0:\n myTurtle.forward(lineLen)\n myTurtle.right(90)\n drawspiral(myTurtle, lineLen-5)\n\n#1. \nlst=[3,5,7,9]\nprint(listSum(lst))\n\n#2. \nprint(tostr(769, 10))\n\ndrawspiral(myTurtle, 100)\nmyWin.exitonclick()","repo_name":"krishnajinka/pydsalgo","sub_path":"recur.py","file_name":"recur.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"21682973945","text":"from typing import Text, Dict, Any, List, Union\nimport logging\nfrom datetime import datetime \nfrom datetime import timedelta\nimport requests\nfrom rasa_sdk.events import SlotSet, BotUttered\nfrom rasa_sdk import Action, Tracker\nfrom graph_db import InMemoryGraph as db, ATTR_MAP, ENT_MAP, ATTR_KEY_MAP, MEN_MAP, equalComp, compareDates, containsComp\nfrom rasa_sdk.executor import CollectingDispatcher\nfrom rasa_sdk.forms import FormAction\nfrom datetime import date\nimport smtplib\nimport ssl\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nimport os\nimport html\nimport time\nimport io\nimport base64\n# import locale\nfrom fuzzywuzzy import fuzz\n# import cProfile\n# import pstats\n# from pstats import SortKey\n\nrasa_env = os.environ.get(\"rasa_env\", None) \n# if (rasa_env!= None): \n# locale.setlocale(locale.LC_ALL, 'de_DE.utf-8') \n# else:\n# locale.setlocale(locale.LC_ALL, 'de_DE.UTF-8')\n\nmailserver_user = os.environ.get(\"email_user\", \"data\")\nmailserver_password = os.environ.get(\"email_password\", \"bl341xbAUxLvq+\")\n\nfuehrung_email = os.environ.get(\"FUEHRUNG_EMAIL\", \"e.ersada@integr8.com\")\nwerk_fragen_email = os.environ.get(\"WERK_FRAGEN_EMAIL\", \"e.ersada@integr8.com\")\npublikation_bestellen_email = os.environ.get(\"PUBLIKATION_BESTELLEN_EMAIL\", \"e.ersada@integr8.com\")\npublikation_fragen_email = os.environ.get(\"PUBLIKATION_FRAGEN_EMAIL\", \"e.ersada@integr8.com\")\nallgemeine_fragen_email = os.environ.get(\"ALLGEMEINE_FRAGEN_EMAIL\", \"e.ersada@integr8.com\")\nfotografie_genehmigung_email = os.environ.get(\"FOTOGRAFIE_GENEHMIGUNG_EMAIL\", \"e.ersada@integr8.com\")\n# fuehrung_email = os.environ.get(\"FUEHRUNG_EMAIL\", \"chatbot_intern@zkm.de\")\n# werk_fragen_email = os.environ.get(\"WERK_FRAGEN_EMAIL\", \"chatbot_intern@zkm.de\")\n# publikation_bestellen_email = os.environ.get(\"PUBLIKATION_BESTELLEN_EMAIL\", \"chatbot_intern@zkm.de\")\n# publikation_fragen_email = os.environ.get(\"PUBLIKATION_FRAGEN_EMAIL\", \"chatbot_intern@zkm.de\")\n# allgemeine_fragen_email = os.environ.get(\"ALLGEMEINE_FRAGEN_EMAIL\", \"chatbot_intern@zkm.de\")\n# fotografie_genehmigung_email = os.environ.get(\"FOTOGRAFIE_GENEHMIGUNG_EMAIL\", \"chatbot_intern@zkm.de\")\n# fotografie_genehmigung_email = os.environ.get(\"FOTOGRAFIE_GENEHMIGUNG_EMAIL\", \"chatbot_intern@zkm.de\")\n##########################\n# Constants Section\n##########################\n\nlogger = logging.getLogger(__name__)\nlogger.debug(\"fuehrung email = {}\".format(fuehrung_email))\nlogger.debug(\"werk fragen = {}\".format(werk_fragen_email))\nlogger.debug(\"publ best= {}\".format(publikation_bestellen_email))\nlogger.debug(\"publ frag = {}\".format(publikation_fragen_email))\nlogger.debug(\"allgemeine_fragen_email {}\".format(allgemeine_fragen_email))\nlogger.debug(\"fotografie_genehmigung {}\".format(fotografie_genehmigung_email))\nschema = {\n \"person\": {\n \"attributes\": [\"nationality\", \"name\", \"nid\"],\n \"representation\": [\"name\", \"nid\"],\n \"key\": \"name\"\n },\n \"event\": {\n \"attributes\": [\"title\", \"location_name\", \"date\", \"time\",\"corr_person\", \"nid\", \"subcategory\", \"link\", \"targetgroup\"],\n \"representation\": [\"title\", \"date\", \"subtitle\", \"image_url\", \"corr_person\", \"nid\", \"link\"],\n \"key\": \"title\"\n },\n \"creation\": {\n \"attributes\": [\"creator_id\", \"corr_event\", \"creation_date\", \"title\", \"creator_name\", \"creator\", \"image_url\", \"link\"],\n \"representation\": [\"creator_id\", \"corr_event\",\"creation_date\", \"image_url\", \"title\", \"creator_name\", \"link\"],\n \"key\": \"title\"\n },\n \"publication\": {\n \"attributes\": [\"title\", \"date\" ],\n \"representation\": [\"title\", \"date\", \"shop_link\", \"cover_image_url\"],\n \"key\": \"title\"\n }\n}\ncalender = {\n 0: \"Montag\",\n 1: \"Dienstag\",\n 2: \"Mittwoch\",\n 3: \"Donnerstag\",\n 4: \"Freitag\",\n 5: \"Samstag\",\n 6: \"Sonntag\",\n}\n\n#######################\n# Interfaces\n#######################\n\nclass Zdate():\n value: date\n\n def __init__(self, value):\n if (value == None):\n raise ValueError\n self.value = value\n \n def toGermanDate(self) -> Text:\n return datetime.strftime(self.value, '%d.%m.%Y')\n def toEnglishDate(self) -> Text:\n return datetime.strftime(self.value, '%Y-%m-%d')\n def getWeekDay(self) -> Text:\n return calender[self.value.weekday()]\n\nclass DbQueryResult():\n \"\"\"\n Result object of querying the knowledge database\n fields error and result are required,\n other fields are: entity type, slots\n \"\"\"\n error: str\n result: Any\n entity_type: str\n attribute: str\n entity_name: str\n slots: List\n\n def __init__(self, error, result, slots=[], entity_type=\"\", entity_name=\"\", attribute=\"\"):\n self.error = error\n self.result = result\n self.slots = slots\n self.entity_type = entity_type\n self.entity_name = entity_name\n self.attribute = attribute\n\nclass EmailInfo():\n message_text: str\n message_html: str\n subject: str\n email_receiver: str\n\n def __init__(self, message_text, message_html, subject, email_receiver):\n self.message_text = message_text\n self.message_html = message_html\n self.subject = subject\n self.email_receiver = email_receiver\n\n\n##########################\n# Utility functions\n##########################\n\ndef get_attributes_of_entity(dbInstance, entity_type, tracker) -> List[Dict[any, any]]:\n attributes = []\n\n if entity_type in schema:\n for attr in schema[entity_type][\"attributes\"]:\n\n value = str(tracker.get_slot(attr))\n value = value if attr == \"time\" and value != 'None' else dbInstance.map(\n ATTR_MAP, value.lower(), attr)\n attr = \"date\" if attr == \"time\" else attr\n compareFunction = get_compare_function_of_attribute(attr)\n\n if value != None:\n attributes.append(\n {'key': attr, 'value': value, 'comp': compareFunction})\n return attributes\n\n\ndef get_compare_function_of_attribute(attr):\n if (attr == \"date\"):\n return compareDates\n return equalComp\n\n\ndef get_attribute(dbInstance, tracker):\n attr = str(tracker.get_slot('attribute'))\n attribute = dbInstance.map(ATTR_KEY_MAP, attr.lower())\n return attribute\n\n\ndef get_entity_name(tracker, entity_type):\n name = tracker.get_slot(entity_type)\n if name != None:\n return name\n name = resolve_mention(tracker)\n if name != None:\n return name\n return None\n\n\ndef get_entity_key(dbInstance, tracker):\n for entity in schema:\n value = tracker.get_slot(entity)\n if value is None:\n continue\n return entity\n\n entityKey = tracker.get_slot('entity_type')\n if entityKey != None:\n value = dbInstance.map(ENT_MAP, entityKey)\n if value != None:\n return value\n\n return None\n\n\ndef reset_attribute_slots(slots, entity_type, tracker):\n \"\"\"\n Reset all slots corresponding to the attributes of a given entity as per the schema.\n parameters:\n slots = slots that you want to keep\n entity_type\n tracker\n \"\"\"\n slots.append(SlotSet(\"time\", None))\n if entity_type in schema:\n for attr in schema[entity_type][\"attributes\"]:\n attr_val = tracker.get_slot(attr)\n if attr_val is not None:\n slots.append(SlotSet(attr, None))\n return slots\n\n\ndef resolve_mention(tracker: Tracker) -> Text:\n \"\"\"\n Resolves a mention of an entity, such as first, to the actual entity.\n If multiple entities are listed during the conversation, the entities\n are stored in the slot 'listed_items' as a list. We resolve the mention,\n such as first, to the list index and retrieve the actual entity.\n :param tracker: tracker\n :return: name of the actually entity\n \"\"\"\n graph_database = db()\n\n mention = tracker.get_slot(\"mention\")\n listed_items = tracker.get_slot(\"listed_items\")\n\n if mention is not None and listed_items is not None:\n idx = int(graph_database.map(MEN_MAP, mention))\n\n if type(idx) is int and idx < len(listed_items):\n return listed_items[idx]\n\n\ndef to_str(entity: Dict[Text, Any], entity_keys: Union[Text, List[Text]]) -> Text:\n \"\"\"\n Converts an entity to a string by concatenating the values of the provided\n entity keys.\n :param entity: the entity with all its attributes\n :param entity_keys: the name of the representation attributes\n :return: a string that represents the entity\n \"\"\"\n if isinstance(entity_keys, str):\n entity_keys = [entity_keys]\n\n result_list = ['{} : {}'.format(attr, entity[attr])\n for attr in entity_keys]\n return \", \".join(result_list)\n\ndef string_to_german_date(date_value) -> Text:\n \"\"\"\n Converts an date (String) into german date (String).\n :param date_value: date as a string\n :return: a string that represents a german date\n \"\"\"\n obj = datetime.strptime(date_value,'%Y-%m-%d %H:%M:%S') #2014-01-06 10:00:00\n date = datetime.strftime(obj, '%d.%m.%Y')\n return date\n\ndef to_dict(entity: Dict[Text, Any], entity_keys: Union[Text, List[Text]]) -> Dict:\n \"\"\"\n Converts an entity to a dic by concatenating the values of the provided\n entity keys.\n :param entity: the entity with all its attributes\n :param entity_keys: the name of the representation attributes\n :return: a dic that represents the entity\n \"\"\"\n if isinstance(entity_keys, str):\n entity_keys = [entity_keys]\n result_dic = {}\n for attr in entity_keys:\n result_dic[attr] = entity[attr]\n return result_dic\n\ndef get_entity_type(tracker) -> Text:\n entity = tracker.get_slot(\"entity_type\")\n if entity is None:\n entity = \"person\" if tracker.get_slot(\"PER\") is not None else None\n return entity\n \ndef get_date(tracker) -> Zdate:\n \"\"\"\n get_date Looks for the date object in the slots time (filled by duckling)\n or Date (filled by our own entities)\n return a Zdate object, or None.\n \"\"\"\n dates = tracker.get_slot(\"time\") or tracker.get_slot(\"date\")\n if (dates == None or dates=='heute'):\n return Zdate(value=date.today())\n try:\n if(rasa_env == 'production'):\n dateObject = datetime.strptime(dates[:10], '%Y-%m-%d').date()\n else:\n dateObject = datetime.strptime(dates[:10], '%d.%m.%Y').date()\n return Zdate(value=dateObject) \n except ValueError:\n return None\n # dateObject = datetime.strptime(dates[:10], '%Y-%m-%d').date()\n # return Zdate(value=dateObject)\n\ndef query_attribute(tracker, attr: Text = \"\", entity: Text = None) -> DbQueryResult:\n graph_database = db()\n\n attribute = attr\n entity_type = entity or get_entity_key(graph_database, tracker)\n\n if entity_type is None:\n return DbQueryResult(error=\"Entity type nicht gefunden\", result=None)\n\n name = get_entity_name(tracker, entity_type)\n\n if (len(attribute) <= 0):\n attribute = get_attribute(graph_database, tracker)\n\n logger.debug(\n \"name of entity {}, attribute requested {}\".format(name, attribute))\n\n if name is None or attribute is None:\n return DbQueryResult(error=\"Slot entity type was None\", result=None)\n\n key_attribute = schema[entity_type][\"key\"]\n value = graph_database.get_attribute_of(\n entity_type, key_attribute, name, attribute\n )\n\n slots = [SlotSet(\"mention\", None), SlotSet(entity_type, name)]\n slots = reset_attribute_slots(slots, entity_type, tracker)\n return DbQueryResult(error=None, result=value,\n slots=slots, entity_type=entity_type,\n attribute=attribute, entity_name=name\n )\n\n\ndef query_entities(tracker, attr: List[Dict[any, any]] = [], entity_type_param: Text = None, limit= int, useIndex=False) -> DbQueryResult:\n \"\"\"\n query_entities will search for every entity of a determine entity type(event, person, etc)\n and given attributes\n :Param tracker: required, attr: attributes with wich the entities will be filtered, if not \n given any, it will search on the attribute slot.\n :Returns a DbQueryResult wich is an object with (error, result, entity_type, slots) fields\n \"\"\"\n\n graph_database = db()\n attributes = attr \n entity_type = entity_type_param if entity_type_param != None else get_entity_type(tracker=tracker)\n # if (entity_type == 'Workshops'):\n # entity_type = 'workshop'\n if entity_type is None:\n entity_type='event'\n logger.debug(entity_type)\n logger.debug(\"query_entities\")\n if entity_type is None:\n return DbQueryResult(error=\"Entity type nicht gefunden\", result=None)\n\n if (len(attributes) <= 0):\n attributes = get_attributes_of_entity(\n graph_database, entity_type, tracker)\n\n if useIndex:\n entities = graph_database.get_entities_op(entity_type, attributes)\n else:\n entities = graph_database.get_entities(entity_type, attributes)\n\n if not entities:\n return DbQueryResult(error=None, result=[])\n\n entity_representation: List[str] = schema[entity_type][\"representation\"]\n\n sorted_entities = [to_dict(e, entity_representation) for e in entities]\n\n entity_key = schema[entity_type][\"key\"]\n\n slots = [\n SlotSet(\"entity_type\", entity_type),\n SlotSet(\"listed_items\", list(\n map(lambda entity: str(entity[entity_key]), entities))),\n ]\n\n return DbQueryResult(\n error=None, result=sorted_entities,\n slots=slots, entity_type=entity_type\n )\n\ndef prepareEmailInfo(tracker)-> EmailInfo:\n\n email_type = tracker.get_slot(\"email_type\") or \"book_guide\"\n if email_type == \"book_guide\":\n\n subject = \"Führung buchen\"\n name = tracker.get_slot('name')\n email = tracker.get_slot('email')\n num_people = tracker.get_slot('num_people')\n phone_number = \"\"\n visit_date = tracker.get_slot('date')\n visit_time = tracker.get_slot('visit_time')\n guide_event = tracker.get_slot('guide_event')\n kind_of_event = tracker.get_slot('kind_of_event')\n\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n Nachricht lautet:
\n -----------------------
\n {}\n {}\n {}\n {} \n {}\n {}\n {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, name, phone_number, guide_event, kind_of_event, num_people, visit_date, visit_time)\n\n message_text = \"\"\"\n Telefonnummer: {}\n Bitte gib eine gültige Telefonnummer für Rückfragen an\n\n Betreff: Anfrage {}\n\n {} möchte gerne eine Führung buchen für {} Personen.\n\n Am {} {}.\n\n Durch die Ausstellung {}.\n \n Die Art der Führung soll eine {} sein.\n \n ---\n Vom ZKM Bot gesendet\n \"\"\".format(phone_number, subject, name, num_people, visit_date, visit_time, guide_event, kind_of_event )\n # .format(subject, name, phone_number, guide_event, kind_of_event, num_people, visit_date, visit_time)\n\n return EmailInfo(message_text=message_text, message_html= message_html, subject=subject, email_receiver=fuehrung_email)\n\n if email_type == \"question_creation\":\n\n creation = tracker.get_slot('creation')\n name = tracker.get_slot('name')\n email = tracker.get_slot('email')\n subject = \"Anfrage zu Werk {}\".format(creation)\n\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n Nachricht lautet:
\n -----------------------
\n \n Liebes ZKM,\n ich würde gerne mehr über Werk {} wissen. Euer Chatbot meinte, ich sollte Euch fragen.\n Herzliche Grüße, {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, creation, name)\n\n message_text = \"\"\"\n Liebes ZKM,\n ich würde gerne mehr über Werk {} wissen. Euer Chatbot meinte, ich sollte Euch fragen.\n Herzliche Grüße, {}\n \"\"\".format(creation, name)\n\n return EmailInfo(message_text=message_text, message_html=message_html,\n subject=subject, email_receiver=werk_fragen_email)\n\n if email_type == \"buy_publication\":\n\n publication = tracker.get_slot('publication')\n name = tracker.get_slot('name')\n email = tracker.get_slot('email')\n subject = publication\n\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n Nachricht lautet:
\n -----------------------
\n \n Sehr geehrte Damen und Herren, ich würde gerne Publikation {} bestellen.\n Bitten teilen Sie mir doch mit, ob die Publikation verfügbar ist und zu welchen Konditionen sie verschickt werden kann.\n Mit freundlichen Grüßen, {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, publication, name)\n\n message_text = \"\"\"\nLiebes ZKM-Team, \nich würde gerne die Publikation {} bestellen.\nBitten teilen Sie mir doch mit, ob die Publikation verfügbar ist und zu welchen Konditionen sie verschickt werden kann.\nMit freundlichen Grüßen, {}\n \"\"\".format(publication, name)\n\n return EmailInfo(message_text=message_text, message_html=message_html,\n subject=subject, email_receiver=publikation_bestellen_email)\n\n if email_type == \"ask_for_publication\":\n\n publication = tracker.get_slot('publication') or \"{*}\"\n name = tracker.get_slot('name')\n email = tracker.get_slot('email')\n subject = publication\n\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n Nachricht lautet:
\n -----------------------
\n \n Liebes ZKM, ich interessiere mich für den Katalog zu {}.\n Ist diese Publikation eventuell noch in Arbeit?\n Mit besten Grüßen, {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, publication, name)\n\n message_text = \"\"\"\n Liebes ZKM, ich interessiere mich für den Katalog zu {}. \n Ist diese Publikation eventuell noch in Arbeit? \n Mit besten Grüßen, {}\n \"\"\".format(publication, name)\n\n return EmailInfo(message_text=message_text, message_html=message_html,\n subject=subject, email_receiver=publikation_fragen_email)\n\n if email_type == \"custom_message\":\n\n message = \"\"\n name = tracker.get_slot('name')\n email = tracker.get_slot('email')\n subject = \"Frage\"\n context = tracker.get_slot('current_context')\n\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n {}
\n Nachricht lautet:
\n -----------------------
\n \n {},\n Mit besten Grüßen, {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, context, message, name)\n\n message_text = \"\"\"\n {}\n Mit besten Grüßen, {}\n \"\"\".format(message, name)\n\n logger.debug(\"message_html\")\n logger.debug(message_html)\n return EmailInfo(message_text=message_text, message_html=message_html,\n subject=subject, email_receiver=allgemeine_fragen_email)\n\n if email_type == \"approval\":\n\n message = \"\"\n name = tracker.get_slot('name')\n email = tracker.get_slot('email')\n subject = \"Fotografie - Genehmigung\"\n\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n Nachricht lautet:
\n -----------------------
\n \n {},\n Mit besten Grüßen,{}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, message, name)\n\n message_text = \"\"\"\n {}\n Mit besten Grüßen, {}\n \"\"\".format(message, name)\n\n return EmailInfo(message_text=message_text, message_html=message_html,\n subject=subject, email_receiver=fotografie_genehmigung_email)\n\ndef build_html_message(subject: Text, email: Text, message: Text, context: Text):\n\n if not context:\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n Nachricht lautet:
\n -----------------------
\n {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, message)\n else:\n message_html = \"\"\"\n \n \n {} \n \n Email von: {}\n
\n {}
\n Nachricht lautet:
\n -----------------------
\n {}\n
\n -----------------------
\n \n \n \"\"\".format(subject, email, context, message)\n return message_html\n # return html.escape(message_html)\n\n#######################\n# Profile\n#######################\n\n#cProfile.runctx(\"\"\"query_entities(tracker=None, entity_type_param=\"event\",attr=[{'key': \"title\", 'value': \"Wittgenstein in Coen Country\", 'comp': equalComp}], useIndex=True)\"\"\", filename=\"profile\", globals=globals(), locals=locals())\n# cProfile.runctx(\"\"\"query_entities(tracker=None, entity_type_param=\"event\",attr=[{'key': \"title\", 'value': \"mihara\", 'comp': equalComp}], useIndex=True)\"\"\", filename=\"profile\", globals=globals(), locals=locals())\n# with io.open(\"profile_result\", mode=\"w\") as file:\n# p = pstats.Stats(\"profile\", stream=file)\n# p.sort_stats(SortKey.CUMULATIVE).print_stats()\n\n#######################\n# Rasa Actions\n#######################\n \nclass ExhibitionFromArtist(Action):\n \"\"\"Action for listing entities.\n The entities might be filtered by specific attributes.\"\"\"\n\n def name(self):\n return \"action_query_exhibition_artist\"\n \n def run(self, dispatcher, tracker, domain):\n artist_name = tracker.get_slot(\"PER\")\n event_name = tracker.get_slot(\"event\")\n\n query_person = query_entities(tracker=tracker, entity_type_param=\"person\",\n attr=[{'key': \"name\", 'value': artist_name, 'comp': equalComp}])\n\n if query_person.error is not None or len(query_person.result) <= 0:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return []\n\n person_nid = query_person.result[0][\"nid\"]\n\n query_event = query_entities(tracker=tracker, entity_type_param=\"event\",\n attr=[{'key': \"title\", 'value': event_name, 'comp': equalComp}], useIndex=True)\n \n if query_event.error is not None and len(query_event.result) <= 0:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return []\n\n event_nid = query_event.result[0][\"nid\"]\n event_corr_person = query_event.result[0][\"corr_person\"]\n\n if \"Writing the History of the Future\" in event_name and person_nid in event_corr_person:\n\n query_creation = query_entities(tracker=tracker, entity_type_param=\"creation\", \n attr=[{'key': \"creator_id\", 'value': person_nid, 'comp': containsComp},\n {'key': \"corr_event\", 'value': event_nid, 'comp': containsComp}])\n\n dispatcher.utter_message(\n f\"In Writing the History of the Future zeigen wir folgende Arbeiten von {artist_name}\"\n )\n creations = query_creation.result\n\n message_to_send = {\n \"type\": query_creation.entity_type,\n \"items\": creations\n }\n\n dispatcher.utter_message(\n json_message=message_to_send\n )\n return []\n\n if not person_nid in event_corr_person:\n dispatcher.utter_message(\n \"Leider nein. Künstler {} ist nicht Teil der Ausstellung {}\".format(artist_name, event_name)\n )\n return []\n \n dispatcher.utter_message(\n \"Ja, wir zeigen {} ist nicht Teil der Ausstellung {}\".format(artist_name, event_name)\n )\n return []\n\nclass ActionChildrenRecommendation(Action):\n def name(self):\n return \"action_beliebt_bei_kindern\"\n\n def run(self, dispatcher, tracker, domain):\n date_object = get_date(tracker)\n if (date_object is None):\n dispatcher.utter_message(\"Bitte geben Sie das Datum im folgenden Format an tt.mm.jjjj\")\n return []\n date_value = date_object.value\n format_date = date_object.toGermanDate()\n\n message_text = \"Hier das Programm für {}, den {}.\".format(date_object.getWeekDay(), format_date)\n attr = []\n attr.append({\"key\": \"targetgroup\", \"value\": \"children\", \"comp\": containsComp})\n\n event_type = tracker.get_slot(\"entity_type\") or None\n if event_type == 'Veranstaltungen':\n event_type = 'event'\n if event_type is None:\n event_type = 'event'\n\n if (fuzz.ratio(event_type, \"Workshop\") >80 or fuzz.ratio(event_type, \"Workshops\") >80):\n attr.append({\"key\": \"subcategory\", \"value\": \"workshop\", \"comp\": equalComp})\n message_text = \"Am {}, den {} finden folgende Workshops für Kinder statt\".format(date_object.getWeekDay(), format_date)\n \n query_result = query_entities(tracker=tracker, entity_type_param=event_type, attr=attr)\n\n end_date = date_value + timedelta(days=7)\n entities = query_result.result\n filtered_entites = []\n for entity in entities:\n if (datetime.strptime(entity[\"date\"][\"end\"][:10], '%Y-%m-%d').date() >= date_value and\n datetime.strptime(entity[\"date\"][\"start\"][:10], '%Y-%m-%d').date() <= end_date):\n filtered_entites.append(entity)\n logger.debug(filtered_entites)\n\n if query_result.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return []\n\n elif not query_result.result or len(query_result.result) <= 0:\n dispatcher.utter_message(\n \"Ich habe keine passende Info gefunden\"\n )\n return []\n elif len(filtered_entites) <=0:\n dispatcher.utter_message(\"Für {}, den {} finden keine Veranstaltungen für Kinder statt\".format(date_object.getWeekDay(), format_date))\n return []\n else:\n dispatcher.utter_message(text=\"Unser Programm ist vielfältig: Kunstausstellungen, Konzerte, Vorträge und vieles mehr! Bitte beachte, dass unser Museum Montags und Dienstags in der Regel geschlossen ist.\")\n dispatcher.utter_message(text=message_text)\n message_to_send = {\n \"type\": query_result.entity_type,\n \"items\": filtered_entites,\n }\n\n dispatcher.utter_message(json_message=message_to_send)\n slots = [*query_result.slots , SlotSet(\"date\", format_date)]\n return slots\n\nclass ActionCreationGeneral(Action):\n\n def name(self):\n return \"action_werke_allgemein\"\n\n def run(self, dispatcher, tracker, domain):\n \n creation_title = tracker.get_slot(\"creation\")\n query_creation = query_entities(tracker=tracker, entity_type_param=\"creation\",\n attr=[{'key': \"title\", 'value': creation_title, 'comp': equalComp}]) \n \n if query_creation.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return [SlotSet(\"email_type\", \"question_creation\")]\n\n if not query_creation.result or len(query_creation.result) <= 0:\n message_to_send = {\n \"text\": \"Leider habe ich keine Informationen zu diesem Werk. Möchtest Du eine Frage an meine Kolleginnen senden?\",\n \"buttons\": [{\"title\": \"Ja\", \"payload\": \"/affirm\"},{\"title\": \"Nein\", \"payload\": \"/deny\"}]\n }\n dispatcher.utter_message(\n json_message=message_to_send\n )\n return [SlotSet(\"email_type\", \"question_creation\")]\n\n creation_title = query_creation.result[0][\"title\"]\n creator_name = query_creation.result[0][\"creator_name\"]\n creation_date = query_creation.result[0][\"creation_date\"]\n \n\n message_to_send = {\n \"links\": [{\n \"payload\": query_creation.result[0][\"link\"],\n \"title\": query_creation.result[0][\"title\"]}],\n \"text\": \"\"\n }\n dispatcher.utter_message(text= f\"\"\"{creation_title} ist ein Kunstwerk von {creator_name} aus dem Jahr {creation_date}.\nHier findest Du weitere Informationen:\"\"\",json_message=message_to_send)\n\n\n slots = reset_attribute_slots([], query_creation.entity_type, tracker)\n dispatcher.utter_message(template=\"utter_antwort_hilfreich_frage\")\n return slots\n\n\nclass CreationYear(Action):\n def name(self):\n return \"action_jahresuebersicht\"\n\n def run(self, dispatcher, tracker, domain): \n search_year = tracker.get_slot(\"year\") or \"1994\"\n attr = [{\"key\": \"creation_date\", \"value\": search_year, \"comp\": equalComp}]\n query_year = query_entities(tracker=tracker,entity_type_param=\"creation\", limit=200,\n attr=attr)\n\n message_to_send = {\n \"type\": query_year.entity_type,\n \"items\": query_year.result\n }\n\n dispatcher.utter_message(text= f\"\"\"Komm, wir gehen auf Zeitreise! Folgende Werke entstanden im Jahr {search_year}.\"\"\", json_message=message_to_send)\n slots = [SlotSet(\"creation_date\", search_year)]\n return slots\n\nclass ProgrammTagesspezifisch(Action):\n \"\"\"Action for querying events or workshops base on a date\n Returns a list of items.\n \"\"\"\n\n def name(self):\n return \"action_query_programm_tagesspezifisch\"\n\n def run(self, dispatcher, tracker, domain):\n\n date_object = get_date(tracker)\n if (date_object is None):\n dispatcher.utter_message(\"Bitte geben Sie das Datum im folgenden Format an tt.mm.jjjj\")\n return []\n date_value = date_object.value\n format_date = date_object.toGermanDate()\n\n message_text = \"Hier das Programm für {}, den {}.\".format(date_object.getWeekDay(), format_date)\n attr = [{\"key\": \"date\", \"value\": date_object.toEnglishDate(), \"comp\": compareDates}]\n\n event_type = tracker.get_slot(\"entity_type\") or None\n if event_type is None:\n event_type = 'event'\n if event_type == 'event':\n event_type = 'ausstellungen'\n if (fuzz.ratio(event_type, \"Ausstellungen\") >80 or fuzz.ratio(event_type, \"Ausstellung\") >80):\n attr.append({\"key\": \"category\", \"value\": \"exhibition\", \"comp\": equalComp})\n message_text = \"Am {}, den {} finden folgende Ausstellungen statt\".format(date_object.getWeekDay(), format_date)\n \n # if event_type == 'Veranstaltungen':\n # event_type = 'event'\n if (fuzz.ratio(event_type, \"Workshop\") >80 or fuzz.ratio(event_type, \"Workshops\") >80):\n attr.append({\"key\": \"subcategory\", \"value\": \"workshop\", \"comp\": equalComp})\n message_text = \"Am {}, den {} finden folgende Workshops statt\".format(date_object.getWeekDay(), format_date)\n # event_type = 'event'\n logger.debug(event_type)\n logger.debug(fuzz.ratio(event_type, \"workshop\"))\n # event_type = 'event'\n # query_result = query_entities(tracker=tracker, entity_type_param=event_type, attr=attr)\n query_result = query_entities(tracker=tracker, entity_type_param=\"event\", attr=attr)\n\n end_date = date_value + timedelta(days=7)\n\n if query_result.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return []\n\n elif not query_result.result or len(query_result.result) <= 0:\n dispatcher.utter_message(\n \"Ich habe keine passende Info gefunden\"\n )\n return []\n else:\n dispatcher.utter_message(text=\"Unser Programm ist vielfältig: Kunstausstellungen, Konzerte, Vorträge und vieles mehr! Bitte beachte, dass unser Museum Montags und Dienstags in der Regel geschlossen ist.\")\n dispatcher.utter_message(text=message_text)\n\n entities = query_result.result\n filtered_entites = []\n for entity in entities:\n if (datetime.strptime(entity[\"date\"][\"end\"][:10], '%Y-%m-%d').date() >= date_value and\n datetime.strptime(entity[\"date\"][\"start\"][:10], '%Y-%m-%d').date() <= end_date):\n filtered_entites.append(entity)\n\n message_to_send = {\n \"type\": query_result.entity_type,\n \"items\": filtered_entites,\n }\n\n dispatcher.utter_message(json_message=message_to_send)\n slots = [*query_result.slots , SlotSet(\"date\", format_date)]\n return slots\n\nclass ProgrammWochenende(Action):\n \"\"\"Action for querying events or workshops based on the upcoming weekend\n Returns a list of items.\n \"\"\"\n\n def name(self):\n return \"action_query_programm_wochenende\"\n\n def run(self, dispatcher, tracker, domain):\n\n date_object = Zdate(value=date.today())\n date_value = date_object.value\n format_date = date_object.toGermanDate()\n\n \n attr = [{\"key\": \"date\", \"value\": date_object.toEnglishDate(), \"comp\": compareDates}]\n\n query_result = query_entities(tracker=tracker, entity_type_param=\"event\", attr=attr)\n\n weekno = datetime.today().weekday()\n start_date = None\n\n if(weekno == 5):\n start_date = date_value\n else:\n diff = 5 - weekno \n start_date = date_value + timedelta(days=diff)\n\n end_date = start_date + timedelta(days=1)\n \n\n event_type = tracker.get_slot(\"entity_type\") or None\n if fuzz.ratio(event_type, \"workshop\") >80:\n attr.append({\"key\": \"subcategory\", \"value\": \"workshop\", \"comp\": equalComp})\n message_text = \"Am Wochenende, {} - {} finden folgende Workshops statt\".format(start_date, end_date)\n\n message_text = \"Hier das Programm für dieses Wochenende, {} - {}.\".format(datetime.strftime(start_date, '%d.%m.%Y'), datetime.strftime(end_date, '%d.%m.%Y'))\n\n if query_result.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return []\n\n elif not query_result.result or len(query_result.result) <= 0:\n dispatcher.utter_message(\n \"Ich habe keine passende Info gefunden\"\n )\n return []\n else:\n dispatcher.utter_message(text=\"Unser Programm ist vielfältig: Kunstausstellungen, Konzerte, Vorträge und vieles mehr! Bitte beachte, dass unser Museum Montags und Dienstags in der Regel geschlossen ist.\")\n dispatcher.utter_message(text=message_text)\n\n entities = query_result.result\n filtered_entites = []\n for entity in entities:\n if (datetime.strptime(entity[\"date\"][\"end\"][:10], '%Y-%m-%d').date() >= start_date and\n datetime.strptime(entity[\"date\"][\"start\"][:10], '%Y-%m-%d').date() <= end_date):\n filtered_entites.append(entity)\n\n message_to_send = {\n \"type\": query_result.entity_type,\n \"items\": filtered_entites,\n }\n\n dispatcher.utter_message(json_message=message_to_send)\n slots = [*query_result.slots , SlotSet(\"date\", format_date)]\n return slots\n\nclass ActionQueryAttribute(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_query_attribute\"\n\n def run(self, dispatcher, tracker, domain):\n graph_database = db()\n entity_type = get_entity_key(graph_database, tracker)\n attribute = None\n if entity_type is None:\n return []\n\n # get name of entity and attribute of interest\n name = get_entity_name(tracker, entity_type)\n # date = get_attribute(tracker, attribute)\n\n attribute = get_attribute(graph_database, tracker)\n\n if name is None or attribute is None:\n return DbQueryResult(error=\"Slot entity type was None\", result=None)\n else:\n slots = [SlotSet(\"mention\", None)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n # query knowledge base\n key_attribute = schema[entity_type][\"key\"]\n value = graph_database.get_attribute_of(\n entity_type, key_attribute, name, attribute\n )\n\n slots = [SlotSet(\"mention\", None), SlotSet(\n entity_type, name), SlotSet(\"eintritt\", value)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n\n\nclass ActionQueryEventDate (Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_query_event_date\"\n\n def run(self, dispatcher, tracker, domain):\n\n # query_result = query_attribute(tracker=tracker, attr=\"date\")\n#______________________________________________________________________________________\n#______________________________________________________________________________________\n#______________________________________________________________________________________\n event_title = tracker.get_slot(\"event\") or tracker.get_slot(\"PER\")\n attr = [{\"key\": \"title\", \"value\": event_title, \"comp\": equalComp}]\n query_result = query_entities(tracker=tracker, attr=attr, entity_type_param=\"event\")\n\n start_value = None\n end_value = None\n\n if query_result.error is not None:\n slots = [SlotSet(\"mention\", None)]\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return slots\n\n if (query_result.result is None or len(query_result.result) <= 0):\n dispatcher.utter_message(\"Nichts gefunden\")\n return []\n\n value = query_result.result\n start_value = value[0][\"date\"][\"start\"]\n start_date = string_to_german_date(start_value)\n end_value = value[0][\"date\"][\"end\"]\n end_date = string_to_german_date(end_value)\n\n message_to_send = {\n \"links\": [{\n \"payload\": value[0][\"link\"],\n \"title\": value[0][\"title\"]}],\n \"text\": \"\"\n }\n\n dispatcher.utter_message(text= f\"\"\"Hier findest du einige Informationen zum Event: \\n{event_title}. Geöffnet von {start_date} bis {end_date}\"\"\",json_message=message_to_send)\n \n slots = [SlotSet(\"date_start\", start_value),\n SlotSet(\"date_end\", end_value)]\n return slots\n\nclass SendCustomEmail (Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_send_custom_email\"\n\n def run(self, dispatcher, tracker, domain):\n\n prepare\n return slots\n\n\nclass ActionResolveEntity(Action):\n \"\"\"Action for resolving a mention.\"\"\"\n\n def name(self):\n return \"action_resolve_entity\"\n\n def run(self, dispatcher, tracker, domain):\n entity_type = tracker.get_slot(\"entity_type\")\n listed_items = tracker.get_slot(\"listed_items\")\n\n if entity_type is None:\n dispatcher.utter_template(\"utter_rephrase\", tracker)\n return []\n\n # Check if entity was mentioned as 'first', 'second', etc.\n mention = tracker.get_slot(\"mention\")\n if mention is not None:\n value = resolve_mention(tracker)\n if value is not None:\n return [SlotSet(entity_type, value), SlotSet(\"mention\", None)]\n\n # Check if NER recognized entity directly\n # (e.g. bank name was mentioned and recognized as 'bank')\n value = tracker.get_slot(entity_type)\n if value is not None and value in listed_items:\n return [SlotSet(entity_type, value), SlotSet(\"mention\", None)]\n\n dispatcher.utter_template(\"utter_rephrase\", tracker)\n return [SlotSet(entity_type, None), SlotSet(\"mention\", None)]\n\n\nclass EntranceFee (Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_query_entrance\"\n\n def run(self, dispatcher, tracker, domain):\n\n query_result = query_attribute(tracker=tracker, attr=\"entrancefee\")\n entrance = query_result.result\n\n general_message = {\n \"links\": [{\"title\": \"Eintrittspreise\",\"payload\": \"https://zkm.de/de/ausstellungen-veranstaltungen/eintrittspreise\"}],\n \"text\": \"\"\"Eine Übersicht aller Ticketpreise findest Du auf unserer Homepage.\"\"\"\n }\n \n if query_result.error is not None:\n dispatcher.utter_message(json_message= general_message)\n\n return query_result.slots\n\n if (query_result.result is None or len(query_result.result) <= 0):\n dispatcher.utter_message(\"Preise nicht gefunden\")\n dispatcher.utter_message(json_message= general_message)\n return []\n \n value = query_result.result\n str_value = \"\".join(value)\n\n message_to_send = {\n \"text\": \"\"\"Der Eintritt für die Ausstellung {} beträgt: \n {}\"\"\".format(query_result.entity_name,str_value)\n }\n\n dispatcher.utter_message(json_message= message_to_send)\n dispatcher.utter_message(json_message= general_message)\n \n\n slots = query_result.slots + [SlotSet(\"eintritt\", str_value)]\n return slots\n\n\n\nclass ActionUserValidation(FormAction):\n\n def name(self) -> Text:\n\n return \"user_form\"\n\n @staticmethod\n def required_slots(tracker: Tracker) -> List[Text]:\n \"\"\"A list of required slots that the form has to fill\"\"\"\n\n return [\"num_people\", \"email\", \"phone_number\", \"visit_time\", \"subject\", \"kind_of_event\"]\n\n def submit(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict]:\n\n dispatcher.utter_message(template=\"utter_submit\")\n return []\n\n\nclass ActionNameValidation(FormAction):\n\n def name(self) -> Text:\n\n\n return \"valid_name_form\"\n\n @staticmethod\n def required_slots(tracker: Tracker) -> List[Text]:\n \"\"\"A list of required slots that the form has to fill\"\"\"\n\n return[\"name\"]\n\n def slot_mappings(self) -> Dict[Text, Union[Dict, List[Dict]]]:\n \"\"\"A dictionary to map required slots to\n - an extracted entity\n - intent: value pairs\n - a whole message\n or a list of them, where a first match will be picked\"\"\"\n\n return {\n \"name\": [\n self.from_entity(entity=\"PER\"),\n self.from_text(),\n ],\n }\n\n def submit(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict]:\n name = tracker.get_slot(\"name\")\n if name is None:\n dispatcher.utter_message(template=\"utter_ask_name\")\n else:\n dispatcher.utter_message(f\"Herzlich willkommen, \" + name + \"!\" )\n dispatcher.utter_message(json_message={\n \"name_provided\": True\n })\n return []\n\n\nclass ActionSlotValues(Action):\n \"\"\"Action for resolving a mention.\"\"\"\n\n def name(self):\n return \"action_slot_values\"\n\n def run(self, dispatcher, tracker, domain):\n email_info = prepareEmailInfo(tracker)\n\n dispatcher.utter_message(json_message={\n \"allowTyping\": True,\n \"inputContact\": True,\n \"text\": email_info.message_text\n })\n return []\n\n\nclass ActionSlotValuesGuides(Action):\n \"\"\"Action for resolving a mention.\"\"\"\n\n def name(self):\n return \"action_slot_values_guides\"\n\n def run(self, dispatcher, tracker, domain):\n email_info = prepareEmailInfo(tracker)\n\n dispatcher.utter_message(json_message={\n \"allowTyping\": True,\n \"inputContact\": True,\n \"isGuideContext\": True,\n \"text\": email_info.message_text\n })\n return []\n\nclass ActionShopOpening(Action):\n \"\"\"Action for showing opening times\"\"\"\n\n def name(self):\n return \"action_shop_opening\"\n\n def run(self, dispatcher, tracker, domain):\n URL = \"https://data.zkm.de/api/open/shop\"\n start_date = date.today()\n end_date = start_date + timedelta(days=10)\n PARAMS = {'date': start_date, 'date2': end_date}\n r = requests.get(url=URL, params=PARAMS)\n if (r.status_code != 200):\n dispatcher.utter_message(text=\"Es ist ein Fehler aufgetreten\")\n return []\n data = r.json()\n print(data)\n message = [None] * 11\n for i in range(len(data)):\n if data[i]['open'] == True:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': ' + data[i]['start'] + ' - ' + data[i]['end'] + ' Uhr'\n else:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': Geschlossen'\n print(message[i])\n dispatcher.utter_message(json_message={\n \"allowTyping\": True,\n \"text\": '\\n'.join(message)\n })\n # TO DO: Array als Liste / Text anzeigen, 1 funktion je endpoint (exhibition, library, infopoint)\n return []\n\nclass ActionUhrzeit(Action):\n \"\"\"Action for showing opening times\"\"\"\n\n def name(self):\n return \"action_uhrzeit\"\n\n def run(self, dispatcher, tracker, domain):\n uhrzeit = datetime.strptime(str(datetime.now()), \"%Y-%m-%d %H:%M:%S.%f\").strftime(\"%H:%M\")\n\n print(\"Uhrzeit: \" + uhrzeit)\n slots = [SlotSet(\"uhrzeit\", uhrzeit)]\n return slots\n\n\nclass ActionInfoPointOpening(Action):\n \"\"\"Action for showing opening times\"\"\"\n\n def name(self):\n return \"action_infopoint_opening\"\n\n def run(self, dispatcher, tracker, domain):\n URL = \"https://data.zkm.de/api/open/infopoint\"\n start_date = date.today()\n end_date = start_date + timedelta(days=10)\n PARAMS = {'date': start_date, 'date2': end_date}\n r = requests.get(url=URL, params=PARAMS)\n data = r.json()\n print(data)\n if (r.status_code != 200):\n dispatcher.utter_message(text=\"Es ist ein Fehler aufgetreten\")\n return []\n message = [None] * 11\n for i in range(len(data)):\n if data[i]['open'] == True:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': ' + \\\n data[i]['start'] + ' - ' + data[i]['end'] + ' Uhr'\n else:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': Geschlossen'\n print(message[i])\n dispatcher.utter_message(json_message={\n \"allowTyping\": True,\n \"text\": '\\n'.join(message)\n })\n # TO DO: Array als Liste / Text anzeigen, 1 funktion je endpoint (exhibition, library, infopoint)\n return []\n\n\nclass ActionLibaryOpening(Action):\n \"\"\"Action for showing opening times\"\"\"\n\n def name(self):\n return \"action_library_opening\"\n\n def run(self, dispatcher, tracker, domain):\n URL = \"https://data.zkm.de/api/open/library\"\n start_date = date.today()\n end_date = start_date + timedelta(days=10)\n PARAMS = {'date': start_date, 'date2': end_date}\n r = requests.get(url=URL, params=PARAMS)\n data = r.json()\n print(data)\n message = [None] * 11\n for i in range(len(data)):\n if data[i]['open'] == True:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': ' + \\\n data[i]['start'] + ' - ' + data[i]['end'] + ' Uhr'\n else:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': Geschlossen'\n print(message[i])\n dispatcher.utter_message(json_message={\n \"allowTyping\": True,\n \"text\": '\\n'.join(message)\n })\n # TO DO: Array als Liste / Text anzeigen, 1 funktion je endpoint (exhibition, library, infopoint)\n return []\n\n\nclass ActionExhibitionOpening(Action):\n \"\"\"Action for showing opening times\"\"\"\n\n def name(self):\n return \"action_exhibition_opening\"\n\n def run(self, dispatcher, tracker, domain):\n URL = \"https://data.zkm.de/api/open/exhibition\"\n start_date = date.today()\n end_date = start_date + timedelta(days=10)\n PARAMS = {'date': start_date, 'date2': end_date}\n r = requests.get(url=URL, params=PARAMS)\n data = r.json()\n print(data)\n message = [None] * 11\n for i in range(len(data)):\n if data[i]['open'] == True:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': ' + \\\n data[i]['start'] + ' - ' + data[i]['end'] + ' Uhr'\n else:\n message[i] = datetime.strptime(data[i]['date'], '%Y-%m-%d').strftime(\"%a, %d.%m.%Y\") + ': Geschlossen'\n print(message[i])\n dispatcher.utter_message(json_message={\n \"allowTyping\": True,\n \"text\": '\\n'.join(message)\n })\n # TO DO: Array als Liste / Text anzeigen, 1 funktion je endpoint (exhibition, library, infopoint)\n return []\n\n # Email senden\n\n\nclass Sendmail(Action):\n\n def name(self) -> Text:\n return \"action_send_mail\"\n\n def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n email_content = tracker.get_slot(\"email_content\")\n email_user = tracker.get_slot(\"email\")\n email_info = prepareEmailInfo(tracker)\n guide = tracker.get_slot(\"guide_title\")\n logger.debug(\"guide sending email\")\n logger.debug(guide)\n logger.debug(email_content)\n logger.debug(email_user)\n logger.debug(email_info)\n\n email_context = tracker.get_slot(\"current_context\")\n html_message = build_html_message(subject=email_info.subject, email=email_user, message=email_content, context=email_context)\n # html_message = email_info.message_html\n try:\n # set up the SMTP server\n email_server = smtplib.SMTP_SSL(host='mail.zkm.de', port=465)\n email_server.set_debuglevel(1)\n email_server.login(mailserver_user, mailserver_password)\n\n sender_email = \"data@zkm.de\"\n\n mail_to_send = MIMEMultipart('alternative')\n mail_to_send[\"subject\"] = email_info.subject\n mail_to_send[\"from\"] = sender_email\n # mail_to_send[\"to\"] = \"e.ersada@integr8.com\"\n mail_to_send[\"to\"] = email_info.email_receiver\n\n email_text = MIMEText(email_info.message_text, 'plain')\n email = MIMEText(html_message, 'html')\n \n mail_to_send.attach(email_text)\n mail_to_send.attach(email)\n\n email_server.sendmail(mail_to_send[\"from\"], mail_to_send[\"to\"], mail_to_send.as_string())\n\n email_server.quit()\n\n dispatcher.utter_message(text=\"Deine E-Mail wurde erfolgreich gesendet.\")\n if(guide == 'labyrinth'):\n dispatcher.utter_message(buttons = [\n {\"payload\":\"/weiter_labyrinth\", \"title\":\"Weiter\"}\n ])\n elif (guide == 'wipe'):\n dispatcher.utter_message(buttons = [\n {\"payload\":\"/weiter_wipe\", \"title\":\"Weiter\"}\n ])\n elif (guide == 'texte'):\n dispatcher.utter_message(buttons = [\n {\"payload\":\"/weiter_texte\", \"title\":\"Weiter\"}\n ])\n elif (guide == 'fuehrung'):\n dispatcher.utter_message(buttons = [\n {\"payload\":\"/weiter_fuehrung\", \"title\":\"Weiter\"}\n ])\n # elif (guide == 'direkt'):\n # dispatcher.utter_message(buttons = [\n # {\"payload\":\"/weiter_direkt\", \"title\":\"Weiter\"}\n # ])\n else:\n dispatcher.utter_message(buttons = [\n {\"payload\":\"/weiter_direkt\", \"title\":\"Weiter\"}\n # {\"payload\":\"/weiter_hilfreich\", \"title\":\"Weiter\"}\n ])\n\n return []\n except Exception as err:\n print(err)\n dispatcher.utter_message(text=\"Die E-mail konnte leider nicht gesendet werden.\")\n return []\n\nclass GuideSendMail(Action):\n\n def name(self) -> Text:\n return \"action_guide_send_mail\"\n\n def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n logger.debug(\"inside action_send_mail_guide\")\n email_content_test = tracker.get_slot(\"email_content\")\n email_user_test = tracker.get_slot(\"email\")\n email_info_test = prepareEmailInfo(tracker)\n\n html_message = build_html_message(subject=email_info_test.subject, email=email_user_test, message=email_content_test)\n\n try:\n # set up the SMTP server\n email_server = smtplib.SMTP_SSL(host='mail.zkm.de', port=465)\n email_server.set_debuglevel(1)\n email_server.login(mailserver_user, mailserver_password)\n\n sender_email = \"data@zkm.de\"\n\n mail_to_send = MIMEMultipart('alternative')\n mail_to_send[\"subject\"] = email_info_test.subject\n mail_to_send[\"from\"] = sender_email\n # mail_to_send[\"to\"] = \"e.ersada@integr8.com\"\n mail_to_send[\"to\"] = email_info_test.email_receiver\n\n email_text = MIMEText(email_info_test.message_text, 'plain')\n email = MIMEText(html_message, 'html')\n \n mail_to_send.attach(email_text)\n mail_to_send.attach(email)\n\n email_server.sendmail(mail_to_send[\"from\"], mail_to_send[\"to\"], mail_to_send.as_string())\n\n email_server.quit()\n\n dispatcher.utter_message(text=\"Deine E-Mail wurde erfolgreich gesendet.\")\n \n return []\n except Exception as err:\n print(err)\n dispatcher.utter_message(text=\"Die E-mail konnte leider nicht gesendet werden.\")\n return []\n \n\n\nclass EventCategoryExibition(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_category_exibition\"\n\n def run(self, dispatcher, tracker, domain):\n\n query_result = query_attribute(tracker=tracker, attr=\"category\")\n\n \n if query_result.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n \n\n if (query_result.result is None or len(query_result.result) <= 0):\n dispatcher.utter_message(\"Nichts gefunden\")\n return []\n\n value = query_result.result\n \n dispatcher.utter_message(\"TEST\")\n\n slots = query_result.slots + [SlotSet(\"category\", value)]\n return slots \n\nclass EventCategoryLecture(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_category_lecture\"\n\n def run(self, dispatcher, tracker, domain):\n graph_database = db()\n\n last_intent = tracker.latest_message['intent']['name']\n\n # first need to know the entity type we are looking for\n entity_type = get_entity_key(graph_database, tracker)\n attribute = None\n if entity_type is None:\n return []\n\n # get name of entity and attribute of interest\n name = get_entity_name(tracker, entity_type)\n # date = get_attribute(tracker, attribute)\n\n if (last_intent == \"kategorie_vortrag\"):\n attribute = \"category\"\n\n # attribute = get_attribute(graph_database, tracker)\n # logger.debug(\n # \"name of entity {}, attribute requested {}\".format(name, attribute))\n\n if name is None or attribute is None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n # dispatcher.utter_template(\"utter_rephrase\", tracker)\n slots = [SlotSet(\"mention\", None)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n # query knowledge base\n key_attribute = schema[entity_type][\"key\"]\n value = graph_database.get_attribute_of(\n entity_type, key_attribute, name, attribute\n )\n\n if value is not None and date is not None:\n dispatcher.utter_message(\n f\"Am Tag {date} finden folgende Vorträge statt: {value}\")\n else:\n dispatcher.utter_message(\n f\"Tut mir Leid! An diesem Tag gibt es leider keine {value}\"\n )\n\n slots = [SlotSet(\"mention\", None), SlotSet(\n entity_type, name), SlotSet(\"category\", value)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n\n\nclass EventCategoryConcert(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_category_concert\"\n\n def run(self, dispatcher, tracker, domain):\n graph_database = db()\n\n last_intent = tracker.latest_message['intent']['name']\n\n # first need to know the entity type we are looking for\n entity_type = get_entity_key(graph_database, tracker)\n attribute = None\n if entity_type is None:\n return []\n\n # get name of entity and attribute of interest\n name = get_entity_name(tracker, entity_type)\n # date = get_attribute(tracker, attribute)\n\n if (last_intent == \"kategorie_konzert\"):\n attribute = \"subcategory\"\n\n # attribute = get_attribute(graph_database, tracker)\n # logger.debug(\n # \"name of entity {}, attribute requested {}\".format(name, attribute))\n\n if name is None or attribute is None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n # dispatcher.utter_template(\"utter_rephrase\", tracker)\n slots = [SlotSet(\"mention\", None)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n # query knowledge base\n key_attribute = schema[entity_type][\"key\"]\n value = graph_database.get_attribute_of(\n entity_type, key_attribute, name, attribute\n )\n\n if value is not None and date is not None:\n dispatcher.utter_message(f\"Am Tag {date} finden Konzerte statt\")\n else:\n dispatcher.utter_message(\n f\"Tut mir Leid! An diesem Tag gibt es leider keine Konzerte\"\n )\n\n slots = [SlotSet(\"mention\", None), SlotSet(\n entity_type, name), SlotSet(\"category\", value)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n\n\nclass EventCategoryWorkshop(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_category_workshop\"\n\n def run(self, dispatcher, tracker, domain):\n graph_database = db()\n\n last_intent = tracker.latest_message['intent']['name']\n\n # first need to know the entity type we are looking for\n entity_type = get_entity_key(graph_database, tracker)\n attribute = None\n if entity_type is None:\n return []\n\n # get name of entity and attribute of interest\n name = get_entity_name(tracker, entity_type)\n # date = get_attribute(tracker, attribute)\n\n if (last_intent == \"kategorie_workshop\"):\n attribute = \"category\"\n\n # attribute = get_attribute(graph_database, tracker)\n # logger.debug(\n # \"name of entity {}, attribute requested {}\".format(name, attribute))\n\n if name is None or attribute is None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n # dispatcher.utter_template(\"utter_rephrase\", tracker)\n slots = [SlotSet(\"mention\", None)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n # query knowledge base\n key_attribute = schema[entity_type][\"key\"]\n value = graph_database.get_attribute_of(\n entity_type, key_attribute, name, attribute\n )\n\n if value is not None and date is not None:\n dispatcher.utter_message(f\"Am Tag {date} finden Workshops statt\")\n else:\n dispatcher.utter_message(\n f\"Tut mir Leid! An diesem Tag gibt es leider keine Workshops\"\n )\n\n slots = [SlotSet(\"mention\", None), SlotSet(\n entity_type, name), SlotSet(\"category\", value)]\n reset_attribute_slots(slots, entity_type, tracker)\n return slots\n\nclass ActionWerkeKuenstler(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_werke_kuenstler\"\n\n def run(self, dispatcher, tracker, domain):\n artist_name = tracker.get_slot(\"person\") or tracker.get_slot(\"PER\")\n attr = [{\"key\": \"creator_name\", \"value\": artist_name, \"comp\": equalComp}]\n query_result = query_entities(tracker=tracker, attr=attr, entity_type_param=\"creation\")\n\n if query_result.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n \n\n if (query_result.result is None or len(query_result.result) <= 0):\n dispatcher.utter_message(\"Leider konnte ich von {} keine Werke in der Sammlung finden.\".format(artist_name))\n return []\n\n entities = query_result.result\n\n message_to_send = {\n \"type\": query_result.entity_type,\n \"items\": entities,\n }\n dispatcher.utter_message(text=\"Von {} haben wir folgende Werke in der Sammlung.\".format(artist_name))\n dispatcher.utter_message(json_message=message_to_send)\n slots = reset_attribute_slots(query_result.slots, query_result.entity_type, tracker)\n return slots\n\nclass ActionAustellungKatalog(Action):\n \"\"\"Action for querying a specific attribute of an entity.\"\"\"\n\n def name(self):\n return \"action_ausstellung_katalog\"\n\n def run(self, dispatcher, tracker, domain):\n publication_name = tracker.get_slot(\"event\")\n # publication_name = \"MultiMediale\" #tracker.get_slot(\"event\")\n attr = [{\"key\": \"title\", \"value\": publication_name, \"comp\": equalComp}]\n query_result = query_entities(tracker=tracker, attr=attr, entity_type_param=\"publication\")\n\n if query_result.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n \n if (query_result.result is None or len(query_result.result) <= 0):\n message_to_send = {\n \"text\": \"Leider gibt es zu dieser Ausstellung keine Publikation. Soll ich bei meinen Kolleginnen nachfragen, ob sie gerade noch daran arbeiten?\",\n \"buttons\": [{\"title\": \"Ja\", \"payload\": \"/affirm\"},{\"title\": \"Nein\", \"payload\": \"/deny\"}]\n }\n dispatcher.utter_message(json_message=message_to_send)\n return [SlotSet(\"email_type\", \"ask_for_publication\"), SlotSet(\"publication\", publication_name)]\n\n publication = query_result.result[0]\n\n message_to_send = {\n \"links\": [{\n \"payload\": \"{}\".format(publication[\"shop_link\"]),\n \"title\": publication_name\n }],\n \"text\": \"Ja, es gibt eine Publikation. Unter dem folgenden Link findest du weitere Informationen.\"\n }\n\n dispatcher.utter_message(json_message=message_to_send)\n\n ask_want_to_buy_message = {\n \"text\": \"Du kannst die Publikation gerne direkt per Mail bei uns bestellen. \",\n \"buttons\": [{\"title\": \"Publikation bestellen\", \"payload\": \"/affirm\"},{\"title\": \"Nein, danke\", \"payload\": \"/deny\"}]\n }\n\n dispatcher.utter_message(json_message=ask_want_to_buy_message)\n return [*query_result.slots, SlotSet(\"email_type\", \"buy_publication\"), SlotSet(\"publication\", publication_name)]\n\nclass ActionWerke(Action):\n \"\"\" Action werke\"\"\"\n\n def name(self):\n return \"action_werke\"\n\n def run(self, dispatcher, tracker, domain):\n event_name = tracker.get_slot(\"event\") or \"\"\n\n if \"Writing the History\" in event_name or \"Gameplay\" in event_name:\n message_to_send = {\n \"links\": [{\"payload\": \"https://zkm.de/de/sammlung-archive/sammlung/die-sammlung-des-zkm\", \"title\": \"Werke des ZKMs\"}],\n \"text\": \"In unserer Computerspiele-Ausstellung {} findest Du folgende Werke\".format(event_name),\n \"img\": \"https://zkm.de/media/bild/2018_zkm_gameplay_ausstellungsansicht_003.jpg\"\n }\n dispatcher.utter_message(json_message=message_to_send)\n return []\n\n message_to_send = {\n \"links\": [{\"payload\": \"https://zkm.de/de/sammlung-archive/sammlung/die-sammlung-des-zkm\", \"title\": \"Werke des ZKMs\"}],\n \"text\": \"Das weiß ich leider nicht genau. Möchtest Du selbst auf der Website zur Ausstellung nachsehen?\"\n }\n\n dispatcher.utter_message(json_message=message_to_send)\n return []\n\nclass GetGuideInfo(Action):\n \"\"\"GetGuideInfo\"\"\"\n\n def name(self):\n return \"action_get_guide_info\"\n\n def run(self, dispatcher, tracker, domain):\n guide = tracker.get_slot(\"guide_title\")\n dispatcher.utter_message(f\"Test Guide {guide}\")\n # dispatcher.utter_message(json_message=[{\n # \"allowTyping\": False,\n # \"text\": guide_name\n # }])\n return []\n\n\nclass ActionTerminWorkshops(Action):\n \"\"\"\n ActionTerminWorkshops will perform the following:\n check if a name was provided, then query the date of a workshop with it.\n check if a date was provided, then query a list of workshops base on it.\n if no name or date provided, then query a list of workshops based on todays date\n \"\"\"\n\n def name(self):\n return \"action_termin_workshops\"\n\n def run(self, dispatcher, tracker, domain):\n\n query_result = query_attribute(tracker=tracker, attr=\"date\")\n\n if query_result.error is not None or query_result.result is None or len(query_result.result) <= 0:\n\n date_object = Zdate(value=date.today())\n date_value = date_object.value\n end_date = date_value + timedelta(days=7)\n format_date = date_object.toGermanDate()\n\n attr = [{\"key\": \"date\", \"value\": date_object.toEnglishDate(), \"comp\": compareDates},\n {\"key\": \"subcategory\", \"value\": \"workshop\", \"comp\": equalComp}]\n query_workshop_list = query_entities(tracker=tracker, entity_type_param=\"event\", attr=attr)\n\n if query_workshop_list.error is not None:\n dispatcher.utter_message(\"Das habe ich nicht verstanden\")\n return []\n\n if not query_workshop_list.result or len(query_workshop_list.result) <= 0:\n dispatcher.utter_message(\n \"Am %s, %s bieten wir leider keine workshops an\" % (date_object.getWeekDay(), format_date)\n )\n\n entities = query_workshop_list.result\n filtered_entites = []\n for entity in entities:\n if (datetime.strptime(entity[\"date\"][\"end\"][:10], '%Y-%m-%d').date() >= date_value and datetime.strptime(entity[\"date\"][\"start\"][:10], '%Y-%m-%d').date() <= end_date):\n filtered_entites.append(entity)\n\n message_to_send = {\n \"type\": query_workshop_list.entity_type,\n \"items\": filtered_entites,\n }\n\n dispatcher.utter_message(json_message=message_to_send)\n slots = [*query_workshop_list.slots , SlotSet(\"date\", format_date)]\n slots = reset_attribute_slots(slots, query_workshop_list.entity_type, tracker)\n return slots\n\n value = query_result.result\n start_value = value[0][\"start\"]\n end_value = value[0][\"end\"]\n\n dispatcher.utter_message(text=\"Der Workshop {} findet am {} bis {} statt. Weitere Informationen findest Du auf unserer Website.\".format(query_result.entity_name, start_value, end_value))\n \n slots = [SlotSet(\"date_start\", start_value), SlotSet(\"date_end\", end_value)]\n slots = reset_attribute_slots(slots, query_result.entity_type, tracker)\n return slots\n\n","repo_name":"leandergebhardti8/ZKM-Chatbot-for-BA","sub_path":"actions/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":70188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"10010759973","text":"# https://www.acmicpc.net/problem/2231\nimport sys\n\nsys.stdin = open(\"2231_분해합.txt\", \"r\")\nN = int(input())\nans = 0\nfor i in range(1, N+1):\n list_ = list(map(int, str(i))) # 1부터 입력받은 숫자까지 리스트로 변환\n # ex) N이 216이라면 (일의자리)[1]~[9]~(십의자리)[1,0]~[9,9]~(백의자리)[1,0,0]~[2,1,6]까지\n ans = i + sum(list_) # i도 1~입력받은숫자까지 순회하는데, \n if ans == N: # i + list_의 합이 = 입력받은 숫자(N)이라면\n print(i)\n break\n if i == N: # 생성자가 없다면\n print(0) ","repo_name":"JeongJinGan/TIL","sub_path":"Multicampus/0726/실습코드/2231_분해합.py","file_name":"2231_분해합.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"ko","doc_type":"code","stars":5,"dataset":"github-code","pt":"2"}
+{"seq_id":"27105806893","text":"import rhinoscriptsyntax as rs\n\nfrom compas.datastructures import Mesh\n\nfrom compas_rhino.artists.meshartist import MeshArtist\nfrom compas_rhino.helpers import mesh_from_guid \nfrom compas_rhino.helpers import mesh_select_edges\nfrom compas_rhino import get_line_coordinates\n\ndef get_parallel_edges(mesh, uv):\n # fist parallel edge is the one selected\n edges = [uv]\n\n u, v = uv\n # process from edge uv in both directions\n for a,b in [(u, v) ,(v, u)]:\n \n # search until a boundary or the starting edge is reached\n while True:\n # face on one side of the edge\n fkey = mesh.halfedge[a][b]\n \n # check for boundary\n if fkey is None:\n break\n \n # get all vertices of the face\n vertices = mesh.face_vertices(fkey)\n \n # for a quad mesh we know exactly the opposite face edge\n i = vertices.index(a)\n a = vertices[i - 1]\n b = vertices[i - 2]\n \n # check if the starting edge is reached\n if (a, b) in edges or (b, a) in edges:\n break\n\n edges.append((a, b))\n \n # check for the right uv tuples. Ordered as in mesh.edges()\n edgeset = set(list(mesh.edges()))\n return [(u, v) if (u, v) in edgeset else (v, u) for u, v in edges]\n\nif __name__ == '__main__':\n \n edge_crvs = rs.GetObjects(\"Select tessellation edges\", 4)\n lines = get_line_coordinates(edge_crvs)\n \n mesh = Mesh.from_lines(lines, delete_boundary_face=True)\n\n # draw edges for selection\n artist = MeshArtist(mesh, layer='joint_lines')\n artist.draw_edges()\n artist.redraw()\n \n # select edge\n rs.HideObjects(edge_crvs)\n edges = mesh_select_edges(mesh)\n rs.ShowObjects(edge_crvs)\n \n # clear edges\n artist.clear_edges()\n \n # find \"every second\" edge (joint lines)\n joint_lines = []\n for i, uv in enumerate(edges):\n para_edges = get_parallel_edges(mesh,uv)\n for u, v in para_edges[i%2::2]:\n joint_lines.append((u,v))\n \n # draw joint lines\n artist.draw_edges(keys=joint_lines, color=(255,0,0))\n artist.redraw()\n","repo_name":"compas-Workshops/AAG2018","sub_path":"src/day_2/day_2_03/01_tessellation_staggered.py","file_name":"01_tessellation_staggered.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"41431522949","text":"price_item1 = float(input('Enter price of item 1: '))\nquantity_item1 = int(input(\"Enter quantity of item 1: \"))\nprice_item2 = float(input('Enter price of item 2: '))\nquantity_item2 = int(input(\"Enter quantity of item 2: \"))\nprice_item3 = float(input('Enter price of item 3: '))\nquantity_item3 = int(input(\"Enter quantity of item 3: \"))\nprice_item4 = float(input('Enter price of item 4: '))\nquantity_item4 = int(input(\"Enter quantity of item 4: \"))\n\ntotal_item1 = price_item1*quantity_item1\ntotal_item2 = price_item2*quantity_item2\ntotal_item3 = price_item3*quantity_item3\ntotal_item4 = price_item4*quantity_item4\n\ntotal_price = total_item1+total_item2+total_item3+total_item4\n\nprint(\"Your total price is \", str(total_price))\n","repo_name":"ad1tyaSharma/Python","sub_path":"Udemy/miniStore.py","file_name":"miniStore.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"34419438168","text":"import os\nfrom .utils import checks\nfrom discord.ext import commands\nfrom cogs.utils.dataIO import dataIO\n\n\nclass InVoice:\n \"\"\"Gives a custom to anyone who enters a voice channel. THIS ROLE MUST EXIST AND THE BOT MUST HAVE THE RIGHTS TO CHANGE ROLES FOR IT TO WORK!\"\"\"\n def __init__(self, bot):\n self.bot = bot\n self.data = dataIO.load_json('data/invoice/settings.json')\n\n async def _save_data(self):\n dataIO.save_json('data/invoice/settings.json', self.data)\n\n async def _on_voice_state_update(self, before, after):\n try:\n server = after.server\n if server.id in self.data:\n server_role = self.data[server.id]['ROLE']\n if server_role:\n for role in server.roles:\n if role.name.lower() == server_role.lower():\n if role in after.roles and after.voice_channel is None:\n await self.bot.remove_roles(after, role)\n elif role not in before.roles and after.voice_channel:\n await self.bot.add_roles(after, role)\n except Exception as e:\n print('Houston, we have a problem: {}'.format(e))\n\n @commands.command(pass_context=True, name='invoicerole')\n @checks.mod_or_permissions(administrator=True)\n async def _invoicerole(self, context, role):\n \"\"\"Set a role\"\"\"\n server = context.message.server\n roles = [r.name.lower() for r in server.roles]\n if role.lower() in roles:\n if server.id not in self.data:\n self.data[server.id] = {}\n self.data[server.id]['ROLE'] = role\n await self._save_data()\n message = 'Role `{}` set for Invoice.'.format(role)\n else:\n message = 'Role `{}` does not exist on this server.'.format(role)\n await self.bot.say(message)\n\n\ndef check_folder():\n if not os.path.exists(\"data/invoice\"):\n print(\"Creating data/invoice folder...\")\n os.makedirs(\"data/invoice\")\n\n\ndef check_file():\n data = {}\n f = \"data/invoice/settings.json\"\n if not dataIO.is_valid_json(f):\n print(\"Creating default settings.json...\")\n dataIO.save_json(f, data)\n\n\ndef setup(bot):\n check_folder()\n check_file()\n n = InVoice(bot)\n bot.add_listener(n._on_voice_state_update, 'on_voice_state_update')\n bot.add_cog(n)\n","repo_name":"PaPiix/Tarik-Bot","sub_path":"invoice.py","file_name":"invoice.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"25612439945","text":"from __future__ import with_statement\n\nimport core.controllers.outputManager as om\n\n# options\nfrom core.data.options.option import option\nfrom core.data.options.optionList import optionList\n\nfrom core.controllers.basePlugin.baseAuditPlugin import baseAuditPlugin\nfrom core.data.fuzzer.fuzzer import createMutants, createRandAlpha\nfrom core.controllers.w3afException import w3afException\nimport core.data.constants.dbms as dbms\n\nimport core.data.kb.knowledgeBase as kb\nimport core.data.kb.vuln as vuln\nimport core.data.constants.severity as severity\n\nimport re\n\nfrom core.controllers.misc.levenshtein import relative_distance\n\n\n# global variable useful for distinguishing threads\nglobal_id = 0;\n\n# global variable useful for distinguishing different Levenshtein distance\nnum = 0\n\n\nclass drame(baseAuditPlugin):\n '''\n Find SQL injection bugs.\n @author: Thibault Desmoulins ( http://www.desmoulins-thibault.fr/ )\n '''\n\n def __init__(self):\n baseAuditPlugin.__init__(self)\n \n # Internal variables\n self._listeType = []\n self._compteur = 0\n \n # The level used in the process of clustering\n self._level = 10\n\n def audit(self, freq):\n '''\n Tester les vulnerabilites d'injection SQL d'une URL\n \n @param freq: Une URL a tester\n '''\n # New audit, everything following will have global_id for identifying\n global global_id\n global_id = global_id + 1\n\n # Recupere la page : OK\n oResponse = self._sendMutant( freq , analyze=False ).getBody()\n \n # Recupere un tableau de string a tester\n drame_str_reject = self.getDrameStrings(\"reject\")\n drame_str_error = self.getDrameStrings(\"error\")\n drame_str_injection = self.getDrameStrings(\"injection\")\n\n # REJECT URL\n mutantsReject = createMutants( freq , drame_str_reject, oResponse=oResponse )\n for mutant in mutantsReject:\n # Only spawn a thread if the mutant has a modified variable\n # that has no reported bugs in the kb\n if self._hasNoBug( 'drame' , 'drame', mutant.getURL() , mutant.getVar() ):\n # resultReject function will be the callback of this _sendMutant\n targs = (mutant,True,True,self._resultReject)\n self._tm.startFunction( target=self._sendMutant, args=targs, ownerObj=self )\n\n # ERROR URL\n mutantsError = createMutants( freq , drame_str_error, oResponse=oResponse )\n for mutant in mutantsError:\n if self._hasNoBug( 'drame' , 'drame', mutant.getURL() , mutant.getVar() ):\n # resultError function will be the callback of this _sendMutant\n targs = ( mutant, True, True, self._resultError )\n self._tm.startFunction( target=self._sendMutant, args=targs, ownerObj=self )\n\n # INJECTION URL\n mutantsInjection = createMutants( freq , drame_str_injection, oResponse=oResponse )\n for mutant in mutantsInjection:\n if self._hasNoBug( 'drame' , 'drame', mutant.getURL() , mutant.getVar() ):\n # resultInjection function will be the callback of this _sendMutant\n targs = (mutant,True,True,self._resultInjection)\n self._tm.startFunction( target=self._sendMutant, args=targs, ownerObj=self )\n\n self._tm.join( self )\n\n\n def extractType(self, number):\n listeType = []\n for t in self._listeType:\n if t.getIdRequest() == number:\n listeType.append(t)\n return listeType\n \n \n def getLD(self, A, B):\n global num\n num = num + 1\n \n result = int(relative_distance(A.getPage(), B.getPage())*100)\n print(\"\\n-->(\" + str(num) + \") \" + str(len(A.getPage())) + \" - \" + str(len(B.getPage())) + \" - \" + str(result) )\n \n return result\n\n def _resultReject(self, mutant, response):\n self._compteur+=1\n p = typeURL( global_id, self._compteur, response.getURI(), 'reject', response.getBody() )\n self._listeType.append( p )\n\n def _resultError(self, mutant, response):\n self._compteur+=1\n p = typeURL( global_id, self._compteur, response.getURI(), 'error', response.getBody() )\n self._listeType.append( p )\n\n def _resultInjection(self, mutant, response):\n self._compteur+=1\n p = typeURL( global_id, self._compteur, response.getURI(), 'injection', response.getBody() )\n self._listeType.append( p )\n\n\n def end(self):\n '''\n This method is called when the plugin wont be used anymore.\n '''\n if len(self._listeType):\n for i in range(global_id+1):\n liste = self.extractType(i)\n if len(liste):\n cl = HierarchicalClustering( liste, self.getLD )\n clusteredData = cl.getlevel( self._level )\n \n print('\\n\\n================')\n n = len(clusteredData)\n for j in range(n):\n print('\\n---------' + str(j+1) + '----------\\n')\n clusteredData[j].affiche()\n print('--------------------\\n')\n \n self._tm.join( self )\n self.printUniq( kb.kb.getData( 'drame', 'drame' ), 'VAR' )\n \n \n def getDrameStrings(self, kind):\n '''\n Gets a list of strings to test against the web app.\n \n @return: A list with all drame strings to test. Example: [ '\\'','\\'\\'']\n '''\n drame_strings = []\n\n if kind == \"reject\":\n drame_strings.append(createRandAlpha(5))\n #drame_strings.append(createRandAlpha(5))\n elif kind == \"error\":\n #drame_strings.append(\"d'z\\\"0\")\n drame_strings.append(\"' or '1'=PLOOP\")\n #drame_strings.append(\"#\")\n elif kind == \"injection\":\n drame_strings.append(\"' or '1'='1\")\n #drame_strings.append(\"' or '1'='1' #\")\n \n return drame_strings\n \n def getOptions(self):\n '''\n @return: A list of option objects for this plugin.\n '''\n d1 = 'The algorithm to use in the comparison of true and false response for blind sql.'\n h1 = 'The options are: \"stringEq\" and \"setIntersection\". '\n h1 += 'Read the long description for details.'\n o1 = option('Hierarchical clustering level', self._level, d1, 'integer', help=h1)\n \n ol = optionList()\n ol.add(o1)\n return ol\n\n def setOptions(self, optionsMap):\n '''\n This method sets all the options that are configured using the user interface \n generated by the framework using the result of getOptions().\n \n @parameter optionsMap: A dictionary with the options for the plugin.\n @return: No value is returned.\n ''' \n self._level = optionsMap['Hierarchical clustering level'].getValue()\n\n def getPluginDeps(self):\n '''\n @return: A list with the names of the plugins that should be runned before the\n current one.\n '''\n return []\n \n def getLongDesc(self):\n '''\n @return: A DETAILED description of the plugin functions and features.\n '''\n return '''\n This plugin finds SQL injections. To find this vulnerabilities the plugin sends the string d'z\"0 to every\n injection point, and searches for SQL errors in the response body.\n '''\n\n\nclass typeURL:\n def __init__(self, idRequest, compteur, uri, kind, page):\n self._idRequest = idRequest\n self._compteur = compteur\n self._uri = uri\n self._kind = kind\n self._page = page\n\n def getIdRequest(self):\n return self._idRequest\n\n def getPage(self):\n return self._page\n\n def getKind(self):\n return self._kind\n\n def getURI(self):\n return self._uri\n\n def getCompteur(self):\n return self._compteur\n\n def affiche(self):\n om.out.debug( '-->ID ' + str(self._idRequest) + ' - OBJET ' + str(self._compteur) + ' (' + self._kind + ') : ' + self._uri)\n\n\nclass Cluster:\n def __init__(self, item1, item2, distance):\n self._cluster = []\n self._distanceMin = distance\n self._distanceMax = distance\n \n self._cluster.append(item1)\n self._cluster.append(item2)\n\n def joinCluster(self, otherCluster):\n items = otherCluster.getItems()\n for item in items:\n self._cluster.append(item)\n \n self.setDistanceMin(otherCluster.getDistanceMin())\n self.setDistanceMax(otherCluster.getDistanceMax())\n\n def estProche(self, otherCluster, level):\n minDist = self._distanceMin - level\n maxDist = self._distanceMax + level\n oMin = otherCluster.getDistanceMin()\n oMax = otherCluster.getDistanceMax()\n if (minDist<=oMin and oMin<=maxDist) or (minDist<=oMax and oMax<=maxDist):\n return True\n else:\n return False\n\n def getDistanceMin(self):\n return self._distanceMin\n \n def getDistanceMax(self):\n return self._distanceMax\n \n def getItems(self):\n return self._cluster\n \n def setDistanceMin(self, newDist):\n if newDist < self._distanceMin:\n self._distanceMin = newDist\n\n def setDistanceMax(self, newDist):\n if newDist > self._distanceMax:\n self._distanceMax = newDist\n\n def affiche(self):\n for item in self._cluster:\n item.affiche()\n\n\nclass HierarchicalClustering:\n def __init__(self, liste, distFunction):\n self._liste = liste\n self._distFunction = distFunction\n\n def getlevel(self, level):\n clusters = self.getClusters()\n\n i = 0\n while i < len(clusters)-1:\n j = i + 1\n while j < len(clusters):\n if clusters[i].estProche(clusters[j], level):\n clusters[i].joinCluster(clusters[j])\n del clusters[j] # the next cluster is joined to that one, so we delete it\n else:\n j = j + 1\n i = i + 1\n\n return clusters\n\n def getClusters(self):\n clusters = []\n n = len(self._liste)\n i = 0\n while i < n-1:\n j = i + 1\n while j < n:\n dist = self._distFunction(self._liste[i], self._liste[j])\n clusters.append( Cluster(self._liste[i], self._liste[j], dist) )\n j = j+ 1\n i = i + 1\n return clusters\n","repo_name":"akiavara/DRAME","sub_path":"drame.py","file_name":"drame.py","file_ext":"py","file_size_in_byte":10672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"31772249521","text":"import os\nfrom src.config import load_yaml_settings\n\nAPP_PATH = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir)).replace('/src', '')\n\nAPP_RESOURCES_DATA_PATH = APP_PATH + '/data/resources/'\n\nLOG_PATH = os.path.join(APP_PATH, 'data/log')\n\nPLUGINS_PATH = os.path.join(APP_PATH, 'src/plugins')\nPLUGINS_CONTRIB_PATH = os.path.join(APP_PATH, 'contrib')\n\nCACHE_PATH = os.path.join(APP_PATH, 'data/cache')\n\nCACHE_WAVE_PATH = CACHE_PATH + '/wave'\n\nCACHE_WAVE_RECORDED = CACHE_WAVE_PATH + '/record_output.wav'\n\"\"\"\n常用的wave声音\n\"\"\"\nWAVE_DING = APP_PATH + '/src/components/snowboy/resources/ding.wav'\nWAVE_DONG = APP_PATH + '/src/components/snowboy/resources/dong.wav'\n\nHOTWORD_MODEL_PATH = APP_PATH + '/src/config/hotword_models'\n\n\ndef get_hotword_models():\n \"\"\"\n 读取当前有多少个可用模型，返回\n \"\"\"\n custom_dir = os.path.expanduser(load_yaml_settings()['custom']['hotwords'])\n file_list = []\n if os.path.exists(custom_dir):\n file_list = file_list + os.listdir(custom_dir)\n file_list = file_list + os.listdir(HOTWORD_MODEL_PATH)\n files = []\n for file in file_list:\n if file.endswith('umdl') or file.endswith('pmdl'): # 限制模型类型\n if os.path.isfile(HOTWORD_MODEL_PATH + '/' + file):\n files.append(HOTWORD_MODEL_PATH + '/' + file)\n if os.path.isfile(custom_dir + '/' + file):\n files.append(custom_dir + '/' + file)\n if len(files) == 0:\n raise Exception('你至少应该有一个热词模型，请参照README.md')\n return files\n\n\nHOTWORD_MODELS = get_hotword_models()\n","repo_name":"mozhou-tech/kim-voice-assistant","sub_path":"src/config/path.py","file_name":"path.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":78,"dataset":"github-code","pt":"2"}
+{"seq_id":"29683776463","text":"from __future__ import print_function\n\nimport os\nimport argparse\n\nimport torch\nimport numpy as np\nimport torch.nn as nn\nfrom torch.optim import Adam, lr_scheduler\n\nimport data\nimport utils\nimport metrics\nimport config_frequentist as cfg\nfrom models.NonBayesianModels.AlexNet import AlexNet\nfrom models.NonBayesianModels.LeNet import LeNet\nfrom models.NonBayesianModels.ThreeConvThreeFC import ThreeConvThreeFC\n\n# CUDA settings\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef getModel(net_type, inputs, outputs):\n if (net_type == 'lenet'):\n return LeNet(outputs, inputs)\n elif (net_type == 'alexnet'):\n return AlexNet(outputs, inputs)\n elif (net_type == '3conv3fc'):\n return ThreeConvThreeFC(outputs, inputs)\n else:\n raise ValueError('Network should be either [LeNet / AlexNet / 3Conv3FC')\n\n\ndef train_model(net, optimizer, criterion, train_loader):\n train_loss = 0.0\n net.train()\n accs = []\n for data, target in train_loader:\n data, target = data.to(device), target.to(device)\n optimizer.zero_grad()\n output = net(data)\n loss = criterion(output, target)\n loss.backward()\n optimizer.step()\n train_loss += loss.item()*data.size(0)\n accs.append(metrics.acc(output.detach(), target))\n return train_loss, np.mean(accs)\n\n\ndef validate_model(net, criterion, valid_loader):\n valid_loss = 0.0\n net.eval()\n accs = []\n for data, target in valid_loader:\n data, target = data.to(device), target.to(device)\n output = net(data)\n loss = criterion(output, target)\n valid_loss += loss.item()*data.size(0)\n accs.append(metrics.acc(output.detach(), target))\n return valid_loss, np.mean(accs)\n\n\ndef run(dataset, net_type):\n\n # Hyper Parameter settings\n n_epochs = cfg.n_epochs\n lr = cfg.lr\n num_workers = cfg.num_workers\n valid_size = cfg.valid_size\n batch_size = cfg.batch_size\n\n trainset, testset, inputs, outputs = data.getDataset(dataset)\n train_loader, valid_loader, test_loader = data.getDataloader(\n trainset, testset, valid_size, batch_size, num_workers)\n net = getModel(net_type, inputs, outputs).to(device)\n\n ckpt_dir = f'checkpoints/{dataset}/frequentist'\n ckpt_name = f'checkpoints/{dataset}/frequentist/model_{net_type}.pt'\n\n if not os.path.exists(ckpt_dir):\n os.makedirs(ckpt_dir, exist_ok=True)\n\n criterion = nn.CrossEntropyLoss()\n optimizer = Adam(net.parameters(), lr=lr)\n lr_sched = lr_scheduler.ReduceLROnPlateau(optimizer, patience=6, verbose=True)\n valid_loss_min = np.Inf\n for epoch in range(1, n_epochs+1):\n\n train_loss, train_acc = train_model(net, optimizer, criterion, train_loader)\n valid_loss, valid_acc = validate_model(net, criterion, valid_loader)\n lr_sched.step(valid_loss)\n\n train_loss = train_loss/len(train_loader.dataset)\n valid_loss = valid_loss/len(valid_loader.dataset)\n \n print('Epoch: {} \\tTraining Loss: {:.4f} \\tTraining Accuracy: {:.4f} \\tValidation Loss: {:.4f} \\tValidation Accuracy: {:.4f}'.format(\n epoch, train_loss, train_acc, valid_loss, valid_acc))\n \n # save model if validation loss has decreased\n if valid_loss <= valid_loss_min:\n print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(\n valid_loss_min, valid_loss))\n torch.save(net.state_dict(), ckpt_name)\n valid_loss_min = valid_loss\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description = \"PyTorch Frequentist Model Training\")\n parser.add_argument('--net_type', default='lenet', type=str, help='model')\n parser.add_argument('--dataset', default='MNIST', type=str, help='dataset = [MNIST/CIFAR10/CIFAR100]')\n args = parser.parse_args()\n\n run(args.dataset, args.net_type)\n","repo_name":"kumar-shridhar/PyTorch-BayesianCNN","sub_path":"main_frequentist.py","file_name":"main_frequentist.py","file_ext":"py","file_size_in_byte":3907,"program_lang":"python","lang":"en","doc_type":"code","stars":1293,"dataset":"github-code","pt":"2"}
+{"seq_id":"18735132060","text":"#!/bin/python3\n\nfrom aocd.models import Puzzle\n\npuzzle = Puzzle(year=2020, day=6);\ninput = puzzle.input_data\ngroups = [group.split(\"\\n\") for group in input.split(\"\\n\\n\")]\n\nans_a = 0\nfor group in groups:\n yes = set()\n for individual in group:\n for chr in individual:\n yes.add(chr)\n ans_a += len(yes)\nprint(f\"Answer A: {ans_a}\")\n\nans_b = 0\nfor group in groups:\n yes = {}\n for individual in group:\n for chr in individual:\n if chr.isalpha():\n if chr in yes:\n yes[chr] += 1\n else:\n yes[chr] = 1\n for k, v in yes.items():\n if v == len(group):\n ans_b += 1\nprint(f\"Answer B: {ans_b}\")\n","repo_name":"Quaqqer/aoc","sub_path":"2020/6/prg.py","file_name":"prg.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"33672591856","text":"\n\ndef coroutine(func):\n def wrap(*args, **kwargs):\n generator = func(*args, **kwargs)\n generator.send(None)\n return generator\n return wrap\n\n\ndef subgen():\n x = 'Ready to accept message'\n message = yield x\n print('Subgen recieved', message)\n\n\nclass StubException(Exception):\n pass\n\n\n@coroutine\ndef avg():\n count = 0\n summ = 0\n avg = None\n\n while True:\n try:\n x = yield avg\n except StopIteration:\n print('done!')\n break\n except StubException:\n print('..................')\n break\n else:\n count += 1\n summ += x\n avg = round(summ / count, 2)\n\n return avg\n","repo_name":"Mastermind-U/tasksio","sub_path":"ioserver/async_coroutines.py","file_name":"async_coroutines.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"26835599002","text":"class Solution:\n def maxSubArray(self, nums: List[int]) -> int:\n # divide and conquer O_nlogn time and O_logn space\n if not nums:\n return float('-inf')\n if len(nums) == 1:\n return nums[0]\n \n m = len(nums) // 2\n\n # from m to the left, continue sum with m\n s = 0\n left_s = float('-inf')\n for i in range(m, -1, -1):\n s += nums[i]\n left_s = max(left_s, s)\n \n # from m to the right, continue sum with m\n s = 0\n right_s = float('-inf')\n for i in range(m+1, len(nums)):\n s += nums[i]\n right_s = max(right_s, s)\n \n # re is cross sum\n re = max(left_s, right_s, left_s + right_s)\n \n return max(self.maxSubArray(nums[:m]),\n self.maxSubArray(nums[m+1:]),\n re)\n \n \"\"\"\n # greedy O_n time and O_1 space\n n = len(nums)\n curr_sum = max_sum = nums[0]\n\n for i in range(1, n):\n curr_sum = max(nums[i], curr_sum + nums[i])\n max_sum = max(max_sum, curr_sum)\n \n return max_sum\n \"\"\"\n\n \"\"\"\n # greedy and dp O_n time and O_1 space\n n = len(nums)\n max_sum = nums[0]\n\n for i in range(1, n):\n if nums[i-1] > 0:\n nums[i] += nums[i-1]\n max_sum = max(max_sum, nums[i])\n \n return max_sum\n \"\"\"\n\n \"\"\"\n # greedy and dp from left and from right O_n time and O_n space\n n = len(nums)\n left_sum = nums[:]\n right_sum = nums[:]\n \n for i in range(1, n):\n left_sum[i] = max(nums[i], left_sum[i-1] + nums[i])\n\n # 多此一举\n for i in range(n-2, -1, -1):\n right_sum[i] = max(nums[i], right_sum[i+1] + nums[i])\n \n # 加号就是连接两个list的意思\n return max(left_sum + right_sum)\n \"\"\"","repo_name":"billgoo/LeetCode_Solution","sub_path":"Top Interview Questions/Array/53. Maximum Subarray.py","file_name":"53. Maximum Subarray.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"2044906093","text":"import json\r\nimport os\r\nimport csv\r\nimport pandas as pd\r\nimport ast\r\nimport functions\r\nfrom file_Handler import FileHandler\r\nimport logging\r\nimport datetime\r\n\r\nlogging.basicConfig(level=logging.DEBUG,\r\n filename='App.log',\r\n filemode='a',\r\n format=\"*** %(asctime)s — %(message)s \\n\",\r\n datefmt='%d-%b-%y %H:%M:%S')\r\nclass StoreManager:\r\n add_stores_to_file = FileHandler(\"Stores.csv\")\r\n read_stores_file = FileHandler(\"Stores.csv\")\r\n read_invoices = FileHandler(\"invoice.csv\")\r\n read_users_file = FileHandler(\"users.csv\")\r\n\r\n def __init__(self,user_name,store_name,open_time,close_time):\r\n self.user_name = user_name\r\n self.store_name = store_name\r\n self.open_time = open_time\r\n self.close_time = close_time\r\n self.products =None\r\n self.block_customer = None\r\n #اینجا چک می کنیم اگر یوزرنیم مدیر فروشگاه وجود نداشت بعد تو فایل اددش کنه\r\n reader = self.read_stores_file.read_file()\r\n bool_result = True\r\n for i in reader:\r\n if i[\"user_name\"] == self.user_name:\r\n bool_result = False\r\n break\r\n if bool_result:\r\n self.add_stores_to_file.add_to_file(self.__dict__)\r\n\r\n def add_product(self):\r\n number_of_product = int(input(\"Enter number of product \"))\r\n product_list = []\r\n for i in range(1, number_of_product+1):\r\n print(f\"product{i}:\")\r\n try:\r\n product_name = input(\"Enter product name\")\r\n barcode = int(input(\" Enter Barcode(just enter number)\"))\r\n price = int(input(\" Enter price\"))\r\n brand = input(\"Enter Brand\")\r\n number_available = int(input(\" Enter number of product\"))\r\n expire_date = input(\" Enter expire Date(00-03-04)\")\r\n datetime.datetime.strptime(expire_date,'%y-%m-%d')\r\n product_list.append({\"product_name\": product_name, \"barcode\": barcode, \"price\": price, \"brand\": brand,\r\n \"number_available\": number_available, \"expire_date\": expire_date})\r\n\r\n except ValueError:\r\n print(\"wrong input\")\r\n\r\n\r\n\r\n#اینجا چون من داخل init اومدم یکبار اطلاعات رو داخل فایل stores ذخیره کردم ،بار اول که اطلاعات ذخیره میشن مقدار محصولات خالی هست\r\n #باید اول چک بشه اگر مقدار محصول خالی هست فایل رو ادیت کن اگرم پر هست مقدار قبلی رو یه جا نگه میداریم و مقدار جدید هم بهش اضافه میکنیم بعد فایل رو ادیت می کنیم\r\n # ادیت فایل دقیقا مثل نوشتن تو فایل با ایت تفائت که مدش a هست یعنی یه دور فایل رو پاک می کنم بعد دوباره با اطلاعات جدید می نویسم\r\n reader=self.read_stores_file.read_file()\r\n final_list = []\r\n for i in reader:\r\n if i[\"user_name\"] != self.user_name:\r\n final_list.append(i)\r\n for item_dict in reader:\r\n if item_dict[\"user_name\"] == self.user_name:\r\n if item_dict[\"products\"] == '':\r\n self.block_customer = item_dict[\"block_customer\"]\r\n self.products = product_list\r\n final_list.append(self.__dict__)\r\n self.add_stores_to_file.edit_to_file(final_list)\r\n logging.info(\" NEW Products Added\")\r\n else:\r\n result = item_dict[\"products\"]\r\n new_product_list = functions.list_parser(result)\r\n #print(new_product_list)\r\n for i in new_product_list:\r\n product_list.append(i)\r\n print(product_list)\r\n self.block_customer = item_dict[\"block_customer\"]\r\n self.products = product_list\r\n final_list.append(self.__dict__)\r\n self.add_stores_to_file.edit_to_file(final_list)\r\n logging.info(\" NEW Products Added\")\r\n\r\n def view_remaining_inventory(self):\r\n self.alarm()\r\n reader = self.read_stores_file.read_file()\r\n for item in reader:\r\n if item[\"user_name\"] == self.user_name:\r\n product_of_store = item['products']\r\n list_product_of_store=functions.list_parser(product_of_store)\r\n break\r\n if list_product_of_store:\r\n print(\"---------------------------------------Product List -----------------------------------------\")\r\n for item_dict in list_product_of_store:\r\n #\r\n # print(item_dict)\r\n # print(type(item_dict))\r\n print(\"Product Name:\",item_dict[\"product_name\"],\"Barcode:\",item_dict['barcode'],\"Price:\",\r\n item_dict[\"price\"],\"Brand:\",item_dict[\"brand\"],\"Number Available:\",item_dict[\"number_available\"],\r\n \"Expire Date:\",item_dict[\"expire_date\"])\r\n else:\r\n print(\"No Product Please Add Product\")\r\n\r\n def update_vlue_of_product_list(self,store_name,product_name,number):\r\n reader = self.read_stores_file.read_file()\r\n final_list = []\r\n product_list =[]\r\n bool_update = False\r\n for i in reader:\r\n if i[\"user_name\"] == self.user_name and i[\"store_name\"] == store_name :\r\n bool_update = True\r\n if bool_update:\r\n for i in reader:\r\n if i[\"user_name\"] != self.user_name:\r\n final_list.append(i)\r\n elif i[\"user_name\"] == self.user_name:\r\n self.block_customer = i[\"block_customer\"]\r\n if i[\"store_name\"] == store_name :\r\n if i[\"products\"]:\r\n result = i[\"products\"]\r\n new_result = functions.list_parser(result)\r\n for i in new_result:\r\n if i[\"product_name\"] == product_name:\r\n if i[\"number_available\"] >= number:\r\n new_available_number = i[\"number_available\"] - number\r\n i[\"number_available\"] = new_available_number\r\n product_list.append(i)\r\n else:\r\n product_list.append(i)\r\n print(product_list)\r\n self.products = product_list\r\n #self.block_customer =\r\n final_list.append(self.__dict__)\r\n self.add_stores_to_file.edit_to_file(final_list)\r\n else:\r\n print(\"product name or user name is wrong\")\r\n\r\n\r\n\r\n def alarm(self):\r\n reader = self.read_stores_file.read_file()\r\n alarm_list = []\r\n for i in reader:\r\n if i[\"user_name\"] == self.user_name:\r\n if i[\"products\"]:\r\n result = i[\"products\"]\r\n new_result = functions.list_parser(result)\r\n #print(new_result)\r\n for j in new_result:\r\n if j[\"number_available\"] <=3:\r\n logging.info(f\"The product{j} is running out\")\r\n alarm_list.append(j)\r\n if alarm_list:\r\n\r\n print(\"---------------------------------------alarm list ---------------------------------------\")\r\n for i in alarm_list:\r\n print(i)\r\n print(\"-----------------------------------------------------------------------------------------\")\r\n\r\n def view_invoices(self):\r\n ivoice_list = self.read_invoices.read_file()\r\n for i in ivoice_list:\r\n invoices=i[\"invoice\"]\r\n #print(invoices)\r\n #print(type(invoices))\r\n list_invoices = functions.list_parser(invoices)\r\n # print(type(list_invoices))\r\n list_invoices_with_store_name =[]\r\n for j in list_invoices :\r\n if j['store_name'] == self.store_name:\r\n list_invoices_with_store_name.append(j)\r\n print(\"username:\",i[\"user_name\"],\",\",\"list_invoices:\",list_invoices_with_store_name)\r\n\r\n # print(list_invoices_with_store_name)\r\n\r\n def view_information_all_customer(self):\r\n list_users = self.read_users_file.read_file()\r\n if list_users:\r\n for item in list_users:\r\n i = item[\"information\"]\r\n convertedDict = ast.literal_eval(i)\r\n if item[\"type\"] == \"Customer\":\r\n print(convertedDict['username'])\r\n print(type(convertedDict['username']))\r\n #todo complete\r\n def block_customer_from_store(self):\r\n block_list = []\r\n self.view_information_all_customer()\r\n choose_block_customer = int(input(\"please enter username that you want delete:\"))\r\n block_list.append(choose_block_customer)\r\n reader = self.read_stores_file.read_file()\r\n final_list = []\r\n for i in reader:\r\n if i[\"user_name\"] != self.user_name:\r\n final_list.append(i)\r\n for item_dict in reader:\r\n if item_dict[\"user_name\"] == self.user_name:\r\n if item_dict[\"block_customer\"] == '':\r\n self.block_customer = block_list\r\n self.products = item_dict['products']\r\n final_list.append(self.__dict__)\r\n self.add_stores_to_file.edit_to_file(final_list)\r\n else:\r\n result = item_dict[\"block_customer\"]\r\n new_block_list = functions.list_parser(result)\r\n print(new_block_list)\r\n for i in new_block_list:\r\n block_list.append(i)\r\n print(block_list)\r\n self.block_customer = block_list\r\n self.products = item_dict['products']\r\n final_list.append(self.__dict__)\r\n self.add_stores_to_file.edit_to_file(final_list)\r\n\r\n @staticmethod\r\n def sign_out():\r\n print(\"you logged out\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# store1 = StoreManager(\"789\",\"kouroush\",1,8)\r\nstore2 = StoreManager(\"123\",\"refah\",2,24)\r\n#store1.alarm()\r\n#store1.view_invoices()\r\n#store2.view_invoices()\r\n#store2.view_information_all_customer()\r\n#store1.block_customer_from_store()\r\n#store1.update_vlue_of_product_list(\"kouroush\",\"rice\",2)\r\n#store2.update_vlue_of_product_list(\"kouroush\",\"apple\",1)\r\nstore2.add_product()\r\n# store1.view_remaining_inventory()\r\n#store2.add_product()\r\n# # store1.save_to_json_file()\r\n#store1.view_remaining_inventory()","repo_name":"haniehparnoon/Final_Project","sub_path":"storemanager.py","file_name":"storemanager.py","file_ext":"py","file_size_in_byte":11051,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"8796249264","text":"import multiprocessing\nfrom settings import Conf\n\nworker_class = 'uvicorn.workers.UvicornWorker'\nworkers = multiprocessing.cpu_count() * 2 + 1\n# threads = multiprocessing.cpu_count() * 2 + 1\n\nbind = [f\"0.0.0.0:{Conf.APP_PORT_EXTERNAL}\"]\naccess_log_format = 'h:%(h)s l:%(l)s u:%(u)s t:%(t)s r:\"%(r)s\" s:%(s)s b:%(b)s f:\"%(f)s\" a:\"%(a)s\"\\n________________\\n'\n# capture_output = True\n# daemon = True\naccesslog = '-'\nerrorlog = '-'\nloglevel = 'debug'\nwsgi_app = \"app:create_app()\"\n\n\n# certfile = '/etc/letsencrypt/live/www.example.com/fullchain.pem'\n# keyfile = '/etc/letsencrypt/live/www.example.com/privkey.pem'\n# ssl_version = # if necessary\n# print_config = True\n# check_config = True\n","repo_name":"ArtemIsmagilov/mm-yc-notify","sub_path":"src/gunicorn.conf.py","file_name":"gunicorn.conf.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"1140764729","text":"import base64\nimport json\nimport logging\nimport requests\nfrom odoo import api, fields, models, tools\nfrom odoo.exceptions import UserError\n\n_logger = logging.getLogger(__name__)\n\n\nclass DingDingSynchronous(models.TransientModel):\n _name = 'dingding.bash.data.synchronous'\n _description = \"基础数据同步\"\n _rec_name = 'employee'\n\n department = fields.Boolean(string=u'钉钉部门', default=True)\n employee = fields.Boolean(string=u'钉钉员工', default=True)\n employee_avatar = fields.Boolean(string=u'替换钉钉员工头像', default=False)\n partner = fields.Boolean(string=u'钉钉联系人', default=True)\n synchronous_dept_detail = fields.Boolean(string=u'部门详情', default=True)\n \n @api.multi\n def start_synchronous_data(self):\n \"\"\"\n 基础数据同步\n :return:\n \"\"\"\n self.ensure_one()\n try:\n if self.department:\n self.synchronous_dingding_department()\n if self.employee:\n self.synchronous_dingding_employee(s_avatar=self.employee_avatar)\n if self.partner:\n self.synchronous_dingding_category() # 同步标签\n self.synchronous_dingding_partner() # 同步员工\n if self.synchronous_dept_detail:\n # 获取部门详情\n self.get_department_details()\n except Exception as e:\n raise UserError(e)\n\n @api.model\n def synchronous_dingding_department(self):\n \"\"\"\n 同步钉钉部门\n :return:\n \"\"\"\n din_client = self.env['dingding.api.tools'].get_client()\n result = din_client.department.list(fetch_child=True)\n dept_ding_ids = list()\n for res in result:\n dept_ding_ids.append(str(res.get('id')))\n data = {\n 'name': res.get('name'),\n 'ding_id': res.get('id'),\n }\n h_department = self.env['hr.department'].search([('ding_id', '=', res.get('id')), ('active', '=', True)])\n if h_department:\n h_department.write(data)\n else:\n self.env['hr.department'].create(data)\n # 情况：当在钉钉端删除了部门后，那么在odoo原还存在该部门，所以在同步完成后，在检查一遍，将存在的保留，不存在的部门进行归档\n departments = self.env['hr.department'].sudo().search(['|', ('active', '=', False), ('active', '=', True)])\n for department in departments:\n ding_id = department.ding_id\n if ding_id not in dept_ding_ids:\n department.write({'active': False})\n else:\n department.write({'active': True})\n return True\n\n @api.model\n def get_department_details(self):\n \"\"\"\n 获取部门详情\n :return:\n \"\"\"\n departments = self.env['hr.department'].sudo().search([('active', '=', True), ('ding_id', '!=', '')])\n din_client = self.env['dingding.api.tools'].get_client()\n for department in departments:\n result = din_client.department.get(department.ding_id)\n if result.get('errcode') == 0:\n if result.get('parentid') != 1:\n partner_department = self.env['hr.department'].search([('ding_id', '=', result.get('parentid'))])\n if partner_department:\n self._cr.execute(\"UPDATE hr_department SET parent_id=%s WHERE id=%s\" % (partner_department.id, department.id))\n dept_manageusers = result.get('deptManagerUseridList')\n if dept_manageusers:\n depts = dept_manageusers.split(\"|\")\n manage_users = self.env['hr.employee'].search([('ding_id', 'in', depts)])\n department.write({'manager_user_ids': [(6, 0, manage_users.ids)], 'manager_id': manage_users[0].id})\n return True\n\n @api.model\n def synchronous_dingding_employee(self, s_avatar=None):\n \"\"\"\n 同步钉钉部门员工列表\n :param s_avatar: 是否同步头像\n :return:\n \"\"\"\n # 获取所有部门\n departments = self.env['hr.department'].sudo().search([('ding_id', '!=', ''), ('active', '=', True)])\n din_client = self.env['dingding.api.tools'].get_client()\n for department in departments.with_progress(msg=\"正在同步部门员工\"):\n emp_offset = 0\n emp_size = 100\n while True:\n logging.info(\">>>开始获取%s部门的员工\", department.name)\n result_state = self.get_dingding_employees(din_client, department, emp_offset, emp_size, s_avatar=s_avatar)\n if result_state:\n emp_offset = emp_offset + 1\n else:\n break\n return True\n\n @api.model\n def get_dingding_employees(self, din_client, department, offset=0, size=100, s_avatar=None):\n \"\"\"\n 获取部门成员（详情）\n :param din_client:\n :param department:\n :param offset:\n :param size:\n :param s_avatar:\n :return:\n \"\"\"\n try:\n result = din_client.user.list(department.ding_id, offset, size, order='custom')\n for user in result.get('userlist'):\n data = {\n 'name': user.get('name'), # 员工名称\n 'ding_id': user.get('userid'), # 钉钉用户Id\n 'din_unionid': user.get('unionid'), # 钉钉唯一标识\n 'mobile_phone': user.get('mobile'), # 手机号\n 'work_phone': user.get('tel'), # 分机号\n 'work_location': user.get('workPlace'), # 办公地址\n 'notes': user.get('remark'), # 备注\n 'job_title': user.get('position'), # 职位\n 'work_email': user.get('email'), # email\n 'din_jobnumber': user.get('jobnumber'), # 工号\n 'department_id': department[0].id, # 部门\n 'din_avatar': user.get('avatar') if user.get('avatar') else '', # 钉钉头像url\n 'din_isSenior': user.get('isSenior'), # 高管模式\n 'din_isAdmin': user.get('isAdmin'), # 是管理员\n 'din_isBoss': user.get('isBoss'), # 是老板\n 'din_isLeader': user.get('isLeader'), # 是部门主管\n 'din_isHide': user.get('isHide'), # 隐藏手机号\n 'din_active': user.get('active'), # 是否激活\n 'din_isLeaderInDepts': user.get('isLeaderInDepts'), # 是否为部门主管\n 'din_orderInDepts': user.get('orderInDepts'), # 所在部门序位\n }\n # 支持显示国际手机号\n if user.get('stateCode') != '86':\n data.update({'mobile_phone': '+{}-{}'.format(user.get('stateCode'), user.get('mobile'))})\n if user.get('hiredDate'):\n time_stamp = self.env['dingding.api.tools'].get_time_stamp(user.get('hiredDate'))\n data.update({'din_hiredDate': time_stamp})\n if s_avatar and user.get('avatar'):\n try:\n binary_data = tools.image_resize_image_big(\n base64.b64encode(requests.get(user.get('avatar')).content))\n data.update({'image': binary_data})\n except Exception as e:\n logging.info(\">>>--------------------------------\")\n logging.info(\">>>SSL异常:%s\", e)\n logging.info(\">>>--------------------------------\")\n if user.get('department'):\n dep_din_ids = user.get('department')\n dep_list = self.env['hr.department'].sudo().search([('ding_id', 'in', dep_din_ids)])\n data.update({'department_ids': [(6, 0, dep_list.ids)]})\n employee = self.env['hr.employee'].search([('ding_id', '=', user.get('userid'))])\n if employee:\n employee.sudo().write(data)\n else:\n self.env['hr.employee'].sudo().create(data)\n return result.get('hasMore')\n except Exception as e:\n raise UserError(e)\n\n # 留着备用\n @api.model\n def synchronous_dingding_employee_v2(self, s_avatar=None):\n \"\"\"\n 同步钉钉部门员工列表\n :param s_avatar: 是否同步头像\n :return:\n \"\"\"\n # 获取所有部门\n departments = self.env['hr.department'].sudo().search([('ding_id', '!=', ''), ('active', '=', True)])\n din_client = self.env['dingding.api.tools'].get_client()\n ding_user_list = list()\n for department in departments.with_progress(msg=\"正在同步部门员工\"):\n emp_offset = 0\n emp_size = 100\n while True:\n logging.info(\">>>开始获取%s部门的员工\", department.name)\n result_state, user_list = self.get_dingding_employees_v2(din_client, department, ding_user_list, emp_offset, emp_size, s_avatar=s_avatar)\n if result_state:\n emp_offset = emp_offset + 1\n ding_user_list = user_list\n else:\n break\n return True\n\n @api.model\n def get_dingding_employees_v2(self, din_client, department, ding_user_list, offset=0, size=100, s_avatar=None):\n \"\"\"\n 获取部门成员（详情）\n :param din_client:\n :param department:\n :param offset:\n :param size:\n :param s_avatar:\n :return:\n \"\"\"\n try:\n result = din_client.user.list(department.ding_id, offset, size, order='custom')\n user_list = list()\n for user in result.get('userlist'):\n data = {\n 'name': user.get('name'), # 员工名称\n 'ding_id': user.get('userid'), # 钉钉用户Id\n 'din_unionid': user.get('unionid'), # 钉钉唯一标识\n 'mobile_phone': user.get('mobile'), # 手机号\n 'work_phone': user.get('tel'), # 分机号\n 'work_location': user.get('workPlace'), # 办公地址\n 'notes': user.get('remark'), # 备注\n 'job_title': user.get('position'), # 职位\n 'work_email': user.get('email'), # email\n 'din_jobnumber': user.get('jobnumber'), # 工号\n 'department_id': department[0].id, # 部门\n 'din_avatar': user.get('avatar') if user.get('avatar') else '', # 钉钉头像url\n 'din_isSenior': user.get('isSenior'), # 高管模式\n 'din_isAdmin': user.get('isAdmin'), # 是管理员\n 'din_isBoss': user.get('isBoss'), # 是老板\n 'din_isLeader': user.get('isLeader'), # 是部门主管\n 'din_isHide': user.get('isHide'), # 隐藏手机号\n 'din_active': user.get('active'), # 是否激活\n 'din_isLeaderInDepts': user.get('isLeaderInDepts'), # 是否为部门主管\n 'din_orderInDepts': user.get('orderInDepts'), # 所在部门序位\n }\n # 支持显示国际手机号\n if user.get('stateCode') != '86':\n data.update({'mobile_phone': '+{}-{}'.format(user.get('stateCode'), user.get('mobile'))})\n if user.get('hiredDate'):\n time_stamp = self.env['dingding.api.tools'].get_time_stamp(user.get('hiredDate'))\n data.update({'din_hiredDate': time_stamp})\n if s_avatar and user.get('avatar'):\n try:\n binary_data = tools.image_resize_image_big(\n base64.b64encode(requests.get(user.get('avatar')).content))\n data.update({'image': binary_data})\n except Exception as e:\n logging.info(\">>>--------------------------------\")\n logging.info(\">>>SSL异常:%s\", e)\n logging.info(\">>>--------------------------------\")\n if user.get('department'):\n dep_din_ids = user.get('department')\n dep_list = self.env['hr.department'].sudo().search([('ding_id', 'in', dep_din_ids)])\n data.update({'department_ids': [(6, 0, dep_list.ids)]})\n if user.get('userid') not in ding_user_list:\n user_list.append(data)\n ding_user_list.append(user.get('userid'))\n # logging.info(\">>>获取钉钉用户集结果%s\", ding_user_list)\n # 批量新建员工，未做已存在记录检查\n self.env['hr.employee'].sudo().create(user_list)\n return result.get('hasMore'), ding_user_list\n except Exception as e:\n raise UserError(e)\n\n @api.model\n def synchronous_dingding_category(self):\n \"\"\"\n 同步钉钉联系人标签\n :return:\n \"\"\"\n logging.info(\">>>同步钉钉联系人标签\")\n din_client = self.env['dingding.api.tools'].get_client()\n try:\n result = din_client.ext.listlabelgroups(offset=0, size=100)\n category_list = list()\n for res in result:\n for labels in res.get('labels'):\n category_list.append({\n 'name': labels.get('name'),\n 'ding_id': labels.get('id'),\n 'din_color': res.get('color'),\n 'din_category_type': res.get('name'),\n })\n for category in category_list:\n res_category = self.env['res.partner.category'].sudo().search([('ding_id', '=', category.get('ding_id'))])\n if res_category:\n res_category.sudo().write(category)\n else:\n self.env['res.partner.category'].sudo().create(category)\n return True\n except Exception as e:\n raise UserError(e)\n\n @api.model\n def synchronous_dingding_partner(self):\n \"\"\"\n 同步钉钉联系人列表\n :return:\n \"\"\"\n logging.info(\">>>同步钉钉联系人列表start\")\n din_client = self.env['dingding.api.tools'].get_client()\n try:\n result = din_client.ext.list(offset=0, size=100)\n logging.info(result)\n for res in result:\n # 获取标签\n label_list = list()\n for label in res.get('labelIds'):\n category = self.env['res.partner.category'].sudo().search([('ding_id', '=', label)])\n if category:\n label_list.append(category[0].id)\n data = {\n 'name': res.get('name'),\n 'function': res.get('title'),\n 'category_id': [(6, 0, label_list)], # 标签\n 'din_userid': res.get('userId'), # 钉钉用户id\n 'comment': res.get('remark'), # 备注\n 'street': res.get('address'), # 地址\n 'mobile': res.get('mobile'), # 手机\n 'phone': res.get('mobile'), # 电话\n 'din_company_name': res.get('company_name'), # 钉钉公司名称\n 'customer': True, # 是否客户\n 'supplier': True, # 是否供应商\n }\n # 获取负责人\n if res.get('follower_user_id'):\n follower_user = self.env['hr.employee'].sudo().search([('ding_id', '=', res.get('follower_user_id'))])\n data.update({'din_employee_id': follower_user[0].id if follower_user else ''})\n # 根据userid查询联系人是否存在\n partner = self.env['res.partner'].sudo().search(\n ['|', ('din_userid', '=', res.get('userId')), ('name', '=', res.get('name'))])\n if partner:\n partner.sudo().write(data)\n else:\n self.env['res.partner'].sudo().create(data)\n return True\n except Exception as e:\n raise UserError(e)\n","repo_name":"claro5/odooDingDing","sub_path":"dingding_base/wizard/synchronous.py","file_name":"synchronous.py","file_ext":"py","file_size_in_byte":16631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"2"}
+{"seq_id":"15617997898","text":"#!/usr/bin/env python3\n\n###################################\n# CS B551 Fall 2018, Assignment #3\n#\n# Author: Parth Naik (naikpa)\n#\n# (Based on skeleton code by D. Crandall)\n#\n#\n####\n\nimport random\nimport math\nimport operator\nimport pdb\nimport numpy\nfrom collections import defaultdict\n\n\nclass Solver:\n\tdef posterior(self, model, sentence, label):\n\t\tif model == \"Simple\":\t\t\t\t\n\t\t\tlog_probs = 0\n\t\t\tfor i in range(len(sentence)):\n\t\t\t\tif sentence[i] in self.emission[label[i]].keys():\n\t\t\t\t\tlog_probs += math.log(self.prior[label[i]]) + math.log(self.emission[label[i]][sentence[i]])\n\t\t\t\telse:\n\t\t\t\t\tlog_probs += math.log(self.prior[label[i]]) + math.log(0.000001)\n\t\t\treturn log_probs\n\t\telif model == \"Complex\":\n\t\t\tlog_probs = 0\n\t\t\tfor i in range(len(sentence)):\n\t\t\t\tword = sentence[i]\n\t\t\t\ttag = label[i]\n\t\t\t\t# Include only emission and prior\n\t\t\t\tif i == 0:\n\t\t\t\t\tif word in self.emission[tag].keys():\n\t\t\t\t\t\tlog_probs += math.log(self.prior[tag]) + math.log(self.emission[tag][word])\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_probs += math.log(self.prior[tag]) + math.log(0.000001)\n\t\t\t\t# Include transition from prev state\n\t\t\t\telif i == 1:\n\t\t\t\t\tprev_tag = label[i-1]\n\t\t\t\t\n\t\t\t\t\tif word in self.emission[tag].keys():\n\t\t\t\t\t\tlog_probs += math.log(self.emission[tag][word])\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_probs += math.log(0.000001)\n\t\t\t\t\t\t\n\t\t\t\t\tif tag in self.transition[prev_tag]:\n\t\t\t\t\t\tlog_probs += math.log(self.transition[prev_tag][tag])\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_probs += math.log(0.000001)\n\t\t\t\t# Include transition from prev to prev state\n\t\t\t\telse:\n\t\t\t\t\tprev_tag = label[i-1]\n\t\t\t\t\tprev_prev_tag = label[i-2]\n\t\t\t\t\t\n\t\t\t\t\tif word in self.emission[tag].keys():\n\t\t\t\t\t\tlog_probs += math.log(self.emission[tag][word])\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_probs += math.log(0.000001)\n\t\t\t\t\t\t\n\t\t\t\t\tif tag in self.transition[prev_tag]:\n\t\t\t\t\t\tlog_probs += math.log(self.transition[prev_tag][tag])\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_probs += math.log(0.000001)\n\t\t\t\t\t\t\n\t\t\t\t\tif tag in self.transition_next_to_next[prev_prev_tag]:\n\t\t\t\t\t\tlog_probs += math.log(self.transition_next_to_next[prev_prev_tag][tag])\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_probs += math.log(0.000001)\n\t\t\treturn log_probs\n\t\telif model == \"HMM\":\n\t\t\treturn self.hmm_posterior\n\t\telse:\n\t\t\tprint(\"Unknown algo!\")\n\t\t\t\n\t# Do the training\n\tdef train(self, lines):\n\t\temission_count = {'adj':{},'adv':{},'adp':{},'conj':{},'det':{},'noun':{},'num':{},'pron':{},'prt':{},'verb':{},'x':{},'.':{}}\n\t\ttag_count = {'adj':0,'adv':0,'adp':0,'conj':0,'det':0,'noun':0,'num':0,'pron':0,'prt':0,'verb':0,'x':0,'.':0}\n\t\ttransition_count = {'adj':{},'adv':{},'adp':{},'conj':{},'det':{},'noun':{},'num':{},'pron':{},'prt':{},'verb':{},'x':{},'.':{}}\n\t\ttransition_next_to_next_count = {'adj':{},'adv':{},'adp':{},'conj':{},'det':{},'noun':{},'num':{},'pron':{},'prt':{},'verb':{},'x':{},'.':{}}\n\t\t\t\n\t\tfor line in lines:\n\t\t\t# Get words in the line\n\t\t\tline = line.split(\" \")\n\t\t\t# Remove ending spaces and /n characters\n\t\t\tline = line[:-3]\n\t\t\t# Previous tag (required to calculate transition probabilities)\n\t\t\tprev_tag = 0\n\t\t\t# Previous to previous tag (required for MCMC)\n\t\t\tprev_prev_tag = 0\n\t\t\t# Loop through to get emission_count and tag_count\n\t\t\tfor i in range(0,len(line),2):\n\t\t\t\t# Word\n\t\t\t\tword = line[i].lower()\n\t\t\t\t# Corresponding tag\n\t\t\t\ttag = line[i+1].lower()\n\t\t\t\t\n\t\t\t\t# Increment the word count in emission_count\n\t\t\t\tif word not in emission_count[tag].keys():\n\t\t\t\t\temission_count[tag][word] = 1\n\t\t\t\telse:\n\t\t\t\t\temission_count[tag][word] += 1\n\t\t\t\t\t\n\t\t\t\t# Increment tag_count\n\t\t\t\ttag_count[tag] += 1\n\t\t\t\t\n\t\t\t\t# Increment tag count in transition_count\n\t\t\t\tif prev_tag!=0:\n\t\t\t\t\tif tag not in transition_count[prev_tag].keys():\n\t\t\t\t\t\ttransition_count[prev_tag][tag] = 1\n\t\t\t\t\telse:\n\t\t\t\t\t\ttransition_count[prev_tag][tag] += 1\n\t\t\t\t\n\t\t\t\tif prev_prev_tag!=0:\n\t\t\t\t\tif tag not in transition_next_to_next_count[prev_tag].keys():\n\t\t\t\t\t\ttransition_next_to_next_count[prev_tag][tag] = 1\n\t\t\t\t\telse:\n\t\t\t\t\t\ttransition_next_to_next_count[prev_tag][tag] += 1\n\t\t\t\t\n\t\t\t\t# Set the prev_tag to current tag\n\t\t\t\tprev_prev_tag = prev_tag\n\t\t\t\tprev_tag = tag\n\t\t\t\t\n\t\t\n\t\t# Calculate prior probabilities\n\t\ttag_probs = tag_count.copy()\n\t\ttotal_tag_count = sum(tag_count.values())\n\t\tfor tag in tag_probs.keys():\n\t\t\ttag_probs[tag] = tag_probs[tag]/total_tag_count\n\t\t\n\t\t# Calculate emission probabilities\n\t\temission_probs = emission_count.copy()\n\t\tfor tag in emission_probs.keys():\n\t\t\tfor word in emission_probs[tag].keys():\n\t\t\t\temission_probs[tag][word] = emission_probs[tag][word]/tag_count[tag]\n\t\t\n\t\t# Calculate transition probabilities\n\t\ttransition_probs = transition_count.copy()\n\t\tfor tag in transition_probs.keys():\n\t\t\ttag_num = sum(transition_probs[tag].values())\n\t\t\tfor next_tag in transition_probs[tag].keys():\n\t\t\t\ttransition_probs[tag][next_tag] = transition_probs[tag][next_tag]/tag_count[tag]\n\t\t\t\t\n\t\t# Calculate transition next to next probabilities\n\t\ttransition_next_to_next_probs = transition_next_to_next_count.copy()\n\t\tfor tag in transition_next_to_next_probs.keys():\n\t\t\ttag_num = sum(transition_next_to_next_probs[tag].values())\n\t\t\tfor next_tag in transition_next_to_next_probs[tag].keys():\n\t\t\t\ttransition_next_to_next_probs[tag][next_tag] = transition_next_to_next_probs[tag][next_tag]/tag_count[tag]\n\t\t\t\t\n\t\t# print(transition_probs['noun']['noun'])\n\t\t# input()\n\t\t\n\t\t# Set the probabilities to local variables\n\t\tself.prior = tag_probs\n\t\tself.emission = emission_probs\n\t\tself.transition = transition_probs\n\t\tself.transition_next_to_next = transition_next_to_next_probs\n\t\t\n\t\tprint(\"Probabilities learnt !\")\n\t\t# print(self.transition['pron'])\n\t\t# input()\n\t\t\n # Functions for each algorithm.\n\tdef simplified(self, sentence):\n\t\t# Predicted sentence tags\n\t\tpredicted_sentence = []\n\t\t\n\t\tfor word in sentence:\n\t\t\ttemp_dict = {'adj':0,'adv':0,'adp':0,'conj':0,'det':0,'noun':0,'num':0,'pron':0,'prt':0,'verb':0,'x':0,'.':0}\n\t\t\tfor tag in temp_dict.keys():\n\t\t\t\t# Check if word has occured as a emission in the training data for a tag.\n\t\t\t\tif word in self.emission[tag].keys():\n\t\t\t\t\tprob = self.emission[tag][word]\n\t\t\t\telse:\n\t\t\t\t\tprob = 0.0000001\n\t\t\t\tprob *= self.prior[tag]\n\t\t\t\ttemp_dict[tag] = prob\n\t\t\t# Maximize the product of prior and emission probabilities.\n\t\t\tpredicted_sentence.append(max(temp_dict.items(), key=operator.itemgetter(1))[0])\n\t\t\n\t\treturn predicted_sentence\n\t\t\n\t\t\n\tdef calculate_intersection_probs(self,sentence,p0,selected_rv_position):\n\t\t\"\"\"Function to calculate intersection probs for MCMC\"\"\"\n\t\t# Variable to store probability\n\t\tprob = 1\n\t\t\n\t\t# For the first word\n\t\tword = sentence[0]\n\t\ttag = p0[0]\n\t\t\n\t\tif word in self.emission[tag].keys():\n\t\t\tprob = prob * self.emission[tag][word] * self.prior[tag]\n\t\telse:\n\t\t\tprob = prob * 0.000001 * self.prior[tag]\n\t\t\n\t\t# For the remaining words upto the previous word\n\t\tfor i in range(1,selected_rv_position):\n\t\t\tword = sentence[i]\n\t\t\tcurr_tag = p0[i]\n\t\t\tprev_tag = p0[i-1]\n\t\t\t\n\t\t\t# Prev to prev transition probability effect only from the third word onwards\n\t\t\tif i > 1:\n\t\t\t\tprev_prev_tag = p0[i-2]\t\n\t\t\t\t\t\t\n\t\t\t# Next to next transition probability effect only till the third last word\n\t\t\tif i < (len(sentence)-3):\n\t\t\t\tnext_next_tag = p0[i+2]\n\t\t\t\t\n\t\t\t# Next transition probability effect only till the second last word\n\t\t\tif i < (len(sentence)-2):\n\t\t\t\tnext_tag = p0[i+1]\n\t\t\t\n\t\t\tif curr_tag in self.transition[prev_tag].keys():\n\t\t\t\tprob *= self.transition[prev_tag][curr_tag]\n\t\t\telse:\n\t\t\t\tprob *= 0.000001\n\t\t\t\t\n\t\t\tif 'prev_prev_tag' in locals():\n\t\t\t\tif curr_tag in self.transition_next_to_next[prev_prev_tag].keys():\n\t\t\t\t\tprob *= self.transition_next_to_next[prev_prev_tag][curr_tag]\n\t\t\t\telse:\n\t\t\t\t\tprob *= 0.000001\n\t\t\t\t\t\n\t\t\tif 'next_tag' in locals():\n\t\t\t\tif next_tag in self.transition[curr_tag].keys():\n\t\t\t\t\tprob *= self.transition[curr_tag][next_tag]\n\t\t\t\telse:\n\t\t\t\t\tprob *= 0.000001\n\t\t\t\n\t\t\tif 'next_next_tag' in locals():\n\t\t\t\tif next_next_tag in self.transition_next_to_next[curr_tag].keys():\n\t\t\t\t\tprob *= self.transition_next_to_next[curr_tag][next_next_tag]\n\t\t\t\telse:\n\t\t\t\t\tprob *= 0.000001\n\t\t\t\n\t\t\tif word in self.emission[curr_tag].keys():\n\t\t\t\tprob *= self.emission[curr_tag][word]\n\t\t\telse:\n\t\t\t\tprob *= 0.000001\n\t\t\t\t\n\t\treturn prob\n\t\t\t\n\t\t\n\tdef sample(self, dict_prob):\n\t\t\"\"\"Function samples and returns a tag given the probabilities of the tags\"\"\"\n\t\t# Make sure the probabilities sum to 1.\n\t\tprobs = numpy.array(list(dict_prob.values()))\n\t\tprobs /= probs.sum()\n\t\t# Return the sampled tag\n\t\treturn numpy.random.choice(a=list(dict_prob.keys()),size=1,p=probs)\n\t\t\n\tdef complex_mcmc(self, sentence):\n\t\t# List of tags\n\t\ttags = ['adj','adv','adp','conj','det','noun','num','pron','prt','verb','x','.']\n\t\t\n\t\t# The first particle\n\t\tp0 = ['noun'] * len(sentence)\n\t\t\n\t\tburnin = 100\n\t\tnumber_of_particles = 100\n\t\tcollected_particles = []\n\t\t\n\t\t# Start burnin\n\t\tfor i in range(burnin+number_of_particles):\n\t\t\t# Select a RV to sample while keeping all the others the same\n\t\t\tfor particle_no in range(len(sentence)):\n\t\t\t\tselected_rv_position = particle_no\n\t\t\t\tword = sentence[selected_rv_position]\n\t\t\t\t# Dictionary to store probs p(X_i|X_1-i)\n\t\t\t\tdict_prob = {}\n\t\t\t\t# For the first word\n\t\t\t\tif selected_rv_position == 0:\n\t\t\t\t\t# Because there is a single word sentence!\n\t\t\t\t\tif len(sentence) > 1:\n\t\t\t\t\t\tnext_tag = p0[1]\n\t\t\t\t\t# Calculate probability for each tag\n\t\t\t\t\tfor tag in tags:\n\t\t\t\t\t\tif sentence[selected_rv_position] in self.emission[tag].keys():\n\t\t\t\t\t\t\tprob = self.emission[tag][word] * self.prior[tag]\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprob = 0.000001 * self.prior[tag]\n\t\t\t\t\t\t\n\t\t\t\t\t\tif 'next_tag' in locals():\n\t\t\t\t\t\t\tif next_tag in self.transition[p0[0]].keys():\n\t\t\t\t\t\t\t\tprob *= self.transition[p0[0]][next_tag]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tprob *= 0.000001\n\t\t\t\t\t\tdict_prob[tag] = prob\n\t\t\t\telse:\n\t\t\t\t\t# Dictionary to store probs p(X_i|X_1-i)\n\t\t\t\t\tdict_prob = {}\n\t\t\t\t\tfor tag in tags:\n\t\t\t\t\t\tprob = self.calculate_intersection_probs(sentence,p0,selected_rv_position)\n\t\t\t\t\t\t\n\t\t\t\t\t\t# Incorporate the transition and emission for all the possible tags for the selected RV.\n\t\t\t\t\t\tprev_tag = p0[selected_rv_position-1]\n\t\t\t\t\t\tcurr_tag = tag\n\t\t\t\t\t\t\n\t\t\t\t\t\tif curr_tag in self.transition[prev_tag].keys():\n\t\t\t\t\t\t\tprob *= self.transition[prev_tag][curr_tag]\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprob *= 0.000001\n\t\t\t\n\t\t\t\t\t\tif word in self.emission[curr_tag].keys():\n\t\t\t\t\t\t\tprob *= self.emission[curr_tag][word]\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprob *= 0.000001\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tdict_prob[tag] = prob\n\t\t\t\t# Change the selected tag to the recieved sample\t\n\t\t\t\tp0[selected_rv_position] = self.sample(dict_prob)[0]\n\t\t\t\n\t\t\t# Append the particle to the list of collected particles.\n\t\t\tcollected_particles.append(p0.copy())\n\t\t\t\t\n\t\t# Throw away particles collected during burnin\n\t\tcollected_particles = collected_particles[burnin:]\n\t\t\n\t\t# pdb.set_trace()\n\t\t\n\t\t# Find probabilities from the collected_particles for each word for each tag\n\t\tprobs_from_samples = defaultdict(dict)\n\t\t\n\t\tfor i in range(len(sentence)):\n\t\t\tword = sentence[i]\n\t\t\tfor tag in tags:\n\t\t\t\tp_tag = len([x for x in collected_particles if x[i]==tag])/len(collected_particles)\n\t\t\t\tprobs_from_samples[word][tag] = p_tag\n\t\t\n\t\t# Now assign tags to words by maximizing observed probabilities\n\t\tpredictions = []\n\t\t\n\t\tfor word in sentence:\n\t\t\tpredictions.append(max(probs_from_samples[word].items(), key=operator.itemgetter(1))[0])\n\t\t\n\t\treturn predictions\n \n\tdef hmm_viterbi(self, sentence):\n\t\t# [[(),()]]\n\t\t# pdb.set_trace()\n\t\t\n\t\t# List of tags\n\t\ttags = ['adj','adv','adp','conj','det','noun','num','pron','prt','verb','x','.']\n\t\t\n\t\t# Viterbi table\n\t\tv_table = []\n\t\t\n\t\t# Initial start for Viterbi\n\t\t# First word\n\t\tword0 = sentence[0]\n\t\t\n\t\t# List of probs for various states\n\t\tv_list = []\n\t\tfor tag in tags:\n\t\t\t# Calculate probs of being in a state\n\t\t\tif word0 in self.emission[tag].keys():\n\t\t\t\tv_tag = math.log(self.emission[tag][word0]) #+ math.log(self.prior[tag])\n\t\t\telse:\n\t\t\t\tv_tag = math.log(0.000001) #+ math.log(self.prior[tag])\n\t\t\t# Append as a tuple (prob,state)\n\t\t\tv_list.append((v_tag,tag,tag))\n\t\t# Append the probs of the first time instant to the table\n\t\tv_table.append(v_list)\n\t\t\n\t\t# Viterbi for the next n-1 states\n\t\tfor i in range(1,len(sentence)):\n\t\t\tword = sentence[i]\n\t\t\t# List of probs for various states\n\t\t\tv_list = []\n\t\t\t# Calculate v values for the present 12 possible states\n\t\t\tfor tag_curr in tags:\n\t\t\t\t# Calculate list of product of prob of prev state and transition probabilities\n\t\t\t\tv_prod_t = []\n\t\t\t\tfor j in range(0,len(tags)):\n\t\t\t\t\ttag_old = v_table[i-1][j][2]\n\t\t\t\t\tif tag_curr in self.transition[tag_old].keys():\n\t\t\t\t\t\tv_prod_t.append((v_table[i-1][j][0]+math.log(self.transition[tag_old][tag_curr]),tag_old,tag_curr))\n\t\t\t\t\telse:\n\t\t\t\t\t\tv_prod_t.append((v_table[i-1][j][0]+math.log(0.000001),tag_old,tag_curr))\n\t\t\t\t\n\t\t\t\t# Calculate probs of being in a state\n\t\t\t\tif word in self.emission[tag_curr].keys():\n\t\t\t\t\tv_tag = math.log(self.emission[tag_curr][word]) + max(v_prod_t, key=operator.itemgetter(0))[0] #+ math.log(self.prior[tag_curr])\"\"\"\n\t\t\t\telse:\n\t\t\t\t\tv_tag = math.log(0.0000001) + max(v_prod_t, key=operator.itemgetter(0))[0] #+ math.log(self.prior[tag_curr])\"\"\"\n\t\t\t\t# Append as a tuple (prob,state)\n\t\t\t\tv_list.append((v_tag,max(v_prod_t, key=operator.itemgetter(0))[1],tag_curr))\n\t\t\t# Append the probs of the first time instant to the table\n\t\t\tv_table.append(v_list)\n\t\t\n\t\t#print(sentence)\n\t\t\n\t\t#for line in v_table:\n\t\t#\tprint(line,'\\n')\n\t\t#input()\n\t\t# Viterbi table has been created\n\t\t\n\t\t# List to store the predictions\n\t\tpredictions = []\n\t\t\n\t\t# Previous tag\n\t\tmax_prob = max(v_table[len(sentence)-1], key=operator.itemgetter(0))\n\t\tpredictions.append(max_prob[2])\n\t\tprev_tag = max_prob[1]\n\t\t# The posterior of MCMC\n\t\tself.hmm_posterior = max_prob[0]\n\t\t\n\t\t# Backtrack to get MAP\n\t\tfor k in range(len(sentence)-2,-1,-1):\n\t\t\tfor table_entry in v_table[k]:\n\t\t\t\tif table_entry[2] == prev_tag:\n\t\t\t\t\tpredictions.append(table_entry[2])\n\t\t\t\t\tprev_tag = table_entry[1]\n\t\t\t\t\tbreak\n\t\n\t\t#print(sentence)\n\t\t\n\t\t#for line in v_table:\n\t\t#\tprint(line,'\\n')\n\t\t#input()\n\t\t\n\t\t#print(predictions[::-1])\n\t\t#input()\n\t\n\t\treturn predictions[::-1]\n\n\n # This solve() method is called by label.py, so you should keep the interface the\n # same, but you can change the code itself. \n # It should return a list of part-of-speech labelings of the sentence, one\n # part of speech per word.\n #\n\t\n\tdef solve(self, model, sentence):\n\t\tif model == \"Simple\":\n\t\t\treturn self.simplified(sentence)\n\t\telif model == \"Complex\":\n\t\t\treturn self.complex_mcmc(sentence)\n\t\telif model == \"HMM\":\n\t\t\treturn self.hmm_viterbi(sentence)\n\t\telse:\n\t\t\tprint(\"Unknown algo!\")\n","repo_name":"parthnaik13/POS_Tagging","sub_path":"pos_solver.py","file_name":"pos_solver.py","file_ext":"py","file_size_in_byte":14340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"24872463896","text":"# 2022-06-25(June 25th, 2022), Sehyun Kim\n\nimport math\n\nif __name__ == '__main__':\n inputInt = int(input())\n\n if inputInt == 1:\n print(1)\n elif inputInt <= 4:\n print(2)\n else:\n # TEST 1\n testNum = math.floor(math.sqrt(inputInt / 3))\n lowLimit = 3 * (testNum - 1) * testNum + 2\n uppLimit = 3 * (testNum + 1) * testNum + 1\n if lowLimit <= inputInt <= uppLimit:\n print(testNum + 1)\n else:\n print(testNum + 2)\n \n\n \n\n ","repo_name":"sehyun-seankim/baekjoon_algorithms","sub_path":"Mathematics/bj_2292_벌집.py","file_name":"bj_2292_벌집.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"38961427755","text":"from typing import Any\nfrom urllib import response\nfrom flask import Flask, Response, request, abort, jsonify\nfrom rates_demo.rate_data import load_rates_from_history\nimport pathlib\n\napp = Flask(__name__)\n\nrates: dict[str,dict[str, Any]] = {}\n\n@app.route(\"/\")\ndef home() -> Response:\n return \"Hello, World! \"\n\n@app.route(\"/check\")\ndef check() -> Response:\n return \"READY\"\n\n@app.route(\"/api/\")\ndef getDates(rate_date: str) -> Response:\n \"\"\" get rates data from locally loaded csv\"\"\"\n rates_by_country = rates.get(rate_date, None)\n response_dict = {\"Invalid Parameters\": \"Ya Nucklehead\"}\n if rates_by_country:\n base_country = request.args.get('base', \"EUR\")\n country_codes = request.args.get('symbols', '').split(',')\n\n rates_by_country_codes = {country_code: rates_by_country[country_code]\n for country_code in country_codes \n if country_code in rates_by_country}\n base_country_rate = rates_by_country.get(base_country, None)\n\n if base_country_rate is None:\n response_dict = {\"Invalid Parameters\": f\"base country rate not found for {base_country}\"}\n elif len(rates_by_country_codes)==0:\n response_dict = {\"Invalid Parameters\": \"no country symbols provided\"}\n else:\n # valid parameters\n country_rates = {country_code: country_rate/base_country_rate\n for (country_code, country_rate) in rates_by_country_codes.items()}\n response_dict = {\n \"date\": rate_date,\n \"base\": base_country,\n \"rates\": country_rates\n }\n else:\n response_dict = {\"Invalid Parameters\": f\"no data found for date {rate_date}\"}\n \n return jsonify(response_dict)\n\ndef start_rates_api() -> None:\n global rates\n\n rates_file_path = pathlib.Path(\"..\", \"data\", \"eurofxref-hist.csv\")\n\n rates = load_rates_from_history(rates_file_path)\n app.run()\n\nif __name__==\"__main__\":\n start_rates_api()","repo_name":"Nirvan66/advaced-python-course","sub_path":"python_demos/rates_demo/rates_api.py","file_name":"rates_api.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"36704913096","text":"from funciones import checkwin, ver, tradletra, cualp\n\ndef play(p):\n if p==2:\n p=1\n else:\n p=2\n\n while True:\n if checkwin(p, 0):\n print('El juego se terminó.')\n break\n d = open('data.txt','r')\n linea=d.readline()\n \n if p==2:\n linea=d.readline() \n else:\n lineamostrada=d.readline()\n lineamostrada=d.readline()\n if p==2:\n lineamostrada=d.readline()\n\n linea=list(linea)\n lineamostrada=list(lineamostrada)\n intro=input('Coordenada: ')\n coo=(int(intro[1:])*10+tradletra(intro[0]))*2\n\n if linea[coo]=='X':\n linea[coo]='U'\n lineamostrada[coo]='X'\n print('Hit!')\n checkwin(p, 1)\n \n elif linea[coo]=='-':\n linea[coo]='O'\n lineamostrada[coo]='O'\n\n line=''\n most=''\n for i in linea:\n line += i\n for i in lineamostrada:\n most += i\n\n d.close()\n cualp(line, most, p)\n ver(p)\n\n if linea[coo]!='U':\n print('Te equivocaste. Se acabó tu turno.')\n break\n\nturn = open('turno.txt', 'r')\nplayer=int(turn.readline())\nturn.close()\nquien=input('¿Quién eres? ')\n\nif (quien=='Julio' and player==1) or (quien=='Karla' and player==2):\n play(player)\n turn = open('turno.txt', 'w')\n if player==1:\n turn.write('2')\n else:\n turn.write('1')\n turn.close()\nelse:\n print('No es tu turno.')","repo_name":"Julio1261/TC1028","sub_path":"jugar.py","file_name":"jugar.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"29127745499","text":"from NewsScraper import NewsScraper\nfrom GUI_CREATE import NewsGUI\n\n# 创建一个停用词列表\nstopwords = [\"新闻\",\"邮箱\",\"网易\",\"新浪\",\"视频\",\"公司\",\"专家\",\"电影\",\"游戏\",\"国际\",\"世界\",\"亮相\",\"媒体\",\"客户端\",\"事件\",\"全国\",\"财经\",\"时尚\",\"汽车\",\"资讯\"]\n\nurls = ['https://news.163.com/', \"https://news.sina.com.cn/\"]\nscraper = NewsScraper(urls,stopwords)\ngui = NewsGUI(scraper)\ngui.create_gui()\n\n","repo_name":"civilcoco/news_tools","sub_path":"news_run.py","file_name":"news_run.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"48555221222","text":"import sys\nimport time\nfrom datetime import datetime\n\nimport argparse\nfrom argparse import ArgumentParser, RawTextHelpFormatter\n\nfrom iotlabcli import experiment\nfrom iotlabcli import helpers\nfrom iotlabcli import rest\nfrom iotlabcli import auth\nfrom iotlabcli.parser import common, help_msgs\n\nEXPERIMENT_PARSER = \"\"\"\n\niotlab-experiment command-line manages experiments on testbed.\n\n\"\"\"\n\n\ndef parse_options():\n \"\"\" Handle iotlab-experiment command-line options with argparse \"\"\"\n parent_parser = common.base_parser()\n\n # We create top level parser\n parser = ArgumentParser(\n description=EXPERIMENT_PARSER,\n parents=[parent_parser],\n epilog=help_msgs.PARSER_EPILOG.format(\n cli='experiment', option='submit'),\n formatter_class=RawTextHelpFormatter)\n\n subparsers = parser.add_subparsers(dest='command')\n subparsers.required = True # not required by default in Python3\n\n # ####### SUBMIT PARSER ###############\n parser_add_submit_subparser(subparsers)\n\n # ####### SCRIPT PARSER ###############\n parser_add_script_subparser(subparsers)\n\n # ####### STOP PARSER ###############\n stop_parser = subparsers.add_parser('stop', help='stop user experiment')\n common.add_expid_arg(stop_parser)\n\n # ####### GET PARSER ###############\n get_parser = subparsers.add_parser(\n 'get',\n epilog=help_msgs.GET_EPILOG,\n help='get user\\'s experiment',\n formatter_class=RawTextHelpFormatter)\n\n common.add_expid_arg(get_parser)\n\n get_group = get_parser.add_mutually_exclusive_group(required=True)\n\n get_group.add_argument(\n '-d', '--deployment', dest='get_cmd', action='store_const',\n const='deployment', help='get an experiment deployment')\n\n get_group.add_argument(\n '-n', '--nodes', dest='get_cmd', action='store_const',\n const='nodes', help='get an experiment nodes list')\n get_group.add_argument(\n '-ni', '--nodes-id', dest='get_cmd', action='store_const',\n const='nodes_ids', help=('get an experiment nodes id list '\n '(EXP_LIST format : 1-34+72)'))\n get_group.add_argument(\n '-p', '--print', dest='get_cmd', action='store_const',\n const='', help='get an experiment description')\n get_group.add_argument(\n '-a', '--archive', dest='get_cmd', action='store_const',\n const='data', help='get an experiment archive (tar.gz)')\n\n # --list with its options\n get_group.add_argument(\n '-l', '--list', dest='get_cmd', action='store_const',\n const='experiment_list', help='get user\\'s experiment list')\n get_group.add_argument(\n '-r', '--resources', dest='get_cmd', action='store_const',\n const='resources', help='DEPRECATED: use -n or --nodes option')\n get_group.add_argument(\n '-ri', '--resources-id', dest='get_cmd', action='store_const',\n const='resources_ids',\n help=('DEPRECATED: use -ni or --nodes-id option'))\n get_group.add_argument(\n '-s', '--exp-state', dest='get_cmd', action='store_const',\n const='state', help='DEPRECATED: use -p or --print option')\n get_group.add_argument(\n '-st', '--start-time', dest='get_cmd', action='store_const',\n const='start_date',\n help='DEPRECATED: use -p or --print option')\n get_parser.add_argument('--offset', default=0, type=int,\n help='experiment list start index')\n\n get_parser.add_argument('--limit', default=0, type=int,\n help='experiment list maximum length')\n\n get_parser.add_argument('--state', help='experiment list state filter')\n\n get_group.add_argument('-e', '--experiments', dest='get_cmd',\n action='store_const',\n const='experiments',\n help='get running experiments ids')\n get_parser.add_argument(\n '--active', action='store_true', default=False,\n help='experiments: include waiting/starting experiments')\n\n # ####### LOAD PARSER ###############\n load_parser = subparsers.add_parser('load', epilog=help_msgs.LOAD_EPILOG,\n help='load and submit user experiment',\n formatter_class=RawTextHelpFormatter)\n\n load_parser.add_argument('-f', '--file', dest='path_file',\n metavar='EXP_JSON', required=True,\n help='experiment path file')\n\n _load_list_help = ('file path for firmware/script/... if not in'\n ' current directory.')\n\n load_parser.add_argument('-l', '--list', metavar='FILEPATH',\n dest='files', default=[],\n type=(lambda s: s.split(',')), action='append',\n help=_load_list_help)\n\n # ####### RELOAD PARSER ###############\n reload_parser = subparsers.add_parser('reload',\n epilog=help_msgs.RELOAD_EPILOG,\n help='reload user experiment',\n formatter_class=RawTextHelpFormatter)\n common.add_expid_arg(reload_parser, required=True)\n\n _parser_add_duration_and_reservation(reload_parser,\n duration_required=False)\n\n # ####### INFO PARSER ###############\n class DeprecateHelpFormatter(argparse.HelpFormatter):\n \"\"\" Add drepecate help formatter \"\"\"\n def add_usage(self, usage, actions, groups, prefix=None):\n helpers.deprecate_warn_cmd('info', 'iotlab-status', 19)\n return super().add_usage(usage, actions, groups, prefix)\n\n help_msg = 'DEPRECATED: use iotlab-status command instead'\n info_parser = subparsers.add_parser('info', help=help_msg,\n formatter_class=DeprecateHelpFormatter)\n\n info_parser.add_argument('--site',\n action='append', dest='info_selection',\n type=lambda x: ('site', x),\n help='nodes list filter by site')\n info_parser.add_argument('--archi',\n action='append', dest='info_selection',\n type=lambda x: ('archi', x),\n help='nodes list filter by architecture')\n info_parser.add_argument('--state', action='append', dest='info_selection',\n type=lambda x: ('state', x),\n help='nodes list filter by state')\n\n # subcommand\n info_group = info_parser.add_mutually_exclusive_group(required=True)\n info_group.add_argument('-l', '--list', dest='list_id',\n action='store_false', help='nodes list')\n info_group.add_argument('-li', '--list-id', dest='list_id',\n action='store_true',\n help=('nodes id list by archi and state '\n '(EXP_LIST format : 1-34+72)'))\n\n # ####### WAIT PARSER ###############\n parser_add_wait_subparser(subparsers, expid_required=False)\n\n return parser\n\n\ndef parser_add_submit_subparser(subparsers):\n \"\"\"Add 'submit' subparser and return it.\"\"\"\n submit_parser = subparsers.add_parser(\n 'submit', help='submit user experiment',\n epilog=help_msgs.SUBMIT_EPILOG, formatter_class=RawTextHelpFormatter)\n\n submit_parser.add_argument('-p', '--print',\n dest='print_json', action='store_true',\n help='print experiment submission')\n\n # General experiment configuration\n conf_parser = submit_parser.add_argument_group('experiment configuration')\n\n conf_parser.add_argument('-n', '--name', help='experiment name')\n _parser_add_duration_and_reservation(conf_parser, duration_required=True)\n\n # Resources\n res_parser = submit_parser.add_argument_group('resource configuration')\n res_parser.add_argument('-l', '--list', action='append',\n dest='nodes_list', required=True,\n type=exp_resources_from_str,\n help=\"experiment list\")\n res_parser.add_argument('-s', '--site-association', action='append',\n type=site_association_from_str,\n help='sites associations')\n\n # Help messages for resources\n help_parser = submit_parser.add_argument_group('advanced help options')\n common.HelpAction.add_help(help_parser, '--help-list',\n 'show help on --list option',\n help_msgs.SUBMIT_LIST_HELP)\n common.HelpAction.add_help(help_parser, '--help-site-association',\n 'show help on --site-association option',\n help_msgs.SUBMIT_SITE_ASSOC_HELP)\n\n\ndef _parser_add_duration_and_reservation( # pylint:disable=invalid-name\n subparser, duration_required):\n \"\"\"Add a 'duration' and a 'reservation' argument to subparser.\n\n :param subparser: subparser instance\n :param duration_required: select if duration is required or optional\n \"\"\"\n subparser.add_argument('-d', '--duration', required=duration_required,\n type=int, help='experiment duration in minutes')\n\n subparser.add_argument('-r', '--reservation', type=int,\n help=('experiment schedule starting : seconds '\n 'since 1970-01-01 00:00:00 UTC'))\n\n\ndef parser_add_wait_subparser(subparsers, expid_required=False):\n \"\"\"Add wait experiment subparser and return it.\"\"\"\n wait_parser = subparsers.add_parser(\n 'wait', help='wait user experiment started',\n epilog=help_msgs.WAIT_EPILOG, formatter_class=RawTextHelpFormatter)\n\n common.add_expid_arg(wait_parser, required=expid_required)\n\n wait_parser.add_argument(\n '--state', default='Running',\n help=\"wait states `State1,State2` or Finished, default 'Running'\")\n wait_parser.add_argument(\n '--step', default=5, type=int,\n help=\"Wait time in seconds between each check\")\n wait_parser.add_argument(\n '--timeout', default=experiment.WAIT_TIMEOUT_DEFAULT, type=float,\n help=\"Max time to wait in seconds\")\n wait_parser.add_argument(\n '--cancel-on-timeout', action='store_true',\n help=\"Cancel experiment if timeout is reached\")\n\n return wait_parser\n\n\ndef parser_add_script_subparser(subparsers):\n \"\"\"Add suparser for 'script'.\"\"\"\n _script_parser = subparsers.add_parser(\n 'script', help='run script on sites frontends',\n epilog=help_msgs.SCRIPT_EPILOG, formatter_class=RawTextHelpFormatter)\n common.add_expid_arg(_script_parser)\n\n script_group = _script_parser.add_argument_group(\"Command\")\n script_group = script_group.add_mutually_exclusive_group(required=True)\n\n script_group.add_argument('--run', type=run_site_association_from_str,\n metavar=RUN_SITE_ASSOCIATION_METAVAR,\n dest='run_script_site', nargs='+',\n help=\"sites association with 'script'\")\n script_group.add_argument('--kill', type=common.site_with_domain_checked,\n metavar='site', dest='kill_sites',\n nargs='*', help='sites list')\n script_group.add_argument('--status', type=common.site_with_domain_checked,\n metavar='site', dest='status_sites',\n nargs='*', help='sites list')\n return _script_parser\n\n\ndef exp_infos_from_str(exp_str):\n \"\"\"Extract nodes and associations.\"\"\"\n try:\n params = exp_str.split(',')\n nodes, params = _extract_firmware_nodes_list(params)\n associations = _extract_associations(params)\n except ValueError as err:\n raise argparse.ArgumentTypeError(\n f'Invalid arguments in experiment list {exp_str!r}: {err}')\n\n return nodes, associations\n\n\ndef exp_resources_from_str(exp_str):\n \"\"\"Extract an 'experiment.exp_resources' from parameter string.\n\n Accepted formats:\n + 9,archi=wsn430:cc1101+site=grenoble,tp.hex,battery,mobility=JHall\n\n + grenoble,m3,1-20,/home/cc1101.hex\n + rocquencourt,a8,1-5,,battery,firmware=a8.elf\n \"\"\"\n nodes, associations = exp_infos_from_str(exp_str)\n firmware_path = associations.pop('firmware', None)\n profile_name = associations.pop('profile', None)\n return experiment.exp_resources(nodes, firmware_path, profile_name,\n **associations)\n\n\ndef site_association_from_str(site_assoc_str):\n \"\"\"Extract site_association from given string.\n\n Format is:\n\n site,name=value\n site1,site2,site3,assoc_name=assoc_value,assoc2=assoc_val2\n\n Sites validity is checked\n\n :raises argparse.ArgumentTypeError: on invalid site_assoc_str\n \"\"\"\n # Decode keywoard associations\n try:\n sites, kwassocs = _args_kwargs(site_assoc_str.split(','))\n\n # Validate site and add domain\n sites = [common.site_with_domain_checked(site) for site in sites]\n return experiment.site_association(*sites, **kwassocs)\n except ValueError as err:\n raise argparse.ArgumentTypeError(f'Invalid site_association: {err}')\n\n\nRUN_SITE_ASSOCIATIONS_STR = ('script=script_path'\n '[,scriptconfig=scriptconfig_path]')\nRUN_SITE_ASSOCIATION_METAVAR = f'site,site,{RUN_SITE_ASSOCIATIONS_STR}'\n\n\ndef _run_associations_arg_check(script, scriptconfig=None):\n # pylint:disable=unused-argument,unnecessary-pass\n \"\"\"To be used with **associations to check given arguments.\"\"\"\n pass\n\n\ndef run_site_association_from_str(site_assoc_str):\n \"\"\"Extract site_association and verify given associations.\n\n 'script' association is mandatory.\n No other associations allowed.\n \"\"\"\n site_association = site_association_from_str(site_assoc_str)\n\n associations = site_association.associations\n try:\n _run_associations_arg_check(**associations)\n except TypeError:\n raise argparse.ArgumentTypeError(\n f\"Invalid associations in {site_assoc_str} \"\n f\"should match {RUN_SITE_ASSOCIATIONS_STR}\"\n )\n\n return site_association\n\n\ndef _valid_param(param):\n \"\"\"Check parameter are valid for _args_kwargs.\n\n * no space\n * a name before '='\n \"\"\"\n if ' ' in param:\n raise ValueError('no space allowed')\n if param.startswith('='):\n raise ValueError(f\"name required for kwarg '{param}'\")\n\n\ndef _args_kwargs(params):\n \"\"\"Separate args and kwargs from params.\n\n `args` must all be at first and `kwargs` at the end\n Space are forbidden as well as incomplete kwargs without name.\n\n >>> expected = (['a', 'b', 'c'], {'e': 'f', 'g': 'h'})\n >>> _args_kwargs(['a', 'b', 'c', 'e=f', 'g=h']) == expected\n True\n\n >>> _args_kwargs(['a', 'b', 'c']) == (['a', 'b', 'c'], {})\n True\n >>> _args_kwargs(['e=f', 'g=h']) == ([], {'e': 'f', 'g': 'h'})\n True\n\n # no value ignored\n >>> _args_kwargs(['e=']) == ([], {})\n True\n\n >>> _args_kwargs(['a==b']) == ([], {'a': '=b'})\n True\n\n # Space in value\n >>> _args_kwargs(['val ue'])\n Traceback (most recent call last):\n ValueError: no space allowed\n\n >>> _args_kwargs(['=f'])\n Traceback (most recent call last):\n ValueError: name required for kwarg '=f'\n\n # Order not respected\n >>> _args_kwargs(['e=f', 'a'])\n Traceback (most recent call last):\n ValueError: got argument after keyword argument\n >>> _args_kwargs(['a', 'e=f', 'b'])\n Traceback (most recent call last):\n ValueError: got argument after keyword argument\n\n >>> _args_kwargs(['e=f', 'e=i'])\n Traceback (most recent call last):\n ValueError: keyword argument \"e\" specified multiple times\n\n :returns: (`args`, `kwargs`)\n \"\"\"\n args, kwargs = [], {}\n\n _check_args_then_kwargs(params)\n\n for param in params:\n _valid_param(param)\n if '=' in param:\n # Parsing kwargs\n key, value = param.split('=', 1)\n _add_key_value(kwargs, key, value)\n else:\n # Parsing args\n args.append(param)\n\n return args, kwargs\n\n\ndef _check_args_then_kwargs(params):\n \"\"\"Check that args are first, and then kwargs only.\"\"\"\n is_kwargs = ['=' in param for param in params]\n # Should be many False then many True\n if is_kwargs != sorted(is_kwargs):\n raise ValueError('got argument after keyword argument')\n\n\ndef _add_key_value(kwargs, key, value=''):\n \"\"\"Add `key`,`value` if value is not empty.\n\n Raise an error if key exists.\n \"\"\"\n error = 'keyword argument \"%s\" specified multiple times'\n if key in kwargs:\n raise ValueError(error % key)\n if value:\n kwargs[key] = value\n\n\ndef _submit_args_to_dict(firmware='', profile=''):\n \"\"\"Return kwargs for this arguments. Remove empty values\"\"\"\n kwargs = {}\n if firmware:\n kwargs['firmware'] = firmware\n if profile:\n kwargs['profile'] = profile\n return kwargs\n\n\ndef _merge_assocs_args_d_kwargs(args_dict, kwargs):\n \"\"\"Merge args_dict and kwargs. Detect duplicate keys.\"\"\"\n\n error_str = 'Association \"%s\" provided by argument and keyword argument'\n for key in args_dict:\n if key in kwargs:\n raise ValueError(error_str % key)\n\n merged = {}\n merged.update(args_dict)\n merged.update(kwargs)\n return merged\n\n\nSUBMIT_ASSOC_ARGS_KWARGS = '[firmware][,profile][,assoc=value][,assoc=...]'\n\n\ndef _extract_associations(params):\n \"\"\"Extract 'associations'.\n\n Firmware, profile at positional args then keyword arguments.\n :raises ValueError: on invalid input.\n \"\"\"\n args, kwargs = _args_kwargs(params)\n try:\n args_dict = _submit_args_to_dict(*args)\n except TypeError:\n raise ValueError(\n \"Invalid positional arguments, \"\n f\"should be {SUBMIT_ASSOC_ARGS_KWARGS}\"\n )\n associations = _merge_assocs_args_d_kwargs(args_dict, kwargs)\n\n return associations\n\n\ndef get_alias_properties(properties_str):\n \"\"\" Extract nodes selection properties from given properties_str\n\n >>> get_alias_properties(\"site=grenoble+archi=wsn430:cc1101+mobile=True\")\n ('grenoble', 'wsn430:cc1101', 'True')\n\n >>> get_alias_properties(\"site=strasbourg+archi=m3:at86rf231\")\n ('strasbourg', 'm3:at86rf231', None)\n\n >>> get_alias_properties(\"site=strasbourg\")\n Traceback (most recent call last):\n ValueError: Properties should be \"archi\", \"site\" and optional \"mobile\".\n\n >>> inval_prop = \"site=strasbourg+archi=val+uknown=test\"\n >>> get_alias_properties(inval_prop)\n Traceback (most recent call last):\n ValueError: Properties should be \"archi\", \"site\" and optional \"mobile\".\n \"\"\"\n properties_dict = _properties_str_to_dict(properties_str)\n\n try:\n properties = _alias_properties_from_kwargs(**properties_dict)\n except TypeError:\n raise ValueError('Properties should be \"archi\", \"site\"'\n ' and optional \"mobile\".')\n\n # Forward check if there are more properties\n site, archi, mobile = properties\n\n return site, archi, mobile\n\n\ndef _alias_properties_from_kwargs(site, archi, mobile=None):\n \"\"\"To be used with **properties, checks given keys.\n\n Returns values.\n \"\"\"\n return site, archi, mobile\n\n\ndef _properties_str_to_dict(properties_str):\n \"\"\"Extract a properties string to a dict:\n\n >>> _properties_str_to_dict('a=1+b=3') == {'a': '1', 'b': '3'}\n True\n\n >>> _properties_str_to_dict('a=1+a=2')\n Traceback (most recent call last):\n ValueError: Property \"a\" should appear only once\n\n >>> _properties_str_to_dict('a=+b=2')\n Traceback (most recent call last):\n ValueError: Invalid empty value for property \"a\"\n \"\"\"\n properties = [p.split('=', 1) for p in properties_str.split('+')]\n\n prop_dict = {}\n\n for key, value in properties:\n if key in prop_dict:\n raise ValueError(f'Property \"{key}\" should appear only once')\n if value == '':\n raise ValueError(f'Invalid empty value for property \"{key}\"')\n prop_dict[key] = value\n\n return prop_dict\n\n\ndef mobile_from_mobile_str(mobile_str=None):\n \"\"\"Return the value to put in experiment json from mobile_str.\n\n >>> mobile_from_mobile_str(None)\n False\n\n >>> mobile_from_mobile_str('true')\n True\n >>> mobile_from_mobile_str('True')\n True\n\n >>> mobile_from_mobile_str('false')\n False\n\n >>> mobile_from_mobile_str('1')\n True\n >>> mobile_from_mobile_str('0')\n False\n\n >>> mobile_from_mobile_str('invalid_value')\n Traceback (most recent call last):\n ValueError: Invalid 'mobile' property: %r. Should be in 'true|false|0|1'\n \"\"\"\n mobile_str = mobile_str or 'False'\n\n for convert_fct in (_mobile_str_true_false, _mobile_str_as_bool):\n try:\n return convert_fct(mobile_str)\n except (KeyError, ValueError):\n pass\n\n raise ValueError(\"Invalid 'mobile' property: %r.\"\n \" Should be in 'true|false|0|1'\")\n\n\ndef _mobile_str_true_false(mobile_str):\n \"\"\"Try checking for 'true', 'false' in any case.\"\"\"\n mobile_str = mobile_str.title() # upper first letter\n return {'True': True, 'False': False}[mobile_str]\n\n\ndef _mobile_str_as_bool(mobile_str):\n \"\"\"Try converting to an int-bool.\"\"\"\n return bool(int(mobile_str))\n\n\ndef _extract_firmware_nodes_list(param_list):\n \"\"\"\n Extract a firmware nodes list from param_list\n param_list is modified by the function call\n :param param_list: can have following formats\n * ['9', 'archi=wsn430:cc1101+site=grenoble', ...] Alias type\n * ['grenoble', 'm3', '1-4+8-12+7', ...] Physical type\n \"\"\"\n\n # list in iotlab-experiment (alias or physical)\n if param_list[0].isdigit(): # alias selection\n # extract parameters\n nb_nodes, properties_str = param_list[0:2]\n param_list = param_list[2:]\n\n # parse parameters\n site, archi, _mobile = get_alias_properties(properties_str)\n mobile = mobile_from_mobile_str(_mobile)\n nodes = experiment.AliasNodes(int(nb_nodes), site, archi, mobile)\n else: # physical selection\n # extract parameters\n site, archi, nodes_str = param_list[0:3]\n param_list = param_list[3:]\n\n # parse parameters\n nodes = common.nodes_list_from_info(site, archi, nodes_str)\n common.check_site_with_server(site)\n return nodes, param_list\n\n\ndef submit_experiment_parser(opts):\n \"\"\" Parse namespace 'opts' and execute requested 'submit' command \"\"\"\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n\n return experiment.submit_experiment(api, opts.name, opts.duration,\n opts.nodes_list, opts.reservation,\n opts.print_json,\n opts.site_association)\n\n\ndef script_parser(opts):\n \"\"\"Parse namespace 'opts' and execute requestes 'run' command.\"\"\"\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n exp_id = helpers.get_current_experiment(api, opts.experiment_id)\n\n command, options = _script_command_options(opts)\n\n return experiment.script_experiment(api, exp_id, command, *options)\n\n\ndef _script_command_options(opts):\n \"\"\"Extract `command` and `options` from argparse 'opts'.\"\"\"\n if opts.run_script_site is not None:\n command = 'run'\n options = opts.run_script_site\n elif opts.kill_sites is not None:\n command = 'kill'\n options = opts.kill_sites\n elif opts.status_sites is not None:\n command = 'status'\n options = opts.status_sites\n else: # pragma: no cover\n raise ValueError('Unknown request')\n\n return command, options\n\n\ndef stop_experiment_parser(opts):\n \"\"\" Parse namespace 'opts' object and execute requested 'stop' command \"\"\"\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n exp_id = helpers.get_current_experiment(api, opts.experiment_id)\n\n return experiment.stop_experiment(api, exp_id)\n\n\ndef get_experiment_parser(opts):\n \"\"\" Parse namespace 'opts' object and execute requested 'get' command \"\"\"\n\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n # pylint:disable=no-else-return\n if opts.get_cmd == 'experiment_list':\n return experiment.get_experiments_list(api, opts.state, opts.limit,\n opts.offset)\n elif opts.get_cmd in ('start_date', 'state'):\n return _get_experiment_attr(api, opts)\n elif opts.get_cmd == 'experiments':\n return experiment.get_active_experiments(api,\n running_only=not opts.active)\n else:\n exp_id = helpers.get_current_experiment(api, opts.experiment_id)\n\n return experiment.get_experiment(api, exp_id,\n _deprecate_cmd(opts.get_cmd))\n\n\ndef _deprecate_cmd(cmd):\n if cmd == 'resources':\n new_cmd = 'nodes'\n helpers.deprecate_warn_cmd(cmd, new_cmd, 8)\n return new_cmd\n if cmd == 'resources_ids':\n helpers.deprecate_warn_cmd('resources-id', 'nodes-ids', 8)\n return 'nodes_ids'\n return cmd\n\n\ndef _get_experiment_attr(api, opts):\n \"\"\" Return start_time or state experiment attribute with old api format\"\"\"\n assert opts.get_cmd in ('state', 'start_date',)\n exp_id = helpers.get_current_experiment(api, opts.experiment_id,\n running_only=False)\n ret = experiment.get_experiment(api, exp_id, '')\n if opts.get_cmd == 'state':\n helpers.deprecate_warn_cmd('exp-state', 'print', 8)\n return {opts.get_cmd: ret[opts.get_cmd]}\n helpers.deprecate_warn_cmd('start-time', 'print', 8)\n # start_date option\n utc_date = datetime.strptime(ret[opts.get_cmd],\n '%Y-%m-%dT%H:%M:%SZ')\n timestamp = (utc_date - datetime(1970, 1, 1)).total_seconds()\n local_date = time.ctime(timestamp) if timestamp else 'Unknown'\n return {'start_time': int(timestamp),\n 'local_date': local_date}\n\n\ndef load_experiment_parser(opts):\n \"\"\" Parse namespace 'opts' object and execute requested 'load' command \"\"\"\n\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n files = helpers.flatten_list_list(opts.files)\n return experiment.load_experiment(api, opts.path_file, files)\n\n\ndef reload_experiment_parser(opts):\n \"\"\"Parse namespace 'opts' object and execute requested 'reload' command.\"\"\"\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n return experiment.reload_experiment(api, opts.experiment_id,\n opts.duration, opts.reservation)\n\n\ndef info_experiment_parser(opts):\n \"\"\" Parse namespace 'opts' object and execute requested 'info' command \"\"\"\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n\n helpers.deprecate_warn_cmd('info', 'iotlab-status', 7)\n selection = dict(opts.info_selection or ())\n return experiment.info_experiment(api, opts.list_id, **selection)\n\n\ndef wait_experiment_parser(opts):\n \"\"\" Parse namespace 'opts' object and execute requested 'wait' command \"\"\"\n\n user, passwd = auth.get_user_credentials(opts.username, opts.password)\n api = rest.Api(user, passwd)\n\n exp_id = helpers.get_current_experiment(\n api, opts.experiment_id, running_only=False)\n\n sys.stderr.write(\n f\"Waiting that experiment {exp_id} gets in state {opts.state}\\n\"\n )\n\n return experiment.wait_experiment(api, exp_id, opts.state,\n opts.step, opts.timeout,\n opts.cancel_on_timeout)\n\n\ndef experiment_parse_and_run(opts):\n \"\"\" Parse namespace 'opts' object and execute requested command\n Return result object\n \"\"\"\n command = {\n 'submit': submit_experiment_parser,\n 'script': script_parser,\n 'stop': stop_experiment_parser,\n 'get': get_experiment_parser,\n 'load': load_experiment_parser,\n 'reload': reload_experiment_parser,\n 'info': info_experiment_parser,\n 'wait': wait_experiment_parser,\n }[opts.command]\n\n return command(opts)\n\n\ndef main(args=None):\n \"\"\"Main command-line execution loop.\"\"\"\n args = args or sys.argv[1:]\n parser = parse_options()\n common.main_cli(experiment_parse_and_run, parser, args)\n","repo_name":"iot-lab/cli-tools","sub_path":"iotlabcli/parser/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":28902,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"20"}
+{"seq_id":"16811354445","text":"import math\n\nL = '123'\nn = 678910\nlog_term = math.log10(2)\nj_list = []\n\nj = 1\nwhile True:\n nlog = j * log_term\n short = str(10 ** (nlog - math.floor(nlog) + 2))\n if short[:3] == L:\n j_list.append(j)\n ntest = len(j_list)\n if ntest == n:\n print(j, 'is the solution')\n break\n j += 1\n if ntest % 10000 == 0:\n print(ntest)","repo_name":"DlabrecquePE/project-euler","sub_path":"src/Problem 686.py","file_name":"Problem 686.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"71461868849","text":"import unittest\nimport numpy as np\nimport dtmm.data as data\nimport dtmm.rotation as rot\nimport dtmm.conf as conf\n\nRx = rot.rotation_matrix_x\nRy = rot.rotation_matrix_y\nRz = rot.rotation_matrix_z\n\nclass TestData(unittest.TestCase):\n \n def setUp(self):\n pass\n \n def test_rotate_director(self):\n \"\"\"\n Tests the rotate_director function using rot90_director.\n This test only verifies 90 degree rotations since other rotations are not exact.\n Returns\n -------\n\n \"\"\"\n # Strings representing the rotation\n rotation_strings = (\"+x\", \"-2x\", \"+2y\", \"-y\", \"y\", \"z\", \"3z\")\n # Rotation functions to use\n rotation_functions = (Rx, Rx, Ry, Ry, Ry, Rz, Rz)\n # Angles used to create rotation matrices\n angles = (np.pi/2, -np.pi, np.pi, -np.pi/2, np.pi/2, np.pi/2, -np.pi/2)\n\n # Number of vertical layers\n n_layers = 5\n # height of each layer\n heigh = 5\n # width of each layer\n width = 5\n\n # Create random test data of size\n test_data = np.random.randn(n_layers, heigh, width, 3)\n # Cast data to correct data type. Done in case single precision is being used\n test_data = np.asarray(test_data, dtype=conf.FDTYPE)\n\n # Iterate through each rotation test\n for rotation_str, rotation_function, angle in zip(rotation_strings, rotation_functions, angles):\n # Calculate exact result using rot90_director\n rotated_data_goal = data.rot90_director(test_data, rotation_str)\n # Create rotation matrix\n rotation_matrix = rotation_function(angle)\n # Rotate director, do not normalize length because initial data is not normalized\n rotated_data = data.rotate_director(rotation_matrix, test_data, norm = False)\n # Compare inner data, without boundaries \n self.assertTrue(np.allclose(rotated_data_goal[1:-1,1:-1,1:-1], rotated_data[1:-1,1:-1,1:-1]))\n \nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"IJSComplexMatter/dtmm","sub_path":"dtmm/test/test_data.py","file_name":"test_data.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"40436771808","text":"import logging\nimport os\n\nfrom PIL import Image\nfrom PIL import ImageFile\nfrom torch.utils.data import Dataset\n\nlogging.getLogger(\"transformers.tokenization_utils\").setLevel(logging.ERROR)\nImageFile.LOAD_TRUNCATED_IMAGES = True\nImage.MAX_IMAGE_PIXELS = None\n\nclass ImageTextDataset(Dataset):\n def __init__(self, data_folder: str, transform):\n self.transform = transform\n IMG_EXTENSIONS = {'.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG'}\n\n # Sorting the elements in the data based on batch length\n self.image_paths = []\n\n self.root_path = os.path.abspath(data_folder)\n\n for f in os.listdir(data_folder):\n f_path = os.path.join(data_folder, f)\n\n if os.path.isdir(f_path):\n for f2 in os.listdir(f_path):\n extension = f2[f2.rfind(\".\"):]\n if extension in IMG_EXTENSIONS:\n self.image_paths.append(f + \"/\" + f2)\n else:\n extension = f[f.rfind(\".\"):]\n if extension in IMG_EXTENSIONS:\n self.image_paths.append(f)\n\n print(\"Loaded image paths\", self.root_path, len(self.image_paths))\n\n def __len__(self):\n return len(self.image_paths)\n\n def __getitem__(self, item: int):\n path = os.path.join(self.root_path, self.image_paths[item])\n try:\n # Make sure not to deal with rgba or grayscale images.\n image = Image.open(path).convert(\"RGB\")\n if self.transform is not None:\n image = self.transform(image)\n return {\"images\": image, \"paths\": self.image_paths[item]}\n except:\n print(\"error in reading\", path)\n return None\n","repo_name":"rasoolims/image_feature_extractor","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"16208727288","text":"from Utils.ParameterParser import ParameterParser\nimport os\n\n\n\n\nclass CaseTagReplacer(ParameterParser):\n @property\n def case_path(self):\n return self._case_path\n \n @property\n def parameters(self):\n return super(CaseTagReplacer, self).parameters\n \n \n def __init__(self, case_path):\n super(CaseTagReplacer, self).__init__()\n self._case_path = case_path\n \n \n def ParseCase(self, param_dict):\n files_to_parse = []\n for root, dirs, files in os.walk(self._case_path, topdown=True):\n for file in files:\n tmp_file_path = os.path.join(root, file)\n files_to_parse.append(tmp_file_path)\n \n \n for file in files_to_parse:\n self.ParseParameters(param_dict, file, file)\n \n \n","repo_name":"alexpec/bes","sub_path":"OFCase/CaseTagReplacer.py","file_name":"CaseTagReplacer.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"70080533171","text":"from django.urls import path \n\nfrom ..post import views\n\n\napp_name = \"post\"\n\nurlpatterns = [\n\t#path(\"Detalle/\", views.detalle, name=\"detalle\"),\n\tpath(\"Detalle//\", views.Detalle.as_view(), name=\"detalle\"),\n\tpath(\"Detalle//comentarios\", views.NuevoComentario.as_view(), name=\"comentarios\"),\n\tpath(\"Detalle//vercomentarios\", views.VerComentario.as_view(), name=\"vercomentarios\"),\n\n\t#path(\"Detalle//\", views.detalle1, name=\"detalle_pk\"),\n\t\n\t# Admin\n\t#path(\"Admin/Listar/\", views.ListarAdmin.as_view(), name=\"admin_listar\"),\n\tpath(\"Admin/Nuevo/\", views.NuevoAdmin.as_view(), name=\"admin_nuevo\"),\n\tpath(\"Admin/Editar//\", views.EditarAdmin.as_view(), name=\"admin_editar\"),\n\tpath(\"nuevo/\", views.NuevoPost.as_view(), name=\"nuevoPost\"),\n\n]","repo_name":"Davydenkox/EcoBlog","sub_path":"EcoBlog/bloginfo/apps/post/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"13589374752","text":"# -*- coding:utf-8 -*-\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution:\n # 快慢指针解决\n def FindKthToTail(self, head, k):\n # 创建两个指针\n q=m=head\n # 快指针先走k步\n for i in range(k):\n if q is None:\n return None\n q=q.next\n # 快慢指针一起走\n while q:\n q=q.next\n m=m.next\n return m\n\n\n ''' def __init__(self):\n self.head = None\n\n def creat_list_tail(self, data):\n self.head = ListNode(data[0])\n p = self.head\n # 循环插入其他节点\n for num in data[1:]:\n node = ListNode(num)\n p.next = node\n p = node\n\n # 获得链表长度\n def lenght(self):\n # if self.isEmpty():\n # exit(0)\n p = self.head\n len = 0\n while p:\n len += 1\n p = p.next\n return len\n\n def FindKthToTail(self, data, k):\n p = self.head\n i = 0\n # 找到倒数第k个节点的index\n index = self.lenght() - k\n\n # 从头开始找下一个节点直到当前节点的index符合条件\n while p.next:\n pre = p\n p = p.next\n i += 1\n if i == index:\n return p.val\n\n # write code here\n\n\ndef main():\n # 怎么自己写测试\n data = [1, 2, 3, 4, 5]\n k=2\n l = Solution()\n l.creat_list_tail(data)\n print(l.FindKthToTail(data,k))\n\n\nif __name__ == \"__main__\":\n main()\n'''\n","repo_name":"gaofan0906/Practise","sub_path":"0816_8.py","file_name":"0816_8.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"46293692667","text":"from __future__ import unicode_literals\n\nimport copy\nimport types\n\n\nclass AttribDict(dict):\n def __init__(self, indict=None, attribute=None):\n if indict is None:\n indict = {}\n\n # Set any attributes here - before initialisation\n # these remain as normal attributes\n self.attribute = attribute\n dict.__init__(self, indict)\n self.__initialised = True\n\n # After initialisation, setting attributes\n # is the same as setting an item\n\n def __getattr__(self, item):\n \"\"\"\n Maps values to attributes\n Only called if there *is NOT* an attribute with this name\n \"\"\"\n\n try:\n return self.__getitem__(item)\n except KeyError:\n raise AttributeError(\"unable to access item '%s'\" % item)\n\n def __setattr__(self, item, value):\n \"\"\"\n Maps attributes to values\n Only if we are initialised\n \"\"\"\n\n # This test allows attributes to be set in the __init__ method\n if \"_AttribDict__initialised\" not in self.__dict__:\n return dict.__setattr__(self, item, value)\n\n # Any normal attributes are handled normally\n elif item in self.__dict__:\n dict.__setattr__(self, item, value)\n\n else:\n self.__setitem__(item, value)\n\n def __getstate__(self):\n return self.__dict__\n\n def __setstate__(self, dict):\n self.__dict__ = dict\n\n def __deepcopy__(self, memo):\n retVal = self.__class__()\n memo[id(self)] = retVal\n\n for attr in dir(self):\n if not attr.startswith('_'):\n value = getattr(self, attr)\n if not isinstance(value, (types.BuiltinFunctionType, types.FunctionType, types.MethodType)):\n setattr(retVal, attr, copy.deepcopy(value, memo))\n\n for key, value in self.items():\n retVal.__setitem__(key, copy.deepcopy(value, memo))\n\n return retVal\n","repo_name":"LandGrey/pydictor","sub_path":"lib/data/datatype.py","file_name":"datatype.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","stars":2995,"dataset":"github-code","pt":"20"}
+{"seq_id":"38568028627","text":"# File: experimentSpace.py\n# Author: Carter Hale, Owen Hichens\n# Date Created: March 24, 2021\n# Last Updated: April 6, 2021\n\n# Element Tree for XML Parsing\nimport xml.etree.ElementTree as ET\n# OS for System Calls to Run GA\nimport os\n\n# Retrieve Settings from XML\ntree = ET.parse('config.xml')\nroot = tree.getroot()\n\n# Set Up XML Tree Root\nrootGA = root.find(\"GeneticAlgo\")\nfitFun = rootGA.find(\"FitnessFunction\")\nidealMet = fitFun.find(\"IdealMetrics\")\n\n# Get user input for changing variable and step size\nvar = input(\"'Active', 'Percent', or 'Alive': \")\nminStep = float(input(\"Min step size: \"))\nmaxStep = float(input(\"Max step size: \"))\n\n# First tests are for Active Metric\nchangingVar = idealMet.find(var)\nchangingMin = changingVar.find(\"Min\")\nchangingMax = changingVar.find(\"Max\")\n\n# Set starting Metrics\nif var == 'Active':\n cMin = 0.0\n cMax = 0.05\nelif var == 'Percent':\n cMin = 0.0\n cMax = 0.4\nelif var == 'Alive':\n cMin = 0.0\n cMax = 0.49\nchangingMin.text = str(cMin)\nchangingMax.text = str(cMax)\ntree.write('config.xml')\n\n# Loop through Search Space\nfor i in range(3):\n # Run GA and save output to logs\n os.system(\"unbuffer ./main.exe | tee ./test_logs/\" + str(i) + \"test.log\")\n\n # Iterate over search space\n cMin = cMin + minStep\n cMax = cMax + maxStep\n\n # Update Config File\n changingMin.text = str(cMin)\n changingMax.text = str(cMax)\n tree.write('config.xml')\n\n # Remove Past Simulation Files\n os.system(\"rm -r /home/CellAutomataGA/Desktop/Golly Patterns/Simulation\")\n","repo_name":"HaleCT2/CellAutomataGA","sub_path":"Testing/experimentSpace.py","file_name":"experimentSpace.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"21893095229","text":"# coding: utf-8\n\nimport six\n\nfrom huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization\n\n\nclass ShowNodesRequest:\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n sensitive_list = []\n\n openapi_types = {\n 'offset': 'int',\n 'limit': 'int',\n 'name': 'str',\n 'workspace_id': 'str',\n 'app_name': 'str',\n 'tags': 'str',\n 'provider': 'str',\n 'cluster_id': 'str',\n 'status': 'str',\n 'active_status': 'str'\n }\n\n attribute_map = {\n 'offset': 'offset',\n 'limit': 'limit',\n 'name': 'name',\n 'workspace_id': 'workspace_id',\n 'app_name': 'app_name',\n 'tags': 'tags',\n 'provider': 'provider',\n 'cluster_id': 'cluster_id',\n 'status': 'status',\n 'active_status': 'active_status'\n }\n\n def __init__(self, offset=None, limit=None, name=None, workspace_id=None, app_name=None, tags=None, provider=None, cluster_id=None, status=None, active_status=None):\n \"\"\"ShowNodesRequest\n\n The model defined in huaweicloud sdk\n\n :param offset: 查询的起始位置，取值范围为非负整数，默认为0\n :type offset: int\n :param limit: 每页显示的条目数量，取值范围1~100，默认为100\n :type limit: int\n :param name: 设备名称，模糊匹配，只允许中文字符、英文字母、数字、下划线、中划线，最大长度64\n :type name: str\n :param workspace_id: 工作空间ID，默认为注册账号/子账号的default工作空间，可通过专业版HiLens控制台展开工作空间列表获取到工作空间ID\n :type workspace_id: str\n :param app_name: 应用名称，模糊匹配，只允许英文小写字母、数字、中划线，最大长度32, 英文小写字母或数字开头和结尾\n :type app_name: str\n :param tags: 标签的key和value通过点连接，多个标签通过逗号连接，如：tags=key1.value1,key2.value2\n :type tags: str\n :param provider: 服务提供者：ief或hilens。不传会查询全部服务类型的设备列表\n :type provider: str\n :param cluster_id: 集群ID，若值为0会过滤出不隶属任何集群的设备\n :type cluster_id: str\n :param status: 状态\n :type status: str\n :param active_status: 设备激活状态，分别是未激活（INACTIVE）、已激活（ACTIVATED）、已到期（EXPIRED）\n :type active_status: str\n \"\"\"\n \n \n\n self._offset = None\n self._limit = None\n self._name = None\n self._workspace_id = None\n self._app_name = None\n self._tags = None\n self._provider = None\n self._cluster_id = None\n self._status = None\n self._active_status = None\n self.discriminator = None\n\n if offset is not None:\n self.offset = offset\n if limit is not None:\n self.limit = limit\n if name is not None:\n self.name = name\n if workspace_id is not None:\n self.workspace_id = workspace_id\n if app_name is not None:\n self.app_name = app_name\n if tags is not None:\n self.tags = tags\n if provider is not None:\n self.provider = provider\n if cluster_id is not None:\n self.cluster_id = cluster_id\n if status is not None:\n self.status = status\n if active_status is not None:\n self.active_status = active_status\n\n @property\n def offset(self):\n \"\"\"Gets the offset of this ShowNodesRequest.\n\n 查询的起始位置，取值范围为非负整数，默认为0\n\n :return: The offset of this ShowNodesRequest.\n :rtype: int\n \"\"\"\n return self._offset\n\n @offset.setter\n def offset(self, offset):\n \"\"\"Sets the offset of this ShowNodesRequest.\n\n 查询的起始位置，取值范围为非负整数，默认为0\n\n :param offset: The offset of this ShowNodesRequest.\n :type offset: int\n \"\"\"\n self._offset = offset\n\n @property\n def limit(self):\n \"\"\"Gets the limit of this ShowNodesRequest.\n\n 每页显示的条目数量，取值范围1~100，默认为100\n\n :return: The limit of this ShowNodesRequest.\n :rtype: int\n \"\"\"\n return self._limit\n\n @limit.setter\n def limit(self, limit):\n \"\"\"Sets the limit of this ShowNodesRequest.\n\n 每页显示的条目数量，取值范围1~100，默认为100\n\n :param limit: The limit of this ShowNodesRequest.\n :type limit: int\n \"\"\"\n self._limit = limit\n\n @property\n def name(self):\n \"\"\"Gets the name of this ShowNodesRequest.\n\n 设备名称，模糊匹配，只允许中文字符、英文字母、数字、下划线、中划线，最大长度64\n\n :return: The name of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._name\n\n @name.setter\n def name(self, name):\n \"\"\"Sets the name of this ShowNodesRequest.\n\n 设备名称，模糊匹配，只允许中文字符、英文字母、数字、下划线、中划线，最大长度64\n\n :param name: The name of this ShowNodesRequest.\n :type name: str\n \"\"\"\n self._name = name\n\n @property\n def workspace_id(self):\n \"\"\"Gets the workspace_id of this ShowNodesRequest.\n\n 工作空间ID，默认为注册账号/子账号的default工作空间，可通过专业版HiLens控制台展开工作空间列表获取到工作空间ID\n\n :return: The workspace_id of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._workspace_id\n\n @workspace_id.setter\n def workspace_id(self, workspace_id):\n \"\"\"Sets the workspace_id of this ShowNodesRequest.\n\n 工作空间ID，默认为注册账号/子账号的default工作空间，可通过专业版HiLens控制台展开工作空间列表获取到工作空间ID\n\n :param workspace_id: The workspace_id of this ShowNodesRequest.\n :type workspace_id: str\n \"\"\"\n self._workspace_id = workspace_id\n\n @property\n def app_name(self):\n \"\"\"Gets the app_name of this ShowNodesRequest.\n\n 应用名称，模糊匹配，只允许英文小写字母、数字、中划线，最大长度32, 英文小写字母或数字开头和结尾\n\n :return: The app_name of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._app_name\n\n @app_name.setter\n def app_name(self, app_name):\n \"\"\"Sets the app_name of this ShowNodesRequest.\n\n 应用名称，模糊匹配，只允许英文小写字母、数字、中划线，最大长度32, 英文小写字母或数字开头和结尾\n\n :param app_name: The app_name of this ShowNodesRequest.\n :type app_name: str\n \"\"\"\n self._app_name = app_name\n\n @property\n def tags(self):\n \"\"\"Gets the tags of this ShowNodesRequest.\n\n 标签的key和value通过点连接，多个标签通过逗号连接，如：tags=key1.value1,key2.value2\n\n :return: The tags of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._tags\n\n @tags.setter\n def tags(self, tags):\n \"\"\"Sets the tags of this ShowNodesRequest.\n\n 标签的key和value通过点连接，多个标签通过逗号连接，如：tags=key1.value1,key2.value2\n\n :param tags: The tags of this ShowNodesRequest.\n :type tags: str\n \"\"\"\n self._tags = tags\n\n @property\n def provider(self):\n \"\"\"Gets the provider of this ShowNodesRequest.\n\n 服务提供者：ief或hilens。不传会查询全部服务类型的设备列表\n\n :return: The provider of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._provider\n\n @provider.setter\n def provider(self, provider):\n \"\"\"Sets the provider of this ShowNodesRequest.\n\n 服务提供者：ief或hilens。不传会查询全部服务类型的设备列表\n\n :param provider: The provider of this ShowNodesRequest.\n :type provider: str\n \"\"\"\n self._provider = provider\n\n @property\n def cluster_id(self):\n \"\"\"Gets the cluster_id of this ShowNodesRequest.\n\n 集群ID，若值为0会过滤出不隶属任何集群的设备\n\n :return: The cluster_id of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._cluster_id\n\n @cluster_id.setter\n def cluster_id(self, cluster_id):\n \"\"\"Sets the cluster_id of this ShowNodesRequest.\n\n 集群ID，若值为0会过滤出不隶属任何集群的设备\n\n :param cluster_id: The cluster_id of this ShowNodesRequest.\n :type cluster_id: str\n \"\"\"\n self._cluster_id = cluster_id\n\n @property\n def status(self):\n \"\"\"Gets the status of this ShowNodesRequest.\n\n 状态\n\n :return: The status of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._status\n\n @status.setter\n def status(self, status):\n \"\"\"Sets the status of this ShowNodesRequest.\n\n 状态\n\n :param status: The status of this ShowNodesRequest.\n :type status: str\n \"\"\"\n self._status = status\n\n @property\n def active_status(self):\n \"\"\"Gets the active_status of this ShowNodesRequest.\n\n 设备激活状态，分别是未激活（INACTIVE）、已激活（ACTIVATED）、已到期（EXPIRED）\n\n :return: The active_status of this ShowNodesRequest.\n :rtype: str\n \"\"\"\n return self._active_status\n\n @active_status.setter\n def active_status(self, active_status):\n \"\"\"Sets the active_status of this ShowNodesRequest.\n\n 设备激活状态，分别是未激活（INACTIVE）、已激活（ACTIVATED）、已到期（EXPIRED）\n\n :param active_status: The active_status of this ShowNodesRequest.\n :type active_status: str\n \"\"\"\n self._active_status = active_status\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n if attr in self.sensitive_list:\n result[attr] = \"****\"\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)\n\n def __repr__(self):\n \"\"\"For `print`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, ShowNodesRequest):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n","repo_name":"huaweicloud/huaweicloud-sdk-python-v3","sub_path":"huaweicloud-sdk-hilens/huaweicloudsdkhilens/v3/model/show_nodes_request.py","file_name":"show_nodes_request.py","file_ext":"py","file_size_in_byte":12011,"program_lang":"python","lang":"en","doc_type":"code","stars":104,"dataset":"github-code","pt":"20"}
+{"seq_id":"33485497072","text":"import streamlit as st\r\nfrom PIL import Image\r\n\r\nimg=Image.open(\"groot.jpg\")\r\nst.image(img)\r\nst.title(\"R.J college\")\r\nst.header(\"masters application\")\r\nn=st.text_input(\"Enter your name:- \")\r\nf=st.text_input(\"Enter your father name:- \")\r\nm=st.text_input(\"Enter your mother name:- \")\r\ng=st.radio(\"gender\",(\"male\",\"female\",\"transgender\",\"others\"))\r\nst.write(\"Qualifications :- \")\r\n\r\noption_1 = st.checkbox(\"ssc\")\r\noption_2 = st.checkbox(\"hsc\")\r\noption_3 = st.checkbox(\"bsc\")\r\noption_4 = st.checkbox(\"msc\")\r\noption_5 = st.checkbox(\"phd\")\r\noption=st.selectbox(\"your field of interest:- \",(\"physics\",\"chemistry\",\"maths\",\"botany\",\"statistics\",\"zoology\"))\r\nst.write(\"you selected\",option)\r\nd= st.date_input(\"DOB\")\r\nst.write(\"your date of birth:\", d)\r\nst.text_area(\"statement of purpose: \", max_chars=50)\r\nif(st.button(\"submit application\")):\r\n st.warning(\"your form has been submited\")\r\n st.write(\"name:- \", n)\r\n st.write(\"father name:- \", f)\r\n st.write(\"mother name:- \",m)\r\n st.write(\"gender:- \", g)\r\n st.write(\"your field of interset:- \", option)","repo_name":"Ranjit242000/demo","sub_path":"form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"10008075364","text":"import ipaddress\nimport sys\n\nimport click\nimport validators\nfrom click import Context\nfrom pynetbox.core.api import Api\n\n\n@click.group()\ndef ip():\n pass\n\n\ndef validate_ipv4(ctx, param, value):\n try:\n return ipaddress.IPv4Address(value)\n except ipaddress.AddressValueError as e:\n raise click.BadParameter(str(e))\n\n\n@ip.command(help='Create or update IP Address records with the given reverse DNS. The subnet mask is automatically '\n 'set to the same as the parent prefix.')\n@click.option('--start', '-s', 'start_ip', help='Start IP', required=True, callback=validate_ipv4)\n@click.option('--end', '-e', 'end_ip', help='End IP', required=True, callback=validate_ipv4)\n@click.option('--format', '-f', 'dns_format', help='Reverse DNS format. Use {1} for octet 1, {2} for octet 2 etc',\n required=True)\n@click.option('--dry-run', '-n', is_flag=True, help='Dry run, don\\'t actually set DNS')\n@click.option('--allow-all', '-a', is_flag=True,\n help='Also allow setting what would be the subnet\\'s network and broadcast addresses')\n@click.pass_context\ndef set_reverse(ctx: Context, start_ip: ipaddress.IPv4Address, end_ip: ipaddress.IPv4Address, dns_format: str,\n dry_run: bool = False, allow_all: bool = False):\n netbox: Api = ctx.obj['netbox']\n\n if start_ip > end_ip:\n print('End address must be equal to or higher than the start address.', file=sys.stderr)\n sys.exit(1)\n\n # Find a prefix that covers start_ip:\n\n prefix = find_longest_prefix_containing_ip(netbox, start_ip)\n\n if not prefix:\n print('No Netbox prefix found that covers %s' % str(start_ip), file=sys.stderr)\n sys.exit(1)\n\n prefix_net = ipaddress.IPv4Network(prefix.prefix)\n prefix_len = prefix_net.prefixlen\n\n # Check that end_ip is in the same prefix\n\n if end_ip not in prefix_net:\n print(\n 'The end address %s is not in the same prefix %s as the start address %s.' % (str(end_ip), str(prefix_net),\n str(start_ip)),\n file=sys.stderr)\n sys.exit(1)\n\n # Check that we're not accidentally setting the network or broadcast addresses\n\n if not allow_all and (start_ip == prefix_net.network_address or start_ip == prefix_net.broadcast_address or\n end_ip == prefix_net.network_address or end_ip == prefix_net.broadcast_address):\n print('You have requested to include the network/broadcast address and not specified --allow-all.',\n file=sys.stderr)\n sys.exit(1)\n\n # Loop the addresses and create or update their IP Address records:\n\n created = updated = unchanged = 0\n ip = start_ip\n while ip <= end_ip:\n ip_int: ipaddress.IPv4Interface = ipaddress.IPv4Interface('%s/%d' % (str(ip), prefix_len))\n\n # get the current record if it exists\n nb_ip = netbox.ipam.ip_addresses.get(address=str(ip_int))\n\n dns_name = dns_format.format(str(ip_int.ip),\n ip_int.ip.packed[0],\n ip_int.ip.packed[1],\n ip_int.ip.packed[2],\n ip_int.ip.packed[3])\n\n if not validators.domain(dns_name):\n print('Given format string would produce illegal reverse DNS \"%s\" for %s.' % (dns_name, str(ip_int)),\n file=sys.stderr)\n sys.exit(1)\n\n if nb_ip:\n if nb_ip.dns_name == dns_name:\n print('UNCHANGED: %s -> %s' % (str(ip_int), dns_name))\n unchanged += 1\n else:\n print('UPDATE: %s -> %s (was %s)' % (str(ip_int), dns_name, nb_ip.dns_name))\n updated += 1\n if not dry_run:\n nb_ip.dns_name = dns_name\n nb_ip.save()\n else:\n print('CREATE: %s -> %s' % (str(ip_int), dns_name))\n created += 1\n if not dry_run:\n netbox.ipam.ip_addresses.create(address=str(ip_int),\n dns_name=dns_name)\n\n ip += 1\n\n print('%d created, %d updated, %d unchanged.' % (created, updated, unchanged))\n if dry_run:\n print('Dry run, nothing was changed.')\n\n\ndef find_longest_prefix_containing_ip(netbox, ip):\n prefixes = netbox.ipam.prefixes.filter(contains=str(ip))\n found_prefix = None\n found_prefix_length = None\n for prefix in prefixes:\n subnet = ipaddress.IPv4Network(prefix.prefix)\n if found_prefix is None or subnet.prefixlen > found_prefix_length:\n found_prefix = prefix\n found_prefix_length = subnet.prefixlen\n return found_prefix\n\n\nCOMMANDS = [ip]\n","repo_name":"davidc/netbox-utils","sub_path":"netbox_utils/nbip/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"116396775","text":"import tensorflow as tf\r\nimport sys\r\nimport matplotlib.pyplot as plt\r\nimport numpy\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\nfrom lime import lime_image\r\nimport time\r\nfrom skimage.segmentation import mark_boundaries\r\nimport lime\r\nfrom lime import lime_image\r\nfrom skimage.segmentation import mark_boundaries\r\nfrom sklearn.preprocessing import LabelEncoder\r\nimport PIL\r\nimport random\r\nimport numpy as np\r\n\r\ndef main():\r\n\tnew_model = tf.keras.models.load_model(\"\", compile=True)\r\n\timg_height = 256\r\n\timg_width = 256\r\n\tdata_labels = ['ignorable','target']\r\n\r\n\r\n\t#Get names of all files that were tested.\r\n\t#2. Load spreadsheet using pandas. Get the extension column and filename and zip them into a dictionary.\r\n\tfile_handle = open(\"\", \"r\")\r\n\tresults = file_handle.readlines()\r\n\tresults = results[1:]\r\n\tfile_handle.close()\r\n\r\n\t#split into data sets.\r\n\ttrue_pos= []\r\n\ttrue_neg= []\r\n\tfalse_pos= []\r\n\tfalse_neg= []\r\n\t\r\n\tfor file in results:\r\n\t\tfile = file.strip(\" \\n\")\r\n\t\tsplit = file.split(\"\\t\")\r\n\t\tif split[-1] == \"ignorable\" and split[-2] == \"ignorable\":\r\n\t\t\ttrue_neg.append(file)\r\n\t\telif split[-1] == \"target\" and split[-2] == \"target\":\r\n\t\t\ttrue_pos.append(file)\r\n\t\telif split[-1] == \"ignorable\" and split[-2] == \"target\":\r\n\t\t\tfalse_pos.append(file)\r\n\t\telse:\r\n\t\t\tfalse_neg.append(file)\r\n\r\n\tprint(\"The length of each list is:\\n\" +\\\r\n\t\t\"True Negative: \" + str(len(true_neg)) + \"\\n\" + \\\r\n\t\t\"True Positive: \" + str(len(true_pos)) + \"\\n\" + \\\r\n\t\t\"False Positive: \" + str(len(false_pos)) + \"\\n\" + \\\r\n\t\t\"False Negative: \" + str(len(false_neg)))\r\n\t\r\n\t#Pick random samples of 50 files for each of true positive, true negative, false positive and false negative.\r\n\tsample_true_pos= random.sample(true_pos,50)\r\n\tsample_true_neg= random.sample(true_neg,50)\r\n\tsample_false_pos= random.sample(false_pos,50)\r\n\tsample_false_neg= random.sample(false_neg,50)\r\n\r\n\t#Process using LIME and save to a file.\r\n\t\r\n\t#Open each file\r\n\tname = \"\"\r\n\ttesting_file_lists = [sample_true_pos,sample_true_neg,sample_false_pos,sample_false_neg]\r\n\tfor samples in testing_file_lists:\r\n\t\tif samples == sample_true_pos:\r\n\t\t\tname = \"sample_true_pos\"\r\n\t\telif samples == sample_true_neg:\r\n\t\t\tname = \"sample_true_neg\"\r\n\t\telif samples == sample_false_pos:\r\n\t\t\tname = \"sample_false_pos\"\r\n\t\telse:\r\n\t\t\tname = \"sample_false_neg\"\r\n\t\tfile_handle = open(\"\\\\\" + name + \".csv\", \"w+\")\r\n\t\tfile_handle.write(\"file,prediction,score,predicted_label\\n\")\r\n\r\n\t\tfor pos in samples:\r\n\t\t\tfile_handle.write(pos)\r\n\t\t\tpos = pos.strip(\" \\n\")\r\n\t\t\tsplit = pos.split(\"\\t\")\r\n\t\t\timg = tf.keras.utils.load_img(\"\" + split[0], target_size=(img_height,img_width))\r\n\t\t\tlime_img_array = tf.keras.utils.img_to_array(img)\r\n\t\t\tpredict_img_array = tf.expand_dims(lime_img_array, 0)\r\n\t\t\texplainer = lime_image.LimeImageExplainer(random_state=42)\r\n\t\t\texplanation = explainer.explain_instance(lime_img_array, new_model.predict, top_labels = 2, hide_color = 0, num_samples =1000, random_seed = 42)\r\n\t\t\tig_image, ig_mask = explanation.get_image_and_mask(new_model.predict(predict_img_array).argmax(axis=0)[0],positive_only=True,hide_rest=False, num_features=5)\r\n\t\t\ttg_image, tg_mask = explanation.get_image_and_mask(new_model.predict(predict_img_array).argmax(axis=1)[0],positive_only=True,hide_rest=False, num_features=5)\r\n\t\r\n\t\t\t#Create the image\r\n\t\t\tfig = plt.figure(figsize=(10, 15))\r\n\t\t\t# setting values to rows and column variables\r\n\t\t\trows = 3\r\n\t\t\tcolumns = 2\r\n\t\r\n\t\t\tfig.add_subplot(rows, columns, 1)\r\n\t\t\tplt.imshow(lime_img_array.astype('uint8'))\r\n\t\t\tplt.axis('off')\r\n\t\t\tplt.title(\"Original\")\r\n\t\r\n\t\r\n\t\t\tfig.add_subplot(rows, columns, 2)\r\n\t\t\tplt.imshow(mark_boundaries(ig_image.astype('uint8'), ig_mask, mode='inner'))\r\n\t\t\tplt.axis('off')\r\n\t\t\tplt.title(\"Fields indicating ignorable\")\r\n\t\r\n\t\t\tfig.add_subplot(rows,columns,3)\r\n\t\t\tplt.imshow(mark_boundaries(tg_image.astype('uint8'), tg_mask, mode='inner'))\r\n\t\t\tplt.axis('off')\r\n\t\t\tplt.title(\"Fields indicating Target\")\r\n\t\r\n\t\t\tfig.add_subplot(rows,columns,5)\r\n\t\t\tplt.text(0, 1, \"Image Name: \" + split[0] + \"\\n\"\\\r\n\t\t\t\t+ \"Expected Result: \" + split[-1] + \"\\n\"\\\r\n\t\t\t\t+ \"Predicted Result: \" + split[-2] + \"\\n\"\r\n\t\t\t\t+ \"Confidence Scores: \" + split[1] + \" [[ignorable, target]]\",\\\r\n\t\t\t\tverticalalignment='top', horizontalalignment='left' )\r\n\t\t\tplt.axis('off')\r\n\t\r\n\t\t\tplt.title(\"Results\")\r\n\t\r\n\t\r\n\t\t\tfig.add_subplot(rows,columns,4)\r\n\t\r\n\t\t\t#Get locations for target files\r\n\t\t\tind = explanation.top_labels[1]\r\n\t\r\n\t\t\tdict_heatmap = dict(explanation.local_exp[ind])\r\n\t\t\theatmap = np.vectorize(dict_heatmap.get) (explanation.segments)\r\n\t\r\n\t\t\tplt.imshow(heatmap, cmap = 'RdBu' , vmin = -heatmap.max(), vmax = heatmap.max())\r\n\t\t\tplt.colorbar()\r\n\t\t\tplt.axis('off')\r\n\t\t\tplt.title(\"Heatmap of fields\")\r\n\t\r\n\t\t\tfig.savefig(\"\\\\\" + name + \"\\\\\" + split[0].replace('.','_') + \".pdf\")\r\n\t\t\tplt.close()\r\n\t\r\n\t\r\n\t\t\t\r\n\t\tfile_handle.close()\r\n\r\n\r\n\r\n\r\n\t\"\"\"\r\n\tFOR TESTING PURPOSES ONLY\r\n\timg = tf.keras.utils.load_img(\"\", target_size=(img_height,img_width))\r\n\tLIME_img_array = tf.keras.utils.img_to_array(img)\r\n\timg = PIL.Image.open(\"\")\r\n\tprint(LIME_img_array.shape) #Shape here is (256, 256, 3) -> required for LIME\r\n\tpredict_img_array = tf.expand_dims(LIME_img_array, 0)\r\n\t#print(predict_img_array.shape) #Shape is now (1,256,256,3) -> required for model prediction\r\n\tprediction = new_model.predict(predict_img_array) #.argmax(axis=1)[0]\r\n\targmax_prediction = new_model.predict(predict_img_array).argmax(axis=1)\r\n\targmax_prediction2 = new_model.predict(predict_img_array).argmax(axis=0)\r\n\tprint(str(prediction))\r\n\tprint(str(argmax_prediction))\r\n\tprint(str(argmax_prediction2))\r\n\r\n\t#Set the explainer\r\n\texplainer = lime_image.LimeImageExplainer(random_state=42)\r\n\r\n\t#Set explanation\r\n\texplanation = explainer.explain_instance(LIME_img_array, new_model.predict, top_labels = 2, hide_color = 0, num_samples =1000, random_seed = 42)\r\n\r\n\t#Get image and mask\r\n\r\n\t#image, mask = explanation.get_image_and_mask(img_array, positive_only = True, hide_rest = False)\r\n\timage, mask = explanation.get_image_and_mask(new_model.predict(predict_img_array).argmax(axis=0)[0],positive_only=True,hide_rest=False, num_features=2)\r\n\t\r\n\r\n\tplt.imshow(mark_boundaries(image.astype('uint8'), mask))\r\n\tplt.show()\r\n\r\n\r\n\r\n\t#Output image with original, boundaries indicating ignorable, boundaries indicating target, expected label and actual label\r\n\t#Actual Image\r\n\tplt.imshow(img)\r\n\tplt.show()\r\n\t\r\n\t#Prediction Image\r\n\tplt.imshow(tf.squeeze(predict_img_array))\r\n\tplt.show()\r\n\t\r\n\tprint(type(mask))\r\n\t# Showing the lime image array - It's not the actual image\r\n\tplt.imshow(LIME_img_array.astype('uint8'))\r\n\tplt.show()\r\n\tplt.imshow(mask)\r\n\tplt.show()\r\n\tplt.show()\r\n\t\"\"\"\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n","repo_name":"swhal3/Proof-Of-Concept-Explainable-AI-in-DF-Research-Paper","sub_path":"all_files_lime.py","file_name":"all_files_lime.py","file_ext":"py","file_size_in_byte":6717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"10488463802","text":"import PIL\nimport os\nfrom io import BytesIO\nfrom PIL import Image\nfrom analyst_tool_objects import tile_operator\nimport math\nfrom analyst_tool_objects import crop_operator\nimport cv2\nimport numpy as np\nimport random\nfrom distutils.dir_util import copy_tree\nfrom tqdm import tqdm\nimport shutil\n\n\nclass ImageIterator:\n\n def __init__(self, imagesFolderPath, imageToProcess, filesManager):\n self.filesManager = filesManager\n self.imagesFolderPath = imagesFolderPath\n\n if filesManager.checkPath(imagesFolderPath + os.sep + imageToProcess) == False:\n self.tiling = False\n self.relativePath = os.sep\n self.imageExtension = self.filesManager.getExtension(imageToProcess)\n self.imageNames = imageToProcess[0:imageToProcess.rfind(self.imageExtension)]\n else:\n self.tiling = True\n self.imageTilePath = self.imagesFolderPath + os.sep + imageToProcess\n self.imageTiles = tile_operator.TileOperator(self.filesManager)\n self.imageTiles.loadOperator(imagesFolderPath + os.sep + imageToProcess)\n self.imageExtension = self.imageTiles.tilingGeneralInfo.imageExtension\n\n def getListOfSubImages(self):\n if self.tiling == False:\n return [self.imagesFolderPath + self.relativePath + self.imageNames + self.imageExtension]\n else:\n return self.imageTiles.getAllImages()\n\n def updateImageExtension(self, extension):\n self.imageExtension = extension\n if self.tiling == True:\n self.imageTiles.tilingGeneralInfo.imageExtension = extension\n self.imageTiles.tilingGeneralInfo.save(self.imageTiles.tilingGeneralInfo.loadedPath)\n\n def updateImageTilingStatus(self):\n self.tiling = True\n\n def updateMetadataInfo(self):\n if self.tiling == True:\n self.imageTiles.updateMetadata()\n\nclass ImageProcessor:\n\n supportedOperations = [\"downsample\", \"convert_format\", \"tiling\"]\n downsample_resizeScale = 0.92\n MAX_VR_IMAGE_SIZE = 2\n supportedImageFormats = [\"jpeg\", \"png\"]\n\n def __init__(self, outputWriter, filesManger):\n self.outputWriter = outputWriter\n self.filesManager = filesManger\n\n def getImageAspectRatio(self, image):\n size = image.size\n gcd = math.gcd(size[0], size[1])\n return (int(size[0]/gcd), int(size[1]/gcd))\n\n def downsampleImageToNetworkSize(self, image, imagePath, parameters):\n image = cv2.resize(image, (224, 224))\n cv2.imwrite(imagePath, image)\n return (True, \"\")\n\n def downsampleImage(self, image, imagePath, parameters):\n maxSize = None\n maxRes = None\n\n if \"max_size\" in parameters:\n maxSize = parameters[\"max_size\"]\n if \"max_resolution\" in parameters:\n maxRes = (parameters[\"max_resolution\"][\"width\"], parameters[\"max_resolution\"][\"height\"])\n\n if maxSize == None and maxRes == None:\n return (False, \"ERROR: No parameter set for image downsample!\")\n\n imageSize = os.path.getsize(imagePath) / (1024.0 * 1024.0)\n\n if maxSize != None and imageSize > maxSize:\n extension = self.filesManager.normalizeExtension(self.filesManager.getExtension(imagePath))\n while imageSize > maxSize:\n bytesFile = BytesIO()\n temp = list(image.size)\n temp[0] = int(round(temp[0] * self.downsample_resizeScale))\n temp[1] = int(round(temp[1] * self.downsample_resizeScale))\n image.thumbnail(temp, Image.LANCZOS)\n image.save(bytesFile, extension)\n imageSize = bytesFile.tell() / (1024.0 * 1024.0)\n\n if maxRes != None and ( maxRes[0] < image.width or maxRes[1] < image.height ):\n extension = self.filesManager.normalizeExtension(self.filesManager.getExtension(imagePath))\n while maxRes[0] < image.width or maxRes[1] < image.height:\n bytesFile = BytesIO()\n temp = list(image.size)\n temp[0] = int(round(temp[0] * self.downsample_resizeScale))\n temp[1] = int(round(temp[1] * self.downsample_resizeScale))\n image.thumbnail(temp, Image.LANCZOS)\n image.save(bytesFile, extension)\n imageSize = bytesFile.tell() / (1024.0 * 1024.0)\n\n return (True, \"\")\n\n def convertFormat(self, image, parameters):\n if \"format\" not in parameters:\n return (\"\", False, \"ERROR: Required key \\\"format\\\" is not set!\")\n\n image.format = self.filesManager.normalizeExtension(parameters[\"format\"])\n return (parameters[\"format\"], True, \"\")\n\n def imageTiling(self, image, imagePath, imageName, extension, parameters):\n tileOperator = tile_operator.TileOperator(self.filesManager)\n tileOperator.create(image, imagePath, imageName, extension, parameters)\n\n return (True, \"\")\n\n def imageCropping(self, image, imagePath, imageName, extension, parameters):\n cropOperator = crop_operator.CropOperator()\n cropOperator.create(image, imagePath, imageName, extension, parameters, self.filesManager)\n\n return (True, \"\")\n\n def AugmentationRotation(self, image, imagePath, imageName, extension, parameters):\n rotationStep = parameters[\"angle_step\"]\n cropp_center = parameters[\"cropp_center\"]\n samples_num = parameters[\"samples_num\"]\n minAngle = parameters[\"min_angle\"]\n\n width, height = image.size\n if cropp_center > 0:\n left = (width - cropp_center) / 2;\n upper = (height - cropp_center) / 2;\n right = left + cropp_center;\n down = upper + cropp_center;\n\n if samples_num == 0:\n if rotationStep == 90:\n image.rotate(0).save(imagePath + os.sep + imageName + \"_\" + str(0) + extension)\n image.transpose(Image.ROTATE_90).save(imagePath + os.sep + imageName + \"_\" + str(90) + extension)\n image.transpose(Image.ROTATE_180).save(imagePath + os.sep + imageName + \"_\" + str(180) + extension)\n image.transpose(Image.ROTATE_270).save(imagePath + os.sep + imageName + \"_\" + str(270) + extension)\n else:\n numberOfRotations = int(360 / rotationStep)\n for rotateIteration in range(1, numberOfRotations + 1):\n rotatedImage = image.rotate(rotateIteration * rotationStep, PIL.Image.BILINEAR)\n if cropp_center > 0:\n rotatedImage = rotatedImage.crop((left, upper, right, down))\n rotatedImage.save(imagePath + os.sep + imageName + \"_\" + str(rotateIteration * rotationStep) + extension)\n else:\n getOrientation = lambda x: -1 if x == 0 else 1\n for index in range(0, samples_num):\n orientation = getOrientation(random.getrandbits(1))\n angle = 0\n while (angle < minAngle):\n angle = random.randint(0, rotationStep)\n rotatedImage = image.rotate(orientation * angle, PIL.Image.BILINEAR)\n if cropp_center > 0:\n rotatedImage = rotatedImage.crop((left, upper, right, down))\n rotatedImage.save(\n imagePath + os.sep + imageName + \"_rotated_\" + str(orientation * angle) + extension)\n\n return True, \"\"\n\n def AugmentationTranslation(self, image, imagePath, imageName, extension, parameters):\n max_translation = parameters[\"max_translation\"]\n min_translation = parameters[\"min_translation\"]\n num_of_images = parameters[\"num_of_images\"]\n cropp_center = parameters[\"cropp_center\"]\n\n getOrientation = lambda x: -1 if x == 0 else 1\n width, height, channels = image.shape\n if cropp_center > 0:\n left = int((width - cropp_center) / 2);\n upper = int((height - cropp_center) / 2);\n\n for translationIndex in range(1, num_of_images + 1):\n xOrientation = getOrientation(random.getrandbits(1))\n yOrientation = getOrientation(random.getrandbits(1))\n transitionX = random.randint(min_translation, max_translation)\n transitionY = random.randint(min_translation, max_translation)\n M = np.float32([[1, 0, xOrientation * transitionX], [0, 1, yOrientation * transitionY]])\n translatedImage = cv2.warpAffine(image, M, (height,width))\n if cropp_center > 0:\n translatedImage = translatedImage[left:left+cropp_center, upper:upper+cropp_center]\n cv2.imwrite(imagePath + os.sep + imageName + \"_\" + \"translation_\" + \"_\" + str(translationIndex) + extension, translatedImage)\n\n return True, \"\"\n\n def processImagesRoutine(self, jsonConfig):\n processingTasks = jsonConfig[\"processing_tasks\"]\n inputFolder = jsonConfig[\"input_folder\"]\n #outputfolder = jsonConfig[\"output_folder\"]\n imagesFolderPath = self.filesManager.removeLastSlashInFolderPath(inputFolder)\n #imagesOutputFolderPath = self.filesManager.removeLastSlashInFolderPath(outputfolder)\n imagesOutputFolderPath = imagesFolderPath\n status = self.filesManager.checkPath(imagesFolderPath)\n if status == False:\n raise Exception(\"ERROR: Images folder does not exist.\")\n\n status = self.filesManager.checkPath(imagesOutputFolderPath)\n if status == False:\n raise Exception(\"ERROR: Images output folder does not exist.\")\n\n #copy_tree(imagesFolderPath, imagesOutputFolderPath)\n\n dirAnalysis = self.filesManager.analyzeDir(imagesOutputFolderPath)\n\n imagesToProcess = dirAnalysis[\"listOfRecognizedImages\"] + dirAnalysis[\"listOfFoldersWithImageTiles\"] + \\\n dirAnalysis[\"listOfFoldersWithImageCrops\"]\n numberOfImagesToProcess = len(imagesToProcess)\n\n self.outputWriter.setNewRecord()\n self.outputWriter.writeTextOutput(\"Performing images processing ...\")\n\n for index in range(len(imagesToProcess)):\n percentage = index / numberOfImagesToProcess * 100\n self.outputWriter.setNewRecord()\n self.outputWriter.writeTextOutput(\"Already processed \" + \"{0:.4f}\".format(percentage) + \" % images.\")\n self.outputWriter.writeTextOutput(\"Processing image: \" + imagesToProcess[index] + \" ...\")\n status = True\n\n imageIterator = ImageIterator(imagesOutputFolderPath, imagesToProcess[index], self.filesManager)\n for imageProcessingTask in processingTasks:\n listOfImagesToProcess = imageIterator.getListOfSubImages()\n\n for image in listOfImagesToProcess:\n openedImage = Image.open(image)\n extension = imageIterator.imageExtension\n imageNameWithoutExtension = self.filesManager.getFileFromPath(image)\n if \"downsample\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Downsampling image ...\")\n status, message = self.downsampleImage(openedImage, image, imageProcessingTask[\"downsample\"])\n if status == False:\n self.riseError(message)\n else:\n openedImage.save(image)\n imageIterator.updateMetadataInfo()\n openedImage.close()\n\n if \"downsampleImageToNetworkSize\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Downsampling image tp NN input size...\")\n openedImage.close()\n openedImage = cv2.imread(image)\n status, message = self.downsampleImageToNetworkSize(openedImage, image, imageProcessingTask[\"downsampleImageToNetworkSize\"])\n if status == False:\n self.riseError(message)\n else:\n imageIterator.updateMetadataInfo()\n\n if \"convert_format\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Converting image ...\")\n extension, status, message = self.convertFormat(openedImage, imageProcessingTask[\"convert_format\"])\n if status == False:\n self.riseError(message)\n else:\n imageIterator.updateImageExtension(extension)\n if extension != self.filesManager.getExtension(image):\n self.filesManager.deleteFile(image)\n openedImage.save(image[0:image.rfind(self.filesManager.getExtension(image))] + extension)\n openedImage.close()\n\n if \"standardize_file_names\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Renaming image ...\")\n openedImage.save(imagesOutputFolderPath + os.sep + \"renamed_\" + str(index) + extension)\n openedImage.close()\n self.filesManager.deleteFile(image)\n\n if \"tiling\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Cropping images into tiles ...\")\n status, message = self.imageTiling(openedImage, imagesOutputFolderPath, imageNameWithoutExtension, extension, imageProcessingTask[\"tiling\"])\n if status == False:\n self.riseError(message)\n else:\n imageIterator.updateImageTilingStatus()\n openedImage.close()\n self.filesManager.deleteFile(image)\n\n if \"cropping\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Cropping parts of the image ...\")\n status, message = self.imageCropping(openedImage, imagesFolderPath, imageNameWithoutExtension, extension, imageProcessingTask[\"cropping\"])\n if status == False:\n self.riseError(message)\n else:\n imageIterator.updateImageTilingStatus()\n openedImage.close()\n self.filesManager.deleteFile(image)\n\n if \"augmentation_rotation\" in imageProcessingTask:\n self.outputWriter.writeTextOutput(\"- Augmenting images with rotation ...\")\n status, message = self.AugmentationRotation(openedImage, imagesFolderPath,\n imageNameWithoutExtension, extension,\n imageProcessingTask[\"augmentation_rotation\"])\n if status == False:\n self.riseError(message)\n else:\n self.filesManager.deleteFile(image)\n\n if \"augmentation_translation\" in imageProcessingTask:\n openedImage.close()\n openedImage = cv2.imread(image)\n self.outputWriter.writeTextOutput(\"- Augmenting images with translation ...\")\n status, message = self.AugmentationTranslation(openedImage, imagesFolderPath,\n imageNameWithoutExtension, extension,\n imageProcessingTask[\"augmentation_translation\"])\n if status == False:\n self.riseError(message)\n\n self.outputWriter.setNewRecord()\n self.outputWriter.writeTextOutput(\"Processing of images is completed!\")\n\n def generateNegativeImagesTiles(self, jsonConfig):\n imagesFolder = jsonConfig[\"input_folder\"]\n outputFolder = jsonConfig[\"output_folder\"]\n imagesFolder = self.filesManager.removeLastSlashInFolderPath(imagesFolder)\n outputFolder = self.filesManager.removeLastSlashInFolderPath(outputFolder)\n\n status = self.filesManager.checkPath(imagesFolder)\n if status == False:\n raise Exception(\"ERROR: Given path to images folder is not folder!\")\n\n status = self.filesManager.checkPath(outputFolder)\n if status == False:\n raise Exception(\"ERROR: Given path to output folder is not folder!\")\n\n self.outputWriter.writeTextOutput(\"Processing generating negative image samples ... \")\n\n copy_tree(imagesFolder, outputFolder)\n jsonConf = {\n \"input_folder\": outputFolder,\n \"processing_tasks\": [{\"tiling\": jsonConfig[\"tiling_conf\"]}]\n }\n\n self.processImagesRoutine(jsonConf)\n\n dirAnalysis = self.filesManager.analyzeDir(outputFolder, verbose=False)\n\n imageIndex = 0\n\n self.outputWriter.setNewRecord()\n self.outputWriter.writeTextOutput(\"Moving images tiles into output folder:\")\n for image in dirAnalysis[\"listOfFoldersWithImageTiles\"]:\n tileOperator = tile_operator.TileOperator(self.filesManager)\n tileOperator.loadOperator(outputFolder + os.sep + image)\n tilesList = tileOperator.getAllImages()\n for tile in tilesList:\n shutil.move(tile,\n outputFolder + os.sep + str(imageIndex) + tileOperator.tilingGeneralInfo.imageExtension)\n imageIndex = imageIndex + 1\n shutil.rmtree(outputFolder + os.sep + image)\n\n","repo_name":"OndrejSzekely/vr_analyst_tool","sub_path":"analyst_tool_objects/image_processor.py","file_name":"image_processor.py","file_ext":"py","file_size_in_byte":17782,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"4652643308","text":"#!/usr/bin/env python3\n\"\"\"YOLO module\"\"\"\n\nfrom tensorflow import keras\n\n\nclass Yolo:\n \"\"\"yolo class algorithm\"\"\"\n def __init__(self, model_path, classes_path,\n class_t, nms_t, anchors):\n \"\"\"init function\"\"\"\n self.model = keras.models.load_model(model_path)\n with open(classes_path, 'r') as f:\n self.class_names = [line.strip() for line in f]\n self.class_t = class_t\n self.nms_t = nms_t\n self.anchors = anchors\n","repo_name":"wolf-coder/holbertonschool-machine_learning","sub_path":"supervised_learning/0x0A-object_detection/0-yolo.py","file_name":"0-yolo.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"25055591216","text":"#File name: Ex3createGPSFeatureClass.py\n#Author: RRyser\n#Python Version: 2.7 or 3.5 or greater\n#Description : creates a fgdb and feature classes, in the current working directory\n\nimport os\nimport sys\nimport arcpy\n\nfrom arcpy import env\nenv.overwriteOutput = True\n\n#list your feature class names here\nfeatureList = ['CapitalCities', 'Landmarks', 'HistoricPlaces', 'StateNames', 'Nationalities', 'Rivers']\n\ncurrent_dir = os.getcwd() \nfgdb_name = 'Exercise3.gdb'\nworkspace_path = current_dir + '\\\\' +fgdb_name\n \n#SR Definition\nSRDefinition=\"PROJCS['WGS_1984_Web_Mercator_Auxiliary_Sphere',GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.257223563]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Mercator_Auxiliary_Sphere'],PARAMETER['False_Easting',0.0],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',0.0],PARAMETER['Standard_Parallel_1',0.0],PARAMETER['Auxiliary_Sphere_Type',0.0],UNIT['Meter',1.0]];-22041257.773878 -33265068.6042249 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision\"\n\n#create a gdb\narcpy.CreateFileGDB_management(current_dir, fgdb_name)\n \nprint('Done creating gdb')\n\n#Create the Feature Classes\n\n#CapitalCities\narcpy.CreateFeatureclass_management(out_path=workspace_path\n , out_name=featureList[0]\n , geometry_type=\"POINT\"\n , template=\"\"\n , has_m=\"DISABLED\"\n , has_z=\"DISABLED\"\n , spatial_reference=SRDefinition\n , config_keyword=\"\"\n , spatial_grid_1=\"0\"\n , spatial_grid_2=\"0\"\n , spatial_grid_3=\"0\"\n )\nprint('Done creating CapitalCities')\n \n#Landmarks\narcpy.CreateFeatureclass_management(out_path=workspace_path\n , out_name=featureList[1]\n , geometry_type=\"POINT\"\n , template=\"\"\n , has_m=\"DISABLED\"\n , has_z=\"DISABLED\"\n , spatial_reference=SRDefinition\n , config_keyword=\"\"\n , spatial_grid_1=\"0\"\n , spatial_grid_2=\"0\"\n , spatial_grid_3=\"0\"\n )\nprint('Done creating Landmarks')\n\n#HistoricPlaces\narcpy.CreateFeatureclass_management(out_path=workspace_path\n , out_name=featureList[2]\n , geometry_type=\"POINT\"\n , template=\"\"\n , has_m=\"DISABLED\"\n , has_z=\"DISABLED\"\n , spatial_reference=SRDefinition\n , config_keyword=\"\"\n , spatial_grid_1=\"0\"\n , spatial_grid_2=\"0\"\n , spatial_grid_3=\"0\"\n )\nprint('Done creating HistoricPlaces')\n\n#StateNames\narcpy.CreateFeatureclass_management(out_path=workspace_path\n , out_name=featureList[3]\n , geometry_type=\"POINT\"\n , template=\"\"\n , has_m=\"DISABLED\"\n , has_z=\"DISABLED\"\n , spatial_reference=SRDefinition\n , config_keyword=\"\"\n , spatial_grid_1=\"0\"\n , spatial_grid_2=\"0\"\n , spatial_grid_3=\"0\"\n )\nprint('Done creating StateNames')\n\n#Nationalities\narcpy.CreateFeatureclass_management(out_path=workspace_path\n , out_name=featureList[4]\n , geometry_type=\"POINT\"\n , template=\"\"\n , has_m=\"DISABLED\"\n , has_z=\"DISABLED\"\n , spatial_reference=SRDefinition\n , config_keyword=\"\"\n , spatial_grid_1=\"0\"\n , spatial_grid_2=\"0\"\n , spatial_grid_3=\"0\"\n )\nprint('Done creating Nationalities')\n\n#Rivers\narcpy.CreateFeatureclass_management(out_path=workspace_path\n , out_name=featureList[5]\n , geometry_type=\"POLYLINE\"\n , template=\"\"\n , has_m=\"DISABLED\"\n , has_z=\"DISABLED\"\n , spatial_reference=SRDefinition\n , config_keyword=\"\"\n , spatial_grid_1=\"0\"\n , spatial_grid_2=\"0\"\n , spatial_grid_3=\"0\"\n )\nprint('Done creating Rivers')\n\nprint('All done. Your fgdb was created at ' + workspace_path)\n","repo_name":"rryser/MASGIS_Ex3","sub_path":"Ex3_5createGDBFeatureClass.py","file_name":"Ex3_5createGDBFeatureClass.py","file_ext":"py","file_size_in_byte":5625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"4740713728","text":"from __future__ import print_function\n\nimport re\nimport uuid\n\nfrom avocado.utils.process import run, CmdError\nfrom command_utils import FormattedParameter, CommandWithParameters\n\n\nclass IorFailed(Exception):\n \"\"\"Raise if Ior failed.\"\"\"\n\n# pylint: disable=attribute-defined-outside-init\nclass IorCommand(CommandWithParameters):\n \"\"\"Defines a object for executing an IOR command.\n\n Example:\n >>> # Typical use inside of a DAOS avocado test method.\n >>> ior_cmd = IorCommand()\n >>> ior_cmd.get_params(self)\n >>> ior_cmd.set_daos_params(self.server_group, self.pool)\n >>> ior_cmd.run(\n self.basepath, len(self.hostlist_clients),\n self.hostfile_clients)\n \"\"\"\n\n def __init__(self):\n \"\"\"Create an IorCommand object.\"\"\"\n super(IorCommand, self).__init__(\"ior\")\n\n # Flags\n self.flags = FormattedParameter(\"{}\")\n\n # Optional arguments\n # -a=POSIX API for I/O [POSIX|DUMMY|MPIIO|MMAP|DAOS|DFS]\n # -b=1048576 blockSize -- contiguous bytes to write per task\n # -d=0 interTestDelay -- delay between reps in seconds\n # -f=STRING scriptFile -- test script name\n # -G=0 setTimeStampSignature -- time stamp signature\n # -i=1 repetitions -- number of repetitions of test\n # -j=0 outlierThreshold -- warn on outlier N sec from mean\n # -J=1 setAlignment -- HDF5 alignment in bytes\n # -l=STRING datapacket type-- type of packet created\n # -M=STRING memoryPerNode -- hog memory on the node\n # -N=0 numTasks -- num of participating tasks in the test\n # -o=testFile testFile -- full name for test\n # -O=STRING string of IOR directives\n # -Q=1 taskPerNodeOffset for read tests\n # -s=1 segmentCount -- number of segments\n # -t=262144 transferSize -- size of transfer in bytes\n # -T=0 maxTimeDuration -- max time in minutes executing\n # repeated test; it aborts only between iterations\n # and not within a test!\n self.api = FormattedParameter(\"-a {}\", \"DAOS\")\n self.block_size = FormattedParameter(\"-b {}\")\n self.test_delay = FormattedParameter(\"-d {}\")\n self.script = FormattedParameter(\"-f {}\")\n self.signatute = FormattedParameter(\"-G {}\")\n self.repetitions = FormattedParameter(\"-i {}\")\n self.outlier_threshold = FormattedParameter(\"-j {}\")\n self.alignment = FormattedParameter(\"-J {}\")\n self.data_packet_type = FormattedParameter(\"-l {}\")\n self.memory_per_node = FormattedParameter(\"-M {}\")\n self.num_tasks = FormattedParameter(\"-N {}\")\n self.test_file = FormattedParameter(\"-o {}\")\n self.directives = FormattedParameter(\"-O {}\")\n self.task_offset = FormattedParameter(\"-Q {}\")\n self.segment_count = FormattedParameter(\"-s {}\")\n self.transfer_size = FormattedParameter(\"-t {}\")\n self.max_duration = FormattedParameter(\"-T {}\")\n\n # Module DAOS\n # Required arguments\n # --daos.pool=STRING pool uuid\n # --daos.svcl=STRING pool SVCL\n # --daos.cont=STRING container uuid\n # Flags\n # --daos.destroy Destroy Container\n # Optional arguments\n # --daos.group=STRING server group\n # --daos.chunk_size=1048576 chunk size\n # --daos.oclass=STRING object class\n self.daos_pool = FormattedParameter(\"--daos.pool {}\")\n self.daos_svcl = FormattedParameter(\"--daos.svcl {}\")\n self.daos_cont = FormattedParameter(\"--daos.cont {}\")\n self.daos_destroy = FormattedParameter(\"--daos.destroy\", True)\n self.daos_group = FormattedParameter(\"--daos.group {}\")\n self.daos_chunk = FormattedParameter(\"--daos.chunk_size {}\", 1048576)\n self.daos_oclass = FormattedParameter(\"--daos.oclass {}\")\n\n def get_param_names(self):\n \"\"\"Get a sorted list of the defined IorCommand parameters.\"\"\"\n # Sort the IOR parameter names to generate consistent ior commands\n all_param_names = super(IorCommand, self).get_param_names()\n\n # List all of the common ior params first followed by any daos-specific\n # params (except when using MPIIO).\n param_names = [name for name in all_param_names if \"daos\" not in name]\n if self.api.value != \"MPIIO\":\n param_names.extend(\n [name for name in all_param_names if \"daos\" in name])\n\n return param_names\n\n def get_params(self, test, path=\"/run/ior/*\"):\n \"\"\"Get values for all of the ior command params using a yaml file.\n\n Sets each BasicParameter object's value to the yaml key that matches\n the assigned name of the BasicParameter object in this class. For\n example, the self.block_size.value will be set to the value in the yaml\n file with the key 'block_size'.\n\n Args:\n test (Test): avocado Test object\n path (str, optional): yaml namespace. Defaults to \"/run/ior/*\".\n\n \"\"\"\n super(IorCommand, self).get_params(test, path)\n\n def set_daos_params(self, group, pool, cont_uuid=None, display=True,\n mpiio_oclass=None):\n \"\"\"Set the IOR parameters for the DAOS group, pool, and container uuid.\n\n Args:\n group (str): DAOS server group name\n pool (DaosPool): DAOS pool API object\n cont_uuid (str, optional): the container uuid. If not specified one\n is generated. Defaults to None.\n display (bool, optional): print updated params. Defaults to True.\n \"\"\"\n self.set_daos_pool_params(pool, display)\n self.daos_group.update(group, \"daos_group\" if display else None)\n self.daos_cont.update(\n cont_uuid if cont_uuid else uuid.uuid4(),\n \"daos_cont\" if display else None)\n\n # assigning obj class as SX in None else\n # the desired one\n if mpiio_oclass is None:\n self.mpiio_oclass = 214\n else:\n self.mpiio_oclass = mpiio_oclass\n\n def set_daos_pool_params(self, pool, display=True):\n \"\"\"Set the IOR parameters that are based on a DAOS pool.\n\n Args:\n pool (DaosPool): DAOS pool API object\n display (bool, optional): print updated params. Defaults to True.\n \"\"\"\n #self.daos_pool.value = pool.uuid\n self.daos_pool.update(\n pool.pool.get_uuid_str(), \"daos_pool\" if display else None)\n self.set_daos_svcl_param(pool, display)\n\n def set_daos_svcl_param(self, pool, display=True):\n \"\"\"Set the IOR daos_svcl param from the ranks of a DAOS pool object.\n\n Args:\n pool (DaosPool): DAOS pool API object\n display (bool, optional): print updated params. Defaults to True.\n \"\"\"\n svcl = \":\".join(\n [str(item) for item in [\n int(pool.pool.svc.rl_ranks[index])\n for index in range(pool.pool.svc.rl_nr)]])\n self.daos_svcl.update(svcl, \"daos_svcl\" if display else None)\n\n def get_aggregate_total(self, processes):\n \"\"\"Get the total bytes expected to be written by ior.\n\n Args:\n processes (int): number of processes running the ior command\n\n Returns:\n int: total number of bytes written\n\n \"\"\"\n power = {\"k\": 1, \"m\": 2, \"g\": 3, \"t\": 4}\n total = processes\n for name in (\"block_size\", \"segment_count\"):\n item = getattr(self, name).value\n if item:\n sub_item = re.split(r\"([^\\d])\", str(item))\n if sub_item > 0:\n total *= int(sub_item[0])\n if len(sub_item) > 1:\n key = sub_item[1].lower()\n if key in power:\n total *= 1024**power[key]\n else:\n raise IorFailed(\n \"Error obtaining the IOR aggregate total from \"\n \"the {} - bad key: value: {}, split: {}, \"\n \"key: {}\".format(name, item, sub_item, key))\n else:\n raise IorFailed(\n \"Error obtaining the IOR aggregate total from the {}: \"\n \"value: {}, split: {}\".format(name, item, sub_item))\n\n # Account for any replicas\n try:\n # Extract the replica quantity from the object class string\n replica_qty = int(re.findall(r\"\\d+\", self.daos_oclass.value)[0])\n except (TypeError, IndexError):\n # If the daos object class is undefined (TypeError) or it does not\n # contain any numbers (IndexError) then there is only one replica\n replica_qty = 1\n finally:\n total *= replica_qty\n\n return total\n\n def get_launch_command(self, manager, attach_info, processes, hostfile):\n \"\"\"Get the process launch command used to run IOR.\n\n Args:\n manager (str): mpi job manager command\n attach_info (str): CART attach info path\n mpi_prefix (str): path for the mpi launch command\n processes (int): number of host processes\n hostfile (str): file defining host names and slots\n\n Raises:\n IorFailed: if an error occured building the IOR command\n\n Returns:\n str: ior launch command\n\n \"\"\"\n print(\"Getting launch command for {}\".format(manager))\n exports = \"\"\n env = {\n \"CRT_ATTACH_INFO_PATH\": attach_info,\n \"MPI_LIB\": \"\\\"\\\"\",\n \"DAOS_SINGLETON_CLI\": 1,\n }\n if manager.endswith(\"mpirun\"):\n env.update({\n \"DAOS_POOL\": self.daos_pool.value,\n \"DAOS_SVCL\": self.daos_svcl.value,\n \"FI_PSM2_DISCONNECT\": 1,\n \"IOR_HINT__MPI__romio_daos_obj_class\": self.mpiio_oclass,\n })\n assign_env = [\"{}={}\".format(key, val) for key, val in env.items()]\n exports = \"export {}; \".format(\"; export \".join(assign_env))\n args = [\n \"-np {}\".format(processes),\n \"-hostfile {}\".format(hostfile),\n # \"-map-by node\",\n ]\n\n elif manager.endswith(\"orterun\"):\n assign_env = [\"{}={}\".format(key, val) for key, val in env.items()]\n args = [\n \"-np {}\".format(processes),\n \"-hostfile {}\".format(hostfile),\n \"-map-by node\",\n ]\n args.extend([\"-x {}\".format(assign) for assign in assign_env])\n\n elif manager.endswith(\"srun\"):\n env.update({\n \"DAOS_POOL\": self.daos_pool.value,\n \"DAOS_SVCL\": self.daos_svcl.value,\n \"FI_PSM2_DISCONNECT\": 1,\n })\n assign_env = [\"{}={}\".format(key, val) for key, val in env.items()]\n args = [\n \"-l\",\n \"--mpi=pmi2\",\n \"--export={}\".format(\",\".join([\"ALL\"] + assign_env)),\n ]\n if processes is not None:\n args.append(\"--ntasks={}\".format(processes))\n args.append(\"--distribution=cyclic\") # --map-by node\n if hostfile is not None:\n args.append(\"--nodefile={}\".format(hostfile))\n\n else:\n raise IorFailed(\"Unsupported job manager: {}\".format(manager))\n\n return \"{}{} {} {}\".format(\n exports, manager, \" \".join(args), self.__str__())\n\n def run(self, manager, attach_info, processes, hostfile, display=True):\n \"\"\"Run the IOR command.\n\n Args:\n manager (str): mpi job manager command\n attach_info (str): CART attach info path\n processes (int): number of host processes\n hostfile (str): file defining host names and slots\n display (bool, optional): print IOR output to the console.\n Defaults to True.\n\n Raises:\n IorFailed: if an error occured runnig the IOR command\n\n \"\"\"\n command = self.get_launch_command(\n manager, attach_info, processes, hostfile)\n if display:\n print(\": {}\".format(command))\n\n # Run IOR\n try:\n run(command, allow_output_check=\"combined\", shell=True)\n\n except CmdError as error:\n print(\" Exception occurred: {}\".format(error))\n raise IorFailed(\"IOR Run process Failed: {}\".format(error))\n","repo_name":"mchaarawi/daos","sub_path":"src/tests/ftest/util/ior_utils.py","file_name":"ior_utils.py","file_ext":"py","file_size_in_byte":12895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"20"}
+{"seq_id":"39986495218","text":"import argparse\nimport os\nimport re\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"path\", help=\"text file including latex equations.\")\n\nargs = parser.parse_args()\npath = args.path\n\ndef convert(text):\n link_indices = [m.span() for m in re.finditer(\"\\[[あ-んぁ-んァ-ン一-龥a-zA-Z0-9!-/:-@[-`{-~]*\\]([a-zA-Z0-9!-/:-@[-`{-~]*)\", text)][::-1]\n links = []\n for i,idx in enumerate(link_indices): # 逆順になっていることに注意\n links.append(text[idx[0]:idx[1]])\n text = text[:idx[0]] + \"@@@\" + text[idx[1]:]\n\n text = text.replace(\"\\\\\\\\\", \"\\\\\\\\\\\\\")\n text = text.replace(\"_\", \"\\\\_\")\n text = text.replace(\"^\", \"\\\\^\")\n text = text.replace(\"[\", \"\\\\[\")\n text = text.replace(\"]\", \"\\\\]\")\n text = text.replace(\"\\\\{\", \"\\\\\\\\{\")\n text = text.replace(\"\\\\}\", \"\\\\\\\\}\")\n text = text.replace(\"*\", \"\\\\*\")\n\n # equations\n text = text.replace(r\"$$\", r\"\")\n N = len([m.span() for m in re.finditer(\"$$\", text)][::-1])\n if N > 0:\n text = \"\"\"[tex: \\displaystyle]\n\"\"\" + text\n \n indices = [m.span() for m in re.finditer(\"\\$\", text)][::-1]\n for i,idx in enumerate(indices): # 逆順になっていることに注意\n if i % 2 == 0:\n # $ -> ]\n text = text[:idx[0]] + \"]\" + text[idx[1]:]\n elif i % 2 == 1:\n text = text[:idx[0]] + \"[tex: \\\\displaystyle \" + text[idx[1]:]\n\n\n link_indices = [m.span() for m in re.finditer(\"@@@\", text)][::-1]\n for i,idx in enumerate(link_indices): # 逆順になっていることに注意\n text = text[:idx[0]] + links[i] + text[idx[1]:]\n\n return text\n\n\nwith open(path) as f:\n text = f.read()\n\nnewtext = convert(text)\n\nwith open(\"hatena.txt\", \"w\") as f:\n f.write(newtext)\n\n\n","repo_name":"sunbluesome/GaussianProcess","sub_path":"tex2hatena.py","file_name":"tex2hatena.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"19676427718","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom tqdm import tqdm\n\n\nclass DynamicsModel:\n def __init__(self, state_dim, act_dim, hidden_size=(64, 64), seed=123,\n fit_lr=1e-3, fit_wd=0.0, device='cpu', activation='relu', **kwargs):\n self.state_dim = state_dim\n self.act_dim = act_dim\n self.device = 'cuda' if device == 'gpu' or device == 'cuda' else 'cpu'\n self.network = DynamicsNet(state_dim, act_dim, hidden_size, seed=seed)\n self.network = self.network.to(self.device)\n self.network.set_transformations()\n if activation == 'tanh':\n print(\"Using tanh activation\")\n self.network.nonlinearity = torch.tanh\n self.optimizer = torch.optim.Adam(self.network.parameters(), lr=fit_lr, weight_decay=fit_wd)\n self.loss_fn = torch.nn.MSELoss()\n\n def to(self, device):\n self.network.to(device)\n\n def is_cuda(self):\n return True if next(self.network.parameters()).is_cuda else False\n\n def forward(self, s, a):\n if type(s) == np.ndarray:\n s = torch.from_numpy(s).float()\n if type(a) == np.ndarray:\n a = torch.from_numpy(a).float()\n s = s.to(self.device)\n a = a.to(self.device)\n return self.network.forward(s, a)\n\n def predict(self, s, a):\n s = torch.from_numpy(s).float()\n a = torch.from_numpy(a).float()\n s = s.to(self.device)\n a = a.to(self.device)\n s_next = self.network.forward(s, a)\n s_next = s_next.to('cpu').data.numpy()\n return s_next\n\n def fit(self, s, a, s_next, fit_mb_size, fit_epochs):\n return fit_model(self.network, s, a, s_next, self.optimizer,\n self.loss_fn, fit_mb_size, fit_epochs, set_transforms=True)\n\n\nclass DynamicsNet(nn.Module):\n def __init__(self, state_dim, act_dim, hidden_sizes=(64,64),\n s_mean = None,\n s_sigma = None,\n a_mean = None,\n a_sigma = None,\n out_mean = None,\n out_sigma = None,\n seed=123,\n ):\n super(DynamicsNet, self).__init__()\n\n torch.manual_seed(seed)\n self.state_dim = state_dim\n self.act_dim = act_dim\n self.hidden_sizes = hidden_sizes\n self.layer_sizes = (state_dim + act_dim, ) + hidden_sizes + (state_dim, )\n # hidden layers\n self.fc_layers = nn.ModuleList([nn.Linear(self.layer_sizes[i], self.layer_sizes[i+1])\n for i in range(len(self.layer_sizes)-1)])\n self.nonlinearity = torch.relu\n self.set_transformations(s_mean, s_sigma, a_mean, a_sigma, out_mean, out_sigma)\n\n # for param in list(self.parameters())[-2:]: # only last layer\n # param.data = 1e-2 * param.data\n\n def set_transformations(self, s_mean=None, s_sigma=None,\n a_mean=None, a_sigma=None,\n out_mean=None, out_sigma=None):\n\n if s_mean is None:\n self.s_mean = torch.zeros(self.state_dim)\n self.s_sigma = torch.ones(self.state_dim)\n self.a_mean = torch.zeros(self.act_dim)\n self.a_sigma = torch.ones(self.act_dim)\n self.out_mean = torch.zeros(self.state_dim)\n self.out_sigma = torch.ones(self.state_dim)\n elif type(s_mean) == torch.Tensor:\n self.s_mean, self.s_sigma = s_mean, s_sigma\n self.a_mean, self.a_sigma = a_mean, a_sigma\n self.out_mean, self.out_sigma = out_mean, out_sigma\n elif type(s_mean) == np.ndarray:\n self.s_mean = torch.from_numpy(np.float32(s_mean))\n self.s_sigma = torch.from_numpy(np.float32(s_sigma))\n self.a_mean = torch.from_numpy(np.float32(a_mean))\n self.a_sigma = torch.from_numpy(np.float32(a_sigma))\n self.out_mean = torch.from_numpy(np.float32(out_mean))\n self.out_sigma = torch.from_numpy(np.float32(out_sigma))\n else:\n print(\"Unknown type for transformations\")\n quit()\n\n device = 'cuda' if next(self.parameters()).is_cuda else 'cpu'\n self.s_mean, self.s_sigma = self.s_mean.to(device), self.s_sigma.to(device)\n self.a_mean, self.a_sigma = self.a_mean.to(device), self.a_sigma.to(device)\n self.out_mean, self.out_sigma = self.out_mean.to(device), self.out_sigma.to(device)\n\n self.transformations = dict(s_mean=self.s_mean, s_sigma=self.s_sigma,\n a_mean=self.a_mean, a_sigma=self.a_sigma,\n out_mean=self.out_mean, out_sigma=self.out_sigma)\n\n def forward(self, s, a):\n if s.dim() != a.dim():\n print(\"State and action inputs should be of the same size\")\n # normalize inputs\n s_in = (s - self.s_mean)/(self.s_sigma + 1e-6)\n a_in = (a - self.a_mean)/(self.a_sigma + 1e-6)\n out = torch.cat([s_in, a_in], -1)\n for i in range(len(self.fc_layers)-1):\n out = self.fc_layers[i](out)\n out = self.nonlinearity(out)\n out = self.fc_layers[-1](out)\n # de-normalize the output with state transformations\n # out = out * (self.out_sigma + 1e-8) + self.out_mean\n # add back the un-normalized state\n # network is thus forced to learn the residual which is easier\n out = s + out\n return out\n\n def get_params(self):\n network_weights = [p.data for p in self.parameters()]\n transforms = (self.s_mean, self.s_sigma,\n self.a_mean, self.a_sigma,\n self.out_mean, self.out_sigma)\n return dict(weights=network_weights, transforms=transforms)\n\n def set_params(self, new_params):\n new_weights = new_params['weights']\n s_mean, s_sigma, a_mean, a_sigma, out_mean, out_sigma = new_params['transforms']\n for idx, p in enumerate(self.parameters()):\n p.data = new_weights[idx]\n self.set_transformations(s_mean, s_sigma, a_mean, a_sigma, out_mean, out_sigma)\n\n\ndef fit_model(nn_model, s, a, s_next, optimizer,\n loss_func, batch_size, epochs,\n set_transforms=True):\n \"\"\"\n :param nn_model: pytorch model of form sp_hat = f(s, a) (class)\n :param s: state at time t\n :param a: action at time t\n :param s_next: state at time t+1\n :param optimizer: optimizer to use\n :param loss_func: loss criterion\n :param batch_size: mini-batch size\n :param epochs: number of epochs\n :param set_transforms: set the model transforms from data (bool)\n :return:\n \"\"\"\n\n assert type(s) == type(a)\n assert type(s) == type(s_next)\n assert s.shape[0] == a.shape[0]\n assert s.shape[0] == s_next.shape[0]\n\n device = 'cuda' if next(nn_model.parameters()).is_cuda else 'cpu'\n\n if type(s) == np.ndarray:\n s = torch.from_numpy(s).float()\n a = torch.from_numpy(a).float()\n s_next = torch.from_numpy(s_next).float()\n\n s = s.to(device)\n a = a.to(device)\n s_next = s_next.to(device)\n\n if set_transforms:\n delta = s_next - s\n s_mean, s_sigma = torch.mean(s, dim=0), torch.std(s, dim=0)\n a_mean, a_sigma = torch.mean(a, dim=0), torch.std(a, dim=0)\n out_mean, out_sigma = torch.mean(delta, dim=0), torch.std(delta, dim=0)\n nn_model.set_transformations(s_mean, s_sigma, a_mean, a_sigma, out_mean, out_sigma)\n\n num_samples = s.shape[0]\n epoch_losses = []\n for ep in tqdm(range(epochs)):\n rand_idx = torch.LongTensor(np.random.permutation(num_samples)).to(device)\n ep_loss = 0.0\n num_steps = int(num_samples // batch_size)\n for mb in range(num_steps):\n data_idx = rand_idx[mb*batch_size:(mb+1)*batch_size]\n batch_s = s[data_idx]\n batch_a = a[data_idx]\n batch_sp = s_next[data_idx]\n optimizer.zero_grad()\n sp_hat = nn_model.forward(batch_s, batch_a)\n loss = loss_func(sp_hat, batch_sp)\n loss.backward()\n optimizer.step()\n ep_loss += loss.to('cpu').data.numpy()\n epoch_losses.append(ep_loss * 1.0/num_steps)\n # print(\"Loss after 1 epoch = %f | Loss after %i epochs = %f\" % (epoch_losses[0], epochs, epoch_losses[-1]))\n return epoch_losses\n","repo_name":"YujieLu10/tslam","sub_path":"mjrl/algos/model_accel/nn_dynamics.py","file_name":"nn_dynamics.py","file_ext":"py","file_size_in_byte":8452,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"20"}
+{"seq_id":"36916785667","text":"import socket\n\nip = socket.gethostbyname(\"google.com\")\nprint(\"구글 아이피:{}\".format(ip))\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nsock.connect((ip, 80))\n\ns = \"GET / HTTP/1.1\\n\\n\"\nsock.send(s.encode())\nrecv = sock.recv(4096)\nprint(recv)","repo_name":"doctornam/PythonProject","sub_path":"SocketSample/google.py","file_name":"google.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"20"}
+{"seq_id":"73389100210","text":"import array\r\nimport math\r\n\r\ndef luhnControlSUM(numberChars):\r\n numNoChsArr = list(numberChars)\r\n firstStepSUM = 0\r\n secondStepSUM = 0\r\n \r\n evenNumSTR = numNoChsArr[1::2]\r\n evenNum = array.array('i',[])\r\n oddNumSTR = numNoChsArr[::2]\r\n oddNum = array.array('i',[])\r\n \r\n switcher = {\r\n 0:'0',\r\n 1:'2',\r\n 2:'4',\r\n 3:'6',\r\n 4:'8',\r\n 5:'1',\r\n 6:'3',\r\n 7:'5',\r\n 8:'7',\r\n 9:'9'\r\n }\r\n for z in range (len(oddNumSTR)):\r\n oddNum.append(int(oddNumSTR[z]))\r\n firstStepSUM = firstStepSUM + oddNum[z]\r\n\r\n n = 0\r\n \r\n for z in evenNumSTR:\r\n evenNumSTR[int(n)] = switcher.get((int(z)))\r\n evenNum.append(int(evenNumSTR[n]))\r\n secondStepSUM = secondStepSUM + evenNum[n]\r\n n =n + 1\r\n \r\n \r\n sum = int(firstStepSUM) + int(secondStepSUM)\r\n controlsum = (math.ceil(sum/10) * 10) - sum\r\n \r\n return controlsum","repo_name":"ExlibrisSN/Python","sub_path":"Luhn.py","file_name":"Luhn.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"1549553283","text":"import sys\nfrom binascii import hexlify\nfrom IPython import embed\n\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.backends import default_backend\n\ndef swap_bytes(data,len):\n data_int = int.from_bytes(data, 'little')\n return (data_int).to_bytes(len, 'big')\n\ndef decrypt_ecb(data,ikek):\n cipher_ecb = Cipher(algorithms.AES(ikek), modes.ECB(), backend=backend)\n ecb_decryptor = cipher_ecb.decryptor()\n return ecb_decryptor.update(data) + ecb_decryptor.finalize()\n\ndef decrypt_cbd(data,iv,key):\n cipher_cbc = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)\n cbc_decryptor= cipher_cbc.decryptor()\n return cbc_decryptor.update(data) + cbc_decryptor.finalize()\n\nbackend = default_backend()\n\ntry:\n with open(sys.argv[1], 'rb') as f:\n ikek = f.read()\n with open(sys.argv[2], 'rb') as f:\n entry = bytes(f.read())\n output_file = sys.argv[3]\nexcept:\n print(f\"USAGE: {sys.argv[0]} \")\n raise\n\nentry_key_enc = entry[0x80:0x90]\nentry_size = int.from_bytes(entry[0x14:0x18],'little')\nentry_iv = entry[0x20:0x30]\n\nentry_key = decrypt_ecb(entry_key_enc, ikek)\n\ncontent = decrypt_cbd(entry[0x100:0x100+entry_size], entry_iv, entry_key)\n\nwith open(output_file, 'wb') as f:\n f.write(content)\n\n","repo_name":"PSPReverse/amd-sp-glitch","sub_path":"payloads/decrypt_firmware_entry.py","file_name":"decrypt_firmware_entry.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"20"}
+{"seq_id":"33386954305","text":"#First solution for the problem.\r\ndef containduplicate(nums):\r\n seen=set()\r\n for num in nums:\r\n if num in seen:\r\n return True\r\n seen.add(num)\r\n return False\r\nnums=[1,2,3,4,5,1]\r\nresults=containduplicate(nums)\r\nif results==True:\r\n print(\"The array contains duplicates.\")\r\nif results==False:\r\n print(\"The array have distinct\")\r\n#Second solution for the problem.\r\ndef containduplicates(nums):\r\n nums.sort()\r\n for i in range(len(nums)-1):\r\n if nums[i]==nums[i+1]:\r\n print(nums,\"Contains duplicate.\")\r\n else :\r\n print(nums,\"are distinct.\")\r\n\r\nnums=[]\r\nn=int(input(\"Enter the limit for an array :\"))\r\nfor i in range(n):\r\n l=int(input())\r\n nums.append(l)\r\n\r\nprint(nums)\r\ncontainduplicates(nums)\r\n#Third solution for the problem.\r\n\r\ndef containduplicates(nums):\r\n list(nums)==set(nums)\r\n if nums==set():\r\n print(nums,\"is distinct.\")\r\n else:\r\n print(nums,\" Has a duplicate value.\")\r\n\r\nnums=[]\r\nn=int(input(\"Enter the limit for an array :\"))\r\nfor i in range(n):\r\n l=int(input())\r\n nums.append(l)\r\ncontaindupliates(nums)\r\n\r\n#fourth solution for the question.\r\ndef duplicate():\r\n array=[]\r\n n=int(input(\"Enter the Limit :\"))\r\n for i in range(n):\r\n array_input=int(input(f\"Enter the {i+1} Input :\"))\r\n array.append(array_input)\r\n print(array)\r\n unique_elements=set(array)\r\n if len(unique_elements)==len(array):\r\n print(\"The Array Don't contains Duplicate.\")\r\n else:\r\n print(\"The Array Contains Duplicate.\")\r\n\r\n\r\n \r\n","repo_name":"Yash-develope/Dsa-question-solutions.","sub_path":"duplicate.py","file_name":"duplicate.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"34069261869","text":"from gurobipy import *\nfrom Game import Game\nfrom Useful_methods import *\n\n\nclass MDP_Solver():\n\n def __init__(self, configuration):\n self.StatesDict = {((i,j), has_sword, has_key, has_treasure) : dict()\n for i in range(configuration.X)\n for j in range(configuration.Y)\n for has_key in [True, False]\n for has_treasure in [True, False]\n for has_sword in [True, False] if (not has_treasure) or has_key}\n self.Reward_tab = dict()\n nb = 0\n self.config = configuration\n for state in self.StatesDict:\n position, has_sword, has_key, has_treasure = state\n self.Reward_tab[state] = dict()\n for action in Action.get_all_actions():\n # print(action)\n dx, dy = action\n x, y = position\n new_x = x+dx\n new_y = y+dy\n if configuration.Dungeon.is_valid_position(new_x, new_y):\n #L : list of new positions and proba\n L, reward = self.list_of_dest_position_proba_and_rewards(new_x, new_y, has_sword, has_key, has_treasure)\n self.Reward_tab[state][action] = reward\n nb += 1\n for etat_destination, proba in L:\n if action not in self.StatesDict[state]:\n self.StatesDict[state][action] = set()\n self.StatesDict[state][action].add((etat_destination, proba))\n\n # print(\"end mdp solver\")\n\n def list_of_dest_position_proba_and_rewards(self, from_x, from_y, has_sword, has_key, has_treasure):\n case_element = self.config.Dungeon.grid[from_x][from_y]\n return case_element.get_list_dest_and_rewards(from_x, from_y, has_treasure, has_sword, has_key)\n\n def __str__(self):\n s = \"\"\n for state, associated_set in self.StatesDict.items():\n s += str(state)\n s += \" : \"\n s += str(associated_set)\n s += \"\\n\"\n return s\n\n def run_value_iteration(self, epsilon):\n max_dif_i = 100 # never choose epsilo > 100\n #initialization\n i = 0\n States_Value_Table = {state : 0. for state in self.StatesDict}\n States_best_actions_Table = {state : None for state in self.StatesDict}\n #iterations\n while max_dif_i > epsilon:\n L = list()\n for state, value in States_Value_Table.items():\n best_action = None\n new_value = 0\n for action, Set_of_couple_of_dest_stat_proba in self.StatesDict[state].items():\n state_action_value = self.Reward_tab[state][action]\n for dest_stat, proba in Set_of_couple_of_dest_stat_proba:\n state_action_value += 0.9 * States_Value_Table[dest_stat]*proba #facteur d'actualisation = 0.9\n if best_action == None or state_action_value > new_value:\n best_action = action\n new_value = state_action_value\n L.append(abs(value - new_value))\n max_dif_i = max(L)\n # print (max_dif_i)\n States_Value_Table[state] = new_value\n States_best_actions_Table[state] = best_action #.append(best_action)\n i += 1\n #calcul of optimal policy\n return States_best_actions_Table\n\n\n def run_linear_programming_resolution(self):\n m = Model(\"MADI\")\n #initialization\n #Dict correspondance state --> num variable\n State2varnumDict = dict()\n num = 0\n x = list() #liste des objets variables gurobi\n for state in self.StatesDict:\n State2varnumDict[state] = num\n # x.append(m.addVar(name=\"x%d\"%num))\n x.append(m.addVar(vtype=GRB.CONTINUOUS, lb=-GRB.INFINITY, name=\"x_%d\"%num))\n # une variable par etat\n m.update()\n num += 1\n m.update()\n #les contraintes\n for state in self.StatesDict:\n for action, Set_of_couple_of_dest_stat_proba in self.StatesDict[state].items():\n c = LinExpr()\n c += x[State2varnumDict[state]]\n for dest_stat, proba in Set_of_couple_of_dest_stat_proba:\n c -= 0.9*proba*x[State2varnumDict[dest_stat]]\n # print(c)\n m.addConstr(c >= self.Reward_tab[state][action], \"Cstr state {} action {}\".format(state, action))\n m.update()\n m.update()\n #l'objectif\n obj = LinExpr()\n\n for i in range(len(x)):\n obj += x[i]\n m.setObjective(obj, GRB.MINIMIZE)\n m.update()\n # print(obj)\n m.optimize()\n print(\"=====> \", m.Runtime)\n # Politique\n States_best_actions_Table = {state : None for state in self.StatesDict}\n for state in self.StatesDict:\n best_action = None\n best_value = 0\n for action, Set_of_couple_of_dest_stat_proba in self.StatesDict[state].items():\n state_action_value = self.Reward_tab[state][action]\n for dest_stat, proba in Set_of_couple_of_dest_stat_proba:\n state_action_value += 0.9 * proba * x[State2varnumDict[dest_stat]].x\n if best_action == None or state_action_value > best_value:\n best_action = action\n best_value = state_action_value\n States_best_actions_Table[state] = best_action\n # print(States_best_actions_Table)\n return States_best_actions_Table\n\n\n\nif __name__ == '__main__':\n\n\n # filename = \"Instances/impossible_to_win\"\n # filename = \"Instances/example_grid\"\n # filename = \"Instances/EASY_10_10\"\n filename = \"Instances/MEDIUM_10_10\"\n # filename = \"Instances/bridge_to_victory\"\n\n G = Game(filename)\n if G.is_winnable():\n mdp_solver = MDP_Solver(G.config)\n # policy = mdp_solver.run_linear_programming_resolution() # LINEAR PROGRAMING\n policy = mdp_solver.run_value_iteration(0.01) #VALUE ITERATION\n # print_policy(policy, G.config.X, G.config.Y)\n G.play_with_policy(policy)\n else:\n print (\"Game can not be won!!!\")\n # ITERATION VALUE\n\n\n #\n\n #\n\n","repo_name":"tetras92/MADMIN","sub_path":"MDP_Solver.py","file_name":"MDP_Solver.py","file_ext":"py","file_size_in_byte":6476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"11066399366","text":"import tensorflow as tf\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers import Layer\nfrom tensorflow.keras import backend\n\nweight_decay = 1E-4\n\n\nclass HASeparator(tf.keras.layers.Layer):\n \"\"\"Custom layer based on Hyperplane-Assisted Softmax separator paper:\n \n I. Kansizoglou, N. Santavas, L. Bampis and A. Gasteratos, \"HASeparator: Hyperplane-Assisted Softmax,\" \n 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), \n 2020, pp. 519-526, doi: 10.1109/ICMLA51294.2020.00087.\n\n \"\"\" \n def __init__(self, num_classes:int, margin: float, scale: float, **kwargs):\n \"\"\"\n Args:\n num_classes (int): Total number of classes\n margin (float): Margin value, above of which extra penalty will be added\n scale (float): Scaler value by which logits space will be multiplied\n \"\"\" \n super(HASeparator, self).__init__(**kwargs) \n self.num_classes = num_classes\n self.logist_scale = scale\n self.margin = margin\n\n def build(self, input_shape):\n\n self.w = self.add_variable(\n \"weights\",\n trainable=True,\n shape=[int(input_shape[-1]), self.num_classes])\n self.inputShape = int(input_shape[-1])\n\n def call(self, embds, labels):\n normed_embds = tf.nn.l2_normalize(embds, axis=1, name='normed_embd')\n normed_w = tf.nn.l2_normalize(self.w, axis=0, name='normed_weights')\n\n labels = tf.cast(labels, tf.int32)\n gr_w = tf.expand_dims(tf.gather(tf.transpose(normed_w), labels,\n axis=0),\n axis=-1)\n temp = tf.tile(tf.expand_dims(normed_w, 0), [tf.shape(embds)[0], 1, 1])\n dw = tf.subtract(gr_w, temp)\n normed_dw = tf.nn.l2_normalize(dw, axis=1, name='normed_dw')\n win = tf.einsum('...ij,...ijl->...il', normed_embds, normed_dw)\n penalties = tf.math.subtract(self.margin,\n tf.math.minimum(self.margin, win))\n\n cos_t = tf.matmul(normed_embds, normed_w, name='cos_t')\n logits = tf.scalar_mul(self.logist_scale, cos_t, 'scaled_logist')\n logits = tf.nn.softmax(logits)\n\n return logits, penalties","repo_name":"nsantavas/HASeparator-Hyperplane-Assisted-Softmax","sub_path":"customLayer.py","file_name":"customLayer.py","file_ext":"py","file_size_in_byte":2268,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"20"}
+{"seq_id":"71725257971","text":"#import the necessary packages\n#-*-coding:utf-8-*-\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nfrom PIL import Image\nimport sys\nsys.path.append('/usr/local/lib/python2.7/site-packages')\nimport time\nimport cv2\nimport requests\nimport json\nimport ast\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n#url = 'http://112.151.162.170:7000/init'\n#pwd = '/home/pi/Hmmteresting/Razberry/crop_image.jpg'\n\n\ndef detect(img, cascade):\n rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30,30),\n flags=cv2.CASCADE_SCALE_IMAGE)\n if len(rects) == 0:\n return []\n rects[:,2:] += rects[:,:2]\n return rects\n\ndef draw_rects(img, rects, color):\n for x1, y1, x2, y2 in rects:\n cv2.imwrite(\"test_image.jpg\", img)\n rect_image = cv2.rectangle(img, (x1-40, y1-40), (x2+40, y2+40), color, 2)\n print(x1, y1, x2, y2) #rectangle coordinate\n cv2.imwrite(\"rect_image.jpg\", rect_image) #save a rectangle-drawn picture\n '''\n crop_image = Image.open('rect_image.jpg')\n crop_image = crop_image.crop((x1-40, y1-40, x2+40, y2+40)) #crop the image inside the rectangle\n crop_image.save('crop_image.jpg') #save crop image\n '''\n return 1\n\n#initialize the camera and grab a reference to the raw camera capture\ncamera = PiCamera()\ncamera.resolution = (612, 816)\n#camera.brightness = 50\ncamera.framerate = 32\nrawCapture = PiRGBArray(camera, size=(612, 816))\n\ncascade = cv2.CascadeClassifier(\"/home/pi/opencv-3.3.0/data/haarcascades/haarcascade_frontalface_alt.xml\")\n\n#allow the camera to warmup\ntime.sleep(0.1)\n\n#capture frames from the camera\nfor frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n #grab the raw Numpy array representing the image, then initialize the timestamp\n #and occupied/unoccupied text\n img = frame.array\n\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n gray = cv2.equalizeHist(gray)\n\n rects = detect(gray, cascade)\n vis = img.copy()\n \n #draw_rects(vis, rects, (0, 255, 0))\n \n draw_rects(vis, rects, (0, 255, 0))\n '''\n if draw_rects(vis, rects, (0, 255, 0)) == 1:\n url = 'http://112.151.162.170:7000/init'\n\tpwd = '/home/pi/Hmmteresting/Razberry/test_image.jpg'\n\tfiles = {'media' : open(pwd, 'rb') }\n\tres = requests.post(url, files = files)\n \n print type(res)\n\n #make json fionoe named test.json\n if res.text == 'cannot find face':\n print res.text\n elif res.text == 'who are you?':\n print res.text\n print('who are you?')\n else :\n print(\"detected!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\")\n print res.text\n\n #json_data = ast.literal_eval(res.text)\n json_data = json.loads(res.text)\n print type(json_data)\n with open('test.json', 'w') as make_file:\n json.dump(json_data, make_file, ensure_ascii=False)\n\n time.sleep(5)\n '''\n #show the frame\n cv2.imshow(\"Frame\", vis)\n\n #clear the stream in preparation for the next frame\n rawCapture.truncate(0)\n\n #if the 'q' key was pressed, break from the loop\n key = cv2.waitKey(1) & 0xFF\n if key == ord(\"q\"):\n break\n\n","repo_name":"LeeKyungwook/Hmmteresting","sub_path":"Razberry/myface.py","file_name":"myface.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"29091083311","text":"import h5py\nimport json\nimport os\nimport numpy as np\n\nfrom PIL import Image\n\nfrom util import log\n\nRANDOM_STATE = np.random.RandomState(123)\n\n\nclass Dataset(object):\n\n def __init__(self, ids, dataset_path, image_dir, vocab_path, num_k,\n width=224, height=224, name='default', is_train=True):\n self._ids = list(ids)\n self.image_dir = image_dir\n self.width = width\n self.height = height\n self.name = name\n self.is_train = is_train\n\n self.vocab = json.load(open(vocab_path, 'r'))\n\n file_name = os.path.join(dataset_path, 'data.hdf5')\n log.info('Reading {} ...'.format(file_name))\n\n self.data = h5py.File(file_name, 'r')\n self.data_info = self.data['data_info']\n self.objects_intseq = self.data_info['objects_intseq'].value\n self.objects_intseq_len = self.data_info['objects_intseq_len'].value\n self.objects_name = []\n for obj, obj_len in zip(self.objects_intseq, self.objects_intseq_len):\n self.objects_name.append(\n ' '.join([self.vocab['vocab'][i] for i in obj[:obj_len]]))\n self.objects_name = np.array(self.objects_name)\n self.num_objects = int(self.data_info['num_unique_objects'].value)\n self.objects_ids = list(range(self.num_objects))\n self.max_name_len = int(self.data_info['max_name_length'].value)\n\n if is_train:\n self.num_k = num_k # number of positive + negative objects\n else: self.num_k = self.num_objects\n\n log.info('Reading Done {}'.format(file_name))\n\n def get_data(self, id):\n \"\"\"\n Returns:\n image: [height, width, channel]\n sampled_objects: [num_k, max_name_len] (first is positive object)\n sampled_objects_len: [num_k]\n ground_truth: [num_k] (one-hot vector with 1 as the first entry\n sampled_objects_name: [num_k]\n \"\"\"\n image_id, id = id.split()\n entry = self.data[image_id][id]\n\n if self.is_train:\n name_ids = list(entry['name_ids'].value)\n negative_ids = list(set(self.objects_ids) - set(name_ids))\n RANDOM_STATE.shuffle(negative_ids)\n\n sampled_ids = (name_ids + negative_ids)[:self.num_k]\n sampled_objects = np.take(self.objects_intseq, sampled_ids, axis=0)\n sampled_objects_len = np.take(\n self.objects_intseq_len, sampled_ids, axis=0)\n sampled_objects_name = []\n for obj, obj_len in zip(sampled_objects, sampled_objects_len):\n sampled_objects_name.append(\n ' '.join([self.vocab['vocab'][i] for i in obj[:obj_len]]))\n sampled_objects_name = np.array(sampled_objects_name)\n num_pos, num_neg = len(name_ids), self.num_k - len(name_ids)\n ground_truth = np.array([1] * num_pos + [0] * num_neg, dtype=np.float32)\n else:\n name_ids = list(entry['name_ids'].value)\n sampled_objects = self.objects_intseq\n sampled_objects_len = self.objects_intseq_len\n sampled_objects_name = self.objects_name\n ground_truth = np.zeros([self.num_objects], dtype=np.float32)\n for name_id in name_ids:\n ground_truth[name_id] = 1.0\n\n x, y = entry['x'].value, entry['y'].value\n w, h = entry['w'].value, entry['h'].value\n\n image_path = os.path.join(self.image_dir, '{}.jpg'.format(image_id))\n image = np.array(\n Image.open(image_path).crop([x, y, x + w, y + h])\n .resize([self.width, self.height]).convert('RGB'), dtype=np.float32\n )\n\n return image, sampled_objects, sampled_objects_len, ground_truth,\\\n sampled_objects_name\n\n def get_data_shapes(self):\n data_shapes = {\n 'image': [self.height, self.width, 3],\n 'sampled_objects': [self.num_k, self.max_name_len],\n 'sampled_objects_len': [self.num_k],\n 'ground_truth': [self.num_k],\n 'sampled_objects_name': [self.num_k],\n }\n return data_shapes\n\n @property\n def ids(self):\n return self._ids\n\n def __len__(self):\n return len(self.ids)\n\n def __repr__(self):\n return 'Dataset ({}, {} examples)'.format(self.name, len(self))\n\n\ndef create_default_splits(dataset_path, image_dir, vocab_path,\n num_k, is_train=True):\n \"\"\"\n Args:\n num_k: number positive + negative object names sampled for training\n \"\"\"\n ids_train, ids_test, ids_val = all_ids(dataset_path, is_train=is_train)\n\n dataset_train = Dataset(ids_train, dataset_path, image_dir, vocab_path,\n num_k, width=224, height=224,\n name='train', is_train=is_train)\n dataset_test = Dataset(ids_test, dataset_path, image_dir, vocab_path,\n num_k, width=224, height=224,\n name='test', is_train=is_train)\n dataset_val = Dataset(ids_val, dataset_path, image_dir, vocab_path,\n num_k, width=224, height=224,\n name='val', is_train=is_train)\n return {\n 'train': dataset_train,\n 'test': dataset_test,\n 'val': dataset_val\n }\n\n\ndef all_ids(dataset_path, is_train=True):\n with h5py.File(os.path.join(dataset_path, 'data.hdf5'), 'r') as f:\n num_train = int(f['data_info']['num_train'].value)\n num_test = int(f['data_info']['num_test'].value)\n num_val = int(f['data_info']['num_val'].value)\n\n with open(os.path.join(dataset_path, 'id.txt'), 'r') as fp:\n ids_total = fp.read().splitlines()\n\n ids_train = ids_total[:num_train]\n ids_test = ids_total[num_train: num_train + num_test]\n ids_val = ids_total[num_train + num_test: num_train + num_test + num_val]\n\n if is_train:\n RANDOM_STATE.shuffle(ids_train)\n RANDOM_STATE.shuffle(ids_test)\n RANDOM_STATE.shuffle(ids_val)\n\n return ids_train, ids_test, ids_val\n","repo_name":"HyeonwooNoh/VQA-Transfer-ExternalData","sub_path":"vlmap_crop_and_run/datasets/dataset_objects.py","file_name":"dataset_objects.py","file_ext":"py","file_size_in_byte":6037,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"20"}
+{"seq_id":"6556609072","text":"# -*- coding: utf-8 -*-\n\"\"\" 对比排序损失 ranking loss \"\"\"\nimport torch\nfrom torch import nn\nimport numpy as np\n\nclass RankingLoss(nn.Module):\n \"\"\" 对比排序损失 \"\"\"\n \n def __init__(self, args):\n \"\"\" 对比排序损失\n :param args: 超参\n \"\"\"\n super(RankingLoss, self).__init__()\n self.margin = args.margin\n self.target = torch.ones(args.batch_size)\n if args.cuda:\n self.target = self.target.cuda(args.gpu)\n\n def forward(self, image_p_batch, _):\n \"\"\" 相邻两张图片的rank loss\n :param image_p_batch: 一个batch图片的推理结果，奇偶数两张是对比图，奇数图质量 > 接下来的偶数图质量\n :param _: DMOS分数占位符，因为是ranking，所以不需要该值\n :return 左图（奇数图像）对右图（偶数图像）的margin_ranking_loss\n \"\"\"\n #image_p_batch = image_p_batch.view(-1).sigmoid()\n #-------------------------------------------------------zhengruidi------------------------------#\n \n #target = torch.tensor([0.0],requires_grad=True,device = 'cuda')\n batch_size = image_p_batch.size()\n output_temp = []\n for i in range(batch_size[0]):\n output_temp.append(image_p_batch[i])\n output1_temp = output_temp[::2]\n output2_temp = output_temp[1::2]\n output1 = torch.stack(output1_temp)\n output2 = torch.stack(output2_temp)\n #-------------------------------------------------------zhengruidi------------------------------#\n #loss = nn.functional.margin_ranking_loss(image_p_batch[0::2], image_p_batch[1::2],\n # self.target, margin=self.margin)\n \n loss = nn.functional.margin_ranking_loss(output2, output1,\n self.target, margin=self.margin)\n return loss\n\n @staticmethod\n def accuracy(outputs: torch.FloatTensor, _) -> \\\n (torch.float32, torch.BoolTensor, torch.FloatTensor):\n \"\"\" 计算准确率和预测结果\n :param outputs: 模型输出\n :param _: scores的占位符，ranking loss不需要\n :return 排序（左图>右图）的准确率 torch.float32，\n 排序预测结果（左图>右图）(batch_size)，\n 左右图的概率(batch_size, 2)\n \"\"\"\n with torch.no_grad():\n #------------------------------zhengruidi---------------------------#\n #outputs = outputs.view(-1).sigmoid()\n \n batch_size = outputs.size()\n output_temp = np.zeros(batch_size[0], dtype = np.float)\n for i in range(batch_size[0]):\n output_temp[i] = outputs[i]\n output1_temp = output_temp[::2]\n output2_temp = output_temp[1::2]\n output1 = torch.from_numpy(output1_temp)\n output2 = torch.from_numpy(output2_temp)\n #------------------------------zhengruidi---------------------------#\n #probs = torch.stack(output1, output2, dim=1)\n probs = (output1 - output2) > 0\n #-------------------------------------------------------zhengruidi------------------------------#\n # outputs_1 = (outputs[0].softmax(dim=-1) * torch.arange(1,1001,device = 'cuda')).sum(dim=-1)/10\n # outputs_2 = (outputs[1].softmax(dim=-1) * torch.arange(1,1001,device = 'cuda')).sum(dim=-1)/10\n # if(outputs_1 > outputs_2):\n # pred =1\n # else:\n # pred =0\n # acc = pred/1*100.0\n #-------------------------------------------------------zhengruidi------------------------------#\n pred = (output2 - output1) > 0\n acc = pred.int().float().mean() * 100.0\n return acc, pred, probs\n \n # outputs = outputs.view(-1).sigmoid()\n # num_classes = len(outputs)\n # outputs_1 = (outputs[0::2].softmax(dim=-1) * torch.arange(1, (num_classes + 1),device = 'cuda')).sum(dim=-1)/10\n # outputs_2 = (outputs[1::2].softmax(dim=-1) * torch.arange(1, (num_classes + 1),device = 'cuda')).sum(dim=-1)/10\n # probs = torch.stack([outputs[0::2], outputs[1::2]], dim=1)\n # pred = (outputs_1 - outputs_2) > 0\n # acc = pred.int().float().mean() * 100.0\n # print(acc)\n # return acc, pred, probs\n\n\nif __name__ == '__main__':\n ranking_loss = RankingLoss()\n p_batch = torch.rand(20, 1)\n my_loss = ranking_loss(p_batch, None)\n print(f'my_loss: {my_loss}')\n my_acc, my_pred, my_probs = ranking_loss.accuracy(p_batch, None)\n print(f'acc: {my_acc.detach().cpu().item()}, \\n'\n f'pred: {my_pred.detach().cpu().numpy()}, \\n'\n f'my_probs: {my_probs.detach().cpu().numpy()}')\n","repo_name":"AlexReadyNico/RankSR","sub_path":"criterions/ranking_loss.py","file_name":"ranking_loss.py","file_ext":"py","file_size_in_byte":4868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"3935919616","text":"from flask import Flask, render_template, request, redirect, jsonify\nimport sqlite3\n\napp = Flask(__name__)\n\ndef apology(message, code=400):\n \"\"\"Render message as an apology to user.\"\"\"\n def escape(s):\n \"\"\"\n Escape special characters.\n\n https://github.com/jacebrowning/memegen#special-characters\n \"\"\"\n for old, new in [(\"-\", \"--\"), (\" \", \"-\"), (\"_\", \"__\"), (\"?\", \"~q\"),\n (\"%\", \"~p\"), (\"#\", \"~h\"), (\"/\", \"~s\"), (\"\\\"\", \"''\")]:\n s = s.replace(old, new)\n return s\n return render_template(\"apology.html\", top=code, bottom=escape(message)), code\n\n@app.route(\"/\")\ndef index():\n return redirect(\"/select\")\n\n@app.route(\"/select\", methods = [\"GET\", \"POST\"])\ndef select():\n\n conn = sqlite3.connect('catalog/catalog.db')\n db = conn.cursor()\n db.execute(\"SELECT DISTINCT model FROM legkovye\")\n\n models = []\n\n for row in db:\n models.append(row[0])\n\n conn.close()\n\n return render_template(\"select.html\", models = models)\n\n@app.route(\"/model_line\")\ndef model_line():\n model = request.args.get(\"model\")\n conn = sqlite3.connect('catalog/catalog.db')\n db = conn.cursor()\n db.execute(\"SELECT DISTINCT model_line FROM legkovye WHERE model = :model\", {'model': model})\n\n model_lines = []\n\n for row in db:\n model_lines.append(row[0])\n\n conn.close()\n\n return jsonify(model_lines)\n\n#from here on I neded to more arguments to pass, but I knoe how to pass only one argument, thus I need to split it into several\n@app.route(\"/model_type\")\ndef model_type():\n argument = request.args.get(\"model_line\")\n model = argument.split('>|<')[0]\n model_line = argument.split('>|<')[1]\n conn = sqlite3.connect('catalog/catalog.db')\n db = conn.cursor()\n db.execute(\"SELECT DISTINCT model_type FROM legkovye WHERE model = :model AND model_line = :model_line\", {'model': model, 'model_line': model_line})\n\n model_types = []\n\n for row in db:\n model_types.append(row[0])\n\n conn.close()\n\n return jsonify(model_types)\n\n@app.route(\"/date\")\ndef date():\n argument = request.args.get(\"model_type\")\n model = argument.split('>|<')[0]\n model_line = argument.split('>|<')[1]\n model_type = argument.split('>|<')[2]\n conn = sqlite3.connect('catalog/catalog.db')\n db = conn.cursor()\n db.execute(\"SELECT DISTINCT f, t FROM legkovye WHERE model = :model AND model_line = :model_line AND model_type = :model_type\", {'model': model, 'model_line': model_line, 'model_type': model_type})\n\n dates = []\n\n for row in db:\n dates.append(row[0])\n dates.append(row[1])\n\n conn.close()\n\n return jsonify(dates)\n\n@app.route(\"/battery\")\ndef battery():\n argument = request.args.get(\"date\")\n model = argument.split('>|<')[0]\n model_line = argument.split('>|<')[1]\n model_type = argument.split('>|<')[2]\n date = argument.split('>|<')[3]\n conn = sqlite3.connect('catalog/catalog.db')\n db = conn.cursor()\n db.execute(\"SELECT DISTINCT ae, prem, plus, econ FROM legkovye WHERE model = :model AND model_line = :model_line AND model_type = :model_type AND t > :date AND f < :date\", {'model': model, 'model_line': model_line, 'model_type': model_type, 'date': date})\n\n battery = []\n\n for row in db:\n for r in row:\n if r != None:\n battery.append(r)\n\n conn.close()\n\n return jsonify(battery)\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=8080)\n","repo_name":"suhanjer/battery_type_by_car_model","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"22589641221","text":"import sys\nimport json\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom transformers import BertJapaneseTokenizer, BertConfig\nfrom src.model import BertPredictor, Perceptron\nfrom src.preprocess import preprocess, mk_batches, normalize_dataset\nfrom src.constant import JUN, GYAKU, NEUTRAL\n\ntweet_filename = './data/tweet.json'\ndataset_filename = './data/sample.txt'\n\n\ndef load_omoshiro_tweets_from_json(filename):\n with open(tweet_filename, 'r') as f:\n tweets = json.load(f)\n\n omoshiro_tweets = []\n\n print('All tweets size : {}'.format(len(tweets)))\n\n for tweet in tweets:\n tweet_text = tweet['tweet']['full_text']\n\n # RT を除去する\n if 'RT @' in tweet_text:\n continue\n\n if '面白い' in tweet_text or '面白すぎ' in tweet_text:\n omoshiro_tweets.append(tweet_text.replace('\\n', ''))\n # print(omoshiro_tweets[-1])\n\n return omoshiro_tweets\n\n\n\ndef load_dataset(filename):\n def _splitter(x):\n tag, sent = x.split(',', 1)\n tag = int(tag)\n return (tag, sent)\n\n with open(dataset_filename, 'r') as f:\n dataset = f.readlines()\n\n dataset = list(map(_splitter, dataset))\n\n return dataset\n\n\ndef main():\n # omoshiro_tweets = load_omoshiro_tweets_from_json(tweet_filename)\n valid_dataset_size = 128\n batch_size = 8\n config_path = 'cl-tohoku/bert-base-japanese-whole-word-masking'\n\n if torch.cuda.is_available():\n device = torch.device('cuda')\n else:\n device = torch.device('cpu')\n\n tokenizer = BertJapaneseTokenizer.from_pretrained(config_path)\n config = BertConfig.from_pretrained(config_path)\n\n pad_token_id = config.pad_token_id\n\n dataset = load_dataset(dataset_filename)\n\n train_dataset = dataset[:-128]\n valid_dataset = dataset[-128:]\n\n train_dataset, train_max_length = preprocess(train_dataset, tokenizer, config, batch_size=batch_size, device=device)\n train_dataset = normalize_dataset(train_dataset)\n valid_dataset, valid_max_length = preprocess(valid_dataset, tokenizer, config, batch_size=batch_size, device=device)\n\n valid_batches = mk_batches(dataset=valid_dataset, max_length=valid_max_length, batch_size=batch_size, device=device, pad=pad_token_id)\n\n print('Train dataset size is {}, Valid dataset size is {}'.format(len(train_dataset), len(valid_dataset)))\n\n model = BertPredictor(config_path=config_path, model_path=config_path)\n # model = Perceptron(vocab_size=tokenizer.vocab_size, hidden_size=128, device=device)\n\n model.to(device)\n\n criterion = torch.nn.CrossEntropyLoss(ignore_index=NEUTRAL)\n\n optimizer = optim.SGD(model.parameters(), lr=0.0001)\n\n for epoch in range(10):\n print('------ Epoch {} ------'.format(epoch + 1))\n\n train_batches = mk_batches(dataset=train_dataset, max_length=train_max_length, batch_size=batch_size, device=device, pad=pad_token_id)\n\n print('Train')\n model.train()\n accuracy = 0.0\n for i, batch in enumerate(train_batches):\n model.zero_grad()\n\n src = batch['src']\n tgt = batch['tgt']\n\n # output = [batch_size, vocab_size]\n output = model(src)\n\n loss = criterion(output, tgt)\n\n labels = torch.argmax(output, dim=-1)\n\n accuracy = ((labels == tgt).sum() + accuracy * i * batch_size) / ((i + 1) * batch_size) \n\n loss.backward()\n optimizer.step()\n\n sys.stdout.write('\\rLoss: {}, Accuracy: {}'.format(loss.item(), accuracy))\n \n # accuracy /= len(train_dataset)\n\n print('\\nTrain accuracy {}'.format(accuracy))\n\n print('Validation')\n model.eval()\n with torch.no_grad():\n accuracy = 0.0\n for batch in valid_batches:\n src = batch['src']\n tgt = batch['tgt']\n\n output = model(src)\n\n labels = torch.argmax(output, dim=-1)\n\n accuracy += (labels == tgt).sum()\n \n accuracy /= valid_dataset_size\n print('Valid accuracy : {}'.format(accuracy))\n\n accuracy = 0.0\n for batch in valid_batches:\n accuracy += (JUN == batch['tgt']).sum()\n \n accuracy /= valid_dataset_size\n\n print('== JUN accuracy : {}'.format(accuracy))\n\nif __name__ == '__main__':\n main()\n","repo_name":"aqla114/omoshiroi_tweet","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"33316274376","text":"f = \"\\033[0;31m\"\r\nw = \"\\033[37m\"\r\n\r\ng = \"\\033[90m\"\r\nre = \"\\033[0m\"\r\n\r\ntry:\r\n import os\r\n import time\r\n import threading\r\n import discord\r\n import httpx\r\n\r\n from discord.ext import commands\r\n from pystyle import System, Center\r\n from requests_futures.sessions import FuturesSession\r\n from itertools import cycle\r\n\r\nexcept ImportError as e:\r\n os.system('cls & mode 80, 20 & title Sketchy - Error')\r\n print(f\"\\033[0;31m-> \\033[37mmissing requirements\\033[0;31m! \\033[37m\")\r\n time.sleep(1)\r\n os.system('pip install -r requirements.txt')\r\n input();os._exit(0)\r\n\r\nos.system('cls & mode 80, 20 & title Sketchy - Login')\r\ntoken = input(f\"\\033[0;31m-> \\033[37mToken\\033[0;31m: \\033[37m\")\r\nSystem.Clear()\r\nguild = int(input(f\"\\033[0;31m-> \\033[37mGuild\\033[0;31m: \\033[37m\"))\r\nSystem.Clear()\r\n\r\ndef headfilter() -> str:\r\n if httpx.get(\"https://discord.com/api/v9/users/@me\", headers = {\"Authorization\": token}).status_code in [200, 201, 204]:\r\n return \"user\"\r\n elif httpx.get(\"https://discord.com/api/v9/users/@me\", headers = {\"Authorization\": f\"Bot {token}\"}).status_code in [200, 201, 204]:\r\n return \"bot\"\r\n else:\r\n print(f\"\\033[0;31m-> \\033[37mInvalid Token\\033[0;31m! \\033[37m\")\r\n token\r\n \r\ndef rr() -> bool:\r\n if headfilter() == \"user\":\r\n return True\r\n else:\r\n return False\r\n \r\ndef hea() -> dict:\r\n if headfilter() == \"user\": \r\n return {\"Authorization\": token}\r\n elif headfilter() == \"bot\":\r\n return {\"Authorization\": f\"Bot {token}\"}\r\n else:\r\n print(f\"\\033[0;31m-> \\033[37mInvalid Token\\033[0;31m! \\033[37m\")\r\n \r\nheaders = hea()\r\n\r\nclient = commands.Bot(\r\n command_prefix = \"sketchy!\",\r\n self_bot = rr()\r\n)\r\n\r\n@client.event\r\nasync def on_ready():\r\n global guildobj\r\n try:\r\n guildobj = client.get_guild(guild)\r\n except:\r\n print(f\"\\033[0;31m-> \\033[37mInvalid Guild\\033[0;31m! \\033[37m\")\r\n time.sleep(1)\r\n System.Clear()\r\n guild\r\n await menu()\r\n\r\nclass sketchy:\r\n\r\n def massban_worker(session, member):\r\n try:\r\n proxies = open('assets/proxies.txt').read().split('\\n')\r\n proxy = cycle(proxies)\r\n api = f\"https://discord.com/api/v9/guilds/{guild}/bans/{member}?reason=SketchyW\"\r\n s = session.put(api, headers = headers, proxies = {\"http\": 'http://' + next(proxy)})\r\n except KeyboardInterrupt:\r\n System.Clear()\r\n os._exit(0)\r\n\r\n async def scrape_request():\r\n await client.wait_until_ready()\r\n \r\n try:\r\n os.remove(\"assets/members.txt\")\r\n os.remove(\"assets/channels.txt\")\r\n os.remove(\"assets/roles.txt\")\r\n except:\r\n pass\r\n \r\n membercount = 0\r\n with open(\"assets/members.txt\", \"a\")as mems:\r\n for mem in guildobj.members:\r\n mems.write(str(mem.id) + \"\\n\")\r\n membercount += 1\r\n\r\nasync def menu():\r\n os.system('cls & mode 80, 20 & title Sketchy - Nuker')\r\n print(f\"\"\"\r\n\r\n {f}╔═╗╦╔═╔═╗╔╦╗╔═╗╦ ╦╦ ╦\r\n {g}╚═╗╠╩╗║╣ ║ ║ ╠═╣╚╦╝\r\n {w}╚═╝╩ ╩╚═╝ ╩ ╚═╝╩ ╩ ╩ \r\n {f}═══════════════════════════════════{w}\r\n {f}═══════════════════════════════════════════{w} \r\n\r\n{f}[{w}?{f}] {w}-> Select An Executable Command Below{f}:\r\n {f}[{w}!{f}] {w}-> Type 'massban' To Ban-All\r\n {f}[{w}!{f}] {w}-> Type 'scrape' To Scrape Info\r\n \r\n \"\"\")\r\n\r\n x = input(f\"\\033[0;31m-> \\033[37mMethod\\033[0;31m: \\033[37m\")\r\n\r\n if x == 'massban':\r\n members = []\r\n amount = 0\r\n session = FuturesSession(max_workers = 350)\r\n print(f\"\\033[0;31m-> \\033[37mBanning Members\\033[0;31m. . .\")\r\n a_file = open('assets/members.txt','r')\r\n for line in a_file:\r\n stripped_line = line.strip()\r\n members.append(stripped_line)\r\n a_file.close()\r\n looping = True\r\n while looping:\r\n try:\r\n threading.Thread(target = sketchy.massban_worker, args = (session, members[amount],)).start()\r\n except:\r\n looping = False\r\n amount += 1\r\n threads = []\r\n for member in members:\r\n thread = threading.Thread(target = sketchy.massban_worker, args = (session, member,)).start()\r\n threads.append(thread)\r\n thread.start()\r\n for thread in threads:\r\n try:\r\n thread.join()\r\n except Exception:\r\n pass\r\n time.sleep(1.5)\r\n await menu()\r\n\r\n if x == 'scrape':\r\n await sketchy.scrape_request()\r\n print(f\"\\033[0;31m-> \\033[37mScraped Guild Info\\033[0;31m! \\033[37m\")\r\n time.sleep(1.5)\r\n\r\ntry:\r\n if headfilter() == \"user\":\r\n client.run(token, bot=False)\r\n elif headfilter() == \"bot\":\r\n client.run(token)\r\nexcept:\r\n print(f'\\033[0;31m-> \\033[37mInvalid Token\\033[0;31m! \\033[37m')\r\n time.sleep(1)\r\n os._exit(0) ","repo_name":"lawaGo/sketchy","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5367,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"74479012530","text":"import asyncio\r\nfrom concurrent.futures import ProcessPoolExecutor\r\nfrom asgiref.sync import async_to_sync, sync_to_async\r\n\r\n\r\n\"\"\"\r\n - Oftentimes, CPU bound tasks will need to be scheduled for execution in an async event loop. When this\r\n is this case, it is optimal to run the CPU bound task in a separate process. This ensures both the python\r\n GIL and async event loop aren't blocked\r\n \r\n - To do this, simply import the _proc function and pass it a SYNCHRONOUS function and any parameters\r\n\"\"\"\r\n\r\nasync def _proc(func, *args):\r\n loop = asyncio.get_running_loop()\r\n result = None\r\n\r\n # --> Run in current process if debugging\r\n debug = False\r\n if debug is True:\r\n return await sync_to_async(func)(*args)\r\n\r\n # --> Run in new process for production\r\n with ProcessPoolExecutor(max_workers=1) as pool:\r\n return await loop.run_in_executor(pool, func, *args)\r\n","repo_name":"seakers/daphne_brain","sub_path":"EOSS/graphql/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"72567615089","text":"#!/usr/bin/python3\n\"\"\"Exports to-do list information for a given employee ID to CVS format.\"\"\"\nimport csv\nimport requests\nimport sys\n\nif __name__ == \"__main__\":\n usr_id = int(sys.argv[1])\n b_url = \"https://jsonplaceholder.typicode.com/\"\n url = \"https://jsonplaceholder.typicode.com/users/{}\".format(usr_id)\n user_info = requests.get(url).json()\n username = user_info.get(\"username\")\n t_url = b_url + \"todos?userId={}\".format(usr_id)\n tasks = requests.get(t_url).json()\n completed_tasks = [task for task in tasks if task.get(\"completed\")]\n\n # Generating CSV file\n csv_file = \"{}.csv\".format(usr_id)\n with open(csv_file, mode=\"w\", newline=\"\") as file:\n writer = csv.writer(file, quoting=csv.QUOTE_ALL)\n for task in tasks:\n writer.writerow(\n [usr_id, username, task[\"completed\"], task[\"title\"]])\n","repo_name":"Anuka1940/alx-system_engineering-devops","sub_path":"0x15-api/1-export_to_CSV.py","file_name":"1-export_to_CSV.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"72690058608","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Tests dict input objects for `cookiecutter.operator.aws.ec2_meta` module.\"\"\"\n\nimport os\nfrom cookiecutter.main import cookiecutter\n\n\ndef test_missing_operator_fails(monkeypatch, tmpdir):\n \"\"\"Verify the operator call works successfully.\"\"\"\n monkeypatch.chdir(os.path.abspath(os.path.dirname(__file__)))\n\n stuff = None\n context = False\n try:\n stuff = cookiecutter('.', no_input=True, output_dir=str(tmpdir))\n except: # noqa\n context = True\n assert not stuff\n assert context\n","repo_name":"bendhouseart/nuki-tmp","sub_path":"tests/test-operators/test-bad-type/test_operator_bad_type.py","file_name":"test_operator_bad_type.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"27306066648","text":"import unittest\nimport tensorflow as tf\nimport numpy as np\n\nfrom _neat import Network, Node, Edge\nfrom _neat import Genome, NodeGene, EdgeGene\n\n\ndef node_to_tuple(node):\n return (node.get_id(), node.bias, node.active)\n\n\ndef edge_to_tuple(edge):\n return (edge.get_input().get_id(), edge.get_output().get_id(), edge.weight, edge.active)\n\n\nclass CGenomeTest(unittest.TestCase):\n\n def test_pointers(self):\n net = Network(2, 4, tf.nn.relu)\n\n edge1 = net.register_edge(0, 2)\n\n gene1 = EdgeGene(edge1)\n gene2 = EdgeGene(edge1)\n\n # Test independence\n self.assertEqual(gene1.weight, 0)\n self.assertEqual(gene2.weight, 0)\n\n gene1.weight = 1.0\n\n self.assertEqual(gene1.weight, 1.0)\n self.assertEqual(gene2.weight, 0)\n\n self.assertFalse(gene1.disabled)\n self.assertFalse(gene2.disabled)\n\n gene2.disabled = True\n\n self.assertFalse(gene1.disabled)\n self.assertTrue(gene2.disabled)\n\n # Test application\n self.assertEqual(edge1.weight, 0)\n\n gene1.apply()\n\n self.assertEqual(edge1.weight, 1.0)\n self.assertTrue(edge1.active)\n\n gene2.apply()\n\n self.assertEqual(edge1.weight, 0.0)\n self.assertFalse(edge1.active)\n\n # Test reference\n self.assertEqual(gene1.get_edge(), edge1)\n self.assertEqual(gene2.get_edge(), edge1)\n self.assertEqual(gene1.get_edge(), gene2.get_edge())\n\n def test_genomes(self):\n net = Network(2, 2, tf.nn.relu)\n\n edge1 = net.register_edge(0, 2)\n edge2 = net.register_edge(1, 3)\n\n edge_left, node, edge_right = net.register_node(1, 3)\n\n print(node)\n\n default_nodes = [(0, 0.0, False), (1, 0.0, False),\n (2, 0.0, False), (3, 0.0, False)]\n\n genome1 = Genome(net, [(4, 0.0, True)] + default_nodes, [\n (0, 2, 1.0, False), (1, 3, 0.5, False)])\n\n genome2 = Genome(net, [(4, 1.0, False)] + default_nodes, [\n (0, 2, -1.0, False), (1, 3, 0.5, True), (1, 4, 1.0, False), (4, 3, 1.0, False)])\n\n # Check references\n self.assertEqual(genome1.get_node_genes()[0].get_node(),\n genome2.get_node_genes()[0].get_node())\n\n self.assertEqual(genome1.get_edge_genes()[0].get_edge(),\n genome2.get_edge_genes()[0].get_edge())\n\n genome1.apply()\n np.testing.assert_array_almost_equal(net.forward([0, 0]), [0, 0])\n np.testing.assert_array_almost_equal(net.forward([1, 1]), [1, 0.5])\n np.testing.assert_array_almost_equal(\n net.forward([1, -1]), [1, -0.5])\n np.testing.assert_array_almost_equal(\n net.forward([12, 1]), [12, 0.5])\n\n genome2.apply()\n np.testing.assert_array_almost_equal(net.forward([0, 0]), [0, 1])\n np.testing.assert_array_almost_equal(net.forward([1, 1]), [-1, 2])\n np.testing.assert_array_almost_equal(\n net.forward([1, -2]), [-1, 0])\n np.testing.assert_array_almost_equal(\n net.forward([12, 1]), [-12, 2])\n\n genome1.apply()\n np.testing.assert_array_almost_equal(net.forward([0, 0]), [0, 0])\n np.testing.assert_array_almost_equal(net.forward([1, 1]), [1, 0.5])\n np.testing.assert_array_almost_equal(\n net.forward([1, -1]), [1, -0.5])\n np.testing.assert_array_almost_equal(\n net.forward([12, 1]), [12, 0.5])\n\n child1 = genome1.crossbreed(genome2)\n child2 = genome1.crossbreed(genome2)\n child3 = genome1.crossbreed(genome2)\n\n child1.mutate_weight_random(-10)\n child1.mutate_weight_random(-10)\n child1.mutate_weight_random(-10)\n\n child2.mutate_weight_random(-10)\n child2.mutate_weight_random(-10)\n child2.mutate_weight_random(-10)\n\n child3.mutate_weight_random(-10)\n child3.mutate_weight_random(-10)\n child3.mutate_weight_random(-10)\n\n child1.mutate_edge(0.5)\n child2.mutate_edge(0.5)\n child3.mutate_edge(0.5)\n\n child1.mutate_node(0.5)\n child2.mutate_node(0.5)\n child3.mutate_node(0.5)\n\n genome2.apply()\n np.testing.assert_array_almost_equal(net.forward([0, 0]), [0, 1])\n np.testing.assert_array_almost_equal(net.forward([1, 1]), [-1, 2])\n np.testing.assert_array_almost_equal(\n net.forward([1, -2]), [-1, 0])\n np.testing.assert_array_almost_equal(\n net.forward([12, 1]), [-12, 2])\n\n genome1.apply()\n np.testing.assert_array_almost_equal(net.forward([0, 0]), [0, 0])\n np.testing.assert_array_almost_equal(net.forward([1, 1]), [1, 0.5])\n np.testing.assert_array_almost_equal(\n net.forward([1, -1]), [1, -0.5])\n np.testing.assert_array_almost_equal(\n net.forward([12, 1]), [12, 0.5])\n\n\nif \"__main__\" == __name__:\n unittest.main()\n","repo_name":"ju-leon/NEATer","sub_path":"tests/c_genome_test.py","file_name":"c_genome_test.py","file_ext":"py","file_size_in_byte":4916,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"20"}
+{"seq_id":"29951635830","text":"#!/usr/bin/env python\n\n__author__ = 'Jas Kalayan'\n__credits__ = ['Jas Kalayan', 'Ismaeel Ramzan', \n 'Neil Burton', 'Richard Bryce']\n__license__ = 'GPL'\n__maintainer__ = 'Jas Kalayan'\n__email__ = 'jkalayan@gmail.com'\n__status__ = 'Development'\n\nfrom datetime import datetime\nimport argparse\nimport numpy as np\nfrom keras.models import Model, load_model \n\nfrom ForcePred import Molecule, OPTParser, NPParser, Converter, \\\n Permuter, AMBLAMMPSParser, AMBERParser, XYZParser, Binner, \\\n Writer, Plotter, Network, Conservation\n\nopenmm = False\nif openmm:\n from ForcePred.calculate.OpenMM import OpenMM\n\nimport sys\n#import numpy as np\n#from itertools import islice\n\n#import os\n#os.environ['OMP_NUM_THREADS'] = '8'\n\n\ndef run_force_pred(input_files='input_files', \n atom_file='atom_file', coord_files='coord_files',\n force_files='force_files', energy_files='energy_files',\n charge_files='charge_files', list_files='list_files'):\n\n\n startTime = datetime.now()\n\n print(startTime)\n molecule = Molecule() #initiate molecule class\n\n if list_files:\n input_files = open(list_files).read().split()\n #OPTParser(input_files, molecule, opt=False) #read in FCEZ for SP\n #print(molecule.charges)\n #print(molecule.charges.shape)\n #Converter.get_interatomic_charges(molecule)\n #sys.exit()\n #OPTParser(input_files, molecule, opt=True) #read in FCEZ for opt\n '''\n AMBLAMMPSParser('molecules.prmtop', '1md.mdcrd',\n coord_files, force_files, energy_files, molecule)\n '''\n\n #NPParser(atom_file, coord_files, force_files, energy_files, molecule)\n\n '''\n NPParser('../types_z', \n ['../train1_aspirin_coordinates'], \n ['../train1_aspirin_forces'], [], molecule)\n '''\n '''\n NPParser('types_z', \n ['train50000_aspirin_coordinates'], \n ['train50000_aspirin_forces'], [], molecule)\n '''\n '''\n AMBERParser('molecules.prmtop', coord_files, force_files, \n molecule)\n '''\n\n '''\n XYZParser(atom_file, coord_files, force_files, \n energy_files, molecule)\n '''\n\n\n NPParser(atom_file, coord_files, force_files, [], molecule)\n\n '''\n Writer.write_xyz(molecule.coords, molecule.atoms, 'ensemble-coords.xyz', 'w')\n Writer.write_xyz(molecule.forces, molecule.atoms, 'ensemble-forces.xyz', 'w')\n np.savetxt('ensemble-energies.txt', molecule.energies)\n np.savetxt('ensemble-charges.txt', molecule.charges)\n sys.exit()\n '''\n\n '''\n Writer.write_gaus_cart(molecule.coords[0:11550:10], molecule.atoms, \n 'FORCE POP=MK INTEGRAL=(GRID=ULTRAFINE)', 'mm-SPoints')\n\n sys.stdout.flush()\n sys.exit()\n '''\n\n\n '''\n Writer.write_amber_inpcrd(molecule.coords[0], 'molecules1.inpcrd')\n\n sys.stdout.flush()\n sys.exit()\n '''\n\n '''\n A = Molecule.find_bonded_atoms(molecule.atoms, molecule.coords[0])\n indices, pairs_dict = Molecule.find_equivalent_atoms(molecule.atoms, A)\n print('\\nZ', molecule.atoms)\n print('A', A) \n print('indices', indices)\n print('pairs_dict', pairs_dict)\n\n sys.stdout.flush()\n #sys.exit()\n '''\n\n '''\n\n def iterate_xyz_files(filename, var):\n for filename in filename:\n input_ = open(filename, 'r')\n for line in input_:\n xyz = clean(extract(1, input_))\n var.append(xyz)\n var = get_3D_array(var, 10)\n return var\n\n def extract(padding, input_):\n return (list(islice(input_, padding + \n 10))[-10:])\n\n def clean(raw):\n cleaned = np.empty(shape=[10, 3])\n for i, atom in enumerate(raw):\n cleaned[i] = atom.strip('\\n').split()[-3:]\n #print(cleaned.shape)\n return np.array(cleaned)\n\n def get_3D_array(np_list, n_atoms):\n return np.reshape(np.vstack(np_list), (-1,n_atoms,3))\n velocities = []\n velocities = iterate_xyz_files(['nn-velocities.xyz'], velocities)\n np.savetxt('nn-velocities.txt', velocities.reshape(-1,30))\n sys.exit()\n '''\n\n #A = Molecule.find_bonded_atoms(molecule.atoms, molecule.coords[0])\n #print(molecule.atoms)\n #print(A)\n\n '''\n open('decomp_E.txt', 'w') \n open('molecular_E.txt', 'w') \n f1 = open('decomp_E.txt', 'ab') \n f2 = open('molecular_E.txt', 'ab') \n n_atoms = len(molecule.atoms)\n _NC2 = int(n_atoms * (n_atoms-1)/2)\n for i in range(len(molecule.coords)):\n decomp_E = Converter.get_energy_decomposition(molecule.atoms, \n molecule.coords[i], molecule.energies[i])\n _E = molecule.energies[i]\n print(_E)\n print(decomp_E)\n np.savetxt(f1, decomp_E.reshape(1,-1))\n np.savetxt(f2, _E)\n sum_decomp = np.sum(decomp_E)\n print(sum_decomp, sum_decomp/_E)\n recomp = Converter.get_recomposed_energy(molecule.coords[i], decomp_E, \n n_atoms, _NC2)\n print(recomp)\n print()\n '''\n\n get_decomp = True\n if get_decomp:\n print(molecule)\n print('\\ncheck and get force decomp')\n unconserved = Molecule.check_force_conservation(molecule) #\n Converter(molecule) #get pairwise forces\n print(datetime.now() - startTime)\n\n '''\n #aspirin\n scale_NRF = 13036.56150157717\n scale_NRF_min = 4.296393449372179 \n scale_F = 114.0951589972158\n scale_F_min = 1.1633573357150429e-06\n '''\n\n #'''\n #malonaldehyde \n scale_NRF = 12364.344210692281\n scale_NRF_min = 15.377554794644805\n scale_F = 102.58035961189837\n scale_F_min = 2.4191760310543486e-06\n #'''\n\n\n '''\n #ethanediol \n scale_NRF = 19911.051275945494\n scale_NRF_min = 11.648288117257646\n scale_F = 105.72339728320982 \n scale_F_min = 0\n '''\n\n check_model = False\n if check_model:\n print('check model error')\n n_atoms = len(molecule.atoms)\n _NC2 = int(n_atoms * (n_atoms-1)/2)\n Converter(molecule) #get pairwise forces\n scaled_input, in_max, in_min = Network.get_scaled_values(\n molecule.mat_NRF, \n scale_NRF, scale_NRF_min, method='A')\n scaled_output, _max, _min = Network.get_scaled_values(molecule.mat_F, \n scale_F, scale_F_min, method='B')\n print('\\nNRF_min: {} NRF_max: {}\\nF_min: {} F_max: {}\\n'.format(\n np.amin(molecule.mat_NRF), np.amax(molecule.mat_NRF),\n np.amin(np.absolute(molecule.mat_F)), \n np.amax(np.absolute(molecule.mat_F))))\n model = load_model('best_ever_model')\n train_prediction_scaled = model.predict(scaled_input)\n train_prediction = Network.get_unscaled_values(\n train_prediction_scaled, \n scale_F, scale_F_min, method='B')\n train_recomp_forces = Network.get_recomposed_forces(\n molecule.coords, train_prediction, n_atoms, _NC2)\n print(molecule.mat_NRF.shape, train_prediction.shape, \n train_recomp_forces.shape)\n train_mae, train_rms = Binner.get_error(molecule.mat_F.flatten(), \n train_prediction.flatten())\n print('\\nTrain MAE: {} \\nTrain RMS: {}'.format(train_mae, train_rms))\n sys.stdout.flush()\n\n decomp_F = True\n if decomp_F:\n n_atoms = len(molecule.atoms)\n NRF_scale_method = 'A'\n model = load_model('best_ever_model')\n dr = 0.01\n open('scaled-forces.txt', 'w')\n open('predicted-forces.txt', 'w')\n open('scaled-qs.txt', 'w')\n open('predicted-qs.txt', 'w')\n open('separate-qs.txt', 'w')\n open('separate-scale-factors.txt', 'w')\n f2 = open('scaled-forces.txt', 'a')\n f3 = open('predicted-forces.txt', 'a')\n f4 = open('scaled-qs.txt', 'a')\n f5 = open('predicted-qs.txt', 'a')\n f6 = open('separate-qs.txt', 'a')\n f7 = open('separate-scale-factors.txt', 'a')\n n_structures = 1 #len(molecule.coords)\n for i in range(n_structures):\n print(i)\n pred_F, scaled_F, pred_q, scaled_q, scaling_factors = \\\n Conservation.get_conservation(i,\n molecule.coords[i], molecule.forces[i], \n molecule.atoms, scale_NRF, 0, scale_F, \n model, molecule, dr, NRF_scale_method)\n scaled_F = scaled_F.reshape(n_atoms,-1)\n pred_F = pred_F.reshape(n_atoms,-1)\n #print(scaled_F)\n #print(pred_F)\n #print()\n np.savetxt(f2, scaled_F)\n np.savetxt(f3, pred_F)\n np.savetxt(f4, scaled_q)\n np.savetxt(f5, pred_q)\n sys.stdout.flush()\n\n displaced_of_displaced = True\n if displaced_of_displaced:\n displaced_coords = np.loadtxt(\n '1_coords.txt').reshape(-1,n_atoms,3)\n print(displaced_coords.shape)\n for j in range(len(displaced_coords)):\n print('j', j)\n pred_F, scaled_F, pred_q, scaled_q, scaling_factors = \\\n Conservation.get_conservation(j,\n displaced_coords[j], None, \n molecule.atoms, scale_NRF, 0, scale_F, \n model, molecule, dr, NRF_scale_method)\n pred_q = pred_q.reshape(1,-1)\n scaling_factors = scaling_factors.reshape(1,-1)\n np.savetxt(f6, pred_q)\n np.savetxt(f7, scaling_factors)\n print('check q', pred_q)\n print('check SF', scaling_factors)\n print()\n f2.close()\n f3.close()\n f4.close()\n f5.close()\n f6.close()\n f7.close()\n pred_F = np.loadtxt('predicted-forces.txt')\n scaled_F = np.loadtxt('scaled-forces.txt')\n pred_q = np.loadtxt('predicted-qs.txt')\n scaled_q = np.loadtxt('scaled-qs.txt')\n print(pred_F.shape)\n pred_mae, pred_rms = Binner.get_error(\n molecule.forces[:n_structures].flatten(), pred_F.flatten())\n scaled_mae, scaled_rms = Binner.get_error(\n molecule.forces[:n_structures].flatten(), scaled_F.flatten())\n print('F prediction MAE: {} RMS: {}'.format(pred_mae, pred_rms))\n print('F scaled MAE: {} RMS: {}'.format(scaled_mae, scaled_rms))\n\n pred_mae, pred_rms = Binner.get_error(\n molecule.mat_F[:n_structures].flatten(), pred_q.flatten())\n scaled_mae, scaled_rms = Binner.get_error(\n molecule.mat_F[:n_structures].flatten(), scaled_q.flatten())\n print('q prediction MAE: {} RMS: {}'.format(pred_mae, pred_rms))\n print('q scaled MAE: {} RMS: {}'.format(scaled_mae, scaled_rms))\n\n each_pred_mae, each_pred_rms = Binner.get_each_error(\n molecule.mat_F[:n_structures].flatten(), pred_q.flatten())\n\n each_pred_mae_F, each_pred_rms_F = Binner.get_each_error(\n molecule.forces[:n_structures].flatten(), pred_F.flatten())\n\n pairs = []\n for i in range(len(molecule.atoms)):\n for j in range(i):\n pairs.append([i, j])\n\n interatomic_measures = Binner()\n interatomic_measures.get_bond_pop(molecule.coords, pairs)\n\n Plotter.xy_scatter(\n [interatomic_measures.rs[:n_structures].T.flatten()], \n [each_pred_rms], [''], ['k'], 'r', 'rms', 2, 'rms_r-q.png')\n Plotter.xy_scatter(\n [interatomic_measures.rs[:n_structures].T.flatten()], \n [pred_q.flatten()], [''], ['k'], 'r', 'qAB', 2, 'q_r.png')\n n_list = list(range(n_atoms))\n n_list = np.array([n_list * n_structures * 3])\n Plotter.xy_scatter([n_list], \n [each_pred_rms_F], [''], ['k'], 'atom index', \n 'rms', 2, 'rms_N-F.png')\n\n '''\n print('\\nget equivalent atoms')\n sorted_N_list, resorted_N_list = Molecule.get_sorted_pairs(\n molecule.mat_NRF, pairs_dict)\n #print('sorted_N_list', sorted_N_list)\n #print('resorted_N_list', resorted_N_list)\n\n #a = np.array([[0,1,2],[3,4,5]])\n #ind = np.array([[0,2,1],[1,2,0]])\n #print(np.take_along_axis(a, ind, axis=1))\n #sys.exit()\n '''\n\n sys.stdout.flush()\n\n get_rotate = False\n if get_rotate:\n print('rotate coords/forces')\n Converter.get_rotated_forces(molecule)\n molecule.forces = molecule.rotated_forces\n molecule.coords = molecule.rotated_coords\n unconserved = Molecule.check_force_conservation(molecule) #\n Converter(molecule) # get pairwise forces\n\n print(np.amax(molecule.mat_NRF), np.amin(molecule.mat_NRF))\n #sys.exit()\n\n Writer.write_xyz(molecule.rotated_coords, \n molecule.atoms, 'rot_coords.xyz', 'w')\n Writer.write_xyz(molecule.rotated_forces, \n molecule.atoms, 'rot_forces.xyz', 'w')\n\n sys.stdout.flush()\n #print(molecule.mat_NRF[0])\n #Converter.get_coords_from_NRF(molecule.mat_NRF[0]/33, molecule.atoms,\n #molecule.coords[0], 33)\n #sys.exit()\n\n #Writer.write_gaus_cart(molecule.coords[0:3], \n #molecule.atoms, 'SP Force', 'ethanediolSP')\n\n '''\n for i in range(5):\n n_atoms = len(molecule.atoms)\n _NC2 = int(n_atoms * (n_atoms-1)/2)\n recomp = Network.get_recomposed_forces(molecule.coords[i], \n molecule.mat_F[i].reshape(1,-1), n_atoms, _NC2)\n #print(molecule.mat_F[0].shape)\n print(molecule.forces[i], '\\n')\n print(recomp,'\\n')\n print()\n '''\n\n sys.stdout.flush()\n\n get_train_from_dih = False\n if get_train_from_dih:\n dihedrals = Binner()\n #list_dih = [[1, 2, 3, 6], [3, 2, 1, 7],\n #[2, 1, 7, 10]]\n '''\n list_dih = [[1,2,3,6], [3,2,1,7], [2,1,7,10], [4,2,3,6], [5,2,3,6],\n [4,2,1,7], [5,2,1,7], [4,2,1,8], [5,2,1,8], [4,2,1,9], \n [5,2,1,9], [8,1,2,3], [9,1,2,3], [8,1,7,10], [9,1,7,10]]\n '''\n list_dih = []\n n_atoms = len(molecule.atoms)\n for i in range(1, n_atoms+1):\n for j in range(i):\n for k in range(j):\n for l in range(k):\n list_dih.append([i,j,k,l])\n #print(list_dih)\n dihedrals.get_dih_pop(molecule.coords, list_dih)\n #print(np.int_(dihedrals.phis.T)) \n #print(dihedrals.phis.shape, dihedrals.phis.T.shape)\n\n u, indices = np.unique(np.int_(dihedrals.phis), axis=0, \n return_index=True)\n #print(u)\n #print(indices)\n train_phis = np.unique(indices)\n print(train_phis)\n\n test_phis = []\n for i in range(dihedrals.phis.shape[0]):\n if i not in train_phis:\n test_phis.append(i)\n else:\n continue\n\n print(test_phis)\n train_phis = np.array(train_phis)\n test_phis = np.array(test_phis)\n print('train_phis', train_phis.shape, 'test_phis', test_phis.shape)\n\n train_NRF = np.take(molecule.mat_NRF, train_phis, axis=0)\n #_NRF = np.take(molecule.mat_NRF, test_phis, axis=0)\n print(train_NRF.shape)\n\n #sys.exit()\n\n\n '''\n dihedrals.get_dih_pop(molecule.coords, list_dih)\n\n phis_1 = [i[0] for i in dihedrals.phis]\n phis_2 = [i[1] for i in dihedrals.phis]\n phis_3 = [i[2] for i in dihedrals.phis]\n\n p1_list = []\n p2_list = []\n p3_list = []\n train_phis = []\n for i, p1, p2, p3 in zip(range(1,len(phis_1)+1), phis_1, \n phis_2, phis_3):\n if int(p1) not in p1_list or int(p2) not in p2_list or \\\n int(p3) not in p3_list:\n train_phis.append(i)\n p1_list.append(int(p1))\n p2_list.append(int(p2))\n p3_list.append(int(p3))\n else:\n continue\n\n test_phis = []\n for i in range(1, len(phis_1)+1):\n if i not in train_phis:\n test_phis.append(i)\n else:\n continue\n\n train_phis = np.array(train_phis)\n test_phis = np.array(test_phis)\n print('train_phis', train_phis.shape, 'test_phis', test_phis.shape)\n '''\n\n sys.stdout.flush()\n\n run_net = False\n split = 5 #2 4 5 20 52 260\n if run_net:\n train = round(len(molecule.coords) / split, 3)\n nodes = 1000\n input = molecule.mat_NRF\n output = molecule.mat_F\n print('\\nget train and test sets, '\\\n 'training set is {} points.'\\\n '\\nNumber of nodes is {}'.format(train, nodes))\n Molecule.make_train_test_old(molecule, molecule.energies.flatten(), \n split) #get train and test sets\n #override train test\n #molecule.train = train_phis\n #molecule.test = test_phis\n print('\\nget ANN model')\n network = Network(molecule) #initiate network class\n Network.get_variable_depth_model(network, molecule, \n nodes, input, output) #train NN\n\n run_mm = False\n if run_mm:\n nsteps=15000 #10000\n print('\\nrun {} step MD with ANN potential'.format(nsteps))\n mm = Network.run_NVE(network, molecule, timestep=0.5, \n nsteps=nsteps)\n\n print('checking mm forces')\n mm.coords = mm.get_3D_array([mm.coords])\n mm.forces = mm.get_3D_array([mm.forces]) \n mm.energies = np.array(mm.energies)\n unconserved = mm.check_force_conservation()\n #sys.exit()\n sys.stdout.flush()\n\n\n check_E_conservation = False\n if check_E_conservation:\n\n #mat_NRF = Network.get_NRF_input([molecule.coords[0]], molecule.atoms, \n #len(molecule.atoms), len(molecule.mat_NRF[0]))\n #print(mat_NRF.shape)\n #print(molecule.mat_F[0])\n\n '''\n scale_NRF = 9650.66147293977\n scale_F = 131.25482358398773\n q_scaled = Conservation.get_conservation(molecule.coords[0], \n molecule.forces[0], molecule.atoms, scale_NRF, 0, scale_F, \n 'best_ever_model', molecule, 0.001)\n print(q_scaled)\n '''\n\n\n\n\n run_mm = False\n if run_mm:\n open('vary_qAB.txt', 'w').close()\n open('ave_delta_F.txt', 'w').close()\n qAB_factor = 1 #0.95\n #print(molecule.forces[0])\n for x in range(1):\n nsteps = 15000 #30000\n print('\\nrun {} step MD with ANN potential'.format(nsteps))\n network = None\n mm = Network.run_NVE(network, molecule, timestep=0.5, \n nsteps=nsteps, qAB_factor=qAB_factor)\n\n print('checking mm forces')\n mm.coords = mm.get_3D_array([mm.coords])\n mm.forces = mm.get_3D_array([mm.forces]) \n mm.energies = np.array(mm.energies)\n unconserved = mm.check_force_conservation()\n qAB_factor += 2\n\n\n\n correct_training = False\n if correct_training:\n #'''\n dr = 0.001\n NRF_scale_method = 'A'\n print('read and check training forces')\n molecule = Molecule()\n XYZParser(atom_file, coord_files, force_files, \n energy_files, molecule)\n\n '''\n ###pick only some structures, hard-coded\n molecule.coords = molecule.coords[::50]\n molecule.forces = molecule.forces[::50]\n molecule.energies = molecule.energies[::50]\n '''\n\n\n n_atoms = len(molecule.atoms)\n _NC2 = int(n_atoms * (n_atoms-1)/2)\n\n\n print(datetime.now() - startTime)\n unconserved = Molecule.check_force_conservation(molecule) #\n if len(unconserved) > 0:\n molecule.remove_variants(unconserved)\n print(datetime.now() - startTime)\n sys.stdout.flush()\n split = 5 #2 4 5 20 52 260\n train = round(len(molecule.coords) / split, 3)\n Molecule.make_train_test_old(molecule, molecule.energies.flatten(), \n split) #get train and test sets\n print('get training decomp forces')\n Converter(molecule) #get pairwise forces\n #scale_NRF = 17495.630534482527\n #scale_F = 214.93048169383425\n train_mat_NRF = np.take(molecule.mat_NRF, molecule.train, axis=0)\n train_mat_F = np.take(molecule.mat_F, molecule.train, axis=0)\n train_F = np.take(molecule.forces, molecule.train, axis=0)\n train_coords = np.take(molecule.coords, molecule.train, axis=0)\n\n scale_input_max = 19911.051275945494 #np.amax(train_mat_NRF)\n scale_input_min = 11.648288117257646 #np.amin(train_mat_NRF)\n scale_output_max = 105.72339728320982 #np.amax(np.absolute(train_mat_F))\n scale_output_min = 0 #np.amin(np.absolute(train_mat_F))\n\n scaled_input, _max, _min = \\\n Network.get_scaled_values(train_mat_NRF, \n scale_input_max, scale_input_min, method='A')\n scaled_output, _max, _min = \\\n Network.get_scaled_values(train_mat_F, \n scale_output_max, scale_output_min, method='B')\n\n model = load_model('../best_ever_model')\n train_prediction_scaled = model.predict(scaled_input)\n #train_prediction = (train_prediction_scaled - 0.5) * \\\n #scale_output_max\n train_prediction = Network.get_unscaled_values(\n train_prediction_scaled, \n scale_output_max, scale_output_min, method='B')\n train_recomp_forces = Network.get_recomposed_forces(\n train_coords, train_prediction, n_atoms, _NC2)\n print(train_mat_NRF.shape, train_prediction.shape, \n train_recomp_forces.shape)\n\n train_mae, train_rms = Binner.get_error(train_mat_F.flatten(), \n train_prediction.flatten())\n print('\\nTrain MAE: {} \\nTrain RMS: {}'.format(train_mae, train_rms))\n print(datetime.now() - startTime)\n\n '''\n corrected_decomp_list = []\n corrected_recomp_list = []\n for i in range(len(train_coords)):\n corrected_prediction = Conservation.get_conservation(\n train_coords[i], [train_prediction[i]], \n molecule.atoms, scale_input_max, scale_input_min, \n scale_output_max, '../best_ever_model', \n molecule, dr, NRF_scale_method)\n corrected_decomp_list.append(corrected_prediction)\n corr_recomp_forces = Network.get_recomposed_forces(\n [train_coords[i]], [corrected_prediction], n_atoms, _NC2)\n corrected_recomp_list.append(corr_recomp_forces)\n corrected_decomp_list = np.array(corrected_decomp_list)\n corrected_recomp_list = np.array(corrected_recomp_list)\n\n corrected_mae, corrected_rms = Binner.get_error(train_mat_F.flatten(),\n corrected_decomp_list.flatten())\n print('\\nCorrected MAE: {} \\nCorrected RMS: {}'.format(\n corrected_mae, corrected_rms))\n '''\n\n\n _L1s = []\n corrected_L1s = []\n for i in range(_NC2):\n bin_edges, hist = Binner.get_scurve(train_mat_F.T[i].flatten(), \n train_prediction.T[i].flatten(), \n 'scurve.txt')\n\n #bin_edges2, hist2 = Binner.get_scurve(train_mat_F.T[i].flatten(),\n #corrected_decomp_list.T[i].flatten(), \n #'scurve.txt')\n print(i, bin_edges[19], hist[19])\n #print(i, bin_edges2[19], hist2[19])\n #print()\n _L1s.append(hist[19])\n #corrected_L1s.append(hist2[19])\n\n #Plotter.plot_2d([range(_NC2), range(_NC2)], \n #[_L1s, corrected_L1s], ['L1', 'L1 with correction'], \n #'Interaction number', 'L1', 'L1.png')\n\n Plotter.plot_2d([range(_NC2)], \n [_L1s], ['L1'], \n 'Interaction number', 'L1', 'L1.png')\n\n\n #print()\n\n\n\n '''\n for i in range(len(train_coords)):\n print(train_mat_F[i])\n print()\n print(corrected_decomp_list[i])\n print()\n print(train_F[i])\n print()\n print(train_recomp_forces[i])\n print()\n print(corrected_recomp_list[i])\n print('\\n\\n')\n '''\n sys.stdout.flush()\n #'''\n\n\n\n #'''\n print('\\nAtom pairs:')\n print('i indexA indexB - atomA atomB')\n _N = -1\n for i in range(len(molecule.atoms)):\n for j in range(i):\n _N += 1\n print(_N, i, j, '-', Converter._ZSymbol[molecule.atoms[i]], \n Converter._ZSymbol[molecule.atoms[j]])\n #print(_N+1, '-', atom_names[i], atom_names[j])\n #sys.exit()\n #'''\n\n\n '''\n NPParser('types_z', \n ['train_asp_coords'], \n ['train_asp_forces'], molecule)\n '''\n\n #OPTParser(input_files, molecule)\n\n '''\n AMBLAMMPSParser('molecules.prmtop', '1md.mdcrd', \n ['Trajectory_npt_1.data'], \n ['Forces_npt_1.data'], \n ['Energy_npt_1.data'], molecule)\n '''\n\n #AMBERParser('molecules.prmtop', '1md.mdcrd', 'ascii.frc', \n #molecule)\n #unconserved = Molecule.check_force_conservation(molecule)\n #Converter(molecule)\n #print(molecule.mat_F)\n #Writer.write_xyz(molecule.coords, molecule.atoms, \n #'test.xyz')\n\n\n\n\n '''\n bonds = Binner()\n list_bonds = [[1,4]]\n bonds.get_bond_pop(molecule.coords, list_bonds)\n '''\n\n '''\n angles = Binner()\n list_angles = [[2, 1, 3]]\n angles.get_angle_pop(molecule.coords, list_angles)\n '''\n\n get_dihs = False\n if get_dihs:\n dihedrals = Binner()\n list_dih = [[1, 2, 3, 6], [3, 2, 1, 7],\n [2, 1, 7, 10]]\n dihedrals.get_dih_pop(molecule.coords, list_dih)\n\n phis_1 = [i[0] for i in dihedrals.phis]\n phis_2 = [i[1] for i in dihedrals.phis]\n phis_3 = [i[2] for i in dihedrals.phis]\n\n _Es = molecule.energies.flatten()\n Molecule.make_train_test(molecule, _Es, split)\n train = molecule.train\n test = molecule.test\n #train = np.array(range(len(molecule.coords)))\n\n Plotter.xyz_scatter(np.take(phis_1, train), np.take(phis_2, train), \n np.take(_Es, train), \n '$\\phi_1$', '$\\phi_2$', '$U$', 'train-12.png')\n Plotter.xyz_scatter(np.take(phis_1, test), np.take(phis_2, test), \n np.take(_Es, test), \n '$\\phi_1$', '$\\phi_2$', '$U$', 'test-12.png')\n\n mm_dihedrals = Binner()\n mm_dihedrals.get_dih_pop(mm.coords, list_dih)\n mm_phis_1 = [i[0] for i in mm_dihedrals.phis]\n mm_phis_2 = [i[1] for i in mm_dihedrals.phis]\n mm_phis_3 = [i[2] for i in mm_dihedrals.phis]\n\n\n Plotter.xy_scatter([np.take(phis_1, train), mm_phis_1], \n [np.take(phis_2, train), mm_phis_2], \n ['train', 'nn-mm'], ['k', 'r'], \n '$\\phi_1$', '$\\phi_2$', 'train-xy-12.png')\n Plotter.xy_scatter([np.take(phis_1, test)], [np.take(phis_2, test)], \n [''], ['k'], '$\\phi_1$', '$\\phi_2$', 'test-xy-12.png')\n Plotter.xy_scatter([np.take(phis_1, train), mm_phis_1], \n [np.take(phis_3, train), mm_phis_3], \n ['train', 'nn-mm'], ['k', 'r'], \n '$\\phi_1$', '$\\phi_3$', 'train-xy-13.png')\n Plotter.xy_scatter([np.take(phis_1, test)], [np.take(phis_3, test)], \n [''], ['k'], '$\\phi_1$', '$\\phi_3$', 'test-xy-13.png')\n\n Plotter.hist_2d(phis_1, phis_2, '$\\phi_1$', '$\\phi_2$', \n 'dih_hist12')\n Plotter.hist_2d(phis_1, phis_3, '$\\phi_1$', '$\\phi_3$', \n 'dih_hist13')\n Plotter.hist_2d(phis_2, phis_3, '$\\phi_2$', '$\\phi_3$', \n 'dih_hist23')\n\n Plotter.xy_scatter([phis_1], [phis_2], [''], ['k'], \n '$\\phi_1$', '$\\phi_2$', 'dih_xy12')\n Plotter.xy_scatter([phis_1], [phis_3], [''], ['k'], \n '$\\phi_1$', '$\\phi_3$', 'dih_xy13')\n Plotter.xy_scatter([phis_2], [phis_3], [''], ['k'], \n '$\\phi_2$', '$\\phi_3$', 'dih_xy23')\n\n\n scurves = False\n if scurves:\n train_bin_edges, train_hist = Binner.get_scurve(\n np.loadtxt('train-actual-decomp-force.txt').flatten(), \n np.loadtxt('train-decomp-force.txt').flatten(), \n 'train-hist.txt')\n\n test_bin_edges, test_hist = Binner.get_scurve(\n np.loadtxt('test-actual-decomp-force.txt').flatten(), \n np.loadtxt('test-decomp-force.txt').flatten(), \n 'test-hist.txt')\n \n Plotter.plot_2d([train_bin_edges, test_bin_edges], \n [train_hist, test_hist], ['train', 'test'], \n 'Error', '% of points below error', 's-curves.png')\n\n '''\n coords = np.take(molecule.coords, molecule.train, axis=0)\n forces = np.take(molecule.forces, molecule.train, axis=0)\n mat_NRF = np.loadtxt('train-NRF.txt')\n decomp = np.loadtxt('train-decomp-force.txt')\n print('\\nmat_NRF', mat_NRF.shape)\n print('\\ndecomp', decomp.shape)\n\n measures = Binner()\n list_bonds = [[1,2]]\n measures.get_bond_pop(coords, list_bonds)\n _CC = decomp[:,0]\n print(measures.rs.shape, _CC.shape)\n Plotter.xy_scatter([measures.rs], [_CC], ['CC bond'], ['k'], \n '$r$', 'decomp F', 'train-bond-CC.png')\n\n list_angles = [[2, 1, 3]]\n measures.get_angle_pop(coords, list_angles)\n _OCC = decomp[:,2]\n Plotter.xy_scatter([measures.thetas], [_OCC], ['OCC angle'], ['k'],\n '$\\\\theta$', 'decomp F', 'train-angle-OCC.png')\n\n list_dih = [[3, 2, 1, 7]]\n measures.get_dih_pop(coords, list_dih)\n _OCCO = decomp[:,17]\n Plotter.xy_scatter([measures.phis], [_OCCO], ['OCCO dih'], ['k'],\n '$\\Phi$', 'decomp F', 'train-dih-OCCO.png')\n\n coords = np.take(molecule.coords, molecule.test, axis=0)\n forces = np.take(molecule.forces, molecule.test, axis=0)\n mat_NRF = np.loadtxt('test-NRF.txt')\n decomp = np.loadtxt('test-decomp-force.txt')\n print('\\nmat_NRF', mat_NRF.shape)\n print('\\ndecomp', decomp.shape)\n\n measures = Binner()\n list_bonds = [[1,2]]\n measures.get_bond_pop(coords, list_bonds)\n _CC = decomp[:,0]\n print(measures.rs.shape, _CC.shape)\n Plotter.xy_scatter([measures.rs], [_CC], ['CC bond'], ['k'], \n '$r$', 'decomp F', 'test-bond-CC.png')\n\n list_angles = [[2, 1, 3]]\n measures.get_angle_pop(coords, list_angles)\n _OCC = decomp[:,2]\n Plotter.xy_scatter([measures.thetas], [_OCC], ['OCC angle'], ['k'], \n '$\\\\theta$', 'decomp F', 'test-angle-OCC.png')\n\n list_dih = [[3, 2, 1, 7]]\n measures.get_dih_pop(coords, list_dih)\n _OCCO = decomp[:,17]\n Plotter.xy_scatter([measures.phis], [_OCCO], ['OCCO dih'], ['k'], \n '$\\Phi$', 'decomp F', 'test-dih-OCCO.png')\n '''\n\n mm_qm_comparison = False\n if mm_qm_comparison:\n print('read and check mm forces')\n mm_molecule = Molecule()\n XYZParser(atom_file, [coord_files[0]], [force_files[0]], \n [energy_files[0]], mm_molecule)\n n_atoms = len(mm_molecule.atoms)\n _NC2 = int(n_atoms * (n_atoms-1)/2)\n print(datetime.now() - startTime)\n #Converter.get_rotated_forces(mm_molecule)\n #mm_molecule.forces = mm_molecule.rotated_forces\n #mm_molecule.coords = mm_molecule.rotated_coords\n mm_unconserved = Molecule.check_force_conservation(mm_molecule) #\n print(datetime.now() - startTime)\n sys.stdout.flush()\n\n print('read and check qm forces')\n qm_molecule = Molecule()\n #XYZParser(atom_file, [coord_files[1]], [force_files[1]], \n #[energy_files[1]], qm_molecule)\n OPTParser(input_files, qm_molecule, opt=False) #read in FCEZ for SP\n print(datetime.now() - startTime)\n qm_unconserved = Molecule.check_force_conservation(qm_molecule) #\n print(datetime.now() - startTime)\n sys.stdout.flush()\n\n #'''\n print('read and check training forces')\n molecule = Molecule()\n XYZParser(atom_file, [coord_files[1]], [force_files[1]], \n [energy_files[1]], molecule)\n\n print(datetime.now() - startTime)\n unconserved = Molecule.check_force_conservation(molecule) #\n if len(unconserved) > 0:\n molecule.remove_variants(unconserved)\n print(datetime.now() - startTime)\n sys.stdout.flush()\n split = 5 #2 4 5 20 52 260\n train = round(len(molecule.coords) / split, 3)\n Molecule.make_train_test_old(molecule, molecule.energies.flatten(), \n split) #get train and test sets\n print('get training decomp forces')\n Converter(molecule) #get pairwise forces\n #scale_NRF = 17495.630534482527\n #scale_F = 214.93048169383425\n train_mat_NRF = np.take(molecule.mat_NRF, molecule.train, axis=0)\n train_mat_F = np.take(molecule.mat_F, molecule.train, axis=0)\n train_F = np.take(molecule.forces, molecule.train, axis=0)\n train_coords = np.take(molecule.coords, molecule.train, axis=0)\n scaled_input, scale_input_max, scale_input_min = \\\n Network.get_scaled_values(train_mat_NRF)\n scaled_output, scale_output_max, scale_output_min = \\\n Network.get_scaled_values(train_mat_F)\n model = load_model('../best_ever_model')\n train_prediction_scaled = model.predict(scaled_input)\n train_prediction = (train_prediction_scaled - 0.5) * \\\n scale_output_max\n train_recomp_forces = Network.get_recomposed_forces(\n train_coords, train_prediction, n_atoms, _NC2)\n print(train_mat_NRF.shape, train_prediction.shape, \n train_recomp_forces.shape)\n print(datetime.now() - startTime)\n sys.stdout.flush()\n #'''\n\n print('read and check adjusted mm forces')\n fix_molecule = Molecule()\n XYZParser(atom_file, [coord_files[2]], [force_files[2]], \n [energy_files[2]], fix_molecule)\n print(datetime.now() - startTime)\n fix_unconserved = Molecule.check_force_conservation(fix_molecule) #\n print(datetime.now() - startTime)\n sys.stdout.flush()\n\n\n print('remove unconserved')\n unconserved = np.concatenate([mm_unconserved, qm_unconserved])\n if len(unconserved) > 0:\n unconserved = np.unique(unconserved)\n mm_molecule.remove_variants(unconserved)\n qm_molecule.remove_variants(unconserved)\n print('unconserved', unconserved)\n print('get mm decomp forces')\n Converter(mm_molecule) #get pairwise forces\n print(datetime.now() - startTime)\n sys.stdout.flush()\n print('get qm decomp forces')\n Converter(qm_molecule) #get pairwise forces\n print(datetime.now() - startTime)\n sys.stdout.flush()\n\n print('compare mm and qm decomp forces')\n print(mm_molecule.coords.shape, qm_molecule.coords.shape)\n print(range(len(mm_molecule.atoms)))\n print(mm_molecule.atoms)\n\n #train_errors = np.reshape(np.loadtxt('train_errors.txt'), (-1,2))\n ts = range(len(mm_molecule.coords))\n\n #for pairwise analysis\n _N = -1\n for i in range(len(mm_molecule.atoms)):\n for j in range(i):\n _N += 1\n #print(_N, i, j, '-', mm_molecule.atoms[i], \n #mm_molecule.atoms[j])\n\n '''\n #train_decompF = train_prediction.T[_N]\n train_mae, train_rms = Binner.get_error(\n [train_prediction.T[_N]], \n [train_mat_F.T[_N]])\n '''\n #train_mae, train_rms = train_errors[_N][0], train_errors[_N][1]\n #print('\\t', train_mae, train_rms)\n\n '''\n #train_min, train_max = np.amin(train_mat_NRF.T[_N]), \\\n #np.amax(train_mat_NRF.T[_N])\n train_min, train_max = np.amin(train_mat_F.T[_N]), \\\n np.amax(train_mat_F.T[_N])\n print(train_min, train_max)\n '''\n\n mm_mae, mm_rms = Binner.get_error(\n [mm_molecule.mat_F.T[_N]], \n [qm_molecule.mat_F.T[_N]])\n #print('\\t', mm_mae, mm_rms)\n\n mae_list, rms_list = [], []\n for t in ts:\n mae, rms = Binner.get_error(\n [mm_molecule.mat_F.T[_N][t]], \n [qm_molecule.mat_F.T[_N][t]])\n mae_list.append(mae)\n rms_list.append(rms)\n\n '''\n Plotter.xy_scatter([mm_molecule.mat_F.T[_N]], \n [qm_molecule.mat_F.T[_N]], \n ['mm vs qm'], ['k'], \n 'mm decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'qm decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'mm_vs_qm_decomp_forces_{}_{}{}'.format(_N, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n '''\n Plotter.xy_scatter([ts, ts],\n [mm_molecule.mat_F.T[_N], qm_molecule.mat_F.T[_N]], \n ['mm', 'qm'], ['k', 'b'], \n 'timestep', \n 'decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'decomp_forces_vs_ts_{}_{}{}_{}{}'.format(_N, i, j, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n '''\n Plotter.xy_scatter([ts, ts, ts, ts],\n [mae_list, rms_list, [train_mae] * len(ts), \n [train_rms] * len(ts)], \n ['mae', 'rms', 'train_mae', 'train_rms'], \n ['k', 'b', 'r', 'm'], \n 'timestep', \n 'error', \n 'errors_vs_ts_{}_{}{}_{}{}'.format(_N, i, j,\n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n '''\n Plotter.xy_scatter([mm_molecule.mat_NRF.T[_N], \n qm_molecule.mat_NRF.T[_N]],\n [mm_molecule.mat_F.T[_N], qm_molecule.mat_F.T[_N]], \n ['ml-md', 'qm'], \n ['k', 'b'], \n 'NRF', \n 'decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'decomp_forces_vs_NRF_{}_{}{}_{}{}'.format(_N, i, j, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n '''\n Plotter.xy_scatter([mm_molecule.mat_NRF.T[_N], \n qm_molecule.mat_NRF.T[_N], train_mat_NRF.T[_N], \n train_mat_NRF.T[_N]],\n [mm_molecule.mat_F.T[_N], qm_molecule.mat_F.T[_N], \n train_prediction.T[_N], train_mat_F.T[_N]], \n ['mm', 'qm', 'nn-train', 'qm-train'], \n ['k', 'b', 'm', 'r'], \n 'NRF', \n 'decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'decomp_forces_vs_NRF_{}_{}{}_{}{}'.format(_N, i, j, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n #'''\n Plotter.xy_scatter([train_mat_NRF.T[_N],\n mm_molecule.mat_NRF.T[_N][:1000], \n mm_molecule.mat_NRF.T[_N][1000:]],\n [train_prediction.T[_N], \n mm_molecule.mat_F.T[_N][:1000], \n mm_molecule.mat_F.T[_N][1000:]], \n ['train', 'first 1000 frames', 'last 250 frames'], \n ['grey', 'k', 'b'], \n 'NRF', \n 'decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'frames_decomp_forces_vs_NRF_{}_{}{}_{}{}'.format(\n _N, i, j, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n #'''\n\n\n '''\n Plotter.xy_scatter([ts, ts, ts], [mm_molecule.mat_NRF.T[_N], \n [train_min] * len(ts), [train_max] * len(ts)], \n ['mm', 'train_min', 'train_max'], \n ['k', 'r', 'm'], \n 'timestep', \n 'NRF', \n 'NRF_vs_ts_{}_{}{}_{}{}'.format(_N, i, j, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n\n '''\n Plotter.xy_scatter([ts, ts, ts], [mm_molecule.mat_F.T[_N], \n [train_min] * len(ts), [train_max] * len(ts)], \n ['mm', 'train_min', 'train_max'], \n ['k', 'r', 'm'], \n 'timestep', \n 'decompF / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'decomp_forces_vs_ts_{}_{}{}_{}{}'.format(_N, i, j, \n mm_molecule.atoms[i], mm_molecule.atoms[j]))\n '''\n\n #for cartesian analysis\n _N = -1\n for i in range(n_atoms):\n for x in range(3):\n _N += 1\n\n '''\n train_mae, train_rms = Binner.get_error(\n [train_recomp_forces.reshape(\n len(train_mat_NRF),-1).T[_N]], \n [train_F.reshape(\n len(train_mat_NRF),-1).T[_N]])\n #train_mae, train_rms = train_errors[_N][0], train_errors[_N][1]\n #print('\\t', train_mae, train_rms)\n\n #train_min, train_max = np.amin(train_mat_NRF.T[_N]), \\\n #np.amax(train_mat_NRF.T[_N])\n train_min, train_max = np.amin(train_recomp_forces.reshape(\n len(train_mat_NRF),-1).T[_N]), \\\n np.amax(train_recomp_forces.reshape(\n len(train_mat_NRF),-1).T[_N])\n #print(train_min, train_max)\n '''\n\n mae_list, rms_list = [], []\n for t in ts:\n mae, rms = Binner.get_error(\n [mm_molecule.forces.reshape(len(ts),-1).T[_N][t]], \n [qm_molecule.forces.reshape(len(ts),-1).T[_N][t]])\n mae_list.append(mae)\n rms_list.append(rms)\n\n\n '''\n Plotter.xy_scatter([ts, ts, ts, ts], \n [mae_list, rms_list, [train_mae] * len(ts), \n [train_rms] * len(ts)], \n ['mae', 'rms', 'train_mae', 'train_rms'], \n ['k', 'b', 'r', 'm'], \n 'timestep', \n 'F / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'Ferrors_vs_ts_{}_{}_{}'.format(_N, i, \n mm_molecule.atoms[i]))\n '''\n\n '''\n Plotter.xy_scatter([ts, ts, ts, ts], \n [mm_molecule.forces.reshape(len(ts),-1).T[_N], \n qm_molecule.forces.reshape(len(ts),-1).T[_N],\n [train_min] * len(ts), [train_max] * len(ts)], \n ['mm', 'qm', 'train_min', 'train_max'], \n ['k', 'b', 'r', 'm'], \n 'timestep', \n 'F / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'forces_vs_ts_{}_{}_{}'.format(_N, i, \n mm_molecule.atoms[i]))\n '''\n\n\n '''\n Plotter.xy_scatter([ts, ts], \n [mm_molecule.forces.reshape(len(ts),-1).T[_N], \n qm_molecule.forces.reshape(len(ts),-1).T[_N]], \n ['mm', 'qm'], \n ['k', 'b'], \n 'timestep', \n 'F / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'forces_vs_ts_{}_{}_{}'.format(_N, i, \n mm_molecule.atoms[i]))\n '''\n\n\n '''\n force_diff = (fix_molecule.forces.T[0][0] / \n mm_molecule.forces[:len(fix_molecule.forces)].T[0][0])\n Plotter.xy_scatter([range(len(force_diff.flatten()))], \n [force_diff.flatten()], \n ['scaling factor'], \n ['k'], \n 'timestep', \n 'scale factor / kcal mol$^{-1}\\ \\AA^{-1}$', \n 'SF_vs_ts_{}_{}_{}'.format(_N, i, \n mm_molecule.atoms[i]))\n '''\n\n\n\n print(datetime.now() - startTime)\n sys.stdout.flush()\n\n '''\n print('compare mm and qm energies')\n Plotter.xy_scatter([mm_molecule.energies], [qm_molecule.energies], \n ['mm vs qm'], ['k'], \n 'mm Etot / kcal mol$^{-1}$', 'qm Etot / kcal mol$^{-1}$', \n 'mm_vs_qm_energies')\n\n print('check mm and qm dihedrals are the same')\n mm_dihedrals = Binner()\n qm_dihedrals = Binner()\n list_dih = [[1, 2, 3, 6], [3, 2, 1, 7],\n [2, 1, 7, 10]]\n mm_dihedrals.get_dih_pop(mm_molecule.coords, list_dih)\n qm_dihedrals.get_dih_pop(mm_molecule.coords, list_dih)\n\n Plotter.xy_scatter([mm_dihedrals.phis.T[1]], [mm_dihedrals.phis.T[1]], \n ['mm vs qm'], ['k'], \n 'mm OCCO dih', 'qm OCCO dih', \n 'mm_vs_qm_dihs')\n '''\n\n\n #Permuter(molecule) #not used yet\n\n print(datetime.now() - startTime)\n\n\ndef main():\n\n try:\n usage = 'runForcePred.py [-h]'\n parser = argparse.ArgumentParser(description='Program for reading '\\\n 'in molecule forces, coordinates and energies for '\\\n 'force prediction.', usage=usage, \n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n group = parser.add_argument_group('Options')\n group = parser.add_argument('-i', '--input_files', nargs='+', \n metavar='file', default=[],\n help='name of file/s containing forces '\\\n 'coordinates and energies.')\n group = parser.add_argument('-a', '--atom_file', \n metavar='file', default=None,\n help='name of file/s containing atom nuclear charges.')\n group = parser.add_argument('-c', '--coord_files', nargs='+', \n metavar='file', default=[],\n help='name of file/s containing coordinates.')\n group = parser.add_argument('-f', '--force_files', nargs='+', \n metavar='file', default=[],\n help='name of file/s containing forces.')\n group = parser.add_argument('-e', '--energy_files', nargs='+', \n metavar='file', default=[],\n help='name of file/s containing energies.')\n group = parser.add_argument('-q', '--charge_files', nargs='+', \n metavar='file', default=[],\n help='name of file/s containing charges.')\n group = parser.add_argument('-l', '--list_files', action='store', \n metavar='file', default=False,\n help='file containing list of file paths.')\n op = parser.parse_args()\n except argparse.ArgumentError:\n logging.error('Command line arguments are ill-defined, '\n 'please check the arguments.')\n raise\n sys.exit(1)\n\n run_force_pred(input_files=op.input_files, atom_file=op.atom_file, \n coord_files=op.coord_files, force_files=op.force_files, \n energy_files=op.energy_files, charge_files=op.charge_files, \n list_files=op.list_files)\n\nif __name__ == '__main__':\n main()\n\n\n","repo_name":"jkalayan/ForcePred","sub_path":"scripts/runForcePred.py","file_name":"runForcePred.py","file_ext":"py","file_size_in_byte":48073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"44341266275","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n__author__ = \"Rui Wang\"\n__email__ = \"wangru25@msu.edu\"\n\nimport math\nimport numpy as np\nimport pandas as pd\nimport time\n\ntic = time.perf_counter()\n\nfrom nn.convlayers import conv_forward, conv_backward\nfrom nn.poolinglayers import max_pooling_forward, max_pooling_backward\nfrom nn.flattenlayers import flatten_forward, flatten_backward\nfrom nn.fclayers import fc_forward, fc_backward\nfrom nn.activations import relu_forward, relu_backward\n\nnp.random.seed(7)\n\n\n'''\nIn this file, we will use dictionary to combine all the seperate layers together\n'''\n\n\ndef read_dataset(feature_file, label_file):\n ''' Read data set in *.csv to data frame in Pandas'''\n df_X = pd.read_csv(feature_file)\n df_y = pd.read_csv(label_file)\n X = df_X.values # convert values in dataframe to numpy array (features)\n y = df_y.values # convert values in dataframe to numpy array (label)\n return X, y\n\n\ndef normalize_features(X_train, X_test):\n from sklearn.preprocessing import StandardScaler #import libaray\n scaler = StandardScaler() # call an object function\n scaler.fit(X_train) # calculate mean, std in X_train\n X_train_norm1 = scaler.transform(X_train) # apply normalization on X_train\n X_test_norm1 = scaler.transform(X_test) # we use the same normalization on X_test\n X_train_norm = np.reshape(X_train_norm1,(-1,1,28,28)) # reshape X to be a 4-D array \n X_test_norm = np.reshape(X_test_norm1,(-1,1,28,28))\n return X_train_norm, X_test_norm\n\n\ndef one_hot_encoder(y_train, y_test):\n ''' convert label to a vector under one-hot-code fashion '''\n from sklearn import preprocessing\n lb = preprocessing.LabelBinarizer()\n lb.fit(y_train)\n y_train_ohe = lb.transform(y_train)\n y_test_ohe = lb.transform(y_test)\n return y_train_ohe, y_test_ohe\n\n\nX_train, y_train = read_dataset('MNIST_X_train.csv', 'MNIST_y_train.csv')\nX_test, y_test = read_dataset('MNIST_X_test.csv', 'MNIST_y_test.csv')\nX_train_norm, X_test_norm = normalize_features(X_train, X_test)\ny_train_ohe, y_test_ohe = one_hot_encoder(y_train, y_test)\n\nprint(X_train_norm.shape)\nprint(X_test_norm.shape)\nprint(y_train_ohe.shape)\nprint(y_test_ohe.shape)\n\n\nclass CNNLayer:\n\n def __init__(self, X, y, filters = 1, weights_scale = 1e-2, fc_nuerons = 100, lr=0.01, Lambda = 0.01, batch_size = 16):\n '''\n : Structure of this CNN layer: conv1--->pooling--->flatten--->fullyconnect2--->fullyconnect\n | |\n relu relu\n : X : input, with shape (N, C, H, W), in this project, (N,1,28,28)\n N: #of Sampels\n C: input channels\n H: Height of input figure\n W: Width of input figure\n : K : filter, with shape (C, D, k1, k2), in this project, (1,filters, 3,3)\n C: input channels\n D: output channels, actually #of filters\n k1: height of filter\n k2: width of filter\n : b : bias, with shape (D,)\n D: output channels, actually #of filters\n '''\n self.X = X\n self.y = y\n self.filters = filters\n self.weights_scale = weights_scale\n self.fc_nuerons = fc_nuerons\n self.lr = lr\n self.Lambda = Lambda\n self.batch_size = batch_size\n self.N, self.C, self.H, self.W = X.shape\n # Initialize filters, weights and bias\n self.K1 = self.weights_scale * np.random.randn(1, self.filters, 3, 3).astype(np.float64)\n self.b1 = np.zeros(self.filters).astype(np.float64)\n self.W2 = self.weights_scale * np.random.randn(self.filters*13*13, self.fc_nuerons).astype(np.float64)\n self.b2 = np.zeros(self.fc_nuerons).astype(np.float64)\n self.W3 = self.weights_scale * np.random.randn(self.fc_nuerons, 10).astype(np.float64)\n self.b3 = np.zeros(10).astype(np.float64)\n\n def forward(self):\n self.n_conv1 = conv_forward(self.X.astype(np.float64), self.K1, self.b1)\n self.n_conv1_relu = relu_forward(self.n_conv1)\n self.n_pool1 = max_pooling_forward(self.n_conv1_relu.astype(np.float64), pooling = (2,2))\n self.n_flatten = flatten_forward(self.n_pool1)\n self.n_fc2 = fc_forward(self.n_flatten, self.W2, self.b2)\n self.n_fc2_relu = relu_forward(self.n_fc2)\n self.n_fc = fc_forward(self.n_fc2_relu, self.W3, self.b3)\n self.y_hat = softmax(self.n_fc)\n\n def backward(self):\n # dy = self.y_hat - self.y\n loss, dy = cross_entropy_loss(self.y_hat, self.y)\n # add regularization in case of overfitting\n self.loss = loss + 0.5 * self.Lambda * (np.sum(self.K1**2)+np.sum(self.W2**2)+np.sum(self.W3**2))\n g_W3, g_b3, g_fc2_relu = fc_backward(dy, self.W3, self.n_fc2_relu)\n g_fc2 = relu_backward(g_fc2_relu, self.n_fc2)\n g_W2, g_b2, g_flatten = fc_backward(g_fc2, self.W2, self.n_flatten)\n g_pool1 = flatten_backward(g_flatten, self.n_pool1)\n # print('g_pool1', g_pool1.shape)\n g_conv1_relu = max_pooling_backward(g_pool1.astype(np.float64), self.n_conv1_relu.astype(np.float64),pooling=(2,2))\n g_conv1 = relu_backward(g_conv1_relu, self.n_conv1)\n # print('g_conv1', g_conv1.shape)\n g_K1, g_b1, _ = conv_backward(g_conv1, self.K1, self.X)\n\n self.K1 = self.K1 - self.lr * g_K1 - self.Lambda * self.K1\n self.b1 = self.b1 - self.lr * g_b1\n self.W2 = self.W2 - self.lr * g_W2 - self.Lambda * self.W2\n self.b2 = self.b2 - self.lr * g_b2\n self.W3 = self.W3 - self.lr * g_W3 - self.Lambda * self.W3\n self.b3 = self.b3 - self.lr * g_b3\n\n def mini_batch_gradient(self):\n X = self.X\n y = self.y\n self.X, self.y = mini_batch(self.X, self.y, self.batch_size)\n self.forward()\n self.backward()\n self.X = X\n self.y = y\n\n def predict(self, X_test):\n n_conv1 = conv_forward(X_test.astype(np.float64), self.K1, self.b1)\n n_conv1_relu = relu_forward(n_conv1)\n n_pool1 = max_pooling_forward(n_conv1_relu.astype(np.float64), pooling = (2,2))\n n_flatten = flatten_forward(n_pool1)\n n_fc2 = fc_forward(n_flatten, self.W2, self.b2)\n n_fc2_relu = relu_forward(n_fc2)\n n_fc = fc_forward(n_fc2_relu, self.W3, self.b3)\n y_hat_test = softmax(n_fc)\n # return ypred\n # the rest is similar to the logistic regression\n labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n num_test_samples = X_test.shape[0]\n # find which index gives us the highest probability\n ypred = np.zeros(num_test_samples, dtype=int)\n for i in range(num_test_samples):\n ypred[i] = labels[np.argmax(y_hat_test[i,:])]\n return ypred\n\ndef softmax(z):\n exp_value = np.exp(z-np.max(z, axis = 1, keepdims=True)) # for stablility\n # keepdims = True means that the output's dimension is the same as of z\n softmax_scores = exp_value / np.sum(exp_value, axis = 1, keepdims=True)\n return softmax_scores\n\ndef cross_entropy_loss(y_hat, y):\n \"\"\"\n Cross entropy loss\n y_hat: predict y after softmax, shape:(M,d), M is the #of samples\n y: shape(M,d)\n \"\"\"\n loss = np.mean(np.sum(- y * np.log(y_hat), axis=-1))\n dy = y_hat - y\n return loss, dy\n\ndef mini_batch(X, y, batch_size):\n N = X.shape[0]\n idx = np.random.choice(N, batch_size)\n return X[idx], y[idx]\n\ndef accuracy(ypred, yexact):\n # p = np.array(ypred == yexact, dtype = int)\n p = np.array(ypred == yexact, dtype = int)\n return np.sum(p)/float(len(yexact))\n\n# Main\nepoch_num = 2000\nmyCNN = CNNLayer(X_train_norm, y_train_ohe, weights_scale = 1e-2, filters = 4 , fc_nuerons = 256, lr=0.1, Lambda = 0.01, batch_size = 64)\nfor i in range(epoch_num):\n myCNN.mini_batch_gradient()\n if ((i+1)%20 == 0):\n print('epoch = %d, current loss = %.5f' % (i+1, myCNN.loss))\n\ny_pred = myCNN.predict(X_test_norm)\nprint('Accuracy of our model ', accuracy(y_pred, y_test.ravel()))\n\ntoc = time.perf_counter()\nprint('Totol time:' + str((toc-tic)/60)+ 'min')\nprint('===============================Finish===================================')\n","repo_name":"weixiaoqimath/machine_learning_exercise","sub_path":"ANN_CNN/CNN.py","file_name":"CNN.py","file_ext":"py","file_size_in_byte":8235,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"36560920188","text":"# Robert Cyril Plata III\n# Sally Kyvernitis\n# 10/29/2015\n# Only enter capital team names for consistency, will not allow lower. Enter capital teams for insert.\n# Case sensitive program \"E\" not \"e\"\n# Features - Print straight from the text file - Print the list version - Search for a key\n# Remove a key and parallel counterparts - Insert a new key with parallel wins/loss - Leave and overwrite the text file\n# This is my UI\ndef menu(prompt): # prompt is below in the main program, it just asks choose an option.\n running = True # loop until false, user can type in wrong option and it will go until good option is fulfilled.\n print(\"Enter 1 : Print (text file)\") # User sees text\n print(\"Enter 2 : Search\") # User sees text\n print(\"Enter 3 : Insert\") # User sees text\n print(\"Enter 4 : Delete\") # User sees text\n print(\"Enter 5 : PrintList (List)\") # User sees text\n print(\"Enter 6 : Leave and Save\") # User sees text\n while running: # Will run until turned False\n userInput = input(prompt)\n if userInput == \"1\": # if option then execute code below.\n userInput = \"Print\" # Changed value for simplicity. It matches the main programs options below.\n running = False # Turned False\n elif userInput == \"2\": # if option then execute code below.\n userInput = \"Search\" # Changed value for simplicity. It matches the main programs options below.\n running = False # Turned False\n elif userInput == \"3\": # if option then execute code below.\n userInput = \"Insert\" # Changed value for simplicity. It matches the main programs options below.\n running = False # Turned False\n elif userInput == \"4\": # if option then execute code below.\n userInput = \"Delete\" # Changed value for simplicity. It matches the main programs options below.\n running = False # Turned False\n elif userInput ==\"5\": # if option then execute code below.\n userInput = \"PrintList\" # Changed value for simplicity. It matches the main programs options below.\n running = False # Turned False\n elif userInput == \"6\": # if option then execute code below.\n userInput = \"Leave\" # Changed value for simplicity. It matches the main programs options below.\n running = False # Turned False\n else: # else for wrong input\n print()\n print(\"Sorry,\", userInput, \"is not an option.\") # notifying user, choice not valid\n print()\n return userInput # returns the final choice (userInput) main program will now match accordingly\n\ndef defineLists():\n for lineOfText in Teams: # for potato in Teams, (Teams is the openFile), do the following below, until complete! potato = lineOfText ;D\n name = lineOfText[START:NAME_LEN] # length that i want an array\n name = name.strip() # removing unnecessary spaces\n teamName.append(name) # Add line to list teamName until completion\n wins = lineOfText[NAME_LEN:WIN_LEN] # length i want the second array\n wins = wins.strip() # removing unnecessary spaces\n teamWins.append(wins) # Add line to list teamWins until completion\n loss = lineOfText[WIN_LEN:LOSS_LEN] # length wanted for third array\n loss = loss.strip() # removing unnecessary spaces\n teamLoss.append(loss) # Add line to list teamLoss until completion\n return (name, wins, loss) # return in case needed.\n\n# Prints List so that way the user can see what the list looks like\ndef printList():\n print()\n for i in range(0,len(teamName)): # prints each line neatly until completion\n if i >= 10: # When i reaches 10 it will now take away a little bit of space so that way everything lines up. Not completely necessary, but for the eyes.\n print(i, format(teamName[i], NAME_FORMAT), \" \", format(teamWins[i], WIN_FORMAT).strip(),\" \",format(teamLoss[i], LOSS_FORMAT).strip())\n if i < 10: # Before i reaches 10, it is the normal format.\n print(i, \"\", format(teamName[i], NAME_FORMAT), \" \", format(teamWins[i], WIN_FORMAT).strip(),\" \",format(teamLoss[i], LOSS_FORMAT).strip())\n \n print()\n print(\"Team /\", \"Wins /\", \"Losses /\", \"index\") # Organization, what each column means\n print()\n\n# Prints straight from the text file if the user wants to see what they started with\ndef printFile(teams):\n for lineOfText in teams: # Prints each line neatly until completion\n lineOfText = lineOfText.strip() # strip for spaces\n teamNames = lineOfText[START:NAME_LEN] # print the range of this list\n record = lineOfText[NAME_LEN:LOSS_LEN]# print the range of this list\n record = record.strip()# print the range of this list\n print(teamNames+format(\"\"+record, '^10s')) # formating the files output on program\n print()\n print(\"Printing Successful!\")\n\ndef searchList(searchName): # Searches the list and returns the parallel equal\n for i in range (START, len(teamName)):\n if teamName[i].startswith(searchName): # if something in teamName starts with the users inputted letter, it will return the location\n #print (i) useful for debugging\n return i # Returns number(location) of the found searchName\n return -1 # returns -1 so the insert function sees that the team doesn't exist yet. When the if loop doesn't find anything it will return -1\n \ndef insert(team): # team is the input arguement the user searched in the main program, whether it exists or not, it is relevant to this function\n if team not in teamName: # if the team arguement is not inside of the list teamName, then it will ask for wins and losses for the new Team that the user entered in the main program\n wins = input(\"Enter Wins:\") # user inputs wins\n loss = input(\"Enter losses:\") # user inputs losses\n for i in range (0,len(teamName)): # for loop \n if teamName[i]>team: # for alphabetical order insertion, if teamName[i] is greater than team alphabetically\n teamName.insert(i, team) # inserts team into location i of list teamName\n teamWins.insert(i, wins) # inserts wins into location i of list teamName\n teamLoss.insert(i, loss) # inserts loss into location i of list teamName\n return # Returns nothing so that way it stops the function. This is actually vital to the program inserting. Without it, this part of the function will not complete.\n if teamName[i]7s')\nWRITE_FORMAT2 = str('>6s')\n\n# Defining Lists\nTeams = open(\"TeamsAndRecords.txt\",\"r\") # will open the file for reading, it is now represented as Teams\nteamName = [] # List defining\nteamWins = [] # List defining\nteamLoss = [] # List defining\n\ndefineLists() #defining all of the lists that are needed. There will now be 3 parallel defined from the file represented as Teams.\n \nON = True # boolean is true\nwhile ON: #boolean, ON is True\n Menu = menu(\"Choose an option.\") # Menu is func menu\n\n # Print option\n if Menu == \"Print\": # Menu = func menu and it returned \"Print\"\n Teams = open(\"TeamsAndRecords.txt\",\"r\")\n print(format(\" Football Teams\"),) # Title\n print()\n print(\"Teams Wins Losses\") # Labels\n print(\"------------------------\") # Border\n printFile(Teams) # Initiate printFile function\n\n # Search option\n elif Menu == \"Search\": # Menu = func menu and it returned \"Search\"\n askSearch = input(\"What are you looking for?\")\n index = searchList(askSearch) # users input is searched\n if index >= 0: # If the user enters a number less than zero throw exception\n print()\n print(\"Found:\", teamName[index], \"with a record of\", teamWins[index], \"and\", teamLoss[index])\n print()\n else:\n print()\n print(\"not found\")\n print()\n\n # Insert option\n elif Menu == \"Insert\": # Menu = func menu and it returned \"Insert\"\n entryTeam = input(\"What Team?\") # variable for user input of the team they enter\n searchInsert = searchList(entryTeam) # search the variable, if found it will return\n insert(entryTeam) # calls function with entryTeam arguement, it will insert if -1 was returned from searchInsert. if 0 or above was returned, it will allow user to change value of win and loss\n\n\n # Delete option\n elif Menu == \"Delete\": # Menu = func menu and it returned \"Delete\"\n element = input(\"What team do you want to remove?\")\n searchDelete = searchList(element)\n if element in teamName: # if element is in the list then..\n deleteItem(searchDelete) #delete that searched element\n print(element, \"deleted\") # Prints the user's new list\n print()\n else: # if element was not in the list..\n print()\n print(element, \"Not in list\") # element wasn't in the list\n print()\n\n # Simple PrintList option\n elif Menu == \"PrintList\": # Menu = func menu and it returned \"PrintList\"\n printList() # printList func called\n\n # Leave option\n elif Menu == \"Leave\": # Menu = func menu and it returned \"Leave\"\n print()\n print(\"Final list is:\") \n printList() # prints the final list one last time\n print()\n print(\"G\")\n print(\" O\")\n print(\" O\")\n print(\" D\")\n print(\" B\")\n print(\" Y\")\n print(\" E\")\n print(\" !!\") # message for leaving\n\n # Beginning process of writing to original text file\n inFile = open(\"TeamsAndRecords.txt\", \"w\") #open TeamsAndRecords for overwriting\n for i in range(START,len(teamName)): # for loop to write to inFile from 0 to the length of list teamName\n inFile.write(format(teamName[i],NAME_FORMAT)) # write the names in teamName list to the document making sure it is formatted correctly\n inFile.write(format(teamWins[i],WRITE_FORMAT1)) # write wins in teamWins using format function to line up where it first started\n inFile.write(format(teamLoss[i],WRITE_FORMAT2)) # write losses in teamLoss using format function as well\n inFile.write(\"\\n\") # '\\n' will created a new line in my file\n inFile.close() # close, without closing the file we are writing to, it will not save.\n print()\n print(\"Overwrite Successful\")\n ON = False # boolean ON is false\n\n","repo_name":"RobbiePlata/PythonLabs","sub_path":"Lab 9 Lists 2 IO/Robert Plata List Lab 9 Week 2/Robert Plata List Lab 9 Week 2.py","file_name":"Robert Plata List Lab 9 Week 2.py","file_ext":"py","file_size_in_byte":13002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"42395070114","text":"import json\n\nfrom web3 import Web3\n\n# In the video, we forget to `install_solc`\n# from solcx import compile_standard\nfrom solcx import compile_standard, install_solc\nimport os\n\nwith open(\"./SimpleStorage.sol\", \"r\") as file:\n simple_storage_file = file.read()\n\n# We add these two lines that we forgot from the video!\nprint(\"Installing...\")\ninstall_solc(\"0.6.0\")\n\n# Solidity source code\ncompiled_sol = compile_standard(\n {\n \"language\": \"Solidity\",\n \"sources\": {\"SimpleStorage.sol\": {\"content\": simple_storage_file}},\n \"settings\": {\n \"outputSelection\": {\n \"*\": {\n \"*\": [\"abi\", \"metadata\", \"evm.bytecode\", \"evm.bytecode.sourceMap\"]\n }\n }\n },\n },\n solc_version=\"0.6.0\",\n)\n\nwith open(\"compiled_code.json\", \"w\") as file:\n json.dump(compiled_sol, file)\n\n# get bytecode\nbytecode = compiled_sol[\"contracts\"][\"SimpleStorage.sol\"][\"SimpleStorage\"][\"evm\"][\n \"bytecode\"\n][\"object\"]\n\n# get abi\nabi = json.loads(\n compiled_sol[\"contracts\"][\"SimpleStorage.sol\"][\"SimpleStorage\"][\"metadata\"]\n)[\"output\"][\"abi\"]\n\n# w3 = Web3(Web3.HTTPProvider(os.getenv(\"RINKEBY_RPC_URL\")))\n# chain_id = 4\n#\n# For connecting to ganache\nw3 = Web3(Web3.HTTPProvider(\"https://rinkeby.infura.io/v3/9df7580fcec44d9bad846e5385a6c95c\"))\nchain_id = 4\nmy_address = \"0x0B7947D864095bE32eFAce1aDe0595646C8AB136\"\nprivate_key = os.getenv(\"PRIVATE_KEY\")\n\n# Create the contract in Python\nSimpleStorage = w3.eth.contract(abi=abi, bytecode=bytecode)\n# Get the latest transaction\nnonce = w3.eth.getTransactionCount(my_address)\n# Submit the transaction that deploys the contract\ntransaction = SimpleStorage.constructor().buildTransaction(\n {\n \"chainId\": chain_id,\n \"gasPrice\": w3.eth.gas_price,\n \"from\": my_address,\n \"nonce\": nonce,\n }\n)\n# Sign the transaction\nsigned_txn = w3.eth.account.sign_transaction(transaction, private_key=private_key)\nprint(\"Deploying Contract!\")\n# Send it!\ntx_hash = w3.eth.send_raw_transaction(signed_txn.rawTransaction)\n# Wait for the transaction to be mined, and get the transaction receipt\nprint(\"Waiting for transaction to finish...\")\ntx_receipt = w3.eth.wait_for_transaction_receipt(tx_hash)\nprint(f\"Done! Contract deployed to {tx_receipt.contractAddress}\")\n\nsimple_storage = w3.eth.contract(address =tx_receipt.contractAddress, abi =abi)\nprint(simple_storage.functions.watch().call())\nprint(simple_storage.functions.store(15).call())\nprint(simple_storage.functions.watch().call()) #This will not do anything new because the store is not \n# a transaction yet. we first need to change the state of the block chain.\n\nstore_transaction = simple_storage.functions.store(15).buildTransaction(\n {\n \"chainId\": chain_id,\n \"gasPrice\": w3.eth.gas_price,\n \"from\": my_address,\n \"nonce\": nonce+1, #Each nonce must be one more than before\n }\n)\nsigntxnStore = w3.eth.account.sign_transaction(store_transaction,private_key = private_key)\nstoretxhash = w3.eth.send_raw_transaction(signtxnStore.rawTransaction)\ntx_receipt_store = w3.eth.wait_for_transaction_receipt(storetxhash)\n\nprint(simple_storage.functions.watch().call())\n","repo_name":"Shreyansh-Rai/BlockChain_Marathon","sub_path":"web3_py_simple_storage/deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":3175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"74266030770","text":"import cv2\nimport numpy as np\nimport sys\nfrom cv2 import aruco\nimport glob\nimport os\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nfrom util import extract_laser, undistort_camera\n\n\n\ndef read_node_matrix( reader, name ):\n node = reader.getNode( name )\n return node.mat()\n\ndef customAruco():\n # define an empty custom dictionary with \n aruco_dict = cv2.aruco.custom_dictionary(0, 5, 1)\n # add empty bytesList array to fill with 3 markers later\n aruco_dict.bytesList = np.empty(shape = (5, 4, 4), dtype = np.uint8)\n # add new marker(s)\n mybits = np.array([[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,1,1,1]], dtype = np.uint8)\n aruco_dict.bytesList[0] = cv2.aruco.Dictionary_getByteListFromBits(mybits)\n mybits = np.array([[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[0,1,0,0,1]], dtype = np.uint8)\n aruco_dict.bytesList[1] = cv2.aruco.Dictionary_getByteListFromBits(mybits)\n mybits = np.array([[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[0,1,1,1,0]], dtype = np.uint8)\n aruco_dict.bytesList[2] = cv2.aruco.Dictionary_getByteListFromBits(mybits)\n mybits = np.array([[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,1,1,1],[1,0,0,0,0]], dtype = np.uint8)\n aruco_dict.bytesList[3] = cv2.aruco.Dictionary_getByteListFromBits(mybits)\n mybits = np.array([[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,1,1,1],[1,0,1,1,1]], dtype = np.uint8)\n aruco_dict.bytesList[4] = cv2.aruco.Dictionary_getByteListFromBits(mybits)\n # adjust dictionary parameters for better marker detection\n parameters = cv2.aruco.DetectorParameters_create()\n parameters.cornerRefinementMethod = 3\n parameters.errorCorrectionRate = 0.2\n return aruco_dict, parameters\n\ndef getArucoDist():\n pass\n\n\ndef main():\n # Initialization value for image. Change to location of image\n # file_name = \"res\\marker_test\\marker_4_bright.png\"\n\n\n laser_imgs = glob.glob('res/pose_samples/plane/aruco_laser_*.png') \n aruco_imgs = glob.glob('res/pose_samples/plane/aruco_undist_*.png') \n\n aruco_dict, arucoParams = customAruco()\n \n '''\n \n aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_100)\n arucoParams = cv2.aruco.DetectorParameters_create()\n board = cv2.aruco.CharucoBoard_create(12,\n 9,\n 60/1000,\n 47/1000,\n aruco_dict)\n '''\n \n with np.load('res/cal_out/cam_params.npz') as X:\n camera_matrix, dist_coeffs = [X[i] for i in ('mtx','dist')]\n \n #print(camera_matrix)\n #print(dist_coeffs)\n '''\n camera_reader = cv2.FileStorage()\n camera_reader.open(\"cameraParameters.xml\",cv2.FileStorage_READ)\n\n camera_matrix = read_node_matrix( camera_reader, \"cameraMatrix\" )\n dist_coeffs = read_node_matrix( camera_reader, \"dist_coeffs\" )\n '''\n #camera_matrix = np.array(\n # [[focal_length, 0, center[0]],\n # [0, focal_length, center[1]],\n # [0, 0, 1]], dtype = \"double\"\n # )\n #dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion\n \n # for file_name in aruco_imgs:\n\n for i in range(0,30):\n file_name = f\"res/pose_samples/plane/aruco_undist_{i}.png\"\n print(file_name)\n frame = cv2.imread(file_name)\n \n h,w = frame.shape[:2]\n new_mtx, roi = cv2.getOptimalNewCameraMatrix(camera_matrix,dist_coeffs,(w,h),1, \n (w,h))\n \n frame = undistort_camera(frame, camera_matrix, new_mtx, roi, dist_coeffs, w, h)\n \n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(gray, aruco_dict, \n parameters=arucoParams)\n #print(ids)\n #ret, ch_corners, ch_ids = aruco.interpolateCornersCharuco(corners, ids, gray, board )\n #print(ch_ids)\n #cv2.aruco.drawDetectedCornersCharuco(frame,ch_corners,ch_ids,(0,0,255) )\n markertvec = []\n if ids:\n #retval, rvec, tvec = cv2.aruco.estimatePoseCharucoBoard( ch_corners, ch_ids, board, camera_matrix, dist_coeffs)\n for i in range(0, len(ids)): # Iterate in markers\n # Estimate pose of each marker and return the values rvec and tvec---different from camera coefficients\n rvec, tvec, markerPoints = cv2.aruco.estimatePoseSingleMarkers(corners[i], 3, new_mtx, dist_coeffs)\n #print(\"Rvec\", rvec)\n #print(\"Tvec\", tvec)\n temp = [ids[i], tvec]\n markertvec.append(temp)\n cv2.aruco.drawDetectedMarkers(frame, corners, ids)\n cv2.aruco.drawAxis(frame, camera_matrix, dist_coeffs, rvec, tvec, 0.001)\n print(\"marker\", ids[i] , \"Tvec: \", tvec)\n print(len(markertvec))\n \n \n # x = 0\n # y = 3\n # print(\"Distance between marker\", markertvec[x][0], \"and\", markertvec[y][0])\n # dist1 = np.linalg.norm(markertvec[x][1]-markertvec[y][1])\n # print(dist1)\n\n\n # Draw image or quit\n cv2.imshow('Image', frame)\n if cv2.waitKey(0) & 0xff == 27:\n cv2.destroyAllWindows()\n\n\nmain()","repo_name":"B33Boy/3D-Scanner","sub_path":"scripts/aruco_tests/pose_estimate.py","file_name":"pose_estimate.py","file_ext":"py","file_size_in_byte":5342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"17106283448","text":"#!/usr/bin/python3\n\nimport sys\nfrom pyfirmata2 import Arduino\nfrom PyQt5.QtWidgets import QApplication\nfrom threading import Thread\n\nfrom AnalogPrinter import AnalogPrinter\nfrom MainWindow import ControlWidget\nfrom QtPanningPlot import QtPanningPlot\nfrom Encoder import Encoder\nfrom Decoder import Decoder\n\n\nRED_PIN = 9\nBLUE_PIN = 10\nGREEN_PIN = 11\n\nPORT = Arduino.AUTODETECT\nboard = Arduino(PORT)\napp = QApplication(sys.argv)\n# signals to all threads in endless loops that we'd like to run these\nrunning = True\n\noriginalPlot = QtPanningPlot(\"Original Signal\")\nfilteredPlot = QtPanningPlot(\"Filtered Signal\")\n\n# get pins of the LED light\nred = board.get_pin(f\"d:{RED_PIN}:p\")\nblue = board.get_pin(f\"d:{BLUE_PIN}:p\")\ngreen = board.get_pin(f\"d:{GREEN_PIN}:p\")\n\nencoder = Encoder(red, green, blue)\nmainWindow = ControlWidget(board, encoder)\ndecoder = Decoder()\nanalogPrinter = AnalogPrinter(board, originalPlot, filteredPlot, decoder)\n\ndecoder.register_on_decode_end(mainWindow.output_to_logbox)\n\nanalogPrinter.start()\nmainWindow.show()\n\napp.exec_()\n\nrunning = False\napp.exit()\nboard.exit()\n","repo_name":"Hannidiot/LightSequencer","sub_path":"realtime_iir_main.py","file_name":"realtime_iir_main.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"1560897183","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom mysite1.settings import STATIC_DIR\nfrom django.core.files.images import ImageFile\n\nimport json, os, datetime\n\nstatic_dirpath = os.path.join(STATIC_DIR, \"image_classifier\")\nimg_dirpath = os.path.join(static_dirpath, \"images\")\nsave_dirpath = os.path.join(static_dirpath, \"saves\")\n\n\nif not os.path.exists(img_dirpath):\n os.makedirs(img_dirpath)\n\nif not os.path.exists(save_dirpath):\n os.makedirs(save_dirpath)\n\n\n\nimg_files = os.listdir(img_dirpath)\nimg_files = sorted(img_files)\n\n\nlabel_files_dict={}\n\nfor file in os.listdir(save_dirpath):\n basename, ext = os.path.splitext(file)\n if ext!=\".json\":\n continue\n \n label_files_dict[basename] = file\n\n\ndef main(request):\n return render(request, \"image_classifier/main.html\")\n\n@csrf_exempt\ndef save_progress(request):\n print(\"inside save_progress\")\n postdata = request.body.decode(\"utf-8\")\n\n print(\"creating postdata\")\n postdata = json.loads(postdata)\n # print(postdata)\n\n\n # when save value is null, then don't save it.\n if postdata[\"class_value\"] is None:\n return HttpResponse(status=200)\n\n imgno = postdata[\"img_index\"]\n\n imgfile = img_files[imgno]\n\n imgbasename, _ = os.path.splitext(imgfile)\n\n del postdata[\"img_index\"]\n\n postdata[\"imgfile\"] = imgfile\n\n imgpath = os.path.join(img_dirpath, imgfile)\n\n \n save_json_filename =\"{}.json\".format(imgbasename)\n save_json_filepath = os.path.join(save_dirpath, save_json_filename)\n\n print(\"wrriting to json file\")\n with open(save_json_filepath, 'w') as fd:\n # json.dump(postdata, fd)\n fd.write(json.dumps(postdata)+\"\\n\")\n\n print(\"adding to label_files_dict\")\n label_files_dict[imgbasename] = save_json_filename\n\n return HttpResponse(status=200)\n\n\ndef fetch_total_image_number(request):\n retjson={}\n retjson['total_image_number'] = len(img_files)\n\n return JsonResponse(retjson)\n\ndef get_image(request, imgno):\n if imgno >= len(img_files) or imgno < 0:\n return HttpResponse(status=400)\n\n selected_imgfile = img_files[imgno]\n\n _, ext = os.path.splitext(selected_imgfile)\n\n if ext==\".jpeg\" or ext == \".jpg\":\n content_type = \"image/jpeg\"\n elif ext == \".png\":\n content_type = \"image/png\"\n else:\n print(\"invalid image ext: {}\".format(ext))\n return HttpResponse(status=400)\n\n sel_imgfile_path = os.path.join(img_dirpath,selected_imgfile)\n with open(sel_imgfile_path,'rb') as fd:\n return HttpResponse(fd.read(), content_type=content_type)\n\n\n\ndef fetchprogress(request, imgno):\n sel_imgfile = img_files[imgno]\n\n imgbasename, _ = os.path.splitext(sel_imgfile)\n print(\"fetching savefile from label_files_dict\")\n fetched_save_file = label_files_dict.get(imgbasename, None)\n\n if fetched_save_file is None:\n retjson = {}\n retjson[\"success\"] = False\n retjson[\"img_no\"] = imgno\n return JsonResponse(retjson)\n\n fetched_save_filepath = os.path.join(save_dirpath, fetched_save_file)\n print(\"fetched_save_filepath:{}\".format(fetched_save_filepath))\n\n with open(fetched_save_filepath, 'r') as fd:\n # retjson = json.load(fd)\n retjson = json.loads(fd.read())\n retjson[\"success\"] = True\n retjson[\"img_no\"] = imgno\n \n \n return JsonResponse(retjson)\n\ndef convert_check(request):\n retjson={}\n if not check_if_label_all_done():\n retjson[\"check_passed\"]=False \n return JsonResponse(retjson)\n \n retjson[\"check_passed\"] = True\n\n return JsonResponse(retjson)\n\n \ndef check_if_label_all_done():\n if len(label_files_dict)!= len(img_files):\n return False\n else:\n return True\n\ndef fetch_dirname_available(dirpath, dirbasename):\n firstcheck = os.path.join(dirpath, dirbasename)\n if not os.path.exists(firstcheck):\n return firstcheck\n \n for i in range(100):\n iterable_dirbasename = \"{}_{:02d}\".format(dirbasename,i)\n newdirpath = os.path.join(dirpath, iterable_dirbasename)\n if not os.path.exists(newdirpath):\n return newdirpath\n \n raise Exception(\"unable to create dir with dirbasename: {}\".format(dirbasename))\n\n\ndef find_unlabeled_index(request):\n retjson = {}\n\n retjson[\"img_index\"] = find_unlabeled_index_main()\n\n return JsonResponse(retjson)\n\n\ndef find_unlabeled_index_main():\n for index, imgfile in enumerate(img_files):\n imgbasename, _ = os.path.splitext(imgfile)\n fetchlabelfile = label_files_dict.get(imgbasename,None)\n\n if fetchlabelfile is None:\n return index\n \n return None\n\n","repo_name":"chadrick-kwag/segmentation_labeling_tool","sub_path":"image_classifier/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4719,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"10385746068","text":"from datetime import datetime, timezone\n# Nama kind untuk entity ini\ndiskusi_KIND = \"DISKUSI\"\n\n\n# Class Permintaan\n#\n# Deklarasi object yang akan disimpan padda datastor.\n#\n# Property:\n# * nama: isian nama yang diisi pada form\n# * ulang: isian ulang yang diisi pada form\n# * id: ID yang dibangkitkan oleh Datastore\n#\n# Method:\n# * __init__: Default constructor\n# * ke_dictionary: Ubah object ke Dictionary agar bisa di serialkan dan simpan\nclass Diskusi:\n\n def __init__(self, platform, keterangan, link, id=None):\n # Default constructor\n self.id = id\n self.platform = platform\n self.keterangan = keterangan\n self.link = link\n\n def ke_dictionary(self):\n # Ubah\n hasil = {}\n\n # Jika id None jangan buat key \"id\"\n if self.id != None:\n hasil[\"id\"] = self.id\n hasil[\"platform\"] = self.platform\n hasil[\"keterangan\"] = self.keterangan\n hasil[\"link\"] = self.link\n\n return hasil\n","repo_name":"Thariannisa/admin_muwafaq_new","sub_path":"admin/api/model/diskusi/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"69842980530","text":"from requests import get\r\nimport mysql.connector\r\nimport matplotlib as plt\r\nimport numpy as np\r\nfrom numpy.polynomial.polynomial import polyfit\r\nimport json,urllib.request\r\nimport pandas as pd\r\nimport time\r\n\r\n# Globals\r\napiKey = \"16b6de16a73966165b3306c35d54fcb6\"\r\nindex = ['SPX']\r\n\r\ndef connectToDb():\r\n db = mysql.connector.connect(\r\n host=\"localhost\",\r\n user=\"root\",\r\n passwd=\"root\",\r\n database=\"stocktracker\"\r\n )\r\n return db\r\n\r\ndef UpdateSectorGroup (ticker):\r\n try:\r\n db = connectToDb()\r\n mycursor = db.cursor(buffered=True)\r\n sql = \"SELECT * FROM stockprofile where ticker = '%s'\" %ticker\r\n mycursor.execute(sql)\r\n Ticker_Profile = mycursor.fetchall()[0]\r\n mycursor.close()\r\n\r\n Ticker_Sector = Ticker_Profile[3]\r\n print(Ticker_Sector)\r\n mycursor = db.cursor(buffered=True)\r\n sql = \"SELECT Sector_Group FROM sector_definition where Industry = '%s'\" %Ticker_Sector\r\n mycursor.execute(sql)\r\n Sector_Group = mycursor.fetchall()[0][0]\r\n db.close()\r\n return Sector_Group\r\n except Exception as ex:\r\n print(ex)\r\n\r\ndef UpdateTaxRate(ticker, apiKey):\r\n try:\r\n\r\n ticker_taxrate = 0.00\r\n url = 'https://datafied.api.edgar-online.com/v2/corefinancials/ann?Appkey=' + apiKey + '&fields=FinancialRatioData&primarysymbols=' + ticker + '&numperiods=5&activecompanies=false&deleted=false&sortby=primarysymbol%20asc&debug=false'\r\n results = (get(url).json())['result']['rows']\r\n for j in range(len(results)):\r\n Data_ratio = results[j]['values']\r\n for i in range(len(Data_ratio)):\r\n if Data_ratio[i]['field'] == 'taxrate':\r\n ticker_taxrate = ticker_taxrate + Data_ratio[i]['value']\r\n\r\n ticker_taxrate = ticker_taxrate / (len(results))\r\n\r\n if ticker_taxrate <= 0.00 or ticker_taxrate == None:\r\n ticker_taxrate = 0.271 #average effective tax rate in US\r\n\r\n print(ticker_taxrate)\r\n return ticker_taxrate\r\n\r\n except Exception as ex:\r\n print(ex)\r\n\r\n#execute results\r\n\r\ndb = connectToDb()\r\nmycursor = db.cursor(buffered=True)\r\nsql = \"SELECT Ticker FROM stockprofile\"\r\nmycursor.execute(sql)\r\nresult = mycursor.fetchall()\r\n# Close the DB and cursor connection\r\nmycursor.close()\r\ndb.close()\r\n\r\nx = 0\r\n\r\nfor ticker in result:\r\n ticker = ticker[0]\r\n print(ticker)\r\n Sector_Group = UpdateSectorGroup(ticker)\r\n Ticker_TaxRate = UpdateTaxRate(ticker, apiKey)\r\n x = x + 1\r\n if x == 4:\r\n time.sleep(5)\r\n x = 0\r\n try:\r\n db = connectToDb()\r\n mycursor = db.cursor(buffered=True)\r\n sql = \"UPDATE stockprofile SET TaxRate = '%s', Sector_Group = '%s' Where Ticker = '%s'\" %(Ticker_TaxRate, Sector_Group, ticker)\r\n print(sql)\r\n mycursor.execute(sql)\r\n db.commit()\r\n mycursor.close()\r\n db.close()\r\n except Exception as ex:\r\n print(ex)\r\n\r\n #UPDATE stocktracker.stockprofile SET TaxRate='0.025' WHERE Ticker ='1';\r\n","repo_name":"sriyegna/StockTrackAPI","sub_path":"Update_Portfolio Table.py","file_name":"Update_Portfolio Table.py","file_ext":"py","file_size_in_byte":3067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"11073667748","text":"n=int(input())\nfor i in range(n):\n l=int(input())\n t=l\n rev=0\n while l>0:\n r=l%10\n l=l//10\n rev=rev*10+r\n if t==rev:\n print('True')\n else:\n print('False')","repo_name":"LokeshHarshaVardhan/codemind-python","sub_path":"Palindrome_Number.py","file_name":"Palindrome_Number.py","file_ext":"py","file_size_in_byte":206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"28950038541","text":"# 1463 1로 만들기\n# 메모리 : 38652KB / 시간 : 476ms / 코드 길이 : 295B\n\ndef solution(n: int):\n \"\"\"\n dynamic programming (bottom up) 방식 사용\n 3이나 2로 나눠지거나 1을 빼는 3가지 경우 중 최솟값을 저장\n \"\"\"\n dp = [0] * (n + 1)\n\n for i in range(2, n + 1):\n # 1을 빼야 하는 경우\n dp[i] = dp[i-1] + 1\n\n # 3으로 나눠지는 경우\n if i % 3 == 0:\n dp[i] = min(dp[i], dp[i//3] + 1)\n \n # 2로 나눠지는 경우\n if i % 2 == 0:\n dp[i] = min(dp[i], dp[i//2] + 1)\n \n return dp[n]\n\nx = int(input())\nprint(solution(x))\n\n\n# 다른 풀이 (다른 분의 풀이)\n# 시간이 훨씬 적게 걸림\n# top down 방식\ndic = {1:0}\ndef f(n,c) :\n if n == 1 :\n return c\n elif n in dic :\n return c + dic[n]\n elif n%6 == 0:\n return min(f(n/3,c+1),f(n/2,c+1))\n elif n%3 == 0:\n return min(f(n/3,c+1),f(n-1,c+1))\n elif n%2 == 0:\n return min(f(n/2,c+1),f(n-1,c+1))\n else:\n return f(n-1,c+1)\nfor n in range(2,min(10000,x)) :\n dic[n] = f(n,0)\nprint(f(x,0))\n","repo_name":"Hyes-y/algorithm","sub_path":"backjoon/1로만들기.py","file_name":"1로만들기.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"11528988086","text":"\"\"\"\nThis module handles all forms for the Shelly-Settings-Wizard\n\"\"\"\n\nimport json\n\nfrom django import forms\nfrom jsonpath_ng import parse\nfrom updates.models import MasterDataShellySettingsMatrix, MasterDataShellySettings, ShellySettings\n\n\nclass SettingsWizardForm(forms.Form):\n \"\"\"\n Wizard-Page 1\n Select Shelly-Type and one or more Shelly the settings will apply to as well as the settings area\n \"\"\"\n\n TYPE_CHOICES = (('', '-- please select --'),) + MasterDataShellySettingsMatrix.SHELLY_DEVICES\n\n shelly_types = forms.ChoiceField(choices=TYPE_CHOICES, label=\"Shelly Type\")\n shelly = forms.MultipleChoiceField(choices=[],\n label=\"Shellies\",\n widget=forms.SelectMultiple)\n shelly_settings_topic = forms.ChoiceField(choices=MasterDataShellySettings.SETTINGS_TYPE_CHOICES,\n label=\"Setting\")\n\n\nclass SettingsValuesForm(forms.Form):\n \"\"\"\n Build a dynamic form for the available settings for the selected Shelly-Type\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.request = kwargs.pop('request', None)\n self.shelly_type = kwargs.pop('shelly_type', None)\n self.settings_type = kwargs.pop('settings_type', None)\n self.shellies = kwargs.pop('shellies', None)\n super(SettingsValuesForm, self).__init__(*args, **kwargs)\n # A Shelly-Type, at least one Shelly and a settings area have to be selected\n if self.shellies and self.shelly_type and self.settings_type:\n # Count Shellies\n shelly_cnt = len(self.shellies)\n\n # Load Settings Fields\n settings = MasterDataShellySettings.objects.filter(settingsmatrix__md_setmatrix_shellytype=self.shelly_type,\n md_settings_type=self.settings_type)\n\n # Load (reference) Shelly settings\n reference_settings = None\n if ShellySettings.objects.filter(shelly_id__id=self.shellies[0]).exists():\n reference_shelly = ShellySettings.objects.get(shelly_id__id=self.shellies[0])\n try:\n reference_settings = json.loads(reference_shelly.shelly_settings_json)\n except:\n pass\n\n # For each setting create a new Field\n for set in settings:\n # Fieldname is created by settings-path, parameter and parameter type\n fieldname = set.md_settings_path + \"-\" + set.md_settings_parameter + \"-\" + set.md_settings_parameter_type\n if set.md_settings_parameter_type == 'bool':\n self.fields[fieldname] = forms.BooleanField(label='%s' % set.md_settings_parameter)\n elif set.md_settings_parameter_type == 'number':\n self.fields[fieldname] = forms.IntegerField(label='%s' % set.md_settings_parameter)\n else:\n self.fields[fieldname] = forms.CharField(label='%s' % set.md_settings_parameter, max_length=200)\n\n # Exclude fields which are only suitable for on Shelly selected (like IP-Address)\n if set.md_settings_single and shelly_cnt and shelly_cnt > 1:\n self.fields[fieldname].disabled=True\n\n self.fields[fieldname].help_text = set.md_settings_description\n self.fields[fieldname].required = False\n\n reference_settings_path = set.md_settings_reference_path\n # If value is POSTed fill this value, else prefill with current value from Shelly (if exists)\n if reference_settings_path and reference_settings:\n json_path = parse('$.' + reference_settings_path)\n matches = json_path.find(reference_settings)\n placeholder = \"\"\n if self.request and self.request.get(fieldname, None):\n self.fields[fieldname].initial = self.request.get(fieldname)\n elif matches:\n if set.md_settings_parameter_type == 'bool':\n self.fields[fieldname].initial = matches[0].value\n else:\n placeholder = matches[0].value\n\n self.fields[fieldname].widget.attrs.update({\n 'placeholder': placeholder\n })\n\n\nclass SettingsPreviewForm(forms.Form):\n \"\"\"\n Create a form with a preview of the settings to be applied\n Show the settings area, a json with the values, a hidden url_encoded field and a approval field\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.request = kwargs.pop('request', None)\n self.settings_path = kwargs.pop('settings_path', None)\n self.settings_json = kwargs.pop('settings_json', None)\n self.settings_encode = kwargs.pop('settings_encode', None)\n super(SettingsPreviewForm, self).__init__(*args, **kwargs)\n self.fields['settings_path'] = forms.CharField(max_length=100, label=\"Settings path\",\n widget=forms.TextInput(attrs={\"readonly\": \"readonly\"}))\n self.fields['settings_json'] = forms.CharField(label=\"Settings to be applied\", widget=forms.Textarea(\n attrs={\"rows\": 15, \"cols\": 100, \"readonly\": \"readonly\"}))\n self.fields['settings_approval'] = forms.BooleanField(\n label=\"I am sure to apply these settings to the above listed Shellies. I know that wrong settings can cause unusability of the shelly!\")\n self.fields['settings_encode'] = forms.CharField(widget=forms.HiddenInput)\n\n if self.request:\n self.fields['settings_path'].initial = self.request.get('settings_path')\n self.fields['settings_json'].initial = self.request.get('settings_json')\n self.fields['settings_encode'].initial = self.request.get('settings_encode')\n self.fields['settings_approval'].initial = self.request.get('settings_approval')\n elif self.settings_path and self.settings_json and self.settings_encode:\n self.fields['settings_path'].initial = self.settings_path\n self.fields['settings_json'].initial = json.dumps(self.settings_json, indent=2)\n self.fields['settings_encode'].initial = self.settings_encode\n","repo_name":"FreddyFFM/shellyupdater","sub_path":"shellyupdater/setter/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":6298,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"20"}
+{"seq_id":"9526990552","text":"import click\nimport contextlib\nimport datetime\nimport logging\n\nfrom itertools import zip_longest\nfrom pathlib import Path\n\nfrom blogtool.itertools import takeuntil\nfrom blogtool.post import Post\n\nLOG = logging.getLogger(__name__)\n\n\n@contextlib.contextmanager\ndef checkout(repo, branch):\n old_branch = repo.head.ref\n\n if old_branch != branch:\n if repo.index.diff(None):\n raise click.ClickException(\n f'attempt to switch {old_branch.name} -> {branch.name} with unstaged changes')\n branch.checkout()\n\n try:\n yield\n finally:\n if old_branch != branch:\n if repo.index.diff(None):\n raise click.ClickException(\n f'attempt to switch {branch.name} -> {old_branch.name} with unstaged changes')\n\n old_branch.checkout()\n\n\ndef find_posts(ctx, branch, merge_base):\n '''find all post/* files between merge_base and branch'''\n\n cur = branch.commit\n posts = set()\n\n # get a list of commits from merge_base to branch.\n commits = list(\n reversed(\n list(\n takeuntil(\n lambda commit: commit == merge_base,\n ctx.repo.iter_commits(branch)\n )\n )\n )\n )\n\n # iterate over (previous, current) commits, looking for\n # added, renamed, or deleted files.\n for prev, cur in zip_longest(commits, commits[1:]):\n if cur is None:\n break\n\n LOG.debug('examining commit %s', cur)\n diff_index = prev.diff(cur)\n for diff in diff_index:\n LOG.debug('change_type %s a_path %s b_path %s',\n diff.change_type, diff.a_path, diff.b_path)\n if diff.change_type in ['A', 'R']:\n if diff.a_path.startswith('post/'):\n LOG.info('found post %s', diff.b_path)\n posts.add(Path(diff.b_path))\n\n if diff.change_type in ['R', 'D']:\n LOG.debug('discarding %s', diff.a_path)\n posts.discard(Path(diff.a_path))\n\n cur = cur.parents[0]\n\n return posts\n\n\n@click.command()\n@click.option('-d', '--date', type=Post.parse_date)\n@click.option('--commit/--no-commit', default=True)\n@click.argument('draft_name', default='HEAD')\n@click.pass_obj\ndef refresh(ctx, date, commit, draft_name):\n if draft_name == 'HEAD':\n draft_name = ctx.repo.head.ref.name\n\n for i in [draft_name, f'draft/{draft_name}']:\n try:\n branch = ctx.repo.refs[draft_name]\n break\n except IndexError:\n pass\n else:\n raise click.ClickException(f'no draft named {draft_name}')\n\n if ctx.repo.index.diff(None):\n raise click.ClickException('cannot operate when there are unstaged changes')\n\n LOG.info('processing branch %s', branch.name)\n with checkout(ctx.repo, branch):\n try:\n merge_base = ctx.repo.merge_base(ctx.main_branch, branch)[0]\n except IndexError:\n raise click.ClickException(f'failed to find merge base for {draft_name}')\n\n LOG.info('found merge base %s', merge_base)\n\n posts = find_posts(ctx, branch, merge_base)\n\n if len(posts) < 1:\n raise click.ClickException(f'no posts in {draft_name}')\n\n if date is None:\n date = datetime.datetime.now()\n\n LOG.info('setting date to %s', date.strftime('%Y-%m-%d'))\n\n for path in posts:\n post = Post.from_file(path)\n post.date = date\n new_path = path.parent / post.filename\n\n if new_path != path:\n LOG.info('renaming %s -> %s', path, new_path)\n path.unlink()\n ctx.repo.git.add(path)\n\n with new_path.open('w') as fd:\n fd.write(post.to_string())\n ctx.repo.git.add(new_path)\n\n if ctx.repo.index.diff('HEAD'):\n if commit:\n LOG.info('committing changes')\n ctx.repo.git.commit(message=f'updated to date {date.strftime(\"%Y-%m-%d\")}')\n else:\n LOG.info('not committing changes')\n else:\n LOG.info('no changes')\n","repo_name":"larsks/blogtool-py","sub_path":"src/blogtool/cmd/refresh.py","file_name":"refresh.py","file_ext":"py","file_size_in_byte":4176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"69904754931","text":"import numpy as np\n\nclass InternalEnvironment:\n def __init__(self,observation_dim, action_dim, num_observation):\n self.observation_dim = observation_dim\n self.action_dim = action_dim\n self.num_observation = num_observation\n\n self.observation_cnt = 0\n self.observation_group = np.zeros((num_observation, observation_dim))\n\n # for the case of augmented observation\n if observation_dim > 24:\n self.reward_observation = 24\n else:\n self.reward_observation = observation_dim\n\n def feed_observation(self, observation):\n \"\"\"\n 1. Feed observation into internal environment\n 2. perform filtering\n 3. calculate reward\n :param observation:\n :return:\n \"\"\"\n\n flt_observation = np.zeros((1, self.observation_dim), dtype=np.float32)\n reward = 0\n # stack observations\n self.observation_group[self.observation_cnt] = observation\n self.observation_cnt += 1\n\n # Apply filter once observation group is fully updated\n # After that, calculate the reward based on filtered observation\n if self.observation_cnt >= self.num_observation:\n self.observation_cnt = 0\n # self.flt_prev_observation = self.flt_observation\n flt_observation = self._filter(self.observation_group)\n is_new_observation = 1\n\n reward = self._cal_reward(flt_observation)\n\n else:\n is_new_observation = 0\n\n return is_new_observation, flt_observation, reward\n\n def take_action(self,action):\n take_action_flag = True\n return take_action_flag, action\n\n def _cal_reward(self, flt_observation):\n \"\"\"\n Calculate the extrinsic rewards based on the filtered observation\n Filtered observation should have same size as observation space\n :return: reward\n \"\"\"\n reward = 0\n for i in range(self.reward_observation):\n reward += flt_observation[i]\n return reward\n\n def _filter(self, signal):\n \"\"\"\n Averaging filter\n\n signal: numpy matrix, one row is one observation\n\n \"\"\"\n return np.mean(signal, axis = 0)\n","repo_name":"daiweiLin/unity_simulator","sub_path":"LASAgent/InternalEnvironment.py","file_name":"InternalEnvironment.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"18710166353","text":"import requests\nfrom constants import CODE_MESSAGES, USER_AGENT\nfrom ScriptResult import EXECUTION_STATE_COMPLETED, EXECUTION_STATE_FAILED\nfrom SiemplifyAction import SiemplifyAction\nfrom SiemplifyDataModel import EntityTypes\nfrom SiemplifyUtils import convert_dict_to_json_result_dict, output_handler\n\nINTEGRATION_NAME = \"GreyNoise\"\n\nSCRIPT_NAME = \"Quick IP Lookup\"\n\n\n@output_handler\ndef main():\n siemplify = SiemplifyAction()\n siemplify.script_name = SCRIPT_NAME\n\n api_key = siemplify.extract_configuration_param(\n provider_name=INTEGRATION_NAME, param_name=\"GN API Key\"\n )\n\n headers = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\",\n \"key\": api_key,\n \"User-Agent\": USER_AGENT,\n }\n\n ips = [\n entity\n for entity in siemplify.target_entities\n if entity.entity_type == EntityTypes.ADDRESS\n ]\n\n output_message = \"Successfully processed: \"\n result_value = True\n status = EXECUTION_STATE_COMPLETED\n output_json = {}\n for ipaddr in ips:\n siemplify.LOGGER.info(\"Started processing IP: {}\".format(ipaddr))\n url = \"https://api.greynoise.io/v2/noise/quick/\"\n url = f\"{url}{ipaddr}\"\n\n res = requests.get(url, headers=headers)\n\n if res.status_code == 401:\n output_message = \"Unable to auth, please check API Key. This action requires a Paid Subscription.\"\n result_value = False\n status = EXECUTION_STATE_FAILED\n siemplify.end(output_message, result_value, status)\n\n output = res.json()\n try:\n output[\"message\"] = CODE_MESSAGES[output[\"code\"]]\n except KeyError:\n output[\"message\"] = \"Code Message Unknown: {}\".format(output[\"code\"])\n\n siemplify.result.add_json(str(ipaddr), output)\n\n output_json[str(ipaddr)] = output\n\n output_message = output_message + \"{},\".format(ipaddr)\n\n if output_json:\n siemplify.result.add_result_json(\n {\"results\": convert_dict_to_json_result_dict(output_json)}\n )\n\n siemplify.end(output_message, result_value, status)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"chronicle/tip-marketplace-uncertified","sub_path":"Integrations/GreyNoise/ActionsScripts/Quick IP Lookup.py","file_name":"Quick IP Lookup.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"9037391553","text":"myMagma = ['#000003', '#140D35', '#3B0F6F', '#63197F', '#8C2980',\n '#B53679', '#DD4968', '#F66E5B', '#FD9F6C', '#FDCD90', '#FBFCBF']\n\ndef setup():\n global akt\n size(800, 640)\n this.surface.setTitle(\"Magma-Akt\")\n akt = loadImage(\"akt.jpg\")\n image(akt, 0, 0)\n noLoop()\n\ndef index(x, y):\n return(x + y*akt.width)\n\ndef draw():\n akt.filter(GRAY)\n akt.filter(POSTERIZE, 11)\n loadPixels()\n for y in range(akt.height):\n for x in range(akt.width):\n pix = akt.pixels[index(x, y)]\n c = floor(red(pix)/20)\n # print(c)\n col = color(myMagma[c])\n # akt.pixels[index(x, y)] = myMagma[c]\n updatePixels()\n image(akt, 400, 0)\n","repo_name":"kantel/processingpy","sub_path":"sketches/magmaakt/magmaakt.pyde","file_name":"magmaakt.pyde","file_ext":"pyde","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"20"}
+{"seq_id":"9110573084","text":"# https://www.acmicpc.net/problem/2171\n# 2차원 평면 위 N개의 점 존재 N[1:5_000]\n# 이 점들 중 서로 다른 네개의 점을 잡아 연결하면 사각형\n# 사각형들 중 X축과 Y축에 평행한 직사각형의 개수를 구해라\n\n# 좌표 범위는 [-1_000_000_000 : 1_000_000_000]\nfrom sys import stdin\n\ninput = stdin.readline\n\nN = int(input())\npos_list = []\npos_set = set()\nfor _ in range(N):\n x, y = map(int, input().split(' '))\n pos_list.append((x, y))\n pos_set.add((x, y))\n\nresult = 0\nfor p1 in range(N):\n for p2 in range(p1 + 1, N):\n if (pos_list[p1][0] == pos_list[p2][0]) or (pos_list[p1][1] == pos_list[p2][1]):\n continue\n target_pos1 = (pos_list[p1][0], pos_list[p2][1])\n target_pos2 = (pos_list[p2][0], pos_list[p1][1])\n\n if (target_pos1 in pos_set) and (target_pos2 in pos_set):\n result += 1\n\nprint(result // 2)\n","repo_name":"nakevin96/AlogirithmPrac","sub_path":"random/2171.py","file_name":"2171.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"24300276701","text":"import logging\nimport os\nfrom typing import Dict, List\n\nimport numpy as np\n\nfrom .. import config\nfrom ..utils import utils_tools\nfrom .train import evaluate, get_features, kfold_cross_validation, train_model\n\nif not config.USE_EFS:\n logging.basicConfig(\n filename=os.path.join(config.OUTPUT_LOG, config.LOG_FILE),\n level=logging.DEBUG,\n format=\"%(asctime)s:%(filename)s:%(funcName)s:%(lineno)d:%(message)s\",\n )\nelse:\n logging.basicConfig(\n level=logging.DEBUG,\n format=\"%(asctime)s:%(filename)s:%(funcName)s:%(lineno)d:%(message)s\",\n )\n\n\nclass AllocatorEngine:\n \"\"\"\n Allocator Engine Class - wrapper class for encapsulating the train, evaluate and serve function of the\n Allocator Classifier\n\n Attributes:\n model: Transformer model for Feature extraction or Sequence Classification\n data_loader: Data loader/ batcher for input data\n dataset: TransformerModelDataset for input data\n sequence_classification: Whether to use transformer model for sequence classification\n (False value will mean using transformer model for feature extraction)\n\n \"\"\"\n\n def __init__(\n self, model, data_loader, dataset, sequence_classification=False\n ) -> None:\n self.model = model\n self.data_loader = data_loader\n self.dataset = dataset\n self.sequence_classification = sequence_classification\n\n def decode_labels(self, labels):\n \"\"\"\n Decoding labels\n\n Parameters:\n labels: list of predictions values\n\n Returns: list of values decoded\n\n \"\"\"\n if isinstance(labels, (float, int)):\n labels = [labels]\n return [config.INVERSE_LABEL_DICT[label] for label in labels]\n\n def train(self, kfold_training: bool = True, func_test: bool = False):\n \"\"\"\n Training method for Allocator (Deadline and Escalation classifier). Saves the model after training of model is completed\n\n Parameters:\n kfold_training: Whether to do the kfold training or not\n func_test: True for functional testing of Allocator (Deadline and Escalation classifier) package\n\n \"\"\"\n # Training using K fold validation by training on k-1 fold and evaluating on 1 fold\n if kfold_training is True:\n # Checking if data samples are less than number of splits in K Fold\n if len(self.dataset) < config.N_SPLITS:\n logging.exception(\n \"Number of samples for training is less than number of splits in K Fold.\"\n \"Set KFOLD_TRAINING=False for simple training...\"\n )\n raise ValueError(\n \"Number of samples for training is less than N_SPLITS in K Fold\"\n )\n\n logging.info(\"Training with K fold cross validation started...\")\n return kfold_cross_validation(\n self.model,\n self.dataset,\n is_train=True,\n sequence_classification=self.sequence_classification,\n func_test=func_test,\n )\n # Training on the complete input data\n else:\n return train_model(\n self.model,\n self.data_loader,\n sequence_classification=self.sequence_classification,\n func_test=func_test,\n )\n\n def evaluate(\n self,\n x_test=None,\n y_test=None,\n kfold_validation: bool = False,\n ) -> Dict[str, float]:\n \"\"\"\n Evaluation method for Allocator (Deadline and Escalation classifier)\n\n Parameters:\n x_test: Input texts for evaluation\n y_test: Labels corresponding to input texts\n kfold_validation: Whether or not to use k fold cross validation\n\n Returns:\n Dictionary of Evaluation Scores\n\n \"\"\"\n scores = {}\n\n # Model Evaluation using K Fold evaluation (Training on K-1 Fold and evaluating on 1 Fold)\n if kfold_validation is True:\n # Checking if num samples is less than Num splits in K Fold\n if len(self.dataset) < config.N_SPLITS:\n logging.exception(\n \"Number of samples for evaluation is less than number of splits in K Fold. \"\n \"Set KFOLD_VALIDATION=False for simple evaluation\"\n )\n raise ValueError(\n \"Number of samples for evaluation is less than N_SPLITS in K Fold\"\n )\n scores = kfold_cross_validation(\n transformer_model=self.model,\n data=self.dataset,\n is_train=False,\n sequence_classification=self.sequence_classification,\n )\n else:\n if (\n x_test is None or y_test is None\n ): # Evaluating on local data, if evaluation data is not passed\n if self.dataset is None:\n scores = {\n \"Scores\": \"No Evaluation data found. Please pass testing data for evaluation scores\"\n }\n logging.warning(\n \"No Evaluation data found for evaluation process...\"\n )\n else:\n x_test = self.dataset.texts\n y_test = self.dataset.labels\n scores = evaluate(self.model, x_test=x_test, y_test=y_test)\n else: # if evaluation data is present\n scores = evaluate(self.model, x_test=x_test, y_test=y_test)\n return scores\n\n def serve(self, save_result: bool = False) -> Dict[str, List]:\n \"\"\"\n Serving method for Allocator (Deadline and Escalation classifier)\n\n Parameters:\n save_result: default False\n Whether or not to save the result locally (True for functional tests)\n\n Returns:\n The output JSON containing process status and details.\n\n \"\"\"\n try:\n logging.debug(\"Generating predictions for input texts...\")\n if self.sequence_classification is True:\n labels_prob = self.model.predict(self.data_loader)\n else:\n # loading ML model for prediction\n ml_model = utils_tools.pickle_load(config.BASE_ML_MODEL)\n\n # getting features from input data\n features, _ = get_features(self.model, self.dataset, is_train=False)\n labels_prob = ml_model.predict(features)\n logging.debug(\"Decoding labels of prediction output...\")\n labels_pred = [np.argmax(array) for array in labels_prob]\n\n # Storing chunk IDs\n chunk_ids = self.dataset.chunk_ids\n # Getting confidence scores for each class\n confidence_scores = [\n round(array[np.argmax(array)] * 100, 2) for array in labels_prob\n ]\n labels = self.decode_labels(labels_pred)\n logging.debug(\"Prediction Done...\")\n\n # If preprocessing is done, then getting the original texts\n text_list = (\n self.dataset.original_texts\n if config.PREPROCESS_TEXT is True\n else self.dataset.texts\n )\n json_data = self.dataset.json_data\n\n # Update the output dict for the API Plugin\n logging.debug(\"Generating response dictionary for API plugin\")\n response = utils_tools.get_response(\n labels, confidence_scores, json_data, status=\"Success\"\n )\n\n # Check if serving is run in the save mode, and store output locally.\n if save_result is True:\n logging.debug(\"Saving results locally...\")\n utils_tools.save_prediction(text_list, labels)\n\n except Exception:\n response = utils_tools.get_response(\n [], [], self.dataset.json_data, status=\"Error\"\n )\n\n # Print the execution traceback for the exception raised\n logging.exception(\n \"Exception occurred while serving the Allocator (Deadline and Escalation classifier) model\",\n exc_info=True,\n )\n\n return response\n","repo_name":"avinash-chaluvadi-dev/pratilipi-mom","sub_path":"AI-NLP/text-classifier/module/Allocator/ml_engine/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":8331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"27565866758","text":"import socket\r\nimport threading\r\nimport random\r\nfrom struct import pack, unpack\r\n\r\n\r\n\r\ndef run_protocol(message, client_ip, client_num):\r\n buffer_len = 1024\r\n student_id = 835\r\n timeout = 3\r\n header_len = 12 \r\n\r\n print(\"Stage A start, client num#\", client_num) \r\n payload_len, psecret, step, student_num = unpack('>IIHH', message[0:12])\r\n payload = message[header_len:header_len+payload_len].decode()\r\n if payload != 'hello world' + '\\0':\r\n return\r\n num = random.randint(1, 20)\r\n the_len = random.randint(1, 20)\r\n udp_port = random.randint(20000, 65535)\r\n while udp_port in udp_ports:\r\n udp_port = random.randint(20000, 65535)\r\n udp_ports.append(udp_port)\r\n secretA = random.randint(0, 4294967295)\r\n header = pack('>IIHH', 16, secretA, 1, student_id)\r\n response = header + pack('>IIII', num, the_len, udp_port, secretA)\r\n\r\n sock1 = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\n sock1.settimeout(timeout)\r\n sock1.sendto(response, client_ip)\r\n sock1.close()\r\n print(\"Stage A end, client num#\", client_num)\r\n\r\n\r\n\r\n print(\"Stage B start, client num#\", client_num)\r\n sock2 = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\n sock2.settimeout(timeout)\r\n sock2.bind((ip, udp_port))\r\n packets_received = 0\r\n received_id = 0\r\n while packets_received < num:\r\n \r\n message, client_ip = sock2.recvfrom(buffer_len)\r\n payload_len, psecret, step, student_num, packet_id = unpack('>IIHHI', message[0:16])\r\n \r\n if psecret != secretA or step != 1 or student_num != student_id:\r\n return\r\n payload = message[16:16+the_len]\r\n all_zero = all(int(elem) == 0 for elem in payload)\r\n true_payload_len = the_len\r\n if the_len%4 != 0:\r\n true_payload_len = the_len + (4-the_len%4)\r\n\r\n rand_num = random.random()\r\n if rand_num < 0.5 and packet_id == received_id and len(message)%4 == 0 and len(message[16:]) == true_payload_len and all_zero:\r\n packets_received += 1\r\n received_id += 1\r\n header = pack('>IIHH', 16, secretA, 1, 444)\r\n response = header + pack('>I', packet_id)\r\n sock2.sendto(response, client_ip)\r\n\r\n if packets_received == num:\r\n tcp_port = random.randint(20000, 65535)\r\n while tcp_port in tcp_ports:\r\n tcp_port = random.randint(20000, 65535)\r\n tcp_ports.append(tcp_port)\r\n secretB = random.randint(0, 4294967295)\r\n header = pack('>IIHH', 8, secretB, 2, student_id)\r\n response = header + pack('>II', tcp_port, secretB)\r\n sock3 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n sock3.bind((ip, tcp_port))\r\n sock3.listen(5)\r\n sock2.sendto(response, client_ip)\r\n sock2.close()\r\n udp_ports.remove(udp_port)\r\n print(\"Stage B end, client num#\", client_num)\r\n\r\n\r\n\r\n print(\"Stage C start, client num#\", client_num)\r\n connection, client_address = sock3.accept()\r\n num2 = random.randint(1, 20)\r\n len2 = random.randint(1, 20)\r\n secretC = random.randint(0, 4294967295)\r\n c = chr(random.randint(33, 126)) \r\n header = pack('>IIHH', 13, secretC, 2, student_id)\r\n message = header + pack('>III', num2, len2, secretC) + c.encode(\"ascii\")\r\n connection.sendto(message, client_address)\r\n print(\"Stage C end, client num#\", client_num)\r\n\r\n\r\n\r\n print(\"Stage D start, client num#\", client_num)\r\n payloads_received = 0\r\n mess_len = len2 + header_len\r\n if len2%4 != 0:\r\n mess_len = len2 + (4-len2%4) + header_len\r\n sock3.settimeout(timeout)\r\n while payloads_received < num2:\r\n try:\r\n message = connection.recv(mess_len)\r\n except:\r\n return\r\n payload_len, psecret, step, student_num = unpack('>IIHH', message[0:12])\r\n if psecret != secretC or step != 1 or student_num != student_id:\r\n return\r\n payload = message[header_len:header_len + len2]\r\n all_c = all(chr(elem) == c for elem in payload)\r\n if len(message)%4 == 0 and len(message) == mess_len and all_c:\r\n payloads_received += 1\r\n\r\n secretD = random.randint(0, 4294967295)\r\n header = pack('>IIHH', 4, secretC, 2, student_id)\r\n response = header + pack('>I', secretD)\r\n connection.sendto(response, client_address)\r\n tcp_ports.remove(tcp_port)\r\n sock3.close()\r\n print(\"Stage D end, client num#\", client_num)\r\n print(f\"================== Complete client #{client_num} ==================\")\r\n\r\nip =\"127.0.0.1\"\r\nudp_port = 12239\r\nudp_ports = []\r\ntcp_ports = []\r\n\r\nprint(\"Start server\")\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\nsock.bind((ip, udp_port))\r\nclient_num = 0\r\nwhile True:\r\n message, client_ip = sock.recvfrom(1024)\r\n client_thread = threading.Thread(target=run_protocol, args=(message, client_ip, client_num))\r\n client_num += 1\r\n client_thread.start()\r\nsock.close()","repo_name":"BobinShihJosh/Network-Projects","sub_path":"SocketsApi/part2/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"7824922469","text":"from SiemplifyUtils import output_handler\nfrom SiemplifyAction import SiemplifyAction\nfrom SysAidManager import SysAidManager\n\n\nPROVIDER = \"SysAid\"\nACTION_NAME = \"SysAid - CloseServiceRequest\"\n\n\n@output_handler\ndef main():\n siemplify = SiemplifyAction()\n siemplify.action_definition_name = ACTION_NAME\n conf = siemplify.get_configuration(PROVIDER)\n verify_ssl = conf.get('Verify SSL').lower() == 'true'\n sysaid_manager = SysAidManager(server_address=conf.get('Api Root'),\n username=conf.get('Username'),\n password=conf.get('Password'),\n verify_ssl=verify_ssl)\n\n sr_id = siemplify.parameters.get('Service Request ID')\n solution = siemplify.parameters.get('Solution')\n\n sysaid_manager.close_service_request(sr_id, solution)\n siemplify.end(\"Successfully closed service request {} with solution: {}.\".format(sr_id, solution), 'true')\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"chronicle/tip-marketplace","sub_path":"Integrations/SysAid/ActionsScripts/CloseServiceRequest.py","file_name":"CloseServiceRequest.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"13275715375","text":"'''\r\nThis project includes:\r\n\r\n1. Analytical solution of Asian option\r\n2. Monte Carlo simulation to price an Asian option\r\n3. Control variate technique\r\n4. Comparision between classic Monte Carlo simulation and Monte Carlo simulation with control variate\r\n'''\r\n\r\n# import packages\r\nimport numpy as np \r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import norm\r\n\r\n\r\n\"\"\"1. Classic MC\"\"\"\r\ndef AsianMC(T,S,K,sigma,r,NPaths,NSteps):\r\n if NPaths <= 1:\r\n print(\"2 sample paths are required at minimum\")\r\n return\r\n dt = T/NSteps\r\n sqrtdt = np.sqrt(dt)\r\n S_vec = S*np.ones([NSteps+1,NPaths])\r\n for t in range(NSteps):\r\n dW = np.random.normal(0,sqrtdt,NPaths)\r\n S_vec[t+1,:] = S_vec[t,:] + r*S_vec[t,:]*dt + sigma * S_vec[t,:] * dW\r\n S_mean = np.mean(S_vec[1:,:],axis=0) #Don't keep S0, only 1 to NSteps\r\n payoff = np.maximum(S_mean - K ,0)\r\n \r\n pv_vec = np.exp(-r*T) * payoff\r\n pv = np.mean(pv_vec)\r\n varpv = np.var(pv_vec,ddof=1)\r\n i1 = pv - 1.96*np.sqrt(varpv)/np.sqrt(NPaths)\r\n i2 = pv + 1.96*np.sqrt(varpv)/np.sqrt(NPaths)\r\n return i1,i2,pv,pv_vec\r\n\r\n\r\n\r\n\"\"\"2. MC with CV\"\"\"\r\n\"\"\"2.1 Implementing the analytical geometric average Asian option price \"\"\"\r\n\r\n#No dividends, m = t = t0 = 0\r\ndef AnalyticalGeom(T,S,K,sigma,r,NSteps):\r\n dt = T/NSteps\r\n \r\n v = r - 0.5*sigma**2 \r\n a = np.log(S) + v*dt + 0.5*v*(T-dt)\r\n b = sigma**2 * dt + (sigma**2 * (T-dt) )/(6*NSteps) * (2*NSteps -1)\r\n x = (a - np.log(K) + b) / np.sqrt(b)\r\n \r\n price = np.exp(-r*T) * (np.exp(a + 0.5*b) * norm.cdf(x) - K * norm.cdf(x-np.sqrt(b)))\r\n return price\r\n\r\n\"\"\"2.2 Pilot simulation to estimate c*\"\"\"\r\ndef MCPilot(T,S,K,sigma,r,NPaths,NSteps): \r\n if NPaths <= 1:\r\n print(\"2 sample paths are required at minimum\")\r\n return\r\n dt = T/NSteps\r\n sqrtdt = np.sqrt(dt)\r\n S_vec = S*np.ones([NSteps+1,NPaths])\r\n for t in range(NSteps):\r\n dW = np.random.normal(0,sqrtdt,NPaths)\r\n S_vec[t+1,:] = S_vec[t,:] + r*S_vec[t,:]*dt + sigma * S_vec[t,:] * dW\r\n ari_price = np.exp(-r*T)*np.maximum(np.mean(S_vec[1:,:],axis=0)-K,0)\r\n geom_price = np.exp(-r*T)*np.maximum(np.exp(np.mean(np.log(S_vec[1:,:]),axis=0))-K , 0)\r\n varcovar = np.cov(ari_price,geom_price)\r\n return(-varcovar[0,1]/np.var(geom_price,ddof=1))\r\n\r\n\"\"\" 2.3 Monte Carlo with CV \"\"\"\r\ndef AsianMCCV(T,S,K,sigma,r,NPaths,NSteps):\r\n if NPaths <= 1:\r\n print(\"2 sample paths are required at minimum\")\r\n return\r\n c_star = MCPilot(T,S,K,sigma,r,NPaths,NSteps)\r\n geom_anal = AnalyticalGeom(T,S,K,sigma,r,NSteps)\r\n \r\n dt = T/NSteps\r\n sqrtdt = np.sqrt(dt)\r\n S_vec = S*np.ones([NSteps+1,NPaths])\r\n for t in range(NSteps):\r\n dW = np.random.normal(0,sqrtdt,NPaths)\r\n S_vec[t+1,:] = S_vec[t,:] + r*S_vec[t,:]*dt + sigma * S_vec[t,:] * dW\r\n ari_price = np.exp(-r*T)*np.maximum(np.mean(S_vec[1:,:],axis=0)-K,0)\r\n geom_price = np.exp(-r*T)*np.maximum(np.exp(np.mean(np.log(S_vec[1:,:]),axis=0))-K , 0)\r\n \r\n pv_vec = ari_price + c_star * (geom_price - geom_anal)\r\n varprice = np.var(pv_vec,ddof=1)\r\n sigsqrtn = varprice/np.sqrt(NPaths) \r\n pv = np.mean(pv_vec)\r\n i1 = pv - 1.96*sigsqrtn\r\n i2= pv + 1.96*sigsqrtn\r\n return i1,i2,pv,pv_vec,c_star\r\n\r\n\"\"\" 3. Price and confidence intervals comparisons \"\"\"\r\nT = 2\r\nS = 20\r\nK = 15\r\nsigma = 0.3\r\nr = 0.05 \r\nNPaths = 10000\r\nNSteps = 200 * T\r\n\r\nMC = AsianMC(T,S,K,sigma,r,NPaths,NSteps)\r\nMC_CV = AsianMCCV(T,S,K,sigma,r,NPaths,NSteps)\r\nprint('------------ Price w/o CV ------------')\r\nprint (\"lower boundary: \",MC[0])\r\nprint (\"Higher boundary: \",MC[1])\r\nprint(\"Price:\",MC[2])\r\n\r\n\r\nprint('------------ Price w/ CV ------------')\r\nprint (\"lower boundary: \",MC_CV[0])\r\nprint (\"Higher boundary: \",MC_CV[1])\r\nprint(\"Price:\",MC_CV[2])\r\nprint(\"c*: \",MC_CV[4])\r\n\r\n\r\n\r\nfig,[ax1,ax2] = plt.subplots(nrows=1, ncols=2,figsize=(10,4))\r\nax1.hist(MC[3],bins = 50,color='red')\r\nax1.set_xlim(0, np.max(MC[3]))\r\nax1.set_title('Under classic MC method')\r\nax1.set_xlabel('Price')\r\nax1.set_ylabel('Frequency')\r\n\r\nax2.hist(MC_CV[3],bins = 50,color='blue')\r\nax2.set_xlim(0, np.max(MC[3]))\r\nax2.set_title('Under MC with CV method')\r\nax2.set_xlabel('Price')\r\nax2.set_ylabel('Frequency')\r\n\r\nfig.suptitle(f'Simulated prices distribution with {NPaths} simulations')\r\nfig.tight_layout()\r\nplt.show()\r\n","repo_name":"shining-cloud/Simulation","sub_path":"Asian option pricing - Control variate.py","file_name":"Asian option pricing - Control variate.py","file_ext":"py","file_size_in_byte":4351,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"30941104085","text":"#Christopher Lara 10/28/2021 Midterm project BeautifulSoup\r\n#IMPORTANT. if it doesn't work as one file for whatever reason, please break it down from each example into seperate files\r\n#or temporarily delete the other examples if you run into an error.\r\nfrom bs4 import BeautifulSoup\r\nimport requests #to take requests from the web\r\nimport re #lets you check if a particular string matches a given regular expression\r\nimport os #to check if the files created exists on current directory.\r\n\r\n\r\n#EXAMPLE #1 Finding the price of an item on a NewEgg webpage.\r\n#Taking the html directly from a website url\r\nurl = input(\"copy paste an item link from NewEgg: \")\r\nresult = requests.get(url)\r\n\r\n#add results of the get request into doc1\r\ndoc = BeautifulSoup(result.text, \"html.parser\")\r\n\r\n#finds a specific word in the website.\r\nprice = doc.find_all(text=\"$\")\r\nname = doc.find(class_=\"product-title\")\r\n#since html works in family we are looking for text=\"$\", and the name of the product is within class_=\"product-title\"\r\nparent = price[0].parent\r\n#once you know the parent tag, you can write can search for the words between that parent. For this case, The price is between Strong and Sup tags.\r\nstrong = parent.find(\"strong\")\r\nsup = parent.find(\"sup\")\r\nprint(\"the price of \" + name.string + \" is: $\" + strong.string + sup.string) #combine them and you have the accurate price of that specific webpage.\r\nprint(\"-----------------------------\")\r\n\r\n#EXAMPLE 2. changing textboxes in html, and creating a new file.\r\ndef doesFileExists(filePathAndName): #Will be used later.\r\n return os.path.exists(filePathAndName)\r\n\r\nurl1 = \"https://www.facebook.com/\"\r\nresult1 = requests.get(url1)\r\ndoc1 = BeautifulSoup(result1.text, \"html.parser\")\r\n\r\nparent1 = doc1.find_all(\"input\", id=\"email\") #Since Email and Password had different id=\"\" definitions I had to make two for loops. Not every website is built the same.\r\npassw = doc1.find_all(\"input\", id=\"pass\")\r\n\r\nfor tags in parent1:\r\n tags['placeholder'] = \"Hello everyone my name is Bowser\"\r\n\r\nfor tags in passw:\r\n tags['placeholder'] = \"I'm the best.\"\r\n\r\nwith open(\"fixed.html\", \"w\", encoding='utf-8') as file: #the encoding argument is so it doesn't try to write in errors into the file.\r\n file.write(str(doc1))\r\n\r\nif doesFileExists('fixed.html'): #Checks to see if the html file exists. and if it does that means it has been modified\r\n print (\"The file has been modified\")\r\nelse:\r\n print ('An error occured')\r\n\r\nprint(\"-----------------------------\")\r\n\r\n\r\n#EXAMPLE #3 Finding the price of various results on NewEgg\r\n#Taking the html directly from a website url\r\nsearch = input(\"What product do you want to search for? \")\r\nurl2 = f\"https://www.newegg.com/p/pl?d={search}&N=4131\"\r\nresult2 = requests.get(url2)\r\n\r\n#add results of the get request into doc1\r\ndoc2 = BeautifulSoup(result2.text, \"html.parser\")\r\n\r\npage_num = doc2.find(class_=\"list-tool-pagination-text\").strong\r\npages = int(str(page_num).split(\"/\")[-2].split(\">\")[-1][:-1]) #[-1][:-1} removes the last element for us.\r\n\r\nitems_found = {} #dictionary for the items we find.\r\n\r\nfor page in range(1, pages + 1):\r\n\turl = f\"https://www.newegg.com/p/pl?d={search}&N=4131&page={page}\"\r\n\tpage = requests.get(url).text\r\n\tdoc2 = BeautifulSoup(page, \"html.parser\")\r\n\r\n#within this class we are only looking through SEARCH RESULTS.\r\n#will match any text that contains the search term within the pages acquired.\r\n\tdiv = doc2.find(class_=\"item-cells-wrap border-cells items-grid-view four-cells expulsion-one-cell\")\r\n\tresults = div.find_all(text=re.compile(search)) #with re.compile it will match items that have more character than the search term.\r\n\r\n#if the item we're looking for is in results.\r\n\tfor item in results: #item for this case will be the item we're looking for. AKA 3080\r\n\t\tparent = item.parent\r\n\t\tif parent.name != \"a\": #check if parent is in \"a\" tag\r\n\t\t\tcontinue\r\n\r\n\t\tlink = parent['href'] #this is the link to the product.\r\n\t\tnext_parent = item.find_parent(class_=\"item-container\") #parent of the parent. this is where item info is located so we need this parent.\r\n\t\ttry:\r\n\t\t\tprice = next_parent.find(class_=\"price-current\").find(\"strong\").string #class_=\"price current\" is where the price in the html was located.\r\n\t\t\titems_found[item] = {\"price\": int(price.replace(\",\", \"\")), \"link\": link} #another dictionary for the price. .replace to remove ','\r\n\t\texcept:\r\n\t\t\tpass\r\n\r\nsorted_items = sorted(items_found.items())\r\n#A basic sort so it looks neat. it sorts the results alphabetically\r\nfor item in sorted_items:\r\n\tprint(item[0])\r\n\tprint(f\"${item[1]['price']}\")\r\n\tprint(item[1]['link'])\r\n\tprint(\"---------------------------------------------------------------------------\")\r\n\r\n#find_parent command finds the parent of the class we're looking for.\r\n\r\n#use .attrs when looking for a tag to find all attributes\r\n","repo_name":"BigPapaSparkles/python-midterm","sub_path":"midterm_code.py","file_name":"midterm_code.py","file_ext":"py","file_size_in_byte":4824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"40993381225","text":"import cv2 \nimport torch\nimport torch.nn as nn\nfrom torchvision import transforms\nimport numpy as np\nfrom Model import DeePixBiS\nfrom Loss import PixWiseBCELoss\nfrom Metrics import predict, test_accuracy, test_loss\nimport argparse\nimport os\nimport pandas as pd \nimport statistics\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = DeePixBiS()\nmodel.load_state_dict(torch.load('./DeePixBiS.pth'))\nmodel.eval()\n\nmodel.to(device)\n\n\ntfms = transforms.Compose([\n transforms.ToPILImage(),\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n])\ndef inference(args):\n result = []\n count = 1\n for file in os.listdir(args.files):\n print(f'{count}: {file}')\n count+=1\n link = args.files+file\n confidence = []\n cap = cv2.VideoCapture(link)\n i = 0\n frame_skip = 10\n frame_count = 0\n while cap.isOpened():\n ret, frame = cap.read()\n if not ret: \n break\n if i > frame_skip - 1:\n frame_count += 1\n faceRegion = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n faceRegion = tfms(faceRegion)\n faceRegion = faceRegion.unsqueeze(0)\n mask, binary = model.forward(faceRegion.to(device))\n res = torch.mean(mask).item()\n confidence.append(res)\n i = 0\n continue\n i += 1\n result.append([file,statistics.median(confidence)])\n df = pd.DataFrame(result, columns = ['fname', 'liveness_score'])\n df.to_csv('./Predict.csv', index = False)\n\n return res \n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--files', type=str, default='video')\n\n args = parser.parse_args()\n inference(args)","repo_name":"huynguyenanh2000/ZALOAI","sub_path":"Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"9565492354","text":"import sys\r\nimport math\r\nN = int (1e4 + 5)\r\nsys.setrecursionlimit (N)\r\n\r\ndef charming () :\r\n x = int (input ())\r\n p = 1; q = N\r\n while p <= q and p * p * p <= x:\r\n while p * p * p + q * q * q > x : q -= 1\r\n if q < p : break\r\n if p * p * p + q * q * q == x : print (\"YES\"); return\r\n p += 1\r\n print (\"NO\")\r\n\r\nfor t in range (int (input ())) : charming ()\r\n","repo_name":"SuperBigFive/Charming_ACM","sub_path":"Codeforces/Div3/702div3(vp)/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"25497149338","text":"\nfrom . import views\nfrom django.urls import include, path\n\nurlpatterns = [\n path('user/', views.getUser, name='user'),\n path('newuser/', views.createUser, name='newuser'),\n path('product//', views.getProducts, name='product'),\n \n \n]\n","repo_name":"alidauda/djangoapi","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"1468536034","text":"import os\nimport json\nimport time\nimport socket\n\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nADDR = ('169.254.200.118', 22222)\ns.bind((ADDR))\ns.listen(5)\nfile_path = 'D:\\chaodawenjian.txt'\nfile_name = file_path.rsplit(os.sep, 1)[1]\nfile_size = os.path.getsize(file_path) # 获取文件内容大小\n# 发送给客户端的头部信息\nheader_data = {\n 'file_name': file_name,\n 'file_size': file_size,\n 'date': time.strftime('%Y-%m-%d %X', time.localtime()),\n 'charset': 'utf-8'\n}\nwhile True:\n conn, addr = s.accept()\n print(\"%s:%s is connect\"% addr)\n request_data = conn.recv(1024)\n print(request_data)\n # 把头部内容发送过去\n conn.send(json.dumps(header_data).encode())\n request_data1 = conn.recv(1024)\n print(request_data1)\n f = open(file_path, 'r')\n content = f.read()\n # 发送文件内容\n conn.sendall(content.encode())\n conn.close()\n","repo_name":"lyric777/TCP-Communication","sub_path":"FileToStringTransmission/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"29735811753","text":"import feedparser\nfrom bunch import bunchify\nfrom dateutil import parser\nfrom lxml import etree\nfrom models import Event\n\ndef _safe(func, default):\n try:\n return func()\n except Exception:\n return default\n\nclass EventRSSSource(object):\n \"\"\" http://web.resource.org/rss/1.0/modules/event/\n \n Currently contains code specific to the OII\n \"\"\"\n def __init__(self, url=None, name=None):\n self.url = url\n self.name = name\n\n @classmethod\n def create(cls, url, name):\n return EventRSSSource(url, name)\n\n def __call__(self, list_manager):\n feed = feedparser.parse(self.url)\n master_list = list_manager.get_or_create_managed_list_by_name(feed.feed.title_detail.value)\n\n for entry in feed.entries:\n location = entry.ev_location\n start_datetime = parser.parse(entry.ev_startdate)\n end_datetime = parser.parse(entry.ev_enddate)\n name = entry.title_detail.value\n summary_blob = entry.summary_detail.value\n\n # OII specific code\n link = entry.id\n summary_soup = etree.fromstring(\"%s \" % summary_blob)\n def _get_speaker():\n speaker, = summary_soup.xpath(\"p[@class='location']/text()\")\n return speaker[len(\"Speakers: \"):]\n def _get_description():\n description, = summary_soup.xpath(\"p[@class='summary']/text()\")\n return \"%s\\nTaken from %s\" % (description, link)\n\n speaker = _safe(_get_speaker, None)\n description = _safe(_get_description, None)\n\n yield Event(name=name, start=start_datetime, end=end_datetime,\n description=description, speaker=speaker, location=location,\n master_list=master_list, lists=[master_list])\n","repo_name":"richardhills/OUEventAggregationSystem","sub_path":"src/sources/rss_event_source.py","file_name":"rss_event_source.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"34170448909","text":"import os\nimport random\nimport string\nfrom pathlib import Path\nfrom uuid import uuid4\nfrom dotenv import load_dotenv\nfrom azure.ai.formrecognizer import DocumentAnalysisClient\nfrom azure.core.credentials import AzureKeyCredential\nimport fitz\nimport io\nfrom PIL import Image\n\nload_dotenv()\nenv = os.environ\n\nkey = env.get('DOC_AI_KEY')\nendpoint = env.get('DOC_AI_ENDPOINT')\n\n\ndef analyse_ID(fileURL):\n document_analysis_client = DocumentAnalysisClient(\n endpoint=endpoint, credential=AzureKeyCredential(key)\n )\n\n poller = document_analysis_client.begin_analyze_document_from_url(\n \"prebuilt-idDocument\", fileURL\n )\n id_documents = poller.result()\n\n for idx, id_document in enumerate(id_documents.documents):\n first_name = id_document.fields.get(\"FirstName\")\n f_name = ''\n if first_name:\n f_name = first_name.value\n\n last_name = id_document.fields.get(\"LastName\")\n l_name = ''\n if last_name:\n l_name = last_name.value\n\n document_number = id_document.fields.get(\"DocumentNumber\")\n doc_num = ''\n if document_number:\n doc_num = document_number.value\n\n dob = id_document.fields.get(\"DateOfBirth\")\n d_o_b = ''\n if dob:\n d_o_b = str(dob.value)\n\n doe = id_document.fields.get(\"DateOfExpiration\")\n d_o_e = ''\n if doe:\n d_o_e = str(doe.value)\n\n sex = id_document.fields.get(\"Sex\")\n sex_ = ''\n if sex:\n sex_ = sex.value\n\n address = id_document.fields.get(\"Address\")\n address_ = ''\n if address:\n address_ = str(address.value)\n\n country_region = id_document.fields.get(\"CountryRegion\")\n country = ''\n if country_region:\n country = str(country_region.value)\n\n region = id_document.fields.get(\"Region\")\n region_ = ''\n if region:\n region_ = str(region.value)\n\n send = {\n 'first_name': f_name,\n 'last_name': l_name,\n 'document_number': doc_num,\n 'DOB': d_o_b,\n 'DOE': d_o_e,\n 'sex': sex_,\n 'address': address_,\n 'country': country,\n 'region': region_\n }\n return send\n\n\ndef PDFtoImage(pdfPath):\n # open the file\n pdf_file = fitz.open(pdfPath)\n print(pdf_file)\n\n # iterate over PDF pages\n path = ''\n for page_index in range(len(pdf_file)):\n # get the page itself\n page = pdf_file[page_index]\n image_list = page.get_images()\n\n # printing number of images found in this page\n if image_list:\n print(f\"[+] Found a total of {len(image_list)} images in page \"\n f\"{page_index}\")\n else:\n print(\"[!] No images found on page\", page_index)\n for image_index, img in enumerate(page.get_images(), start=1):\n # get the XREF of the image\n xref = img[0]\n # extract the image bytes\n base_image = pdf_file.extract_image(xref)\n image_bytes = base_image[\"image\"]\n # get the image extension\n image_ext = base_image[\"ext\"]\n # load it to PIL\n image = Image.open(io.BytesIO(image_bytes))\n # save it to local disk\n rand_strings = ''.join(random.choice(string.ascii_lowercase\n + string.digits\n + string.ascii_uppercase)\n for i in range(5))\n file_name = f\"{rand_strings}{uuid4().hex}.{image_ext}\"\n BASE_DIR = Path(__file__).resolve(strict=True).parent.parent\n path = f'{BASE_DIR}/media/pdf_images/{file_name}'\n image.save(open(path, \"wb\"))\n\n return path\n","repo_name":"MrKintu/buzzhub","sub_path":"backend/authy/DocHandler.py","file_name":"DocHandler.py","file_ext":"py","file_size_in_byte":3850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"72196882930","text":"from datetime import datetime\nfrom django.shortcuts import render\nfrom rest_framework.response import Response\nfrom .serializers import (BillSerializer, SmallIssueSerializer, BigBillSerializer,\n SmallFreelancerMemberSerializer, SmallProjectSerializer, BigProjectSerializer,\n BigInvoiceSerializer, SmallBillSerializer)\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework import status\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom rest_framework.decorators import api_view, permission_classes\nfrom .actions import sync_user_issues, generate_freelancer_bill, generate_project_invoice\nfrom .models import (Bill, Freelancer, Issue, BILL_STATUS, AcademyFreelanceProject, FreelanceProjectMember,\n ProjectInvoice)\nfrom .tasks import async_repository_issue_github\nfrom rest_framework.views import APIView\nfrom breathecode.utils.views import private_view\nfrom breathecode.notify.actions import get_template_content\nfrom breathecode.utils.decorators import capable_of\nfrom breathecode.utils.validation_exception import ValidationException\nfrom breathecode.admissions.models import Academy\nfrom breathecode.utils.api_view_extensions.api_view_extensions import APIViewExtensions\nfrom django.http import HttpResponse\n\n\n@private_view()\ndef render_html_all_bills(request, token):\n\n def map_status(_status):\n status_maper = {\n 'DUE': 'under review',\n 'APPROVED': 'ready to pay',\n 'PAID': 'already paid',\n 'IGNORED': 'ignored',\n }\n if _status not in status_maper: return _status\n return status_maper[_status]\n\n lookup = {}\n\n status = 'APPROVED'\n if 'status' in request.GET:\n status = request.GET.get('status')\n lookup['status'] = status.upper()\n\n if 'academy' in request.GET:\n lookup['academy__id__in'] = request.GET.get('academy').split(',')\n\n items = Bill.objects.filter(**lookup).exclude(academy__isnull=True)\n serializer = BigBillSerializer(items, many=True)\n\n total_price = 0\n for bill in serializer.data:\n total_price += bill['total_price']\n\n data = {\n 'status': status,\n 'token': token.key,\n 'title': f'Payments {map_status(status)}',\n 'possible_status': [(key, map_status(key)) for key, label in BILL_STATUS],\n 'bills': serializer.data,\n 'total_price': total_price\n }\n template = get_template_content('bills', data)\n return HttpResponse(template['html'])\n\n\n@api_view(['GET'])\n@permission_classes([AllowAny])\ndef render_html_bill(request, id=None):\n item = Bill.objects.filter(id=id).first()\n if item is None:\n template = get_template_content('message', {'message': 'Bill not found'})\n return HttpResponse(template['html'])\n else:\n serializer = BigBillSerializer(item, many=False)\n status_map = {'DUE': 'UNDER_REVIEW', 'APPROVED': 'READY_TO_PAY', 'PAID': 'ALREADY PAID'}\n data = {\n **serializer.data, 'issues':\n SmallIssueSerializer(item.issue_set.all(), many=True).data,\n 'status':\n status_map[serializer.data['status']],\n 'title':\n f'Freelancer { serializer.data[\"freelancer\"][\"user\"][\"first_name\"] } { serializer.data[\"freelancer\"][\"user\"][\"last_name\"] } - Invoice { item.id }'\n }\n template = get_template_content('invoice', data)\n return HttpResponse(template['html'])\n\n\n# Create your views here.\nclass BillView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n\n def get(self, request, format=None):\n\n items = Bill.objects.all()\n lookup = {}\n\n if 'freelancer' in self.request.GET:\n userId = self.request.GET.get('freelancer')\n lookup['freelancer__id'] = userId\n\n if 'user' in self.request.GET:\n userId = self.request.GET.get('user')\n lookup['freelancer__user__id'] = userId\n\n if 'status' in self.request.GET:\n status = self.request.GET.get('status')\n lookup['status'] = status\n\n items = items.filter(**lookup).order_by('-created_at')\n\n serializer = BillSerializer(items, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = BillSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass AcademyBillView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n\n extensions = APIViewExtensions(sort='-created_at', paginate=True)\n\n @capable_of('read_freelancer_bill')\n def get(self, request, academy_id, bill_id=None):\n\n def get_freelancer_by_name_or_email(query_name, query):\n for term in query_name.split():\n query = query.filter(\n Q(freelancer__user__first_name__icontains=term)\n | Q(freelancer__user__last_name__icontains=term)\n | Q(freelancer__user__email__icontains=term))\n return query\n\n if bill_id is not None:\n item = Bill.objects.filter(id=id).first()\n if item is None:\n raise serializers.ValidationError('Bill not found', code=404)\n else:\n serializer = BillSerializer(item, many=False)\n return Response(serializer.data)\n\n handler = self.extensions(request)\n items = Bill.objects.filter(academy__id=academy_id)\n lookup = {}\n\n like = request.GET.get('like', None)\n if like is not None:\n items = get_freelancer_by_name_or_email(like, items)\n\n freelancer = self.request.GET.get('freelancer', None)\n if freelancer is not None:\n lookup['freelancer__id'] = freelancer.id\n\n status = self.request.GET.get('status', '')\n if status != '':\n lookup['status__in'] = status.split(',')\n\n user_id = self.request.GET.get('user', None)\n if user_id is not None:\n lookup['freelancer__user__id'] = user_id\n\n reviewer = self.request.GET.get('reviewer', None)\n if reviewer is not None:\n lookup['reviewer__id'] = reviewer.id\n\n sort = self.request.GET.get('sort', '-created_at')\n items = items.filter(**lookup).order_by(sort)\n\n items = handler.queryset(items)\n serializer = SmallBillSerializer(items, many=True)\n return handler.response(serializer.data)\n\n @capable_of('crud_freelancer_bill')\n def put(self, request, bill_id=None, academy_id=None):\n # Bulk Action to Modify Status\n if bill_id is None:\n billStatus = request.data['status']\n bills = request.data['bills']\n\n if billStatus is None or billStatus == '':\n raise ValidationException('Status not found in the body of the request', code=404)\n\n if billStatus not in ['DUE', 'APPROVED', 'PAID', 'IGNORED']:\n raise ValidationException(f'Status provided ({billStatus}) is not a valid status', code=404)\n\n if bills is None or len(bills) == 0:\n raise ValidationException('Bills not found in the body of the request', code=404)\n\n for bill in bills:\n item = Bill.objects.filter(id=bill, academy__id=academy_id).first()\n if item is None:\n raise ValidationException('Bill not found for this academy', code=404)\n item.status = billStatus\n item.save()\n bill = item\n\n return Response(f\"Bills' status successfully updated to {billStatus}\",\n status=status.HTTP_201_CREATED)\n\n item = Bill.objects.filter(id=bill_id, academy__id=academy_id).first()\n if item is None:\n raise ValidationException('Bill not found for this academy', code=404)\n\n serializer = BillSerializer(item, data=request.data, many=False)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass AcademyProjectView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n\n @capable_of('read_freelance_projects')\n def get(self, request, academy_id, project_id=None):\n\n if project_id is not None:\n item = AcademyFreelanceProject.objects.filter(id=project_id, academy__id=academy_id).first()\n if item is None:\n raise ValidationException('Project not found on this academy', 404)\n serializer = BigProjectSerializer(item, many=False)\n return Response(serializer.data)\n\n items = AcademyFreelanceProject.objects.filter(academy__id=academy_id)\n lookup = {}\n\n if 'like' in self.request.GET:\n like = self.request.GET.get('like')\n lookup['title__icontains'] = like\n\n if 'repository' in self.request.GET:\n repository = self.request.GET.get('repository')\n lookup['repository__icontains'] = repository\n\n items = items.filter(**lookup).order_by('-title')\n\n serializer = BigProjectSerializer(items, many=True)\n return Response(serializer.data)\n\n\nclass AcademyProjectMemberView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n\n @capable_of('read_freelance_projects')\n def get(self, request, academy_id):\n\n items = FreelanceProjectMember.objects.filter(project__academy__id=academy_id)\n lookup = {}\n\n def find_user_by_name(query_name, qs):\n for term in query_name.split():\n qs = qs.filter(Q(first_name__icontains=term) | Q(last_name__icontains=term))\n return qs\n\n if 'like' in self.request.GET:\n like = self.request.GET.get('like')\n if '@' in like:\n items = items.filter(Q(freelancer__user__email__icontains=like))\n else:\n for term in like.split():\n items = items.filter(\n Q(freelancer__user__first_name__icontains=term)\n | Q(freelancer__user__last_name__icontains=term))\n\n if 'project' in self.request.GET:\n project = self.request.GET.get('project')\n lookup['project__id'] = project\n\n items = items.filter(**lookup).order_by('-freelancer__user__first_name')\n\n serializer = SmallFreelancerMemberSerializer(items, many=True)\n return Response(serializer.data)\n\n\nclass AcademyInvoiceMemberView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n permission_classes = [AllowAny]\n\n def get(self, request, invoice_id):\n\n invoice = ProjectInvoice.objects.filter(id=invoice_id).first()\n if invoice is None:\n raise ValidationException(f'No invoice with id {invoice_id}', slug='invoice-not-found')\n\n items = FreelanceProjectMember.objects.filter(project__id=invoice.project.id)\n lookup = {}\n\n def find_user_by_name(query_name, qs):\n for term in query_name.split():\n qs = qs.filter(Q(first_name__icontains=term) | Q(last_name__icontains=term))\n return qs\n\n if 'like' in self.request.GET:\n like = self.request.GET.get('like')\n if '@' in like:\n items = items.filter(Q(freelancer__user__email__icontains=like))\n else:\n for term in like.split():\n items = items.filter(\n Q(freelancer__user__first_name__icontains=term)\n | Q(freelancer__user__last_name__icontains=term))\n\n if 'project' in self.request.GET:\n project = self.request.GET.get('project')\n lookup['project__id'] = project\n\n items = items.filter(**lookup).order_by('-freelancer__user__first_name')\n\n serializer = SmallFreelancerMemberSerializer(items, many=True)\n return Response(serializer.data)\n\n\nclass AcademyProjectInvoiceView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n\n @capable_of('read_project_invoice')\n def get(self, request, invoice_id=None, academy_id=None, project_id=None):\n\n if invoice_id is not None:\n item = ProjectInvoice.objects.filter(project__academy__id=academy_id, id=invoice_id).first()\n if item is None:\n raise ValidationException('Project Invoice user not found', 404)\n serializer = BigInvoiceSerializer(item, many=False)\n return Response(serializer.data)\n\n items = ProjectInvoice.objects.filter(project__academy__id=academy_id)\n lookup = {}\n\n if 'like' in self.request.GET:\n like = self.request.GET.get('like')\n lookup['project__title__icontains'] = like\n\n project = self.request.GET.get('project', project_id)\n if project:\n lookup['project__id'] = project\n\n if 'status' in self.request.GET:\n status = self.request.GET.get('status')\n lookup['status__iexact'] = status\n\n if 'after' in self.request.GET:\n after = self.request.GET.get('after')\n after = datetime.strptime(after, '%Y-%m-%d').date()\n items = items.filter(created_at__gte=after)\n if 'before' in self.request.GET:\n before = self.request.GET.get('before')\n before = datetime.strptime(before, '%Y-%m-%d').date()\n items = items.filter(created_at__lte=before)\n\n items = items.filter(**lookup).order_by('-created_at')\n\n serializer = BillSerializer(items, many=True)\n return Response(serializer.data)\n\n @capable_of('crud_project_invoice')\n def post(self, request, academy_id=None, project_id=None):\n\n if project_id is None:\n raise ValidationException('Missing project ID on the URL', code=404, slug='argument-not-provided')\n\n project = AcademyFreelanceProject.objects.filter(id=project_id, academy__id=academy_id).first()\n if project is None:\n raise ValidationException('This project does not exist for this academy',\n code=404,\n slug='not-found')\n\n invoices = generate_project_invoice(project)\n serializer = BigInvoiceSerializer(invoices, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n\nclass SingleInvoiceView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n permission_classes = [AllowAny]\n\n def get(self, request, id):\n item = ProjectInvoice.objects.filter(id=id).first()\n if item is None:\n raise ValidationException('Invoice not found', slug='invoice-not-found', code=404)\n else:\n serializer = BigInvoiceSerializer(item, many=False)\n return Response(serializer.data)\n\n\nclass SingleBillView(APIView):\n \"\"\"\n List all snippets, or create a new snippet.\n \"\"\"\n\n def get(self, request, id):\n item = Bill.objects.filter(id=id).first()\n if item is None:\n raise serializers.ValidationError('Bill not found', code=404)\n else:\n serializer = BigBillSerializer(item, many=False)\n return Response(serializer.data)\n\n @capable_of('crud_freelancer_bill')\n def put(self, request, id=None, academy_id=None):\n item = Bill.objects.filter(id=id, academy__id=academy_id).first()\n if item is None:\n raise serializers.ValidationError('Bill not found for this academy', code=404)\n\n serializer = BillSerializer(item, data=request.data, many=False)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n# Create your views here.\n@api_view(['GET'])\ndef sync_user_issues(request):\n tags = sync_user_issues()\n return Response(tags, status=status.HTTP_200_OK)\n\n\n# Create your views here.\n@api_view(['GET'])\ndef get_issues(request):\n issues = Issue.objects.all()\n\n lookup = {}\n\n if 'freelancer' in request.GET:\n userId = request.GET.get('freelancer')\n lookup['freelancer__id'] = userId\n\n if 'bill' in request.GET:\n _id = request.GET.get('bill')\n lookup['bill__id'] = _id\n\n if 'status' in request.GET:\n _status = request.GET.get('status')\n lookup['status'] = _status.upper()\n\n issues = issues.filter(**lookup).order_by('-created_at')\n\n serializer = SmallIssueSerializer(issues, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n\n# Create your views here.\n@api_view(['GET'])\ndef get_latest_bill(request, user_id=None):\n freelancer = Freelancer.objects.filter(user__id=user_id).first()\n\n if freelancer is None or reviewer is None:\n raise serializers.ValidationError('Freelancer or reviewer not found', code=404)\n\n open_bills = generate_freelancer_bill(freelancer)\n return Response(open_bills, status=status.HTTP_200_OK)\n","repo_name":"breatheco-de/apiv2","sub_path":"breathecode/freelance/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17357,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"20"}
+{"seq_id":"73466120688","text":"import numpy as np\nimport pickle\nfrom Board import *\n\nHEIGHT = 7\nWIDTH = 7\nclass Agent:\n def __init__(self, name, exp_rate=0.3):\n self.states = []\n self.lr = 0.2\n self.exp_rate = exp_rate\n self.decay_gamma = 0.9\n self.states_value = {}\n self.name = name\n\n def choose_action(self, positions,current_board,symbol):\n if np.random.uniform(0, 1) <= self.exp_rate:\n # take random action\n idx = np.random.choice(len(positions))\n action = positions[idx]\n else:\n value_max = -999\n for p in positions:\n next_board = current_board.copy()\n next_board[p] = symbol\n next_board_hash = self.get_hash(next_board)\n value = 0 if self.states_value.get(next_board_hash) is None else self.states_value.get(next_board_hash)\n if value >= value_max:\n value_max = value\n action = p\n return action\n\n def feed_reward(self, reward):\n for st in reversed(self.states):\n if self.states_value.get(st) is None:\n self.states_value[st] = 0\n self.states_value[st] += self.lr * (self.decay_gamma * reward - self.states_value[st])\n reward = self.states_value[st]\n\n def reset(self):\n self.states = []\n\n # append a hash state\n def add_state(self, state):\n self.states.append(state)\n\n # hash state\n def get_hash(self, board):\n board_hash = str(board.reshape(HEIGHT * WIDTH))\n return board_hash\n\n def save_policy(self):\n fw = open('for_' + str(self.name), 'wb')\n pickle.dump(self.states_value, fw)\n fw.close()\n\n def load_policy(self, file):\n fr = open(file, 'rb')\n self.states_value = pickle.load(fr)\n fr.close()\n\n\n\n\n\n","repo_name":"KubilayGazioglu/Board-Game-with-Q-Learning","sub_path":"AI.py","file_name":"AI.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"74010390449","text":"import attr\nfrom enum import auto, Enum\nfrom functools import reduce\n\nfrom aoc.utils import local_path\nfrom aoc.utils.spatial import Direction, Orientation, Point2D, rotate\n\n\nclass Action(Enum):\n ABSOLUTE = auto()\n RELATIVE = auto()\n ROTATION = auto()\n\n\n@attr.s\nclass Ship:\n position = attr.ib()\n direction = attr.ib()\n\n\ndef parse_instruction(raw_instruction):\n action_char = raw_instruction[0]\n number = int(raw_instruction[1:])\n\n if action_char in 'NSEW':\n action = Action.ABSOLUTE\n\n units = {\n 'N': Point2D(0, 1),\n 'S': Point2D(0, -1),\n 'E': Point2D(1, 0),\n 'W': Point2D(-1, 0),\n }\n distance = number\n param = distance * units[action_char]\n elif action_char in 'LR':\n action = Action.ROTATION\n\n orientations = {\n 'L': Orientation.COUNTERCLOCKWISE,\n 'R': Orientation.CLOCKWISE,\n }\n degrees = number\n param = (orientations[action_char], (degrees % 360) // 90)\n else:\n action = Action.RELATIVE\n param = number\n\n return (action, param)\n\n\ndef parse_input(path_to_file):\n with open(path_to_file) as input_file:\n return [parse_instruction(line.strip()) for line in input_file]\n\n\ndef apply_instruction(ship, instruction):\n action, param = instruction\n\n if action == Action.ABSOLUTE:\n return Ship(ship.position + param, ship.direction)\n elif action == Action.RELATIVE:\n return Ship(\n ship.position.move_by(ship.direction, param),\n ship.direction\n )\n else:\n orientation, times = param\n new_direction = ship.direction\n\n for _ in range(times):\n new_direction = rotate(new_direction, orientation)\n\n return Ship(ship.position, new_direction)\n\n\ndef main():\n input_filename = '../input'\n instructions = parse_input(local_path(__file__, input_filename))\n ship = Ship(Point2D(0, 0), Direction.R)\n\n final_ship = reduce(apply_instruction, instructions, ship)\n\n print(abs(final_ship.position))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"cronos2/aoc-2020","sub_path":"src/aoc/day12/first/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"3057804082","text":"from datetime import datetime as dt\nimport pandas as pd\nfrom hft import HFT\nfrom pprint import pprint\nimport random\n\n\nif __name__ == '__main__':\n\n hft = HFT(ratings_filename=r'Data\\ratings.npz', reviews_filename=r'Data\\reviews.npz')\n\n start_time = dt.now()\n grads = hft.get_gradients()\n print('1. Finished get gradients in', (dt.now() - start_time).seconds, 'seconds')\n\n start_time = dt.now()\n hft.rating_model.get_predicted_ratings()\n print('2. Finished get predicted ratings in', (dt.now() - start_time).seconds, 'seconds')\n\n start_time = dt.now()\n hft.review_model.Gibbsampler()\n print('3. Finished performing Gibbs sampling in', (dt.now() - start_time).seconds, 'seconds')\n\n l = hft.review_model.loglikelihood()\n\n start_time = dt.now()\n hft.update()\n print('4. Finished updating parameters in', (dt.now() - start_time).seconds, 'seconds')\n\n start_time = dt.now()\n\n hft.learn()\n\n print('5. Finished updating parameters in', (dt.now() - start_time).seconds, 'seconds')\n\n # print(len(hft.business_dict[0]))\n\n file = open(r'Data\\test.dat', 'rU', encoding='UTF-8')\n wrt = open(r'Data\\result.dat', 'w', encoding='UTF-8')\n record = [0 for x in range(6)]\n print(record)\n for line in file:\n line = line.split(' ')\n u = line[0]\n i = line[1][:-1] # remove '\\n'\n\n if u in hft.user_dict.keys() and i in hft.business_dict.keys():\n unum = hft.user_dict[u]\n inum = hft.business_dict[i]\n rat = hft.predict(unum, inum)\n rat = int(rat + 0.5) if 0 <= rat <= 5 else (0 if rat < 0 else 5)\n record[rat] += 1\n s = ('%s %s %s\\n' % (u, i, rat))\n wrt.write(s)\n\n file.close()\n wrt.close()\n print(record)\n\n","repo_name":"mazeyang/DataMiningLab","sub_path":"HFT/main_test.py","file_name":"main_test.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"37514231887","text":"## 模块\n\nfrom fibo import fibo1,fibo2\n'''\nwith open('txt.txt','w') as f:\n f.write('ssss')\n'''\nwith open('txt.txt','r') as f:\n print(f.read())\n\nimport json\n\njson_data=json.dumps([1,'zhangsan','list'])\nf=open('json.txt','w')\njson.dump([1,'zhangsan','list'],f)\nf.close()\nprint(json_data)\n\nf2=open('json.txt','r')\njson1=json.load(f2)\nprint('json1:%s'%json1)\nf2.close()","repo_name":"zhangbailong945/PyQt5Projects","sub_path":"pachong/study/study3.py","file_name":"study3.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"42027578534","text":"\"\"\"\nCaches the match info and timeline for each match in the text file.\n\"\"\"\n\nimport os\nimport pickle\nfrom tracemalloc import start\nfrom constants import *\n\n# Load current matches\nwith open(match_filepath, 'r') as match_file:\n matches = [line.strip() for line in match_file.readlines()]\n\ndef save_match(name, info, timeline):\n print(f'Caching match data for {name}...')\n with open(f'{match_cache_folder}/{name}.pkl', 'wb') as fout:\n pickle.dump(info, fout)\n with open(f'{timeline_cache_folder}/{name}.pkl', 'wb') as fout:\n pickle.dump(timeline, fout)\n print('Save complete!')\n\nprint(f'Loaded {len(matches)} matches')\nstart_match = int(input('Match to start from (0-indexed): '))\n\n# Process matches for each name\nmatches = matches[start_match:]\nto_process = len(matches)\nfor match in matches:\n if os.path.isfile(f'{match_cache_folder}/{match}.pkl') and os.path.isfile(f'{timeline_cache_folder}/{match}.pkl'):\n print(f'Skipping match {match} (already processed)')\n to_process -= 1\n continue\n print(f'Processing match {match}...')\n\n # Query the Riot API\n info = lol_watcher.match.by_id(region, match)\n timeline = lol_watcher.match.timeline_by_match(region, match)\n\n save_match(match, info, timeline)\n to_process -= 1\n print(f'To process: {to_process}\\n')\n\nprint('All matches processed!')\n","repo_name":"Giantpizzahead/league-win-predictor","sub_path":"api/cache_match.py","file_name":"cache_match.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"22473024439","text":"n = int(input())\r\n\r\ndit = {}\r\nfor i in range(n):\r\n word = input()\r\n if word not in dit:\r\n dit[word] = 1\r\n else:\r\n dit[word] += 1\r\n# print(dit)\r\narr = dict(sorted(dit.items()))\r\n# print(arr)\r\nfor k,v in arr.items():\r\n if max(arr.values()) == v:\r\n print(k)\r\n break","repo_name":"dnjsxo0616/algorithm","sub_path":"백준/Silver/1302. 베스트셀러/베스트셀러.py","file_name":"베스트셀러.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"25861402238","text":"\"\"\"\nSetting manager based on environment.\n\nOne module for each env should exist - env_name.py\n\"\"\"\nimport os\n\n\ndef get_environment():\n \"\"\"\n Get the current application environment based on environment\n variable or hostname. Returns 'local' by default.\n \"\"\"\n os_env = os.environ.get('ENVIRONMENT', '')\n\n if os_env.lower() == 'production':\n env = 'production'\n elif os_env.lower() == 'development':\n env = 'development'\n else:\n env = 'local'\n\n return env\n\n\nENVIRONMENT = get_environment()\n\n#####\n\nimport importlib\n\n# Replace me\nCONFIG_PATH = '.'\n\ntry:\n settings = importlib.import_module('.%s' % ENVIRONMENT, CONFIG_PATH)\nexcept ImportError as e:\n raise RuntimeError('Unable to import config module {}'.format(ENVIRONMENT))\n","repo_name":"eBLDR/Resources","sub_path":"EnvironmentConfig/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"41721666357","text":"def listeMots(s):\n for i in [\",\",\".\",\";\",\"!\",\"?\"]:\n s = s.replace(i,\"\")\n return s.split(\" \")\n\ndef nbMots(s):\n return len(listeMots(s))\n\ndef motsLesPlusLongs(s):\n maxi = 0\n lsMaxi = []\n for i in listeMots(s):\n if(len(i) > maxi):\n maxi = len(i)\n\n for i in listeMots(s):\n if(len(i) == maxi):\n lsMaxi.append(i)\n\n return lsMaxi\n\nprint(motsLesPlusLongs(\"Ceci est une phrase à tester.\"))","repo_name":"Proxyfil/PolytechInfo","sub_path":"Semestre 1/TP6/ex10.py","file_name":"ex10.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"35645402988","text":"from pyspark import SparkContext\n\ndef mutual(record):\n id = record[0]\n friends = record[1].split(\",\")\n mutuals = []\n for friend in friends:\n if friend != '' and friend != id:\n if int(id) >= int(friend):\n pair = (friend + \",\" + id, set(friends)) \n else:\n pair = (id + \",\" + friend, set(friends)) \n mutuals.append(pair)\n return mutuals\n\nif __name__ == \"__main__\":\n sc = SparkContext.getOrCreate();\n data = sc.textFile(\"./dataset/mutual.txt\")\n users = data.map(lambda record: record.split(\"\\t\")).filter(lambda record: len(record) == 2).flatMap(mutual) \n mf = users.reduceByKey(lambda x, y: x.intersection(y)) \n mf.map(lambda x:\"{0}\\t{1}\".format(x[0],len(x[1]))).sortBy(lambda x: x.split(\"\\t\")[0]).sortBy(lambda x: x.split(\"\\t\")[1]).saveAsTextFile(\"output_mutual\")\n","repo_name":"nairvnv/Spark-Scala-matrixMul-mutualFriends-userReviews","sub_path":"mutual.py","file_name":"mutual.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"17446466048","text":"from models import *\nfrom helpers import *\nfrom render import *\nfrom training import *\n \n# load modules\nimport numpy as np\nimport torch\nimport wandb\n\nuse_cuda = True\nuse_cuda = False if not use_cuda else torch.cuda.is_available()\ndevice = torch.device('cuda:0' if use_cuda else 'cpu')\ntorch.cuda.get_device_name(device) if use_cuda else 'cpu'\nprint('Using device', device)\n#device = \"cpu\"\n\n#####################################################\n# set seed\ntorch.manual_seed(123)\nnp.random.seed(123)\nmodel_name = \"decoder_only\" \n\nparams = {\n# set sizes\n\"batch_size\": 1500,\n\"batch_size_val\": 50000,\n\"sample_size\" : 150000,\n\n# model parameters\n\"epochs\" : 10,\n\"lr\" : 0.001,\n\"num_nodes\" : 512, # nodes in decoder\n\"num_nodes_first\" : 512, # nodes in first layer of decoder\n\"num_layer\" : 6, # layer of decoder (incl. first one) \n\"num_blocks\" : 5, # number of convolutional blocks\n\"dropout\" : 0.0, \n\"weight_decay\" : 0.0,\n\"patience\" : 20, # disabled\n\"num_encoding_functions\" : 10, # number of fourier transforms for each coordinate (E in paper)\n\"num_feat\" : 256, # number of latent features per pair of lungs\n\n# data\n\"num_lungs\" : 332,\n\"val_lungs\" : [0, 1, 2, 3, 4, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331],\n#\"val_lungs\" : [],\n\"test_lungs\" : [5, 6, 7, 8, 9, 86, 87, 88, 178, 179, 180, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304],\n#\"test_lungs\" : [],\n\n# resolutions\n\"point_resolution\" : 128, # resolution of masks for sampling\n\"img_resolution\" : 128, # resolution of ct images\n\"shape_resolution\" : 128, # resolution of reconstructions\n\"proportion\" : 0.5, # portion of samples from surface of lungs\n\n# model type\n\"augmentations\": False, # True: enable shift and zoom augmentations\n\"pos_encoding\" : True, # True: use positional encoding in the decoder\n\"skips\" : True, # True: use skip conenction in the decoder\n\"spatial_feat\" : True, # True: use xy features\n\"global_feat\" : False, # True: use global features (pooling over slice features)\n\"layer_norm\" : True, # True: use layernorm in the encoder\n\"batch_norm\" : False, # True: use batchnorm in the encoder\n\"verbose\" : True, # True: give console output\n\"pe_freq\" : \"2\", # set frequency bands of fourier transforms (\"2pi\" - Mildenhal et al.; \"2\" - see paper)\n\"no_encoder\" : True, # True: use training procedure without encoder \n\n# path to model files\n\"model_name\" : model_name,\n}\n\n\n#####################################################\nwandb.init(project = \"decoder_only\", config = params, name = model_name)\n\n# setup model \nmodel = MLP_with_feat(**params)\nmodel.train()\nmodel.to(device)\n\n# training\n#model, acc, iou_, dice, iou_val = train(model = model, wandb = wandb, device = device, **params)\n\n########################################## TEST #####################################################\n# load model\nmodel.load_state_dict(torch.load(\"model_checkpoints/final_models/\" + params[\"model_name\"] +\".pt\", map_location = device))\nprint(\"Model loaded.\")\nmodel.eval()\n\nlungs = params[\"test_lungs\"]\n#lungs = [6,284]\n\n# load lungs to visualize\n_, img_list, mask_list = load_data(lungs, \n train = False,\n point_resolution = 128,\n img_resolution = 128,\n return_mask = True,\n augmentations=False\n )\n\n# save metrics\niou_list = []\ndice_list = []\nacc_list = []\n\n# loop over lungs\nfor i, img, mask in zip(lungs, img_list, mask_list):\n img_raw = img\n mask_raw = mask\n\n slice_index = torch.arange(0,int(img_raw.shape[0]),1)\n slice_max = img_raw.shape[0]\n\n img = img_raw[slice_index]\n\n mask = mask.moveaxis(0,-1)\n \n pred = model_to_voxel(model,lung = i, device=device, img = img, resolution = mask.shape[1], z_resolution= mask.shape[2], max_batch = params[\"batch_size_val\"], slice_index = slice_index, slice_max = slice_max, no_encoder=True) \n\n pred = pred.cpu().numpy()\n pred = np.moveaxis(pred,-1,0)\n # get_ply(mask = pred, ply_filename = \"dump/ISAS_\"+model_name+\"_lung_\"+str(i), from_mask = True, resolution = params[\"shape_resolution\"], \n # device = device)\n \n pred = torch.round(torch.sigmoid(torch.from_numpy(pred)))\n \n pred = pred.moveaxis(0,-1)\n dice = dice_coef(pred,mask)\n iou_value = iou(pred, mask)\n acc = torch.sum(pred.flatten() == mask.flatten())/len(mask.flatten())\n\n iou_list.append(iou_value.numpy())\n dice_list.append(dice.numpy())\n acc_list.append(acc.numpy())\n\n\n print(\"Dice Coeff.: \", dice.numpy())\n print(\"IoU: \",iou_value.numpy())\n\niou_test = np.mean(np.array(iou_list))\ndice_test = np.mean(np.array(dice_list))\nacc_test = np.mean(np.array(acc_list))\n\n\nprint(\"IoU Test: \", iou_test)\nprint(\"Dice Test \", dice_test)\nprint(\"Acc Test: \", acc_test)\n\nresults = pd.DataFrame({\"Lung\": lungs,\n \"Accuracy\" : acc_list,\n \"Dice\" : dice_list,\n \"IoU\" : iou_list})\n\nresults.to_csv(\"results/TEST_NODECODER_metrics.csv\")","repo_name":"dmnk1308/isas_model","sub_path":"decoder_only.py","file_name":"decoder_only.py","file_ext":"py","file_size_in_byte":5112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"12069239441","text":"import pyautogui as p\nimport time as t\nimport pynput\nimport sys\n\np.PAUSE = 0.05\np.FAILSAFE = True\n\n\ndef on_click(x, y, button, pressed):\n global start_time\n print('{0} at {1}'.format('Pressed' if pressed else 'Released', (x, y)))\n\n # Getting which button to press/release\n button = str(button)\n if button[-2] == \"f\":\n button = \"left\"\n elif button[-2] == \"h\":\n button = \"right\"\n else:\n button = \"middle\"\n\n # Click and drag the mouse\n if pressed:\n press_time = t.time()\n sleep_time = press_time - start_time\n start_time = press_time\n with open(name+\".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"p.mouseDown({str(x)}, {str(y)}, button=\\\"{button}\\\")\\n\")\n f_r.close()\n else:\n press_time = t.time()\n sleep_time = press_time - start_time\n start_time = press_time\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"p.mouseUp({str(x)}, {str(y)}, button=\\\"{button}\\\")\\n\")\n f_r.close()\n\n\ndy_all = 0\n\n\ndef on_scroll(x, y, dx, dy):\n global start_time\n global dy_all\n if dx != 0:\n return False\n scroll_time = t.time()\n sleep_time = scroll_time - start_time\n\n # Quick scroll\n if sleep_time < 0.1:\n dy_all += dy\n else:\n dy_all += dy\n\n start_time = scroll_time\n print('Scrolled {0} at {1}'.format('down' if dy_all < 0 else 'up', (x, y)))\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"p.moveTo({str(x)}, {str(y)})\\n\")\n f_r.write(\"ctr.scroll({0}, {1})\\n\".format(str(dx), str(dy_all)))\n f_r.close()\n dy_all = 0\n\n\ndef on_press(key):\n global start_time\n # Stop recording by pressing f8\n if key == pynput.keyboard.Key.f8:\n sys.exit()\n\n print(\"key {} is pressed\".format(key))\n press_time = t.time()\n sleep_time = press_time - start_time\n start_time = press_time\n if type(key) is not pynput.keyboard.KeyCode:\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"keyboard.press(pynput.keyboard.{key})\\n\")\n f_r.close()\n else:\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"keyboard.press({key})\\n\")\n f_r.close()\n\n\nenter_release = False\n\n\ndef on_release(key):\n global start_time\n global enter_release\n if enter_release:\n print(\"key {} is released\".format(key))\n press_time = t.time()\n sleep_time = press_time - start_time\n start_time = press_time\n if type(key) is not pynput.keyboard.KeyCode:\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"keyboard.release(pynput.keyboard.{key})\\n\")\n f_r.close()\n else:\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f_r:\n f_r.write(f\"t.sleep({str(sleep_time)})\\n\")\n f_r.write(f\"keyboard.release({key})\\n\")\n f_r.close()\n\n # Neglecting the first release of \"enter\"\n if key == pynput.keyboard.Key.enter:\n enter_release = True\n\n\ndef record(name_to_save):\n # Initializing a file\n f_r = open(name_to_save + \".pyw\", \"w\", encoding=\"utf-8\")\n f_r.write('import pyautogui as p\\nimport time as t\\nimport pynput\\n')\n f_r.write(\"p.PAUSE = 0.05\\np.FAILSAFE = True\\n\")\n f_r.write(\"ctr = pynput.mouse.Controller()\\n\")\n f_r.write(\"keyboard = pynput.keyboard.Controller()\\n\")\n f_r.close()\n\n # Collecting events\n mouse_listener = pynput.mouse.Listener(on_click=on_click,\n on_release=on_release,\n on_scroll=on_scroll)\n mouse_listener.start()\n with pynput.keyboard.Listener(on_press=on_press,\n on_release=on_release) as keyboard_listener:\n keyboard_listener.join()\n\n\ndo = p.confirm(text=\"请选择操作\", title=\"键盘和鼠标脚本生成器\", buttons=[\"记录\", \"运行\", \"取消\"])\n# if __name__ == \"__main__\":\nif do == \"记录\":\n name = p.prompt(text=\"请输入要保存的脚本文件名(无需加后缀)：\\n按enter确认\",\n title=\"鼠标和键盘脚本生成器\",\n default=\"auto_gui\")\n start_time = t.time()\n name = str(name)\n if name != \"\":\n # time3 = [t.localtime().tm_hour, t.localtime().tm_min, t.localtime().tm_sec]\n record(name)\n p.alert(text=\"录制已结束。\", title=\"键盘和鼠标脚本生成器\")\n with open(name + \".pyw\", \"a\", encoding=\"utf-8\") as f:\n f.write(\"p.alert(text=\\\"运行完成。\\\", title=\\\"键盘和鼠标控制器\\\")\")\n f.close()\nelif do == \"运行\":\n path = p.prompt(text=\"请输入脚本路径\", title=\"鼠标和键盘脚本生成器\", default=\"D:\\\\\")\n while True:\n try:\n f_r = open(path, \"r\", encoding=\"utf-8\")\n f_lines = f_r.readlines()\n n = 0\n ctr = pynput.mouse.Controller()\n keyboard = pynput.keyboard.Controller()\n for i in f_lines:\n if n > 6:\n print(n)\n eval(i)\n n = n + 1\n f_r.close()\n break\n except Exception as r:\n print(r)\n path = p.prompt(text=\"运行出错啦！请重新输入脚本路径\",\n title=\"鼠标和键盘脚本生成器\", default=\"D:\\\\\")\n","repo_name":"wzh811/py--","sub_path":"脚本生成器.py","file_name":"脚本生成器.py","file_ext":"py","file_size_in_byte":5798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"28414586143","text":"from fastapi import APIRouter, Request, HTTPException, Depends\nfrom models.server import Group\nfrom schemas.group import ResGroups, ResOneGroup, ResGroupChannels, Group_Pydantic, GroupIn_Pydantic\nfrom utils import Response200\nfrom common.logger import logger\n\ngroup = APIRouter()\n\n\n@group.post(\"/\", summary=\"创建一个分区\", response_model=Group_Pydantic)\nasync def create_one_group(g: GroupIn_Pydantic):\n logger.info(f\"创建一个服务器-{g.name}\")\n ch_obj = await Group.create(**g.dict(exclude_unset=True))\n return Response200(data=ch_obj)\n\n\n@group.get(\"/all\", summary=\"获取所有分区\", response_model=ResGroups)\nasync def get_all_group():\n logger.info(\"获取所有分区\")\n all_group = await Group.all()\n return Response200(data=all_group)\n\n\n@group.get(\"/{group_id}\", summary=\"获取一个分组信息\", response_model=ResOneGroup)\nasync def get_one_group(group_id: int):\n logger.info(f\"获取一个分区信息-{group_id}\")\n g = await Group.get(id=group_id)\n return Response200(data=g)\n\n\n@group.put(\"/{group_id}\", summary=\"更新一个分区\", response_model=ResOneGroup)\nasync def update_one_group(group_id: int, g: GroupIn_Pydantic):\n logger.info(f\"更新一个渠道-{group_id}\")\n await Group.filter(id=group_id).update(**g.dict(exclude_unset=True))\n\n # 返回更新后的模型数据\n data = await Group_Pydantic.from_queryset_single(Group.get(id=group_id))\n return Response200(data=data)\n\n\n@group.delete(\"/{group_id}\", summary=\"删除一个分区\", response_model=ResOneGroup)\nasync def delete_one_group(group_id: int):\n logger.info(f\"删除一个分区 - {group_id}\")\n del_group = await Group.filter(id=group_id)\n if not del_group:\n raise HTTPException(status_code=404, detail=f'Group {group_id} not found')\n else:\n group = del_group[0]\n await group.delete()\n return Response200(data=group)\n\n\n@group.get(\"/groups_channels/all\", summary=\"获取所有分区对应的渠道\", response_model=ResGroupChannels)\nasync def get_groups_channels():\n data = {}\n groups = await Group.all()\n print(groups)\n for group in groups:\n print(group)\n data[group.group_key] = await group.channel.all()\n print(data)\n return Response200(data=data)\n\n\n@group.get(\"/group_channels/{group_id}\", summary=\"获取单个分区下的渠道\", response_model=ResGroupChannels)\nasync def get_group_channels(group_id: int):\n data = {}\n group = await Group.get(id=group_id)\n data[group.name] = await group.channel.all()\n return Response200(data=data)\n","repo_name":"kadycui/fast-tortoise","sub_path":"views/group.py","file_name":"group.py","file_ext":"py","file_size_in_byte":2561,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"26823104138","text":"def inp(to_int=True):\n if not type(to_int) == bool:\n raise Exception()\n l = input().split()\n return list(map(lambda x: int(x), l)) if to_int else l\n\n\ndef inps(n, to_int=True):\n if not type(n) == int:\n raise Exception()\n return [inp(to_int) for _ in [0] * n]\n\n\ndef main():\n t, n = inp()\n\n x = ((100 / t) * n) + n\n if 100 % t == 0:\n x = int(x) - 1\n else:\n x = int(x)\n\n print(x)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"pheni-18/atcoder","sub_path":"arc118/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"4739045929","text":"import numpy as np\r\nfrom utils import(\r\n or_lstsq,\r\n timethis,\r\n profile,\r\n)\r\n\r\n\r\nclass Interior_Orientation:\r\n def __init__(self, img_x, img_y, pixel_x, pixel_y) -> None:\r\n self.img_x = img_x\r\n self.img_y = img_y\r\n self.pixel_x = pixel_x\r\n self.pixel_y = pixel_y\r\n\r\n def cal_coords(self) -> None:\r\n # take counterclockwise as positive direction\r\n self.img_coords_x = np.array(\r\n [[-self.img_x/2, -self.img_x/2, self.img_x/2, self.img_x/2]], dtype=np.float64)\r\n self.img_coords_y = np.array(\r\n [[self.img_y/2, -self.img_y/2, -self.img_y/2, self.img_y/2]], dtype=np.float64)\r\n self.pixel_coords_x = np.array(\r\n [[0, 0, self.pixel_x, self.pixel_x]], dtype=np.float64)\r\n self.pixel_coords_y = np.array(\r\n [[0, self.pixel_y, self.pixel_y, 0]], dtype=np.float64)\r\n\r\n @profile\r\n @timethis\r\n def inter_ori(self):\r\n # calculate interior orientation parameters\r\n self.cal_coords()\r\n L = np.append(self.img_coords_x, self.img_coords_y, axis=0).T\r\n A = np.concatenate(\r\n (np.ones((1, 4)), self.pixel_coords_x, self.pixel_coords_y), axis=0).T\r\n inter_ori_parameters = or_lstsq(A, L)\r\n self.inter_ori_parameters = inter_ori_parameters\r\n return self.inter_ori_parameters\r\n\r\n def pixel_to_img(self, pixel):\r\n return np.dot(self.inter_ori_parameters, pixel)\r\n","repo_name":"spirit-man/Photogrammetry_Fieldwork","sub_path":"摄影测量实习成果/program_changed/interior_orientation.py","file_name":"interior_orientation.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"39892789154","text":"\"\"\" demo file for flooding of variable bedding \"\"\"\n\nfrom __future__ import division\n\nfrom firedrake import *\nfrom flooddrake import *\n\n\nn = 40\nmesh = SquareMesh(n, n, 1)\n\n# mixed function space\nv_h = FunctionSpace(mesh, \"DG\", 1)\nv_mu = FunctionSpace(mesh, \"DG\", 1)\nv_mv = FunctionSpace(mesh, \"DG\", 1)\nV = v_h * v_mu * v_mv\n\n# parameters\nparameters[\"flooddrake\"].update({\"eps2\": 8e-3})\n\n# setup free surface depth\ng = Function(V)\nx = SpatialCoordinate(V.mesh())\ng.sub(0).assign(0.05)\n\n# setup bed\nbed = Function(V)\nx = SpatialCoordinate(V.mesh())\nbed.sub(0).interpolate(conditional(x[0] > (50 / 50), (1.5 / 50) * (15 / 50), conditional(x[0] < 15 / 50, (10 / 50) * ((15 / 50) - x[0]), conditional(x[0] > (25 / 50), (15 / 50) * (x[0] - (25 / 50)), 0.0))))\n\n# setup state\nstate = State(V, g, bed)\n\n# setup source (is only a depth function)\nsource = Function(v_h).assign(0.00025)\n\n# timestep\nsolution = Timestepper(V, state.bed, source, 0.5)\n\nsolution.stepper(0, 100, state.w, 0.5)\n","repo_name":"firedrakeproject/flooddrake","sub_path":"examples/demo_flooding_land.py","file_name":"demo_flooding_land.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"20"}
+{"seq_id":"22901167527","text":"from __future__ import print_function\r\nfrom itertools import islice\r\n\r\ndef getStressValues(fileName):\r\n with open(fileName,'r') as out:\r\n # the stress values always start at line 753\r\n # and will always be 10 lines long\r\n stress = []\r\n for line in islice(out,752,762):\r\n a = line.split(' ')\r\n stress.append(float(a[-1]))\r\n return stress\r\n\r\ndef write_areas(areas):\r\n with open('TenBarModArea.inp', 'r') as procTemplate, open('TenBarArea.inp', 'w') as procOutput:\r\n data = procTemplate.read()\r\n changeVar = data.replace('area01', str(areas[0])).replace('area02', str(areas[1])).replace('area03', str(areas[2])).replace('area04', str(areas[3])).replace('area05', str(areas[4])).replace('area06', str(areas[5])).replace('area07', str(areas[6])).replace('area08', str(areas[7])).replace('area09', str(areas[8])).replace('area10', str(areas[9]))\r\n procOutput.write(changeVar)\r\n \r\nwrite_areas((0.5, 0.5, 0.5, 0.6, 0.7, 0.25, 0.25, 0.4, 0.3, 0.7))\r\nstress = getStressValues('TenBarArea.dat')\r\nprint(stress)","repo_name":"cjekel/Introduction-to-Python-Numerical-Analysis-for-Engineers-and-Scientist","sub_path":"lectures/lecture09/code/hw09.py","file_name":"hw09.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","stars":126,"dataset":"github-code","pt":"20"}
+{"seq_id":"72183294449","text":"import sys\nimport numpy as np\nimport os\nimport itertools\nimport pickle\nimport multiprocessing as mp\nimport timeit\nimport getopt\n\n#This is created while running Zeisel_wrapper.py\nfile_list='./SRR_in_3005.txt'\n\ncell_files=sorted(np.loadtxt(file_list,dtype=str))\n\n\ntry:\n opts, args = getopt.getopt(sys.argv[1:],\"k:s:r:h:\",[\"hacked-kallisto-path\",\"seed=\",\"reference-transcriptome\",\"mouse-genome-for-hisat\"])\nexcept getopt.GetoptError:\n print (\"getopterrror\")\n print ('usage is : \\n python time_test.py -k hacked-kallisto-path -r mouse-reference-transcriptome -h path-to-file-containing-paths-to-mouse-genome [-s seed]')\n sys.exit(1)\n\nkallipso_path=''\nref_transcriptome=''\nref_genome=''\nnpseed=100\n\nfor opt,arg in opts:\n if opt in (\"-s\", \"--seed\"):\n npseed=int(arg)\n elif opt in (\"-k\",\"--hacked-kallisto-path\"):\n kallipso_path=arg\n elif opt in (\"-r\",\"--reference-transcriptome\"):\n ref_transcriptome=arg\n elif opt in (\"-h\",\"--mouse-genome-for-hisat\"):\n ref_genome=arg\n\nif (not kallipso_path) or (not ref_transcriptome) or (not ref_genome):\n print ('usage is : \\n python time_test.py -k hacked-kallisto-path -r mouse-reference-transcriptome -h path-to-file-containing-paths-to-mouse-genome [-s seed]')\n sys.exit(1)\n\ntest_SRA_dir='./SRA/'\ntest_read_dir='./reads/'\ntest_kallisto_dir='./kallisto/'\ntest_kallipso_dir='./TCC/'\ntest_bowtie1_dir='./bowtie1/'\ntest_hisat_dir='./hisat/'\ntest_wc_dir='./wc/'\nkallisto_index='./kallisto_index/'\nbowtie_index='./bowtie_index/'\nhisat_index='./hisat_index/'\n\nos.system('rm -rf '+test_SRA_dir)\nos.system('rm -rf '+test_read_dir)\nos.system('rm -rf '+test_kallisto_dir)\nos.system('rm -rf '+test_kallipso_dir)\nos.system('rm -rf '+test_bowtie1_dir)\nos.system('rm -rf '+test_hisat_dir)\nos.system('rm -rf '+test_wc_dir)\nos.system('rm -rf '+kallisto_index)\nos.system('rm -rf '+bowtie_index)\nos.system('rm -rf '+hisat_index)\n\nos.system('mkdir -p '+test_SRA_dir)\nos.system('mkdir -p '+test_read_dir)\nos.system('mkdir -p '+test_kallisto_dir)\nos.system('mkdir -p '+test_kallipso_dir)\nos.system('mkdir -p '+test_bowtie1_dir)\nos.system('mkdir -p '+test_hisat_dir)\nos.system('mkdir -p '+test_wc_dir)\nos.system('mkdir -p '+kallisto_index)\nos.system('mkdir -p '+bowtie_index)\nos.system('mkdir -p '+hisat_index)\n\nnp.random.seed(seed=100)\nfiles_picked=np.random.choice(cell_files,10,replace=False)\n\n\nprint('Copying over SRA files...')\nbase_cmd1=\"wget -O ./SRA/\"\nbase_cmd2=\"ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/SRP/SRP045/SRP045452/\"\nfor flnames in files_picked:\n cmd=base_cmd1+flnames+\".sra \"+base_cmd2+flnames+\"/\"+flnames+'.sra'\n os.system(cmd)\n \nprint('Converting SRA to fastq.gz files')\nfor flname in files_picked:\n cmd='fastq-dump --gzip '+ './SRA/'+flname+'.sra' ' -O '+ test_read_dir\n os.system(cmd)\n\nprint('Building kallisto index...')\nkallisto_index_path=kallisto_index+'Zeisel_index.idx'\nos.system(kallipso_path+' index -i '+kallisto_index_path+' '+ref_transcriptome)\n\nprint('Building hisat index...')\nhisat_ip_paths=''\nwith open('hisat_chr_path_list.txt','r') as f:\n hisat_ip_paths=f.readline()\nhisat_index_path=hisat_index+'Zeisel_index'\nos.system('hisat-build --offrate 5 '+hisat_ip_paths+' '+hisat_index_path)\n\nprint('Getting bowtie indices...')\nbowtie_index_dir='./bowtie_index/'\nos.system('mkdir -p '+bowtie_index_dir)\nbowtie_index_path=bowtie_index_dir+'Zeisel_index.all'\nos.system('bowtie-build --offrate=5 '+ref_transcriptome+' '+bowtie_index_path)\n\n\nprint('Timing kallisto...')\ndef run_kallisto():\n test_kallisto_dir='./kallisto/'\n test_read_dir='./reads/'\n transcriptome_path='./kallisto_index/Zeisel_index.idx'\n \n flnames=sorted(os.listdir(test_read_dir))\n for fls in flnames:\n cellname=fls.split('.')[0]\n out_dir=test_kallisto_dir+cellname\n read_fl=test_read_dir+fls\n cmd=\"\"\"kallisto quant -i\"\"\"+transcriptome_path+ \"\"\" -o \"\"\" +out_dir +\"\"\" --single -l 200 -s 100 \"\"\"+read_fl\n #print cmd\n os.system(cmd)\n\nx=timeit.timeit(run_kallisto,number=1)\nop_file=test_kallisto_dir+'time.time'\nwith open(op_file,'w') as f:\n f.write(str(x))\n \nprint('Timing hacked kallisto...')\ndef make_kallipso_path_global():\n global kallipso_path\nmake_kallipso_path_global()\ndef run_kallipso():\n test_kallipso_dir='./TCC/'\n ref_path='./kallisto_index/Zeisel_index.idx'\n test_read_dir='./reads/'\n \n flnames=sorted(os.listdir(test_read_dir))\n for flname in flnames:\n read_path=test_read_dir+flname\n cellname=flname.split('.')[0]\n command = kallipso_path+' pseudoalign -i '+ ref_path+ ' -o ' + test_kallipso_dir+cellname+'.counts' + ' ' + read_path\n #print command\n os.system(command)\nx=timeit.timeit(run_kallipso,number=1)\nop_file=test_kallipso_dir+'time.time'\nwith open(op_file,'w') as f:\n f.write(str(x))\n \nprint('Timing word count...')\ndef run_word_count():\n test_read_dir='./reads/'\n flnames=sorted(os.listdir(test_read_dir))\n for flname in flnames:\n read_path=test_read_dir+flname\n os.system('wc '+read_path)\nx=timeit.timeit(run_word_count,number=1)\nop_file=test_wc_dir+'time.time'\nwith open(op_file,'w') as f:\n f.write(str(x)) \n\nprint('Timing hisat...')\ndef run_hisat():\n test_hisat_dir='./hisat/'\n test_read_dir='./reads/'\n transcriptome_path='./hisat_index/Zeisel_index'\n \n \n flnames=sorted(os.listdir(test_read_dir))\n for fls in flnames:\n cellname=fls.split('.')[0]\n out_sam=test_hisat_dir+cellname+'.sam'\n out_bam=test_hisat_dir+cellname+'.bam'\n read_fl=test_read_dir+fls\n cmd=\"\"\"hisat -p 1 -x\"\"\"+transcriptome_path+ \"\"\" -U \"\"\" +test_read_dir+fls+\" -S \"+out_sam\n #print cmd\n cmd1=\"samtools view -bS \"+out_sam+\" > \"+out_bam\n #print cmd1\n os.system(cmd)\n os.system(cmd1)\n \nx=timeit.timeit(run_hisat,number=1)\nop_file=test_hisat_dir+'time.time'\nwith open(op_file,'w') as f:\n f.write(str(x))\n\nprint('Timing bowtie...')\ndef run_bowtie1():\n index = './bowtie_index/Zeisel_index.all'\n test_bowtie1_dir='./bowtie1/'\n test_read_dir='./reads/'\n \n flnames=sorted(os.listdir(test_read_dir))\n for flname in flnames:\n out = test_bowtie1_dir + flname.split('.')[0]\n os.system('mkdir -p ' + out)\n BTcmd = 'gzip -dc '+test_read_dir+flname+' | bowtie -p 1 -aS --offrate 1 '+index+' - | samtools view -Sb - > '+out+'/hits.bam'\n #print BTcmd\n os.system(BTcmd)\n \nx=timeit.timeit(run_bowtie1,number=1)\nop_file=test_bowtie1_dir+'time.time'\nwith open(op_file,'w') as f:\n f.write(str(x)) \n\n ","repo_name":"govinda-kamath/clustering_on_transcript_compatibility_counts","sub_path":"Timing_pipeline/time_test.py","file_name":"time_test.py","file_ext":"py","file_size_in_byte":6617,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"20"}
+{"seq_id":"3109761347","text":"# -*- coding: utf-8 -*-\r\nimport pandas as pd\r\nfrom collections import Counter\r\nfrom torch.utils import data\r\nfrom typing import Dict, Tuple\r\nimport transE_model as model_definition\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport dataset as ds\r\nimport numpy as np\r\nimport pickle\r\nimport pandas as pd\r\nimport random\r\nimport json\r\nfrom torch.utils import data as torch_data\r\nimport generate_data\r\nimport inside_category\r\n\r\ntrain_dict,valid_dict,test_dict = generate_data.get_data()\r\n\r\ndf = pd.read_csv(\"../data/new_transE_3.csv\") \r\nuser_length = df['User_id'].max()+1\r\nitem_length = df['Item_id'].max()+1\r\n\r\ntime_length = df['new_time'].max()+1\r\ncategory_length = df['L2_Category_name'].max()+1\r\ncluster_length = df['clusters'].max()+1\r\npoi_type_length = df['POI_Type'].max()+1\r\n\r\nrelation_count_list = []\r\nrelation_count_list.append(time_length)\r\nrelation_count_list.append(category_length)\r\nrelation_count_list.append(cluster_length)\r\nrelation_count_list.append(poi_type_length)\r\n\r\nentity_count_list = []\r\nentity_count_list.append(user_length)\r\nentity_count_list.append(item_length)\r\n\r\n\r\nvector_length = 50\r\nmargin = 1.0\r\ndevice = torch.device('cuda') \r\nnorm = 2\r\nlearning_rate = 0.001\r\nmodel_item = model_definition.Attention_item_level(input_dim= 2 * vector_length, dim1=64, output_dim=1)\r\nmodel_relation = model_definition.Attention_relation_level(input_dim= 2 * vector_length, dim1=64, output_dim=1)\r\n\r\n\r\nmodel = model_definition.TransE( relation_count_list, entity_count_list, device=device, dim=vector_length,\r\n margin=margin, norm=norm, item_att_model = model_item, relation_att_model=model_relation)\r\nmodel = model.to(device)\r\noptimizer = optim.SGD(model.parameters(), lr=learning_rate)\r\nbest_score = 0.0\r\nstep = 0\r\nsequence_list = []\r\n\r\nf = open(\"new_train.txt\", \"w\")\r\nfor key in train_dict.keys():\r\n num_total_sequences = len(train_dict[key][0])\r\n for i in range(num_total_sequences):\r\n sequence_length = len(train_dict[key][0][i])\r\n sequence_list.append(sequence_length)\r\n for j in range(sequence_length):\r\n row = ''\r\n row += str(train_dict[key][0][i][j]) + ' '+ str(train_dict[key][8][i][j])+',' + str(train_dict[key][3][i][j]) +',' + \\\r\n str(train_dict[key][4][i][j]) +',' + str(train_dict[key][5][i][j]) + ' ' + str(train_dict[key][1][i][j]) + '\\n'\r\n f.write(row)\r\nf.close()\r\n\r\n\r\ntrain_set = ds.Dataset('new_train.txt',None)\r\n\r\npath = \"../weight/new_transE.pt\"\r\n\r\nsequence_index = 0\r\ncurrent_sequence = 0\r\nembedding_list = []\r\nAttention_item_list = []\r\nitem_context_embedding_list = []\r\nsoftmax_list = []\r\nweighted_list = []\r\n\r\nmodel.train()\r\n\r\nfor epoch in range(20):\r\n print ('epoch: ', epoch)\r\n for i in range(len(train_set)): \r\n if (sequence_index == sequence_list[current_sequence]-1):\r\n local_heads, local_relation_time,local_relation_category, local_relation_cluster, local_relation_type, local_tails = train_set[i]\r\n local_heads = torch.LongTensor([local_heads])\r\n local_relation_time = torch.LongTensor([local_relation_time])\r\n local_relation_category = torch.LongTensor([local_relation_category])\r\n local_relation_cluster = torch.LongTensor([local_relation_cluster])\r\n local_relation_type = torch.LongTensor([local_relation_type])\r\n local_tails = torch.LongTensor([local_tails])\r\n local_heads, local_relation_time, local_relation_category, local_relation_cluster, local_relation_type, local_tails = \\\r\n (torch.LongTensor([local_heads]).to(device), torch.LongTensor([local_relation_time]).to(device),torch.LongTensor([local_relation_category]).to(device), \r\n torch.LongTensor([local_relation_cluster]).to(device), torch.LongTensor([local_relation_type]).to(device),torch.LongTensor([local_tails]).to(device)) \r\n \r\n item_triples = torch.stack((local_heads,local_relation_time, local_relation_category, local_relation_cluster, local_relation_type,local_tails), dim=1)\r\n context_embedding = model.cal_context_embedding(item_triples) # cal relation_1\r\n \r\n softmax = torch.nn.Softmax(dim = 1)\r\n softmax_value = softmax(torch.Tensor([Attention_item_list]))\r\n softmax_list = softmax_value.detach().cpu().numpy()[0].tolist()\r\n\r\n for i in softmax_list:\r\n weighted_item_context_embedding = item_context_embedding_list[softmax_list.index(i)] * i\r\n weighted_list.append(weighted_item_context_embedding)\r\n \r\n relation_2_embedding = sum(weighted_list) #relation 2\r\n \r\n concat_relation_embedding_1 = torch.cat((model.entities_emb_head(local_heads),context_embedding), 1) \r\n concat_relation_embedding_2 = torch.cat((model.entities_emb_head(local_heads),relation_2_embedding),1) \r\n\r\n relation_weight_1 = model.relation_att_model(concat_relation_embedding_1)\r\n relation_weight_2 = model.relation_att_model(concat_relation_embedding_2)\r\n relation = relation_weight_1*context_embedding + relation_weight_2 * relation_2_embedding\r\n \r\n optimizer.zero_grad()\r\n\r\n broken_tails = torch.randint(high=item_length, size=(1,), device=device)\r\n \r\n positive_item_list = [local_heads, relation, local_tails]\r\n negative_item_list = [local_heads, relation, broken_tails]\r\n \r\n lsigmoid = nn.LogSigmoid()\r\n diff,_,_ = model.forward2(positive_item_list,negative_item_list)\r\n loss = - lsigmoid(diff).sum(dim=0)\r\n # print(loss.data.cpu())\r\n loss.backward()\r\n \r\n optimizer.step()\r\n # step += 1 \r\n\r\n embedding_list.clear()\r\n Attention_item_list.clear()\r\n item_context_embedding_list.clear()\r\n softmax_list.clear()\r\n weighted_list.clear()\r\n sequence_index = 0\r\n current_sequence += 1\r\n# j+=1\r\n else:\r\n local_heads, local_relation_time,local_relation_category, local_relation_cluster, local_relation_type, local_tails = train_set[i]\r\n local_heads = torch.LongTensor([local_heads])\r\n local_relation_time = torch.LongTensor([local_relation_time])\r\n local_relation_category = torch.LongTensor([local_relation_category])\r\n local_relation_cluster = torch.LongTensor([local_relation_cluster])\r\n local_relation_type = torch.LongTensor([local_relation_type])\r\n local_tails = torch.LongTensor([local_tails]) \r\n \r\n if int(local_relation_type.item()) == 0: \r\n local_heads, local_relation_time, local_relation_category, local_relation_cluster, local_relation_type, local_tails = \\\r\n (torch.LongTensor([local_heads]).to(device), torch.LongTensor([local_relation_time]).to(device),torch.LongTensor([local_relation_category]).to(device), \r\n torch.LongTensor([local_relation_cluster]).to(device), torch.LongTensor([local_relation_type]).to(device),torch.LongTensor([local_tails]).to(device)) \r\n \r\n item_triples = torch.stack((local_heads, local_relation_time, local_relation_category, local_relation_cluster, local_relation_type, local_tails), dim=1)\r\n \r\n item_context_embedding, concat_embedding = model.cal_item_context_embedding(item_triples)\r\n item_context_embedding_list.append(item_context_embedding)\r\n \r\n concat_embedding = concat_embedding.to(device)\r\n \r\n output = model.item_att_model(concat_embedding)\r\n Attention_item_list.append(output)\r\n \r\n embedding_list.append(concat_embedding) \r\n \r\n sequence_index += 1\r\n else:\r\n categorys, ratios = inside_category.getvalues()\r\n local_relation_category = torch.zeros([1,50]).to(device)\r\n for i in range(len(categorys)):\r\n cat = categorys[i]\r\n cat = torch.LongTensor([cat]).to(device)\r\n rat = torch.Tensor([ratios[i]]).to(device)\r\n rat = rat.expand(1,vector_length)\r\n local_relation_category += model.relations_emb_category(cat) * rat\r\n \r\n local_heads, local_relation_time, local_relation_cluster, local_relation_type, local_tails = \\\r\n (torch.LongTensor([local_heads]).to(device), torch.LongTensor([local_relation_time]).to(device),\r\n torch.LongTensor([local_relation_cluster]).to(device), torch.LongTensor([local_relation_type]).to(device),torch.LongTensor([local_tails]).to(device)) \r\n\r\n \r\n item_context_embedding = model.entities_emb_tail(local_tails) + model.relations_emb_time(local_relation_time) + local_relation_category + \\\r\n model.relations_emb_clusters(local_relation_cluster) + model.relations_emb_poi_type(local_relation_type)\r\n \r\n concat_embedding = torch.cat((model.entities_emb_head(local_heads),item_context_embedding), 1)\r\n item_context_embedding_list.append(item_context_embedding)\r\n \r\n concat_embedding = concat_embedding.to(device)\r\n \r\n output = model.item_att_model(concat_embedding)\r\n Attention_item_list.append(output)\r\n \r\n embedding_list.append(concat_embedding) \r\n \r\n sequence_index += 1 \r\n \r\n current_sequence = 0\r\n\r\ntorch.save(model.state_dict(),path)\r\n\r\n ","repo_name":"CART2020/CART","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":9866,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"7804365489","text":"from SiemplifyUtils import output_handler\nfrom TIPCommon import extract_connector_param\n# ============================================================================#\n# title :ArcsightESMConnector.py\n# description :This Module contain all Arcsight ESM connector functionality\n# author :avital@siemplify.co\n# date :26-03-2018\n# python_version :3.7\n# ============================================================================#\n\n# ============================= IMPORTS ===================================== #\n\nimport csv\nimport sys\nimport json\nimport os\nimport uuid\nfrom SiemplifyUtils import dict_to_flat\nfrom SiemplifyConnectors import CaseInfo, SiemplifyConnectorExecution\nfrom ArcsightManager import ArcsightManager\nfrom exceptions import ArcsightLoginError, UnableToParseException\nfrom FileRetentionManager import FileRetentionManager\nimport re\n\n# ============================== CONSTS ===================================== #\nEVENT_ID_FIELD_NAME = \"eventId\"\nCORRELATION_ID_FIELD_NAME = \"event.eventId\"\nCORRELATION_MANAGER_RECEIPT_TIME = \"event.managerReceiptTime\"\nARCSIGHT_SOURCE_ENUM = \"1\"\nRULE_GENERATOR_SEPARATOR = r\"/\"\nCSV_EXTENTION = \".done.csv\"\nCORRELATION_NAME_FIELD_NAME = \"#event.name\"\nARCSIGHT = \"arcsight\"\nSLICED_PATTERN = \"_sliced_\"\nDEFAULT_DONE_FILES_RETENTION_DAYS = 3\nDEFAULT_ERROR_FILES_RETENTION_DAYS = 14\n\nDEFAULT_BLACKLIST = [\n \"^Connector Raw Event Statistics$\",\n \"Device Receipt Time is smaller than Agent Receipt Time\"\n]\n\n# ============================= CLASSES ===================================== #\n\n\nclass ArcsightESMConnectorException(Exception):\n \"\"\"\n Arcsight ESM Connector Exception\n \"\"\"\n pass\n\n\nclass ArcsightESMConnector(object):\n \"\"\"\n Arcsight ESM Connector\n \"\"\"\n\n def __init__(self, connector_scope, arcsight_manager, event_limit,\n alerts_limit,\n siemshare_path, device_product_field_name,\n secondary_device_product_field_name,\n environment_field_name, alert_custom_fields_names,\n done_retention_days=DEFAULT_DONE_FILES_RETENTION_DAYS,\n error_retention_days=DEFAULT_ERROR_FILES_RETENTION_DAYS):\n # Document what is this connector_scope and its type\n self.connector_scope = connector_scope\n self.logger = connector_scope.LOGGER\n self.arcsight_manager = arcsight_manager\n self.event_limit = event_limit\n self.alerts_limit = alerts_limit\n self.siemshare_path = siemshare_path\n self.error_folder = os.path.join(self.siemshare_path, \"Error\")\n self.done_folder = os.path.join(self.siemshare_path, \"Done\")\n self.sliced_folder = os.path.join(self.siemshare_path, \"Slice Original\")\n self.device_product_field_name = device_product_field_name\n self.secondary_device_product_field_name = secondary_device_product_field_name\n self.environment_field_name = environment_field_name\n self.map_file = os.path.join(self.connector_scope.run_folder,\n 'map.json')\n self.blacklist_file = os.path.join(self.connector_scope.run_folder,\n 'blacklist.json')\n self.alert_custom_fields_names = alert_custom_fields_names\n self.done_retention_hours = int(done_retention_days) * 24 # Convert days into hours\n self.error_retention_hours = int(error_retention_days) * 24 # Convert days into hours\n\n self.retention_manager = FileRetentionManager(self.logger)\n\n try:\n # Make Done folder if doesn't exist\n if not os.path.exists(self.done_folder):\n os.makedirs(self.done_folder)\n except Exception as e:\n self.logger.error(\"Unable to create Done folder: {}\".format(str(e)))\n self.logger.exception(e)\n\n try:\n # Make Error folder if doesn't exist\n if not os.path.exists(self.error_folder):\n os.makedirs(self.error_folder)\n except Exception as e:\n self.logger.error(\"Unable to create Error folder: {}\".format(str(e)))\n self.logger.exception(e)\n\n try:\n # Make Sliced Original folder if doesn't exist\n if not os.path.exists(self.sliced_folder):\n os.makedirs(self.sliced_folder)\n except Exception as e:\n self.logger.error(\"Unable to create Sliced Original folder: {}\".format(str(e)))\n self.logger.exception(e)\n\n try:\n if not os.path.exists(self.map_file):\n with open(self.map_file, 'w+') as map_file:\n map_file.write(json.dumps(\n {\"Original environment name\": \"Desired environment name\",\n \"Env1\": \"MyEnv1\"}))\n self.connector_scope.LOGGER.info(\n \"Mapping file was created at {}\".format(self.map_file))\n except Exception as e:\n self.logger.error(\"Unable to create mapping file: {}\".format(str(e)))\n self.logger.exception(e)\n\n try:\n if not os.path.exists(self.blacklist_file):\n with open(self.blacklist_file, 'w+') as blacklist_file:\n blacklist_file.write(json.dumps(DEFAULT_BLACKLIST))\n self.connector_scope.LOGGER.info(\n \"Blacklist file was created at {}\".format(self.blacklist_file))\n except Exception as e:\n self.logger.error(\"Unable to create blacklist file: {}\".format(str(e)))\n self.logger.exception(e)\n\n try:\n with open(self.blacklist_file, 'r+') as blacklist_file:\n self.blacklist = json.loads(blacklist_file.read())\n except Exception as e:\n self.logger.error(\"Unable to read blacklist. Default blacklist will be used.\")\n self.logger.exception(e)\n self.blacklist = DEFAULT_BLACKLIST\n\n def is_blacklisted(self, correlation_name):\n \"\"\"\n Check if a correlation name is blacklisted\n :param correlation_name: {str} The name of the correlation\n :return:\n \"\"\"\n for blacklist_pattern in self.blacklist:\n pattern = re.compile(blacklist_pattern)\n if pattern.search(correlation_name):\n return blacklist_pattern\n\n def parse_csv(self, csv_file):\n \"\"\"\n Parse a csv\n :param csv_file: {str} The path to the csv\n :return: {[dict]} List of found correlations (dicts)\n \"\"\"\n self.logger.info(\"Parsing {}\".format(csv_file))\n\n correlations_list = []\n correlations = []\n\n # Read csv line by line\n with open(csv_file, \"r\") as f:\n reader = csv.reader(f)\n for row in reader:\n correlations_list.append(row)\n\n # Check if there are any correlations (first row is headers row)\n if len(correlations_list) <= 1:\n # No correlations were found in csv\n return []\n\n # Construct human readable correlation objects (using headers and info)\n for correlation in correlations_list[1:]:\n correlation_info = {}\n for i, item in enumerate(correlation):\n # Construct Arcsight alert info dict\n correlation_info[correlations_list[0][i]] = item\n # Append alert info dict to alerts list\n correlation_info['raw'] = \",\".join(correlation)\n correlations.append(correlation_info)\n\n return correlations\n\n @staticmethod\n def create_csv(save_path, correlations):\n \"\"\"\n Create a new csv with given correlations at given path\n :param save_path: {str} The path to save the csv at\n :param correlations: {list} The list of correlations to save\n :return:\n \"\"\"\n # Remove the raw field from the correlations\n for correlation in correlations:\n if 'raw' in correlation:\n del correlation['raw']\n\n # Write the csv\n with open(save_path, 'w') as output_file:\n dict_writer = csv.DictWriter(output_file, correlations[0].keys())\n dict_writer.writeheader()\n dict_writer.writerows(correlations)\n\n def get_first_correlation(self, correlation_id, events):\n \"\"\"\n Find the event that has matching Id to the given correlation_id\n :param correlation_id: {int} The id to search for\n :param events: {[dict]} List of event info dicts.\n :return: {dict} The found event\n \"\"\"\n for event in events:\n if event.get(EVENT_ID_FIELD_NAME) == correlation_id:\n return event\n\n def split_large_csv_file(self, csv_file):\n \"\"\"\n Split a large csv to multiple smaller csvs files, save the original csv in the sliced_file_name\n :param csv_file: {str} The path of the csv file\n \"\"\"\n arcsight_correlations = self.parse_csv(os.path.join(self.siemshare_path, csv_file))\n # Split correlations list into sub lists under the limit restrictions\n correlations_chunks = [arcsight_correlations[i:i + self.alerts_limit] for i in\n range(0, len(arcsight_correlations), self.alerts_limit)]\n\n self.logger.info(\"Creating {} sliced file from {}\".format(len(correlations_chunks), csv_file))\n for correlations_chunk in correlations_chunks:\n sliced_file_name = \"{}{}{}{}\".format(\n csv_file.split(CSV_EXTENTION)[0],\n SLICED_PATTERN,\n uuid.uuid4().hex,\n CSV_EXTENTION)\n\n self.logger.info(\"Creating sliced file - {}\".format(sliced_file_name))\n self.create_csv(os.path.join(self.siemshare_path, sliced_file_name), correlations_chunk)\n\n try:\n # Backup the original file to Sliced Originals folder\n self.retention_manager.retensify_file(os.path.join(self.siemshare_path, csv_file),\n self.sliced_folder,\n self.error_retention_hours)\n except Exception as e:\n self.logger.error(\"Could not move {} to sliced folder\".format(csv_file))\n raise\n\n def get_arcsight_alerts(self, test_run):\n \"\"\"\n Get Arcsight alerts\n :return: {[CaseInfo]} List of CaseInfos.\n \"\"\"\n alerts = []\n count = 0\n logged_in = False\n\n # Iterate files in SiemShare folder\n for csv_file in os.listdir(self.siemshare_path):\n try:\n if csv_file.endswith(CSV_EXTENTION):\n # Process only files with .done.csv extension.\n self.logger.info(\"Parsing {}\".format(csv_file))\n\n try:\n # Parse the csv file\n arcsight_correlations = self.parse_csv(os.path.join(self.siemshare_path, csv_file))\n\n # Validate file size limit\n if len(arcsight_correlations) > self.alerts_limit:\n self.logger.info(\"Current csv file contains {}\".format(len(arcsight_correlations)))\n self.logger.info(\"Splitting {}\".format(csv_file))\n self.split_large_csv_file(csv_file)\n\n except Exception:\n done_folder = FileRetentionManager.create_retention_folder_name(self.done_folder)\n # Check if the file was already moved to the Done folder\n if os.path.exists(os.path.join(done_folder, os.path.basename(os.path.join(self.siemshare_path, csv_file)))):\n self.logger.info(u\"File \\\"{}\\\" wasn't found in the Cases folder: \\\"{}\\\" but was found in the Done folder: \\\"{}\\\" \"\n u\"continuing...\".format(os.path.join(self.siemshare_path, csv_file), self.siemshare_path, done_folder))\n continue\n self.logger.error(\"Unable to parse csv {}.\".format(csv_file))\n raise\n\n if count + len(arcsight_correlations) > self.alerts_limit:\n # Alerts limit reached - slice\n self.logger.info(\"Reached alerts limit. stopping iteration\")\n break\n\n # Start processing the csv file\n else:\n count += len(arcsight_correlations)\n self.logger.info(\"Processing {}\".format(csv_file))\n # Initiate inner failure flag - to mark if one of the\n # cases in the current csv has failed (to move the entire\n # csv to errors folder)\n inner_failure = False\n\n self.logger.info(\"Found {} correlations.\".format(len(arcsight_correlations)))\n # Iterate the found correlations from the csv\n for correlation in arcsight_correlations:\n try:\n blacklist_pattern = self.is_blacklisted(correlation.get(CORRELATION_NAME_FIELD_NAME))\n\n if blacklist_pattern:\n self.logger.info(\n \"Correlation {} is blacklisted by rule :{}\".format(\n correlation.get(CORRELATION_ID_FIELD_NAME),\n blacklist_pattern\n )\n )\n self.logger.info(\n \"Blacklisted correlation name: {}\".format(\n correlation.get(CORRELATION_NAME_FIELD_NAME)\n )\n )\n self.logger.info(\n \"Skipping correlation {}\".format(\n correlation.get(CORRELATION_ID_FIELD_NAME)\n )\n )\n\n else:\n if not logged_in:\n self.arcsight_manager.login()\n logged_in = True\n\n is_overflow = False\n\n try:\n is_overflow = self.connector_scope.is_overflowed_alert(\n environment=self.get_mapped_environment(\n correlation.get(\n self.environment_field_name,\n self.connector_scope.context.connector_info.environment\n )\n ),\n alert_identifier=str(\n correlation[CORRELATION_ID_FIELD_NAME]\n ),\n alert_name=str(\n correlation[CORRELATION_NAME_FIELD_NAME]\n )\n )\n\n except Exception as e:\n self.logger.error(\n \"Failed to detect overflow for Alert {}\".format(\n str(correlation[CORRELATION_NAME_FIELD_NAME]))\n )\n self.logger.exception(e)\n\n if not is_overflow:\n # Generate case info for the correlation\n case = self.create_case_info(correlation)\n alerts.append(case)\n\n # In case of test run return one alert\n if test_run:\n return alerts\n\n else:\n self.logger.warn(\n \"{alertname}-{alertid}-{environ} found as overflow alert, skipping this alert.\".format(\n alertname=str(\n correlation[CORRELATION_NAME_FIELD_NAME]\n ),\n alertid=str(\n correlation[CORRELATION_ID_FIELD_NAME]\n ),\n environ=self.get_mapped_environment(\n correlation.get(\n self.environment_field_name,\n self.connector_scope.context.connector_info.environment\n )\n ),\n )\n )\n\n except ArcsightLoginError as e:\n raise ArcsightLoginError(\n \"Failed login error for given creds.\\n{}. \\nCheck Logs for more details.\".format(\n str(e)))\n\n except Exception as e:\n # The correlation has failed\n self.logger.error(\n \"Failed to create CaseInfo for Correlation {}\".format(\n correlation.get(CORRELATION_ID_FIELD_NAME))\n )\n self.logger.error(\"Error Message: {}\".format(str(e)))\n self.logger.exception(e)\n\n inner_failure = True\n\n if inner_failure:\n # At least one of the inner correlations have failed -\n # raise an exception\n raise ArcsightESMConnectorException(\n \"Failure occured when processing {}. Check Logs for details.\".format(csv_file))\n\n # Completed processing - move to Done folder\n self.logger.info(\"Completed - {}\".format(csv_file))\n\n if not test_run:\n try:\n # Check if the file was already moved to the Done folder\n done_folder = FileRetentionManager.create_retention_folder_name(self.done_folder)\n if os.path.exists(os.path.join(done_folder, os.path.basename(os.path.join(self.siemshare_path, csv_file)))):\n self.logger.info(u\"File \\\"{}\\\" wasn't found in the Cases folder: \\\"{}\\\" but was found in the Done folder: \\\"{}\\\" \"\n u\"continuing...\".format(os.path.join(self.siemshare_path, csv_file), self.siemshare_path, \n done_folder))\n continue\n self.retention_manager.retensify_file(os.path.join(self.siemshare_path, csv_file),\n self.done_folder,\n self.done_retention_hours)\n\n except Exception as e:\n self.logger.error(\n \"Unable to move {} to Done folder.\".format(csv_file))\n self.logger.error(str(e))\n self.logger.exception(e)\n\n except ArcsightLoginError as e:\n raise\n\n except Exception as e:\n # Execution failed - move to error folder\n self.logger.error(\"Error occurred in creating alerts loop\")\n self.logger.exception(e)\n\n if not test_run:\n try:\n self.retention_manager.retensify_file(os.path.join(self.siemshare_path, csv_file),\n self.error_folder,\n self.error_retention_hours)\n\n except Exception as e:\n self.logger.error(\n \"Unable to move {} to Error folder.\".format(csv_file))\n self.logger.exception(e)\n\n return alerts\n\n def get_priority_value(self, correlation):\n \"\"\"\n Get priority from correlation\n :param correlation: {dict} The correlation info\n :return: {int} The maching priority (Siemplify matching)\n \"\"\"\n try:\n priority = float(correlation.get('priority', -1))\n\n if priority in [9, 10]:\n return 100 # Critical\n elif priority in [7, 8]:\n return 80 # High\n elif priority in [5, 6]:\n return 60 # Medium\n elif priority in [3, 4]:\n return 40 # Low\n\n except Exception as e:\n # Exception in conversion - return Informative priority level\n self.logger.warn(\n \"Couldn't calculate priority of correlation {}\".format(correlation.get(EVENT_ID_FIELD_NAME))\n )\n self.logger.warn(str(e))\n\n return -1 # Informative\n\n def get_rule_generator(self, value):\n \"\"\"\n Get rule generator from given generator uri\n :param value: {str} generator uri field in an Arcsight event\n :return: {str} the rule generator\n \"\"\"\n return value.split(RULE_GENERATOR_SEPARATOR)[-1]\n\n def create_case_info(self, correlation):\n \"\"\"\n Get alerts from Arcsight ESM\n :param correlation: {dict} An arcsight correlation info\n :return: {list} List of the newly created alerts\n \"\"\"\n correlation_id = int(correlation[CORRELATION_ID_FIELD_NAME])\n\n self.logger.info(\n \"Processing correlation {}\".format(correlation_id))\n\n events, error_message = self.arcsight_manager.get_security_events(\n [correlation_id],\n self.event_limit)\n\n if error_message:\n self.logger.error(\"An error occurred while fetching events: {}\".format(error_message))\n self.logger.warn(\"Case will be created anyway with found events.\")\n\n # Get the base correlation info from events (an event that matches\n # the given correlation_id)\n first_correlation = self.get_first_correlation(correlation_id, events)\n\n # Remove first correlation from events (first correlation is\n # an alert in Siemplify)\n if first_correlation in events:\n events.remove(first_correlation)\n\n if not first_correlation:\n # Base event was not found - case cannot be created\n self.logger.error(\"Found events: {}\".format(str(events).encode().decode()))\n raise ArcsightESMConnectorException(\n \"Correlation was not found in events. Cannot create case.\")\n\n self.logger.info(\n \"Creating Case for Arcsight Correlation {}\".format(correlation_id))\n\n try:\n # Create the CaseInfo\n case_info = CaseInfo()\n case_info.name = first_correlation.get('name')\n case_info.ticket_id = first_correlation[EVENT_ID_FIELD_NAME]\n\n try:\n rule_generator = first_correlation.get('generator', {}).get('uri', '')\n except Exception as e:\n self.logger.error(\"Unable to get rule generator: {}\".format(str(e)))\n self.logger.exception(e)\n rule_generator = \"\"\n\n case_info.rule_generator = self.get_rule_generator(rule_generator)\n case_info.display_id = first_correlation.get(EVENT_ID_FIELD_NAME)\n\n try:\n device_product = first_correlation.get('device', {}).get('product')\n except Exception as e:\n self.logger.error(\"Unable to get device product: {}\".format(str(e)))\n self.logger.exception(e)\n device_product = \"\"\n\n case_info.device_product = device_product\n\n try:\n device_vendor = first_correlation.get('device', {}).get('vendor')\n except Exception as e:\n self.logger.error(\"Unable to get device vendor: {}\".format(str(e)))\n self.logger.exception(e)\n device_vendor = \"\"\n\n case_info.device_vendor = device_vendor\n\n case_info.start_time = first_correlation.get(\"startTime\", 1)\n case_info.end_time = first_correlation.get(\"endTime\", 1)\n case_info.source_system_name = ARCSIGHT_SOURCE_ENUM\n case_info.priority = self.get_priority_value(first_correlation)\n # Take env name from given field in settings or if it is not in csv\n # take the default environment name. Then try to replace that\n # with the mappings.\n # If replacement fails stay with current env.\n case_info.environment = self.get_mapped_environment(\n correlation.get(\n self.environment_field_name,\n self.connector_scope.context.connector_info.environment))\n\n # Pull custom field values from ArcSight into the alert section on context details\n flat_first_correlation = dict_to_flat(first_correlation)\n\n if self.alert_custom_fields_names:\n for alert_custom_field_name in self.alert_custom_fields_names:\n if flat_first_correlation.get(alert_custom_field_name):\n case_info.extensions.update(\n {\n alert_custom_field_name: flat_first_correlation.get(alert_custom_field_name)\n }\n )\n else:\n self.logger.error(\"{0} doesn't exist\".format(\n alert_custom_field_name)\n )\n if case_info.extensions:\n try:\n self.arcsight_manager.remove_invalid_values([case_info.extensions])\n self.arcsight_manager.parse_ip_addresses([case_info.extensions])\n except Exception as e:\n self.logger.error(\"Unable to remove invalid values from extensions\")\n self.logger.exception(e)\n except KeyError as e:\n raise KeyError(\"Mandatory key is missing: {}\".format(e.message))\n\n # Flatten events\n flat_events = []\n\n for event in events:\n flat_events.append(dict_to_flat(event))\n\n try:\n self.arcsight_manager.remove_invalid_values(flat_events)\n except Exception as e:\n self.logger.error(\"Unable to remove invalid values from events\")\n self.logger.exception(e)\n\n try:\n self.arcsight_manager.parse_ip_addresses(flat_events)\n except UnableToParseException as e:\n key = str(e.key)\n value = str(e.value)\n self.logger.info(\"Value - \\'{}\\' in key \\'{}\\' wasn't converted to the IP address\".format(value, key))\n\n for flat_event in flat_events:\n if flat_event.get(self.device_product_field_name, \"\").lower() == ARCSIGHT and \\\n flat_event.get('device_vendor', \"\").lower() == ARCSIGHT and \\\n flat_event.get('type', \"\").upper() == 'BASE':\n # Event is a base event - try to fetch real product\n # from secondary product field\n if flat_event.get(self.secondary_device_product_field_name):\n # Replace the original product field (from connector's\n # DeviceProductField settings) with the value from\n # secondary product field\n flat_event[self.device_product_field_name] = flat_event[\n self.secondary_device_product_field_name]\n self.logger.info(\n 'Product Field was replaced in event {0}'.format(flat_event.get('eventId')))\n\n case_info.events = flat_events\n\n return case_info\n\n def get_mapped_environment(self, original_env):\n try:\n with open(self.map_file, 'r+') as map_file:\n mappings = json.loads(map_file.read())\n except Exception as e:\n self.connector_scope.LOGGER.error(\n \"Unable to read environment mappings: {}\".format(str(e)))\n mappings = {}\n\n if not isinstance(mappings, dict):\n self.connector_scope.LOGGER.error(\n \"Mappings are not in valid format. Environment will not be mapped.\")\n return original_env\n\n return mappings.get(original_env, original_env)\n\n\n@output_handler\ndef main(test_run=False):\n \"\"\"\n Main execution - Arcsight ESM Connector\n \"\"\"\n connector_scope = SiemplifyConnectorExecution()\n output_variables = {}\n log_items = []\n result_params = {}\n\n connector_scope.script_name = 'Arcsight ESM Connector'\n connector_scope.LOGGER.info(\"====================Starting Connector====================\")\n if test_run:\n connector_scope.LOGGER.info(\"====================Test====================\")\n\n try:\n connector_scope.LOGGER.info(\"Connecting to Arcsight ESM\")\n server_address = connector_scope.parameters.get('Server Address')\n username = connector_scope.parameters.get('Username')\n password = connector_scope.parameters.get('Password')\n verify_ssl = extract_connector_param(connector_scope, param_name='Verify SSL', input_type=bool)\n ca_certificate_file = extract_connector_param(connector_scope, param_name='CA Certificate File')\n event_limit = int(connector_scope.parameters['Events Count Limit'])\n alerts_limit = int(connector_scope.parameters['Alerts Count Limit'])\n siemshare_path = connector_scope.parameters.get('Cases Folder Path')\n done_retention_days = int(connector_scope.parameters.get('Done files retention days', DEFAULT_DONE_FILES_RETENTION_DAYS))\n error_retention_days = int(connector_scope.parameters.get('Error files retention days', DEFAULT_ERROR_FILES_RETENTION_DAYS))\n\n device_product_field_name = connector_scope.parameters.get('DeviceProductField')\n secondary_device_product_field_name = connector_scope.parameters.get('Secondary Device Product Field', '')\n environment_field_name = connector_scope.parameters.get('Environment Field Name')\n alert_custom_fields_names = connector_scope.parameters.get(\"Alert Custom Fields Names\", [])\n alert_custom_fields_names = alert_custom_fields_names.split(\",\") if alert_custom_fields_names else []\n\n # Connect to Arcsight\n arcsight_manager = ArcsightManager(server_address, username, password, verify_ssl=verify_ssl,\n ca_certificate_file=ca_certificate_file, logger=connector_scope.LOGGER)\n\n arcsight_connector = ArcsightESMConnector(connector_scope,\n arcsight_manager,\n event_limit,\n alerts_limit,\n siemshare_path,\n device_product_field_name,\n secondary_device_product_field_name,\n environment_field_name,\n alert_custom_fields_names,\n done_retention_days=done_retention_days,\n error_retention_days=error_retention_days)\n\n # Get alerts\n connector_scope.LOGGER.info(\"Collecting correlations from Arcsight ESM.\")\n cases = arcsight_connector.get_arcsight_alerts(test_run=test_run)\n\n connector_scope.LOGGER.info(\"Completed. Found {} cases.\".format(len(cases)))\n\n try:\n # Logout from arcsight\n arcsight_manager.logout()\n connector_scope.LOGGER.info(\"Logout from Arcsight successfully\")\n\n except Exception as e:\n connector_scope.LOGGER.error(\"Unable to logout from Arcsight: {}\".format(str(e)))\n connector_scope.LOGGER.exception(e)\n if test_run:\n raise\n\n connector_scope.LOGGER.info(\"====================Connector Finished====================\")\n\n # Return data\n connector_scope.return_package(cases, output_variables, log_items)\n\n except ArcsightLoginError as e:\n connector_scope.LOGGER.error(\"Failed login error for given creds.\")\n connector_scope.LOGGER.exception(e)\n raise\n\n except Exception as e:\n connector_scope.LOGGER.error(\"Error in main handler\")\n connector_scope.LOGGER.exception(e)\n if test_run:\n raise\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 2 or sys.argv[1] == 'True':\n print(\"Main execution started\")\n main(test_run=False)\n else:\n print(\"Test execution started\")\n main(test_run=True)\n","repo_name":"chronicle/tip-marketplace","sub_path":"Integrations/Arcsight/ConnectorsScripts/Arcsight ESM Connector.py","file_name":"Arcsight ESM Connector.py","file_ext":"py","file_size_in_byte":34226,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"7442678932","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nimport pyttsx3\nimport PyPDF2\nimport warnings\nimport threading\n\nwarnings.filterwarnings('ignore')\n\n\nspeaker = pyttsx3.init()\nstart=0\nnum=0\nt1=None\n\n\n##class Worker(QtCore.QRunnable):\n## '''\n## Worker thread\n## '''\n##\n## def __init__(self, file,rate,start):\n## super(Worker, self).__init__()\n## self.file=file\n## self.rate=rate\n## self.start=start\n##\n## @QtCore.pyqtSlot()\n## def run(self):\n## \n## book = open(self.file, 'rb')\n## pdfReader = PyPDF2.PdfFileReader(book)\n## pages = pdfReader.numPages\n## \n## speaker.setProperty('rate',self.rate)\n##\n## text=''\n##\n## for num in range(self.start,pages+1):\n##\n## \n## page = pdfReader.getPage(num)\n## text = page.extractText()\n##\n##\n## speaker.say(text)\n## speaker.runAndWait()\n\n \n\n\n\nclass Ui_MainWindow(object):\n\n## def __init__(self):\n## self.threadpool = QtCore.QThreadPool()\n## print(\"Multithreading with maximum %d threads\" % self.threadpool.maxThreadCount())\n\n\n\n \n def setupUi(self, MainWindow):\n MainWindow.setObjectName(\"MainWindow\")\n MainWindow.setEnabled(True)\n MainWindow.resize(500, 150)\n MainWindow.setMinimumSize(QtCore.QSize(500, 150))\n MainWindow.setMaximumSize(QtCore.QSize(500, 150))\n font = QtGui.QFont()\n font.setFamily(\"Arial\")\n MainWindow.setFont(font)\n icon = QtGui.QIcon()\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Normal, QtGui.QIcon.On)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Disabled, QtGui.QIcon.Off)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Disabled, QtGui.QIcon.On)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Active, QtGui.QIcon.Off)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Active, QtGui.QIcon.On)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Selected, QtGui.QIcon.Off)\n icon.addPixmap(QtGui.QPixmap(\"blue-music-icon.png\"), QtGui.QIcon.Selected, QtGui.QIcon.On)\n MainWindow.setWindowIcon(icon)\n self.centralwidget = QtWidgets.QWidget(MainWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)\n self.lineEdit.setGeometry(QtCore.QRect(20, 30, 371, 20))\n self.lineEdit.setStyleSheet(\"border-radius:4px;\")\n self.lineEdit.setObjectName(\"lineEdit\")\n self.Browse = QtWidgets.QPushButton(self.centralwidget)\n self.Browse.setGeometry(QtCore.QRect(410, 30, 75, 23))\n self.Browse.setStyleSheet(\"QPushButton#Browse{\\n\"\n\" color: rgb(255, 255, 255);\\n\"\n\" alternate-background-color: rgb(0, 0, 255);\\n\"\n\" background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(72, 132, 238, 255), stop:1 rgba(6, 188, 251, 255));\\n\"\n\" border-radius:5px;\\n\"\n\"}\\n\"\n\"\\n\"\n\"QPushButton#Browse:pressed{\\n\"\n\" \\n\"\n\" background-color: rgb(255, 255, 255);\\n\"\n\" \\n\"\n\" color: rgb(0, 0, 255);\\n\"\n\"}\")\n self.Browse.setObjectName(\"Browse\")\n self.Stop = QtWidgets.QPushButton(self.centralwidget)\n self.Stop.setGeometry(QtCore.QRect(20, 100, 75, 23))\n self.Stop.setStyleSheet(\"QPushButton#Stop{\\n\"\n\" color: rgb(255, 255, 255);\\n\"\n\" alternate-background-color: rgb(0, 0, 255);\\n\"\n\" background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(72, 132, 238, 255), stop:1 rgba(6, 188, 251, 255));\\n\"\n\" border-radius:5px;\\n\"\n\"}\\n\"\n\"\\n\"\n\"QPushButton#Stop:pressed{\\n\"\n\" \\n\"\n\" background-color: rgb(255, 255, 255);\\n\"\n\" \\n\"\n\" color: rgb(0, 0, 255);\\n\"\n\"}\")\n self.Stop.setObjectName(\"Stop\")\n self.Play = QtWidgets.QPushButton(self.centralwidget)\n self.Play.setGeometry(QtCore.QRect(410, 100, 75, 23))\n self.Play.setStyleSheet(\"QPushButton#Play{\\n\"\n\" color: rgb(255, 255, 255);\\n\"\n\" alternate-background-color: rgb(0, 0, 255);\\n\"\n\" background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(72, 132, 238, 255), stop:1 rgba(6, 188, 251, 255));\\n\"\n\" border-radius:5px;\\n\"\n\"}\\n\"\n\"\\n\"\n\"QPushButton#Play:pressed{\\n\"\n\" \\n\"\n\" background-color: rgb(255, 255, 255);\\n\"\n\" \\n\"\n\" color: rgb(0, 0, 255);\\n\"\n\"}\")\n self.Play.setObjectName(\"Play\")\n self.Pause = QtWidgets.QPushButton(self.centralwidget)\n self.Pause.setGeometry(QtCore.QRect(320, 100, 75, 23))\n self.Pause.setStyleSheet(\"QPushButton#Pause{\\n\"\n\" color: rgb(255, 255, 255);\\n\"\n\" alternate-background-color: rgb(0, 0, 255);\\n\"\n\" background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(72, 132, 238, 255), stop:1 rgba(6, 188, 251, 255));\\n\"\n\" border-radius:5px;\\n\"\n\"}\\n\"\n\"\\n\"\n\"QPushButton#Pause:pressed{\\n\"\n\" \\n\"\n\" background-color: rgb(255, 255, 255);\\n\"\n\" \\n\"\n\" color: rgb(0, 0, 255);\\n\"\n\"}\")\n self.Pause.setObjectName(\"Pause\")\n self.spinBox = QtWidgets.QSpinBox(self.centralwidget)\n self.spinBox.setGeometry(QtCore.QRect(130, 100, 42, 22))\n self.spinBox.setMaximum(300)\n self.spinBox.setProperty(\"value\", 120)\n self.spinBox.setObjectName(\"spinBox\")\n self.label = QtWidgets.QLabel(self.centralwidget)\n self.label.setGeometry(QtCore.QRect(180, 110, 60, 13))\n self.label.setObjectName(\"label\")\n MainWindow.setCentralWidget(self.centralwidget)\n\n\n #BUTTON FUCTIONALITY\n self.Browse.clicked.connect(self.browse)\n self.Play.clicked.connect(self.play)\n self.Pause.clicked.connect(self.pause)\n\n\n \n\n self.retranslateUi(MainWindow)\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n \n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"Read Aloud\"))\n self.lineEdit.setPlaceholderText(_translate(\"MainWindow\", \"Enter File Path\"))\n self.Browse.setText(_translate(\"MainWindow\", \"Browse\"))\n self.Stop.setText(_translate(\"MainWindow\", \"Stop\"))\n self.Play.setText(_translate(\"MainWindow\", \"Play\"))\n self.Pause.setText(_translate(\"MainWindow\", \"Pause\"))\n self.label.setText(_translate(\"MainWindow\", \"words / min\"))\n\n def browse(self): \n fm = QtWidgets.QFileDialog.getOpenFileName(None,\"Open File\",\"\",\"Pdf (*.pdf)\")\n filename = fm[0]\n self.lineEdit.setText(filename)\n\n \n def play(self):\n file=self.lineEdit.text()\n rate=self.spinBox.value()\n self.SpeakOut(file,rate,start)\n print(start,t1)\n## worker = Worker()\n## self.threadpool.start(worker)\n \n #t1=threading.Thread(target=self.SpeakOut,args=(file,rate,start,))\n #t1.start()\n\n def SpeakOut(self,file,rate,start):\n \n print(1)\n book = open(file, 'rb')\n pdfReader = PyPDF2.PdfFileReader(book)\n pages = pdfReader.numPages\n print(2)\n speaker.setProperty('rate',rate)\n\n text=''\n\n for num in range(start,pages+1):\n\n \n page = pdfReader.getPage(num)\n text = page.extractText()\n\n\n speaker.say(text)\n speaker.runAndWait()\n \n\n \n\n\n def SpeakPause(self):\n speaker.stop()\n start=num\n playing=False\n \n\n def pause(self):\n self.SpeakPause()\n \n \n \n \n\n\nif __name__ == \"__main__\":\n import sys\n app = QtWidgets.QApplication(sys.argv)\n MainWindow = QtWidgets.QMainWindow()\n ui = Ui_MainWindow()\n ui.setupUi(MainWindow)\n MainWindow.show()\n sys.exit(app.exec_())\n\n","repo_name":"suvam-sky/PDF-to-AudioBook","sub_path":"UI.py","file_name":"UI.py","file_ext":"py","file_size_in_byte":7912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"15625021318","text":"import numpy as np\n\n\ndef print_summary(flow):\n \"\"\"\n Print a summary of the trainable varaibles of a flow model\n\n \"\"\"\n\n print('_' * 100)\n print('Network \\t Layer \\t Shape \\t\\t\\t\\t\\t\\t #parameters')\n print('=' * 100)\n total_count = 0\n curr_network_name = ''\n curr_layer_name = ''\n curr_print_shape = ''\n curr_count = 0\n not_first_print = False\n\n for elt in flow.trainable_variables:\n name = elt.name\n names = name.split('/')\n kernel_bias = names[-1].split(':')[0]\n\n if len(names) == 1:\n network_name = ''\n layer_name = kernel_bias\n elif len(names) > 4:\n network_name = '/'.join([names[-5], names[-4], names[-3]])\n layer_name = names[-2]\n elif len(names) >= 4:\n network_name = names[-3]\n layer_name = names[-2]\n else:\n layer_name = names[-2]\n network_name = ''\n\n shape = elt.shape\n count = np.prod(shape)\n\n if layer_name == curr_layer_name:\n curr_print_shape += ' | ' + kernel_bias + ' ' + str(shape)\n curr_count += count\n else:\n if total_count != 0:\n print('\\t', curr_layer_name, '\\t\\t',\n curr_print_shape, '\\t\\t', curr_count)\n curr_layer_name = layer_name\n curr_print_shape = kernel_bias + ' ' + str(shape)\n curr_count = count\n if (network_name != curr_network_name) or network_name == '':\n if not_first_print is True:\n print('_' * 100)\n else:\n not_first_print = True\n print(network_name)\n curr_network_name = network_name\n\n total_count += count\n\n print('\\t', curr_layer_name, '\\t\\t', curr_print_shape, '\\t\\t', curr_count)\n print('=' * 100)\n print(\"Total Trainable Variables: \", total_count)\n\n\ndef total_trainable_variables(flow):\n count_total = 0\n for var in flow.trainable_variables:\n count_total += np.prod(var.shape)\n\n return count_total\n","repo_name":"SamArgt/AudioSourceSep","sub_path":"flow_models/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"20"}
+{"seq_id":"39702894078","text":"from django.shortcuts import render\nfrom rest_framework.views import APIView, Request, Response, status\nfrom users.permissions import CustomPermission\nfrom rest_framework_simplejwt.authentication import JWTAuthentication\nfrom rest_framework.permissions import IsAuthenticated\nfrom .serializers import MovieSerializer, MovieOrderSerializer\nfrom .models import Movie\nfrom django.shortcuts import get_object_or_404\nfrom rest_framework.pagination import PageNumberPagination\n\n\nclass MoviesView(APIView, PageNumberPagination):\n authentication_classes = [JWTAuthentication]\n permission_classes = [CustomPermission]\n\n def get(self, request):\n movies = Movie.objects.all()\n\n result_page = self.paginate_queryset(movies, request, view=self)\n\n movies_serializer = MovieSerializer(result_page, many=True)\n\n return self.get_paginated_response(movies_serializer.data)\n\n def post(self, request):\n movie_serializer = MovieSerializer(data=request.data)\n movie_serializer.is_valid(raise_exception=True)\n movie_serializer.save(user=request.user)\n\n return Response(movie_serializer.data, status.HTTP_201_CREATED)\n\n\nclass MoviesDetailView(APIView):\n authentication_classes = [JWTAuthentication]\n permission_classes = [CustomPermission]\n\n def get(self, request, movie_id):\n movie = get_object_or_404(Movie, id=movie_id)\n movie_serializer = MovieSerializer(movie)\n\n return Response(movie_serializer.data, status.HTTP_200_OK)\n\n def delete(self, request, movie_id):\n movie = get_object_or_404(Movie, id=movie_id)\n movie.delete()\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass MoviesOrderDetailView(APIView):\n authentication_classes = [JWTAuthentication]\n permission_classes = [IsAuthenticated]\n\n def post(self, request, movie_id):\n movie = get_object_or_404(Movie, id=movie_id)\n\n movie_serializer = MovieOrderSerializer(data=request.data)\n movie_serializer.is_valid(raise_exception=True)\n movie_serializer.save(user=request.user, movie=movie)\n\n return Response(movie_serializer.data, status.HTTP_201_CREATED)\n","repo_name":"ramonriotinto/Projeto-API-Kenzie-Buster","sub_path":"movies/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"19417023035","text":"from typing import Optional, Tuple, List\n\nfrom app.data.repositories import ReadersRepository, CirculationsRepository, RecommendationsRepository\n\n\nclass RecommendationSystem:\n \"\"\"\n Recommendation system core class\n Interface to interact with controllers\n \"\"\"\n\n _readers: ReadersRepository\n _circulations: CirculationsRepository\n _recommendations: RecommendationsRepository\n\n def __init__(self,\n readers: ReadersRepository,\n circulations: CirculationsRepository,\n recommendations: RecommendationsRepository):\n self._readers = readers\n self._circulations = circulations\n self._recommendations = recommendations\n\n def get_recommendations_for_reader(self, reader_id: Optional[int] = None) -> Tuple[List[int], List[int]]:\n reader = None\n if reader_id:\n reader = self._readers.get_reader_by_id(reader_id=reader_id)\n if reader:\n recommended_books, history = self._interact_with_existing_reader(reader)\n\n else:\n recommended_books, history = self._interact_with_unknown_reader()\n return recommended_books, history\n\n def _interact_with_existing_reader(self, reader) -> Tuple[List[int], List[int]]:\n recommended_books = self._recommendations.get_recommendations_by_reader_id(reader[0])\n known_books = self._circulations.get_circulations_by_reader(reader[0])\n recommended_books_ids = [x[1] for x in recommended_books]\n known_books_ids = [x[1] for x in known_books]\n return recommended_books_ids, known_books_ids\n\n def _interact_with_unknown_reader(self, n_rec_items=5) -> Tuple[List[int], List[int]]:\n return self._circulations.get_n_popular_books(n_rec_items), []\n","repo_name":"dmitriyl99/bookly","sub_path":"app/core/recommendation.py","file_name":"recommendation.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"74743246770","text":"import random\nimport math\nimport os\nimport art\n\ncards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n\n\ndef calculate_score(card_list):\n current_score = int(math.fsum(card_list))\n\n if 11 in card_list and current_score > 21:\n card_list.remove(11)\n card_list.append(1)\n\n return int(math.fsum(card_list))\n\n\ndef deal_card():\n return int(random.choice(cards))\n\n\ndef compare_results(user_result, computer_result):\n if user_result == computer_result:\n return \"It's a draw!\"\n if user_result > 21 and computer_result > 21:\n return \"It's a draw!\"\n if user_result > 21:\n return \"You went over it!! You lose!\"\n if computer_result > 21:\n return \"Computer bust!! You win!!\"\n elif user_result > computer_result:\n return \"You win!!\"\n else:\n return \"Computer cards are higher. You lose!\"\n\n\nshould_continue = True\n\n\ndef play_blackjack():\n print(art.logo)\n\n user_cards = [deal_card(), deal_card()]\n computer_cards = [deal_card(), deal_card()]\n print(f\"Your cards are {user_cards}. Current score is {calculate_score(user_cards)}.\")\n print(f\"Computer first cards is {computer_cards[0]}\")\n while calculate_score(user_cards) < 21 and input(\"Type 'y' to get another card, type 'n' to pass: \") == 'y':\n user_cards.append(deal_card())\n print(f\" Your current hand is {user_cards}. Current score is {calculate_score(user_cards)}.\")\n while calculate_score(computer_cards) < 17:\n print(f\" Computer's current hand is {computer_cards}. Current score is {calculate_score(computer_cards)}.\")\n print(\" will draw another card.\")\n computer_cards.append(deal_card())\n print(f\"\\nComputer's final hand is {computer_cards}. Final score is {calculate_score(computer_cards)}.\")\n print(f\"User final hand is {user_cards}. Final score is {calculate_score(user_cards)}.\\n\")\n final_user_result = calculate_score(user_cards)\n final_computer_result = calculate_score(computer_cards)\n\n print(compare_results(final_user_result, final_computer_result))\n\n\nwhile input(\"Do you want to play a game o Blackjack? Type 'y' or 'n': \") == 'y':\n os.system(\"clear\")\n play_blackjack()\n","repo_name":"pedrocruzeiro/python-sandbox","sub_path":"sandbox/day11/blackjack.py","file_name":"blackjack.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"15171024643","text":"import asyncio\nfrom datetime import datetime\n\nfrom . import Command, CommandTime\nfrom ..api.structures import RustChatMessage\nfrom ..commands.command_options import CommandOptions\nfrom ..commands.command_data import CommandData\nfrom ..api.remote.events import RegisteredListener\n\n\nclass CommandHandler:\n def __init__(self, command_options: CommandOptions, api) -> None:\n self.command_options = command_options\n self.commands = {}\n self.api = api\n\n def register_command(self, data: CommandData) -> None:\n if not asyncio.iscoroutinefunction(data.coro):\n raise TypeError(\"The event registered must be a coroutine\")\n\n self.commands[data.coro.__name__] = data\n\n async def run_command(self, message: RustChatMessage, prefix) -> None:\n if prefix == self.command_options.prefix:\n command = message.message.split(\" \")[0][len(prefix) :]\n else:\n command = prefix\n\n if command in self.commands:\n data = self.commands[command]\n\n await data.coro(\n Command(\n message.name,\n message.steam_id,\n CommandTime(datetime.utcfromtimestamp(message.time), message.time),\n command,\n message.message.split(\" \")[1:],\n )\n )\n else:\n for command_name, data in self.commands.items():\n # Loop through all the commands and see if the command is in the data aliases list\n # or if it matches the callable function\n\n if command in data.aliases or data.callable_func(command):\n await data.coro(\n Command(\n message.name,\n message.steam_id,\n CommandTime(\n datetime.utcfromtimestamp(message.time), message.time\n ),\n command,\n message.message.split(\" \")[1:],\n ),\n )\n break\n\n def has_command(self, listener: RegisteredListener) -> bool:\n return listener.listener_id in self.commands\n\n def remove_command(self, listener: RegisteredListener) -> None:\n try:\n del self.commands[listener.listener_id]\n except KeyError:\n pass\n","repo_name":"olijeffers0n/rustplus","sub_path":"rustplus/commands/command_handler.py","file_name":"command_handler.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"20"}
+{"seq_id":"23990270343","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Case 3: Elektrische voertuigen\n\n# #### Namen en groepsnummer:\n\n# Groep 18: Irina van Dam, Pien van Dongen, Kevin Linders & Floor Wesselink\n\n# # Importing Packages\n\n# In[125]:\n\n\nimport requests\nimport json\nimport pandas as pd\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport streamlit as st\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom shapely.geometry import Point, Polygon\nimport geopandas as gpd\nimport folium\nfrom folium.plugins import MarkerCluster\nimport branca\nimport streamlit_folium as st_folium\nfrom streamlit_folium import folium_static\n\n\n# ## Streamlit codes\n\n# In[150]:\n\n\nst.header('Toename van elektrisch vervoer')\n\n\n# In[151]:\n\n\nst.write('Dit dashboard geeft informatie over het aantal voertuigen in Nederland per brandstof categorie en is gefocust op de elektrische voertuigen. Zo geeft het dashboard de groei van elektrische voertuigen weer over de afgelopen jaren en word er gekeken naar de toekomst. Daarnaast geeft het dashboard ook informatie over de laadpalen/laadpunten die in Nederland actief zijn. (Deze data is gebaseerd op de database van het rdw. bron: https://opendata.rdw.nl/ )')\n\n\n# # Regressie analyse\n\n# ## Streamlit codes\n\n# In[179]:\n\n\nst.subheader('Regressie Analyse')\n\n\n# In[196]:\n\n\nst.write('Aan de hand van de barplot hieronder wordt er laten zien of het aantal elektrische voertuigen over de komende jaren zal toenemen')\n\n\n# ## Data inladen, inspecteren en manipuleren\n\n# De code hieronder hebben wij tot comments gemaakt omdat streamlit de data anders niet in kan laden. Dat is vanwege het feit dat de hoeveelheid data uit de response teveel is om in te laden voor streamlit. \n# Hieronder is te zien hoe wij de data hebben ingeladen met de API's van het RDW en hoe wij deze data hebben geanalyseerd.\n\n# In[181]:\n\n\n#Inladen van alle elektrische auto's\n#response = requests.get('https://opendata.rdw.nl/resource/8ys7-d773.json?$$app_token=qXFPxMddONVr63MCrFtrxLUrN&brandstof_omschrijving=Elektriciteit&$select=kenteken, brandstof_omschrijving&$limit=1100000')\n\n\n# In[182]:\n\n\n#data = response.json()\n#df = pd.DataFrame.from_dict(data)\n\n\n# In[183]:\n\n\n#Inladen van alle kentekens met bijbehorende kenteken van eerste toelating\n#response3 = requests.get('https://opendata.rdw.nl/resource/m9d7-ebf2.json?$$app_token=qXFPxMddONVr63MCrFtrxLUrN&$select=kenteken, datum_eerste_toelating_dt&$limit=15300000')\n\n\n# In[184]:\n\n\n#data3 = response3.json()\n#df3 = pd.DataFrame.from_dict(data3)\n\n\n# In[185]:\n\n\n#De invoeringsdatums koppelen aan de elektrische auto's\n#elektrisch = df.merge(df3, on = 'kenteken', how = 'left')\n\n\n# In[187]:\n\n\n#elektrisch_datum = elektrisch[['datum_eerste_toelating_dt']]\n\n\n# In[188]:\n\n\n#Datums omzetten naar datetime\n#elektrisch_datum['to_date_time'] = pd.to_datetime(elektrisch_datum['datum_eerste_toelating_dt'], format = '%Y-%m-%dT00:00:00.000')\n\n\n# In[189]:\n\n\n#Het jaar selecteren van de datum\n#elektrisch_datum['jaar'] = elektrisch_datum['to_date_time'].dt.year\n\n\n# In[190]:\n\n\n#Tellen van het aantal auto's per jaar\n#toename_perjaar = elektrisch_datum.value_counts(subset = 'jaar')\n\n\n# In[191]:\n\n\n#Kijken naar auto's die sinds 2000 bestaan\n#toename_perjaar_2000 = toename_perjaar[toename_perjaar > 2000]\n\n#df_jaar = pd.DataFrame(toename_perjaar_2000).reset_index()\n#df4 = df_jaar.rename(columns={0: \"Aantal voertuigen\"}).sort_values(by = 'jaar', ascending = True)\n#df4.to_csv('df4.csv', index = False)\n\n\n# ## Code voor het maken van de barplot\n\n# In[193]:\n\n\ndf5 = pd.read_csv('df4.csv')\n\n\n# In[195]:\n\n\nfig, ax = plt.subplots()\n\nsns.barplot(data = df5, x = 'jaar', y = 'Aantal voertuigen', color = 'green')\nax.set_title('Aantal elektrische voertuigen door de jaren heen')\nax.set_xlabel('Tijd (in jaren)')\nax.set_ylabel('Aantal voertuigen')\nax.set_xticklabels(labels = df5['jaar'], rotation = 90)\nst.pyplot(fig)\n\n\n# In[152]:\n\n\nst.subheader('Lijnplot aantal voertuigen')\n\n\n# In[153]:\n\n\nst.write('De lijnplot laat de hoeveelheid voertuigen per brandstoftype per maand zien.')\n\n\n# # Lijngrafiek aantal voertuigen over de jaren\n\n# ## Data Analyse\n\n# De code hieronder hebben wij tot comments gemaakt omdat streamlit de data anders niet in kan laden. Dat is vanwege het feit dat de hoeveelheid data uit de response teveel is om in te laden voor streamlit. \n# Hieronder is te zien hoe wij de data hebben ingeladen met de API's van het RDW en hoe wij deze data hebben geanalyseerd.\n\n# In[3]:\n\n\n#Inladen van alle auto's vanaf het jaar 2015, met de variabelen kenteken en datum_eerste_tenaamstelling_in_nederland\n\n#response1 = requests.get('https://opendata.rdw.nl/resource/m9d7-ebf2.json?$$app_token=qXFPxMddONVr63MCrFtrxLUrN&$where=datum_eerste_tenaamstelling_in_nederland>20141231&$select=kenteken, datum_eerste_tenaamstelling_in_nederland&$limit=7000000') \n#data1 = response1.json()\n#df1 = pd.DataFrame.from_dict(data1) \n\n\n# In[4]:\n\n\n#Inladen van kenteken, brandstof omschrijving en klasse hybride elektrisch voertuig\n\n#response2 = requests.get('https://opendata.rdw.nl/resource/8ys7-d773.json?$$app_token=qXFPxMddONVr63MCrFtrxLUrN&$select=kenteken, brandstof_omschrijving, klasse_hybride_elektrisch_voertuig&$limit=15000000')\n#data2 = response2.json()\n#df2 = pd.DataFrame.from_dict(data2)\n\n\n# In[5]:\n\n\n#Brandstof koppelen aan de juiste auto's\n#auto_brandstof = df1.merge(df2, on = 'kenteken', how = 'left')\n\n\n# In[6]:\n\n\n#Data bekijken\n#auto_brandstof.head()\n\n\n# In[7]:\n\n\n#Data verder inspecteren\n#auto_brandstof.info()\n\n\n# ## Data manipuleren\n\n# In[8]:\n\n\n#Datum naar datetime omzetten\n\n#auto_brandstof['datum'] = pd.to_datetime(auto_brandstof['datum_eerste_tenaamstelling_in_nederland'], format = '%Y%m%d')\n#auto_brandstof['jaar'] = auto_brandstof['datum'].dt.year\n#auto_brandstof['maand'] = auto_brandstof['datum'].dt.month\n#auto_brandstof['dag'] = 1\n#auto_brandstof['datum'] = pd.to_datetime(dict(year = auto_brandstof.jaar, month = auto_brandstof.maand, day = auto_brandstof.dag))\n#auto_brandstof.head()\n\n\n# In[9]:\n\n\n#df = auto_brandstof[['brandstof_omschrijving', 'datum']]\n\n\n# In[10]:\n\n\n#Per brandstofsoort een dataframe aanmaken.\n\n#benzine = df[df['brandstof_omschrijving'] == 'Benzine']\n#diesel = df[df['brandstof_omschrijving'] == 'Diesel']\n#elektriciteit = df[df['brandstof_omschrijving'] == 'Elektriciteit']\n#lpg = df[df['brandstof_omschrijving'] == 'LPG']\n#cng = df[df['brandstof_omschrijving'] == 'CNG']\n#alcohol = df[df['brandstof_omschrijving'] == 'Alcohol']\n#lng = df[df['brandstof_omschrijving'] == 'LNG']\n#waterstof = df[df['brandstof_omschrijving'] == 'Waterstof']\n\n\n# In[11]:\n\n\n#Deze functie telt het aantal auto's dat binnen een maand aangeschaft wordt.\n\n#def counting(colom, list_name):\n #list_name = {}\n #for entry in colom:\n #if entry in list_name.keys():\n #list_name[entry] = list_name[entry] + 1\n #else:\n #list_name[entry] = 1\n #return list_name\n\n\n# In[12]:\n\n\n#Een lege lijst per brandstofsoort aanmaken voor het tellen\n\n#counts1 = {}\n#counts2 = {}\n#counts3 = {}\n#counts4 = {}\n#counts5 = {}\n#counts6 = {}\n#counts7 = {}\n#counts8 = {}\n\n\n# In[13]:\n\n\n#Hier wordt per brandstofsoort het aantal auto's per maand geteld en opgeslagen.\n\n#counts1 = counting(benzine['datum'], counts1)\n#counts2 = counting(diesel['datum'], counts2)\n#counts3 = counting(elektriciteit['datum'], counts3)\n#counts4 = counting(lpg['datum'], counts4)\n#counts5 = counting(cng['datum'], counts5)\n#counts6 = counting(alcohol['datum'], counts6)\n#counts7 = counting(lng['datum'], counts7)\n#counts8 = counting(waterstof['datum'], counts8)\n\n\n# In[14]:\n\n\n#Hier worden de dictionarys naar dataframes omgezet\n\n#benzine1 = pd.DataFrame(counts1.items(), columns = ['Datum', 'Aantal'])\n#diesel1 = pd.DataFrame(counts2.items(), columns = ['Datum', 'Aantal'])\n#elektriciteit1 = pd.DataFrame(counts3.items(), columns = ['Datum', 'Aantal'])\n#lpg1 = pd.DataFrame(counts4.items(), columns = ['Datum', 'Aantal'])\n#cng1 = pd.DataFrame(counts5.items(), columns = ['Datum', 'Aantal'])\n#alcohol1 = pd.DataFrame(counts6.items(), columns = ['Datum', 'Aantal'])\n#lng1 = pd.DataFrame(counts7.items(), columns = ['Datum', 'Aantal'])\n#waterstof1 = pd.DataFrame(counts8.items(), columns = ['Datum', 'Aantal'])\n\n\n# In[15]:\n\n\n#Hier wordt per dataframe een kolom toegevoegd met de cummulatieve som toegevoegd\n\n#benzine1['Cumsum'] = benzine1['Aantal'].cumsum()\n#diesel1['Cumsum'] = diesel1['Aantal'].cumsum()\n#elektriciteit1['Cumsum'] = elektriciteit1['Aantal'].cumsum()\n#lpg1['Cumsum'] = lpg1['Aantal'].cumsum()\n#cng1['Cumsum'] = cng1['Aantal'].cumsum()\n#alcohol1['Cumsum'] = alcohol1['Aantal'].cumsum()\n#lng1['Cumsum'] = lng1['Aantal'].cumsum()\n#waterstof1['Cumsum'] = waterstof1['Aantal'].cumsum()\n\n\n# In[16]:\n\n\n#De brandstofsoort wordt per dataframe toegevoegd.\n\n#benzine1['Brandstoftype'] = 'Benzine'\n#diesel1['Brandstoftype'] = 'Diesel'\n#elektriciteit1['Brandstoftype'] = 'Elektriciteit'\n#lpg1['Brandstoftype'] = 'LPG'\n#cng1['Brandstoftype'] = 'CNG'\n#alcohol1['Brandstoftype'] = 'Alcohol'\n#lng1['Brandstoftype'] = 'LNG'\n#waterstof1['Brandstoftype'] = 'Waterstof'\n\n\n# ## Code voor het maken van de Lijngrafiek \n\n# Hier hebben we vanwege streamlit alle dataframes die we gebruiken in de lijngrafiek omgezet naar csv bestanden om die daarna met streamlit via Github in te laden.\n\n# In[120]:\n\n\n#Dataframes naar csv bestanden\n\n#benzine1.to_csv('benzine2.csv')\n#diesel1.to_csv('diesel2.csv')\n#elektriciteit1.to_csv('elektriciteit2.csv')\n#lpg1.to_csv('lpg2.csv')\n#cng1.to_csv('cng2.csv')\n#alcohol1.to_csv('alcohol2.csv')\n#lng1.to_csv('lng2.csv')\n#waterstof1.to_csv('waterstof2.csv')\n\n\n# In[121]:\n\n\n#Csv bestanden inladen\nbenzine2 = pd.read_csv('benzine2.csv')\ndiesel2 = pd.read_csv('diesel2.csv')\nelektriciteit2 = pd.read_csv('elektriciteit2.csv')\nlpg2 = pd.read_csv('lpg2.csv')\ncng2 = pd.read_csv('cng2.csv')\nalcohol2 = pd.read_csv('alcohol2.csv')\nlng2 = pd.read_csv('lng2.csv')\nwaterstof2 = pd.read_csv('waterstof2.csv')\n\n\n# In[122]:\n\n\n#Alle dataframes in een lijst.\ndataframes = [benzine2, diesel2, elektriciteit2, lpg2, cng2, alcohol2, lng2, waterstof2]\n\n#Alle dataframes weer aan elkaar toevoegen en brandstoftype omzetten naar een string.\ndf2 = pd.concat(dataframes)\ndf2['Brandstoftype'] = df2['Brandstoftype'].astype('string')\n\n\n# In[147]:\n\n\n#Een checkbox om de schaal in de lijnplot aan te passen\ncheckbox = st.checkbox(label = 'Logaritmische schaal')\n\n#Code voor de lijnplot\nif checkbox:\n fig = px.line(x = df2['Datum'], y = df2['Cumsum'], color = df2['Brandstoftype'], log_y = True, \n title = 'Toename aantal voertuigen per brandstofcategorie', \n labels = {'x':'Tijd (in jaren)', 'y':'Aantal voertuigen'})\n st.plotly_chart(fig)\nelse:\n fig = px.line(x = df2['Datum'], y = df2['Cumsum'], color = df2['Brandstoftype'], \n title = 'Toename aantal voertuigen per brandstofcategorie', \n labels = {'x':'Tijd (in jaren)', 'y':'Aantal voertuigen'})\n st.plotly_chart(fig)\n\n\n# \n# \n\n# # Histogram over de Laadpaaldata\n\n# ### Streamlit codes\n\n# In[154]:\n\n\nst.subheader('Histogram laadpaaldata')\n\n\n# In[155]:\n\n\nst.write('Hieronder is een histogram te zien over de laaddata van elektrische voertuigen. Het histogram laat zien hoe lang deze voertuigen over het algemeen laden.')\n\n\n# ## Data inladen, inspecteren en manipuleren\n\n# In[19]:\n\n\n#Inladen van de data\ndf3 = pd.read_csv('laadpaaldata.csv')\n\n#Bekijken van de data\n#df3.info()\n\n\n# In[20]:\n\n\n#De onderstaande code geeft een error omdat er verschillende rijen die aangeven op 29 februari 2018 te laden, \n#echter bestaat die datum niet.\n\n#df3['Started'] = pd.to_datetime(df3['Started'])\n#df3['Ended'] = pd.to_datetime(df3['Ended'])\n\n#Zoeken naar verschillende rijen die aangeven op 29 februari 2018 te laden, echter bestaat die datum niet.\n#De onderstaande regels zijn de regels met 29 februari 2018\n#df3.loc[1731]\n#df3.loc[1732]\n\n\n# In[21]:\n\n\n#Droppen van rijen met ongeldige waarden.\ndf3 = df3.drop([1731,1732])\n\n\n# In[22]:\n\n\n#Omzetten naar datetime.\ndf3['Started'] = pd.to_datetime(df3['Started'])\ndf3['Ended'] = pd.to_datetime(df3['Ended'])\n\n\n# In[23]:\n\n\n#Filteren zodat de eindtijd na de starttijd ligt.\ndf3 = df3[df3['Started'] < df3['Ended']]\n#df3\n\n\n# In[24]:\n\n\n#De Charge time moet altijd kleiner of gelijk zijn aan de Connected time\n#Hier worden de rijen gedropt waarbij dit niet het geval is\ndf3 = df3[df3['ChargeTime'] <= df3['ConnectedTime']]\n#df3\n\n\n# In[25]:\n\n\n#Onderzoeken van de kolom Charge Time.\n#df3['ChargeTime'].describe()\n\n\n# In[26]:\n\n\n#Negatieve Charge Time droppen.\ndf3 = df3[df3['ChargeTime'] > 0]\n\n#Waarde boven 20 droppen\ndf3 = df3[df3['ChargeTime'] <= 15]\n\n\n# In[27]:\n\n\n#Kolom Connected Time inspecteren\n#df3['ConnectedTime'].describe()\n\n\n# In[28]:\n\n\n#Hier wordt er gefilterd zodat de connected time onder de 30 uur zit.\ndf3 = df3[df3['ConnectedTime'] <= 30]\n\n\n# ## Code voor het maken van het Histogram\n\n# In[29]:\n\n\n#Hier worden strings aangemaakt voor de histogram\nstring1 = 'Mean: ' + str(round(df3['ConnectedTime'].mean(), 4))\nstring2 = 'Median: ' + str(round(df3['ConnectedTime'].median(), 4))\nstring3 = 'Mean: ' + str(round(df3['ChargeTime'].mean(), 4))\nstring4 = 'Median: ' + str(round(df3['ChargeTime'].median(), 4))\n\n\n# In[133]:\n\n\n#Hier wordt een histogram gemaakt voor zowel de Charge time en Connected time. \nfig, ax = plt.subplots()\n#Histogram vormen\nsns.histplot(data = df3, x = 'ConnectedTime', bins = 39, label = 'Connected time', color = 'Blue')\nsns.histplot(data = df3, x = 'ChargeTime', bins = 20, label = 'Charge time', color = 'Red')\n#Titels aanmaken voor het histogram\nax.set_title('Histogram met de verdeling van tijd voor opladen van voertuigen')\nax.set_xlabel('Laadtijd in uren')\nax.set_ylabel('Aantal voertuigen')\n#Annotaties aanmaken voor het histogram\nplt.annotate(string1, xy = (10, 2000), color = 'Blue')\nplt.annotate(string2, xy = (10, 1850), color = 'Blue')\nplt.annotate(string3, xy = (10, 1600), color = 'Red')\nplt.annotate(string4, xy = (10, 1450), color = 'Red')\n\nplt.legend()\n\nst.pyplot(fig)\n\n\n# Uit de plot valt te halen, dat veel auto's langer connected zijn dan dat er daadwerkelijk geladen wordt.\n\n# # Kaart laadpunten Nederland\n\n# ### Streamlit codes \n\n# In[156]:\n\n\nst.subheader('Kaart laadpunten Nederland')\n\n\n# In[157]:\n\n\nst.write('Deze kaart weergeeft hoeveel laadpunten er zich bevinden in een provincie. Daarnaast geeft de kaart ook weer waar de laadpunten zich bevinden per provincie.')\n\n\n# ### Data inladen, inspecteren en manipuleren\n\n# In[158]:\n\n\n#Alle data inladen voor de kaart\nurl = 'https://api.openchargemap.io/v3/poi'\n\nquerystring = {'countrycode':'NL','latitude':'52.848781','longitude':'5.838421','distanceunit':'km','key':'444', 'maxresults': 10000}\n\nheaders = {'Content-Type': 'application/json'}\n\nresponse = requests.request('GET', url, headers=headers, params=querystring)\n\ndata = response.json()\n\ndf = pd.DataFrame.from_dict(data)\n\n\n# In[57]:\n\n\n#Informatie uit de ingeladen data die we nodig hebben voor de markers.\ndf_map = df[['AddressInfo', 'StatusType', 'Connections', 'NumberOfPoints']]\n\n\n# In[58]:\n\n\n#df_map.info()\n\n\n# In[59]:\n\n\n#Welke waarde missen we bij Number of points.\n#df_map[df_map['NumberOfPoints'].isna()]\n\n\n# In[68]:\n\n\n#Nagaan welke rijen alleen een lege lijst hebben bij de variabele connections.\nconnections_lengte = []\n\nfor i in list(df_map.index):\n connections_lengte.append(len(df_map['Connections'][i]))\n\nlengte = pd.DataFrame(connections_lengte, columns = ['lengte'])\n\ndrop_list = list(lengte[lengte['lengte']==0].index)\n\n\n# Rij met index 5561 zit beide, dus we droppen alle bovenstaande rijen.\n\n# In[72]:\n\n\n#De rijen droppen waarvoor er geen connection data beschikbaar is en waar geen numberofpoints data beschikbaar voor is.\ndf_map = df_map.drop(drop_list)\n\n\n# In[159]:\n\n\n#StatusType \ntitle_status = []\n\nfor i in df_map['StatusType']:\n title_status.append(i['Title'])\n \nlen(title_status)\n\n\n# In[160]:\n\n\n#ID\nID = []\n\nfor i in df_map['AddressInfo']:\n ID.append(i['ID'])\n\n\n# In[161]:\n\n\n#Adres info\nadres_info = []\n\nfor i in df_map['AddressInfo']:\n adres_info.append(i['Title'])\n \n#len(adres_info)\n\n\n# In[162]:\n\n\n#ConnectionType\nconnectiontype_title = []\n\nfor i in df_map['Connections']:\n connectiontype_title.append(i[0]['ConnectionType']['Title'])\n#len(connectiontype_title)\n\n\n# In[163]:\n\n\n#Power KW\npower_kw = []\n\nfor i in df_map['Connections']:\n power_kw.append(i[0]['PowerKW'])\n#len(power_kw)\n\n\n# In[164]:\n\n\n#Current type\ncurrent_type = []\n\nfor i in df_map['Connections']:\n if i[0]['CurrentType'] == None:\n current_type.append('Unknown')\n else:\n current_type.append(i[0]['CurrentType']['Title'])\n\n#len(current_type)\n\n\n# In[165]:\n\n\n#De hierboven gegenereerde lijsten samenvoegen tot een dataframe. \nmarker_info = {'ID': ID, 'SatusType': title_status, 'AdresInfo': adres_info, 'ConnectionType': connectiontype_title, 'Power_kw': power_kw, 'Current_type': current_type}\ndf_marker_info = pd.DataFrame(data = marker_info, index = list(df_map.index))\n\n\n# In[80]:\n\n\n#df_marker_info\n\n\n# In[81]:\n\n\n#Provincie grenzen inladen\nprovincies = gpd.read_file('provincies4.geojson')\n\n\n# In[82]:\n\n\n#Adres info inladen om de markers te plotten op de juiste plek.\naddressinfo = df['AddressInfo']\n\n\n# In[166]:\n\n\n#Lijst maken van de info die we nodig hebben om te plotten.\nlijst = []\nfor adres in addressinfo:\n info = [adres['ID'], adres['Longitude'], adres['Latitude']]\n lijst.append(info)\n\n\n# In[84]:\n\n\n#Omzetten naar dataframe\nlat_long = pd.DataFrame(lijst, columns = ['ID', 'Lng', 'Lat'] )\n\n\n# In[85]:\n\n\n#Mergen van data, zodat er 1 dataframe is met alle benodige info\nlat_long2 = lat_long.merge(df_marker_info, on = 'ID')\n\n\n# In[168]:\n\n\n#Bepalen in welke provincie een laadpunt zit.\nprovincie = []\nfor index, row in lat_long2.iterrows():\n plaats = Point(row['Lng'], row['Lat'])\n if plaats.within(provincies['geometry'][0]) == True:\n provincie.append('Drenthe')\n elif plaats.within(provincies['geometry'][1]) == True:\n provincie.append('Flevoland') \n elif plaats.within(provincies['geometry'][2]) == True:\n provincie.append('Friesland (Fryslân)') \n elif plaats.within(provincies['geometry'][3]) == True:\n provincie.append('Gelderland') \n elif plaats.within(provincies['geometry'][4]) == True:\n provincie.append('Groningen') \n elif plaats.within(provincies['geometry'][5]) == True:\n provincie.append('Limburg') \n elif plaats.within(provincies['geometry'][6]) == True:\n provincie.append('Noord-Brabant')\n elif plaats.within(provincies['geometry'][7]) == True:\n provincie.append('Noord-Holland') \n elif plaats.within(provincies['geometry'][8]) == True:\n provincie.append('Overijssel') \n elif plaats.within(provincies['geometry'][9]) == True:\n provincie.append('Utrecht') \n elif plaats.within(provincies['geometry'][10]) == True:\n provincie.append('Zeeland') \n elif plaats.within(provincies['geometry'][11]) == True:\n provincie.append('Zuid-Holland') \n\n\n# In[169]:\n\n\n#Provincies toevoegen aan het dataframe\nlat_long2['Provincie'] = provincie\n\n\n# In[170]:\n\n\n#De totale dataframe groeperen per provincie\n\nFriesland = lat_long2[lat_long2['Provincie']=='Friesland (Fryslân)']\nGroningen = lat_long2[lat_long2['Provincie']=='Groningen']\nDrenthe = lat_long2[lat_long2['Provincie']=='Drenthe']\nGelderland = lat_long2[lat_long2['Provincie']=='Gelderland']\nFlevoland = lat_long2[lat_long2['Provincie']=='Flevoland']\nNoord_Holland = lat_long2[lat_long2['Provincie']=='Noord-Holland']\nZuid_Holland = lat_long2[lat_long2['Provincie']=='Zuid-Holland']\nUtrecht = lat_long2[lat_long2['Provincie']=='Utrecht']\nZeeland = lat_long2[lat_long2['Provincie']=='Zeeland']\nLimburg = lat_long2[lat_long2['Provincie']=='Limburg']\nNoord_Brabant = lat_long2[lat_long2['Provincie']=='Noord-Brabant']\nOverijssel = lat_long2[lat_long2['Provincie']=='Overijssel']\n\n\n# In[171]:\n\n\n#Het aantal laadpunten per provincie bepalen\nper_provincie = lat_long2.groupby(by = 'Provincie' ).count()[['ID']]\nper_provincie['Aantal laadpunten'] = per_provincie['ID']\nper_provincie = per_provincie[['Aantal laadpunten']].reset_index()\n\n\n# ## Code voor het maken van de Kaart\n\n# In[90]:\n\n\n#Geojson file inladen met informatie over de provincies.\ngeo = r'provincies4.geojson'\nwith open(geo) as geo_file:\n geo_json = json.load(geo_file)\n\n\n# In[172]:\n\n\n#Het middelstepunt van de provincies bepalen. Vervolgens een dataframe creeren met de provincie, het middelstepunt en het aantal laadpunten.\nprovincies['midden'] = provincies['geometry'].centroid\nmarker_midden = provincies[['name', 'midden']]\nmarker_midden['Aantal laadpunten'] = per_provincie['Aantal laadpunten']\n\n\n# In[92]:\n\n\n#Dit is de code die gebruikt is om de legenda aan te maken\nhtml = \"\"\"\n\n\n\n\n\n\n\n\n
Provincies
\n
\n
\n \n
\n
\n
Laadpunt: {} \"\"\".format(adres_info) + \"\"\"\n\n\n \n Status \n Connection type \n Power \n \n \n {} \"\"\".format(status) + \"\"\"\n {} \"\"\".format(connection) + \"\"\"\n {} Kw \"\"\".format(power) + \"\"\"\n \n
\n\n\n\n\n\"\"\"\n return html2\n\n\n# In[173]:\n\n\n#Het maken van een map\nm = folium.Map(location=[52.090736, 5.121420], zoom_start=7, min_zoom = 7, tiles = 'CartoDB Positron', width = 500, height = 700)\n\n#Eerste map: Verdeling Nederland \nfolium.Choropleth(\n geo_data = geo_json,\n name = 'choropleth',\n data = per_provincie,\n columns = ['Provincie', 'Aantal laadpunten'],\n key_on = 'feature.properties.name',\n fill_color = 'Greens',\n fill_opacity = 0.8,\n line_opacity = 0.5,\n legend_name = 'Aantal laadpunten'\n).add_to(m)\n\n##Markers maken voor elke provincie!\n\n#Markers Friesland\n\n\nmarker_cluster_friesland = MarkerCluster(name = 'Friesland', show = False).add_to(m)\nfor index, row in Friesland.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2,width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'blue', icon = 'flash')).add_to(marker_cluster_friesland)\n\npunt_friesland = marker_midden[marker_midden['name'] == 'Friesland (Fryslân)']['midden']\nlaadpunten_friesland = int(marker_midden[marker_midden['name'] == 'Friesland (Fryslân)']['Aantal laadpunten'])\n\nfolium.Marker(location=[punt_friesland.y, punt_friesland.x],\n popup=f\"In Friesland zijn er {laadpunten_friesland} laadpunten\",\n icon=folium.Icon(color = 'blue', icon = 'flash')).add_to(m)\n\n#Markers Groningen\nmarker_cluster_groningen = MarkerCluster(name = 'Groningen', show = False).add_to(m)\nfor index, row in Groningen.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'green', icon='flash')).add_to(marker_cluster_groningen)\n\npunt_groningen = marker_midden[marker_midden['name'] == 'Groningen']['midden']\nlaadpunten_groningen = int(marker_midden[marker_midden['name'] == 'Groningen']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_groningen.y, punt_groningen.x],\n popup = f\"In Groningen zijn er {laadpunten_groningen} laadpunten\",\n icon = folium.Icon(color = 'green', icon = 'flash')).add_to(m)\n\n\n#Markers Drenthe\nmarker_cluster_drenthe = MarkerCluster(name = \"Drenthe\", show = False).add_to(m)\nfor index, row in Drenthe.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script=True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'pink', icon = 'flash')).add_to(marker_cluster_drenthe)\n\npunt_drenthe = marker_midden[marker_midden['name'] == 'Drenthe']['midden']\nlaadpunten_drenthe = int(marker_midden[marker_midden['name'] == 'Drenthe']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_drenthe.y, punt_drenthe.x],\n popup = f\"In Drenthe zijn er {laadpunten_drenthe} laadpunten\",\n icon = folium.Icon(color = 'pink', icon = 'flash')).add_to(m)\n\n#Markers Overijssel\nmarker_cluster_overijssel = MarkerCluster(name = 'Overijssel', show = False).add_to(m)\nfor index, row in Overijssel.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'lightblue', icon = 'flash')).add_to(marker_cluster_overijssel)\n\npunt_overijssel = marker_midden[marker_midden['name'] == 'Overijssel']['midden']\nlaadpunten_overijssel = int(marker_midden[marker_midden['name'] == 'Overijssel']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_overijssel.y, punt_overijssel.x],\n popup = f\"In Overijssel zijn er {laadpunten_overijssel} laadpunten\",\n icon = folium.Icon(color = 'lightblue', icon = 'flash')).add_to(m)\n\n#Markers Gelderland\nmarker_cluster_gelderland = MarkerCluster(name = 'Gelderland', show = False).add_to(m)\nfor index, row in Gelderland.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'purple', icon = 'flash')).add_to(marker_cluster_gelderland)\n\npunt_gelderland = marker_midden[marker_midden['name'] == 'Gelderland']['midden']\nlaadpunten_gelderland = int(marker_midden[marker_midden['name'] == 'Gelderland']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_gelderland.y, punt_gelderland.x],\n popup = f\"In Gelderland zijn er {laadpunten_gelderland} laadpunten\",\n icon = folium.Icon(color = 'purple', icon = 'flash')).add_to(m)\n\n\n#Markers Utrecht\nmarker_cluster_utrecht = MarkerCluster(name = \"Utrecht\", show = False).add_to(m)\nfor index, row in Utrecht.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'gray', icon='flash')).add_to(marker_cluster_utrecht)\n\npunt_utrecht = marker_midden[marker_midden['name'] == 'Utrecht']['midden']\nlaadpunten_utrecht = int(marker_midden[marker_midden['name'] == 'Utrecht']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_utrecht.y, punt_utrecht.x],\n popup = f\"In Utrecht zijn er {laadpunten_utrecht} laadpunten\",\n icon = folium.Icon(color = 'gray', icon = 'flash')).add_to(m)\n\n#Markers Noord-Holland\nmarker_cluster_noord_holland = MarkerCluster(name = 'Noord-Holland', show = False).add_to(m)\nfor index, row in Noord_Holland.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'red', icon = 'flash')).add_to(marker_cluster_noord_holland)\n\npunt_noord_holland = marker_midden[marker_midden['name'] == 'Noord-Holland']['midden']\nlaadpunten_noord_holland = int(marker_midden[marker_midden['name'] == 'Noord-Holland']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_noord_holland.y, punt_noord_holland.x],\n popup = f\"In Noord-Holland zijn er {laadpunten_noord_holland} laadpunten\",\n icon = folium.Icon(color = 'red', icon = 'flash')).add_to(m)\n\n#Markers Zuid-holland\nmarker_cluster_zuid_holland = MarkerCluster(name = 'Zuid-Holland', show = False).add_to(m)\nfor index, row in Zuid_Holland.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'orange', icon = 'flash')).add_to(marker_cluster_zuid_holland)\n\npunt_zuid_holland = marker_midden[marker_midden['name'] == 'Zuid-Holland']['midden']\nlaadpunten_zuid_holland = int(marker_midden[marker_midden['name'] == 'Zuid-Holland']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_zuid_holland.y, punt_zuid_holland.x],\n popup = f\"In Zuid-Holland zijn er {laadpunten_zuid_holland} laadpunten\",\n icon = folium.Icon(color = 'orange', icon = 'flash')).add_to(m)\n\n\n#Markers Limburg\nmarker_cluster_limburg = MarkerCluster(name = 'Limburg', show = False).add_to(m)\nfor index, row in Limburg.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'lightred', icon = 'flash')).add_to(marker_cluster_limburg)\n\npunt_limburg = marker_midden[marker_midden['name'] == 'Limburg']['midden']\nlaadpunten_limburg = int(marker_midden[marker_midden['name'] == 'Limburg']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_limburg.y, punt_limburg.x],\n popup = f\"In Limburg zijn er {laadpunten_limburg} laadpunten\",\n icon = folium.Icon(color = 'lightred', icon = 'flash')).add_to(m)\n\n#Markers Noord-Brabant\nmarker_cluster_noord_brabant = MarkerCluster(name = 'Noord-Brabant', show = False).add_to(m)\nfor index, row in Noord_Brabant.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'darkblue', icon = 'flash')).add_to(marker_cluster_noord_brabant)\n\npunt_noord_brabant = marker_midden[marker_midden['name'] == 'Noord-Brabant']['midden']\nlaadpunten_noord_brabant = int(marker_midden[marker_midden['name'] == 'Noord-Brabant']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_noord_brabant.y, punt_noord_brabant.x],\n popup = f\"In Noord-Brabant zijn er {laadpunten_noord_brabant} laadpunten\",\n icon = folium.Icon(color = 'darkblue', icon = 'flash')).add_to(m)\n\n#Markers Zeeland\nmarker_cluster_zeeland = MarkerCluster(name = 'Zeeland', show = False).add_to(m)\nfor index, row in Zeeland.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'cadetblue', icon='flash')).add_to(marker_cluster_zeeland)\n\npunt_zeeland = marker_midden[marker_midden['name'] == 'Zeeland']['midden']\nlaadpunten_zeeland = int(marker_midden[marker_midden['name'] == 'Zeeland']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_zeeland.y, punt_zeeland.x],\n popup = f\"In Zeeland zijn er {laadpunten_zeeland} laadpunten\",\n icon = folium.Icon(color = 'cadetblue', icon='flash')).add_to(m)\n\n#Markers Flevoland\nmarker_cluster_flevoland = MarkerCluster(name = 'Flevoland', show = False).add_to(m)\nfor index, row in Flevoland.iterrows():\n html2 = popup_html(index)\n iframe = branca.element.IFrame(html = html2, width = 510, height = 280)\n popup = folium.Popup(folium.Html(html2, script = True), max_width = 500)\n folium.Marker(location = [row['Lat'], row['Lng']],\n popup = popup,\n icon = folium.Icon(color = 'darkred', icon = 'flash')).add_to(marker_cluster_flevoland)\n\npunt_flevoland = marker_midden[marker_midden['name'] == 'Flevoland']['midden']\nlaadpunten_flevoland = int(marker_midden[marker_midden['name'] == 'Flevoland']['Aantal laadpunten'])\n\nfolium.Marker(location = [punt_flevoland.y, punt_flevoland.x],\n popup = f\"In Flevoland zijn er {laadpunten_flevoland} laadpunten\",\n icon = folium.Icon(color = 'darkred', icon='flash'), tooltip = 'Click here for legend').add_to(m)\n\n\n\n#Legenda de markers per provincie\niframe = branca.element.IFrame(html = html, width = 510, height = 280)\npopup = folium.Popup(folium.Html(html, script = True), max_width = 500)\nfolium.Marker(location = [52.244261833472144, 3.2201231832041888], popup = popup, icon = folium.Icon(icon = 'info-sign'), draggable = True).add_to(m)\n\nfolium.LayerControl().add_to(m)\n\nfolium_static(m)\n\n","repo_name":"KL13579/Case-3","sub_path":"Case3Versie2.py","file_name":"Case3Versie2.py","file_ext":"py","file_size_in_byte":35693,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"14489203790","text":"import argparse\nimport json\nfrom pathlib import Path\nimport re\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--infile_dir', type=str, default='downloaded/')\n parser.add_argument('--infile_prefix', type=str, default='id_to_metadata')\n args = parser.parse_args()\n\n filename_pattern = re.compile(f'{args.infile_prefix}_(\\d+).json')\n json_files = []\n for file in Path(args.infile_dir).iterdir():\n match = filename_pattern.match(file.name)\n if match:\n json_files.append((str(file), int(match.group(1))))\n\n json_files.sort(key=lambda x: x[1])\n\n result = dict()\n for json_file in json_files:\n with open(json_file[0], 'r') as f:\n file_dict = json.load(f)\n for k, v in file_dict.items():\n result[k] = v\n\n with open(f'{args.infile_prefix}.json', 'w', encoding='utf-8') as f:\n json.dump(result, f, indent=2)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"junoWutopia/Wutopia-app","sub_path":"metadata/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"74288663088","text":"from os.path import realpath, dirname, isfile, join, isdir\nimport requests\nimport json\n\nfrom unittest import skip\n\n# Base test class\n#\nfrom tabular_test_base import TestTabularAPIBase\n\n# Validation forms from https://github.com/IQSS/shared-dataverse-information\n#\nfrom shared_dataverse_information.shapefile_import.forms import ShapefileImportDataForm\nfrom shared_dataverse_information.dataverse_info.forms_existing_layer import CheckForExistingLayerForm\nfrom shared_dataverse_information.map_layer_metadata.forms import MapLayerMetadataValidationForm\n\nfrom geonode.contrib.msg_util import *\n\n\nclass TestWorldMapShapefileImport(TestTabularAPIBase):\n\n @classmethod\n def tearDownClass(cls):\n # We don't need the initial shapefile to work with (tabular does)\n pass\n\n @classmethod\n def setUpClass(cls):\n cls.createDataverseUserAndGroup(add_user_to_dv_group=False)\n\n\n def setUp(self):\n super(TestWorldMapShapefileImport, self).setUp()\n\n msgt('........ set up 1 (base) ................')\n\n # Create database\n #\n #call_command('syncdb', interactive = False)\n\n self.TEST_FILE_DIR = join(dirname(realpath(__file__)), 'input')\n\n # Verify/load dataverse test info\n #\n dataverse_info_test_fixture_fname = join(self.TEST_FILE_DIR, 'dataverse_info_test_fixture.json')\n assert isfile(dataverse_info_test_fixture_fname), \"Dataverse test fixture file not found: %s\" % dataverse_info_test_fixture_fname\n self.dataverse_test_info = json.loads(open(dataverse_info_test_fixture_fname, 'r').read())\n\n # Verify/load shapefile test info\n #\n shapefile_info_test_fixture_fname = join(self.TEST_FILE_DIR, 'shapefile_info_test_fixture.json')\n assert isfile(shapefile_info_test_fixture_fname), \"Shapefile test fixture file not found: %s\" % shapefile_info_test_fixture_fname\n self.shapefile_test_info = json.loads(open(shapefile_info_test_fixture_fname, 'r').read())\n\n # Verify that test shapefile exists (good file)\n #\n self.test_shapefile_name = join(self.TEST_FILE_DIR, 'social_disorder_in_boston_yqh.zip')\n assert isfile(self.test_shapefile_name), \"Test shapefile not found: %s\" % self.test_shapefile_name\n\n # Verify that test shapefile exists (bad file)\n #\n self.test_bad_file = join(self.TEST_FILE_DIR, 'meditation-gray-matter-rebuild.pdf')\n assert isfile(self.test_bad_file), '\"Bad\"\" test shapefile not found: %s' % self.test_bad_file\n\n # Verify/load geotiff test information\n geotiff_info_test_fixture_fname = join(self.TEST_FILE_DIR, 'geotiff_info_test.json')\n assert isfile(geotiff_info_test_fixture_fname), \"GeoTiff test fixture file not found: %s\" % geotiff_info_test_fixture_fname\n self.geotiff_test_info = json.loads(open(geotiff_info_test_fixture_fname, 'r').read())\n\n # Verify that test geotiff exists (good file)\n #\n self.test_geotiff_fname = join(self.TEST_FILE_DIR, 'Neighborhoods_BRA_1.tiff')\n assert isfile(self.test_geotiff_fname), \"Test geotiff not found: %s\" % self.test_geotiff_fname\n\n\n\n\n def tearDown(self):\n pass\n #os.remove(realpath(join('test-scratch', 'scratch.db3')))\n\n #@skip(\"skipping\")\n def test01_bad_shapefile_imports(self):\n\n #-----------------------------------------------------------\n msgt(\"--- Shapefile imports (that should fail) ---\")\n #-----------------------------------------------------------\n api_url = self.add_shapefile_url\n\n #-----------------------------------------------------------\n msgn(\"(1a) Test WorldMap shapefile import API but without any payload (GET instead of POST)\")\n #-----------------------------------------------------------\n msg('api_url: %s' % api_url)\n try:\n r = requests.post(api_url,\n auth=self.get_creds_for_http_basic_auth())\n except requests.exceptions.ConnectionError as e:\n msgx('Connection error: %s' % e.message); return\n except:\n msgx(\"Unexpected error: %s\" % sys.exc_info()[0]); return\n\n msg(r.status_code)\n\n self.assertEqual(r.status_code, 401, \"Should receive 401 error. Received: %s\\n%s\" % (r.status_code, r.text))\n expected_msg = 'The request must be a POST.'\n self.assertEqual(r.json().get('message'), expected_msg\\\n , 'Should receive message: \"%s\". Received: %s' % (expected_msg, r.text))\n\n\n #-----------------------------------------------------------\n msgn(\"(1b) Test WorldMap shapefile import API but use a BAD credentials\")\n #-----------------------------------------------------------\n\n # Test WorldMap shapefile import API but use a BAD TOKEN\n #\n #r = requests.post(api_url, data=json.dumps( self.get_worldmap_random_token_dict() ))\n try:\n r = requests.post(api_url,\n auth=('not-a-real-user-789', 'with-not-a-real-password-123'))\n except requests.exceptions.ConnectionError as e:\n msgx('Connection error: %s' % e.message); return\n except:\n msgx(\"Unexpected error: %s\" % sys.exc_info()[0]); return\n\n msg(r.status_code)\n\n self.assertEqual(r.status_code, 401, \"Should receive 401 error. Received: %s\\n%s\" % (r.status_code, r.text))\n try:\n json_resp = r.json()\n except:\n self.assertTrue(False, \"Failed to convert response text to JSON. Text:\\n%s\" % r.text)\n return\n\n expected_msg = 'Login failed'\n self.assertEqual(json_resp.get('message'), expected_msg,\n 'Should receive message: \"%s\". Received: %s' % (expected_msg, r.text))\n\n\n #-----------------------------------------------------------\n msgn(\"(1c) Test WorldMap shapefile import API but FAIL to include a file\")\n #-----------------------------------------------------------\n # Get basic shapefile info\n shapefile_api_form = ShapefileImportDataForm(self.shapefile_test_info)\n self.assertTrue(shapefile_api_form.is_valid())\n params = shapefile_api_form.cleaned_data\n test_shapefile_info = params.copy()\n # add dv info\n params.update(self.dataverse_test_info)\n\n # Test WorldMap shapefile import API but FAIL to include a file\n #\n msg('api url: %s' % api_url)\n try:\n r = requests.post(api_url,\n data=params,\n auth=self.get_creds_for_http_basic_auth())\n except requests.exceptions.ConnectionError as e:\n msgx('Connection error: %s' % e.message); return\n except:\n msgx(\"Unexpected error: %s\" % sys.exc_info()[0]); return\n\n msg(r.status_code)\n #msg(r.text)\n self.assertEqual(r.status_code, 400, \"Should receive 400 error. Received: %s\\n%s\" % (r.status_code, r.text))\n expected_msg = 'File not found. Did you send a file?'\n self.assertEqual(r.json().get('message'), expected_msg\\\n , 'Should receive message: \"%s\". Received: %s' % (expected_msg, r.text))\n\n #-----------------------------------------------------------\n msgn(\"(1d) Test WorldMap shapefile import API but send 2 files instead of 1\")\n #-----------------------------------------------------------\n files = { 'file': open( self.test_shapefile_name, 'rb')\\\n , 'file1': open( self.test_shapefile_name, 'rb')\\\n }\n\n # Test WorldMap shapefile import API but send 2 files instead of 1\n #\n try:\n r = requests.post(api_url,\n data=test_shapefile_info,\n files=files,\n auth=self.get_creds_for_http_basic_auth())\n except requests.exceptions.ConnectionError as e:\n msgx('Connection error: %s' % e.message); return\n except:\n msgx(\"Unexpected error: %s\" % sys.exc_info()[0]); return\n\n msg(r.status_code)\n\n self.assertEqual(r.status_code, 400, \"Should receive 400 error. Received: %s\\n%s\" % (r.status_code, r.text))\n\n try:\n json_resp = r.json()\n except:\n self.assertTrue(False, \"Failed to convert response to JSON. Received: %s\" % r.text)\n\n\n expected_msg = 'This request only accepts a single file'\n self.assertTrue(json_resp.get('message','').startswith(expected_msg),\n 'Should receive message: \"%s\". Received: %s' % (expected_msg, r.text))\n\n\n\n #-----------------------------------------------------------\n msgn(\"(1e) Test WorldMap shapefile import API with payload except file (metadata is not given)\")\n #-----------------------------------------------------------\n # prep file\n files = {'file': open( self.test_shapefile_name, 'rb')}\n\n test_shapefile_info_missing_pieces = test_shapefile_info.copy()\n test_shapefile_info_missing_pieces.pop('dv_user_email')\n test_shapefile_info_missing_pieces.pop('abstract')\n test_shapefile_info_missing_pieces.pop('shapefile_name')\n test_shapefile_info_missing_pieces.pop('title')\n\n # Test WorldMap shapefile import API with payload except file (metadata is not given)\n #\n try:\n r = requests.post(api_url,\n data=test_shapefile_info_missing_pieces,\n files=files,\n auth=self.get_creds_for_http_basic_auth())\n except requests.exceptions.ConnectionError as e:\n msgx('Connection error: %s' % e.message); return\n except:\n msgx(\"Unexpected error: %s\" % sys.exc_info()[0]); return\n\n msg(r.status_code)\n\n self.assertEqual(r.status_code, 400, \"Should receive 400 error. Received: %s\\n%s\" % (r.status_code, r.text))\n expected_msg = \"Incorrect params for ShapefileImportDataForm: dv_user_email abstract shapefile_name title \"\n\n @property \n def find_job_name(self):\n locator = JobLocator.JOB_NAME\n job_link = self.parent.select_one(locator)\n if job_link != None:\n job_name = job_link.attrs['title']\n return(job_name)\n\n @property\n def find_job_link(self):\n locator = JobLocator.JOB_LINK\n job_link = self.parent.select_one(locator)\n if job_link != None:\n return(job_link.attrs['href'])\n\n @property\n def employer(self): \n \"\"\"reaturns a tuple (employer name and employer link) \"\"\"\n # aLSO RETURNS LINK TO THE EMPLOYERS SITE\n locator = JobLocator.EMPLOYER\n emp_name = self.parent.select_one(locator)\n\n # emp_link = self.parent.select_one(locator).attrs['href']emp_link == None andemp_link\n if emp_name == None:\n pass\n else:\n return(emp_name.string.strip())\n\n \n\n @property\n def location(self):\n locator = JobLocator.LOCATION\n location = self.parent.select_one(locator).string.strip()\n return(location)\n\n @property\n def job_type(self):\n locator = JobLocator.JOB_TYPE\n type = self.parent.select_one(locator)\n if type != None:\n return(type.string.strip())\n\n @property\n def salary(self):\n locator = JobLocator.SALARY\n salary = self.parent.select_one(locator)\n if salary != None:\n salary = salary.text.strip().split()\n salary = ' '.join(map(str, salary))\n return(salary)\n\n @property\n def job_function(self): \n \"\"\"returns a tuple (job function and time stamp ) \"\"\"\n # ALSO RETURNS THE TIME STAMP\n locator = JobLocator.JOB_FUNCTION\n locator2 = JobLocator.TIME_STAMP\n function = self.parent.select_one(locator)\n time_stamp = self.parent.select_one(locator2)\n if(function, time_stamp) != (None, None):\n return(function.string.strip(), time_stamp.text.strip())\n\n @property\n def summary(self):\n locator = JobLocator.SUMMARY\n summ = self.parent.select_one(locator)\n if summ != None:\n return(summ.string.strip())\n","repo_name":"Michael-Lyon/ClassDemo","sub_path":"webScrape/scraping_jobs/parsers/job_parser.py","file_name":"job_parser.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"74742945330","text":"\"\"\"\nTravel Pass.\nCalculate the amount of time it takes to travel.\n\"\"\"\n\n\ndef calculate_time(a, b, s):\n ones = s.count('1')\n zeros = s.count('0')\n\n return (zeros * a) + (ones * b)\n\n\nif __name__ == \"__main__\":\n number_of_testcases = int(input())\n for _ in range(number_of_testcases):\n length_of_string, inter_district_time, inter_state_time = \\\n map(int, input().rstrip().split())\n string_to_check = str(input())\n result = calculate_time(inter_district_time, inter_state_time, string_to_check)\n print(result)\n","repo_name":"Oshalb/Code-Chef-Contests","sub_path":"Sept Long Challenge 2021/travelps.py","file_name":"travelps.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"3392058021","text":"'''\nProblem 18\n\nBy starting at the top of the triangle below and moving to adjacent numbers on the row below, the maximum \ntotal from top to bottom is 23.\n\n 3\n 7 4\n 2 4 6\n 8 5 9 3\n\nThat is, 3 + 7 + 4 + 9 = 23.\n\nFind the maximum total from top to bottom of the triangle below:\n\nNOTE: As there are only 16384 routes, it is possible to solve this problem by trying every route.\n\nAns: 1074\n'''\nfrom functools import lru_cache\nfrom typing import List\nimport re \n\n\ndef str2List(string: str) -> List[str]:\n '''\n Parameters\n ----------\n string : str\n A triangle of integers with rows separated by newlines.\n\n Returns\n -------\n cleaned : list\n Converts the triangle to nested list of strings representing integers.\n Example:\n >>s = '75\\n 95 64\\n 17 47 82'\n >>str2List(s)\n [['75'],['95','64'],['17','47','82']]\n '''\n temp = string.replace('\\n',',')\n rgx = r\" {2,}\"\n temp = re.sub(rgx, '', temp).split(',') \n cleaned = [row.split(' ') for row in temp]\n return cleaned\n\n\ndef str2IntElements(lst: List[str]) -> List[int]:\n '''Converts list of str to list of ints'''\n for i in range(len(lst)):\n for j in range(len(lst[i])):\n lst[i][j] = int(lst[i][j])\n return lst\n\n\ndef findPathGreedy(matrix: List[List[int]]) -> List[int]:\n '''\n Parameters\n ----------\n matrix : List[List[int]]\n The tree of ints to consider. e.g. [[1], [3, 19], [32, 2, 45]]\n Returns\n -------\n List[int]\n The Greedy solution path yielding a maximum total from top to bottom \n of the triangle matrix input. May NOT be the global maximum.\n '''\n path = [matrix[0][0]] # Set the initial starting points\n for i in range(1,len(matrix)): # Iterate over each row in matrix, save for the first \n idx = matrix[i-1].index(path[i-1]) # Record the index of the previous path max \n valid_idxs = list(set([idx, idx+1])) # List indices of the adjacent elements \n if valid_idxs[-1] >= len(matrix[i]): # If a candidate is outside the bounds, drop it\n valid_idxs.pop(-1) \n choices = []\n for k in valid_idxs: \n choices.append(matrix[i][k]) # Record values of the 2-3 candidates \n v = max(choices) # Select the largest path element candidate\n path.append(v)\n return path\n\n############################################################################################################################\n\ndef _A179477(n: int) -> int:\n '''\n Parameters\n ----------\n n : int\n Must be that n >= -1. \n Returns\n -------\n int\n Generates the corresponding term in sequence A179477: 1, 10, 99, 988, 9877, ...\n '''\n return (10 + 2**(5+n) * 5**(2+n) + 9*n) // 81\n\n@lru_cache()\ndef _diffPattern(N: int) -> List[int]:\n '''\n Parameters\n ----------\n N : int\n Must be that n >= 2.\n Returns\n -------\n List[int]\n Returns a list of the differences between subsequent terms of the indices\n of valid traversals of elements in the tree: \n a [[a],\n b c [b,c], \n d e f [d,e,f]\n ... ... ] \n For example if N=3, then the valid paths and their list of indices would be: \n a->b->d [[0,0,0], \n a->b->e [0,0,1], \n a->c->e [0,1,1], \n a->c->f [0,1,2]]\n and the differences between those lists taken as whole numbers would \n be [1, 10, 1]. For N=4 it would be [1, 10, 1, 99, 1, 10, 1], and so on.\n '''\n if N < 2:\n raise ValueError(\"It must be that N >= 2\")\n elif N == 2:\n return [_A179477(-1)]\n else:\n return _diffPattern(N-1) + [_A179477(N-3)] + _diffPattern(N-1) \n \ndef _list2int(array: List[int]) -> int:\n '''E.g. _list2int([1,2,3,4]) returns 1234''' \n num = ''\n for item in array:\n num += str(item)\n return int(num)\n\ndef _int2list(num: int) -> List[int]:\n '''E.g. _int2list(12345678910) returns [1,2,3,4,5,6,7,8,9,10]'''\n preprocessed = list(str(num)) # e.g. ['9','1','0','1','1']\n postprocessed = [int(preprocessed[0])] # e.g. [9]\n i, k = 1, 1\n while i < len(preprocessed):\n if postprocessed[-1] == 9 and preprocessed[i:i+k+1] == ['1','0']: # If we transition to 2 digits\n k = 2 # Slice every 2 digits \n if k == 1:\n sliced = [int(preprocessed[i:i+k][0])]\n else:\n a,b = preprocessed[i:i+k]\n sliced = [int(a)*10 + int(b)]\n i += 1\n postprocessed += sliced \n i += 1\n return postprocessed\n \n \ndef _addAsHex(x: int, y: int) -> int:\n '''Unnecessary for trees under 10 rows'''\n h1, h2 = hex(x), hex(y)\n return int(hex(int(h1, 16) + int(h2, 16))[2:], 16) \n \n# The problem is traced to here. Need to resolve the transition from one state to the next being the addition \n# of the difference pattern, but each index_path needs to be in base 10 afterwards. \ndef _transition(old_path, difference)-> List[int]:\n '''BRITTLE AND BUGGY'''\n converted = _addAsHex(_list2int(old_path), difference)\n padding = len(old_path) - len(str(converted))\n result = [0]*padding + _int2list(converted) \n return result\n\ndef bruteForce(tree: List[List[int]]) -> int:\n '''\n Parameters\n ----------\n tree : List[List[int]]\n The tree of ints to consider. e.g. [[1], [3, 19], [32, 2, 45]].\n Returns\n -------\n int\n The sum of the path that generates the maximal sum, out of all 2**(n-1) paths.\n BREAKS FOR TREES DEEPER THAN 10 ROWS CURRENTLY!!!!!!!!!!!!!\n '''\n length = len(tree)\n differences = _diffPattern(length) # Precompute the difference pattern e.g. [1, 10, 1, 99, 1, 10, 1]\n idx_paths = [[0 for i in range(length)]] # Initialize the zero path: [array([0., 0., 0., 0., 0.])]\n for i in range(1, len(differences) + 1): # Iterate 2**(n-1) - 1 times\n idx_paths.append(_transition(idx_paths[i-1], differences[i-1]))\n paths = [list(map(lambda row, idx: row[idx], tree, idx_paths[i])) for i in range(len(idx_paths))] \n return max(sum(path) for path in paths)\n \n############################################################################################################################\n\ndef findPathDynamic(matrix: List[List[int]]) -> List[int]:\n '''\n Parameters\n ----------\n matrix : List[List[int]]\n The tree of ints to consider. e.g. [[1], [3, 19], [32, 2, 45]].\n Returns\n -------\n List[int]\n The Dynamic Programming solution to find the path that maximizes the \n sum of the elements traversed.\n '''\n pass\n \n \nif __name__ == \"__main__\":\n tri ='''75\n 95 64\n 17 47 82\n 18 35 87 10\n 20 04 82 47 65\n 19 01 23 75 03 34\n 88 02 77 73 07 63 67\n 99 65 04 28 06 16 70 92\n 41 41 26 56 83 40 80 70 33\n 41 48 72 33 47 32 37 16 94 29\n 53 71 44 65 25 43 91 52 97 51 14\n 70 11 33 28 77 73 17 78 39 68 17 57\n 91 71 52 38 17 14 91 43 58 50 27 29 48\n 63 66 04 68 89 53 67 30 73 16 69 87 40 31\n 04 62 98 27 23 09 70 98 73 93 38 53 60 04 23'''\n print(bruteForce(str2IntElements(str2List(tri))))","repo_name":"VictorCannestro/Project_Euler","sub_path":"src/pe18_weird.py","file_name":"pe18_weird.py","file_ext":"py","file_size_in_byte":7567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"31655842463","text":"'''Write a function to find out the prime factors of a number. Example: prime factors of 84 -\n[2, 2, 3, 7]'''\n\nimport math\n\n\ndef get_prime_factors(number):\n prime_factors = []\n prime_num = 2\n\n while prime_num <= math.sqrt(number):\n while number % prime_num == 0:\n prime_factors.append(int(prime_num))\n number /= prime_num\n\n prime_num += 1\n\n if number > 1:\n # Here is the crucial part.\n # First quick refreshment on the two key mathematical conjectures of Prime factorization of any non-Prime number\n # Which is - 1. If n is not a prime number AT-LEAST one Prime factor would be less than sqrt(n)\n # And - 2. If n is not a prime number - There can be AT-MOST 1 prime factor of n greater than sqrt(n).\n # Like 7 is a prime-factor for 14 which is greater than sqrt(14)\n # But if the above loop DOES NOT go beyond square root of the initial n.\n # Then how does that greater than sqrt(n) prime-factor\n # will be captured in my prime factorization function.\n # ANS to that is - in my first for-loop I am dividing n with the prime number if that prime is a factor of n.\n # Meaning, after this first for-loop gets executed completely, the adjusted initial n should become\n # either 1 or greater than 1\n # And if n has NOT become 1 after the previous 2 while-loop, that means that\n # The remaining n is that prime factor which is greater that the square root of initial n.\n # And that's why in the next part of my algorithm, I need to check whether n becomes 1 or not,\n prime_factors.append(int(number))\n\n return prime_factors\n\n\nprint(get_prime_factors(84))\n","repo_name":"rohan-paul/MachineLearning-DeepLearning-Code-for-my-YouTube-Channel","sub_path":"Python_Challenges/Prime-Numbers/primeFactors.py","file_name":"primeFactors.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","stars":306,"dataset":"github-code","pt":"20"}
+{"seq_id":"599773546","text":"from django.shortcuts import render,redirect,get_object_or_404,reverse\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nfrom django.views.generic import (\n CreateView, ListView,\n DetailView, RedirectView,\n)\nfrom jobs.models import Job, JobProposal\nfrom user.models import User\nfrom direct_messages.services import MessagingService\nfrom direct_messages.models import ChatRoom\n\n# Create your views here.\n\n\nclass JobListView(ListView):\n \"\"\"\n 任务列表\n \"\"\"\n model = Job\n ordering = 'job_title'\n context_object_name = 'jobs'\n template_name = 'jobs/job_list.html'\n queryset = Job.objects.all()\n\n\n@method_decorator([login_required], name='dispatch')\nclass JobCreateView(CreateView):\n \"\"\"\n 创建任务\n \"\"\"\n model = Job\n fields = ('job_title', 'job_description', 'price', 'tags', 'document')\n template_name = 'jobs/job_add_form.html'\n\n def form_valid(self, form):\n job = form.save(commit=False)\n job.owner = self.request.user\n\n job.save()\n form.save_m2m()\n return redirect('jobs:job_detail', job.pk)\n\n\n@method_decorator([login_required], name='dispatch')\nclass JobDetailView(DetailView):\n \"\"\"\n 任务详细信息\n \"\"\"\n model = Job\n template_name = 'jobs/job_detail.html'\n context_object_name = 'job'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n job_id = self.kwargs.get('pk')\n job = Job.objects.get(pk=job_id)\n if job.owner != self.request.user and self.request.user in job.freelancers:\n context['current_proposal'] = JobProposal.objects.get(\n job__pk=job_id,\n freelancer=self.request.user\n )\n return context\n\n\n@method_decorator([login_required], name='dispatch')\nclass JobApplyView(CreateView):\n \"\"\"\n 任务申请\n \"\"\"\n model = JobProposal\n fields = ('proposal',)\n template_name = 'jobs/job_apply_form.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['jobs'] = Job.objects.get(pk=self.kwargs.get('pk'))\n return context\n\n def form_valid(self, form):\n proposal = form.save(commit=False)\n proposal.job = Job.objects.get(pk=self.kwargs.get('pk'))\n proposal.freelancer = self.request.user\n\n proposal.save()\n return redirect('user:job_profile', self. request.user.username)\n\n\n@method_decorator([login_required], name='dispatch')\nclass ProposalAcceptView(RedirectView):\n \"\"\"\n\n \"\"\"\n permanent = False\n query_string = True\n pattern_name = 'jobs:job_detail'\n\n def get_redirect_url(self, *args, **kwargs):\n job = get_object_or_404(Job, pk=kwargs['pk'])\n job.freelancer = User.objects.get(username=kwargs.get('username'))\n job.status = 'working'\n job.save()\n\n return super().get_redirect_url(*args,pk=kwargs['pk'])\n\n\n@method_decorator([login_required], name='dispatch')\nclass JobCloseView(RedirectView):\n \"\"\"\n 关闭任务\n \"\"\"\n permanent = False\n pattern_name = 'home'\n\n def get_redirect_url(self, *args, **kwargs):\n job = get_object_or_404(Job, pk=kwargs['pk'])\n job.status = 'ended'\n job.save()\n\n return super().get_redirect_url(*args)\n\n\nclass ProposalAcceptView(RedirectView):\n\n permanent = False\n query_string = True\n pattern_name = 'jobs:job_detail'\n\n def get_redirect_url(self, *args, **kwargs):\n job = get_object_or_404(Job,pk=kwargs['pk'])\n job.freelancer = User.objects.get(username=kwargs.get('username'))\n job.status = 'working'\n job.save()\n\n is_chatroom = False\n try:\n chatromm = ChatRoom.objects.get(sender=self.request.user, recipient=job.freelancer)\n is_chatroom = True\n\n except:\n pass\n\n if not is_chatroom:\n try:\n chatromm = ChatRoom.objects.create(sender=self.request.user, recipient=job.freelancer)\n\n except:\n pass\n\n if not is_chatroom:\n chatromm = ChatRoom.objects.create(sender=self.request.user, recipient=job.freelancer)\n\n MessagingService().send_messages(\n sender=self.request.user,\n recipient=job.freelancer,\n message='''\n Hi {username},\n\n Your proposal is accepted.\n\n project details : {job} \n '''.format(username=job.freelancer.username,\n url=reverse(\"jobs:job_detail\", kwargs={\"pk\": job.pk}),\n job=job.job_title)\n )\n messages.success(\n self.request, 'User : {} is assiged to your project'.format(kwargs.get('username')))\n\n return super().get_redirect_url(*args,pk=kwargs['pk'])\n\n\n\n\n\n\n\n\n","repo_name":"REds45/allwork","sub_path":"jobs/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"141217220","text":"#https://leetcode.com/problems/house-robber-ii/\n#Title: 213. House Robber II\n#Difficulty: Medium\n#Language: Python3\n#Author: Pranay Kotian\n\nclass Solution:\n def rob(self, nums: List[int]) -> int:\n \n n = len(nums)\n if n < 2:\n return nums[0]\n \n if (n%2 == 0):\n arr = [0 for i in range(n)]\n arr[0] = nums[0]\n arr[1] = max(nums[0], nums[1])\n\n for i in range(2, n):\n arr[i] = max(arr[i-1], arr[i-2] + nums[i])\n\n return arr[n-1]\n \n else:\n nums1 = nums[1:n]\n nums2 = nums[0:n-1]","repo_name":"PranayKotian/programming_practice","sub_path":"LeetCode/Python/rob2.py","file_name":"rob2.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"8866060059","text":"import reader\nimport tensorflow as tf\n\n# 数据路径\nDATA_PATH = 'simple-examples/data/'\n\n# 读取原始数据\ntrain_data, valid_data, test_data, _ = reader.ptb_raw_data(DATA_PATH)\n\n# 将数据组织成batch大小为4，截断长度为5的数据组，要放在开启多线程之前\nbatch = reader.ptb_producer(train_data, 4, 5)\n\nwith tf.Session() as sess:\n tf.global_variables_initializer().run()\n\n # 开启多线程\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(coord=coord)\n\n # 读取前两个batch，其中包括每个时刻的输入和对应的答案，ptb_producer()会自动迭代\n for i in range(2):\n x, y = sess.run(batch)\n print('x:', x)\n print('y:', y)\n\n # 关闭多线程\n coord.request_stop()\n coord.join(threads)\n","repo_name":"widiot/tensorflow-practices","sub_path":"lstm/nl-modeling/reader_test.py","file_name":"reader_test.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"2"}
+{"seq_id":"72793322607","text":"#!/usr/bin/python3\n\n\"\"\"\nsend a POST request to a URL with\nemail as a parameter and display the\nbody of the respobse(decoded in utf-8)\n\"\"\"\n\n\nimport urllib.request\nimport urllib.parse\nimport sys\n\n\ndef main():\n # Fetch the url and email\n url = sys.argv[1]\n email = sys.argv[2]\n data = urllib.parse.urlencode({\"email\": email}).encode(\"utf-8\")\n\n try:\n with urllib.request.urlopen(url, data) as response:\n print(response.read().decode(\"utf-8\"))\n except urllib.error.HTTPError as e:\n print(f\"Error code: {e.code}\")\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"LevittC17/alx-higher_level_programming","sub_path":"0x11-python-network_1/2-post_email.py","file_name":"2-post_email.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"35824422478","text":"#! encoding = utf8\n''' Quickly convert space delimited catalog file into comma delimited '''\nimport argparse\nimport re\n\ndef comma_del(line):\n ''' Insert comma at certain places following JPL catalog format '''\n comma_indices = [13, 22, 31, 34, 45, 49, 61, 74]\n for index in comma_indices:\n line = line[:index] + ',' + line[index:]\n return line\n\nparser = argparse.ArgumentParser(description=__doc__,\n epilog='--- Luyao Zou, May 2015 ---')\nparser.add_argument('file', nargs='+', help='File names')\nargs = parser.parse_args()\n\nfor file_name in args.file:\n try:\n output = re.sub('cat$', 'csv', file_name)\n catalog_out = []\n with open(file_name, 'r') as cat_file:\n catalog = cat_file.readlines()\n for line in catalog:\n # insert comma at given places \n line_out = comma_del(line)\n # remove all spaces\n line_out = re.sub(' +', '', line_out)\n catalog_out.append(line_out)\n with open(output, 'w') as cat_out:\n for line in catalog_out:\n cat_out.write(line)\n print('{:s} saved!'.format(output))\n except FileNotFoundError:\n print('{:s} not found! Skip'.format(file_name))\n except UnicodeDecodeError:\n print('File format error. Skip')\n except ValueError:\n print('Import error. Skip')\n except:\n print('Unexpected error. Skip')\n raise\n\n\n","repo_name":"luyaozou/gizmo","sub_path":"cat2csv.py","file_name":"cat2csv.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"40208678625","text":"import re\nfrom re import sub\n\ndef gen_sorted_bracket_pairs(expr): #ordered bracket pairs\n open_brackets = [m.start() for m in re.finditer('\\(', expr)]\n close_brackets = [m.start() for m in re.finditer('\\)', expr)]\n # copyOpen = open_brackets\n taken = list()\n bracket_pairs = list()\n \n all_open_brackets = [[open,'o'] for open in open_brackets]\n all_close_brackets = [[close,'c'] for close in close_brackets]\n all_brackets = sorted(all_open_brackets + all_close_brackets)\n\n if len(all_open_brackets) != len(all_close_brackets):\n raise \"Error: Non-even brackets.\"\n\n for b in range(len(all_brackets)):\n if all_brackets[b][1] == \"c\":\n i = 1\n while True:\n if all_brackets[b-i][1] == \"o\" and b-i not in taken:\n bracket_pairs.append([all_brackets[b-i][0],all_brackets[b][0]])\n taken.append(b-i)\n break\n i+=1\n sorted_bracket_pairs = sorted(bracket_pairs)\n # print(sorted(sorted_bracket_pairs)) ### BRACKET PAIRS\n return sorted_bracket_pairs\n\ndef gen_func_bps(func_name, expr, sorted_bracket_pairs): #bracket pair after function namefunction opening and closing bracket pairs in str\n funcs = [m.start() for m in re.finditer(func_name, expr)]\n master_func_bracket_pairs = list()\n\n for i in range(len(sorted_bracket_pairs)):\n # print(bracket_pairs[i])\n for func in funcs:\n if func > sorted_bracket_pairs[i][0] and func < sorted_bracket_pairs[i+1][0]:\n master_func_bracket_pairs.append(sorted_bracket_pairs[i+1])\n return master_func_bracket_pairs\n\ndef gen_func_sub_bps(func_bps,sorted_bracket_pairs): \n \"\"\"function sub bracket pairs\"\"\"\n #Find bracket pairs inside func_bps:\n func_sub_bps = list()\n for fbp in func_bps:\n temp_sub_bps = list()\n for bp in sorted_bracket_pairs:\n if bp[0]>fbp[0] and bp[1] fbp[0] and comma < fbp[1]: #if comma within function brackets\n AND_counter = list()\n for sfbp in func_sub_bps[i]: # if comma not within any bracket pair within function\n if comma < sfbp[0] or comma > sfbp[1]: # is comma outside of this sub-bracket?\n AND_counter.append(1)\n else:\n AND_counter.append(0)\n if sum(AND_counter) == len(AND_counter): # append comma only when outside all sub-brackets\n temp_comma.append(comma)\n if comma:\n func_commas.append(temp_comma)\n i+=1\n return func_commas\n\ndef gen_ranges2protect(func_bps, func_comma):\n #generating list of ranges to protect\n zipped_list = list(zip(func_bps,func_comma))\n ranges2protect = list()\n i=0\n for func, fcommas in zipped_list:\n i=0\n mini_r2p=list()\n for fcomma in fcommas:\n if (len(fcommas)-i)>1:\n # print(expr[fcommas[i]+1:fcommas[i+1]])\n mini_r2p.append([fcommas[i]+1, fcommas[i+1]])\n else:\n # print(expr[fcomma+1:func[1]])\n mini_r2p.append([fcomma+1,func[1]])\n i+=1\n ranges2protect.append(mini_r2p)\n return ranges2protect\n\ndef gen_protect_zones_copy(expr, ranges2protect):\n protect_zones_copy = list()\n for a in ranges2protect:\n for b in a:\n # print(expr[b[0]:b[1]])\n protect_zones_copy.append(expr[b[0]:b[1]])\n return protect_zones_copy\n\ndef gen_protect_zones(expr, func_list):\n protect_zones = list()\n protect_zone_ranges= list()\n for func in func_list:\n sorted_bracket_pairs = gen_sorted_bracket_pairs(expr)\n func_bps = gen_func_bps(func, expr, sorted_bracket_pairs)\n func_sub_bps = gen_func_sub_bps(func_bps,sorted_bracket_pairs)\n func_commas = gen_func_commas(expr, func_bps, func_sub_bps)\n ranges2protect = gen_ranges2protect(func_bps, func_commas)\n func_protect_zones = gen_protect_zones_copy(expr, ranges2protect)\n protect_zones = protect_zones + func_protect_zones\n protect_zone_ranges = protect_zone_ranges + ranges2protect\n\n #flatten list\n protect_zones_ranges_copy = list()\n for a in protect_zone_ranges:\n for b in a:\n # print(expr[b[0]:b[1]])\n protect_zones_ranges_copy.append([b[0],b[1]])\n\n zipped_list = sorted(list(zip(protect_zones_ranges_copy,protect_zones)))\n sorted_protect_zones = [item[1] for item in zipped_list]\n sorted_protect_zones.reverse()\n sorted_protect_zones_ranges_copy = [item[0] for item in zipped_list]\n sorted_protect_zones_ranges_copy.reverse()\n\n # return protect_zones\n # return list(zip(protect_zones_copy,protect_zones))\n return sorted_protect_zones, sorted_protect_zones_ranges_copy\n\ndef gen_replace_zones(expr, func_list):\n replace_zones = list()\n for func in func_list:\n sorted_bracket_pairs = gen_sorted_bracket_pairs(expr)\n func_bps = gen_func_bps(func, expr, sorted_bracket_pairs)\n func_sub_bps = gen_func_sub_bps(func_bps,sorted_bracket_pairs)\n func_commas = gen_func_commas(expr, func_bps, func_sub_bps)\n ranges2replace = gen_ranges2protect(func_bps, func_commas)\n replace_zones = replace_zones + ranges2replace\n\n replace_zones_copy = list()\n for a in replace_zones:\n for b in a:\n # print(expr[b[0]:b[1]])\n replace_zones_copy.append([b[0],b[1]])\n return replace_zones_copy\n\ndef stringMutation(old, new, replacement_range):\n #slice into 3 parts\n pre = old[:replacement_range[0]]\n # cen = old[replacement_range[0]:replacement_range[1]]\n suf = old[replacement_range[1]:]\n return pre + new + suf\n\ndef preSympifySub(expr,**kwargs):\n \"\"\"\n Designed this as a reponse to the fact that sympify evaluates expressions when performed and therefore would integrate and/or differentiate before I would have a chance to perform substitution.\n \n Usage:\n preSympifySub(r'Eq(v, L*diff(i,t))',i=\"10*t*exp(-5*t)\")\n \"\"\"\n\n for k,v in kwargs.items():\n v=str(v)\n\n func_list = [\"integrate\", \"diff\"]\n func_pres = list()\n\n # Check if any function listed is present in expr\n for func in func_list:\n if func in str(expr):\n func_pres.append(1)\n else:\n func_pres.append(0)\n\n if sum(func_pres) >= 1:\n protect_zones, _ = gen_protect_zones(str(expr), func_list)\n for k,v in kwargs.items():\n regex = rf'(?0.5]\n emedium=[(u,v) for (u,v,d) in G.edges(data=True) if (d['weight'] <=0.5 and d['weight'] >0.1)]\n esmall=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] <=0.1]\n # nodes\n nx.draw_networkx_nodes(G,pos,node_size=30)\n # edges\n nx.draw_networkx_edges(G,pos,edgelist=elarge,\n width=1.5)\n nx.draw_networkx_edges(G,pos,edgelist=emedium,\n width=0.8,alpha=0.5,edge_color='b')\n nx.draw_networkx_edges(G,pos,edgelist=esmall,\n width=0.8,alpha=0.2,edge_color='g')\n #nx.draw(G)\n plt.axis('off')\n plt.show()\n\n def graph_statistics(self):\n with open(self.connectivity_path) as f:\n C = [map(float, line.split()) for line in f] \n C = C/np.max(C)\n G=nx.from_numpy_matrix(C)\n\n print(\"Radius (minimum excentricity): %d\" % nx.radius(G))\n print(\"Diameter (shortes distance between most distant nodes): %d\" % nx.diameter(G))\n print(\"Density: %s\" % nx.density(G))\n print(\"Number of edges: %s\" % G.number_of_edges())\n print(\"Avg. path length: %s\" % nx.average_shortest_path_length(G))\n print(\"Avg. degree: %s\" % str(sum(G.degree().values())/float(len(G))))\n print(\"Avg. clustering: %f\" % nx.average_clustering(G))\n\n#model = Patient('APOE-3_02')\n\n#model.draw_graph()\n#model.graph_statistics()\n\n\n\n\nsignal = np.genfromtxt('test.txt', delimiter=',')\n\nsignal = signal-2.5\n\nps = np.abs(np.fft.fft(signal))**2\n\ntime_step = 0.1/1000\nfreqs = np.fft.fftfreq(signal.size, time_step)\nidx = np.argsort(freqs)\n\n\n\nplt.plot(freqs[idx], ps[idx])\n\nplt.yscale('log')\n\n\nplt.show()","repo_name":"pabloruiziba/aging_oscillators","sub_path":"patient.py","file_name":"patient.py","file_ext":"py","file_size_in_byte":6355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"8159227679","text":"#!/usr/bin/env python3\n# encoding: utf-8\nfrom __future__ import absolute_import, print_function, unicode_literals\nimport os\n\nfrom gozokia import Gozokia\nfrom gozokia.core.rules import RuleBase\nfrom gozokia.rules import GreetingRaise, GreetingObjetive\nfrom my_class import MyClassObjetive\n# First, declare our settings file:\nos.environ.setdefault(\"GOZOKIA_SETTINGS_MODULE\", \"settings\")\n\n# Initialize\ngoz = Gozokia()\n\n# Register our class rules\ngoz.rule(type=goz.OBJETIVE_COND, rank=2)(MyClassObjetive)\n#goz.rule(name='greeting', type=goz.OBJETIVE_COND, rank=1)(Greeting)\ngoz.rule(name='greeting', type=goz.RAISE_COND, rank=100)(GreetingRaise)\n# Register our class rules\n\n\n@goz.rule(type=goz.RAISE_COND, rank=3)\nclass MyClass2(RuleBase):\n\n def condition_raise(self, *args, **kwargs):\n return False\n\n def response(self, *args, **kwargs):\n self.response_output = 'My Class2'\n\n# Run Gozokia console\nif __name__ == '__main__':\n goz.initialize()\n print('\\n*** Gozokia ***')\n goz.console()\n","repo_name":"avara1986/gozokia","sub_path":"example/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"17447636279","text":"from quest.examples.island import IslandAdventure\nfrom quest.sprite import Player\nfrom quest.contrib.hit_points import HitPointsMixin\nimport arcade\n\nclass MortalPlayer(HitPointsMixin, Player):\n \"\"\"MortalPlayer uses `HitPointsMixin` to add hit point\n properties and methods like `change_hit_points`. Here, we re-define\n `on_collision` to lower the player's hit points whenever she bumps\n into another sprite (like a wall). Because the default behavior of\n `on_min_hit_points` is to kill the sprite, the player will be removed\n when she runs out of hit points.\n \"\"\"\n\n max_hit_points = 3\n hit_points = 3\n\n def on_collision(self, other_sprite, game):\n self.change_hit_points(-1)\n \n\nclass DangerousIsland(IslandAdventure):\n \"\"\"Extends `IslandAdventure` to use `MortalPlayer` and to \n constantly report the player's hit points.\n \"\"\"\n\n def setup_player(self):\n \"\"\"Creates the player sprite. Almost the same as the regular \n `setup_player`, except uses `MortalPlayer` instead of `Player`.\n \"\"\"\n self.player = MortalPlayer(self.player_sprite_image, self.player_scaling)\n self.player.center_x = self.player_initial_x\n self.player.center_y = self.player_initial_y\n self.player.speed = self.player_speed\n self.player_list = arcade.SpriteList()\n self.player_list.append(self.player)\n\n def message(self):\n return \"Hit points: {}/{}\".format(\n self.player.hit_points,\n self.player.max_hit_points,\n )\n \nif __name__ == '__main__':\n game = DangerousIsland()\n game.run()\n","repo_name":"the-isf-academy/quest","sub_path":"quest/examples/contrib/hit_points.py","file_name":"hit_points.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"31433669804","text":"import random\n\nclass Player:\n def __init__(self):\n self.roll = \"\"\n self.rollsCount = 0\n self.atStartup = True\n self.winner = False\n self.loser = False\n\n def rollDice(self):\n if self.atStartup:\n self.roll = self._rollDice()\n self.atStartup = False\n else:\n self.roll = self._rollDice()\n if self.roll == \"7\":\n self.loser = True\n elif self.roll == self.firstRoll:\n self.winner = True\n self.rollsCount += 1\n return tuple(map(int, self.roll.split(\",\")))\n\n def getNumberOfRolls(self):\n return self.rollsCount\n\n def isWinner(self):\n return self.winner\n\n def isLoser(self):\n return self.loser\n\n def _rollDice(self):\n dice1 = random.randint(1, 6)\n dice2 = random.randint(1, 6)\n total = dice1 + dice2\n return f\"{dice1},{dice2}\"\n\ndef playOneGame():\n player = Player()\n while not player.isWinner() and not player.isLoser():\n input(\"Press Enter to roll the dice...\")\n dice_values = player.rollDice()\n print(f\"Roll {player.getNumberOfRolls()}: {dice_values[0]} + {dice_values[1]} = {sum(dice_values)}\")\n if player.isWinner():\n print(\"Congratulations! You win!\")\n else:\n print(\"Oops! You rolled a 7. You lose!\")\n\ndef playManyGames(num_games):\n for game in range(num_games):\n print(f\"\\nGame {game+1}:\")\n playOneGame()\n\n# Example usage:\nplayManyGames(3)\n","repo_name":"akomvictory/pyproject","sub_path":"dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"16536974354","text":"n = int(input())\nlst = []\nres = []\ncnt = 1\ntemp = False\n\nfor i in range(n):\n num = int(input())\n while cnt <= num:\n lst.append(cnt)\n res.append('+')\n cnt += 1\n if lst[-1] == num:\n lst.pop()\n res.append('-')\n else:\n temp = True\nif temp:\n print('NO')\nelse:\n for i in res:\n print(i)\n","repo_name":"hodurie/Algorithm","sub_path":"Baekjoon/Silver/1874.py","file_name":"1874.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"74356613166","text":"# -*- coding:utf-8 -*-\n\n__author__ = 'catleer'\n\n\"\"\"\n循环只有for和while 没有do...while\n\"\"\"\n\n# 杨辉三角形?递归？生成器？\ndef triangles():\n L = [1]\n while True:\n yield L \n L = [1]+[L[i] + L[i+1] for i in range(len(L)-1)]+[1]\n\nt = triangles()\nn = 0\nfor t in triangles():\n print(t)\n n = n + 1\n if n == 15:\n break\n\n ","repo_name":"hlb9071/deeptest","sub_path":"第一期/成都-乐大爷/task02/02_Day2/for.py","file_name":"for.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"43114862993","text":"from datasets import load_dataset\nimport numpy as np\nimport pandas as pd\nimport requests\n\nurl = \"https://huggingface.co/datasets/mstz/heart_failure/raw/main/heart_failure_clinical_records_dataset.csv\"\ndataset = load_dataset(\"mstz/heart_failure\")\ndata = np.array (dataset[\"train\"])\n\n'''\nPromedio edades de las personas participantes\n'''\n# suma = 0\n# conteo = 0\n# for item in data:\n# suma += item ['age']\n# conteo += 1\n# print (suma/conteo)\n\n\n'''\nPROCESANDO INFORMACION EN BRUTO\n'''\n\ndef recibirUrl(url,nombre_archivo):\n\n response = requests.get(url)\n\n if response.status_code == 200:\n with open(nombre_archivo,'w') as archivo:\n archivo.write(response.text)\n print(\"Archivo creado exitosamente\")\n return f\"Datos descargados y guardados en '{archivo}' exitosamente.\"\n \n else:\n return f\"Error en la solicitud. Código de estado: {response.status_code}\"\nrecibirUrl(url,\"data.csv\")\ndataframe = pd.read_csv(\"data.csv\")\n\n'''\nConvirtiendo estructura a DataFrame\n'''\n\nconversion_dataframe = pd.DataFrame(dataset)\nprint(conversion_dataframe)\n\n'''\nDivision de DataFrame segun condicion is_dead\n'''\ncondicion1 = conversion_dataframe['train'].apply(lambda x : x ['is_dead']==1)\npacientes_is_dead = conversion_dataframe[condicion1]\nprint (pacientes_is_dead)\n\n\ncondicion2 = conversion_dataframe['train'].apply(lambda x : x ['is_dead']==0)\npacientes_not_is_dead = conversion_dataframe[condicion2]\nprint (pacientes_not_is_dead)\n\n\n'''\nCALCULO PROMEDIO EDADES\n'''\npromedio_edades1 = pacientes_is_dead['train'].apply(lambda x : x ['age']).mean()\nprint (promedio_edades1)\n\npromedio_edades2 = pacientes_not_is_dead['train'].apply(lambda x : x ['age']).mean()\nprint (promedio_edades2)\n\n\n'''\nDIVISION DATAFRAME A 299 FILAS Y 13 COLUMNAS\n'''\n\ndef obtener_valores(conversion_dataframe):\n def obtener_valor(fila, columna):\n return fila.get(columna, None)\n\n columnas_deseadas = ['age', 'has_anaemia', 'creatinine_phosphokinase_concentration_in_blood',\n 'has_diabetes', 'heart_ejection_fraction', 'has_high_blood_pressure',\n 'platelets_concentration_in_blood', 'serum_creatinine_concentration_in_blood',\n 'serum_sodium_concentration_in_blood', 'is_male', 'is_smoker',\n 'days_in_study', 'is_dead']\n\n for columna in columnas_deseadas:\n conversion_dataframe[columna] = conversion_dataframe['train'].apply(lambda x: obtener_valor(x, columna))\n\n conversion_dataframe = conversion_dataframe.drop('train', axis=1)\n return conversion_dataframe\n\n# Uso de la función\nconversion_dataframe = obtener_valores(conversion_dataframe)\nprint(conversion_dataframe)\n\n'''\nConsultando tipo de datos correctos\n'''\nprint(conversion_dataframe.dtypes)\n\n\n'''\nCALCULO HOMBRES VS MUJERES FUMADORAS\n'''\n\nhfumadores_vs_mfumadoras = conversion_dataframe.groupby(['is_male', 'is_smoker']).size().unstack()\nprint(\"\\nresultados:\")\nprint(hfumadores_vs_mfumadoras)\n\n\n'''\nLIMPIEZA Y PREPARACION DE DATOS\n'''\n#Verificando que no existan valores faltantes\nconversion_dataframe.info()\n\n#Verificando que no existan filas repetidas\nprint (conversion_dataframe.duplicated())\n\n#Verificando si existen valores atípicos y eliminarlos en la columna 'age' (0 errores atipicos)\ndef limpiarPrepararDatos(dataframe):\n conversion_dataframe = dataframe\n df_ordenado_age = conversion_dataframe.sort_values(by='age', ascending=True)\n\n Q1 = df_ordenado_age['age'].quantile(0.25)\n Q3 = df_ordenado_age['age'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n val_atipicos_age = df_ordenado_age[(df_ordenado_age['age'] >= lower_bound) & (df_ordenado_age['age'] <= upper_bound)]\n # print (val_atipicos_age)\n\n #Verificando si existen valores atípicos y eliminarlos en la columna 'creatinine_phosphokinase_concentration_in_blood'(29 errores atipicos)\n df_ordenado_creatinine = conversion_dataframe.sort_values(by='creatinine_phosphokinase_concentration_in_blood', ascending=True)\n\n Q1 = df_ordenado_creatinine['creatinine_phosphokinase_concentration_in_blood'].quantile(0.25)\n Q3 = df_ordenado_creatinine['creatinine_phosphokinase_concentration_in_blood'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n val_atipicos_creatinine = df_ordenado_creatinine[(df_ordenado_creatinine['creatinine_phosphokinase_concentration_in_blood'] >= lower_bound) & (df_ordenado_creatinine['creatinine_phosphokinase_concentration_in_blood'] <= upper_bound)]\n # print (\"Errores atipicos de creatinine :\",val_atipicos_creatinine)\n\n #Verificando si existen valores atípicos y eliminarlos en la columna 'heart_ejection_fraction'(2 errores atipicos)\n df_ordenado_heart = val_atipicos_creatinine.sort_values(by='heart_ejection_fraction', ascending=True)\n\n Q1 = df_ordenado_heart['heart_ejection_fraction'].quantile(0.25)\n Q3 = df_ordenado_heart['heart_ejection_fraction'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n val_atipicos_heart = df_ordenado_heart[(df_ordenado_heart['heart_ejection_fraction'] >= lower_bound) & (df_ordenado_heart['heart_ejection_fraction'] <= upper_bound)]\n # print (\"Errores atipicos de heart :\",val_atipicos_heart)\n\n #Verificando si existen valores atípicos y eliminarlos en la columna 'platelets_concentration_in_blood' (18 errores atipicos)\n df_ordenado_platelets = val_atipicos_heart.sort_values(by='platelets_concentration_in_blood', ascending=True)\n\n Q1 = df_ordenado_platelets['platelets_concentration_in_blood'].quantile(0.25)\n Q3 = df_ordenado_platelets['platelets_concentration_in_blood'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n val_atipicos_platelets = df_ordenado_platelets[(df_ordenado_platelets['platelets_concentration_in_blood'] >= lower_bound) & (df_ordenado_platelets['platelets_concentration_in_blood'] <= upper_bound)]\n # print (\"Errores atipicos de platelets :\",val_atipicos_platelets)\n\n #Verificando si existen valores atípicos y eliminarlos en la columna 'serum_creatinine_concentration_in_blood'(23 errores atipicos)\n df_ordenado_serum = val_atipicos_platelets.sort_values(by='serum_creatinine_concentration_in_blood', ascending=True)\n\n Q1 = df_ordenado_serum['serum_creatinine_concentration_in_blood'].quantile(0.25)\n Q3 = df_ordenado_serum['serum_creatinine_concentration_in_blood'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n val_atipicos_serum = df_ordenado_serum[(df_ordenado_serum['serum_creatinine_concentration_in_blood'] >= lower_bound) & (df_ordenado_serum['serum_creatinine_concentration_in_blood'] <= upper_bound)]\n # print (\"Errores atipicos de serum :\",val_atipicos_serum)\n\n #Verificando si existen valores atípicos y eliminarlos en la columna 'serum_sodium_concentration_in_blood' (3 errores atipicos)\n df_ordenado_sodio = val_atipicos_serum.sort_values(by='serum_sodium_concentration_in_blood', ascending=True)\n\n Q1 = df_ordenado_sodio['serum_sodium_concentration_in_blood'].quantile(0.25)\n Q3 = df_ordenado_sodio['serum_sodium_concentration_in_blood'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n df_limpio = df_ordenado_sodio[(df_ordenado_sodio['serum_sodium_concentration_in_blood'] >= lower_bound) & (df_ordenado_sodio['serum_sodium_concentration_in_blood'] <= upper_bound)]\n print (\"dataframe limpio :\",df_limpio)\n\n #Verificando si existen valores atípicos y eliminarlos en la columna 'days_in_study' (0 errores atipicos)\n df_ordenado_dias_en_estudio = conversion_dataframe.sort_values(by='days_in_study', ascending=True)\n\n Q1 = df_ordenado_dias_en_estudio['days_in_study'].quantile(0.25)\n Q3 = df_ordenado_dias_en_estudio['days_in_study'].quantile(0.75)\n IQR = Q3 - Q1\n\n lower_bound = Q1 - 1.5 * IQR\n upper_bound = Q3 + 1.5 * IQR\n\n val_atipicos_dias_en_estudio = df_ordenado_dias_en_estudio[(df_ordenado_dias_en_estudio['days_in_study'] >= lower_bound) & (df_ordenado_dias_en_estudio['days_in_study'] <= upper_bound)]\n # print (val_atipicos_dias_en_estudio)\n '''\n CREACION DE COLUMNA CATEGORIZANDO POR EDADES\n '''\n edades = [0, 12,19, 39, 59, float('inf')]\n\n etiquetas = ['Niño', 'Adolescente', 'Jovenes adulto', 'Adulto', 'Adulto mayor']\n #import pdb;pdb.set_trace()\n df_limpio = df_limpio.copy()\n df_limpio['Categoría de Edad'] = pd.cut(df_limpio['age'], bins=edades, labels=etiquetas, right=False)\n print (df_limpio)\n\n df_limpio.to_csv(\"resultado_df_limpio.csv\", index = False)\nlimpiarPrepararDatos(conversion_dataframe)\n\n ","repo_name":"Brian10032011/Proyecto_procesamiento_de_datos","sub_path":"fallo_cardiaco.py","file_name":"fallo_cardiaco.py","file_ext":"py","file_size_in_byte":8776,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"73454077485","text":"\ndef factorial(n):\n if n == 0:\n return 1\n elif n == 1:\n return 1\n elif n == 2:\n return 2\n elif n == 3:\n return 6\n elif n == 4:\n return 24\n elif n == 5:\n return 120\n elif n == 6:\n return 720\n elif n == 7:\n return 720 * 7\n elif n == 8:\n return 720 * 7 * 8\n elif n == 9:\n return 720 * 7 * 8 * 9\n\ndef fact_sum(n):\n sum = 0\n while n != 0:\n sum += factorial(n % 10)\n n = n // 10\n return sum\n\ncnt = 0\nfor i in range(1, 1000000):\n chain = []\n n = i\n chain_cnt = 0\n while True:\n if n not in chain:\n chain_cnt += 1\n chain.append(n)\n n = fact_sum(n)\n else:\n break\n if chain_cnt == 60:\n print(i, chain)\n cnt += 1\n\nprint(cnt)","repo_name":"crapas/ep","sub_path":"061_090/074/74.py","file_name":"74.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"41397397694","text":"# If False, use an r-uniform sparse prior\nUSE_SUPERFLAT_PRIOR = False\nDO_ASCENT = True\nDO_NORMALIZATION = True\nUSE_TLI_ESTIMATE = True\n\n# document sizes to use for synthetic experiments\ntest_D = range(200, 2000, 200)\nDOCS = 200\n#DOCS = 200\n\n# Sparsity parameter; for Dirichlet, we set alpha = r/K\nr = 5\n# Inversion parameter: allowed bias\ndelta = 0\n\n# This setting matters mostly for MAP computation\n# For experiments we used 8e-4 for NY times, 2e-3 for other datasets\nbase_step_size = 2e-3\niters = 1600\n\n#LP Solver to use, either glpk or mosek. Must install cvxopt package\n# with appropriate support.\nLP_SOLVER = 'glpk'\n\n# max number of processes to use\nPROC = 16\n","repo_name":"frkoehle/topic-inference","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"2"}
+{"seq_id":"37433044987","text":"import pyinputplus as pyip\n\n\ndef addsUpToTen(numbers):\n numbersList = list(numbers)\n for i, digit in enumerate(numbersList):\n numbersList[i] = int(digit)\n if sum(numbersList) != 10:\n raise Exception(\"The digits must add up to 10, not %s.\" % (sum(numbersList)))\n return int(numbers) # Return an int form of numbers.\n\n\nresponse = pyip.inputCustom(addsUpToTen)\nprint(response)\n","repo_name":"Sylruilshu/automate-the-boring-stuff","sub_path":"chapter-8/Programs/inputCustom.py","file_name":"inputCustom.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"74471954287","text":"# 130. Surrounded Regions\nclass Solution:\n def solve(self, board: List[List[str]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n visit = set()\n row,col = len(board),len(board[0])\n def dfs(r, c):\n if (\n r not in range(row)\n or c not in range(col)\n or board[r][c] == \"X\"\n or (r, c) in visit\n ):\n return\n\n visit.add((r, c))\n directions = [[0, 1], [0, -1], [1, 0], [-1, 0]]\n for dr, dc in directions:\n dfs(r + dr, c + dc)\n # 0,0 0,1 0,n \n # 1,0 1,n\n # n,0 n,1 n,n\n #top\n for r in [0,row-1]:\n for c in range(col):\n if board[r][c] == 'O' and (r,c) not in visit:\n dfs(r,c)\n # bottom\n for c in [0,col-1]:\n for r in range(row):\n if board[r][c] == 'O' and (r,c) not in visit:\n dfs(r,c)\n for r in range(row):\n for c in range(col):\n if board[r][c] == 'O' and (r,c) not in visit:\n board[r][c]='X'\n return","repo_name":"farukhsk09/Algorithms","sub_path":"graph/surrounding_regions.py","file_name":"surrounding_regions.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"29512681707","text":"\n#!/usr/bin/env python3\nimport threading\nimport traceback\nfrom contextlib import contextmanager\n\nimport smach\n\n__all__ = ['Iterator']\n\nclass Iterator(smach.container.Container):\n \"\"\"Sequence Container\n\n This container inherits functionality from L{smach.StateMachine} and adds\n some auto-generated transitions that create a sequence of states from the\n order in which said states are added to the container.\n \"\"\"\n def __init__(self,\n outcomes,\n input_keys,\n output_keys,\n it = [],\n it_label = 'it_data',\n exhausted_outcome = 'exhausted'):\n \"\"\"Constructor.\n\n @type outcomes: list of string\n @param outcomes: The potential outcomes of this container.\n\n @type it: iterable\n @param iteritems: Items to iterate over on each cycle\n\n @type it_label: string\n @param iteritems_label: The label that the item in the current\n iteration will be given when it is put into the container's local\n userdata.\n \"\"\"\n if exhausted_outcome not in outcomes:\n outcomes.append(exhausted_outcome)\n smach.container.Container.__init__(self, outcomes, input_keys, output_keys)\n\n self._items = it\n self._items_label = it_label\n\n self._is_running = False\n\n self._state_label = ''\n self._state = None\n self._loop_outcomes = []\n self._break_outcomes = []\n self._final_outcome_map = {}\n self._exhausted_outcome = exhausted_outcome\n\n\n ### Construction Methods\n @staticmethod\n def set_iteritems(it, it_label='it_data'):\n \"\"\"Set the list or generator for the iterator to iterate over.\n \n @type it: iterable\n @param iteritems: Items to iterate over on each cycle\n\n @type it_label: string\n @param iteritems_label: The label that the item in the current\n iteration will be given when it is put into the container's local\n userdata.\n\n @type exhausted_outcome: string\n @param exhausted_outcome: If the iterable is exhausted without a break\n condition this outcome is emitted by the container.\n \"\"\"\n # Get currently opened container\n self = Iterator._currently_opened_container()\n self._items = it\n self._items_label = it_label\n\n @staticmethod\n def set_contained_state(\n label,\n state,\n loop_outcomes = [],\n break_outcomes = [],\n final_outcome_map = {}):\n \"\"\"Set the contained state\n \n @type label: string\n @param label: The label of the state being added.\n \n @type state: L{smach.State}\n @param state: An instance of a class implementing the L{smach.State} interface.\n\n @param loop_outcomes: List of contained state outcomes that should cause\n the iterator to continue. If this is empty, all outcomes that are not\n in the break_outcomes list will cause the iterator to continue to\n iterate. NOTE: loop_outcomes will be overriden by break_outcomes if both\n parameters are used.\n\n @param break_outcomes: List of contained state outcomes that should\n cause the iterator to break. When the contained state emits an outcome\n in this list, the container will terminate and return either that\n outcome or the outcome it is mapped to in final_outcome_map. NOTE:\n loop_outcomes will be overriden by break_outcomes if both\n parameters are used.\n\n @param final_outcome_map: A map from contained state outcomes to container\n outcomes. On termination of the iterator (either from finishing or from\n a break) this map will be used to translate contained state outcomes to\n container outcomes.\n Unspecified contained state outcomes will fall through as\n container outcomes.\n \"\"\"\n # Get currently opened container\n self = Iterator._currently_opened_container()\n\n self._state_label = label\n self._state = state\n\n # Get potential state outcomes\n state_outcomes = state.get_registered_outcomes()\n\n # Check for loop and break outcomes\n if loop_outcomes and break_outcomes:\n smach.logwarn('Both loop_outcomes and break_outcomes were specified when constructing SMACH iterator container.')\n\n if break_outcomes:\n self._break_outcomes = break_outcomes\n for outcome in state_outcomes:\n if outcome not in break_outcomes:\n self._loop_outcomes.append(outcome)\n else:\n self._loop_outcomes = loop_outcomes\n for outcome in state_outcomes:\n if outcome not in loop_outcomes:\n self._break_outcomes.append(outcome)\n\n self._final_outcome_map = final_outcome_map\n\n ### State interface\n def execute(self, parent_ud = None):\n self._is_running = True\n\n # Copy input keys\n self._copy_input_keys(parent_ud, self.userdata)\n\n self.call_start_cbs()\n\n # Iterate over items\n outcome = self._exhausted_outcome\n\n if hasattr(self._items,'__call__'):\n it = self._items().__iter__()\n else:\n it = self._items.__iter__()\n\n while not smach.is_shutdown():\n try:\n item = next(it)\n except:\n outcome = self._exhausted_outcome\n break\n smach.loginfo(\"Iterating %s of %s\" % (str(item), str(self._items)))\n self.userdata[self._items_label] = item\n # Enter the contained state\n try:\n outcome = self._state.execute(self.userdata)\n except smach.InvalidUserCodeError as ex:\n smach.logerr(\"Could not execute Iterator state '%s'\" % self._state_label)\n raise ex\n except:\n raise smach.InvalidUserCodeError(\"Could not execute iterator state '%s' of type '%s': \" % ( self._state_label, self._state) + traceback.format_exc())\n \n\n\n # Check if we should stop preemptively\n if self._preempt_requested\\\n or outcome in self._break_outcomes\\\n or (len(self._loop_outcomes) > 0 and outcome not in self._loop_outcomes):\n self._preempt_requested = False\n break\n self.call_transition_cbs()\n\n # Remap the outcome if necessary\n if outcome in self._final_outcome_map:\n outcome = self._final_outcome_map[outcome]\n\n # Copy output keys\n self._copy_output_keys(self.userdata, parent_ud)\n\n self._is_running = False\n\n self.call_termination_cbs(self._state_label,outcome)\n\n return outcome\n\n def request_preempt(self):\n self._preempt_requested = True\n if self._is_running:\n self._state.request_preempt()\n \n ### Container interface\n def get_children(self):\n return {self._state_label: self._state}\n\n def __getitem__(self,key):\n if key != self._state_label:\n smach.logerr(\"Attempting to get state '%s' from Iterator container. The only available state is '%s'.\" % (key, self._state_label))\n raise KeyError()\n return self._state\n\n def get_initial_states(self):\n return [self._state_label]\n\n def set_initial_state(self, initial_states, userdata):\n # Check initial state\n if len(initial_states) > 1:\n smach.logwarn(\"Attempting to set initial state to include more than one state, but Iterator container can only have one initial state.\" % (self._state_label))\n\n if len(initial_states) > 0:\n if initial_states[0] != self._state_label:\n smach.logwarn(\"Attempting to set state '%s' as initial state in Iterator container. The only available state is '%s'.\" % (initial_states[0], self._state_label))\n raise KeyError()\n\n # Set local userdata\n self.userdata.update(userdata)\n\n def get_active_states(self):\n if self._is_running:\n return [self._state_label]\n return []\n\n def get_internal_edges(self):\n int_edges = []\n \n for outcome in self._loop_outcomes:\n int_edges.append([outcome, self._state_label, self._state_label])\n\n for outcome in self._break_outcomes:\n container_outcome = outcome\n if outcome in self._final_outcome_map:\n container_outcome = self._final_outcome_map[outcome]\n if outcome == container_outcome:\n int_edges.append((outcome, self._state_label, None))\n else:\n int_edges.append((outcome, self._state_label, container_outcome))\n\n return int_edges\n\n def check_consistency(self):\n pass\n","repo_name":"ros/executive_smach","sub_path":"smach/smach/iterator.py","file_name":"iterator.py","file_ext":"py","file_size_in_byte":8871,"program_lang":"python","lang":"en","doc_type":"code","stars":153,"dataset":"github-code","pt":"2"}
+{"seq_id":"36093911877","text":"import trio\n\nimport pyctor\nfrom pyctor.behaviors import Behaviors\nfrom pyctor.types import Behavior, BehaviorSetup, Context, SpawnOptions\n\n\ndef test_restart():\n counter = 0\n restart_counter = -1\n\n async def setup(_: Context[int]) -> BehaviorSetup[int]:\n nonlocal counter\n nonlocal restart_counter\n\n internal_counter = 0\n\n counter += 1\n restart_counter += 1\n\n async def setup_handler(msg: int) -> Behavior[int]:\n nonlocal internal_counter\n nonlocal counter\n nonlocal restart_counter\n\n internal_counter += 1\n counter += 1\n\n if internal_counter == 3:\n if restart_counter == 3:\n return Behaviors.Stop\n\n return Behaviors.Restart\n\n return Behaviors.Same\n\n yield Behaviors.receive(setup_handler)\n\n async def main() -> None:\n with trio.fail_after(1):\n setup_behavior = Behaviors.setup(setup)\n async with pyctor.open_nursery() as n:\n setup_ref = await n.spawn(\n setup_behavior, options=SpawnOptions(name=\"setup\")\n )\n for i in range(12):\n setup_ref.send(i)\n\n trio.run(main)\n assert counter == 16\n\n\nif __name__ == \"__main__\":\n test_restart()\n","repo_name":"Chumper/pyctor","sub_path":"src/tests/examples/func/restart_test.py","file_name":"restart_test.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"72783396205","text":"# -*- coding: utf-8 -*-\r\n\r\nimport random\r\nimport pickle\r\nimport argparse\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom tqdm import tqdm\r\nfrom scipy.io import loadmat\r\nimport torch\r\nimport torchvision\r\nimport torch.utils.data as Data\r\nimport torch.nn as nn\r\ntorch.backends.cudnn.enabled = False\r\n\r\nrandom.seed(1234)\r\n\r\nworkdir = 'datasets/'\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('--dataset', default='Epinions', help='dataset name: Ciao/Epinions/Flixster')\r\n\r\nparser.add_argument('--test_prop', default=0.2, help='the proportion of data used for test')\r\nargs = parser.parse_args()\r\n\r\ndf =np.dtype([('uid',np.int32),('iid',np.int32),('rating',np.float32)])\r\n\r\n\r\nif args.dataset == 'Ciao':\r\n click_f = np.loadtxt(workdir + 'Ciao/my_rating.txt', dtype=np.int32)\r\n trust_f = np.loadtxt(workdir + 'Ciao/trust.txt', dtype=np.int32)\r\nelif args.dataset == 'Epinions':\r\n click_f = np.loadtxt(workdir + 'Epinions/my_rating.txt', dtype=np.int32)\r\n trust_f = np.loadtxt(workdir + 'Epinions/link.txt', dtype=np.int32)\r\nelif args.dataset == 'Flixster':\r\n click_f = np.loadtxt(workdir + 'Flixster/my_rating.txt', dtype=df)\r\n trust_f = np.loadtxt(workdir + 'Flixster/my_trust.txt', dtype=np.int32)\r\nelse:\r\n pass\r\n\r\n\r\nclick_list = []\r\ntrust_list = []\r\ntrust_temp = []\r\n\r\nu_items_list = []\r\nu_users_list = []\r\nu_users_items_list = []\r\ni_users_list = []\r\n\r\npos_u_items_list = []\r\npos_i_users_list = []\r\n\r\nuser_count = 0\r\nitem_count = 0\r\nrate_count = 0\r\n\r\nfor s in click_f:\r\n uid = s[0]\r\n iid = s[1]\r\n\r\n if args.dataset == 'Ciao':\r\n label = s[3]\r\n elif args.dataset == 'Epinions':\r\n label = s[3]\r\n elif args.dataset == 'Flixster':\r\n label = s[2]\r\n\r\n if uid > user_count:\r\n user_count = uid\r\n if iid > item_count:\r\n item_count = iid\r\n if label > rate_count:\r\n rate_count = label\r\n\r\n click_list.append([uid, iid, label])\r\n\r\nfor s in trust_f:\r\n uid = s[0]\r\n trust_temp.append(uid)\r\n\r\n\r\npos_list = []\r\npos_trust = []\r\nfor i in range(len(click_list)):\r\n pos_list.append((click_list[i][0], click_list[i][1], click_list[i][2]))\r\nfor i in trust_temp:\r\n pos_trust.append(i)\r\n\r\npos_list = list(set(pos_list))\r\npos_trust = list(set(pos_trust))\r\n\r\npos_df = pd.DataFrame(pos_list, columns=['uid', 'iid', 'label'])\r\nfilter_pos_list = []\r\nuser_in_set, user_out_set = set(), set()\r\n\r\nfor u in tqdm(range(user_count + 1)):\r\n hist = pos_df[pos_df['uid'] == u]\r\n\r\n if len(hist) < 5:\r\n continue\r\n\r\n user_in_set.add(u)\r\n\r\n u_items = hist['iid'].tolist()\r\n u_ratings = hist['label'].tolist()\r\n filter_pos_list.extend([(u, iid, rating) for iid, rating in zip(u_items, u_ratings)])\r\n\r\nprint('user in and out size: ', len(user_in_set), len(user_out_set))\r\nprint('data size before and after filtering: ', len(pos_list), len(filter_pos_list))\r\n\r\n\r\nprint('test prop: ', args.test_prop)\r\npos_list = filter_pos_list\r\n\r\nrandom.shuffle(pos_list)\r\nnum_test = int(len(pos_list) * args.test_prop)\r\n\r\n\r\ntest_set = pos_list[:num_test]\r\n\r\nvalid_set = pos_list[num_test:2 * num_test]\r\n\r\ntrain_set = pos_list[2 * num_test:]\r\n\r\n\r\nprint('Train samples: {}, Valid samples: {}, Test samples: {}, Total samples: {}'.format(len(train_set), len(valid_set),\r\n len(test_set), len(pos_list)))\r\n\r\nwith open(workdir + args.dataset + '/dataset_filter5.pkl', 'wb') as f:\r\n pickle.dump(train_set, f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump(valid_set, f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump(test_set, f, pickle.HIGHEST_PROTOCOL)\r\n\r\npos_df = pd.DataFrame(pos_list, columns=['uid', 'iid', 'label'])\r\ntrain_df = pd.DataFrame(train_set, columns=['uid', 'iid', 'label'])\r\nvalid_df = pd.DataFrame(valid_set, columns=['uid', 'iid', 'label'])\r\ntest_df = pd.DataFrame(test_set, columns=['uid', 'iid', 'label'])\r\n\r\nclick_df = pd.DataFrame(click_list, columns=['uid', 'iid', 'label'])\r\ntrain_df = train_df.sort_values(axis=0, ascending=True, by='uid')\r\npos_df = pos_df.sort_values(axis=0, ascending=True, by='uid')\r\n\r\n\r\nfor u in tqdm(range(user_count + 1)):\r\n hist = train_df[train_df['uid'] == u]\r\n u_items = hist['iid'].tolist()\r\n u_ratings = hist['label'].tolist()\r\n if u_items == []:\r\n u_items_list.append([(0, 0)])\r\n else:\r\n u_items_list.append([(iid, rating) for iid, rating in zip(u_items, u_ratings)])\r\n\r\n\r\ntrain_df = train_df.sort_values(axis=0, ascending=True, by='iid')\r\n\r\n\r\nuserful_item_set = set()\r\nfor i in tqdm(range(item_count + 1)):\r\n hist = train_df[train_df['iid'] == i]\r\n i_users = hist['uid'].tolist()\r\n i_ratings = hist['label'].tolist()\r\n\r\n\r\n if i_ratings == []:\r\n i_users_list.append([(0, 0)])\r\n else:\r\n i_users_list.append([(uid, rating) for uid, rating in zip(i_users, i_ratings)])\r\n userful_item_set.add(i)\r\n\r\nprint('item size before and after filtering: ', item_count, len(userful_item_set))\r\n\r\nwith open(workdir + args.dataset + '/effective_users_and items_filter5.pkl', 'wb') as f:\r\n pickle.dump(list(user_in_set), f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump(list(userful_item_set), f, pickle.HIGHEST_PROTOCOL)\r\n\r\ncount_f_origin, count_f_filter = 0, 0\r\nfor s in trust_f:\r\n uid = s[0]\r\n fid = s[1]\r\n count_f_origin += 1\r\n if uid > user_count or fid > user_count:\r\n continue\r\n if uid in user_out_set or fid in user_out_set:\r\n continue\r\n\r\n trust_list.append([uid, fid])\r\n count_f_filter += 1\r\n\r\nprint('u-u relation filter size changes: ', count_f_origin, count_f_filter)\r\ntrust_df = pd.DataFrame(trust_list, columns=['uid', 'fid'])\r\ntrust_df = trust_df.sort_values(axis=0, ascending=True, by='uid')\r\n\r\ncount_0, count_1 = 0, 0\r\nfor u in tqdm(range(user_count + 1)):\r\n hist = trust_df[trust_df['uid'] == u]\r\n\r\n u_users = hist['fid'].unique().tolist()\r\n if u_users == []:\r\n u_users_list.append([0])\r\n u_users_items_list.append([[(0, 0)]])\r\n count_0 += 1\r\n continue\r\n else:\r\n u_users_list.append(u_users)\r\n uu_items = []\r\n for uid in u_users:\r\n\r\n uu_items.append(u_items_list[uid])\r\n u_users_items_list.append(uu_items)\r\n count_1 += 1\r\n\r\nprint('trust user with items size: ', count_0, count_1)\r\n\r\nwith open(workdir + args.dataset + '/list_filter5.pkl', 'wb') as f:\r\n pickle.dump(u_items_list, f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump(u_users_list, f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump(u_users_items_list, f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump(i_users_list, f, pickle.HIGHEST_PROTOCOL)\r\n pickle.dump((user_count, item_count, rate_count), f, pickle.HIGHEST_PROTOCOL)\r\n","repo_name":"XXY2211068/GSFR","sub_path":"preprocess_filter5.py","file_name":"preprocess_filter5.py","file_ext":"py","file_size_in_byte":6664,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"2"}
+{"seq_id":"4321264796","text":"#!/usr/bin/env python\nfrom enum import Enum\n\nimport rospy\nfrom visualization_msgs.msg import Marker\nfrom tf.listener import TransformListener\nfrom move_base_msgs.msg import MoveBaseAction, MoveBaseGoal\nfrom actionlib import SimpleActionClient, GoalStatus\nfrom perspectives_msgs.srv import StartFact, EndFact\nfrom head_manager.msg import CoordinationSignal, TargetWithExpiration\n\nNODE_NAME = \"nathan_expe_supervisor\"\nNS = \"nathan_expe\"\n\nMAP_FRAME = \"map\"\nHUMAN_FRAME = \"mocap_human-1_footprint\"\nHUMAN_HEAD_FRAME = \"mocap_human-1\"\n\nHUMAN_TRIGGER_MARKER_PUB = \"{}/human_trigger_marker\".format(NS)\nCOORD_SIGNAL_PUB = \"head_manager/head_coordination_signals\"\nMOVE_BASE_ACTION_SRV = \"/move_base\"\nSTART_FACT_SRV_NAME = \"/uwds_ros_bridge/start_fact\"\nSTOP_FACT_SRV_NAME = \"/uwds_ros_bridge/end_fact\"\n\nDURATION_BETWEEN_COORD_SIGNAL = 1.9 # ----|##Coordination signal##|<------this duration------>|###Another coordination signal###|-----> time\nCOORD_SIGNAL_DURATION = 1.9\n\nclass ExpeSupervisor:\n class State(Enum):\n IDLE = 0\n RUNNING = 1\n ENDED = 2\n\n def __init__(self):\n self.marker_pub = rospy.Publisher(HUMAN_TRIGGER_MARKER_PUB, Marker, queue_size=10)\n self.coord_signal = rospy.Publisher(COORD_SIGNAL_PUB, CoordinationSignal, queue_size=1)\n self.tf_listener = TransformListener()\n self.move_base = SimpleActionClient(MOVE_BASE_ACTION_SRV, MoveBaseAction)\n rospy.loginfo(\"{} waiting for move_base action server.\".format(NODE_NAME))\n self.move_base.wait_for_server()\n rospy.loginfo(\"{} found move_base action server.\".format(NODE_NAME))\n\n self.navigating_fact_id = None\n rospy.loginfo(NODE_NAME + \" waiting for underworlds start fact service server.\")\n self.start_fact = rospy.ServiceProxy(START_FACT_SRV_NAME, StartFact)\n self.start_fact.wait_for_service()\n rospy.loginfo(NODE_NAME + \" found underworlds start fact service server.\")\n rospy.loginfo(NODE_NAME + \" waiting for underworlds stop fact service server.\")\n self.end_fact = rospy.ServiceProxy(STOP_FACT_SRV_NAME, EndFact)\n self.end_fact.wait_for_service()\n rospy.loginfo(NODE_NAME + \" found underworlds stop fact service server.\")\n\n self.coordination_human_timer = None\n self.trigger_zone_bb = BoundingBox(3.0, 13.0, 5.0, 16.0)\n self.state = self.State.IDLE\n rospy.Timer(rospy.Duration(1.0), lambda _: self.draw_trigger_zone(False), oneshot=True)\n rospy.Timer(rospy.Duration(0.1), self.loop)\n\n def draw_trigger_zone(self, activated):\n marker = Marker()\n marker.header.frame_id = MAP_FRAME\n marker.header.stamp = rospy.Time(0)\n marker.lifetime = rospy.Duration(0)\n marker.ns = NS\n marker.id = 0\n marker.type = Marker.CUBE\n marker.action = Marker.ADD\n marker.pose.position.x = (self.trigger_zone_bb[0][0] + self.trigger_zone_bb[1][0]) / 2\n marker.pose.position.y = (self.trigger_zone_bb[0][1] + self.trigger_zone_bb[1][1]) / 2\n marker.pose.position.z = 0.25\n marker.pose.orientation.x = 0.0\n marker.pose.orientation.y = 0.0\n marker.pose.orientation.z = 0.0\n marker.pose.orientation.w = 1.0\n marker.scale.x = abs(self.trigger_zone_bb[0][0] - self.trigger_zone_bb[1][0]) / 2\n marker.scale.y = abs(self.trigger_zone_bb[0][1] - self.trigger_zone_bb[1][1]) / 2\n marker.scale.z = 0.5\n marker.color.a = 0.5\n marker.color.r = 0.09 if activated else 0.18\n marker.color.g = 0.15 if activated else 0.26\n marker.color.b = 0.34 if activated else 0.46\n self.marker_pub.publish(marker)\n\n def send_look_at_human(self):\n coord = CoordinationSignal()\n t = TargetWithExpiration()\n t.target.header.stamp = rospy.Time.now()\n t.target.header.frame_id = HUMAN_HEAD_FRAME\n t.duration = COORD_SIGNAL_DURATION\n t.regex_end_condition = \"\"\n t.target.point.x = 0.0\n t.target.point.y = 0.0\n t.target.point.z = 0.0\n\n coord.header.frame_id = HUMAN_HEAD_FRAME\n coord.header.stamp = rospy.Time.now()\n coord.priority = 255\n coord.expiration = rospy.Time.now() + rospy.Duration(3)\n coord.predicate = \"\"\n coord.regex_end_condition = \"\"\n coord.targets = [t]\n self.coord_signal.publish()\n\n def start_navigation(self):\n goal = MoveBaseGoal()\n goal.target_pose.header.stamp = rospy.Time.now()\n goal.target_pose.header.frame_id = MAP_FRAME\n goal.target_pose.pose.position.x = 8.0\n goal.target_pose.pose.position.y = 15.5\n goal.target_pose.pose.orientation.z = 0.0\n goal.target_pose.pose.orientation.w = 1.0\n\n self.start_fact()\n resp = self.start_fact(\"base\", \"isNavigating(robot)\", rospy.Time.now().to_sec(), False)\n if resp.success:\n self.navigating_fact_id = resp.fact_id\n\n self.coordination_human_timer = rospy.Timer(\n rospy.Duration(DURATION_BETWEEN_COORD_SIGNAL + COORD_SIGNAL_DURATION), self.send_look_at_human)\n self.move_base.send_goal(goal)\n\n def stop_navigation(self):\n s = self.move_base.get_state()\n if s != GoalStatus.SUCCEEDED or s != GoalStatus.ABORTED or s != GoalStatus.PREEMPTED:\n self.move_base.cancel_all_goals()\n\n if self.navigating_fact_id is not None:\n resp = self.end_fact(\"base\", self.move_to_fact_id)\n if resp.success:\n self.move_to_fact_id = None\n\n if self.coordination_human_timer is not None:\n self.coordination_human_timer.shutdown()\n\n def loop(self, _):\n t_human, r_human = self.tf_listener.lookupTransform(HUMAN_FRAME, MAP_FRAME, rospy.Time(0))\n if self.state == self.State.IDLE:\n if self.trigger_zone_bb.is_in(t_human[0], t_human[1]):\n self.state = self.State.RUNNING\n self.start_navigation()\n rospy.loginfo(\"{} idle -> running\".format(NODE_NAME))\n elif self.state == self.State.RUNNING:\n s = self.move_base.get_state()\n if s == GoalStatus.SUCCEEDED or s == GoalStatus.ABORTED or s == GoalStatus.PREEMPTED:\n self.stop_navigation()\n rospy.loginfo(\"{} running -> ended\".format(NODE_NAME))\n self.state = self.State.ENDED\n\n def run(self):\n rospy.loginfo(\"{} online\".format(NODE_NAME))\n rospy.spin()\n\n\nclass BoundingBox:\n def __init__(self, x0, y0, x1, y1):\n self.bb = None\n self.set_bounds(x0, y0, x1, y1)\n\n def set_bounds(self, x0, y0, x1, y1):\n self.bb = ((min(x0, x1), min(y0, y1)), (max(x0, x1), max(y0, y1)))\n\n def is_in(self, x, y):\n return self.bb[0][0] <= x <= self.bb[1][0] and self.bb[0][1] <= y <= self.bb[1][1]\n\n\n\n\nif __name__ == '__main__':\n rospy.init_node(NODE_NAME)\n ExpeSupervisor().run()\n\n\n","repo_name":"guilhembn/nathan_expe","sub_path":"scripts/expe_nathan.py","file_name":"expe_nathan.py","file_ext":"py","file_size_in_byte":6909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"72247221808","text":"# encoding: UTF-8\n\nimport tensorflow as tf\n\n\ndef average_pooling_1d(inputs, pool_size, stride, padding, data_format='channels_first', name='average_pooling_1d'):\n with tf.compat.v1.variable_scope(name):\n if data_format == 'channels_first':\n #### inputs shape is n_batches * n_channels * n_frames\n #### expand dimension on last axis\n pool_size = (1, 1, ) + (pool_size,) + (1,)\n strides = (1, 1, ) + (stride, ) + (1,)\n reshaped_inputs = tf.expand_dims(inputs, -1)\n res = tf.nn.avg_pool2d(input=reshaped_inputs, ksize=pool_size, strides=strides, padding=padding,\n data_format='NCHW', name=name)\n return tf.squeeze(res, axis=-1)\n else:\n #### inputs shape is n_batches * n_frames * n_channels\n reshaped_inputs = tf.expand_dims(inputs, 2)\n pool_size = (1, ) + (pool_size, ) + (1, 1, )\n strides = (1, ) + (stride,) + (1, 1, )\n res = tf.nn.avg_pool2d(input=reshaped_inputs, ksize=pool_size, strides=strides, padding=padding,\n data_format='NHWC', name=name)\n return tf.squeeze(res, axis=2)","repo_name":"dhhjx880713/DeepMotionModeling","sub_path":"nn/pooling.py","file_name":"pooling.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"70348148206","text":"import operator\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions import Categorical\n\nfrom itlearn.utils.misc import cuda\nfrom itlearn.models.modules import ArgsModule\n\n\nclass RNNDec(ArgsModule):\n def __init__(self, args):\n super(RNNDec, self).__init__(args)\n\n self.emb = nn.Embedding(args.voc_sz_trg, args.D_emb, padding_idx=0)\n self.rnn = nn.GRU(args.D_emb, args.D_hid, args.n_layers, \\\n dropout=args.drop_ratio, batch_first=True)\n\n self.ctx_to_h0 = nn.Linear(args.n_dir * args.D_hid, args.n_layers * args.D_hid)\n self.out = nn.Linear(args.D_hid, args.voc_sz_trg, bias=True)\n\n if args.tie_emb:\n self.emb.weight = self.out.weight\n\n def forward(self, h_ctx, y):\n # h_ctx : (batch_size, n_dir * D_hid )\n # y : (batch_size, y_seq_len)\n batch_size, y_seq_len = y.size()\n\n h_0 = self.ctx_to_h0(h_ctx).view(batch_size, self.n_layers, self.D_hid)\\\n .transpose(0,1).contiguous()\n input = self.emb( y )\n input = F.dropout( input, p=self.drop_ratio, training=self.training )\n\n # input (batch_size, y_seq_len, D_emb)\n # h_0 (n_layers, batch_size, D_hid)\n output, _ = self.rnn(input, h_0)\n # output (batch_size, y_seq_len, D_hid)\n # h_n (n_layers, batch_size, D_hid)\n\n logits = self.out( output.contiguous().view(-1, self.D_hid) ) # (batch_size * y_seq_len, voc_sz_trg)\n\n return logits\n\n def send(self, h_ctx, h_len, y_len, send_method):\n # h_ctx : (batch_size, n_dir * D_hid)\n batch_size = h_ctx.size()[0]\n\n y_seq_len = int(trg_len.max().item() * self.msg_len_ratio) if self.msg_len_ratio > 0 else self.max_len_gen\n\n done = cuda( torch.zeros(batch_size).long() )\n seq_lens = cuda( torch.zeros(batch_size).fill_(y_seq_len).long() ) # (max_len)\n max_seq_lens = cuda( torch.zeros(batch_size).fill_(y_seq_len).long() ) # (max_len)\n eos_tensor = cuda( torch.zeros(batch_size).fill_( self.eos_token )).long()\n\n input = cuda( torch.full((batch_size, 1), self.init_token ).long() ) # (batch_size, 1)\n input = self.emb( input )\n msg = []\n self.log_probs = [] # (batch_size, seq_len)\n\n hid = self.ctx_to_h0(h_ctx).view(batch_size, self.n_layers, self.D_hid)\\\n .transpose(0,1).contiguous()\n\n batch_logits = 0\n\n for idx in range(y_seq_len):\n # input (batch_size, 1, D_emb)\n # hid (n_layers, batch_size, D_hid)\n output, hid = self.rnn( input, hid )\n # output (batch_size, 1, D_hid)\n # hid (n_layers, batch_size, D_hid)\n\n output = self.out( output.view(-1, self.D_hid) )\n # (batch_size, voc_sz_trg)\n\n if send_method == \"argmax\":\n tokens = output.data.max(dim=1)[1] # (batch_size)\n\n elif send_method == \"reinforce\":\n cat = Categorical(logits=output)\n tokens = cat.sample()\n self.log_probs.append( cat.log_prob(tokens) )\n\n if self.entropy:\n batch_logits += (output * (1-done)[:,None].float()).sum(dim=0)\n\n msg.append(tokens.unsqueeze(dim=1))\n\n is_next_eos = ( tokens == eos_tensor ).long() # (batch_size)\n # (1 if eos, 0 otherwise) \n done = (done + is_next_eos).clamp(min=0, max=1).long()\n new_seq_lens = max_seq_lens.clone().masked_fill_(mask=is_next_eos.byte(), \\\n value=float(idx+1)) # either max or idx if next is eos\n # max_seq_lens : (batch_size)\n seq_lens = torch.min(seq_lens, new_seq_lens)\n\n if done.sum() == batch_size:\n break\n\n input = self.emb(tokens)[:,None,:] # (batch_size, 1, D_emb)\n\n msg = torch.cat(msg, dim=1)\n if send_method == \"reinforce\": # want to sum per-token log prob to yield log prob for the whole message sentence\n self.log_probs = torch.stack(self.log_probs, dim=1) # (batch_size, seq_len)\n\n # (batch_size, seq_len) if argmax or reinforce\n # (batch_size, seq_len, voc_sz_trg) if gumbel\n results = {\"msg\":msg, \"seq_lens\":seq_lens}\n if send_method == \"reinforce\" and self.entropy:\n results.update( {\"batch_logits\":batch_logits} )\n return results\n\n def beam_search(self, h_ctx, h_len, width): # (batch_size, n_dir * D_hid)\n voc_size, batch_size = self.voc_sz_trg, h_ctx.size()[0]\n live = [ [ ( 0.0, [ self.init_token ], 0 ) ] for ii in range(batch_size) ]\n # live : a list of 3-tuples\n # a : cumulative log prob\n # b : list of tokens\n # c : last beam index\n dead = [ [] for ii in range(batch_size) ]\n n_dead = [0 for ii in range(batch_size)]\n\n y_seq_len = int(trg_len.max().item() * self.msg_len_ratio) if self.msg_len_ratio > 0 else self.max_len_gen\n\n input = cuda( torch.full((batch_size, 1), self.init_token ).long() ) # (batch_size, 1)\n input = self.emb( input )\n\n hid = self.ctx_to_h0(h_ctx).view(batch_size, self.n_layers, self.D_hid).transpose(0,1)\\\n .contiguous()\n\n for tidx in range(y_seq_len):\n # input (batch_size * width, 1, D_emb)\n # h_0 (n_layers, batch_size * width, D_hid)\n output, hid = self.rnn( input, hid )\n # output (batch_size * width, 1, D_hid)\n # h_n (n_layers, batch_size * width, D_hid)\n\n cur_prob = F.log_softmax( self.out( output.view(-1, self.D_hid) ), dim=1 )\\\n .view(batch_size, -1, voc_size) # (batch_size, width * voc_sz_trg)\n pre_prob = cuda( torch.FloatTensor( [ [ x[0] for x in ee ] for ee in live ] ).view(batch_size, -1, 1) ) \\\n # (batch_size, width * 1)\n total_prob = cur_prob + pre_prob # (batch_size, width * voc_size)\n total_prob = total_prob.view(batch_size, -1)\n\n _, topi_s = total_prob.topk( width, dim=1)\n topv_s = cur_prob.view(batch_size, -1).gather(1, topi_s)\n # (batch_size, width)\n\n new_live = [ [] for ii in range(batch_size) ]\n for bidx in range(batch_size):\n n_live = width - n_dead[bidx]\n if n_live > 0:\n tis = topi_s[bidx][:n_live].cpu().numpy().tolist()\n tvs = topv_s[bidx][:n_live].cpu().numpy().tolist()\n for eidx, (topi, topv) in enumerate(zip(tis, tvs)): # NOTE max width times\n if topi % voc_size == self.eos_token :\n dead[bidx].append( ( live[bidx][ topi // voc_size ][0] + topv, \\\n live[bidx][ topi // voc_size ][1] + [ topi % voc_size ],\\\n topi) )\n n_dead[bidx] += 1\n else:\n new_live[bidx].append( ( live[bidx][ topi // voc_size ][0] + topv, \\\n live[bidx][ topi // voc_size ][1] + [ topi % voc_size ],\\\n topi) )\n while len(new_live[bidx]) < width:\n new_live[bidx].append( ( -99999999, \\\n [0],\\\n 0) )\n live = new_live\n\n if n_dead == [width for ii in range(batch_size)]:\n break\n\n in_vocab_idx = cuda( torch.LongTensor( [ [ x[2] % voc_size for x in ee ] for ee in live ] ) )# NOTE batch_size first\n input = self.emb( in_vocab_idx.view(-1) ).view(-1, 1, self.D_emb)\n # input (batch_size * width, 1, D_emb)\n\n bb = 1 if tidx == 0 else width\n in_width_idx = cuda( torch.LongTensor( [ [ x[2] // voc_size + bbidx * bb for x in ee ] for bbidx, ee in enumerate(live) ] ) ) \\\n # live : (batch_size, width)\n hid = hid.index_select( 1, in_width_idx.view(-1) ).view(self.n_layers, -1, self.D_hid)\n # h_0 (n_layers, batch_size * width, D_hid)\n\n for bidx in range(batch_size):\n if n_dead[bidx] < width:\n for didx in range( width - n_dead[bidx] ):\n (a, b, c) = live[bidx][didx]\n dead[bidx].append( (a, b, c) )\n\n dead_ = [ [ ( a / ( math.pow(5+len(b), self.beam_alpha) / math.pow(5+1, self.beam_alpha) ) , b, c) for (a,b,c) in ee] for ee in dead]\n ans = []\n for dd_ in dead_:\n dd = sorted( dd_, key=operator.itemgetter(0), reverse=True )\n ans.append( dd[0][1] )\n return ans\n\n","repo_name":"JACKHAHA363/SILTranslationGame","sub_path":"itlearn/models/dec_rnn.py","file_name":"dec_rnn.py","file_ext":"py","file_size_in_byte":8854,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"2"}
+{"seq_id":"29414407853","text":"import argparse\nimport ast\nimport json\nimport logging\nimport os\nimport pathlib\nimport sqlite3\nimport subprocess\nfrom typing import Optional, Tuple, List\n\nimport pandas as pd\n\nSCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))\nKG2C_DIR = f\"{SCRIPT_DIR}/../\"\nSYNONYMIZER_BUILD_DIR = f\"{KG2C_DIR}/synonymizer_build\"\nlogging.basicConfig(level=logging.INFO,\n format='%(asctime)s %(levelname)s: %(message)s',\n handlers=[logging.StreamHandler()])\n\n\ndef get_cluster_id(node_id: str, cursor) -> Optional[str]:\n answer = cursor.execute(f\"SELECT cluster_id FROM nodes WHERE id = '{node_id}'\")\n matching_row = answer.fetchone()\n if matching_row:\n cluster_id = matching_row[0]\n logging.debug(f\"Cluster ID for {node_id} is {cluster_id}\")\n else:\n logging.warning(f\"No node with id '{node_id}' exists in the match graph\")\n cluster_id = None\n return cluster_id\n\n\ndef get_member_ids(cluster_id: str, cursor) -> Tuple[List[str], List[str]]:\n answer = cursor.execute(f\"SELECT member_ids, intra_cluster_edge_ids FROM clusters WHERE cluster_id = '{cluster_id}'\")\n matching_row = answer.fetchone()\n if matching_row:\n member_ids = ast.literal_eval(matching_row[0]) # Lists are stored as strings in sqlite\n intra_cluster_edge_ids_str = \"[]\" if matching_row[1] == \"nan\" else matching_row[1]\n intra_cluster_edge_ids = ast.literal_eval(intra_cluster_edge_ids_str) # Lists are stored as strings in sqlite\n logging.debug(f\"Cluster {cluster_id} has member IDs {member_ids} and \"\n f\"intra cluster edge IDs {intra_cluster_edge_ids}\")\n else:\n logging.error(f\"There is no cluster with ID {cluster_id}\")\n member_ids, intra_cluster_edge_ids = [], []\n return member_ids, intra_cluster_edge_ids\n\n\ndef _load_records_into_dict(item_ids: List[str], table_name: str, cursor) -> List[dict]:\n ids_as_str = \", \".join([f\"'{item_id}'\" for item_id in item_ids])\n answer = cursor.execute(f\"SELECT * FROM {table_name} WHERE id IN ({ids_as_str})\")\n matching_rows = answer.fetchall()\n if matching_rows:\n column_info = cursor.execute(f\"PRAGMA table_info({table_name})\").fetchall()\n column_names = [column_info[1] for column_info in column_info]\n matching_items_df = pd.DataFrame(matching_rows, columns=column_names)\n items_as_dicts = matching_items_df.to_dict(orient=\"records\")\n return items_as_dicts\n else:\n return []\n\n\ndef get_graph(node_ids: List[str], edge_ids: List[str], cursor, hide_predicate: bool = False) -> dict:\n nodes_list = _load_records_into_dict(node_ids, \"nodes\", cursor)\n edges_list = _load_records_into_dict(edge_ids, \"edges\", cursor)\n\n cluster_graph = {\"nodes\": nodes_list, \"edges\": edges_list}\n if hide_predicate and \"predicate\" in cluster_graph[\"edges\"][0]:\n for edge in cluster_graph[\"edges\"]:\n edge[\"predicate_hidden\"] = edge[\"predicate\"]\n del edge[\"predicate\"]\n\n # Choose one name (SRI or KG2) for each node\n for node in cluster_graph[\"nodes\"]:\n node[\"name\"] = node[\"name_sri\"] if node.get(\"name_sri\") else node[\"name_kg2pre\"]\n\n logging.info(f\"Cluster graph has {len(cluster_graph['nodes'])} nodes, {len(cluster_graph['edges'])} edges.\")\n return cluster_graph\n\n\ndef compute_cluster_density(cluster_graph: any) -> float:\n num_nodes = len(cluster_graph[\"nodes\"])\n total_possible_edges = (num_nodes * (num_nodes - 1)) / 2\n weighted_edge_count = sum([edge.get(\"weight\", 1) for edge in cluster_graph[\"edges\"]])\n if total_possible_edges:\n density = weighted_edge_count / total_possible_edges\n else:\n density = 1.0\n # TODO: Account for fact that we have a multigraph...\n return density\n\n\ndef convert_to_check_mark(some_bool: bool) -> str:\n return \"X\" if some_bool else \"\"\n\n\ndef main():\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument(\"node_id\")\n arg_parser.add_argument('--full', dest='full', action='store_true')\n args = arg_parser.parse_args()\n\n sqlite_path = f\"{SYNONYMIZER_BUILD_DIR}/node_synonymizer.sqlite\"\n cluster_graphs_path = f\"{SYNONYMIZER_BUILD_DIR}/cluster_debug_graphs\"\n if not pathlib.Path(cluster_graphs_path).exists():\n subprocess.check_call([\"mkdir\", cluster_graphs_path])\n db_connection = sqlite3.connect(sqlite_path)\n cursor = db_connection.cursor()\n\n cluster_id = get_cluster_id(args.node_id, cursor)\n member_node_ids, intra_cluster_edge_ids = get_member_ids(cluster_id, cursor)\n cluster_graph = get_graph(member_node_ids, intra_cluster_edge_ids, cursor)\n cluster_rep = next(node for node in cluster_graph[\"nodes\"] if node[\"id\"] == cluster_id)\n\n # Print out the cluster edges in tabular format\n if args.full:\n column_names = [\"id\", \"subject\", \"predicate\", \"object\", \"upstream_resource_id\", \"primary_knowledge_source\", \"weight\"]\n cluster_edge_rows = [[edge[\"id\"], edge[\"subject\"], edge[\"predicate\"], edge[\"object\"],\n edge[\"upstream_resource_id\"],\n edge[\"primary_knowledge_source\"] if edge[\"primary_knowledge_source\"] else \"\",\n edge[\"weight\"]]\n for edge in cluster_graph[\"edges\"]]\n else:\n column_names = [\"subject\", \"predicate\", \"object\", \"upstream_resource_id\", \"primary_knowledge_source\"]\n cluster_edge_rows = [[edge[\"subject\"], edge[\"predicate\"], edge[\"object\"],\n edge[\"upstream_resource_id\"],\n edge[\"primary_knowledge_source\"] if edge[\"primary_knowledge_source\"] else \"\"]\n for edge in cluster_graph[\"edges\"]]\n cluster_edges_df = pd.DataFrame(cluster_edge_rows, columns=column_names).sort_values(by=\"upstream_resource_id\")\n print(f\"\\nCluster for {args.node_id} ({cluster_id}) has {cluster_edges_df.shape[0]} edges:\")\n print(f\"\\n{cluster_edges_df.to_markdown(index=False)}\\n\")\n\n # Print out the cluster nodes in tabular format\n if args.full:\n column_names = [\"id\", \"category\", \"major_branch\", \"name\",\n \"in_SRI\", \"category_sri\", \"name_sri\",\n \"in_KG2pre\", \"category_kg2pre\", \"name_kg2pre\",\n \"is_cluster_rep\"]\n cluster_node_rows = [[node[\"id\"], node[\"category\"], node[\"major_branch\"], node[\"name\"],\n convert_to_check_mark(node[\"category_sri\"] is not None),\n node[\"category_sri\"], node[\"name_sri\"],\n convert_to_check_mark(node[\"category_kg2pre\"] is not None),\n node[\"category_kg2pre\"], node[\"name_kg2pre\"],\n convert_to_check_mark(node[\"id\"] == cluster_id)]\n for node in cluster_graph[\"nodes\"]]\n else:\n column_names = [\"id\", \"category\", \"name\", \"in_SRI\", \"in_KG2pre\", \"is_cluster_rep\"]\n cluster_node_rows = [[node[\"id\"], node[\"category\"], node[\"name\"],\n convert_to_check_mark(node[\"category_sri\"] is not None),\n convert_to_check_mark(node[\"category_kg2pre\"] is not None),\n convert_to_check_mark(node[\"id\"] == cluster_id)]\n for node in cluster_graph[\"nodes\"]]\n cluster_nodes_df = pd.DataFrame(cluster_node_rows, columns=column_names).sort_values(by=\"id\")\n cluster_id_str = f\" ({cluster_id})\" if args.node_id != cluster_id else \"\"\n print(f\"\\nCluster for {args.node_id}{cluster_id_str} has {cluster_nodes_df.shape[0]} nodes:\")\n print(f\"\\n{cluster_nodes_df.to_markdown(index=False)}\\n\")\n\n # Compute the density of this cluster\n density = compute_cluster_density(cluster_graph)\n print(f\"With {len(cluster_graph['nodes'])} nodes and {len(cluster_graph['edges'])} edges, \"\n f\"density of cluster is: {density}\\n\")\n\n # Save the cluster graph in a JSON file\n cluster_file_path = f\"{cluster_graphs_path}/{cluster_rep['name']}_{cluster_id}.json\"\n with open(cluster_file_path, \"w+\") as cluster_file:\n json.dump(cluster_graph, cluster_file, indent=2)\n print(f\"Cluster saved in Biolink format to: {cluster_file_path}\\n\")\n\n cursor.close()\n db_connection.close()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"RTXteam/RTX","sub_path":"code/kg2c/synonymizer_build/inspect_clusters.py","file_name":"inspect_clusters.py","file_ext":"py","file_size_in_byte":8346,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"2"}
+{"seq_id":"10949341496","text":"T = int(input())\n# 여러개의 테스트 케이스가 주어지므로, 각각을 처리합니다.\nfor test_case in range(1, T + 1):\n a = int(input())\n b = list(map(int, input().split()))\n b.sort()\n print('#{}'.format(test_case),end=' ')\n for i in b :\n print(i, end=' ')\n print()","repo_name":"taeyeon5362/codingTest_python","sub_path":"SWEA/D2/1966. 숫자를 정렬하자/숫자를 정렬하자.py","file_name":"숫자를 정렬하자.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"7710995582","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 ('games', '0001_initial'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='game',\n old_name='max_type',\n new_name='max_time',\n ),\n ]\n","repo_name":"rnelson/library","sub_path":"games/migrations/0002_auto_20150725_1613.py","file_name":"0002_auto_20150725_1613.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"22696151764","text":"import numpy as np\nimport scipy.sparse\nfrom scipy.optimize import fmin_l_bfgs_b\n\nclass LinearRegressionGD:\n def __init__(self, lambda_=1, **fmin_args):\n self.lambda_ = lambda_\n self.fmin_args = fmin_args\n\n def cost_grad(self, theta, X, y):\n m = X.shape[0]\n theta1 = theta[1:]\n\n t = X.dot(theta) - y\n j = (t.dot(t) + self.lambda_ * theta1.dot(theta1)) / (2.0 * m)\n\n grad = X.T.dot(t) / m\n grad[1:] += self.lambda_ * theta1 / m\n\n return j, grad\n\n def fit(self, X, y):\n theta = np.zeros(X.shape[1])\n self.theta, j, ret = fmin_l_bfgs_b(self.cost_grad, theta, args=(X, y),\n **self.fmin_args)\n\n def predict(self, X):\n return X.dot(self.theta)\n","repo_name":"lostsoul3/Short_answer_scoring","sub_path":"lr.py","file_name":"lr.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"39327380646","text":"from collections import defaultdict\n\nfrom rig.machine import Machine, Links\nfrom rig.netlist import Net\nfrom rig.place_and_route.constraints import \\\n LocationConstraint, ReserveResourceConstraint, RouteEndpointConstraint\nfrom rig.place_and_route.routing_tree import RoutingTree\nfrom rig.routing_table import Routes\nfrom six import iteritems\n\nfrom pacman.model.constraints.placer_constraints\\\n .placer_chip_and_core_constraint import PlacerChipAndCoreConstraint\nfrom pacman.model.constraints.placer_constraints\\\n .placer_radial_placement_from_chip_constraint \\\n import PlacerRadialPlacementFromChipConstraint\nfrom pacman.model.graphs.abstract_fpga_vertex import AbstractFPGAVertex\nfrom pacman.model.graphs.abstract_virtual_vertex import AbstractVirtualVertex\nfrom pacman.model.graphs.abstract_spinnaker_link_vertex \\\n import AbstractSpiNNakerLinkVertex\nfrom pacman.model.placements.placement import Placement\nfrom pacman.model.placements.placements import Placements\nfrom pacman.model.routing_table_by_partition\\\n .multicast_routing_table_by_partition \\\n import MulticastRoutingTableByPartition\nfrom pacman.model.routing_table_by_partition\\\n .multicast_routing_table_by_partition_entry \\\n import MulticastRoutingTableByPartitionEntry\nfrom pacman.utilities import constants\n\n# A lookup from link name (string) to Links enum entry.\nLINK_LOOKUP = {l.name: l for l in Links}\nROUTE_LOOKUP = {\"core_{}\".format(r.core_num) if r.is_core else r.name: r\n for r in Routes}\n\nCHIP_HOMOGENEOUS_CORES = 18\nCHIP_HOMOGENEOUS_SDRAM = 119275520\nCHIP_HOMOGENEOUS_SRAM = 24320\nCHIP_HOMOGENEOUS_TAGS = 0\nROUTER_MAX_NUMBER_OF_LINKS = 6\nROUTER_HOMOGENEOUS_ENTRIES = 1023\n\nN_CORES_PER_VERTEX = 1\n\n\ndef convert_to_rig_machine(machine):\n\n chip_resources = dict()\n chip_resources['cores'] = CHIP_HOMOGENEOUS_CORES\n chip_resources['sdram'] = CHIP_HOMOGENEOUS_SDRAM\n chip_resources['sram'] = CHIP_HOMOGENEOUS_SRAM\n chip_resources[\"router_entries\"] = ROUTER_HOMOGENEOUS_ENTRIES\n chip_resources['tags'] = CHIP_HOMOGENEOUS_TAGS\n\n # handle exceptions\n dead_chips = list()\n dead_links = list()\n chip_resource_exceptions = list()\n\n # write dead chips\n for x_coord in range(0, machine.max_chip_x + 1):\n for y_coord in range(0, machine.max_chip_y + 1):\n if (not machine.is_chip_at(x_coord, y_coord) or\n machine.get_chip_at(x_coord, y_coord).virtual):\n dead_chips.append([x_coord, y_coord])\n else:\n chip = machine.get_chip_at(x_coord, y_coord)\n\n # write dead links\n for link_id in range(0, ROUTER_MAX_NUMBER_OF_LINKS):\n router = chip.router\n if not router.is_link(link_id):\n dead_links.append(\n [x_coord, y_coord, \"{}\".format(\n constants.EDGES(link_id).name.lower())])\n\n # Fix the number of processors when there are less\n resource_exceptions = dict()\n n_processors = len([\n processor for processor in chip.processors])\n if n_processors < CHIP_HOMOGENEOUS_CORES:\n resource_exceptions[\"cores\"] = n_processors\n\n # Add tags if Ethernet chip\n if chip.ip_address is not None:\n resource_exceptions[\"tags\"] = len(chip.tag_ids)\n\n if len(resource_exceptions) > 0:\n chip_resource_exceptions.append(\n (x_coord, y_coord, resource_exceptions))\n\n return Machine(\n width=machine.max_chip_x + 1,\n height=machine.max_chip_y + 1,\n chip_resources=chip_resources,\n chip_resource_exceptions={\n (x, y): {\n resource: r.get(\n resource, chip_resources[resource])\n for resource in chip_resources}\n for x, y, r in chip_resource_exceptions},\n dead_chips=set((x, y) for x, y in dead_chips),\n dead_links=set(\n (x, y, LINK_LOOKUP[link])\n for x, y, link in dead_links))\n\n\ndef convert_to_rig_graph(machine_graph):\n\n vertices_resources = dict()\n edges_resources = defaultdict()\n\n for vertex in machine_graph.vertices:\n\n # handle external devices\n if isinstance(vertex, AbstractVirtualVertex):\n vertex_resources = dict()\n vertices_resources[vertex] = vertex_resources\n vertex_resources[\"cores\"] = 0\n\n # handle standard vertices\n else:\n vertex_resources = dict()\n vertices_resources[vertex] = vertex_resources\n vertex_resources[\"cores\"] = N_CORES_PER_VERTEX\n vertex_resources[\"sdram\"] = int(\n vertex.resources_required.sdram.get_value())\n vertex_outgoing_partitions = \\\n machine_graph.get_outgoing_edge_partitions_starting_at_vertex(\n vertex)\n\n # handle the vertex edges\n for partition in vertex_outgoing_partitions:\n hyper_edge_dict = dict()\n edges_resources[partition] = hyper_edge_dict\n hyper_edge_dict[\"source\"] = vertex\n\n sinks = list()\n weight = 0\n for edge in partition.edges:\n sinks.append(edge.post_vertex)\n weight += edge.traffic_weight\n hyper_edge_dict['sinks'] = sinks\n hyper_edge_dict[\"weight\"] = weight\n hyper_edge_dict[\"type\"] = partition.traffic_type.name.lower()\n\n net_names = {\n Net(edge[\"source\"], edge[\"sinks\"], edge[\"weight\"]): name\n for name, edge in iteritems(edges_resources)\n }\n nets = list(net_names)\n\n return vertices_resources, nets, net_names\n\n\ndef create_rig_graph_constraints(machine_graph, machine):\n\n constraints = []\n for vertex in machine_graph.vertices:\n for constraint in vertex.constraints:\n if isinstance(constraint, (\n PlacerChipAndCoreConstraint,\n PlacerRadialPlacementFromChipConstraint)):\n constraints.append(LocationConstraint(\n vertex, (constraint.x, constraint.y)))\n\n if isinstance(vertex, AbstractVirtualVertex):\n link_data = None\n if isinstance(vertex, AbstractFPGAVertex):\n link_data = machine.get_fpga_link_with_id(\n vertex.fpga_id, vertex.fpga_link_id, vertex.board_address)\n elif isinstance(vertex, AbstractSpiNNakerLinkVertex):\n link_data = machine.get_spinnaker_link_with_id(\n vertex.spinnaker_link_id, vertex.board_address)\n constraints.append(LocationConstraint(\n vertex,\n (link_data.connected_chip_x, link_data.connected_chip_y)))\n constraints.append(RouteEndpointConstraint(\n vertex,\n LINK_LOOKUP[constants.EDGES(\n link_data.connected_link).name.lower()]))\n return constraints\n\n\ndef create_rig_machine_constraints(machine):\n constraints = []\n for chip in machine.chips:\n for processor in chip.processors:\n if processor.is_monitor:\n constraints.append(ReserveResourceConstraint(\n \"cores\",\n slice(processor.processor_id, processor.processor_id + 1),\n (chip.x, chip.y)))\n return constraints\n\n\ndef convert_to_rig_placements(placements, machine):\n rig_placements = dict()\n for placement in placements:\n if not isinstance(placement.vertex, AbstractVirtualVertex):\n rig_placements[placement.vertex] = (placement.x, placement.y)\n else:\n link_data = None\n vertex = placement.vertex\n if isinstance(vertex, AbstractFPGAVertex):\n link_data = machine.get_fpga_link_with_id(\n vertex.fpga_id, vertex.fpga_link_id, vertex.board_address)\n elif isinstance(vertex, AbstractSpiNNakerLinkVertex):\n link_data = machine.get_spinnaker_link_with_id(\n vertex.spinnaker_link_id, vertex.board_address)\n rig_placements[placement.vertex] = (\n link_data.connected_chip_x, link_data.connected_chip_y\n )\n\n core_allocations = {\n p.vertex: {\"cores\": slice(p.p, p.p + 1)}\n for p in placements.placements\n if not isinstance(p.vertex, AbstractVirtualVertex)}\n\n return rig_placements, core_allocations\n\n\ndef convert_from_rig_placements(\n rig_placements, rig_allocations, machine_graph):\n placements = Placements()\n for vertex in rig_placements:\n if isinstance(vertex, AbstractVirtualVertex):\n placements.add_placement(Placement(\n vertex, vertex.virtual_chip_x, vertex.virtual_chip_y,\n None))\n else:\n x, y = rig_placements[vertex]\n p = rig_allocations[vertex][\"cores\"].start\n placements.add_placement(Placement(vertex, x, y, p))\n\n return placements\n\n\ndef convert_from_rig_routes(rig_routes, machine_graph):\n routing_tables = MulticastRoutingTableByPartition()\n for partition in rig_routes:\n partition_route = rig_routes[partition]\n _convert_next_route(\n routing_tables, partition, 0, None, partition_route)\n return routing_tables\n\n\ndef _convert_next_route(\n routing_tables, partition, incoming_processor, incoming_link,\n partition_route):\n x, y = partition_route.chip\n\n next_hops = list()\n processor_ids = list()\n link_ids = list()\n for (route, next_hop) in partition_route.children:\n if route is not None:\n link = None\n if isinstance(route, Routes):\n if route.is_core:\n processor_ids.append(route.core_num)\n else:\n link = route.value\n link_ids.append(link)\n elif isinstance(route, Links):\n link = route.value\n link_ids.append(link)\n if isinstance(next_hop, RoutingTree):\n next_incoming_link = None\n if link is not None:\n next_incoming_link = (link + 3) % 6\n next_hops.append((next_hop, next_incoming_link))\n\n routing_tables.add_path_entry(MulticastRoutingTableByPartitionEntry(\n link_ids, processor_ids, incoming_processor,\n incoming_link), x, y, partition)\n\n for next_hop, next_incoming_link in next_hops:\n _convert_next_route(\n routing_tables, partition, None, next_incoming_link, next_hop)\n","repo_name":"Erzcoder/Spiking-Neural-Networks-on-Robotino","sub_path":"src/spinnaker_nn/venv/lib/python2.7/site-packages/pacman/utilities/rig_converters.py","file_name":"rig_converters.py","file_ext":"py","file_size_in_byte":10664,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"2"}
+{"seq_id":"26852123760","text":"from functools import reduce\nfrom operator import mul\nfrom itertools import islice\n\n\ndef take(n, iterable):\n \"Return first n items of the iterable as a list\"\n return list(islice(iterable, n))\n\n\ndef parse(it):\n matrix = []\n for line in it:\n if line.strip():\n matrix.append(list(map(int, list(line.strip()))))\n return matrix\n\n\ndef get(matrix, x, y, default):\n if x < 0 or y < 0:\n return default\n try:\n return matrix[x][y]\n except IndexError:\n return default\n\n\ndef basin(matrix, r, c):\n maxrow = len(matrix) - 1\n maxcol = len(matrix[0]) - 1\n\n frontier = set([(r, c)])\n seen = set()\n size = 0\n while len(frontier):\n r, c = frontier.pop()\n seen.add((r, c))\n if matrix[r][c] != 9:\n size += 1\n if r > 0 and (r - 1, c) not in seen:\n frontier.add((r - 1, c))\n if c > 0 and (r, c - 1) not in seen:\n frontier.add((r, c - 1))\n if r < maxrow and (r + 1, c) not in seen:\n frontier.add((r + 1, c))\n if c < maxcol and (r, c + 1) not in seen:\n frontier.add((r, c + 1))\n\n return size\n\n\ndef mins(matrix):\n mins = 0\n sum_ = 0\n basins = []\n for r in range(len(matrix)):\n for c in range(len(matrix[0])):\n if all(\n matrix[r][c] < x\n for x in (\n get(matrix, r - 1, c, 99),\n get(matrix, r, c - 1, 99),\n get(matrix, r + 1, c, 99),\n get(matrix, r, c + 1, 99),\n )\n ):\n mins += 1\n sum_ += matrix[r][c] + 1\n basins.append(basin(matrix, r, c))\n return mins, sum_, reduce(mul, take(3, sorted(basins, reverse=True)))\n\n\nprint(mins(parse(open(\"small.txt\"))))\nprint(mins(parse(open(\"input.txt\"))))\n","repo_name":"llimllib/personal_code","sub_path":"misc/advent/2021/09/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"2"}
+{"seq_id":"71057575726","text":"import heapq\r\n\r\n\r\nclass Node:\r\n def __init__(self, data):\r\n self.data = data\r\n self.left = None\r\n self.right = None\r\n\r\n\r\nclass Solution:\r\n def constructBSTInorder_util(self, inorder, low, high):\r\n if low > high:\r\n return None\r\n \r\n mid = (low + high) // 2\r\n root = Node(inorder[mid])\r\n\r\n root.left = self.constructBSTInorder_util(inorder, low, mid-1)\r\n root.right = self.constructBSTInorder_util(inorder, mid+1, high)\r\n\r\n return root\r\n\r\n\r\n def constructBSTInorder(self, inorder, size):\r\n return self.constructBSTInorder_util(inorder, 0, size-1)\r\n\r\n # =================================================\r\n\r\n def storeInorder(self, root, inOrder):\r\n if root:\r\n self.storeInorder(root.left, inOrder)\r\n inOrder.append(root.data)\r\n self.storeInorder(root.right, inOrder)\r\n return inOrder\r\n\r\n def mergeTwoBST(self, root1, root2):\r\n # edge cases ======================\r\n if root1 is None and root2:\r\n return root2\r\n \r\n if root1 and not root2:\r\n return root1\r\n\r\n if not root1 and not root2:\r\n return None\r\n # =================================\r\n\r\n root1_inorder = self.storeInorder(root1, [])\r\n root2_inorder = self.storeInorder(root2, [])\r\n\r\n # merge two arrays\r\n new_inorder = list(heapq.merge(root1_inorder, root2_inorder, key=lambda x: x))\r\n\r\n # built tree \r\n return self.constructBSTInorder(new_inorder, len(new_inorder))\r\n\r\n# utility ============================================\r\n\r\ndef insert(root, data):\r\n if not root:\r\n return Node(data)\r\n if root.data == data:\r\n return root\r\n elif root.data > data:\r\n root.left = insert(root.left, data)\r\n else:\r\n root.right = insert(root.right, data)\r\n return root\r\n\r\ndef printAll(root):\r\n print(\"PreOrder\")\r\n preorder(root)\r\n\r\n print(\"\\nInOrder\")\r\n inorder(root)\r\n\r\n print(\"\\nPostOrder\")\r\n postorder(root)\r\n print()\r\n print('=' * 50)\r\n\r\ndef preorder(root):\r\n if root is None:\r\n return\r\n\r\n print(root.data, end=' ')\r\n preorder(root.left)\r\n preorder(root.right)\r\n\r\ndef inorder(root):\r\n if root is None:\r\n return\r\n\r\n inorder(root.left)\r\n print(root.data, end=' ')\r\n inorder(root.right)\r\n\r\ndef postorder(root):\r\n if root is None:\r\n return\r\n\r\n postorder(root.left)\r\n postorder(root.right)\r\n print(root.data, end=' ')\r\n# =====================================================\r\nif __name__ == '__main__':\r\n root1 = root2 = None\r\n\r\n root1 = insert(root1, 100)\r\n root1 = insert(root1, 50)\r\n root1 = insert(root1, 300)\r\n root1 = insert(root1, 20)\r\n root1 = insert(root1, 70)\r\n printAll(root1) \r\n \r\n root2 = insert(root2, 80)\r\n root2 = insert(root2, 40)\r\n root2 = insert(root2, 120)\r\n printAll(root2)\r\n\r\n root3 = Solution().mergeTwoBST(root1, root2)\r\n printAll(root3) ","repo_name":"ikaushikpal/DS-450-python","sub_path":"Binary Search Trees/Merge Two Balanced Binary Search Trees.py","file_name":"Merge Two Balanced Binary Search Trees.py","file_ext":"py","file_size_in_byte":3030,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"2"}
+{"seq_id":"14489327604","text":"import networkx as nx\nimport matplotlib.pyplot as plt\nfrom collections import defaultdict\nimport sys\n\nnome = str(sys.argv[1])\n#input(\"Informe o nome do arquivo: \")\npasta = \"grafos/\"+nome\n\nf = open(pasta,\"r\")\na = {} \na = f.readline()\nquant = int(a[:3].strip(\" \"))\na = f.readline()\nb = str(a[:3].strip(\" \"))\nn = int(b)\nmat = f.read()\nsoma = 0\n#print(n)\nfor ob in range(0,quant):\n result = \"imagens/\"+nome.strip(\".dat\")+\"_\"+str(ob)+\".png\"\n linha = 0\n lin = defaultdict(list)\n G=nx.Graph()\n #print(mat)\n \n init = (n*(n*5)+n) \n print(init)\n if ob>0:\n soma = 24\n\n for coln in range(ob*init+(soma*ob),init+ob*init+(soma*ob)):\n if mat[coln] != \" \":\n if mat[coln] == \"\\n\":\n print(lin[linha])\n linha = linha + 1\n elif mat[coln-1] == \" \":\n lin[linha].append(int(mat[coln:coln+2].strip(\" \")))\n \n \n\n \n labels = {}\n edge_labels = {}\n\n for linha in range(0,n):\n #print(lin[linha])\n G.add_node(linha)\n labels = linha\n for col in range(0,n):\n if lin[linha][col] != 0:\n G.add_edge(linha,col,weight=lin[linha][col])\n edge_labels = lin[linha][col]\n\n\n lin.clear()\n\n aux= nx.shell_layout(G,scale=0.5)\n\n nnode=0\n labels = {}\n pos={}\n for turma in G.node:\n labels[nnode]=turma\n pos[nnode]=aux[turma]\n pos[nnode][1]=pos[nnode][1]+0.1\n nnode+=1\n\n label = nx.get_edge_attributes(G,'weight')\n nx.draw_networkx_edge_labels(G, pos, edge_labels=label, alpha=0.5)\n nx.draw_networkx(G, pos, node_color=\"k\", font_color=\"w\")\n plt.axis('off')\n plt.savefig(result)\n plt.close()\n G.clear()\n aux.clear()\n label.clear()\n pos.clear()\n labels.clear()\n #plt.show()\n \nf.close\n","repo_name":"FilipeRodrigues3003/coloracao-tcc","sub_path":"plotarGrafo.py","file_name":"plotarGrafo.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"14513790381","text":"import streamlit as st\nimport base64\nimport tempfile\nimport subprocess\nimport numpy as np\nimport pandas as pd\nimport json\nimport os\nimport sys\nimport io\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nfrom io import BytesIO\nfrom pathlib import Path\nfrom PIL import Image, ImageDraw\nfrom pycocotools import mask\nimport time\nimport shutil\n\nst.markdown(\"## Generate Segmnetations\")\nst.divider()\n\nst.subheader(\"Saliency Heatmap Segmentations\")\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"CheXlocalize\"))\n\nfrom eval_constants import CHEXPERT_TASKS, LOCALIZATION_TASKS\nfrom utils import CPU_Unpickler, encode_segmentation, parse_pkl_filename\nfrom heatmap_to_segmentation import cam_to_segmentation, pkl_to_mask, heatmap_to_mask\n\n# File Upload Required\nuploaded_files = st.file_uploader('Upload Heatmaps', type='pkl', accept_multiple_files=True, help=\"**Input:** Pickle files containing heatmaps (see Example Input).\\n\\n**Output:** JSON of binary segmentations.\")\n\nst.write(\"\")\nst.write(\"Optional Parameters\")\n\n# Optional File Uploads\nthreshold_file = st.file_uploader(\"Upload Thresholds\", type=[\"csv\"], help=\"**Input:** CSV of Thresholds.\\n\\n**Output:** JSON of binary segmentations.\")\nprobability_threshold_file = st.file_uploader(\"Upload Probability Thresholds\", type=[\"csv\"], help=\"**Input:** CSV of Probability Thresholds.\\n\\n**Output:** JSON of binary segmentations.\")\n\n# Optional Parameters\nif_smoothing = st.checkbox(\"Apply Smoothing\", help=\"Check to smooth the pixelated heatmaps via box filtering\")\nkernel_size = st.number_input(\"Kernel Size\", min_value=0, max_value=10, value=0, help=\"Kernel size used for box filtering. Make sure to check Smoothing, otherwise no smoothing will be performed.\")\n\n# Example Input\nwith st.expander(\"Example Input\"):\n st.code(\"\"\"\n{\n# DenseNet121 + Grad-CAM heatmap of shape (1, 1, h, w)\n'map': tensor([[[[1.4711e-06, 1.4711e-06, 1.4711e-06, ..., 5.7636e-06, 5.7636e-06, 5.7636e-06],\n [1.4711e-06, 1.4711e-06, 1.4711e-06, ..., 5.7636e-06, 5.7636e-06, 5.7636e-06],\n ...,\n [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 7.9709e-05, 7.9709e-05, 7.9709e-05],\n [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 7.9709e-05, 7.9709e-05, 7.9709e-05]]]]),\n\n# model probability (float)\n'prob': 0.02029409697279334,\n\n# one of the ten possible pathologies (string)\n'task': Consolidation,\n\n# 0 if ground-truth label for 'task' is negative, 1 if positive (int)\n'gt': 0,\n\n# original cxr image\n'cxr_img': tensor([[[0.7490, 0.7412, 0.7490, ..., 0.8196, 0.8196, 0.8118],\n [0.6627, 0.6627, 0.6706, ..., 0.7373, 0.7137, 0.6941],\n [0.5137, 0.5176, 0.5294, ..., 0.6000, 0.5686, 0.5255],\n ...,\n [0.7294, 0.7725, 0.7804, ..., 0.2941, 0.2549, 0.2078],\n [0.7804, 0.8157, 0.8157, ..., 0.3216, 0.2824, 0.2510],\n [0.8353, 0.8431, 0.8549, ..., 0.3725, 0.3412, 0.3137]],\n ...\n [[0.7490, 0.7412, 0.7490, ..., 0.8196, 0.8196, 0.8118],\n [0.6627, 0.6627, 0.6706, ..., 0.7373, 0.7137, 0.6941],\n [0.5137, 0.5176, 0.5294, ..., 0.6000, 0.5686, 0.5255],\n ...,\n [0.7294, 0.7725, 0.7804, ..., 0.2941, 0.2549, 0.2078],\n [0.7804, 0.8157, 0.8157, ..., 0.3216, 0.2824, 0.2510],\n [0.8353, 0.8431, 0.8549, ..., 0.3725, 0.3412, 0.3137]]]),\n\n# dimensions of original cxr (w, h)\n'cxr_dims': (2022, 1751)\n}\n\n \"\"\", language=\"python\")\n\n# Output Path\noutput_path = os.path.join(tempfile.gettempdir(), \"saliency_segmentations.json\")\n\n# Check if the session state already exists\nif 'download_clicked' not in st.session_state:\n st.session_state.download_clicked = False\n\n# Check if the session state already exists for the temporary directory\nif 'temp_dir' not in st.session_state:\n st.session_state.temp_dir = None\n\n# Run Button\nif uploaded_files:\n run_button = st.button(\"Run\", help=\"Generating Segmentations\")\n if run_button:\n with st.spinner(\"Running\"):\n if uploaded_files:\n st.spinner(\"Running\")\n\n # Cleanup the previous temporary directory if it exists\n if st.session_state.temp_dir:\n shutil.rmtree(st.session_state.temp_dir)\n \n # Create a new temporary directory\n temp_dir = tempfile.mkdtemp()\n st.session_state.temp_dir = temp_dir\n \n # Save uploaded files to temporary directory\n for file in uploaded_files:\n file_path = os.path.join(temp_dir, file.name)\n with open(file_path, \"wb\") as f:\n f.write(file.getbuffer())\n \n # Build the command for subprocess\n command = [\"python\", \"heatmap_to_segmentation.py\", f\"--map_dir={temp_dir}\", f\"--output_path={output_path}\"]\n \n if threshold_file:\n threshold_path = os.path.join(temp_dir, threshold_file.name)\n with open(threshold_path, \"wb\") as f:\n f.write(threshold_file.getbuffer())\n command.append(f\"--threshold_path={threshold_path}\")\n \n if probability_threshold_file:\n prob_threshold_path = os.path.join(temp_dir, probability_threshold_file.name)\n with open(prob_threshold_path, \"wb\") as f:\n f.write(probability_threshold_file.getbuffer())\n command.append(f\"--probability_threshold_path={prob_threshold_path}\")\n \n if if_smoothing:\n command.append(\"--if_smoothing\")\n command.append(f\"--k={kernel_size}\")\n \n # Execute the command using subprocess\n subprocess.run(command)\n \n # Set the session state to indicate that the download button has been clicked\n st.session_state.download_clicked = True\n\n# Download Button\nif st.session_state.download_clicked:\n st.markdown(\"---\")\n st.markdown(\"### Download\")\n st.download_button(\"Download Segmentations\", data=open(output_path, \"rb\").read(), file_name=\"saliency_segmentations.json\")\n\n # Cleanup the temporary directory\n if st.session_state.temp_dir:\n shutil.rmtree(st.session_state.temp_dir)\n st.session_state.temp_dir = None\n\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n\nst.divider()\nst.subheader(\"Human Annotation Segmentations\")\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"CheXlocalize\"))\n\nfrom eval_constants import LOCALIZATION_TASKS\nfrom utils import encode_segmentation\nfrom annotation_to_segmentation import create_mask, ann_to_mask\n\ndef convert_annotations_to_segmentations(annotations):\n segmentations = {}\n for img_id, img_data in annotations.items():\n if img_id not in segmentations:\n segmentations[img_id] = {}\n for task, polygons in img_data.items():\n img_dims = img_data['img_size']\n try:\n segm_map = create_mask(polygons, img_dims)\n encoded_map = encode_segmentation(segm_map)\n segmentations[img_id][task] = encoded_map\n except TypeError:\n segmentations[img_id][task] = None\n return segmentations\n\ndef save_segmentations(segmentations, output_path):\n with open(output_path, \"w\") as outfile:\n json.dump(segmentations, outfile)\n \ndef download_json(output_json, output_path):\n with open(output_path, 'w') as f:\n f.write(output_json)\n with open(output_path, 'r') as f:\n data = f.read()\n st.markdown(\"---\")\n st.markdown(\"### Download\")\n st.download_button(\n label=\"Download Generated Segmentations\",\n data=data,\n file_name=output_path,\n mime=\"application/json\"\n )\n\n# File uploader\nuploaded_file = st.file_uploader('Upload', type='json', help=\"**Input:** JSON Human annotations file (see Example Input).\\n\\n**Output:** JSON of segmentations.\")\n\n# Example Input\nwith st.expander(\"Example Input\"):\n st.code(\"\"\"\n{\n 'patient64622_study1_view1_frontal': {\n 'img_size': [2320, 2828], # (h, w)\n 'Support Devices': [[[1310.68749, 194.47059],\n [1300.45214, 194.47059],\n [1290.21691, 201.29412],\n ...\n [1310.68749, 191.05883],\n [1300.45214, 197.88236],\n [1293.62865, 211.52943]]],\n 'Cardiomegaly': [[[1031.58047, 951.35314],\n [1023.92373, 957.09569],\n [1012.43856, 964.75249],\n ...\n [1818.31313, 960.92406],\n [1804.91384, 955.1815],\n [1789.60024, 951.35314]]],\n ...\n },\n 'patient64542_study1_view2_lateral': {\n ...\n }\n}\n \"\"\", language=\"python\")\n\n# File Processing\nif uploaded_file is not None:\n run_button = st.button('Run', key='segmentation_run_button', help=\"Generating Segmentations\")\n \n if run_button:\n with st.spinner('Running...'):\n annotations = json.load(uploaded_file)\n segmentations = convert_annotations_to_segmentations(annotations)\n st.session_state[\"segmentations\"] = segmentations\n\n# Download button\nif \"segmentations\" in st.session_state:\n segmentations = st.session_state[\"segmentations\"]\n with tempfile.NamedTemporaryFile(suffix=\".json\", delete=False) as temp_output_file:\n temp_output_path = temp_output_file.name\n save_segmentations(segmentations, temp_output_path)\n\n # Read JSON file\n with open(temp_output_path, 'r') as f:\n output_json = f.read()\n \n # Download button for output\n download_json(output_json, temp_output_path)\n","repo_name":"Health-Universe/CheXlocalize","sub_path":"pages/3_Generate_Segmentations.py","file_name":"3_Generate_Segmentations.py","file_ext":"py","file_size_in_byte":9959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"15314261930","text":"import httplib\nimport logging\nimport mimetypes\nimport os\nimport re\nimport sys\nimport time\nfrom itertools import izip\n\nfrom bottle import abort, get, post, put, delete, local, request, response\nfrom codalab.bundles import get_bundle_subclass, UploadedBundle\nfrom codalab.common import precondition, UsageError, NotFoundError\nfrom codalab.lib import canonicalize, spec_util, worksheet_util\nfrom codalab.lib.server_util import (\n bottle_patch as patch,\n json_api_include,\n query_get_json_api_include_set,\n json_api_meta,\n query_get_bool,\n query_get_list,\n query_get_type,\n)\nfrom codalab.objects.permission import (\n check_bundles_have_all_permission,\n check_bundles_have_read_permission,\n check_worksheet_has_all_permission,\n)\nfrom codalab.rest.schemas import (\n BundleSchema,\n BundlePermissionSchema,\n BUNDLE_CREATE_RESTRICTED_FIELDS,\n BUNDLE_UPDATE_RESTRICTED_FIELDS,\n WorksheetSchema,\n)\nfrom codalab.rest.users import UserSchema\nfrom codalab.rest.util import get_bundle_infos, get_resource_ids, resolve_owner_in_keywords\nfrom codalab.server.authenticated_plugin import AuthenticatedPlugin\nfrom codalabworker.bundle_state import State\n\nlogger = logging.getLogger(__name__)\n\n\n@get('/bundles/' % spec_util.UUID_STR)\ndef _fetch_bundle(uuid):\n \"\"\"\n Fetch bundle by UUID.\n\n Query parameters:\n\n - `include_display_metadata`: `1` to include additional metadata helpful\n for displaying the bundle info, `0` to omit them. Default is `0`.\n - `include`: comma-separated list of related resources to include, such as \"owner\"\n \"\"\"\n document = build_bundles_document([uuid])\n precondition(len(document['data']) == 1, \"data should have exactly one element\")\n document['data'] = document['data'][0] # Flatten data list\n return document\n\n\n@get('/bundles')\ndef _fetch_bundles():\n \"\"\"\n Fetch bundles by bundle `specs` OR search `keywords`. Behavior is undefined\n when both `specs` and `keywords` are provided.\n\n Query parameters:\n\n - `worksheet`: UUID of the base worksheet. Required when fetching by specs.\n - `specs`: Bundle spec of bundle to fetch. May be provided multiples times\n to fetch multiple bundle specs. A bundle spec is either:\n 1. a UUID (8 or 32 hex characters with a preceding '0x')\n 2. a bundle name referring to the last bundle with that name on the\n given base worksheet\n 3. or a reverse index of the form `^N` referring to the Nth-to-last\n bundle on the given base worksheet.\n - `keywords`: Search keyword. May be provided multiples times for multiple\n keywords. Bare keywords match the names and descriptions of bundles.\n Examples of other special keyword forms:\n - `name= ` : More targeted search of using metadata fields.\n - `size=.sort ` : Sort by a particular field.\n - `size=.sort- ` : Sort by a particular field in reverse.\n - `size=.sum ` : Compute total of a particular field.\n - `.mine ` : Match only bundles I own.\n - `.floating ` : Match bundles that aren't on any worksheet.\n - `.count ` : Count the number of bundles.\n - `.limit=10 ` : Limit the number of results to the top 10.\n - `include_display_metadata`: `1` to include additional metadata helpful\n for displaying the bundle info, `0` to omit them. Default is `0`.\n - `include`: comma-separated list of related resources to include, such as \"owner\"\n\n When aggregation keywords such as `.count` are used, the resulting value\n is returned as:\n ```\n {\n \"meta\": {\n \"results\": \n }\n }\n ```\n\n \"\"\"\n keywords = query_get_list('keywords')\n specs = query_get_list('specs')\n worksheet_uuid = request.query.get('worksheet')\n descendant_depth = query_get_type(int, 'depth', None)\n\n if keywords:\n # Handle search keywords\n keywords = resolve_owner_in_keywords(keywords)\n search_result = local.model.search_bundles(request.user.user_id, keywords)\n # Return simple dict if scalar result (e.g. .sum or .count queries)\n if search_result['is_aggregate']:\n return json_api_meta({}, {'result': search_result['result']})\n # If not aggregate this is a list\n bundle_uuids = search_result['result']\n elif specs:\n # Resolve bundle specs\n bundle_uuids = canonicalize.get_bundle_uuids(\n local.model, request.user, worksheet_uuid, specs\n )\n else:\n abort(\n httplib.BAD_REQUEST,\n \"Request must include either 'keywords' \" \"or 'specs' query parameter\",\n )\n\n # Find all descendants down to the provided depth\n if descendant_depth is not None:\n bundle_uuids = local.model.get_self_and_descendants(bundle_uuids, depth=descendant_depth)\n\n return build_bundles_document(bundle_uuids)\n\n\ndef build_bundles_document(bundle_uuids):\n include_set = query_get_json_api_include_set(\n supported={'owner', 'group_permissions', 'children', 'host_worksheets'}\n )\n\n bundles_dict = get_bundle_infos(\n bundle_uuids,\n get_children='children' in include_set,\n get_permissions='group_permissions' in include_set,\n get_host_worksheets='host_worksheets' in include_set,\n ignore_not_found=False,\n )\n\n # Create list of bundles in original order\n bundles = [bundles_dict[uuid] for uuid in bundle_uuids]\n\n # Build response document\n document = BundleSchema(many=True).dump(bundles).data\n\n # Shim in display metadata used by the front-end application\n if query_get_bool('include_display_metadata', default=False):\n for bundle, data in izip(bundles, document['data']):\n bundle_class = get_bundle_subclass(bundle['bundle_type'])\n json_api_meta(\n data,\n {\n 'editable_metadata_keys': worksheet_util.get_editable_metadata_fields(\n bundle_class\n ),\n 'metadata_type': worksheet_util.get_metadata_types(bundle_class),\n },\n )\n\n if 'owner' in include_set:\n owner_ids = set(b['owner_id'] for b in bundles if b['owner_id'] is not None)\n json_api_include(document, UserSchema(), local.model.get_users(owner_ids))\n\n if 'group_permissions' in include_set:\n for bundle in bundles:\n json_api_include(\n document, BundlePermissionSchema(), bundle.get('group_permissions', [])\n )\n\n if 'children' in include_set:\n for bundle in bundles:\n json_api_include(document, BundleSchema(), bundle.get('children', []))\n\n if 'host_worksheets' in include_set:\n for bundle in bundles:\n json_api_include(document, WorksheetSchema(), bundle.get('host_worksheets', []))\n\n return document\n\n\n@post('/bundles', apply=AuthenticatedPlugin())\ndef _create_bundles():\n \"\"\"\n Bulk create bundles.\n\n Query parameters:\n - `worksheet`: UUID of the parent worksheet of the new bundle, add to\n this worksheet if not detached or shadowing another bundle. The new\n bundle also inherits permissions from this worksheet.\n - `shadow`: UUID of the bundle to \"shadow\" (the new bundle will be added\n as an item immediately after this bundle in its parent worksheet).\n - `detached`: 1 if should not add new bundle to any worksheet,\n or 0 otherwise. Default is 0.\n - `wait_for_upload`: 1 if the bundle state should be initialized to\n \"uploading\" regardless of the bundle type, or 0 otherwise. Used when\n copying bundles from another CodaLab instance, this prevents these new\n bundles from being executed by the BundleManager. Default is 0.\n \"\"\"\n worksheet_uuid = request.query.get('worksheet')\n shadow_parent_uuid = request.query.get('shadow')\n detached = query_get_bool('detached', default=False)\n if worksheet_uuid is None:\n abort(\n httplib.BAD_REQUEST,\n \"Parent worksheet id must be specified as\" \"'worksheet' query parameter\",\n )\n\n # Deserialize bundle fields\n bundles = (\n BundleSchema(strict=True, many=True, dump_only=BUNDLE_CREATE_RESTRICTED_FIELDS)\n .load(request.json)\n .data\n )\n\n # Check for all necessary permissions\n worksheet = local.model.get_worksheet(worksheet_uuid, fetch_items=False)\n check_worksheet_has_all_permission(local.model, request.user, worksheet)\n worksheet_util.check_worksheet_not_frozen(worksheet)\n request.user.check_quota(need_time=True, need_disk=True)\n\n created_uuids = []\n for bundle in bundles:\n # Prep bundle info for saving into database\n # Unfortunately cannot use the `construct` methods because they don't\n # provide a uniform interface for constructing bundles for all types\n # Hopefully this can all be unified after REST migration is complete\n bundle_uuid = bundle.setdefault('uuid', spec_util.generate_uuid())\n created_uuids.append(bundle_uuid)\n bundle_class = get_bundle_subclass(bundle['bundle_type'])\n bundle['owner_id'] = request.user.user_id\n if issubclass(bundle_class, UploadedBundle) or query_get_bool('wait_for_upload', False):\n bundle['state'] = State.UPLOADING\n else:\n bundle['state'] = State.CREATED\n bundle['is_anonymous'] = worksheet.is_anonymous # inherit worksheet anonymity\n bundle.setdefault('metadata', {})['created'] = int(time.time())\n for dep in bundle.setdefault('dependencies', []):\n dep['child_uuid'] = bundle_uuid\n\n # Create bundle object\n bundle = bundle_class(bundle, strict=False)\n\n # Save bundle into model\n local.model.save_bundle(bundle)\n\n # Inherit worksheet permissions\n group_permissions = local.model.get_group_worksheet_permissions(\n request.user.user_id, worksheet_uuid\n )\n set_bundle_permissions(\n [\n {\n 'object_uuid': bundle_uuid,\n 'group_uuid': p['group_uuid'],\n 'permission': p['permission'],\n }\n for p in group_permissions\n ]\n )\n\n # Add as item to worksheet\n if not detached:\n if shadow_parent_uuid is None:\n local.model.add_worksheet_item(\n worksheet_uuid, worksheet_util.bundle_item(bundle_uuid)\n )\n else:\n local.model.add_shadow_worksheet_items(shadow_parent_uuid, bundle_uuid)\n\n # Get created bundles\n bundles_dict = get_bundle_infos(created_uuids)\n\n # Return bundles in original order\n bundles = [bundles_dict[uuid] for uuid in created_uuids]\n return BundleSchema(many=True).dump(bundles).data\n\n\n@patch('/bundles', apply=AuthenticatedPlugin())\ndef _update_bundles():\n \"\"\"\n Bulk update bundles.\n \"\"\"\n bundle_updates = (\n BundleSchema(strict=True, many=True, dump_only=BUNDLE_UPDATE_RESTRICTED_FIELDS)\n .load(request.json, partial=True)\n .data\n )\n\n # Check permissions\n bundle_uuids = [b.pop('uuid') for b in bundle_updates]\n check_bundles_have_all_permission(local.model, request.user, bundle_uuids)\n bundles = local.model.batch_get_bundles(uuid=bundle_uuids)\n\n # Update bundles\n for bundle, update in izip(bundles, bundle_updates):\n local.model.update_bundle(bundle, update)\n\n # Get updated bundles\n bundles_dict = get_bundle_infos(bundle_uuids)\n\n # Create list of bundles in original order\n updated_bundles = [bundles_dict[uuid] for uuid in bundle_uuids]\n\n return BundleSchema(many=True).dump(updated_bundles).data\n\n\n@delete('/bundles', apply=AuthenticatedPlugin())\ndef _delete_bundles():\n \"\"\"\n Delete the bundles specified.\n\n Query parameters:\n - `force`: 1 to allow deletion of bundles that have descendants or that\n appear across multiple worksheets, or 0 to throw an error if any of the\n specified bundles have multiple references. Default is 0.\n - `recursive`: 1 to remove all bundles downstream too, or 0 otherwise.\n Default is 0.\n - `data-only`: 1 to only remove contents of the bundle(s) from the bundle\n store and leave the bundle metadata intact, or 0 to remove both the\n bundle contents and the bundle metadata. Default is 0.\n - `dry-run`: 1 to just return list of bundles that would be deleted with\n the given parameters without actually deleting them, or 0 to perform\n the deletion. Default is 0.\n \"\"\"\n uuids = get_resource_ids(request.json, 'bundles')\n force = query_get_bool('force', default=False)\n recursive = query_get_bool('recursive', default=False)\n data_only = query_get_bool('data-only', default=False)\n dry_run = query_get_bool('dry-run', default=False)\n deleted_uuids = delete_bundles(\n uuids, force=force, recursive=recursive, data_only=data_only, dry_run=dry_run\n )\n\n # Return list of deleted ids as meta\n return json_api_meta({}, {'ids': deleted_uuids})\n\n\n@post('/bundle-permissions', apply=AuthenticatedPlugin())\ndef _set_bundle_permissions():\n \"\"\"\n Bulk set bundle permissions.\n\n A bundle permission created on a bundle-group pair will replace any\n existing permissions on the same bundle-group pair.\n \"\"\"\n new_permissions = BundlePermissionSchema(strict=True, many=True).load(request.json).data\n set_bundle_permissions(new_permissions)\n return BundlePermissionSchema(many=True).dump(new_permissions).data\n\n\n@get('/bundles//contents/info/' % spec_util.UUID_STR, name='fetch_bundle_contents_info')\n@get(\n '/bundles//contents/info/' % spec_util.UUID_STR,\n name='fetch_bundle_contents_info',\n)\ndef _fetch_bundle_contents_info(uuid, path=''):\n \"\"\"\n Fetch metadata of the bundle contents or a subpath within the bundle.\n\n Query parameters:\n - `depth`: recursively fetch subdirectory info up to this depth.\n Default is 0.\n\n Response format:\n ```\n {\n \"data\": {\n \"name\": \"\",\n \"link\": \"\",\n \"type\": \"\",\n \"size\": ,\n \"perm\": ,\n \"contents\": [\n {\n \"name\": ...,\n \n },\n ...\n ]\n }\n }\n ```\n \"\"\"\n depth = query_get_type(int, 'depth', default=0)\n if depth < 0:\n abort(httplib.BAD_REQUEST, \"Depth must be at least 0\")\n\n check_bundles_have_read_permission(local.model, request.user, [uuid])\n try:\n info = local.download_manager.get_target_info(uuid, path, depth)\n except NotFoundError as e:\n abort(httplib.NOT_FOUND, e.message)\n except Exception as e:\n abort(httplib.BAD_REQUEST, e.message)\n\n return {'data': info}\n\n\n@put('/bundles//netcat//' % spec_util.UUID_STR, name='netcat_bundle')\ndef _netcat_bundle(uuid, port):\n \"\"\"\n Send a raw bytestring into the specified port of the running bundle with uuid.\n Return the response from this bundle.\n \"\"\"\n check_bundles_have_read_permission(local.model, request.user, [uuid])\n bundle = local.model.get_bundle(uuid)\n print(bundle.state)\n if bundle.state != State.RUNNING:\n abort(httplib.FORBIDDEN, 'Cannot netcat bundle, bundle not running.')\n info = local.download_manager.netcat(uuid, port, request.json['message'])\n return {'data': info}\n\n\n@post(\n '/bundles//netcurl//' % spec_util.UUID_STR,\n name='netcurl_bundle',\n)\n@put(\n '/bundles//netcurl//' % spec_util.UUID_STR,\n name='netcurl_bundle',\n)\n@delete(\n '/bundles//netcurl//' % spec_util.UUID_STR,\n name='netcurl_bundle',\n)\n@get(\n '/bundles//netcurl//' % spec_util.UUID_STR,\n name='netcurl_bundle',\n)\n@patch(\n '/bundles//netcurl//' % spec_util.UUID_STR,\n name='netcurl_bundle',\n)\ndef _netcurl_bundle(uuid, port, path=''):\n \"\"\"\n Forward an HTTP request into the specified port of the running bundle with uuid.\n Return the HTTP response from this bundle.\n \"\"\"\n check_bundles_have_read_permission(local.model, request.user, [uuid])\n bundle = local.model.get_bundle(uuid)\n if bundle.state in State.FINAL_STATES:\n abort(httplib.FORBIDDEN, 'Cannot netcurl bundle, bundle already finalized.')\n\n try:\n request.path_shift(4) # shift away the routing parts of the URL\n\n headers_string = [\n '{}: {}'.format(h, request.headers.get(h)) for h in request.headers.keys()\n ]\n message = \"{} {} HTTP/1.1\\r\\n\".format(request.method, request.path)\n message += \"\\r\\n\".join(headers_string) + \"\\r\\n\"\n message += \"\\r\\n\"\n message += request.body.read()\n\n info = local.download_manager.netcat(uuid, port, message)\n except Exception:\n print >>sys.stderr, \"{}\".format(request.environ)\n raise\n finally:\n request.path_shift(-4) # restore the URL\n\n return info\n\n\n@get('/bundles//contents/blob/' % spec_util.UUID_STR, name='fetch_bundle_contents_blob')\n@get(\n '/bundles//contents/blob/' % spec_util.UUID_STR,\n name='fetch_bundle_contents_blob',\n)\ndef _fetch_bundle_contents_blob(uuid, path=''):\n \"\"\"\n API to download the contents of a bundle or a subpath within a bundle.\n\n For directories, this method always returns a tarred and gzipped archive of\n the directory.\n\n For files, if the request has an Accept-Encoding header containing gzip,\n then the returned file is gzipped. Otherwise, the file is returned as-is.\n\n HTTP Request headers:\n - `Range: bytes=-`: fetch bytes from the range\n `[, )`.\n - `Accept-Encoding: `: indicate that the client can accept\n encoding ``. Currently only `gzip` encoding is supported.\n\n Query parameters:\n - `head`: number of lines to fetch from the beginning of the file.\n Default is 0, meaning to fetch the entire file.\n - `tail`: number of lines to fetch from the end of the file.\n Default is 0, meaning to fetch the entire file.\n - `max_line_length`: maximum number of characters to fetch from each line,\n if either `head` or `tail` is specified. Default is 128.\n\n HTTP Response headers (for single-file targets):\n - `Content-Disposition: inline; filename=`\n - `Content-Type: `\n - `Content-Encoding: [gzip|identity]`\n - `Target-Type: file`\n\n HTTP Response headers (for directories):\n - `Content-Disposition: attachment; filename=.tar.gz`\n - `Content-Type: application/gzip`\n - `Content-Encoding: identity`\n - `Target-Type: directory`\n \"\"\"\n byte_range = get_request_range()\n head_lines = query_get_type(int, 'head', default=0)\n tail_lines = query_get_type(int, 'tail', default=0)\n truncation_text = query_get_type(str, 'truncation_text', default='')\n max_line_length = query_get_type(int, 'max_line_length', default=128)\n check_bundles_have_read_permission(local.model, request.user, [uuid])\n bundle = local.model.get_bundle(uuid)\n\n try:\n target_info = local.download_manager.get_target_info(uuid, path, 0)\n except NotFoundError as e:\n abort(httplib.NOT_FOUND, e.message)\n except Exception as e:\n abort(httplib.BAD_REQUEST, e.message)\n\n # Figure out the file name.\n if not path and bundle.metadata.name:\n filename = bundle.metadata.name\n else:\n filename = target_info['name']\n\n if target_info['type'] == 'directory':\n if byte_range:\n abort(httplib.BAD_REQUEST, 'Range not supported for directory blobs.')\n if head_lines or tail_lines:\n abort(httplib.BAD_REQUEST, 'Head and tail not supported for directory blobs.')\n # Always tar and gzip directories\n gzipped_stream = False # but don't set the encoding to 'gzip'\n mimetype = 'application/gzip'\n filename += '.tar.gz'\n fileobj = local.download_manager.stream_tarred_gzipped_directory(uuid, path)\n elif target_info['type'] == 'file':\n # Let's gzip to save bandwidth.\n # For simplicity, we do this even if the file is already a packed\n # archive (which should be relatively rare).\n # The browser will transparently decode the file.\n gzipped_stream = request_accepts_gzip_encoding()\n\n # Since guess_type() will interpret '.tar.gz' as an 'application/x-tar' file\n # with 'gzip' encoding, which would usually go into the Content-Encoding\n # header. But if the bundle contents is actually a packed archive, we don't\n # want the client to automatically decompress the file, so we don't want to\n # set the Content-Encoding header. Instead, if guess_type() detects an\n # archive, we just set mimetype to indicate an arbitrary binary file.\n mimetype, encoding = mimetypes.guess_type(filename, strict=False)\n if encoding is not None:\n mimetype = 'application/octet-stream'\n\n if byte_range and (head_lines or tail_lines):\n abort(httplib.BAD_REQUEST, 'Head and range not supported on the same request.')\n elif byte_range:\n start, end = byte_range\n fileobj = local.download_manager.read_file_section(\n uuid, path, start, end - start + 1, gzipped_stream\n )\n elif head_lines or tail_lines:\n fileobj = local.download_manager.summarize_file(\n uuid, path, head_lines, tail_lines, max_line_length, truncation_text, gzipped_stream\n )\n else:\n fileobj = local.download_manager.stream_file(uuid, path, gzipped_stream)\n else:\n # Symlinks.\n abort(httplib.FORBIDDEN, 'Cannot download files of this type (%s).' % target_info['type'])\n\n # Set headers.\n response.set_header('Content-Type', mimetype or 'text/plain')\n response.set_header('Content-Encoding', 'gzip' if gzipped_stream else 'identity')\n if target_info['type'] == 'file':\n response.set_header('Content-Disposition', 'inline; filename=\"%s\"' % filename)\n else:\n response.set_header('Content-Disposition', 'attachment; filename=\"%s\"' % filename)\n response.set_header('Target-Type', target_info['type'])\n\n return fileobj\n\n\n@put(\n '/bundles//contents/blob/' % spec_util.UUID_STR,\n name='update_bundle_contents_blob',\n apply=AuthenticatedPlugin(),\n)\ndef _update_bundle_contents_blob(uuid):\n \"\"\"\n Update the contents of the given running or uploading bundle.\n\n Query parameters:\n - `urls`: (optional) comma-separated list of URLs from which to fetch data\n to fill the bundle, using this option will ignore any uploaded file data\n - `git`: (optional) 1 if URL should be interpreted as git repos to clone\n or 0 otherwise, default is 0.\n - `filename`: (optional) filename of the uploaded file, used to indicate\n whether or not it is an archive, default is 'contents'\n - `unpack`: (optional) 1 if the uploaded file should be unpacked if it is\n an archive, or 0 otherwise, default is 1\n - `simplify`: (optional) 1 if the uploaded file should be 'simplified' if\n it is an archive, or 0 otherwise, default is 1.\n - `finalize_on_failure`: (optional) 1 if bundle state should be set\n to 'failed' in the case of a failure during upload, or 0 if the bundle\n state should not change on failure. Default is 0.\n - `finalize_on_success`: (optional) 1 if bundle state should be set\n to 'state_on_success' when the upload finishes successfully. Default is\n True\n - `state_on_success`: (optional) Update the bundle state to this state if\n the upload completes successfully. Must be either 'ready' or 'failed'.\n Default is 'ready'.\n \"\"\"\n check_bundles_have_all_permission(local.model, request.user, [uuid])\n bundle = local.model.get_bundle(uuid)\n if bundle.state in State.FINAL_STATES:\n abort(httplib.FORBIDDEN, 'Contents cannot be modified, bundle already finalized.')\n\n # Get and validate query parameters\n finalize_on_failure = query_get_bool('finalize_on_failure', default=False)\n finalize_on_success = query_get_bool('finalize_on_success', default=True)\n final_state = request.query.get('state_on_success', default=State.READY)\n if finalize_on_success and final_state not in State.FINAL_STATES:\n abort(\n httplib.BAD_REQUEST, 'state_on_success must be one of %s' % '|'.join(State.FINAL_STATES)\n )\n\n # If this bundle already has data, remove it.\n if local.upload_manager.has_contents(bundle):\n local.upload_manager.cleanup_existing_contents(bundle)\n\n # Store the data.\n try:\n if request.query.urls:\n sources = query_get_list('urls')\n else:\n filename = request.query.get('filename', default='contents')\n sources = [(filename, request['wsgi.input'])]\n\n local.upload_manager.upload_to_bundle_store(\n bundle,\n sources=sources,\n follow_symlinks=False,\n exclude_patterns=None,\n remove_sources=False,\n git=query_get_bool('git', default=False),\n unpack=query_get_bool('unpack', default=True),\n simplify_archives=query_get_bool('simplify', default=True),\n ) # See UploadManager for full explanation of 'simplify'\n\n local.upload_manager.update_metadata_and_save(bundle, enforce_disk_quota=True)\n\n except UsageError as err:\n # This is a user error (most likely disk quota overuser) so raise a client HTTP error\n if local.upload_manager.has_contents(bundle):\n local.upload_manager.cleanup_existing_contents(bundle)\n msg = \"Upload failed: %s\" % err\n local.model.update_bundle(\n bundle, {'state': State.FAILED, 'metadata': {'failure_message': msg}}\n )\n abort(httplib.BAD_REQUEST, msg)\n\n except Exception as e:\n # Upload failed: cleanup, update state if desired, and return HTTP error\n if local.upload_manager.has_contents(bundle):\n local.upload_manager.cleanup_existing_contents(bundle)\n\n msg = \"Upload failed: %s\" % e\n\n # The client may not want to finalize the bundle on failure, to keep\n # open the possibility of retrying the upload in the case of transient\n # failure.\n # Workers also use this API endpoint to upload partial contents of\n # running bundles, and they should use finalize_on_failure=0 to avoid\n # letting transient errors during upload fail the bundles prematurely.\n if finalize_on_failure:\n local.model.update_bundle(\n bundle, {'state': State.FAILED, 'metadata': {'failure_message': msg}}\n )\n\n abort(httplib.INTERNAL_SERVER_ERROR, msg)\n\n else:\n if finalize_on_success:\n # Upload succeeded: update state\n local.model.update_bundle(bundle, {'state': final_state})\n\n\n#############################################################\n# BUNDLE HELPER FUNCTIONS\n#############################################################\n\n\ndef get_request_range():\n \"\"\"\n Parses header of the form:\n Range: bytes=START-END\n into tuple:\n (int(START), int(END))\n \"\"\"\n if 'Range' not in request.headers:\n return None\n\n m = re.match(r'bytes=(\\d+)-(\\d+)', request.headers['Range'].strip())\n if m is None:\n abort(httplib.BAD_REQUEST, \"Range must be 'bytes=START-END'.\")\n\n start, end = m.groups()\n return int(start), int(end)\n\n\ndef request_accepts_gzip_encoding():\n # See rules for parsing here: https://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html\n # Browsers silently decode gzipped files, so we save some bandwidth.\n if 'Accept-Encoding' not in request.headers:\n return False\n for encoding in request.headers['Accept-Encoding'].split(','):\n encoding = encoding.strip().split(';')\n if encoding[0] == 'gzip':\n if len(encoding) > 1 and encoding[1] == 'q=0':\n return False\n else:\n return True\n return False\n\n\ndef delete_bundles(uuids, force, recursive, data_only, dry_run):\n \"\"\"\n Delete the bundles specified by |uuids|.\n If |force|, allow deletion of bundles that have descendants or that appear across multiple worksheets.\n If |recursive|, add all bundles downstream too.\n If |data_only|, only remove from the bundle store, not the bundle metadata.\n \"\"\"\n relevant_uuids = local.model.get_self_and_descendants(uuids, depth=sys.maxint)\n if not recursive:\n # If any descendants exist, then we only delete uuids if force = True.\n if (not force) and set(uuids) != set(relevant_uuids):\n relevant = local.model.batch_get_bundles(uuid=(set(relevant_uuids) - set(uuids)))\n raise UsageError(\n 'Can\\'t delete bundles %s because the following bundles depend on them:\\n %s'\n % (' '.join(uuids), '\\n '.join(bundle.simple_str() for bundle in relevant))\n )\n relevant_uuids = uuids\n check_bundles_have_all_permission(local.model, request.user, relevant_uuids)\n\n # Make sure we don't delete bundles which are active.\n states = local.model.get_bundle_states(uuids)\n logger.debug('delete states: %s', states)\n active_uuids = [uuid for (uuid, state) in states.items() if state in State.ACTIVE_STATES]\n logger.debug('delete actives: %s', active_uuids)\n if len(active_uuids) > 0:\n raise UsageError(\n 'Can\\'t delete bundles: %s. ' % (' '.join(active_uuids))\n + 'For run bundles, kill them first. '\n + 'Bundles stuck not running will eventually '\n + 'automatically be moved to a state where they '\n + 'can be deleted.'\n )\n\n # Make sure that bundles are not referenced in multiple places (otherwise, it's very dangerous)\n result = local.model.get_host_worksheet_uuids(relevant_uuids)\n for uuid, host_worksheet_uuids in result.items():\n worksheets = local.model.batch_get_worksheets(fetch_items=False, uuid=host_worksheet_uuids)\n frozen_worksheets = [worksheet for worksheet in worksheets if worksheet.frozen]\n if len(frozen_worksheets) > 0:\n raise UsageError(\n \"Can't delete bundle %s because it appears in frozen worksheets \"\n \"(need to delete worksheet first):\\n %s\"\n % (uuid, '\\n '.join(worksheet.simple_str() for worksheet in frozen_worksheets))\n )\n if not force and len(host_worksheet_uuids) > 1:\n raise UsageError(\n \"Can't delete bundle %s because it appears in multiple worksheets \"\n \"(--force to override):\\n %s\"\n % (uuid, '\\n '.join(worksheet.simple_str() for worksheet in worksheets))\n )\n\n # Delete the actual bundle\n if not dry_run:\n if data_only:\n # Just remove references to the data hashes\n local.model.remove_data_hash_references(relevant_uuids)\n else:\n # Actually delete the bundle\n local.model.delete_bundles(relevant_uuids)\n\n # Update user statistics\n local.model.update_user_disk_used(request.user.user_id)\n\n # Delete the data.\n for uuid in relevant_uuids:\n # check first is needs to be deleted\n bundle_location = local.bundle_store.get_bundle_location(uuid)\n if os.path.lexists(bundle_location):\n local.bundle_store.cleanup(uuid, dry_run)\n\n return relevant_uuids\n\n\ndef set_bundle_permissions(new_permissions):\n # Check if current user has permission to set bundle permissions\n check_bundles_have_all_permission(\n local.model, request.user, [p['object_uuid'] for p in new_permissions]\n )\n # Sequentially set bundle permissions\n for p in new_permissions:\n local.model.set_group_bundle_permission(p['group_uuid'], p['object_uuid'], p['permission'])\n","repo_name":"Matt-F-Wu/codalab_cl","sub_path":"codalab/rest/bundles.py","file_name":"bundles.py","file_ext":"py","file_size_in_byte":32589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"39290161814","text":"#!/usr/bin/env python\n\nimport os\nimport sys\nimport logging\nimport argparse\nCWD = os.path.dirname(os.path.realpath(__file__))\nsys.path.insert(0, os.path.join(CWD, 'src'))\n\nfrom chatbot.client import Client\n\nHR_CHATBOT_AUTHKEY = os.environ.get('HR_CHATBOT_AUTHKEY', 'AAAAB3NzaD')\n\nif __name__ == '__main__':\n logging.basicConfig()\n logging.getLogger().setLevel(logging.WARN)\n\n parser = argparse.ArgumentParser(\n description='Run the chatbot client')\n parser.add_argument(\n 'botname', help='Name of the bot')\n parser.add_argument(\n 'host', help='Host of the chatbot server')\n args = parser.parse_args()\n\n client = Client(HR_CHATBOT_AUTHKEY, botname=args.botname, host=args.host)\n client.cmdloop()\n","repo_name":"opencog/loving-ai","sub_path":"chatbot-server/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"2"}
+{"seq_id":"23200101742","text":"from fastapi import FastAPI\nimport requests\nfrom pydantic import BaseModel\nfrom database import filter_add_uniques, get_last_added, create_tables\nfrom utils import validate_response\n\napp = FastAPI()\n\n\nclass Question(BaseModel):\n questions_num: int\n\n\n@app.post(\"/api/v1/questions\")\ndef get_questions(request_data: Question):\n last_added = get_last_added()\n added_num = 0\n\n while added_num < request_data.questions_num:\n response = requests.get(\n f\"https://jservice.io/api/random?count={request_data.questions_num - added_num}\").json()\n\n if not validate_response(response):\n return {'result': 'error'}\n\n added_num_in = filter_add_uniques(response)\n added_num += added_num_in\n\n return {'result': 'success', 'data': last_added}\n\n\n@app.on_event(\"startup\")\nasync def startup_event():\n create_tables()\n","repo_name":"bucin98/FastApi_Postgre_1","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"75259930606","text":"from datetime import timedelta\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom django.core.files.uploadedfile import SimpleUploadedFile\nfrom core import services\nfrom core.models import FileUploadRecord, EscalaRecord\n\n\ndef test_upload_multiple_files(client, mocker):\n mocker.patch(\"core.services.handle_csv_files\")\n file = get_CSV_example_as_uploadedFile()\n file2 = get_CSV_example_as_uploadedFile()\n\n res = client.post(\"/api/files/\", {\"files\": [file, file2]}, format=\"multipart\")\n data = res.json()\n\n assert res.status_code == 200\n assert data[\"success\"] is True\n\n\ndef test_correctly_record_number_of_rows_in_file():\n file = get_CSV_example_as_uploadedFile()\n\n services.handle_csv_files([file]) # type: ignore\n register = FileUploadRecord.objects.all().first()\n\n assert register is not None\n assert register.file == \"file.csv\"\n assert register.number_of_entries == 208\n\n\ndef test_record_escala_from_file():\n file = get_CSV_example_as_uploadedFile()\n\n services.handle_csv_files([file]) # type: ignore\n escalas = EscalaRecord.objects.all()\n files = FileUploadRecord.objects.all()\n\n assert len(escalas) == 208\n assert len(files) == 1\n assert escalas[0].matricula_colaborador\n assert escalas[0].timestamp\n assert escalas[0].file == files[0]\n\n\ndef get_CSV_example_as_uploadedFile():\n f = open(\n settings.BASE_DIR / \"core\" / \"example_data\" / \"exemplo_arquivo_marcacoes.csv\",\n \"r\",\n )\n return SimpleUploadedFile(\"file.csv\", f.read().encode(), content_type=\"text/csv\")\n\n\ndef test_get_escalar_for_a_day(client):\n today = timezone.now()\n escala_criada = EscalaRecord.objects.create(\n matricula_colaborador=\"123\",\n timestamp=today,\n file=FileUploadRecord.objects.create(\n file=\"file.csv\",\n number_of_entries=1,\n ),\n )\n\n res = client.get(\n \"/api/escalas/\",\n {\"initDay\": today.date(), \"endDay\": (today + timedelta(days=1)).date()},\n )\n data = res.json()\n escalas = data[\"escalas\"]\n\n assert escalas[0][\"matricula_colaborador\"] == \"123\"\n assert escalas[0][\"timestamp\"] == today.strftime(\"%Y-%m-%dT%H:%M:%S.%f\")[:-3] + \"Z\"\n assert escalas[0][\"file\"] == {\"id\": escala_criada.file_id, \"file_name\": \"file.csv\"}\n\n\ndef test_list_file_uploads(client):\n file_register = FileUploadRecord.objects.create(\n file=\"file.csv\",\n number_of_entries=1,\n )\n\n res = client.get(\"/api/files/\")\n data = res.json()\n files = data[\"files\"]\n\n assert len(files) == 1\n assert files[0][\"file_name\"] == file_register.file.name\n assert files[0][\"id\"] == file_register.id\n assert files[0][\"number_of_entries\"] == file_register.number_of_entries\n","repo_name":"danielpassy/desafio_moavi","sub_path":"backend/core/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"20297788207","text":"import csv\nimport os\n\n# new_path = \"0. 데이터셋 준비/train_origin.tsv\" # 셔플 전의 데이터 전체 모음\n# new_f = open(new_path, 'w', encoding='UTF-8', newline=\"\") # 쓰기 모드\n\n\ncurrent_path = os.getcwd()\nprint(current_path, ' ******************** ')\n\nfile_list = os.listdir(\"0. Dataset\")\nfile_list.sort()\n\nnew_list = [] ####\n\n# with open(path, 'r', encoding='UTF-8') as f:\n# tw = csv.writer(f, delimiter=\"\\t\")\n\n\nprint(file_list) ## (예) ['.DS_Store', '127715_ko_nia_normal_squad_all_question_mun.txt', '15111_ko_wiki_v1_squad_question_mun.txt', '18748_ko_nia_clue0529_squad_all_question_mun.txt', '44162_ko_nia_noanswer_squad_all_question_mun.txt',\n# '5435_KorQuAD_v1.0_dev_question_mun.txt', '55879_KorQuAD_v1.0_train_question_mun.txt']\n\nprint()\n\nfor file_name in file_list[1:]:\n print(' ----- \" {0} \" 파일 시작 -----'.format(file_name))\n path = current_path + \"/0. Dataset/\" + str(file_name)\n\n # 1. 파일 읽기\n f = open(path, 'r', encoding='UTF-8') # 읽기 모드\n ###\n add_list = []\n\n for each_set in f:\n '''\n # 2. label 정보 tab으로 붙이기\n line = line + '\\t' + str(label)\n\n # 3. .tsv 파일로 저장하기\n new_f.write(line + \"\\n\")\n '''\n\n sentence, label = each_set.split('\\t')\n add_list.append(list([sentence, int(label)])) # [\"~~ 문장 ?\", label 값]\n\n new_list.extend(add_list)\n print(\"완 성 !! \", \" ----- 파일 크기 (문장의 개수) 는 ----- : \", str(len(add_list)))\n\n#####print(new_list)\nprint(len(new_list))\n\n\n\n# 3. .tsv 파일로 저장하기\nnew_path = current_path + \"/0. Dataset/\" + \"train_origin.tsv\" ###\nnew_path_train = current_path + \"/0. Dataset/\" + \"train.tsv\"\nnew_path_dev = current_path + \"/0. Dataset/\" + \"dev.tsv\"\nnew_path_test = current_path + \"/0. Dataset/\" + \"test.tsv\"\n\nl = len(new_list)\n\nwith open(new_path, 'w') as new_f:\n tw = csv.writer(new_f, delimiter=\"\\t\")\n for each_set in new_list:\n tw.writerow(each_set)\n\n\n# (셔플)\nimport random\n\nrandom.seed(42)\nrandom.shuffle(new_list)\nprint(\"--- 셔플 완료 ---\")\n#####(new_list)\n\n\n\nwith open(new_path_train, 'w') as new_f:\n tw = csv.writer(new_f, delimiter=\"\\t\")\n for each_set in new_list[:int(l * 0.8)]:\n tw.writerow(each_set)\n\nwith open(new_path_dev, 'w') as new_f:\n tw = csv.writer(new_f, delimiter=\"\\t\")\n\n for each_set in new_list[int(l * 0.8): int(l * 0.9)]:\n tw.writerow(each_set)\n\nwith open(new_path_test, 'w') as new_f:\n tw = csv.writer(new_f, delimiter=\"\\t\")\n\n for each_set in new_list[int(l * 0.9):]:\n tw.writerow(each_set)\n\nprint(int(l * 0.8), int(l * 0.9), l)\n# 733535 825227 916919\n\nprint(len(new_list[:int(l * 0.8)]), len(new_list[int(l * 0.8): int(l * 0.9)]), len(new_list[int(l * 0.9):]))\n# 733535 91692 91692\n\n# new_f = open(new_path, 'w') # 쓰기 모드\n#\n# for line, label in new_list:\n# f.write(line + \"\\n\")\n\n","repo_name":"sallyy1/Padded-MLM-for-Style-Transfer","sub_path":"0_make_first_datasets_to train test dev split.py","file_name":"0_make_first_datasets_to train test dev split.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"73819330925","text":"import sys\r\n\r\nsys.setrecursionlimit(10**6)\r\n\r\nn = int(input())\r\ngraph = []\r\nresult = 1e9\r\nvisited = [False] * n\r\nfor _ in range(n):\r\n graph.append(list(map(int, input().split())))\r\n\r\ndef dfs(graph, start, now, cost):\r\n global result\r\n if start == now and visited.count(False) == 0:\r\n result = min(result, cost)\r\n\r\n for i in range(n):\r\n if graph[now][i] != 0 and not visited[i]:\r\n visited[i] = True\r\n dfs(graph, start, i, cost + graph[now][i])\r\n visited[i] = False\r\n\r\ndfs(graph, 0, 0, 0)\r\nprint(result)\r\n","repo_name":"dlsrks0631/Problem_Solving","sub_path":"백준/Silver/10971. 외판원 순회 2/외판원 순회 2.py","file_name":"외판원 순회 2.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"32506866818","text":"from Clase_canciones import Canciones\nfrom Clase_artistas import Artista\nfrom Clase_discografica import Discografica\nfrom Clase_autores import Autores\nfrom Ejercicio5.Clase_album import Album\n\n\n\ndiscografica = Discografica()\nalbum = Album()\n\n\ncancion = Canciones()\ncancion2 = Canciones()\ncancion3 = Canciones()\ncancion4 = Canciones()\n\nartista = Artista()\nartista2 = Artista()\nartista3 = Artista()\nartista4 = Artista()\nartista5 = Artista()\nautor = Autores()\nautor2 = Autores()\nautor3 = Autores()\nalbum.titulo = input(\"albun nuebooo xddd: \")\ndiscografica.AgregarAlbum(album)\n\nargentina = input(\"pais del artista mas influyente: \")\ndiscografica.ListaCancionesPais(argentina)\n\ncancion.titulo = input(\"Titulo de la cancion: \")\nalbum.AgregarCanciones(cancion)\n\nfor item in range(5):\n nombre_artista = input(\"Artista a a a a: \")\n artista.SetNombre(nombre_artista)\n cancion.AgregarArtistas(artista)\nfor item in range(3):\n nombre_autor = input(\"Autor: \")\n autor.SetNombre(nombre_autor)\n\n\ncancion.AgregarAutores(autor)\n\n\nprint (autor.nombre)\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"politecnicomodelopoo2018/Guia-ejercicios-1-Cami-Aye","sub_path":"Ejercicio5/Main_ejercicio5.py","file_name":"Main_ejercicio5.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"6003797838","text":"import copy\n\n\n# Node {{{1\nclass Node:\n def __init__(self, key, *, data=None, next=None):\n self.key = key\n self.data = data\n self.next = next\n\n\n# LinkedList {{{1\nclass LinkedList:\n # List creation {{{2\n def __init__(self, key, data, *, reverse=False):\n start = Node(key[0], data=data[0]) # create root node\n end = start\n for k, d in zip(key[1:], data[1:]):\n if reverse:\n end = self._prepend(end, Node(k, data=d))\n else:\n end = self._append(end, Node(k, data=d))\n self.first = end if reverse else start\n\n @staticmethod\n def _append(anchor, node):\n anchor.next = node\n return node\n\n @staticmethod\n def _prepend(anchor, node):\n node.next = anchor\n return node\n\n # Sequence properties {{{2\n def __getitem__(self, key):\n _, node = self._getitem(key)\n return node\n\n def __len__(self):\n node = self.first\n count = 0\n while node != None:\n count += 1\n node = node.next\n return count\n\n def __str__(self):\n node = self.first\n pretty = \"\"\n while node != None:\n pretty += f\"node: {hex(id(node))} : key={node.key} : data={node.data}\\n\"\n node = node.next\n return pretty\n\n def _getitem(self, key):\n \"\"\"Private method to retrieve a list element given a key.\n Arguments:\n key: identifies the node\n Returns:\n Tuple with reference to predecessor node and reference to node\n with given key\n \"\"\"\n node = self.first\n prev = node\n while node != None:\n if node.key == key:\n return (prev, node)\n prev = node\n node = node.next\n raise KeyError(f\"Key '{key}' not found\")\n\n # Node insertion {{{2\n def insert(self, key, node, *, before=False):\n anchor = self.__getitem__(key)\n if before: # insert before\n tmp = copy.copy(anchor) # beware!\n anchor.key = node.key\n anchor.data = node.data\n node = tmp\n anchor.next = node\n else: # insert after\n assert anchor is not None\n node.next = anchor.next\n anchor.next = node\n return node\n\n # Node removal {{{2\n def remove(self, key):\n if self.first.key == key:\n del_node = self.first\n self.first = self.first.next\n else:\n prev, del_node = self._getitem(key)\n prev.next = del_node.next\n del del_node\n\n # Iterator {{{2\n def __iter__(self):\n node = self.first\n while node != None:\n yield node\n node = node.next\n\n\n# main() {{{1\ndef main():\n # create a list\n linked_list = LinkedList(range(5), list('abcde'))\n\n # iter(ll) returns a generator object\n print(type(iter(linked_list)))\n for node in linked_list:\n print(f'{node} : key={node.key} value={node.data}')\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"geoJackSheehan/CS107_class","sub_path":"lecture/code/lecture19/linked_list.py","file_name":"linked_list.py","file_ext":"py","file_size_in_byte":3090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"42141709941","text":"from twisted.web.server import Site\nfrom twisted.web.resource import Resource\nfrom twisted.internet.protocol import Protocol, Factory\n\nfrom superwiser.common.jinja_manager import JinjaTemplateManager\n\n\nclass SuperwiserHome(Resource):\n isLeaf = True\n\n def __init__(self, sauron):\n self.template_manager = JinjaTemplateManager()\n self.sauron = sauron\n\n def get_process_states(self):\n all_procs = {}\n all_nodes = []\n\n # Gather all the node details\n nodes = self.sauron.eye.list_nodes()\n for node in nodes:\n all_nodes.append({\n 'name': node,\n 'node_ip': self.sauron.eye.get_node_details(node)\n })\n\n # Gather all the process details for each node\n for node in all_nodes:\n procs = self.sauron.eye.list_processes(node['name'])\n for proc in procs:\n num_procs = procs[proc]['numprocs']\n if proc in all_procs:\n all_procs[proc]['numprocs'] += num_procs\n all_procs[proc]['nodes'].append(node)\n continue\n proc_details = {}\n proc_details.update(procs[proc])\n proc_details['name'] = proc\n proc_details['nodes'] = [node]\n all_procs[proc] = proc_details\n return all_procs, all_nodes\n\n def render_GET(self, request):\n all_procs, all_nodes = self.get_process_states()\n context = {'all_procs': all_procs.values(),\n 'all_nodes': all_nodes}\n return self.template_manager.render_template('index.html',\n context)\n\n\nclass SuperwiserWebFactory(object):\n def make_site(self, sauron):\n root = SuperwiserHome(sauron)\n site = Site(root)\n return site\n\n\nclass SuperwiserTCP(Protocol):\n def parse_command(self, data):\n cmd, args = None, []\n try:\n cmd, rest = data.split('(')\n if cmd in ['increase_procs', 'decrease_procs']:\n prog, factor = rest.split(',')\n prog = prog.strip()\n factor = factor.strip().rstrip(')')\n factor = int(factor)\n args = [prog, factor]\n elif cmd in ['update_conf', 'stop_program',\n 'start_program', 'restart_program']:\n rest = rest.strip().rstrip(')')\n args = [rest]\n except:\n pass\n\n return (cmd, args)\n\n def dataReceived(self, data):\n cmd, args = self.parse_command(data)\n if cmd is None:\n self.transport.write('Invalid operation\\n')\n elif cmd == 'increase_procs':\n result = self.overlord.increase_procs(*args)\n self.transport.write(str(result) + '\\n')\n elif cmd == 'decrease_procs':\n result = self.overlord.decrease_procs(*args)\n self.transport.write(str(result) + '\\n')\n elif cmd == 'update_conf':\n conf = requests.get(*args).content\n result = self.overlord.update_conf(conf)\n self.transport.write(str(result) + '\\n')\n elif cmd == 'stop_program':\n result = self.overlord.stop_program(*args)\n self.transport.write(str(result) + '\\n')\n elif cmd == 'start_program':\n result = self.overlord.start_program(*args)\n self.transport.write(str(result) + '\\n')\n elif cmd == 'restart_program':\n result = self.overlord.restart_program(*args)\n self.transport.write(str(result) + '\\n')\n\n def connectionMade(self):\n self.transport.write('Hello\\n')\n\n def connectionLost(self, reason):\n self.transport.write('Goodbye\\n')\n\n\nclass SuperwiserTCPFactory(Factory):\n def __init__(self, overlord):\n self.overlord = overlord\n\n def buildProtocol(self, addr):\n prot = SuperwiserTCP()\n prot.overlord = self.overlord\n return prot\n","repo_name":"ksrvtsa/superwiser","sub_path":"superwiser/master/endpoints.py","file_name":"endpoints.py","file_ext":"py","file_size_in_byte":3997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"23264204442","text":"import datetime\nfrom flask import Flask, render_template, jsonify, request\nimport os\nimport openai\nimport json\n\nfrom controllers.AppOrchestrator import AppOrchestrator\n\nfrom controllers.CloudRunSpecificInMemoryOpenAiKeyHandling import CloudRunSpecificInMemoryOpenAiKeyHandling\n\ncloud_run_spec = CloudRunSpecificInMemoryOpenAiKeyHandling()\napp = Flask(__name__)\n\n# Initialize AppOrchestrator\norchestrator = AppOrchestrator()\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n eula_o_k = os.environ.get('EULA_O_K')\n openai_api_key = os.environ.get('OPENAI_API_KEY')\n\n if request.method == 'POST':\n if 'agree' in request.form:\n eula_o_k = 'TRUE'\n openai_api_key = request.form.get('openai_api_key')\n if openai_api_key:\n cloud_run_spec.save_api_key(openai_api_key)\n os.environ['EULA_O_K'] = eula_o_k\n return render_template('graph.html')\n else:\n return render_template('eula_agreement.html', error=\"Please enter the OpenAI API key.\")\n else:\n return render_template('disagreement_page.html')\n\n if eula_o_k != 'TRUE' or not openai_api_key:\n return render_template('eula_agreement.html')\n else:\n return render_template('graph.html')\n\n\n@app.route('/get-openai-models', methods=['GET'])\ndef get_openai_models():\n try:\n # openai.api_key = os.getenv('OPENAI_API_KEY')\n openai.api_key = cloud_run_spec.get_api_key()\n models = openai.Model.list()\n model_options = [{'label': model['id'], 'value': model['id']} for model in models['data'] if\n 'annalect' not in model['id']]\n return jsonify(model_options)\n except Exception as e:\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/get-graph-data', methods=['POST'])\ndef get_graph_data():\n try:\n graph_id = request.json['graph_id']\n graph_data = orchestrator.get_graph_data(graph_id)\n return jsonify(graph_data)\n except Exception as e:\n orchestrator.logger.error(f\"Error fetching graph data: {e}\")\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/get-available-graphs', methods=['GET'])\ndef get_available_graphs():\n try:\n available_graphs = orchestrator.get_available_graphs()\n return jsonify(available_graphs)\n except Exception as e:\n orchestrator.logger.error(f\"Error loading available graphs: {e}\")\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/save-graph', methods=['POST'])\ndef save_graph():\n try:\n graph_data = request.json\n orchestrator.save_graph(graph_data)\n return jsonify({\"status\": \"success\", \"message\": 200})\n except Exception as e:\n orchestrator.logger.error(f\"Error saving graph: {e}\")\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/return-gpt-agent-answer-to-graph', methods=['POST'])\ndef matrix_sudoku_approach():\n try:\n graph_data = request.json\n updated_graph = orchestrator.matrix_sudoku_approach(graph_data)\n random_answer = orchestrator.select_random_answer_nodes(graph_data) # Get the random answer\n return jsonify({\"updated_graph\": updated_graph, \"saved_name\": random_answer}) # Include it in the response\n except Exception as e:\n orchestrator.logger.error(f\"Error in matrix sudoku approach: {e}\")\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/get-json-files', methods=['GET'])\ndef get_json_files():\n json_dir = 'json_blueprints' # Directory where JSON files are stored\n try:\n json_files = [f for f in os.listdir(json_dir) if f.endswith('.json')]\n return jsonify(json_files)\n except Exception as e:\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/load-json-file', methods=['POST'])\ndef load_json_file():\n data = request.json\n filename = data.get('filename')\n json_dir = 'json_blueprints' # Directory where JSON files are stored\n try:\n with open(os.path.join(json_dir, filename), 'r') as file:\n graph_data = json.load(file)\n return jsonify(graph_data)\n except Exception as e:\n return jsonify({\"status\": \"error\", \"message\": str(e)})\n\n\n@app.route('/impressum', methods=['GET', 'POST'])\ndef impressum():\n return render_template('impressum.html')\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"kilian-lm/graph_to_agent","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"23027226125","text":"from argparse import ArgumentParser\n\nparser = ArgumentParser()\n\nparser.add_argument(\"prnumber\",\n nargs=1,\n help=\"Pull request number\",\n type=int)\nparser.add_argument(\"-v\", \"--verbosity\", action=\"count\",\n help=\"increase output verbosity\")\n\n\ndef init_args():\n return parser.parse_args()\n","repo_name":"miskopo/nightwatch","sub_path":"nightwatch/cli_arguments.py","file_name":"cli_arguments.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"38940935431","text":"from __future__ import absolute_import, print_function\n\nimport os\n\nimport ROOT\nfrom HiggsAnalysis.CombinedLimit.PhysicsModel import *\nfrom HiggsAnalysis.CombinedLimit.SMHiggsBuilder import SMHiggsBuilder\n\n\nclass FermiophobicHiggs(SMLikeHiggsModel):\n \"assume the SM coupling but let the Higgs mass to float\"\n\n def __init__(self):\n SMLikeHiggsModel.__init__(self) # not using 'super(x,self).__init__' since I don't understand it\n self.mHRange = [\"115\", \"135\"] # default\n\n def setPhysicsOptions(self, physOptions):\n for po in physOptions:\n if po.startswith(\"higgsMassRange=\"):\n self.mHRange = po.replace(\"higgsMassRange=\", \"\").split(\",\")\n print(\"The Higgs mass range:\", self.mHRange)\n if len(self.mHRange) != 2:\n raise RuntimeError(\"Higgs mass range definition requires two extrema\")\n elif float(self.mHRange[0]) >= float(self.mHRange[1]):\n raise RuntimeError(\"Extrama for Higgs mass range defined with inverterd order. Second must be larger the first\")\n\n def doParametersOfInterest(self):\n \"\"\"Create POI out of signal strength and MH\"\"\"\n # --- Signal Strength as only POI ---\n self.modelBuilder.doVar(\"r[1,0,20]\")\n if self.modelBuilder.out.var(\"MH\"):\n self.modelBuilder.out.var(\"MH\").setRange(float(self.mHRange[0]), float(self.mHRange[1]))\n self.modelBuilder.out.var(\"MH\").setConstant(False)\n else:\n self.modelBuilder.doVar(\"MH[%s,%s]\" % (self.mHRange[0], self.mHRange[1]))\n self.modelBuilder.doSet(\"POI\", \"r,MH\")\n self.SMH = SMHiggsBuilder(self.modelBuilder)\n self.setup()\n\n def setup(self):\n ## Add FP BRs\n datadir = os.environ[\"CMSSW_BASE\"] + \"/src/HiggsAnalysis/CombinedLimit/data/lhc-hxswg\"\n self.SMH.textToSpline(\"FP_BR_hww\", os.path.join(datadir, \"fp/BR.txt\"), ycol=4)\n self.SMH.textToSpline(\"FP_BR_hzz\", os.path.join(datadir, \"fp/BR.txt\"), ycol=5)\n self.SMH.textToSpline(\"FP_BR_hgg\", os.path.join(datadir, \"fp/BR.txt\"), ycol=2)\n self.SMH.textToSpline(\"FP_BR_hzg\", os.path.join(datadir, \"fp/BR.txt\"), ycol=3)\n\n for decay in [\"hww\", \"hzz\", \"hgg\", \"hzg\"]:\n self.SMH.makeBR(decay)\n self.modelBuilder.factory_('expr::FP_BRScal_%s(\"@0*@1/@2\",r,FP_BR_%s,SM_BR_%s)' % (decay, decay, decay))\n\n self.modelBuilder.out.Print()\n\n def getHiggsSignalYieldScale(self, production, decay, energy):\n if production not in [\"VH\", \"WH\", \"ZH\", \"qqH\"]:\n return 0\n if decay not in [\"hww\", \"hzz\", \"hgg\", \"hzg\"]:\n return 0\n return \"FP_BRScal_%s\" % decay\n\n\nfp = FermiophobicHiggs()\n","repo_name":"cms-analysis/HiggsAnalysis-CombinedLimit","sub_path":"python/HiggsFermiophobic.py","file_name":"HiggsFermiophobic.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"2"}
+{"seq_id":"72213001648","text":"\"\"\"Script for extracting gazetteers from json data\n\nInputs: path to input json\nOutputs: title.txt, cast.txt, director.txt, genre.txt\n\"\"\"\n\nimport argparse\nimport json\nimport logging\nimport os\nimport sys\n\nimport pycountry\n\nlogging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n\n\ndef write_gazes(objs, output_dir, filename):\n output_file = os.path.join(output_dir, filename)\n with open(output_file, 'w') as fp:\n for gaz, count in sorted(objs.items(), key=lambda x: x[0]):\n fp.write(str(count) + '\\t' + gaz + '\\n')\n\n\ndef add_count(count_dict, entity):\n if not entity:\n return\n entity = str(entity).strip()\n entity = entity.replace('\\t', '')\n entity = entity.replace(' ', ' ')\n count = count_dict.get(entity, 0)\n count_dict[entity] = count + 1\n\n\ndef add_list_count(count_dict, entities):\n if not entities:\n return\n [add_count(count_dict, entity) for entity in entities]\n\n\ndef expand_country_names(country_codes):\n country_names = []\n for country_code in country_codes:\n country_names.append(country_code)\n try:\n country = pycountry.countries.get(alpha_2=country_code)\n except Exception:\n logging.error('Cannot find country name for {}'.format(country_code))\n continue\n country_names.append(country.name)\n return country_names\n\n\ndef main(args):\n input_file = args.input_file\n output_dir = args.output_dir\n\n if not os.path.exists(output_dir):\n logging.info('Folder {} not exist, creating one.'.format(output_dir))\n os.mkdir(output_dir)\n\n titles = {}\n casts = {}\n directors = {}\n genres = {}\n countries = {}\n\n logging.info('Reading data from {}.'.format(input_file))\n with open(input_file, 'r') as fp:\n for line in fp:\n json_text = line\n parsed_json = json.loads(json_text)\n add_count(titles, parsed_json['title'])\n\n add_list_count(casts, parsed_json['cast'])\n add_list_count(directors, parsed_json['directors'])\n add_list_count(genres, parsed_json['genres'])\n country_names = expand_country_names(parsed_json['countries'])\n add_list_count(countries, country_names)\n\n logging.info('Writing gazes to folder {}.'.format(output_dir))\n write_gazes(titles, output_dir, 'title.txt')\n write_gazes(casts, output_dir, 'cast.txt')\n write_gazes(directors, output_dir, 'director.txt')\n write_gazes(genres, output_dir, 'genre.txt')\n write_gazes(countries, output_dir, 'country.txt')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(dest=\"input_file\", type=str, help=\"Please provide input file path\")\n parser.add_argument(\"-o\", dest=\"output_dir\", type=str, help=\"Please provide output dir path\",\n default='gazes')\n args = parser.parse_args()\n main(args)\n","repo_name":"CiscoDevNet/mindmeld-blueprints","sub_path":"blueprints/video_discovery/scripts/extract_gazetteer.py","file_name":"extract_gazetteer.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"2"}
+{"seq_id":"6245704245","text":"import settings\nimport pytz\nfrom database import Domains\nfrom datetime import datetime\nimport web3\n\ndef get_price(address, duration):\n return float(settings.Web3.fromWei(\n settings.CONTRACT_ENS.functions.rentPrice(address, duration).call(),\n \"gwei\"))\n\ndef get_domains(address):\n filter = settings.CONTRACT_ENS_WS.events.NameRegistered.createFilter(\n fromBlock=0,\n toBlock='latest',\n argument_filters={\n \"owner\": address\n }\n )\n rt = []\n for data in filter.get_all_entries():\n name = data[\"args\"][\"name\"]\n expires = datetime.fromtimestamp(data[\"args\"][\"expires\"], tz=pytz.UTC)\n block = data[\"blockNumber\"]\n\n q = settings.CONTRACT_ENS_WS.events.NameRenewed.createFilter(\n fromBlock=block,\n toBlock='latest',\n argument_filters={\n \"name\": name\n }\n )\n for renewal in q.get_all_entries():\n newtimestamp = datetime.fromtimestamp(renewal[\"args\"][\"expires\"], tz=pytz.UTC)\n if newtimestamp > expires:\n expires = newtimestamp\n block = renewal[\"blockNumber\"]\n\n rt.append(Domains(\n domain=name,\n expires=expires,\n block=block\n ))\n\n return rt","repo_name":"Evert0x/ENSbot","sub_path":"ensdata/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"7107238709","text":"from selenium.webdriver import Firefox\nfrom selenium.webdriver.common.alert import Alert\n\nbrowser = Firefox()\n\nbrowser.get('http://selenium.dunossauro.live/aula_11_a')\n\nbrowser.find_element_by_id('alert').click()\n\nalerta = Alert(browser) # Liadar com erros\n\nalerta = browser.switch_to.alert # Não lida com erros\n\n\nalerta.accept() # Confirma, clica no OK\nalerta.dismiss() # Confirma, clica no OK\n","repo_name":"dunossauro/curso-python-selenium","sub_path":"codigo_das_aulas/aula_11/aula_11_02.py","file_name":"aula_11_02.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"pt","doc_type":"code","stars":283,"dataset":"github-code","pt":"2"}
+{"seq_id":"116377935","text":"import numpy as np\nimport copy\nfrom tensorflow.keras.utils import Sequence\nimport matplotlib.pyplot as plt\nfrom Utils.utils import file_reader, instance_generator\nfrom Orchestrator.orchestrator_v2 import Orchestrator\n\n\nclass DataGenerator(Sequence):\n \"\"\"\n Generates batches of images and corresponding labels for training and validation\n \"\"\"\n def __init__(self, batch_size, img_list_path, processing_graph):\n super(DataGenerator, self).__init__()\n self.batch_size = batch_size\n self.data_list = self.generate_data_list(img_list_path)\n self.data_processing_graph = processing_graph\n self.orchestrator = Orchestrator(copy.deepcopy(self.data_processing_graph))\n\n @staticmethod\n def generate_callable(function_dict):\n \"\"\"\n Instantiates the factory/function defined in function_Def\n :param function_dict:\n :return:\n \"\"\"\n if function_dict.get(\"factory\") is not None and function_dict.get(\"function\") is not None:\n raise Exception(\"Either factory or function path should be provided, not both\")\n elif function_dict.get(\"factory\") is None and function_dict.get(\"function\") is None:\n raise Exception(\"Either factory or function path should be provided\")\n elif function_dict.get(\"factory\") is not None:\n raise instance_generator(factory=function_dict[\"factory\"], params=function_dict[\"params\"])\n elif function_dict.get(\"function\") is not None:\n instance_generator(factory=function_dict[\"function\"])\n\n @staticmethod\n def generate_data_list(image_list_path):\n \"\"\"\n Create a data list from the given image list path\n :param image_list_path:\n :return:\n \"\"\"\n img_list = file_reader(image_list_path)\n data_list = dict()\n data_list[\"img\"] = img_list\n return data_list\n\n def load_data(self, idx):\n \"\"\"\n Generate batches of data\n :param idx:\n :return:\n \"\"\"\n batch_info = dict()\n batch_info[\"img\"] = self.data_list[\"img\"][idx * self.batch_size: (idx + 1) * self.batch_size]\n return batch_info\n\n def process_data(self, item):\n batch_values = self.load_data(item)\n refined_images = []\n refined_labels = []\n external_inputs = dict()\n for current_img_path in batch_values[\"img\"]:\n external_inputs[\"img\"] = current_img_path\n # print(\"Processing: \", current_img_path)\n outputs = self.orchestrator(external_inputs)\n refined_images.append(outputs[\"img\"])\n refined_labels.append(outputs[\"lbl\"])\n return np.asarray(refined_images), np.asarray(refined_labels)\n\n def __len__(self):\n \"\"\"\n Computes the length of the data samples\n :return:\n \"\"\"\n return int(np.ceil(len(self.data_list[\"img\"]) / self.batch_size))\n\n def __getitem__(self, item):\n \"\"\"\n Returns a batch of images and labels\n :param item:\n :return:\n \"\"\"\n x, y = self.process_data(item)\n return x, y\n","repo_name":"Sumukha21/DeepLearningFramework","sub_path":"Data_Generators/data_generator_classifier.py","file_name":"data_generator_classifier.py","file_ext":"py","file_size_in_byte":3104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"7665139234","text":"#!/usr/bin/python3.6\n\nimport enum\nimport glob\nimport os\nimport sys\nimport re\n\n\ndef excepthook(type, value, traceback):\n print(value)\n\n#sys.excepthook = excepthook\n\n\ndef throw(msg):\n raise Exception(msg)\n\n\ndef int_try_parse(num):\n try:\n out = int(num)\n except ValueError:\n out = None\n\n return out\n\n\nclass Token(enum.Enum):\n T_COMMENT = 0\n T_KEYWORD = 1\n T_SYMBOL = 2\n T_IDENTIFIER = 3\n T_INT_CONST = 4\n T_STRING_CONST = 5\n\n\nclass K(enum.Enum):\n K_NONE = 0\n K_STATIC = 1\n K_FIELD = 2\n K_ARG = 3\n K_VAR = 4\n\n\nclass Opp(enum.Enum):\n O_ADD = \"add\"\n O_SUB = \"sub\"\n O_NEG = \"neg\"\n O_EQ = \"eq\"\n O_GT = \"gt\"\n O_LT = \"lt\"\n O_AND = \"and\"\n O_OR = \"or\"\n O_NOT = \"not\"\n O_MUL = \"call Math.multiply 2\"\n O_DIV = \"call Math.divide 2\"\n\n\nclass JackTokenizer:\n # open input filestream and get ready to tokenize it\n def __init__(self, input_file):\n with open(input_file, \"r\") as file:\n self.file_list = file.read()\n\n self.current_token = \"\"\n self.current_index = 0\n self.key_words = ['class', 'constructor', 'function', 'method', 'field', 'static', 'var',\n 'int', 'char', 'boolean', 'void', 'true', 'false', 'null', 'this', 'let',\n 'do', 'if', 'else', 'while', 'return']\n self.symbols = ['{', '}', '(', ')', '[', ']', '.', ',',\n ';', '+', '-', '*', '/', '&', '|', '<', '>', '=', '~']\n self.terminals = self.key_words + self.symbols\n self.tokenize()\n return\n\n # intial tokenization\n def tokenize(self):\n temp_list = []\n out_list = []\n\n # remove comments\n\n comment_re = re.compile(\n r'(^)?[^\\S\\n]*/(?:\\*(.*?)\\*/[^\\S\\n]*|/[^\\n]*)($)?',\n re.DOTALL | re.MULTILINE\n )\n\n def comment_replacer(match):\n start, mid, end = match.group(1, 2, 3)\n if mid is None:\n return ''\n elif start is not None or end is not None:\n return ''\n elif '\\n' in mid:\n return '\\n'\n else:\n return ' '\n\n def remove_comments(text):\n return comment_re.sub(comment_replacer, text)\n\n self.file_list = '\\n'.join(\n remove_comments(self.file_list).splitlines())\n\n # remove whitespace\n self.file_list = self.file_list.split()\n\n for item in self.file_list:\n if item in self.terminals:\n temp_list.append(item)\n else:\n for symbol in self.symbols:\n if symbol in item:\n replacement = \" \" + symbol + \" \"\n item = item.replace(symbol, replacement)\n\n for temp in item.split():\n temp_list.append(temp)\n\n # second pass to rejoin string constants\n in_string = False\n temp_string = \"\"\n for item in temp_list:\n if item[0] == '\"' or in_string:\n if not in_string:\n temp_string = item\n in_string = True\n elif '\"' in item:\n temp_string += \" \" + item\n out_list.append(temp_string)\n temp_string = \"\"\n in_string = False\n else:\n temp_string += \" \" + item\n else:\n out_list.append(item)\n\n self.file_list = out_list\n return\n\n # are there any more tokens in the input?\n def hasMoreTokens(self):\n return self.current_index < len(self.file_list)\n\n # gets next token from input and makes current token\n # only called if hasMoreTokens() is true\n def advance(self):\n self.current_token = self.file_list[self.current_index]\n self.current_index = self.current_index + 1\n return\n\n # handy macro to make coding a little smoother\n def backtrack(self):\n self.current_index = self.current_index - 1\n self.current_token = self.file_list[self.current_index]\n\n # returns the type of the current token\n def tokenType(self):\n if self.current_token in self.symbols:\n return Token.T_SYMBOL\n elif self.current_token in self.key_words:\n return Token.T_KEYWORD\n elif self.current_token[0] == '\"':\n return Token.T_STRING_CONST\n elif isinstance(int_try_parse(self.current_token), int):\n return Token.T_INT_CONST\n else:\n return Token.T_IDENTIFIER\n\n # returns the keyword which is the current token\n # only called if tokenType() == T_KEYWORD\n def keyWord(self):\n return self.current_token\n\n # returns the character which is the current token\n # only called if tokenType() == T_SYMBOL\n def symbol(self):\n return self.current_token\n\n # returns the identifier which is the current token\n # only called if tokenType() == T_IDENTIFIER\n def identifier(self):\n return self.current_token\n\n # returns the integer value of the current token\n # only called if tokenType() == T_INT_CONST\n def intVal(self):\n return int_try_parse(self.current_token)\n\n # returns the string value of the current token\n # only called if tokenType() == T_STRING_CONST\n def stringVal(self):\n return self.current_token[1:-1]\n\n # helpful macro for making less code in compiler\n def getXml(self):\n token_type = self.tokenType()\n\n if token_type == Token.T_KEYWORD:\n return \" {} \\n\".format(self.keyWord())\n elif token_type == Token.T_IDENTIFIER:\n return \" {} \\n\".format(self.identifier())\n elif token_type == Token.T_INT_CONST:\n return \" {} \\n\".format(str(self.intVal()))\n elif token_type == Token.T_STRING_CONST:\n return \" {} \\n\".format(self.stringVal())\n elif token_type == Token.T_SYMBOL:\n symbol = self.symbol()\n if symbol == \"<\":\n symbol = \"<\"\n elif symbol == \">\":\n symbol = \">\"\n elif symbol == \"&\":\n symbol = \"&\"\n\n return \" {} \\n\".format(symbol)\n\n\nclass SymbolTable:\n def __init__(self):\n self.class_scope = []\n self.subroutine_scope = []\n return\n\n # starts a new subroutine scope\n def startSubroutine(self):\n self.subroutine_scope = []\n return\n\n # defines a new identifier of a given:\n # name, type and kind and assigns it a\n # running index static and field have class scope\n # arg and var have subroutine scope\n def define(self, name, v_type, kind):\n index = self.varcount(kind)\n if kind in [K.K_STATIC, K.K_FIELD]:\n self.class_scope.append({\n 'index': index,\n 'name': name,\n 'type': v_type,\n 'kind': kind\n })\n else:\n self.subroutine_scope.append({\n 'index': index,\n 'name': name,\n 'type': v_type,\n 'kind': kind\n })\n return\n\n # returns number of variables of the\n # given kind already defined in current\n # scope\n def varcount(self, kind):\n temp = self.subroutine_scope + self.class_scope\n of_kind = list(filter(lambda x: x['kind'] == kind, temp))\n return len(of_kind)\n\n # returns the kind of the named identifier\n # in the current scope if identifier is\n # unknown, returns NONE\n def kindof(self, name):\n temp = self.subroutine_scope + self.class_scope\n temp = list(filter(lambda x: x['name'] == name, temp))\n if len(temp) > 0:\n return temp[0]['kind']\n else:\n return K.K_NONE\n\n # returns type of named identifier in\n # current scope\n def typeof(self, name):\n temp = self.subroutine_scope + self.class_scope\n temp = list(filter(lambda x: x['name'] == name, temp))\n if len(temp) > 0:\n return temp[0]['type']\n else:\n return \"\"\n\n # returns the index assigned to the\n # named identifier\n\n def indexof(self, name):\n temp = self.subroutine_scope + self.class_scope\n temp = list(filter(lambda x: x['name'] == name, temp))\n if len(temp) > 0:\n return temp[0]['index']\n else:\n return 0\n\n\nclass VMWriter:\n def __init__(self, file):\n self.file = file\n return\n\n def writePush(self, segment, index):\n output = \"push {} {}\\n\".format(segment, str(index))\n self.file.write(output)\n return\n\n def writePop(self, segment, index):\n output = \"pop {} {}\\n\".format(segment, str(index))\n self.file.write(output)\n return\n\n def writeArithmetic(self, command: Opp):\n output = \"{}\\n\".format(command.value)\n self.file.write(output)\n return\n\n def writeLabel(self, label):\n output = \"label {}\\n\".format(label)\n self.file.write(output)\n return\n\n def writeGoto(self, label):\n output = \"goto {}\\n\".format(label)\n self.file.write(output)\n return\n\n def writeIf(self, label):\n output = \"if-goto {}\\n\".format(label)\n self.file.write(output)\n return\n\n def writeFunction(self, name, nLocals):\n output = \"function {} {}\\n\".format(name, str(nLocals))\n self.file.write(output)\n return\n\n def writeReturn(self):\n output = \"return\\n\"\n self.file.write(output)\n return\n\n def writeCall(self, name, nArgs):\n output = \"call {} {}\\n\".format(name, str(nArgs))\n self.file.write(output)\n return\n\n def close(self):\n self.file.close()\n return\n\n\nclass CompilationEngine:\n def __init__(self, input_file, output_file):\n self.symbolTable = SymbolTable()\n self.tokenizer = JackTokenizer(input_file)\n self.file = open(output_file, \"w\")\n self.vmWriter = VMWriter(self.file)\n self.class_name = \"\"\n self.expression_count = 0\n self.local_count = 0\n self.class_var_count = 0\n self.loop_label_count = 0\n self.if_label_count = 0\n self.compileClass()\n #print(self.symbolTable.class_scope + self.symbolTable.subroutine_scope)\n return\n\n # macros for getting next token with error checking\n # pretty much just to cut down on bloat\n def getNextKeyword(self, location, expected):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n if not self.tokenizer.tokenType() == Token.T_KEYWORD:\n throw(\"Syntax Error! Expecting type: {} in {}\".format(\n str(Token.T_KEYWORD), location))\n keyword = self.tokenizer.keyWord()\n if not keyword == expected and not keyword in expected:\n throw(\"Syntax Error! Expecting: {} in {}\".format(\n str(expected), location))\n\n # self.file.write(self.tokenizer.getXml())\n\n def getNextSymbol(self, location, expected):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n if not self.tokenizer.tokenType() == Token.T_SYMBOL:\n throw(\"Syntax Error! Expecting type: {} in {}\".format(\n str(Token.T_SYMBOL), location))\n symbol = self.tokenizer.symbol()\n if not symbol == expected and not symbol in expected:\n throw(\"Syntax Error! Expecting: {} in {}\".format(\n str(expected), location))\n\n # self.file.write(self.tokenizer.getXml())\n\n def getNextIdentifier(self, location, definition=False, k_type=K.K_NONE, v_type=\"\"):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n if not self.tokenizer.tokenType() == Token.T_IDENTIFIER:\n throw(\"Syntax Error! Expecting type: {} in {}\".format(\n str(Token.T_IDENTIFIER), location))\n\n if definition:\n self.defineVar(k_type, v_type)\n\n name = self.tokenizer.identifier()\n kind_of = self.symbolTable.kindof(name)\n isVar = kind_of != K.K_NONE\n index = self.symbolTable.indexof(name)\n v_type = self.symbolTable.typeof(name)\n\n # determine category\n if kind_of == K.K_ARG:\n category = \"argument\"\n elif kind_of == K.K_FIELD:\n category = \"this\"\n elif kind_of == K.K_STATIC:\n category = \"static\"\n elif kind_of == K.K_VAR:\n category = \"local\"\n else:\n # peek next token\n token_type = self.doAdvance(\"getting category\")\n if token_type == Token.T_SYMBOL and self.tokenizer.symbol() == \"(\":\n category = \"subroutine\"\n else:\n category = \"class\"\n self.tokenizer.backtrack()\n\n return {'name': name, 'category': category, 'isDefinition': definition,\n 'isVar': isVar, 'index': index, 'kind': kind_of, 'type': v_type }\n\n def getNextKeyOrIdentifier(self, location, expected=[], definition=False, k_type=K.K_NONE, v_type=\"\"):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n token_type = self.tokenizer.tokenType()\n if token_type == Token.T_IDENTIFIER:\n self.tokenizer.backtrack()\n self.getNextIdentifier(location, definition, k_type, v_type)\n elif token_type == Token.T_KEYWORD:\n keyword = self.tokenizer.keyWord()\n if keyword in expected:\n # self.file.write(self.tokenizer.getXml())\n self.tokenizer.getXml()\n else:\n throw(\"Syntax Error! Expecting token: {} in {}\".format(\n str(expected), location))\n else:\n throw(\"Syntax Error! Expecting type: {} in {}\".format(\n str([Token.T_IDENTIFIER, Token.T_KEYWORD]), location))\n\n def getNextInt(self, location):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n if not self.tokenizer.tokenType() == Token.T_INT_CONST:\n throw(\"Syntax Error! Expecting type: {} in {}\".format(\n str(Token.T_INT_CONST), location))\n\n # self.file.write(self.tokenizer.getXml())\n\n def getNextStr(self, location):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n if not self.tokenizer.tokenType() == Token.T_STRING_CONST:\n throw(\"Syntax Error! Expecting type: {} in {}\".format(\n str(Token.T_STRING_CONST), location))\n\n # self.file.write(self.tokenizer.getXml())\n\n def defineVar(self, k_type, v_type):\n # declare in symbol table, then write to output\n self.tokenizer.backtrack()\n self.doAdvance(\"symbol table add\")\n name = self.tokenizer.identifier()\n self.symbolTable.define(name, v_type, k_type)\n return\n\n def doAdvance(self, location):\n if not self.tokenizer.hasMoreTokens():\n throw(\"Unexpected EOF! Expecting in {}\".format(location))\n self.tokenizer.advance()\n return self.tokenizer.tokenType()\n\n def compileClass(self):\n self.getNextKeyword(\"class declaration\", \"class\")\n details = self.getNextIdentifier(\"class declaration\")\n self.class_name = details['name']\n self.getNextSymbol(\"class declaration\", \"{\")\n\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"class declaration\")\n\n if token_type == Token.T_KEYWORD:\n keyword = self.tokenizer.keyWord()\n if keyword in [\"function\", \"constructor\", \"method\"]:\n self.tokenizer.backtrack()\n self.compileSubroutine()\n elif keyword in [\"field\", \"static\"]:\n self.tokenizer.backtrack()\n self.compileClassVarDesc()\n else:\n throw(\"Unexpected keyword: {} in class declaration\".format(keyword))\n elif token_type == Token.T_SYMBOL and self.tokenizer.symbol() == \"}\":\n self.tokenizer.backtrack()\n break\n else:\n throw(\"Unexpected token type: {} in class declaration\".format(\n str(Token.T_KEYWORD)))\n\n self.getNextSymbol(\"class declaration\", \"}\")\n return\n\n def compileClassVarDesc(self):\n self.getNextKeyword(\"class var declaration\", [\"field\", \"static\"])\n # hacky fix for getting type :/, didn't wanna fix it\n v_type = self.tokenizer.file_list[self.tokenizer.current_index]\n # get kind\n kind = self.tokenizer.keyWord()\n\n if kind == \"static\":\n kind = K.K_STATIC\n else:\n kind = K.K_FIELD\n\n self.getNextKeyOrIdentifier(\"class var declaration\", [\"int\", \"boolean\", \"char\"])\n self.getNextIdentifier(\"class var declaration\", True, kind, v_type)\n self.class_var_count += 1\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"class var declaration\")\n\n if not token_type == Token.T_SYMBOL:\n throw(\"Syntax Error! Unexpected token of type {} in class var declaration\".format(\n str(Token.T_SYMBOL)))\n else:\n symbol = self.tokenizer.symbol()\n if symbol == \";\":\n self.tokenizer.backtrack()\n break\n elif symbol == \",\":\n self.getNextIdentifier(\"class var declaration\", True, kind, v_type)\n self.class_var_count += 1\n else:\n throw(\n \"Syntax Error! Unexpected symbol: {} in class var declaration\".format(symbol))\n\n self.getNextSymbol(\"class var declaration\", \";\")\n return\n\n def compileSubroutine(self):\n self.loop_label_count = 0\n self.if_label_count = 0\n self.symbolTable.startSubroutine()\n self.local_count = 0\n self.getNextKeyword(\"subroutine declaration\", [\n \"function\", \"method\", \"constructor\"])\n \n self.function_type = self.tokenizer.keyWord()\n\n self.getNextKeyOrIdentifier(\"subroutine declaration\", [\n \"int\", \"void\", \"boolean\", \"char\"])\n\n if self.function_type == \"method\":\n self.defineVar(K.K_ARG, self.class_name)\n\n details = self.getNextIdentifier(\"subroutine declaration\")\n self.getNextSymbol(\"subroutine declaration\", \"(\")\n self.compileParameterList()\n self.getNextSymbol(\"subroutine declaration\", \")\")\n self.getNextSymbol(\"subroutine declaration\", \"{\")\n\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"subroutine Body\")\n\n if not token_type == Token.T_KEYWORD:\n throw(\"Unexpected token type: {} in subroutine body\".format(\n str(token_type)))\n else:\n keyword = self.tokenizer.keyWord()\n if keyword == \"var\":\n self.tokenizer.backtrack()\n self.compileVarDec()\n elif keyword in [\"let\", \"while\", \"do\", \"if\", \"return\"]:\n self.tokenizer.backtrack()\n break\n else:\n throw(\"Unexpected keyword {} in subroutine body\".format(\n str(token_type)))\n\n # declare function\n name = self.class_name + \".\" + details['name']\n var_count = self.symbolTable.varcount(K.K_VAR)\n self.vmWriter.writeFunction(name, var_count)\n\n if self.function_type == \"constructor\":\n count = self.symbolTable.varcount(K.K_FIELD)\n self.vmWriter.writePush(\"constant\", count)\n self.vmWriter.writeCall(\"Memory.alloc\", 1)\n self.vmWriter.writePop(\"pointer\", 0)\n elif self.function_type == \"method\":\n self.vmWriter.writePush(\"argument\", 0)\n self.vmWriter.writePop(\"pointer\", 0)\n\n self.compileStatements()\n self.getNextSymbol(\"subroutine body\", \"}\")\n return\n\n def compileParameterList(self):\n token_type = self.doAdvance(\"parameter list\")\n\n if token_type == Token.T_SYMBOL and self.tokenizer.symbol() == \")\":\n self.tokenizer.backtrack()\n else:\n self.tokenizer.backtrack()\n self.getNextKeyOrIdentifier(\n \"parameter list\", [\"int\", \"boolean\", \"char\"])\n\n v_type = self.tokenizer.identifier()\n self.getNextIdentifier(\"paramenter list\", True, K.K_ARG, v_type)\n\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"parameter list\")\n\n if not token_type == Token.T_SYMBOL:\n throw(\"Unexpected token type: {} in parameter list\".format(\n str(token_type)))\n else:\n symbol = self.tokenizer.symbol()\n if symbol == \",\":\n # self.file.write(self.tokenizer.getXml())\n self.getNextKeyOrIdentifier(\n \"parameter list\", [\"int\", \"boolean\", \"char\"])\n\n v_type = self.tokenizer.identifier()\n self.getNextIdentifier(\"parameter list\", True, K.K_ARG, v_type)\n elif symbol == \")\":\n self.tokenizer.backtrack()\n break\n else:\n throw(\"Unexpected symbol: {} in parameter list\".format(\n str(symbol)))\n \n return\n\n def compileVarDec(self):\n self.getNextKeyword(\"var declaration\", \"var\")\n # hacky fix for getting type :/, didn't wanna fix it\n v_type = self.tokenizer.file_list[self.tokenizer.current_index]\n self.getNextKeyOrIdentifier(\n \"var declaration\", [\"boolean\", \"int\", \"char\"])\n self.getNextIdentifier(\"var declaration\", True, K.K_VAR, v_type)\n self.local_count += 1\n\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"class var declaration\")\n\n if not token_type == Token.T_SYMBOL:\n throw(\"Syntax Error! Unexpected token of type {} in class var declaration\".format(\n str(Token.T_SYMBOL)))\n else:\n symbol = self.tokenizer.symbol()\n if symbol == \";\":\n self.tokenizer.backtrack()\n break\n elif symbol == \",\":\n # self.file.write(self.tokenizer.getXml())\n self.getNextIdentifier(\n \"var declaration\", True, K.K_VAR, v_type)\n self.local_count += 1\n else:\n throw(\n \"Syntax Error! Unexpected symbol: {} in class var declaration\".format(symbol))\n\n self.getNextSymbol(\"var declaration\", \";\")\n return\n\n def compileStatements(self):\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"statements\")\n\n if token_type == Token.T_KEYWORD:\n keyword = self.tokenizer.keyWord()\n if keyword == \"let\":\n self.tokenizer.backtrack()\n self.compileLet()\n elif keyword == \"while\":\n self.tokenizer.backtrack()\n self.compileWhile()\n elif keyword == \"do\":\n self.tokenizer.backtrack()\n self.compileDo()\n elif keyword == \"if\":\n self.tokenizer.backtrack()\n self.compileIf()\n elif keyword == \"return\":\n self.tokenizer.backtrack()\n self.compileReturn()\n else:\n throw(\"Unexpected keyword {} in statements\".format(keyword))\n elif token_type == Token.T_SYMBOL and self.tokenizer.symbol() == \"}\":\n self.tokenizer.backtrack()\n break\n else:\n throw(\"Unexpected token type: {} in statements\".format(\n str(token_type)))\n return\n\n def compileLet(self):\n self.getNextKeyword(\"let statement\", \"let\")\n details = self.getNextIdentifier(\"let statement\")\n segment = details['category']\n index = details['index']\n is_array = False\n\n # deal with a = x vs a[1] = x\n token_type = self.doAdvance(\"let statement\")\n if token_type == Token.T_SYMBOL:\n symbol = self.tokenizer.symbol()\n if symbol == \"[\":\n self.compileExpression()\n self.vmWriter.writePush(segment, index)\n self.getNextSymbol(\"let statement\", \"]\")\n self.vmWriter.writeArithmetic(Opp.O_ADD)\n is_array = True\n elif symbol == \"=\":\n self.tokenizer.backtrack()\n else:\n throw(\"Unexpected symbol: {} in let statement\".format(symbol))\n else:\n throw(\"Unexepected token type: {}\".format(str(token_type)))\n\n self.getNextSymbol(\"let statement\", \"=\")\n self.compileExpression()\n self.getNextSymbol(\"let statement\", \";\")\n\n if is_array:\n self.vmWriter.writePop(\"temp\", 0)\n self.vmWriter.writePop(\"pointer\", 1)\n self.vmWriter.writePush(\"temp\", 0)\n self.vmWriter.writePop(\"that\", 0)\n else:\n self.vmWriter.writePop(segment, index)\n return\n\n def compileIf(self):\n if_label = \"IF_TRUE\" + str(self.if_label_count)\n else_label = \"IF_FALSE\" + str(self.if_label_count)\n end_label = \"IF_END\" + str(self.if_label_count)\n self.if_label_count += 1\n self.getNextKeyword(\"if statement\", \"if\")\n self.getNextSymbol(\"if statement\", \"(\")\n self.compileExpression()\n self.vmWriter.writeIf(if_label)\n self.vmWriter.writeGoto(else_label)\n self.vmWriter.writeLabel(if_label)\n self.getNextSymbol(\"if statement\", \")\")\n self.getNextSymbol(\"if statement\", \"{\")\n self.compileStatements()\n self.getNextSymbol(\"if statement\", \"}\")\n\n # check for else statement\n token_type = self.doAdvance(\"if statement\")\n if token_type == Token.T_KEYWORD:\n keyword = self.tokenizer.keyWord()\n if keyword == \"else\":\n self.tokenizer.backtrack()\n self.vmWriter.writeGoto(end_label)\n self.vmWriter.writeLabel(else_label)\n self.getNextKeyword(\"if statement\", \"else\")\n self.getNextSymbol(\"if statement\", \"{\")\n self.compileStatements()\n self.getNextSymbol(\"if statement\", \"}\")\n self.vmWriter.writeLabel(end_label)\n else:\n self.tokenizer.backtrack()\n self.vmWriter.writeLabel(else_label)\n else:\n self.tokenizer.backtrack()\n self.vmWriter.writeLabel(else_label)\n\n return\n\n def compileWhile(self):\n label_start = \"WHILE_EXP\" + str(self.loop_label_count)\n label_end = \"WHILE_END\" + str(self.loop_label_count)\n self.loop_label_count += 1\n\n self.getNextKeyword(\"while statement\", \"while\")\n self.getNextSymbol(\"while statement\", \"(\")\n self.vmWriter.writeLabel(label_start)\n self.compileExpression()\n self.vmWriter.writeArithmetic(Opp.O_NOT)\n self.vmWriter.writeIf(label_end)\n self.getNextSymbol(\"while statement\", \")\")\n self.getNextSymbol(\"while statement\", \"{\")\n self.compileStatements()\n self.vmWriter.writeGoto(label_start)\n self.vmWriter.writeLabel(label_end)\n self.getNextSymbol(\"while statement\", \"}\")\n return\n\n def compileDo(self):\n self.getNextKeyword(\"do statement\", \"do\")\n details = self.getNextIdentifier(\"do statment\")\n subroutine_name = details['name']\n instance_type = details['type']\n this_index = details['index']\n this_segment = details['category']\n name = self.class_name + \".\" + subroutine_name\n is_this_class_method = True\n is_instance_method = False\n token_type = self.doAdvance(\"do statement\")\n\n # differentiate between a() and a.b()\n if token_type == Token.T_SYMBOL:\n symbol = self.tokenizer.symbol()\n if symbol == \"(\":\n self.tokenizer.backtrack()\n elif symbol == \".\":\n details = self.getNextIdentifier(\"do statement\")\n if instance_type == \"\":\n instance_type = subroutine_name\n else:\n is_instance_method = True\n name = instance_type + \".\" + details['name']\n is_this_class_method = False\n else:\n throw(\"Unexpected symbol: {} in do statement\".format(symbol))\n else:\n throw(\"Unexpected token type: {} in do statement\".format(str(token_type)))\n\n if is_this_class_method:\n self.vmWriter.writePush(\"pointer\", 0)\n self.expression_count += 1\n elif is_instance_method:\n self.vmWriter.writePush(this_segment, this_index)\n\n self.getNextSymbol(\"do statement\", \"(\")\n self.compileExpressionList()\n self.getNextSymbol(\"do statement\", \")\")\n self.getNextSymbol(\"do statement\", \";\")\n \n if is_this_class_method or is_instance_method:\n self.expression_count += 1\n \n self.vmWriter.writeCall(name, self.expression_count)\n self.vmWriter.writePop(\"temp\", 0)\n\n return\n\n def compileReturn(self):\n self.getNextKeyword(\"return statement\", \"return\")\n\n token_type = self.doAdvance(\"return\")\n\n if token_type == Token.T_SYMBOL and self.tokenizer.symbol() == \";\":\n self.tokenizer.backtrack()\n self.vmWriter.writePush(\"constant\", 0)\n else:\n self.tokenizer.backtrack()\n self.compileExpression()\n\n self.getNextSymbol(\"return statement\", \";\")\n self.vmWriter.writeReturn()\n return\n\n def compileExpression(self):\n self.compileTerm()\n\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"expression\")\n symbol = self.tokenizer.symbol()\n is_op = symbol in [\n \"+\", \"-\", \"/\", \"*\", \"&\", \"|\", \"=\", \">\", \"<\"]\n\n mapping = {\n \"+\": Opp.O_ADD, \"-\": Opp.O_SUB,\n \"/\": Opp.O_DIV, \"*\": Opp.O_MUL,\n \"&\": Opp.O_AND, \"|\": Opp.O_OR,\n \">\": Opp.O_GT, \"<\": Opp.O_LT,\n \"=\": Opp.O_EQ\n }\n\n if token_type == Token.T_SYMBOL and is_op:\n self.compileTerm()\n op = mapping[symbol]\n self.vmWriter.writeArithmetic(op)\n else:\n self.tokenizer.backtrack()\n break\n\n # self.file.write(\"\\n\")\n return\n\n def compileTerm(self):\n token_type = self.doAdvance(\"term\")\n\n if token_type == Token.T_INT_CONST:\n self.tokenizer.backtrack()\n self.getNextInt(\"term\")\n intval = self.tokenizer.intVal()\n self.vmWriter.writePush(\"constant\", intval)\n elif token_type == Token.T_STRING_CONST:\n self.tokenizer.backtrack()\n self.getNextStr(\"term\")\n strconst = self.tokenizer.stringVal()\n strlen = len(strconst)\n self.vmWriter.writePush(\"constant\", strlen)\n self.vmWriter.writeCall(\"String.new\", 1)\n for char in strconst:\n code = ord(char)\n self.vmWriter.writePush(\"constant\", code)\n self.vmWriter.writeCall(\"String.appendChar\", 2)\n elif token_type == Token.T_KEYWORD:\n self.tokenizer.backtrack()\n self.getNextKeyword(\"term\", [\"this\", \"false\", \"true\", \"null\"])\n keyword = self.tokenizer.keyWord()\n if keyword == \"true\":\n self.vmWriter.writePush(\"constant\", 1)\n self.vmWriter.writeArithmetic(Opp.O_NEG)\n elif keyword == \"false\" or keyword == \"null\":\n self.vmWriter.writePush(\"constant\", 0)\n elif keyword == \"this\":\n self.vmWriter.writePush(\"pointer\", 0)\n elif token_type == Token.T_IDENTIFIER:\n next_token_type = self.doAdvance(\"term\")\n if next_token_type == Token.T_SYMBOL:\n symbol = self.tokenizer.symbol()\n self.tokenizer.backtrack()\n if symbol == \"[\":\n # handle array accessing\n self.tokenizer.backtrack()\n first_var = self.getNextIdentifier(\"term\")\n self.tokenizer.backtrack()\n self.getNextIdentifier(\"term\")\n self.getNextSymbol(\"term\", \"[\")\n self.compileExpression()\n self.getNextSymbol(\"term\", \"]\")\n self.vmWriter.writePush(first_var['category'], first_var['index'])\n self.vmWriter.writeArithmetic(Opp.O_ADD)\n self.vmWriter.writePop(\"pointer\", 1)\n self.vmWriter.writePush(\"that\", 0)\n elif symbol == \"(\":\n # handle subroutine call\n self.tokenizer.backtrack()\n details = self.getNextIdentifier(\"term\")\n self.vmWriter.writePush(\"pointer\", 0)\n self.getNextSymbol(\"term\", \"(\")\n self.compileExpressionList()\n self.getNextSymbol(\"term\", \")\")\n name = self.class_name + \".\" + details['name'] \n self.vmWriter.writeCall(name, self.expression_count)\n elif symbol == \".\":\n # handle subroutine call\n self.tokenizer.backtrack()\n details = self.getNextIdentifier(\"term\")\n class_name = details['type']\n this_segment = details['category']\n this_index = details['index']\n is_instance_method = False\n if class_name == \"\":\n class_name = details['name']\n else:\n is_instance_method = True\n self.vmWriter.writePush(this_segment, this_index)\n self.getNextSymbol(\"term\", \".\")\n details = self.getNextIdentifier(\"term\")\n name = class_name + \".\" + details['name']\n self.getNextSymbol(\"term\", \"(\")\n self.compileExpressionList()\n self.getNextSymbol(\"term\", \")\")\n if is_instance_method:\n self.expression_count += 1\n self.vmWriter.writeCall(name, self.expression_count)\n else:\n self.tokenizer.backtrack()\n details = self.getNextIdentifier(\"term\")\n segment = details['category']\n index = details['index']\n self.vmWriter.writePush(segment, index)\n else:\n self.tokenizer.backtrack()\n self.getNextIdentifier(\"term\")\n else:\n symbol = self.tokenizer.symbol()\n if symbol == \"(\":\n # new expression\n self.tokenizer.backtrack()\n self.getNextSymbol(\"term\", \"(\")\n self.compileExpression()\n self.getNextSymbol(\"term\", \")\")\n elif symbol in [\"-\", \"~\"]:\n # self.file.write(self.tokenizer.getXml())\n self.compileTerm()\n op = Opp.O_NEG\n if symbol == \"~\":\n op = Opp.O_NOT\n self.vmWriter.writeArithmetic(op)\n elif symbol in [\")\", \"]\", \"}\"]:\n self.tokenizer.backtrack()\n else:\n throw(\"Unexpected symbol: {} in term\".format(symbol))\n\n # self.file.write(\"\\n\")\n return\n\n def compileExpressionList(self):\n self.expression_count = 0\n token_type = self.doAdvance(\"expression list\")\n if token_type == Token.T_SYMBOL and self.tokenizer.symbol() == \")\":\n self.tokenizer.backtrack()\n else:\n self.tokenizer.backtrack()\n self.compileExpression()\n self.expression_count += 1\n\n while self.tokenizer.hasMoreTokens():\n token_type = self.doAdvance(\"expression list\")\n if token_type == Token.T_SYMBOL:\n symbol = self.tokenizer.symbol()\n if symbol == \")\":\n self.tokenizer.backtrack()\n break\n elif symbol == \",\":\n self.expression_count += 1\n self.compileExpression()\n else:\n throw(\n \"Unexpected symbol: {} in expression list\".format(symbol))\n else:\n self.tokenizer.backtrack()\n self.compileExpression()\n return\n\n\nclass JackCompiler:\n def __init__(self, input_file):\n self.filename = input_file\n self.compile()\n return\n\n def compile(self):\n file_name = self.filename\n if not os.path.exists(file_name):\n print(\"[+] File not found at {}\".format(file_name))\n sys.exit()\n\n abs_path = os.path.abspath(file_name)\n is_dir = os.path.isdir(abs_path)\n\n if not is_dir and not abs_path.endswith('.jack'):\n print(\"[+] Provided file must be a .jack file\")\n\n if is_dir:\n glob_search = abs_path + '/*.jack'\n file_list = glob.glob(glob_search)\n for globbed_filename in file_list:\n output_filename = globbed_filename[0:-5] + \".vm\"\n CompilationEngine(globbed_filename, output_filename)\n else:\n output_filename = abs_path[0:-5] + \".vm\"\n CompilationEngine(abs_path, output_filename)\n\n\nif len(sys.argv) < 2:\n print(\"[+] Missing required argument filename\")\n print(\"[+] Please provide either filename or directory\")\n\nfile_name = sys.argv[1]\nJackCompiler(file_name)\n","repo_name":"Furbreeze/Nand2Tetris","sub_path":"11/Dev/JackCompiler.py","file_name":"JackCompiler.py","file_ext":"py","file_size_in_byte":39157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"10080810342","text":"import pandas as pd \n# pandas is short for panel data\n# it is a library, like numpy, for data science\n# it is built on numpy\n\n# one of the major shortcomings of using lists\n# for data science, is the lack of label-based indexing\n# e.g. the header!!\n# also really great builtin functionality for indexing, cleaning,\n# stats, etc....\n\n# there are two main objects in pandas\n# 1D data: pandas Series\n# 2D data: pandas DataFrame (every column is a series)\n\n# now, join demo with pandas' DataFrames!!\n\n# first trace example (single key)\n# adapted from SQL examples at https://www.diffen.com/difference/Inner_Join_vs_Outer_Join\nheader_left = [\"Product\", \"Price\"]\ndata_left = [[\"Potatoes\", 3.0],\n [\"Avacodos\", 4.0],\n [\"Kiwis\", 2.0],\n [\"Onions\", 1.0],\n [\"Melons\", 5.0],\n [\"Oranges\", 5.0],\n [\"Tomatoes\", 6.0]]\nheader_right = [\"Product\", \"Quantity\"]\ndata_right = [[\"Potatoes\", 45.0],\n [\"Avacodos\", 63.0],\n [\"Kiwis\", 19.0],\n [\"Onions\", 20.0],\n [\"Melons\", 66.0],\n [\"Broccoli\", 27.0],\n [\"Squash\", 92.0]]\n\n# pandas dataframe demo\ndf_left = pd.DataFrame(data_left, columns=header_left)\ndf_right = pd.DataFrame(data_right, columns=header_right)\n\n# inner join\ndf_inner_joined = df_left.merge(df_right, how=\"inner\", on=[\"Product\"])\nprint(\"Inner Join:\")\nprint(df_inner_joined)\nprint()\n\n# outer join\ndf_outer_joined = df_left.merge(df_right, how=\"outer\", on=[\"Product\"])\nprint(\"Outer Join:\")\nprint(df_outer_joined)\nprint()","repo_name":"GonzagaCPSC322/U3-Data-Analysis","sub_path":"DataPreparationFun/pandas_join.py","file_name":"pandas_join.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"16179788078","text":"import json\n\nimport click\nfrom click.testing import CliRunner\nimport pytest\n\nfrom rocrate.cli import cli\nfrom rocrate.model import File\nfrom rocrate.model.metadata import TESTING_EXTRA_TERMS\nfrom rocrate.rocrate import ROCrate\n\n\ndef get_command_paths(command):\n \"\"\"\\\n Return a list of full command paths for all leaf commands that are part of\n the given root command.\n\n For example, for a root command that has two subcommands ``command_a`` and\n ``command_b`` where ``command_b`` in turn has two subcommands, the\n returned list will have the form::\n\n [\n ['command_a'],\n ['command_b', 'subcommand_a'],\n ['command_b', 'subcommand_b'],\n ]\n\n :param command: The root command.\n :return: A list of lists, where each element is the full command path to a\n leaf command.\n \"\"\"\n\n def resolve_commands(command, command_path, commands):\n if isinstance(command, click.MultiCommand):\n for subcommand in command.commands.values():\n command_subpath = command_path + [subcommand.name]\n resolve_commands(subcommand, command_subpath, commands)\n else:\n commands.append(command_path)\n\n commands = []\n resolve_commands(command, [], commands)\n\n return commands\n\n\n@pytest.mark.parametrize('command_path', get_command_paths(cli))\ndef test_cli_help(command_path):\n \"\"\"\\\n Test that invoking any CLI command with ``--help`` prints the help string\n and exits normally.\n\n This is a regression test for:\n https://github.com/ResearchObject/ro-crate-py/issues/97\n\n Note that we cannot simply invoke the actual leaf :class:`click.Command`\n that we are testing, because the test runner follows a different path then\n when actually invoking the command from the command line. This means that\n any code that is in the groups that the command is part of, won't be\n executed. This in turn means that a command could actually be broken when\n invoked from the command line but would not be detected by the test. The\n workaround is to invoke the full command path. For example when testing\n ``add workflow --help``, we cannot simply invoke ``workflow`` with\n ``['--help']`` as argument but we need to invoke the base command with\n ``['add', 'workflow', '--help']``.\n \"\"\"\n runner = CliRunner()\n result = runner.invoke(cli, command_path + ['--help'])\n assert result.exit_code == 0, result.output\n assert 'Usage:' in result.output\n\n\n@pytest.mark.parametrize(\"gen_preview,cwd\", [(False, False), (False, True), (True, False), (True, True)])\ndef test_cli_init(test_data_dir, helpers, monkeypatch, cwd, gen_preview):\n crate_dir = test_data_dir / \"ro-crate-galaxy-sortchangecase\"\n metadata_path = crate_dir / helpers.METADATA_FILE_NAME\n preview_path = crate_dir / helpers.PREVIEW_FILE_NAME\n metadata_path.unlink()\n\n runner = CliRunner()\n args = [\"init\"]\n if cwd:\n monkeypatch.chdir(str(crate_dir))\n else:\n args.extend([\"-c\", str(crate_dir)])\n if gen_preview:\n args.append(\"--gen-preview\")\n result = runner.invoke(cli, args)\n assert result.exit_code == 0\n assert metadata_path.is_file()\n if gen_preview:\n assert preview_path.is_file()\n else:\n assert not preview_path.exists()\n\n json_entities = helpers.read_json_entities(crate_dir)\n assert \"sort-and-change-case.ga\" in json_entities\n assert json_entities[\"sort-and-change-case.ga\"][\"@type\"] == \"File\"\n\n\ndef test_cli_init_exclude(test_data_dir, helpers):\n crate_dir = test_data_dir / \"ro-crate-galaxy-sortchangecase\"\n (crate_dir / helpers.METADATA_FILE_NAME).unlink()\n exclude = \"test,README.md\"\n runner = CliRunner()\n args = [\"init\", \"-c\", str(crate_dir), \"-e\", exclude]\n result = runner.invoke(cli, args)\n assert result.exit_code == 0\n crate = ROCrate(crate_dir)\n for p in \"LICENSE\", \"sort-and-change-case.ga\":\n assert isinstance(crate.dereference(p), File)\n for p in exclude.split(\",\") + [\"test/\"]:\n assert not crate.dereference(p)\n for e in crate.data_entities:\n assert not e.id.startswith(\"test\")\n\n\n@pytest.mark.parametrize(\"cwd\", [False, True])\ndef test_cli_add_workflow(test_data_dir, helpers, monkeypatch, cwd):\n # init\n crate_dir = test_data_dir / \"ro-crate-galaxy-sortchangecase\"\n runner = CliRunner()\n assert runner.invoke(cli, [\"init\", \"-c\", str(crate_dir)]).exit_code == 0\n json_entities = helpers.read_json_entities(crate_dir)\n assert \"sort-and-change-case.ga\" in json_entities\n assert json_entities[\"sort-and-change-case.ga\"][\"@type\"] == \"File\"\n # add\n wf_path = crate_dir / \"sort-and-change-case.ga\"\n args = [\"add\", \"workflow\"]\n if cwd:\n monkeypatch.chdir(str(crate_dir))\n wf_path = wf_path.relative_to(crate_dir)\n else:\n args.extend([\"-c\", str(crate_dir)])\n for lang in \"cwl\", \"galaxy\":\n extra_args = [\"-l\", lang, str(wf_path)]\n result = runner.invoke(cli, args + extra_args)\n assert result.exit_code == 0\n json_entities = helpers.read_json_entities(crate_dir)\n helpers.check_wf_crate(json_entities, wf_path.name)\n assert \"sort-and-change-case.ga\" in json_entities\n lang_id = f\"https://w3id.org/workflowhub/workflow-ro-crate#{lang}\"\n assert lang_id in json_entities\n assert json_entities[\"sort-and-change-case.ga\"][\"programmingLanguage\"][\"@id\"] == lang_id\n\n\n@pytest.mark.parametrize(\"cwd\", [False, True])\ndef test_cli_add_test_metadata(test_data_dir, helpers, monkeypatch, cwd):\n # init\n crate_dir = test_data_dir / \"ro-crate-galaxy-sortchangecase\"\n runner = CliRunner()\n assert runner.invoke(cli, [\"init\", \"-c\", str(crate_dir)]).exit_code == 0\n json_entities = helpers.read_json_entities(crate_dir)\n def_id = \"test/test1/sort-and-change-case-test.yml\"\n assert def_id in json_entities\n assert json_entities[def_id][\"@type\"] == \"File\"\n # add workflow\n wf_path = crate_dir / \"sort-and-change-case.ga\"\n assert runner.invoke(cli, [\"add\", \"workflow\", \"-c\", str(crate_dir), \"-l\", \"galaxy\", str(wf_path)]).exit_code == 0\n # add test suite\n result = runner.invoke(cli, [\"add\", \"test-suite\", \"-c\", str(crate_dir)])\n assert result.exit_code == 0\n suite_id = result.output.strip()\n json_entities = helpers.read_json_entities(crate_dir)\n assert suite_id in json_entities\n # add test instance\n result = runner.invoke(cli, [\"add\", \"test-instance\", \"-c\", str(crate_dir), suite_id, \"http://example.com\", \"-r\", \"jobs\"])\n assert result.exit_code == 0\n instance_id = result.output.strip()\n json_entities = helpers.read_json_entities(crate_dir)\n assert instance_id in json_entities\n # add test definition\n def_path = crate_dir / def_id\n args = [\"add\", \"test-definition\"]\n if cwd:\n monkeypatch.chdir(str(crate_dir))\n def_path = def_path.relative_to(crate_dir)\n else:\n args.extend([\"-c\", str(crate_dir)])\n extra_args = [\"-e\", \"planemo\", \"-v\", \">=0.70\", suite_id, str(def_path)]\n result = runner.invoke(cli, args + extra_args)\n assert result.exit_code == 0\n json_entities = helpers.read_json_entities(crate_dir)\n assert def_id in json_entities\n assert set(json_entities[def_id][\"@type\"]) == {\"File\", \"TestDefinition\"}\n # check extra terms\n metadata_path = crate_dir / helpers.METADATA_FILE_NAME\n with open(metadata_path, \"rt\") as f:\n json_data = json.load(f)\n assert \"@context\" in json_data\n context = json_data[\"@context\"]\n assert isinstance(context, list)\n assert len(context) > 1\n extra_terms = context[1]\n assert set(TESTING_EXTRA_TERMS.items()).issubset(extra_terms.items())\n\n\n@pytest.mark.parametrize(\"hash_\", [False, True])\ndef test_cli_add_test_metadata_explicit_ids(test_data_dir, helpers, monkeypatch, hash_):\n # init\n crate_dir = test_data_dir / \"ro-crate-galaxy-sortchangecase\"\n runner = CliRunner()\n assert runner.invoke(cli, [\"init\", \"-c\", str(crate_dir)]).exit_code == 0\n # add workflow\n wf_path = crate_dir / \"sort-and-change-case.ga\"\n assert runner.invoke(cli, [\"add\", \"workflow\", \"-c\", str(crate_dir), \"-l\", \"galaxy\", str(wf_path)]).exit_code == 0\n # add test suite\n suite_id = \"#foo\"\n cli_suite_id = suite_id if hash_ else suite_id[1:]\n result = runner.invoke(cli, [\"add\", \"test-suite\", \"-c\", str(crate_dir), \"-i\", cli_suite_id])\n assert result.exit_code == 0\n assert result.output.strip() == suite_id\n json_entities = helpers.read_json_entities(crate_dir)\n assert suite_id in json_entities\n # add test instance\n instance_id = \"#bar\"\n cli_instance_id = instance_id if hash_ else instance_id[1:]\n args = [\n \"add\", \"test-instance\", cli_suite_id, \"http://example.com\", \"-c\", str(crate_dir), \"-r\", \"jobs\", \"-i\", cli_instance_id\n ]\n result = runner.invoke(cli, args)\n assert result.exit_code == 0\n assert result.output.strip() == instance_id\n json_entities = helpers.read_json_entities(crate_dir)\n assert instance_id in json_entities\n # add test definition\n def_id = \"test/test1/sort-and-change-case-test.yml\"\n def_path = crate_dir / def_id\n args = [\"add\", \"test-definition\", \"-c\", str(crate_dir), \"-e\", \"planemo\", \"-v\", \">=0.70\", cli_suite_id, str(def_path)]\n result = runner.invoke(cli, args)\n assert result.exit_code == 0\n json_entities = helpers.read_json_entities(crate_dir)\n assert def_id in json_entities\n assert set(json_entities[def_id][\"@type\"]) == {\"File\", \"TestDefinition\"}\n\n\n@pytest.mark.parametrize(\"cwd\", [False, True])\ndef test_cli_write_zip(test_data_dir, monkeypatch, cwd):\n crate_dir = test_data_dir / \"ro-crate-galaxy-sortchangecase\"\n runner = CliRunner()\n assert runner.invoke(cli, [\"init\", \"-c\", str(crate_dir)]).exit_code == 0\n wf_path = crate_dir / \"sort-and-change-case.ga\"\n args = [\"add\", \"workflow\", str(wf_path), \"-c\", str(crate_dir)]\n assert runner.invoke(cli, args).exit_code == 0\n\n output_zip_path = test_data_dir / \"test-zip-archive.zip\"\n args = [\"write-zip\"]\n if cwd:\n monkeypatch.chdir(str(crate_dir))\n else:\n args.extend([\"-c\", str(crate_dir)])\n args.append(str(output_zip_path))\n\n result = runner.invoke(cli, args)\n assert result.exit_code == 0\n assert output_zip_path.is_file()\n\n crate = ROCrate(output_zip_path)\n assert crate.mainEntity is not None\n","repo_name":"ResearchObject/ro-crate-py","sub_path":"test/test_cli.py","file_name":"test_cli.py","file_ext":"py","file_size_in_byte":10401,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"2"}
+{"seq_id":"26011181425","text":"from itertools import cycle\n\nimport pyqtgraph as pg\nfrom traits.api import Instance, List\n\nfrom extensions.models.metro import MetroTwinXGraphModel\nfrom extensions.utils import (\n add_twinx, get_array_data, get_node_value, guess_path)\nfrom karabo.common.scenemodel.api import build_model_config\nfrom karabogui.binding.api import (\n NDArrayBinding, VectorNumberBinding, WidgetNodeBinding)\nfrom karabogui.controllers.api import (\n BaseBindingController, register_binding_controller, with_display_type)\nfrom karabogui.graph.common.api import KaraboLegend, get_pen_cycler\nfrom karabogui.graph.plots.api import (\n KaraboPlotView, VectorBarGraphPlot, generate_down_sample, get_view_range)\n\nfrom .utils import PlotData\n\n\n@register_binding_controller(\n ui_name='Metro TwinX Graph',\n klassname='MetroTwinXGraph',\n binding_type=WidgetNodeBinding,\n is_compatible=with_display_type('WidgetNode|Metro-TwinXGraph'),\n priority=0, can_show_nothing=False)\nclass MetroTwinXGraph(BaseBindingController):\n \"\"\"The controller for the XAS graph display from a Metro output\n \"\"\"\n model = Instance(MetroTwinXGraphModel, args=())\n _main_plot = Instance(PlotData, args=())\n _other_plots = List(Instance(PlotData))\n _legend = Instance(pg.LegendItem)\n _pens = Instance(cycle, allow_none=False)\n\n def create_widget(self, parent):\n widget = KaraboPlotView(parent=parent)\n widget.stateChanged.connect(self._change_model)\n widget.add_cross_target()\n widget.add_toolbar()\n\n # Main axis\n self._main_plot.item = widget.add_curve_item(pen=next(self._pens))\n\n # Second y-axis\n viewBox = add_twinx(plotItem=widget.plotItem, y_label='XAS')\n for plot in self._other_plots:\n plot.item = data_item = pg.PlotDataItem(pen=next(self._pens))\n viewBox.addItem(data_item)\n\n # Add legend manually\n self._legend = legend = KaraboLegend(offset=(5, 5))\n legend.setBrush(None)\n legend.setParentItem(widget.plotItem.vb)\n\n # Finalize\n widget.restore(build_model_config(self.model))\n return widget\n\n def binding_update(self, proxy):\n for plot in self._plots:\n excluded = [ex.path for ex in set(self._plots) - {plot}]\n plot.path = guess_path(proxy,\n klass=(NDArrayBinding, VectorNumberBinding),\n excluded=excluded,\n output=True)\n # Refresh the legends\n self._legend.removeItem(plot.item)\n self._legend.addItem(plot.item, name=plot.path)\n\n def value_update(self, proxy):\n for plot in self._plots:\n self._plot_data(plot, proxy)\n\n def __pens_default(self):\n return get_pen_cycler()\n\n def __other_plots_default(self):\n return [PlotData() for _ in range(2)]\n\n # ----------------------------------------------------------------\n # Helpers\n\n def _plot_data(self, plot, proxy):\n if proxy.value is None:\n return\n prop = get_node_value(proxy, key=plot.path)\n x, _ = get_array_data(get_node_value(prop, key='x'), default=[])\n y, _ = get_array_data(get_node_value(prop, key='y0'), default=[])\n if not len(y) or len(x) != len(y):\n plot.item.setData([], [])\n return\n\n rect = get_view_range(plot.item)\n x, y = generate_down_sample(y, x=x, rect=rect, deviation=True)\n if isinstance(plot.item, VectorBarGraphPlot):\n plot.item.opts['width'] = 0.8 * (x[1] - x[0])\n plot.item.setData(x, y)\n\n def _change_model(self, content):\n self.model.trait_set(**content)\n\n @property\n def _plots(self):\n return [self._main_plot] + self._other_plots\n","repo_name":"European-XFEL/guiextensions","sub_path":"src/extensions/metro/twinx_graph.py","file_name":"twinx_graph.py","file_ext":"py","file_size_in_byte":3790,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"26025914465","text":"import datrie\nimport string\nimport random\nfrom copy import deepcopy\nfrom curses import wrapper, A_REVERSE, curs_set\n\nLOG = open('log', 'w')\n\ndef filter_dict_into_trie(words, trie):\n lowercase_letters = set(string.ascii_lowercase)\n for line in words:\n line = line[:-1]\n # exclude proper nouns and punctuation\n # and words of 2 letters and less\n if len(line) <= 2 and not set(line).issubset(lowercase_letters):\n continue\n trie[line] = 1\n return trie\n\ndef build_trie(dict_file = '/usr/share/dict/words'):\n trie = datrie.BaseTrie(string.ascii_lowercase)\n with open(dict_file, 'r') as words:\n filter_dict_into_trie(words, trie)\n return trie\n\ndef random_letter():\n return random.choice(string.ascii_lowercase + '*')\n\nclass Word:\n def __init__(self, dictionary, grid):\n self.grid = grid\n self.coords = []\n self.trie = dictionary\n self.tried_coords = []\n def __len__(self): return len(self.coords)\n def clear_tries(self):\n self.tried_coords = []\n def push(self, x,y):\n if len(self.coords) == 0 or \\\n ((abs(x - self.coords[-1][0]) <= 1 and abs(y - self.coords[-1][1]) <= 1) and not \\\n (x == self.coords[-1][0] and y == self.coords[-1][1])):\n self.coords.append((x,y))\n self.tried_coords.append((x,y))\n def pop(self):\n self.coords = self.coords[:-1]\n def letters(self):\n return ''.join([self.grid[coord] for coord in self.coords])\n def tried(self, coord):\n for c in self.tried_coords:\n if c == coord:\n return True\n return False\n def member(self, coord):\n for c in self.coords:\n if c == coord:\n return True\n return False\n def is_valid_or_prefix(self):\n return self.trie.has_keys_with_prefix(self.letters())\n def is_valid(self):\n return len(self.coords) >= 3 and self.letters() in self.trie\n\nclass Grid:\n def __init__(self, dictionary, x=8,y=12):\n self.grid = []\n self._random_grid(x,y)\n self.x, self.y = x,y\n self.dictionary = dictionary\n def _valid_neighbours(self, coords):\n (i,j) = coords\n return [(x,y) for (x,y) in\n [(i-1,j-1), (i-1,j), (i,j-1),\n # (i,j),\n (i,j+1), (i+1,j), (i+1, j+1)] \n if 0 <= x < self.x and 0 <= y < self.y]\n\n def _random_grid(self, x,y):\n # 8x12\n self.grid = []\n for _ in range(y):\n row = []\n for _ in range(x):\n row.append(random_letter())\n self.grid.append(row)\n def __getitem__(self, coords):\n return self.grid[coords[1]][coords[0]]\n def __setitem__(self, coords, letter):\n self.grid[coords[1]][coords[0]] = letter\n def __repr__(self):\n return '\\n'.join([' '.join(row) for row in self.grid]) \n def _words_r(self, word, words):\n for (x,y) in self._valid_neighbours(word.coords[-1]):\n # don't use an already visited letter\n if not word.member((x,y)):\n # add a new letter\n word.push(x,y)\n # check that the letter makes a potentially valid word\n if word.is_valid_or_prefix():\n # if it's a word, add it\n if word.is_valid():\n words.append(deepcopy(word))\n # try adding more letters\n self._words_r(word, words)\n # remove the added letter and keep looking\n word.pop()\n \n def find_words(self):\n words = []\n for i in range(self.x):\n for j in range(self.y):\n word = Word(self.dictionary, self)\n word.push(i,j)\n self._words_r(word, words)\n return words\n def clear(self):\n for i in range(self.x):\n for j in range(self.y):\n self[(i,j)] = ' '\n def apply_gravity(self):\n for j in range(self.y-1, -1, -1):\n for i in range(self.x):\n if self[(i,j)] == ' ':\n print('%s, %s'%(i,j), file=LOG)\n for up in range(j, -1, -1):\n if self[(i, up)] != ' ':\n print('move %s,%s -> %s, %s'%(i,j,i,up), file=LOG)\n self[(i,j)] = self[(i,up)]\n self[(i,up)] = ' '\n break\n def eliminate_word(self, word):\n for coord in word.coords:\n self[coord] = ' '\n # eliminate cardinal neighbours\n for (x,y) in self._valid_neighbours(coord):\n if len(word.coords) > 4 or self[(x,y)] == '*':\n self[(x,y)] = ' '\n self.apply_gravity()\n \n\nclass GridUI:\n def __init__(self, grid, stdscr):\n self.grid = grid\n self.stdscr = stdscr\n self.word = False\n self.words = []\n self._regen_wordlist()\n curs_set(False)\n def _regen_wordlist(self):\n self.word = 0\n self.words = sorted(self.grid.find_words(), key=len, reverse=True)[0:10]\n def mainloop(self):\n self.update()\n while True:\n key = self.stdscr.getkey()\n\n self.stdscr.addstr(18, 0, key)\n if key == \"e\" or key == \"E\":\n if len(self.words) > 0:\n self.grid.eliminate_word(self.words[self.word])\n self._regen_wordlist()\n elif key == 'r' or key == 'R':\n self._regen_wordlist()\n elif key == 'q' or key == 'Q':\n return\n elif key == 'n' or key == 'N':\n self.editloop()\n elif key == 'KEY_UP':\n self.word = self.word - 1 if self.word > 0 else len(self.words)-1\n elif key == 'KEY_DOWN':\n self.word = self.word + 1 if self.word < len(self.words)-1 else 0\n\n self.update(key=key)\n def editloop(self):\n self.grid.clear()\n self.words = []\n self.word = -1 # turn off word hilighing\n self.update()\n i, j = 0,0\n while True:\n i = 0\n while i < self.grid.x:\n self.update()\n self.stdscr.addstr(16, 0, \"Enter letters, use arrow keys, then hit (End) \")\n self.stdscr.addstr(j,i*2, self.grid[(i,j)], A_REVERSE)\n\n key = self.stdscr.getkey()\n if key in set(string.ascii_lowercase + '*' + ' '):\n self.grid[(i,j)] = key\n i +=1\n elif key == 'KEY_LEFT':\n i = i-1 if i > 0 else i\n elif key == 'KEY_RIGHT':\n i = i+1 if i+1 < self.grid.x else i\n elif key == 'KEY_UP':\n j = j-1 if j > 0 else j\n elif key == 'KEY_DOWN':\n j = j+1 if j+1 < self.grid.y else j\n elif key == 'KEY_END':\n self.grid.apply_gravity()\n return\n\n j = j+1 if j+1 < self.grid.y else j\n\n\n\n def update(self, key=''):\n self.stdscr.clear()\n for i in range(self.grid.x):\n for j in range(self.grid.y):\n self.stdscr.addstr(j, i*2, '%s '%(self.grid[(i,j)]))\n self.show_wordlist()\n self.show_promptline()\n self.stdscr.addstr(18, 0, key)\n self.stdscr.refresh()\n\n def show_promptline(self):\n self.stdscr.addstr(16, 0, \"(R)egenerate Wordlist, (E)liminate Word, (Up/Down) Select Word, (N)ew Tower, (Q)uit\")\n def show_wordlist(self):\n if len(self.words) <= 0: return\n for i in range(len(self.words)):\n self.stdscr.addstr(i, 16, '| ' + self.words[i].letters())\n\n if self.word >= 0:\n self.stdscr.addstr(self.word, 18, self.words[self.word].letters(), A_REVERSE)\n for coord in self.words[self.word].coords:\n self.stdscr.addstr(coord[1], coord[0]*2, '%s '%(self.grid[coord]), A_REVERSE)\n\n\n \n \n\ndef main(stdscr):\n trie = build_trie(dict_file = '/usr/share/dict/cracklib-small')\n grid = Grid(trie)\n ui = GridUI(grid, stdscr)\n ui.mainloop()\n\nif __name__ == \"__main__\":\n wrapper(main)\n","repo_name":"charlieb/spelltower-solver","sub_path":"sps.py","file_name":"sps.py","file_ext":"py","file_size_in_byte":8373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"33586396103","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\np1=plt.figure(figsize=(10,10))\nplt.rcParams['font.sans-serif']='SimHei' #将默认字体修改为仿宋\nplt.rcParams['axes.unicode_minus']=False\n\n\ndatas=np.load(r'E:\\DataAnalyst\\数据分析表\\populations.npz',allow_pickle=True)\nprint(list(datas))\n\ndata=datas['data']\nfeature_names=datas['feature_names']\n# print(data)\n\n\n\np1.add_subplot(2,2,1)\nlabels=['城镇人口','乡村人口']\nexplode=[0.01,0.01]\nplt.pie(data[0,4:6],explode=explode,labels=labels,autopct='%1.1f%%',colors=['r','b'])\nplt.title(\"城乡人口变化图\")\n\n\np1.add_subplot(2,2,2)\nlabels=['男','女']\nexplode=[0.01,0.01]\nplt.pie(data[1,3:5],explode=explode,labels=labels,autopct='%1.1f%%',colors=['r','b'])\nplt.title(\"男女人口变化图\")\n\n\np1.add_subplot(3,2,3)\nx=np.linspace(0,20,20)\ny=data[-3::-1,1]\nplt.plot(x,y,marker=\"*\",)\nplt.title('总人��变化图')\nplt.xlabel('年份')\nplt.ylabel('人口')\nplt.xticks(x,data[-3::-1,0],rotation=45)\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"hdqlife/DataAnalyst","sub_path":"作业.py","file_name":"作业.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"6913927060","text":"\"\"\"\nLeetCode\n2542. Maximum Subsequence Score\nMay 2023 Challenge\njramaswami\n\"\"\"\n\n\nimport heapq\nimport math\nimport operator\nfrom typing import *\n\n\nclass Solution:\n def maxScore(self, nums1: List[int], nums2: List[int], k: int) -> int:\n heap = []\n curr_sum = 0\n soln = -math.inf\n values = list(zip(nums1, nums2))\n values.sort(key=operator.itemgetter(1), reverse=True)\n for num1, num2 in values:\n curr_sum += num1\n heapq.heappush(heap, num1)\n while len(heap) > k:\n curr_sum -= heapq.heappop(heap)\n if len(heap) == k:\n soln = max(soln, num2 * curr_sum)\n return soln\n\n\ndef test_1():\n nums1 = [1,3,3,2]\n nums2 = [2,1,3,4]\n k = 3\n expected = 12\n assert Solution().maxScore(nums1,nums2, k) == expected\n\n\ndef test_2():\n nums1 = [4,2,3,1,1]\n nums2 = [7,5,10,9,6]\n k = 1\n expected = 30\n assert Solution().maxScore(nums1,nums2, k) == expected\n\n\ndef test_3():\n nums1 = [2,1,14,12]\n nums2 = [11,7,13,6]\n k = 3\n expected = 168\n assert Solution().maxScore(nums1,nums2, k) == expected\n","repo_name":"jramaswami/LeetCode_Python","sub_path":"maximum_subsequence_score.py","file_name":"maximum_subsequence_score.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"29103863491","text":"# 1\nmy_list_1 = [2, 5, 8, 2, 12, 12, 4, 2, 2, 2, 2, 2, 2, 2, 2]\nmy_list_2 = [2, 7, 12, 3]\n\nfor item in my_list_2:\n while item in my_list_1:\n my_list_1.remove(item)\n\nprint(my_list_1)\n\n# 2\ndate = '02.11.2013'\nmm_dict = {\n '01': 'January',\n '02': 'February',\n '03': 'March',\n '04': 'April',\n '05': 'May',\n '06': 'June',\n '07': 'July',\n '08': 'August',\n '09': 'September',\n '10': 'October',\n '11': 'November',\n '12': 'December'}\ndd_dict = {'01': 'первое', '02': 'второе', '03': 'третье', '04': 'четвёртое', '05': 'пятое', '06': 'шестое',\n '07': 'седьмое', '08': 'восьмое', '09': 'девятое', '10': 'десятое', '11': 'одиннадцатое',\n '12': 'двенадцатое', '13': 'тренадцатое', '14': 'четырнадцатое', '15': 'пятнадцатое',\n '16': 'шестнадцатое', '17': 'семнадцатое', '18': 'восемнадцатое', '19': 'девятнадцатое',\n '20': 'двадцатое', '21': 'двадцать первое', '22': 'двадцать второе', '23': 'двадцать третье',\n '24': 'двадцать четвёртое', '25': 'двадцать пятое', '26': 'двадцать шестое', '27': 'двадцать седьмое',\n '28': 'двадцать восьмое', '29': 'двадцать девятое', '30': 'тридцатое', '31': 'тридцать первое'\n }\n\nprint('{} = {} {} {}'.format(date, dd_dict.get(date[:2]), mm_dict.get(date[3:5]), date[-4:]))\n\n# 3\nmy_list_1 = [2, 2, 5, 12, 8, 2, 12]\nmy_list_unique = list()\n\nfor i in set(my_list_1):\n if my_list_1.count(i) == 1:\n my_list_unique.append(i)\n\nprint(my_list_unique)\n","repo_name":"Alex-Sky-Q/Python-git","sub_path":"Python_Intro-GB/helloworld/4-Lists.py","file_name":"4-Lists.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"12648038666","text":"\"\"\"\n主要是排序\n\"\"\"\n\n'''\n冒泡排序\n'''\ndef maopao(l):\n for i in range(len(l)-1):\n for j in range(len(l)-1-i):\n if l[j]>l[j+1]:\n l[j],l[j+1]=l[j+1],l[j]\n return l\n\n'''\n快速排序\n'''\ndef quit(l):\n if len(l)<2:\n return l\n else:\n m=l[0]\n left=[i for i in l[1:] if i<=m]\n right=[j for j in l[1:] if j>m]\n return quit(left)+[m]+quit(right)\n\n'''\n选择排序\n'''\ndef select(l):\n for i in range(len(l)-1):\n minone=i\n for j in range(i,len(l)):\n if l[minone]>l[j]:\n minone=j\n l[i],l[minone]=l[minone],l[i]\n return l\n\n'''\n插入排序???\n'''\ndef insert(l):\n for i in range(len(l)-1):\n minone=i\n for j in range(i,len(l)):\n if l[minone]>l[j]:\n minone=j\n for n in range(i,minone):\n l[n+1]=l[n]\n l[i]=l[minone]\n return l\n\n\n\nif __name__==\"__main__\":\n l=[8,9,6,1,92,56]\n\n\n print(insert(l))\n\n\n","repo_name":"fanwenpeng93/selfleetcode","sub_path":"suanfa/test01.py","file_name":"test01.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"7757193650","text":"\"\"\"\nZ\n\"\"\"\n\nN, r, c = map(int, input().split())\ntop, bottom, left, right = 0, 2**N - 1, 0, 2**N - 1\ns, e = 0, (2**N)**2 - 1\nwhile N > 1:\n row = (top + bottom) // 2\n col = (left + right) // 2\n d = (e-s+1) // 4\n if r <= row and c <= col:\n bottom, right = row, col\n s, e = s, s+d-1\n elif r <= row and c > col:\n bottom, left = row, col+1\n s, e = s+d, s+d+d-1\n elif r > row and c <= col:\n top, right = row+1, col\n s, e = s+d+d, s+d+d+d-1\n elif r > row and c > col:\n top, left = row+1, col+1\n s, e = s+d+d+d, s+d+d+d+d-1\n N -= 1\nif r == top and c == left:\n print(s)\nelif r == top and c == right:\n print(s+1)\nelif r == bottom and c == left:\n print(s+2)\nelif r == bottom and c == right:\n print(s+3)","repo_name":"2mingyu/Baekjoon","sub_path":"python/1074.py","file_name":"1074.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"40878095720","text":"\n#%%\n# Таблица умножения\n'''На вход программе подаются два натуральных числа \nn и m — количество строк и столбцов в матрице. \nСоздайте матрицу mult размером n × m и заполните \nеё таблицей умножения по формуле mult[i][j] = i * j.\n'''\n\nn = int(input())\nm = int(input())\nmatrix = [[i*j for j in range(m)] for i in range(n)]\nfor i in range(n):\n for j in range(m):\n print(str(matrix[i][j]).ljust(3, ' '), end='')\n print()\n\n\n#%%\n# Максимум в таблице\n'''\nНа вход программе подаются два натуральных числа \nn и m — количество строк и столбцов в матрице, \nзатем n строк по m целых чисел в каждой, \nотделенных символом пробела.\nНапишите программу, которая находит индексы \n(строку и столбец) первого вхождения максимального элемента.\n'''\n\nm1 = [[1, 2], [3, 4], [5, 6]]\nm2 = [[0, 1, 2], [3, 6, 9], [2, 2, 4],[1, 6, 3]]\nmatrix = m2\nn = len(matrix)\nm = len(matrix[0])\n'''\n'''\nn = int(input())\nm = int(input())\nmatrix = [[int(elem) for elem in input().split()] for _ in range(n)]\n\n[print(row) for row in matrix]\n\nmax_i = 0\nmax_j = 0\nmax = matrix[0][0]\nfor i in range(n):\n for j in range(m):\n if matrix[i][j] > max:\n max = matrix[i][j]\n max_i = i\n max_j = j\n#print(f'matrix[{max_i}][{max_j}] = ', max)\nprint(max_i, max_j)\n\n\n#%%\n# Обмен столбцов\n'''\nНапишите программу, которая меняет местами столбцы в матрице.\nФормат входных данных\nНа вход программе на разных строках подаются два натуральных числа n и m — количество строк и столбцов в матрице, затем элементы матрицы построчно через пробел, затем числа i и j — номера столбцов, подлежащих обмену.\n'''\n\nn = int(input())\nm = int(input())\nmatrix = [[int(elem) for elem in input().split()] for _ in range(n)]\n#i = int(input()) # обмен столбца\n#j = int(input()) # обмен столбца\n\n\nprint('до замены')\n[print(row) for row in matrix]\n\nfor row in range(n):\n matrix[row][i], matrix[row][j] = matrix[row][j], matrix[row][i]\n\nprint('после замены')\n[print(row) for row in matrix]\n\n\n\n#%%\n# Симметричная матрица\n'''\nНапишите программу, которая проверяет симметричность квадратной матрицы относительно главной диагонали.\n'''\n\nn = int(input())\nmatrix = [[int(elem) for elem in input().split()] for _ in range(n)]\nanswer = 'YES'\nfor i in range(1, n):\n for j in range(i):\n if matrix[i][j] != matrix[j][i]:\n answer = 'NO'\n break\n if answer == 'NO':\n break\nprint(answer)\n\n\n#%%\n# Обмен диагоналей\n'''\nДана квадратная матрица чисел. Напишите программу, которая меняет местами элементы, стоящие на главной и побочной диагонали, при этом каждый элемент должен остаться в том же столбце (то есть в каждом столбце нужно поменять местами элемент на главной диагонали и на побочной диагонали).\n'''\n\nn = int(input())\nmatrix = [[int(elem) for elem in input().split()] for _ in range(n)]\n\nprint('before')\n[print(row) for row in matrix]\n\nfor i in range(n):\n matrix[i][i], matrix[~i][i] = matrix[~i][i], matrix[i][i]\n\nprint('after')\n[print(row) for row in matrix]\n\n\n#%%\n# Зеркальное отображение\n'''\nДана квадратная матрица чисел. Напишите программу, которая зеркально отображает её элементы относительно горизонтальной оси симметрии.\n'''\n\nn = int(input())\nmx = [[int(elem) for elem in input().split()] for _ in range(n)]\n\n[print(row) for row in mx]\n\nfor j in range(n):\n for i in range(n//2):\n mx[i][j], mx[~i][j] = mx[~i][j], mx[i][j]\n\n[print(row) for row in mx]\n\n#%%\n# Поворот матрицы\n'''\nНапишите программу, которая поворачивает квадратную матрицу чисел на 90 градусов по часовой стрелке.\nФормат входных данных\nНа вход программе подаётся натуральное число n — количество строк и столбцов в матрице, затем элементы матрицы построчно через пробел.\nФормат выходных данных\nПрограмма должна вывести результат на экран, числа должны быть разделены одним пробелом.\n'''\n\n#n = int(input())\n#mx = [[int(el) for el in input().split()] for _ in range(n)]\nmx1 = [[1, 2, 3], \n [4, 5, 6],\n [7, 8, 9]]\n\nmx = mx1\nn=len(mx)\nmx_r=[[0]*n for _ in range(n)]\n\n[print(row) for row in mx]\nprint()\n\nfor i in range(n):\n\tfor j in range(n):\n\t\tmx_r[i][j]=mx[~j][i]\n\n[print(row) for row in mx_r]\n\n\n# %%\n# Ход коня\n'''\nНа шахматной доске \n8×8 стоит конь. Напишите программу, которая отмечает положение коня на доске и все клетки, которые бьет конь. Клетку, где стоит конь, отметьте английской буквой N, клетки, которые бьет конь, отметьте символами *, остальные клетки заполните точками.\n'''\nn = 8\nchess_desk = [['.']*n for _ in range(n)]\n\n\n# красивый вывод поля с обозначением номеров строк и букв столбцов\ndef print_chess(chess_desk):\n desk = [[elem for elem in row] for row in chess_desk]\n for i in range(1, 9):\n desk[n-i].insert(0, i)\n desk.insert(0, [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n [print(*row) for row in desk]\n\n\n# добавдяем коня\npos = input().lower()\nind_i = n - int(pos[1])\nind_j = ord(pos[0]) - 97\nchess_desk[ind_i][ind_j] = 'N'\n \n# обозначим, куда конь может ходить\nsteps = [[ind_i - 2, ind_j - 1],\n [ind_i - 2, ind_j + 1],\n [ind_i - 1, ind_j - 2],\n [ind_i - 1, ind_j + 2],\n [ind_i + 1, ind_j - 2],\n [ind_i + 1, ind_j + 2],\n [ind_i + 2, ind_j - 1],\n [ind_i + 2, ind_j + 1]]\n\n# добавим точки возможных ходов на поле\nfor p in steps:\n if 0 <= p[0] < 8 and 0 <= p[1] < 8:\n chess_desk[p[0]][p[1]] = '*'\n\n[print(*row) for row in chess_desk]\n\nprint_chess(chess_desk)\n# %%\n# v2\n# рисуем поле\nx,y = input()\ndesk = [['.']*8 for _ in range(8)]\n\n# добавдяем коня\nind_i = n - int(y)\nind_j = ord(x) - 97\ndesk[ind_i][ind_j] = 'N'\n\n# расставляем возможные ходы для коня\nfor i in range(8):\n for j in range(8):\n if abs(ind_i - i)*abs(ind_j - j) == 2:\n desk[i][j] = '*'\n\n\n[print(*row) for row in desk]\n\n# %%\n# Магический квадрат\n'''\nМагическим квадратом порядка n называется квадратная \nтаблица размера n x n составленная из всех чисел \n1, 2, 3... n^2 так, что суммы по каждому столбцу, \nкаждой строке и каждой из двух диагоналей равны \nмежду собой. Напишите программу, которая проверяет, \nявляется ли заданная квадратная матрица магическим квадратом.\nНа вход программе подаётся натуральное число \nn — количество строк и столбцов в матрице, затем \nэлементы матрицы: n строк, по n чисел в каждой, \nразделённые пробелами.\n'''\n# v1 \n# n = input()\n# matrix = [[int(elem) for elem in input().split()] for _ in range(n)]\nflag = True\n\n\nm1 = [[8, 1, 6], [3, 5, 7], [4, 9, 2]]\nm2 = [[8, 2, 6], [3, 5, 7], [4, 9, 1]]\nm3 = [[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]]\nm4 = [[5, 5, 5], [5, 5, 5], [5, 5, 5]]\nmatrix = m4\nn = len(matrix)\n\ndef check_elems(li):\n current_sum = li[0]\n for elem in li[1:]:\n if current_sum != elem:\n return False\n else:\n return True\n\n# проверяем, что квадрат состоит из чисел от 1 до n^2\nall_numbers = [i for i in range(1, 1 + n**2)]\nfor i in range(n):\n for j in range(n):\n if matrix[i][j] in all_numbers:\n all_numbers.remove(matrix[i][j])\nif len(all_numbers) > 0:\n flag = False\nelse:\n flag = True\n\n\n# проверяем суммы строк\nsum_row = []\nfor i in range(n):\n sum_current = 0\n for j in range(n):\n sum_current += matrix[i][j]\n sum_row.append(sum_current)\nsum_current = sum_row[0]\n\n# # выводим список сумм\nprint('sum_row:', sum_row)\nprint(check_elems(sum_row))\n\n# обновляем флаг\nflag = flag and check_elems(sum_row)\n\n\n# проверяем суммы столбцов\nsum_col = []\nfor j in range(n):\n current_sum = 0\n for i in range(n):\n current_sum += matrix[i][j]\n sum_col.append(current_sum)\n\n# # выводим список сумм\nprint('sum_col:', sum_col)\nprint(check_elems(sum_col))\n\nflag = flag and check_elems(sum_col)\n\n\n# проверяем сумму главной диагонали\nsum_diag = [0, 0]\nfor i in range(n):\n sum_diag[0] += matrix[i][i]\n sum_diag[1] += matrix[i][~i]\n\n# # выводим сумму главной диагонали\nprint('main diagonal:', sum_diag[0])\nprint(check_elems(sum_diag))\n\n# # выводим сумму побочной диагонали\nprint('second diagonal:', sum_diag[1])\n\nflag = flag and check_elems(sum_diag)\n\nprint('final flag:', flag)\n\nprint('YES' if flag else 'NO')\n","repo_name":"xxxAleksandrxxx/Stepik","sub_path":"Python generation lvl 2/Stepik_lvl2_04.5.py","file_name":"Stepik_lvl2_04.5.py","file_ext":"py","file_size_in_byte":10405,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"33594183833","text":"#while true:\n #cos tam\n\nwhile True:\n print(\"lewix :D\")\n break\n print(\"sad\")\n\n#----------------------------\nlicznik = 0\nwhile licznik < 10:\n print(f\"lewis = {licznik}\")\n licznik += 1\n\nzdanie = \"lewis ma kota i rzepunka\"\nindex = 0\ndlugosc = len(zdanie)\nwhile index < dlugosc:\n print(zdanie[index])\n index += 1\n","repo_name":"Lewixx/lewypython","sub_path":"dzien3/petla-while.py","file_name":"petla-while.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"35039881298","text":"import arcpy\nprint (\"Imported...\")\n\narcpy.env.workspace = \"O:/RO_GEOGRAPHY/FLD_GSB/CollectionGEO/Shapefiles/PDB_2018\"\n\n#counties = [] - list of every county in country\n\nfor co in counties:\n \n inFeatures = \"/tract73_2018/tract73_2018_\"+co+\"_tract_v73.shp\"\n inLayer = \"tract73_2018_\"+co+\"_tract\"\n in_field = \"TRACTCE\"\n joinTable = \"/pdb2018/\"+co+\"_pdb.csv\"\n join_field = \"Tract\"\n\n arcpy.MakeFeatureLayer_management(inFeatures, inLayer)\n print (\"Layer \",co)\n joinLayer = arcpy.AddJoin_management(inLayer, in_field, joinTable, join_field)\n print (\"Joined\")\n arcpy.FeatureClassToGeodatabase_conversion(joinLayer,'testing.gdb')\n #arcpy.CopyFeatures_management(joinLayer,\"justatest\")\n arcpy.Delete_management(inLayer)\n arcpy.Delete_management(joinLayer)\n print (\"Finished \",co)","repo_name":"jmjfisher/python-tasks","sub_path":"Random_ArcPy/join_gdb.py","file_name":"join_gdb.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"26401734464","text":"import threading\nimport tkinter as tk\nfrom tkinter import ttk\nfrom node import node\n\nclass gui_runner(object):\n\n GENERAL_MODE = \"GENERAL_MODE\"\n DETAILED_MODE = \"DETAILED_MODE\"\n\n COVER = '#53FF53'\n GREEN = '#53FF53'\n RED = '#FF5151'\n\n def __init__(self):\n self.node = []\n self.mode = self.GENERAL_MODE\n self.is_runner_ready = False\n self.is_pause_ready = False\n self.is_script_select = False\n self.is_keyword_tree_select = False\n self.is_render_ready = False\n self.can_step = False\n self.block_next_keyword = False\n self.block_next_node = False\n\n def run(self):\n self.thread = threading.Thread(target=self.show)\n self.thread.start()\n\n def show(self):\n self.set_window_configuration()\n self.make_vision()\n self.window.mainloop()\n\n def set_window_configuration(self):\n self.window = tk.Tk()\n self.window.protocol(\"WM_DELETE_WINDOW\", self.on_closing)\n self.window.title(\"Runner\")\n self.window.resizable(True, True)\n self.window.attributes(\"-alpha\", 0.8)\n self.window.attributes(\"-topmost\", True)\n\n def set_window_title(self, title):\n self.window.title(title)\n\n def on_closing(self):\n self.is_pause_ready = False\n self.window.quit()\n\n def make_vision(self):\n def make_steps_vision(window):\n self.script = tk.Listbox(window, width='50', height='7', selectmode=tk.MULTIPLE, font=('Comic Sans MS', 12), exportselection=0)\n self.script.grid(row=1, columnspan=3, sticky=\"nsew\")\n\n def make_keyword_tree_vision(window):\n self.keyword_tree = tk.Listbox(window, width='50', height='7', selectmode=tk.MULTIPLE, font=('Comic Sans MS', 12), exportselection=0)\n self.keyword_tree.grid(row=2, columnspan=3, sticky=\"nsew\")\n\n if self.node:\n self.keyword_tree.insert('end', self.node[self.current_node_index].payload)\n self.insert_tree_vision(self.node[self.current_node_index], self.keyword_tree)\n\n def make_function(window):\n self.function_resume = tk.Button(window, text='Resume', command=self.function_resume_callback)\n self.function_pause = tk.Button(window, text='Pause', command=self.function_pause_callback)\n self.function_step_into = tk.Button(window, text='Step Into', command=self.step_into_callback)\n self.function_step_over = tk.Button(window, text='Step Over', command=self.step_over_callback)\n self.render_text = tk.StringVar()\n self.render_text.set(\"Render / Off\")\n self.render = tk.Button(window, textvariable=self.render_text, command=self.render_callback)\n self.transparency_scale = ttk.Scale(window, from_=25, to=100, orient='horizontal', command=self.update_transparency)\n\n self.function_resume.grid(row=3, column=0, sticky=\"nsew\")\n self.function_pause.grid(row=4, column=0, sticky=\"nsew\")\n self.function_step_into.grid(row=3, column=1, sticky=\"nsew\")\n self.function_step_over.grid(row=4, column=1, sticky=\"nsew\")\n self.render.grid(row=3, column=2, sticky=\"nsew\")\n self.transparency_scale.grid(row=4, column=2, sticky=\"nsew\")\n self.transparency_scale.set(80)\n\n def set_grid_config():\n self.window.grid_columnconfigure(0, weight=1)\n self.window.grid_rowconfigure(1, weight=1)\n self.window.grid_columnconfigure(1, weight=1)\n self.window.grid_columnconfigure(2, weight=1)\n\n def switch_vision():\n if self.radio.get() == 0:\n self.keyword_tree.grid_forget()\n self.window.grid_rowconfigure(2, weight=0)\n self.mode = self.GENERAL_MODE\n elif self.radio.get() == 1:\n self.window.grid_rowconfigure(2, weight=1)\n make_keyword_tree_vision(self.window)\n self.mode = self.DETAILED_MODE\n self.keyword_tree.itemconfig(self.keyword_tree_index, bg=self.COVER)\n self.keyword_tree.see(self.keyword_tree_index)\n\n self.radio = tk.IntVar()\n tk.Radiobutton(self.window, text=\"General\", variable=self.radio, value=0, command=switch_vision).grid(row=0, column=0, columnspan=2)\n tk.Radiobutton(self.window, text=\"Detailed\", variable=self.radio, value=1, command=switch_vision).grid(row=0, column=1, columnspan=2)\n\n make_steps_vision(self.window)\n make_function(self.window)\n set_grid_config()\n if self.mode == self.DETAILED_MODE:\n self.radio.set(1)\n make_keyword_tree_vision(self.window)\n \n self.is_runner_ready = True\n\n def set_node_list(self, node):\n self._disabled_button(self.function_resume, self.function_pause, self.function_step_into, self.function_step_over)\n\n self.is_start_node = True\n self.node = node\n self.current_node_index, self.keyword_tree_index = 0, 0\n self.script.delete(0, 'end')\n\n for n in self.node:\n if n.keyword_type == \"setup\":\n self.script.insert('end', \" + \" + n.payload)\n elif n.keyword_type == \"teardown\":\n self.script.insert('end', \" - \" + n.payload)\n else:\n self.script.insert('end', n.payload)\n\n if self.node:\n self.script.itemconfig(self.current_node_index, bg=self.COVER)\n\n self._pause_ready()\n self.is_pause_ready = True\n\n if self.mode == self.DETAILED_MODE:\n if self.node:\n self.set_start_keyword_tree_vision()\n\n def set_start_keyword_tree_vision(self):\n self.keyword_tree.delete(0, 'end')\n self.keyword_tree.insert('end', self.node[self.current_node_index].payload)\n self.insert_tree_vision(self.node[self.current_node_index], self.keyword_tree)\n self.keyword_tree.itemconfig(0, bg=self.COVER)\n\n def next_node(self):\n self.current_node_index += 1\n self.keyword_tree_index = 0\n\n if self.current_node_index > 0:\n self.script.itemconfig(self.current_node_index-1, bg='')\n self.script.itemconfig(self.current_node_index, bg=self.COVER)\n self.script.see(self.current_node_index)\n\n if self.mode == self.DETAILED_MODE:\n self.set_start_keyword_tree_vision()\n\n if self.is_select(self.script, self.current_node_index):\n self.is_script_select = True\n\n if self.block_next_node:\n self.block_next_node = False\n self._pause_ready()\n self.is_pause_ready = True\n\n def next_keyword(self, name, args):\n def is_next(node, name, args):\n if node:\n return node.name == name and node.args == list(args)\n return False\n if not is_next(self.node[self.current_node_index].get_node_by_id(self.keyword_tree_index+1), name, args):\n return\n self.keyword_tree_index += 1\n if self.mode == self.DETAILED_MODE:\n if self.keyword_tree_index > 0:\n self.keyword_tree.itemconfig(self.keyword_tree_index-1, bg='')\n self.keyword_tree.itemconfig(self.keyword_tree_index, bg=self.COVER)\n self.keyword_tree.see(self.keyword_tree_index)\n if self.is_select(self.keyword_tree, self.keyword_tree_index):\n self.is_keyword_tree_select = True\n if self.block_next_keyword:\n self.block_next_keyword = False\n self._pause_ready()\n self.is_pause_ready = True\n\n def is_select(self, listbox, index):\n if index in listbox.curselection():\n self._pause_ready()\n self.is_pause_ready = True\n return True\n return False\n\n def clear_selection(self):\n if self.is_script_select:\n self.script.selection_clear(0, self.current_node_index)\n elif self.is_keyword_tree_select:\n if self.mode == self.DETAILED_MODE:\n self.keyword_tree.selection_clear(0, self.keyword_tree_index)\n\n def insert_tree_vision(self, node, vision, indentation=\" | \"):\n for child in node.children:\n vision.insert('end', indentation + child.payload)\n if len(child.children):\n self.insert_tree_vision(child, vision, indentation + \" | \")\n\n def function_pause_callback(self):\n self._pause_ready()\n self.is_pause_ready = True\n\n def function_resume_callback(self):\n self._pause_prepare()\n self.is_pause_ready = False\n\n def step_into_callback(self):\n if self.can_step and self.mode == self.DETAILED_MODE:\n self._pause_prepare()\n self.is_pause_ready = False\n self.block_next_keyword = True\n\n def step_over_callback(self):\n if self.can_step:\n self._pause_prepare()\n self.is_pause_ready = False\n self.block_next_node = True\n\n def render_callback(self):\n if not self.is_render_ready:\n self.is_render_ready = True\n self.render_text.set(\"Render / On\")\n else:\n self.is_render_ready = False\n self.render_text.set(\"Render / Off\")\n\n def update_transparency(self, event):\n self.window.attributes(\"-alpha\", (float)(self.transparency_scale.get())/100.0)\n\n def _enabled_button(self, *buttons):\n for button in buttons:\n button['state'] = tk.NORMAL\n\n def _disabled_button(self, *buttons):\n for button in buttons:\n button['state'] = tk.DISABLED\n\n def _pause_ready(self):\n self._disabled_button(self.function_pause)\n self._enabled_button(self.function_resume, self.function_step_into, self.function_step_over)\n\n def _pause_prepare(self):\n self._enabled_button(self.function_pause)\n self._disabled_button(self.function_resume, self.function_step_into, self.function_step_over)\n\n def on_fail(self):\n self.COVER = self.RED\n self.cover()\n\n def on_pass(self):\n self.COVER = self.GREEN\n self.cover()\n\n def cover(self):\n self.script.itemconfig(self.current_node_index, bg=self.COVER)\n if self.mode == self.DETAILED_MODE:\n self.keyword_tree.itemconfig(self.keyword_tree_index, bg=self.COVER)\n\nif __name__ == \"__main__\":\n gui_runner = gui_runner()\n n1 = node( \"keyword A\", \"\", [], ['a1', 'a2'], \"\" )\n n2 = node( \"keyword B\", \"\", [], ['b1', 'b2'], \"\" )\n n3 = node( \"keyword C\", \"\", [], ['c1', 'c2'], \"\" )\n\n node_list = [n1, n2, n3]\n gui_runner.run()\n while not gui_runner.is_runner_ready:\n pass\n gui_runner.set_node_list(node_list)","repo_name":"baodian123/robotframework-visualize","sub_path":"rfvisualize/gui_runner.py","file_name":"gui_runner.py","file_ext":"py","file_size_in_byte":10736,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"24687055835","text":"from tkinter import *\nimport socket\nimport atexit\nimport threading\nmsgrecv = \"\"\nroot = Tk()\nroot.title(\"client\")\ndef send():\n msg = entry.get()\n t.insert(END, \"Client: \" +msg + \"\\n\")\n s.sendall(msg.encode())\ndef handle_exit():\n print(\"This runs after keyboard interrupt\")\n s.close()\natexit.register(handle_exit)\n\n\nhost = 'localhost' #IP Address, do ipconfig, get IPV4\nport = 12345\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.connect((host, port))\n\nprint(\"connected\")\n\n\n\n\nt = Text(root, bg = \"black\", fg = \"red\", height=20, width=40)\nt.pack()\n\nentry = Entry(root, width = 53)\nentry.pack()\n\nsend_button = Button(root, text = \"Send\", height=1, width=45, command=send)\nsend_button.pack()\ndef get_data():\n global msgrecv\n while True:\n data = s.recv(1024)\n msgrecv = data.decode()\n t.insert(END, \"Server: \"+ msgrecv + \"\\n\")\nrecp = threading.Thread(target = get_data)\nrecp.start()\nroot.mainloop()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#Start Server then Client\n#Close Client then Server\n","repo_name":"kingaryaprince/pythoncode","sub_path":"Sockets/Full Duplex/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"69919726451","text":"from fastapi_users import FastAPIUsers\nfrom fastapi_users.authentication import BearerTransport, AuthenticationBackend\nfrom fastapi_users.authentication import JWTStrategy\nfrom ..auth.models import User\nfrom ..auth.manager import get_user_manager\n\nbearer_transport = BearerTransport(tokenUrl=\"auth/login\")\nSECRET = 'olimpiad_forever' # Поставить в файл .env\n\n\ndef get_jwt_strategy() -> JWTStrategy:\n return JWTStrategy(secret=SECRET, lifetime_seconds=72000)\n\nauth_backend = AuthenticationBackend(\n name=\"jwt\",\n transport=bearer_transport,\n get_strategy=get_jwt_strategy,\n)\n\nfastapi_users = FastAPIUsers[User, int](\n get_user_manager,\n [auth_backend],\n)\n\ncurrent_user = fastapi_users.current_user()\n","repo_name":"Chief-tain/AI_News_NaturaLP","sub_path":"backend/src/auth/base_config.py","file_name":"base_config.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"32995545762","text":"import sys\n\nfrom PyQt5.QtCore import QPoint\nfrom PyQt5.QtCore import Qt, QSize\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtWidgets import QHBoxLayout\nfrom PyQt5.QtWidgets import QVBoxLayout\nfrom PyQt5.QtWidgets import QLabel\nfrom PyQt5.QtWidgets import QGridLayout\nfrom PyQt5.QtWidgets import QTableWidget\nfrom PyQt5.QtWidgets import QPushButton\nfrom PyQt5.QtWidgets import QSizePolicy\nfrom PyQt5.QtWidgets import QWidget, QStyle, QStyleOption, QScrollArea\nfrom PyQt5.QtGui import QPixmap, QIcon, QFont, QCursor, QPainter\n\n# from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QHBoxLayout, QGroupBox, QDialog, QVBoxLayout, QGridLayout short\n\nimport img\n\nclass MainWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n\n self.inputRowCounter = 0\n\n self.buttonsRegistry = {\n \"fileGroupButtons\": {} # signature buttonsRegistry.fileGroupButtons[ inputRowCounter ][ btnName ] = btn\n }\n\n self.layout = QVBoxLayout()\n self.setStyleSheet(\"\"\"\n background-color: #FFFFFF;\n \"\"\")\n self.layout.addWidget(MyBar(self))\n self.layout.addWidget(TopContentBlock(self))\n self.layout.addWidget(ProgramTitleBar(self))\n self.layout.addWidget(BottomContentBlock(self))\n\n self.setLayout(self.layout)\n self.layout.setContentsMargins(0,0,0,74)\n self.layout.setSpacing(0)\n # self.layout.addStretch(-1)\n self.setMinimumSize(640,400)\n self.setWindowFlags(Qt.FramelessWindowHint)\n\n\n # self.pressing = False\n\n\nclass TopContentBlock(QWidget):\n\n def __init__(self, parent):\n super().__init__()\n self.test_1 = 200\n self.parent = parent\n self.layout = QVBoxLayout()\n self.layout.setContentsMargins(0, 0, 0, 0)\n\n # self.content = QWidget()\n self.setFixedSize(640, 270)\n self.setStyleSheet(\"\"\"\n background-color: #67BEC3;\n \"\"\")\n\n self.img = QLabel()\n self.img.setAlignment(Qt.AlignCenter)\n\n\n pixmap = QPixmap(':img/main_illustration.png')\n # self.img.setPixmap(pixmap)\n self.img.setPixmap(pixmap)\n print(pixmap.width(), pixmap.height())\n # self.img.resize(pixmap.width(), pixmap.height())\n #self.img.setFixedSize(pixmap.width(), pixmap.height())\n\n self.layout.addWidget(self.img)\n # self.layout.addWidget(self.content)\n self.setLayout(self.layout)\n\nclass ProgramTitleBar(QWidget):\n def __init__(self, parent):\n super().__init__()\n self.parent = parent\n self.layout = QVBoxLayout()\n self.layout.setAlignment(Qt.AlignCenter)\n self.layout.setContentsMargins(0, 70, 0, 30)\n\n self.title = QLabel(\"Business\")\n self.title.setAlignment(Qt.AlignCenter)\n self.title.setStyleSheet(\"\"\"\n font-weight: bold;\n font-family: Segoe UI;\n text-transform: uppercase;\n font-size: 40px;\n text-align: center;\n color: #303030;\n \"\"\")\n\n self.description = QLabel(\"select the programs you want run simultaneously\")\n self.description.setStyleSheet(\"\"\"\n font-size: 16px;\n color: rgba(0, 0, 0, 0.3);\n \"\"\")\n\n self.layout.addWidget(self.title)\n self.layout.addWidget(self.description)\n\n self.setLayout(self.layout)\n\nclass BottomContentBlock(QWidget):\n\n # base amount of the input rows. We can't delete a field if there are only 2 fields.\n inputRowsBaseAmount = 2\n # amount of the generated fields. Need to generate an id\n #inputRowCounter = 0\n\n inputRowPrefix = \"inputRow_\"\n btnAddPrefix = \"btnAddFile_\"\n btnRemoveFilePrefix = \"btnRemoveFile_\"\n btnMinHeight = 32\n # a left margin of the remove and add buttons\n BTN_LEFT_MARGIN = 15\n\n def __init__(self, parent):\n super().__init__()\n self.parent = parent\n # assign to the parent the createInputRow method\n self.parent.createInputRow = self.createInputRow\n self.parent.deleteInputRow = self.deleteInputRow\n print(parent)\n # base amount of the input rows. We can't delete a field if there are only 2 fields.\n #self.parent.inputRowsBaseAmount = 2\n # amount of the generated fields. Need to generate an id\n #self.parent.inputRowCounter = 0\n #self.parent.inputRowPrefix = \"inputRow_\"\n self.parent.btnAddPrefix = self.btnAddPrefix\n self.parent.btnRemoveFilePrefix = self.btnRemoveFilePrefix\n\n self.layout = QVBoxLayout()\n self.layout.setContentsMargins(86, 0, 56, 0)\n\n self.content = QWidget()\n\n\n # create a grid\n self.inputGrid = QGridLayout()\n\n #self.inputGrid.addLayout(self.createInputRow(), 0, 0)\n #self.inputGrid.addLayout(self.createInputRow(), 1, 0)\n # creating 2 base rows\n self.createInputRow()\n self.createInputRow()\n\n\n #self.inputGrid.addWidget(ActionPanel(self), 2, 0)\n\n\n # scrollArea.addWidget(self.inputGrid)\n\n self.content.setLayout(self.inputGrid)\n\n\n\n\n\n\n scrollArea = QScrollArea()\n scrollArea.setStyleSheet(\"\"\"\n QScrollArea {\n border: none;\n }\n QScrollBar:vertical {\n border: none;\n background: transparent;\n width: 4;\n margin: 0 0 0 0;\n height: 100%\n }\n QScrollBar::handle:vertical {\n background: #C4C4C4;\n min-height: 20px;\n border-radius: 2px;\n }\n QScrollBar::add-line:vertical {\n height: 0;\n }\n \n QScrollBar::sub-line:vertical {\n height: 0;\n \n }\n QScrollBar::up-arrow:vertical, QScrollBar::down-arrow:vertical {\n border: none;\n background: none;\n height: 0;\n }\n \n QScrollBar::add-page:vertical, QScrollBar::sub-page:vertical {\n background: none;\n }\n \"\"\")\n\n scrollArea.setWidget(self.content)\n scrollArea.setWidgetResizable(True)\n scrollArea.setFixedHeight(92)\n\n self.layout.addWidget(scrollArea)\n self.layout.addWidget(ActionPanel(parent))\n self.setLayout(self.layout)\n # self.content.setLayout(scrollArea)\n\n # create a table\n # self.inputTable = QTableWidget(2, 3)\n\n def createInputRow(self, withRemoveBtn=False):\n # create a row\n\n inputName = self.inputRowPrefix + str(self.parent.inputRowCounter)\n addBtnName = self.btnAddPrefix + str(self.parent.inputRowCounter)\n removeBtnName = self.btnRemoveFilePrefix + str(self.parent.inputRowCounter)\n # TODO: refactor it\n #inputName = self.inputRowPrefix\n #addBtnName = self.btnAddPrefix\n #removeBtnName = self.btnRemoveFilePrefix\n\n inputLayout = QHBoxLayout()\n inputLayout.setSpacing(self.BTN_LEFT_MARGIN)\n\n addBtn = QPushButton('Click to select a Program or a File')\n addBtn.setMinimumHeight(self.btnMinHeight)\n addBtn.setObjectName(addBtnName)\n addBtn.setCursor(Qt.PointingHandCursor)\n addBtn.setStyleSheet(\"\"\"\n text-align: left;\n padding-left: 15px;\n background-color: #EFF3F4;\n border: none;\n border-radius: 2px;\n \"\"\")\n\n inputLayout.addWidget(addBtn, 1)\n #print(self.parent.buttonsRegistry)\n self.parent.buttonsRegistry[\"fileGroupButtons\"][self.parent.inputRowCounter] = {addBtnName: addBtn}\n #print(self.parent.buttonsRegistry)\n\n if withRemoveBtn:\n removeBtn = QPushButton();\n removeBtn.setObjectName(removeBtnName)\n removeBtn.setMinimumHeight(self.btnMinHeight)\n removeBtn.setCursor(Qt.PointingHandCursor)\n removeBtn.setStyleSheet(\"\"\"\n width: 14px;\n border: none;\n background-color: transparent;\n \"\"\")\n\n icon = QIcon()\n icon.addPixmap(QPixmap(\":/img/close_ic_dark.png\"), QIcon.Normal, QIcon.Off)\n\n removeBtn.setIcon(icon)\n inputLayout.addWidget(removeBtn, 0)\n self.parent.buttonsRegistry[\"fileGroupButtons\"][self.parent.inputRowCounter][removeBtnName] = removeBtn;\n #self.parent.buttonsRegistry[self.parent.inputRowCounter][removeBtnName] = removeBtn\n #setattr(self, removeBtnName, removeBtn)\n\n\n\n\n\n # we need to add these objects like a class prop to provide access for them\n #setattr(self, addBtnName, addBtn)\n #setattr(self, inputName, inputLayout)\n\n #print(self.inputRowCounter);\n\n self.inputGrid.addLayout(inputLayout, self.parent.inputRowCounter, 0)\n\n # updating counter to achieve an unique id for the next row\n self.parent.inputRowCounter += 1\n\n return inputLayout\n\n def deleteInputRow(self, fieldId):\n print(\"input grid {}\".format(self.inputGrid.rowCount()))\n print(\"fieldId {}\".format(fieldId))\n print(\"fieldId {}\".format(fieldId))\n h_box_layout = self.inputGrid.itemAtPosition(int(fieldId), 0)\n\n add_btn_layout_item = h_box_layout.itemAt(0)\n remove_btn_layout_item = h_box_layout.itemAt(1)\n\n #h_box_layout.removeWidget(add_btn_layout_item.widget())\n #h_box_layout.removeWidget(remove_btn_layout_item.widget())\n\n # remove widgets\n add_btn_widget = add_btn_layout_item.widget()\n add_btn_widget.setParent(None)\n add_btn_widget.deleteLater()\n\n remove_btn_widget = remove_btn_layout_item.widget()\n remove_btn_widget.setParent(None)\n remove_btn_widget.deleteLater()\n\n # remove a layout from the grid row\n self.inputGrid.removeItem(h_box_layout)\n\n # decrement row counter is necessary because QGrid can't remove it's row|column\n # so after widget deletion we still have an 0-height empty row. So it means that new HBoxLayout\n # must be inserted into this empty row. We don't want to create a new one till we have the empty row\n self.parent.inputRowCounter -= 1\n print(\"input grid {}\".format(self.inputGrid.rowCount()))\n\n\nclass ActionPanel(QWidget):\n\n def __init__(self, parent):\n super().__init__()\n self.parent = parent # it's a MainWindow\n self.actionPanel = QVBoxLayout()\n\n self.actionPanel.setContentsMargins(0, 0, 15 + 15, 0)\n\n self.actionPanel.setObjectName(\"actionPanel\")\n self.addFileGroupBtn = QPushButton(\"add one more\")\n self.addFileGroupBtn.setObjectName(\"addFileGroup\")\n sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Maximum)\n sizePolicy.setHorizontalStretch(1)\n sizePolicy.setVerticalStretch(0)\n sizePolicy.setHeightForWidth(self.addFileGroupBtn.sizePolicy().hasHeightForWidth())\n self.addFileGroupBtn.setSizePolicy(sizePolicy)\n font = QFont()\n font.setFamily(\"Segoe UI\")\n font.setPointSize(14)\n font.setItalic(False)\n self.addFileGroupBtn.setFont(font)\n\n self.addFileGroupBtn.setCursor(QCursor(Qt.PointingHandCursor))\n self.addFileGroupBtn.setMouseTracking(False)\n self.addFileGroupBtn.setStyleSheet(\"\"\"\n background-color: #FFFFFF;\n color: #9FB9B9;\n border: 2px dashed #EFF3F4;\n margin-bottom: 34px;\n height: 32px;\n min-height: 32px;\n \"\"\")\n\n icon = QIcon()\n icon.addPixmap(QPixmap(\":/img/add_one_more.png\"), QIcon.Normal, QIcon.Off)\n\n self.addFileGroupBtn.setIcon(icon)\n self.addFileGroupBtn.setAutoExclusive(False)\n self.addFileGroupBtn.setObjectName(\"addFileGroupBtn\")\n\n self.savePresetBtn = QPushButton(\"Save preset\")\n self.savePresetBtn.setObjectName(\"savePresetBtn\")\n sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Maximum)\n sizePolicy.setHorizontalStretch(1)\n sizePolicy.setVerticalStretch(0)\n sizePolicy.setHeightForWidth(self.addFileGroupBtn.sizePolicy().hasHeightForWidth())\n self.savePresetBtn.setSizePolicy(sizePolicy)\n font = QFont()\n font.setFamily(\"Segoe UI\")\n font.setPointSize(16)\n font.setItalic(False)\n self.savePresetBtn.setFont(font)\n self.savePresetBtn.setCursor(QCursor(Qt.PointingHandCursor))\n self.savePresetBtn.setMouseTracking(False)\n self.savePresetBtn.setStyleSheet(\"\"\"\n background-color: #67BEC3; \n text-transform: uppercase; \n padding: 10px 6px; \n color: #FFFFFF; \n border: none;\n \"\"\")\n\n self.actionPanel.addWidget(self.addFileGroupBtn)\n self.actionPanel.addWidget(self.savePresetBtn)\n\n self.parent.buttonsRegistry[\"addFileGroupBtn\"] = self.addFileGroupBtn\n self.parent.buttonsRegistry[\"savePresetBtn\"] = self.savePresetBtn\n\n self.setLayout(self.actionPanel)\n\n\nclass MyBar(QWidget):\n\n\n def __init__(self, parent):\n super().__init__()\n self.setStyleSheet(\"\"\"\n background-color: #67BEC3;\n \"\"\")\n self.parent = parent\n self.layout = QHBoxLayout()\n self.layout.setContentsMargins(0,0,0,0)\n self.title = QLabel(\"Multi Launcher - Business\")\n\n btn_size = 32\n\n self.btn_min = QPushButton()\n self.btn_min.setCursor(Qt.PointingHandCursor)\n self.btn_min.clicked.connect(self.btn_min_clicked)\n self.btn_min.setFixedSize(btn_size, btn_size)\n self.btn_min.setStyleSheet(\"\"\"\n background: url(:/img/hide_ic.png) no-repeat center;\n border: none;\n color: red;\n \"\"\")\n\n self.btn_close = QPushButton()\n self.btn_close.setCursor(Qt.PointingHandCursor)\n self.btn_close.clicked.connect(self.btn_close_clicked)\n self.btn_close.setFixedSize(btn_size, btn_size)\n self.btn_close.setStyleSheet(\"\"\"\n background: url(:/img/close_ic.png) no-repeat center;\n border: none;\n \"\"\")\n\n # self.btn_max = QPushButton(\"+\")\n # self.btn_max.clicked.connect(self.btn_max_clicked)\n # self.btn_max.setFixedSize(btn_size, btn_size)\n # self.btn_max.setStyleSheet(\"background-color: gray;\")\n\n self.title.setFixedHeight(32)\n self.title.setAlignment(Qt.AlignLeft)\n self.title.setAlignment(Qt.AlignVCenter)\n self.layout.addWidget(self.title)\n self.layout.addWidget(self.btn_min)\n # self.layout.addWidget(self.btn_max)\n self.layout.addWidget(self.btn_close)\n\n self.title.setStyleSheet(\"\"\"\n padding-left: 22px;\n font-size: 16px;\n color: #FFFFFF;\n \"\"\")\n self.setLayout(self.layout)\n\n self.start = QPoint(0, 0)\n self.pressing = False\n\n def resizeEvent(self, QResizeEvent):\n super(MyBar, self).resizeEvent(QResizeEvent)\n self.title.setFixedWidth(self.parent.width())\n\n def mousePressEvent(self, event):\n self.start = self.mapToGlobal(event.pos())\n self.pressing = True\n\n def mouseMoveEvent(self, event):\n if self.pressing:\n self.end = self.mapToGlobal(event.pos())\n self.movement = self.end-self.start\n self.parent.setGeometry(self.mapToGlobal(self.movement).x(),\n self.mapToGlobal(self.movement).y(),\n self.parent.width(),\n self.parent.height())\n self.start = self.end\n\n def mouseReleaseEvent(self, QMouseEvent):\n self.pressing = False\n\n\n def btn_close_clicked(self):\n self.parent.close()\n\n def btn_max_clicked(self):\n self.parent.showMaximized()\n\n def btn_min_clicked(self):\n self.parent.showMinimized()\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n mw = MainWindow()\n mw.show()\n sys.exit(app.exec_())\n\n\n","repo_name":"velidan/multi-launcher","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":16352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"71550495411","text":"import pandas as pd\r\nimport numpy as np\r\n# import seaborn as sns\r\n# import matplotlib as plt\r\n\r\n# sns.pointplot('Sex', 'Survived', 'Pclass', data_train, palette={1: 'blue', 2: 'red', 3: 'green'})\r\n\r\n\r\ndef simplify_ages(df):\r\n \"\"\" 将Age分块 \"\"\"\r\n df.Age = df.Age.fillna(-0.5)\r\n bins = [-1, 0, 5, 12, 18, 25, 35, 60, 100]\r\n group_names = ['unknown', 'baby', 'child', 'Teenager', 'student', 'young', 'adult', 'senior']\r\n categeries = pd.cut(df.Age, bins, labels=group_names)\r\n df.Age = categeries\r\n return df\r\n\r\n\r\ndef simplify_cabins(df):\r\n \"\"\" 将Cabin取第一个字母，N代表无值 \"\"\"\r\n df.Cabin = df.Cabin.fillna('N')\r\n\r\n # 取Cabin的第一个字母\r\n df.Cabin = df.Cabin.apply(lambda x: x[0])\r\n\r\n return df\r\n\r\n\r\ndef simplify_fares(df):\r\n \"\"\" 对fare票价进行分块 \"\"\"\r\n df.Fare = df.Fare.fillna(-0.5)\r\n bins = [-1, 0, 8, 15, 32, 600]\r\n group_names = ['unknown', '1_qua', '2_qua', '3_qua', '4_qua']\r\n categeries = pd.cut(df.Fare, bins, labels=group_names)\r\n df.Fare = categeries\r\n return df\r\n\r\n\r\ndef format_names(df):\r\n \"\"\" 对name进行划分为两列 \"\"\"\r\n df['L_name'] = df.Name.apply(lambda x: x.split(' ')[0])\r\n df['Prefix_name'] = df.Name.apply(lambda x: x.split(' ')[1])\r\n return df\r\n\r\n\r\ndef drop_features(df):\r\n \"\"\" 丢弃无用特征 \"\"\"\r\n return df.drop(['Ticket', 'Name', 'Embarked'], axis=1)\r\n\r\n\r\ndef transform_features(df):\r\n \"\"\" 格式化特征 \"\"\"\r\n df = simplify_ages(df)\r\n df = simplify_cabins(df)\r\n df = simplify_fares(df)\r\n df = format_names(df)\r\n df = drop_features(df)\r\n return df\r\n\r\n\r\n# noinspection PyUnresolvedReferences\r\ndef encode_features(df_train, df_test):\r\n \"\"\" 将离散特征编码 \"\"\"\r\n from sklearn import preprocessing\r\n\r\n features = ['Fare', 'Cabin', 'Age', 'Sex', 'L_name', 'Prefix_name']\r\n df_combined = pd.concat([df_train, df_test])\r\n\r\n for feature in features:\r\n le = preprocessing.LabelEncoder()\r\n le = le.fit(df_combined[feature])\r\n df_train[feature] = le.transform(df_train[feature])\r\n df_test[feature] = le.transform(df_test[feature])\r\n\r\n return df_train, df_test\r\n\r\n\r\ndef run_kflod(clf, x_all, y_all):\r\n \"\"\" K折验证\r\n\r\n Args:\r\n clf: 分类器\r\n x_all: 训练集特征数据\r\n y_all: 训练集标签数据\r\n\r\n Returns: None\r\n\r\n \"\"\"\r\n from sklearn.model_selection import KFold\r\n\r\n kf = KFold(10)\r\n outcomes = list()\r\n fold = 0\r\n\r\n for train_index, test_index in kf.split(x_all):\r\n fold += 1\r\n x_train, x_test = x_all.values[train_index], x_all.values[test_index]\r\n y_train, y_test = y_all.values[train_index], y_all.values[test_index]\r\n\r\n clf.fit(x_train, y_train)\r\n predic = clf.predict(x_test)\r\n accuracy = accuracy_score(y_test, predic)\r\n outcomes.append(accuracy)\r\n print('Fold {0} accuracy: {1}'.format(fold, accuracy))\r\n\r\n mean_outcomes = np.mean(outcomes)\r\n print('Mean accuracy: {0}'.format(mean_outcomes))\r\n\r\n\r\ndef preprocessing():\r\n # 数据预处理 ########################################################################################################\r\n #\r\n file_path = 'd:/work/source/kaggle/titanic/'\r\n data_train = pd.read_csv(file_path + 'train.csv')\r\n data_test = pd.read_csv(file_path + 'test.csv')\r\n\r\n data_train = transform_features(data_train)\r\n data_test = transform_features(data_test)\r\n\r\n data_train, data_test = encode_features(data_train, data_test)\r\n\r\n # 将训练数据分成train+valid #########################################################################################\r\n #\r\n from sklearn.model_selection import train_test_split\r\n\r\n x_all = data_train.drop(['Survived', 'PassengerId'], axis=1)\r\n y_all = data_train['Survived']\r\n\r\n num_test = 0.2\r\n x_train, x_test, y_train, y_test = train_test_split(x_all, y_all, test_size=num_test, random_state=23)\r\n return x_train, x_test, y_train, y_test\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n # 数据预处理 ########################################################################################################\r\n #\r\n file_path = 'd:/work/source/kaggle/titanic/'\r\n data_train = pd.read_csv(file_path + 'train.csv')\r\n data_test = pd.read_csv(file_path + 'test.csv')\r\n\r\n data_train = transform_features(data_train)\r\n data_test = transform_features(data_test)\r\n\r\n data_train, data_test = encode_features(data_train, data_test)\r\n\r\n # 将训练数据分成train+valid #########################################################################################\r\n #\r\n from sklearn.model_selection import train_test_split\r\n\r\n x_all = data_train.drop(['Survived', 'PassengerId'], axis=1)\r\n y_all = data_train['Survived']\r\n\r\n num_test = 0.2\r\n x_train, x_test, y_train, y_test = train_test_split(x_all, y_all, test_size=num_test, random_state=23)\r\n\r\n # 训练 #############################################################################################################\r\n #\r\n from sklearn.ensemble import RandomForestClassifier\r\n from sklearn.metrics import make_scorer, accuracy_score\r\n from sklearn.model_selection import GridSearchCV\r\n\r\n clf = RandomForestClassifier()\r\n\r\n # 选择一些参数进行尝试\r\n paramters = {\r\n 'n_estimators': [4, 6, 9],\r\n 'max_features': ['log2', 'sqrt'],\r\n 'criterion': ['entropy', 'gini'],\r\n 'max_depth': [2, 3, 5, 10],\r\n 'min_samples_split': [2, 3, 5],\r\n 'min_samples_leaf': [1, 5, 8]\r\n }\r\n acc_scorer = make_scorer(accuracy_score)\r\n\r\n grid_obj = GridSearchCV(clf, paramters, scoring=acc_scorer)\r\n grid_obj = grid_obj.fit(x_train, y_train)\r\n clf = grid_obj.best_estimator_\r\n clf.fit(x_train, y_train)\r\n\r\n predic = clf.predict(x_test)\r\n print(accuracy_score(y_test, predic))\r\n\r\n # k折验证 ##########################################################################################################\r\n #\r\n run_kflod(clf, x_all, y_all)\r\n\r\n # 预测测试数据 ######################################################################################################\r\n #\r\n ids = data_test['PassengerId']\r\n predic = clf.predict(data_test.drop(['PassengerId'], axis=1))\r\n\r\n output = pd.DataFrame({'PassengerId': ids, 'Survived': predic})\r\n","repo_name":"deeplxx/titanic","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"16808438485","text":"import requests\nimport json\nimport traceback\nimport time\nfrom threading import Thread, current_thread\nfrom json import JSONDecodeError\n\nfrom datetime import datetime\n\nfrom tg_bot import message, send_text_message\n\n\ndef get_data(url):\n headers = {\n \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.69 Safari/537.36\"\n }\n\n req = requests.get(url, headers)\n res = json.loads(req.text)\n return res\n\n\ndef sort_workers(workers):\n global workers_notice_list\n text = False\n for worker in workers:\n if not worker['isSold']:\n if worker['nftData']['level'] == 2:\n text, workers_notice_list = worker_level_two(worker, text, workers_notice_list)\n if worker['nftData']['level'] == 3:\n text, workers_notice_list = worker_level_three(worker, text, workers_notice_list)\n if text:\n send_text_message(message, text)\n print(text, worker)\n text = False\n\n\ndef worker_level_two(worker, text, workers_notice_list):\n if 60 > worker['nftData']['minePower'] >= 50:\n if 11000000000000000 <= float(worker['price']) <= fifty_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 70 > worker['nftData']['minePower'] >= 60:\n if float(worker['price']) <= sixty_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 80 > worker['nftData']['minePower'] >= 70:\n if float(worker['price']) <= seventy_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 90 > worker['nftData']['minePower'] >= 80:\n if float(worker['price']) <= eighty_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 100 >= worker['nftData']['minePower'] >= 90:\n if float(worker['price']) <= ninety_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n return text, workers_notice_list\n\n\ndef worker_level_three(worker, text, workers_notice_list):\n if 110 > worker['nftData']['minePower'] >= 100:\n if float(worker['price']) <= hundred_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 120 > worker['nftData']['minePower'] >= 110:\n if float(worker['price']) <= hundred_ten_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 130 > worker['nftData']['minePower'] >= 120:\n if float(worker['price']) <= hundred_twenty_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 140 > worker['nftData']['minePower'] >= 130:\n if float(worker['price']) <= hundred_thirty_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n elif 150 >= worker['nftData']['minePower'] >= 140:\n if float(worker['price']) <= hundred_forty_mp * price_coeff:\n text, workers_notice_list = create_text(worker['nftData']['minePower'], float(worker['price']),\n worker['marketId'], worker['_id'], workers_notice_list)\n return text, workers_notice_list\n\n\ndef sort_spaceships(spaceships):\n global spaceships_notice_list\n text = False\n for spaceship in spaceships:\n if spaceship['nftData']['level'] == 3:\n if float(spaceship['price']) < 10000000000000000000:\n text, spaceships_notice_list = create_text(spaceship['nftData']['level'], float(spaceship['price']),\n spaceship['marketId'], spaceship['_id'], spaceships_notice_list)\n if text:\n send_text_message(message, text)\n print(text)\n text = False\n\n\ndef create_text(mine_power, price, market_id, id, notice_list):\n if [market_id, id] not in notice_list:\n text = f\"MP {mine_power}, price {price/1000000000000000000} ETL\"\n notice_list.append([market_id, id])\n return text, notice_list\n else:\n return False, notice_list\n\n\ndef search_workers(stream_number):\n errors = []\n while True:\n try:\n workers = get_data(\"https://api.cryptomines.app/api/workers\")\n sort_workers(workers)\n except JSONDecodeError:\n print(\"JSONDecodeError\")\n except:\n print(traceback.print_exc())\n if traceback not in errors:\n send_text_message(message, traceback.format_exc())\n errors.append(traceback)\n\n\ndef search_spaceships():\n errors = []\n while True:\n try:\n spaceships = get_data(f\"https://api.cryptomines.app/api/spaceships\")\n sort_spaceships(spaceships)\n except JSONDecodeError:\n print(\"JSONDecodeError\")\n except:\n print(traceback.print_exc())\n if traceback not in errors:\n send_text_message(message, traceback.format_exc())\n errors.append(traceback)\n\n\ndef search_fleets(ranks):\n errors = []\n while True:\n for rank in ranks:\n try:\n fleets = get_data(f\"https://api.cryptomines.app/api/fleets?rank={rank}\")\n sort_fleets(fleets, trading_fleets_list)\n except JSONDecodeError:\n print(\"JSONDecodeError\")\n except:\n print(traceback.print_exc())\n if traceback not in errors:\n send_text_message(message, traceback.format_exc())\n errors.append(traceback)\n\n\ndef sort_fleets(fleets, trading_fleets_list):\n global fleets_notice_list\n text = False\n for trading_fleet in trading_fleets_list:\n for fleet in fleets:\n if fleet['nftData']['rank'] == trading_fleet[0]\\\n and fleet['nftData']['minePower'] >= trading_fleet[1]\\\n and float(fleet['price']) < trading_fleet[2] * price_coeff:\n text, fleets_notice_list = create_text(fleet['nftData']['minePower'], float(fleet['price']),\n fleet['marketId'], fleet['_id'], fleets_notice_list)\n if text:\n send_text_message(message, text)\n print(text)\n text = False\n\n\ndef search_cheap_fleet(ranks):\n errors = []\n for rank in ranks:\n try:\n fleets = get_data(f\"https://api.cryptomines.app/api/fleets?rank={rank}\")\n cheapest_fleets = sort_cheapest_fleets(fleets, rank)\n cheapest_fleets_text = create_cheapest_fleets_text(cheapest_fleets)\n send_text_message(message, cheapest_fleets_text)\n except JSONDecodeError:\n print(\"JSONDecodeError\")\n except:\n print(traceback.print_exc())\n if traceback not in errors:\n # send_text_message(message, traceback.format_exc())\n errors.append(traceback)\n\n\ndef sort_cheapest_fleets(fleets, rank):\n cheapest_fleets_list = []\n cheapest_fleet = 9999000000000000000000\n for interval in range(200, 3500, 100):\n for fleet in fleets:\n if interval - 100 <= fleet['nftData']['minePower'] < interval:\n if cheapest_fleet > float(fleet['price']):\n cheapest_fleet = float(fleet['price'])\n if fleets[fleets.index(fleet)] == fleets[-1]:\n if cheapest_fleet != 9999000000000000000000:\n cheapest_fleets_list.append([rank, interval - 100, cheapest_fleet / 1000000000000000000])\n cheapest_fleet = 9999000000000000000000\n return cheapest_fleets_list\n\n\ndef create_cheapest_fleets_text(cheapest_fleets_list):\n rank = \"not found\"\n if cheapest_fleets_list[0][0] == '2':\n rank = \"C\"\n elif cheapest_fleets_list[0][0] == '3':\n rank = \"B\"\n elif cheapest_fleets_list[0][0] == '4':\n rank = \"A\"\n result_text = f\"Rank {rank}\\n\"\n for fleet in cheapest_fleets_list:\n result_text += f\"{fleet[1]} MP за {fleet[2]} ETL\\n\"\n return result_text\n\n\ndef search_page():\n fleets = get_data(f\"https://api.cryptomines.app/api/workers?level=2&cursor=0&limit=30000\")\n print(len(fleets))\n print(fleets[-1])\n for fleet in fleets:\n if (fleet['nftData']['minePower'] > 49 and float(fleet['price']) < fifty_mp * 1000000000000000001)\\\n or (fleet['nftData']['minePower'] > 59 and float(fleet['price']) < sixty_mp * 1000000000000000001)\\\n or (fleet['nftData']['minePower'] > 69 and float(fleet['price']) < seventy_mp * 1000000000000000001)\\\n or (fleet['nftData']['minePower'] > 79 and float(fleet['price']) < eighty_mp * 1000000000000000001)\\\n or (fleet['nftData']['minePower'] > 89 and float(fleet['price']) < ninety_mp * 1000000000000000001):\n print(f\"MP {fleet['nftData']['minePower']}, price {float(fleet['price'])/1000000000000000000} ETL на \"\n f\"{(int((fleets.index(fleet)-1)/8))+1} странице\")\n print(fleets.index(fleet))\n\n\nif __name__ == \"__main__\":\n price_coeff = 1000000000000000001\n fifty_mp = 0\n sixty_mp = 0\n seventy_mp = 0\n eighty_mp = 0\n ninety_mp = 0.02\n\n hundred_mp = 0\n hundred_ten_mp = 0\n hundred_twenty_mp = 0\n hundred_thirty_mp = 0\n hundred_forty_mp = 0\n\n trading_fleets_list = [[2, 1400, 2.2], [3, 1600, 3,5], [4, 1600, 10], [4, 3000, 2,2], [5, 3000, 17]]\n\n workers_notice_list = []\n fleets_notice_list = []\n spaceships_notice_list = []\n\n search_page()\n\n # fleet_ranks = [['2', '3', '4']]\n # op_cheap_fleet_1 = Thread(target=search_cheap_fleet, args=(fleet_ranks))\n # op_cheap_fleet_1.start()\n\n op_fleet_1 = Thread(target=search_fleets, args=(['2', '3', '4']))\n op_fleet_1.start()\n\n # op_spaceship_1 = Thread(target=search_spaceships)\n # op_spaceship_1.start()\n\n # time.sleep(0.4)\n # op_worker_1 = Thread(target=search_workers, args=('1'))\n # op_worker_1.start()\n # time.sleep(0.4)\n # op_worker_2 = Thread(target=search_workers, args=('2'))\n # op_worker_2.start()\n # time.sleep(0.4)\n # op_worker_3 = Thread(target=search_workers, args=('3'))\n # op_worker_3.start()\n # time.sleep(0.4)\n # op_worker_4 = Thread(target=search_workers, args=('4'))\n # op_worker_4.start()\n # time.sleep(0.4)\n # op_worker_5 = Thread(target=search_workers, args=('5'))\n # op_worker_5.start()\n","repo_name":"Dmitry254/CryptoMinesPublic","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"35329292923","text":"import time\n#for time.sleep command to pause between words\nimport os\n#for os.system command to clear the screen between words\n\n# to identify the terminals clear command\ncommand = 'clear'\nif os.name in ('nt', 'dos'): # If Machine is running on Windows, use cls\n command = 'cls'\n os.system(command)\n\nprint('-'*50)\nprint('Remember the Word')\nprint('-'*50)\n\nfilename = \"instructions.txt\"\nfilemode = \"r\" # read-only\nfile = open(filename, filemode)\ncontents = file.read()\nfile.close()\nprint(contents)\n\ninput(\"Press enter key to display the words\")\nos.system ('clear')\n\n#input allows user to input something before showing more things\n\nwords = ['orange', 'chair', 'mouse', 'sandwitch']\nfor word in words:\n print(word)\n time.sleep(2)\n os.system('clear')\n\n\nusers_guess = input('What word begins with the letter c?')\nif users_guess == 'chair':\n print('Congratulations, you are correct.')\nelse:\n print('Sorry, you entered', users_guess)\n print('The answer was chair.')\n\n","repo_name":"josephhdu/cmput-174","sub_path":"remember_the_wordv2.py","file_name":"remember_the_wordv2.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"41225565799","text":"\"\"\"\nhttps://www.hackerrank.com/challenges/electronics-shop/problem\n\"\"\"\n\n\ndef getMoneySpent(keyboards, drives, b):\n keyboards_sorted = sorted([k for k in keyboards if k <= b], reverse=True)\n drives_sorted = sorted([d for d in drives if d <= b], reverse=True)\n\n max_money_spent = -1\n for k in keyboards_sorted:\n for d in drives_sorted:\n if k + d < max_money_spent or k + d > b:\n continue\n else:\n max_money_spent = k + d\n\n return max_money_spent\n\n\nif __name__ == '__main__':\n bnm = input().split()\n b = int(bnm[0])\n n = int(bnm[1])\n m = int(bnm[2])\n\n keyboards = list(map(int, input().rstrip().split()))\n drives = list(map(int, input().rstrip().split()))\n\n moneySpent = getMoneySpent(keyboards, drives, b)\n\n print(moneySpent)\n","repo_name":"maheshhingane/HackerRank","sub_path":"Algorithms/Implementation/ElectronicsShop.py","file_name":"ElectronicsShop.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"70948662771","text":"\"\"\"\nA set of tests for energy minimization\n\"\"\"\n\nimport copy\nimport numpy as np\nimport json, os, shutil\nfrom . import addons\nimport geometric\nimport pytest\nimport itertools\nimport time\nimport subprocess\n\nlocalizer = addons.in_folder\ndatad = addons.datad\nexampled = addons.exampled\n\n@addons.using_psi4\ndef test_hcccn_minimize_psi4(localizer):\n \"\"\"\n Optimize a linear HCCCN molecule\n \"\"\"\n shutil.copy2(os.path.join(datad, 'hcccn.psi4in'), os.path.join(os.getcwd(), 'hcccn.psi4in'))\n progress = geometric.optimize.run_optimizer(engine='psi4', input='hcccn.psi4in', converge=['gmax', '1.0e-5'], \n nt=4, reset=False, trust=0.1, tmax=0.3)\n e_ref = -167.6136203991\n assert progress.qm_energies[-1] < (e_ref + 1e-5)\n # Check that the optimization converged in less than 10 steps\n assert len(progress) < 10\n\n@addons.using_quick\ndef test_water2_minimize_quick(localizer):\n \"\"\"\n Optimize a water dimer \n \"\"\"\n shutil.copy2(os.path.join(exampled, \"1-simple-examples\",\"water2_quick\",\"Water2.qkin\"), os.path.join(os.getcwd(), \"Water2.qkin\"))\n progress = geometric.optimize.run_optimizer(engine='quick', input='Water2.qkin', converge=['gmax', '1.0e-5'],\n nt=4, reset=False, trust=0.1, tmax=0.3)\n e_ref = -149.9412443430\n assert progress.qm_energies[-1] < (e_ref + 1e-5)\n # Check that the optimization converged in less than 25 steps (took 20 steps in LPW's local test.)\n assert len(progress) < 25\n\n@addons.using_quick\ndef test_water2_minimize_quick_converge_maxiter(localizer):\n \"\"\"\n Optimize a water dimer, but \"converge\" when maximum number of iterations (5) is reached.\n \"\"\"\n shutil.copy2(os.path.join(exampled, \"1-simple-examples\",\"water2_quick\",\"Water2.qkin\"), os.path.join(os.getcwd(), \"Water2.qkin\"))\n progress = geometric.optimize.run_optimizer(engine='quick', input='Water2.qkin', converge=['gmax', '1.0e-5', 'maxiter'],\n nt=4, reset=False, trust=0.1, tmax=0.3, maxiter=5)\n assert len(progress) == 6\n # e_ref = -149.9412443430\n # assert progress.qm_energies[-1] < (e_ref + 1e-5)\n # # Check that the optimization converged in less than 25 steps (took 20 steps in LPW's local test.)\n # assert len(progress) < 25\n","repo_name":"leeping/geomeTRIC","sub_path":"geometric/tests/test_minimize.py","file_name":"test_minimize.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","stars":118,"dataset":"github-code","pt":"20"}
+{"seq_id":"19712519533","text":"import os, sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as tck\nimport random\nimport math\nimport time\nimport h5py\n\ndef get_data(path):\n\tos.chdir(path)\n\tinfo = []\n\tw=[]\n\tprint('reading encoded data')\n\tfor file in os.listdir():\n\t\tinput=np.load(file)\n\t\ts,t,d=np.shape(input)\n\t\tif d==4:\n\t\t\trest=np.ones((128,8))*-55\n\t\t\tfor n in range(t):\n\t\t\t\trest[n][:4]=input[0][n]\n\t\t\tw.append(rest)\n\t\telse:\n\t\t\tw.append(input[0])\n\t\t#w=torch.load(file)\n\t\t#w=w.detach().cpu().numpy()\n\t\t#w=np.transpose(w[0])\n\t\tinfo.append(w)\n\t\tw=[]\n\treturn info\n\n\ndef norm_matrix(datain):\n ns,_,_,_=np.shape(datain)\n scaled_in=np.copy(datain)\n #scaled_out=np.copy(dataout)\n #data=np.append(datain,dataout,axis=0)\n data=datain\n s,n,t,d=np.shape(data)\n print('dimensions',s,' ',n,' ',t,' ',d)\n max_now=0\n min_now=0\n for m in range(s):\n max_past=data[m][0].max()\n min_past=data[m][0].min()\n if max_past>max_now:\n max_now=max_past\n if min_pastmax_now:\n max_now=max_past\n if min_pastMSE2: MSE=MSE2\n else: MSE=MSE1\n TEX[j][0]=TEX[j][0]+MSE\n\n else:\n MSE = np.square(np.subtract(inp[m][j],obj[m][j])).mean()\n TEX[j][0]=TEX[j][0]+MSE\n \n TEX=TEX/ns\n return TEX\n\ndef norm_from_spine(dat0,dat1,dat2,dat3,dat4):\n refx0=dat0[2][0]\n refy0=dat0[2][1]\n refx1=dat1[2][0]\n refy1=dat1[2][1]\n refx2=dat2[2][0]\n refy2=dat2[2][1]\n refx3=dat3[2][0]\n refy3=dat3[2][1]\n refx4=dat4[2][0]\n refy4=dat4[2][1]\n \n joints,xyc=np.shape(dat0)\n print('joints',joints,'xy',xyc)\n \n for ns in range(joints):\n dat0[ns][0]=dat0[ns][0]-refx0\n dat0[ns][1]=dat0[ns][1]-refy0\n dat1[ns][0]=dat1[ns][0]-refx1\n dat1[ns][1]=dat1[ns][1]-refy1\n dat2[ns][0]=dat2[ns][0]-refx2\n dat2[ns][1]=dat2[ns][1]-refy2\n dat3[ns][0]=dat3[ns][0]-refx3\n dat3[ns][1]=dat3[ns][1]-refy3\n dat4[ns][0]=dat4[ns][0]-refx4\n dat4[ns][1]=dat4[ns][1]-refy4\n\n x0,y0=x_y_one(dat0)\n x1,y1=x_y_one(dat1)\n x2,y2=x_y_one(dat2)\n x3,y3=x_y_one(dat3)\n x4,y4=x_y_one(dat4)\n print(x0)\n return x0,y0,x1,y1,x2,y2,x3,y3,x4,y4\n\n\ndef norm_from_spine2(dat0,dat1):\n refx0=dat0[2][0]\n refy0=dat0[2][1]\n refx1=dat1[2][0]\n refy1=dat1[2][1]\n \n joints,xyc=np.shape(dat0)\n print('joints',joints,'xy',xyc)\n \n for ns in range(joints):\n dat0[ns][0]=dat0[ns][0]-refx0\n dat0[ns][1]=dat0[ns][1]-refy0\n dat1[ns][0]=dat1[ns][0]-refx1\n dat1[ns][1]=dat1[ns][1]-refy1\n\n x0,y0=x_y_one(dat0)\n x1,y1=x_y_one(dat1)\n\n print(x0)\n return x0,y0,x1,y1\n\n\n\n","repo_name":"Chr1sus/Predictor-of-human-posture-with-2D-data","sub_path":"ops/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"22379501026","text":"import sys\n\ninputFile = sys.argv[1]\noutputFile = sys.argv[2]\n\ndef convToFlp():\n print(\"Converting .bin to .flp...\\n\")\n with open(inputFile, 'rb') as infile:\n binData = infile.read()\n with open(outputFile, \"wb\") as outfile:\n print(outfile.write(binData))\n print(\"Done converting.\")\n\nconvToFlp()\n \n","repo_name":"masen-f/maseOS-lite","sub_path":"to_floppy.py","file_name":"to_floppy.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"11432099665","text":"#!/usr/bin/env python3\r\n\r\n__author__ = \"tarik0\"\r\n\r\nimport pickle\r\nfrom datetime import datetime\r\nfrom json import loads\r\nfrom random import uniform\r\nfrom threading import Event, Thread\r\nfrom time import sleep\r\n\r\nfrom requests import Session, Response\r\n\r\n# You can get it from ex. https://subdomain.almscloud.com/Activity/Index/COURSE_ID\r\nCOURSE_ID = \"\"\r\n# Your ALMS username.\r\nUSERNAME = \"\"\r\n# Your ALMS password.\r\nPASSWORD = \"\"\r\n# Your ALMS host.\r\nALMS_HOST = \"subdomain.almscloud.com\"\r\n\r\n\r\ndef iso_format(dt):\r\n \"\"\" Convert datetime object to Javascript like ISO date string. \"\"\"\r\n try:\r\n utc = dt + dt.utcoffset()\r\n except TypeError as e:\r\n utc = dt\r\n isostring = datetime.strftime(utc, '%Y-%m-%dT%H:%M:%S.{0}Z')\r\n return isostring.format(int(round(utc.microsecond / 1000.0)))\r\n\r\n\r\nclass ALMSClient:\r\n \"\"\"\r\n ALMSClient:\r\n A HTTP wrapper to communicate with the ALMS.\r\n \"\"\"\r\n\r\n PING_INTERVAL_SEC = 5\r\n\r\n def __init__(self, username: str, password: str):\r\n \"\"\" Construct the class. \"\"\"\r\n self.__username = username\r\n self.__password = password\r\n self.__disposed = False\r\n self.__first_ping_event = Event()\r\n self.__auth_event = Event()\r\n self.__ping_timer = Thread(target=self.__ping_interval, args=())\r\n self.__session = Session()\r\n self.__session.headers = {\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \"\r\n \"Chrome/89.0.4389.90 Safari/537.36 \",\r\n \"sec-ch-ua\": '\"Chromium\";v=\"89\", \";Not A Brand\";v=\"99\"',\r\n \"sec-ch-ua-mobile\": \"?0\",\r\n \"Upgrade-Insecure-Requests\": \"1\",\r\n \"Connection\": \"close\"\r\n }\r\n\r\n def login(self, timeout=15):\r\n \"\"\" Login to the ALMS. \"\"\"\r\n # Get necessary cookies.\r\n res = self.__session.get(f\"https://{ALMS_HOST}/Account/LoginBefore\")\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n # Start the ping interval and wait for the first ping to succeed.\r\n self.__ping_timer.start()\r\n self.__first_ping_event.wait(timeout)\r\n\r\n # Enter username.\r\n res = self.__session.post(\r\n f\"https://{ALMS_HOST}/Account/LoginBefore?returnUrl=%2FHome%2FIndex\",\r\n data={\r\n \"LocationName\": \"\",\r\n \"Latitude\": \"\",\r\n \"Longitude\": \"\",\r\n \"LoginChannel\": \"\",\r\n \"TimeZoneOffset\": -180, # This is hardcoded on the website too.\r\n \"UserName\": self.__username\r\n }\r\n )\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n # Enter password.\r\n self.__session.cookies.set(\"CookUserName\", self.__username)\r\n res = self.__session.post(\r\n f\"https://{ALMS_HOST}/?returnUrl=%2FHome%2FIndex\",\r\n data={\r\n \"LocationName\": \"\",\r\n \"Latitude\": \"\",\r\n \"Longitude\": \"\",\r\n \"LoginChannel\": \"\",\r\n \"TimeZoneOffset\": -180, # This is hardcoded on the website too.\r\n \"UserName\": \"\",\r\n \"Password\": self.__password\r\n }\r\n )\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n self.__auth_event.set()\r\n self.save_to_file()\r\n\r\n def login_via_cache(self):\r\n \"\"\" Login with the previous saved cache. \"\"\"\r\n with open(f\"{self.__username}.cache\", \"rb\") as f:\r\n self.__session = pickle.load(f)\r\n\r\n def save_to_file(self):\r\n \"\"\" Save session to a file to re-use it without authenticating. \"\"\"\r\n with open(f\"{self.__username}.cache\", \"wb\") as f:\r\n pickle.dump(self.__session, f)\r\n\r\n def get_class_progress(self, course_id: str) -> dict:\r\n \"\"\" Get class progresses. \"\"\"\r\n res = self.__session.get(f\"https://{ALMS_HOST}/Activity/Index/{course_id}\")\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n tmp = loads(res.text.split(\"var datasource = \")[1].split(\"};\")[0] + \"}\")\r\n return tmp\r\n\r\n def get_flow_data(self, activity_id: str, attempt_id: str) -> dict:\r\n \"\"\" Get flow player data. \"\"\"\r\n res = self.__session.post(\r\n f\"https://{ALMS_HOST}/Video/ManageInteraction?id={activity_id}&attemptId={attempt_id}\",\r\n data={\r\n \"/Video/ManageInteraction?id\": activity_id,\r\n \"attemptId\": attempt_id\r\n }\r\n )\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n return res.json()\r\n\r\n def submit_track(self, activity_id: str, enroll_id: str):\r\n \"\"\" Simulate that we have finished the activity. \"\"\"\r\n # Get attempt id from the page.\r\n res = self.__session.get(\r\n f\"https://{ALMS_HOST}/Video/Play\",\r\n data={\r\n \"id\": activity_id,\r\n \"eId\": enroll_id,\r\n \"isPartialView\": True\r\n }\r\n )\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n # Parse the output and get attempt id.\r\n attempt_id = res.text.split(\"attemptId=\")[1].split(\"',\")[0]\r\n\r\n # Get video data.\r\n flow_data = self.get_flow_data(activity_id, attempt_id)\r\n video_duration = flow_data[\"Meta\"][\"Duration\"]\r\n\r\n # That means we don't have a video to watch.\r\n if video_duration == 0:\r\n return \"Derse video yüklenmemiş!\"\r\n\r\n # Send tracking information.\r\n date = iso_format(datetime.utcnow())\r\n res = self.__session.post(\r\n f\"https://{ALMS_HOST}/Video/SaveTracking?TrackingData=&id={activity_id}&AttemptId={attempt_id}&EnrollId={enroll_id}\",\r\n data={\r\n \"date\": date,\r\n \"duration\": video_duration,\r\n \"response\": None,\r\n \"totalPartSec\": video_duration,\r\n \"viewParts\": f\"[\\\"0-{video_duration}\\\"]\"\r\n }\r\n )\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n return res.text\r\n\r\n def dispose(self):\r\n \"\"\" Close the ping interval and dispose the class. \"\"\"\r\n if self.__first_ping_event.is_set():\r\n self.__disposed = True\r\n self.__ping_timer.join()\r\n\r\n def __ping_interval(self):\r\n \"\"\" We gotta send a ping request per 5 seconds. \"\"\"\r\n while not self.__disposed:\r\n self.__send_ping()\r\n if not self.__first_ping_event.is_set():\r\n self.__first_ping_event.set()\r\n sleep(ALMSClient.PING_INTERVAL_SEC)\r\n if not self.__auth_event.is_set():\r\n self.__auth_event.wait()\r\n\r\n def __send_ping(self) -> Response:\r\n \"\"\" Send ping request. \"\"\"\r\n tmp = uniform(0, 1)\r\n res = self.__session.get(f\"https://{ALMS_HOST}/System/Ping?r={tmp}\")\r\n if res.status_code != 200:\r\n res.raise_for_status()\r\n\r\n return res\r\n\r\n\r\nif __name__ == '__main__':\r\n # Get inputs.\r\n COURSE_ID = input(\"Kurs ID'si: \")\r\n USERNAME = input(\"ALMS Kullanıcı Adınız: \")\r\n PASSWORD = input(\"ALMS Şifreniz: \")\r\n\r\n # Authenticate with ALMS\r\n print(f\"{USERNAME} olarak ALMS'ye giriş yapılyor...\")\r\n alms = ALMSClient(USERNAME, PASSWORD)\r\n alms.login()\r\n\r\n print(\"Başarıyla giriş yapıldı! Ders bilgileri alınıyor...\")\r\n\r\n unfinished_lessons = []\r\n finished_lessons = []\r\n\r\n # Get lesson counts.\r\n progresses = alms.get_class_progress(COURSE_ID)\r\n\r\n k = progresses[\"activities\"]\r\n for activity in progresses[\"activities\"]:\r\n # Skip non-video lessons.\r\n if not (\"status\" in activity) or \\\r\n not (\"type\" in activity) or \\\r\n not activity[\"isActive\"] or \\\r\n activity[\"type\"] != \"Video\" or \\\r\n activity[\"completionType\"] != \"View\":\r\n continue\r\n\r\n # Check progress\r\n if activity[\"status\"][\"progress\"] >= 90:\r\n finished_lessons.append(activity)\r\n else:\r\n unfinished_lessons.append(activity)\r\n\r\n print(\"Programın çalışması için derslerin web sayfalarını önceden görüntülemeniz lazımdır!\")\r\n\r\n # Print lesson statuses.\r\n print(\"\\nDers bilgileri alındı;\")\r\n print(f\" Bitirilmiş ders sayısı: {len(finished_lessons)}\")\r\n print(f\" Bitirilmemiş ders sayısı: {len(unfinished_lessons)}\\n\")\r\n\r\n # Print lessons\r\n print(\"Bitirilmiş dersler;\")\r\n for activity in finished_lessons:\r\n print(f\" {activity['addedDate']} | {activity['UserName']} - {activity['name']}\")\r\n\r\n print(\"\\nBitirilmemiş dersler;\")\r\n for activity in unfinished_lessons:\r\n print(f\" {activity['addedDate']} | {activity['UserName']} - {activity['name']}\")\r\n\r\n # Finish lessons\r\n print(\"\\nDersler bitiriliyor;\")\r\n for activity in unfinished_lessons:\r\n enroll_id = activity[\"enrollmentId\"]\r\n activity_id = activity[\"id\"]\r\n result = alms.submit_track(activity_id, enroll_id)\r\n print(f\" Bitirildi - {activity['addedDate']} | {activity['UserName']} - {activity['name']}: {result}\")\r\n sleep(1)\r\n\r\n print(\"\\nBütün dersler bitirildi!\")\r\n alms.dispose()\r\n","repo_name":"tarik0/almsbot","sub_path":"alms.py","file_name":"alms.py","file_ext":"py","file_size_in_byte":9312,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"24889930083","text":"#1. По двум заданным числам проверить является ли одно квадратом второго\n#print('Введите первое число')\n#a = int(input())\n#print('Введите второе число')\n#b = int(input())\n#if a == b**2:\n # print(f'Число {a} является квадратом числа {b}')\n#elif b == a**2:\n# print(f'Число {b} является квадратом числа {a}')\n#else:\n # print (f'Числа {a} и {b} не являются квадратом друг друга')\n\n#2. Найти максимальное из пяти чисел\n#print(max([int(input()) for _ in range(int(input()))]))\n#list1 = [3, 2, 8, 5, 10]\n#max_number = max(list1)\n#print(\"Наибольшее число:\", max_number)\n\n#3.Вывести на экран числа от -N до N\n#print('Введите число')\n#N = int(input())\n#for i in range(-N,N+1):\n # print(i)\n\n#4.Показать первую цифру дробной части числа\n#print('Введите число')\n#N = float(input())\n#d = float((N*10) % 10)\n#print(d)\n\n#from random import uniform\n#def get_frst_fract_digit (n):\n# return int(n * 10 % 10)\n#n = uniform(10,1000)\n#print(n)\n#print(get_frst_fract_digit(n))\n\n#5.Дано число. Проверить кратно ли оно 5 и 10 или 15 но не 30\n#n = int(input('Введите число: '))\n#def check_mult(n):\n# return (n % 5 == 0 or n % 10 == 0 or n % 15 == 0) and n % 30 !=0\n#print(check_mult(n))\n\n#6.Дано число обозначающее день недели.\n# Вывести его название и указать является ли он выходным.\n\n#from calendar import weekday\n#from datetime import datetime\n\n\n#week_days = ('Понедельник', 'Вторник', 'Среда', 'Четверг',\n# 'Пятница', 'Суббота', 'Воскресенье')\n#number = int(input('Введите номер дня недели: '))\n\n\n#def Find_day(list, num):\n#\n# if 0 < num < 6:\n# return week_days[num-1]\n# elif 5 < num < 7:\n# return f'{week_days[num-1]}, выходной день'\n\n#day_name = Find_day(week_days, number)\n#print(day_name)\n\n# 7. Проверить истинность утверждения ¬(X ⋁ Y ⋁ Z) = ¬X ⋀ ¬Y ⋀ ¬Z для всех значений предикат\n\n#x = 0\n#y = 1\n#left = not (x or y)\n#right = not x and not y\n#statement = left == right\n#print (statement)\n\n\n# 8. Сообщить в какой четверти координатной плоскости\n# или на какой оси находится точка с координатами Х и У\n\n#from random import randint\n\n#x = randint (-100, 100)\n#y = randint (-100, 100)\n\n#def get_quarter_number (x, y):\n# if x != 0 and y != 0:\n# if x * y > 0:\n# if x > 0: return 1\n# else: return 3\n# else:\n# if x < 0: return 2\n# else: return 4\n# else:\n# if x == 0: return 'OY'\n# else: return 'OX'\n\n#print (f'Точка: {x, y}')\n#print (f'Четверть: {get_quarter_number(x, y)}')\n\n\n\n# 9. Указав номер четверти прямоугольной системы координат,\n# показать допустимые значения координат для точек этой четверти\n\n#from random import randint\n\n#quarter = randint(1, 4)\n#diapasones = ['(x > 0, y > 0)', '(x < 0, y > 0)', '(x < 0, y < 0)', '(x > 0, y < 0)']\n\n#def show_diapasone (quart, diapasones):\n# return diapasones[quart-1]\n\n#print (f'{quarter}: {diapasones[quarter-1]}')\n\n# 10. Найти расстояние между двумя точками пространства\n\n#from random import randint\n#import math\n\n#def Get_coordinates(num_plan, left, right): # координаты точек\n# return tuple([randint(left, right) for i in range(num_plan)])\n\n#def Find_dist(a, b): # расстояние между точками\n# return round(math.sqrt(sum((x - y)**2 for x, y in zip(a, b))), 3)\n\n#num_plan = 3 # количество осей координат\n#left = -10\n#right = 10\n\n#point_A = Get_coordinates(num_plan, left, right)\n#point_B = Get_coordinates(num_plan,left, right)\n\n#print(f'A {point_A}, B {point_B}')\n#print (f'Расстояние между точками: {Find_dist(point_A, point_B)}')\n\nstr = input(\"Enter first message: \")\nstr2 = input(\"Enter second message: \")\n\nprint(str2.count(str))\nprint(str.count(str2))\n\n#Напишите алгоритм, который берет массив и перемещает все нули в конец,\n# сохраняя порядок других элементов.\n\nif i.isdigit():\n print(f'Все окей, число есть {i}')\n exit()\n\n\nspisok_1 = [\"йцу\", \"фыв\", \"ячс\", \"цук\", \"йцукен\", \"йцу\"]\nstroka_1 = \"йцу\"\ncount = 0\nprint(spisok_1.count(stroka_1))\nif spisok_1.count(stroka_1) <2:\n count = -1\nelse:\n for i in range(len(spisok_1)):\n if spisok_1[i] == stroka_1:\n count+=1\n if count ==2:\n count = i\n break\nprint(f'Искомое {stroka_1}, ответ {count}')\n\n\n\n\nspisok_1 = [1, 654, 45, 85, 9, 6, 4, 53, 4]\nsum = 0\nfor i in range(1, len(spisok_1), 2):\n sum += spisok_1[i]\nprint(sum)\n\n\nfloat_spisok = [1.1, 1.2, 3.1, 5.002, 10.000001, 11.13, 10.11, 122324.52, 0.14]\n# i=0\n# while float_spisok[i]>1:\nmax = 0.000\nmin = 1.000\nfor i in range(len(float_spisok)):\n float_spisok[i]=round(float_spisok[i]-int(float_spisok[i]),10)\n if maxfloat_spisok[i]:\n min = float_spisok[i]\nx=max-min\n# print(float_spisok)\n# print(max(float_spisok))\nprint(max,min)\nprint(type(x))\nprint(x)\n","repo_name":"VladimirZherdochkin/python","sub_path":"tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":5829,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"18226254092","text":"from django.conf.urls import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'LivinWater.views.home', name='home'),\n # url(r'^LivinWater/', include('LivinWater.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n\turl(r'^admin/', include(admin.site.urls)),\n\turl(r'^$', 'LivinWater.views.index'),\n\turl(r'^home$', 'LivinWater.views.index'),\n\turl(r'^registration$', 'LivinWater.views.registration'),\n\turl(r'^login$', 'LivinWater.views.login'),\n\turl(r'^logout$', 'LivinWater.views.logout'),\n\turl(r'^auth$', 'LivinWater.views.authorization'),\n\turl(r'^signup$', 'LivinWater.views.signup'),\n\turl(r'^manage$', 'LivinWater.views.manage'),\n\turl(r'^manage/users$', 'LivinWater.views.user_list'),\n\turl(r'^manage/users/(?P\\d+)/$', 'LivinWater.views.user_info_by_id'),\n\turl(r'^manage/groups$', 'LivinWater.views.groups_list'),\n\turl(r'^manage/groups/add$', 'LivinWater.views.group_add'),\n\turl(r'^manage/add_group$', 'LivinWater.views.add_group'),\n\turl(r'^news/', include('news.urls')),\n\turl(r'^user/info/$', 'LivinWater.views.user_info'),\n\turl(r'^user/edit$', 'LivinWater.views.user_edit'),\n)\n\nurlpatterns += patterns('',\n\t(r'^media/(?P.*)$', 'django.views.static.serve', {'document_root':'./media/'}),\n)","repo_name":"Saithiril/livingwater","sub_path":"LivinWater/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"35166781954","text":"from Bio import SeqIO\nimport burrows_wheeler as bw\n\n\ndef search_fasta_file(file,pattern,p,q,N):\n '''\n This function search for a pattern in fasta input file, N serves as upper bound for\n size of text that is being searched for at once, because this algorithm uses\n O(n^2) space.\n :param file: fasta file in which we search for pattern\n :param pattern: pattern that is searched\n :param p: tally factor for algorithm\n :param q: suffix array factor for algorithm\n :param N: the upper limit for chunk of string that is searched at once\n :return: list of list of pattern positions , in all reads in a input file\n '''\n positions_list=[]\n for record in SeqIO.parse(file, \"fasta\"):\n text=str(record.seq).replace(\"\\n\",\"\")\n\n if len(text) < N:\n positions_list.append(bw.burrows_wheeler(text,pattern,(p,q)))\n else:\n positions_list.append(search_large_text(text,pattern,p,q,N))\n return positions_list\n\n\ndef search_large_text(text,pattern,p,q,N):\n '''\n This is just a wrapper for burrows-wheeler algorithm, it uses upper bound for size of text\n that is searched at once because burrows-wheleer has O(n^2) space consumption.\n :param text: input text\n :param pattern: pattern that is searched\n :param p: tally matrix factor\n :param q: suffix array factor\n :param N: the upper limit for chunk of string that is searched at once\n :return: list of positions of pattern in text\n '''\n pattern_len=len(pattern)\n text_len=len(text)\n positions=[]\n begin_index=0\n end_index=N\n while end_index < text_len:\n positions.extend(bw.burrows_wheeler(text[begin_index:end_index],pattern,(p,q)))\n begin_index=end_index-pattern_len+1\n end_index=begin_index+N\n\n\n if begin_index < text_len and (text_len-begin_index) > pattern_len:\n positions.extend(bw.burrows_wheeler(text[begin_index:],pattern,(p,q)))\n\n return positions\n\n\n","repo_name":"nikojov/Burrows-Wheeler","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"72049778929","text":"import requests\nimport json\nimport os\nfrom html.parser import HTMLParser\n\n_url = \"http://d.defold.com/stable/\"\n_latest = \"http://d.defold.com/stable/info.json\"\n_beta = \"http://d.defold.com/beta/info.json\"\nversion_json = os.path.join(os.path.expanduser(\"~\"), \".builder\", \"cache\", \"version.json\")\n\n\nclass _UpdateVersionJSON(HTMLParser):\n def handle_starttag(self, tag, attrs):\n pass\n\n def handle_endtag(self, tag):\n pass\n\n def handle_data(self, raw_data):\n if \"var model\" in raw_data[:100]:\n new = {\"versions\": []}\n data = raw_data[raw_data.find(\"{\"):]\n data = data[:data.find(r\"\\n\")]\n json_data = json.loads(data)\n for x in json_data[\"releases\"]:\n entry = {\"version\":x[\"tag\"], \"sha1\":x[\"sha1\"]}\n new[\"versions\"].append(entry)\n with open(version_json, \"w\") as fp:\n json.dump(new, fp, indent=4, sort_keys=True)\n\n\ndef update():\n response = requests.get(_url)\n if response.status_code in [200]:\n parser = _UpdateVersionJSON()\n parser.feed(str(response.content))\n\n\ndef latest():\n response = requests.get(_latest)\n if response.status_code in [200]:\n return response.json()[\"version\"]\n\n\ndef update_required():\n if os.path.exists(version_json):\n new_version = latest()\n with open(version_json, \"r\") as fp:\n version_data = json.load(fp)\n\n if new_version not in version_data:\n return True\n return False\n\n\ndef beta():\n beta_info = requests.get(_beta).json()\n return beta_info[\"sha1\"], beta_info[\"version\"]\n\n\ndef get(auto_update=False):\n version_data = {}\n if os.path.exists(version_json):\n with open(version_json, \"r\") as fp:\n version_data = json.load(fp)\n elif auto_update:\n update()\n return get()\n return version_data","repo_name":"Jerakin/DefBuild","sub_path":"defbuild/versions.py","file_name":"versions.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"18188526937","text":"import subprocess\nimport sys, os\n# scrapy crawl my_scraper -o data-science.jl -a tag='data-science' -a date='2018/01/01'\ndef init():\n if not os.path.exists('/home/ubuntu/temp'):\n subprocess.call([\"mkdir\", \"temp\"])\n\nif __name__ == \"__main__\":\n #for i in range(31):\n # subprocess.call([\"cd\", \"mediumScraper\"])\n init()\n tag = sys.argv[1]\n print(\"Start scraping tag {}\".format(tag))\n\n filepath = \"temp/{}.jl\".format(tag)\n tag_arg = \"tag={}\".format(tag)\n month_days = dict({1:31, 2:28, 3:31, 4:30, 5:31, 6:30, 7:30, 8:24})\n\n for month in range(1, 9):\n for day in range(1, month_days[month]+1):\n if day < 10:\n day = \"0\" + str(day)\n else:\n day = str(day)\n print(\"start crawling 2018/0{month}/{day}\".format(month=month, day=day))\n date_arg = \"date=2018/0{month}/{day}\".format(month=month, day=day)\n subprocess.call([\"scrapy\", \"crawl\", \"my_scraper\", \"-o\", filepath, \"-a\", tag_arg, \"-a\", date_arg])\n\n\n subprocess.call([\"aws\", \"s3\", \"mv\", filepath, \"s3://mediumscraper/{}/\".format(tag)])\n","repo_name":"KevinLiao159/MediumBlog","sub_path":"src/scraper/call_crawler.py","file_name":"call_crawler.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"36769575411","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 9 20:38:51 2017\n\n@author: Thomas Pingel\n\"\"\"\nimport time\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport rasterio\nimport scipy.ndimage as ndi\nfrom skimage.util import apply_parallel\n#%%\nimport neilpy\n#%%\n\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\n \n \n#%%\n\nwith rasterio.open('../neilpy_data/poland_30m.tif') as src:\n Z = src.read(1)\n Zt = src.transform\n \n#%% 42 mins\nthen = time.time()\nG = get_geomorphon_from_openness(Z,Zt[0],10,1)\nnow = time.time()\nprint(now-then)\n\n#%% at 1000, 26 minutes\n\n# Calculate the geomorphons (a numeric code, 1-10)\nthen = time.time()\ncellsize = Zt[0]\nlookup_pixels = 50\nthreshold_angle = 1\ndef gm_wrap(I):\n this_G = get_geomorphon_from_openness(I,cellsize,lookup_pixels,threshold_angle)\n return this_G\nG = apply_parallel(gm_wrap,Z.copy(),1000,lookup_pixels)\nnow = time.time()\nprint(now-then)\n\n#%%\n\nim = Image.fromarray(G,mode='L')\nim.putpalette(geomorphon_cmap())\nplt.imshow(im)\nplt.show()\n\n\nim.save('../neilpy_data/poland_30m_geomorphons.png')\n#%%\nwrite_worldfile(Zt,'../neilpy_data/poland_30m_geomorphons.pgw')\n\n#%% SMRF TESTING\nfns = glob.glob(r'C:\\Temp\\Reference\\*.txt')\ntotal_error = np.zeros(len(fns))\nfor i,fn in enumerate(fns):\n df = pd.read_csv(fn,header=None,names=['x','y','z','g'],delimiter='\\t')\n x,y,z = df.x.values,df.y.values,df.z.values\n windows = np.arange(18) + 1\n cellsize= 1\n slope_threshold = .15\n elevation_threshold = .5\n elevation_scaler = 1.25\n \n result = neilpy.smrf(x,y,z,windows,cellsize,slope_threshold,elevation_threshold,elevation_scaler)\n \n total_error[i] = 1 - np.sum(result[3] == df.g) / len(df)\n \n print(fn,':',total_error[i])\n\nprint('Mean total error',np.mean(total_error))\nprint('Median total error',np.median(total_error))\n\n#%%\nplt.imshow(Zpro)\n\n\n#%%\nvalues = [f(p[0],p[1])[0][0] for p in zip(row,col)]\n\n#%% progressive_filter\nheader, df = read_las('DK22_partial.las')\nZ,t = create_dem(df.x,df.y,df.z,resolution=5,bin_type='min');\nZ = apply_parallel(inpaint_nans_by_springs,Z.copy(),100,10)\n\n\n#%%\nslope_threshold = .15\nwindows = np.arange(1,10,2)\ncellsize = 5\nOC = progressive_filter(Z,np.arange(1,10),cellsize=5,slope_threshold=.2)\nplt.imshow(is_object_cell) \n \n#%%\na = np.arange(3) # cols, x\nb = np.arange(3) + 1 # rows, y\nc = np.arange(9).reshape((3,3))\nprint(c)\ng = interpolate.RegularGridInterpolator((a,b),c)\nprint(g((2,3))) #col/x \"2\" and row/y \"3\"\n\n#%%\nc,r = ~t * (df.x.values,df.y.values)\nf = interpolate.RectBivariateSpline(row_centers,col_centers,Zpro)\nvalues = [f(p[0],p[1])[0][0] for p in zip(r,c)]\n\n#%%\n\n#Z = np.arange(9).reshape((3,3))\ndef vipmask(Z,cellsize=1):\n heights = np.zeros(np.size(Z))\n dlist = np.array([np.sqrt(2),1])\n for direction in range(4):\n dist = dlist[direction % 2]\n h0 = ashift(Z,direction) - Z\n h1 = ashift(Z,direction+4) - Z\n heights += triangle_height(h0.ravel(),h1.ravel(),dist*cellsize)\n return heights.reshape(np.shape(Z))\n#print(heights)\n\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\n \nV = vipmask(Z)\n \n\n#%% third go\n#h0 = np.array([-1,0,0])\n#h1 = np.array([1,1,1])\n#x_dist = 1\n\n'''\nh0 is the height of the neighbor pixel in one direction, relative to the center\nh1 is the height of the pixel on the other size of the center pixel (same dir)\nxdist is the real distance between them (as some neighbors are diagnonal)\n'''\n \ndef triangle_height(h0,h1,x_dist=1):\n n = np.shape(h0)\n\n # The area of the triangle is half of the cross product \n h0 = np.column_stack((-x_dist*np.ones(n),h0))\n h1 = np.column_stack(( x_dist*np.ones(n),h1))\n cp = np.abs(np.cross(h0,h1))\n \n # Find the base from the original coords\n base = np.sqrt( (2*x_dist)**2 + (h1[:,1]-h0[:,1])**2 )\n \n # Triangle height is the cross product divided by the base\n return cp/base\n \n\n\n#%% second go\n \ny = np.array([[0,1,2],[2,2,3],[4,4,5]])\nz_diff = np.diff(y)\nz_diff[:,0] = -z_diff[:,0]\n\nn = np.shape(z_diff)[0]\n\nxdist = 1\n\na = np.ones(np.shape(z_diff))\nb = np.ones(np.shape(z_diff))\na[:,0] = -xdist\nb[:,0] = xdist\na[:,1] = z_diff[:,0]\nb[:,1] = z_diff[:,1]\n\ncp = np.sqrt(np.abs(np.cross(a,b)))\n\n# Calculate base\nbase = np.sqrt((2**xdist*np.ones(n))**2 + (z_diff[:,1])**2)\n# Calculate height\nh = cp/base\nprint(h)\n\n#%% First go\ny = np.array([[0,1,2],[2,2,3],[4,4,5]])\n# y = np.random.rand(100,3)\nxdist = 1\n\n\nn = np.shape(y)[0]\n\n# Calculate cross-product\na = np.hstack((-xdist*np.ones((n,1)),np.reshape(y[:,0]-y[:,1],(n,1)),np.zeros((n,1))))\nb = np.hstack((xdist*np.ones((n,1)),np.reshape(y[:,2]-y[:,1],(n,1)),np.zeros((n,1))))\ncp = np.abs(np.cross(a,b))\nprint(cp)\n#del a,b\ncp = np.sqrt(np.sum(cp**2,axis=1))\n# Calculate base\nbase = np.sqrt((2**xdist*np.ones(n))**2 + (y[:,2] - y[:,1])**2)\n# Calculate height\nh = cp/base\nprint(h)\n\n\n#%%\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\nplt.imshow(Z,cmap='terrain',vmin=-500,vmax=2000)\nplt.show()\n#%%\n\nG = get_geomorphon_from_openness(Z,cellsize=Zt[0],lookup_pixels=25,threshold_angle=1,enhance=False)\n\n#%%\nG2 = get_geomorphon_from_openness(Z,cellsize=Zt[0],lookup_pixels=6,threshold_angle=1,enhance=True)\n# repair peaks\nG[G==2] = G2[G==2]\n# repair ridges\nG[G==3] = G2[G==3]\n#%%\n# Apply a \"standard\" colormap and display the image\nim = Image.fromarray(G,mode='L')\nim.putpalette(geomorphon_cmap())\nplt.imshow(im)\nplt.show()\n\n#%%\n\nwith rasterio.open('../sample_data/sample_dem_geomorphons.tif') as src:\n G3 = src.read(1)\nnp.sum(G==G3) / np.size(G3) \n \n#%% Develop for Swiss Shading\n\n# Uh, awesome one!\n\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\n \n\"\"\"\ncolor_table = np.zeros((2,2,3),dtype=np.uint8)\ncolor_table[0,0,:] = [110,120,117] # Top Left\ncolor_table[0,1,:] = [242,245,173] # Top Right\ncolor_table[1,0,:] = [128,148,138] # Bottom Left\ncolor_table[1,1,:] = [196,201,168] # Bottom Right\n\n# Top Left, Top Right, Bottom Left, Bottom Right\nR = ndi.zoom(np.array([[110,242],[128,196]]).astype(np.uint8),8)\nG = ndi.zoom(np.array([[120,245],[148,138]]).astype(np.uint8),8)\nB = ndi.zoom(np.array([[117,173],[138,168]]).astype(np.uint8),8)\n\"\"\"\nlut = plt.imread('swiss_shading_lookup_flipped.png')[:,:,:3]\nlut = (255*lut).astype(np.uint8)\n\n# Subtract Z_norm from 255 here to invert the colormap\nZ_norm = np.round(255 * (Z - np.min(Z)) / (np.max(Z) - np.min(Z))).astype(np.uint8)\nH = hillshade(Z,cellsize,return_uint8=True)\n\nRGB = np.zeros((np.shape(Z)[0],np.shape(Z)[1],3))\nRGB[:,:,0] = lut[:,:,0][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\nRGB[:,:,1] = lut[:,:,1][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\nRGB[:,:,2] = lut[:,:,2][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\n\nplt.imshow(RGB)\n\n#%% Develop for Swiss Shading; got it!\n\n# Uh, awesome one!\n\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\n \n\"\"\"\ncolor_table = np.zeros((2,2,3),dtype=np.uint8)\ncolor_table[0,0,:] = [110,120,117] # Top Left\ncolor_table[0,1,:] = [242,245,173] # Top Right\ncolor_table[1,0,:] = [128,148,138] # Bottom Left\ncolor_table[1,1,:] = [196,201,168] # Bottom Right\n\n# Top Left, Top Right, Bottom Left, Bottom Right\nR = ndi.zoom(np.array([[110,242],[128,196]]).astype(np.uint8),8)\nG = ndi.zoom(np.array([[120,245],[148,138]]).astype(np.uint8),8)\nB = ndi.zoom(np.array([[117,173],[138,168]]).astype(np.uint8),8)\n\"\"\"\nlut = plt.imread('swiss_shading_lookup.png')[:,:,:3]\nlut = (255*lut).astype(np.uint8)\n\n# Subtract 255 here to invert the colormap!\n# \nZ_norm = np.round(255 * (Z - np.min(Z)) / (np.max(Z) - np.min(Z))).astype(np.uint8)\nH = hillshade(Z,cellsize,return_uint8=True)\n\nRGB = np.zeros((np.shape(Z)[0],np.shape(Z)[1],3))\nRGB[:,:,0] = lut[:,:,0][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\nRGB[:,:,1] = lut[:,:,1][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\nRGB[:,:,2] = lut[:,:,2][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\n\nplt.imshow(RGB)\n\n\n#%%\n\nfig = plt.imshow(RGB)\nplt.axis('off')\nfig.axes.get_xaxis().set_visible(False)\nfig.axes.get_yaxis().set_visible(False)\nplt.margins(0,0)\nplt.savefig('just_the_image.png',bbox_inches='tight',pad_inches=0.0)\n\n\n\n#%% play around\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\n \n\"\"\"\ncolor_table = np.zeros((2,2,3),dtype=np.uint8)\ncolor_table[0,0,:] = [110,120,117] # Top Left\ncolor_table[0,1,:] = [242,245,173] # Top Right\ncolor_table[1,0,:] = [128,148,138] # Bottom Left\ncolor_table[1,1,:] = [196,201,168] # Bottom Right\n\"\"\"\n# Top Left, Top Right, Bottom Left, Bottom Right\n#R = ndi.zoom(np.array([[40,116],[90,95]]).astype(np.uint8),128)\n#G = ndi.zoom(np.array([[38,102],[74,77]]).astype(np.uint8),128)\n#B = ndi.zoom(np.array([[74,109],[84,85]]).astype(np.uint8),128)\n#lut = np.stack((R,G,B),axis=2)\nspec = np.array([[90,74,84],[95,77,85],[40,38,74],[116,102,109]]) # dark\n#spec = np.array([[129,137,131],[190,192,173],[117,124,121],[244,244,190]]) # swiss\nspec = np.array([])\nlut = np.zeros((256,256,3),dtype=np.uint8)\nlut[:,:,0] = ndi.zoom([[spec[0,0],spec[1,0]],[spec[2,0],spec[3,0]]],128)\nlut[:,:,1] = ndi.zoom([[spec[0,1],spec[1,1]],[spec[2,1],spec[3,1]]],128)\nlut[:,:,2] = ndi.zoom([[spec[0,2],spec[1,2]],[spec[2,2],spec[3,2]]],128)\n\n\n#lut = plt.imread('swiss_shading_test.png')[:,:,:3]\n#lut = (255*lut).astype(np.uint8)\n\nZ_norm = np.round(255 * (Z - np.min(Z)) / (np.max(Z) - np.min(Z))).astype(np.uint8)\nH = hillshade(Z,cellsize,return_uint8=True)\n\n# this is the key\n# without the uint8, it's processed as a float, andthe resulting image is\n# very different (and cool! \\/\nRGB = np.zeros((np.shape(Z)[0],np.shape(Z)[1],3),dtype=np.uint8)\nRGB[:,:,0] = lut[:,:,0][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\nRGB[:,:,1] = lut[:,:,1][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\nRGB[:,:,2] = lut[:,:,2][Z_norm.ravel(),H.ravel()].reshape(np.shape(Z))\n\nplt.imshow(RGB)\n\n#%%\nfrom scipy.misc import imsave\nimsave('lut.png',lut)\nimsave('orig5.png',RGB)\n\n\n#%%\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\n\nname = 'gray_high_contrast'\nRGB = colortable_shade(Z,name,Zt[0])\nimsave(name + '.png',RGB)\n\n#%%\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\nI = plt.imread('gray_mash.png')\nI = (255 * I[:,:,0]).astype(np.uint8)\nH = hillshade(Z,cellsize,return_uint8=True)\nZn = np.round(255 * (Z - np.min(Z)) / (np.max(Z) - np.min(Z))).astype(np.uint8)\n\n#%%\n\nO = np.zeros((256,256),dtype=np.float)\nO[:] = np.nan\nfor z in range(256):\n print(z)\n for h in range(256):\n pixels = I[(Zn==z) & (H==h)]\n if len(pixels) > 0:\n m = np.median(pixels)\n else:\n m = np.nan\n O[z,h] = m\n\n#%%\n\nfrom scipy import interpolate\n\nk = np.random.choice(np.size(Z),1000,replace=False)\n\n# Alternatively, interp2 also works, but is designed for unstructured data and can run more slowly\nf = interpolate.interp2d(H.flatten()[k],Z.flatten()[k],I.flatten()[k],kind='cubic')\n\n#%%\n\ndef inpaint_nearest(X):\n idx = np.isfinite(X)\n RI,CI = np.meshgrid(np.arange(X.shape[0]),np.arange(X.shape[1]))\n f_near = interpolate.NearestNDInterpolator((RI[idx],CI[idx]),X[idx])\n idx = ~idx\n X[idx] = f_near(RI[idx],CI[idx])\n return X\n\n\n#%%\nX = O.copy()\nidx = np.isfinite(X)\nRI,CI = np.meshgrid(np.arange(X.shape[0]),np.arange(X.shape[1]))\nf = interpolate.interp2d(RI[idx],CI[idx],X[idx],kind='cubic')\n\n#%%\ndef logfit(x,k=12.5,low=0,high=0):\n low_idx = x < low\n high_idx = x > 1 - high\n\n idx = ~((low_idx) | (high_idx))\n \n a = x[idx]\n a = (a - a.min()) / (a.max() - a.min())\n x[idx] = a\n \n \n y = 1 / (1 + np.e **(-k * (x - .5)))\n y[low_idx] = 0\n y[high_idx] = 1\n \n y = (y - y.min()) / (y.max() - y.min())\n\n \n return y\n\ndef roundfit(x):\n y = (x) ** 2\n return y\n\n#%%\n \n#%%\n\ndef ifit(x,lows=(.1,0),highs=(.9,1),kind='quadratic'):\n a = np.array([0,lows[0],highs[0],1])\n b = np.array([lows[1],lows[1],highs[1],highs[1]])\n if lows[0]==0:\n a,b=a[1:],b[1:]\n if highs[0]==1:\n a,b=a[:-1],b[:-1]\n f = interpolate.interp1d(a,b,kind)\n result = f(x)\n result[result < lows[1]] = lows[1]\n result[result > highs[1]] = highs[1]\n return result\n\nx = np.linspace(0,1)\ny = ifit(x,lows = (.1,.5),highs=(.9,.8))\nplt.plot(x,y)\n\n\n\n\n#%%\n#def loglut():\nspec = np.array([[119,119,119],[255,255,255]])\nlut = np.zeros((256,256),dtype=np.float)\nlut[:] = np.nan\nlut[-1,:] = ifit(np.linspace(0,1,256),(.3,0),(.8,spec[1,0]))\nlut[:,-1] = ifit(np.linspace(0,1,256),(.05,spec[0,0]),(.7,spec[1,0]))\nlut[0,:] = ifit(np.linspace(0,1,256),(.35,0),(.975,spec[0,0]))\nlut[np.arange(256),np.arange(256)] = ifit(np.linspace(0,1,256),(.35,0),(.85,spec[1,0]))\nlut[:,0] = 0\nlut = inpaint_nans_by_springs(lut)\n\nRGB = colortable_shade(Z,lut,Zt[0])\nimsave('gray.png',RGB)\nimsave('gray_lut_exp.png',lut)\n\n#%%\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\n\nname = 'gray_nice.png'\nRGB = colortable_shade(Z,name,Zt[0])\nimsave(name + '.png',RGB)\n\n#%%\nwith rasterio.open('../sample_data/sample_dem.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\n\n#%%\nlookup_pixels = 20\nO = ndi.filters.generic_filter(Z,openness_filter,size=2*lookup_pixels+1,extra_keywords={'cellsize':cellsize})\n\n#%%\nlookup_pixels = 20\nSV = ndi.filters.generic_filter(Z,skyview_filter,size=2*lookup_pixels+1,extra_keywords={'cellsize':cellsize})\n\n#%%\nOsk = ndi.filters.generic_filter(Z,openness_filter,size=2*lookup_pixels+1,extra_keywords={'cellsize':cellsize,'skyview':True})\n\n#%%\nO2 = neilpy.openness(Z,cellsize,lookup_pixels=20)\n\n#%%\n\n\nimport numpy as np\nfrom skimage import graph\nimport matplotlib.pyplot as plt\n\n#%%\ncs = Z\ncellSize = Zt[0]\nlg = graph.MCP_Geometric(cs, sampling=(cellSize, cellSize))\nstartCell = (5, 5)\nlcd = lg.find_costs(starts=[startCell])[0]\n\n#%%\n\nI = np.array([[1,1,1],[1,100,1],[1,1,1]])\nlg = graph.MCP_Geometric(I)\nstartCell = (0,0)\ncumCost, tb = lg.find_costs(starts=[startCell])\nprint(cumCost)\n\n#%%\n\nimport numpy as np\nimport skimage.graph.mcp as mcp\n\na = np.ones((8, 8), dtype=np.float32)\na[1::2] *= 2.0\n\n\nclass FlexibleMCP(mcp.MCP_Flexible):\n \"\"\" Simple MCP subclass that allows the front to travel \n a certain distance from the seed point, and uses a constant\n cost factor that is independant of the cost array.\n \"\"\"\n \n def _reset(self):\n mcp.MCP_Flexible._reset(self)\n self._distance = np.zeros((8, 8), dtype=np.float32).ravel()\n \n def goal_reached(self, index, cumcost):\n if self._distance[index] > 4:\n return 2\n else:\n return 0\n \n def travel_cost(self, index, new_index, offset_length):\n return 1.0 # fixed cost\n \n def examine_neighbor(self, index, new_index, offset_length):\n pass # We do not test this\n \n def update_node(self, index, new_index, offset_length):\n self._distance[new_index] = self._distance[index] + 1\n\n#%%\n\ndef test_flexible():\n # Create MCP and do a traceback\n mcp = FlexibleMCP(a)\n costs, traceback = mcp.find_costs([(0, 0)])\n \n # Check that inner part is correct. This basically\n # tests whether travel_cost works.\n assert_array_equal(costs[:4, :4], [[1, 2, 3, 4],\n [2, 2, 3, 4],\n [3, 3, 3, 4],\n [4, 4, 4, 4]])\n \n # Test that the algorithm stopped at the right distance.\n # Note that some of the costs are filled in but not yet frozen,\n # so we take a bit of margin\n assert np.all(costs[-2:, :] == np.inf)\n assert np.all(costs[:, -2:] == np.inf)\n \n#%%\n \nfrom skimage import graph\n\nwith rasterio.open('../../neilpy_data/peru.tif') as src:\n Z = src.read(1)\n Zt = src.affine\ncellsize = Zt[0]\nzf = z_factor(Zt[5])\n\n#%%\n\nG = 100 * slope(Z,cellsize,z_factor=zf,return_as='percent')\nG[Z==src.nodata] = np.inf\n#%%\nW = 50\nL = 0\ntc = 1.1\nV = 1.2\n\nMW = 1.5*W + 2.0 * (W + L) * ((L/W)**2) + tc * (W+L) * (1.5 * V**2 + .35 * V * G)\n\n#%%\n\nstart = np.round(~Zt * (-72.872,-16.509)).astype(int)[::-1]\nend = np.round(~Zt * (-72.708,-15.123)).astype(int)[::-1]\nroute, cost = graph.route_through_array(MW,start,end)\n\nroute = np.array(route)\n#%%\nplt.imshow(Z,cmap='terrain',vmin=-500,vmax=5000)\nplt.plot(route[:,1],route[:,0],'r-')\nplt.show()\n\n\n#%%\n\n\ndef svf2(Z,cellsize=1,lookup_pixels=1):\n\n nrows, ncols = np.shape(Z)\n neighbors=np.arange(8) \n # neighbor directions are clockwise from top left,starting at zero\n\n # This will sum the max angles \n sum_matrix = np.zeros_like(Z,dtype=np.float)\n \n # Define an array to calculate distances to neighboring pixels\n dlist = np.array([np.sqrt(2),1])\n\n # Calculate minimum angles \n for direction in neighbors:\n max_angles = np.zeros_like(Z,dtype=np.float)\n z_shift = Z.copy()\n for L in range(1,lookup_pixels+1):\n # Map distance to this pixel:\n dist = dlist[direction % 2]\n dist = cellsize * L * dist\n # Angle is the arctan of the difference in elevations, divided by distance\n z_shift = ashift(z_shift,direction,1)\n these_angles = np.clip(np.arctan((z_shift-Z)/dist),0,np.inf)\n max_angles = np.nanmax(np.stack((max_angles,these_angles),axis=0),axis=0)\n sum_matrix += np.sin(max_angles)\n sum_matrix = 1 - sum_matrix / 8\n\n return sum_matrix\n\n#%%\n \ndef openness(Z,cellsize=1,lookup_pixels=1,neighbors=np.arange(8),skyview=False):\n\n nrows, ncols = np.shape(Z)\n \n # neighbor directions are clockwise from top left,starting at zero\n # neighbors = np.arange(8) \n \n # Define a (fairly large) 3D matrix to hold the minimum angle for each pixel\n # for each of the requested directions (usually 8)\n opn = np.Inf * np.ones((len(neighbors),nrows,ncols))\n \n # Define an array to calculate distances to neighboring pixels\n dlist = np.array([np.sqrt(2),1])\n\n # Calculate minimum angles \n for L in np.arange(1,lookup_pixels+1):\n for i,direction in enumerate(neighbors):\n # Map distance to this pixel:\n dist = dlist[direction % 2]\n dist = cellsize * L * dist\n # Angle is the arctan of the difference in elevations, divided by distance\n these_angles = (np.pi/2) - np.arctan((ashift(Z,direction,L)-Z)/dist)\n this_layer = opn[i,:,:]\n this_layer[these_angles < this_layer] = these_angles[these_angles < this_layer]\n opn[i,:,:] = this_layer\n\n # Openness is definted as the mean of the minimum angles of all 8 neighbors \n return np.mean(opn,0)\n\n#%%\ntic = time.time()\nlookup_pixels = 20\nSV = ndi.filters.generic_filter(Z,skyview_filter,size=2*lookup_pixels+1,extra_keywords={'cellsize':cellsize})\ntoc = time.time()\nprint(toc-tic)\n","repo_name":"thomaspingel/neilpy","sub_path":"neilpy/test_neilpy.py","file_name":"test_neilpy.py","file_ext":"py","file_size_in_byte":19218,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"20"}
+{"seq_id":"40823902978","text":"\"\"\"\nSubmits a bunch of jobs for execution to a server interface for Waterflow.\n\"\"\"\nimport json\nimport requests\n\nfrom waterflow.core import load_local_config\nfrom waterflow import to_base64_str, StopWatch\n\n\ndef submit_job(url_base, work_queue, job_input):\n\n job_name = \"TestJob\"\n job_input64 = to_base64_str(job_input)\n request = {\n \"job_name\": job_name,\n \"job_input64\": job_input64,\n \"job_input64_v\": 0,\n \"service_pointer\": \"123\",\n \"tags\": [\"type=spark\"],\n }\n\n # create the job\n resp = requests.post(f\"{url_base}/api/submit_job/{work_queue}\", json=request)\n job_id = json.loads(resp.content)[\"job_id\"]\n print(f\"submitted job {job_id}\")\n\n\nif __name__ == \"__main__\":\n config = load_local_config(\"worker_config.json\")\n url_base = config[\"server_url_base\"]\n stw = StopWatch()\n\n # JOB_COUNT = 14000\n JOB_COUNT = 64\n\n for i in range(JOB_COUNT):\n submit_job(url_base, 0, str(i))\n print(f\"{JOB_COUNT} jobs added in {stw}\")","repo_name":"theroadninja/waterflow","sub_path":"waterflow/tools/flask_job_submitter.py","file_name":"flask_job_submitter.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"13129002683","text":"#!/usr/bin/env python\n\"\"\"\nOpen a MIDI file and print every message in every track.\nSupport for MIDI files is still experimental.\n\"\"\"\nimport sys\nimport os\nfrom mido import MidiFile\nimport math\nimport copy\nfrom time import time\n\nfrom FloppySequences import TestStep, Direction, FloppySequences\n\n# Parse Midi File to Floppy Sequence\nclass MidiParser:\n tune_factor = 1\n arduino_freq = 10000 # Arduino Timer1 Frequency\n midi_file = None\n channels = 0\n serial = None\n\n MAXLEN = 50 # Maximum number of tones\n\n def __init__(self,filename, serial):\n if not os.path.isfile(filename):\n print(\"File not found \" + filename)\n exit\n\n self.serial = serial\n self.midi_file = MidiFile(filename)\n self.channels = len(self.midi_file.tracks) - 1 # First Channel is for Meta-Information only I guess\n\n def convert_to_tone(self,n):\n # Convert time to 1/10 milliseconds\n time = round(n['time'] * self.arduino_freq)\n\n # Convert note from midi note number to frequency\n # https://newt.phys.unsw.edu.au/jw/notes.html\n frequency = math.pow(2,(n['note']/12)) * 440 if n['note'] >= 0 else 0 \n \n # Scale down frequency, so it can be played on a floppy drive, always double the factor to tune down in octaves\n while (frequency / self.tune_factor > 440):\n self.tune_factor*=2\n\n frequency /= self.tune_factor\n\n # Get time per period of frequency, set to zero if frequency is zero\n p = self.arduino_freq/frequency if frequency > 0 else 0\n\n # Get high and low times from period time, duty cycle 50%\n if(frequency > 0):\n hp = round(p/2) if round(p/2) > 0 else 1\n lp = hp\n else: # This is for pause\n hp = 0\n lp = 1\n\n # Calculate how much steps we need\n steps = round(time / (hp + lp))\n\n # Get TestStep Object, divide by compensation factor because we use ms in TestStep, not 100us\n return TestStep(steps,hp/TestStep.arduinoTimerCompensation,lp/TestStep.arduinoTimerCompensation,Direction.AUTO, 0)\n\n def parse(self, channel = 0,numberOfTracks=40):\n tones = {}\n\n # Tune down frequencies by factor to ensure it is playable by floppy drives\n # Reset when start parsing\n self.tune_factor = 1\n\n # Add Note to tone Array, do neccessary conversions\n def add_note(n):\n if(n['channel'] not in tones):\n tones[n['channel']] = []\n \n # Ignore notes where time equals zero\n if(n['time'] == 0): return\n \n step = self.convert_to_tone(n)\n\n # Done! Append it\n tones[n['channel']].append(step)\n \n m = {}\n current_time = 0\n for msg in self.midi_file:\n # Only process not events\n if not hasattr(msg,'note'):\n continue\n current_time += msg.time\n\n # Set initial values if new channel appears\n if(msg.channel not in m):\n m[msg.channel] = {}\n m[msg.channel]['time'] = 0\n m[msg.channel]['note'] = -1\n m[msg.channel]['channel'] = msg.channel\n\n # Process Note on and Note off events in order to transform them to frequency/time objects\n if msg.type == 'note_off':\n # Tone END\n m[msg.channel]['time'] = current_time - m[msg.channel]['time']\n add_note(m[msg.channel])\n\n # Pause START\n m[msg.channel]['time'] = current_time\n m[msg.channel]['note'] = -1\n\n if msg.type == 'note_on':\n # Pause END\n m[msg.channel]['time'] = current_time - m[msg.channel]['time']\n add_note(m[msg.channel])\n\n # Tone START\n m[msg.channel]['time'] = current_time\n m[msg.channel]['note'] = msg.note\n\n # Create Sequence for index 0 TODO: Let user select sequence id\n floppy = FloppySequences(0,\"\",False,False,numberOfTracks,False)\n floppy.steps = tones[channel] # TODO: Warn user if song is longer as maxlen\n return floppy\n\n def play(self, channel,numberOfTracks=40):\n m = {}\n self.tune_factor = 1\n for msg in self.midi_file.play():\n if(msg.channel != channel or not hasattr(msg, 'note')):\n continue\n if msg.type == 'note_on':\n m['time'] = time()\n elif msg.type == 'note_off':\n diff = time() - m['time']\n\n # Extend time by 10%, it tends to sound better that way TODO: test and delete \n m['time'] = diff\n m['note'] = msg.note\n\n tone = self.convert_to_tone(m)\n floppy = FloppySequences(0,\"\",False,False,numberOfTracks, True)\n floppy.steps = [tone]\n cmds = floppy.GetCommand()\n self.serial.SendCommands(cmds)\n else:\n continue","repo_name":"muelmx/FloppyTester","sub_path":"ConsoleApp/MidiParser.py","file_name":"MidiParser.py","file_ext":"py","file_size_in_byte":5081,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"29327862489","text":"import sqlite3\nimport dataset\nimport os, stat, datetime\nfrom datetime import timedelta\n\nfrom config import Config\nfrom modules import common\nfrom modules.device import Device\nconfig = Config()\n\nif (not os.path.exists(config.PATH_DB_INTEGRATION)) :\n print (\"Integration DB is not initialized!\")\n exit(1)\n\nprint (\"Export Pegasus-DB!\")\ndb_integration = dataset.connect(f\"sqlite:///{config.PATH_DB_INTEGRATION}\")\ntb_integration_devices = db_integration[\"cfg_devices\"]\ntb_integration_games = db_integration[\"cfg_games\"]\ntb_integration_paths = db_integration[\"paths\"]\ntb_integration_histories = db_integration[\"histories\"]\nnowTime = datetime.datetime.now()\n\ndef main (): \n # デバイス毎に処理を実施する\n for device in tb_integration_devices.find(db_path={'!=': ''}): \n print (f\"device -> {device['name']}\")\n deviceCntl = Device(device['db_path'])\n\n # pegasus_frontend出力DBの更新が10分以内の場合，更新しない\n if deviceCntl.get_db_updated_time() > (nowTime + timedelta(minutes=-10)):\n print (f\"device DB is using now. cant update. >> SKIP\")\n continue\n\n # バックアップを作成する\n deviceCntl.create_db_backup()\n\n # デバイスDB (デバイス毎の pegasus-stats DB)と接続\n original_db = deviceCntl.connect_db()\n if (not original_db):\n print (f\"device DB couldnot connect ! => {device['name']} / {device['db_path']} \")\n continue\n original_db.begin()\n\n\n # DBのデータ削除\n original_db['plays'].delete()\n\n # 統合DBから，デバイスに登録済のゲーム履歴を取得し，インサートする\n insertPlays = []\n games = map(lambda path: {\\\n \"game_id\": path['game_id'], \\\n \"original_path_id\": path['original_path_id'],\\\n }, \\\n tb_integration_paths.find(device_id=device['id']) \\\n )\n for game in games:\n insertPlays.extend(\\\n list(\n map(\n lambda history: {\\\n \"start_time\": history['played_time'], \\\n \"duration\": history['played_duration'], \\\n \"path_id\": game['original_path_id']\\\n }, \\\n tb_integration_histories.find(game_id=game['game_id'])\n )\n )\n )\n insertPlays.sort(key=lambda x: x['start_time'])\n\n original_db['plays'].insert_many(insertPlays)\n original_db.commit()\n original_db.close()\n\n print ('done!')\n\n\nmain()\ndb_integration.close()\nprint (\"Export Pegasus-DB COMPLETE!\")\n","repo_name":"hagumeta/Python_PegasusSyncer","sub_path":"source/exportStatsDB.py","file_name":"exportStatsDB.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"16016497021","text":"# Level 2\n\ndef solution(n):\n answer = \"\"\n oneTwoFour = ['4', '1', '2']\n\n if n <= 3:\n # 문자열의 (n%3)번째 요소\n answer = '412'[n % 3]\n else:\n while n > 0:\n answer = oneTwoFour[n % 3] + answer\n\n if n % 3 == 0:\n n = n // 3 - 1\n else:\n n //= 3\n\n return answer","repo_name":"nsbg/ALGORITHM","sub_path":"programmers/pg_017.py","file_name":"pg_017.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"20"}
+{"seq_id":"12611452155","text":"import logging\nimport math\n\nimport cv2\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.contrib.framework.python.ops import add_arg_scope\n\nfrom neuralgym.ops.layers import resize\nfrom neuralgym.ops.layers import *\nfrom neuralgym.ops.loss_ops import *\nfrom neuralgym.ops.gan_ops import *\nfrom neuralgym.ops.summary_ops import *\n\n\nlogger = logging.getLogger()\nnp.random.seed(2018)\n\n@add_arg_scope\ndef gen_conv(x, cnum, ksize, stride=1, rate=1, name='conv',\n padding='SAME', activation=tf.nn.elu, training=True):\n \"\"\"Define conv for generator.\n\n Args:\n x: Input.\n cnum: Channel number.\n ksize: Kernel size.\n Stride: Convolution stride.\n Rate: Rate for or dilated conv.\n name: Name of layers.\n padding: Default to SYMMETRIC.\n activation: Activation function after convolution.\n training: If current graph is for training or inference, used for bn.\n\n Returns:\n tf.Tensor: output\n\n \"\"\"\n assert padding in ['SYMMETRIC', 'SAME', 'REFELECT']\n if padding == 'SYMMETRIC' or padding == 'REFELECT':\n p = int(rate*(ksize-1)/2)\n x = tf.pad(x, [[0,0], [p, p], [p, p], [0,0]], mode=padding)\n padding = 'VALID'\n x = tf.layers.conv2d(\n x, cnum, ksize, stride, dilation_rate=rate,\n activation=None, padding=padding, name=name)\n if cnum == 3 or activation is None:\n # conv for output\n return x\n x, y = tf.split(x, 2, 3)\n x = activation(x)\n y = tf.nn.sigmoid(y)\n x = x * y\n return x\n\n@add_arg_scope\ndef gen_deconv(x, cnum, name='upsample', padding='SAME', training=True):\n \"\"\"Define deconv for generator.\n The deconv is defined to be a x2 resize_nearest_neighbor operation with\n additional gen_conv operation.\n\n Args:\n x: Input.\n cnum: Channel number.\n name: Name of layers.\n training: If current graph is for training or inference, used for bn.\n\n Returns:\n tf.Tensor: output\n\n \"\"\"\n with tf.variable_scope(name):\n x = resize(x, func=tf.image.resize_nearest_neighbor)\n x = gen_conv(\n x, cnum, 3, 1, name=name+'_conv', padding=padding,\n training=training)\n return x\n\ndef resize_mask_like(mask, x):\n \"\"\"Resize mask like shape of x.\n\n Args:\n mask: Original mask.\n x: To shape of x.\n\n Returns:\n tf.Tensor: resized mask\n\n \"\"\"\n mask_resize = resize(\n mask, to_shape=x.get_shape().as_list()[1:3],\n func=tf.image.resize_nearest_neighbor)\n return mask_resize\n\ndef contextual_attention(f, b, mask=None, ksize=3, stride=1, rate=1,\n fuse_k=3, softmax_scale=10., training=True, fuse=True):\n \"\"\" Contextual attention layer implementation.\n\n Contextual attention is first introduced in publication:\n Generative Image Inpainting with Contextual Attention, Yu et al.\n\n Args:\n x: Input feature to match (foreground).\n t: Input feature for match (background).\n mask: Input mask for t, indicating patches not available.\n ksize: Kernel size for contextual attention.\n stride: Stride for extracting patches from t.\n rate: Dilation for matching.\n softmax_scale: Scaled softmax for attention.\n training: Indicating if current graph is training or inference.\n\n Returns:\n tf.Tensor: output\n\n \"\"\"\n # get shapes\n raw_fs = tf.shape(f)\n raw_int_fs = f.get_shape().as_list()\n raw_int_bs = b.get_shape().as_list()\n # extract patches from background with stride and rate\n kernel = 2*rate\n raw_w = tf.extract_image_patches(\n b, [1,kernel,kernel,1], [1,rate*stride,rate*stride,1], [1,1,1,1], padding='SAME')\n raw_w = tf.reshape(raw_w, [raw_int_bs[0], -1, kernel, kernel, raw_int_bs[3]])\n raw_w = tf.transpose(raw_w, [0, 2, 3, 4, 1]) # transpose to b*k*k*c*hw\n # downscaling foreground option: downscaling both foreground and\n # background for matching and use original background for reconstruction.\n f = resize(f, scale=1./rate, func=tf.image.resize_nearest_neighbor)\n b = resize(b, to_shape=[int(raw_int_bs[1]/rate), int(raw_int_bs[2]/rate)], func=tf.image.resize_nearest_neighbor) # https://github.com/tensorflow/tensorflow/issues/11651\n if mask is not None:\n mask = resize(mask, scale=1./rate, func=tf.image.resize_nearest_neighbor)\n fs = tf.shape(f)\n int_fs = f.get_shape().as_list()\n f_groups = tf.split(f, int_fs[0], axis=0)\n # from t(H*W*C) to w(b*k*k*c*h*w)\n bs = tf.shape(b)\n int_bs = b.get_shape().as_list()\n w = tf.extract_image_patches(\n b, [1,ksize,ksize,1], [1,stride,stride,1], [1,1,1,1], padding='SAME')\n w = tf.reshape(w, [int_fs[0], -1, ksize, ksize, int_fs[3]])\n w = tf.transpose(w, [0, 2, 3, 4, 1]) # transpose to b*k*k*c*hw\n # process mask\n if mask is None:\n mask = tf.zeros([1, bs[1], bs[2], 1])\n m = tf.extract_image_patches(\n mask, [1,ksize,ksize,1], [1,stride,stride,1], [1,1,1,1], padding='SAME')\n m = tf.reshape(m, [1, -1, ksize, ksize, 1])\n m = tf.transpose(m, [0, 2, 3, 4, 1]) # transpose to b*k*k*c*hw\n m = m[0]\n mm = tf.cast(tf.equal(tf.reduce_mean(m, axis=[0,1,2], keep_dims=True), 0.), tf.float32)\n w_groups = tf.split(w, int_bs[0], axis=0)\n raw_w_groups = tf.split(raw_w, int_bs[0], axis=0)\n y = []\n offsets = []\n k = fuse_k\n scale = softmax_scale\n fuse_weight = tf.reshape(tf.eye(k), [k, k, 1, 1])\n for xi, wi, raw_wi in zip(f_groups, w_groups, raw_w_groups):\n # conv for compare\n wi = wi[0]\n wi_normed = wi / tf.maximum(tf.sqrt(tf.reduce_sum(tf.square(wi), axis=[0,1,2])), 1e-4)\n yi = tf.nn.conv2d(xi, wi_normed, strides=[1,1,1,1], padding=\"SAME\")\n\n # conv implementation for fuse scores to encourage large patches\n if fuse:\n yi = tf.reshape(yi, [1, fs[1]*fs[2], bs[1]*bs[2], 1])\n yi = tf.nn.conv2d(yi, fuse_weight, strides=[1,1,1,1], padding='SAME')\n yi = tf.reshape(yi, [1, fs[1], fs[2], bs[1], bs[2]])\n yi = tf.transpose(yi, [0, 2, 1, 4, 3])\n yi = tf.reshape(yi, [1, fs[1]*fs[2], bs[1]*bs[2], 1])\n yi = tf.nn.conv2d(yi, fuse_weight, strides=[1,1,1,1], padding='SAME')\n yi = tf.reshape(yi, [1, fs[2], fs[1], bs[2], bs[1]])\n yi = tf.transpose(yi, [0, 2, 1, 4, 3])\n yi = tf.reshape(yi, [1, fs[1], fs[2], bs[1]*bs[2]])\n\n # softmax to match\n yi *= mm # mask\n yi = tf.nn.softmax(yi*scale, 3)\n yi *= mm # mask\n\n offset = tf.argmax(yi, axis=3, output_type=tf.int32)\n offset = tf.stack([offset // fs[2], offset % fs[2]], axis=-1)\n # deconv for patch pasting\n # 3.1 paste center\n wi_center = raw_wi[0]\n yi = tf.nn.conv2d_transpose(yi, wi_center, tf.concat([[1], raw_fs[1:]], axis=0), strides=[1,rate,rate,1]) / 4.\n y.append(yi)\n offsets.append(offset)\n y = tf.concat(y, axis=0)\n y.set_shape(raw_int_fs)\n offsets = tf.concat(offsets, axis=0)\n offsets.set_shape(int_bs[:3] + [2])\n # case1: visualize optical flow: minus current position\n h_add = tf.tile(tf.reshape(tf.range(bs[1]), [1, bs[1], 1, 1]), [bs[0], 1, bs[2], 1])\n w_add = tf.tile(tf.reshape(tf.range(bs[2]), [1, 1, bs[2], 1]), [bs[0], bs[1], 1, 1])\n offsets = offsets - tf.concat([h_add, w_add], axis=3)\n # to flow image\n flow = flow_to_image_tf(offsets)\n # # case2: visualize which pixels are attended\n # flow = highlight_flow_tf(offsets * tf.cast(mask, tf.int32))\n if rate != 1:\n flow = resize(flow, scale=rate, func=tf.image.resize_bilinear)\n return y, flow\n\ndef flow_to_image_tf(flow, name='flow_to_image'):\n \"\"\"Tensorflow ops for computing flow to image.\n \"\"\"\n with tf.variable_scope(name), tf.device('/cpu:0'):\n img = tf.py_func(flow_to_image, [flow], tf.float32, stateful=False)\n img.set_shape(flow.get_shape().as_list()[0:-1]+[3])\n img = img / 127.5 - 1.\n return img\n\ndef flow_to_image(flow):\n \"\"\"Transfer flow map to image.\n Part of code forked from flownet.\n \"\"\"\n out = []\n maxu = -999.\n maxv = -999.\n minu = 999.\n minv = 999.\n maxrad = -1\n for i in range(flow.shape[0]):\n u = flow[i, :, :, 0]\n v = flow[i, :, :, 1]\n idxunknow = (abs(u) > 1e7) | (abs(v) > 1e7)\n u[idxunknow] = 0\n v[idxunknow] = 0\n maxu = max(maxu, np.max(u))\n minu = min(minu, np.min(u))\n maxv = max(maxv, np.max(v))\n minv = min(minv, np.min(v))\n rad = np.sqrt(u ** 2 + v ** 2)\n maxrad = max(maxrad, np.max(rad))\n u = u/(maxrad + np.finfo(float).eps)\n v = v/(maxrad + np.finfo(float).eps)\n img = compute_color(u, v)\n out.append(img)\n return np.float32(np.uint8(out))\n\ndef highlight_flow(flow):\n \"\"\"Convert flow into middlebury color code image.\n \"\"\"\n out = []\n s = flow.shape\n for i in range(flow.shape[0]):\n img = np.ones((s[1], s[2], 3)) * 144.\n u = flow[i, :, :, 0]\n v = flow[i, :, :, 1]\n for h in range(s[1]):\n for w in range(s[1]):\n ui = u[h,w]\n vi = v[h,w]\n img[ui, vi, :] = 255.\n out.append(img)\n return np.float32(np.uint8(out))\n\ndef highlight_flow_tf(flow, name='flow_to_image'):\n \"\"\"Tensorflow ops for highlight flow.\n \"\"\"\n with tf.variable_scope(name), tf.device('/cpu:0'):\n img = tf.py_func(highlight_flow, [flow], tf.float32, stateful=False)\n img.set_shape(flow.get_shape().as_list()[0:-1]+[3])\n img = img / 127.5 - 1.\n return img","repo_name":"nichapman/Object-Remover-Backend","sub_path":"inpaint_ops.py","file_name":"inpaint_ops.py","file_ext":"py","file_size_in_byte":9635,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"21877509369","text":"# coding: utf-8\n\nimport six\n\nfrom huaweicloudsdkcore.sdk_response import SdkResponse\nfrom huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization\n\n\nclass BatchShowEdgeAppsResponse(SdkResponse):\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n sensitive_list = []\n\n openapi_types = {\n 'count': 'int',\n 'edge_apps': 'list[QueryApplicationBriefResponseDTO]'\n }\n\n attribute_map = {\n 'count': 'count',\n 'edge_apps': 'edge_apps'\n }\n\n def __init__(self, count=None, edge_apps=None):\n \"\"\"BatchShowEdgeAppsResponse\n\n The model defined in huaweicloud sdk\n\n :param count: **参数说明**：总记录数。\n :type count: int\n :param edge_apps: **参数说明**：列举每条记录。\n :type edge_apps: list[:class:`huaweicloudsdkdris.v1.QueryApplicationBriefResponseDTO`]\n \"\"\"\n \n super(BatchShowEdgeAppsResponse, self).__init__()\n\n self._count = None\n self._edge_apps = None\n self.discriminator = None\n\n if count is not None:\n self.count = count\n if edge_apps is not None:\n self.edge_apps = edge_apps\n\n @property\n def count(self):\n \"\"\"Gets the count of this BatchShowEdgeAppsResponse.\n\n **参数说明**：总记录数。\n\n :return: The count of this BatchShowEdgeAppsResponse.\n :rtype: int\n \"\"\"\n return self._count\n\n @count.setter\n def count(self, count):\n \"\"\"Sets the count of this BatchShowEdgeAppsResponse.\n\n **参数说明**：总记录数。\n\n :param count: The count of this BatchShowEdgeAppsResponse.\n :type count: int\n \"\"\"\n self._count = count\n\n @property\n def edge_apps(self):\n \"\"\"Gets the edge_apps of this BatchShowEdgeAppsResponse.\n\n **参数说明**：列举每条记录。\n\n :return: The edge_apps of this BatchShowEdgeAppsResponse.\n :rtype: list[:class:`huaweicloudsdkdris.v1.QueryApplicationBriefResponseDTO`]\n \"\"\"\n return self._edge_apps\n\n @edge_apps.setter\n def edge_apps(self, edge_apps):\n \"\"\"Sets the edge_apps of this BatchShowEdgeAppsResponse.\n\n **参数说明**：列举每条记录。\n\n :param edge_apps: The edge_apps of this BatchShowEdgeAppsResponse.\n :type edge_apps: list[:class:`huaweicloudsdkdris.v1.QueryApplicationBriefResponseDTO`]\n \"\"\"\n self._edge_apps = edge_apps\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n if attr in self.sensitive_list:\n result[attr] = \"****\"\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)\n\n def __repr__(self):\n \"\"\"For `print`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, BatchShowEdgeAppsResponse):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n","repo_name":"huaweicloud/huaweicloud-sdk-python-v3","sub_path":"huaweicloud-sdk-dris/huaweicloudsdkdris/v1/model/batch_show_edge_apps_response.py","file_name":"batch_show_edge_apps_response.py","file_ext":"py","file_size_in_byte":4354,"program_lang":"python","lang":"en","doc_type":"code","stars":104,"dataset":"github-code","pt":"20"}
+{"seq_id":"70021198770","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\n\nfrom mysite.blog.models import Post\nfrom mysite.blog.forms import PostForm\n\n\ndef post_list(request):\n posts = Post.objects.filter(published_at__lte=timezone.now()).order_by('published_at')\n context = {\n 'posts': posts\n }\n \n return render(request=request, template_name='blog/post_list.html', context=context)\n\ndef post_show(request, pk):\n post = get_object_or_404(Post, pk=pk)\n print(post)\n context = {\n 'post': post\n }\n\n return render(request=request, template_name='blog/post_show.html', context=context)\n\ndef post_save(request):\n if request.method == 'POST':\n form = PostForm(request.POST)\n if form.is_valid():\n post = form.save(commit=False)\n post.author = request.user\n post.published_at = timezone.now()\n post.save()\n return redirect(to='blog:post_show', pk=post.pk) \n else:\n form = PostForm()\n return render(request=request, template_name='blog/post_create.html', context={'form': form})\n\ndef post_edit(request, pk):\n post = get_object_or_404(Post, pk=pk)\n if request.method == 'POST':\n form = PostForm(request.POST, instance=post)\n if form.is_valid():\n post = form.save(commit=False)\n post.author = request.user\n post.published_at = timezone.now()\n post.save()\n return redirect(to='blog:post_show', pk=post.pk) \n else:\n form = PostForm(instance=post)\n return render(request=request, template_name='blog/post_edit.html', context={'form': form})","repo_name":"anderson89marques/djangoGirlTutorial","sub_path":"mysite/blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"74749985650","text":"import pygame, sys\nfrom pygame.locals import *\nfrom random import randrange, random\n\npygame.init()\n\nSIZE = 620\nN_CELLS = 31\nFPS = 25\nDIM = SIZE // N_CELLS\nfont = pygame.font.SysFont(\"Arial\", 16)\nscreen = pygame.display.set_mode((SIZE, SIZE))\n\npygame.display.set_caption(\"Pacman\")\nscreen.fill(\"brown\")\n\nclock = pygame.time.Clock()\ndone = False\ndirections = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n\n\nclass Maze:\n def __init__(self):\n self.cells = {}\n for i in range(N_CELLS):\n for j in range(N_CELLS):\n if i % 10 == 0 or j % 10 == 0:\n self.cells[(i, j)] = \"f\"\n else:\n self.cells[(i, j)] = \"w\"\n def draw(self):\n for i in range(N_CELLS):\n\n for j in range(N_CELLS):\n if self.cells[(i, j)] == \"w\":\n pygame.draw.rect(screen, \"brown\", (i * DIM, j * DIM, i * DIM + DIM, j * DIM + DIM), 0)\n\n elif self.cells[(i, j)] == \"f\":\n pygame.draw.rect(screen, \"white\", (i * DIM, j * DIM, i * DIM + DIM, j * DIM + DIM), 0)\n pygame.draw.circle(screen, \"green\", (i * DIM + DIM // 2, j * DIM + DIM // 2), 3, 0)\n pygame.draw.circle(screen, \"darkgreen\", (i * DIM + DIM // 2, j * DIM + DIM // 2), 4, 1)\n\n else:\n pygame.draw.rect(screen, \"white\", (i * DIM, j * DIM, i * DIM + DIM, j * DIM + DIM), 0)\n\n def is_valid(self, x, y):\n if not (x, y) in self.cells:\n return False\n if self.cells[(x, y)] == \"f\" or self.cells[(x, y)] == \"r\":\n return True\n else:\n return False\n\n\nclass Ghost:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n def draw(self):\n pygame.draw.circle(screen, \"blue\", (self.x * DIM + DIM // 2, self.y * DIM + DIM // 2), DIM // 2, 0)\n pygame.draw.rect(screen, \"blue\", (self.x * DIM, self.y * DIM + DIM // 2\n , DIM\n , DIM // 2), 0)\n pygame.draw.rect(screen, \"white\", (self.x * DIM + DIM // 5\n , self.y * DIM + DIM - DIM // 4\n , DIM // 5\n , DIM // 4), 0)\n pygame.draw.rect(screen, \"white\", (self.x * DIM + 3 * DIM // 5\n , self.y * DIM + DIM - DIM // 4\n , DIM // 5\n , DIM // 4), 0)\n pygame.draw.circle(screen, \"white\", (self.x * DIM + DIM // 2, self.y * DIM + DIM // 2), DIM // 8, 0)\n pygame.draw.circle(screen, \"white\", (self.x * DIM + 3 * DIM // 4, self.y * DIM + DIM // 2), DIM // 8, 0)\n\n def move(self, m):\n while True:\n (dx, dy) = directions[randrange(0, 4)]\n if m.is_valid(self.x + dx, self.y + dy):\n self.x += dx\n self.y += dy\n break\n\n\nclass Pacman:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n self.is_alive = True\n self.score = 0\n\n def draw(self):\n if self.is_alive:\n pygame.draw.circle(screen, \"gold\", (self.x * DIM + DIM // 2, self.y * DIM + DIM // 2), DIM // 2, 0)\n pygame.draw.circle(screen, \"black\", (self.x * DIM + DIM // 4, self.y * DIM + DIM // 4), DIM // 16, 0)\n pygame.draw.circle(screen, \"black\", (self.x * DIM + 3 * DIM // 4, self.y * DIM + DIM // 4), DIM // 16, 0)\n pygame.draw.ellipse(screen, \"red\", (self.x * DIM + DIM // 4, self.y * DIM + DIM // 2, DIM // 2, DIM // 4), 0)\n else:\n pygame.draw.circle(screen, \"red\", (self.x * DIM + DIM // 2, self.y * DIM + DIM // 2), DIM // 2, 0)\n pygame.draw.circle(screen, \"black\", (self.x * DIM + DIM // 4, self.y * DIM + DIM // 4), DIM // 16, 0)\n pygame.draw.circle(screen, \"black\", (self.x * DIM + 3 * DIM // 4, self.y * DIM + DIM // 4), DIM // 16, 0)\n pygame.draw.ellipse(screen, \"gold\", (self.x * DIM + DIM // 4, self.y * DIM + DIM // 2, DIM // 2, DIM // 4), 0)\n\n def collides_with(self, g):\n if self.x == g.x and self.y == g.y:\n return True\n else:\n return False\n\n def move(self, maze, dx, dy):\n if maze.is_valid(self.x + dx, self.y + dy):\n if maze.cells[(self.x + dx, self.y + dy)] == \"f\":\n self.score += 1\n maze.cells[(self.x + dx, self.y + dy)] = \"r\"\n self.x += dx\n self.y += dy\n return True\n else:\n return False\n\n\ndef get_random_pos(maze):\n if randrange(1, 2) == 1:\n x = randrange(1, 3) * 10\n y = randrange(1, 30)\n return x, y\n else:\n x = randrange(1, 30)\n y = randrange(1, 3) * 10\n return x, y\n\n\ndef still_some_food(maze):\n for i in range(N_CELLS):\n for j in range(N_CELLS):\n if maze.cells[i][j] == \"f\":\n return False\n return True\n\n\nm = Maze()\np = Pacman(0, 0)\nghosts = []\nfor i in range(4):\n x, y = get_random_pos(m)\n ghosts.append(Ghost(x, y))\n\n\nfinished = 0\n\nwhile not done:\n for event in pygame.event.get():\n if event.type == QUIT:\n done = True\n if not finished or p.score == 232:\n keys = pygame.key.get_pressed()\n if keys[K_w]:\n p.move(m, 0, -1)\n elif keys[K_s]:\n p.move(m, 0, 1)\n elif keys[K_a]:\n p.move(m, -1, 0)\n elif keys[K_d]:\n p.move(m, 1, 0)\n\n if not finished or p.score == 232:\n m.draw()\n\n for ghost in ghosts:\n ghost.draw()\n if p.collides_with(ghost):\n p.is_alive = False\n finished = 1\n continue\n ghost.move(m)\n \n \n p.draw()\n if finished:\n if p.score == 232:\n text = font.render(f\"YOU WON! SCORE:{p.score}\", True , \"green\")\n else:\n text = font.render(f\"YOU DIED! SCORE:{p.score}\", True , \"orange\")\n screen.blit(text, (SIZE // 2 - 65, SIZE // 2))\n\n pygame.display.update()\n clock.tick(FPS)\n\npygame.quit()\nsys.exit()","repo_name":"ChickTheNugget/InfoExamen","sub_path":"pacman.py","file_name":"pacman.py","file_ext":"py","file_size_in_byte":6262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"32528707119","text":"#!/usr/bin/python3\n#-*- encoding: Utf-8 -*-\nfrom collections import OrderedDict, defaultdict\nfrom os.path import exists, dirname, realpath\nfrom google.protobuf.message import Message\nfrom importlib import import_module, reload\nfrom tempfile import TemporaryDirectory\nfrom inspect import getmembers, isclass\nfrom sys import platform, path as PATH\nfrom os import environ, makedirs, sep\nfrom importlib.util import find_spec\nfrom argparse import ArgumentParser\nfrom urllib.parse import urlparse\nfrom platform import architecture\nfrom subprocess import run, PIPE\nfrom json import dump, load\nfrom re import findall, sub\nfrom pathlib import Path\nfrom shutil import which\n\n# Constructing paths - local data\n\nif platform != 'win32':\n BASE_PATH = Path(environ['HOME']) / '.pbtk'\nelse:\n BASE_PATH = Path(environ['APPDATA']) / 'pbtk'\nmakedirs(str(BASE_PATH / 'protos'), exist_ok=True)\nmakedirs(str(BASE_PATH / 'endpoints'), exist_ok=True)\n\n# Constructing paths - executables\n\nexternal = Path(dirname(realpath(__file__))) / 'external'\narch = '64' * (architecture()[0] == '64bit')\n\nprotoc = str(external / 'protoc' / ('protoc' + {'win32': '.exe', 'darwin': '_osx'}.get(platform, arch)))\ndex2jar = str(external / 'dex2jar' / ('d2j-dex2jar.' + {'win32': 'bat'}.get(platform, 'sh')))\njad = str(external / 'jad' / ('jad' + {'win32': '.exe', 'darwin': '_osx'}.get(platform, '')))\n\n# Disable the C++ extension for Python-Protobuf (for consistent behaviour) [1]\n# [1] https://github.com/google/protobuf/blob/cf1418/python/google/protobuf/internal/api_implementation.py#L72\n\nenviron['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'\n\n# Decorators for registering pluggable modules, documented at [2]\n# [2] https://github.com/marin-m/pbtk#source-code-structure\n\nextractors = OrderedDict()\n\"\"\"\ndef register_extractor(name = None, # Used to refer to internally\n desc = None, # Used to describe extractor in GUI\n pick_url = False, # Pick URL rather than file\n depends = None): # kwargs for assert_installed()\n\"\"\"\ndef register_extractor(**kwargs):\n def register_extractor_decorate(func):\n extractors[kwargs['name']] = {'func': func, **kwargs}\n return func\n return register_extractor_decorate\n\ntransports = OrderedDict()\n\"\"\"\ndef register_transport(name, # Used to refer to in JSON data files\n desc, # Used to describe protocol in GUI\n ui_tab = None, # Used to name the protocol data tab in fuzzer GUI (if any)\n ui_data_form = None, # Used to describe the nature of protocol data\n enforce_int_parameter = False): # Whether keys in protocol data are integer\n\"\"\"\ndef register_transport(**kwargs):\n def register_transport_decorate(func):\n transports[kwargs['name']] = {'func': func, **kwargs}\n return func\n return register_transport_decorate\n\n# General utility functions\n\ndef assert_installed(win=None, modules=[], binaries=[]):\n missing = defaultdict(list)\n for items, what, func in ((modules, 'modules', find_spec),\n (binaries, 'binaries', which)):\n for item in items:\n if not func(item):\n missing[what].append(item)\n if missing:\n msg = []\n for subject, names in missing.items():\n if len(names) == 1:\n subject = {'modules': 'module', 'binaries': 'binary'}[subject]\n msg.append('%s \"%s\"' % (subject, '\", \"'.join(names)))\n msg = 'You are missing the %s for this.' % ' and '.join(msg)\n if win:\n from PyQt5.QtWidgets import QMessageBox\n QMessageBox.warning(win, ' ', msg)\n else:\n raise ImportError(msg)\n return not missing\n\ndef insert_endpoint(base_path, obj):\n url = obj['request']['url']\n path = base_path / (urlparse(url).hostname + '.json')\n \n json = []\n if exists(str(path)):\n with open(str(path)) as fd:\n json = load(fd, object_pairs_hook=OrderedDict)\n \n # Try to merge objects\n inserted = False\n for obj2 in json:\n if obj2['request']['url'] == obj['request']['url'] and \\\n obj2['request'].get('pb_param') == obj['request'].get('pb_param'):\n \n # Try to merge data samples\n if 'samples' in obj2['request'] and 'samples' in obj['request']:\n \n if obj2['request']['transport'] == 'pburl_private':\n new_samples = []\n lite_samples = []\n \n for i in obj2['request']['samples'] + obj['request'].pop('samples'):\n # Simplify Protobuf-URL payloads\n lite = {k: sub('(!\\d+[^esz]|!\\d+s(?=\\d+|0x[a-f0-9]+:0x[a-f0-9]+(!|$)))[^!]+', r'\\1', v)\n if v.startswith('!') else v\n for k, v in i.items()}\n if lite not in lite_samples:\n new_samples.append(i)\n lite_samples.append(lite)\n \n obj2['request']['samples'] = new_samples\n \n else:\n for sample in obj['request'].pop('samples'):\n if sample not in obj2['request']['samples']:\n obj2['request']['samples'].append(sample)\n \n obj2['request'].update(obj['request'])\n if 'response' in obj:\n obj2['response'] = obj['response']\n inserted = True\n break\n \n if not inserted:\n json.append(obj)\n \n makedirs(str(path.parent), exist_ok=True)\n with open(str(path), 'w') as fd:\n dump(json, fd, ensure_ascii=False, indent=4)\n\n# Turn a .proto input into Python classes.\n\ndef load_proto_msgs(proto_path, ret_source_info=False):\n # List imports that we need to specify to protoc for the necessary *_pb2.py to be generated\n \n proto_dir = Path(proto_path).parent\n arg_proto_path = proto_dir\n arg_proto_files = []\n to_import = [str(proto_path)]\n \n while to_import:\n next_import = to_import.pop()\n while not exists(str(arg_proto_path / next_import)) and arg_proto_path.name:\n arg_proto_path = arg_proto_path.parent\n next_import = str(arg_proto_path / next_import)\n \n if next_import not in arg_proto_files:\n arg_proto_files.insert(0, next_import)\n with open(next_import) as fd:\n for prior_import in reversed(findall('import(?:\\s*weak|public)?\\s*\"(.+?)\"\\s*;', fd.read())):\n to_import.append(prior_import)\n \n # Execute protoc and import the actual module from a tmp\n \n with TemporaryDirectory() as arg_python_out:\n args = [protoc, '--proto_path=%s' % arg_proto_path, '--python_out=' + arg_python_out, *arg_proto_files]\n if ret_source_info:\n args += ['-o%s' % (Path(arg_python_out) / 'desc_info'), '--include_source_info', '--include_imports']\n \n cmd = run(args, stderr=PIPE)\n if cmd.returncode:\n raise ValueError(cmd.stderr.decode('utf8'))\n \n if ret_source_info:\n from google.protobuf.descriptor_pb2 import FileDescriptorSet\n\n with open(str(Path(arg_python_out) / 'desc_info'), 'rb') as fd:\n yield FileDescriptorSet.FromString(fd.read()), arg_proto_path\n return\n \n # Do actual import\n \n module_name = str(proto_dir).replace(str(arg_proto_path), '').strip('/\\\\').replace(sep, '.')\n if module_name:\n module_name += '.'\n module_name += Path(proto_path).stem.replace('-', '_') + '_pb2'\n\n PATH.append(arg_python_out)\n module = import_module(module_name)\n reload(module)\n PATH.remove(arg_python_out)\n \n # Recursively iterate over class members to list Protobuf messages\n\n yield from iterate_proto_msg(module, '')\n\ndef iterate_proto_msg(module, base):\n for name, cls in getmembers(module):\n if isclass(cls) and issubclass(cls, Message):\n yield base + name, cls\n yield from iterate_proto_msg(cls, base + name + '.')\n\n# Routine for saving data returned by an extractor\n\ndef extractor_save(base_path, folder, outputs):\n nb_written = 0\n name_to_path = {}\n wrote_endpoints = False\n \n for name, contents in outputs:\n if '.proto' in name:\n if folder:\n path = base_path / 'protos' / folder / name\n else:\n path = base_path / name\n \n makedirs(str(path.parent), exist_ok=True)\n with open(str(path), 'w') as fd:\n fd.write(contents)\n \n if name not in name_to_path:\n nb_written += 1\n name_to_path[name] = str(path)\n \n elif name.endswith('.sample'):\n endpoint = contents\n \n name = name.replace('.sample', '.proto')\n endpoint['proto_path'] = name_to_path[name]\n endpoint['proto_msg'] = name.replace('.proto', '')\n \n wrote_endpoints = True\n if folder:\n insert_endpoint(base_path / 'endpoints', {'request': endpoint})\n else:\n insert_endpoint(base_path, {'request': endpoint})\n \n return nb_written, wrote_endpoints\n\n# CLI entry point when calling an extractor as an individual script\n\ndef extractor_main(extractor):\n extractor = extractors[extractor]\n \n if assert_installed(**extractor.get('depends', {})):\n parser = ArgumentParser(description=extractor['desc'])\n if extractor.get('pick_url'):\n parser.add_argument('input_', metavar='input_url')\n else:\n parser.add_argument('input_', metavar='input_file')\n parser.add_argument('output_dir', type=Path, default='.', nargs='?')\n args = parser.parse_args()\n \n nb_written, wrote_endpoints = extractor_save(args.output_dir, '', extractor['func'](args.input_))\n if nb_written:\n print('\\n[+] Wrote %s .proto files to \"%s\".\\n' % (nb_written, args.output_dir))\n","repo_name":"marin-m/pbtk","sub_path":"utils/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":10247,"program_lang":"python","lang":"en","doc_type":"code","stars":1236,"dataset":"github-code","pt":"20"}
+{"seq_id":"72423475571","text":"import pathlib\nimport typing\n\n\nclass NewMonkey(typing.NamedTuple):\n monkey_id: int\n item_value: int\n\n\nclass Operation:\n _value: typing.Optional[int]\n\n def __init__(self, value: str) -> None:\n if value == \"old\":\n self._value = None\n else:\n self._value = int(value)\n\n def __call__(self, old: int) -> int:\n return self._do(old)\n\n def _do(self, old: int) -> int:\n raise NotImplementedError\n\n\nclass Sum(Operation):\n\n def _do(self, old) -> int:\n return old + self._value\n\n\nclass Multiply(Operation):\n\n def _do(self, old: int) -> int:\n if self._value is None:\n return old * old\n return old * self._value\n\n\nclass Monkey:\n def __init__(self, init_values: list[str]) -> None:\n self._id = int(init_values[0].split()[1][:-1])\n self._items: list[int] = list(map(int, init_values[1].strip()[len('starting items:'):].split(', ')))\n self._operation: Operation = self.__createOperation(init_values[2])\n self._divisible_by: int = int(init_values[3].split()[-1])\n self._if_true_throw_to: int = int(init_values[4].split()[-1])\n self._if_false_throw_to: int = int(init_values[5].split()[-1])\n\n self._inspected = 0\n self._initial_items = list(self._items)\n\n def reset(self):\n self._inspected = 0\n self._items = list(self._initial_items)\n\n @staticmethod\n def __createOperation(line: str) -> Operation:\n v = line[len('Operation: new = '):]\n value = v.split()[-1]\n if '+' in v:\n return Sum(value=value)\n return Multiply(value=value)\n\n def __repr__(self) -> str:\n return f\"Monkey {self._id}: items {self._items}\"\n\n def inspectItems(self, i_feel_fine=False) -> list[NewMonkey]:\n new_values: list[NewMonkey] = []\n\n divisor = 1 if i_feel_fine else 3\n\n for old_value in self._items:\n self._inspected += 1\n new_value = self._operation(old_value) // divisor\n if not (new_value % self._divisible_by):\n new_values.append(NewMonkey(self._if_true_throw_to, new_value))\n else:\n new_values.append(NewMonkey(self._if_false_throw_to, new_value))\n\n self._items = []\n\n return new_values\n\n def addItem(self, new_item: int) -> None:\n self._items.append(new_item)\n\n def totalInspected(self) -> tuple[int, int]:\n return self._id, self._inspected\n\n\ndef main():\n monkeys: list[Monkey] = []\n\n with open(pathlib.Path(__file__, '..', 'input.txt').resolve()) as f:\n init_values: list[str] = []\n for line in f:\n if not line.strip():\n monkeys.append(Monkey(init_values))\n init_values = []\n else:\n init_values.append(line.strip())\n monkeys.append(Monkey(init_values))\n\n all_divisible = 1\n for monkey in monkeys:\n all_divisible *= monkey._divisible_by\n\n for i in range(20):\n\n for monkey in monkeys:\n for item in monkey.inspectItems():\n monkeys[item.monkey_id].addItem(item.item_value)\n\n more_inspected_step1 = sorted([monkey.totalInspected() for monkey in monkeys], key=lambda x: x[1], reverse=True)[:2]\n\n for monkey in monkeys:\n monkey.reset()\n\n for i in range(10000):\n\n for monkey in monkeys:\n for item in monkey.inspectItems(i_feel_fine=True):\n value = item.item_value % all_divisible\n if value == 0:\n value = all_divisible\n monkeys[item.monkey_id].addItem(value)\n\n more_inspected_step2 = sorted([monkey.totalInspected() for monkey in monkeys], key=lambda x: x[1], reverse=True)[:2]\n\n return more_inspected_step1[0][1] * more_inspected_step1[1][1], more_inspected_step2[0][1] * more_inspected_step2[1][1]\n\n\nif __name__ == \"__main__\":\n print(f\"Result: {main()}\")\n","repo_name":"bara86/aoc-2022","sub_path":"day11/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"27055694814","text":"import unittest\nimport os\n\nfrom flask import current_app\nfrom app import create_app\nfrom config import basedir\n\nclass TestDevelopmentConfig(unittest.TestCase):\n def test_app_is_development(self):\n \"\"\" Check if application is running in development mode \"\"\"\n app = create_app(\"development\") # uyg dev başlatabiliyor muyum\n\n self.assertFalse(app.config[\"SECRET_KEY\"] == \"GahNooSlasHLinUcks\") # dev başlattığım uygulamada secret key şuna sahip mi\n self.assertTrue(app.config[\"DEBUG\"]) # dev başlattığım uygulamada debug true mi\n self.assertFalse(current_app is None) # dev başlattığım uygulamada current_app null mı\n self.assertTrue(\n app.config[\"SQLALCHEMY_DATABASE_URI\"]\n == \"postgresql+psycopg2://user:123456@localhost:5432/burgerzilla\"\n )\n\n\nclass TestProductionConfig(unittest.TestCase):\n def test_app_is_production(self):\n \"\"\" Check if application is running in production mode \"\"\"\n app = create_app(\"production\")\n\n self.assertTrue(app.config[\"DEBUG\"] is False)\n","repo_name":"Yemeksepeti-Python-Bootcamp/burgerzilla_iremdoganci","sub_path":"tests/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"20"}
+{"seq_id":"43875164682","text":"from random import *\nfrom PathManager import PathManager\nfrom MapGenerator import *\nfrom PySide2.QtCore import Qt, Slot, QSize, Signal, QObject\nfrom PySide2.QtGui import QPixmap, QImage, qRgb\nfrom PySide2.QtWidgets import *\n\nclass Widget(QWidget):\n\n def __init__(self):\n QWidget.__init__(self)\n self.generator = MapGenerator()\n\n self.mainLayout = QVBoxLayout()\n self.topBar = QWidget()\n self.topBarLayout = QHBoxLayout()\n self.topBar.setLayout(self.topBarLayout)\n self.display = QLabel()\n self.mainLayout.addWidget(self.topBar)\n self.mainLayout.addWidget(self.display)\n self.setLayout(self.mainLayout)\n\n self.topBar.setMaximumHeight(80)\n self.startButton = QPushButton(\"Generate\")\n self.progressBar = QProgressBar()\n self.progressBar.setRange(0,200)\n self.generator.updateProgress.connect(self.progressBar.setValue)\n self.topBarLayout.addWidget(self.startButton)\n self.topBarLayout.addWidget(self.progressBar)\n\n self.generator.displayCompleted.connect(self.updateImage)\n self.generator.printUpdate.connect(self.printUpdateToConsole)\n #self.display.setPixmap(QPixmap.fromImage(self.generator.image))\n\n scale = 0.25\n size = (int(3000 * scale), int(2000 * scale))\n self.generator.configure(size=size)\n\n def generate(self):\n self.generator.start()\n\n def printUpdateToConsole(self, message):\n print(message)\n\n def updateImage(self):\n self.display.setPixmap(QPixmap.fromImage(self.generator.image))\n\n def save(self):\n print(self.generator.image.save(PathManager.path(\"images\") + self.generator.seedname + \" - \" + str(self.generator.faultCount) + \".png\"))\n\nclass MainWindow(QMainWindow):\n\n def __init__(self, widget):\n QMainWindow.__init__(self)\n self.widget = widget\n #geometry = app.desktop().availableGeometry(self)\n #self.setFixedSize(geometry.width() * 0.95, geometry.height() * 0.90)\n self.setFixedSize(3000,2000)\n self.setWindowTitle(\"MapSavvy\")\n\n PathManager.insureFolder(PathManager.libraryPath+\"images\")\n\n widget.setFixedSize(self.size())\n\n # Menu\n self.menu = self.menuBar()\n self.file_menu = self.menu.addMenu(\"File\")\n\n # Exit QAction\n exit_action = QAction(\"Exit\", self)\n exit_action.setShortcut(\"Ctrl+Q\")\n exit_action.triggered.connect(self.exit_app)\n\n # Saving the Image\n save_action = QAction(\"Save\", self)\n #save_action.setShortcut(\"Ctrl+S\")\n save_action.triggered.connect(self.save)\n\n self.file_menu.addAction(save_action)\n self.file_menu.addAction(exit_action)\n\n # Status Bar\n self.status = self.statusBar()\n self.status.showMessage(\"Loaded and plotted\")\n\n self.setCentralWidget(widget)\n\n def save(self):\n self.widget.save()\n\n @Slot()\n def exit_app(self, checked):\n sys.exit()\n","repo_name":"Zellenon/MapSavvy","sub_path":"src/MapGUI.py","file_name":"MapGUI.py","file_ext":"py","file_size_in_byte":2994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"22691463018","text":"from data_files import data\nimport random\nfrom art import logo, vs\nimport os\nimport time\n\ndef random_():\n alegere = random.randint(0, len(data) - 1)\n return data[alegere]\n\n\n\ndef jocul():\n scor = 0\n game0 = True\n game0_1 = False\n alegere_1 = random_()\n alegere_2 = random_()\n scor_provizoriu = 0\n while game0:\n print(logo)\n if game0_1 == True:\n print(\"Nu am inteles!....Te rog repeta\")\n if scor > 0:\n if game0_1 == True:\n print(f\"Your current score: {scor}\")\n else:\n print(f\"You're right! Current score: {scor}.\")\n print(\"Compare A: \" + alegere_1[\"name\"] + \", \" + alegere_1[\"description\"] + \", \" + alegere_1[\"country\"])\n print(vs)\n print(\"Compare B: \" + alegere_2[\"name\"] + \", \" + alegere_2[\"description\"] + \", \" + alegere_2[\"country\"])\n alegere = input(\"Who has more followers? Type 'A' or 'B':\").lower()\n if alegere == \"a\":\n if alegere_1[\"follower_count\"] >= alegere_2[\"follower_count\"]:\n alegere_1 = alegere_2\n scor = scor + 1\n alegere_2 = random_()\n else:\n game0 = False\n elif alegere == \"b\":\n if alegere_2[\"follower_count\"] >= alegere_1[\"follower_count\"]:\n alegere_1 = alegere_2\n scor = scor + 1\n alegere_2 = random_()\n else:\n game0 = False\n elif alegere != \"a\" or alegere != \"b\":\n game0_1 = True\n continue\n if game0 == False:\n if scor > 0:\n return print(f\"Sorry, that's wrong. Final score: {scor}\")\n else:\n return print(\"Sorry, that's wrong\")\n os.system('cls')\n \njocul()\ngame1 = True\ninput(\"Apasa \\\"ENTER\\\" pentru a continua\")\nwhile game1:\n os.system('cls')\n alegere = input(\"Doresti sa mai joci?...Scrie \\\"da\\\" sau \\\"nu\\\": \").lower()\n if alegere == \"da\":\n jocul()\n input(\"Apasa \\\"ENTER\\\" pentru a continua\")\n elif alegere == \"nu\":\n os.system('cls')\n print(\"Bine.....La revedere...\")\n time.sleep(2)\n game1 = False\n else:\n print(\"Nu am inteleses....\")\n continue\n\n","repo_name":"cadfrunze/higher_lower_game","sub_path":"jocul.py","file_name":"jocul.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"20"}
+{"seq_id":"14600085765","text":"# 拿页面源代码->解析\n\nimport requests\nfrom lxml import etree\n\nurl = \"https://supei.zbj.com/search/service?kw=saas&r=2\"\nresp = requests.get(url)\nprint(resp.text)\n# print(resp.text)\n\nhtml = etree.HTML(resp.text)\ndivs= html.xpath(\"/html/body/div[2]/div/div/div[2]/div/div[2]/div[3]/div[1]/div\")\nfor div in divs:\n price = div.xpath(\"./div[2]/div/span[1]/text()\")\n print(div.xpath(\"./text()\"))\n\n\nprice = html.xpath(\"/html/body/div[2]/div/div/div[2]/div/div[2]/div[3]/div[1]/div[1]/div[2]/div/span[1]/text()\")\nprint(price)","repo_name":"HideOnHeart0507/Personal_Skills","sub_path":"spider/part2/xpath案例.py","file_name":"xpath案例.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"20"}
+{"seq_id":"15604448409","text":"import cv2\n\n\ndef draw_rect(img, boxes, color=(255, 0, 0), box_thickness=2):\n if isinstance(boxes, list):\n for (x, y, w, h) in boxes:\n cv2.rectangle(img, (x, y), (x + w, y + h), color, box_thickness)\n elif isinstance(boxes, tuple):\n x, y, w, h = boxes\n cv2.rectangle(img, (x, y), (x + w, y + h), color, box_thickness)\n return img\n","repo_name":"YanzheL/CatchU","sub_path":"utils/drawer.py","file_name":"drawer.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"26"}
+{"seq_id":"33863807947","text":"import os\nfrom os import walk\n#input_directory = \"./swabian/20200824_sample/33db\"\n#input_directory = \"./test\"\n#input_directory = \"/media/tanvir/QubeSat1_64/23_10_2020\"\n#input_directory = \"/media/tanvir/SpooqyLab/Table_Top_Demo/30sep2020\"\n#input_directory = \"/media/tanvir/QubeSat1_64/23_10_2020\"\ninput_directory = \"./dark\"\ncoincidence_window = 1500 #pico-seconds\n\n\nwork_directory = \"23Oct2020_dark\"\n\nlog_file_name = \"result_\" + work_directory+\"_cw\"+str(coincidence_window)+\"ps.csv\"\n\ninput_files = []\nfor (dirpath, dirnames, filenames) in walk(input_directory):\n input_files.extend(filenames)\n break\n\nos.system(\"mkdir -p \"+work_directory)\n\nprint(input_directory)\nprint(input_files)\n\nfp = open(work_directory+\"/\"+log_file_name,\"w\")\n#print(work_directory+log_file_name)\nfp.write(\"input_filename, alice_singles_rate, bob_singles_rate, coincidence_window(ps), coincidence_count_rate, sifted_key_length, num_error, QBER, hv_count,ad_count,alice_efficiency(%), bob_effeciency(%),duration(s),hv_QBER,ad_QBER\\n\")\nfp.close()\n\nfile_count = 0\nfor file in input_files:\n if (\"B.ttbin\" in file):\n file_count +=1 \ni = 0\nfor file in input_files:\n if (\"B.ttbin\" in file):\n print (file_count-i, \"files to go\")\n print (\"working on: \"+file)\n i+=1\n #./run.sh ./swabian/20200714_Swabian_Timestamp/data_2 4_1uW0dB.1.ttbin 14july0db \n #os.system( \"print '\"+file+\",'>> input_directory+\"/\"+log_file_name\")\n os.system(\"./run.sh \"+input_directory+\" \"+file+\" \"+work_directory+\" \"+log_file_name+\" \"+str(coincidence_window)+\" \"+\"runbatch\")\n print (file,\"[Done]\")\n \n","repo_name":"tanvirulz/satQKD2","sub_path":"runbatch.py","file_name":"runbatch.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"72778045508","text":"from flask import render_template, redirect, request, url_for, flash, make_response\r\nfrom . import outpatient\r\nfrom .form import OpCheckForm, OpCheckinForm, OpExamForm, OpRecipeForm, OpIndexFrom\r\nfrom ..model import OpCheck, OpCheckAfford, OpCheckin, OpCheckinAfford, OpExam, OpExamAfford, OpRecipe, OpRecipeAfford, Medicine, Price, UserInfo, OpCost\r\nfrom .. import db\r\nfrom ..decorator import is_login\r\nimport datetime\r\n\r\n\r\n@outpatient.route('/outpatient/checkin', methods=['GET', 'POST'])\r\n@is_login\r\ndef checkin(name):\r\n patientcheckin = OpCheckin()\r\n form = OpCheckinForm()\r\n price = OpCheckinAfford()\r\n if request.method == 'GET':\r\n return render_template('outpatient/checkin.html', form= form, name= name)\r\n else:\r\n if form.validate_on_submit():\r\n response = make_response(redirect(url_for('outpatient.opindex', name= name)))\r\n prepatient = OpCheckin.query.order_by(OpCheckin.opcheckinid.desc()).first()\r\n patientcheckin.opcheckinid = prepatient.opcheckinid + 1\r\n\r\n nowpcheckid = patientcheckin.opcheckinid\r\n response.set_cookie('nopcid', str(nowpcheckid))\r\n\r\n patientcheckin.patientid = form.patientid.data\r\n patientcheckin.doctorid = form.doctorname.data\r\n docid = UserInfo.query.filter_by(id= form.doctorname.data).first()\r\n patientcheckin.doctortype = docid.rank\r\n patientcheckin.jips = False\r\n\r\n price.opcheckinid = patientcheckin.opcheckinid\r\n price.opid = form.patientid.data\r\n priceinfo = Price.query.filter_by(optionid= docid.rank).first()\r\n price.price = priceinfo.price\r\n\r\n db.session.add(patientcheckin)\r\n db.session.add(price)\r\n db.session.commit()\r\n\r\n return response\r\n # print(nowpcheckid)\r\n # return redirect(url_for('outpatient.opindex'))\r\n \r\n\r\n@outpatient.route('/outpatient/opindex', methods= ['GET', 'POST'])\r\n@is_login\r\ndef opindex(name):\r\n form = OpIndexFrom()\r\n nowpcheckid = int(request.cookies.get('nopcid'))\r\n print('11')\r\n if request.method == 'GET':\r\n # nowpcheckid = request.cookies.get('nopcid')\r\n # print('2', type(int(nowpcheckid)), int(nowpcheckid))\r\n return render_template('outpatient/opindex.html', form=form, name=name)\r\n else:\r\n if form.inpatientcheck.data == True:\r\n opcheckininfo = OpCheckin.query.filter_by(\r\n opcheckinid=nowpcheckid).first()\r\n opcheckininfo.jips = True\r\n db.session.commit()\r\n else:\r\n pass\r\n return render_template('outpatient/opindex.html', form=form, name=name)\r\n\r\n\r\n@outpatient.route('/outpatient/exam', methods=['GET', 'POST'])\r\n@is_login\r\ndef exam(name):\r\n patientexam = OpExam()\r\n form = OpExamForm()\r\n price = OpExamAfford()\r\n nowpcheckid = request.cookies.get('nopcid')\r\n if request.method == 'GET':\r\n return render_template('outpatient/exam.html', form=form, name=name)\r\n else:\r\n if form.validate_on_submit():\r\n patientexam.opcheckinid = int(nowpcheckid)\r\n patientexam.opid = form.opid.data\r\n patientexam.examitems = ','.join(form.examitems.data)\r\n\r\n price.opcheckinid = patientexam.opcheckinid\r\n price.opid = form.opid.data\r\n count = 0\r\n for item in form.examitems.data:\r\n examinfo = Price.query.filter_by(optionid=int(item)).first()\r\n count = count + examinfo.price\r\n price.price = count\r\n\r\n db.session.add(patientexam)\r\n db.session.add(price)\r\n db.session.commit()\r\n flash('选择的检验项目已经上传')\r\n return redirect(url_for('outpatient.opindex', name=name))\r\n\r\n\r\n@outpatient.route('/outpatient/check', methods=['GET', 'POST'])\r\n@is_login\r\ndef check(name):\r\n patientcheck = OpCheck()\r\n form = OpCheckForm()\r\n price = OpCheckAfford()\r\n nowpcheckid = request.cookies.get('nopcid')\r\n if request.method == 'GET':\r\n return render_template('outpatient/check.html', form=form, name=name)\r\n else:\r\n if form.validate_on_submit():\r\n patientcheck.opcheckinid = int(nowpcheckid)\r\n patientcheck.opid = form.opid.data\r\n patientcheck.checkitems = ','.join(form.checkitems.data)\r\n\r\n price.opcheckinid = patientcheck.opcheckinid\r\n price.opid = form.opid.data\r\n count = 0\r\n for item in form.checkitems.data:\r\n checkinfo = Price.query.filter_by(optionid=int(item)).first()\r\n count = count + checkinfo.price\r\n price.price = count\r\n\r\n db.session.add(patientcheck)\r\n db.session.add(price)\r\n db.session.commit()\r\n flash('选择的检查项目已经上传')\r\n return redirect(url_for('outpatient.opindex', name=name))\r\n\r\n\r\n@outpatient.route('/outpatient/recipe', methods=['GET', 'POST'])\r\n@is_login\r\ndef recipe(name):\r\n patientrecipe = OpRecipe()\r\n form = OpRecipeForm()\r\n nowpcheckid = request.cookies.get('nopcid')\r\n if request.method == 'GET':\r\n return render_template('/outpatient/medicine.html', form=form, name=name)\r\n else:\r\n if form.validate_on_submit():\r\n patientrecipe.opcheckinid = int(nowpcheckid)\r\n patientrecipe.opid = form.opid.data\r\n patientrecipe.medicinenames = ','.join(form.medicines.data)\r\n db.session.add(patientrecipe)\r\n db.session.commit()\r\n return redirect(url_for('outpatient.recipenum', name=name))\r\n\r\n\r\n@outpatient.route('/outpatient/recipenum', methods=['GET', 'POST'])\r\n@is_login\r\ndef recipenum(name):\r\n nowpcheckid = request.cookies.get('nopcid')\r\n price = OpRecipeAfford()\r\n if request.method == 'GET':\r\n patientcheckinid = int(nowpcheckid)\r\n selectedinfo = OpRecipe.query.filter_by(\r\n opcheckinid=patientcheckinid).first()\r\n medicinenames = selectedinfo.medicinenames\r\n medslist = medicinenames.split(',')\r\n medsnlist = []\r\n for item in medslist:\r\n med = Medicine.query.filter_by(id=item).first()\r\n medname = med.medicinename\r\n medsnlist.append(medname)\r\n return render_template('outpatient/recipenum.html', medsnlist=medsnlist, name=name)\r\n else:\r\n patientcheckinid = int(nowpcheckid)\r\n oprecipe = OpRecipe.query.filter(\r\n OpRecipe.opcheckinid == patientcheckinid).first()\r\n mednumbers = []\r\n d = request.values.to_dict()\r\n for number in d.keys():\r\n mednumbers.append(d.get(number))\r\n oprecipe.medicinenumbers = ','.join(mednumbers)\r\n db.session.commit()\r\n\r\n price.opcheckinid = patientcheckinid\r\n opreinfo = OpRecipe.query.filter_by(\r\n opcheckinid=patientcheckinid).first()\r\n price.opid = opreinfo.opid\r\n recipeinfo = OpRecipe.query.filter_by(\r\n opcheckinid=patientcheckinid).first()\r\n recipemdname = recipeinfo.medicinenames\r\n recipemdnamel = recipemdname.split(',')\r\n recipenum = recipeinfo.medicinenumbers\r\n recipenuml = recipenum.split(',')\r\n count = 0\r\n zipinfo = zip(recipemdnamel, recipenuml)\r\n for item in zipinfo:\r\n medinfo = Price.query.filter_by(optionid=int(item[0])).first()\r\n count = count + medinfo.price * int(item[1])\r\n # for item in recipemdnamel:\r\n # medinfo = Price.query.filter_by(optionid= int(item)).first()\r\n # count = count + medinfo.price *\r\n price.price = count\r\n\r\n db.session.add(price)\r\n db.session.commit()\r\n flash('处方已经上传完成')\r\n return redirect(url_for('outpatient.opindex', name=name))\r\n\r\n\r\n@outpatient.route('/outpatient/cost', methods=['GET', 'POST'])\r\n@is_login\r\ndef cost(name):\r\n cost = OpCost()\r\n nowpcheckid = int(request.cookies.get('nopcid'))\r\n if request.method == 'GET':\r\n opcheckininfo = OpCheckinAfford.query.filter_by(\r\n opcheckinid=nowpcheckid).first()\r\n opexaminfo = OpExamAfford.query.filter_by(\r\n opcheckinid=nowpcheckid).first()\r\n opcheckinfo = OpCheckAfford.query.filter_by(\r\n opcheckinid=nowpcheckid).first()\r\n oprecipeinfo = OpRecipeAfford.query.filter_by(\r\n opcheckinid=nowpcheckid).first()\r\n ociprice = opcheckininfo.price\r\n oeprice = opexaminfo.price\r\n ocprice = opcheckinfo.price\r\n orprice = oprecipeinfo.price\r\n\r\n cost.opcheckinid = nowpcheckid\r\n cost.price = ociprice + oeprice + ocprice + orprice\r\n\r\n price = cost.price\r\n\r\n db.session.add(cost)\r\n db.session.commit()\r\n return render_template('outpatient/cost.html', price=price, name=name)\r\n","repo_name":"zh58815519/HospitalManagement","sub_path":"app/outpatient/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"35516212990","text":"import re\nfrom argparse import ArgumentParser\nfrom raspyrfm import *\nimport threading\nimport time\nimport json\n\nPARAM_ID = ('i', 'id')\nPARAM_HOUSE = ('o', 'house')\nPARAM_GROUP = ('g', 'group')\nPARAM_UNIT = ('u', 'unit')\nPARAM_COMMAND = ('a', 'command')\nPARAM_CODE = ('c', 'code')\nPARAM_DIPS = ('d', 'dips')\n\nCLASS_RCSWITCH = \"switch\"\nCLASS_RCWEATHER = \"weather\"\n\nclass RcPulse:\n\tdef __init__(self):\n\t\tself.__numbits = 0\n\t\tself._ookdata = bytearray()\n\t\tself.__bbuf = 0\n\t\tself.__bval = 0\n\t\tself._parser = ArgumentParser()\n\t\tself._lastdecode = None\n\t\tself._lastdecodetime = 0\n\n\t\tsymset = set()\n\t\tfor k in self._symbols:\n\t\t\tsymset |= set(self._symbols[k])\n\n\t\tsymlist = list(symset)\n\t\tsymlist.sort()\n\t\tq = 0\n\t\tfor i in range(len(symlist) - 1):\n\t\t\tif 1.0 * symlist[i] / symlist[i+1] > q:\n\t\t\t\tq = 1.0 * symlist[i] / symlist[i+1]\n\t\t\t\tp1 = 1.0 * symlist[i]\n\t\t\t\tp2 = 1.0 * symlist[i+1]\n\n\t\tif q > 0:\n\t\t\tf = (p2-p1) / (p2+p1)\n\t\t\tself._minwidth = self._timebase * (1 - f)\n\t\t\tself._maxwidth = self._timebase * (1 + f)\n\n\tdef _reset(self):\n\t\tself.__numbits = 0\n\t\tself._ookdata = bytearray()\n\t\tself.__bbuf = 0\n\t\tself.__bval = 0\n\n\tdef _add_pulses(self, numbits):\n\t\tfor num in numbits:\n\t\t\tself.__bval ^= 1\n\t\t\tfor i in range(num):\n\t\t\t\tself.__bbuf <<= 1\n\t\t\t\tself.__bbuf |= self.__bval\n\t\t\t\tself.__numbits += 1\n\t\t\t\tif self.__numbits == 8:\n\t\t\t\t\tself._ookdata.append(self.__bbuf)\n\t\t\t\t\tself.__bbuf = 0\n\t\t\t\t\tself.__numbits = 0\n\n\tdef _add_finish(self):\n\t\tif (self.__numbits > 0):\n\t\t\tself.__bval ^= 1\n\t\t\tself._add_pulses([8 - self.__numbits])\n\n\tdef _add_symbols(self, symbols):\n\t\tfor s in symbols:\n\t\t\tsym = self._symbols[s]\n\t\t\tfor pulse in sym:\n\t\t\t\tself._add_pulses([pulse])\n\n\tdef _match_symbol(self, pulsetrain, symbol):\n\t\tif len(pulsetrain) != len(symbol):\n\t\t\treturn False\n\n\t\tsumpulse = 0\n\t\tsumstep = 0\n\t\tfor i, v in enumerate(symbol):\n\t\t\tif not (self._minwidth <= 1.0 * pulsetrain[i] / v <= self._maxwidth):\n\t\t\t\treturn\n\t\t\tsumpulse += pulsetrain[i]\n\t\t\tsumstep += v\n\t\treturn (sumpulse, sumstep)\n\n\tdef _decode_symbols(self, pulsetrain):\n\t\t#match symbols\n\t\tdec = \"\"\n\t\tpos = 0\n\t\tsumpulse = 0\n\t\tsumstep = 0\n\t\twhile pos < len(pulsetrain):\n\t\t\tmatch = None\n\t\t\tfor s in self._symbols:\n\t\t\t\tslen = len(self._symbols[s])\n\t\t\t\tmatch = self._match_symbol(pulsetrain[pos:pos+slen], self._symbols[s])\n\t\t\t\tif match:\n\t\t\t\t\tdec += s\n\t\t\t\t\tpos += slen\n\t\t\t\t\tsumpulse += match[0]\n\t\t\t\t\tsumstep += match[1]\n\t\t\t\t\tbreak\n\n\t\t\tif not match:\n\t\t\t\treturn None, None, None\n\n\t\tif re.match(\"^\" + self._pattern + \"$\", dec):\n\t\t\trep = True\n\t\t\tif (self._lastdecode != dec) or (time.time() - self._lastdecodetime > 0.5):\n\t\t\t\tself._lastdecode = dec\n\t\t\t\trep = False\n\t\t\tself._lastdecodetime = time.time()\n\t\t\treturn dec, int(1.0 * sumpulse / sumstep), rep\n\n\t\treturn None, None, None\n\n\tdef _encode_command(self, command):\n\t\tfor key in self._commands:\n\t\t\tif key.lower() == command.lower():\n\t\t\t\treturn self._commands[key]\n\t\traise Exception(\"Invalid command '\" + str(command) + \"'\")\n\n\tdef _decode_command(self, symbols):\n\t\tfor k in self._commands:\n\t\t\tif self._commands[k] == symbols:\n\t\t\t\treturn k\n\t\traise Exception(\"Unknown command\")\n\n\tdef _build_frame(self, symbols, timebase=None, repetitions=None):\n\t\tself._reset()\n\n\t\tif hasattr(self, '_header'):\n\t\t\tself._add_pulses(self._header)\n\t\tself._add_symbols(symbols)\n\t\tif hasattr(self, '_footer'):\n\t\t\tself._add_pulses(self._footer)\n\t\tself._add_finish()\n\t\tif repetitions is None:\n\t\t\trepetitions = self._repetitions\n\t\treturn self._ookdata * repetitions, timebase if timebase else self._timebase\n\n\tdef decode(self, pulsetrain):\n\t\tpass\n\n\tdef encode(self, params):\n\t\tpass\n\n\tdef get_decode_dict(self, decresult):\n\t\tif not hasattr(self, \"_params\"):\n\t\t\treturn None\n\t\tresult = {}\n\t\tfor i in range(len(decresult)):\n\t\t\tresult[self._params[i][1]] = decresult[i]\n\t\treturn result\n\n\tdef get_mqtt_from_dict(self, li):\n\t\ttopic = \"\"\n\t\tfor i in range(len(self._params[:-1])):\n\t\t\ttopic += \"/\" + str(li[self._params[i][1]])\n\t\tpay = li[self._params[-1][1]]\n\t\tif type(pay) is str:\n\t\t\tpay = pay.upper()\n\t\treturn topic, pay\n\nclass RcRaw(RcPulse):\n\tdef __init__(self, timebase):\n\t\tself._timebase = timebase\n\t\tself._symbols = {}\n\t\tRcPulse.__init__(self)\n\n\tdef build_raw(self, pulsetrain, repetitions):\n\t\tself._reset()\n\t\tfor p in pulsetrain:\n\t\t\tself._add_pulses([round(p / self._timebase)])\n\t\tself._add_finish()\n\t\treturn self._ookdata * repetitions\n\nclass TristateBase(RcPulse): #Baseclass for old intertechno, Brennenstuhl, ...\n\tdef __init__(self):\n\t\tif not hasattr(self, \"_timebase\"):\n\t\t\tself._timebase = 300\n\t\tself._repetitions = 4\n\t\tself._pattern = \"[01F]{12}\"\n\t\tself._symbols = {\n\t\t\t'0': [1, 3, 1, 3],\n\t\t\t'1': [3, 1, 3, 1],\n\t\t\t'F': [1, 3, 3, 1],\n\t\t}\n\t\tself._footer = [1, 31]\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\n\tdef _encode_int(self, ival, digits):\n\t\tcode = \"\"\n\t\tfor i in range(digits):\n\t\t\tcode += \"F\" if (ival & 0x01) > 0 else \"0\"\n\t\t\tival >>= 1\n\t\treturn code\n\n\tdef _decode_int(self, tristateval):\n\t\ti = 0\n\t\twhile tristateval != \"\":\n\t\t\ti <<= 1\n\t\t\tif tristateval[-1] == \"F\":\n\t\t\t\ti |= 1\n\t\t\ttristateval = tristateval[:-1]\n\t\treturn i\n\n\nclass Tristate(TristateBase):\n\tdef __init__(self):\n\t\tself._name = \"tristate\"\n\t\tTristateBase.__init__(self)\n\t\tself._params = [PARAM_CODE]\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\treturn self._build_frame(params[\"code\"].upper(), timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\treturn {\"code\": symbols}, tb, rep, [], symbols\n\nclass ITTristate(TristateBase): #old intertechno\n\tdef __init__(self):\n\t\tself._name = \"ittristate\"\n\t\tTristateBase.__init__(self)\n\t\tself._pattern = \"[0F]{8}0F(FF|F0)\"\n\t\tself._params = [PARAM_HOUSE, PARAM_GROUP, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._commands = {\"on\": \"FF\", \"off\": \"F0\"}\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\thouse = params[\"house\"].upper()[0]\n\t\tsymbols += self._encode_int(ord(house) - ord('A'), 4)\n\t\tsymbols += self._encode_int(int(params[\"unit\"]) - 1, 2)\n\t\tsymbols += self._encode_int(int(params[\"group\"]) - 1, 2)\n\t\tsymbols += \"0F\"\n\t\tsymbols += self._encode_command(params[\"command\"])\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\thouse = chr(self._decode_int(symbols[:4]) + ord('A'))\n\t\t\tunit = self._decode_int(symbols[4:6]) + 1\n\t\t\tgroup = self._decode_int(symbols[6:8]) + 1\n\t\t\tcommand = self._decode_command(symbols[10:12])\n\t\t\treturn [house, group, unit, command], tb, rep\n\nclass Brennenstuhl(TristateBase):\n\tdef __init__(self):\n\t\tself._name = \"brennenstuhl\"\n\t\tTristateBase.__init__(self)\n\t\tself._pattern = \"[0F]{5}(0FFF|F0FF|FF0F|FFF0)F(0F|F0)\"\n\t\tself._params = [PARAM_DIPS, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._commands = {\"on\": \"0F\", \"off\": \"F0\"}\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\tfor c in params[\"dips\"]:\n\t\t\tsymbols += '0' if c == '1' else 'F'\n\t\tfor i in range(4):\n\t\t\tsymbols += '0' if (int(params[\"unit\"]) - 1) == i else 'F'\n\t\tsymbols += \"F\"\n\t\tsymbols += self._encode_command(params[\"command\"])\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\tdips = \"\"\n\t\t\tfor s in symbols[0:5]:\n\t\t\t\tdips += \"1\" if s == '0' else \"0\"\n\t\t\tunit = 0\n\t\t\tfor u in symbols[5:9]:\n\t\t\t\tunit += 1\n\t\t\t\tif u == '0':\n\t\t\t\t\tbreak\n\n\t\t\tcommand = self._decode_command(symbols[10:12].upper())\n\t\t\treturn [dips, unit, command], tb, rep\n\nclass PPM1(RcPulse): #Intertechno, Hama, Nexa, Telldus, ...\n\t'''\n\tPDM1: Pulse Position Modulation\n\tEvery bit consists of 2 shortpulses. Long distance between these pulses 2 pulses -> 1, else -> 0\n\tFrame: header, payload, footer\n\tUsed by Intertechno self learning, Hama, ...\n\t'''\n\tdef __init__(self):\n\t\tself._timebase = 250\n\t\tself._repetitions = 4\n\t\tself._pattern = \"[01]{32}\"\n\t\tself._header = [1, 11]\n\t\tself._symbols = {\n\t\t\t'0': [1, 1, 1, 5],\n\t\t\t'1': [1, 5, 1, 1]\n\t\t}\n\t\tself._footer = [1, 39]\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\nclass Intertechno(PPM1):\n\tdef __init__(self):\n\t\tself._name = \"intertechno\"\n\t\tself._params = [PARAM_ID, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._commands = {\"on\": \"1\", \"off\": \"0\"}\n\t\tself._timebase = 275\n\t\tPPM1.__init__(self)\n\t\tself._symbols['X'] = [1, 1, 1, 1] #tristate symbol for dimmer\n\t\tself._pattern = \"([01]{32})|([01]{27}X[01]{8})\"\n\n\tdef _encode_unit(self, unit):\n\t\treturn \"{:04b}\".format(int(unit) - 1)\n\n\tdef _decode_unit(self, unit):\n\t\treturn int(unit, 2) + 1\n\n\tdef _encode_dim(self, dim):\n\t\tdim = int(round(15*dim/100.0))\n\t\tif dim > 15:\n\t\t\tdim = 15\n\t\treturn \"{:04b}\".format(dim)\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\tsymbols += \"{:026b}\".format(int(params[\"id\"]))\n\t\tsymbols += \"0\" #group\n\t\tdim = None\n\t\ttry:\n\t\t\tdim = int(params[\"command\"])\n\t\t\tsymbols += \"X\"\n\t\texcept:\n\t\t\tsymbols += self._encode_command(params[\"command\"])\n\t\tsymbols += self._encode_unit(params[\"unit\"])\n\t\tif dim:\n\t\t\tsymbols += self._encode_dim(dim)\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[2:-2])\n\t\tif symbols:\n\t\t\tid = int(symbols[:26], 2)\n\t\t\tunit = self._decode_unit(symbols[28:32])\n\t\t\tif symbols[27] == 'X': #dimmer command\n\t\t\t\tcommand = int(round(int(symbols[32:36], 2) * 100 / 15.0))\n\t\t\telse:\n\t\t\t\tcommand = self._decode_command(symbols[27])\n\t\t\treturn [id, unit, command], tb, rep\n\nclass Hama(Intertechno):\n\tdef __init__(self):\n\t\tIntertechno.__init__(self)\n\t\tself._name = \"hama\"\n\t\tself._timebase = 250\n\n\tdef _encode_unit(self, unit):\n\t\treturn \"{:04b}\".format(16 - int(unit))\n\n\tdef _decode_unit(self, unit):\n\t\treturn 16 - int(unit, 2)\n\n\nclass PWM1(RcPulse):\n\t'''\n\tPWM1: Pulse Width Modulation 24 bit\n\tWide pulse -> 1, small pulse -> 0\n\tFrame: header, payload, footer\n\tUsed by Emylo, Logilight, ...\n\t'''\n\tdef __init__(self):\n\t\tself._timebase = 300\n\t\tself._repetitions = 6\n\t\tself._pattern = \"[01]{24}\"\n\t\tself._symbols = {\n\t\t\t'1': [3, 1],\n\t\t\t'0': [1, 3],\n\t\t}\n\t\tself._footer = [1, 31]\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\nclass Logilight(PWM1):\n\tdef __init__(self):\n\t\tself._name = \"logilight\"\n\t\tself._params = [PARAM_ID, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._commands = {\"on\": \"1\", \"learn\": \"1\", \"off\": \"0\"}\n\t\tPWM1.__init__(self)\n\n\tdef _encode_unit(self, unit):\n\t\tres = \"\"\n\t\tmask = 0x01\n\t\twhile mask < 0x08:\n\t\t\tres += '1' if ((int(unit) - 1) & mask) == 0 else '0'\n\t\t\tmask <<= 1\n\t\treturn res\n\n\tdef _decode_unit(self, unit):\n\t\ti = 0\n\t\twhile unit:\n\t\t\ti <<= 1\n\t\t\tif unit[-1] == '0':\n\t\t\t\ti |= 1\n\t\t\tunit = unit[:-1]\n\t\treturn i + 1\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\tsymbols += \"{:020b}\".format(int(params[\"id\"]))\n\t\tsymbols += self._encode_command(params[\"command\"])\n\t\tsymbols += self._encode_unit(params[\"unit\"])\n\t\tif (params[\"command\"].lower() == \"learn\"):\n\t\t\trepetitions = 10\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[:-2])\n\t\tif symbols:\n\t\t\tid = int(symbols[:20], 2)\n\t\t\tunit = self._decode_unit(symbols[21:24])\n\t\t\tcommand = self._decode_command(symbols[20])\n\t\t\treturn [id, unit, command], tb, rep\n\nclass Emylo(PWM1):\n\tdef __init__(self):\n\t\tself._name = \"emylo\"\n\t\tself._params = [PARAM_ID, PARAM_COMMAND]\n\t\tself._commands = {'A': '0001', 'B': '0010', 'C': '0100', 'D': '1000'}\n\t\tPWM1.__init__(self)\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\tsymbols += \"{:020b}\".format(int(params[\"id\"]))\n\t\tsymbols += self._encode_command(params[\"command\"])\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[:-2])\n\t\tif symbols:\n\t\t\tid = int(symbols[:20], 2)\n\t\t\tcommand = self._decode_command(symbols[-4:])\n\t\t\treturn [id, command], tb, rep\n\nclass FS20(RcPulse):\n\tdef __init__(self):\n\t\tself._name = \"fs20\"\n\t\tself._timebase = 200\n\t\tself._repetitions = 6\n\t\tself._pattern = \"0000000000001[01]{45}\"\n\t\tself._symbols = {\n\t\t\t'0': [2, 2],\n\t\t\t'1': [3, 3],\n\t\t}\n\t\tself._header = [2, 2] * 12 + [3, 3]\n\t\tself._footer = [1, 100]\n\t\tself._params = [PARAM_ID, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\n\tdef __encode_byte(self, b):\n\t\tb &= 0xFF\n\t\tresult = '{:08b}'.format(b)\n\t\tpar = 0\n\t\twhile b:\n\t\t\tpar ^= 1\n\t\t\tb &= b-1\n\t\tresult += '1' if par != 0 else '0'\n\t\treturn result\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\tid = int(params[\"id\"])\n\t\tunit = int(params[\"unit\"]) - 1\n\t\tcommand = int(params[\"command\"])\n\t\tsymbols += self.__encode_byte((id >> 8))\n\t\tsymbols += self.__encode_byte(id)\n\t\tsymbols += self.__encode_byte(unit)\n\t\tsymbols += self.__encode_byte(command)\n\t\tq = 0x06 + (id >> 8) + (id & 0xFF) + unit + command\n\t\tsymbols += self.__encode_byte(q)\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\tid = int(symbols[13:21] + symbols[22:30], 2)\n\t\t\tunit = int(symbols[31:39], 2) + 1\n\t\t\tcommand = int(symbols[40:48], 2)\n\t\t\treturn [id, unit, command], tb, rep\n\nclass Voltcraft(RcPulse):\n\t'''\n\tPPM: Pulse Position Modulation\n\tPulse in middle of a symbol: 0, end of symbol: 1\n\tUsed by Voltcraft RC30\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"voltcraft\"\n\t\tself._timebase = 600\n\t\tself._repetitions = 4\n\t\tself._pattern = \"[01]{20}\"\n\t\tself._symbols = {\n\t\t\t'0': [1, 2],\n\t\t\t'1': [2, 1],\n\t\t}\n\t\tself._header = [1]\n\t\tself._footer = [132]\n\t\tself._params = [PARAM_ID, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._commands = {\"off\": \"000\", \"alloff\": \"100\", \"on\": \"010\", \"allon\": \"110\", \"dimup\": \"101\", \"dimdown\": \"111\"}\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tif params[\"command\"].lower() in [\"on\", \"off\"]:\n\t\t\tunit = int(params[\"unit\"])-1\n\t\telse:\n\t\t\tunit = 3\n\n\t\tsymbols = \"{:012b}\".format(int(params[\"id\"]))[::-1]\n\t\tsymbols += \"{:02b}\".format(unit)[::-1]\n\t\tsymbols += self._encode_command(params[\"command\"])\n\t\tsymbols += \"0\"\n\t\tsymbols += \"1\" if (symbols[12] == \"1\") ^ (symbols[14] == \"1\") ^ (symbols[16] == \"1\") else \"0\"\n\t\tsymbols += \"1\" if (symbols[13] == \"1\") ^ (symbols[15] == \"1\") ^ (symbols[17] == \"1\") else \"0\"\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[1:-1])\n\t\tif symbols:\n\t\t\tid = int(symbols[0:12][::-1], 2)\n\t\t\tunit = int(symbols[12:14][::-1], 2) + 1\n\t\t\tcommand = self._decode_command(symbols[14:17])\n\t\t\treturn [id, unit, command], tb, rep\n\nclass PilotaCasa(RcPulse):\n\t'''\n\tPulse Width Modulation 32 bit\n\tWide pulse -> 0, small pulse -> 1\n\t'''\n\t__codes = {\n\t\t'110001': (1, 1, 'on'), '111110': (1, 1, 'off'),\n\t\t'011001': (1, 2, 'on'), '010001': (1, 2, 'off'),\n\t\t'101001': (1, 3, 'on'), '100001': (1, 3, 'off'),\n\t\t'111010': (2, 1, 'on'), '110010': (2, 1, 'off'),\n\t\t'010110': (2, 2, 'on'), '011010': (2, 2, 'off'),\n\t\t'100110': (2, 3, 'on'), '101010': (2, 3, 'off'),\n\t\t'110111': (3, 1, 'on'), '111011': (3, 1, 'off'),\n\t\t'011111': (3, 2, 'on'), '010111': (3, 2, 'off'),\n\t\t'101111': (3, 3, 'on'), '100111': (3, 3, 'off'),\n\t\t'111101': (4, 1, 'on'), '110101': (4, 1, 'off'),\n\t\t'010011': (4, 2, 'on'), '011101': (4, 2, 'off'),\n\t\t'100011': (4, 3, 'on'), '101101': (4, 3, 'off'),\n\t\t'101100': (-1, -1 , 'allon'), '011100': (-1, -1, 'alloff')\n\t}\n\n\tdef __init__(self):\n\t\tself._name = \"pilota\"\n\t\tself._timebase = 550\n\t\tself._repetitions = 5\n\t\tself._pattern = \"[01]{32}\"\n\t\tself._symbols = {\n\t\t\t'1': [1, 2],\n\t\t\t'0': [2, 1],\n\t\t}\n\t\tself._footer = [1, 12]\n\t\tself._params = [PARAM_ID, PARAM_GROUP, PARAM_UNIT, PARAM_COMMAND]\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = '01'\n\t\tu = None\n\t\tcmd = params[\"command\"].lower()\n\t\tif cmd == \"learn\":\n\t\t\trepetitions = 20\n\t\t\tcmd = \"on\"\n\t\telif cmd in [\"allon\", \"alloff\"]:\n\t\t\tparams[\"unit\"] = -1\n\t\t\tparams[\"group\"] = -1\n\t\tfor k, v in self.__codes.items():\n\t\t\tif v[0] == int(params[\"group\"]) and v[1] == int(params[\"unit\"]) and v[2] == cmd:\n\t\t\t\tu = k\n\t\t\t\tbreak\n\t\tsymbols += u\n\t\tsymbols += \"{:016b}\".format(int(params[\"id\"]))[::-1]\n\t\tsymbols += \"11111111\"\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[:-2])\n\t\tif symbols and (symbols[2:8] in self.__codes):\n\t\t\tc = self.__codes[symbols[2:8]]\n\t\t\tid = int(symbols[8:24][::-1], 2)\n\t\t\treturn [id, c[0], c[1], c[2]], tb, rep\n\n\nclass PCPIR(TristateBase): #pilota casa PIR sensor\n\t'''\n\tPilota Casa IR sensor\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"pcpir\"\n\t\tself._params = [PARAM_ID, PARAM_COMMAND]\n\t\tself._timebase = 500\n\t\tself._commands = {\"off\": \"0\", \"on\": \"F\"}\n\t\tTristateBase.__init__(self)\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"\"\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\tid = self._decode_int(symbols[5:10])\n\t\t\tunit = self._decode_int(symbols[0:5])\n\t\t\tcommand = self._decode_command(symbols[11:12])\n\t\t\treturn [id, unit, command], tb, rep\n\n\nclass REVRitterShutter(RcPulse):\n\t'''\n\tPulse Width Modulation 24 bit, \n\tShort pulse: 0, long pulse: 1\n\tUsed by REV Ritter shutter contact SA-MC08-N04-D\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"revshutter\"\n\t\tself._timebase = 200\n\t\tself._repetitions = 4\n\t\tself._pattern = \"[01]{24}\"\n\t\tself._symbols = {\n\t\t\t'0': [1, 3],\n\t\t\t'1': [3, 1],\n\t\t}\n\t\tself._footer = [1, 85]\n\t\tself._params = [PARAM_ID]\n\t\tself._class = CLASS_RCSWITCH\n\t\tRcPulse.__init__(self)\n\n\tdef encode(self, params, timebase=None, repetitions=None):\n\t\tsymbols = \"{:024b}\".format(int(params[\"id\"]))\n\t\treturn self._build_frame(symbols, timebase, repetitions)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\tid = int(symbols[0:24], 2)\n\t\t\treturn [id], tb, rep\n\nclass WH2(RcPulse):\n\t'''\n\tTemperature & humidity sensor WH2, Telldus\n\tPulse Duration Modulation\n\tShort, middle = 1, long middle = 0\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"wh2\"\n\t\tself._class = CLASS_RCWEATHER\n\t\tself._timebase = 500\n\t\tself._pattern = \"11111111[01]{39}\"\n\t\tself._symbols = {\n\t\t\t'1': [1, 2],\n\t\t\t'0': [3, 2],\n\t\t}\n\t\tself._footer = [2, 49]\n\t\tRcPulse.__init__(self)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\tsymbols += \"0\"\n\n\t\t\tid = \"{:02x}\".format(int(symbols[12:20], 2))\n\t\t\tT = int(symbols[20:32], 2)\n\t\t\tif T >= 1<<11:\n\t\t\t\tT -= 1<<12\n\t\t\tT /= 10.0\n\n\t\t\tres = [id, T]\n\t\t\tRH = int(symbols[32:40], 2)\n\t\t\tif RH != 0xFF:\n\t\t\t\tres.append(RH)\n\n\t\t\treturn res, tb, rep\n\n\tdef get_decode_dict(self, decresult):\n\t\tres = {\n\t\t\t\"id\": decresult[0],\n\t\t\t\"T\": decresult[1]\n\t\t}\n\t\tif len(decresult) >= 3:\n\t\t\tres[\"RH\"] = decresult[2]\n\n\t\treturn res\n\n\tdef get_mqtt_from_dict(self, di):\n\t\ttopic = \"/\" + di[\"id\"]\n\t\tmsg = {\n\t\t\t\"T\": di[\"T\"]\n\t\t}\n\t\tif \"RH\" in di:\n\t\t\tmsg[\"RH\"] = di[\"RH\"]\n\t\treturn topic, json.dumps(msg)\n\n\nclass WS7000(RcPulse):\n\t'''\n\tTemperature & humidity sensor LaCrosee/ELV\n\tPulse Width Modulation\n\tShort = 1, long = 2\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"ws7000\"\n\t\tself._class = CLASS_RCWEATHER\n\t\tself._timebase = 400\n\t\tself._pattern = \"^0{5,}1([01]{4}1){6,}[01]{4,5}$\"\n\t\tself._symbols = {\n\t\t\t'1': [1, 2],\n\t\t\t'0': [2, 1],\n\t\t}\n\t\tself._footer = [2, 49]\n\t\tRcPulse.__init__(self)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:-2])\n\t\tif symbols:\n\t\t\tsymbols = symbols[symbols.find(\"000001\") + 6:]\n\t\t\tn = [] #nibbles\n\t\t\ti = 0\n\t\t\tcheck = 0\n\t\t\twhile i <= len(symbols) - 4:\n\t\t\t\tn.append(int(symbols[i:i+4][::-1], 2))\n\t\t\t\ti += 5\n\t\t\t\tif i <= len(symbols) - 4:\n\t\t\t\t\tcheck ^= n[-1]\n\t\t\tif check != 0:\n\t\t\t\treturn\n\n\t\t\tres = {}\n\t\t\tres[\"id\"] = (n[0] << 4) | (n[1] & 0x07)\n\n\t\t\tif n[0] == 1: #WS7000 has subtypes\n\t\t\t\t#WS7000-22/25\n\t\t\t\tt = n[2] * 0.1 + n[3] * 1 + n[4] * 10\n\t\t\t\tif (n[1] & 0x8) == 0x8:\n\t\t\t\t\tt = -t\n\t\t\t\tres[\"T\"] = t\n\t\t\t\tres[\"unit\"] = n[1] & 0x7\n\t\t\t\th = n[5] * 0.1 + n[6] * 1 + n[7] * 10\n\t\t\t\tif h > 0:\n\t\t\t\t\tres[\"RH\"] = h\n\t\t\t\treturn res, tb, rep\n\nclass TX141TH(RcPulse):\n\t'''\n\tTemperature & humidity sensor LaCrosse TX141TH / TFA Dostmann 30.3221.02\n\tPulse Width Modulation\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"TX141TH\"\n\t\tself._class = CLASS_RCWEATHER\n\t\tself._timebase = 250\n\t\tself._pattern = \"^[01]{40}$\"\n\t\tself._symbols = {\n\t\t\t'0': [1, 2],\n\t\t\t'1': [2, 1]\n\t\t}\n\t\tRcPulse.__init__(self)\n\n\tdef check_crc(self, symbols):\n\t\ta = []\n\t\tsum = 0\n\t\tkey = 0xf4\n\t\tfor i in range(0, 32, 8):\n\t\t\tb = int(symbols[24-i:32-i], 2)\n\t\t\tfor j in range(8):\n\t\t\t\tif ((b >> j) & 1) != 0:\n\t\t\t\t\tsum ^= key\n\n\t\t\t\tif (key & 0x80) != 0:\n\t\t\t\t\tkey = (key << 1) ^ 0x31\n\t\t\t\telse:\n\t\t\t\t\tkey = key << 1\n\n\t\treturn (sum & 0xff) == int(symbols[32:40], 2)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[8:88])\n\t\tif symbols:\n\t\t\tif self.check_crc(symbols):\n\t\t\t\tres = {}\n\t\t\t\tid = int(symbols[0:8], 2)\n\t\t\t\tch = int(symbols[10:12], 2) + 1\n\t\t\t\tT = (int(symbols[12:24], 2) - 500) / 10\n\t\t\t\tRH = int(symbols[24:32], 2)\n\t\t\t\treturn [id, ch, T, RH], tb, rep\n\n\tdef get_decode_dict(self, decresult):\n\t\tres = {\n\t\t\t\"id\": decresult[0],\n\t\t\t\"ch\": decresult[1],\n\t\t\t\"T\": decresult[2],\n\t\t\t\"RH\": decresult[3]\n\t\t}\n\t\treturn res\n\n\tdef get_mqtt_from_dict(self, di):\n\t\ttopic = \"/\" + str(di[\"id\"]) + \"/\" + str(di[\"ch\"])\n\t\tmsg = {\n\t\t\t\"T\": di[\"T\"],\n\t\t\t\"RH\": di[\"RH\"]\n\t\t}\n\t\treturn topic, json.dumps(msg)\n\nclass TFA30_3120_90(RcPulse):\n\t'''\n\tTemperature & humidity sensor TFA 30.3120.90\n\tPulse Width Modulation\n\t'''\n\tdef __init__(self):\n\t\tself._name = \"TFA_30_3120_90\"\n\t\tself._class = CLASS_RCWEATHER\n\t\tself._timebase = 250\n\t\tself._pattern = \"^[01]{44}$\"\n\t\tself._symbols = {\n\t\t\t'0': [5, 4],\n\t\t\t'1': [2, 4]\n\t\t}\n\t\tRcPulse.__init__(self)\n\n\tdef decode(self, pulsetrain):\n\t\tsymbols, tb, rep = self._decode_symbols(pulsetrain[0:87] + [1000])\n\t\tif symbols:\n\t\t\tchk = 0\n\t\t\tfor i in range(0, 40, 4):\n\t\t\t\tchk += int(symbols[i:i+4], 2)\n\t\t\tif (chk & 0x0F) != int(symbols[40:44], 2):\n\t\t\t\treturn\n\t\t\tid = int(symbols[12:19], 2)\n\t\t\tmsgtype = int(symbols[8:12], 2)\n\n\t\t\tif msgtype == 0x00:\n\t\t\t\tT = int(symbols[20:24], 2) * 10 + int(symbols[24:28], 2) + int(symbols[28:32], 2) / 10 - 50\n\t\t\t\treturn [id, T], tb, rep\n\t\t\telif msgtype == 0x0E:\n\t\t\t\tpass\n\n\tdef get_decode_dict(self, decresult):\n\t\tres = {\n\t\t\t\"id\": decresult[0],\n\t\t\t\"T\": decresult[1]\n\t\t}\n\t\treturn res\n\n\tdef get_mqtt_from_dict(self, di):\n\t\ttopic = \"/\" + str(di[\"id\"])\n\t\tmsg = {\n\t\t\t\"T\": di[\"T\"]\n\t\t}\n\t\treturn topic, json.dumps(msg)\n\n\nprotocols = [\n\tTristate(),\n\tITTristate(),\n\tBrennenstuhl(),\n\tIntertechno(),\n\tHama(),\n\tLogilight(),\n\tEmylo(),\n\tVoltcraft(),\n\tPCPIR(),\n\tFS20(),\n\tPilotaCasa(),\n\tREVRitterShutter(),\n\tWH2(),\n\tWS7000(),\n\tTX141TH(),\n\tTFA30_3120_90()\n]\n\ndef get_protocol(name):\n\tfor p in protocols:\n\t\tif p._name == name:\n\t\t\treturn p\n\treturn None\n\nRXDATARATE = 20.0 #kbit/s\nclass RfmPulseTRX(threading.Thread):\n\tdef __init__(self, module, rxcb, frequency):\n\t\tself.__rfm = RaspyRFM(module, RFM69)\n\t\tself.__rfm.set_params(\n\t\t\tFreq = frequency, # MHz\n\t\t\tBandwidth = 500, # kHz\n\t\t\tSyncPattern = [],\n\t\t\tRssiThresh = -70, # dBm\n\t\t\tModulationType = rfm69.OOK,\n\t\t\tOokThreshType = 1, # peak thresh\n\t\t\tOokPeakThreshDec = 3, # decrement once every 8 chips\n\t\t\tOokFixedThresh = 125, # floor threshold in peak mode\n\t\t\tPreamble = 0,\n\t\t\tTxPower = 13\n\t\t)\n\t\tself.__rxevent = threading.Event()\n\t\tself.__rxevent.set()\n\t\tself.__rfmlock = threading.Lock()\n\n\t\tif rxcb is not None:\n\t\t\tself.__rxtraincb = rxcb\n\t\t\tthreading.Thread.__init__(self)\n\t\t\tself.daemon = True\n\t\t\tself.start()\n\n\tdef run(self):\n\t\twhile True:\n\t\t\tself.__rxevent.wait()\n\t\t\tself.__rfmlock.acquire()\n\t\t\tself.__rfm.set_params(Datarate = RXDATARATE)\n\t\t\tself.__rfm.receive_start(0) # unlimited length\n\t\t\tself.__receive()\n\t\t\tself.__rfm.receive_end()\n\t\t\tself.__rfmlock.release()\n\t\t\tcontinue\n\n\tdef __receive(self):\n\t\tbit = False\n\t\tcnt = 1\n\t\ttrain = []\n\t\twhile True:\n\t\t\tfifo = self.__rfm.read_fifo_wait(64)\n\t\t\tif len(fifo) == 0:\n\t\t\t\treturn\n\n\t\t\tfor b in fifo:\n\t\t\t\t#print \"{0:08b}\".format(b),\n\t\t\t\t#continue\n\t\t\t\tmask = 0x80\n\t\t\t\twhile mask != 0:\n\t\t\t\t\tnewbit = (b & mask) != 0\n\n\t\t\t\t\t''' filter\n\t\t\t\t\tbitfifo <<= 1\n\t\t\t\t\tif newbit:\n\t\t\t\t\t\tbitfifo |= 1\n\t\t\t\t\tv = bitfifo & 0x3\n\t\t\t\t\tc = 0\n\t\t\t\t\twhile v > 0:\n\t\t\t\t\t\tv &= v - 1\n\t\t\t\t\t\tc += 1\n\t\t\t\t\t'''\n\n\t\t\t\t\tif newbit == bit:\n\t\t\t\t\t\tcnt += 1\n\t\t\t\t\t\tif cnt > 100000*RXDATARATE/1000:\n\t\t\t\t\t\t\ttrain.append(cnt)\n\t\t\t\t\t\t\tself.__rxtraincb(train)\n\t\t\t\t\t\t\treturn\n\t\t\t\t\telse:\n\t\t\t\t\t\tif bit or len(train) > 0:\n\t\t\t\t\t\t\ttrain.append(cnt)\n\t\t\t\t\t\tif cnt < 150*RXDATARATE/1000:\n\t\t\t\t\t\t\ttrain = []\n\t\t\t\t\t\telif cnt > 4000*RXDATARATE/1000:\n\t\t\t\t\t\t\tself.__rxtraincb(train)\n\t\t\t\t\t\t\ttrain = []\n\t\t\t\t\t\tcnt = 1\n\t\t\t\t\t\tbit = newbit\n\n\t\t\t\t\tmask >>= 1\n\n\tdef send(self, train, timebase):\n\t\tself.__rxevent.clear()\n\t\tself.__rfm.receive_stop()\n\t\tself.__rfmlock.acquire()\n\t\tself.__rfm.set_params(\n\t\t\tDatarate = 1000.0 / timebase\n\t\t)\n\t\tself.__rfm.send(train)\n\t\tself.__rfmlock.release()\n\t\tself.__rxevent.set()\n\nclass RcTransceiver(threading.Thread):\n\tdef __init__(self, module, frequency, rxcallback, statecallback = None):\n\t\tthreading.Thread.__init__(self)\n\t\tself.__lock = threading.Lock()\n\t\tself.__event = threading.Event()\n\t\tself.__trainbuf = []\n\t\tself.daemon = True\n\t\tcb = None if rxcallback is None else self.__pushPulseTrain\n\t\tif cb is not None:\n\t\t\tself.start()\n\t\tself.__rxcb = rxcallback\n\t\tself.__statecb = statecallback\n\t\tself.__rfmtrx = RfmPulseTRX(module, cb, frequency)\n\n\tdef __del__(self):\n\t\tdel self.__rfmtrx\n\n\tdef __pushPulseTrain(self, train):\n\t\tif len(train) < 20:\n\t\t\treturn\n\t\tself.__lock.acquire()\n\t\tself.__trainbuf.append(list(train))\n\t\tself.__event.set()\n\t\tself.__lock.release()\n\n\tdef __decode(self, train):\n\t\tres = []\n\t\tsucc = False\n\t\tfor p in protocols:\n\t\t\ttry:\n\t\t\t\tdec, tb, rep = p.decode(train)\n\t\t\t\tif dec:\n\t\t\t\t\tsucc = True\n\t\t\t\t\tif not rep:\n\t\t\t\t\t\tdecdict = p.get_decode_dict(dec)\n\t\t\t\t\t\tresitem = {\n\t\t\t\t\t\t\t\"protocol\": p._name,\n\t\t\t\t\t\t\t\"class\": p._class,\n\t\t\t\t\t\t\t\"params\": decdict\n\t\t\t\t\t\t}\n\t\t\t\t\t\tres.append(resitem)\n\n\t\t\t\t\t\tif self.__statecb:\n\t\t\t\t\t\t\ttopic, msg = p.get_mqtt_from_dict(decdict)\n\t\t\t\t\t\t\ttopic = p._name + topic\n\t\t\t\t\t\t\tself.__statecb(topic, msg)\n\n\t\t\texcept Exception as e:\n\t\t\t\tpass\n\n\t\tself.__rxcb(res if succ else None, train)\n\n\tdef send(self, protocol, params, timebase=None, repeats=None):\n\t\tproto = get_protocol(protocol)\n\t\tif proto:\n\t\t\ttry:\n\t\t\t\tif not type(params) is dict:\n\t\t\t\t\tparams = proto.get_decode_dict(params)\n\t\t\t\ttxdata, tb = proto.encode(params, timebase, repeats)\n\t\t\t\tself.__rfmtrx.send(txdata, tb)\n\t\t\t\tif self.__statecb:\n\t\t\t\t\ttopic, msg = proto.get_mqtt_from_dict(params)\n\t\t\t\t\ttopic = proto._name + topic\n\t\t\t\t\tself.__statecb(topic, msg)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"Encode error: \" + str(e))\n\n\tdef run(self):\n\t\twhile True:\n\t\t\tself.__event.wait()\n\t\t\tself.__lock.acquire()\n\t\t\ttrain = None\n\t\t\tif len(self.__trainbuf) > 0:\n\t\t\t\ttrain = self.__trainbuf.pop()\n\t\t\telse:\n\t\t\t\tself.__event.clear()\n\t\t\tself.__lock.release()\n\t\t\tif (train != None):\n\t\t\t\tfor i, v in enumerate(train):\n\t\t\t\t\ttrain[i] = int(v * 1000 / RXDATARATE) #convert to microseconds\n\t\t\t\tself.__decode(train)\n","repo_name":"Phunkafizer/RaspyRFM","sub_path":"apps/rcprotocols.py","file_name":"rcprotocols.py","file_ext":"py","file_size_in_byte":27006,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"26"}
+{"seq_id":"10822182547","text":"def record(arr, n):\n minVal = arr[0]\n maxVal = arr[0]\n maxCount = 0\n minCount = 0\n\n for i in range(len(arr)):\n if arr[i] > maxVal:\n maxVal = arr[i]\n maxCount += 1\n elif arr[i] < minVal:\n minVal = arr[i]\n minCount += 1\n\n print([maxCount, minCount])\n\narr = [10, 5, 20, 20, 4, 5, 2, 25, 1]\nn = 9\nrecord(arr, n)","repo_name":"khaledfahmy123/IEEE-ZSB-Technical-Rookies-22","sub_path":"Task-3/#5.py","file_name":"#5.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"16040725974","text":"FONT = (\"Courier\", 24, \"normal\")\nfrom turtle import Turtle\n\nclass Scoreboard(Turtle):\n def __init__(self):\n super().__init__()\n self.level = 0\n self.penup()\n self.goto(-220, 250)\n self.color(\"white\")\n self.write(f\"LEVEL: {self.level}\", False, align=\"center\", font=FONT)\n self.hideturtle()\n\n def game_over(self):\n self.goto(0, 0)\n self.color(\"white\")\n self.write(f\"GAME OVER\", False, align=\"center\", font=FONT)\n\n def increase_score(self):\n self.level += 1\n self.color(\"white\")\n self.clear()\n self.write(f\"LEVEL: {self.level}\", False, align=\"center\", font=FONT)\n","repo_name":"BiteTheBullet47/MyPythonProject","sub_path":"Turtle Crossing/scoreboard.py","file_name":"scoreboard.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"22710204455","text":"import torch\nfrom torch import nn\nfrom torch.optim import Adam, lr_scheduler\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import WeightedRandomSampler\n\nfrom processing.utils import make_weights_for_balanced_classes\nfrom processing.train_utils import supervised_training \nfrom typing import Tuple\n \ndef train(model : torch.nn.Module, datasets : list, split : str, settings : dict, batch_sizes : Tuple):\n # Check which device we are working on\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n \n # Dataloaders\n dataloaders = obtain_dataloaders(datasets, batch_sizes, settings)\n\n # Loss function, optimizer and scheduler\n loss_fn, optimizer, scheduler = obtain_training_tools(model, settings)\n train_tools = [loss_fn, optimizer, scheduler]\n \n supervised_training(dataloaders, model, device, train_tools,\n settings, num_epochs=250, split=split)\n\ndef obtain_dataloaders(datasets : list , batch_sizes : Tuple, settings : dict):\n # Separate datasets from tuple\n dataset_train = datasets[0]\n dataset_val = datasets[1]\n\n # Separate batch sizes from tuple\n batch_size_train = batch_sizes[0]\n batch_size_test = batch_sizes[1]\n \n # Obtain weights for balance classes\n weights_train, _ = make_weights_for_balanced_classes(\n dataset_train.df.label)\n weights_train = torch.FloatTensor(weights_train)\n sampler_train = WeightedRandomSampler(\n weights_train, len(weights_train), replacement=True)\n\n # Instantiate loaders\n loader_train = DataLoader(\n dataset_train, batch_size=batch_size_train,\n sampler=sampler_train, num_workers=32, drop_last=False)\n\n # and for validation (without sampler)\n loader_val = DataLoader(\n dataset_val, batch_size=batch_size_test, num_workers=32, shuffle=False, drop_last=False)\n\n return {'train': loader_train, 'val': loader_val}\n\ndef obtain_training_tools(model : torch.nn.Module , settings : dict):\n loss_fn = nn.CrossEntropyLoss()\n print('We are using {} as loss function'.format(loss_fn))\n optimizer = Adam(model.parameters(),\n lr=settings['learning_rate'], weight_decay=settings['regularization'])\n scheduler = lr_scheduler.CosineAnnealingWarmRestarts(\n optimizer, 20, T_mult=1)\n return loss_fn, optimizer, scheduler\n","repo_name":"CarlosHernandezP/BCN-20k","sub_path":"processing/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"74262671109","text":"#!/usr/bin/python3\n\ndef count(orbits, orbiter):\n total = 0\n while orbiter != 'COM':\n orbiter = orbits[orbiter]\n total += 1\n return total\n\n\ndef path(orbits, orbiter):\n result = []\n while orbiter != 'COM':\n orbiter = orbits[orbiter]\n result.append(orbiter)\n return result\n\ndef intersection(path_one, path_two):\n for place in path_one:\n if place in path_two: return place\n\n\n# part 1\norbits = dict()\n\nwith open('input', 'r') as f:\n for line in f:\n orbitee, orbiter = line.strip().split(')')\n orbits[orbiter] = orbitee\n\ntotal = 0\nfor orbiter in orbits.keys():\n total += count(orbits, orbiter)\nprint(total)\n\n\n# part 2\nyou_path = path(orbits, 'YOU')\nsan_path = path(orbits, 'SAN')\nplace = intersection(you_path, san_path)\nprint(you_path.index(place) + san_path.index(place))\n\n","repo_name":"madacoo/aoc","sub_path":"2019/day06/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"45055805720","text":"from setuptools import setup, find_packages\nfrom setuptools.command.sdist import sdist\n\nVERSION = '0.0.1'\n\nwith open('README.md', 'r') as f:\n long_description = f.read()\n\ndependencies = [\n 'apscheduler >= 3.0.0',\n 'flask >= 1.0.0',\n 'flask_socketio >= 4.0.0'\n]\n\n\nclass SdistWithWebpack(sdist):\n \"\"\"\n We need to extend the sdist command to run webpack in production mode before building the package for distribution.\n \"\"\"\n\n def run(self):\n import os\n import subprocess\n\n if 'CI' not in os.environ:\n if not os.path.exists('frontend/node_build_env'):\n subprocess.check_call(['nodeenv', 'node_build_env'], cwd='frontend')\n\n subprocess.check_call(\n ['./node_build_env/bin/activate && npm install && npx webpack -p'],\n shell=True,\n cwd='frontend'\n )\n\n sdist.run(self)\n\n\nsetup(\n name='apschedulerui',\n version=VERSION,\n description='',\n long_description=long_description,\n long_description_content_type='text/markdown',\n author='Federico Schmidt',\n author_email='schmidt.fdr@gmail.com',\n url='https://github.com/schmidtfederico/apscheduler-ui',\n packages=find_packages(exclude=['tests']),\n install_requires=dependencies,\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n \"Programming Language :: Python :: 3.5\",\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Operating System :: OS Independent',\n 'Topic :: Internet'\n ],\n python_requires='>=3.5, <4',\n extras_require={\n 'testing': ['requests'],\n 'testing:python_version == \"3.5\"': ['mock']\n },\n cmdclass={'sdist': SdistWithWebpack},\n include_package_data=True\n)\n","repo_name":"schmidtfederico/apscheduler-ui","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"26"}
+{"seq_id":"25092619016","text":"from sqlalchemy.orm import Session\nfrom sqlalchemy.orm import load_only\n\nfrom media.auth.auth_handler import decode_jwt\nfrom media.models import User\n\n\ndef is_admin_user(authorization: str, db: Session):\n user_id = get_user_id_from_header(authorization)\n\n user = db.query(User).filter_by(id=user_id).options(load_only(\"id\", \"email\", \"username\", \"firstname\", \"lastname\")).first()\n\n for role in user.roles:\n if 'admin' == role.role:\n return True\n\n return False\n\n\ndef is_current_or_admin_user(authorization: str, user_id: int, db: Session):\n auth = False\n\n auth_user_id = get_user_id_from_header(authorization)\n if int(auth_user_id) == user_id:\n auth = True\n else:\n if is_admin_user(authorization, db):\n auth = True\n return auth\n\n\ndef get_user_id_from_header(authorization: str):\n token = get_token(authorization)\n\n return token['sub']\n\n\ndef get_token(authorization: str):\n if not authorization:\n raise Exception('jwt token missing')\n # Split the authorization header into the Bearer token\n token = authorization.split(\"Bearer \")[1]\n decoded_token = decode_jwt(token)\n\n return decoded_token\n\n\ndef split_title_on_colon(title):\n if ':' in title:\n pts = title.split(':')\n return pts[0]\n return title\n\n\ndef shorten_title(title, max_length):\n title = split_title_on_colon(title)\n if len(title) <= max_length:\n return title\n return title[0:max_length-3] + '...'\n # else:\n # words = title.split()\n # shortened_words = []\n # length = 0\n # for word in words:\n # shortened_words.append(word)\n # length += len(word)\n # if length >= max_length:\n # break\n # shortened_title = \" \".join(shortened_words)\n # if len(shortened_title) < len(title):\n # shortened_title += \"...\"\n # elif len(shortened_title) > max_length + 2:\n # shortened_title = shortened_title[0:max_length] + \"...\"\n #\n # return shortened_title\n\n\n","repo_name":"mudstrand/couchranking","sub_path":"media/util/general_utils.py","file_name":"general_utils.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"26"}
+{"seq_id":"41437739584","text":"from collections import Counter\nfrom cv2 import imread, cvtColor, COLOR_BGR2RGB\nfrom funcs_classes import TranslationConfig, Translation, Color\nfrom os import path\nfrom pdf2image import convert_from_path\nfrom pytesseract import image_to_string\nfrom re import sub\nfrom tempfile import mkdtemp\n\ntessdata_dir_config = r'--tessdata-dir \"/Users/nolanwelch/homebrew/Cellar/tesseract/5.2.0/share/tessdata\"'\n\n\ndef run(tr_cfg: TranslationConfig) -> None:\n temp_path: str = path.join(mkdtemp(), 'temp.jpg')\n\n original_pdf_path: str = tr_cfg.originalFileName\n original_image_list: list = convert_from_path(original_pdf_path)\n \n page: list\n for page_ind, page in enumerate(original_image_list):\n if page_ind == len(tr_cfg.translations):\n break\n\n page.save(temp_path, 'JPEG')\n page_img = imread(temp_path)\n \n cfg_page: list = tr_cfg.translations[page_ind]\n\n translation: Translation\n for translation in cfg_page:\n # Cropping the image to only include the selected region\n end_x: int = translation.xPos + translation.width\n end_y: int = translation.yPos + translation.height\n cropped_page_img = page_img[translation.yPos:end_y, translation.xPos:end_x]\n cropped_page_img = cvtColor(cropped_page_img, COLOR_BGR2RGB)\n # Using pytesseract to extract the text from the cropped region and adding it to the Translation\n original_text: str = image_to_string(cropped_page_img, config=tessdata_dir_config)\n original_text = sub('\\n', ' ', original_text)\n original_text = sub('\\u2019', '\\'', original_text)\n translation.originalText = original_text.strip()\n # Finds the most- and second-most common colors to be the background and foreground colors respectively\n img_height, img_width, _ = cropped_page_img.shape\n color_list: list = []\n for x in range(img_width):\n for y in range(img_height):\n color_list.append(Color(int(cropped_page_img[y,x][0]), int(cropped_page_img[y,x][1]),\\\n int(cropped_page_img[y,x][2])))\n # Sort color_list by number of occurrences and get unique values\n color_list.sort(key=Counter(color_list).get, reverse=True)\n color_list = list(dict.fromkeys(color_list))\n bg_color: Color = color_list[0]\n translation.backgroundColor = bg_color\n # Remove any colors where any component is within 30 of that of the background color\n color_list = [c for c in color_list if abs(c.R-bg_color.R) >= 30 and abs(c.G-bg_color.G) >= 30\\\n and abs(c.B-bg_color.B) >= 30]\n translation.textColor = color_list[0] if len(color_list) != 0 else Color(0, 0, 0) # Default textColor is black\n # Save to file after each data extraction\n tr_cfg.save()\n\n ","repo_name":"nolanwelch/PDFormatter","sub_path":"app/extract_image_data.py","file_name":"extract_image_data.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"39342189229","text":"import random, pprint\n\nclass Graph:\n def __init__(self,v):\n self.v = len(v)\n self.graph = v\n \n def make_matrix(self, v):\n n = range(len(v))\n maze = [[0 for x in n] for y in n]\n \n for x,y in v.items():\n for items in y:\n maze[x][items] = 1\n maze[items][x] = 1\n self.graph = maze\n return maze\n \n def show_graph(self):\n pprint.pprint(self.graph)\n \n\ndef house_colors(adj_list, magic_num, colors=[]):\n # end case\n if len(colors) == len(adj_list):\n return True, colors\n # try each option for every single house\n for i in range(magic_num):\n colors.append(i)\n # check to see conditions are met\n if is_safe(adj_list, colors):\n # driver code via recursion\n if house_colors(adj_list, magic_num, colors):\n return True, colors\n colors.pop()\n return False\n\ndef is_safe(adj_list, colors):\n last_color = colors[-1]\n last_vertex = len(colors)-1\n \n neighbors = [x for x in adj_list[last_vertex] if x < last_vertex]\n \n for edge in neighbors:\n if colors[edge] == last_color:\n return False\n return True\n\n\nadj_list = {\n0: [1],\n1: [2],\n2: [3,1],\n3: [4],\n4: [5],\n5: [0]\n}\n\nprint(house_colors(adj_list,2,))","repo_name":"kyle1james/2019_Lessons","sub_path":"backtracking/house_color.py","file_name":"house_color.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"27137995483","text":"import gi\n\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, GLib\n\nimport time\nimport threading\nfrom App import XMLSoup\n\nGLADE_FILE = \"view/OSM2BD.glade\"\n\nglobal flgt\nflgt = True\n\nclass interface():\n\n\n def __init__(self):\n self.builder = Gtk.Builder()\n self.builder.add_from_file(GLADE_FILE)\n self.builder.connect_signals(self)\n\n self.arq = \"\"\n\n self.window = self.builder.get_object(\"main_window\")\n self.val_search = self.builder.get_object(\"entry\")\n self.progressBar = self.builder.get_object(\"progressBar\")\n self.status = self.builder.get_object(\"status\")\n self.bt_start = self.builder.get_object(\"bt_start\")\n\n self.window.connect(\"delete-event\", Gtk.main_quit)\n self.activity_mode = True\n self.window.show_all()\n\n\n def bt_search_clicked(self, widget):\n print(\"search\")\n dialog = Gtk.FileChooserDialog(\"Please choose a file\", None,\n Gtk.FileChooserAction.OPEN,\n (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n Gtk.STOCK_OPEN, Gtk.ResponseType.OK))\n\n dialog.set_modal(True)\n response = dialog.run()\n if response == Gtk.ResponseType.OK:\n self.arq = dialog.get_filename()\n self.val_search.set_text(dialog.get_filename())\n self.bt_start.set_sensitive(True)\n\n elif response == Gtk.ResponseType.CANCEL:\n print(\"Cancel clicked\")\n\n dialog.destroy()\n\n def bt_start_clicked(self, wigdet):\n print(\"clicked\")\n app_main(self)\n\ndef app_main(self):\n\n progress = self.progressBar\n status = self.status\n arq = self.arq\n\n def update_progress():\n progress.pulse()\n progress.set_text(\"Process...\")\n return False\n\n def example_target():\n while flgt:\n GLib.idle_add(update_progress)\n time.sleep(0.3)\n\n def task():\n global flgt\n XMLSoup.start(arq)\n print(\"fim\")\n flgt = False\n progress.set_fraction(1)\n progress.set_text(\"100% completed\")\n status.push(1,\"files were generated and stored in the Results directory.\")\n\n thread1 = threading.Thread(target=example_target)\n thread1.daemon = True\n thread1.start()\n\n thread2 = threading.Thread(target=task)\n thread2.daemon = True\n thread2.start()\n\n\nif __name__ == '__main__':\n interface()\n Gtk.main()\n exit(0)\n","repo_name":"vinisperandio/OSM2Diagram_optmizated","sub_path":"view/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":2502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"17760954076","text":"'''\n本程序用来计算各供应商送货期间，每月26号到次月25日为一个期间\n'''\nimport re\n\ndef qijian(riqi):\n\n r = '(?P\\d{4})-(?P0[1-9]|1[012])-(?P0[1-9]|[12]\\d|3[01])'\n pattern = re.compile(r)\n mat = pattern.match(riqi)\n if mat != None :\n\n #print(mat)\n #print(mat.group('year'))\n #print(mat.group('month'))\n #print(mat.group('day'))\n year = int(mat.group('year'))\n month = int(mat.group('month'))\n day = int(mat.group('day'))\n #print(year,month,day)\n\n #计算期间\n if day>25 :\n if month == 12 :\n yue = 1\n year = year +1\n\n else :\n yue = month +1\n\n else:\n yue = month\n year = year\n\n\n yue= '%02d'%yue\n qijian = str(year)+'-'+ str(yue)\n #print('日期',year,yue,day)\n #print('期间',qijian)\n else:\n print('日期有误，匹配失败')\n\n return qijian\n\n\ndef main():\n riqi = '2021-11-30'\n\n qijia= qijian(riqi)\n print(qijia)\n\nif __name__=='__main__':\n\n main()\n\n\n","repo_name":"reddatetea/fish","sub_path":"qijianchuli.py","file_name":"qijianchuli.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"20357048951","text":"from mutagen.wave import WAVE\nfrom datetime import timedelta\nimport pathlib\nfrom datetime import datetime\nfrom datetime import date\n\n\ndef audio_duration(length: int):\n audio_hours = length // 3600 # calculate in hours\n length %= 3600\n audio_minutes = length // 60 # calculate in minutes\n length %= 60\n audio_seconds = length # calculate in seconds\n\n return str(int(audio_hours)) + ':' + str(int(audio_minutes)) + ':' + str(int(audio_seconds)) # returns the duration\n\n\ndef creation_date(folder_location: str, file_name: str):\n # assign the creation time in milliseconds to create_time\n create_time = int(pathlib.Path(folder_location + file_name).stat().st_ctime)\n # compare to the epoch time to find the date it was uploaded\n converted_utc = datetime.fromtimestamp(create_time)\n # return value adjusted for timezone differences\n return converted_utc\n\n\ndef creation_date2(folder_location: str, file_name: str):\n # assign the creation time in milliseconds to create_time\n create_time = int(pathlib.Path(folder_location + file_name).stat().st_ctime)\n # compare to the epoch time to find the date it was uploaded\n converted_utc = datetime.fromtimestamp(create_time)\n converted_utc = converted_utc.strftime(\"%y-%m-%d_%H.%M.%S\")\n # return value adjusted for timezone differences\n return converted_utc\n\n\ndef obtain_length_of_call(file_location: str):\n # define the variable audio to be the wave file at file_location\n audio = WAVE(file_location + \".wav\")\n # using the mutagen.wave function .info.length cast the float value to an int and assign it to duration\n duration = int(audio.info.length)\n # return the integer with the length of the call\n return audio_duration(duration)\n\n\ndef complete_time():\n time_now = datetime.now()\n current_time = time_now.strftime(\"%H.%M.%S\")\n date_now = date.today()\n current_date = date_now.strftime(\"%y-%m-%d_\")\n complete_time_return = current_date + current_time\n return complete_time_return\n","repo_name":"mhebrado20/404-Team28-GroupRepo","sub_path":"Scott/ECEN404Project/NCID_Database/services/obtain_call_length.py","file_name":"obtain_call_length.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"26"}
+{"seq_id":"74502240708","text":"import gc\nimport math\nimport torch\nimport difflib\nimport Levenshtein\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nfrom sklearn.neighbors import NearestNeighbors\nfrom numba import jit\n\n\ndef get_n_sample_from_each_class_with_minimum_m(n, m, df, target_col, seed):\n size = df.groupby(target_col).size()\n target = df.groupby(target_col).sample(\n n, random_state=seed)\n has_more_than_n_samples = size >= m\n has_more_than_n_samples.index = target.index\n target = target[has_more_than_n_samples]\n return target\n\n\ndef create_text_embd(df, model, tokenizer, device):\n vectors = []\n\n # Split dataframe into chunks\n chunks = [df[i:i+128] for i in range(0, len(df), 128)]\n\n for chunk in tqdm(chunks):\n input_ids = []\n attention_masks = []\n\n for index, row in chunk.iterrows():\n text = [row['name'], row['address'], row['city'], row['state'],\n row['zip'], row['country'], row['url'], row['phone'], row['categories']]\n text = [str(x) for x in text]\n text = ' '.join(text)\n input = tokenizer(\n text,\n truncation=True,\n max_length=128,\n padding='max_length',\n return_tensors=\"pt\"\n )\n input_ids.append(input['input_ids'])\n attention_masks.append(input['attention_mask'])\n\n input_ids = torch.cat(input_ids, dim=0)\n attention_masks = torch.cat(attention_masks, dim=0)\n\n with torch.no_grad():\n embd = model(input_ids.to(device), attention_masks.to(device))\n\n vectors.append(embd)\n\n vectors = torch.cat(vectors, 0)\n vectors = torch.nn.functional.normalize(vectors, p=2, dim=-1)\n return vectors\n\n\ndef get_geospatial_neightbors(k, df):\n knn = NearestNeighbors(n_neighbors=k, metric='haversine', n_jobs=-1)\n knn.fit(np.deg2rad(df[['latitude', 'longitude']]), df.index)\n dists, nears = knn.kneighbors(df[['latitude', 'longitude']])\n return dists, nears\n\n\ndef get_cos_sims(k, vectors, nears):\n topk_indices = []\n cos_sims_word = []\n cos_sims_dist = []\n\n for i in tqdm(range(math.ceil(vectors.shape[0]/1000))):\n end = min(1000*(i+1), vectors.shape[0])\n dist = torch.mm(vectors[1000*i:end], vectors.t())\n value, indices = torch.topk(dist, k, dim=-1)\n\n temp = []\n for j in range(1000*i, end):\n temp.append(dist[j-1000*i][nears[j]])\n temp = torch.stack(temp, 0)\n\n cos_sims_dist.append(temp)\n cos_sims_word.append(value)\n topk_indices.append(indices)\n\n topk_indices = torch.cat(topk_indices, 0).cpu().numpy()\n cos_sims_word = torch.cat(cos_sims_word, 0).cpu().numpy()\n cos_sims_dist = torch.cat(cos_sims_dist, 0).cpu().numpy()\n\n return topk_indices, cos_sims_word, cos_sims_dist\n\n\ndef create_pair_df(df, k, topk_indices, cos_sims_word, cos_sims_dist, nears, dists):\n test_df = []\n\n for i in tqdm(range(k)):\n # Add based on geospatial distance\n cur_df = df[['id']]\n cur_df['match_id'] = df['id'].values[nears[:, i]]\n cur_df['dist'] = dists[:, i]\n cur_df['cos_sim'] = cos_sims_dist[:, i]\n cur_df['geospatial_k'] = k-i\n if i > 10:\n cur_df = cur_df[cur_df['dist'] < 2]\n cur_df = cur_df.drop(['dist'], axis=1)\n test_df.append(cur_df)\n\n # Add based on word similarity\n cur_df = df[['id']]\n cur_df['match_id'] = df['id'].values[topk_indices[:, i]]\n cur_df['cos_sim'] = cos_sims_word[:, i]\n cur_df['cos_sim_k'] = k-i\n if i > 10:\n cur_df = cur_df[cur_df['cos_sim'] > 0.5]\n test_df.append(cur_df)\n\n test_df = pd.concat(test_df)\n test_df = test_df.fillna(0)\n test_df = test_df.drop_duplicates()\n\n # Remove duplicate pairs and self-match \n test_df = test_df[~test_df[['id', 'match_id']].apply(frozenset, axis=1).duplicated()]\n test_df = test_df[test_df['id']!=test_df['match_id']]\n\n return test_df\n\n\n@jit(nopython=True, cache=True)\ndef LCS(S, T):\n dp = np.zeros((len(S) + 1, len(T) + 1), dtype=np.int32)\n for i in range(len(S)):\n for j in range(len(T)):\n cost = (int)(S[i] == T[j])\n v1 = dp[i, j] + cost\n v2 = dp[i + 1, j]\n v3 = dp[i, j + 1]\n v4 = dp[i + 1, j + 1]\n dp[i + 1, j + 1] = max((v1,v2,v3,v4))\n return dp[len(S)][len(T)]\n \n\ndef create_features(df, test_df):\n df = df[['id', 'longitude', 'latitude', 'point_of_interest']]\n test_df = test_df.merge(df, on='id')\n test_df = test_df.rename(columns={\n \"longitude\": \"longitude_1\", 'latitude': 'latitude_1', 'point_of_interest': 'point_of_interest_1'})\n test_df = test_df.merge(df, left_on='match_id', right_on='id')\n test_df = test_df.rename(columns={\"longitude\": \"longitude_2\", 'latitude': 'latitude_2',\n 'id_x': 'id', 'point_of_interest': 'point_of_interest_2'})\n test_df = test_df.drop(['id_y'], axis=1)\n\n test_df['longitude_diff'] = abs(\n test_df['longitude_2'] - test_df['longitude_1'])\n test_df['latitude_diff'] = abs(\n test_df['latitude_2'] - test_df['latitude_1'])\n test_df['euc_dist'] = (test_df['longitude_diff'] ** 2 + test_df['latitude_diff'] ** 2) ** 0.5\n test_df['manhattan_dist'] = test_df['longitude_diff'] + \\\n test_df['latitude_diff']\n return test_df\n\n\nfeat_columns = ['name', 'address', 'city',\n 'state', 'zip', 'url',\n 'phone', 'categories', 'country']\nvec_columns = ['name', 'categories', 'address',\n 'state', 'url', 'country']\n\ndef reduce_memory(df):\n for col in df.columns:\n col_type = df[col].dtypes\n if col_type != object:\n cmin = df[col].min()\n cmax = df[col].max()\n if str(col_type)[:3] == 'int':\n if cmin > np.iinfo(np.int8).min and cmax < np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif cmin > np.iinfo(np.int16).min and cmax < np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif cmin > np.iinfo(np.int32).min and cmax < np.iinfo(np.int32).max:\n df[col] = df[col].astype(np.int32)\n elif cmin > np.iinfo(np.int64).min and cmax < np.iinfo(np.int64).max:\n df[col] = df[col].astype(np.int64)\n else:\n # if cmin > np.finfo(np.float16).min and cmax < np.finfo(np.float16).max:\n # df[col] = df[col].astype(np.float16)\n # el\n if cmin > np.finfo(np.float32).min and cmax < np.finfo(np.float32).max:\n df[col] = df[col].astype(np.float32)\n else:\n df[col] = df[col].astype(np.float64)\n return df\n\ndef create_more_features(df, test_df, tfidf_d, id2index_d):\n for col in feat_columns:\n if col in vec_columns:\n tv_fit = tfidf_d[col]\n indexs = [id2index_d[i] for i in test_df['id']]\n match_indexs = [id2index_d[i] for i in test_df['match_id']]\n test_df[f'{col}_sim'] = np.array(tv_fit[indexs].multiply(\n tv_fit[match_indexs]).sum(axis=1)).ravel()\n\n col_values = df.loc[test_df['id']][col].values.astype(str)\n matcol_values = df.loc[test_df['match_id']][col].values.astype(str)\n\n geshs = []\n levens = []\n jaros = []\n lcss = []\n for s, match_s in zip(col_values, matcol_values):\n if s != 'nan' and match_s != 'nan':\n geshs.append(difflib.SequenceMatcher(None, s, match_s).ratio())\n levens.append(Levenshtein.distance(s, match_s))\n jaros.append(Levenshtein.jaro_winkler(s, match_s))\n lcss.append(LCS(str(s), str(match_s)))\n else:\n geshs.append(np.nan)\n levens.append(np.nan)\n jaros.append(np.nan)\n lcss.append(np.nan)\n\n test_df[f'{col}_gesh'] = geshs\n test_df[f'{col}_leven'] = levens\n test_df[f'{col}_jaro'] = jaros\n test_df[f'{col}_lcs'] = lcss\n\n if col not in ['phone', 'zip']:\n test_df[f'{col}_len'] = list(map(len, col_values))\n test_df[f'match_{col}_len'] = list(map(len, matcol_values))\n test_df[f'{col}_len_diff'] = np.abs(\n test_df[f'{col}_len'] - test_df[f'match_{col}_len'])\n test_df[f'{col}_nleven'] = test_df[f'{col}_leven'] / \\\n test_df[[f'{col}_len', f'match_{col}_len']].max(axis=1)\n\n test_df[f'{col}_nlcsk'] = test_df[f'{col}_lcs'] / test_df[f'match_{col}_len']\n test_df[f'{col}_nlcs'] = test_df[f'{col}_lcs'] / test_df[f'{col}_len']\n\n test_df = test_df.drop(f'{col}_len', axis=1)\n test_df = test_df.drop(f'match_{col}_len', axis=1)\n\n gc.collect()\n \n return test_df\n\n\n\n\n","repo_name":"dongkyuk/Kaggle-Foursquare","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8982,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"26"}
+{"seq_id":"33192919257","text":"from sqlalchemy.orm import Session\nimport typing\n\nfrom tracim_backend.applications.collaborative_document_edition.wopi.models import (\n WopiLastModifiedTime,\n)\nfrom tracim_backend.applications.collaborative_document_edition.wopi.models import WopiCheckFileInfo\nfrom tracim_backend.config import CFG\nfrom tracim_backend.models.auth import User\nfrom tracim_backend.models.data import Content\nfrom tracim_backend.models.roles import WorkspaceRoles\n\n\nclass WopiLib(object):\n \"\"\"\n Manager providing methods to allow support of WOPI protocol\n \"\"\"\n\n def __init__(self, current_user: typing.Optional[User], session: Session, config: CFG) -> None:\n self._session = session\n self._user = current_user\n self._config = config\n\n def check_file_info(self, content: Content) -> WopiCheckFileInfo:\n size = 0\n if content.depot_file.file:\n size = content.depot_file.file.content_length\n user_can_write = bool(\n content.workspace.get_user_role(self._user) >= WorkspaceRoles.CONTRIBUTOR.level\n )\n return WopiCheckFileInfo(\n last_modified_time=content.updated,\n base_file_name=content.file_name,\n size=size,\n owner_id=content.owner_id,\n user_id=self._user.user_id,\n user_friendly_name=self._user.display_name,\n user_can_write=user_can_write,\n version=content.cached_revision_id,\n user_can_not_write_relative=True,\n )\n\n def last_modified_time(self, content: Content) -> WopiLastModifiedTime:\n return WopiLastModifiedTime(last_modified_time=content.updated)\n","repo_name":"tracim/tracim","sub_path":"backend/tracim_backend/applications/collaborative_document_edition/wopi/lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":193,"dataset":"github-code","pt":"26"}
+{"seq_id":"23970907930","text":"import serial\r\nimport settings\r\nimport time\r\nimport consoleClass\r\n\r\nspeed = settings.serialSpeed\r\nserialLoc = settings.serialAddy\r\n\r\nreset = settings.pwr_cycle\r\nhomies = settings.homing\r\npullout = settings.pulloff_pos\r\n\r\ndef stopit():\r\n consoleClass.thread5 = \"qick exit initiated\"\r\n s = serial.Serial(serialLoc, speed)\r\n \r\n s.write(reset.encode())\r\n consoleClass.thread5 = \"reset sent\"\r\n \r\n time.sleep(2)\r\n s.write(homies.encode())\r\n consoleClass.thread5 = \"homing\"\r\n \r\n #s.write(pullout.encode())\r\n\r\nif __name__ == \"__main__\":\r\n stopit()","repo_name":"WorkVerbDesign/BTS2","sub_path":"bts2/ohShit.py","file_name":"ohShit.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"17929840474","text":"import click\n\nfrom habu.lib.xor import xor\n\n\n@click.command()\n@click.option('-k', default='0', help='Encryption key')\n@click.option('-i', type=click.File('rb'), required=True, help='Input file')\n@click.option('-o', type=click.File('wb'), required=True, help='Output file')\ndef cmd_xor(k, i, o):\n \"\"\"XOR cipher\"\"\"\n o.write(xor(i.read(), k.encode()))\n\n\nif __name__ == '__main__':\n cmd_xor()\n","repo_name":"seclib/habu","sub_path":"habu/cli/cmd_xor.py","file_name":"cmd_xor.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"26"}
+{"seq_id":"38307588330","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 27 23:59:24 2020\n\n@author: aguasharo\n\"\"\"\n\nfrom recognitionFunctions import *\n\n\nclass RecognitionModel:\n \n num_gestures = 6\n \n def __init__(self,version,user):\n self.user = user\n self.version = version\n \n \n def preProcessingData(self):\n sample_type = self.version+'Samples'\n # Reading the training samples\n train_samples = self.user[sample_type]\n # Preprocessing\n train_segment_X = [get_x_train(self.user,sample) for sample in train_samples] \n \n return train_segment_X \n \n def featureExtraction(self, train_data): \n # Finding the EMG that is the center of each class\n centers = bestCenter_Class(train_data) \n # Feature extraction by computing the DTW distance between each training\n # example and the center of each cluster \n # Preprocessing the feature vectors \n X_train = getFeatureExtraction(train_data, centers)\n \n return X_train, centers\n \n \n def trainSoftmaxNN(self, X_train):\n sample_type = self.version+'Samples'\n # Reading the training samples\n train_samples = self.user[sample_type] \n # Training the feed-forward NN\n y_train = decode_targets(get_y_train(train_samples)) \n X_val, y_val = get_xy_val(X_train, get_y_train(train_samples)) \n estimator = trainFeedForwardNetwork(X_train, y_train, X_val, y_val) \n \n return estimator\n \n \n def classifyGestures(self,version, estimator, centers) :\n \n sample_type = self.version+'Samples'\n # Reading the testing samples \n test_samples = self.user[sample_type] \n # Concatenating the predictions of all the users for computing the\n # errors\n response = ([testing_prediction(self.user, sample, centers, estimator) for sample in test_samples]) \n \n results = recognition_results(response)\n \n return results\n ","repo_name":"laboratorioAI/DTW_ANN_EPN_Python","sub_path":"RecognitionModel.py","file_name":"RecognitionModel.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"26"}
+{"seq_id":"15152552537","text":"#!/usr/bin/env python3\n\nimport socket\nfrom gpiozero import LEDBoard\nfrom bs4 import BeautifulSoup\n\n\nHOST = '0.0.0.0'\nPORT = 8000\n\nSUPPORTED_PINS = {\n 'D1': 6,\n 'D2': 13,\n 'D3': 12,\n 'D4': 26,\n 'D5': 20,\n 'D6': 21,\n 'D7': 4\n}\n\n\ndef main():\n '''\n HTTP Server designed to run on a Raspberry Pi to control LEDs on a Pi Hat\n\n The supported LEDs are found in `SUPPORTED_PINS`\n '''\n board = LEDBoard(*(SUPPORTED_PINS[led_name] for led_name in SUPPORTED_PINS), active_high=False)\n\n mysocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n mysocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 2)\n mysocket.bind((HOST, PORT))\n\n mysocket.listen()\n print(f'Listening on port {PORT}...')\n while True:\n conn, addr = mysocket.accept()\n\n # get HTTP request\n request_line = conn.recv(1024).decode().splitlines()[0]\n command, query, version = request_line.rstrip().split(\" \")\n\n # parse GET queries\n full_query = query.split('?')\n if len(full_query) > 1:\n path, queries = full_query\n handle_pin_queries(board, queries)\n else:\n path = full_query[0]\n\n send_response(conn, path)\n\n\ndef send_response(conn, path):\n if path == '/':\n path = '/index.html'\n path = path.lstrip('/')\n response_code = 200\n status = 'OK'\n try:\n f = open(path, 'r')\n except OSError:\n response_code = 404\n status = 'NOT FOUND'\n f = open('404.html', 'r')\n content = f.read()\n response = f'HTTP/1.1 {response_code} {status}\\r\\n' \\\n 'Connection: close\\r\\n' \\\n f'Content-Length: {len(content)}\\r\\n\\r\\n' \\\n f'{content}'\n conn.sendall(response.encode())\n conn.close()\n f.close()\n\ndef handle_pin_queries(board, queries):\n '''\n Turn LEDs on/off according to GET request queries\n '''\n queries = queries.split(\"&\")\n\n # determine which pins should be on\n if queries[0] is not '':\n on_leds = list(map(lambda token: int(token.rstrip('=on').lstrip('D')) - 1, queries))\n else:\n on_leds = []\n board.on(*on_leds)\n\n # determine which pins should be off\n off_leds = [led for led in range(len(board)) if led not in on_leds]\n board.off(*off_leds)\n update_html(on_leds)\n\n\ndef update_html(on_leds):\n '''\n Update html file to have checked boxes persist\n '''\n with open(\"index.html\", 'r+') as f:\n soup = BeautifulSoup(f.read(), 'html.parser')\n for checkbox in soup.find_all('input', {'type':'checkbox'}):\n led_idx = int(checkbox['name'].lstrip('D')) - 1\n if led_idx in on_leds:\n checkbox['checked'] = None\n else:\n del checkbox['checked']\n f.truncate(0)\n f.seek(0)\n f.write(soup.prettify())\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"rockboynton/pi-IoT","sub_path":"pi-iot/http_server.py","file_name":"http_server.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"72096940870","text":"import textwrap\nfrom os.path import basename, dirname\n\nfrom PySide6.QtCore import Qt, QDir, QSize\nfrom PySide6.QtWidgets import QMessageBox, QPushButton, QFileDialog, QDialog, QSlider, QVBoxLayout, QHBoxLayout, QLabel, \\\n QCheckBox, QFormLayout, QLineEdit, QDialogButtonBox, QScrollArea, QProgressDialog\n\nfrom bLUeTop import Gui\nfrom bLUeTop.utils import QbLUeSlider\n\n##################\n# file extension constants\nBLUE_FILE_EXTENSIONS = (\".blu\", \".BLU\", \".bLU\")\nIMAGE_FILE_EXTENSIONS = (\".jpg\", \".JPG\", \".png\", \".PNG\", \".tif\", \".TIF\", \".bmp\", \".BMP\")\nRAW_FILE_EXTENSIONS = (\".nef\", \".NEF\", \".dng\", \".DNG\", \".cr2\", \".CR2\")\nSVG_FILE_EXTENSIONS = (\".svg\", \".SVG\")\nIMAGE_FILE_NAME_FILTER = ['Image Files (*.jpg *.png *.tif *.blu *.JPG *.PNG *.TIF *.BLU)']\n\n\n#################\n\n\nclass dimsInputDialog(QDialog):\n \"\"\"\n Simple input Dialog for image width and height.\n \"\"\"\n\n def __init__(self, w, h, keepAspectRatio=True, keepBox=False, parent=Gui.window):\n \"\"\"\n\n :param w:\n :type w:\n :param h:\n :type h:\n :param keepAspectRatio:\n :type keepAspectRatio:\n :param keepBox:\n :type keepBox:\n :param parent:\n :type parent:\n \"\"\"\n super().__init__(parent=parent)\n self.setAttribute(Qt.WA_DeleteOnClose)\n self.setWindowTitle('Image Dimensions')\n self.dims = {'w': w, 'h': h, 'kr': keepAspectRatio}\n self.r = 1.0\n if w > 0:\n self.r = h / w\n fLayout = QFormLayout()\n self.fields = []\n self.checkBox = None\n for i in range(2):\n lineEdit = QLineEdit()\n label = \"Width (px)\" if i == 0 else \"Height (px)\"\n lineEdit.setText(str(w) if i == 0 else str(h))\n fLayout.addRow(label, lineEdit)\n self.fields.append(lineEdit)\n lineEdit.textEdited.connect(self.keepRatioW if i == 0 else self.keepRatioH)\n if keepBox:\n label1 = \"Keep Aspect Ratio\"\n self.checkBox = QCheckBox()\n fLayout.addRow(label1, self.checkBox)\n buttonBox = QDialogButtonBox(QDialogButtonBox.Ok, Qt.Horizontal)\n fLayout.addRow(buttonBox)\n self.setLayout(fLayout)\n self.onAccept = lambda: None\n buttonBox.accepted.connect(self.accept)\n\n def keepRatioH(self, text):\n if self.checkBox is None:\n return\n elif not self.checkBox.isChecked():\n return\n try:\n w1, h1 = int(self.fields[0].text()), int(self.fields[1].text())\n except ValueError:\n return\n w1 = int(h1 / self.r)\n self.fields[0].setText(str(w1))\n\n def keepRatioW(self, text):\n if self.checkBox is None:\n return\n elif not self.checkBox.isChecked():\n return\n try:\n w1, h1 = int(self.fields[0].text()), int(self.fields[1].text())\n except ValueError:\n return\n h1 = int(w1 * self.r)\n self.fields[1].setText(str(h1))\n\n def accept(self):\n \"\"\"\n button slot\n \"\"\"\n try:\n self.dims['w'] = int(self.fields[0].text())\n self.dims['h'] = int(self.fields[1].text())\n if self.checkBox is not None:\n self.dims['kr'] = self.checkBox.isChecked()\n except ValueError:\n return\n super().accept()\n self.onAccept()\n\n\ndef dlgInfo(text, info='', parent=Gui.window):\n \"\"\"\n Shows a simple information dialog.\n\n :param parent:\n :type parent: QWidget\n :param text:\n :type text: str\n :param info:\n :type info: str\n \"\"\"\n msg = QMessageBox(parent=parent)\n msg.setWindowTitle('Information')\n msg.setIcon(QMessageBox.Information)\n msg.setText(text)\n msg.setInformativeText(info)\n msg.exec()\n\n\ndef dlgWarn(text, info='', parent=Gui.window):\n \"\"\"\n Shows a simple warning dialog.\n\n :param parent:\n :type parent: QWidget\n :param text:\n :type text: str\n :param info:\n :type info: str\n \"\"\"\n msg = QMessageBox(parent=parent)\n msg.setWindowTitle('Warning')\n msg.setIcon(QMessageBox.Warning)\n msg.setText(text)\n msg.setInformativeText(info)\n msg.exec()\n\n\ndef workInProgress(title, parent=None):\n \"\"\"\n Inits a modal QProgressDialog\n\n :param title:\n :type title: str\n :param parent:\n :type parent:\n :return:\n :rtype: QProgressDialog\n \"\"\"\n progress = QProgressDialog(parent=parent)\n progress.setFixedSize(300, 80)\n progress.setStyleSheet(\"\"\"background-color: rgb(20,20,100);\n color: rgb(220,220,220);\"\"\")\n progress.setWindowModality(Qt.ApplicationModal)\n progress.setAttribute(Qt.WA_DeleteOnClose)\n progress.setWindowFlags(Qt.Dialog | Qt.WindowStaysOnTopHint | Qt.CustomizeWindowHint)\n # del cancel button\n progress.setCancelButtonText('')\n progress.setLabelText(title)\n return progress\n\n\ndef saveChangeDialog(img, parent=Gui.window):\n \"\"\"\n Save/discard dialog. Returns the chosen button.\n\n :param img: image to save\n :type img: vImage\n :param parent:\n :type parent:\n :return:\n :rtype: QMessageBox.StandardButton\n \"\"\"\n reply = QMessageBox(parent=parent)\n reply.setText(\"%s was modified\" % img.meta.name if len(img.meta.name) > 0 else 'unnamed image')\n reply.setInformativeText(\"Save your changes ?\")\n reply.setStandardButtons(QMessageBox.Save | QMessageBox.Discard | QMessageBox.Cancel)\n reply.setDefaultButton(QMessageBox.Save)\n ret = reply.exec()\n return ret\n\n\nclass savingDialog(QDialog):\n \"\"\"\n File dialog with options.\n We use a standard QFileDialog as a child widget and we\n forward its methods to the top level.\n \"\"\"\n\n def __init__(self, parent, text, lastDir):\n \"\"\"\n\n :param parent:\n :type parent: QObject\n :param text:\n :type text: str\n :param lastDir:\n :type lastDir:str\n \"\"\"\n # QDialog __init__\n super().__init__(parent=parent)\n self.setWindowTitle(text)\n # File Dialog\n self.dlg = QFileDialog(caption=text, directory=lastDir)\n self.dlg.setOption(QFileDialog.DontUseNativeDialog)\n self.metaOption = QCheckBox('Remove Meta')\n # sliders\n self.sliderComp = QbLUeSlider(Qt.Horizontal)\n self.sliderComp.setTickPosition(QSlider.TicksBelow)\n self.sliderComp.setRange(0, 9)\n self.sliderComp.setSingleStep(1)\n self.sliderComp.setValue(3) # 3 = default opencv imwrite value\n self.sliderQual = QbLUeSlider(Qt.Horizontal)\n self.sliderQual.setTickPosition(QSlider.TicksBelow)\n self.sliderQual.setRange(0, 100)\n self.sliderQual.setSingleStep(10)\n self.sliderQual.setValue(95) # 95 = default opencv imwrite value\n self.dlg.setVisible(True)\n l = QVBoxLayout()\n h = QHBoxLayout()\n l.addWidget(self.dlg)\n h.addWidget(self.metaOption)\n h.addWidget(QLabel(\"Quality\"))\n h.addWidget(self.sliderQual)\n h.addWidget(QLabel(\"Compression\"))\n h.addWidget(self.sliderComp)\n l.addLayout(h)\n self.setLayout(l)\n\n # file dialog close event handler\n def f():\n self.close()\n\n self.dlg.finished.connect(f)\n\n def exec(self):\n # QDialog exec_\n super().exec()\n # forward file dialog result\n return self.dlg.result()\n\n def selectFile(self, fileName):\n self.dlg.selectFile(fileName)\n\n def selectedFiles(self):\n return self.dlg.selectedFiles()\n\n def directory(self):\n return self.dlg.directory()\n\n\nclass labelDlg(QDialog):\n \"\"\"\n Displays a floating modal text window.\n If search is True, a search field editor is added on top of the window.\n \"\"\"\n\n def __init__(self, parent=None, title='', wSize=QSize(500, 500), scroll=True, search=False, modal=True):\n super().__init__(parent)\n self.setWindowTitle(parent.tr(title))\n self.setStyleSheet(\" * {background-color: rgb(220, 220, 220); color: black;}\\\n QLabel {selection-background-color: blue; selection-color: white}\\\n QLineEdit {background-color: white;}\")\n self.setModal(modal)\n self.label = QLabel()\n self.label.setAlignment(Qt.AlignTop)\n vl = QVBoxLayout()\n if search:\n ed = QLineEdit()\n ed.setMaximumWidth(300)\n ed.setPlaceholderText('Search')\n vl = QVBoxLayout()\n hl = QHBoxLayout()\n hl.addWidget(ed)\n button = QPushButton('Next')\n button.setAutoDefault(False);\n button.setMaximumWidth(60)\n hl.addWidget(button)\n vl.addLayout(hl)\n matches = []\n current = 0\n\n def f(searchedText):\n import re\n nonlocal current\n matches.clear()\n current = 0\n matches.extend([m.span() for m in\n re.finditer(searchedText, self.label.text(), re.IGNORECASE | re.MULTILINE | re.DOTALL)])\n if matches:\n item = matches[0]\n self.label.setSelection(item[0], item[1] - item[0])\n metrics = self.label.fontMetrics()\n tabSize = 4\n rect = metrics.boundingRect(0, 0, 150000, 150000, self.label.alignment() | Qt.TextExpandTabs,\n self.label.text()[:item[1]], tabSize)\n scarea.ensureVisible(0, rect.height())\n\n def g():\n nonlocal current\n if not matches or not button.isDown():\n return\n current = (current + 1) % len(matches)\n item = matches[current]\n self.label.setSelection(item[0], item[1] - item[0])\n metrics = self.label.fontMetrics()\n tabSize = 4\n rect = metrics.boundingRect(0, 0, 150000, 150000, self.label.alignment() | Qt.TextExpandTabs,\n self.label.text()[:item[1]], tabSize)\n scarea.ensureVisible(0, rect.height())\n\n button.pressed.connect(g)\n ed.textEdited.connect(f)\n\n if scroll:\n scarea = QScrollArea()\n scarea.setWidget(self.label)\n scarea.setWidgetResizable(True)\n vl.addWidget(scarea)\n else:\n vl.addWidget(self.label)\n self.setLayout(vl)\n self.setFixedSize(wSize)\n\n def wrapped(self, s):\n \"\"\"\n Returns wrapped text, according to the current font and size of label.\n NOT updated when these parameters are modified.\n\n :param s: text to wrap\n :type s: str\n :return:\n :rtype: list of str\n \"\"\"\n metrics = self.label.fontMetrics()\n tabSize = 4\n # get max character count per line\n testText = 'WWWWWWWWWWWWWWW' # start from a minimum width !\n while metrics.boundingRect(0, 0, 150000, 150000, self.label.alignment() | Qt.TextExpandTabs,\n testText, tabSize).width() < self.label.width():\n testText += 'W'\n # wrap text while keeping existing newlines\n s = '\\n'.join(['\\n'.join(textwrap.wrap(line, len(testText), break_long_words=False, replace_whitespace=False))\n for line in s.splitlines()])\n return s\n\n\ndef saveDlg(img, mainForm, ext='jpg', selected=True, parent=None):\n \"\"\"\n Image saving dialog.\n If selected is False, initially the filename box is left empty and no file is selected.\n A ValueError exception is raised if the dialog is aborted.\n\n :param img:\n :type img: vImage\n :param mainForm:\n :type mainForm: QWidget\n :param selected:\n :type selected: boolean\n :return:filename, quality, compression, metaOption\n :rtype: str, int, int, boolean\n \"\"\"\n # get last accessed dir\n lastDir = str(mainForm.settings.value(\"paths/dlgsavedir\", QDir.currentPath()))\n # file dialogs\n dlg = savingDialog(parent, \"Save\", lastDir)\n if selected:\n # default saving format jpg\n dlg.selectFile(basename(img.filename)[:-3] + ext)\n filename = ''\n if dlg.exec():\n newDir = dlg.directory().absolutePath()\n mainForm.settings.setValue('paths/dlgsavedir', newDir)\n filenames = dlg.selectedFiles()\n if filenames:\n filename = filenames[0]\n else:\n raise ValueError(\"You must select a file\")\n return filename, dlg.sliderQual.value(), dlg.sliderComp.value(), not dlg.metaOption.isChecked()\n\n\ndef openDlg(mainForm, ask=True, multiple=False, key='dlgdir', parent=None):\n \"\"\"\n if multiple is true returns a list of file names,\n otherwise returns a file name or None.\n\n :param mainForm:\n :type mainForm:\n :param ask:\n :type ask:\n :param multiple:\n :type multiple: boolean\n :param key: QSettings key\n :type key: str\n :return: file name or list of file names\n :rtype: string or list of strings\n \"\"\"\n if ask and mainForm.label.img.isModified:\n ret = saveChangeDialog(mainForm.label.img)\n if ret == QMessageBox.Yes:\n try:\n saveDlg(mainForm.label.img, mainForm, parent=parent)\n except (ValueError, IOError) as e:\n dlgWarn(str(e))\n return\n elif ret == QMessageBox.Cancel:\n return\n # don't ask again for saving\n mainForm.label.img.isModified = False\n lastDir = str(mainForm.settings.value(key, '.'))\n filter = \"Images ( *\" + \" *\".join(IMAGE_FILE_EXTENSIONS) + \\\n \" *\" + \" *\".join(RAW_FILE_EXTENSIONS) + \\\n \" *\" + \" *\".join(BLUE_FILE_EXTENSIONS) + \\\n \" *\" + \" *\".join(SVG_FILE_EXTENSIONS) + \")\"\n if multiple:\n # allow multiple selections\n filenames = QFileDialog.getOpenFileNames(mainForm, \"select\", lastDir, filter)\n names = filenames[0]\n if names:\n newDir = dirname(names[0])\n mainForm.settings.setValue(key, newDir)\n return names\n # select a single file\n dlg = QFileDialog(mainForm, \"select\", lastDir, filter)\n if dlg.exec():\n filenames = dlg.selectedFiles()\n newDir = dlg.directory().absolutePath()\n mainForm.settings.setValue(key, newDir)\n return filenames[0]\n return None\n","repo_name":"bvirxx/bLUe_PYSIDE","sub_path":"bLUeGui/dialog.py","file_name":"dialog.py","file_ext":"py","file_size_in_byte":14476,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"26"}
+{"seq_id":"2637298073","text":"import sys\nsys.path.append(\"..\")\nimport NeedsLimitsFramework.plots as plots\nimport agentpy as ap\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\ndef qol_income_dist(results, y, ylabel, hue=None, color=None): \n plots.agent_dist(\n results=results,\n x='income', y=y, \n xlabel='Income', ylabel=ylabel,\n xbins=30, ybins=11,\n ybinrange=(0,1),\n hue=hue,\n color=color,\n obj_types=('Individual')\n )\n\n \ndef growth_redist():\n results_growth = ap.DataDict.load('growth', 0, 'results', display=False)\n results_redist = ap.DataDict.load('redist', 0, 'results', display=False)\n data1 = results_growth.arrange_reporters()\n data2 = results_redist.arrange_reporters()\n data1['growth'] *= 100\n data2['redist'] *= 100\n\n series = [\n ('Q', 'Quality of life (QOL)', 'tab:purple'),\n ('CES', 'CES Utility', 'tab:red'),\n ('I', 'Income', 'tab:blue'),\n ]\n \n fig, rows = plt.subplots(2, 2, figsize=(6, 6), sharex='col', sharey='row')\n axs = [ax for row in rows for ax in row]\n for X, f_label, c in series: \n \n x_keys = ['growth', 'redist'] * 2\n y_keys = [f'M({X})'] * 2 + [f'G({X})'] * 2\n datas = [data1, data2] * 2\n\n for ax, x, y, data in zip(axs, x_keys, y_keys, datas): \n sns.lineplot(data=data, x=x, y=y, ax=ax, color=c, label=f_label)\n \n for ax in (0,2,3):\n axs[ax].get_legend().remove()\n \n xlabels = ('Income growth [%]', 'Redistribution [%]')\n for ax, label in zip(rows[1], xlabels):\n ax.set_xlabel(label) # Bottom row\n\n ylabels = (f'Average - Well-being', f'Gini coefficient - Well-being')\n for row, label in zip(rows, ylabels):\n row[0].set_ylabel(label) # Left column\n \n plt.tight_layout()\n \n \ndef carbon_tax():\n results = ap.DataDict.load('ctax', 0, 'results', display=None)\n fig, rows = plt.subplots(2, 2, figsize=(6, 6), sharex='col')\n axs = [ax for row in rows for ax in row]\n hue = 'ctax_redist' \n y_keys = ('M(E)', 'M(Q)', 'G(E)', 'G(Q)')\n data = results.arrange_reporters()\n for ax, y in zip(axs, y_keys): \n sns.lineplot(data=data, x='ctax', hue=hue, y=y, ax=ax)\n if hue:\n for ax in axs:\n ax.get_legend().remove()\n plt.tight_layout()\n for row, X in zip(rows, ('Average', 'Gini coefficient')):\n for ax, Y in zip(row, ('Emissions', 'QOL')):\n ax.set_ylabel(f'{X} - {Y}')\n for ax in rows[1]:\n ax.set_xlabel('Carbon tax rate')\n \n \ndef multiplier(): \n fig, axs = plt.subplots(2, 1, figsize=(6, 6), sharex='col')\n ax1, ax2 = axs\n \n results = ap.DataDict.load('multiplier', 0, 'results', display=False)\n df = results.arrange_reporters()\n hue='imp_norms_recreation'\n x='imp_norms'\n y_keys = ('M(E)', 'M(Q)')\n \n for ax, y in zip(axs, y_keys):\n sns.lineplot(data=df, x=x, y=y, hue=hue, ax=ax)\n \n ax1.legend(title='Perceived\\nimportance\\nof recreation\\n& rest', \n bbox_to_anchor=(1,1), loc=\"upper left\")\n ax2.get_legend().remove()\n \n ax1.set_ylabel('Average - Emissions')\n ax2.set_ylabel('Average - QOL')\n ax2.set_xlabel('Perceived importance of social norms')\n \n plt.tight_layout()\n","repo_name":"JoelForamitti/NeedsAndLimitsFramework","sub_path":"TimeIncomeScenario/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":3298,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"26"}
+{"seq_id":"42193129332","text":"import requests\nimport re\nfrom lxml import etree\n\n\nclass CaijingSpider:\n def __init__(self):\n self.url = 'http://finance.ce.cn/rolling/index.shtml'\n self.headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.7 Safari/537.36'}\n\n def get_html(self, url):\n html = requests.get(url=url, headers=self.headers).content.decode('gb2312', 'ignore')\n return html\n\n def parse_html(self, html, regex):\n p = etree.HTML(html)\n\n pattern = re.compile(regex, re.S)\n result = pattern.findall(html)\n return result\n\n def get_data(self):\n html = self.get_html(self.url)\n pattern = r'.*?td height=\"28\".*?'\n res = self.parse_html(html, pattern)\n for url in res:\n if url[0] == 'h':\n self.get_data_2(url)\n elif url[0] == '.' and url[1] == '.':\n self.get_data_2('http://finance.ce.cn' + url[2:])\n else:\n self.get_data_2('http://finance.ce.cn/rolling' + url[1:])\n\n def get_data_2(self, url):\n html = self.get_html(url)\n pattern = r'(.*)。.*?责任编辑：'\n # pattern = r'
(.*?).*?id=\"articleTime\">(.*?).*?id=\"articleSource\">(.*?).*?
(.*?)
'\n res = self.parse_html(html, pattern)\n for item in res:\n result = re.sub('[0-9a-zA-Z]', ' ', item, re.S)\n # result = re.sub('\\s+', ' ', result, re.S)\n print(result)\n print('**************************************')\n # print(item[1].strip().strip('\\n'))\n # print(item[2].strip().strip('\\n'))\n # print(item[3].strip().replace('', '').replace('
', '\\n'))\n\n def run(self):\n self.get_data()\n\n\nif __name__ == '__main__':\n spider = CaijingSpider()\n spider.run()\n","repo_name":"ShaoxiongYuan/PycharmProjects","sub_path":"4. Python 爬虫工程师/1. 爬虫/Extra/财经.py","file_name":"财经.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"26"}
+{"seq_id":"36810054569","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 2 15:14:34 2019\n\n@author: aahunter\n\"\"\"\nimport serial\nimport io\n\n\ndev = \"/dev/cu.usbserial-DA01233R\"\nbaudRate = 56700\nser = serial.Serial(dev,baudRate,timeout=1) #open serial port\nsio = io.TextIOWrapper(io.BufferedRWPair(ser, ser),encoding='ascii')\n\ndef drive(u_l,u_r):\n v = (int(u_l),int(u_r))\n commandStr = '[{0[0]:d},{0[1]:d}]\\n'.format(v)\n# commandStr = '-50,50 \\n'\n# command = bytes(commandStr, 'ascii')\n # sio.flush()\n # ser.reset_output_buffer()\n sio.write(commandStr)\n sio.flush()\n sio.write(commandStr)\n sio.flush()\n\n","repo_name":"2ahunter/Serial_Drive_Robot","sub_path":"Controller/drive_test.py","file_name":"drive_test.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"28271179506","text":"from typing import List\n\n\nclass Solution:\n def minPathSum(self, grid: List[List[int]]) -> int:\n m = len(grid)\n if not m:\n return 0\n n = len(grid[0])\n if not n:\n return 0\n # 状态定义：dp[i] 表示从 (0, 0) 到达第 i - 1 行的最短路径值\n dp = [0] * n\n # 状态初始化\n dp[0] = grid[0][0]\n # 状态转移\n for i in range(m):\n for j in range(n):\n if i == 0 and j != 0: # 顶部横着计算， 前一列的值 + 当前位置的值\n dp[j] = dp[j - 1] + grid[i][j]\n elif i != 0 and j == 0: # 左侧竖着计算， dp[j]是上一行的值 + 当前位置的值\n dp[j] = dp[j] + grid[i][j]\n elif i != 0 and j != 0:\n dp[j] = min(dp[j], dp[j - 1]) + grid[i][j]\n return dp[-1]\n","repo_name":"issone/leetcode","sub_path":"0064_minimum-path-sum/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"13078430853","text":"\"\"\"\nPerfect Squares\n\nGiven an integer n, return the least number of perfect square numbers that sum to n.\n\nA perfect square is an integer that is the square of an integer; in other words, it is the product of some integer\nwith itself. For example, 1, 4, 9, and 16 are perfect squares while 3 and 11 are not.\n\n\n\nExample 1:\n\nInput: n = 12\nOutput: 3\nExplanation: 12 = 4 + 4 + 4.\nExample 2:\n\nInput: n = 13\nOutput: 2\nExplanation: 13 = 4 + 9.\nWorth read: https://leetcode.com/problems/perfect-squares/solution/\n\"\"\"\nfrom collections import deque\n\n\nclass Solution:\n def numSquares(self, n: int) -> int:\n sq_nums = [i ** i for i in range((n // 2) + 1)]\n q = deque([n])\n level = 0\n while q:\n level += 1\n # use set() instead of list() to eliminate the redundancy, leads to 5-times speedup, 200ms vs. 1000ms.\n next_q = set()\n # construct the queue for the next level\n for remain in q:\n for sq_num in sq_nums:\n if remain == sq_num:\n # found the node\n return level\n elif remain < sq_num:\n break\n else:\n next_q.add(remain - sq_num)\n q = next_q\n return level\n","repo_name":"kuntalchandra/ds-guide","sub_path":"random_selection/perfect_squares.py","file_name":"perfect_squares.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"40155502790","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 22 12:45:24 2019\n\n@author: abigailbernhardt\n\"\"\"\n#%%\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#lambda is one simple line of code \nf_n = lambda n: n**5+1\ng_n = lambda n: n**4+n**2.0+1\n\ndef mainfunc():\n z = 40 #change these numbers\n b = 1 #change these numbers\n divlist = [1]\n bList = [1]\n for ii in range (1,z):\n divlist.append(((1+(f_n(ii)/(g_n(ii)))))*(divlist[ii-1]))\n bList.append((1+b**ii)*bList[ii-1])\n \n listx = np.linspace(1,len(divlist), len(divlist))\n \n print(\"The first 15 terms of the sequence are: \\n \", divlist[:15])\n print()\n print(\"The last 15 terms of the sequence are: \\n \", divlist[-15:])\n print()\n print(\"The first 15 terms of the sequence are: \\n \", bList[:15])\n print()\n print(\"The last 15 terms of the sequence are: \\n \", bList[-15:]) \n\nmainfunc()","repo_name":"abbybernhardt/Bernhardt-MATH361B","sub_path":"IntroToProgramming/I9_PartialProd2_Bernhardt.py","file_name":"I9_PartialProd2_Bernhardt.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"42389918887","text":"import codecs\nimport os\nimport re\n\nimport numpy as np\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\n\ndef read_vocab(filename):\n vocab = dict()\n filepath = os.path.join(FLAGS.data_dir, filename)\n with tf.gfile.FastGFile(filepath) as vocab_file:\n for line in vocab_file:\n match = re.search(r'(\\d+)\\. (.*)\\s*', line)\n vocab[int(match.group(1))] = match.group(2)\n\n pairs = sorted(vocab.items(), key=lambda a: a[0])\n indices = [a for a, b in pairs]\n words = [b for a, b in pairs]\n assert indices == list(range(len(indices)))\n return words\n\n\ndef read_corpus(filename, encoding=\"utf-8\"):\n \"\"\"Reads a textfile corpus.\n\n Args:\n filename (String) : The path to the file containing the corpus.\n encoding (String ): The encoding on the file. (default: {\"utf-8\"})\n\n Returns:\n corpus (np.ndarray) : An array of integers representing the corpus.\n mappings (dict) : A dictionary of mappings where the keys are characters\n and the values are the corresponding integers.\n \"\"\"\n corpus = None\n mappings = None\n with codecs.open(filename, \"r\", encoding) as file:\n data = file.read()\n chars = set(data)\n mappings = {c: i for i, c in enumerate(chars)}\n corpus = np.array([mappings[ch] for ch in data])\n return corpus, mappings\n","repo_name":"for-ai/CipherGAN","sub_path":"train_utils/vocab_utils.py","file_name":"vocab_utils.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"26"}
+{"seq_id":"16448239451","text":"import scrapy\nfrom bs4 import BeautifulSoup\n\nclass MoreComments(scrapy.Spider):\n name='more_comments'\n user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246'\n start_urls = [l.strip() for l in open('urls.csv').readlines()]\n\n custom_settings = {'FEED_FORMAT': 'jsonlines', 'FEED_URI': '/root/more.json', 'LOG_LEVEL': 'INFO'}\n\n def parse(self, response):\n doc = BeautifulSoup(response.text)\n comments = doc.find_all('article', {\"class\": \"comment\"})\n res = []\n for c in comments:\n body = c.find('div', {'class': 'comment__body'}).get_text().strip()\n date = c.find('a', {'class': 'comment-meta__date'}).get_text().strip()\n res.append( {'text': body, 'date': date} )\n yield {'url': response.url, 'comments': res}\n","repo_name":"jfilter/ptf-kommentare","sub_path":"notebooks/03_scrape/scrape/more_comments_spider.py","file_name":"more_comments_spider.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"26"}
+{"seq_id":"14101954208","text":"import os\nimport requests\nimport time\n\nBASE_URL = r'https://wishlist-flask-api.herokuapp.com/'\n\ndef tempo_decorrido(f):\n def wrapper():\n t0 = time.time()\n f()\n t1 = time.time()\n\n t = t1 - t0\n \n print(f\"\\nTempo decorrido: {t:.3f} segundos. \\n\")\n return wrapper\n\n\n@tempo_decorrido\ndef put_itens():\n data = [{\"item 1\":{\"desc\": \"descrição item 1\"}},\n {\"item 2\":{\"desc\": \"descrição item 2\"}},\n {\"item 3\":{\"desc\": \"descrição item 3\"}}]\n\n for i,item in enumerate(data):\n for j in item:\n response = requests.put(BASE_URL + \"wishlist/\" + j, data[i][j])\n print(response, response.json())\n\n@tempo_decorrido\ndef get_itens():\n data = [\"item 1\", \"item 2\",\"item 3\"]\n\n for i in data:\n response = requests.get(BASE_URL + \"wishlist/\" + i)\n print(response, response.json())\n\n@tempo_decorrido\ndef patch_itens():\n data = [{\"item 1\":{\"desc\": \"nova descrição item 1\"}},\n {\"item 2\":{\"desc\": \"nova descrição item 2\"}},\n {\"item 3\":{\"desc\": \"nova descrição item 3\"}}]\n\n for i,item in enumerate(data):\n for j in item:\n response = requests.patch(BASE_URL + \"wishlist/\" + j, data[i][j])\n print(response, response.json())\n\n@tempo_decorrido\ndef patch_status():\n data = [\"item 1\", \"item 2\",\"item 3\"]\n\n for i in data:\n response = requests.patch(BASE_URL + \"wishlist/\" + i + \"/owned/\")\n print(response, response.json())\n\n@tempo_decorrido\ndef get_random():\n response = requests.get(BASE_URL + \"wishlist/\" + \"random/\")\n print(response, response.json())\n\n@tempo_decorrido\ndef del_itens():\n data = [\"item 1\", \"item 2\",\"item 3\"]\n\n for i in data:\n response = requests.delete(BASE_URL + \"wishlist/\" + i)\n print(response)\n\n\ndef teste_api():\n t0 = time.time()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE GET \n \"\"\"\n print(\"Teste 1: Tenta acessar 3 itens que não existem na lista \\n \\nStatus esperado: 404 (não encontrado)\")\n get_itens()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE PUT \n \"\"\"\n print(\"Teste 2: Adiciona 3 itens que não existem na lista \\n \\nStatus esperado: 200 (sucesso)\")\n put_itens()\n print(\"----------------------------------------------\")\n print(\"Teste 3: Adiciona 3 itens que já existem na lista \\n \\nStatus esperado: 409 (item já existe)\")\n put_itens()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE PATCH \n \"\"\"\n print(\"Teste 4: Altera a descrição de 3 itens que existem na lista \\n \\nStatus esperado: 200 (sucesso)\")\n patch_itens()\n print(\"----------------------------------------------\")\n print(\"Teste 5: Altera o status de 3 itens que existem na lista \\n \\nStatus esperado: 200 (sucesso)\")\n patch_status()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE GET \n \"\"\"\n print(\"Teste 6: Tenta acessar 3 itens que existem na lista \\n \\nStatus esperado: 200 (sucesso)\")\n get_itens()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE GET (RANDOM)\n \"\"\"\n print(\"Teste 7: Acessa um item disponível aleatório da lista \\n \\nStatus esperado: 200 (sucesso)\")\n get_random()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE DELETE\n \"\"\"\n print(\"Teste 8: Deleta os 3 itens criados anteriormente \\n \\nStatus esperado: 204 (sucesso)\")\n del_itens()\n print(\"----------------------------------------------\")\n print(\"Teste 9: Deleta 3 itens que não existem na lista \\n \\nStatus esperado: 404 (item não encontrado)\")\n del_itens()\n print(\"----------------------------------------------\")\n \"\"\" \n TESTE PATCH \n \"\"\"\n print(\"Teste 10: Altera a descrição de 3 itens que não existem na lista \\n \\nStatus esperado: 404 (item não encontrado)\")\n patch_itens()\n print(\"----------------------------------------------\")\n print(\"Teste 11: Altera o status de 3 itens que não existem na lista \\n \\nStatus esperado: 404 (item não encontrado)\")\n patch_status()\n print(\"----------------------------------------------\")\n t1 = time.time()\n t = t1 - t0\n t_ideal = 0.25*3*11\n print(f\"\\nTestes realizados: 11 \\nTempo decorrido: {t:.3f} segundos.\")","repo_name":"cyberfvnk/wishlist-rest-api","sub_path":"tests/testes_import.py","file_name":"testes_import.py","file_ext":"py","file_size_in_byte":4351,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"567905158","text":"\"\"\" Module containing functions for clustering and cluster indices generation from samples of data \"\"\"\nimport logging\n\nimport numpy as np\nimport tqdm\n\nfrom .utils import read_bag_file, extract_bag_index\nfrom .internal_indices import InternalIndices, INTERNAL_INDICES_METHOD_NAMES_DICT\nfrom .external_indices import ExternalIndices, EXTERNAL_INDICES_METHOD_NAMES_DICT\n\n\nlogging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',\n\t\t\tdatefmt = '%m/%d/%Y %H:%M:%S',\n\t\t\tlevel = logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nKMEANS_CHOOSEN_CLUSTER_INDICES = {\n\n 'internal_indices' : [\n # 'WGSS',\n # 'BGSS',\n # 'Ball-Hall',\n # 'Banfeld-Raftery',\n # 'Calinski-Harabasz',\n # 'Det-Ratio',\n # 'Ksq-DetW',\n # 'Log-Det-Ratio',\n # 'Log-SS-Ratio',\n ## 'Scott-Symons',\n 'Silhouette',\n # 'Trace-WiB',\n 'C',\n 'Dunn',\n # 'Davies-Bouldin',\n # 'Ray-Turi',\n # 'PBM',\n 'Score',\n ],\n\n 'external_indices' : [\n 'Entropy',\n 'Purity',\n 'Precision',\n 'Recall',\n 'F',\n 'Weighted-F',\n 'Folkes-Mallows',\n 'Rand',\n 'Adjusted-Rand',\n 'Adjusted-Mutual-Info',\n 'Normalized-Mutual-Info',\n 'Homogeneity',\n 'Completeness',\n 'V-Measure',\n 'Jaccard',\n 'Hubert Γ̂',\n 'Kulczynski',\n # 'McNemar',\n 'Phi',\n 'Russel-Rao',\n 'Rogers-Tanimoto',\n 'Sokal-Sneath1',\n 'Sokal-Sneath2',\n ],\n\n}\n\n\nHIERARCHICAL_CHOOSEN_CLUSTER_INDICES = {\n\n 'internal_indices' : [\n # 'WGSS',\n # 'BGSS',\n # 'Ball-Hall',\n # 'Banfeld-Raftery',\n # 'Calinski-Harabasz',\n # 'Det-Ratio',\n # 'Ksq-DetW',\n # 'Log-Det-Ratio',\n # 'Log-SS-Ratio',\n ## 'Scott-Symons',\n 'Silhouette',\n # 'Trace-WiB',\n 'C',\n 'Dunn',\n # 'Davies-Bouldin',\n # 'Ray-Turi',\n # 'PBM',\n 'Score',\n ],\n\n 'external_indices' : [\n 'Entropy',\n 'Purity',\n 'Precision',\n 'Recall',\n 'F',\n 'Weighted-F',\n 'Folkes-Mallows',\n 'Rand',\n 'Adjusted-Rand',\n 'Adjusted-Mutual-Info',\n 'Normalized-Mutual-Info',\n 'Homogeneity',\n 'Completeness',\n 'V-Measure',\n 'Jaccard',\n 'Hubert Γ̂',\n 'Kulczynski',\n # 'McNemar',\n 'Phi',\n 'Russel-Rao',\n 'Rogers-Tanimoto',\n 'Sokal-Sneath1',\n 'Sokal-Sneath2',\n ],\n\n}\n\n\nSPECTRAL_CHOOSEN_CLUSTER_INDICES = {\n\n 'internal_indices' : [\n # 'WGSS',\n # 'BGSS',\n # 'Ball-Hall',\n # 'Banfeld-Raftery',\n # 'Calinski-Harabasz',\n # 'Det-Ratio',\n # 'Ksq-DetW',\n # 'Log-Det-Ratio',\n # 'Log-SS-Ratio',\n ## 'Scott-Symons',\n 'Silhouette',\n # 'Trace-WiB',\n 'C',\n 'Dunn',\n 'Davies-Bouldin',\n 'Ray-Turi',\n # 'PBM',\n 'Score',\n ],\n\n 'external_indices' : [\n 'Entropy',\n 'Purity',\n 'Precision',\n 'Recall',\n 'F',\n 'Weighted-F',\n 'Folkes-Mallows',\n 'Rand',\n 'Adjusted-Rand',\n 'Adjusted-Mutual-Info',\n 'Normalized-Mutual-Info',\n 'Homogeneity',\n 'Completeness',\n 'V-Measure',\n 'Jaccard',\n 'Hubert Γ̂',\n 'Kulczynski',\n # 'McNemar',\n 'Phi',\n 'Russel-Rao',\n 'Rogers-Tanimoto',\n 'Sokal-Sneath1',\n 'Sokal-Sneath2',\n ],\n\n}\n\n\nHDBSCAN_CHOOSEN_CLUSTER_INDICES = {\n\n 'internal_indices' : [\n # 'WGSS',\n # 'BGSS',\n # 'Ball-Hall',\n # 'Banfeld-Raftery',\n # 'Calinski-Harabasz',\n # 'Det-Ratio',\n # 'Ksq-DetW',\n # 'Log-Det-Ratio',\n 'Log-SS-Ratio',\n ## 'Scott-Symons',\n 'Silhouette',\n # 'Trace-WiB',\n # 'C',\n 'Dunn',\n # 'Davies-Bouldin',\n # 'Ray-Turi',\n # 'PBM',\n 'Score',\n ],\n\n 'external_indices' : [\n 'Entropy',\n 'Purity',\n 'Precision',\n 'Recall',\n 'F',\n 'Weighted-F',\n 'Folkes-Mallows',\n 'Rand',\n 'Adjusted-Rand',\n 'Adjusted-Mutual-Info',\n 'Normalized-Mutual-Info',\n 'Homogeneity',\n 'Completeness',\n 'V-Measure',\n 'Jaccard',\n 'Hubert Γ̂',\n 'Kulczynski',\n # 'McNemar',\n 'Phi',\n 'Russel-Rao',\n 'Rogers-Tanimoto',\n 'Sokal-Sneath1',\n 'Sokal-Sneath2',\n ],\n\n}\n\n\nFEATURE_VECTOR_CLUSTER_INDICES_ORDER_TRIPLES = [\n\n # ('kmeans', 'internal_indices', 'WGSS'),\n # ('kmeans', 'internal_indices', 'BGSS'),\n # ('kmeans', 'internal_indices', 'Ball-Hall'),\n # ('kmeans', 'internal_indices', 'Banfeld-Raftery'),\n # ('kmeans', 'internal_indices', 'Calinski-Harabasz'),\n # ('kmeans', 'internal_indices', 'Det-Ratio'),\n # ('kmeans', 'internal_indices', 'Ksq-DetW'),\n # ('kmeans', 'internal_indices', 'Log-Det-Ratio'),\n # ('kmeans', 'internal_indices', 'Log-SS-Ratio'),\n ## ('kmeans', 'internal_indices', 'Scott-Symons'),\n ('kmeans', 'internal_indices', 'Silhouette'),\n # ('kmeans', 'internal_indices', 'Trace-WiB'),\n ('kmeans', 'internal_indices', 'C'),\n ('kmeans', 'internal_indices', 'Dunn'),\n # ('kmeans', 'internal_indices', 'Davies-Bouldin'),\n # ('kmeans', 'internal_indices', 'Ray-Turi'),\n # ('kmeans', 'internal_indices', 'PBM'),\n ('kmeans', 'internal_indices', 'Score'),\n\n ('kmeans', 'external_indices', 'Entropy'),\n ('kmeans', 'external_indices', 'Purity'),\n ('kmeans', 'external_indices', 'Precision'),\n ('kmeans', 'external_indices', 'Recall'),\n ('kmeans', 'external_indices', 'F'),\n ('kmeans', 'external_indices', 'Weighted-F'),\n ('kmeans', 'external_indices', 'Folkes-Mallows'),\n ('kmeans', 'external_indices', 'Rand'),\n ('kmeans', 'external_indices', 'Adjusted-Rand'),\n ('kmeans', 'external_indices', 'Adjusted-Mutual-Info'),\n ('kmeans', 'external_indices', 'Normalized-Mutual-Info'),\n ('kmeans', 'external_indices', 'Homogeneity'),\n ('kmeans', 'external_indices', 'Completeness'),\n ('kmeans', 'external_indices', 'V-Measure'),\n ('kmeans', 'external_indices', 'Jaccard'),\n ('kmeans', 'external_indices', 'Hubert Γ̂'),\n ('kmeans', 'external_indices', 'Kulczynski'),\n # ('kmeans', 'external_indices', 'McNemar'),\n ('kmeans', 'external_indices', 'Phi'),\n ('kmeans', 'external_indices', 'Russel-Rao'),\n ('kmeans', 'external_indices', 'Rogers-Tanimoto'),\n ('kmeans', 'external_indices', 'Sokal-Sneath1'),\n ('kmeans', 'external_indices', 'Sokal-Sneath2'),\n\n\n # ('hierarchical', 'internal_indices', 'WGSS'),\n # ('hierarchical', 'internal_indices', 'BGSS'),\n # ('hierarchical', 'internal_indices', 'Ball-Hall'),\n # ('hierarchical', 'internal_indices', 'Banfeld-Raftery'),\n # ('hierarchical', 'internal_indices', 'Calinski-Harabasz'),\n # ('hierarchical', 'internal_indices', 'Det-Ratio'),\n # ('hierarchical', 'internal_indices', 'Ksq-DetW'),\n # ('hierarchical', 'internal_indices', 'Log-Det-Ratio'),\n # ('hierarchical', 'internal_indices', 'Log-SS-Ratio'),\n ## ('hierarchical', 'internal_indices', 'Scott-Symons'),\n ('hierarchical', 'internal_indices', 'Silhouette'),\n # ('hierarchical', 'internal_indices', 'Trace-WiB'),\n ('hierarchical', 'internal_indices', 'C'),\n ('hierarchical', 'internal_indices', 'Dunn'),\n # ('hierarchical', 'internal_indices', 'Davies-Bouldin'),\n # ('hierarchical', 'internal_indices', 'Ray-Turi'),\n # ('hierarchical', 'internal_indices', 'PBM'),\n ('hierarchical', 'internal_indices', 'Score'),\n\n ('hierarchical', 'external_indices', 'Entropy'),\n ('hierarchical', 'external_indices', 'Purity'),\n ('hierarchical', 'external_indices', 'Precision'),\n ('hierarchical', 'external_indices', 'Recall'),\n ('hierarchical', 'external_indices', 'F'),\n ('hierarchical', 'external_indices', 'Weighted-F'),\n ('hierarchical', 'external_indices', 'Folkes-Mallows'),\n ('hierarchical', 'external_indices', 'Rand'),\n ('hierarchical', 'external_indices', 'Adjusted-Rand'),\n ('hierarchical', 'external_indices', 'Adjusted-Mutual-Info'),\n ('hierarchical', 'external_indices', 'Normalized-Mutual-Info'),\n ('hierarchical', 'external_indices', 'Homogeneity'),\n ('hierarchical', 'external_indices', 'Completeness'),\n ('hierarchical', 'external_indices', 'V-Measure'),\n ('hierarchical', 'external_indices', 'Jaccard'),\n ('hierarchical', 'external_indices', 'Hubert Γ̂'),\n ('hierarchical', 'external_indices', 'Kulczynski'),\n # ('hierarchical', 'external_indices', 'McNemar'),\n ('hierarchical', 'external_indices', 'Phi'),\n ('hierarchical', 'external_indices', 'Russel-Rao'),\n ('hierarchical', 'external_indices', 'Rogers-Tanimoto'),\n ('hierarchical', 'external_indices', 'Sokal-Sneath1'),\n ('hierarchical', 'external_indices', 'Sokal-Sneath2'),\n\n\n # ('spectral', 'internal_indices', 'WGSS'),\n # ('spectral', 'internal_indices', 'BGSS'),\n # ('spectral', 'internal_indices', 'Ball-Hall'),\n # ('spectral', 'internal_indices', 'Banfeld-Raftery'),\n # ('spectral', 'internal_indices', 'Calinski-Harabasz'),\n # ('spectral', 'internal_indices', 'Det-Ratio'),\n # ('spectral', 'internal_indices', 'Ksq-DetW'),\n # ('spectral', 'internal_indices', 'Log-Det-Ratio'),\n # ('spectral', 'internal_indices', 'Log-SS-Ratio'),\n ## ('spectral', 'internal_indices', 'Scott-Symons'),\n ('spectral', 'internal_indices', 'Silhouette'),\n # ('spectral', 'internal_indices', 'Trace-WiB'),\n ('spectral', 'internal_indices', 'C'),\n ('spectral', 'internal_indices', 'Dunn'),\n ('spectral', 'internal_indices', 'Davies-Bouldin'),\n ('spectral', 'internal_indices', 'Ray-Turi'),\n # ('spectral', 'internal_indices', 'PBM'),\n ('spectral', 'internal_indices', 'Score'),\n\n ('spectral', 'external_indices', 'Entropy'),\n ('spectral', 'external_indices', 'Purity'),\n ('spectral', 'external_indices', 'Precision'),\n ('spectral', 'external_indices', 'Recall'),\n ('spectral', 'external_indices', 'F'),\n ('spectral', 'external_indices', 'Weighted-F'),\n ('spectral', 'external_indices', 'Folkes-Mallows'),\n ('spectral', 'external_indices', 'Rand'),\n ('spectral', 'external_indices', 'Adjusted-Rand'),\n ('spectral', 'external_indices', 'Adjusted-Mutual-Info'),\n ('spectral', 'external_indices', 'Normalized-Mutual-Info'),\n ('spectral', 'external_indices', 'Homogeneity'),\n ('spectral', 'external_indices', 'Completeness'),\n ('spectral', 'external_indices', 'V-Measure'),\n ('spectral', 'external_indices', 'Jaccard'),\n ('spectral', 'external_indices', 'Hubert Γ̂'),\n ('spectral', 'external_indices', 'Kulczynski'),\n # ('spectral', 'external_indices', 'McNemar'),\n ('spectral', 'external_indices', 'Phi'),\n ('spectral', 'external_indices', 'Russel-Rao'),\n ('spectral', 'external_indices', 'Rogers-Tanimoto'),\n ('spectral', 'external_indices', 'Sokal-Sneath1'),\n ('spectral', 'external_indices', 'Sokal-Sneath2'),\n\n\n # ('hdbscan', 'internal_indices', 'WGSS'),\n # ('hdbscan', 'internal_indices', 'BGSS'),\n # ('hdbscan', 'internal_indices', 'Ball-Hall'),\n # ('hdbscan', 'internal_indices', 'Banfeld-Raftery'),\n # ('hdbscan', 'internal_indices', 'Calinski-Harabasz'),\n # ('hdbscan', 'internal_indices', 'Det-Ratio'),\n # ('hdbscan', 'internal_indices', 'Ksq-DetW'),\n # ('hdbscan', 'internal_indices', 'Log-Det-Ratio'),\n ('hdbscan', 'internal_indices', 'Log-SS-Ratio'),\n ## ('hdbscan', 'internal_indices', 'Scott-Symons'),\n ('hdbscan', 'internal_indices', 'Silhouette'),\n # ('hdbscan', 'internal_indices', 'Trace-WiB'),\n # ('hdbscan', 'internal_indices', 'C'),\n ('hdbscan', 'internal_indices', 'Dunn'),\n # ('hdbscan', 'internal_indices', 'Davies-Bouldin'),\n # ('hdbscan', 'internal_indices', 'Ray-Turi'),\n # ('hdbscan', 'internal_indices', 'PBM'),\n ('hdbscan', 'internal_indices', 'Score'),\n\n ('hdbscan', 'external_indices', 'Entropy'),\n ('hdbscan', 'external_indices', 'Purity'),\n ('hdbscan', 'external_indices', 'Precision'),\n ('hdbscan', 'external_indices', 'Recall'),\n ('hdbscan', 'external_indices', 'F'),\n ('hdbscan', 'external_indices', 'Weighted-F'),\n ('hdbscan', 'external_indices', 'Folkes-Mallows'),\n ('hdbscan', 'external_indices', 'Rand'),\n ('hdbscan', 'external_indices', 'Adjusted-Rand'),\n ('hdbscan', 'external_indices', 'Adjusted-Mutual-Info'),\n ('hdbscan', 'external_indices', 'Normalized-Mutual-Info'),\n ('hdbscan', 'external_indices', 'Homogeneity'),\n ('hdbscan', 'external_indices', 'Completeness'),\n ('hdbscan', 'external_indices', 'V-Measure'),\n ('hdbscan', 'external_indices', 'Jaccard'),\n ('hdbscan', 'external_indices', 'Hubert Γ̂'),\n ('hdbscan', 'external_indices', 'Kulczynski'),\n # ('hdbscan', 'external_indices', 'McNemar'),\n ('hdbscan', 'external_indices', 'Phi'),\n ('hdbscan', 'external_indices', 'Russel-Rao'),\n ('hdbscan', 'external_indices', 'Rogers-Tanimoto'),\n ('hdbscan', 'external_indices', 'Sokal-Sneath1'),\n ('hdbscan', 'external_indices', 'Sokal-Sneath2'),\n\n]\n\n\ndef generate_kmeans_cluster_indices(dataset, choosen_indices, n_jobs=None):\n\n cluster_labels = dataset.perform_kmeans_clustering(n_clusters='n_classes', n_jobs=n_jobs)\n\n internal_indices_values = dict()\n internal_validation = InternalIndices(dataset.data, cluster_labels)\n\n choosen_internal_indices = choosen_indices['internal_indices']\n for internal_index in choosen_internal_indices:\n internal_index_method = getattr(internal_validation, INTERNAL_INDICES_METHOD_NAMES_DICT[internal_index])\n internal_indices_values[internal_index] = internal_index_method()\n\n external_indices_values = dict()\n external_validation = ExternalIndices(dataset.target, cluster_labels)\n\n choosen_external_indices = choosen_indices['external_indices']\n for external_index in choosen_external_indices:\n external_index_method = getattr(external_validation, EXTERNAL_INDICES_METHOD_NAMES_DICT[external_index])\n external_indices_values[external_index] = external_index_method()\n\n indices_values = {\n 'internal_indices' : internal_indices_values,\n 'external_indices' : external_indices_values,\n }\n\n return indices_values\n\n\ndef generate_hierarchical_cluster_indices(dataset, choosen_indices, n_jobs=None):\n\n cluster_labels = dataset.perform_hierarchical_clustering(n_clusters='n_classes')\n\n internal_indices_values = dict()\n internal_validation = InternalIndices(dataset.data, cluster_labels)\n\n choosen_internal_indices = choosen_indices['internal_indices']\n for internal_index in choosen_internal_indices:\n internal_index_method = getattr(internal_validation, INTERNAL_INDICES_METHOD_NAMES_DICT[internal_index])\n internal_indices_values[internal_index] = internal_index_method()\n\n external_indices_values = dict()\n external_validation = ExternalIndices(dataset.target, cluster_labels)\n\n choosen_external_indices = choosen_indices['external_indices']\n for external_index in choosen_external_indices:\n external_index_method = getattr(external_validation, EXTERNAL_INDICES_METHOD_NAMES_DICT[external_index])\n external_indices_values[external_index] = external_index_method()\n\n indices_values = {\n 'internal_indices' : internal_indices_values,\n 'external_indices' : external_indices_values,\n }\n\n return indices_values\n\n\ndef generate_spectral_cluster_indices(dataset, choosen_indices, n_jobs=None):\n\n cluster_labels = dataset.perform_spectral_clustering(n_clusters='n_classes', n_jobs=n_jobs)\n\n internal_indices_values = dict()\n internal_validation = InternalIndices(dataset.data, cluster_labels)\n\n choosen_internal_indices = choosen_indices['internal_indices']\n for internal_index in choosen_internal_indices:\n internal_index_method = getattr(internal_validation, INTERNAL_INDICES_METHOD_NAMES_DICT[internal_index])\n internal_indices_values[internal_index] = internal_index_method()\n\n external_indices_values = dict()\n external_validation = ExternalIndices(dataset.target, cluster_labels)\n\n choosen_external_indices = choosen_indices['external_indices']\n for external_index in choosen_external_indices:\n external_index_method = getattr(external_validation, EXTERNAL_INDICES_METHOD_NAMES_DICT[external_index])\n external_indices_values[external_index] = external_index_method()\n\n indices_values = {\n 'internal_indices' : internal_indices_values,\n 'external_indices' : external_indices_values,\n }\n\n return indices_values\n\n\ndef generate_hdbscan_cluster_indices(dataset, choosen_indices, n_jobs=None):\n\n cluster_labels = dataset.perform_hdbscan_clustering(core_dist_n_jobs=(n_jobs if n_jobs is not None else 4))\n\n internal_indices_values = dict()\n internal_validation = InternalIndices(dataset.data, cluster_labels)\n\n choosen_internal_indices = choosen_indices['internal_indices']\n for internal_index in choosen_internal_indices:\n internal_index_method = getattr(internal_validation, INTERNAL_INDICES_METHOD_NAMES_DICT[internal_index])\n internal_indices_values[internal_index] = internal_index_method()\n\n external_indices_values = dict()\n external_validation = ExternalIndices(dataset.target, cluster_labels)\n\n choosen_external_indices = choosen_indices['external_indices']\n for external_index in choosen_external_indices:\n external_index_method = getattr(external_validation, EXTERNAL_INDICES_METHOD_NAMES_DICT[external_index])\n external_indices_values[external_index] = external_index_method()\n\n indices_values = {\n 'internal_indices' : internal_indices_values,\n 'external_indices' : external_indices_values,\n }\n\n return indices_values\n\n\ndef bag_generate_cluster_indices(bag_filename, n_jobs=1):\n \"\"\" Perform clustering on the bags and generate cluster indices evaluating the quality of clusters \"\"\"\n\n bag_index = extract_bag_index(bag_filename)\n dataset = read_bag_file(bag_filename)\n\n logger.info(\"Bag %2d : performing kmeans clustering, generating cluster indices\", bag_index)\n kmeans_cluster_indices = generate_kmeans_cluster_indices(dataset, KMEANS_CHOOSEN_CLUSTER_INDICES, n_jobs=n_jobs)\n logger.info(\"Bag %2d : performing hierarchical clustering, generating cluster indices\", bag_index)\n hierarchical_cluster_indices = generate_hierarchical_cluster_indices(dataset, HIERARCHICAL_CHOOSEN_CLUSTER_INDICES, n_jobs=n_jobs)\n logger.info(\"Bag %2d : performing spectral clustering, generating cluster indices\", bag_index)\n spectral_cluster_indices = generate_spectral_cluster_indices(dataset, SPECTRAL_CHOOSEN_CLUSTER_INDICES, n_jobs=n_jobs)\n logger.info(\"Bag %2d : performing hdbscan clustering, generating cluster indices\", bag_index)\n hdbscan_cluster_indices = generate_hdbscan_cluster_indices(dataset, HDBSCAN_CHOOSEN_CLUSTER_INDICES, n_jobs=n_jobs)\n\n cluster_indices = {\n 'kmeans' : kmeans_cluster_indices,\n 'hierarchical' : hierarchical_cluster_indices,\n 'spectral' : spectral_cluster_indices,\n 'hdbscan' : hdbscan_cluster_indices,\n }\n\n return cluster_indices\n\n\ndef convert_cluster_indices_to_features(cluster_indices):\n \"\"\" Convert the cluster indices into a flat feature vector \"\"\"\n\n feature_vector = list(map(\n lambda keys_triple: cluster_indices[keys_triple[0]][keys_triple[1]][keys_triple[2]],\n FEATURE_VECTOR_CLUSTER_INDICES_ORDER_TRIPLES\n ))\n\n feature_vector = np.array(feature_vector)\n\n return feature_vector\n","repo_name":"BUDDI-AI/CIAMS","sub_path":"src/automs/cluster_indices_generation.py","file_name":"cluster_indices_generation.py","file_ext":"py","file_size_in_byte":23299,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"26"}
+{"seq_id":"74092461829","text":"import datetime\nimport os\nimport os.path as osp\nimport sys\nimport tempfile\n\nimport cv2\nfrom moviepy.audio.io.AudioFileClip import AudioFileClip\nfrom moviepy.video.io.ffmpeg_writer import FFMPEG_VideoWriter\nfrom moviepy.video.io.VideoFileClip import VideoFileClip\nfrom tqdm import tqdm\n\nfrom jsk_rosbag_tools.bag_to_audio import bag_to_audio\nfrom jsk_rosbag_tools.extract import extract_image_topic\nfrom jsk_rosbag_tools.extract import get_image_topic_names\nfrom jsk_rosbag_tools.image_utils import \\\n resize_keeping_aspect_ratio_wrt_target_size\nfrom jsk_rosbag_tools.info import get_topic_dict\nfrom jsk_rosbag_tools.makedirs import makedirs\nfrom jsk_rosbag_tools.topic_name_utils import topic_name_to_file_name\n\n\ndef bag_to_video(input_bagfile,\n output_filepath=None,\n output_dirpath=None,\n image_topic=None,\n image_topics=None,\n fps=30,\n samplerate=16000,\n channels=1,\n audio_topic='/audio',\n show_progress_bar=True):\n \"\"\"Create video from rosbag file.\n\n Specify only either output_filepath or output_dirpath.\n If output_filepath is specified, specify image_topic.\n If output_dirpath is specified, image_topics can be specified.\n If image_topic_names is None, make all color images into video.\n\n \"\"\"\n if not os.path.exists(input_bagfile):\n print('[bag_to_video] Input bagfile {} not exists.'\n .format(input_bagfile))\n sys.exit(1)\n\n if output_filepath is not None and output_dirpath is not None:\n raise ValueError(\n 'Specify only either output_filepath or output_dirpath.')\n\n output_filepaths = []\n target_image_topics = []\n candidate_topics = get_image_topic_names(input_bagfile)\n\n if output_filepath is not None:\n if image_topic is None:\n raise ValueError(\n 'If output_filepath is specified, specify image_topic.')\n output_filepaths.append(output_filepath)\n target_image_topics.append(image_topic)\n wav_outpath = tempfile.NamedTemporaryFile(suffix='.wav').name\n else:\n # output_dirpath is specified case.\n if image_topics is None:\n # record all color topics\n image_topics = get_image_topic_names(input_bagfile,\n rgb_only=True)\n target_image_topics = image_topics\n\n for image_topic in target_image_topics:\n output_filepaths.append(\n osp.join(\n output_dirpath,\n topic_name_to_file_name(image_topic) + '.mp4'))\n wav_outpath = osp.join(output_dirpath, '{}.wav'.format(\n topic_name_to_file_name(audio_topic)))\n\n # check topics exist.\n not_exists_topics = list(filter(\n lambda tn: tn not in candidate_topics, target_image_topics))\n if len(not_exists_topics) > 0:\n raise ValueError(\n 'Topics that are not included in the rosbag are specified.'\n ' {}'.format(list(not_exists_topics)))\n\n print('[bag_to_video] Extracting audio from rosbag file.')\n audio_exists = bag_to_audio(input_bagfile, wav_outpath,\n samplerate=samplerate,\n channels=channels,\n topic_name=audio_topic)\n\n dt = 1.0 / fps\n for image_topic, output_filepath in zip(target_image_topics,\n output_filepaths):\n print('[bag_to_video] Creating video of {} from rosbag file {}.'\n .format(image_topic, input_bagfile))\n filepath_dir = osp.dirname(output_filepath)\n if filepath_dir:\n makedirs(filepath_dir)\n if audio_exists:\n tmp_videopath = tempfile.NamedTemporaryFile(suffix='.mp4').name\n else:\n tmp_videopath = output_filepath\n\n images = extract_image_topic(input_bagfile, image_topic)\n topic_info_dict = get_topic_dict(input_bagfile)[image_topic]\n n_frame = topic_info_dict['messages']\n\n if show_progress_bar:\n progress = tqdm(total=n_frame)\n\n # remove 0 time stamp\n stamp = 0.0\n try:\n while stamp == 0.0:\n stamp, _, img, _ = next(images)\n if show_progress_bar:\n progress.update(1)\n except StopIteration:\n print('[bag_to_video] No timestamp found in all images')\n print('[bag_to_video] Skipping {}'.format(image_topic))\n break\n start_stamp = stamp\n width, height = img.shape[1], img.shape[0]\n\n creation_time = datetime.datetime.utcfromtimestamp(start_stamp)\n time_format = '%y-%m-%d %h:%M:%S'\n writer = FFMPEG_VideoWriter(\n tmp_videopath,\n (width, height),\n fps, logfile=None,\n ffmpeg_params=[\n '-metadata',\n 'creation_time={}'.format(\n creation_time.strftime(time_format)),\n ])\n\n current_time = 0.0\n cur_img = resize_keeping_aspect_ratio_wrt_target_size(\n cv2.cvtColor(img, cv2.COLOR_BGR2RGB), width=width, height=height)\n for i, (stamp, _, bgr_img, _) in enumerate(images):\n if show_progress_bar:\n progress.update(1)\n aligned_stamp = stamp - start_stamp\n # write the current image until the next frame's timestamp.\n while current_time < aligned_stamp:\n current_time += dt\n writer.write_frame(cur_img)\n # set current image.\n cur_img = resize_keeping_aspect_ratio_wrt_target_size(\n cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB),\n width=width, height=height)\n writer.close()\n\n if show_progress_bar:\n progress.close()\n\n if audio_exists:\n print('[bag_to_video] Combine video and audio')\n clip_output = VideoFileClip(tmp_videopath).subclip().\\\n set_audio(AudioFileClip(wav_outpath))\n clip_output.write_videofile(\n output_filepath,\n verbose=False,\n logger='bar' if show_progress_bar else None)\n print('[bag_to_video] Created video is saved to {}'\n .format(output_filepath))\n","repo_name":"jsk-ros-pkg/jsk_common","sub_path":"jsk_rosbag_tools/python/jsk_rosbag_tools/bag_to_video.py","file_name":"bag_to_video.py","file_ext":"py","file_size_in_byte":6373,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"26"}
+{"seq_id":"8535674526","text":"from airflow import DAG\nfrom datetime import datetime, timedelta\nfrom airflow.operators.python_operator import PythonOperator\nfrom airflow.operators.http_operator import SimpleHttpOperator\nfrom airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.hooks.mysql_hook import MySqlHook\nfrom psycopg2.extras import execute_values\nfrom airflow.models import Variable\nimport requests\n\ndefault_args = {\n 'owner': 'Airflow',\n \"retries\": 1,\n \"retry_delay\": timedelta(minutes=5),\n 'start_date': datetime.today().strftime('%Y-%m-%d'),\n 'schedule_interval': '0 0 * * 0'\n \n}\n\ndef fetch_bankruptcy_data_function(ds, **kwargs):\n last_scheduler_run = None\n last_dag_run = dag.get_last_dagrun()\n if last_dag_run is None:\n last_scheduler_run = datetime.today().strftime('%Y-%m-%d')\n else:\n last_scheduler_run = last_dag_run.execution_date.strftime(\"%Y-%m-%d\")\n postgresQuery = \"\"\"select * from notices where date BETWEEN %s and %s;\"\"\"\n postgres_hook = PostgresHook(postgres_conn_id='postgres_conn', schema='airflow')\n postgres_conn = postgres_hook.get_conn()\n postgres_cursor = postgres_conn.cursor()\n postgres_cursor.execute(postgresQuery, (last_scheduler_run, datetime.today().strftime('%Y-%m-%d'))) \n kwargs['ti'].xcom_push(key ='postgresRecords', value = postgres_cursor.fetchall())\n postgres_cursor.close()\n postgres_conn.close()\n \n \ndef fetch_customer_data_function(ds, **kwargs):\n mysqlQuery = \"select * from customer\"\n mysql_hook = MySqlHook(mysql_conn_id='mysql_conn', schema='airflow')\n mysql_conn = mysql_hook.get_conn()\n mysql_cursor = mysql_conn.cursor()\n mysql_cursor.execute(mysqlQuery)\n kwargs['ti'].xcom_push(key ='mysqlRecords', value = mysql_cursor.fetchall())\n mysql_cursor.close()\n mysql_conn.close()\ndef filter_bankruptcy_data_function(ds, **kwargs):\n result = []\n mysqlRecords = kwargs['ti'].xcom_pull(key ='mysqlRecords')\n postgresRecords = kwargs['ti'].xcom_pull(key ='postgresRecords')\n print(\"Customer data is \")\n for x in mysqlRecords :\n print(x)\n print(\"Bankcrupty data is \")\n for x in postgresRecords:\n print(x)\n if( postgresRecords):\n for x in postgresRecords:\n hkId = x[2][ 0 : 4 ]\n name = x[1]\n result.extend([i for i in mysqlRecords if i[0].startswith(hkId) and i[1]== name] )\n\n if len(result) == 0:\n for x in postgresRecords:\n hkId = x[2][ 0 : 4 ]\n result.extend([i for i in mysqlRecords if i[0].startswith(hkId)] )\n\n if len(result) > 0: \n result = list(set([i for i in result]))\n kwargs['ti'].xcom_push(key ='matched_records', value = result)\n \n else:\n last_scheduler_run = None\n last_dag_run = dag.get_last_dagrun()\n if last_dag_run is None:\n last_scheduler_run = datetime.today().strftime('%Y-%m-%d')\n else:\n last_scheduler_run = last_dag_run.execution_date.strftime(\"%Y-%m-%d\")\n print(\"No Data found in between this date \" + last_scheduler_run +\" to \" + datetime.today().strftime('%Y-%m-%d'))\n\ndef create_process_for_matched_data(ds, **kwargs):\n matchedRecords = kwargs['ti'].xcom_pull(key ='matched_records')\n if matchedRecords is not None and len(matchedRecords) > 0 :\n obj = {\"name\": \"Testing 1\",\"processDefinitionKey\": \"testingprocess\"}\n r = requests.post('http://111.93.191.82:9595/activiti-app/api/enterprise/process-instances', auth=('admin@app.activiti.com', 'admin'),data= obj)\n # connetion = SimpleHttpOperator(http_conn_id =\"http_conn\",\"/activiti-app/api/enterprise/process-instances\",\"POST\", data)\n print(\" ststaus is \" + str(r.status_code))\n \nwith DAG('bankruptcy-airflow-job', default_args=default_args) as dag:\n t1 = PythonOperator(task_id='fetch-bankruptcy-latest-record', python_callable=fetch_bankruptcy_data_function, on_failure_callback=fetch_bankruptcy_data_function,\n provide_context=True, dag=dag)\n t2 = PythonOperator(task_id='fetch-all-customer-records', python_callable=fetch_customer_data_function, on_failure_callback=fetch_customer_data_function,\n provide_context=True, dag=dag)\n t3 = PythonOperator(task_id='filter-bankruptcy-records', python_callable=filter_bankruptcy_data_function, on_failure_callback=filter_bankruptcy_data_function,\n provide_context=True, dag=dag)\n t4 = PythonOperator(task_id='create-process-for-customer', python_callable=create_process_for_matched_data, on_failure_callback=create_process_for_matched_data,\n provide_context=True, dag=dag)\n t1>>t2>>t3>>t4\n","repo_name":"Dharmraj12/airflow","sub_path":"dags/hooks.py","file_name":"hooks.py","file_ext":"py","file_size_in_byte":4591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"7970112779","text":"import pandas as pd\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.losses import categorical_crossentropy\nfrom tensorflow.keras.optimizers import SGD\nfrom tensorflow.keras.regularizers import l1_l2\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras import backend as K\nimport numpy as np\n\nfrom base import Benchmark\n\n\ndef preprocess_data(\n verbose=True,\n L=0,\n algorithm=\"topsbm\",\n directory=\"breast\",\n label=\"Subtype_Selected_Lum\",\n):\n # get the reduced space matrix\n filename = f\"{directory}/{algorithm}/{algorithm}_level_{L}_topic-dist.csv\"\n df_topics = pd.read_csv(filename)\n df_topics = df_topics.set_index(\"doc\").drop(\"i_doc\", axis=1)\n filename = \"%s/%s/%s_level_%d_word-dist.csv\" % (directory, algorithm, algorithm, L)\n df_words = pd.read_csv(filename, index_col=0)\n\n # clean gene ENSG names\n df_words.index = [g[:15] for g in df_words.index]\n\n # read original space data and normalise\n filename = \"%s/mainTable.csv\" % (directory)\n df = pd.read_csv(filename, index_col=0).reindex(index=df_words.index)\n df = df.divide(df.sum(0), 1).transpose().fillna(0)\n df_files = pd.read_csv(\"%s/files.dat\" % (directory), index_col=0)\n\n # get the tumour subtype (tissue) label\n df_topics.insert(0, \"tissue\", df_files.reindex(index=df_topics.index)[label])\n df_topics = df_topics[df_topics[\"tissue\"] != \"unknown\"]\n df_labels = df_files.copy()\n df_labels = df_labels.reindex(index=df_topics.index)\n X = df_topics.drop(\"tissue\", 1)\n\n # feature scaling to use SGD\n X = (\n X.subtract(X.mean(0), 1)\n .divide(0.5 * (X.max(0) - X.min(0)), 1)\n .values.astype(float)\n ) # SGD transform\n Y = to_categorical(np.unique(df_labels[label], return_inverse=True)[1])\n\n if verbose:\n print(X.shape, Y.shape)\n\n return X, Y\n\n\ndef recall_m(y_true, y_pred):\n \"\"\"\n Estimate recall.\n\n recall = TP / (TP + FN)\n \"\"\"\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n recall = true_positives / (possible_positives + K.epsilon())\n return recall\n\n\ndef precision_m(y_true, y_pred):\n \"\"\"\n Estimate precision.\n\n precision = TP / (TP + FP)\n \"\"\"\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n precision = true_positives / (predicted_positives + K.epsilon())\n return precision\n\n\ndef f1(y_true, y_pred):\n \"\"\"\n F1 score in tensorflow.\n\n f1 is the harmonic average of precision and recall\n \"\"\"\n precision = precision_m(y_true, y_pred)\n recall = recall_m(y_true, y_pred)\n return 2 * ((precision * recall) / (precision + recall + K.epsilon()))\n\n\ndef create_model(\n opt,\n l1,\n l2,\n hidden,\n input_dim,\n output_dim,\n loss=categorical_crossentropy,\n activation_func=\"softmax\",\n verbose=True,\n):\n \"\"\"Create a tf.keras.Sequential() model.\"\"\"\n K.clear_session()\n\n model = Sequential()\n model.add(\n Dense(\n units=hidden,\n input_dim=input_dim,\n use_bias=True,\n bias_initializer=\"ones\",\n activation=\"relu\",\n kernel_regularizer=l1_l2(l1=l1, l2=l2),\n )\n )\n model.add(Dense(units=output_dim, activation=activation_func))\n model.compile(loss=loss, optimizer=opt, metrics=[\"accuracy\", \"AUC\", f1])\n\n if verbose:\n print(model.summary())\n return model\n\n\nif __name__ == \"__main__\":\n bench = Benchmark(\"TCGA\")\n\n bench.set_data(*preprocess_data(True))\n\n bench.set_model(\n create_model(\n SGD(0.001, momentum=0.99),\n l1=0.0001,\n l2=0.0001,\n hidden=50,\n input_dim=bench.X.shape[1],\n output_dim=bench.Y.shape[1],\n loss=categorical_crossentropy,\n activation_func=\"softmax\",\n verbose=True,\n )\n )\n\n bench.run()\n","repo_name":"Open-ForBC/OpenForBC-Benchmark","sub_path":"benchmarks/tensorflow_benchmark/tcga.py","file_name":"tcga.py","file_ext":"py","file_size_in_byte":4028,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"26"}
+{"seq_id":"41185530650","text":"'''\nPython provides two unique built-in functions that allow us to check\nif all elements in an iterable are True, and if any element is True.\nThese functions are aptly named all and any.\n'''\n\n\n#if student[s] == \"python\" and student[s] == english:\n\nall_true = [True, True, 1, 'hello']\nany_true = [0, False, True, '', []]\n# all(all_true) == True\n# any(all_true) == True\n# all(any_true) == False\n# any(any_true) == True\n\nprint(all_true,all(all_true),any(all_true),all(any_true),any(any_true))\n\nour_list = [1, 2, 3]\na, b, c = our_list\nprint(a,b,c)","repo_name":"gridvisi/Python_workspace","sub_path":"4 ddm_course/逻辑符/Any and All.py","file_name":"Any and All.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"2993930070","text":"from django.core.validators import MinValueValidator, MaxValueValidator\nfrom django.db import models\n\nfrom Dynamic_Champs.models import Marque, Categorie\nfrom Traitement.compression import compression_size_photo\nfrom Vendeur.models import Boutique\n\n\ndef produit_directory_path(instance, filename):\n return 'Produits/{0}/{1}/'.format(instance.boutique_prod_id.nom_bout, filename)\n\n\nclass Produit(models.Model):\n ETAT = (\n ('N', \"Nouveau\"),\n ('O', \"Occasion\")\n )\n\n boutique_prod_id = models.ForeignKey(\n Boutique,\n on_delete=models.CASCADE,\n )\n titre_prod = models.CharField(\n max_length=254\n )\n ref_prod = models.CharField(\n max_length=254\n )\n marque_prod_id = models.ForeignKey(\n Marque,\n on_delete=models.CASCADE,\n )\n categorie_prod_id = models.ForeignKey(\n Categorie,\n on_delete=models.CASCADE,\n )\n description_prod = models.TextField(\n blank=True\n )\n prix_original_prod = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n validators=[\n MinValueValidator(\n limit_value=0,\n message=\"Le prix ne peut pas être négative!!\"\n )\n ]\n )\n pourcentage_solde_prod = models.DecimalField(\n max_digits=6,\n decimal_places=3,\n blank=True,\n default=0,\n validators=[\n MinValueValidator(\n limit_value=0,\n message=\"Le pourcentage ne peut pas être négative!!\"\n ),\n MaxValueValidator(\n limit_value=99,\n message=\"Valeur de pourcentage très élevé\"\n )\n ]\n )\n quantite_stock_prod = models.PositiveSmallIntegerField()\n quantite_vendue_prod = models.PositiveSmallIntegerField(\n default=0\n )\n condition_prod = models.CharField(\n max_length=1,\n choices=ETAT\n )\n image_prod = models.ImageField(\n upload_to=produit_directory_path\n )\n nbr_avis_prod = models.PositiveSmallIntegerField(\n default=0\n )\n note_avis_prod = models.DecimalField(\n max_digits=2,\n decimal_places=1,\n default=0\n )\n\n def __str__(self):\n return self.titre_prod.capitalize()\n\n def save(self, *args, **kwargs):\n try:\n this = Produit.objects.get(id=self.id)\n if this.image_prod != self.image_prod:\n this.image_prod.delete(save=False)\n except:\n pass\n super(Produit, self).save(*args, **kwargs)\n # Compression du l'image du produit (max_widh = max_height = 512px)\n compression_size_photo(src=self.image_prod, size=512)\n\n def delete(self, using=None, keep_parents=False):\n this = Produit.objects.get(id=self.id)\n this.image_prod.delete(save=False)\n super(Produit, self).delete()\n\n\n\nclass Specification_Produit(models.Model):\n kle_value_spec_prod = models.TextField()\n produit_spec_prod = models.OneToOneField(\n Produit,\n on_delete=models.CASCADE,\n )","repo_name":"FrikhaMohamedSaid/Projet-Marketplace","sub_path":"Article/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"26604786501","text":"import discord\nfrom discord.ext import commands\nimport asyncio\nimport sqlite3\nimport sys\nimport json\nimport urllib\nimport urllib.request\nimport math\nimport pytz\nimport pytz_dict\n\nfrom datetime import datetime, timedelta\nfrom pytz import timezone\n\npytz_tz = pytz_dict.get_dict()\nbot = commands.Bot(command_prefix=\"!ez\")\nEZAPI = \"https://easyallies.com/api/site/getHome\"\nEZLinks = {\"Twitch\" : \"[Twitch](https://www.twitch.tv/easyallies)\", \n \"Youtube\" : \"[YouTube](https://www.youtube.com/channel/UCZrxXp1reP8E353rZsB3jaA)\",\n\t\t\t\t\t \"Patreon\" : \"[Patreon](https://www.patreon.com/EasyAllies)\"}\n\nTZFMT = \"%Y-%m-%d %H:%M %Z\"\n@bot.event\nasync def on_ready():\n\tprint('Logged in as')\n\tprint(bot.user.name)\n\tprint(bot.user.id)\n\tprint('------')\n\n@bot.command()\nasync def schedule(ctx, tz = None):\n\ttmp = await ctx.send('Fetching Schedule...')\n\twith urllib.request.urlopen(EZAPI) as response:\n\t\tdata = response.read()\n\n\tif data is None:\n\t\tawait ctx.send('Failed to fetch schedule.')\n\tdata = data.decode(\"utf-8\")\n\tdecoded = json.loads(data)\n\tdecoded = decoded[\"schedule\"]\n\tupcoming = \"\"\n\tfor event in decoded:\n\t\ttime = datetime.strptime(event[\"date\"], '%Y-%m-%dT%H:%M:%S.%fZ')\n\t\ttimeTo = time - datetime.utcnow()\n\t\tserviceStr = EZLinks.get(event[\"service\"], \"\")\n\t\tif tz is None:\n\t\t\tif timeTo.days == 0:\n\t\t\t\tupcoming += \"**{}**: {}h {}min. {}\\n\".format(event[\"title\"], math.floor(timeTo.seconds / 3600), math.floor((timeTo.seconds / 60) % 60), serviceStr)\n\t\t\telif timeTo.days < 3 and timeTo.days > 0:\n\t\t\t\tupcoming += \"**{}**: {} day(s) {}h {}min. {}\\n\".format(event[\"title\"], timeTo.days, math.floor(timeTo.seconds / 3600), math.floor((timeTo.seconds / 60) % 60), serviceStr)\n\t\telse:\n\t\t\ttry:\n\t\t\t\tset_tz = pytz_tz.get(tz.upper(), None)\n\t\t\t\tif set_tz is None:\n\t\t\t\t\tset_tz = timezone(tz)\n\t\t\texcept:\n\t\t\t\tawait client.edit_message(tmp, \"Unknown timezone.\")\n\t\t\t\treturn\n\t\t\ttime = time.replace(tzinfo=pytz.utc)\n\t\t\ttime = time.astimezone(set_tz)\n\t\t\tif timeTo.days < 3 and timeTo.days >= 0:\n\t\t\t\tupcoming += \"**{}**: {} {}\\n\".format(event[\"title\"], time.strftime(TZFMT) , serviceStr)\n\n\tem = discord.Embed(title='Upcoming events.', color=0xbe0121, description = upcoming)\n\tawait tmp.edit(content=\"Schedule loaded.\", embed=em)\n\n@bot.command()\nasync def update(ctx):\n\tpytz_tz = pytz_dict.get_dict()\n\tawait ctx.send('Internal data updated.')\n\t\nbot.run(sys.argv[1])\n","repo_name":"Drewol/EZBot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"42684043487","text":"from tkinter import BooleanVar, StringVar, IntVar\nfrom configparser import ConfigParser\nimport os\nimport typing as T\nfrom .module_manifest import ModuleManifest\n\nclass Parameters :\n\tdef __init__(self, master = None):\n\t\tself.var_keep_git = BooleanVar(master, False)\n\n\t\tself.var_cleanup_debug_symbols = BooleanVar(master,True)\n\t\tself.var_use_links = BooleanVar(master,False)\n\t\tself.var_replace_main = BooleanVar(master,False)\n\n\t\tself.var_cproject_path = StringVar(master,None)\n\t\tself.var_sool_destination_path = StringVar(master,None)\n\t\tself.var_sool_path = StringVar(master, None)\n\t\tself.var_sool_chip = StringVar(master, None)\n\t\tself.var_module_path= StringVar(master,None)\n\n\t\tself.modules_list : T.List[ModuleManifest] = list()\n\t\tself.modules_selected_list : T.List[ModuleManifest] = list()\n\n\t\tself.read_parameters()\n\n\tdef print(self):\n\n\t\tprint(\"CProject file : \", self.cproject_path)\n\t\tprint(\"Sool dest path : \", self.sool_destination_path)\n\t\tprint(\"SooL path : \", self.sool_path)\n\t\tprint(\"SooL Chip : \", self.sool_chip)\n\t\tprint(\"Used modules : \" + \"\\n : \".join([x.name for x in self.modules_selected_list]))\n\n\tdef read_parameters(self,path = os.path.dirname(os.path.abspath(__file__)) + \"/config.ini\"):\n\t\tconfig_parser = ConfigParser()\n\t\tif os.path.exists(path) :\n\t\t\tconfig_parser.read(path)\n\n\t\tself.cleanup_debug_symbols = config_parser.getboolean(\"General\",\"CleanupSymbols\", fallback=True)\n\t\tself.use_links = config_parser.getboolean(\"General\",\"UseLinks\",fallback=False)\n\t\tself.replace_main = config_parser.getboolean(\"General\",\"ReplaceMain\",fallback=True)\n\t\tself.keep_git = config_parser.getboolean(\"General\", \"KeepGit\", fallback=False)\n\n\t\tself.cproject_path = config_parser.get(\"Paths\",\"CProject\",fallback=\"\")\n\t\tself.sool_destination_path = config_parser.get(\"Paths\",\"SoolDestination\", fallback=\"./sool\")\n\t\tself.sool_path = config_parser.get(\"Paths\",\"SoolSource\", fallback=\"\")\n\t\tself.modules_destination_path = config_parser.get(\"Paths\", \"ModulesDestination\", fallback=\"./modules\")\n\t\tself.sool_chip = config_parser.get(\"SooL\",\"Chip\", fallback=\"\")\n\t\tself.modules_list = [ModuleManifest(x) for x in config_parser.get(\"Paths\",\"SoolModules\", fallback=\"\").split(\";\") if os.path.exists(x) and os.path.isfile(x)]\n\t\tself.modules_selected_list = [ModuleManifest(x) for x in config_parser.get(\"Paths\", \"SoolModulesUsed\", fallback=\"\").split(\";\") if os.path.exists(x) and os.path.isfile(x)]\n\n\t\tfor module in self.modules_list :\n\t\t\tmodule.read()\n\t\tfor module in self.modules_selected_list :\n\t\t\tmodule.read()\n\n\n\n\tdef write_parameters(self, path=os.path.dirname(os.path.abspath(__file__)) + \"/config.ini\"):\n\t\tconfig_parser = ConfigParser()\n\n\t\tconfig_parser.add_section(\"Modules\")\n\t\tconfig_parser.add_section(\"General\")\n\t\tconfig_parser.add_section(\"Paths\")\n\t\tconfig_parser.add_section(\"SooL\")\n\n\t\tconfig_parser.set(\"General\",\"CleanupSymbols\", \tstr(self.cleanup_debug_symbols))\n\t\tconfig_parser.set(\"General\",\"UseLinks\",\t\t\tstr(self.use_links))\n\t\tconfig_parser.set(\"General\",\"ReplaceMain\",\t\tstr(self.replace_main))\n\t\tconfig_parser.set(\"General\", \"KeepGit\", \t\tstr(self.keep_git))\n\n\t\tconfig_parser.set(\"Paths\",\"CProject\",\t\t\tself.var_cproject_path.get())\n\n\t\tconfig_parser.set(\"Paths\",\"SoolDestination\", \tself.sool_destination_path)\n\t\tconfig_parser.set(\"Paths\",\"SoolSource\", \t\tself.sool_path)\n\t\tconfig_parser.set(\"SooL\",\"Chip\", \t\t\t\tself.sool_chip)\n\t\tconfig_parser.set(\"Paths\",\"SoolModules\", \t\t\";\".join([x.path for x in self.modules_list]))\n\t\tconfig_parser.set(\"Paths\",\"SoolModulesUsed\",\t\";\".join([x.path for x in self.modules_selected_list]))\n\t\tconfig_parser.set(\"Paths\", \"ModulesDestination\", self.modules_destination_path)\n\n\t\twith open(path,\"w\") as f :\n\t\t\tconfig_parser.write(f)\n\n\t@property\n\tdef project_dir(self) -> str:\n\t\treturn os.path.dirname(self.cproject_path)\n\n\t@property\n\tdef project_sool_dir(self) -> str:\n\t\treturn os.path.abspath(f\"{self.project_dir}/{self.sool_destination_path}\")\n\n\t@property\n\tdef use_links(self) -> bool:\n\t\treturn self.var_use_links.get()\n\n\t@use_links.setter\n\tdef use_links(self, val: bool):\n\t\tself.var_use_links.set(val)\n\t\t\n\t@property\n\tdef cleanup_debug_symbols(self) -> bool:\n\t\treturn self.var_cleanup_debug_symbols.get()\n\n\t@cleanup_debug_symbols.setter\n\tdef cleanup_debug_symbols(self,val : bool):\n\t\tself.var_cleanup_debug_symbols.set(val)\n\n\t@property\n\tdef keep_git(self) -> bool:\n\t\treturn self.var_keep_git.get()\n\n\t@keep_git.setter\n\tdef keep_git(self,val : bool):\n\t\tself.var_keep_git.set(val)\n\n\n\t@property\n\tdef replace_main(self) -> bool:\n\t\treturn self.var_replace_main.get()\n\n\t@replace_main.setter\n\tdef replace_main(self,val : bool):\n\t\tself.var_replace_main.set(val)\n\n\t@property\n\tdef cproject_path(self) -> str:\n\t\treturn self.var_cproject_path.get() + \"/.cproject\"\n\n\t@property\n\tdef project_path(self) -> str:\n\t\treturn self.var_cproject_path.get() + \"/.project\"\n\n\n\t@cproject_path.setter\n\tdef cproject_path(self, val: str):\n\t\tself.var_cproject_path.set(val)\n\t\n\t@property\n\tdef sool_destination_path(self) -> str:\n\t\treturn self.var_sool_destination_path.get()\n\n\t@sool_destination_path.setter\n\tdef sool_destination_path(self, val: str):\n\t\tself.var_sool_destination_path.set(val)\n\t\t\n\t@property\n\tdef sool_path(self) -> str:\n\t\treturn self.var_sool_path.get()\n\n\t@sool_path.setter\n\tdef sool_path(self, val: str):\n\t\tself.var_sool_path.set(val)\n\t\t\n\t@property\n\tdef sool_chip(self) -> str:\n\t\treturn self.var_sool_chip.get()\n\n\t@sool_chip.setter\n\tdef sool_chip(self, val: str):\n\t\tself.var_sool_chip.set(val)\n\n\t@property\n\tdef modules_destination_path(self) -> str:\n\t\treturn self.var_module_path.get()\n\n\t@modules_destination_path.setter\n\tdef modules_destination_path(self, val : str):\n\t\tself.var_module_path.set(val)\n\n\tdef get_module_root_destination_path(self,module_name = None):\n\t\tret = f\"{self.sool_destination_path}/{self.modules_destination_path}\"\n\t\tif module_name is not None :\n\t\t\tret += f\"/{module_name}\"\n\t\treturn ret","repo_name":"SooL/usertools-cubeide-patcher","sub_path":"app/backend/ParametersHandler.py","file_name":"ParametersHandler.py","file_ext":"py","file_size_in_byte":5853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"27634763288","text":"from model import Model\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport time\nrng = np.random\n\nxls = pd.ExcelFile('./data.xlsx')\nread_data = pd.read_excel(xls, 'Sheet1')\nread_data = read_data.values\n\ndata = []\nfor d in read_data:\n data.append(np.array([[d[0], d[1]]]))\ntraining = data[:26]\ntesting = data[-15:]\nfunding = 10000\nm = Model(episode=100)\nplot_x = []\nresult = []\nepisode_result = []\nfor index in range(100):\n # funding = 10000\n for i in range(len(training)):\n if i == len(training)-1:\n break\n action = m.choose_action()\n\n reward = m.calculate_true_return(action, training[i])\n m.store_transition(training[i], action, reward, training[i+1])\n m.aka_learn()\n m.reset_episode()\n\nfor index in range(100):\n funding = 10000\n episode_result = [funding]\n for i in range(len(testing)):\n # if i == len(testing):\n # break\n action = m.choose_action()\n\n reward = m.calculate_true_return(action, testing[i])\n m.store_test_transition(testing[i], action, reward)\n funding += funding * reward.item()\n episode_result.append(funding)\n # print(\"{}|{}\".format(funding, reward.item()))\n result.append(episode_result)\n plot_x.append(funding)\n # print(funding)\n print(\"---------\")\n\na2 = np.transpose([[0.25, 0.75]])\na2_funding = 10000\na2_result = [a2_funding]\n\na3 = np.transpose([[0.5, 0.5]])\na3_funding = 10000\na3_result = [a3_funding]\n\na4 = np.transpose([[0.75, 0.25]])\na4_funding = 10000\na4_result = [a4_funding]\n\nfor i in range(len(testing)):\n reward2 = m.calculate_true_return(a2, testing[i])\n reward3 = m.calculate_true_return(a3, testing[i])\n reward4 = m.calculate_true_return(a4, testing[i])\n\n a2_funding += a2_funding * reward2.item()\n a2_result.append(a2_funding)\n\n a3_funding += a3_funding * reward3.item()\n a3_result.append(a3_funding)\n\n a4_funding += a4_funding * reward4.item()\n a4_result.append(a4_funding)\n\n\nmax_result_index = 0\nmax_result = 0\nfor i in range(len(result)):\n if result[i][-1] > max_result:\n max_result = result[i][-1]\n max_result_index = i\n\nprint(sum(plot_x)/len(plot_x))\nprint(max(plot_x))\nprint(min(plot_x))\nprint(np.std(plot_x))\n\nm.manual_plot(a2_result, a3_result, a4_result, result[max_result_index])\n\n","repo_name":"lordknight1904/TradingAllocation","sub_path":"ska.py","file_name":"ska.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"4829728644","text":"import sys\nsys.stdin = open(\"in15.txt\",\"rt\")\na = input()\nanswer = 1e9\nfor step in range(1,len(a)//2+1):\n compressed = \"\"\n prev = a[0:step]\n count = 1\n for j in range(step,len(a),step):\n if prev == a[j:j + step]:\n count +=1\n print(\"이전의 수 : \",prev)\n print(\"압축 가능 카운트: \", count)\n else:\n compressed += str(count) + prev if count >=2 else prev\n prev = a[j:j + step]\n count = 1\n print(\"압축문자열 : \" ,compressed)\n compressed += str(count) + prev if count >= 2 else prev\n answer = min(answer,len(compressed))\nprint(answer)\n","repo_name":"LeeJongAnn/Even-earthworms-can-understand-Algorithms","sub_path":"구현/문자열 압축.py","file_name":"문자열 압축.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"29681855980","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport cgi\n\nimport webencodings\nimport cchardet\n\nfrom cosrlib import re\nfrom .parsers import GUMBOCY_PARSER_HEAD\n\n\n_RE_XML_ENCODING = re.compile(r'^\\s*\\<\\?xml\\s+version\\=\"1\\.0\"\\s+encoding\\=\"([^\"]+)\"\\?\\>')\n\n\ndef get_encoding_from_content_type(content_type):\n _, params = cgi.parse_header(content_type.decode(\"ascii\", \"ignore\"))\n if params.get(\"charset\"):\n detected = webencodings.lookup(params[\"charset\"])\n if detected:\n return detected.codec_info\n\n\nclass HTMLEncoding(object):\n \"\"\" This class deals with the many different encoding and quirks found in pages on the web,\n and tries to normalize everything in UTF-8 \"\"\"\n\n def __init__(self, document):\n self.doc = document\n self.parser = None\n self.detected = None\n\n def ensure_utf8(self):\n \"\"\" Makes sure we have only UTF-8 in source_data \"\"\"\n\n self.detected = self.detect()\n\n # No encoding detected, let's hope we are utf-8\n if self.detected is None or self.detected.name == \"utf-8\":\n return\n\n # If the source_encoding is not utf-8, reencode the source_data!\n self.doc.source_data = self.detected.decode(\n self.doc.source_data, \"ignore\"\n )[0].encode(\"utf-8\", \"ignore\")\n\n def detect(self):\n \"\"\" Returns the python-compatible encoding of the doc as a codecs.CodecInfo object\n\n We attempt to detect the encoding in the following order:\n - BOM presence (https://en.wikipedia.org/wiki/Byte_order_mark)\n - HTTP header\n - meta charset or meta http-equiv\n - XML prolog\n - Auto-detection with chardet\n - utf-8\n \"\"\"\n\n # See http://html5lib.readthedocs.org/en/latest/movingparts.html#encoding-discovery for more\n\n bom_encoding = self.detect_bom()\n if bom_encoding:\n return bom_encoding\n\n if self.doc.source_headers.get(\"content-type\"):\n header_encoding = get_encoding_from_content_type(self.doc.source_headers[\"content-type\"])\n if header_encoding:\n return header_encoding\n\n # Create a temporary parser to look in meta tags\n GUMBOCY_PARSER_HEAD.parse(self.doc.source_data)\n\n meta_encoding = self.detect_meta_charset()\n if meta_encoding:\n return meta_encoding\n\n xml_encoding = self.detect_xml_encoding()\n if xml_encoding:\n return xml_encoding\n\n # Use slow auto-detection!\n guess = self.guess_encoding()\n return guess\n\n def guess_encoding(self):\n \"\"\" Makes an expensive guess of the charset with the chardet library \"\"\"\n\n # TODO: would it be faster to look only in the first N thousand bytes?\n detected = cchardet.detect(self.doc.source_data)\n if detected.get(\"encoding\"):\n c = webencodings.lookup(detected.get(\"encoding\"))\n if c:\n return c.codec_info\n\n def detect_meta_charset(self):\n \"\"\" Returns the encoding found in meta tags in the doc \"\"\"\n\n for node in GUMBOCY_PARSER_HEAD.listnodes():\n if node[1] == \"meta\" and len(node) > 2:\n if node[2].get(\"charset\"):\n detected = webencodings.lookup(node[2][\"charset\"])\n if detected:\n return detected.codec_info\n elif node[2].get(\"http-equiv\", \"\").lower().strip() == \"content-type\":\n meta_encoding = get_encoding_from_content_type(node[2].get(\"content\", \"\"))\n if meta_encoding:\n return meta_encoding\n\n def detect_xml_encoding(self):\n \"\"\" Detects an encoding from an XML prolog \"\"\"\n\n match = _RE_XML_ENCODING.search(self.doc.source_data)\n if match:\n detected = webencodings.lookup(match.group(1))\n if detected:\n return detected.codec_info\n\n def detect_bom(self):\n \"\"\" Returns the encoding if a BOM was found. \"\"\"\n\n if self.doc.source_data.startswith(b'\\xFF\\xFE'):\n return webencodings._UTF16LE.codec_info # pylint: disable=protected-access\n elif self.doc.source_data.startswith(b'\\xFE\\xFF'):\n return webencodings._UTF16BE.codec_info # pylint: disable=protected-access\n elif self.doc.source_data.startswith(b'\\xEF\\xBB\\xBF'):\n return webencodings.UTF8.codec_info\n # TODO utf-32?\n return None\n","repo_name":"commonsearch/cosr-back","sub_path":"cosrlib/document/html/htmlencoding.py","file_name":"htmlencoding.py","file_ext":"py","file_size_in_byte":4513,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"26"}
+{"seq_id":"38901026672","text":"# Author: Zbigniew Dynowski\nimport pandas as pd\nimport numpy as np\n\nfrom multiprocessing import Pool\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import accuracy_score, matthews_corrcoef\nfrom random import randrange, choice, uniform\nfrom typing import Dict, Tuple, Any, List, Optional, Union\n\nimport config as cfg\nfrom config import ALPHA, BETA, EXPECTED_TREE_HEIGHT, NUM_SPLITS, NUM_REPEATS\n\n\ndef cross_validate(model, data: pd.DataFrame, labels: pd.Series, num_splits: int = NUM_SPLITS,\n num_repeats: int = NUM_REPEATS, encode: bool = False) -> Tuple[float, float, float, float]:\n accuracies: List[float] = []\n if encode:\n data = pd.get_dummies(data)\n for k in range(num_repeats):\n kf = KFold(num_splits, shuffle=True)\n for i, (train_index, test_index) in enumerate(kf.split(data)):\n train_data: pd.DataFrame = data.iloc[train_index]\n train_labels: pd.Series = labels.iloc[train_index]\n test_data: pd.DataFrame = data.iloc[test_index]\n test_labels: pd.Series = labels.iloc[test_index]\n model.fit(train_data, train_labels)\n accuracies.append(model.score(test_data, test_labels))\n return sum(accuracies) / (num_repeats * num_splits), np.std(accuracies).item(), max(accuracies), min(accuracies)\n\n\ndef cross_validate_parallel(model, data: pd.DataFrame, labels: pd.Series, num_splits: int = NUM_SPLITS,\n num_repeats: int = NUM_REPEATS) -> Tuple[float, float, float, float]:\n accuracies: List[float] = []\n args_data: List[Tuple['EvolutionaryDecisionTree', pd.DataFrame, pd.Series, pd.DataFrame, pd.Series]]= []\n for k in range(num_repeats):\n kf = KFold(num_splits, shuffle=True)\n for train_index, test_index in kf.split(data):\n train_data: pd.DataFrame = data.iloc[train_index]\n train_labels: pd.Series = labels.iloc[train_index]\n test_data: pd.DataFrame = data.iloc[test_index]\n test_labels: pd.Series = labels.iloc[test_index]\n args_data.append((model, train_data, train_labels, test_data, test_labels))\n with Pool() as pool:\n scores: List[float] = pool.starmap(_fit_and_eval, args_data)\n accuracies += scores\n return sum(accuracies) / (num_repeats * num_splits), np.std(accuracies).item(), max(accuracies), min(accuracies)\n\n\ndef _fit_and_eval(model, train_data: pd.DataFrame, train_labels: pd.Series,\n test_data: pd.DataFrame, test_labels: pd.Series) -> float:\n model.fit(train_data, train_labels)\n score = model.score(test_data, test_labels)\n return score\n\n\ndef choose_node_split_params(attributes_info: Dict[str, Dict[str, Any]]) -> Tuple[str, int, Union[str, float]]:\n attributes: List[str] = list(attributes_info.keys())\n attribute_idx: int = randrange(len(attributes))\n attribute: str = attributes[attribute_idx]\n attr_data: Dict[str, Any] = attributes_info[attribute]\n threshold: Optional[Union[float, str]] = None\n if attr_data['is_string']:\n threshold: str = choice(attr_data['possible_values'])\n else:\n threshold: float = round(uniform(attr_data['min_value'], attr_data['max_value']), 3)\n return attribute, attribute_idx, threshold\n\n\ndef evaluate_candidates(population: List['CandidateTree'], data: pd.DataFrame, labels: pd.Series) -> None:\n for individual in population:\n error: float = ALPHA * (1 - accuracy_score(labels.to_list(), individual.predict(data)))\n # changed so that smaller trees are not penalized if they have good score\n height_penalty: float = BETA * individual.height() / EXPECTED_TREE_HEIGHT\n individual.fitness = round(error + height_penalty, 3)\n","repo_name":"Dynovski/UMA-Ewolucyjne-Drzewo-Decyzyjne","sub_path":"algorithm/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"3614685321","text":"from Game import *\nfrom Player import *\nfrom Neuron import *\nimport pickle\ngame = Game(15)\nplayer1 = CPUPlayer(\"player1\", 'hard', 15)\nplayer2 = CPUPlayer(\"player2\", 'hard', 15)\nfor i in range(20000) :\n game.start(player1, player2, False)\nprint(player1.getNbWin())\nprint(player2.getNbWin())\nplayer1.getNeuronNetwork().printAllConnections()\nplayer2.getNeuronNetwork().printAllConnections()\nwith open('NeronNetwork.txt', 'wb') as output : pickle.dump(player1.getNeuronNetwork(), output, pickle.HIGHEST_PROTOCOL)","repo_name":"abderrahim1992/r-seau-de-neurones-appliqu-au-jeu-des-b-tons-","sub_path":"Nbr_winned_parts.py","file_name":"Nbr_winned_parts.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"7315603374","text":"input_file = \"inLife.txt\"\r\noutput_file = \"outLife.txt\"\r\ngrid = []\r\ngeneration = 0\r\nrow = 0\r\ncol = 0\r\n\r\ndef getInputFile():\r\n global input_file\r\n global grid\r\n global generation\r\n global row\r\n global col\r\n \r\n infile = open(input_file, \"r\" )\r\n temp = [[n for n in list(line)] for line in infile]\r\n generation = int(temp[0][0])\r\n row = len(temp) - 1\r\n col = len(temp[row])\r\n for i in range(1, row + 1):\r\n grid.append([])\r\n for j in range(col):\r\n grid[i-1].append(int(temp[i][j]))\r\n \r\ndef pendingOutFile(gen):\r\n global grid\r\n global row\r\n global col\r\n toReturn = \"Generation \" + str(gen) + \"\\n\"\r\n #toReturn = \"\"\r\n \r\n for i in range(row):\r\n for j in range(col):\r\n toReturn = toReturn + str(grid[i][j])\r\n toReturn = toReturn + \"\\n\"\r\n return toReturn\r\n\r\ndef writeOutFile(pendingStr):\r\n global output_file\r\n with open(output_file, \"a\") as f:\r\n f.write(pendingStr + \"\\n\")\r\n \r\n \r\ndef find_8_neighbours(t_row, t_col):\r\n global grid\r\n\r\n count = 0 # count alive neighbours\r\n neighbours = [(0, 1), (0, -1), (1, 0), (-1, 0), (1, 1), (1, -1), (-1, -1), (-1, 1)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n return count\r\n\r\n\r\ndef find_5_neighbours(t_row, t_col):\r\n global grid\r\n global row\r\n global col\r\n\r\n count = 0 # count alive neighbours\r\n if t_row == 0:\r\n # neighbours of points on top\r\n neighbours = [(0, -1), (1, -1), (1, 0), (1, 1), (0, 1)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n elif t_row == row - 1:\r\n # neighbours of points at button\r\n neighbours = [(0, -1), (-1, -1), (-1, 0), (-1, 1), (0, 1)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n elif t_col == 0:\r\n # neighbours of points on left\r\n neighbours = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n elif t_col == col -1:\r\n # neighbours of points on right\r\n neighbours = [(-1, 0), (-1, -1), (0, -1), (1, -1), (1, 0)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n\r\n return count\r\n \r\n\r\ndef find_3_neighbours(t_row, t_col):\r\n global grid\r\n global row\r\n global col\r\n\r\n count = 0 # count alive neighbours\r\n if t_row == 0 and t_col == 0:\r\n # neighbours of points on top left corner\r\n neighbours = [(0, 1), (1, 1), (1, 0)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n elif t_row == 0 and t_col == col - 1:\r\n # neighbours of points on top right corner\r\n neighbours = [(0, -1), (1, -1), (1, 0)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n elif t_row == row - 1 and t_col == 0:\r\n # neighbours of points on button left corner\r\n neighbours = [(-1, 0), (-1, 1), (0, 1)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n elif t_row == row - 1 and t_col == col - 1:\r\n # neighbours of points on button right corner\r\n neighbours = [(0, -1), (-1, -1), (-1, 0)]\r\n for i in neighbours:\r\n if grid[t_row + i[0]][t_col + i[1]] == 1:\r\n count = count + 1\r\n return count\r\n \r\n\r\ndef gridcpy():\r\n global grid\r\n global row\r\n global col\r\n temp = []\r\n \r\n for i in range(row):\r\n temp.append([])\r\n for j in range(col):\r\n temp[i].append(int(grid[i][j]))\r\n return temp\r\n \r\ndef gameOfLife():\r\n global grid\r\n global row\r\n global col\r\n global generation\r\n\r\n for gen in range(generation):\r\n temp = gridcpy()\r\n for t_row in range(row):\r\n for t_col in range(col):\r\n # four corners\r\n if (t_row == 0 and t_col == 0) or (t_row == 0 and t_col == col - 1) or (t_row == row - 1 and t_col ==0 ) or (t_row == row - 1 and t_col == col - 1):\r\n alive = find_3_neighbours(t_row, t_col)\r\n # target is alive\r\n if grid[t_row][t_col] == 1:\r\n if alive < 2 or alive > 3:\r\n temp[t_row][t_col] = 0\r\n # target is dead\r\n else:\r\n if alive == 3:\r\n temp[t_row][t_col] = 1\r\n # boundary\r\n elif t_row == 0 or t_col == 0 or t_row == row - 1 or t_col == col - 1:\r\n alive = find_5_neighbours(t_row, t_col)\r\n # target is alive\r\n if grid[t_row][t_col] == 1:\r\n if alive < 2 or alive > 3:\r\n temp[t_row][t_col] = 0\r\n # target is dead\r\n else:\r\n if alive == 3:\r\n temp[t_row][t_col] = 1\r\n # center\r\n else:\r\n alive = find_8_neighbours(t_row, t_col)\r\n # target is alive\r\n if grid[t_row][t_col] == 1:\r\n if alive < 2 or alive > 3:\r\n temp[t_row][t_col] = 0\r\n # target is dead\r\n else:\r\n if alive == 3:\r\n temp[t_row][t_col] = 1\r\n grid = temp\r\n writeOutFile(pendingOutFile(gen + 1))\r\n \r\n \r\n \r\n # end ith generation loop\r\n \r\n \r\n \r\n\r\n\r\n\r\ndef main():\r\n global row\r\n global col\r\n getInputFile()\r\n writeOutFile(pendingOutFile(0))\r\n #print(find_3_neighbours(0, 4))\r\n gameOfLife()\r\n\r\n \r\n \r\n\r\nmain()\r\n","repo_name":"PinkViolet/boid-gameOfLife","sub_path":"life.py","file_name":"life.py","file_ext":"py","file_size_in_byte":6105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"70295298630","text":"from __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport argparse\nimport os\nimport time\nimport csv\nimport copy\nimport gc\nfrom core50.dataset import CORE50\nimport torch\nimport numpy as np\nfrom utils.train_test import train_net, test_multitask\nfrom core50.dataset import preprocess_imgs\nimport torchvision.models as models\nfrom models.resnet_drl import resnet18, resnet50\nfrom models.drloss import cl_dr_loss_softmax,cl_dr_loss_sigmoid\nfrom utils.common import create_code_snapshot\n\n\n\ndef shuffle_data(*X):\n \n N = X[0].shape[0]\n idx = np.arange(N)\n \n np.random.shuffle(idx) \n rt = [x[idx] for x in X]\n return rt\n\ndef one_hot_encoder(label,H=None,out=None):\n \n N = label.shape[0]\n if H is None:\n H = int(np.max(label)+1)\n label = label.astype(np.int32)\n \n if out is None:\n Y = np.zeros((N,H),dtype=np.float32)\n else:\n Y = out * 0\n \n Y[range(N),label] = 1\n\n \n return Y\n\ndef main(args):\n\n # print args recap\n print(args, end=\"\\n\\n\")\n # directory with the code snapshot to generate the results\n sub_dir = os.path.join('submissions', args.sub_dir, '-'.join(time.ctime().replace(':','').split(' '))) \n if not os.path.exists(sub_dir):\n os.makedirs(sub_dir)\n\n with open(os.path.join(sub_dir,'configures.txt'),'w') as f:\n f.write(str(args))\n # do not remove this line\n start = time.time()\n\n # Create the dataset object for example with the \"ni, multi-task-nc, or nic\n # tracks\" and assuming the core50 location in ./core50/data/\n dataset = CORE50(root=args.data_dir, scenario=args.scenario,\n preload=args.preload_data,train_size=args.train_size)\n\n # Get the validation set\n print(\"Recovering validation set...\")\n full_valdidset = dataset.get_full_valid_set()\n print('val len',len(full_valdidset),len(full_valdidset[0][0][0]),full_valdidset[0][1])\n for i,vt in enumerate(full_valdidset):\n val_x = preprocess_imgs(vt[0][0])\n val_y = vt[0][1]\n full_valdidset[i] = [(val_x,val_y),vt[1]]\n print('val len after',len(full_valdidset),len(full_valdidset[0][0][0]),full_valdidset[0][1])\n\n # model\n one_hot, incl_fc = False, True\n if args.drl_lmb > 0:\n if args.sigmoid and args.scenario != 'ni':\n criterion = cl_dr_loss_sigmoid\n one_hot = True\n\n else:\n criterion = cl_dr_loss_softmax\n else:\n if args.sigmoid and args.scenario != 'ni':\n criterion = torch.nn.BCELoss()\n one_hot = True\n\n else:\n criterion = torch.nn.CrossEntropyLoss()\n\n if args.classifier == 'ResNet18':\n classifier = resnet18(pretrained=True,incl_fc=incl_fc) if args.drl_lmb>0 else models.resnet18(pretrained=True) \n if one_hot:\n classifier.fc = torch.nn.Sequential(torch.nn.Linear(512, args.n_classes),torch.nn.Sigmoid())\n else:\n classifier.fc = torch.nn.Linear(512, args.n_classes)\n elif args.classifier == 'ResNet50':\n classifier = resnet50(pretrained=True,incl_fc=incl_fc) if args.drl_lmb>0 else models.resnet50(pretrained=True)\n if one_hot:\n classifier.fc = torch.nn.Sequential(torch.nn.Linear(2048, args.n_classes),torch.nn.Sigmoid())\n else:\n classifier.fc = torch.nn.Linear(2048, args.n_classes)\n\n elif args.classifier == 'ResNetst':\n if args.drl_lmb > 0:\n from models.resnet_drl import resnest50\n else:\n from ResNeSt.resnest.torch import resnest50\n #torch.hub.list('zhanghang1989/ResNeSt', force_reload=True)\n classifier = resnest50(pretrained=True)#torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True)\n if one_hot:\n classifier.fc = torch.nn.Sequential(torch.nn.Linear(2048, args.n_classes),torch.nn.Sigmoid())\n else:\n classifier.fc = torch.nn.Linear(2048, args.n_classes)\n\n############################################### fix layers ###########################################\n fix_layer = []\n for l in range(1,4):\n if str(l) in args.fix:\n fix_layer.append('layer'+str(l))\n if l == 1:\n fix_layer+=['conv1','bn1']\n print('fix layers: {}'.format(fix_layer))\n for name, value in classifier.named_parameters():\n name = name.split('.')\n if name[0] in fix_layer:\n value.requires_grad = False\n\n\n############################################# config optimizer #########################################\n if args.opt == 'sgd':\n opt = torch.optim.SGD(classifier.parameters(), lr=args.lr,momentum=args.mmt,nesterov=args.nestro)\n elif args.opt == 'adam':\n opt = torch.optim.Adam(classifier.parameters(),lr=args.lr,betas=(args.beta1,args.beta2))\n elif args.opt == 'adamw':\n opt = torch.optim.AdamW(classifier.parameters(),lr=args.lr,betas=(args.beta1,args.beta2))\n elif args.opt == 'adagrad':\n opt = torch.optim.Adagrad(classifier.parameters(),lr=args.lr,lr_decay=args.lr_decay)\n elif args.opt == 'rmsprop':\n opt = torch.optim.RMSprop(classifier.parameters(),lr=args.lr,alpha=args.alpha,momentum=args.mmt)\n else:\n raise NotImplementedError('Please add implementation for optimizer {} first.'.format(args.opt))\n \n \n\n #criterion = cl_dr_loss if args.drl_lmb > 0 else torch.nn.CrossEntropyLoss()\n\n # vars to update over time\n valid_acc = []\n ext_mem_sz = []\n ram_usage = []\n heads = []\n ext_mem = {} \n # loop over the training incremental batches (x, y, t)\n for i, train_batch in enumerate(dataset):\n train_x, train_y, t = train_batch\n #train_x, train_y = train_x[:200],train_y[:200]\n #if i > 2:\n # break\n train_x = preprocess_imgs(train_x)\n if one_hot:\n train_y = one_hot_encoder(train_y,H=args.n_classes)\n\n # adding eventual replay patterns to the current batch\n if args.scenario!='offline':\n if args.fix_budget:\n if not ext_mem:\n total_mem = args.replay_examples\n else:\n args.replay_examples = total_mem//(len(ext_mem)+1)\n\n \n idxs_cur = np.random.choice(\n train_x.shape[0], min(args.replay_examples,train_x.shape[0]), replace=False\n )\n if args.replay_examples > 0:\n \n mem_x = train_x[idxs_cur]\n mem_y = train_y[idxs_cur]\n ext_mem.update({i:[mem_x, mem_y]})\n \n if i > 0 and not args.ER_type:\n \n train_x = np.concatenate([train_x]+ [ext_mem[k][0] for k in range(i)])\n train_y = np.concatenate([train_y]+ [ext_mem[k][1] for k in range(i)])\n \n \n if args.final:\n if args.scenario != 'multi-task-nc' and i == len(dataset.tasks_id)-1:\n print('add final')\n val_x,val_y = full_valdidset[0][0][0],full_valdidset[0][0][1]\n\n train_x = np.concatenate([train_x,val_x])\n train_y = np.concatenate([train_y,val_y]) \n if one_hot:\n val_y = one_hot_encoder(val_y,H=args.n_classes) \n\n if args.scenario == 'multi-task-nc':\n print('add final')\n val_x,val_y = full_valdidset[i][0][0],full_valdidset[i][0][1] \n train_x = np.concatenate([train_x,val_x])\n train_y = np.concatenate([train_y,val_y]) \n if one_hot:\n val_y = one_hot_encoder(val_y,H=args.n_classes) \n \n \n print(\"----------- batch {0} -------------\".format(i))\n print(\"x shape: {0}, y shape: {1}\"\n .format(train_x.shape, train_y.shape))\n print(\"Task Label: \", t)\n\n # train the classifier on the current batch/task\n pad = False if args.scenario=='offline' else True\n _, _, stats = train_net(\n opt, classifier, criterion, args.batch_size, train_x, train_y, i,\n args.epochs, preproc=None, use_cuda=args.cuda, pad=pad,\n ER_type=args.ER_type, ext_mem=ext_mem, mem_size=args.replay_examples,\n drl_lmb=args.drl_lmb, out_dim=args.n_classes,scenario=args.scenario,\n one_hot=one_hot\n )\n if args.scenario == \"multi-task-nc\" and not args.singlehead:\n heads.append(copy.deepcopy(classifier.fc))\n\n # collect statistics\n ext_mem_sz += stats['disk']\n ram_usage += stats['ram']\n\n # test on the validation set\n stats, _ = test_multitask(\n classifier, full_valdidset, args.batch_size,\n preproc=None, multi_heads=heads, verbose=False, one_hot=one_hot\n )\n\n valid_acc += stats['acc']\n print(\"------------------------------------------\")\n print(\"Avg. acc: {}\".format(stats['acc']))\n print(\"------------------------------------------\")\n\n \n if args.fix_budget and i > 0:\n print('fix budget update',args.replay_examples)\n for i, mem in ext_mem.items():\n ext_mem[i] = [mem[0][:args.replay_examples],mem[1][:args.replay_examples]]\n \n gc.collect()\n\n print(\"Avg. acc for all batches:\")\n print(np.mean(valid_acc))\n\n # Generate submission.zip\n\n # copy code\n create_code_snapshot(\".\", sub_dir + \"/code_snapshot\")\n\n # generating metadata.txt: with all the data used for the CLScore\n elapsed = (time.time() - start) / 60\n print(\"Training Time: {}m\".format(elapsed))\n with open(sub_dir + \"/metadata.txt\", \"w\") as wf:\n for obj in [\n np.average(valid_acc), elapsed, np.average(ram_usage),\n np.max(ram_usage), np.average(ext_mem_sz), np.max(ext_mem_sz)\n ]:\n wf.write(str(obj) + \"\\n\")\n\n with open(sub_dir+'valid_accuracy.csv','w') as f:\n writer = csv.writer(f,delimiter=',')\n writer.writerow(valid_acc)\n\n # test_preds.txt: with a list of labels separated by \"\\n\"\n \n print(\"Final inference on test set...\")\n full_testset = dataset.get_full_test_set()\n stats, preds = test_multitask(\n classifier, full_testset, args.batch_size, preproc=preprocess_imgs,\n multi_heads=heads, verbose=True,one_hot=one_hot\n )\n print(\"------------------------------------------\")\n print(\"Test acc: {}\".format(stats['acc']))\n print(\"------------------------------------------\")\n\n with open(sub_dir + \"/test_preds.txt\", \"w\") as wf:\n for pred in preds:\n wf.write(str(pred) + \"\\n\")\n\n print(\"Experiment completed.\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser('CVPR Continual Learning Challenge')\n\n # General\n parser.add_argument('--seed',type=int,default=0)\n parser.add_argument('--scenario', type=str, default=\"multi-task-nc\",\n choices=['ni', 'multi-task-nc', 'nic','offline'])\n parser.add_argument('--preload_data', type=bool, default=True,\n help='preload data into RAM')\n parser.add_argument('--train_size', type=int,default=-1,\n help='training size for offline tests,-1 use all data')\n parser.add_argument('--singlehead', default=False, action='store_true')\n parser.add_argument('--final',default=False,action='store_true',\n help='add valid set to training set in final training')\n parser.add_argument('--sigmoid', default=False, action='store_true',\n help='use sigmoid corss entropy except ni task')\n\n # Model\n parser.add_argument('-cls', '--classifier', type=str, default='ResNet50',\n choices=['ResNet18','ResNet50','ResNetst'])\n parser.add_argument('-noc', '--no_cuda', default=False,action='store_true',\n help='use cuda or not')\n parser.add_argument('--fix', type=str, default='',\n help='specify which layer to be fixed with pre-trained parameters.')\n\n\n # Optimization\n parser.add_argument('--lr', type=float, default=0.01,\n help='learning rate')\n parser.add_argument('--batch_size', type=int, default=32,\n help='batch_size')\n parser.add_argument('--epochs', type=int, default=1,\n help='number of epochs')\n parser.add_argument('--opt', type=str, default='sgd',choices=['sgd','adam','adamw','adagrad','rmsprop'],\n help='type of optimizer')\n parser.add_argument('--mmt', type=float, default=0.,\n help='momemtum of optimizer sgd or rmsprop')\n parser.add_argument('--nestro', default=False,action='store_true',\n help='nesterov of optimizer sgd')\n parser.add_argument('--beta1', type=float, default=0.9,\n help='beta1 of optimizer adam*')\n parser.add_argument('--beta2', type=float, default=0.999,\n help='beta2 of optimizer adam')\n parser.add_argument('--lr_decay', type=float, default=0.,\n help='lr_decay of optimizer adagrad')\n parser.add_argument('--alpha', type=float, default=0.99,\n help='alpha of optimizer rmsprop')\n\n\n # Continual Learning\n parser.add_argument('--replay_examples', type=int, default=0,\n help='data examples to keep in memory for each batch '\n 'for replay.')\n parser.add_argument('--ER_type', type=str, default=None,choices=['ER','BER'],\n help='use BER or ER')\n\n parser.add_argument('--fix_budget', default=False, action='store_true',\n help='fix memory budget or not')\n\n # Misc\n parser.add_argument('--sub_dir', type=str, default=\"multi-task-nc\",\n help='directory of the submission file for this exp.')\n parser.add_argument('--data_dir',type=str,default='core50/data/',help='folder of keeping core50 data')\n\n # DRL config\n parser.add_argument('--drl_lmb',type=float,default=0.,help='lambda of DRL')\n\n \n args = parser.parse_args()\n args.n_classes = 50\n args.input_size = [3, 128, 128]\n\n args.cuda = not args.no_cuda#torch.cuda.is_available()\n args.device = 'cuda:0' if args.cuda else 'cpu'\n\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed_all(args.seed)\n\n main(args)\n","repo_name":"Pervasive-AI-Lab/cvpr_clvision_challenge","sub_path":"finalists/yc14600/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":14549,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"26"}
+{"seq_id":"42267650396","text":"from __future__ import print_function\n__author__ = 'Elliot, David, Jacob'\nimport numpy as np\n\nclass LpModel:\n def __init__(self, numConstraints, numVars):\n self.numConstraints = numConstraints\n self.numVars = numVars\n self.constrNames = np.zeros((self.numConstraints,1))\n self.Constraints = np.zeros((self.numConstraints, self.numVars))\n self.RHS = np.zeros((self.numConstraints, 1))\n self.objectiveFunction = np.zeros((1, self.numVars))\n self.listOfColumns = []\n self.__makeIndexes(self.numVars)\n\n def setColumn(self, colNum, values):\n # self.listOfColumns[colNum]= np.matrix(values)\n self._addRow(self.indexes, colNum, values)\n\n def setObjfn(self, values):\n self.Objfn = np.zeros((1, self.numVars))\n for a in range(0, self.numVars):\n self.Objfn.put(a, values[a])\n\n def setDimNames(self,dimNames):\n self.constrNames = dimNames\n\n def setConstrType(self, constrType):\n self.ConstrType = np.zeros((self.numConstraints, 1))\n for a in range(0, self.numConstraints):\n ctype = constrType[a]\n if ctype == '<=':\n const = -2\n if ctype == '>=':\n const = 2\n if ctype == '<':\n const = -1\n if ctype == '>':\n const = 1\n if ctype == '=':\n const = 0\n self.ConstrType.put(a,const)\n\n def setRHS(self, values):\n self.RHS = np.zeros((self.numConstraints, 1))\n for a in range(0, self.numConstraints):\n self.RHS.put(a, values[a])\n\n def lpControl(self, minmax):\n if minmax.lower == 'min':\n self.lpControl = 0\n if minmax.lower == 'max':\n self.lpControl = 1\n\n def __makeIndexes(self, numValues):\n self.indexes = np.zeros((1, numValues))\n for a in range(numValues):\n self.indexes.put(a, a)\n self.indexes = self.indexes.astype(int)\n\n def _addRow(self, indexes, rowNumber, values):\n indexes += ((rowNumber - 1) * self.numVars)\n low = ((rowNumber - 1) * self.numVars)\n high = (rowNumber * self.numVars)\n counter = 0\n # for a in indexes:\n for a in range(low, high):\n self.Constraints.put(a, values[counter])\n counter += 1\n\n def printLp(self):\n\n for i in range(0, self.numVars):\n print(\"\\t\\t\"+self.constrNames[i], end=\"\")\n if self.lpControl== 1:\n print(\"\\nMax\",end=\"\\t\")\n else:\n print(\"\\nMin\",end=\"\\t\")\n for i in range(0, self.numVars):\n if len(str(self.Objfn[0][i]))<4:\n print(\"\\t\\t\"+str(self.Objfn[0][i]), end=\"\")\n else:\n print(\"\\t\"+str(self.Objfn[0][i]), end=\"\")\n print(\"\\r\")\n for i in range(0, self.numConstraints):\n if len(str(i))<4:\n print(\"R\"+str(i)+\"\", end=\"\\t\\t\")\n else:\n print(\"R\"+str(i)+\"\", end=\"\\t\")\n for j in range(0, self.numVars):\n if len(str(self.Constraints[i][j]))<4:\n print(self.Constraints[i][j], end=\"\\t\\t\")\n else:\n print(self.Constraints[i][j], end=\"\\t\")\n if self.ConstrType[i]== 0:\n print(\"=\", end=\"\")\n elif self.ConstrType[i]== 1:\n print(\">\", end=\"\")\n elif self.ConstrType[i]== 2:\n print(\">=\", end=\"\")\n elif self.ConstrType[i]== -1:\n print(\"<\", end=\"\")\n elif self.ConstrType[i]== -2:\n print(\"<=\", end=\"\")\n print(\"\\r\")\n\n\n\"\"\"test = LpModel(4, 2)\ntest.setColumn(1, [3, 4])\ntest.setColumn(2, [5, 5])\ntest.setColumn(3, [1, 1])\ntest.setColumn(4, [-5, 0])\ntest.setObjfn([444, 567])\ntest.lpControl(\"max\")\ntest.setRHS([15, 20, 50, 1000])\ntest.setConstrType([\"=\",\"<=\",\">\",\"<\"])\ntest.setDimNames([\"x\",\"y\"])\ntest.printLp()\"\"\"\n","repo_name":"etsandfort/lpsolve","sub_path":"LpModel.py","file_name":"LpModel.py","file_ext":"py","file_size_in_byte":3961,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"26"}
+{"seq_id":"12972493233","text":"import pathlib\nimport numpy as np\nimport tensorflow as tf\nimport keras\nimport matplotlib.pyplot as plt\nimport os\nimport time\nimport matplotlib\n\ndef preprocess(image):\n image = tf.cast(image, tf.float32)\n image = image/255\n image = tf.image.resize(image, (64, 64))\n image = image[None, ...]\n if image.shape[-1] != 3:\n image = tf.image.grayscale_to_rgb(image)\n return image\n\ndef create_adversarial_pattern(input_image, input_label, pretrained_model):\n loss_object = tf.keras.losses.CategoricalCrossentropy()\n with tf.GradientTape() as tape:\n tape.watch(input_image)\n prediction = pretrained_model(input_image)\n loss = loss_object(input_label, prediction)\n\n gradient = tape.gradient(loss, input_image)\n\n signed_grad = tf.sign(gradient)\n return signed_grad\n\n\ndef get_label(file_path):\n parts = tf.strings.split(file_path, os.path.sep)\n\n with open('./data/tiny-imagenet-200/val/val_annotations.txt', 'r') as labels_file:\n labels_lines = [line.split()[0] for line in labels_file]\n\n return parts[-1] == labels_lines\n\ndef process_path(file_path):\n label = get_label(file_path)\n img = tf.io.read_file(file_path)\n img = tf.image.decode_image(img)\n\n return img, label\n\ndef main():\n pretrained_model = tf.keras.models.load_model('./models/mobilenet_10.h5')\n data_dir = pathlib.Path('./data/tiny-imagenet-200/train/')\n val_dir = pathlib.Path('./data/tiny-imagenet-200/val/images/')\n CLASSES = sorted([item.name for item in data_dir.glob('*')])\n\n with open('./data/tiny-imagenet-200/val/val_annotations.txt', 'r') as labels_file:\n labels_lines = [line.split() for line in labels_file]\n\n list_ds = tf.data.Dataset.list_files(str(val_dir/'*'), shuffle=False)\n labeled_ds = list_ds.map(process_path, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n\n\n for image, label in labeled_ds:\n start_time = time.time()\n image = preprocess(image)\n s = np.argmax(label.numpy())\n\n label = tf.one_hot(CLASSES.index(labels_lines[s][1]), len(CLASSES))\n label = tf.reshape(label, (1, len(CLASSES)))\n\n perturbations = create_adversarial_pattern(image, label, pretrained_model)\n eps = 0.1\n adv_x = image + eps * perturbations\n adv_x = tf.clip_by_value(adv_x, 0, 1)\n image = adv_x.numpy()\n matplotlib.image.imsave('./data/tiny-imagenet-200/new_val/val_{}.JPEG'.format(s), image[0])\n\n print('took:', time.time() - start_time)\n\nif __name__ == '__main__':\n main()\n","repo_name":"abhi1345/cs-182-final-project","sub_path":"CS182-Spring2020-Vision-Project/fool_image.py","file_name":"fool_image.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"26"}
+{"seq_id":"31618446165","text":"import setuptools\n\nwith open('README.md', 'r') as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name='orchestra',\n version='0.0.14',\n author='Christoph Berger',\n author_email='c.berger@tum.de',\n description='Bulk segmentation for the BraTS challenge',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url='https://bitbucket.org/christophantonberger/orchestra',\n packages=['orchestra'],\n zip_safe=False,\n install_requires=[\n 'SimpleITK',\n 'numpy'\n ],\n entry_points={\n 'console_scripts': [\n 'brats-segment = orchestra.cli:segmentation',\n 'brats-fuse = orchestra.cli:fusion',\n 'brats-orchestra = orchestra.cli:segmentation'\n ],\n },\n classifiers=[\n 'Programming Language :: Python :: 3.7',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n ],\n)\n","repo_name":"christophbrgr/brats-orchestra","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"26"}
+{"seq_id":"42934988288","text":"from . import client\nfrom . import login\nfrom flask import render_template, request, abort\nfrom database import Tag, Post\n\n\n@client.route('/')\ndef homepage():\n\treturn render_template('homepage.html')\n\n@client.route('/posts')\ndef posts():\n\ttag = request.args.get('tag')\n\tpage = request.args.get('page',1, type=int)\n\tpagination = None\n\tif tag:\n\t\ttag = Tag.query.filter_by(name=tag.strip(' ')).first()\n\t\tif tag:\n\t\t\tpagination = Post.query.filter(Post.tags.contains(tag)).paginate(page,12,error_out=False)\n\t\n\tif pagination == None:\n\t\tpagination = Post.query.order_by(Post.id.desc()).paginate(page,12,error_out=False)\n\n\treturn render_template('tutorials.html',pagination=pagination)\n\n@client.route('/posts/.html')\ndef read(title):\n\tpost = Post.query.filter_by(title=title).first()\n\tif not post:\n\t\tabort(404)\n\n\treturn render_template('read.html',post=post)\n\n@client.route('/contact')\ndef contact():\n\treturn render_template('contact.html')\n","repo_name":"seth-blenks/pytut","sub_path":"modules/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"31678290462","text":"import sys\nimport MySQLdb\n\ntry:\n conn = MySQLdb.connect(host=\"localhost\",\n user=\"user1\",\n passwd=\"password1\",\n db=\"shop\")\nexcept MySQLdb.Error:\n print(\"Connection with Base Data error!\")\n sys.exit(1)\ncursor = conn.cursor()\np = int(input(\"Enter Product ID: \"))\ncursor.execute(\"SELECT * from products where prod_id=%d\" % p)\nrow = cursor.fetchone()\nif row == None:\n print(\"Нет такого ID в таблице\")\nelse:\n print(\"Есть такая информайия о продукте с ID %d :\" % p)\n print(\"Product ID\\tProduct Name\\tQuantity\\tPrice\")\n print(\"%d\\t\\t\\t%s\\t\\t\\t%d\\t\\t\\t%0.2f\" % (row[0], row[1], row[2], row[3]))\n k = input(\"Подтвердите что хотите удалить эту запись, yes/no: \")\n if k.upper() == \"YES\":\n cursor.execute(\"DELETE from products where prod_id=%d\" % p)\n print(\"Продукт с номером ID %d удален!\" % p)\n cursor.close()\n conn.commit()\n conn.close()","repo_name":"turamant/ToolKit","sub_path":"MySQL_CRUD/mysql_delete.py","file_name":"mysql_delete.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"24608112349","text":"from ChestXrayDataset import ChestXrayDataset\nfrom MyModel import MyModel\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\ndef main():\n print(\"Caricamento dataset...\")\n\n #INSERIRE PERCORSO DEL DATASET\n dataset = ChestXrayDataset(img_dir='')\n print(\"Dataset caricato\")\n\n num_train = dataset.get_trainset().__len__()\n num_test = dataset.get_testset().__len__()\n num_val = dataset.get_valset().__len__()\n\n print('Numero di immagini nel train set: ', num_train)\n print('Numero di immagini nel validation set: ', num_val)\n print('Numero di immagini nel test set: ', num_test)\n\n # Creare i DataLoader per ogni set\n batch_size = 32\n #setup device to use the GPU\n dev = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n loaders = dataset.get_loaders(batch_size=batch_size, shuffle=True, drop_last=True, num_workers=2) \n\n print(\"Vuoi allenare il modello o testarlo?\")\n print(\"1. Allenare il modello\")\n print(\"2. Testare il modello\")\n #scansione dell'input\n scelta = input()\n while scelta != \"1\" and scelta != \"2\":\n print(\"Input non valido\")\n scelta = input()\n\n\n if scelta == \"1\":\n model= MyModel(num_classes=3, Trained=False)\n # Define an optimizer\n optimizer = optim.SGD(model.resnet.fc.parameters(), lr = 0.005, weight_decay = 0.008)\n # Define a loss\n criterion = nn.CrossEntropyLoss()\n # Train the model\n model.train(loaders, optimizer, criterion, epochs=200, save_param = True, dev=dev)\n elif scelta == \"2\":\n model = MyModel(num_classes=3, Trained=True)\n # Test the model\n print(\"Modello caricato\")\n print(\"Test in corso...\")\n test_loss, test_accuracy, confusion_matrix, num_classes, f1, recall = model.test(loaders, criterion=nn.CrossEntropyLoss(), dev=dev)\n print(f\"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n print(f\"F1: {f1:.4f}\")\n print(f\"Recall: {recall:.4f}\")\n print(\"Chiudere la matrice di confusione per continuare\")\n # Plot confusion matrix\n plt.figure(figsize=(10, 8))\n sns.heatmap(confusion_matrix, annot=True, fmt=\".2f\", cmap=\"Blues\", xticklabels=num_classes, yticklabels=num_classes)\n plt.title(\"Confusion Matrix\")\n plt.xlabel(\"Predicted Label\")\n plt.ylabel(\"True Label\")\n plt.show()\n print(\"Test completato\")\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"alestrange01/MLProject","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"31854353649","text":"from pwn import *\n\ndef exploit():\n # p = process(\"./return-to-what\")\n p = remote(\"chal.duc.tf\", 30003)\n binary = ELF(\"./return-to-what\")\n\n main = binary.symbols['main']\n puts_plt = binary.plt['puts']\n puts_got = binary.got['puts']\n pop_rdi = 0x000000000040122b\n\n payload = ''\n payload += 'A' * 56\n payload += p64(pop_rdi)\n payload += p64(puts_got)\n payload += p64(puts_plt)\n payload += p64(main)\n\n p.sendline(payload)\n\n p.recvuntil(\"to?\\n\")\n\n libc_leak = u64(p.recvline()[:-1].ljust(8, '\\x00'))\n log.info(\"Libc leak: {}\".format(hex(libc_leak)))\n libc_base = libc_leak - 0x0809c0\n log.info(\"Libc base: {}\".format(hex(libc_base)))\n one_gadget = libc_base + 0x10a38c\n log.info(\"One gadget addr: {}\".format(hex(one_gadget)))\n\n payload = ''\n payload += 'A' * 56\n payload += p64(one_gadget)\n \n p.sendline(payload)\n p.interactive()\n\nif __name__ == \"__main__\":\n exploit()","repo_name":"ChaO-0/WriteUps","sub_path":"ductf/2020/pwn/return_to_what/exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"26"}
+{"seq_id":"11739141721","text":"# Question 8\n'''\nname:Clark\nclass:net182\nI D:201810701580047\n'''\ndict1 = {'Malaysia': 38.4, 'China': 36.4, 'Australia': 22.5, 'New Zealand': 18.2}\nx = 0\ny = 0\nfor a in dict1.values():\n x += a\nfor b in dict1:\n print(b, ':', dict1[b])\nfor c in dict1.values():\n if 20 < c < 30:\n y += 1\nprint('Average temperature:', '{0:.2f}'.format(x/len(dict1)))\nprint('Maximum temperature:', '{0:.2f}'.format(max(dict1.values())))\nprint('Number of temperatures between 20 and 30:', y)\n","repo_name":"loukjltd/JhcBackup","sub_path":"Python_OOP/Assessment/Assessment 01/201810701580047 - Clark/q-8.py","file_name":"q-8.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"26"}
+{"seq_id":"19219469293","text":"import random\nimport string\nfrom django.core.mail import send_mail\n\n\ndef get_random_string():\n length = 20\n letters_and_digits = string.ascii_letters + string.digits\n result = ''.join((random.choice(letters_and_digits) for i in range(length)))\n return result\n\n\ndef send_magic_link(request, to_email, token):\n send_mail(\n \"Your magic link\",\n f\"http://{request.get_host()}/home/token={token}\",\n \"MagicLinkCreationApp@gmail.com\",\n [to_email],\n )","repo_name":"danilovpy/MagicLinkApp","sub_path":"link/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"2006296652","text":"\"\"\"Event System\n\nThis module provides a central event bus to decouple the app from the individual\nmodules. It is possible to register callback functions, that are executed when\na specific event is fired.\n\"\"\"\n\nfrom typing import Any, Callable\n\nimport PySimpleGUI as sg\n\n\n# ------------------------------------------------------------------------------\n\n\nEventName = str\nEventListener = Callable[[sg.Window, EventName, Any], None]\n\n\ndef clear():\n \"\"\"Remove all registered events\"\"\"\n _listener.clear()\n\n\ndef fire(window: sg.Window, event: EventName, values: Any):\n \"\"\"Fires an event.\n\n All registrars will be notified immediately.\n \"\"\"\n if _is_paused:\n return\n\n callbacks = _listener.get(event, [_unknown_event])\n for callback in callbacks:\n callback(window, event, values)\n\n\ndef pause():\n \"\"\"Pauses all events until resumed.\"\"\"\n global _is_paused\n _is_paused = True\n\n\ndef resume():\n \"\"\"Resumes the event bus after pausing.\"\"\"\n global _is_paused\n _is_paused = False\n\n\ndef register(event_name: EventName, callback: EventListener):\n \"\"\"Register an event.\n\n Events are registered under a specific name with a callback that is executed\n when the event is fired.\n \"\"\"\n if _listener.get(event_name) == None:\n _listener[event_name] = [callback]\n else:\n _listener[event_name].append(callback)\n\n\ndef unregister(event_name: EventName, callback: EventListener = None):\n \"\"\"Removes a registered event.\"\"\"\n if not _listener.get(event_name):\n return\n\n if callback == None:\n del _listener[event_name]\n\n try:\n _listener[event_name].remove(callback)\n if len(_listener[event_name]) == 0:\n del _listener[event_name]\n except:\n return\n\n\n# ------------------------------------------------------------------------------s\n\n\ndef _unknown_event(_: sg.Window, event: EventName, values: Any):\n print(\"An unknown event was fired!\")\n print(\" Event:\", event)\n print(\" Values:\", values)\n\n\n_is_paused = False\n_listener: dict[EventName, list[EventListener]] = dict()\n","repo_name":"hd-code/pattern-learning-xai-ls","sub_path":"src/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"8530463772","text":"# Crie um programa que leia o ano de nascimento\n# de sete pessoas. No final, mostre quantas\n# pessoas ainda não atingiram a maioridade e quantas já são maiores.\n\n# BIBLIOTECA PARA PUXAR A DATA ATUAL\nfrom datetime import date\n# ARMAZENA ANO ATUAL\nano_atual = date.today().year\n\nmaior = 0\nmenor = 0\ncontador = 0\n\nfor c in range(1, 9):\n contador = contador + 1\n pessoa = int(input(' qual idade da {}ª pessoa: '.format(contador)))\n pessoa = int(pessoa + 1)\n if (ano_atual - pessoa) >= 21:\n maior += 1\n else:\n menor += 1\nprint('Tem {} pessoas maiores de idade: '.format(maior))\nprint('Tem {} pessoas menores de idade: '.format(menor))","repo_name":"JonasFarias/Um-Pouco-Sobre-Python","sub_path":"Exercicios/Curso em video/Mundo 2/Exercicios part 2/Exercicio 9 - Grupo da Maior Idade.py","file_name":"Exercicio 9 - Grupo da Maior Idade.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"11585486090","text":"# ----------------------------------------------------------------------------\n# CLASSES: nightly\n#\n# Test Case: Fluent.py\n#\n# Programmer: Hank Childs\n# Date: September 8, 2006\n#\n# Modifications:\n# Jeremy Meredith, Tue Jul 15 10:43:58 EDT 2008\n# Changed number of vectors in vector plot to match the old behavior.\n# (We now account for how many domains there are.)\n#\n# ----------------------------------------------------------------------------\n\n\nOpenDatabase(data_path(\"Fluent_test_data/Gas2OxyEntSlurry3d30m_MSmods_Ar_Wen_Moist_CO2_WGS_HCl_COdevol.cas\"))\n\nAddPlot(\"Pseudocolor\", \"TEMPERATURE\")\nDrawPlots()\nTest(\"Fluent_01\")\n\nAddPlot(\"Mesh\", \"Mesh\")\nDrawPlots()\nTest(\"Fluent_02\")\n\nDeleteAllPlots()\n\nAddPlot(\"Vector\", \"DPMS_MOM\")\nv = VectorAttributes()\nv.nVectors = 400*5\nSetPlotOptions(v)\nDrawPlots()\nTest(\"Fluent_03\")\n\nDeleteAllPlots()\n\nAddPlot(\"Subset\", \"blocks\")\nDrawPlots()\nTest(\"Fluent_04\")\n\nExit()\n","repo_name":"visit-dav/visit","sub_path":"src/test/tests/databases/Fluent.py","file_name":"Fluent.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":349,"dataset":"github-code","pt":"23"}
+{"seq_id":"818938630","text":"from django.conf.urls import patterns, url\n\nfrom tourneys import views\n\nurlpatterns = patterns('',\n url(r'^$', views.index, name='index'),\n url(r'^profile/$' , views.profile, name='profile'),\n url(r'^save_profile/$' , views.save_profile, name='save_profile'),\n url(r'^add_console/$' , views.add_console, name='add_console'),\n url(r'^edit_console/$' , views.edit_console, name='edit_console'),\n url(r'^my_tourneys/$' , views.my_tourneys, name='my_tourneys'),\n url(r'^add_tourney/$' , views.add_tourney, name='add_tourney'),\n # ex: /tourneys/5/\n url(r'^(?P<tourney_id>[0-9]+)/$', views.view_tourney, name='view_tourney'),\n url(r'^sign_up/$' , views.sign_up, name='sign_up'),\n url(r'^createMatch/$' , views.createMatch, name='createMatch')\n \n)","repo_name":"Peteringo2/Tourney","sub_path":"Tourney Manager/Tourney_Manager/tourneys/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"34776180533","text":"'''Input : arr[] = {1, 3, 5, 5, 5, 5, 67, 123, 125}\r\n x = 5\r\nOutput : First Occurrence = 2\r\n Last Occurrence = 5\r\n\r\nInput : arr[] = {1, 3, 5, 5, 5, 5, 7, 123, 125 }\r\n x = 7\r\nOutput : First Occurrence = 6\r\n Last Occurrence = 6\r\n'''\r\nlist = [1, 3, 5, 5, 5, 5, 67, 123, 125]\r\nx = 5\r\nfirst = -1\r\nlast = -1\r\nfor i in range(0,len((list))):\r\n if x != list[i]:\r\n continue\r\n\r\n\r\n if first == -1:\r\n first = i\r\n last = i\r\n\r\nif first != -1:\r\n print(\"First Occurence is \", first)\r\n print(\"Last Occurence is\",last)\r\nelse:\r\n print(\"Not Found\")\r\n\r\n","repo_name":"monstermahi982/competitive-coding","sub_path":"Searching & Sorting Tech/prob_1.py","file_name":"prob_1.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"27858772905","text":"\"\"\"\n첫째 줄부터 4개의 줄에 각 칸의 들어있는 물고기의 정보가 1번 행부터 순서대로 주어진다. \n물고기의 정보는 두 정수 ai, bi로 이루어져 있고, ai는 물고기의 번호, bi는 방향을 의미한다. \n방향 bi는 8보다 작거나 같은 자연수를 의미하고, 1부터 순서대로 ↑, ↖, ←, ↙, ↓, ↘, →, ↗ 를 의미한다.\n상어가 먹을 수 있는 물고기 번호의 합의 최댓값을 출력한다.\n\n\"\"\"\ninput_data = [list(map(int,input().split())) for _ in range(4)]\nfish_dic = {} \nspace = [[0 for _ in range(4)] for _ in range(4)]\n\nfor i in range(4) : \n for j in range(0,8,2) :\n space[i][j//2] = input_data[i][j] \n fish_dic[input_data[i][j]] = [i,j//2,input_data[i][j+1] - 1]\n\n# 1부터 순서대로 ↑, ↖, ←, ↙, ↓, ↘, →, ↗\ndirection = [(-1,0),(-1,-1), (0,-1), (1,-1),(1,0),(1,1),(0,1),(-1,1)]\n\n\n\ndef copy_dic(ori):\n ret = {} \n for key in ori.keys():\n ret[key] = ori[key][:]\n return ret \n\ndef copy_space(ori):\n temp = [[0 for _ in range(4)] for _ in range(4)]\n for i in range(4):\n for j in range(4):\n temp[i][j] = ori[i][j] \n return temp\n\nfrom collections import deque \n\ndef fish_move(ts1,tfk1): \n ts = copy_space(ts1)\n tfk = copy_dic(tfk1)\n for key in range(1,17): \n if tfk.get(key):\n x,y,d = tfk[key]\n nx,ny = x+direction[d][0], y+direction[d][1]\n succ = False \n for _ in range(8): \n if 0<=nx<4 and 0<=ny<4 and ts[nx][ny] != -1 :\n succ = True \n break \n d+=1\n nx,ny = x+direction[d%8][0], y+direction[d%8][1]\n if succ:\n if tfk.get(ts[nx][ny]):\n tfk[ts[nx][ny]][0], tfk[ts[nx][ny]][1] = x,y\n tfk[key][0], tfk[key][1], tfk[key][2] = nx,ny,d%8\n ts[x][y],ts[nx][ny] = ts[nx][ny],ts[x][y]\n return ts,tfk\n\n# space, fish_dic = fish_move(space,fish_dic) # first move \n\ndef shark_move(shark_position,space): \n x,y,d = shark_position\n ret = 0 \n if (x,y) == (0,0):\n del fish_dic[space[0][0]]\n ret += space[0][0]\n space[0][0] = -1\n temp_space,temp_fish_dic = fish_move(space,fish_dic)\n \n q = deque([(x,y,d,temp_space,ret,temp_fish_dic)]) \n while q :\n x,y,d,temp_space,point, temp_fish_dic= q.popleft()\n if ret<point : \n ret= point \n temp_space[x][y] = 0 # 이동 \n px,py = direction[d]\n nx,ny = x,y\n while 0<=nx + px<4 and 0<=ny+py<4 : \n nx,ny = nx+px,ny+py\n if 0<=nx<4 and 0<=ny<4 and temp_space[nx][ny] != 0 :\n key = temp_space[nx][ny] \n fx,fy,fd = temp_fish_dic[key]\n nd=fd \n del temp_fish_dic[key]\n temp_space[nx][ny] = -1\n ts,tfk = fish_move(temp_space,temp_fish_dic)\n q.append((nx,ny,nd,ts,point+key, tfk))\n temp_space[nx][ny] = key \n temp_fish_dic[key] = [fx,fy,fd]\n return ret \n\nshark_position = [0,0,fish_dic[space[0][0]][2]]\n\nprint(shark_move(shark_position,space))\n\n\n\n\n\"\"\"\n7 6 2 3 15 6 9 8\n3 1 1 8 14 7 10 1\n6 1 13 6 4 3 11 4\n16 1 8 7 5 2 12 2\n\n16 7 1 4 4 3 12 8\n14 7 7 6 3 4 10 2\n5 2 15 2 8 3 6 4\n11 8 2 4 13 5 9 4\n\"\"\"","repo_name":"JODONG2/ALGORITHM","sub_path":"samsung/19236.py","file_name":"19236.py","file_ext":"py","file_size_in_byte":3355,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"12735894185","text":"import requests\nimport json\n\n# Prompt the user for a search query\nquery = input(\"Enter your search query: \")\n\n# Send a GET request to the GitHub Search API\nresponse = requests.get(f\"https://api.github.com/search/repositories?q={query}\")\n\n# Parse the search results as a JSON object\nresults = response.json()\n\n# Find all of the repository links in the search results\nrepo_links = [repo[\"html_url\"] for repo in results[\"items\"]]\n\n# Filter the repository links to only include those that point to github.com\ngithub_links = [link for link in repo_links if \"github.com\" in link]\n\n# Print the github links\nfor link in github_links:\n print(link)\n","repo_name":"Laka795/Github-Repo-Scraper","sub_path":"RepoScrape.py","file_name":"RepoScrape.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"23"}
+{"seq_id":"14790099232","text":"####################################################################################################################\n# Script que permite ejecutar la prueba de concepto desde Python\n####################################################################################################################\nimport rpy2.robjects as robjects\nimport gc\nr_setwd = robjects.r['setwd']\nr_source = robjects.r['source']\n\nrobjects.r('memory.size()')\nrobjects.r('memory.limit()')\nrobjects.r('memory.limit(size=12200)')\n\nrobjects.r('rm(list=ls())')\ndata = \"C:/Users/Camilo Andres/Desktop/Variety/i2t/[data]/\"\ncode = \"C:/Users/Camilo Andres/Desktop/Variety/i2t/[code]/\"\nplots = \"C:/Users/Camilo Andres/Desktop/Variety/i2t/plots/test_v1/\"\nreport = \"C:/Users/Camilo Andres/Desktop/Variety/i2t/plots/test_v1/[hourly_v1].txt\"\nr_setwd(code)\n\nr_source(\"prototype_v2.R\")\nr_main_research_line = robjects.r['main_research_line']\ngc.collect()\nr_main_research_line(data,code,plots,report)\n\n# Consulta de los tiempos de ejecucion\nfor t in list(range(2, 13)):\n s = str('t'+str(t)+'-'+'t'+str(t-1))\n print(str(t)+' '+str(robjects.r(s)))\n","repo_name":"camicoding/energy_consumption","sub_path":"setup_v1.py","file_name":"setup_v1.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"70391268539","text":"import pandas as pd\r\nimport numpy as np\r\nimport os \r\nfrom PIL import Image\r\nimport cv2\r\nimport os\r\nfrom sklearn import tree\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\npath = 'D:/Braille Dataset'\r\ntraining_data = []\r\nfor img in os.listdir(path):\r\n pic = cv2.imread(os.path.join(path,img))\r\n pic1 = cv2.cvtColor(pic, cv2.COLOR_BGR2GRAY)\r\n pic2 = pic1.flatten()\r\n training_data.append(pic2)\r\ndf = pd.DataFrame(training_data)\r\ntarget = []\r\nfor i in range(0,26):\r\n for j in range(0,60):\r\n target.append(i)\r\ndf['target'] = target\r\ntarget = df['target']\r\ninputs = df.drop('target',axis='columns')\r\nmodel = tree.DecisionTreeClassifier(max_depth=100,criterion='entropy')\r\nx_train, x_test, y_train, y_test = train_test_split(inputs, target, test_size=0.25, random_state=0)\r\nmodel.fit(x_train,y_train)\r\nprint(model.score(x_test,y_test))","repo_name":"SHRE2303/Braille_Dataset","sub_path":"decision tree.py","file_name":"decision tree.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"46721558536","text":"import sympy as sym\nimport numpy as np\n\nf = open(\"Linear_classification.txt\", 'r') # 파일 오픈\nlines = f.readlines() # 각 데이터서 개행 단위로 요소를 만들어서 list로 바꿈\ndata_list = [] # data들을 저장할 리스트\n\n# linear classification 에서 읽은 파일을 data_list에 옮기는 과정\nfor line in lines:\n line = line.strip() # 개행 문자 제거\n temp_list = line.split(' ') # 공백글자에 따라 데이터를 나눔\n data_list.append(temp_list) # data_list에 추가\n\n# 사용할 Symbol 정의\na = sym.Symbol('a')\nb = sym.Symbol('b')\nc = sym.Symbol('c')\nz = sym.Symbol('z')\nx = sym.Symbol('x')\ny = sym.Symbol('y')\n\n# 최소 자승법으로 해 구하기\n\n\ndef least_square_method(datalist):\n # 오차의 합을 저장할 변수 선언\n error_summation_a = 0*a\n error_summation_b = 0*b\n error_summation_c = 0*c\n\n # 오차의 누적 합\n for element in data_list:\n # 오차 구하기\n element_error = int(element[1])*a + \\\n int(element[2])*b + c - int(element[3])\n element_error = element_error*element_error # 오차 제곱\n # Ra 미분, z가 0이므로 미분한 값만 더해주면 됨\n de_element_error_a = sym.diff(element_error, a)\n # 누적합 변수에 더해준다.\n error_summation_a = error_summation_a + de_element_error_a\n\n # b와 c에도 위 과정 반복\n de_element_error_b = sym.diff(element_error, b)\n error_summation_b = error_summation_b + de_element_error_b\n\n de_element_error_c = sym.diff(element_error, c)\n error_summation_c = error_summation_c + de_element_error_c\n\n # 구한 R 을 행렬계산을 위해 계수를 추출하는 과정.\n # 다항식으로 바꿔준 후,\n coeffi_Ra = sym.Poly(error_summation_a, a, b, c)\n coeffi_Rb = sym.Poly(error_summation_b, a, b, c)\n coeffi_Rc = sym.Poly(error_summation_c, a, b, c)\n\n # 각각 리스트를 선언하고 저장해준다.\n coeffi_Ra_list = coeffi_Ra.coeffs()\n coeffi_Rb_list = coeffi_Rb.coeffs()\n coeffi_Rc_list = coeffi_Rc.coeffs()\n\n # 행렬 계산을 하기위해 각각의 리스트를 만들어 줌\n matrix_x = [float(coeffi_Ra_list[0]), float(\n coeffi_Rb_list[0]), float(coeffi_Rc_list[0])]\n matrix_y = [float(coeffi_Ra_list[1]), float(\n coeffi_Rb_list[1]), float(coeffi_Rc_list[1])]\n matrix_z = [float(coeffi_Ra_list[2]), float(\n coeffi_Rb_list[2]), float(coeffi_Rc_list[2])]\n matrix_b = [(-1)*float(coeffi_Ra_list[3]), (-1) *\n float(coeffi_Rb_list[3]), (-1)*float(coeffi_Rc_list[3])]\n\n # 3x3 행렬\n matrix_A = [matrix_x, matrix_y, matrix_z]\n\n inv_A = np.linalg.inv(matrix_A) # 역행렬 구하기\n result = np.dot(inv_A, matrix_b) # 구하려는 해\n\n # 리턴\n return result\n\n\n# 위에서 정의한 최소자승법으로 구한 모델의 계수를 리스트 형식으로 저장함.\nlinear_classification_coeffs = least_square_method(data_list)\n\n# sympy 를 이용해서, 방정식 꼴로 바꿔줌. ax+by+c=z 에서,z를 이항해주고, ax+by+c-z = 0 꼴로 나타내, 좌변만 출력함.\n# linear_equation_answer = linear_classification_coeffs[0]*x + linear_classification_coeffs[1]*y + linear_classification_coeffs[2] - z\n\n# 팔, 다리 길이 정의\narm_length = 50\nleg_length = 30\n\n# 각 x, y 에 다리, 팔 값을 넣어 줌. 여기서 나오는 z값으로 암컷인지 수컷인지 분류 가능.\nz_value = float(linear_classification_coeffs[0])*leg_length + \\\n float(linear_classification_coeffs[1])*arm_length + \\\n float(linear_classification_coeffs[2])\n\nsex = \"\" # 성별 변수\n\n# 성별 정하기\nif z_value > 0:\n sex = \"수컷\"\nelif z_value < 0:\n sex = \"암컷\"\n\n# 답안 출력\nprint(\"입력한 동물은 학습 모델에 의해 \" + sex + \"으로 판단 됩니다.\")\n\n\nf.close() # 파일 닫기\n","repo_name":"2yunseong/AI-basic","sub_path":"Linear-Classification/LinearClassification.py","file_name":"LinearClassification.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"21680036666","text":"from src.lexer import Lexer\r\nfrom src.parser import Parser\r\n\r\nf = open(\"notas.roberson\", \"r\")\r\ntext_input = f.read()\r\n\r\nlexer = Lexer().get_lexer()\r\ntokens = lexer.lex(text_input)\r\ntokens2 = lexer.lex(text_input)\r\n\r\nout_tokens = open(\"lista_tokens.txt\", \"w\")\r\nfor token in tokens2:\r\n out_tokens.write(str(token) + \"\\n\")\r\n\r\npg = Parser()\r\npg.parse()\r\nparser = pg.get_parser()\r\nres = parser.parse(tokens, state=Parser())\r\nprint(res.eval(Parser()))\r\n","repo_name":"fred-direne/ecom06_compilador","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"71675491579","text":"from enum import Enum\nfrom functools import wraps\nfrom typing import Dict, Iterable, Optional\n\nimport attr\nimport ujson\nfrom aiohttp import web\nfrom apispec import APISpec # type: ignore\nfrom apispec.ext.marshmallow import MarshmallowPlugin # type: ignore\nfrom apispec.utils import validate_spec # type: ignore\nfrom apispec.yaml_utils import load_yaml_from_docstring # type: ignore\nfrom marshmallow import Schema\n\n\nclass ParameterIn(Enum):\n cookies = \"cookies\"\n header = \"header\"\n path = \"path\"\n query = \"query\"\n\n\n@attr.dataclass(slots=True, kw_only=True)\nclass Parameter:\n name: str\n schema: Dict[str, str]\n in_: ParameterIn = ParameterIn.query\n required: bool = False\n\n\n@attr.dataclass(slots=True, kw_only=True)\nclass RequestBody:\n description: str\n schema: Schema\n required: bool = False\n\n\n@attr.dataclass(slots=True, kw_only=True)\nclass Response:\n description: str\n content_type: str\n status_code: int = 200\n\n\n@attr.dataclass(slots=True, kw_only=True)\nclass JSONResponse(Response):\n schema: Schema\n content_type: str = \"application/json\"\n\n\nResponses = Iterable[Response]\n\n\ndef register_operation(\n description: str,\n responses: Responses,\n parameters: Optional[Iterable[Parameter]] = None,\n request_body: Optional[RequestBody] = None,\n):\n def wrapper(f):\n if not hasattr(f, \"spec\"):\n f.spec = {\n \"operation\": {\"description\": description, \"responses\": {}}\n }\n\n operation_from_doc = load_yaml_from_docstring(f.__doc__)\n\n if operation_from_doc:\n f.spec[\"operation\"] = operation_from_doc\n\n if description:\n f.spec[\"operation\"][\"description\"] = description\n\n if request_body:\n f.spec[\"operation\"][\"requestBody\"] = {\n \"description\": request_body.description,\n \"content\": {\n \"application/json\": {\"schema\": request_body.schema}\n },\n }\n if request_body.required:\n f.spec[\"operation\"][\"requestBody\"][\"required\"] = True\n\n if parameters:\n f.spec[\"operation\"][\"parameters\"] = [\n {\n \"in\": parameter.in_.value,\n \"name\": parameter.name,\n \"schema\": parameter.schema,\n \"required\": parameter.required,\n }\n for parameter in parameters\n ]\n\n for response in responses:\n f.spec[\"operation\"][\"responses\"][str(response.status_code)] = {\n \"description\": response.description,\n \"content\": {response.content_type: {\"schema\": response.schema}},\n }\n\n @wraps(f)\n async def wrapped(*args, **kwargs):\n return await f(*args, **kwargs)\n\n return wrapped\n\n return wrapper\n\n\nasync def handler(request: web.Request) -> web.Response:\n \"\"\"\n Expose API specification to the world\n \"\"\"\n\n return web.json_response(request.app[\"spec\"].to_dict(), dumps=ujson.dumps)\n\n\ndef setup(\n app: web.Application,\n *,\n title: str,\n version: str,\n description: str,\n openapi_version: str = \"3.0.2\",\n path: str = \"/api/spec.json\",\n validate: bool = False\n) -> None:\n\n app[\"spec\"] = APISpec(\n title=title,\n version=version,\n openapi_version=openapi_version,\n info={\"description\": description},\n plugins=[MarshmallowPlugin()],\n )\n\n app.router.add_get(path, handler, name=\"api.spec\")\n\n for route in app.router.routes():\n if hasattr(route.handler, \"spec\") and route.resource:\n operation_spec = route.handler.spec[\"operation\"] # type: ignore\n app[\"spec\"].path(\n path=route.resource.canonical,\n operations={route.method.lower(): operation_spec},\n )\n\n if validate:\n validate_spec(app[\"spec\"])\n","repo_name":"clayman083/aiohttp-openapi","sub_path":"src/aiohttp_openapi/openapi.py","file_name":"openapi.py","file_ext":"py","file_size_in_byte":3911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"28402362806","text":"import matplotlib.pyplot as plt # type: ignore\nimport numpy as np\n\n\ndef _standard_error_mean(size: int, sigma: float = 1.0) -> float:\n return float(sigma / np.sqrt(size))\n\n\ndef _mc_error_mean(size: int, sigma: float = 1.0, nmc: int = 1000) -> float:\n return float(\n np.std(\n [\n np.average(np.random.normal(size=size, scale=sigma)) - 0.0\n for _ in range(nmc)\n ]\n )\n )\n\n\ndef _standard_error_std(size: int, sigma: float = 1.0) -> float:\n return float(np.sqrt(sigma**2 / (2.0 * size)))\n\n\ndef _mc_error_std(size: int, sigma: float = 1.0, nmc: int = 1000) -> float:\n return float(\n np.std(\n [\n np.std(np.random.normal(size=size, scale=sigma)) - sigma\n for _ in range(nmc)\n ]\n )\n )\n\n\ndef test_standarderror() -> None:\n fig = plt.figure()\n ax = fig.add_subplot(211)\n X = np.arange(10, 100, 10)\n sigma = 2.0\n # check mean\n Y = [_mc_error_mean(size=x, sigma=sigma) for x in X]\n ax.plot(X, Y, label=\"MC\")\n Y = [_standard_error_mean(size=x, sigma=sigma) for x in X]\n ax.plot(X, Y, label=\"Analytic\")\n ax.legend()\n # check std dev\n ax = fig.add_subplot(212)\n Y = [_mc_error_std(size=x, sigma=sigma) for x in X]\n ax.plot(X, Y, label=\"MC\")\n Y = [_standard_error_std(size=x, sigma=sigma) for x in X]\n ax.plot(X, Y, label=\"Analytic\")\n ax.legend()\n plt.show()\n return\n","repo_name":"davehadley/bootstraphistogram","sub_path":"tests/test_standarderror.py","file_name":"test_standarderror.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"23"}
+{"seq_id":"28396724488","text":"from tkinter import *\nimport sqlite3\nimport ast\n\noutput_label = None\n\ndef create_book_win():\n book_win = Tk()\n book_win.title('Add Book')\n book_win.geometry(\"1000x1000\")\n\n #gui components\n # create text boxes\n title = Entry(book_win, width=30)\n title.grid(row=1, column=1)\n\n publisher = Entry(book_win, width=30)\n publisher.grid(row=2, column=1)\n\n author = Entry(book_win, width=30)\n author.grid(row=3, column=1)\n\n #create labels\n title_label = Label(book_win, text='Title:')\n title_label.grid(row=1, column=0)\n\n publisher_label = Label(book_win, text='Publisher:')\n publisher_label.grid(row=2, column=0)\n\n author_label = Label(book_win, text='Author:')\n author_label.grid(row=3, column=0)\n\n #create button\n submit_btn = Button(book_win, text='Add Book', command=lambda: submit_book(book_win, title.get(), publisher.get(), author.get()))\n submit_btn.grid(row=4, column=1)\n\n global output_label\n output_label = Label(book_win)\n output_label.grid(row=9, column=1)\n\ndef submit_book(book_win, title, publisher_name, author_name):\n # Check if the input values are not empty\n if not title or not publisher_name or not author_name:\n return\n\n #establish connection\n submit_conn = sqlite3.connect('../LMS.sql')\n submit_cur = submit_conn.cursor()\n\n #the new book's book_id = num_books + 1\n book_id = submit_cur.execute(\"SELECT count(*) from book\").fetchone()[0] + 1\n\n\n #perform queries to add book, author, and copies\n submit_cur.execute(\"INSERT INTO book (book_id, title, publisher_name) VALUES (:book_id, :title, :publisher_name) \",\n {\n 'book_id': book_id,\n 'title': title,\n 'publisher_name': publisher_name\n })\n\n submit_cur.execute(\"INSERT INTO BOOK_AUTHORS (Book_Id, Author_Name) VALUES (:Book_Id, :Author_Name) \",\n {\n 'Book_Id': book_id,\n 'Author_Name': author_name\n })\n\n # Query branch_id values from LIBRARY_BRANCH table\n submit_cur.execute(\"SELECT branch_id FROM library_branch\")\n branch_ids = submit_cur.fetchall()\n\n # Loop over all book_id and branch_id combinations, and insert No_Of_Copies = 5\n for branch_id_as_tuple in branch_ids:\n branch_id, = branch_id_as_tuple\n submit_cur.execute(\"INSERT INTO BOOK_COPIES (Book_Id, Branch_Id, No_Of_Copies) VALUES (:book_id, :branch_id, :no_of_copies)\", \n {\n 'book_id' : str(book_id),\n 'branch_id' : str(branch_id),\n 'no_of_copies' : '5'\n })\n\n #output table\n\n submit_cur.execute(\"SELECT bc.branch_id, b.book_id, b.title, b.publisher_name, ba.Author_Name, bc.No_Of_Copies FROM book b JOIN BOOK_AUTHORS ba ON b.book_id = ba.Book_Id JOIN BOOK_COPIES bc ON bc.book_id = b.book_id\")\n output_records = submit_cur.fetchall()\n print_record = ''\n for output_record in output_records:\n print_record += str(\"branch_id (\" + str(output_record[0]) + \") | book_id(\" + str(output_record[1]) + \") | \" + str(output_record[2]) + \" | \" + str(output_record[3]) + \"|\" + str(output_record[4]) + \" | No_Of_Copies(\" + str(output_record[5]) + ')\\n')\n global output_label\n if output_label != None:\n output_label.destroy()\n output_label = Label(book_win, text = print_record)\n output_label.grid(row = 9, column = 1)\n\n\n #commit changes\n submit_conn.commit()\n\n #close the DB connection\n submit_conn.close()\n","repo_name":"benMonty2001/cse3330","sub_path":"book_win.py","file_name":"book_win.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"45057285211","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# -------------------------------------------------------------------------------------\n# Instruction:\n# This is a example script of controling measurement instruments via GPIB or Serial interface.\n# It is checked on Ubuntu 14.04 LTS(32-bit) and 16.04 LTS (32-bit).\n# Mac and Windows are also available when using LAN/GPIB and LAN/Serial interface,\n# but unavailable when using USB/GPIB and USB/Serial interface.\n# See info.md to install requirements.\n#\n# NOTE:\n# - Some warning messages often arise when finishing the script without deleting the ResourceManager instance.\n# - Althogh these warnings are negligible, add 'del <instance_name>' at the end of the script to avoid this .\n# -------------------------------------------------------------------------------------\n\nimport sys, atexit, time, visa\n\n\n#------------------------------------------------------------------\n# Parental class to control an instrument via GPIB/Serial interface\n#------------------------------------------------------------------\nclass VISA_Instrument(object):\n \"\"\"\n This class controls instruments via GPIB/serial connection.\n Check the port and address (GPIB only) of the instrument in advance.\n\n If you use LAN - GPIB/serial interface, the ip address of the LAN is necessary as an input.\n\n If you use USB - GPIB/serial adapter to connect them to the computer,\n you need to make connection in advance.\n Use linux-gpib to establish connection.\n\n I did not check GPIB/USB connection with Mac,\n since Mac linux-gpib does not support Mac.\n If you use NI-VISA, it may work by setting setPyVisaBackend('@ni'),\n but I didn't check it.\n \"\"\"\n\n def __init__(self, port=0, address=0, ip=None):\n self.setPort(port) # GPIB/Serial port\n self.setAddress(address) # GPIB address\n self.setIP(ip) # IP address of LAN-GPIB/Serial adapter.\n self.setPyVisaBackend('@py') # '@py' means using pyvisa-py backend.\n self.updateResourceManager()\n\n def setPyVisaBackend(self, backend):\n # Run updateResourceManager() to apply the change.\n self.backend = backend\n\n def updateResourceManager(self):\n self.rm = visa.ResourceManager(self.backend)\n\n def setPort(self, port):\n self.port = port\n\n def setAddress(self, address):\n self.address = address\n\n def setIP(self, ip):\n self.ip = ip\n\n def openGpib(self):\n if self.ip is None:\n self.inst = self.rm.open_resource(\n 'GPIB' + str(self.port) + '::' + str(self.address) + '::INSTR')\n else:\n self.inst = self.rm.open_resource(\n 'TCPIP::' + self.ip + '::gpib' + str(self.port) + ',' +\n str(self.address) + '::INSTR')\n\n def openSerial(self):\n if self.ip is None:\n self.inst = self.rm.open_resource(\n 'ASRL/dev/ttyUSB' + str(self.port) + '::INSTR')\n else:\n self.inst = self.rm.open_resource(\n 'TCPIP::' + self.ip + '::COM' + str(self.port) + '::INSTR')\n\n def read(self):\n return self.inst.read()\n\n def write(self, string):\n self.inst.write(string)\n return\n\n def query(self, string):\n return self.inst.query(string)\n\n def getIDN(self):\n self.write(\"*IDN?\")\n return self.read()\n\n\n#------------------------------------------------------------------\n# Instruments inheriting \"VISA_Instrument\" class\n#------------------------------------------------------------------\n# GPIB\nclass Keithley_2602B(VISA_Instrument):\n \"\"\"\n Source meter\n \"\"\"\n\n def __init__(self, port=0, address=26, ip=None):\n super().__init__(port, address, ip)\n self.openGpib()\n atexit.register(self.at_exit)\n\n def measure_I(self):\n return float(self.query(\"print(smua.measure.i())\"))\n\n def measure_V(self):\n return float(self.query(\"print(smua.measure.v())\"))\n\n def measure_R(self):\n return float(self.query(\"print(smua.measure.r())\"))\n\n def average_R(self, average=100):\n # reset\n self.write(\"smua.reset()\")\n # 4 probe method.\n self.write(\"smua.sense = smua.SENSE_REMOTE\")\n # Voltage source\n self.write(\"smua.source.func = smua.OUTPUT_DCVOLTS\")\n # Source limit (1A).\n self.write(\"smua.source.limiti = 1\")\n # Source range (100 mV)\n self.write(\"smua.measure.rangev = 100e-3\")\n # Set V = 2 mV\n self.write(\"smua.source.levelv = 2e-3\")\n # Output on\n self.write(\"smua.source.output = smua.OUTPUT_ON\")\n R = 0.\n for i in range(average):\n R += self.measure_R()\n return R / average * 1.\n\n def measure_IV(self, probe=4, output=None):\n # Measurement setting.\n v_end = 10e-3\n step = 150\n # reset\n self.write(\"smua.reset()\")\n #src_mtr.write(\"smub.reset()\")\n if probe == 4:\n # 4 probe method.\n self.write(\"smua.sense = smua.SENSE_REMOTE\")\n elif probe == 2:\n # 2 probe method.\n self.write(\"smua.sense = smua.SENSE_LOCAL\")\n else:\n print(\"probe must be 2 or 4.\")\n sys.exit(1)\n # V\n self.write(\"smua.source.func = smua.OUTPUT_DCVOLTS\")\n # Source limit (10 mV, 10mA).\n # Ignored in IV sweep?\n self.write(\"smua.source.limitv = 10e-3\")\n self.write(\"smua.source.limiti = 10e-3\")\n # Source range (100 mV, 1 mA)\n self.write(\"smua.measure.rangev = 100e-3\")\n self.write(\"smua.measure.rangei = 5e-3\")\n # Timeout (IMPORTANT)\n self.timeout = 100000\n\n self.write(\"SweepVLinMeasureI(smua, \" + str(-1. * v_end) + \",\" +\n str(v_end) + \", 0.001,\" + str(step) + \")\")\n self.write(\"smua.source.output = smua.OUTPUT_ON\")\n self.write(\"waitcomplete()\")\n current = [0 for i in range(step)]\n for i in range(1, step + 1):\n current[i - 1] = self.query(\"printbuffer(\" + str(i) + \",\" + str(\n i) + \",smua.nvbuffer1.readings)\").rstrip()\n if output is None:\n for i in range(0, step):\n print(\n str(-1. * v_end + v_end * 2. / step * i) + \"\\t\" +\n str(current[i] if float(current[i]) < 5.0e-3 else \"0\"))\n else:\n with open(output, 'w') as f:\n for i in range(0, step):\n f.write(\n str(-1. * v_end + v_end * 2. / step * i) + \"\\t\" +\n str(current[i]\n if float(current[i]) < 5.0e-3 else \"0\") + '\\n')\n\n def at_exit(self):\n # Turn off output of source meter.\n # It should be registered to \"atexit\" when connecting this machine\n self.write(\"smua.source.output = smua.OUTPUT_OFF\")\n\n\n# GPIB\nclass Lakeshore_LSCI218(VISA_Instrument):\n \"\"\"\n Thermometer\n \"\"\"\n\n def __init__(self, port=0, address=11, ip=None):\n super().__init__(port, address, ip)\n self.openGpib()\n atexit.register(self.at_exit)\n\n def measure_T(self, num=1):\n # num=2: Inside the dewar\n # num=5: Nine HEB mixers\n return float(self.query(\"KRDG? \" + str(num)))\n\n def at_exit(self):\n pass\n\n\n# Serial\nclass Lakeshore_LSCI218S(VISA_Instrument):\n \"\"\"\n Thermometer\n - baudrate=300/1200/9600\n - startbits=1\n - databits=7\n - stopbits=1\n - parity=ODD\n - terminator=CR+LF\n \"\"\"\n\n def __init__(self, port=0, address=None, ip=None):\n super().__init__(port, address, ip)\n self.openSerial()\n # Tuning serial interface constants\n self.inst.baud_rate = 9600\n self.inst.data_bits = 7\n self.inst.timeout = 1000\n self.inst.parity = visa.constants.Parity.odd\n self.inst.stop_bits = visa.constants.StopBits.one\n\n def measure_T(self, window=1):\n if window == 0:\n return map(float, (self.query('KRDG? 0')).split(','))\n elif window > 0 and window < 9:\n return float(self.query('KRDG? ' + str(window)))\n else:\n print(\n 'Error! num must be 1~8, or 0 to meausre all simultaneously.')\n\n\n# GPIB\nclass Agilent_4418B(VISA_Instrument):\n \"\"\"\n Power meter\n \"\"\"\n\n def __init__(self, port=0, address=20, ip=None):\n super().__init__(port, address, ip)\n self.openGpib()\n atexit.register(self.at_exit)\n\n def at_exit(self):\n pass\n\n def measure_Power(self, sleep=0.1):\n time.sleep(sleep)\n return float(self.query('MEAS?'))\n\n def average_Power(self, average=30):\n self.write('*RST')\n self.write('CONF')\n self.write('SENS:AVER ON')\n self.write('SENS:AVER:COUNT:AUTO OFF')\n self.write('SENS:AVER:COUNT ' + str(average))\n return float(self.query('READ?'))\n\n\n# Serial\nclass Pfeiffer_TPG262(VISA_Instrument):\n \"\"\"\n Manometer\n - baudrate=9600\n - startbits=1\n - databits=8\n - paritybits=None\n - stopbits=1\n - terminator=CR+LF\n NOTE that sending '\\x05' is necessary to receive data.\n \"\"\"\n\n def __init__(self, port=0, address=None, ip=None):\n super().__init__(port, address, ip)\n self.openSerial()\n # Tuning serial interface constants\n self.inst.baud_rate = 9600\n self.inst.data_bits = 8\n self.inst.timeout = 1000\n self.inst.parity = visa.constants.Parity.none\n self.inst.stop_bits = visa.constants.StopBits.one\n self.ENQ = '\\x05'\n\n def measure_Pressure(self):\n self.query('PR1')\n data = self.query(self.ENQ)\n return float(data.split(',')[1].strip())\n\n\ndef measuer_IV_Power(pow_average=30):\n \"\"\"\n Measure IV curve and IF power simultaneously.\n \"\"\"\n # Connect\n src_mtr = Keithley_2602B()\n pow_mtr = Agilent_4418B()\n\n # Initialization\n src_mtr.write(\"smua.reset()\")\n src_mtr.write(\"smua.sense = smua.SENSE_REMOTE\")\n src_mtr.write(\"smua.source.limitv = 10e-3\")\n src_mtr.write(\"smua.source.limiti = 10e-3\")\n src_mtr.write(\"smua.measure.rangev = 100e-3\")\n src_mtr.write(\"smua.measure.rangei = 5e-3\")\n src_mtr.write(\"smua.source.output = smua.OUTPUT_ON\")\n src_mtr.write(\"waitcomplete()\")\n\n v_end = 9.e-3\n step = 200\n\n for i in range(step):\n v_bias = -v_end + v_end * 2. / step * i\n # set bias voltage\n src_mtr.write('smua.source.levelv = ' + str(v_bias))\n V = src_mtr.measure_V(src_mtr)\n I = src_mtr.measure_I(src_mtr)\n P = pow_mtr.average_Power(pow_average)\n print(str(V) + \" \" + str(I) + \" \" + str(P))\n","repo_name":"yurutaso/pyvisa-example","sub_path":"instruments.py","file_name":"instruments.py","file_ext":"py","file_size_in_byte":10703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"16086057759","text":"import requests\nfrom requests.exceptions import HTTPError\n\napi_key = 'Ebqy46zKgPyDOZTjgMkkETTPECqfO7fpKC4jwlOn'\nbill_type_mapper = {'J': 'jres', 'C': 'conres', 'R': 'res' }\n\n# e.g.\n# H.R.1234 => hr1234\n# S. 5568 => s5568\n# H.Res.41 => hres41\n# S.Res.66 => sres66\n# H.J.Res.1=> hjres1\n# S.ConRes1=> sconres1\ndef parse_bill(bill_string):\n chamber, bill_type, bill_num = bill_string[1:-1].split(',')\n if bill_type == '':\n if chamber == 'H':\n return 'hr', bill_num\n elif chamber == 'S':\n return 's', bill_num\n else:\n raise ValueError('Invalid bill chamber')\n\n return chamber.lower() + bill_type_mapper[bill_type], bill_num\n\ndef request_bill_info(bill_type, bill_number, congress_num):\n bill = bill_type + str(bill_number)\n url = 'https://api.propublica.org/congress/v1/{}/bills/{}.json'.format(congress_num, bill)\n\n try:\n response = requests.get(url, headers={'X-API-Key': api_key})\n except HTTPError as http_err:\n print(f'HTTP error occurred: {http_err}')\n except Exception as err:\n print(f'Other error occurred: {err}')\n\n if response.status_code == 200:\n return response.json()\n else:\n return response.status_code\n\ndef parse_response(json_obj):\n bill_info = json_obj['results'][0]\n bill_num = bill_info['number']\n title = bill_info['title']\n short_title = bill_info['short_title']\n\n return bill_num, title, short_title\n\ndef get_bill_info(bill_string, congress_num):\n bill_type, bill_num = parse_bill(bill_string)\n request_response = request_bill_info(bill_type, bill_num, congress_num)\n if type(request_response) != int:\n return parse_response(request_response)\n else:\n return (request_response, request_response, request_response)\n\ndef short_test():\n # H.Con.Res.8, 111th congress\n print(get_bill_info('(H,C,8)', 106))\n print(get_bill_info('(S,J,11)', 111))\n print(get_bill_info('(S,R,28)', 107))\n print(get_bill_info('(H,,7610)', 116))\n print(get_bill_info('(S,,347)', 108))\n\nshort_test()\n","repo_name":"reliu375/data_labeler","sub_path":"bill_api.py","file_name":"bill_api.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"10530667239","text":"def main():\n nota1 = int(input('Digite sua 1º nota: '))\n nota2 = int(input('Digite sua 2º nota: '))\n\n aprovacao(nota1, nota2)\n\n\ndef aprovacao(nota1, nota2):\n media = (nota1 + nota2) / 2\n if media > 7:\n print('Aprovado')\n else:\n recuperacao = int(input('Digite a nota da recuperacao: '))\n if recuperacao > 5:\n print('Aprovado')\n else:\n print('reprovado')\n\nmain()","repo_name":"Matheus654/ifpi_ads_2020","sub_path":"Q16_F2a_aprovado.py","file_name":"Q16_F2a_aprovado.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"6065595251","text":"from random import randint\n\n# Local imports\nimport pyswmm.toolkitapi as tka\nfrom pyswmm import Links, Nodes, Simulation\nfrom pyswmm.tests.data import MODEL_WEIR_SETTING_PATH\n\n\ndef test_simulation_1():\n sim = Simulation(MODEL_WEIR_SETTING_PATH)\n print(f'system units: {sim.system_units}')\n print(f'swmm version: {sim._model.swmm_getVersion()}')\n allow_ponding = sim._model.getSimOptionSetting(tka.SimAnalysisSettings.AllowPonding)\n routing_step = sim._model.getSimAnalysisSetting(tka.SimulationParameters.RouteStep)\n print(f'analysis setting: {allow_ponding}')\n print(f'analysis param: {routing_step}')\n assert sim.system_units == 'US'\n assert not allow_ponding\n assert routing_step == 1\n for ind, step in enumerate(sim):\n print(sim.current_time)\n sim.step_advance(randint(300, 900))\n\n sim.close()\n\n\ndef test_simulation_2():\n with Simulation(MODEL_WEIR_SETTING_PATH) as sim:\n for ind, step in enumerate(sim):\n print(sim.current_time)\n sim.step_advance(randint(300, 900))\n\n\ndef test_simulation_3():\n sim = Simulation(MODEL_WEIR_SETTING_PATH)\n sim.execute()\n\n\ndef test_simulation_4():\n with Simulation(MODEL_WEIR_SETTING_PATH) as sim:\n for ind, step in enumerate(sim):\n print(sim.percent_complete)\n sim.step_advance(randint(300, 900))\n\n\ndef test_simulation_iter():\n with Simulation(MODEL_WEIR_SETTING_PATH) as sim:\n c1c2 = Links(sim)[\"C1:C2\"]\n\n sim.step_advance(300)\n for ind, step in enumerate(sim):\n print(c1c2.flow, c1c2.target_setting)\n if c1c2.flow > 9.19:\n c1c2.target_setting = 0.9\n\n\ndef test_simulation_9():\n with Simulation(MODEL_WEIR_SETTING_PATH) as sim:\n J1 = Nodes(sim)[\"J1\"]\n\n def init_function():\n J1.initial_depth = 15\n\n sim.initial_conditions(init_function)\n for ind, step in enumerate(sim):\n if ind == 0:\n assert J1.depth > 14\n\n\ndef test_simulation_callback_1():\n LIST = []\n with Simulation(MODEL_WEIR_SETTING_PATH) as sim:\n LIST.append(\"OPENED\")\n\n def before_start1():\n LIST.append(\"before_start1\")\n\n sim.add_before_start(before_start1)\n\n def before_step1():\n if \"before_step1\" not in LIST:\n LIST.append(\"before_step1\")\n\n sim.add_before_step(before_step1)\n\n def after_step1():\n if \"after_step1\" not in LIST:\n LIST.append(\"after_step1\")\n\n sim.add_after_step(after_step1)\n\n def before_end1():\n if \"before_end1\" not in LIST:\n LIST.append(\"before_end1\")\n\n sim.add_before_end(before_end1)\n\n def after_end1():\n if \"after_end1\" not in LIST:\n LIST.append(\"after_end1\")\n\n sim.add_after_end(after_end1)\n\n def after_close1():\n if \"after_close1\" not in LIST:\n LIST.append(\"after_close1\")\n\n sim.add_after_close(after_close1)\n\n for ind, step in enumerate(sim):\n if ind == 0:\n LIST.append(\"SIM_STEP\")\n\n assert LIST == [\n \"OPENED\", \"before_start1\", \"before_step1\", \"after_step1\", \"SIM_STEP\",\n \"before_end1\", \"after_end1\", \"after_close1\"\n ]\n print(LIST)\n\n\ndef test_simulation_terminate():\n with Simulation(MODEL_WEIR_SETTING_PATH) as sim:\n i = 0\n for ind, step in enumerate(sim):\n i += 1\n if ind == 10:\n sim.terminate_simulation()\n assert(i == 11)\n\n","repo_name":"pyswmm/pyswmm","sub_path":"pyswmm/tests/test_simulation.py","file_name":"test_simulation.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","stars":251,"dataset":"github-code","pt":"23"}
+{"seq_id":"11945631281","text":"#!/usr/bin/env python\nimport argparse\nimport os\nimport sys\nfrom openpilot.common.basedir import BASEDIR\nfrom openpilot.tools.lib.logreader import MultiLogIterator\nfrom openpilot.tools.lib.route import Route\n\nos.environ['BASEDIR'] = BASEDIR\n\n\ndef get_arg_parser():\n parser = argparse.ArgumentParser(\n description=\"Unlogging and save to file\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n parser.add_argument(\"data_dir\", nargs='?',\n help=\"Path to directory in which log and camera files are located.\")\n parser.add_argument(\"route_name\", type=(lambda x: x.replace(\"#\", \"|\")), nargs=\"?\",\n help=\"The route whose messages will be published.\")\n parser.add_argument(\"--out_path\", nargs='?', default='/data/ubloxRaw.stream',\n help=\"Output pickle file path\")\n return parser\n\n\ndef main(argv):\n args = get_arg_parser().parse_args(sys.argv[1:])\n if not args.data_dir:\n print('Data directory invalid.')\n return\n\n if not args.route_name:\n # Extract route name from path\n args.route_name = os.path.basename(args.data_dir)\n args.data_dir = os.path.dirname(args.data_dir)\n\n route = Route(args.route_name, args.data_dir)\n lr = MultiLogIterator(route.log_paths())\n\n with open(args.out_path, 'wb') as f:\n try:\n done = False\n i = 0\n while not done:\n msg = next(lr)\n if not msg:\n break\n smsg = msg.as_builder()\n typ = smsg.which()\n if typ == 'ubloxRaw':\n f.write(smsg.to_bytes())\n i += 1\n except StopIteration:\n print('All done')\n print(f'Writed {i} msgs')\n\n\nif __name__ == \"__main__\":\n sys.exit(main(sys.argv[1:]))\n","repo_name":"commaai/openpilot","sub_path":"tools/scripts/save_ubloxraw_stream.py","file_name":"save_ubloxraw_stream.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":44085,"dataset":"github-code","pt":"23"}
+{"seq_id":"18863956282","text":"#!/usr/bin/env python\n\nimport pandas as pd\nimport nibabel as nib\nimport numpy as np\n\ndef relabel(data, db):\n aff = data.get_affine()\n hdr = data.get_header()\n img = data.get_data()\n out = np.zeros(img.shape)\n\n # find all nodes in each network\n for network in np.unique(db['Network']):\n nodes = db.loc[db['Network'] == network, 'Node']\n\n # find all voxels in each node\n for node in nodes:\n idx = np.where(img == node)\n out[idx] = network\n\n out = nib.nifti1.Nifti1Image(out, aff, header=hdr)\n return(out)\n\ndb = pd.read_csv('shen_268_parcellation_networklabels.csv')\n\nd1 = nib.load('shen_1mm_268_parcellation.nii.gz')\nd2 = nib.load('shen_2mm_268_parcellation.nii.gz')\n\nd1 = relabel(d1, db)\nd2 = relabel(d2, db)\n\nd1.to_filename('shen_1mm_268_networks.nii.gz')\nd2.to_filename('shen_2mm_268_networks.nii.gz')\n","repo_name":"josephdviviano/epitome","sub_path":"assets/make-shen-networks.py","file_name":"make-shen-networks.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"23"}
+{"seq_id":"22241555258","text":"class Solution(object):\r\n def addDigits(self, num):\r\n \"\"\"\r\n :type num: int\r\n :rtype: int\r\n \"\"\"\r\n snum = str(num)\r\n while len(snum) != 1:\r\n rsl = 0\r\n for w in snum:\r\n rsl += int(w)\r\n snum = str(rsl)\r\n\r\n return int(snum)","repo_name":"Vinceri/leetcode","sub_path":"258.AddDigits.py","file_name":"258.AddDigits.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"16893302633","text":"import sys, os\nimport uproot\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nimport pandas as pd\nimport numpy as np\nimport splitlib as sp\n\n# To launch script with all the arguments:\n# python colsdistrib_binned.py\n\nfilename = \"/scratch/mmarcheg/lumi_data/clusters_V1_Run2017C_Fill6061.root\"\nplot_dir = \"../ntuplesPixel/plots/colsdistrib/\"\nif not os.path.exists(plot_dir):\n\tos.makedirs(plot_dir)\nprint(\"Plots will be saved in \" + plot_dir)\nprint(\"Opening %s\" % filename)\nfile = uproot.open(filename)\ntree = file[b'a/tree;1']\nprint(str(tree.name) + \" contains \" + str(len(tree)) + \" entries\")\n\nentrystop = 53605236\nnentries = len(tree)\nread_entry = 0\ni = 0\ndf_list = []\nwhile read_entry < nentries:\n\tprint(\"Reading entries between %d and %d\" % (read_entry, read_entry + entrystop))\n\tdf = tree.pandas.df([b'ladder', b'pos_x', b'pos_y', b'size', b'cols', b'rows', b'global_eta', b'global_phi', b'instaLumi', b'bx', b'tres'], entrystart=read_entry, entrystop=read_entry + entrystop)\n\tdf_list.append(df)\n\tprint(\"Saving chunk number %d of dataframe\" % (i+1))\n\t#filename = \"/scratch/mmarcheg/csv_files/combined2017_0\" + str(i+1) + \".csv\"\n\t#df.to_csv(filename, index=False)\n\tread_entry += entrystop\n\ti += 1\n\nprint(\"Readed dataframe in %d chunks\" % i)\nlumi_bins = [] # Luminosity quantiles\n\nbins_cols = np.linspace(0.5, 20.5, 21)\ncentroids = np.linspace(1, 20, 20)\neta_list = ['abs(global_eta) < 1', '(abs(global_eta) > 1) & (abs(global_eta) < 2)', 'abs(global_eta) > 2']\neta_range = ['|$\\\\eta$| < 1', '1 < |$\\\\eta$| < 2', '|$\\\\eta$| > 2']\ncolors = ['red', 'blue', 'green']\nn_tot_eta = []\nfor (j, eta_query) in enumerate(eta_list):\n\tn_tot = []\n\tfor (i, df) in enumerate(df_list):\n\t\tdf_eta = df.query(eta_query)\n\t\tn, bins, patches = plt.hist(df_eta['cols'], bins=bins_cols)\n\t\tn_tot = sp.list_sum(n_tot, n)\n\t\tplt.close()\n\tn_tot_eta.append(n_tot)\n\nfor (j, eta_query) in enumerate(eta_list):\n\tplt.hist(centroids, bins=bins_cols, weights=n_tot_eta[j], color=colors[j], ec=colors[j], histtype='step', label=eta_range[j])\n\nplt.xlabel(\"cols\")\nplt.ylabel(\"# clusters\")\nplt.legend(loc=\"upper right\")\nplt.title(\"length of clusters\")\nplt.xticks(centroids)\nax = plt.gca()\nax.xaxis.set_major_locator(MaxNLocator(integer=True))\nplt.savefig(plot_dir + \"cols_distribution.png\", format=\"png\", dpi=300)\nplt.close()\n\nfor (j, eta_query) in enumerate(eta_list):\n\tplt.hist(centroids, bins=bins_cols, weights=n_tot_eta[j], color=colors[j], ec=colors[j], histtype='step', label=eta_range[j], log=True)\n\nplt.xlabel(\"cols\")\nplt.ylabel(\"# clusters\")\nplt.legend(loc=\"upper right\")\nplt.title(\"length of clusters\")\nplt.xticks(centroids)\nax = plt.gca()\nax.xaxis.set_major_locator(MaxNLocator(integer=True))\nplt.savefig(plot_dir + \"cols_distribution_log.png\", format=\"png\", dpi=300)\nplt.close()\n","repo_name":"mmarchegiani/L1-BrokenClusters","sub_path":"colsdistrib_binned.py","file_name":"colsdistrib_binned.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"833894948","text":"import pygame\nimport time\n\nfrom Functions import *\nfrom States.baseclass import Base\nfrom constants import *\n\nclass CountDown(Base):\n\n def __init__(self):\n super().__init__()\n\n self.count = 4\n\n \n def render(self):\n text, textRect = print_text(100, str(self.count), RED, GRAY)\n textRect.center = (WINDOW_WIDTH // 2, WINDOW_HEIGHT // 2)\n\n SCREEN.blit(text, textRect)\n\n def update(self):\n\n self.count -= 1\n pygame.time.wait(1000)\n\n if self.count <= 0 : GAME_STATE.change(\"play\")\n\n self.render()\n\n def enter(self):\n self.count = 4","repo_name":"Rishikesh-kumar-7258/SlidePuzzle","sub_path":"States/countdownstate.py","file_name":"countdownstate.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"2347377886","text":"import json\nimport boto3\nfrom botocore.exceptions import ClientError\n\ndynamodb = boto3.resource('dynamodb')\ntable = dynamodb.Table('simple-chat-conversations')\n\n\ndef lambda_handler(event, context):\n # Ensure message has a body\n if 'body' not in event:\n return {'statusCode': 400}\n\n data = json.loads(event['body'])\n username = event['requestContext']['authorizer']['jwt']['claims']['cognito:username']\n\n # Verify client input\n if 'conversation' not in data:\n return {'statusCode': 400}\n\n conversation_id = data['conversation']\n\n # Verify client input types\n if type(conversation_id) is not str:\n return {'statusCode': 400}\n\n key = {'id': conversation_id}\n\n try:\n response = table.get_item(Key=key, AttributesToGet=['users', 'admins'])\n\n if 'Item' not in response:\n return {'statusCode': 404, 'body': 'No such conversation'}\n\n admins = response['Item']['admins']\n users = response['Item']['users']\n\n # Ensure user is in conversation\n if username not in users:\n return {'statusCode': 401}\n\n # Ensure user isn't the only admin\n if username in admins and len(admins) == 1:\n return {'statusCode': 200, 'body': 'Can\\'t remove the only admin from a conversation'}\n\n admins.remove(username)\n users.remove(username)\n\n # Update conversation\n table.update_item(\n Key=key,\n UpdateExpression=\"set #users=:users, admins=:admins\",\n ExpressionAttributeValues={\n \":users\": users,\n \":admins\": admins\n },\n ExpressionAttributeNames={'#users': 'users'}\n )\n return 'OK'\n except ClientError as e:\n print(e.response['Error']['Message'])\n return {'statusCode': 500}\n","repo_name":"TheLogicMaster/simple-chat-app","sub_path":"server/conversation-leave.py","file_name":"conversation-leave.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"7873942452","text":"import logging\nimport re\nimport json\nfrom django.db import models\nfrom marto_python.util import as_datetime\nfrom marto_python.strings import as_int\nfrom marto_python.email.email import send_email_to_admins\nfrom .util import update_list_of, update_object_from_data, \\\n unescape_func_not_null, url_func, unescape_func, unescape_dict_val_func, valid_email_func\nfrom . import models\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef update_from_data(obj, data, data_obj_mapping):\n \"\"\"\n update an object from weebly data, returns true if changed\n \"\"\"\n changed = False\n for mapping in data_obj_mapping:\n data_attr = mapping[0]\n obj_attr = mapping[1]\n mapping_fn = mapping[2] if len(mapping) >= 3 else None\n\n json_val = data[data_attr]\n old_val = getattr(obj, obj_attr)\n new_val = mapping_fn(json_val) if mapping_fn else json_val\n\n if old_val != new_val:\n changed = True\n setattr(obj, obj_attr, new_val)\n return changed\n\n\ndef refresh_user(user, weebly_auth):\n if not weebly_auth:\n weebly_auth = user.get_default_weebly_auth()\n rv, resp_json = weebly_auth.weebly_request('/v1/user',\n params={'user_id': user.user_id},\n action_name='getting user details')\n if 'error' not in rv:\n changed = update_from_data(user, resp_json, [\n ('user_id', 'user_id', as_int),\n ('name', 'name', unescape_func),\n ('email', 'email', valid_email_func),\n ])\n if changed: user.save()\n return rv\n\n\ndef refresh_site(site, weebly_auth=None):\n if not weebly_auth:\n weebly_auth = site.get_default_weebly_auth()\n rv, resp_json = weebly_auth.weebly_request(f'/v1/user/sites/{site.site_id}',\n action_name='getting site details',\n expected_errors=['Site not found'])\n changed = False\n if 'error' not in rv:\n def lang_func(l):\n if not isinstance(l, str):\n logger.error(f'weebly is returning {l} as language - json: {resp_json}')\n return site.language\n return l\n\n changed = update_from_data(site, resp_json, [\n ('site_id', 'site_id', as_int),\n ('site_title', 'site_title', unescape_func),\n ('domain', 'domain'),\n ('is_published', 'is_published'),\n ('language', 'language', lang_func),\n ('user_id', 'user', lambda val: models.WeeblyUser.get_or_create(val))\n ])\n if not site.is_found:\n site.is_found = True\n changed = True\n elif 'Site not found' in rv['error']:\n if site.is_found:\n site.is_found = False\n changed = True\n if changed:\n logger.info(f'{site} - changed, saving and sending signal')\n site.save()\n site.signal_change()\n return rv\n\n\ndef refresh_pages(site, weebly_auth=None, signal_change=False):\n if not weebly_auth:\n weebly_auth = site.get_default_weebly_auth()\n logger.info(f'{site} - refreshing pages')\n path = f'/v1/user/sites/{site.site_id}/pages'\n rv, response_json = weebly_auth.weebly_request_paginated(path, action_name='requesting pages')\n if 'error' in rv:\n return rv\n return refresh_pages_from_data(site, response_json, signal_change=signal_change)\n\n\ndef refresh_pages_from_data(site, pages_data, signal_change=False):\n rv = update_list_of(\n site,\n pages_data,\n 'page', 'page_id', 'page_id',\n lambda: models.WeeblyPage(site=site),\n site.pages,\n [\n ('title', 'title', unescape_func_not_null),\n ('page_url', 'page_url', url_func),\n ('page_order', 'page_order', None),\n ('parent_id', 'parent_id', as_int),\n ]\n )\n changes = 'changes' in rv and rv['changes']\n if signal_change and changes: site.signal_change()\n return rv\n\n\ndef refresh_blogs(site, refresh_posts=False):\n weebly_auth = site.get_default_weebly_auth()\n path = f'/v1/user/sites/{site.site_id}/blogs'\n expected_errors = ['access to the requested user information'] # TODO: remove expected_errors when permissions are ok\n rv, resp_json = weebly_auth.weebly_request(path, action_name='getting blogs', expected_errors=expected_errors)\n if 'error' in rv:\n return rv\n rv = update_list_of(\n site,\n resp_json,\n 'blog', 'blog_id', 'blog_id',\n lambda: models.WeeblyBlog(site=site),\n site.blogs,\n [\n ('blog_id', 'blog_id', as_int),\n ('page_id', 'page_id', as_int),\n ('title', 'title', unescape_func),\n ]\n )\n if 'error' in rv:\n return rv\n changes = rv.get('changes', False)\n if refresh_posts:\n for blog in site.blogs.all():\n rv = blog.refresh_posts()\n changes = changes or rv.get('changes', False)\n if 'error' in rv:\n return rv\n for blog in site.blogs.all():\n for post in blog.posts.all():\n rv = post.refresh_from_weebly()\n if 'error' in rv:\n return rv\n changes = changes or rv.get('changes', False)\n return {'changes': changes}\n\n\ndef refresh_posts(blog):\n site = blog.site\n weebly_auth = site.get_default_weebly_auth()\n path = f'/v1/user/sites/{site.site_id}/blogs/{blog.blog_id}/posts'\n rv, resp_json = weebly_auth.weebly_request(path, action_name='getting blog posts')\n if 'error' in rv:\n return rv\n rv = update_list_of(site, resp_json, 'post', 'post_id', 'post_id', lambda: models.WeeblyBlogPost(blog=blog),\n blog.posts,\n [\n ('post_id', 'post_id', as_int),\n ('post_title', 'title', unescape_func),\n ('created_date', 'created_date', as_datetime)\n ])\n return rv\n\n\ndef refresh_post(post):\n site = post.blog.site\n weebly_auth = site.get_default_weebly_auth()\n path = f'/v1/user/sites/{site.site_id}/blogs/{post.blog.blog_id}/posts/{post.post_id}'\n rv, resp_json = weebly_auth.weebly_request(path, action_name='getting blog post details')\n\n def tags_fn(tags):\n if not tags: tags = {}\n tags = unescape_dict_val_func(tags)\n return json.dumps(tags)\n\n if 'error' not in rv:\n changes = update_from_data(post, resp_json, [\n ('post_id', 'post_id', as_int),\n ('post_title', 'title', unescape_func),\n ('created_date', 'created_date', as_datetime),\n ('updated_date', 'updated_date', as_datetime),\n ('post_body', 'body', unescape_func),\n ('post_link', 'link', unescape_func),\n ('post_url', 'url', unescape_func),\n ('seo_title', 'seo_title', unescape_func),\n ('seo_description', 'seo_description', unescape_func),\n ('tags', 'tags', tags_fn),\n ])\n if changes:\n post.save()\n rv = {'changes': changes}\n return rv\n\n\ndef refresh_store(site, weebly_auth=None, signal_change=False):\n if not weebly_auth:\n weebly_auth = site.get_default_weebly_auth()\n rv = site.refresh_store_products(weebly_auth)\n if 'error' in rv: return rv\n changes = 'changes' in rv and rv['changes']\n for product in site.store_products.all():\n rv = product.refresh_from_weebly(weebly_auth)\n if 'error' in rv: return rv\n changes = changes or ('changes' in rv and rv['changes'])\n rv = site.refresh_store_categories(weebly_auth=weebly_auth)\n if 'error' in rv: return rv\n changes = changes or ('changes' in rv and rv['changes'])\n if signal_change and changes: site.signal_change()\n return {'changes': changes}\n\n\ndef refresh_store_products(site, weebly_auth=None, signal_change=False):\n if not weebly_auth:\n weebly_auth = site.get_default_weebly_auth()\n logger.info(f'{site} - refreshing store products')\n path = f'/v1/user/sites/{site.site_id}/store/products'\n rv, response_json = weebly_auth.weebly_request_paginated(path, action_name='requesting store products')\n if 'error' in rv: return rv\n rv = update_list_of(\n site,\n response_json,\n 'store product',\n 'product_id', 'product_id',\n lambda: models.WeeblyStoreProduct(site=site),\n site.store_products,\n [\n ('name', 'name', unescape_func_not_null),\n ('url', 'url', None)\n ]\n )\n changes = 'changes' in rv and rv['changes']\n if signal_change and changes: site.signal_change()\n return rv\n\n\ndef refresh_store_product(product, weebly_auth):\n site = product.site\n logger.info(f'{site} - refreshing product {product.product_id}')\n path = f'/v1/user/sites/{site.site_id}/store/products/{product.product_id}'\n rv, resp_json = weebly_auth.weebly_request(path, action_name=f'requesting product info - {product.product_id}')\n if 'error' in rv: return rv\n properties_mapping = [\n ('name', 'name', unescape_func_not_null),\n ('url', 'url', None),\n ('short_description', 'description', None),\n ]\n save = update_object_from_data(product, properties_mapping, resp_json)\n if save:\n logger.info(f'{site} - saving product {product.product_id}')\n product.save()\n rv2 = refresh_product_options_from_data(product, resp_json['options'])\n if 'error' in rv2: return rv2\n rv['changes'] = save or ('changes' in rv2 and rv2['changes'])\n return rv\n\n\ndef refresh_product_options(product, weebly_auth):\n \"\"\"\n This function is not being used. Refreshing a product refreshes the options.\n Leaving it for now.\n \"\"\"\n site = product.site\n logger.info(f'{site} - refreshing product {product.product_id} - options')\n path = f'/v1/user/sites/{site.site_id}/store/products/{product.product_id}/options'\n rv, resp_json = weebly_auth.weebly_request(path, action_name='requesting product options')\n if 'error' in rv: return rv\n return refresh_product_options_from_data(product, resp_json)\n\n\ndef refresh_product_options_from_data(product, data):\n color_option_regex = re.compile('(.+)(<#.+>)')\n\n def choices_func(choices):\n new_choices = []\n for choice in choices:\n if choice.startswith('Text:'): continue\n match_color = re.search(color_option_regex, choice)\n if match_color:\n choice = match_color.group(1)\n new_choices.append(choice)\n return json.dumps(new_choices)\n\n return update_list_of(\n product.site, data,\n 'store product option',\n 'option_id', 'product_option_id',\n lambda: models.WeeblyStoreProductOption(product=product),\n product.options,\n [\n ('name', 'name', unescape_func_not_null),\n ('choice_order', 'choices', choices_func)\n ])\n\n\ndef refresh_store_categories(site, weebly_auth=None, signal_change=False):\n if not weebly_auth:\n weebly_auth = site.get_default_weebly_auth()\n logger.info(f'{site} - refreshing store categories')\n path = f'/v1/user/sites/{site.site_id}/store/categories'\n rv, response_json = weebly_auth.weebly_request_paginated(path, action_name='requesting store categories')\n if 'error' in rv: return rv\n\n loops = 0\n changes = False\n while True:\n missing_cats = []\n\n def parent_category_func(parent_category_id):\n if not parent_category_id: return None\n parent_category_id = int(parent_category_id)\n parent_cat = models.WeeblyStoreCategory.objects.filter(site=site, category_id=parent_category_id)\n if parent_cat.exists():\n return parent_cat.first()\n else:\n msg = f'{site} - reference to a category that doesn\\'t exist, probably defined later'\n logger.warning(msg)\n missing_cats.append(parent_category_id)\n return None\n\n site.refresh_from_db() # FIXME: lets see if this fixes everything :P\n rv = update_list_of(\n site,\n response_json,\n 'store category',\n 'category_id',\n 'category_id',\n lambda: models.WeeblyStoreCategory(site=site),\n site.store_categories,\n [\n ('name', 'name', unescape_func_not_null),\n ('parent_category_id', 'parent_category', parent_category_func)\n ]\n )\n if 'error' in rv: return rv\n changes = changes or ('changes' in rv and rv['changes'])\n loops += 1\n if not missing_cats: break\n if loops == 3:\n # FIXME: this really shouldnt happen, but it does, so need to understand what's going on\n logger.warning('already 3 times looping, breaking')\n send_email_to_admins('already 3 times looping', json.dumps(response_json))\n break\n if signal_change and changes: site.signal_change()\n return {'changes': changes}\n","repo_name":"newearthmartin/django-weebly","sub_path":"src/weebly/refresh.py","file_name":"refresh.py","file_ext":"py","file_size_in_byte":13147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"70387968699","text":"#!env/bin/python\nimport os\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nfrom crop_eye import find_eye\n\nDATA_PATH = \"./data/glasses-detection\"\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\ndef trainModel():\n\tIMG_SIZE = (4, 4)\n\tclass trainingCallback(tf.keras.callbacks.Callback):\n\t\tdef on_epoch_end(self, epoch, logs={}, threshold=0.99):\n\t\t\tif(logs.get('acc') >= threshold):\n\t\t\t\tself.model.stop_training = True\n\n\ttrain_glasses_dir = os.path.join(DATA_PATH, \"train/glasses\")\n\ttrain_regular_dir = os.path.join(DATA_PATH, \"train/regular\")\n\tvalidation_glasses_dir = os.path.join(DATA_PATH, \"validation/glasses\")\n\tvalidation_regular_dir = os.path.join(DATA_PATH, \"validation/regular\")\n\n\tcallbacks = trainingCallback()\n\n\tmodel = tf.keras.models.Sequential([\n\t\ttf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(4, 4, 3)),\n\t\ttf.keras.layers.MaxPooling2D(2, 2),\n\t\ttf.keras.layers.Flatten(), \n\t\ttf.keras.layers.Dense(128, activation='relu'),\n\t\ttf.keras.layers.Dense(1, activation='sigmoid')\n\t])\n\n\tmodel.summary()\n\n\tmodel.compile(loss='binary_crossentropy',\n\t\t\t\t optimizer=RMSprop(lr=0.001),\n\t\t\t\t metrics=['acc'])\n\n\t# preprocessing_function=find_eye,\n\ttrain_datagen = ImageDataGenerator(rescale=1/255)\n\tvalidation_datagen = ImageDataGenerator(rescale=1/255)\n\n\ttrain_generator = train_datagen.flow_from_directory(\n\t\tos.path.join(DATA_PATH, \"train\"),\n\t\ttarget_size=IMG_SIZE,\n\t\tbatch_size = 4, \n\t\tclass_mode='binary'\n\t)\n\n\tvalidation_generator = validation_datagen.flow_from_directory(\n\t\tos.path.join(DATA_PATH, \"validation\"),\n\t\ttarget_size=IMG_SIZE,\n\t\tbatch_size = 4, \n\t\tclass_mode='binary'\n\t)\n\n\thistory = model.fit(\n\t\ttrain_generator,\n\t\tepochs=10,\n\t\tcallbacks=[callbacks],\n\t\tverbose=1,\n\t\tvalidation_data = validation_generator,\n\t\tvalidation_steps = 2\n\t)\n\nif __name__ == \"__main__\":\n\tprint(\"Tensorflow version= \" + tf.__version__)\n\ttrainModel()\n","repo_name":"keving3ng/face-recog","sub_path":"src/glasses_detection.py","file_name":"glasses_detection.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"5831627292","text":"from __future__ import print_function\nfrom datetime import datetime\nimport requests\nimport xmltodict\nfrom googleapiclient.discovery import build\nfrom google.oauth2 import service_account\n\nfrom db_home.models import db, Order\n\n\ndef update_sheet(bag):\n \"\"\"\n Function for update from database to my google sheet\n REQUEST_METHOD = POST\n BODY = { }\n \"\"\"\n SERVICE_ACCOUNT_FILE = '../kanal.json'\n SCOPES = ['https://www.googleapis.com/auth/spreadsheets']\n creds = service_account.Credentials.from_service_account_file(\n SERVICE_ACCOUNT_FILE, scopes=SCOPES\n )\n\n # The ID of my spreadsheet.\n SAMPLE_SPREADSHEET_ID = '1WqF3fiyrt3sWPd_fRGVdDKk98P4q_6Y23TJu8xWBkfI'\n\n service = build('sheets', 'v4', credentials=creds)\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n orders = db.session.query(Order).all()\n for o in orders:\n data = [[o.id, o.order_id, o.amount, str(o.shipping_date), o.amount]]\n\n request = sheet.values().append(spreadsheetId=SAMPLE_SPREADSHEET_ID,\n range='Sheet1!A2:D2', valueInputOption='USER_ENTERED',\n insertDataOption='INSERT_ROWS',\n body={'values': data}).execute()\n\n\ndef save_data(d):\n \"\"\"\n Function for save data to Postgres database\n \"\"\"\n order = db.session.query(Order).filter(Order.order_id == d['заказ №']).first()\n if not order:\n o = Order()\n date = datetime.strptime(d['срок поставки'], '%d/%m/%Y')\n o.order_id = d['заказ №']\n o.cost = d['стоимость в $']\n o.shipping_date = date\n o.amount = d['стоимость в RUB']\n\n db.session.add(o)\n db.session.commit()\n\n\ndef get_currency(i):\n \"\"\"\n Function for get currency from currency API\n \"\"\"\n new_list = []\n\n date = datetime.strptime(i[3], '%d.%m.%Y')\n reformat_date = date.strftime('%d/%m/%Y') # formatting date because url not accepting 'divider'\n\n url = 'https://www.cbr.ru/scripts/XML_daily.asp'\n params = {\"date_req\": reformat_date}\n currencies = requests.get(url, params=params)\n parser = xmltodict.parse(currencies.text) # parsing xml to dict\n\n items = parser['ValCurs']['Valute'] if parser['ValCurs'] else []\n\n for k in items:\n if k.get('NumCode') == '840': # choosing USD by international currency iso code\n new_list.append({\n '№': int(i[0]),\n 'заказ №': i[1],\n 'стоимость в $': i[2],\n 'срок поставки': reformat_date,\n 'стоимость в RUB': float(i[2]) * float(k.get('Value').replace(\",\", \".\"))\n })\n\n for d in new_list:\n save_data(d)\n\n\ndef get_sheet(bag):\n \"\"\"\n Function for get a sheet from google account by sheet id\n REQUEST_METHOD = POST\n BODY = { }\n \"\"\"\n SERVICE_ACCOUNT_FILE = '../kanal.json'\n SCOPES = ['https://www.googleapis.com/auth/spreadsheets']\n creds = service_account.Credentials.from_service_account_file(\n SERVICE_ACCOUNT_FILE, scopes=SCOPES\n )\n\n # ID of a spreadsheet.\n SAMPLE_SPREADSHEET_ID = '1f-qZEX1k_3nj5cahOzntYAnvO4ignbyesVO7yuBdv_g'\n\n service = build('sheets', 'v4', credentials=creds)\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n result = sheet.values().get(spreadsheetId=SAMPLE_SPREADSHEET_ID,\n range='Лист1!A2:D51').execute()\n\n for i in result['values']:\n get_currency(i)\n","repo_name":"jetigenov/kanal","sub_path":"manager/service/import.py","file_name":"import.py","file_ext":"py","file_size_in_byte":3580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"18724140763","text":"from datetime import datetime, timedelta\nfrom users.models import user_reg\nfrom rest_framework import serializers\nfrom django.db import models\nfrom .models import *\n\n\nclass UserSerializer(serializers.ModelSerializer):\n class Meta:\n model = user_reg\n fields = ['id', 'groups']\n\n\nclass TournamentSerializer(serializers.ModelSerializer):\n total_player = models.IntegerField(null= False, blank= False)\n # match_type = serializers.SerializerMethodField()\n #\n # def get_match_type(self, object):\n # return object.match_type\n\n def validate_tournament_name(self, object):\n t1 = Tournament.objects.filter(tournament_name=object)\n if t1:\n raise serializers.ValidationError(\"tournament name must be unique\")\n return object\n\n def validate_Max_score(self, data):\n if data < 11:\n raise serializers.ValidationError(\"Max score must be more then 11\")\n return data\n\n def validate_start_date(self, data):\n if data < datetime.today().date():\n raise serializers.ValidationError(\"start date must be after today\")\n return data\n\n def validate_registration_end(self, data):\n startdate = self.initial_data.get('start_date')\n if startdate is None and self.instance:\n startdate = self.instance.start_date\n\n if startdate:\n if type(startdate) == str:\n startdate = datetime.strptime(startdate, '%Y-%m-%d').date()\n if startdate < data:\n raise serializers.ValidationError({\"registration_start\": \"start date must be after registration date\"})\n if data <= datetime.today().date():\n raise serializers.ValidationError(\"registration ends after today\")\n return data\n raise serializers.ValidationError(\"missing required data\")\n\n\n class Meta:\n model = Tournament\n fields = '__all__'\n\n def to_representation(self, instance):\n response = super().to_representation(instance)\n total = int(instance.total_players)\n for i in range(1, total):\n if pow(2, i) == total:\n enddate = instance.start_date + timedelta(days=i - 1)\n response['end_date'] = enddate\n instance.end_date = enddate\n instance.save()\n enddate = response['end_date']\n if instance.start_date > datetime.today().date():\n response['status'] = 'upcoming'\n elif enddate < datetime.today().date():\n response['status'] = \"previous\"\n else:\n response['status'] = \"ongoing\"\n response['match_type'] = response['match_type']\n return response\n\nclass MatchSerializer(serializers.ModelSerializer):\n player1_username = serializers.SerializerMethodField(read_only=True, required=False)\n player1_image = serializers.SerializerMethodField(read_only=True, required=False)\n\n player2_image = serializers.SerializerMethodField(read_only=True, required=False)\n player2_username = serializers.SerializerMethodField(read_only=True, required=False)\n\n winner_image = serializers.SerializerMethodField(read_only=True, required=False)\n winner_username = serializers.SerializerMethodField(read_only=True, required=False)\n\n status = serializers.SerializerMethodField(read_only=True, required=False)\n\n def get_player1_username(self, object):\n return object.player1.username\n\n def get_player1_image(self, object):\n return '/media/' + str(object.player1.image) if str(object.player1.image) else None\n\n def get_player2_username(self, object):\n return object.player2.username\n\n def get_player2_image(self, object):\n return '/media/' + str(object.player2.image) if str(object.player2.image) else None\n\n def get_winner_username(self, object):\n return object.winner.username if object.winner_id else None\n\n def get_winner_image(self, object):\n return '/media/' + str(object.winner.image) if object.winner_id and str(object.winner.image) else None\n\n def validate_game_date(self, data):\n if data < datetime.today().date():\n raise serializers.ValidationError(\"match date must be after today\")\n return data\n\n def get_status(self,object):\n if object.game_date > datetime.today().date():\n status = \"upcoming\"\n elif object.game_date == datetime.today().date():\n status = \"ongoing\"\n else:\n status = \"previous\"\n return status\n\n def validate(self, data):\n player = int(\n Tournament.objects.filter(tournament_name=data['tournament']).values('total_players')[0]['total_players'])\n a = pow(2, data['round_number'])\n player = pow(2, player)\n if a > player:\n raise serializers.ValidationError(\"invalid round number\")\n return data\n\n class Meta:\n model = MatchData\n fields = '__all__'\n\n\nclass SetSerializer(serializers.ModelSerializer):\n class Meta:\n model = SetData\n fields = '__all__'\n","repo_name":"gupta97p/ttapp","sub_path":"tournament/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":5059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"22383057322","text":"# from selenium.webdriver.chrome import webdriver\n#\n#\n# class Base_test():\n#\n# def initial(self):\n# self.driver = webdriver.Chrome()\n# self.driver.get(\"https://www.demoblaze.com/\")\n# self.driver.maximize_window()\n# return self.driver\n#\n# def window_close(self):\n# self.driver.quite()\n# self.driver.close()\n\nimport pytest\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.chrome.service import Service as ChromeService\nfrom selenium import webdriver\n\n\nclass Base_test:\n driver = None\n @pytest.fixture(autouse=True)\n def set_up(self):\n\n print(\"Initiating Chrome driver\")\n self.driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()))\n self.driver.get(\"https://www.demoblaze.com/index.html\")\n print(\"-----------------------------------------\")\n print(\"Test is started\")\n self.driver.implicitly_wait(10)\n self.driver.maximize_window()\n\n yield self.driver\n if self.driver is not None:\n print(\"-----------------------------------------\")\n print(\"Tests is finished\")\n self.driver.quit()","repo_name":"NigatuBeyene/Demoblaze_Auto_Selenium_Project","sub_path":"Web/Base_test/base_test.py","file_name":"base_test.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"36257095389","text":"import logging\nimport math\nfrom typing import Collection, Dict, FrozenSet, List, Mapping, Optional, Set, Union\n\nimport attr\nfrom sortedcontainers import SortedDict\n\nfrom synapse.util import caches\n\nlogger = logging.getLogger(__name__)\n\n# for now, assume all entities in the cache are strings\nEntityType = str\n\n\n@attr.s(auto_attribs=True, frozen=True, slots=True)\nclass AllEntitiesChangedResult:\n \"\"\"Return type of `get_all_entities_changed`.\n\n Callers must check that there was a cache hit, via `result.hit`, before\n using the entities in `result.entities`.\n\n This specifically does *not* implement helpers such as `__bool__` to ensure\n that callers do the correct checks.\n \"\"\"\n\n _entities: Optional[List[EntityType]]\n\n @property\n def hit(self) -> bool:\n return self._entities is not None\n\n @property\n def entities(self) -> List[EntityType]:\n assert self._entities is not None\n return self._entities\n\n\nclass StreamChangeCache:\n \"\"\"\n Keeps track of the stream positions of the latest change in a set of entities.\n\n The entity will is typically a room ID or user ID, but can be any string.\n\n Can be queried for whether a specific entity has changed after a stream position\n or for a list of changed entities after a stream position. See the individual\n methods for more information.\n\n Only tracks to a maximum cache size, any position earlier than the earliest\n known stream position must be treated as unknown.\n \"\"\"\n\n def __init__(\n self,\n name: str,\n current_stream_pos: int,\n max_size: int = 10000,\n prefilled_cache: Optional[Mapping[EntityType, int]] = None,\n ) -> None:\n self._original_max_size: int = max_size\n self._max_size = math.floor(max_size)\n\n # map from stream id to the set of entities which changed at that stream id.\n self._cache: SortedDict[int, Set[EntityType]] = SortedDict()\n # map from entity to the stream ID of the latest change for that entity.\n #\n # Must be kept in sync with _cache.\n self._entity_to_key: Dict[EntityType, int] = {}\n\n # the earliest stream_pos for which we can reliably answer\n # get_all_entities_changed. In other words, one less than the earliest\n # stream_pos for which we know _cache is valid.\n #\n self._earliest_known_stream_pos = current_stream_pos\n\n self.name = name\n self.metrics = caches.register_cache(\n \"cache\", self.name, self._cache, resize_callback=self.set_cache_factor\n )\n\n if prefilled_cache:\n for entity, stream_pos in prefilled_cache.items():\n self.entity_has_changed(entity, stream_pos)\n\n def set_cache_factor(self, factor: float) -> bool:\n \"\"\"\n Set the cache factor for this individual cache.\n\n This will trigger a resize if it changes, which may require evicting\n items from the cache.\n\n Returns:\n Whether the cache changed size or not.\n \"\"\"\n new_size = math.floor(self._original_max_size * factor)\n if new_size != self._max_size:\n self.max_size = new_size\n self._evict()\n return True\n return False\n\n def has_entity_changed(self, entity: EntityType, stream_pos: int) -> bool:\n \"\"\"\n Returns True if the entity may have been updated after stream_pos.\n\n Args:\n entity: The entity to check for changes.\n stream_pos: The stream position to check for changes after.\n\n Return:\n True if the entity may have been updated, this happens if:\n * The given stream position is at or earlier than the earliest\n known stream position.\n * The given stream position is earlier than the latest change for\n the entity.\n\n False otherwise:\n * The entity is unknown.\n * The given stream position is at or later than the latest change\n for the entity.\n \"\"\"\n assert isinstance(stream_pos, int)\n\n # _cache is not valid at or before the earliest known stream position, so\n # return that the entity has changed.\n if stream_pos <= self._earliest_known_stream_pos:\n self.metrics.inc_misses()\n return True\n\n # If the entity is unknown, it hasn't changed.\n latest_entity_change_pos = self._entity_to_key.get(entity, None)\n if latest_entity_change_pos is None:\n self.metrics.inc_hits()\n return False\n\n # This is a known entity, return true if the stream position is earlier\n # than the last change.\n if stream_pos < latest_entity_change_pos:\n self.metrics.inc_misses()\n return True\n\n # Otherwise, the stream position is after the latest change: return false.\n self.metrics.inc_hits()\n return False\n\n def get_entities_changed(\n self, entities: Collection[EntityType], stream_pos: int\n ) -> Union[Set[EntityType], FrozenSet[EntityType]]:\n \"\"\"\n Returns the subset of the given entities that have had changes after the given position.\n\n Entities unknown to the cache will be returned.\n\n If the position is too old it will just return the given list.\n\n Args:\n entities: Entities to check for changes.\n stream_pos: The stream position to check for changes after.\n\n Return:\n A subset of entities which have changed after the given stream position.\n\n This will be all entities if the given stream position is at or earlier\n than the earliest known stream position.\n \"\"\"\n cache_result = self.get_all_entities_changed(stream_pos)\n if cache_result.hit:\n # We now do an intersection, trying to do so in the most efficient\n # way possible (some of these sets are *large*). First check in the\n # given iterable is already a set that we can reuse, otherwise we\n # create a set of the *smallest* of the two iterables and call\n # `intersection(..)` on it (this can be twice as fast as the reverse).\n if isinstance(entities, (set, frozenset)):\n result = entities.intersection(cache_result.entities)\n elif len(cache_result.entities) < len(entities):\n result = set(cache_result.entities).intersection(entities)\n else:\n result = set(entities).intersection(cache_result.entities)\n self.metrics.inc_hits()\n else:\n result = set(entities)\n self.metrics.inc_misses()\n\n return result\n\n def has_any_entity_changed(self, stream_pos: int) -> bool:\n \"\"\"\n Returns true if any entity has changed after the given stream position.\n\n Args:\n stream_pos: The stream position to check for changes after.\n\n Return:\n True if any entity has changed after the given stream position or\n if the given stream position is at or earlier than the earliest\n known stream position.\n\n False otherwise.\n \"\"\"\n assert isinstance(stream_pos, int)\n\n # _cache is not valid at or before the earliest known stream position, so\n # return that an entity has changed.\n if stream_pos <= self._earliest_known_stream_pos:\n self.metrics.inc_misses()\n return True\n\n # If the cache is empty, nothing can have changed.\n if not self._cache:\n self.metrics.inc_misses()\n return False\n\n self.metrics.inc_hits()\n return stream_pos < self._cache.peekitem()[0]\n\n def get_all_entities_changed(self, stream_pos: int) -> AllEntitiesChangedResult:\n \"\"\"\n Returns all entities that have had changes after the given position.\n\n If the stream change cache does not go far enough back, i.e. the\n position is too old, it will return None.\n\n Returns the entities in the order that they were changed.\n\n Args:\n stream_pos: The stream position to check for changes after.\n\n Return:\n A class indicating if we have the requested data cached, and if so\n includes the entities in the order they were changed.\n \"\"\"\n assert isinstance(stream_pos, int)\n\n # _cache is not valid at or before the earliest known stream position, so\n # return None to mark that it is unknown if an entity has changed.\n if stream_pos <= self._earliest_known_stream_pos:\n return AllEntitiesChangedResult(None)\n\n changed_entities: List[EntityType] = []\n\n for k in self._cache.islice(start=self._cache.bisect_right(stream_pos)):\n changed_entities.extend(self._cache[k])\n return AllEntitiesChangedResult(changed_entities)\n\n def entity_has_changed(self, entity: EntityType, stream_pos: int) -> None:\n \"\"\"\n Informs the cache that the entity has been changed at the given position.\n\n Args:\n entity: The entity to mark as changed.\n stream_pos: The stream position to update the entity to.\n \"\"\"\n assert isinstance(stream_pos, int)\n\n # For a change before _cache is valid (e.g. at or before the earliest known\n # stream position) there's nothing to do.\n if stream_pos <= self._earliest_known_stream_pos:\n return\n\n old_pos = self._entity_to_key.get(entity, None)\n if old_pos is not None:\n if old_pos >= stream_pos:\n # nothing to do\n return\n e = self._cache[old_pos]\n e.remove(entity)\n if not e:\n # cache at this point is now empty\n del self._cache[old_pos]\n\n e1 = self._cache.get(stream_pos)\n if e1 is None:\n e1 = self._cache[stream_pos] = set()\n e1.add(entity)\n self._entity_to_key[entity] = stream_pos\n self._evict()\n\n def _evict(self) -> None:\n \"\"\"\n Ensure the cache has not exceeded the maximum size.\n\n Evicts entries until it is at the maximum size.\n \"\"\"\n # if the cache is too big, remove entries\n while len(self._cache) > self._max_size:\n k, r = self._cache.popitem(0)\n self._earliest_known_stream_pos = max(k, self._earliest_known_stream_pos)\n for entity in r:\n self._entity_to_key.pop(entity, None)\n\n def get_max_pos_of_last_change(self, entity: EntityType) -> int:\n \"\"\"Returns an upper bound of the stream id of the last change to an\n entity.\n\n Args:\n entity: The entity to check.\n\n Return:\n The stream position of the latest change for the given entity or\n the earliest known stream position if the entitiy is unknown.\n \"\"\"\n return self._entity_to_key.get(entity, self._earliest_known_stream_pos)\n","repo_name":"matrix-org/synapse","sub_path":"synapse/util/caches/stream_change_cache.py","file_name":"stream_change_cache.py","file_ext":"py","file_size_in_byte":11038,"program_lang":"python","lang":"en","doc_type":"code","stars":11638,"dataset":"github-code","pt":"23"}
+{"seq_id":"42243768483","text":"#Python program to performa a global search over the whole input string using re.findall() method.\n#matchList = re.findall(pattern, input_str, flags=0)\n\nimport re\n#Regex to match a few date strings\nregex = r'[a-zA-Z]+ \\d+'\nmatches = re.findall(regex,'October 02, September 23, Dec 1')\nfor match in matches:\n\tprint('Full match: %s' %(match))\n\t\n#To capture specific monthg of each date we can use the following pattern\nregex1 = r'[a-zA-Z]+ \\d+'\nmatches1 = re.findall(regex1,'October 02, September 23, Dec 1')\nfor match in matches1:\n\tprint('Match Month: %s'%(match))\n\t\n#if we need the exact position of each match\n\t\nregex2 = r'[a-zA-Z]+ \\d+'\nmatches2 = re.finditer(regex2,'October 02, September 23, Dec 1')\nfor match in matches2:\n\tprint('Match is at index: %s,%s' %(match.start(),match.end()))","repo_name":"kamit17/Python","sub_path":"AutomateTheBoringStuff/RegularExpressions/capturinggroups.py","file_name":"capturinggroups.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"32361682182","text":"import argparse, logging, re, smtplib, configparser, sys\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\ndef level_count(read_FH):\n '''\n Method will Extract all the valid IP Addresses and returns it's count\n return: Dictionary\n '''\n ip_count = {}\n for line in read_FH:\n match = re.search(\n r'(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)',\n line)\n if match:\n if match.group() in ip_count:\n ip_count[match.group()] += 1\n else:\n ip_count[match.group()] = 1\n return ip_count\n\ndef config_reader(filepath = None):\n '''\n Method parses config file, return dictionary of all the sections\n return: dictionary\n '''\n dict_option = {}\n config = configparser.ConfigParser()\n config.read(filepath)\n sections = config.sections()\n options = config.options(sections[0])\n for option in options:\n try:\n dict_option[option] = config.get(sections[0], option)\n if dict_option[option] == -1:\n logger.error(\"Please check Threshold Configuration File\")\n except:\n dict_option[option] = None\n return dict_option\n\ndef send_mail(user, password, to_list, subject, body):\n '''\n Method sends mail to List of users using GMAIL credentials\n '''\n msg = MIMEMultipart()\n msg['From'] = user\n msg['To'] = \",\".join(to_list)\n msg['Subject'] = subject\n\n msg_body = MIMEText(body, 'html')\n msg.attach(msg_body)\n\n try:\n server = smtplib.SMTP('smtp.gmail.com', 587)\n server.ehlo()\n server.starttls()\n server.ehlo()\n server.login(user, password)\n server.sendmail(user, \",\".join(to_list), msg.as_string())\n server.quit()\n logger.info(\"Email Sent!!\")\n except Exception as e:\n logger.error(\"Error: {}\".format(e))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n requiredNamed = parser.add_argument_group('Required Named arguments')\n requiredNamed.add_argument('-e', '--email', dest='email_id', required=True,\n type=str, nargs='+')\n args = parser.parse_args()\n\n # Logger Configuration\n logger = logging.getLogger()\n formatter = logging.Formatter('%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')\n fileHandler = logging.FileHandler(filename='app.log') # Relative Path\n fileHandler.setFormatter(formatter)\n logger.setLevel(level=logging.DEBUG) # Logging Level\n\n streamHandler = logging.StreamHandler()\n streamHandler.setFormatter(formatter)\n\n logger.addHandler(fileHandler)\n logger.addHandler(streamHandler)\n\n # read Gmail UserName and Password from config File\n dict_config = config_reader('config.ini') # Relative Path\n\n ip_count = {}\n try:\n with open('access_log.txt') as infile: #Relative Path\n ip_count = level_count(infile)\n except Exception as e:\n logger.error(\"File Exception {}\".format(e))\n sys.exit(1)\n\n logger.debug(\"IP Count: {}\".format(ip_count))\n msg_body = '''\\\n <html>\n <head></head>\n <body>\n <p>Hey {},<br>\n <br>\n Following is the Log Message Count:<br>\\\n '''.format(\",\".join(args.email_id))\n for key in ip_count:\n msg_body += \"<b>\"+key+\" -> \"+str(ip_count[key])+\"<br></b>\"\n\n msg_body += '''\\\n </p>\n </body>\n </html>\n '''\n logger.debug(\"Message Body {}\".format(msg_body))\n send_mail(dict_config['gmail_user'], dict_config['gmail_password'], args.email_id,\n 'Apache Log Parser Results', msg_body)","repo_name":"codingnest/ApacheLogParser","sub_path":"apachelog_parser.py","file_name":"apachelog_parser.py","file_ext":"py","file_size_in_byte":3829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"16097024975","text":"import numpy as np\nimport matplotlib.pylab as plt\n\n# Funcion lambda para hacer la norma 1 a una matriz\nnorma_1 = lambda V, sum: np.matrix([x.item(0)/sum for x in V]).transpose()\n\ndef plot(X, name=\"ColorBar\"):\n fig = plt.figure(figsize=(6, 6))\n ax = fig.add_subplot(111)\n ax.set_title(name)\n ax.imshow(X, interpolation='nearest', cmap=plt.cm.ocean,\n extent=(0.5,np.shape(X)[0]+0.5,0.5,np.shape(X)[1]+0.5))\n ax.set_aspect('equal')\n\ndef create_B(M, size):\n \"\"\"\n Coge una matriz y su tamaño en columnas\n y crea la matriz B.\n \"\"\"\n new = []\n n_m = 0\n for i in range(size):\n for j in range(i+1, size):\n new.append([0 for _ in range(size)])\n new[n_m][i] = -1\n new[n_m][j] = M.item(i,j)\n n_m += 1\n new.append([1 for _ in range(size)])\n return np.matrix(new)\n\ndef create_W(V):\n \"\"\"\n Coge un vector y crea la matriz W a partir de ese vector.\n \"\"\"\n new = []\n size = np.size(V,0)\n for i in range(size):\n new.append([0 for _ in range(size)])\n for j in range(size):\n new[i][j] = V.item(i) / V.item(j)\n return np.matrix(new)\n\ndef res_min_cuad_ponderada(M):\n \"\"\"\n aplica los minimos cruadrados a una matriz.\n \"\"\"\n M = np.matrix(M, np.float)\n\n size = np.size(M,0)\n B = create_B(M, size)\n print(\"Matriz Original:\")\n print(M)\n print(\"-\"*20)\n print(\"Sistema de ecuaciones lineal B:\")\n print(B)\n print(\"-\"*20)\n\n b = np.matrix([0 for _ in range(np.size(B,0)-1)] + [1])\n b = b.transpose()\n print(\"b:\")\n print(b)\n print(\"-\"*20)\n\n V,residuals_auto,A,_ = np.linalg.lstsq(B,b)\n V = norma_1(V,V.sum())\n residuals_auto = residuals_auto.item(0)\n\n print(\"Vector de pesos normalizado V:\")\n print(V)\n print(\"-\"*20)\n\n W = create_W(V)\n print(\"Matriz W creada a partir de V:\")\n print(W)\n print(\"-\"*20)\n\n diff = M - W\n plot(M, \"M\")\n plot(W, \"W\")\n\n print(\"Matriz diferencia (M-W)\")\n print(diff)\n print(\"-\"*20)\n suma = sum([abs(x) for y in diff.tolist() for x in y])\n print(\"Suma de los valores de la matriz diferencia\")\n print(suma)\n print(\"-\"*20)\n return V\n\n\nM2 = [[1,2/3,2,1/2],[3/2,1,3,3/4],[1/2,1/3,1,1/4],[2,4/3,4,1]]\n\n\nprint(\"DATOS PARA M2\")\nV = res_min_cuad_ponderada(M2)\n\n\n\nplt.show()\n","repo_name":"rockneurotiko/rockneurotiko.github.io","sub_path":"Universidad/AplicacionesNumericas/aplNum3.py","file_name":"aplNum3.py","file_ext":"py","file_size_in_byte":2315,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"23"}
+{"seq_id":"5380118857","text":"# %%\n\nimport replicate\nimport os\n\n# %%\nos.environ[\"REPLICATE_API_TOKEN\"] = \"cdfdd32a0bd5e1b2df1f9fb6768c6ad3eb16ad41\"\n\n\n# %%\ndef stylize(image_path=\"static/output.png\", prompt: str = \"aestheic\"):\n print(\"Stylizing image with prompt:\", prompt)\n output = replicate.run(\n \"rossjillian/controlnet:d55b9f2dcfb156089686b8f767776d5b61b007187a4e1e611881818098100fbb\",\n input={\n \"image\": open(image_path, \"rb\"),\n \"prompt\": prompt,\n \"structure\": \"canny\",\n },\n )\n print(\"Stylized image:\", output[0])\n\n return output[0]\n\n\n# %%\nif __name__ == \"__main__\":\n stylize(prompt=\"aesthetic solar system\")\n\n# %%\n","repo_name":"viksit/placegpt","sub_path":"placegpt/replicate_api.py","file_name":"replicate_api.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"42313439487","text":"import json_numpy\njson_numpy.patch()\nimport json\n\nimport gym\n\nloads = json.loads\n\ndef deserialize_space(space_def):\n if space_def['type'] == 'Discrete':\n return gym.spaces.Discrete(space_def['n'])\n elif space_def['type'] == 'Box':\n return gym.spaces.Box(\n low=space_def['low'],\n high=space_def['high'],\n dtype=space_def['dtype']\n )\n elif space_def['type'] == 'Dict':\n return gym.spaces.Dict({k: deserialize_space(v) for k, v in space_def['spaces'].items()})\n else:\n raise ValueError(f'Unknown space type: {space_def[\"type\"]}')\n\ndef serialize_space(space):\n if isinstance(space, gym.spaces.Discrete):\n return {\n 'type': 'Discrete',\n 'n': space.n\n }\n elif isinstance(space, gym.spaces.Box):\n return {\n 'type': 'Box',\n 'low': space.low,\n 'high': space.high,\n 'dtype': str(space.dtype)\n }\n elif isinstance(space, gym.spaces.Dict):\n return {\n 'type': 'Dict',\n 'spaces': {k: serialize_space(v) for k, v in space.spaces.items()}\n }\n else:\n raise ValueError(f'Unknown space type: {type(space)}')","repo_name":"dibyaghosh/remote_gym","sub_path":"remote_gym/serialization.py","file_name":"serialization.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"36719232006","text":"from clearml import PipelineController\nfrom config import AppConfig\n\n\ndef main(config: AppConfig):\n\n pipe = PipelineController(\n name=config.task_name, project=config.project, version=config.version\n )\n\n pipe.set_default_execution_queue(\"default\")\n\n pipe.add_step(\n name=\"preparation_step\",\n base_task_project=config.project,\n base_task_name=config.task_name_preparation,\n )\n pipe.add_step(\n name=\"training_step\",\n parents=[\n \"preparation_step\",\n ],\n base_task_project=config.project,\n base_task_name=config.task_name_train,\n parameter_override={\n \"General/dataset_id\": \"${preparation_step.parameters.General/output_dataset_id}\"\n },\n )\n pipe.start_locally(run_pipeline_steps_locally=True)\n\n\nif __name__ == \"__main__\":\n config = config = AppConfig.parse_raw()\n main(config=config)\n","repo_name":"efffna/mlops_itmo","sub_path":"clear_ml/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"2268063150","text":"from datetime import datetime, timedelta\nfrom decimal import Decimal\n\n\nDATE_FORMAT = '%Y-%m-%d'\n\n\nDATETIME_FORMAT = DATE_FORMAT + 'T%H:%M:%S'\nDATETIME_FORMAT_MICRO = DATETIME_FORMAT + '.%f'\n\n\nDURATION_FORMAT = 'PT%02dH%02dM%02dS'\n\n\n\nclass Boolean(object):\n\n @staticmethod\n def decode(data):\n if data == 'true':\n return True\n elif data == 'false':\n return False\n raise ValueError('boolean \"%s\" is invalid' % data)\n\n\n @staticmethod\n def encode(value):\n if value is True or str(value).lower() == \"true\":\n return \"true\"\n elif value is False or str(value).lower() == \"false\":\n return \"false\"\n raise TypeError('\"%s\" is not a boolean' % value)\n\n\n\nclass Date(object):\n\n @staticmethod\n def decode(data):\n return datetime.strptime(data, DATE_FORMAT)\n\n\n @staticmethod\n def encode(value):\n return value.strftime(DATE_FORMAT)\n\n\n\nclass DateTime(object):\n\n @staticmethod\n def decode(data):\n # XXX \"Z\" means a UTC datetime, convert it ??\n # Cf http://en.wikipedia.org/wiki/ISO_8601\n if data.endswith('Z'):\n data = data[:-1]\n try:\n return datetime.strptime(data, DATETIME_FORMAT_MICRO)\n except ValueError:\n return datetime.strptime(data, DATETIME_FORMAT)\n\n\n @staticmethod\n def encode(value):\n return value.strftime(DATETIME_FORMAT)\n\n\n\nclass Duration(object):\n \"\"\"ISO 8601 format.\n \"\"\"\n\n @staticmethod\n def decode(data):\n if data.startswith('P'):\n sign = 1\n elif data.startswith('-P'):\n sign = -1\n else:\n raise ValueError(\"duration not valid\")\n\n days = 0\n hours = 0\n minutes = 0\n seconds = 0\n\n buffer = ''\n for c in data:\n if c.isdigit():\n buffer += c\n elif c == 'D':\n days = int(buffer)\n buffer = ''\n elif c == 'H':\n hours = int(buffer)\n buffer = ''\n elif c == 'M':\n minutes = int(buffer)\n buffer = ''\n elif c == 'S':\n seconds = int(buffer)\n buffer = ''\n break\n if buffer != '':\n raise ValueError(\"duration not valid\")\n\n return timedelta(days=sign*days,\n hours=sign*hours,\n minutes=sign*minutes,\n seconds=sign*seconds)\n\n\n @staticmethod\n def encode(value):\n if type(value) is not timedelta:\n raise TypeError(\"duration must be a timedelta\")\n\n days = value.days\n if days < 0:\n microseconds = -((days * 24 * 60 *60 + value.seconds) * 1000000 +\n value.microseconds)\n sign = '-'\n else:\n microseconds = ((days * 24 * 60 *60 + value.seconds) * 1000000 +\n value.microseconds)\n sign = ''\n\n hours = microseconds / ( 60 * 60 * 1000000)\n microseconds %= 60 * 60 * 1000000\n\n minutes = microseconds / ( 60 * 1000000)\n microseconds %= 60 * 1000000\n\n seconds = microseconds / 1000000\n\n return sign + DURATION_FORMAT % (hours, minutes, seconds)\n\n\n# I chose not to inherit from Decimal to avoid operations on different units\nclass Unit(object):\n\n def __init__(self, value, unit='cm'):\n if isinstance(value, basestring):\n digits = []\n nondigits = []\n for char in value:\n if char.isdigit() or char == '.':\n digits.append(char)\n else:\n nondigits.append(char)\n value = ''.join(digits)\n if nondigits:\n unit = ''.join(nondigits)\n elif isinstance(value, float):\n value = str(value)\n self.value = Decimal(value)\n self.unit = unit\n\n\n def __str__(self):\n return str(self.value) + self.unit\n\n\n def __repr__(self):\n return \"%s %s\" % (object.__repr__(self), self)\n\n\n def __cmp__(self, other):\n if type(other) is not type(self):\n raise ValueError (\"can only compare Unit\")\n if self.unit != other.unit:\n raise NotImplementedError(\"no conversion yet\")\n return cmp(self.value, other.value)\n\n\n def convert(self, unit, dpi=72):\n if unit == 'px':\n if self.unit == 'in':\n return Unit(int(self.value * dpi), 'px')\n elif self.unit == 'cm':\n return Unit(int(self.value / Decimal('2.54') * dpi), 'px')\n raise NotImplementedError(\"%s\" % self.unit)\n raise NotImplementedError(\"%s\" % unit)\n","repo_name":"lpod/lpod-python","sub_path":"lpod/datatype.py","file_name":"datatype.py","file_ext":"py","file_size_in_byte":4760,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"23"}
+{"seq_id":"11181386139","text":"log = 10\nimport binary, common, ichilog\nreload(binary)\n\nPackedStruct = binary.PackedStruct\ncheck_args = common.check_args\nInvariant = common.Invariant\nInvariantCheck = common.InvariantCheck\ngetlogger = ichilog.getlogger \n\n__all__ = ['log', 'PackedStruct', 'check_args', 'getlogger', 'ichilog', 'Invariant', 'InvariantCheck']\n","repo_name":"Falaina/Ichiko","sub_path":"ichiko/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"9183439919","text":"#stack structure => based on list\n# 'last in first out'\nimport csv\nclass Stack:\n _stack = []\n def push(self,item):\n self._stack.append(item)\n def pop(self):\n self._stack.pop()\n def show(self):\n print(self._stack)\n# stack gives us => \"last in , first out\"\n# play essential rule in computer => call stack\nBooks = Stack()\n#get list of books\nwith open(\"books.csv\",\"r\",newline='') as book_list:\n reader = csv.reader(book_list,delimiter=',')\n for row in reader:\n str = row[0] + \" by \" +row[1].replace(',',' ')\n Books.push(str)\n\n# Books.show()\n# Books.pop()\n# Books.pop()\n# Books.show()\n# Books.push('Idiot, The by Dostoevsky Fyodor')\n\n# Queues\n# it can come handy for handling a program for orders eg\n# it comes handy when handling Buffer\nclass Queue:\n _queue = []\n def push(self,item):\n self._queue.insert(len(self._queue),item)\n def pop(self):\n self._queue.pop(0)\n def isEmpty(self):\n return (len(self._queue)) == 0\n\n# =============================================================================\n############################## Dictionary #####################################\n# Hash table Maps a large of data values into smaller number of indices (called slots) \n# It uses chaining which consists of maling a list of everything in a particular slot \n# Hash function : function to convert a key phrase into a number to index into an array (list)\n# =============================================================================\nprepare_data = []\nbooks_dict = dict()\nwith open(\"books_new.csv\",\"r\",newline='') as file:\n new_reader = csv.reader(file,delimiter=',')\n for row in new_reader:\n prepare_data.append(row)\n\ntitles = []\nauthors = []\ngenres = []\nsubgenres = []\nheights = []\npublishers = []\nfor i in range(1,len(prepare_data)-1,1):\n titles.append(prepare_data[i][0])\n authors.append(prepare_data[i][1])\n genres.append(prepare_data[i][2])\n subgenres.append(prepare_data[i][3])\n heights.append(prepare_data[i][4])\n publishers.append(prepare_data[i][5])\n books_dict['Title'] = titles\n books_dict['Author'] = authors\n books_dict['Genres'] = genres\n books_dict['Subgenres'] = subgenres\n books_dict['Heights'] = heights\n books_dict['Publishers'] = publishers\n \n\n# print(books_dict)\n# #del books_dict['Title'] #used to delete a key with it is values\n# print('Title' in books_dict)\n# =============================================================================\n# SET : Collection of items stored using a hash-table does mathematical\n# operations checks membership quickly\n# =============================================================================\ningredients = set(['flour','sugar','eggs','milk'])\ningredient_1 = set(['salt','chicken','eggs','sauce'])\n\nif 'milk' in ingredients:\n print('it exists')\ningredients.add('apple')\nprint(ingredients)\ningredients.remove('apple')\nprint(ingredients)\nprint(ingredients - ingredient_1)\nprint(ingredients | ingredient_1)\nprint(ingredients & ingredient_1)\nprint(ingredients ^ ingredient_1) #exclusive or \n","repo_name":"saidworks/TTC","sub_path":"chapter19_data_structure/playground.py","file_name":"playground.py","file_ext":"py","file_size_in_byte":3080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"5593371298","text":"n_people = int(input('Введите количество человек: '))\nd_family = dict()\nd_height = dict()\nfor i in range(n_people - 1):\n print(i + 1, 'пара:', end='')\n child, parent = input().split()\n d_family[child] = parent\nfor parent in d_family.values():\n if parent not in d_family.keys():\n d_height[parent] = 0\nfor child in d_family.keys():\n d_height[child] = d_height[d_family[child]] + 1\nprint('\"Высота\" каждого члена семьи:')\n[print(key, value) for key, value in sorted(d_height.items())]\n\n\n\n# Введите количество человек: 9\n# 1 пара: Alexei Peter_I\n# 2 пара: Anna Peter_I\n# 3 пара: Elizabeth Peter_I\n# 4 пара: Peter_II Alexei\n# 5 пара: Peter_III Anna\n# 6 пара: Paul_I Peter_III\n# 7 пара: Alexander_I Paul_I\n# 8 пара: Nicholaus_I Paul_I\n#\n# “Высота” каждого члена семьи:\n# Alexander_I 4\n# Alexei 1\n# Anna 1\n# Elizabeth 1\n# Nicholaus_I 4\n# Paul_I 3\n# Peter_I 0\n# Peter_II 2\n# Peter_III 2\n","repo_name":"RudyKhabib/Python","sub_path":"12. Dictionaries/12.09.py","file_name":"12.09.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"37862858782","text":"from utils import *\nimport visualization as visual\nclass KUKA_KR10_R1100_2:\n def __init__(self, T_base=None, T_tool=None):\n self.links_dimensions = KUKA_KR10_R1100_2_configs.get_links_dimensions()\n self.joint_limits = KUKA_KR10_R1100_2_configs.get_joints_limits()\n # self.zero_config = []\n self.T_base = translation_x(0) if T_base is None else T_base\n self.T_tool = translation_x(0) if T_tool is None else T_tool\n\n\n def print_frame(self, f):\n print(f\"Homogeneous Matrix:\\n{f}\")\n print(\"Rotation:\\n\", get_rotation(f))\n print(\"Position:\\n\", get_position(f)) \n\n def print_frames(self, frames):\n print(f\"Note: Frame #{len(frames)-1} -> Tool\")\n for i, f in enumerate(frames):\n print(f\"Frame #{i}:\")\n if(i == len(frames) - 1):\n print(f\"--- Tool Frame ---\")\n else:\n print(f\"--- Joint #{i} Frame ---\")\n self.print_frame(f)\n print(\"-----------\")\n\n def print_angles(self, q):\n for i, angle in enumerate(q):\n print(f\"Joint #{i+1}: {angle} rad ---> {angle*180/np.pi} degrees\")\n\n def plot_robot(self, T):\n vis = visual.RobotVisualization_vpython(rate=10, scale=0.0002, radius={\"link\":0.007, \"joint\":0.008, \"node\":0.01, \"axe\":0.003})\n frame = []\n links = []\n joints = []\n node = get_position(T[-1])\n for i in range(1,len(T)):\n links.append((get_position(T[i-1]), get_position(T[i])))\n joints.append(get_position(T[i]))\n for i, l in enumerate(links):\n # i == (shift_link-1)\n if(i == 0): # because of the physical shift without link (if I was using DH it would be much easier)\n p1 = l[0]\n p2 = l[1]\n # print(p2)\n p1_1 = p1.copy()\n p2_1 = p2.copy()\n p2_2 = p2.copy()\n p2_1[0] = 0.0\n p1_2 = p2_1.copy()\n frame.append([\"link\", p1_1, p2_1])\n frame.append([\"link\", p1_2, p2_2])\n continue\n frame.append([\"link\", l[0], l[1]])\n for j in joints:\n frame.append([\"joint\", j])\n frame.append([\"node\", node])\n frame.append([\"time\", 0, 0])\n # print(frame)\n while True:\n vis.render_frame(frame, axis=False)\n\n def plot_robot_multi_frames(self, Ts):\n while True:\n for idx, T in enumerate(Ts):\n # print(T)\n vis = visual.RobotVisualization_vpython(rate=1, scale=0.0002, radius={\"link\":0.007, \"joint\":0.008, \"node\":0.01, \"axe\":0.003})\n frame = []\n links = []\n joints = []\n node = get_position(T[-1])\n for i in range(1,len(T)):\n links.append((get_position(T[i-1]), get_position(T[i])))\n joints.append(get_position(T[i]))\n for i, l in enumerate(links):\n # i == (shift_link-1)\n if(i == 0): # because of the physical shift without link (if I was using DH it would be much easier)\n p1 = l[0]\n p2 = l[1]\n # print(p2)\n p1_1 = p1.copy()\n p2_1 = p2.copy()\n p2_2 = p2.copy()\n p2_1[0] = 0.0\n p1_2 = p2_1.copy()\n frame.append([\"link\", p1_1, p2_1])\n frame.append([\"link\", p1_2, p2_2])\n continue\n frame.append([\"link\", l[0], l[1]])\n for j in joints:\n frame.append([\"joint\", j])\n frame.append([\"node\", node])\n frame.append([\"time\", idx, 0])\n # print(frame)\n vis.render_frame(frame, axis=False)\n\n def forward_kinematics(self, q, plot=True, debug=True, return_all=False):\n from FK import FK\n T = FK(q, T_base=self.T_base, T_tool=self.T_tool, return_frames=(plot or debug or return_all))\n \n if(debug == True):\n self.print_frames(T) # just print the result in a good way\n if(plot == True):\n self.plot_robot(T) # plot the result\n if(return_all == True):\n return T\n # We need only to return the end-effector\n if(not(plot or debug) == True):\n # print(T) # only end_effector\n return T\n return T[-1] # end_effector\n\n\n def inverse_kinematics(self, T, m=-1, plot=True, debug=True):\n from IK import IK\n\n q, status = IK(T, T_base=self.T_base, T_tool=self.T_tool, m=m, debug=True)\n\n if(plot == True):\n self.plot_robot(T) # plot the result\n if(debug == True):\n # self.print_frame(T) # just print the result in a good way\n self.print_angles(q)\n print(status)\n\n if(not debug == True):\n # print(q)\n return q\n\n return q\n\nclass KUKA_KR10_R1100_2_configs:\n @staticmethod\n def get_links_dimensions():\n return [400,25,560,25,515,90]\n\n @staticmethod\n def get_joints_limits():\n deg = [(-170, 170), (-190, 45), (-120, 156), (-185, 185), (-120, 120), (-350, 350)]\n rad = []\n for (i,j) in deg:\n rad.append(((i*np.pi/180), (j*np.pi/180)))\n return rad\n\nif __name__ == \"__main__\":\n # print(KUKA_KR10_R1100_2_configs.get_joints_limits())\n robot = KUKA_KR10_R1100_2()\n q = np.zeros((6,))\n # q[1] = -np.pi/4\n # q = [0, np.pi/4, 0, 1, 1, 0]\n # T = robot.forward_kinematics(q, plot=False, return_all=True)\n # # robot.print_frame(T)\n # q_calc = robot.inverse_kinematics(T, plot=False)\n # T_calc = robot.forward_kinematics(q_calc, debug=False)\n\n # print(q, q_calc)\n # robot.print_frame(T)\n # robot.print_frame(T_calc)\n\n # print(f\"Error using FK then IK then FK -> {calc_error(T, T_calc)}\")","repo_name":"hany606/Introduction-to-Manipulators-FoR_Fall20IU","sub_path":"assignment1_kinematics/src/robot.py","file_name":"robot.py","file_ext":"py","file_size_in_byte":6046,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"23"}
+{"seq_id":"72116540540","text":"from functools import partial\nfrom typing import Any, Generator\n\nfrom . import _mp4v2\n\n\nclass Mp4Handle(object):\n \"\"\"Wraps the mp4v2 mp4 file handle object.\"\"\"\n\n def __init__(self, filename: str):\n \"\"\"Initialize and create partial functions.\"\"\"\n self._mp4_handle = _mp4v2.MP4FileHandle(_mp4v2.MP4Read(filename, 0))\n\n self.track_read_sample = partial(\n _mp4v2.MP4ReadSample,\n self._mp4_handle\n )\n self.track_es_configuration = partial(\n _mp4v2.MP4GetTrackESConfiguration,\n self._mp4_handle\n )\n self.track_type = partial(_mp4v2.MP4GetTrackType, self._mp4_handle)\n self.track_esds_object_type = partial(\n _mp4v2.MP4GetTrackEsdsObjectTypeId,\n self._mp4_handle\n )\n self.track_audio_mpeg4_type = partial(\n _mp4v2.MP4GetTrackAudioMpeg4Type,\n self._mp4_handle\n )\n self.track_sample_count = partial(\n _mp4v2.MP4GetTrackNumberOfSamples,\n self._mp4_handle\n )\n self.track_rate = partial(_mp4v2.MP4GetTrackBitRate, self._mp4_handle)\n self.track_name = partial(_mp4v2.MP4GetTrackName, self._mp4_handle)\n\n self._closed = False\n\n self._number_of_tracks = _mp4v2.MP4GetNumberOfTracks(\n self._mp4_handle,\n None,\n 0\n )\n self._tags = _mp4v2.MP4TagsAlloc()\n _mp4v2.MP4TagsFetch(self._tags, self._mp4_handle)\n\n def close(self):\n \"\"\"Close the mp4 handle.\"\"\"\n if not self._closed:\n _mp4v2.MP4Close(\n self._mp4_handle,\n _mp4v2.MP4_CLOSE_DO_NOT_COMPUTE_BITRATE\n )\n _mp4v2.MP4TagsFree(self._tags)\n\n self._closed = True\n\n @property\n def tags(self) -> Any:\n \"\"\"Get the mp4 tags.\"\"\"\n return self._tags\n\n @property\n def track_count(self) -> int:\n \"\"\"Get the number of tracks in this mp4.\"\"\"\n return self._number_of_tracks\n\n def tracks(self) -> Generator[Any, None, None]:\n \"\"\"Yield a track object for each track.\"\"\"\n for track in range(1, self.track_count + 1):\n yield Mp4Track(self, track)\n\n def get_aac_track(self) -> Any:\n \"\"\"Return the AAC track in the mp4 or None.\"\"\"\n # Tracks start at 1.\n for track in self.tracks():\n track_type = track.type.decode()\n\n # Only use audio tracks.\n if not track_type or _mp4v2.MP4_IS_AUDIO_TRACK_TYPE(track_type):\n continue\n\n object_type = track.object_type\n\n # Only return audio if it is AAC encoded.\n if object_type == _mp4v2.MP4_MPEG4_AUDIO_TYPE:\n object_type = track.audio_mpeg4_type\n\n # Check for AAC encoding.\n if _mp4v2.MP4_IS_MPEG4_AAC_AUDIO_TYPE(object_type):\n return track\n elif _mp4v2.MP4_IS_AAC_AUDIO_TYPE(object_type):\n return track\n\n # An invalid track.\n raise(Exception('Invalid AAC track.'))\n\n\nclass Mp4Track(object):\n \"\"\"Wraps the mp4v2 track object.\"\"\"\n\n def __init__(self, mp4_handle: Any, track: int = 1):\n \"\"\"Initialize the track and create partial functions.\"\"\"\n self._mp4_handle = mp4_handle\n self._mp4_track = track\n self._mp4_track_id = _mp4v2.MP4TrackId(track)\n\n self._read_sample = partial(mp4_handle.track_read_sample,\n self._mp4_track_id)\n self._es_configuration = partial(mp4_handle.track_es_configuration,\n self._mp4_track_id)\n\n self._type = mp4_handle.track_type(self._mp4_track_id)\n self._esds_object_type = mp4_handle.track_esds_object_type(\n self._mp4_track_id)\n self._audio_mpeg4_type = mp4_handle.track_audio_mpeg4_type(\n self._mp4_track_id)\n\n self._sample_count = mp4_handle.track_sample_count(self._mp4_track_id)\n self._rate = mp4_handle.track_rate(self._mp4_track_id)\n\n name = _mp4v2.c_char_p()\n _ = mp4_handle.track_name(self._mp4_track_id, name)\n\n self._name = name\n\n @property\n def track_id(self) -> int:\n \"\"\"Get the mp4 track id.\"\"\"\n return self._mp4_track_id.value\n\n def read_sample(self, sample_id: int) -> Any:\n \"\"\"Return the sample and its size.\"\"\"\n # Is this the last sample.\n last = (sample_id == self._sample_count)\n\n data_buffer = _mp4v2.POINTER(_mp4v2.c_uint8)()\n buffer_size = _mp4v2.c_uint32()\n\n # Don't read past the end of the file.\n if sample_id <= self._sample_count:\n self._read_sample(\n sample_id,\n _mp4v2.byref(data_buffer),\n _mp4v2.byref(buffer_size),\n None,\n None,\n None,\n None\n )\n\n # Return a sample object.\n return Mp4Sample(sample_id, data_buffer, buffer_size, last)\n\n def get_configuration(self) -> tuple[Any, Any]:\n \"\"\"Get the configuration.\n\n Return a buffer and size to use with faad init functions to find the\n sample rate and channels.\n \"\"\"\n data_buffer = _mp4v2.POINTER(_mp4v2.c_ubyte)()\n buffer_size = _mp4v2.c_uint32()\n\n ret = self._es_configuration(_mp4v2.byref(data_buffer), buffer_size)\n\n # Reset the buffer and size if there was no configuration data.\n if not ret:\n data_buffer = _mp4v2.POINTER(_mp4v2.c_ubyte)()\n buffer_size = _mp4v2.c_uint32()\n\n return (data_buffer, _mp4v2.c_ulong(buffer_size.value))\n\n @property\n def sample_count(self) -> int:\n \"\"\"Get the number of samples in the track.\"\"\"\n return self._sample_count\n\n @property\n def type(self) -> bytes:\n \"\"\"Get the type of the current track.\"\"\"\n return self._type\n\n @property\n def object_type(self) -> int:\n \"\"\"Get the track object type.\"\"\"\n return self._esds_object_type\n\n @property\n def audio_mpeg4_type(self) -> int:\n \"\"\"Get the type of mpeg4 audio for the track.\"\"\"\n return self._audio_mpeg4_type\n\n\nclass Mp4Sample(object):\n \"\"\"An mp4 sample contains the data and size.\"\"\"\n\n def __init__(self, sample_id: int, data: Any, size: Any,\n last: bool = False):\n \"\"\"Initialize the sample.\"\"\"\n self._mp4_sample_id = _mp4v2.MP4SampleId(sample_id)\n\n self._data = data\n self._size = size\n self._id = sample_id\n self._last = last\n\n @property\n def sample_id(self) -> int:\n \"\"\"Get the mp4 sample id.\"\"\"\n return self._mp4_sample_id.value\n\n def islast(self) -> bool:\n \"\"\"Return True if this is a the last sample.\"\"\"\n return self._last\n\n @property\n def id(self) -> int:\n \"\"\"Get the current sample id.\"\"\"\n return self._id\n\n @property\n def data(self) -> bytes:\n \"\"\"Get the sample data.\"\"\"\n return self._data\n\n @property\n def size(self) -> int:\n \"\"\"Get the size of the sample.\"\"\"\n return self._size\n\n\nclass Mp4(object):\n \"\"\"Provides easy access to the AAC audio in mp4s.\"\"\"\n\n def __init__(self, filename: str):\n \"\"\"Initialize class variables.\"\"\"\n self._mp4_handle = Mp4Handle(filename)\n\n self._aac_track = self._mp4_handle.get_aac_track()\n if not self._aac_track:\n raise Exception(f\"No AAC track in {filename}\")\n\n self._sample_count = self._aac_track.sample_count\n self._current_sample = 1\n\n def close(self):\n \"\"\"Close the mp4.\"\"\"\n self._mp4_handle.close()\n del(self._mp4_handle)\n\n def get_tag_dict(self) -> dict:\n \"\"\"Return a dictionary of tags from the mp4 or an empty dict.\"\"\"\n tag_dict = {}\n\n tags = self._mp4_handle.tags\n for i in dir(tags.contents):\n item = getattr(tags.contents, i)\n if item:\n if hasattr(item, 'contents'):\n tag_dict[i] = item.contents\n elif hasattr(item, 'value'):\n tag_dict[i] = item.value\n else:\n tag_dict[i] = item\n # try:\n # if item.contents:\n # tag_dict[i] = item.contents\n # except Exception as err:\n # try:\n # if item.value:\n # tag_dict[i] = item.value\n # except Exception as err:\n # if item:\n # tag_dict[i] = item\n\n return tag_dict\n\n def get_configuration(self):\n \"\"\"Get config.\n\n Return a buffer and size to use with faad init functions to find the\n sample rate and channels.\n \"\"\"\n return self._aac_track.get_configuration()\n\n def read(self) -> Mp4Sample:\n \"\"\"Read the next sample from the aac audio in the open mp4.\"\"\"\n sample = self._aac_track.read_sample(self._current_sample)\n\n self._current_sample += 1\n\n return sample\n\n @property\n def current_sample(self) -> int:\n \"\"\"Get the next sample to read.\"\"\"\n return self._current_sample\n\n @current_sample.setter\n def current_sample(self, value: int):\n \"\"\"Set the next sample to read.\"\"\"\n if value in range(1, self._sample_count):\n self._current_sample = value\n else:\n self._current_sample = 1\n\n @property\n def sample_count(self) -> int:\n \"\"\"Get the number of samples in the aac track.\"\"\"\n return self._sample_count\n","repo_name":"zepto/musio","sub_path":"musio/mp4v2/_mp4v2_wrapper.py","file_name":"_mp4v2_wrapper.py","file_ext":"py","file_size_in_byte":9603,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"23"}
+{"seq_id":"72488373180","text":"import sys\ninput = sys.stdin.readline\n\npoem = input().strip().split()\nspace = int(input())\nalpha = list(map(int, input().split()))\nresult = True\n\nif len(poem)-1 > space:\n result = False\nelse:\n s = len(poem)\n for i in range(s):\n string_i = poem[i]\n for j in range(len(string_i)):\n num = ord(string_i[j].lower())-97\n alpha[num] -= 1\n if j>=1 and (string_i[j] == string_i[j-1]):\n alpha[num] += 1\n if alpha[num] <0:\n result = False\n if result == True:\n string = ''\n for i in range(s):\n string = string + poem[i][0].upper()\n for i in range(len(string)):\n num = ord(string[i].lower())-97\n alpha[num] -= 1\n if i>=1 and (string[i] == string[i-1]):\n alpha[num] += 1\n if alpha[num] <0:\n result = False\nif result == True:\n print(string)\nelse:\n print(-1)","repo_name":"juyoung810/idecs-algorithm-study","sub_path":"junyoung/19948/19948_음유시인_영재.py","file_name":"19948_음유시인_영재.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"23"}
+{"seq_id":"664379216","text":"import twill \nfrom twill.namespaces import get_twill_glocals \nfrom xmlrpclib import Server as XMLRPCServer \nimport urllib \nfrom logging import log_warn\n\n__all__ = ['create_doc']\n\n\ndef get_twill_var(varname): \n twill_globals, twill_locals = get_twill_glocals()\n return twill_globals.get(varname)\n\ndef create_doc(container, admin_user, admin_pw, id, title, body, revision=None): \n base_url = get_twill_var('base_url')\n prepath = get_twill_var('prepath')\n\n log_warn(\"(zope) Creating document %s in %s\" % (id, container))\n scheme, uri = urllib.splittype(base_url) \n host, path = urllib.splithost(uri)\n if prepath is not None:\n path = prepath + path\n auth_url = \"%s://%s:%s@%s%s/\" % (scheme, admin_user, admin_pw, host, path)\n portal = XMLRPCServer(auth_url)\n getattr(portal, container).invokeFactory('Document', id)\n item = getattr(getattr(portal, container), id)\n item.setTitle(title)\n item.setText(body)\n\n # XXX getting error:\n # TypeError: cannot marshal <type 'instancemethod'> objects\n# if revision:\n# getattr(portal, 'portal_repository').save(item, revision)\n\n item.reindexObject()\n","repo_name":"socialplanning/flunc","sub_path":"flunc/zope_create_doc.py","file_name":"zope_create_doc.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"23"}
+{"seq_id":"39438768294","text":"#!/usr/bin/env python\nimport urllib2\n\ndef CheckAdmin():\n \"\"\"\n For Plex Home : Only the main users token is accepted at http://127.0.0.1:32400/myplex/account\n For All Else : Only the main users token is accepted at https://plex.tv/users/account\n \"\"\"\n\n url = 'https://plex.tv/users/account' if Prefs['plextv'] else 'http://127.0.0.1:32400/myplex/account'\n\n Log.Debug('*' * 80)\n Log.Debug('* Checking if user is Admin')\n Log.Debug('* Auth URL = {}'.format(url))\n\n ptoken = Request.Headers.get('X-Plex-Token', '')\n if not ptoken:\n Log.Error('* NO Plex Token available for validation')\n Log.Debug('* Assuming current user is Admin')\n Log.Debug('*' * 80)\n return True\n else:\n Log.Debug('* Plex Token is available for validation')\n try:\n req = urllib2.Request(url, headers={'X-Plex-Token': ptoken})\n res = urllib2.urlopen(req)\n if res.read():\n Log.Debug('* Current User is Admin')\n Log.Debug('*' * 80)\n return True\n except Exception as e:\n Log.Error('* Current User is NOT Admin')\n Log.Error('* CheckAdmin: User denied access: {}'.format(e))\n Log.Debug('*' * 80)\n return False\n","repo_name":"Twoure/KissNetwork.bundle","sub_path":"Contents/Code/AuthTools.py","file_name":"AuthTools.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":95,"dataset":"github-code","pt":"23"}
+{"seq_id":"7845697537","text":"from .views import *\n\nfrom django.urls import path\n\napp_name = 'product'\n\nurlpatterns = [\n # Topic\n path('topic/', TopicList.as_view(), name='topicList'),\n path('topic/<int:pk>/', TopicDetail.as_view(), name='topicDetail'),\n\n # SubTopic\n path('subtopic/', SubTopicList.as_view(), name='subTopicList'),\n path('subtopic/<int:pk>/', SubTopicDetail.as_view(), name='subTopicDetail'),\n\n # Question\n path('question/', QuestionList.as_view(), name='questionList'),\n path('question/<int:pk>/', QuestionDetail.as_view(), name='questionDetail'),\n\n # Answer\n path('answer/', AnswerList.as_view(), name='answerList'),\n path('answer/<int:pk>/', AnswerDetail.as_view(), name='answerDetail'),\n\n # # Nested (Category)\n # path(\n # 'category/<int:cat>/product/',\n # CategoryProductList.as_view(),\n # name='categoryProductList'\n # ),\n\n # # Nested (Subject)\n # path(\n # 'subject/<int:sub>/product/',\n # SubjectProductList.as_view(),\n # name='subjectProductList'\n # ),\n\n # # Nested (Category Subject)\n # path(\n # 'category/<int:cat>/subject/<int:sub>/product/',\n # CategorySubjectProductList.as_view(),\n # name='categorySubjectProductList'\n # ),\n]\n","repo_name":"AbdullahJaswal/Examination","sub_path":"content/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"23"}
+{"seq_id":"22677491330","text":"import numpy as np\nimport pytest\nfrom astromodels import Gaussian, Line, Log_normal, Model, PointSource\n\nfrom threeML.bayesian.bayesian_analysis import BayesianAnalysis\nfrom threeML.classicMLE.joint_likelihood import JointLikelihood\nfrom threeML.data_list import DataList\nfrom threeML.plugins.UnbinnedPoissonLike import (EventObservation,\n UnbinnedPoissonLike)\n\nfrom .conftest import event_observation_contiguous, event_observation_split\n\n\ndef test_event_observation(event_observation_contiguous, event_observation_split):\n\n assert not event_observation_contiguous.is_multi_interval\n\n assert event_observation_split.is_multi_interval\n\n # test all exists\n for obs in [event_observation_split, event_observation_contiguous]:\n\n obs.exposure\n obs.start\n obs.stop\n obs.events\n\n assert isinstance(event_observation_contiguous.start, float)\n assert isinstance(event_observation_contiguous.stop, float)\n\n for a, b in zip(event_observation_split.start, event_observation_split.stop):\n\n assert a < b\n\n with pytest.raises(AssertionError):\n\n EventObservation([0, 1, 2, 3], exposure=1, start=10, stop=1)\n\n\ndef test_ubinned_poisson_full(event_observation_contiguous, event_observation_split):\n\n s = Line()\n\n ps = PointSource(\"s\", 0, 0, spectral_shape=s)\n\n s.a.bounds = (0, None)\n s.a.value = .1\n s.b.value = .1\n\n s.a.prior = Log_normal(mu=np.log(10), sigma=1)\n s.b.prior = Gaussian(mu=0, sigma=1)\n\n m = Model(ps)\n\n ######\n ######\n ######\n\n \n ub1 = UnbinnedPoissonLike(\"test\", observation=event_observation_contiguous)\n\n jl = JointLikelihood(m, DataList(ub1))\n\n jl.fit(quiet=True)\n\n np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5)\n\n ba = BayesianAnalysis(m, DataList(ub1))\n\n ba.set_sampler(\"emcee\")\n\n ba.sampler.setup(n_burn_in=500, n_walkers=50, n_iterations=500)\n\n ba.sample(quiet=True)\n\n ba.restore_median_fit()\n\n np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5)\n\n ######\n ######\n ######\n\n ub2 = UnbinnedPoissonLike(\"test\", observation=event_observation_split)\n\n jl = JointLikelihood(m, DataList(ub2))\n\n jl.fit(quiet=True)\n\n np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5)\n\n ba = BayesianAnalysis(m, DataList(ub2))\n\n ba.set_sampler(\"emcee\")\n\n ba.sampler.setup(n_burn_in=500, n_walkers=100, n_iterations=500)\n\n ba.sample(quiet=True)\n\n ba.restore_median_fit()\n\n np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=10)\n","repo_name":"threeML/threeML","sub_path":"threeML/test/test_unbinned_poisson_like.py","file_name":"test_unbinned_poisson_like.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"2"}
+{"seq_id":"74167241327","text":"# GUardado con el nombre de 08_Grafia2SEparadas\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np \r\n\r\nfig = plt.figure() # create a plot figure\r\n\r\nx = np.linspace(0, 10, 100)\r\n\r\n# crear el primero de dos paneles y establecer el eje actual\r\n# (filas, columnas, número de panel)\r\nplt.subplot(2, 1, 1) \r\nplt.plot(x, np.sin(x),'b--d')\r\n\r\n# crea el segundo panel y establecer el eje actual\r\nplt.subplot(2, 1, 2)\r\nplt.plot(x, np.cos(x), 'g--d');\r\n\r\nprint(\"Dos Gráficas separadas en una exposición\") \r\nprint(\"----- Fin del Programa -----\") ","repo_name":"rafaelespinosacastaneda/Data-Science-and-Mathematics-for-Decision-Making-","sub_path":"BasicVisualization/08_Grafica2Separadas.py","file_name":"08_Grafica2Separadas.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"39073189276","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport subprocess\nimport shlex\n\ndef check_nextclade(thisdir,workflow):\n snakefile = os.path.join(thisdir, \"bin\", \"rules\", f\"{workflow}.smk\")\n print(\n \"Checking whether nextclade needs to be updated...\"\n )\n print(f\"Working with: {snakefile}\")\n update_file = os.path.join(thisdir, \"bin\", \"scripts\", \"get_latest_tag.sh\")\n cmd = f'grep \"docker://nextstrain\" {snakefile} | uniq | tr -d \" \" | sed \"s|docker://nextstrain/nextclade:||\" | tr -d \"\\n\"'\n current_version = subprocess.check_output(cmd, shell=True).decode('utf-8').replace('\"','')\n cmd = f\"bash {update_file} --source dockerhub --repo nextstrain --image nextclade --no-alphabet\"\n newest_version = subprocess.check_output(cmd, shell=True).decode('utf-8')\n if current_version != newest_version:\n cmd = f'sed -i \"s|docker://nextstrain/nextclade:{current_version}|docker://nextstrain/nextclade:{newest_version}|g\" {snakefile}'\n update_proc = subprocess.Popen(\n shlex.split(cmd),\n shell=False,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n print(\n f\"Updated nextclade version dependency to {newest_version}\"\n )\n else:\n print(\n f\"Nextclade version dependency is already at the newest version: {current_version}\"\n )\n","repo_name":"charlesfoster/covid-illumina-snakemake","sub_path":"CIS/bin/scripts/check_nextclade.py","file_name":"check_nextclade.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"2"}
+{"seq_id":"19254363929","text":"def targetindex(arr,target):\n ans = []\n n = len(arr)\n arr.sort()\n for i in range(n):\n if arr[i]==target:\n ans.append(i)\n \n return ans\nprint(targetindex([5,3,4,2,7,8], 5))\n\n","repo_name":"NamiqPlanov/Python-practice","sub_path":"leetcode tasks/targetIndices.py","file_name":"targetIndices.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"30909631075","text":"import sys\nfrom PyQt6.QtCore import QRect\nfrom overlay_animations import animation\nfrom overlay_objects import overlayRect\nfrom overlay_animations import animator\nfrom overlay_objects.overlayObject import OverlayObject\n\n\ndef createTestObjectAndApplyAnimation(_animator: animator.Animator) -> OverlayObject:\n \"\"\"\n 윈도우에 draw 되는 오브젝트를 하나 만들고, 해당 오브젝트에 적용되는 여러 animation을 생성 후, animator에 추가하는 메소드\n\n :param _animator: main window의 animator\n :return: 애니메이션이 적용된 OverlayObject\n \"\"\"\n\n # Window에 draw되는 사각형 오브젝트.\n obj = overlayRect.OverlayRect()\n\n # --------------------------------------------------\n # First Animation\n # --------------------------------------------------\n\n # custom Easing 함수 지정시 필요. https://easings.net/ 참고\n easeInOutQuint = lambda x: (16 * x * x * x * x * x) if x < 0.5 else (1 - pow(-2 * x + 2, 5) / 2)\n\n # obj의 멤버 변수를 변형시킬 첫번째 애니메이션\n # 사전에 정의된 make_rect_anim 을 이용해 Animation 객체 생성\n anim = animation.make_rect_anim(obj.setRect, QRect(100, 100, 200, 200), QRect(500, 500, 500, 500), 2000,\n easeInOutQuint)\n\n # --------------------------------------------------\n # Second, Third Animation\n # --------------------------------------------------\n\n # 첫번째 애니메이션이 끝난 직후 실행될 두번째 애니메이션. 첫번째와 달리 Animation 생성자를 직접 호출하여 객체를 생성할 예정\n # Animation 객체 직접 생성시 update callback method 지정 필요\n updateCallback = lambda q1, q2, t: obj.setRect(QRect(\n (1 - t) * q1.topLeft() + t * q2.topLeft(),\n (1 - t) * q1.bottomRight() + t * q2.bottomRight()))\n\n # 두번째 Animation 객체 생성\n anim2 = animation.Animation(QRect(500, 500, 500, 500), QRect(200, 200, 0, 0), updateCallback, 1000, easeInOutQuint)\n\n # 두번째 애니메이션과 동시에 페이드 아웃 효과를 부여할 애니메이션. obj 객체의 멤버 변수 opacity를 변경하는 애니메이션\n # 세번째 Animation 객체는 사전에 정의된 make_var_anim 을 이용해 생성.\n anim3 = animation.make_var_anim(obj.setOpacity, 0.9, 0, 2000)\n\n # anim 종료 후, animator에 anim2와 anim3를 추가(실행) 하고,\n anim.after = lambda: [_animator.addAnim(anim2), _animator.addAnim(anim3)]\n # anim3 종료 후, obj를 파괴하시오\n anim3.after = lambda: obj.destroy()\n\n # anim을 애니메이터에 추가\n _animator.addAnim(anim)\n return obj\n","repo_name":"ksc1008/team4","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":2712,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"3653466828","text":"import torch\n\nfrom vel.api import Learner, ModelConfig, EpochInfo, TrainingInfo\n\n\nclass SimpleTrainCommand:\n \"\"\" Very simple training command - just run the supplied generators \"\"\"\n\n def __init__(self, model_config: ModelConfig, model_factory, epochs, optimizer_factory, scheduler_factory,\n callbacks, source, storage):\n self.epochs = epochs\n self.callbacks = callbacks if callbacks is not None else []\n self.optimizer_factory = optimizer_factory\n self.scheduler_factory = scheduler_factory\n self.model_factory = model_factory\n self.source = source\n self.model_config = model_config\n self.storage = storage\n\n def run(self):\n \"\"\" Run the command with supplied configuration \"\"\"\n device = torch.device(self.model_config.device)\n learner = Learner(device, self.model_factory.instantiate())\n optimizer = self.optimizer_factory.instantiate(learner.model)\n\n # All callbacks used for learning\n callbacks = self.gather_callbacks(optimizer)\n\n # Metrics to track through this training\n metrics = learner.metrics()\n\n # Check if training was already started and potentially continue where we left off\n training_info = self.resume_training(learner, optimizer, callbacks, metrics)\n\n training_info.on_train_begin()\n\n for global_epoch_idx in range(training_info.start_epoch_idx + 1, self.epochs + 1):\n epoch_info = EpochInfo(\n training_info=training_info,\n global_epoch_idx=global_epoch_idx,\n batches_per_epoch=self.source.train_iterations_per_epoch(),\n optimizer=optimizer\n )\n\n # Execute learning\n learner.run_epoch(epoch_info, self.source)\n\n self.storage.checkpoint(epoch_info, learner.model)\n\n training_info.on_train_end()\n\n return training_info\n\n def gather_callbacks(self, optimizer) -> list:\n \"\"\" Gather all the callbacks to be used in this training run \"\"\"\n callbacks = []\n\n if self.scheduler_factory is not None:\n callbacks.append(self.scheduler_factory.instantiate(optimizer))\n\n callbacks.extend(self.callbacks)\n callbacks.extend(self.storage.streaming_callbacks())\n\n return callbacks\n\n def resume_training(self, learner, optimizer, callbacks, metrics) -> TrainingInfo:\n \"\"\" Possibly resume training from a saved state from the storage \"\"\"\n if self.model_config.reset:\n start_epoch, hidden_state = 0, {}\n else:\n start_epoch, hidden_state = self.storage.resume_learning(learner.model)\n\n training_info = TrainingInfo(start_epoch_idx=start_epoch, metrics=metrics, callbacks=callbacks)\n\n if start_epoch > 0:\n self.restore_state(hidden_state, optimizer, callbacks)\n training_info.restore(hidden_state)\n\n return training_info\n\n def restore_state(self, hidden_state, optimizer, callbacks):\n \"\"\" Load state into optimizer and callbacks \"\"\"\n optimizer.load_state_dict(hidden_state['optimizer'])\n\n for callback in callbacks:\n callback.load_state_dict(hidden_state)\n\n\ndef create(model_config, epochs, optimizer, model, source, storage, scheduler=None, callbacks=None):\n \"\"\" Simply train the model \"\"\"\n return SimpleTrainCommand(\n model_config=model_config,\n model_factory=model,\n epochs=epochs,\n optimizer_factory=optimizer,\n scheduler_factory=scheduler,\n callbacks=callbacks,\n source=source,\n storage=storage,\n )\n","repo_name":"Helicqin/vel","sub_path":"vel/commands/train_command.py","file_name":"train_command.py","file_ext":"py","file_size_in_byte":3635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"2"}
+{"seq_id":"12321451142","text":"from tkinter import *\r\nimport mysql.connector\r\nfrom tkinter import ttk\r\nfrom tkinter import ttk,messagebox\r\nfrom tkcalendar import DateEntry\r\nfrom PIL import Image,ImageTk\r\ndef book_issue():\r\n rt.destroy()\r\n import issuebook\r\ndef renew_book():\r\n rt.destroy()\r\n import rw_rew\r\ndef fine():\r\n rt.destroy()\r\n import fine\r\ndef show_book():\r\n rt.destroy()\r\n import Show_Update_add\r\ndef exit():\r\n MsgBox = messagebox.askquestion('Exit Application', 'Are you sure you want to exit the application',\r\n icon='warning')\r\n if MsgBox == 'yes':\r\n rt.quit()\r\n\r\nrt=Tk()\r\nrt.title(\"HOME PAGE\")\r\nrt.geometry(\"1600x800+0+0\")\r\nto_im=ImageTk.PhotoImage(file=\"top_frame.jpg\")\r\ntop_frame=Frame(rt,bd=10,relief=GROOVE)\r\ntop_frame.place(x=0,y=0,width=1590,height=200)\r\nto_i_l=Label(top_frame,image=to_im)\r\nto_i_l.place(x=0,y=0,relwidth=1,relheight=1)\r\nfront_text=Label(top_frame,relief=RAISED,bd=10,text=\"Library Management system\",font=(\"Comic Sans MS\",30,\"bold\"),fg=\"white\",bg=\"black\")\r\nfront_text.place(x=390,y=60,width=800)\r\ns_frame=Frame(rt,bd=4,relief=RIDGE,bg=\"light blue\")\r\ns_frame.place(x=0,y=200,width=400,height=800)\r\nsf_im=ImageTk.PhotoImage(file=\"s_pic.jpg\")\r\nsf_i_l=Label(s_frame,image=sf_im)\r\nsf_i_l.place(x=0,y=0,relwidth=1,relheight=1)\r\n#s_title=Label(s_frame,text=\"Actions\",font=(\"Arial Black\",20,\"bold\",\"underline\"),fg=\"green\",bg=\"light blue\")\r\n#s_title.place(x=70,y=20,width=200)\r\nm_frame=Frame(rt,bd=2,relief=GROOVE)\r\nm_frame.place(x=400,y=202,width=1125,height=560)\r\nm_f_im=ImageTk.PhotoImage(file=\"back.jpg\")\r\nm_f_Im=Label(m_frame,image=m_f_im)\r\nm_f_Im.place(x=0,y=0,relwidth=1,relheight=1)\r\nbki_bt=Button(s_frame,text=\"Issue Book\",relief=GROOVE,font=(\"Arial Black\",13),fg=\"white\",bg=\"black\",command=book_issue,width=18).place(x=120,y=100,width=150)\r\n#bki_bt.pack()\r\nren_bt=Button(s_frame,text=\"Renew/Return\",relief=GROOVE,font=(\"Arial Black\",13),fg=\"white\",bg=\"black\",command=renew_book,width=18).place(x=120,y=190,width=150)\r\n#Button.pack()\r\nshow_bt=Button(s_frame,text=\"Show\",relief=GROOVE,font=(\"Arial Black\",13),fg=\"white\",bg=\"black\",command=show_book,width=18).place(x=120,y=280,width=150)\r\n#Button.pack()\r\nfines_bt=Button(s_frame,text=\"Fines\",relief=GROOVE,font=(\"Arial Black\",13),fg=\"white\",bg=\"black\",command=fine,width=18).place(x=120,y=370,width=150)\r\n#Button.pack()\r\nexit_bt=Button(s_frame,text=\"Exit\",relief=GROOVE,font=(\"Arial Black\",13),fg=\"white\",bg=\"black\",command=exit,width=18).place(x=120,y=450,width=150)\r\n#Button.pack()\r\nrt.mainloop()\r\n","repo_name":"somosree21/Library-Management-System","sub_path":"Libary Management System/DEMO.py","file_name":"DEMO.py","file_ext":"py","file_size_in_byte":2531,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"11012731256","text":"import numpy as np\nimport random\nimport time\nfrom partVch1_1 import cllm_utils\nfrom partVch1_1.network import *\n\n#%% 测试抽样\n\n# #读入数据集\n# data = open(\"dinos.txt\",\"r\").read()\n# data = data.lower()\n# chars = list(set(data))\n#\n# #创建字典\n# char_to_ix = {ch:i for i, ch in enumerate(sorted(chars))}\n# ix_to_char = {i:ch for i,ch in enumerate(sorted(chars))}\n#\n# # 获取大小信息\n# data_size, vocab_size = len(data), len(chars)\n#\n# np.random.seed(2)\n# _, n_a = 20, 100\n# Wax, Waa, Wya = np.random.randn(n_a, vocab_size), np.random.randn(n_a, n_a), np.random.randn(vocab_size, n_a)\n# b, by = np.random.randn(n_a, 1), np.random.randn(vocab_size, 1)\n# parameters = {\"Wax\": Wax, \"Waa\": Waa, \"Wya\": Wya, \"b\": b, \"by\": by}\n#\n#\n# indices = sample(parameters, char_to_ix, 0)\n# print(\"Sampling:\")\n# print(\"list of sampled indices:\", indices)\n# print(\"list of sampled characters:\", [ix_to_char[i] for i in indices])\n\n# %%测试优化\nnp.random.seed(1)\nvocab_size, n_a = 27, 100\na_prev = np.random.randn(n_a, 1)\nWax, Waa, Wya = np.random.randn(n_a, vocab_size), np.random.randn(n_a, n_a), np.random.randn(vocab_size, n_a)\nb, by = np.random.randn(n_a, 1), np.random.randn(vocab_size, 1)\nparameters = {\"Wax\": Wax, \"Waa\": Waa, \"Wya\": Wya, \"b\": b, \"by\": by}\nX = [12,3,5,11,22,3]\nY = [4,14,11,22,25, 26]\n\nloss, gradients, a_last = optimize(X, Y, a_prev, parameters, learning_rate = 0.01)\nprint(\"Loss =\", loss)\nprint(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients[\"dWaa\"][1][2])\nprint(\"np.argmax(gradients[\\\"dWax\\\"]) =\", np.argmax(gradients[\"dWax\"]))\nprint(\"gradients[\\\"dWya\\\"][1][2] =\", gradients[\"dWya\"][1][2])\nprint(\"gradients[\\\"db\\\"][4] =\", gradients[\"db\"][4])\nprint(\"gradients[\\\"dby\\\"][1] =\", gradients[\"dby\"][1])\nprint(\"a_last[4] =\", a_last[4])","repo_name":"nobodypengium/PythonLearning","sub_path":"partVch1_1/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"25955156618","text":"from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter\n\n\nclass Command(BaseXpressDemocracyClubCsvImporter):\n council_id = \"E07000202\"\n addresses_name = \"parl.2019-12-12/Version 2/ipswich.gov.uk-1573574354000-.CSV\"\n stations_name = \"parl.2019-12-12/Version 2/ipswich.gov.uk-1573574354000-.CSV\"\n elections = [\"parl.2019-12-12\"]\n allow_station_point_from_postcode = False\n\n def station_record_to_dict(self, record):\n\n # Sikh Temple\n if record.polling_place_id == \"6642\":\n record = record._replace(polling_place_uprn=\"10035058265\")\n\n # Castle Hill United Reformed Church\n if record.polling_place_id == \"6629\":\n record = record._replace(polling_place_uprn=\"100091483837\")\n\n # St Thomas the Apostle Church\n if record.polling_place_id == \"6638\":\n record = record._replace(\n polling_place_uprn=\"10004564181\", polling_place_postcode=\"IP1 5BS\"\n )\n\n # Stoke Green Baptist Church Hall\n if record.polling_place_id == \"6827\":\n record = record._replace(polling_place_uprn=\"10004567047\")\n\n # Ascension Hall\n if record.polling_place_id == \"6655\":\n record = record._replace(polling_place_uprn=\"200001930783\")\n\n # Broomhill Library\n if record.polling_place_id == \"6659\":\n record = record._replace(polling_place_uprn=\"10004565452\")\n\n # St Mark`s RC Church Hall\n if record.polling_place_id == \"6834\":\n record = record._replace(polling_place_easting=\"614239\")\n record = record._replace(polling_place_northing=\"243310\")\n\n # Belstead Arms Public House\n if record.polling_place_id == \"6863\":\n record = record._replace(\n polling_place_uprn=\"10004566897\", polling_place_postcode=\"IP2 9QU\"\n )\n\n return super().station_record_to_dict(record)\n\n def address_record_to_dict(self, record):\n uprn = record.property_urn.strip().lstrip(\"0\")\n\n if uprn in [\n \"100091636692\", # IP42AU -> IP42AT : 39 St Margarets Street, Ipswich\n ]:\n rec = super().address_record_to_dict(record)\n rec[\"accept_suggestion\"] = True\n return rec\n\n # UPRNs not in addressbase\n if record.post_code in [\"IP\", \"IP1\", \"IP2\"]:\n return None\n\n if uprn in [\"10093555944\"]:\n return None\n\n return super().address_record_to_dict(record)\n","repo_name":"mbateman/UK-Polling-Stations","sub_path":"polling_stations/apps/data_collection/management/commands/import_ipswich.py","file_name":"import_ipswich.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"2"}
+{"seq_id":"18886006088","text":"from stdoutWrapper import StdoutWrapper\r\nfrom time import sleep\r\n\r\n# put the process output into a buffer array\r\nbuff_size = 10\r\nbuff = []\r\ndef callback(stdout_line: str):\r\n global buff\r\n buff.append(int(stdout_line))\r\n buff = buff[-buff_size:]\r\n\r\n\r\ndef main():\r\n # create a StdoutWrapper instance, when count_up.exe outputs a new line on stdout, the callback will be called\r\n stw = StdoutWrapper('./count_up.exe', callback_output=callback)\r\n # start the execution, this will be done in a new thread, not blocking the current thread.\r\n stw.start()\r\n while True:\r\n try:\r\n sleep(1)\r\n print(buff)\r\n except KeyboardInterrupt:\r\n break\r\n stw.stop()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"mathklk/PyStdoutWrapper","sub_path":"example/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"16457700050","text":"import time\nimport numpy as np\nimport serial\nfrom collections import OrderedDict\nfrom .hardware import format,BaseHardware\n\nthis_format = format\nwrite_params = []\n\nPORT_NAME=\"COM10\"\n\n\nclass Hardware(BaseHardware):\n def __init__(self):\n super(Hardware,self).__init__(name = 'relay_switcher',\n format=this_format,\n write_params = write_params,\n refresh_time = 100)\n self.config = {}\n self.actuator_status = {}\n self.actuator_status_needed = True\n self.load_config()\n\n self.mapping = {'switch_actuator':self.switch_actuator}\n\n def load_config(self):\n sc=open(u'\\\\\\\\cern.ch\\\\dfs\\\\Users\\\\c\\\\CRIS\\\\Documents\\\\Networked-Data-Acquisition\\\\config\\\\24V_config.ini','r')\n for line in sc:\n act, num = [x.strip() for x in line.split(',')]\n self.config[act] = num\n self.actuator_status[act] = False\n if act == 'IRTurbo':\n self.actuator_status[act] = True\n\n self.actuator_status_needed = True\n\n def switch_actuator(self, args):\n actuator_info = args['actuator_info']\n for act in self.actuator_status.keys():\n on = actuator_info[act]\n if on:\n self.relay_write(self.config[act],\"on\") \n else:\n self.relay_write(self.config[act],\"off\")\n\n self.actuator_status_needed = True\n\n def relay_write(self,relayNum,relayCmd):\n serPort = serial.Serial(PORT_NAME, 19200, timeout=1)\n\n if (int(relayNum) < 10):\n relayIndex = str(relayNum)\n else:\n relayIndex = chr(55 + int(relayNum))\n\n serPort.write(bytes(\"relay \"+ str(relayCmd) +\" \"+ relayIndex + \"\\n\\r\",'utf8'))\n\n serPort.close()\n return True\n\n def read_status(self):\n status_data = self.actuator_status\n if self.actuator_status_needed:\n for act, on in self.actuator_status.items():\n self.actuator_status[act] = self.relay_read(self.config[act])\n self.actuator_status_needed = False\n\n self.ns.status_data = status_data\n\n def relay_read(self,relayNum):\n serPort = serial.Serial(PORT_NAME, 19200, timeout=1)\n\n if (int(relayNum) < 10):\n relayIndex = str(relayNum)\n else:\n relayIndex = chr(55 + int(relayNum))\n\n serPort.write(bytes(\"relay read \"+ str(relayIndex) + \"\\n\\r\",'utf8'))\n\n response = serPort.read(25).decode(\"utf-8\")\n serPort.close()\n\n if(response.find(\"on\") > 0):\n return True\n elif(response.find(\"off\") > 0):\n return False\n","repo_name":"rubendegroote/Networked-Data-Acquisition","sub_path":"backend/acquire_files/relay_switcher.py","file_name":"relay_switcher.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"7286379079","text":"from __future__ import print_function\n\nimport argparse\nimport logging\nimport math\nimport os\nimport re\nfrom typing import List, Set, Optional\n\nimport pysam\nimport sys\nfrom pathlib import Path\nfrom datetime import datetime, timedelta\n\nfrom . import __version__\n\n\n\nLOOKUP = []\n\nfor q in range(100):\n LOOKUP.append(pow(10, -0.1 * q))\n\n\nchannel_regex_pattern = re.compile(\"(^|\\s)ch=(?P<channel>\\d+)\")\n\"\"\"\nsimple pattern for integer `channel` id in fastq comment section\n\"\"\"\nchannel_range_regex_pattern = re.compile(\"^(?P<c1>\\d+)(-(?P<c2>\\d+))?$\")\n\"\"\"\npatter for integer entry (`c1`) or range (`c1-c2`) \n\"\"\"\n\n\ndef _compute_mean_qscore(scores):\n \"\"\"Returns the phred score corresponding to the mean of the probabilities\n associated with the phred scores provided.\n\n :param scores: Iterable of phred scores.\n\n :returns: Phred score corresponding to the average error rate, as\n estimated from the input phred scores.\n \"\"\"\n if not scores:\n return 0.0\n sum_prob = 0.0\n for val in scores:\n sum_prob += LOOKUP[val]\n mean_prob = sum_prob / len(scores)\n return -10.0 * math.log10(mean_prob)\n\n\ndef _parse_channels_input(channels_input: str) -> List[int]:\n match = channel_range_regex_pattern.search(channels_input.strip())\n if match is None:\n raise ValueError(f\"Channels input '{channels_input}' does not specify a[-b] single[range] integer pattern\")\n le = int(match.group('c1'))\n he = le + 1\n if match.group('c2') is not None:\n he = int(match.group('c2')) + 1\n if he <= le:\n raise ValueError(f\"Channels input '{channels_input}' clopen range has higher end '{he}' <= than lower end '{le}'\")\n return list(range(le, he))\n\n\ndef get_channels_set(channels_input_list: List[str]) -> Set[int]:\n result: Set[int] = set()\n for entry in channels_input_list:\n for channel in _parse_channels_input(channels_input=entry):\n result.add(channel)\n return result\n\n\ndef get_channel_from_comment(comment: str) -> Optional[int]:\n if not comment:\n return None\n match = channel_regex_pattern.search(comment)\n if match is not None:\n return int(match.group('channel'))\n return None\n\n\ndef parse_args(argv):\n \"\"\"\n Commandline parser\n\n :param argv: Command line arguments\n :type argv: List\n \"\"\"\n usage = \"Cat long lists of FASTQ files\"\n parser = argparse.ArgumentParser(\n description=usage, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n\n parser.add_argument(\n \"--log\",\n dest=\"log\",\n choices=[\n \"DEBUG\",\n \"INFO\",\n \"WARNING\",\n \"ERROR\",\n \"CRITICAL\",\n \"debug\",\n \"info\",\n \"warning\",\n \"error\",\n \"critical\",\n ],\n default=\"INFO\",\n help=\"Print debug information\",\n )\n parser.add_argument(\n \"-o\",\n \"--output\",\n dest=\"OUT\",\n type=str,\n default=\"/dev/stdout\",\n help=\"Output file. (default: stdout)\",\n )\n parser.add_argument(\n \"-l\", \"--min-length\", dest=\"MIN_LEN\", type=int, default=0, help=\"Minimum read length\"\n )\n parser.add_argument(\n \"-q\", \"--min-qscore\", dest=\"MIN_QSCORE\", type=int, default=0, help=\"Minimum q-score\"\n )\n parser.add_argument(\n \"--max-sequencing-time\", dest=\"MAX_SEQ_TIME\", type=int, default=None, help=\"Only output reads that where sequenced at or up to the given time (minutes).\"\n )\n parser.add_argument(\n \"--min-sequencing-time\", dest=\"MIN_SEQ_TIME\", type=int, default=None, help=\"Only output reads that where sequenced at or after the given time (minutes).\"\n )\n parser.add_argument(\n \"--start-time\", dest=\"START_TIME\", type=str, default=None, help=\"Starttime of the run as guppy time stamp (only required with --sequencing-time). If 'min' is given as argument the minimal time is detected automatically.\"\n )\n\n parser.add_argument(\n \"--filter-id\", dest=\"FILTER_ID\", type=str, default=None, help=\"Only print reads with IDs present in file.\"\n )\n\n parser.add_argument(\n \"--channels\", dest=\"channels_input\", type=str, nargs=\"*\",\n help=\"List of individual `a`/ranges `a-b` of integer channel ids to print reads from. \"\n \"Ranges are inclusive on both sides. First `ch=\\\\d+` entry in header is considered.\",\n )\n\n parser.add_argument(\n \"--filter-as\", dest=\"FILTER_AS\", type=str, default=None, help=\"Adaptive sampling CSV file created by guppy.\"\n )\n\n parser.add_argument(\n \"--filter-as-state\", dest=\"FILTER_AS_STATE\", default=None, action='append', choices=['unblock', 'unblock_hit_outside_bed', 'stop_receiving', 'no_decision'], help=\"Fiter reads by adaptiver sampling decision. (requires --filter-as)\"\n )\n\n parser.add_argument(\n \"--print-start-time\", dest=\"PRINT_START_TIME\", action=\"store_true\", help=\"Print the minimal start_time of all fastq files\"\n )\n\n parser.add_argument(\n \"-n\", \"--max_n\", dest=\"MAX_N\", type=int, default=0, help=\"Stop after <max_n> reads\"\n )\n parser.add_argument(\n \"-b\", \"--max_mbp\", dest=\"MAX_BP\", type=int, default=0, help=\"Stop after <max_bp> mega base pairs\"\n )\n parser.add_argument(\n \"-r\",\n \"--recursive\",\n dest=\"RECURSIVE\",\n action=\"store_true\",\n help=\"Search folders recursively\",\n )\n\n parser.add_argument(\n \"-d\",\n \"--dedup\",\n dest=\"DEDUP\",\n action=\"store_true\",\n help=\"Remove duplicated reads.\",\n )\n\n parser.add_argument(\n \"--comments\",\n choices=['forward', 'skip', 'wrap'],\n default='wrap',\n help=\"How to treat FASTQ header comments. \"\n \"`forward` them 'as is', `wrap` them into 'CO:Z:xxx' tag, `skip` them in the output.\",\n )\n\n parser.add_argument(\n \"FASTQ\",\n nargs=\"+\",\n type=str,\n help=\"FASTQ files or folders containing FASTQ files\",\n )\n\n parser.add_argument(\n \"-v\",\n '--version',\n action='version',\n version='catfish ' + __version__,\n help=\"Print version\",\n )\n\n\n args = parser.parse_args(argv)\n\n return args\n\n\ndef find_file_in_folder(\n folder,\n recursive=True,\n patterns=[\n \"*.fastq\",\n \"*.fastq.gz\",\n \"*.fasta\",\n \"*.fasta.gz\",\n \"*.fa\",\n \"*.fa.gz\",\n \"*.fq\",\n \"*.fq.gz\",\n ],\n):\n if os.path.isfile(folder):\n return folder\n files = []\n\n glob = Path(folder).glob\n if recursive:\n glob = Path(folder).rglob\n\n for pattern in patterns:\n for file in glob(pattern):\n files.append(file)\n\n if len(files) == 0:\n logging.warning(\"Could not find {} files in {}\".format(pattern, folder))\n return files\n\n\ndef parse_timestamp(time_str):\n try:\n time_obj = datetime.strptime(time_str,'%Y-%m-%dT%H:%M:%SZ')\n except ValueError:\n time_obj = datetime.strptime(time_str,'%Y-%m-%dT%H:%M:%S.%f%z').replace(tzinfo=None)\n return time_obj\n\n\ndef check_seq_time(comment, max_start_time,min_start_time):\n #This tests if the start time of the respective read is between\n #max_sequencing_time and min_sequencing_time\n #If one of the times is not given the condition is automatically considered true\n if (max_start_time == None and min_start_time == None):\n return True\n else:\n try:\n matchObj = re.search( r'start_time=([^ ]+)', comment, re.M|re.I)\n start_time_str = matchObj.group(1)\n except AttributeError:\n matchObj = re.search( r'st:Z:([^ ]+)', comment, re.M|re.I)\n start_time_str = matchObj.group(1)\n start = parse_timestamp(start_time_str)\n\n bool_min=0\n bool_max=0\n\n if (max_start_time == None or start<=max_start_time):\n bool_max=1\n if (min_start_time == None or start>=min_start_time):\n bool_min=1\n\n if (bool_min == 1 and bool_max == 1):\n return True\n else:\n return False\n\ndef compare_start_time(comment,min_start_time):\n #Checks if a given min start time is smaller than the time of an entry\n #The smaller time is returned\n try:\n matchObj = re.search( r'start_time=([^ ]+)', comment, re.M|re.I)\n start_time_str = matchObj.group(1)\n except AttributeError:\n matchObj = re.search( r'st:Z:([^ ]+)', comment, re.M|re.I)\n start_time_str = matchObj.group(1)\n start_time = parse_timestamp(start_time_str)\n\n if(min_start_time==0):\n return start_time\n elif(min_start_time<=start_time):\n return min_start_time\n else:\n return start_time\n\n\ndef parse_fastqs(filename, min_len=0, min_qscore=0, max_start_time=None, min_start_time=None, comments='wrap',\n channels: Optional[Set[int]] = None):\n with pysam.FastxFile(filename) as fh:\n for entry in fh:\n if min_len and len(entry.sequence) < min_len:\n continue\n if (\n min_qscore\n and _compute_mean_qscore(entry.get_quality_array()) < min_qscore\n ):\n continue\n if not check_seq_time(entry.comment, max_start_time, min_start_time):\n continue\n if entry.comment and comments == 'wrap':\n entry.comment = \"CO:Z:{}\".format(entry.comment)\n elif comments == 'skip':\n entry.comment = None\n if channels is not None and get_channel_from_comment(entry.comment) not in channels:\n continue\n yield entry\n\n\ndef get_file_names(path, recursive):\n filenames = []\n if os.path.exists(path):\n filenames = [path]\n if os.path.isdir(path):\n logging.debug(\"Searching {} for FASTQ files\".format(path))\n filenames = find_file_in_folder(path, recursive=recursive)\n else:\n logging.warning(\"Could not find {}\".format(path))\n\n return filenames\n\ndef get_start_time(paths,recursive=False):\n \"\"\"\n Only print the start time.\n This function automatically detects the minmal start_time of\n all the given fastq files\n\n\n :param paths: Input FASTQ files or folders containing FASTQ files\n :return: min_start_time\n \"\"\"\n start = None\n max_start_time = None\n min_start_time=0\n for path in paths:\n filenames = get_file_names(path,recursive)\n for filename in filenames:\n with pysam.FastxFile(filename) as fh:\n for entry in fh:\n min_start_time=compare_start_time(entry.comment,min_start_time)\n return min_start_time\n\n\ndef format_fq(paths, out_filename, min_len=0, min_qscore=0, max_n=0, max_bp=0, recursive=False, dedup=False,\n max_seq_time=0, min_seq_time=0, start_time=0, filter_read_ids_file=None, comments='wrap',\n filter_read_as_file=None, filter_read_as_decision=None, channels: Optional[Set[int]]=None):\n \"\"\"\n Concatenate FASTQ files\n\n :param paths: Input FASTQ files or folders containing FASTQ files\n :param out_filename: Output FASTQ file\n :return: None\n \"\"\"\n start = None\n max_start_time = None\n min_start_time = None\n\n keep_ids = None\n if filter_read_ids_file:\n keep_ids = set()\n with open(filter_read_ids_file, \"r\") as fh:\n for line in fh:\n read_id = line.strip()\n keep_ids.add(read_id)\n logging.info(\"Found {} read ids.\".format(len(keep_ids)))\n\n if filter_read_as_file and filter_read_as_decision:\n if not keep_ids:\n keep_ids = set()\n with open(filter_read_as_file, \"r\") as fh:\n for line in fh:\n cols = line.strip().split(',')\n read_id = cols[4]\n read_decision = cols[6]\n if read_decision in filter_read_as_decision:\n keep_ids.add(read_id)\n logging.info(\"Found {} reads with {}.\".format(len(keep_ids), filter_read_as_decision))\n\n if start_time:\n if not start_time==\"min\":\n start = parse_timestamp(start_time)\n\n if(max_seq_time):\n max_start_time = start + timedelta(minutes=max_seq_time)\n if(min_seq_time):\n min_start_time = start + timedelta(minutes=min_seq_time)\n else:\n #This option allows to automatically use the minmal start_time of\n #all the given fastq files as input for --start-time\n start=get_start_time(paths,recursive)\n\n if(max_seq_time):\n max_start_time = start + timedelta(minutes=max_seq_time)\n if(min_seq_time):\n min_start_time = start + timedelta(minutes=min_seq_time)\n read_ids = set()\n\n n = 0\n n_bp = 0\n with open(out_filename, mode=\"w\") as fout:\n for path in paths:\n filenames = get_file_names(path, recursive)\n logging.debug(\"Found {} files\".format(len(filenames)))\n for filename in filenames:\n for entry in parse_fastqs(\n filename, min_len=min_len, min_qscore=min_qscore, max_start_time=max_start_time,\n min_start_time=min_start_time, comments=comments, channels=channels,\n ):\n if dedup and entry.name in read_ids:\n continue\n\n if keep_ids is not None and entry.name not in keep_ids:\n continue\n\n fout.write(str(entry) + \"\\n\")\n if dedup:\n read_ids.add(entry.name)\n n += 1\n n_bp += len(entry.sequence)\n if max_n and n >= max_n or max_bp and n_bp > max_bp:\n return\n\n\ndef main(argv=sys.argv[1:]):\n \"\"\"\n Basic command line interface to telemap.\n\n :param argv: Command line arguments\n :type argv: list\n :return: None\n :rtype: NoneType\n \"\"\"\n args = parse_args(argv=argv)\n\n numeric_level = getattr(logging, args.log.upper(), None)\n if not isinstance(numeric_level, int):\n raise ValueError(\"Invalid log level: %s\" % args.log.upper())\n logging.basicConfig(level=numeric_level, format=\"%(message)s\")\n\n if(args.PRINT_START_TIME):\n min_start_time=get_start_time(args.FASTQ,args.RECURSIVE)\n print(min_start_time.strftime('%Y-%m-%dT%H:%M:%SZ'))\n else:\n\n if args.FILTER_AS and not args.FILTER_AS_STATE or not args.FILTER_AS and args.FILTER_AS_STATE:\n logging.error(\"--filter-as-state and --filter-as must either be both specified or both skipped.\")\n return\n\n channels_set: Optional[Set[int]] = get_channels_set(args.channels_input) if args.channels_input else None\n\n format_fq(\n args.FASTQ,\n args.OUT,\n min_len=args.MIN_LEN,\n min_qscore=args.MIN_QSCORE,\n max_n=args.MAX_N,\n max_bp=args.MAX_BP * 1000 * 1000,\n recursive=args.RECURSIVE,\n dedup=args.DEDUP,\n max_seq_time=args.MAX_SEQ_TIME,\n min_seq_time=args.MIN_SEQ_TIME,\n start_time=args.START_TIME,\n filter_read_ids_file=args.FILTER_ID,\n filter_read_as_file=args.FILTER_AS,\n filter_read_as_decision=args.FILTER_AS_STATE,\n comments=args.comments,\n channels=channels_set,\n )\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"philres/catfishq","sub_path":"catfishq/cat_fastq.py","file_name":"cat_fastq.py","file_ext":"py","file_size_in_byte":15447,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"2"}
+{"seq_id":"37239999919","text":"# -*- coding: utf-8 -*-\nimport calendar\nimport hashlib\nimport logging\nimport os\nimport time\n\nimport datetime\nimport markdown2\nfrom jinja2 import Template\n\nimport docktors\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger('nginx-sample')\n\n# The source content of the static web site\nSOURCE_DIR = os.path.join(os.path.dirname(__file__), 'content')\n\n# The target directory where will be mount the nginx volume\nTARGET_DIR = os.path.join(os.path.dirname(__file__), '.tmp')\n\n# In the nginx.conf, we just define .md file to be serve as html\nNGINX_CONF = os.path.join(os.path.dirname(__file__), 'nginx.conf')\n\n# The second to wait between 2 refresh\nREFRESH_DELAY = 5\n\n# The Jinja template to wrap markdown content\nTEMPLATE = Template(\"\"\"\n<!DOCTYPE html>\n<html lang=\"en\">\n <head>\n <meta charset=\"utf-8\">\n <title>{{title}} \n