement was\n split by seeds, not by cliffords. Meaning that each measurement file\n contains data for all the Cliffords in sweep_points, but for a subset\n of the total seeds.\n :param plot_all_shots: bool specifying whether to produce a raw plot of\n of all the shots vs cliffords. SEE WARNING ABOVE.\n :param sweep_type: dict of the form\n {'cliffords': sweep_dim, 'seeds': sweep_dim} where sweep_dim is either\n 0 or 1 and specifies whether the measurement was run with seeds in the\n fast dimension (0) and cliffords in the slow dimensino (1), or the other\n way around.\n :param processing_pipeline: ProcessingPipeline instance to which this\n function will append.\n :return: the unresolved ProcessingPipeline\n \"\"\"\n if sweep_type is None:\n sweep_type = {'cliffords': 0, 'seeds': 1}\n slow_cliffords = sweep_type['cliffords'] == 1\n\n sweep_points = sp_mod.SweepPoints(sweep_points)\n if cal_points is None:\n num_cal_states = 0\n else:\n if isinstance(cal_points, str):\n cal_points = cp_mod.CalibrationPoints.from_string(cal_points)\n num_cal_states = len(cal_points.states)\n if slow_cliffords:\n # n_segments = nr_seeds + nr_cal_segments\n n_segments = nreps*(sweep_points.length(sweep_type['seeds']) +\n num_cal_states)\n # n_sequences = nr_cliffords\n n_sequences = sweep_points.length(sweep_type['cliffords'])\n else:\n # n_segments = nr_cliffords + nr_cal_segments\n n_segments = nreps*(sweep_points.length(sweep_type['cliffords']) +\n num_cal_states)\n # n_sequences = nr_seeds\n n_sequences = sweep_points.length(sweep_type['seeds'])\n\n if processing_pipeline is None:\n processing_pipeline = pp_mod.ProcessingPipeline()\n if nreps > 1:\n processing_pipeline.add_node('combine_datafiles_split_by_seeds',\n keys_in='raw',\n n_shots=n_shots,\n meas_obj_names=meas_obj_names)\n keys_in = 'previous combine_datafiles_split_by_seeds' if nreps > 1 else 'raw'\n processing_pipeline.add_node('threshold_data',\n keys_in=keys_in,\n ro_thresholds=ro_thresholds,\n meas_obj_names=meas_obj_names)\n processing_pipeline.add_node('average_data',\n # shape=(n_shots, n_segments*n_sequences),\n # averaging_axis=0,\n shape=(n_sequences, n_shots, n_segments),\n averaging_axis=1,\n keys_in='previous threshold_data',\n meas_obj_names=meas_obj_names)\n for label in ['rb']:\n pp = pp_mod.ProcessingPipeline(keys_out_container=label)\n pp.add_node('average_data',\n shape=(n_sequences, n_segments),\n averaging_axis=-1 if slow_cliffords else 0,\n keys_in='previous average_data',\n meas_obj_names=meas_obj_names)\n pp.add_node('get_std_deviation',\n shape=(n_sequences, n_segments) ,\n averaging_axis=-1 if slow_cliffords else 0,\n keys_in='previous average_data',\n meas_obj_names=meas_obj_names)\n pp.add_node('rb_analysis',\n d=dim_hilbert,\n sweep_type=sweep_type,\n keys_in=f'previous {label}.average_data',\n keys_in_std=f'previous {label}.get_std_deviation',\n keys_in_all_seeds_data='previous average_data',\n do_plotting=False,\n keys_out=None,\n meas_obj_names=meas_obj_names)\n for mobjn in meas_obj_names:\n cliffords = sweep_points.get_sweep_params_property(\n 'values', sweep_type['cliffords'], mospm[mobjn][\n sweep_type['cliffords']])\n if plot_all_shots:\n pp.add_node('prepare_1d_raw_data_plot_dicts',\n sp_name=mospm[mobjn][sweep_type['cliffords']],\n xvals=np.repeat(cliffords, n_segments*n_shots\n if slow_cliffords else n_sequences*n_shots),\n do_plotting=False,\n figname_suffix=f'shots_{label}',\n title_suffix=' - All shots',\n plot_params={'linestyle': 'none'},\n keys_in=keys_in,\n keys_out=None,\n meas_obj_names=mobjn)\n if slow_cliffords:\n xvals = np.repeat(cliffords, n_segments)\n else:\n xvals = np.tile(cliffords, n_sequences)\n pp.add_node('prepare_1d_raw_data_plot_dicts',\n sp_name=mospm[mobjn][sweep_type['cliffords']],\n xvals=xvals,\n do_plotting=False,\n figname_suffix=f'{label}',\n title_suffix=' - All seeds',\n plot_params={'linestyle': 'none'},\n ylabel='Probability, $P(|e\\\\rangle)$',\n yunit='',\n keys_in='previous average_data',\n keys_out=None,\n meas_obj_names=mobjn)\n processing_pipeline += pp\n\n # do plotting of all plot_dicts in the data_dict\n processing_pipeline.add_node('plot')\n\n return processing_pipeline\n\n\ndef pipeline_interleaved_rb_irb_classif(meas_obj_names, mospm, sweep_points,\n dim_hilbert, cal_points=None, nreps=1,\n sweep_type=None,\n processing_pipeline=None):\n \"\"\"\n Wrapper to create the standard processing pipeline for an interleaved RB/RIB\n measurement, measured with a the classifier detector with qutrit readout\n :param meas_obj_names: list of measured object names\n :param mospm: meas_obj_sweep_points_map\n :param sweep_points: SweepPoints object (of one file if the measurement\n was split into several files)\n :param dim_hilbert: dimension of Hilebert space. 4 for 2QB RB, 2 for 1QB RB\n :param cal_points: CalibrationPoints object\n :param nreps: int specifying the number of files to combine into one\n measurement. IMPORTANT! This feature only works if the measurement was\n split by seeds, not by cliffords. Meaning that each measurement file\n contains data for all the Cliffords in sweep_points, but for a subset\n of the total seeds.\n :param sweep_type: dict of the form\n {'cliffords': sweep_dim, 'seeds': sweep_dim} where sweep_dim is either\n 0 or 1 and specifies whether the measurement was run with seeds in the\n fast dimension (0) and cliffords in the slow dimensino (1), or the other\n way around.\n :param processing_pipeline: ProcessingPipeline instance to which this\n function will append.\n :return: the unresolved ProcessingPipeline\n \"\"\"\n if sweep_type is None:\n sweep_type = {'cliffords': 0, 'seeds': 1}\n slow_cliffords = sweep_type['cliffords'] == 1\n\n sweep_points = sp_mod.SweepPoints(sweep_points)\n if cal_points is None:\n num_cal_states = 0\n else:\n if isinstance(cal_points, str):\n cal_points = cp_mod.CalibrationPoints.from_string(cal_points)\n num_cal_states = len(cal_points.states)\n\n if slow_cliffords:\n # n_segments = nr_seeds + nr_cal_segments\n n_segments = nreps*(sweep_points.length(sweep_type['seeds'])\n + num_cal_states)\n # n_sequences = nr_cliffords\n n_sequences = sweep_points.length(sweep_type['cliffords'])\n else:\n # n_segments = nr_cliffords + nr_cal_segments\n n_segments = nreps*(sweep_points.length(sweep_type['cliffords'])\n + num_cal_states)\n # n_sequences = nr_seeds\n n_sequences = sweep_points.length(sweep_type['seeds'])\n\n if processing_pipeline is None:\n processing_pipeline = pp_mod.ProcessingPipeline()\n if nreps > 1:\n processing_pipeline.add_node('combine_datafiles_split_by_seeds',\n keys_in='raw',\n interleaved_irb=True,\n sweep_type=sweep_type,\n meas_obj_names=meas_obj_names)\n for label in ['rb', 'irb']:\n pp = pp_mod.ProcessingPipeline(global_keys_out_container=label)\n keys_in = 'previous combine_datafiles_split_by_seeds' if \\\n nreps > 1 else 'raw'\n pp.add_node('submsmt_data_from_interleaved_msmt', msmt_name=label,\n keys_in=keys_in, meas_obj_names=meas_obj_names)\n pp.add_node('average_data',\n shape=(n_sequences, n_segments),\n averaging_axis=-1 if slow_cliffords else 0,\n keys_in=f'previous {label}.submsmt_'\n f'data_from_interleaved_msmt',\n meas_obj_names=meas_obj_names)\n pp.add_node('get_std_deviation',\n shape=(n_sequences, n_segments),\n averaging_axis=-1 if slow_cliffords else 0,\n keys_in=f'previous {label}.submsmt_'\n f'data_from_interleaved_msmt',\n meas_obj_names=meas_obj_names)\n pp.add_node('rb_analysis',\n d=dim_hilbert,\n keys_in=f'previous {label}.average_data',\n keys_in_std=f'previous {label}.get_std_deviation',\n keys_in_all_seeds_data=f'previous {label}.submsmt_'\n f'data_from_interleaved_msmt',\n do_plotting=False,\n keys_out=None,\n meas_obj_names=meas_obj_names)\n for mobjn in meas_obj_names:\n cliffords = sweep_points.get_sweep_params_property(\n 'values', sweep_type['cliffords'], mospm[mobjn][\n sweep_type['cliffords']])\n pp.add_node('prepare_1d_raw_data_plot_dicts',\n sp_name=mospm[mobjn][-1],\n xvals=np.repeat(cliffords, n_segments),\n do_plotting=False,\n figname_suffix=f'{label}',\n title_suffix=' - All seeds',\n plot_params={'linestyle': 'none'},\n ylabel='Probability, $P(|ee\\\\rangle)$' if\n mobjn=='correlation_object' else None,\n yunit='',\n keys_in=f'previous {label}.submsmt_'\n f'data_from_interleaved_msmt',\n keys_out=None,\n meas_obj_names=mobjn)\n processing_pipeline += pp\n\n # calculate interleaved gate error\n processing_pipeline.add_node('irb_gate_error',\n meas_obj_names='correlation_object',\n d=dim_hilbert)\n\n # do plotting of all plot_dicts in the data_dict\n processing_pipeline.add_node('plot')\n\n return processing_pipeline\n\n\ndef pipeline_ssro_measurement(meas_obj_names, mospm, sweep_points, n_shots,\n dim_hilbert, ro_thresholds=None,\n nreps=1, interleaved_irb=False, sweep_type=None,\n plot_all_shots=False, processing_pipeline=None,\n compression_factor=1, **params):\n\n \"\"\"\n Wrapper to create the standard processing pipeline for an interleaved RB/RIB\n measurement, measured in SSRO.\n WARNING: if you use plot_all_shots=True, disable data saving. It will try\n to save a huge string of the large numpy array this node will generate.\n :param meas_obj_names: list of measured object names\n :param mospm: meas_obj_sweep_points_map\n :param sweep_points: SweepPoints object (of one file if the measurement\n was split into several files)\n :param n_shots: number of shots\n :param dim_hilbert: dimension of Hilebert space. 4 for 2QB RB, 2 for 1QB RB\n :param cal_points: CalibrationPoints object\n :param ro_thresholds: optional (the threshold_data node can also extract\n them from the data_dict. See docstring there).\n Dict with meas_obj_names as keys and their readout thresholds as values.\n :param nreps: int specifying the number of files to combine into one\n measurement. IMPORTANT! This feature only works if the measurement was\n split by seeds, not by cliffords. Meaning that each measurement file\n contains data for all the Cliffords in sweep_points, but for a subset\n of the total seeds.\n :param interleaved_irb: bool specifying whether the measurement was\n IRB with RB and IRB interleaved.\n :param plot_all_shots: bool specifying whether to produce a raw plot of\n of all the shots vs cliffords. SEE WARNING ABOVE.\n :param sweep_type: dict of the form\n {'cliffords': sweep_dim, 'seeds': sweep_dim} where sweep_dim is either\n 0 or 1 and specifies whether the measurement was run with seeds in the\n fast dimension (0) and cliffords in the slow dimensino (1), or the other\n way around.\n :param compression_factor: sequence compression factor\n :param processing_pipeline: ProcessingPipeline instance to which this\n function will append.\n :return: the unresolved ProcessingPipeline\n \"\"\"\n if sweep_type is None:\n sweep_type = {'cliffords': 0, 'seeds': 1}\n slow_cliffords = sweep_type['cliffords'] == 1\n\n nr_swpts0 = sweep_points.length(0)\n nr_swpts1 = sweep_points.length(1)\n n_segments = nr_swpts0 * compression_factor\n n_sequences = (nr_swpts1 * (interleaved_irb + 1)) // compression_factor\n\n if processing_pipeline is None:\n processing_pipeline = pp_mod.ProcessingPipeline()\n if nreps > 1:\n processing_pipeline.add_node('combine_datafiles_split_by_seeds',\n keys_in='raw',\n n_shots=n_shots,\n sweep_type=sweep_type,\n interleaved_irb=interleaved_irb,\n meas_obj_names=meas_obj_names)\n keys_in = 'previous combine_datafiles_split_by_seeds' if nreps > 1 else 'raw'\n processing_pipeline.add_node('threshold_data',\n keys_in=keys_in,\n ro_thresholds=ro_thresholds,\n meas_obj_names=meas_obj_names)\n processing_pipeline.add_node('average_data',\n shape=(n_sequences, n_shots, n_segments),\n final_shape=(n_sequences*n_segments),\n averaging_axis=1,\n keys_in='previous threshold_data',\n meas_obj_names=meas_obj_names)\n if plot_all_shots:\n for mobjn in meas_obj_names:\n cliffords = sweep_points.get_sweep_params_property(\n 'values', sweep_type['cliffords'], mospm[mobjn])[0]\n keys_in = 'previous combine_datafiles_split_by_seeds' \\\n if nreps > 1 else 'raw'\n if slow_cliffords:\n xvals = np.repeat(cliffords, 2*n_segments*n_shots if\n interleaved_irb else n_segments*n_shots)\n else:\n xvals = np.repeat(cliffords, n_sequences*n_shots)\n processing_pipeline.add_node('prepare_1d_raw_data_plot_dicts',\n sp_name=mospm[mobjn][-1],\n xvals=xvals,\n do_plotting=False,\n figname_suffix=f'shots',\n title_suffix=' - All shots',\n plot_params={'linestyle': 'none'},\n keys_in=keys_in,\n keys_out=None,\n meas_obj_names=mobjn)\n\n if dim_hilbert == 4:\n processing_pipeline.add_node('correlate_qubits',\n keys_in='previous threshold_data',\n meas_obj_names=meas_obj_names,\n joint_processing=True, num_keys_out=1,\n keys_out_container='correlation_object',\n add_mobjn_container=False)\n processing_pipeline.add_node('average_data',\n shape=(n_sequences, n_shots, n_segments),\n final_shape=(n_sequences*n_segments),\n averaging_axis=1,\n keys_in='previous correlate_qubits',\n meas_obj_names=['correlation_object'])\n\n meas_obj_names = deepcopy(meas_obj_names)\n meas_obj_names += ['correlation_object']\n mospm['correlation_object'] = list(mospm.values())[0]\n labels = ['rb', 'irb'] if interleaved_irb else ['rb']\n for label in labels:\n pp = pp_mod.ProcessingPipeline(keys_out_container=label)\n keys_in_0 = 'previous average_data'\n if interleaved_irb:\n pp.add_node('submsmt_data_from_interleaved_msmt',\n msmt_name=label,\n keys_in='previous average_data',\n meas_obj_names=meas_obj_names)\n keys_in_0 = f'previous {label}.submsmt_data_from_interleaved_msmt'\n pp.add_node('average_data',\n shape=(nr_swpts1, nr_swpts0),\n averaging_axis=-1 if slow_cliffords else 0,\n keys_in=keys_in_0,\n meas_obj_names=meas_obj_names)\n pp.add_node('get_std_deviation',\n shape=(nr_swpts1, nr_swpts0),\n averaging_axis=-1 if slow_cliffords else 0,\n keys_in=keys_in_0,\n meas_obj_names=meas_obj_names)\n pp.add_node('rb_analysis',\n d=dim_hilbert,\n sweep_type=sweep_type,\n msmt_type=label,\n state_prob_name='e' if dim_hilbert==2 else None,\n keys_in=f'previous {label}.average_data',\n keys_in_std=f'previous {label}.get_std_deviation',\n keys_in_all_seeds_data=keys_in_0,\n do_plotting=False,\n keys_out=None,\n meas_obj_names=meas_obj_names)\n for mobjn in meas_obj_names:\n cliffords = sweep_points.get_sweep_params_property(\n 'values', sweep_type['cliffords'], mospm[mobjn])[0]\n xvals = np.repeat(cliffords, nr_swpts0) if slow_cliffords else \\\n np.tile(cliffords, nr_swpts1)\n pp.add_node('prepare_1d_raw_data_plot_dicts',\n sp_name=mospm[mobjn][-1],\n xvals=xvals,\n do_plotting=False,\n figname_suffix=f'{label}',\n title_suffix=' - All seeds',\n plot_params={'linestyle': 'none'},\n ylabel='Probability, ' + ('$P(|ee\\\\rangle)$' if\n mobjn=='correlation_object' else '$P(|e\\\\rangle)$'),\n yunit='',\n keys_in=keys_in_0,\n keys_out=None,\n meas_obj_names=mobjn)\n processing_pipeline += pp\n\n if interleaved_irb:\n # calculate interleaved gate error\n processing_pipeline.add_node(\n 'irb_gate_error', meas_obj_names='correlation_object' if\n dim_hilbert == 4 else meas_obj_names, d=dim_hilbert)\n\n # do plotting of all plot_dicts in the data_dict\n if params.get('do_plotting', True):\n processing_pipeline.add_node('plot')\n\n return processing_pipeline\n\n\n# nodes related to extracting data\ndef combine_datafiles_split_by_seeds(data_dict, keys_in, keys_out,\n interleaved_irb=False, **params):\n \"\"\"\n NOT FULLY IMPLEMENTED FOR slow_cliffords == True!!!\n Combines the data from an (interleaved) RB/IRB measurement that was saved in\n multiple files into one data set that would look as if it had all been\n taken in one measurement (one file).\n :param data_dict: OrderedDict containing data to be processed and where\n processed data is to be stored\n :param keys_in: list of key names or dictionary keys paths in\n data_dict for the data to be processed\n :param keys_out: list of key names or dictionary keys paths in\n data_dict for the processed data to be saved into\n :param interleaved_irb: bool specifying whether the measurement was\n IRB with RB and IRB interleaved.\n :param params: keyword arguments:\n Should contain 'exp_metadata_list', 'n_shots', 'mospm', 'rev_movnm',\n 'cp' if they are not in data_dict\n ToDo: put n_shots info in the metadata (27.07.2020)\n :return:\n\n Assumptions:\n - ASSUMES MEASUREMENT WAS SPLIT BY SEEDS NOT BY CLIFFORDS. Meaning that\n each measurement file contains data for all the Cliffords in\n sweep_points, but for a subset of the total seeds.\n\n \"\"\"\n assert len(keys_in) == len(keys_out)\n\n n_shots = hlp_mod.get_param('n_shots', data_dict, default_value=1, **params)\n mospm, rev_movnm, cp, mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['mospm', 'rev_movnm', 'cp', 'mobjn'],\n **params)\n metadata_list = hlp_mod.get_param('exp_metadata_list', data_dict,\n raise_error=True, **params)\n sp_list = [hlp_mod.get_param('sweep_points', mdl, raise_error=True)\n for mdl in metadata_list]\n sp0 = sp_mod.SweepPoints(sp_list[0])\n\n nr_segments = sp0.length(0) + len(cp.states)\n nr_uploads = sp0.length(1)\n chunk = nr_segments*n_shots\n\n data_to_proc_dict = hlp_mod.get_data_to_process(data_dict, keys_in)\n for keyi, keyo in zip(keys_in, keys_out):\n data = data_to_proc_dict[keyi]\n if np.ndim(data) != 2:\n raise ValueError(f'Data corresponding to {keyi} is not 2D.')\n # take the segment_chunk * n_shots for each clifford from each row\n # (corresponding to data from one data file) in data and concatenate\n # them. Put all the nr_cliffords concatenations in the\n # list data_combined\n data_combined = [np.concatenate(\n [d[m * chunk + j * nr_segments: m * chunk + (j + 1) * nr_segments]\n for d in data])\n for m in np.arange((interleaved_irb + 1)*nr_uploads)\n for j in np.arange(n_shots)]\n # concatenate all the lists in data_combined to get one complete\n # array of data\n data_combined = np.concatenate(data_combined)\n hlp_mod.add_param(keyo, data_combined, data_dict, **params)\n\n # update the sweep_points if they were a list\n nr_sp0 = sp0.length(0)\n nr_exp = len(sp_list)\n sp_all_vals_list = [np.zeros(nr_exp*nr_sp0, dtype=int) for _\n in range(len(sp0.get_sweep_dimension(0)))]\n\n for i, sp in enumerate(sp_list):\n sp = sp_mod.SweepPoints(sp)\n sp_vals_list = sp.get_sweep_params_property('values', 0, 'all')\n for j, sp_vals in enumerate(sp_vals_list):\n sp_all_vals_list[j][i::nr_exp] = sp_vals\n\n sweep_points = sp_mod.SweepPoints()\n for i, sp_name in enumerate(sp0.get_sweep_dimension(0)):\n sweep_points.add_sweep_parameter(\n sp_name, sp_all_vals_list[i],\n sp0.get_sweep_params_property('unit', 0, sp_name),\n sp0.get_sweep_params_property('label', 0, sp_name))\n sweep_points += [sp0.get_sweep_dimension(1)]\n hlp_mod.add_param('exp_metadata.sweep_points', sweep_points,\n data_dict, add_param_method='replace')\n\n\ndef submsmt_data_from_interleaved_msmt(data_dict, keys_in, msmt_name,\n keys_out=None, sweep_type=None,\n **params):\n start_index = (msmt_name.lower() != 'rb')\n if sweep_type is None:\n sweep_type = {'cliffords': 0, 'seeds': 1}\n slow_cliffords = sweep_type['cliffords'] == 1\n\n n_shots = hlp_mod.get_param('n_shots', data_dict, default_value=1, **params)\n data_to_proc_dict = hlp_mod.get_data_to_process(data_dict, keys_in)\n sp, cp = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['sp', 'cp'], **params)\n nr_seeds = sp.length(sweep_type['seeds']) + len(cp.states)\n nr_cliffords = sp.length(sweep_type['cliffords'])\n nr_segments = (nr_seeds if slow_cliffords else nr_cliffords) + len(cp.states)\n nr_uploads = (nr_cliffords if slow_cliffords else nr_seeds)\n\n if keys_out is None:\n keys_out = [f'{msmt_name}_data_from_interleaved_msmt.{s}'\n for s in keys_in]\n for keyi, keyo in zip(keys_in, keys_out):\n data = data_to_proc_dict[keyi]\n if len(data) != nr_segments * (2 * nr_uploads):\n raise ValueError(f'The data has the wrong size of {len(data)}, '\n f'which is not expected for {nr_segments} '\n f'segments and {nr_uploads} uploads.')\n selected_data = np.concatenate([\n data[j*nr_segments*n_shots:(j+1)*nr_segments*n_shots]\n for j in np.arange(2*nr_uploads)[start_index::2]])\n hlp_mod.add_param(\n keyo, selected_data, data_dict, **params)\n\n\ndef rb_analysis(data_dict, keys_in, sweep_type=None, **params):\n \"\"\"\n Does single qubit RB analysis. Prepares fits and plots, and extracts\n errors per clifford.\n :param data_dict: OrderedDict containing data to be processed and where\n processed data is to be stored\n :param keys_in: list of key names or dictionary keys paths in\n data_dict for the data to be processed\n\n Assumptions:\n - cal_points, sweep_points, qb_sweep_points_map, qb_name exist in\n metadata or params\n - expects a 2d sweep with nr_seeds on innermost sweep and cliffords\n on outermost\n - if active reset was used, 'filter' must be in the key names of the\n filtered data if you want the filtered raw data to be plotted\n \"\"\"\n data_to_proc_dict = hlp_mod.get_data_to_process(data_dict, keys_in)\n keys_in = list(data_to_proc_dict)\n\n prep_fit_dicts = hlp_mod.pop_param('prep_fit_dicts', data_dict,\n default_value=True, node_params=params)\n do_fitting = hlp_mod.pop_param('do_fitting', data_dict,\n default_value=True, node_params=params)\n prepare_plotting = hlp_mod.pop_param('prepare_plotting', data_dict,\n default_value=True, node_params=params)\n do_plotting = hlp_mod.pop_param('do_plotting', data_dict,\n default_value=True, node_params=params)\n\n sp, mospm, mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['sp', 'mospm', 'mobjn'], **params)\n if sweep_type is None:\n sweep_type = {'cliffords': 0, 'seeds': 1}\n nr_seeds = sp.length(sweep_type['seeds'])\n if len(data_dict['timestamps']) > 1:\n nr_seeds *= len(data_dict['timestamps'])\n cliffords = sp.get_sweep_params_property('values', sweep_type['cliffords'],\n mospm[mobjn])[0]\n\n # prepare fitting\n if prep_fit_dicts:\n prepare_rb_fitting(data_dict, data_to_proc_dict, cliffords, nr_seeds,\n **params)\n\n if do_fitting:\n getattr(fit_mod, 'run_fitting')(data_dict, keys_in=list(\n data_dict['fit_dicts']),**params)\n # extract EPC, leakage, and seepage from fits and save to\n # data_dict[meas_obj_name]\n analyze_rb_fit_results(data_dict, keys_in, **params)\n\n # prepare plots\n if prepare_plotting:\n prepare_rb_plots(data_dict, keys_in, sweep_type, **params)\n if do_plotting:\n getattr(plot_mod, 'plot')(data_dict, keys_in=list(\n data_dict['plot_dicts']), **params)\n\n\ndef prepare_rb_fitting(data_dict, data_to_proc_dict, cliffords, nr_seeds,\n **params):\n cp, mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['cp', 'mobjn'], **params)\n conf_level = hlp_mod.get_param('conf_level', data_dict,\n default_value=0.68, **params)\n do_simple_fit = hlp_mod.get_param(\n 'do_simple_fit', data_dict, default_value=True, **params)\n d = hlp_mod.get_param('d', data_dict, raise_error=True, **params)\n print('d: ', d)\n guess_pars = {'A': {'value': 1},\n 'p': {'value': 0.99},\n 'B': {'value': 0}}\n fit_guess_params = hlp_mod.get_param('fit_guess_params', data_dict,\n default_value={}, **params)\n guess_pars.update(fit_guess_params)\n\n fit_dicts = OrderedDict()\n rb_mod = lmfit.Model(fit_mods.RandomizedBenchmarkingDecay)\n rb_mod.set_param_hint('Amplitude', **guess_pars['A'])\n rb_mod.set_param_hint('p', **guess_pars['p'])\n rb_mod.set_param_hint('offset', **guess_pars['B'])\n rb_mod.set_param_hint('fidelity_per_Clifford',\n expr=f'1-(({d}-1)*(1-p)/{d})')\n rb_mod.set_param_hint('error_per_Clifford',\n expr='1-fidelity_per_Clifford')\n gate_decomp = hlp_mod.get_param('gate_decomp', data_dict,\n default_value='HZ', **params)\n if gate_decomp == 'XY':\n rb_mod.set_param_hint('fidelity_per_gate',\n expr='fidelity_per_Clifford**(1./1.875)')\n elif gate_decomp == 'HZ':\n rb_mod.set_param_hint('fidelity_per_gate',\n expr='fidelity_per_Clifford**(1./1.125)')\n else:\n raise ValueError('Gate decomposition not recognized.')\n rb_mod.set_param_hint('error_per_gate', expr='1-fidelity_per_gate')\n guess_pars = rb_mod.make_params()\n\n keys_in_std = hlp_mod.get_param('keys_in_std', data_dict, raise_error=False,\n **params)\n if keys_in_std is None:\n keys_in_std = [''] * len(data_to_proc_dict)\n if len(keys_in_std) != len(data_to_proc_dict):\n raise ValueError('keys_in_std and keys_in do not have '\n 'the same length.')\n for keyi, keys in zip(data_to_proc_dict, keys_in_std):\n if 'pf' in keyi:\n # if this is the |f> state population data, then do an additional\n # fit based on the Google style\n fit_mod.prepare_rbleakage_fit_dict(\n data_dict, [keyi], indep_var_array=cliffords,\n fit_name='rbleak_fit', **params)\n\n # do standard fit to A*p**m + B\n key = 'rb_fit' + keyi\n data_fit = hlp_mod.get_msmt_data(data_to_proc_dict[keyi], cp, mobjn)\n\n model = deepcopy(rb_mod)\n fit_dicts[key] = {\n 'fit_fn': fit_mods.RandomizedBenchmarkingDecay,\n 'fit_xvals': {'numCliff': cliffords},\n 'fit_yvals': {'data': np.array(data_fit).flatten()},\n 'guess_pars': guess_pars}\n\n if do_simple_fit:\n fit_kwargs = {}\n elif keys is not None:\n stds = np.array(hlp_mod.get_param(keys, data_dict)).flatten()\n fit_kwargs = {'scale_covar': False,\n 'weights': 1/stds}\n else:\n # Run once to get an estimate for the error per Clifford\n fit_res = model.fit(data_fit, numCliff=cliffords,\n params=guess_pars)\n # Use the found error per Clifford to standard errors for\n # the data points fro Helsen et al. (2017)\n epsilon_guess = hlp_mod.get_param('epsilon_guess', data_dict,\n default_value=0.01, **params)\n epsilon = calculate_rb_confidence_intervals(\n nr_seeds=nr_seeds,\n nr_cliffords=cliffords,\n depolariz_param=fit_res.best_values['p'],\n conf_level=conf_level,\n epsilon_guess=epsilon_guess, d=2)\n\n hlp_mod.add_param(\n keys, epsilon, data_dict,\n add_param_method=params.get('add_param_method', None))\n # Run fit again with scale_covar=False, and\n # weights = 1/epsilon if an entry in epsilon_sqrd is 0,\n # replace it with half the minimum value in the epsilon_sqrd\n # array\n idxs = np.where(epsilon == 0)[0]\n epsilon[idxs] = min([eps for eps in epsilon if eps != 0])/2\n fit_kwargs = {'scale_covar': False, 'weights': 1/epsilon}\n fit_dicts[key]['fit_kwargs'] = fit_kwargs\n\n hlp_mod.add_param('fit_dicts', fit_dicts, data_dict,\n add_param_method='update')\n\n\ndef analyze_rb_fit_results(data_dict, keys_in, **params):\n mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['mobjn'], **params)\n msmt_type = hlp_mod.get_param('msmt_type', data_dict, **params)\n keys_out_container = hlp_mod.get_param('keys_out_container', data_dict,\n default_value='', **params)\n if not len(keys_out_container) or keys_out_container is None:\n keys_out_container = f'{mobjn}.{msmt_type}'\n\n fit_dicts = hlp_mod.get_param('fit_dicts', data_dict, raise_error=True)\n for keyi in keys_in:\n fit_res = fit_dicts['rb_fit' + keyi]['fit_res']\n hlp_mod.add_param(f'{keys_out_container}.EPC value',\n fit_res.params['error_per_Clifford'].value,\n data_dict, add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.EPC stderr',\n fit_res.params['fidelity_per_Clifford'].stderr,\n data_dict, add_param_method='replace')\n hlp_mod.add_param(\n f'{keys_out_container}.depolarization parameter value',\n fit_res.params['p'].value, data_dict,\n add_param_method='replace')\n hlp_mod.add_param(\n f'{keys_out_container}.depolarization parameter stderr',\n fit_res.params['p'].stderr, data_dict,\n add_param_method='replace')\n\n if 'pf' in keyi:\n A = fit_res.best_values['Amplitude']\n Aerr = fit_res.params['Amplitude'].stderr\n p = fit_res.best_values['p']\n perr = fit_res.params['p'].stderr\n # IBM-style leakage and seepage:\n # https://journals.aps.org/pra/abstract/10.1103/PhysRevA.97.032306\n hlp_mod.add_param(f'{keys_out_container}.IBM-style leakage value',\n A*(1-p),\n data_dict,\n add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.IBM-style leakage stderr',\n np.sqrt((A*perr)**2 + (Aerr*(p-1))**2),\n data_dict,\n add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.IBM-style seepage value',\n (1-A)*(1-p),\n data_dict,\n add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.IBM-style seepage stderr',\n np.sqrt((Aerr*(p-1))**2 + ((A-1)*perr)**2),\n data_dict,\n add_param_method='replace')\n\n # Google-style leakage and seepage:\n # https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.116.020501\n fit_res = fit_dicts['rbleak_fit' + keyi]['fit_res']\n hlp_mod.add_param(f'{keys_out_container}.Google-style leakage value',\n fit_res.best_values['pu'],\n data_dict,\n add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.Google-style leakage stderr',\n fit_res.params['pu'].stderr,\n data_dict,\n add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.Google-style seepage value',\n fit_res.best_values['pd'],\n data_dict,\n add_param_method='replace')\n hlp_mod.add_param(f'{keys_out_container}.Google-style seepage stderr',\n fit_res.params['pd'].stderr,\n data_dict,\n add_param_method='replace')\n\n if hlp_mod.get_param('plot_T1_lim', data_dict, default_value=False,\n **params):\n # get T1, T2, gate length from HDF file\n get_meas_obj_coh_times(data_dict, **params)\n F_T1, p_T1 = calc_rb_coherence_limited_fidelity(\n hlp_mod.get_param(f'{mobjn}.T1', data_dict, **params),\n hlp_mod.get_param(f'{mobjn}.T2', data_dict, **params),\n hlp_mod.get_param(f'{mobjn}.ge_sigma', data_dict, **params) *\n hlp_mod.get_param(f'{mobjn}.ge_nr_sigma', data_dict, **params),\n hlp_mod.get_param('gate_decomp', data_dict,\n default_value='HZ', **params))\n hlp_mod.add_param(f'{keys_out_container}.EPC coh_lim', 1-F_T1,\n data_dict, add_param_method='replace')\n hlp_mod.add_param(\n f'{keys_out_container}.depolarization parameter coh_lim', p_T1,\n data_dict, add_param_method='replace')\n\n\ndef prepare_rb_plots(data_dict, keys_in, sweep_type, **params):\n sp, cp, mospm, mobjn, movnm = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['sp', 'cp', 'mospm', 'mobjn', 'movnm'],\n **params)\n\n plot_dicts = OrderedDict()\n keys_in_std = hlp_mod.get_param('keys_in_std', data_dict, raise_error=False,\n **params)\n stpn = hlp_mod.get_param(\n 'state_prob_name', data_dict,\n default_value='gg' if 'corr' in mobjn else 'e', **params)\n classified_msmt = any([v == 3 for v in [len(chs) for chs in movnm.values()]])\n lw = plot_mod.get_default_plot_params(\n set_params=False, return_full_rc_params=True)['lines.linewidth']\n ms = plot_mod.get_default_plot_params(\n set_params=False, return_full_rc_params=True)['lines.markersize']\n llsp = plot_mod.get_default_plot_params(\n set_params=False, return_full_rc_params=True)['legend.labelspacing']\n lcsp = plot_mod.get_default_plot_params(\n set_params=False, return_full_rc_params=True)['legend.columnspacing']\n\n ylabel = hlp_mod.pop_param('ylabel', data_dict, node_params=params)\n if ylabel is None:\n if isinstance(stpn, (tuple, list)):\n # assumed of the form ('gg', '+', 'ee', '-', 'ge', '-', 'eg')\n prob_states = stpn[0::2]\n prob_labels = [f'$P(|{{{p}}}\\\\rangle)$' for p in prob_states]\n ylabel = (2*len(prob_states)-1)*['']\n ylabel[0::2] = prob_labels\n ylabel[1::2] = list(stpn[1::2])\n ylabel = ''.join(ylabel)\n else:\n ylabel = f'Probability, $P(|{{{stpn}}}\\\\rangle)$'\n figure_name_suffix = hlp_mod.get_param('figure_name_suffix', data_dict,\n default_value='', **params)\n for keyi, keys in zip(keys_in, keys_in_std):\n figure_name = f'RB_{keyi}_{mobjn}{figure_name_suffix}'\n sp_name = [p for p in mospm[mobjn] if 'clifford' in p][0]\n\n # plot data\n pd = \\\n plot_mod.prepare_1d_plot_dicts(data_dict=data_dict, keys_in=[keyi],\n figure_name=figure_name,\n ylabel=ylabel,\n sp_name=sp_name,\n yerr_key=keys,\n data_labels=['avg.'],\n plot_params={\n 'zorder': 2, 'marker': 'o',\n 'legend_ncol': 3,\n 'line_kws': {\n 'elinewidth': lw+3,\n 'markersize': ms+1,\n 'alpha_errorbars': 0.25}},\n do_plotting=False, **params)\n plot_dicts.update(pd)\n\n # plot all seeds\n keys_in_all_seeds_data = hlp_mod.get_param('keys_in_all_seeds_data',\n data_dict, **params)\n clf_dim = sweep_type['cliffords']\n seeds_dim = sweep_type['seeds']\n cliffords = sp.get_sweep_params_property('values', clf_dim, sp_name)\n xvals = np.repeat(cliffords, sp.length(seeds_dim)) if clf_dim == 1 \\\n else np.tile(cliffords, sp.length(seeds_dim))\n if keys_in_all_seeds_data is not None:\n\n pd = \\\n plot_mod.prepare_1d_plot_dicts(data_dict=data_dict,\n keys_in=keys_in_all_seeds_data,\n figure_name=figure_name,\n xvals=xvals,\n ylabel=ylabel,\n sp_name=sp_name,\n data_labels=['seeds'],\n plot_params={\n 'linestyle': 'none',\n 'marker': '.',\n 'color': 'gray',\n 'line_kws': {'alpha': 0.5},\n 'zorder': 1},\n do_plotting=False, **params)\n plot_dicts.update(pd)\n\n if len(cp.states) != 0:\n # plot cal states\n plot_dicts.update(\n plot_mod.prepare_cal_states_plot_dicts(data_dict=data_dict,\n keys_in=[keyi],\n figure_name=figure_name,\n sp_name=sp_name,\n do_plotting=False,\n **params))\n\n if 'fit_dicts' in data_dict:\n # plot fits\n fit_dicts = data_dict['fit_dicts']\n textstr = ''\n if 'pf' in keyi:\n # plot Google-style leakage fit + textbox\n plot_dicts.update(plot_mod.prepare_fit_plot_dicts(\n data_dict=data_dict,\n figure_name=figure_name,\n fit_names=['rbleak_fit' + keyi],\n plot_params={'color': 'C1',\n 'setlabel': 'fit - Google',\n 'legend_ncol': 3},\n do_plotting=False, **params))\n textstr += get_rb_textbox_properties(\n data_dict, fit_dicts['rbleak_fit' + keyi]['fit_res'],\n textstr_style=['leakage_google'],\n **params)[0]\n\n # plot fit trace\n pd = plot_mod.prepare_fit_plot_dicts(\n data_dict=data_dict,\n figure_name=figure_name,\n fit_names=['rb_fit' + keyi],\n plot_params={'color': 'C0',\n 'setlabel': 'fit - IBM' if 'pf' in keyi else 'fit',\n 'legend_ncol': 3},\n do_plotting=False, **params)\n plot_dicts.update(pd)\n\n # plot coherence-limit\n fit_res = fit_dicts['rb_fit' + keyi]['fit_res']\n if hlp_mod.get_param('plot_T1_lim', data_dict,\n default_value=False, **params) and 'pf' not in keyi:\n keys_out_container = hlp_mod.get_param('keys_out_container',\n data_dict,\n default_value=mobjn,\n **params)\n epc_T1 = hlp_mod.get_param(f'{keys_out_container}.EPC coh_lim',\n data_dict, **params)\n p_T1 = hlp_mod.get_param(\n f'{keys_out_container}.depolarization parameter coh_lim',\n data_dict, **params)\n clfs_fine = np.linspace(cliffords[0], cliffords[-1], 1000)\n T1_limited_curve = fit_res.model.func(\n clfs_fine, fit_res.best_values['Amplitude'], p_T1,\n fit_res.best_values['offset'])\n plot_dicts['t1Lim_' + keyi] = {\n 'fig_id': figure_name,\n 'plotfn': 'plot_line',\n 'xvals': clfs_fine,\n 'yvals': T1_limited_curve,\n 'setlabel': 'coh-lim',\n 'do_legend': True,\n 'linestyle': '--',\n 'line_kws': {'linewidth': lw-0.5},\n 'zorder': 0,\n 'marker': ''}\n else:\n epc_T1 = None\n\n # add texbox\n va_text = hlp_mod.get_param('va_text', data_dict, **params)\n if va_text is None:\n va_text = 'top' if 'g' in stpn else 'bottom'\n textstr, ha, hp, va, vp = get_rb_textbox_properties(\n data_dict, fit_res, epc_T1=None if 'pf' in keyi else epc_T1,\n va=va_text,\n textstr_style='leakage_ibm' if 'pf' in keyi else 'regular',\n textstr=textstr if 'pf' in keyi else '', **params)\n plot_dicts['text_msg_' + keyi] = {\n 'fig_id': figure_name,\n 'plotfn': 'plot_text',\n 'ypos': vp,\n 'xpos': hp,\n 'horizontalalignment': ha,\n 'verticalalignment': va,\n 'box_props': None,\n 'text_string': textstr}\n\n plot_dicts[list(plot_dicts)[-2]].update({\n 'legend_bbox_to_anchor': (1.025, -0.15),\n 'legend_pos': 'upper right',\n 'legend_labelspacing': llsp-0.25,\n 'legend_columnspacing': lcsp-1,\n 'legend_ncol': 1 if 'pf' in keyi else 2,\n 'yrange': hlp_mod.get_param('yrange', data_dict, **params)\n })\n hlp_mod.add_param('plot_dicts', plot_dicts, data_dict,\n add_param_method='update')\n\n\ndef prepare_irb_plot(data_dict, plot_dict_names_irb_plot=None,\n figure_name=None, **params):\n\n plot_dicts_updated = OrderedDict()\n do_plotting = params.pop('do_plotting', False)\n if figure_name is None:\n figure_name = 'IRB'\n\n mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['mobjn'],\n **params)\n\n if plot_dict_names_irb_plot is None:\n plot_dict_names_irb_plot = hlp_mod.get_param(\n 'plot_dict_names_irb_plot', data_dict, **params)\n\n plot_dicts = hlp_mod.get_param('plot_dicts', data_dict, **params)\n for label in ['rb', 'irb']:\n epc_value = hlp_mod.get_param(f'{mobjn}.{label}.EPC value',\n data_dict, **params)\n print(epc_value)\n leg_label = ''\n if epc_value is not None:\n epc_stderr = hlp_mod.get_param(f'{mobjn}.{label}.EPC stderr',\n data_dict, **params)\n leg_label = f'{label.upper()}:\\t' \\\n f'{100*epc_value:.2f}%$\\\\pm${100*epc_stderr:.2f}% EPC'\n print(leg_label)\n plot_dicts_updated[f'legend_data_IRB_{label}'] = {\n 'fig_id': figure_name,\n 'plotfn': 'plot_line',\n 'xvals': [],\n 'yvals': [],\n 'color': 'C0' if label == 'rb' else 'C1',\n 'marker': 'o',\n 'linestyle': '-',\n 'setlabel': leg_label,\n }\n\n pd_plot_type = [pdn for pdn in plot_dict_names_irb_plot['rb']\n if 'seeds' in pdn]\n if len(pd_plot_type):\n pd_name = plot_dict_names_irb_plot['rb'][pd_plot_type[0]]\n plot_dicts_updated['legend_seeds_IRB'] = \\\n deepcopy(plot_dicts[f'{pd_name}'])\n plot_dicts_updated['legend_seeds_IRB'].update({\n 'xvals': [], 'yvals': [], 'yerr': None,\n 'setlabel': 'all seeds'})\n\n cz_err = hlp_mod.get_param('cz_err_value', data_dict, **params)\n if cz_err is None:\n cz_err = hlp_mod.get_param(\n 'correlation_object.average_gate_error_CZ value', data_dict)\n cz_err_stderr = hlp_mod.get_param('cz_err_stderr', data_dict, **params)\n if cz_err_stderr is None:\n cz_err_stderr = hlp_mod.get_param(\n 'correlation_object.average_gate_error_CZ stderr', data_dict)\n if cz_err is not None:\n textstr = \\\n f'Gate error:\\n{100*cz_err:.2f}%$\\\\pm${100*cz_err_stderr:.2f}%'\n plot_dicts_updated['text_msg_IRB'] = {\n 'fig_id': figure_name,\n 'plotfn': 'plot_text',\n 'ypos': 0.05,\n 'xpos': 0.4,\n 'horizontalalignment': 'left',\n 'verticalalignment': 'bottom',\n 'box_props': None,\n 'text_string': textstr}\n\n for label in ['rb', 'irb']:\n for plot_type in list(plot_dict_names_irb_plot[label])[::-1]:\n pd_name = plot_dict_names_irb_plot[label][plot_type]\n plot_dicts_updated[f'{pd_name} IRB'] = deepcopy(plot_dicts[pd_name])\n updated_vals = {'fig_id': figure_name,\n 'color': 'C0' if label == 'rb' else 'C1',\n 'setlabel': '', 'legend_ncol': 1}\n plot_dicts_updated[f'{pd_name} IRB'].update(updated_vals)\n\n plotsize = plot_mod.get_default_plot_params(\n set_params=False, return_full_rc_params=True)['figure.figsize']\n plotsize = (plotsize[0], 3*plotsize[1])\n last_pd = plot_dicts_updated[list(plot_dicts_updated)[-1]]\n last_pd.update({'legend_bbox_to_anchor': (0.35, 0.08),\n 'legend_ncol': 1,\n 'legend_pos': 'lower left',\n 'plotsize': plotsize})\n\n hlp_mod.add_param('plot_dicts', plot_dicts_updated, data_dict,\n add_param_method='update')\n if do_plotting:\n getattr(plot_mod, 'plot')(data_dict, keys_in=list(plot_dicts),\n **params)\n\n\ndef get_rb_leakage_ibm_textstr(data_dict, fit_res=None, **params):\n mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['mobjn'], **params)\n msmt_type = hlp_mod.get_param('msmt_type', data_dict, **params)\n keys_out_container = hlp_mod.get_param('keys_out_container', data_dict,\n default_value='', **params)\n if not len(keys_out_container) or keys_out_container is None:\n keys_out_container = f'{mobjn}.{msmt_type}'\n textstr = 'IBM style:'\n p_value = hlp_mod.get_param(\n f'{keys_out_container}.depolarization parameter value', data_dict,\n raise_error=True)\n p_stderr = hlp_mod.get_param(\n f'{keys_out_container}.depolarization parameter stderr', data_dict)\n textstr += f'\\np = {100*p_value:.4f}%'\n if p_stderr is not None:\n textstr += f'$\\pm$ {100*p_stderr:.3f}%'\n\n L_value = hlp_mod.get_param(\n f'{keys_out_container}.IBM-style leakage value', data_dict,\n raise_error=True)\n textstr += f'\\nL = {100*L_value:.4f}%'\n L_stderr = hlp_mod.get_param(\n f'{keys_out_container}.IBM-style leakage stderr', data_dict)\n if L_stderr is not None:\n textstr += f'$\\pm$ {100*L_stderr:.3f}%'\n\n S_value = hlp_mod.get_param(\n f'{keys_out_container}.IBM-style seepage value', data_dict,\n raise_error=True)\n textstr += f'\\nS = {100*S_value:.4f}%'\n S_stderr = hlp_mod.get_param(\n f'{keys_out_container}.IBM-style seepage stderr', data_dict)\n if S_stderr is not None:\n textstr += f'$\\pm$ {100*S_stderr:.3f}%'\n return textstr\n\n\ndef get_rb_leakage_google_textstr(fit_res, **params):\n textstr = 'Google style:'\n textstr += ('\\n$p_{\\\\uparrow}$' +\n ' = {:.4f}% $\\pm$ {:.3f}%'.format(\n fit_res.params['pu'].value*100,\n fit_res.params['pu'].stderr*100) +\n '\\n$p_{\\\\downarrow}$' +\n ' = {:.4f}% $\\pm$ {:.3f}%'.format(\n fit_res.params['pd'].value*100,\n fit_res.params['pd'].stderr*100) +\n '\\n$p_0$' + ' = {:.2f}% $\\pm$ {:.2f}%\\n'.format(\n fit_res.params['p0'].value,\n fit_res.params['p0'].stderr))\n return textstr\n\n\ndef get_rb_regular_textstr(fit_res, epc_T1=None, **params):\n textstr = ('$r_{\\mathrm{Cl}}$' + ' = {:.4f}% $\\pm$ {:.3f}%'.format(\n (1-fit_res.params['fidelity_per_Clifford'].value)*100,\n fit_res.params['fidelity_per_Clifford'].stderr*100))\n if epc_T1 is not None:\n textstr += ('\\n$r_{\\mathrm{coh-lim}}$ = ' +\n '{:.3f}%'.format(epc_T1*100))\n textstr += ('\\n' + 'p = {:.4f}% $\\pm$ {:.3f}%'.format(\n fit_res.params['p'].value*100, fit_res.params['p'].stderr*100))\n textstr += ('\\n' + r'$\\langle \\sigma_z \\rangle _{m=0}$ = ' +\n '{:.2f} $\\pm$ {:.2f}'.format(\n fit_res.params['Amplitude'].value +\n fit_res.params['offset'].value,\n np.sqrt(fit_res.params['offset'].stderr**2 +\n fit_res.params['Amplitude'].stderr**2)))\n return textstr\n\n\ndef get_cz_irb_textstr(fit_res, epc_T1=None, **params):\n suffix = params.get('suffix', 'RB')\n textstr = (f'$r_{{\\mathrm{{Cl}}, {{{suffix}}}}}$' +\n ' = {:.4f}% $\\pm$ {:.3f}%'.format(\n (1-fit_res.params['fidelity_per_Clifford'].value)*100,\n fit_res.params['fidelity_per_Clifford'].stderr*100))\n if epc_T1 is not None:\n textstr += ('\\n$r_{\\mathrm{coh-lim}}$ = ' +\n '{:.3f}%'.format(epc_T1*100))\n textstr += (f'\\n$p_{{\\\\uparrow, {suffix}}}$' +\n ' = {:.4f}% $\\pm$ {:.3f}%'.format(\n fit_res.params['pu'].value*100,\n fit_res.params['pu'].stderr*100) +\n f'\\n$p_{{\\\\downarrow, {suffix}}}$' +\n ' = {:.4f}% $\\pm$ {:.3f}%'.format(\n fit_res.params['pd'].value*100,\n fit_res.params['pd'].stderr*100))\n return textstr\n\n\ndef get_rb_textbox_properties(data_dict, fit_res, epc_T1=None,\n textstr_style=(), textstr='', **params):\n if len(textstr_style) != 0:\n textstr += '\\n'\n if 'regular' in textstr_style:\n textstr += get_rb_regular_textstr(fit_res, epc_T1, **params)\n if 'leakage_google' in textstr_style:\n textstr += get_rb_leakage_google_textstr(fit_res, **params)\n if 'leakage_ibm' in textstr_style:\n textstr += get_rb_leakage_ibm_textstr(data_dict, **params)\n if 'irb' in textstr_style:\n textstr += get_cz_irb_textstr(fit_res, **params)\n if len(textstr) == 0:\n raise NotImplementedError(f'The textstring style {textstr_style} '\n f'has not been implemented yet.')\n\n va = 'top'\n vp = -0.15\n ha = 'left'\n hp = -0.12\n\n return textstr, ha, hp, va, vp\n\n\ndef irb_gate_error(data_dict, keys_container_rb, keys_container_irb, **params):\n \"\"\"\n Calculates the average gate error from a set of RB-IRB measurements and\n saves it in data_dict.\n :param data_dict: OrderedDict containing the results of running rb_analysis\n node.\n :param params: keyword arguments:\n meas_obj_names (str): name of the measurement object\n for which to calculate average gate error.\n Should be correlation_object for a two-qubit RB.\n d (int): dimension of the Hilbert space\n interleaved_gate (str or int): the interleaved gate for which to\n calculate average gate error.\n\n Assumptions:\n - meas_obj_names, d, interleaved_gate must exist wither in data_dict,\n metadata, or params\n \"\"\"\n mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['mobjn'], **params)\n d = hlp_mod.get_param('d', data_dict, raise_error=True, **params)\n interleaved_gate = hlp_mod.get_param(\n 'interleaved_gate', data_dict, raise_error=True, **params)\n if interleaved_gate == 4368:\n interleaved_gate = 'CZ'\n\n keys_out_container = hlp_mod.get_param('keys_out_container', data_dict,\n default_value='', **params)\n prb = hlp_mod.get_param(\n f'{keys_container_rb}.depolarization parameter value', data_dict,\n raise_error=True, **params)\n prb_err = hlp_mod.get_param(\n f'{keys_container_rb}.depolarization parameter stderr', data_dict,\n raise_error=True, **params)\n pirb = hlp_mod.get_param(\n f'{keys_container_irb}.depolarization parameter value', data_dict,\n raise_error=True, **params)\n pirb_err = hlp_mod.get_param(\n f'{keys_container_irb}.depolarization parameter stderr', data_dict,\n raise_error=True, **params)\n\n if not len(keys_out_container) or keys_out_container is None:\n keys_out_container = f'{mobjn}.average_gate_error_{interleaved_gate}'\n if mobjn not in keys_out_container:\n keys_out_container = f'{mobjn}.{keys_out_container}'\n hlp_mod.add_param(f'{keys_out_container}.value',\n ((d-1)/d)*(1 - pirb/prb),\n data_dict, **params)\n hlp_mod.add_param(f'{keys_out_container}.stderr',\n ((d-1)/d)*np.sqrt((pirb_err*prb)**2 +\n (prb_err*pirb)**2)/(prb**2),\n data_dict, **params)\n\n\ndef calc_rb_coherence_limited_fidelity(T1, T2, pulse_length, gate_decomp='HZ'):\n \"\"\"\n Formula from Asaad et al. (2016):\n https://www.nature.com/articles/npjqi201629\n\n Returns:\n F_cl (float): decoherence limited fildelity\n p (float): decoherence limited depolarization parameter\n \"\"\"\n # Np = 1.875 # Avg. number of gates per Clifford for XY decomposition\n # Np = 1.125 # Avg. number of gates per Clifford for HZ decomposition\n if gate_decomp == 'HZ':\n Np = 1.125\n elif gate_decomp == 'XY':\n Np = 1.875\n else:\n raise ValueError('Gate decomposition not recognized.')\n\n F_cl = (1/6*(3 + 2*np.exp(-1*pulse_length/(T2)) +\n np.exp(-pulse_length/T1)))**Np\n p = 2*F_cl - 1\n\n return F_cl, p\n\n\ndef get_meas_obj_coh_times(data_dict, extract_T2s=True, **params):\n mobjn = hlp_mod.get_measurement_properties(\n data_dict, props_to_extract=['mobjn'], **params)\n # Get from the hdf5 file any parameters specified in\n # params_dict and numeric_params.\n params_dict = {}\n s = 'Instrument settings.' + mobjn\n for trans_name in ['', '_ef']:\n if hlp_mod.get_param(f'{mobjn}.T1{trans_name}', data_dict) is None:\n params_dict[f'{mobjn}.T1{trans_name}'] = s + f'.T1{trans_name}'\n if hlp_mod.get_param(f'{mobjn}.T2{trans_name}', data_dict) is None:\n params_dict[f'{mobjn}.T2{trans_name}'] = s + (\n f'.T2_star{trans_name}' if extract_T2s else f'.T2{trans_name}')\n for trans_name in ['ge', 'ef']:\n if hlp_mod.get_param(f'{mobjn}.T1{trans_name}', data_dict) is None and \\\n hlp_mod.get_param(f'{mobjn}.T1{trans_name}', data_dict) is None:\n params_dict[f'{mobjn}.{trans_name}_sigma'] = \\\n s + f'.{trans_name}_sigma'\n params_dict[f'{mobjn}.{trans_name}_nr_sigma'] = \\\n s + f'.{trans_name}_nr_sigma'\n if len(params_dict) > 0:\n hlp_mod.get_params_from_hdf_file(data_dict, params_dict=params_dict,\n numeric_params=list(params_dict),\n **params)\n\n\ndef calculate_rb_confidence_intervals(\n nr_seeds, nr_cliffords, conf_level=0.68, depolariz_param=1,\n epsilon_guess=0.01, d=2):\n\n # From Helsen et al. (2017)\n # For each number of cliffords in nr_cliffords (array), finds epsilon\n # such that with probability greater than conf_level, the true value of\n # the survival probability, p_N_m, for a given N=nr_seeds and\n # m=nr_cliffords, is in the interval\n # [p_N_m_measured-epsilon, p_N_m_measured+epsilon]\n # See Helsen et al. (2017) for more details.\n\n # eta is the SPAM-dependent prefactor defined in Helsen et al. (2017)\n epsilon = []\n delta = 1-conf_level\n infidelity = (d-1)*(1-depolariz_param)/d\n\n for n_cl in nr_cliffords:\n if n_cl == 0:\n epsilon.append(0)\n else:\n if d == 2:\n V_short_n_cl = (13*n_cl*infidelity**2)/2\n V_long_n_cl = 7*infidelity/2\n V = min(V_short_n_cl, V_long_n_cl)\n else:\n V_short_n_cl = \\\n (0.25*(-2+d**2)/((d-1)**2)) * (infidelity**2) + \\\n (0.5*n_cl*(n_cl-1)*(d**2)/((d-1)**2)) * (infidelity**2)\n V1 = 0.25*((-2+d**2)/((d-1)**2))*n_cl*(infidelity**2) * \\\n depolariz_param**(n_cl-1) + ((d/(d-1))**2) * \\\n (infidelity**2)*(\n (1+(n_cl-1)*(depolariz_param**(2*n_cl)) -\n n_cl*(depolariz_param**(2*n_cl-2))) /\n (1-depolariz_param**2)**2 )\n V = min(V1, V_short_n_cl)\n H = lambda eps: (1/(1-eps))**((1-eps)/(V+1)) * \\\n (V/(V+eps))**((V+eps)/(V+1)) - \\\n (delta/2)**(1/nr_seeds)\n epsilon.append(optimize.fsolve(H, epsilon_guess)[0])\n return np.asarray(epsilon)\n\n\n","sub_path":"pycqed/analysis_v3/randomized_benchmarking_analysis.py","file_name":"randomized_benchmarking_analysis.py","file_ext":"py","file_size_in_byte":65189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"191991695","text":"import logging\n\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import LinearSVC\nfrom sklearn.utils import check_random_state\n\nfrom csrank.learner import Learner\nfrom csrank.util import print_dictionary\n\n\nclass PairwiseSVM(Learner):\n def __init__(\n self,\n C=1.0,\n tol=1e-4,\n normalize=True,\n fit_intercept=True,\n random_state=None,\n **kwargs,\n ):\n \"\"\" Create an instance of the PairwiseSVM model for any preference learner.\n\n Parameters\n ----------\n C : float, optional\n Penalty parameter of the error term\n tol : float, optional\n Optimization tolerance\n normalize : bool, optional\n If True, the data will be normalized before fitting.\n fit_intercept : bool, optional\n If True, the linear model will also fit an intercept.\n random_state : int, RandomState instance or None, optional\n Seed of the pseudorandom generator or a RandomState instance\n **kwargs\n Keyword arguments for the algorithms\n\n References\n ----------\n [1] Joachims, T. (2002, July). \"Optimizing search engines using clickthrough data.\", Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133-142). ACM.\n \"\"\"\n self.normalize = normalize\n self.C = C\n self.tol = tol\n self.logger = logging.getLogger(\"RankSVM\")\n self.random_state = random_state\n self.threshold_instances = int(1e10)\n self.fit_intercept = fit_intercept\n self.weights = None\n self.model = None\n\n def fit(self, X, Y, **kwargs):\n \"\"\"\n Fit a generic preference learning model on a provided set of queries.\n The provided queries can be of a fixed size (numpy arrays).\n\n Parameters\n ----------\n X : numpy array, shape (n_samples, n_objects, n_features)\n Feature vectors of the objects\n Y : numpy array, shape (n_samples, n_objects, n_features)\n Preferences in form of Orderings or Choices for given n_objects\n **kwargs\n Keyword arguments for the fit function\n\n \"\"\"\n self.random_state_ = check_random_state(self.random_state)\n _n_instances, self.n_objects_fit_, self.n_object_features_fit_ = X.shape\n x_train, y_single = self._convert_instances_(X, Y)\n if x_train.shape[0] > self.threshold_instances:\n self.model = LogisticRegression(\n C=self.C,\n tol=self.tol,\n fit_intercept=self.fit_intercept,\n random_state=self.random_state_,\n )\n self.logger.info(\"Logistic Regression model \")\n else:\n self.model = LinearSVC(\n C=self.C,\n tol=self.tol,\n fit_intercept=self.fit_intercept,\n random_state=self.random_state_,\n )\n self.logger.info(\"Linear SVC model \")\n\n if self.normalize:\n std_scalar = StandardScaler()\n x_train = std_scalar.fit_transform(x_train)\n self.logger.debug(\"Finished Creating the model, now fitting started\")\n\n self.model.fit(x_train, y_single)\n self.weights = self.model.coef_.flatten()\n if self.fit_intercept:\n self.weights = np.append(self.weights, self.model.intercept_)\n self.logger.debug(\"Fitting Complete\")\n\n def _predict_scores_fixed(self, X, **kwargs):\n assert X.shape[-1] == self.n_object_features_fit_\n self.logger.info(\n \"For Test instances {} objects {} features {}\".format(*X.shape)\n )\n if self.fit_intercept:\n scores = np.dot(X, self.weights[:-1])\n else:\n scores = np.dot(X, self.weights)\n self.logger.info(\"Done predicting scores\")\n return np.array(scores)\n\n def _convert_instances_(self, X, Y):\n raise NotImplementedError\n\n def set_tunable_parameters(self, C=1.0, tol=1e-4, **point):\n \"\"\"\n Set tunable parameters of the PairwiseSVM model to the values provided.\n\n Parameters\n ----------\n C : float\n Penalty parameter of the error term of the SVM classifier\n tol : float\n Optimization tolerance of the SVM classifier\n point: dict\n Dictionary containing parameter values which are not tuned for the network\n \"\"\"\n self.tol = tol\n self.C = C\n if len(point) > 0:\n self.logger.warning(\n \"This ranking algorithm does not support\"\n \" tunable parameters\"\n \" called: {}\".format(print_dictionary(point))\n )\n","sub_path":"csrank/core/pairwise_svm.py","file_name":"pairwise_svm.py","file_ext":"py","file_size_in_byte":4913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"79737682","text":"import numpy as np\nfrom tools.writing import wp_dump\nfrom tools.plotting import *\nfrom pushers.boris_sdc import boris_SDC\nfrom pushers.coll import coll\n# from pushers.rel_col18 import implicit_coll\nfrom pushers.gauss_legendre import CollGaussLegendre\nfrom pushers.gauss_lobatto import CollGaussLobatto\n\nfrom penning import config\n\n\nsims = [10,20,40,80,160,320,640]\ntend = 10\n\nM = 3\nK_range = [2]\n\nc = 10\nq = 1\nlabel = \"A\"\n# gamma_max = 1.0000000000005\ngamma_max = 5.\nbeta_max = np.sqrt(1-1./gamma_max**2.)\nuy_max = beta_max*c\n\nconf = config(q=q,c=c)\n\nnew = True\n\nfor K in K_range:\n new = True\n for Nt in sims:\n dt = tend/Nt\n\n nq = 1\n\n pos = np.zeros((nq,3),dtype=np.float)\n vel = np.zeros((nq,3),dtype=np.float)\n\n vel[:,1] = np.linspace(uy_max/8,uy_max,nq)\n vel[:,2] = np.sqrt(1-1./gamma_max**2.)\n\n pos = np.array([[10.,0.,0.]])\n vel = np.array([[100.,0.,100.]])\n\n # gamma = gu(vel,c=c)\n # lfreq = -q*Bfield/(1*c*gamma)\n # larmor = vel[:,1]/gamma/lfreq\n # #larmor = 1*vel[:,1]/(-q*B)\n # pos[:,0] = larmor\n\n t = 0\n\n x_array = [pos]\n x2_array = [pos]\n v_array = [vel]\n t_array = [t]\n\n col = coll(CollGaussLobatto,dt,nq,K=K,M=M,predictor=True)\n rx_array = [np.linalg.norm(col.Rx,axis=1)]\n rv_array = [np.linalg.norm(col.Rv,axis=1)]\n\n # Collocation solution stuff\n # posc = np.copy(pos)\n # velc = np.copy(vel)\n # colc = coll(CollGaussLobatto,dt,nq,M=5,K=1,c=c,q=q)\n\n for ti in range(1,Nt+1):\n t = ti*dt\n\n pos, vel, col = boris_SDC(pos,vel,col,conf)\n # print(G(vel,c=c)/c*100)\n # posc, velc, colc = implicit_coll(posc,velc,colc)\n rx_array.append(np.linalg.norm(col.Rx,axis=1))\n rv_array.append(np.linalg.norm(col.Rv,axis=1))\n # x2_array.append(posc)\n x_array.append(pos)\n v_array.append(vel)\n t_array.append(t)\n\n # colc.calc_residual_2018(1)\n # col.calc_residual_2018(4)\n # errorx = np.abs(col.x[2:,0,:]-np.around(colc.x[2:,0,:],14))/np.abs(np.around(colc.x[2:,0,:],14))\n # errorf = np.abs(col.F[2:,0,:]-np.around(colc.F[2:,0,:],14))/np.abs(np.around(colc.F[2:,0,:],14))\n # erroru = np.abs(col.u[2:,0,:]-np.around(colc.u[2:,0,:],14))/np.abs(np.around(colc.u[2:,0,:],14))\n # print(\"Diff in x: {0}\".format(errorx))\n # print(\"Diff in F: {0}\".format(errorf))\n # print(\"Diff in u: {0}\".format(erroru))\n # print(\"SDC solution: {0}\".format(col.Rv))\n # print(\"Collocation solution: {0}\".format(colc.Rv))\n rx_array = np.array(rx_array)\n rv_array = np.array(rv_array)\n x_array = np.array(x_array)\n # x2_array = np.array(x2_array)\n v_array = np.array(v_array)\n t_array = np.array(t_array)\n\n if col.predictor == True:\n rhs = (M-1)*(K+1)*Nt\n else:\n rhs = (M-1)*K*Nt\n\n wp_dump(t_array,x_array,v_array,dt,\"sdc_M{0}K{1}_wp_{2}.h5\".format(M,K,label),rhs=rhs,new=new)\n new = False\n\n plot_xres(t_array,rx_array,\"sdc_M{0}K{1}_\".format(M,K)+str(Nt))\n plot_vres(t_array,rv_array,\"sdc_M{0}K{1}_\".format(M,K)+str(Nt))\n plot_isotraj(x_array,\"sdc_\"+str(Nt),label=\"sim\")\n # plot_isotraj(x2_array,\"col2_\"+str(Nt),label=\"sim\")\n plot_vel(t_array,v_array,\"sdc_\"+str(Nt),label=\"sim\")\n","sub_path":"projects/penning/rvv_sdc.py","file_name":"rvv_sdc.py","file_ext":"py","file_size_in_byte":3422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"398837782","text":"########## hamoon의 풀이 ##########\nimport sys\nsys.stdin = open('input1874.txt')\nT = int(input())\ns = [] # 스택 초기값 설정\nans = [] # 결과값 한번에 출력용 리스트 초기값 설정\ncount = 1 # 카운트 초기값 설정\nTOF = True # 수열을 만들수 있는지 여부 초기 상태값 설정\n# 입력받은 수열의 길이만큼 반복하면서\nfor i in range(T):\n num = int(input())\n # 입력받은 수가 count보다 크거나 같을 경우\n while count <= num:\n s.append(count) # 스택에 쌓는다.\n ans.append('+') # '+' 출력\n count += 1 # count횟수를 1 증가시킨다.\n \n # 입력받은 수가 count보다 작을 경우\n # 만약 스택의 마지막 인덱스와 입력받은 값이 같다면\n if s[-1] == num:\n s.pop() # 스택의 마지막 인덱스를 뺀다.\n ans.append('-') # '-'출력\n # 스택의 마지막 인덱스와 입력받은 값이 다르다면\n else:\n TOF = False # 상태값을 False로 변경\n# 해당 수열을 만들 수 없으므로 'NO' 출력\nif TOF == False:\n print('NO')\n# 만들 수 있는 경우 결과값 출력\nelse:\n for i in ans:\n print(i)","sub_path":"알고리즘 스터디/큐&스택/백준_1874_스택수열.py","file_name":"백준_1874_스택수열.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"608608011","text":"#!/usr/bin/env python3\n\n\nimport argparse\nimport os, os.path\nimport random\nimport sys\nimport subprocess\nimport hashlib\nfrom gtts import gTTS\n\ntopics = []\n\ndef get_text_hash(text):\n return hashlib.md5(text.encode(\"utf-8\")).hexdigest()\n\n\ndef get_topics(path):\n for root, dirs, files in os.walk(path):\n return files\n\n\ndef get_random_sentence(topic):\n with open(topic) as file:\n sentences = [line for line in file]\n audio_files = []\n for sentence in sentences:\n audio_files.append(get_text_to_speech_file(sentence))\n return random.choice(audio_files)\n\n\ndef get_text_to_speech_file(text):\n hashstr = get_text_hash(text)\n tmp_file_path = '/tmp/{path}.mp3'.format(path=hashstr)\n if not os.path.isfile(tmp_file_path):\n tts = gTTS(text=text, lang=\"es\")\n tts.save(tmp_file_path)\n return tmp_file_path\n\n\ndef reproduce_file(_file):\n subprocess.call([\"cvlc\", \"--play-and-exit\", _file], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-t\", \"--text\", type=str, help=\"Text to reproduce\")\n parser.add_argument(\"-r\", \"--random\", action=\"store_true\", help=\"Reproduce random sentence from files in ./topics\")\n parser.add_argument(\"-w\", \"--what\", type=str, help=\"Specify topic to reproduce random sentence\")\n parser.add_argument(\"-g\", \"--generate\", action=\"store_true\", help=\"Generate mp3 files for precharged sentences\")\n parser.add_argument(\"-l\", \"--list\", action=\"store_true\", help=\"List available topics\")\n args = parser.parse_args()\n args = vars(args) \n\n myself = os.path.abspath(sys.argv[0])\n dir = os.path.dirname(myself)\n\n if args[\"text\"]:\n text = args[\"text\"]\n reproduce_file(get_text_to_speech_file(text))\n exit\n\n if args[\"random\"]:\n topics = get_topics(dir + '/topics')\n if args[\"what\"]:\n what = args[\"what\"]\n exist = False\n for item in topics:\n #print(item)\n if what in item:\n audio_file = get_random_sentence(dir + '/topics/' + item)\n exist = True\n if not exist:\n print (\"Choosen topic doesn't exist\")\n quit()\n else:\n audio_file = get_random_sentence(dir + '/topics/' + random.choice(topics))\n reproduce_file(audio_file)\n exit\n\n if args[\"generate\"]:\n topics = get_topics(dir + '/topics')\n for topic in topics:\n with open(dir + '/topics/' + random.choice(topics)) as file:\n sentences = [line for line in file]\n for sentence in sentences:\n generated_file = get_text_to_speech_file(sentence)\n print (\"Creating file %s...\" %generated_file)\n exit\n\n if args[\"list\"]:\n topics = get_topics(dir + '/topics')\n print (\"This is the list of available topics:\")\n for item in topics:\n print (item[:-4])\n exit\n\n","sub_path":"speech/speech.py","file_name":"speech.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"345564337","text":"# coding: utf-8\n\nimport time\nfrom os import path\nimport decimal\nfrom logging import getLogger\n\nimport boto3\nimport rds_config\nimport pymysql\nfrom jinja2 import Environment, FileSystemLoader\n\nlogger = getLogger()\nenv = Environment(loader=FileSystemLoader(path.join(path.dirname(__file__), 'template'), encoding='utf8'))\n\nrds_host = \"recipe.csuoosoe1wmb.us-west-2.rds.amazonaws.com\"\nname = rds_config.db_username\npassword = rds_config.db_password\ndb_name = rds_config.db_name\n\ndef handle(event, context):\n # init\n recipe_id = event[\"pathParameters\"][\"id\"]\n result = {}\n\n # set query parameters\n selected_trouble = 0\n selected_cook_time = 0\n if event[\"queryStringParameters\"] is not None and \"trouble\" in event[\"queryStringParameters\"]:\n selected_trouble = int(event[\"queryStringParameters\"][\"trouble\"])\n result[\"selected_trouble\"] = selected_trouble\n if event[\"queryStringParameters\"] is not None and \"cook_time\" in event[\"queryStringParameters\"]:\n selected_cook_time = int(event[\"queryStringParameters\"][\"cook_time\"])\n result[\"selected_cook_time\"] = selected_cook_time\n\n # connect DB\n connect = pymysql.connect(rds_host, user=name, passwd=password, db=db_name, charset='utf8', connect_timeout=5)\n cursor = connect.cursor() \n\n # get recipe detail\n sql = 'select recipe_id, recipe_name, comment, cook_time, serving, image_name'\n sql += ' from recipes'\n sql += ' where recipe_id = %s'\n cursor.execute(sql, (recipe_id,))\n for row in cursor:\n result[\"recipe_detail\"] = {\n \"recipe_id\": row[0],\n \"recipe_name\": row[1],\n \"comment\": row[2],\n \"cook_time\": row[3],\n \"serving\": row[4],\n \"image_name\": row[5]\n }\n\n # get directions\n sql = 'select direction_comment from directions'\n sql += ' where recipe_id = %s'\n sql += ' order by direction_sequence'\n cursor.execute(sql, (recipe_id,))\n result[\"directions\"] = []\n for row in cursor:\n direction = {\n \"direction_comment\": row[0]\n }\n result[\"directions\"].append(direction)\n\n # get ingredients\n sql = \"select f.food_name, f.category_id, i.ingredient_quantity\"\n sql += \" from ingredients i\"\n sql += \" join foods f on i.food_id = f.food_id\"\n sql += \" where i.recipe_id = %s\"\n sql += \" order by f.category_id\"\n cursor.execute(sql, (recipe_id,))\n result[\"ingredients\"] = []\n for row in cursor:\n ingredient = {\n \"food_name\": row[0],\n \"ingredient_category\": row[1],\n \"ingredient_quantity\": row[2]\n }\n result[\"ingredients\"].append(ingredient)\n\n # set troubles for sidemenu\n sql = 'select trouble_id, trouble_name from troubles'\n cursor.execute(sql)\n result[\"troubles\"] = []\n for row in cursor:\n trouble = {\n \"trouble_id\": row[0],\n \"trouble_name\": row[1]\n }\n result[\"troubles\"].append(trouble)\n\n # set cook_time for side menu\n result[\"cook_times\"] = [5, 10, 15, 20, 30, 45]\n\n # close DB\n cursor.close()\n connect.close()\n \n # set return body\n template = env.get_template('recipe.html')\n html = template.render(result=result)\n\n return {\n \"statusCode\": 200,\n \"headers\": {\n \"Content-Type\": \"text/html\",\n \"Access-Control-Allow-Headers\":\"Content-Type,X-Amz-Date,Authorization\",\n \"Access-Control-Allow-Methods\":\"GET\",\n \"Access-Control-Allow-Origin\": \"*\"\n },\n \"body\": html\n }\n","sub_path":"functions/getRecipe/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"101664576","text":"# Copyright 2020, Digi International Inc.\n#\n# Permission to use, copy, modify, and/or distribute this software for any\n# purpose with or without fee is hereby granted, provided that the above\n# copyright notice and this permission notice appear in all copies.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES\n# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF\n# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR\n# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES\n# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN\n# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF\n# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.\n\nfrom digi.xbee.models.mode import APIOutputModeBit\nfrom digi.xbee.util import utils\nfrom digidevice import xbee\n\n\ndef main():\n print(\" +-------------------------------------------+\")\n print(\" | XBee Gateway Receive Explicit Data Sample |\")\n print(\" +-------------------------------------------+\\n\")\n\n device = xbee.get_device()\n\n try:\n device.open()\n\n device.set_api_output_mode_value(\n APIOutputModeBit.calculate_api_output_mode_value(\n device.get_protocol(), {APIOutputModeBit.EXPLICIT}))\n\n def explicit_data_callback(explicit_xbee_message):\n print(\"From %s >> %s\"\n % (explicit_xbee_message.remote_device.get_64bit_addr(),\n explicit_xbee_message.data.decode()))\n print(\" - Source endpoint: %s\"\n % utils.hex_to_string(utils.int_to_length(explicit_xbee_message.source_endpoint)))\n print(\" - Destination endpoint: %s\"\n % utils.hex_to_string(utils.int_to_length(explicit_xbee_message.dest_endpoint)))\n print(\" - Cluster ID: %s\"\n % utils.hex_to_string(utils.int_to_length(explicit_xbee_message.cluster_id)))\n print(\" - Profile ID: %s\"\n % utils.hex_to_string(utils.int_to_length(explicit_xbee_message.profile_id)))\n\n device.flush_queues()\n device.add_expl_data_received_callback(explicit_data_callback)\n\n print(\"Waiting for data in explicit format...\\n\")\n\n input()\n\n finally:\n if device.is_open():\n device.close()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"samples/xbee/communication/explicit/ReceiveExplicitDataSample/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"230373748","text":"from os import getcwd, listdir\nfrom sys import path\n\nfrom foo.pictureR import pictureFind\nfrom foo.win import toast\nfrom common2 import adb\n\nclass Task:\n def __init__(self, cwd, ico, listGoTo):\n self.cwd = cwd\n self.switch = False\n self.icon = ico\n #self.screenShot = self.cwd + '/bin/adb/arktemp.png'\n self.task = pictureFind.picRead(self.cwd + \"/res/panel/other/task.png\")\n self.get = pictureFind.picRead(self.cwd + \"/res/panel/other/get.png\")\n self.daySel = pictureFind.picRead(self.cwd + \"/res/panel/other/dailyTaskSelect.png\")\n self.actSel = pictureFind.picRead(self.cwd + \"/res/panel/other/actSelect.png\")\n self.weekUnSel = pictureFind.picRead(self.cwd + \"/res/panel/other/weeklyTaskUnSelect.png\")\n self.weekSel = pictureFind.picRead(self.cwd + \"/res/panel/other/weeklyTaskSelect.png\")\n self.back = pictureFind.picRead(self.cwd + '/res/panel/other/back.png')\n self.rewardFinish = pictureFind.picRead(self.cwd + '/res/panel/other/rewardFinish.png')\n self.collectAll = pictureFind.picRead(self.cwd + '/res/panel/other/collectAll.png')\n\n self.listGoTo = listGoTo\n self.mainpage = self.listGoTo[0]\n self.home = self.listGoTo[1]\n self.mainpageMark = self.listGoTo[2]\n\n def goToMainpage(self):\n listGoToTemp = self.listGoTo.copy()\n tryCount = 0\n while self.switch:\n screenshot = adb.getScreen_std()\n for eachStep in listGoToTemp:\n bInfo = pictureFind.matchImg(screenshot, eachStep)\n if bInfo != None:\n listGoToTemp.remove(eachStep)\n break\n else:\n listGoToTemp = self.listGoTo.copy()\n tryCount += 1\n if tryCount > 5:\n return False\n\n if bInfo != None:\n if bInfo['obj'] == 'act.png':\n return True\n else:\n adb.click(bInfo['result'][0], bInfo['result'][1])\n\n\n def checkTask(self):\n tryCount = 0\n cInfo = pictureFind.matchImg(adb.getScreen_std(), self.task)\n if cInfo == None:\n print('无法检测到任务交付入口，中断任务交付后续')\n return False\n else:\n while self.switch:\n adb.click(cInfo['result'][0], cInfo['result'][1])\n \n screenshot = adb.getScreen_std()\n if pictureFind.matchImg(screenshot, self.daySel) != None:\n return True\n elif pictureFind.matchImg(screenshot, self.actSel) != None:\n return True\n else:\n tryCount += 1\n if tryCount > 5:\n return False\n\n def submitTask(self):\n #交付当前栏的任务\n endCount = 0\n while self.switch:\n screenshot = adb.getScreen_std()\n #gInfo = pictureFind.matchImg(screenshot, self.get, 0.9)\n collectAllInfo = pictureFind.matchImg(screenshot, self.collectAll, 0.8)\n backInfo = pictureFind.matchImg(screenshot, self.back, 0.9)\n #rewardFinishInfo = pictureFind.matchMultiImg(screenshot, self.rewardFinish, \n # confidencevalue = adb.getTagConfidence())[0]\n #if rewardFinishInfo != None:\n # rewardFinishInfo.sort(key = lambda x:x[1])\n # if rewardFinishInfo[0][1] < 250:#该栏任务交付全部完成\n # return True\n if collectAllInfo != None: #有任务待交付\n endCount = 0\n adb.click(collectAllInfo['result'][0], collectAllInfo['result'][1])\n continue\n elif backInfo != None: #没有任务待交付\n endCount += 1\n if endCount > 3:\n return True\n else:\n continue\n else: #获取了奖励\n endCount = 0\n adb.click(720, 120)\n continue\n\n def oneByOne(self):\n #adb.screenShot()\n tryCount = 0\n self.submitTask()\n while self.switch:\n #切换到每周任务\n wInfo = pictureFind.matchImg(adb.getScreen_std(), self.weekUnSel)\n adb.click(wInfo['result'][0], wInfo['result'][1])\n wInfo = pictureFind.matchImg(adb.getScreen_std(), self.weekSel)\n if wInfo != None:\n break\n else:\n tryCount += 1\n if tryCount > 5:\n return False\n self.submitTask()\n\n\n\n def run(self, switchI):\n self.switch = switchI\n condition0 = self.goToMainpage()\n if condition0:\n condition1 = self.checkTask()\n if condition1:\n self.oneByOne()\n if self.switch and (not condition0):\n toast.broadcastMsg(\"ArkHelper\", \"任务交付出错\", self.icon)\n\n elif self.switch and (not condition1):\n toast.broadcastMsg(\"ArkHelper\", \"无需任务交付\", self.icon)\n \n elif self.switch:\n self.goToMainpage()\n toast.broadcastMsg(\"ArkHelper\", \"任务交付完成\", self.icon)\n \n self.switch = False\n\n def stop(self):\n self.switch = False","sub_path":"foo/arknight/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":5404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"2166187","text":"# encoding: utf-8\n\"\"\"\n@author: julse@qq.com\n@time: 2020/4/18 17:15\n@desc:\n\"\"\"\nimport os\nimport numpy as np\n# from keras.utils import to_categorical\n\nfrom common import check_path, handleBygroup, getPairs\n\n\nclass Feature_type:\n V_PSSM='V_PSSM'\n H_PSSM='H_PSSM'\n SEQ_1D = 'SEQ_1D'\n SEQ_1D_OH = 'SEQ_1D_OH' # onehot\n SEQ_2D = 'SEQ_2D'\nclass BaseFeature:\n def base_compose(self,dirout_feature,fin_pair,dir_feature_db,feature_type='V_PSSM',fout_pair=''):\n check_path(dirout_feature)\n fo = open(fout_pair,'w') if fout_pair!='' else None\n row = 0\n for pairs in getPairs(fin_pair):\n a = pairs[0]\n b = pairs[1]\n # print(pairs) # ['O35668', 'P00516']\n fa = os.path.join(dir_feature_db, a + '.npy')\n fb = os.path.join(dir_feature_db, b + '.npy')\n row = row + 1\n print('loading %d th feature pair'%row)\n if not (os.access(fa, os.F_OK) and os.access(fb, os.F_OK)):\n print('===============features of pairs not found %s %s================' % (a, b), os.access(fa, os.F_OK),\n os.access(fb, os.F_OK))\n continue\n pa = np.load(fa,allow_pickle=True)\n pb = np.load(fb,allow_pickle=True)\n if (len(pa)<50 or len(pa)>2000 or max(pa)>20) or (len(pb)<50 or len(pb)>2000 or max(pb)>20):\n print('wrong length or x')\n continue\n if fo!=None:\n fo.write('%s\\t%s\\n'%(a,b))\n fo.flush()\n # padding\n if feature_type == Feature_type.V_PSSM:pc = self.padding_PSSM(pa,pb,vstack=True)\n elif feature_type == Feature_type.H_PSSM:pc = self.padding_PSSM(pa,pb,vstack=False)\n elif feature_type == Feature_type.SEQ_1D:pc = self.padding_seq1D(pa,pb,vstack=False)\n # elif feature_type == Feature_type.SEQ_1D_OH:pc = self.padding_seq1D(pa,pb,vstack=False)\n elif feature_type == Feature_type.SEQ_2D:pc = self.padding_seq2D(pa,pb)\n else:\n print('incoreect feature_type')\n return\n # 保存padding后的成对特征\n fout = os.path.join(dirout_feature, \"%s_%s.npy\" % (a, b))\n np.save(fout, pc)\n del pc, pa, pb\n if fo != None:\n fo.close()\n def padding_PSSM(self,pa,pb,vstack=True,shape=(2000,21)):\n pa_pad_col = np.pad(pa, ((0, 0), (0, shape[1]-pa.shape[1])), 'constant', constant_values=(0, 1))\n pb_pad_col = np.pad(pb, ((0, 0), (0, shape[1]-pb.shape[1])), 'constant', constant_values=(0, 1))\n # 前期工作不够严谨，估计有超过两千长度的蛋白\n pa_pad_row = np.pad(pa_pad_col, ((0, shape[0] - pa.shape[0]), (0, 0)), 'constant')\n pb_pad_row = np.pad(pb_pad_col, ((0, shape[0] - pb.shape[0]), (0, 0)), 'constant')\n pc = np.vstack([pa_pad_row, pb_pad_row]) if vstack else np.hstack([pa_pad_row, pb_pad_row])\n return pc\n def padding_seq1D(self,pa,pb,vstack=True,shape=(2000,)):\n # data.shape = (4000,)\n # warring padding number not appear in origin data\n pa_pad_col = np.pad(pa, ((0, shape[0]-pa.shape[0])), 'constant', constant_values=0)\n pb_pad_col = np.pad(pb, ((0, shape[0]-pb.shape[0])), 'constant', constant_values=0)\n pc = np.vstack([pa_pad_col, pb_pad_col]) if vstack else np.hstack([pa_pad_col, pb_pad_col])\n return pc\n\n def padding_seq2D(self,pa,pb,shape=(2000*2000,21*21)):\n pa_pad_col = np.pad(pa, ((0, 2000 - pa.shape[0])), 'constant', constant_values=0)\n pb_pad_col = np.pad(pb, ((0, 2000 - pb.shape[0])), 'constant', constant_values=0)\n pc = np.zeros((2000,2000))\n lookUpTable = self.constructLookUpTable()\n for idx, x in enumerate(pa_pad_col):\n for idy, y in enumerate(pb_pad_col):\n pc[idx, idy] = lookUpTable.index(x*100+y)\n # tmp_sp_2D[i,idx,idy,0] = lookUpTable.index(x*100+y)[0] # todo\n return pc\n # support\n def constructLookUpTable(self):\n lookUpTable = [0] * 21 * 21\n aminos = [i for i in range(21)]\n cell = 0\n for i in aminos:\n for j in aminos:\n lookUpTable[cell] = i * 100 + j\n cell = cell + 1\n return lookUpTable\ndef getGroupFeature(group_dir_pair,dir_feature_db,feature_type=Feature_type.SEQ_1D):\n src = 'pairdata'\n des = 'feature'\n func = BaseFeature().base_compose\n handleBygroup(group_dir_pair, src, des, func,dir_feature_db,feature_type=feature_type)\n # dir_feature_db = '/home/jjhnenu/data/PPI/release/featuredb/seq_feature_1D/'\n # group_dir_pair = '/home/jjhnenu/data/PPI/release/data/group/'\n\nif __name__ == '__main__':\n print()\n cloud = 'jjhnenu'\n\n # filename = 'positiveV1_fswissprot_Composi_5'\n # basepath = '/home/%s/data/PPI/stage2/processPair2445/pair/%s' % (cloud, filename)\n # # dir_std_pssm = '%s/PSSM/output_std_head' % basepath\n '''\n pssm\n '''\n # dir_std_pssm = '/home/jjhnenu/data/PPI/stage2/processPair2445/pair/positiveV1_fswissprot_Composi_5/PSSM/output_std_head'\n # dir_pair = '/home/%s/data/PPI/release/data/p_fp_fw_2_1_1/'%cloud\n # for eachfile in os.listdir(dir_pair):\n # print(eachfile)\n # finpair = os.path.join(dir_pair, eachfile)\n # dir_foutFeaturePair = '/home/%s/data/PPI/release/feature/pssm_feature_2D_vstack/p_fp_fw_2_1_1/%s/' % (cloud,eachfile.split('.')[0])\n # BaseFeature().compose_PSSM(dir_foutFeaturePair,finpair,dir_std_pssm)\n '''\n seq1D\n '''\n # dir_feature_db = '/home/jjhnenu/data/PPI/release/featuredb/seq_feature_1D/'\n # dir_pair = '/home/%s/data/PPI/release/data/p_fp_fw_2_1_1/'%cloud\n # for eachfile in os.listdir(dir_pair):\n # print(eachfile)\n # fin_pair = os.path.join(dir_pair, eachfile)\n # dirout_feature = '/home/%s/data/PPI/release/feature/seq_feature_1D/p_fp_fw_2_1_1/%s/' % (cloud,eachfile.split('.')[0])\n # BaseFeature().base_compose(dirout_feature,fin_pair,dir_feature_db,feature_type=Feature_type.SEQ_1D)\n '''\n seq1D_onehot\n just use seq1D\n '''\n\n '''\n seq2D\n too large\n '''\n # dir_feature_db = '/home/jjhnenu/data/PPI/release/featuredb/seq_feature_1D/'\n # dir_pair = '/home/%s/data/PPI/release/data/p_fp_fw_2_1_1/'%cloud\n # for eachfile in os.listdir(dir_pair):\n # print(eachfile)\n # fin_pair = os.path.join(dir_pair, eachfile)\n # dirout_feature = '/home/%s/data/PPI/release/feature/seq_feature_2D/p_fp_fw_2_1_1/%s/' % (cloud,eachfile.split('.')[0])\n # BaseFeature().base_compose(dirout_feature,fin_pair,dir_feature_db,feature_type=Feature_type.SEQ_2D)\n\n '''\n pssm hstack\n '''\n # dir_feature_db = '/home/jjhnenu/data/PPI/stage2/processPair2445/pair/positiveV1_fswissprot_Composi_5/PSSM/output_std_head'\n # dir_pair = '/home/%s/data/PPI/release/data/p_fp_fw_2_1_1/'%cloud\n # for eachfile in os.listdir(dir_pair):\n # print(eachfile)\n # fin_pair = os.path.join(dir_pair, eachfile)\n # dirout_feature = '/home/%s/data/PPI/release/feature/pssm_feature_2D_hstack/p_fp_fw_2_1_1/%s/' % (cloud,eachfile.split('.')[0])\n # BaseFeature().base_compose(dirout_feature,fin_pair,dir_feature_db,feature_type=Feature_type.H_PSSM)\n\n '''\n pssm 400 hstack\n '''\n # dir_feature_db = '/home/jjhnenu/data/PPI/stage2/processPair2445/pair/positiveV1_fswissprot_Composi_5/PSSM/output_std_400_1'\n # dir_pair = '/home/%s/data/PPI/release/data/p_fp_fw_2_1_1/'%cloud\n # for eachfile in os.listdir(dir_pair):\n # print(eachfile)\n # fin_pair = os.path.join(dir_pair, eachfile)\n # dirout_feature = '/home/%s/data/PPI/release/feature/pssm400_feature_1D/p_fp_fw_2_1_1/%s/' % (cloud,eachfile.split('.')[0])\n # BaseFeature().base_compose(dirout_feature,fin_pair,dir_feature_db,feature_type=Feature_type.SEQ_1D)\n\n '''\n group seq1D\n '''\n # dir_feature_db = '/home/jjhnenu/data/PPI/release/featuredb/seq_feature_1D/'\n # group_dir_pair = '/home/jjhnenu/data/PPI/release/pairdata/group/'\n # getGroupFeature(group_dir_pair, dir_feature_db, feature_type=Feature_type.SEQ_1D)\n\n\n '''\n feature pair \n positive \n swissprot\n '''\n # dir_feature_db = '/home/19jiangjh/data/PPI/release/featuredb/seq_feature_1D/'\n # dir_pair = '/home/19jiangjh/data/PPI/release/pairdata'\n # for eachfile in ['negative_fswissprot_7177.txt','negative_fpositive_10245.txt','positive_2049.txt']:\n # print(eachfile)\n # fin_pair = os.path.join(dir_pair, eachfile)\n # dirout_feature = '/home/19jiangjh/data/PPI/release/feature/p_fp_fw'\n # BaseFeature().base_compose(dirout_feature,fin_pair,dir_feature_db,feature_type=Feature_type.SEQ_1D)\n\n dir_feature_db = '/home/19jjhnenu/Data/SeqTMPPI2W/featuredb/'\n dir_pair = '/home/19jjhnenu/Data/SeqTMPPI2W/pair/p_fw_13349/0'\n for eachfile in ['negative_fswissprot_7177.txt','negative_fpositive_10245.txt','positive_2049.txt']:\n print(eachfile)\n fin_pair = os.path.join(dir_pair, eachfile)\n dirout_feature = '/home/19jiangjh/data/PPI/release/feature/p_fp_fw'\n BaseFeature().base_compose(dirout_feature,fin_pair,dir_feature_db,feature_type=Feature_type.SEQ_1D)","sub_path":"FeatureDealer.py","file_name":"FeatureDealer.py","file_ext":"py","file_size_in_byte":9275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"357660249","text":"\"\"\"\nastroHOG Statistical tests\n\"\"\"\n\nimport numpy as np\n\n# ------------------------------------------------------------------------------------------------------------------------\ndef HOG_PRS(phi):\n # Calculates the projected Rayleigh statistic of the distributions of angles phi.\n #\n # INPUTS\n # phi - angles between -pi/2 and pi/2\n #\n # OUTPUTS\n # Zx - value of the projected Rayleigh statistic \n # s_Zx - \n # meanPhi -\n\n angles=phi #2.*phi\n\n Zx=np.sum(np.cos(angles))/np.sqrt(np.size(angles)/2.)\n temp=np.sum(np.cos(angles)*np.cos(angles))\n s_Zx=np.sqrt((2.*temp-Zx*Zx)/np.size(angles))\n\n Zy=np.sum(np.sin(angles))/np.sqrt(np.size(angles)/2.)\n temp=np.sum(np.sin(angles)*np.sin(angles))\n s_Zx=np.sqrt((2.*temp-Zy*Zy)/np.size(angles))\n\n meanPhi=0.5*np.arctan2(Zy, Zx)\n\n return Zx, s_Zx, meanPhi\n\n# ------------------------------------------------------------------------------------------------------------------------------\ndef HOG_AM(phi):\n # Calculate the alignment measure.\n #\n # INPUTS\n # phi - angles between -pi/2 and pi/2\n #\n # OUTPUTS\n #AM - value of the alignment measure. \n \n angles=phi\n\n ami=2.*np.cos(phi)-1.\n am=np.mean(ami)\n\n return am\n\n\n","sub_path":"statests.py","file_name":"statests.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"426384530","text":"#this will allow you to pick two items and plot them\n#options are all within parm array\n#options: z, Nf, Nx, alphaX, Mmin, avg temp\n\nfileglob = '/home/amber/data/alpha0/*'\nfileblob = '/home/amber/data/alpha1/*'\n#looks at all files within specified alpha folder\n\nfrom twentyonecmfast_tools import *\n#imports everything within directory\n \nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#load_andre_models returns 4 sets of data\n#this assigns variables to the 4 sets\np,k,d,e=load_andre_models(fileglob)\nP,K,D,E=load_andre_models(fileblob)\n#p is the parm array\n#k is the k array\n#d is the delta2 array\n#e is the delta2 error array\n\n#p has the option for plotting\n#shape of p is 83 data points for each of the 6\n#row 0 is z\n#row 1 is Nf\n#row 2 is Nx\n#row 3 is alphaX\n#row 4 is Mmin\n#row 5 is avg temp\n\n#fixed bottom axis of plot\nx=np.argsort(p[:,0])\nX=np.argsort(P[:,0])\n\n#fixed data vs other item in same order\nplt.plot(p[x,0],p[x,5], color='r', label='alpha0')\nplt.plot(P[X,0],P[X,5], color='b', label='alpha1')\n\n#customize plot\nplt.ylabel('avg temp')\nplt.xlabel('z')\nplt.title('z vs. avg temp')\nplt.legend(loc='lower right')\n\n#display plot\nplt.show()\n","sub_path":"plottwoparms.py","file_name":"plottwoparms.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"249644732","text":"import streamlit as st\r\nfrom scipy.integrate import odeint\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n#st.set_page_config(layout=\"wide\")\r\nst.set_page_config(page_title='simulatingSars',page_icon='')\r\nst.title('Modelo de Infección por SARS-CoV-2')\r\n\r\n@st.cache\r\ndef deriv(y, t, N, beta, alfaa, alfag, alfah, alfai, deltaa, deltai, gammaa, gammai, gammah, gammag, sigmah, sigmag, pe, pa, pi, ph, pg, w, mu, v, lamda, q):\r\n #Ecuaciones del cambio para solucionar\r\n S, E, A, I, H, G, R, V, FV, D = y\r\n dSdt = (-beta(t) * S * (pe*E+pa*A+pi*I+ph*H+pg*G) / N) -(v(t)*w*S) +lamda -(mu*S) #v(t)*S vacunados en tiempo t, solo vacunación sobre susceptibles es efectiva\r\n dEdt = (beta(t) * (S+FV) * (pe*E+pa*A+pi*I+ph*H+pg*G) / N) - ((deltaa+deltai+mu) * E)\r\n dAdt = deltaa*E - (gammaa+alfaa+mu)*A\r\n dIdt = deltai * E - (sigmah +sigmag+gammai+alfai+mu)*I\r\n dHdt = sigmah*I - (gammah+alfah+mu)*H*mu # el mu hace que crezca mucho\r\n dGdt = sigmag*I - (gammag+alfag+mu)*G\r\n dRdt = gammaa*A + gammai * I + gammah*H + gammag*G - mu*R\r\n dVdt = v(t)*q*w*S- mu*V #las vacunaciones tienen una componente temporal\r\n dFVdt= v(t)*(1-q)*w*S - (beta(t)/N)*FV*(pe*E+pa*A+pi*I+ph*H+pg*G) - mu*FV #comp temporal\r\n N = S+E+A+I+H+G+R+V+FV\r\n dDdt = mu * N\r\n\r\n return dSdt, dEdt, dAdt, dIdt, dHdt, dGdt, dRdt, dVdt, dFVdt, dDdt\r\n\r\n#La sidebar NO se actualiza cada vez que recarga el archivo!!!\r\nN = st.sidebar.number_input('Población Total',min_value=1_000,max_value=100_000_000,value=1_000_000,step=1_000)\r\n\r\nbeta_0 = st.sidebar.number_input('Tasa de Contacto Potencialmente Peligroso \\u03B2',min_value=0.0,max_value=1.0,value=0.7)\r\nlamda = st.sidebar.number_input('Tasa de Natalidad \\u039B',min_value=0.0,max_value=1.0,value=0.01)\r\nalfag = st.sidebar.number_input('Tasa de Mortalidad inducida por enfermedad para pacientes de UCI \\u03B1₉',min_value=0.0,max_value=1.0,value=0.01)\r\nsigmag = st.sidebar.number_input('Tasa de Ingreso a UCI \\u03C3₉',min_value=0.0,max_value=1.0,value=0.01)\r\n\r\n\r\n#Para el encerramiento:\r\n\r\ndef beta(t):\r\n return beta_0\r\n\r\nif st.sidebar.checkbox('¿Hay vacunación?'):\r\n vac = st.sidebar.number_input('Tasa de Vacunación \\u03BD',min_value=0.0,max_value=1.0, value=5*(10e-3))\r\n dia = st.sidebar.number_input('Comienzo de Vacunación (Día)',1,None,50,1)\r\n q = st.sidebar.number_input('Eficacia q',min_value=0.0,max_value=1.0,value=0.9)\r\n w = st.sidebar.number_input('Adhesión a la campaña w',min_value=0.0,max_value=1.0,value=0.6)\r\nelse:\r\n vac=0\r\n dia=0\r\n q=0\r\n w=0\r\ndef v(t):\r\n return vac if (t>=dia) else 0.0 #Tasa de vacunación\r\n#&(t 388:\r\n print(\"{} {}\".format('answer:',outline))\r\n time.sleep(1)\r\n\r\n\r\n\r\ndef main():\r\n id = input('请输入问题的url:')\r\n id = id.split('/') # 字符串切片\r\n id = id[-1] # 取倒数第一个切片\r\n list_url = get_urls(id)\r\n print_content(list_url)\r\n\r\nmain()","sub_path":"zhihu/zhihureping.py","file_name":"zhihureping.py","file_ext":"py","file_size_in_byte":1786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"371827317","text":"import Zero\nimport Events\nimport Property\nimport VectorMath\nimport math\n\nVector3 = VectorMath.Vec3\nQuaternion = VectorMath.Quaternion\n\nclass SedrickBody:\n def DefineProperties(self):\n self.IsRight = Property.Bool(False)\n self.IsLeft = Property.Bool(False)\n self.LeftRight = Property.Bool(False)\n \n self.Start = Property.Bool(True)\n \n self.BodyX = Property.Float(0)\n self.BodyX = Property.Float(0)\n self.BodyZ = Property.Float(0)\n\n def Initialize(self, initializer):\n Zero.Connect(self.Space, Events.LogicUpdate, self.OnLogicUpdate)\n\n def OnLogicUpdate(self, UpdateEvent):\n Parent = self.Owner.Parent\n Position = self.Owner.Transform.Translation\n \n MouseScreenPosition = Zero.Mouse.ScreenPosition\n WorldMousePosition = self.LevelSettings.CameraViewport.ScreenToWorldZPlane(MouseScreenPosition, 0)\n Distance = WorldMousePosition - self.Owner.Transform.WorldTranslation\n \n RotationAngles = Quaternion(0, 0, 0)\n \n if(self.Start is True):\n self.BodyX = Position.x\n self.BodyY = Position.y\n self.BodyZ = Position.z\n self.Start = False\n \n if(Parent.SedrickPlayerController.Side is True):\n \n self.Owner.Orientation.LookAtPoint(Vector3(WorldMousePosition.x, WorldMousePosition.y, 0))\n \n if(Parent.SedrickPlayerController.Side2 is True):\n self.Owner.Orientation.LookAtPoint(Vector3(WorldMousePosition.x, WorldMousePosition.y, 0))\n \n #print(self.Owner.Orientation.AbsoluteAngle)\n #print(self.Owner.Orientation.Rotation)\n \n if(Distance.x < 0):\n self.IsLeft = True\n self.IsRight = False\n if(self.IsLeft is True):\n if(self.LeftRight is True):\n #print(\"Right\")\n RotationAngles.y = 1\n self.Owner.Transform.Rotation = RotationAngles\n self.LeftRight = False\n \n if(Distance.x > 0):\n self.IsLeft = False\n self.IsRight = True\n if(self.IsRight is True):\n if(self.LeftRight is False):\n #print(\"Left\")\n RotationAngles.y = 0\n self.Owner.Transform.Rotation = RotationAngles\n self.LeftRight = True\n \n if(Zero.Keyboard.KeyIsDown(Zero.Keys.S) and not Zero.Keyboard.KeyIsDown(Zero.Keys.A) and not Zero.Keyboard.KeyIsDown(Zero.Keys.D)):\n self.Owner.Transform.Translation = Vector3(self.BodyX, self.BodyY - 0.09, self.BodyZ)\n Parent.BoxCollider.Offset = Vector3(0.02, 0.07, 0)\n Parent.BoxCollider.Size = Vector3(0.1, 0.3, 10)\n \n elif(Zero.Keyboard.KeyIsDown(Zero.Keys.Space) or Parent.SedrickPlayerController.OnGround is False):\n self.Owner.Transform.Translation = Vector3(self.BodyX, self.BodyY - 0.02, self.BodyZ)\n \n else:\n self.Owner.Transform.Translation = Vector3(self.BodyX, self.BodyY, self.BodyZ)\n Parent.BoxCollider.Offset = Vector3(0.02, 0.15, 0)\n Parent.BoxCollider.Size = Vector3(0.1, 0.5, 10)\n\nZero.RegisterComponent(\"SedrickBody\", SedrickBody)","sub_path":"Content/SedrickBody.py","file_name":"SedrickBody.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"555112700","text":"\"\"\"Support for MelView device sensors.\"\"\"\nimport logging\n\nfrom pymelview import DEVICE_TYPE_ATA\n\nfrom homeassistant.const import (\n DEVICE_CLASS_TEMPERATURE,\n TEMP_CELSIUS,\n STATE_ON,\n STATE_OFF\n)\nfrom homeassistant.components.binary_sensor import DEVICE_CLASS_PROBLEM\nfrom homeassistant.helpers.entity import Entity\n\nfrom . import MelViewDevice\nfrom .const import DOMAIN, MEL_DEVICES\n\nATTR_MEASUREMENT_NAME = \"measurement_name\"\nATTR_ICON = \"icon\"\nATTR_UNIT = \"unit\"\nATTR_DEVICE_CLASS = \"device_class\"\nATTR_VALUE_FN = \"value_fn\"\nATTR_ENABLED_FN = \"enabled\"\n\nATTR_STATE_DEVICE_ID = \"device_id\"\nATTR_STATE_DEVICE_LAST_SEEN = \"last_communication\"\n\nATA_SENSORS = {\n \"room_temperature\": {\n ATTR_MEASUREMENT_NAME: \"Room Temperature\",\n ATTR_ICON: \"mdi:thermometer\",\n ATTR_UNIT: TEMP_CELSIUS,\n ATTR_DEVICE_CLASS: DEVICE_CLASS_TEMPERATURE,\n ATTR_VALUE_FN: lambda x: x.device.room_temperature,\n ATTR_ENABLED_FN: lambda x: True,\n },\n}\n\nATA_BINARY_SENSORS = {\n \"error_state\": {\n ATTR_MEASUREMENT_NAME: \"Error State\",\n ATTR_ICON: None,\n ATTR_UNIT: None,\n ATTR_DEVICE_CLASS: DEVICE_CLASS_PROBLEM,\n ATTR_VALUE_FN: lambda x: x.error_state,\n ATTR_ENABLED_FN: lambda x: True,\n },\n}\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_sensors(hass, entry, async_add_entities, type_binary, init_status=False):\n \"\"\"Set up MELView device sensors and bynary sensor based on config_entry.\"\"\"\n entry_config = hass.data[DOMAIN][entry.entry_id]\n ata_sensors = ATA_BINARY_SENSORS if type_binary else ATA_SENSORS\n\n mel_devices = entry_config.get(MEL_DEVICES)\n async_add_entities(\n [\n MelDeviceSensor(mel_device, measurement, definition, type_binary)\n for measurement, definition in ata_sensors.items()\n for mel_device in mel_devices[DEVICE_TYPE_ATA]\n if definition[ATTR_ENABLED_FN](mel_device)\n ],\n init_status,\n )\n\n\nasync def async_setup_entry(hass, entry, async_add_entities):\n \"\"\"Set up MELView device sensors based on config_entry.\"\"\"\n await async_setup_sensors(hass, entry, async_add_entities, False)\n\n\nclass MelDeviceSensor(Entity):\n \"\"\"Representation of a Sensor.\"\"\"\n\n def __init__(self, device: MelViewDevice, measurement, definition, isbinary):\n \"\"\"Initialize the sensor.\"\"\"\n self._api = device\n self._name_slug = device.name\n self._measurement = measurement\n self._def = definition\n self._isbinary = isbinary\n\n @property\n def unique_id(self):\n \"\"\"Return a unique ID.\"\"\"\n return f\"melview_custom_heatpump_{self._api.device.device_id}\"\n\n @property\n def icon(self):\n \"\"\"Return the icon to use in the frontend, if any.\"\"\"\n return self._def[ATTR_ICON]\n\n @property\n def name(self):\n \"\"\"Return the name of the sensor.\"\"\"\n return f\"{self._name_slug} {self._def[ATTR_MEASUREMENT_NAME]}\"\n\n @property\n def is_on(self):\n \"\"\"Return the state of the binary sensor.\"\"\"\n if self._isbinary:\n return self._def[ATTR_VALUE_FN](self._api)\n\n return False\n\n @property\n def state(self):\n \"\"\"Return the state of the sensor.\"\"\"\n if self._isbinary:\n return STATE_ON if self.is_on else STATE_OFF\n\n return self._def[ATTR_VALUE_FN](self._api)\n\n @property\n def unit_of_measurement(self):\n \"\"\"Return the unit of measurement.\"\"\"\n return self._def[ATTR_UNIT]\n\n @property\n def device_class(self):\n \"\"\"Return device class.\"\"\"\n return self._def[ATTR_DEVICE_CLASS]\n\n async def async_update(self):\n \"\"\"Retrieve latest state.\"\"\"\n await self._api.async_update()\n\n @property\n def device_info(self):\n \"\"\"Return a device description for device registry.\"\"\"\n return self._api.device_info\n\n @property\n def state_attributes(self):\n \"\"\"Return the optional state attributes.\"\"\"\n data = {\n ATTR_STATE_DEVICE_ID: self._api.device_id,\n }\n return data\n","sub_path":"custom_components/melview_custom/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":4094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"420282816","text":"import stdio\n\nstdio.writeln('Nhap so: ')\nso_input=stdio.readInt()\n\nsodao=0\ntam=so_input\n\nwhile tam>0:\n\t\n\tsodao= sodao*10 + tam%10\n\ttam=tam//10\n\t\t\nif so_input==sodao:\n\tstdio.writeln('So %d la do doi xung' %so_input)\nelse:\n\tstdio.writeln('So %d khong phai la so doi xung' %so_input)\t","sub_path":"basic/Tuan5/Bai2.py","file_name":"Bai2.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"304562456","text":"from airflow import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom datetime import datetime, timedelta\nfrom airflow.contrib.hooks.ssh_hook import SSHHook \nfrom airflow.operators.latest_only_operator import LatestOnlyOperator\nfrom airflow.contrib.operators.ssh_operator import SSHOperator\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': datetime(2019, 10, 2),\n 'email': ['airflow@example.com'],\n 'email_on_failure': False,\n 'email_on_retry': False,\n 'retries': 1,\n 'retry_delay': timedelta(minutes=5),\n # 'queue': 'bash_queue',\n # 'pool': 'backfill',\n # 'priority_weight': 10,\n # 'end_date': datetime(2016, 1, 1),\n}\n\ndag = DAG(\n 'KillerWF3-v1.0.1', \n default_args=default_args, \n schedule_interval='0 0 2 * *'\n )\n\nlatest_only = LatestOnlyOperator(\n task_id='latest_only', \n dag=dag\n )\n\ninfa_wait_WF_10_bash =\"\"\"\ncd /cygdrive/c/Users/DRECRAP/Desktop\n./WFwrapper.sh DRECRAP Wait_WF_10; echo $?\n\"\"\"\n\nop1 = SSHOperator(\n task_id=\"01_infa_wait_WF_10\",\n ssh_hook=SSHHook(ssh_conn_id='infa_ssh'),\n command=infa_wait_WF_10_bash,\n dag=dag)\n\nop2 = SSHOperator(\n task_id=\"02_infa_wait_WF_10\",\n ssh_hook=SSHHook(ssh_conn_id='infa_ssh'),\n command=infa_wait_WF_10_bash,\n dag=dag)\n\nlatest_only >> op1 >> op2","sub_path":"KillerWF3-v1.0.1.py","file_name":"KillerWF3-v1.0.1.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"23697540","text":"from bilibili import bilibili\nfrom statistics import Statistics\nfrom printer import Printer\nimport utils\nimport asyncio\nimport random\n\n\nclass Rafflehandler:\n __slots__ = ('queue_raffle',)\n instance = None\n\n def __new__(cls, *args, **kw):\n if not cls.instance:\n cls.instance = super(Rafflehandler, cls).__new__(cls, *args, **kw)\n cls.instance.queue_raffle = asyncio.Queue()\n return cls.instance\n \n async def run(self):\n while True:\n raffle = await self.queue_raffle.get()\n await asyncio.sleep(3)\n list_raffle0 = [self.queue_raffle.get_nowait() for i in range(self.queue_raffle.qsize())]\n list_raffle0.append(raffle)\n list_raffle = list(set(list_raffle0))\n \n # print('过滤完毕')\n # if len(list_raffle) != len(list_raffle0):\n # print('过滤机制起作用')\n \n tasklist = []\n for i in list_raffle:\n task = asyncio.ensure_future(i[0](*i[1]))\n tasklist.append(task)\n \n await asyncio.wait(tasklist, return_when=asyncio.ALL_COMPLETED)\n \n @staticmethod\n def Put2Queue(func, value):\n # print('welcome to appending')\n Rafflehandler.instance.queue_raffle.put_nowait((func, value))\n # print('appended')\n return\n \n @staticmethod\n def getlist():\n print('目前TV任务队列状况', Rafflehandler.instance.queue_raffle.qsize())\n \n\nasync def handle_1_TV_raffle(num, real_roomid, raffleid):\n # print('参与')\n await asyncio.sleep(random.uniform(0.5, min(30, num * 1.3)))\n json_response2 = await bilibili.get_gift_of_TV(real_roomid, raffleid)\n Printer().printlist_append(['join_lottery', '小电视', 'user', f'参与了房间{real_roomid:^9}的小电视抽奖'], True)\n Printer().printlist_append(\n ['join_lottery', '小电视', 'user', \"# 小电视道具抽奖状态: \", json_response2['msg']])\n # -400不存在\n # -500繁忙\n if not json_response2['code']:\n Statistics.append_to_TVlist(raffleid, real_roomid)\n return True\n elif json_response2['code'] == -500:\n print('# -500繁忙，稍后重试')\n return False\n else:\n print(json_response2)\n return True\n \n \nasync def handle_1_captain_raffle(num, roomid, raffleid):\n await asyncio.sleep(random.uniform(0.5, min(30, num * 1.3)))\n json_response2 = await bilibili.get_gift_of_captain(roomid, raffleid)\n if not json_response2['code']:\n print(\"# 获取到房间 %s 的总督奖励: \" %(roomid), json_response2['data']['message'])\n Statistics.append_to_captainlist()\n else:\n print(json_response2)\n return True\n \n \nasync def handle_1_activity_raffle(num, giftId, text1, text2, raffleid):\n # print('参与')\n await asyncio.sleep(random.uniform(0.5, min(30, num * 1.3)))\n json_response1 = await bilibili.get_gift_of_events_app(text1, text2, raffleid)\n json_pc_response = await bilibili.get_gift_of_events_web(text1, text2, raffleid)\n \n Printer().printlist_append(['join_lottery', '', 'user', f'参与了房间{text1:^9}的{bilibili.get_giftids_raffle(str(giftId))}活动抽奖'], True)\n\n if not json_response1['code']:\n Printer().printlist_append(['join_lottery', '', 'user', \"# 移动端活动抽奖结果: \",\n json_response1['data']['gift_desc']])\n Statistics.add_to_result(*(json_response1['data']['gift_desc'].split('X')))\n else:\n print(json_response1)\n Printer().printlist_append(['join_lottery', '', 'user', \"# 移动端活动抽奖结果: \", json_response1['message']])\n \n Printer().printlist_append(\n ['join_lottery', '', 'user', \"# 网页端活动抽奖状态: \", json_pc_response['message']])\n if not json_pc_response['code']:\n Statistics.append_to_activitylist(raffleid, text1)\n else:\n print(json_pc_response)\n return True\n\n \nasync def handle_1_room_TV(real_roomid):\n await asyncio.sleep(random.uniform(0.5, 1.5))\n result = await utils.check_room_true(real_roomid)\n if True in result:\n Printer().printlist_append(['join_lottery', '钓鱼提醒', 'user', f'WARNING:检测到房间{real_roomid:^9}的钓鱼操作'], True)\n else:\n # print(True)\n await bilibili.post_watching_history(real_roomid)\n json_response = await bilibili.get_giftlist_of_TV(real_roomid)\n # print(json_response['data']['list'])\n checklen = json_response['data']['list']\n list_available_raffleid = []\n for j in checklen:\n # await asyncio.sleep(random.uniform(0.5, 1))\n # resttime = j['dtime']\n raffleid = j['raffleId']\n status = j['status']\n if status == 1:\n # print('未参加')\n list_available_raffleid.append(raffleid)\n elif status == 2:\n # print('过滤')\n pass\n else:\n print(checklen)\n tasklist = []\n num_available = len(list_available_raffleid)\n for raffleid in list_available_raffleid:\n task = asyncio.ensure_future(handle_1_TV_raffle(num_available, real_roomid, raffleid))\n tasklist.append(task)\n if tasklist:\n raffle_results = await asyncio.gather(*tasklist)\n if False in raffle_results:\n print('有繁忙提示，稍后重新尝试')\n Rafflehandler.Put2Queue(handle_1_room_TV, (real_roomid,))\n\nasync def handle_1_room_activity(giftId, text1, text2):\n await asyncio.sleep(random.uniform(0.5, 1.5))\n result = await utils.check_room_true(text1)\n if True in result:\n Printer().printlist_append(['join_lottery', '钓鱼提醒', 'user', f'WARNING:检测到房间{text1:^9}的钓鱼操作'], True)\n else:\n # print(True)\n await bilibili.post_watching_history(text1)\n json_response = await bilibili.get_giftlist_of_events(text1)\n checklen = json_response['data']\n list_available_raffleid = []\n for j in checklen:\n # await asyncio.sleep(random.uniform(0.5, 1))\n resttime = j['time']\n raffleid = j['raffleId']\n if Statistics.check_activitylist(text1, raffleid):\n list_available_raffleid.append(raffleid)\n tasklist = []\n num_available = len(list_available_raffleid)\n for raffleid in list_available_raffleid:\n task = asyncio.ensure_future(handle_1_activity_raffle(num_available, giftId, text1, text2, raffleid))\n tasklist.append(task)\n if tasklist:\n raffle_results = await asyncio.gather(*tasklist)\n if False in raffle_results:\n print('有繁忙提示，稍后重新尝试')\n Rafflehandler.Put2Queue(handle_1_room_activity, (giftId, text1, text2))\n \n\nasync def handle_1_room_captain(roomid):\n await asyncio.sleep(random.uniform(0.5, 1.5))\n result = await utils.check_room_true(roomid)\n if True in result:\n Printer().printlist_append(['join_lottery', '钓鱼提醒', 'user', f'WARNING:检测到房间{roomid:^9}的钓鱼操作'], True)\n else:\n # print(True)\n await bilibili.post_watching_history(roomid)\n num = 0\n while True:\n json_response1 = await bilibili.get_giftlist_of_captain(roomid)\n # print(json_response1)\n num = len(json_response1['data']['guard'])\n if not num:\n await asyncio.sleep(5)\n else:\n break\n \n list_available_raffleid = []\n # guard这里领取后，list对应会消失，其实就没有status了，这里是为了统一\n for j in json_response1['data']['guard']:\n id = j['id']\n status = j['status']\n if status == 1:\n # print('未参加')\n list_available_raffleid.append(id)\n elif status == 2:\n # print('过滤')\n pass\n else:\n print(json_response1)\n \n tasklist = []\n num_available = len(list_available_raffleid)\n for raffleid in list_available_raffleid:\n task = asyncio.ensure_future(handle_1_captain_raffle(num_available, roomid, raffleid))\n tasklist.append(task)\n if tasklist:\n raffle_results = await asyncio.gather(*tasklist)\n if False in raffle_results:\n print('有繁忙提示，稍后重新尝试')\n Rafflehandler.Put2Queue(handle_1_room_captain, (roomid,))\n \n \n\n","sub_path":"rafflehandler.py","file_name":"rafflehandler.py","file_ext":"py","file_size_in_byte":8824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"293281540","text":"################################\n# 已废弃\n################################\n\nfrom common import redis_cli\n\nUSER_CACHE_LIMIT = 10000\nUSER_FOLLOWING_CACHE_LIMIT = 10000\nUSER_FANS_CACHE_LIMIT = 10000\nUSER_ARTICLE_CACHE_LIMIT = 10000\n\n\nARTICLE_COMMENT_CACHE_LIMIT = 10000 # 热门评论的文章缓存数量\nCOMMENT_REPLY_CACHE_LIMIT = 10000 # 热门回复的评论缓存数量\nCOMMENT_CONTENT_CACHE_LIMIT = 100 # 评论的缓存限制\n\nARTICLE_CACHE_LIMIT = 50000\n\n\ndef clear_user_cache():\n \"\"\"\n 清理用户数据缓存，仅保留有限的最近活跃用户\n \"\"\"\n r = redis_cli['user_cache']\n size = r.zcard('user')\n if size <= USER_CACHE_LIMIT:\n return\n\n end_index = size - USER_CACHE_LIMIT\n user_id_li = r.zrange('user', 0, end_index-1)\n user_cache_keys = []\n for user_id in user_id_li:\n user_cache_keys.append('user:{}'.format(user_id))\n pl = r.pipeline()\n pl.delete(*user_cache_keys)\n pl.zrem('user', *user_id_li)\n pl.execute()\n\n\ndef clear_user_following_cache():\n \"\"\"\n 清理用户关注数据\n \"\"\"\n r = redis_cli['user_cache']\n size = r.zcard('user:following')\n if size <= USER_FOLLOWING_CACHE_LIMIT:\n return\n\n end_index = size - USER_FOLLOWING_CACHE_LIMIT\n user_id_li = r.zrange('user:following', 0, end_index - 1)\n user_cache_keys = []\n for user_id in user_id_li:\n user_cache_keys.append('user:{}:following'.format(user_id))\n pl = r.pipeline()\n pl.delete(*user_cache_keys)\n pl.zrem('user:following', *user_id_li)\n pl.execute()\n\n\ndef clear_user_fans_cache():\n \"\"\"\n 清理用户粉丝数据\n \"\"\"\n r = redis_cli['user_cache']\n size = r.zcard('user:fans')\n if size <= USER_FANS_CACHE_LIMIT:\n return\n\n end_index = size - USER_FANS_CACHE_LIMIT\n user_id_li = r.zrange('user:fans', 0, end_index - 1)\n user_cache_keys = []\n for user_id in user_id_li:\n user_cache_keys.append('user:{}:fans'.format(user_id))\n pl = r.pipeline()\n pl.delete(*user_cache_keys)\n pl.zrem('user:fans', *user_id_li)\n pl.execute()\n\n\ndef clear_user_article_cache():\n \"\"\"\n 清理用户文章数据\n \"\"\"\n r = redis_cli['user_cache']\n size = r.zcard('user:art')\n if size <= USER_ARTICLE_CACHE_LIMIT:\n return\n\n end_index = size - USER_ARTICLE_CACHE_LIMIT\n user_id_li = r.zrange('user:art', 0, end_index - 1)\n user_cache_keys = []\n for user_id in user_id_li:\n user_cache_keys.append('user:{}:art'.format(user_id))\n pl = r.pipeline()\n pl.delete(*user_cache_keys)\n pl.zrem('user:art', *user_id_li)\n pl.execute()\n\n\ndef clear_comment_cache():\n \"\"\"\n 清理评论（包括评论回复）的缓存，仅保留有限的最热评论数据\n \"\"\"\n r = redis_cli['comm_cache']\n pl = r.pipeline()\n\n # 清理文章评论\n size = r.zcard('art:comm')\n\n # 清理非热门评论\n if size > ARTICLE_COMMENT_CACHE_LIMIT:\n end_index = size - ARTICLE_COMMENT_CACHE_LIMIT\n article_id_li = r.zrange('art:comm', 0, end_index-1)\n delete_keys = []\n if article_id_li:\n for article_id in article_id_li:\n\n comment_id_li = r.zrange('art:{}:comm'.format(article_id), 0, -1)\n if comment_id_li:\n for comment_id in comment_id_li:\n delete_keys.append('comm:{}'.format(comment_id))\n\n delete_keys.append('art:{}:comm'.format(article_id))\n delete_keys.append('art:{}:comm:figure'.format(article_id))\n\n if delete_keys:\n pl.delete(*delete_keys)\n pl.zrem('art:comm', *article_id_li)\n pl.execute()\n\n # 清理热门评论数量\n delete_keys = []\n article_id_li = r.zrange('art:comm', 0, -1)\n if article_id_li:\n for article_id in article_id_li:\n size = r.zcard('art:{}:comm'.format(article_id))\n if size > COMMENT_CONTENT_CACHE_LIMIT:\n end_index = size - ARTICLE_COMMENT_CACHE_LIMIT\n comment_id_li = r.zrange('art:{}:comm'.format(article_id), 0, end_index - 1)\n if comment_id_li:\n for comment_id in comment_id_li:\n delete_keys.append('comm:{}'.format(comment_id))\n if delete_keys:\n r.delete(*delete_keys)\n\n # 清理评论回复\n size = r.zcard('comm:reply')\n\n # 清理非热门评论回复\n if size > COMMENT_REPLY_CACHE_LIMIT:\n end_index = size - COMMENT_REPLY_CACHE_LIMIT\n comment_id_li = r.zrange('comm:reply', 0, end_index-1)\n delete_keys = []\n if comment_id_li:\n for comment_id in comment_id_li:\n\n reply_id_li = r.zrange('comm:{}:reply'.format(comment_id), 0, -1)\n if reply_id_li:\n for reply_id in reply_id_li:\n delete_keys.append('comm:{}'.format(reply_id))\n\n delete_keys.append('comm:{}:reply'.format(comment_id))\n delete_keys.append('comm:{}:reply:figure'.format(comment_id))\n\n if delete_keys:\n pl.delete(*delete_keys)\n\n pl.zrem('comm:reply', *comment_id_li)\n pl.execute()\n\n # 清理热门评论回复数量\n delete_keys = []\n comment_id_li = r.zrange('comm:reply', 0, -1)\n if comment_id_li:\n for comment_id in comment_id_li:\n size = r.zcard('comm:{}:reply'.format(comment_id))\n if size > COMMENT_CONTENT_CACHE_LIMIT:\n end_index = size - ARTICLE_COMMENT_CACHE_LIMIT\n reply_id_li = r.zrange('comm:{}:reply'.format(comment_id), 0, end_index - 1)\n if reply_id_li:\n for reply_id in reply_id_li:\n delete_keys.append('comm:{}'.format(reply_id))\n if delete_keys:\n r.delete(*delete_keys)\n\n\ndef clear_article_cache():\n \"\"\"\n 清理文章缓存\n \"\"\"\n r = redis_cli['art_cache']\n size = r.zcard('art')\n if size <= ARTICLE_CACHE_LIMIT:\n return\n\n end_index = size - ARTICLE_CACHE_LIMIT\n article_id_li = r.zrange('art', 0, end_index - 1)\n article_cache_keys = []\n for article_id in article_id_li:\n article_cache_keys.append('art:{}:info'.format(article_id.decode()))\n # TODO 清理文章detail缓存\n pl = r.pipeline()\n pl.delete(*article_cache_keys)\n pl.zrem('art', *article_id_li)\n pl.execute()\n\n\n\n","sub_path":"schedule/clear_cache.py","file_name":"clear_cache.py","file_ext":"py","file_size_in_byte":6406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"571676379","text":"import subprocess\nimport sys\nfrom argparse import ArgumentParser\n\nfrom mpi4py import MPI\n\nfrom ._plugin import MPI_SESSION_ARGUMENT\nfrom ._test_name_converter import get_filename\n\nparser = ArgumentParser()\nparser.add_argument(\"test_name\")\n\nargs = parser.parse_args()\n\ntry:\n subprocess.check_output(\n [sys.executable, \"-m\", \"pytest\", \"--color=yes\", MPI_SESSION_ARGUMENT]\n + [args.test_name],\n stderr=subprocess.STDOUT,\n universal_newlines=True,\n )\nexcept subprocess.CalledProcessError as error:\n with open(\n f\"{get_filename(args.test_name)}_{MPI.COMM_WORLD.Get_rank()}\", \"w\"\n ) as f:\n test_name = args.test_name.split(\":\")[-1]\n output = error.output.split(\"\\n\")\n for i in range(len(output)):\n if test_name in output[i]:\n break\n\n output = \"\\n\".join(output[i + 1 : -2])\n f.write(output)\n sys.exit(error.returncode)\n","sub_path":"pytest_MPI/_print_capture.py","file_name":"_print_capture.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"429717138","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom datetime import datetime\n\n\nclass Message:\n\n def __init__(self, db, **kwargs):\n self.collection = db['messages']\n\n async def save(self, user, msg, **kw):\n result = await self.collection.insert({'user': user, 'msg': msg, 'time': datetime.now()})\n return result\n\n async def get_messages(self):\n messages = self.collection.find().sort([('time', 1)])\n result = []\n for message in await messages.to_list(length=None):\n result.append({\n '_id': str(message['_id']),\n 'user': message['user'],\n 'msg': message['msg'],\n 'time': str(message['time']),\n })\n return result\n","sub_path":"gbserver/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"45023363","text":"from django.utils.translation import ugettext_lazy as _\nfrom django.conf import settings\n\n# Default settings.. overwrite in your own settings.py\n\n# Planned feature.\nWIKI_USE_MARKUP_WIDGET = True\n\n####################\n# LOGIN PROTECTION #\n####################\n# Before setting the below parameters, please note that permissions can\n# be set in the django permission system on individual articles and their\n# child articles. In this way you can add a user group and give them\n# special permissions, be it on the root article or some other. Permissions\n# are inherited on lower levels.\n\n# Adds standard django login protection for viewing\nWIKI_REQUIRE_LOGIN_VIEW = getattr(settings, 'SIMPLE_WIKI_REQUIRE_LOGIN_VIEW',\n False)\n\n# Adds standard django login protection for editing\nWIKI_REQUIRE_LOGIN_EDIT = getattr(settings, 'SIMPLE_WIKI_REQUIRE_LOGIN_EDIT',\n True)\n\n####################\n# ATTACHMENTS #\n####################\n\n# This should be a directory that's writable for the web server.\n# It's relative to the MEDIA_ROOT.\nWIKI_ATTACHMENTS = getattr(settings, 'SIMPLE_WIKI_ATTACHMENTS',\n 'simplewiki/attachments/')\n\n# If false, attachments will completely disappear\nWIKI_ALLOW_ATTACHMENTS = getattr(settings, 'SIMPLE_WIKI_ALLOW_ATTACHMENTS',\n True)\n\n# If WIKI_REQUIRE_LOGIN_EDIT is False, then attachments can still be disallowed\nWIKI_ALLOW_ANON_ATTACHMENTS = getattr(settings, 'SIMPLE_WIKI_ALLOW_ANON_ATTACHMENTS', False)\n\n# Attachments are automatically stored with a dummy extension and delivered\n# back to the user with their original extension.\n# This setting does not add server security, but might add user security\n# if set -- or force users to use standard formats, which might also\n# be a good idea.\n# Example: ('pdf', 'doc', 'gif', 'jpeg', 'jpg', 'png')\nWIKI_ATTACHMENTS_ALLOWED_EXTENSIONS = getattr(settings, 'SIMPLE_WIKI_ATTACHMENTS_ALLOWED_EXTENSIONS',\n None)\n\n# At the moment this variable should not be modified, because\n# it breaks compatibility with the normal Django FileField and uploading\n# from the admin interface.\nWIKI_ATTACHMENTS_ROOT = settings.MEDIA_ROOT\n\n# Bytes! Default: 1 MB.\nWIKI_ATTACHMENTS_MAX = getattr(settings, 'SIMPLE_WIKI_ATTACHMENTS_MAX',\n 1 * 1024 * 1024)\n \n####################\n# AESTHETICS #\n####################\n\n# Planned features\nWIKI_PAGE_WIDTH = getattr(settings, 'SIMPLE_WIKI_PAGE_WIDTH', \"100%\")\nWIKI_PAGE_ALIGN = getattr(settings, 'SIMPLE_WIKI_PAGE_ALIGN', \"center\")\n","sub_path":"infolab/simplewiki/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"532684866","text":"import argparse\nimport logging\nimport re\nfrom Bio import SeqIO\n\ndef get_args():\n \"\"\"\n Get arguments from command line with argparse.\n \"\"\"\n parser = argparse.ArgumentParser(\n prog='Sort-Fasta-Records-Biopython.py',\n description=\"\"\"Sort all entries in a fasta file by order of record name.\"\"\")\n\n parser.add_argument(\"-f\", \"--fasta\",\n required=True,\n help=\"The FASTA file to sort.\")\n \n parser.add_argument(\"-o\", \"--outfile\",\n required=True,\n help=\"The name of the output FASTA file (example: output.fasta).\")\n \n parser.add_argument(\"-l\", \"--logfile\",\n required=True,\n help=\"The name of the log file to write.\")\n\n return parser.parse_args()\n\ndef setup_logging(logfile):\n # set up logging to file\n logging.basicConfig(filename=logfile,\n format=\"%(levelname)s: %(asctime)s: %(message)s\",\n datefmt='%d-%b-%y %H:%M:%S',\n level=logging.DEBUG)\n\ndef index_fasta(f):\n \"\"\"\n Use SeqIO to index the fasta file (f), which may be too big\n to parse into a list. Returns a dictionary-like structure,\n with seq names as keys and seqs as values.\n\n :param f: path to fasta file\n :return: dictionary-like structure of fasta records\n \"\"\"\n logging.info(\"index_fasta: Beginning to index fasta file.\")\n records = SeqIO.index(f, \"fasta\")\n logging.info(\"index_fasta: Found {:,} records.\".format(len(records)))\n logging.info(\"index_fasta: Completed indexing.\")\n\n return records\n\ndef sort_records(records):\n \"\"\"\n Returns a sorted list of the sequence names in fasta.\n\n :param records: records obtained from index_fasta\n :return: list of sorted sequence names\n \"\"\"\n logging.info(\"sort_records: Beginning sequence name sorting.\")\n convert = lambda text: int(text) if text.isdigit() else text\n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]\n ordered_names = sorted(list(records), key = alphanum_key)\n logging.info(\"sort_records: Completed sorting.\")\n #for n in ordered_names:\n # print(\"{:,}\".format(int(n.split('/')[1])))\n return ordered_names\n\ndef write_records(records, ordered_names, outfile):\n \"\"\"\n Write output fasta.\n :param records: dictionary-like structure from index_fasta\n :param ordered_names: list of sequence names, in sorted order\n :param outfile: name of output file to write\n :return: None\n \"\"\"\n logging.info(\"write_records: Beginning to write fasta file.\")\n rec_list = []\n rec_count = int(1)\n with open(outfile, 'a') as fhout:\n for n in ordered_names:\n rec_list.append(records[n].format(\"fasta\"))\n if len(rec_list) > 50000:\n fhout.write(\"\".join(rec_list))\n rec_list = []\n rec_count += 1\n fhout.write(\"\".join(rec_list))\n \n logging.info(\"write_records: Completed writing.\")\n\n\ndef main():\n args = get_args()\n setup_logging(args.logfile)\n records = index_fasta(args.fasta)\n ordered_names = sort_records(records)\n write_records(records, ordered_names, args.outfile)\n\nif __name__ == '__main__':\n main()\n","sub_path":"Taxonomic-Profiling-Nucleotide/scripts/Sort-Fasta-Records-BioPython.py","file_name":"Sort-Fasta-Records-BioPython.py","file_ext":"py","file_size_in_byte":3326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"514330690","text":"import os\nimport tornado.web\nfrom views import Start,Shops,Login,Register,Filter\nfrom webAPI import LoginWeb,move,RegisterWeb,FilterWeb\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\nSETTINGS = {\n \"template_path\": os.path.join(BASE_DIR, \"templates\"),\n \"static_path\": os.path.join(BASE_DIR, \"static\")\n}\n\nHANDLERS = [\n (r\"/\", Start),\n (r\"/login\", Login),\n (r\"/ajax/login\", LoginWeb),\n\t(r\"/shop\", Start),\n\t(r\"/ajax/shop\", move),\n\t(r\"/register\",Register),\n\t(r\"/ajax/register\",RegisterWeb),\n (r\"/filter\",Filter),\n (r\"/ajax/filter\",FilterWeb),\n]\n\nUI_MODULES={\n\t'Shop': Shops\n}\n\napplication = tornado.web.Application(\n handlers = HANDLERS,\n ui_modules=UI_MODULES,\n**SETTINGS)\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"311938000","text":"import random\nimport time\nimport sys\n\ndef random_guessing():\n number=random.randint(1,10)\n print('random number ',number)\n while True:\n try:\n my_input=int(input('Input '))\n if my_input>=1 and my_input<=10:\n if my_input==number:\n print('Hurrrayyy..... You are a Genius ...')\n break\n else:\n print('Try Again')\n continue\n else:\n print('Hey bro, I said Enter 1~10')\n continue\n except ValueError:\n print('Please enter a number')\n continue\n\n\nrandom_guessing()\n\n\nwhile True:\n try:\n x=input('You want to play again [Y/N]').lower()\n if x=='y':\n random_guessing()\n continue\n elif x=='n':\n print('Exit')\n sys.exit()\n except ValueError:\n print('Try to enter Y/N')\n continue\n \ntime.sleep(5)\n \n\n\n","sub_path":"Guessing_Game.py","file_name":"Guessing_Game.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"399561678","text":"import re\nimport gzip\nimport shutil\nimport requests\nimport json\nimport numpy as np\nimport pandas as pd\nimport os\nimport collections\nimport h5py\nfrom scipy import sparse\nimport csv\n\n#main gdc api querry function\ndef get_projects_info(project_names):\n '''\n a method for retrieveing data about cases and their related samples for specified gdc projects.\n The method looks for which of the projects specified actually exist in the GDC and then for each\n of the project retrieves all the cases with data about them like demographic and data about\n samples related to each case.\n \n Input:\n List of project names for which data should be retrieved\n \n Output:\n dict:\n data dict: data in the form of a dictionary with the structure: {project_name: {case_id: {data about the case}}}\n image to sample: mapping from image file name to sample id for matching with labels\n case to images: mapping from case id to list of associated images for dataset creation\n labels: label dataframe\n mutational signatures: mutatiuonal signatures dataframe\n hugo symbols: hugo symbols dataframe\n '''\n \n #check if project_names is a list of strings\n if not(isinstance(project_names,list) and all(isinstance(i,str) for i in project_names)):\n raise TypeError(\"project_names expects a list of strings\")\n \n #define api endpoints\n cases_endpt = 'https://api.gdc.cancer.gov/cases'\n projects_endpt = 'https://api.gdc.cancer.gov/projects'\n \n \n #check which of the specified project names are in gdc\n print(\"looking for project names in GDC database.\")\n \n filters = {\n \"op\":\"in\",\n \"content\":{\n \"field\":\"project_id\",\n \"value\":project_names\n }\n }\n params = {\n \"filters\": json.dumps(filters),\n \"fields\":\"project_id\",\n \"format\":\"json\",\n \"pretty\":\"true\",\n }\n \n response = requests.get(projects_endpt, params = params)\n response = json.loads(response.content.decode(\"utf-8\"))\n print(80*'-')\n \n if not(response['warnings']=={}):\n print(\"warnings:\")\n print(response['warnings'])\n \n found_projects = list(map(lambda x: x['project_id'],response['data']['hits']))\n print(\"Projects found in GDC:\",found_projects)\n out_samples = []\n projects_data={}\n image_to_sample={}\n case_to_images={}\n out_hugos = []\n all_barcodes = []\n \n #mutational signature file for the entire tcga dataset\n signatures = pd.read_csv(os.path.join(\"manifest\", 'TCGA_WES_sigProfiler_SBS_signatures_in_samples.csv'))\n signatures['case_id'] = signatures['Sample Names'].apply(lambda x : '-'.join(x.split('-')[:3]))\n signatures = signatures.iloc[:,3:]\n #search for cases for each project\n print(80*'-')\n for project in found_projects:\n \n print(\"looking for cases for project:\",project)\n \n filters = {\n \"op\":\"=\",\n \"content\":{\n \"field\":\"project.project_id\",\n \"value\":project\n }\n }\n \n fields = ['case_id','project.project_id',\"submitter_id\",\"files.file_id\",\"files.file_name\"]\n fields = ','.join(fields)\n \n params = {\n \"filters\":json.dumps(filters),\n \"fields\" :fields,\n \"format\" :\"json\",\n \"pretty\" :\"true\",\n \"size\" : \"1100\",\n \"expand\" : \"demographic,samples,files,diagnoses\"\n }\n\n response = requests.get(cases_endpt, params = params)\n cases = json.loads(response.content.decode(\"utf-8\"))\n print(\"retrieved\",cases['data']['pagination']['count'],\"cases in the\",project,\"project\")\n \n \n #add cases to dictionary\n found_cases = cases['data']['hits']\n out_cases = {}\n for case in found_cases:\n \n #section for creating a dataframe\n for sample in case['samples']:\n sample_dict = {}\n sample_dict['case_barcode'] = case['submitter_id']\n sample_dict['project'] = case['project']['project_id']\n sample_dict['case_id'] = case['case_id']\n\n #add demographic data for sample\n demographic_dict = case.get('demographic',{})\n for key in demographic_dict:\n sample_dict[\"demographic.\"+key] = demographic_dict[key]\n\n #add diagnose data for sample\n diagnoses_dict = case.get('diagnoses',{})\n #the diagnostic value is a list with either length 0 or 1 if empty list then change it to empty\n #dict for convenience\n if len(diagnoses_dict)>0:\n diagnoses_dict = diagnoses_dict[0]\n else:\n diagnoses_dict = {}\n for key in diagnoses_dict:\n sample_dict[\"diagnose.\"+key] = diagnoses_dict[key]\n sample_dict['sample.barcode']=sample['submitter_id']\n #print(sample['submitter_id'])\n for key in sample:\n if key != 'submitter_id':\n sample_dict['sample.'+key]=sample[key]\n #print(sample_dict['sample.barcode'])\n out_samples.append(sample_dict)\n \n \n #dict section\n demographic = case.get('demographic',{})\n samples = case.get('samples',[])\n mutational_signatures = signatures[signatures['case_id']==case['submitter_id']]\n \n if mutational_signatures.shape[0] != 0:\n mutational_signatures = mutational_signatures.iloc[0,:-1].to_list()\n else:\n mutational_signatures = []\n\n out_cases[case['submitter_id']]={\"demographic\":demographic,\"samples\":samples,\n \"case_id\":case['case_id'],\"hugo_symbols\":[],\n \"mutational_signature\":mutational_signatures,\n }\n \n #add slide image file names to cases for mapping from cases to images\n for sample in out_cases[case['submitter_id']]['samples']:\n #sample_mut_sig = \n sample['image_files']=[]\n \n images_for_case = []\n for file in case['files']:\n if file['data_format']=='SVS':\n sample_id = '-'.join(file['submitter_id'].split('-')[:4])\n images_for_case.append(file['file_name'])\n image_to_sample[file['file_name']] = sample_id\n for sample in out_cases[case['submitter_id']]['samples']:\n if sample['submitter_id']==sample_id:\n sample['image_files'].append(file['file_name'])\n #add list of image file names to mapping for that case \n if images_for_case != []:\n case_to_images[case['submitter_id']]=images_for_case\n \n #delete samples with no image files\n for i,sample in enumerate(out_cases[case['submitter_id']]['samples']):\n if sample['image_files']==[]:\n del out_cases[case['submitter_id']]['samples'][i]\n \n #delete cases with no samples\n if out_cases[case['submitter_id']]['samples'] == []:\n del out_cases[case['submitter_id']]\n \n #download maf file to add hugo symbols to each case\n print(\"downloading maf file for project\",project)\n zip_file,maf_file = download_maf_for_proj(project)\n \n #if maf file for the given project was found and extracted then add it to \n if maf_file != None:\n print(\"downloaded file:\",zip_file)\n print(\"file extracted to:\",maf_file)\n\n with open(maf_file) as maf:\n rd = csv.reader(maf,delimiter='\\t', quotechar='\"')\n #skip headers\n for i in range(6):\n next(rd)\n #add hugo symbols to each case\n for line in rd:\n symbol = line[0]\n case_id = '-'.join(line[15].split('-')[:3])\n out_cases[case_id]['hugo_symbols'].append(symbol)\n\n #add cases to hugo symbol dataframe\n all_barcodes = all_barcodes+list(out_cases.keys())\n for case in out_cases:\n symbols = out_cases[case]['hugo_symbols']\n symbols = dict(collections.Counter(symbols))\n out_hugos.append(symbols)\n \n #add the cases doctionary for given project to the projects dictionary\n projects_data[project]=out_cases\n print(80*'-')\n \n #create sparse dataframe for hugos\n hugos = dicts_to_sparse(out_hugos)\n hugos['case_barcode'] = all_barcodes\n\n samples = pd.DataFrame.from_records(out_samples)\n samples = samples.fillna(np.nan)\n print(\"done\") \n return {\"data dict\":projects_data,\"image to sample\":image_to_sample,\n \"case to images\":case_to_images,\"labels\":samples, \"mutational signatures\" : signatures,\n \"hugo symbols\" : hugos}\n\n#file download functionality\ndef download_extract(file_id,project_name):\n download_endpt = \"https://api.gdc.cancer.gov/data/{}\".format(file_id)\n response = requests.get(download_endpt, headers = {\"Content-Type\": \"application/json\"})\n response_head_cd = response.headers[\"Content-Disposition\"]\n file_name = os.path.join(\"manifest\", re.findall(\"filename=(.+)\", response_head_cd)[0])\n out_name = os.path.join(\"manifest\", project_name+'-maf.tsv')\n \n with open(file_name, \"wb\") as output_file:\n output_file.write(response.content)\n \n with gzip.open(file_name, 'rb') as f_in:\n with open(out_name, 'wb') as f_out:\n shutil.copyfileobj(f_in, f_out)\n return file_name,out_name\n\ndef download_maf_for_proj(project_name):\n files_endpt = \"https://api.gdc.cancer.gov/files\"\n\n workflow = {\"op\":\"=\",\n \"content\":{\n \"field\":\"analysis.workflow_type\",\n \"value\":\"MuSE Variant Aggregation and Masking\"\n }\n }\n\n category = {\"op\":\"=\",\n \"content\":{\n \"field\":\"data_category\",\n \"value\":\"Simple Nucleotide Variation\"\n }\n }\n\n dat_type = {\"op\":\"=\",\n \"content\":{\n \"field\":\"data_type\",\n \"value\":\"Masked Somatic Mutation\"\n }\n }\n proj = {\"op\":\"=\",\n \"content\":{\n \"field\":\"cases.project.project_id\",\n \"value\":project_name\n }\n }\n filters = {\n \"op\":\"and\",\n \"content\":[workflow,dat_type,category,proj]\n }\n params = {\n \"filters\":json.dumps(filters),\n \"fields\" : \"data_type,data_category,file_id,cases.project.project_id\",\n \"format\" :\"json\",\n \"pretty\" :\"true\",\n \"size\" : \"5\",\n \"expand\" : \"analysis\",\n }\n\n response = requests.get(files_endpt, params = params)\n file_data = json.loads(response.content.decode(\"utf-8\"))\n \n #if no maf file found do nothing\n if(file_data['data']['pagination']['total']==0):\n print(\"no maf file found for project\",project_name)\n return None,None\n \n file_id = file_data['data']['hits'][0]['file_id']\n return download_extract(file_id,project_name)\n\ndef download_image(file_name,path=\"\"):\n file_path = os.path.join(path,file_name)\n #check if image already exists\n if not os.path.exists(file_path):\n if not os.path.exists(path):\n os.makedirs(path)\n files_endpt = \"https://api.gdc.cancer.gov/files\"\n\n context = {\"op\":\"=\",\n \"content\":{\n \"field\":\"file_name\",\n \"value\":file_name\n }\n }\n params = {\n \"filters\":json.dumps(context),\n \"fields\" : \"file_id\",\n \"format\" :\"json\",\n \"pretty\" :\"true\",\n \"size\" : \"5\",\n }\n\n response = requests.get(files_endpt, params = params)\n file_data = json.loads(response.content.decode(\"utf-8\"))\n file_id = file_data['data']['hits'][0]['file_id']\n\n data_endpt = \"https://api.gdc.cancer.gov/data/{}\".format(file_id)\n print(\"downloading image {} to path {}\".format(file_name,file_path))\n response = requests.get(data_endpt, headers = {\"Content-Type\": \"application/json\"})\n\n with open(file_path, \"wb\") as output_file:\n output_file.write(response.content)\n else:\n print(\"{} already exists, not downloading anything\".format(file_path))\n\ndef dicts_to_sparse(dicts):\n symbol_to_col = {}\n idx = 0\n #create maping from hugo symbol to col in coo matrix\n for case in dicts:\n for symbol in case:\n if symbol not in symbol_to_col:\n symbol_to_col[symbol]=idx\n idx+=1\n row = []\n col = []\n data = []\n #build arrays\n row_idx=0\n for case in dicts:\n for symbol in case:\n row.append(row_idx)\n col.append(symbol_to_col[symbol])\n data.append(case[symbol])\n row_idx += 1\n #build coo\n coo = sparse.coo_matrix((data,(row,col)))\n #convert coo to df\n df = pd.DataFrame.sparse.from_spmatrix(coo,columns=list(symbol_to_col.keys()))\n return df\n\ndef store_hugo(file,hugo,overwrite=False):\n #store a hugo symbol dataframe in an existing h5 file\n hugo_counts = hugo.drop(\"case_barcode\",axis=1)\n hugo_barcodes = hugo['case_barcode'].to_numpy().astype('S')\n hugo_counts_coo = hugo_counts.sparse.to_coo()\n\n #del existing hugo symbols group if overwrite is true\n if 'hugo_symbols' in file:\n print('hugo symbols already stored in this file') \n if overwrite:\n print('overwriting')\n del file['hugo_symbols']\n else:\n return\n\n #store hugos in h5 file\n file.create_group('hugo_symbols')\n hugo = file['hugo_symbols']\n #store values\n hugo.create_dataset('data', data=hugo_counts_coo.data)\n hugo.create_dataset('col', data=hugo_counts_coo.col)\n hugo.create_dataset('row', data=hugo_counts_coo.row)\n hugo.attrs['shape'] = hugo_counts.shape\n #store barcodes\n hugo.create_dataset('barcodes',data=hugo_barcodes)\n #store names\n hugo.create_dataset('names',data=hugo_counts.columns.to_numpy().astype('S'))\n\ndef load_hugo(file):\n #reconstruct hugo symbol dataframe from opened h5 file\n if not 'hugo_symbols' in file:\n print('hugo symbols data not in file')\n return None\n else:\n hugo = file['hugo_symbols']\n #restore the count values\n matrix = sparse.coo_matrix((hugo['data'],(hugo['row'],hugo['col'])),hugo.attrs['shape'])\n cols = np.array(hugo['names']).astype(str)\n df = pd.DataFrame.sparse.from_spmatrix(matrix,columns=cols)\n #add barcodes\n barcodes = np.array(hugo['barcodes']).astype(str)\n df['case_barcode']=barcodes\n return df ","sub_path":"src/labeling_util.py","file_name":"labeling_util.py","file_ext":"py","file_size_in_byte":15351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"257253261","text":"# -*- coding: utf-8 -*-\nimport json\nfrom celery_app import app\nfrom celery.utils.log import get_task_logger\nfrom celery_tasks import SAK_URL, SAK_VERSION\nimport requests\nfrom flod_common.session.utils import make_superuser_auth_cookie\n\nlogger = get_task_logger(__name__)\n\n\ndef send_reminder(url, minutes=None, reminder_slack=None, min_minutes_since_last_reminder=None):\n if minutes is None or not isinstance(minutes, int):\n logger.error(\"send_soknad_reminder_task - antall minutter ikke satt\")\n return\n\n data = {'minutes': minutes}\n if reminder_slack is not None:\n data.update(reminder_slack=reminder_slack)\n if min_minutes_since_last_reminder is not None:\n data.update(min_minutes_since_last_reminder=min_minutes_since_last_reminder)\n\n auth_token_cookie = make_superuser_auth_cookie()\n headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}\n\n return requests.post(url, data=json.dumps(data), cookies=auth_token_cookie, headers=headers)\n\n\n@app.task\ndef send_soknad_reminder_task(minutes=None, reminder_slack=None, min_minutes_since_last_reminder=None):\n url = '%s/api/%s/purringer/soknader/' % (SAK_URL, SAK_VERSION)\n response = send_reminder(url, minutes, reminder_slack, min_minutes_since_last_reminder)\n\n if response.status_code != 201:\n logger.error(\"Could not trigger the sending of soknad reminders, response has status %s!\\n response=%s\"\n % (response.status_code, response))\n\n\n@app.task\ndef send_rapport_reminder_task(minutes=None, reminder_slack=None, min_minutes_since_last_reminder=None):\n url = '%s/api/%s/purringer/rapporter/' % (SAK_URL, SAK_VERSION)\n response = send_reminder(url, minutes, reminder_slack, min_minutes_since_last_reminder)\n\n if response.status_code != 201:\n logger.error(\"Could not trigger the sending of rapport reminders, response has status %s!\\n response=%s\"\n % (response.status_code, response))\n","sub_path":"flod_tasks/celery_tasks/reminder_tasks.py","file_name":"reminder_tasks.py","file_ext":"py","file_size_in_byte":1967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"122619973","text":"###################################\n#\n# \n#\n###################################\n\nimport requests\n\nHost = 'http://127.0.0.1:8000/'\n\nurls_update = 'api/updates/'\nupdate_date = '2018-08-12'\nupdate_time = '13:00:00'\n#update_hook = update_time + '@' + update_time\npayload_update = {'notes_text': 'rilevazione di prova da client', 'pub_date': update_date, 'pub_hour': update_time}\nr = requests.post(Host + urls_update, data = payload_update)\nprint('URL request' + r.url)\nprint('Request status code: ' + str(r.status_code))\nprint('Request status code: ' + str(r.status_code))\nprint('Response header:' + str(r.headers))\nupdate_content = r.json()\nupdate_pk = update_content['pk']\nprint('Content response:' + str(update_content))\nprint('Content pk value:' + str(update_pk))\n\nurls_category = 'api/categories/'\npayload_category = {'name': 'Titoli gevernativi', 'description': 'Tassi e Spread principali titoli benchmark', 'update': update_pk}\nr = requests.post(Host + urls_category, data = payload_category)\nprint('URL request' + r.url)\nprint('Request status code: ' + str(r.status_code))\n\n#payload_category = {'name': 'Tassi', 'description': 'Principali punti della curva EUR IRS', 'update': update_hook}\n#r = requests.post(Host + urls_category, data = payload_category)\n#print('URL request' + r.url)\n#print('Request status code: ' + str(r.status_code))\n\n\n#urls_symbol = 'api/symbol/'\n#payload_symbol = {'notes_text': 'rilevazione di prova da client', 'pub_date': '2018-08-03', 'pub_hour': '08:00:00'}\n#r = requests.post(Host + urls_symbol, data = payload_symbol)\n#print('URL request' + r.url)\n#print('Request status code: ' + str(r.status_code))\n\n#urls_value = 'api/value/'\n#payload_value = {'notes_text': 'rilevazione di prova da client', 'pub_date': '2018-08-03', 'pub_hour': '08:00:00'}\n#r = requests.post(Host + urls_value, data = payload_value)\n#print('URL request' + r.url)\n#print('Request status code: ' + str(r.status_code))","sub_path":"old/marketupdate_rest_client.py","file_name":"marketupdate_rest_client.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"386048967","text":"\"\"\"lab1_webproject URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom django.views.generic import TemplateView\nfrom rest_framework import routers\nfrom rest_framework.schemas import get_schema_view\nfrom lab1_app import views\n\nrouter = routers.DefaultRouter()\nrouter.register(r'users', views.UserViewSet)\nrouter.register(r'groups', views.GroupViewSet)\nrouter.register(r'language', views.LanguageViewSet, 'language')\nrouter.register(r'word', views.WordViewSet, 'word')\n\n\nurlpatterns = [\n path('', include(router.urls)),\n path('account/register', views.UserCreate.as_view()),\n path('admin/', admin.site.urls),\n path('api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n path('lab/', include('lab1_app.urls')),\n path('openapi',\n get_schema_view(\n title=\"lab1_app\",\n description=\"API for Dictionary, \"\n \"the API can store words and their translation\",\n version=\"1.0.0\"\n ),\n name='openapi-schema'),\n path('redoc/',\n TemplateView.as_view(\n template_name='redoc.html',\n extra_context={'schema_url': 'openapi-schema'}\n ),\n name='redoc'),\n\n path('swagger-ui/',\n TemplateView.as_view(\n template_name='swagger-ui.html',\n extra_context={'schema_url': 'openapi-schema'}\n ),\n name='swagger-ui'),\n]\n","sub_path":"lab1_webproject/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"152392901","text":"from flask import Flask, request\nimport random\nfrom werkzeug.serving import run_simple\napp = Flask(__name__)\napp.debug = True\n\n#######################################################\n#\n# Ruta: http://localhost:5001/\n# Descripcion:\n# Esta ruta muestra informacion sobre el WebServer Rastreo\n#\n#######################################################\n@app.route('/')\ndef Principal():\n return 'Este es el WebService Rastreo!'\n\n#######################################################\n#\n# Ruta: 'http://localhost:5001/SolicitudRastreoCliente/\n# Descripcion:\n# Esta ruta toma el codigo del cliente del cual se solicita\n# el rastreo, devuelve su longitud y latitud.\n#\n#######################################################\n@app.route('/SolicitudRastreoCliente/')\ndef SolicitudRastreoCliente():\n codigoCliente=request.args['codigoCliente']\n longitud = str(random.randint(1, 50000))\n latitud = str(random.randint(1, 50000))\n return \"El cliente [\"+codigoCliente+\"] esta en la siguiente ubicacion. longitud [\"+longitud+\"] y latitud [\"+latitud+\"] \"\n\n#######################################################\n#\n# Ruta: 'http://localhost:5001/SolicitudRastreoPiloto/\n# Descripcion:\n# Esta ruta toma el codigo del piloto del cual se solicita\n# el rastreo, devuelve su longitud y latitud.\n#\n#######################################################\n@app.route('/SolicitudRastreoPiloto/')\ndef SolicitudRastreoPiloto():\n codigoPiloto=request.args['codigoPiloto']\n longitud = str(random.randint(1, 50000))\n latitud = str(random.randint(1, 50000))\n return \"El piloto [\"+codigoPiloto+\"] esta en la siguiente ubicacion. longitud [\"+longitud+\"] y latitud [\"+latitud+\"] \"\n\nif __name__ == \"__main__\":\n run_simple('localhost', 5001, app,\n use_reloader=True, use_debugger=True, use_evalex=True)","sub_path":"Rastreo/microservicioRastreo.py","file_name":"microservicioRastreo.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"254067077","text":"\"\"\"-------------------------------------------------------------------------------------------------\nScript Name: Speech to Text GCloud\nScript Author: Andoni Sooklaris\nDate: 2019-02-04\nUpdated:\n -2019-09-24\n\nTranscribe an audio file using the Google Cloud Speech engine.\n-------------------------------------------------------------------------------------------------\"\"\"\n\nimport argparse\nfrom os.path import join, expanduser\nfrom pydoni.scripts.audio import SpeechToText\n\ndef initialize_argparser():\n \"\"\"\n Initiate program argument parser.\n\n Returns:\n Namespace\n \"\"\"\n\n parser = argparse.ArgumentParser(description='Program argument parser')\n parser._action_groups.pop()\n\n required = parser.add_argument_group('required arguments')\n required.add_argument('--audiofile',type=str, required=True,\n help='Audio file to transcribe')\n required.add_argument('--outfile',type=str, required=True,\n help='Output textfile')\n required.add_argument('--method',type=str, required=False, default='gcs',\n help=\"Method to use for audiofile transcription, one of ['gcs'] (default: {gcs})\")\n required.add_argument('--gcs_split_threshold',type=str, required=False, default=55,\n help='Maximum audio clip size in seconds, if clip exceeds this length it will be split using class method `split()` (default: {55})')\n required.add_argument('--google_application_credentials_json',type=str, required=False,\n default=join(expanduser('~'), 'google-cloud-sdk', 'doni-speech-to-text-ebfb6aece133.json'),\n help='Path to google application credentials file')\n\n optional = parser.add_argument_group('optional arguments')\n optional.add_argument('--apply_correction', action='store_true', default=True,\n help='Apply smart dictation, smart capitalize, remove excess spaces and any manual corrections to transcript (default: {True})')\n optional.add_argument('--verbose', action='store_true', default=False,\n help='Print messages to STDOUT (default: {False})')\n\n return parser.parse_args()\n\n\nns = initialize_argparser()\nSpeechToText(\n audiofile=ns.audiofile,\n outfile=ns.outfile,\n method=ns.method,\n gcs_split_threshold=ns.gcs_split_threshold,\n google_application_credentials_json=ns.google_application_credentials_json,\n apply_correction=ns.apply_correction,\n verbose=ns.verbose\n).run()\n","sub_path":"audio/SpeechToText.py","file_name":"SpeechToText.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"458087440","text":"# forward stage\nfrom __future__ import division\ndef data(confidence, Param, iteration, fcf_Operator): \n\n #import InputFilesForw\n import pyomo.environ as pyomo\n from pyomo.opt import SolverFactory\n from pyomo.core import Suffix\n from utils.parameters import pyomoset, pyomohydro, pyomogates\n from scripts.pyomo_model import obj_function\n from scripts.pyomo_model import load_balance\n from scripts.pyomo_model import energy_conservationF as energy_conservation\n from scripts.pyomo_model import storage_function, costtogo\n from scripts.pyomo_model import variables\n from utils.saveresults import saveiter\n from utils import solver\n import progressbar\n import pickle\n\n # Cost-to-go function\n if fcf_Operator is True:\n dict_fcf = pickle.load(open(\"savedata/fcfdataIter.p\", \"rb\"))\n else:\n dict_fcf = pickle.load(open(\"savedata/fcfdata.p\", \"rb\"))\n fcf_backward = dict_fcf['fcf_backward']\n \n # progress analysis\n bar = progressbar.ProgressBar(max_value = Param.seriesForw*Param.stages, \\\n widgets=[progressbar.Bar('=', '[', ']'), ' Forward stage - iteration '+str(iteration+1)+' ', \n progressbar.Percentage()])\n bar.start(); count = 0\n \n # import data\n dict_hydro = pickle.load(open(\"savedata/hydro_save.p\", \"rb\"))\n dh_factor = dict_hydro['prodFactor']\n dh_limits = dict_hydro['u_limit']\n dict_batt = pickle.load(open(\"savedata/batt_save.p\", \"rb\"))\n dict_lines = pickle.load(open(\"savedata/lines_save.p\", \"rb\"))\n dict_windenergy = pickle.load(open(\"savedata/windspeed_save.p\", \"rb\"))\n dict_wind = pickle.load(open(\"savedata/wind_hat_0.p\", \"rb\"))\n dict_format = pickle.load(open(\"savedata/format_save.p\", \"rb\"))\n df_demand = dict_format['demand']\n df_thmin = dict_format['thermalMin']\n df_thmax = dict_format['thermalMax']\n df_opcost = dict_format['opCost']\n dict_data = pickle.load(open(\"savedata/data_save_iter.p\", \"rb\"))\n dd_rationing = dict_data['rationingData']\n dd_emissions = dict_data['emissionsData']\n b_storageData = dict_data['b_storageData']\n\n # data from dictionaries\n numAreas = dict_data['numAreas']\n numBlocks = dict_format['numBlocks']\n blocksData = dict_data['blocksData']\n thermalPlants = dict_data['thermalPlants']\n smallData = dict_data['smallData']\n smallPlants = dict_data['smallPlants']\n batteries = dict_data['batteries']\n hydroPlants = dict_data['hydroPlants']\n volData = dict_data['volData']\n thermalData = dict_data['thermalData']\n demandArea = dict_format['demandArea']\n rnwArea = dict_windenergy['RnwArea']\n \n circuits = dict_data['linesData']\n fcircuits = list(range(1, len(circuits)+1))\n\n # print results\n lenblk = range(numBlocks)\n lenstg = range(Param.stages); lensc = range(Param.seriesForw)\n\n genThermal = [[[[[] for bl in lenblk] for z in thermalPlants] for x in lenstg] for y in lensc]\n genSmall = [[[[[] for bl in lenblk] for z in smallPlants] for x in lenstg] for y in lensc]\n genHydro = [[[[[] for bl in lenblk] for z in hydroPlants] for x in lenstg] for y in lensc]\n genwind = [[[[[] for bl in lenblk] for z in range(numAreas)] for x in lenstg] for y in lensc]\n spillwind = [[[[[] for bl in lenblk] for z in range(numAreas)] for x in lenstg] for y in lensc]\n genBatteries = [[[[[] for bl in lenblk] for z in batteries] for x in lenstg] for y in lensc]\n genDeficit = [[[[[] for bl in lenblk] for x in lenstg] for y in lensc] for x in range(numAreas)]\n emissCurve = [[[[] for bl in lenblk] for x in lenstg] for y in lensc]\n loadBatteries = [[[[[] for bl in lenblk] for z in batteries] for x in lenstg] for y in lensc]\n lvlBatteries = [[[[] for z in batteries] for x in lenstg] for y in lensc]\n lvlHydro = [[[[] for z in hydroPlants] for x in lenstg] for y in lensc]\n spillHydro = [[[[[] for bl in lenblk] for z in hydroPlants] for x in lenstg] for y in lensc]\n linTransfer = [[[[[] for a in range(numAreas)] for z in range(numAreas)] for x in lenstg] for y in lensc]\n genRnws = [[[[[] for bl in lenblk] for z in range(numAreas)] for x in lenstg] for y in lensc]\n \n # Define solver\n opt = solver.gurobi_solver(SolverFactory)\n #opt = solver.glpk_solver(SolverFactory)\n #opt = solver.cplex_solver(SolverFactory)\n #opt = solver.ipopt_solver(SolverFactory)\n #opt = solver.cbc_solver(SolverFactory)\n #opt = solver.xpress_solver(SolverFactory)\n \n # Define abstract model\n model = pyomo.ConcreteModel()\n\n # SETS\n # set of demand blocks\n model.Blocks = pyomo.Set(initialize=list(range(1, numBlocks+1)))\n # set of state/cut\n model.Cuts = pyomo.Set(initialize= list(range(1, iteration+2)))\n #model.Cuts = pyomo.Set(initialize=[1])\n # set of hydroelectric plants / reservoirs\n model.Hydro = pyomo.Set(initialize=hydroPlants)\n # set of hydro plants with reservoirs\n setData = pyomohydro(hydroPlants, dict_data['hydroReservoir'])\n model.resHydro = pyomo.Set(initialize=setData)\n # generation chains: set of spill and turbining arcs\n setData = pyomoset(dict_hydro['S-downstream'])\n model.SpillArcs = pyomo.Set(initialize= setData, dimen=2)\n setData = pyomoset(dict_hydro['T-downstream'])\n model.TurbiningArcs = pyomo.Set(initialize= setData, dimen=2)\n # set of thermal plants\n model.Thermal = pyomo.Set(initialize=thermalPlants)\n # set of thermal plants\n model.Small = pyomo.Set(initialize=smallPlants)\n # set of wind farms\n model.Wind = pyomo.Set(initialize=dict_data['windPlants'])\n # set of battery units\n model.Batteries = pyomo.Set(initialize=batteries)\n # set of areas in the system\n model.Areas = pyomo.Set(initialize=list(range(1, numAreas+1)))\n model.AreasDmd = pyomo.Set(initialize= demandArea)\n model.AreasRnw = pyomo.Set(initialize= rnwArea)\n # set of lines in the system\n model.Circuits = pyomo.Set(initialize= fcircuits)\n \n # Plants by areas\n def In_init(model, area):\n retval = [];\n for lines_def in range(len(circuits)):\n lines_area = circuits[lines_def][0]\n if lines_area == area:\n retval.append(lines_def+1)\n return retval\n model.linesAreaIn = pyomo.Set(model.Areas, initialize= In_init)\n def Out_init(model, area):\n retval = [];\n for lines_def in range(len(circuits)):\n lines_area = circuits[lines_def][1]\n if lines_area == area:\n retval.append(lines_def+1)\n return retval\n model.linesAreaOut = pyomo.Set(model.Areas, initialize= Out_init)\n \n def thermalArea_init(model, area):\n retval = []\n for i in model.Thermal:\n th_indx = thermalPlants.index(i)\n are_pl = dict_format['area_thermal'][th_indx]\n if are_pl == area:\n retval.append(i)\n return retval\n model.ThermalArea = pyomo.Set(model.Areas, initialize= thermalArea_init)\n \n def hydroArea_init(model, area):\n retval = []\n for i in model.Hydro:\n hy_indx = hydroPlants.index(i)\n are_pl = dict_format['area_hydro'][hy_indx]\n if are_pl == area:\n retval.append(i)\n return retval\n model.HydroArea = pyomo.Set(model.Areas, initialize= hydroArea_init)\n \n def smallArea_init(model, area):\n retval = []\n for i in model.Small:\n sm_indx = smallPlants.index(i)\n are_pl = dict_format['area_small'][sm_indx]\n if are_pl == area:\n retval.append(i)\n return retval\n model.SmallArea = pyomo.Set(model.Areas, initialize= smallArea_init)\n \n # PARAMETERS\n # cost of thermal production\n model.cost = pyomo.Param(model.Thermal, mutable=True)\n # cost of energy rationing\n model.rationing = pyomo.Param(model.Areas, mutable=True)\n # demand for each stage\n model.demand = pyomo.Param(model.Areas, model.Blocks, mutable=True)\n # inflows for each stage\n model.inflows = pyomo.Param(model.Hydro, mutable=True)\n # wind inflows for each stage\n model.meanWind = pyomo.Param(model.Areas, model.Blocks, mutable=True)\n # production factor for each hydro plant\n model.factorH = pyomo.Param(model.Hydro, mutable=True)\n # Hydro plants by area\n # production cost (CxC) for each hydro plant\n dictplant = {hydroPlants[z]: dict_hydro['oymcost'][z] for z in range(len(hydroPlants))}\n model.hydroCost = pyomo.Param(model.Hydro, initialize=dictplant)\n # production factor for each battery unit\n model.factorB = pyomo.Param(model.Batteries, mutable=True)\n # Batteries by area\n dictplant = {batteries[z]: dict_batt['b_area'][z] for z in range(len(batteries))}\n model.BatteriesArea = pyomo.Param(model.Batteries, initialize=dictplant)\n # coeficcient of lineal segments in the future cost function\n model.coefcTerm = pyomo.Param(model.Hydro, model.Cuts, mutable=True)\n # constant term of lineal segments in the future cost function\n model.constTerm = pyomo.Param(model.Cuts, mutable=True)\n # coeficcient of lineal segments in the future cost function\n model.coefcBatt = pyomo.Param(model.Batteries, model.Cuts, mutable=True)\n # Initial volume in reservoirs\n model.iniVol = pyomo.Param(model.Hydro, mutable=True)\n # Initial storage in batteries\n model.iniLvl = pyomo.Param(model.Batteries, mutable=True)\n model.iniLvlBlk = pyomo.Param(model.Batteries, model.Blocks, mutable=True)\n\n # BOUNDS\n # bounds (min and max) on hydro generation\n #model.minGenH = pyomo.Param(model.Hydro, model.Blocks, mutable=True)\n model.maxGenH = pyomo.Param(model.Hydro, model.Blocks, mutable=True)\n # bounds (min and max) on thermal generation\n model.minGenT = pyomo.Param(model.Thermal, model.Blocks, mutable=True)\n model.maxGenT = pyomo.Param(model.Thermal, model.Blocks, mutable=True)\n # bounds (min and max) on thermal generation\n model.minGenS = pyomo.Param(model.Small, model.Blocks, mutable=True)\n model.maxGenS = pyomo.Param(model.Small, model.Blocks, mutable=True)\n # bounds (min and max) on batteries generation\n #model.minGenB = pyomo.Param(model.Batteries, model.Blocks, mutable=True)\n model.maxGenB = pyomo.Param(model.Batteries, model.Blocks, mutable=True)\n # bounds (min and max) on wind area generation\n model.maxGenW = pyomo.Param(model.Areas, model.Blocks, mutable=True)\n # bounds (min and max) on capacity of reservoirs\n dictplant = {hydroPlants[z]: dict_hydro['volmin'][z] for z in range(len(hydroPlants))}\n model.minVolH = pyomo.Param(model.Hydro, initialize=dictplant)\n dictplant = {hydroPlants[z]: dict_hydro['volmax'][z] for z in range(len(hydroPlants))}\n model.maxVolH = pyomo.Param(model.Hydro, initialize=dictplant)\n # bounds (min and max) on capacity of batteries\n model.maxlvl = pyomo.Param(model.Batteries, mutable=True)\n model.maxlvlB = pyomo.Param(model.Batteries, mutable=True)\n # bounds (max) on capacity of lines\n model.lineLimit = pyomo.Param(model.Circuits, model.Blocks, mutable=True)\n \n ###########################################################################\n \n # Bounds and DECISION VARIABLES\n variables(model, pyomo)\n \n ###########################################################################\n \n # conditional constraints\n \n if Param.dist_free is True:\n \n dict_pleps = pickle.load(open(\"savedata/pleps_save.p\", \"rb\"))\n residual = dict_pleps['p_points']\n \n numPleps = dict_pleps['plepcount']\n model.plepNum = pyomo.Set(initialize= list(range(1, numPleps+1)))\n \n # coeficient pleps variables\n model.factorPlep = pyomo.Var(model.Areas, model.Blocks, model.plepNum, domain=pyomo.NonNegativeReals)\n # aggregated renewables production\n model.RnwLoad = pyomo.Var(model.Areas, model.Blocks)\n \n # p_efficient points\n model.plep = pyomo.Param(model.Areas, model.Blocks, model.plepNum, mutable=True)\n \n if Param.param_opf is True:\n dict_intensity = pickle.load(open(\"savedata/matrixbeta_save.p\", \"rb\"))\n \n # generation chains: set of spill and turbining arcs\n setData = pyomoset(dict_intensity['matrixLineBus'][0])\n model.linebus = pyomo.Set(initialize= setData, dimen=2)\n \n # lines intensities OPF\n model.flines = pyomo.Param(model.Circuits, model.Areas, mutable=True)\n \n # flowgates parameters\n if Param.flow_gates is True:\n \n numGates = dict_format['numGates']\n gatesets = dict_lines['gatesets']\n gateslimit = dict_lines['gateslimit']\n \n # set of gates flow in the system\n model.Gates = pyomo.Set(initialize= list(range(1, numGates+1)))\n \n # flow gates set\n setData = pyomogates(gatesets)\n model.gateLines = pyomo.Set(initialize=setData, dimen=3)\n \n # defining limit of gate flows\n model.gateLimt = pyomo.Param(model.Gates, model.Blocks, mutable=True)\n \n # emissions consideration\n if Param.emissions is True:\n \n # emission factor for each hydro plant\n dictplant = {hydroPlants[z]: volData[15][z] for z in range(len(hydroPlants))}\n model.hydroFE = pyomo.Param(model.Hydro, initialize=dictplant)\n # emission factor for each thermal plant\n dictplant = {thermalPlants[z]: (Param.thermal_co2[0]*thermalData[13][z])+\n (Param.thermal_co2[1]*thermalData[12][z]*thermalData[11][z]) for z in range(len(thermalPlants))}\n model.thermalFE = pyomo.Param(model.Thermal, initialize=dictplant)\n # emission factor for each small plant\n dictplant = {smallPlants[z]: smallData[8][z] for z in range(len(smallPlants))}\n model.smallFE = pyomo.Param(model.Small, initialize=dictplant)\n # cost of CO2 emissions\n model.co2cost = pyomo.Param(mutable=True)\n \n ################### Optimization model ####################################\n \n # OBJ FUNCTION\n obj_function(Param, model, pyomo)\n \n # CONSTRAINTS\n # define constraint: demand must be served in each block and stage\n load_balance(Param, model, pyomo)\n \n # define constraint: energy conservation\n energy_conservation(Param, model, pyomo)\n\n # define constraint: Wind production conservation\n storage_function(Param, model, pyomo)\n\n # define constraint: future cost funtion\n costtogo(Param, model, pyomo)\n\n # Creating instance\n model.dual = Suffix(direction=Suffix.IMPORT)\n\n ################ Forward analysis #############################\n\n sol_cost_scn = [[],[],[]] # Save operation csot and cost-to-go value\n\n # Save results\n sol_vol_iter = [[0 for x in hydroPlants] for x in lenstg] # Hydro iteration\n sol_lvl_batt = [[0 for x in batteries] for x in lenstg] # Batteries iteration\n\n # Save new state for the current iteration\n sol_scn = [[] for x in lensc] # Hydro iteration\n marg_costs = [[[] for x in lenstg] for x in lensc] # Hydro iteration\n\n # Create a model instance and optimize\n for k in lensc: # Iteration by scenarios\n\n # Update the initial volume at each stage\n for i, plant in enumerate(hydroPlants): \n model.iniVol[plant] = dict_data['volData'][0][i]\n for i, plant in enumerate(batteries): \n model.iniLvl[plant] = dict_data['battData'][0][i]*dict_batt[\"b_storage\"][i][0][0]\n for y in lenblk:\n model.iniLvlBlk[plant, y+1] = dict_data['battData'][0][i]*dict_batt[\"b_storage\"][i][0][0]/numBlocks\n\n sol_cost = [[],[],[]] # Save operation cost and cost-to-go value\n\n for s in lenstg: # Iteration by stages\n\n for z in range(len(circuits)):\n for y in lenblk:\n model.lineLimit[z+1, y+1] = dict_lines['l_limits'][s][circuits[z][0]-1][circuits[z][1]-1]*blocksData[0][y]\n \n # opf matriz restrictions\n if Param.param_opf is True:\n for z in range(len(circuits)):\n for area1 in range(numAreas):\n model.flines[z+1,area1+1]= dict_intensity['matrixbeta'][s][z][area1]\n # flowgates constraints\n if Param.flow_gates is True:\n for gate1 in range(numGates):\n for y in lenblk:\n model.gateLimt[gate1+1, y+1] = gateslimit[gate1][s]*blocksData[0][y]\n\n InflowsHydro = []\n InflowsHydro += [dict_format['inflow_hydro'][n][1][s][k] for n in range(len(hydroPlants))]\n\n for z, plant in enumerate(hydroPlants):\n model.factorH[plant] = dh_factor[z][s]\n model.inflows[plant] = InflowsHydro[z]\n for y in lenblk:\n model.maxGenH[plant, y+1] = dh_limits[z][s]*blocksData[0][y]\n \n if Param.emissions is True:\n model.co2cost = dd_emissions[0][s]\n \n for y in range(numAreas):\n model.rationing[y+1] = dd_rationing[y][s]\n \n \n if Param.dist_free is True:\n # update rationing cost and demand values by stage\n for area1 in range(numAreas):\n for y in lenblk:\n for plp in range(numPleps):\n model.plep[area1+1, y+1, plp+1] = residual[s][k][area1][y][plp]\n else:\n # wind energy\n for z in lenblk:\n for y in range(numAreas):\n model.meanWind[y+1,z+1] = dict_windenergy['windenergy_area'][y][s][k][z]\n\n for z in lenblk:\n for y in range(numAreas):\n model.demand[y+1,z+1] = df_demand[y][s][z]\n model.maxGenW[y+1,z+1] = dict_wind['hat_area'][y][s]*blocksData[0][z]\n \n # Update the generation cost\n for i, plant in enumerate(thermalPlants):\n model.cost[plant] = df_opcost[i][s]\n for y in lenblk:\n model.minGenT[plant, y+1] = df_thmin[s][i]*blocksData[0][y]\n model.maxGenT[plant, y+1] = df_thmax[s][i]*blocksData[0][y]\n\n # Update small plants limits\n for i, plant in enumerate(smallPlants):\n for y in lenblk:\n model.minGenS[plant, y+1] = 0\n model.maxGenS[plant, y+1] = dict_format['smallMax'][s][i]*blocksData[0][y]\n\n # Update batteries limits\n for i, plant in enumerate(batteries):\n model.maxlvl[plant] = dict_batt[\"b_storage\"][i][s][1]\n model.maxlvlB[plant] = dict_batt[\"b_storage\"][i][s][0]*blocksData[0][y]*b_storageData[i][y]\n model.factorB[plant] = dict_data['battData'][4][i]\n for y in lenblk:\n model.maxGenB[plant, y+1] = dict_batt['b_limit'][i][s]*blocksData[0][y]*b_storageData[i][y] # restrictions\n\n # Update the cost-to-go function\n #model.Cuts.clear()\n for z in range(iteration+1): # len(fcf_backward[s+1])):\n #model.Cuts.add(z+1)\n if s+1 == Param.stages:\n model.constTerm[z+1] = fcf_backward[s+1][0][2]\n for y, plant in enumerate(batteries):\n model.coefcBatt[plant, z+1] = fcf_backward[s+1][0][1][y]\n for y, plant in enumerate(hydroPlants):\n model.coefcTerm[plant, z+1] = fcf_backward[s+1][0][0][y]\n else:\n model.constTerm[z+1] = fcf_backward[s+1][z][2]\n for y, plant in enumerate(batteries):\n model.coefcBatt[plant, z+1] = fcf_backward[s+1][z][1][y]\n for y, plant in enumerate(hydroPlants):\n model.coefcTerm[plant, z+1] = fcf_backward[s+1][z][0][y]\n #model.ctFcf.reconstruct()\n\n # solver\n opt.solve(model)#, symbolic_solver_labels=False) #, tee=True)\n #with open('pyomo_model.txt', 'w') as f:\n # model.pprint(ostream=f)\n # instance.display()\n\n # objective function value\n sol_objective = model.OBJ(); costtogo = model.futureCost()\n sol_cost[0].append(sol_objective-costtogo); sol_cost[1].append(sol_objective)\n sol_scn[k].append(sol_objective-costtogo)\n #print(sol_objective)\n\n vol_f_stage = [] # Save results of initial volume - v_t+1\n for vol_fin in [model.vol]:\n varobject = getattr(model, str(vol_fin))\n vol_f_stage += [varobject[i].value for i in hydroPlants]\n\n lvl_f_stage = [] # Save results of initial level - l_t+1\n for lvl_fin in [model.lvl]:\n varobject = getattr(model, str(lvl_fin))\n lvl_f_stage += [varobject[i].value for i in batteries]\n\n # Svael volt_t+1 for the next backward iteration\n sol_vol_iter[s] = [sum(x) for x in zip(sol_vol_iter[s], vol_f_stage)]\n sol_lvl_batt[s] = [sum(x) for x in zip(sol_lvl_batt[s], lvl_f_stage )]\n\n # Update the initial volume at each stage\n for i, plant in enumerate(hydroPlants):\n model.iniVol[plant] = vol_f_stage[i]\n for i, plant in enumerate(batteries): \n model.iniLvl[plant] = lvl_f_stage[i]\n for y in lenblk:\n model.iniLvlBlk[plant,y+1] = lvl_f_stage[i]/numBlocks\n\n # marginal cost of each area\n if (iteration + 1 == Param.max_iter or confidence == 1):\n duals_dmd = [[[] for x in lenblk] for y in range(numAreas)]\n d_object = getattr(model, 'ctDemand')\n for areadem in range(numAreas):\n for i in lenblk:\n duals_dmd[areadem][i].append(model.dual[d_object[areadem+1,i+1]])\n marg_costs[k][s] = duals_dmd\n\n # RESULTS\n if ((iteration + 1 == Param.max_iter or confidence == 1) and Param.results is True):\n\n (genThermal,genHydro,genBatteries,genDeficit,loadBatteries,lvlBatteries,\n lvlHydro,linTransfer,spillHydro,genwind,spillwind,genSmall,emsscurve,genRnws) = saveiter(k,\n s,lenblk,thermalPlants,model,genThermal,hydroPlants,batteries,genHydro,\n genBatteries,loadBatteries,lvlBatteries,lvlHydro,genDeficit,linTransfer,\n circuits,spillHydro,genwind,spillwind,genSmall,smallPlants,rnwArea,Param,\n emissCurve,genRnws)\n\n # progress of the analysis\n bar.update(count+1)\n count += 1\n\n # Operation costs by scenarios\n sol_cost_scn[0].append(sum(sol_cost[0]))\n sol_cost_scn[1].append(sol_cost[1][0])\n sol_cost_scn[2].append(sum(sol_cost[0][:(Param.stages-Param.bnd_stages)]))\n\n# for s in lenstg:\n# # save the last solutions\n# last_sol = [x/dataParam.sf for x in sol_vol_iter[s]]\n# last_batt = [x/dataParam.sf for x in sol_lvl_batt[s]]\n# if last_sol not in sol_vol[s+1] or last_batt not in sol_lvl[s+1]:\n# sol_vol[s+1].append([x/dataParam.sf for x in sol_vol_iter[s]])\n# sol_lvl[s+1].append([x/dataParam.sf for x in sol_lvl_batt[s]])\n\n # Export results\n if ((iteration + 1 == Param.max_iter or confidence == 1) and Param.results is True):\n\n # export data\n DataDictionary = { \"genThermal\":genThermal,\"genHydro\":genHydro,\"genSmall\":genSmall,\n \"genBatteries\":genBatteries,\"genDeficit\":genDeficit,\"loadBatteries\":loadBatteries,\n \"lvlBatteries\":lvlBatteries,\"lvlHydro\":lvlHydro,\"linTransfer\":linTransfer,\n \"spillHydro\":spillHydro,\"genwind\":genwind,\"spillwind\":spillwind,\"marg_costs\":marg_costs,\n \"emsscurve\":emsscurve,\"genRnws\":genRnws}\n\n pickle.dump(DataDictionary, open( \"savedata/results_save.p\", \"wb\" ) )\n\n return (sol_vol_iter, sol_lvl_batt, sol_cost_scn, sol_scn)\n\n","sub_path":"scripts/forward2.py","file_name":"forward2.py","file_ext":"py","file_size_in_byte":24066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"483132953","text":"from __future__ import print_function\n\nfrom keras.datasets import mnist\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\n\nimport numpy as np\nfrom keras.utils.np_utils import to_categorical\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.optimizers import SGD\n\nfrom keras.models import load_model\n\n\nfrom keras.datasets import mnist\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Convolution2D, MaxPooling2D, Convolution3D, MaxPooling3D\nfrom keras.layers import Merge\nfrom keras.utils import np_utils\n\nfrom keras.models import model_from_json\nfrom keras import backend as K\n\n\nimport random as rdm\nimport nibabel as nib\nimport image_manager as imm\nimport gc\n\nimport tensorflow as tf\nimport json\n\n#############################################\n\nmodel_name\t= \"paralel_v1_2D\"\n\n#### default: takes 3 imag types -> less context so as the input size of \n ### the NN is equal, and the comparison is fair\n\n\nbatch_size = 128\nnb_classes = 2\nnb_epoch = 30\n# input image dimensions\ninp_dim_2d = 33\ninp_dim_3d = 65\nstep = 8\n\n# number of convolutional filters to use\nnb_filters = 45 ### lets change it manually, increasing it \n# size of pooling area for max pooling\npool_size_2d = (2, 2)\n# convolution kernel size\nkernel_size_2d = (3, 3)\n\n\n# exp1\nimg_types = [\"flair\",\"FA\",\"anatomica\"]\n\n\n\n# prepare input for the \ninput_shape_2d = (inp_dim_2d, inp_dim_2d, len(img_types))\ninput_shape_3d = (inp_dim_3d, inp_dim_3d, len(img_types))\n\n\n\n\n\n\n## paralel NN, y\nmodel_y = Sequential()\n\n\nmodel_y.add(Convolution2D(nb_filters, kernel_size_2d[0], kernel_size_2d[1],\n border_mode='valid',\n input_shape=input_shape_2d))\nmodel_y.add(Activation('relu'))\n\nmodel_y.add(Convolution2D(nb_filters, kernel_size_2d[0], kernel_size_2d[1]))\nmodel_y.add(Activation('relu'))\n\nmodel_y.add(MaxPooling2D(pool_size=pool_size_2d))\n#model_y.add(Dropout(0.25))\n\nmodel_y.add(Convolution2D(nb_filters, kernel_size_2d[0], kernel_size_2d[1]))\nmodel_y.add(Activation('relu'))\n\nmodel_y.add(MaxPooling2D(pool_size=pool_size_2d))\n#model_y.add(Dropout(0.25))\n\nmodel_y.add(Flatten())\n\n\n## paralel NN, z\nmodel_z = Sequential()\n\nmodel_z.add(Convolution2D(nb_filters, kernel_size_2d[0], kernel_size_2d[1],\n border_mode='valid',\n input_shape=input_shape_3d))\nmodel_z.add(Activation('relu'))\n\nmodel_z.add(Convolution2D(nb_filters, kernel_size_2d[0], kernel_size_2d[1]))\nmodel_z.add(Activation('relu'))\n\nmodel_z.add(MaxPooling2D(pool_size=pool_size_2d))\n#model_z.add(Dropout(0.25))\n\nmodel_z.add(Convolution2D(nb_filters, kernel_size_2d[0], kernel_size_2d[1]))\nmodel_z.add(Activation('relu'))\n\nmodel_z.add(MaxPooling2D(pool_size=pool_size_2d))\n#model_z.add(Dropout(0.25))\n\nmodel_z.add(Flatten())\n\n\n\nmerged = Merge([model_y, model_z], mode='concat')\nfinal_model = Sequential()\n\nfinal_model.add(merged)\n# print(\"Output shape after merge:\", final_model.output_shape)\n# final_model.add(Dense(1024))\n# final_model.add(Activation('relu'))\n# final_model.add(Dropout(0.5))\nprint(\"Output shape after fully connected(dropout0.5):\", final_model.output_shape)\nfinal_model.add(Dense(128))\nfinal_model.add(Activation('relu'))\nfinal_model.add(Dropout(0.5))\nprint(\"Output shape after dully connected(dropout0.5):\", final_model.output_shape)\nfinal_model.add(Dense(nb_classes))\nfinal_model.add(Activation('softmax'))\nprint(\"Output shape after softmax (2 classes):\", final_model.output_shape)\n\n#train_data partition\nbrains = [\"tka002\",\"tka003\",\"tka004\",\"tka005\",\"tka006\",\"tka007\",\"tka009\",\"tka010\",\"tka011\",\"tka012\",\"tka013\",\"tka015\",\"tka016\",\"tka017\",\"tka018\",\"tka019\",\"tka020\",\"tka021\"]\n\n#balance proportion\nbal_train = 10\nbal_test = 200\n\ntr = imm.ImageManager() # load training data\nres = []\ndef evaluate(model,X_test,y_test):\n\ty_pred = model.predict(X_test)\n\tmat = [[0,0],[0,0]] # [[TP,FP],[FN,TN]]\n\tfor i in range(len(y_pred)):\n\t\tif y_test[i][1] == 0: # real negative\n\t\t\tmat[1][1] += y_pred[i][0] #TN\n\t\t\tmat[0][1] += y_pred[i][1] #FP\n\t\telse:\n\t\t\tmat[1][0] += y_pred[i][0] #FN\n\t\t\tmat[0][0] += y_pred[i][1] #TP\n\n\t# mat[0][0] /= len(y_pred)\n\t# mat[0][1] /= len(y_pred)\n\t# mat[1][0] /= len(y_pred)\n\t# mat[1][1] /= len(y_pred)\n\n\tTPR = mat[0][0] / (mat[0][0] + mat[1][0])\n\tTNR = mat[1][1] / (mat[1][1] + mat[0][1])\n\treturn(mat,TPR,TNR)\t\n\nfor i in range(len(brains)/4):\n\tfor it in range(1):\n\t\ttrain_brain = brains[:i*4]+brains[(i+1)*4:]\n\t\ttest_brain = brains[i*4:(i+1)*4]\n\n\t\t## load training data\n\t\ttr.reset()\n\t\ttr.init(train_brain)\n\t\ttr.createSlices(step=step)\n\t\ttr.balance(bal_train)\n\t\ttr.split(1) # we will select the hole brain\n\n\t\tX_train_y = tr.getData(img_types, \"2dy\", inp_dim_2d)[0]\n\t\ty_train = X_train_y[1]\n\t\tX_train_y = X_train_y[0]\n\n\t\tX_train_z = tr.getData(img_types, \"2dy\", inp_dim_3d)[0][0]\n\n\n\n\n\t\tfinal_model.compile(loss='binary_crossentropy',\n\t\t optimizer='adadelta',\n\t\t metrics=['accuracy'])\n\n\t\tcv = final_model.fit([X_train_y, X_train_z], y_train, batch_size=batch_size, validation_split=0.1, nb_epoch=nb_epoch,verbose=2)\n\t\tfinal_model.save(\"../models/model_\" + model_name +\"_\"+ str(i) + \".mdl\")\n\n\t\twith open(\"hist_\"+model_name+\"_\"+str(i)+\".json\",\"w\") as tf:\n\t\t\ttf.write(json.dumps(cv.history))\n\n\t\t#model = load_model(\"../models/model_0.mdl\")\n\t\ttr.reset()\n\t\t### test stuff\n\n\t\ttt = imm.ImageManager() # load training data\n\t\ttt.init(test_brain)\n\t\ttt.createSlices(step=step+1)\n\t\ttt.balance(bal_test)\n\t\ttt.split(1) # we will select the hole brain\n\n\t\tX_test_y = tt.getData(img_types, \"2dy\", inp_dim_2d)[0]\n\t\ty_test = X_test_y[1]\n\t\tX_test_y = X_test_y[0]\n\n\t\tX_test_z = tt.getData(img_types, \"2dy\", inp_dim_3d)[0][0]\t\n\n\t\tscore = evaluate(final_model,[X_test_y, X_test_z],y_test)\n\t\tres.append((score,train_brain, test_brain))\n\t\tprint(\"###########################################\")\n\t\tprint(\"Cross Validation:\",i, \"\\tIteration:\",it)\n\t\tprint(\"balance:\", bal_test, \"(\", y_test.shape[0], \")\")\n\t\tprint(score[0][0])\n\t\tprint(score[0][1])\n\t\tprint(\"TPR:\", score[1])\n\t\tprint(\"TNR:\", score[2])\n\t\ttt.reset()\n\tprint(\"\")\n\tprint(\"########################################\")\n\tprint(\"### Total (Average) cv: \"+i+\" ###\")\n\tprint(\"########################################\")\n\tTP = 0\n\tTN = 0\n\tFP = 0\n\tFN = 0\n\tfor el in res[i*1:(i*1)+1]:\n\t\tTP += el[0][0][0][0]\n\t\tTN += el[0][0][1][1]\n\t\tFP += el[0][0][0][1]\n\t\tFN += el[0][0][1][0]\n\ttotal = TP+TN+FP+FN\n\tTP = TP / float(total)\n\tTN = TN / float(total)\n\tFP = FP / float(total)\n\tFN = FN / float(total)\n\tprint([TP,FP], [\"TP\",\"FP\"])\n\tprint([FN,TN], [\"FN\",\"TN\"])\n\tprint(\"\")\n\tprint(\"Accuracy:\", TP+TN)\n\tprint(\"TPR:\", TP / float(TP + FN))\n\tprint(\"TNR:\", TN / float(TN + FP))\n\n\nprint(\"\")\nprint(\"########################################\")\nprint(\"### Total (Average) ###\")\nprint(\"########################################\")\nTP = 0\nTN = 0\nFP = 0\nFN = 0\nfor el in res:\n\tTP += el[0][0][0][0]\n\tTN += el[0][0][1][1]\n\tFP += el[0][0][0][1]\n\tFN += el[0][0][1][0]\ntotal = TP+TN+FP+FN\nTP = TP / float(total)\nTN = TN / float(total)\nFP = FP / float(total)\nFN = FN / float(total)\nprint([TP,FP], [\"TP\",\"FP\"])\nprint([FN,TN], [\"FN\",\"TN\"])\nprint(\"\")\nprint(\"Accuracy:\", TP+TN)\nprint(\"TPR:\", TP / float(TP + FN))\nprint(\"TNR:\", TN / float(TN + FP))\n\n\n\n","sub_path":"src/dnn_1_2d.py","file_name":"dnn_1_2d.py","file_ext":"py","file_size_in_byte":7226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"469552824","text":"from classifier import classifier\nfrom numpy import *\n\n\nclass SVM(classifier):\n def __init__(self, c=1.0, tolerance=0.0001, max_iter=10000):\n self.X = None # X observations (nxp matrix)\n self.Y = None # labels\n self.m = None # max number of rows/observations in X\n self.n = None\n self.w = None\n self.alphas = None # support vectors\n self.eCache = None # first column is valid flag TODO: ???\n self.b = 0\n self.C = c # budget for amount that the margin can be violated\n self.tol = tolerance # tolerance for stopping criteria?\n self.max_iter = max_iter\n\n def fit(self, x, y):\n self.X = mat(x)\n self.Y = mat(y).transpose()\n self.m = shape(x)[0]\n self.m, n = shape(self.X)\n self.w = zeros((n, 1))\n self.alphas = mat(zeros((self.m, 1)))\n self.eCache = mat(zeros((self.m, 2)))\n n_iter = 0\n entire = True\n alpha_pairs_changed = 0\n\n while (n_iter < self.max_iter) and ((alpha_pairs_changed > 0) or entire):\n alpha_pairs_changed = 0\n if entire: # go over all\n for i in range(self.m): # iterate over number of observations\n alpha_pairs_changed += self.inner_l_k(i)\n print(\"fullSet, iter: %d i:%d, pairs changed %d\" % (n_iter, i, alpha_pairs_changed))\n n_iter += 1\n else: # go over non-bound (railed) alphas\n non_bound_is = nonzero((self.alphas.A > 0) * (self.alphas.A < self.C))[0]\n for i in non_bound_is:\n alpha_pairs_changed += self.inner_l_k(i)\n print(\"non-bound, iter: %d i:%d, pairs changed %d\" % (n_iter, i, alpha_pairs_changed))\n n_iter += 1\n\n if entire:\n entire = False # toggle entire set loop\n elif alpha_pairs_changed == 0:\n entire = True\n print(\"iteration number: %d\" % n_iter)\n\n return self.calc_ws()\n\n def predict(self, X):\n fx = dot(self.w.T, X.T) + self.b\n hyp = apply_along_axis(lambda x: 1 if x >= 0 else -1, 0, fx)\n return hyp\n\n def calc_ws(self):\n for i in range(self.m):\n self.w += multiply(self.alphas[i] * self.Y[i], self.X[i, :].T)\n return self.w\n\n def calc_ek_k(self, k):\n \"\"\"\n Calculates weight for observation, k\n :param k:\n :return:\n \"\"\"\n # f = w^T * x\n # f = (a * y)T * (X*(X_k)T) + b\n f = float(multiply(self.alphas, self.Y).T * (self.X * self.X[k, :].T)) + self.b\n # w = f - y\n w = f - float(self.Y[k])\n return w\n\n def select_j_k(self, i, e_i): #this is the second choice -heurstic, and calcs Ej\n max_k = -1\n max_delta_e = 0\n e_j = 0\n self.eCache[i] = [1, e_i] #set valid #choose the alpha that gives the maximum delta E\n valid_ecache_list = nonzero(self.eCache[:, 0].A)[0]\n if (len(valid_ecache_list)) > 1:\n for k in valid_ecache_list: #loop through valid Ecache values and find the one that maximizes delta E\n if k == i:\n continue #don't calc for i, waste of time\n e_k = self.calc_ek_k(k)\n delta_e = abs(e_i - e_k)\n if delta_e > max_delta_e:\n max_k = k\n max_delta_e = delta_e\n e_j = e_k\n return max_k, e_j\n else: #in this case (first time around) we don't have any valid eCache values\n j = select_j_rand(i, self.m)\n e_j = self.calc_ek_k(j)\n return j, e_j\n\n def update_ek_k(self, k): # after any alpha has changed update the new value in the cache\n e_k = self.calc_ek_k(k)\n self.eCache[k] = [1, e_k]\n\n def inner_l_k(self, i):\n\n Ei = self.calc_ek_k(i)\n\n if ((self.Y[i]*Ei < -self.tol) and (self.alphas[i] < self.C)) or \\\n ((self.Y[i]*Ei > self.tol) and (self.alphas[i] > 0)):\n\n j, Ej = self.select_j_k(i, Ei) # this has been changed from selectJrand\n\n alpha_i_old = self.alphas[i].copy()\n alpha_j_old = self.alphas[j].copy()\n\n if self.Y[i] != self.Y[j]:\n l = max(0, self.alphas[j] - self.alphas[i])\n h = min(self.C, self.C + self.alphas[j] - self.alphas[i])\n else:\n l = max(0, self.alphas[j] + self.alphas[i] - self.C)\n h = min(self.C, self.alphas[j] + self.alphas[i])\n if l == h:\n print(\"L==H\")\n return 0\n\n # eta = 2 * X_i * X_j - X_i * (X_i)T - X_j * (X_j)T\n eta = (2.0 * self.X[i, :] * self.X[j, :].T) - (self.X[i, :] * self.X[i, :].T) - (self.X[j, :] * self.X[j, :].T)\n if eta >= 0:\n print(\"eta>=0\")\n return 0\n\n self.alphas[j] -= self.Y[j]*(Ei - Ej)/eta\n self.alphas[j] = clip_alpha(j, h, l)\n self.update_ek_k(j) # added this for the Ecache\n if abs(self.alphas[j] - alpha_j_old) < 0.00001:\n print(\"j not moving enough\"); return 0\n self.alphas[i] += self.Y[j]*self.Y[i]*(alpha_j_old - self.alphas[j])#update i by the same amount as j\n self.update_ek_k(i) # added this for the Ecache #the update is in the oppselftie direction\n b1 = self.b - Ei - self.Y[i]*(self.alphas[i]-alpha_i_old)*self.X[i, :]*self.X[i, :].T - self.Y[j]*(self.alphas[j]-alpha_j_old)*self.X[i,:]*self.X[j, :].T\n b2 = self.b - Ej - self.Y[i]*(self.alphas[i]-alpha_i_old)*self.X[i, :]*self.X[j, :].T - self.Y[j]*(self.alphas[j]-alpha_j_old)*self.X[j,:]*self.X[j, :].T\n if (0 < self.alphas[i]) and (self.C > self.alphas[i]):\n self.b = b1\n elif (0 < self.alphas[j]) and (self.C > self.alphas[j]):\n self.b = b2\n else:\n self.b = (b1 + b2)/2.0\n return 1\n else:\n return 0\n\n\ndef clip_alpha(aj, H, L):\n if aj > H:\n aj = H\n if L > aj:\n aj = L\n return aj\n\n\ndef select_j_rand(i, m):\n j=i #we want to select any J not equal to i\n while (j==i):\n j = int(random.uniform(0,m))\n return j\n\n\n\n","sub_path":"svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":6415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"290046159","text":"import argparse\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport mlrose_hiive as mlrose\nimport numpy as np\nimport time\n\nSIZE = 60\n\nalgs = {\n 'hill_climbing': mlrose.random_hill_climb,\n 'simulated_annealing': mlrose.simulated_annealing,\n 'genetic_alg': mlrose.genetic_alg,\n 'mimic': mlrose.mimic\n}\n\n# Define alternative N-Queens fitness function for maximization problem\ndef queens_max(state):\n \n # Initialize counter\n fitness = 0\n \n # For all pairs of queens\n for i in range(len(state) - 1):\n for j in range(i + 1, len(state)):\n \n # Check for horizontal, diagonal-up and diagonal-down attacks\n if (state[j] != state[i]) \\\n and (state[j] != state[i] + (j - i)) \\\n and (state[j] != state[i] - (j - i)):\n \n # If no attacks, then increment counter\n fitness += 1\n\n return fitness\n\n# note: genetic alg should excel at four peaks,\n# while RHC and annealing are likely to get stuck in the local optima with bigger basins (all 1s or all 0s).\n# annealing and RHC will perform well on onemax because there is only one optimum and it's global (wide basin).\n# MIMIC will perform best on kcolor, per Charles Isbell's paper\n# call_counter_array = np.zeros(100)\n# call_counter_idx = 0\n\nfitness_evals = []\n\ndef get_score(alg, problem, params, num_seeds = 30):\n fitnesses = []\n fitness_curves = []\n for random_seed in range(1,num_seeds+1):\n params.update({'random_state': random_seed, 'curve': True})\n\n global fitness_evals\n fitness_evals = []\n\n state, fitness, curve = alg(**params)\n \n fitnesses.append(fitness)\n fitness_curves.append(curve)\n \n print(state)\n \n avg_fitness_curve = [ np.mean( [ c[i] for c in fitness_curves ] ) for i in range(min([len(c) for c in fitness_curves])) ]\n return np.mean(fitnesses), avg_fitness_curve\n\n# def get_hyperparam_score(alg, problem, params):\n# fitness, curve = get_score(alg, problem, params, 20)\n# return fitness\n\ndef get_hyperparam_score(alg, problem, params, num_seeds = 20):\n fitnesses = []\n for random_seed in range(1, num_seeds+1):\n params.update({'random_state': random_seed, 'curve': True})\n state, fitness, curve = alg(**params)\n fitnesses.append(fitness)\n\n return np.mean(fitnesses)\n\ndef get_fitness_eval_curve():\n fitness_eval_x = []\n fitness_eval_y = []\n maximum = 0\n for i in range(len(fitness_evals)):\n if fitness_evals[i] > maximum:\n maximum = fitness_evals[i]\n fitness_eval_x.append(i)\n fitness_eval_y.append(fitness_evals[i])\n return fitness_eval_x, fitness_eval_y\n\ndef get_final_score(learner, fitness, hyperparams):\n problem = mlrose.DiscreteOpt(length = SIZE, fitness_fn = fitness, maximize = True)\n params = hyperparams.copy()\n params.update({'problem': problem})\n\n start = time.process_time()\n score, curve = get_score(alg, problem, params)\n print( 'Time taken to learn:', time.process_time() - start )\n\n print( 'Average Best Score over 30 random seeds:', score )\n\n plt.plot( curve )\n plt.xlabel( 'Iterations' )\n plt.ylabel( 'Average Fitness Score' )\n plt.suptitle( learner )\n plt.show()\n\n x, y = get_fitness_eval_curve()\n plt.plot(x, y)\n plt.xlabel( 'Fitness Function Evaluations' )\n plt.ylabel( 'Fitness Score' )\n plt.suptitle( learner )\n plt.show()\n return score\n\ndef find_best_hyperparameter(alg, fitness, params, param_name, possible_values, show_graph = True):\n best_avg_score = 0\n best_val = None\n scores = []\n for v in possible_values:\n problem = mlrose.DiscreteOpt(length = SIZE, fitness_fn = fitness, maximize = True)\n problem.set_mimic_fast_mode( True )\n cur_params = params.copy()\n cur_params.update({param_name: v, 'problem': problem})\n score = get_hyperparam_score(alg, problem, cur_params)\n scores.append(score)\n if score > best_avg_score:\n best_avg_score = score\n best_val = v\n \n # plot it\n if show_graph:\n plt.plot(possible_values, scores)\n plt.xlabel( f'{param_name}' )\n plt.ylabel( 'Average Fitness Score' )\n plt.suptitle( f'{param_name}' )\n plt.show()\n\n return best_val \n\ndef optimize_hyperparams(title, alg, fitness, params, hyperparams):\n best_hyperparams = {}\n for param_name, (possible_values, show_graph) in hyperparams.items():\n print( 'optimizing hyperparameter %s' % param_name )\n best_hyperparams[param_name] = find_best_hyperparameter(alg, fitness, params, param_name, possible_values, show_graph)\n print( 'optimal value found: %s' % best_hyperparams[param_name] )\n \n print('parameters optimized.')\n \n # print('parameters optimized. plotting learning curve with optimal params.')\n\n # problem = mlrose.DiscreteOpt(length = SIZE, fitness_fn = fitness, maximize = True)\n # temp_params = hyperparams.copy()\n # temp_params.update({'problem': problem})\n # score, curve = get_score(alg, problem, temp_params)\n # print('Score:', score)\n # plt.plot( curve )\n # plt.xlabel( 'Iterations' )\n # plt.ylabel( 'Average fitness score' )\n # plt.suptitle( title )\n # plt.show()\n \n return best_hyperparams\n\ndef optimize_hill_climbing_hyperparams(fitness):\n # params = { 'max_attempts': 100,\n # 'max_iters': 100,\n # 'restarts': 20 }\n params = { 'max_attempts': 200, 'restarts': 2000 }\n # mlrose.random_hill_climb()\n hyperparams = {}\n return params\n # return optimize_hyperparams( 'Random Hill Climbing', mlrose.random_hill_climb, fitness, params )\n\ndef optimize_simulated_annealing_hyperparams(fitness):\n params = { 'max_attempts': 100,\n 'max_iters': 1000 }\n hyperparams = {}\n return params\n # return optimize_hyperparams( 'Simulated Annualing', mlrose.simulated_annealing, fitness, params )\n\ndef optimize_genetic_alg_hyperparams(fitness):\n params = { 'max_attempts': 100 }\n if fitness == mlrose.OneMax:\n hyperparams = { 'pop_breed_percent': 0.7 }\n params.update(hyperparams)\n return params\n hyperparams = { 'pop_size': ( [ 100 + 20*i for i in range(11) ], True ),\n 'pop_breed_percent': ( [ 0.7 + 0.01*i for i in range(11) ], True ),\n 'elite_dreg_ratio': ( [ 0.95 + 0.01*i for i in range(5) ], True ),\n 'mutation_prob': ( [ 0.2 * i for i in range(1, 5) ], True ) }\n \n optimized = optimize_hyperparams( 'Genetic Alg', mlrose.genetic_alg, fitness, params, hyperparams )\n params.update(optimized)\n return params\n\ndef optimize_mimic_hyperparams(fitness):\n # params = { 'fast_mimic': True }\n params = {}\n if isinstance(fitness, mlrose.OneMax):\n hyperparams = {}\n params.update(hyperparams)\n return params\n elif isinstance(fitness, mlrose.MaxKColor):\n hyperparams = {}\n params.update(hyperparams)\n return params\n elif isinstance(fitness, mlrose.FourPeaks):\n hyperparams = { 'pop_size': 1200, 'keep_pct': 0.3 }\n params.update(hyperparams)\n return params\n hyperparams = { 'pop_size': ( [ 100 + 20*i for i in range(11) ], True ),\n 'keep_pct': ( [ 0.1 + 0.02*i for i in range(11) ], True ) }\n \n optimized = optimize_hyperparams( 'MIMIC', mlrose.mimic, fitness, params, hyperparams )\n params.update(optimized)\n return params\n\ndef get_custom_fitness(fitness):\n def counting_fitness(state):\n global fitness_evals\n score = fitness.evaluate(state)\n fitness_evals.append( score )\n return score\n return counting_fitness\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument( '--learner', action = 'store', dest = 'learner', required = True )\n parser.add_argument( '--problem', action = 'store', dest = 'problem', required = True )\n # parser.add_argument( '--size', action = 'store', dest = 'size', required = False, default = 8)\n args = parser.parse_args()\n\n # size = int(args.size)\n\n if args.problem == 'queens':\n fitness = mlrose.CustomFitness(queens_max)\n elif args.problem == 'onemax':\n fitness = mlrose.OneMax()\n elif args.problem == 'four_peaks':\n fitness = mlrose.FourPeaks(t_pct = 0.15)\n elif args.problem == 'kcolor':\n edges = [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4)]\n fitness = mlrose.MaxKColor(edges)\n problem = mlrose.DiscreteOpt(length = 5, fitness_fn = fitness)\n init_state = np.array([0, 1, 0, 1, 1])\n else:\n raise RuntimeError(\"Invalid problem argument\")\n\n if args.learner == 'hill_climbing':\n hyperparams = optimize_hill_climbing_hyperparams(fitness)\n elif args.learner == 'simulated_annealing':\n hyperparams = optimize_simulated_annealing_hyperparams(fitness)\n elif args.learner == 'genetic_alg':\n hyperparams = optimize_genetic_alg_hyperparams(fitness)\n elif args.learner == 'mimic':\n hyperparams = optimize_mimic_hyperparams(fitness)\n else:\n raise RuntimeError(\"Invalid learner argument\")\n\n # hyperparams = {'max_attempts': 100, 'mutation_prob': 0.4}\n\n print( 'found hyperparams:', hyperparams )\n\n custom_fitness_function = get_custom_fitness(fitness)\n custom_fitness = mlrose.CustomFitness(custom_fitness_function)\n\n alg = algs[args.learner]\n \n get_final_score(args.learner, custom_fitness, hyperparams)\n \n # print( 'call counter array:', call_counter_array )\n # avg_best_score, avg_fitness_curve = get_score()\n\n # best_fitnesses = []\n # fitness_curves = []\n\n # for random_seed in range(30):\n # if args.learner == 'simulated_annealing':\n # # Define decay schedule\n # schedule = mlrose.ExpDecay()\n # best_state, best_fitness, fitness_curve = mlrose.simulated_annealing(problem, schedule = schedule, max_attempts = 100, max_iters = 1000, curve = True, random_state = random_seed)\n # elif args.learner == 'hill_climbing':\n # best_state, best_fitness, fitness_curve = mlrose.random_hill_climb(problem, restarts = 100, max_attempts = 100, max_iters = 100, curve = True, random_state = random_seed)\n # elif args.learner == 'genetic_alg':\n # best_state, best_fitness, fitness_curve = mlrose.genetic_alg(problem, max_attempts = 100, curve = True)\n # elif args.learner == 'mimic':\n # best_state, best_fitness, fitness_curve = mlrose.mimic(problem, curve = True, random_state = random_seed)\n # else:\n # raise ValueError('Invalid learner argument')\n\n # best_fitnesses.append(best_fitness)\n # fitness_curves.append(fitness_curve)\n # print('The curve is: ', fitness_curve)\n # print(len( fitness_curve ))\n\n # avg_best_fitness = np.mean( best_fitnesses )\n # avg_fitness_curve = [ np.mean( [ c[i] for c in fitness_curves ] ) for i in range(100) ]\n # plt.plot( avg_fitness_curve )\n # plt.xlabel( 'Iterations' )\n # plt.ylabel( 'Average fitness score' )\n # plt.suptitle( args.learner )\n # plt.show()","sub_path":"ro.py","file_name":"ro.py","file_ext":"py","file_size_in_byte":11252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"461490653","text":"import os\nimport numpy as np\nimport pandas as pd\nfrom tensorflow.keras import models\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport gc\n\ndef get_images():\n \n with open(\"mfr_list.csv\", \"r\") as mfr_list:\n \n mfrs = mfr_list.readlines()\n mfrs = mfrs[:-1]\n \n missions = []\n \n for i in range(1,len(mfrs)):\n missions.append(mfrs[i].split('-E-'))\n mfrs[i] = mfrs[i].strip(\"\\n\")\n\n mfr_list.close()\n \n os.chdir(\"../dataset/mfr_processing/\")\n \n for i in range(1, 5001):\n os.system(\"wget https://eol.jsc.nasa.gov/DatabaseImages/ESC/small/\"+missions[i][0]+\"/\"+mfrs[i]+\".JPG\")\n \n os.chdir(\"../../scripts/\")\n \n return \n\nif os.path.exists(\"../dataset/mfr_processing/ISS038-E-65727.JPG\"):\n print(\"Images Available\")\nelse:\n get_images()\n\nimage_names = pd.read_csv(\"mfr_list.csv\")\nimage_names = image_names.apply(lambda x: x+\".JPG\")\n\nmodel = models.load_model(\"../models/darkskies_model_VGG16.h5\")\n#model.load_weights(\"../models/\")\n\ntest_datagen=ImageDataGenerator(rescale=1./255.)\n \ntest_generator=test_datagen.flow_from_dataframe(dataframe=image_names[:5001],\n directory=\"../dataset/mfr_processing/\",\n x_col=\"MFR\",\n batch_size=1,\n seed=42,\n shuffle=False,\n class_mode=None,\n target_size=(300,300))\n\npredictions=[]\n\nfor batch in range(len(test_generator)):\n pred=model.predict(test_generator[batch], verbose = 1)\n if np.argmax(pred) == 0:\n predictions.append(\"Aurora\")\n elif np.argmax(pred) == 1:\n predictions.append(\"Black\")\n elif np.argmax(pred) == 2:\n predictions.append(\"City\")\n elif np.argmax(pred) == 3:\n predictions.append(\"Astronomical\")\n else:\n continue\n\nfilenames=test_generator.filenames\nresults=pd.DataFrame({\"Filename\":filenames,\n \"Predictions\":predictions})\n\nresults[\"Filename\"] = results[\"Filename\"].apply(lambda x: x.rstrip(\".JPG\"))\nresults.to_csv(\"results.csv\", index=False)\n\ngc.collect()\n\nresults[\"Filename\"] = results[\"Filename\"].apply(lambda x: x+\".JPG\")\n\nos.chdir(\"../dataset/mfr_processing/\")\n\nfor result in range(len(results[\"Predictions\"])):\n \n if results[\"Predictions\"][result] == \"Aurora\":\n os.system(\"mv \"+results[\"Filename\"][result]+\" Aurora/\")\n elif results[\"Predictions\"][result] == \"Black\":\n os.system(\"mv \"+results[\"Filename\"][result]+\" Black/\")\n elif results[\"Predictions\"][result] == \"City\":\n os.system(\"mv \"+results[\"Filename\"][result]+\" City/\")\n else:\n os.system(\"mv \"+results[\"Filename\"][result]+\" Astronomical/\")\n\n\n\n\n","sub_path":"testing_with_mfr.py","file_name":"testing_with_mfr.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"594146195","text":"def divide(dividend, divisor):\n if divisor == 0:\n raise ZeroDivisionError(\"Divisor cannot be 0.\")\n\n return dividend / divisor\n\ngrades = []\n\nprint(\"Welcome to the average grade program.\")\ntry:\n average = divide(sum(grades), len(grades))\n print(f\"The average grade is {average}.\")\nexcept ZeroDivisionError as e:\n print(\"There are no grades yet in your list.\")\n\n\nstudents = [\n {\"name\": \"Bob\", \"grades\": [75,90]},\n {\"name\": \"Rolf\", \"grades\": [3,40]},\n {\"name\": \"Jen\", \"grades\": [100,90]}\n]\n\ntry:\n for student in students:\n name = student[\"name\"]\n grades = student[\"grades\"]\n average = divide(sum(grades), len(grades))\n print(f\"{name} averaged {average}.\")\nexcept ZeroDivisionError:\n print(f\"ERROR: {name} has no grades!\")\nelse:\n print(\"-- All student averages calculated --\")\nfinally:\n print(\"-- End of student average calculation --\")\n\nclass TooManyPagesReadError(ValueError):\n pass\n\n\nclass Book:\n def __init__(self, name: str, page_count:int):\n self.name = name\n self.page_count = page_count\n self.pages_read = 0\n\n def __repr__(self):\n return(\n f\"\"\n )\n\n def read(self, pages:int):\n if self.pages_read + pages > self.page_count:\n raise TooManyPagesReadError(\n f\"You tried to read {self.pages_read + pages} pages, but this book only has {self.page_count} pages.\"\n )\n self.pages_read += pages\n print (f\"You have now read {self.pages_read} pages out of {self.page_count}\")\n\npython101 = Book(\"Python 101\", 50)\npython101.read(35)\npython101.read(50)","sub_path":"exercise44_errors.py","file_name":"exercise44_errors.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"540090738","text":"class Tree:\n def __init__(self, val=None):\n self.val = val\n self.right = None\n self.left = None\n\n def add(self, val):\n if self.val is None:\n self.val = val\n elif val > self.val:\n if self.right is None:\n self.right = Tree(val)\n else:\n self.right.add(val)\n else:\n if self.left is None:\n self.left = Tree(val)\n else:\n self.left.add(val)\n\n\nn_test, layers = map(int, input().split())\nshapes = set()\nfor i in range(n_test):\n tr = Tree()\n for val in list(map(int,input().split())):\n tr.add(val)\n q = [tr]\n string = str()\n while len(q):\n node = q.pop()\n string += 'l' if node.left else 'n'\n string += 'r' if node.right else 'n'\n if node.left:\n q.append(node.left)\n if node.right:\n q.append(node.right)\n shapes.add(string)\nprint(len(shapes))\n\n","sub_path":"ceiling function_2.py","file_name":"ceiling function_2.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"89676652","text":"import sqlite3\r\nfrom pprint import pprint\r\nfrom common_functions import *\r\nfrom cdp_work import *\r\nfrom yed_work import *\r\nimport random\r\n\r\n\r\nmap_name = \"RH 6.graphml\"\r\n\r\ndef do_i_want_this(host):\r\n\ti_want_these = [\r\n\t\"peb_4506_06\",\r\n\t]\r\n\tfor i_want in i_want_these:\r\n\t\tif i_want in host:\r\n\t\t\treturn True\r\n\treturn False\r\n\r\n\r\nconnected_hosts = []\r\ndef it_is_dupe(existing_conns, this_connectoin):\r\n if this_connection in existing_conns:\r\n return True\r\n swapped_direction = {}\r\n swapped_direction[\"local_interface\"] =this_connectoin[\"remote_interface\"]\r\n swapped_direction[\"remote_interface\"] =this_connectoin[\"local_interface\"]\r\n swapped_direction[\"origin_host\"] =this_connectoin[\"other_host\"]\r\n swapped_direction[\"other_host\"] =this_connectoin[\"origin_host\"]\r\n if swapped_direction in existing_conns:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef sanatize_hostname(host):\r\n\tbad_charicters = [\"&\"]\r\n\r\n\tfor bad_char in bad_charicters:\r\n\t\tif bad_char in host:\r\n\t\t\tprint (host)\r\n\t\t\thost = host.replace(bad_char,\"\")\r\n\treturn host\r\n\r\n\r\ndef pull_ips_from_running_config(running):\r\n\trunning = running.split(\"\\n\")\r\n\tlocal_ips=[]\r\n\tip_lines = find_child_text (running, \"ip address\")\r\n\tfor ip_line in ip_lines:\r\n\t\tfor each_line in ip_line:\r\n\t\t\t#print (each_line)\r\n\t\t\ttry:\r\n\t\t\t\tlocal_ips.append(get_ip(each_line)[0])\r\n\t\t\texcept:\r\n\t\t\t\tfor ip in get_ip(each_line):\r\n\t\t\t\t\tlocal_ips.append(ip)\r\n\treturn (local_ips)\r\n\r\ndef pull_count(db_name):\r\n\tconn = sqlite3.connect(db_name)\r\n\tcur = conn.cursor()\r\n\tcommand =\"\"\"select count(site_name) from devices;\r\n\t\"\"\"\r\n\toutput = cur.execute(command)\r\n\treturn output\r\n\r\n\r\ndef pull_cdp_info(db_name):\r\n\tconn = sqlite3.connect(db_name)\r\n\tcur = conn.cursor()\r\n\tcommand =\"\"\" select site_name,CDP_nei from devices;\"\"\"\r\n\toutput = cur.execute(command)\r\n\treturn output\r\n\r\n\r\ndef put_in_xml_start(map):\r\n\tmap = map_start\r\n\treturn map\r\n\r\n\r\n\r\n#def dont_put_this_in_map(hostname):\r\n#\tbad_hostnames = [\"SEP\"]\r\n#\tfor bad_host in bad_hostnames:\r\n#\t\tif bad_host in hostname:\r\n#\t\t\treturn True\r\n#\treturn False\r\n\r\ndef put_in_nodes(hosts,node ):\r\n\tmap = ''\r\n\tfor host in hosts:\r\n\t\tif is_it_a_phone(host) == True:\r\n\t\t\tcontinue\r\n\r\n\r\n\t\thost = sanatize_hostname(host)\r\n\t\tmap = map +node.format(host, host, \"rectangle\")\r\n\treturn map\r\n\r\ndef put_in_connections(map,connections_data,link):\r\n\tmap = \"\"\r\n\tkeys = []\r\n\tfor connection in connections_data:\r\n\t\t# #ID,Source, Target, description, description\r\n\t\tif is_it_a_phone(connection['origin_host']) == True:\r\n\t\t\tcontinue\r\n\t\tif is_it_a_phone(connection['other_host']) == True:\r\n\t\t\tcontinue\r\n\r\n\r\n\t\tif connection['origin_host'] not in connected_hosts:\r\n\t\t\tconnected_hosts.append(connection['origin_host'])\r\n\t\tif \tconnection['other_host'] not in connected_hosts:\r\n\t\t\tconnected_hosts.append(connection['other_host'])\r\n\r\n\t\tconnection['origin_host'] = sanatize_hostname(connection['origin_host'])\r\n\t\tconnection['other_host'] = sanatize_hostname(connection['other_host'])\r\n\t\tid = 10\r\n\r\n\t\tnew_id_needed = True\r\n\t\twhile new_id_needed == True:\r\n\t\t\tid = str(random.randint(1, 100000000))\r\n\t\t\tif id not in keys:\r\n\t\t\t\tkeys.append(id)\r\n\t\t\t\tnew_id_needed = False\r\n\r\n\t\tdescription = str(connection['local_interface'])+\" \"+str(connection['remote_interface'])\r\n\t\tmap = map+link.format(id,connection['origin_host'],connection['other_host'],description,description)\r\n\treturn map\r\n\r\n\r\n\r\n\r\ndb_name = 'Network_info.db'\r\n\r\nconnections = []\r\npprint (\"Pulling CDP data from database\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nall_cpd_data = pull_cdp_info(db_name)\r\npprint (\"Parsing CDP Data\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nfor tmp_site_cdp_data in all_cpd_data:\r\n\thostname = tmp_site_cdp_data[0]\r\n\tsite_cdp_data= tmp_site_cdp_data[1]\r\n\tcdp_neigh_data = cdpNeighbors(site_cdp_data)\r\n\t#print (hostname)\r\n\t#pprint(cdp_neigh_data)\r\n\tfor each in cdp_neigh_data:\r\n\t\t#pprint (each)\r\n\t\tconnections.append([hostname,each])\r\n\t#print (\"\\n\\n\\n\\n\\n\\n\\n\")\r\n\r\nprint (\"Building connection list\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nconnections_data = []\r\nfor each in connections:\r\n\ti_want = False\r\n\tthis_connection = {}\r\n\tthis_connection['origin_host'] = each[0]\r\n\tif do_i_want_this(this_connection['origin_host']) == True:\r\n\t\ti_want = True\r\n\tcdp_data = each[1]\r\n\tthis_connection['other_host'] = cdp_data[\"deviceId\"]\r\n\tif do_i_want_this(this_connection['other_host']) == True:\r\n\t\ti_want = True\r\n\tif i_want == False:\r\n\t\tcontinue\r\n\tthis_connection['other_host'] = remove_end(this_connection['other_host'],\"\\.\")\r\n\tthis_connection['local_interface'] = cdp_data[\"localInterface\"]\r\n\tthis_connection['remote_interface'] = cdp_data[\"interface\"]\r\n\tif len(connections_data) == 0:\r\n\t\tconnections_data.append(this_connection)\r\n\telse:\r\n\t\tif it_is_dupe(connections_data, this_connection) == False:\r\n\t\t\tconnections_data.append(this_connection)\r\n\r\n\r\nhosts = []\r\nprint (\"Building host list\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nfor this_connection in connections_data:\r\n#\tprint (this_connection)\r\n#\tprint (len(hosts))\r\n\tif this_connection['origin_host'] not in hosts:\r\n\t\thosts.append(this_connection['origin_host'])\r\n\tif this_connection['other_host'] not in hosts:\r\n\t\thosts.append(this_connection['other_host'])\r\n\t\t\r\n#pprint (hosts)\r\n#print (len(hosts))\r\n\r\npprint (\"putting in connections\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nconnections = put_in_connections(map,connections_data,link)\r\n\r\n\r\n\r\nprint (\"Making the map.\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nmap = \"\"\r\n\r\nmap = put_in_xml_start (map)\r\npprint (\"putting in nodes\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nnodes = put_in_nodes(connected_hosts,node )\r\n\r\n\r\n\r\nmap = put_in_xml_start (map)+nodes+connections+map_end\r\n\r\n\r\nprint (\"Writting map to drive\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\nto_doc_w(map_name,map)\r\nprint (\"Done\")\r\nprint (get_time())\r\nprint (\"\\n\")\r\n\r\n\r\npprint (connected_hosts)","sub_path":"Build physical connection small map.py","file_name":"Build physical connection small map.py","file_ext":"py","file_size_in_byte":5784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"546460453","text":"from django.shortcuts import render, redirect\nfrom django.conf import settings\nfrom django.core.files.storage import FileSystemStorage\n\nfrom uploads.core.models import Document\nfrom uploads.core.forms import DocumentForm\nfrom django.http import JsonResponse\nfrom django.core import serializers\n\nimport json\nimport random\nimport pandas as pd\n\n\n\ndef home(request):\n documents = Document.objects.all()\n return render(request, 'core/home.html', { 'documents': documents })\n\n\ndef simple_upload(request):\n if request.method == 'POST' and request.FILES['myfile']:\n myfile = request.FILES['myfile']\n fs = FileSystemStorage()\n filename = fs.save(myfile.name, myfile)\n uploaded_file_url = fs.url(filename)\n return render(request, 'core/simple_upload.html', {\n 'uploaded_file_url': uploaded_file_url\n })\n return render(request, 'core/simple_upload.html')\n\n\ndef model_form_upload(request):\n if request.method == 'POST':\n form = DocumentForm(request.POST, request.FILES)\n if form.is_valid():\n form.save()\n return redirect('home')\n else:\n form = DocumentForm()\n return render(request, 'core/model_form_upload.html', {\n 'form': form\n })\n\ndef d3_data(request):\n #data = [50,100,150,200,250,130,210]\n data = random.sample(range(50, 300), 20)\n data_json= json.dumps(data)\n return render(request, 'core/d3Chart.html',{\"data_json\" : data_json})\n\ndef chordPlot(request):\n ChordData = [[11975, 5871, 8916, 2868],[ 1951, 10048, 2060, 6171],[ 8010, 16145, 8090, 8045],[ 1013, 990, 940, 6907]]\n #ChordData = [150,100,75,50,100,150,200,250,130,210]\n ChordData_json= json.dumps(ChordData)\n return render(request, 'core/chordDiagrame.html',{\"ChordData_json\" : ChordData_json})\n\n\ndef simple_upload(request):\n if request.method == 'POST' and request.FILES['myfile']:\n myfile = request.FILES['myfile']\n df = pd.read_csv(myfile)\n matrix = df.values.tolist()\n matrix_json = json.dumps(matrix)\n return render(request, 'core/chordDiagrame.html', {\n 'matrix_json': matrix_json\n })\n return render(request, 'core/simple_upload.html')\n\ndef Ichord(request):\n if request.method == 'POST' and request.FILES['myfile']:\n myfile = request.FILES['myfile']\n df = pd.read_csv(myfile)\n df1 = pd.DataFrame({\"name\": df.columns, \"color\": randomColor(len(df.columns))})\n names = [{k: df1.values[i][v] for v, k in enumerate(df1.columns)} for i in range(len(df1))]\n #names = list(df.columns)\n names_json = json.dumps(names,ensure_ascii= False).encode('utf8')\n\n matrix = df.values.tolist()\n matrix_json = json.dumps(matrix)\n return render(request, 'core/IntChord.html', {\n 'matrix_json': matrix_json,\n 'names_json': names_json\n })\n return render(request, 'core/simple_upload.html')\n\n\ndef randomColor(n):\n color = [\"#\"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])\n for i in range(n)]\n return color\n\n#clist = [\"#000000\",\"#800000\",\"#008000\",\"#808000\",\"#000080\",\"#800080\",\"#008080\",\"#c0c0c0\",\"#808080\",\"#ff0000\",\"#00ff00\",\"#ffff00\",\"#0000ff\",\"#ff00ff\",\"#00ffff\",\"#ffaf00\",\"#ffd7d7\",\"#d1fde9\",\"#B10DC9\",\"#FF4136\",\"#4f45c0\",\"#ffff66\",\"#c4c1ea\",\"#00cccc\",\"#7c9d45\",\"#57389f\"]\n#color= random.sample(clist, 15)","sub_path":"uploads/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"143956841","text":"# a^b (modulo c)\n# https://discuss.codechef.com/questions/20451/a-tutorial-on-fast-modulo-multiplication-exponential-squaring\ndef exponent(a,b,c):\n\tresult=1\n\tp=a\n\twhile b>0:\n\t\tif(b%2 == 1):\n\t\t\tresult=(result*p)%c\n\t\tp=(p*p)%c\n\t\tb=b/2\n\treturn result\n","sub_path":"Python/fast-modulo-exponentiation.py","file_name":"fast-modulo-exponentiation.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"364525387","text":"import torch\n\nfrom recsys_metrics.precision import precision\nfrom torchmetrics.functional import retrieval_precision\n\nfrom benchmark.benchmark import benchmark, benchmark_batch, sanity_check\n\n\n# ns = [128, 256, 512, 1024, 2048, 4096]\n# ks = [1, 50, 100]\ndef bench_precision_single(ns, ks, number=10_000, seed=42):\n def init_callback(n, k):\n torch.manual_seed(seed)\n preds = torch.rand(n)\n target = torch.randint(0, 2, size=(n,))\n return preds, target\n\n def base_callback(n, k, init_out):\n preds, target = init_out\n return retrieval_precision(preds, target, k=k)\n\n def our_callback(n, k, init_out):\n preds, target = init_out\n return precision(preds, target, k=k, reduction='mean')\n\n sanity_check(ns[-1], ks[-1], init_callback, base_callback, our_callback)\n\n df, g = benchmark(ns, ks, init_callback, base_callback, our_callback, number=number)\n g.fig.subplots_adjust(top=.88)\n g.fig.suptitle('Benchmark - Single Example: Precision', fontsize=16)\n return df, g\n\n# n=512,\n# k=50,\n# batch_sizes=[32, 64, 128, 256, 512],\ndef bench_precision_batch(n, k, batch_sizes, number=1_000, seed=42):\n def init_callback(n, k, batch_size):\n torch.manual_seed(seed)\n preds = torch.rand(batch_size, n)\n target = torch.randint(0, 2, size=(batch_size, n))\n return preds, target\n\n def base_callback(n, k, init_out):\n preds, target = init_out\n out = torch.stack([\n retrieval_precision(_preds, _target, k=k)\n for _preds, _target in zip(preds, target)\n ]).mean(0)\n return out\n\n def our_callback(n, k, init_out):\n preds, target = init_out\n return precision(preds, target, k=k, reduction='mean')\n\n df, g = benchmark_batch(\n n=n,\n k=k,\n batch_sizes=batch_sizes,\n init_callback=init_callback,\n base_callback=base_callback,\n our_callback=our_callback,\n number=number\n )\n g.fig.subplots_adjust(top=.88)\n g.fig.suptitle('Benchmark - Mini-Batch: Precision', fontsize=16)\n return df, g\n","sub_path":"benchmark/precision.py","file_name":"precision.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"312669051","text":"import numpy as np\nimport cv2\nimport math\n\ncam = cv2.VideoCapture(0)\n\ndef empty():\n pass\n\ncv2.namedWindow(\"HSV\")\ncv2.resizeWindow(\"HSV\",640,240)\ncv2.createTrackbar(\"Hue Min\", \"HSV\", 0, 179, empty)\ncv2.createTrackbar(\"Hue Max\", \"HSV\", 0, 179, empty)\ncv2.createTrackbar(\"Sat Min\", \"HSV\", 0, 255, empty)\ncv2.createTrackbar(\"Sat Max\", \"HSV\", 255, 255, empty)\ncv2.createTrackbar(\"Val Min\", \"HSV\", 0, 255, empty)\ncv2.createTrackbar(\"Val Max\", \"HSV\", 255, 255, empty)\n\ncv2.namedWindow(\"Trackbars\")\nhh='Max'\nhl='Min'\nwnd = 'Colorbars'\n\n\nwhile True:\n _, imgFrame = cam.read()\n hueF = cv2.cvtColor(imgFrame, cv2.COLOR_BGR2HSV)\n # EXIT\n\n h_min = cv2.getTrackbarPos(\"Hue Min\", \"HSV\")\n h_max = cv2.getTrackbarPos(\"Hue Max\", \"HSV\")\n s_min = cv2.getTrackbarPos(\"Sat Min\", \"HSV\")\n s_max = cv2.getTrackbarPos(\"Sat Max\", \"HSV\")\n v_min = cv2.getTrackbarPos(\"Val Min\", \"HSV\")\n v_max = cv2.getTrackbarPos(\"Val Max\", \"HSV\")\n\n lower = np.array([h_min, s_min, v_min], np.uint8)\n upper = np.array([h_max, s_max, v_max], np.uint8)\n mask = cv2.inRange(hueF, lower, upper)\n kernal = np.ones((5, 5), \"uint8\")\n\n mask2 = cv2.dilate(mask, kernal)\n res = cv2.bitwise_and(imgFrame, imgFrame, mask=mask2)\n\n # # Define the red threshold & apply mask\n # redLow = np.array([136, 87, 111], np.uint8)\n # redHigh = np.array([180, 255, 255], np.uint8)\n # redMask = cv2.inRange(hueF, redLow, redHigh)\n\n cv2.imshow(\"orig\", imgFrame)\n #cv2.imshow(\"hsv\", hueF)\n #cv2.imshow(\"mask\", mask2)\n cv2.imshow(\"res\", res)\n\n contours, hierarchy = cv2.findContours(mask2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n # detect red\n for pic, contour in enumerate(contours):\n area = cv2.contourArea(contour)\n #\n # if (area > 300):\n # x, y, w, h = cv2.boundingRect(contour)\n # imgFrame = cv2.rectangle(imgFrame, (x, y), (x + w, y + h), (0, 0, 255), 2)\n #\n # cv2.putText(imgFrame, \"Color\", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255))\n\n M = cv2.moments(contour)\n\n if M[\"m00\"] > 0:\n cX = int(M[\"m10\"] / M[\"m00\"])\n cY = int(M[\"m01\"] / M[\"m00\"])\n\n # Use contour if size is bigger then 1000 and smaller then 50000\n if area > 1000:\n if area < 50000:\n x, y, w, h = cv2.boundingRect(contour)\n approx = cv2.approxPolyDP(contour, 0.001 * cv2.arcLength(contour, True), True)\n # draw contour\n cv2.drawContours(imgFrame, contour, -1, (0, 255, 0), 3)\n # draw circle on center of contour\n # cv2.circle(imgFrame, (cX, cY), 7, (255, 255, 255), -1)\n # perimeter = cv2.arcLength(contour, True)\n # approx = cv2.approxPolyDP(contour, 0.04 * perimeter, True)\n # # fit elipse\n # _, _, angle = cv2.fitEllipse(contour)\n # P1x = cX\n # P1y = cY\n # length = 35\n #\n # # calculate vector line at angle of bounding box\n # P2x = int(P1x + length * math.cos(math.radians(angle)))\n # P2y = int(P1y + length * math.sin(math.radians(angle)))\n # # draw vector line\n # cv2.line(imgFrame, (cX, cY), (P2x, P2y), (255, 255, 255), 5)\n #\n # # output center of contour\n # print(angle)\n\n # detect bounding box\n rect = cv2.minAreaRect(contour)\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # draw bounding box\n cv2.drawContours(imgFrame, [box], 0, (0, 0, 255), 2)\n cv2.putText(imgFrame, \"Points: \" + str((x, y)), (x + w + 20, y + 20),\n cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 0), 2)\n\n # EXIT\n cv2.imshow(\"multiple colors\", imgFrame)\n if cv2.waitKey(10) & 0xFF == ord('q'):\n cap.release()\n cv2.destroyAllWindows()\n break","sub_path":"tag-bot-main/FINAL_CODE/PyCVCode/Final/ColorTest.py","file_name":"ColorTest.py","file_ext":"py","file_size_in_byte":4013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"401686101","text":"import os\nimport scipy.misc\nimport sis_utils\nimport ersa_utils\nfrom rst_utils import misc_utils\nfrom evaluate_utils import local_maxima_suppression\nfrom post_processing_utils import get_edge_info, connect_lines, prune_lines, prune_towers, \\\n visualize_results, towers_online, linked_length, break_lines, get_samples_between, load_model, \\\n run_inference_for_single_image, get_tower_truth_pred, update_connected_pairs\n\ncity_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']\n\n\ndef load_data(dirs, model_name, city_id, tile_id, merge_range=100):\n conf_dict = {0: 2, 1: 1, 2: 0, 3: 3}\n pred_file_name = os.path.join(dirs['task'], model_name + '_v2', 'USA_{}_{}.txt'.format(city_list[city_id], tile_id))\n preds = ersa_utils.load_file(pred_file_name)\n raw_rgb = ersa_utils.load_file(os.path.join(dirs['raw'], 'USA_{}_{}.tif'.format(city_list[city_id], tile_id)))\n conf_img = ersa_utils.load_file(os.path.join(dirs['conf'].format(conf_dict[city_id]),\n '{}{}.png'.format(city_list[city_id].split('_')[1], tile_id)))\n line_gt = ersa_utils.load_file(os.path.join(dirs['line'], '{}{}_GT.png'.format(city_list[city_id].split('_')[1],\n tile_id)))\n tower_gt = get_tower_truth_pred(dirs, city_id, tile_id)\n tower_pred, tower_conf, _ = local_maxima_suppression(preds, th=merge_range)\n conf_img = scipy.misc.imresize(conf_img, line_gt.shape)\n return preds, raw_rgb, conf_img, line_gt, tower_gt, tower_pred, tower_conf\n\n\nif __name__ == '__main__':\n # directories\n img_dir, task_dir = sis_utils.get_task_img_folder()\n ersa_utils.make_dir_if_not_exist(os.path.join(img_dir, 'new_annotation'))\n dirs = {\n 'task': task_dir,\n 'image': os.path.join(img_dir, 'new_annotation'),\n 'raw': r'/home/lab/Documents/bohao/data/transmission_line/raw2',\n 'conf': r'/media/ei-edl01/user/bh163/tasks/2018.11.16.transmission_line/confmap_uab_UnetCrop_linesv3_city0_'\n r'pw50_0_PS(572, 572)_BS5_EP100_LR0.0001_DS80_DR0.1_SFN32',\n 'line': r'/media/ei-edl01/data/uab_datasets/lines_v3/data/Original_Tiles'\n }\n\n # settings\n merge_range = 100\n radius = [1500]\n width = 5\n th = 8\n step = 5\n patch_size = (500, 500)\n for model_name in ['faster_rcnn']:\n model_list_1 = [\n '{}_Tucson_2019-06-30_12-51-03'.format(model_name),\n '{}_Colwich_2019-07-01_10-47-22'.format(model_name),\n '{}_NZ_2019-07-01_18-33-39'.format(model_name),\n ]\n\n model_list_2 = [\n '{}_Tucson_2019-07-03_00-05-04'.format(model_name),\n '{}_Colwich_2019-07-03_00-04-11'.format(model_name),\n '{}_NZ_2019-07-01_18-33-39'.format(model_name),\n ]\n\n model_list_3 = [\n '{}_Tucson_2019-07-02_14-53-17'.format(model_name),\n '{}_Colwich_2019-07-02_18-10-38'.format(model_name),\n '{}_NZ_2019-07-01_18-33-39'.format(model_name),\n ]\n\n if model_name == 'faster_rcnn':\n model_list = model_list_1\n elif model_name == 'faster_rcnn_res101':\n model_list = model_list_2\n elif model_name == 'faster_rcnn_res50':\n model_list = model_list_3\n\n '''model_list = [\n '{}_2019-02-12_09-30-35'.format(model_name),\n '{}_2019-02-12_09-33-08'.format(model_name),\n '{}_2019-02-12_09-34-45'.format(model_name),\n '{}_2019-02-12_09-36-15'.format(model_name),\n ]'''\n '''model_list = [\n '{}_2019-02-12_09-30-35'.format(model_name),\n '{}_2019-02-12_09-33-08'.format(model_name),\n '{}_2019-02-12_09-34-45'.format(model_name),\n '{}_2019-02-12_09-36-15'.format(model_name),\n ]'''\n model_id = 25000\n gpu = 1\n\n for city_id in range(2):\n detection_graph, category_index = load_model(model_list[city_id], model_id, gpu)\n for tile_id in [1, 2, 3]:\n print('Evaluating city {} tile {}'.format(city_id, tile_id))\n\n # load data\n preds, raw_rgb, conf_img, line_gt, tower_gt, tower_pred, tower_conf = \\\n load_data(dirs, model_name, city_id, tile_id, merge_range=merge_range)\n\n # get line confidences\n connected_pairs, connected_towers, unconnected_towers = None, None, None\n for r in radius:\n tower_pairs, tower_dists, line_confs = \\\n get_edge_info(tower_pred, conf_img, radius=r, width=width,\n tile_min=(0, 0), tile_max=raw_rgb.shape)\n\n # connect lines\n connected_pairs = connect_lines(tower_pairs, line_confs, th, cut_n=2)\n connected_pairs, unconnected_pairs = prune_lines(connected_pairs, tower_pred)\n\n # get towers that are not connected\n connected_towers, unconnected_towers = prune_towers(connected_pairs, tower_pred)\n\n # search line\n try:\n connected_towers, unconnected_towers, connected_pairs = \\\n towers_online(tower_pred, connected_towers, unconnected_towers, connected_pairs)\n except ValueError:\n pass\n\n # update towers\n break_lines(connected_pairs, tower_pred)\n # tower_pred = [tower_pred[a] for a in connected_towers]\n # tower_conf = [tower_conf[a] for a in connected_towers]\n\n # check the connection length\n line_length_list, attention_pair = linked_length(tower_pred, connected_pairs, dirs, city_id, tile_id)\n for ap in attention_pair:\n pred = []\n for sample_patch, top_left in get_samples_between(raw_rgb, tower_pred[ap[0]], tower_pred[ap[1]], step, patch_size):\n # Actual detection.\n output_dict = run_inference_for_single_image(sample_patch, detection_graph)\n\n for db, dc, ds in zip(output_dict['detection_boxes'], output_dict['detection_classes'],\n output_dict['detection_scores']):\n left = int(db[1] * patch_size[1]) + top_left[1]\n top = int(db[0] * patch_size[0]) + top_left[0]\n right = int(db[3] * patch_size[1]) + top_left[1]\n bottom = int(db[2] * patch_size[0]) + top_left[0]\n confidence = ds\n class_name = category_index[dc]['name']\n if confidence > 0.5:\n pred.append('{} {} {} {} {} {}\\n'.format(class_name, confidence, left, top, right, bottom))\n center_list, conf_list, _ = local_maxima_suppression(pred, th=20)\n tower_pred.extend(center_list)\n tower_conf.extend(conf_list)\n\n tower_pairs, tower_dists, line_confs = \\\n get_edge_info(tower_pred, conf_img, radius=radius[-1], width=width,\n tile_min=(0, 0), tile_max=raw_rgb.shape)\n\n # connect lines\n connected_pairs = connect_lines(tower_pairs, line_confs, th, cut_n=2)\n connected_pairs, unconnected_pairs = prune_lines(connected_pairs, tower_pred)\n\n # get towers that are not connected\n connected_towers, unconnected_towers = prune_towers(connected_pairs, tower_pred)\n\n # search line\n try:\n connected_towers, unconnected_towers, connected_pairs = \\\n towers_online(tower_pred, connected_towers, unconnected_towers, connected_pairs)\n except ValueError:\n pass\n\n # update towers\n '''break_lines(connected_pairs, tower_pred)\n connected_pairs = update_connected_pairs(connected_pairs, tower_pred, connected_towers)\n tower_pred = [tower_pred[a] for a in connected_towers]\n tower_conf = [tower_conf[a] for a in connected_towers]\n\n assert len(tower_pred) == len(tower_conf)\n save_file_name = os.path.join(task_dir, 'post_{}_{}_{}_pred_v2.npy'.format(model_name, city_id, tile_id))\n misc_utils.save_file(save_file_name, tower_pred)\n save_file_name = os.path.join(task_dir, 'post_{}_{}_{}_conf_v2.npy'.format(model_name, city_id, tile_id))\n misc_utils.save_file(save_file_name, tower_conf)\n save_file_name = os.path.join(task_dir, 'post_{}_{}_{}_conn_v2.npy'.format(model_name, city_id, tile_id))\n misc_utils.save_file(save_file_name, connected_pairs)'''\n\n # visualize results\n visualize_results(dirs['image'], city_id, tile_id, raw_rgb, line_gt, tower_pred, tower_gt, connected_pairs,\n connected_towers, unconnected_towers, save_fig=True, post_str='_{}'.format(model_name), close_file=True)\n","sub_path":"]tasks/2019.02.11.tower_evaluate/iterative_post_processing_v2.py","file_name":"iterative_post_processing_v2.py","file_ext":"py","file_size_in_byte":9306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"76800095","text":"import os, sys\nimport pexpect\nimport threading\nimport time\nimport subprocess\nfrom bson.objectid import ObjectId\nfrom pathlib import Path\nfrom flask import request, send_from_directory\nfrom flask_restplus import Resource\n\nfrom app.main.model.database import *\nfrom ..util.dto import CertDto\nfrom ..util.errors import *\nfrom ..config import get_config\n\napi = CertDto.api\ncert_key = CertDto.cert_key\n\nUPLOAD_DIR = Path(get_config().UPLOAD_ROOT)\n\nif os.name == 'nt':\n EASY_RSA_PATH = Path('..') / 'easy-rsa' / 'Windows'\n KEYS_PATH = EASY_RSA_PATH / 'keys'\n LINE_BEGIN = ''\n LINE_END = os.linesep * 2\nelif os.name == 'posix':\n EASY_RSA_PATH = Path('..') / 'easy-rsa' / 'Linux'\n KEYS_PATH = EASY_RSA_PATH / 'keys'\n LINE_BEGIN = './'\n LINE_END = os.linesep\nelse:\n print('Unsupported platform')\n sys.exit(1)\n\ndef is_file_valid(file):\n return os.path.exists(file) and os.path.getsize(file) > 0\n\ndef get_pexpect_child():\n if os.name == 'nt':\n from pexpect import popen_spawn\n\n shell = 'cmd.exe'\n child = popen_spawn.PopenSpawn(shell)\n child.expect('>')\n child.sendline('chcp 65001')\n child.expect(LINE_END)\n elif os.name == 'posix':\n shell = '/bin/bash'\n child = pexpect.spawn(shell)\n\n return child\n\nclass build_keys(threading.Thread):\n def __init__(self):\n threading.Thread.__init__(self)\n\n def run(self):\n if not is_file_valid(KEYS_PATH / 'ca.key') or not is_file_valid(KEYS_PATH / 'ca.crt'):\n print('Start to build CA key')\n start = time.time()\n child = get_pexpect_child()\n child.sendline('cd {}'.format(EASY_RSA_PATH))\n child.expect(LINE_END)\n\n if os.name == 'nt':\n child.sendline(LINE_BEGIN + 'vars')\n elif os.name == 'posix':\n child.sendline('source vars')\n child.expect(LINE_END)\n\n child.sendline(LINE_BEGIN + 'build-ca')\n child.expect(']:', timeout=30) # Country Name\n child.send('\\n')\n\n child.expect(']:') # State or Province Name\n child.sendline()\n\n child.expect(']:') # Locality Name\n child.sendline()\n\n child.expect(']:') # Organization Name\n child.sendline()\n\n child.expect(']:') # Organizational Unit Name\n child.sendline()\n\n child.expect(']:') # Common Name\n child.sendline('OpenVPN-CA')\n\n child.expect(']:') # Name\n child.sendline()\n\n child.expect(']:') # Email Address\n child.sendline()\n child.expect(os.linesep, timeout=30) # only one line feed works on Windows\n\n time.sleep(1)\n child.kill(9)\n\n if is_file_valid(KEYS_PATH / 'ca.key') and is_file_valid(KEYS_PATH / 'ca.crt'):\n print('Succeeded to build CA key, time consumed: {}'.format(time.time() - start))\n else:\n print('Failed to build CA key')\n\n if not is_file_valid(KEYS_PATH / 'ta.key'):\n print('Start to build TA key')\n start = time.time()\n child = get_pexpect_child()\n child.sendline('cd {}'.format(EASY_RSA_PATH))\n child.expect(LINE_END)\n\n if os.name == 'nt':\n child.sendline(LINE_BEGIN + 'vars')\n elif os.name == 'posix':\n child.sendline('source vars')\n child.expect(LINE_END)\n\n child.sendline(LINE_BEGIN + 'build-ta')\n child.expect(LINE_END, timeout=30)\n\n time.sleep(1)\n child.kill(9)\n\n if is_file_valid(KEYS_PATH / 'ta.key'):\n print('Succeeded to build TA key, time consumed: {}'.format(time.time() - start))\n else:\n print('Failed to build TA key')\n\n if not is_file_valid(KEYS_PATH / 'server.key') or not is_file_valid(KEYS_PATH / 'server.crt'):\n print('Start to build server key')\n start = time.time()\n child = get_pexpect_child()\n child.sendline('cd {}'.format(EASY_RSA_PATH))\n child.expect(LINE_END)\n\n if os.name == 'nt':\n child.sendline(LINE_BEGIN + 'vars')\n elif os.name == 'posix':\n child.sendline('source vars')\n child.expect(LINE_END)\n\n child.sendline(LINE_BEGIN + 'build-key-server server')\n child.expect(']:', timeout=30) # Country Name\n child.send('\\n')\n\n child.expect(']:') # State or Province Name\n child.sendline()\n\n child.expect(']:') # Locality Name\n child.sendline()\n\n child.expect(']:') # Organization Name\n child.sendline()\n\n child.expect(']:') # Organizational Unit Name\n child.sendline()\n\n child.expect(']:') # Common Name\n child.sendline('server')\n\n child.expect(']:') # Name\n child.sendline()\n\n child.expect(']:') # Email Address\n child.sendline()\n\n child.expect(']:') # A challenge password\n child.send('\\n') # don't know why only '\\n' works\n\n child.expect(']:') # An optional company name\n child.sendline()\n\n child.expect(r'\\[y/n\\]:')\n child.sendline('y')\n\n try:\n child.expect('\\[y/n\\]', timeout=2)\n except pexpect.exceptions.TIMEOUT:\n print('Signing certificate failed possibly due to repeated CSR requests')\n child.sendline('y')\n\n child.expect(LINE_END, timeout=30)\n\n time.sleep(1)\n child.kill(9)\n\n if is_file_valid(KEYS_PATH / 'server.key') and is_file_valid(KEYS_PATH / 'server.crt'):\n print('Succeeded to build server key, time consumed: {}'.format(time.time() - start))\n else:\n print('Failed to build server key')\n\n if not is_file_valid(KEYS_PATH / 'dh2048.pem'):\n print('Start to build DH key')\n start = time.time()\n child = get_pexpect_child()\n child.sendline('cd {}'.format(EASY_RSA_PATH))\n child.expect(LINE_END)\n\n if os.name == 'nt':\n child.sendline(LINE_BEGIN + 'vars')\n elif os.name == 'posix':\n child.sendline('source vars')\n child.expect(LINE_END)\n\n child.sendline(LINE_BEGIN + 'build-dh')\n child.expect(LINE_END, timeout=600)\n\n time.sleep(1)\n child.kill(9)\n\n if is_file_valid(KEYS_PATH / 'dh2048.pem'):\n print('Succeeded to build DH key, time consumed: {}'.format(time.time() - start))\n else:\n print('Failed to build DH key')\n\nif not 'thread' in globals():\n thread = build_keys()\n thread.daemon = True\n thread.start()\n\n@api.route('/csr')\nclass certificate_signing_request(Resource):\n @api.doc('certificate signing request')\n def post(self):\n \"\"\"\n Certificate signing request\n \"\"\"\n filename = None\n for name, file in request.files.items():\n if file.filename.endswith('.csr'):\n temp_id = str(ObjectId())\n filename = KEYS_PATH / (temp_id + '.csr')\n # file.save(str(filename))\n file.save(str(filename))\n break\n\n if filename:\n child = get_pexpect_child()\n child.sendline('cd {}'.format(EASY_RSA_PATH))\n child.expect(LINE_END)\n\n if os.name == 'nt':\n child.sendline(LINE_BEGIN + 'vars')\n elif os.name == 'posix':\n child.sendline('source vars')\n child.expect(LINE_END)\n\n if not is_file_valid(KEYS_PATH / 'ca.key'):\n return 'CA not found', 404\n\n cert_name = os.path.basename(filename).split('.')[0]\n child.sendline(LINE_BEGIN + 'sign-req {}'.format(cert_name))\n child.expect(r'\\[y/n\\]:')\n child.sendline('y')\n\n try:\n child.expect('\\[y/n\\]', timeout=2)\n except pexpect.exceptions.TIMEOUT:\n return 'Signing certificate failed possibly due to repeated CSR requests', 404\n child.sendline('y')\n\n child.expect(LINE_END)\n\n return send_from_directory(Path(os.getcwd()) / os.path.dirname(filename), cert_name + '.crt')\n return 'CSR request is invalid', 404\n\n@api.route('/ca')\nclass certificate_authority_request(Resource):\n @api.doc('certificate authority request')\n def get(self):\n \"\"\"\n Certificate authority request\n \"\"\"\n if not is_file_valid(KEYS_PATH / 'ca.key'):\n return 'CA not found', 404\n\n return send_from_directory(Path(os.getcwd()) / KEYS_PATH, 'ca.crt')\n","sub_path":"webrobot/app/main/controller/cert_controller.py","file_name":"cert_controller.py","file_ext":"py","file_size_in_byte":8911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"371536436","text":"import configparser\nimport logging\nfrom os import getpid\nfrom pathlib import Path\nfrom socket import gethostname\n\nimport configargparse\n\n\nclass VersionArgParser(configargparse.ArgumentParser):\n def error(self, message: str):\n \"\"\"\n Override the default implementation so nothing gets printed to screen\n \"\"\"\n raise RuntimeError(\"Did not ask for --version\")\n\n def _print_message(self, message: str, file: None = None):\n \"\"\"\n Override the default implementation so nothing gets printed to screen\n \"\"\"\n raise RuntimeError(\"Did not ask for --version\")\n\n\ndef get_version() -> str:\n \"\"\"\n Gets the current version from the setup.cfg file\n \"\"\"\n config = configparser.ConfigParser()\n path = Path(__file__).parent.parent / \"setup.cfg\"\n config.read(path)\n return str(config[\"metadata\"][\"version\"])\n\n\ndef _print_version_if_requested():\n version_arg_parser = VersionArgParser()\n version_arg_parser.add_argument(\n \"--version\",\n required=True,\n action=\"store_true\",\n help=\"Print application version and exit\",\n env_var=\"VERSION\",\n )\n try:\n version_arg_parser.parse_args()\n print(get_version())\n exit()\n except RuntimeError:\n pass\n\n\ndef parse_args():\n _print_version_if_requested()\n\n parser = configargparse.ArgumentParser(\n description=\"Forwards EPICS PVs to Apache Kafka. Part of the ESS data streaming pipeline.\"\n )\n parser.add_argument(\n \"--version\",\n action=\"store_true\",\n help=\"Print application version and exit\",\n env_var=\"VERSION\",\n )\n parser.add_argument(\n \"--config-topic\",\n required=True,\n help=\" Kafka broker/topic to listen for commands\",\n type=str,\n env_var=\"CONFIG_TOPIC\",\n )\n parser.add_argument(\n \"--status-topic\",\n required=True,\n help=\" Kafka broker/topic to publish status updates on\",\n type=str,\n env_var=\"STATUS_TOPIC\",\n )\n parser.add_argument(\n \"--output-broker\",\n required=True,\n help=\" Kafka broker to forward data into\",\n type=str,\n env_var=\"OUTPUT_BROKER\",\n )\n parser.add_argument(\n \"--storage-topic\",\n required=False,\n help=\" Kafka broker/topic for storage of the \"\n \"last known forwarding details\",\n type=str,\n env_var=\"STORAGE_TOPIC\",\n )\n parser.add_argument(\n \"-s\",\n \"--skip-retrieval\",\n action=\"store_true\",\n help=\"Ignore the stored configuration on startup\",\n )\n parser.add_argument(\n \"--graylog-logger-address\",\n required=False,\n help=\" Log to Graylog\",\n type=str,\n env_var=\"GRAYLOG_LOGGER_ADDRESS\",\n )\n parser.add_argument(\n \"--grafana-carbon-address\",\n required=False,\n help=\" Address to the Grafana (Carbon) metrics server\",\n type=str,\n )\n parser.add_argument(\n \"--log-file\", required=False, help=\"Log filename\", type=str, env_var=\"LOG_FILE\"\n )\n parser.add_argument(\n \"-c\",\n \"--config-file\",\n required=False,\n is_config_file=True,\n help=\"Read configuration from an ini file\",\n env_var=\"CONFIG_FILE\",\n )\n parser.add_argument(\n \"--pv-update-period\",\n required=False,\n help=\"If set then PV value will be sent with this interval even if unchanged (units=milliseconds)\",\n env_var=\"PV_UPDATE_PERIOD\",\n type=int,\n )\n parser.add_argument(\n \"--service-id\",\n required=False,\n help='Identifier for this particular instance of the Forwarder, defaults to \"Forwarder..',\n default=f\"Forwarder.{gethostname()}.{getpid()}\",\n env_var=\"SERVICE_ID\",\n type=str,\n )\n parser.add_argument(\n \"--fake-pv-period\",\n required=False,\n help=\"Period for random generated PV updates when channel_provider_type is set to 'fake' (units=milliseconds)\",\n env_var=\"FAKE_PV_PERIOD\",\n type=int,\n default=1000,\n )\n log_choice_to_enum = {\n \"Trace\": logging.DEBUG,\n \"Debug\": logging.DEBUG,\n \"Warning\": logging.WARNING,\n \"Error\": logging.ERROR,\n \"Critical\": logging.CRITICAL,\n }\n parser.add_argument(\n \"-v\",\n \"--verbosity\",\n required=False,\n help=\"Set logging level\",\n choices=log_choice_to_enum.keys(),\n default=\"Error\",\n env_var=\"VERBOSITY\",\n )\n optargs = parser.parse_args()\n optargs.verbosity = log_choice_to_enum[optargs.verbosity]\n return optargs\n","sub_path":"forwarder/parse_commandline_args.py","file_name":"parse_commandline_args.py","file_ext":"py","file_size_in_byte":4736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"303652643","text":"from gec_backend.controller import UserManager, SenManager, TypeManager\nfrom django.test import TestCase\n\n\n# Create your tests here.\n# !!! includes test for SenManager and TypeManager\nclass TestSenManager(TestCase):\n\n def setUp(self):\n err, self.user = UserManager.createUser(\"test1\", \"test\")\n self.assertEquals(err, \"succeed\")\n\n def testAddSen(self):\n \"\"\"\n test if addSentences function could run correctly\n \"\"\"\n print(\"======= test add\")\n err2, newsen = SenManager.addSentences(self.user.id, 'org sentence', 'correct sentence',\n ['spell', 'case', 'verb', 'replace'])\n self.assertEquals(err2, \"succeed\")\n for type in list(newsen.error_type.all()):\n print(type.type)\n err3, newsen = SenManager.addSentences(self.user.id, 'org sentence1', 'correct sentence1',\n ['spell', 'case', 'verb'])\n err4, lists = SenManager.getSentencesByUserID(self.user.id)\n self.assertEqual(err4, 'succeed')\n for l in lists:\n print(l.org_sen, l.corr_sen, l.is_delete, [t.type for t in l.error_type.all()], l.dateTime)\n\n err0, type = TypeManager.getTypeByName('spell', self.user.id)\n self.assertEqual(err0, 'succeed')\n print('newuser==============')\n err0, newuser = UserManager.createUser('ttt', 'ppp')\n err3, newsen = SenManager.addSentences(newuser.id, 'org sentence222', 'correct sentence222',\n ['spell', 'case', 'verb'])\n err, type = TypeManager.getTypeByName('spell', newuser.id)\n err5, resList = SenManager.getSentencesByTypeName('replace', self.user.id)\n self.assertEqual(err5, 'succeed')\n for l in resList:\n print(l.org_sen, l.corr_sen, l.is_delete, [t.type for t in l.error_type.all()], l.dateTime)\n err0, type = TypeManager.getTypeByName('spell', newuser.id)\n\n print(\"===\")\n err = TypeManager.updateType('spell', self.user.id, 10)\n err, lists = TypeManager.getTypeCntRank(self.user.id)\n for l in lists:\n print(l.type, l.error_counts)\n print(\"+++\")\n err, typelist = TypeManager.getTypeBySentence(newsen.id)\n for l in typelist:\n print(l.type)\n def testDel(self):\n print(\"======= test delete\")\n err2, newsen = SenManager.addSentences(self.user.id, 'org sentence', 'correct sentence',\n ['spell', 'case', 'verb', 'replace'])\n err3, newsen = SenManager.addSentences(self.user.id, 'org sentence1', 'correct sentence1',\n ['spell', 'case', 'verb'])\n\n err0, newuser = UserManager.createUser('ttt', 'ppp')\n err3, newsen1 = SenManager.addSentences(newuser.id, 'org sentence1', 'correct sentence1',\n ['spell', 'case', 'verb'])\n self.assertNotEqual(newsen1.id, newsen.id)\n err3 = SenManager.delSentences(newsen.id)\n self.assertEqual(err3, 'succeed')\n err4, sen = SenManager.getSentencesByID(newsen.id)\n self.assertNotEqual(err4, 'succeed')\n err4, lists = SenManager.getSentencesByUserID(newuser.id)\n self.assertEqual(err4, 'succeed')\n for l in lists:\n print(l.user.userName, l.org_sen, l.corr_sen, l.is_delete, [t.type for t in l.error_type.all()], l.dateTime)\n\n err4, lists = SenManager.getSentencesByUserID(self.user.id)\n self.assertEqual(err4, 'succeed')\n for l in lists:\n print(l.org_sen, l.corr_sen, l.is_delete, [t.type for t in l.error_type.all()], l.dateTime)\n err0, type = TypeManager.getTypeByName('case', self.user.id)\n print(\"++++\")\n err5, resList = SenManager.getSentencesByTypeName('case', self.user.id)\n self.assertEqual(err5, 'succeed')\n for l in resList:\n print(l.org_sen, l.corr_sen, l.is_delete, [t.type for t in l.error_type.all()], l.dateTime)\n","sub_path":"gec_backend/test/testSentence.py","file_name":"testSentence.py","file_ext":"py","file_size_in_byte":4067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}
+{"seq_id":"327401355","text":"# -*- coding:ascii -*-\nfrom mako import runtime, filters, cache\nUNDEFINED = runtime.UNDEFINED\n__M_dict_builtin = dict\n__M_locals_builtin = locals\n_magic_number = 10\n_modified_time = 1414172190.393\n_enable_loop = True\n_template_filename = 'C:\\\\DjangoMako\\\\ITILITY\\\\homepage\\\\templates/edit_location.html'\n_template_uri = 'edit_location.html'\n_source_encoding = 'ascii'\nimport os, os.path, re\n_exports = [u'content']\n\n\ndef _mako_get_namespace(context, name):\n try:\n return context.namespaces[(__name__, name)]\n except KeyError:\n _mako_generate_namespaces(context)\n return context.namespaces[(__name__, name)]\ndef _mako_generate_namespaces(context):\n pass\ndef _mako_inherit(template, context):\n _mako_generate_namespaces(context)\n return runtime._inherit_from(context, u'base.htm', _template_uri)\ndef render_body(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n __M_locals = __M_dict_builtin(pageargs=pageargs)\n def content():\n return render_content(context._locals(__M_locals))\n form = context.get('form', UNDEFINED)\n __M_writer = context.writer()\n __M_writer(u'\\n\\n')\n if 'parent' not in context._data or not hasattr(context._data['parent'], 'content'):\n context['self'].content(**pageargs)\n \n\n __M_writer(u'\\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\ndef render_content(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n def content():\n return render_content(context)\n form = context.get('form', UNDEFINED)\n __M_writer = context.writer()\n __M_writer(u'\\n\\n
![](\"\\/images\\/icon\\/new_icon.gif){kind=icon}
([0-9]{1,3})(.*)>(.*)![](\"\\/images\\/icon\\/new_icon.gif'\n\n","sub_path":"MilitaryAffairs/ChangeDetector.py","file_name":"ChangeDetector.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"63"}){kind=icon}
\"python -m myfile\"\n\"\"\"\nWhen use python -m xxx, xxx.py is loaded as a module (with a __package__ of prefix with ., non-NONE, and similar to be loaded with import), \nthen run as the top-level script.\nthe xxx.py directory will NOT be added as first [0] of sys.path.\n\n有两种方式加载一个 py 文件：作为 top-level 脚本或者作为 module。前者指的是直接运行脚本，比如 python myfile.py。\n如果执行 python -m myfile，或者在其它 py 文件中用 import 语句来加载，那么它就会被当作一个 module。有且只能有一个 top-level 脚本，\n就是最开始执行的那个（比如 python myfile.py 中的 myfile.py，译者注）。\n\n当一个 py 文件被加载之后，它会被赋予一个名字，保存在 __name__ 属性中。如果是 top-level 脚本，那么名字就是 __main__。如果是作为 module，\n名字就是把它所在的 packages/subpackages 和文件名用 . 连接起来。\n\n====> import 是否成功，取决于：\n1）where to type 'python abcxyz'\n2) how to run 'abcxyz' , (-m, -c, etc)\n\"\"\"\n\n# Fibonacci numbers module\n\ndef fib(n): # write Fibonacci series up to n\n print(\"name: \", __name__)\n # when in python interactive mode, imoport test_name as fibo, fibo.fib(1), name is 'test_name'\n # just type fibo.__name__\n # when in terminal, python [-m] test_name.py 3, it is '__main__'\n a, b = 0, 1\n while b < n:\n #print(b, end=\" \")\n print(b, \" \")\n a, b = b, a+b\n print()\n\ndef fib2(n): # return Fibonacci series up to n\n print(\"this is fib2 in test_name.\")\n print(\"name: \", __name__)\n print(\"__package__: \", __package__)\n result = []\n a, b = 0, 1\n while b < n:\n result.append(b)\n a, b = b, a+b\n return result\n\n\"\"\"\n\nLingLins-MacBook-Pro:test linglin$ python3\nPython 3.6.2 (default, Jul 17 2017, 16:44:45) \n[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.42)] on darwin\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import test_name\n>>> fibo.fibo()\nTraceback (most recent call last):\n File \"![](\"./graphs/%s.png\")
' % sen.senid_\n senid = '