0:\n\t\t\towner_data.append(getattr(q[0], attribute))\n\t\telse:\n\t\t\towner_data.append(None)\n\treturn owner_data","title":""},{"docid":"5884df060898fb58e6ea54fe34543efd","score":"0.457715","text":"def GettingSkillsforCandidates(*args, **kwargs):\n listofcandidates = kwargs['listofcandidates']\n final_dictio = {}\n username = kwargs['public_Id']\n listofskills = kwargs['skills']\n candidatesforchecking = []\n gettingweight = []\n skillsandcandidates = {}\n for skill in listofskills:\n skillsandcandidates[skill] = []\n for candidates in listofcandidates:\n candidatesforchecking.append(userDataSkills(public_Id=candidates))\n for skill in listofskills:\n for candidate_data in candidatesforchecking:\n if any(d['skill_name'] == skill for d in candidate_data['skills']):\n skillsandcandidates[skill].append(candidate_data)\n for candidate in skillsandcandidates[skill]:\n for checking in candidate['skills']:\n if checking['skill_name'] == skill:\n gettingweight.append(checking['weight'])\n if len(gettingweight) == 0:\n return {'message': 'No matches with the skills provided'}\n maxbyskill = max(gettingweight)\n position = gettingweight.index(maxbyskill)\n final_dictio[skill] = skillsandcandidates[skill][position]\n gettingweight.clear()\n return (final_dictio)","title":""},{"docid":"112f0ffdda08d849bb3143c7490a6807","score":"0.4573652","text":"def get_password_data(self, context, instance_id):","title":""},{"docid":"d0bf921d55c8c0f23bea385f2acd24ba","score":"0.45733482","text":"def getSessionData(create=1):","title":""},{"docid":"8c82d4e090853ef725dd33b242cdfe05","score":"0.45731208","text":"def get_queryset(self):\n return Feedback.objects.filter(user=self.request.user).values('id', 'saloon__id', 'rate',\n 'description', 'user__id')","title":""},{"docid":"327b15d2596bb070e87d355e31be2a0e","score":"0.45705187","text":"def user_info(self) -> dict:\n return self.provide_session()[\"user\"]","title":""},{"docid":"46b22420f957f6a2d5f8c3e248611b5b","score":"0.45661145","text":"async def get_source(\n obj_id,\n user_id,\n session,\n include_thumbnails=False,\n include_comments=False,\n include_analyses=False,\n include_photometry=False,\n include_photometry_exists=False,\n include_spectrum_exists=False,\n include_comment_exists=False,\n include_period_exists=False,\n include_detection_stats=False,\n include_labellers=False,\n is_token_request=False,\n include_requested=False,\n requested_only=False,\n include_color_mag=False,\n):\n user = session.scalar(sa.select(User).where(User.id == user_id))\n\n options = []\n if include_thumbnails:\n options.append(joinedload(Obj.thumbnails))\n if include_detection_stats:\n options.append(joinedload(Obj.photstats))\n\n s = session.scalars(\n Obj.select(user, options=options).where(Obj.id == obj_id)\n ).first()\n if s is None:\n raise ValueError(\"Source not found\")\n\n source_info = s.to_dict()\n source_info[\"followup_requests\"] = (\n session.scalars(\n FollowupRequest.select(\n user,\n options=[\n joinedload(FollowupRequest.allocation).joinedload(\n Allocation.instrument\n ),\n joinedload(FollowupRequest.allocation).joinedload(Allocation.group),\n joinedload(FollowupRequest.requester),\n joinedload(FollowupRequest.watchers),\n ],\n )\n .where(FollowupRequest.obj_id == obj_id)\n .where(FollowupRequest.status != \"deleted\")\n )\n .unique()\n .all()\n )\n source_info[\"assignments\"] = session.scalars(\n ClassicalAssignment.select(\n user,\n options=[\n joinedload(ClassicalAssignment.run)\n .joinedload(ObservingRun.instrument)\n .joinedload(Instrument.telescope)\n ],\n ).where(ClassicalAssignment.obj_id == obj_id)\n ).all()\n point = ca.Point(ra=s.ra, dec=s.dec)\n # Check for duplicates (within 4 arcsecs)\n galaxies = session.scalars(\n Galaxy.select(user).where(Galaxy.within(point, 10 / 3600))\n ).all()\n if len(galaxies) > 0:\n source_info[\"galaxies\"] = list({galaxy.name for galaxy in galaxies})\n else:\n source_info[\"galaxies\"] = None\n\n # Check for nearby galaxies (within 10 arcsecs)\n duplicate_objs = (\n Obj.select(user)\n .where(Obj.within(point, 4 / 3600))\n .where(Obj.id != s.id)\n .subquery()\n )\n duplicates = session.scalars(\n Source.select(user).join(duplicate_objs, Source.obj_id == duplicate_objs.c.id)\n ).all()\n if len(duplicates) > 0:\n source_info[\"duplicates\"] = list({dup.obj_id for dup in duplicates})\n else:\n source_info[\"duplicates\"] = None\n\n if 'photstats' in source_info:\n photstats = source_info[\"photstats\"]\n for photstat in photstats:\n if (\n hasattr(photstat, 'first_detected_mjd')\n and photstat.first_detected_mjd is not None\n ):\n source_info[\"first_detected\"] = Time(\n photstat.first_detected_mjd, format='mjd'\n ).isot\n if (\n hasattr(photstat, 'last_detected_mjd')\n and photstat.last_detected_mjd is not None\n ):\n source_info[\"last_detected\"] = Time(\n photstat.last_detected_mjd, format='mjd'\n ).isot\n\n if s.host_id:\n source_info[\"host\"] = s.host.to_dict()\n source_info[\"host_offset\"] = s.host_offset.deg * 3600.0\n source_info[\"host_distance\"] = s.host_distance.value\n\n if is_token_request:\n # Logic determining whether to register front-end request as view lives in front-end\n sv = SourceView(\n obj_id=obj_id,\n username_or_token_id=user.id,\n is_token=True,\n )\n session.add(sv)\n # To keep loaded relationships from being cleared in verify_and_commit:\n source_info = recursive_to_dict(source_info)\n session.commit()\n\n if include_thumbnails:\n existing_thumbnail_types = [thumb.type for thumb in s.thumbnails]\n thumbnails = list({\"ps1\", \"ls\", \"sdss\"} - set(existing_thumbnail_types))\n if len(thumbnails) > 0:\n post_thumbnails([obj_id])\n\n if include_comments:\n comments = (\n session.scalars(\n Comment.select(\n user,\n options=[\n joinedload(Comment.author),\n joinedload(Comment.groups),\n ],\n ).where(Comment.obj_id == obj_id)\n )\n .unique()\n .all()\n )\n source_info[\"comments\"] = sorted(\n (\n {\n **{k: v for k, v in c.to_dict().items() if k != \"attachment_bytes\"},\n \"author\": {\n **c.author.to_dict(),\n \"gravatar_url\": c.author.gravatar_url,\n },\n }\n for c in comments\n ),\n key=lambda x: x[\"created_at\"],\n reverse=True,\n )\n if include_analyses:\n analyses = (\n session.scalars(\n ObjAnalysis.select(\n user,\n ).where(ObjAnalysis.obj_id == obj_id)\n )\n .unique()\n .all()\n )\n source_info[\"analyses\"] = [analysis.to_dict() for analysis in analyses]\n if include_period_exists:\n annotations = session.scalars(\n Annotation.select(user).where(Annotation.obj_id == obj_id)\n ).all()\n period_str_options = ['period', 'Period', 'PERIOD']\n source_info[\"period_exists\"] = any(\n [\n isinstance(an.data, dict) and period_str in an.data\n for an in annotations\n for period_str in period_str_options\n ]\n )\n if include_labellers:\n labels_subquery = (\n SourceLabel.select(session.user_or_token)\n .where(SourceLabel.obj_id == obj_id)\n .subquery()\n )\n\n users = (\n session.scalars(\n User.select(session.user_or_token).join(\n labels_subquery,\n User.id == labels_subquery.c.labeller_id,\n )\n )\n .unique()\n .all()\n )\n source_info[\"labellers\"] = [user.to_dict() for user in users]\n\n annotations = sorted(\n session.scalars(\n Annotation.select(user)\n .options(joinedload(Annotation.author))\n .where(Annotation.obj_id == obj_id)\n )\n .unique()\n .all(),\n key=lambda x: x.origin,\n )\n source_info[\"annotations\"] = [\n {**annotation.to_dict(), 'type': 'source'} for annotation in annotations\n ]\n readable_classifications = (\n session.scalars(\n Classification.select(user).where(Classification.obj_id == obj_id)\n )\n .unique()\n .all()\n )\n\n readable_classifications_json = []\n for classification in readable_classifications:\n classification_dict = classification.to_dict()\n classification_dict['groups'] = [g.to_dict() for g in classification.groups]\n classification_dict['votes'] = [g.to_dict() for g in classification.votes]\n readable_classifications_json.append(classification_dict)\n\n source_info[\"classifications\"] = readable_classifications_json\n source_info[\"gal_lat\"] = s.gal_lat_deg\n source_info[\"gal_lon\"] = s.gal_lon_deg\n source_info[\"luminosity_distance\"] = s.luminosity_distance\n source_info[\"dm\"] = s.dm\n source_info[\"angular_diameter_distance\"] = s.angular_diameter_distance\n source_info[\"ebv\"] = s.ebv\n\n if include_photometry:\n photometry = (\n session.scalars(\n Photometry.select(\n user, options=[joinedload(Photometry.annotations)]\n ).where(Photometry.obj_id == obj_id)\n )\n .unique()\n .all()\n )\n source_info[\"photometry\"] = [\n serialize(phot, 'ab', 'flux') for phot in photometry\n ]\n if include_photometry_exists:\n source_info[\"photometry_exists\"] = check_if_obj_has_photometry(\n obj_id, user, session\n )\n\n if include_spectrum_exists:\n source_info[\"spectrum_exists\"] = (\n session.scalars(\n Spectrum.select(user).where(Spectrum.obj_id == obj_id)\n ).first()\n is not None\n )\n if include_comment_exists:\n source_info[\"comment_exists\"] = (\n session.scalars(\n Comment.select(user).where(Comment.obj_id == obj_id)\n ).first()\n is not None\n )\n source_query = Source.select(user).where(Source.obj_id == source_info[\"id\"])\n source_query = apply_active_or_requested_filtering(\n source_query, include_requested, requested_only\n )\n source_subquery = source_query.subquery()\n groups = session.scalars(\n Group.select(user).join(source_subquery, Group.id == source_subquery.c.group_id)\n ).all()\n source_info[\"groups\"] = [g.to_dict() for g in groups]\n for group in source_info[\"groups\"]:\n source_table_row = session.scalars(\n Source.select(user)\n .where(Source.obj_id == s.id)\n .where(Source.group_id == group[\"id\"])\n ).first()\n if source_table_row is not None:\n group[\"active\"] = source_table_row.active\n group[\"requested\"] = source_table_row.requested\n group[\"saved_at\"] = source_table_row.saved_at\n group[\"saved_by\"] = (\n source_table_row.saved_by.to_dict()\n if source_table_row.saved_by is not None\n else None\n )\n if include_color_mag:\n source_info[\"color_magnitude\"] = get_color_mag(annotations)\n\n source_info = recursive_to_dict(source_info)\n return source_info","title":""},{"docid":"30badafe730a2f84abfdec24d0f6ca2e","score":"0.45581418","text":"def getUserInfo(user_id):\n session = DBSession()\n user = session.query(User).filter_by(id=user_id).one()\n return user","title":""},{"docid":"479e0258781c025b08321feb627485d4","score":"0.45506266","text":"def get_users_with_checkout(self, fbo_id, make_and_model):\n selected_users = []\n users = self.get_users(fbo_id)\n for user in users: \n if ('aircraft_checkouts' in user and user[\"aircraft_checkouts\"] != None):\n checkouts = json.loads(user[\"aircraft_checkouts\"])\n for checkout in checkouts:\n if (checkout[\"Make_Model\"] == make_and_model):\n selected_users.append(user)\n return selected_users","title":""},{"docid":"93c3dc43f1d1dc3d3be5b83c4d816b87","score":"0.45486704","text":"def getSessionsByMultipleInequalities(self, request):\n\n # This function is not supposed to be useful\n # It only demonstrates how to filter my multiple inequalities\n\n # create 7pm time object for filtering\n time = datetime.strptime('19:00', \"%H:%M\").time()\n\n sessions = Session.query()\n sessions = sessions.filter(Session.startTime <= time)\n\n sessionsArray = [session for session in sessions if session.typeOfSession != 'workshop']\n\n # return set of Session objects per Session\n return SessionForms(\n items=[self._copySessionToForm(session) for session in sessionsArray]\n )","title":""},{"docid":"edbe64621d4068552e82dd32c368e08f","score":"0.45476386","text":"def check_data(user_input,path):\n\tret = {}\n\tif(CONF_COOKIE in user_input):\n\t\ttry:\n\t\t\t# sadly config flow will not allow to have a multiline text field\n\t\t\t# we get a looong string that we've to rearrange into multiline for ytmusic\n\n\t\t\t# the only thing we need is cookie + x-goog-authuser, lets try to cut this out\n\t\t\t# so the fields are written like 'identifier':'value', but some values actually have ':' inside, bummer.\n\t\t\tc = user_input[CONF_COOKIE]\n\t\t\tclean_cookie = \"\"\n\t\t\tclean_x_goog_authuser = \"\"\n\t\t\t## lets try to find the cookie part\n\t\t\tcookie_pos = c.lower().find('cookie')\n\t\t\tif(cookie_pos>=0):\n\t\t\t\t#_LOGGER.debug(\"found cookie in text\")\n\t\t\t\tcookie_end = c[cookie_pos:]\n\t\t\t\tcookie_end_split = cookie_end.split(':')\n\t\t\t\tif(len(cookie_end_split)>=3):\n\t\t\t\t\t#_LOGGER.debug(\"found three or more sections\")\n\t\t\t\t\tcookie_length_last_field = cookie_end_split[2].rfind(' ')\n\t\t\t\t\tif(cookie_length_last_field>=0):\n\t\t\t\t\t\t#_LOGGER.debug(\"found a space\")\n\t\t\t\t\t\tcookie_length = len(cookie_end_split[0])+1+len(cookie_end_split[1])+1+cookie_length_last_field\n\t\t\t\t\t\tclean_cookie = c[cookie_pos:cookie_pos+cookie_length]\n\t\t\t\t\t\t#_LOGGER.debug(clean_cookie)\n\t\t\t## lets try to find x-auth-part\n\t\t\txauth_pos = c.lower().find('x-goog-authuser: ')\n\t\t\tif(xauth_pos>=0):\n\t\t\t\t#_LOGGER.debug(\"found x-goog-authuser in text\")\n\t\t\t\t#_LOGGER.debug(c[xauth_pos+len('x-goog-authuser: '):])\n\t\t\t\txauth_len = c[xauth_pos+len('x-goog-authuser: '):].find(' ')\n\t\t\t\t#_LOGGER.debug(xauth_len)\n\t\t\t\tif(xauth_len>=0):\n\t\t\t\t\t#_LOGGER.debug(\"found space in text\")\n\t\t\t\t\tclean_x_goog_authuser = c[xauth_pos:(xauth_pos+len('x-goog-authuser: ')+xauth_len)]\n\t\t\t\t\t#_LOGGER.debug(clean_x_goog_authuser)\n\t\t\t## lets see what we got\n\t\t\tif(clean_cookie!=\"\" and clean_x_goog_authuser!=\"\"):\n\t\t\t\t# woop woop, this COULD be it\n\t\t\t\tc = clean_cookie+\"\\n\"+clean_x_goog_authuser+\"\\n\"\n\t\t\telse:\n\t\t\t\t# well we've failed to find the cookie, the only thing we can do is at least to help with some breaks\n\t\t\t\tc = c.replace('cookie','\\ncookie')\n\t\t\t\tc = c.replace('Cookie','\\nCookie')\n\t\t\t\tc = c.replace('x-goog-authuser','\\nx-goog-authuser')\n\t\t\t\tc = c.replace('X-Goog-AuthUser','\\nX-Goog-AuthUser')\n\t\t\t#_LOGGER.debug(\"feeding with: \")\n\t\t\t#_LOGGER.debug(c)\n\t\t\tYTMusic.setup(filepath = path, headers_raw = c)\n\t\t\treturn {}\n\t\texcept Exception:\n\t\t\tc = user_input[CONF_COOKIE].lower()\n\t\t\tif(c.find('cookie') == -1 ):\n\t\t\t\t_LOGGER.error(\"Field 'Cookie' not found!\")\n\t\t\t\tret[\"base\"] = ERROR_COOKIE\n\t\t\telif(c.find('X-Goog-AuthUser') == -1 ):\n\t\t\t\t_LOGGER.error(\"Field 'X-Goog-AuthUser' not found!\")\n\t\t\t\tret[\"base\"] = ERROR_AUTH_USER\n\t\t\telse:\n\t\t\t\t_LOGGER.error(\"Generic setup error, please see below\")\n\t\t\t\t_LOGGER.error(traceback.format_exc())\n\t\t\t\tret[\"base\"] = ERROR_GENERIC\n\t\t\treturn ret","title":""},{"docid":"8b3b96b9f6d98ec849efdb8895708c59","score":"0.45462644","text":"def get_user_info_query_variables(args):\n info = args[1]\n user = info.context.user\n query = FavoriteThing.get_query(info)\n return (info, user, query)","title":""},{"docid":"dfc6147c078e8b512d574b763b3ab4e2","score":"0.45443237","text":"def matches(id):","title":""},{"docid":"1865fb606f3f71050f9504119040a113","score":"0.45430806","text":"def src_user_features(self):\n response_time_metrics = self.src_dest_response_time_avgs()\n return {\n 'src_num_directed_dest': self.src_num_directed_dest(),\n 'src_avg_positive_sentiment_directed_dest': self.src_avg_positive_sentiment_directed_dest(),\n 'src_avg_negative_sentiment_directed_dest': self.src_avg_negative_sentiment_directed_dest(),\n 'src_num_with_media': number_of_tweets_with_media(self.src_user, self.node_collection),\n 'src_num_with_hashtags': number_of_tweets_with_hashtags(self.src_user, self.node_collection),\n 'src_num_with_urls': number_of_tweets_with_urls(self.src_user, self.node_collection),\n 'event_is_positive': self.event_is_positive(self.src_user),\n 'event_is_negative': self.event_is_negative(self.src_user),\n 'event_is_directed': self.event_is_directed_to(self.src_user, self.dest_user),\n 'event_has_hashtags': self.event_has_hashtags(self.src_user),\n 'event_has_media': self.event_has_media(self.src_user),\n 'event_has_url': self.event_has_url(self.src_user),\n 'num_event_responses': self.num_event_responses(self.src_user, self.dest_user),\n 'event_response_time': self.event_response_time(self.src_user, self.dest_user),\n 'src_mean_response_time_to_dest': response_time_metrics[0],\n 'src_median_response_time_to_dest': response_time_metrics[1],\n 'src_max_response_time_to_dest': response_time_metrics[2],\n 'src_mean_response_time': self.mean_response_time(self.src_user),\n 'src_median_response_time': self.median_response_time(self.src_user),\n 'src_max_response_time': self.max_response_time(self.src_user),\n }","title":""},{"docid":"fce9755fad4abd1470600df8db070693","score":"0.4542985","text":"async def get_player_battle_info(pool, user_id : int):\r\n base_info = await get_attack_crit_hp(pool, user_id)\r\n\r\n acolyte1, acolyte2 = await getAcolyteFromPlayer(pool, user_id)\r\n if acolyte1 is not None:\r\n acolyte1_info = await getAcolyteByID(pool, acolyte1)\r\n else:\r\n acolyte1_info = empty_acolyte_dict()\r\n\r\n if acolyte2 is not None:\r\n acolyte2_info = await getAcolyteByID(pool, acolyte2)\r\n else:\r\n acolyte2_info = empty_acolyte_dict()\r\n\r\n return {\r\n 'ID' : user_id,\r\n 'Name' : await getPlayerName(pool, user_id),\r\n 'Attack' : base_info['Attack'],\r\n 'Crit' : base_info['Crit'],\r\n 'HP' : base_info['HP'],\r\n 'Max_HP' : base_info['HP'] * 2,\r\n 'Class' : await getClass(pool, user_id),\r\n 'Acolyte1' : acolyte1_info,\r\n 'Acolyte2' : acolyte2_info,\r\n 'Strategy' : await getStrategy(pool, user_id)\r\n }","title":""},{"docid":"24736302e27414ed5ed0bf8e86c029fa","score":"0.45406625","text":"def get_uservars(self, user=None):\n\n if user is None:\n # All the users!\n return self._users\n elif user in self._users:\n # Just this one!\n return self._users[user]\n else:\n # No info.\n return None","title":""},{"docid":"5e0fd6d1fc4dd02c2e5f96d217b59924","score":"0.45312223","text":"def _query_attribute(\n self, dispatcher: CollectingDispatcher, tracker: Tracker\n ) -> List[Dict]:\n object_type = tracker.get_slot(SLOT_OBJECT_TYPE)\n attribute = tracker.get_slot(SLOT_ATTRIBUTE)\n\n object_name = self._get_object_name(\n tracker,\n self.knowledge_base.ordinal_mention_mapping,\n self.use_last_object_mention,\n )\n\n\n\n logger.info(\"_query_attribute [object_type]:\"+str(object_type) + \" [attribute]:\"+str(attribute)+\" [object_name]:\"+str(object_name))\n\n if not object_name or not attribute:\n logger.info(\"object_name or attribute not available\")\n dispatcher.utter_template(\"utter_ask_rephrase\", tracker)\n return [SlotSet(SLOT_MENTION, None)]\n\n object_attributes = self.knowledge_base.get_attributes_of_object(object_type)\n\n # get all set attribute slots of the object type to be able to filter the\n # list of objects\n attributes = get_attribute_slots(tracker, object_attributes)\n # query the knowledge base\n\n objects = self.knowledge_base.get_objects(object_type,attributes,object_name)\n key_attribute = self.knowledge_base.get_key_attribute_of_object(object_type)\n\n if not objects or attribute not in objects[0]:\n logger.info(\"object not found or attribute not in objects[0]\")\n dispatcher.utter_template(\"utter_ask_rephrase\", tracker)\n return [SlotSet(SLOT_MENTION, None)] + reset_attribute_slots(tracker, object_attributes)\n\n if len(objects) >1 :\n dispatcher.utter_message(\"Ho trovato più di un risultato:\")\n \n for object_of_interest in objects:\n value = object_of_interest[attribute]\n repr_function = self.knowledge_base.get_representation_function_of_object(\n object_type\n )\n object_representation = repr_function(object_of_interest)\n key_attribute = self.knowledge_base.get_key_attribute_of_object(object_type)\n object_identifier = object_of_interest[key_attribute]\n\n self.utter_attribute_value(dispatcher, object_representation, attribute, value)\n\n slots = [\n SlotSet(SLOT_OBJECT_TYPE, object_type),\n SlotSet(SLOT_ATTRIBUTE, None),\n SlotSet(SLOT_MENTION, None),\n SlotSet(SLOT_LAST_OBJECT, object_identifier),\n SlotSet(SLOT_LAST_OBJECT_TYPE, object_type),\n ]\n\n return slots + reset_attribute_slots(tracker, object_attributes)","title":""},{"docid":"7e872b8b133b932a11f1b58cd3ac2b29","score":"0.45271084","text":"def karma_given_by(user):\n from qon.base import get_group_database, get_user_database\n \n results = []\n \n def check_karma(item):\n p = item.karma_points_from(user)\n if p:\n results.append((item, p))\n \n def check_blog_karma(blog):\n for i in blog.get_items():\n check_karma(i)\n for c in i.get_all_comments():\n check_karma(c)\n \n for id, u in get_user_database().root.iteritems():\n check_karma(u)\n check_blog_karma(u.blog)\n \n for id, g in get_group_database().root.iteritems():\n check_blog_karma(g.blog)\n \n for id, page in g.wiki.pages.iteritems():\n check_karma(page)\n check_blog_karma(page.blog)\n\n return results","title":""},{"docid":"3ee6d40f6aba6439a4aa93d93b3e8bd1","score":"0.45251662","text":"def query_chapinfo_by_target(self, target_name, username):\n url = 'resource/target/get_chap_list'\n data = {\"targetName\": target_name}\n result = self.run(url, 'POST', data=data)\n self._assert_restapi_result(result, _(\"Query chapinfo \"\n \"by target error.\"))\n\n result = result.get('data') or []\n chapinfo = [c for c in result if c.get(\"user\") == username]\n if chapinfo:\n return chapinfo[0]\n return None","title":""},{"docid":"904b742f252f071d11d5b01c6bea9eb0","score":"0.45208624","text":"def get(cls, user_id: int):\n user = User.query.get_or_404(user_id)\n if not user:\n return response_with(resp.SERVER_ERROR_404)\n\n return (\n {\n \"current_time\": int(time()),\n \"confirmation\": [\n confirmation_schema.dump(each)\n for each in user.confirmation.order_by(Confirmation.expire_at)\n ],\n },\n 200,\n )","title":""},{"docid":"c01c96d09cf419a56d809760bf77fbf0","score":"0.45193732","text":"def get_user_statistics(user):\n # fetch some status labels in Submissions\n # here, we only concern about COMPILE_ERROR, RESTRICTED_FUNCTION,\n # and JUDGE_ERROR since ACCEPTED, NOT_ACCEPTED, etc will appear in\n # SubmissionDetail.VERDICT_CHOICE\n status_labels = [\n Submission.COMPILE_ERROR,\n Submission.RESTRICTED_FUNCTION,\n Submission.JUDGE_ERROR\n ]\n # find all verdict in SubmissionDetail.VERDICT_CHOICE\n verdict_labels = [x[0] for x in SubmissionDetail.VERDICT_CHOICE]\n statistics = []\n\n # fetch Submission of the given user\n submissions = Submission.objects.filter(user=user)\n for label in status_labels:\n statistics += [{\n 'label': label,\n 'value': submissions.filter(status=label).count()\n }]\n\n # fetch Submission of the given user\n submissions_id = map(lambda submission: submission.id, submissions)\n submission_details = SubmissionDetail.objects.filter(\n sid__in=submissions_id)\n for label in verdict_labels:\n statistics += [{\n 'label': label,\n 'value': submission_details.filter(verdict=label).count()\n }]\n\n return statistics","title":""},{"docid":"e35f0ce8538fbf61d958ed4a12aca2bf","score":"0.451922","text":"def get_all_session_dicts(session, filter_data):\n session_objs = session_generate_query(session, filter_data).all()\n results = []\n for session_obj in session_objs:\n if session_obj:\n results.append(session_obj.to_dict())\n return results","title":""}],"string":"[\n {\n \"docid\": \"2a68dcc3efe7a2dbc0131b7093d361ce\",\n \"score\": \"0.5634904\",\n \"text\": \"def example_data():\\n import datetime\\n\\n # Amazing. A dimension where all proper nouns begin with \\\"Schmla\\\":\\n u1 = User(username='Shmlony', password='123', email='shmlony@email.com', \\n phone='111-222-3333')\\n u2 = User(username='Shmlantha', password='123', email='shmlantha@email.com', \\n phone='111-222-3333')\\n u3 = User(username='Schmlona', password='123', email='schmlona@email.com', \\n phone='111-222-3333')\\n u4 = User(username='Schmlove', password='123', email='schmlove@email.com', \\n phone='111-222-3333')\\n u5 = User(username='Schmlandula', password='123', email='schmlandula@email.com', \\n phone='111-222-3333')\\n u6 = User(username='Schmlonathan', password='123', email='schmlonathan@email.com', \\n phone='111-222-3333')\\n\\n accepted = datetime.datetime(2017, 3, 30)\\n # you shouldn't be able to accept two challenges at the exact same instant \\n accepted2 = datetime.datetime(2017, 4, 30) \\n completed = datetime.datetime.now() # does not take into account time travel\\n\\n # Starting with user_id 2 because user_id 1 should have no challenges\\n # user 2, one pending\\n uc1 = UserChallenge(user_id=2, challenge_id=1, is_completed=False, \\n accepted_timestamp=accepted)\\n # user 3, two pending\\n uc2 = UserChallenge(user_id=3, challenge_id=1, is_completed=False, \\n accepted_timestamp=accepted)\\n uc3 = UserChallenge(user_id=3, challenge_id=2, is_completed=False, \\n accepted_timestamp=accepted2)\\n # user 4, pending and complete\\n uc4 = UserChallenge(user_id=4, challenge_id=1, is_completed=False, \\n accepted_timestamp=accepted )\\n uc5 = UserChallenge(user_id=4, challenge_id=2, is_completed=True, \\n accepted_timestamp=accepted2, completed_timestamp=completed, \\n image_path='static/images/currency.png')\\n # user 5, one complete\\n uc6 = UserChallenge(user_id=5, challenge_id=2, is_completed=True, \\n accepted_timestamp=accepted, completed_timestamp=completed, \\n image_path='static/images/currency.png')\\n # user 6, many complete\\n uc7 = UserChallenge(user_id=6, challenge_id=1, is_completed=True, \\n accepted_timestamp=accepted, completed_timestamp=completed, \\n image_path='static/images/butter.png')\\n uc8 = UserChallenge(user_id=6, challenge_id=2, is_completed=True, \\n accepted_timestamp=accepted2, completed_timestamp=completed,\\n image_path='static/images/currency.png')\\n\\n # Challenges\\n\\n c1 = Challenge(title='Existential Crisis', \\n description='Build an unnecessarily complex robot to do a simple task', \\n difficulty=5, image_path='/static/images/butter.png')\\n c2 = Challenge(title='Bring Down the Federation', \\n description='Take down the Galactic Federation by changing a 1 to a 0', \\n difficulty=2, image_path='/static/images/currency.png')\\n\\n # Challenge categories\\n\\n cc1 = ChallengeCategory(challenge_id=1, category_id=1)\\n cc2 = ChallengeCategory(challenge_id=1, category_id=2)\\n cc3 = ChallengeCategory(challenge_id=1, category_id=3)\\n cc4 = ChallengeCategory(challenge_id=1, category_id=4)\\n cc5 = ChallengeCategory(challenge_id=2, category_id=6)\\n cc6 = ChallengeCategory(challenge_id=2, category_id=1)\\n cc7 = ChallengeCategory(challenge_id=2, category_id=5)\\n cc8 = ChallengeCategory(challenge_id=2, category_id=7)\\n\\n # Categories\\n\\n cg1 = Category(tag='cartoon')\\n cg2 = Category(tag='art')\\n cg3 = Category(tag='machine')\\n cg4 = Category(tag='illustration')\\n cg5 = Category(tag='font')\\n cg6 = Category(tag='text')\\n cg7 = Category(tag='games')\\n\\n\\n db.session.add_all([u1, u2, u3, u4, u5, u6,\\n uc1, uc2, uc3, uc4, uc5, uc6, uc7, uc8,\\n c1, c2, cc1, cc2, cc3, cc4, cc5, cc6, cc7, cc8,\\n cg1, cg2, cg3, cg4, cg5, cg6, cg7])\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ab04c95c0f5b6adb7f147e39454e26\",\n \"score\": \"0.5623507\",\n \"text\": \"def get_trial_user_attrs(self, trial_id: int) -> Dict[str, Any]:\\n return self.get_trial(trial_id).user_attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71514d2f481ef38b5766157cb0b93cf7\",\n \"score\": \"0.55468446\",\n \"text\": \"def get_profile_page_info(user_id):\\n info = db.session.query(UserChallenge, \\n Challenge).join(Challenge).filter(UserChallenge.user_id==user_id)\\n return info.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49183853a05c7102065e612389554a37\",\n \"score\": \"0.53029066\",\n \"text\": \"def get_study_user_attrs(self, study_id: int) -> Dict[str, Any]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2fa17727e4a0959d753c6df39c287f2\",\n \"score\": \"0.52586186\",\n \"text\": \"def get_unverified_user_record_by_challenge(connection, challenge):\\r\\n\\tcursor = connection.cursor(cursor_factory=psycopg2.extras.DictCursor)\\r\\n\\tcursor.execute(\\\"SELECT * FROM unverified_users WHERE challenge=%s\\\", (challenge,))\\r\\n\\trecord = cursor.fetchone()\\r\\n\\treturn dict(record) if record is not None else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787dd7251f569464f594b2d3b225402e\",\n \"score\": \"0.5224856\",\n \"text\": \"def get_quiz_results_for_user_by_session(user_id, session_id):\\n return get_db().execute(\\n \\\"\\\"\\\"SELECT *\\n FROM quiz_results\\n WHERE user_id = (?) AND quiz_session_id = (?)\\\"\\\"\\\",\\n (user_id, session_id),\\n ).fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a422dfb1418faf7baf80869d035b02e\",\n \"score\": \"0.5189436\",\n \"text\": \"async def get_player_info(self, user_id: int):\\n for row in self.user_cur.execute(\\n 'SELECT \\\"userID\\\",\\\"name\\\",\\\"oculus\\\",\\\"pubs\\\",\\\"eloshow\\\",\\\"elo0\\\",\\\"elo1\\\",\\\"elo2\\\",\\\"elo3\\\" FROM'\\n '\\\"main\\\".\\\"users\\\" WHERE \\\"userID\\\" = '\\n + str(user_id)\\n ):\\n user = {\\n key: row[i] for i, key in enumerate(\\n (\\\"id\\\", \\\"name\\\", \\\"oculus\\\",\\\"pubs\\\",\\\"eloshow\\\",\\\"elo0\\\", \\\"elo1\\\", \\\"elo2\\\", \\\"elo3\\\")\\n )\\n }\\n #print(user)\\n return user\\n\\n return await self.save_new_user(user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15fbdeb9107cd1815a0eaafd8be463f\",\n \"score\": \"0.51578593\",\n \"text\": \"def get(self, user):\\n return get_current_challenge(user=user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc253e0f6200c3e390e61b83c86045e\",\n \"score\": \"0.5115934\",\n \"text\": \"def get_associated_samples(self, ctx, GenericParams):\\n # ctx is the context object\\n # return variables are: returnVal\\n #BEGIN get_associated_samples\\n user_id=ctx.get('user_id')\\n if not user_id:\\n raise InvalidUserError\\n GenericParams[\\\"user_id\\\"]=user_id\\n validated_params=misc.validate_params(GenericParams, 'get_associated_samples')\\n results = re_api.query(\\\"get_associated_samples\\\", validated_params)\\n\\n returnVal={\\\"stats\\\": results[\\\"stats\\\"], \\\"ts\\\": validated_params[\\\"ts\\\"], \\\"ns\\\": validated_params[\\\"ns\\\"]}\\n if results.get('error'):\\n returnVal[\\\"results\\\"]=[]\\n returnVal[\\\"total_count\\\"]=0\\n returnVal[\\\"total_accessible_count\\\"]=0\\n returnVal[\\\"error\\\"]=results.get('error')\\n else:\\n returnVal[\\\"total_count\\\"]=results[\\\"results\\\"][0][\\\"total_count\\\"]\\n returnVal[\\\"total_accessible_count\\\"]=results[\\\"results\\\"][0][\\\"total_accessible_count\\\"]\\n returnVal[\\\"results\\\"]=[]\\n for x in results[\\\"results\\\"][0][\\\"results\\\"]:\\n _sample_access=x.get(\\\"sample_access\\\")\\n _sample_metadata_key=x.get(\\\"sample_metadata_key\\\")\\n _sample=x.get(\\\"sample\\\")\\n if None in [_sample, _sample_metadata_key, _sample_access]:\\n returnVal[\\\"results\\\"].append({})\\n continue\\n sample={\\\"id\\\": _sample[\\\"id\\\"], \\\"save_date\\\": _sample[\\\"saved\\\"], \\n \\\"version\\\": _sample[\\\"ver\\\"], \\\"user\\\": _sample_access[\\\"acls\\\"][\\\"owner\\\"]}\\n node_tree={\\\"id\\\": _sample[\\\"name\\\"], \\\"type\\\": _sample[\\\"type\\\"], \\\"parent\\\": _sample[\\\"parent\\\"]}\\n meta_controlled={}\\n for m in _sample.get(\\\"cmeta\\\", []):\\n if m[\\\"ok\\\"] not in meta_controlled:\\n meta_controlled[m[\\\"ok\\\"]]={}\\n meta_controlled[m[\\\"ok\\\"]][m[\\\"k\\\"]]=m[\\\"v\\\"]\\n sample_name=meta_controlled.get(\\\"name\\\")\\n sample[\\\"name\\\"]=sample_name[\\\"value\\\"] if sample_name is not None else None\\n node_tree[\\\"meta_controlled\\\"]=meta_controlled\\n meta_user={}\\n for m in _sample.get(\\\"ucmeta\\\", []):\\n if m[\\\"ok\\\"] not in meta_user:\\n meta_user[m[\\\"ok\\\"]]={}\\n meta_user[m[\\\"ok\\\"]][m[\\\"k\\\"]]=m[\\\"v\\\"]\\n node_tree[\\\"meta_user\\\"]=meta_user\\n source_meta={}\\n for m in _sample.get(\\\"smeta\\\", []):\\n source_meta[\\\"key\\\"]=m[\\\"k\\\"]\\n source_meta[\\\"skey\\\"]=m[\\\"sk\\\"]\\n source_meta[\\\"svalue\\\"]=m[\\\"v\\\"]\\n node_tree[\\\"source_meta\\\"]=source_meta\\n sample[\\\"node_tree\\\"]=[node_tree]\\n returnVal[\\\"results\\\"].append({\\\"sample\\\": sample, \\\"sample_metadata_key\\\": _sample_metadata_key})\\n #END get_associated_samples\\n\\n # At some point might do deeper type checking...\\n if not isinstance(returnVal, dict):\\n raise ValueError('Method get_associated_samples return value ' +\\n 'returnVal is not type dict as required.')\\n # return the results\\n return [returnVal]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e786895fee3d12613e705429898dda5\",\n \"score\": \"0.51083285\",\n \"text\": \"def on_challenge(self, session, challenge):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0e40ae8c927f52caf971d8fb6338c5\",\n \"score\": \"0.50963753\",\n \"text\": \"def getPropertiesForUser(self, user, request=None):\\n if not request:\\n return {}\\n sessionInfo = request.SESSION.get(Auth0.session_key)\\n if not sessionInfo:\\n log.debug(\\\"No Auth0 session - not getting properties for user\\\")\\n return {}\\n userid = getattr(sessionInfo, \\\"userid\\\", None)\\n if not userid:\\n log.debug(\\\"No 'userid' in Auth0 session\\\")\\n return {}\\n # userid is not always an email; see method storeToken\\n if email_pattern.match(userid):\\n return {\\\"email\\\": userid}\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff4704e20427f5c203fd93d0b82083dc\",\n \"score\": \"0.5094332\",\n \"text\": \"def user_by_session(session_id):\\n cursor, conn = db_sql.connect('users.db')\\n sql = \\\"SELECT * FROM users WHERE session_ids LIKE ?\\\"\\n cursor.execute(sql, ('%\\\"' + session_id + '\\\"%', ))\\n temp = cursor.fetchone()\\n if temp != None:\\n user = dict(temp)\\n else:\\n user = None\\n conn.close()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffbdef076a676390101b122b2e7fd0c9\",\n \"score\": \"0.505652\",\n \"text\": \"def _get_answers(self):\\n\\t\\tif not self.form.username.data:\\n\\t\\t\\treturn None\\n\\t\\tquery = {'username' : str(self.form.username.data.lower())}\\n\\t\\treturn Record.get_secret_answers(collection='forgotten_password', query=query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae44df6155a3b0b96ea2249e5615930a\",\n \"score\": \"0.5042543\",\n \"text\": \"def respose_query_user_info():\\n response = {\\n 'userID': [],\\n 'userName': [],\\n 'age': [],\\n 'gender': [],\\n 'rfid': [],\\n 'roomNo': [],\\n 'berthNo': [],\\n 'diseaseType': [],\\n 'diseaseDec': [],\\n }\\n query_patient_info_db = DB()\\n ret = query_patient_info_db.query_patient_info()\\n if ret is None:\\n return response\\n\\n for i in range(0, len(ret)):\\n # 9 columns: 0 userID,1 userName,2 age\\n # 3 gender, 4 rfid, 5 roomNo, 6 berthNo\\n # 7 diseaseType 8 diseaseDec\\n response['userID'].append(ret[i][0])\\n response['userName'].append(ret[i][1])\\n response['age'].append(ret[i][2])\\n response['gender'].append(ret[i][3])\\n response['rfid'].append(ret[i][4])\\n response['roomNo'].append(ret[i][5])\\n response['berthNo'].append(ret[i][6])\\n response['diseaseType'].append(ret[i][7])\\n response['diseaseDec'].append(ret[i][8])\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa8172d0943f049ca2887e68e709a17\",\n \"score\": \"0.5019687\",\n \"text\": \"def get_relevant_user_data(client_name):\\n\\n try:\\n # Get the user from the database\\n user = Users.objects.get(name=client_name)\\n\\n except ObjectDoesNotExist:\\n # Catch the exception and pass it along, handle in parent\\n raise ObjectDoesNotExist\\n\\n try:\\n user_goals = Goals.objects.get(user_id=user.user_id)\\n user_data = dict()\\n today_info = get_today_macros(user)\\n\\n # Add goals and other stats to user, create space for logged entries\\n user_data[\\\"username\\\"] = user.name\\n user_data[\\\"user_score\\\"] = user.points\\n user_data[\\\"streak\\\"] = user.sprint\\n\\n user_data[\\\"breakfast_foods\\\"] = []\\n user_data[\\\"lunch_foods\\\"] = []\\n user_data[\\\"dinner_foods\\\"] = []\\n\\n # Add macro quanities, goals and percentages\\n user_data[\\\"curr_user_carbs\\\"] = today_info[\\\"carb_grams\\\"]\\n user_data[\\\"user_carbs_goal\\\"] = user_goals.carb_grams\\n user_data[\\\"user_carbs_percentage\\\"] = int((today_info[\\\"carb_grams\\\"] / user_goals.carb_grams) * 100)\\n\\n user_data[\\\"curr_user_fat\\\"] = today_info[\\\"fat_grams\\\"]\\n user_data[\\\"user_fat_goal\\\"] = user_goals.fat_grams\\n user_data[\\\"user_fat_percentage\\\"] = int((today_info[\\\"fat_grams\\\"] / user_goals.fat_grams) * 100)\\n\\n user_data[\\\"curr_user_protein\\\"] = today_info[\\\"protein_grams\\\"]\\n user_data[\\\"user_protein_goal\\\"] = user_goals.protein_grams\\n user_data[\\\"user_protein_percentage\\\"] = int((today_info[\\\"protein_grams\\\"] / user_goals.protein_grams) * 100)\\n\\n user_data[\\\"curr_user_cals\\\"] = today_info[\\\"kilocalories\\\"]\\n user_data[\\\"user_cals_goal\\\"] = user_goals.kilocalories\\n user_data[\\\"user_cals_percentage\\\"] = int((today_info[\\\"kilocalories\\\"] / user_goals.kilocalories) * 100)\\n\\n user_data[\\\"curr_user_water\\\"] = today_info[\\\"water_ml\\\"]\\n user_data[\\\"user_water_goal\\\"] = user_goals.water_ml\\n user_data[\\\"user_water_percentage\\\"] = int((today_info[\\\"water_ml\\\"] / user_goals.water_ml) * 100)\\n\\n if today_info:\\n # Add meals and food consumed today is any exist\\n start = datetime.now().date()\\n end = start + timedelta(1)\\n\\n today_start = datetime.combine(start, time())\\n today_end = datetime.combine(end, time())\\n\\n user_food_list = Entry.objects.filter(user_id=user)\\\\\\n .filter(is_water=False)\\\\\\n .filter(time_of_creation__range=[today_start, today_end])\\n\\n if user_food_list.exists():\\n for entry in user_food_list:\\n user_data[which_meal(entry.time_of_creation)].append(entry.entry_name)\\n\\n return user_data\\n\\n except Exception as e:\\n print(e.__class__.__name__)\\n print(e)\\n raise ObjectDoesNotExist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3f243d78d7302f21803532c24c148a8\",\n \"score\": \"0.50126487\",\n \"text\": \"def get_users_with_cracked_passwords(client: Client):\\n\\n args = demisto.args()\\n timeout = args.get(\\\"timeout\\\", 60)\\n timeout = int(timeout)\\n\\n mitigations_results = client.get_mitigations(timeout=timeout)\\n users_formatted = []\\n table_headers = [\\\"Username\\\"]\\n procedures_ids: Set[str] = set()\\n mitigation_id = None\\n privileged_users = []\\n unprivileged_users = []\\n\\n privileged = argToBoolean(args.get(\\\"privileged\\\", \\\"True\\\"))\\n\\n for mitigation in mitigations_results[\\\"mitigations\\\"]:\\n\\n if mitigation[\\\"name\\\"] == \\\"Brute Force\\\":\\n mitigation_id = mitigation[\\\"id\\\"]\\n break\\n\\n if mitigation_id:\\n l_mitigations = client.get_mitigation_by_id(mitigation_id)\\n\\n for subtechnique in l_mitigations[\\\"subtechniques\\\"]:\\n if subtechnique[\\\"name\\\"] == \\\"Password Guessing\\\":\\n procedures_ids.add(subtechnique[\\\"id\\\"])\\n\\n if procedures_ids:\\n\\n for proc_id in procedures_ids:\\n l_users = client.get_mitigations_subtechnique_procedure_by_id(proc_id)\\n\\n if l_users:\\n l_users = l_users[0][\\\"targets\\\"]\\n for user in l_users:\\n if \\\"Domain\\\" in user[\\\"labels\\\"]:\\n username_dict = user[\\\"name\\\"]\\n if privileged and (\\\"DomainAdmin\\\" in user[\\\"labels\\\"] or \\\"ComputerAdmin\\\" in user[\\\"labels\\\"]):\\n privileged_users.append(username_dict)\\n else:\\n unprivileged_users.append(username_dict)\\n\\n if privileged_users:\\n for username in privileged_users:\\n users_formatted.append({\\\"Username\\\": username})\\n readable_output = tableToMarkdown('Privileged Users With Cracked Passwords', privileged_users,\\n headers=table_headers)\\n elif unprivileged_users:\\n for username in unprivileged_users:\\n users_formatted.append({\\\"Username\\\": username})\\n readable_output = tableToMarkdown('Unprivileged Users With Cracked Passwords', unprivileged_users,\\n headers=table_headers)\\n else:\\n readable_output = tableToMarkdown('Users With Cracked Passwords', unprivileged_users,\\n headers=table_headers)\\n\\n command_results = CommandResults(\\n outputs_prefix=\\\"Cymptom.CrackedUsers\\\",\\n outputs_key_field=\\\"Username\\\",\\n readable_output=readable_output,\\n outputs=users_formatted,\\n )\\n\\n return command_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acae412b5d81d8956afe86db40deb94a\",\n \"score\": \"0.4995696\",\n \"text\": \"async def reward_users(self):\\r\\n async def get_object(r, server):\\r\\n \\\"\\\"\\\"\\r\\n Get channel or role from a reward.\\r\\n\\r\\n :param r: dict - reward\\r\\n :param server:\\r\\n :return:\\r\\n \\\"\\\"\\\"\\r\\n _m, _c, _r = await Utils(self.data).finder(server, r['id'], channel=True, role=True)\\r\\n if _c is not None:\\r\\n obj = _c\\r\\n else:\\r\\n obj = _r\\r\\n if obj is not None:\\r\\n i = \\\\\\r\\n self.data.servers[server_id].config['cookies']['cookie_rewards'][str(value)] \\\\\\r\\n .index(r)\\r\\n self.data.servers[server_id] \\\\\\r\\n .config['cookies']['cookie_rewards'][str(value)][i] = {\\r\\n 'type': r['type'], 'id': obj.id, 'name': obj.name,\\r\\n 'permanent': r['permanent']\\r\\n }\\r\\n self.data.servers[server_id].write_config()\\r\\n return obj\\r\\n\\r\\n for server_id, Server in self.data.servers.items():\\r\\n\\r\\n distribution = Server.config['cookies']['cookie_distribution']\\r\\n if len(distribution) == 0:\\r\\n continue\\r\\n\\r\\n reward_data, embeds = self.calculate_cookie_reward(Server)\\r\\n hundred, zero, diff = reward_data\\r\\n\\r\\n if Server.server.large:\\r\\n await self.data.client.request_offline_members(Server.server)\\r\\n\\r\\n for member in Server.server.members:\\r\\n\\r\\n user = self.check_user_config(member)\\r\\n\\r\\n highest = 0\\r\\n for value in Server.config['cookies']['cookie_rewards']:\\r\\n\\r\\n value = float(value)\\r\\n if value*diff/100 > user.config['cookies']['total_cookies']:\\r\\n continue\\r\\n\\r\\n if value*diff/100 < user.config['cookies']['total_cookies']:\\r\\n if value > highest:\\r\\n highest = value\\r\\n\\r\\n for value, rewards in Server.config['cookies']['cookie_rewards'].items():\\r\\n\\r\\n value = float(value)\\r\\n\\r\\n if value > highest:\\r\\n continue\\r\\n\\r\\n # When user has grown out of the reward\\r\\n if value < highest:\\r\\n\\r\\n for reward in rewards:\\r\\n\\r\\n if reward['permanent']:\\r\\n continue\\r\\n if reward['type'] == 'role':\\r\\n role = await get_object(reward, Server.server)\\r\\n if role is None:\\r\\n continue\\r\\n if role in user.user.roles:\\r\\n await self.data.client.remove_roles(user.user, role)\\r\\n\\r\\n if reward['type'] == 'channel':\\r\\n channel = await get_object(reward, Server.server)\\r\\n if channel is None:\\r\\n continue\\r\\n overwrites = channel.overwrites_for(user.user)\\r\\n overwrites.update(read_messages=False)\\r\\n await self.data.client.edit_channel_permissions(channel, user.user, overwrites)\\r\\n #\\r\\n if value == highest:\\r\\n\\r\\n for reward in rewards:\\r\\n\\r\\n if reward['type'] == 'role':\\r\\n role = await get_object(reward, Server.server)\\r\\n if role is None:\\r\\n continue\\r\\n if role not in user.user.roles:\\r\\n await self.data.client.add_roles(user.user, role)\\r\\n\\r\\n if reward['type'] == 'channel':\\r\\n channel = await get_object(reward, Server.server)\\r\\n if channel is None:\\r\\n continue\\r\\n overwrites = channel.overwrites_for(user.user)\\r\\n if not overwrites.read_messages:\\r\\n overwrites.update(read_messages=True)\\r\\n await self.data.client.edit_channel_permissions(channel, user.user, overwrites)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3490f76ea51ef49c111c1522fc40c835\",\n \"score\": \"0.49911833\",\n \"text\": \"def view_me(self, request):\\n\\n if request.user.id:\\n serializer = UserDetailSerializer(request.user, data=request.data)\\n\\n if serializer.is_valid() and request.method == \\\"PATCH\\\":\\n serializer.save()\\n serializer = UserDetailSerializer(request.user)\\n return Response(serializer.data, status=status.HTTP_200_OK)\\n guest = request.session.session_key\\n result = Result.objects.filter(session_id=guest)\\n serializer = ResultUserSerializer(result, many=True)\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac64b1d3206508a58b303e5e1bcb2b3\",\n \"score\": \"0.4960477\",\n \"text\": \"def _retrieve_user(self, context=None):\\n context = dict(context or {}, active_test=False)\\n res_users = self.env['res.users']\\n res = {}\\n for i in self:\\n domain = [('partner_id', '=', i)]\\n user_ids = res_users.search(domain)\\n user_id = False\\n if user_ids:\\n user_id = user_ids[0]\\n res[i] = user_id\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9343a84cb2a5928c9e5780fc6f2c243f\",\n \"score\": \"0.49550226\",\n \"text\": \"def get_sr_for_user(self,user):\\n assert(self.requests)\\n for req in self.requests:\\n if req.user == user:\\n return req\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3bbc1a02319b3b0e3639ba5591035f\",\n \"score\": \"0.49539447\",\n \"text\": \"def get_data_by_user_id(self):\\n rides_list = InvoiceGenerator.ride_list\\n self.user_ride_list.clear()\\n #print(rides_list)\\n for single_ride in rides_list:\\n if self.id_user == single_ride[0]:\\n self.user_ride_list.append(single_ride)\\n #print(self.user_ride_list)\\n self.display_user_data()\\n return self.user_ride_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3482f0ab782a832f3b7213f487afa69\",\n \"score\": \"0.49493283\",\n \"text\": \"def get_user_details():\\n list_of_questions = ['login', 'password', 'name', 'surname', 'email']\\n user_data = []\\n\\n for question in list_of_questions:\\n user_input_is_valid = False\\n user_input = None\\n\\n while user_input_is_valid is False:\\n view.clear_window()\\n user_input = view.get_inputs([question])\\n user_input_is_valid = validate_user_personal_data(question, user_input)\\n\\n user_data.append(user_input)\\n\\n return user_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b1d524b46ee24ef50a3e5bd653fbc7\",\n \"score\": \"0.49214286\",\n \"text\": \"def brute_force_user_status(self, realm, userId):\\n\\n path = \\\"/{realm}/attack-detection/brute-force/users/{userId}\\\".format(\\n realm=realm, userId=userId\\n )\\n params = {\\n \\n }\\n headers = {\\n \\n }\\n return self.session.request(\\n method=\\\"GET\\\",\\n endpoint=self.session._admurl(path),\\n params=params,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b09c71e9cbb29da8299a974f4d85711\",\n \"score\": \"0.4907587\",\n \"text\": \"def revealed_attrs(proof: dict) -> dict:\\n\\n rv = {}\\n for sub_index in range(len(proof['identifiers'])):\\n cd_id = proof['identifiers'][sub_index]['cred_def_id']\\n rv[cd_id] = {\\n attr: decode(proof['proof']['proofs'][sub_index]['primary_proof']['eq_proof']['revealed_attrs'][attr])\\n for attr in proof['proof']['proofs'][sub_index]['primary_proof']['eq_proof']['revealed_attrs']\\n }\\n\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b09c71e9cbb29da8299a974f4d85711\",\n \"score\": \"0.4907587\",\n \"text\": \"def revealed_attrs(proof: dict) -> dict:\\n\\n rv = {}\\n for sub_index in range(len(proof['identifiers'])):\\n cd_id = proof['identifiers'][sub_index]['cred_def_id']\\n rv[cd_id] = {\\n attr: decode(proof['proof']['proofs'][sub_index]['primary_proof']['eq_proof']['revealed_attrs'][attr])\\n for attr in proof['proof']['proofs'][sub_index]['primary_proof']['eq_proof']['revealed_attrs']\\n }\\n\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0813229526fd0f15c7cfcde50d705996\",\n \"score\": \"0.49054447\",\n \"text\": \"def get_user_details():\\n\\n questions_list = ['password', 'Name', 'Surname', 'email']\\n user_details = []\\n\\n for question in questions_list:\\n user_input = None\\n\\n while not user_input:\\n user_input = codecooler_view.get_inputs('', [question])[0]\\n try:\\n user_input = additional_filters_for_user_details(question, user_input)\\n except ValueError as err:\\n print(err)\\n user_input = None\\n\\n user_details.append(user_input)\\n\\n today = date.today()\\n today = \\\"{}.{}.{}\\\".format(today.day, today.month, today.year)\\n user_details.append(today)\\n\\n return user_details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702525c2618dbfdf3ad9ac3785b3a19c\",\n \"score\": \"0.48794073\",\n \"text\": \"def get_user_details(self, response):\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702525c2618dbfdf3ad9ac3785b3a19c\",\n \"score\": \"0.48794073\",\n \"text\": \"def get_user_details(self, response):\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8699148e31dfb232aeae98692cd4ef\",\n \"score\": \"0.48652795\",\n \"text\": \"def user_details(user_id):\\n \\n user = User.query.get(user_id)\\n movies = Movie.query.all()\\n user_truths = Truth.query.filter_by(user_id=user_id).all()\\n user_replies = Reply.query.filter_by(user_id=user_id).all()\\n\\n\\n return render_template(\\\"user.html\\\",\\n movies=movies,\\n user=user,\\n user_truths=user_truths,\\n user_replies=user_replies)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612742d1edadf0b8311a7c05fd6c3ba3\",\n \"score\": \"0.4855388\",\n \"text\": \"def __get_data(self, target=None):\\n if target is None:\\n target = self.user_id\\n r = requests.get(f'https://api.spreaker.com/v2/users/{target}')\\n response = r.json().get('response')\\n if r.status_code == 200:\\n user_info = response.get('user')\\n return user_info\\n else:\\n raise Exception('Code {code}: {messages}'.format(**r.get('error')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54848104d7547b67c49401fc89662c78\",\n \"score\": \"0.48307657\",\n \"text\": \"def get_needinfo_query(username):\\n # type: (str) -> dict\\n query = dict(f1='requestees.login_name',\\n o1='equals',\\n v1='{}{}'.format(username, common_data.RHDT))\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec6534fbf8c135f2bd9257d133c0e09\",\n \"score\": \"0.48293883\",\n \"text\": \"def get_sessions_by_user(user):\\n\\n check_environment()\\n\\n query = \\\"select DISTINCT a.idsesion,b.dsesion from ut_st_storage A, ut_st_sesion B where ccuenta= \\\" + comillas(user) + \\\" and a.idsesion=b.idsesion\\\"\\n cursor = bbdd_execute(query)\\n rows = cursor.fetchall()\\n\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34a1e5be42635b2958d67c52ada1ba2\",\n \"score\": \"0.4818256\",\n \"text\": \"def get_queryset(self):\\n hash_key = self.request.query_params.get('session')\\n if hash_key is not None and hash_key:\\n try:\\n event = EventSession.objects.get(\\n hash_key=hash_key,\\n )\\n return Exam.objects.by_user_perms(self.request.user).filter(\\n event=event\\n ).prefetch_related('comment_set').prefetch_related('usersession_set')\\n except EventSession.DoesNotExist:\\n return Exam.objects.filter(pk__lt=0).prefetch_related('comment_set').prefetch_related('usersession_set')\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35351d0ead92aade38ab28445ab4f8e3\",\n \"score\": \"0.4817109\",\n \"text\": \"def retrieve_user(self, user_id):\\n pk_exp = Key('pk').eq('person')\\n\\n for lsi_key in self.lsi_key_combinations:\\n sk_exp = Key('lsi').eq(lsi_key + '#' + user_id)\\n key_exp = pk_exp & sk_exp\\n # Retrieve user with ID\\n user = DDB_TABLE.query(\\n IndexName='user-id-index',\\n KeyConditionExpression=key_exp\\n )\\n if user.get('Count', 0) == 0:\\n continue\\n else:\\n return user['Items'][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb6890570f39ca4d2af7ee77a86779ad\",\n \"score\": \"0.481015\",\n \"text\": \"def get_users_data():\\n response = client.list_users()['Users']\\n for user in response:\\n list_of_checks = {\\n \\\"LoginProfile\\\" : client.get_login_profile,\\n \\\"PolicyNames\\\" : client.list_user_policies,\\n \\\"AttachedPolicies\\\" : client.list_attached_user_policies,\\n \\\"AccessKeyMetadata\\\" : client.list_access_keys,\\n \\\"Groups\\\" : client.list_groups_for_user,\\n \\\"MFADevices\\\" : client.list_mfa_devices,\\n \\\"Certificates\\\" : client.list_signing_certificates,\\n \\\"SSHPublicKeys\\\" : client.list_ssh_public_keys,\\n \\\"User\\\" : client.get_user,\\n }\\n for key in list_of_checks:\\n try:\\n user[key] = list_of_checks[key](UserName=user['UserName'])[key]\\n except Exception as e:\\n logging.warning(f\\\"Failed with {e}; skipping.\\\")\\n\\n iam_output['Users'] = response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5f7285a21c09e46794cb054210a536\",\n \"score\": \"0.4799769\",\n \"text\": \"def extract_users(data_path):\\n d = load_json(data_path)\\n profs = [ent['user'] for ent in d]\\n return profs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f253d8744666323c4f3f91267087a56a\",\n \"score\": \"0.47994873\",\n \"text\": \"def get_raters(self):\\n raw_users = self.circuit_ratings.values('user')\\n users = []\\n for elem in raw_users:\\n users.append(elem['user'])\\n\\n return User.objects.filter(id_in=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b58dbdcf49b709788b76bcc84da132e\",\n \"score\": \"0.4781437\",\n \"text\": \"def user_get_details(*, db: Session = Depends(dependencies.get_db), id: str):\\n results_proxy = db.execute(f\\\"select * from users where (id = '{id}')\\\")\\n return [{**row} for row in results_proxy]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a677ef24d8f1ee708583d6b42e4d0441\",\n \"score\": \"0.47808662\",\n \"text\": \"def results():\\n #answers shouldn't really be part of the exam_result table. fix this\\n exam_answers = {}\\n exam_practical_answers = {}\\n\\n # exam_questions = ExamQuestions.query.filter(\\n # and_(\\n # ExamQuestions.question_category==\\\"theory\\\",\\n # ExamQuestions.exam_level==current_user.level\\n # )\\n # )\\n \\n theory_answers = ExamQuestions.query.filter_by(question_category=\\\"theory\\\")\\n # theory_answers = ExamQuestions.query.filter_by(question_category=\\\"theory\\\").all()\\n practical_answers = ExamPractialAnswers.query.all()\\n \\n for theory_answer in theory_answers:\\n exam_answers[f\\\"{theory_answer.question_id}\\\"] = {\\n \\\"answer\\\":theory_answer.answer,\\n \\\"level\\\":theory_answer.exam_level\\n }\\n\\n for practical_answer in practical_answers:\\n exam_practical_answers[f\\\"{practical_answer.id}\\\"] = practical_answer.control_score\\n\\n exam_result = []\\n participants = [x.sagf_id for x in User.query.filter(User.level.in_([\\\"1\\\",\\\"2\\\"]))]\\n results = ExamResult.query.filter(ExamResult.sagf_id.in_(participants))\\n for result in results:\\n r = {}\\n r[\\\"name\\\"] = f\\\"{result.linked_user.name} {result.linked_user.surname}\\\"\\n r[\\\"sagf_id\\\"] = result.linked_user.sagf_id\\n #Filter bases on level\\n exam_answer_copy = {}\\n for k,v in exam_answers.items():\\n if v[\\\"level\\\"] == result.linked_user.level:\\n exam_answer_copy[k] = v[\\\"answer\\\"]\\n #calculate the theory result\\n percent, missed = calculate_theory_score(json.loads(result.theory_answer), exam_answer_copy)\\n r[\\\"theory\\\"] = percent\\n r[\\\"theory_missed\\\"] = missed\\n #practical answer cannot be None \\n if not result.practical_answer: result.practical_answer = '{\\\"answer_1\\\":\\\"0\\\"}'\\n practical_percent, practical_calculated_answer = calculate_practical_score(json.loads(result.practical_answer), practical_answers)\\n r[\\\"practical\\\"] = practical_percent\\n r[\\\"practical_answers\\\"] = practical_calculated_answer\\n r[\\\"level\\\"] = result.linked_user.level\\n date_taken = result.exam_start_date\\n date_diff = datetime.datetime.today()-date_taken\\n if date_diff.days <= 2:\\n r[\\\"recent\\\"] = \\\"1\\\"\\n else:\\n r[\\\"recent\\\"] = \\\"-1\\\"\\n\\n exam_result.append(r)\\n\\n return render_template(\\\"exam/results.html\\\", title=\\\"Exam Results\\\", exam_result=exam_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf8f977ed0f22812f74224229dc53ba\",\n \"score\": \"0.47792172\",\n \"text\": \"def _items_of_user(self, user):\\n items = []\\n\\n for i in self.interactions_list:\\n if i[0] == user:\\n items.append(i[1])\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb43d1486d0b37118f2e85607822d9a2\",\n \"score\": \"0.47731897\",\n \"text\": \"def getSessionDataByKey(key):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03bdb8624cea83f461b9efc9d4261579\",\n \"score\": \"0.47713858\",\n \"text\": \"def get_user_goals(self):\\n selected_goal_ids = self.user.usergoal_set.filter(goal__action=self.action)\\n selected_goal_ids = selected_goal_ids.values_list(\\\"goal\\\", flat=True)\\n return Goal.objects.filter(id__in=selected_goal_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a606d5a1a98afeca9337159352409a\",\n \"score\": \"0.47691235\",\n \"text\": \"def session_length(\\n self, \\n user_id: Optional[str] = None, \\n verbose: Optional[int] = 0\\n ) -> Dict:\\n if self._time_statistics: # if the statistics have already been written\\n if user_id: # if the request is for a single user\\n if user_id not in self._time_statistics.keys(): \\n raise ValueError('Invalid user id (perhaps the user is not in the data)')\\n\\n return self._time_statistics[user_id]['session_length']\\n return {user: stat['session_length'] for user, stat in self._time_statistics.items()}\\n else:\\n self.time_statistics(verbose = verbose)\\n\\n if user_id:\\n if user_id not in self._time_statistics.keys():\\n raise ValueError('Invalid user id (perhaps the user is not in the data)')\\n return self._time_statistics[user_id]['session_length']\\n \\n return {user: stat['session_length'] for user, stat in self._time_statistics.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7de42df08831742ac2e63db0da65107\",\n \"score\": \"0.47516194\",\n \"text\": \"def get(self):\\n header_token = request.headers.get('Authorization')\\n if header_token:\\n user_token = header_token.split(\\\" \\\")[1]\\n user_id = User.decode_authentication_token(user_token)\\n\\n if isinstance(user_id, int):\\n # then get all rides posted by this person using the got id\\n user_rides=[ride for ride in rideslist if ride[\\\"user_id\\\"]==user_id]\\n\\n # get all the ride requests by this person\\n user_requests=[request for request in ride_requests if request[\\\"user_id\\\"]==user_id]\\n # get the requests details\\n return {\\\"status\\\":\\\"success\\\", \\\"message\\\":\\\"successful return\\\", \\\"my_rides\\\":user_rides,\\\"my_requests\\\":user_requests }\\n\\n return {\\\"status\\\":\\\"fail\\\",\\\"message\\\":\\\"unregistered user\\\"}, 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94fbf9e4fcb56b128efe146ff9b848f\",\n \"score\": \"0.47504535\",\n \"text\": \"def __find_user_info(\\n self, user_information: Dict[int, str]\\n ) -> Dict[bool, List[Users]]:\\n\\n user_found = None\\n user_params = user_information.keys()\\n\\n print(user_params)\\n\\n if \\\"user_id\\\" in user_params and \\\"user_name\\\" in user_params:\\n user_found = self.find_user.by_id_and_name(\\n user_information[\\\"user_id\\\"], user_information[\\\"user_name\\\"]\\n )\\n elif \\\"user_id\\\" not in user_params and \\\"user_name\\\" in user_params:\\n user_found = self.find_user.by_name(user_information[\\\"user_name\\\"])\\n elif \\\"user_id\\\" in user_params and \\\"user_name\\\" not in user_params:\\n user_found = self.find_user.by_id(user_information[\\\"user_id\\\"])\\n else:\\n return {\\\"Success\\\": False, \\\"Data\\\": None}\\n\\n return user_found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d3e3e9fb5349993e9365ee5bcf83f5\",\n \"score\": \"0.47461018\",\n \"text\": \"def getSecurityInfoFromUser(self,_user):\\n\\n query = \\\"SELECT PublicKey,G,P from user where UserName = %s\\\"\\n try:\\n #Executing the query\\n self.cursor.execute(query,(_user))\\n rows = self.cursor.fetchall()\\n return [rows[0][0],rows[0][1],rows[0][2]]\\n except Exception as e:\\n #rollback to the previous operations\\n self.db.rollback()\\n print (e)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4b4397ef7582bd15959f7e6f4545f41\",\n \"score\": \"0.47437233\",\n \"text\": \"def get_authentication_session(self, contextid, userid):\\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'users/view/getAuthenticationSession/', {'contextId': contextid, 'userId': userid})))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e11361e6c6b6722331ae9bc5a4296d\",\n \"score\": \"0.4740214\",\n \"text\": \"def FindCredentials(self,challenge):\\n\\t\\tlogging.debug(\\\"HTTPRequestManager searching for credentials in %s with challenge %s\\\",challenge.protectionSpace,str(challenge))\\n\\t\\twith self.managerLock:\\n\\t\\t\\tfor c in self.credentials:\\n\\t\\t\\t\\tif c.MatchChallenge(challenge):\\n\\t\\t\\t\\t\\treturn c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7cfe85512b25f2413048de7d0af946a\",\n \"score\": \"0.47316742\",\n \"text\": \"def match(self, request, gathering_pk):\\n return get_matches(request, gathering_pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc7b98be53328a4c6fa5eb9fa642370c\",\n \"score\": \"0.47298035\",\n \"text\": \"def _get_user(self, args:Dict[str,str], users:'DataFrame') -> 'DataFrame':\\r\\n return users.loc[(users['username'] == args['username'])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17859cad43832f1d5aacab9f4844a920\",\n \"score\": \"0.47252014\",\n \"text\": \"def getUserInfo(user_id): \\n session = connect_to_database()\\n user = session.query(User).filter_by(id=user_id).one()\\n session.close()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22333fa5eb978f4836836465fe4b1011\",\n \"score\": \"0.47081572\",\n \"text\": \"def get_all_play_session_infos(self, game: str, version: int) -> List[Tuple[UserID, ValidatedDict]]:\\n sql = (\\n \\\"SELECT id, time, userid, data FROM playsession \\\"\\n \\\"WHERE game = :game AND version = :version \\\"\\n \\\"AND time > :time\\\"\\n )\\n cursor = self.execute(\\n sql,\\n {\\n 'game': game,\\n 'version': version,\\n 'time': Time.now() - Time.SECONDS_IN_HOUR,\\n },\\n )\\n\\n ret = []\\n for result in cursor.fetchall():\\n data = ValidatedDict(self.deserialize(result['data']))\\n data['id'] = result['id']\\n data['time'] = result['time']\\n ret.append((UserID(result['userid']), data))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc8ced45ff0b607aa8884d21c4448fe\",\n \"score\": \"0.4707702\",\n \"text\": \"def userinfo(claims: Dict[str, Any], user: User) -> Dict[str, Any]:\\n claims[\\\"name\\\"] = user.name\\n claims[\\\"given_name\\\"] = user.name\\n claims[\\\"family_name\\\"] = user.name\\n claims[\\\"email\\\"] = user.email\\n\\n return claims\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a2ea1616dfd1dfdb1029cf0ffdccb4\",\n \"score\": \"0.47041073\",\n \"text\": \"def _get_user(self, args:Dict[str,str], users:'DataFrame', action:str='login') -> 'DataFrame':\\r\\n if action == 'login':\\r\\n return users.loc[(users['username'] == args['username']) & \\r\\n (users['password'] == args['password'])]\\r\\n elif action == 'signup':\\r\\n return users.loc[(users['username'] == args['username'])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f458b89a548b0e4b644f6afe39a3cc0e\",\n \"score\": \"0.47019228\",\n \"text\": \"def challenge_details(id):\\n username = get_user_by_id(session['user_id']).username\\n challenge = db.session.query(Challenge, ChallengeCategory).filter(Challenge.id==id).join(ChallengeCategory).all()\\n winners = leaderboard(id)\\n print winners, \\\"route print\\\"\\n return render_template('challenge.html', challenge=challenge, username=username, winners=winners)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5babd084785e76019b05579ded7d71\",\n \"score\": \"0.4687548\",\n \"text\": \"def user_mfa_check(cache: dict, session, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:\\n iam = session.client(\\\"iam\\\")\\n # ISO Time\\n iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()\\n for users in get_iam_users(cache, session):\\n # B64 encode all of the details for the Asset\\n assetJson = json.dumps(users,default=str).encode(\\\"utf-8\\\")\\n assetB64 = base64.b64encode(assetJson)\\n userName = users[\\\"UserName\\\"]\\n userArn = users[\\\"Arn\\\"]\\n # check if the user has a password - override MFA passing if not\\n if \\\"PasswordLastUsed\\\" not in users:\\n passwordMfaPassing = True\\n else:\\n if not iam.list_mfa_devices(UserName=userName)[\\\"MFADevices\\\"]:\\n passwordMfaPassing = False\\n else:\\n passwordMfaPassing = True\\n\\n if passwordMfaPassing is False:\\n # this is a failing check\\n finding = {\\n \\\"SchemaVersion\\\": \\\"2018-10-08\\\",\\n \\\"Id\\\": f\\\"{userArn}/iam-user-mfa-check\\\",\\n \\\"ProductArn\\\": f\\\"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default\\\",\\n \\\"GeneratorId\\\": userArn,\\n \\\"AwsAccountId\\\": awsAccountId,\\n \\\"Types\\\": [\\\"Software and Configuration Checks/AWS Security Best Practices\\\"],\\n \\\"FirstObservedAt\\\": iso8601Time,\\n \\\"CreatedAt\\\": iso8601Time,\\n \\\"UpdatedAt\\\": iso8601Time,\\n \\\"Severity\\\": {\\\"Label\\\": \\\"HIGH\\\"},\\n \\\"Confidence\\\": 99,\\n \\\"Title\\\": \\\"[IAM.3] IAM users with passwords should have Multi-Factor Authentication (MFA) enabled\\\",\\n \\\"Description\\\": f\\\"IAM user {userName} does not have MFA enabled. For increased security, AWS recommends that you configure multi-factor authentication (MFA) to help protect your AWS resources. Passwords are the most common method of authenticating a sign-in to a computer or online service, but they're also the most vulnerable. People can choose easy passwords and use the same passwords for multiple sign-ins to different computers and services. To provide an extra level of security for sign-ins, you must use multifactor authentication (MFA), which uses both a password, which should be strong, and an additional verification method based on either something you have with you that isn't easily duplicated, such as Time-based One Time Password (TOTP) generation application such as Google Authenticator or using a FIDO2 hardware key such as a Yubikey. The additional verification method isn't employed until after the user's password has been verified. With MFA, even if a strong user password is compromised, the attacker doesn't have your smart phone or your fingerprint to complete the sign-in. Refer to the remediation section if this behavior is not intended.\\\",\\n \\\"Remediation\\\": {\\n \\\"Recommendation\\\": {\\n \\\"Text\\\": \\\"For information on MFA refer to the Using Multi-Factor Authentication (MFA) in AWS section of the AWS IAM User Guide\\\",\\n \\\"Url\\\": \\\"https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_mfa.html\\\"\\n }\\n },\\n \\\"ProductFields\\\": {\\n \\\"ProductName\\\": \\\"ElectricEye\\\",\\n \\\"Provider\\\": \\\"AWS\\\",\\n \\\"ProviderType\\\": \\\"CSP\\\",\\n \\\"ProviderAccountId\\\": awsAccountId,\\n \\\"AssetRegion\\\": global_region_generator(awsPartition),\\n \\\"AssetDetails\\\": assetB64,\\n \\\"AssetClass\\\": \\\"Identity & Access Management\\\",\\n \\\"AssetService\\\": \\\"AWS IAM\\\",\\n \\\"AssetComponent\\\": \\\"User\\\"\\n },\\n \\\"Resources\\\": [\\n {\\n \\\"Type\\\": \\\"AwsIamUser\\\",\\n \\\"Id\\\": userArn,\\n \\\"Partition\\\": awsPartition,\\n \\\"Region\\\": awsRegion,\\n \\\"Details\\\": {\\n \\\"AwsIamUser\\\": {\\n \\\"UserName\\\": userName\\n }\\n }\\n }\\n ],\\n \\\"Compliance\\\": {\\n \\\"Status\\\": \\\"FAILED\\\",\\n \\\"RelatedRequirements\\\": [\\n \\\"NIST CSF V1.1 PR.AC-1\\\",\\n \\\"NIST SP 800-53 Rev. 4 AC-1\\\",\\n \\\"NIST SP 800-53 Rev. 4 AC-2\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-1\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-2\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-3\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-4\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-5\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-6\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-7\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-8\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-9\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-10\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-11\\\",\\n \\\"AICPA TSC CC6.1\\\",\\n \\\"AICPA TSC CC6.2\\\",\\n \\\"ISO 27001:2013 A.9.2.1\\\",\\n \\\"ISO 27001:2013 A.9.2.2\\\",\\n \\\"ISO 27001:2013 A.9.2.3\\\",\\n \\\"ISO 27001:2013 A.9.2.4\\\",\\n \\\"ISO 27001:2013 A.9.2.6\\\",\\n \\\"ISO 27001:2013 A.9.3.1\\\",\\n \\\"ISO 27001:2013 A.9.4.2\\\",\\n \\\"ISO 27001:2013 A.9.4.3\\\",\\n \\\"CIS Amazon Web Services Foundations Benchmark V1.5 1.10\\\"\\n ]\\n },\\n \\\"Workflow\\\": {\\\"Status\\\": \\\"NEW\\\"},\\n \\\"RecordState\\\": \\\"ACTIVE\\\"\\n }\\n yield finding\\n else:\\n finding = {\\n \\\"SchemaVersion\\\": \\\"2018-10-08\\\",\\n \\\"Id\\\": f\\\"{userArn}/iam-user-mfa-check\\\",\\n \\\"ProductArn\\\": f\\\"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default\\\",\\n \\\"GeneratorId\\\": userArn,\\n \\\"AwsAccountId\\\": awsAccountId,\\n \\\"Types\\\": [\\\"Software and Configuration Checks/AWS Security Best Practices\\\"],\\n \\\"FirstObservedAt\\\": iso8601Time,\\n \\\"CreatedAt\\\": iso8601Time,\\n \\\"UpdatedAt\\\": iso8601Time,\\n \\\"Severity\\\": {\\\"Label\\\": \\\"INFORMATIONAL\\\"},\\n \\\"Confidence\\\": 99,\\n \\\"Title\\\": \\\"[IAM.3] IAM users with passwords should have Multi-Factor Authentication (MFA) enabled\\\",\\n \\\"Description\\\": f\\\"IAM user {userName} has MFA enabled or does not have a password.\\\",\\n \\\"Remediation\\\": {\\n \\\"Recommendation\\\": {\\n \\\"Text\\\": \\\"For information on MFA refer to the Using Multi-Factor Authentication (MFA) in AWS section of the AWS IAM User Guide\\\",\\n \\\"Url\\\": \\\"https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_mfa.html\\\"\\n }\\n },\\n \\\"ProductFields\\\": {\\n \\\"ProductName\\\": \\\"ElectricEye\\\",\\n \\\"Provider\\\": \\\"AWS\\\",\\n \\\"ProviderType\\\": \\\"CSP\\\",\\n \\\"ProviderAccountId\\\": awsAccountId,\\n \\\"AssetRegion\\\": global_region_generator(awsPartition),\\n \\\"AssetDetails\\\": assetB64,\\n \\\"AssetClass\\\": \\\"Identity & Access Management\\\",\\n \\\"AssetService\\\": \\\"AWS IAM\\\",\\n \\\"AssetComponent\\\": \\\"User\\\"\\n },\\n \\\"Resources\\\": [\\n {\\n \\\"Type\\\": \\\"AwsIamUser\\\",\\n \\\"Id\\\": userArn,\\n \\\"Partition\\\": awsPartition,\\n \\\"Region\\\": awsRegion,\\n \\\"Details\\\": {\\n \\\"AwsIamUser\\\": {\\n \\\"UserName\\\": userName\\n }\\n }\\n }\\n ],\\n \\\"Compliance\\\": {\\n \\\"Status\\\": \\\"PASSED\\\",\\n \\\"RelatedRequirements\\\": [\\n \\\"NIST CSF V1.1 PR.AC-1\\\",\\n \\\"NIST SP 800-53 Rev. 4 AC-1\\\",\\n \\\"NIST SP 800-53 Rev. 4 AC-2\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-1\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-2\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-3\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-4\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-5\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-6\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-7\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-8\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-9\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-10\\\",\\n \\\"NIST SP 800-53 Rev. 4 IA-11\\\",\\n \\\"AICPA TSC CC6.1\\\",\\n \\\"AICPA TSC CC6.2\\\",\\n \\\"ISO 27001:2013 A.9.2.1\\\",\\n \\\"ISO 27001:2013 A.9.2.2\\\",\\n \\\"ISO 27001:2013 A.9.2.3\\\",\\n \\\"ISO 27001:2013 A.9.2.4\\\",\\n \\\"ISO 27001:2013 A.9.2.6\\\",\\n \\\"ISO 27001:2013 A.9.3.1\\\",\\n \\\"ISO 27001:2013 A.9.4.2\\\",\\n \\\"ISO 27001:2013 A.9.4.3\\\",\\n \\\"CIS Amazon Web Services Foundations Benchmark V1.5 1.10\\\",\\n ]\\n },\\n \\\"Workflow\\\": {\\\"Status\\\": \\\"RESOLVED\\\"},\\n \\\"RecordState\\\": \\\"ARCHIVED\\\"\\n }\\n yield finding\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f5af9f282367e841a6036cda4186ea\",\n \"score\": \"0.46828836\",\n \"text\": \"def demo_matching(user_id):\\n find_match(user_id, is_queued=False)\\n return {}, 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfea9aec73e23e6fc15a7a58cead582e\",\n \"score\": \"0.46725953\",\n \"text\": \"def get(self, uid):\\n result = {}\\n for entity, attribute, value in self.facts:\\n if entity == uid:\\n result[attribute] = value\\n return result or None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7571f12973835fc72f396d14af30f6df\",\n \"score\": \"0.4671931\",\n \"text\": \"def reveiveSessionAttributeVerificationRequest(self, pWhat, pKey):\\n log.log(cons.TK_LOG_LEVEL_DEBUG, \\\"receive verification request: %s, %s\\\" % (pWhat, \\\"key\\\"))\\n # resend stuff to server\\n self._timekprClientIndicator.verifySessionAttributes(pWhat, pKey)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73c9e036fb3563f397fa760dfaa5fa5\",\n \"score\": \"0.46698922\",\n \"text\": \"def get_user_logic(user_id, query_param):\\n '''# Check if we have given user in our database\\n if not check_if_user_exists(user_id):\\n raise HTTPException(status_code=400, detail=f\\\"User {user_id} does not exist!\\\")\\n\\n user = getattr(users_module, user_mapper[user_id])\\n\\n # Check if value of parameter exists in database. If not return list of most similar values.\\n is_valid_param, param = app.database.daos.check_if_param_exists(\\n user_id=user_id,\\n query_param=query_param,\\n date_from=None,\\n date_to=None\\n )\\n if not is_valid_param:\\n raise HTTPException(status_code=400,\\n detail=f\\\"There is no {list(query_param.keys())[0]}: {list(query_param.values())[0]}, try some of these: {param}\\\")\\n\\n user_data = user.get_users(\\n user_id=user_id,\\n query_param=query_param,\\n )\\n if user_data == {}:\\n return JSONResponse(status_code=204, content={\\n \\\"message\\\": \\\"Query returned empty results !\\\"\\n })\\n return {\\n \\\"user_id\\\": user_id,\\n list(query_param.keys())[0]: list(query_param.values())[0],\\n \\\"user_data\\\": user_data\\n }'''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdddc1a77f22df0b552a316bb5ede5cd\",\n \"score\": \"0.46627873\",\n \"text\": \"def get_item_by_session_id(self, session_id):\\n\\n log.info('in user db, retrieving item using session id')\\n\\n response = self._table.scan(\\n FilterExpression=Attr('sessionId').eq(session_id),\\n ProjectionExpression='email,firstName,lastName,phoneNumber,#tk,profilePicUrl,#rl,rocketChatPassword',\\n ExpressionAttributeNames={'#rl': 'role','#tk':'token'}\\n )\\n\\n if response['Count'] < 1:\\n return {}\\n return response['Items']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db3f91b1750ca5e2e9127776ffa7f32\",\n \"score\": \"0.46548545\",\n \"text\": \"def getUsers(current_user): \\n users = User.query.all()\\n output = []\\n\\n for user in users:\\n user_data = {}\\n user_data['public_id'] = user.public_id\\n user_data['name'] = user.name\\n user_data['email'] = user.email\\n user_data['password'] = user.password\\n user_data['reviews'] = user.reviews\\n output.append(user_data)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340da106bd7555fcdc1ad58a1c8f7663\",\n \"score\": \"0.46523702\",\n \"text\": \"def get_user_session_data(parent_folder, user_id, user_session_id):\\n colume_names_1 = ['Systime', 'EventTime', 'ActivityID', 'X_a', 'Y_a', 'Z_a', 'Phone_orientation']\\n colume_names_2 = ['Systime', 'EventTime', 'ActivityID', 'X_g', 'Y_g', 'Z_g', 'Phone_orientation']\\n\\n sub_data1 = read_file(parent_folder, user_id, user_session_id, 'Accelerometer'+'.csv', colume_names_1)\\n sub_data2 = read_file(parent_folder, user_id, user_session_id, 'Gyroscope'+'.csv', colume_names_2)\\n\\n\\n sub_data1.drop(['Systime', 'Phone_orientation'], axis=1, inplace = True)\\n sub_data2.drop(['Systime', 'Phone_orientation'], axis=1, inplace = True)\\n\\n data = sub_data1.merge(sub_data2, on = ['EventTime', 'ActivityID'], how = 'outer')\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1de555a2ba1dd880b6180581a8e14eb\",\n \"score\": \"0.46490407\",\n \"text\": \"def get_fields():\\n # return result\\n if request.method == 'POST':\\n if request.form['id_game'].encode('utf8'):\\n kwargs = { 'id_game': request.form['id_game'].encode('utf8')}\\n result = return_data_field(**kwargs)\\n if result: \\n return result\\n game_status = get_game_status(request.form['id_game'].encode('utf8'))\\n if game_status == 3 or game_status == 1:\\n return jsonify(result=\\\"1\\\",\\n game_status=game_status)\\n return jsonify(result=\\\"0\\\")\\n else:\\n if request.cookies.has_key('session_id'):\\n cookie_session = request.cookies.get('session_id')\\n user = get_user_by_session(cookie_session)\\n kwargs = { 'id_game': str(user.game.id)}\\n result = return_data_field(str(user.id), **kwargs )\\n if result:\\n return result\\n game_status = get_game_status(kwargs['id_game'])\\n if game_status == 3 or game_status == 1:\\n return jsonify(result=\\\"1\\\",\\n game_status=game_status)\\n return jsonify(result=\\\"0\\\")\\n return jsonify(result=\\\"0\\\")\\n return jsonify(result=\\\"0\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c74c1f220df06099b54a1c3e92fd81f\",\n \"score\": \"0.46431288\",\n \"text\": \"def get(self, user_id):\\n payload = {}\\n try:\\n if user_id:\\n # Retrieve user details\\n user = self.retrieve_user(user_id)\\n if user:\\n payload = {\\n 'username': user['username'],\\n 'age': int(user['age']),\\n 'fruits': list(\\n user['fruits']) if user['fruits'] else None,\\n 'vegetables': list(\\n user['vegetables']) if user['vegetables'] else None\\n }\\n status_code = 200\\n else:\\n payload = {\\n 'Error': 'User being retrieved does not exist'}\\n status_code = 404\\n else:\\n if request.args:\\n if 'user1' in request.args and 'user2' in request.args:\\n # Retreive details of users\\n user1 = self.retrieve_user(request.args['user1'])\\n user2 = self.retrieve_user(request.args['user2'])\\n if user1 and user2:\\n required_keys = (\\n 'fullname', 'age', 'address', 'phone')\\n # Cast age as int since DynamoDB\\n # casts it as Decimal and it is not serializable\\n user1['age'] = int(user1['age'])\\n user2['age'] = int(user2['age'])\\n # Prepare set of friends\\n user1_friends = set(user1['friends'])\\n user2_friends = set(user2['friends'])\\n # Get intersection of friends\\n # to find common\\n common_friends = user1_friends.intersection(\\n user2_friends)\\n # Retrieve all common friends details\\n friends_dframe = self.retrieve_common_friends(\\n common_friends)\\n # If empty just return empty\\n if friends_dframe.empty:\\n friends_dframe = []\\n else:\\n friends_dframe['age'] = friends_dframe[\\n 'age'].astype(int)\\n friends_dframe = friends_dframe[\\n [\\n 'user_id', 'fullname', 'age',\\n 'address', 'phone', 'eyeColor',\\n 'has_died',\\n ]\\n ].to_dict(\\n orient='records'\\n )\\n payload = {\\n 'user1': {\\n k: user1[k] for k in required_keys\\n },\\n 'user2': {\\n k: user2[k] for k in required_keys\\n },\\n 'common_friends': friends_dframe\\n }\\n status_code = 200\\n else:\\n # Return error if at least one of the users\\n # is/are not existing\\n payload = {\\n 'Error': 'User(s) requested are not existing:'\\n 'Existing User({})?: {},'\\n ' Existing User({})?: {}'.format(\\n request.args['user1'],\\n user1 is not None,\\n request.args['user2'],\\n user2 is not None,\\n )\\n }\\n status_code = 400\\n else:\\n # Return error if one of the required parameters\\n # user1 or user2 is missing\\n payload = {\\n 'Error':\\n 'One of the required query '\\n 'parameters missing. Existing keys: {}'.format(\\n request.args.keys()\\n )\\n }\\n status_code = 400\\n else:\\n # Return error if there are no query parameters to parse\\n # or no user ID to retrieve\\n payload = {\\n 'Error': 'No query parameters'\\n }\\n status_code = 400\\n except Exception as ex:\\n logging.error('Unknown error occured', exc_info=True)\\n payload = {'Error': 'Unknown error occured'}\\n status_code = 500\\n return jsonify(payload), status_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca826da00a1aa4087f6783faf050c7f0\",\n \"score\": \"0.46425718\",\n \"text\": \"def getObjectUser(username):\\n # KAMUS LOKAL\\n # dataUser: userTable\\n # objUser: user\\n\\n # ALGORITMA\\n dataUser = getTable(\\\"user\\\")\\n for i in range(dataUser[\\\"row_number\\\"]):\\n objUser = dataUser[\\\"data\\\"][i]\\n\\n if(objUser[\\\"username\\\"] == username):\\n return objUser\\n \\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39695b0ddef612564c12cd245906f83\",\n \"score\": \"0.46409473\",\n \"text\": \"def getUserInfo(user_id):\\n user = session.query(User).filter_by(id=user_id).one()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39695b0ddef612564c12cd245906f83\",\n \"score\": \"0.46409473\",\n \"text\": \"def getUserInfo(user_id):\\n user = session.query(User).filter_by(id=user_id).one()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f97fd3ea99594958747718240a56fa7\",\n \"score\": \"0.46403623\",\n \"text\": \"def ExtractFeaturesFromSessions(self):\\n \\n self.boolSession = True\\n \\n # Imputation of the columns of sessions data table\\n self.sessions[['user_id', 'action', 'action_type', 'action_detail']] = self.sessions[['user_id', 'action', 'action_type', 'action_detail']].fillna('Missing')\\n self.sessions['secs_elapsed'] = self.sessions['secs_elapsed'].fillna(self.sessions['secs_elapsed'].mean())\\n\\n # extracting features using 'action' column\\n sess_action_nunique = pd.Series(self.sessions.groupby('user_id')['action'].nunique(), name='action_nunique')\\n sess_action_count = pd.Series(self.sessions.groupby('user_id')['action'].count(), name='action_count')\\n\\n\\n # extracting features using 'action_type' column\\n sess_action_type_nunique = pd.Series(self.sessions.groupby('user_id')['action_type'].nunique(), name='action_type_nunique')\\n sess_action_type_count = pd.Series(self.sessions.groupby('user_id')['action_type'].count(), name='action_type_count')\\n\\n\\n # extracting features using 'action_detail' column\\n sess_action_detail_nunique = pd.Series(self.sessions.groupby('user_id')['action_detail'].nunique(), name='action_detail_nunique')\\n sess_action_detail_count = pd.Series(self.sessions.groupby('user_id')['action_detail'].count(), name='action_detail_count')\\n\\n # extracting features using 'secs_elapsed' column\\n sess_secs_elapsed_nunique = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].nunique(), name='sec_elapsed_nunique')\\n sess_secs_elapsed_count = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].count(), name='sec_elapsed_count')\\n sess_secs_elapsed_mean = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.mean), name='sec_elapsed_mean')\\n sess_secs_elapsed_median = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.median), name='sec_elapsed_median')\\n sess_secs_elapsed_std = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.std), name='sec_elapsed_std')\\n sess_secs_elapsed_skew = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.skew), name='sec_elapsed_skew')\\n sess_secs_elapsed_kurt = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.kurtosis), name='sec_elapsed_kurt')\\n sess_secs_elapsed_min = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.min), name='sec_elapsed_min')\\n sess_secs_elapsed_max = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.max), name='sec_elapsed_max')\\n sess_secs_elapsed_sum = pd.Series(self.sessions.groupby('user_id')['secs_elapsed'].agg(pd.Series.sum), name='sec_elapsed_sum')\\n\\n # concating all the features generated so far\\n sess_ext = pd.concat([sess_action_nunique, sess_action_count,\\n sess_action_type_nunique, sess_action_type_count,\\n sess_action_detail_nunique, sess_action_detail_count,\\n sess_secs_elapsed_nunique, sess_secs_elapsed_count, sess_secs_elapsed_mean, \\n sess_secs_elapsed_median, sess_secs_elapsed_std, sess_secs_elapsed_skew, sess_secs_elapsed_kurt, \\n sess_secs_elapsed_min, sess_secs_elapsed_max, sess_secs_elapsed_sum], axis='columns')\\n \\n def extract_single_top(data):\\n \\\"\\\"\\\"\\n If a list is obtained as a feature, take the first value as the particular value.\\n \\\"\\\"\\\"\\n if(type(data)==np.ndarray):\\n return data[0]\\n return data \\n \\n \\n # take the unique values if had obtained a list\\n for col in list(sess_ext.columns):\\n sess_ext[col] = sess_ext[col].apply(lambda x: extract_single_top(x))\\n \\n self.listCol = ['action_nunique',\\n 'action_count', 'action_type_nunique',\\n 'action_type_count', 'action_detail_nunique',\\n 'action_detail_count', 'sec_elapsed_nunique',\\n 'sec_elapsed_count', 'sec_elapsed_mean',\\n 'sec_elapsed_median', 'sec_elapsed_std', 'sec_elapsed_skew',\\n 'sec_elapsed_kurt', 'sec_elapsed_min', 'sec_elapsed_max',\\n 'sec_elapsed_sum']\\n\\n\\n # Merge the sessions data with train and with test according to the user ids\\n \\n self.train = self.train.set_index('id')\\n self.train = self.train.join(sess_ext, how='inner')\\n self.train = self.train.reset_index()\\n self.train = self.train.rename(columns={'index':'id'})\\n self.test = self.test.set_index('id')\\n self.test = self.test.join(sess_ext)\\n self.test = self.test.reset_index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e32bf23f94ccd9e0d559327a785e39\",\n \"score\": \"0.4627288\",\n \"text\": \"def _getSessionsInWishlist(self):\\n user = self.get_authed_user()\\n user_id = getUserId(user)\\n prof_key = ndb.Key(Profile, user_id)\\n\\n wish_keys = Wishlist.query(ancestor=prof_key)\\n sess_keys = [wish_key.session for wish_key in wish_keys]\\n\\n if sess_keys in (None, []):\\n raise endpoints.BadRequestException(\\n 'No wishlist found: {}'.format(sess_keys))\\n return ndb.get_multi(sess_keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"184af7fc2d7aadc3c7db28a0ced7acf2\",\n \"score\": \"0.46252325\",\n \"text\": \"def authorized_download_context_claims(user_name, user_id):\\n aud = ['access', 'data', 'user', 'openid']\\n iss = capp.config['BASE_URL']\\n jti = new_jti()\\n iat, exp = iat_and_exp()\\n return {\\n 'aud': aud,\\n 'sub': user_id,\\n 'iss': iss,\\n 'iat': iat,\\n 'exp': exp,\\n 'jti': jti,\\n 'pur': 'access',\\n 'context': {\\n 'user': {\\n 'name': user_name,\\n 'projects': {\\n \\\"phs000178\\\": [\\\"read\\\"],\\n \\\"phs000218\\\": [\\\"read\\\", \\\"read-storage\\\"],\\n },\\n },\\n },\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1ca0ff3f927394813e2c666a753a15\",\n \"score\": \"0.4624385\",\n \"text\": \"def find_supplements():\\n\\n user_id = session.get(\\\"user_id\\\")\\n user = User.query.filter_by(user_id=user_id).first()\\n all_user_vitamins = User_Vitamin.query.filter_by(user_id=user_id).all()\\n\\n actives_list = []\\n \\n for item in all_user_vitamins: \\n\\n item_info = {}\\n item_info[\\\"uv_id\\\"] = item.uv_id\\n item_info[\\\"id\\\"] = item.vitamin.label_id\\n item_info[\\\"name\\\"] = item.vitamin.product_name\\n item_info[\\\"serving_size\\\"] = item.vitamin.serving_size_quantity\\n item_info[\\\"serving_unit\\\"] = item.vitamin.serving_size_unit\\n item_info[\\\"use\\\"] = item.vitamin.use\\n item_info[\\\"start_date\\\"] = item.start_date\\n item_info[\\\"discontinue_date\\\"] = item.discontinue_date\\n item_info[\\\"container_amount\\\"] = item.vitamin.net_contents\\n item_info[\\\"container_unit\\\"] = item.vitamin.net_content_unit\\n item_info[\\\"active\\\"] = item.active\\n item_info[\\\"run_out\\\"] = item.run_out_date\\n item_info[\\\"rating\\\"] = item.user_rating\\n actives_list.append(item_info)\\n\\n return jsonify(actives_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d50a525f943aaca8998d1d023f03b17\",\n \"score\": \"0.46226838\",\n \"text\": \"def get_creds(user_id):\\n creds = User.query.filter_by(user_id=user_id).first_or_404()\\n return creds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91cfbce123d300a6277c26073f851c2b\",\n \"score\": \"0.46211922\",\n \"text\": \"def lineup_user(self, userid):\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\", \\\"Accept\\\": \\\"text/plain\\\",\\n 'Referer': 'http://' + self.domain + '/standings.phtml', \\\"User-Agent\\\": user_agent}\\n req = self.session.get('http://' + self.domain + '/playerInfo.phtml?pid=' + userid, headers=headers).content\\n soup = BeautifulSoup(req, \\\"html.parser\\\")\\n info = list()\\n for i in soup.find_all('td', {'class': 'name_cont'}):\\n info.append(i.text.strip())\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce03d71fa1c83292eef5234512ec65f\",\n \"score\": \"0.46152017\",\n \"text\": \"def get_api_data():\\r\\n matches = None\\r\\n logging.info(\\\"start get_api_data\\\")\\r\\n name = config_functions.get_faceit_name_from_db()\\r\\n faceit_data = FaceitData(FACEIT_API)\\r\\n\\r\\n user = faceit_data.player_details(name)\\r\\n user_stats = faceit_data.player_stats(user[\\\"player_id\\\"], \\\"csgo\\\")\\r\\n match = faceit_data.player_matches(user[\\\"player_id\\\"], \\\"csgo\\\", return_items=50)\\r\\n if match['items']:\\r\\n try:\\r\\n matches = faceit_data.match_stats(match['items'][0]['match_id'])\\r\\n except ValueError:\\r\\n logging.error(\\\"Faceit Matches are broken !\\\")\\r\\n else:\\r\\n matches = None\\r\\n return user, user_stats, matches, match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177135e905a6f29686b83c66de923a1e\",\n \"score\": \"0.46060473\",\n \"text\": \"def getSession(self, uid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e90f5fa497bec2e36b4776544aeabd5\",\n \"score\": \"0.4601523\",\n \"text\": \"def user_by_height(user, session):\\n athletes = session.query(Athelete).all()\\n atlhete_id_height = {athlete.id: athlete.height for athlete in athletes}\\n\\n user_height = user.height\\n min_dist = None\\n athlete_id = None\\n athlete_height = None\\n\\n for id_, height in atlhete_id_height.items():\\n if height is None:\\n continue\\n\\n dist = abs(user_height - height)\\n if not min_dist or dist < min_dist:\\n min_dist = dist\\n athlete_id = id_\\n athlete_height = height\\n \\n return athlete_id, athlete_height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de55da0144b8f5aa4103fb8c4c1c5de3\",\n \"score\": \"0.4588841\",\n \"text\": \"def get_user_details(self, response):\\n\\n global_role = getattr(settings, 'KEYCLOAK_GLOBAL_ROLE', False)\\n roles = []\\n\\n if global_role:\\n if 'realm_access' in response and 'roles' in response['realm_access']:\\n roles = response['realm_access']['roles']\\n else:\\n if 'resource_access' in response and self.CLIENT_ID in response['resource_access'] and 'roles' in response['resource_access'][self.CLIENT_ID]:\\n roles = response['resource_access'][self.CLIENT_ID]['roles']\\n\\n superuser = any(role.strip().lower() == \\\"admin\\\" for role in roles)\\n group_roles = [role.strip().lower() for role in roles if role.strip().lower() != \\\"admin\\\"]\\n\\n return {\\n 'username': response.get('preferred_username'),\\n 'email': response.get('email') or '',\\n 'fullname': response.get('name') or '',\\n 'first_name': response.get('given_name') or '',\\n 'last_name': response.get('family_name') or '',\\n 'is_superuser': superuser,\\n 'is_staff': superuser,\\n 'roles': group_roles\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1f16fac69a98996c254ba285046700\",\n \"score\": \"0.45800152\",\n \"text\": \"def get_owner_data(request_id, attributes = [\\\"alias\\\"]):\\n\\towner_data = []\\n\\tif not request_id:\\n\\t\\treturn owner_data\\n\\tq = db.session.query(User).join(Owner, User.id == Owner.user_id).join(Request, Owner.id == Request.current_owner).filter(Request.id == request_id).all()\\n\\tfor attribute in attributes:\\n\\t\\tif len(q) > 0:\\n\\t\\t\\towner_data.append(getattr(q[0], attribute))\\n\\t\\telse:\\n\\t\\t\\towner_data.append(None)\\n\\treturn owner_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5884df060898fb58e6ea54fe34543efd\",\n \"score\": \"0.457715\",\n \"text\": \"def GettingSkillsforCandidates(*args, **kwargs):\\n listofcandidates = kwargs['listofcandidates']\\n final_dictio = {}\\n username = kwargs['public_Id']\\n listofskills = kwargs['skills']\\n candidatesforchecking = []\\n gettingweight = []\\n skillsandcandidates = {}\\n for skill in listofskills:\\n skillsandcandidates[skill] = []\\n for candidates in listofcandidates:\\n candidatesforchecking.append(userDataSkills(public_Id=candidates))\\n for skill in listofskills:\\n for candidate_data in candidatesforchecking:\\n if any(d['skill_name'] == skill for d in candidate_data['skills']):\\n skillsandcandidates[skill].append(candidate_data)\\n for candidate in skillsandcandidates[skill]:\\n for checking in candidate['skills']:\\n if checking['skill_name'] == skill:\\n gettingweight.append(checking['weight'])\\n if len(gettingweight) == 0:\\n return {'message': 'No matches with the skills provided'}\\n maxbyskill = max(gettingweight)\\n position = gettingweight.index(maxbyskill)\\n final_dictio[skill] = skillsandcandidates[skill][position]\\n gettingweight.clear()\\n return (final_dictio)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"112f0ffdda08d849bb3143c7490a6807\",\n \"score\": \"0.4573652\",\n \"text\": \"def get_password_data(self, context, instance_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0bf921d55c8c0f23bea385f2acd24ba\",\n \"score\": \"0.45733482\",\n \"text\": \"def getSessionData(create=1):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c82d4e090853ef725dd33b242cdfe05\",\n \"score\": \"0.45731208\",\n \"text\": \"def get_queryset(self):\\n return Feedback.objects.filter(user=self.request.user).values('id', 'saloon__id', 'rate',\\n 'description', 'user__id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"327b15d2596bb070e87d355e31be2a0e\",\n \"score\": \"0.45705187\",\n \"text\": \"def user_info(self) -> dict:\\n return self.provide_session()[\\\"user\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b22420f957f6a2d5f8c3e248611b5b\",\n \"score\": \"0.45661145\",\n \"text\": \"async def get_source(\\n obj_id,\\n user_id,\\n session,\\n include_thumbnails=False,\\n include_comments=False,\\n include_analyses=False,\\n include_photometry=False,\\n include_photometry_exists=False,\\n include_spectrum_exists=False,\\n include_comment_exists=False,\\n include_period_exists=False,\\n include_detection_stats=False,\\n include_labellers=False,\\n is_token_request=False,\\n include_requested=False,\\n requested_only=False,\\n include_color_mag=False,\\n):\\n user = session.scalar(sa.select(User).where(User.id == user_id))\\n\\n options = []\\n if include_thumbnails:\\n options.append(joinedload(Obj.thumbnails))\\n if include_detection_stats:\\n options.append(joinedload(Obj.photstats))\\n\\n s = session.scalars(\\n Obj.select(user, options=options).where(Obj.id == obj_id)\\n ).first()\\n if s is None:\\n raise ValueError(\\\"Source not found\\\")\\n\\n source_info = s.to_dict()\\n source_info[\\\"followup_requests\\\"] = (\\n session.scalars(\\n FollowupRequest.select(\\n user,\\n options=[\\n joinedload(FollowupRequest.allocation).joinedload(\\n Allocation.instrument\\n ),\\n joinedload(FollowupRequest.allocation).joinedload(Allocation.group),\\n joinedload(FollowupRequest.requester),\\n joinedload(FollowupRequest.watchers),\\n ],\\n )\\n .where(FollowupRequest.obj_id == obj_id)\\n .where(FollowupRequest.status != \\\"deleted\\\")\\n )\\n .unique()\\n .all()\\n )\\n source_info[\\\"assignments\\\"] = session.scalars(\\n ClassicalAssignment.select(\\n user,\\n options=[\\n joinedload(ClassicalAssignment.run)\\n .joinedload(ObservingRun.instrument)\\n .joinedload(Instrument.telescope)\\n ],\\n ).where(ClassicalAssignment.obj_id == obj_id)\\n ).all()\\n point = ca.Point(ra=s.ra, dec=s.dec)\\n # Check for duplicates (within 4 arcsecs)\\n galaxies = session.scalars(\\n Galaxy.select(user).where(Galaxy.within(point, 10 / 3600))\\n ).all()\\n if len(galaxies) > 0:\\n source_info[\\\"galaxies\\\"] = list({galaxy.name for galaxy in galaxies})\\n else:\\n source_info[\\\"galaxies\\\"] = None\\n\\n # Check for nearby galaxies (within 10 arcsecs)\\n duplicate_objs = (\\n Obj.select(user)\\n .where(Obj.within(point, 4 / 3600))\\n .where(Obj.id != s.id)\\n .subquery()\\n )\\n duplicates = session.scalars(\\n Source.select(user).join(duplicate_objs, Source.obj_id == duplicate_objs.c.id)\\n ).all()\\n if len(duplicates) > 0:\\n source_info[\\\"duplicates\\\"] = list({dup.obj_id for dup in duplicates})\\n else:\\n source_info[\\\"duplicates\\\"] = None\\n\\n if 'photstats' in source_info:\\n photstats = source_info[\\\"photstats\\\"]\\n for photstat in photstats:\\n if (\\n hasattr(photstat, 'first_detected_mjd')\\n and photstat.first_detected_mjd is not None\\n ):\\n source_info[\\\"first_detected\\\"] = Time(\\n photstat.first_detected_mjd, format='mjd'\\n ).isot\\n if (\\n hasattr(photstat, 'last_detected_mjd')\\n and photstat.last_detected_mjd is not None\\n ):\\n source_info[\\\"last_detected\\\"] = Time(\\n photstat.last_detected_mjd, format='mjd'\\n ).isot\\n\\n if s.host_id:\\n source_info[\\\"host\\\"] = s.host.to_dict()\\n source_info[\\\"host_offset\\\"] = s.host_offset.deg * 3600.0\\n source_info[\\\"host_distance\\\"] = s.host_distance.value\\n\\n if is_token_request:\\n # Logic determining whether to register front-end request as view lives in front-end\\n sv = SourceView(\\n obj_id=obj_id,\\n username_or_token_id=user.id,\\n is_token=True,\\n )\\n session.add(sv)\\n # To keep loaded relationships from being cleared in verify_and_commit:\\n source_info = recursive_to_dict(source_info)\\n session.commit()\\n\\n if include_thumbnails:\\n existing_thumbnail_types = [thumb.type for thumb in s.thumbnails]\\n thumbnails = list({\\\"ps1\\\", \\\"ls\\\", \\\"sdss\\\"} - set(existing_thumbnail_types))\\n if len(thumbnails) > 0:\\n post_thumbnails([obj_id])\\n\\n if include_comments:\\n comments = (\\n session.scalars(\\n Comment.select(\\n user,\\n options=[\\n joinedload(Comment.author),\\n joinedload(Comment.groups),\\n ],\\n ).where(Comment.obj_id == obj_id)\\n )\\n .unique()\\n .all()\\n )\\n source_info[\\\"comments\\\"] = sorted(\\n (\\n {\\n **{k: v for k, v in c.to_dict().items() if k != \\\"attachment_bytes\\\"},\\n \\\"author\\\": {\\n **c.author.to_dict(),\\n \\\"gravatar_url\\\": c.author.gravatar_url,\\n },\\n }\\n for c in comments\\n ),\\n key=lambda x: x[\\\"created_at\\\"],\\n reverse=True,\\n )\\n if include_analyses:\\n analyses = (\\n session.scalars(\\n ObjAnalysis.select(\\n user,\\n ).where(ObjAnalysis.obj_id == obj_id)\\n )\\n .unique()\\n .all()\\n )\\n source_info[\\\"analyses\\\"] = [analysis.to_dict() for analysis in analyses]\\n if include_period_exists:\\n annotations = session.scalars(\\n Annotation.select(user).where(Annotation.obj_id == obj_id)\\n ).all()\\n period_str_options = ['period', 'Period', 'PERIOD']\\n source_info[\\\"period_exists\\\"] = any(\\n [\\n isinstance(an.data, dict) and period_str in an.data\\n for an in annotations\\n for period_str in period_str_options\\n ]\\n )\\n if include_labellers:\\n labels_subquery = (\\n SourceLabel.select(session.user_or_token)\\n .where(SourceLabel.obj_id == obj_id)\\n .subquery()\\n )\\n\\n users = (\\n session.scalars(\\n User.select(session.user_or_token).join(\\n labels_subquery,\\n User.id == labels_subquery.c.labeller_id,\\n )\\n )\\n .unique()\\n .all()\\n )\\n source_info[\\\"labellers\\\"] = [user.to_dict() for user in users]\\n\\n annotations = sorted(\\n session.scalars(\\n Annotation.select(user)\\n .options(joinedload(Annotation.author))\\n .where(Annotation.obj_id == obj_id)\\n )\\n .unique()\\n .all(),\\n key=lambda x: x.origin,\\n )\\n source_info[\\\"annotations\\\"] = [\\n {**annotation.to_dict(), 'type': 'source'} for annotation in annotations\\n ]\\n readable_classifications = (\\n session.scalars(\\n Classification.select(user).where(Classification.obj_id == obj_id)\\n )\\n .unique()\\n .all()\\n )\\n\\n readable_classifications_json = []\\n for classification in readable_classifications:\\n classification_dict = classification.to_dict()\\n classification_dict['groups'] = [g.to_dict() for g in classification.groups]\\n classification_dict['votes'] = [g.to_dict() for g in classification.votes]\\n readable_classifications_json.append(classification_dict)\\n\\n source_info[\\\"classifications\\\"] = readable_classifications_json\\n source_info[\\\"gal_lat\\\"] = s.gal_lat_deg\\n source_info[\\\"gal_lon\\\"] = s.gal_lon_deg\\n source_info[\\\"luminosity_distance\\\"] = s.luminosity_distance\\n source_info[\\\"dm\\\"] = s.dm\\n source_info[\\\"angular_diameter_distance\\\"] = s.angular_diameter_distance\\n source_info[\\\"ebv\\\"] = s.ebv\\n\\n if include_photometry:\\n photometry = (\\n session.scalars(\\n Photometry.select(\\n user, options=[joinedload(Photometry.annotations)]\\n ).where(Photometry.obj_id == obj_id)\\n )\\n .unique()\\n .all()\\n )\\n source_info[\\\"photometry\\\"] = [\\n serialize(phot, 'ab', 'flux') for phot in photometry\\n ]\\n if include_photometry_exists:\\n source_info[\\\"photometry_exists\\\"] = check_if_obj_has_photometry(\\n obj_id, user, session\\n )\\n\\n if include_spectrum_exists:\\n source_info[\\\"spectrum_exists\\\"] = (\\n session.scalars(\\n Spectrum.select(user).where(Spectrum.obj_id == obj_id)\\n ).first()\\n is not None\\n )\\n if include_comment_exists:\\n source_info[\\\"comment_exists\\\"] = (\\n session.scalars(\\n Comment.select(user).where(Comment.obj_id == obj_id)\\n ).first()\\n is not None\\n )\\n source_query = Source.select(user).where(Source.obj_id == source_info[\\\"id\\\"])\\n source_query = apply_active_or_requested_filtering(\\n source_query, include_requested, requested_only\\n )\\n source_subquery = source_query.subquery()\\n groups = session.scalars(\\n Group.select(user).join(source_subquery, Group.id == source_subquery.c.group_id)\\n ).all()\\n source_info[\\\"groups\\\"] = [g.to_dict() for g in groups]\\n for group in source_info[\\\"groups\\\"]:\\n source_table_row = session.scalars(\\n Source.select(user)\\n .where(Source.obj_id == s.id)\\n .where(Source.group_id == group[\\\"id\\\"])\\n ).first()\\n if source_table_row is not None:\\n group[\\\"active\\\"] = source_table_row.active\\n group[\\\"requested\\\"] = source_table_row.requested\\n group[\\\"saved_at\\\"] = source_table_row.saved_at\\n group[\\\"saved_by\\\"] = (\\n source_table_row.saved_by.to_dict()\\n if source_table_row.saved_by is not None\\n else None\\n )\\n if include_color_mag:\\n source_info[\\\"color_magnitude\\\"] = get_color_mag(annotations)\\n\\n source_info = recursive_to_dict(source_info)\\n return source_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30badafe730a2f84abfdec24d0f6ca2e\",\n \"score\": \"0.45581418\",\n \"text\": \"def getUserInfo(user_id):\\n session = DBSession()\\n user = session.query(User).filter_by(id=user_id).one()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"479e0258781c025b08321feb627485d4\",\n \"score\": \"0.45506266\",\n \"text\": \"def get_users_with_checkout(self, fbo_id, make_and_model):\\n selected_users = []\\n users = self.get_users(fbo_id)\\n for user in users: \\n if ('aircraft_checkouts' in user and user[\\\"aircraft_checkouts\\\"] != None):\\n checkouts = json.loads(user[\\\"aircraft_checkouts\\\"])\\n for checkout in checkouts:\\n if (checkout[\\\"Make_Model\\\"] == make_and_model):\\n selected_users.append(user)\\n return selected_users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c3dc43f1d1dc3d3be5b83c4d816b87\",\n \"score\": \"0.45486704\",\n \"text\": \"def getSessionsByMultipleInequalities(self, request):\\n\\n # This function is not supposed to be useful\\n # It only demonstrates how to filter my multiple inequalities\\n\\n # create 7pm time object for filtering\\n time = datetime.strptime('19:00', \\\"%H:%M\\\").time()\\n\\n sessions = Session.query()\\n sessions = sessions.filter(Session.startTime <= time)\\n\\n sessionsArray = [session for session in sessions if session.typeOfSession != 'workshop']\\n\\n # return set of Session objects per Session\\n return SessionForms(\\n items=[self._copySessionToForm(session) for session in sessionsArray]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edbe64621d4068552e82dd32c368e08f\",\n \"score\": \"0.45476386\",\n \"text\": \"def check_data(user_input,path):\\n\\tret = {}\\n\\tif(CONF_COOKIE in user_input):\\n\\t\\ttry:\\n\\t\\t\\t# sadly config flow will not allow to have a multiline text field\\n\\t\\t\\t# we get a looong string that we've to rearrange into multiline for ytmusic\\n\\n\\t\\t\\t# the only thing we need is cookie + x-goog-authuser, lets try to cut this out\\n\\t\\t\\t# so the fields are written like 'identifier':'value', but some values actually have ':' inside, bummer.\\n\\t\\t\\tc = user_input[CONF_COOKIE]\\n\\t\\t\\tclean_cookie = \\\"\\\"\\n\\t\\t\\tclean_x_goog_authuser = \\\"\\\"\\n\\t\\t\\t## lets try to find the cookie part\\n\\t\\t\\tcookie_pos = c.lower().find('cookie')\\n\\t\\t\\tif(cookie_pos>=0):\\n\\t\\t\\t\\t#_LOGGER.debug(\\\"found cookie in text\\\")\\n\\t\\t\\t\\tcookie_end = c[cookie_pos:]\\n\\t\\t\\t\\tcookie_end_split = cookie_end.split(':')\\n\\t\\t\\t\\tif(len(cookie_end_split)>=3):\\n\\t\\t\\t\\t\\t#_LOGGER.debug(\\\"found three or more sections\\\")\\n\\t\\t\\t\\t\\tcookie_length_last_field = cookie_end_split[2].rfind(' ')\\n\\t\\t\\t\\t\\tif(cookie_length_last_field>=0):\\n\\t\\t\\t\\t\\t\\t#_LOGGER.debug(\\\"found a space\\\")\\n\\t\\t\\t\\t\\t\\tcookie_length = len(cookie_end_split[0])+1+len(cookie_end_split[1])+1+cookie_length_last_field\\n\\t\\t\\t\\t\\t\\tclean_cookie = c[cookie_pos:cookie_pos+cookie_length]\\n\\t\\t\\t\\t\\t\\t#_LOGGER.debug(clean_cookie)\\n\\t\\t\\t## lets try to find x-auth-part\\n\\t\\t\\txauth_pos = c.lower().find('x-goog-authuser: ')\\n\\t\\t\\tif(xauth_pos>=0):\\n\\t\\t\\t\\t#_LOGGER.debug(\\\"found x-goog-authuser in text\\\")\\n\\t\\t\\t\\t#_LOGGER.debug(c[xauth_pos+len('x-goog-authuser: '):])\\n\\t\\t\\t\\txauth_len = c[xauth_pos+len('x-goog-authuser: '):].find(' ')\\n\\t\\t\\t\\t#_LOGGER.debug(xauth_len)\\n\\t\\t\\t\\tif(xauth_len>=0):\\n\\t\\t\\t\\t\\t#_LOGGER.debug(\\\"found space in text\\\")\\n\\t\\t\\t\\t\\tclean_x_goog_authuser = c[xauth_pos:(xauth_pos+len('x-goog-authuser: ')+xauth_len)]\\n\\t\\t\\t\\t\\t#_LOGGER.debug(clean_x_goog_authuser)\\n\\t\\t\\t## lets see what we got\\n\\t\\t\\tif(clean_cookie!=\\\"\\\" and clean_x_goog_authuser!=\\\"\\\"):\\n\\t\\t\\t\\t# woop woop, this COULD be it\\n\\t\\t\\t\\tc = clean_cookie+\\\"\\\\n\\\"+clean_x_goog_authuser+\\\"\\\\n\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# well we've failed to find the cookie, the only thing we can do is at least to help with some breaks\\n\\t\\t\\t\\tc = c.replace('cookie','\\\\ncookie')\\n\\t\\t\\t\\tc = c.replace('Cookie','\\\\nCookie')\\n\\t\\t\\t\\tc = c.replace('x-goog-authuser','\\\\nx-goog-authuser')\\n\\t\\t\\t\\tc = c.replace('X-Goog-AuthUser','\\\\nX-Goog-AuthUser')\\n\\t\\t\\t#_LOGGER.debug(\\\"feeding with: \\\")\\n\\t\\t\\t#_LOGGER.debug(c)\\n\\t\\t\\tYTMusic.setup(filepath = path, headers_raw = c)\\n\\t\\t\\treturn {}\\n\\t\\texcept Exception:\\n\\t\\t\\tc = user_input[CONF_COOKIE].lower()\\n\\t\\t\\tif(c.find('cookie') == -1 ):\\n\\t\\t\\t\\t_LOGGER.error(\\\"Field 'Cookie' not found!\\\")\\n\\t\\t\\t\\tret[\\\"base\\\"] = ERROR_COOKIE\\n\\t\\t\\telif(c.find('X-Goog-AuthUser') == -1 ):\\n\\t\\t\\t\\t_LOGGER.error(\\\"Field 'X-Goog-AuthUser' not found!\\\")\\n\\t\\t\\t\\tret[\\\"base\\\"] = ERROR_AUTH_USER\\n\\t\\t\\telse:\\n\\t\\t\\t\\t_LOGGER.error(\\\"Generic setup error, please see below\\\")\\n\\t\\t\\t\\t_LOGGER.error(traceback.format_exc())\\n\\t\\t\\t\\tret[\\\"base\\\"] = ERROR_GENERIC\\n\\t\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3b96b9f6d98ec849efdb8895708c59\",\n \"score\": \"0.45462644\",\n \"text\": \"def get_user_info_query_variables(args):\\n info = args[1]\\n user = info.context.user\\n query = FavoriteThing.get_query(info)\\n return (info, user, query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc6147c078e8b512d574b763b3ab4e2\",\n \"score\": \"0.45443237\",\n \"text\": \"def matches(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1865fb606f3f71050f9504119040a113\",\n \"score\": \"0.45430806\",\n \"text\": \"def src_user_features(self):\\n response_time_metrics = self.src_dest_response_time_avgs()\\n return {\\n 'src_num_directed_dest': self.src_num_directed_dest(),\\n 'src_avg_positive_sentiment_directed_dest': self.src_avg_positive_sentiment_directed_dest(),\\n 'src_avg_negative_sentiment_directed_dest': self.src_avg_negative_sentiment_directed_dest(),\\n 'src_num_with_media': number_of_tweets_with_media(self.src_user, self.node_collection),\\n 'src_num_with_hashtags': number_of_tweets_with_hashtags(self.src_user, self.node_collection),\\n 'src_num_with_urls': number_of_tweets_with_urls(self.src_user, self.node_collection),\\n 'event_is_positive': self.event_is_positive(self.src_user),\\n 'event_is_negative': self.event_is_negative(self.src_user),\\n 'event_is_directed': self.event_is_directed_to(self.src_user, self.dest_user),\\n 'event_has_hashtags': self.event_has_hashtags(self.src_user),\\n 'event_has_media': self.event_has_media(self.src_user),\\n 'event_has_url': self.event_has_url(self.src_user),\\n 'num_event_responses': self.num_event_responses(self.src_user, self.dest_user),\\n 'event_response_time': self.event_response_time(self.src_user, self.dest_user),\\n 'src_mean_response_time_to_dest': response_time_metrics[0],\\n 'src_median_response_time_to_dest': response_time_metrics[1],\\n 'src_max_response_time_to_dest': response_time_metrics[2],\\n 'src_mean_response_time': self.mean_response_time(self.src_user),\\n 'src_median_response_time': self.median_response_time(self.src_user),\\n 'src_max_response_time': self.max_response_time(self.src_user),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce9755fad4abd1470600df8db070693\",\n \"score\": \"0.4542985\",\n \"text\": \"async def get_player_battle_info(pool, user_id : int):\\r\\n base_info = await get_attack_crit_hp(pool, user_id)\\r\\n\\r\\n acolyte1, acolyte2 = await getAcolyteFromPlayer(pool, user_id)\\r\\n if acolyte1 is not None:\\r\\n acolyte1_info = await getAcolyteByID(pool, acolyte1)\\r\\n else:\\r\\n acolyte1_info = empty_acolyte_dict()\\r\\n\\r\\n if acolyte2 is not None:\\r\\n acolyte2_info = await getAcolyteByID(pool, acolyte2)\\r\\n else:\\r\\n acolyte2_info = empty_acolyte_dict()\\r\\n\\r\\n return {\\r\\n 'ID' : user_id,\\r\\n 'Name' : await getPlayerName(pool, user_id),\\r\\n 'Attack' : base_info['Attack'],\\r\\n 'Crit' : base_info['Crit'],\\r\\n 'HP' : base_info['HP'],\\r\\n 'Max_HP' : base_info['HP'] * 2,\\r\\n 'Class' : await getClass(pool, user_id),\\r\\n 'Acolyte1' : acolyte1_info,\\r\\n 'Acolyte2' : acolyte2_info,\\r\\n 'Strategy' : await getStrategy(pool, user_id)\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24736302e27414ed5ed0bf8e86c029fa\",\n \"score\": \"0.45406625\",\n \"text\": \"def get_uservars(self, user=None):\\n\\n if user is None:\\n # All the users!\\n return self._users\\n elif user in self._users:\\n # Just this one!\\n return self._users[user]\\n else:\\n # No info.\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0fd6d1fc4dd02c2e5f96d217b59924\",\n \"score\": \"0.45312223\",\n \"text\": \"def _query_attribute(\\n self, dispatcher: CollectingDispatcher, tracker: Tracker\\n ) -> List[Dict]:\\n object_type = tracker.get_slot(SLOT_OBJECT_TYPE)\\n attribute = tracker.get_slot(SLOT_ATTRIBUTE)\\n\\n object_name = self._get_object_name(\\n tracker,\\n self.knowledge_base.ordinal_mention_mapping,\\n self.use_last_object_mention,\\n )\\n\\n\\n\\n logger.info(\\\"_query_attribute [object_type]:\\\"+str(object_type) + \\\" [attribute]:\\\"+str(attribute)+\\\" [object_name]:\\\"+str(object_name))\\n\\n if not object_name or not attribute:\\n logger.info(\\\"object_name or attribute not available\\\")\\n dispatcher.utter_template(\\\"utter_ask_rephrase\\\", tracker)\\n return [SlotSet(SLOT_MENTION, None)]\\n\\n object_attributes = self.knowledge_base.get_attributes_of_object(object_type)\\n\\n # get all set attribute slots of the object type to be able to filter the\\n # list of objects\\n attributes = get_attribute_slots(tracker, object_attributes)\\n # query the knowledge base\\n\\n objects = self.knowledge_base.get_objects(object_type,attributes,object_name)\\n key_attribute = self.knowledge_base.get_key_attribute_of_object(object_type)\\n\\n if not objects or attribute not in objects[0]:\\n logger.info(\\\"object not found or attribute not in objects[0]\\\")\\n dispatcher.utter_template(\\\"utter_ask_rephrase\\\", tracker)\\n return [SlotSet(SLOT_MENTION, None)] + reset_attribute_slots(tracker, object_attributes)\\n\\n if len(objects) >1 :\\n dispatcher.utter_message(\\\"Ho trovato più di un risultato:\\\")\\n \\n for object_of_interest in objects:\\n value = object_of_interest[attribute]\\n repr_function = self.knowledge_base.get_representation_function_of_object(\\n object_type\\n )\\n object_representation = repr_function(object_of_interest)\\n key_attribute = self.knowledge_base.get_key_attribute_of_object(object_type)\\n object_identifier = object_of_interest[key_attribute]\\n\\n self.utter_attribute_value(dispatcher, object_representation, attribute, value)\\n\\n slots = [\\n SlotSet(SLOT_OBJECT_TYPE, object_type),\\n SlotSet(SLOT_ATTRIBUTE, None),\\n SlotSet(SLOT_MENTION, None),\\n SlotSet(SLOT_LAST_OBJECT, object_identifier),\\n SlotSet(SLOT_LAST_OBJECT_TYPE, object_type),\\n ]\\n\\n return slots + reset_attribute_slots(tracker, object_attributes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e872b8b133b932a11f1b58cd3ac2b29\",\n \"score\": \"0.45271084\",\n \"text\": \"def karma_given_by(user):\\n from qon.base import get_group_database, get_user_database\\n \\n results = []\\n \\n def check_karma(item):\\n p = item.karma_points_from(user)\\n if p:\\n results.append((item, p))\\n \\n def check_blog_karma(blog):\\n for i in blog.get_items():\\n check_karma(i)\\n for c in i.get_all_comments():\\n check_karma(c)\\n \\n for id, u in get_user_database().root.iteritems():\\n check_karma(u)\\n check_blog_karma(u.blog)\\n \\n for id, g in get_group_database().root.iteritems():\\n check_blog_karma(g.blog)\\n \\n for id, page in g.wiki.pages.iteritems():\\n check_karma(page)\\n check_blog_karma(page.blog)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee6d40f6aba6439a4aa93d93b3e8bd1\",\n \"score\": \"0.45251662\",\n \"text\": \"def query_chapinfo_by_target(self, target_name, username):\\n url = 'resource/target/get_chap_list'\\n data = {\\\"targetName\\\": target_name}\\n result = self.run(url, 'POST', data=data)\\n self._assert_restapi_result(result, _(\\\"Query chapinfo \\\"\\n \\\"by target error.\\\"))\\n\\n result = result.get('data') or []\\n chapinfo = [c for c in result if c.get(\\\"user\\\") == username]\\n if chapinfo:\\n return chapinfo[0]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"904b742f252f071d11d5b01c6bea9eb0\",\n \"score\": \"0.45208624\",\n \"text\": \"def get(cls, user_id: int):\\n user = User.query.get_or_404(user_id)\\n if not user:\\n return response_with(resp.SERVER_ERROR_404)\\n\\n return (\\n {\\n \\\"current_time\\\": int(time()),\\n \\\"confirmation\\\": [\\n confirmation_schema.dump(each)\\n for each in user.confirmation.order_by(Confirmation.expire_at)\\n ],\\n },\\n 200,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01c96d09cf419a56d809760bf77fbf0\",\n \"score\": \"0.45193732\",\n \"text\": \"def get_user_statistics(user):\\n # fetch some status labels in Submissions\\n # here, we only concern about COMPILE_ERROR, RESTRICTED_FUNCTION,\\n # and JUDGE_ERROR since ACCEPTED, NOT_ACCEPTED, etc will appear in\\n # SubmissionDetail.VERDICT_CHOICE\\n status_labels = [\\n Submission.COMPILE_ERROR,\\n Submission.RESTRICTED_FUNCTION,\\n Submission.JUDGE_ERROR\\n ]\\n # find all verdict in SubmissionDetail.VERDICT_CHOICE\\n verdict_labels = [x[0] for x in SubmissionDetail.VERDICT_CHOICE]\\n statistics = []\\n\\n # fetch Submission of the given user\\n submissions = Submission.objects.filter(user=user)\\n for label in status_labels:\\n statistics += [{\\n 'label': label,\\n 'value': submissions.filter(status=label).count()\\n }]\\n\\n # fetch Submission of the given user\\n submissions_id = map(lambda submission: submission.id, submissions)\\n submission_details = SubmissionDetail.objects.filter(\\n sid__in=submissions_id)\\n for label in verdict_labels:\\n statistics += [{\\n 'label': label,\\n 'value': submission_details.filter(verdict=label).count()\\n }]\\n\\n return statistics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35f0ce8538fbf61d958ed4a12aca2bf\",\n \"score\": \"0.451922\",\n \"text\": \"def get_all_session_dicts(session, filter_data):\\n session_objs = session_generate_query(session, filter_data).all()\\n results = []\\n for session_obj in session_objs:\\n if session_obj:\\n results.append(session_obj.to_dict())\\n return results\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1578,"cells":{"query_id":{"kind":"string","value":"ed0eee55e45052cf7e06916c07afd9bc"},"query":{"kind":"string","value":"Helps to list all specified files in package including files in directories since `package_data` ignores directories."},"positive_passages":{"kind":"list like","value":[{"docid":"09bad0013a0dd4d825a9a8815b823eca","score":"0.822155","text":"def get_package_data_files(package, data, package_dir=None):\n if package_dir is None:\n package_dir = os.path.join(*package.split('.'))\n all_files = []\n for f in data:\n path = os.path.join(package_dir, f)\n if os.path.isfile(path):\n all_files.append(f)\n continue\n for root, _dirs, files in os.walk(path, followlinks=True):\n root = os.path.relpath(root, package_dir)\n for file in files:\n file = os.path.join(root, file)\n if file not in all_files:\n all_files.append(file)\n return all_files","title":""}],"string":"[\n {\n \"docid\": \"09bad0013a0dd4d825a9a8815b823eca\",\n \"score\": \"0.822155\",\n \"text\": \"def get_package_data_files(package, data, package_dir=None):\\n if package_dir is None:\\n package_dir = os.path.join(*package.split('.'))\\n all_files = []\\n for f in data:\\n path = os.path.join(package_dir, f)\\n if os.path.isfile(path):\\n all_files.append(f)\\n continue\\n for root, _dirs, files in os.walk(path, followlinks=True):\\n root = os.path.relpath(root, package_dir)\\n for file in files:\\n file = os.path.join(root, file)\\n if file not in all_files:\\n all_files.append(file)\\n return all_files\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9b9c693e91e50e7f0a9168a1f2600086","score":"0.76190716","text":"def find_package_data(data_root, package_root):\n files = []\n for root, dirnames, filenames in os.walk(data_root):\n for fn in filenames:\n files.append(relpath(join(root, fn), package_root))\n return files","title":""},{"docid":"eae72636da107d2f1b8ce60790612736","score":"0.752957","text":"def find_package_data(package):\n walk = [(dirpath.replace(package + os.sep, '', 1), filenames)\n for dirpath, dirnames, filenames in os.walk(package)]\n\n filepaths = []\n for base, filenames in walk:\n filepaths.extend([os.path.join(base, filename)\n for filename in filenames])\n return filepaths","title":""},{"docid":"157c33276d4765d7d39c308cb7c9ee04","score":"0.72842246","text":"def get_package_data(package):\n walk = [(dirpath.replace(package + os.sep, '', 1), filenames)\n for dirpath, dirnames, filenames in os.walk(package)\n if not os.path.exists(os.path.join(dirpath, '__init__.py'))]\n\n filepaths = []\n for base, filenames in walk:\n filepaths.extend([os.path.join(base, filename)\n for filename in filenames])\n return {package: filepaths}","title":""},{"docid":"723955cc0b5247ccb057e5d61bde2cfa","score":"0.7275809","text":"def find_data_files_distutils(self, package, src_dir):\n from glob import glob\n import os\n from distutils.util import convert_path\n\n globs = (self.package_data.get('', [])\n + self.package_data.get(package, []))\n files = []\n for pattern in globs:\n # Each pattern has to be converted to a platform-specific path\n filelist = glob(os.path.join(src_dir, convert_path(pattern)))\n # Files that match more than one pattern are only added once\n files.extend([fn for fn in filelist if fn not in files\n and (os.path.isfile(fn) or os.path.islink(fn))])\n return files","title":""},{"docid":"910c0737657f583889b91ec7ccf10212","score":"0.7213643","text":"def get_package_data(package):\n walk = [\n (dirpath.replace(package + os.sep, '', 1), filenames)\n for dirpath, dirnames, filenames in os.walk(package)\n if not os.path.exists(\n os.path.join(dirpath, '__init__.py')\n )\n ]\n\n filepaths = []\n for base, filenames in walk:\n filepaths.extend([\n os.path.join(base, filename) for filename in filenames\n ])\n return {package: filepaths}","title":""},{"docid":"8933fa7d4f4a37dfcd08472465a2ebfb","score":"0.71410495","text":"def getDataFiles(directoryName):\r\n \r\n return listdir(directoryName)","title":""},{"docid":"91fda1e976937def7ac29f17901f5f79","score":"0.6989218","text":"def get_data_files():\n\n data_files = []\n for d, dirs, filenames in os.walk(share_jupyterhub):\n rel_d = os.path.relpath(d, here)\n data_files.append((rel_d, [os.path.join(rel_d, f) for f in filenames]))\n return data_files","title":""},{"docid":"206508a8389e5cddfbb7fb737ea34c3a","score":"0.695029","text":"def find_data_files_setuptools(self, package, src_dir):\n from glob import glob\n import itertools\n import os\n\n patterns = self._get_platform_patterns(\n self.package_data,\n package,\n src_dir,\n )\n globs_expanded = map(glob, patterns)\n # flatten the expanded globs into an iterable of matches\n globs_matches = itertools.chain.from_iterable(globs_expanded)\n glob_files = [e for e in globs_matches if os.path.isfile(e) or os.path.islink(e)]\n files = itertools.chain(\n self.manifest_files.get(package, []),\n glob_files,\n )\n return self.exclude_data_files(package, src_dir, files)","title":""},{"docid":"63a61e99748093a0cd14f0c0223ae9dc","score":"0.6948258","text":"def get_data_files():\n return [\n ('share/jupyter/nbextensions/{}'.format(PY_PACKAGE), TARGETS),\n ('share/jupyter/lab/extensions', [\n os.path.relpath(f, '.') for f in glob.glob(TAR_PATH)\n ])\n ]","title":""},{"docid":"6f4916a09c32b35ccc9689ba4c87a37a","score":"0.68480045","text":"def find_data(self):\n data_list = []\n for root, dirs, files in os.walk(pathfinder.data_path()):\n for name in files:\n data_list.append(os.path.join(root, name))\n return data_list","title":""},{"docid":"04d54869ce8518d1c1450d92d8e9e751","score":"0.68318033","text":"def get_data_files():\n data_files = []\n\n # Walk through the data directory, adding all files\n data_generator = os.walk('pypeit/data')\n for path, directories, files in data_generator:\n for f in files:\n data_path = '/'.join(path.split('/')[1:])\n data_files.append(os.path.join(data_path, f))\n\n # Add pipeline and spectrograph settings\n settings = glob.glob('pypeit/settings/settings.*')\n settings = ['/'.join(path.split('/')[1:]) for path in settings]\n data_files.extend(settings)\n\n return data_files","title":""},{"docid":"4b16f16f7115ba28720cc88a6c658b67","score":"0.6805526","text":"def getFilesList(data):\n\n filesList = []\n\n if os.path.isdir(data):\n logging.info(\"Using files from \" + data)\n #Create a list containing the file names\n for root, dirs, files in os.walk(data):\n for filename in files:\n filesList.append(os.path.join(root,filename))\n\n else:\n logging.info(\"Using file \" + data)\n filesList.append(os.path.abspath(data))\n\n return sorted(filesList)","title":""},{"docid":"89c7b5aff632d5de59264c13e8535392","score":"0.67001873","text":"def list_all():\n if os.path.exists(DATA_DIR):\n return os.listdir(DATA_DIR)\n return []","title":""},{"docid":"a80ca1643274764680908b7a1843cfb3","score":"0.66871184","text":"def get_data_files(main_directory):\n print('************************************')\n print('Log data list')\n print('************************************')\n log_files_list = globlin(main_directory + '/*/*.json' , recursive=True)\n song_files_list = globlin(main_directory + '/*/*/*/*/*.json', recursive=True)\n print(log_files_list)\n print('************************************')\n print('Song data list')\n print('************************************')\n print(song_files_list)\n return log_files_list, song_files_list","title":""},{"docid":"105a555b616da5a8c5f1b1d1f95083ab","score":"0.6596991","text":"def find_package_data(module, path):\n files = []\n exclude = re.compile(\"\\.pyc$|~$\")\n for dirpath, dirnames, filenames in os.walk(os.path.join(module,path)):\n for filename in filenames:\n if not exclude.search(filename):\n files.append(os.path.relpath(os.path.join(dirpath,filename),module))\n return {module:files}","title":""},{"docid":"1020c3d9903e9a4cfac184549587f340","score":"0.65608156","text":"def getAllFiles(self):\n\n\t\treturn self.getFilesForDirs([])","title":""},{"docid":"9c6da788db83acce0f7ca1a7fa1cff8f","score":"0.65192974","text":"def get_files(data_dir, extension):\n files = []\n if data_dir and os.path.exists(data_dir):\n files = glob.glob(data_dir + \"/*.\" + extension)\n return files","title":""},{"docid":"d35ecadb57b8dd2871dcfd68aeb72b89","score":"0.65102446","text":"def get_package_files(_dir):\n _files = []\n for root, _, files in os.walk(_dir):\n if('git' in root or '/tests' in root):\n continue\n for _name in files:\n _files.append((root, _name))\n return _files","title":""},{"docid":"35f12893e48bc58559558ead32b4aab7","score":"0.6429552","text":"def get_data_files(dirname):\r\n flist = []\r\n for dirpath, _dirnames, filenames in os.walk(dirname):\r\n for fname in filenames:\r\n flist.append(osp.join(dirpath, fname))\r\n return flist","title":""},{"docid":"fcdfe4068b318838fe806c1f64a39d3b","score":"0.6428221","text":"def _package_files(directory: str, suffix: str) -> list:\n\n paths = []\n\n for (path, directories, filenames) in os.walk(directory):\n for filename in filenames:\n if filename.endswith(suffix):\n paths.append(os.path.join('..', path, filename))\n\n return paths","title":""},{"docid":"1b47cf029b936f3cce984904718e1960","score":"0.64271677","text":"def directory_readlines(package, filename):\n\n try:\n return open(os.path.join(package, filename), 'rt').readlines()\n except:\n pass\n\n return []","title":""},{"docid":"f4eb4764ba0d5c81ccc6e176601aecdd","score":"0.6410961","text":"def listFiles(self):\n pass","title":""},{"docid":"e2976a260741ef2664ba549ecd2d95b2","score":"0.63944864","text":"def _get_package_data() -> list[list[str]]:\n moddata = []\n modlist: tuple[str, ...] = (\n \"click\",\n \"cryptography\",\n \"globus_cli\",\n \"globus_sdk\",\n \"jmespath\",\n \"requests\",\n )\n if verbosity() < 2:\n modlist = (\"globus_cli\", \"globus_sdk\", \"requests\")\n\n for mod in modlist:\n cur = [mod]\n try:\n loaded_mod = __import__(mod)\n except ImportError:\n loaded_mod = None\n\n for attr in (\"__version__\", \"__file__\", \"__path__\"):\n # if loading failed, be sure to pad with error messages\n if loaded_mod is None:\n cur.append(\"[import failed]\")\n continue\n\n try:\n attrval = getattr(loaded_mod, attr)\n except AttributeError:\n attrval = \"\"\n cur.append(attrval)\n moddata.append(cur)\n\n return moddata","title":""},{"docid":"67e1005cec61f6b5bd092035b1205520","score":"0.63850534","text":"def data_files(self, pattern=None, regex=None):\n return self._files_in_subdir(self.data_dir, pattern, regex)","title":""},{"docid":"c4a00fcc60bf3815526036ead22c8259","score":"0.6376269","text":"def get_package_data(dataname, asfile=False):\n import inspect\n import os\n dirpath = os.path.dirname(inspect.stack()[1][1])\n datapath = dirpath+'/data/'+dataname\n print datapath\n return get_data(datapath, asfile=asfile)","title":""},{"docid":"53b72469fe25a4dd11b6828dbd0596bb","score":"0.63617796","text":"def _get_nodl_files_from_package_share(*, package_name: str) -> List[Path]:\n package_share_directory = Path(get_package_share_directory(package_name))\n nodl_paths = [\n path for path in package_share_directory.glob('*' + _FILE_EXTENSION) if path.is_file()\n ]\n if not nodl_paths:\n raise NoNoDLFilesError(package_name)\n return nodl_paths","title":""},{"docid":"cea32fdf5802a0b657948f83f7e1ca7d","score":"0.62963617","text":"def list_dir(self, path):","title":""},{"docid":"dc6bf82df3c9201ad96e23fdde17c299","score":"0.6276334","text":"def returnFiles(input_data, fmt=IMAGE_FORMATS):\r\n if type(input_data) is list:\r\n return [returnFiles(element) for element in input_data]\r\n else:\r\n if not os.path.exists(input_data):\r\n raise OSError(\"invalid input path: {}\".format(input_data))\r\n return [pjoin(input_data, name) for name in os.listdir(\r\n input_data) if os.path.splitext(name)[1][1:] in fmt]","title":""},{"docid":"4b1da223cbc5a849eeb63cfea9ede3c9","score":"0.625607","text":"def find_files_for_package(self, package, absolute_path=False):\n package_files = []\n for p in package.package_versions:\n package_files.extend(self.find_files_for_packageversion(p, absolute_path))\n return package_files","title":""},{"docid":"65d9fc43636252a2d4962892e1bf81a7","score":"0.6233622","text":"def files(self):\n files = []\n if self.package_type == 'package':\n file_data = dict([(k, self[k]) \\\n for k in ['size', 'sha1', 'sha256', 'md5sum']])\n file_data['name'] = self['filename'].split('/')[-1]\n files.append(file_data)\n else:\n for d in self['files']:\n file_data = d.copy()\n # Get checksum data as well...\n for key in ['sha1', 'sha256']:\n for data in self['checksums-' + key]:\n if file_data['name'] == data['name']:\n file_data[key] = data[key]\n files.append(file_data)\n return files","title":""},{"docid":"3ae228f1162ef4f144f6a7a85b722d7b","score":"0.6211653","text":"def get_dataset_filelist(dataset):\n\n query = {\n \"_source\": {\n \"includes\": [\"info.directory\", \"info.name\"]\n },\n \"query\": {\n \"match_phrase_prefix\": {\n \"info.directory.analyzed\": dataset\n }\n }\n }\n\n es = CEDAElasticsearchClient()\n results = scan(es, query=query, index='opensearch-files')\n\n file_list = [\n os.path.join(\n item['_source']['info']['directory'],\n item['_source']['info']['name']\n ) for item in results\n ]\n\n return file_list","title":""},{"docid":"048fefd577ed465f474a7d116912e11b","score":"0.61826205","text":"def files_list(directory: str) -> list:\n files = os.listdir(directory)\n\n return files","title":""},{"docid":"c10f9421a4e40a93193a3640b4895b3d","score":"0.61763304","text":"def find_package_data(packages):\n package_data = {}\n for package in packages:\n package_data[package] = []\n for subdir in find_subdirectories(package):\n if '.'.join((package, subdir)) in packages: # skip submodules\n logging.debug(\"skipping submodule %s/%s\" % (package, subdir))\n continue\n if skip_tests and (subdir == 'tests'): # skip tests\n logging.debug(\"skipping tests %s/%s\" % (package, subdir))\n continue\n package_data[package] += subdir_findall(package_to_path(package), subdir)\n return package_data","title":""},{"docid":"eabe27341adf057b7a3f75ad88ee7fe7","score":"0.61759526","text":"def list_files(directories):\n return exec_fn(lambda: _list_dir(directories))","title":""},{"docid":"8dc6be5ea3c113fcc32d424447e92639","score":"0.6162045","text":"def command_package_ls(*args):\n\n # Setup build, install, and data directories\n package_dirs = os.listdir(packages_path())\n\n for packname in package_dirs:\n # Filter to directories with config files\n if not os.path.isdir(package_path(packname)): continue\n if not package_load_config(packname): continue\n\n # Check for installation\n installed_flag = ''\n installdir = install_dir(packname)\n bindir = os.path.join(installdir, 'bin')\n # This is just a very basic sanity check for binaries we\n # require\n binfiles = (os.path.exists(bindir) and os.listdir(bindir)) or []\n if ( ('space' in binfiles or 'space_d' in binfiles) and\n ('cppoh' in binfiles or 'cppoh_d' in binfiles) ):\n installed_flag = '*'\n\n print packname, installed_flag\n\n return 0","title":""},{"docid":"ea89ce953406746f802208804d167cf5","score":"0.61299103","text":"def get_all_file_paths_labels(data_root: str) -> list:\n\n speaker_dirs = os.listdir(data_root)\n all_files = []\n i = 0\n for d in speaker_dirs:\n files = glob.iglob(data_root + '/' + d + '/**/*.wav', recursive=True)\n files = [[f, i] for f in files]\n all_files += files\n i += 1\n all_files = sorted(all_files, key=lambda x:x[0], reverse=False)\n\n return all_files","title":""},{"docid":"bc16e0302943cf8dd292c736826e281e","score":"0.6100568","text":"def get_packages_in_current_dir() -> list:\n from os import listdir\n\n pkgs = []\n ext = ('.tgz', '.txz')\n for file_in_current_dir in sorted(listdir()):\n if file_in_current_dir.endswith(ext):\n pkgs.append(file_in_current_dir)\n\n return pkgs","title":""},{"docid":"d4dbf285bc8de0f17e62294f6b1c8931","score":"0.6099843","text":"def list_source_files(\n data_json: str,\n root_dir: str,\n show_tested_files: str,\n output_file: str = None\n) -> None:\n tested_files_filter = ShowTestedFilesOption.UNSPECIFIED\n if show_tested_files == 'all':\n tested_files_filter = ShowTestedFilesOption.ALL_TESTED\n if show_tested_files == 'some':\n tested_files_filter = ShowTestedFilesOption.ANY_TESTED\n if show_tested_files == 'none':\n tested_files_filter = ShowTestedFilesOption.NOT_TESTED\n\n invocation = cli_list_source_files_datatypes.ListSourceFilesInvocation(\n data_json,\n root_dir,\n tested_files_filter\n )\n result = cli_list_source_files.process_list_source_files(invocation)\n output_lines = (\n cli_list_source_files.format_list_source_files(invocation, result))\n\n _write_output(output_lines, output_file)","title":""},{"docid":"c492521108b61c2ede1ad6780bdecf57","score":"0.6092376","text":"def listf(d=None):\n\tif d:\n\t\tdir=str(d)\n\telse:\n\t\tdir=os.getcwd()\n\tfor item in os.listdir(dir):\n\t\tprint item","title":""},{"docid":"c01c1977da2f0ddbbbdc5e86284e8d9d","score":"0.60829717","text":"def get_filestream(data_dir: str, ext: str) -> Iterable[str]:\n return listdir(data_dir) \\\n | where(lambda p: p.endswith(ext)) \\\n | select( lambda p: join(data_dir,p))","title":""},{"docid":"f05f315c7e7ac518ba7d89633937d260","score":"0.6075903","text":"def get_packages(package):\n return [\n dirpath\n for dirpath, dirnames, filenames in os.walk(package)\n if os.path.exists(os.path.join(dirpath, '__init__.py'))\n ]","title":""},{"docid":"1ef99b49556f5d692c3199de583eb7a5","score":"0.6073497","text":"def get_packages(package):\n return [dirpath\n for dirpath, dirnames, filenames in os.walk(package)\n if os.path.exists(os.path.join(dirpath, '__init__.py'))]","title":""},{"docid":"1ef99b49556f5d692c3199de583eb7a5","score":"0.6073497","text":"def get_packages(package):\n return [dirpath\n for dirpath, dirnames, filenames in os.walk(package)\n if os.path.exists(os.path.join(dirpath, '__init__.py'))]","title":""},{"docid":"1ef99b49556f5d692c3199de583eb7a5","score":"0.6073497","text":"def get_packages(package):\n return [dirpath\n for dirpath, dirnames, filenames in os.walk(package)\n if os.path.exists(os.path.join(dirpath, '__init__.py'))]","title":""},{"docid":"06de9c79c665a398dfc3ac8d30bbc06c","score":"0.60666007","text":"def walk_package(pkgname, root):\n dirs = []\n files = []\n for name in pkg_resources.resource_listdir(pkgname, str(root)):\n fullname = root / name\n if pkg_resources.resource_isdir(pkgname, str(fullname)):\n dirs.append(fullname)\n else:\n files.append(Path(name))\n for new_path in dirs:\n yield from walk_package(pkgname, new_path)\n yield root, dirs, files","title":""},{"docid":"9f1ccad400736e9ed61c79184a702637","score":"0.6063","text":"def get_all_files(cwd):\n return os.listdir(cwd)","title":""},{"docid":"3b4728b7bb1fdff2ec93b79712b898c3","score":"0.6058638","text":"def getFiles(self):\n\t\treturn os.listdir(self.getPath())","title":""},{"docid":"f1fb52b8b2f7b95650ee8abc16cb5c33","score":"0.60514724","text":"def scan_directories(data_dir, file_filter):\n\n root = os.walk(data_dir)\n\n print('Scanning for files...')\n output = []\n\n for directory in root:\n\n files = directory[2]\n\n # Valid dataset contains video files of both halves and an accompanying label\n if file_filter(files):\n output.append(directory[0])\n\n print('Done')\n\n return output","title":""},{"docid":"b7d5d7fde6bc6d983e3d0fdb5e809021","score":"0.6043484","text":"def my_find_packages(*args):\n import os\n packages = []\n for root_module_dir in args:\n for root, dirs, files in os.walk(root_module_dir):\n if '__init__.py' in files:\n packages.append(root)\n return packages","title":""},{"docid":"cb9fb8defa07feb18e672c290304c137","score":"0.60390246","text":"def get_data_from_files(path, filename):\n\n data_files = []\n\n if path:\n list_of_files = os.listdir(path)\n print(\"List of data files:\", list_of_files)\n\n for file in list_of_files:\n if filename in file:\n full_filepath = path + \"/\" + file\n data_files.append(full_filepath)\n #print(data_files)\n\n else:\n data_files = []\n #print(data_files)\n return data_files","title":""},{"docid":"f59bdfca2fb1d60754657a748fa70230","score":"0.6030516","text":"def get_all_files_by_name(self, data_indir, file_name):\n directory_files = glob.glob('{}\\\\*\\\\'.format(data_indir))\n allfiles = []\n for file in directory_files:\n l = self.get_files_by_name(file, file_name)\n allfiles.append(l)\n return allfiles","title":""},{"docid":"0b981d3e82b7537e06eb1a75c4f3c091","score":"0.60171884","text":"def list_packages ( self, name_only=False ):\n if name_only:\n for name, subdir in self._subdirs.items():\n if not subdir.empty():\n yield name\n else:\n for name, subdir in self._subdirs.items():\n if not subdir.empty():\n yield self.name + os.sep + name","title":""},{"docid":"79b01aa21d3e78c5ba5aaa861c69b5ec","score":"0.60168976","text":"def _find_data_files(pattern: str) -> List[str]:\n file_list = glob.glob(pattern)\n if not file_list:\n raise ValueError('No files found matching: ' + str(pattern))\n sorted_file_list = sorted(file_list, key=numerical_sort)\n return sorted_file_list","title":""},{"docid":"6c8ab566244e60d304c0911969c82a2b","score":"0.6005327","text":"def find_data_files(pattern: str) -> List[str]:\n file_list = glob.glob(pattern)\n if not file_list:\n raise ValueError('No files found matching: ' + str(pattern))\n sorted_file_list = sorted(file_list, key=numerical_sort)\n return sorted_file_list","title":""},{"docid":"8c6a62015057abe4faf63dbf84f38dca","score":"0.6004021","text":"def get_package_data(self, prefix=None):\n\n # glob all the files in dist_dir then filter out py & ext files\n root = _Path(self._get_dist_dir(prefix))\n excludes = [\".py\", sysconfig.get_config_var(\"EXT_SUFFIX\")]\n files = [\n f\n for f in root.rglob(\"**/*\")\n if f.is_file() and not any(f.name.endswith(e) for e in excludes)\n ]\n\n # find the parent package of each file and add to the package_data\n package_data = {}\n for f in files:\n pkg_dir = next(d for d in f.parents if (d / \"__init__.py\").is_file())\n pkg_name = _dir_to_pkg(\n self.package_name, pkg_dir.relative_to(root).as_posix()\n )\n pkg_path = f.relative_to(pkg_dir).as_posix()\n if pkg_name in package_data:\n package_data[pkg_name].append(pkg_path)\n else:\n package_data[pkg_name] = [pkg_path]\n\n return package_data","title":""},{"docid":"6552e13acdc3b131238265955de25038","score":"0.59727776","text":"def packages():","title":""},{"docid":"4d4b56afc8e1144a139391adcf14df4e","score":"0.59676534","text":"def get_listfile(self, datadir):\n return []","title":""},{"docid":"d095a1c4cc4f9645e15d71052d5a1909","score":"0.5967452","text":"def list_files(directory) -> List:\n return sorted(f for f in listdir(directory) if f.endswith('.py') and '__init__.py' not in f)","title":""},{"docid":"f3503229020c00def9f5f859db7b31f4","score":"0.5958508","text":"def list_raw_file_directory(\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n extension: Optional[str] = None,\n levels: Optional[int] = 1,\n only_filename: Optional[bool] = False,\n with_prefix: Optional[bool] = True,\n):\n\n file_list = []\n\n if raw_file_dir is None:\n raw_file_dir = prms.Paths.rawdatadir\n\n # 'dressing' the raw_file_dir in a list in case we want to\n # search in several folders (not implemented yet):\n if not isinstance(raw_file_dir, (list, tuple)):\n raw_file_dir = [OtherPath(raw_file_dir)]\n else:\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\n\n if project_dir is not None:\n raw_file_dir = [r / project_dir for r in raw_file_dir]\n\n for d in raw_file_dir:\n _file_list = d.listdir(levels=levels)\n if extension is not None:\n logging.debug(f\"filtering for extension: {extension}\")\n _file_list = fnmatch.filter(_file_list, f\"*.{extension}\")\n if only_filename:\n logging.debug(\"only returning the file names\")\n _file_list = [f.name for f in _file_list]\n elif with_prefix:\n logging.debug(\"adding prefix to file names\")\n logging.debug(f\"{d.pathlike_location=}\")\n _file_list = [d.pathlike_location / f for f in _file_list]\n\n file_list.extend(_file_list)\n\n return file_list","title":""},{"docid":"3010c968f676aa30f1d90f4ff38bcc9f","score":"0.5949623","text":"def list_all_files(dir):\n\n result = []\n for root, _, filenames in os.walk(dir):\n for name in filenames:\n filename, ext = os.path.splitext(name)\n if ext == '.cs' or ext == '.xaml':\n result.append(os.path.join(root, name))\n return result","title":""},{"docid":"3d208d62c393e668e9cfd40e333efd05","score":"0.59430313","text":"def files_in( d ):\n return [ join(d,f) for f in os.listdir(d) if isfile(join(d,f)) ]","title":""},{"docid":"735ca90477065eebc07ea6fd16e76236","score":"0.5940161","text":"def files(self) -> Generator[Path, None, None]:\n return Path(self.package).resolve(strict=True).glob(self.glob)","title":""},{"docid":"06af77e88b0a7590e5cd27efecf80b72","score":"0.59388995","text":"def _get_files(self):\n # pylint: disable=unused-variable\n for dirpath, __, filenames in os.walk(self.start_location):\n for file_ in filenames:\n if file_.endswith('.py'):\n yield \"{0}{1}\".format(dirpath, file_)","title":""},{"docid":"564e71e1582a1a97bf5c917f4ec4541e","score":"0.59324783","text":"def retrieve_all_files(self):\n result = utilities.rscandir(\n self.folder(), ignore_dirs=[\".git\"])\n\n return result","title":""},{"docid":"c2e0c40b0eda05212a503782a8d95b1c","score":"0.5932272","text":"def get_data_files(dir_path):\n fnames = list()\n # dir_path = Path(dir_path)\n if dir_path.exists():\n # if list(dir_path.glob('.*')):\n # for item in list(dir_path.glob('.*')):\n # fnames.append(item.name[1:])\n for f in dir_path.iterdir():\n if f.suffix == '.seis' or f.suffix == '.well':\n fnames.append(f.stem)\n return fnames","title":""},{"docid":"051012e790f4993e435b30761e426280","score":"0.5915267","text":"def list_and_filter(self, pattern, root_path):\n for path, dirs, files in os.walk(os.path.abspath(root_path)):\n for filename in fnmatch.filter(files, pattern):\n yield os.path.join(path, filename)","title":""},{"docid":"2b83956f0008770dfd2e55a5a5c2c09e","score":"0.5914717","text":"def get_all_files(self):\n dp = FileSystemDataProvider.FileSystemDataProvider(self.folder)\n filenames = dp.getFileNames()\n htmlOut = \"available files:\"+\", \".join(filenames)\n return htmlOut","title":""},{"docid":"a7ec23e15dad275f3f665cd568e22a00","score":"0.59104496","text":"def get_file_list(rootdir): #{{{\n file_list = []\n for f in os.listdir(rootdir):\n if f == None or not f.endswith(\".csv\"):\n continue\n file_list.append(os.path.join(rootdir, f))\n \n return file_list","title":""},{"docid":"2e50b39aa9062d6054b55976445bd787","score":"0.5899876","text":"def list_files(path=None):\n if path == None:\n return glob.glob('Data/*.csv')\n else:\n return glob.glob(path+'*.csv')","title":""},{"docid":"fcc5a93319fa0a142d9c4462047ab5ef","score":"0.5897903","text":"def get_library_content(self):\n from glob import glob\n try:\n os.path.isdir(self.source)\n lst = glob(self.source + '/*')\n except TypeError:\n lst = self.source\n dircheck = True\n while dircheck is True:\n dircheck = False\n newlst = []\n for entry in lst:\n if os.path.isdir(entry):\n newlst.extend(glob(entry + '/*'))\n dircheck = True\n else:\n newlst.append(entry)\n lst = newlst\n return lst","title":""},{"docid":"b91c72cff125826d395008115b3a2fe4","score":"0.5897566","text":"def extract_from_dir(self, data_dir):\n data_paths = []\n for(dirpath, dirnames, filenames) in os.walk(data_dir):\n for file in filenames:\n # Make sure that we don't get garbage files\n file_type = file.split('.')\n if len(file_type) == 1 or (file_type[1] not in ['wav', 'txt']):\n continue\n\n data_paths.append(os.path.join(dirpath, file))\n\n return data_paths","title":""},{"docid":"0a78ed7f5c66cad17b0795b08c5937f4","score":"0.5888215","text":"def listFiles(root):\n for dirpath, dirnames, filenames in os.walk(root):\n for file in filenames:\n yield os.path.join(dirpath, file)","title":""},{"docid":"9d9aeb30cfab09224ccfda0bc1ea4434","score":"0.58853084","text":"def list_packages():\n\n shelf_dir = settings.shelf_dir\n\n package_list = os.listdir(shelf_dir)\n\n package_list.sort()\n\n return package_list","title":""},{"docid":"23aab1f6bab792143e2c5ff84c802cc9","score":"0.5879018","text":"def _import_package_files():\n\n\timport os\n\texports = []\n\tglobals_, locals_ = globals(), locals()\n\tpackage_path = os.path.dirname(__file__)\n\tpackage_name = os.path.basename(package_path)\n\n\tfor filename in os.listdir(package_path):\n\t\tmodulename, ext = os.path.splitext(filename)\n\t\tif modulename[0] != '_' and ext in ('.py', '.pyw'):\n\t\t\tsubpackage = '%s.%s' % (package_name, modulename) # package relative\n\t\t\tmodule = __import__(subpackage, globals_, locals_, [modulename])\n\t\t\tmodict = module.__dict__\n\t\t\tnames = (modict['__all__'] if '__all__' in modict else\n\t\t\t\t[name for name in modict if name[0] != '_']) # all public\n\t\t\texports.extend(names)\n\t\t\tglobals_.update((name, modict[name]) for name in names)\n\n\treturn exports","title":""},{"docid":"d03f4886b89b3c8f17899a04f47371fd","score":"0.5873897","text":"def getFiles(directory, showName, extension):\n os.chdir(directory)\n \n list = []\n \n for file in glob.glob(\"*\"+showName+\"*.\"+extension):\n list.append(file)\n \n return list","title":""},{"docid":"4120da720a95a6d3816e67e54888ec4d","score":"0.5871083","text":"def getFiles(folderToProcess,filter):\n\n print(f\"Parsing {folderToProcess} for {filter} files\")\n\n if debug:\n for path in Path(folderToProcess).rglob(filter):\n print(f\"Found {path}\")\n\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \n\n return all_files","title":""},{"docid":"1227eede4f400a1e0bd646f07abd6506","score":"0.5870355","text":"def getFileList(*args, filespec: AnyStr=\"\", folder: AnyStr=\"\", **kwargs)->List[AnyStr]:\n pass","title":""},{"docid":"4fbe6200434124e55eee09b99d7f0e26","score":"0.5866699","text":"def getFiles(searchDir = './', extension = 'source'):\n from glob import glob \n\n return glob(searchDir+'/*.'+extension)","title":""},{"docid":"67b5b535be06d405d1f01123cc282c9f","score":"0.5857886","text":"def listdir(self, name, source, test_data=()):\n assert isinstance(source, config_types.Path)\n self.m.path.assert_absolute(source)\n result = self._run(\n name, ['listdir', source],\n lambda: self.test_api.listdir(test_data),\n self.m.raw_io.output_text())\n ret = [source.join(x) for x in result.stdout.splitlines()]\n result.presentation.logs['listdir'] = map(str, ret)\n return ret","title":""},{"docid":"ce8736e229c1e48618c9ec42122730b8","score":"0.58537245","text":"def get_packages():\n\tapps=[]\n\tlspackages = glob.glob(u'/home/wpkg/packages/*.xml')\n\tfor fic in lspackages:\n #print fic\n\t\tif os.path.isfile(fic):\n\t\t\ttry:\n\t\t\t\txml = etree.parse(fic)\n\t\t\texcept:\n\t\t\t\t#si erreur on tente de corriger le fichier\n\t\t\t\toput = open(\"output.txt\",\"w\")\n\t\t\t\tdata = open(fic).read()\n\t\t\t\toput.write( re.sub('&(?!amp;|quot;|nbsp;|gt;|lt;|laquo;|raquo;|copy;|reg;|bul;|rsquo;)', '&', data) )\n\t\t\t\toput.close()\n\t\t\t\tshutil.move(\"output.txt\", fic)\n\n\t\t\tfinally:\n\t\t\t\tparser = etree.XMLParser(remove_comments=False)\n\t\t\t\txml = etree.parse(fic,parser)\n\t\t\t\tstrxml = etree.tostring(xml.getroot())\n\t\t\t\tfor group in xml.getiterator(u'package'):\n\t\t\t\t\tuid = group.get(u'id')\n\t\t\t\t\tname = group.get(u'name')\n\t\t\t\t\tdvars = dict()\n\t\t\t\t\tfor vars in xml.getiterator('variable'):\n\t\t\t\t\t\tdvars[vars.get('name').lower()] = vars.get('value').lower()\n\n\t\t\t\t#on remplit le dico des appli dispo\n\t\t\t\t#print fic,uid,name\n\t\t\t\tapps.append((fic,uid,name))\n\n\treturn apps","title":""},{"docid":"82e1184c149651d255b64bb91477500f","score":"0.58508915","text":"def getAllFiles(dataSets):\n\n\tfiles = []\n\tfor dataSet in dataSets:\n\t\tif dataSet not in _dataSets:\n\t\t\traise ValueError(\"Not a valid data set: \" + dataSet)\n\n\t\tfiles += sorted(map(lambda x: _dataSets[dataSet] + \"test/\" + x,\n\t\t\t\t\t\t\tfilter(lambda x: x.endswith(\".txt\"), os.listdir(_dataSets[dataSet] + \"test/\"))))\n\t\tfiles += sorted(map(lambda x: _dataSets[dataSet] + x,\n\t\t\t\t\t\t\tfilter(lambda x: x.endswith(\".txt\"), os.listdir(_dataSets[dataSet]))))\n\n\treturn files","title":""},{"docid":"fd1be884d99bfe1931d924e2adff0755","score":"0.5847845","text":"def list_files(tag=None, inst_id=None, data_path=None, format_str=None):\n if format_str is None:\n # user did not supply an alternative format template string\n format_str = '???c{day:03d}{hour:1d}.{year:02d}?.Z'\n # we use a pysat provided function to grab list of files from the\n # local file system that match the format defined above\n file_list = pysat.Files.from_os(data_path=data_path, format_str=format_str,\n two_digit_year_break=90)\n\n return file_list","title":""},{"docid":"d9815471979ee926223741dc7fff31a0","score":"0.5835521","text":"def _filenames(self, dir_or_file):\n if os.path.isdir(dir_or_file):\n return glob(os.path.join(dir_or_file, \"*.txt\"))\n else:\n return [dir_or_file]","title":""},{"docid":"8f352b271d3e1d733d3ec53b10a11a45","score":"0.5834985","text":"def get_file_list():\n wb = xw.Workbook.caller()\n path_input = xw.Range('Macro', 'FilePath').value\n l_file_path = glob.glob(path_input + '[!~]*.*')\n l_file_name = [l.split('/')[-1] for l in l_file_path]\n xw.Range('Macro', 'FileField').clear_contents()\n xw.Range('Macro', 'C_FilePath').options(transpose=True).value = l_file_path\n xw.Range('Macro', 'C_FileName').options(transpose=True).value = l_file_name\n xw.Sheet('Macro').activate()\n wb.macro('ShowMsg')(\"Choose DataType for all the listed files\")","title":""},{"docid":"3bb4f949aacd25409f641741b493b1de","score":"0.58344376","text":"def get_filelist(import_path, extension):\n filelist = []\n for root, dirs, files in os.walk(import_path):\n filelist += glob.glob(os.path.join(root, '*.' + extension))\n return filelist","title":""},{"docid":"aac0ff1e48fba87d00475fc23aecf431","score":"0.5834244","text":"def get_my_files():\n return [file for file in os.listdir(os.getcwd()) if os.path.isfile(file)]","title":""},{"docid":"385974fe321f03b4485b4709ce1ab760","score":"0.583288","text":"def __gather_package_data(arguments):\n ignore_patterns, packages_path, search_packages_path = _resolve_arguments(\n arguments.ignore_patterns,\n arguments.packages_path,\n arguments.search_packages_path,\n )\n rez_packages = set(arguments.rez_packages)\n\n package_finder = registry.get_package_finder(arguments.command)\n\n found_packages = []\n packages, invalid_packages, skips = package_finder(\n paths=packages_path + search_packages_path\n )\n\n for package in packages:\n if rez_packages and package.name not in rez_packages:\n skips.append(package)\n else:\n found_packages.append(package)\n\n ignored_packages, other_packages = _split_the_ignored_packages(\n found_packages, ignore_patterns\n )\n\n other_packages = sorted(other_packages, key=operator.attrgetter(\"name\"))\n\n return ignored_packages, other_packages, invalid_packages, skips","title":""},{"docid":"dff6a041428501b77cfb976ea4b1eca9","score":"0.58267784","text":"def list_package(all: bool = False) -> List[List[str]]:\n if not all:\n pkgs_info = read_installation_records()\n else:\n pkgs_info = []\n for pkg in pkg_resources.working_set:\n pkgs_info.append([pkg.project_name, pkg.version])\n\n return pkgs_info","title":""},{"docid":"5ee7f31dda2e8d8e8de6fc4b318c84c6","score":"0.582648","text":"def list_resource_files(resource_name: str) -> List[str]:\n\n files = []\n dirs = []\n\n def inner_recursion(resource_name: str):\n\n if not pkg_resources.resource_exists(__name__, resource_name):\n raise RuntimeError(\n f\"The resource {resource_name}, does not seem to be available \"\n )\n\n # base case\n if not pkg_resources.resource_isdir(__name__, resource_name):\n files.append(resource_name)\n\n # recursive case\n else:\n if not resource_name.endswith(\"/\"):\n resource_name += \"/\"\n\n dirs.append(resource_name)\n children = pkg_resources.resource_listdir(__name__, resource_name)\n\n resource_names_extended = [f\"{resource_name}{c}\" for c in children]\n\n for child_resource in resource_names_extended:\n inner_recursion(child_resource)\n\n inner_recursion(resource_name)\n\n return files","title":""},{"docid":"b3fe2f2cc17f594ef8833f8debca7c61","score":"0.58206064","text":"def get_package_resources(self):\n resources = []\n for pkg in self.packages:\n resource_data = self.get_resource_data()\n resources.extend(pkg.get_resources(resource_data))\n return resources","title":""},{"docid":"bb0bd22bb088346a36de9e9dd9f0ee11","score":"0.5818124","text":"def GetFilesForTool(self):\n raise NotImplementedError()","title":""},{"docid":"ef8197b5ca3f2c26ba62ace67eb12fa1","score":"0.5816224","text":"def pdbfile_list():\n \n import glob, os\n os.chdir(\"../Data\")\n file_list = []\n for file in glob.glob(\"*.pdb\"):\n file_list.append(file)\n return file_list","title":""},{"docid":"c45c99af7e0cb813728f08cf4c351030","score":"0.58138263","text":"def pdbfile_list():\n import glob, os\n os.chdir(\"../Data\")\n file_list = []\n for file in glob.glob(\"*.pdb\"):\n file_list.append(file)\n return file_list","title":""},{"docid":"8e29a19ded966f77ed2314da6782f408","score":"0.5810797","text":"def get_data_filenames(data_dirs, full_paths=False):\n full_filenames = []\n for data_dir in data_dirs:\n files = os.listdir(os.path.join(data_dir, 'Images'))\n if full_paths:\n full_filenames.extend(map(lambda f: os.path.join(data_dir, f), files))\n else:\n full_filenames.extend(files)\n return full_filenames","title":""},{"docid":"0d22c1dd52e2bb6c8162a933384c5f5c","score":"0.58062553","text":"def get_setup_data_files(self):\n data_files = CMakeBuilder.default_setup_data_files\n if gitutil.has_submodules():\n data_files.append(\".gitmodules\")\n data_files.append(\n os.path.join(self.dist_dir, gitutil.gitmodules_status_name)\n )\n return [(\"\", data_files)]","title":""},{"docid":"e8cd43708ca69506fcc1246123266acc","score":"0.5804019","text":"def get_test_files(dirname):\n if not os.path.isdir(dirname):\n return []\n path = dirname + \"/{}\"\n return list(map(path.format, sorted(os.listdir(dirname))))","title":""},{"docid":"df16251f0afdb394329826a9f21fb078","score":"0.5803129","text":"def _get_all_files(self):\n file_pattern = _FILE_PATTERN\n file_pattern = os.path.join(self.dataset_dir,\n file_pattern % self.split_name)\n return tf.gfile.Glob(file_pattern)","title":""},{"docid":"b3ccdedee8ae1963eb422acbc3265465","score":"0.5787606","text":"def copy_package_data(self, package, target_dir):\n exts = [ i[0] for i in imp.get_suffixes() ]\n exts.append('.py')\n exts.append('.pyc')\n exts.append('.pyo')\n def datafilter(item):\n for e in exts:\n if item.endswith(e):\n return False\n return True\n\n target_dir = os.path.join(target_dir, *(package.identifier.split('.')))\n for dname in package.packagepath:\n filenames = list(filter(datafilter, zipio.listdir(dname)))\n for fname in filenames:\n if fname in ('.svn', 'CVS', '.hg', '.git'):\n # Scrub revision manager junk\n continue\n if fname in ('__pycache__',):\n # Ignore PEP 3147 bytecode cache\n continue\n if fname.startswith('.') and fname.endswith('.swp'):\n # Ignore vim(1) temporary files\n continue\n if fname.endswith('~') or fname.endswith('.orig'):\n # Ignore backup files for common tools (hg, emacs, ...)\n continue\n pth = os.path.join(dname, fname)\n\n # Check if we have found a package, exclude those\n if zipio.isdir(pth):\n # XXX: the 'and not' part is wrong, need to fix zipio.isdir\n for p in zipio.listdir(pth):\n if p.startswith('__init__.') and p[8:] in exts:\n break\n\n else:\n if os.path.isfile(pth):\n # Avoid extracting a resource file that happens\n # to be zipfile.\n # XXX: Need API in zipio for nicer code.\n copy_file(pth, os.path.join(target_dir, fname))\n else:\n copy_tree(pth, os.path.join(target_dir, fname))\n continue\n\n elif zipio.isdir(pth) and (\n zipio.isfile(os.path.join(pth, '__init__.py'))\n or zipio.isfile(os.path.join(pth, '__init__.pyc'))\n or zipio.isfile(os.path.join(pth, '__init__.pyo'))):\n # Subdirectory is a python package, these will get included later on\n # when the subpackage itself is included, ignore for now.\n pass\n\n else:\n copy_file(pth, os.path.join(target_dir, fname))","title":""},{"docid":"52452dc78690459971d93805523ad3fc","score":"0.5785907","text":"def gather_files(path_specs, file_name_pattern, recursively=False):\n files = []\n for path_spec in path_specs:\n if os.path.isdir(path_spec):\n files.extend(IOUtils.find_files(path_spec, file_name_pattern, recursively))\n elif os.path.isfile(path_spec):\n files.append(path_spec)\n return files","title":""},{"docid":"ae7f54bac9d739762b0ca81cc4a1c9c3","score":"0.57830936","text":"def gather_test_files(file_args):\n for dir_or_file in file_args:\n if path.isdir(dir_or_file):\n for current_dir, _, files in os.walk(dir_or_file):\n for file in filter(lambda f: f.endswith(\".09\"), files):\n yield TestFile(dir_or_file, path.join(current_dir, file))\n else:\n yield TestFile(path.dirname(dir_or_file), dir_or_file)","title":""}],"string":"[\n {\n \"docid\": \"9b9c693e91e50e7f0a9168a1f2600086\",\n \"score\": \"0.76190716\",\n \"text\": \"def find_package_data(data_root, package_root):\\n files = []\\n for root, dirnames, filenames in os.walk(data_root):\\n for fn in filenames:\\n files.append(relpath(join(root, fn), package_root))\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eae72636da107d2f1b8ce60790612736\",\n \"score\": \"0.752957\",\n \"text\": \"def find_package_data(package):\\n walk = [(dirpath.replace(package + os.sep, '', 1), filenames)\\n for dirpath, dirnames, filenames in os.walk(package)]\\n\\n filepaths = []\\n for base, filenames in walk:\\n filepaths.extend([os.path.join(base, filename)\\n for filename in filenames])\\n return filepaths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157c33276d4765d7d39c308cb7c9ee04\",\n \"score\": \"0.72842246\",\n \"text\": \"def get_package_data(package):\\n walk = [(dirpath.replace(package + os.sep, '', 1), filenames)\\n for dirpath, dirnames, filenames in os.walk(package)\\n if not os.path.exists(os.path.join(dirpath, '__init__.py'))]\\n\\n filepaths = []\\n for base, filenames in walk:\\n filepaths.extend([os.path.join(base, filename)\\n for filename in filenames])\\n return {package: filepaths}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723955cc0b5247ccb057e5d61bde2cfa\",\n \"score\": \"0.7275809\",\n \"text\": \"def find_data_files_distutils(self, package, src_dir):\\n from glob import glob\\n import os\\n from distutils.util import convert_path\\n\\n globs = (self.package_data.get('', [])\\n + self.package_data.get(package, []))\\n files = []\\n for pattern in globs:\\n # Each pattern has to be converted to a platform-specific path\\n filelist = glob(os.path.join(src_dir, convert_path(pattern)))\\n # Files that match more than one pattern are only added once\\n files.extend([fn for fn in filelist if fn not in files\\n and (os.path.isfile(fn) or os.path.islink(fn))])\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"910c0737657f583889b91ec7ccf10212\",\n \"score\": \"0.7213643\",\n \"text\": \"def get_package_data(package):\\n walk = [\\n (dirpath.replace(package + os.sep, '', 1), filenames)\\n for dirpath, dirnames, filenames in os.walk(package)\\n if not os.path.exists(\\n os.path.join(dirpath, '__init__.py')\\n )\\n ]\\n\\n filepaths = []\\n for base, filenames in walk:\\n filepaths.extend([\\n os.path.join(base, filename) for filename in filenames\\n ])\\n return {package: filepaths}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8933fa7d4f4a37dfcd08472465a2ebfb\",\n \"score\": \"0.71410495\",\n \"text\": \"def getDataFiles(directoryName):\\r\\n \\r\\n return listdir(directoryName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91fda1e976937def7ac29f17901f5f79\",\n \"score\": \"0.6989218\",\n \"text\": \"def get_data_files():\\n\\n data_files = []\\n for d, dirs, filenames in os.walk(share_jupyterhub):\\n rel_d = os.path.relpath(d, here)\\n data_files.append((rel_d, [os.path.join(rel_d, f) for f in filenames]))\\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"206508a8389e5cddfbb7fb737ea34c3a\",\n \"score\": \"0.695029\",\n \"text\": \"def find_data_files_setuptools(self, package, src_dir):\\n from glob import glob\\n import itertools\\n import os\\n\\n patterns = self._get_platform_patterns(\\n self.package_data,\\n package,\\n src_dir,\\n )\\n globs_expanded = map(glob, patterns)\\n # flatten the expanded globs into an iterable of matches\\n globs_matches = itertools.chain.from_iterable(globs_expanded)\\n glob_files = [e for e in globs_matches if os.path.isfile(e) or os.path.islink(e)]\\n files = itertools.chain(\\n self.manifest_files.get(package, []),\\n glob_files,\\n )\\n return self.exclude_data_files(package, src_dir, files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a61e99748093a0cd14f0c0223ae9dc\",\n \"score\": \"0.6948258\",\n \"text\": \"def get_data_files():\\n return [\\n ('share/jupyter/nbextensions/{}'.format(PY_PACKAGE), TARGETS),\\n ('share/jupyter/lab/extensions', [\\n os.path.relpath(f, '.') for f in glob.glob(TAR_PATH)\\n ])\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4916a09c32b35ccc9689ba4c87a37a\",\n \"score\": \"0.68480045\",\n \"text\": \"def find_data(self):\\n data_list = []\\n for root, dirs, files in os.walk(pathfinder.data_path()):\\n for name in files:\\n data_list.append(os.path.join(root, name))\\n return data_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d54869ce8518d1c1450d92d8e9e751\",\n \"score\": \"0.68318033\",\n \"text\": \"def get_data_files():\\n data_files = []\\n\\n # Walk through the data directory, adding all files\\n data_generator = os.walk('pypeit/data')\\n for path, directories, files in data_generator:\\n for f in files:\\n data_path = '/'.join(path.split('/')[1:])\\n data_files.append(os.path.join(data_path, f))\\n\\n # Add pipeline and spectrograph settings\\n settings = glob.glob('pypeit/settings/settings.*')\\n settings = ['/'.join(path.split('/')[1:]) for path in settings]\\n data_files.extend(settings)\\n\\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b16f16f7115ba28720cc88a6c658b67\",\n \"score\": \"0.6805526\",\n \"text\": \"def getFilesList(data):\\n\\n filesList = []\\n\\n if os.path.isdir(data):\\n logging.info(\\\"Using files from \\\" + data)\\n #Create a list containing the file names\\n for root, dirs, files in os.walk(data):\\n for filename in files:\\n filesList.append(os.path.join(root,filename))\\n\\n else:\\n logging.info(\\\"Using file \\\" + data)\\n filesList.append(os.path.abspath(data))\\n\\n return sorted(filesList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c7b5aff632d5de59264c13e8535392\",\n \"score\": \"0.67001873\",\n \"text\": \"def list_all():\\n if os.path.exists(DATA_DIR):\\n return os.listdir(DATA_DIR)\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80ca1643274764680908b7a1843cfb3\",\n \"score\": \"0.66871184\",\n \"text\": \"def get_data_files(main_directory):\\n print('************************************')\\n print('Log data list')\\n print('************************************')\\n log_files_list = globlin(main_directory + '/*/*.json' , recursive=True)\\n song_files_list = globlin(main_directory + '/*/*/*/*/*.json', recursive=True)\\n print(log_files_list)\\n print('************************************')\\n print('Song data list')\\n print('************************************')\\n print(song_files_list)\\n return log_files_list, song_files_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105a555b616da5a8c5f1b1d1f95083ab\",\n \"score\": \"0.6596991\",\n \"text\": \"def find_package_data(module, path):\\n files = []\\n exclude = re.compile(\\\"\\\\.pyc$|~$\\\")\\n for dirpath, dirnames, filenames in os.walk(os.path.join(module,path)):\\n for filename in filenames:\\n if not exclude.search(filename):\\n files.append(os.path.relpath(os.path.join(dirpath,filename),module))\\n return {module:files}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1020c3d9903e9a4cfac184549587f340\",\n \"score\": \"0.65608156\",\n \"text\": \"def getAllFiles(self):\\n\\n\\t\\treturn self.getFilesForDirs([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6da788db83acce0f7ca1a7fa1cff8f\",\n \"score\": \"0.65192974\",\n \"text\": \"def get_files(data_dir, extension):\\n files = []\\n if data_dir and os.path.exists(data_dir):\\n files = glob.glob(data_dir + \\\"/*.\\\" + extension)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35ecadb57b8dd2871dcfd68aeb72b89\",\n \"score\": \"0.65102446\",\n \"text\": \"def get_package_files(_dir):\\n _files = []\\n for root, _, files in os.walk(_dir):\\n if('git' in root or '/tests' in root):\\n continue\\n for _name in files:\\n _files.append((root, _name))\\n return _files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f12893e48bc58559558ead32b4aab7\",\n \"score\": \"0.6429552\",\n \"text\": \"def get_data_files(dirname):\\r\\n flist = []\\r\\n for dirpath, _dirnames, filenames in os.walk(dirname):\\r\\n for fname in filenames:\\r\\n flist.append(osp.join(dirpath, fname))\\r\\n return flist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcdfe4068b318838fe806c1f64a39d3b\",\n \"score\": \"0.6428221\",\n \"text\": \"def _package_files(directory: str, suffix: str) -> list:\\n\\n paths = []\\n\\n for (path, directories, filenames) in os.walk(directory):\\n for filename in filenames:\\n if filename.endswith(suffix):\\n paths.append(os.path.join('..', path, filename))\\n\\n return paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b47cf029b936f3cce984904718e1960\",\n \"score\": \"0.64271677\",\n \"text\": \"def directory_readlines(package, filename):\\n\\n try:\\n return open(os.path.join(package, filename), 'rt').readlines()\\n except:\\n pass\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4eb4764ba0d5c81ccc6e176601aecdd\",\n \"score\": \"0.6410961\",\n \"text\": \"def listFiles(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2976a260741ef2664ba549ecd2d95b2\",\n \"score\": \"0.63944864\",\n \"text\": \"def _get_package_data() -> list[list[str]]:\\n moddata = []\\n modlist: tuple[str, ...] = (\\n \\\"click\\\",\\n \\\"cryptography\\\",\\n \\\"globus_cli\\\",\\n \\\"globus_sdk\\\",\\n \\\"jmespath\\\",\\n \\\"requests\\\",\\n )\\n if verbosity() < 2:\\n modlist = (\\\"globus_cli\\\", \\\"globus_sdk\\\", \\\"requests\\\")\\n\\n for mod in modlist:\\n cur = [mod]\\n try:\\n loaded_mod = __import__(mod)\\n except ImportError:\\n loaded_mod = None\\n\\n for attr in (\\\"__version__\\\", \\\"__file__\\\", \\\"__path__\\\"):\\n # if loading failed, be sure to pad with error messages\\n if loaded_mod is None:\\n cur.append(\\\"[import failed]\\\")\\n continue\\n\\n try:\\n attrval = getattr(loaded_mod, attr)\\n except AttributeError:\\n attrval = \\\"\\\"\\n cur.append(attrval)\\n moddata.append(cur)\\n\\n return moddata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e1005cec61f6b5bd092035b1205520\",\n \"score\": \"0.63850534\",\n \"text\": \"def data_files(self, pattern=None, regex=None):\\n return self._files_in_subdir(self.data_dir, pattern, regex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4a00fcc60bf3815526036ead22c8259\",\n \"score\": \"0.6376269\",\n \"text\": \"def get_package_data(dataname, asfile=False):\\n import inspect\\n import os\\n dirpath = os.path.dirname(inspect.stack()[1][1])\\n datapath = dirpath+'/data/'+dataname\\n print datapath\\n return get_data(datapath, asfile=asfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53b72469fe25a4dd11b6828dbd0596bb\",\n \"score\": \"0.63617796\",\n \"text\": \"def _get_nodl_files_from_package_share(*, package_name: str) -> List[Path]:\\n package_share_directory = Path(get_package_share_directory(package_name))\\n nodl_paths = [\\n path for path in package_share_directory.glob('*' + _FILE_EXTENSION) if path.is_file()\\n ]\\n if not nodl_paths:\\n raise NoNoDLFilesError(package_name)\\n return nodl_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea32fdf5802a0b657948f83f7e1ca7d\",\n \"score\": \"0.62963617\",\n \"text\": \"def list_dir(self, path):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc6bf82df3c9201ad96e23fdde17c299\",\n \"score\": \"0.6276334\",\n \"text\": \"def returnFiles(input_data, fmt=IMAGE_FORMATS):\\r\\n if type(input_data) is list:\\r\\n return [returnFiles(element) for element in input_data]\\r\\n else:\\r\\n if not os.path.exists(input_data):\\r\\n raise OSError(\\\"invalid input path: {}\\\".format(input_data))\\r\\n return [pjoin(input_data, name) for name in os.listdir(\\r\\n input_data) if os.path.splitext(name)[1][1:] in fmt]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b1da223cbc5a849eeb63cfea9ede3c9\",\n \"score\": \"0.625607\",\n \"text\": \"def find_files_for_package(self, package, absolute_path=False):\\n package_files = []\\n for p in package.package_versions:\\n package_files.extend(self.find_files_for_packageversion(p, absolute_path))\\n return package_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d9fc43636252a2d4962892e1bf81a7\",\n \"score\": \"0.6233622\",\n \"text\": \"def files(self):\\n files = []\\n if self.package_type == 'package':\\n file_data = dict([(k, self[k]) \\\\\\n for k in ['size', 'sha1', 'sha256', 'md5sum']])\\n file_data['name'] = self['filename'].split('/')[-1]\\n files.append(file_data)\\n else:\\n for d in self['files']:\\n file_data = d.copy()\\n # Get checksum data as well...\\n for key in ['sha1', 'sha256']:\\n for data in self['checksums-' + key]:\\n if file_data['name'] == data['name']:\\n file_data[key] = data[key]\\n files.append(file_data)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae228f1162ef4f144f6a7a85b722d7b\",\n \"score\": \"0.6211653\",\n \"text\": \"def get_dataset_filelist(dataset):\\n\\n query = {\\n \\\"_source\\\": {\\n \\\"includes\\\": [\\\"info.directory\\\", \\\"info.name\\\"]\\n },\\n \\\"query\\\": {\\n \\\"match_phrase_prefix\\\": {\\n \\\"info.directory.analyzed\\\": dataset\\n }\\n }\\n }\\n\\n es = CEDAElasticsearchClient()\\n results = scan(es, query=query, index='opensearch-files')\\n\\n file_list = [\\n os.path.join(\\n item['_source']['info']['directory'],\\n item['_source']['info']['name']\\n ) for item in results\\n ]\\n\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"048fefd577ed465f474a7d116912e11b\",\n \"score\": \"0.61826205\",\n \"text\": \"def files_list(directory: str) -> list:\\n files = os.listdir(directory)\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10f9421a4e40a93193a3640b4895b3d\",\n \"score\": \"0.61763304\",\n \"text\": \"def find_package_data(packages):\\n package_data = {}\\n for package in packages:\\n package_data[package] = []\\n for subdir in find_subdirectories(package):\\n if '.'.join((package, subdir)) in packages: # skip submodules\\n logging.debug(\\\"skipping submodule %s/%s\\\" % (package, subdir))\\n continue\\n if skip_tests and (subdir == 'tests'): # skip tests\\n logging.debug(\\\"skipping tests %s/%s\\\" % (package, subdir))\\n continue\\n package_data[package] += subdir_findall(package_to_path(package), subdir)\\n return package_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabe27341adf057b7a3f75ad88ee7fe7\",\n \"score\": \"0.61759526\",\n \"text\": \"def list_files(directories):\\n return exec_fn(lambda: _list_dir(directories))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc6be5ea3c113fcc32d424447e92639\",\n \"score\": \"0.6162045\",\n \"text\": \"def command_package_ls(*args):\\n\\n # Setup build, install, and data directories\\n package_dirs = os.listdir(packages_path())\\n\\n for packname in package_dirs:\\n # Filter to directories with config files\\n if not os.path.isdir(package_path(packname)): continue\\n if not package_load_config(packname): continue\\n\\n # Check for installation\\n installed_flag = ''\\n installdir = install_dir(packname)\\n bindir = os.path.join(installdir, 'bin')\\n # This is just a very basic sanity check for binaries we\\n # require\\n binfiles = (os.path.exists(bindir) and os.listdir(bindir)) or []\\n if ( ('space' in binfiles or 'space_d' in binfiles) and\\n ('cppoh' in binfiles or 'cppoh_d' in binfiles) ):\\n installed_flag = '*'\\n\\n print packname, installed_flag\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea89ce953406746f802208804d167cf5\",\n \"score\": \"0.61299103\",\n \"text\": \"def get_all_file_paths_labels(data_root: str) -> list:\\n\\n speaker_dirs = os.listdir(data_root)\\n all_files = []\\n i = 0\\n for d in speaker_dirs:\\n files = glob.iglob(data_root + '/' + d + '/**/*.wav', recursive=True)\\n files = [[f, i] for f in files]\\n all_files += files\\n i += 1\\n all_files = sorted(all_files, key=lambda x:x[0], reverse=False)\\n\\n return all_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc16e0302943cf8dd292c736826e281e\",\n \"score\": \"0.6100568\",\n \"text\": \"def get_packages_in_current_dir() -> list:\\n from os import listdir\\n\\n pkgs = []\\n ext = ('.tgz', '.txz')\\n for file_in_current_dir in sorted(listdir()):\\n if file_in_current_dir.endswith(ext):\\n pkgs.append(file_in_current_dir)\\n\\n return pkgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4dbf285bc8de0f17e62294f6b1c8931\",\n \"score\": \"0.6099843\",\n \"text\": \"def list_source_files(\\n data_json: str,\\n root_dir: str,\\n show_tested_files: str,\\n output_file: str = None\\n) -> None:\\n tested_files_filter = ShowTestedFilesOption.UNSPECIFIED\\n if show_tested_files == 'all':\\n tested_files_filter = ShowTestedFilesOption.ALL_TESTED\\n if show_tested_files == 'some':\\n tested_files_filter = ShowTestedFilesOption.ANY_TESTED\\n if show_tested_files == 'none':\\n tested_files_filter = ShowTestedFilesOption.NOT_TESTED\\n\\n invocation = cli_list_source_files_datatypes.ListSourceFilesInvocation(\\n data_json,\\n root_dir,\\n tested_files_filter\\n )\\n result = cli_list_source_files.process_list_source_files(invocation)\\n output_lines = (\\n cli_list_source_files.format_list_source_files(invocation, result))\\n\\n _write_output(output_lines, output_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c492521108b61c2ede1ad6780bdecf57\",\n \"score\": \"0.6092376\",\n \"text\": \"def listf(d=None):\\n\\tif d:\\n\\t\\tdir=str(d)\\n\\telse:\\n\\t\\tdir=os.getcwd()\\n\\tfor item in os.listdir(dir):\\n\\t\\tprint item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01c1977da2f0ddbbbdc5e86284e8d9d\",\n \"score\": \"0.60829717\",\n \"text\": \"def get_filestream(data_dir: str, ext: str) -> Iterable[str]:\\n return listdir(data_dir) \\\\\\n | where(lambda p: p.endswith(ext)) \\\\\\n | select( lambda p: join(data_dir,p))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05f315c7e7ac518ba7d89633937d260\",\n \"score\": \"0.6075903\",\n \"text\": \"def get_packages(package):\\n return [\\n dirpath\\n for dirpath, dirnames, filenames in os.walk(package)\\n if os.path.exists(os.path.join(dirpath, '__init__.py'))\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef99b49556f5d692c3199de583eb7a5\",\n \"score\": \"0.6073497\",\n \"text\": \"def get_packages(package):\\n return [dirpath\\n for dirpath, dirnames, filenames in os.walk(package)\\n if os.path.exists(os.path.join(dirpath, '__init__.py'))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef99b49556f5d692c3199de583eb7a5\",\n \"score\": \"0.6073497\",\n \"text\": \"def get_packages(package):\\n return [dirpath\\n for dirpath, dirnames, filenames in os.walk(package)\\n if os.path.exists(os.path.join(dirpath, '__init__.py'))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef99b49556f5d692c3199de583eb7a5\",\n \"score\": \"0.6073497\",\n \"text\": \"def get_packages(package):\\n return [dirpath\\n for dirpath, dirnames, filenames in os.walk(package)\\n if os.path.exists(os.path.join(dirpath, '__init__.py'))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06de9c79c665a398dfc3ac8d30bbc06c\",\n \"score\": \"0.60666007\",\n \"text\": \"def walk_package(pkgname, root):\\n dirs = []\\n files = []\\n for name in pkg_resources.resource_listdir(pkgname, str(root)):\\n fullname = root / name\\n if pkg_resources.resource_isdir(pkgname, str(fullname)):\\n dirs.append(fullname)\\n else:\\n files.append(Path(name))\\n for new_path in dirs:\\n yield from walk_package(pkgname, new_path)\\n yield root, dirs, files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1ccad400736e9ed61c79184a702637\",\n \"score\": \"0.6063\",\n \"text\": \"def get_all_files(cwd):\\n return os.listdir(cwd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b4728b7bb1fdff2ec93b79712b898c3\",\n \"score\": \"0.6058638\",\n \"text\": \"def getFiles(self):\\n\\t\\treturn os.listdir(self.getPath())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1fb52b8b2f7b95650ee8abc16cb5c33\",\n \"score\": \"0.60514724\",\n \"text\": \"def scan_directories(data_dir, file_filter):\\n\\n root = os.walk(data_dir)\\n\\n print('Scanning for files...')\\n output = []\\n\\n for directory in root:\\n\\n files = directory[2]\\n\\n # Valid dataset contains video files of both halves and an accompanying label\\n if file_filter(files):\\n output.append(directory[0])\\n\\n print('Done')\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d5d7fde6bc6d983e3d0fdb5e809021\",\n \"score\": \"0.6043484\",\n \"text\": \"def my_find_packages(*args):\\n import os\\n packages = []\\n for root_module_dir in args:\\n for root, dirs, files in os.walk(root_module_dir):\\n if '__init__.py' in files:\\n packages.append(root)\\n return packages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9fb8defa07feb18e672c290304c137\",\n \"score\": \"0.60390246\",\n \"text\": \"def get_data_from_files(path, filename):\\n\\n data_files = []\\n\\n if path:\\n list_of_files = os.listdir(path)\\n print(\\\"List of data files:\\\", list_of_files)\\n\\n for file in list_of_files:\\n if filename in file:\\n full_filepath = path + \\\"/\\\" + file\\n data_files.append(full_filepath)\\n #print(data_files)\\n\\n else:\\n data_files = []\\n #print(data_files)\\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f59bdfca2fb1d60754657a748fa70230\",\n \"score\": \"0.6030516\",\n \"text\": \"def get_all_files_by_name(self, data_indir, file_name):\\n directory_files = glob.glob('{}\\\\\\\\*\\\\\\\\'.format(data_indir))\\n allfiles = []\\n for file in directory_files:\\n l = self.get_files_by_name(file, file_name)\\n allfiles.append(l)\\n return allfiles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b981d3e82b7537e06eb1a75c4f3c091\",\n \"score\": \"0.60171884\",\n \"text\": \"def list_packages ( self, name_only=False ):\\n if name_only:\\n for name, subdir in self._subdirs.items():\\n if not subdir.empty():\\n yield name\\n else:\\n for name, subdir in self._subdirs.items():\\n if not subdir.empty():\\n yield self.name + os.sep + name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b01aa21d3e78c5ba5aaa861c69b5ec\",\n \"score\": \"0.60168976\",\n \"text\": \"def _find_data_files(pattern: str) -> List[str]:\\n file_list = glob.glob(pattern)\\n if not file_list:\\n raise ValueError('No files found matching: ' + str(pattern))\\n sorted_file_list = sorted(file_list, key=numerical_sort)\\n return sorted_file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8ab566244e60d304c0911969c82a2b\",\n \"score\": \"0.6005327\",\n \"text\": \"def find_data_files(pattern: str) -> List[str]:\\n file_list = glob.glob(pattern)\\n if not file_list:\\n raise ValueError('No files found matching: ' + str(pattern))\\n sorted_file_list = sorted(file_list, key=numerical_sort)\\n return sorted_file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6a62015057abe4faf63dbf84f38dca\",\n \"score\": \"0.6004021\",\n \"text\": \"def get_package_data(self, prefix=None):\\n\\n # glob all the files in dist_dir then filter out py & ext files\\n root = _Path(self._get_dist_dir(prefix))\\n excludes = [\\\".py\\\", sysconfig.get_config_var(\\\"EXT_SUFFIX\\\")]\\n files = [\\n f\\n for f in root.rglob(\\\"**/*\\\")\\n if f.is_file() and not any(f.name.endswith(e) for e in excludes)\\n ]\\n\\n # find the parent package of each file and add to the package_data\\n package_data = {}\\n for f in files:\\n pkg_dir = next(d for d in f.parents if (d / \\\"__init__.py\\\").is_file())\\n pkg_name = _dir_to_pkg(\\n self.package_name, pkg_dir.relative_to(root).as_posix()\\n )\\n pkg_path = f.relative_to(pkg_dir).as_posix()\\n if pkg_name in package_data:\\n package_data[pkg_name].append(pkg_path)\\n else:\\n package_data[pkg_name] = [pkg_path]\\n\\n return package_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6552e13acdc3b131238265955de25038\",\n \"score\": \"0.59727776\",\n \"text\": \"def packages():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d4b56afc8e1144a139391adcf14df4e\",\n \"score\": \"0.59676534\",\n \"text\": \"def get_listfile(self, datadir):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d095a1c4cc4f9645e15d71052d5a1909\",\n \"score\": \"0.5967452\",\n \"text\": \"def list_files(directory) -> List:\\n return sorted(f for f in listdir(directory) if f.endswith('.py') and '__init__.py' not in f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3503229020c00def9f5f859db7b31f4\",\n \"score\": \"0.5958508\",\n \"text\": \"def list_raw_file_directory(\\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n extension: Optional[str] = None,\\n levels: Optional[int] = 1,\\n only_filename: Optional[bool] = False,\\n with_prefix: Optional[bool] = True,\\n):\\n\\n file_list = []\\n\\n if raw_file_dir is None:\\n raw_file_dir = prms.Paths.rawdatadir\\n\\n # 'dressing' the raw_file_dir in a list in case we want to\\n # search in several folders (not implemented yet):\\n if not isinstance(raw_file_dir, (list, tuple)):\\n raw_file_dir = [OtherPath(raw_file_dir)]\\n else:\\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\\n\\n if project_dir is not None:\\n raw_file_dir = [r / project_dir for r in raw_file_dir]\\n\\n for d in raw_file_dir:\\n _file_list = d.listdir(levels=levels)\\n if extension is not None:\\n logging.debug(f\\\"filtering for extension: {extension}\\\")\\n _file_list = fnmatch.filter(_file_list, f\\\"*.{extension}\\\")\\n if only_filename:\\n logging.debug(\\\"only returning the file names\\\")\\n _file_list = [f.name for f in _file_list]\\n elif with_prefix:\\n logging.debug(\\\"adding prefix to file names\\\")\\n logging.debug(f\\\"{d.pathlike_location=}\\\")\\n _file_list = [d.pathlike_location / f for f in _file_list]\\n\\n file_list.extend(_file_list)\\n\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3010c968f676aa30f1d90f4ff38bcc9f\",\n \"score\": \"0.5949623\",\n \"text\": \"def list_all_files(dir):\\n\\n result = []\\n for root, _, filenames in os.walk(dir):\\n for name in filenames:\\n filename, ext = os.path.splitext(name)\\n if ext == '.cs' or ext == '.xaml':\\n result.append(os.path.join(root, name))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d208d62c393e668e9cfd40e333efd05\",\n \"score\": \"0.59430313\",\n \"text\": \"def files_in( d ):\\n return [ join(d,f) for f in os.listdir(d) if isfile(join(d,f)) ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"735ca90477065eebc07ea6fd16e76236\",\n \"score\": \"0.5940161\",\n \"text\": \"def files(self) -> Generator[Path, None, None]:\\n return Path(self.package).resolve(strict=True).glob(self.glob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06af77e88b0a7590e5cd27efecf80b72\",\n \"score\": \"0.59388995\",\n \"text\": \"def _get_files(self):\\n # pylint: disable=unused-variable\\n for dirpath, __, filenames in os.walk(self.start_location):\\n for file_ in filenames:\\n if file_.endswith('.py'):\\n yield \\\"{0}{1}\\\".format(dirpath, file_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564e71e1582a1a97bf5c917f4ec4541e\",\n \"score\": \"0.59324783\",\n \"text\": \"def retrieve_all_files(self):\\n result = utilities.rscandir(\\n self.folder(), ignore_dirs=[\\\".git\\\"])\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e0c40b0eda05212a503782a8d95b1c\",\n \"score\": \"0.5932272\",\n \"text\": \"def get_data_files(dir_path):\\n fnames = list()\\n # dir_path = Path(dir_path)\\n if dir_path.exists():\\n # if list(dir_path.glob('.*')):\\n # for item in list(dir_path.glob('.*')):\\n # fnames.append(item.name[1:])\\n for f in dir_path.iterdir():\\n if f.suffix == '.seis' or f.suffix == '.well':\\n fnames.append(f.stem)\\n return fnames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051012e790f4993e435b30761e426280\",\n \"score\": \"0.5915267\",\n \"text\": \"def list_and_filter(self, pattern, root_path):\\n for path, dirs, files in os.walk(os.path.abspath(root_path)):\\n for filename in fnmatch.filter(files, pattern):\\n yield os.path.join(path, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b83956f0008770dfd2e55a5a5c2c09e\",\n \"score\": \"0.5914717\",\n \"text\": \"def get_all_files(self):\\n dp = FileSystemDataProvider.FileSystemDataProvider(self.folder)\\n filenames = dp.getFileNames()\\n htmlOut = \\\"available files:\\\"+\\\", \\\".join(filenames)\\n return htmlOut\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7ec23e15dad275f3f665cd568e22a00\",\n \"score\": \"0.59104496\",\n \"text\": \"def get_file_list(rootdir): #{{{\\n file_list = []\\n for f in os.listdir(rootdir):\\n if f == None or not f.endswith(\\\".csv\\\"):\\n continue\\n file_list.append(os.path.join(rootdir, f))\\n \\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e50b39aa9062d6054b55976445bd787\",\n \"score\": \"0.5899876\",\n \"text\": \"def list_files(path=None):\\n if path == None:\\n return glob.glob('Data/*.csv')\\n else:\\n return glob.glob(path+'*.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc5a93319fa0a142d9c4462047ab5ef\",\n \"score\": \"0.5897903\",\n \"text\": \"def get_library_content(self):\\n from glob import glob\\n try:\\n os.path.isdir(self.source)\\n lst = glob(self.source + '/*')\\n except TypeError:\\n lst = self.source\\n dircheck = True\\n while dircheck is True:\\n dircheck = False\\n newlst = []\\n for entry in lst:\\n if os.path.isdir(entry):\\n newlst.extend(glob(entry + '/*'))\\n dircheck = True\\n else:\\n newlst.append(entry)\\n lst = newlst\\n return lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91c72cff125826d395008115b3a2fe4\",\n \"score\": \"0.5897566\",\n \"text\": \"def extract_from_dir(self, data_dir):\\n data_paths = []\\n for(dirpath, dirnames, filenames) in os.walk(data_dir):\\n for file in filenames:\\n # Make sure that we don't get garbage files\\n file_type = file.split('.')\\n if len(file_type) == 1 or (file_type[1] not in ['wav', 'txt']):\\n continue\\n\\n data_paths.append(os.path.join(dirpath, file))\\n\\n return data_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a78ed7f5c66cad17b0795b08c5937f4\",\n \"score\": \"0.5888215\",\n \"text\": \"def listFiles(root):\\n for dirpath, dirnames, filenames in os.walk(root):\\n for file in filenames:\\n yield os.path.join(dirpath, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9aeb30cfab09224ccfda0bc1ea4434\",\n \"score\": \"0.58853084\",\n \"text\": \"def list_packages():\\n\\n shelf_dir = settings.shelf_dir\\n\\n package_list = os.listdir(shelf_dir)\\n\\n package_list.sort()\\n\\n return package_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23aab1f6bab792143e2c5ff84c802cc9\",\n \"score\": \"0.5879018\",\n \"text\": \"def _import_package_files():\\n\\n\\timport os\\n\\texports = []\\n\\tglobals_, locals_ = globals(), locals()\\n\\tpackage_path = os.path.dirname(__file__)\\n\\tpackage_name = os.path.basename(package_path)\\n\\n\\tfor filename in os.listdir(package_path):\\n\\t\\tmodulename, ext = os.path.splitext(filename)\\n\\t\\tif modulename[0] != '_' and ext in ('.py', '.pyw'):\\n\\t\\t\\tsubpackage = '%s.%s' % (package_name, modulename) # package relative\\n\\t\\t\\tmodule = __import__(subpackage, globals_, locals_, [modulename])\\n\\t\\t\\tmodict = module.__dict__\\n\\t\\t\\tnames = (modict['__all__'] if '__all__' in modict else\\n\\t\\t\\t\\t[name for name in modict if name[0] != '_']) # all public\\n\\t\\t\\texports.extend(names)\\n\\t\\t\\tglobals_.update((name, modict[name]) for name in names)\\n\\n\\treturn exports\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03f4886b89b3c8f17899a04f47371fd\",\n \"score\": \"0.5873897\",\n \"text\": \"def getFiles(directory, showName, extension):\\n os.chdir(directory)\\n \\n list = []\\n \\n for file in glob.glob(\\\"*\\\"+showName+\\\"*.\\\"+extension):\\n list.append(file)\\n \\n return list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4120da720a95a6d3816e67e54888ec4d\",\n \"score\": \"0.5871083\",\n \"text\": \"def getFiles(folderToProcess,filter):\\n\\n print(f\\\"Parsing {folderToProcess} for {filter} files\\\")\\n\\n if debug:\\n for path in Path(folderToProcess).rglob(filter):\\n print(f\\\"Found {path}\\\")\\n\\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \\n\\n return all_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1227eede4f400a1e0bd646f07abd6506\",\n \"score\": \"0.5870355\",\n \"text\": \"def getFileList(*args, filespec: AnyStr=\\\"\\\", folder: AnyStr=\\\"\\\", **kwargs)->List[AnyStr]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fbe6200434124e55eee09b99d7f0e26\",\n \"score\": \"0.5866699\",\n \"text\": \"def getFiles(searchDir = './', extension = 'source'):\\n from glob import glob \\n\\n return glob(searchDir+'/*.'+extension)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b5b535be06d405d1f01123cc282c9f\",\n \"score\": \"0.5857886\",\n \"text\": \"def listdir(self, name, source, test_data=()):\\n assert isinstance(source, config_types.Path)\\n self.m.path.assert_absolute(source)\\n result = self._run(\\n name, ['listdir', source],\\n lambda: self.test_api.listdir(test_data),\\n self.m.raw_io.output_text())\\n ret = [source.join(x) for x in result.stdout.splitlines()]\\n result.presentation.logs['listdir'] = map(str, ret)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8736e229c1e48618c9ec42122730b8\",\n \"score\": \"0.58537245\",\n \"text\": \"def get_packages():\\n\\tapps=[]\\n\\tlspackages = glob.glob(u'/home/wpkg/packages/*.xml')\\n\\tfor fic in lspackages:\\n #print fic\\n\\t\\tif os.path.isfile(fic):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\txml = etree.parse(fic)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\t#si erreur on tente de corriger le fichier\\n\\t\\t\\t\\toput = open(\\\"output.txt\\\",\\\"w\\\")\\n\\t\\t\\t\\tdata = open(fic).read()\\n\\t\\t\\t\\toput.write( re.sub('&(?!amp;|quot;|nbsp;|gt;|lt;|laquo;|raquo;|copy;|reg;|bul;|rsquo;)', '&', data) )\\n\\t\\t\\t\\toput.close()\\n\\t\\t\\t\\tshutil.move(\\\"output.txt\\\", fic)\\n\\n\\t\\t\\tfinally:\\n\\t\\t\\t\\tparser = etree.XMLParser(remove_comments=False)\\n\\t\\t\\t\\txml = etree.parse(fic,parser)\\n\\t\\t\\t\\tstrxml = etree.tostring(xml.getroot())\\n\\t\\t\\t\\tfor group in xml.getiterator(u'package'):\\n\\t\\t\\t\\t\\tuid = group.get(u'id')\\n\\t\\t\\t\\t\\tname = group.get(u'name')\\n\\t\\t\\t\\t\\tdvars = dict()\\n\\t\\t\\t\\t\\tfor vars in xml.getiterator('variable'):\\n\\t\\t\\t\\t\\t\\tdvars[vars.get('name').lower()] = vars.get('value').lower()\\n\\n\\t\\t\\t\\t#on remplit le dico des appli dispo\\n\\t\\t\\t\\t#print fic,uid,name\\n\\t\\t\\t\\tapps.append((fic,uid,name))\\n\\n\\treturn apps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e1184c149651d255b64bb91477500f\",\n \"score\": \"0.58508915\",\n \"text\": \"def getAllFiles(dataSets):\\n\\n\\tfiles = []\\n\\tfor dataSet in dataSets:\\n\\t\\tif dataSet not in _dataSets:\\n\\t\\t\\traise ValueError(\\\"Not a valid data set: \\\" + dataSet)\\n\\n\\t\\tfiles += sorted(map(lambda x: _dataSets[dataSet] + \\\"test/\\\" + x,\\n\\t\\t\\t\\t\\t\\t\\tfilter(lambda x: x.endswith(\\\".txt\\\"), os.listdir(_dataSets[dataSet] + \\\"test/\\\"))))\\n\\t\\tfiles += sorted(map(lambda x: _dataSets[dataSet] + x,\\n\\t\\t\\t\\t\\t\\t\\tfilter(lambda x: x.endswith(\\\".txt\\\"), os.listdir(_dataSets[dataSet]))))\\n\\n\\treturn files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1be884d99bfe1931d924e2adff0755\",\n \"score\": \"0.5847845\",\n \"text\": \"def list_files(tag=None, inst_id=None, data_path=None, format_str=None):\\n if format_str is None:\\n # user did not supply an alternative format template string\\n format_str = '???c{day:03d}{hour:1d}.{year:02d}?.Z'\\n # we use a pysat provided function to grab list of files from the\\n # local file system that match the format defined above\\n file_list = pysat.Files.from_os(data_path=data_path, format_str=format_str,\\n two_digit_year_break=90)\\n\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9815471979ee926223741dc7fff31a0\",\n \"score\": \"0.5835521\",\n \"text\": \"def _filenames(self, dir_or_file):\\n if os.path.isdir(dir_or_file):\\n return glob(os.path.join(dir_or_file, \\\"*.txt\\\"))\\n else:\\n return [dir_or_file]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f352b271d3e1d733d3ec53b10a11a45\",\n \"score\": \"0.5834985\",\n \"text\": \"def get_file_list():\\n wb = xw.Workbook.caller()\\n path_input = xw.Range('Macro', 'FilePath').value\\n l_file_path = glob.glob(path_input + '[!~]*.*')\\n l_file_name = [l.split('/')[-1] for l in l_file_path]\\n xw.Range('Macro', 'FileField').clear_contents()\\n xw.Range('Macro', 'C_FilePath').options(transpose=True).value = l_file_path\\n xw.Range('Macro', 'C_FileName').options(transpose=True).value = l_file_name\\n xw.Sheet('Macro').activate()\\n wb.macro('ShowMsg')(\\\"Choose DataType for all the listed files\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb4f949aacd25409f641741b493b1de\",\n \"score\": \"0.58344376\",\n \"text\": \"def get_filelist(import_path, extension):\\n filelist = []\\n for root, dirs, files in os.walk(import_path):\\n filelist += glob.glob(os.path.join(root, '*.' + extension))\\n return filelist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac0ff1e48fba87d00475fc23aecf431\",\n \"score\": \"0.5834244\",\n \"text\": \"def get_my_files():\\n return [file for file in os.listdir(os.getcwd()) if os.path.isfile(file)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"385974fe321f03b4485b4709ce1ab760\",\n \"score\": \"0.583288\",\n \"text\": \"def __gather_package_data(arguments):\\n ignore_patterns, packages_path, search_packages_path = _resolve_arguments(\\n arguments.ignore_patterns,\\n arguments.packages_path,\\n arguments.search_packages_path,\\n )\\n rez_packages = set(arguments.rez_packages)\\n\\n package_finder = registry.get_package_finder(arguments.command)\\n\\n found_packages = []\\n packages, invalid_packages, skips = package_finder(\\n paths=packages_path + search_packages_path\\n )\\n\\n for package in packages:\\n if rez_packages and package.name not in rez_packages:\\n skips.append(package)\\n else:\\n found_packages.append(package)\\n\\n ignored_packages, other_packages = _split_the_ignored_packages(\\n found_packages, ignore_patterns\\n )\\n\\n other_packages = sorted(other_packages, key=operator.attrgetter(\\\"name\\\"))\\n\\n return ignored_packages, other_packages, invalid_packages, skips\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff6a041428501b77cfb976ea4b1eca9\",\n \"score\": \"0.58267784\",\n \"text\": \"def list_package(all: bool = False) -> List[List[str]]:\\n if not all:\\n pkgs_info = read_installation_records()\\n else:\\n pkgs_info = []\\n for pkg in pkg_resources.working_set:\\n pkgs_info.append([pkg.project_name, pkg.version])\\n\\n return pkgs_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee7f31dda2e8d8e8de6fc4b318c84c6\",\n \"score\": \"0.582648\",\n \"text\": \"def list_resource_files(resource_name: str) -> List[str]:\\n\\n files = []\\n dirs = []\\n\\n def inner_recursion(resource_name: str):\\n\\n if not pkg_resources.resource_exists(__name__, resource_name):\\n raise RuntimeError(\\n f\\\"The resource {resource_name}, does not seem to be available \\\"\\n )\\n\\n # base case\\n if not pkg_resources.resource_isdir(__name__, resource_name):\\n files.append(resource_name)\\n\\n # recursive case\\n else:\\n if not resource_name.endswith(\\\"/\\\"):\\n resource_name += \\\"/\\\"\\n\\n dirs.append(resource_name)\\n children = pkg_resources.resource_listdir(__name__, resource_name)\\n\\n resource_names_extended = [f\\\"{resource_name}{c}\\\" for c in children]\\n\\n for child_resource in resource_names_extended:\\n inner_recursion(child_resource)\\n\\n inner_recursion(resource_name)\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3fe2f2cc17f594ef8833f8debca7c61\",\n \"score\": \"0.58206064\",\n \"text\": \"def get_package_resources(self):\\n resources = []\\n for pkg in self.packages:\\n resource_data = self.get_resource_data()\\n resources.extend(pkg.get_resources(resource_data))\\n return resources\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0bd22bb088346a36de9e9dd9f0ee11\",\n \"score\": \"0.5818124\",\n \"text\": \"def GetFilesForTool(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef8197b5ca3f2c26ba62ace67eb12fa1\",\n \"score\": \"0.5816224\",\n \"text\": \"def pdbfile_list():\\n \\n import glob, os\\n os.chdir(\\\"../Data\\\")\\n file_list = []\\n for file in glob.glob(\\\"*.pdb\\\"):\\n file_list.append(file)\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45c99af7e0cb813728f08cf4c351030\",\n \"score\": \"0.58138263\",\n \"text\": \"def pdbfile_list():\\n import glob, os\\n os.chdir(\\\"../Data\\\")\\n file_list = []\\n for file in glob.glob(\\\"*.pdb\\\"):\\n file_list.append(file)\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e29a19ded966f77ed2314da6782f408\",\n \"score\": \"0.5810797\",\n \"text\": \"def get_data_filenames(data_dirs, full_paths=False):\\n full_filenames = []\\n for data_dir in data_dirs:\\n files = os.listdir(os.path.join(data_dir, 'Images'))\\n if full_paths:\\n full_filenames.extend(map(lambda f: os.path.join(data_dir, f), files))\\n else:\\n full_filenames.extend(files)\\n return full_filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d22c1dd52e2bb6c8162a933384c5f5c\",\n \"score\": \"0.58062553\",\n \"text\": \"def get_setup_data_files(self):\\n data_files = CMakeBuilder.default_setup_data_files\\n if gitutil.has_submodules():\\n data_files.append(\\\".gitmodules\\\")\\n data_files.append(\\n os.path.join(self.dist_dir, gitutil.gitmodules_status_name)\\n )\\n return [(\\\"\\\", data_files)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8cd43708ca69506fcc1246123266acc\",\n \"score\": \"0.5804019\",\n \"text\": \"def get_test_files(dirname):\\n if not os.path.isdir(dirname):\\n return []\\n path = dirname + \\\"/{}\\\"\\n return list(map(path.format, sorted(os.listdir(dirname))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df16251f0afdb394329826a9f21fb078\",\n \"score\": \"0.5803129\",\n \"text\": \"def _get_all_files(self):\\n file_pattern = _FILE_PATTERN\\n file_pattern = os.path.join(self.dataset_dir,\\n file_pattern % self.split_name)\\n return tf.gfile.Glob(file_pattern)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ccdedee8ae1963eb422acbc3265465\",\n \"score\": \"0.5787606\",\n \"text\": \"def copy_package_data(self, package, target_dir):\\n exts = [ i[0] for i in imp.get_suffixes() ]\\n exts.append('.py')\\n exts.append('.pyc')\\n exts.append('.pyo')\\n def datafilter(item):\\n for e in exts:\\n if item.endswith(e):\\n return False\\n return True\\n\\n target_dir = os.path.join(target_dir, *(package.identifier.split('.')))\\n for dname in package.packagepath:\\n filenames = list(filter(datafilter, zipio.listdir(dname)))\\n for fname in filenames:\\n if fname in ('.svn', 'CVS', '.hg', '.git'):\\n # Scrub revision manager junk\\n continue\\n if fname in ('__pycache__',):\\n # Ignore PEP 3147 bytecode cache\\n continue\\n if fname.startswith('.') and fname.endswith('.swp'):\\n # Ignore vim(1) temporary files\\n continue\\n if fname.endswith('~') or fname.endswith('.orig'):\\n # Ignore backup files for common tools (hg, emacs, ...)\\n continue\\n pth = os.path.join(dname, fname)\\n\\n # Check if we have found a package, exclude those\\n if zipio.isdir(pth):\\n # XXX: the 'and not' part is wrong, need to fix zipio.isdir\\n for p in zipio.listdir(pth):\\n if p.startswith('__init__.') and p[8:] in exts:\\n break\\n\\n else:\\n if os.path.isfile(pth):\\n # Avoid extracting a resource file that happens\\n # to be zipfile.\\n # XXX: Need API in zipio for nicer code.\\n copy_file(pth, os.path.join(target_dir, fname))\\n else:\\n copy_tree(pth, os.path.join(target_dir, fname))\\n continue\\n\\n elif zipio.isdir(pth) and (\\n zipio.isfile(os.path.join(pth, '__init__.py'))\\n or zipio.isfile(os.path.join(pth, '__init__.pyc'))\\n or zipio.isfile(os.path.join(pth, '__init__.pyo'))):\\n # Subdirectory is a python package, these will get included later on\\n # when the subpackage itself is included, ignore for now.\\n pass\\n\\n else:\\n copy_file(pth, os.path.join(target_dir, fname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52452dc78690459971d93805523ad3fc\",\n \"score\": \"0.5785907\",\n \"text\": \"def gather_files(path_specs, file_name_pattern, recursively=False):\\n files = []\\n for path_spec in path_specs:\\n if os.path.isdir(path_spec):\\n files.extend(IOUtils.find_files(path_spec, file_name_pattern, recursively))\\n elif os.path.isfile(path_spec):\\n files.append(path_spec)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7f54bac9d739762b0ca81cc4a1c9c3\",\n \"score\": \"0.57830936\",\n \"text\": \"def gather_test_files(file_args):\\n for dir_or_file in file_args:\\n if path.isdir(dir_or_file):\\n for current_dir, _, files in os.walk(dir_or_file):\\n for file in filter(lambda f: f.endswith(\\\".09\\\"), files):\\n yield TestFile(dir_or_file, path.join(current_dir, file))\\n else:\\n yield TestFile(path.dirname(dir_or_file), dir_or_file)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1579,"cells":{"query_id":{"kind":"string","value":"a4f43756e38427187545affc135b545a"},"query":{"kind":"string","value":"Scripted pipeline to process FluoXAS data"},"positive_passages":{"kind":"list like","value":[{"docid":"f548e1d390f916c338f7efeac824c7ee","score":"0.0","text":"def tasks(**parameters):\n tasks = []\n\n # Common parameters\n commonparams = task_parameters(parameters, \"common\")\n sinstrument = require_pop(commonparams, \"instrument\")\n coutputparent = commonparams.pop(\"outputparent\", None)\n ensure_parameter(commonparams, \"stackdim\", 0)\n ensure_parameter(commonparams, \"default\", None)\n instrument = getinstrument(instrument=sinstrument)\n\n # Calibrate geometry\n params = task_parameters(parameters, \"geometry\")\n ensure_parameter(params, \"outputparent\", default=None)\n geometry = ensure_parameter(params, \"geometry\", None)\n if geometry:\n init = ensure_parameter(params, \"init\", {})\n init[\"instrument\"] = instrument\n task = create_task(method=\"xrfgeometry\", name=\"xrfgeometry\", **params)\n tasks.append(task)\n else:\n task = None\n\n # XRF fit and counter maps\n params = task_parameters(parameters, \"resample\")\n encodercor = params.pop(\"encodercor\", False)\n params = task_parameters(parameters, \"pymca\")\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n xrfparameters(params, instrument, include_encoders=encodercor, quant=bool(geometry))\n params.update(commonparams)\n task = create_task(method=\"pymca\", name=\"pymca\", dependencies=task, **params)\n tasks.append(task)\n\n # Normalization\n params = task_parameters(parameters, \"prealignnormalize\")\n counter = params.pop(\"counter\", None)\n dtcor = False # No longer needed\n if dtcor or counter:\n copy = [\n {\"method\": \"regex\", \"pattern\": prefix}\n for prefix in instrument.counterdict[\"counters\"]\n ]\n # Create normalization expression\n if dtcor:\n icr = instrument.counterdict[\"xrficr\"]\n ocr = instrument.counterdict[\"xrfocr\"]\n if counter:\n expression = \"{{}}*nanone({{{}}}/({{{}}}*{{{}}}))\".format(\n icr, ocr, counter\n )\n else:\n expression = \"{{}}*nanone({{{}}}/{{{}}})\".format(icr, ocr)\n else:\n expression = \"{{}}/{{{}}}\".format(counter)\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n ensure_parameter(params, \"name\", \"prenormalize\")\n task = create_task(\n dependencies=task,\n method=\"expression\",\n name=\"normalize\",\n expression=expression,\n copy=copy,\n **commonparams\n )\n tasks.append(task)\n\n # Correct for encoder positions\n if encodercor:\n params = task_parameters(parameters, \"resample\")\n params.update(commonparams)\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n ensure_parameter(params, \"name\", \"resample\")\n encoders = instrument.encoderinfo\n task = create_task(\n dependencies=task, method=\"resample\", encoders=encoders, **params\n )\n tasks.append(task)\n\n # Alignment\n params = task_parameters(parameters, \"align\")\n alignmethod = ensure_parameter(params, \"alignmethod\", None)\n reference = ensure_parameter(params, \"reference\", None)\n if alignmethod and reference is not None:\n params.update(commonparams)\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n ensure_parameter(params, \"name\", \"align\")\n ensure_parameter(params, \"refimageindex\", -1)\n ensure_parameter(params, \"roi\", None)\n ensure_parameter(params, \"plot\", False)\n ensure_parameter(params, \"crop\", False)\n ensure_parameter(params, \"name\", \"postnormalize\")\n task = create_task(dependencies=task, method=\"align\", **params)\n tasks.append(task)\n\n # Post normalization\n params = task_parameters(parameters, \"postalignnormalize\")\n counter = params.pop(\"counter\", None)\n if counter:\n copy = [\n {\"method\": \"regexparent\", \"pattern\": prefix}\n for prefix in instrument.counterdict[\"counters\"]\n ]\n expression = \"{{}}/{{{}}}\".format(counter)\n params.update(commonparams)\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n ensure_parameter(params, \"name\", \"postnormalize\")\n task = create_task(\n dependencies=task,\n method=\"expression\",\n expression=expression,\n copy=copy,\n **params\n )\n tasks.append(task)\n\n # Remove NaN's\n params = task_parameters(parameters, \"replacenan\")\n replacenan = params.pop(\"replacenan\", False)\n if replacenan:\n params.update(commonparams)\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n ensure_parameter(params, \"name\", \"replace\")\n ensure_parameter(params, \"org\", np.nan)\n ensure_parameter(params, \"new\", 0)\n tmp = create_task(dependencies=task, method=\"replace\", **params)\n tasks.append(tmp)\n\n # Crop\n params = task_parameters(parameters, \"crop\")\n cropafter = params.pop(\"crop\", False)\n if cropafter:\n params.update(commonparams)\n ensure_parameter(params, \"outputparent\", default=coutputparent)\n ensure_parameter(params, \"name\", \"crop\")\n ensure_parameter(params, \"cropfull\", True)\n params[\"nanfull\"] = params.pop(\"cropfull\")\n ensure_parameter(params, \"nanval\", np.nan)\n ensure_parameter(params, \"reference\", reference)\n tmp = create_task(dependencies=task, method=\"crop\", **params)\n tasks.append(tmp)\n\n return tasks","title":""}],"string":"[\n {\n \"docid\": \"f548e1d390f916c338f7efeac824c7ee\",\n \"score\": \"0.0\",\n \"text\": \"def tasks(**parameters):\\n tasks = []\\n\\n # Common parameters\\n commonparams = task_parameters(parameters, \\\"common\\\")\\n sinstrument = require_pop(commonparams, \\\"instrument\\\")\\n coutputparent = commonparams.pop(\\\"outputparent\\\", None)\\n ensure_parameter(commonparams, \\\"stackdim\\\", 0)\\n ensure_parameter(commonparams, \\\"default\\\", None)\\n instrument = getinstrument(instrument=sinstrument)\\n\\n # Calibrate geometry\\n params = task_parameters(parameters, \\\"geometry\\\")\\n ensure_parameter(params, \\\"outputparent\\\", default=None)\\n geometry = ensure_parameter(params, \\\"geometry\\\", None)\\n if geometry:\\n init = ensure_parameter(params, \\\"init\\\", {})\\n init[\\\"instrument\\\"] = instrument\\n task = create_task(method=\\\"xrfgeometry\\\", name=\\\"xrfgeometry\\\", **params)\\n tasks.append(task)\\n else:\\n task = None\\n\\n # XRF fit and counter maps\\n params = task_parameters(parameters, \\\"resample\\\")\\n encodercor = params.pop(\\\"encodercor\\\", False)\\n params = task_parameters(parameters, \\\"pymca\\\")\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n xrfparameters(params, instrument, include_encoders=encodercor, quant=bool(geometry))\\n params.update(commonparams)\\n task = create_task(method=\\\"pymca\\\", name=\\\"pymca\\\", dependencies=task, **params)\\n tasks.append(task)\\n\\n # Normalization\\n params = task_parameters(parameters, \\\"prealignnormalize\\\")\\n counter = params.pop(\\\"counter\\\", None)\\n dtcor = False # No longer needed\\n if dtcor or counter:\\n copy = [\\n {\\\"method\\\": \\\"regex\\\", \\\"pattern\\\": prefix}\\n for prefix in instrument.counterdict[\\\"counters\\\"]\\n ]\\n # Create normalization expression\\n if dtcor:\\n icr = instrument.counterdict[\\\"xrficr\\\"]\\n ocr = instrument.counterdict[\\\"xrfocr\\\"]\\n if counter:\\n expression = \\\"{{}}*nanone({{{}}}/({{{}}}*{{{}}}))\\\".format(\\n icr, ocr, counter\\n )\\n else:\\n expression = \\\"{{}}*nanone({{{}}}/{{{}}})\\\".format(icr, ocr)\\n else:\\n expression = \\\"{{}}/{{{}}}\\\".format(counter)\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n ensure_parameter(params, \\\"name\\\", \\\"prenormalize\\\")\\n task = create_task(\\n dependencies=task,\\n method=\\\"expression\\\",\\n name=\\\"normalize\\\",\\n expression=expression,\\n copy=copy,\\n **commonparams\\n )\\n tasks.append(task)\\n\\n # Correct for encoder positions\\n if encodercor:\\n params = task_parameters(parameters, \\\"resample\\\")\\n params.update(commonparams)\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n ensure_parameter(params, \\\"name\\\", \\\"resample\\\")\\n encoders = instrument.encoderinfo\\n task = create_task(\\n dependencies=task, method=\\\"resample\\\", encoders=encoders, **params\\n )\\n tasks.append(task)\\n\\n # Alignment\\n params = task_parameters(parameters, \\\"align\\\")\\n alignmethod = ensure_parameter(params, \\\"alignmethod\\\", None)\\n reference = ensure_parameter(params, \\\"reference\\\", None)\\n if alignmethod and reference is not None:\\n params.update(commonparams)\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n ensure_parameter(params, \\\"name\\\", \\\"align\\\")\\n ensure_parameter(params, \\\"refimageindex\\\", -1)\\n ensure_parameter(params, \\\"roi\\\", None)\\n ensure_parameter(params, \\\"plot\\\", False)\\n ensure_parameter(params, \\\"crop\\\", False)\\n ensure_parameter(params, \\\"name\\\", \\\"postnormalize\\\")\\n task = create_task(dependencies=task, method=\\\"align\\\", **params)\\n tasks.append(task)\\n\\n # Post normalization\\n params = task_parameters(parameters, \\\"postalignnormalize\\\")\\n counter = params.pop(\\\"counter\\\", None)\\n if counter:\\n copy = [\\n {\\\"method\\\": \\\"regexparent\\\", \\\"pattern\\\": prefix}\\n for prefix in instrument.counterdict[\\\"counters\\\"]\\n ]\\n expression = \\\"{{}}/{{{}}}\\\".format(counter)\\n params.update(commonparams)\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n ensure_parameter(params, \\\"name\\\", \\\"postnormalize\\\")\\n task = create_task(\\n dependencies=task,\\n method=\\\"expression\\\",\\n expression=expression,\\n copy=copy,\\n **params\\n )\\n tasks.append(task)\\n\\n # Remove NaN's\\n params = task_parameters(parameters, \\\"replacenan\\\")\\n replacenan = params.pop(\\\"replacenan\\\", False)\\n if replacenan:\\n params.update(commonparams)\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n ensure_parameter(params, \\\"name\\\", \\\"replace\\\")\\n ensure_parameter(params, \\\"org\\\", np.nan)\\n ensure_parameter(params, \\\"new\\\", 0)\\n tmp = create_task(dependencies=task, method=\\\"replace\\\", **params)\\n tasks.append(tmp)\\n\\n # Crop\\n params = task_parameters(parameters, \\\"crop\\\")\\n cropafter = params.pop(\\\"crop\\\", False)\\n if cropafter:\\n params.update(commonparams)\\n ensure_parameter(params, \\\"outputparent\\\", default=coutputparent)\\n ensure_parameter(params, \\\"name\\\", \\\"crop\\\")\\n ensure_parameter(params, \\\"cropfull\\\", True)\\n params[\\\"nanfull\\\"] = params.pop(\\\"cropfull\\\")\\n ensure_parameter(params, \\\"nanval\\\", np.nan)\\n ensure_parameter(params, \\\"reference\\\", reference)\\n tmp = create_task(dependencies=task, method=\\\"crop\\\", **params)\\n tasks.append(tmp)\\n\\n return tasks\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"229a4bdd48a9e744c6b38a6794e0beb5","score":"0.7169673","text":"def pipeline():\n indexTargetsProcesses()\n #Start the mapping operations\n mappingProcesses()\n extractingProcesses()\n graphingProcesses()\n formatOutput()","title":""},{"docid":"20e842fa35aa495c31a96db571f3ecbc","score":"0.64032096","text":"def build_pipeline(self):","title":""},{"docid":"c98b2bcb355df437910dd6a54a49ba67","score":"0.6330437","text":"def main(_args):\n # download_packages() #TODO automate the package check\n # drop_all_tables()\n metrics = Metrics()\n start_time = datetime.today().strftime('%Y-%m-%d-%H:%M')\n print(\"START TIME = {}\".format(start_time))\n processes = []\n\n # reads data from parser\n data_raw = read_data(args)\n # data_raw = read_data_from_blob(args)\n data = data_raw\n # data = aggregated_data(data_raw)\n\n # multiprocessing\n for rf in data.parameters.region_filters:\n if data.parameters.parallelize == 'Yes':\n p = Process(target=run_pipeline, args=(rf, data, metrics, start_time))\n p.start()\n processes.append(p)\n\n else:\n run_pipeline(rf, data, metrics, start_time)\n\n for p in processes:\n p.join()\n\n end_time = datetime.now()\n print(\"END TIME = {}\".format(end_time))\n metrics.print()","title":""},{"docid":"3eb58a844096ecea03f045c8038eb61c","score":"0.6173508","text":"def run_pipeline():\n\n def get_root_path():\n \"\"\"\n return the root path\n \"\"\"\n p = Path.cwd()\n while p.name != 'backend':\n p = p.parent\n return p\n\n def download_file(url: str, path: str):\n \"\"\"\n download a file using urllib3\n \"\"\"\n nonlocal root_path\n file_path = root_path / path\n file_path.parent.mkdir(parents=True, exist_ok=True)\n http = urllib3.PoolManager()\n r = http.request('GET', url, preload_content=False)\n with open(file_path, 'w+b') as out:\n while True:\n data = r.read()\n if not data:\n break\n out.write(data)\n logging.debug(f\"downloaded {file_path.name}\")\n r.release_conn()\n\n def get_download_list():\n \"\"\"\n return the list of objects to download;\n Alameda County parcel polygons and Oakland zoning polygons\n are not included.\n \"\"\"\n return [\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/air_district/Oakland+Sources.xlsx\",\n \"path\": \"data/raw/air_district/air_district_sources.xlsx\"},\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/air_district/Oakland+NOV.xlsx\",\n \"path\": \"data/raw/air_district/air_district_novs.xlsx\"},\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/air_district/Oakland+Complaints.xlsx\",\n \"path\": \"data/raw/air_district/air_district_complaints.xlsx\"},\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/assessor/IE670-10-01-19.TXT\",\n \"path\": \"data/raw/assessor/assessor_ownership.TXT\"},\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/business_licenses/PRR+%23+19-4745+all+Business+Accts+2019.xls\",\n \"path\": \"data/raw/business_licenses/business_licenses.xls\"},\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/scraping_out/accela_zc_summary.csv\",\n \"path\": \"data/pre-scraped/zoning_clearances.csv\"},\n {\"url\": \"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/scraping_out/hwts_summary.csv\",\n \"path\": \"data/pre-scraped/hazardous_transfers.csv\"},\n ]\n\n def upload_data():\n \"\"\"\n recursively upload all files in ./data/parcel_added folder\n \"\"\"\n nonlocal root_path\n data_path = root_path / 'data/parcel_added'\n for data in data_path.glob('**/*'):\n if data.is_file():\n relative_path = data.relative_to(data_path)\n target_path = Path('latest_data') / relative_path\n upload_file(str(data), 'eo-lu-inventory', str(target_path))\n\n def upload_file(file_name: str, bucket: str, object_name: str = None):\n \"\"\"Upload a file to an S3 bucket\n (copied from boto3 docs)\n\n :param file_name: File to upload\n :param bucket: Bucket to upload to\n :param object_name: S3 object name.\n If not specified then file_name is used\n :return: True if file was uploaded, else False\n \"\"\"\n\n # If S3 object_name was not specified, use file_name\n if object_name is None:\n object_name = file_name\n\n # Upload the file\n s3_client = boto3.client('s3')\n try:\n logging.debug(f\"uploading {file_name} to {object_name}\")\n s3_client.upload_file(file_name, bucket, object_name)\n except ClientError as e:\n logging.error(e)\n return False\n return True\n\n logging.basicConfig(level=logging.DEBUG)\n root_path = get_root_path()\n # download_list = get_download_list()\n # for file in download_list:\n # download_file(file['url'], file['path'])\n upload_data()","title":""},{"docid":"6cea7c6ce6f47f3e3755689f995aaf2c","score":"0.6123348","text":"def run_pipeline(files, flat_field=True):\n\n # Create a name for image3 association and drizzled files\n detector = fits.getheader(files[0])['DETECTOR'].lower()\n fltr = fits.getheader(files[0])['FILTER'].lower()\n pupil = fits.getheader(files[0])['PUPIL'].lower()\n target = fits.getheader(files[0])['TARGNAME'].lower()\n name = '{}_{}_{}_{}'.format(detector, fltr, pupil, target)\n\n # Run detector1\n for f in files:\n m = Detector1Pipeline()\n #m.jump.override_readnoise = 'jwst_nircam_readnoise_0024_psub.fits'\n m.refpix.odd_even_rows = False # only for MIRI\n m.ipc.skip = True\n m.persistence.skip = True\n m.save_results = True\n m.output_dir = os.getcwd()\n m.run(f)\n\n # Run image2\n files = [f.replace('_uncal.fits', '_rate.fits') for f in files]\n for f in files:\n m = Image2Pipeline()\n if not flat_field:\n m.flat_field.skip = True\n m.save_results = True\n m.output_dir = os.getcwd()\n m.run(f)\n\n # Run image3\n files = [f.replace('_rate.fits', '_cal.fits') for f in files]\n asn = asn_from_list(files, rule=Asn_Lv3Image, product_name=name)\n asn_file = '{}.json'.format(name)\n with open(asn_file, 'w') as f:\n f.write(asn.dump()[1])\n m = Image3Pipeline()\n m.save_results = True\n m.output_dir = os.getcwd()\n m.run(asn_file)\n\n return '{}_i2d.fits'.format(name)","title":""},{"docid":"b2ca5b8551edc0b3318a8581241c1d9a","score":"0.6102607","text":"def runPipeline(plConf, X):\n pl = build3StagePipe(plConf)\n pl.setX(X)\n pl.runStages()\n Y = pl.finalOut[\"Y\"]\n return Y","title":""},{"docid":"7d745a51c7cd5150c0753af0a7bf4e4f","score":"0.60049415","text":"def run_pipeline(jail, rtdata_module, request):\n rtdata = rtdata_module\n\n # Get the spec3 ASN and its members\n rtdata.get_asn(\"miri/lrs/jw01530-o005_20221202t204827_spec3_00001_asn.json\")\n root_file = \"jw01530-o005_t004_miri_p750l_\"\n rtdata.get_truth(\"truth/test_miri_lrs_slit_spec3/jw01530-o005_t004_miri_p750l_s2d.fits\")\n\n args = [\n \"calwebb_spec3\",\n rtdata.input\n ]\n\n rtdata.custom_wcs_mode = request.param\n\n if request.param != \"default_wcs\":\n dm = datamodels.open(rtdata.truth)\n af = asdf.AsdfFile({\"wcs\": dm.meta.wcs})\n wcs_file = rtdata.truth[:-8] + 'wcs.asdf'\n af.write_to(wcs_file)\n args.append(f\"--steps.resample_spec.output_wcs={wcs_file}\")\n\n if request.param == \"user_wcs+shape\":\n output_shape = ','.join(map(str, dm.data.shape[::-1]))\n args.append(f\"--steps.resample_spec.output_shape={output_shape}\")\n\n elif request.param == \"user_wcs+shape1\":\n output_shape = ','.join(map(str, (d + 1 for d in dm.data.shape[::-1])))\n args.append(f\"--steps.resample_spec.output_shape={output_shape}\")\n output_file = root_file + 'shape1.fits'\n args.append(f\"--steps.resample_spec.output_file={output_file}\")\n\n # Run the calwebb_spec3 pipeline; save results from intermediate steps\n Step.from_cmdline(args)","title":""},{"docid":"c6a5c9aed3739ce33c56220402451727","score":"0.59742904","text":"def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,\r\n metadata_path: Text) -> pipeline.Pipeline:\r\n\r\n # Brings data into the pipeline or otherwise joins/converts training data.\r\n example_gen = CsvExampleGen(input_base=data_root)\r\n\r\n # Computes statistics over data for visualization and example validation.\r\n statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])\r\n\r\n # inferes a schema\r\n schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)\r\n\r\n # modifies infered schema with use of udf `schema_fn` defined in module file\r\n schema_curation = component.SchemaCuration(schema=schema_gen.outputs['schema'],\r\n module_file=os.path.join('schemacomponent','example','module_file.py')\r\n\r\n\r\n return pipeline.Pipeline(\r\n pipeline_name=pipeline_name,\r\n pipeline_root=pipeline_root,\r\n components=[example_gen, statistics_gen, schema_gen, schema_curation],\r\n enable_cache=True,\r\n metadata_connection_config=metadata.sqlite_metadata_connection_config(\r\n metadata_path))\r\n\r\n\r\n# To run this pipeline from the python CLI:\r\n# $python taxi_pipeline_hello.py\r\nif __name__ == '__main__':\r\n absl.logging.set_verbosity(absl.logging.INFO)\r\n local_dag_runner.LocalDagRunner().run(\r\n _create_pipeline(\r\n pipeline_name=_pipeline_name,\r\n pipeline_root=_pipeline_root,\r\n data_root=_data_root,\r\n metadata_path=_metadata_path))","title":""},{"docid":"79672ebdb824b17395d930311fb2177d","score":"0.59379274","text":"def run(self):\n logger.info(\"Running SFM pipeline.\")\n\n self.extract_cameras()\n self.extract_features()\n self.match_features()\n\n self.create_tracks()\n\n base_pair = self.generate_base_pair()\n self.base_reconstruction(base_pair)\n\n # TODO: Get views from tracks.\n views = []\n\n for view in views:\n self.add_view(view)\n\n return self.export_as_ply()","title":""},{"docid":"22a5d57c44a1891ad96d646b590df07b","score":"0.59039646","text":"def main(parsed_args: argparse.Namespace) -> None:\n incremental = not parsed_args.full\n\n if parsed_args.scan_type == 'all':\n selected_scan_types = list(beam_tables.ALL_SCAN_TYPES)\n else:\n selected_scan_types = [parsed_args.scan_type]\n\n firehook_runner = get_firehook_beam_pipeline_runner()\n\n if parsed_args.env == 'user':\n username = pwd.getpwuid(os.getuid()).pw_name\n run_user_pipelines(firehook_runner, username, selected_scan_types,\n incremental)\n elif parsed_args.env == 'prod':\n run_parallel_pipelines(firehook_runner, beam_tables.PROD_DATASET_NAME,\n selected_scan_types, incremental)","title":""},{"docid":"e6e1b29fbc7d5ee713a961ba502f7863","score":"0.59008193","text":"def run_pipeline(step):\n bxc_list = []\n cxb_list = []\n for i in os.listdir('BxC'):\n if os.path.isdir(os.path.join('BxC', i)):\n if i.isdigit or i[2:].isdigit:\n if i == 'mat' or i == 'pat' or i == 'genome':\n continue\n bxc_list.append(os.path.join('BxC', i))\n for i in os.listdir('CxB'):\n if os.path.isdir(os.path.join('CxB', i)):\n if i.isdigit or i[2:].isdigit:\n if i == 'mat' or i == 'pat' or i == 'genome':\n continue\n cxb_list.append(os.path.join('CxB', i))\n bxc = {}\n cxb = {}\n # Make fastas\n if step == 1:\n print(\"Submitting FastQ Filter\")\n for readdir in bxc_list:\n bxc[readdir] = {'filter':\n filter_fastqs(readdir)}\n for readdir in cxb_list:\n cxb[readdir] = {'filter':\n filter_fastqs(readdir)}\n step += 1\n # Run bowtie\n if step == 2:\n print(\"Submitting mapping jobs\")\n for readdir in bxc_list:\n # bxc[readdir]['bowtie'] = run_bowtie(readdir,\n # bxc[readdir]['filter'])\n try:\n bxc[readdir]['star'] = run_star(readdir,\n bxc[readdir]['filter'])\n except KeyError:\n bxc[readdir] = {}\n bxc[readdir]['star'] = run_star(readdir,\n None)\n for readdir in cxb_list:\n # cxb[readdir]['bowtie'] = run_bowtie(readdir,\n # cxb[readdir]['filter'])\n try:\n cxb[readdir]['star'] = run_star(readdir,\n cxb[readdir]['filter'])\n except KeyError:\n cxb[readdir] = {}\n cxb[readdir]['star'] = run_star(readdir,\n None)\n step += 1\n if step == 3:\n print(\"Submitting cleaning jobs\")\n for readdir in bxc_list:\n # bxc[readdir]['samtools'] = clean_bowtie(readdir,\n # bxc[readdir]['bowtie'])\n try:\n bxc[readdir]['clean'] = clean_star(readdir,\n bxc[readdir]['star'])\n except KeyError:\n bxc[readdir] = {}\n bxc[readdir]['clean'] = clean_star(readdir, None)\n for readdir in cxb_list:\n # cxb[readdir]['samtools'] = clean_bowtie(readdir,\n # cxb[readdir]['bowtie'])\n try:\n cxb[readdir]['clean'] = clean_star(readdir,\n cxb[readdir]['star'])\n except KeyError:\n cxb[readdir] = {}\n cxb[readdir]['clean'] = clean_star(readdir, None)\n step += 1\n # Merge bowtie\n if step == 4:\n print(\"Submitting AlleleSeq bowtie mat/pat merge\")\n for readdir in bxc_list:\n try:\n bxc[readdir]['merge'] = merge(readdir,\n bxc[readdir]['clean'], 'BxC')\n except KeyError:\n bxc[readdir] = {}\n bxc[readdir]['merge'] = merge(readdir, None, 'BxC')\n for readdir in cxb_list:\n try:\n cxb[readdir]['merge'] = merge(readdir,\n cxb[readdir]['clean'], 'CxB')\n except KeyError:\n cxb[readdir] = {}\n cxb[readdir]['merge'] = merge(readdir, None, 'CxB')\n step += 1\n # Count reads\n if step == 5:\n print(\"Submitting AlleleSeq allele counting script\")\n for readdir in bxc_list:\n try:\n bxc[readdir]['count'] = count(readdir,\n bxc[readdir]['merge'], 'BxC')\n except KeyError:\n bxc[readdir] = {}\n bxc[readdir]['count'] = count(readdir, None, 'BxC')\n for readdir in cxb_list:\n try:\n cxb[readdir]['count'] = count(readdir,\n cxb[readdir]['merge'], 'CxB')\n except KeyError:\n cxb[readdir] = {}\n cxb[readdir]['count'] = count(readdir, None, 'CxB')\n\n print(\"Job mapping:\")\n with open('alleleseq_jobs.pickle', 'wb') as outf:\n pickle.dump([bxc, cxb], outf)\n print(bxc)\n print(cxb)","title":""},{"docid":"71847dc204e3af8c41cae2c42f4395ae","score":"0.58906186","text":"def main():\n\n # parse arguments\n parser = argparse.ArgumentParser(\n description='''A pipeline to analyze shotgun metagenomics NGS Illumina data.''',\n epilog='''Please read the documentation for more details.''')\n parser.add_argument('-c', '--config', nargs=1, type=file, required=True,\n help='configuration file for the pipeline', metavar='shotgun_config.cfg',\n dest='config')\n parser.add_argument('-q', '--fastqc', action='store_true',\n help='run fastqc to check the sequence quality', dest='q')\n parser.add_argument('-t', '--trimmomatic', action='store', nargs='*',\n help='run trimmomatic to clean the sequences', choices=[\"n\", \"r\", \"f\"], dest='t')\n parser.add_argument('-r', '--randomlysample', action='store_true',\n help='run randomlysample to rarefy the reads number to the lowest among the samples', dest='r')\n parser.add_argument('-a', '--assembly', action='store', nargs='*',\n help='run assembly tools to assemble the reads to contigs', choices=[\"n\", \"f\", \"r\", \"rf\", \"soap\",\n \"idba\", \"ray\"], dest='a')\n parser.add_argument('-p', '--ppsp', action='store', nargs='*', help='run ppsp to perform taxonomic binning',\n choices=[\"m\", \"n\", \"f\"], dest='p')\n parser.add_argument('-x', '--taxator', action='store_true', help='run taxator-tk to perform taxaonomic binning',\n dest='x')\n parser.add_argument('-f', '--annotation', action='store', help='run functional annotation',\n choices=[None, \"h\"], dest='f', nargs='*')\n\n\n args = parser.parse_args()\n \n\n # read configuration file\n config = Config(args.config[0], 'SMP')\n\n\n # pipeline directory\n pipelineDir = os.path.normpath(config.get('pipelineDir'))\n if not os.path.isdir(pipelineDir):\n print(\"Pipeline directory doesn't exist:\", pipelineDir)\n return\n\n # create the following dirs in the pipeline directory if they do not exist\n workingDir = os.path.join(pipelineDir, 'working')\n outputDir = os.path.join(pipelineDir, 'output')\n profileDir = os.path.join(pipelineDir, 'profile')\n fastqcDir = os.path.join(outputDir, 'fastqc')\n trimmomaticDir = os.path.join(outputDir, 'trimmomatic')\n trimmomaticProfileDir = os.path.join(profileDir, 'trimmomatic')\n randomlysampleDir = os.path.join(outputDir, 'randomlysample')\n #randomlysampleProfileDir = os.path.join(profileDir, 'randomlysample')\n randomlysampleWorkingDir = os.path.join(workingDir, 'randomlysample')\n filterreadsDir = os.path.join(outputDir, 'filterreads')\n filterreadsWorkingDir = os.path.join(workingDir, 'filterreads')\n forAssemblyWorkingDir = os.path.join(workingDir, 'forAssembly')\n soapdenovoDir = os.path.join(outputDir, 'soapdenovo')\n soapdenovoWorkingDir = os.path.join(forAssemblyWorkingDir, 'soapdenovo')\n idbaDir = os.path.join(outputDir,'IDBA-UD')\n idbaWorkingDir = os.path.join(forAssemblyWorkingDir,'IDBA-UD')\n rayDir = os.path.join(outputDir,'Ray')\n rayWorkingDir = os.path.join(forAssemblyWorkingDir, 'Ray')\n assemblyDir = os.path.join(outputDir, 'assembly')\n ppspDir = os.path.join(outputDir, 'PPSP')\n #taxatorDir = os.path.join(outputDir, 'taxator')\n annotationDir = os.path.join(outputDir, 'annotation')\n orfDir = os.path.join(annotationDir, 'ORFs')\n hmmDir = os.path.join(annotationDir, 'HMM')\n\n\n\n dirArray = [workingDir, outputDir, fastqcDir, trimmomaticDir, profileDir, trimmomaticProfileDir,\n randomlysampleDir, randomlysampleWorkingDir, filterreadsDir, filterreadsWorkingDir,\n forAssemblyWorkingDir, soapdenovoDir, idbaDir, rayDir, assemblyDir, ppspDir, annotationDir, orfDir, hmmDir]\n\n\n for dirPath in dirArray:\n if not os.path.isdir(dirPath):\n try:\n os.mkdir(dirPath)\n except OSError:\n print (\"Can't create the directory\", dirPath)\n return\n\n # set up log file\n logDir = os.path.join(outputDir, 'log')\n if not os.path.exists(logDir):\n try:\n os.mkdir(logDir)\n except OSError:\n print(\"Can't create log directory:\", logDir)\n return\n\n # read multiple input raw fastqc reads\n #def readInputForward():\n # count = 1\n # list1 = []\n #list2 = []\n #for i in range(10000):\n # inputFastqFileForward = config.get('inputFastqFileForward'+str(count))\n #inputFastqFileReverse = config.get('inputFastqFileReverse'+str(count))\n #if inputFastqFileForward is not None:\n # count += 1\n # list1.append(inputFastqFileForward)\n #list2.append(inputFastqFileReverse)\n #listf = list1\n #listr = list2\n #if listf != '':\n # return listf\n\n #Judge if you need to split the original File, only work for fastq File\n #inputFileJudge = config.get('pairedOrNot')\n #if inputFileJudge == 'True':\n # SplitPairedFile(os.path.normpath(config.get('inputPiredFile')), os.path.dirname)\n\n\n\n count = 1\n list1 = []\n list2 = []\n for i in range(10000):\n inputFastqFileForward = config.get('inputFastqFileForward'+str(count))\n inputFastqFileReverse = config.get('inputFastqFileReverse'+str(count))\n if inputFastqFileForward is None:\n break\n else:\n list1.append(inputFastqFileForward)\n list2.append(inputFastqFileReverse)\n count += 1\n listf = list1\n listr = list2\n\n # run fastqc\n if args.q is not None:\n fastqcInstallDir = os.path.normpath(config.get('fastqcInstallDir'))\n\n for i, j in zip(listf, listr):\n if j is not None:\n cmd = str(os.path.join(fastqcInstallDir, 'fastqc') + ' -o ' + fastqcDir + ' -f fastq ' +\n os.path.normpath(i) + ' ' + os.path.normpath(j))\n else: #for single reads\n cmd = str(os.path.join(fastqcInstallDir, 'fastqc') + ' -o ' + fastqcDir + ' -f fastq ' +\n os.path.normpath(i))\n proc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=fastqcDir)\n proc.wait() #check proc.wait\n\n\n\n # run trimmomatic-0.32 to trim the adapters and clean the sequences according to quality score and the seq length.\n # The program first cut the adapters then cleans the seqs with quality scores and in the end discard the seqs with\n # length shorter than the minimum length.\n # Yao's ubuntu's version, needs to change to server version.\n if args.t is not None:\n trimmomaticInstallDir = os.path.normpath(config.get('trimmomaticInstallDir'))\n #lista=[]\n #listb=[]\n for i, j in zip(listf, listr):\n cmd = str('java -jar' + ' ' + os.path.join(trimmomaticInstallDir, 'trimmomatic-0.32.jar') + ' '\n + config.get('seqType') + ' -phred' + config.get('phred') + ' -trimlog ' +\n os.path.join(logDir, os.path.basename(i).split('.')[0]+'trimmomatic.log')\n + ' ' + os.path.normpath(i) + ' ' + os.path.normpath(j) + ' ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]\n + '_paired.fq') + ' ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0] + '_unpaired.fq') + ' ' + os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]\n + '_paired.fq') + ' ' + os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0] + '_unpaired.fq') + ' ' +'ILLUMINACLIP:'\n + trimmomaticProfileDir + '/' + config.get('adapter') + ':' + config.get('seedMismatches') + ':'\n + config.get('palindromeClipThreshold') + ':' + config.get('simpleClipThreshold') + ' '\n + 'LEADING:' + config.get('leading') + ' ' + 'TRAILING:' + config.get('trailing') + ' '\n + 'SLIDINGWINDOW:' + config.get('windowSize') + ':' + config.get('requiredQuality') + ' '\n + 'MINLEN:' + config.get('minLength'))\n #print cmd\n proc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=trimmomaticDir)\n proc.wait()\n\n # unify the sequences ids pattern (there are old and new versions of seq ids, after this step, the id pattern will be \"A /1\" or \"A 1:N:0:GA...\" )\n seqidSuffix((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_temp_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_temp_paired.fq')))\n\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\n\n if 'n' in args.t:\n fastqTofasta((os.path.join(trimmomaticDir, (os.path.basename(i).split('.')[0] + '_paired.fq'))), 1,\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward.fa')))\n fastqTofasta((os.path.join(trimmomaticDir, (os.path.basename(j).split('.')[0] + '_paired.fq'))), 2,\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse.fa')))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_noN.fa')), config.get('removeNlen'))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_noN.fa')), config.get('removeNlen'))\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_consensus.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_consensus.fa')))\n\n #print args.t\n #First run RandomlySample then run FilterReads-P(MPI-version available) -\n # yao's ubuntu's version, needs to change to server version.\n elif 'r' in args.t and ('f' in args.t):\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\n\n randomlysampleInstallDir = os.path.normpath(config.get('randomlysampleInstallDir'))\n filterreadsInstallDir = os.path.normpath(config.get('filterreadsInstallDir'))\n cmd2 = str(os.path.join(randomlysampleInstallDir, 'RandomlySample') + ' --ignore-quality=1 --by-pair=1 --num-samples=' +\n config.get('randomlySampleNum-samples') + ' --format-output=1' + ' --fastq-base-quality=33 ' + ' --temp-dir=' +\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp1') + ' --keep-temp-dir=' +\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp2') +\n ' --input-file ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq') +\n ' --output-file ' + os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_random.fa'))\n proc2 = subprocess.Popen(cmd2, shell=True, bufsize=-1, cwd=randomlysampleDir)\n proc2.wait()\n print \"RandomlySample return code:\", proc2.returncode\n if proc2.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (proc2.returncode, cmd2))\n print \"Filtering reads based on kmer counting is going to start.\"\n cmd3 = str('export LD_LIBRARY_PATH=/home/yao/Softwares/openmpi-1.6.5/lib:$LD_LIBRARY_PATH;mpirun -np ' + config.get('numberOfProcess_mpi') + ' ' + os.path.join(filterreadsInstallDir, 'FilterReads-P')\n + ' --histogram-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_histogram')\n + ' --size-history-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_sizeHistory')\n + ' --ignore-quality=1 --format-output=1 --keep-read-comment=1' + ' --kmer-size=' + config.get('kmerSize_filterReads')\n + ' --min-depth=' + config.get('minDepth_filterReads') + ' --separate-output=0 --min-passing-in-pair=2'\n + ' --min-read-length=' + config.get('minReadLen_filterReads') + ' --max-kmer-output-depth=-1 '\n + ' --skip-artifact-filter=1 --dedup-single=1 --dedup-edit-distance=0' + ' --input-file '\n + os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_random.fa')\n + ' --output-file ' + os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_random_filter.fa')\n + ' --log-file ' + os.path.join(logDir, os.path.basename(i).split('.')[0]+'_filterReads_log'))\n proc3 = subprocess.Popen(cmd3, shell=True, bufsize=-1, cwd=filterreadsDir)\n proc3.wait()\n print \"Filterreads return code:\", proc3.returncode\n if proc3.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (proc3.returncode, cmd3))\n #\n splitPairedReads((os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_random_filter.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_random_filter_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_random_filter_reverse.fa')))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward_noN.fa')), config.get('removeNlen'))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_reverse.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_filter_reverse_noN.fa')), config.get('removeNlen'))\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_filter_reverse_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward_consensus.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_filter_reverse_consensus.fa')))\n\n\n\n # Only run RandomlySample (without FilterReads-P)\n elif 'r' in args.t:\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\n\n randomlysampleInstallDir = os.path.normpath(config.get('randomlysampleInstallDir'))\n cmd2 = str(os.path.join(randomlysampleInstallDir, 'RandomlySample') + ' --ignore-quality=1 --by-pair=1 --num-samples=' +\n config.get('randomlySampleNum-samples') + ' --format-output=1' + ' --fastq-base-quality=33 ' + ' --temp-dir=' +\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp1') + ' --keep-temp-dir=' +\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp2') +\n ' --input-file ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq') +\n ' --output-file ' + os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_onlyRandom.fa'))\n proc2 = subprocess.Popen(cmd2, shell=True, bufsize=-1, cwd=randomlysampleDir)\n proc2.wait()\n print \"RandomlySample return code:\", proc2.returncode\n if proc2.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (proc2.returncode, cmd2))\n print \"You only rarefied the reads and you did not want to filter the reads, right?\"\n\n splitPairedReads((os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_onlyRandom.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_onlyRandom_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_OnlyRandom_reverse.fa')))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_noN.fa')), config.get('removeNlen'))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse_noN.fa')), config.get('removeNlen'))\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_consensus.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse_consensus.fa')))\n\n #Only run FilterReads-P(MPI-version available)--yao's ubuntu's version, needs to change to server version.\n elif 'f' in args.t:\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\n\n filterreadsInstallDir = os.path.normpath(config.get('filterreadsInstallDir'))\n #cmd3 = str('export LD_LIBRARY_PATH=/home/yao/Softwares/openmpi-1.6.5/lib:$LD_LIBRARY_PATH;mpirun -np '\n cmd3 = str('mpirun -np '\n + config.get('numberOfProcess_mpi') + ' ' + os.path.join(filterreadsInstallDir, 'FilterReads-P')\n + ' --histogram-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_onlyFiltering_histogram')\n + ' --size-history-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_onlyFiltering_sizeHistory')\n + ' --ignore-quality=1 --format-output=1 --keep-read-comment=1' + ' --kmer-size=' + config.get('kmerSize_filterReads')\n + ' --min-depth=' + config.get('minDepth_filterReads') + ' --separate-output=0 --min-passing-in-pair=2'\n + ' --min-read-length=' + config.get('minReadLen_filterReads') + ' --max-kmer-output-depth=-1 '\n + ' --skip-artifact-filter=1 --dedup-single=1 --dedup-edit-distance=0' + ' --input-file '\n + os.path.join(os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq'))\n + ' --output-file ' + os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_onlyFilter.fa')\n + ' --log-file ' + os.path.join(logDir, os.path.basename(i).split('.')[0]+'_onlyFilterReadslog'))\n proc3 = subprocess.Popen(cmd3, shell=True, bufsize=-1, cwd=filterreadsDir)\n proc3.wait()\n print \"Filterreads return code:\", proc3.returncode\n if proc3.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (proc3.returncode, cmd3))\n print \"You only filtered the reads, you did not want to rarefy the reads, right?\"\n\n splitPairedReads((os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_onlyFilter.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_onlyFilter_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_OnlyFilter_reverse.fa')))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_noN.fa')), config.get('removeNlen'))\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse_noN.fa')), config.get('removeNlen'))\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse_noN.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_consensus.fa')),\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse_consensus.fa')))\n\n #lista.append(os.path.basename(i).split('.')[0] + '_paired.fq ')\n #listb.append(os.path.basename(j).split('.')[0] + '_paired.fq ')\n #print lista, listb\n\n #sys.stdout.write(cmd3)\n\n\n fileHolder = FileHolder(outputTrimForward=[], outputTrimReverse=[], outputRandomForward=[], outputRandomReverse=[],\n outputFilterForward=[], outputFilterReverse=[], outputRandomFilterForward=[],\n outputRandomFilterReverse=[])\n\n if args.a:\n if \"soap\" in args.a:\n def soapAssembly(file1,file2):\n counter = 1\n for i in range(1000):\n readsNumberPerSample = config.get('sample'+str(counter))\n\n if readsNumberPerSample is not None:\n soapdenovoPerSampleDir = os.path.join(soapdenovoDir, ('sample'+str(counter)))\n if not os.path.exists(soapdenovoPerSampleDir):\n try:\n os.mkdir(soapdenovoPerSampleDir)\n except OSError:\n print(\"Can't create soapdenovoPerSample directory:\", soapdenovoPerSampleDir)\n soapdenovoPerSampleWorkingDir = os.path.join(forAssemblyWorkingDir, ('soap_'+'sample'+str(counter)))\n if not os.path.exists(soapdenovoPerSampleWorkingDir):\n try:\n os.mkdir(soapdenovoPerSampleWorkingDir)\n except OSError:\n print(\"Can't create soapdenovoPerSample directory:\", soapdenovoPerSampleWorkingDir)\n\n soapConfigFile = open(os.path.join(soapdenovoPerSampleDir, ('sample'+str(counter) + '.config')), 'w')\n soapConfigFile.write('max_rd_len=' + config.get('sample'+str(counter)+'_' + 'maxReadLen') + '\\n'\n + '[LIB]' + '\\n' + '#average insert size'\n + '\\n' + 'avg_ins=' + config.get('sample'+str(counter)+'_'+'averageInsertSize')\n + '\\n' +'#if sequence needs to be reversed'\n + '\\n' + 'reverse_seq=' + config.get('sample'+str(counter)+'_' + 'reverseSeq')\n + '\\n' + '#in which part(s) the reads are used'\n + '\\n' + 'asm_flags=' + config.get('sample'+str(counter)+'_' + 'asmFlag')\n + '\\n' + '#paired data files' + '\\n')\n\n for k, l in zip(file1[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2],\n (file2[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2])):\n\n shutil.copy(os.path.join(forAssemblyWorkingDir, k), soapdenovoPerSampleWorkingDir)\n shutil.copy(os.path.join(forAssemblyWorkingDir, l), soapdenovoPerSampleWorkingDir)\n #print 'p=',k\n #print 'p=',l\n soapConfigFile.write('p='+k+'\\n'+'p='+l+'\\n')\n soapConfigFile.close()\n\n\n soapdenovoInstallDir = os.path.normpath(config.get('soapdenovoInstallDir'))\n runSoapdenovo = str('export PATH=/net/programs/Debian-6.0.3-x86_64/soapdenovo-2.04:$PATH'+';' + ' '\n +os.path.join(soapdenovoInstallDir, config.get('soapdenovoMethod')) + ' all -s '\n + os.path.join(os.path.normpath(soapdenovoPerSampleDir), 'sample'+str(counter) + '.config')\n + ' -K '+config.get('assemblyKmerSize') + ' -R' + ' -p ' + config.get('n_cpu') + ' -o '\n + 'soapdenovo_sample' + str(counter) + '_' + ' ' + '1>'+'sample'+str(counter)+'_assembly.log' + ' '\n + '2>'+'sample'+str(counter) + '_assembly.error')\n #print runSoapdenovo\n procRunSoapdenovo = subprocess.Popen(runSoapdenovo, shell=True, bufsize=-1, cwd=soapdenovoPerSampleDir)\n procRunSoapdenovo.wait()\n print \"Soapdenovo return code:\", procRunSoapdenovo.returncode\n if procRunSoapdenovo.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (procRunSoapdenovo.returncode, runSoapdenovo))\n print \"Soapdenovo is finished.\"\n shutil.copy(os.path.join(soapdenovoPerSampleDir, 'soapdenovo_sample' + str(counter) + '_'+'.contig'),\n os.path.join(assemblyDir, 'soapdenovo_sample'+str(counter)+'.contig.fa'))\n counter += 1\n\n if \"idba\" in args.a:\n def idbaAssembly(file1, file2):\n counter = 1\n for i in range(1000):\n readsNumberPerSample = config.get('sample'+str(counter))\n\n if readsNumberPerSample is not None:\n idbaPerSampleDir = os.path.join(idbaDir, ('sample'+str(counter)))\n if not os.path.exists(idbaPerSampleDir):\n try:\n os.mkdir(idbaPerSampleDir)\n except OSError:\n print(\"Can't create idbaPerSample directory:\", idbaPerSampleDir)\n if readsNumberPerSample is not None:\n idbaPerSampleWorkingDir = os.path.join(forAssemblyWorkingDir, ('idba_'+'sample'+str(counter)))\n if not os.path.exists(idbaPerSampleWorkingDir):\n try:\n os.mkdir(idbaPerSampleWorkingDir)\n except OSError:\n print(\"Can't create idbaPerSample directory:\", idbaPerSampleWorkingDir)\n\n outputFile1 = open(os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa'),'w')\n outputFile2 = open(os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa'),'w')\n\n for k, l in zip((file1[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2]),\n (file2[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2])):\n\n forwardDir = os.path.join(idbaPerSampleWorkingDir, 'forward')\n reverseDir = os.path.join(idbaPerSampleWorkingDir, 'reverse')\n if not os.path.exists(forwardDir):\n try:\n os.mkdir(forwardDir)\n except OSError:\n print (\"Can't create forward directory:\", forwardDir)\n shutil.copy(os.path.join(forAssemblyWorkingDir, k), forwardDir)\n inputFile1 = open(os.path.normpath(k), 'r')\n lines = inputFile1.readlines()\n #while True:\n if len(lines) == 0:\n break\n for line in lines:\n outputFile1.write(line)\n\n if not os.path.exists(reverseDir):\n try:\n os.mkdir(reverseDir)\n except OSError:\n print (\"Can't create reverse directory:\", reverseDir)\n\n shutil.copy(os.path.join(forAssemblyWorkingDir, l), reverseDir)\n inputFile2 = open(os.path.normpath(l), 'r')\n lines = inputFile2.readlines()\n #while True:\n if len(lines) == 0:\n break\n for line in lines:\n outputFile2.write(line)\n\n outputFile1.close()\n outputFile2.close()\n\n combinePairedEndFileFasta(os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa'),\n os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa'),\n os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.mergePaired.fa'))\n\n idbaInstallDir = os.path.normpath(config.get('idbaInstallDir'))\n\n runIdba = str('export PATH=/net/programs/Debian-6.0.3-x86_64/idba-1.1.1/bin:$PATH'+';' + os.path.join(idbaInstallDir, 'idba_ud') + ' '\n + '-' + config.get('idbaReadsLen') + ' '\n + os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.mergePaired.fa')\n + ' -o ' + idbaPerSampleDir + ' --num_threads ' + config.get('idbaNumThreads'))\n #print runIdba\n procRunIdba = subprocess.Popen(runIdba, shell=True, bufsize=-1, cwd=idbaPerSampleDir)\n procRunIdba.wait()\n print \"IDBA_UD return code:\", procRunIdba.returncode\n if procRunIdba.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (procRunIdba.returncode, runIdba))\n print \"sample\" + str(counter) + \"IDBA_UD is finished.\"\n shutil.copy(os.path.join(idbaPerSampleDir, 'contig.fa'), os.path.join(idbaPerSampleDir, 'idba_sample'+str(counter)+'.contig.fa'))\n shutil.copy(os.path.join(idbaPerSampleDir, 'idba_sample'+str(counter)+'.contig.fa'), assemblyDir)\n counter += 1\n\n if \"ray\" in args.a:\n def rayAssembly(file1,file2):\n counter = 1\n for i in range(1000):\n readsNumberPerSample = config.get('sample'+str(counter))\n\n if readsNumberPerSample is not None:\n rayPerSampleDir = os.path.join(rayDir, ('sample'+str(counter)))\n if not os.path.exists(rayPerSampleDir):\n try:\n os.mkdir(rayPerSampleDir)\n except OSError:\n print(\"Can't create idbaPerSample directory:\", rayPerSampleDir)\n #if readsNumberPerSample is not None:\n rayPerSampleWorkingDir = os.path.join(forAssemblyWorkingDir, ('ray_'+'sample'+str(counter)))\n if not os.path.exists(rayPerSampleWorkingDir):\n try:\n os.mkdir(rayPerSampleWorkingDir)\n except OSError:\n print(\"Can't create idbaPerSample directory:\", rayPerSampleWorkingDir)\n\n outputFile1 = open(os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa'),'w')\n outputFile2 = open(os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa'),'w')\n\n for k, l in zip((file1[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2]),\n (file2[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2])):\n\n inputFile1 = open(os.path.normpath(k), 'r')\n lines = inputFile1.readlines()\n #while True:\n if len(lines) == 0:\n break\n for line in lines:\n outputFile1.write(line)\n\n inputFile2 = open(os.path.normpath(l), 'r')\n lines = inputFile2.readlines()\n #while True:\n if len(lines) == 0:\n break\n for line in lines:\n outputFile2.write(line)\n\n outputFile1.close()\n outputFile2.close()\n\n rayInstallDir = os.path.normpath(config.get('rayInstallDir'))\n rayPerSampleDirOutput = os.path.join(rayPerSampleDir, 'output')\n runRay = str('export PATH=/net/programs/Debian-6.0.3-x86_64/Ray-2.3.0:$PATH'+';' + 'mpirun -np '\n + config.get('numProcess_mpi_ray') + ' ' + os.path.join(rayInstallDir, 'Ray')\n + ' -k ' + config.get('kmerSize_ray') + ' -p '\n + os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa')\n + ' ' + os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa')\n + ' -o ' + rayPerSampleDirOutput)\n #print runRay\n procRunRay = subprocess.Popen(runRay, shell=True, bufsize=-1, cwd=rayPerSampleDir)\n procRunRay.wait()\n print \"Ray return code:\", procRunRay.returncode\n if procRunRay.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (procRunRay.returncode, runRay))\n print \"Ray is finished.\"\n shutil.copy(os.path.join(rayPerSampleDirOutput, 'Contigs.fasta'), os.path.join(rayPerSampleDir, 'ray_sample'+str(counter)+'.contig.fa'))\n shutil.copy(os.path.join(rayPerSampleDir, 'ray_sample'+str(counter)+'.contig.fa'), assemblyDir)\n counter += 1\n\n if \"n\" in args.a:\n for i, j in zip(listf, listr):\n fileHolder.outputTrimForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_consensus.fa'))\n fileHolder.outputTrimReverse.append(os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_consensus.fa'))\n file1 = fileHolder.outputTrimForward\n file2 = fileHolder.outputTrimReverse\n\n if \"soap\" in args.a:\n soapAssembly(file1, file2)\n if \"idba\" in args.a:\n #starttime = time.clock()\n idbaAssembly(file1, file2)\n #endtime = time.clock()\n #print (endtime-starttime)\n if \"ray\" in args.a:\n rayAssembly(file1, file2)\n\n if \"r\" in args.a:\n for i, j in zip(listf,listr):\n fileHolder.outputRandomForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_consensus.fa'))\n fileHolder.outputRandomReverse.append(os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_forward_consensus.fa'))\n file1 = fileHolder.outputRandomForward\n file2 = fileHolder.outputRandomReverse\n if \"soap\" in args.a:\n soapAssembly(file1, file2)\n # return fileHolder.outputRandomForward, fileHolder.outputRandomReverse\n if \"idba\" in args.a:\n idbaAssembly(file1, file2)\n if \"ray\" in args.a:\n rayAssembly(file1, file2)\n if \"f\" in args.a:\n for i, j in zip(listf, listr):\n fileHolder.outputFilterForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_consensus.fa'))\n fileHolder.outputFilterForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_reverse_consensus.fa'))\n file1 = fileHolder.outputFilterForward\n file2 = fileHolder.outputFilterReverse\n if \"soap\" in args.a:\n soapAssembly(file1, file2)\n if \"idba\" in args.a:\n idbaAssembly(file1, file2)\n if \"ray\" in args.a:\n rayAssembly(file1, file2)\n # return fileHolder.outputFilterForward, fileHolder.outputFilterReverse\n if \"rf\" in args.a:\n for i, j in zip(listf, listr):\n fileHolder.outputRandomFilterForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] +'_random_filter_forward_consensus.fa'))\n fileHolder.outputRandomFilterReverse.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] +'_random_filter_reverse_consensus.fa'))\n file1 = fileHolder.outputRandomFilterForward\n file2 = fileHolder.outputRandomFilterReverse\n if \"soap\" in args.a:\n soapAssembly(file1, file2)\n if \"idba\" in args.a:\n idbaAssembly(file1, file2)\n if \"ray\" in args.a:\n rayAssembly(file1, file2)\n # return fileHolder.outputRandomFilterForward, fileHolder.outputRandomFilterReverse\n\n if args.p:\n counter = 1\n for i in range(1000):\n ppspPerSampleDir = os.path.join(ppspDir, ('sample'+str(counter)))\n if not os.path.exists(ppspPerSampleDir):\n try:\n os.mkdir(ppspPerSampleDir)\n except OSError:\n print(\"Can't create ppspPerSample directory:\", ppspPerSampleDir)\n\n filterReadLen(os.path.join(assemblyDir, config.get('ppspAssemblyMethod') + '_sample' + str(counter)+'.contig.fa'),\n os.path.join(assemblyDir, config.get('ppspAssemblyMethod') + '_sample' + str(counter)+'.contig.ppspfilter.fa'), 1000)\n\n ppspConfigFile = open(os.path.join(ppspPerSampleDir, ('sample'+str(counter) + '.cfg')), 'w')\n ppspConfigFile.write('[PhyloPythiaS_Plus]' + '\\n' + '# Configuration file of the PhyloPythiaS Plus pipeline.'\n + '\\n' + 'pipelineDir=' + os.path.normpath(ppspPerSampleDir) + '\\n' + '# INPUT FILES' + '\\n' +\n 'inputFastaFile=' + os.path.join(assemblyDir, config.get('ppspAssemblyMethod') + '_sample'\n + str(counter)+'.contig.ppspfilter.fa') + '\\n' + '# Fasta file with scaffolds (optional)'\n + '\\n' + 'inputFastaScaffoldsFile=' + noneToEmptyStr(config.get('inputFastaScaffoldsFile')) + '\\n'\n + '# Scaffold-contig mapping, tab separated file; map: scaffold -> contigs; (optional)'\n + ' \\n' + 'scaffoldsToContigsMapFile=' + noneToEmptyStr(config.get('scaffoldsToContigsMapFile')) + '\\n'\n + '# Reference prediction file in PPS (*.out) format (optional)' + '\\n'\n + 'referencePlacementFileOut=' + noneToEmptyStr(config.get('referencePlacementFileOut')) + '\\n'\n + '# REFERENCE\\n' + 'databaseFile=/net/metagenomics/projects/PPSmg/taxonomy/20121122\\n'\n + 'refSeq=/net/metagenomics/projects/PPSmg/data/nobackup/NCBI20121122/sequences\\n'\n + '# Exclude reference sequences according to the reference prediction file at a given '\n 'rank (optional)\\n# allowed ranks are: phylum, class, order, family, genus, species, strain\\n'\n + 'excludeRefSeqRank=' + noneToEmptyStr(config.get('excludeRefSeqRank')) + '\\n'\n + 's16Database=/net/metagenomics/projects/PPSmg/database/silva111' + '\\n'\n + 'mgDatabase=/net/metagenomics/projects/PPSmg/database/mg3' + '\\n' + 'excludeRefMgRank='\n + noneToEmptyStr(config.get('excludeRefMgRank')) + '\\n' + '# TOOLS\\n'\n + 'ppsInstallDir=/net/metagenomics/projects/PPSmg/tools/PhyloPythiaS/vm/1_3\\n'\n + 'mothurInstallDir=/net/metagenomics/projects/PPSmg/tools/mothur/mothur_1_23\\n'\n + 'hmmerBinDir=/net/metagenomics/projects/PPSmg/tools/hmmer-3.0/binaries\\n'\n + 'rnaHmmInstallDir=/net/metagenomics/projects/PPSmg/tools/rna_hmm3\\n'\n + '#BASIC SETTINGS\\n' + '# e.g.: 1 ~ phylum, 2 ~ class, 3 ~ order, 4 ~ family, 5 ~ genus, 6 ~ species\\n'\n + 'rankIdCut=' + config.get('rankIdCut') + '\\n' + 'maxLeafClades=' + config.get('maxLeafClades')\n + '\\n' + 'minPercentInLeaf=' + config.get('minPercentInLeaf') + '\\n'\n + 'minSeqLen=' + config.get('minSeqLen') + '\\n' + 'parallelPPSmodels=' + config.get('parallelPPSmodels')\n + '\\n' + '# ADVANCED SETTINGS\\n' + '# It is not recommended to change the advanced settings, ' +\n 'however it is possible to give you more control.\\n' + 'placeContigsFromTheSameScaffold=True\\n'\n + 'agThreshold=0.3\\n' + 'assignedPartThreshold=0.5\\n' + 'rankIdAll=0\\n' + 'weightStayAll=60.0\\n'\n + 'rankIdCutMinBp=100\\n' + 'overrideWithPPSPlacements=True\\n' + 'minBpToModel=100000\\n'\n + 'minSSDfileSize=100\\n' + 'maxSSDfileSize=400000\\n' + 'minGenomesWgs=1\\n' + 'minBpPerSpecies=2000000\\n'\n + 'candidatePlTopPercentThreshold=0.1\\n' + 'outputFileContigSubPattern=^(.*)\\n'\n + 'mothurClassifyParamOther=method=bayesian, cutoff=80, iters=300' + '\\n' + 'configPPS=' + '\\n'\n + 'recallMinFracClade=0.001' + '\\n' + 'precisionMinFracPred=0.001' + '\\n' + 'correctLabelThreshold=0.9')\n ppspConfigFile.close() #Close file is important\n\n #only run maker gene analysis not the whole ppsp pipeline\n if \"m\" in args.p:\n runPpsp = str('export PYTHONPATH=/net/metagenomics/projects/PPSmg/scripts/current:$PYTHONPATH'+';' + ' '\n + 'python /net/metagenomics/projects/PPSmg/scripts/current/algbioi/core/run.py'\n + ' -c ' + 'sample'+str(counter) + '.cfg' + ' -n -g -o s16 mg -s')\n #run whole ppsp pipeline without inputFastaScaffoldsFile setting in ppsp configuration file\n if \"n\" in args.p:\n runPpsp = str('export PYTHONPATH=/net/metagenomics/projects/PPSmg/scripts/current:$PYTHONPATH'+';' + ' ' + 'python /net/metagenomics/projects/PPSmg/scripts/current/algbioi/core/run.py'\n + ' -c ' + os.path.join(ppspPerSampleDir,'sample'+str(counter) + '.cfg') + ' -n -g -o s16 mg -t -a -p c -r -s')\n print runPpsp\n #run whole ppsp pipeline with inputFastaScaffoldsFile setting in ppsp configuration file\n if \"f\" in args.p:\n runPpsp = str('export PYTHONPATH=/net/metagenomics/projects/PPSmg/scripts/current:$PYTHONPATH'+';' + ' ' + 'python /net/metagenomics/projects/PPSmg/scripts/current/algbioi/core/run.py'\n + ' -c ' + 'sample'+str(counter) + '.cfg' + ' -n -g -o s16 mg -t -a -p c s v -r -s')\n\n procRunPpsp = subprocess.Popen(runPpsp, shell=True, bufsize=-1, cwd=ppspPerSampleDir)\n procRunPpsp.wait()\n print \"PPSP return code:\", procRunPpsp.returncode\n if procRunPpsp.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (procRunPpsp.returncode, runPpsp))\n else:\n print \"sample\"+str(counter),\"PPSP is finished.\"\n\n counter += 1\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\n pass\n else:\n break\n\n if args.x:\n counter = 1\n for i in range(1000):\n #taxatorPerSampleDir = os.path.join(taxatorDir, ('sample'+str(counter)))\n # if not os.path.exists(taxatorPerSampleDir):\n # try:\n # os.mkdir(taxatorPerSampleDir)\n # except OSError:\n # print(\"Can't create taxatorPerSample directory:\", taxatorPerSampleDir)\n renameReads(os.path.join(assemblyDir, config.get('taxatorAssemblyMethod') + '_sample' + str(counter)+'.contig.fa'),\n os.path.join(assemblyDir, config.get('taxatorAssemblyMethod') + '_sample' + str(counter)+'.contig.rename.fa'))\n taxatorInstallDir = os.path.normpath(config.get('taxatorInstallDir'))\n #shutil.copy(os.path.join(taxatorInstallDir, 'binning-working-fasta-blast.bash'), taxatorPerSampleDir)\n # runTaxator = str(os.path.join(taxatorInstallDir, 'binning-workflow-fasta-blast.bash') + ' '\n runTaxator = str(os.path.join(taxatorInstallDir, 'binning-workflow-fasta-last.sh') + ' '\n + os.path.join(assemblyDir, config.get('taxatorAssemblyMethod') + '_sample' + str(counter)+'.contig.rename.fa'))\n # print runTaxator\n procRunTaxator = subprocess.Popen(runTaxator, shell=True, bufsize=-1, cwd=taxatorInstallDir)\n procRunTaxator.wait()\n print \"Taxator-tk return code:\", procRunTaxator.returncode\n if procRunTaxator.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (procRunTaxator.returncode, runTaxator))\n else:\n print \"sample\"+str(counter), \"Taxator-tk is finished.\"\n\n counter += 1\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\n pass\n else:\n break\n\n if args.f is not None:\n counter = 1\n for i in range(1000):\n orfPerSampleDir = os.path.join(orfDir, ('sample'+str(counter)))\n hmmPerSampleDir = os.path.join(hmmDir, ('sample'+str(counter)))\n if not os.path.exists(orfPerSampleDir):\n try:\n os.mkdir(orfPerSampleDir)\n except OSError:\n print(\"Can't create orfPerSampleDir:\", orfPerSampleDir)\n if not os.path.exists(hmmPerSampleDir):\n try:\n os.mkdir(hmmPerSampleDir)\n except OSError:\n print(\"Can't create hmmPerSampleDir:\", hmmPerSampleDir)\n\n filterReadLen(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa'),\n os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.annofilter.fa'), int(config.get('annoSeqLen')))\n\n MetaGeneMarkInstallDir = os.path.normpath(config.get('MetaGeneMarkInstallDir'))\n runMetaGeneMark = str(os.path.join(MetaGeneMarkInstallDir, 'gmhmmp') + ' -m '\n + os.path.join(MetaGeneMarkInstallDir, 'MetaGeneMark_v1.mod') + ' '\n + os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.annofilter.fa')\n + ' -f G -a -d -r -o ' + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.ntaa.gff')\n + ';' + os.path.join(MetaGeneMarkInstallDir, 'aa_from_gff.pl') + ' < '\n + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.ntaa.gff')\n + ' > ' + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.gff.faa')\n + ';' + os.path.join(MetaGeneMarkInstallDir, 'nt_from_gff.pl') + ' < '\n + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.ntaa.gff')\n + ' > ' + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.gff.fna'))\n procRunMetaGeneMark = subprocess.Popen(runMetaGeneMark, shell=True, bufsize=-1, cwd=orfPerSampleDir)\n procRunMetaGeneMark.wait()\n print \"ORFs calling return code:\", procRunMetaGeneMark.returncode\n if procRunMetaGeneMark.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status: %s\" % (procRunMetaGeneMark.returncode, runMetaGeneMark))\n else:\n print \"sample\"+str(counter), \"ORFs calling is finished.\"\n f1 = open(os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'ORF_aa.txt'), 'w')\n f1.write(os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.gff.faa'))\n f1.close()\n\n if None in args.f:\n counter += 1\n #\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\n pass\n else:\n break\n\n\n\n if \"h\" in args.f:\n hmmScriptInstallDir = os.path.normpath(config.get('hmmScriptInstallDir'))\n runAnnotationScriptPath = os.path.normpath(\"/net/metagenomics/\") #at the moment, Aaron's pipeline only support to run under the /net/metagenomics/\n arrayOutDir = os.path.join(hmmPerSampleDir, (config.get('assemblyMethod') + '_sample' + str(counter) + '_array_out'))\n if not os.path.exists(arrayOutDir):\n try:\n os.mkdir(arrayOutDir)\n except OSError:\n print(\"Can't create arrayOutDir:\", arrayOutDir)\n runHmm = str('importpackage parallel; python ' + os.path.join(hmmScriptInstallDir, 'write_annot_array_script.py') + ' -d '\n + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'ORF_aa.txt')[len(runAnnotationScriptPath)+1:]\n + ' -t ' + os.path.normpath(hmmPerSampleDir)[len(runAnnotationScriptPath)+1:] + ' -f '\n + os.path.normpath(arrayOutDir)[len(runAnnotationScriptPath)+1:] + ' -s ' + config.get('annotationSource') + ' -h ')\n # print runHmm\n procRunHmm = subprocess.Popen(runHmm, shell=True, bufsize=-1, cwd=runAnnotationScriptPath)\n procRunHmm.wait()\n print \"Hmm annotation return code:\", procRunHmm.returncode\n if procRunHmm.returncode != 0:\n raise Exception(\"Command returned with non-zero %s status : %s\" % (procRunHmm.returncode, runHmm))\n else:\n print \"sample\"+str(counter), \"Hmm annotation is finished.\"\n #\n counter += 1\n #\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\n pass\n else:\n break\n #if \"s\" in args.f:","title":""},{"docid":"a9bcf6c29174951e9173c01b3594c846","score":"0.58679366","text":"def run(self, pipeline: pipeline_py.Pipeline) -> Optional[Any]:\n pass","title":""},{"docid":"a71cc839ed60fe44d705ba29c1b3a879","score":"0.5863825","text":"def main():\n for gpx_file in FILES:\n transform(gpx_file)","title":""},{"docid":"eae37600e81b4ed83185a9a8fafef30f","score":"0.586028","text":"def process(self, env, data):\n pass","title":""},{"docid":"08b5c6e35850c271a459acec8531620f","score":"0.58322805","text":"def pipeline_casero(data, preprocessing=[], creation=[], reduction=[], selection=[], models=[]):\n print(\"Beginning pipeline at {}\\n\".format(datetime.now()))\n print(\"Performing preprocessing steps...\")\n data = update_df(data, preprocessing)\n print(\"Preprocessing completed at {}, performed {} steps\".format(datetime.now(), len(preprocessing)))\n print(\"New Shape of data: {0}\\n\".format(len(data.columns)))\n\n print(\"Performing feature creation...\")\n data = update_df(data, creation)\n print(\"Feature Creation completed at {}, performed {} steps\".format(datetime.now(), len(creation)))\n print(\"New Shape of data: {0}\\n\".format(len(data.columns)))\n\n print(\"Dummifying...\")\n data = dd.get_dummies(data)\n print(\"New Shape of data: {0}\\n\".format(len(data.columns)))\n\n\n print(\"Performing dimensionality reduction...\")\n data = update_df(data, reduction)\n print(\"Dimensionality reduction completed at {}, performed {} steps\".format(datetime.now(), len(reduction)))\n print(\"New Shape of data: {0}\\n\".format(len(data.columns)))\n\n train = get_train(data)\n holdout = get_holdout(data)\n print(\"Performing feature selection...\")\n train = update_df(train, selection)\n print(\"Feature Selection completed at {}, performed {} steps\".format(datetime.now(), len(selection)))\n print(\"New Shape of train: {0}\\n\".format(len(train.columns)))\n\n holdout = holdout[train.columns]\n\n # The issue here is this assumes the model need the data in the same format, unless we submit pipelines\n # here which seems silly....\n\n print(\"Scoring models....\")\n\n best_model = select_best_model(models, train)\n\n print(\"Evaluating model on the holdout...\")\n final_r2 = r2_score(holdout.cnt, best_model.predict(holdout.drop(\"cnt\", axis=1)))\n print(\"Final R2: {0}\".format(final_r2))\n print(\"\\nPipeline finished! Completed execution at {}. Returning model...\".format(datetime.now()))\n\n return best_model","title":""},{"docid":"9b2263b95c0fd4f749c1d6c8872d4b0b","score":"0.5829256","text":"def pipelineComponents(self, *args):\n\t\taaeDesc = createEngineDescription(args);\n\t\tself.processEngine = createEngine(aaeDesc);","title":""},{"docid":"26426c2960fdeea6cf5773304b0eb6a3","score":"0.5825872","text":"def main():\n\n args = arg_parser()\n threads = args.threads\n LOGGER.info(\"{} threads flagged to run each software.\".format(threads))\n\n LOGGER.info(\"Pipeline beginning.\")\n log_file = open('UPEC.log', 'a+')\n\n # Storing all constants in lists for efficient looping within large functions\n # maintains the same order, and ensures no missing files\n\n # input for ncbi wget, holds ftp paths\n fasta_ftp_list = [HM27_FILES[0], HM46_FILES[0], HM65_FILES[0], HM69_FILES[0], \\\n HM27_FILES[1], HM46_FILES[1], HM65_FILES[1], HM69_FILES[1]]\n\n # output from ncbi wget\n fasta_file_list = [HM27_FASTA, HM46_FASTA, HM65_FASTA, HM69_FASTA]\n\n # output from fastq-dump\n fastq_tuple_list = [(HM27_FASTQ_1, HM27_FASTQ_2), \\\n (HM46_FASTQ_1, HM46_FASTQ_2), \\\n (HM65_FASTQ_1, HM65_FASTQ_2), \\\n (HM69_FASTQ_1, HM69_FASTQ_2)]\n\n # output from prokka software\n gff_list = [HM27_GFF_FILE, HM46_GFF_FILE, HM65_GFF_FILE, HM69_GFF_FILE]\n\n # output from tophat2\n bam_file_list = [HM27_BAM, HM46_BAM, HM65_BAM, HM69_BAM]\n\n # output from samtools sort and tophat2\n sorted_bam_list = [HM27_SORTED_BAM, HM46_SORTED_BAM, HM65_SORTED_BAM, HM69_SORTED_BAM]\n\n # grabbing ftp files from ncbi using wget method\n LOGGER.info(\"Beginning to find FTP files.\")\n wget_gunzip_fasta(fasta_ftp_list)\n\n # Parsing the FASTA and counting number of contigs and base pairs > 1000 in length\n LOGGER.info(\"Parsing FASTA and writing to log file.\")\n parse_seqio_fasta(fasta_file_list, log_file)\n\n # NEED TO FIX THE DIRECTORY PATH FOR WRITING TO THE LOG FILE!!\n\n # fix prokka to use less cpus and add the -usegenus flag\n LOGGER.info(\"Starting gene annotation with Prokka\")\n build_prokka(fasta_file_list, log_file)\n\n # LOGGER.info(\"Grabbing SRA files and converting to FASTQ\")\n # prefetch_fastq_decomp()\n\n LOGGER.info(\"Beginning alignment process.\")\n build_tophat_alignment(fasta_file_list, fastq_tuple_list, bam_file_list, \\\n sorted_bam_list, threads)\n\n LOGGER.info(\"Creating assembly file for cuffmerge.\")\n with open('ecoli_assemblies.txt', 'w') as assemble_file:\n assemble_file.write(\"./hm27_cuff/transcripts.gtf\\n\")\n assemble_file.write(\"./hm46_cuff/transcripts.gtf\\n\")\n assemble_file.write(\"./hm65_cuff/transcripts.gtf\\n\")\n assemble_file.write(\"./hm69_cuff/transcripts.gtf\\n\")\n\n LOGGER.info(\"Beginning to run cufflinks\")\n run_cufflinks_suite(gff_list, bam_file_list, 'ecoli_assemblies.txt', MERGED_GTF, threads)\n\n LOGGER.info(\"Pipeline Complete.\")\n log_file.close()","title":""},{"docid":"c8cb787443828cba22935bb3a8911c97","score":"0.57822245","text":"def pipeline(root):\n for split in [\"train\", \"dev\"]:\n input_path = os.path.join(FLAGS.input_dir,\n \"%s/nq-%s-??.jsonl.gz\" % (split, split))\n output_path = os.path.join(FLAGS.output_dir, \"nq-%s.tfrecords\" % split)\n serialized_examples = (\n root\n | \"Read-\" + split >> beam.io.textio.ReadFromText(\n input_path, validate=False)\n | \"Shuffle-\" + split >> beam.transforms.Reshuffle()\n | \"Prep-\" + split >> beam.ParDo(\n preproc_nq_lib.NQPreprocessFn(\n FLAGS.stride, FLAGS.seq_len, FLAGS.global_seq_len,\n FLAGS.question_len, FLAGS.vocab_file, FLAGS.do_lower_case,\n FLAGS.global_token_types, FLAGS.spm_model_path,\n FLAGS.tokenizer_type, split == \"train\",\n FLAGS.predict_la_when_no_sa, FLAGS.include_unknown_rate,\n FLAGS.include_unknown_rate_for_unanswerable, FLAGS.fixed_blocks,\n FLAGS.fixed_block_size))\n | \"Split-\" + split >> beam.ParDo(\n preproc_nq_lib.NQSplitFn(\n FLAGS.stride, FLAGS.seq_len, FLAGS.global_seq_len,\n FLAGS.question_len, FLAGS.vocab_file, FLAGS.do_lower_case,\n FLAGS.global_token_types, FLAGS.spm_model_path,\n FLAGS.tokenizer_type, split == \"train\",\n FLAGS.predict_la_when_no_sa, FLAGS.include_unknown_rate,\n FLAGS.include_unknown_rate_for_unanswerable, FLAGS.fixed_blocks,\n FLAGS.fixed_block_size))\n | \"Shuffle2-\" + split >> beam.transforms.Reshuffle())\n _ = (\n serialized_examples\n | \"WriteTFExample-\" + split >> beam.io.WriteToTFRecord(\n output_path,\n num_shards=FLAGS.num_shards,\n compression_type=beam.io.filesystem.CompressionTypes.GZIP))\n\n # Write count to file.\n _ = (\n serialized_examples\n | \"Count-\" + split >> beam.combiners.Count.Globally()\n | \"WriteCount-\" + split >> beam.io.WriteToText(\n os.path.join(FLAGS.output_dir, \"%s_count.txt\" % split),\n shard_name_template=\"\", # To force unsharded output.\n ))","title":""},{"docid":"8862001d3e966e17f630ab409a2a862d","score":"0.5770933","text":"def run(self):\n self.set_pipeline()\n self.pipeline.fit(self.X, self.y)","title":""},{"docid":"93a1eaa3e8f3494613c61cac9876bca9","score":"0.5768495","text":"def main_pipeline(args):\n initialize(args)\n dataset_list = args.d\n offline_toggle = args.offline\n set_debug_simple(args.etl)\n raw_bool = args.raw\n #Load logging information\n set_log_folder(f'{args.local_directory}/logging') # folder for logs\n set_target_folder_name(args.local_directory)\n if not os.path.exists(args.local_directory):\n os.mkdir(args.local_directory)\n if not os.path.exists(get_log_folder()):\n os.mkdir(get_log_folder())\n #Create offline folder - always required for chemaxon/rdkit...\n set_offline_folder(f'./{get_target_folder()}/offline')\n if not os.path.exists(get_offline_folder()):\n os.mkdir(get_offline_folder())\n\n #Create Loggers\n main_logger = f'{get_log_folder()}/REPORT_LOG.txt' \n warning_logger = f'{get_log_folder()}/REPORT_WARNING_LOG.txt' \n ingredient_logger = f'{get_log_folder()}/REPORT_INGREDIENT_LOG.txt' \n logger.setup_logger('mainlog', main_logger)\n logger.setup_logger('warning', warning_logger, level=logging.WARN, stream=True)\n logger.setup_logger('ilog', ingredient_logger) #ingredient log\n modlog = logging.getLogger(f'mainlog.{__name__}')\n\n # Initial reporting signaling successful code kickoff\n modlog.info(f'{dataset_list} selected as the dataset target(s) for this run')\n print(f'{dataset_list} selected as the dataset target(s) for this run')\n print(f'{len(dataset_list)} set(s) of downloads will occur, one per dataset, please be patient!')\n modlog.info(f'{len(dataset_list)} set(s) of downloads will occur, one for dataset, please be patient!')\n modlog.info(f'Developer Option: \"offline_toggle\" set to {offline_toggle}')\n\n # Gather data from targeted directory (google UID from devcongfig datasets)\n chemdf_dict = get_remote_data(dataset_list, \n offline_toggle)\n\n report_df = report_pipeline(chemdf_dict, \n raw_bool,\n dataset_list, \n offline_toggle)\n\n debug_header = f\"# Report version {__version__}; Created on {datetime.datetime.now()}; Dataset(s) targeted {dataset_list}\\n\"\n set_debug_header(debug_header)\n if args.debug:\n # Export dataframes of initial parsing and chemical inventories for ETL to ESCALATEV3\n report_csv_filename = f'REPORT_{get_target_folder().upper()}.csv'\n write_debug_file(report_df, report_csv_filename, write_index=True) #turn to True to generate for testing\n for name, chemicaldf in chemdf_dict.items():\n inventory_name = f'REPORT_{name.upper()}_INVENTORY.csv'\n write_debug_file(chemicaldf, inventory_name)\n\n # Perform feature calculations / gather physicochemical descriptors\n compound_ingredient_objects_df = ingredient_pipeline(report_df,\n chemdf_dict,\n args.debug)\n\n inchi_key_indexed_features_df = feat_pipeline(report_df,\n chemdf_dict,\n args.debug)\n\n amounts_df,\\\n ratios_df = ratio_pipeline(report_df,\n compound_ingredient_objects_df,\n chemdf_dict,\n args.debug) \n\n #calc_out_df.to_csv(f'./{get_offline_folder()}/REPORT_CALCOUT.csv')\n #calc_out_df = pd.read_csv(f'./{get_offline_folder()}/REPORT_CALCOUT.csv')\n if not args.disablecalcs:\n calcs_df = calc_pipeline(report_df,\n amounts_df,\n ratios_df,\n inchi_key_indexed_features_df,\n args.debug)\n else:\n calcs_df = pd.DataFrame()\n\n # Export dataframe\n escalate_final_df = construct_2d_view(report_df,\n amounts_df,\n inchi_key_indexed_features_df, \n ratios_df,\n calcs_df,\n args.debug,\n args.raw)\n\n escalate_final_df.to_csv(f'{get_target_folder()}.csv')\n\n # Additional variations on dataframe export (for escalation / versioned data repo)\n if ('state' in vars(args)):\n templink = str(args.state)\n link = templink.split('.')[0] + '.link.csv'\n pass\n else:\n modlog.error('User MUST specify a stateset during version data repo\\\n upload preparation!')\n sys.exit()\n\n if args.verdata is not None:\n export_to_repo.prepareexport(escalate_final_df, args.state, link, args.verdata, get_target_folder())\n modlog.info(f'Exporting {args.verdata}: {args.verdata}.{get_target_folder()}.csv was generated')\n print(f'Exporting {args.verdata}: {args.verdata}.{get_target_folder()}.csv was generated')\n \n modlog.info(f'Clean Exit: {get_target_folder()}.csv was generated')\n print(f'Clean Exit: {get_target_folder()}.csv was generated')\n\n # Postrun cleanup\n if args.offline == 0: \n os.remove('./mycred.txt') #cleanup automatic authorization","title":""},{"docid":"09a64f15315f446efb7bba9a08e679d9","score":"0.57667524","text":"def exec_before_job(app, inp_data, out_data, param_dict, tool=None):\n job_name = param_dict.get( 'name', 'PlinkFbat' )\n outxls = ['tabular','%s_fbat.xls' % job_name]\n logtxt = ['txt','%s_fbat.log' % job_name]\n lookup={}\n lookup['out_file1'] = outxls\n lookup['logf'] = logtxt\n for name in lookup.keys():\n data = out_data[name]\n data_type,newname = lookup[name]\n data = app.datatypes_registry.change_datatype(data, data_type)\n data.name = newname\n out_data[name] = data","title":""},{"docid":"9abc6f4b0c6ecf5733c204113f245029","score":"0.57398516","text":"def call_pipeline(met,input_file,cent,corr,conditioning,total,incr,weight,c_prop,\n thresh,min,plot_metric,create_graph,plot_pca,name_file,run_number):\n #try:\n proc_id = os.getpid()\n t_start = time.perf_counter()\n pipeline.main(False, input_file, '', met, conditioning, min, total, incr, None,\n None, None, cent, thresh, corr, weight, c_prop, 'kl_divergence',plot_metric,\n create_graph, plot_pca,name_file, run_number)\n t_end = time.perf_counter()\n print('runtime = ', t_end - t_start)\n #except:\n # pass\n return","title":""},{"docid":"2b78b6ba27a16e8510c4d894e61b36d7","score":"0.5738241","text":"def make_pipeline():\n\n # Create our mean reversion factor by taking the negative of a momentum factor\n \n reversion = Reversion()\n\n # Classify all securities by sector so that we can enforce sector neutrality later\n sector = Sector()\n\n # Screen out non-desirable securities by defining our universe.\n universe = Q1500US()\n\n # By applying a mask to the rank computations, we remove any stocks that failed\n # to meet our initial criteria **before** computing ranks. This means that the\n # stock with rank 10.0 is the 10th-lowest stock that was included in the Q1500US.\n # 標準偏差\n # filter のかけかたを mask で行ってしまう\n factor_rank = reversion.rank(mask=universe).zscore()\n\n # Build Filters representing the top and bottom 150 stocks by our ranking system.\n # We'll use these as our tradeable universe each day.\n longs = factor_rank.top(NUM_LONG_POSITIONS)\n shorts = factor_rank.bottom(NUM_SHORT_POSITIONS)\n\n # The final output of our pipeline should only include\n # the top/bottom 300 stocks by our criteria\n long_short_screen = (longs | shorts)\n\n # Define any risk factors that we will want to neutralize\n # We are chiefly interested in market beta as a risk factor so we define it using\n # Bloomberg's beta calculation\n # Ref: https://www.lib.uwo.ca/business/betasbydatabasebloombergdefinitionofbeta.html\n beta = 0.66 * RollingLinearRegressionOfReturns(\n target=sid(8554),\n returns_length=5,\n regression_length=260,\n mask=long_short_screen\n ).beta + 0.33*1.0\n\n # Create pipeline\n pipe = Pipeline(\n columns={\n 'longs': longs,\n 'shorts': shorts,\n 'factor_rank': factor_rank,\n 'reversion': reversion,\n 'sector': sector,\n 'market_beta': beta\n },\n screen=long_short_screen\n )\n return pipe","title":""},{"docid":"c313321bfaa7fcc8c2964a3ef8505b36","score":"0.57319045","text":"def transform_data():\n pass","title":""},{"docid":"84981d796d43daf713d6c3ad26d27de8","score":"0.5726708","text":"def runpipe():","title":""},{"docid":"f06ce5c66b68337cd0e42f59c2276283","score":"0.5698271","text":"def main(lims, args, logger):\n p = Process(lims,id = args.pid)\n samplenb=0\n errnb=0\n summary={}\n logart=None\n for output_artifact in p.all_outputs():\n #filter to only keep solo sample demultiplexing output artifacts\n if output_artifact.type=='Analyte' and len(output_artifact.samples)==1:\n sample=output_artifact.samples[0]\n samplenb+=1\n #update the total number of reads\n total_reads=sumreads(sample, summary)\n sample.udf['Total Reads (M)']=total_reads\n output_artifact.udf['Set Total Reads']=total_reads\n logging.info(\"Total reads is {0} for sample {1}\".format(sample.udf['Total Reads (M)'],sample.name))\n try:\n if sample.udf['Reads Min'] >= sample.udf['Total Reads (M)']:\n sample.udf['Status (auto)']=\"In Progress\"\n sample.udf['Passed Sequencing QC']=\"False\"\n elif sample.udf['Reads Min'] < sample.udf['Total Reads (M)'] : \n sample.udf['Passed Sequencing QC']=\"True\"\n sample.udf['Status (auto)']=\"Finished\"\n except KeyError as e:\n print e\n logging.warning(\"No reads minimum found, cannot set the status auto flag for sample {0}\".format(sample.name))\n errnb+=1\n\n #commit the changes\n sample.put()\n output_artifact.put()\n elif(output_artifact.type=='Analyte') and len(output_artifact.samples)!=1:\n logging.error(\"Found {0} samples for the ouput analyte {1}, that should not happen\".format(len(output_artifact.samples()),output_artifact.id))\n elif(output_artifact.type==\"ResultFile\" and output_artifact.name==\"AggregationLog\"):\n logart=output_artifact\n\n\n #write the csv file, separated by pipes, no cell delimiter\n with open(\"AggregationLog.csv\", \"w\") as f:\n f.write(\"sep=,\\n\")\n f.write('sample name,number of flowcells,number of lanes,flowcell1:lane1|lane2;flowcell2:lane1|lane2|lane3 ...\\n')\n for sample in summary:\n view=[]\n totfc=len(summary[sample])\n totlanes=0\n for fc in summary[sample]:\n view.append(\"{0}:{1}\".format(fc, \"|\".join(summary[sample][fc])))\n totlanes+=len(summary[sample][fc])\n f.write('{0},{1},{2},{3}\\n'.format(sample, totfc, totlanes, \";\".join(view)))\n try:\n attach_file(os.path.join(os.getcwd(), \"AggregationLog.csv\"), logart)\n logging.info(\"updated {0} samples with {1} errors\".format(samplenb, errnb))\n except AttributeError:\n #happens if the log artifact does not exist, if the step has been started before the configuration changes\n logging.info(\"Could not upload the log file\")","title":""},{"docid":"79873034b23627420e4ab4a53158cf4c","score":"0.56886816","text":"def main_pipeline_part_1():\n taxonomy_download_dir = get_local_folder_for(settings.generated_dir_name, FileType.TaxonomyData)\n taxon_file_path = download_ncbi_taxonomy_as_xml(taxonomy_download_dir, settings.virus_taxon_id)\n organism = OrganismWrapperClass(taxon_file_path)\n\n def get_virus_id_and_nuc_reference_sequence(session: database.Session):\n # get virus ID\n virus = vcm.get_virus(session, organism)\n if virus:\n _virus_id = virus.virus_id\n else:\n logger.trace(f'inserting new organism { settings.virus_taxon_id} into VIRUS table')\n _virus_id = vcm.create_or_get_virus(session, organism)\n # get nuc_reference_sequence\n logger.trace(f'organism txid { settings.virus_taxon_id} already in VIRUS table.')\n refseq_objects = vcm.get_reference_sequence_of_virus(session, _virus_id)\n if refseq_objects:\n ref_seq_obj, ref_nuc_seq_obj = refseq_objects\n return _virus_id, ref_nuc_seq_obj.nucleotide_sequence, ref_seq_obj.accession_id\n else:\n logger.trace('caching the nucleotide sequence of the reference')\n downloaded_ref_sample = _reference_sample_from_organism(SampleWrapperClass)\n return _virus_id, downloaded_ref_sample.nucleotide_sequence(), downloaded_ref_sample.primary_accession_number()\n\n return database.try_py_function(get_virus_id_and_nuc_reference_sequence)","title":""},{"docid":"207a1972b3f018fe7ce7e4a001fbea61","score":"0.5674275","text":"def build_own_pipeline() -> Pipeline:\n pipeline = None\n ##### Write code here #######\n\n pipeline = build_linear_svc() \n\n ##### End of your work ######\n return pipeline","title":""},{"docid":"2d35253ceab432e1175a7489e97ffd62","score":"0.5670093","text":"def stage(self, data):","title":""},{"docid":"c5d1505b8db1e1676fb91bd112fa1df9","score":"0.56636786","text":"def main():\n ## Process Each Dataset in Serial\n for d, dataset in enumerate(DATASETS):\n logger.info(f\"Processing dataset {d+1}/{len(DATASETS)}: `{dataset}`\")\n _ = process_dataset(dataset)","title":""},{"docid":"60c0dc2011a19bb7007765c116f91c3c","score":"0.5652783","text":"def main():\n args = BeutelArgs().parse_args()\n train, test = load_data_from_flags(args)\n save_transformations(train_and_transform(train, test, args), args)","title":""},{"docid":"1eaf3053e0bb915860ca6eef2fb46bc9","score":"0.56264156","text":"def main():\n args = VfaeArgs(explicit_bool=True).parse_args()\n train, test = load_data_from_flags(args)\n save_transformations(train_and_transform(train, test, args), args)","title":""},{"docid":"88eb20e49779b01d1d60d5a62d617a49","score":"0.5623422","text":"def main(args):\n #%% Step 0: Set basic parameters\n metric_sets = [args.metric_name]\n metric_parameters = {\"problem\": args.problem, \"label_name\": [args.label_name]}\n\n #%% Step 1: Upload Dataset\n # File names\n data_directory = \"../datasets/data/\" + args.data_name + \"/\" + args.data_name + \"_\"\n\n data_loader_training = CSVLoader(\n static_file=data_directory + \"static_train_data.csv.gz\",\n temporal_file=data_directory + \"temporal_train_data_eav.csv.gz\",\n )\n\n data_loader_testing = CSVLoader(\n static_file=data_directory + \"static_test_data.csv.gz\",\n temporal_file=data_directory + \"temporal_test_data_eav.csv.gz\",\n )\n\n dataset_training = data_loader_training.load()\n dataset_testing = data_loader_testing.load()\n\n print(\"Finish data loading.\")\n\n #%% Step 2: Preprocess Dataset\n # (0) filter out negative values (Automatically)\n negative_filter = FilterNegative()\n # (1) one-hot encode categorical features\n onehot_encoder = OneHotEncoder(one_hot_encoding_features=[args.one_hot_encoding])\n # (2) Normalize features: 3 options (minmax, standard, none)\n normalizer = Normalizer(args.normalization)\n\n filter_pipeline = PipelineComposer(negative_filter, onehot_encoder, normalizer)\n\n dataset_training = filter_pipeline.fit_transform(dataset_training)\n dataset_testing = filter_pipeline.transform(dataset_testing)\n\n print(\"Finish preprocessing.\")\n\n #%% Step 3: Define Problem\n problem_maker = ProblemMaker(\n problem=args.problem, label=[args.label_name], max_seq_len=args.max_seq_len, treatment=args.treatment\n )\n\n dataset_training = problem_maker.fit_transform(dataset_training)\n dataset_testing = problem_maker.fit_transform(dataset_testing)\n\n print(\"Finish defining problem.\")\n\n #%% Step 4: Impute Dataset\n static_imputation = Imputation(imputation_model_name=args.static_imputation_model, data_type=\"static\")\n temporal_imputation = Imputation(imputation_model_name=args.temporal_imputation_model, data_type=\"temporal\")\n\n imputation_pipeline = PipelineComposer(static_imputation, temporal_imputation)\n\n dataset_training = imputation_pipeline.fit_transform(dataset_training)\n dataset_testing = imputation_pipeline.transform(dataset_testing)\n\n print(\"Finish imputation.\")\n\n #%% Step 5: Feature selection (4 options)\n static_feature_selection = FeatureSelection(\n feature_selection_model_name=args.static_feature_selection_model,\n feature_type=\"static\",\n feature_number=args.static_feature_selection_number,\n task=args.task,\n metric_name=args.metric_name,\n metric_parameters=metric_parameters,\n )\n\n temporal_feature_selection = FeatureSelection(\n feature_selection_model_name=args.temporal_feature_selection_model,\n feature_type=\"temporal\",\n feature_number=args.temporal_feature_selection_number,\n task=args.task,\n metric_name=args.metric_name,\n metric_parameters=metric_parameters,\n )\n\n feature_selection_pipeline = PipelineComposer(static_feature_selection, temporal_feature_selection)\n\n dataset_training = feature_selection_pipeline.fit_transform(dataset_training)\n dataset_testing = feature_selection_pipeline.transform(dataset_testing)\n\n print(\"Finish feature selection.\")\n\n #%% Step 6: Bayesian Optimization\n ## Model define\n # RNN model\n rnn_parameters = {\n \"model_type\": \"lstm\",\n \"epoch\": args.epochs,\n \"static_mode\": args.static_mode,\n \"time_mode\": args.time_mode,\n \"verbose\": False,\n }\n\n general_rnn = GeneralRNN(task=args.task)\n general_rnn.set_params(**rnn_parameters)\n\n # CNN model\n cnn_parameters = {\n \"epoch\": args.epochs,\n \"static_mode\": args.static_mode,\n \"time_mode\": args.time_mode,\n \"verbose\": False,\n }\n temp_cnn = TemporalCNN(task=args.task)\n temp_cnn.set_params(**cnn_parameters)\n\n # Transformer\n transformer = TransformerPredictor(\n task=args.task, epoch=args.epochs, static_mode=args.static_mode, time_mode=args.time_mode\n )\n\n # Attention model\n attn_parameters = {\n \"model_type\": \"lstm\",\n \"epoch\": args.epochs,\n \"static_mode\": args.static_mode,\n \"time_mode\": args.time_mode,\n \"verbose\": False,\n }\n attn = Attention(task=args.task)\n attn.set_params(**attn_parameters)\n\n # model_class_list = [general_rnn, attn, temp_cnn, transformer]\n model_class_list = [general_rnn, attn]\n\n # train_validate split\n dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.1)\n\n # Bayesian Optimization Start\n metric = BOMetric(metric=\"auc\", fold=0, split=\"test\")\n\n ens_model_list = []\n\n # Run BO for each model class\n for m in model_class_list:\n BO_model = automl.model.AutoTS(dataset_training, m, metric, model_path=\"tmp/\")\n models, bo_score = BO_model.training_loop(num_iter=args.bo_itr)\n auto_ens_model = AutoEnsemble(models, bo_score)\n ens_model_list.append(auto_ens_model)\n\n # Load all ensemble models\n for ens in ens_model_list:\n for m in ens.models:\n m.load_model(BO_model.model_path + \"/\" + m.model_id + \".h5\")\n\n # Stacking algorithm\n stacking_ens_model = StackingEnsemble(ens_model_list)\n stacking_ens_model.fit(dataset_training, fold=0, train_split=\"val\")\n\n # Prediction\n assert not dataset_testing.is_validation_defined\n test_y_hat = stacking_ens_model.predict(dataset_testing, test_split=\"test\")\n test_y = dataset_testing.label\n\n print(\"Finish AutoML model training and testing.\")\n\n #%% Step 7: Visualize Results\n idx = np.random.permutation(len(test_y_hat))[:2]\n\n # Evaluate predictor model\n result = Metrics(metric_sets, metric_parameters).evaluate(test_y, test_y_hat)\n print(\"Finish predictor model evaluation.\")\n\n # Visualize the output\n # (1) Performance\n print(\"Overall performance\")\n print_performance(result, metric_sets, metric_parameters)\n # (2) Predictions\n print(\"Each prediction\")\n print_prediction(test_y_hat[idx], metric_parameters)\n\n return","title":""},{"docid":"ab27aa4f0de17a2e4deecf3fc8d97275","score":"0.56216466","text":"def process(self):","title":""},{"docid":"ab27aa4f0de17a2e4deecf3fc8d97275","score":"0.56216466","text":"def process(self):","title":""},{"docid":"ab27aa4f0de17a2e4deecf3fc8d97275","score":"0.56216466","text":"def process(self):","title":""},{"docid":"ab27aa4f0de17a2e4deecf3fc8d97275","score":"0.56216466","text":"def process(self):","title":""},{"docid":"ab27aa4f0de17a2e4deecf3fc8d97275","score":"0.56216466","text":"def process(self):","title":""},{"docid":"ab27aa4f0de17a2e4deecf3fc8d97275","score":"0.56216466","text":"def process(self):","title":""},{"docid":"b8aeab9d4753095a17b727a3899ff5df","score":"0.5620898","text":"def protein_functional_profiling_pipeline(params):\n\n work_dir = os.path.join(params['work_dir'], str(uuid.uuid4()))\n os.mkdir(work_dir)\n\n config_file = write_config_file(work_dir)\n project_file = write_project_file(params['input_proteins'], params['reference'], work_dir)\n project = Project(config_file=config_file, project_file=project_file)\n # Run Fama\n project = protein_pipeline(project)\n\n out_dir = os.path.join(work_dir, 'out')\n os.mkdir(out_dir)\n # export_reads\n output = {}\n output['krona_charts'] = {}\n\n # Generate output\n report_files = {}\n metric = 'proteincount'\n project_xlsx_report = sanitize_file_name(os.path.join(\n project.options.work_dir,\n project.options.project_name + '_' + metric + '_functions.xlsx'\n ))\n if os.path.exists(project_xlsx_report):\n report_files[project_xlsx_report] = 'Functional_profiles_combined.xlsx'\n else:\n print('Project XLSX file not found:', project_xlsx_report)\n project_xlsx_report = sanitize_file_name(os.path.join(\n project.options.work_dir,\n project.options.project_name + '_' + metric + '_functions_taxonomy.xlsx'\n ))\n if os.path.exists(project_xlsx_report):\n report_files[project_xlsx_report] = 'Function_taxonomy_profiles_combined.xlsx'\n else:\n print('Project XLSX file not found:', project_xlsx_report)\n project_text_report = sanitize_file_name(os.path.join(\n project.options.work_dir,\n 'all_proteins.list.txt'\n ))\n if os.path.exists(project_text_report):\n report_files[project_text_report] = 'proteins_list.txt'\n else:\n print('Proteins list not found:', project_text_report)\n\n featureset_elements = {}\n featureset_element_ordering = []\n objects_created = []\n genome_names = {}\n # Get Domain Model Set reference\n dms_ref = get_dms(params['reference'],\n project.config.get_functions_file(project.collection),\n params['ws_name'],\n params['ws_client']\n )\n\n for sample_id in project.list_samples():\n annotation_obj_ref, feature_ids, genome_name = \\\n save_domain_annotations(project, dms_ref, params['ws_name'],\n params['ws_client'], params['annotation_prefix'],\n sample_id, params['name2ref'][sample_id])\n genome_names[sample_id] = genome_name\n objects_created.append({'ref': annotation_obj_ref,\n 'description': 'Functional annotations for genome '\n + project.samples[sample_id].sample_name})\n for feature_id in feature_ids:\n if feature_id not in featureset_elements:\n featureset_elements[feature_id] = []\n featureset_elements[feature_id].append(params['name2ref'][sample_id])\n featureset_element_ordering.append(feature_id)\n\n sample_xlsx_report = sanitize_file_name(os.path.join(\n project.options.work_dir,\n sample_id + '_' + metric + '_functions_taxonomy.xlsx'\n ))\n if os.path.exists(sample_xlsx_report):\n report_files[sample_xlsx_report] = \\\n sanitize_file_name(genome_name + '_function_taxonomy_profile_long.xlsx')\n else:\n print('Sample XLSX file not found:', sample_xlsx_report)\n krona_file = sanitize_file_name(os.path.join(\n project.options.work_dir,\n sample_id + '_' + metric + '_functional_taxonomy_profile.xml.html'\n ))\n\n if os.path.exists(krona_file):\n krona_output = \\\n sanitize_file_name(os.path.join(out_dir, genome_name +\n '_function_taxonomy_profile_chart.html'))\n shutil.copy2(krona_file, krona_output)\n with zipfile.ZipFile(krona_output + '.zip', 'w',\n zipfile.ZIP_DEFLATED,\n allowZip64=True) as zip_file:\n zip_file.write(krona_output,\n sanitize_file_name(genome_name +\n '_function_taxonomy_profile_chart.html'))\n report_files[krona_output] = \\\n sanitize_file_name(genome_name + '_function_taxonomy_profile_chart.html')\n output['krona_charts'][krona_output + '.zip'] = \\\n (sanitize_file_name(genome_name + '_function_taxonomy_profile_chart.html'),\n sample_id + ' function taxonomy chart')\n else:\n print('Krona diagram file not found:', krona_file)\n\n feature_set_data = {'description': 'FeatureSet generated by Fama protein profiling',\n 'element_ordering': featureset_element_ordering,\n 'elements': featureset_elements}\n\n out_report = os.path.join(out_dir, 'fama_report.html')\n generate_protein_html_report(out_report, project, params['name2ref'])\n with zipfile.ZipFile(out_report + '.zip', 'w',\n zipfile.ZIP_DEFLATED,\n allowZip64=True) as zip_file:\n zip_file.write(out_report, 'fama_report.html')\n output['html_report'] = out_report + '.zip'\n report_files[out_report] = 'fama_report.html'\n\n output_files = list()\n result_file = os.path.join(project.options.work_dir, 'Fama_result.zip')\n with zipfile.ZipFile(result_file, 'w',\n zipfile.ZIP_DEFLATED,\n allowZip64=True) as zip_file:\n for filename in report_files:\n zip_file.write(filename, report_files[filename])\n output_files.append({'path': result_file,\n 'name': os.path.basename(result_file),\n 'label': os.path.basename(result_file),\n 'description': 'Files generated by Fama App'})\n output['report_files'] = output_files\n output['project'] = project\n output['feature_set_data'] = feature_set_data\n output['objects_created'] = objects_created\n return output","title":""},{"docid":"210bda854b7dc1ee0ac3082390d2e403","score":"0.56162924","text":"def _PTransformFn(pcoll_or_pipeline: Any):\n return (\n pcoll_or_pipeline\n | \"RawRecordBeamSource\" >> self.RawRecordBeamSource()\n | \"RawRecordToRecordBatch\" >> self.RawRecordToRecordBatch(batch_size))","title":""},{"docid":"2c1949be9ca8db1a5fe64b08a09a26ab","score":"0.55996466","text":"def run_model_pipeline(process_number, uuid_list):\n\n\n\n\n\n\tfor uuid in uuid_list:\n\t\tif uuid is None:\n\t\t\tcontinue\n\n\t\ttry: \n\t\t\trun_model_pipeline_for_user(uuid)\n\t\texcept Exception as e:\n\t\t\tprint(\"dang flabbit failed on error %s\" % e)","title":""},{"docid":"2f5c8dad9f24d757d3de0ad12b850e69","score":"0.55933267","text":"def setup(self):\n ### Set Names\n '''\n NAMES:\n Set the internal name for the function and the procedure identifier to be incorporated in output file names. \n name: The name should be all lower-case and should be identical to \n the module name (the module name is used to identify pipe steps\n in the config file, so if it doesn’t match case an error will occur). \n procname: The procname should be all upper-case and, if possible, \n restricted to three or four alphabetic characters(the code would \n automatically capitalize, but it’s good form to also capitalize it here).\n '''\n # Name of the pipeline reduction step\n self.name='srcextpy'\n # Shortcut for pipeline reduction step and identifier for\n # saved file names.\n self.procname = 'SEP'\n # Set Logger for this pipe step\n self.log = logging.getLogger('pipe.step.%s' % self.name)\n ### Set Parameter list\n '''\n PARAMETERS:\n Parameters are stored in a list containing the following information:\n name: The name for the parameter. This name is used when calling the \n pipe step from command line or python shell. It is also used to \n identify the parameter in the pipeline configuration file.\n default: A default value for the parameter. \n If nothing, set ‘ ’ for strings 0 for integers, and 0.0 for floats.\n help: A short description of the parameter.\n '''\n # Clear Parameter list\n self.paramlist = []\n # Append parameters\n self.paramlist.append(['sourcetable',False,\n 'Flag for making txt table of all sources'])\n self.paramlist.append(['sourcetableformat','csv',\n 'txt table format (see astropy.io.ascii for options)'])\n self.paramlist.append(['bkg_maskthreshold', 0.0,\n 'mask threshold for background detection'])\n self.paramlist.append(['bkg_wh', [16,16],\n 'background box width and height for background detection'])\n self.paramlist.append(['bkg_filterwh', [3,3],\n 'filter width and height for background detection'])\n self.paramlist.append(['bkg_fthreshold', 0.0,\n 'filter threshold for background detection'])\n self.paramlist.append(['ext_thresh', 2.0,\n 'extraction threshold for source extration'])\n self.paramlist.append(['ext_bfactor', 10.0,\n 'brightness factor for creating highlevel threshold'])\n self.paramlist.append(['ext_deblend', 256,\n 'deblend threshold for source extraction'])\n self.paramlist.append(['phot_kronf', 2.5,\n 'factor multiplied into kronrad to get radius for integration'])\n self.paramlist.append(['save_background', True,\n 'option to save the background as a seprate hdu'])\n #self.paramlist.append(['byte_swap', False,\n # \t 'says if the bytes should be swapped or not for the image'])\n # confirm end of setup\n self.log.debug('Setup: done')","title":""},{"docid":"2d3521b86747b932030927e0514afb12","score":"0.5582054","text":"def run(self, stream):\n \n # Run any preparations that need to exist before the handler runs\n self.prepare()\n \n for line in stream:\n if self.terminate_early:\n break \n\n line = line.rstrip('\\n')\n \n if line.startswith('##'):\n self.process_meta(line)\n else:\n fields = line.split('\\t')\n \n # Do we have a genotype field?\n if line.startswith('#'):\n self.has_genotype_field = 'FORMAT' in fields\n if self.has_genotype_field:\n individuals = fields[FIXED_FIELDS_LENGTH+1:]\n else:\n individuals = fields[FIXED_FIELDS_LENGTH:]\n self.process_individuals(individuals)\n \n else:\n fixed_fields = fields[:FIXED_FIELDS_LENGTH]\n if self.has_genotype_field:\n format_field = fields[FIXED_FIELDS_LENGTH]\n data_fields = fields[FIXED_FIELDS_LENGTH+1:]\n else:\n format_field = ''\n data_fields = fields[FIXED_FIELDS_LENGTH:]\n \n self.process_data(fixed_fields, format_field, data_fields)\n \n self.terminate()","title":""},{"docid":"3c592a8ae39158ae799e3d436e27c44c","score":"0.55707747","text":"def create_pipeline(self) -> None:\n pass","title":""},{"docid":"68a756ee93440f8223c5234e7c6b31af","score":"0.55685365","text":"def data_process(self, data):\n pass","title":""},{"docid":"1b8af7fad6192c8fdb1b99c8dfc1b612","score":"0.5535488","text":"def process_data(app):\n data_path_fn = data_path(app)\n _process_shops(data_path_fn)\n _process_products(data_path_fn)\n _process_tags(data_path_fn)","title":""},{"docid":"bdf012acd44a35a6ffb9625e9ffe2697","score":"0.5532902","text":"def main(args):\n\n # Look up the type of args, close the verified file handle we were passed\n # in, and begin data processing\n\n folder = None\n outfile = None\n\n destination = args.DEST[0]\n if os.path.isdir(destination):\n DEBUG('Output is to folder %s' % destination)\n folder = destination\n else:\n DEBUG('Output is to file %s' % destination)\n outfile = destination\n\n for handle in args.SOURCE:\n handle.close() # we know it exists b/c it's filetype\n process(handle.name, args.LAYER[0], outfile, folder)","title":""},{"docid":"8eb74512072676ebca6541f58b0a1642","score":"0.5532351","text":"def main():\n logger = logging.getLogger(__name__)\n # logger.info('downloading the raw data')\n # download_raw_data()\n logger.info('processing the raw data')\n SplitAndScale()\n logger.info('planing the mass')\n GetPlaningWeights()\n logger.info('Do the PCA rotations')\n PCA()","title":""},{"docid":"1ccf0d7aa98e8c6a30775cce5e190a42","score":"0.5504588","text":"def run(argv=None):\n parser = argparse.ArgumentParser()\n parser.add_argument('--input',\n dest='input',\n required=True,\n default='gs://dataflow-samples/shakespeare/kinglear.txt',\n help='Input file to process.')\n parser.add_argument('--output',\n dest='output',\n required=True,\n default='gs://dataflow-samples/',\n help='Output file to write results to.')\n parser.add_argument('--topic',\n dest='topic',\n required=True,\n help='Topic for message.')\n parser.add_argument('--subscription',\n dest='subscription',\n required=True,\n help='Subscription for message.')\n parser.add_argument('--entity_type',\n dest='entity_type',\n required=True,\n help='Entity Type for message.')\n parser.add_argument('--event_type',\n dest='event_type',\n required=True,\n help='Event Type for message.') \n parser.add_argument('--outputFilenamePrefix',\n dest='outputFilenamePrefix',\n required=True,\n help='Output Filename Prefix Type for message.') \n parser.add_argument('--outputFilenameSuffix',\n dest='outputFilenameSuffix',\n required=True,\n help='Output Filename Suffix Type for message.') \n\n known_args, pipeline_args = parser.parse_known_args(argv)\n\n # We use the save_main_session option because one or more DoFn's in this\n # workflow rely on global context (e.g., a module imported at module level).\n pipeline_options = PipelineOptions(pipeline_args)\n pipeline_options.view_as(SetupOptions).save_main_session = True\n pipeline_options.view_as(StandardOptions).streaming= True\n p = beam.Pipeline(options=pipeline_options)\n\n if known_args.subscription:\n messages = (p \n | ReadFromPubSub(subscription=known_args.subscription, with_attributes=True))\n else:\n messages = (p \n | ReadFromPubSub(subscription=known_args.topic, with_attributes=True))\n\n def printattr(msg):\n print(msg.attributes)\n\n (messages \n | \"Print Attributes\" >> beam.Map(printattr)\n | beam.WindowInto(window.FixedWindows(120,0))\n | beam.io.WriteToText(known_args.output + known_args.outputFilenamePrefix, \n file_name_suffix=known_args.outputFilenameSuffix,\n num_shards=1))\n\n result = p.run()\n result.wait_until_finish()","title":""},{"docid":"ad132a892dd8b1e3cab0dd484e65ea88","score":"0.548488","text":"def transform(self, *args, **kwargs):\n return self.pipeline.transform(*args, **kwargs)","title":""},{"docid":"890ad26c357e40e6349a6d8a782cb4f4","score":"0.5483538","text":"def __call__(self, **args):\n return self.pipeline[:self.name](**args)","title":""},{"docid":"2ea7eb15408029479fdc27aee50db850","score":"0.5481434","text":"def build_own_pipeline() -> Pipeline:\n ##### Write code here #######\n pipeline = None\n SVM = SVC(C=4, kernel=\"linear\")\n estimator = []\n estimator.append((\"CountVectorizer\", CountVectorizer()))\n estimator.append((\"SVC\", SVM))\n pipeline = Pipeline(estimator)\n ##### End of your work ######\n return pipeline","title":""},{"docid":"6e0a9562a2d30840820449040a6f32e8","score":"0.547682","text":"def process(self, raw_data, raw_data_filenames=None):\n pass","title":""},{"docid":"2b41eac949e2c40eaf032887f2e451d9","score":"0.546774","text":"def __call__(self, *args) -> Tuple[Any]:\n # Check if no data supplied to pipeline\n if not args:\n raise ValueError(\n 'No data supplied to pipeline')\n\n # Create `Variable` tuple from input arguments.\n vargs = []\n for arg in args:\n vtype = type(arg).__name__\n vargs.append(Variable(vtype, arg))\n vargs = tuple(vargs)\n\n # Process the data through the pipeline.\n in_df = DataFrame('_in_df')\n in_df.put(vargs)\n out_df = self._pipeline(in_df)\n\n # Extract values from output tuple\n res = []\n for v in out_df.get(0, out_df.size)[1][0]:\n res.append(v.value)\n\n return tuple(res)","title":""},{"docid":"d8f60a8a77c3bdd0c20432a5620eb76a","score":"0.5464261","text":"def process_data(self):\n pass","title":""},{"docid":"ee7f7ccbb6819646915105052a873d3a","score":"0.54573524","text":"def make_pipeline():\n # Pipeline constants ----\n\n # Used to filer the strength threshold for Bullish sentiment.\n st_strength = 0.25\n\n # Pipeline Factors -----\n\n # Base universe set to the Q500US\n base_universe = Q1500US()\n\n # Messages\n message_volume = MessageVolume()\n\n # Bull messages\n bull_messages = BullMessages()\n\n # Bull message percentage\n bull_score = (bull_messages / message_volume)\n\n # Sentiment Filters\n bull_sentiment = bull_score > st_strength\n\n # Pipeline Creation ----\n\n pipe = Pipeline(\n screen=(base_universe &\n bull_sentiment),\n columns={\n 'Message Volume': message_volume,\n 'Bull Messages': bull_messages,\n 'Bull % of messages': bull_score,\n }\n )\n return pipe","title":""},{"docid":"e1ffbd9aac2a9286cdee5e39ffc6b85c","score":"0.5454726","text":"def run(self):\n self.set_pipeline()\n return self.pipeline.fit(self.X, self.y)","title":""},{"docid":"e8713faa23c9ad8a061316ac70a37379","score":"0.5445825","text":"def run_processing(self, inputs, transdir, id_='unknown', parameters=[]):\n\n args = docopt(usage_extra, argv=parameters)\n\n\n # Send all output to STDERR (so that jsawrapdr collects it).\n os.dup2(sys.stderr.fileno(), sys.stdout.fileno())\n\n outputs = transient_analysis(\n inputfiles=inputs,\n reductiontype=args['<reduction_type>'],\n no_450_cat=args['--no-450-cat'],\n as_ref_cat=args['--as-ref-cat'],\n dimmconfig_850=args['--dimmconfig850'],\n dimmconfig_450=args['--dimmconfig450'],\n fixed_dra=args['--dra'],\n fixed_ddec=args['--ddec'],\n mask_suffix=args['--mask'])\n\n for filename in outputs:\n (base_name, extension) = os.path.splitext(filename)\n\n if extension == '.sdf':\n logger.info('Storing SDF file \"%s\"', filename)\n filename_edited = re.sub(\n '_cal$', '', base_name) + '.sdf'\n filename_trans = os.path.join(transdir, filename_edited)\n subprocess.check_call(\n [\n os.path.join(\n os.environ['STARLINK_DIR'],\n 'bin', 'h5repack'),\n '-i', filename,\n '-o', filename_trans,\n '-f', 'NONE',\n ],\n shell=False)\n if not args['--nozip']:\n subprocess.check_call(\n [\n '/bin/gzip',\n '--best',\n filename_trans,\n ],\n shell=False)\n\n elif extension == '.txt':\n logger.info('Storing text file \"%s\"', filename)\n shutil.copy(filename, transdir)\n\n elif extension == '.png':\n logger.info('Storing preview image \"%s\"', filename)\n shutil.copy(filename, transdir)\n\n elif extension == '.FIT':\n filename_edited = re.sub(\n '_crop.*', '_cat', base_name) + '.fits'\n logger.info('Storing FITS file \"%s\" as \"%s\"',\n filename, filename_edited)\n shutil.copyfile(\n filename, os.path.join(transdir, filename_edited))\n\n else:\n raise Exception('Unexpected output file \"{}\"'.format(filename))","title":""},{"docid":"02edee2583ab16c9a4b07afd3ff1696a","score":"0.54428303","text":"def main():\r\n\r\n logging.debug(\"Initialization of a Spark session...\")\r\n spark = create_sparksession()\r\n\r\n logging.debug(\"Start processing source data\")\r\n sdw = SourceDataWrangling(spark)\r\n sdw.process_data()","title":""},{"docid":"0e5730a4c2d55890c19f0063432e3e2c","score":"0.54194224","text":"def test_pipeline_transform(\n primary_model_num_feature_generators: int,\n metalabeling_model_num_feature_generators: int,\n dollar_bar_dataframe: pd.DataFrame,\n bar_feature_generator,\n):\n # create dict structure for primary model feature generator\n feature_transformers_primary_model = {\n f\"primary_{i}\": bar_feature_generator\n for i in range(primary_model_num_feature_generators)\n }\n\n # create dict structure for metalabeling model feature generator\n feature_transformers_metalabeling_model = {\n f\"metalabeling_{i}\": bar_feature_generator\n for i in range(metalabeling_model_num_feature_generators)\n }\n\n align_how_primary = {\n f\"primary_{i}\": \"mean\" for i in range(primary_model_num_feature_generators)\n }\n align_how_secondary = {\n f\"metalabeling_{i}\": \"mean\"\n for i in range(metalabeling_model_num_feature_generators)\n }\n\n pipeline = StrategySignalPipeline(\n feature_transformers_primary_model=feature_transformers_primary_model,\n feature_transformers_metalabeling_model=feature_transformers_metalabeling_model,\n align_on=\"primary_0\",\n align_how={**align_how_primary, **align_how_secondary},\n )\n\n # transform data with pipeline\n X_features_pipeline = pipeline.transform(\n {\n **{\n f\"primary_{i}\": dollar_bar_dataframe\n for i in range(primary_model_num_feature_generators)\n },\n **{\n f\"metalabeling_{i}\": dollar_bar_dataframe\n for i in range(metalabeling_model_num_feature_generators)\n },\n }\n )\n\n # check expected output\n X_features_expected = bar_feature_generator.transform(dollar_bar_dataframe)\n if primary_model_num_feature_generators > 0:\n for df in X_features_pipeline[\"primary_model_features\"].values():\n pd.testing.assert_frame_equal(df, X_features_expected)\n if metalabeling_model_num_feature_generators > 0:\n for df in X_features_pipeline[\"metalabeling_model_features\"].values():\n pd.testing.assert_frame_equal(df, X_features_expected)","title":""},{"docid":"28bba944d1602df153d4f2c8e4afcf0c","score":"0.54061794","text":"def feed_data(self, data: Any):\n self.concurrent_pipelines = 1\n self.step(data)","title":""},{"docid":"c29f9687b595e5568f447b931ae90261","score":"0.54011166","text":"def handle(self, data, **kwargs):\n return data.map(lambda x: self.model.load(x))\\\n .filter(lambda x: x is not None)\\\n .map(lambda x: x.transform())","title":""},{"docid":"b0fda8ab5a8b63877b9f8d834eb0996a","score":"0.5399969","text":"def main():\n args = get_parsed_args()\n use_gpu = torch.cuda.is_available()\n\n model = Checkpoint.load_model_from_dir(args.dir)\n if use_gpu:\n model.cuda()\n\n # load prior\n if args.prior:\n log_prior = args.prior_scale * read_log_prior(args.prior)\n else:\n log_prior = 0\n\n feat = {\n 'rspec': args.feat_rspec,\n 'transform': args.transform,\n 'skip_frame': args.skip_frame,\n 'target_delay': args.target_delay\n }\n\n data_rspecs = [[feat], [], [], []]\n\n collector = SequenceCollector(args.stream_size, args.frame_limit)\n utt_reader = UtteranceReader(data_rspecs, random_sweep=False)\n data_queue = BatchLoader(utt_reader, collector)\n\n decode(model, data_queue, log_prior, args.temperature)","title":""},{"docid":"1423b80869043c71bb6e430f0b16b42e","score":"0.53983605","text":"def runner(self):\n printtime('Starting {} analysis pipeline'.format(self.analysistype), self.starttime)\n if not self.pipeline:\n # If the metadata has been passed from the method script, self.pipeline must still be false in order to\n # get Sippr() to function correctly, but the metadata shouldn't be recreated\n try:\n _ = vars(self.runmetadata)['samples']\n except KeyError:\n # Create the objects to be used in the analyses\n objects = Objectprep(self)\n objects.objectprep()\n self.runmetadata = objects.samples\n self.threads = int(self.cpus / len(self.runmetadata.samples)) \\\n if self.cpus / len(self.runmetadata.samples) > 1 \\\n else 1\n # Use a custom sippr method to use the full reference database as bait, and run mirabait against the FASTQ\n # reads - do not perform reference mapping yet\n SixteenSBait(self, self.cutoff)\n # Subsample 1000 reads from the FASTQ files\n self.subsample()\n # Convert the subsampled FASTQ files to FASTA format\n self.fasta()\n # Create BLAST databases if required\n self.makeblastdb()\n # Run BLAST analyses of the subsampled FASTA files against the NCBI 16S reference database\n self.blast()\n # Parse the BLAST results\n self.blastparse()\n # Feed the BLAST results into a modified sippr method to perform reference mapping using the calculated\n # genus of the sample as the mapping file\n SixteenSSipper(self, self.cutoff)\n # Create reports\n self.reporter()","title":""},{"docid":"5dec2af9cbaca96c263ba26d66335aea","score":"0.5396622","text":"def run(self):\n self.pipeline = self.set_pipeline()\n return self.pipeline.fit(self.X, self.y)","title":""},{"docid":"0c12ff9ff68b4bcdecb51b17162f27ce","score":"0.5396334","text":"def __push_samples_into_relevant_pipelines__(self,configs,mockdb):\n if configs[\"system\"].get(\"Logging\",\"debug\") is \"True\":\n print \" Starting post casava sample pipelines for \" + self.flowcell_key\n print \" Determining Sample dirs\"\n sample_dirs = list_project_sample_dirs(self.output_dir.split(\":\"))\n if configs[\"system\"].get(\"Logging\",\"debug\") is \"True\":\n print \" Samples: \" + str(sample_dirs) \n flowcell_dir_name = os.path.basename(self.output_dir)\n automation_parameters_config = MyConfigParser()\n automation_parameters_config.read(configs[\"system\"].get(\"Filenames\",\"automation_config\"))\n fastqc_pipeline_config = MyConfigParser()\n fastqc_pipeline_config.read(configs[\"system\"].get(\"Pipeline\",\"FastQCPipeline\"))\n for project in sample_dirs:\n for sample in sample_dirs[project]:\n #running_location = identify_running_location_with_most_currently_available(configs,storage_devices)\n running_location = \"Speed\"\n parsed = parse_sample_sheet(configs['system'],mockdb,sample_dirs[project][sample][0])\n if configs[\"system\"].get(\"Logging\",\"debug\") is \"True\":\n print \" Pushing fastqc pipeline for \" + sample\n fastqc_pipeline = mockdb[\"FastQCPipeline\"].__new__(configs['system'],input_dir=sample_dirs[project][sample][0],flowcell_dir_name=flowcell_dir_name,project=parsed['project_name'],pipeline_config=fastqc_pipeline_config,seq_run_key=self.seq_run_key,**parsed)\n description_dict = parse_description_into_dictionary(parsed['description'])\n if 'Pipeline' in description_dict:\n pipeline_key = description_dict['Pipeline']\n else:\n description_pieces = parsed['description'].split('-')\n pipeline_key = description_pieces[-1]\n pipeline_name = automation_parameters_config.safe_get(\"Pipeline\",pipeline_key)\n if pipeline_name is None:\n continue\n if configs[\"system\"].get(\"Logging\",\"debug\") is \"True\":\n print \"Starting \" + pipeline_name + \" for \" + sample\n pipeline = mockdb[pipeline_name].__new__(configs['system'],input_dir=sample_dirs[project][sample][0],pipeline_key=pipeline_key,seq_run_key=self.seq_run_key,project=parsed['project_name'],flowcell_dir_name=flowcell_dir_name,**parsed)","title":""},{"docid":"4cdcf851c6a7f3f7b9ee8a1a502ff54b","score":"0.53907794","text":"def execute_pipeline(self, input_, mode):\n if not self.transformer_sequence:\n raise Exception(\n \"run() must be called to extract/create a TransformerSequence\"\n )\n \n if not isinstance(mode, PipelineModeType):\n raise Exception(\"mode must be of type PipelineModeType Enum object.\")\n\n for transformer in self.transformer_sequence.transformers():\n\n if mode == PipelineModeType.FIT:\n\n transformer.fit(input_)\n\n elif mode == PipelineModeType.TRANSFORM:\n\n input_ = transformer.transform(input_)\n\n elif mode == PipelineModeType.FIT_TRANSFORM:\n\n input_ = transformer.fit_transform(input_)\n\n return input_","title":""},{"docid":"982bc7078ce663a4d379c242536e0eb9","score":"0.5390698","text":"def main():\r\n \r\n name = sys.argv[1]\r\n# name = \"input.yaml\"\r\n \r\n \r\n hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out = bc.set_temp_boundary_conditions(name)\r\n \r\n h_cold, area_cold, h_hot, area_hot = bc.set_flow_boundary_conditions(name)\r\n\r\n \r\n q_fin_counter, fin_counter_vars = hx.q_fin(hx.log_mean_temp_diff_counter(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\r\n q_fin_parallel, fin_parallel_vars = hx.q_fin(hx.log_mean_temp_diff_parallel(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\r\n \r\n q_tube_counter, tube_counter_vars = hx.q_tube(hx.log_mean_temp_diff_counter(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\r\n q_tube_parallel, tube_parallel_vars = hx.q_tube(hx.log_mean_temp_diff_parallel(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\r\n \r\n max_counter_fin = []\r\n max_parallel_fin = []\r\n \r\n max_counter_tube = []\r\n max_parallel_tube = []\r\n \r\n \r\n \r\n for i in range(len(q_fin_counter)):\r\n if q_fin_counter[i] == max(q_fin_counter):\r\n max_counter_fin.append(fin_counter_vars[i])\r\n if q_fin_parallel[i] == max(q_fin_parallel):\r\n max_parallel_fin.append(fin_parallel_vars[i])\r\n \r\n \r\n for i in range(len(q_tube_counter)):\r\n if q_tube_counter[i] == max(q_tube_counter):\r\n max_counter_tube.append(tube_counter_vars[i])\r\n if q_tube_parallel[i] == max(q_tube_parallel):\r\n max_parallel_tube.append(tube_parallel_vars[i])\r\n \r\n print(max(q_fin_counter))\r\n print(max(q_fin_parallel))\r\n print(max(q_tube_counter))\r\n print(max(q_tube_parallel))\r\n \r\n print(max_counter_fin)\r\n print(max_parallel_fin)\r\n print(max_counter_tube)\r\n print(max_parallel_tube)\r\n \r\n \r\n \r\n plt.scatter(range(len(q_fin_counter)), q_fin_counter, label = \"Finned Counter-Flow\")\r\n plt.title('Finned Counter-Flow')\r\n plt.xlabel('Input Variable Permutations')\r\n plt.ylabel('Heat Rate (W)')\r\n plt.legend()\r\n plt.savefig(\"q_fin_counter.png\")\r\n plt.show()\r\n plt.close()\r\n \r\n plt.scatter(range(len(q_fin_parallel)), q_fin_parallel, label = \"Finned Parallel Flow\")\r\n plt.title('Finned Parallel Flow')\r\n plt.xlabel('Input Variable Permutations')\r\n plt.ylabel('Heat Rate (W)')\r\n plt.legend()\r\n plt.savefig(\"q_fin_parallel.png\")\r\n plt.show()\r\n plt.close()\r\n \r\n plt.scatter(range(len(q_tube_counter)), q_tube_counter, label = \"Tubed Counter-Flow\")\r\n plt.title('Tubed Counter-Flow')\r\n plt.xlabel('Input Variable Permutations')\r\n plt.ylabel('Heat Rate (W)')\r\n plt.legend()\r\n plt.savefig(\"q_tube_counter.png\")\r\n plt.show()\r\n plt.close()\r\n \r\n plt.scatter(range(len(q_tube_parallel)), q_tube_parallel, label = \"Tubed Parallel Flow\")\r\n plt.title('Tubed Parallel-Flow')\r\n plt.xlabel('Input Variable Permutations')\r\n plt.ylabel('Heat Rate (W)')\r\n plt.legend()\r\n plt.savefig(\"q_tube_parallel.png\")\r\n plt.show()\r\n plt.close()","title":""},{"docid":"938efc26626ac693c09a2e350c6f4985","score":"0.5387147","text":"def preprocess(pipeline, training_data, eval_data, predict_data, output_dir,\n frequency_threshold):\n\n # 1) The schema can be either defined in-memory or read from a configuration\n # file, in this case we are creating the schema in-memory.\n input_schema = make_bq_sql.make_input_schema()\n\n # 2) Read from BigQuery or from CSV.\n train_data = pipeline | 'ReadTrainingData' >> _ReadData(training_data)\n evaluate_data = pipeline | 'ReadEvalData' >> _ReadData(eval_data)\n\n input_metadata = dataset_metadata.DatasetMetadata(schema=input_schema)\n\n _ = (input_metadata\n | 'WriteInputMetadata' >> tft_beam_io.WriteMetadata(\n os.path.join(output_dir, path_constants.RAW_METADATA_DIR),\n pipeline=pipeline))\n\n preprocessing_fn = make_bq_sql.make_preprocessing_fn(frequency_threshold)\n transform_fn = ((train_data, input_metadata)\n | 'Analyze' >> tft.AnalyzeDataset(preprocessing_fn))\n\n # WriteTransformFn writes transform_fn and metadata to fixed subdirectories\n # of output_dir, which are given by path_constants.TRANSFORM_FN_DIR and\n # path_constants.TRANSFORMED_METADATA_DIR.\n _ = (transform_fn\n | 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(output_dir))\n\n @beam.ptransform_fn\n def TransformAndWrite(pcoll, path): # pylint: disable=invalid-name\n pcoll |= 'Shuffle' >> _Shuffle() # pylint: disable=no-value-for-parameter\n (dataset, metadata) = (((pcoll, input_metadata), transform_fn)\n | 'Transform' >> tft.TransformDataset())\n coder = coders.ExampleProtoCoder(metadata.schema)\n _ = (dataset\n | 'SerializeExamples' >> beam.Map(coder.encode)\n | 'WriteExamples' >> beam.io.WriteToTFRecord(\n os.path.join(output_dir, path), file_name_suffix='.tfrecord.gz'))\n\n _ = train_data | 'TransformAndWriteTraining' >> TransformAndWrite( # pylint: disable=no-value-for-parameter\n path_constants.TRANSFORMED_TRAIN_DATA_FILE_PREFIX)\n\n _ = evaluate_data | 'TransformAndWriteEval' >> TransformAndWrite( # pylint: disable=no-value-for-parameter\n path_constants.TRANSFORMED_EVAL_DATA_FILE_PREFIX)\n\n # TODO(b/35300113) Remember to eventually also save the statistics.\n\n if predict_data:\n predict_mode = tf.contrib.learn.ModeKeys.INFER\n predict_schema = make_bq_sql.make_input_schema(mode=predict_mode)\n predict_coder = coders.ExampleProtoCoder(predict_schema)\n\n serialized_examples = (pipeline\n | 'ReadPredictData' >> _ReadData(\n predict_data, mode=predict_mode)\n # TODO(b/35194257) Obviate the need for this explicit\n # serialization.\n | 'EncodePredictData' >> beam.Map(\n predict_coder.encode))\n _ = (serialized_examples\n | 'WritePredictDataAsTFRecord' >> beam.io.WriteToTFRecord(\n os.path.join(output_dir,\n path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),\n file_name_suffix='.tfrecord.gz'))\n _ = (serialized_examples\n | 'EncodePredictAsB64Json' >> beam.Map(_encode_as_b64_json)\n | 'WritePredictDataAsText' >> beam.io.WriteToText(\n os.path.join(output_dir,\n path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),\n file_name_suffix='.txt'))","title":""},{"docid":"29b8d6d933b66d37a281d95332f2b097","score":"0.5386972","text":"def execute(self):\n self.data = self.extract()\n self.records = self.transform()\n self.path = self._load()\n print(\"Extracted, transformed, and loaded for %s\" % self.name)","title":""},{"docid":"e7bb90271b982aae2e208fc8e37b3f8b","score":"0.538034","text":"def run(self):\n ret = True \n while ret is not False:\n if self.input is None: # it's a faucet\n ret = self.fn()\n elif hasattr(self.input, 'get'): # it's a pipe\n ob = self.input.get()\n if ob is False:\n self.put(ob)\n break\n ret = self.fn(ob)\n if ret is not None: self.put(ret)","title":""},{"docid":"c0834db06d5d37a827e22e3fbd9c7e13","score":"0.5380174","text":"def setup(self):\n ### Set Names\n # Name of the pipeline reduction step\n self.name='masterflat'\n # Shortcut for pipeline reduction step and identifier for\n # saved file names.\n self.procname = 'MFLAT'\n # Set Logger for this pipe step\n self.log = logging.getLogger('pipe.step.%s' % self.name)\n ### Set Parameter list\n # Clear Parameter list\n self.paramlist = []\n # Append parameters !!!! WHAT PARAMETERS ARE NEEDED ????? !!!!!\n self.paramlist.append(['combinemethod','median',\n 'Specifies how the files should be combined - options are\\\n median , average, sum'])\n self.paramlist.append(['outputfolder','',\n 'Output directory location - default is the folder of \\\n the input files'])\n # Get parameters for StepLoadAux, replace auxfile with biasfile\n self.loadauxsetup('bias')\n # Get parameters for StepLoadAux, replace auxfile with darkfile\n self.loadauxsetup('dark')","title":""},{"docid":"a8befd29df0b73e6beaffa6d42f5875f","score":"0.537462","text":"def pipeline(*flags,**kwargs):\n\n\tflags = list(flags)\n\t#---script and quit are protected and get popped\n\tscript = kwargs.pop('script',None)\n\tquit = kwargs.pop('quit',None)\n\tif not script and len(flags)>0: script = flags.pop(0)\n\tif not script:\n\t\tpipeline_regex = '^pipeline-(.+)\\.py$'\n\t\tprint \"[USAGE] make pipeline <name>\"\n\t\tprint \"[USAGE] available pipelines: \\n > \"+'\\n > '.join([re.findall(pipeline_regex,i)[0] \n\t\t\tfor i in [os.path.basename(j) for j in glob.glob(\"./calcs/pipeline-*.py\")]\n\t\t\tif re.match(pipeline_regex,i)])\n\t\treturn\n\t#---drop into the pipeline code but load the header first\n\tscript_fn = \"./calcs/pipeline-%s.py\"%script\n\tprint \"[STATUS] starting pipeline via %s\"%script_fn\n\tif not os.path.isfile(script_fn): raise Exception(\"[ERROR] cannot find \"+script_fn)\n\t#---all flags and arguments pass through\n\textra_args = \"import os,sys;\"\n\tfor flag in flags: extra_args += 'sys.argv.append(\\\"%s\\\");'%flag\n\tfor key,val in kwargs.items(): extra_args += 'sys.argv.append(\\\"%s=%s\\\");'%(key,val)\n\tos.system('python %s -c \\'%sexecfile(\"./omni/base/header.py\");execfile(\"%s\")\\''%(\n\t\t'-i' if not quit else '',extra_args,script_fn))","title":""},{"docid":"b11f555455f17596d1c91cd4cd20f799","score":"0.53695756","text":"def main():\r\n nColSize, nRowSize, lstData, lstGraphLabels = pipe.retrieveData()\r\n aaData = _processData(nColSize, nRowSize, lstData) \r\n _saveImage(aaData, lstGraphLabels)\r\n pipe.updateStatus()","title":""},{"docid":"2972e18b20842728d7edc9eb046f6694","score":"0.53688323","text":"def pipeline(request):\n pipeline = gpipy.PipelineDriver()\n return pipeline","title":""},{"docid":"5faaabc3906ef47b251b867bed02a71c","score":"0.53661937","text":"def main():\n farser = FastaParser(sys.argv[1])\n genes = farser.parse()\n\n #handle_codon_usage(genes)\n #store_low_CAA_genes(genes)\n #stretch_pos_histogram(genes)\n stretch_codu_histogram(genes)","title":""},{"docid":"760b5592a1f8d3a97f6f99ae51d317ca","score":"0.5364469","text":"def main(input_filepath, output_filepath):\n #logger.info(\"Running generic data processing and cleanup scripts...\")\n print_hihi(\"VALVAL\")","title":""},{"docid":"3a5308e2a8ae073ddc473537e3ea41a2","score":"0.53594476","text":"def pre_process(dataset, args):\n dataset = step_1(dataset, args)\n dataset, eval_res_tuple = step_2(dataset, args)\n return step_3(dataset, args)","title":""},{"docid":"dc81e970b8a1c6054947967398109896","score":"0.5356594","text":"def main():\n parser = argparse.ArgumentParser(\n description='process data with Spark, using Kafka as the transport')\n parser.add_argument('--in', dest='input_topic',\n help='the kafka topic to read data from')\n parser.add_argument('--out', dest='output_topic',\n help='the kafka topic to publish data to')\n parser.add_argument('--count', dest='count_topic',\n help='the kafka topic to publish count information to')\n parser.add_argument('--servers', help='the kafka brokers')\n args = parser.parse_args()\n\n processor = FilterStreamProcessor(\n input_topic = args.input_topic,\n output_topic = args.output_topic,\n count_topic = args.count_topic,\n servers = args.servers,\n duration = 3)\n\n processor.start_and_await_termination()","title":""},{"docid":"91564e1b41e078c442db8f4c4ab514d1","score":"0.53558266","text":"def run_full_pipeline():\n t0 = time.time()\n mol = input('Which line (HCN, HCO, CS, or CO)?\\n').lower()\n cut = input('Cut baselines for better signal (y/n)?\\n').lower()\n cut_baselines = True if cut == 'y' else False\n remake = input('Remake everything (y/n)?\\n')\n remake_all = True if remake.lower() == 'y' else False\n\n # Paths to the data\n jonas = '/Volumes/disks/jonas/'\n raw_data_path = jonas + 'raw_data/'\n final_data_path = jonas + 'modeling/data/' + mol + '/'\n name = mol\n if cut_baselines is True:\n name += '-short' + str(lines[mol]['baseline_cutoff'])\n\n # Establish a string for the log file to be made at the end\n log = 'Files created on ' + today + '\\n\\n'\n\n if remake_all is True:\n # This doesn't work yet.\n print(\"Remaking everything; emptied line dir and remaking.\")\n remove(final_data_path + '*')\n log += \"Full remake occured; all files are fresh.\\n\\n\"\n else:\n log += \"Some files already existed and so were not remade.\\n\"\n log += \"Careful for inconsistencies.\\n\\n\"\n\n print(\"Now processing data....\")\n casa_sequence(mol, raw_data_path,\n final_data_path + name, cut_baselines)\n\n print(\"Running varvis....\\n\\n\")\n if already_exists(final_data_path + name + '.uvf') is False:\n # Note that var_vis takes in mol_exportuvfits, returns mol.uvf\n var_vis(final_data_path + name)\n print(\"Finished varvis; converting uvf to vis now....\\n\\n\")\n\n # Note that this is different than lines[mol][chan0_freq] bc\n # it's dealing with the chopped vis set\n restfreq = lines[mol]['restfreq']\n f = fits.getheader(final_data_path + name + '.uvf')\n\n # chan0_freq = (f['CRVAL4'] - (f['CRPIX4']-1) * f['CDELT4']) * 1e-9\n # Using the same math as in lines 130-135\n # chan0_vel = c * (chan0_freq - restfreq)/restfreq\n data, header = fits.getdata(final_data_path + name + '.uvf', header=True)\n header['RESTFREQ'] = restfreq * 1e9\n fits.writeto(final_data_path + name + '.uvf', data, header, overwrite=True)\n if already_exists(final_data_path + name + '.vis') is False:\n sp.Popen(['fits',\n 'op=uvin',\n 'in={}.uvf'.format(name),\n # DONT PUT THIS BACK IN\n # Or if you do, flip the sign of chan0_vel to pos\n # 'velocity=lsr,{},1'.format(chan0_vel),\n 'out={}.vis'.format(name)],\n cwd=final_data_path).wait()\n\n\n print(\"Convolving data to get image, converting output to .fits\\n\\n\")\n if already_exists(final_data_path + name + '.cm') is False:\n icr(final_data_path + name, mol=mol)\n\n print(\"Deleting the junk process files...\\n\\n\")\n fpath = final_data_path + name\n files_to_remove = [fpath + '.bm', fpath + '_split.*',\n fpath + '.cl', fpath + '_cvel.*',\n fpath + '.mp', fpath + '_exportuvfits.*',\n 'casa*.log', '*.last']\n remove(files_to_remove)\n\n tf = time.time()\n t_total = (tf - t0)/60\n log += '\\nThis processing took ' + str(t_total) + ' minutes.'\n with open(final_data_path + 'file_log.txt', 'w') as f:\n f.write(log)\n print(\"All done! This processing took \" + str(t_total) + \" minutes.\")","title":""},{"docid":"d77b0b0b48255c32f7fe5f9e858ceedd","score":"0.5352431","text":"def main():\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=DESC,\n epilog=EPILOG\n )\n parser.add_argument('name', help='the streamer\\'s name')\n parser.add_argument('-c', '--config', metavar='configfile',\n help='Path of a JSON file containing a valid config.')\n args = parser.parse_args()\n if args.config is None: # process single file read from stdin\n streamers = [{'name': args.name, 'code': sys.stdin.read()}]\n else: # process one or more files from config\n with open(args.config) as cf:\n streamers = json.load(cf)\n\n flowcharts = (StreamerFlowchart(**s) for s in streamers)\n figures = (latex.make_figure(f) for f in flowcharts)\n document = latex.make_document(figures)\n print(document)","title":""},{"docid":"8c877989b50d1af9a4acf74cb854aeff","score":"0.53516805","text":"def execute(self):\n # input module\n spl_cmd = SplInput().get_input() # search repo=\"mytest\"\n\n # parse module\n # input -> pipe_cmd\n # output -> collection of opts\n antlr_parser = AntlrParser(\"antlr\", spl_cmd)\n antlr_parser.validate()\n opts = antlr_parser.parse()\n\n # schedule module\n scheduler = Scheduler(\"scheduler\")\n scheduler.schedule(opts)","title":""},{"docid":"5493f2c29ba09f717fe1fd888948430d","score":"0.5351004","text":"def execute(self):\n \n # Make sure that input data has the same length\n self.verify_input()\n \n for beam in self.eqbeams:\n a = Assembly()\n \n # Add a splined blade planform from the input data\n a.add('pf_splines', SplinedBladePlanform(True))\n a.driver.workflow.add('pf_splines')\n a.pf_splines.nC = 3\n a.pf_splines.pfIn = beam\n a.pf_splines.configure_splines()\n \n a.create_passthrough('pf_splines.blade_length')\n a.create_passthrough('pf_splines.span_ni')\n \n a.add('blade_surface', LoftedBladeSurface())\n a.driver.workflow.add('blade_surface')\n \n a.connect('pf_splines.pfOut', 'blade_surface.pf')\n a.connect('span_ni', 'blade_surface.span_ni')\n \n # load the planform file\n a.span_ni = self.span_ni\n \n b = a.blade_surface\n \n # distribute 200 points evenly along the airfoil sections\n b.chord_ni = 200\n for f in self.airfoils:\n b.base_airfoils.append(np.loadtxt(f))\n b.blend_var = np.array([0.25, 0.75])\n \n self.wingsurf.append(a)","title":""},{"docid":"c3d17dcb9c07dec2aa57940394815aeb","score":"0.53436625","text":"def run(self):\n\t\t# print(self.params)\n\t\ttransformed_data = self.convert_json()\n\t\tmsf.pickle_dump(transformed_data, self.file_out)","title":""},{"docid":"fdb3bb02ff6a09759128c269e77afe34","score":"0.5342858","text":"def _process_data(self):\n self.output_file.reset_data() # reset data in case processor instance is used multiple times\n\n while True:\n data = self._get_next_chunk()\n if data is None:\n break\n\n self.processing_index_start = data.index[0]\n self.processing_index_end = data.index[-1]\n\n data = self._apply_field_mapping(data) # Map inputs to outputs\n data = self._validate_and_prepare_data(data) # Validate data by handling any nulls + type-casting\n data = self._apply_field_transformations(data) # Process data transformations for all columns\n self._flush_data(data)\n\n self._write_errors_and_warnings()\n\n self.stage = self.OUTPUT_DATA\n self.output_file.generate_output() # append all data to output file or return processed data for python configs","title":""},{"docid":"d900ef664d8b8a2757c7d9632cfe8bc5","score":"0.5342623","text":"def update_pipeline(self) -> None:\n pass","title":""},{"docid":"95ee3f6727ec9940d7f86e1bb5832c9d","score":"0.5342543","text":"def __feature_sequence_extraction(self, inputdata):\n conv1 = self.__conv_stage(inputdata=inputdata, out_dims=64, name='conv1') # batch*16*50*64\n conv2 = self.__conv_stage(inputdata=conv1, out_dims=128, name='conv2') # batch*8*25*128\n conv3 = self.conv2d(inputdata=conv2, out_channel=256, kernel_size=3, stride=1, use_bias=False, name='conv3') # batch*8*25*256\n relu3 = self.relu(conv3) # batch*8*25*256\n conv4 = self.conv2d(inputdata=relu3, out_channel=256, kernel_size=3, stride=1, use_bias=False, name='conv4') # batch*8*25*256\n relu4 = self.relu(conv4) # batch*8*25*256\n max_pool4 = self.maxpooling(inputdata=relu4, kernel_size=[2, 1], stride=[2, 1], padding='VALID') # batch*4*25*256\n conv5 = self.conv2d(inputdata=max_pool4, out_channel=512, kernel_size=3, stride=1, use_bias=False, name='conv5') # batch*4*25*512\n relu5 = self.relu(conv5) # batch*4*25*512\n if self.phase.lower() == 'train':\n bn5 = self.layerbn(inputdata=relu5, is_training=True)\n else:\n bn5 = self.layerbn(inputdata=relu5, is_training=False) # batch*4*25*512\n conv6 = self.conv2d(inputdata=bn5, out_channel=512, kernel_size=3, stride=1, use_bias=False, name='conv6') # batch*4*25*512\n relu6 = self.relu(conv6) # batch*4*25*512\n if self.phase.lower() == 'train':\n bn6 = self.layerbn(inputdata=relu6, is_training=True)\n else:\n bn6 = self.layerbn(inputdata=relu6, is_training=False) # batch*4*25*512\n max_pool6 = self.maxpooling(inputdata=bn6, kernel_size=[2, 1], stride=[2, 1]) # batch*2*25*512\n conv7 = self.conv2d(inputdata=max_pool6, out_channel=512, kernel_size=2, stride=[2, 1], use_bias=False, name='conv7') # batch*1*25*512\n relu7 = self.relu(conv7) # batch*1*25*512\n return relu7","title":""},{"docid":"1bc18758b6a8fc51d3287a718bcdee23","score":"0.5339686","text":"def pipeline(root):\n tf_examples = (\n root\n | \"ReadFromJSON\" >> beam.io.textio.ReadFromText(FLAGS.input_json_filename)\n | \"ParseHotpotQAExamples\" >> beam.FlatMap(lib.HotpotQAExample.from_json)\n | \"Reshuffle\" >> beam.transforms.Reshuffle()\n | \"ConvertToTfExample\" >> beam.ParDo(\n lib.HotpotQAExampleToTfExamplesFn(\n FLAGS.global_seq_length, FLAGS.long_seq_length, FLAGS.is_training,\n FLAGS.answer_encoding_method, FLAGS.spm_model_file,\n FLAGS.vocab_file, FLAGS.debug)))\n\n _ = (\n tf_examples\n | \"CountTfExamples\" >> beam.combiners.Count.Globally()\n | beam.Map(lambda x: json.dumps(x, indent=2))\n | beam.io.WriteToText(\n FLAGS.output_tf_examples_stat_filename,\n shard_name_template=\"\", # To force unsharded output.\n ))\n _ = (\n tf_examples\n | \"WriteTFExample\" >> beam.io.WriteToTFRecord(\n FLAGS.output_tf_examples_file_path_prefix,\n coder=beam.coders.ProtoCoder(tf.train.Example),\n num_shards=FLAGS.num_shards))","title":""},{"docid":"54329bce75553ed12a2ace230ae2c821","score":"0.5334392","text":"def create_test_pipeline():\n pipeline_name = \"iris\"\n iris_root = \"iris_root\"\n serving_model_dir = os.path.join(iris_root, \"serving_model\", pipeline_name)\n tfx_root = \"tfx_root\"\n data_path = os.path.join(tfx_root, \"data_path\")\n pipeline_root = os.path.join(tfx_root, \"pipelines\", pipeline_name)\n\n example_gen = CsvExampleGen(input_base=data_path)\n\n statistics_gen = StatisticsGen(examples=example_gen.outputs[\"examples\"])\n\n my_importer = importer.Importer(\n source_uri=\"m/y/u/r/i\",\n properties={\n \"split_names\": \"['train', 'eval']\",\n },\n custom_properties={\n \"int_custom_property\": 42,\n \"str_custom_property\": \"42\",\n },\n artifact_type=standard_artifacts.Examples).with_id(\"my_importer\")\n another_statistics_gen = StatisticsGen(\n examples=my_importer.outputs[\"result\"]).with_id(\"another_statistics_gen\")\n\n schema_gen = SchemaGen(statistics=statistics_gen.outputs[\"statistics\"])\n\n example_validator = ExampleValidator(\n statistics=statistics_gen.outputs[\"statistics\"],\n schema=schema_gen.outputs[\"schema\"])\n\n trainer = Trainer(\n # Use RuntimeParameter as module_file to test out RuntimeParameter in\n # compiler.\n module_file=data_types.RuntimeParameter(\n name=\"module_file\",\n default=os.path.join(iris_root, \"iris_utils.py\"),\n ptype=str),\n custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),\n examples=example_gen.outputs[\"examples\"],\n schema=schema_gen.outputs[\"schema\"],\n train_args=trainer_pb2.TrainArgs(num_steps=2000),\n # Attaching `TrainerArgs` as platform config is not sensible practice,\n # but is only for testing purpose.\n eval_args=trainer_pb2.EvalArgs(num_steps=5)).with_platform_config(\n config=trainer_pb2.TrainArgs(num_steps=2000))\n\n model_resolver = resolver.Resolver(\n strategy_class=latest_blessed_model_strategy.LatestBlessedModelStrategy,\n model=Channel(\n type=standard_artifacts.Model, producer_component_id=trainer.id),\n model_blessing=Channel(type=standard_artifacts.ModelBlessing)).with_id(\n \"latest_blessed_model_resolver\")\n\n eval_config = tfma.EvalConfig(\n model_specs=[tfma.ModelSpec(signature_name=\"eval\")],\n slicing_specs=[tfma.SlicingSpec()],\n metrics_specs=[\n tfma.MetricsSpec(\n thresholds={\n \"sparse_categorical_accuracy\":\n tfma.MetricThreshold(\n value_threshold=tfma.GenericValueThreshold(\n lower_bound={\"value\": 0.6}),\n change_threshold=tfma.GenericChangeThreshold(\n direction=tfma.MetricDirection.HIGHER_IS_BETTER,\n absolute={\"value\": -1e-10}))\n })\n ])\n evaluator = Evaluator(\n examples=example_gen.outputs[\"examples\"],\n model=trainer.outputs[\"model\"],\n baseline_model=model_resolver.outputs[\"model\"],\n eval_config=eval_config)\n\n pusher = Pusher(\n model=trainer.outputs[\"model\"],\n model_blessing=evaluator.outputs[\"blessing\"],\n push_destination=pusher_pb2.PushDestination(\n filesystem=pusher_pb2.PushDestination.Filesystem(\n base_directory=serving_model_dir)))\n\n return pipeline.Pipeline(\n pipeline_name=pipeline_name,\n pipeline_root=pipeline_root,\n components=[\n example_gen,\n statistics_gen,\n another_statistics_gen,\n my_importer,\n schema_gen,\n example_validator,\n trainer,\n model_resolver,\n evaluator,\n pusher,\n ],\n enable_cache=True,\n beam_pipeline_args=[\"--my_testing_beam_pipeline_args=foo\"],\n # Attaching `TrainerArgs` as platform config is not sensible practice,\n # but is only for testing purpose.\n platform_config=trainer_pb2.TrainArgs(num_steps=2000),\n execution_mode=pipeline.ExecutionMode.SYNC)","title":""},{"docid":"7d4fb2430bbaf9ef98061575bdc5faae","score":"0.5311707","text":"def run(args):\n\n if tf.io.gfile.exists(args.embed_output_dir):\n print('Removing embedding output directory...')\n tf.io.gfile.rmtree(args.embed_output_dir)\n print('Creating empty output directory...')\n tf.io.gfile.makedirs(args.embed_output_dir)\n\n options = beam.options.pipeline_options.PipelineOptions(**vars(args))\n\n original_dim = hub.load(args.module_url)(['']).shape[1]\n\n random_projection_matrix = generate_random_projection_weights(\n original_dim, args.projected_dim, args.embed_output_dir)\n\n print('Starting the Beam pipeline...')\n with beam.Pipeline(runner=_RUNNER, options=options) as pipeline:\n _ = (\n pipeline\n | 'Read sentences from files' >>\n beam.io.ReadFromText(file_pattern=args.data_file_pattern)\n | 'Batch elements' >> util.BatchElements(\n min_batch_size=_BATCH_SIZE / 2, max_batch_size=_BATCH_SIZE)\n | 'Generate embeddings' >> beam.Map(\n generate_embeddings, args.module_url, random_projection_matrix)\n | 'Encode to tf example' >> beam.FlatMap(to_tf_example)\n | 'Write to TFRecords files' >> beam.io.WriteToTFRecord(\n file_path_prefix='{}/emb'.format(args.embed_output_dir),\n file_name_suffix='.tfrecords')\n )\n\n print('Beam pipeline completed.')","title":""},{"docid":"940ec5718136e888ba6ce41658a5d3e8","score":"0.5303702","text":"def forward(self, example):\n # voxels = example[\"voxels\"]\n dev_pillar_x_ = example['dev_pillar_x_']\n dev_pillar_y_ = example['dev_pillar_y_']\n dev_pillar_z_ = example['dev_pillar_z_']\n dev_pillar_i_ = example['dev_pillar_i_']\n dev_x_coors_for_sub_shaped_ = example['dev_x_coors_for_sub_shaped_']\n dev_y_coors_for_sub_shaped_ = example['dev_y_coors_for_sub_shaped_']\n dev_pillar_feature_mask_ = example['dev_pillar_feature_mask_']\n dev_num_points_per_pillar_ = example[\"dev_num_points_per_pillar_\"]\n coors = example[\"coordinates\"]\n batch_anchors = example[\"anchors\"]\n batch_size_dev = batch_anchors.shape[0]\n t = time.time()\n # features: [num_voxels, max_num_points_per_voxel, 7]\n # num_points: [num_voxels]\n # coors: [num_voxels, 4]\n voxel_features = self.voxel_feature_extractor(dev_pillar_x_, dev_pillar_y_, dev_pillar_z_, dev_pillar_i_,\n dev_num_points_per_pillar_,\n dev_x_coors_for_sub_shaped_, dev_y_coors_for_sub_shaped_,\n dev_pillar_feature_mask_)\n\n spatial_features = self.middle_feature_extractor(voxel_features, coors, batch_size_dev)\n \"\"\"\n if self._use_bev:\n preds_dict = self.rpn(spatial_features, example[\"bev_map\"])\n else:\n preds_dict = self.rpn(spatial_features)\n # preds_dict[\"voxel_features\"] = voxel_features\n # preds_dict[\"spatial_features\"] = spatial_features\n box_preds = preds_dict[\"box_preds\"]\n cls_preds = preds_dict[\"cls_preds\"]\n \"\"\"\n if self._use_bev:\n box_preds, cls_preds, dir_cls_preds = self.rpn(spatial_features, example[\"bev_map\"])\n else:\n box_preds, cls_preds, dir_cls_preds = self.rpn(spatial_features)\n # preds_dict[\"voxel_features\"] = voxel_features\n # preds_dict[\"spatial_features\"] = spatial_features\n preds_dict = {}\n preds_dict[\"box_preds\"] = box_preds\n preds_dict[\"cls_preds\"] = cls_preds\n preds_dict[\"dir_cls_preds\"] = dir_cls_preds\n\n self._total_forward_time += time.time() - t\n if self.training:\n labels = example['labels']\n reg_targets = example['reg_targets']\n\n cls_weights, reg_weights, cared = prepare_loss_weights(\n labels,\n pos_cls_weight=self._pos_cls_weight,\n neg_cls_weight=self._neg_cls_weight,\n loss_norm_type=self._loss_norm_type,\n dtype=dev_pillar_x_.dtype)\n cls_targets = labels * cared.type_as(labels)\n cls_targets = cls_targets.unsqueeze(-1)\n\n loc_loss, cls_loss = create_loss(\n self._loc_loss_ftor,\n self._cls_loss_ftor,\n box_preds=box_preds,\n cls_preds=cls_preds,\n cls_targets=cls_targets,\n cls_weights=cls_weights,\n reg_targets=reg_targets,\n reg_weights=reg_weights,\n num_class=self._num_class,\n encode_rad_error_by_sin=self._encode_rad_error_by_sin,\n encode_background_as_zeros=self._encode_background_as_zeros,\n box_code_size=self._box_coder.code_size,\n )\n loc_loss_reduced = loc_loss.sum() / batch_size_dev\n loc_loss_reduced *= self._loc_loss_weight\n cls_pos_loss, cls_neg_loss = _get_pos_neg_loss(cls_loss, labels)\n cls_pos_loss /= self._pos_cls_weight\n cls_neg_loss /= self._neg_cls_weight\n\n cls_loss_reduced = cls_loss.sum() / batch_size_dev\n cls_loss_reduced *= self._cls_loss_weight\n loss = loc_loss_reduced + cls_loss_reduced\n if self._use_direction_classifier:\n dir_targets = get_direction_target(example['anchors'],\n reg_targets)\n dir_logits = preds_dict[\"dir_cls_preds\"].view(\n batch_size_dev, -1, 2)\n weights = (labels > 0).type_as(dir_logits)\n weights /= torch.clamp(weights.sum(-1, keepdim=True), min=1.0)\n dir_loss = self._dir_loss_ftor(\n dir_logits, dir_targets, weights=weights)\n dir_loss = dir_loss.sum() / batch_size_dev\n loss += dir_loss * self._direction_loss_weight\n\n return {\n \"loss\": loss,\n \"cls_loss\": cls_loss,\n \"loc_loss\": loc_loss,\n \"cls_pos_loss\": cls_pos_loss,\n \"cls_neg_loss\": cls_neg_loss,\n \"cls_preds\": cls_preds,\n \"dir_loss_reduced\": dir_loss,\n \"cls_loss_reduced\": cls_loss_reduced,\n \"loc_loss_reduced\": loc_loss_reduced,\n \"cared\": cared,\n }\n else:\n return self.predict(example, preds_dict)","title":""},{"docid":"2ab728aaab3c4cb3dff792f9a8b2be07","score":"0.52977425","text":"def create_pipelines(self):\n self._run_init_steps(self.hparams.encode_pipeline)\n self._run_init_steps(self.hparams.decode_pipeline)\n self.encode_pipeline = DataPipeline(static_data_keys=self.INPUT_STATIC_KEYS, dynamic_items=self.hparams.encode_pipeline['steps'], output_keys=self.hparams.encode_pipeline['output_keys'])\n self.decode_pipeline = DataPipeline(static_data_keys=self.hparams.model_output_keys, dynamic_items=self.hparams.decode_pipeline['steps'], output_keys=self.OUTPUT_KEYS)","title":""},{"docid":"b67aee2e7ab8f384042c86f04ef15bcc","score":"0.529499","text":"def processes(self):","title":""},{"docid":"287aa99fe6d15980b19144ed11ce7add","score":"0.5293246","text":"def main():\n args = parse_arguments()\n\n res = resdk.Resolwe()\n\n parent = res.data.get(args.data_id)\n parent_user = parent.contributor['username']\n\n permissions = {\n 'users': {\n 'add': {\n parent_user: ['download', 'edit', 'owner', 'share', 'view']\n }\n }\n }\n\n demux_data = _tsv_to_dict(args.annotation_file)\n barcodes = demux_data.keys()\n\n adapters_temp = list(set([demux_data[barcode][\"3' adapter\"] for barcode in barcodes]))\n if len(adapters_temp) != 1:\n print('{\"proc.error\":\"Only one adapter type is allowed per demultiplexing job.\"}')\n exit(1)\n\n adapter = ''.join([adpr.split('_')[0] for adpr in adapters_temp[0].split(',')])\n\n demux_inputs = {\n 'reads': res.data.get(args.reads_id),\n 'annotation': res.data.get(args.annotation_id)\n }\n\n demux = res.run('icount-demultiplex-samples', input=demux_inputs, collections=parent.collections)\n\n # set demux permissions\n demux.api(demux.id).permissions.post(permissions)\n\n while demux.status not in ['ER', 'OK']:\n sleep(10)\n demux.update()\n\n if demux.status == 'ER':\n print('{\"proc.error\":\"Only one adapter type is allowed per demultiplexing job.\"}')\n exit(1)\n\n demux_reads = res.data.filter(parents=demux.id)\n\n for read in demux_reads:\n read.api(read.id).permissions.post(permissions)\n if read.sample:\n barcode = _get_barcode(read.name)\n if barcode in demux_data.keys():\n print(\"ANNOTATING SAMPLE:\", read.sample.name)\n read.descriptor_schema = 'reads-iclip'\n read.descriptor = create_reads_descriptor(demux_data, barcode)\n read.save()\n read.sample.descriptor_schema = 'sample-iclip'\n read.sample.descriptor = create_sample_descriptor(demux_data, barcode)\n read.sample.name = demux_data[barcode]['Renamed fastq on iCount']\n read.sample.slug = demux_data[barcode]['Renamed fastq on iCount']\n read.sample.save()\n read.sample.confirm_is_annotated()\n\n read.sample.api(read.sample.id).permissions.post(permissions)\n\n for pc in parent.collections:\n pc.add_samples(read.sample)\n\n print(\"RUNNING PRIMARY ANALYSIS FOR SAMPLE:\", read.sample.name)\n\n genome = res.data.filter(slug=GENOME_MAP[demux_data[barcode]['mapto']])\n segmentation = res.data.filter(slug=SEGMENTATION_MAP[demux_data[barcode]['mapto']])\n\n if len(genome) == 0:\n print('{\"proc.warning\":\"STAR index not found in the database.\"}')\n\n if len(segmentation) == 0:\n print('{\"proc.warning\":\"Segmentation map not found in the database.\"}')\n\n if len(genome) == 1 and len(segmentation) == 1:\n icount_inputs = {\n 'reads': read.id,\n 'index': res.data.get(slug=GENOME_MAP[demux_data[barcode]['mapto']]),\n 'segmentation': res.data.get(SEGMENTATION_MAP[demux_data[barcode]['mapto']])\n }\n\n icount_analysis = res.run('workflow-icount', input=icount_inputs, collections=parent.collections)\n icount_analysis.api(icount_analysis.id).permissions.post(permissions)\n\n for child in res.data.filter(parents=icount_analysis.id):\n child.api(child.id).permissions.post(permissions)\n\n else:\n print('{\"proc.warning\":\"Primary analysis not triggered.\"}')","title":""},{"docid":"2ce936ace36c40bba4f5db784850782e","score":"0.52930874","text":"def create_pipeline(\n pipeline_name: Text,\n pipeline_root: Text,\n query: Text,\n preprocessing_fn: Text,\n run_fn: Text,\n train_args: trainer_pb2.TrainArgs,\n eval_args: trainer_pb2.EvalArgs,\n model_serve_dir: Text,\n metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,\n beam_pipeline_args: Optional[Dict[Text, Any]] = None,\n ai_platform_training_args: Optional[Dict[Text, Text]] = None,\n ai_platform_serving_args: Optional[Dict[Text, Any]] = None,\n enable_cache: Optional[bool] = False,\n system_config: Optional[Dict[Text, Any]] = None,\n model_config: Optional[Dict[Text, Any]] = None,\n) -> pipeline.Pipeline:\n\n components = []\n # %%\n # ExampleGen: Load the graph data from bigquery\n query_str = load_query_string(\n query,\n field_dict={\n \"GOOGLE_CLOUD_PROJECT\": system_config[\"GOOGLE_CLOUD_PROJECT\"],\n \"DEBUG_SETTINGS\": model_config[\"query_debug_settings\"],\n },\n )\n\n output_config = example_gen_pb2.Output(\n split_config=example_gen_pb2.SplitConfig(\n splits=[ # Generate no splitting, as we need to load everything\n example_gen_pb2.SplitConfig.Split(name=\"train\", hash_buckets=1),\n ],\n )\n )\n\n example_gen = BigQueryExampleGen(query=query_str, output_config=output_config)\n components.append(example_gen)\n\n # %%\n # StatisticsGen:\n # Computes statistics over data for visualization and example validation.\n statistics_gen = StatisticsGen(examples=example_gen.outputs[\"examples\"])\n components.append(statistics_gen)\n\n # %%\n # SchemaGen:\n # Generates schema based on statistics files.\n schema_gen = SchemaGen(\n statistics=statistics_gen.outputs[\"statistics\"], infer_feature_shape=True\n )\n components.append(schema_gen)\n\n # %%\n # ExampleValidator:\n # Performs anomaly detection based on statistics and data schema.\n # example_validator = ExampleValidator( # pylint: disable=unused-variable\n # statistics=statistics_gen.outputs[\"statistics\"],\n # schema=schema_gen.outputs[\"schema\"],\n # )\n # components.append(example_validator)\n\n # %%\n # Transform:\n # Performs transformations and feature engineering in training and serving.\n transform = Transform(\n examples=example_gen.outputs[\"examples\"],\n schema=schema_gen.outputs[\"schema\"],\n preprocessing_fn=preprocessing_fn,\n )\n components.append(transform)\n\n # %%\n # Trainer\n # Uses user-provided Python function that implements a model using TF-Learn.\n trainer_args = {\n \"run_fn\": run_fn,\n \"transformed_examples\": transform.outputs[\"transformed_examples\"],\n \"schema\": schema_gen.outputs[\"schema\"],\n \"transform_graph\": transform.outputs[\"transform_graph\"],\n \"train_args\": train_args,\n \"eval_args\": eval_args,\n \"custom_config\": {\"model_config\": model_config, \"system_config\": system_config},\n \"custom_executor_spec\": executor_spec.ExecutorClassSpec(\n trainer_executor.GenericExecutor\n ),\n }\n\n if ai_platform_training_args is not None:\n trainer_args[\"custom_executor_spec\"] = executor_spec.ExecutorClassSpec(\n ai_platform_trainer_executor.GenericExecutor\n )\n\n trainer_args[\"custom_config\"][\n ai_platform_trainer_executor.TRAINING_ARGS_KEY\n ] = ai_platform_training_args\n\n # Lowercase and replace illegal characters in labels.\n # This is the job ID that will be shown in the platform\n # See https://cloud.google.com/compute/docs/naming-resources.\n trainer_args[\"custom_config\"][\n ai_platform_trainer_executor.JOB_ID_KEY\n ] = \"tfx_{}_{}\".format(\n re.sub(r\"[^a-z0-9\\_]\", \"_\", pipeline_name.lower()),\n datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\"),\n )[\n -63:\n ]\n logging.info(\"trainer arguments\")\n logging.info(trainer_args)\n\n trainer = Trainer(**trainer_args)\n components.append(trainer)\n\n # %%\n # ResolveNode:\n # Get the latest blessed model for model validation.\n model_resolver = ResolverNode(\n instance_name=\"latest_blessed_model_resolver\",\n resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,\n model=Channel(type=Model),\n model_blessing=Channel(type=ModelBlessing),\n )\n # components.append(model_resolver)\n\n # %%\n # Evaluator:\n # Uses TFMA to compute a evaluation statistics over features of a model and\n # perform quality validation of a candidate model (compared to a baseline).\n eval_config = tfma.EvalConfig(\n model_specs=[tfma.ModelSpec(label_key=\"big_tipper\")],\n slicing_specs=[tfma.SlicingSpec()],\n metrics_specs=[\n tfma.MetricsSpec(\n metrics=[\n tfma.MetricConfig(\n class_name=\"BinaryAccuracy\",\n threshold=tfma.MetricThreshold(\n value_threshold=tfma.GenericValueThreshold(\n lower_bound={\"value\": 0.1}\n ),\n change_threshold=tfma.GenericChangeThreshold(\n direction=tfma.MetricDirection.HIGHER_IS_BETTER,\n absolute={\"value\": -1e-10},\n ),\n ),\n )\n ]\n )\n ],\n )\n evaluator = Evaluator(\n examples=example_gen.outputs[\"examples\"],\n model=trainer.outputs[\"model\"],\n baseline_model=model_resolver.outputs[\"model\"],\n # Change threshold will be ignored if there is no baseline (first run).\n eval_config=eval_config,\n )\n # components.append(evaluator)\n\n # %%\n # Pusher:\n # Checks whether the model passed the validation steps and pushes the model\n # to a file destination if check passed.\n pusher_args = {\n \"model\": trainer.outputs[\"model\"],\n \"model_blessing\": evaluator.outputs[\"blessing\"],\n \"push_destination\": pusher_pb2.PushDestination(\n filesystem=pusher_pb2.PushDestination.Filesystem(\n base_directory=model_serve_dir,\n )\n ),\n }\n if ai_platform_serving_args is not None:\n pusher_args.update(\n {\n \"custom_executor_spec\": executor_spec.ExecutorClassSpec(\n ai_platform_pusher_executor.Executor\n ),\n \"custom_config\": {\n ai_platform_pusher_executor.SERVING_ARGS_KEY: ai_platform_serving_args\n },\n }\n )\n pusher = Pusher(**pusher_args) # pylint: disable=unused-variable\n # components.append(pusher)\n\n # %%\n return pipeline.Pipeline(\n pipeline_name=pipeline_name,\n pipeline_root=pipeline_root,\n components=components,\n enable_cache=enable_cache,\n metadata_connection_config=metadata_connection_config,\n beam_pipeline_args=( # parse beam pipeline into a list of commands\n [f\"--{key}={val}\" for key, val in beam_pipeline_args.items()]\n if beam_pipeline_args is not None\n else None\n ),\n )","title":""},{"docid":"575690199a098c8dca56052378a29d71","score":"0.5290334","text":"def build_model_pipeline():\n \n # Read data\n train_df = loader.read_data_set('train')\n \n # Validate basic structure of data set\n try:\n loader.validate_data_set(train_df)\n except:\n raise\n \n # Clean data set\n try:\n train_df = clean_data_set(train_df)\n except:\n raise\n \n # Impute missing values\n # Missing values are only allowed in Age and Fare columns\n try:\n train_df = impute_data_set(train_df)\n except:\n raise\n \n # ---------------------- MODELING ---------------------\n # This is the core part of this module, so for the most part it is not \n # embedded in functions\n \n # Remove ID column\n train_df.drop(['PassengerId'], axis=1, inplace=True)\n \n # Split into dependent and independet variables\n y = train_df['Survived'].values\n X = train_df.drop('Survived', axis=1).values\n \n # Create training and test sets\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=38)\n \n # Get optimal hyperparameters and set up model\n h_params = get_hyperparameters(X_train, y_train)\n logreg = LogisticRegression(C=h_params['C'], penalty=h_params['penalty'])\n \n # Fit the classifier to the training data\n logreg.fit(X_train, y_train)\n \n # Save model\n try: \n pickle.dump(logreg, open('model.pkl', 'wb'))\n loader.upload_file_to_s3('model.pkl', 'model')\n except:\n raise\n \n # Predict outcomes in the testing data\n y_pred = logreg.predict(X_test)\n \n # Save model parameters, confusion matrix and classification report\n try:\n with open('model_stats.txt', 'w') as text_file:\n with redirect_stdout(text_file):\n print('MODEL:')\n print(logreg)\n print('\\nCONFUSION MATRIX:')\n print(confusion_matrix(y_test, y_pred))\n print('\\nCLASSIFICATION REPORT:')\n print(classification_report(y_test, y_pred))\n loader.upload_file_to_s3('model_stats.txt', 'model')\n except: \n raise\n \n # Save ROC curve\n try: \n y_pred_prob = logreg.predict_proba(X_test)[:,1]\n fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n plt.plot([0, 1], [0, 1], 'k--')\n plt.plot(fpr, tpr)\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('ROC Curve')\n plt.savefig('model_roc.png')\n loader.upload_file_to_s3('model_roc.png', 'model')\n except:\n raise","title":""},{"docid":"9738aa304d4c3681d250cfcbae7ad0ca","score":"0.5287862","text":"def main():\n #time.sleep(7)\n # Grab command line args\n args = build_argparser().parse_args()\n\n #Perform inference on the input stream\n infer_on_stream(args)","title":""},{"docid":"587ba2ebe1a59380f6b51d0457f12ca1","score":"0.5285097","text":"def _setup_pipeline_cfg(self):","title":""},{"docid":"81ce60616afea86e96e6912d58ed0902","score":"0.5283076","text":"def main(argv=None):\n if not argv:\n argv = sys.argv[1:]\n\n parser = argparse.ArgumentParser(\n description=__doc__,\n formatter_class=argparse.RawDescriptionHelpFormatter)\n\n\n parser.add_argument('--step', choices=[1, 2, 3, 4, 5], default=1, type=int,\n help='Step to start from. 1=filter, 2=mapping, ' +\n '3=Map clean, 4=Merging, 5=Counting.')\n\n args = parser.parse_args(argv)\n\n run_pipeline(args.step)\n\n return 0","title":""}],"string":"[\n {\n \"docid\": \"229a4bdd48a9e744c6b38a6794e0beb5\",\n \"score\": \"0.7169673\",\n \"text\": \"def pipeline():\\n indexTargetsProcesses()\\n #Start the mapping operations\\n mappingProcesses()\\n extractingProcesses()\\n graphingProcesses()\\n formatOutput()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e842fa35aa495c31a96db571f3ecbc\",\n \"score\": \"0.64032096\",\n \"text\": \"def build_pipeline(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98b2bcb355df437910dd6a54a49ba67\",\n \"score\": \"0.6330437\",\n \"text\": \"def main(_args):\\n # download_packages() #TODO automate the package check\\n # drop_all_tables()\\n metrics = Metrics()\\n start_time = datetime.today().strftime('%Y-%m-%d-%H:%M')\\n print(\\\"START TIME = {}\\\".format(start_time))\\n processes = []\\n\\n # reads data from parser\\n data_raw = read_data(args)\\n # data_raw = read_data_from_blob(args)\\n data = data_raw\\n # data = aggregated_data(data_raw)\\n\\n # multiprocessing\\n for rf in data.parameters.region_filters:\\n if data.parameters.parallelize == 'Yes':\\n p = Process(target=run_pipeline, args=(rf, data, metrics, start_time))\\n p.start()\\n processes.append(p)\\n\\n else:\\n run_pipeline(rf, data, metrics, start_time)\\n\\n for p in processes:\\n p.join()\\n\\n end_time = datetime.now()\\n print(\\\"END TIME = {}\\\".format(end_time))\\n metrics.print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb58a844096ecea03f045c8038eb61c\",\n \"score\": \"0.6173508\",\n \"text\": \"def run_pipeline():\\n\\n def get_root_path():\\n \\\"\\\"\\\"\\n return the root path\\n \\\"\\\"\\\"\\n p = Path.cwd()\\n while p.name != 'backend':\\n p = p.parent\\n return p\\n\\n def download_file(url: str, path: str):\\n \\\"\\\"\\\"\\n download a file using urllib3\\n \\\"\\\"\\\"\\n nonlocal root_path\\n file_path = root_path / path\\n file_path.parent.mkdir(parents=True, exist_ok=True)\\n http = urllib3.PoolManager()\\n r = http.request('GET', url, preload_content=False)\\n with open(file_path, 'w+b') as out:\\n while True:\\n data = r.read()\\n if not data:\\n break\\n out.write(data)\\n logging.debug(f\\\"downloaded {file_path.name}\\\")\\n r.release_conn()\\n\\n def get_download_list():\\n \\\"\\\"\\\"\\n return the list of objects to download;\\n Alameda County parcel polygons and Oakland zoning polygons\\n are not included.\\n \\\"\\\"\\\"\\n return [\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/air_district/Oakland+Sources.xlsx\\\",\\n \\\"path\\\": \\\"data/raw/air_district/air_district_sources.xlsx\\\"},\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/air_district/Oakland+NOV.xlsx\\\",\\n \\\"path\\\": \\\"data/raw/air_district/air_district_novs.xlsx\\\"},\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/air_district/Oakland+Complaints.xlsx\\\",\\n \\\"path\\\": \\\"data/raw/air_district/air_district_complaints.xlsx\\\"},\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/assessor/IE670-10-01-19.TXT\\\",\\n \\\"path\\\": \\\"data/raw/assessor/assessor_ownership.TXT\\\"},\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/raw_inputs/business_licenses/PRR+%23+19-4745+all+Business+Accts+2019.xls\\\",\\n \\\"path\\\": \\\"data/raw/business_licenses/business_licenses.xls\\\"},\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/scraping_out/accela_zc_summary.csv\\\",\\n \\\"path\\\": \\\"data/pre-scraped/zoning_clearances.csv\\\"},\\n {\\\"url\\\": \\\"https://eo-lu-inventory.s3-us-west-1.amazonaws.com/scraping_out/hwts_summary.csv\\\",\\n \\\"path\\\": \\\"data/pre-scraped/hazardous_transfers.csv\\\"},\\n ]\\n\\n def upload_data():\\n \\\"\\\"\\\"\\n recursively upload all files in ./data/parcel_added folder\\n \\\"\\\"\\\"\\n nonlocal root_path\\n data_path = root_path / 'data/parcel_added'\\n for data in data_path.glob('**/*'):\\n if data.is_file():\\n relative_path = data.relative_to(data_path)\\n target_path = Path('latest_data') / relative_path\\n upload_file(str(data), 'eo-lu-inventory', str(target_path))\\n\\n def upload_file(file_name: str, bucket: str, object_name: str = None):\\n \\\"\\\"\\\"Upload a file to an S3 bucket\\n (copied from boto3 docs)\\n\\n :param file_name: File to upload\\n :param bucket: Bucket to upload to\\n :param object_name: S3 object name.\\n If not specified then file_name is used\\n :return: True if file was uploaded, else False\\n \\\"\\\"\\\"\\n\\n # If S3 object_name was not specified, use file_name\\n if object_name is None:\\n object_name = file_name\\n\\n # Upload the file\\n s3_client = boto3.client('s3')\\n try:\\n logging.debug(f\\\"uploading {file_name} to {object_name}\\\")\\n s3_client.upload_file(file_name, bucket, object_name)\\n except ClientError as e:\\n logging.error(e)\\n return False\\n return True\\n\\n logging.basicConfig(level=logging.DEBUG)\\n root_path = get_root_path()\\n # download_list = get_download_list()\\n # for file in download_list:\\n # download_file(file['url'], file['path'])\\n upload_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cea7c6ce6f47f3e3755689f995aaf2c\",\n \"score\": \"0.6123348\",\n \"text\": \"def run_pipeline(files, flat_field=True):\\n\\n # Create a name for image3 association and drizzled files\\n detector = fits.getheader(files[0])['DETECTOR'].lower()\\n fltr = fits.getheader(files[0])['FILTER'].lower()\\n pupil = fits.getheader(files[0])['PUPIL'].lower()\\n target = fits.getheader(files[0])['TARGNAME'].lower()\\n name = '{}_{}_{}_{}'.format(detector, fltr, pupil, target)\\n\\n # Run detector1\\n for f in files:\\n m = Detector1Pipeline()\\n #m.jump.override_readnoise = 'jwst_nircam_readnoise_0024_psub.fits'\\n m.refpix.odd_even_rows = False # only for MIRI\\n m.ipc.skip = True\\n m.persistence.skip = True\\n m.save_results = True\\n m.output_dir = os.getcwd()\\n m.run(f)\\n\\n # Run image2\\n files = [f.replace('_uncal.fits', '_rate.fits') for f in files]\\n for f in files:\\n m = Image2Pipeline()\\n if not flat_field:\\n m.flat_field.skip = True\\n m.save_results = True\\n m.output_dir = os.getcwd()\\n m.run(f)\\n\\n # Run image3\\n files = [f.replace('_rate.fits', '_cal.fits') for f in files]\\n asn = asn_from_list(files, rule=Asn_Lv3Image, product_name=name)\\n asn_file = '{}.json'.format(name)\\n with open(asn_file, 'w') as f:\\n f.write(asn.dump()[1])\\n m = Image3Pipeline()\\n m.save_results = True\\n m.output_dir = os.getcwd()\\n m.run(asn_file)\\n\\n return '{}_i2d.fits'.format(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ca5b8551edc0b3318a8581241c1d9a\",\n \"score\": \"0.6102607\",\n \"text\": \"def runPipeline(plConf, X):\\n pl = build3StagePipe(plConf)\\n pl.setX(X)\\n pl.runStages()\\n Y = pl.finalOut[\\\"Y\\\"]\\n return Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d745a51c7cd5150c0753af0a7bf4e4f\",\n \"score\": \"0.60049415\",\n \"text\": \"def run_pipeline(jail, rtdata_module, request):\\n rtdata = rtdata_module\\n\\n # Get the spec3 ASN and its members\\n rtdata.get_asn(\\\"miri/lrs/jw01530-o005_20221202t204827_spec3_00001_asn.json\\\")\\n root_file = \\\"jw01530-o005_t004_miri_p750l_\\\"\\n rtdata.get_truth(\\\"truth/test_miri_lrs_slit_spec3/jw01530-o005_t004_miri_p750l_s2d.fits\\\")\\n\\n args = [\\n \\\"calwebb_spec3\\\",\\n rtdata.input\\n ]\\n\\n rtdata.custom_wcs_mode = request.param\\n\\n if request.param != \\\"default_wcs\\\":\\n dm = datamodels.open(rtdata.truth)\\n af = asdf.AsdfFile({\\\"wcs\\\": dm.meta.wcs})\\n wcs_file = rtdata.truth[:-8] + 'wcs.asdf'\\n af.write_to(wcs_file)\\n args.append(f\\\"--steps.resample_spec.output_wcs={wcs_file}\\\")\\n\\n if request.param == \\\"user_wcs+shape\\\":\\n output_shape = ','.join(map(str, dm.data.shape[::-1]))\\n args.append(f\\\"--steps.resample_spec.output_shape={output_shape}\\\")\\n\\n elif request.param == \\\"user_wcs+shape1\\\":\\n output_shape = ','.join(map(str, (d + 1 for d in dm.data.shape[::-1])))\\n args.append(f\\\"--steps.resample_spec.output_shape={output_shape}\\\")\\n output_file = root_file + 'shape1.fits'\\n args.append(f\\\"--steps.resample_spec.output_file={output_file}\\\")\\n\\n # Run the calwebb_spec3 pipeline; save results from intermediate steps\\n Step.from_cmdline(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a5c9aed3739ce33c56220402451727\",\n \"score\": \"0.59742904\",\n \"text\": \"def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,\\r\\n metadata_path: Text) -> pipeline.Pipeline:\\r\\n\\r\\n # Brings data into the pipeline or otherwise joins/converts training data.\\r\\n example_gen = CsvExampleGen(input_base=data_root)\\r\\n\\r\\n # Computes statistics over data for visualization and example validation.\\r\\n statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])\\r\\n\\r\\n # inferes a schema\\r\\n schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)\\r\\n\\r\\n # modifies infered schema with use of udf `schema_fn` defined in module file\\r\\n schema_curation = component.SchemaCuration(schema=schema_gen.outputs['schema'],\\r\\n module_file=os.path.join('schemacomponent','example','module_file.py')\\r\\n\\r\\n\\r\\n return pipeline.Pipeline(\\r\\n pipeline_name=pipeline_name,\\r\\n pipeline_root=pipeline_root,\\r\\n components=[example_gen, statistics_gen, schema_gen, schema_curation],\\r\\n enable_cache=True,\\r\\n metadata_connection_config=metadata.sqlite_metadata_connection_config(\\r\\n metadata_path))\\r\\n\\r\\n\\r\\n# To run this pipeline from the python CLI:\\r\\n# $python taxi_pipeline_hello.py\\r\\nif __name__ == '__main__':\\r\\n absl.logging.set_verbosity(absl.logging.INFO)\\r\\n local_dag_runner.LocalDagRunner().run(\\r\\n _create_pipeline(\\r\\n pipeline_name=_pipeline_name,\\r\\n pipeline_root=_pipeline_root,\\r\\n data_root=_data_root,\\r\\n metadata_path=_metadata_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79672ebdb824b17395d930311fb2177d\",\n \"score\": \"0.59379274\",\n \"text\": \"def run(self):\\n logger.info(\\\"Running SFM pipeline.\\\")\\n\\n self.extract_cameras()\\n self.extract_features()\\n self.match_features()\\n\\n self.create_tracks()\\n\\n base_pair = self.generate_base_pair()\\n self.base_reconstruction(base_pair)\\n\\n # TODO: Get views from tracks.\\n views = []\\n\\n for view in views:\\n self.add_view(view)\\n\\n return self.export_as_ply()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a5d57c44a1891ad96d646b590df07b\",\n \"score\": \"0.59039646\",\n \"text\": \"def main(parsed_args: argparse.Namespace) -> None:\\n incremental = not parsed_args.full\\n\\n if parsed_args.scan_type == 'all':\\n selected_scan_types = list(beam_tables.ALL_SCAN_TYPES)\\n else:\\n selected_scan_types = [parsed_args.scan_type]\\n\\n firehook_runner = get_firehook_beam_pipeline_runner()\\n\\n if parsed_args.env == 'user':\\n username = pwd.getpwuid(os.getuid()).pw_name\\n run_user_pipelines(firehook_runner, username, selected_scan_types,\\n incremental)\\n elif parsed_args.env == 'prod':\\n run_parallel_pipelines(firehook_runner, beam_tables.PROD_DATASET_NAME,\\n selected_scan_types, incremental)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6e1b29fbc7d5ee713a961ba502f7863\",\n \"score\": \"0.59008193\",\n \"text\": \"def run_pipeline(step):\\n bxc_list = []\\n cxb_list = []\\n for i in os.listdir('BxC'):\\n if os.path.isdir(os.path.join('BxC', i)):\\n if i.isdigit or i[2:].isdigit:\\n if i == 'mat' or i == 'pat' or i == 'genome':\\n continue\\n bxc_list.append(os.path.join('BxC', i))\\n for i in os.listdir('CxB'):\\n if os.path.isdir(os.path.join('CxB', i)):\\n if i.isdigit or i[2:].isdigit:\\n if i == 'mat' or i == 'pat' or i == 'genome':\\n continue\\n cxb_list.append(os.path.join('CxB', i))\\n bxc = {}\\n cxb = {}\\n # Make fastas\\n if step == 1:\\n print(\\\"Submitting FastQ Filter\\\")\\n for readdir in bxc_list:\\n bxc[readdir] = {'filter':\\n filter_fastqs(readdir)}\\n for readdir in cxb_list:\\n cxb[readdir] = {'filter':\\n filter_fastqs(readdir)}\\n step += 1\\n # Run bowtie\\n if step == 2:\\n print(\\\"Submitting mapping jobs\\\")\\n for readdir in bxc_list:\\n # bxc[readdir]['bowtie'] = run_bowtie(readdir,\\n # bxc[readdir]['filter'])\\n try:\\n bxc[readdir]['star'] = run_star(readdir,\\n bxc[readdir]['filter'])\\n except KeyError:\\n bxc[readdir] = {}\\n bxc[readdir]['star'] = run_star(readdir,\\n None)\\n for readdir in cxb_list:\\n # cxb[readdir]['bowtie'] = run_bowtie(readdir,\\n # cxb[readdir]['filter'])\\n try:\\n cxb[readdir]['star'] = run_star(readdir,\\n cxb[readdir]['filter'])\\n except KeyError:\\n cxb[readdir] = {}\\n cxb[readdir]['star'] = run_star(readdir,\\n None)\\n step += 1\\n if step == 3:\\n print(\\\"Submitting cleaning jobs\\\")\\n for readdir in bxc_list:\\n # bxc[readdir]['samtools'] = clean_bowtie(readdir,\\n # bxc[readdir]['bowtie'])\\n try:\\n bxc[readdir]['clean'] = clean_star(readdir,\\n bxc[readdir]['star'])\\n except KeyError:\\n bxc[readdir] = {}\\n bxc[readdir]['clean'] = clean_star(readdir, None)\\n for readdir in cxb_list:\\n # cxb[readdir]['samtools'] = clean_bowtie(readdir,\\n # cxb[readdir]['bowtie'])\\n try:\\n cxb[readdir]['clean'] = clean_star(readdir,\\n cxb[readdir]['star'])\\n except KeyError:\\n cxb[readdir] = {}\\n cxb[readdir]['clean'] = clean_star(readdir, None)\\n step += 1\\n # Merge bowtie\\n if step == 4:\\n print(\\\"Submitting AlleleSeq bowtie mat/pat merge\\\")\\n for readdir in bxc_list:\\n try:\\n bxc[readdir]['merge'] = merge(readdir,\\n bxc[readdir]['clean'], 'BxC')\\n except KeyError:\\n bxc[readdir] = {}\\n bxc[readdir]['merge'] = merge(readdir, None, 'BxC')\\n for readdir in cxb_list:\\n try:\\n cxb[readdir]['merge'] = merge(readdir,\\n cxb[readdir]['clean'], 'CxB')\\n except KeyError:\\n cxb[readdir] = {}\\n cxb[readdir]['merge'] = merge(readdir, None, 'CxB')\\n step += 1\\n # Count reads\\n if step == 5:\\n print(\\\"Submitting AlleleSeq allele counting script\\\")\\n for readdir in bxc_list:\\n try:\\n bxc[readdir]['count'] = count(readdir,\\n bxc[readdir]['merge'], 'BxC')\\n except KeyError:\\n bxc[readdir] = {}\\n bxc[readdir]['count'] = count(readdir, None, 'BxC')\\n for readdir in cxb_list:\\n try:\\n cxb[readdir]['count'] = count(readdir,\\n cxb[readdir]['merge'], 'CxB')\\n except KeyError:\\n cxb[readdir] = {}\\n cxb[readdir]['count'] = count(readdir, None, 'CxB')\\n\\n print(\\\"Job mapping:\\\")\\n with open('alleleseq_jobs.pickle', 'wb') as outf:\\n pickle.dump([bxc, cxb], outf)\\n print(bxc)\\n print(cxb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71847dc204e3af8c41cae2c42f4395ae\",\n \"score\": \"0.58906186\",\n \"text\": \"def main():\\n\\n # parse arguments\\n parser = argparse.ArgumentParser(\\n description='''A pipeline to analyze shotgun metagenomics NGS Illumina data.''',\\n epilog='''Please read the documentation for more details.''')\\n parser.add_argument('-c', '--config', nargs=1, type=file, required=True,\\n help='configuration file for the pipeline', metavar='shotgun_config.cfg',\\n dest='config')\\n parser.add_argument('-q', '--fastqc', action='store_true',\\n help='run fastqc to check the sequence quality', dest='q')\\n parser.add_argument('-t', '--trimmomatic', action='store', nargs='*',\\n help='run trimmomatic to clean the sequences', choices=[\\\"n\\\", \\\"r\\\", \\\"f\\\"], dest='t')\\n parser.add_argument('-r', '--randomlysample', action='store_true',\\n help='run randomlysample to rarefy the reads number to the lowest among the samples', dest='r')\\n parser.add_argument('-a', '--assembly', action='store', nargs='*',\\n help='run assembly tools to assemble the reads to contigs', choices=[\\\"n\\\", \\\"f\\\", \\\"r\\\", \\\"rf\\\", \\\"soap\\\",\\n \\\"idba\\\", \\\"ray\\\"], dest='a')\\n parser.add_argument('-p', '--ppsp', action='store', nargs='*', help='run ppsp to perform taxonomic binning',\\n choices=[\\\"m\\\", \\\"n\\\", \\\"f\\\"], dest='p')\\n parser.add_argument('-x', '--taxator', action='store_true', help='run taxator-tk to perform taxaonomic binning',\\n dest='x')\\n parser.add_argument('-f', '--annotation', action='store', help='run functional annotation',\\n choices=[None, \\\"h\\\"], dest='f', nargs='*')\\n\\n\\n args = parser.parse_args()\\n \\n\\n # read configuration file\\n config = Config(args.config[0], 'SMP')\\n\\n\\n # pipeline directory\\n pipelineDir = os.path.normpath(config.get('pipelineDir'))\\n if not os.path.isdir(pipelineDir):\\n print(\\\"Pipeline directory doesn't exist:\\\", pipelineDir)\\n return\\n\\n # create the following dirs in the pipeline directory if they do not exist\\n workingDir = os.path.join(pipelineDir, 'working')\\n outputDir = os.path.join(pipelineDir, 'output')\\n profileDir = os.path.join(pipelineDir, 'profile')\\n fastqcDir = os.path.join(outputDir, 'fastqc')\\n trimmomaticDir = os.path.join(outputDir, 'trimmomatic')\\n trimmomaticProfileDir = os.path.join(profileDir, 'trimmomatic')\\n randomlysampleDir = os.path.join(outputDir, 'randomlysample')\\n #randomlysampleProfileDir = os.path.join(profileDir, 'randomlysample')\\n randomlysampleWorkingDir = os.path.join(workingDir, 'randomlysample')\\n filterreadsDir = os.path.join(outputDir, 'filterreads')\\n filterreadsWorkingDir = os.path.join(workingDir, 'filterreads')\\n forAssemblyWorkingDir = os.path.join(workingDir, 'forAssembly')\\n soapdenovoDir = os.path.join(outputDir, 'soapdenovo')\\n soapdenovoWorkingDir = os.path.join(forAssemblyWorkingDir, 'soapdenovo')\\n idbaDir = os.path.join(outputDir,'IDBA-UD')\\n idbaWorkingDir = os.path.join(forAssemblyWorkingDir,'IDBA-UD')\\n rayDir = os.path.join(outputDir,'Ray')\\n rayWorkingDir = os.path.join(forAssemblyWorkingDir, 'Ray')\\n assemblyDir = os.path.join(outputDir, 'assembly')\\n ppspDir = os.path.join(outputDir, 'PPSP')\\n #taxatorDir = os.path.join(outputDir, 'taxator')\\n annotationDir = os.path.join(outputDir, 'annotation')\\n orfDir = os.path.join(annotationDir, 'ORFs')\\n hmmDir = os.path.join(annotationDir, 'HMM')\\n\\n\\n\\n dirArray = [workingDir, outputDir, fastqcDir, trimmomaticDir, profileDir, trimmomaticProfileDir,\\n randomlysampleDir, randomlysampleWorkingDir, filterreadsDir, filterreadsWorkingDir,\\n forAssemblyWorkingDir, soapdenovoDir, idbaDir, rayDir, assemblyDir, ppspDir, annotationDir, orfDir, hmmDir]\\n\\n\\n for dirPath in dirArray:\\n if not os.path.isdir(dirPath):\\n try:\\n os.mkdir(dirPath)\\n except OSError:\\n print (\\\"Can't create the directory\\\", dirPath)\\n return\\n\\n # set up log file\\n logDir = os.path.join(outputDir, 'log')\\n if not os.path.exists(logDir):\\n try:\\n os.mkdir(logDir)\\n except OSError:\\n print(\\\"Can't create log directory:\\\", logDir)\\n return\\n\\n # read multiple input raw fastqc reads\\n #def readInputForward():\\n # count = 1\\n # list1 = []\\n #list2 = []\\n #for i in range(10000):\\n # inputFastqFileForward = config.get('inputFastqFileForward'+str(count))\\n #inputFastqFileReverse = config.get('inputFastqFileReverse'+str(count))\\n #if inputFastqFileForward is not None:\\n # count += 1\\n # list1.append(inputFastqFileForward)\\n #list2.append(inputFastqFileReverse)\\n #listf = list1\\n #listr = list2\\n #if listf != '':\\n # return listf\\n\\n #Judge if you need to split the original File, only work for fastq File\\n #inputFileJudge = config.get('pairedOrNot')\\n #if inputFileJudge == 'True':\\n # SplitPairedFile(os.path.normpath(config.get('inputPiredFile')), os.path.dirname)\\n\\n\\n\\n count = 1\\n list1 = []\\n list2 = []\\n for i in range(10000):\\n inputFastqFileForward = config.get('inputFastqFileForward'+str(count))\\n inputFastqFileReverse = config.get('inputFastqFileReverse'+str(count))\\n if inputFastqFileForward is None:\\n break\\n else:\\n list1.append(inputFastqFileForward)\\n list2.append(inputFastqFileReverse)\\n count += 1\\n listf = list1\\n listr = list2\\n\\n # run fastqc\\n if args.q is not None:\\n fastqcInstallDir = os.path.normpath(config.get('fastqcInstallDir'))\\n\\n for i, j in zip(listf, listr):\\n if j is not None:\\n cmd = str(os.path.join(fastqcInstallDir, 'fastqc') + ' -o ' + fastqcDir + ' -f fastq ' +\\n os.path.normpath(i) + ' ' + os.path.normpath(j))\\n else: #for single reads\\n cmd = str(os.path.join(fastqcInstallDir, 'fastqc') + ' -o ' + fastqcDir + ' -f fastq ' +\\n os.path.normpath(i))\\n proc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=fastqcDir)\\n proc.wait() #check proc.wait\\n\\n\\n\\n # run trimmomatic-0.32 to trim the adapters and clean the sequences according to quality score and the seq length.\\n # The program first cut the adapters then cleans the seqs with quality scores and in the end discard the seqs with\\n # length shorter than the minimum length.\\n # Yao's ubuntu's version, needs to change to server version.\\n if args.t is not None:\\n trimmomaticInstallDir = os.path.normpath(config.get('trimmomaticInstallDir'))\\n #lista=[]\\n #listb=[]\\n for i, j in zip(listf, listr):\\n cmd = str('java -jar' + ' ' + os.path.join(trimmomaticInstallDir, 'trimmomatic-0.32.jar') + ' '\\n + config.get('seqType') + ' -phred' + config.get('phred') + ' -trimlog ' +\\n os.path.join(logDir, os.path.basename(i).split('.')[0]+'trimmomatic.log')\\n + ' ' + os.path.normpath(i) + ' ' + os.path.normpath(j) + ' ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]\\n + '_paired.fq') + ' ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0] + '_unpaired.fq') + ' ' + os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]\\n + '_paired.fq') + ' ' + os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0] + '_unpaired.fq') + ' ' +'ILLUMINACLIP:'\\n + trimmomaticProfileDir + '/' + config.get('adapter') + ':' + config.get('seedMismatches') + ':'\\n + config.get('palindromeClipThreshold') + ':' + config.get('simpleClipThreshold') + ' '\\n + 'LEADING:' + config.get('leading') + ' ' + 'TRAILING:' + config.get('trailing') + ' '\\n + 'SLIDINGWINDOW:' + config.get('windowSize') + ':' + config.get('requiredQuality') + ' '\\n + 'MINLEN:' + config.get('minLength'))\\n #print cmd\\n proc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=trimmomaticDir)\\n proc.wait()\\n\\n # unify the sequences ids pattern (there are old and new versions of seq ids, after this step, the id pattern will be \\\"A /1\\\" or \\\"A 1:N:0:GA...\\\" )\\n seqidSuffix((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_temp_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_temp_paired.fq')))\\n\\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\\n\\n if 'n' in args.t:\\n fastqTofasta((os.path.join(trimmomaticDir, (os.path.basename(i).split('.')[0] + '_paired.fq'))), 1,\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward.fa')))\\n fastqTofasta((os.path.join(trimmomaticDir, (os.path.basename(j).split('.')[0] + '_paired.fq'))), 2,\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse.fa')))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_noN.fa')), config.get('removeNlen'))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_noN.fa')), config.get('removeNlen'))\\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_consensus.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_consensus.fa')))\\n\\n #print args.t\\n #First run RandomlySample then run FilterReads-P(MPI-version available) -\\n # yao's ubuntu's version, needs to change to server version.\\n elif 'r' in args.t and ('f' in args.t):\\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\\n\\n randomlysampleInstallDir = os.path.normpath(config.get('randomlysampleInstallDir'))\\n filterreadsInstallDir = os.path.normpath(config.get('filterreadsInstallDir'))\\n cmd2 = str(os.path.join(randomlysampleInstallDir, 'RandomlySample') + ' --ignore-quality=1 --by-pair=1 --num-samples=' +\\n config.get('randomlySampleNum-samples') + ' --format-output=1' + ' --fastq-base-quality=33 ' + ' --temp-dir=' +\\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp1') + ' --keep-temp-dir=' +\\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp2') +\\n ' --input-file ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq') +\\n ' --output-file ' + os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_random.fa'))\\n proc2 = subprocess.Popen(cmd2, shell=True, bufsize=-1, cwd=randomlysampleDir)\\n proc2.wait()\\n print \\\"RandomlySample return code:\\\", proc2.returncode\\n if proc2.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (proc2.returncode, cmd2))\\n print \\\"Filtering reads based on kmer counting is going to start.\\\"\\n cmd3 = str('export LD_LIBRARY_PATH=/home/yao/Softwares/openmpi-1.6.5/lib:$LD_LIBRARY_PATH;mpirun -np ' + config.get('numberOfProcess_mpi') + ' ' + os.path.join(filterreadsInstallDir, 'FilterReads-P')\\n + ' --histogram-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_histogram')\\n + ' --size-history-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_sizeHistory')\\n + ' --ignore-quality=1 --format-output=1 --keep-read-comment=1' + ' --kmer-size=' + config.get('kmerSize_filterReads')\\n + ' --min-depth=' + config.get('minDepth_filterReads') + ' --separate-output=0 --min-passing-in-pair=2'\\n + ' --min-read-length=' + config.get('minReadLen_filterReads') + ' --max-kmer-output-depth=-1 '\\n + ' --skip-artifact-filter=1 --dedup-single=1 --dedup-edit-distance=0' + ' --input-file '\\n + os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_random.fa')\\n + ' --output-file ' + os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_random_filter.fa')\\n + ' --log-file ' + os.path.join(logDir, os.path.basename(i).split('.')[0]+'_filterReads_log'))\\n proc3 = subprocess.Popen(cmd3, shell=True, bufsize=-1, cwd=filterreadsDir)\\n proc3.wait()\\n print \\\"Filterreads return code:\\\", proc3.returncode\\n if proc3.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (proc3.returncode, cmd3))\\n #\\n splitPairedReads((os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_random_filter.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_random_filter_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_random_filter_reverse.fa')))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward_noN.fa')), config.get('removeNlen'))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_reverse.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_filter_reverse_noN.fa')), config.get('removeNlen'))\\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_filter_reverse_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_random_filter_forward_consensus.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_random_filter_reverse_consensus.fa')))\\n\\n\\n\\n # Only run RandomlySample (without FilterReads-P)\\n elif 'r' in args.t:\\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\\n\\n randomlysampleInstallDir = os.path.normpath(config.get('randomlysampleInstallDir'))\\n cmd2 = str(os.path.join(randomlysampleInstallDir, 'RandomlySample') + ' --ignore-quality=1 --by-pair=1 --num-samples=' +\\n config.get('randomlySampleNum-samples') + ' --format-output=1' + ' --fastq-base-quality=33 ' + ' --temp-dir=' +\\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp1') + ' --keep-temp-dir=' +\\n os.path.join(randomlysampleWorkingDir, 'randomlysampletmp2') +\\n ' --input-file ' + os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq') +\\n ' --output-file ' + os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_onlyRandom.fa'))\\n proc2 = subprocess.Popen(cmd2, shell=True, bufsize=-1, cwd=randomlysampleDir)\\n proc2.wait()\\n print \\\"RandomlySample return code:\\\", proc2.returncode\\n if proc2.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (proc2.returncode, cmd2))\\n print \\\"You only rarefied the reads and you did not want to filter the reads, right?\\\"\\n\\n splitPairedReads((os.path.join(randomlysampleDir, os.path.basename(i).split('.')[0]+'_combined_onlyRandom.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_onlyRandom_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_OnlyRandom_reverse.fa')))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_noN.fa')), config.get('removeNlen'))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse_noN.fa')), config.get('removeNlen'))\\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_consensus.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_reverse_consensus.fa')))\\n\\n #Only run FilterReads-P(MPI-version available)--yao's ubuntu's version, needs to change to server version.\\n elif 'f' in args.t:\\n # combine separated forward and reverse files to one single fastaq file (/1,/2,/1,/2).\\n combinePairedEndFileFastq((os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(j).split('.')[0]+'_paired.fq')),\\n (os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq')))\\n\\n filterreadsInstallDir = os.path.normpath(config.get('filterreadsInstallDir'))\\n #cmd3 = str('export LD_LIBRARY_PATH=/home/yao/Softwares/openmpi-1.6.5/lib:$LD_LIBRARY_PATH;mpirun -np '\\n cmd3 = str('mpirun -np '\\n + config.get('numberOfProcess_mpi') + ' ' + os.path.join(filterreadsInstallDir, 'FilterReads-P')\\n + ' --histogram-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_onlyFiltering_histogram')\\n + ' --size-history-file ' + os.path.join(filterreadsWorkingDir, os.path.basename(i).split('.')[0]+'_onlyFiltering_sizeHistory')\\n + ' --ignore-quality=1 --format-output=1 --keep-read-comment=1' + ' --kmer-size=' + config.get('kmerSize_filterReads')\\n + ' --min-depth=' + config.get('minDepth_filterReads') + ' --separate-output=0 --min-passing-in-pair=2'\\n + ' --min-read-length=' + config.get('minReadLen_filterReads') + ' --max-kmer-output-depth=-1 '\\n + ' --skip-artifact-filter=1 --dedup-single=1 --dedup-edit-distance=0' + ' --input-file '\\n + os.path.join(os.path.join(trimmomaticDir, os.path.basename(i).split('.')[0]+'_combined.fq'))\\n + ' --output-file ' + os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_onlyFilter.fa')\\n + ' --log-file ' + os.path.join(logDir, os.path.basename(i).split('.')[0]+'_onlyFilterReadslog'))\\n proc3 = subprocess.Popen(cmd3, shell=True, bufsize=-1, cwd=filterreadsDir)\\n proc3.wait()\\n print \\\"Filterreads return code:\\\", proc3.returncode\\n if proc3.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (proc3.returncode, cmd3))\\n print \\\"You only filtered the reads, you did not want to rarefy the reads, right?\\\"\\n\\n splitPairedReads((os.path.join(filterreadsDir, os.path.basename(i).split('.')[0]+'_combined_onlyFilter.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_onlyFilter_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0]+'_OnlyFilter_reverse.fa')))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_noN.fa')), config.get('removeNlen'))\\n removeN((os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse_noN.fa')), config.get('removeNlen'))\\n getConsensusSeqs((os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse_noN.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_consensus.fa')),\\n (os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyFilter_reverse_consensus.fa')))\\n\\n #lista.append(os.path.basename(i).split('.')[0] + '_paired.fq ')\\n #listb.append(os.path.basename(j).split('.')[0] + '_paired.fq ')\\n #print lista, listb\\n\\n #sys.stdout.write(cmd3)\\n\\n\\n fileHolder = FileHolder(outputTrimForward=[], outputTrimReverse=[], outputRandomForward=[], outputRandomReverse=[],\\n outputFilterForward=[], outputFilterReverse=[], outputRandomFilterForward=[],\\n outputRandomFilterReverse=[])\\n\\n if args.a:\\n if \\\"soap\\\" in args.a:\\n def soapAssembly(file1,file2):\\n counter = 1\\n for i in range(1000):\\n readsNumberPerSample = config.get('sample'+str(counter))\\n\\n if readsNumberPerSample is not None:\\n soapdenovoPerSampleDir = os.path.join(soapdenovoDir, ('sample'+str(counter)))\\n if not os.path.exists(soapdenovoPerSampleDir):\\n try:\\n os.mkdir(soapdenovoPerSampleDir)\\n except OSError:\\n print(\\\"Can't create soapdenovoPerSample directory:\\\", soapdenovoPerSampleDir)\\n soapdenovoPerSampleWorkingDir = os.path.join(forAssemblyWorkingDir, ('soap_'+'sample'+str(counter)))\\n if not os.path.exists(soapdenovoPerSampleWorkingDir):\\n try:\\n os.mkdir(soapdenovoPerSampleWorkingDir)\\n except OSError:\\n print(\\\"Can't create soapdenovoPerSample directory:\\\", soapdenovoPerSampleWorkingDir)\\n\\n soapConfigFile = open(os.path.join(soapdenovoPerSampleDir, ('sample'+str(counter) + '.config')), 'w')\\n soapConfigFile.write('max_rd_len=' + config.get('sample'+str(counter)+'_' + 'maxReadLen') + '\\\\n'\\n + '[LIB]' + '\\\\n' + '#average insert size'\\n + '\\\\n' + 'avg_ins=' + config.get('sample'+str(counter)+'_'+'averageInsertSize')\\n + '\\\\n' +'#if sequence needs to be reversed'\\n + '\\\\n' + 'reverse_seq=' + config.get('sample'+str(counter)+'_' + 'reverseSeq')\\n + '\\\\n' + '#in which part(s) the reads are used'\\n + '\\\\n' + 'asm_flags=' + config.get('sample'+str(counter)+'_' + 'asmFlag')\\n + '\\\\n' + '#paired data files' + '\\\\n')\\n\\n for k, l in zip(file1[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2],\\n (file2[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2])):\\n\\n shutil.copy(os.path.join(forAssemblyWorkingDir, k), soapdenovoPerSampleWorkingDir)\\n shutil.copy(os.path.join(forAssemblyWorkingDir, l), soapdenovoPerSampleWorkingDir)\\n #print 'p=',k\\n #print 'p=',l\\n soapConfigFile.write('p='+k+'\\\\n'+'p='+l+'\\\\n')\\n soapConfigFile.close()\\n\\n\\n soapdenovoInstallDir = os.path.normpath(config.get('soapdenovoInstallDir'))\\n runSoapdenovo = str('export PATH=/net/programs/Debian-6.0.3-x86_64/soapdenovo-2.04:$PATH'+';' + ' '\\n +os.path.join(soapdenovoInstallDir, config.get('soapdenovoMethod')) + ' all -s '\\n + os.path.join(os.path.normpath(soapdenovoPerSampleDir), 'sample'+str(counter) + '.config')\\n + ' -K '+config.get('assemblyKmerSize') + ' -R' + ' -p ' + config.get('n_cpu') + ' -o '\\n + 'soapdenovo_sample' + str(counter) + '_' + ' ' + '1>'+'sample'+str(counter)+'_assembly.log' + ' '\\n + '2>'+'sample'+str(counter) + '_assembly.error')\\n #print runSoapdenovo\\n procRunSoapdenovo = subprocess.Popen(runSoapdenovo, shell=True, bufsize=-1, cwd=soapdenovoPerSampleDir)\\n procRunSoapdenovo.wait()\\n print \\\"Soapdenovo return code:\\\", procRunSoapdenovo.returncode\\n if procRunSoapdenovo.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (procRunSoapdenovo.returncode, runSoapdenovo))\\n print \\\"Soapdenovo is finished.\\\"\\n shutil.copy(os.path.join(soapdenovoPerSampleDir, 'soapdenovo_sample' + str(counter) + '_'+'.contig'),\\n os.path.join(assemblyDir, 'soapdenovo_sample'+str(counter)+'.contig.fa'))\\n counter += 1\\n\\n if \\\"idba\\\" in args.a:\\n def idbaAssembly(file1, file2):\\n counter = 1\\n for i in range(1000):\\n readsNumberPerSample = config.get('sample'+str(counter))\\n\\n if readsNumberPerSample is not None:\\n idbaPerSampleDir = os.path.join(idbaDir, ('sample'+str(counter)))\\n if not os.path.exists(idbaPerSampleDir):\\n try:\\n os.mkdir(idbaPerSampleDir)\\n except OSError:\\n print(\\\"Can't create idbaPerSample directory:\\\", idbaPerSampleDir)\\n if readsNumberPerSample is not None:\\n idbaPerSampleWorkingDir = os.path.join(forAssemblyWorkingDir, ('idba_'+'sample'+str(counter)))\\n if not os.path.exists(idbaPerSampleWorkingDir):\\n try:\\n os.mkdir(idbaPerSampleWorkingDir)\\n except OSError:\\n print(\\\"Can't create idbaPerSample directory:\\\", idbaPerSampleWorkingDir)\\n\\n outputFile1 = open(os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa'),'w')\\n outputFile2 = open(os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa'),'w')\\n\\n for k, l in zip((file1[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2]),\\n (file2[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2])):\\n\\n forwardDir = os.path.join(idbaPerSampleWorkingDir, 'forward')\\n reverseDir = os.path.join(idbaPerSampleWorkingDir, 'reverse')\\n if not os.path.exists(forwardDir):\\n try:\\n os.mkdir(forwardDir)\\n except OSError:\\n print (\\\"Can't create forward directory:\\\", forwardDir)\\n shutil.copy(os.path.join(forAssemblyWorkingDir, k), forwardDir)\\n inputFile1 = open(os.path.normpath(k), 'r')\\n lines = inputFile1.readlines()\\n #while True:\\n if len(lines) == 0:\\n break\\n for line in lines:\\n outputFile1.write(line)\\n\\n if not os.path.exists(reverseDir):\\n try:\\n os.mkdir(reverseDir)\\n except OSError:\\n print (\\\"Can't create reverse directory:\\\", reverseDir)\\n\\n shutil.copy(os.path.join(forAssemblyWorkingDir, l), reverseDir)\\n inputFile2 = open(os.path.normpath(l), 'r')\\n lines = inputFile2.readlines()\\n #while True:\\n if len(lines) == 0:\\n break\\n for line in lines:\\n outputFile2.write(line)\\n\\n outputFile1.close()\\n outputFile2.close()\\n\\n combinePairedEndFileFasta(os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa'),\\n os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa'),\\n os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.mergePaired.fa'))\\n\\n idbaInstallDir = os.path.normpath(config.get('idbaInstallDir'))\\n\\n runIdba = str('export PATH=/net/programs/Debian-6.0.3-x86_64/idba-1.1.1/bin:$PATH'+';' + os.path.join(idbaInstallDir, 'idba_ud') + ' '\\n + '-' + config.get('idbaReadsLen') + ' '\\n + os.path.join(idbaPerSampleWorkingDir, 'sample' + str(counter) + '.mergePaired.fa')\\n + ' -o ' + idbaPerSampleDir + ' --num_threads ' + config.get('idbaNumThreads'))\\n #print runIdba\\n procRunIdba = subprocess.Popen(runIdba, shell=True, bufsize=-1, cwd=idbaPerSampleDir)\\n procRunIdba.wait()\\n print \\\"IDBA_UD return code:\\\", procRunIdba.returncode\\n if procRunIdba.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (procRunIdba.returncode, runIdba))\\n print \\\"sample\\\" + str(counter) + \\\"IDBA_UD is finished.\\\"\\n shutil.copy(os.path.join(idbaPerSampleDir, 'contig.fa'), os.path.join(idbaPerSampleDir, 'idba_sample'+str(counter)+'.contig.fa'))\\n shutil.copy(os.path.join(idbaPerSampleDir, 'idba_sample'+str(counter)+'.contig.fa'), assemblyDir)\\n counter += 1\\n\\n if \\\"ray\\\" in args.a:\\n def rayAssembly(file1,file2):\\n counter = 1\\n for i in range(1000):\\n readsNumberPerSample = config.get('sample'+str(counter))\\n\\n if readsNumberPerSample is not None:\\n rayPerSampleDir = os.path.join(rayDir, ('sample'+str(counter)))\\n if not os.path.exists(rayPerSampleDir):\\n try:\\n os.mkdir(rayPerSampleDir)\\n except OSError:\\n print(\\\"Can't create idbaPerSample directory:\\\", rayPerSampleDir)\\n #if readsNumberPerSample is not None:\\n rayPerSampleWorkingDir = os.path.join(forAssemblyWorkingDir, ('ray_'+'sample'+str(counter)))\\n if not os.path.exists(rayPerSampleWorkingDir):\\n try:\\n os.mkdir(rayPerSampleWorkingDir)\\n except OSError:\\n print(\\\"Can't create idbaPerSample directory:\\\", rayPerSampleWorkingDir)\\n\\n outputFile1 = open(os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa'),'w')\\n outputFile2 = open(os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa'),'w')\\n\\n for k, l in zip((file1[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2]),\\n (file2[(int(readsNumberPerSample.split('-')[0])-1)/2:int(readsNumberPerSample.split('-')[-1])/2])):\\n\\n inputFile1 = open(os.path.normpath(k), 'r')\\n lines = inputFile1.readlines()\\n #while True:\\n if len(lines) == 0:\\n break\\n for line in lines:\\n outputFile1.write(line)\\n\\n inputFile2 = open(os.path.normpath(l), 'r')\\n lines = inputFile2.readlines()\\n #while True:\\n if len(lines) == 0:\\n break\\n for line in lines:\\n outputFile2.write(line)\\n\\n outputFile1.close()\\n outputFile2.close()\\n\\n rayInstallDir = os.path.normpath(config.get('rayInstallDir'))\\n rayPerSampleDirOutput = os.path.join(rayPerSampleDir, 'output')\\n runRay = str('export PATH=/net/programs/Debian-6.0.3-x86_64/Ray-2.3.0:$PATH'+';' + 'mpirun -np '\\n + config.get('numProcess_mpi_ray') + ' ' + os.path.join(rayInstallDir, 'Ray')\\n + ' -k ' + config.get('kmerSize_ray') + ' -p '\\n + os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.forwardMerge.fa')\\n + ' ' + os.path.join(rayPerSampleWorkingDir, 'sample' + str(counter) + '.reverseMerge.fa')\\n + ' -o ' + rayPerSampleDirOutput)\\n #print runRay\\n procRunRay = subprocess.Popen(runRay, shell=True, bufsize=-1, cwd=rayPerSampleDir)\\n procRunRay.wait()\\n print \\\"Ray return code:\\\", procRunRay.returncode\\n if procRunRay.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (procRunRay.returncode, runRay))\\n print \\\"Ray is finished.\\\"\\n shutil.copy(os.path.join(rayPerSampleDirOutput, 'Contigs.fasta'), os.path.join(rayPerSampleDir, 'ray_sample'+str(counter)+'.contig.fa'))\\n shutil.copy(os.path.join(rayPerSampleDir, 'ray_sample'+str(counter)+'.contig.fa'), assemblyDir)\\n counter += 1\\n\\n if \\\"n\\\" in args.a:\\n for i, j in zip(listf, listr):\\n fileHolder.outputTrimForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyTrim_forward_consensus.fa'))\\n fileHolder.outputTrimReverse.append(os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyTrim_reverse_consensus.fa'))\\n file1 = fileHolder.outputTrimForward\\n file2 = fileHolder.outputTrimReverse\\n\\n if \\\"soap\\\" in args.a:\\n soapAssembly(file1, file2)\\n if \\\"idba\\\" in args.a:\\n #starttime = time.clock()\\n idbaAssembly(file1, file2)\\n #endtime = time.clock()\\n #print (endtime-starttime)\\n if \\\"ray\\\" in args.a:\\n rayAssembly(file1, file2)\\n\\n if \\\"r\\\" in args.a:\\n for i, j in zip(listf,listr):\\n fileHolder.outputRandomForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyRandom_forward_consensus.fa'))\\n fileHolder.outputRandomReverse.append(os.path.join(forAssemblyWorkingDir, os.path.basename(j).split('.')[0] + '_onlyRandom_forward_consensus.fa'))\\n file1 = fileHolder.outputRandomForward\\n file2 = fileHolder.outputRandomReverse\\n if \\\"soap\\\" in args.a:\\n soapAssembly(file1, file2)\\n # return fileHolder.outputRandomForward, fileHolder.outputRandomReverse\\n if \\\"idba\\\" in args.a:\\n idbaAssembly(file1, file2)\\n if \\\"ray\\\" in args.a:\\n rayAssembly(file1, file2)\\n if \\\"f\\\" in args.a:\\n for i, j in zip(listf, listr):\\n fileHolder.outputFilterForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_forward_consensus.fa'))\\n fileHolder.outputFilterForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] + '_onlyFilter_reverse_consensus.fa'))\\n file1 = fileHolder.outputFilterForward\\n file2 = fileHolder.outputFilterReverse\\n if \\\"soap\\\" in args.a:\\n soapAssembly(file1, file2)\\n if \\\"idba\\\" in args.a:\\n idbaAssembly(file1, file2)\\n if \\\"ray\\\" in args.a:\\n rayAssembly(file1, file2)\\n # return fileHolder.outputFilterForward, fileHolder.outputFilterReverse\\n if \\\"rf\\\" in args.a:\\n for i, j in zip(listf, listr):\\n fileHolder.outputRandomFilterForward.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] +'_random_filter_forward_consensus.fa'))\\n fileHolder.outputRandomFilterReverse.append(os.path.join(forAssemblyWorkingDir, os.path.basename(i).split('.')[0] +'_random_filter_reverse_consensus.fa'))\\n file1 = fileHolder.outputRandomFilterForward\\n file2 = fileHolder.outputRandomFilterReverse\\n if \\\"soap\\\" in args.a:\\n soapAssembly(file1, file2)\\n if \\\"idba\\\" in args.a:\\n idbaAssembly(file1, file2)\\n if \\\"ray\\\" in args.a:\\n rayAssembly(file1, file2)\\n # return fileHolder.outputRandomFilterForward, fileHolder.outputRandomFilterReverse\\n\\n if args.p:\\n counter = 1\\n for i in range(1000):\\n ppspPerSampleDir = os.path.join(ppspDir, ('sample'+str(counter)))\\n if not os.path.exists(ppspPerSampleDir):\\n try:\\n os.mkdir(ppspPerSampleDir)\\n except OSError:\\n print(\\\"Can't create ppspPerSample directory:\\\", ppspPerSampleDir)\\n\\n filterReadLen(os.path.join(assemblyDir, config.get('ppspAssemblyMethod') + '_sample' + str(counter)+'.contig.fa'),\\n os.path.join(assemblyDir, config.get('ppspAssemblyMethod') + '_sample' + str(counter)+'.contig.ppspfilter.fa'), 1000)\\n\\n ppspConfigFile = open(os.path.join(ppspPerSampleDir, ('sample'+str(counter) + '.cfg')), 'w')\\n ppspConfigFile.write('[PhyloPythiaS_Plus]' + '\\\\n' + '# Configuration file of the PhyloPythiaS Plus pipeline.'\\n + '\\\\n' + 'pipelineDir=' + os.path.normpath(ppspPerSampleDir) + '\\\\n' + '# INPUT FILES' + '\\\\n' +\\n 'inputFastaFile=' + os.path.join(assemblyDir, config.get('ppspAssemblyMethod') + '_sample'\\n + str(counter)+'.contig.ppspfilter.fa') + '\\\\n' + '# Fasta file with scaffolds (optional)'\\n + '\\\\n' + 'inputFastaScaffoldsFile=' + noneToEmptyStr(config.get('inputFastaScaffoldsFile')) + '\\\\n'\\n + '# Scaffold-contig mapping, tab separated file; map: scaffold -> contigs; (optional)'\\n + ' \\\\n' + 'scaffoldsToContigsMapFile=' + noneToEmptyStr(config.get('scaffoldsToContigsMapFile')) + '\\\\n'\\n + '# Reference prediction file in PPS (*.out) format (optional)' + '\\\\n'\\n + 'referencePlacementFileOut=' + noneToEmptyStr(config.get('referencePlacementFileOut')) + '\\\\n'\\n + '# REFERENCE\\\\n' + 'databaseFile=/net/metagenomics/projects/PPSmg/taxonomy/20121122\\\\n'\\n + 'refSeq=/net/metagenomics/projects/PPSmg/data/nobackup/NCBI20121122/sequences\\\\n'\\n + '# Exclude reference sequences according to the reference prediction file at a given '\\n 'rank (optional)\\\\n# allowed ranks are: phylum, class, order, family, genus, species, strain\\\\n'\\n + 'excludeRefSeqRank=' + noneToEmptyStr(config.get('excludeRefSeqRank')) + '\\\\n'\\n + 's16Database=/net/metagenomics/projects/PPSmg/database/silva111' + '\\\\n'\\n + 'mgDatabase=/net/metagenomics/projects/PPSmg/database/mg3' + '\\\\n' + 'excludeRefMgRank='\\n + noneToEmptyStr(config.get('excludeRefMgRank')) + '\\\\n' + '# TOOLS\\\\n'\\n + 'ppsInstallDir=/net/metagenomics/projects/PPSmg/tools/PhyloPythiaS/vm/1_3\\\\n'\\n + 'mothurInstallDir=/net/metagenomics/projects/PPSmg/tools/mothur/mothur_1_23\\\\n'\\n + 'hmmerBinDir=/net/metagenomics/projects/PPSmg/tools/hmmer-3.0/binaries\\\\n'\\n + 'rnaHmmInstallDir=/net/metagenomics/projects/PPSmg/tools/rna_hmm3\\\\n'\\n + '#BASIC SETTINGS\\\\n' + '# e.g.: 1 ~ phylum, 2 ~ class, 3 ~ order, 4 ~ family, 5 ~ genus, 6 ~ species\\\\n'\\n + 'rankIdCut=' + config.get('rankIdCut') + '\\\\n' + 'maxLeafClades=' + config.get('maxLeafClades')\\n + '\\\\n' + 'minPercentInLeaf=' + config.get('minPercentInLeaf') + '\\\\n'\\n + 'minSeqLen=' + config.get('minSeqLen') + '\\\\n' + 'parallelPPSmodels=' + config.get('parallelPPSmodels')\\n + '\\\\n' + '# ADVANCED SETTINGS\\\\n' + '# It is not recommended to change the advanced settings, ' +\\n 'however it is possible to give you more control.\\\\n' + 'placeContigsFromTheSameScaffold=True\\\\n'\\n + 'agThreshold=0.3\\\\n' + 'assignedPartThreshold=0.5\\\\n' + 'rankIdAll=0\\\\n' + 'weightStayAll=60.0\\\\n'\\n + 'rankIdCutMinBp=100\\\\n' + 'overrideWithPPSPlacements=True\\\\n' + 'minBpToModel=100000\\\\n'\\n + 'minSSDfileSize=100\\\\n' + 'maxSSDfileSize=400000\\\\n' + 'minGenomesWgs=1\\\\n' + 'minBpPerSpecies=2000000\\\\n'\\n + 'candidatePlTopPercentThreshold=0.1\\\\n' + 'outputFileContigSubPattern=^(.*)\\\\n'\\n + 'mothurClassifyParamOther=method=bayesian, cutoff=80, iters=300' + '\\\\n' + 'configPPS=' + '\\\\n'\\n + 'recallMinFracClade=0.001' + '\\\\n' + 'precisionMinFracPred=0.001' + '\\\\n' + 'correctLabelThreshold=0.9')\\n ppspConfigFile.close() #Close file is important\\n\\n #only run maker gene analysis not the whole ppsp pipeline\\n if \\\"m\\\" in args.p:\\n runPpsp = str('export PYTHONPATH=/net/metagenomics/projects/PPSmg/scripts/current:$PYTHONPATH'+';' + ' '\\n + 'python /net/metagenomics/projects/PPSmg/scripts/current/algbioi/core/run.py'\\n + ' -c ' + 'sample'+str(counter) + '.cfg' + ' -n -g -o s16 mg -s')\\n #run whole ppsp pipeline without inputFastaScaffoldsFile setting in ppsp configuration file\\n if \\\"n\\\" in args.p:\\n runPpsp = str('export PYTHONPATH=/net/metagenomics/projects/PPSmg/scripts/current:$PYTHONPATH'+';' + ' ' + 'python /net/metagenomics/projects/PPSmg/scripts/current/algbioi/core/run.py'\\n + ' -c ' + os.path.join(ppspPerSampleDir,'sample'+str(counter) + '.cfg') + ' -n -g -o s16 mg -t -a -p c -r -s')\\n print runPpsp\\n #run whole ppsp pipeline with inputFastaScaffoldsFile setting in ppsp configuration file\\n if \\\"f\\\" in args.p:\\n runPpsp = str('export PYTHONPATH=/net/metagenomics/projects/PPSmg/scripts/current:$PYTHONPATH'+';' + ' ' + 'python /net/metagenomics/projects/PPSmg/scripts/current/algbioi/core/run.py'\\n + ' -c ' + 'sample'+str(counter) + '.cfg' + ' -n -g -o s16 mg -t -a -p c s v -r -s')\\n\\n procRunPpsp = subprocess.Popen(runPpsp, shell=True, bufsize=-1, cwd=ppspPerSampleDir)\\n procRunPpsp.wait()\\n print \\\"PPSP return code:\\\", procRunPpsp.returncode\\n if procRunPpsp.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (procRunPpsp.returncode, runPpsp))\\n else:\\n print \\\"sample\\\"+str(counter),\\\"PPSP is finished.\\\"\\n\\n counter += 1\\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\\n pass\\n else:\\n break\\n\\n if args.x:\\n counter = 1\\n for i in range(1000):\\n #taxatorPerSampleDir = os.path.join(taxatorDir, ('sample'+str(counter)))\\n # if not os.path.exists(taxatorPerSampleDir):\\n # try:\\n # os.mkdir(taxatorPerSampleDir)\\n # except OSError:\\n # print(\\\"Can't create taxatorPerSample directory:\\\", taxatorPerSampleDir)\\n renameReads(os.path.join(assemblyDir, config.get('taxatorAssemblyMethod') + '_sample' + str(counter)+'.contig.fa'),\\n os.path.join(assemblyDir, config.get('taxatorAssemblyMethod') + '_sample' + str(counter)+'.contig.rename.fa'))\\n taxatorInstallDir = os.path.normpath(config.get('taxatorInstallDir'))\\n #shutil.copy(os.path.join(taxatorInstallDir, 'binning-working-fasta-blast.bash'), taxatorPerSampleDir)\\n # runTaxator = str(os.path.join(taxatorInstallDir, 'binning-workflow-fasta-blast.bash') + ' '\\n runTaxator = str(os.path.join(taxatorInstallDir, 'binning-workflow-fasta-last.sh') + ' '\\n + os.path.join(assemblyDir, config.get('taxatorAssemblyMethod') + '_sample' + str(counter)+'.contig.rename.fa'))\\n # print runTaxator\\n procRunTaxator = subprocess.Popen(runTaxator, shell=True, bufsize=-1, cwd=taxatorInstallDir)\\n procRunTaxator.wait()\\n print \\\"Taxator-tk return code:\\\", procRunTaxator.returncode\\n if procRunTaxator.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (procRunTaxator.returncode, runTaxator))\\n else:\\n print \\\"sample\\\"+str(counter), \\\"Taxator-tk is finished.\\\"\\n\\n counter += 1\\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\\n pass\\n else:\\n break\\n\\n if args.f is not None:\\n counter = 1\\n for i in range(1000):\\n orfPerSampleDir = os.path.join(orfDir, ('sample'+str(counter)))\\n hmmPerSampleDir = os.path.join(hmmDir, ('sample'+str(counter)))\\n if not os.path.exists(orfPerSampleDir):\\n try:\\n os.mkdir(orfPerSampleDir)\\n except OSError:\\n print(\\\"Can't create orfPerSampleDir:\\\", orfPerSampleDir)\\n if not os.path.exists(hmmPerSampleDir):\\n try:\\n os.mkdir(hmmPerSampleDir)\\n except OSError:\\n print(\\\"Can't create hmmPerSampleDir:\\\", hmmPerSampleDir)\\n\\n filterReadLen(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa'),\\n os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.annofilter.fa'), int(config.get('annoSeqLen')))\\n\\n MetaGeneMarkInstallDir = os.path.normpath(config.get('MetaGeneMarkInstallDir'))\\n runMetaGeneMark = str(os.path.join(MetaGeneMarkInstallDir, 'gmhmmp') + ' -m '\\n + os.path.join(MetaGeneMarkInstallDir, 'MetaGeneMark_v1.mod') + ' '\\n + os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.annofilter.fa')\\n + ' -f G -a -d -r -o ' + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.ntaa.gff')\\n + ';' + os.path.join(MetaGeneMarkInstallDir, 'aa_from_gff.pl') + ' < '\\n + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.ntaa.gff')\\n + ' > ' + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.gff.faa')\\n + ';' + os.path.join(MetaGeneMarkInstallDir, 'nt_from_gff.pl') + ' < '\\n + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.ntaa.gff')\\n + ' > ' + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.gff.fna'))\\n procRunMetaGeneMark = subprocess.Popen(runMetaGeneMark, shell=True, bufsize=-1, cwd=orfPerSampleDir)\\n procRunMetaGeneMark.wait()\\n print \\\"ORFs calling return code:\\\", procRunMetaGeneMark.returncode\\n if procRunMetaGeneMark.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status: %s\\\" % (procRunMetaGeneMark.returncode, runMetaGeneMark))\\n else:\\n print \\\"sample\\\"+str(counter), \\\"ORFs calling is finished.\\\"\\n f1 = open(os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'ORF_aa.txt'), 'w')\\n f1.write(os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.gff.faa'))\\n f1.close()\\n\\n if None in args.f:\\n counter += 1\\n #\\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\\n pass\\n else:\\n break\\n\\n\\n\\n if \\\"h\\\" in args.f:\\n hmmScriptInstallDir = os.path.normpath(config.get('hmmScriptInstallDir'))\\n runAnnotationScriptPath = os.path.normpath(\\\"/net/metagenomics/\\\") #at the moment, Aaron's pipeline only support to run under the /net/metagenomics/\\n arrayOutDir = os.path.join(hmmPerSampleDir, (config.get('assemblyMethod') + '_sample' + str(counter) + '_array_out'))\\n if not os.path.exists(arrayOutDir):\\n try:\\n os.mkdir(arrayOutDir)\\n except OSError:\\n print(\\\"Can't create arrayOutDir:\\\", arrayOutDir)\\n runHmm = str('importpackage parallel; python ' + os.path.join(hmmScriptInstallDir, 'write_annot_array_script.py') + ' -d '\\n + os.path.join(orfPerSampleDir, config.get('assemblyMethod') + '_sample' + str(counter)+'ORF_aa.txt')[len(runAnnotationScriptPath)+1:]\\n + ' -t ' + os.path.normpath(hmmPerSampleDir)[len(runAnnotationScriptPath)+1:] + ' -f '\\n + os.path.normpath(arrayOutDir)[len(runAnnotationScriptPath)+1:] + ' -s ' + config.get('annotationSource') + ' -h ')\\n # print runHmm\\n procRunHmm = subprocess.Popen(runHmm, shell=True, bufsize=-1, cwd=runAnnotationScriptPath)\\n procRunHmm.wait()\\n print \\\"Hmm annotation return code:\\\", procRunHmm.returncode\\n if procRunHmm.returncode != 0:\\n raise Exception(\\\"Command returned with non-zero %s status : %s\\\" % (procRunHmm.returncode, runHmm))\\n else:\\n print \\\"sample\\\"+str(counter), \\\"Hmm annotation is finished.\\\"\\n #\\n counter += 1\\n #\\n if os.path.exists(os.path.join(assemblyDir, config.get('assemblyMethod') + '_sample' + str(counter)+'.contig.fa')):\\n pass\\n else:\\n break\\n #if \\\"s\\\" in args.f:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9bcf6c29174951e9173c01b3594c846\",\n \"score\": \"0.58679366\",\n \"text\": \"def run(self, pipeline: pipeline_py.Pipeline) -> Optional[Any]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71cc839ed60fe44d705ba29c1b3a879\",\n \"score\": \"0.5863825\",\n \"text\": \"def main():\\n for gpx_file in FILES:\\n transform(gpx_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eae37600e81b4ed83185a9a8fafef30f\",\n \"score\": \"0.586028\",\n \"text\": \"def process(self, env, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08b5c6e35850c271a459acec8531620f\",\n \"score\": \"0.58322805\",\n \"text\": \"def pipeline_casero(data, preprocessing=[], creation=[], reduction=[], selection=[], models=[]):\\n print(\\\"Beginning pipeline at {}\\\\n\\\".format(datetime.now()))\\n print(\\\"Performing preprocessing steps...\\\")\\n data = update_df(data, preprocessing)\\n print(\\\"Preprocessing completed at {}, performed {} steps\\\".format(datetime.now(), len(preprocessing)))\\n print(\\\"New Shape of data: {0}\\\\n\\\".format(len(data.columns)))\\n\\n print(\\\"Performing feature creation...\\\")\\n data = update_df(data, creation)\\n print(\\\"Feature Creation completed at {}, performed {} steps\\\".format(datetime.now(), len(creation)))\\n print(\\\"New Shape of data: {0}\\\\n\\\".format(len(data.columns)))\\n\\n print(\\\"Dummifying...\\\")\\n data = dd.get_dummies(data)\\n print(\\\"New Shape of data: {0}\\\\n\\\".format(len(data.columns)))\\n\\n\\n print(\\\"Performing dimensionality reduction...\\\")\\n data = update_df(data, reduction)\\n print(\\\"Dimensionality reduction completed at {}, performed {} steps\\\".format(datetime.now(), len(reduction)))\\n print(\\\"New Shape of data: {0}\\\\n\\\".format(len(data.columns)))\\n\\n train = get_train(data)\\n holdout = get_holdout(data)\\n print(\\\"Performing feature selection...\\\")\\n train = update_df(train, selection)\\n print(\\\"Feature Selection completed at {}, performed {} steps\\\".format(datetime.now(), len(selection)))\\n print(\\\"New Shape of train: {0}\\\\n\\\".format(len(train.columns)))\\n\\n holdout = holdout[train.columns]\\n\\n # The issue here is this assumes the model need the data in the same format, unless we submit pipelines\\n # here which seems silly....\\n\\n print(\\\"Scoring models....\\\")\\n\\n best_model = select_best_model(models, train)\\n\\n print(\\\"Evaluating model on the holdout...\\\")\\n final_r2 = r2_score(holdout.cnt, best_model.predict(holdout.drop(\\\"cnt\\\", axis=1)))\\n print(\\\"Final R2: {0}\\\".format(final_r2))\\n print(\\\"\\\\nPipeline finished! Completed execution at {}. Returning model...\\\".format(datetime.now()))\\n\\n return best_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2263b95c0fd4f749c1d6c8872d4b0b\",\n \"score\": \"0.5829256\",\n \"text\": \"def pipelineComponents(self, *args):\\n\\t\\taaeDesc = createEngineDescription(args);\\n\\t\\tself.processEngine = createEngine(aaeDesc);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26426c2960fdeea6cf5773304b0eb6a3\",\n \"score\": \"0.5825872\",\n \"text\": \"def main():\\n\\n args = arg_parser()\\n threads = args.threads\\n LOGGER.info(\\\"{} threads flagged to run each software.\\\".format(threads))\\n\\n LOGGER.info(\\\"Pipeline beginning.\\\")\\n log_file = open('UPEC.log', 'a+')\\n\\n # Storing all constants in lists for efficient looping within large functions\\n # maintains the same order, and ensures no missing files\\n\\n # input for ncbi wget, holds ftp paths\\n fasta_ftp_list = [HM27_FILES[0], HM46_FILES[0], HM65_FILES[0], HM69_FILES[0], \\\\\\n HM27_FILES[1], HM46_FILES[1], HM65_FILES[1], HM69_FILES[1]]\\n\\n # output from ncbi wget\\n fasta_file_list = [HM27_FASTA, HM46_FASTA, HM65_FASTA, HM69_FASTA]\\n\\n # output from fastq-dump\\n fastq_tuple_list = [(HM27_FASTQ_1, HM27_FASTQ_2), \\\\\\n (HM46_FASTQ_1, HM46_FASTQ_2), \\\\\\n (HM65_FASTQ_1, HM65_FASTQ_2), \\\\\\n (HM69_FASTQ_1, HM69_FASTQ_2)]\\n\\n # output from prokka software\\n gff_list = [HM27_GFF_FILE, HM46_GFF_FILE, HM65_GFF_FILE, HM69_GFF_FILE]\\n\\n # output from tophat2\\n bam_file_list = [HM27_BAM, HM46_BAM, HM65_BAM, HM69_BAM]\\n\\n # output from samtools sort and tophat2\\n sorted_bam_list = [HM27_SORTED_BAM, HM46_SORTED_BAM, HM65_SORTED_BAM, HM69_SORTED_BAM]\\n\\n # grabbing ftp files from ncbi using wget method\\n LOGGER.info(\\\"Beginning to find FTP files.\\\")\\n wget_gunzip_fasta(fasta_ftp_list)\\n\\n # Parsing the FASTA and counting number of contigs and base pairs > 1000 in length\\n LOGGER.info(\\\"Parsing FASTA and writing to log file.\\\")\\n parse_seqio_fasta(fasta_file_list, log_file)\\n\\n # NEED TO FIX THE DIRECTORY PATH FOR WRITING TO THE LOG FILE!!\\n\\n # fix prokka to use less cpus and add the -usegenus flag\\n LOGGER.info(\\\"Starting gene annotation with Prokka\\\")\\n build_prokka(fasta_file_list, log_file)\\n\\n # LOGGER.info(\\\"Grabbing SRA files and converting to FASTQ\\\")\\n # prefetch_fastq_decomp()\\n\\n LOGGER.info(\\\"Beginning alignment process.\\\")\\n build_tophat_alignment(fasta_file_list, fastq_tuple_list, bam_file_list, \\\\\\n sorted_bam_list, threads)\\n\\n LOGGER.info(\\\"Creating assembly file for cuffmerge.\\\")\\n with open('ecoli_assemblies.txt', 'w') as assemble_file:\\n assemble_file.write(\\\"./hm27_cuff/transcripts.gtf\\\\n\\\")\\n assemble_file.write(\\\"./hm46_cuff/transcripts.gtf\\\\n\\\")\\n assemble_file.write(\\\"./hm65_cuff/transcripts.gtf\\\\n\\\")\\n assemble_file.write(\\\"./hm69_cuff/transcripts.gtf\\\\n\\\")\\n\\n LOGGER.info(\\\"Beginning to run cufflinks\\\")\\n run_cufflinks_suite(gff_list, bam_file_list, 'ecoli_assemblies.txt', MERGED_GTF, threads)\\n\\n LOGGER.info(\\\"Pipeline Complete.\\\")\\n log_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8cb787443828cba22935bb3a8911c97\",\n \"score\": \"0.57822245\",\n \"text\": \"def pipeline(root):\\n for split in [\\\"train\\\", \\\"dev\\\"]:\\n input_path = os.path.join(FLAGS.input_dir,\\n \\\"%s/nq-%s-??.jsonl.gz\\\" % (split, split))\\n output_path = os.path.join(FLAGS.output_dir, \\\"nq-%s.tfrecords\\\" % split)\\n serialized_examples = (\\n root\\n | \\\"Read-\\\" + split >> beam.io.textio.ReadFromText(\\n input_path, validate=False)\\n | \\\"Shuffle-\\\" + split >> beam.transforms.Reshuffle()\\n | \\\"Prep-\\\" + split >> beam.ParDo(\\n preproc_nq_lib.NQPreprocessFn(\\n FLAGS.stride, FLAGS.seq_len, FLAGS.global_seq_len,\\n FLAGS.question_len, FLAGS.vocab_file, FLAGS.do_lower_case,\\n FLAGS.global_token_types, FLAGS.spm_model_path,\\n FLAGS.tokenizer_type, split == \\\"train\\\",\\n FLAGS.predict_la_when_no_sa, FLAGS.include_unknown_rate,\\n FLAGS.include_unknown_rate_for_unanswerable, FLAGS.fixed_blocks,\\n FLAGS.fixed_block_size))\\n | \\\"Split-\\\" + split >> beam.ParDo(\\n preproc_nq_lib.NQSplitFn(\\n FLAGS.stride, FLAGS.seq_len, FLAGS.global_seq_len,\\n FLAGS.question_len, FLAGS.vocab_file, FLAGS.do_lower_case,\\n FLAGS.global_token_types, FLAGS.spm_model_path,\\n FLAGS.tokenizer_type, split == \\\"train\\\",\\n FLAGS.predict_la_when_no_sa, FLAGS.include_unknown_rate,\\n FLAGS.include_unknown_rate_for_unanswerable, FLAGS.fixed_blocks,\\n FLAGS.fixed_block_size))\\n | \\\"Shuffle2-\\\" + split >> beam.transforms.Reshuffle())\\n _ = (\\n serialized_examples\\n | \\\"WriteTFExample-\\\" + split >> beam.io.WriteToTFRecord(\\n output_path,\\n num_shards=FLAGS.num_shards,\\n compression_type=beam.io.filesystem.CompressionTypes.GZIP))\\n\\n # Write count to file.\\n _ = (\\n serialized_examples\\n | \\\"Count-\\\" + split >> beam.combiners.Count.Globally()\\n | \\\"WriteCount-\\\" + split >> beam.io.WriteToText(\\n os.path.join(FLAGS.output_dir, \\\"%s_count.txt\\\" % split),\\n shard_name_template=\\\"\\\", # To force unsharded output.\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8862001d3e966e17f630ab409a2a862d\",\n \"score\": \"0.5770933\",\n \"text\": \"def run(self):\\n self.set_pipeline()\\n self.pipeline.fit(self.X, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a1eaa3e8f3494613c61cac9876bca9\",\n \"score\": \"0.5768495\",\n \"text\": \"def main_pipeline(args):\\n initialize(args)\\n dataset_list = args.d\\n offline_toggle = args.offline\\n set_debug_simple(args.etl)\\n raw_bool = args.raw\\n #Load logging information\\n set_log_folder(f'{args.local_directory}/logging') # folder for logs\\n set_target_folder_name(args.local_directory)\\n if not os.path.exists(args.local_directory):\\n os.mkdir(args.local_directory)\\n if not os.path.exists(get_log_folder()):\\n os.mkdir(get_log_folder())\\n #Create offline folder - always required for chemaxon/rdkit...\\n set_offline_folder(f'./{get_target_folder()}/offline')\\n if not os.path.exists(get_offline_folder()):\\n os.mkdir(get_offline_folder())\\n\\n #Create Loggers\\n main_logger = f'{get_log_folder()}/REPORT_LOG.txt' \\n warning_logger = f'{get_log_folder()}/REPORT_WARNING_LOG.txt' \\n ingredient_logger = f'{get_log_folder()}/REPORT_INGREDIENT_LOG.txt' \\n logger.setup_logger('mainlog', main_logger)\\n logger.setup_logger('warning', warning_logger, level=logging.WARN, stream=True)\\n logger.setup_logger('ilog', ingredient_logger) #ingredient log\\n modlog = logging.getLogger(f'mainlog.{__name__}')\\n\\n # Initial reporting signaling successful code kickoff\\n modlog.info(f'{dataset_list} selected as the dataset target(s) for this run')\\n print(f'{dataset_list} selected as the dataset target(s) for this run')\\n print(f'{len(dataset_list)} set(s) of downloads will occur, one per dataset, please be patient!')\\n modlog.info(f'{len(dataset_list)} set(s) of downloads will occur, one for dataset, please be patient!')\\n modlog.info(f'Developer Option: \\\"offline_toggle\\\" set to {offline_toggle}')\\n\\n # Gather data from targeted directory (google UID from devcongfig datasets)\\n chemdf_dict = get_remote_data(dataset_list, \\n offline_toggle)\\n\\n report_df = report_pipeline(chemdf_dict, \\n raw_bool,\\n dataset_list, \\n offline_toggle)\\n\\n debug_header = f\\\"# Report version {__version__}; Created on {datetime.datetime.now()}; Dataset(s) targeted {dataset_list}\\\\n\\\"\\n set_debug_header(debug_header)\\n if args.debug:\\n # Export dataframes of initial parsing and chemical inventories for ETL to ESCALATEV3\\n report_csv_filename = f'REPORT_{get_target_folder().upper()}.csv'\\n write_debug_file(report_df, report_csv_filename, write_index=True) #turn to True to generate for testing\\n for name, chemicaldf in chemdf_dict.items():\\n inventory_name = f'REPORT_{name.upper()}_INVENTORY.csv'\\n write_debug_file(chemicaldf, inventory_name)\\n\\n # Perform feature calculations / gather physicochemical descriptors\\n compound_ingredient_objects_df = ingredient_pipeline(report_df,\\n chemdf_dict,\\n args.debug)\\n\\n inchi_key_indexed_features_df = feat_pipeline(report_df,\\n chemdf_dict,\\n args.debug)\\n\\n amounts_df,\\\\\\n ratios_df = ratio_pipeline(report_df,\\n compound_ingredient_objects_df,\\n chemdf_dict,\\n args.debug) \\n\\n #calc_out_df.to_csv(f'./{get_offline_folder()}/REPORT_CALCOUT.csv')\\n #calc_out_df = pd.read_csv(f'./{get_offline_folder()}/REPORT_CALCOUT.csv')\\n if not args.disablecalcs:\\n calcs_df = calc_pipeline(report_df,\\n amounts_df,\\n ratios_df,\\n inchi_key_indexed_features_df,\\n args.debug)\\n else:\\n calcs_df = pd.DataFrame()\\n\\n # Export dataframe\\n escalate_final_df = construct_2d_view(report_df,\\n amounts_df,\\n inchi_key_indexed_features_df, \\n ratios_df,\\n calcs_df,\\n args.debug,\\n args.raw)\\n\\n escalate_final_df.to_csv(f'{get_target_folder()}.csv')\\n\\n # Additional variations on dataframe export (for escalation / versioned data repo)\\n if ('state' in vars(args)):\\n templink = str(args.state)\\n link = templink.split('.')[0] + '.link.csv'\\n pass\\n else:\\n modlog.error('User MUST specify a stateset during version data repo\\\\\\n upload preparation!')\\n sys.exit()\\n\\n if args.verdata is not None:\\n export_to_repo.prepareexport(escalate_final_df, args.state, link, args.verdata, get_target_folder())\\n modlog.info(f'Exporting {args.verdata}: {args.verdata}.{get_target_folder()}.csv was generated')\\n print(f'Exporting {args.verdata}: {args.verdata}.{get_target_folder()}.csv was generated')\\n \\n modlog.info(f'Clean Exit: {get_target_folder()}.csv was generated')\\n print(f'Clean Exit: {get_target_folder()}.csv was generated')\\n\\n # Postrun cleanup\\n if args.offline == 0: \\n os.remove('./mycred.txt') #cleanup automatic authorization\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a64f15315f446efb7bba9a08e679d9\",\n \"score\": \"0.57667524\",\n \"text\": \"def exec_before_job(app, inp_data, out_data, param_dict, tool=None):\\n job_name = param_dict.get( 'name', 'PlinkFbat' )\\n outxls = ['tabular','%s_fbat.xls' % job_name]\\n logtxt = ['txt','%s_fbat.log' % job_name]\\n lookup={}\\n lookup['out_file1'] = outxls\\n lookup['logf'] = logtxt\\n for name in lookup.keys():\\n data = out_data[name]\\n data_type,newname = lookup[name]\\n data = app.datatypes_registry.change_datatype(data, data_type)\\n data.name = newname\\n out_data[name] = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9abc6f4b0c6ecf5733c204113f245029\",\n \"score\": \"0.57398516\",\n \"text\": \"def call_pipeline(met,input_file,cent,corr,conditioning,total,incr,weight,c_prop,\\n thresh,min,plot_metric,create_graph,plot_pca,name_file,run_number):\\n #try:\\n proc_id = os.getpid()\\n t_start = time.perf_counter()\\n pipeline.main(False, input_file, '', met, conditioning, min, total, incr, None,\\n None, None, cent, thresh, corr, weight, c_prop, 'kl_divergence',plot_metric,\\n create_graph, plot_pca,name_file, run_number)\\n t_end = time.perf_counter()\\n print('runtime = ', t_end - t_start)\\n #except:\\n # pass\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b78b6ba27a16e8510c4d894e61b36d7\",\n \"score\": \"0.5738241\",\n \"text\": \"def make_pipeline():\\n\\n # Create our mean reversion factor by taking the negative of a momentum factor\\n \\n reversion = Reversion()\\n\\n # Classify all securities by sector so that we can enforce sector neutrality later\\n sector = Sector()\\n\\n # Screen out non-desirable securities by defining our universe.\\n universe = Q1500US()\\n\\n # By applying a mask to the rank computations, we remove any stocks that failed\\n # to meet our initial criteria **before** computing ranks. This means that the\\n # stock with rank 10.0 is the 10th-lowest stock that was included in the Q1500US.\\n # 標準偏差\\n # filter のかけかたを mask で行ってしまう\\n factor_rank = reversion.rank(mask=universe).zscore()\\n\\n # Build Filters representing the top and bottom 150 stocks by our ranking system.\\n # We'll use these as our tradeable universe each day.\\n longs = factor_rank.top(NUM_LONG_POSITIONS)\\n shorts = factor_rank.bottom(NUM_SHORT_POSITIONS)\\n\\n # The final output of our pipeline should only include\\n # the top/bottom 300 stocks by our criteria\\n long_short_screen = (longs | shorts)\\n\\n # Define any risk factors that we will want to neutralize\\n # We are chiefly interested in market beta as a risk factor so we define it using\\n # Bloomberg's beta calculation\\n # Ref: https://www.lib.uwo.ca/business/betasbydatabasebloombergdefinitionofbeta.html\\n beta = 0.66 * RollingLinearRegressionOfReturns(\\n target=sid(8554),\\n returns_length=5,\\n regression_length=260,\\n mask=long_short_screen\\n ).beta + 0.33*1.0\\n\\n # Create pipeline\\n pipe = Pipeline(\\n columns={\\n 'longs': longs,\\n 'shorts': shorts,\\n 'factor_rank': factor_rank,\\n 'reversion': reversion,\\n 'sector': sector,\\n 'market_beta': beta\\n },\\n screen=long_short_screen\\n )\\n return pipe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c313321bfaa7fcc8c2964a3ef8505b36\",\n \"score\": \"0.57319045\",\n \"text\": \"def transform_data():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84981d796d43daf713d6c3ad26d27de8\",\n \"score\": \"0.5726708\",\n \"text\": \"def runpipe():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06ce5c66b68337cd0e42f59c2276283\",\n \"score\": \"0.5698271\",\n \"text\": \"def main(lims, args, logger):\\n p = Process(lims,id = args.pid)\\n samplenb=0\\n errnb=0\\n summary={}\\n logart=None\\n for output_artifact in p.all_outputs():\\n #filter to only keep solo sample demultiplexing output artifacts\\n if output_artifact.type=='Analyte' and len(output_artifact.samples)==1:\\n sample=output_artifact.samples[0]\\n samplenb+=1\\n #update the total number of reads\\n total_reads=sumreads(sample, summary)\\n sample.udf['Total Reads (M)']=total_reads\\n output_artifact.udf['Set Total Reads']=total_reads\\n logging.info(\\\"Total reads is {0} for sample {1}\\\".format(sample.udf['Total Reads (M)'],sample.name))\\n try:\\n if sample.udf['Reads Min'] >= sample.udf['Total Reads (M)']:\\n sample.udf['Status (auto)']=\\\"In Progress\\\"\\n sample.udf['Passed Sequencing QC']=\\\"False\\\"\\n elif sample.udf['Reads Min'] < sample.udf['Total Reads (M)'] : \\n sample.udf['Passed Sequencing QC']=\\\"True\\\"\\n sample.udf['Status (auto)']=\\\"Finished\\\"\\n except KeyError as e:\\n print e\\n logging.warning(\\\"No reads minimum found, cannot set the status auto flag for sample {0}\\\".format(sample.name))\\n errnb+=1\\n\\n #commit the changes\\n sample.put()\\n output_artifact.put()\\n elif(output_artifact.type=='Analyte') and len(output_artifact.samples)!=1:\\n logging.error(\\\"Found {0} samples for the ouput analyte {1}, that should not happen\\\".format(len(output_artifact.samples()),output_artifact.id))\\n elif(output_artifact.type==\\\"ResultFile\\\" and output_artifact.name==\\\"AggregationLog\\\"):\\n logart=output_artifact\\n\\n\\n #write the csv file, separated by pipes, no cell delimiter\\n with open(\\\"AggregationLog.csv\\\", \\\"w\\\") as f:\\n f.write(\\\"sep=,\\\\n\\\")\\n f.write('sample name,number of flowcells,number of lanes,flowcell1:lane1|lane2;flowcell2:lane1|lane2|lane3 ...\\\\n')\\n for sample in summary:\\n view=[]\\n totfc=len(summary[sample])\\n totlanes=0\\n for fc in summary[sample]:\\n view.append(\\\"{0}:{1}\\\".format(fc, \\\"|\\\".join(summary[sample][fc])))\\n totlanes+=len(summary[sample][fc])\\n f.write('{0},{1},{2},{3}\\\\n'.format(sample, totfc, totlanes, \\\";\\\".join(view)))\\n try:\\n attach_file(os.path.join(os.getcwd(), \\\"AggregationLog.csv\\\"), logart)\\n logging.info(\\\"updated {0} samples with {1} errors\\\".format(samplenb, errnb))\\n except AttributeError:\\n #happens if the log artifact does not exist, if the step has been started before the configuration changes\\n logging.info(\\\"Could not upload the log file\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79873034b23627420e4ab4a53158cf4c\",\n \"score\": \"0.56886816\",\n \"text\": \"def main_pipeline_part_1():\\n taxonomy_download_dir = get_local_folder_for(settings.generated_dir_name, FileType.TaxonomyData)\\n taxon_file_path = download_ncbi_taxonomy_as_xml(taxonomy_download_dir, settings.virus_taxon_id)\\n organism = OrganismWrapperClass(taxon_file_path)\\n\\n def get_virus_id_and_nuc_reference_sequence(session: database.Session):\\n # get virus ID\\n virus = vcm.get_virus(session, organism)\\n if virus:\\n _virus_id = virus.virus_id\\n else:\\n logger.trace(f'inserting new organism { settings.virus_taxon_id} into VIRUS table')\\n _virus_id = vcm.create_or_get_virus(session, organism)\\n # get nuc_reference_sequence\\n logger.trace(f'organism txid { settings.virus_taxon_id} already in VIRUS table.')\\n refseq_objects = vcm.get_reference_sequence_of_virus(session, _virus_id)\\n if refseq_objects:\\n ref_seq_obj, ref_nuc_seq_obj = refseq_objects\\n return _virus_id, ref_nuc_seq_obj.nucleotide_sequence, ref_seq_obj.accession_id\\n else:\\n logger.trace('caching the nucleotide sequence of the reference')\\n downloaded_ref_sample = _reference_sample_from_organism(SampleWrapperClass)\\n return _virus_id, downloaded_ref_sample.nucleotide_sequence(), downloaded_ref_sample.primary_accession_number()\\n\\n return database.try_py_function(get_virus_id_and_nuc_reference_sequence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"207a1972b3f018fe7ce7e4a001fbea61\",\n \"score\": \"0.5674275\",\n \"text\": \"def build_own_pipeline() -> Pipeline:\\n pipeline = None\\n ##### Write code here #######\\n\\n pipeline = build_linear_svc() \\n\\n ##### End of your work ######\\n return pipeline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d35253ceab432e1175a7489e97ffd62\",\n \"score\": \"0.5670093\",\n \"text\": \"def stage(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d1505b8db1e1676fb91bd112fa1df9\",\n \"score\": \"0.56636786\",\n \"text\": \"def main():\\n ## Process Each Dataset in Serial\\n for d, dataset in enumerate(DATASETS):\\n logger.info(f\\\"Processing dataset {d+1}/{len(DATASETS)}: `{dataset}`\\\")\\n _ = process_dataset(dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60c0dc2011a19bb7007765c116f91c3c\",\n \"score\": \"0.5652783\",\n \"text\": \"def main():\\n args = BeutelArgs().parse_args()\\n train, test = load_data_from_flags(args)\\n save_transformations(train_and_transform(train, test, args), args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eaf3053e0bb915860ca6eef2fb46bc9\",\n \"score\": \"0.56264156\",\n \"text\": \"def main():\\n args = VfaeArgs(explicit_bool=True).parse_args()\\n train, test = load_data_from_flags(args)\\n save_transformations(train_and_transform(train, test, args), args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88eb20e49779b01d1d60d5a62d617a49\",\n \"score\": \"0.5623422\",\n \"text\": \"def main(args):\\n #%% Step 0: Set basic parameters\\n metric_sets = [args.metric_name]\\n metric_parameters = {\\\"problem\\\": args.problem, \\\"label_name\\\": [args.label_name]}\\n\\n #%% Step 1: Upload Dataset\\n # File names\\n data_directory = \\\"../datasets/data/\\\" + args.data_name + \\\"/\\\" + args.data_name + \\\"_\\\"\\n\\n data_loader_training = CSVLoader(\\n static_file=data_directory + \\\"static_train_data.csv.gz\\\",\\n temporal_file=data_directory + \\\"temporal_train_data_eav.csv.gz\\\",\\n )\\n\\n data_loader_testing = CSVLoader(\\n static_file=data_directory + \\\"static_test_data.csv.gz\\\",\\n temporal_file=data_directory + \\\"temporal_test_data_eav.csv.gz\\\",\\n )\\n\\n dataset_training = data_loader_training.load()\\n dataset_testing = data_loader_testing.load()\\n\\n print(\\\"Finish data loading.\\\")\\n\\n #%% Step 2: Preprocess Dataset\\n # (0) filter out negative values (Automatically)\\n negative_filter = FilterNegative()\\n # (1) one-hot encode categorical features\\n onehot_encoder = OneHotEncoder(one_hot_encoding_features=[args.one_hot_encoding])\\n # (2) Normalize features: 3 options (minmax, standard, none)\\n normalizer = Normalizer(args.normalization)\\n\\n filter_pipeline = PipelineComposer(negative_filter, onehot_encoder, normalizer)\\n\\n dataset_training = filter_pipeline.fit_transform(dataset_training)\\n dataset_testing = filter_pipeline.transform(dataset_testing)\\n\\n print(\\\"Finish preprocessing.\\\")\\n\\n #%% Step 3: Define Problem\\n problem_maker = ProblemMaker(\\n problem=args.problem, label=[args.label_name], max_seq_len=args.max_seq_len, treatment=args.treatment\\n )\\n\\n dataset_training = problem_maker.fit_transform(dataset_training)\\n dataset_testing = problem_maker.fit_transform(dataset_testing)\\n\\n print(\\\"Finish defining problem.\\\")\\n\\n #%% Step 4: Impute Dataset\\n static_imputation = Imputation(imputation_model_name=args.static_imputation_model, data_type=\\\"static\\\")\\n temporal_imputation = Imputation(imputation_model_name=args.temporal_imputation_model, data_type=\\\"temporal\\\")\\n\\n imputation_pipeline = PipelineComposer(static_imputation, temporal_imputation)\\n\\n dataset_training = imputation_pipeline.fit_transform(dataset_training)\\n dataset_testing = imputation_pipeline.transform(dataset_testing)\\n\\n print(\\\"Finish imputation.\\\")\\n\\n #%% Step 5: Feature selection (4 options)\\n static_feature_selection = FeatureSelection(\\n feature_selection_model_name=args.static_feature_selection_model,\\n feature_type=\\\"static\\\",\\n feature_number=args.static_feature_selection_number,\\n task=args.task,\\n metric_name=args.metric_name,\\n metric_parameters=metric_parameters,\\n )\\n\\n temporal_feature_selection = FeatureSelection(\\n feature_selection_model_name=args.temporal_feature_selection_model,\\n feature_type=\\\"temporal\\\",\\n feature_number=args.temporal_feature_selection_number,\\n task=args.task,\\n metric_name=args.metric_name,\\n metric_parameters=metric_parameters,\\n )\\n\\n feature_selection_pipeline = PipelineComposer(static_feature_selection, temporal_feature_selection)\\n\\n dataset_training = feature_selection_pipeline.fit_transform(dataset_training)\\n dataset_testing = feature_selection_pipeline.transform(dataset_testing)\\n\\n print(\\\"Finish feature selection.\\\")\\n\\n #%% Step 6: Bayesian Optimization\\n ## Model define\\n # RNN model\\n rnn_parameters = {\\n \\\"model_type\\\": \\\"lstm\\\",\\n \\\"epoch\\\": args.epochs,\\n \\\"static_mode\\\": args.static_mode,\\n \\\"time_mode\\\": args.time_mode,\\n \\\"verbose\\\": False,\\n }\\n\\n general_rnn = GeneralRNN(task=args.task)\\n general_rnn.set_params(**rnn_parameters)\\n\\n # CNN model\\n cnn_parameters = {\\n \\\"epoch\\\": args.epochs,\\n \\\"static_mode\\\": args.static_mode,\\n \\\"time_mode\\\": args.time_mode,\\n \\\"verbose\\\": False,\\n }\\n temp_cnn = TemporalCNN(task=args.task)\\n temp_cnn.set_params(**cnn_parameters)\\n\\n # Transformer\\n transformer = TransformerPredictor(\\n task=args.task, epoch=args.epochs, static_mode=args.static_mode, time_mode=args.time_mode\\n )\\n\\n # Attention model\\n attn_parameters = {\\n \\\"model_type\\\": \\\"lstm\\\",\\n \\\"epoch\\\": args.epochs,\\n \\\"static_mode\\\": args.static_mode,\\n \\\"time_mode\\\": args.time_mode,\\n \\\"verbose\\\": False,\\n }\\n attn = Attention(task=args.task)\\n attn.set_params(**attn_parameters)\\n\\n # model_class_list = [general_rnn, attn, temp_cnn, transformer]\\n model_class_list = [general_rnn, attn]\\n\\n # train_validate split\\n dataset_training.train_val_test_split(prob_val=0.2, prob_test=0.1)\\n\\n # Bayesian Optimization Start\\n metric = BOMetric(metric=\\\"auc\\\", fold=0, split=\\\"test\\\")\\n\\n ens_model_list = []\\n\\n # Run BO for each model class\\n for m in model_class_list:\\n BO_model = automl.model.AutoTS(dataset_training, m, metric, model_path=\\\"tmp/\\\")\\n models, bo_score = BO_model.training_loop(num_iter=args.bo_itr)\\n auto_ens_model = AutoEnsemble(models, bo_score)\\n ens_model_list.append(auto_ens_model)\\n\\n # Load all ensemble models\\n for ens in ens_model_list:\\n for m in ens.models:\\n m.load_model(BO_model.model_path + \\\"/\\\" + m.model_id + \\\".h5\\\")\\n\\n # Stacking algorithm\\n stacking_ens_model = StackingEnsemble(ens_model_list)\\n stacking_ens_model.fit(dataset_training, fold=0, train_split=\\\"val\\\")\\n\\n # Prediction\\n assert not dataset_testing.is_validation_defined\\n test_y_hat = stacking_ens_model.predict(dataset_testing, test_split=\\\"test\\\")\\n test_y = dataset_testing.label\\n\\n print(\\\"Finish AutoML model training and testing.\\\")\\n\\n #%% Step 7: Visualize Results\\n idx = np.random.permutation(len(test_y_hat))[:2]\\n\\n # Evaluate predictor model\\n result = Metrics(metric_sets, metric_parameters).evaluate(test_y, test_y_hat)\\n print(\\\"Finish predictor model evaluation.\\\")\\n\\n # Visualize the output\\n # (1) Performance\\n print(\\\"Overall performance\\\")\\n print_performance(result, metric_sets, metric_parameters)\\n # (2) Predictions\\n print(\\\"Each prediction\\\")\\n print_prediction(test_y_hat[idx], metric_parameters)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27aa4f0de17a2e4deecf3fc8d97275\",\n \"score\": \"0.56216466\",\n \"text\": \"def process(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27aa4f0de17a2e4deecf3fc8d97275\",\n \"score\": \"0.56216466\",\n \"text\": \"def process(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27aa4f0de17a2e4deecf3fc8d97275\",\n \"score\": \"0.56216466\",\n \"text\": \"def process(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27aa4f0de17a2e4deecf3fc8d97275\",\n \"score\": \"0.56216466\",\n \"text\": \"def process(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27aa4f0de17a2e4deecf3fc8d97275\",\n \"score\": \"0.56216466\",\n \"text\": \"def process(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab27aa4f0de17a2e4deecf3fc8d97275\",\n \"score\": \"0.56216466\",\n \"text\": \"def process(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8aeab9d4753095a17b727a3899ff5df\",\n \"score\": \"0.5620898\",\n \"text\": \"def protein_functional_profiling_pipeline(params):\\n\\n work_dir = os.path.join(params['work_dir'], str(uuid.uuid4()))\\n os.mkdir(work_dir)\\n\\n config_file = write_config_file(work_dir)\\n project_file = write_project_file(params['input_proteins'], params['reference'], work_dir)\\n project = Project(config_file=config_file, project_file=project_file)\\n # Run Fama\\n project = protein_pipeline(project)\\n\\n out_dir = os.path.join(work_dir, 'out')\\n os.mkdir(out_dir)\\n # export_reads\\n output = {}\\n output['krona_charts'] = {}\\n\\n # Generate output\\n report_files = {}\\n metric = 'proteincount'\\n project_xlsx_report = sanitize_file_name(os.path.join(\\n project.options.work_dir,\\n project.options.project_name + '_' + metric + '_functions.xlsx'\\n ))\\n if os.path.exists(project_xlsx_report):\\n report_files[project_xlsx_report] = 'Functional_profiles_combined.xlsx'\\n else:\\n print('Project XLSX file not found:', project_xlsx_report)\\n project_xlsx_report = sanitize_file_name(os.path.join(\\n project.options.work_dir,\\n project.options.project_name + '_' + metric + '_functions_taxonomy.xlsx'\\n ))\\n if os.path.exists(project_xlsx_report):\\n report_files[project_xlsx_report] = 'Function_taxonomy_profiles_combined.xlsx'\\n else:\\n print('Project XLSX file not found:', project_xlsx_report)\\n project_text_report = sanitize_file_name(os.path.join(\\n project.options.work_dir,\\n 'all_proteins.list.txt'\\n ))\\n if os.path.exists(project_text_report):\\n report_files[project_text_report] = 'proteins_list.txt'\\n else:\\n print('Proteins list not found:', project_text_report)\\n\\n featureset_elements = {}\\n featureset_element_ordering = []\\n objects_created = []\\n genome_names = {}\\n # Get Domain Model Set reference\\n dms_ref = get_dms(params['reference'],\\n project.config.get_functions_file(project.collection),\\n params['ws_name'],\\n params['ws_client']\\n )\\n\\n for sample_id in project.list_samples():\\n annotation_obj_ref, feature_ids, genome_name = \\\\\\n save_domain_annotations(project, dms_ref, params['ws_name'],\\n params['ws_client'], params['annotation_prefix'],\\n sample_id, params['name2ref'][sample_id])\\n genome_names[sample_id] = genome_name\\n objects_created.append({'ref': annotation_obj_ref,\\n 'description': 'Functional annotations for genome '\\n + project.samples[sample_id].sample_name})\\n for feature_id in feature_ids:\\n if feature_id not in featureset_elements:\\n featureset_elements[feature_id] = []\\n featureset_elements[feature_id].append(params['name2ref'][sample_id])\\n featureset_element_ordering.append(feature_id)\\n\\n sample_xlsx_report = sanitize_file_name(os.path.join(\\n project.options.work_dir,\\n sample_id + '_' + metric + '_functions_taxonomy.xlsx'\\n ))\\n if os.path.exists(sample_xlsx_report):\\n report_files[sample_xlsx_report] = \\\\\\n sanitize_file_name(genome_name + '_function_taxonomy_profile_long.xlsx')\\n else:\\n print('Sample XLSX file not found:', sample_xlsx_report)\\n krona_file = sanitize_file_name(os.path.join(\\n project.options.work_dir,\\n sample_id + '_' + metric + '_functional_taxonomy_profile.xml.html'\\n ))\\n\\n if os.path.exists(krona_file):\\n krona_output = \\\\\\n sanitize_file_name(os.path.join(out_dir, genome_name +\\n '_function_taxonomy_profile_chart.html'))\\n shutil.copy2(krona_file, krona_output)\\n with zipfile.ZipFile(krona_output + '.zip', 'w',\\n zipfile.ZIP_DEFLATED,\\n allowZip64=True) as zip_file:\\n zip_file.write(krona_output,\\n sanitize_file_name(genome_name +\\n '_function_taxonomy_profile_chart.html'))\\n report_files[krona_output] = \\\\\\n sanitize_file_name(genome_name + '_function_taxonomy_profile_chart.html')\\n output['krona_charts'][krona_output + '.zip'] = \\\\\\n (sanitize_file_name(genome_name + '_function_taxonomy_profile_chart.html'),\\n sample_id + ' function taxonomy chart')\\n else:\\n print('Krona diagram file not found:', krona_file)\\n\\n feature_set_data = {'description': 'FeatureSet generated by Fama protein profiling',\\n 'element_ordering': featureset_element_ordering,\\n 'elements': featureset_elements}\\n\\n out_report = os.path.join(out_dir, 'fama_report.html')\\n generate_protein_html_report(out_report, project, params['name2ref'])\\n with zipfile.ZipFile(out_report + '.zip', 'w',\\n zipfile.ZIP_DEFLATED,\\n allowZip64=True) as zip_file:\\n zip_file.write(out_report, 'fama_report.html')\\n output['html_report'] = out_report + '.zip'\\n report_files[out_report] = 'fama_report.html'\\n\\n output_files = list()\\n result_file = os.path.join(project.options.work_dir, 'Fama_result.zip')\\n with zipfile.ZipFile(result_file, 'w',\\n zipfile.ZIP_DEFLATED,\\n allowZip64=True) as zip_file:\\n for filename in report_files:\\n zip_file.write(filename, report_files[filename])\\n output_files.append({'path': result_file,\\n 'name': os.path.basename(result_file),\\n 'label': os.path.basename(result_file),\\n 'description': 'Files generated by Fama App'})\\n output['report_files'] = output_files\\n output['project'] = project\\n output['feature_set_data'] = feature_set_data\\n output['objects_created'] = objects_created\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210bda854b7dc1ee0ac3082390d2e403\",\n \"score\": \"0.56162924\",\n \"text\": \"def _PTransformFn(pcoll_or_pipeline: Any):\\n return (\\n pcoll_or_pipeline\\n | \\\"RawRecordBeamSource\\\" >> self.RawRecordBeamSource()\\n | \\\"RawRecordToRecordBatch\\\" >> self.RawRecordToRecordBatch(batch_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1949be9ca8db1a5fe64b08a09a26ab\",\n \"score\": \"0.55996466\",\n \"text\": \"def run_model_pipeline(process_number, uuid_list):\\n\\n\\n\\n\\n\\n\\tfor uuid in uuid_list:\\n\\t\\tif uuid is None:\\n\\t\\t\\tcontinue\\n\\n\\t\\ttry: \\n\\t\\t\\trun_model_pipeline_for_user(uuid)\\n\\t\\texcept Exception as e:\\n\\t\\t\\tprint(\\\"dang flabbit failed on error %s\\\" % e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5c8dad9f24d757d3de0ad12b850e69\",\n \"score\": \"0.55933267\",\n \"text\": \"def setup(self):\\n ### Set Names\\n '''\\n NAMES:\\n Set the internal name for the function and the procedure identifier to be incorporated in output file names. \\n name: The name should be all lower-case and should be identical to \\n the module name (the module name is used to identify pipe steps\\n in the config file, so if it doesn’t match case an error will occur). \\n procname: The procname should be all upper-case and, if possible, \\n restricted to three or four alphabetic characters(the code would \\n automatically capitalize, but it’s good form to also capitalize it here).\\n '''\\n # Name of the pipeline reduction step\\n self.name='srcextpy'\\n # Shortcut for pipeline reduction step and identifier for\\n # saved file names.\\n self.procname = 'SEP'\\n # Set Logger for this pipe step\\n self.log = logging.getLogger('pipe.step.%s' % self.name)\\n ### Set Parameter list\\n '''\\n PARAMETERS:\\n Parameters are stored in a list containing the following information:\\n name: The name for the parameter. This name is used when calling the \\n pipe step from command line or python shell. It is also used to \\n identify the parameter in the pipeline configuration file.\\n default: A default value for the parameter. \\n If nothing, set ‘ ’ for strings 0 for integers, and 0.0 for floats.\\n help: A short description of the parameter.\\n '''\\n # Clear Parameter list\\n self.paramlist = []\\n # Append parameters\\n self.paramlist.append(['sourcetable',False,\\n 'Flag for making txt table of all sources'])\\n self.paramlist.append(['sourcetableformat','csv',\\n 'txt table format (see astropy.io.ascii for options)'])\\n self.paramlist.append(['bkg_maskthreshold', 0.0,\\n 'mask threshold for background detection'])\\n self.paramlist.append(['bkg_wh', [16,16],\\n 'background box width and height for background detection'])\\n self.paramlist.append(['bkg_filterwh', [3,3],\\n 'filter width and height for background detection'])\\n self.paramlist.append(['bkg_fthreshold', 0.0,\\n 'filter threshold for background detection'])\\n self.paramlist.append(['ext_thresh', 2.0,\\n 'extraction threshold for source extration'])\\n self.paramlist.append(['ext_bfactor', 10.0,\\n 'brightness factor for creating highlevel threshold'])\\n self.paramlist.append(['ext_deblend', 256,\\n 'deblend threshold for source extraction'])\\n self.paramlist.append(['phot_kronf', 2.5,\\n 'factor multiplied into kronrad to get radius for integration'])\\n self.paramlist.append(['save_background', True,\\n 'option to save the background as a seprate hdu'])\\n #self.paramlist.append(['byte_swap', False,\\n # \\t 'says if the bytes should be swapped or not for the image'])\\n # confirm end of setup\\n self.log.debug('Setup: done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d3521b86747b932030927e0514afb12\",\n \"score\": \"0.5582054\",\n \"text\": \"def run(self, stream):\\n \\n # Run any preparations that need to exist before the handler runs\\n self.prepare()\\n \\n for line in stream:\\n if self.terminate_early:\\n break \\n\\n line = line.rstrip('\\\\n')\\n \\n if line.startswith('##'):\\n self.process_meta(line)\\n else:\\n fields = line.split('\\\\t')\\n \\n # Do we have a genotype field?\\n if line.startswith('#'):\\n self.has_genotype_field = 'FORMAT' in fields\\n if self.has_genotype_field:\\n individuals = fields[FIXED_FIELDS_LENGTH+1:]\\n else:\\n individuals = fields[FIXED_FIELDS_LENGTH:]\\n self.process_individuals(individuals)\\n \\n else:\\n fixed_fields = fields[:FIXED_FIELDS_LENGTH]\\n if self.has_genotype_field:\\n format_field = fields[FIXED_FIELDS_LENGTH]\\n data_fields = fields[FIXED_FIELDS_LENGTH+1:]\\n else:\\n format_field = ''\\n data_fields = fields[FIXED_FIELDS_LENGTH:]\\n \\n self.process_data(fixed_fields, format_field, data_fields)\\n \\n self.terminate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c592a8ae39158ae799e3d436e27c44c\",\n \"score\": \"0.55707747\",\n \"text\": \"def create_pipeline(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68a756ee93440f8223c5234e7c6b31af\",\n \"score\": \"0.55685365\",\n \"text\": \"def data_process(self, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8af7fad6192c8fdb1b99c8dfc1b612\",\n \"score\": \"0.5535488\",\n \"text\": \"def process_data(app):\\n data_path_fn = data_path(app)\\n _process_shops(data_path_fn)\\n _process_products(data_path_fn)\\n _process_tags(data_path_fn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf012acd44a35a6ffb9625e9ffe2697\",\n \"score\": \"0.5532902\",\n \"text\": \"def main(args):\\n\\n # Look up the type of args, close the verified file handle we were passed\\n # in, and begin data processing\\n\\n folder = None\\n outfile = None\\n\\n destination = args.DEST[0]\\n if os.path.isdir(destination):\\n DEBUG('Output is to folder %s' % destination)\\n folder = destination\\n else:\\n DEBUG('Output is to file %s' % destination)\\n outfile = destination\\n\\n for handle in args.SOURCE:\\n handle.close() # we know it exists b/c it's filetype\\n process(handle.name, args.LAYER[0], outfile, folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eb74512072676ebca6541f58b0a1642\",\n \"score\": \"0.5532351\",\n \"text\": \"def main():\\n logger = logging.getLogger(__name__)\\n # logger.info('downloading the raw data')\\n # download_raw_data()\\n logger.info('processing the raw data')\\n SplitAndScale()\\n logger.info('planing the mass')\\n GetPlaningWeights()\\n logger.info('Do the PCA rotations')\\n PCA()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ccf0d7aa98e8c6a30775cce5e190a42\",\n \"score\": \"0.5504588\",\n \"text\": \"def run(argv=None):\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--input',\\n dest='input',\\n required=True,\\n default='gs://dataflow-samples/shakespeare/kinglear.txt',\\n help='Input file to process.')\\n parser.add_argument('--output',\\n dest='output',\\n required=True,\\n default='gs://dataflow-samples/',\\n help='Output file to write results to.')\\n parser.add_argument('--topic',\\n dest='topic',\\n required=True,\\n help='Topic for message.')\\n parser.add_argument('--subscription',\\n dest='subscription',\\n required=True,\\n help='Subscription for message.')\\n parser.add_argument('--entity_type',\\n dest='entity_type',\\n required=True,\\n help='Entity Type for message.')\\n parser.add_argument('--event_type',\\n dest='event_type',\\n required=True,\\n help='Event Type for message.') \\n parser.add_argument('--outputFilenamePrefix',\\n dest='outputFilenamePrefix',\\n required=True,\\n help='Output Filename Prefix Type for message.') \\n parser.add_argument('--outputFilenameSuffix',\\n dest='outputFilenameSuffix',\\n required=True,\\n help='Output Filename Suffix Type for message.') \\n\\n known_args, pipeline_args = parser.parse_known_args(argv)\\n\\n # We use the save_main_session option because one or more DoFn's in this\\n # workflow rely on global context (e.g., a module imported at module level).\\n pipeline_options = PipelineOptions(pipeline_args)\\n pipeline_options.view_as(SetupOptions).save_main_session = True\\n pipeline_options.view_as(StandardOptions).streaming= True\\n p = beam.Pipeline(options=pipeline_options)\\n\\n if known_args.subscription:\\n messages = (p \\n | ReadFromPubSub(subscription=known_args.subscription, with_attributes=True))\\n else:\\n messages = (p \\n | ReadFromPubSub(subscription=known_args.topic, with_attributes=True))\\n\\n def printattr(msg):\\n print(msg.attributes)\\n\\n (messages \\n | \\\"Print Attributes\\\" >> beam.Map(printattr)\\n | beam.WindowInto(window.FixedWindows(120,0))\\n | beam.io.WriteToText(known_args.output + known_args.outputFilenamePrefix, \\n file_name_suffix=known_args.outputFilenameSuffix,\\n num_shards=1))\\n\\n result = p.run()\\n result.wait_until_finish()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad132a892dd8b1e3cab0dd484e65ea88\",\n \"score\": \"0.548488\",\n \"text\": \"def transform(self, *args, **kwargs):\\n return self.pipeline.transform(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"890ad26c357e40e6349a6d8a782cb4f4\",\n \"score\": \"0.5483538\",\n \"text\": \"def __call__(self, **args):\\n return self.pipeline[:self.name](**args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ea7eb15408029479fdc27aee50db850\",\n \"score\": \"0.5481434\",\n \"text\": \"def build_own_pipeline() -> Pipeline:\\n ##### Write code here #######\\n pipeline = None\\n SVM = SVC(C=4, kernel=\\\"linear\\\")\\n estimator = []\\n estimator.append((\\\"CountVectorizer\\\", CountVectorizer()))\\n estimator.append((\\\"SVC\\\", SVM))\\n pipeline = Pipeline(estimator)\\n ##### End of your work ######\\n return pipeline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0a9562a2d30840820449040a6f32e8\",\n \"score\": \"0.547682\",\n \"text\": \"def process(self, raw_data, raw_data_filenames=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b41eac949e2c40eaf032887f2e451d9\",\n \"score\": \"0.546774\",\n \"text\": \"def __call__(self, *args) -> Tuple[Any]:\\n # Check if no data supplied to pipeline\\n if not args:\\n raise ValueError(\\n 'No data supplied to pipeline')\\n\\n # Create `Variable` tuple from input arguments.\\n vargs = []\\n for arg in args:\\n vtype = type(arg).__name__\\n vargs.append(Variable(vtype, arg))\\n vargs = tuple(vargs)\\n\\n # Process the data through the pipeline.\\n in_df = DataFrame('_in_df')\\n in_df.put(vargs)\\n out_df = self._pipeline(in_df)\\n\\n # Extract values from output tuple\\n res = []\\n for v in out_df.get(0, out_df.size)[1][0]:\\n res.append(v.value)\\n\\n return tuple(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8f60a8a77c3bdd0c20432a5620eb76a\",\n \"score\": \"0.5464261\",\n \"text\": \"def process_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee7f7ccbb6819646915105052a873d3a\",\n \"score\": \"0.54573524\",\n \"text\": \"def make_pipeline():\\n # Pipeline constants ----\\n\\n # Used to filer the strength threshold for Bullish sentiment.\\n st_strength = 0.25\\n\\n # Pipeline Factors -----\\n\\n # Base universe set to the Q500US\\n base_universe = Q1500US()\\n\\n # Messages\\n message_volume = MessageVolume()\\n\\n # Bull messages\\n bull_messages = BullMessages()\\n\\n # Bull message percentage\\n bull_score = (bull_messages / message_volume)\\n\\n # Sentiment Filters\\n bull_sentiment = bull_score > st_strength\\n\\n # Pipeline Creation ----\\n\\n pipe = Pipeline(\\n screen=(base_universe &\\n bull_sentiment),\\n columns={\\n 'Message Volume': message_volume,\\n 'Bull Messages': bull_messages,\\n 'Bull % of messages': bull_score,\\n }\\n )\\n return pipe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ffbd9aac2a9286cdee5e39ffc6b85c\",\n \"score\": \"0.5454726\",\n \"text\": \"def run(self):\\n self.set_pipeline()\\n return self.pipeline.fit(self.X, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8713faa23c9ad8a061316ac70a37379\",\n \"score\": \"0.5445825\",\n \"text\": \"def run_processing(self, inputs, transdir, id_='unknown', parameters=[]):\\n\\n args = docopt(usage_extra, argv=parameters)\\n\\n\\n # Send all output to STDERR (so that jsawrapdr collects it).\\n os.dup2(sys.stderr.fileno(), sys.stdout.fileno())\\n\\n outputs = transient_analysis(\\n inputfiles=inputs,\\n reductiontype=args['<reduction_type>'],\\n no_450_cat=args['--no-450-cat'],\\n as_ref_cat=args['--as-ref-cat'],\\n dimmconfig_850=args['--dimmconfig850'],\\n dimmconfig_450=args['--dimmconfig450'],\\n fixed_dra=args['--dra'],\\n fixed_ddec=args['--ddec'],\\n mask_suffix=args['--mask'])\\n\\n for filename in outputs:\\n (base_name, extension) = os.path.splitext(filename)\\n\\n if extension == '.sdf':\\n logger.info('Storing SDF file \\\"%s\\\"', filename)\\n filename_edited = re.sub(\\n '_cal$', '', base_name) + '.sdf'\\n filename_trans = os.path.join(transdir, filename_edited)\\n subprocess.check_call(\\n [\\n os.path.join(\\n os.environ['STARLINK_DIR'],\\n 'bin', 'h5repack'),\\n '-i', filename,\\n '-o', filename_trans,\\n '-f', 'NONE',\\n ],\\n shell=False)\\n if not args['--nozip']:\\n subprocess.check_call(\\n [\\n '/bin/gzip',\\n '--best',\\n filename_trans,\\n ],\\n shell=False)\\n\\n elif extension == '.txt':\\n logger.info('Storing text file \\\"%s\\\"', filename)\\n shutil.copy(filename, transdir)\\n\\n elif extension == '.png':\\n logger.info('Storing preview image \\\"%s\\\"', filename)\\n shutil.copy(filename, transdir)\\n\\n elif extension == '.FIT':\\n filename_edited = re.sub(\\n '_crop.*', '_cat', base_name) + '.fits'\\n logger.info('Storing FITS file \\\"%s\\\" as \\\"%s\\\"',\\n filename, filename_edited)\\n shutil.copyfile(\\n filename, os.path.join(transdir, filename_edited))\\n\\n else:\\n raise Exception('Unexpected output file \\\"{}\\\"'.format(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02edee2583ab16c9a4b07afd3ff1696a\",\n \"score\": \"0.54428303\",\n \"text\": \"def main():\\r\\n\\r\\n logging.debug(\\\"Initialization of a Spark session...\\\")\\r\\n spark = create_sparksession()\\r\\n\\r\\n logging.debug(\\\"Start processing source data\\\")\\r\\n sdw = SourceDataWrangling(spark)\\r\\n sdw.process_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5730a4c2d55890c19f0063432e3e2c\",\n \"score\": \"0.54194224\",\n \"text\": \"def test_pipeline_transform(\\n primary_model_num_feature_generators: int,\\n metalabeling_model_num_feature_generators: int,\\n dollar_bar_dataframe: pd.DataFrame,\\n bar_feature_generator,\\n):\\n # create dict structure for primary model feature generator\\n feature_transformers_primary_model = {\\n f\\\"primary_{i}\\\": bar_feature_generator\\n for i in range(primary_model_num_feature_generators)\\n }\\n\\n # create dict structure for metalabeling model feature generator\\n feature_transformers_metalabeling_model = {\\n f\\\"metalabeling_{i}\\\": bar_feature_generator\\n for i in range(metalabeling_model_num_feature_generators)\\n }\\n\\n align_how_primary = {\\n f\\\"primary_{i}\\\": \\\"mean\\\" for i in range(primary_model_num_feature_generators)\\n }\\n align_how_secondary = {\\n f\\\"metalabeling_{i}\\\": \\\"mean\\\"\\n for i in range(metalabeling_model_num_feature_generators)\\n }\\n\\n pipeline = StrategySignalPipeline(\\n feature_transformers_primary_model=feature_transformers_primary_model,\\n feature_transformers_metalabeling_model=feature_transformers_metalabeling_model,\\n align_on=\\\"primary_0\\\",\\n align_how={**align_how_primary, **align_how_secondary},\\n )\\n\\n # transform data with pipeline\\n X_features_pipeline = pipeline.transform(\\n {\\n **{\\n f\\\"primary_{i}\\\": dollar_bar_dataframe\\n for i in range(primary_model_num_feature_generators)\\n },\\n **{\\n f\\\"metalabeling_{i}\\\": dollar_bar_dataframe\\n for i in range(metalabeling_model_num_feature_generators)\\n },\\n }\\n )\\n\\n # check expected output\\n X_features_expected = bar_feature_generator.transform(dollar_bar_dataframe)\\n if primary_model_num_feature_generators > 0:\\n for df in X_features_pipeline[\\\"primary_model_features\\\"].values():\\n pd.testing.assert_frame_equal(df, X_features_expected)\\n if metalabeling_model_num_feature_generators > 0:\\n for df in X_features_pipeline[\\\"metalabeling_model_features\\\"].values():\\n pd.testing.assert_frame_equal(df, X_features_expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28bba944d1602df153d4f2c8e4afcf0c\",\n \"score\": \"0.54061794\",\n \"text\": \"def feed_data(self, data: Any):\\n self.concurrent_pipelines = 1\\n self.step(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29f9687b595e5568f447b931ae90261\",\n \"score\": \"0.54011166\",\n \"text\": \"def handle(self, data, **kwargs):\\n return data.map(lambda x: self.model.load(x))\\\\\\n .filter(lambda x: x is not None)\\\\\\n .map(lambda x: x.transform())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0fda8ab5a8b63877b9f8d834eb0996a\",\n \"score\": \"0.5399969\",\n \"text\": \"def main():\\n args = get_parsed_args()\\n use_gpu = torch.cuda.is_available()\\n\\n model = Checkpoint.load_model_from_dir(args.dir)\\n if use_gpu:\\n model.cuda()\\n\\n # load prior\\n if args.prior:\\n log_prior = args.prior_scale * read_log_prior(args.prior)\\n else:\\n log_prior = 0\\n\\n feat = {\\n 'rspec': args.feat_rspec,\\n 'transform': args.transform,\\n 'skip_frame': args.skip_frame,\\n 'target_delay': args.target_delay\\n }\\n\\n data_rspecs = [[feat], [], [], []]\\n\\n collector = SequenceCollector(args.stream_size, args.frame_limit)\\n utt_reader = UtteranceReader(data_rspecs, random_sweep=False)\\n data_queue = BatchLoader(utt_reader, collector)\\n\\n decode(model, data_queue, log_prior, args.temperature)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1423b80869043c71bb6e430f0b16b42e\",\n \"score\": \"0.53983605\",\n \"text\": \"def runner(self):\\n printtime('Starting {} analysis pipeline'.format(self.analysistype), self.starttime)\\n if not self.pipeline:\\n # If the metadata has been passed from the method script, self.pipeline must still be false in order to\\n # get Sippr() to function correctly, but the metadata shouldn't be recreated\\n try:\\n _ = vars(self.runmetadata)['samples']\\n except KeyError:\\n # Create the objects to be used in the analyses\\n objects = Objectprep(self)\\n objects.objectprep()\\n self.runmetadata = objects.samples\\n self.threads = int(self.cpus / len(self.runmetadata.samples)) \\\\\\n if self.cpus / len(self.runmetadata.samples) > 1 \\\\\\n else 1\\n # Use a custom sippr method to use the full reference database as bait, and run mirabait against the FASTQ\\n # reads - do not perform reference mapping yet\\n SixteenSBait(self, self.cutoff)\\n # Subsample 1000 reads from the FASTQ files\\n self.subsample()\\n # Convert the subsampled FASTQ files to FASTA format\\n self.fasta()\\n # Create BLAST databases if required\\n self.makeblastdb()\\n # Run BLAST analyses of the subsampled FASTA files against the NCBI 16S reference database\\n self.blast()\\n # Parse the BLAST results\\n self.blastparse()\\n # Feed the BLAST results into a modified sippr method to perform reference mapping using the calculated\\n # genus of the sample as the mapping file\\n SixteenSSipper(self, self.cutoff)\\n # Create reports\\n self.reporter()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dec2af9cbaca96c263ba26d66335aea\",\n \"score\": \"0.5396622\",\n \"text\": \"def run(self):\\n self.pipeline = self.set_pipeline()\\n return self.pipeline.fit(self.X, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c12ff9ff68b4bcdecb51b17162f27ce\",\n \"score\": \"0.5396334\",\n \"text\": \"def __push_samples_into_relevant_pipelines__(self,configs,mockdb):\\n if configs[\\\"system\\\"].get(\\\"Logging\\\",\\\"debug\\\") is \\\"True\\\":\\n print \\\" Starting post casava sample pipelines for \\\" + self.flowcell_key\\n print \\\" Determining Sample dirs\\\"\\n sample_dirs = list_project_sample_dirs(self.output_dir.split(\\\":\\\"))\\n if configs[\\\"system\\\"].get(\\\"Logging\\\",\\\"debug\\\") is \\\"True\\\":\\n print \\\" Samples: \\\" + str(sample_dirs) \\n flowcell_dir_name = os.path.basename(self.output_dir)\\n automation_parameters_config = MyConfigParser()\\n automation_parameters_config.read(configs[\\\"system\\\"].get(\\\"Filenames\\\",\\\"automation_config\\\"))\\n fastqc_pipeline_config = MyConfigParser()\\n fastqc_pipeline_config.read(configs[\\\"system\\\"].get(\\\"Pipeline\\\",\\\"FastQCPipeline\\\"))\\n for project in sample_dirs:\\n for sample in sample_dirs[project]:\\n #running_location = identify_running_location_with_most_currently_available(configs,storage_devices)\\n running_location = \\\"Speed\\\"\\n parsed = parse_sample_sheet(configs['system'],mockdb,sample_dirs[project][sample][0])\\n if configs[\\\"system\\\"].get(\\\"Logging\\\",\\\"debug\\\") is \\\"True\\\":\\n print \\\" Pushing fastqc pipeline for \\\" + sample\\n fastqc_pipeline = mockdb[\\\"FastQCPipeline\\\"].__new__(configs['system'],input_dir=sample_dirs[project][sample][0],flowcell_dir_name=flowcell_dir_name,project=parsed['project_name'],pipeline_config=fastqc_pipeline_config,seq_run_key=self.seq_run_key,**parsed)\\n description_dict = parse_description_into_dictionary(parsed['description'])\\n if 'Pipeline' in description_dict:\\n pipeline_key = description_dict['Pipeline']\\n else:\\n description_pieces = parsed['description'].split('-')\\n pipeline_key = description_pieces[-1]\\n pipeline_name = automation_parameters_config.safe_get(\\\"Pipeline\\\",pipeline_key)\\n if pipeline_name is None:\\n continue\\n if configs[\\\"system\\\"].get(\\\"Logging\\\",\\\"debug\\\") is \\\"True\\\":\\n print \\\"Starting \\\" + pipeline_name + \\\" for \\\" + sample\\n pipeline = mockdb[pipeline_name].__new__(configs['system'],input_dir=sample_dirs[project][sample][0],pipeline_key=pipeline_key,seq_run_key=self.seq_run_key,project=parsed['project_name'],flowcell_dir_name=flowcell_dir_name,**parsed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cdcf851c6a7f3f7b9ee8a1a502ff54b\",\n \"score\": \"0.53907794\",\n \"text\": \"def execute_pipeline(self, input_, mode):\\n if not self.transformer_sequence:\\n raise Exception(\\n \\\"run() must be called to extract/create a TransformerSequence\\\"\\n )\\n \\n if not isinstance(mode, PipelineModeType):\\n raise Exception(\\\"mode must be of type PipelineModeType Enum object.\\\")\\n\\n for transformer in self.transformer_sequence.transformers():\\n\\n if mode == PipelineModeType.FIT:\\n\\n transformer.fit(input_)\\n\\n elif mode == PipelineModeType.TRANSFORM:\\n\\n input_ = transformer.transform(input_)\\n\\n elif mode == PipelineModeType.FIT_TRANSFORM:\\n\\n input_ = transformer.fit_transform(input_)\\n\\n return input_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982bc7078ce663a4d379c242536e0eb9\",\n \"score\": \"0.5390698\",\n \"text\": \"def main():\\r\\n \\r\\n name = sys.argv[1]\\r\\n# name = \\\"input.yaml\\\"\\r\\n \\r\\n \\r\\n hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out = bc.set_temp_boundary_conditions(name)\\r\\n \\r\\n h_cold, area_cold, h_hot, area_hot = bc.set_flow_boundary_conditions(name)\\r\\n\\r\\n \\r\\n q_fin_counter, fin_counter_vars = hx.q_fin(hx.log_mean_temp_diff_counter(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\\r\\n q_fin_parallel, fin_parallel_vars = hx.q_fin(hx.log_mean_temp_diff_parallel(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\\r\\n \\r\\n q_tube_counter, tube_counter_vars = hx.q_tube(hx.log_mean_temp_diff_counter(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\\r\\n q_tube_parallel, tube_parallel_vars = hx.q_tube(hx.log_mean_temp_diff_parallel(hot_temp_in, hot_temp_out, cold_temp_in, cold_temp_out),name)\\r\\n \\r\\n max_counter_fin = []\\r\\n max_parallel_fin = []\\r\\n \\r\\n max_counter_tube = []\\r\\n max_parallel_tube = []\\r\\n \\r\\n \\r\\n \\r\\n for i in range(len(q_fin_counter)):\\r\\n if q_fin_counter[i] == max(q_fin_counter):\\r\\n max_counter_fin.append(fin_counter_vars[i])\\r\\n if q_fin_parallel[i] == max(q_fin_parallel):\\r\\n max_parallel_fin.append(fin_parallel_vars[i])\\r\\n \\r\\n \\r\\n for i in range(len(q_tube_counter)):\\r\\n if q_tube_counter[i] == max(q_tube_counter):\\r\\n max_counter_tube.append(tube_counter_vars[i])\\r\\n if q_tube_parallel[i] == max(q_tube_parallel):\\r\\n max_parallel_tube.append(tube_parallel_vars[i])\\r\\n \\r\\n print(max(q_fin_counter))\\r\\n print(max(q_fin_parallel))\\r\\n print(max(q_tube_counter))\\r\\n print(max(q_tube_parallel))\\r\\n \\r\\n print(max_counter_fin)\\r\\n print(max_parallel_fin)\\r\\n print(max_counter_tube)\\r\\n print(max_parallel_tube)\\r\\n \\r\\n \\r\\n \\r\\n plt.scatter(range(len(q_fin_counter)), q_fin_counter, label = \\\"Finned Counter-Flow\\\")\\r\\n plt.title('Finned Counter-Flow')\\r\\n plt.xlabel('Input Variable Permutations')\\r\\n plt.ylabel('Heat Rate (W)')\\r\\n plt.legend()\\r\\n plt.savefig(\\\"q_fin_counter.png\\\")\\r\\n plt.show()\\r\\n plt.close()\\r\\n \\r\\n plt.scatter(range(len(q_fin_parallel)), q_fin_parallel, label = \\\"Finned Parallel Flow\\\")\\r\\n plt.title('Finned Parallel Flow')\\r\\n plt.xlabel('Input Variable Permutations')\\r\\n plt.ylabel('Heat Rate (W)')\\r\\n plt.legend()\\r\\n plt.savefig(\\\"q_fin_parallel.png\\\")\\r\\n plt.show()\\r\\n plt.close()\\r\\n \\r\\n plt.scatter(range(len(q_tube_counter)), q_tube_counter, label = \\\"Tubed Counter-Flow\\\")\\r\\n plt.title('Tubed Counter-Flow')\\r\\n plt.xlabel('Input Variable Permutations')\\r\\n plt.ylabel('Heat Rate (W)')\\r\\n plt.legend()\\r\\n plt.savefig(\\\"q_tube_counter.png\\\")\\r\\n plt.show()\\r\\n plt.close()\\r\\n \\r\\n plt.scatter(range(len(q_tube_parallel)), q_tube_parallel, label = \\\"Tubed Parallel Flow\\\")\\r\\n plt.title('Tubed Parallel-Flow')\\r\\n plt.xlabel('Input Variable Permutations')\\r\\n plt.ylabel('Heat Rate (W)')\\r\\n plt.legend()\\r\\n plt.savefig(\\\"q_tube_parallel.png\\\")\\r\\n plt.show()\\r\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938efc26626ac693c09a2e350c6f4985\",\n \"score\": \"0.5387147\",\n \"text\": \"def preprocess(pipeline, training_data, eval_data, predict_data, output_dir,\\n frequency_threshold):\\n\\n # 1) The schema can be either defined in-memory or read from a configuration\\n # file, in this case we are creating the schema in-memory.\\n input_schema = make_bq_sql.make_input_schema()\\n\\n # 2) Read from BigQuery or from CSV.\\n train_data = pipeline | 'ReadTrainingData' >> _ReadData(training_data)\\n evaluate_data = pipeline | 'ReadEvalData' >> _ReadData(eval_data)\\n\\n input_metadata = dataset_metadata.DatasetMetadata(schema=input_schema)\\n\\n _ = (input_metadata\\n | 'WriteInputMetadata' >> tft_beam_io.WriteMetadata(\\n os.path.join(output_dir, path_constants.RAW_METADATA_DIR),\\n pipeline=pipeline))\\n\\n preprocessing_fn = make_bq_sql.make_preprocessing_fn(frequency_threshold)\\n transform_fn = ((train_data, input_metadata)\\n | 'Analyze' >> tft.AnalyzeDataset(preprocessing_fn))\\n\\n # WriteTransformFn writes transform_fn and metadata to fixed subdirectories\\n # of output_dir, which are given by path_constants.TRANSFORM_FN_DIR and\\n # path_constants.TRANSFORMED_METADATA_DIR.\\n _ = (transform_fn\\n | 'WriteTransformFn' >> tft_beam_io.WriteTransformFn(output_dir))\\n\\n @beam.ptransform_fn\\n def TransformAndWrite(pcoll, path): # pylint: disable=invalid-name\\n pcoll |= 'Shuffle' >> _Shuffle() # pylint: disable=no-value-for-parameter\\n (dataset, metadata) = (((pcoll, input_metadata), transform_fn)\\n | 'Transform' >> tft.TransformDataset())\\n coder = coders.ExampleProtoCoder(metadata.schema)\\n _ = (dataset\\n | 'SerializeExamples' >> beam.Map(coder.encode)\\n | 'WriteExamples' >> beam.io.WriteToTFRecord(\\n os.path.join(output_dir, path), file_name_suffix='.tfrecord.gz'))\\n\\n _ = train_data | 'TransformAndWriteTraining' >> TransformAndWrite( # pylint: disable=no-value-for-parameter\\n path_constants.TRANSFORMED_TRAIN_DATA_FILE_PREFIX)\\n\\n _ = evaluate_data | 'TransformAndWriteEval' >> TransformAndWrite( # pylint: disable=no-value-for-parameter\\n path_constants.TRANSFORMED_EVAL_DATA_FILE_PREFIX)\\n\\n # TODO(b/35300113) Remember to eventually also save the statistics.\\n\\n if predict_data:\\n predict_mode = tf.contrib.learn.ModeKeys.INFER\\n predict_schema = make_bq_sql.make_input_schema(mode=predict_mode)\\n predict_coder = coders.ExampleProtoCoder(predict_schema)\\n\\n serialized_examples = (pipeline\\n | 'ReadPredictData' >> _ReadData(\\n predict_data, mode=predict_mode)\\n # TODO(b/35194257) Obviate the need for this explicit\\n # serialization.\\n | 'EncodePredictData' >> beam.Map(\\n predict_coder.encode))\\n _ = (serialized_examples\\n | 'WritePredictDataAsTFRecord' >> beam.io.WriteToTFRecord(\\n os.path.join(output_dir,\\n path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),\\n file_name_suffix='.tfrecord.gz'))\\n _ = (serialized_examples\\n | 'EncodePredictAsB64Json' >> beam.Map(_encode_as_b64_json)\\n | 'WritePredictDataAsText' >> beam.io.WriteToText(\\n os.path.join(output_dir,\\n path_constants.TRANSFORMED_PREDICT_DATA_FILE_PREFIX),\\n file_name_suffix='.txt'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b8d6d933b66d37a281d95332f2b097\",\n \"score\": \"0.5386972\",\n \"text\": \"def execute(self):\\n self.data = self.extract()\\n self.records = self.transform()\\n self.path = self._load()\\n print(\\\"Extracted, transformed, and loaded for %s\\\" % self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7bb90271b982aae2e208fc8e37b3f8b\",\n \"score\": \"0.538034\",\n \"text\": \"def run(self):\\n ret = True \\n while ret is not False:\\n if self.input is None: # it's a faucet\\n ret = self.fn()\\n elif hasattr(self.input, 'get'): # it's a pipe\\n ob = self.input.get()\\n if ob is False:\\n self.put(ob)\\n break\\n ret = self.fn(ob)\\n if ret is not None: self.put(ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0834db06d5d37a827e22e3fbd9c7e13\",\n \"score\": \"0.5380174\",\n \"text\": \"def setup(self):\\n ### Set Names\\n # Name of the pipeline reduction step\\n self.name='masterflat'\\n # Shortcut for pipeline reduction step and identifier for\\n # saved file names.\\n self.procname = 'MFLAT'\\n # Set Logger for this pipe step\\n self.log = logging.getLogger('pipe.step.%s' % self.name)\\n ### Set Parameter list\\n # Clear Parameter list\\n self.paramlist = []\\n # Append parameters !!!! WHAT PARAMETERS ARE NEEDED ????? !!!!!\\n self.paramlist.append(['combinemethod','median',\\n 'Specifies how the files should be combined - options are\\\\\\n median , average, sum'])\\n self.paramlist.append(['outputfolder','',\\n 'Output directory location - default is the folder of \\\\\\n the input files'])\\n # Get parameters for StepLoadAux, replace auxfile with biasfile\\n self.loadauxsetup('bias')\\n # Get parameters for StepLoadAux, replace auxfile with darkfile\\n self.loadauxsetup('dark')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8befd29df0b73e6beaffa6d42f5875f\",\n \"score\": \"0.537462\",\n \"text\": \"def pipeline(*flags,**kwargs):\\n\\n\\tflags = list(flags)\\n\\t#---script and quit are protected and get popped\\n\\tscript = kwargs.pop('script',None)\\n\\tquit = kwargs.pop('quit',None)\\n\\tif not script and len(flags)>0: script = flags.pop(0)\\n\\tif not script:\\n\\t\\tpipeline_regex = '^pipeline-(.+)\\\\.py$'\\n\\t\\tprint \\\"[USAGE] make pipeline <name>\\\"\\n\\t\\tprint \\\"[USAGE] available pipelines: \\\\n > \\\"+'\\\\n > '.join([re.findall(pipeline_regex,i)[0] \\n\\t\\t\\tfor i in [os.path.basename(j) for j in glob.glob(\\\"./calcs/pipeline-*.py\\\")]\\n\\t\\t\\tif re.match(pipeline_regex,i)])\\n\\t\\treturn\\n\\t#---drop into the pipeline code but load the header first\\n\\tscript_fn = \\\"./calcs/pipeline-%s.py\\\"%script\\n\\tprint \\\"[STATUS] starting pipeline via %s\\\"%script_fn\\n\\tif not os.path.isfile(script_fn): raise Exception(\\\"[ERROR] cannot find \\\"+script_fn)\\n\\t#---all flags and arguments pass through\\n\\textra_args = \\\"import os,sys;\\\"\\n\\tfor flag in flags: extra_args += 'sys.argv.append(\\\\\\\"%s\\\\\\\");'%flag\\n\\tfor key,val in kwargs.items(): extra_args += 'sys.argv.append(\\\\\\\"%s=%s\\\\\\\");'%(key,val)\\n\\tos.system('python %s -c \\\\'%sexecfile(\\\"./omni/base/header.py\\\");execfile(\\\"%s\\\")\\\\''%(\\n\\t\\t'-i' if not quit else '',extra_args,script_fn))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b11f555455f17596d1c91cd4cd20f799\",\n \"score\": \"0.53695756\",\n \"text\": \"def main():\\r\\n nColSize, nRowSize, lstData, lstGraphLabels = pipe.retrieveData()\\r\\n aaData = _processData(nColSize, nRowSize, lstData) \\r\\n _saveImage(aaData, lstGraphLabels)\\r\\n pipe.updateStatus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2972e18b20842728d7edc9eb046f6694\",\n \"score\": \"0.53688323\",\n \"text\": \"def pipeline(request):\\n pipeline = gpipy.PipelineDriver()\\n return pipeline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5faaabc3906ef47b251b867bed02a71c\",\n \"score\": \"0.53661937\",\n \"text\": \"def main():\\n farser = FastaParser(sys.argv[1])\\n genes = farser.parse()\\n\\n #handle_codon_usage(genes)\\n #store_low_CAA_genes(genes)\\n #stretch_pos_histogram(genes)\\n stretch_codu_histogram(genes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760b5592a1f8d3a97f6f99ae51d317ca\",\n \"score\": \"0.5364469\",\n \"text\": \"def main(input_filepath, output_filepath):\\n #logger.info(\\\"Running generic data processing and cleanup scripts...\\\")\\n print_hihi(\\\"VALVAL\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5308e2a8ae073ddc473537e3ea41a2\",\n \"score\": \"0.53594476\",\n \"text\": \"def pre_process(dataset, args):\\n dataset = step_1(dataset, args)\\n dataset, eval_res_tuple = step_2(dataset, args)\\n return step_3(dataset, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc81e970b8a1c6054947967398109896\",\n \"score\": \"0.5356594\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(\\n description='process data with Spark, using Kafka as the transport')\\n parser.add_argument('--in', dest='input_topic',\\n help='the kafka topic to read data from')\\n parser.add_argument('--out', dest='output_topic',\\n help='the kafka topic to publish data to')\\n parser.add_argument('--count', dest='count_topic',\\n help='the kafka topic to publish count information to')\\n parser.add_argument('--servers', help='the kafka brokers')\\n args = parser.parse_args()\\n\\n processor = FilterStreamProcessor(\\n input_topic = args.input_topic,\\n output_topic = args.output_topic,\\n count_topic = args.count_topic,\\n servers = args.servers,\\n duration = 3)\\n\\n processor.start_and_await_termination()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91564e1b41e078c442db8f4c4ab514d1\",\n \"score\": \"0.53558266\",\n \"text\": \"def run_full_pipeline():\\n t0 = time.time()\\n mol = input('Which line (HCN, HCO, CS, or CO)?\\\\n').lower()\\n cut = input('Cut baselines for better signal (y/n)?\\\\n').lower()\\n cut_baselines = True if cut == 'y' else False\\n remake = input('Remake everything (y/n)?\\\\n')\\n remake_all = True if remake.lower() == 'y' else False\\n\\n # Paths to the data\\n jonas = '/Volumes/disks/jonas/'\\n raw_data_path = jonas + 'raw_data/'\\n final_data_path = jonas + 'modeling/data/' + mol + '/'\\n name = mol\\n if cut_baselines is True:\\n name += '-short' + str(lines[mol]['baseline_cutoff'])\\n\\n # Establish a string for the log file to be made at the end\\n log = 'Files created on ' + today + '\\\\n\\\\n'\\n\\n if remake_all is True:\\n # This doesn't work yet.\\n print(\\\"Remaking everything; emptied line dir and remaking.\\\")\\n remove(final_data_path + '*')\\n log += \\\"Full remake occured; all files are fresh.\\\\n\\\\n\\\"\\n else:\\n log += \\\"Some files already existed and so were not remade.\\\\n\\\"\\n log += \\\"Careful for inconsistencies.\\\\n\\\\n\\\"\\n\\n print(\\\"Now processing data....\\\")\\n casa_sequence(mol, raw_data_path,\\n final_data_path + name, cut_baselines)\\n\\n print(\\\"Running varvis....\\\\n\\\\n\\\")\\n if already_exists(final_data_path + name + '.uvf') is False:\\n # Note that var_vis takes in mol_exportuvfits, returns mol.uvf\\n var_vis(final_data_path + name)\\n print(\\\"Finished varvis; converting uvf to vis now....\\\\n\\\\n\\\")\\n\\n # Note that this is different than lines[mol][chan0_freq] bc\\n # it's dealing with the chopped vis set\\n restfreq = lines[mol]['restfreq']\\n f = fits.getheader(final_data_path + name + '.uvf')\\n\\n # chan0_freq = (f['CRVAL4'] - (f['CRPIX4']-1) * f['CDELT4']) * 1e-9\\n # Using the same math as in lines 130-135\\n # chan0_vel = c * (chan0_freq - restfreq)/restfreq\\n data, header = fits.getdata(final_data_path + name + '.uvf', header=True)\\n header['RESTFREQ'] = restfreq * 1e9\\n fits.writeto(final_data_path + name + '.uvf', data, header, overwrite=True)\\n if already_exists(final_data_path + name + '.vis') is False:\\n sp.Popen(['fits',\\n 'op=uvin',\\n 'in={}.uvf'.format(name),\\n # DONT PUT THIS BACK IN\\n # Or if you do, flip the sign of chan0_vel to pos\\n # 'velocity=lsr,{},1'.format(chan0_vel),\\n 'out={}.vis'.format(name)],\\n cwd=final_data_path).wait()\\n\\n\\n print(\\\"Convolving data to get image, converting output to .fits\\\\n\\\\n\\\")\\n if already_exists(final_data_path + name + '.cm') is False:\\n icr(final_data_path + name, mol=mol)\\n\\n print(\\\"Deleting the junk process files...\\\\n\\\\n\\\")\\n fpath = final_data_path + name\\n files_to_remove = [fpath + '.bm', fpath + '_split.*',\\n fpath + '.cl', fpath + '_cvel.*',\\n fpath + '.mp', fpath + '_exportuvfits.*',\\n 'casa*.log', '*.last']\\n remove(files_to_remove)\\n\\n tf = time.time()\\n t_total = (tf - t0)/60\\n log += '\\\\nThis processing took ' + str(t_total) + ' minutes.'\\n with open(final_data_path + 'file_log.txt', 'w') as f:\\n f.write(log)\\n print(\\\"All done! This processing took \\\" + str(t_total) + \\\" minutes.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77b0b0b48255c32f7fe5f9e858ceedd\",\n \"score\": \"0.5352431\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n description=DESC,\\n epilog=EPILOG\\n )\\n parser.add_argument('name', help='the streamer\\\\'s name')\\n parser.add_argument('-c', '--config', metavar='configfile',\\n help='Path of a JSON file containing a valid config.')\\n args = parser.parse_args()\\n if args.config is None: # process single file read from stdin\\n streamers = [{'name': args.name, 'code': sys.stdin.read()}]\\n else: # process one or more files from config\\n with open(args.config) as cf:\\n streamers = json.load(cf)\\n\\n flowcharts = (StreamerFlowchart(**s) for s in streamers)\\n figures = (latex.make_figure(f) for f in flowcharts)\\n document = latex.make_document(figures)\\n print(document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c877989b50d1af9a4acf74cb854aeff\",\n \"score\": \"0.53516805\",\n \"text\": \"def execute(self):\\n # input module\\n spl_cmd = SplInput().get_input() # search repo=\\\"mytest\\\"\\n\\n # parse module\\n # input -> pipe_cmd\\n # output -> collection of opts\\n antlr_parser = AntlrParser(\\\"antlr\\\", spl_cmd)\\n antlr_parser.validate()\\n opts = antlr_parser.parse()\\n\\n # schedule module\\n scheduler = Scheduler(\\\"scheduler\\\")\\n scheduler.schedule(opts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5493f2c29ba09f717fe1fd888948430d\",\n \"score\": \"0.5351004\",\n \"text\": \"def execute(self):\\n \\n # Make sure that input data has the same length\\n self.verify_input()\\n \\n for beam in self.eqbeams:\\n a = Assembly()\\n \\n # Add a splined blade planform from the input data\\n a.add('pf_splines', SplinedBladePlanform(True))\\n a.driver.workflow.add('pf_splines')\\n a.pf_splines.nC = 3\\n a.pf_splines.pfIn = beam\\n a.pf_splines.configure_splines()\\n \\n a.create_passthrough('pf_splines.blade_length')\\n a.create_passthrough('pf_splines.span_ni')\\n \\n a.add('blade_surface', LoftedBladeSurface())\\n a.driver.workflow.add('blade_surface')\\n \\n a.connect('pf_splines.pfOut', 'blade_surface.pf')\\n a.connect('span_ni', 'blade_surface.span_ni')\\n \\n # load the planform file\\n a.span_ni = self.span_ni\\n \\n b = a.blade_surface\\n \\n # distribute 200 points evenly along the airfoil sections\\n b.chord_ni = 200\\n for f in self.airfoils:\\n b.base_airfoils.append(np.loadtxt(f))\\n b.blend_var = np.array([0.25, 0.75])\\n \\n self.wingsurf.append(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3d17dcb9c07dec2aa57940394815aeb\",\n \"score\": \"0.53436625\",\n \"text\": \"def run(self):\\n\\t\\t# print(self.params)\\n\\t\\ttransformed_data = self.convert_json()\\n\\t\\tmsf.pickle_dump(transformed_data, self.file_out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb3bb02ff6a09759128c269e77afe34\",\n \"score\": \"0.5342858\",\n \"text\": \"def _process_data(self):\\n self.output_file.reset_data() # reset data in case processor instance is used multiple times\\n\\n while True:\\n data = self._get_next_chunk()\\n if data is None:\\n break\\n\\n self.processing_index_start = data.index[0]\\n self.processing_index_end = data.index[-1]\\n\\n data = self._apply_field_mapping(data) # Map inputs to outputs\\n data = self._validate_and_prepare_data(data) # Validate data by handling any nulls + type-casting\\n data = self._apply_field_transformations(data) # Process data transformations for all columns\\n self._flush_data(data)\\n\\n self._write_errors_and_warnings()\\n\\n self.stage = self.OUTPUT_DATA\\n self.output_file.generate_output() # append all data to output file or return processed data for python configs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d900ef664d8b8a2757c7d9632cfe8bc5\",\n \"score\": \"0.5342623\",\n \"text\": \"def update_pipeline(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ee3f6727ec9940d7f86e1bb5832c9d\",\n \"score\": \"0.5342543\",\n \"text\": \"def __feature_sequence_extraction(self, inputdata):\\n conv1 = self.__conv_stage(inputdata=inputdata, out_dims=64, name='conv1') # batch*16*50*64\\n conv2 = self.__conv_stage(inputdata=conv1, out_dims=128, name='conv2') # batch*8*25*128\\n conv3 = self.conv2d(inputdata=conv2, out_channel=256, kernel_size=3, stride=1, use_bias=False, name='conv3') # batch*8*25*256\\n relu3 = self.relu(conv3) # batch*8*25*256\\n conv4 = self.conv2d(inputdata=relu3, out_channel=256, kernel_size=3, stride=1, use_bias=False, name='conv4') # batch*8*25*256\\n relu4 = self.relu(conv4) # batch*8*25*256\\n max_pool4 = self.maxpooling(inputdata=relu4, kernel_size=[2, 1], stride=[2, 1], padding='VALID') # batch*4*25*256\\n conv5 = self.conv2d(inputdata=max_pool4, out_channel=512, kernel_size=3, stride=1, use_bias=False, name='conv5') # batch*4*25*512\\n relu5 = self.relu(conv5) # batch*4*25*512\\n if self.phase.lower() == 'train':\\n bn5 = self.layerbn(inputdata=relu5, is_training=True)\\n else:\\n bn5 = self.layerbn(inputdata=relu5, is_training=False) # batch*4*25*512\\n conv6 = self.conv2d(inputdata=bn5, out_channel=512, kernel_size=3, stride=1, use_bias=False, name='conv6') # batch*4*25*512\\n relu6 = self.relu(conv6) # batch*4*25*512\\n if self.phase.lower() == 'train':\\n bn6 = self.layerbn(inputdata=relu6, is_training=True)\\n else:\\n bn6 = self.layerbn(inputdata=relu6, is_training=False) # batch*4*25*512\\n max_pool6 = self.maxpooling(inputdata=bn6, kernel_size=[2, 1], stride=[2, 1]) # batch*2*25*512\\n conv7 = self.conv2d(inputdata=max_pool6, out_channel=512, kernel_size=2, stride=[2, 1], use_bias=False, name='conv7') # batch*1*25*512\\n relu7 = self.relu(conv7) # batch*1*25*512\\n return relu7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc18758b6a8fc51d3287a718bcdee23\",\n \"score\": \"0.5339686\",\n \"text\": \"def pipeline(root):\\n tf_examples = (\\n root\\n | \\\"ReadFromJSON\\\" >> beam.io.textio.ReadFromText(FLAGS.input_json_filename)\\n | \\\"ParseHotpotQAExamples\\\" >> beam.FlatMap(lib.HotpotQAExample.from_json)\\n | \\\"Reshuffle\\\" >> beam.transforms.Reshuffle()\\n | \\\"ConvertToTfExample\\\" >> beam.ParDo(\\n lib.HotpotQAExampleToTfExamplesFn(\\n FLAGS.global_seq_length, FLAGS.long_seq_length, FLAGS.is_training,\\n FLAGS.answer_encoding_method, FLAGS.spm_model_file,\\n FLAGS.vocab_file, FLAGS.debug)))\\n\\n _ = (\\n tf_examples\\n | \\\"CountTfExamples\\\" >> beam.combiners.Count.Globally()\\n | beam.Map(lambda x: json.dumps(x, indent=2))\\n | beam.io.WriteToText(\\n FLAGS.output_tf_examples_stat_filename,\\n shard_name_template=\\\"\\\", # To force unsharded output.\\n ))\\n _ = (\\n tf_examples\\n | \\\"WriteTFExample\\\" >> beam.io.WriteToTFRecord(\\n FLAGS.output_tf_examples_file_path_prefix,\\n coder=beam.coders.ProtoCoder(tf.train.Example),\\n num_shards=FLAGS.num_shards))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54329bce75553ed12a2ace230ae2c821\",\n \"score\": \"0.5334392\",\n \"text\": \"def create_test_pipeline():\\n pipeline_name = \\\"iris\\\"\\n iris_root = \\\"iris_root\\\"\\n serving_model_dir = os.path.join(iris_root, \\\"serving_model\\\", pipeline_name)\\n tfx_root = \\\"tfx_root\\\"\\n data_path = os.path.join(tfx_root, \\\"data_path\\\")\\n pipeline_root = os.path.join(tfx_root, \\\"pipelines\\\", pipeline_name)\\n\\n example_gen = CsvExampleGen(input_base=data_path)\\n\\n statistics_gen = StatisticsGen(examples=example_gen.outputs[\\\"examples\\\"])\\n\\n my_importer = importer.Importer(\\n source_uri=\\\"m/y/u/r/i\\\",\\n properties={\\n \\\"split_names\\\": \\\"['train', 'eval']\\\",\\n },\\n custom_properties={\\n \\\"int_custom_property\\\": 42,\\n \\\"str_custom_property\\\": \\\"42\\\",\\n },\\n artifact_type=standard_artifacts.Examples).with_id(\\\"my_importer\\\")\\n another_statistics_gen = StatisticsGen(\\n examples=my_importer.outputs[\\\"result\\\"]).with_id(\\\"another_statistics_gen\\\")\\n\\n schema_gen = SchemaGen(statistics=statistics_gen.outputs[\\\"statistics\\\"])\\n\\n example_validator = ExampleValidator(\\n statistics=statistics_gen.outputs[\\\"statistics\\\"],\\n schema=schema_gen.outputs[\\\"schema\\\"])\\n\\n trainer = Trainer(\\n # Use RuntimeParameter as module_file to test out RuntimeParameter in\\n # compiler.\\n module_file=data_types.RuntimeParameter(\\n name=\\\"module_file\\\",\\n default=os.path.join(iris_root, \\\"iris_utils.py\\\"),\\n ptype=str),\\n custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),\\n examples=example_gen.outputs[\\\"examples\\\"],\\n schema=schema_gen.outputs[\\\"schema\\\"],\\n train_args=trainer_pb2.TrainArgs(num_steps=2000),\\n # Attaching `TrainerArgs` as platform config is not sensible practice,\\n # but is only for testing purpose.\\n eval_args=trainer_pb2.EvalArgs(num_steps=5)).with_platform_config(\\n config=trainer_pb2.TrainArgs(num_steps=2000))\\n\\n model_resolver = resolver.Resolver(\\n strategy_class=latest_blessed_model_strategy.LatestBlessedModelStrategy,\\n model=Channel(\\n type=standard_artifacts.Model, producer_component_id=trainer.id),\\n model_blessing=Channel(type=standard_artifacts.ModelBlessing)).with_id(\\n \\\"latest_blessed_model_resolver\\\")\\n\\n eval_config = tfma.EvalConfig(\\n model_specs=[tfma.ModelSpec(signature_name=\\\"eval\\\")],\\n slicing_specs=[tfma.SlicingSpec()],\\n metrics_specs=[\\n tfma.MetricsSpec(\\n thresholds={\\n \\\"sparse_categorical_accuracy\\\":\\n tfma.MetricThreshold(\\n value_threshold=tfma.GenericValueThreshold(\\n lower_bound={\\\"value\\\": 0.6}),\\n change_threshold=tfma.GenericChangeThreshold(\\n direction=tfma.MetricDirection.HIGHER_IS_BETTER,\\n absolute={\\\"value\\\": -1e-10}))\\n })\\n ])\\n evaluator = Evaluator(\\n examples=example_gen.outputs[\\\"examples\\\"],\\n model=trainer.outputs[\\\"model\\\"],\\n baseline_model=model_resolver.outputs[\\\"model\\\"],\\n eval_config=eval_config)\\n\\n pusher = Pusher(\\n model=trainer.outputs[\\\"model\\\"],\\n model_blessing=evaluator.outputs[\\\"blessing\\\"],\\n push_destination=pusher_pb2.PushDestination(\\n filesystem=pusher_pb2.PushDestination.Filesystem(\\n base_directory=serving_model_dir)))\\n\\n return pipeline.Pipeline(\\n pipeline_name=pipeline_name,\\n pipeline_root=pipeline_root,\\n components=[\\n example_gen,\\n statistics_gen,\\n another_statistics_gen,\\n my_importer,\\n schema_gen,\\n example_validator,\\n trainer,\\n model_resolver,\\n evaluator,\\n pusher,\\n ],\\n enable_cache=True,\\n beam_pipeline_args=[\\\"--my_testing_beam_pipeline_args=foo\\\"],\\n # Attaching `TrainerArgs` as platform config is not sensible practice,\\n # but is only for testing purpose.\\n platform_config=trainer_pb2.TrainArgs(num_steps=2000),\\n execution_mode=pipeline.ExecutionMode.SYNC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d4fb2430bbaf9ef98061575bdc5faae\",\n \"score\": \"0.5311707\",\n \"text\": \"def run(args):\\n\\n if tf.io.gfile.exists(args.embed_output_dir):\\n print('Removing embedding output directory...')\\n tf.io.gfile.rmtree(args.embed_output_dir)\\n print('Creating empty output directory...')\\n tf.io.gfile.makedirs(args.embed_output_dir)\\n\\n options = beam.options.pipeline_options.PipelineOptions(**vars(args))\\n\\n original_dim = hub.load(args.module_url)(['']).shape[1]\\n\\n random_projection_matrix = generate_random_projection_weights(\\n original_dim, args.projected_dim, args.embed_output_dir)\\n\\n print('Starting the Beam pipeline...')\\n with beam.Pipeline(runner=_RUNNER, options=options) as pipeline:\\n _ = (\\n pipeline\\n | 'Read sentences from files' >>\\n beam.io.ReadFromText(file_pattern=args.data_file_pattern)\\n | 'Batch elements' >> util.BatchElements(\\n min_batch_size=_BATCH_SIZE / 2, max_batch_size=_BATCH_SIZE)\\n | 'Generate embeddings' >> beam.Map(\\n generate_embeddings, args.module_url, random_projection_matrix)\\n | 'Encode to tf example' >> beam.FlatMap(to_tf_example)\\n | 'Write to TFRecords files' >> beam.io.WriteToTFRecord(\\n file_path_prefix='{}/emb'.format(args.embed_output_dir),\\n file_name_suffix='.tfrecords')\\n )\\n\\n print('Beam pipeline completed.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"940ec5718136e888ba6ce41658a5d3e8\",\n \"score\": \"0.5303702\",\n \"text\": \"def forward(self, example):\\n # voxels = example[\\\"voxels\\\"]\\n dev_pillar_x_ = example['dev_pillar_x_']\\n dev_pillar_y_ = example['dev_pillar_y_']\\n dev_pillar_z_ = example['dev_pillar_z_']\\n dev_pillar_i_ = example['dev_pillar_i_']\\n dev_x_coors_for_sub_shaped_ = example['dev_x_coors_for_sub_shaped_']\\n dev_y_coors_for_sub_shaped_ = example['dev_y_coors_for_sub_shaped_']\\n dev_pillar_feature_mask_ = example['dev_pillar_feature_mask_']\\n dev_num_points_per_pillar_ = example[\\\"dev_num_points_per_pillar_\\\"]\\n coors = example[\\\"coordinates\\\"]\\n batch_anchors = example[\\\"anchors\\\"]\\n batch_size_dev = batch_anchors.shape[0]\\n t = time.time()\\n # features: [num_voxels, max_num_points_per_voxel, 7]\\n # num_points: [num_voxels]\\n # coors: [num_voxels, 4]\\n voxel_features = self.voxel_feature_extractor(dev_pillar_x_, dev_pillar_y_, dev_pillar_z_, dev_pillar_i_,\\n dev_num_points_per_pillar_,\\n dev_x_coors_for_sub_shaped_, dev_y_coors_for_sub_shaped_,\\n dev_pillar_feature_mask_)\\n\\n spatial_features = self.middle_feature_extractor(voxel_features, coors, batch_size_dev)\\n \\\"\\\"\\\"\\n if self._use_bev:\\n preds_dict = self.rpn(spatial_features, example[\\\"bev_map\\\"])\\n else:\\n preds_dict = self.rpn(spatial_features)\\n # preds_dict[\\\"voxel_features\\\"] = voxel_features\\n # preds_dict[\\\"spatial_features\\\"] = spatial_features\\n box_preds = preds_dict[\\\"box_preds\\\"]\\n cls_preds = preds_dict[\\\"cls_preds\\\"]\\n \\\"\\\"\\\"\\n if self._use_bev:\\n box_preds, cls_preds, dir_cls_preds = self.rpn(spatial_features, example[\\\"bev_map\\\"])\\n else:\\n box_preds, cls_preds, dir_cls_preds = self.rpn(spatial_features)\\n # preds_dict[\\\"voxel_features\\\"] = voxel_features\\n # preds_dict[\\\"spatial_features\\\"] = spatial_features\\n preds_dict = {}\\n preds_dict[\\\"box_preds\\\"] = box_preds\\n preds_dict[\\\"cls_preds\\\"] = cls_preds\\n preds_dict[\\\"dir_cls_preds\\\"] = dir_cls_preds\\n\\n self._total_forward_time += time.time() - t\\n if self.training:\\n labels = example['labels']\\n reg_targets = example['reg_targets']\\n\\n cls_weights, reg_weights, cared = prepare_loss_weights(\\n labels,\\n pos_cls_weight=self._pos_cls_weight,\\n neg_cls_weight=self._neg_cls_weight,\\n loss_norm_type=self._loss_norm_type,\\n dtype=dev_pillar_x_.dtype)\\n cls_targets = labels * cared.type_as(labels)\\n cls_targets = cls_targets.unsqueeze(-1)\\n\\n loc_loss, cls_loss = create_loss(\\n self._loc_loss_ftor,\\n self._cls_loss_ftor,\\n box_preds=box_preds,\\n cls_preds=cls_preds,\\n cls_targets=cls_targets,\\n cls_weights=cls_weights,\\n reg_targets=reg_targets,\\n reg_weights=reg_weights,\\n num_class=self._num_class,\\n encode_rad_error_by_sin=self._encode_rad_error_by_sin,\\n encode_background_as_zeros=self._encode_background_as_zeros,\\n box_code_size=self._box_coder.code_size,\\n )\\n loc_loss_reduced = loc_loss.sum() / batch_size_dev\\n loc_loss_reduced *= self._loc_loss_weight\\n cls_pos_loss, cls_neg_loss = _get_pos_neg_loss(cls_loss, labels)\\n cls_pos_loss /= self._pos_cls_weight\\n cls_neg_loss /= self._neg_cls_weight\\n\\n cls_loss_reduced = cls_loss.sum() / batch_size_dev\\n cls_loss_reduced *= self._cls_loss_weight\\n loss = loc_loss_reduced + cls_loss_reduced\\n if self._use_direction_classifier:\\n dir_targets = get_direction_target(example['anchors'],\\n reg_targets)\\n dir_logits = preds_dict[\\\"dir_cls_preds\\\"].view(\\n batch_size_dev, -1, 2)\\n weights = (labels > 0).type_as(dir_logits)\\n weights /= torch.clamp(weights.sum(-1, keepdim=True), min=1.0)\\n dir_loss = self._dir_loss_ftor(\\n dir_logits, dir_targets, weights=weights)\\n dir_loss = dir_loss.sum() / batch_size_dev\\n loss += dir_loss * self._direction_loss_weight\\n\\n return {\\n \\\"loss\\\": loss,\\n \\\"cls_loss\\\": cls_loss,\\n \\\"loc_loss\\\": loc_loss,\\n \\\"cls_pos_loss\\\": cls_pos_loss,\\n \\\"cls_neg_loss\\\": cls_neg_loss,\\n \\\"cls_preds\\\": cls_preds,\\n \\\"dir_loss_reduced\\\": dir_loss,\\n \\\"cls_loss_reduced\\\": cls_loss_reduced,\\n \\\"loc_loss_reduced\\\": loc_loss_reduced,\\n \\\"cared\\\": cared,\\n }\\n else:\\n return self.predict(example, preds_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab728aaab3c4cb3dff792f9a8b2be07\",\n \"score\": \"0.52977425\",\n \"text\": \"def create_pipelines(self):\\n self._run_init_steps(self.hparams.encode_pipeline)\\n self._run_init_steps(self.hparams.decode_pipeline)\\n self.encode_pipeline = DataPipeline(static_data_keys=self.INPUT_STATIC_KEYS, dynamic_items=self.hparams.encode_pipeline['steps'], output_keys=self.hparams.encode_pipeline['output_keys'])\\n self.decode_pipeline = DataPipeline(static_data_keys=self.hparams.model_output_keys, dynamic_items=self.hparams.decode_pipeline['steps'], output_keys=self.OUTPUT_KEYS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b67aee2e7ab8f384042c86f04ef15bcc\",\n \"score\": \"0.529499\",\n \"text\": \"def processes(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287aa99fe6d15980b19144ed11ce7add\",\n \"score\": \"0.5293246\",\n \"text\": \"def main():\\n args = parse_arguments()\\n\\n res = resdk.Resolwe()\\n\\n parent = res.data.get(args.data_id)\\n parent_user = parent.contributor['username']\\n\\n permissions = {\\n 'users': {\\n 'add': {\\n parent_user: ['download', 'edit', 'owner', 'share', 'view']\\n }\\n }\\n }\\n\\n demux_data = _tsv_to_dict(args.annotation_file)\\n barcodes = demux_data.keys()\\n\\n adapters_temp = list(set([demux_data[barcode][\\\"3' adapter\\\"] for barcode in barcodes]))\\n if len(adapters_temp) != 1:\\n print('{\\\"proc.error\\\":\\\"Only one adapter type is allowed per demultiplexing job.\\\"}')\\n exit(1)\\n\\n adapter = ''.join([adpr.split('_')[0] for adpr in adapters_temp[0].split(',')])\\n\\n demux_inputs = {\\n 'reads': res.data.get(args.reads_id),\\n 'annotation': res.data.get(args.annotation_id)\\n }\\n\\n demux = res.run('icount-demultiplex-samples', input=demux_inputs, collections=parent.collections)\\n\\n # set demux permissions\\n demux.api(demux.id).permissions.post(permissions)\\n\\n while demux.status not in ['ER', 'OK']:\\n sleep(10)\\n demux.update()\\n\\n if demux.status == 'ER':\\n print('{\\\"proc.error\\\":\\\"Only one adapter type is allowed per demultiplexing job.\\\"}')\\n exit(1)\\n\\n demux_reads = res.data.filter(parents=demux.id)\\n\\n for read in demux_reads:\\n read.api(read.id).permissions.post(permissions)\\n if read.sample:\\n barcode = _get_barcode(read.name)\\n if barcode in demux_data.keys():\\n print(\\\"ANNOTATING SAMPLE:\\\", read.sample.name)\\n read.descriptor_schema = 'reads-iclip'\\n read.descriptor = create_reads_descriptor(demux_data, barcode)\\n read.save()\\n read.sample.descriptor_schema = 'sample-iclip'\\n read.sample.descriptor = create_sample_descriptor(demux_data, barcode)\\n read.sample.name = demux_data[barcode]['Renamed fastq on iCount']\\n read.sample.slug = demux_data[barcode]['Renamed fastq on iCount']\\n read.sample.save()\\n read.sample.confirm_is_annotated()\\n\\n read.sample.api(read.sample.id).permissions.post(permissions)\\n\\n for pc in parent.collections:\\n pc.add_samples(read.sample)\\n\\n print(\\\"RUNNING PRIMARY ANALYSIS FOR SAMPLE:\\\", read.sample.name)\\n\\n genome = res.data.filter(slug=GENOME_MAP[demux_data[barcode]['mapto']])\\n segmentation = res.data.filter(slug=SEGMENTATION_MAP[demux_data[barcode]['mapto']])\\n\\n if len(genome) == 0:\\n print('{\\\"proc.warning\\\":\\\"STAR index not found in the database.\\\"}')\\n\\n if len(segmentation) == 0:\\n print('{\\\"proc.warning\\\":\\\"Segmentation map not found in the database.\\\"}')\\n\\n if len(genome) == 1 and len(segmentation) == 1:\\n icount_inputs = {\\n 'reads': read.id,\\n 'index': res.data.get(slug=GENOME_MAP[demux_data[barcode]['mapto']]),\\n 'segmentation': res.data.get(SEGMENTATION_MAP[demux_data[barcode]['mapto']])\\n }\\n\\n icount_analysis = res.run('workflow-icount', input=icount_inputs, collections=parent.collections)\\n icount_analysis.api(icount_analysis.id).permissions.post(permissions)\\n\\n for child in res.data.filter(parents=icount_analysis.id):\\n child.api(child.id).permissions.post(permissions)\\n\\n else:\\n print('{\\\"proc.warning\\\":\\\"Primary analysis not triggered.\\\"}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ce936ace36c40bba4f5db784850782e\",\n \"score\": \"0.52930874\",\n \"text\": \"def create_pipeline(\\n pipeline_name: Text,\\n pipeline_root: Text,\\n query: Text,\\n preprocessing_fn: Text,\\n run_fn: Text,\\n train_args: trainer_pb2.TrainArgs,\\n eval_args: trainer_pb2.EvalArgs,\\n model_serve_dir: Text,\\n metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,\\n beam_pipeline_args: Optional[Dict[Text, Any]] = None,\\n ai_platform_training_args: Optional[Dict[Text, Text]] = None,\\n ai_platform_serving_args: Optional[Dict[Text, Any]] = None,\\n enable_cache: Optional[bool] = False,\\n system_config: Optional[Dict[Text, Any]] = None,\\n model_config: Optional[Dict[Text, Any]] = None,\\n) -> pipeline.Pipeline:\\n\\n components = []\\n # %%\\n # ExampleGen: Load the graph data from bigquery\\n query_str = load_query_string(\\n query,\\n field_dict={\\n \\\"GOOGLE_CLOUD_PROJECT\\\": system_config[\\\"GOOGLE_CLOUD_PROJECT\\\"],\\n \\\"DEBUG_SETTINGS\\\": model_config[\\\"query_debug_settings\\\"],\\n },\\n )\\n\\n output_config = example_gen_pb2.Output(\\n split_config=example_gen_pb2.SplitConfig(\\n splits=[ # Generate no splitting, as we need to load everything\\n example_gen_pb2.SplitConfig.Split(name=\\\"train\\\", hash_buckets=1),\\n ],\\n )\\n )\\n\\n example_gen = BigQueryExampleGen(query=query_str, output_config=output_config)\\n components.append(example_gen)\\n\\n # %%\\n # StatisticsGen:\\n # Computes statistics over data for visualization and example validation.\\n statistics_gen = StatisticsGen(examples=example_gen.outputs[\\\"examples\\\"])\\n components.append(statistics_gen)\\n\\n # %%\\n # SchemaGen:\\n # Generates schema based on statistics files.\\n schema_gen = SchemaGen(\\n statistics=statistics_gen.outputs[\\\"statistics\\\"], infer_feature_shape=True\\n )\\n components.append(schema_gen)\\n\\n # %%\\n # ExampleValidator:\\n # Performs anomaly detection based on statistics and data schema.\\n # example_validator = ExampleValidator( # pylint: disable=unused-variable\\n # statistics=statistics_gen.outputs[\\\"statistics\\\"],\\n # schema=schema_gen.outputs[\\\"schema\\\"],\\n # )\\n # components.append(example_validator)\\n\\n # %%\\n # Transform:\\n # Performs transformations and feature engineering in training and serving.\\n transform = Transform(\\n examples=example_gen.outputs[\\\"examples\\\"],\\n schema=schema_gen.outputs[\\\"schema\\\"],\\n preprocessing_fn=preprocessing_fn,\\n )\\n components.append(transform)\\n\\n # %%\\n # Trainer\\n # Uses user-provided Python function that implements a model using TF-Learn.\\n trainer_args = {\\n \\\"run_fn\\\": run_fn,\\n \\\"transformed_examples\\\": transform.outputs[\\\"transformed_examples\\\"],\\n \\\"schema\\\": schema_gen.outputs[\\\"schema\\\"],\\n \\\"transform_graph\\\": transform.outputs[\\\"transform_graph\\\"],\\n \\\"train_args\\\": train_args,\\n \\\"eval_args\\\": eval_args,\\n \\\"custom_config\\\": {\\\"model_config\\\": model_config, \\\"system_config\\\": system_config},\\n \\\"custom_executor_spec\\\": executor_spec.ExecutorClassSpec(\\n trainer_executor.GenericExecutor\\n ),\\n }\\n\\n if ai_platform_training_args is not None:\\n trainer_args[\\\"custom_executor_spec\\\"] = executor_spec.ExecutorClassSpec(\\n ai_platform_trainer_executor.GenericExecutor\\n )\\n\\n trainer_args[\\\"custom_config\\\"][\\n ai_platform_trainer_executor.TRAINING_ARGS_KEY\\n ] = ai_platform_training_args\\n\\n # Lowercase and replace illegal characters in labels.\\n # This is the job ID that will be shown in the platform\\n # See https://cloud.google.com/compute/docs/naming-resources.\\n trainer_args[\\\"custom_config\\\"][\\n ai_platform_trainer_executor.JOB_ID_KEY\\n ] = \\\"tfx_{}_{}\\\".format(\\n re.sub(r\\\"[^a-z0-9\\\\_]\\\", \\\"_\\\", pipeline_name.lower()),\\n datetime.datetime.now().strftime(\\\"%Y%m%d%H%M%S\\\"),\\n )[\\n -63:\\n ]\\n logging.info(\\\"trainer arguments\\\")\\n logging.info(trainer_args)\\n\\n trainer = Trainer(**trainer_args)\\n components.append(trainer)\\n\\n # %%\\n # ResolveNode:\\n # Get the latest blessed model for model validation.\\n model_resolver = ResolverNode(\\n instance_name=\\\"latest_blessed_model_resolver\\\",\\n resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,\\n model=Channel(type=Model),\\n model_blessing=Channel(type=ModelBlessing),\\n )\\n # components.append(model_resolver)\\n\\n # %%\\n # Evaluator:\\n # Uses TFMA to compute a evaluation statistics over features of a model and\\n # perform quality validation of a candidate model (compared to a baseline).\\n eval_config = tfma.EvalConfig(\\n model_specs=[tfma.ModelSpec(label_key=\\\"big_tipper\\\")],\\n slicing_specs=[tfma.SlicingSpec()],\\n metrics_specs=[\\n tfma.MetricsSpec(\\n metrics=[\\n tfma.MetricConfig(\\n class_name=\\\"BinaryAccuracy\\\",\\n threshold=tfma.MetricThreshold(\\n value_threshold=tfma.GenericValueThreshold(\\n lower_bound={\\\"value\\\": 0.1}\\n ),\\n change_threshold=tfma.GenericChangeThreshold(\\n direction=tfma.MetricDirection.HIGHER_IS_BETTER,\\n absolute={\\\"value\\\": -1e-10},\\n ),\\n ),\\n )\\n ]\\n )\\n ],\\n )\\n evaluator = Evaluator(\\n examples=example_gen.outputs[\\\"examples\\\"],\\n model=trainer.outputs[\\\"model\\\"],\\n baseline_model=model_resolver.outputs[\\\"model\\\"],\\n # Change threshold will be ignored if there is no baseline (first run).\\n eval_config=eval_config,\\n )\\n # components.append(evaluator)\\n\\n # %%\\n # Pusher:\\n # Checks whether the model passed the validation steps and pushes the model\\n # to a file destination if check passed.\\n pusher_args = {\\n \\\"model\\\": trainer.outputs[\\\"model\\\"],\\n \\\"model_blessing\\\": evaluator.outputs[\\\"blessing\\\"],\\n \\\"push_destination\\\": pusher_pb2.PushDestination(\\n filesystem=pusher_pb2.PushDestination.Filesystem(\\n base_directory=model_serve_dir,\\n )\\n ),\\n }\\n if ai_platform_serving_args is not None:\\n pusher_args.update(\\n {\\n \\\"custom_executor_spec\\\": executor_spec.ExecutorClassSpec(\\n ai_platform_pusher_executor.Executor\\n ),\\n \\\"custom_config\\\": {\\n ai_platform_pusher_executor.SERVING_ARGS_KEY: ai_platform_serving_args\\n },\\n }\\n )\\n pusher = Pusher(**pusher_args) # pylint: disable=unused-variable\\n # components.append(pusher)\\n\\n # %%\\n return pipeline.Pipeline(\\n pipeline_name=pipeline_name,\\n pipeline_root=pipeline_root,\\n components=components,\\n enable_cache=enable_cache,\\n metadata_connection_config=metadata_connection_config,\\n beam_pipeline_args=( # parse beam pipeline into a list of commands\\n [f\\\"--{key}={val}\\\" for key, val in beam_pipeline_args.items()]\\n if beam_pipeline_args is not None\\n else None\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575690199a098c8dca56052378a29d71\",\n \"score\": \"0.5290334\",\n \"text\": \"def build_model_pipeline():\\n \\n # Read data\\n train_df = loader.read_data_set('train')\\n \\n # Validate basic structure of data set\\n try:\\n loader.validate_data_set(train_df)\\n except:\\n raise\\n \\n # Clean data set\\n try:\\n train_df = clean_data_set(train_df)\\n except:\\n raise\\n \\n # Impute missing values\\n # Missing values are only allowed in Age and Fare columns\\n try:\\n train_df = impute_data_set(train_df)\\n except:\\n raise\\n \\n # ---------------------- MODELING ---------------------\\n # This is the core part of this module, so for the most part it is not \\n # embedded in functions\\n \\n # Remove ID column\\n train_df.drop(['PassengerId'], axis=1, inplace=True)\\n \\n # Split into dependent and independet variables\\n y = train_df['Survived'].values\\n X = train_df.drop('Survived', axis=1).values\\n \\n # Create training and test sets\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=38)\\n \\n # Get optimal hyperparameters and set up model\\n h_params = get_hyperparameters(X_train, y_train)\\n logreg = LogisticRegression(C=h_params['C'], penalty=h_params['penalty'])\\n \\n # Fit the classifier to the training data\\n logreg.fit(X_train, y_train)\\n \\n # Save model\\n try: \\n pickle.dump(logreg, open('model.pkl', 'wb'))\\n loader.upload_file_to_s3('model.pkl', 'model')\\n except:\\n raise\\n \\n # Predict outcomes in the testing data\\n y_pred = logreg.predict(X_test)\\n \\n # Save model parameters, confusion matrix and classification report\\n try:\\n with open('model_stats.txt', 'w') as text_file:\\n with redirect_stdout(text_file):\\n print('MODEL:')\\n print(logreg)\\n print('\\\\nCONFUSION MATRIX:')\\n print(confusion_matrix(y_test, y_pred))\\n print('\\\\nCLASSIFICATION REPORT:')\\n print(classification_report(y_test, y_pred))\\n loader.upload_file_to_s3('model_stats.txt', 'model')\\n except: \\n raise\\n \\n # Save ROC curve\\n try: \\n y_pred_prob = logreg.predict_proba(X_test)[:,1]\\n fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\\n plt.plot([0, 1], [0, 1], 'k--')\\n plt.plot(fpr, tpr)\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('ROC Curve')\\n plt.savefig('model_roc.png')\\n loader.upload_file_to_s3('model_roc.png', 'model')\\n except:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9738aa304d4c3681d250cfcbae7ad0ca\",\n \"score\": \"0.5287862\",\n \"text\": \"def main():\\n #time.sleep(7)\\n # Grab command line args\\n args = build_argparser().parse_args()\\n\\n #Perform inference on the input stream\\n infer_on_stream(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587ba2ebe1a59380f6b51d0457f12ca1\",\n \"score\": \"0.5285097\",\n \"text\": \"def _setup_pipeline_cfg(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ce60616afea86e96e6912d58ed0902\",\n \"score\": \"0.5283076\",\n \"text\": \"def main(argv=None):\\n if not argv:\\n argv = sys.argv[1:]\\n\\n parser = argparse.ArgumentParser(\\n description=__doc__,\\n formatter_class=argparse.RawDescriptionHelpFormatter)\\n\\n\\n parser.add_argument('--step', choices=[1, 2, 3, 4, 5], default=1, type=int,\\n help='Step to start from. 1=filter, 2=mapping, ' +\\n '3=Map clean, 4=Merging, 5=Counting.')\\n\\n args = parser.parse_args(argv)\\n\\n run_pipeline(args.step)\\n\\n return 0\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1580,"cells":{"query_id":{"kind":"string","value":"46a7ecaa379a016942a993503469a8bd"},"query":{"kind":"string","value":"Sets up _client as a test emulation of DynamoDB. Creates the full database schema, a test user, and two devices (one for mobile, one for webapplication)."},"positive_passages":{"kind":"list like","value":[{"docid":"b8c6dbd3d74a7ed974009238bb7c1f6d","score":"0.0","text":"def setUp(self):\n super(LocalClientTestCase, self).setUp()\n options.options.localdb_dir = ''\n self._client = LocalClient(test_SCHEMA)\n test_SCHEMA.VerifyOrCreate(self._client, self.stop)\n self.wait()","title":""}],"string":"[\n {\n \"docid\": \"b8c6dbd3d74a7ed974009238bb7c1f6d\",\n \"score\": \"0.0\",\n \"text\": \"def setUp(self):\\n super(LocalClientTestCase, self).setUp()\\n options.options.localdb_dir = ''\\n self._client = LocalClient(test_SCHEMA)\\n test_SCHEMA.VerifyOrCreate(self._client, self.stop)\\n self.wait()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"215dfd1ba1c84a029e7042c351b19542","score":"0.7453459","text":"def setUp(self):\r\n super(DynamoDBClientTestCase, self).setUp()\r\n options.options.domain = 'goviewfinder.com'\r\n secrets.InitSecretsForTest()\r\n self._client = dynamodb_client.DynamoDBClient(schema=vf_schema.SCHEMA)\r\n self._ClearTestTable(self.stop)\r\n self.wait(timeout=30)","title":""},{"docid":"687d93e47caae798bc147ec831211ea6","score":"0.7204757","text":"def setUp(self):\r\n super(DynamoDBReadOnlyClientTestCase, self).setUp()\r\n options.options.domain = 'goviewfinder.com'\r\n secrets.InitSecretsForTest()\r\n self._client = dynamodb_client.DynamoDBClient(schema=vf_schema.SCHEMA, read_only=True)\r\n\r\n self._client._read_only = False\r\n self._RunAsync(self._ClearTestTable)\r\n self._RunAsync(self._client.PutItem, table=_table.name, key=DBKey(hash_key='1', range_key=2), attributes={'a1': 1})\r\n self._client._read_only = True","title":""},{"docid":"14cdfe188f5a8c28e0aa30c0b8646015","score":"0.7136222","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n # Initialization of the test_client\n self.client = self.app.test_client\n # This is the user test json data\n self.user_data = {\n 'email': 'test@example.com',\n 'password': 'test_password'\n }\n\n with self.app.app_context():\n # create all tables\n db.session.close()\n db.drop_all()\n db.create_all()","title":""},{"docid":"f32fdd283165a22c116bb0ac1114c49d","score":"0.7050845","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n app.config['SECRET_KEY'] = 'supersecret'\n\n connect_to_db(app, \"postgresql:///testdb\")\n db.create_all()\n create_example_data()\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['current_user'] = 'thodas'\n sess['current_user_id'] = 2","title":""},{"docid":"c983222d9e72b310e447008182bdd367","score":"0.7013796","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.user = {\n \"email\": \"test_script@test.com\",\n \"password\": \"abcd1234%\",\n \"location\": \"Durham\",\n \"quip\": \"witty test remark\",\n \"photo\": \"http://photoimage.com\",\n \"user_name\": \"test_name\"\n }\n\n with self.app.app_context():\n db.create_all()","title":""},{"docid":"c505ba5f1585dc1c1c9adf885b0f9da8","score":"0.68929213","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"capstone_test\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.menu_item = {\n \"name\": 'mac and cheese',\n \"ingredients\": '[{\"ingredient_name\": \"noodle\", \"ingredient_amount\": 10},'\\\n '{\"ingredient_name\": \"cheese\", \"ingredient_amount\": 5}]',\n \"category\": 1,\n \"price\": 10\n }\n self.category = {\n \"type\": \"breakfast\"\n }\n self.chef_header = {'Authorization': 'Bearer {}'.format('eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6InZDV042TnNTa21jU293ajliZVMtVCJ9.eyJpc3MiOiJodHRwczovL2NocmlzLWNhbXBvcy51cy5hdXRoMC5jb20vIiwic3ViIjoiYXV0aDB8NWY3Y2Q2ZGE4MTU1MWYwMDZlNDMwYmY2IiwiYXVkIjoiZmRtIiwiaWF0IjoxNjAyMDI4ODM5LCJleHAiOjE2MDIwMzYwMzksImF6cCI6IjNkQW02a1pZZmVhQ3hINmVoR1VIUjNWNk1NcmJYbGowIiwic2NvcGUiOiIiLCJwZXJtaXNzaW9ucyI6WyJkZWxldGU6bWVudV9pdGVtIiwiZ2V0OmNhdGVnb3JpZXMiLCJnZXQ6bWVudV9pdGVtIiwicGF0Y2g6bWVudV9pdGVtIiwicG9zdDptZW51X2l0ZW0iXX0.JFbQTbfjOfAlQeDDOzbSO6fZ3wbW2JPU4YMcskY0ZVjL_ntWY3kPGiBabtl8brfiIYEj3VDpAUzfAD8YtjeH1OsAUDaOl_z3H06MIo0Z0p2pxA034ifiATw06KVc15lV5FWnaoPSwkEt8s22qAy9ZWdN443-qK4ziRnJOd2jo7ZmzpXtUC9yEhd2OrLlLArNf7fESmSesLKr8Tpdsonn7rSKKDQO99fuE4HJylbb6c0KGOV3-MwR723_P00RBT7N4Gng5OdFCrMJq3Zdu4EaGgjTE8MhcddOLWJTskHeUjveCvdddP_iwLz_8ya7BA-40aC6wGTSzHLMttQIwJouxQ')}\n self.owner_header = {'Authorization': 'Bearer {}'.format('eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6InZDV042TnNTa21jU293ajliZVMtVCJ9.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.gKSYMzVGkNoV61_QkuVEqfK_MC1TvAjFCMJMiq-TAKnvOPJXtOYtYrkVYByAzDjh05ic4D_FiVjUKT1Ov2MoPxDmjJ3boZzAWbj_PFvaCU2h9_Kixe9WE6tZRGZFKuXOAR1HO-tqJ8aZvdzHIaXD2S2mDD4ldf8u7CHDraI-crFfdcrumnmbFzt9hmvpApWGZyMAO8yKW_tTplHAH4ni65Lc_2MIfMC9gl-lISZuCNAPRQnR_XsLQhANTBtaUf64BOXTBcy1J1WWJbQmggTzQNAUPgvPJQkiC_Aw5POHIS1yrbJBtUFaEw9u9bp0doEub7C2P7Y_kg7bYPxQQ3ckgQ')}\n #if os.environ['CHEF_TOKEN']:\n # self.chef_header = {'Authorization': \"Bearer {}\".format(os.environ['CHEF_TOKEN'])}\n #if os.environ['OWNER_TOKEN']:\n # self.owner_header = {'Authorization': \"Bearer {}\".format(os.environ['OWNER_TOKEN'])}\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"f2794ac5d724c4f6cbf35a11d166cedc","score":"0.68868893","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n db.create_all()\n create_example_data()","title":""},{"docid":"3f19d5976e822a5de2740853d49c461a","score":"0.68550247","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()\n\n add_test_user_to_session(self.client)","title":""},{"docid":"3f19d5976e822a5de2740853d49c461a","score":"0.68550247","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()\n\n add_test_user_to_session(self.client)","title":""},{"docid":"3f19d5976e822a5de2740853d49c461a","score":"0.68550247","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()\n\n add_test_user_to_session(self.client)","title":""},{"docid":"3f19d5976e822a5de2740853d49c461a","score":"0.68550247","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()\n\n add_test_user_to_session(self.client)","title":""},{"docid":"66914dbcc34f5755900d09e6c7eded2b","score":"0.6847757","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()","title":""},{"docid":"66914dbcc34f5755900d09e6c7eded2b","score":"0.6847757","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()","title":""},{"docid":"66914dbcc34f5755900d09e6c7eded2b","score":"0.6847757","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n connect_to_db(app, \"postgresql:///testdb\")\n\n db.create_all()\n example_data()","title":""},{"docid":"3149d69aa119be4a1dd3a2d2e01a1b13","score":"0.6843024","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"capstone_test\"\n #self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres', 'postgres', 'localhost:5432', self.database_name)\n self.database_path = \"postgres://saqmdnbucthosu:5e6ec82b00af5309ba7c19ccc534a2de33673b4fbe7304fbe467714e2be777d6@ec2-52-0-155-79.compute-1.amazonaws.com:5432/d3nuuppk8gou7m\"\n setup_db(self.app, self.database_path)\n\n \n \n self.Casting_Assistant ={\n 'Authorization': os.environ.get('Casting_Assistant')\n }\n\n self.Casting_Director ={\n 'Authorization': os.environ.get('Casting_Director')\n }\n\n self.Executive_Producer ={\n 'Authorization': os.environ.get('Executive_Producer')\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"6c0318dfe17814282018034858464367","score":"0.6838229","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n\n # Connect to \n database = SetDatabase('..\\\\config_test.json')\n database_conn_string = database.connection_string\n setup_db(self.app, database_conn_string)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"9ed5e6702976a48c09488e1013e5eb64","score":"0.6830688","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///test_database\")\n\n # Create tables and add sample data\n db.create_all()\n example_data()","title":""},{"docid":"8e4272f03588a6c354fb79da6a095b8f","score":"0.6821161","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n # Connect to test database\n connect_to_db(app, uri=\"postgresql:///testdb\")\n # Create tables and add sample data\n db.create_all()\n example_data()","title":""},{"docid":"8e4272f03588a6c354fb79da6a095b8f","score":"0.6821161","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config['TESTING'] = True\n # Connect to test database\n connect_to_db(app, uri=\"postgresql:///testdb\")\n # Create tables and add sample data\n db.create_all()\n example_data()","title":""},{"docid":"03fb895d62d5017cd2c3b0649c2d3fa9","score":"0.6781941","text":"def setUp(self):\n\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n\n os.system(\"dropdb pixtestdb\")\n os.system(\"createdb pixtestdb\")\n\n # Connect to test database (uncomment when testing database)\n connect_to_db(app, \"postgresql:///pixtestdb\")\n\n # Create tables and add sample data (uncomment when testing database)\n db.create_all()\n sample_data()","title":""},{"docid":"e19cafefbfc84da7622397b494c90fc7","score":"0.6685032","text":"def setUp(self):\n\t\tself.app = create_app(\"testing\")\n\t\tself.client = self.app.test_client\n\t\tself.user_data = {\n\t\t\t'f_name': 'Peter',\n\t\t\t'l_name': 'Mwaura',\n\t\t\t'u_name': 'Pemwa',\n\t\t\t'email': 'pemwa@gm.com',\n\t\t\t'password': '12345'\n\t\t}\n\n\t\t\"\"\"\n\t\tBinding the app with the current context\n\t\tand creating all tables\n\t\t\"\"\"\n\t\twith self.app.app_context():\n\t\t\tdb.session.close()\n\t\t\tdb.drop_all()\n\t\t\tdb.create_all()","title":""},{"docid":"d47c0106f669ae88da86dd74b0b6b1d4","score":"0.665801","text":"def setUp(self):\n self.app = create_app()\n self.app.testing = True\n self.app_context = self.app.test_request_context()\n self.app_context.push()\n self.client = self.app.test_client()\n self.app.db.create_all()","title":""},{"docid":"27d3fd679478da9766ce2e60d49b1c1e","score":"0.6655897","text":"def setUp(self):\n db.drop_all()\n db.create_all()\n\n self.uid = 12345\n u = User.signup(\"testing\", \"password\")\n u.id = self.uid\n db.session.commit()\n\n self.user = User.query.get(self.uid)\n\n self.song_uid = 11111\n song = Song.create_song(\"test title\", \"test artist\", \"test lyrics\", self.uid)\n song.id = self.song_uid\n self.song = Song.query.get(self.song_uid)\n db.session.commit()\n\n user_song = UserSong.create_user_song(self.song_uid, self.uid)\n\n self.stash_uid = 22222\n stash = Stash.create_stash(\"test stash name\", self.uid)\n stash.id = self.stash_uid\n self.stash = Stash.query.get(self.stash_uid)\n db.session.commit()\n\n user_stash = UserStash.create_user_stash(self.stash_uid, self.uid)\n stash_song = StashSong.create_stash_song(self.song_uid, self.stash_uid)\n db.session.commit()\n\n self.annotation_uid = 33333\n annotation = Annotation.create_annotation(\"test annotation\", 5, self.uid, self.song_uid)\n annotation.id = self.annotation_uid\n self.annotation = annotation.query.get(self.annotation_uid)\n db.session.commit()\n\n SongAnnotation.create_song_annotation(self.annotation_uid, self.song_uid)\n db.session.commit()\n\n self.client = app.test_client()","title":""},{"docid":"d8c7d8bac341bf3d11b13ecc29f0b872","score":"0.66393816","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.testuser = User.register(User(username=\"test1\", password=\"password\", first_name=\"John\", last_name=\"Doe\", email=\"email@email.com\"))\n self.testuser_id = 8989\n self.testuser.id = self.testuser_id\n \n self.u1 = User(username=\"testtesttest\", password=\"password\", first_name=\"John\", last_name=\"Doe\", email=\"test@test.com\")\n self.u1_id = 778\n self.u1.id = self.u1_id\n self.u2 = User.register(User(username=\"test2\", password=\"password\", first_name=\"John\", last_name=\"Doe\", email=\"email2@email.com\"))\n self.u2_id = 884\n self.u2.id = self.u2_id\n self.u3 = User.register(User(username=\"test3\", password=\"password\", first_name=\"John\", last_name=\"Doe\", email=\"email3@email.com\"))\n self.u4 = User.register(User(username=\"test4\", password=\"password\", first_name=\"John\", last_name=\"Doe\", email=\"email4@email.com\"))\n\n db.session.add_all([self.u1, self.u2, self.u3, self.u4])\n db.session.commit()","title":""},{"docid":"45e6b6784211131200dc4709b57586b0","score":"0.66277075","text":"def setUp(self):\n self.db_fd, project.app.config['DATABASE'] = tempfile.mkstemp()\n project.app.testing = True\n self.app = project.app.test_client()\n with project.app.app_context():\n db.Database().create_tables()\n user.createUser('adminTest', 'tcg1134', '', '')\n user.createUser('adminTest2', 'gct2334', '', '')\n Group.createGroup('TestGroup', 'adminTest2')\n topic.insertTopic('TestTopic', 'adminTest2', '1')\n post.insertPost(1, 'adminTest2', 'TestPost', 'Some random text')\n subscription.addToSub(1,1)\n notification.sendNotif(\"post\", 1)\n Votes.upvote(1, 1)","title":""},{"docid":"8451946637bc0f39bb57f3af0a19356e","score":"0.66239786","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n # create a sample user\n user = User.signup(\"test\", \"test@test.com\", \"testpass\", None)\n self.uid = 111\n user.id = self.uid\n\n db.session.commit()\n\n self.user = User.query.get(self.uid)\n\n self.client = app.test_client()","title":""},{"docid":"1e846b066c7a718589c949633182e909","score":"0.6623845","text":"def setUp(self):\n\n # Get the Flask test client\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///vidtester\")\n\n # Create tables and add sample data\n db.create_all()\n example_data()","title":""},{"docid":"37a5c44f5131f15e39389aa801788c6b","score":"0.6622711","text":"def setUp(self) -> None:\n app.config['TESTING'] = True\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\n self.client = app.test_client()\n with app.app_context():\n db.create_all()","title":""},{"docid":"37a5c44f5131f15e39389aa801788c6b","score":"0.6622711","text":"def setUp(self) -> None:\n app.config['TESTING'] = True\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\n self.client = app.test_client()\n with app.app_context():\n db.create_all()","title":""},{"docid":"d03bf558a6c9aa5ee4e41d231894ba5f","score":"0.66059357","text":"def setUp(self):\n\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = 'capstonefsnd_test'\n self.database_path = 'postgresql://{}:{}@{}/{}'.format(\n 'postgres', 'admin',\n 'localhost:5432', self.database_name)\n\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n self.db.drop_all()\n\n # create all tables\n\n self.db.create_all()\n\n # initiate the test database with dummy records.\n\n for i in range(5):\n new_actor = self.create_actor()\n actor = Actor(name=new_actor['name'],\n age=new_actor['age'],\n gender=new_actor['gender'])\n actor.insert()\n\n new_movie = self.create_movie()\n movie = Movie(title=new_movie['title'],\n release=new_movie['release'])\n movie.insert()","title":""},{"docid":"827dc19def914d85175852e709681e48","score":"0.66024137","text":"def setup(self):\n self.app = create_app(TEST_SETTINGS)\n self.client = self.app.test_client()\n with self.app.test_request_context():\n db.create_all()\n db.session.commit()","title":""},{"docid":"9a7770b749b02c450c1044c096f3cb5b","score":"0.6601224","text":"def setUp(self):\r\n options.options.localdb = True\r\n options.options.fileobjstore = True\r\n options.options.localdb_dir = ''\r\n\r\n super(DBBaseTestCase, self).setUp()\r\n options.options.localdb_dir = ''\r\n self._client = local_client.LocalClient(vf_schema.SCHEMA)\r\n object_store.InitObjectStore(temporary=True)\r\n self._temp_dir = tempfile.mkdtemp()\r\n server_log.LogBatchPersistor.SetInstance(server_log.LogBatchPersistor(backup_dir=self._temp_dir))\r\n IdAllocator.ResetState()\r\n\r\n options.options.domain = 'goviewfinder.com'\r\n secrets.InitSecretsForTest()\r\n\r\n # Set deterministic testing timestamp used in place of time.time() in server code.\r\n util._TEST_TIME = time.time()\r\n\r\n self._RunAsync(vf_schema.SCHEMA.VerifyOrCreate, self._client)\r\n OpManager.SetInstance(OpManager(op_map=DB_OPERATION_MAP, client=self._client, scan_ops=True))\r\n\r\n # Create users with linked email identity and default viewpoint.\r\n self._user, self._mobile_dev = self._CreateUserAndDevice(user_dict={'user_id': 1, 'name': 'Spencer Kimball'},\r\n ident_dict={'key': 'Email:spencer.kimball@emailscrubbed.com',\r\n 'authority': 'Google'},\r\n device_dict={'device_id': 1, 'name': 'Spencer\\'s iPhone'})\r\n\r\n self._user2, _ = self._CreateUserAndDevice(user_dict={'user_id': 2, 'name': 'Peter Mattis'},\r\n ident_dict={'key': 'Email:peter.mattis@emailscrubbed.com',\r\n 'authority': 'Google'},\r\n device_dict=None)","title":""},{"docid":"a0bf0b1d440dc6d44570bb091700a03c","score":"0.65988797","text":"def setUp(self):\n testDatabase.createAll()\n\t \n # Carer\n carerClient = testDatabase.Client.insert(\n username = \"carer\",\n email = \"justhealth@richlogan.co.uk\",\n dob = \"03/03/1993\",\n verified = True,\n accountlocked = False,\n loginattempts = 0,\n accountdeactivated = False)\n carerCarer = testDatabase.Carer.insert(\n username = \"carer\",\n firstname = \"carer\",\n ismale = True,\n nhscarer = True,\n surname = \"carer\")\n carerPassword = testDatabase.uq8LnAWi7D.insert(\n expirydate = '01/01/2020',\n iscurrent = True,\n password = sha256_crypt.encrypt('test'),\n username = \"carer\")\n carerClient.execute()\n carerCarer.execute()\n carerPassword.execute()\n\n # Patient\n patientClient = testDatabase.Client.insert(\n username = \"patient\",\n email = \"justhealth123@richlogan.co.uk\",\n dob = \"03/03/1993\",\n verified = True,\n accountlocked = False,\n loginattempts = 0,\n accountdeactivated = False)\n testPatient = testDatabase.Patient.insert(\n username = \"patient\",\n firstname = \"patient\",\n surname = \"patient\",\n ismale = True)\n patientPassword = testDatabase.uq8LnAWi7D.insert(\n expirydate = '01/01/2020',\n iscurrent = True,\n password = sha256_crypt.encrypt('test'),\n username = \"patient\")\n patientClient.execute()\n testPatient.execute()\n patientPassword.execute()\n\n testRelationship = testDatabase.Relationship.insert(\n code = 1234,\n requestor = \"carer\",\n requestortype = \"Carer\",\n target = \"patient\",\n targettype = \"Patient\")\n testRelationship.execute()","title":""},{"docid":"283a987e3b3ebd68b42f38aa628bca3b","score":"0.6598439","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.Character = {\n \"name\" : 'Ness',\n \"hp\" : 100,\n 'mana' : 45,\n \"attack\" : 24,\n \"defense\" : 18,\n 'intelligence' : 20,\n 'luck' : 16\n }\n self.Character2 = { \n 'name' : 'Paula',\n 'hp' : 110,\n 'mana' : 60,\n 'attack' : 20,\n 'defense' : 20,\n 'intelligence' : 22,\n 'luck' : 12\n } \n # binds the app to the current context\n with self.app.app_context():\n # create all tables\n db.create_all()","title":""},{"docid":"bfd54dc2f701b246b990941109bd0848","score":"0.6582792","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n u1 = User.signup(\"testuser1\", \"test1@test.com\", \"password1\", None)\n u1.bio = \"testbio1\"\n u1.location = \"testlocation1\"\n uid1 = 1\n u1.id = uid1\n\n u2 = User.signup(\"testuser2\", \"test2@test.com\", \"password2\", None)\n u2.bio = \"testbio2\"\n u2.location = \"testlocation2\"\n uid2 = 2\n u2.id = uid2\n\n db.session.commit()\n\n self.u1 = User.query.get(uid1)\n self.u2 = User.query.get(uid2)\n\n self.uid1 = uid1\n self.uid2 = uid2\n\n self.client = app.test_client()","title":""},{"docid":"f9c63cd64a2f180c0133f735aa0cfe3b","score":"0.65791297","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.incidences = {\n \"createdBy\": 1,\n \"type\": \"RED-FLAG\",\n \"comment\": \"comment\",\n \"location\": \"12N3E\"\n }\n\n self.regular_user = {\n \"firstname\": \"john\",\n \"lastname\": \"doe\",\n \"othernames\": \"foo\",\n \"email\": \"joe@test.com\",\n \"phoneNumber\": \"0700000000\",\n \"username\": \"jondo\",\n \"isAdmin\": False,\n \"password\": \"12345\"\n }\n\n self.regular_user_login = {\n \"username\": \"jondo\",\n \"password\": \"12345\"\n }\n\n self.admin_user = {\n \"firstname\": \"jane\",\n \"lastname\": \"doe\",\n \"othernames\": \"bar\",\n \"email\": \"jane@test.com\",\n \"phoneNumber\": \"0711111111\",\n \"username\": \"bando\",\n \"isAdmin\": True,\n \"password\": \"56789\"\n }\n\n self.admin_user_login = {\n \"username\": \"bando\",\n \"password\": \"56789\"\n }","title":""},{"docid":"aaf8e99233c79b35a0ddde0559090b11","score":"0.6578678","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"ourhome_test\"\n self.database_path = \"postgresql://{}/{}\".format(\n 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_menu = {\n 'menu': 'test',\n 'description': \"test_desc\",\n 'cuisine': 5,\n 'preference': 1\n }\n\n self.updated_menu = {\n 'menu': 'test_updated',\n 'description': 'test_desc_updated',\n 'cuisine': 4,\n 'preference': 2\n }\n\n self.search_term = {\n 'searchTerm': 'beef'\n }\n\n self.menu_cuisine = {\n 'menu_cuisine': '1'\n }\n\n nutritionist_token = os.environ['NUTRITIONIST_TOKEN']\n\n self.nutritionist = {\n 'Authorization': 'Bearer ' + nutritionist_token\n }\n\n student_token = os.environ['STUDENT_TOKEN']\n\n self.student = {\n 'Authorization': 'Bearer ' + student_token\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"46f4dffc78341033f612d7e3e99178d1","score":"0.65745157","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"fsnd_capstone\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432',\n self.database_name)\n setup_db(self.app, self.database_path)\n db_drop_and_create_all()\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"024e4c75f8d4309fd21caa51228c3b98","score":"0.65733886","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres','host123','localhost:5432', self.database_name)\n\n setup_db(self.app, self.database_path)\n\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"40b158c1ea9e35acc6d1c237e541b8a7","score":"0.6573055","text":"def setUp(self):\n\n # Get the Flask test client\n self.client = app.test_client()\n\n # Create secret key to access session\n app.secret_key = \"ABC\"\n\n # Connect to fake database\n connect_to_db(app, 'postgresql://localhost/eatablefaker')\n\n app.config['TESTING'] = True","title":""},{"docid":"08a688e4eccf99d0883de33295dda50c","score":"0.6570717","text":"def setUp(self):\n self.app = app\n self.client = self.app.test_client \n database_path = 'postgresql+psycopg2://{}:{}@{}/{}'.format(DB_USER, DB_PASSWORD, DB_HOST, DB_NAME)\n\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"e07337dd1c550f974480f13c5ea1ef7e","score":"0.6564815","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"easylist_test\"\n DB_USERNAME = \"postgres\"\n DB_PASSWORD = \"postgres123\"\n self.database_path = \"postgresql://{}:{}@{}/{}\".format(\n DB_USERNAME, DB_PASSWORD, 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"f74a31d99183da4586ed594839bca6d3","score":"0.6561662","text":"def setUp(self):\n # Get the Flask test client\n app.config['TESTING'] = True\n self.client = app.test_client()\n\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n db.create_all()\n #Example data to seed images paths into testDB\n example_data()","title":""},{"docid":"17150a8be560744f7ce9f1558e88a103","score":"0.655628","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.app_context = self.app.app_context()\n self.app_context.push()\n db.create_all()","title":""},{"docid":"9e68de679908ab4edbcf103991aeebbf","score":"0.65556425","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n setup_db(self.app, database_name=self.database_name)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"64992838c74185e114bc2629638a8731","score":"0.6554274","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres','1234','localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"69358b4c1dfa6feac7188d4744f42c98","score":"0.6552557","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n #self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n ######################################################################\n ############## Change username and password and whatever you need ##########################\n username = \"postgres\"\n password = \"2021\"\n self.database_path = \"postgresql://{}:{}@{}/{}\".format(username, password,'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"49bd0cd9e38c456e71691c50ddded42e","score":"0.65489686","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = DATABASE_PATH\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"a035022971811a70d9f51fc2cf6eeb55","score":"0.6548439","text":"def setUp(self):\n self.db_fd, app.config['DATABASE'] = tempfile.mkstemp()\n app.config['Testing'] = True\n self.client = app.test_client()","title":""},{"docid":"eabe12712b422919b2d05eda893834aa","score":"0.65378356","text":"def setUp(self):\n os.environ['APP_MODE'] = \"test\"\n self.app = create_app()\n self.client = self.app.test_client\n self.guest = {'name': 'John and Jane Doe', 'total_attendees': 2, \"phone_number\": \"5555555555\"}\n self.invalid_guest = {'name': 'Steve and Dora Explora', 'total_attendees': 'blurple'}\n\n # binds the app to the current context\n with self.app.app_context():\n # create all tables\n db.create_all()","title":""},{"docid":"aa8aa54eedbad6884c00d2034bdc8514","score":"0.6536112","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.uid = 123456789\n user = User.signup(\"testuser\", \"test@email.com\", \"password\", None)\n user.id = self.uid\n\n db.session.commit()\n\n self.user = User.query.get(self.uid)\n\n self.client = app.test_client()","title":""},{"docid":"83e100ee8aeccecc78fafc717abf6158","score":"0.65273595","text":"def setUp(self):\r\n self.app = create_app()\r\n self.client = self.app.test_client\r\n self.database_name = \"trivia_test\"\r\n self.database_path = 'postgresql://postgres:Gofran_333@localhost:5432/trivia_test'\r\n setup_db(self.app, self.database_path)\r\n\r\n # binds the app to the current context\r\n with self.app.app_context():\r\n self.db = SQLAlchemy()\r\n self.db.init_app(self.app)\r\n # create all tables\r\n self.db.create_all()","title":""},{"docid":"09c82a29ce79a105e1af4139ff6d70eb","score":"0.652082","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_path = os.environ['DATABASE_URL']\n setup_db(self.app, self.database_path)\n\n self.jwt_all_permissions = os.environ['JWT_TOKEN']\n self.jwt_casting_assistant = os.environ['JWT_CASTING_ASSIST']\n self.jwt_casting_director = os.environ['JWT_CASTING_DIRECTOR']\n self.jwt_executive_producer = os.environ['JWT_EXECUTIVE_PRODUCER']\n\n self.new_movie = {\n 'title': 'test movie',\n 'release_date': '1999-01-01',\n }\n\n self.new_actor = {\n 'name': 'test actor',\n 'age': '25',\n 'gender': 'male',\n }\n\n self.new_movie_missing_attribute = {\n 'title': 'test movie',\n }\n\n self.new_actor_missing_attribute = {\n 'name': 'test actor',\n 'age': '25',\n }\n\n self.update_movie_date = {\n 'release_date': '1999-01-01',\n }\n\n self.update_actor_age = {\n 'age': 35,\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"0ffa24e1e5314ce57a4d09ec406be5f3","score":"0.6508347","text":"def setUp(self):\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.testuser = User.signup(username=\"testuser1\", password=\"testuserpassword\", email=\"testuser1@email.com\", goal_cal=1000, city=\"Houston\", state=\"TX\")\n self.testuser_id = 909090\n self.testuser.id = self.testuser_id\n\n self.user1 = User.signup(username=\"user1\", password=\"user1user1\", email=\"user1@gmail.com\", goal_cal=1110, city=\"New Orleans\", state=\"LA\")\n self.user1_id = 90909\n self.user1.id = self.user1_id\n self.user2 = User.signup(username=\"user2\", password=\"user2pwpw\", email=\"user2@yahoo.com\", goal_cal=9999, city=\"Miami\", state=\"FL\")\n self.user2_id = 909099\n self.user2.id = self.user2_id\n self.user3 = User.signup(\"user3\", \"user33user3\", \"user3@user.com\", 1234, \"Lincoln\", \"NE\")\n\n db.session.commit()","title":""},{"docid":"bf60e184208079ce231cde5856565a3c","score":"0.6506584","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n my_password = \"Asdfasdf1!\"\n self.database_path = \"postgresql://{}:{}@{}/{}\".format('postgres', my_password, 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"2aec7151246993866552e366503ef355","score":"0.64933604","text":"def setUp(self):\n super().setUp()\n self.test_user_email = \"testWill@gmail.com\"\n self.test_user_password = \"timeTotest12\"\n self.client = Client()\n\n self.create_test_entity_types()\n # A set of generic personal details used for a few of the tests\n self.personal_details = {\n \"name\": \"TestUser\",\n \"age\": 20,\n \"id_number\": 10001,\n \"id_document\": \"NRIC\",\n \"religion\": \"Christianity\",\n \"real_estate_type\": \"HDB_FLAT\",\n \"postal_code\": 123456,\n \"floor_number\": 23,\n \"unit_number\": 23,\n \"block_number\": \"B123\",\n \"street_name\": \"Crazy Street\",\n \"country\": \"SG\",\n }","title":""},{"docid":"8061423dc271fbc53f334d8c745649c0","score":"0.6492227","text":"def setUp(self):\n \n app = create_app()\n\n db.drop_all()\n db.create_all()\n\n seed_db()\n \n db.session.commit()\n\n self.client = app.test_client()","title":""},{"docid":"fec317bcd401b1c10a14adece8b5683f","score":"0.64856344","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgresql://postgres:123@{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"7b725771767e94eba969b3664d901e3e","score":"0.64852583","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_path = 'postgresql://postgres:admin@localhost:5432/trivia_test'\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"2980549dc3cea958aa14561258982f59","score":"0.64704","text":"def setUp(self):\n\n self.app = create_app()\n self.client = self.app.test_client\n\n self.database_path = \"postgres://{}/{}\".format(\n 'localhost:5432', \"agency_test\")\n\n setup_db(self.app, self.database_path)\n db_drop_and_create()\n\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"f0aa53fbfb383a48c186459cbedd2bb6","score":"0.6468383","text":"def setUp(self):\n self.app = app\n self.client = self.app.test_client\n self.database_path = database_path\n setup_db(self.app, self.database_path)\n # JWT from environment variable\n self.user_jwt = {'Authorization': 'Bearer {}'.format(os.environ['USER_TOKEN'])}\n self.author_jwt = {'Authorization': 'Bearer {}'.format(os.environ['AUTHOR_TOKEN'])}\n self.invalid_token = {'Authorization': 'Bearer {}'.format(os.environ['INVALID_TOKEN'])}\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n self.db.create_all()","title":""},{"docid":"469fdb64e8ce81bc0fb3d305519a50a0","score":"0.6468015","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.testuser = User.signup(username=\"testuser\",\n email=\"test@test.com\",\n password=\"testuser\")\n \n self.testuser_id = 9999\n self.testuser.id = self.testuser_id\n\n self.u1 = User.signup(\"abc\", \"test1@test.com\", \"password\")\n self.u1_id = 789\n self.u1.id = self.u1_id\n self.u2 = User.signup(\"efg\", \"test2@test.com\", \"password\")\n self.u2_id = 678\n self.u2.id = self.u2_id\n self.u3 = User.signup(\"hij\", \"test3@test.com\", \"password\")\n self.u3_id = 4567\n self.u3.id = self.u3_id\n\n db.session.commit()","title":""},{"docid":"8adbc2627866d22896e8d1885fed7d86","score":"0.64580613","text":"def setUp(self):\n db.drop_all()\n db.create_all()\n\n u1 = User.signup(\"testuser1\", \"testuser@gmail.com\", \"testUser12\", None)\n id1 = 3333\n u1.id = id1\n\n image_url = \"https://cdn.pixabay.com/photo/2014/11/30/14/11/cat-551554__340.jpg\"\n u2 = User.signup(\"testuser2\", \"testuser2@gmail.com\", \"tesUSer45\", image_url) \n id2 = 4444\n u2.id = id2\n\n db.session.commit()\n\n self.user1 = User.query.get(id1)\n self.user2 = User.query.get(id2)\n\n self.u1id = id1\n self.u2id = id2\n \n\n self.client = app.test_client()","title":""},{"docid":"3a58ad01ace77951ef35debe77d8d75f","score":"0.64489305","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format(\n 'postgres', '11112015', 'localhost:5432', self.database_name\n )\n\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"e1b4edceb7b1d0b4fe839f2d4614e6f1","score":"0.6445891","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.testuser = User.signup(\n username=\"testuser\", email=\"test@test.com\", password=\"testpw\",\n )\n self.testuser_id = 100\n self.testuser.id = self.testuser_id\n\n self.otheruser = User.signup(\n username=\"otheruser\", email=\"other@test.com\", password=\"otherpw\",\n )\n\n self.otheruser_id = 200\n self.otheruser.id = self.otheruser_id\n\n self.testproject = Project(\n name=\"Project_Test\", description=\"Project_Test test description.\",\n )\n self.testproject_name = self.testproject.name\n self.testproject_id = 110\n self.testproject.id = self.testproject_id\n db.session.add(self.testproject)\n\n self.testplot = Plot(\n name=\"Plot_Test\",\n width=5,\n length=10,\n description=\"Plot_Test test description.\",\n )\n self.testplot_name = self.testplot.name\n self.testplot_id = 120\n self.testplot.id = self.testplot_id\n db.session.add(self.testplot)\n\n self.testplantlist = PlantList(\n name=\"Plantlist_Test\", description=\"Plantlist_Test test description.\",\n )\n self.testplantlist_name = self.testplantlist.name\n self.testplantlist_id = 130\n self.testplantlist.id = self.testplantlist_id\n db.session.add(self.testplantlist)\n\n self.testplant = Plant(\n trefle_id=1231,\n slug=\"plantus-slugs1\",\n common_name=\"common plant1\",\n scientific_name=\"plantus testus1\",\n family=\"Plantaceae1\",\n family_common_name=\"Plant Family1\",\n )\n self.testplant_common_name = self.testplant.common_name\n self.testplant_id = 140\n self.testplant.id = self.testplant_id\n\n db.session.add(self.testplant)\n\n self.testsymbol = Symbol(\n symbol=\"<i class='symbol fas fa-seedling' style='color:#228B22;'></i>\"\n )\n\n self.testsymbol_id = 1\n self.testsymbol.id = self.testsymbol_id\n db.session.add(self.testsymbol)\n\n # Connections\n self.testuser.projects.append(self.testproject)\n self.testuser.plots.append(self.testplot)\n self.testuser.plantlists.append(self.testplantlist)\n self.testproject.plots.append(self.testplot)\n self.testproject.plantlists.append(self.testplantlist)\n self.testplot.plantlists.append(self.testplantlist)\n self.testplantlist.plants.append(self.testplant)\n\n db.session.commit()","title":""},{"docid":"88afba32e98c568ed3a62d3cf913934c","score":"0.6440165","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format(\n 'postgres', 'admin', 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"9ed32f7e3bbc3fc4af96b1f864d399d8","score":"0.6430592","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"casptone_test\"\n setup_db(self.app)\n\n self.new_actor = {\n \"name\": \"Manuela Mercado\",\n \"age\": 25,\n \"gender\": \"F\",\n \"movies\": [1, 2]\n }\n\n self.new_movie = {\n \"title\": \"Manuela Mercado\",\n \"release_year\": \"Fri, 15 May 2020 00:00:00 GMT\",\n \"actors\": [1, 2]\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"3f0389efb4b8e175d458b03e1414910d","score":"0.64297104","text":"def setUp(self):\n\n self.app = create_app()\n self.client = self.app.test_client\n self.database_path = \"postgres://sxvwypvcewiwft:71a5b4af53ca61d9936511c46f96b61fa9d3b9b589f87be97bb49cc98d66aab5@ec2-52-87-58-157.compute-1.amazonaws.com:5432/db28be7l9velkt\"\n setup_db(self.app, self.database_path)\n db_drop_and_create_all()\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"1617529d3c6e9e50bc2f176e5d49ed36","score":"0.6426215","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"1617529d3c6e9e50bc2f176e5d49ed36","score":"0.6426215","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"d154957ab2d384fb364ec4b189e34e21","score":"0.6424833","text":"def setUp(self):\n db.drop_all()\n db.create_all()\n\n j1 = Jobseeker.signup(first_name=\"testuser\",\n last_name=\"testuser\",\n username=\"testuser\",\n email=\"test@me.com\",\n password=\"testuser\",\n profile_img=None,\n bio=\"bio\",\n location=\"location\")\n jid1 = 1111\n j1.id = jid1\n\n r1 = Recruiter.signup(username=\"testR\",\n email=\"testR\",\n company_name=\"testR\",\n password=\"testR\",\n profile_img=None,\n about=\"testR\",\n location=\"testR\")\n rid1 = 2222\n r1.id = rid1\n\n db.session.commit()\n\n j1 = Jobseeker.query.get(jid1)\n r1 = Recruiter.query.get(rid1)\n\n self.j1 = j1\n self.jid1 = jid1\n\n self.r1 = r1\n self.rid1 = rid1\n\n self.client = app.test_client()","title":""},{"docid":"8fcdad9e0e3b97bda44c82dc2360840e","score":"0.64246064","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.quotes = {'title': 'Happiness'}\n\n # binds the app to the current context\n with self.app.app_context():\n # create all tables\n db.create_all()","title":""},{"docid":"f0c91b8b3a1dd406ffea39a9d7203cee","score":"0.6418258","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_path = database_path\n setup_db(self.app, self.database_path)\n db_drop_create_initialize()\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"17f14c64be9abe890de5300bb0e53218","score":"0.641815","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.testuser = User.signup(username=\"testuser\",\n email=\"test@test.com\",\n password=\"testuser\",\n image_url=None)\n self.testuser.id = 12345\n\n self.u1 = User.signup(\"u1\", \"test1@test1.com\", \"pwd1\", None)\n self.u1.id = 11111\n self.u2 = User.signup(\"u2\", \"test2@test2.com\", \"pwd2\", None)\n self.u2.id = 22222\n # self.u3 = User.signup(\"u3\", \"test3@test3.com\", \"pwd3\", None)\n # self.u4 = User.signup(\"u4\", \"test4@test4.com\", \"pwd4\", None)\n\n db.session.commit()","title":""},{"docid":"24e86d38569c842b3787bd27732578ec","score":"0.641675","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_user = \"postgres\"\n self.database_pw = \"postgres\"\n\n # Note I'm using port 5433 due to the way my system is set up\n self.database_path =\\\n \"postgres://{}:{}@{}/{}\".format(self.database_user,\n self.database_pw,\n 'localhost:5433',\n self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"1ed96261cffa54e360dfe323bf4269cd","score":"0.6416701","text":"def setUp(self):\n self.test_db, test_db_name = tempfile.mkstemp()\n test_config = dict(\n ENV='development',\n TESTING=True,\n SECRET_KEY='test',\n DATABASE=test_db_name,\n DEBUG = True)\n self.app = create_app(test_config=test_config)\n self.client = self.app.test_client()\n\n with self.app.app_context():\n db.init_db()","title":""},{"docid":"b9e467da30ded194d3a7f7cc820cbe69","score":"0.6413648","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n\n self.database_path = os.environ['DATABASE_URL']\n setup_db(self.app, self.database_path)\n\n self.token_CA = os.environ['token_CA']\n self.token_CD = os.environ['token_CD']\n self.token_EP = os.environ['token_EP']\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n\n self.new_actor = {\n 'name': 'actor_json_name',\n 'age': 1,\n 'gender': 'female',\n 'description': 'actor_json_name age1 gender female'\n }\n\n self.new_movie = {\n 'title': 'movie_title',\n 'released_date': '2021-07-23',\n 'genre': 'SF',\n 'description': 'movie_title 2021-07-23 SF'\n }","title":""},{"docid":"cb506b36c9c5e70123c31250a067681a","score":"0.6412848","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"movies_test\"\n self.database_path = os.getenv(\"DATABASE_URL\")\n self.test_movie = {\n 'title': 'Inception 2',\n 'release_date': '2024-04-04'\n }\n # Casting Assistant (can view movies and actors)\n CA_TOKEN = os.getenv(\"CA_TOKEN\")\n # Casting Director (CA role + can add, delete, patch actors and movies)\n CD_TOKEN = os.getenv(\"CD_TOKEN\")\n setup_db(self.app, self.database_path)\n self.director_headers = {\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer \" + CD_TOKEN,\n }\n self.assistant_headers = {\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer \" + CA_TOKEN,\n }\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"fca3d6751acdfa3db5e259d5d48adc52","score":"0.64096457","text":"def setUp(self):\n try:\n self.alice = self.create_user('alice', 'password')\n except IntegrityError:\n self.skipTest(\"Unable to create the test user.\")\n else:\n self.device = self.alice.totpdevice_set.create(\n key='2a2bbba1092ffdd25a328ad1a0a5f5d61d7aacc4', step=30,\n t0=int(time() - (30 * 3)), digits=6, tolerance=0, drift=0\n )","title":""},{"docid":"ea625cade68ed1f841a38ef16c07aa3f","score":"0.6406738","text":"def setUp(self):\n self.starting_db = [(1, 'Bob', 'Bobbo', '12 Green St', '1112223344',\n 'bobbo@python.org', False, 85000),\n (2, 'Jane', 'Janeo', '1550 Red Rd', '1118675309',\n 'jane@therealjane.com', True, 150000),\n (5, 'Wilson', 'Volleyball', '1 Castaway Island', '0000000000',\n 'wilson@ImLost.com', True, 0)\n ]\n database.init(TEST_DATABASE)\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON')\n database.create_tables([Customer])\n with database.transaction():\n Customer.delete().execute()\n Customer.insert_many(self.starting_db, fields=[Customer.customer_id, Customer.name,\n Customer.lastname, Customer.home_address,\n Customer.phone_number,\n Customer.email_address,\n Customer.active_status,\n Customer.credit_limit\n ]).execute()\n database.close()","title":""},{"docid":"bc7e7f83527163419adf6a7685b7d657","score":"0.6396226","text":"def setUp(self):\n\n self.db_handle, app.app.config['DATABASE'] = tempfile.mkstemp()\n self.app = app.app.test_client()\n with app.app.app_context():\n app.init_db() # nope","title":""},{"docid":"2ee9656993214c6b32f21245eb7bd0ed","score":"0.638305","text":"def setUp(self):\n db.drop_all()\n db.create_all()\n\n User.query.delete()\n Message.query.delete()\n Follows.query.delete()\n\n self.client = app.test_client()","title":""},{"docid":"5a666372b2aab8a20af0803bf197ed37","score":"0.63763547","text":"def setUp(self):\r\n self.app = create_app()\r\n self.client = self.app.test_client\r\n self.database_path = os.environ.get('DATABASE_URL')\r\n if self.database_path and self.database_path.startswith(\"postgres://\"):\r\n self.database_path = self.database_path.replace(\"postgres://\", \"postgresql://\", 1)\r\n else:\r\n self.database_path =os.environ.get('Test_database_name')\r\n setup_db(self.app, self.database_path)\r\n\r\n self.new_actor = {\r\n 'name': 'mohammed',\r\n 'age': '25',\r\n 'gender': 'male'\r\n }\r\n\r\n self.new_movie = {\r\n \"title\": \"catch me\",\r\n \"date\": \"03/25/2006\"\r\n }\r\n # binds the app to the current context\r\n with self.app.app_context():\r\n self.db = SQLAlchemy()\r\n self.db.init_app(self.app)\r\n # create all tables\r\n self.db.create_all()","title":""},{"docid":"dda6209b1615eb0914f04759b26acf8e","score":"0.63751256","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"28bb93f7d9ee09d4d62c3f879c5905d4","score":"0.63602","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.review = {\"opinion\": \"Good business with good food\", \"rating\": 5}\n self.wrong_review = {\"rating\": 5}\n self.wrong_rating = {\"opinion\": \"Good business with good food\"}\n self.business = {'name': 'crastycrab', 'description': 'Fastfood', 'contact': '0702848032', 'category': 'fastfood', 'location': 'atlantic'}\n # bind app to the current context\n with self.app.app_context():\n # create db tables\n db.create_all()","title":""},{"docid":"b703cdc632162a1c78b085a68d71df42","score":"0.635609","text":"def setUp(self):\n self.app = create_app(config_name=\"testing\")\n self.client = self.app.test_client\n self.produtor = {\n \"nrDocumento\": \"083.843.589-07\",\n \"nmProdutor\": \"Fernando Paladini\",\n \"nrTelefone\": \"(48) 99845-9684\",\n \"dsEmail\": \"paladini@1doc.com.br\",\n \"cdEstabelecimento\": 0\n }\n self.estabelecimento = {\n \"nmEstabelecimento\": \"Granja Santa Clara\",\n \"nrCodigoOficial\": \"42-0003256\",\n \"idPais\": 1,\n \"idUf\": 24,\n \"idMunicipio\": 2812,\n \"nmLocalidade\": \"Rua Retangular, número 182. Bairro Santa Clara.\",\n \"nrLatitude\": -28.098883,\n \"nrLongitude\": -28.098883,\n \"stAtivo\": True,\n \"idCliente\": 10\n }\n\n # binds the app to the current context\n with self.app.app_context():\n # create all tables\n db.create_all()","title":""},{"docid":"4478846d7eac95eea7bb0dea2d607eaa","score":"0.6356084","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"casting_agency\"\n self.database_path = \"postgresql://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_movie = {\n 'title': 'Anansi Boys2',\n 'release_date': '10-19-2009'\n }\n\n self.new_actor = {\n \"name\": \"Emile Hirsch2\",\n \"age\": 35,\n \"gender\": \"male\"\n }\n\n self.executive_producer = {'Authorization': os.environ['EXECUTIVE_PRODUCER_TOKEN']}\n\n self.casting_director = {'Authorization': os.environ['CASTING_DIRECTOR_TOKEN']}\n\n self.casting_assistant = {'Authorization': os.environ['CASTING_ASSISTANT_TOKEN']}\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"f0cd994396e04362f077b62f70ee5444","score":"0.6355797","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.user1 = User.signup(**USER_DATA1)\n self.user1.id = 100\n\n db.session.add(self.user1)\n db.session.commit()","title":""},{"docid":"f0cd994396e04362f077b62f70ee5444","score":"0.6355797","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.user1 = User.signup(**USER_DATA1)\n self.user1.id = 100\n\n db.session.add(self.user1)\n db.session.commit()","title":""},{"docid":"f0cd994396e04362f077b62f70ee5444","score":"0.6355797","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.user1 = User.signup(**USER_DATA1)\n self.user1.id = 100\n\n db.session.add(self.user1)\n db.session.commit()","title":""},{"docid":"f0cd994396e04362f077b62f70ee5444","score":"0.6355797","text":"def setUp(self):\n\n db.drop_all()\n db.create_all()\n\n self.client = app.test_client()\n\n self.user1 = User.signup(**USER_DATA1)\n self.user1.id = 100\n\n db.session.add(self.user1)\n db.session.commit()","title":""},{"docid":"541e0ddad670f652c3a8d269a5f0bd97","score":"0.6344583","text":"def setUp(self):\n config_name = config.TestingConfig\n app.config.from_object(config_name)\n self.app = app\n self.test_client = self.app.test_client()\n self.app_context = self.app.app_context()\n self.app_context.push()\n self.name = 'test user'\n self.email = 'testuser@example.com'\n self.test_username = 'testuser'\n self.test_password = 'test1234'\n with self.app_context:\n db.create_all()","title":""},{"docid":"7465031dd1cacc20262b4739a893dce1","score":"0.63431394","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432',\n self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"29985517ffc879757a352ec4b31afd6e","score":"0.6335297","text":"def setUp(self):\n server.init_db()\n db.drop_all() # clean up the last tests\n db.create_all() # create new tables\n date = datetime.now()\n\n\n order = Order(customer_id=1, date=date, status = 'processing').save()\n order = Order(customer_id=2, date=date, status = 'processing').save()\n\n order1 = Order()\n order1 = order1.find_by_customer_id(1)[0]\n order2 = Order()\n order2 = order2.find_by_customer_id(2)[0]\n\n item = Item(order_id=order1.id, product_id=1, name='hammer', quantity=1, price=11.50).save()\n item = Item(order_id=order1.id, product_id=2, name='toilet paper', quantity=2, price=2.50).save()\n item = Item(order_id=order2.id, product_id=3, name='beer', quantity=2, price=10.50).save()\n self.app = server.app.test_client()","title":""},{"docid":"b12e10de2ba9f92f4d40cacbfb77a5a1","score":"0.6332886","text":"def setUp(self):\n db.drop_all()\n db.create_all()\n\n User.query.delete()\n Message.query.delete()\n Follows.query.delete()\n\n self.test1 = User.signup(\n email=\"test1@test.com\",\n username=\"testuser1\",\n password=\"HASHED_PASSWORD1\",\n image_url = None\n )\n self.test1_id = 1010\n self.test1.id = self.test1_id \n\n self.test2 = User.signup(\n email=\"test2@test.com\",\n username=\"testuser2\",\n password=\"HASHED_PASSWORD2\",\n image_url = None\n )\n self.test2_id = 2020\n self.test2.id = self.test2_id \n\n db.session.commit()\n self.client = app.test_client()","title":""},{"docid":"7604c6ec2b992e03bb3d2da90f0aebdd","score":"0.6323764","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format(\n 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n\n def tearDown(self):\n \"\"\"Executed after reach test\"\"\"\n pass","title":""},{"docid":"5287b55bbe23081e3b0863062095f499","score":"0.6313915","text":"def setUp(self):\n self.app = app\n self.client = self.app.test_client\n self.database_name = \"booksnap_db\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres','password','localhost:5432', self.database_name)\n self.app.config['WTF_CSRF_ENABLED'] = False\n self.app.config['TESTING'] = True","title":""},{"docid":"72c468331453f895d19b023c1a9d9d76","score":"0.62994677","text":"def setUp(self):\n # Get the Flask test client\n self.client = app.test_client()\n\n # Show Flask errors that happen during tests\n app.config['TESTING'] = True\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///testdb\")\n\n # Create tables from model\n db.create_all()\n\n # Import different types of data from seed file\n seed.load_users()\n seed.load_groups()\n seed.load_usergroups()\n seed.load_patterns()\n seed.load_invites()\n\n # Reset auto incrementing primary keys to start after seed data\n seed.set_val_user_id()\n seed.set_val_group_id()\n seed.set_val_usergroup_id()\n seed.set_val_pattern_id()\n seed.set_val_invite_id()\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['user_id'] = 1\n c.set_cookie('localhost', 'MYCOOKIE', 'cookie_value')","title":""},{"docid":"1e01c981ae3ddac4e30c578f30536c3d","score":"0.62909126","text":"def setUp(self):\n application.app.config['TESTING'] = True\n self.app = application.app.test_client()\n self.drop_database()","title":""},{"docid":"83a9e7858579b4d7c684ca997bde3d3f","score":"0.62820077","text":"def setUp(self):\n self.app = app.create_app()\n self.client = self.app.test_client\n self.database_name = \"castapp_test\"\n self.database_path = settings.LOCAL_DATABASE_PATH\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"cd1807356f575a608c62460cc2efb3b1","score":"0.6281231","text":"def setUp(self):\n # Get the Flask test client\n self.client = app.test_client()\n\n # Show Flask errors that happen during tests\n app.config['TESTING'] = True\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///testdb\")\n\n # Create tables from model\n db.create_all()\n\n # Import different types of data from seed file\n seed.load_users()\n seed.load_groups()\n seed.load_usergroups()\n seed.load_patterns()\n seed.load_invites()\n \n # Reset auto incrementing primary keys to start after seed data\n seed.set_val_user_id()\n seed.set_val_group_id()\n seed.set_val_usergroup_id()\n seed.set_val_pattern_id()\n seed.set_val_invite_id()\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['user_id'] = 1\n c.set_cookie('localhost', 'MYCOOKIE', 'cookie_value')","title":""}],"string":"[\n {\n \"docid\": \"215dfd1ba1c84a029e7042c351b19542\",\n \"score\": \"0.7453459\",\n \"text\": \"def setUp(self):\\r\\n super(DynamoDBClientTestCase, self).setUp()\\r\\n options.options.domain = 'goviewfinder.com'\\r\\n secrets.InitSecretsForTest()\\r\\n self._client = dynamodb_client.DynamoDBClient(schema=vf_schema.SCHEMA)\\r\\n self._ClearTestTable(self.stop)\\r\\n self.wait(timeout=30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687d93e47caae798bc147ec831211ea6\",\n \"score\": \"0.7204757\",\n \"text\": \"def setUp(self):\\r\\n super(DynamoDBReadOnlyClientTestCase, self).setUp()\\r\\n options.options.domain = 'goviewfinder.com'\\r\\n secrets.InitSecretsForTest()\\r\\n self._client = dynamodb_client.DynamoDBClient(schema=vf_schema.SCHEMA, read_only=True)\\r\\n\\r\\n self._client._read_only = False\\r\\n self._RunAsync(self._ClearTestTable)\\r\\n self._RunAsync(self._client.PutItem, table=_table.name, key=DBKey(hash_key='1', range_key=2), attributes={'a1': 1})\\r\\n self._client._read_only = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14cdfe188f5a8c28e0aa30c0b8646015\",\n \"score\": \"0.7136222\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n # Initialization of the test_client\\n self.client = self.app.test_client\\n # This is the user test json data\\n self.user_data = {\\n 'email': 'test@example.com',\\n 'password': 'test_password'\\n }\\n\\n with self.app.app_context():\\n # create all tables\\n db.session.close()\\n db.drop_all()\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32fdd283165a22c116bb0ac1114c49d\",\n \"score\": \"0.7050845\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n app.config['SECRET_KEY'] = 'supersecret'\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n db.create_all()\\n create_example_data()\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess['current_user'] = 'thodas'\\n sess['current_user_id'] = 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c983222d9e72b310e447008182bdd367\",\n \"score\": \"0.7013796\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.user = {\\n \\\"email\\\": \\\"test_script@test.com\\\",\\n \\\"password\\\": \\\"abcd1234%\\\",\\n \\\"location\\\": \\\"Durham\\\",\\n \\\"quip\\\": \\\"witty test remark\\\",\\n \\\"photo\\\": \\\"http://photoimage.com\\\",\\n \\\"user_name\\\": \\\"test_name\\\"\\n }\\n\\n with self.app.app_context():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c505ba5f1585dc1c1c9adf885b0f9da8\",\n \"score\": \"0.68929213\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"capstone_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.menu_item = {\\n \\\"name\\\": 'mac and cheese',\\n \\\"ingredients\\\": '[{\\\"ingredient_name\\\": \\\"noodle\\\", \\\"ingredient_amount\\\": 10},'\\\\\\n '{\\\"ingredient_name\\\": \\\"cheese\\\", \\\"ingredient_amount\\\": 5}]',\\n \\\"category\\\": 1,\\n \\\"price\\\": 10\\n }\\n self.category = {\\n \\\"type\\\": \\\"breakfast\\\"\\n }\\n self.chef_header = {'Authorization': 'Bearer {}'.format('eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6InZDV042TnNTa21jU293ajliZVMtVCJ9.eyJpc3MiOiJodHRwczovL2NocmlzLWNhbXBvcy51cy5hdXRoMC5jb20vIiwic3ViIjoiYXV0aDB8NWY3Y2Q2ZGE4MTU1MWYwMDZlNDMwYmY2IiwiYXVkIjoiZmRtIiwiaWF0IjoxNjAyMDI4ODM5LCJleHAiOjE2MDIwMzYwMzksImF6cCI6IjNkQW02a1pZZmVhQ3hINmVoR1VIUjNWNk1NcmJYbGowIiwic2NvcGUiOiIiLCJwZXJtaXNzaW9ucyI6WyJkZWxldGU6bWVudV9pdGVtIiwiZ2V0OmNhdGVnb3JpZXMiLCJnZXQ6bWVudV9pdGVtIiwicGF0Y2g6bWVudV9pdGVtIiwicG9zdDptZW51X2l0ZW0iXX0.JFbQTbfjOfAlQeDDOzbSO6fZ3wbW2JPU4YMcskY0ZVjL_ntWY3kPGiBabtl8brfiIYEj3VDpAUzfAD8YtjeH1OsAUDaOl_z3H06MIo0Z0p2pxA034ifiATw06KVc15lV5FWnaoPSwkEt8s22qAy9ZWdN443-qK4ziRnJOd2jo7ZmzpXtUC9yEhd2OrLlLArNf7fESmSesLKr8Tpdsonn7rSKKDQO99fuE4HJylbb6c0KGOV3-MwR723_P00RBT7N4Gng5OdFCrMJq3Zdu4EaGgjTE8MhcddOLWJTskHeUjveCvdddP_iwLz_8ya7BA-40aC6wGTSzHLMttQIwJouxQ')}\\n self.owner_header = {'Authorization': 'Bearer {}'.format('eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6InZDV042TnNTa21jU293ajliZVMtVCJ9.eyJpc3MiOiJodHRwczovL2NocmlzLWNhbXBvcy51cy5hdXRoMC5jb20vIiwic3ViIjoiZ29vZ2xlLW9hdXRoMnwxMTE4NDQ5MzUzNjc3ODE3Mjk3OTgiLCJhdWQiOlsiZmRtIiwiaHR0cHM6Ly9jaHJpcy1jYW1wb3MudXMuYXV0aDAuY29tL3VzZXJpbmZvIl0sImlhdCI6MTYwMjAyODkxOCwiZXhwIjoxNjAyMDM2MTE4LCJhenAiOiIzZEFtNmtaWWZlYUN4SDZlaEdVSFIzVjZNTXJiWGxqMCIsInNjb3BlIjoib3BlbmlkIHByb2ZpbGUgZW1haWwiLCJwZXJtaXNzaW9ucyI6WyJkZWxldGU6Y2F0ZWdvcmllcyIsImRlbGV0ZTptZW51X2l0ZW0iLCJnZXQ6Y2F0ZWdvcmllcyIsImdldDptZW51X2l0ZW0iLCJwYXRjaDptZW51X2l0ZW0iLCJwb3N0OmNhdGVnb3JpZXMiLCJwb3N0Om1lbnVfaXRlbSJdfQ.gKSYMzVGkNoV61_QkuVEqfK_MC1TvAjFCMJMiq-TAKnvOPJXtOYtYrkVYByAzDjh05ic4D_FiVjUKT1Ov2MoPxDmjJ3boZzAWbj_PFvaCU2h9_Kixe9WE6tZRGZFKuXOAR1HO-tqJ8aZvdzHIaXD2S2mDD4ldf8u7CHDraI-crFfdcrumnmbFzt9hmvpApWGZyMAO8yKW_tTplHAH4ni65Lc_2MIfMC9gl-lISZuCNAPRQnR_XsLQhANTBtaUf64BOXTBcy1J1WWJbQmggTzQNAUPgvPJQkiC_Aw5POHIS1yrbJBtUFaEw9u9bp0doEub7C2P7Y_kg7bYPxQQ3ckgQ')}\\n #if os.environ['CHEF_TOKEN']:\\n # self.chef_header = {'Authorization': \\\"Bearer {}\\\".format(os.environ['CHEF_TOKEN'])}\\n #if os.environ['OWNER_TOKEN']:\\n # self.owner_header = {'Authorization': \\\"Bearer {}\\\".format(os.environ['OWNER_TOKEN'])}\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2794ac5d724c4f6cbf35a11d166cedc\",\n \"score\": \"0.68868893\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n db.create_all()\\n create_example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f19d5976e822a5de2740853d49c461a\",\n \"score\": \"0.68550247\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\\n\\n add_test_user_to_session(self.client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f19d5976e822a5de2740853d49c461a\",\n \"score\": \"0.68550247\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\\n\\n add_test_user_to_session(self.client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f19d5976e822a5de2740853d49c461a\",\n \"score\": \"0.68550247\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\\n\\n add_test_user_to_session(self.client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f19d5976e822a5de2740853d49c461a\",\n \"score\": \"0.68550247\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\\n\\n add_test_user_to_session(self.client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66914dbcc34f5755900d09e6c7eded2b\",\n \"score\": \"0.6847757\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66914dbcc34f5755900d09e6c7eded2b\",\n \"score\": \"0.6847757\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66914dbcc34f5755900d09e6c7eded2b\",\n \"score\": \"0.6847757\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3149d69aa119be4a1dd3a2d2e01a1b13\",\n \"score\": \"0.6843024\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"capstone_test\\\"\\n #self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres', 'postgres', 'localhost:5432', self.database_name)\\n self.database_path = \\\"postgres://saqmdnbucthosu:5e6ec82b00af5309ba7c19ccc534a2de33673b4fbe7304fbe467714e2be777d6@ec2-52-0-155-79.compute-1.amazonaws.com:5432/d3nuuppk8gou7m\\\"\\n setup_db(self.app, self.database_path)\\n\\n \\n \\n self.Casting_Assistant ={\\n 'Authorization': os.environ.get('Casting_Assistant')\\n }\\n\\n self.Casting_Director ={\\n 'Authorization': os.environ.get('Casting_Director')\\n }\\n\\n self.Executive_Producer ={\\n 'Authorization': os.environ.get('Executive_Producer')\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c0318dfe17814282018034858464367\",\n \"score\": \"0.6838229\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n\\n # Connect to \\n database = SetDatabase('..\\\\\\\\config_test.json')\\n database_conn_string = database.connection_string\\n setup_db(self.app, database_conn_string)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed5e6702976a48c09488e1013e5eb64\",\n \"score\": \"0.6830688\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///test_database\\\")\\n\\n # Create tables and add sample data\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e4272f03588a6c354fb79da6a095b8f\",\n \"score\": \"0.6821161\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n # Connect to test database\\n connect_to_db(app, uri=\\\"postgresql:///testdb\\\")\\n # Create tables and add sample data\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e4272f03588a6c354fb79da6a095b8f\",\n \"score\": \"0.6821161\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n # Connect to test database\\n connect_to_db(app, uri=\\\"postgresql:///testdb\\\")\\n # Create tables and add sample data\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03fb895d62d5017cd2c3b0649c2d3fa9\",\n \"score\": \"0.6781941\",\n \"text\": \"def setUp(self):\\n\\n self.client = app.test_client()\\n app.config[\\\"TESTING\\\"] = True\\n\\n os.system(\\\"dropdb pixtestdb\\\")\\n os.system(\\\"createdb pixtestdb\\\")\\n\\n # Connect to test database (uncomment when testing database)\\n connect_to_db(app, \\\"postgresql:///pixtestdb\\\")\\n\\n # Create tables and add sample data (uncomment when testing database)\\n db.create_all()\\n sample_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e19cafefbfc84da7622397b494c90fc7\",\n \"score\": \"0.6685032\",\n \"text\": \"def setUp(self):\\n\\t\\tself.app = create_app(\\\"testing\\\")\\n\\t\\tself.client = self.app.test_client\\n\\t\\tself.user_data = {\\n\\t\\t\\t'f_name': 'Peter',\\n\\t\\t\\t'l_name': 'Mwaura',\\n\\t\\t\\t'u_name': 'Pemwa',\\n\\t\\t\\t'email': 'pemwa@gm.com',\\n\\t\\t\\t'password': '12345'\\n\\t\\t}\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tBinding the app with the current context\\n\\t\\tand creating all tables\\n\\t\\t\\\"\\\"\\\"\\n\\t\\twith self.app.app_context():\\n\\t\\t\\tdb.session.close()\\n\\t\\t\\tdb.drop_all()\\n\\t\\t\\tdb.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47c0106f669ae88da86dd74b0b6b1d4\",\n \"score\": \"0.665801\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.app.testing = True\\n self.app_context = self.app.test_request_context()\\n self.app_context.push()\\n self.client = self.app.test_client()\\n self.app.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d3fd679478da9766ce2e60d49b1c1e\",\n \"score\": \"0.6655897\",\n \"text\": \"def setUp(self):\\n db.drop_all()\\n db.create_all()\\n\\n self.uid = 12345\\n u = User.signup(\\\"testing\\\", \\\"password\\\")\\n u.id = self.uid\\n db.session.commit()\\n\\n self.user = User.query.get(self.uid)\\n\\n self.song_uid = 11111\\n song = Song.create_song(\\\"test title\\\", \\\"test artist\\\", \\\"test lyrics\\\", self.uid)\\n song.id = self.song_uid\\n self.song = Song.query.get(self.song_uid)\\n db.session.commit()\\n\\n user_song = UserSong.create_user_song(self.song_uid, self.uid)\\n\\n self.stash_uid = 22222\\n stash = Stash.create_stash(\\\"test stash name\\\", self.uid)\\n stash.id = self.stash_uid\\n self.stash = Stash.query.get(self.stash_uid)\\n db.session.commit()\\n\\n user_stash = UserStash.create_user_stash(self.stash_uid, self.uid)\\n stash_song = StashSong.create_stash_song(self.song_uid, self.stash_uid)\\n db.session.commit()\\n\\n self.annotation_uid = 33333\\n annotation = Annotation.create_annotation(\\\"test annotation\\\", 5, self.uid, self.song_uid)\\n annotation.id = self.annotation_uid\\n self.annotation = annotation.query.get(self.annotation_uid)\\n db.session.commit()\\n\\n SongAnnotation.create_song_annotation(self.annotation_uid, self.song_uid)\\n db.session.commit()\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c7d8bac341bf3d11b13ecc29f0b872\",\n \"score\": \"0.66393816\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.testuser = User.register(User(username=\\\"test1\\\", password=\\\"password\\\", first_name=\\\"John\\\", last_name=\\\"Doe\\\", email=\\\"email@email.com\\\"))\\n self.testuser_id = 8989\\n self.testuser.id = self.testuser_id\\n \\n self.u1 = User(username=\\\"testtesttest\\\", password=\\\"password\\\", first_name=\\\"John\\\", last_name=\\\"Doe\\\", email=\\\"test@test.com\\\")\\n self.u1_id = 778\\n self.u1.id = self.u1_id\\n self.u2 = User.register(User(username=\\\"test2\\\", password=\\\"password\\\", first_name=\\\"John\\\", last_name=\\\"Doe\\\", email=\\\"email2@email.com\\\"))\\n self.u2_id = 884\\n self.u2.id = self.u2_id\\n self.u3 = User.register(User(username=\\\"test3\\\", password=\\\"password\\\", first_name=\\\"John\\\", last_name=\\\"Doe\\\", email=\\\"email3@email.com\\\"))\\n self.u4 = User.register(User(username=\\\"test4\\\", password=\\\"password\\\", first_name=\\\"John\\\", last_name=\\\"Doe\\\", email=\\\"email4@email.com\\\"))\\n\\n db.session.add_all([self.u1, self.u2, self.u3, self.u4])\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e6b6784211131200dc4709b57586b0\",\n \"score\": \"0.66277075\",\n \"text\": \"def setUp(self):\\n self.db_fd, project.app.config['DATABASE'] = tempfile.mkstemp()\\n project.app.testing = True\\n self.app = project.app.test_client()\\n with project.app.app_context():\\n db.Database().create_tables()\\n user.createUser('adminTest', 'tcg1134', '', '')\\n user.createUser('adminTest2', 'gct2334', '', '')\\n Group.createGroup('TestGroup', 'adminTest2')\\n topic.insertTopic('TestTopic', 'adminTest2', '1')\\n post.insertPost(1, 'adminTest2', 'TestPost', 'Some random text')\\n subscription.addToSub(1,1)\\n notification.sendNotif(\\\"post\\\", 1)\\n Votes.upvote(1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8451946637bc0f39bb57f3af0a19356e\",\n \"score\": \"0.66239786\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n # create a sample user\\n user = User.signup(\\\"test\\\", \\\"test@test.com\\\", \\\"testpass\\\", None)\\n self.uid = 111\\n user.id = self.uid\\n\\n db.session.commit()\\n\\n self.user = User.query.get(self.uid)\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e846b066c7a718589c949633182e909\",\n \"score\": \"0.6623845\",\n \"text\": \"def setUp(self):\\n\\n # Get the Flask test client\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///vidtester\\\")\\n\\n # Create tables and add sample data\\n db.create_all()\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37a5c44f5131f15e39389aa801788c6b\",\n \"score\": \"0.6622711\",\n \"text\": \"def setUp(self) -> None:\\n app.config['TESTING'] = True\\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\\n self.client = app.test_client()\\n with app.app_context():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37a5c44f5131f15e39389aa801788c6b\",\n \"score\": \"0.6622711\",\n \"text\": \"def setUp(self) -> None:\\n app.config['TESTING'] = True\\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\\n self.client = app.test_client()\\n with app.app_context():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03bf558a6c9aa5ee4e41d231894ba5f\",\n \"score\": \"0.66059357\",\n \"text\": \"def setUp(self):\\n\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = 'capstonefsnd_test'\\n self.database_path = 'postgresql://{}:{}@{}/{}'.format(\\n 'postgres', 'admin',\\n 'localhost:5432', self.database_name)\\n\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n self.db.drop_all()\\n\\n # create all tables\\n\\n self.db.create_all()\\n\\n # initiate the test database with dummy records.\\n\\n for i in range(5):\\n new_actor = self.create_actor()\\n actor = Actor(name=new_actor['name'],\\n age=new_actor['age'],\\n gender=new_actor['gender'])\\n actor.insert()\\n\\n new_movie = self.create_movie()\\n movie = Movie(title=new_movie['title'],\\n release=new_movie['release'])\\n movie.insert()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827dc19def914d85175852e709681e48\",\n \"score\": \"0.66024137\",\n \"text\": \"def setup(self):\\n self.app = create_app(TEST_SETTINGS)\\n self.client = self.app.test_client()\\n with self.app.test_request_context():\\n db.create_all()\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a7770b749b02c450c1044c096f3cb5b\",\n \"score\": \"0.6601224\",\n \"text\": \"def setUp(self):\\r\\n options.options.localdb = True\\r\\n options.options.fileobjstore = True\\r\\n options.options.localdb_dir = ''\\r\\n\\r\\n super(DBBaseTestCase, self).setUp()\\r\\n options.options.localdb_dir = ''\\r\\n self._client = local_client.LocalClient(vf_schema.SCHEMA)\\r\\n object_store.InitObjectStore(temporary=True)\\r\\n self._temp_dir = tempfile.mkdtemp()\\r\\n server_log.LogBatchPersistor.SetInstance(server_log.LogBatchPersistor(backup_dir=self._temp_dir))\\r\\n IdAllocator.ResetState()\\r\\n\\r\\n options.options.domain = 'goviewfinder.com'\\r\\n secrets.InitSecretsForTest()\\r\\n\\r\\n # Set deterministic testing timestamp used in place of time.time() in server code.\\r\\n util._TEST_TIME = time.time()\\r\\n\\r\\n self._RunAsync(vf_schema.SCHEMA.VerifyOrCreate, self._client)\\r\\n OpManager.SetInstance(OpManager(op_map=DB_OPERATION_MAP, client=self._client, scan_ops=True))\\r\\n\\r\\n # Create users with linked email identity and default viewpoint.\\r\\n self._user, self._mobile_dev = self._CreateUserAndDevice(user_dict={'user_id': 1, 'name': 'Spencer Kimball'},\\r\\n ident_dict={'key': 'Email:spencer.kimball@emailscrubbed.com',\\r\\n 'authority': 'Google'},\\r\\n device_dict={'device_id': 1, 'name': 'Spencer\\\\'s iPhone'})\\r\\n\\r\\n self._user2, _ = self._CreateUserAndDevice(user_dict={'user_id': 2, 'name': 'Peter Mattis'},\\r\\n ident_dict={'key': 'Email:peter.mattis@emailscrubbed.com',\\r\\n 'authority': 'Google'},\\r\\n device_dict=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bf0b1d440dc6d44570bb091700a03c\",\n \"score\": \"0.65988797\",\n \"text\": \"def setUp(self):\\n testDatabase.createAll()\\n\\t \\n # Carer\\n carerClient = testDatabase.Client.insert(\\n username = \\\"carer\\\",\\n email = \\\"justhealth@richlogan.co.uk\\\",\\n dob = \\\"03/03/1993\\\",\\n verified = True,\\n accountlocked = False,\\n loginattempts = 0,\\n accountdeactivated = False)\\n carerCarer = testDatabase.Carer.insert(\\n username = \\\"carer\\\",\\n firstname = \\\"carer\\\",\\n ismale = True,\\n nhscarer = True,\\n surname = \\\"carer\\\")\\n carerPassword = testDatabase.uq8LnAWi7D.insert(\\n expirydate = '01/01/2020',\\n iscurrent = True,\\n password = sha256_crypt.encrypt('test'),\\n username = \\\"carer\\\")\\n carerClient.execute()\\n carerCarer.execute()\\n carerPassword.execute()\\n\\n # Patient\\n patientClient = testDatabase.Client.insert(\\n username = \\\"patient\\\",\\n email = \\\"justhealth123@richlogan.co.uk\\\",\\n dob = \\\"03/03/1993\\\",\\n verified = True,\\n accountlocked = False,\\n loginattempts = 0,\\n accountdeactivated = False)\\n testPatient = testDatabase.Patient.insert(\\n username = \\\"patient\\\",\\n firstname = \\\"patient\\\",\\n surname = \\\"patient\\\",\\n ismale = True)\\n patientPassword = testDatabase.uq8LnAWi7D.insert(\\n expirydate = '01/01/2020',\\n iscurrent = True,\\n password = sha256_crypt.encrypt('test'),\\n username = \\\"patient\\\")\\n patientClient.execute()\\n testPatient.execute()\\n patientPassword.execute()\\n\\n testRelationship = testDatabase.Relationship.insert(\\n code = 1234,\\n requestor = \\\"carer\\\",\\n requestortype = \\\"Carer\\\",\\n target = \\\"patient\\\",\\n targettype = \\\"Patient\\\")\\n testRelationship.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283a987e3b3ebd68b42f38aa628bca3b\",\n \"score\": \"0.6598439\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.Character = {\\n \\\"name\\\" : 'Ness',\\n \\\"hp\\\" : 100,\\n 'mana' : 45,\\n \\\"attack\\\" : 24,\\n \\\"defense\\\" : 18,\\n 'intelligence' : 20,\\n 'luck' : 16\\n }\\n self.Character2 = { \\n 'name' : 'Paula',\\n 'hp' : 110,\\n 'mana' : 60,\\n 'attack' : 20,\\n 'defense' : 20,\\n 'intelligence' : 22,\\n 'luck' : 12\\n } \\n # binds the app to the current context\\n with self.app.app_context():\\n # create all tables\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfd54dc2f701b246b990941109bd0848\",\n \"score\": \"0.6582792\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n u1 = User.signup(\\\"testuser1\\\", \\\"test1@test.com\\\", \\\"password1\\\", None)\\n u1.bio = \\\"testbio1\\\"\\n u1.location = \\\"testlocation1\\\"\\n uid1 = 1\\n u1.id = uid1\\n\\n u2 = User.signup(\\\"testuser2\\\", \\\"test2@test.com\\\", \\\"password2\\\", None)\\n u2.bio = \\\"testbio2\\\"\\n u2.location = \\\"testlocation2\\\"\\n uid2 = 2\\n u2.id = uid2\\n\\n db.session.commit()\\n\\n self.u1 = User.query.get(uid1)\\n self.u2 = User.query.get(uid2)\\n\\n self.uid1 = uid1\\n self.uid2 = uid2\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c63cd64a2f180c0133f735aa0cfe3b\",\n \"score\": \"0.65791297\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.incidences = {\\n \\\"createdBy\\\": 1,\\n \\\"type\\\": \\\"RED-FLAG\\\",\\n \\\"comment\\\": \\\"comment\\\",\\n \\\"location\\\": \\\"12N3E\\\"\\n }\\n\\n self.regular_user = {\\n \\\"firstname\\\": \\\"john\\\",\\n \\\"lastname\\\": \\\"doe\\\",\\n \\\"othernames\\\": \\\"foo\\\",\\n \\\"email\\\": \\\"joe@test.com\\\",\\n \\\"phoneNumber\\\": \\\"0700000000\\\",\\n \\\"username\\\": \\\"jondo\\\",\\n \\\"isAdmin\\\": False,\\n \\\"password\\\": \\\"12345\\\"\\n }\\n\\n self.regular_user_login = {\\n \\\"username\\\": \\\"jondo\\\",\\n \\\"password\\\": \\\"12345\\\"\\n }\\n\\n self.admin_user = {\\n \\\"firstname\\\": \\\"jane\\\",\\n \\\"lastname\\\": \\\"doe\\\",\\n \\\"othernames\\\": \\\"bar\\\",\\n \\\"email\\\": \\\"jane@test.com\\\",\\n \\\"phoneNumber\\\": \\\"0711111111\\\",\\n \\\"username\\\": \\\"bando\\\",\\n \\\"isAdmin\\\": True,\\n \\\"password\\\": \\\"56789\\\"\\n }\\n\\n self.admin_user_login = {\\n \\\"username\\\": \\\"bando\\\",\\n \\\"password\\\": \\\"56789\\\"\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf8e99233c79b35a0ddde0559090b11\",\n \"score\": \"0.6578678\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"ourhome_test\\\"\\n self.database_path = \\\"postgresql://{}/{}\\\".format(\\n 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_menu = {\\n 'menu': 'test',\\n 'description': \\\"test_desc\\\",\\n 'cuisine': 5,\\n 'preference': 1\\n }\\n\\n self.updated_menu = {\\n 'menu': 'test_updated',\\n 'description': 'test_desc_updated',\\n 'cuisine': 4,\\n 'preference': 2\\n }\\n\\n self.search_term = {\\n 'searchTerm': 'beef'\\n }\\n\\n self.menu_cuisine = {\\n 'menu_cuisine': '1'\\n }\\n\\n nutritionist_token = os.environ['NUTRITIONIST_TOKEN']\\n\\n self.nutritionist = {\\n 'Authorization': 'Bearer ' + nutritionist_token\\n }\\n\\n student_token = os.environ['STUDENT_TOKEN']\\n\\n self.student = {\\n 'Authorization': 'Bearer ' + student_token\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f4dffc78341033f612d7e3e99178d1\",\n \"score\": \"0.65745157\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"fsnd_capstone\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432',\\n self.database_name)\\n setup_db(self.app, self.database_path)\\n db_drop_and_create_all()\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"024e4c75f8d4309fd21caa51228c3b98\",\n \"score\": \"0.65733886\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres','host123','localhost:5432', self.database_name)\\n\\n setup_db(self.app, self.database_path)\\n\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b158c1ea9e35acc6d1c237e541b8a7\",\n \"score\": \"0.6573055\",\n \"text\": \"def setUp(self):\\n\\n # Get the Flask test client\\n self.client = app.test_client()\\n\\n # Create secret key to access session\\n app.secret_key = \\\"ABC\\\"\\n\\n # Connect to fake database\\n connect_to_db(app, 'postgresql://localhost/eatablefaker')\\n\\n app.config['TESTING'] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a688e4eccf99d0883de33295dda50c\",\n \"score\": \"0.6570717\",\n \"text\": \"def setUp(self):\\n self.app = app\\n self.client = self.app.test_client \\n database_path = 'postgresql+psycopg2://{}:{}@{}/{}'.format(DB_USER, DB_PASSWORD, DB_HOST, DB_NAME)\\n\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07337dd1c550f974480f13c5ea1ef7e\",\n \"score\": \"0.6564815\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"easylist_test\\\"\\n DB_USERNAME = \\\"postgres\\\"\\n DB_PASSWORD = \\\"postgres123\\\"\\n self.database_path = \\\"postgresql://{}:{}@{}/{}\\\".format(\\n DB_USERNAME, DB_PASSWORD, 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74a31d99183da4586ed594839bca6d3\",\n \"score\": \"0.6561662\",\n \"text\": \"def setUp(self):\\n # Get the Flask test client\\n app.config['TESTING'] = True\\n self.client = app.test_client()\\n\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n db.create_all()\\n #Example data to seed images paths into testDB\\n example_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17150a8be560744f7ce9f1558e88a103\",\n \"score\": \"0.655628\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.app_context = self.app.app_context()\\n self.app_context.push()\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e68de679908ab4edbcf103991aeebbf\",\n \"score\": \"0.65556425\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n setup_db(self.app, database_name=self.database_name)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64992838c74185e114bc2629638a8731\",\n \"score\": \"0.6554274\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres','1234','localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69358b4c1dfa6feac7188d4744f42c98\",\n \"score\": \"0.6552557\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n #self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n ######################################################################\\n ############## Change username and password and whatever you need ##########################\\n username = \\\"postgres\\\"\\n password = \\\"2021\\\"\\n self.database_path = \\\"postgresql://{}:{}@{}/{}\\\".format(username, password,'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bd0cd9e38c456e71691c50ddded42e\",\n \"score\": \"0.65489686\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = DATABASE_PATH\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a035022971811a70d9f51fc2cf6eeb55\",\n \"score\": \"0.6548439\",\n \"text\": \"def setUp(self):\\n self.db_fd, app.config['DATABASE'] = tempfile.mkstemp()\\n app.config['Testing'] = True\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabe12712b422919b2d05eda893834aa\",\n \"score\": \"0.65378356\",\n \"text\": \"def setUp(self):\\n os.environ['APP_MODE'] = \\\"test\\\"\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.guest = {'name': 'John and Jane Doe', 'total_attendees': 2, \\\"phone_number\\\": \\\"5555555555\\\"}\\n self.invalid_guest = {'name': 'Steve and Dora Explora', 'total_attendees': 'blurple'}\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n # create all tables\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa8aa54eedbad6884c00d2034bdc8514\",\n \"score\": \"0.6536112\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.uid = 123456789\\n user = User.signup(\\\"testuser\\\", \\\"test@email.com\\\", \\\"password\\\", None)\\n user.id = self.uid\\n\\n db.session.commit()\\n\\n self.user = User.query.get(self.uid)\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83e100ee8aeccecc78fafc717abf6158\",\n \"score\": \"0.65273595\",\n \"text\": \"def setUp(self):\\r\\n self.app = create_app()\\r\\n self.client = self.app.test_client\\r\\n self.database_name = \\\"trivia_test\\\"\\r\\n self.database_path = 'postgresql://postgres:Gofran_333@localhost:5432/trivia_test'\\r\\n setup_db(self.app, self.database_path)\\r\\n\\r\\n # binds the app to the current context\\r\\n with self.app.app_context():\\r\\n self.db = SQLAlchemy()\\r\\n self.db.init_app(self.app)\\r\\n # create all tables\\r\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c82a29ce79a105e1af4139ff6d70eb\",\n \"score\": \"0.652082\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_path = os.environ['DATABASE_URL']\\n setup_db(self.app, self.database_path)\\n\\n self.jwt_all_permissions = os.environ['JWT_TOKEN']\\n self.jwt_casting_assistant = os.environ['JWT_CASTING_ASSIST']\\n self.jwt_casting_director = os.environ['JWT_CASTING_DIRECTOR']\\n self.jwt_executive_producer = os.environ['JWT_EXECUTIVE_PRODUCER']\\n\\n self.new_movie = {\\n 'title': 'test movie',\\n 'release_date': '1999-01-01',\\n }\\n\\n self.new_actor = {\\n 'name': 'test actor',\\n 'age': '25',\\n 'gender': 'male',\\n }\\n\\n self.new_movie_missing_attribute = {\\n 'title': 'test movie',\\n }\\n\\n self.new_actor_missing_attribute = {\\n 'name': 'test actor',\\n 'age': '25',\\n }\\n\\n self.update_movie_date = {\\n 'release_date': '1999-01-01',\\n }\\n\\n self.update_actor_age = {\\n 'age': 35,\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ffa24e1e5314ce57a4d09ec406be5f3\",\n \"score\": \"0.6508347\",\n \"text\": \"def setUp(self):\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.testuser = User.signup(username=\\\"testuser1\\\", password=\\\"testuserpassword\\\", email=\\\"testuser1@email.com\\\", goal_cal=1000, city=\\\"Houston\\\", state=\\\"TX\\\")\\n self.testuser_id = 909090\\n self.testuser.id = self.testuser_id\\n\\n self.user1 = User.signup(username=\\\"user1\\\", password=\\\"user1user1\\\", email=\\\"user1@gmail.com\\\", goal_cal=1110, city=\\\"New Orleans\\\", state=\\\"LA\\\")\\n self.user1_id = 90909\\n self.user1.id = self.user1_id\\n self.user2 = User.signup(username=\\\"user2\\\", password=\\\"user2pwpw\\\", email=\\\"user2@yahoo.com\\\", goal_cal=9999, city=\\\"Miami\\\", state=\\\"FL\\\")\\n self.user2_id = 909099\\n self.user2.id = self.user2_id\\n self.user3 = User.signup(\\\"user3\\\", \\\"user33user3\\\", \\\"user3@user.com\\\", 1234, \\\"Lincoln\\\", \\\"NE\\\")\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf60e184208079ce231cde5856565a3c\",\n \"score\": \"0.6506584\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n my_password = \\\"Asdfasdf1!\\\"\\n self.database_path = \\\"postgresql://{}:{}@{}/{}\\\".format('postgres', my_password, 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aec7151246993866552e366503ef355\",\n \"score\": \"0.64933604\",\n \"text\": \"def setUp(self):\\n super().setUp()\\n self.test_user_email = \\\"testWill@gmail.com\\\"\\n self.test_user_password = \\\"timeTotest12\\\"\\n self.client = Client()\\n\\n self.create_test_entity_types()\\n # A set of generic personal details used for a few of the tests\\n self.personal_details = {\\n \\\"name\\\": \\\"TestUser\\\",\\n \\\"age\\\": 20,\\n \\\"id_number\\\": 10001,\\n \\\"id_document\\\": \\\"NRIC\\\",\\n \\\"religion\\\": \\\"Christianity\\\",\\n \\\"real_estate_type\\\": \\\"HDB_FLAT\\\",\\n \\\"postal_code\\\": 123456,\\n \\\"floor_number\\\": 23,\\n \\\"unit_number\\\": 23,\\n \\\"block_number\\\": \\\"B123\\\",\\n \\\"street_name\\\": \\\"Crazy Street\\\",\\n \\\"country\\\": \\\"SG\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8061423dc271fbc53f334d8c745649c0\",\n \"score\": \"0.6492227\",\n \"text\": \"def setUp(self):\\n \\n app = create_app()\\n\\n db.drop_all()\\n db.create_all()\\n\\n seed_db()\\n \\n db.session.commit()\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec317bcd401b1c10a14adece8b5683f\",\n \"score\": \"0.64856344\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgresql://postgres:123@{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b725771767e94eba969b3664d901e3e\",\n \"score\": \"0.64852583\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_path = 'postgresql://postgres:admin@localhost:5432/trivia_test'\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2980549dc3cea958aa14561258982f59\",\n \"score\": \"0.64704\",\n \"text\": \"def setUp(self):\\n\\n self.app = create_app()\\n self.client = self.app.test_client\\n\\n self.database_path = \\\"postgres://{}/{}\\\".format(\\n 'localhost:5432', \\\"agency_test\\\")\\n\\n setup_db(self.app, self.database_path)\\n db_drop_and_create()\\n\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0aa53fbfb383a48c186459cbedd2bb6\",\n \"score\": \"0.6468383\",\n \"text\": \"def setUp(self):\\n self.app = app\\n self.client = self.app.test_client\\n self.database_path = database_path\\n setup_db(self.app, self.database_path)\\n # JWT from environment variable\\n self.user_jwt = {'Authorization': 'Bearer {}'.format(os.environ['USER_TOKEN'])}\\n self.author_jwt = {'Authorization': 'Bearer {}'.format(os.environ['AUTHOR_TOKEN'])}\\n self.invalid_token = {'Authorization': 'Bearer {}'.format(os.environ['INVALID_TOKEN'])}\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469fdb64e8ce81bc0fb3d305519a50a0\",\n \"score\": \"0.6468015\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.testuser = User.signup(username=\\\"testuser\\\",\\n email=\\\"test@test.com\\\",\\n password=\\\"testuser\\\")\\n \\n self.testuser_id = 9999\\n self.testuser.id = self.testuser_id\\n\\n self.u1 = User.signup(\\\"abc\\\", \\\"test1@test.com\\\", \\\"password\\\")\\n self.u1_id = 789\\n self.u1.id = self.u1_id\\n self.u2 = User.signup(\\\"efg\\\", \\\"test2@test.com\\\", \\\"password\\\")\\n self.u2_id = 678\\n self.u2.id = self.u2_id\\n self.u3 = User.signup(\\\"hij\\\", \\\"test3@test.com\\\", \\\"password\\\")\\n self.u3_id = 4567\\n self.u3.id = self.u3_id\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8adbc2627866d22896e8d1885fed7d86\",\n \"score\": \"0.64580613\",\n \"text\": \"def setUp(self):\\n db.drop_all()\\n db.create_all()\\n\\n u1 = User.signup(\\\"testuser1\\\", \\\"testuser@gmail.com\\\", \\\"testUser12\\\", None)\\n id1 = 3333\\n u1.id = id1\\n\\n image_url = \\\"https://cdn.pixabay.com/photo/2014/11/30/14/11/cat-551554__340.jpg\\\"\\n u2 = User.signup(\\\"testuser2\\\", \\\"testuser2@gmail.com\\\", \\\"tesUSer45\\\", image_url) \\n id2 = 4444\\n u2.id = id2\\n\\n db.session.commit()\\n\\n self.user1 = User.query.get(id1)\\n self.user2 = User.query.get(id2)\\n\\n self.u1id = id1\\n self.u2id = id2\\n \\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a58ad01ace77951ef35debe77d8d75f\",\n \"score\": \"0.64489305\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format(\\n 'postgres', '11112015', 'localhost:5432', self.database_name\\n )\\n\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b4edceb7b1d0b4fe839f2d4614e6f1\",\n \"score\": \"0.6445891\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.testuser = User.signup(\\n username=\\\"testuser\\\", email=\\\"test@test.com\\\", password=\\\"testpw\\\",\\n )\\n self.testuser_id = 100\\n self.testuser.id = self.testuser_id\\n\\n self.otheruser = User.signup(\\n username=\\\"otheruser\\\", email=\\\"other@test.com\\\", password=\\\"otherpw\\\",\\n )\\n\\n self.otheruser_id = 200\\n self.otheruser.id = self.otheruser_id\\n\\n self.testproject = Project(\\n name=\\\"Project_Test\\\", description=\\\"Project_Test test description.\\\",\\n )\\n self.testproject_name = self.testproject.name\\n self.testproject_id = 110\\n self.testproject.id = self.testproject_id\\n db.session.add(self.testproject)\\n\\n self.testplot = Plot(\\n name=\\\"Plot_Test\\\",\\n width=5,\\n length=10,\\n description=\\\"Plot_Test test description.\\\",\\n )\\n self.testplot_name = self.testplot.name\\n self.testplot_id = 120\\n self.testplot.id = self.testplot_id\\n db.session.add(self.testplot)\\n\\n self.testplantlist = PlantList(\\n name=\\\"Plantlist_Test\\\", description=\\\"Plantlist_Test test description.\\\",\\n )\\n self.testplantlist_name = self.testplantlist.name\\n self.testplantlist_id = 130\\n self.testplantlist.id = self.testplantlist_id\\n db.session.add(self.testplantlist)\\n\\n self.testplant = Plant(\\n trefle_id=1231,\\n slug=\\\"plantus-slugs1\\\",\\n common_name=\\\"common plant1\\\",\\n scientific_name=\\\"plantus testus1\\\",\\n family=\\\"Plantaceae1\\\",\\n family_common_name=\\\"Plant Family1\\\",\\n )\\n self.testplant_common_name = self.testplant.common_name\\n self.testplant_id = 140\\n self.testplant.id = self.testplant_id\\n\\n db.session.add(self.testplant)\\n\\n self.testsymbol = Symbol(\\n symbol=\\\"<i class='symbol fas fa-seedling' style='color:#228B22;'></i>\\\"\\n )\\n\\n self.testsymbol_id = 1\\n self.testsymbol.id = self.testsymbol_id\\n db.session.add(self.testsymbol)\\n\\n # Connections\\n self.testuser.projects.append(self.testproject)\\n self.testuser.plots.append(self.testplot)\\n self.testuser.plantlists.append(self.testplantlist)\\n self.testproject.plots.append(self.testplot)\\n self.testproject.plantlists.append(self.testplantlist)\\n self.testplot.plantlists.append(self.testplantlist)\\n self.testplantlist.plants.append(self.testplant)\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88afba32e98c568ed3a62d3cf913934c\",\n \"score\": \"0.6440165\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format(\\n 'postgres', 'admin', 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed32f7e3bbc3fc4af96b1f864d399d8\",\n \"score\": \"0.6430592\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"casptone_test\\\"\\n setup_db(self.app)\\n\\n self.new_actor = {\\n \\\"name\\\": \\\"Manuela Mercado\\\",\\n \\\"age\\\": 25,\\n \\\"gender\\\": \\\"F\\\",\\n \\\"movies\\\": [1, 2]\\n }\\n\\n self.new_movie = {\\n \\\"title\\\": \\\"Manuela Mercado\\\",\\n \\\"release_year\\\": \\\"Fri, 15 May 2020 00:00:00 GMT\\\",\\n \\\"actors\\\": [1, 2]\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0389efb4b8e175d458b03e1414910d\",\n \"score\": \"0.64297104\",\n \"text\": \"def setUp(self):\\n\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_path = \\\"postgres://sxvwypvcewiwft:71a5b4af53ca61d9936511c46f96b61fa9d3b9b589f87be97bb49cc98d66aab5@ec2-52-87-58-157.compute-1.amazonaws.com:5432/db28be7l9velkt\\\"\\n setup_db(self.app, self.database_path)\\n db_drop_and_create_all()\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1617529d3c6e9e50bc2f176e5d49ed36\",\n \"score\": \"0.6426215\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1617529d3c6e9e50bc2f176e5d49ed36\",\n \"score\": \"0.6426215\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d154957ab2d384fb364ec4b189e34e21\",\n \"score\": \"0.6424833\",\n \"text\": \"def setUp(self):\\n db.drop_all()\\n db.create_all()\\n\\n j1 = Jobseeker.signup(first_name=\\\"testuser\\\",\\n last_name=\\\"testuser\\\",\\n username=\\\"testuser\\\",\\n email=\\\"test@me.com\\\",\\n password=\\\"testuser\\\",\\n profile_img=None,\\n bio=\\\"bio\\\",\\n location=\\\"location\\\")\\n jid1 = 1111\\n j1.id = jid1\\n\\n r1 = Recruiter.signup(username=\\\"testR\\\",\\n email=\\\"testR\\\",\\n company_name=\\\"testR\\\",\\n password=\\\"testR\\\",\\n profile_img=None,\\n about=\\\"testR\\\",\\n location=\\\"testR\\\")\\n rid1 = 2222\\n r1.id = rid1\\n\\n db.session.commit()\\n\\n j1 = Jobseeker.query.get(jid1)\\n r1 = Recruiter.query.get(rid1)\\n\\n self.j1 = j1\\n self.jid1 = jid1\\n\\n self.r1 = r1\\n self.rid1 = rid1\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fcdad9e0e3b97bda44c82dc2360840e\",\n \"score\": \"0.64246064\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.quotes = {'title': 'Happiness'}\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n # create all tables\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c91b8b3a1dd406ffea39a9d7203cee\",\n \"score\": \"0.6418258\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_path = database_path\\n setup_db(self.app, self.database_path)\\n db_drop_create_initialize()\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f14c64be9abe890de5300bb0e53218\",\n \"score\": \"0.641815\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.testuser = User.signup(username=\\\"testuser\\\",\\n email=\\\"test@test.com\\\",\\n password=\\\"testuser\\\",\\n image_url=None)\\n self.testuser.id = 12345\\n\\n self.u1 = User.signup(\\\"u1\\\", \\\"test1@test1.com\\\", \\\"pwd1\\\", None)\\n self.u1.id = 11111\\n self.u2 = User.signup(\\\"u2\\\", \\\"test2@test2.com\\\", \\\"pwd2\\\", None)\\n self.u2.id = 22222\\n # self.u3 = User.signup(\\\"u3\\\", \\\"test3@test3.com\\\", \\\"pwd3\\\", None)\\n # self.u4 = User.signup(\\\"u4\\\", \\\"test4@test4.com\\\", \\\"pwd4\\\", None)\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e86d38569c842b3787bd27732578ec\",\n \"score\": \"0.641675\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_user = \\\"postgres\\\"\\n self.database_pw = \\\"postgres\\\"\\n\\n # Note I'm using port 5433 due to the way my system is set up\\n self.database_path =\\\\\\n \\\"postgres://{}:{}@{}/{}\\\".format(self.database_user,\\n self.database_pw,\\n 'localhost:5433',\\n self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed96261cffa54e360dfe323bf4269cd\",\n \"score\": \"0.6416701\",\n \"text\": \"def setUp(self):\\n self.test_db, test_db_name = tempfile.mkstemp()\\n test_config = dict(\\n ENV='development',\\n TESTING=True,\\n SECRET_KEY='test',\\n DATABASE=test_db_name,\\n DEBUG = True)\\n self.app = create_app(test_config=test_config)\\n self.client = self.app.test_client()\\n\\n with self.app.app_context():\\n db.init_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e467da30ded194d3a7f7cc820cbe69\",\n \"score\": \"0.6413648\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n\\n self.database_path = os.environ['DATABASE_URL']\\n setup_db(self.app, self.database_path)\\n\\n self.token_CA = os.environ['token_CA']\\n self.token_CD = os.environ['token_CD']\\n self.token_EP = os.environ['token_EP']\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n\\n self.new_actor = {\\n 'name': 'actor_json_name',\\n 'age': 1,\\n 'gender': 'female',\\n 'description': 'actor_json_name age1 gender female'\\n }\\n\\n self.new_movie = {\\n 'title': 'movie_title',\\n 'released_date': '2021-07-23',\\n 'genre': 'SF',\\n 'description': 'movie_title 2021-07-23 SF'\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb506b36c9c5e70123c31250a067681a\",\n \"score\": \"0.6412848\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"movies_test\\\"\\n self.database_path = os.getenv(\\\"DATABASE_URL\\\")\\n self.test_movie = {\\n 'title': 'Inception 2',\\n 'release_date': '2024-04-04'\\n }\\n # Casting Assistant (can view movies and actors)\\n CA_TOKEN = os.getenv(\\\"CA_TOKEN\\\")\\n # Casting Director (CA role + can add, delete, patch actors and movies)\\n CD_TOKEN = os.getenv(\\\"CD_TOKEN\\\")\\n setup_db(self.app, self.database_path)\\n self.director_headers = {\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Authorization\\\": \\\"Bearer \\\" + CD_TOKEN,\\n }\\n self.assistant_headers = {\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Authorization\\\": \\\"Bearer \\\" + CA_TOKEN,\\n }\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca3d6751acdfa3db5e259d5d48adc52\",\n \"score\": \"0.64096457\",\n \"text\": \"def setUp(self):\\n try:\\n self.alice = self.create_user('alice', 'password')\\n except IntegrityError:\\n self.skipTest(\\\"Unable to create the test user.\\\")\\n else:\\n self.device = self.alice.totpdevice_set.create(\\n key='2a2bbba1092ffdd25a328ad1a0a5f5d61d7aacc4', step=30,\\n t0=int(time() - (30 * 3)), digits=6, tolerance=0, drift=0\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea625cade68ed1f841a38ef16c07aa3f\",\n \"score\": \"0.6406738\",\n \"text\": \"def setUp(self):\\n self.starting_db = [(1, 'Bob', 'Bobbo', '12 Green St', '1112223344',\\n 'bobbo@python.org', False, 85000),\\n (2, 'Jane', 'Janeo', '1550 Red Rd', '1118675309',\\n 'jane@therealjane.com', True, 150000),\\n (5, 'Wilson', 'Volleyball', '1 Castaway Island', '0000000000',\\n 'wilson@ImLost.com', True, 0)\\n ]\\n database.init(TEST_DATABASE)\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON')\\n database.create_tables([Customer])\\n with database.transaction():\\n Customer.delete().execute()\\n Customer.insert_many(self.starting_db, fields=[Customer.customer_id, Customer.name,\\n Customer.lastname, Customer.home_address,\\n Customer.phone_number,\\n Customer.email_address,\\n Customer.active_status,\\n Customer.credit_limit\\n ]).execute()\\n database.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7e7f83527163419adf6a7685b7d657\",\n \"score\": \"0.6396226\",\n \"text\": \"def setUp(self):\\n\\n self.db_handle, app.app.config['DATABASE'] = tempfile.mkstemp()\\n self.app = app.app.test_client()\\n with app.app.app_context():\\n app.init_db() # nope\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee9656993214c6b32f21245eb7bd0ed\",\n \"score\": \"0.638305\",\n \"text\": \"def setUp(self):\\n db.drop_all()\\n db.create_all()\\n\\n User.query.delete()\\n Message.query.delete()\\n Follows.query.delete()\\n\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a666372b2aab8a20af0803bf197ed37\",\n \"score\": \"0.63763547\",\n \"text\": \"def setUp(self):\\r\\n self.app = create_app()\\r\\n self.client = self.app.test_client\\r\\n self.database_path = os.environ.get('DATABASE_URL')\\r\\n if self.database_path and self.database_path.startswith(\\\"postgres://\\\"):\\r\\n self.database_path = self.database_path.replace(\\\"postgres://\\\", \\\"postgresql://\\\", 1)\\r\\n else:\\r\\n self.database_path =os.environ.get('Test_database_name')\\r\\n setup_db(self.app, self.database_path)\\r\\n\\r\\n self.new_actor = {\\r\\n 'name': 'mohammed',\\r\\n 'age': '25',\\r\\n 'gender': 'male'\\r\\n }\\r\\n\\r\\n self.new_movie = {\\r\\n \\\"title\\\": \\\"catch me\\\",\\r\\n \\\"date\\\": \\\"03/25/2006\\\"\\r\\n }\\r\\n # binds the app to the current context\\r\\n with self.app.app_context():\\r\\n self.db = SQLAlchemy()\\r\\n self.db.init_app(self.app)\\r\\n # create all tables\\r\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda6209b1615eb0914f04759b26acf8e\",\n \"score\": \"0.63751256\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28bb93f7d9ee09d4d62c3f879c5905d4\",\n \"score\": \"0.63602\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.review = {\\\"opinion\\\": \\\"Good business with good food\\\", \\\"rating\\\": 5}\\n self.wrong_review = {\\\"rating\\\": 5}\\n self.wrong_rating = {\\\"opinion\\\": \\\"Good business with good food\\\"}\\n self.business = {'name': 'crastycrab', 'description': 'Fastfood', 'contact': '0702848032', 'category': 'fastfood', 'location': 'atlantic'}\\n # bind app to the current context\\n with self.app.app_context():\\n # create db tables\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b703cdc632162a1c78b085a68d71df42\",\n \"score\": \"0.635609\",\n \"text\": \"def setUp(self):\\n self.app = create_app(config_name=\\\"testing\\\")\\n self.client = self.app.test_client\\n self.produtor = {\\n \\\"nrDocumento\\\": \\\"083.843.589-07\\\",\\n \\\"nmProdutor\\\": \\\"Fernando Paladini\\\",\\n \\\"nrTelefone\\\": \\\"(48) 99845-9684\\\",\\n \\\"dsEmail\\\": \\\"paladini@1doc.com.br\\\",\\n \\\"cdEstabelecimento\\\": 0\\n }\\n self.estabelecimento = {\\n \\\"nmEstabelecimento\\\": \\\"Granja Santa Clara\\\",\\n \\\"nrCodigoOficial\\\": \\\"42-0003256\\\",\\n \\\"idPais\\\": 1,\\n \\\"idUf\\\": 24,\\n \\\"idMunicipio\\\": 2812,\\n \\\"nmLocalidade\\\": \\\"Rua Retangular, número 182. Bairro Santa Clara.\\\",\\n \\\"nrLatitude\\\": -28.098883,\\n \\\"nrLongitude\\\": -28.098883,\\n \\\"stAtivo\\\": True,\\n \\\"idCliente\\\": 10\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n # create all tables\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4478846d7eac95eea7bb0dea2d607eaa\",\n \"score\": \"0.6356084\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"casting_agency\\\"\\n self.database_path = \\\"postgresql://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_movie = {\\n 'title': 'Anansi Boys2',\\n 'release_date': '10-19-2009'\\n }\\n\\n self.new_actor = {\\n \\\"name\\\": \\\"Emile Hirsch2\\\",\\n \\\"age\\\": 35,\\n \\\"gender\\\": \\\"male\\\"\\n }\\n\\n self.executive_producer = {'Authorization': os.environ['EXECUTIVE_PRODUCER_TOKEN']}\\n\\n self.casting_director = {'Authorization': os.environ['CASTING_DIRECTOR_TOKEN']}\\n\\n self.casting_assistant = {'Authorization': os.environ['CASTING_ASSISTANT_TOKEN']}\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cd994396e04362f077b62f70ee5444\",\n \"score\": \"0.6355797\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.user1 = User.signup(**USER_DATA1)\\n self.user1.id = 100\\n\\n db.session.add(self.user1)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cd994396e04362f077b62f70ee5444\",\n \"score\": \"0.6355797\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.user1 = User.signup(**USER_DATA1)\\n self.user1.id = 100\\n\\n db.session.add(self.user1)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cd994396e04362f077b62f70ee5444\",\n \"score\": \"0.6355797\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.user1 = User.signup(**USER_DATA1)\\n self.user1.id = 100\\n\\n db.session.add(self.user1)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cd994396e04362f077b62f70ee5444\",\n \"score\": \"0.6355797\",\n \"text\": \"def setUp(self):\\n\\n db.drop_all()\\n db.create_all()\\n\\n self.client = app.test_client()\\n\\n self.user1 = User.signup(**USER_DATA1)\\n self.user1.id = 100\\n\\n db.session.add(self.user1)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"541e0ddad670f652c3a8d269a5f0bd97\",\n \"score\": \"0.6344583\",\n \"text\": \"def setUp(self):\\n config_name = config.TestingConfig\\n app.config.from_object(config_name)\\n self.app = app\\n self.test_client = self.app.test_client()\\n self.app_context = self.app.app_context()\\n self.app_context.push()\\n self.name = 'test user'\\n self.email = 'testuser@example.com'\\n self.test_username = 'testuser'\\n self.test_password = 'test1234'\\n with self.app_context:\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7465031dd1cacc20262b4739a893dce1\",\n \"score\": \"0.63431394\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432',\\n self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29985517ffc879757a352ec4b31afd6e\",\n \"score\": \"0.6335297\",\n \"text\": \"def setUp(self):\\n server.init_db()\\n db.drop_all() # clean up the last tests\\n db.create_all() # create new tables\\n date = datetime.now()\\n\\n\\n order = Order(customer_id=1, date=date, status = 'processing').save()\\n order = Order(customer_id=2, date=date, status = 'processing').save()\\n\\n order1 = Order()\\n order1 = order1.find_by_customer_id(1)[0]\\n order2 = Order()\\n order2 = order2.find_by_customer_id(2)[0]\\n\\n item = Item(order_id=order1.id, product_id=1, name='hammer', quantity=1, price=11.50).save()\\n item = Item(order_id=order1.id, product_id=2, name='toilet paper', quantity=2, price=2.50).save()\\n item = Item(order_id=order2.id, product_id=3, name='beer', quantity=2, price=10.50).save()\\n self.app = server.app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b12e10de2ba9f92f4d40cacbfb77a5a1\",\n \"score\": \"0.6332886\",\n \"text\": \"def setUp(self):\\n db.drop_all()\\n db.create_all()\\n\\n User.query.delete()\\n Message.query.delete()\\n Follows.query.delete()\\n\\n self.test1 = User.signup(\\n email=\\\"test1@test.com\\\",\\n username=\\\"testuser1\\\",\\n password=\\\"HASHED_PASSWORD1\\\",\\n image_url = None\\n )\\n self.test1_id = 1010\\n self.test1.id = self.test1_id \\n\\n self.test2 = User.signup(\\n email=\\\"test2@test.com\\\",\\n username=\\\"testuser2\\\",\\n password=\\\"HASHED_PASSWORD2\\\",\\n image_url = None\\n )\\n self.test2_id = 2020\\n self.test2.id = self.test2_id \\n\\n db.session.commit()\\n self.client = app.test_client()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7604c6ec2b992e03bb3d2da90f0aebdd\",\n \"score\": \"0.6323764\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format(\\n 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n\\n def tearDown(self):\\n \\\"\\\"\\\"Executed after reach test\\\"\\\"\\\"\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5287b55bbe23081e3b0863062095f499\",\n \"score\": \"0.6313915\",\n \"text\": \"def setUp(self):\\n self.app = app\\n self.client = self.app.test_client\\n self.database_name = \\\"booksnap_db\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres','password','localhost:5432', self.database_name)\\n self.app.config['WTF_CSRF_ENABLED'] = False\\n self.app.config['TESTING'] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c468331453f895d19b023c1a9d9d76\",\n \"score\": \"0.62994677\",\n \"text\": \"def setUp(self):\\n # Get the Flask test client\\n self.client = app.test_client()\\n\\n # Show Flask errors that happen during tests\\n app.config['TESTING'] = True\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables from model\\n db.create_all()\\n\\n # Import different types of data from seed file\\n seed.load_users()\\n seed.load_groups()\\n seed.load_usergroups()\\n seed.load_patterns()\\n seed.load_invites()\\n\\n # Reset auto incrementing primary keys to start after seed data\\n seed.set_val_user_id()\\n seed.set_val_group_id()\\n seed.set_val_usergroup_id()\\n seed.set_val_pattern_id()\\n seed.set_val_invite_id()\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess['user_id'] = 1\\n c.set_cookie('localhost', 'MYCOOKIE', 'cookie_value')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e01c981ae3ddac4e30c578f30536c3d\",\n \"score\": \"0.62909126\",\n \"text\": \"def setUp(self):\\n application.app.config['TESTING'] = True\\n self.app = application.app.test_client()\\n self.drop_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a9e7858579b4d7c684ca997bde3d3f\",\n \"score\": \"0.62820077\",\n \"text\": \"def setUp(self):\\n self.app = app.create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"castapp_test\\\"\\n self.database_path = settings.LOCAL_DATABASE_PATH\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1807356f575a608c62460cc2efb3b1\",\n \"score\": \"0.6281231\",\n \"text\": \"def setUp(self):\\n # Get the Flask test client\\n self.client = app.test_client()\\n\\n # Show Flask errors that happen during tests\\n app.config['TESTING'] = True\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables from model\\n db.create_all()\\n\\n # Import different types of data from seed file\\n seed.load_users()\\n seed.load_groups()\\n seed.load_usergroups()\\n seed.load_patterns()\\n seed.load_invites()\\n \\n # Reset auto incrementing primary keys to start after seed data\\n seed.set_val_user_id()\\n seed.set_val_group_id()\\n seed.set_val_usergroup_id()\\n seed.set_val_pattern_id()\\n seed.set_val_invite_id()\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess['user_id'] = 1\\n c.set_cookie('localhost', 'MYCOOKIE', 'cookie_value')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1581,"cells":{"query_id":{"kind":"string","value":"e5f2f01e2cb575c68541ea2684aa4630"},"query":{"kind":"string","value":"Performs wavelet analysis on a dataset"},"positive_passages":{"kind":"list like","value":[{"docid":"9374964ad45102ff7b307bf9cb0a8173","score":"0.646736","text":"def analyze(data, wavelet=DEFAULT_WAVELET, scales=None, dt=1., dj=0.125, \n mask_coi=False, frequency=False, axis=-1):\n n = data.shape[axis]\n if scales is None:\n s0 = find_s0(wavelet, dt)\n scales = find_optimal_scales(s0, dt, dj, n)\n if frequency:\n scalogram = cwt_tc_frequency_jit(data, wavelet.frequency, scales, dt, axis)\n else:\n scalogram = cwt_tc_time_jit(data, wavelet.time, scales, dt, axis)\n scales = jnp.asarray(scales)\n return WaveletAnalysis(data=data, wavelet=wavelet, \n dt=dt, dj=dj, mask_coi=mask_coi,\n frequency=frequency, axis=axis, scales=scales, \n scalogram=scalogram)","title":""}],"string":"[\n {\n \"docid\": \"9374964ad45102ff7b307bf9cb0a8173\",\n \"score\": \"0.646736\",\n \"text\": \"def analyze(data, wavelet=DEFAULT_WAVELET, scales=None, dt=1., dj=0.125, \\n mask_coi=False, frequency=False, axis=-1):\\n n = data.shape[axis]\\n if scales is None:\\n s0 = find_s0(wavelet, dt)\\n scales = find_optimal_scales(s0, dt, dj, n)\\n if frequency:\\n scalogram = cwt_tc_frequency_jit(data, wavelet.frequency, scales, dt, axis)\\n else:\\n scalogram = cwt_tc_time_jit(data, wavelet.time, scales, dt, axis)\\n scales = jnp.asarray(scales)\\n return WaveletAnalysis(data=data, wavelet=wavelet, \\n dt=dt, dj=dj, mask_coi=mask_coi,\\n frequency=frequency, axis=axis, scales=scales, \\n scalogram=scalogram)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9ac1013ef45a01eb02ad55a7f60cad55","score":"0.65809494","text":"def model_data(self,wav,dt,t0,minf,maxf,vel,ref,jf=1,nrmax=3,eps=0.,dtmax=5e-05,time=True,\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\n # Save wavelet temporal parameters\n nt = wav.shape[0]; it0 = int(t0/dt)\n\n # Create the input frequency domain source and get original frequency axis\n self.__nwo,self.__ow,self.__dw,wfft = self.fft1(wav,dt,minf=minf,maxf=maxf)\n wfftd = wfft[::jf]\n self.__nwc = wfftd.shape[0] # Get the number of frequencies to compute\n self.__dwc = jf*self.__dw\n\n if(sverb or wverb): print(\"Frequency axis: nw=%d ow=%f dw=%f\"%(self.__nwc,self.__ow,self.__dwc))\n\n # Single square root object\n ssf = ssr3(self.__nx ,self.__ny,self.__nz , # Spatial Sizes\n self.__dx ,self.__dy,self.__dz , # Spatial Samplings\n self.__nwc,self.__ow,self.__dwc,eps, # Frequency axis\n ntx,nty,px,py, # Taper and padding\n dtmax,nrmax,nthrds) # Reference velocities\n\n # Compute slowness and reference slownesses\n slo = 1/vel\n ssf.set_slows(slo)\n\n # Allocate output data (surface wavefield)\n datw = np.zeros([self.__nexp,self.__nwc,self.__ny,self.__nx],dtype='complex64')\n\n # Allocate the source for one shot\n sou = np.zeros([self.__nwc,self.__ny,self.__nx],dtype='complex64')\n\n # Loop over sources\n k = 0\n for icrd in progressbar(self.__scoords,\"nexp:\",verb=sverb):\n # Get the source coordinates\n sy = icrd[0]; sx = icrd[1]\n # Create the source for this shot\n sou[:] = 0.0\n sou[:,sy,sx] = wfftd[:]\n # Downward continuation\n ssf.modallw(ref,sou,datw[k],wverb)\n k += 1\n\n # Reshape output data\n datwr = datw.reshape([self.__nsy,self.__nsx,self.__nwc,self.__ny,self.__nx])\n\n if(time):\n # Inverse fourier transform\n datt = self.data_f2t(datwr,self.__nwo,self.__ow,self.__dwc,nt,it0)\n return datt\n else:\n return datwr","title":""},{"docid":"6f267e019f19f50707df0d23604d3626","score":"0.6510147","text":"def wavelet_features(X):\n N_train = X.shape[0]\n nchannels = 3\n X_dwt = np.zeros(X.shape)\n print(\"generating wavelet features\")\n for image in tqdm.tqdm(range(N_train)):\n for channel in range(nchannels):\n coeffs = pywt.dwt2(X[image, :, :, channel], 'haar')\n LL, (LH, HL, HH) = coeffs\n X_dwt[image, :, :, channel] = np.block([[LL, LH], [HL, HH]])\n \n return X_dwt","title":""},{"docid":"81d483d00d559aed9fe2343ac0d7e07a","score":"0.6369924","text":"def wave():","title":""},{"docid":"303cd897c993be8216ca8c2ab77b4359","score":"0.62218803","text":"def wavelet(self, period, period_unit = 'y', cut = 1, ts = None, pool = None, save_wave = False,\n regress_amp_to_data = False, k0 = 6., cut_time = False, continuous_phase = False,\n phase_fluct = False, cut_data = False):\n\n delta = self.time[1] - self.time[0]\n if delta == 1:\n # daily data\n if period_unit == 'y':\n y = 365.25\n elif period_unit == 'd':\n y = 1.\n elif period_unit == 'm':\n raise Exception(\"For daily data is hard to enter wavelet period in months...\")\n else:\n raise Exception(\"Unknown type.\")\n elif abs(delta - 30) < 3.0:\n # monthly data\n if period_unit == 'y':\n y = 12.\n elif period_unit == 'm':\n y = 1.\n elif period_unit == 'd':\n raise Exception(\"For monthly data doesn't make sense to enter wavelet period in days.\")\n else:\n raise Exception(\"Unknown type.\")\n elif delta == 365 or delta == 366:\n # annual data\n if period_unit == 'y':\n y = 1.\n elif period_unit == 'm':\n raise Exception(\"For monthly data doesn't make sense to enter wavelet period in days.\")\n elif period_unit == 'd':\n raise Exception(\"For monthly data doesn't make sense to enter wavelet period in days.\")\n else:\n raise Exception(\"Unknown type.\")\n else:\n raise Exception('Unknown temporal sampling in the field.')\n\n fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))\n per = period * y # frequency of interest\n self.frequency = per\n self.omega = 2*np.pi / (y*period)\n s0 = per / fourier_factor # get scale\n\n if phase_fluct:\n continuous_phase = True\n\n if cut is not None:\n to_cut = int(y*cut)\n else:\n to_cut = None\n\n if ts is None:\n\n if self.data.ndim > 2:\n num_lats = self.lats.shape[0]\n num_lons = self.lons.shape[0]\n elif self.data.ndim == 2: # lot of station data\n num_lats = self.lats.shape[0]\n num_lons = 1\n self.data = self.data[:, :, np.newaxis]\n else:\n num_lats = 1\n num_lons = 1\n self.data = self.data[:, np.newaxis, np.newaxis]\n\n self.phase = np.zeros_like(self.data) if cut is None else np.zeros([self.data.shape[0] - 2*to_cut] + self.get_spatial_dims())\n self.amplitude = np.zeros_like(self.data) if cut is None else np.zeros([self.data.shape[0] - 2*to_cut] + self.get_spatial_dims())\n if save_wave:\n self.wave = np.zeros_like(self.data, dtype = np.complex64) if cut is None else np.zeros([self.data.shape[0] - 2*to_cut] + self.get_spatial_dims(), dtype = np.complex64)\n\n job_args = [ (i, j, s0, self.data[:, i, j], save_wave, regress_amp_to_data, k0, continuous_phase, to_cut) for i in range(num_lats) for j in range(num_lons) ]\n\n if pool is None:\n job_result = map(self._get_oscillatory_modes, job_args)\n elif pool is not None:\n job_result = pool.map(self._get_oscillatory_modes, job_args)\n del job_args\n\n for i, j, res in job_result:\n self.phase[:, i, j] = res[0]\n self.amplitude[:, i, j] = res[1]\n if save_wave:\n self.wave[:, i, j] = res[2]\n\n del job_result\n\n if cut is not None and cut_time:\n self.time = self.time[to_cut:-to_cut]\n\n if cut is not None and cut_data:\n self.data = self.data[to_cut:-to_cut, ...]\n\n self.data = np.squeeze(self.data)\n if phase_fluct:\n ph0 = self.phase[0, ...]\n sin = np.arange(0, self.phase.shape[0])[:, np.newaxis, np.newaxis] * self.omega + ph0\n self.phase -= sin\n self.phase = np.squeeze(self.phase)\n else:\n self.phase = np.squeeze(self.phase)\n self.amplitude = np.squeeze(self.amplitude)\n if save_wave:\n self.wave = np.squeeze(self.wave)\n\n else:\n res = self._get_oscillatory_modes([0, 0, s0, ts, save_wave, regress_amp_to_data, k0, continuous_phase, to_cut])[-1]\n # add phase fluct!!!\n return res","title":""},{"docid":"5e670e79563c8f8141a8c85a2da45f08","score":"0.60416424","text":"def wavelet(X, mask, morlet_fb, gaussian_fb, row, segment_len, prefix):\n nchannel = X.shape[1]\n \n for k in range(morlet_fb.shape[1]):\n # calculate wavelet coefficients\n W, W_autocorr = wavelet_coef(X, morlet_fb[:, k], MORLET_FILTER_LENGTH[k], NUM_PAD, segment_len)\n phase = np.angle(W)\n amplitude = np.abs(W)\n auto_spectra = np.square(amplitude)\n \n basename = \"wavelet_\" + MORLET_BAND[k]\n \n for i in range(nchannel):\n \n # univariate wavelet features\n basic_stats(amplitude[mask[:, i]==1, i], row, prefix + basename, \"_\" + CHANNEL_ID[i], True, False)\n \n # auto correlation features over wavelet coefficients\n indices = np.where(np.diff(np.sign(W_autocorr[:, i])))[0]\n index = W_autocorr.shape[0] if len(indices) == 0 else indices[0]\n row[prefix + basename + \"_autocorr_decay_\" + CHANNEL_ID[i]] = float(index) / float(SAMPLING_RATE) * 1000.0\n\n for i in range(nchannel-1):\n for j in range(i+1, nchannel):\n # bivariate features\n channel_id_pair = \"_\" + CHANNEL_ID[i] + CHANNEL_ID[j]\n phase_delta = phase[:, i] - phase[:, j]\n mask_union = ((mask[:, i]==1) & (mask[:, j]==1)) == 1\n count = np.sum(mask_union)\n \n # calculate SPLV and correlation coefficients of wavelet coefficients\n splv = np.nan\n if count > 0:\n splv = np.abs(np.sum(np.exp(1.0j * phase_delta)[mask_union]) / float(count))\n row[prefix + basename + \"_splv\" + channel_id_pair] = splv\n row[prefix + basename + \"_corrcoef\" + channel_id_pair] = scipy.stats.pearsonr(amplitude[mask_union, i], amplitude[mask_union, j])[0]\n\n # extract statistics of phase\n basic_stats(phase_delta[mask_union], row, prefix + basename + \"_phase_original\", channel_id_pair, False, True)\n basic_stats(np.unwrap(phase_delta)[mask_union], row, prefix + basename + \"_phase_unwrapped\", channel_id_pair, True, True)\n \n # extract statistics of wavelet coherence\n coherence = wavelet_coherence(W[:, i], W[:, j], auto_spectra[:, i], auto_spectra[:, j], mask_union, gaussian_fb[k])\n basic_stats(coherence, row, prefix + basename + \"_coherence\", channel_id_pair, False, False)","title":""},{"docid":"398de2e4eea4e6c6d22a4a1579e4ad19","score":"0.59884363","text":"def filter_data(expname, iband, fband):\n datainfo = experiments[expname]\n\n f = h5py.File(datainfo.dpath + datainfo.name + '.hdf5', 'r+')\n print datainfo.dpath + datainfo.name + '.hdf5'\n wtime = 120e-3 # Window length in miliseconds\n sampling = datainfo.sampling #/ 6.0\n Tw = int(2 * np.round(wtime*sampling/2))\n\n for df in datainfo.datafiles:\n print df\n d = f[df + '/Raw']\n samp = f[df + '/Raw'].attrs['Sampling']\n data = d[()]\n freq = samp * 0.5\n b, a = butter(3, [iband/freq, fband/freq], btype='band')\n filtered = np.zeros(data.shape)\n for i in range(data.shape[1]):\n filtered[:, i] = filtfilt(b, a, data[:, i])\n d = f.require_dataset(df + '/RawFiltered', filtered.shape, dtype='f', data=filtered, compression='gzip')\n d[()] = filtered\n f[df + '/RawFiltered'].attrs['Low'] = iband\n f[df + '/RawFiltered'].attrs['high'] = fband\n for s in datainfo.sensors:\n i = datainfo.sensors.index(s)\n times = f[df + '/' + s + '/TimeClean']\n rawpeaks = np.zeros((times.shape[0], Tw))\n print times.shape[0]\n for j in range(times.shape[0]):\n tstart = times[j] - np.floor(Tw / 2)\n tstop = tstart + Tw\n if tstart > 0 and tstop < filtered.shape[0]:\n rawpeaks[j, :] = filtered[tstart:tstop, i]\n elif tstart < 0:\n rawpeaks[j, :] = np.hstack((np.zeros(np.abs(tstart)), filtered[0:tstop, i]))\n else:\n rawpeaks[j, :] = np.hstack((filtered[tstart:tstop, i], np.zeros(tstop-filtered.shape[0])))\n #show_signal(rawpeaks[j])\n # Peak Data\n dfilter = f[df + '/' + s]\n #del dfilter['PeaksFilter']\n dfilter.require_dataset('PeaksFilter', rawpeaks.shape, dtype='f', data=rawpeaks,\n compression='gzip')\n\n\n\n f.close()","title":""},{"docid":"f968e6ec2a12fbaba17779ebdc37bd3a","score":"0.5986626","text":"def goEstimateWaveletFromGather(name,tmin,tmax,funcert,luncert):\n print name\n if name == \"syn1\": # Synthetic with one reflector\n st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first\n sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first\n nref,vnmo = 1,2.0 # number of reflectors and NMO velocity\n tran,tbed = False,False\n freq,decay = 20.0,0.05 # peak frequency and decay for wavelet\n fmin,fmax,sfac = 0.0,50.0,1.00\n texp,tbal = 0.00,0\n \n zp = False# zero-phase?\n if zp:\n na,ka = 5,-2\n nh,kh = 251,-125\n decay *= 4.0\n else:\n na,ka = 11,0\n nh,kh = 151,-25\n hsyn = getArWavelet(freq,decay,st,nh,kh,zp)\n elif name == \"synr\": # Synthetic with random reflectors\n st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first\n sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first\n tran,tbed = True,False\n nref,vnmo = 40,2.0 # number of reflectors and NMO velocity\n freq,decay = 20.0,0.05 # peak frequency and decay for wavelet\n fmin,fmax,sfac = 0.0,50.0,1.00\n texp,tbal = 0.00,0\n zp = False # zero-phase?\n na,ka = 11,0 # sampling for inverse wavelet a\n nh,kh = 151,-25 # sampling for wavelet h\n hsyn = getArWavelet(freq,decay,st,nh,kh)\n elif name == \"synt\": # Synthetic with random thin beds\n st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first\n sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first\n tran,tbed = True,True\n nref,vnmo = 40,2.0 # number of reflectors and NMO velocity\n freq,decay = 20.0,0.05 # peak frequency and decay for wavelet\n fmin,fmax,sfac = 0.0,50.0,1.0001\n texp,tbal = 0.00,0\n zp = True # zero-phase?\n if zp:\n na,ka = 5,-2\n nh,kh = 251,-125\n decay *= 4.0\n else:\n na,ka = 11,0\n nh,kh = 151,-25\n hsyn = getArWavelet(freq,decay,st,nh,kh,zp)\n\n f = zerofloat(nt,nx)\n p = makeCmpReflections(vnmo,nref,st,sx,random=tran,thinBeds=tbed)\n f = addArWavelet(freq,decay,st,sx,p,zp)\n f = tpow(texp,st,f)\n if tbal>0:\n f = balance(tbal,f)\n\n #for whole gather\n #tmin = 0.00\n #tmax = 2.00\n #itmin = int(tmin/dt)\n #itmax = int(tmax/dt)\n #nw = itmax-itmin+1\n #w = rampWeights(funcert,luncert,nw)\n #inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,\n # na,ka,nh,kh,hsyn,tmin,tmax,w=w)\n itmin = int(tmin/dt)\n itmax = int(tmax/dt)\n nw = itmax-itmin+1\n w = zerofloat(nw,3)\n w[0] = fillfloat(1.0,nw)\n w[1] = rampWeights(funcert,luncert,nw)\n w[2] = rampWeights(luncert,funcert,nw)\n uncerts = [\"Constant Uncertainty\",\"Ramp Up Uncertainty\",\"Ramp Down Uncertainty\"]\n hpefs = zerofloat(nh,3)\n hnmos = zerofloat(nh,3)\n for u in range(0,3):\n hpef,hnmo = inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,\n na,ka,nh,kh,hsyn,tmin,tmax,uncerts[u],w=w[u])\n #hpef,hnmo = inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,\n #na,ka,nh,kh,hsyn,tmin,tmax)\n hnmos[u] = hnmo\n hpefs[u] = hpef\n title = \"Constant Uncert., Uncert. \"+str(funcert)+\" \"+str(luncert)+\", Uncert. \"+str(luncert)+\" \"+str(funcert)\n titlePEF = \"PEF \"+title\n titleNMO = \"NMO \"+title\n\n hmax = max(hpefs)\n if hmax < max(hnmos):\n hmax = max(hnmos)\n\n plotTimeWindowWavelets(Sampling(nh,st.delta,kh*st.delta),hpefs,hsyn,hmax,\n title=titlePEF,pngDir=pngDir)\n plotTimeWindowWavelets(Sampling(nh,st.delta,kh*st.delta),hnmos,hsyn,hmax,\n title=titleNMO,pngDir=pngDir)","title":""},{"docid":"ab4dc324be0e67e4d99f983876c8da69","score":"0.59141636","text":"def exec(self, data):\n env = Environment.get()\n tod_buffer_length = env.tod_buffer_length()\n\n for obs in data.obs:\n tod = obs[\"tod\"]\n\n # compute effective sample rate\n\n times = tod.local_times()\n dt = np.mean(times[1:-1] - times[0:-2])\n rate = 1.0 / dt\n del times\n\n offset, nsamp = tod.local_samples\n\n hwpang = None\n try:\n hwpang = tod.local_hwp_angle()\n except:\n hwpang = None\n raise RuntimeError(\"Can't run this operator if hwpang is None \")\n\n # read the common flags and apply bitmask\n\n common = None\n if self._apply_flags:\n common = tod.local_common_flags(self._common_flag_name)\n common = common & self._common_flag_mask\n else:\n common = np.zeros(nsamp, dtype=np.uint8)\n\n for det in tod.local_dets:\n eps = 0.0\n if self._epsilon is not None:\n eps = self._epsilon[det]\n\n cal = 1.0\n if self._cal is not None:\n cal = self._cal[det]\n\n # Create cache objects and use that memory directly\n\n pixelsname = \"{}_{}\".format(self._pixels, det)\n weightsname = \"{}_{}\".format(self._weights, det)\n\n pixelsref = None\n weightsref = None\n\n if tod.cache.exists(pixelsname):\n pixelsref = tod.cache.reference(pixelsname)\n else:\n pixelsref = tod.cache.create(pixelsname, np.int64, (nsamp,))\n\n if tod.cache.exists(weightsname):\n weightsref = tod.cache.reference(weightsname)\n else:\n weightsref = tod.cache.create(\n weightsname, np.float64, (nsamp, self._nnz)\n )\n\n pdata = None\n if self._keep_quats:\n # We are keeping the detector quaternions, so cache\n # them now for the full sample range.\n pdata = tod.local_pointing(det)\n\n xaxis = np.array([1.0, 0.0, 0.0])\n zaxis = np.array([0.0, 0.0, 1.0])\n nullquat = np.array([0.0, 0.0, 0.0, 1.0])\n eta = (1.0 - eps) / (1.0 + eps)\n\n pdata[common] = nullquat\n\n dir = rotate(pdata, zaxis)\n pixelsref = hp.vec2pix(\n nside=self._nside, x=dir[:, 0], y=dir[:, 1], z=dir[:, 2], nest=True\n )\n\n pixelsref[common] = -1\n # import pdb\n # pdb.set_trace()\n\n T = self._hwp_parameters_set[0]\n c = self._hwp_parameters_set[1]\n rho = self._hwp_parameters_set[2]\n s = self._hwp_parameters_set[3]\n cos2hwp = np.cos(2 * hwpang)\n\n if self._mode == \"I\":\n weightsref = cal * (T + eta * rho * cos2hwp)\n elif self._mode == \"IQU\":\n orient = rotate(pdata, xaxis)\n\n by = orient[:, 0] * dir[:, 1] - orient[:, 1] * dir[:, 0]\n bx = (\n orient[:, 0] * (-dir[:, 2] * dir[:, 0])\n + orient[:, 1] * (-dir[:, 2] * dir[:, 1])\n + orient[:, 2] * (dir[:, 0] * dir[:, 0] + dir[:, 1] * dir[:, 1])\n )\n\n detang = np.arctan2(by, bx)\n\n sindetang = np.sin(2 * detang)\n cosdetang = np.cos(2 * detang)\n\n ang4 = 2 * detang + 4 * hwpang\n ang2 = 2 * detang + 2 * hwpang\n\n sin4 = np.sin(ang4)\n sin2 = np.sin(ang2)\n cos4 = np.cos(ang4)\n cos2 = np.cos(ang2)\n\n weightsref[:, 0] = cal * (T + (eta * rho * cos2hwp))\n weightsref[:, 1] = cal * (\n (rho * cos2)\n + eta * (T + c) / 2.0 * cosdetang\n + eta * (T - c) / 2.0 * cos4\n )\n weightsref[:, 2] = cal * (\n (rho * sin2)\n + eta * (T + c) / 2.0 * sindetang\n + eta * (T - c) / 2.0 * sin4\n )\n else:\n raise RuntimeError(\"Unknown healpix pointing matrix mode\")\n\n if self._single_precision:\n pixels = pixelsref.astype(np.int32)\n del pixelsref\n pixelsref = tod.cache.put(pixelsname, pixels, replace=True)\n del pixels\n weights = weightsref.astype(np.float32)\n del weightsref\n weightsref = tod.cache.put(weightsname, weights, replace=True)\n del weights\n\n self._hit_submaps[pixelsref // self._npix_submap] = True\n\n del pixelsref\n del weightsref\n del pdata\n\n del common\n\n # Store the local submaps in the data object under the same name\n # as the pixel numbers\n\n if self._single_precision:\n dtype = np.int32\n else:\n dtype = np.int64\n\n local_submaps = np.arange(self._nsubmap, dtype=dtype)[self._hit_submaps]\n submap_name = \"{}_local_submaps\".format(self._pixels)\n data[submap_name] = local_submaps\n npix_submap_name = \"{}_npix_submap\".format(self._pixels)\n data[npix_submap_name] = self._npix_submap\n nsubmap_name = \"{}_nsubmap\".format(self._pixels)\n data[nsubmap_name] = self._nsubmap\n npix_name = \"{}_npix\".format(self._pixels)\n data[npix_name] = 12 * self._nside ** 2\n\n return","title":""},{"docid":"e1f4d409274ad8a57a0acf42df6ce6c2","score":"0.5890701","text":"def store_wavelet_transform(elec_series, processing, filters='rat', hg_only=True, abs_only=True,\n npad='fast', post_resample_rate=None, chunked=True, precision='single',\n source_series=None):\n X = elec_series.data[:]\n X_dtype = dtype(X, precision)\n X = X.astype(X_dtype, copy=False)\n rate = elec_series.rate\n if source_series is None:\n source_series = elec_series\n\n final_rate = rate\n if post_resample_rate is not None:\n final_rate = post_resample_rate\n\n if chunked:\n if not abs_only:\n raise NotImplementedError(\"Phase is not implemented for chunked wavelet transform.\")\n X_wvlt_abs = ChannelBandIterator(X, rate, filters=filters, npad=npad, hg_only=hg_only,\n post_resample_rate=post_resample_rate, precision=precision)\n cfs = X_wvlt_abs.cfs\n sds = X_wvlt_abs.sds\n elec_series_wvlt_amp = DecompositionSeries('wvlt_amp_' + elec_series.name,\n H5DataIO(X_wvlt_abs,\n compression=True,\n shuffle=True,\n fletcher32=True),\n metric='amplitude',\n source_timeseries=source_series,\n starting_time=elec_series.starting_time,\n rate=final_rate,\n description=('Wavlet: ' +\n elec_series.description))\n output_series = [elec_series_wvlt_amp]\n X_wvlt = None\n else:\n X_wvlt, _, cfs, sds = wavelet_transform(X, rate, filters=filters, hg_only=hg_only,\n npad=npad, precision=precision)\n amplitude = abs(X_wvlt)\n if post_resample_rate is not None:\n amplitude = resample(amplitude, post_resample_rate, rate, precision=precision)\n X_wvlt = amplitude\n rate = post_resample_rate\n elec_series_wvlt_amp = DecompositionSeries('wvlt_amp_' + elec_series.name,\n H5DataIO(amplitude,\n compression=True,\n shuffle=True,\n fletcher32=True),\n metric='amplitude',\n source_timeseries=source_series,\n starting_time=elec_series.starting_time,\n rate=final_rate,\n description=('Wavlet: ' +\n elec_series.description))\n output_series = [elec_series_wvlt_amp]\n if not abs_only:\n if post_resample_rate is not None:\n raise ValueError('Wavelet phase should not be resampled.')\n elec_series_wvlt_phase = DecompositionSeries('wvlt_phase_' + elec_series.name,\n H5DataIO(np.angle(X_wvlt),\n compression=True,\n shuffle=True,\n fletcher32=True),\n metric='phase',\n source_timeseries=source_series,\n starting_time=elec_series.starting_time,\n rate=final_rate,\n description=('Wavlet: ' +\n elec_series.description))\n output_series.append(elec_series_wvlt_phase)\n\n for es in output_series:\n for ii, (cf, sd) in enumerate(zip(cfs, sds)):\n es.add_band(band_name=str(ii), band_mean=cf,\n band_stdev=sd, band_limits=(-1, -1))\n\n processing.add(es)\n return X_wvlt, output_series","title":""},{"docid":"4e9d7c712f549f6483fd8660d46e2a31","score":"0.58704555","text":"def test_wstat(self):\n on_vector = self.src.copy()\n on_vector.data += self.bkg.data\n acceptance = RegionNDMap.from_geom(self.src.geom, data=1)\n acceptance_off = RegionNDMap.from_geom(self.bkg.geom, data=1 / self.alpha)\n\n dataset = SpectrumDatasetOnOff(\n counts=on_vector,\n counts_off=self.off,\n exposure=self.exposure,\n acceptance=acceptance,\n acceptance_off=acceptance_off,\n )\n dataset.models = self.source_model\n\n self.source_model.parameters.index = 1.12\n\n fit = Fit()\n result = fit.run(datasets=[dataset])\n pars = self.source_model.parameters\n\n assert_allclose(pars[\"index\"].value, 1.997342, rtol=1e-3)\n assert_allclose(pars[\"amplitude\"].value, 100245.187067, rtol=1e-3)\n assert_allclose(result.total_stat, 30.022316, rtol=1e-3)","title":""},{"docid":"3feb13a87e5c37f3a1e5835b4f53cc40","score":"0.5869362","text":"def do_wavelet(x, y, fr, mom = 'Morlet', f0 = 6., dj = .125, s0 = -1, J = -1,\n slevel = .95, plts = True, rectify=False):\n std = y.std()\n var = std ** 2\n y = (y - y.mean()) / std\n N = len(y)\n dt = np.diff(x).mean()\n\n alpha, _, _ = wavelet.ar1(y)\n \n # the wavelet transform\n mother = eval('wavelet.' + mom + '(' + str(f0) + ')')\n wave, scales, freqs, coi, FFT, FFTfreqs = wavelet.cwt(y, dt, dj, s0, J,\\\n mother)\n iwave = wavelet.icwt(wave, scales, dt, dj, mother) # inverse wavelet transform\n\n power = (np.abs(wave)) ** 2 # Normalized wavelet power spectrum\n fft_power = var * np.abs(FFT) ** 2 # FFT power spectrum\n period = 1. / freqs\n # this rectification may be bullshit, plus if used power must be re-scaled again further down\n if rectify:\n power = power / np.ones([1, N]) * scales[:, None]\n signif, fft_theor = wavelet.significance(1.0, dt, scales, 0, alpha,\\\n significance_level=slevel, wavelet=mother)\n sig = np.ones([1, N]) * signif[:, None]\n sig = power / sig # Where ratio > 1, power is significant\n\n glbl_power = var * power.mean(axis = 1)\n dof = N - scales # Correction for padding at edges\n glbl_sig, tmp = wavelet.significance(var, dt, scales, 1, alpha,\\\n significance_level=slevel, dof=dof, wavelet=mother)\n \n # Scale average between avg1 and avg2 periods and significance level\n sel = find((freqs >= fr[0]) & (freqs <= fr[1]))\n Cdelta = mother.cdelta\n scale_avg = (scales * np.ones((N, 1))).transpose()\n # As in Torrence and Compo (1998) equation 24\n scale_avg = power / scale_avg\n scale_avg = var * dj * dt / Cdelta * scale_avg[sel, :].sum(axis = 0)\n scale_avg_sig, tmp = wavelet.significance(var, dt, scales, 2, alpha,\n significance_level=slevel, dof=[scales[sel[0]],\n scales[sel[-1]]], wavelet=mother)\n \n if plts == True:\n figprops = dict(figsize=(11, 8), dpi=72)\n figure(**figprops)\n \n ax = axes([0.1, 0.75, 0.65, 0.2])\n ax.plot(x, y, 'k', lw = 1.5)\n ax.plot(x, iwave, '-', lw = 1, color=[0.5, 0.5, 0.5])\n ax.set_title('Normalized Anomaly timeseries and its inverse wavelet transform (gray)')\n \n bx = axes([0.1, 0.37, 0.65, 0.28], sharex = ax)\n bx.contourf(x, freqs, power, extend='both')\n bx.contour(x, freqs, sig, [-99, 1], colors='k', lw = 2.) \n bx.fill(np.concatenate([[x[0]-dt, x[0]-dt], x[0:-1], [x[-1]+dt, x[-1]+dt]]),\n np.concatenate([[1e-9, 1./coi[0]], 1./coi[0:-1], [1./coi[-2], 1e-9]]),\n 'k' , alpha='0.3', hatch='x')\n bx.set_ylim([0, 2*fr[1]])\n bx.set_ylabel('Frequency')\n bx.set_title('Wavelet power spectrum (' + mom + ')')\n \n cx = axes([0.77, 0.37, 0.2, 0.28])\n cx.semilogx(fft_power, FFTfreqs, '-', color=[0.7, 0.7, 0.7])\n cx.semilogx(glbl_power, freqs, 'k-', linewidth=1.5)\n cx.plot(glbl_sig, freqs, 'k--')\n cx.set_ylim([0, 2*fr[1]])\n cx.set_title('Global wavelet spec (thick) vs FFT spec')\n cx.set_xlabel('Power')\n cx.set_yticklabels(cx.get_yticklabels(), visible = False)\n \n dx = axes([0.1, 0.07, 0.65, 0.2], sharex=ax)\n dx.plot(x, scale_avg, 'k-', linewidth=1.5)\n dx.axhline(scale_avg_sig, color='k', linestyle='--', linewidth=1.)\n dx.set_title('$%f$-$%f$ freq band scale-averaged power' % (fr[0], fr[1]))\n dx.set_ylabel('Average variance')\n draw()\n show()\n \n return freqs, power, sig, coi, iwave, glbl_power, glbl_sig, scale_avg, scale_avg_sig, scales","title":""},{"docid":"8948cb2e7f0950087a4ef23aea06ffe6","score":"0.58554786","text":"def main() -> None:\n params = TsParams(nperseg=64, noverlap=48)\n da = DataArray(fs=100, nchannels=12, ntrials=20, simulate=True)\n ds = Dataset(da, params)\n print(ds)","title":""},{"docid":"95a2ca764992318b9defb3f2f22b2e60","score":"0.578665","text":"def main():\n\n #\n # Generate waveform\n #\n\n print 'reading waveform...'\n waveform=read_waveformfile(sys.argv[1])\n\n # Pick some extrinsic parameters\n ext_params = ExtParams(distance=1, ra=0.0, dec=0.0, polarization=0.0,\n inclination=0.0, phase=0.0, geocent_peak_time=2.0)\n\n\n #\n # Generate IFO data\n #\n det1_data = DetData(waveform=waveform, ext_params=ext_params)\n\n from scipy import signal\n import pylab as pl\n\n pl.figure()\n pl.plot(det1_data.td_response.sample_times,det1_data.td_response.data)\n pl.plot(det1_data.td_signal.sample_times,det1_data.td_signal.data)\n\n pl.figure()\n f,p = signal.welch(det1_data.td_response.data, fs=1./det1_data.delta_t,\n nperseg=512)\n pl.loglog(f,np.sqrt(p))\n\n f,p = signal.welch(det1_data.td_signal.data, fs=1./det1_data.delta_t,\n nperseg=512)\n pl.loglog(f,np.sqrt(p))\n pl.ylim(1e-25,1e-21)\n pl.show()","title":""},{"docid":"3cab1c33c185d047dfaafd81767f1820","score":"0.57759106","text":"def exec(self, data):\n env = Environment.get()\n tod_buffer_length = env.tod_buffer_length()\n\n for obs in data.obs:\n tod = obs[\"tod\"]\n\n # compute effective sample rate\n\n times = tod.local_times()\n dt = np.mean(times[1:-1] - times[0:-2])\n rate = 1.0 / dt\n del times\n\n offset, nsamp = tod.local_samples\n\n hwpang = None\n try:\n hwpang = tod.local_hwp_angle()\n except:\n hwpang = None\n\n # read the common flags and apply bitmask\n\n common = None\n if self._apply_flags:\n common = tod.local_common_flags(self._common_flag_name)\n common = common & self._common_flag_mask\n else:\n common = np.zeros(nsamp, dtype=np.uint8)\n\n for det in tod.local_dets:\n eps = 0.0\n if self._epsilon is not None:\n eps = self._epsilon[det]\n\n cal = 1.0\n if self._cal is not None:\n cal = self._cal[det]\n\n # Create cache objects and use that memory directly\n\n pixelsname = \"{}_{}\".format(self._pixels, det)\n weightsname = \"{}_{}\".format(self._weights, det)\n\n pixelsref = None\n weightsref = None\n\n if tod.cache.exists(pixelsname):\n pixelsref = tod.cache.reference(pixelsname)\n else:\n pixelsref = tod.cache.create(pixelsname, np.int64, (nsamp,))\n\n if tod.cache.exists(weightsname):\n weightsref = tod.cache.reference(weightsname)\n else:\n weightsref = tod.cache.create(\n weightsname, np.float64, (nsamp, self._nnz)\n )\n\n pdata = None\n if self._keep_quats:\n # We are keeping the detector quaternions, so cache\n # them now for the full sample range.\n pdata = tod.local_pointing(det)\n\n buf_off = 0\n buf_n = tod_buffer_length\n while buf_off < nsamp:\n if buf_off + buf_n > nsamp:\n buf_n = nsamp - buf_off\n bslice = slice(buf_off, buf_off + buf_n)\n\n detp = None\n if pdata is None:\n # Read and discard\n detp = tod.read_pntg(detector=det, local_start=buf_off, n=buf_n)\n else:\n # Use cached version\n detp = pdata[bslice, :]\n\n hslice = None\n if hwpang is not None:\n hslice = hwpang[bslice].reshape(-1)\n fslice = common[bslice].reshape(-1)\n\n pointing_matrix_healpix(\n self.hpix,\n self._nest,\n eps,\n cal,\n self._mode,\n detp.reshape(-1),\n hslice,\n fslice,\n pixelsref[bslice].reshape(-1),\n weightsref[bslice, :].reshape(-1),\n )\n buf_off += buf_n\n\n if self._single_precision:\n pixels = pixelsref.astype(np.int32)\n del pixelsref\n pixelsref = tod.cache.put(pixelsname, pixels, replace=True)\n del pixels\n weights = weightsref.astype(np.float32)\n del weightsref\n weightsref = tod.cache.put(weightsname, weights, replace=True)\n del weights\n\n self._hit_submaps[pixelsref // self._npix_submap] = True\n\n del pixelsref\n del weightsref\n del pdata\n\n del common\n\n # Store the local submaps in the data object under the same name\n # as the pixel numbers\n\n if self._single_precision:\n dtype = np.int32\n else:\n dtype = np.int64\n\n local_submaps = np.arange(self._nsubmap, dtype=dtype)[self._hit_submaps]\n submap_name = \"{}_local_submaps\".format(self._pixels)\n data[submap_name] = local_submaps\n npix_submap_name = \"{}_npix_submap\".format(self._pixels)\n data[npix_submap_name] = self._npix_submap\n nsubmap_name = \"{}_nsubmap\".format(self._pixels)\n data[nsubmap_name] = self._nsubmap\n npix_name = \"{}_npix\".format(self._pixels)\n data[npix_name] = 12 * self._nside ** 2\n\n return","title":""},{"docid":"bfcdd55bf266206cd019233a009b7b52","score":"0.57688946","text":"def scalogram(filename, savename):\n\n #signal reading\n (rate,signal) = wav.read(filename)\n\n #ignore other bands for primary treatment\n if signal.shape[1] > 1:\n signal = signal[:,0]\n\n #clip the signal\n max_energy = max(energy)\n start_frame = 0\n for k in range(len(energy)):\n if energy[k] >= max_energy*0.01:\n start_frame = k\n break\n\n end_frame = start_frame\n for k in range(start_frame,len(energy)):\n if energy[k] < max_energy*0.001:\n end_frame = k\n break\n\n if(end_frame == start_frame):\n for k in range(start_frame,len(energy)):\n if energy[k] < max_energy*0.01:\n end_frame = k\n break\n\n samples_per_frame = rate * 0.01\n signal = signal[start_frame*samples_per_frame:end_frame*samples_per_frame]\n\n\n wavelet=DOG4\n maxscale=10\n notes=100\n scaling='log'#\"log\" #or \"linear\"\n plotpower2d=True\n\n Ns=1024\n #limits of analysis\n Nlo=0 \n Nhi=Ns\n\n # Wavelet transform\n cw=wavelet(signal,maxscale,notes,scaling=scaling)\n scales=cw.getscales() \n cwt=cw.getdata()\n # power spectrum\n pwr=cw.getpower()\n scalespec=np.sum(pwr,axis=1)/scales # calculate scale spectrum\n # scales\n y=cw.fourierwl*scales\n x=np.arange(Nlo*1.0,Nhi*1.0,1.0)\n \n #mpl.tight_layout()\n mpl.axis('off')\n fig=mpl.figure(1)\n\n # 2-d coefficient plot\n plotcwt=np.clip(np.fabs(cwt.real), 0., 1000.)\n if plotpower2d: plotcwt=pwr\n im=mpl.imshow(plotcwt,cmap=mpl.cm.jet,extent=[x[0],x[-1],y[-1],y[0]],aspect='auto')\n mpl.ylim(y[0],y[-1])\n theposition=mpl.gca().get_position()\n\n mpl.tight_layout()\n mpl.savefig(savename)","title":""},{"docid":"bb7f0d37c8821de30d443f715f55133d","score":"0.57541025","text":"def _process_wavelengths(self):\r\n aot_cols = [c for c in self.data.columns if 'AOT' in c]\r\n wvs = [int(x.replace('AOT', '')) for x in aot_cols]\r\n\r\n self.wavelengths = wvs","title":""},{"docid":"66ae559e9181dafc87ab38845473dd30","score":"0.57334787","text":"def wt(y,t,dj = 1/32, mother = 'morlet',pad = 1, param = -1, freqScale = 'log',ar1 = 'auto', **kwargs): \n import numpy as np\n \n # Default values\n maxScale = None\n \n n = len(y)\n sigma2 = np.var(y)\n \n if np.ndim(t)==0:\n dt = t\n t = np.arange(n)*dt\n else:\n dt = t[1]-t[0]\n \n for key in kwargs.keys():\n if key.lower() == 'maxscale':\n maxScale = kwargs[key] \n\n s0= kwargs.get('s0')\n if s0 == None:\n s0 = 2*dt\n \n siglvl = kwargs.get('siglvl')\n if siglvl == None:\n siglvl = 0.95\n \n J1 = kwargs.get('J1') \n if J1 == None:\n if maxScale == None:\n maxScale = (n*0.17)*2*dt\n J1 = np.round(np.log2(maxScale/s0)/dj)\n if ar1.lower()== 'auto':\n ar1 = ar1nv(y)[0]\n \n wave,period,scale,coi = wavelet(y,t,dj = dj, mother = mother ,param = param,\n freqScale = freqScale, **kwargs)\n power = np.abs(wave)**2\n signif = wave_signif(1.0,dt,scale,sigtest=0,lag1=ar1,siglvl= siglvl,mother = mother)\n sig95 = signif[0].reshape((-1,1))*np.ones((1,n))\n sig95 = power/(sigma2*sig95)\n \n return wave, period, scale, coi, sig95","title":""},{"docid":"78a47ab07d9f80c3fe8e695555baeb70","score":"0.57304037","text":"def _plot_wavelet(datas):\n \n # Declare a starlet object (and performs the transform)\n Sw = scarlet.Starlet(datas, lvl=5, direct=True)\n # This is the starlet transform as an array\n w = Sw.coefficients\n # The inverse starlet transform of w (new object otherwise, the tranform is not used)\n iw = Sw.image\n\n # TODO: Clean this code up using plt.subplots()\n # The wavelet transform of the first slice of images in pictures\n lvl = w.shape[1]\n plt.figure(figsize=(lvl*5+5,5))\n plt.suptitle('Wavelet coefficients')\n for i in range(lvl):\n plt.subplot(1, lvl, i+1)\n plt.title('scale' + str(i+1))\n plt.imshow(w[0,i], cmap='inferno')\n plt.colorbar()\n plt.show()\n\n # Making sure we recover the original image\n plt.figure(figsize=(30,10))\n plt.subplot(131)\n plt.title('Original image', fontsize=20)\n plt.imshow(datas[0], cmap='inferno')\n plt.colorbar()\n plt.subplot(132)\n plt.title('Starlet-reconstructed image', fontsize=20)\n plt.imshow(iw[0], cmap='inferno')\n plt.colorbar()\n plt.subplot(133)\n plt.title('Absolute difference', fontsize=20)\n plt.imshow((np.abs(iw[0]-datas[0])), cmap='inferno')\n plt.colorbar()\n plt.show()\n \n return","title":""},{"docid":"61eb8a9921b0ba4239fa160abfc4c1fd","score":"0.5722021","text":"def run(self):\n self.check_filename(\".txt\")\n self.spec = self.spec_hw.spec\n self.total_time = self.spec_hw.settings['intg_time'] * self.settings['scans_to_avg'] #ms\n while not self.interrupt_measurement_called:\n self._read_spectrometer()\n self.save_array[:,1] = self.y\n if self.ui.save_every_spec_checkBox.isChecked(): #while interrupt not called, inside, have an if for if interrupt is called \n self.save_array[:,0] = self.spec.wavelengths()\n np.savetxt(self.app.settings['save_dir']+\"/\"+self.app.settings['sample']+str(self.point_counter)+\".txt\", self.save_array, fmt = '%.5f', header = 'Wavelength (nm), Intensity (counts)', delimiter = ' ')\n self.point_counter += 1\n if self.interrupt_measurement_called:\n break","title":""},{"docid":"958047e235027295f278d93f7190514a","score":"0.566877","text":"def pco2w_instrument(ds):\n # drop some variables:\n # record_type == not used\n # record_time == internal_timestamp == time, redundant so can remove\n # absorbance_ratio_*_qc_results == incorrectly set tests, ignoring\n # absorbance_ratio_*_qc_executed == incorrectly set tests, ignoring\n ds = ds.reset_coords()\n drop_vars = ['record_type', 'record_time', 'internal_timestamp',\n 'absorbance_ratio_434_qc_results', 'absorbance_ratio_434_qc_executed',\n 'absorbance_ratio_620_qc_results', 'absorbance_ratio_620_qc_executed']\n for v in drop_vars:\n if v in ds.variables:\n ds = ds.drop(v)\n\n # rename some variables for better clarity\n rename = {\n 'voltage_battery': 'raw_battery_voltage',\n 'thermistor_raw': 'raw_thermistor',\n 'pco2w_thermistor_temperature': 'thermistor_temperature',\n 'pco2w_thermistor_temperature_qc_executed': 'thermistor_temperature_qc_executed',\n 'pco2w_thermistor_temperature_qc_results': 'thermistor_temperature_qc_results',\n }\n ds = ds.rename(rename)\n\n # now we need to reset the light array to named variables that will be more meaningful and useful in\n # the final data files\n light = ds.light_measurements.astype('int32')\n dark_reference = light[:, [0, 8]].values # dark reference\n dark_signal = light[:, [1, 9]].values # dark signal\n reference_434 = light[:, [2, 10]].values # reference signal, 434 nm\n signal_434 = light[:, [3, 11]].values # signal intensity, 434 nm\n reference_620 = light[:, [4, 12]].values # reference signal, 620 nm\n signal_620 = light[:, [5, 13]].values # signal intensity, 620 nm\n\n # create a data set with the duplicate measurements for each variable\n data = xr.Dataset({\n 'dark_reference': (['time', 'duplicates'], dark_reference),\n 'dark_signal': (['time', 'duplicates'], dark_signal),\n 'reference_434': (['time', 'duplicates'], reference_434),\n 'signal_434': (['time', 'duplicates'], signal_434),\n 'reference_620': (['time', 'duplicates'], reference_620),\n 'signal_620': (['time', 'duplicates'], signal_620)\n }, coords={'time': ds['time'], 'duplicates': np.arange(0, 2).astype('int32')})\n ds = ds.drop(['spectrum', 'light_measurements'])\n\n # merge the data sets back together\n ds = ds.merge(data)\n\n # calculate the battery voltage\n ds['battery_voltage'] = ds['raw_battery_voltage'] * 15. / 4096.\n\n # reset some data types\n data_types = ['deployment', 'raw_thermistor', 'raw_battery_voltage',\n 'absorbance_blank_434', 'absorbance_blank_620', 'absorbance_ratio_434',\n 'absorbance_ratio_620']\n for v in data_types:\n ds[v] = ds[v].astype('int32')\n\n data_types = ['thermistor_temperature', 'pco2_seawater']\n for v in data_types:\n ds[v] = ds[v].astype('float32')\n\n # test the data quality\n ds['pco2_seawater_quality_flag'] = quality_checks(ds)\n\n # reset some attributes\n for key, value in ATTRS.items():\n for atk, atv in value.items():\n if key in ds.variables:\n ds[key].attrs[atk] = atv\n\n # add the original variable name as an attribute, if renamed\n for key, value in rename.items():\n ds[value].attrs['ooinet_variable_name'] = key\n\n return ds","title":""},{"docid":"7ad4e3da23dac78d5a29f923f0b55175","score":"0.5666068","text":"def calculate_with_sampler(self):\n self.calculate()\n # results = []\n #print \"calculating lambda=\" + str(lambda_um)\n with open(osp.join(DIR_NAME, \"params.json\"),\"w\") as f:\n json.dump(self.params, f) \n process = subprocess.Popen([osp.join(DIR_NAME, \"S4.exe\"), osp.join(DIR_NAME, \"sampler.lua\")], shell=True, stdout=subprocess.PIPE)\n process.wait()\n if process.returncode:\n raise ValueError(\"S4 didn't return code 0\")\n self.results = np.loadtxt(osp.join(DIR_NAME, \"tempfile.txt\"))\n # if len(self.dat.shape) == 1:\n # results.append(self.dat[1:])\n # else:\n# results.append(self.dat)\n# self.results = results\n return np.array(self.results)","title":""},{"docid":"3dd3c51fe86dc8ea1660008b1d7821d6","score":"0.5653361","text":"def cwt(data, wavelet_name, sampling_frequency=1., voices_per_octave=10):\n\n # Currently only supported for Morlet wavelets\n if wavelet_name == 'morl':\n data -= np.mean(data)\n n_orig = data.size\n nv = voices_per_octave\n ds = 1 / nv\n fs = sampling_frequency\n dt = 1 / fs\n\n # Pad data symmetrically\n padvalue = n_orig // 2\n x = np.concatenate((np.flipud(data[0:padvalue]), data, np.flipud(data[-padvalue:])))\n n = x.size\n\n # Define scales\n _, _, wavscales = cwt_filterbank(wavelet_name, n_orig, ds)\n\n wavscales = wavscales[:]\n num_scales = wavscales.size\n\n # Frequency vector sampling the Fourier transform of the wavelet\n omega = np.arange(1, math.floor(n / 2) + 1, dtype=np.float64)\n omega *= (2 * np.pi) / n\n omega = np.concatenate((np.array([0]), omega, -omega[np.arange(math.floor((n - 1) / 2), 0, -1, dtype=int) - 1]))\n\n # Compute FFT of the (padded) time series\n f = np.fft.fft(x)\n\n # Loop through all the scales and compute wavelet Fourier transform\n psift, freq = waveft(wavelet_name, omega, wavscales)\n\n\n # Inverse transform to obtain the wavelet coefficients.\n cwtcfs = np.fft.ifft(np.kron(np.ones([num_scales, 1]), f) * psift)\n cfs = cwtcfs[:, padvalue:padvalue + n_orig]\n freq = freq * fs\n\n return cfs, freq\n else:\n raise Exception","title":""},{"docid":"e127a11f3cbecce79280802bd176cbc5","score":"0.5650203","text":"def mainloop(self, data):\n\n # total = Input().number_of_chr(data)\n \n resultframe =pd.DataFrame(columns=['genome','width','mode','chr','biosource','tf','means','covariances', 'weights']) \n \n # i = 0\n # loop all biosources\n print(\"analyse_main.py: unpacking\")\n for biosource, b_value in data.items():\n print(\"unpacking: \" + biosource)\n #loop all transcription factors\n for tf, tf_value in b_value.items():\n \n print('analysing: '+ tf)\n \n scoresarray = []\n #combine all scores of the chromosomes into vector-format list\n for chromosome in tf_value.values():\n \n for array in chromosome:\n \n scoresarray.append([array[-1], array[-2]]) \n \n # scaled_scores = TF_analyser.scale(self, scoresarray)\n distribution = np.array(scoresarray)\n \n mode = 'manual'\n \n if self.evaluate_n == True:\n \n mode = 'auto'\n print(\"mode auto: number of components is evaluated (components_fit.py)\")\n #automated number of components evaluation \n all_diffs = GmFit.getDifference(self, distribution, self.eval_size)\n self.n_components = GmFit.evaluate(self, all_diffs)\n #plt.plot(all_diffs)\n \n single_result = EMA().emAnalyse(distribution, self.n_components)\n \n single_result.insert(0,'tf',tf)\n single_result.insert(0,'chr', \", \".join(self.chr))\n single_result.insert(0,'biosource',biosource)\n single_result.insert(0, 'mode', mode)\n single_result.insert(0,'width', self.width)\n single_result.insert(0,'genome', self.genome)\n \n #visualization and saving plots \n v= VD(self.path_results, tf, self.genome, biosource, self.chr)\n path = v.displayDensityScatter(distribution, tf)\n \n v.altitudePlot(distribution, self.n_components, tf)\n z,filename = v.contourPlot(distribution, self.n_components, tf)\n \n #Add z axis to scoresarray:\n for i in range(0,len(z)):\n scoresarray[i].append(z[i])\n \n #save data\n np.savetxt(path + '/' + tf + '.csv', scoresarray, delimiter=',')\n \n single_result.insert(9, 'path', path)\n single_result.insert(10, 'time', time.time())\n single_result.insert(11, 'vis_filename', filename)\n \n resultframe = pd.concat([resultframe, single_result])\n\n parameters = [self.genome, self.width, mode, \", \".join(self.chr), biosource, tf]\n modifyCSV(self.path_result_csv).compare(parameters)\n print (tf + \" Done\")\n \n\n #Save resultframe\n Repository().save_csv(resultframe)\n \n return resultframe","title":""},{"docid":"f7f3f33e528829fcc738e9ae6a6cbf33","score":"0.55947524","text":"def run():\n\n import matplotlib.pyplot as plt\n\n anomalies_t = []\n anomalies_v = []\n anomalies_c = []\n\n all_t = []\n all_v = []\n\n rows = []\n for i, row in dataSet.iterrows():\n\n inputData = row.to_dict()\n\n detectorValues = handleRecord(inputData)\n\n if (detectorValues[0] > 0.65):\n anomalies_t.append(inputData[\"timestamp\"])\n anomalies_v.append(inputData[\"value\"])\n anomalies_c.append(detectorValues[0])\n\n all_t.append(inputData[\"timestamp\"])\n all_v.append(inputData[\"value\"])\n\n outputRow = list(row) + list(detectorValues)\n\n rows.append(outputRow)\n\n # Progress report\n if (i % 1000) == 0:\n print \".\",\n sys.stdout.flush()\n\n fig, ax = plt.subplots()\n\n ax.plot(all_t, all_v)\n ax.plot(anomalies_t, anomalies_v, 'ro')\n\n plt.show()\n\n ans = pandas.DataFrame(rows)\n return ans","title":""},{"docid":"225c777ea9ee9b4cc194e8a2fa73ea17","score":"0.5591327","text":"def main():\n parser = get_parser()\n args = parser.parse_args()\n\n # set logger\n logfmt = \"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\"\n if args.verbose > 0:\n logging.basicConfig(level=logging.INFO, format=logfmt)\n else:\n logging.basicConfig(level=logging.WARN, format=logfmt)\n logging.info(get_commandline_args())\n\n os.makedirs(args.figdir, exist_ok=True)\n\n with kaldiio.ReadHelper(args.rspecifier) as reader, codecs.open(\n args.wspecifier, \"w\", encoding=\"utf-8\"\n ) as f:\n for utt_id, (rate, array) in reader:\n array = array.astype(numpy.float32)\n if args.normalize is not None and args.normalize != 1:\n array = array / (1 << (args.normalize - 1))\n if rate != args.fs:\n array = resampy.resample(array, rate, args.fs, axis=0)\n array_trim, idx = librosa.effects.trim(\n y=array,\n top_db=args.threshold,\n frame_length=args.win_length,\n hop_length=args.shift_length,\n )\n start, end = idx / args.fs\n\n # save figure\n if args.figdir is not None:\n plt.subplot(2, 1, 1)\n plt.plot(array)\n plt.title(\"Original\")\n plt.subplot(2, 1, 2)\n plt.plot(array_trim)\n plt.title(\"Trim\")\n plt.tight_layout()\n plt.savefig(args.figdir + \"/\" + utt_id + \".png\")\n plt.close()\n\n # added minimum silence part\n start = max(0.0, start - args.min_silence)\n end = min(len(array) / args.fs, end + args.min_silence)\n\n # write to segments file\n segment = \"%s %s %f %f\\n\" % (utt_id, utt_id, start, end)\n f.write(segment)","title":""},{"docid":"c724224d374c4c0478134066f76f5b06","score":"0.55848354","text":"def provide_dataset(self):\n audio_length = int(self._config['audio_length'] * self._config['sample_rate'])\n\n pitch_counts = self.get_pitch_counts()\n pitches = sorted(pitch_counts.keys())\n print('LENGTH OF PITCH ONE HOT')\n print(len(pitches))\n label_index_table = tf.contrib.lookup.index_table_from_tensor(\n sorted(pitches), dtype=tf.int64)\n \n # Get audio file names\n wav_directory = self._config['train_data_path']\n file_names = os.listdir(wav_directory)\n \n # Create seed arrays\n\n # STEREO\n if (self._channel_mode == 'stereo'):\n wavs = np.empty(shape = [1, audio_length, 2])\n \n # MONO\n if (self._channel_mode == 'mono'):\n wavs = np.empty(shape = [1, audio_length, 1])\n\n labels = 60\n\n # Build audio and pitch arrays\n for file_name in file_names:\n # Read audio\n unused_sample_rate, wav = read_audio(wav_directory + '/' + file_name)\n \n print('WAV SHAPE--AFTER READ FROM DIRECTORY, BEFORE LABEL PARSE')\n print(wav.shape)\n \n # Parse MIDI note number of \".wav\" file name\n # Follow nsynth file name formatting if MONO, slowcore if STEREO \n if (wav.ndim == 1):\n label = int(file_name[-11:-8]) - 24\n else:\n label = int(file_name[-7:-4]) - 24\n \n # mono to stereo\n if (self._channel_mode == 'stereo' and wav.ndim == 1):\n wav = np.expand_dims(wav, axis=1)\n wav = np.concatenate((wav, wav), axis=1)\n\n if (self._channel_mode == 'mono' and wav.ndim == 1):\n # MONO (none, maybe)\n wav = np.expand_dims(wav, axis=1)\n \n # stereo to mono\n if (self._channel_mode == 'mono' and wav.ndim == 2):\n wav = np.mean(wav, axis=1)\n wav = np.expand_dims(wav, axis=1)\n \n # crop\n wav = wav[0:audio_length,:]\n\n # Put in range [-1, 1]\n float_normalizer = float(np.iinfo(np.int16).max)\n wav = wav / float_normalizer\n\n # Convert to float32\n wav = np.float32(wav)\n \n # Create batch dimension\n wav = np.expand_dims(wav, axis=0) \n\n # Create vertical array of wavs\n \n # STEREO\n if (self._channel_mode == 'stereo'):\n wavs = np.concatenate((wavs, wav), axis=0) \n \n # MONO\n if (self._channel_mode == 'mono'):\n wavs = np.vstack((wavs, wav)) \n\n # Create vertical array of pitches\n labels = np.vstack((labels, label))\n\n # Remove seed arrays\n \n # STEREO\n if (self._channel_mode == 'stereo'):\n wavs = tf.slice(wavs, [1, 0, 0], [len(file_names), audio_length, 2])\n\n # MONO\n if (self._channel_mode == 'mono'):\n wavs = tf.slice(wavs, [1, 0, 0], [len(file_names), audio_length, 1])\n \n labels = tf.slice(labels, [1, 0], [len(file_names), 1])\n\n # Translate MIDI note numbers to one-hot vectors\n def _one_hot(wavs, labels):\n labels = tf.one_hot(labels, depth=len(pitches))[0]\n # labels = tf.Print(labels, [labels], message=\"One hot labels: \")\n labels = tf.dtypes.cast(labels, tf.int64)\n wavs = tf.dtypes.cast(wavs, tf.float32)\n return wavs, labels\n\n # Create and map dataset\n dataset = tf.data.Dataset.from_tensor_slices((wavs, labels))\n dataset = dataset.map(_one_hot, num_parallel_calls=4)\n\n return dataset","title":""},{"docid":"49db3bc8f6a8a3c19389d31924d88f6a","score":"0.5580731","text":"def get_real_result_waves(self):","title":""},{"docid":"debfa44378f6d219adef895154156343","score":"0.5561195","text":"def applyTo(self, photon_array):\n photon_array.wavelength = self.sed.sampleWavelength(\n photon_array.size(), self.bandpass, rng=self.rng, npoints=self.npoints)","title":""},{"docid":"8e45d868176625190d0dce75f01e2e05","score":"0.55299485","text":"def procelemdata(self, event):\n dlg = wx.FileDialog(self, \"Choose a file\", \"\", \"\", \"*.s7k\", wx.OPEN)\n if dlg.ShowModal() == wx.ID_OK:\n self.rawfile = dlg.GetFilename()\n dirname = dlg.GetDirectory()\n havefile = True\n else: havefile = False\n dlg.Destroy()\n if havefile:\n r = prr.x7kRead(os.path.join(dirname, self.rawfile))\n r.mapfile()\n print \"mapping complete. Processing data.\"\n # make sure there are 7038 records in the file\n if r.map.packdir.has_key('7038'):\n r.getrecord(7000, 0)\n frequency = r.packet.subpack.header[3]\n samplerate = r.packet.subpack.header[4]\n r.getrecord(7038, 0)\n # assuming the same number of samples throughout the file\n maxsamples = r.packet.subpack.header[4]\n numelements = r.packet.subpack.numelements\n # initialize stuff\n gainlist = {}\n mags = {}\n num7038 = len(r.map.packdir['7038'])\n dir7000 = np.asarray(r.map.packdir['7000'])\n # get the number of pings at each gain setting\n for pingnum in range(num7038):\n tstamp = r.map.packdir['7038'][pingnum][1]\n idx = np.nonzero(dir7000[:,1] == tstamp)[0]\n if len(idx) == 1:\n r.getrecord(7000, idx[0])\n gain = str(r.packet.subpack.header[15])\n if gainlist.has_key(gain):\n gainlist[gain].append(pingnum)\n else:\n gainlist[gain] = [pingnum]\n # inialize arrays for all gain settings\n for gain in gainlist:\n num = len(gainlist[gain])\n mags[gain] = np.zeros((maxsamples * num, numelements))\n # get data from all pings\n pingcount = 0\n for gain in gainlist:\n pointer = 0\n for n,pingnum in enumerate(gainlist[gain]):\n try:\n\t\t\t\t\t\t\tr.getrecord(7038,pingnum)\n\t\t\t\t\t\t\tnumsamples = r.packet.subpack.numsamples\n\t\t\t\t\t\t\tpingcount +=1\n\t\t\t\t\t\t\tcomplete = str(int(100.0 * pingcount / num7038))\n\t\t\t\t\t\t\tb = (len(complete) + 2) * '\\b'\n\t\t\t\t\t\t\tprint b + complete + '%',\n\t\t\t\t\t\t\tend = pointer + numsamples\n\t\t\t\t\t\t\tmag = np.abs(r.packet.subpack.r.reshape(-1,numelements))\n\t\t\t\t\t\t\tmags[gain][pointer:end, :] = mag\n except:\n mags[gain][pointer:end, :] = np.nan\n pointer += numsamples\n mags[gain] = mags[gain][:pointer, :]\n print '\\n',\n # reusing a variable name, sorry. I'm not very creative.\n gainlist = [float(g) for g in mags.keys()]\n gainlist.sort()\n aveMag = np.zeros((len(gainlist), numelements))\n targetplotgain = 40 # the closest gain to this value is plotted\n lastval = 100 # just picked a large value...\n for idx, gain in enumerate(gainlist):\n g_amp = mags[str(gain)]\n #FFT by Sam Greenaway\n #one side fft of magnitude, treat each element independently\n C = np.average(g_amp, axis=0)\t\n #Tile average to remove average mag value before fft \n D = np.tile(C,(len(g_amp),1))\n W = np.tile(np.hanning(len(g_amp)),(numelements,1)).T\n aveMag[idx,:] = np.average(g_amp, axis = 0)\n testval = np.abs(gain - targetplotgain)\n if testval < lastval:\n lastval = testval\n A = (8/3)*(2/(samplerate*len(g_amp)))*np.abs(np.fft.rfft(np.multiply(W,(g_amp-D)), axis=0))**2\n midg_amp = g_amp\n midgain = str(gain)\n #average PSD - equivalent to ensemble avergaing across elements\n aA = np.average(A, axis=1)\n # the frequencies\n fn1S = np.linspace(0,samplerate/2,np.size(aA))\n # get rid of some warnings...\n idx = np.nonzero(midg_amp == 0)\n midg_amp[idx[0],idx[1]] = 1\n idx = np.nonzero(aveMag == 0)\n aveMag[idx[0],idx[1]] = 1\n # Plotting also by Sam... mostly\n f=plt.figure(figsize = (15,10))\n f.suptitle(self.rawfile)\n plt.subplot(2,2,1)\n plt.imshow(20*np.log10(midg_amp), aspect = 'auto')\n plt.title('Amplitude, ' + midgain + 'dB gain')\n plt.xlabel('element')\n plt.ylabel('sample')\n plt.colorbar()\n\n plt.subplot(2,2,2)\n plt.plot(gainlist,20*np.log10(aveMag))\n plt.title('Average Amplitude by Gain')\n plt.xlabel('gain')\n plt.ylabel('dB re 7125 Units')\n plt.grid()\n\n ax = plt.subplot(2,2,3)\n linelist = ax.plot(20*np.log10(aveMag).T)\n ax.set_title('Average Element Amplitude by Gain')\n ax.set_xlabel('element')\n ax.set_ylabel('dB re 7125 Units')\n ax.set_xlim([0, numelements])\n ax.grid()\n box = ax.get_position()\n ax.set_position([box.x0, box.y0, box.width*0.8, box.height])\n ax.legend(linelist, gainlist, loc = 'center left', bbox_to_anchor=(1,0.5))\n\n plt.subplot(2,2,4)\n plt.plot(fn1S,20*np.log10(aA))\n plt.title('One Sided PSD, '+ midgain +'dB Gain, ensemble averaged across elements')\n plt.xlabel('Hz')\n plt.ylabel('dB re 7125 Units/ Hz')\n plt.grid()\n\n plt.draw()\n print \"Thanks Sam.\"\n else:\n print 'No 7038 data found. Make sure the latest Reson Feature Pack is installed.'","title":""},{"docid":"b469c98a1354fff7ded37704929b5794","score":"0.5512731","text":"def run_analysis(self, analysis_kwargs=None, **kw):\n if analysis_kwargs is None:\n analysis_kwargs = {}\n\n self.analysis = tda.FluxAmplitudeSweepAnalysis(\n qb_names=self.meas_obj_names, options_dict=dict(TwoD=True),\n t_start=self.timestamp, **analysis_kwargs)","title":""},{"docid":"23992559b691c78640552342d8f24c59","score":"0.55057263","text":"def image_data(self,dat,dt,minf,maxf,vel,jf=1,nhx=0,nhy=0,sym=True,nrmax=3,eps=0.0,dtmax=5e-05,wav=None,\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\n # Get temporal axis\n nt = dat.shape[-1]\n\n # Create frequency domain source\n if(wav is None):\n wav = np.zeros(nt,dtype='float32')\n wav[0] = 1.0\n self.__nwo,self.__ow,self.__dw,wfft = self.fft1(wav,dt,minf=minf,maxf=maxf)\n wfftd = wfft[::jf]\n self.__nwc = wfftd.shape[0] # Get the number of frequencies for imaging\n self.__dwc = self.__dw*jf\n\n if(sverb or wverb): print(\"Frequency axis: nw=%d ow=%f dw=%f\"%(self.__nwc,self.__ow,self.__dwc))\n\n # Create frequency domain data\n _,_,_,dfft = self.fft1(dat,dt,minf=minf,maxf=maxf)\n dfftd = dfft[:,::jf]\n datt = np.transpose(dfftd,(0,1,4,2,3)) # [nsy,nsx,ny,nx,nwc] -> [nsy,nsx,nwc,ny,nx]\n datw = np.ascontiguousarray(datt.reshape([self.__nexp,self.__nwc,self.__ny,self.__nx]))\n\n # Single square root object\n ssf = ssr3(self.__nx ,self.__ny,self.__nz , # Spatial Sizes\n self.__dx ,self.__dy,self.__dz , # Spatial Samplings\n self.__nwc,self.__ow,self.__dwc,eps, # Frequency axis\n ntx,nty,px,py, # Taper and padding\n dtmax,nrmax,nthrds) # Reference velocities\n\n # Compute slowness and reference slownesses\n slo = 1/vel\n ssf.set_slows(slo)\n\n # Allocate partial image array\n if(nhx == 0 and nhy == 0):\n imgar = np.zeros([self.__nexp,self.__nz,self.__ny,self.__nx],dtype='float32')\n else:\n if(sym):\n # Create axes\n self.__rnhx = 2*nhx+1; self.__ohx = -nhx*self.__dx; self.__dhx = self.__dx\n self.__rnhy = 2*nhy+1; self.__ohy = -nhy*self.__dy; self.__dhy = self.__dy\n # Allocate image array\n imgar = np.zeros([self.__nexp,self.__rnhy,self.__rnhx,self.__nz,self.__ny,self.__nx],dtype='float32')\n else:\n # Create axes\n self.__rnhx = nhx+1; self.__ohx = 0; self.__dhx = self.__dx\n self.__rnhy = nhy+1; self.__ohy = 0; self.__dhy = self.__dy\n # Allocate image array\n imgar = np.zeros([self.__nexp,self.__rnhy,self.__rnhx,self.__nz,self.__ny,self.__nx],dtype='float32')\n # Allocate memory necessary for extension\n ssf.set_ext(nhy,nhx,sym)\n\n # Allocate the source for one shot\n sou = np.zeros([self.__nwc,self.__ny,self.__nx],dtype='complex64')\n\n # Loop over sources\n k = 0\n for icrd in progressbar(self.__scoords,\"nexp:\",verb=sverb):\n # Get the source coordinates\n sy = icrd[0]; sx = icrd[1]\n # Create the source for this shot\n sou[:] = 0.0\n sou[:,sy,sx] = wfftd[:]\n if(nhx == 0 and nhy == 0):\n # Conventional imaging\n ssf.migallw(datw[k],sou,imgar[k],wverb)\n else:\n # Extended imaging\n ssf.migoffallw(datw[k],sou,imgar[k],wverb)\n k += 1\n\n # Sum over all partial images\n img = np.sum(imgar,axis=0)\n\n # Free memory for extension\n if(nhx != 0 or nhy != 0):\n ssf.del_ext()\n\n return img","title":""},{"docid":"1a37546017a1588dcac3258f95e5b83f","score":"0.5502482","text":"def run(self):\n # Creation of the spectral shape\n norm = 1 * u.Unit('cm-2 s-1 TeV-1')\n index = self.slope\n ref = 1 * u.TeV\n model = PowerLaw(index=index, amplitude=norm, reference=ref)\n\n # Get the bins in reconstructed energy\n reco_energy = self.irf.bkg.energy\n\n # Start the computation\n bkg_counts = self.get_bkg(self.irf.bkg)\n if self.random < 1:\n excess_counts = self.get_excess(bkg_counts)\n else:\n ex = self.get_excess(np.random.poisson(bkg_counts))\n for ii in range(self.random - 1):\n ex += self.get_excess(np.random.poisson(bkg_counts))\n excess_counts = ex / float(self.random)\n\n phi_0 = self.get_1TeV_differential_flux(excess_counts, model, self.irf.aeff, self.irf.rmf)\n energy = reco_energy.log_center()\n dnde_model = model.evaluate(energy=energy, index=index, amplitude=1, reference=ref)\n diff_flux = (phi_0 * dnde_model * energy ** 2).to('erg / (cm2 s)')\n\n self.energy = reco_energy.log_center()\n self.diff_sens = diff_flux","title":""},{"docid":"9acda5d47b6563c544ab6cc2718d9772","score":"0.55020845","text":"def main(datadir, outfile=\"wave.mp4\"):\n files = os.listdir(datadir)\n nframe = len(files)\n tframe = np.loadtxt(\"%s/data0\" % datadir)\n m = tframe.shape[0]\n X, Y = np.meshgrid(np.linspace(-1, 1, m), np.linspace(-1, 1, m), indexing='ij')\n fig = plt.figure(figsize=(10,10))\n\n def plot_frame(filename):\n ax = fig.add_subplot(111, projection='3d')\n ax.set_zlim(0, 2)\n H = np.loadtxt(filename)\n ax.plot_surface(X, Y, H, color='w', rstride=5, cstride=5)\n return ax\n\n metadata = dict(title='Wave animation', artist='Matplotlib')\n if outfile[-4:] == \".mp4\":\n# Writer = manimation.writers['ffmpeg']\n Writer = manimation.writers['avconv']\n writer = Writer(fps=15, metadata=metadata,\n extra_args=[\"-r\", \"30\",\n \"-c:v\", \"libx264\",\n \"-pix_fmt\", \"yuv420p\"])\n elif outfile[-4:] == \".gif\":\n Writer = manimation.writers['imagemagick']\n writer = Writer(fps=15, metadata=metadata)\n\n with writer.saving(fig, outfile, nframe):\n for i in range(0, nframe):\n print \"plot frame\", i\n ax = plot_frame(\"%s/data%d\" % (datadir, i))\n writer.grab_frame()\n plt.delaxes(ax)","title":""},{"docid":"389ae06c10000e5352d3b48196c396aa","score":"0.5486174","text":"def wf_analysis(x):\n # Get the \"raw\" spectral power\n z2 = wf.spacetime_power(x, segsize=96, noverlap=96-30, spd=1, latitude_bounds=(-15,15), dosymmetries=True, rmvLowFrq=True)\n z2avg = z2.mean(dim='component')\n z2.loc[{'frequency':0}] = np.nan # get rid of spurious power at \\nu = 0\n # the background is supposed to be derived from both symmetric & antisymmetric\n background = wf.smooth_wavefreq(z2avg, kern=wf.simple_smooth_kernel(), nsmooth=50, freq_name='frequency')\n # separate components\n z2_sym = z2[0,...]\n z2_asy = z2[1,...]\n # normalize\n nspec_sym = z2_sym / background \n nspec_asy = z2_asy / background\n return nspec_sy, nspec_asy","title":""},{"docid":"5632fbeb446de88255f887d9a6689065","score":"0.5462022","text":"def run(skillfile, itemfile, datafile):\n\tanalysis = Analyser(skillfile, itemfile, datafile)\n\n\tprint(\"Welcome to MATMAN, an analyser of the MatMat.cz dataset.\")\n\tprint(\"Extracting the data...\")\n\n\tanalysis.build()\n\tanalysis.run()\n\n\tcount_entries = analysis.get_num_entries()\n\tvalidity = 1 - round((count_entries['invalid'] + count_entries['fake']) / count_entries['all'], 2)\n\tprint(\"Number of all entries:\", count_entries['all'], \"| Invalid entries:\", count_entries['invalid'], \"| Fake entries:\", count_entries['fake'], \"(Valid samples make up:\", validity*100, \"%)\")\n\n\tprint(\"Proceeding with the analysis...\")\n\n\tstudents = analysis.analyse_students()\n\n\tprint(\"Average response time:\", round(students[0], 2), \"| Average success rate:\", round(students[1], 2), \"| Average error:\", round(students[2], 2))\n\tprint(\"Success rate standard deviation:\", round(students[3], 2))\n\tprint(\"Number of students:\", len(analysis.get_students()))\n\tprint(\"Estimated number of fakers:\", students[4], \"| Under average perf.:\", students[5], \"| Over average perf.:\", students[6])\n\n\titems_diff = analysis.analyse_items()\n\tskills_diff = analysis.analyse_skills()\n\n\t# Plot the results\n\tplotter = Plotter(analysis)\n\tplotter.run()\n\n\tprint(\"Done. You can see the results in the project's root folder.\")","title":""},{"docid":"ce69cfd4c2abdc854d0b0d70ba329c5d","score":"0.54612726","text":"def getTrainSamples(\n self, layout=..., compressSamples=..., compressVars=...\n ) -> retval:\n ...","title":""},{"docid":"b22fa8b597d03e5bd01517fc8365e4d2","score":"0.5457275","text":"def extract_xy(self, audio):\n sample_rate, signal = wavfile.read(str(self.data_folder / audio))\n speaker_id = self.mapping[audio]\n # Frame configuration\n frame_size = 0.025\n frame_stride = 0.01\n # Convert from seconds to samples\n frame_length = int(round(frame_size * sample_rate))\n frame_step = int(round(frame_stride * sample_rate))\n num_frames = int(np.ceil(float(np.abs(signal_length - \\\n frame_length)) / frame_step))\n\n indices = np.tile(np.arange(0, frame_length),\n (num_frames, 1)) + np.tile(np.arange(0,\n num_frames * frame_step,\n frame_step),\n (frame_length, 1)).T\n frames = signal[indices.astype(np.int32, copy=False)]\n\n frames *= np.hamming(frame_length)\n \n NFFT = 512\n # Magnitude of the FFT\n frame_magnitude = np.absolute(np.fft.rfft(frames, NFFT))\n # Power Spectrum\n frame_power = ((1.0 / NFFT) * ((frame_magnitude) ** 2))\n\n num_filters = 40\n mel_low_freq = 0\n # Convert Hz to Mel\n mel_high_freq = (2595 * np.log10(1 + (sample_rate / 2) / 700))\n # Equally spaced in Mel scale\n mel_points = np.linspace(mel_low_freq, mel_high_freq, num_filters + 2)\n # Convert Mel to Hz\n hz_points = (700 * (10**(mel_points / 2595) - 1))\n bins = np.floor((NFFT + 1) * hz_points / sample_rate)\n\n filter_bank = np.zeros((num_filters, int(np.floor(NFFT / 2 + 1))))\n\n for i in range(1, num_filters + 1):\n filter_left = int(bins[i - 1]) # Left\n filter_i = int(bins[i]) # Center\n filter_right = int(bins[i + 1]) # Right\n\n for j in range(filter_left, filter_i):\n filter_bank[i - 1, j] = (j - bins[i - 1]) / (bins[i] - bins[i - 1])\n for j in range(filter_left, filter_right):\n filter_bank[i - 1, j] = (bins[i + 1] - j) / (bins[i + 1] - bins[i])\n\n filter_banks = np.dot(frame_power, filter_bank.T)\n # Numerical Stability\n filter_banks = np.where(filter_banks == 0, np.finfo(float).eps,\n filter_banks)\n # dB\n filter_banks = 20 * np.log10(filter_banks)\n\n if self.ftype == 'fbank':\n return filter_banks, speaker_id * (filter_banks.size)\n else:\n num_ceps = 12\n mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)] # Keep 2-13\n mfcc = mfcc.flatten()\n return mfcc, speaker_id * (filter_banks.size)","title":""},{"docid":"ad694897c7295c7207964146a5155388","score":"0.5445063","text":"def run(self):\n # Set Options and get parameters\n # Chops to exclude from the beginning of the file\n skip_start = self.getarg('skip_start')\n # Chops to exclude from the end of the file\n skip_end = self.getarg('skip_end')\n # Raw data threshold for dead pixles\n bad_dead = self.getarg('bad_dead')\n # Raw data threshold for ramping pixels. Set ramping as low as\n # possible, but if < 5e5 you may start to lose some good pixels\n bad_ramping = self.getarg('bad_ramping')\n # threshold for HIGH STD of DMD SIGNAL to exclude pixels\n normstd = self.getarg('normstd')\n # threshold to elliminate pixels with LOW SIGNAL\n ynormlowlim = self.getarg('ynormlowlim')\n # threshold for pixels with HIGH NORMALIZED SIGNAL\n ynormhighlim = self.getarg('ynormhighlim')\n # Scale factor for T/R flatfield\n t_to_r = self.getarg('TtoR')\n # option to skip flat generation entirely and make the INTCAL\n # product only\n skip_flat = self.getarg('dcl_only')\n\n # Loop through input datasets\n self.dataout = []\n self.auxout = []\n for pd in self.datain:\n # Store R/T Real/Imag in indata\n end = len(pd.table) - skip_end\n signals = ['R array', 'T array', 'R array Imag', 'T array Imag']\n indata = np.zeros((4, end - skip_start, 41, 64))\n invar = np.zeros((4, end - skip_start, 41, 64))\n for i in range(4):\n indata[i] = pd.table[signals[i]][skip_start:end, :, :]\n invar[i] = pd.table[signals[i] + ' VAR'][skip_start:end, :, :]\n\n # Store R/T Raw averages in rawavg\n rawavg = np.zeros((2, end - skip_start, 41, 64))\n signals = ['R array AVG', 'T array AVG']\n for i in [0, 1]:\n rawavg[i] = pd.table[signals[i]][skip_start:end, :, :]\n\n # Make raw stdevs\n # 2, 41, 64 array\n rawstd = np.nanstd(rawavg, axis=1)\n\n # Set Dead / Ramping pixels as NaN in indata\n # This next section finds and masks dead and\n # ramping pixels, so they won't bias the medians\n # calculated below. This may be redundant if they have\n # already been eliminated by the pipeline in StepNoah.\n # If so, it won't hurt to do it again, and it\n # allows tightening the criteria beyond those\n # set in StepNoah, if desired.\n nbad = np.ones((4, 41, 64))\n\n # Identify bad and ramping pixels by looking low and high rawstd\n # Dead pixels\n nbad[np.where(rawstd < bad_dead)] = np.NaN\n # Ramping pixels\n nbad[np.where(rawstd > bad_ramping)] = np.NaN\n\n # Mark all bad pixels with NaN in indata\n # This sets the T1 array signal values to nan.\n nbad[1, :, 32:64] = np.NaN\n nbad.shape = (4, 1, 41, 64)\n # multiply a (4, n, 41, 64) array by (4, 1, 41, 64) array\n indata *= nbad\n invar *= nbad\n\n # Calculate Modulus, phase and the medians for R and T array\n\n # initialize arrays\n modulus = np.zeros((2, indata.shape[1], 41, 64))\n phase = np.zeros((2, indata.shape[1], 41, 64))\n modvar = np.zeros((2, invar.shape[1], 41, 64))\n # phasevar = np.zeros((2, invar.shape[1], 41, 64))\n\n # Loop over R and T array\n for i in range(2):\n rld = indata[i]\n imd = indata[i + 2]\n rlv = invar[i]\n imv = invar[i + 2]\n\n # m = sqrt(r^2 + i^2)\n # Vm = (1/m^2)(r^2 Vr + i^2 Vi + 2 r i sqrt(Vr Vi))\n msq = (rld**2 + imd**2)\n modulus[i] = np.sqrt(msq)\n modvar[i] = (1 / msq) * (rld**2 * rlv\n + imd**2 * imv\n + 2 * rld * imd * np.sqrt(rlv * imv))\n\n # p = arctan(i / r)\n # Vp = (r^2 + i^2)^(-2) * (i^2 Vr + Vi - 2 sqrt(Vr Vi))\n # (phase variance not needed at this time)\n phase[i] = np.arctan(imd / rld)\n # phasevar[i] = (rld**2 + imd**2)^(-2) * \\\n # (imd**2 * rlv + imv - 2 * np.sqrt(rlv * imv))\n\n modmap = np.nanmedian(modulus, axis=1)\n phasemap = np.nanmedian(phase, axis=1)\n\n # variance of median is approximately pi/2 * variance of mean\n with np.errstate(invalid='ignore'):\n modmapvar = (np.pi / 2.) \\\n * (np.nansum(modvar, axis=1)\n / np.count_nonzero(~np.isnan(modulus), axis=1) ** 2)\n\n # Generate temporary subarrays to iterate over data\n subs = np.zeros((3, 41, 32))\n subs[0] = modmap[0, :, 0:32]\n subs[1] = modmap[0, :, 32:64]\n subs[2] = modmap[1, :, 0:32]\n\n # Set limits then iterate over them\n lowlim = (np.nanmedian(modmap[0, :, 0:32]) / 5,\n np.nanmedian(modmap[1, :, 0:32]) / 5,\n np.nanmedian(modmap[0, :, 32:64]) / 5)\n highlim = (200000., 200000., 200000.)\n outs = []\n for i in range(4):\n # Operates on the real parts of the signals\n outs = self._histogram3d(subs, lowlim, highlim)\n lowlim = outs[0] / 2\n highlim = 2 * outs[0]\n ymeds = outs[0]\n\n # Normalized modulus map and its stdev -> ynorm, ynormstd\n\n # Make normalized signal maps\n # Alias modmap to ymean to make it easier to re-use\n # code for making flats\n ymean = modmap\n ynorm = np.zeros((2, 41, 64))\n ynorm[0, :, 0:32] = ymean[0, :, 0:32] / ymeds[0]\n ynorm[0, :, 32:64] = ymean[0, :, 32:64] / ymeds[1]\n ynorm[1, :, 0:32] = ymean[1, :, 0:32] / ymeds[2]\n ynorm[1, :, 32:64] = np.NaN\n\n # Make normalized std maps\n ystd = np.nanstd(modulus, axis=1)\n ynormstd = np.zeros((2, 41, 64))\n ynormstd[0, :, 0:32] = ystd[0, :, 0:32] / ymean[0, :, 0:32]\n ynormstd[0, :, 32:64] = ystd[0, :, 32:64] / ymean[0, :, 32:64]\n ynormstd[1, :, 0:32] = ystd[1, :, 0:32] / ymean[1, :, 0:32]\n\n # Create bad pixel map and flatfield\n # using various selection criteria\n\n # Badstack datacube holds information about\n # which bad-pixel criteria have been detected.\n badstack = np.zeros((6, 2, 41, 64))\n\n # Badstack will be flattened into badsum\n # below (by addition). In badsum, bits in\n # a binary number indicate which criteria\n # have been triggered.\n\n # Criteria (1/1) This criterion identifies DEAD\n # pixels by looking for those with essentially zero rawstd.\n ybad = np.zeros((2, 41, 64))\n # dead has already been set above in section creating nbad.\n with np.errstate(invalid='ignore'):\n ybad[np.where(rawstd < bad_dead)] = 1\n badstack[0] = ybad.copy()\n\n # Criteria (2/2) This criterion identifies RAMPING\n # pixels by looking for very high rawstd\n ybad = np.zeros((2, 41, 64))\n # ramping has already been set above in section creating nbad.\n ybad[np.where(rawstd > bad_ramping)] = 2\n badstack[1] = ybad.copy()\n\n # Criteria (3/4) This criterion looks for very HIGH STD\n # of the demodulated signal.\n # May not be completely redundant with lower limit\n # on rawstd above. Need to check this with more data.\n ybad = np.zeros((2, 41, 64))\n with np.errstate(invalid='ignore'):\n ybad[np.where(ynormstd > normstd)] = 4\n badstack[2] = ybad.copy()\n\n # One could define a criterion that looks for low rawstd\n # not to identify dead pixels but to identify pixels\n # with very low signal\n # (e.g., those on the \"normal\" resistance part of the IV curve).\n # For example, Rbad[where(rstd < 1e1)]=xx. However this\n # may be redundant with rnorm < 0.x below. May want to take\n # a closer look at this later.\n\n # Criteria (4/8) This criterion attempts to identify\n # and eliminate pixels with very LOW SIGNAL\n # (e.g., those on the \"normal\" part of the IV curve).\n ybad = np.zeros((2, 41, 64))\n with np.errstate(invalid='ignore'):\n ybad[0, :, 0:32][np.where(ynorm[0, :, 0:32]\n < ynormlowlim[0])] = 8\n ybad[0, :, 32:64][np.where(ynorm[0, :, 32:64]\n < ynormlowlim[1])] = 8\n ybad[1, :, 0:32][np.where(ynorm[1, :, 0:32]\n < ynormlowlim[2])] = 8\n badstack[3] = ybad.copy()\n\n # Criteria (5/16) This criterion identifies pixels\n # with very HIGH NORMALIZED SIGNAL.\n # Need to check whether this might eliminate some \"good\" pixels.\n # Might not be needed. Might be redundant with\n # (and more prone to eliminate \"good\" pixels than)\n # the criterion on ynormstd above.\n ybad = np.zeros((2, 41, 64))\n with np.errstate(invalid='ignore'):\n ybad[0, :, 0:32][np.where(ynorm[0, :, 0:32]\n > ynormhighlim[0])] = 16\n ybad[0, :, 32:64][np.where(ynorm[0, :, 32:64]\n > ynormhighlim[1])] = 16\n ybad[1, :, 0:32][np.where(ynorm[1, :, 0:32]\n > ynormhighlim[2])] = 16\n badstack[4] = ybad.copy()\n\n # Criteria (6/32) This one ELIMINATES ROW 40\n # (the row with multiplexer but no active IR pixels).\n # This one may not be needed if the bad pixel mask ingested\n # at the beginning of this algorithm has already set\n # these rows to \"bad\". On the other hand, this doesn't\n # take long to do here, so we could leave it\n # for now, just in case we change approaches later.\n ybad = np.zeros((2, 41, 64))\n ybad[:, 40, :] = 32\n badstack[5] = ybad.copy()\n\n # Create bad pixel map (value labels how the pixel is bad)\n # This step combines all the bad pixel identifications\n # into a single map with binary values for which the bits\n # each indicate that a particular selection criterion\n # has been triggered.\n badsum = np.sum(badstack, axis=0)\n\n # Create bad pixel mask (with nans) to apply\n # to flatfields. Add this to the target map\n # to mask the bad pixels.\n bad = np.zeros((2, 41, 64))\n with np.errstate(invalid='ignore'):\n bad[np.where(badsum != 0)] = np.NaN\n\n # Make flatfields (Rcal, Tcal) and medians array\n\n # Make DCAL type flat fields, based on time-averaged\n # modulus (bad pixels set to NaN)\n rcal = 10000. / ymean[0] + bad[0]\n tcal = 10000. * t_to_r / ymean[1] + bad[1]\n\n # Propagate variance\n rvar = (10000. / (ymean[0]**2))**2 * modmapvar[0] + bad[0]\n tvar = (10000. * t_to_r / (ymean[1]**2))**2 * modmapvar[1] + bad[1]\n\n # Make an array to hold information on the\n # median intensities and masked map medians of\n # normalized std's, rawavg, and rawavg std's.\n medians = np.zeros((4, 3))\n medians[0] = ymeds\n\n ynormstdm = ynormstd + bad\n ynormstdmmed = np.zeros(3)\n ynormstdmmed[0] = np.nanmedian(ynormstdm[0, :, 0:32])\n ynormstdmmed[1] = np.nanmedian(ynormstdm[0, :, 32:64])\n ynormstdmmed[2] = np.nanmedian(ynormstdm[1, :, 0:32])\n medians[1] = ynormstdmmed\n\n rawmedianm = np.nanmedian(rawavg, axis=1) + bad\n rawmedianmmed = np.zeros(3)\n rawmedianmmed[0] = np.nanmedian(rawmedianm[0, :, 0:32])\n rawmedianmmed[1] = np.nanmedian(rawmedianm[0, :, 32:64])\n rawmedianmmed[2] = np.nanmedian(rawmedianm[1, :, 0:32])\n medians[2] = rawmedianmmed\n\n rawstdm = rawstd + bad\n rawstdmmed = np.zeros(3)\n rawstdmmed[0] = np.nanmedian(rawstdm[0, :, 0:32])\n rawstdmmed[1] = np.nanmedian(rawstdm[0, :, 32:64])\n rawstdmmed[2] = np.nanmedian(rawstdm[1, :, 0:32])\n medians[3] = rawstdmmed\n\n # Save to output object.\n\n # This will save a DCAL named after input file\n # with 'DCAL' in file-type identifier field.\n # Rcal and Tcal correct detector gains to the\n # values that would give a signal of 10000 ADU\n # when exposed to the IR-50 internal calibrator source.\n # The factor TorR is a first-order correction for the\n # difference in the IR-50 illumination of the R and T\n # detectors. Higher-order corrections for the\n # detector to detector differences when exposed to\n # radiation from the sky must be derived from direct\n # measurements of astronomical sources.\n # t_to_r = 2.0 is a first-order guess based on\n # limited observations of astronomical source\n # signals. It could be refined by additional\n # observations, or the remaining differences could\n # be subsumed in the intcal-to-source-flat\n # transformation coefficients. The number 10000\n # was chosen to make the median R0 subarray signal\n # approximately equal to what it would be if no\n # corrections were applied (based on observations\n # with \"typical\" biases used in commissioning flights).\n\n # Get Configuration image from DMD file\n imgconfig = pd.imageget('CONFIGURATION')\n\n # Convert phases to time delay\n chopfreq = pd.getheadval('CHPFREQ')\n rphase = phasemap[0] * (-1 / (2 * np.pi * chopfreq)) + bad[0]\n tphase = phasemap[1] * (-1 / (2 * np.pi * chopfreq)) + bad[1]\n\n # Shift the phases to center around zero\n rphase = rphase - (-0.055)\n tphase = tphase - (-0.055)\n\n # Set NaN pixels to median\n # (not used at this time)\n\n # R0medianm = np.nanmedian(rphase[:, 0:32])\n # R1medianm = np.nanmedian(rphase[:, 32:64])\n # T0medianm = np.nanmedian(tphase[:, 0:32])\n # nanR0 = np.where(np.isnan(rphase[:, 0:32]))\n # nanT = np.where(np.isnan(tphase))\n # rphase[nanR0]=R0medianm\n # tphase[nanT]=T0medianm\n # nanR1 = np.where(np.isnan(rphase))\n # rphase[nanR1] = R1medianm\n\n # Make and fill output data\n outd = DataFits(config=pd.config)\n outd.filename = pd.filename\n outd.imageset(rcal, 'R ARRAY GAIN')\n outd.imageset(tcal, 'T ARRAY GAIN')\n outd.imageset(rvar, 'R ARRAY GAIN VAR')\n outd.imageset(tvar, 'T ARRAY GAIN VAR')\n outd.imageset(badsum[0], 'R BAD PIXEL MASK')\n outd.imageset(badsum[1], 'T BAD PIXEL MASK')\n outd.imageset(medians, 'SUBARRAY MEDIANS')\n outd.imageset(imgconfig, 'CONFIGURATION')\n outd.imageset(rphase, 'RPHASE')\n outd.imageset(tphase, 'TPHASE')\n outd.imageset(rawmedianm[0], 'R RAWAVG')\n outd.imageset(rawmedianm[1], 'T RAWAVG')\n outd.header = pd.header\n\n # Write the configuration header from pd into\n # the corresponding HDU in outd.\n outd.imgheads[5] = pd.imgheads[1]\n\n # Append to dataout\n self.dataout.append(outd)\n\n # END of loop over input datasets\n\n # Make groups of output files -> datagroups\n groupkey = self.getarg('groupkey')\n datagroups = []\n\n # Make output folder if it doesn't exist\n outfolder = self.getarg('flatoutfolder')\n if len(outfolder) > 0:\n if not os.path.isabs(outfolder):\n # if outfolder is not an absolute path, make it\n # relative to the output data location\n outfolder = os.path.join(\n os.path.dirname(self.dataout[0].filename),\n outfolder)\n try:\n os.makedirs(outfolder)\n except OSError:\n if not os.path.isdir(outfolder):\n log.error('Run: Failed to make flatoutfolder = %s' %\n outfolder)\n raise\n\n # if desired, just save DCL files to flats folder and quit\n if skip_flat:\n for data in self.dataout:\n dclname = data.filenamebegin + 'DCL' + data.filenameend\n if len(outfolder) > 0:\n dclname = os.path.join(outfolder,\n os.path.basename(dclname))\n data.save(dclname)\n self.auxout.append(dclname)\n log.info('Saved result %s' % dclname)\n self.dataout = []\n return\n\n # Loop over datasets in datain\n for data in self.dataout:\n groupind = 0\n # Loop over groups until group match found or end reached\n while groupind < len(datagroups):\n # Check if data fits group: Get first group element\n gdata = datagroups[groupind][0]\n\n # Get key from group and new data - format if needed\n dkey = data.getheadval(groupkey)\n gkey = gdata.getheadval(groupkey)\n\n # Match -> add to group\n if dkey == gkey:\n datagroups[groupind].append(data)\n break\n\n # Not found -> increase group index\n groupind += 1\n # If not in any group -> make new group\n if groupind == len(datagroups):\n datagroups.append([data, ])\n\n # info messages\n log.debug(\" Found %d data groups\" % len(datagroups))\n for groupind in range(len(datagroups)):\n group = datagroups[groupind]\n msg = \" Group %d len=%d\" % (groupind, len(group))\n msg += \" %s = %s\" % (groupkey, group[0].getheadval(groupkey))\n log.debug(msg)\n\n # Make and save groupflats\n\n # loop over datagroups\n for groupind in range(len(datagroups)):\n data0 = datagroups[groupind][0]\n\n # Load masterflat for this datagroup -> mflat\n mflat = self.loadauxfile(data=data0)\n\n # Average intcal R/T Array Gains\n filenum = []\n rlist = []\n tlist = []\n rvlist = []\n tvlist = []\n for data in datagroups[groupind]:\n rg = data.imageget('R ARRAY GAIN')\n if np.any(~np.isnan(rg)):\n rlist.append(rg)\n rvlist.append(data.imageget('R ARRAY GAIN VAR'))\n found_r = True\n else:\n found_r = False\n\n tg = data.imageget('T ARRAY GAIN')\n if np.any(~np.isnan(tg)):\n tlist.append(tg)\n tvlist.append(data.imageget('T ARRAY GAIN VAR'))\n found_t = True\n else:\n found_t = False\n\n if found_r or found_t:\n if data.filenum is not None:\n filenum.extend(data.filenum.split('-'))\n else:\n log.warning('Excluding bad file %s' % data.filename)\n\n if not rlist or not tlist:\n msg = 'No good flat files found.'\n log.error(msg)\n raise ValueError(msg)\n\n rgainavg = np.nanmean(np.array(rlist), axis=0)\n tgainavg = np.nanmean(np.array(tlist), axis=0)\n rgainvar = np.nansum(np.array(rvlist), axis=0) / len(rvlist) ** 2\n tgainvar = np.nansum(np.array(tvlist), axis=0) / len(tvlist) ** 2\n\n # Multiply masterflat -> groupflat\n gflatr = mflat.imageget('R ARRAY GAIN') * rgainavg\n gflatt = mflat.imageget('T ARRAY GAIN') * tgainavg\n gflatrvar = (mflat.imageget('R ARRAY GAIN'))**2 * rgainvar\n gflattvar = (mflat.imageget('T ARRAY GAIN'))**2 * tgainvar\n\n # Groupflat bad pixel mask: see where there are NaNs\n gbadr = np.zeros(gflatr.shape)\n gbadt = np.zeros(gflatt.shape)\n gbadr[np.where(np.isnan(gflatr))] = 1.0\n gbadt[np.where(np.isnan(gflatt))] = 2.0\n\n # Make groupflat filename: Filename of\n # first file with OBSFLAT (OFT)\n gflatname = data0.filenamebegin + 'OFT' + data0.filenameend\n\n # Split file, make sure outfolder is valid\n if len(outfolder) > 0:\n gflatname = os.path.split(gflatname)[1]\n else:\n outfolder, gflatname = os.path.split(gflatname)\n\n # Update file number to be a range\n if len(filenum) > 1:\n fn = sorted(filenum)\n filenums = fn[0] + '-' + fn[-1]\n match = re.search(self.config['data']['filenum'], gflatname)\n if match is not None:\n # regex may contain multiple possible matches --\n # for middle or end of filename\n for i, g in enumerate(match.groups()):\n if g is not None:\n gbegin = gflatname[:match.start(i + 1)]\n gend = gflatname[match.end(i + 1):]\n gflatname = gbegin + filenums + gend\n break\n gflatname = os.path.join(outfolder, gflatname)\n\n # Make groupflat data\n gflat = DataFits(config=self.datain[0].config)\n gflat.header = data0.header.copy()\n\n # Make header\n for data in datagroups[groupind][1:]:\n gflat.mergehead(data)\n for data in datagroups[groupind]:\n gflat.setheadval('PRODTYPE', 'obsflat')\n gflat.setheadval('PROCSTAT', 'LEVEL_2')\n gflat.setheadval('HISTORY', 'MakeFlat Source: %s' %\n (os.path.split(data.filename)[1], ))\n # Fill data\n gflat.imageset(gflatr, 'R ARRAY GAIN')\n gflat.imageset(gflatt, 'T ARRAY GAIN')\n gflat.imageset(gflatrvar, 'R ARRAY GAIN VAR')\n gflat.imageset(gflattvar, 'T ARRAY GAIN VAR')\n gflat.imageset(gbadr, 'R BAD PIXEL MASK')\n gflat.imageset(gbadt, 'T BAD PIXEL MASK')\n\n # Save groupflat data\n gflat.save(gflatname)\n self.auxout.append(gflatname)\n log.info('Saved result %s' % gflatname)","title":""},{"docid":"ef3b90c4d99edad1dbd72942e1691244","score":"0.5434679","text":"def signal_extract(func_data=None,confounds=None,atlas_img=None,masker_type='Spheres',smoothing_fwhm=6,high_pass=0.01,low_pass=0.1,t_r=2.2,detrend=False,saveas='file'):\r\n subjects_ts=[]\r\n \r\n if os.path.exists(saveas):\r\n \r\n subjects_ts=np.load(saveas)['arr_0']\r\n \r\n else:\r\n \r\n if \r\n \r\n if masker_type== 'Spheres':\r\n masker = NiftiSpheresMasker(\r\n seeds=atlas_img, smoothing_fwhm=smoothing_fwhm, radius=4 ,mask_img=brainmask,\r\n detrend=False, standardize=True, low_pass=low_pass, high_pass=high_pass, t_r=t_r\r\n )\r\n elif masker_type == 'Maps':\r\n masker = NiftiMapsMasker(\r\n maps_img=atlas_img,mask_img=brainmask,standardize=True,\r\n low_pass=low_pass, high_pass=high_pass, t_r=t_r,\r\n memory_level=2,smoothing_fwhm=smoothing_fwhm,resampling_target='data',\r\n memory=mem,verbose=5\r\n )\r\n elif masker_type == 'Labels':\r\n masker = NiftiLabelsMasker(\r\n labels_img=atlas_img,mask_img=brainmask,standardize=True,\r\n high_pass=high_pass,low_pass=low_pass,detrend=False,t_r=t_r,\r\n memory_level=2,smoothing_fwhm=smoothing_fwhm,resampling_target='data',\r\n memory=mem,verbose=5\r\n )\r\n \r\n else:\r\n raise ValueError(\"Please provide masker type\")\r\n \r\n if confounds is not None: \r\n for func_file, confound_file in zip(func_data,confounds):\r\n time_series = masker.fit_transform(func_file,confounds=confound_file)\r\n subjects_ts.append(time_series)\r\n np.savez(saveas,subjects_ts)\r\n else:\r\n for func_file in data:\r\n time_series = masker.fit_transform(func_file)\r\n subjects_ts.append(time_series)\r\n np.savez(saveas,subjects_ts) \r\n \r\n return subjects_ts","title":""},{"docid":"1989216b52a61e6a040f72ec26270137","score":"0.54088956","text":"def process(self):\n\n d = self.dataset\n self.outputs[0].frequency = d['TWC_DET'].frequency\n self.outputs[1].frequency = d['TWC_DET'].frequency\n\n tfull = d['TWC_DET'].data.copy()\n tfullx = tfull.times2d.ravel()\n\n sh = tfull.shape\n vf = tfull.flag\n\n F = 0.93\n Kv = 427.0\n p0 = 1013.2\n uO = 0.2095\n\n if len(d[self.fit].data) == 2:\n\n fit = np.array(d[self.fit].data)\n print('Applying FIT={}'.format(fit))\n\n ans = np.polyval(fit, tfull)\n\n px = d['PS_RVSM'].data.ravel()\n p1 = px.interp(times=tfullx).reshape(sh)\n\n tx = d['TWC_TSAM'].data.ravel()\n t2 = tx.interp(times=tfullx).reshape(sh)\n\n KO = 0.304 + 0.351 * p1 * F / p0\n vpo = (ans - (KO * uO * p1 / (Kv * t2))) * t2\n vmro = vp2vmr(vpo, p1)\n mmr = vmr_mmr(vmro)\n dp = vp2dp(vpo.ravel()).reshape(sh)\n\n else:\n dp = np.zeros(sh)\n mmr = tfull\n vf[:] = 3\n\n self.outputs[0].data = flagged_data(dp, tfull.times, vf)\n self.outputs[1].data = flagged_data(mmr, tfull.times, vf)","title":""},{"docid":"11ffeeb24d926c9dde8dff10194d3090","score":"0.54030704","text":"def main():\n #print(\"le bhai data\")\n collect_data()","title":""},{"docid":"5b6185c2b11947ddd8b1d587b9d1b2fa","score":"0.54015166","text":"def signal_processing(da,\n varname = None,\n agg_to_year = False,\n add_global_warming_signal = False,\n apply_standard_scaler=True,\n demean_all_time=False,\n apply_area_weighting=True,\n normalise_by_year = False,\n normalise_by_rolling_year = False,\n normalise_by_month=True):\n\n if agg_to_year:\n da = aggregate_to_year(da, deviation = True)\n\n if add_global_warming_signal:\n da = da + np.linspace(0,1, da.shape[0])[:,np.newaxis, np.newaxis]\n\n if not agg_to_year:\n\n if normalise_by_year:\n da = deviation_from_year_means(da, normalise_by_rolling_year)\n\n if normalise_by_month:\n da = deviation_from_monthly_means(da)\n \n if demean_all_time:\n da = da - da.mean(dim='time')\n\n if apply_standard_scaler:\n #ss = StandardScaler() # probably overkill\n # decide here whether you will pre-normalise or not\n da = da - da.mean(dim='time')\n da = da/da.std(dim='time')\n\n if apply_area_weighting:\n coslat = np.abs(np.cos(np.deg2rad(da.latitude)))**0.5\n weights = coslat #/ coslat.sum(dim=('latitude', 'longitude'))\n da = da*weights\n \n if varname is not None:\n da.name = varname\n \n return da","title":""},{"docid":"8273806bc85b338e726a04070d6db74e","score":"0.54005605","text":"def filtering(wave,spec,filters):\n\n\tw = h5py.File(dir_file+'filters_w.hdf5', 'r')\n\tt = h5py.File(dir_file+'filters_t.hdf5', 'r')\n\n\tnbands = len(filters)\n\tfluxes = np.zeros(nbands)\n\tfor bb in range(0,nbands):\n\t\tfil_w = w[filters[bb]][:]\n\t\tfil_t = t[filters[bb]][:]\n\n\t\tmin_wave = int(min(fil_w))\n\t\tmax_wave = int(max(fil_w))\n\n\t\tgwave = np.linspace(min_wave,max_wave,max_wave-min_wave+1)\n\n\t\tfil_trans = np.interp(gwave, fil_w, fil_t)\n\t\tspec_flux = np.interp(gwave, wave, spec)\n\n\t\ttot_u = np.sum(spec_flux*gwave*fil_trans)\n\t\ttot_l = np.sum(gwave*fil_trans)\n\n\t\tfluxes[bb] = tot_u/tot_l\n\n\tw.close()\n\tt.close()\n\n\treturn fluxes","title":""},{"docid":"3a9d8962b4c22dbe9496f5e34689cd9d","score":"0.53904307","text":"def ba_events(data):\n data_2=calc_aiphil(data)\n data_all=vstack([data, array(data_2)])\n ddata=zeros((50,100,int(data_all[-1].max()*10)+1), dtype=float)\n for i, di in enumerate(data_all.T):\n if i%100==0:\n print(i)\n w=di[0]\n ai,phi,l=di[-3:]\n res=run_simulation(l, ai*180/pi, phi*180/pi).array()\n ddata[:,:,int(l*10)]+=w*res\n return ddata","title":""},{"docid":"7132925ac77afc085170603510bbe1a7","score":"0.538609","text":"def trace_extract(self, data, r=0, bin=0, ivar=1., dy0=0):\n dy = np.cast[int](np.round(self.ytrace+dy0))\n aper = np.zeros_like(self.model)\n y0 = self.sh_beam[0] // 2\n for d in range(-r, r+1):\n for i in range(self.sh_beam[1]):\n aper[y0+d+dy[i]-1, i] = 1\n\n var = 1./ivar\n if not np.isscalar(ivar):\n var[ivar == 0] = 0\n\n opt_flux = np.sum(data*aper, axis=0)\n opt_var = np.sum(var*aper, axis=0)\n\n if bin > 1:\n kern = np.ones(bin, dtype=float)/bin\n opt_flux = nd.convolve(opt_flux, kern)[bin // 2::bin]\n opt_var = nd.convolve(opt_var, kern**2)[bin // 2::bin]\n wave = self.lam[bin // 2::bin]\n else:\n wave = self.lam\n\n opt_rms = np.sqrt(opt_var)\n\n return wave, opt_flux, opt_rms","title":""},{"docid":"6a2f1ff85b7b3740a4e28c674264c170","score":"0.5382624","text":"def run_pls_selector(data, produce, wavelengths):\n\n # produce_spectras = utils.load_spectra(produce)\n # data = [spectra[0:2] for spectra in produce_spectras]\n select = PLSDASelector(data, wavelengths)\n\n wave, ind = select.find_wavelengths()\n return wave, ind","title":""},{"docid":"c3ddbb123983b4a85763862ec5729323","score":"0.5377325","text":"def data_loader(test=False, preemph=0.95, need_length=False):\r\n lendata = {\r\n 'name' : [],\r\n 'length' : []\r\n }\r\n\r\n\r\n ## Sub function : wav read & data shaping\r\n def wavloader(filename, length, name='wav', get_lendata=False):\r\n\r\n # Error\r\n num = len(filename)\r\n if num == 0:\r\n print('Dataset Error : no wave files.')\r\n\r\n i = 1\r\n filedata = []\r\n for filename_ in filename:\r\n file_ = wave.open(filename_, 'rb')\r\n wavdata = np.frombuffer(file_.readframes(-1), dtype='int16')\r\n if get_lendata:\r\n lendata['name'].append(filename_)\r\n lendata['length'].append(len(wavdata))\r\n filedata.append(wavdata)\r\n file_.close()\r\n print(' Loading {0} wav... #{1} / {2}'.format(name, i, num))\r\n i+=1\r\n\r\n filedata = np.concatenate(filedata, axis=0) # Serializing\r\n filedata = filedata - preemph * np.roll(filedata, 1) # Pre-enphasis\r\n filedata = filedata.astype(np.float32) # Data Compressing (float64 -> float32)\r\n L = length // 2 # Half of Input Size (init: 8192 samples)\r\n D = len(filedata) // L # No. of 0.5s blocks\r\n if len(filedata) % (D*L) != 0:\r\n fdata = []\r\n for f in filedata:\r\n fdata.append(f)\r\n zeros = np.zeros(shape=(len(filedata) - L*D), dtype=np.float32)\r\n for z in zeros:\r\n fdata.append(z)\r\n filedata = np.array(fdata, dtype=np.float32)\r\n filedata = filedata[:D * L].reshape(D, L) # Split data for each half of input size : (1,:) --> (D, 8192)\r\n return filedata\r\n\r\n\r\n\t# Load settings\r\n args = settings()\r\n\r\n # Make folder\r\n if not os.path.exists(args.model_save_path): # Folder of model\r\n os.makedirs(args.model_save_path)\r\n\r\n if not os.path.exists(args.wav_save_path): # Folder of model\r\n os.makedirs(args.wav_save_path)\r\n\r\n if not os.path.exists(args.train_pkl_path): # Folder of train pkl\r\n os.makedirs(args.train_pkl_path)\r\n\r\n if not os.path.exists(args.test_pkl_path): # Folder of test pkl\r\n os.makedirs(args.test_pkl_path)\r\n\r\n # File name\r\n if not test:\r\n wav_clean = args.clean_train_path + '/*.wav'\r\n wav_noisy = args.noisy_train_path + '/*.wav'\r\n pkl_clean = args.train_pkl_path + '/' + args.train_pkl_clean\r\n pkl_noisy = args.train_pkl_path + '/' + args.train_pkl_noisy\r\n else:\r\n wav_clean = args.clean_test_path + '/*.wav'\r\n wav_noisy = args.noisy_test_path + '/*.wav'\r\n pkl_clean = args.test_pkl_path + '/' + args.test_pkl_clean\r\n pkl_noisy = args.test_pkl_path + '/' + args.test_pkl_noisy\r\n pkl_length = args.test_pkl_path + '/' + args.test_pkl_length\r\n\r\n\r\n ## No pkl files -> read wav + create pkl files\r\n ## -------------------------------------------------\r\n if not (os.access(pkl_clean, os.F_OK) and os.access(pkl_noisy, os.F_OK)):\r\n\r\n ## Wav files\r\n print(' Load wav file...')\r\n\r\n\t # Get file path\r\n cname = glob.glob(wav_clean)\r\n nname = glob.glob(wav_noisy)\r\n\r\n # Get wave data\r\n cdata = wavloader(cname, args.len, name='clean', get_lendata=True) # Clean wav\r\n ndata = wavloader(nname, args.len, name='noisy') # Noisy wav\r\n\r\n ## Pkl files\r\n print(' Create Pkl file...')\r\n\r\n\t\t# Create clean pkl file\r\n with open(pkl_clean, 'wb') as f:\r\n joblib.dump(cdata, f, protocol=-1,compress=3)\r\n\r\n # Create noisy pkl file\r\n with open(pkl_noisy, 'wb') as f:\r\n joblib.dump(ndata, f, protocol=-1,compress=3)\r\n\r\n if test:\r\n if (not os.access(pkl_length, os.F_OK)):\r\n # Create length pkl file\r\n with open(pkl_length, 'wb') as f:\r\n joblib.dump(lendata, f, protocol=-1,compress=3)\r\n\r\n\t## Pkl files exist -> Load\r\n ## -------------------------------------------------\r\n else:\r\n # Load clean pkl file\r\n print(' Load Clean Pkl...')\r\n with open(pkl_clean, 'rb') as f:\r\n cdata = joblib.load(f)\r\n\r\n # Load noisy pkl file\r\n print(' Load Noisy Pkl...')\r\n with open(pkl_noisy, 'rb') as f:\r\n ndata = joblib.load(f)\r\n\r\n if test:\r\n # Load length pkl file\r\n print(' Load Noisy Pkl...')\r\n with open(pkl_length, 'rb') as f:\r\n lendata = joblib.load(f)\r\n\r\n if not test:\r\n return cdata, ndata\r\n else:\r\n return cdata, ndata, lendata","title":""},{"docid":"24a070460a1621564256aaac1cfd6c06","score":"0.5368832","text":"def run(met_data, prof_data, param_kwds=None, overwrite=True, diagnostics=False, suffix='', save_plots=False):\n \n #TODO: move this to the helper file\n \n #close all figures\n plt.close('all')\n \n #start timer\n t0 = timeit.default_timer()\n \n ## Get surface forcing and profile data \n # These are x-ray datasets, but you can treat them as dicts. \n # Do met_dset.keys() to explore the data fields\n met_dset = xray.open_dataset('input_data/%s' %met_data) \n prof_dset = xray.open_dataset('input_data/%s' %prof_data)\n \n ## get model parameters and constants (read docs for set_params function)\n lat = prof_dset['lat'] #needed to compute internal wave dissipation\n if param_kwds is None:\n params = phf.set_params(lat=lat) \n else:\n param_kwds['lat'] = lat\n params = phf.set_params(**param_kwds)\n \n ## prep forcing and initial profile data for model run (see prep_data function for more details)\n forcing, pwp_out, params = phf.prep_data(met_dset, prof_dset, params)\n \n ## run the model\n pwp_out = pwpgo(forcing, params, pwp_out, diagnostics)\n \n #check timer\n tnow = timeit.default_timer()\n t_elapsed = (tnow - t0) \n print(\"Time elapsed: %i minutes and %i seconds\" %(np.floor(t_elapsed/60), t_elapsed%60))\n \n ## write output to disk\n if overwrite:\n time_stamp = ''\n else:\n #use unique time stamp\n time_stamp = datetime.now().strftime(\"_%Y%m%d_%H%M\")\n \n if len(suffix)>0 and suffix[0] != '_':\n suffix = '_%s' %suffix\n \n # save output as netCDF file\n pwp_out_ds = xray.Dataset({'temp': (['z', 'time'], pwp_out['temp']), 'sal': (['z', 'time'], pwp_out['sal']), \n 'uvel': (['z', 'time'], pwp_out['uvel']), 'vvel': (['z', 'time'], pwp_out['vvel']),\n 'dens': (['z', 'time'], pwp_out['dens']), 'mld': (['time'], pwp_out['mld'])}, \n coords={'z': pwp_out['z'], 'time': pwp_out['time']})\n\n pwp_out_ds.to_netcdf(\"output/pwp_output%s%s.nc\" %(suffix, time_stamp))\n\n # also output and forcing as pickle file\n pickle.dump(forcing, open( \"output/forcing%s%s.p\" %(suffix, time_stamp), \"wb\" ))\n pickle.dump(pwp_out, open( \"output/pwp_out%s%s.p\" %(suffix, time_stamp), \"wb\" ))\n \n ## do analysis of the results\n phf.makeSomePlots(forcing, pwp_out, suffix=suffix, save_plots=save_plots)\n \n return forcing, pwp_out","title":""},{"docid":"75972f241192e742087c53fdd27e0b1d","score":"0.5366451","text":"def prepare_wave_data(fname):\n wh = iris.load_cube(fname, 'Significant height of combined wind waves and swell')\n wd = iris.load_cube(fname, 'Mean wave direction')\n wp = iris.load_cube(fname, 'Mean wave period')\n return wd, wh, wp","title":""},{"docid":"ad68ef2ae0d42ce3312b68989ea4b97f","score":"0.53548145","text":"def filters(array, sample_frequency):\n strain = TimeSeries(array, sample_rate=int(sample_frequency))\n white_data = strain.whiten(fftlength=4, fduration=4)\n bp_data = white_data.bandpass(50, 250)\n return bp_data.value","title":""},{"docid":"85c4756098fc291bbbd85bcd1bfc2b14","score":"0.5349286","text":"def quality_stats():\n datapath_mri = op.join('data', 'raw_eyegaze', 'sub-*', 'ses-movie', 'func',\n 'sub-*_ses-movie_task-movie_run-*_recording-eyegaze_physio.tsv.gz')\n datapath_lab = op.join('data', 'raw_eyegaze', 'sub-*', 'beh',\n 'sub-*_task-movie_run-*_recording-eyegaze_physio.tsv.gz')\n\n for (data, assoc) in [(datapath_lab, 'lab'),\n (datapath_mri, 'mri')]:\n infiles = glob(data)\n for f in infiles:\n datalad_get(f)\n # make sure we have 15 subjects' data\n assert len(infiles) == 120\n print(\"Currently processing data from {} sample\".format(assoc))\n # set sampling rate and px2deg\n px2deg = 0.0266711972026 if assoc == 'lab' else 0.0185581232561\n sr = 1000\n # calculate percent signal loss across subjects and runs\n losses = []\n vels = []\n for f in infiles:\n data = np.recfromcsv(f,\n delimiter='\\t',\n names=['x', 'y', 'pupil', 'frame'])\n # all periods of signal loss are marked as nan in the data\n signal_loss = np.sum(np.isnan(data['x'])) / len(data['x'])\n losses.append(signal_loss)\n velocities = cal_velocities(data=data,\n px2deg=px2deg,\n sr=sr)\n vels.append(velocities)\n print(\"Calculated velocities and losses for {} sample\".format(assoc))\n # average across signal losses in sample (mri or lab)\n loss = np.nanmean(losses)\n # print results as Latex command using 'assoc' as sample identifier in name\n label_loss = 'avgloss{}'.format(assoc)\n rsout('\\\\newcommand{\\\\%s}{%s}'\n % (label_loss, loss))\n # vels is a list of arrays atm\n v = np.concatenate(vels).ravel()\n if assoc == 'lab':\n v_lab = v\n elif assoc == 'mri':\n v_mri = v\n\n # plot velocities in a histogram on logscale\n # create non-linear non-equal bin sizes, as x axis will be log\n hist, bins, _ = plt.hist(v[~np.isnan(v)], bins=40)\n plt.close()\n logbins = np.logspace(1, # don't start with 0, does not make sense in logspace\n np.log10(bins[-1]),\n len(bins))\n fig, ax = plt.subplots()\n fig.set_figheight(3)\n fig.set_figwidth(5)\n ax.set_ylabel('frequency')\n ax.set_xlabel('velocities (deg/s)')\n plt.hist(v_mri[~np.isnan(v_mri)],\n weights=np.zeros_like(v_mri[~np.isnan(v_mri)]) + 1. / (v_mri[~np.isnan(v_mri)]).size,\n bins=logbins,\n histtype='bar',\n color='orangered',\n alpha=0.5,\n label='mri')\n plt.hist(v_lab[~np.isnan(v_lab)],\n weights=np.zeros_like(v_lab[~np.isnan(v_lab)]) + 1. / (v_lab[~np.isnan(v_lab)]).size,\n bins=logbins,\n histtype='bar',\n color='darkslategrey',\n alpha=0.5,\n label='lab')\n plt.legend(loc='upper right')\n plt.xscale('log')\n plt.savefig(op.join('img', 'velhist.svg'),\n transparent=True,\n bbox_inches=\"tight\",\n metadata={'Date': None})","title":""},{"docid":"32e11f21dd5a4e3cdd55f3512225bd33","score":"0.5347797","text":"def calculate_spectrum(obs_freqs,v0,radex_params,radex=True):\n\n\n\t#user supplies the observed frequency so doppler shift to emitted\n\t#tau dist makes this unnecessary\n\temit_freqs=obs_freqs*(1.0+v_0/light_speed)\n\t\n\t#we'll return a dataframe of Frequency, Intensity\n\tnew_df=DataFrame({\"Frequency\":obs_freqs})\n\tnew_df[\"Intensity\"]=0.0\n\t\n\t#solve the radex model and get all line properties\n\tif radex:\n\t\ttau_0_df=get_radex_taus(radex_params)\n\telse:\n\t\ttau_0_df=get_lte_taus(col_dens,gas_temp,delta_v)\n\t\t\t\t\t\t\t \n\t#now loop through line and build up the tau weighted radiation temperature average\n\tfor i,line in tau_0_df.iterrows():\n\t\t#get the relative velocity of all the emitting frequencies\n\t\tvelocities=((line[\"freq\"]/obs_freqs)-1.0)*light_speed\n\t\t\n\t\t#use that to get the tau values at those frequencies\n\t\ttaus=get_tau_dist(v_0,delta_v,line[\"tau\"],velocities)\n\t\t\n\t\t#store tau weighted radiation temp\n\t\tnew_df[f\"{line.freq:.3f}\"]=rad_temp(line[\"T_ex\"],emit_freqs)*taus\n\t\t\n\t\t#and add tau to running total\n\t\tnew_df[\"Intensity\"]+=taus\n\t\t\n\t\n\t#sum our tau weighted temperatures and divide by sum of taus\n\tline_cols=[x for x in new_df if x not in [\"Intensity\",\"Frequency\"]]\n\tnew_df[\"temp\"]=new_df[line_cols].sum(axis=1)/new_df[\"Intensity\"]\n\t#now get brightness temperature as a function of frequency\n\tnew_df[\"Intensity\"]=(new_df[\"temp\"]-rad_temp(2.73,emit_freqs))*(1.0-np.exp(-new_df[\"Intensity\"]))\n\tnew_df[\"Intensity\"]=new_df[\"Intensity\"].fillna(0.0)\n\treturn new_df[\"Intensity\"].values","title":""},{"docid":"6d73e9324c816981f4ae0c2601f921b8","score":"0.53431565","text":"def run(self):\n\n logging.info('Begin logging for wata_monitor')\n\n # define WATA variables\n self.instrument = \"nirspec\"\n self.aperture = \"NRS_S1600A1_SLIT\"\n\n # Identify which database tables to use\n self.identify_tables()\n\n # Get the output directory and setup a directory to store the data\n self.output_dir = os.path.join(get_config()['outputs'], 'wata_monitor')\n ensure_dir_exists(self.output_dir)\n # Set up directories for the copied data\n ensure_dir_exists(os.path.join(self.output_dir, 'data'))\n self.data_dir = os.path.join(self.output_dir,\n 'data/{}_{}'.format(self.instrument.lower(),\n self.aperture.lower()))\n ensure_dir_exists(self.data_dir)\n\n # Locate the record of most recent MAST search; use this time\n self.query_start = self.most_recent_search()\n # get the data of the plots previously created and set the query start date\n self.prev_data = None\n self.output_file_name = os.path.join(self.output_dir, \"wata_layout.html\")\n logging.info('\\tNew output plot file will be written as: {}'.format(self.output_file_name))\n if os.path.isfile(self.output_file_name):\n self.prev_data, self.query_start = self.get_data_from_html(self.output_file_name)\n logging.info('\\tPrevious data read from html file: {}'.format(self.output_file_name))\n # copy this plot to a previous version\n shutil.copyfile(self.output_file_name, os.path.join(self.output_dir, \"prev_wata_layout.html\"))\n # fail save - start from the beginning if there is no html file\n else:\n self.query_start = self.query_very_beginning\n logging.info('\\tPrevious output html file not found. Starting MAST query from Jan 28, 2022 == First JWST images (MIRI)')\n\n # Use the current time as the end time for MAST query\n self.query_end = Time.now().mjd\n logging.info('\\tQuery times: {} {}'.format(self.query_start, self.query_end))\n\n # Query for data using the aperture and the time of the\n # most recent previous search as the starting time\n\n # via MAST:\n # new_entries = monitor_utils.mast_query_ta(\n # self.instrument, self.aperture, self.query_start, self.query_end)\n\n # via django model:\n new_entries = monitor_utils.model_query_ta(\n self.instrument, self.aperture, self.query_start, self.query_end)\n wata_entries = len(new_entries)\n logging.info('\\tQuery has returned {} WATA files for {}, {}.'.format(wata_entries, self.instrument, self.aperture))\n\n # Filter new entries to only keep uncal files\n new_entries = self.pull_filenames(new_entries)\n new_entries = self.get_uncal_names(new_entries)\n wata_entries = len(new_entries)\n logging.info('\\tThere are {} uncal TA files to run the WATA monitor.'.format(wata_entries))\n\n # Get full paths to the files\n new_filenames = []\n for filename_of_interest in new_entries:\n if (self.prev_data is not None\n and filename_of_interest in self.prev_data['filename'].values):\n logging.warning('\\t\\tFile {} already in previous data. Skipping.'.format(filename_of_interest))\n continue\n try:\n new_filenames.append(filesystem_path(filename_of_interest))\n logging.warning('\\tFile {} included for processing.'.format(filename_of_interest))\n except FileNotFoundError:\n logging.warning('\\t\\tUnable to locate {} in filesystem. Not including in processing.'.format(filename_of_interest))\n\n if len(new_filenames) == 0:\n logging.warning('\\t\\t ** Unable to locate any file in filesystem. Nothing to process. ** ')\n\n # Run the monitor on any new files\n self.script, self.div, self.wata_data = None, None, None\n monitor_run = False\n if len(new_filenames) > 0: # new data was found\n # get the data\n self.new_wata_data, no_ta_ext_msgs = self.get_wata_data(new_filenames)\n if len(no_ta_ext_msgs) >= 1:\n for item in no_ta_ext_msgs:\n logging.info(item)\n if self.new_wata_data is not None:\n # concatenate with previous data\n if self.prev_data is not None:\n self.wata_data = pd.concat([self.prev_data, self.new_wata_data])\n logging.info('\\tData from previous html output file and new data concatenated.')\n else:\n self.wata_data = self.new_wata_data\n logging.info('\\tOnly new data was found - no previous html file.')\n else:\n logging.info('\\tWATA monitor skipped. No WATA data found.')\n # make sure to return the old data if no new data is found\n elif self.prev_data is not None:\n self.wata_data = self.prev_data\n logging.info('\\tNo new data found. Using data from previous html output file.')\n # do the plots if there is any data\n if self.wata_data is not None:\n self.script, self.div = self.mk_plt_layout()\n monitor_run = True\n logging.info('\\tOutput html plot file created: {}'.format(self.output_file_name))\n wata_files_used4plots = len(self.wata_data['visit_id'])\n logging.info('\\t{} WATA files were used to make plots.'.format(wata_files_used4plots))\n # update the list of successful and failed TAs\n self.update_ta_success_txtfile()\n logging.info('\\tWATA status file was updated')\n else:\n logging.info('\\tWATA monitor skipped.')\n\n # Update the query history\n new_entry = {'instrument': self.instrument,\n 'aperture': self.aperture,\n 'start_time_mjd': self.query_start,\n 'end_time_mjd': self.query_end,\n 'entries_found': wata_entries,\n 'files_found': len(new_filenames),\n 'run_monitor': monitor_run,\n 'entry_date': datetime.now()}\n\n with engine.begin() as connection:\n connection.execute(self.query_table.__table__.insert(), new_entry)\n logging.info('\\tUpdated the query history table')\n\n logging.info('WATA Monitor completed successfully.')","title":""},{"docid":"d5bbfbbe34f60117d0d64047a7f68605","score":"0.53421396","text":"def analyze(self, event):\n\n nllnnloW = -1\n nllW = -1\n nllW_Rup = -1\n nllW_Qup = -1\n nllW_Rdown = -1\n nllW_Qdown = -1\n gen_mww = -1\n gen_ptww = -1\n\n genParticles = Collection(event, \"GenPart\")\n \n ptV1 = -1\n ptV2 = -1\n phiV1 = -1\n phiV2 = -1\n etaV1 = -1\n etaV2 = -1\n \n for particle in genParticles :\n # 24 = W+/-\n # == 13 : isLastCopy\n if abs(particle.pdgId) == 24 and (particle.statusFlags >> 13 & 1):\n \n if ptV1 == -1 :\n ptV1 = particle.pt\n phiV1 = particle.phi\n etaV1 = particle.eta\n elif ptV2 == -1 :\n ptV2 = particle.pt\n phiV2 = particle.phi\n etaV2 = particle.eta\n \n\n if ptV1 != -1 and ptV2 != -1 :\n self.wwNLL.SetPTWW(ptV1, phiV1, etaV1, ptV2, phiV2, etaV2)\n\n \n gen_ptww = self.wwNLL.GetPTWW()\n gen_mww = self.wwNLL.GetMWW()\n \n nllnnloW = self.wwNLL.nllnnloWeight(0) \n nllW = self.wwNLL.nllWeight(0) \n nllW_Rup = self.wwNLL.nllWeight(1,1) \n nllW_Qup = self.wwNLL.nllWeight(1,0) \n nllW_Rdown = self.wwNLL.nllWeight(-1,1) \n nllW_Qdown = self.wwNLL.nllWeight(-1,0) \n \n \n self.out.fillBranch(\"gen_ptww\", gen_ptww)\n self.out.fillBranch(\"gen_mww\", gen_mww)\n\n self.out.fillBranch(\"nllnnloW\", nllnnloW)\n self.out.fillBranch(\"nllW\", nllW)\n self.out.fillBranch(\"nllW_Rup\", nllW_Rup)\n self.out.fillBranch(\"nllW_Qup\", nllW_Qup)\n self.out.fillBranch(\"nllW_Rdown\", nllW_Rdown)\n self.out.fillBranch(\"nllW_Qdown\", nllW_Qdown)\n\n\n return True","title":""},{"docid":"1203a6f9c412f728f4f91692e9d72dca","score":"0.5340376","text":"def load_wave_bouy_spectrograms(arg):\n pass","title":""},{"docid":"1be1feb1e86713760604f0b1fee95476","score":"0.53308433","text":"def syntheticSeismogram(\n d, rho, v, wavf, wavA=1.0, usingT=True, wavtyp=\"RICKER\", dt=0.0001, dmax=200\n):\n\n v, rho, d = (\n np.array(v, dtype=float),\n np.array(rho, dtype=float),\n np.array(d, dtype=float),\n )\n usingT = np.array(usingT, dtype=bool)\n\n _, t = getTimeDepth(d, v, dmax)\n rseries, R = getReflectivity(d, rho, v)\n\n # time for reflectivity series\n tref = t[1:-1]\n\n # create time vector\n t = np.arange(t.min(), t.max(), dt)\n\n # make wavelet\n twav = np.arange(-2.0 / np.min(wavf), 2.0 / np.min(wavf), dt)\n\n # Get source wavelet\n wav = {\"RICKER\": getRicker, \"ORMSBY\": getOrmsby, \"KLAUDER\": getKlauder}[wavtyp](\n wavf, twav\n )\n wav = wavA * wav\n\n rseriesconv = np.zeros(len(t))\n for i in range(len(tref)):\n index = np.abs(t - tref[i]).argmin()\n rseriesconv[index] = rseries[i]\n\n # Do the convolution\n seis = np.convolve(wav, rseriesconv)\n tseis = np.min(twav) + dt * np.arange(len(seis))\n index = np.logical_and(tseis >= 0, tseis <= np.max(t))\n tseis = tseis[index]\n seis = seis[index]\n\n return tseis, seis, twav, wav, tref, rseries","title":""},{"docid":"f5808402970a69f63f45c6b8f520b532","score":"0.53300315","text":"def _run(example_file_names, plot_shortwave, output_dir_name):\n\n file_system_utils.mkdir_recursive_if_necessary(\n directory_name=output_dir_name\n )\n\n example_dicts = []\n\n for this_file_name in example_file_names:\n print('Reading data from: \"{0:s}\"...'.format(this_file_name))\n this_example_dict = example_io.read_file(\n netcdf_file_name=this_file_name,\n max_shortwave_heating_k_day01=numpy.inf,\n min_longwave_heating_k_day01=-1 * numpy.inf,\n max_longwave_heating_k_day01=numpy.inf\n )\n example_dicts.append(this_example_dict)\n\n example_dict = example_utils.concat_examples(example_dicts)\n\n heating_rate_matrix_k_day01 = example_utils.get_field_from_dict(\n example_dict=example_dict,\n field_name=\n example_utils.SHORTWAVE_HEATING_RATE_NAME if plot_shortwave\n else example_utils.LONGWAVE_HEATING_RATE_NAME\n )\n heating_rate_percentiles_k_day01 = numpy.percentile(\n heating_rate_matrix_k_day01, PERCENTILE_LEVELS\n )\n\n for a, b in zip(PERCENTILE_LEVELS, heating_rate_percentiles_k_day01):\n print('{0:.1f}th-percentile heating rate = {1:.4f} K day^-1'.format(\n a, b\n ))\n print(SEPARATOR_STRING)\n\n _plot_by_cloud_regime(\n example_dict=example_dict, plot_shortwave=plot_shortwave,\n output_dir_name=output_dir_name\n )\n _plot_by_latitude(\n example_dict=example_dict, plot_shortwave=plot_shortwave,\n output_dir_name=output_dir_name\n )\n _plot_by_zenith_angle(\n example_dict=example_dict, plot_shortwave=plot_shortwave,\n output_dir_name=output_dir_name\n )\n\n mean_heating_rates_k_day01 = numpy.mean(heating_rate_matrix_k_day01, axis=1)\n sort_indices = numpy.argsort(-mean_heating_rates_k_day01)\n figure_object, axes_object = _plot_profiles(\n example_dict=example_dict, plot_shortwave=plot_shortwave,\n example_indices=sort_indices[:NUM_EXTREME_PROFILES]\n )\n axes_object.set_title(\n '{0:d} profiles with max average heating rates'.format(\n NUM_EXTREME_PROFILES\n )\n )\n output_file_name = '{0:s}/max_average_heating_rates.jpg'.format(\n output_dir_name\n )\n print('Saving figure to: \"{0:s}\"...'.format(output_file_name))\n figure_object.savefig(\n output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight'\n )\n pyplot.close(figure_object)\n\n sort_indices = numpy.argsort(mean_heating_rates_k_day01)\n figure_object, axes_object = _plot_profiles(\n example_dict=example_dict,\n plot_shortwave=plot_shortwave,\n example_indices=sort_indices[:NUM_EXTREME_PROFILES]\n )\n axes_object.set_title(\n '{0:d} profiles with minimum average heating rates'.format(\n NUM_EXTREME_PROFILES\n )\n )\n output_file_name = '{0:s}/min_average_heating_rates.jpg'.format(\n output_dir_name\n )\n print('Saving figure to: \"{0:s}\"...'.format(output_file_name))\n figure_object.savefig(\n output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight'\n )\n pyplot.close(figure_object)\n\n max_heating_rates_k_day01 = numpy.max(heating_rate_matrix_k_day01, axis=1)\n sort_indices = numpy.argsort(-max_heating_rates_k_day01)\n figure_object, axes_object = _plot_profiles(\n example_dict=example_dict,\n plot_shortwave=plot_shortwave,\n example_indices=sort_indices[:NUM_EXTREME_PROFILES]\n )\n axes_object.set_title(\n '{0:d} profiles with max max heating rates'.format(\n NUM_EXTREME_PROFILES\n )\n )\n output_file_name = '{0:s}/max_max_heating_rates.jpg'.format(\n output_dir_name\n )\n print('Saving figure to: \"{0:s}\"...'.format(output_file_name))\n figure_object.savefig(\n output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\n bbox_inches='tight'\n )\n pyplot.close(figure_object)","title":""},{"docid":"c07b95d4fca1cd711fbc3812807e61dd","score":"0.5327413","text":"def plotwav(samp, data, filt_type='bandpass', filt_freqlim=[12, 18], \n filt_order=4, window_size=4, overlap=.95, window_type='hann',\n plotflag=True, scale_func=defaultScaleFunction,ylim=[12, 18]):\n\n PLT_TIMESERIES = 1\n FIGSIZE = [9, 3]\n FILTER_OFFSET = 10\n\n #filter data to spectral bands where B-call is\n [b, a] = sig.butter(filt_order, np.array(filt_freqlim)/samp, filt_type, 'ba') \n filtered_data = sig.filtfilt(b, a, data)\n\n \n datalength = data.size\n times = (np.arange(datalength)/samp) \n\n #plot timeseries on upper axis\n if plotflag == True:\n plt.figure(PLT_TIMESERIES, figsize=FIGSIZE)\n plt.subplot(211)\n plt.plot(times[FILTER_OFFSET:],filtered_data[FILTER_OFFSET:])\n plt.axis([min(times), max(times), min(filtered_data[FILTER_OFFSET:]), \n max(filtered_data[FILTER_OFFSET:])])\n plt.xlabel('Seconds')\n plt.ylabel('Amplitude')\n\n #plot spectrogram on lower axis\n [f, t, Sxx] = sig.spectrogram(filtered_data, int(samp), \n window_type,int(samp*window_size),int(samp*window_size*overlap))\n \n if plotflag == True:\n cmap = plt.get_cmap('magma')\n vmin, vmax = scale_func(Sxx)\n norm = color.Normalize(vmin=vmin, vmax=vmax)\n plt.subplot(212)\n plt.pcolormesh(t, f, 10*np.log10(Sxx), cmap=cmap, norm=norm) \n plt.ylabel('Frequency [Hz]')\n plt.xlabel('Time [sec]')\n plt.ylim(ylim)\n plt.show(PLT_TIMESERIES)\n #plt.clf()\n \n return [f, t, Sxx]","title":""},{"docid":"18757a2f731d39fd7c0004a774af913f","score":"0.53065217","text":"def Analysis_data():\n\t_scatter_plot('user ID','channel ID')\n\t_histogram_plot('user ID','genre ID')\n\t_scatter_plot('user ID','duration')\n\t_histogram_plot('user ID','slot')\n\t_histogram_plot('channel ID','slot')\n\t_histogram_plot('genre ID','duration')\n\t_box_plot('user ID','program ID')\n\t_bar_chart('user ID')\n\t_bar_chart('channel ID')\n\t_bar_chart('slot')\n\t_bar_chart('genre ID')\n\t_bar_chart('duration')\n\t_bar_chart('subGenre ID')\n\t_bar_chart('week')","title":""},{"docid":"a39c1dd85c3a1f580ead85d77ef006e4","score":"0.5300061","text":"def run(self):\n tTag = self.training.sampleTag\n sTag = self.samples.sampleTag\n training = self.training.asDataset()\n samples = self.samples.asDataset()\n alpha = max(0.05, .5/len(samples))\n varNames = self.variables\n numVars = len(varNames)\n # use the len of macro, cluster from samples to build plots\n macro = samples[self.macroName]\n figCounter = 0\n for m, mac in enumerate(macro):\n figCounter += 1\n fig, axes = plt.subplots(numVars, 1, sharex=True)\n if numVars == 1:\n axes = [axes]\n axes[-1].set_xlabel(self.microName)\n\n mSamples = samples.sel({self.macroName: mac}, drop=True)\n mTraining = None\n if self.macroName in training:\n if int(mac) in training[self.macroName]:\n if self.macroName in training.dims:\n mTraining = training.drop_sel({self.macroName: mac})\n else:\n mTraining = training.where(training[self.macroName]==mac, drop=True).squeeze()\n for v, var in enumerate(varNames):\n ax = axes[v]\n # plot cloud of sample data\n for s in mSamples[sTag].values:\n samp = mSamples[{sTag: s}]\n ax.plot(samp[self.microName].values, samp[var].values, 'b-.', alpha=alpha)\n ax.set_title(f'{var}, {self.macroName} {int(mac)}')\n ax.set_ylabel(var)\n if mTraining is not None:\n ax.plot(mTraining[self.microName].values, mTraining[var].values, 'k-.')\n\n filename = f'{self.name}_{m}.png'\n plt.savefig(filename)\n self.raiseAMessage(f'Wrote \"{filename}\".')","title":""},{"docid":"30849743348e120f95b310e8a542adda","score":"0.52973163","text":"def extract_wavelet(signals, config={}):\n if not config:\n raise Exception('empty config in extract_mfcc(...)')\n wavelets = []\n for data in signals:\n wavelet = signal.cwt(data, signal.ricker, np.arange(1, config['widths'] + 1))\n wavelets.append(wavelet)\n wavelets = np.array(wavelets)\n return wavelets","title":""},{"docid":"d307237a828f4c9bf1dbf1d8ed2c0817","score":"0.52964026","text":"def main():\n \n import argparse\n import pickle\n\n parser = argparse.ArgumentParser()\n \n # normal operation \n parser.add_argument(\"num\", type=int, default=1000, \n help=\"number of data points to be generated (1000)\")\n parser.add_argument(\"mod\", type=str, default='qpsk', \n help=\"modulation scheme: 'bspk', 'qpsk', '8psk', '16qam' (qpsk))\")\n parser.add_argument(\"--dir\", type=str, default='./generated_data', \n help=\"directory/filename to save output to (./generated_data)\")\n \n # for pulseshaped data \n parser.add_argument('--pulse', action='store_true', help=\"generate pulse shaped data\")\n parser.add_argument(\"-os\", type=int, default=16, \n help=\"oversampling factor (power of 2)\")\n parser.add_argument(\"-len\", type=int, default=128, \n help=\"filter length (power of 2)\")\n\n args = parser.parse_args()\n num, mod, directory = (args.num, args.mod, args.dir)\n pulse, os, length = (args.pulse, args.os, args.len)\n \n generator = Data_generator()\n\n # check input arguments\n try:\n pck_file = open(directory+\".pck\", \"w\")\n except IOError as error:\n print(\"Check if the output directory exists\")\n raise\n \n # check if os and length are powers of 2\n assert os&(os-1) == 0\n assert length&(length-1) == 0\n \n if (pulse): # generate pulse shaped data\n \n data = generator.get_pulseshaped_data(num, mod, os)\n # data = (data_raw, data_mod, data_os, data_shaped, rc)\n\n # save the data \n pickle.dump(data, pck_file)\n pck_file.close()\n\n print(\"Generated %d random samples with '%s' modulation.\" % (num, mod))\n print(\"Signal is oversampled by a factor of %d and filtered with a rc filter of length %d\" % (os, length))\n print(\"Data has been saved in %s.pck\" % directory)\n\n else: # generate normal data\n \n data = generator.get_random_data(num, mod)\n \n # save the data \n pickle.dump(data, pck_file)\n pck_file.close()\n\n print(\"Generated %d random samples with '%s' modulation.\" % (num, mod))\n print(\"Data has been saved in %s.pck\" % directory)","title":""},{"docid":"25c6b52588c32ecba054cfbb97c51078","score":"0.5294081","text":"def test_var_measure_single_wire(self):\n state = np.array([[1, -1j], [0, 0]]) / np.sqrt(2)\n shots = qml.measurements.Shots(100)\n\n mp0 = qml.var(qml.PauliZ(0))\n mp1 = qml.var(qml.PauliY(1))\n\n result0 = measure_with_samples(mp0, state, shots=shots)\n result1 = measure_with_samples(mp1, state, shots=shots)\n\n assert result0.shape == ()\n assert result1.shape == ()\n assert result0 == 0\n assert result1 == 0","title":""},{"docid":"40b66d8d1bf7dbdc107494d0b8bda245","score":"0.52934396","text":"def fwemva(self,dslo,dat,dt,minf,maxf,vel,jf=1,nrmax=3,eps=0.0,dtmax=5e-05,wav=None,\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\n # Get temporal axis\n nt = dat.shape[-1]\n\n # Create frequency domain source\n if(wav is None):\n wav = np.zeros(nt,dtype='float32')\n wav[0] = 1.0\n self.__nwo,self.__ow,self.__dw,wfft = self.fft1(wav,dt,minf=minf,maxf=maxf)\n wfftd = wfft[::jf]\n self.__nwc = wfftd.shape[0] # Get the number of frequencies for imaging\n self.__dwc = self.__dw*jf\n\n if(sverb or wverb): print(\"Frequency axis: nw=%d ow=%f dw=%f\"%(self.__nwc,self.__ow,self.__dwc))\n\n # Create frequency domain data\n _,_,_,dfft = self.fft1(dat,dt,minf=minf,maxf=maxf)\n dfftd = dfft[:,::jf]\n datt = np.transpose(dfftd,(0,1,4,2,3)) # [nsy,nsx,ny,nx,nwc] -> [nsy,nsx,nwc,ny,nx]\n datw = np.ascontiguousarray(datt.reshape([self.__nexp,self.__nwc,self.__ny,self.__nx]))\n\n # Single square root object\n ssf = ssr3(self.__nx ,self.__ny,self.__nz , # Spatial Sizes\n self.__dx ,self.__dy,self.__dz , # Spatial Samplings\n self.__nwc,self.__ow,self.__dwc,eps, # Frequency axis\n ntx,nty,px,py, # Taper and padding\n dtmax,nrmax,nthrds) # Reference velocities\n\n # Compute slowness and reference slownesses\n slo = 1/vel\n ssf.set_slows(slo)\n\n dimgar = np.zeros([self.__nexp,self.__nz,self.__ny,self.__nx],dtype='complex64')\n\n # Allocate the source for one shot\n sou = np.zeros([self.__nwc,self.__ny,self.__nx],dtype='complex64')\n\n # Loop over sources\n k = 0\n for icrd in progressbar(self.__scoords,\"nexp:\",verb=sverb):\n # Get the source coordinates\n sy = icrd[0]; sx = icrd[1]\n # Create the source for this shot\n sou[:] = 0.0\n sou[:,sy,sx] = wfftd[:]\n ssf.fwemvaallw(sou,datw[k],dslo,dimgar[k],verb=wverb)\n k += 1\n\n # Sum over all partial images\n dimg = np.sum(dimgar,axis=0)\n\n return np.real(dimg)","title":""},{"docid":"ff2a20b7d52e0fafc2e3328b9d229b19","score":"0.52859616","text":"def get_terror_waves_info(self):","title":""},{"docid":"2e3baf45d31bc7686677fb88c61886ee","score":"0.528461","text":"def test_ripples_haar_lvl3():\n\n class MyHaarFilterBank(object):\n @property\n def filter_bank(self):\n return (\n [1 / 2, 1 / 2.0],\n [-1 / 2.0, 1 / 2.0],\n [1 / 2.0, 1 / 2.0],\n [1 / 2.0, -1 / 2.0],\n )\n\n data = [56.0, 40.0, 8.0, 24.0, 48.0, 48.0, 40.0, 16.0]\n wavelet = pywt.Wavelet(\"unscaled Haar Wavelet\",\n filter_bank=MyHaarFilterBank())\n ptdata = torch.tensor(data).unsqueeze(0).unsqueeze(0)\n coeffs = wavedec(ptdata, wavelet, level=3)\n # print(coeffs)\n assert torch.squeeze(coeffs[0]).numpy() == 35.0\n assert torch.squeeze(coeffs[1]).numpy() == -3.0\n assert (torch.squeeze(coeffs[2]).numpy() == [16.0, 10.0]).all()\n assert (torch.squeeze(coeffs[3]).numpy() == [8.0, -8.0, 0.0, 12.0]).all()","title":""},{"docid":"b909fa3dd3f57e701bf13658e49272fa","score":"0.5266676","text":"def dataset(verbose=False):\n \n s = Sample()\n finite = np.isfinite(s.data['Fe'])\n selection = finite & s.giant & s.spectype[-1] & (s.cf>0) & (s.data['age']-s.data['eage']>1)\n \n fep = np.percentile(s.data['Fe'][selection], [33,66])\n fei = np.empty((3, np.sum(selection)), dtype=np.bool)\n fei[0] = s.data['Fe'][selection]<fep[0]\n fei[1] = (s.data['Fe'][selection]>=fep[0]) & (s.data['Fe'][selection]<fep[1])\n fei[2] = s.data['Fe'][selection]>=fep[1]\n \n z_bins = np.arange(0, 4, 0.2)\n z = myutils.bincen(z_bins)\n Nb = np.size(z)\n \n for i, ind in enumerate(fei):\n hz, be = np.histogram(np.abs(s.x[:,2][selection][ind].value), bins=z_bins, weights=1/s.cf[selection][ind])\n nz, be = np.histogram(np.abs(s.x[:,2][selection][ind].value), bins=z_bins)\n idx = np.digitize(np.abs(s.x[:,2][selection][ind].value), bins=z_bins)\n \n n = np.zeros(Nb)\n vz = np.empty(Nb)\n sz = np.empty(Nb)\n vze = np.empty(Nb)\n sze = np.empty(Nb)\n sza = np.empty(Nb)\n \n for l in range(Nb):\n if verbose: print(i, l)\n #plt.sca(ax[int(l/Ncol)][l%Ncol])\n if np.sum(idx==l+1):\n n[l] = np.sum(idx==l+1)\n sampler = np.load('../data/chains/vz_feh{}_z{}.chain.npz'.format(i,l))\n p = np.percentile(sampler['chain'], [16,50,84], axis=0)\n vz[l] = p[1,0]\n sz[l] = p[1,1]\n vze[l] = 0.5*(p[2,0] - p[0,0])\n sze[l] = 0.5*(p[2,1] - p[0,1])\n \n vmin, vmax = np.percentile(s.v[:,2][selection][ind][idx==l+1].value, [15,85])\n i_ = (s.v[:,2][selection][ind][idx==l+1].value>vmin) & (s.v[:,2][selection][ind][idx==l+1].value<vmax)\n sza[l] = np.std(s.v[:,2][selection][ind][idx==l+1][i_].value)\n #sza[l] = np.std(s.v[:,2][selection][ind][idx==l+1].value)\n \n t = Table([z, hz, nz, vz, vze, sz, sze, sza], names=('z', 'nu', 'n', 'vz', 'vze', 'sz', 'sze', 'sza'))\n t.write('../data/profile_mgiants_feh{}.fits'.format(i), overwrite=True)","title":""},{"docid":"df8f8821d5ec85bf4e97e7e3cb6f8352","score":"0.5263289","text":"def visualize_wave(self, y):\r\n if self.current_freq_detects[\"beat\"]:\r\n output = np.zeros((3,config.settings[\"devices\"][self.board][\"configuration\"][\"N_PIXELS\"]))\r\n output[0][:]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_flash\"]][0]\r\n output[1][:]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_flash\"]][1]\r\n output[2][:]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_flash\"]][2]\r\n self.wave_wipe_count = config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"wipe_len\"]\r\n else:\r\n output = np.copy(self.prev_output)\r\n #for i in range(len(self.prev_output)):\r\n # output[i] = np.hsplit(self.prev_output[i],2)[0]\r\n output = np.multiply(self.prev_output,config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"decay\"])\r\n for i in range(self.wave_wipe_count):\r\n output[0][i]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_wave\"]][0]\r\n output[0][-i]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_wave\"]][0]\r\n output[1][i]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_wave\"]][1]\r\n output[1][-i]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_wave\"]][1]\r\n output[2][i]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_wave\"]][2]\r\n output[2][-i]=config.settings[\"colors\"][config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"color_wave\"]][2]\r\n #output = np.concatenate([output,np.fliplr(output)], axis=1)\r\n if self.wave_wipe_count > config.settings[\"devices\"][self.board][\"configuration\"][\"N_PIXELS\"]//2:\r\n self.wave_wipe_count = config.settings[\"devices\"][self.board][\"configuration\"][\"N_PIXELS\"]//2\r\n self.wave_wipe_count += config.settings[\"devices\"][self.board][\"effect_opts\"][\"Wave\"][\"wipe_speed\"]\r\n return output","title":""},{"docid":"572c6cd02e4cda53c363e1e9d1caec70","score":"0.52579087","text":"def main():\n\n # Load data and template\n npzfile = np.load(\"./data/optimumInputs.npz\")\n rl, tl = npzfile['arr_0'], npzfile['arr_1']\n wave, waveTS, dataE, dataST = rl[0], rl[1], rl[2], rl[3]\n temp, tempTS, tempE, tempST = tl[0], tl[1], tl[2], tl[3]\n\n # Window the fit around rising edge - start time calculator method\n loWin, hiWin = dataST - 1000, dataST + 4000 # ns\n if loWin < waveTS[0] or hiWin > waveTS[-1]:\n print \"Window out of range! dataST: %.1f loWin %.1f hiWin %.1f\" % (dataST,loWin,hiWin)\n idx = np.where((waveTS >= loWin) & (waveTS <= hiWin))\n data = wave[idx]\n dataTS = waveTS[idx]\n\n # Pack into lists\n dataNoise = 2. # just a guess - 1 sigma baseline adc values\n rawList = [wave, waveTS, dataE, dataST]\n dataList = [data, dataTS, dataE, dataST, loWin, hiWin, dataNoise]\n tempList = [temp, tempTS, tempE, tempST]\n\n # Recreate the guess and the guess's rising edge\n guessFull, guessFullTS = wm.MakeModel(rawList, tempList, [dataST,dataE,1.], opt=\"full\")\n guess, guessTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\"!fancy\")\n\n # Make an \"almost complete\" guess - no MCMC\n # st, en, slo = dataST-100, dataE, 5\n # InterpFn = interpolate.interp1d(tempTS, temp, kind=\"linear\", copy=\"False\", assume_sorted=\"True\")\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n # Fit with MCMC and get best-fit parameters\n numSteps, burnIn = 3000, 1800 # default: 10000, 5000. fast: 3000, 1800 long test: 20000,10000\n wfModel = wm.TemplateModel( dataList, dataNoise, tempList )\n M = pymc.MCMC( pymc.Model( wfModel ) )\n M.use_step_method(pymc.Metropolis, M.startTime, proposal_sd=100., proposal_distribution='Normal')\n M.use_step_method(pymc.Metropolis, M.energy, proposal_sd=1., proposal_distribution='Normal')\n M.use_step_method(pymc.Metropolis, M.slowness, proposal_sd=100., proposal_distribution='Normal')\n M.sample(iter=numSteps, verbose=0)\n st = np.median(M.trace(\"startTime\")[:])\n en = np.median(M.trace(\"energy\")[:])\n slo = np.median(M.trace(\"slowness\")[:])\n InterpFn = interpolate.interp1d(tempTS, temp, kind=\"linear\", copy=\"False\", assume_sorted=\"True\")\n model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n print \"MCMC:\",st,en,slo\n\n # Fit with SciPy minimizer\n MakeTracesGlobal() # creates 3 global arrays: startTrace, enTrace, sloTrace\n floats = [dataST, dataE, 1]\n print \"Minimizer guesses:\",floats\n datas = [dataList,tempList,InterpFn]\n result = minimize(findLnLike, floats, args=datas, method=\"Nelder-Mead\")\n st, en, slo = result[\"x\"]\n print \"Minimizer: %.1f %.1f %.1f Success: %s. %s\" % (st, en, slo, result[\"success\"], result[\"message\"])\n minimizer, minimizerTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n # plots\n fig = plt.figure(figsize=(11,7), facecolor='w')\n p1 = plt.subplot2grid((6,7), (0,0), colspan=4, rowspan=2) # original\n p2 = plt.subplot2grid((6,7), (2,0), colspan=4, rowspan=3) # rising edge\n p3 = plt.subplot2grid((6,7), (0,4), colspan=3, rowspan=2 ) # trace 1\n p4 = plt.subplot2grid((6,7), (2,4), colspan=3, rowspan=2, sharex=p3) # trace 2\n p5 = plt.subplot2grid((6,7), (4,4), colspan=3, rowspan=2, sharex=p3) # trace 3\n\n # p1 = plt.subplot(211)\n p1.set_title(\"Energy %.1f keV Start Time %.0f ns\" % (dataE, dataST))\n p1.set_ylabel(\"ADC [A.U.]\",y=0.95, ha='right')\n p1.set_xlabel(\"Time (ns)\",x=0.95, ha='right')\n p1.plot(waveTS,wave,color='blue',alpha=0.8,label='Data WF')\n p1.plot(guessFullTS,guessFull,color='orange',alpha=0.8,label='Guess WF')\n p1.axvline(x=dataST,color='green')\n p1.legend(loc=4)\n\n # p2 = plt.subplot(212)\n p2.plot(dataTS, data, color='blue',label='Data')\n p2.plot(guessTS, guess, color='orange',label='Guess')\n p2.plot(modelTS, model, color='red',linewidth=4,alpha=0.8,label='MCMC')\n p2.plot(minimizerTS, minimizer, color='cyan',linewidth=1,label='Nelder-Mead')\n p2.legend(loc=4)\n\n p3.cla()\n p3.set_title(\"startTime %.1f Energy %.2f Slow %.1f\" % (st,en,slo))\n p3.plot(stTrace[1:])\n p3.set_ylabel('startTime')\n\n p4.cla()\n p4.plot(enTrace[1:])\n p4.set_ylabel('energy')\n\n p5.cla()\n p5.plot(sloTrace[1:])\n p5.set_ylabel('slowness')\n\n plt.tight_layout()\n plt.subplots_adjust(hspace=0.35)\n # plt.show(block=False)\n # plt.show()\n plt.savefig(\"./plots/minimizer-test.pdf\")","title":""},{"docid":"7dff0d949e86fc706bf79d623e50b616","score":"0.5253615","text":"def process(self, df):\n output = self.accumulator.identity()\n dataset = df[\"dataset\"]\n cfg = loadConfig()\n # We can access the data frame as usual\n # The dataset is written into the data frame\n # outside of this function\n\n output['totalEvents']['all'] += len(df['weight'])\n\n met_pt = df[\"MET_pt\"]\n met_phi = df[\"MET_phi\"]\n\n Jet = JaggedCandidateArray.candidatesfromcounts(\n df['nJet'],\n pt = df['Jet_pt'].content,\n eta = df['Jet_eta'].content,\n phi = df['Jet_phi'].content,\n mass = df['Jet_mass'].content,\n goodjet = df['Jet_isGoodJetAll'].content,\n bjet = df['Jet_isGoodBJet'].content,\n jetId = df['Jet_jetId'].content,\n puId = df['Jet_puId'].content,\n )\n\n lepton = JaggedCandidateArray.candidatesfromcounts(\n df['nLepton'],\n pt = df['Lepton_pt'].content,\n eta = df['Lepton_eta'].content,\n phi = df['Lepton_phi'].content,\n mass = df['Lepton_mass'].content,\n pdgId = df['Lepton_pdgId'].content,\n )\n \n alljet = Jet[(Jet['goodjet']==1)] # all jets with pt>25 and pt>60 in 2.7<|eta|<3.0 (noise suppression)\n b = Jet[Jet['bjet']==1]\n nonb = Jet[(Jet['goodjet']==1) & (Jet['bjet']==0)]\n leading_nonb = nonb[:,:6] # first six non-b-tagged jets\n lead_light = nonb[nonb.pt.argmax()]\n spectator = Jet[(abs(Jet.eta)>2.0) & (abs(Jet.eta)<4.7) & (Jet.pt>25) & (Jet['puId']>=7) & (Jet['jetId']>=6)] # 40 GeV seemed good. let's try going lower\n \n bj_pair = b.cross(nonb)\n jj_pair = nonb.cross(nonb)\n lb_pair = lepton.cross(b)\n lj_pair = lepton.cross(nonb)\n ht = Jet[Jet['goodjet']==1].pt.sum()\n st = Jet[Jet['goodjet']==1].pt.sum() + lepton.pt.sum() + met_pt\n \n leading_lepton = lepton[lepton.pt.argmax()]\n\n ## calculate mt\n mt_lep_met = mt(leading_lepton.pt, leading_lepton.phi, met_pt, met_phi)\n\n ### define selections here, using the objects defined above\n singlelep = ((df['nLepton']==1) & (df['nVetoLepton']==1))\n sixjet = (alljet.counts >= 6 )\n sevenjet = (alljet.counts >= 7)\n two_b = ( b.counts >= 2)\n eta_lead = ((lead_light.eta>-.5).counts>0) & ((lead_light.eta<0.5).counts>0)\n \n # selections used for the histograms below\n event_selection = (Jet.counts>5) & (b.counts>=2) & (nonb.counts>=4) & (df['nLepton']==1) & (df['nVetoLepton']==1)\n tight_selection = (Jet.counts>5) & (b.counts>=2) & (nonb.counts>=4) & (df['nLepton']==1) & (df['nVetoLepton']==1) & (df['MET_pt']>50) & (ht>500) & (df['MT']>50) & (spectator.counts>=1) & (spectator.pt.max()>50) & (st>600) & (bj_pair.mass.max()>300) & (jj_pair.mass.max()>300)\n\n ### work on the cutflow\n addRowToCutFlow( output, df, cfg, 'skim', None ) # entry point\n addRowToCutFlow( output, df, cfg, 'singlelep', singlelep )\n addRowToCutFlow( output, df, cfg, 'sixjet', singlelep & sixjet )\n addRowToCutFlow( output, df, cfg, 'sevenjet', singlelep & sevenjet )\n addRowToCutFlow( output, df, cfg, 'twob', singlelep & sevenjet & two_b )\n addRowToCutFlow( output, df, cfg, 'etalead', singlelep & sevenjet & eta_lead ) # this is a weird cut\n addRowToCutFlow( output, df, cfg, 'everything', singlelep & sevenjet & two_b & eta_lead ) # this is a weird cut\n \n ### fill all the histograms\n \n output['MET_pt'].fill(dataset=dataset, pt=df[\"MET_pt\"][singlelep].flatten(), weight=df['weight'][singlelep]*cfg['lumi'])\n output['MT'].fill(dataset=dataset, pt=df[\"MT\"][singlelep].flatten(), weight=df['weight'][singlelep]*cfg['lumi'])\n output['N_b'].fill(dataset=dataset, multiplicity=df[\"nGoodBTag\"][singlelep], weight=df['weight'][singlelep]*cfg['lumi'] )\n output['N_jet'].fill(dataset=dataset, multiplicity=df[\"nGoodJet\"][singlelep], weight=df['weight'][singlelep]*cfg['lumi'] )\n \n output['mbj_max'].fill(dataset=dataset, mass=bj_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n output['mjj_max'].fill(dataset=dataset, mass=jj_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n output['mlb_min'].fill(dataset=dataset, mass=lb_pair[event_selection].mass.min().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n output['mlb_max'].fill(dataset=dataset, mass=lb_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n output['mlj_min'].fill(dataset=dataset, mass=lj_pair[event_selection].mass.min().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n output['mlj_max'].fill(dataset=dataset, mass=lj_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n\n output['HT'].fill(dataset=dataset, ht=ht[event_selection].flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n output['ST'].fill(dataset=dataset, ht=st[event_selection].flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\n\n # forward stuff\n output['N_spec'].fill(dataset=dataset, multiplicity=spectator[event_selection].counts, weight=df['weight'][event_selection]*cfg['lumi'])\n output['pt_spec_max'].fill(dataset=dataset, pt=spectator[event_selection & (spectator.counts>0)].pt.max().flatten(), weight=df['weight'][event_selection & (spectator.counts>0)]*cfg['lumi'])\n\n\n ### event shape variables - neglect for now\n \n #output['FWMT1'].fill(dataset=dataset, norm=FWMT(leading_nonb)[1][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\n #output['FWMT2'].fill(dataset=dataset, norm=FWMT(leading_nonb)[2][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\n #output['FWMT3'].fill(dataset=dataset, norm=FWMT(leading_nonb)[3][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\n #output['FWMT4'].fill(dataset=dataset, norm=FWMT(leading_nonb)[4][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\n #output['FWMT5'].fill(dataset=dataset, norm=FWMT(leading_nonb)[5][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\n ##output['S'].fill(dataset=dataset, norm=sphericityBasic(alljet)[event_selection], weight=df['weight'][event_selection]*cfg['lumi'])\n\n #all_obj = mergeArray(alljet, lepton)\n #output['S_lep'].fill(dataset=dataset, norm=sphericityBasic(all_obj)[event_selection], weight=df['weight'][event_selection]*cfg['lumi'])\n \n \n return output","title":""},{"docid":"869fcaa45c2d0c524bff1e3c96f633d2","score":"0.5243887","text":"def main():\n logger = logging.getLogger(__name__)\n logger.info(\"making final data set from raw data\")\n load_data()","title":""},{"docid":"78857a618c25b76991ab25f47ad40d09","score":"0.52398384","text":"def applyTo(self, photon_array, local_wcs=None, rng=None):\n rng = rng if rng is not None else self.rng\n if photon_array.hasAllocatedWavelengths():\n galsim_warn(\"Wavelengths already set before applying WavelengthSampler. \"\n \"This is most likely an error.\")\n photon_array.wavelength = self.sed.sampleWavelength(\n photon_array.size(), self.bandpass, rng=rng, npoints=self.npoints)","title":""},{"docid":"f4db61b489f1741b8b4cdecb2daf986a","score":"0.5237792","text":"def model_fulldata(self,vel,wav,dtd,dtu=0.001,nthrds=4,verb=False):\n if(vel.shape[1] != self.__nx or vel.shape[0] != self.__nz):\n raise Exception(\"Velocity must have same shape as passed to constructor\")\n\n # Pad the velocity model\n velp = self.pad_model(vel)\n\n # Temporal axis\n ntu = wav.shape[0]\n fact = int(dtd/dtu); ntd = int(ntu/fact)\n\n # Build the wave propagation object\n sca = sca2d.scaas2d(ntd, self.__nxp, self.__nzp, # Sizes\n dtd, self.__dx, self.__dz,dtu, # Samplings\n self.__bx, self.__bz, self.__alpha) # Absorbing boundary\n # Create input wavelet array\n allsrcs = np.zeros([self.__nsx,1,ntu],dtype='float32') # One source per shot (same length as wavefield)\n for isx in range(self.__nsx):\n allsrcs[isx,0,:] = wav[:]\n # Create output data\n allshot = np.zeros([self.__nsx,ntd,self.__nrx],dtype='float32')\n # Forward modeling\n sca.fwdprop_multishot(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\n self.allrecx,self.allrecz,self.nrec, # Receiver information\n self.__nsx,velp,allshot,nthrds,verb) # Velocity and output\n\n return allshot","title":""},{"docid":"c17b0421f4001be6bd4c0ee797155649","score":"0.52371895","text":"def load_data( band_id):\n band = photons[photons.band==band_id] #.query('band== @band_id')\n wt_table = wt_tables[band_id]\n nside = wt_table['nside']\n new_weights = wt_table['wts'].astype(np.float16)\n to_shift = int(2*np.log2(data_nside//nside))\n data_pixels = np.right_shift(band.nest_index, to_shift)\n wt_pixels=wt_table['pixels']\n good = np.isin( data_pixels, wt_pixels)\n if self.config.verbose>2:\n print(f'\\t {band_id:2}: {len(band):8,} -> {sum(good ):8,}')\n return data_pixels, new_weights, good","title":""},{"docid":"4a219a6deae64601dfd8f9d27bbcae2e","score":"0.52176845","text":"def get_wavelength(ins, filtname):\n\n YJfile = pkg_resources.resource_stream(\n \"amical\", \"internal_data/ifs_wave_YJ.fits\")\n YJHfile = pkg_resources.resource_stream(\n \"amical\", \"internal_data/ifs_wave_YJH.fits\")\n wave_YJ = fits.open(YJfile)[0].data\n wave_YJH = fits.open(YJHfile)[0].data\n\n dic_filt = {'NIRISS': {'F277W': [2.776, 0.715],\n 'F380M': [3.828, 0.205],\n 'F430M': [4.286, 0.202],\n 'F480M': [4.817, 0.298]\n },\n 'SPHERE': {'H2': [1.593, 0.052],\n 'H3': [1.667, 0.054],\n 'H4': [1.733, 0.057],\n 'K1': [2.110, 0.102],\n 'K2': [2.251, 0.109],\n 'CntH': [1.573, 0.023],\n 'CntK1': [2.091, 0.034],\n 'CntK2': [2.266, 0.032]\n },\n 'SPHERE-IFS': {'YJ': wave_YJ,\n 'YH': wave_YJH\n },\n 'GLINT': {'F155': [1.55, 0.01],\n 'F430': [4.3, 0.01]\n },\n 'VISIR': {'10_5_SAM': [10.56, 0.37],\n '11_3_SAM': [11.23, 0.55]},\n 'VAMPIRES': {'750-50': [0.75, 0.05]}\n }\n\n if ins not in dic_filt.keys():\n cprint(\"--- Error: instrument <%s> not found ---\" % ins, 'red')\n cprint('Available: %s' % list(dic_filt.keys()), 'red')\n wl = np.NaN\n\n try:\n wl = np.array(dic_filt[ins][filtname]) * 1e-6\n except KeyError:\n cprint(\"--- Error: filtname <%s> not found for %s ---\" %\n (filtname, ins), 'red')\n cprint('Available: %s' % list(dic_filt[ins].keys()), 'red')\n wl = np.NaN\n return wl","title":""},{"docid":"9b1c334f36458c371c18433ee4637f8b","score":"0.5214118","text":"def SE_data_gen(is_tr):\n file_idx = 0\n sum_temp = None\n std_temp = None\n total_sample_cnt = 0\n if is_tr == 'tr':\n tf_list = c.TRAIN_TF_LIST\n random.shuffle(c.snr_list)\n snr = c.snr_list[0]\n elif is_tr == 'cv':\n tf_list = c.VALID_TF_LIST\n elif is_tr == 'tt':\n tf_list = c.TEST_TF_LIST\n\n anechoic_path = 'anechoic_mono_speech_' + is_tr + '.flist'\n with open(anechoic_path, 'r') as f:\n all_wavs = f.readlines()\n num_of_wavs = len(all_wavs)\n \n with open(anechoic_path, 'r') as f:\n for path in all_wavs:\n file_idx += 1\n # print(file_idx)\n # if file_idx <= 1735:\n # continue\n print(is_tr, file_idx)\n content = path.strip().split(' ')\n adr, label = content[0], content[1]\n sample_rate, wav_data_temp = wav.read(adr)\n assert sample_rate == c.fs\n\n index = int(label) - 1\n random.shuffle(tf_list)\n TF_path = tf_list[0]\n TF = load_mat(TF_path)\n #_mmax = np.max(TF[index][0], axis=0)\n #_mmin = np.min(TF[index][0], axis=0)\n wav_data = get_array_signal(wav_data_temp, TF[index][0]).astype(np.float32)\n if is_tr == 'cv' or is_tr == 'tt':\n thres = num_of_wavs / len(c.snr_list)\n if file_idx < thres:\n snr = c.snr_list[0]\n elif thres <= file_idx < 2 * thres:\n snr = c.snr_list[1]\n elif 2 * thres <= file_idx < 3 * thres:\n snr = c.snr_list[2]\n else:\n snr = c.snr_list[3]\n #_mmax = np.max(wav_data, axis=0)\n #_mmin = np.min(wav_data, axis=0)\n wav_data = awgn(wav_data, snr)\n #mmax = np.max(wav_data, axis=0)\n #mmin = np.min(wav_data, axis=0)\n angles = get_goal_angle(index * 5)\n extracted_sig = extract_sig(wav_data, angles)\n assert extracted_sig.shape[1] == 5\n extracted_sig = extracted_sig[0:len(wav_data_temp), :]\n fig, ax = plt.subplots(6, 1)\n ax[0].plot(wav_data_temp)\n for i in range(5): ax[i+1].plot(extracted_sig[:, i])\n plt.show()\n # frames of multichannel, the 1st dimension is channel\n multi_frames = []\n for i in range(5):\n frames_each_chan = sig2frames(extracted_sig[:, i])\n multi_frames.append(frames_each_chan)\n X = torch.from_numpy(np.array(multi_frames).transpose([1, 0, 2]))\n Y = torch.from_numpy(sig2frames(wav_data_temp))\n torch.save({'X': X, 'Y': Y}, c.SE_data_save_path + is_tr + '/SEdata{}.pt'.format(file_idx))","title":""},{"docid":"0f4b5a766612415afe168390d637e19e","score":"0.5213889","text":"def wem(self,vel,dat,wav,dtd,dtu=0.001,nh=None,trunc=True,lap=False,nthrds=4,verb=False):\n if(vel.shape[1] != self.__nx or vel.shape[0] != self.__nz):\n raise Exception(\"Velocity must have same shape as passed to constructor\")\n\n if(dat.shape[0] != self.__nsx or dat.shape[2] != self.__nrx):\n raise Exception(\"Data must have same shape as acqusition passed to constructor\")\n\n # Pad the model and create the output image\n velp = self.pad_model(vel)\n if(nh == None):\n imgp = np.zeros([self.__nzp,self.__nxp],dtype='float32')\n else:\n self.rnh = 2*nh + 1; self.oh = -self.__dx*nh; self.dh = self.__dx\n imgp = np.zeros([self.rnh,self.__nzp,self.__nxp],dtype='float32')\n\n # Temporal axis\n ntu = wav.shape[0]\n fact = int(dtd/dtu); ntd = int(ntu/fact)\n\n # Build the wave propagation object\n sca = sca2d.scaas2d(ntd, self.__nxp, self.__nzp, # Sizes\n dtd, self.__dx, self.__dz,dtu, # Samplings\n self.__bx, self.__bz, self.__alpha) # Absorbing boundary\n # Create input wavelet array\n allsrcs = np.zeros([self.__nsx,1,ntu],dtype='float32') # One source per shot (same length as wavefield)\n for isx in range(self.__nsx):\n allsrcs[isx,0,:] = wav[:]\n\n # Form the image\n if(nh == None):\n sca.brnadj(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\n self.allrecx,self.allrecz,self.nrec, # Receiver information\n self.__nsx,velp,imgp,dat,nthrds,verb) # Velocity and output image\n if(self.tap is not None):\n # Apply taper\n imgp *= self.tap\n if(lap):\n # Apply laplacian\n imgl = np.zeros(imgp.shape,dtype='float32')\n sca.lapimg(imgp,imgl)\n else:\n imgl = imgp\n if(trunc):\n # Trucate the padding\n img = self.trunc_model(imgl)\n else:\n img = imgl\n # Extended image\n else:\n sca.brnoffadj(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\n self.allrecx,self.allrecz,self.nrec, # Receiver information\n self.__nsx,velp,self.rnh,imgp,dat,nthrds,verb) # Velocity and output image\n if(self.tap is not None):\n # Apply taper\n for ih in range(self.rnh):\n imgp[ih,:,:] *= self.tap\n if(lap):\n # Apply laplacian\n imgl = np.zeros(imgp.shape,dtype='float32')\n for ih in range(self.rnh):\n sca.lapimg(imgp[ih,:,:],imgl[ih,:,:])\n else:\n imgl = imgp\n if(trunc):\n img = self.trunc_model(imgl)\n else:\n img = imgl\n\n return img","title":""},{"docid":"f1c38687eebeaaa985973ee1eac75f97","score":"0.5211603","text":"def run():\r\n dl_extract_tar()\r\n get_melspec_data()\r\n get_spectral_values()\r\n print(\"Preprocessing complete\")","title":""},{"docid":"142b07bbf59f7639d4076a71647eefa8","score":"0.5209897","text":"def run_a_sample(data_dir):\n # sensitivity analysis settings\n s_a_s = global_settings.get_s_a_setting(data_dir)\n\n # global k file name, all together\n g_k_f_n = os.path.join(data_dir, \"output\", \"k_global.csv\")\n if os.path.isfile(g_k_f_n):\n os.remove(g_k_f_n)\n # global target file name, all together, target is ignition delay time (ign) here\n g_t_f_n = os.path.join(data_dir, \"output\", \"ign_global.csv\")\n if os.path.isfile(g_t_f_n):\n os.remove(g_t_f_n)\n # local target file name\n l_t_f_n = os.path.join(data_dir, \"output\", \"ign_local.csv\")\n\n u_u = get_uniform_uncertainties(\n s_a_s['n_dim'], s_a_s['default_uncertainty'], s_a_s['exclude'])\n # save constant uncertainty to file\n f_n_u_const = os.path.join(data_dir, \"output\", \"uncertainties_const.csv\")\n np.savetxt(f_n_u_const, u_u, fmt='%.18e', delimiter=',', newline='\\n')\n\n for _ in range(s_a_s['n_run']):\n r_c = get_random_coef(uniform_uncertainties=u_u)\n\n spe_idx_conc = copy.deepcopy(s_a_s['spe_idx_conc'])\n print(spe_idx_conc)\n for s_i in spe_idx_conc:\n if int(s_i) >= 0 and int(s_i) < len(r_c):\n spe_idx_conc[s_i] *= r_c[int(s_i)]\n\n us.update_s_a_setting(data_dir,\n init_temp=s_a_s['init_temp'],\n critical_temp=s_a_s['critical_temp'],\n target_temp=s_a_s['target_temp'],\n end_temp=s_a_s['end_temp'],\n spe_idx_conc=spe_idx_conc)\n\n flag = job_drivers.make_run_timeout(data_dir, timeout=s_a_s['timeout'])\n\n # local target time\n local_t_t = np.loadtxt(l_t_f_n, dtype=float, delimiter=',')\n local_t_t = [local_t_t]\n\n # is successfully run a sample, save to file\n if flag is True:\n r_c = r_c.reshape((1, len(r_c)))\n with open(g_k_f_n, 'ab') as f_handler:\n np.savetxt(f_handler, r_c, fmt='%.18e',\n delimiter=',', newline='\\n')\n with open(g_t_f_n, 'ab') as f_handler:\n np.savetxt(f_handler, local_t_t, fmt='%.18e',\n delimiter=',', newline='\\n')","title":""},{"docid":"65b99bf5f52d67a13cf3091aa536bac4","score":"0.520932","text":"def main():\n # Grupos de 5 minutos\n step = 12\n\n wfiles = ['90/45142', '90/45143', '90/45229', '90/45230']\n vars = ['wind_speed', 'density', 'pressure', 'wind_direction']\n mdata = {}\n for d, wf in enumerate(wfiles):\n print(\"/home/bejar/storage/Data/Wind/files/%s.nc\" % wf)\n nc_fid = Dataset(\"/home/bejar/storage/Data/Wind/files/%s.nc\" % wf, 'r')\n if d == 0:\n nint = nc_fid.dimensions['time'].size\n stime = nc_fid.getncattr('start_time')\n samp = nc_fid.getncattr('sample_period')\n hour = np.array(\n [t.tm_hour * 60 + t.tm_min for t in [time.gmtime(stime + (i * samp)) for i in range(0, nint, step)]])\n month = np.array([t.tm_mon for t in [time.gmtime(stime + (i * samp)) for i in range(0, nint, step)]])\n ldata = []\n for v in vars:\n data = nc_fid.variables[v]\n print(data.shape)\n\n end = data.shape[0]\n length = int(end / step)\n print(length)\n data30 = np.zeros((length))\n\n for i in range(0, end, step):\n data30[i / step] = np.sum(data[i: i + step]) / step\n\n ldata.append((data30))\n if d == 0:\n ldata.append(hour)\n ldata.append(month)\n\n data30 = np.stack(ldata, axis=1)\n print(data30.shape)\n mdata[wf.replace('/', '-')] = data30\n np.savez_compressed('/home/bejar/Wind%d.npz' % (step * 5), **mdata)","title":""},{"docid":"5dbf54d91490b55adc7ccd74e3efe2c6","score":"0.52065533","text":"def main_func(_thisDir, expInfo):\n print('####### starting analysis #########')\n\n # get all tsv files in subject directory\n directory = _thisDir + '/data/'+expInfo['participant'] +'/*.tsv'\n os.chdir(_thisDir + '/data/'+expInfo['participant'] + '/')\n\n # create png directory to store plots in\n try:\n os.makedirs('pngs/')\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n # define directory to save plots in\n outdir = _thisDir + '/data/'+expInfo['participant'] + '/pngs/'\n # get subject name\n subject = expInfo['participant']\n\n # create a counter to keep track of which loop-iteration we're in\n counter = 0\n\n # iterate over all files in subject directory, exclude the ones we're not\n # interested in\n for idx, filename in enumerate(glob.glob(directory)):\n if 'trial_start' in filename:\n #print('skipped')\n continue\n elif 'stimuli_onset' in filename:\n #print('skipped')\n continue\n elif '.json' in filename:\n #print('skipped')\n continue\n elif '.html' in filename:\n #print('skipped')\n continue\n elif '.png' in filename:\n #print('skipped')\n continue\n elif 'stats' in filename:\n #print('skipped')\n continue\n elif 'appended' in filename:\n #print('skipped')\n continue\n else:\n # if we're at the first iteration of loop read Data into DataFrame\n if counter == 0:\n df = pd.read_csv(filename, sep='\\t',\n encoding=\"utf8\", engine='python')\n # if we're in a sucessive loop read Data and append to DataFrame\n elif counter > 0:\n df2 = pd.read_csv(filename, sep='\\t',\n encoding=\"utf8\", engine='python')\n df = df.append(df2, ignore_index=True)\n counter += 1 # update counter\n\n # get a overview of results\n plot_overview_linegraph(df, outdir, subject)\n\n # divide DataFrame by acquisition_scheme\n df_base,df_tr1, df_tr2, df_t1w = plot_mri_settings_scatter(df, outdir, subject)\n\n # if \"epi fast\" acquisition_scheme was in DataFrame plot results compared to\n # frequency spectrum of acoustic scanner noise of specific acquisition_scheme\n if df_tr1 is not None:\n df_tr1 = rename_columns(df_tr1)\n df_tr1_mean = get_stats(df_tr1)\n\n x_coordinates, y_coordinates, coordinates, scaled = analyze_mri_noise(_thisDir+'/plotting/t2_tr1_spectrum.txt',\n half_height =False)\n plot_mri_overlay(x_coordinates, y_coordinates, coordinates, df_tr1, scaled, outdir, subject, setting='epi_fast(tr1)')\n\n # if \"epi_standard\" acquisition_scheme was in DataFrame plot results compared to\n # frequency spectrum of acoustic scanner noise of specific acquisition_scheme\n if df_tr2 is not None:\n df_tr2 = rename_columns(df_tr2)\n df_tr2_mean = get_stats(df_tr2)\n\n x_coordinates, y_coordinates, coordinates, scaled = analyze_mri_noise(_thisDir+'/plotting/t2_tr2_spectrum.txt',\n half_height =False)\n plot_mri_overlay(x_coordinates, y_coordinates, coordinates, df_tr2,scaled, outdir, subject, setting='epi_standard(tr2)')\n\n # if \"mprage\" acquisition_scheme was in DataFrame plot results compared to\n # frequency spectrum of acoustic scanner noise of specific acquisition_scheme\n if df_t1w is not None:\n df_t1w = rename_columns(df_t1w)\n df_t1w_mean = get_stats(df_t1w)\n x_coordinates, y_coordinates, coordinates, scaled = analyze_mri_noise(_thisDir+'/plotting/t1w_spectrum.txt',\n half_height =False)\n plot_mri_overlay(x_coordinates, y_coordinates, coordinates, df_t1w,scaled, outdir, subject, setting='mprage(T1w)')\n\n html_name = outdir+subject+'.html'\n htmlFile = open(outdir+subject+'.html','w')\n\n message = \"\"\"<html>\n <head><strong><font face = \"helvetica\" size = \"19\"><center>\n Aint no sound loud enough!</center></font></strong></head>\n <body>\n <p>\n <br><br>\n <font face = \"helvetica\" size = \"17\">Overview for subject</font>\n <br><br>\n <img src=\"outdir_overview_complete.png\" alt=\"overview\" height=\"400\" width=\"2300\">\n <br><br>\n <img src=\"outdir_mprage.png\" alt=\"Lineplot-placeholder\" height=\"400\" width=\"2300\">\n <br><br>\n <img src=\"outdir_baseline.png\" alt=\"Lineplot-placeholder\" height=\"400\" width=\"2300\">\n <br><br>\n <img src=\"outdir_epi_fast.png\" alt=\"Lineplot-placeholder height=\"400\" width=\"2300\">\n <br><br>\n <img src=\"outdir_epi_standard.png\" alt=\"Lineplot-placeholder\"height=\"400\" width=\"2300\">\n <br><br>\n <br><br>\n <font face = \"helvetica\" size = \"17\"><center>\n Mri-noise vs. stimuli\n </center></font>\n <br><br>\n <img src=\"outdir_mprage(t1w)mri_noise_overlay.png\" alt=\"Lineplot-placeholder\" height=\"700\" width=\"2300\">\n <br><br>\n <img src=\"outdir_epi_fast(tr1)mri_noise_overlay.png\" alt=\"Lineplot-placeholder\" height=\"700\" width=\"2300\">\n <br><br>\n <img src=\"outdir_epi_standard(tr2)mri_noise_overlay.png\" alt=\"Lineplot-placeholder\" height=\"700\" width=\"2300\">\n <br><br>\n </p></body></html>\"\"\"\n\n htmlFile.write(message)\n htmlFile.close()\n\n with open(html_name) as html:\n text = html.read().replace('subject', subject)\n text2 = text.replace('outdir', outdir+subject)\n\n with open(html_name, \"w\") as html:\n html.write(text2)","title":""},{"docid":"455845dc44f33fdf1e155d183a7b03f6","score":"0.5204826","text":"def samples_extraction(source_dir, save_dir, data_list_dir, window_size):\n hsi_data, hsi_gt = h5_loader(source_dir)\n hsi_data = max_min_normalization(hsi_data)\n s = window_size\n hsi_data = padding(hsi_data, s)\n\n [m, n] = hsi_gt.shape\n\n make_if_not_exist(save_dir)\n delete_if_exist(data_list_dir)\n\n for i in range(m):\n for j in range(n):\n if hsi_gt[i, j] > 0:\n label = hsi_gt[i, j]\n data = hsi_data[i:i + s, j:j + s, :].transpose([2, 0, 1])[np.newaxis]\n save_name = save_dir + 'samples_{}_{}.npy'.format(i + 1, j + 1)\n np.save(save_name, data)\n with open(data_list_dir, 'a') as f:\n f.write(save_name + ' {}\\n'.format(label))","title":""},{"docid":"0efa2c950182245968764b41c3c79ba4","score":"0.52036667","text":"def measure(self, mat, counts, w0, w1):\n pass","title":""},{"docid":"72017de3edf596622dd3e6f3769bbe68","score":"0.52022755","text":"def model_lindata(self,vel,dvel,wav,dtd,dtu=0.001,nthrds=4,verb=False):\n if(vel.shape[1] != self.__nx or vel.shape[0] != self.__nz):\n raise Exception(\"Velocity must have same shape as passed to constructor\")\n\n if(vel.shape[1] != dvel.shape[1] or vel.shape[0] != dvel.shape[0]):\n raise Exception(\"Velocity and perturbation must have same shape\")\n\n # Pad the models\n velp = self.pad_model(vel); dvelp = self.pad_model(dvel)\n\n # Temporal axis\n ntu = wav.shape[0]\n fact = int(dtd/dtu); ntd = int(ntu/fact)\n\n # Build the wave propagation object\n sca = sca2d.scaas2d(ntd, self.__nxp, self.__nzp, # Sizes\n dtd, self.__dx, self.__dz,dtu, # Samplings\n self.__bx, self.__bz, self.__alpha) # Absorbing boundary\n # Create input wavelet array\n allsrcs = np.zeros([self.__nsx,1,ntu],dtype='float32') # One source per shot (same length as wavefield)\n for isx in range(self.__nsx):\n allsrcs[isx,0,:] = wav[:]\n # Create output data\n allshot = np.zeros([self.__nsx,ntd,self.__nrx],dtype='float32')\n\n sca.brnfwd(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\n self.allrecx,self.allrecz,self.nrec, # Receiver information\n self.__nsx,velp,dvelp,allshot,nthrds,verb) # Velocity and output\n\n return allshot","title":""},{"docid":"11c69d722b12286baa5fe66cbd2e8c04","score":"0.5200596","text":"def run(self):\n # Log the value of sample parameter\n self.log.debug(\"Sample Parameter = %.2f\" % self.getarg('sampar'))\n # Copy datain to dataout\n self.dataout = self.datain","title":""},{"docid":"412bd7ef3910d75a3225dfcd4bfae8bc","score":"0.5194018","text":"def analyze(self, event):\n electrons = Collection(event, \"Electron\")\n muons = Collection(event, \"Muon\")\n jets = Collection(event, \"Jet\")\n photons = Collection(event, \"Photon\")\n fsrPhoton = Collection(event, \"FsrPhoton\")\n\n eventSum = ROOT.TLorentzVector()\n \n mu1_charge = 0\n count_mu = 0\n dimuonSelection = False\n RaffaeleRequest = False\n mu1 = ROOT.TLorentzVector()\n mu2 = ROOT.TLorentzVector()\n dimuonMass = 0\n \n \n selectedJet = {}\n selectedJet[\"criteria1\"] = {}\n selectedJet[\"criteria2\"] = {}\n \n\n\n\n if self.data :\n self.jesVariation = ['pt']\n \n \n jet1_jesTotalDown = ROOT.TLorentzVector()\n jet2_jesTotalDown = ROOT.TLorentzVector()\n jet1_jerUp = ROOT.TLorentzVector()\n jet2_jerUp = ROOT.TLorentzVector()\n jet1_jerDown = ROOT.TLorentzVector()\n jet2_jerDown = ROOT.TLorentzVector()\n \n if len(filter(self.muSel,muons)) < 2:\n return False\n muonNumber = len(filter(self.muSel,muons))\n for lep in muons :\n muon_corrected_p4 = ROOT.TLorentzVector()\n lep.FSR_p4 = ROOT.TLorentzVector()\n muon_corrected_p4.SetPtEtaPhiM(lep.corrected_pt , lep.eta, lep.phi, lep.mass)\n lep.iso = lep.pfRelIso04_all\n lep.correctedROC_pt = lep.corrected_pt\n #if lep.iso_FSR < 0.8 : \n #lep.FSR_p4.SetPtEtaPhiM(lep.pt_FSR , lep.eta_FSR, lep.phi_FSR, 0)\n #lep.correctedROC_pt = (muon_corrected_p4 + lep.FSR_p4).Pt()\n #lep.iso = (lep.pfRelIso04_all*lep.pt-lep.pt_FSR)/lep.correctedROC_pt\n if lep.fsrPhotonIdx!=-1 and fsrPhoton[ lep.fsrPhotonIdx].relIso03 <0.8 and fsrPhoton[ lep.fsrPhotonIdx].dROverEt2 < 0.019 :\n lep.FSR_p4.SetPtEtaPhiM(fsrPhoton[ lep.fsrPhotonIdx].pt,fsrPhoton[\n lep.fsrPhotonIdx].eta,fsrPhoton[ lep.fsrPhotonIdx].phi, 0)\n lep.correctedROC_pt = (muon_corrected_p4 + lep.FSR_p4).Pt()\n lep.iso = (lep.pfRelIso04_all*lep.pt-lep.FSR_p4.Pt())/lep.correctedROC_pt\n \n eventSum += lep.p4()\n if lep.iso<0.25 and abs(lep.pdgId)==13 and lep.mediumId and lep.correctedROC_pt>20 and not dimuonSelection :\n\n if count_mu == 1 and (lep.charge*mu1_charge)<0: # and lep.mediumId \n mu2.SetPtEtaPhiM(lep.corrected_pt,lep.eta, lep.phi, 0.105658375)\n dimuonSelection = True\n if count_mu == 0 :\n mu1.SetPtEtaPhiM(lep.corrected_pt,lep.eta, lep.phi, 0.105658375)#105,6583745(24) MeV\n mu1_charge = lep.charge\n count_mu +=1\n \n #if not dimuonSelection : return False \n #if max(mu1.Pt(), mu2.Pt())<28 : return False # muons are ordered in lep.pt, not in lep.pt_corrected\n #if min(mu1.Pt(), mu2.Pt())<9 : return False # muons are ordered in lep.pt, not in lep.pt_corrected\n \n\n \n #dimuon = mu1 + mu2\n dimuonMass = 0\n if dimuonSelection : dimuonMass = (mu1 + mu2).M()\n #if dimuon.M()<70 or dimuon.M()>110 :\n #if dimuonMass<0.1 :\n #return False\n \n\n\n muonfilter = lambda j : (j.muonIdx1==-1 or muons[j.muonIdx1].iso>0.25 or not muons[j.muonIdx1].mediumId or muons[j.muonIdx1].correctedROC_pt<20) and (j.muonIdx2==-1 or muons[j.muonIdx2].iso>0.25 or not muons[j.muonIdx2].mediumId or muons[j.muonIdx2].correctedROC_pt<20)\n #electronfilter = lambda j : (j.electronIdx1==-1 or electrons[j.electronIdx1].pfRelIso03_all>0.25 or abs(electrons[j.electronIdx1].dz) > 0.2 or abs(electrons[j.electronIdx1].dxy) > 0.05) and(j.electronIdx2==-1 or electrons[j.electronIdx2].pfRelIso03_all>0.25 or abs(electrons[j.electronIdx2].dz) > 0.2 or abs(electrons[j.electronIdx2].dxy) > 0.05)\n \n \n \n jetFilter1 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId>0 ) and abs(j.eta)<4.7 and (abs(j.eta)<2.5 or j.puId>6 or j.pt>50))\n jetFilter2 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId>0 ) and abs(j.eta)<4.7 )\n jetFilter2_2017 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId17>0) and abs(j.eta)<4.7 )\n jetFilter3_2017 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId17>0) and abs(j.eta)<4.7 ) and (j.puId17 > 6 or abs(j.eta) < 2.6 or abs(j.eta) > 3.0 )\n jetFilter4_2017 = lambda j : (j.jetId>0 and (j.PT>50 or j.puId17>0) and abs(j.eta)<4.7 ) and (j.puId17 > 6 or abs(j.eta) < 2.6 or abs(j.eta) > 3.0 ) \n \n jetsNolep = [j for j in jets if muonfilter(j)]\n\n \n if len(jetsNolep) < 2:\n return False\n \n if self.year!=\"2017\" : \n for j in jetsNolep : j.puId17 = j.puId\n\n\n jerBinName = []\n if not self.data:\n etabins=[0,1.93,2.5,3.139]\n ptbins=[0,50]\n jerBinName = [\"JEReta%spt%s\"%(n,m) for n in range(len(etabins)) for m in range(len(ptbins))]\n \n for j in jetsNolep:\n binEta = len(etabins)-1\n binPt = len(ptbins)-1\n for n in range(len(etabins)-1) : \n if abs(j.eta) > etabins[n] and abs(j.eta) < etabins[n+1] : binEta = n\n for m in range(len(ptbins)-1) : \n if j.pt > ptbins[m] and j.pt < ptbins[m+1] : binPt = m\n #print \"test pteta \", abs(j.eta), \" \\t\", j.pt, \" \\t\", binEta, \" \\t\", binPt, \" \\t\", \"JEReta%spt%s\"%(binEta,binPt), \" \\t \", jerBinName\n \n for k in jerBinName : setattr(j, k, j.pt_nom if k == \"JEReta%spt%s\"%(binEta,binPt) else j.pt)\n\n \n \n \n passAtLeastOne=False\n for vn in self.jesVariation + jerBinName:\n for j in jetsNolep:\n j.PT=getattr(j, vn)\n sortedJets=sorted(jetsNolep,key=lambda j : j.PT, reverse=True)\n jetsCriteria1=[j for j in sortedJets if jetFilter1(j)]\n jetsCriteria2=[j for j in sortedJets if jetFilter2(j)]\n jetsCriteria2_2017=[j for j in sortedJets if jetFilter2_2017(j)]\n jetsCriteria3_2017=[j for j in sortedJets if jetFilter3_2017(j)]\n jetsCriteria4_2017=[j for j in sortedJets if jetFilter4_2017(j)]\n if ( len(jetsCriteria1)>=2 and jetsCriteria1[0].PT > 35 and jetsCriteria1[1].PT > 25 and self.mqq(jetsCriteria1) > 250 ) : passAtLeastOne=True\n if ( len(jetsCriteria2)>=2 and jetsCriteria2[0].PT > 35 and jetsCriteria2[1].PT > 25 and self.mqq(jetsCriteria2) > 250 ) : passAtLeastOne=True\n if ( len(jetsCriteria2_2017)>=2 and jetsCriteria2_2017[0].PT > 35 and jetsCriteria2_2017[1].PT > 25 and self.mqq(jetsCriteria2_2017) > 250 ) : passAtLeastOne=True\n if ( len(jetsCriteria3_2017)>=2 and jetsCriteria3_2017[0].PT > 35 and jetsCriteria3_2017[1].PT > 25 and self.mqq(jetsCriteria3_2017) > 250 ) : passAtLeastOne=True\n if ( len(jetsCriteria4_2017)>=2 and jetsCriteria4_2017[0].PT > 35 and jetsCriteria4_2017[1].PT > 25 and self.mqq(jetsCriteria4_2017) > 250 ) : passAtLeastOne=True\n \n dijetMass = 0 \n \n if not passAtLeastOne : return False\n\n\n #self.out.fillBranch(\"EventMass\",eventSum.M())\n self.out.fillBranch(\"MuonMass\",dimuonMass)\n self.out.fillBranch(\"qqMass\",dijetMass)\n #self.out.fillBranch(\"jetIdx1\",jetIdx1)\n #self.out.fillBranch(\"jetIdx2\",jetIdx2)\n #self.out.fillBranch(\"jetNumber\",jetNumber)\n #self.out.fillBranch(\"bjetNumber\",bjetNumber)\n self.out.fillBranch(\"muonNumber\",muonNumber)\n #self.out.fillBranch(\"Jet_photonIdx1\",Jet_photonIdx1)\n #self.out.fillBranch(\"Jet_photonIdx2\",Jet_photonIdx2)\n #self.out.fillBranch(\"Jet_VBFselected\",VBFselectedJet)\n return True","title":""},{"docid":"fd3cb9dbdadd84282996c7ac71f48990","score":"0.5188413","text":"def _mlperftiny_test_dataset_ad(device_transport, dataset, timeout):\n samples, _ = dataset\n result_output = np.zeros(samples[0].shape[0])\n\n for slice in range(0, 40):\n _mlperftiny_write_sample(device_transport, samples[0][slice, :].flatten().tolist(), timeout)\n _mlperftiny_infer(device_transport, 1, 0, timeout)\n results = aot_transport_find_message(\n device_transport, MLPERFTINY_RESULT_MSG, timeout_sec=timeout\n )\n m = re.search(r\"m\\-results\\-\\[([A-Za-z0-9_,.]+)\\]\", results)\n results = m.group(1).split(\",\")\n results_val = [float(x) for x in results]\n result_output[slice] = np.array(results_val)\n return np.average(result_output)","title":""},{"docid":"bfa1dd54d9eddaa51fe57b0efcb6833c","score":"0.5187075","text":"def gen_data(self):\n train_data = []\n val_data = []\n test_data = []\n\n total_traj = sum(self.n_traj)\n\n freqs = np.random.uniform(self.freq[0], self.freq[1], total_traj)\n amps = np.random.uniform(self.amp[0], self.amp[1], total_traj)\n\n if self.phase:\n phase = np.random.uniform(0, 2, total_traj)\n else:\n phase = [0] * total_traj\n\n for i in range(self.n_traj[0]):\n train_d = transform_sine(self.train_time, freqs[i], amps[i],\n phase[i], noise=self.noise)\n train_data.append(train_d)\n\n for i in range(self.n_traj[0], self.n_traj[0]+self.n_traj[1]):\n val_d = transform_sine(self.val_time, freqs[i], amps[i],\n phase[i], noise=self.noise)\n val_data.append(val_d)\n\n for i in range(self.n_traj[0]+self.n_traj[1], total_traj):\n test_d = transform_sine(self.test_time, freqs[i], amps[i],\n phase[i], noise=self.noise)\n test_data.append(test_d)\n\n train_data = np.stack(train_data, 0)\n train_data = np.concatenate(train_data, 0)\n\n val_data = np.stack(val_data, 0)\n val_data = np.concatenate(val_data, 0)\n\n test_data = np.stack(test_data, 0)\n test_data = np.concatenate(test_data, 0)\n\n return train_data, val_data, test_data","title":""},{"docid":"d4276b2fb48b883daaaedb29afa5c784","score":"0.5187028","text":"def make_simple_sample_data_2D(data_type='iris'):\n from iris.cube import Cube\n from iris.coords import DimCoord,AuxCoord\n\n t_0=datetime.datetime(2000,1,1,12,0,0)\n \n x=np.arange(0,100e3,1000)\n y=np.arange(0,50e3,1000)\n t=t_0+np.arange(0,100,1)*datetime.timedelta(minutes=1)\n xx,yy=np.meshgrid(x,y) \n \n\n t_temp=np.arange(0,60,1)\n track1_t=t_0+t_temp*datetime.timedelta(minutes=1)\n x_0_1=10e3\n y_0_1=10e3\n track1_x=x_0_1+30*t_temp*60\n track1_y=y_0_1+14*t_temp*60\n track1_magnitude=10*np.ones(track1_x.shape)\n\n data=np.zeros((t.shape[0],y.shape[0],x.shape[0]))\n for i_t,t_i in enumerate(t):\n if np.any(t_i in track1_t):\n x_i=track1_x[track1_t==t_i]\n y_i=track1_y[track1_t==t_i]\n mag_i=track1_magnitude[track1_t==t_i]\n data[i_t]=data[i_t]+mag_i*np.exp(-np.power(xx - x_i,2.) / (2 * np.power(10e3, 2.)))*np.exp(-np.power(yy - y_i, 2.) / (2 * np.power(10e3, 2.)))\n\n t_start=datetime.datetime(1970,1,1,0,0)\n t_points=(t-t_start).astype(\"timedelta64[ms]\").astype(int) / 1000\n t_coord=DimCoord(t_points,standard_name='time',var_name='time',units='seconds since 1970-01-01 00:00')\n x_coord=DimCoord(x,standard_name='projection_x_coordinate',var_name='x',units='m')\n y_coord=DimCoord(y,standard_name='projection_y_coordinate',var_name='y',units='m')\n lat_coord=AuxCoord(24+1e-5*xx,standard_name='latitude',var_name='latitude',units='degree')\n lon_coord=AuxCoord(150+1e-5*yy,standard_name='longitude',var_name='longitude',units='degree')\n sample_data=Cube(data,dim_coords_and_dims=[(t_coord, 0),(y_coord, 1),(x_coord, 2)],aux_coords_and_dims=[(lat_coord, (1,2)),(lon_coord, (1,2))],var_name='w',units='m s-1') \n\n if data_type=='xarray':\n sample_data=DataArray.from_iris(sample_data)\n \n return sample_data","title":""},{"docid":"d3bbdab2cf1c3afc491c627f74d59039","score":"0.51864505","text":"def wavelet_denoise(array, wavelet, threshold, levels, thrmode='hard'):\n if not array.ndim==2:\n raise TypeError('Input array is not a frame or 2d array')\n \n WC = pywt.wavedec2(array, wavelet, level=levels)\n if thrmode=='hard':\n NWC = map(lambda x: pywt.thresholding.hard(x, threshold), WC)\n elif thrmode=='soft':\n NWC = map(lambda x: pywt.thresholding.soft(x, threshold), WC)\n else:\n raise ValueError('Threshold mode not recognized')\n array_filtered = pywt.waverec2(NWC, wavelet)\n \n return array_filtered","title":""},{"docid":"36e07d071ea24f7aa3257dcb7c5792b3","score":"0.5180787","text":"def main():\n\n\tpathToResults = \"/tmp/results/\"\n\tname = \"_SS_pyNN_\"\n\tnorm = '1'\n\teesAmplitude = '235'\n\teesFrequency = '40'\n\tdelay = 2\n\tweights_1 = np.array([0.091])\n\tweights_2 = np.linspace(0.03,0.1,20)\n\tweights_3 = np.array([0.046])\n\tweights_4 = np.linspace(-.001,-.010,20)\n\tweights_5 = np.array([-0.002])\n\n\tfig, ax = plt.subplots(weights_2.size, weights_4.size,figsize=(22,12),sharex='col',sharey='col')\n\tfor w1 in weights_1:\n\t\tfor i2,w2 in enumerate(weights_2):\n\t\t\tfor w3 in weights_3:\n\t\t\t\tfor i4,w4 in enumerate(weights_4):\n\t\t\t\t\tfor w5 in weights_5:\n\t\t\t\t\t\tpattern = \"*%suA_%sHz_Delay_%dms%s_w1_%f_w2_%f_w3_%f_w4_%f_w5_%f.p\" % (eesAmplitude,eesFrequency,delay,name,w1,w2,w3,w4,w5)\n\t\t\t\t\t\tresultFile = gt.find(pattern,pathToResults)\n\t\t\t\t\t\tif not resultFile: continue\n\t\t\t\t\t\twith open(resultFile[0], 'r') as pickle_file:\n\t\t\t\t\t\t\t_ = pickle.load(pickle_file)\n\t\t\t\t\t\t\tmeanFr = pickle.load(pickle_file)\n\n\t\t\t\t\t\tax[i2,i4].plot(meanFr['TA']['IaInt'],'r')\n\t\t\t\t\t\tax[i2,i4].plot(meanFr['GM']['IaInt'],'b')\n\t\t\t\t\t\tax[i2,i4].xaxis.set_ticklabels([])\n\t\t\t\t\t\tax[i2,i4].yaxis.set_ticklabels([])\n\t\t\t\t\t\tax[i2,i4].xaxis.set_ticks([])\n\t\t\t\t\t\tax[i2,i4].yaxis.set_ticks([])\n\t\t\t\t\t\tif i4==0:ax[i2,i4].set_ylabel(\"w2\\n%.4f\"%(w2),fontsize=6)\n\t\t\t\t\t\tif i2==weights_2.size-1:ax[i2,i4].set_xlabel(\"w4\\n%.4f\"%(w4),fontsize=6)\n\tplt.show()","title":""},{"docid":"3ba7c78dcd1c61cf1bae765b95a8bc89","score":"0.51759034","text":"def cwvlt(\n da,\n s,\n dim=None,\n t0=5 * 365 * 86400,\n a=1.0,\n wtype=\"morlet\",\n):\n\n if dim is None:\n dim = list(da.dims)\n else:\n if isinstance(dim, str):\n dim = [dim]\n\n if len(dim) != 1:\n raise ValueError(\"The transformed dimension should be one-dimensional.\")\n\n sdim = s.dims[0]\n\n # the axes along which to take wavelets\n axis_num = [da.get_axis_num(d) for d in dim]\n\n N = [da.shape[n] for n in axis_num]\n\n delta_t = _delta(da, dim)\n\n # grid parameters\n if len(dim) == 1:\n t = da[da.dims[axis_num[0]]] - da[da.dims[axis_num[0]]].mean()\n else:\n raise NotImplementedError(\n \"Only one-dimensional transforms are implemented for now.\"\n )\n\n if wtype == \"morlet\":\n wavelet = _morlet(t0, a, s, t)\n else:\n raise NotImplementedError(\"Only the Morlet wavelet is implemented for now.\")\n\n dawt = (da * np.conj(wavelet)).sum(dim, skipna=True) * delta_t / np.sqrt(np.abs(s))\n dawt = dawt.drop_vars(sdim)\n dawt[sdim] = t0 * s\n\n return dawt","title":""},{"docid":"40c3cd67f1c2a62046a165376af04b90","score":"0.5174303","text":"def analyze(self):\n self._setup_datastructs()\n self._calculate_powers()\n self._calculate_interpolants()\n self._calculate_energies()","title":""},{"docid":"88b21fea069e7ef42c0e42a4c0c93ffe","score":"0.51703465","text":"def extract_drums(attack, decay, onset_array, wavefile):\n read_data = wavio.readwav(wavefile)\n filename = os.path.splitext(wavefile)[0]\n read_array = read_data[2] # a list of sample values in sequential order for the wavefile\n output_csv_filename = '{0}_samples_to_onset.csv'.format(filename)\n samples_to_onset_array = []\n output_dir = 'separated_input'\n for i in range(len(onset_array) - 1): \n if onset_array[i + 1] >= len(read_array) and onset_array[i] - attack < 0:\n start = 0 \n write_array = read_array[0:]\n elif onset_array[i] - attack < 0:\n start = 0 \n write_array = read_array[0: onset_array[i + 1]] \n elif onset_array[i + 1] >= len(read_array):\n start = onset_array[i] - attack\n write_array = read_array[onset_array[i] - attack:]\n else:\n start = onset_array[i] - attack\n write_array = read_array[onset_array[i] - attack: onset_array[i+1]]\n if len(write_array) - attack >= decay: #if the drumhit file is long enough, write it into the unclassified_drums directory\n output_filename = '{0}_{1}.wav'.format(filename, \"%05d\" % i)\n wavio.writewav24(output_filename, read_data[0], write_array)\n shutil.move(output_filename, '{0}'.format(output_dir))\n samples_to_onset = onset_array[i] - start\n samples_to_onset_array.append([output_filename, samples_to_onset])\n with open(output_csv_filename, 'w') as csvfile:\n a = csv.writer(csvfile)\n a.writerows(samples_to_onset_array)\n shutil.move(output_csv_filename, '{0}'.format(output_dir))","title":""},{"docid":"38682cceb9b9e7403eb5bb0681e0cfd3","score":"0.51696014","text":"def analyze(self):\n self.calculate_FOM()\n self.generate_FOM_image()\n self.generate_plot()\n ##self.generate_spot_mask()","title":""},{"docid":"a740f2baf7a940f3283e121014541413","score":"0.51629245","text":"def run(input_file_path, output_file_path):\n\n # 1. Read required data to generate W matrices\n print \"Reading file:\", input_file_path\n sensor_1_name, sensor_2_name, time1, time2, uR1, uR2, scanline_time, weights = read_matchup_data(input_file_path)\n\n if sensor_2_name in MHS_sensors:\n sensor_2_instrument = \"MHS\"\n elif sensor_2_name in AMSUB_sensors:\n sensor_2_instrument = \"AMSUB\"\n else:\n raise ValueError(\"Sensor 2 Unknown - \"+sensor_2_name)\n\n if sensor_1_name in MHS_sensors:\n sensor_1_instrument = \"MHS\"\n elif sensor_1_name in AMSUB_sensors:\n sensor_1_instrument = \"AMSUB\"\n elif sensor_1_name in ref_sensors:\n sensor_1_instrument = \"ref\"\n else:\n raise ValueError(\"Sensor 1 Unknown - \"+sensor_1_name)\n\n # 2. Generate required W matrix variables\n print \"Generating W Matrix Variables from data...\"\n\n w_matrix_val, w_matrix_row, w_matrix_col,\\\n w_matrix_nnz, u_matrix_row_count, u_matrix_val, \\\n w_matrix_use1, w_matrix_use2, u_matrix_use1, \\\n u_matrix_use2 = return_w_matrix_variables_MW(time1, time2, scanline_time, uR1, uR2, weights,\n sensor_1_instrument, sensor_2_instrument)\n\n # 3. Update file to include W matrix variables\n print \"Writing to new file:\", output_file_path\n\n # a. Copy old file to new location\n copyfile(input_file_path, output_file_path)\n\n # b. Amend existing variables\n dataset = Dataset(output_file_path, 'a')\n\n # + uncertainty_type1/2\n if sensor_1_instrument == \"ref\":\n uncertainty_type1 = array([1], dtype=int8)\n elif sensor_1_instrument == \"AMSUB\":\n uncertainty_type1 = array([3, 3, 4, 4, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\n elif sensor_1_instrument == \"MHS\":\n uncertainty_type1 = array([3, 3, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\n if sensor_2_instrument == \"AMSUB\":\n uncertainty_type2 = array([3, 3, 4, 4, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\n elif sensor_2_instrument == \"MHS\":\n uncertainty_type2 = array([3, 3, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\n\n dataset.variables['uncertainty_type1'][:] = uncertainty_type1\n dataset.variables['uncertainty_type2'][:] = uncertainty_type2\n\n # + Ur1/2\n Ur2 = dataset.variables['Ur2'][:]\n if sensor_2_instrument == \"AMSUB\":\n Ur2[:, 0:9] = 0\n elif sensor_2_instrument == \"MHS\":\n Ur2[:, 0:7] = 0\n dataset.variables['Ur2'][:] = Ur2\n\n Ur1 = dataset.variables['Ur1'][:]\n if sensor_1_instrument == \"AMSUB\":\n Ur1[:, :9] = 0\n elif sensor_1_instrument == \"MHS\":\n Ur1[:, :7] = 0\n dataset.variables['Ur1'][:] = Ur1\n\n dataset.close()\n\n # c. Add w matrix variables to updated file\n append_W_to_input_file(output_file_path,\n w_matrix_val, w_matrix_row, w_matrix_col, w_matrix_nnz,\n u_matrix_row_count, u_matrix_val,\n w_matrix_use1, w_matrix_use2, u_matrix_use1, u_matrix_use2)\n\n # 4. Test harmonisation\n test_input_file(output_file_path)\n\n return 0","title":""},{"docid":"3c71d7a2d7dc7e565ddcb02026dfb6d7","score":"0.5162724","text":"def waveletTransform(signalRaw, level=4, wavelet='db2', order='freq'):\n wp = pywt.WaveletPacket(signalRaw, wavelet, 'symmetric', maxlevel=level)\n nodes = wp.get_level(level, order=order)\n yWT = np.array([n.data for n in nodes], 'd')\n yWT = abs(yWT)\n return wp.data, yWT","title":""},{"docid":"8718568fe6c1e819e0198e3473ae7973","score":"0.51623017","text":"def assign_wavelengths(self, input_spectra):\n\n # Save these, so we'll know what data type to use for the output.\n # The types used for accumulating sums and taking averages may not\n # be the same as these types.\n self.wavelength_dtype = input_spectra[0].wavelength.dtype\n self.net_dtype = input_spectra[0].net.dtype\n self.dq_dtype = input_spectra[0].dq.dtype\n\n nwl = 0\n for in_spec in input_spectra:\n nwl += in_spec.nelem\n\n # Create an array with all the input wavelengths (i.e. the union\n # of the input wavelengths).\n wl = np.zeros(nwl, dtype=np.float)\n i = 0\n for in_spec in input_spectra:\n nelem = in_spec.nelem\n # Concatenate current input wavelengths to wl array.\n wl[i:i + nelem] = in_spec.wavelength.copy()\n i += nelem\n wl.sort()\n\n # count_input will be the number of input spectra that cover the\n # corresponding wavelength in wl.\n count_input = np.zeros(nwl, dtype=np.int64)\n for in_spec in input_spectra:\n input_wl = in_spec.wavelength\n # wl0 and wl1 will be about a half pixel wider on either side\n # of the wavelength range for the current input spectrum.\n if input_wl[1] > input_wl[0]: # wavelengths are increasing\n wl0 = input_wl[0] - 0.5 * (input_wl[1] - input_wl[0])\n wl1 = input_wl[-1] + 0.5 * (input_wl[-1] - input_wl[-2])\n elif input_wl[1] < input_wl[0]: # wavelengths are decreasing\n wl0 = input_wl[-1] - 0.5 * (input_wl[-2] - input_wl[-1])\n wl1 = input_wl[0] + 0.5 * (input_wl[0] - input_wl[1])\n else:\n raise RuntimeError(\"Wavelength increment must not be zero.\")\n temp = np.where(wl >= wl0, 1, 0)\n temp = np.where(wl >= wl1, 0, temp)\n count_input += temp\n del temp\n # This shouldn't happen.\n if np.any(count_input <= 0.):\n raise RuntimeError(\"Problem with input wavelengths.\")\n\n self.wavelength = self.compute_output_wl(wl, count_input)\n\n self.wcs = create_spectral_wcs(input_spectra[0].right_ascension[0],\n input_spectra[0].declination[0],\n self.wavelength)","title":""},{"docid":"bd51685fce806c00f93458ce8c34cb21","score":"0.5159673","text":"def analyze_spectrum(measurement, background, reference):\n sample_data = []\n for isotope in isotope_list:\n info = analyze_isotope(measurement, background, reference, isotope)\n sample_data.extend(info)\n return sample_data","title":""},{"docid":"7a24b332bb4c95c7afdc7f63e51d56b8","score":"0.51534945","text":"def __analyze_dataset(self):\n info_file = os.path.join(self.work_dir, self.data_name + '_info.txt')\n\n #if not analyzed before, analyze\n if os.path.exists(info_file) == False :\n logging.info('analyze data %s' %(self.data_path))\n if pysol.analyze_data(self.data_path, self.dtype, info_file) != 0:\n sys.exit()\n\n #parse data num\n pattern = re.compile(r'data number\\s*:\\s*(\\d+)')\n result_list = pattern.findall(open(info_file,'r').read())\n if len(result_list) != 1:\n logging.error('parse data number failed, result_list is %s' %(str(result_list)))\n sys.exit()\n\n self.data_num = (int)(result_list[0])\n\n #parse dimension\n pattern = re.compile(r'dimension\\s*:\\s*(\\d+)')\n result_list = pattern.findall(open(info_file,'r').read())\n if len(result_list) != 1:\n print result_list\n print 'parse dimension failed'\n sys.exit()\n\n self.dim = (int)(result_list[0])\n\n #parse class number\n pattern = re.compile(r'class num\\s*:\\s*(\\d+)')\n result_list = pattern.findall(open(info_file,'r').read())\n if len(result_list) != 1:\n logging.error('parse class num failed, result_list is %s' %(str(result_list)))\n sys.exit()\n\n self.class_num = (int)(result_list[0])","title":""}],"string":"[\n {\n \"docid\": \"9ac1013ef45a01eb02ad55a7f60cad55\",\n \"score\": \"0.65809494\",\n \"text\": \"def model_data(self,wav,dt,t0,minf,maxf,vel,ref,jf=1,nrmax=3,eps=0.,dtmax=5e-05,time=True,\\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\\n # Save wavelet temporal parameters\\n nt = wav.shape[0]; it0 = int(t0/dt)\\n\\n # Create the input frequency domain source and get original frequency axis\\n self.__nwo,self.__ow,self.__dw,wfft = self.fft1(wav,dt,minf=minf,maxf=maxf)\\n wfftd = wfft[::jf]\\n self.__nwc = wfftd.shape[0] # Get the number of frequencies to compute\\n self.__dwc = jf*self.__dw\\n\\n if(sverb or wverb): print(\\\"Frequency axis: nw=%d ow=%f dw=%f\\\"%(self.__nwc,self.__ow,self.__dwc))\\n\\n # Single square root object\\n ssf = ssr3(self.__nx ,self.__ny,self.__nz , # Spatial Sizes\\n self.__dx ,self.__dy,self.__dz , # Spatial Samplings\\n self.__nwc,self.__ow,self.__dwc,eps, # Frequency axis\\n ntx,nty,px,py, # Taper and padding\\n dtmax,nrmax,nthrds) # Reference velocities\\n\\n # Compute slowness and reference slownesses\\n slo = 1/vel\\n ssf.set_slows(slo)\\n\\n # Allocate output data (surface wavefield)\\n datw = np.zeros([self.__nexp,self.__nwc,self.__ny,self.__nx],dtype='complex64')\\n\\n # Allocate the source for one shot\\n sou = np.zeros([self.__nwc,self.__ny,self.__nx],dtype='complex64')\\n\\n # Loop over sources\\n k = 0\\n for icrd in progressbar(self.__scoords,\\\"nexp:\\\",verb=sverb):\\n # Get the source coordinates\\n sy = icrd[0]; sx = icrd[1]\\n # Create the source for this shot\\n sou[:] = 0.0\\n sou[:,sy,sx] = wfftd[:]\\n # Downward continuation\\n ssf.modallw(ref,sou,datw[k],wverb)\\n k += 1\\n\\n # Reshape output data\\n datwr = datw.reshape([self.__nsy,self.__nsx,self.__nwc,self.__ny,self.__nx])\\n\\n if(time):\\n # Inverse fourier transform\\n datt = self.data_f2t(datwr,self.__nwo,self.__ow,self.__dwc,nt,it0)\\n return datt\\n else:\\n return datwr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f267e019f19f50707df0d23604d3626\",\n \"score\": \"0.6510147\",\n \"text\": \"def wavelet_features(X):\\n N_train = X.shape[0]\\n nchannels = 3\\n X_dwt = np.zeros(X.shape)\\n print(\\\"generating wavelet features\\\")\\n for image in tqdm.tqdm(range(N_train)):\\n for channel in range(nchannels):\\n coeffs = pywt.dwt2(X[image, :, :, channel], 'haar')\\n LL, (LH, HL, HH) = coeffs\\n X_dwt[image, :, :, channel] = np.block([[LL, LH], [HL, HH]])\\n \\n return X_dwt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d483d00d559aed9fe2343ac0d7e07a\",\n \"score\": \"0.6369924\",\n \"text\": \"def wave():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303cd897c993be8216ca8c2ab77b4359\",\n \"score\": \"0.62218803\",\n \"text\": \"def wavelet(self, period, period_unit = 'y', cut = 1, ts = None, pool = None, save_wave = False,\\n regress_amp_to_data = False, k0 = 6., cut_time = False, continuous_phase = False,\\n phase_fluct = False, cut_data = False):\\n\\n delta = self.time[1] - self.time[0]\\n if delta == 1:\\n # daily data\\n if period_unit == 'y':\\n y = 365.25\\n elif period_unit == 'd':\\n y = 1.\\n elif period_unit == 'm':\\n raise Exception(\\\"For daily data is hard to enter wavelet period in months...\\\")\\n else:\\n raise Exception(\\\"Unknown type.\\\")\\n elif abs(delta - 30) < 3.0:\\n # monthly data\\n if period_unit == 'y':\\n y = 12.\\n elif period_unit == 'm':\\n y = 1.\\n elif period_unit == 'd':\\n raise Exception(\\\"For monthly data doesn't make sense to enter wavelet period in days.\\\")\\n else:\\n raise Exception(\\\"Unknown type.\\\")\\n elif delta == 365 or delta == 366:\\n # annual data\\n if period_unit == 'y':\\n y = 1.\\n elif period_unit == 'm':\\n raise Exception(\\\"For monthly data doesn't make sense to enter wavelet period in days.\\\")\\n elif period_unit == 'd':\\n raise Exception(\\\"For monthly data doesn't make sense to enter wavelet period in days.\\\")\\n else:\\n raise Exception(\\\"Unknown type.\\\")\\n else:\\n raise Exception('Unknown temporal sampling in the field.')\\n\\n fourier_factor = (4 * np.pi) / (k0 + np.sqrt(2 + np.power(k0,2)))\\n per = period * y # frequency of interest\\n self.frequency = per\\n self.omega = 2*np.pi / (y*period)\\n s0 = per / fourier_factor # get scale\\n\\n if phase_fluct:\\n continuous_phase = True\\n\\n if cut is not None:\\n to_cut = int(y*cut)\\n else:\\n to_cut = None\\n\\n if ts is None:\\n\\n if self.data.ndim > 2:\\n num_lats = self.lats.shape[0]\\n num_lons = self.lons.shape[0]\\n elif self.data.ndim == 2: # lot of station data\\n num_lats = self.lats.shape[0]\\n num_lons = 1\\n self.data = self.data[:, :, np.newaxis]\\n else:\\n num_lats = 1\\n num_lons = 1\\n self.data = self.data[:, np.newaxis, np.newaxis]\\n\\n self.phase = np.zeros_like(self.data) if cut is None else np.zeros([self.data.shape[0] - 2*to_cut] + self.get_spatial_dims())\\n self.amplitude = np.zeros_like(self.data) if cut is None else np.zeros([self.data.shape[0] - 2*to_cut] + self.get_spatial_dims())\\n if save_wave:\\n self.wave = np.zeros_like(self.data, dtype = np.complex64) if cut is None else np.zeros([self.data.shape[0] - 2*to_cut] + self.get_spatial_dims(), dtype = np.complex64)\\n\\n job_args = [ (i, j, s0, self.data[:, i, j], save_wave, regress_amp_to_data, k0, continuous_phase, to_cut) for i in range(num_lats) for j in range(num_lons) ]\\n\\n if pool is None:\\n job_result = map(self._get_oscillatory_modes, job_args)\\n elif pool is not None:\\n job_result = pool.map(self._get_oscillatory_modes, job_args)\\n del job_args\\n\\n for i, j, res in job_result:\\n self.phase[:, i, j] = res[0]\\n self.amplitude[:, i, j] = res[1]\\n if save_wave:\\n self.wave[:, i, j] = res[2]\\n\\n del job_result\\n\\n if cut is not None and cut_time:\\n self.time = self.time[to_cut:-to_cut]\\n\\n if cut is not None and cut_data:\\n self.data = self.data[to_cut:-to_cut, ...]\\n\\n self.data = np.squeeze(self.data)\\n if phase_fluct:\\n ph0 = self.phase[0, ...]\\n sin = np.arange(0, self.phase.shape[0])[:, np.newaxis, np.newaxis] * self.omega + ph0\\n self.phase -= sin\\n self.phase = np.squeeze(self.phase)\\n else:\\n self.phase = np.squeeze(self.phase)\\n self.amplitude = np.squeeze(self.amplitude)\\n if save_wave:\\n self.wave = np.squeeze(self.wave)\\n\\n else:\\n res = self._get_oscillatory_modes([0, 0, s0, ts, save_wave, regress_amp_to_data, k0, continuous_phase, to_cut])[-1]\\n # add phase fluct!!!\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e670e79563c8f8141a8c85a2da45f08\",\n \"score\": \"0.60416424\",\n \"text\": \"def wavelet(X, mask, morlet_fb, gaussian_fb, row, segment_len, prefix):\\n nchannel = X.shape[1]\\n \\n for k in range(morlet_fb.shape[1]):\\n # calculate wavelet coefficients\\n W, W_autocorr = wavelet_coef(X, morlet_fb[:, k], MORLET_FILTER_LENGTH[k], NUM_PAD, segment_len)\\n phase = np.angle(W)\\n amplitude = np.abs(W)\\n auto_spectra = np.square(amplitude)\\n \\n basename = \\\"wavelet_\\\" + MORLET_BAND[k]\\n \\n for i in range(nchannel):\\n \\n # univariate wavelet features\\n basic_stats(amplitude[mask[:, i]==1, i], row, prefix + basename, \\\"_\\\" + CHANNEL_ID[i], True, False)\\n \\n # auto correlation features over wavelet coefficients\\n indices = np.where(np.diff(np.sign(W_autocorr[:, i])))[0]\\n index = W_autocorr.shape[0] if len(indices) == 0 else indices[0]\\n row[prefix + basename + \\\"_autocorr_decay_\\\" + CHANNEL_ID[i]] = float(index) / float(SAMPLING_RATE) * 1000.0\\n\\n for i in range(nchannel-1):\\n for j in range(i+1, nchannel):\\n # bivariate features\\n channel_id_pair = \\\"_\\\" + CHANNEL_ID[i] + CHANNEL_ID[j]\\n phase_delta = phase[:, i] - phase[:, j]\\n mask_union = ((mask[:, i]==1) & (mask[:, j]==1)) == 1\\n count = np.sum(mask_union)\\n \\n # calculate SPLV and correlation coefficients of wavelet coefficients\\n splv = np.nan\\n if count > 0:\\n splv = np.abs(np.sum(np.exp(1.0j * phase_delta)[mask_union]) / float(count))\\n row[prefix + basename + \\\"_splv\\\" + channel_id_pair] = splv\\n row[prefix + basename + \\\"_corrcoef\\\" + channel_id_pair] = scipy.stats.pearsonr(amplitude[mask_union, i], amplitude[mask_union, j])[0]\\n\\n # extract statistics of phase\\n basic_stats(phase_delta[mask_union], row, prefix + basename + \\\"_phase_original\\\", channel_id_pair, False, True)\\n basic_stats(np.unwrap(phase_delta)[mask_union], row, prefix + basename + \\\"_phase_unwrapped\\\", channel_id_pair, True, True)\\n \\n # extract statistics of wavelet coherence\\n coherence = wavelet_coherence(W[:, i], W[:, j], auto_spectra[:, i], auto_spectra[:, j], mask_union, gaussian_fb[k])\\n basic_stats(coherence, row, prefix + basename + \\\"_coherence\\\", channel_id_pair, False, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398de2e4eea4e6c6d22a4a1579e4ad19\",\n \"score\": \"0.59884363\",\n \"text\": \"def filter_data(expname, iband, fband):\\n datainfo = experiments[expname]\\n\\n f = h5py.File(datainfo.dpath + datainfo.name + '.hdf5', 'r+')\\n print datainfo.dpath + datainfo.name + '.hdf5'\\n wtime = 120e-3 # Window length in miliseconds\\n sampling = datainfo.sampling #/ 6.0\\n Tw = int(2 * np.round(wtime*sampling/2))\\n\\n for df in datainfo.datafiles:\\n print df\\n d = f[df + '/Raw']\\n samp = f[df + '/Raw'].attrs['Sampling']\\n data = d[()]\\n freq = samp * 0.5\\n b, a = butter(3, [iband/freq, fband/freq], btype='band')\\n filtered = np.zeros(data.shape)\\n for i in range(data.shape[1]):\\n filtered[:, i] = filtfilt(b, a, data[:, i])\\n d = f.require_dataset(df + '/RawFiltered', filtered.shape, dtype='f', data=filtered, compression='gzip')\\n d[()] = filtered\\n f[df + '/RawFiltered'].attrs['Low'] = iband\\n f[df + '/RawFiltered'].attrs['high'] = fband\\n for s in datainfo.sensors:\\n i = datainfo.sensors.index(s)\\n times = f[df + '/' + s + '/TimeClean']\\n rawpeaks = np.zeros((times.shape[0], Tw))\\n print times.shape[0]\\n for j in range(times.shape[0]):\\n tstart = times[j] - np.floor(Tw / 2)\\n tstop = tstart + Tw\\n if tstart > 0 and tstop < filtered.shape[0]:\\n rawpeaks[j, :] = filtered[tstart:tstop, i]\\n elif tstart < 0:\\n rawpeaks[j, :] = np.hstack((np.zeros(np.abs(tstart)), filtered[0:tstop, i]))\\n else:\\n rawpeaks[j, :] = np.hstack((filtered[tstart:tstop, i], np.zeros(tstop-filtered.shape[0])))\\n #show_signal(rawpeaks[j])\\n # Peak Data\\n dfilter = f[df + '/' + s]\\n #del dfilter['PeaksFilter']\\n dfilter.require_dataset('PeaksFilter', rawpeaks.shape, dtype='f', data=rawpeaks,\\n compression='gzip')\\n\\n\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f968e6ec2a12fbaba17779ebdc37bd3a\",\n \"score\": \"0.5986626\",\n \"text\": \"def goEstimateWaveletFromGather(name,tmin,tmax,funcert,luncert):\\n print name\\n if name == \\\"syn1\\\": # Synthetic with one reflector\\n st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first\\n sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first\\n nref,vnmo = 1,2.0 # number of reflectors and NMO velocity\\n tran,tbed = False,False\\n freq,decay = 20.0,0.05 # peak frequency and decay for wavelet\\n fmin,fmax,sfac = 0.0,50.0,1.00\\n texp,tbal = 0.00,0\\n \\n zp = False# zero-phase?\\n if zp:\\n na,ka = 5,-2\\n nh,kh = 251,-125\\n decay *= 4.0\\n else:\\n na,ka = 11,0\\n nh,kh = 151,-25\\n hsyn = getArWavelet(freq,decay,st,nh,kh,zp)\\n elif name == \\\"synr\\\": # Synthetic with random reflectors\\n st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first\\n sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first\\n tran,tbed = True,False\\n nref,vnmo = 40,2.0 # number of reflectors and NMO velocity\\n freq,decay = 20.0,0.05 # peak frequency and decay for wavelet\\n fmin,fmax,sfac = 0.0,50.0,1.00\\n texp,tbal = 0.00,0\\n zp = False # zero-phase?\\n na,ka = 11,0 # sampling for inverse wavelet a\\n nh,kh = 151,-25 # sampling for wavelet h\\n hsyn = getArWavelet(freq,decay,st,nh,kh)\\n elif name == \\\"synt\\\": # Synthetic with random thin beds\\n st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first\\n sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first\\n tran,tbed = True,True\\n nref,vnmo = 40,2.0 # number of reflectors and NMO velocity\\n freq,decay = 20.0,0.05 # peak frequency and decay for wavelet\\n fmin,fmax,sfac = 0.0,50.0,1.0001\\n texp,tbal = 0.00,0\\n zp = True # zero-phase?\\n if zp:\\n na,ka = 5,-2\\n nh,kh = 251,-125\\n decay *= 4.0\\n else:\\n na,ka = 11,0\\n nh,kh = 151,-25\\n hsyn = getArWavelet(freq,decay,st,nh,kh,zp)\\n\\n f = zerofloat(nt,nx)\\n p = makeCmpReflections(vnmo,nref,st,sx,random=tran,thinBeds=tbed)\\n f = addArWavelet(freq,decay,st,sx,p,zp)\\n f = tpow(texp,st,f)\\n if tbal>0:\\n f = balance(tbal,f)\\n\\n #for whole gather\\n #tmin = 0.00\\n #tmax = 2.00\\n #itmin = int(tmin/dt)\\n #itmax = int(tmax/dt)\\n #nw = itmax-itmin+1\\n #w = rampWeights(funcert,luncert,nw)\\n #inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,\\n # na,ka,nh,kh,hsyn,tmin,tmax,w=w)\\n itmin = int(tmin/dt)\\n itmax = int(tmax/dt)\\n nw = itmax-itmin+1\\n w = zerofloat(nw,3)\\n w[0] = fillfloat(1.0,nw)\\n w[1] = rampWeights(funcert,luncert,nw)\\n w[2] = rampWeights(luncert,funcert,nw)\\n uncerts = [\\\"Constant Uncertainty\\\",\\\"Ramp Up Uncertainty\\\",\\\"Ramp Down Uncertainty\\\"]\\n hpefs = zerofloat(nh,3)\\n hnmos = zerofloat(nh,3)\\n for u in range(0,3):\\n hpef,hnmo = inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,\\n na,ka,nh,kh,hsyn,tmin,tmax,uncerts[u],w=w[u])\\n #hpef,hnmo = inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,\\n #na,ka,nh,kh,hsyn,tmin,tmax)\\n hnmos[u] = hnmo\\n hpefs[u] = hpef\\n title = \\\"Constant Uncert., Uncert. \\\"+str(funcert)+\\\" \\\"+str(luncert)+\\\", Uncert. \\\"+str(luncert)+\\\" \\\"+str(funcert)\\n titlePEF = \\\"PEF \\\"+title\\n titleNMO = \\\"NMO \\\"+title\\n\\n hmax = max(hpefs)\\n if hmax < max(hnmos):\\n hmax = max(hnmos)\\n\\n plotTimeWindowWavelets(Sampling(nh,st.delta,kh*st.delta),hpefs,hsyn,hmax,\\n title=titlePEF,pngDir=pngDir)\\n plotTimeWindowWavelets(Sampling(nh,st.delta,kh*st.delta),hnmos,hsyn,hmax,\\n title=titleNMO,pngDir=pngDir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4dc324be0e67e4d99f983876c8da69\",\n \"score\": \"0.59141636\",\n \"text\": \"def exec(self, data):\\n env = Environment.get()\\n tod_buffer_length = env.tod_buffer_length()\\n\\n for obs in data.obs:\\n tod = obs[\\\"tod\\\"]\\n\\n # compute effective sample rate\\n\\n times = tod.local_times()\\n dt = np.mean(times[1:-1] - times[0:-2])\\n rate = 1.0 / dt\\n del times\\n\\n offset, nsamp = tod.local_samples\\n\\n hwpang = None\\n try:\\n hwpang = tod.local_hwp_angle()\\n except:\\n hwpang = None\\n raise RuntimeError(\\\"Can't run this operator if hwpang is None \\\")\\n\\n # read the common flags and apply bitmask\\n\\n common = None\\n if self._apply_flags:\\n common = tod.local_common_flags(self._common_flag_name)\\n common = common & self._common_flag_mask\\n else:\\n common = np.zeros(nsamp, dtype=np.uint8)\\n\\n for det in tod.local_dets:\\n eps = 0.0\\n if self._epsilon is not None:\\n eps = self._epsilon[det]\\n\\n cal = 1.0\\n if self._cal is not None:\\n cal = self._cal[det]\\n\\n # Create cache objects and use that memory directly\\n\\n pixelsname = \\\"{}_{}\\\".format(self._pixels, det)\\n weightsname = \\\"{}_{}\\\".format(self._weights, det)\\n\\n pixelsref = None\\n weightsref = None\\n\\n if tod.cache.exists(pixelsname):\\n pixelsref = tod.cache.reference(pixelsname)\\n else:\\n pixelsref = tod.cache.create(pixelsname, np.int64, (nsamp,))\\n\\n if tod.cache.exists(weightsname):\\n weightsref = tod.cache.reference(weightsname)\\n else:\\n weightsref = tod.cache.create(\\n weightsname, np.float64, (nsamp, self._nnz)\\n )\\n\\n pdata = None\\n if self._keep_quats:\\n # We are keeping the detector quaternions, so cache\\n # them now for the full sample range.\\n pdata = tod.local_pointing(det)\\n\\n xaxis = np.array([1.0, 0.0, 0.0])\\n zaxis = np.array([0.0, 0.0, 1.0])\\n nullquat = np.array([0.0, 0.0, 0.0, 1.0])\\n eta = (1.0 - eps) / (1.0 + eps)\\n\\n pdata[common] = nullquat\\n\\n dir = rotate(pdata, zaxis)\\n pixelsref = hp.vec2pix(\\n nside=self._nside, x=dir[:, 0], y=dir[:, 1], z=dir[:, 2], nest=True\\n )\\n\\n pixelsref[common] = -1\\n # import pdb\\n # pdb.set_trace()\\n\\n T = self._hwp_parameters_set[0]\\n c = self._hwp_parameters_set[1]\\n rho = self._hwp_parameters_set[2]\\n s = self._hwp_parameters_set[3]\\n cos2hwp = np.cos(2 * hwpang)\\n\\n if self._mode == \\\"I\\\":\\n weightsref = cal * (T + eta * rho * cos2hwp)\\n elif self._mode == \\\"IQU\\\":\\n orient = rotate(pdata, xaxis)\\n\\n by = orient[:, 0] * dir[:, 1] - orient[:, 1] * dir[:, 0]\\n bx = (\\n orient[:, 0] * (-dir[:, 2] * dir[:, 0])\\n + orient[:, 1] * (-dir[:, 2] * dir[:, 1])\\n + orient[:, 2] * (dir[:, 0] * dir[:, 0] + dir[:, 1] * dir[:, 1])\\n )\\n\\n detang = np.arctan2(by, bx)\\n\\n sindetang = np.sin(2 * detang)\\n cosdetang = np.cos(2 * detang)\\n\\n ang4 = 2 * detang + 4 * hwpang\\n ang2 = 2 * detang + 2 * hwpang\\n\\n sin4 = np.sin(ang4)\\n sin2 = np.sin(ang2)\\n cos4 = np.cos(ang4)\\n cos2 = np.cos(ang2)\\n\\n weightsref[:, 0] = cal * (T + (eta * rho * cos2hwp))\\n weightsref[:, 1] = cal * (\\n (rho * cos2)\\n + eta * (T + c) / 2.0 * cosdetang\\n + eta * (T - c) / 2.0 * cos4\\n )\\n weightsref[:, 2] = cal * (\\n (rho * sin2)\\n + eta * (T + c) / 2.0 * sindetang\\n + eta * (T - c) / 2.0 * sin4\\n )\\n else:\\n raise RuntimeError(\\\"Unknown healpix pointing matrix mode\\\")\\n\\n if self._single_precision:\\n pixels = pixelsref.astype(np.int32)\\n del pixelsref\\n pixelsref = tod.cache.put(pixelsname, pixels, replace=True)\\n del pixels\\n weights = weightsref.astype(np.float32)\\n del weightsref\\n weightsref = tod.cache.put(weightsname, weights, replace=True)\\n del weights\\n\\n self._hit_submaps[pixelsref // self._npix_submap] = True\\n\\n del pixelsref\\n del weightsref\\n del pdata\\n\\n del common\\n\\n # Store the local submaps in the data object under the same name\\n # as the pixel numbers\\n\\n if self._single_precision:\\n dtype = np.int32\\n else:\\n dtype = np.int64\\n\\n local_submaps = np.arange(self._nsubmap, dtype=dtype)[self._hit_submaps]\\n submap_name = \\\"{}_local_submaps\\\".format(self._pixels)\\n data[submap_name] = local_submaps\\n npix_submap_name = \\\"{}_npix_submap\\\".format(self._pixels)\\n data[npix_submap_name] = self._npix_submap\\n nsubmap_name = \\\"{}_nsubmap\\\".format(self._pixels)\\n data[nsubmap_name] = self._nsubmap\\n npix_name = \\\"{}_npix\\\".format(self._pixels)\\n data[npix_name] = 12 * self._nside ** 2\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f4d409274ad8a57a0acf42df6ce6c2\",\n \"score\": \"0.5890701\",\n \"text\": \"def store_wavelet_transform(elec_series, processing, filters='rat', hg_only=True, abs_only=True,\\n npad='fast', post_resample_rate=None, chunked=True, precision='single',\\n source_series=None):\\n X = elec_series.data[:]\\n X_dtype = dtype(X, precision)\\n X = X.astype(X_dtype, copy=False)\\n rate = elec_series.rate\\n if source_series is None:\\n source_series = elec_series\\n\\n final_rate = rate\\n if post_resample_rate is not None:\\n final_rate = post_resample_rate\\n\\n if chunked:\\n if not abs_only:\\n raise NotImplementedError(\\\"Phase is not implemented for chunked wavelet transform.\\\")\\n X_wvlt_abs = ChannelBandIterator(X, rate, filters=filters, npad=npad, hg_only=hg_only,\\n post_resample_rate=post_resample_rate, precision=precision)\\n cfs = X_wvlt_abs.cfs\\n sds = X_wvlt_abs.sds\\n elec_series_wvlt_amp = DecompositionSeries('wvlt_amp_' + elec_series.name,\\n H5DataIO(X_wvlt_abs,\\n compression=True,\\n shuffle=True,\\n fletcher32=True),\\n metric='amplitude',\\n source_timeseries=source_series,\\n starting_time=elec_series.starting_time,\\n rate=final_rate,\\n description=('Wavlet: ' +\\n elec_series.description))\\n output_series = [elec_series_wvlt_amp]\\n X_wvlt = None\\n else:\\n X_wvlt, _, cfs, sds = wavelet_transform(X, rate, filters=filters, hg_only=hg_only,\\n npad=npad, precision=precision)\\n amplitude = abs(X_wvlt)\\n if post_resample_rate is not None:\\n amplitude = resample(amplitude, post_resample_rate, rate, precision=precision)\\n X_wvlt = amplitude\\n rate = post_resample_rate\\n elec_series_wvlt_amp = DecompositionSeries('wvlt_amp_' + elec_series.name,\\n H5DataIO(amplitude,\\n compression=True,\\n shuffle=True,\\n fletcher32=True),\\n metric='amplitude',\\n source_timeseries=source_series,\\n starting_time=elec_series.starting_time,\\n rate=final_rate,\\n description=('Wavlet: ' +\\n elec_series.description))\\n output_series = [elec_series_wvlt_amp]\\n if not abs_only:\\n if post_resample_rate is not None:\\n raise ValueError('Wavelet phase should not be resampled.')\\n elec_series_wvlt_phase = DecompositionSeries('wvlt_phase_' + elec_series.name,\\n H5DataIO(np.angle(X_wvlt),\\n compression=True,\\n shuffle=True,\\n fletcher32=True),\\n metric='phase',\\n source_timeseries=source_series,\\n starting_time=elec_series.starting_time,\\n rate=final_rate,\\n description=('Wavlet: ' +\\n elec_series.description))\\n output_series.append(elec_series_wvlt_phase)\\n\\n for es in output_series:\\n for ii, (cf, sd) in enumerate(zip(cfs, sds)):\\n es.add_band(band_name=str(ii), band_mean=cf,\\n band_stdev=sd, band_limits=(-1, -1))\\n\\n processing.add(es)\\n return X_wvlt, output_series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e9d7c712f549f6483fd8660d46e2a31\",\n \"score\": \"0.58704555\",\n \"text\": \"def test_wstat(self):\\n on_vector = self.src.copy()\\n on_vector.data += self.bkg.data\\n acceptance = RegionNDMap.from_geom(self.src.geom, data=1)\\n acceptance_off = RegionNDMap.from_geom(self.bkg.geom, data=1 / self.alpha)\\n\\n dataset = SpectrumDatasetOnOff(\\n counts=on_vector,\\n counts_off=self.off,\\n exposure=self.exposure,\\n acceptance=acceptance,\\n acceptance_off=acceptance_off,\\n )\\n dataset.models = self.source_model\\n\\n self.source_model.parameters.index = 1.12\\n\\n fit = Fit()\\n result = fit.run(datasets=[dataset])\\n pars = self.source_model.parameters\\n\\n assert_allclose(pars[\\\"index\\\"].value, 1.997342, rtol=1e-3)\\n assert_allclose(pars[\\\"amplitude\\\"].value, 100245.187067, rtol=1e-3)\\n assert_allclose(result.total_stat, 30.022316, rtol=1e-3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3feb13a87e5c37f3a1e5835b4f53cc40\",\n \"score\": \"0.5869362\",\n \"text\": \"def do_wavelet(x, y, fr, mom = 'Morlet', f0 = 6., dj = .125, s0 = -1, J = -1,\\n slevel = .95, plts = True, rectify=False):\\n std = y.std()\\n var = std ** 2\\n y = (y - y.mean()) / std\\n N = len(y)\\n dt = np.diff(x).mean()\\n\\n alpha, _, _ = wavelet.ar1(y)\\n \\n # the wavelet transform\\n mother = eval('wavelet.' + mom + '(' + str(f0) + ')')\\n wave, scales, freqs, coi, FFT, FFTfreqs = wavelet.cwt(y, dt, dj, s0, J,\\\\\\n mother)\\n iwave = wavelet.icwt(wave, scales, dt, dj, mother) # inverse wavelet transform\\n\\n power = (np.abs(wave)) ** 2 # Normalized wavelet power spectrum\\n fft_power = var * np.abs(FFT) ** 2 # FFT power spectrum\\n period = 1. / freqs\\n # this rectification may be bullshit, plus if used power must be re-scaled again further down\\n if rectify:\\n power = power / np.ones([1, N]) * scales[:, None]\\n signif, fft_theor = wavelet.significance(1.0, dt, scales, 0, alpha,\\\\\\n significance_level=slevel, wavelet=mother)\\n sig = np.ones([1, N]) * signif[:, None]\\n sig = power / sig # Where ratio > 1, power is significant\\n\\n glbl_power = var * power.mean(axis = 1)\\n dof = N - scales # Correction for padding at edges\\n glbl_sig, tmp = wavelet.significance(var, dt, scales, 1, alpha,\\\\\\n significance_level=slevel, dof=dof, wavelet=mother)\\n \\n # Scale average between avg1 and avg2 periods and significance level\\n sel = find((freqs >= fr[0]) & (freqs <= fr[1]))\\n Cdelta = mother.cdelta\\n scale_avg = (scales * np.ones((N, 1))).transpose()\\n # As in Torrence and Compo (1998) equation 24\\n scale_avg = power / scale_avg\\n scale_avg = var * dj * dt / Cdelta * scale_avg[sel, :].sum(axis = 0)\\n scale_avg_sig, tmp = wavelet.significance(var, dt, scales, 2, alpha,\\n significance_level=slevel, dof=[scales[sel[0]],\\n scales[sel[-1]]], wavelet=mother)\\n \\n if plts == True:\\n figprops = dict(figsize=(11, 8), dpi=72)\\n figure(**figprops)\\n \\n ax = axes([0.1, 0.75, 0.65, 0.2])\\n ax.plot(x, y, 'k', lw = 1.5)\\n ax.plot(x, iwave, '-', lw = 1, color=[0.5, 0.5, 0.5])\\n ax.set_title('Normalized Anomaly timeseries and its inverse wavelet transform (gray)')\\n \\n bx = axes([0.1, 0.37, 0.65, 0.28], sharex = ax)\\n bx.contourf(x, freqs, power, extend='both')\\n bx.contour(x, freqs, sig, [-99, 1], colors='k', lw = 2.) \\n bx.fill(np.concatenate([[x[0]-dt, x[0]-dt], x[0:-1], [x[-1]+dt, x[-1]+dt]]),\\n np.concatenate([[1e-9, 1./coi[0]], 1./coi[0:-1], [1./coi[-2], 1e-9]]),\\n 'k' , alpha='0.3', hatch='x')\\n bx.set_ylim([0, 2*fr[1]])\\n bx.set_ylabel('Frequency')\\n bx.set_title('Wavelet power spectrum (' + mom + ')')\\n \\n cx = axes([0.77, 0.37, 0.2, 0.28])\\n cx.semilogx(fft_power, FFTfreqs, '-', color=[0.7, 0.7, 0.7])\\n cx.semilogx(glbl_power, freqs, 'k-', linewidth=1.5)\\n cx.plot(glbl_sig, freqs, 'k--')\\n cx.set_ylim([0, 2*fr[1]])\\n cx.set_title('Global wavelet spec (thick) vs FFT spec')\\n cx.set_xlabel('Power')\\n cx.set_yticklabels(cx.get_yticklabels(), visible = False)\\n \\n dx = axes([0.1, 0.07, 0.65, 0.2], sharex=ax)\\n dx.plot(x, scale_avg, 'k-', linewidth=1.5)\\n dx.axhline(scale_avg_sig, color='k', linestyle='--', linewidth=1.)\\n dx.set_title('$%f$-$%f$ freq band scale-averaged power' % (fr[0], fr[1]))\\n dx.set_ylabel('Average variance')\\n draw()\\n show()\\n \\n return freqs, power, sig, coi, iwave, glbl_power, glbl_sig, scale_avg, scale_avg_sig, scales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8948cb2e7f0950087a4ef23aea06ffe6\",\n \"score\": \"0.58554786\",\n \"text\": \"def main() -> None:\\n params = TsParams(nperseg=64, noverlap=48)\\n da = DataArray(fs=100, nchannels=12, ntrials=20, simulate=True)\\n ds = Dataset(da, params)\\n print(ds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a2ca764992318b9defb3f2f22b2e60\",\n \"score\": \"0.578665\",\n \"text\": \"def main():\\n\\n #\\n # Generate waveform\\n #\\n\\n print 'reading waveform...'\\n waveform=read_waveformfile(sys.argv[1])\\n\\n # Pick some extrinsic parameters\\n ext_params = ExtParams(distance=1, ra=0.0, dec=0.0, polarization=0.0,\\n inclination=0.0, phase=0.0, geocent_peak_time=2.0)\\n\\n\\n #\\n # Generate IFO data\\n #\\n det1_data = DetData(waveform=waveform, ext_params=ext_params)\\n\\n from scipy import signal\\n import pylab as pl\\n\\n pl.figure()\\n pl.plot(det1_data.td_response.sample_times,det1_data.td_response.data)\\n pl.plot(det1_data.td_signal.sample_times,det1_data.td_signal.data)\\n\\n pl.figure()\\n f,p = signal.welch(det1_data.td_response.data, fs=1./det1_data.delta_t,\\n nperseg=512)\\n pl.loglog(f,np.sqrt(p))\\n\\n f,p = signal.welch(det1_data.td_signal.data, fs=1./det1_data.delta_t,\\n nperseg=512)\\n pl.loglog(f,np.sqrt(p))\\n pl.ylim(1e-25,1e-21)\\n pl.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cab1c33c185d047dfaafd81767f1820\",\n \"score\": \"0.57759106\",\n \"text\": \"def exec(self, data):\\n env = Environment.get()\\n tod_buffer_length = env.tod_buffer_length()\\n\\n for obs in data.obs:\\n tod = obs[\\\"tod\\\"]\\n\\n # compute effective sample rate\\n\\n times = tod.local_times()\\n dt = np.mean(times[1:-1] - times[0:-2])\\n rate = 1.0 / dt\\n del times\\n\\n offset, nsamp = tod.local_samples\\n\\n hwpang = None\\n try:\\n hwpang = tod.local_hwp_angle()\\n except:\\n hwpang = None\\n\\n # read the common flags and apply bitmask\\n\\n common = None\\n if self._apply_flags:\\n common = tod.local_common_flags(self._common_flag_name)\\n common = common & self._common_flag_mask\\n else:\\n common = np.zeros(nsamp, dtype=np.uint8)\\n\\n for det in tod.local_dets:\\n eps = 0.0\\n if self._epsilon is not None:\\n eps = self._epsilon[det]\\n\\n cal = 1.0\\n if self._cal is not None:\\n cal = self._cal[det]\\n\\n # Create cache objects and use that memory directly\\n\\n pixelsname = \\\"{}_{}\\\".format(self._pixels, det)\\n weightsname = \\\"{}_{}\\\".format(self._weights, det)\\n\\n pixelsref = None\\n weightsref = None\\n\\n if tod.cache.exists(pixelsname):\\n pixelsref = tod.cache.reference(pixelsname)\\n else:\\n pixelsref = tod.cache.create(pixelsname, np.int64, (nsamp,))\\n\\n if tod.cache.exists(weightsname):\\n weightsref = tod.cache.reference(weightsname)\\n else:\\n weightsref = tod.cache.create(\\n weightsname, np.float64, (nsamp, self._nnz)\\n )\\n\\n pdata = None\\n if self._keep_quats:\\n # We are keeping the detector quaternions, so cache\\n # them now for the full sample range.\\n pdata = tod.local_pointing(det)\\n\\n buf_off = 0\\n buf_n = tod_buffer_length\\n while buf_off < nsamp:\\n if buf_off + buf_n > nsamp:\\n buf_n = nsamp - buf_off\\n bslice = slice(buf_off, buf_off + buf_n)\\n\\n detp = None\\n if pdata is None:\\n # Read and discard\\n detp = tod.read_pntg(detector=det, local_start=buf_off, n=buf_n)\\n else:\\n # Use cached version\\n detp = pdata[bslice, :]\\n\\n hslice = None\\n if hwpang is not None:\\n hslice = hwpang[bslice].reshape(-1)\\n fslice = common[bslice].reshape(-1)\\n\\n pointing_matrix_healpix(\\n self.hpix,\\n self._nest,\\n eps,\\n cal,\\n self._mode,\\n detp.reshape(-1),\\n hslice,\\n fslice,\\n pixelsref[bslice].reshape(-1),\\n weightsref[bslice, :].reshape(-1),\\n )\\n buf_off += buf_n\\n\\n if self._single_precision:\\n pixels = pixelsref.astype(np.int32)\\n del pixelsref\\n pixelsref = tod.cache.put(pixelsname, pixels, replace=True)\\n del pixels\\n weights = weightsref.astype(np.float32)\\n del weightsref\\n weightsref = tod.cache.put(weightsname, weights, replace=True)\\n del weights\\n\\n self._hit_submaps[pixelsref // self._npix_submap] = True\\n\\n del pixelsref\\n del weightsref\\n del pdata\\n\\n del common\\n\\n # Store the local submaps in the data object under the same name\\n # as the pixel numbers\\n\\n if self._single_precision:\\n dtype = np.int32\\n else:\\n dtype = np.int64\\n\\n local_submaps = np.arange(self._nsubmap, dtype=dtype)[self._hit_submaps]\\n submap_name = \\\"{}_local_submaps\\\".format(self._pixels)\\n data[submap_name] = local_submaps\\n npix_submap_name = \\\"{}_npix_submap\\\".format(self._pixels)\\n data[npix_submap_name] = self._npix_submap\\n nsubmap_name = \\\"{}_nsubmap\\\".format(self._pixels)\\n data[nsubmap_name] = self._nsubmap\\n npix_name = \\\"{}_npix\\\".format(self._pixels)\\n data[npix_name] = 12 * self._nside ** 2\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcdd55bf266206cd019233a009b7b52\",\n \"score\": \"0.57688946\",\n \"text\": \"def scalogram(filename, savename):\\n\\n #signal reading\\n (rate,signal) = wav.read(filename)\\n\\n #ignore other bands for primary treatment\\n if signal.shape[1] > 1:\\n signal = signal[:,0]\\n\\n #clip the signal\\n max_energy = max(energy)\\n start_frame = 0\\n for k in range(len(energy)):\\n if energy[k] >= max_energy*0.01:\\n start_frame = k\\n break\\n\\n end_frame = start_frame\\n for k in range(start_frame,len(energy)):\\n if energy[k] < max_energy*0.001:\\n end_frame = k\\n break\\n\\n if(end_frame == start_frame):\\n for k in range(start_frame,len(energy)):\\n if energy[k] < max_energy*0.01:\\n end_frame = k\\n break\\n\\n samples_per_frame = rate * 0.01\\n signal = signal[start_frame*samples_per_frame:end_frame*samples_per_frame]\\n\\n\\n wavelet=DOG4\\n maxscale=10\\n notes=100\\n scaling='log'#\\\"log\\\" #or \\\"linear\\\"\\n plotpower2d=True\\n\\n Ns=1024\\n #limits of analysis\\n Nlo=0 \\n Nhi=Ns\\n\\n # Wavelet transform\\n cw=wavelet(signal,maxscale,notes,scaling=scaling)\\n scales=cw.getscales() \\n cwt=cw.getdata()\\n # power spectrum\\n pwr=cw.getpower()\\n scalespec=np.sum(pwr,axis=1)/scales # calculate scale spectrum\\n # scales\\n y=cw.fourierwl*scales\\n x=np.arange(Nlo*1.0,Nhi*1.0,1.0)\\n \\n #mpl.tight_layout()\\n mpl.axis('off')\\n fig=mpl.figure(1)\\n\\n # 2-d coefficient plot\\n plotcwt=np.clip(np.fabs(cwt.real), 0., 1000.)\\n if plotpower2d: plotcwt=pwr\\n im=mpl.imshow(plotcwt,cmap=mpl.cm.jet,extent=[x[0],x[-1],y[-1],y[0]],aspect='auto')\\n mpl.ylim(y[0],y[-1])\\n theposition=mpl.gca().get_position()\\n\\n mpl.tight_layout()\\n mpl.savefig(savename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7f0d37c8821de30d443f715f55133d\",\n \"score\": \"0.57541025\",\n \"text\": \"def _process_wavelengths(self):\\r\\n aot_cols = [c for c in self.data.columns if 'AOT' in c]\\r\\n wvs = [int(x.replace('AOT', '')) for x in aot_cols]\\r\\n\\r\\n self.wavelengths = wvs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ae559e9181dafc87ab38845473dd30\",\n \"score\": \"0.57334787\",\n \"text\": \"def wt(y,t,dj = 1/32, mother = 'morlet',pad = 1, param = -1, freqScale = 'log',ar1 = 'auto', **kwargs): \\n import numpy as np\\n \\n # Default values\\n maxScale = None\\n \\n n = len(y)\\n sigma2 = np.var(y)\\n \\n if np.ndim(t)==0:\\n dt = t\\n t = np.arange(n)*dt\\n else:\\n dt = t[1]-t[0]\\n \\n for key in kwargs.keys():\\n if key.lower() == 'maxscale':\\n maxScale = kwargs[key] \\n\\n s0= kwargs.get('s0')\\n if s0 == None:\\n s0 = 2*dt\\n \\n siglvl = kwargs.get('siglvl')\\n if siglvl == None:\\n siglvl = 0.95\\n \\n J1 = kwargs.get('J1') \\n if J1 == None:\\n if maxScale == None:\\n maxScale = (n*0.17)*2*dt\\n J1 = np.round(np.log2(maxScale/s0)/dj)\\n if ar1.lower()== 'auto':\\n ar1 = ar1nv(y)[0]\\n \\n wave,period,scale,coi = wavelet(y,t,dj = dj, mother = mother ,param = param,\\n freqScale = freqScale, **kwargs)\\n power = np.abs(wave)**2\\n signif = wave_signif(1.0,dt,scale,sigtest=0,lag1=ar1,siglvl= siglvl,mother = mother)\\n sig95 = signif[0].reshape((-1,1))*np.ones((1,n))\\n sig95 = power/(sigma2*sig95)\\n \\n return wave, period, scale, coi, sig95\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78a47ab07d9f80c3fe8e695555baeb70\",\n \"score\": \"0.57304037\",\n \"text\": \"def _plot_wavelet(datas):\\n \\n # Declare a starlet object (and performs the transform)\\n Sw = scarlet.Starlet(datas, lvl=5, direct=True)\\n # This is the starlet transform as an array\\n w = Sw.coefficients\\n # The inverse starlet transform of w (new object otherwise, the tranform is not used)\\n iw = Sw.image\\n\\n # TODO: Clean this code up using plt.subplots()\\n # The wavelet transform of the first slice of images in pictures\\n lvl = w.shape[1]\\n plt.figure(figsize=(lvl*5+5,5))\\n plt.suptitle('Wavelet coefficients')\\n for i in range(lvl):\\n plt.subplot(1, lvl, i+1)\\n plt.title('scale' + str(i+1))\\n plt.imshow(w[0,i], cmap='inferno')\\n plt.colorbar()\\n plt.show()\\n\\n # Making sure we recover the original image\\n plt.figure(figsize=(30,10))\\n plt.subplot(131)\\n plt.title('Original image', fontsize=20)\\n plt.imshow(datas[0], cmap='inferno')\\n plt.colorbar()\\n plt.subplot(132)\\n plt.title('Starlet-reconstructed image', fontsize=20)\\n plt.imshow(iw[0], cmap='inferno')\\n plt.colorbar()\\n plt.subplot(133)\\n plt.title('Absolute difference', fontsize=20)\\n plt.imshow((np.abs(iw[0]-datas[0])), cmap='inferno')\\n plt.colorbar()\\n plt.show()\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61eb8a9921b0ba4239fa160abfc4c1fd\",\n \"score\": \"0.5722021\",\n \"text\": \"def run(self):\\n self.check_filename(\\\".txt\\\")\\n self.spec = self.spec_hw.spec\\n self.total_time = self.spec_hw.settings['intg_time'] * self.settings['scans_to_avg'] #ms\\n while not self.interrupt_measurement_called:\\n self._read_spectrometer()\\n self.save_array[:,1] = self.y\\n if self.ui.save_every_spec_checkBox.isChecked(): #while interrupt not called, inside, have an if for if interrupt is called \\n self.save_array[:,0] = self.spec.wavelengths()\\n np.savetxt(self.app.settings['save_dir']+\\\"/\\\"+self.app.settings['sample']+str(self.point_counter)+\\\".txt\\\", self.save_array, fmt = '%.5f', header = 'Wavelength (nm), Intensity (counts)', delimiter = ' ')\\n self.point_counter += 1\\n if self.interrupt_measurement_called:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958047e235027295f278d93f7190514a\",\n \"score\": \"0.566877\",\n \"text\": \"def pco2w_instrument(ds):\\n # drop some variables:\\n # record_type == not used\\n # record_time == internal_timestamp == time, redundant so can remove\\n # absorbance_ratio_*_qc_results == incorrectly set tests, ignoring\\n # absorbance_ratio_*_qc_executed == incorrectly set tests, ignoring\\n ds = ds.reset_coords()\\n drop_vars = ['record_type', 'record_time', 'internal_timestamp',\\n 'absorbance_ratio_434_qc_results', 'absorbance_ratio_434_qc_executed',\\n 'absorbance_ratio_620_qc_results', 'absorbance_ratio_620_qc_executed']\\n for v in drop_vars:\\n if v in ds.variables:\\n ds = ds.drop(v)\\n\\n # rename some variables for better clarity\\n rename = {\\n 'voltage_battery': 'raw_battery_voltage',\\n 'thermistor_raw': 'raw_thermistor',\\n 'pco2w_thermistor_temperature': 'thermistor_temperature',\\n 'pco2w_thermistor_temperature_qc_executed': 'thermistor_temperature_qc_executed',\\n 'pco2w_thermistor_temperature_qc_results': 'thermistor_temperature_qc_results',\\n }\\n ds = ds.rename(rename)\\n\\n # now we need to reset the light array to named variables that will be more meaningful and useful in\\n # the final data files\\n light = ds.light_measurements.astype('int32')\\n dark_reference = light[:, [0, 8]].values # dark reference\\n dark_signal = light[:, [1, 9]].values # dark signal\\n reference_434 = light[:, [2, 10]].values # reference signal, 434 nm\\n signal_434 = light[:, [3, 11]].values # signal intensity, 434 nm\\n reference_620 = light[:, [4, 12]].values # reference signal, 620 nm\\n signal_620 = light[:, [5, 13]].values # signal intensity, 620 nm\\n\\n # create a data set with the duplicate measurements for each variable\\n data = xr.Dataset({\\n 'dark_reference': (['time', 'duplicates'], dark_reference),\\n 'dark_signal': (['time', 'duplicates'], dark_signal),\\n 'reference_434': (['time', 'duplicates'], reference_434),\\n 'signal_434': (['time', 'duplicates'], signal_434),\\n 'reference_620': (['time', 'duplicates'], reference_620),\\n 'signal_620': (['time', 'duplicates'], signal_620)\\n }, coords={'time': ds['time'], 'duplicates': np.arange(0, 2).astype('int32')})\\n ds = ds.drop(['spectrum', 'light_measurements'])\\n\\n # merge the data sets back together\\n ds = ds.merge(data)\\n\\n # calculate the battery voltage\\n ds['battery_voltage'] = ds['raw_battery_voltage'] * 15. / 4096.\\n\\n # reset some data types\\n data_types = ['deployment', 'raw_thermistor', 'raw_battery_voltage',\\n 'absorbance_blank_434', 'absorbance_blank_620', 'absorbance_ratio_434',\\n 'absorbance_ratio_620']\\n for v in data_types:\\n ds[v] = ds[v].astype('int32')\\n\\n data_types = ['thermistor_temperature', 'pco2_seawater']\\n for v in data_types:\\n ds[v] = ds[v].astype('float32')\\n\\n # test the data quality\\n ds['pco2_seawater_quality_flag'] = quality_checks(ds)\\n\\n # reset some attributes\\n for key, value in ATTRS.items():\\n for atk, atv in value.items():\\n if key in ds.variables:\\n ds[key].attrs[atk] = atv\\n\\n # add the original variable name as an attribute, if renamed\\n for key, value in rename.items():\\n ds[value].attrs['ooinet_variable_name'] = key\\n\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad4e3da23dac78d5a29f923f0b55175\",\n \"score\": \"0.5666068\",\n \"text\": \"def calculate_with_sampler(self):\\n self.calculate()\\n # results = []\\n #print \\\"calculating lambda=\\\" + str(lambda_um)\\n with open(osp.join(DIR_NAME, \\\"params.json\\\"),\\\"w\\\") as f:\\n json.dump(self.params, f) \\n process = subprocess.Popen([osp.join(DIR_NAME, \\\"S4.exe\\\"), osp.join(DIR_NAME, \\\"sampler.lua\\\")], shell=True, stdout=subprocess.PIPE)\\n process.wait()\\n if process.returncode:\\n raise ValueError(\\\"S4 didn't return code 0\\\")\\n self.results = np.loadtxt(osp.join(DIR_NAME, \\\"tempfile.txt\\\"))\\n # if len(self.dat.shape) == 1:\\n # results.append(self.dat[1:])\\n # else:\\n# results.append(self.dat)\\n# self.results = results\\n return np.array(self.results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd3c51fe86dc8ea1660008b1d7821d6\",\n \"score\": \"0.5653361\",\n \"text\": \"def cwt(data, wavelet_name, sampling_frequency=1., voices_per_octave=10):\\n\\n # Currently only supported for Morlet wavelets\\n if wavelet_name == 'morl':\\n data -= np.mean(data)\\n n_orig = data.size\\n nv = voices_per_octave\\n ds = 1 / nv\\n fs = sampling_frequency\\n dt = 1 / fs\\n\\n # Pad data symmetrically\\n padvalue = n_orig // 2\\n x = np.concatenate((np.flipud(data[0:padvalue]), data, np.flipud(data[-padvalue:])))\\n n = x.size\\n\\n # Define scales\\n _, _, wavscales = cwt_filterbank(wavelet_name, n_orig, ds)\\n\\n wavscales = wavscales[:]\\n num_scales = wavscales.size\\n\\n # Frequency vector sampling the Fourier transform of the wavelet\\n omega = np.arange(1, math.floor(n / 2) + 1, dtype=np.float64)\\n omega *= (2 * np.pi) / n\\n omega = np.concatenate((np.array([0]), omega, -omega[np.arange(math.floor((n - 1) / 2), 0, -1, dtype=int) - 1]))\\n\\n # Compute FFT of the (padded) time series\\n f = np.fft.fft(x)\\n\\n # Loop through all the scales and compute wavelet Fourier transform\\n psift, freq = waveft(wavelet_name, omega, wavscales)\\n\\n\\n # Inverse transform to obtain the wavelet coefficients.\\n cwtcfs = np.fft.ifft(np.kron(np.ones([num_scales, 1]), f) * psift)\\n cfs = cwtcfs[:, padvalue:padvalue + n_orig]\\n freq = freq * fs\\n\\n return cfs, freq\\n else:\\n raise Exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e127a11f3cbecce79280802bd176cbc5\",\n \"score\": \"0.5650203\",\n \"text\": \"def mainloop(self, data):\\n\\n # total = Input().number_of_chr(data)\\n \\n resultframe =pd.DataFrame(columns=['genome','width','mode','chr','biosource','tf','means','covariances', 'weights']) \\n \\n # i = 0\\n # loop all biosources\\n print(\\\"analyse_main.py: unpacking\\\")\\n for biosource, b_value in data.items():\\n print(\\\"unpacking: \\\" + biosource)\\n #loop all transcription factors\\n for tf, tf_value in b_value.items():\\n \\n print('analysing: '+ tf)\\n \\n scoresarray = []\\n #combine all scores of the chromosomes into vector-format list\\n for chromosome in tf_value.values():\\n \\n for array in chromosome:\\n \\n scoresarray.append([array[-1], array[-2]]) \\n \\n # scaled_scores = TF_analyser.scale(self, scoresarray)\\n distribution = np.array(scoresarray)\\n \\n mode = 'manual'\\n \\n if self.evaluate_n == True:\\n \\n mode = 'auto'\\n print(\\\"mode auto: number of components is evaluated (components_fit.py)\\\")\\n #automated number of components evaluation \\n all_diffs = GmFit.getDifference(self, distribution, self.eval_size)\\n self.n_components = GmFit.evaluate(self, all_diffs)\\n #plt.plot(all_diffs)\\n \\n single_result = EMA().emAnalyse(distribution, self.n_components)\\n \\n single_result.insert(0,'tf',tf)\\n single_result.insert(0,'chr', \\\", \\\".join(self.chr))\\n single_result.insert(0,'biosource',biosource)\\n single_result.insert(0, 'mode', mode)\\n single_result.insert(0,'width', self.width)\\n single_result.insert(0,'genome', self.genome)\\n \\n #visualization and saving plots \\n v= VD(self.path_results, tf, self.genome, biosource, self.chr)\\n path = v.displayDensityScatter(distribution, tf)\\n \\n v.altitudePlot(distribution, self.n_components, tf)\\n z,filename = v.contourPlot(distribution, self.n_components, tf)\\n \\n #Add z axis to scoresarray:\\n for i in range(0,len(z)):\\n scoresarray[i].append(z[i])\\n \\n #save data\\n np.savetxt(path + '/' + tf + '.csv', scoresarray, delimiter=',')\\n \\n single_result.insert(9, 'path', path)\\n single_result.insert(10, 'time', time.time())\\n single_result.insert(11, 'vis_filename', filename)\\n \\n resultframe = pd.concat([resultframe, single_result])\\n\\n parameters = [self.genome, self.width, mode, \\\", \\\".join(self.chr), biosource, tf]\\n modifyCSV(self.path_result_csv).compare(parameters)\\n print (tf + \\\" Done\\\")\\n \\n\\n #Save resultframe\\n Repository().save_csv(resultframe)\\n \\n return resultframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f3f33e528829fcc738e9ae6a6cbf33\",\n \"score\": \"0.55947524\",\n \"text\": \"def run():\\n\\n import matplotlib.pyplot as plt\\n\\n anomalies_t = []\\n anomalies_v = []\\n anomalies_c = []\\n\\n all_t = []\\n all_v = []\\n\\n rows = []\\n for i, row in dataSet.iterrows():\\n\\n inputData = row.to_dict()\\n\\n detectorValues = handleRecord(inputData)\\n\\n if (detectorValues[0] > 0.65):\\n anomalies_t.append(inputData[\\\"timestamp\\\"])\\n anomalies_v.append(inputData[\\\"value\\\"])\\n anomalies_c.append(detectorValues[0])\\n\\n all_t.append(inputData[\\\"timestamp\\\"])\\n all_v.append(inputData[\\\"value\\\"])\\n\\n outputRow = list(row) + list(detectorValues)\\n\\n rows.append(outputRow)\\n\\n # Progress report\\n if (i % 1000) == 0:\\n print \\\".\\\",\\n sys.stdout.flush()\\n\\n fig, ax = plt.subplots()\\n\\n ax.plot(all_t, all_v)\\n ax.plot(anomalies_t, anomalies_v, 'ro')\\n\\n plt.show()\\n\\n ans = pandas.DataFrame(rows)\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"225c777ea9ee9b4cc194e8a2fa73ea17\",\n \"score\": \"0.5591327\",\n \"text\": \"def main():\\n parser = get_parser()\\n args = parser.parse_args()\\n\\n # set logger\\n logfmt = \\\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\\\"\\n if args.verbose > 0:\\n logging.basicConfig(level=logging.INFO, format=logfmt)\\n else:\\n logging.basicConfig(level=logging.WARN, format=logfmt)\\n logging.info(get_commandline_args())\\n\\n os.makedirs(args.figdir, exist_ok=True)\\n\\n with kaldiio.ReadHelper(args.rspecifier) as reader, codecs.open(\\n args.wspecifier, \\\"w\\\", encoding=\\\"utf-8\\\"\\n ) as f:\\n for utt_id, (rate, array) in reader:\\n array = array.astype(numpy.float32)\\n if args.normalize is not None and args.normalize != 1:\\n array = array / (1 << (args.normalize - 1))\\n if rate != args.fs:\\n array = resampy.resample(array, rate, args.fs, axis=0)\\n array_trim, idx = librosa.effects.trim(\\n y=array,\\n top_db=args.threshold,\\n frame_length=args.win_length,\\n hop_length=args.shift_length,\\n )\\n start, end = idx / args.fs\\n\\n # save figure\\n if args.figdir is not None:\\n plt.subplot(2, 1, 1)\\n plt.plot(array)\\n plt.title(\\\"Original\\\")\\n plt.subplot(2, 1, 2)\\n plt.plot(array_trim)\\n plt.title(\\\"Trim\\\")\\n plt.tight_layout()\\n plt.savefig(args.figdir + \\\"/\\\" + utt_id + \\\".png\\\")\\n plt.close()\\n\\n # added minimum silence part\\n start = max(0.0, start - args.min_silence)\\n end = min(len(array) / args.fs, end + args.min_silence)\\n\\n # write to segments file\\n segment = \\\"%s %s %f %f\\\\n\\\" % (utt_id, utt_id, start, end)\\n f.write(segment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c724224d374c4c0478134066f76f5b06\",\n \"score\": \"0.55848354\",\n \"text\": \"def provide_dataset(self):\\n audio_length = int(self._config['audio_length'] * self._config['sample_rate'])\\n\\n pitch_counts = self.get_pitch_counts()\\n pitches = sorted(pitch_counts.keys())\\n print('LENGTH OF PITCH ONE HOT')\\n print(len(pitches))\\n label_index_table = tf.contrib.lookup.index_table_from_tensor(\\n sorted(pitches), dtype=tf.int64)\\n \\n # Get audio file names\\n wav_directory = self._config['train_data_path']\\n file_names = os.listdir(wav_directory)\\n \\n # Create seed arrays\\n\\n # STEREO\\n if (self._channel_mode == 'stereo'):\\n wavs = np.empty(shape = [1, audio_length, 2])\\n \\n # MONO\\n if (self._channel_mode == 'mono'):\\n wavs = np.empty(shape = [1, audio_length, 1])\\n\\n labels = 60\\n\\n # Build audio and pitch arrays\\n for file_name in file_names:\\n # Read audio\\n unused_sample_rate, wav = read_audio(wav_directory + '/' + file_name)\\n \\n print('WAV SHAPE--AFTER READ FROM DIRECTORY, BEFORE LABEL PARSE')\\n print(wav.shape)\\n \\n # Parse MIDI note number of \\\".wav\\\" file name\\n # Follow nsynth file name formatting if MONO, slowcore if STEREO \\n if (wav.ndim == 1):\\n label = int(file_name[-11:-8]) - 24\\n else:\\n label = int(file_name[-7:-4]) - 24\\n \\n # mono to stereo\\n if (self._channel_mode == 'stereo' and wav.ndim == 1):\\n wav = np.expand_dims(wav, axis=1)\\n wav = np.concatenate((wav, wav), axis=1)\\n\\n if (self._channel_mode == 'mono' and wav.ndim == 1):\\n # MONO (none, maybe)\\n wav = np.expand_dims(wav, axis=1)\\n \\n # stereo to mono\\n if (self._channel_mode == 'mono' and wav.ndim == 2):\\n wav = np.mean(wav, axis=1)\\n wav = np.expand_dims(wav, axis=1)\\n \\n # crop\\n wav = wav[0:audio_length,:]\\n\\n # Put in range [-1, 1]\\n float_normalizer = float(np.iinfo(np.int16).max)\\n wav = wav / float_normalizer\\n\\n # Convert to float32\\n wav = np.float32(wav)\\n \\n # Create batch dimension\\n wav = np.expand_dims(wav, axis=0) \\n\\n # Create vertical array of wavs\\n \\n # STEREO\\n if (self._channel_mode == 'stereo'):\\n wavs = np.concatenate((wavs, wav), axis=0) \\n \\n # MONO\\n if (self._channel_mode == 'mono'):\\n wavs = np.vstack((wavs, wav)) \\n\\n # Create vertical array of pitches\\n labels = np.vstack((labels, label))\\n\\n # Remove seed arrays\\n \\n # STEREO\\n if (self._channel_mode == 'stereo'):\\n wavs = tf.slice(wavs, [1, 0, 0], [len(file_names), audio_length, 2])\\n\\n # MONO\\n if (self._channel_mode == 'mono'):\\n wavs = tf.slice(wavs, [1, 0, 0], [len(file_names), audio_length, 1])\\n \\n labels = tf.slice(labels, [1, 0], [len(file_names), 1])\\n\\n # Translate MIDI note numbers to one-hot vectors\\n def _one_hot(wavs, labels):\\n labels = tf.one_hot(labels, depth=len(pitches))[0]\\n # labels = tf.Print(labels, [labels], message=\\\"One hot labels: \\\")\\n labels = tf.dtypes.cast(labels, tf.int64)\\n wavs = tf.dtypes.cast(wavs, tf.float32)\\n return wavs, labels\\n\\n # Create and map dataset\\n dataset = tf.data.Dataset.from_tensor_slices((wavs, labels))\\n dataset = dataset.map(_one_hot, num_parallel_calls=4)\\n\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49db3bc8f6a8a3c19389d31924d88f6a\",\n \"score\": \"0.5580731\",\n \"text\": \"def get_real_result_waves(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"debfa44378f6d219adef895154156343\",\n \"score\": \"0.5561195\",\n \"text\": \"def applyTo(self, photon_array):\\n photon_array.wavelength = self.sed.sampleWavelength(\\n photon_array.size(), self.bandpass, rng=self.rng, npoints=self.npoints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e45d868176625190d0dce75f01e2e05\",\n \"score\": \"0.55299485\",\n \"text\": \"def procelemdata(self, event):\\n dlg = wx.FileDialog(self, \\\"Choose a file\\\", \\\"\\\", \\\"\\\", \\\"*.s7k\\\", wx.OPEN)\\n if dlg.ShowModal() == wx.ID_OK:\\n self.rawfile = dlg.GetFilename()\\n dirname = dlg.GetDirectory()\\n havefile = True\\n else: havefile = False\\n dlg.Destroy()\\n if havefile:\\n r = prr.x7kRead(os.path.join(dirname, self.rawfile))\\n r.mapfile()\\n print \\\"mapping complete. Processing data.\\\"\\n # make sure there are 7038 records in the file\\n if r.map.packdir.has_key('7038'):\\n r.getrecord(7000, 0)\\n frequency = r.packet.subpack.header[3]\\n samplerate = r.packet.subpack.header[4]\\n r.getrecord(7038, 0)\\n # assuming the same number of samples throughout the file\\n maxsamples = r.packet.subpack.header[4]\\n numelements = r.packet.subpack.numelements\\n # initialize stuff\\n gainlist = {}\\n mags = {}\\n num7038 = len(r.map.packdir['7038'])\\n dir7000 = np.asarray(r.map.packdir['7000'])\\n # get the number of pings at each gain setting\\n for pingnum in range(num7038):\\n tstamp = r.map.packdir['7038'][pingnum][1]\\n idx = np.nonzero(dir7000[:,1] == tstamp)[0]\\n if len(idx) == 1:\\n r.getrecord(7000, idx[0])\\n gain = str(r.packet.subpack.header[15])\\n if gainlist.has_key(gain):\\n gainlist[gain].append(pingnum)\\n else:\\n gainlist[gain] = [pingnum]\\n # inialize arrays for all gain settings\\n for gain in gainlist:\\n num = len(gainlist[gain])\\n mags[gain] = np.zeros((maxsamples * num, numelements))\\n # get data from all pings\\n pingcount = 0\\n for gain in gainlist:\\n pointer = 0\\n for n,pingnum in enumerate(gainlist[gain]):\\n try:\\n\\t\\t\\t\\t\\t\\t\\tr.getrecord(7038,pingnum)\\n\\t\\t\\t\\t\\t\\t\\tnumsamples = r.packet.subpack.numsamples\\n\\t\\t\\t\\t\\t\\t\\tpingcount +=1\\n\\t\\t\\t\\t\\t\\t\\tcomplete = str(int(100.0 * pingcount / num7038))\\n\\t\\t\\t\\t\\t\\t\\tb = (len(complete) + 2) * '\\\\b'\\n\\t\\t\\t\\t\\t\\t\\tprint b + complete + '%',\\n\\t\\t\\t\\t\\t\\t\\tend = pointer + numsamples\\n\\t\\t\\t\\t\\t\\t\\tmag = np.abs(r.packet.subpack.r.reshape(-1,numelements))\\n\\t\\t\\t\\t\\t\\t\\tmags[gain][pointer:end, :] = mag\\n except:\\n mags[gain][pointer:end, :] = np.nan\\n pointer += numsamples\\n mags[gain] = mags[gain][:pointer, :]\\n print '\\\\n',\\n # reusing a variable name, sorry. I'm not very creative.\\n gainlist = [float(g) for g in mags.keys()]\\n gainlist.sort()\\n aveMag = np.zeros((len(gainlist), numelements))\\n targetplotgain = 40 # the closest gain to this value is plotted\\n lastval = 100 # just picked a large value...\\n for idx, gain in enumerate(gainlist):\\n g_amp = mags[str(gain)]\\n #FFT by Sam Greenaway\\n #one side fft of magnitude, treat each element independently\\n C = np.average(g_amp, axis=0)\\t\\n #Tile average to remove average mag value before fft \\n D = np.tile(C,(len(g_amp),1))\\n W = np.tile(np.hanning(len(g_amp)),(numelements,1)).T\\n aveMag[idx,:] = np.average(g_amp, axis = 0)\\n testval = np.abs(gain - targetplotgain)\\n if testval < lastval:\\n lastval = testval\\n A = (8/3)*(2/(samplerate*len(g_amp)))*np.abs(np.fft.rfft(np.multiply(W,(g_amp-D)), axis=0))**2\\n midg_amp = g_amp\\n midgain = str(gain)\\n #average PSD - equivalent to ensemble avergaing across elements\\n aA = np.average(A, axis=1)\\n # the frequencies\\n fn1S = np.linspace(0,samplerate/2,np.size(aA))\\n # get rid of some warnings...\\n idx = np.nonzero(midg_amp == 0)\\n midg_amp[idx[0],idx[1]] = 1\\n idx = np.nonzero(aveMag == 0)\\n aveMag[idx[0],idx[1]] = 1\\n # Plotting also by Sam... mostly\\n f=plt.figure(figsize = (15,10))\\n f.suptitle(self.rawfile)\\n plt.subplot(2,2,1)\\n plt.imshow(20*np.log10(midg_amp), aspect = 'auto')\\n plt.title('Amplitude, ' + midgain + 'dB gain')\\n plt.xlabel('element')\\n plt.ylabel('sample')\\n plt.colorbar()\\n\\n plt.subplot(2,2,2)\\n plt.plot(gainlist,20*np.log10(aveMag))\\n plt.title('Average Amplitude by Gain')\\n plt.xlabel('gain')\\n plt.ylabel('dB re 7125 Units')\\n plt.grid()\\n\\n ax = plt.subplot(2,2,3)\\n linelist = ax.plot(20*np.log10(aveMag).T)\\n ax.set_title('Average Element Amplitude by Gain')\\n ax.set_xlabel('element')\\n ax.set_ylabel('dB re 7125 Units')\\n ax.set_xlim([0, numelements])\\n ax.grid()\\n box = ax.get_position()\\n ax.set_position([box.x0, box.y0, box.width*0.8, box.height])\\n ax.legend(linelist, gainlist, loc = 'center left', bbox_to_anchor=(1,0.5))\\n\\n plt.subplot(2,2,4)\\n plt.plot(fn1S,20*np.log10(aA))\\n plt.title('One Sided PSD, '+ midgain +'dB Gain, ensemble averaged across elements')\\n plt.xlabel('Hz')\\n plt.ylabel('dB re 7125 Units/ Hz')\\n plt.grid()\\n\\n plt.draw()\\n print \\\"Thanks Sam.\\\"\\n else:\\n print 'No 7038 data found. Make sure the latest Reson Feature Pack is installed.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b469c98a1354fff7ded37704929b5794\",\n \"score\": \"0.5512731\",\n \"text\": \"def run_analysis(self, analysis_kwargs=None, **kw):\\n if analysis_kwargs is None:\\n analysis_kwargs = {}\\n\\n self.analysis = tda.FluxAmplitudeSweepAnalysis(\\n qb_names=self.meas_obj_names, options_dict=dict(TwoD=True),\\n t_start=self.timestamp, **analysis_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23992559b691c78640552342d8f24c59\",\n \"score\": \"0.55057263\",\n \"text\": \"def image_data(self,dat,dt,minf,maxf,vel,jf=1,nhx=0,nhy=0,sym=True,nrmax=3,eps=0.0,dtmax=5e-05,wav=None,\\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\\n # Get temporal axis\\n nt = dat.shape[-1]\\n\\n # Create frequency domain source\\n if(wav is None):\\n wav = np.zeros(nt,dtype='float32')\\n wav[0] = 1.0\\n self.__nwo,self.__ow,self.__dw,wfft = self.fft1(wav,dt,minf=minf,maxf=maxf)\\n wfftd = wfft[::jf]\\n self.__nwc = wfftd.shape[0] # Get the number of frequencies for imaging\\n self.__dwc = self.__dw*jf\\n\\n if(sverb or wverb): print(\\\"Frequency axis: nw=%d ow=%f dw=%f\\\"%(self.__nwc,self.__ow,self.__dwc))\\n\\n # Create frequency domain data\\n _,_,_,dfft = self.fft1(dat,dt,minf=minf,maxf=maxf)\\n dfftd = dfft[:,::jf]\\n datt = np.transpose(dfftd,(0,1,4,2,3)) # [nsy,nsx,ny,nx,nwc] -> [nsy,nsx,nwc,ny,nx]\\n datw = np.ascontiguousarray(datt.reshape([self.__nexp,self.__nwc,self.__ny,self.__nx]))\\n\\n # Single square root object\\n ssf = ssr3(self.__nx ,self.__ny,self.__nz , # Spatial Sizes\\n self.__dx ,self.__dy,self.__dz , # Spatial Samplings\\n self.__nwc,self.__ow,self.__dwc,eps, # Frequency axis\\n ntx,nty,px,py, # Taper and padding\\n dtmax,nrmax,nthrds) # Reference velocities\\n\\n # Compute slowness and reference slownesses\\n slo = 1/vel\\n ssf.set_slows(slo)\\n\\n # Allocate partial image array\\n if(nhx == 0 and nhy == 0):\\n imgar = np.zeros([self.__nexp,self.__nz,self.__ny,self.__nx],dtype='float32')\\n else:\\n if(sym):\\n # Create axes\\n self.__rnhx = 2*nhx+1; self.__ohx = -nhx*self.__dx; self.__dhx = self.__dx\\n self.__rnhy = 2*nhy+1; self.__ohy = -nhy*self.__dy; self.__dhy = self.__dy\\n # Allocate image array\\n imgar = np.zeros([self.__nexp,self.__rnhy,self.__rnhx,self.__nz,self.__ny,self.__nx],dtype='float32')\\n else:\\n # Create axes\\n self.__rnhx = nhx+1; self.__ohx = 0; self.__dhx = self.__dx\\n self.__rnhy = nhy+1; self.__ohy = 0; self.__dhy = self.__dy\\n # Allocate image array\\n imgar = np.zeros([self.__nexp,self.__rnhy,self.__rnhx,self.__nz,self.__ny,self.__nx],dtype='float32')\\n # Allocate memory necessary for extension\\n ssf.set_ext(nhy,nhx,sym)\\n\\n # Allocate the source for one shot\\n sou = np.zeros([self.__nwc,self.__ny,self.__nx],dtype='complex64')\\n\\n # Loop over sources\\n k = 0\\n for icrd in progressbar(self.__scoords,\\\"nexp:\\\",verb=sverb):\\n # Get the source coordinates\\n sy = icrd[0]; sx = icrd[1]\\n # Create the source for this shot\\n sou[:] = 0.0\\n sou[:,sy,sx] = wfftd[:]\\n if(nhx == 0 and nhy == 0):\\n # Conventional imaging\\n ssf.migallw(datw[k],sou,imgar[k],wverb)\\n else:\\n # Extended imaging\\n ssf.migoffallw(datw[k],sou,imgar[k],wverb)\\n k += 1\\n\\n # Sum over all partial images\\n img = np.sum(imgar,axis=0)\\n\\n # Free memory for extension\\n if(nhx != 0 or nhy != 0):\\n ssf.del_ext()\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a37546017a1588dcac3258f95e5b83f\",\n \"score\": \"0.5502482\",\n \"text\": \"def run(self):\\n # Creation of the spectral shape\\n norm = 1 * u.Unit('cm-2 s-1 TeV-1')\\n index = self.slope\\n ref = 1 * u.TeV\\n model = PowerLaw(index=index, amplitude=norm, reference=ref)\\n\\n # Get the bins in reconstructed energy\\n reco_energy = self.irf.bkg.energy\\n\\n # Start the computation\\n bkg_counts = self.get_bkg(self.irf.bkg)\\n if self.random < 1:\\n excess_counts = self.get_excess(bkg_counts)\\n else:\\n ex = self.get_excess(np.random.poisson(bkg_counts))\\n for ii in range(self.random - 1):\\n ex += self.get_excess(np.random.poisson(bkg_counts))\\n excess_counts = ex / float(self.random)\\n\\n phi_0 = self.get_1TeV_differential_flux(excess_counts, model, self.irf.aeff, self.irf.rmf)\\n energy = reco_energy.log_center()\\n dnde_model = model.evaluate(energy=energy, index=index, amplitude=1, reference=ref)\\n diff_flux = (phi_0 * dnde_model * energy ** 2).to('erg / (cm2 s)')\\n\\n self.energy = reco_energy.log_center()\\n self.diff_sens = diff_flux\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9acda5d47b6563c544ab6cc2718d9772\",\n \"score\": \"0.55020845\",\n \"text\": \"def main(datadir, outfile=\\\"wave.mp4\\\"):\\n files = os.listdir(datadir)\\n nframe = len(files)\\n tframe = np.loadtxt(\\\"%s/data0\\\" % datadir)\\n m = tframe.shape[0]\\n X, Y = np.meshgrid(np.linspace(-1, 1, m), np.linspace(-1, 1, m), indexing='ij')\\n fig = plt.figure(figsize=(10,10))\\n\\n def plot_frame(filename):\\n ax = fig.add_subplot(111, projection='3d')\\n ax.set_zlim(0, 2)\\n H = np.loadtxt(filename)\\n ax.plot_surface(X, Y, H, color='w', rstride=5, cstride=5)\\n return ax\\n\\n metadata = dict(title='Wave animation', artist='Matplotlib')\\n if outfile[-4:] == \\\".mp4\\\":\\n# Writer = manimation.writers['ffmpeg']\\n Writer = manimation.writers['avconv']\\n writer = Writer(fps=15, metadata=metadata,\\n extra_args=[\\\"-r\\\", \\\"30\\\",\\n \\\"-c:v\\\", \\\"libx264\\\",\\n \\\"-pix_fmt\\\", \\\"yuv420p\\\"])\\n elif outfile[-4:] == \\\".gif\\\":\\n Writer = manimation.writers['imagemagick']\\n writer = Writer(fps=15, metadata=metadata)\\n\\n with writer.saving(fig, outfile, nframe):\\n for i in range(0, nframe):\\n print \\\"plot frame\\\", i\\n ax = plot_frame(\\\"%s/data%d\\\" % (datadir, i))\\n writer.grab_frame()\\n plt.delaxes(ax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"389ae06c10000e5352d3b48196c396aa\",\n \"score\": \"0.5486174\",\n \"text\": \"def wf_analysis(x):\\n # Get the \\\"raw\\\" spectral power\\n z2 = wf.spacetime_power(x, segsize=96, noverlap=96-30, spd=1, latitude_bounds=(-15,15), dosymmetries=True, rmvLowFrq=True)\\n z2avg = z2.mean(dim='component')\\n z2.loc[{'frequency':0}] = np.nan # get rid of spurious power at \\\\nu = 0\\n # the background is supposed to be derived from both symmetric & antisymmetric\\n background = wf.smooth_wavefreq(z2avg, kern=wf.simple_smooth_kernel(), nsmooth=50, freq_name='frequency')\\n # separate components\\n z2_sym = z2[0,...]\\n z2_asy = z2[1,...]\\n # normalize\\n nspec_sym = z2_sym / background \\n nspec_asy = z2_asy / background\\n return nspec_sy, nspec_asy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5632fbeb446de88255f887d9a6689065\",\n \"score\": \"0.5462022\",\n \"text\": \"def run(skillfile, itemfile, datafile):\\n\\tanalysis = Analyser(skillfile, itemfile, datafile)\\n\\n\\tprint(\\\"Welcome to MATMAN, an analyser of the MatMat.cz dataset.\\\")\\n\\tprint(\\\"Extracting the data...\\\")\\n\\n\\tanalysis.build()\\n\\tanalysis.run()\\n\\n\\tcount_entries = analysis.get_num_entries()\\n\\tvalidity = 1 - round((count_entries['invalid'] + count_entries['fake']) / count_entries['all'], 2)\\n\\tprint(\\\"Number of all entries:\\\", count_entries['all'], \\\"| Invalid entries:\\\", count_entries['invalid'], \\\"| Fake entries:\\\", count_entries['fake'], \\\"(Valid samples make up:\\\", validity*100, \\\"%)\\\")\\n\\n\\tprint(\\\"Proceeding with the analysis...\\\")\\n\\n\\tstudents = analysis.analyse_students()\\n\\n\\tprint(\\\"Average response time:\\\", round(students[0], 2), \\\"| Average success rate:\\\", round(students[1], 2), \\\"| Average error:\\\", round(students[2], 2))\\n\\tprint(\\\"Success rate standard deviation:\\\", round(students[3], 2))\\n\\tprint(\\\"Number of students:\\\", len(analysis.get_students()))\\n\\tprint(\\\"Estimated number of fakers:\\\", students[4], \\\"| Under average perf.:\\\", students[5], \\\"| Over average perf.:\\\", students[6])\\n\\n\\titems_diff = analysis.analyse_items()\\n\\tskills_diff = analysis.analyse_skills()\\n\\n\\t# Plot the results\\n\\tplotter = Plotter(analysis)\\n\\tplotter.run()\\n\\n\\tprint(\\\"Done. You can see the results in the project's root folder.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce69cfd4c2abdc854d0b0d70ba329c5d\",\n \"score\": \"0.54612726\",\n \"text\": \"def getTrainSamples(\\n self, layout=..., compressSamples=..., compressVars=...\\n ) -> retval:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b22fa8b597d03e5bd01517fc8365e4d2\",\n \"score\": \"0.5457275\",\n \"text\": \"def extract_xy(self, audio):\\n sample_rate, signal = wavfile.read(str(self.data_folder / audio))\\n speaker_id = self.mapping[audio]\\n # Frame configuration\\n frame_size = 0.025\\n frame_stride = 0.01\\n # Convert from seconds to samples\\n frame_length = int(round(frame_size * sample_rate))\\n frame_step = int(round(frame_stride * sample_rate))\\n num_frames = int(np.ceil(float(np.abs(signal_length - \\\\\\n frame_length)) / frame_step))\\n\\n indices = np.tile(np.arange(0, frame_length),\\n (num_frames, 1)) + np.tile(np.arange(0,\\n num_frames * frame_step,\\n frame_step),\\n (frame_length, 1)).T\\n frames = signal[indices.astype(np.int32, copy=False)]\\n\\n frames *= np.hamming(frame_length)\\n \\n NFFT = 512\\n # Magnitude of the FFT\\n frame_magnitude = np.absolute(np.fft.rfft(frames, NFFT))\\n # Power Spectrum\\n frame_power = ((1.0 / NFFT) * ((frame_magnitude) ** 2))\\n\\n num_filters = 40\\n mel_low_freq = 0\\n # Convert Hz to Mel\\n mel_high_freq = (2595 * np.log10(1 + (sample_rate / 2) / 700))\\n # Equally spaced in Mel scale\\n mel_points = np.linspace(mel_low_freq, mel_high_freq, num_filters + 2)\\n # Convert Mel to Hz\\n hz_points = (700 * (10**(mel_points / 2595) - 1))\\n bins = np.floor((NFFT + 1) * hz_points / sample_rate)\\n\\n filter_bank = np.zeros((num_filters, int(np.floor(NFFT / 2 + 1))))\\n\\n for i in range(1, num_filters + 1):\\n filter_left = int(bins[i - 1]) # Left\\n filter_i = int(bins[i]) # Center\\n filter_right = int(bins[i + 1]) # Right\\n\\n for j in range(filter_left, filter_i):\\n filter_bank[i - 1, j] = (j - bins[i - 1]) / (bins[i] - bins[i - 1])\\n for j in range(filter_left, filter_right):\\n filter_bank[i - 1, j] = (bins[i + 1] - j) / (bins[i + 1] - bins[i])\\n\\n filter_banks = np.dot(frame_power, filter_bank.T)\\n # Numerical Stability\\n filter_banks = np.where(filter_banks == 0, np.finfo(float).eps,\\n filter_banks)\\n # dB\\n filter_banks = 20 * np.log10(filter_banks)\\n\\n if self.ftype == 'fbank':\\n return filter_banks, speaker_id * (filter_banks.size)\\n else:\\n num_ceps = 12\\n mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)] # Keep 2-13\\n mfcc = mfcc.flatten()\\n return mfcc, speaker_id * (filter_banks.size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad694897c7295c7207964146a5155388\",\n \"score\": \"0.5445063\",\n \"text\": \"def run(self):\\n # Set Options and get parameters\\n # Chops to exclude from the beginning of the file\\n skip_start = self.getarg('skip_start')\\n # Chops to exclude from the end of the file\\n skip_end = self.getarg('skip_end')\\n # Raw data threshold for dead pixles\\n bad_dead = self.getarg('bad_dead')\\n # Raw data threshold for ramping pixels. Set ramping as low as\\n # possible, but if < 5e5 you may start to lose some good pixels\\n bad_ramping = self.getarg('bad_ramping')\\n # threshold for HIGH STD of DMD SIGNAL to exclude pixels\\n normstd = self.getarg('normstd')\\n # threshold to elliminate pixels with LOW SIGNAL\\n ynormlowlim = self.getarg('ynormlowlim')\\n # threshold for pixels with HIGH NORMALIZED SIGNAL\\n ynormhighlim = self.getarg('ynormhighlim')\\n # Scale factor for T/R flatfield\\n t_to_r = self.getarg('TtoR')\\n # option to skip flat generation entirely and make the INTCAL\\n # product only\\n skip_flat = self.getarg('dcl_only')\\n\\n # Loop through input datasets\\n self.dataout = []\\n self.auxout = []\\n for pd in self.datain:\\n # Store R/T Real/Imag in indata\\n end = len(pd.table) - skip_end\\n signals = ['R array', 'T array', 'R array Imag', 'T array Imag']\\n indata = np.zeros((4, end - skip_start, 41, 64))\\n invar = np.zeros((4, end - skip_start, 41, 64))\\n for i in range(4):\\n indata[i] = pd.table[signals[i]][skip_start:end, :, :]\\n invar[i] = pd.table[signals[i] + ' VAR'][skip_start:end, :, :]\\n\\n # Store R/T Raw averages in rawavg\\n rawavg = np.zeros((2, end - skip_start, 41, 64))\\n signals = ['R array AVG', 'T array AVG']\\n for i in [0, 1]:\\n rawavg[i] = pd.table[signals[i]][skip_start:end, :, :]\\n\\n # Make raw stdevs\\n # 2, 41, 64 array\\n rawstd = np.nanstd(rawavg, axis=1)\\n\\n # Set Dead / Ramping pixels as NaN in indata\\n # This next section finds and masks dead and\\n # ramping pixels, so they won't bias the medians\\n # calculated below. This may be redundant if they have\\n # already been eliminated by the pipeline in StepNoah.\\n # If so, it won't hurt to do it again, and it\\n # allows tightening the criteria beyond those\\n # set in StepNoah, if desired.\\n nbad = np.ones((4, 41, 64))\\n\\n # Identify bad and ramping pixels by looking low and high rawstd\\n # Dead pixels\\n nbad[np.where(rawstd < bad_dead)] = np.NaN\\n # Ramping pixels\\n nbad[np.where(rawstd > bad_ramping)] = np.NaN\\n\\n # Mark all bad pixels with NaN in indata\\n # This sets the T1 array signal values to nan.\\n nbad[1, :, 32:64] = np.NaN\\n nbad.shape = (4, 1, 41, 64)\\n # multiply a (4, n, 41, 64) array by (4, 1, 41, 64) array\\n indata *= nbad\\n invar *= nbad\\n\\n # Calculate Modulus, phase and the medians for R and T array\\n\\n # initialize arrays\\n modulus = np.zeros((2, indata.shape[1], 41, 64))\\n phase = np.zeros((2, indata.shape[1], 41, 64))\\n modvar = np.zeros((2, invar.shape[1], 41, 64))\\n # phasevar = np.zeros((2, invar.shape[1], 41, 64))\\n\\n # Loop over R and T array\\n for i in range(2):\\n rld = indata[i]\\n imd = indata[i + 2]\\n rlv = invar[i]\\n imv = invar[i + 2]\\n\\n # m = sqrt(r^2 + i^2)\\n # Vm = (1/m^2)(r^2 Vr + i^2 Vi + 2 r i sqrt(Vr Vi))\\n msq = (rld**2 + imd**2)\\n modulus[i] = np.sqrt(msq)\\n modvar[i] = (1 / msq) * (rld**2 * rlv\\n + imd**2 * imv\\n + 2 * rld * imd * np.sqrt(rlv * imv))\\n\\n # p = arctan(i / r)\\n # Vp = (r^2 + i^2)^(-2) * (i^2 Vr + Vi - 2 sqrt(Vr Vi))\\n # (phase variance not needed at this time)\\n phase[i] = np.arctan(imd / rld)\\n # phasevar[i] = (rld**2 + imd**2)^(-2) * \\\\\\n # (imd**2 * rlv + imv - 2 * np.sqrt(rlv * imv))\\n\\n modmap = np.nanmedian(modulus, axis=1)\\n phasemap = np.nanmedian(phase, axis=1)\\n\\n # variance of median is approximately pi/2 * variance of mean\\n with np.errstate(invalid='ignore'):\\n modmapvar = (np.pi / 2.) \\\\\\n * (np.nansum(modvar, axis=1)\\n / np.count_nonzero(~np.isnan(modulus), axis=1) ** 2)\\n\\n # Generate temporary subarrays to iterate over data\\n subs = np.zeros((3, 41, 32))\\n subs[0] = modmap[0, :, 0:32]\\n subs[1] = modmap[0, :, 32:64]\\n subs[2] = modmap[1, :, 0:32]\\n\\n # Set limits then iterate over them\\n lowlim = (np.nanmedian(modmap[0, :, 0:32]) / 5,\\n np.nanmedian(modmap[1, :, 0:32]) / 5,\\n np.nanmedian(modmap[0, :, 32:64]) / 5)\\n highlim = (200000., 200000., 200000.)\\n outs = []\\n for i in range(4):\\n # Operates on the real parts of the signals\\n outs = self._histogram3d(subs, lowlim, highlim)\\n lowlim = outs[0] / 2\\n highlim = 2 * outs[0]\\n ymeds = outs[0]\\n\\n # Normalized modulus map and its stdev -> ynorm, ynormstd\\n\\n # Make normalized signal maps\\n # Alias modmap to ymean to make it easier to re-use\\n # code for making flats\\n ymean = modmap\\n ynorm = np.zeros((2, 41, 64))\\n ynorm[0, :, 0:32] = ymean[0, :, 0:32] / ymeds[0]\\n ynorm[0, :, 32:64] = ymean[0, :, 32:64] / ymeds[1]\\n ynorm[1, :, 0:32] = ymean[1, :, 0:32] / ymeds[2]\\n ynorm[1, :, 32:64] = np.NaN\\n\\n # Make normalized std maps\\n ystd = np.nanstd(modulus, axis=1)\\n ynormstd = np.zeros((2, 41, 64))\\n ynormstd[0, :, 0:32] = ystd[0, :, 0:32] / ymean[0, :, 0:32]\\n ynormstd[0, :, 32:64] = ystd[0, :, 32:64] / ymean[0, :, 32:64]\\n ynormstd[1, :, 0:32] = ystd[1, :, 0:32] / ymean[1, :, 0:32]\\n\\n # Create bad pixel map and flatfield\\n # using various selection criteria\\n\\n # Badstack datacube holds information about\\n # which bad-pixel criteria have been detected.\\n badstack = np.zeros((6, 2, 41, 64))\\n\\n # Badstack will be flattened into badsum\\n # below (by addition). In badsum, bits in\\n # a binary number indicate which criteria\\n # have been triggered.\\n\\n # Criteria (1/1) This criterion identifies DEAD\\n # pixels by looking for those with essentially zero rawstd.\\n ybad = np.zeros((2, 41, 64))\\n # dead has already been set above in section creating nbad.\\n with np.errstate(invalid='ignore'):\\n ybad[np.where(rawstd < bad_dead)] = 1\\n badstack[0] = ybad.copy()\\n\\n # Criteria (2/2) This criterion identifies RAMPING\\n # pixels by looking for very high rawstd\\n ybad = np.zeros((2, 41, 64))\\n # ramping has already been set above in section creating nbad.\\n ybad[np.where(rawstd > bad_ramping)] = 2\\n badstack[1] = ybad.copy()\\n\\n # Criteria (3/4) This criterion looks for very HIGH STD\\n # of the demodulated signal.\\n # May not be completely redundant with lower limit\\n # on rawstd above. Need to check this with more data.\\n ybad = np.zeros((2, 41, 64))\\n with np.errstate(invalid='ignore'):\\n ybad[np.where(ynormstd > normstd)] = 4\\n badstack[2] = ybad.copy()\\n\\n # One could define a criterion that looks for low rawstd\\n # not to identify dead pixels but to identify pixels\\n # with very low signal\\n # (e.g., those on the \\\"normal\\\" resistance part of the IV curve).\\n # For example, Rbad[where(rstd < 1e1)]=xx. However this\\n # may be redundant with rnorm < 0.x below. May want to take\\n # a closer look at this later.\\n\\n # Criteria (4/8) This criterion attempts to identify\\n # and eliminate pixels with very LOW SIGNAL\\n # (e.g., those on the \\\"normal\\\" part of the IV curve).\\n ybad = np.zeros((2, 41, 64))\\n with np.errstate(invalid='ignore'):\\n ybad[0, :, 0:32][np.where(ynorm[0, :, 0:32]\\n < ynormlowlim[0])] = 8\\n ybad[0, :, 32:64][np.where(ynorm[0, :, 32:64]\\n < ynormlowlim[1])] = 8\\n ybad[1, :, 0:32][np.where(ynorm[1, :, 0:32]\\n < ynormlowlim[2])] = 8\\n badstack[3] = ybad.copy()\\n\\n # Criteria (5/16) This criterion identifies pixels\\n # with very HIGH NORMALIZED SIGNAL.\\n # Need to check whether this might eliminate some \\\"good\\\" pixels.\\n # Might not be needed. Might be redundant with\\n # (and more prone to eliminate \\\"good\\\" pixels than)\\n # the criterion on ynormstd above.\\n ybad = np.zeros((2, 41, 64))\\n with np.errstate(invalid='ignore'):\\n ybad[0, :, 0:32][np.where(ynorm[0, :, 0:32]\\n > ynormhighlim[0])] = 16\\n ybad[0, :, 32:64][np.where(ynorm[0, :, 32:64]\\n > ynormhighlim[1])] = 16\\n ybad[1, :, 0:32][np.where(ynorm[1, :, 0:32]\\n > ynormhighlim[2])] = 16\\n badstack[4] = ybad.copy()\\n\\n # Criteria (6/32) This one ELIMINATES ROW 40\\n # (the row with multiplexer but no active IR pixels).\\n # This one may not be needed if the bad pixel mask ingested\\n # at the beginning of this algorithm has already set\\n # these rows to \\\"bad\\\". On the other hand, this doesn't\\n # take long to do here, so we could leave it\\n # for now, just in case we change approaches later.\\n ybad = np.zeros((2, 41, 64))\\n ybad[:, 40, :] = 32\\n badstack[5] = ybad.copy()\\n\\n # Create bad pixel map (value labels how the pixel is bad)\\n # This step combines all the bad pixel identifications\\n # into a single map with binary values for which the bits\\n # each indicate that a particular selection criterion\\n # has been triggered.\\n badsum = np.sum(badstack, axis=0)\\n\\n # Create bad pixel mask (with nans) to apply\\n # to flatfields. Add this to the target map\\n # to mask the bad pixels.\\n bad = np.zeros((2, 41, 64))\\n with np.errstate(invalid='ignore'):\\n bad[np.where(badsum != 0)] = np.NaN\\n\\n # Make flatfields (Rcal, Tcal) and medians array\\n\\n # Make DCAL type flat fields, based on time-averaged\\n # modulus (bad pixels set to NaN)\\n rcal = 10000. / ymean[0] + bad[0]\\n tcal = 10000. * t_to_r / ymean[1] + bad[1]\\n\\n # Propagate variance\\n rvar = (10000. / (ymean[0]**2))**2 * modmapvar[0] + bad[0]\\n tvar = (10000. * t_to_r / (ymean[1]**2))**2 * modmapvar[1] + bad[1]\\n\\n # Make an array to hold information on the\\n # median intensities and masked map medians of\\n # normalized std's, rawavg, and rawavg std's.\\n medians = np.zeros((4, 3))\\n medians[0] = ymeds\\n\\n ynormstdm = ynormstd + bad\\n ynormstdmmed = np.zeros(3)\\n ynormstdmmed[0] = np.nanmedian(ynormstdm[0, :, 0:32])\\n ynormstdmmed[1] = np.nanmedian(ynormstdm[0, :, 32:64])\\n ynormstdmmed[2] = np.nanmedian(ynormstdm[1, :, 0:32])\\n medians[1] = ynormstdmmed\\n\\n rawmedianm = np.nanmedian(rawavg, axis=1) + bad\\n rawmedianmmed = np.zeros(3)\\n rawmedianmmed[0] = np.nanmedian(rawmedianm[0, :, 0:32])\\n rawmedianmmed[1] = np.nanmedian(rawmedianm[0, :, 32:64])\\n rawmedianmmed[2] = np.nanmedian(rawmedianm[1, :, 0:32])\\n medians[2] = rawmedianmmed\\n\\n rawstdm = rawstd + bad\\n rawstdmmed = np.zeros(3)\\n rawstdmmed[0] = np.nanmedian(rawstdm[0, :, 0:32])\\n rawstdmmed[1] = np.nanmedian(rawstdm[0, :, 32:64])\\n rawstdmmed[2] = np.nanmedian(rawstdm[1, :, 0:32])\\n medians[3] = rawstdmmed\\n\\n # Save to output object.\\n\\n # This will save a DCAL named after input file\\n # with 'DCAL' in file-type identifier field.\\n # Rcal and Tcal correct detector gains to the\\n # values that would give a signal of 10000 ADU\\n # when exposed to the IR-50 internal calibrator source.\\n # The factor TorR is a first-order correction for the\\n # difference in the IR-50 illumination of the R and T\\n # detectors. Higher-order corrections for the\\n # detector to detector differences when exposed to\\n # radiation from the sky must be derived from direct\\n # measurements of astronomical sources.\\n # t_to_r = 2.0 is a first-order guess based on\\n # limited observations of astronomical source\\n # signals. It could be refined by additional\\n # observations, or the remaining differences could\\n # be subsumed in the intcal-to-source-flat\\n # transformation coefficients. The number 10000\\n # was chosen to make the median R0 subarray signal\\n # approximately equal to what it would be if no\\n # corrections were applied (based on observations\\n # with \\\"typical\\\" biases used in commissioning flights).\\n\\n # Get Configuration image from DMD file\\n imgconfig = pd.imageget('CONFIGURATION')\\n\\n # Convert phases to time delay\\n chopfreq = pd.getheadval('CHPFREQ')\\n rphase = phasemap[0] * (-1 / (2 * np.pi * chopfreq)) + bad[0]\\n tphase = phasemap[1] * (-1 / (2 * np.pi * chopfreq)) + bad[1]\\n\\n # Shift the phases to center around zero\\n rphase = rphase - (-0.055)\\n tphase = tphase - (-0.055)\\n\\n # Set NaN pixels to median\\n # (not used at this time)\\n\\n # R0medianm = np.nanmedian(rphase[:, 0:32])\\n # R1medianm = np.nanmedian(rphase[:, 32:64])\\n # T0medianm = np.nanmedian(tphase[:, 0:32])\\n # nanR0 = np.where(np.isnan(rphase[:, 0:32]))\\n # nanT = np.where(np.isnan(tphase))\\n # rphase[nanR0]=R0medianm\\n # tphase[nanT]=T0medianm\\n # nanR1 = np.where(np.isnan(rphase))\\n # rphase[nanR1] = R1medianm\\n\\n # Make and fill output data\\n outd = DataFits(config=pd.config)\\n outd.filename = pd.filename\\n outd.imageset(rcal, 'R ARRAY GAIN')\\n outd.imageset(tcal, 'T ARRAY GAIN')\\n outd.imageset(rvar, 'R ARRAY GAIN VAR')\\n outd.imageset(tvar, 'T ARRAY GAIN VAR')\\n outd.imageset(badsum[0], 'R BAD PIXEL MASK')\\n outd.imageset(badsum[1], 'T BAD PIXEL MASK')\\n outd.imageset(medians, 'SUBARRAY MEDIANS')\\n outd.imageset(imgconfig, 'CONFIGURATION')\\n outd.imageset(rphase, 'RPHASE')\\n outd.imageset(tphase, 'TPHASE')\\n outd.imageset(rawmedianm[0], 'R RAWAVG')\\n outd.imageset(rawmedianm[1], 'T RAWAVG')\\n outd.header = pd.header\\n\\n # Write the configuration header from pd into\\n # the corresponding HDU in outd.\\n outd.imgheads[5] = pd.imgheads[1]\\n\\n # Append to dataout\\n self.dataout.append(outd)\\n\\n # END of loop over input datasets\\n\\n # Make groups of output files -> datagroups\\n groupkey = self.getarg('groupkey')\\n datagroups = []\\n\\n # Make output folder if it doesn't exist\\n outfolder = self.getarg('flatoutfolder')\\n if len(outfolder) > 0:\\n if not os.path.isabs(outfolder):\\n # if outfolder is not an absolute path, make it\\n # relative to the output data location\\n outfolder = os.path.join(\\n os.path.dirname(self.dataout[0].filename),\\n outfolder)\\n try:\\n os.makedirs(outfolder)\\n except OSError:\\n if not os.path.isdir(outfolder):\\n log.error('Run: Failed to make flatoutfolder = %s' %\\n outfolder)\\n raise\\n\\n # if desired, just save DCL files to flats folder and quit\\n if skip_flat:\\n for data in self.dataout:\\n dclname = data.filenamebegin + 'DCL' + data.filenameend\\n if len(outfolder) > 0:\\n dclname = os.path.join(outfolder,\\n os.path.basename(dclname))\\n data.save(dclname)\\n self.auxout.append(dclname)\\n log.info('Saved result %s' % dclname)\\n self.dataout = []\\n return\\n\\n # Loop over datasets in datain\\n for data in self.dataout:\\n groupind = 0\\n # Loop over groups until group match found or end reached\\n while groupind < len(datagroups):\\n # Check if data fits group: Get first group element\\n gdata = datagroups[groupind][0]\\n\\n # Get key from group and new data - format if needed\\n dkey = data.getheadval(groupkey)\\n gkey = gdata.getheadval(groupkey)\\n\\n # Match -> add to group\\n if dkey == gkey:\\n datagroups[groupind].append(data)\\n break\\n\\n # Not found -> increase group index\\n groupind += 1\\n # If not in any group -> make new group\\n if groupind == len(datagroups):\\n datagroups.append([data, ])\\n\\n # info messages\\n log.debug(\\\" Found %d data groups\\\" % len(datagroups))\\n for groupind in range(len(datagroups)):\\n group = datagroups[groupind]\\n msg = \\\" Group %d len=%d\\\" % (groupind, len(group))\\n msg += \\\" %s = %s\\\" % (groupkey, group[0].getheadval(groupkey))\\n log.debug(msg)\\n\\n # Make and save groupflats\\n\\n # loop over datagroups\\n for groupind in range(len(datagroups)):\\n data0 = datagroups[groupind][0]\\n\\n # Load masterflat for this datagroup -> mflat\\n mflat = self.loadauxfile(data=data0)\\n\\n # Average intcal R/T Array Gains\\n filenum = []\\n rlist = []\\n tlist = []\\n rvlist = []\\n tvlist = []\\n for data in datagroups[groupind]:\\n rg = data.imageget('R ARRAY GAIN')\\n if np.any(~np.isnan(rg)):\\n rlist.append(rg)\\n rvlist.append(data.imageget('R ARRAY GAIN VAR'))\\n found_r = True\\n else:\\n found_r = False\\n\\n tg = data.imageget('T ARRAY GAIN')\\n if np.any(~np.isnan(tg)):\\n tlist.append(tg)\\n tvlist.append(data.imageget('T ARRAY GAIN VAR'))\\n found_t = True\\n else:\\n found_t = False\\n\\n if found_r or found_t:\\n if data.filenum is not None:\\n filenum.extend(data.filenum.split('-'))\\n else:\\n log.warning('Excluding bad file %s' % data.filename)\\n\\n if not rlist or not tlist:\\n msg = 'No good flat files found.'\\n log.error(msg)\\n raise ValueError(msg)\\n\\n rgainavg = np.nanmean(np.array(rlist), axis=0)\\n tgainavg = np.nanmean(np.array(tlist), axis=0)\\n rgainvar = np.nansum(np.array(rvlist), axis=0) / len(rvlist) ** 2\\n tgainvar = np.nansum(np.array(tvlist), axis=0) / len(tvlist) ** 2\\n\\n # Multiply masterflat -> groupflat\\n gflatr = mflat.imageget('R ARRAY GAIN') * rgainavg\\n gflatt = mflat.imageget('T ARRAY GAIN') * tgainavg\\n gflatrvar = (mflat.imageget('R ARRAY GAIN'))**2 * rgainvar\\n gflattvar = (mflat.imageget('T ARRAY GAIN'))**2 * tgainvar\\n\\n # Groupflat bad pixel mask: see where there are NaNs\\n gbadr = np.zeros(gflatr.shape)\\n gbadt = np.zeros(gflatt.shape)\\n gbadr[np.where(np.isnan(gflatr))] = 1.0\\n gbadt[np.where(np.isnan(gflatt))] = 2.0\\n\\n # Make groupflat filename: Filename of\\n # first file with OBSFLAT (OFT)\\n gflatname = data0.filenamebegin + 'OFT' + data0.filenameend\\n\\n # Split file, make sure outfolder is valid\\n if len(outfolder) > 0:\\n gflatname = os.path.split(gflatname)[1]\\n else:\\n outfolder, gflatname = os.path.split(gflatname)\\n\\n # Update file number to be a range\\n if len(filenum) > 1:\\n fn = sorted(filenum)\\n filenums = fn[0] + '-' + fn[-1]\\n match = re.search(self.config['data']['filenum'], gflatname)\\n if match is not None:\\n # regex may contain multiple possible matches --\\n # for middle or end of filename\\n for i, g in enumerate(match.groups()):\\n if g is not None:\\n gbegin = gflatname[:match.start(i + 1)]\\n gend = gflatname[match.end(i + 1):]\\n gflatname = gbegin + filenums + gend\\n break\\n gflatname = os.path.join(outfolder, gflatname)\\n\\n # Make groupflat data\\n gflat = DataFits(config=self.datain[0].config)\\n gflat.header = data0.header.copy()\\n\\n # Make header\\n for data in datagroups[groupind][1:]:\\n gflat.mergehead(data)\\n for data in datagroups[groupind]:\\n gflat.setheadval('PRODTYPE', 'obsflat')\\n gflat.setheadval('PROCSTAT', 'LEVEL_2')\\n gflat.setheadval('HISTORY', 'MakeFlat Source: %s' %\\n (os.path.split(data.filename)[1], ))\\n # Fill data\\n gflat.imageset(gflatr, 'R ARRAY GAIN')\\n gflat.imageset(gflatt, 'T ARRAY GAIN')\\n gflat.imageset(gflatrvar, 'R ARRAY GAIN VAR')\\n gflat.imageset(gflattvar, 'T ARRAY GAIN VAR')\\n gflat.imageset(gbadr, 'R BAD PIXEL MASK')\\n gflat.imageset(gbadt, 'T BAD PIXEL MASK')\\n\\n # Save groupflat data\\n gflat.save(gflatname)\\n self.auxout.append(gflatname)\\n log.info('Saved result %s' % gflatname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef3b90c4d99edad1dbd72942e1691244\",\n \"score\": \"0.5434679\",\n \"text\": \"def signal_extract(func_data=None,confounds=None,atlas_img=None,masker_type='Spheres',smoothing_fwhm=6,high_pass=0.01,low_pass=0.1,t_r=2.2,detrend=False,saveas='file'):\\r\\n subjects_ts=[]\\r\\n \\r\\n if os.path.exists(saveas):\\r\\n \\r\\n subjects_ts=np.load(saveas)['arr_0']\\r\\n \\r\\n else:\\r\\n \\r\\n if \\r\\n \\r\\n if masker_type== 'Spheres':\\r\\n masker = NiftiSpheresMasker(\\r\\n seeds=atlas_img, smoothing_fwhm=smoothing_fwhm, radius=4 ,mask_img=brainmask,\\r\\n detrend=False, standardize=True, low_pass=low_pass, high_pass=high_pass, t_r=t_r\\r\\n )\\r\\n elif masker_type == 'Maps':\\r\\n masker = NiftiMapsMasker(\\r\\n maps_img=atlas_img,mask_img=brainmask,standardize=True,\\r\\n low_pass=low_pass, high_pass=high_pass, t_r=t_r,\\r\\n memory_level=2,smoothing_fwhm=smoothing_fwhm,resampling_target='data',\\r\\n memory=mem,verbose=5\\r\\n )\\r\\n elif masker_type == 'Labels':\\r\\n masker = NiftiLabelsMasker(\\r\\n labels_img=atlas_img,mask_img=brainmask,standardize=True,\\r\\n high_pass=high_pass,low_pass=low_pass,detrend=False,t_r=t_r,\\r\\n memory_level=2,smoothing_fwhm=smoothing_fwhm,resampling_target='data',\\r\\n memory=mem,verbose=5\\r\\n )\\r\\n \\r\\n else:\\r\\n raise ValueError(\\\"Please provide masker type\\\")\\r\\n \\r\\n if confounds is not None: \\r\\n for func_file, confound_file in zip(func_data,confounds):\\r\\n time_series = masker.fit_transform(func_file,confounds=confound_file)\\r\\n subjects_ts.append(time_series)\\r\\n np.savez(saveas,subjects_ts)\\r\\n else:\\r\\n for func_file in data:\\r\\n time_series = masker.fit_transform(func_file)\\r\\n subjects_ts.append(time_series)\\r\\n np.savez(saveas,subjects_ts) \\r\\n \\r\\n return subjects_ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1989216b52a61e6a040f72ec26270137\",\n \"score\": \"0.54088956\",\n \"text\": \"def process(self):\\n\\n d = self.dataset\\n self.outputs[0].frequency = d['TWC_DET'].frequency\\n self.outputs[1].frequency = d['TWC_DET'].frequency\\n\\n tfull = d['TWC_DET'].data.copy()\\n tfullx = tfull.times2d.ravel()\\n\\n sh = tfull.shape\\n vf = tfull.flag\\n\\n F = 0.93\\n Kv = 427.0\\n p0 = 1013.2\\n uO = 0.2095\\n\\n if len(d[self.fit].data) == 2:\\n\\n fit = np.array(d[self.fit].data)\\n print('Applying FIT={}'.format(fit))\\n\\n ans = np.polyval(fit, tfull)\\n\\n px = d['PS_RVSM'].data.ravel()\\n p1 = px.interp(times=tfullx).reshape(sh)\\n\\n tx = d['TWC_TSAM'].data.ravel()\\n t2 = tx.interp(times=tfullx).reshape(sh)\\n\\n KO = 0.304 + 0.351 * p1 * F / p0\\n vpo = (ans - (KO * uO * p1 / (Kv * t2))) * t2\\n vmro = vp2vmr(vpo, p1)\\n mmr = vmr_mmr(vmro)\\n dp = vp2dp(vpo.ravel()).reshape(sh)\\n\\n else:\\n dp = np.zeros(sh)\\n mmr = tfull\\n vf[:] = 3\\n\\n self.outputs[0].data = flagged_data(dp, tfull.times, vf)\\n self.outputs[1].data = flagged_data(mmr, tfull.times, vf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ffeeb24d926c9dde8dff10194d3090\",\n \"score\": \"0.54030704\",\n \"text\": \"def main():\\n #print(\\\"le bhai data\\\")\\n collect_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6185c2b11947ddd8b1d587b9d1b2fa\",\n \"score\": \"0.54015166\",\n \"text\": \"def signal_processing(da,\\n varname = None,\\n agg_to_year = False,\\n add_global_warming_signal = False,\\n apply_standard_scaler=True,\\n demean_all_time=False,\\n apply_area_weighting=True,\\n normalise_by_year = False,\\n normalise_by_rolling_year = False,\\n normalise_by_month=True):\\n\\n if agg_to_year:\\n da = aggregate_to_year(da, deviation = True)\\n\\n if add_global_warming_signal:\\n da = da + np.linspace(0,1, da.shape[0])[:,np.newaxis, np.newaxis]\\n\\n if not agg_to_year:\\n\\n if normalise_by_year:\\n da = deviation_from_year_means(da, normalise_by_rolling_year)\\n\\n if normalise_by_month:\\n da = deviation_from_monthly_means(da)\\n \\n if demean_all_time:\\n da = da - da.mean(dim='time')\\n\\n if apply_standard_scaler:\\n #ss = StandardScaler() # probably overkill\\n # decide here whether you will pre-normalise or not\\n da = da - da.mean(dim='time')\\n da = da/da.std(dim='time')\\n\\n if apply_area_weighting:\\n coslat = np.abs(np.cos(np.deg2rad(da.latitude)))**0.5\\n weights = coslat #/ coslat.sum(dim=('latitude', 'longitude'))\\n da = da*weights\\n \\n if varname is not None:\\n da.name = varname\\n \\n return da\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8273806bc85b338e726a04070d6db74e\",\n \"score\": \"0.54005605\",\n \"text\": \"def filtering(wave,spec,filters):\\n\\n\\tw = h5py.File(dir_file+'filters_w.hdf5', 'r')\\n\\tt = h5py.File(dir_file+'filters_t.hdf5', 'r')\\n\\n\\tnbands = len(filters)\\n\\tfluxes = np.zeros(nbands)\\n\\tfor bb in range(0,nbands):\\n\\t\\tfil_w = w[filters[bb]][:]\\n\\t\\tfil_t = t[filters[bb]][:]\\n\\n\\t\\tmin_wave = int(min(fil_w))\\n\\t\\tmax_wave = int(max(fil_w))\\n\\n\\t\\tgwave = np.linspace(min_wave,max_wave,max_wave-min_wave+1)\\n\\n\\t\\tfil_trans = np.interp(gwave, fil_w, fil_t)\\n\\t\\tspec_flux = np.interp(gwave, wave, spec)\\n\\n\\t\\ttot_u = np.sum(spec_flux*gwave*fil_trans)\\n\\t\\ttot_l = np.sum(gwave*fil_trans)\\n\\n\\t\\tfluxes[bb] = tot_u/tot_l\\n\\n\\tw.close()\\n\\tt.close()\\n\\n\\treturn fluxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9d8962b4c22dbe9496f5e34689cd9d\",\n \"score\": \"0.53904307\",\n \"text\": \"def ba_events(data):\\n data_2=calc_aiphil(data)\\n data_all=vstack([data, array(data_2)])\\n ddata=zeros((50,100,int(data_all[-1].max()*10)+1), dtype=float)\\n for i, di in enumerate(data_all.T):\\n if i%100==0:\\n print(i)\\n w=di[0]\\n ai,phi,l=di[-3:]\\n res=run_simulation(l, ai*180/pi, phi*180/pi).array()\\n ddata[:,:,int(l*10)]+=w*res\\n return ddata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7132925ac77afc085170603510bbe1a7\",\n \"score\": \"0.538609\",\n \"text\": \"def trace_extract(self, data, r=0, bin=0, ivar=1., dy0=0):\\n dy = np.cast[int](np.round(self.ytrace+dy0))\\n aper = np.zeros_like(self.model)\\n y0 = self.sh_beam[0] // 2\\n for d in range(-r, r+1):\\n for i in range(self.sh_beam[1]):\\n aper[y0+d+dy[i]-1, i] = 1\\n\\n var = 1./ivar\\n if not np.isscalar(ivar):\\n var[ivar == 0] = 0\\n\\n opt_flux = np.sum(data*aper, axis=0)\\n opt_var = np.sum(var*aper, axis=0)\\n\\n if bin > 1:\\n kern = np.ones(bin, dtype=float)/bin\\n opt_flux = nd.convolve(opt_flux, kern)[bin // 2::bin]\\n opt_var = nd.convolve(opt_var, kern**2)[bin // 2::bin]\\n wave = self.lam[bin // 2::bin]\\n else:\\n wave = self.lam\\n\\n opt_rms = np.sqrt(opt_var)\\n\\n return wave, opt_flux, opt_rms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a2f1ff85b7b3740a4e28c674264c170\",\n \"score\": \"0.5382624\",\n \"text\": \"def run_pls_selector(data, produce, wavelengths):\\n\\n # produce_spectras = utils.load_spectra(produce)\\n # data = [spectra[0:2] for spectra in produce_spectras]\\n select = PLSDASelector(data, wavelengths)\\n\\n wave, ind = select.find_wavelengths()\\n return wave, ind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ddbb123983b4a85763862ec5729323\",\n \"score\": \"0.5377325\",\n \"text\": \"def data_loader(test=False, preemph=0.95, need_length=False):\\r\\n lendata = {\\r\\n 'name' : [],\\r\\n 'length' : []\\r\\n }\\r\\n\\r\\n\\r\\n ## Sub function : wav read & data shaping\\r\\n def wavloader(filename, length, name='wav', get_lendata=False):\\r\\n\\r\\n # Error\\r\\n num = len(filename)\\r\\n if num == 0:\\r\\n print('Dataset Error : no wave files.')\\r\\n\\r\\n i = 1\\r\\n filedata = []\\r\\n for filename_ in filename:\\r\\n file_ = wave.open(filename_, 'rb')\\r\\n wavdata = np.frombuffer(file_.readframes(-1), dtype='int16')\\r\\n if get_lendata:\\r\\n lendata['name'].append(filename_)\\r\\n lendata['length'].append(len(wavdata))\\r\\n filedata.append(wavdata)\\r\\n file_.close()\\r\\n print(' Loading {0} wav... #{1} / {2}'.format(name, i, num))\\r\\n i+=1\\r\\n\\r\\n filedata = np.concatenate(filedata, axis=0) # Serializing\\r\\n filedata = filedata - preemph * np.roll(filedata, 1) # Pre-enphasis\\r\\n filedata = filedata.astype(np.float32) # Data Compressing (float64 -> float32)\\r\\n L = length // 2 # Half of Input Size (init: 8192 samples)\\r\\n D = len(filedata) // L # No. of 0.5s blocks\\r\\n if len(filedata) % (D*L) != 0:\\r\\n fdata = []\\r\\n for f in filedata:\\r\\n fdata.append(f)\\r\\n zeros = np.zeros(shape=(len(filedata) - L*D), dtype=np.float32)\\r\\n for z in zeros:\\r\\n fdata.append(z)\\r\\n filedata = np.array(fdata, dtype=np.float32)\\r\\n filedata = filedata[:D * L].reshape(D, L) # Split data for each half of input size : (1,:) --> (D, 8192)\\r\\n return filedata\\r\\n\\r\\n\\r\\n\\t# Load settings\\r\\n args = settings()\\r\\n\\r\\n # Make folder\\r\\n if not os.path.exists(args.model_save_path): # Folder of model\\r\\n os.makedirs(args.model_save_path)\\r\\n\\r\\n if not os.path.exists(args.wav_save_path): # Folder of model\\r\\n os.makedirs(args.wav_save_path)\\r\\n\\r\\n if not os.path.exists(args.train_pkl_path): # Folder of train pkl\\r\\n os.makedirs(args.train_pkl_path)\\r\\n\\r\\n if not os.path.exists(args.test_pkl_path): # Folder of test pkl\\r\\n os.makedirs(args.test_pkl_path)\\r\\n\\r\\n # File name\\r\\n if not test:\\r\\n wav_clean = args.clean_train_path + '/*.wav'\\r\\n wav_noisy = args.noisy_train_path + '/*.wav'\\r\\n pkl_clean = args.train_pkl_path + '/' + args.train_pkl_clean\\r\\n pkl_noisy = args.train_pkl_path + '/' + args.train_pkl_noisy\\r\\n else:\\r\\n wav_clean = args.clean_test_path + '/*.wav'\\r\\n wav_noisy = args.noisy_test_path + '/*.wav'\\r\\n pkl_clean = args.test_pkl_path + '/' + args.test_pkl_clean\\r\\n pkl_noisy = args.test_pkl_path + '/' + args.test_pkl_noisy\\r\\n pkl_length = args.test_pkl_path + '/' + args.test_pkl_length\\r\\n\\r\\n\\r\\n ## No pkl files -> read wav + create pkl files\\r\\n ## -------------------------------------------------\\r\\n if not (os.access(pkl_clean, os.F_OK) and os.access(pkl_noisy, os.F_OK)):\\r\\n\\r\\n ## Wav files\\r\\n print(' Load wav file...')\\r\\n\\r\\n\\t # Get file path\\r\\n cname = glob.glob(wav_clean)\\r\\n nname = glob.glob(wav_noisy)\\r\\n\\r\\n # Get wave data\\r\\n cdata = wavloader(cname, args.len, name='clean', get_lendata=True) # Clean wav\\r\\n ndata = wavloader(nname, args.len, name='noisy') # Noisy wav\\r\\n\\r\\n ## Pkl files\\r\\n print(' Create Pkl file...')\\r\\n\\r\\n\\t\\t# Create clean pkl file\\r\\n with open(pkl_clean, 'wb') as f:\\r\\n joblib.dump(cdata, f, protocol=-1,compress=3)\\r\\n\\r\\n # Create noisy pkl file\\r\\n with open(pkl_noisy, 'wb') as f:\\r\\n joblib.dump(ndata, f, protocol=-1,compress=3)\\r\\n\\r\\n if test:\\r\\n if (not os.access(pkl_length, os.F_OK)):\\r\\n # Create length pkl file\\r\\n with open(pkl_length, 'wb') as f:\\r\\n joblib.dump(lendata, f, protocol=-1,compress=3)\\r\\n\\r\\n\\t## Pkl files exist -> Load\\r\\n ## -------------------------------------------------\\r\\n else:\\r\\n # Load clean pkl file\\r\\n print(' Load Clean Pkl...')\\r\\n with open(pkl_clean, 'rb') as f:\\r\\n cdata = joblib.load(f)\\r\\n\\r\\n # Load noisy pkl file\\r\\n print(' Load Noisy Pkl...')\\r\\n with open(pkl_noisy, 'rb') as f:\\r\\n ndata = joblib.load(f)\\r\\n\\r\\n if test:\\r\\n # Load length pkl file\\r\\n print(' Load Noisy Pkl...')\\r\\n with open(pkl_length, 'rb') as f:\\r\\n lendata = joblib.load(f)\\r\\n\\r\\n if not test:\\r\\n return cdata, ndata\\r\\n else:\\r\\n return cdata, ndata, lendata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24a070460a1621564256aaac1cfd6c06\",\n \"score\": \"0.5368832\",\n \"text\": \"def run(met_data, prof_data, param_kwds=None, overwrite=True, diagnostics=False, suffix='', save_plots=False):\\n \\n #TODO: move this to the helper file\\n \\n #close all figures\\n plt.close('all')\\n \\n #start timer\\n t0 = timeit.default_timer()\\n \\n ## Get surface forcing and profile data \\n # These are x-ray datasets, but you can treat them as dicts. \\n # Do met_dset.keys() to explore the data fields\\n met_dset = xray.open_dataset('input_data/%s' %met_data) \\n prof_dset = xray.open_dataset('input_data/%s' %prof_data)\\n \\n ## get model parameters and constants (read docs for set_params function)\\n lat = prof_dset['lat'] #needed to compute internal wave dissipation\\n if param_kwds is None:\\n params = phf.set_params(lat=lat) \\n else:\\n param_kwds['lat'] = lat\\n params = phf.set_params(**param_kwds)\\n \\n ## prep forcing and initial profile data for model run (see prep_data function for more details)\\n forcing, pwp_out, params = phf.prep_data(met_dset, prof_dset, params)\\n \\n ## run the model\\n pwp_out = pwpgo(forcing, params, pwp_out, diagnostics)\\n \\n #check timer\\n tnow = timeit.default_timer()\\n t_elapsed = (tnow - t0) \\n print(\\\"Time elapsed: %i minutes and %i seconds\\\" %(np.floor(t_elapsed/60), t_elapsed%60))\\n \\n ## write output to disk\\n if overwrite:\\n time_stamp = ''\\n else:\\n #use unique time stamp\\n time_stamp = datetime.now().strftime(\\\"_%Y%m%d_%H%M\\\")\\n \\n if len(suffix)>0 and suffix[0] != '_':\\n suffix = '_%s' %suffix\\n \\n # save output as netCDF file\\n pwp_out_ds = xray.Dataset({'temp': (['z', 'time'], pwp_out['temp']), 'sal': (['z', 'time'], pwp_out['sal']), \\n 'uvel': (['z', 'time'], pwp_out['uvel']), 'vvel': (['z', 'time'], pwp_out['vvel']),\\n 'dens': (['z', 'time'], pwp_out['dens']), 'mld': (['time'], pwp_out['mld'])}, \\n coords={'z': pwp_out['z'], 'time': pwp_out['time']})\\n\\n pwp_out_ds.to_netcdf(\\\"output/pwp_output%s%s.nc\\\" %(suffix, time_stamp))\\n\\n # also output and forcing as pickle file\\n pickle.dump(forcing, open( \\\"output/forcing%s%s.p\\\" %(suffix, time_stamp), \\\"wb\\\" ))\\n pickle.dump(pwp_out, open( \\\"output/pwp_out%s%s.p\\\" %(suffix, time_stamp), \\\"wb\\\" ))\\n \\n ## do analysis of the results\\n phf.makeSomePlots(forcing, pwp_out, suffix=suffix, save_plots=save_plots)\\n \\n return forcing, pwp_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75972f241192e742087c53fdd27e0b1d\",\n \"score\": \"0.5366451\",\n \"text\": \"def prepare_wave_data(fname):\\n wh = iris.load_cube(fname, 'Significant height of combined wind waves and swell')\\n wd = iris.load_cube(fname, 'Mean wave direction')\\n wp = iris.load_cube(fname, 'Mean wave period')\\n return wd, wh, wp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad68ef2ae0d42ce3312b68989ea4b97f\",\n \"score\": \"0.53548145\",\n \"text\": \"def filters(array, sample_frequency):\\n strain = TimeSeries(array, sample_rate=int(sample_frequency))\\n white_data = strain.whiten(fftlength=4, fduration=4)\\n bp_data = white_data.bandpass(50, 250)\\n return bp_data.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c4756098fc291bbbd85bcd1bfc2b14\",\n \"score\": \"0.5349286\",\n \"text\": \"def quality_stats():\\n datapath_mri = op.join('data', 'raw_eyegaze', 'sub-*', 'ses-movie', 'func',\\n 'sub-*_ses-movie_task-movie_run-*_recording-eyegaze_physio.tsv.gz')\\n datapath_lab = op.join('data', 'raw_eyegaze', 'sub-*', 'beh',\\n 'sub-*_task-movie_run-*_recording-eyegaze_physio.tsv.gz')\\n\\n for (data, assoc) in [(datapath_lab, 'lab'),\\n (datapath_mri, 'mri')]:\\n infiles = glob(data)\\n for f in infiles:\\n datalad_get(f)\\n # make sure we have 15 subjects' data\\n assert len(infiles) == 120\\n print(\\\"Currently processing data from {} sample\\\".format(assoc))\\n # set sampling rate and px2deg\\n px2deg = 0.0266711972026 if assoc == 'lab' else 0.0185581232561\\n sr = 1000\\n # calculate percent signal loss across subjects and runs\\n losses = []\\n vels = []\\n for f in infiles:\\n data = np.recfromcsv(f,\\n delimiter='\\\\t',\\n names=['x', 'y', 'pupil', 'frame'])\\n # all periods of signal loss are marked as nan in the data\\n signal_loss = np.sum(np.isnan(data['x'])) / len(data['x'])\\n losses.append(signal_loss)\\n velocities = cal_velocities(data=data,\\n px2deg=px2deg,\\n sr=sr)\\n vels.append(velocities)\\n print(\\\"Calculated velocities and losses for {} sample\\\".format(assoc))\\n # average across signal losses in sample (mri or lab)\\n loss = np.nanmean(losses)\\n # print results as Latex command using 'assoc' as sample identifier in name\\n label_loss = 'avgloss{}'.format(assoc)\\n rsout('\\\\\\\\newcommand{\\\\\\\\%s}{%s}'\\n % (label_loss, loss))\\n # vels is a list of arrays atm\\n v = np.concatenate(vels).ravel()\\n if assoc == 'lab':\\n v_lab = v\\n elif assoc == 'mri':\\n v_mri = v\\n\\n # plot velocities in a histogram on logscale\\n # create non-linear non-equal bin sizes, as x axis will be log\\n hist, bins, _ = plt.hist(v[~np.isnan(v)], bins=40)\\n plt.close()\\n logbins = np.logspace(1, # don't start with 0, does not make sense in logspace\\n np.log10(bins[-1]),\\n len(bins))\\n fig, ax = plt.subplots()\\n fig.set_figheight(3)\\n fig.set_figwidth(5)\\n ax.set_ylabel('frequency')\\n ax.set_xlabel('velocities (deg/s)')\\n plt.hist(v_mri[~np.isnan(v_mri)],\\n weights=np.zeros_like(v_mri[~np.isnan(v_mri)]) + 1. / (v_mri[~np.isnan(v_mri)]).size,\\n bins=logbins,\\n histtype='bar',\\n color='orangered',\\n alpha=0.5,\\n label='mri')\\n plt.hist(v_lab[~np.isnan(v_lab)],\\n weights=np.zeros_like(v_lab[~np.isnan(v_lab)]) + 1. / (v_lab[~np.isnan(v_lab)]).size,\\n bins=logbins,\\n histtype='bar',\\n color='darkslategrey',\\n alpha=0.5,\\n label='lab')\\n plt.legend(loc='upper right')\\n plt.xscale('log')\\n plt.savefig(op.join('img', 'velhist.svg'),\\n transparent=True,\\n bbox_inches=\\\"tight\\\",\\n metadata={'Date': None})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e11f21dd5a4e3cdd55f3512225bd33\",\n \"score\": \"0.5347797\",\n \"text\": \"def calculate_spectrum(obs_freqs,v0,radex_params,radex=True):\\n\\n\\n\\t#user supplies the observed frequency so doppler shift to emitted\\n\\t#tau dist makes this unnecessary\\n\\temit_freqs=obs_freqs*(1.0+v_0/light_speed)\\n\\t\\n\\t#we'll return a dataframe of Frequency, Intensity\\n\\tnew_df=DataFrame({\\\"Frequency\\\":obs_freqs})\\n\\tnew_df[\\\"Intensity\\\"]=0.0\\n\\t\\n\\t#solve the radex model and get all line properties\\n\\tif radex:\\n\\t\\ttau_0_df=get_radex_taus(radex_params)\\n\\telse:\\n\\t\\ttau_0_df=get_lte_taus(col_dens,gas_temp,delta_v)\\n\\t\\t\\t\\t\\t\\t\\t \\n\\t#now loop through line and build up the tau weighted radiation temperature average\\n\\tfor i,line in tau_0_df.iterrows():\\n\\t\\t#get the relative velocity of all the emitting frequencies\\n\\t\\tvelocities=((line[\\\"freq\\\"]/obs_freqs)-1.0)*light_speed\\n\\t\\t\\n\\t\\t#use that to get the tau values at those frequencies\\n\\t\\ttaus=get_tau_dist(v_0,delta_v,line[\\\"tau\\\"],velocities)\\n\\t\\t\\n\\t\\t#store tau weighted radiation temp\\n\\t\\tnew_df[f\\\"{line.freq:.3f}\\\"]=rad_temp(line[\\\"T_ex\\\"],emit_freqs)*taus\\n\\t\\t\\n\\t\\t#and add tau to running total\\n\\t\\tnew_df[\\\"Intensity\\\"]+=taus\\n\\t\\t\\n\\t\\n\\t#sum our tau weighted temperatures and divide by sum of taus\\n\\tline_cols=[x for x in new_df if x not in [\\\"Intensity\\\",\\\"Frequency\\\"]]\\n\\tnew_df[\\\"temp\\\"]=new_df[line_cols].sum(axis=1)/new_df[\\\"Intensity\\\"]\\n\\t#now get brightness temperature as a function of frequency\\n\\tnew_df[\\\"Intensity\\\"]=(new_df[\\\"temp\\\"]-rad_temp(2.73,emit_freqs))*(1.0-np.exp(-new_df[\\\"Intensity\\\"]))\\n\\tnew_df[\\\"Intensity\\\"]=new_df[\\\"Intensity\\\"].fillna(0.0)\\n\\treturn new_df[\\\"Intensity\\\"].values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d73e9324c816981f4ae0c2601f921b8\",\n \"score\": \"0.53431565\",\n \"text\": \"def run(self):\\n\\n logging.info('Begin logging for wata_monitor')\\n\\n # define WATA variables\\n self.instrument = \\\"nirspec\\\"\\n self.aperture = \\\"NRS_S1600A1_SLIT\\\"\\n\\n # Identify which database tables to use\\n self.identify_tables()\\n\\n # Get the output directory and setup a directory to store the data\\n self.output_dir = os.path.join(get_config()['outputs'], 'wata_monitor')\\n ensure_dir_exists(self.output_dir)\\n # Set up directories for the copied data\\n ensure_dir_exists(os.path.join(self.output_dir, 'data'))\\n self.data_dir = os.path.join(self.output_dir,\\n 'data/{}_{}'.format(self.instrument.lower(),\\n self.aperture.lower()))\\n ensure_dir_exists(self.data_dir)\\n\\n # Locate the record of most recent MAST search; use this time\\n self.query_start = self.most_recent_search()\\n # get the data of the plots previously created and set the query start date\\n self.prev_data = None\\n self.output_file_name = os.path.join(self.output_dir, \\\"wata_layout.html\\\")\\n logging.info('\\\\tNew output plot file will be written as: {}'.format(self.output_file_name))\\n if os.path.isfile(self.output_file_name):\\n self.prev_data, self.query_start = self.get_data_from_html(self.output_file_name)\\n logging.info('\\\\tPrevious data read from html file: {}'.format(self.output_file_name))\\n # copy this plot to a previous version\\n shutil.copyfile(self.output_file_name, os.path.join(self.output_dir, \\\"prev_wata_layout.html\\\"))\\n # fail save - start from the beginning if there is no html file\\n else:\\n self.query_start = self.query_very_beginning\\n logging.info('\\\\tPrevious output html file not found. Starting MAST query from Jan 28, 2022 == First JWST images (MIRI)')\\n\\n # Use the current time as the end time for MAST query\\n self.query_end = Time.now().mjd\\n logging.info('\\\\tQuery times: {} {}'.format(self.query_start, self.query_end))\\n\\n # Query for data using the aperture and the time of the\\n # most recent previous search as the starting time\\n\\n # via MAST:\\n # new_entries = monitor_utils.mast_query_ta(\\n # self.instrument, self.aperture, self.query_start, self.query_end)\\n\\n # via django model:\\n new_entries = monitor_utils.model_query_ta(\\n self.instrument, self.aperture, self.query_start, self.query_end)\\n wata_entries = len(new_entries)\\n logging.info('\\\\tQuery has returned {} WATA files for {}, {}.'.format(wata_entries, self.instrument, self.aperture))\\n\\n # Filter new entries to only keep uncal files\\n new_entries = self.pull_filenames(new_entries)\\n new_entries = self.get_uncal_names(new_entries)\\n wata_entries = len(new_entries)\\n logging.info('\\\\tThere are {} uncal TA files to run the WATA monitor.'.format(wata_entries))\\n\\n # Get full paths to the files\\n new_filenames = []\\n for filename_of_interest in new_entries:\\n if (self.prev_data is not None\\n and filename_of_interest in self.prev_data['filename'].values):\\n logging.warning('\\\\t\\\\tFile {} already in previous data. Skipping.'.format(filename_of_interest))\\n continue\\n try:\\n new_filenames.append(filesystem_path(filename_of_interest))\\n logging.warning('\\\\tFile {} included for processing.'.format(filename_of_interest))\\n except FileNotFoundError:\\n logging.warning('\\\\t\\\\tUnable to locate {} in filesystem. Not including in processing.'.format(filename_of_interest))\\n\\n if len(new_filenames) == 0:\\n logging.warning('\\\\t\\\\t ** Unable to locate any file in filesystem. Nothing to process. ** ')\\n\\n # Run the monitor on any new files\\n self.script, self.div, self.wata_data = None, None, None\\n monitor_run = False\\n if len(new_filenames) > 0: # new data was found\\n # get the data\\n self.new_wata_data, no_ta_ext_msgs = self.get_wata_data(new_filenames)\\n if len(no_ta_ext_msgs) >= 1:\\n for item in no_ta_ext_msgs:\\n logging.info(item)\\n if self.new_wata_data is not None:\\n # concatenate with previous data\\n if self.prev_data is not None:\\n self.wata_data = pd.concat([self.prev_data, self.new_wata_data])\\n logging.info('\\\\tData from previous html output file and new data concatenated.')\\n else:\\n self.wata_data = self.new_wata_data\\n logging.info('\\\\tOnly new data was found - no previous html file.')\\n else:\\n logging.info('\\\\tWATA monitor skipped. No WATA data found.')\\n # make sure to return the old data if no new data is found\\n elif self.prev_data is not None:\\n self.wata_data = self.prev_data\\n logging.info('\\\\tNo new data found. Using data from previous html output file.')\\n # do the plots if there is any data\\n if self.wata_data is not None:\\n self.script, self.div = self.mk_plt_layout()\\n monitor_run = True\\n logging.info('\\\\tOutput html plot file created: {}'.format(self.output_file_name))\\n wata_files_used4plots = len(self.wata_data['visit_id'])\\n logging.info('\\\\t{} WATA files were used to make plots.'.format(wata_files_used4plots))\\n # update the list of successful and failed TAs\\n self.update_ta_success_txtfile()\\n logging.info('\\\\tWATA status file was updated')\\n else:\\n logging.info('\\\\tWATA monitor skipped.')\\n\\n # Update the query history\\n new_entry = {'instrument': self.instrument,\\n 'aperture': self.aperture,\\n 'start_time_mjd': self.query_start,\\n 'end_time_mjd': self.query_end,\\n 'entries_found': wata_entries,\\n 'files_found': len(new_filenames),\\n 'run_monitor': monitor_run,\\n 'entry_date': datetime.now()}\\n\\n with engine.begin() as connection:\\n connection.execute(self.query_table.__table__.insert(), new_entry)\\n logging.info('\\\\tUpdated the query history table')\\n\\n logging.info('WATA Monitor completed successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5bbfbbe34f60117d0d64047a7f68605\",\n \"score\": \"0.53421396\",\n \"text\": \"def analyze(self, event):\\n\\n nllnnloW = -1\\n nllW = -1\\n nllW_Rup = -1\\n nllW_Qup = -1\\n nllW_Rdown = -1\\n nllW_Qdown = -1\\n gen_mww = -1\\n gen_ptww = -1\\n\\n genParticles = Collection(event, \\\"GenPart\\\")\\n \\n ptV1 = -1\\n ptV2 = -1\\n phiV1 = -1\\n phiV2 = -1\\n etaV1 = -1\\n etaV2 = -1\\n \\n for particle in genParticles :\\n # 24 = W+/-\\n # == 13 : isLastCopy\\n if abs(particle.pdgId) == 24 and (particle.statusFlags >> 13 & 1):\\n \\n if ptV1 == -1 :\\n ptV1 = particle.pt\\n phiV1 = particle.phi\\n etaV1 = particle.eta\\n elif ptV2 == -1 :\\n ptV2 = particle.pt\\n phiV2 = particle.phi\\n etaV2 = particle.eta\\n \\n\\n if ptV1 != -1 and ptV2 != -1 :\\n self.wwNLL.SetPTWW(ptV1, phiV1, etaV1, ptV2, phiV2, etaV2)\\n\\n \\n gen_ptww = self.wwNLL.GetPTWW()\\n gen_mww = self.wwNLL.GetMWW()\\n \\n nllnnloW = self.wwNLL.nllnnloWeight(0) \\n nllW = self.wwNLL.nllWeight(0) \\n nllW_Rup = self.wwNLL.nllWeight(1,1) \\n nllW_Qup = self.wwNLL.nllWeight(1,0) \\n nllW_Rdown = self.wwNLL.nllWeight(-1,1) \\n nllW_Qdown = self.wwNLL.nllWeight(-1,0) \\n \\n \\n self.out.fillBranch(\\\"gen_ptww\\\", gen_ptww)\\n self.out.fillBranch(\\\"gen_mww\\\", gen_mww)\\n\\n self.out.fillBranch(\\\"nllnnloW\\\", nllnnloW)\\n self.out.fillBranch(\\\"nllW\\\", nllW)\\n self.out.fillBranch(\\\"nllW_Rup\\\", nllW_Rup)\\n self.out.fillBranch(\\\"nllW_Qup\\\", nllW_Qup)\\n self.out.fillBranch(\\\"nllW_Rdown\\\", nllW_Rdown)\\n self.out.fillBranch(\\\"nllW_Qdown\\\", nllW_Qdown)\\n\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1203a6f9c412f728f4f91692e9d72dca\",\n \"score\": \"0.5340376\",\n \"text\": \"def load_wave_bouy_spectrograms(arg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be1feb1e86713760604f0b1fee95476\",\n \"score\": \"0.53308433\",\n \"text\": \"def syntheticSeismogram(\\n d, rho, v, wavf, wavA=1.0, usingT=True, wavtyp=\\\"RICKER\\\", dt=0.0001, dmax=200\\n):\\n\\n v, rho, d = (\\n np.array(v, dtype=float),\\n np.array(rho, dtype=float),\\n np.array(d, dtype=float),\\n )\\n usingT = np.array(usingT, dtype=bool)\\n\\n _, t = getTimeDepth(d, v, dmax)\\n rseries, R = getReflectivity(d, rho, v)\\n\\n # time for reflectivity series\\n tref = t[1:-1]\\n\\n # create time vector\\n t = np.arange(t.min(), t.max(), dt)\\n\\n # make wavelet\\n twav = np.arange(-2.0 / np.min(wavf), 2.0 / np.min(wavf), dt)\\n\\n # Get source wavelet\\n wav = {\\\"RICKER\\\": getRicker, \\\"ORMSBY\\\": getOrmsby, \\\"KLAUDER\\\": getKlauder}[wavtyp](\\n wavf, twav\\n )\\n wav = wavA * wav\\n\\n rseriesconv = np.zeros(len(t))\\n for i in range(len(tref)):\\n index = np.abs(t - tref[i]).argmin()\\n rseriesconv[index] = rseries[i]\\n\\n # Do the convolution\\n seis = np.convolve(wav, rseriesconv)\\n tseis = np.min(twav) + dt * np.arange(len(seis))\\n index = np.logical_and(tseis >= 0, tseis <= np.max(t))\\n tseis = tseis[index]\\n seis = seis[index]\\n\\n return tseis, seis, twav, wav, tref, rseries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5808402970a69f63f45c6b8f520b532\",\n \"score\": \"0.53300315\",\n \"text\": \"def _run(example_file_names, plot_shortwave, output_dir_name):\\n\\n file_system_utils.mkdir_recursive_if_necessary(\\n directory_name=output_dir_name\\n )\\n\\n example_dicts = []\\n\\n for this_file_name in example_file_names:\\n print('Reading data from: \\\"{0:s}\\\"...'.format(this_file_name))\\n this_example_dict = example_io.read_file(\\n netcdf_file_name=this_file_name,\\n max_shortwave_heating_k_day01=numpy.inf,\\n min_longwave_heating_k_day01=-1 * numpy.inf,\\n max_longwave_heating_k_day01=numpy.inf\\n )\\n example_dicts.append(this_example_dict)\\n\\n example_dict = example_utils.concat_examples(example_dicts)\\n\\n heating_rate_matrix_k_day01 = example_utils.get_field_from_dict(\\n example_dict=example_dict,\\n field_name=\\n example_utils.SHORTWAVE_HEATING_RATE_NAME if plot_shortwave\\n else example_utils.LONGWAVE_HEATING_RATE_NAME\\n )\\n heating_rate_percentiles_k_day01 = numpy.percentile(\\n heating_rate_matrix_k_day01, PERCENTILE_LEVELS\\n )\\n\\n for a, b in zip(PERCENTILE_LEVELS, heating_rate_percentiles_k_day01):\\n print('{0:.1f}th-percentile heating rate = {1:.4f} K day^-1'.format(\\n a, b\\n ))\\n print(SEPARATOR_STRING)\\n\\n _plot_by_cloud_regime(\\n example_dict=example_dict, plot_shortwave=plot_shortwave,\\n output_dir_name=output_dir_name\\n )\\n _plot_by_latitude(\\n example_dict=example_dict, plot_shortwave=plot_shortwave,\\n output_dir_name=output_dir_name\\n )\\n _plot_by_zenith_angle(\\n example_dict=example_dict, plot_shortwave=plot_shortwave,\\n output_dir_name=output_dir_name\\n )\\n\\n mean_heating_rates_k_day01 = numpy.mean(heating_rate_matrix_k_day01, axis=1)\\n sort_indices = numpy.argsort(-mean_heating_rates_k_day01)\\n figure_object, axes_object = _plot_profiles(\\n example_dict=example_dict, plot_shortwave=plot_shortwave,\\n example_indices=sort_indices[:NUM_EXTREME_PROFILES]\\n )\\n axes_object.set_title(\\n '{0:d} profiles with max average heating rates'.format(\\n NUM_EXTREME_PROFILES\\n )\\n )\\n output_file_name = '{0:s}/max_average_heating_rates.jpg'.format(\\n output_dir_name\\n )\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(output_file_name))\\n figure_object.savefig(\\n output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\\n\\n sort_indices = numpy.argsort(mean_heating_rates_k_day01)\\n figure_object, axes_object = _plot_profiles(\\n example_dict=example_dict,\\n plot_shortwave=plot_shortwave,\\n example_indices=sort_indices[:NUM_EXTREME_PROFILES]\\n )\\n axes_object.set_title(\\n '{0:d} profiles with minimum average heating rates'.format(\\n NUM_EXTREME_PROFILES\\n )\\n )\\n output_file_name = '{0:s}/min_average_heating_rates.jpg'.format(\\n output_dir_name\\n )\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(output_file_name))\\n figure_object.savefig(\\n output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\\n\\n max_heating_rates_k_day01 = numpy.max(heating_rate_matrix_k_day01, axis=1)\\n sort_indices = numpy.argsort(-max_heating_rates_k_day01)\\n figure_object, axes_object = _plot_profiles(\\n example_dict=example_dict,\\n plot_shortwave=plot_shortwave,\\n example_indices=sort_indices[:NUM_EXTREME_PROFILES]\\n )\\n axes_object.set_title(\\n '{0:d} profiles with max max heating rates'.format(\\n NUM_EXTREME_PROFILES\\n )\\n )\\n output_file_name = '{0:s}/max_max_heating_rates.jpg'.format(\\n output_dir_name\\n )\\n print('Saving figure to: \\\"{0:s}\\\"...'.format(output_file_name))\\n figure_object.savefig(\\n output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,\\n bbox_inches='tight'\\n )\\n pyplot.close(figure_object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07b95d4fca1cd711fbc3812807e61dd\",\n \"score\": \"0.5327413\",\n \"text\": \"def plotwav(samp, data, filt_type='bandpass', filt_freqlim=[12, 18], \\n filt_order=4, window_size=4, overlap=.95, window_type='hann',\\n plotflag=True, scale_func=defaultScaleFunction,ylim=[12, 18]):\\n\\n PLT_TIMESERIES = 1\\n FIGSIZE = [9, 3]\\n FILTER_OFFSET = 10\\n\\n #filter data to spectral bands where B-call is\\n [b, a] = sig.butter(filt_order, np.array(filt_freqlim)/samp, filt_type, 'ba') \\n filtered_data = sig.filtfilt(b, a, data)\\n\\n \\n datalength = data.size\\n times = (np.arange(datalength)/samp) \\n\\n #plot timeseries on upper axis\\n if plotflag == True:\\n plt.figure(PLT_TIMESERIES, figsize=FIGSIZE)\\n plt.subplot(211)\\n plt.plot(times[FILTER_OFFSET:],filtered_data[FILTER_OFFSET:])\\n plt.axis([min(times), max(times), min(filtered_data[FILTER_OFFSET:]), \\n max(filtered_data[FILTER_OFFSET:])])\\n plt.xlabel('Seconds')\\n plt.ylabel('Amplitude')\\n\\n #plot spectrogram on lower axis\\n [f, t, Sxx] = sig.spectrogram(filtered_data, int(samp), \\n window_type,int(samp*window_size),int(samp*window_size*overlap))\\n \\n if plotflag == True:\\n cmap = plt.get_cmap('magma')\\n vmin, vmax = scale_func(Sxx)\\n norm = color.Normalize(vmin=vmin, vmax=vmax)\\n plt.subplot(212)\\n plt.pcolormesh(t, f, 10*np.log10(Sxx), cmap=cmap, norm=norm) \\n plt.ylabel('Frequency [Hz]')\\n plt.xlabel('Time [sec]')\\n plt.ylim(ylim)\\n plt.show(PLT_TIMESERIES)\\n #plt.clf()\\n \\n return [f, t, Sxx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18757a2f731d39fd7c0004a774af913f\",\n \"score\": \"0.53065217\",\n \"text\": \"def Analysis_data():\\n\\t_scatter_plot('user ID','channel ID')\\n\\t_histogram_plot('user ID','genre ID')\\n\\t_scatter_plot('user ID','duration')\\n\\t_histogram_plot('user ID','slot')\\n\\t_histogram_plot('channel ID','slot')\\n\\t_histogram_plot('genre ID','duration')\\n\\t_box_plot('user ID','program ID')\\n\\t_bar_chart('user ID')\\n\\t_bar_chart('channel ID')\\n\\t_bar_chart('slot')\\n\\t_bar_chart('genre ID')\\n\\t_bar_chart('duration')\\n\\t_bar_chart('subGenre ID')\\n\\t_bar_chart('week')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a39c1dd85c3a1f580ead85d77ef006e4\",\n \"score\": \"0.5300061\",\n \"text\": \"def run(self):\\n tTag = self.training.sampleTag\\n sTag = self.samples.sampleTag\\n training = self.training.asDataset()\\n samples = self.samples.asDataset()\\n alpha = max(0.05, .5/len(samples))\\n varNames = self.variables\\n numVars = len(varNames)\\n # use the len of macro, cluster from samples to build plots\\n macro = samples[self.macroName]\\n figCounter = 0\\n for m, mac in enumerate(macro):\\n figCounter += 1\\n fig, axes = plt.subplots(numVars, 1, sharex=True)\\n if numVars == 1:\\n axes = [axes]\\n axes[-1].set_xlabel(self.microName)\\n\\n mSamples = samples.sel({self.macroName: mac}, drop=True)\\n mTraining = None\\n if self.macroName in training:\\n if int(mac) in training[self.macroName]:\\n if self.macroName in training.dims:\\n mTraining = training.drop_sel({self.macroName: mac})\\n else:\\n mTraining = training.where(training[self.macroName]==mac, drop=True).squeeze()\\n for v, var in enumerate(varNames):\\n ax = axes[v]\\n # plot cloud of sample data\\n for s in mSamples[sTag].values:\\n samp = mSamples[{sTag: s}]\\n ax.plot(samp[self.microName].values, samp[var].values, 'b-.', alpha=alpha)\\n ax.set_title(f'{var}, {self.macroName} {int(mac)}')\\n ax.set_ylabel(var)\\n if mTraining is not None:\\n ax.plot(mTraining[self.microName].values, mTraining[var].values, 'k-.')\\n\\n filename = f'{self.name}_{m}.png'\\n plt.savefig(filename)\\n self.raiseAMessage(f'Wrote \\\"{filename}\\\".')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30849743348e120f95b310e8a542adda\",\n \"score\": \"0.52973163\",\n \"text\": \"def extract_wavelet(signals, config={}):\\n if not config:\\n raise Exception('empty config in extract_mfcc(...)')\\n wavelets = []\\n for data in signals:\\n wavelet = signal.cwt(data, signal.ricker, np.arange(1, config['widths'] + 1))\\n wavelets.append(wavelet)\\n wavelets = np.array(wavelets)\\n return wavelets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d307237a828f4c9bf1dbf1d8ed2c0817\",\n \"score\": \"0.52964026\",\n \"text\": \"def main():\\n \\n import argparse\\n import pickle\\n\\n parser = argparse.ArgumentParser()\\n \\n # normal operation \\n parser.add_argument(\\\"num\\\", type=int, default=1000, \\n help=\\\"number of data points to be generated (1000)\\\")\\n parser.add_argument(\\\"mod\\\", type=str, default='qpsk', \\n help=\\\"modulation scheme: 'bspk', 'qpsk', '8psk', '16qam' (qpsk))\\\")\\n parser.add_argument(\\\"--dir\\\", type=str, default='./generated_data', \\n help=\\\"directory/filename to save output to (./generated_data)\\\")\\n \\n # for pulseshaped data \\n parser.add_argument('--pulse', action='store_true', help=\\\"generate pulse shaped data\\\")\\n parser.add_argument(\\\"-os\\\", type=int, default=16, \\n help=\\\"oversampling factor (power of 2)\\\")\\n parser.add_argument(\\\"-len\\\", type=int, default=128, \\n help=\\\"filter length (power of 2)\\\")\\n\\n args = parser.parse_args()\\n num, mod, directory = (args.num, args.mod, args.dir)\\n pulse, os, length = (args.pulse, args.os, args.len)\\n \\n generator = Data_generator()\\n\\n # check input arguments\\n try:\\n pck_file = open(directory+\\\".pck\\\", \\\"w\\\")\\n except IOError as error:\\n print(\\\"Check if the output directory exists\\\")\\n raise\\n \\n # check if os and length are powers of 2\\n assert os&(os-1) == 0\\n assert length&(length-1) == 0\\n \\n if (pulse): # generate pulse shaped data\\n \\n data = generator.get_pulseshaped_data(num, mod, os)\\n # data = (data_raw, data_mod, data_os, data_shaped, rc)\\n\\n # save the data \\n pickle.dump(data, pck_file)\\n pck_file.close()\\n\\n print(\\\"Generated %d random samples with '%s' modulation.\\\" % (num, mod))\\n print(\\\"Signal is oversampled by a factor of %d and filtered with a rc filter of length %d\\\" % (os, length))\\n print(\\\"Data has been saved in %s.pck\\\" % directory)\\n\\n else: # generate normal data\\n \\n data = generator.get_random_data(num, mod)\\n \\n # save the data \\n pickle.dump(data, pck_file)\\n pck_file.close()\\n\\n print(\\\"Generated %d random samples with '%s' modulation.\\\" % (num, mod))\\n print(\\\"Data has been saved in %s.pck\\\" % directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c6b52588c32ecba054cfbb97c51078\",\n \"score\": \"0.5294081\",\n \"text\": \"def test_var_measure_single_wire(self):\\n state = np.array([[1, -1j], [0, 0]]) / np.sqrt(2)\\n shots = qml.measurements.Shots(100)\\n\\n mp0 = qml.var(qml.PauliZ(0))\\n mp1 = qml.var(qml.PauliY(1))\\n\\n result0 = measure_with_samples(mp0, state, shots=shots)\\n result1 = measure_with_samples(mp1, state, shots=shots)\\n\\n assert result0.shape == ()\\n assert result1.shape == ()\\n assert result0 == 0\\n assert result1 == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b66d8d1bf7dbdc107494d0b8bda245\",\n \"score\": \"0.52934396\",\n \"text\": \"def fwemva(self,dslo,dat,dt,minf,maxf,vel,jf=1,nrmax=3,eps=0.0,dtmax=5e-05,wav=None,\\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\\n # Get temporal axis\\n nt = dat.shape[-1]\\n\\n # Create frequency domain source\\n if(wav is None):\\n wav = np.zeros(nt,dtype='float32')\\n wav[0] = 1.0\\n self.__nwo,self.__ow,self.__dw,wfft = self.fft1(wav,dt,minf=minf,maxf=maxf)\\n wfftd = wfft[::jf]\\n self.__nwc = wfftd.shape[0] # Get the number of frequencies for imaging\\n self.__dwc = self.__dw*jf\\n\\n if(sverb or wverb): print(\\\"Frequency axis: nw=%d ow=%f dw=%f\\\"%(self.__nwc,self.__ow,self.__dwc))\\n\\n # Create frequency domain data\\n _,_,_,dfft = self.fft1(dat,dt,minf=minf,maxf=maxf)\\n dfftd = dfft[:,::jf]\\n datt = np.transpose(dfftd,(0,1,4,2,3)) # [nsy,nsx,ny,nx,nwc] -> [nsy,nsx,nwc,ny,nx]\\n datw = np.ascontiguousarray(datt.reshape([self.__nexp,self.__nwc,self.__ny,self.__nx]))\\n\\n # Single square root object\\n ssf = ssr3(self.__nx ,self.__ny,self.__nz , # Spatial Sizes\\n self.__dx ,self.__dy,self.__dz , # Spatial Samplings\\n self.__nwc,self.__ow,self.__dwc,eps, # Frequency axis\\n ntx,nty,px,py, # Taper and padding\\n dtmax,nrmax,nthrds) # Reference velocities\\n\\n # Compute slowness and reference slownesses\\n slo = 1/vel\\n ssf.set_slows(slo)\\n\\n dimgar = np.zeros([self.__nexp,self.__nz,self.__ny,self.__nx],dtype='complex64')\\n\\n # Allocate the source for one shot\\n sou = np.zeros([self.__nwc,self.__ny,self.__nx],dtype='complex64')\\n\\n # Loop over sources\\n k = 0\\n for icrd in progressbar(self.__scoords,\\\"nexp:\\\",verb=sverb):\\n # Get the source coordinates\\n sy = icrd[0]; sx = icrd[1]\\n # Create the source for this shot\\n sou[:] = 0.0\\n sou[:,sy,sx] = wfftd[:]\\n ssf.fwemvaallw(sou,datw[k],dslo,dimgar[k],verb=wverb)\\n k += 1\\n\\n # Sum over all partial images\\n dimg = np.sum(dimgar,axis=0)\\n\\n return np.real(dimg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff2a20b7d52e0fafc2e3328b9d229b19\",\n \"score\": \"0.52859616\",\n \"text\": \"def get_terror_waves_info(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3baf45d31bc7686677fb88c61886ee\",\n \"score\": \"0.528461\",\n \"text\": \"def test_ripples_haar_lvl3():\\n\\n class MyHaarFilterBank(object):\\n @property\\n def filter_bank(self):\\n return (\\n [1 / 2, 1 / 2.0],\\n [-1 / 2.0, 1 / 2.0],\\n [1 / 2.0, 1 / 2.0],\\n [1 / 2.0, -1 / 2.0],\\n )\\n\\n data = [56.0, 40.0, 8.0, 24.0, 48.0, 48.0, 40.0, 16.0]\\n wavelet = pywt.Wavelet(\\\"unscaled Haar Wavelet\\\",\\n filter_bank=MyHaarFilterBank())\\n ptdata = torch.tensor(data).unsqueeze(0).unsqueeze(0)\\n coeffs = wavedec(ptdata, wavelet, level=3)\\n # print(coeffs)\\n assert torch.squeeze(coeffs[0]).numpy() == 35.0\\n assert torch.squeeze(coeffs[1]).numpy() == -3.0\\n assert (torch.squeeze(coeffs[2]).numpy() == [16.0, 10.0]).all()\\n assert (torch.squeeze(coeffs[3]).numpy() == [8.0, -8.0, 0.0, 12.0]).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b909fa3dd3f57e701bf13658e49272fa\",\n \"score\": \"0.5266676\",\n \"text\": \"def dataset(verbose=False):\\n \\n s = Sample()\\n finite = np.isfinite(s.data['Fe'])\\n selection = finite & s.giant & s.spectype[-1] & (s.cf>0) & (s.data['age']-s.data['eage']>1)\\n \\n fep = np.percentile(s.data['Fe'][selection], [33,66])\\n fei = np.empty((3, np.sum(selection)), dtype=np.bool)\\n fei[0] = s.data['Fe'][selection]<fep[0]\\n fei[1] = (s.data['Fe'][selection]>=fep[0]) & (s.data['Fe'][selection]<fep[1])\\n fei[2] = s.data['Fe'][selection]>=fep[1]\\n \\n z_bins = np.arange(0, 4, 0.2)\\n z = myutils.bincen(z_bins)\\n Nb = np.size(z)\\n \\n for i, ind in enumerate(fei):\\n hz, be = np.histogram(np.abs(s.x[:,2][selection][ind].value), bins=z_bins, weights=1/s.cf[selection][ind])\\n nz, be = np.histogram(np.abs(s.x[:,2][selection][ind].value), bins=z_bins)\\n idx = np.digitize(np.abs(s.x[:,2][selection][ind].value), bins=z_bins)\\n \\n n = np.zeros(Nb)\\n vz = np.empty(Nb)\\n sz = np.empty(Nb)\\n vze = np.empty(Nb)\\n sze = np.empty(Nb)\\n sza = np.empty(Nb)\\n \\n for l in range(Nb):\\n if verbose: print(i, l)\\n #plt.sca(ax[int(l/Ncol)][l%Ncol])\\n if np.sum(idx==l+1):\\n n[l] = np.sum(idx==l+1)\\n sampler = np.load('../data/chains/vz_feh{}_z{}.chain.npz'.format(i,l))\\n p = np.percentile(sampler['chain'], [16,50,84], axis=0)\\n vz[l] = p[1,0]\\n sz[l] = p[1,1]\\n vze[l] = 0.5*(p[2,0] - p[0,0])\\n sze[l] = 0.5*(p[2,1] - p[0,1])\\n \\n vmin, vmax = np.percentile(s.v[:,2][selection][ind][idx==l+1].value, [15,85])\\n i_ = (s.v[:,2][selection][ind][idx==l+1].value>vmin) & (s.v[:,2][selection][ind][idx==l+1].value<vmax)\\n sza[l] = np.std(s.v[:,2][selection][ind][idx==l+1][i_].value)\\n #sza[l] = np.std(s.v[:,2][selection][ind][idx==l+1].value)\\n \\n t = Table([z, hz, nz, vz, vze, sz, sze, sza], names=('z', 'nu', 'n', 'vz', 'vze', 'sz', 'sze', 'sza'))\\n t.write('../data/profile_mgiants_feh{}.fits'.format(i), overwrite=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df8f8821d5ec85bf4e97e7e3cb6f8352\",\n \"score\": \"0.5263289\",\n \"text\": \"def visualize_wave(self, y):\\r\\n if self.current_freq_detects[\\\"beat\\\"]:\\r\\n output = np.zeros((3,config.settings[\\\"devices\\\"][self.board][\\\"configuration\\\"][\\\"N_PIXELS\\\"]))\\r\\n output[0][:]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_flash\\\"]][0]\\r\\n output[1][:]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_flash\\\"]][1]\\r\\n output[2][:]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_flash\\\"]][2]\\r\\n self.wave_wipe_count = config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"wipe_len\\\"]\\r\\n else:\\r\\n output = np.copy(self.prev_output)\\r\\n #for i in range(len(self.prev_output)):\\r\\n # output[i] = np.hsplit(self.prev_output[i],2)[0]\\r\\n output = np.multiply(self.prev_output,config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"decay\\\"])\\r\\n for i in range(self.wave_wipe_count):\\r\\n output[0][i]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_wave\\\"]][0]\\r\\n output[0][-i]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_wave\\\"]][0]\\r\\n output[1][i]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_wave\\\"]][1]\\r\\n output[1][-i]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_wave\\\"]][1]\\r\\n output[2][i]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_wave\\\"]][2]\\r\\n output[2][-i]=config.settings[\\\"colors\\\"][config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"color_wave\\\"]][2]\\r\\n #output = np.concatenate([output,np.fliplr(output)], axis=1)\\r\\n if self.wave_wipe_count > config.settings[\\\"devices\\\"][self.board][\\\"configuration\\\"][\\\"N_PIXELS\\\"]//2:\\r\\n self.wave_wipe_count = config.settings[\\\"devices\\\"][self.board][\\\"configuration\\\"][\\\"N_PIXELS\\\"]//2\\r\\n self.wave_wipe_count += config.settings[\\\"devices\\\"][self.board][\\\"effect_opts\\\"][\\\"Wave\\\"][\\\"wipe_speed\\\"]\\r\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572c6cd02e4cda53c363e1e9d1caec70\",\n \"score\": \"0.52579087\",\n \"text\": \"def main():\\n\\n # Load data and template\\n npzfile = np.load(\\\"./data/optimumInputs.npz\\\")\\n rl, tl = npzfile['arr_0'], npzfile['arr_1']\\n wave, waveTS, dataE, dataST = rl[0], rl[1], rl[2], rl[3]\\n temp, tempTS, tempE, tempST = tl[0], tl[1], tl[2], tl[3]\\n\\n # Window the fit around rising edge - start time calculator method\\n loWin, hiWin = dataST - 1000, dataST + 4000 # ns\\n if loWin < waveTS[0] or hiWin > waveTS[-1]:\\n print \\\"Window out of range! dataST: %.1f loWin %.1f hiWin %.1f\\\" % (dataST,loWin,hiWin)\\n idx = np.where((waveTS >= loWin) & (waveTS <= hiWin))\\n data = wave[idx]\\n dataTS = waveTS[idx]\\n\\n # Pack into lists\\n dataNoise = 2. # just a guess - 1 sigma baseline adc values\\n rawList = [wave, waveTS, dataE, dataST]\\n dataList = [data, dataTS, dataE, dataST, loWin, hiWin, dataNoise]\\n tempList = [temp, tempTS, tempE, tempST]\\n\\n # Recreate the guess and the guess's rising edge\\n guessFull, guessFullTS = wm.MakeModel(rawList, tempList, [dataST,dataE,1.], opt=\\\"full\\\")\\n guess, guessTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\\\"!fancy\\\")\\n\\n # Make an \\\"almost complete\\\" guess - no MCMC\\n # st, en, slo = dataST-100, dataE, 5\\n # InterpFn = interpolate.interp1d(tempTS, temp, kind=\\\"linear\\\", copy=\\\"False\\\", assume_sorted=\\\"True\\\")\\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n # Fit with MCMC and get best-fit parameters\\n numSteps, burnIn = 3000, 1800 # default: 10000, 5000. fast: 3000, 1800 long test: 20000,10000\\n wfModel = wm.TemplateModel( dataList, dataNoise, tempList )\\n M = pymc.MCMC( pymc.Model( wfModel ) )\\n M.use_step_method(pymc.Metropolis, M.startTime, proposal_sd=100., proposal_distribution='Normal')\\n M.use_step_method(pymc.Metropolis, M.energy, proposal_sd=1., proposal_distribution='Normal')\\n M.use_step_method(pymc.Metropolis, M.slowness, proposal_sd=100., proposal_distribution='Normal')\\n M.sample(iter=numSteps, verbose=0)\\n st = np.median(M.trace(\\\"startTime\\\")[:])\\n en = np.median(M.trace(\\\"energy\\\")[:])\\n slo = np.median(M.trace(\\\"slowness\\\")[:])\\n InterpFn = interpolate.interp1d(tempTS, temp, kind=\\\"linear\\\", copy=\\\"False\\\", assume_sorted=\\\"True\\\")\\n model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n print \\\"MCMC:\\\",st,en,slo\\n\\n # Fit with SciPy minimizer\\n MakeTracesGlobal() # creates 3 global arrays: startTrace, enTrace, sloTrace\\n floats = [dataST, dataE, 1]\\n print \\\"Minimizer guesses:\\\",floats\\n datas = [dataList,tempList,InterpFn]\\n result = minimize(findLnLike, floats, args=datas, method=\\\"Nelder-Mead\\\")\\n st, en, slo = result[\\\"x\\\"]\\n print \\\"Minimizer: %.1f %.1f %.1f Success: %s. %s\\\" % (st, en, slo, result[\\\"success\\\"], result[\\\"message\\\"])\\n minimizer, minimizerTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n # plots\\n fig = plt.figure(figsize=(11,7), facecolor='w')\\n p1 = plt.subplot2grid((6,7), (0,0), colspan=4, rowspan=2) # original\\n p2 = plt.subplot2grid((6,7), (2,0), colspan=4, rowspan=3) # rising edge\\n p3 = plt.subplot2grid((6,7), (0,4), colspan=3, rowspan=2 ) # trace 1\\n p4 = plt.subplot2grid((6,7), (2,4), colspan=3, rowspan=2, sharex=p3) # trace 2\\n p5 = plt.subplot2grid((6,7), (4,4), colspan=3, rowspan=2, sharex=p3) # trace 3\\n\\n # p1 = plt.subplot(211)\\n p1.set_title(\\\"Energy %.1f keV Start Time %.0f ns\\\" % (dataE, dataST))\\n p1.set_ylabel(\\\"ADC [A.U.]\\\",y=0.95, ha='right')\\n p1.set_xlabel(\\\"Time (ns)\\\",x=0.95, ha='right')\\n p1.plot(waveTS,wave,color='blue',alpha=0.8,label='Data WF')\\n p1.plot(guessFullTS,guessFull,color='orange',alpha=0.8,label='Guess WF')\\n p1.axvline(x=dataST,color='green')\\n p1.legend(loc=4)\\n\\n # p2 = plt.subplot(212)\\n p2.plot(dataTS, data, color='blue',label='Data')\\n p2.plot(guessTS, guess, color='orange',label='Guess')\\n p2.plot(modelTS, model, color='red',linewidth=4,alpha=0.8,label='MCMC')\\n p2.plot(minimizerTS, minimizer, color='cyan',linewidth=1,label='Nelder-Mead')\\n p2.legend(loc=4)\\n\\n p3.cla()\\n p3.set_title(\\\"startTime %.1f Energy %.2f Slow %.1f\\\" % (st,en,slo))\\n p3.plot(stTrace[1:])\\n p3.set_ylabel('startTime')\\n\\n p4.cla()\\n p4.plot(enTrace[1:])\\n p4.set_ylabel('energy')\\n\\n p5.cla()\\n p5.plot(sloTrace[1:])\\n p5.set_ylabel('slowness')\\n\\n plt.tight_layout()\\n plt.subplots_adjust(hspace=0.35)\\n # plt.show(block=False)\\n # plt.show()\\n plt.savefig(\\\"./plots/minimizer-test.pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dff0d949e86fc706bf79d623e50b616\",\n \"score\": \"0.5253615\",\n \"text\": \"def process(self, df):\\n output = self.accumulator.identity()\\n dataset = df[\\\"dataset\\\"]\\n cfg = loadConfig()\\n # We can access the data frame as usual\\n # The dataset is written into the data frame\\n # outside of this function\\n\\n output['totalEvents']['all'] += len(df['weight'])\\n\\n met_pt = df[\\\"MET_pt\\\"]\\n met_phi = df[\\\"MET_phi\\\"]\\n\\n Jet = JaggedCandidateArray.candidatesfromcounts(\\n df['nJet'],\\n pt = df['Jet_pt'].content,\\n eta = df['Jet_eta'].content,\\n phi = df['Jet_phi'].content,\\n mass = df['Jet_mass'].content,\\n goodjet = df['Jet_isGoodJetAll'].content,\\n bjet = df['Jet_isGoodBJet'].content,\\n jetId = df['Jet_jetId'].content,\\n puId = df['Jet_puId'].content,\\n )\\n\\n lepton = JaggedCandidateArray.candidatesfromcounts(\\n df['nLepton'],\\n pt = df['Lepton_pt'].content,\\n eta = df['Lepton_eta'].content,\\n phi = df['Lepton_phi'].content,\\n mass = df['Lepton_mass'].content,\\n pdgId = df['Lepton_pdgId'].content,\\n )\\n \\n alljet = Jet[(Jet['goodjet']==1)] # all jets with pt>25 and pt>60 in 2.7<|eta|<3.0 (noise suppression)\\n b = Jet[Jet['bjet']==1]\\n nonb = Jet[(Jet['goodjet']==1) & (Jet['bjet']==0)]\\n leading_nonb = nonb[:,:6] # first six non-b-tagged jets\\n lead_light = nonb[nonb.pt.argmax()]\\n spectator = Jet[(abs(Jet.eta)>2.0) & (abs(Jet.eta)<4.7) & (Jet.pt>25) & (Jet['puId']>=7) & (Jet['jetId']>=6)] # 40 GeV seemed good. let's try going lower\\n \\n bj_pair = b.cross(nonb)\\n jj_pair = nonb.cross(nonb)\\n lb_pair = lepton.cross(b)\\n lj_pair = lepton.cross(nonb)\\n ht = Jet[Jet['goodjet']==1].pt.sum()\\n st = Jet[Jet['goodjet']==1].pt.sum() + lepton.pt.sum() + met_pt\\n \\n leading_lepton = lepton[lepton.pt.argmax()]\\n\\n ## calculate mt\\n mt_lep_met = mt(leading_lepton.pt, leading_lepton.phi, met_pt, met_phi)\\n\\n ### define selections here, using the objects defined above\\n singlelep = ((df['nLepton']==1) & (df['nVetoLepton']==1))\\n sixjet = (alljet.counts >= 6 )\\n sevenjet = (alljet.counts >= 7)\\n two_b = ( b.counts >= 2)\\n eta_lead = ((lead_light.eta>-.5).counts>0) & ((lead_light.eta<0.5).counts>0)\\n \\n # selections used for the histograms below\\n event_selection = (Jet.counts>5) & (b.counts>=2) & (nonb.counts>=4) & (df['nLepton']==1) & (df['nVetoLepton']==1)\\n tight_selection = (Jet.counts>5) & (b.counts>=2) & (nonb.counts>=4) & (df['nLepton']==1) & (df['nVetoLepton']==1) & (df['MET_pt']>50) & (ht>500) & (df['MT']>50) & (spectator.counts>=1) & (spectator.pt.max()>50) & (st>600) & (bj_pair.mass.max()>300) & (jj_pair.mass.max()>300)\\n\\n ### work on the cutflow\\n addRowToCutFlow( output, df, cfg, 'skim', None ) # entry point\\n addRowToCutFlow( output, df, cfg, 'singlelep', singlelep )\\n addRowToCutFlow( output, df, cfg, 'sixjet', singlelep & sixjet )\\n addRowToCutFlow( output, df, cfg, 'sevenjet', singlelep & sevenjet )\\n addRowToCutFlow( output, df, cfg, 'twob', singlelep & sevenjet & two_b )\\n addRowToCutFlow( output, df, cfg, 'etalead', singlelep & sevenjet & eta_lead ) # this is a weird cut\\n addRowToCutFlow( output, df, cfg, 'everything', singlelep & sevenjet & two_b & eta_lead ) # this is a weird cut\\n \\n ### fill all the histograms\\n \\n output['MET_pt'].fill(dataset=dataset, pt=df[\\\"MET_pt\\\"][singlelep].flatten(), weight=df['weight'][singlelep]*cfg['lumi'])\\n output['MT'].fill(dataset=dataset, pt=df[\\\"MT\\\"][singlelep].flatten(), weight=df['weight'][singlelep]*cfg['lumi'])\\n output['N_b'].fill(dataset=dataset, multiplicity=df[\\\"nGoodBTag\\\"][singlelep], weight=df['weight'][singlelep]*cfg['lumi'] )\\n output['N_jet'].fill(dataset=dataset, multiplicity=df[\\\"nGoodJet\\\"][singlelep], weight=df['weight'][singlelep]*cfg['lumi'] )\\n \\n output['mbj_max'].fill(dataset=dataset, mass=bj_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n output['mjj_max'].fill(dataset=dataset, mass=jj_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n output['mlb_min'].fill(dataset=dataset, mass=lb_pair[event_selection].mass.min().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n output['mlb_max'].fill(dataset=dataset, mass=lb_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n output['mlj_min'].fill(dataset=dataset, mass=lj_pair[event_selection].mass.min().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n output['mlj_max'].fill(dataset=dataset, mass=lj_pair[event_selection].mass.max().flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n\\n output['HT'].fill(dataset=dataset, ht=ht[event_selection].flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n output['ST'].fill(dataset=dataset, ht=st[event_selection].flatten(), weight=df['weight'][event_selection]*cfg['lumi'])\\n\\n # forward stuff\\n output['N_spec'].fill(dataset=dataset, multiplicity=spectator[event_selection].counts, weight=df['weight'][event_selection]*cfg['lumi'])\\n output['pt_spec_max'].fill(dataset=dataset, pt=spectator[event_selection & (spectator.counts>0)].pt.max().flatten(), weight=df['weight'][event_selection & (spectator.counts>0)]*cfg['lumi'])\\n\\n\\n ### event shape variables - neglect for now\\n \\n #output['FWMT1'].fill(dataset=dataset, norm=FWMT(leading_nonb)[1][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\\n #output['FWMT2'].fill(dataset=dataset, norm=FWMT(leading_nonb)[2][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\\n #output['FWMT3'].fill(dataset=dataset, norm=FWMT(leading_nonb)[3][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\\n #output['FWMT4'].fill(dataset=dataset, norm=FWMT(leading_nonb)[4][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\\n #output['FWMT5'].fill(dataset=dataset, norm=FWMT(leading_nonb)[5][tight_selection], weight=df['weight'][tight_selection]*cfg['lumi'])\\n ##output['S'].fill(dataset=dataset, norm=sphericityBasic(alljet)[event_selection], weight=df['weight'][event_selection]*cfg['lumi'])\\n\\n #all_obj = mergeArray(alljet, lepton)\\n #output['S_lep'].fill(dataset=dataset, norm=sphericityBasic(all_obj)[event_selection], weight=df['weight'][event_selection]*cfg['lumi'])\\n \\n \\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869fcaa45c2d0c524bff1e3c96f633d2\",\n \"score\": \"0.5243887\",\n \"text\": \"def main():\\n logger = logging.getLogger(__name__)\\n logger.info(\\\"making final data set from raw data\\\")\\n load_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78857a618c25b76991ab25f47ad40d09\",\n \"score\": \"0.52398384\",\n \"text\": \"def applyTo(self, photon_array, local_wcs=None, rng=None):\\n rng = rng if rng is not None else self.rng\\n if photon_array.hasAllocatedWavelengths():\\n galsim_warn(\\\"Wavelengths already set before applying WavelengthSampler. \\\"\\n \\\"This is most likely an error.\\\")\\n photon_array.wavelength = self.sed.sampleWavelength(\\n photon_array.size(), self.bandpass, rng=rng, npoints=self.npoints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4db61b489f1741b8b4cdecb2daf986a\",\n \"score\": \"0.5237792\",\n \"text\": \"def model_fulldata(self,vel,wav,dtd,dtu=0.001,nthrds=4,verb=False):\\n if(vel.shape[1] != self.__nx or vel.shape[0] != self.__nz):\\n raise Exception(\\\"Velocity must have same shape as passed to constructor\\\")\\n\\n # Pad the velocity model\\n velp = self.pad_model(vel)\\n\\n # Temporal axis\\n ntu = wav.shape[0]\\n fact = int(dtd/dtu); ntd = int(ntu/fact)\\n\\n # Build the wave propagation object\\n sca = sca2d.scaas2d(ntd, self.__nxp, self.__nzp, # Sizes\\n dtd, self.__dx, self.__dz,dtu, # Samplings\\n self.__bx, self.__bz, self.__alpha) # Absorbing boundary\\n # Create input wavelet array\\n allsrcs = np.zeros([self.__nsx,1,ntu],dtype='float32') # One source per shot (same length as wavefield)\\n for isx in range(self.__nsx):\\n allsrcs[isx,0,:] = wav[:]\\n # Create output data\\n allshot = np.zeros([self.__nsx,ntd,self.__nrx],dtype='float32')\\n # Forward modeling\\n sca.fwdprop_multishot(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\\n self.allrecx,self.allrecz,self.nrec, # Receiver information\\n self.__nsx,velp,allshot,nthrds,verb) # Velocity and output\\n\\n return allshot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17b0421f4001be6bd4c0ee797155649\",\n \"score\": \"0.52371895\",\n \"text\": \"def load_data( band_id):\\n band = photons[photons.band==band_id] #.query('band== @band_id')\\n wt_table = wt_tables[band_id]\\n nside = wt_table['nside']\\n new_weights = wt_table['wts'].astype(np.float16)\\n to_shift = int(2*np.log2(data_nside//nside))\\n data_pixels = np.right_shift(band.nest_index, to_shift)\\n wt_pixels=wt_table['pixels']\\n good = np.isin( data_pixels, wt_pixels)\\n if self.config.verbose>2:\\n print(f'\\\\t {band_id:2}: {len(band):8,} -> {sum(good ):8,}')\\n return data_pixels, new_weights, good\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a219a6deae64601dfd8f9d27bbcae2e\",\n \"score\": \"0.52176845\",\n \"text\": \"def get_wavelength(ins, filtname):\\n\\n YJfile = pkg_resources.resource_stream(\\n \\\"amical\\\", \\\"internal_data/ifs_wave_YJ.fits\\\")\\n YJHfile = pkg_resources.resource_stream(\\n \\\"amical\\\", \\\"internal_data/ifs_wave_YJH.fits\\\")\\n wave_YJ = fits.open(YJfile)[0].data\\n wave_YJH = fits.open(YJHfile)[0].data\\n\\n dic_filt = {'NIRISS': {'F277W': [2.776, 0.715],\\n 'F380M': [3.828, 0.205],\\n 'F430M': [4.286, 0.202],\\n 'F480M': [4.817, 0.298]\\n },\\n 'SPHERE': {'H2': [1.593, 0.052],\\n 'H3': [1.667, 0.054],\\n 'H4': [1.733, 0.057],\\n 'K1': [2.110, 0.102],\\n 'K2': [2.251, 0.109],\\n 'CntH': [1.573, 0.023],\\n 'CntK1': [2.091, 0.034],\\n 'CntK2': [2.266, 0.032]\\n },\\n 'SPHERE-IFS': {'YJ': wave_YJ,\\n 'YH': wave_YJH\\n },\\n 'GLINT': {'F155': [1.55, 0.01],\\n 'F430': [4.3, 0.01]\\n },\\n 'VISIR': {'10_5_SAM': [10.56, 0.37],\\n '11_3_SAM': [11.23, 0.55]},\\n 'VAMPIRES': {'750-50': [0.75, 0.05]}\\n }\\n\\n if ins not in dic_filt.keys():\\n cprint(\\\"--- Error: instrument <%s> not found ---\\\" % ins, 'red')\\n cprint('Available: %s' % list(dic_filt.keys()), 'red')\\n wl = np.NaN\\n\\n try:\\n wl = np.array(dic_filt[ins][filtname]) * 1e-6\\n except KeyError:\\n cprint(\\\"--- Error: filtname <%s> not found for %s ---\\\" %\\n (filtname, ins), 'red')\\n cprint('Available: %s' % list(dic_filt[ins].keys()), 'red')\\n wl = np.NaN\\n return wl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1c334f36458c371c18433ee4637f8b\",\n \"score\": \"0.5214118\",\n \"text\": \"def SE_data_gen(is_tr):\\n file_idx = 0\\n sum_temp = None\\n std_temp = None\\n total_sample_cnt = 0\\n if is_tr == 'tr':\\n tf_list = c.TRAIN_TF_LIST\\n random.shuffle(c.snr_list)\\n snr = c.snr_list[0]\\n elif is_tr == 'cv':\\n tf_list = c.VALID_TF_LIST\\n elif is_tr == 'tt':\\n tf_list = c.TEST_TF_LIST\\n\\n anechoic_path = 'anechoic_mono_speech_' + is_tr + '.flist'\\n with open(anechoic_path, 'r') as f:\\n all_wavs = f.readlines()\\n num_of_wavs = len(all_wavs)\\n \\n with open(anechoic_path, 'r') as f:\\n for path in all_wavs:\\n file_idx += 1\\n # print(file_idx)\\n # if file_idx <= 1735:\\n # continue\\n print(is_tr, file_idx)\\n content = path.strip().split(' ')\\n adr, label = content[0], content[1]\\n sample_rate, wav_data_temp = wav.read(adr)\\n assert sample_rate == c.fs\\n\\n index = int(label) - 1\\n random.shuffle(tf_list)\\n TF_path = tf_list[0]\\n TF = load_mat(TF_path)\\n #_mmax = np.max(TF[index][0], axis=0)\\n #_mmin = np.min(TF[index][0], axis=0)\\n wav_data = get_array_signal(wav_data_temp, TF[index][0]).astype(np.float32)\\n if is_tr == 'cv' or is_tr == 'tt':\\n thres = num_of_wavs / len(c.snr_list)\\n if file_idx < thres:\\n snr = c.snr_list[0]\\n elif thres <= file_idx < 2 * thres:\\n snr = c.snr_list[1]\\n elif 2 * thres <= file_idx < 3 * thres:\\n snr = c.snr_list[2]\\n else:\\n snr = c.snr_list[3]\\n #_mmax = np.max(wav_data, axis=0)\\n #_mmin = np.min(wav_data, axis=0)\\n wav_data = awgn(wav_data, snr)\\n #mmax = np.max(wav_data, axis=0)\\n #mmin = np.min(wav_data, axis=0)\\n angles = get_goal_angle(index * 5)\\n extracted_sig = extract_sig(wav_data, angles)\\n assert extracted_sig.shape[1] == 5\\n extracted_sig = extracted_sig[0:len(wav_data_temp), :]\\n fig, ax = plt.subplots(6, 1)\\n ax[0].plot(wav_data_temp)\\n for i in range(5): ax[i+1].plot(extracted_sig[:, i])\\n plt.show()\\n # frames of multichannel, the 1st dimension is channel\\n multi_frames = []\\n for i in range(5):\\n frames_each_chan = sig2frames(extracted_sig[:, i])\\n multi_frames.append(frames_each_chan)\\n X = torch.from_numpy(np.array(multi_frames).transpose([1, 0, 2]))\\n Y = torch.from_numpy(sig2frames(wav_data_temp))\\n torch.save({'X': X, 'Y': Y}, c.SE_data_save_path + is_tr + '/SEdata{}.pt'.format(file_idx))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4b5a766612415afe168390d637e19e\",\n \"score\": \"0.5213889\",\n \"text\": \"def wem(self,vel,dat,wav,dtd,dtu=0.001,nh=None,trunc=True,lap=False,nthrds=4,verb=False):\\n if(vel.shape[1] != self.__nx or vel.shape[0] != self.__nz):\\n raise Exception(\\\"Velocity must have same shape as passed to constructor\\\")\\n\\n if(dat.shape[0] != self.__nsx or dat.shape[2] != self.__nrx):\\n raise Exception(\\\"Data must have same shape as acqusition passed to constructor\\\")\\n\\n # Pad the model and create the output image\\n velp = self.pad_model(vel)\\n if(nh == None):\\n imgp = np.zeros([self.__nzp,self.__nxp],dtype='float32')\\n else:\\n self.rnh = 2*nh + 1; self.oh = -self.__dx*nh; self.dh = self.__dx\\n imgp = np.zeros([self.rnh,self.__nzp,self.__nxp],dtype='float32')\\n\\n # Temporal axis\\n ntu = wav.shape[0]\\n fact = int(dtd/dtu); ntd = int(ntu/fact)\\n\\n # Build the wave propagation object\\n sca = sca2d.scaas2d(ntd, self.__nxp, self.__nzp, # Sizes\\n dtd, self.__dx, self.__dz,dtu, # Samplings\\n self.__bx, self.__bz, self.__alpha) # Absorbing boundary\\n # Create input wavelet array\\n allsrcs = np.zeros([self.__nsx,1,ntu],dtype='float32') # One source per shot (same length as wavefield)\\n for isx in range(self.__nsx):\\n allsrcs[isx,0,:] = wav[:]\\n\\n # Form the image\\n if(nh == None):\\n sca.brnadj(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\\n self.allrecx,self.allrecz,self.nrec, # Receiver information\\n self.__nsx,velp,imgp,dat,nthrds,verb) # Velocity and output image\\n if(self.tap is not None):\\n # Apply taper\\n imgp *= self.tap\\n if(lap):\\n # Apply laplacian\\n imgl = np.zeros(imgp.shape,dtype='float32')\\n sca.lapimg(imgp,imgl)\\n else:\\n imgl = imgp\\n if(trunc):\\n # Trucate the padding\\n img = self.trunc_model(imgl)\\n else:\\n img = imgl\\n # Extended image\\n else:\\n sca.brnoffadj(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\\n self.allrecx,self.allrecz,self.nrec, # Receiver information\\n self.__nsx,velp,self.rnh,imgp,dat,nthrds,verb) # Velocity and output image\\n if(self.tap is not None):\\n # Apply taper\\n for ih in range(self.rnh):\\n imgp[ih,:,:] *= self.tap\\n if(lap):\\n # Apply laplacian\\n imgl = np.zeros(imgp.shape,dtype='float32')\\n for ih in range(self.rnh):\\n sca.lapimg(imgp[ih,:,:],imgl[ih,:,:])\\n else:\\n imgl = imgp\\n if(trunc):\\n img = self.trunc_model(imgl)\\n else:\\n img = imgl\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c38687eebeaaa985973ee1eac75f97\",\n \"score\": \"0.5211603\",\n \"text\": \"def run():\\r\\n dl_extract_tar()\\r\\n get_melspec_data()\\r\\n get_spectral_values()\\r\\n print(\\\"Preprocessing complete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142b07bbf59f7639d4076a71647eefa8\",\n \"score\": \"0.5209897\",\n \"text\": \"def run_a_sample(data_dir):\\n # sensitivity analysis settings\\n s_a_s = global_settings.get_s_a_setting(data_dir)\\n\\n # global k file name, all together\\n g_k_f_n = os.path.join(data_dir, \\\"output\\\", \\\"k_global.csv\\\")\\n if os.path.isfile(g_k_f_n):\\n os.remove(g_k_f_n)\\n # global target file name, all together, target is ignition delay time (ign) here\\n g_t_f_n = os.path.join(data_dir, \\\"output\\\", \\\"ign_global.csv\\\")\\n if os.path.isfile(g_t_f_n):\\n os.remove(g_t_f_n)\\n # local target file name\\n l_t_f_n = os.path.join(data_dir, \\\"output\\\", \\\"ign_local.csv\\\")\\n\\n u_u = get_uniform_uncertainties(\\n s_a_s['n_dim'], s_a_s['default_uncertainty'], s_a_s['exclude'])\\n # save constant uncertainty to file\\n f_n_u_const = os.path.join(data_dir, \\\"output\\\", \\\"uncertainties_const.csv\\\")\\n np.savetxt(f_n_u_const, u_u, fmt='%.18e', delimiter=',', newline='\\\\n')\\n\\n for _ in range(s_a_s['n_run']):\\n r_c = get_random_coef(uniform_uncertainties=u_u)\\n\\n spe_idx_conc = copy.deepcopy(s_a_s['spe_idx_conc'])\\n print(spe_idx_conc)\\n for s_i in spe_idx_conc:\\n if int(s_i) >= 0 and int(s_i) < len(r_c):\\n spe_idx_conc[s_i] *= r_c[int(s_i)]\\n\\n us.update_s_a_setting(data_dir,\\n init_temp=s_a_s['init_temp'],\\n critical_temp=s_a_s['critical_temp'],\\n target_temp=s_a_s['target_temp'],\\n end_temp=s_a_s['end_temp'],\\n spe_idx_conc=spe_idx_conc)\\n\\n flag = job_drivers.make_run_timeout(data_dir, timeout=s_a_s['timeout'])\\n\\n # local target time\\n local_t_t = np.loadtxt(l_t_f_n, dtype=float, delimiter=',')\\n local_t_t = [local_t_t]\\n\\n # is successfully run a sample, save to file\\n if flag is True:\\n r_c = r_c.reshape((1, len(r_c)))\\n with open(g_k_f_n, 'ab') as f_handler:\\n np.savetxt(f_handler, r_c, fmt='%.18e',\\n delimiter=',', newline='\\\\n')\\n with open(g_t_f_n, 'ab') as f_handler:\\n np.savetxt(f_handler, local_t_t, fmt='%.18e',\\n delimiter=',', newline='\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b99bf5f52d67a13cf3091aa536bac4\",\n \"score\": \"0.520932\",\n \"text\": \"def main():\\n # Grupos de 5 minutos\\n step = 12\\n\\n wfiles = ['90/45142', '90/45143', '90/45229', '90/45230']\\n vars = ['wind_speed', 'density', 'pressure', 'wind_direction']\\n mdata = {}\\n for d, wf in enumerate(wfiles):\\n print(\\\"/home/bejar/storage/Data/Wind/files/%s.nc\\\" % wf)\\n nc_fid = Dataset(\\\"/home/bejar/storage/Data/Wind/files/%s.nc\\\" % wf, 'r')\\n if d == 0:\\n nint = nc_fid.dimensions['time'].size\\n stime = nc_fid.getncattr('start_time')\\n samp = nc_fid.getncattr('sample_period')\\n hour = np.array(\\n [t.tm_hour * 60 + t.tm_min for t in [time.gmtime(stime + (i * samp)) for i in range(0, nint, step)]])\\n month = np.array([t.tm_mon for t in [time.gmtime(stime + (i * samp)) for i in range(0, nint, step)]])\\n ldata = []\\n for v in vars:\\n data = nc_fid.variables[v]\\n print(data.shape)\\n\\n end = data.shape[0]\\n length = int(end / step)\\n print(length)\\n data30 = np.zeros((length))\\n\\n for i in range(0, end, step):\\n data30[i / step] = np.sum(data[i: i + step]) / step\\n\\n ldata.append((data30))\\n if d == 0:\\n ldata.append(hour)\\n ldata.append(month)\\n\\n data30 = np.stack(ldata, axis=1)\\n print(data30.shape)\\n mdata[wf.replace('/', '-')] = data30\\n np.savez_compressed('/home/bejar/Wind%d.npz' % (step * 5), **mdata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dbf54d91490b55adc7ccd74e3efe2c6\",\n \"score\": \"0.52065533\",\n \"text\": \"def main_func(_thisDir, expInfo):\\n print('####### starting analysis #########')\\n\\n # get all tsv files in subject directory\\n directory = _thisDir + '/data/'+expInfo['participant'] +'/*.tsv'\\n os.chdir(_thisDir + '/data/'+expInfo['participant'] + '/')\\n\\n # create png directory to store plots in\\n try:\\n os.makedirs('pngs/')\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n\\n # define directory to save plots in\\n outdir = _thisDir + '/data/'+expInfo['participant'] + '/pngs/'\\n # get subject name\\n subject = expInfo['participant']\\n\\n # create a counter to keep track of which loop-iteration we're in\\n counter = 0\\n\\n # iterate over all files in subject directory, exclude the ones we're not\\n # interested in\\n for idx, filename in enumerate(glob.glob(directory)):\\n if 'trial_start' in filename:\\n #print('skipped')\\n continue\\n elif 'stimuli_onset' in filename:\\n #print('skipped')\\n continue\\n elif '.json' in filename:\\n #print('skipped')\\n continue\\n elif '.html' in filename:\\n #print('skipped')\\n continue\\n elif '.png' in filename:\\n #print('skipped')\\n continue\\n elif 'stats' in filename:\\n #print('skipped')\\n continue\\n elif 'appended' in filename:\\n #print('skipped')\\n continue\\n else:\\n # if we're at the first iteration of loop read Data into DataFrame\\n if counter == 0:\\n df = pd.read_csv(filename, sep='\\\\t',\\n encoding=\\\"utf8\\\", engine='python')\\n # if we're in a sucessive loop read Data and append to DataFrame\\n elif counter > 0:\\n df2 = pd.read_csv(filename, sep='\\\\t',\\n encoding=\\\"utf8\\\", engine='python')\\n df = df.append(df2, ignore_index=True)\\n counter += 1 # update counter\\n\\n # get a overview of results\\n plot_overview_linegraph(df, outdir, subject)\\n\\n # divide DataFrame by acquisition_scheme\\n df_base,df_tr1, df_tr2, df_t1w = plot_mri_settings_scatter(df, outdir, subject)\\n\\n # if \\\"epi fast\\\" acquisition_scheme was in DataFrame plot results compared to\\n # frequency spectrum of acoustic scanner noise of specific acquisition_scheme\\n if df_tr1 is not None:\\n df_tr1 = rename_columns(df_tr1)\\n df_tr1_mean = get_stats(df_tr1)\\n\\n x_coordinates, y_coordinates, coordinates, scaled = analyze_mri_noise(_thisDir+'/plotting/t2_tr1_spectrum.txt',\\n half_height =False)\\n plot_mri_overlay(x_coordinates, y_coordinates, coordinates, df_tr1, scaled, outdir, subject, setting='epi_fast(tr1)')\\n\\n # if \\\"epi_standard\\\" acquisition_scheme was in DataFrame plot results compared to\\n # frequency spectrum of acoustic scanner noise of specific acquisition_scheme\\n if df_tr2 is not None:\\n df_tr2 = rename_columns(df_tr2)\\n df_tr2_mean = get_stats(df_tr2)\\n\\n x_coordinates, y_coordinates, coordinates, scaled = analyze_mri_noise(_thisDir+'/plotting/t2_tr2_spectrum.txt',\\n half_height =False)\\n plot_mri_overlay(x_coordinates, y_coordinates, coordinates, df_tr2,scaled, outdir, subject, setting='epi_standard(tr2)')\\n\\n # if \\\"mprage\\\" acquisition_scheme was in DataFrame plot results compared to\\n # frequency spectrum of acoustic scanner noise of specific acquisition_scheme\\n if df_t1w is not None:\\n df_t1w = rename_columns(df_t1w)\\n df_t1w_mean = get_stats(df_t1w)\\n x_coordinates, y_coordinates, coordinates, scaled = analyze_mri_noise(_thisDir+'/plotting/t1w_spectrum.txt',\\n half_height =False)\\n plot_mri_overlay(x_coordinates, y_coordinates, coordinates, df_t1w,scaled, outdir, subject, setting='mprage(T1w)')\\n\\n html_name = outdir+subject+'.html'\\n htmlFile = open(outdir+subject+'.html','w')\\n\\n message = \\\"\\\"\\\"<html>\\n <head><strong><font face = \\\"helvetica\\\" size = \\\"19\\\"><center>\\n Aint no sound loud enough!</center></font></strong></head>\\n <body>\\n <p>\\n <br><br>\\n <font face = \\\"helvetica\\\" size = \\\"17\\\">Overview for subject</font>\\n <br><br>\\n <img src=\\\"outdir_overview_complete.png\\\" alt=\\\"overview\\\" height=\\\"400\\\" width=\\\"2300\\\">\\n <br><br>\\n <img src=\\\"outdir_mprage.png\\\" alt=\\\"Lineplot-placeholder\\\" height=\\\"400\\\" width=\\\"2300\\\">\\n <br><br>\\n <img src=\\\"outdir_baseline.png\\\" alt=\\\"Lineplot-placeholder\\\" height=\\\"400\\\" width=\\\"2300\\\">\\n <br><br>\\n <img src=\\\"outdir_epi_fast.png\\\" alt=\\\"Lineplot-placeholder height=\\\"400\\\" width=\\\"2300\\\">\\n <br><br>\\n <img src=\\\"outdir_epi_standard.png\\\" alt=\\\"Lineplot-placeholder\\\"height=\\\"400\\\" width=\\\"2300\\\">\\n <br><br>\\n <br><br>\\n <font face = \\\"helvetica\\\" size = \\\"17\\\"><center>\\n Mri-noise vs. stimuli\\n </center></font>\\n <br><br>\\n <img src=\\\"outdir_mprage(t1w)mri_noise_overlay.png\\\" alt=\\\"Lineplot-placeholder\\\" height=\\\"700\\\" width=\\\"2300\\\">\\n <br><br>\\n <img src=\\\"outdir_epi_fast(tr1)mri_noise_overlay.png\\\" alt=\\\"Lineplot-placeholder\\\" height=\\\"700\\\" width=\\\"2300\\\">\\n <br><br>\\n <img src=\\\"outdir_epi_standard(tr2)mri_noise_overlay.png\\\" alt=\\\"Lineplot-placeholder\\\" height=\\\"700\\\" width=\\\"2300\\\">\\n <br><br>\\n </p></body></html>\\\"\\\"\\\"\\n\\n htmlFile.write(message)\\n htmlFile.close()\\n\\n with open(html_name) as html:\\n text = html.read().replace('subject', subject)\\n text2 = text.replace('outdir', outdir+subject)\\n\\n with open(html_name, \\\"w\\\") as html:\\n html.write(text2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"455845dc44f33fdf1e155d183a7b03f6\",\n \"score\": \"0.5204826\",\n \"text\": \"def samples_extraction(source_dir, save_dir, data_list_dir, window_size):\\n hsi_data, hsi_gt = h5_loader(source_dir)\\n hsi_data = max_min_normalization(hsi_data)\\n s = window_size\\n hsi_data = padding(hsi_data, s)\\n\\n [m, n] = hsi_gt.shape\\n\\n make_if_not_exist(save_dir)\\n delete_if_exist(data_list_dir)\\n\\n for i in range(m):\\n for j in range(n):\\n if hsi_gt[i, j] > 0:\\n label = hsi_gt[i, j]\\n data = hsi_data[i:i + s, j:j + s, :].transpose([2, 0, 1])[np.newaxis]\\n save_name = save_dir + 'samples_{}_{}.npy'.format(i + 1, j + 1)\\n np.save(save_name, data)\\n with open(data_list_dir, 'a') as f:\\n f.write(save_name + ' {}\\\\n'.format(label))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0efa2c950182245968764b41c3c79ba4\",\n \"score\": \"0.52036667\",\n \"text\": \"def measure(self, mat, counts, w0, w1):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72017de3edf596622dd3e6f3769bbe68\",\n \"score\": \"0.52022755\",\n \"text\": \"def model_lindata(self,vel,dvel,wav,dtd,dtu=0.001,nthrds=4,verb=False):\\n if(vel.shape[1] != self.__nx or vel.shape[0] != self.__nz):\\n raise Exception(\\\"Velocity must have same shape as passed to constructor\\\")\\n\\n if(vel.shape[1] != dvel.shape[1] or vel.shape[0] != dvel.shape[0]):\\n raise Exception(\\\"Velocity and perturbation must have same shape\\\")\\n\\n # Pad the models\\n velp = self.pad_model(vel); dvelp = self.pad_model(dvel)\\n\\n # Temporal axis\\n ntu = wav.shape[0]\\n fact = int(dtd/dtu); ntd = int(ntu/fact)\\n\\n # Build the wave propagation object\\n sca = sca2d.scaas2d(ntd, self.__nxp, self.__nzp, # Sizes\\n dtd, self.__dx, self.__dz,dtu, # Samplings\\n self.__bx, self.__bz, self.__alpha) # Absorbing boundary\\n # Create input wavelet array\\n allsrcs = np.zeros([self.__nsx,1,ntu],dtype='float32') # One source per shot (same length as wavefield)\\n for isx in range(self.__nsx):\\n allsrcs[isx,0,:] = wav[:]\\n # Create output data\\n allshot = np.zeros([self.__nsx,ntd,self.__nrx],dtype='float32')\\n\\n sca.brnfwd(allsrcs,self.allsrcx,self.allsrcz,self.nsrc, # Source information\\n self.allrecx,self.allrecz,self.nrec, # Receiver information\\n self.__nsx,velp,dvelp,allshot,nthrds,verb) # Velocity and output\\n\\n return allshot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c69d722b12286baa5fe66cbd2e8c04\",\n \"score\": \"0.5200596\",\n \"text\": \"def run(self):\\n # Log the value of sample parameter\\n self.log.debug(\\\"Sample Parameter = %.2f\\\" % self.getarg('sampar'))\\n # Copy datain to dataout\\n self.dataout = self.datain\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412bd7ef3910d75a3225dfcd4bfae8bc\",\n \"score\": \"0.5194018\",\n \"text\": \"def analyze(self, event):\\n electrons = Collection(event, \\\"Electron\\\")\\n muons = Collection(event, \\\"Muon\\\")\\n jets = Collection(event, \\\"Jet\\\")\\n photons = Collection(event, \\\"Photon\\\")\\n fsrPhoton = Collection(event, \\\"FsrPhoton\\\")\\n\\n eventSum = ROOT.TLorentzVector()\\n \\n mu1_charge = 0\\n count_mu = 0\\n dimuonSelection = False\\n RaffaeleRequest = False\\n mu1 = ROOT.TLorentzVector()\\n mu2 = ROOT.TLorentzVector()\\n dimuonMass = 0\\n \\n \\n selectedJet = {}\\n selectedJet[\\\"criteria1\\\"] = {}\\n selectedJet[\\\"criteria2\\\"] = {}\\n \\n\\n\\n\\n if self.data :\\n self.jesVariation = ['pt']\\n \\n \\n jet1_jesTotalDown = ROOT.TLorentzVector()\\n jet2_jesTotalDown = ROOT.TLorentzVector()\\n jet1_jerUp = ROOT.TLorentzVector()\\n jet2_jerUp = ROOT.TLorentzVector()\\n jet1_jerDown = ROOT.TLorentzVector()\\n jet2_jerDown = ROOT.TLorentzVector()\\n \\n if len(filter(self.muSel,muons)) < 2:\\n return False\\n muonNumber = len(filter(self.muSel,muons))\\n for lep in muons :\\n muon_corrected_p4 = ROOT.TLorentzVector()\\n lep.FSR_p4 = ROOT.TLorentzVector()\\n muon_corrected_p4.SetPtEtaPhiM(lep.corrected_pt , lep.eta, lep.phi, lep.mass)\\n lep.iso = lep.pfRelIso04_all\\n lep.correctedROC_pt = lep.corrected_pt\\n #if lep.iso_FSR < 0.8 : \\n #lep.FSR_p4.SetPtEtaPhiM(lep.pt_FSR , lep.eta_FSR, lep.phi_FSR, 0)\\n #lep.correctedROC_pt = (muon_corrected_p4 + lep.FSR_p4).Pt()\\n #lep.iso = (lep.pfRelIso04_all*lep.pt-lep.pt_FSR)/lep.correctedROC_pt\\n if lep.fsrPhotonIdx!=-1 and fsrPhoton[ lep.fsrPhotonIdx].relIso03 <0.8 and fsrPhoton[ lep.fsrPhotonIdx].dROverEt2 < 0.019 :\\n lep.FSR_p4.SetPtEtaPhiM(fsrPhoton[ lep.fsrPhotonIdx].pt,fsrPhoton[\\n lep.fsrPhotonIdx].eta,fsrPhoton[ lep.fsrPhotonIdx].phi, 0)\\n lep.correctedROC_pt = (muon_corrected_p4 + lep.FSR_p4).Pt()\\n lep.iso = (lep.pfRelIso04_all*lep.pt-lep.FSR_p4.Pt())/lep.correctedROC_pt\\n \\n eventSum += lep.p4()\\n if lep.iso<0.25 and abs(lep.pdgId)==13 and lep.mediumId and lep.correctedROC_pt>20 and not dimuonSelection :\\n\\n if count_mu == 1 and (lep.charge*mu1_charge)<0: # and lep.mediumId \\n mu2.SetPtEtaPhiM(lep.corrected_pt,lep.eta, lep.phi, 0.105658375)\\n dimuonSelection = True\\n if count_mu == 0 :\\n mu1.SetPtEtaPhiM(lep.corrected_pt,lep.eta, lep.phi, 0.105658375)#105,6583745(24) MeV\\n mu1_charge = lep.charge\\n count_mu +=1\\n \\n #if not dimuonSelection : return False \\n #if max(mu1.Pt(), mu2.Pt())<28 : return False # muons are ordered in lep.pt, not in lep.pt_corrected\\n #if min(mu1.Pt(), mu2.Pt())<9 : return False # muons are ordered in lep.pt, not in lep.pt_corrected\\n \\n\\n \\n #dimuon = mu1 + mu2\\n dimuonMass = 0\\n if dimuonSelection : dimuonMass = (mu1 + mu2).M()\\n #if dimuon.M()<70 or dimuon.M()>110 :\\n #if dimuonMass<0.1 :\\n #return False\\n \\n\\n\\n muonfilter = lambda j : (j.muonIdx1==-1 or muons[j.muonIdx1].iso>0.25 or not muons[j.muonIdx1].mediumId or muons[j.muonIdx1].correctedROC_pt<20) and (j.muonIdx2==-1 or muons[j.muonIdx2].iso>0.25 or not muons[j.muonIdx2].mediumId or muons[j.muonIdx2].correctedROC_pt<20)\\n #electronfilter = lambda j : (j.electronIdx1==-1 or electrons[j.electronIdx1].pfRelIso03_all>0.25 or abs(electrons[j.electronIdx1].dz) > 0.2 or abs(electrons[j.electronIdx1].dxy) > 0.05) and(j.electronIdx2==-1 or electrons[j.electronIdx2].pfRelIso03_all>0.25 or abs(electrons[j.electronIdx2].dz) > 0.2 or abs(electrons[j.electronIdx2].dxy) > 0.05)\\n \\n \\n \\n jetFilter1 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId>0 ) and abs(j.eta)<4.7 and (abs(j.eta)<2.5 or j.puId>6 or j.pt>50))\\n jetFilter2 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId>0 ) and abs(j.eta)<4.7 )\\n jetFilter2_2017 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId17>0) and abs(j.eta)<4.7 )\\n jetFilter3_2017 = lambda j : (j.jetId>0 and (j.pt>50 or j.puId17>0) and abs(j.eta)<4.7 ) and (j.puId17 > 6 or abs(j.eta) < 2.6 or abs(j.eta) > 3.0 )\\n jetFilter4_2017 = lambda j : (j.jetId>0 and (j.PT>50 or j.puId17>0) and abs(j.eta)<4.7 ) and (j.puId17 > 6 or abs(j.eta) < 2.6 or abs(j.eta) > 3.0 ) \\n \\n jetsNolep = [j for j in jets if muonfilter(j)]\\n\\n \\n if len(jetsNolep) < 2:\\n return False\\n \\n if self.year!=\\\"2017\\\" : \\n for j in jetsNolep : j.puId17 = j.puId\\n\\n\\n jerBinName = []\\n if not self.data:\\n etabins=[0,1.93,2.5,3.139]\\n ptbins=[0,50]\\n jerBinName = [\\\"JEReta%spt%s\\\"%(n,m) for n in range(len(etabins)) for m in range(len(ptbins))]\\n \\n for j in jetsNolep:\\n binEta = len(etabins)-1\\n binPt = len(ptbins)-1\\n for n in range(len(etabins)-1) : \\n if abs(j.eta) > etabins[n] and abs(j.eta) < etabins[n+1] : binEta = n\\n for m in range(len(ptbins)-1) : \\n if j.pt > ptbins[m] and j.pt < ptbins[m+1] : binPt = m\\n #print \\\"test pteta \\\", abs(j.eta), \\\" \\\\t\\\", j.pt, \\\" \\\\t\\\", binEta, \\\" \\\\t\\\", binPt, \\\" \\\\t\\\", \\\"JEReta%spt%s\\\"%(binEta,binPt), \\\" \\\\t \\\", jerBinName\\n \\n for k in jerBinName : setattr(j, k, j.pt_nom if k == \\\"JEReta%spt%s\\\"%(binEta,binPt) else j.pt)\\n\\n \\n \\n \\n passAtLeastOne=False\\n for vn in self.jesVariation + jerBinName:\\n for j in jetsNolep:\\n j.PT=getattr(j, vn)\\n sortedJets=sorted(jetsNolep,key=lambda j : j.PT, reverse=True)\\n jetsCriteria1=[j for j in sortedJets if jetFilter1(j)]\\n jetsCriteria2=[j for j in sortedJets if jetFilter2(j)]\\n jetsCriteria2_2017=[j for j in sortedJets if jetFilter2_2017(j)]\\n jetsCriteria3_2017=[j for j in sortedJets if jetFilter3_2017(j)]\\n jetsCriteria4_2017=[j for j in sortedJets if jetFilter4_2017(j)]\\n if ( len(jetsCriteria1)>=2 and jetsCriteria1[0].PT > 35 and jetsCriteria1[1].PT > 25 and self.mqq(jetsCriteria1) > 250 ) : passAtLeastOne=True\\n if ( len(jetsCriteria2)>=2 and jetsCriteria2[0].PT > 35 and jetsCriteria2[1].PT > 25 and self.mqq(jetsCriteria2) > 250 ) : passAtLeastOne=True\\n if ( len(jetsCriteria2_2017)>=2 and jetsCriteria2_2017[0].PT > 35 and jetsCriteria2_2017[1].PT > 25 and self.mqq(jetsCriteria2_2017) > 250 ) : passAtLeastOne=True\\n if ( len(jetsCriteria3_2017)>=2 and jetsCriteria3_2017[0].PT > 35 and jetsCriteria3_2017[1].PT > 25 and self.mqq(jetsCriteria3_2017) > 250 ) : passAtLeastOne=True\\n if ( len(jetsCriteria4_2017)>=2 and jetsCriteria4_2017[0].PT > 35 and jetsCriteria4_2017[1].PT > 25 and self.mqq(jetsCriteria4_2017) > 250 ) : passAtLeastOne=True\\n \\n dijetMass = 0 \\n \\n if not passAtLeastOne : return False\\n\\n\\n #self.out.fillBranch(\\\"EventMass\\\",eventSum.M())\\n self.out.fillBranch(\\\"MuonMass\\\",dimuonMass)\\n self.out.fillBranch(\\\"qqMass\\\",dijetMass)\\n #self.out.fillBranch(\\\"jetIdx1\\\",jetIdx1)\\n #self.out.fillBranch(\\\"jetIdx2\\\",jetIdx2)\\n #self.out.fillBranch(\\\"jetNumber\\\",jetNumber)\\n #self.out.fillBranch(\\\"bjetNumber\\\",bjetNumber)\\n self.out.fillBranch(\\\"muonNumber\\\",muonNumber)\\n #self.out.fillBranch(\\\"Jet_photonIdx1\\\",Jet_photonIdx1)\\n #self.out.fillBranch(\\\"Jet_photonIdx2\\\",Jet_photonIdx2)\\n #self.out.fillBranch(\\\"Jet_VBFselected\\\",VBFselectedJet)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3cb9dbdadd84282996c7ac71f48990\",\n \"score\": \"0.5188413\",\n \"text\": \"def _mlperftiny_test_dataset_ad(device_transport, dataset, timeout):\\n samples, _ = dataset\\n result_output = np.zeros(samples[0].shape[0])\\n\\n for slice in range(0, 40):\\n _mlperftiny_write_sample(device_transport, samples[0][slice, :].flatten().tolist(), timeout)\\n _mlperftiny_infer(device_transport, 1, 0, timeout)\\n results = aot_transport_find_message(\\n device_transport, MLPERFTINY_RESULT_MSG, timeout_sec=timeout\\n )\\n m = re.search(r\\\"m\\\\-results\\\\-\\\\[([A-Za-z0-9_,.]+)\\\\]\\\", results)\\n results = m.group(1).split(\\\",\\\")\\n results_val = [float(x) for x in results]\\n result_output[slice] = np.array(results_val)\\n return np.average(result_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa1dd54d9eddaa51fe57b0efcb6833c\",\n \"score\": \"0.5187075\",\n \"text\": \"def gen_data(self):\\n train_data = []\\n val_data = []\\n test_data = []\\n\\n total_traj = sum(self.n_traj)\\n\\n freqs = np.random.uniform(self.freq[0], self.freq[1], total_traj)\\n amps = np.random.uniform(self.amp[0], self.amp[1], total_traj)\\n\\n if self.phase:\\n phase = np.random.uniform(0, 2, total_traj)\\n else:\\n phase = [0] * total_traj\\n\\n for i in range(self.n_traj[0]):\\n train_d = transform_sine(self.train_time, freqs[i], amps[i],\\n phase[i], noise=self.noise)\\n train_data.append(train_d)\\n\\n for i in range(self.n_traj[0], self.n_traj[0]+self.n_traj[1]):\\n val_d = transform_sine(self.val_time, freqs[i], amps[i],\\n phase[i], noise=self.noise)\\n val_data.append(val_d)\\n\\n for i in range(self.n_traj[0]+self.n_traj[1], total_traj):\\n test_d = transform_sine(self.test_time, freqs[i], amps[i],\\n phase[i], noise=self.noise)\\n test_data.append(test_d)\\n\\n train_data = np.stack(train_data, 0)\\n train_data = np.concatenate(train_data, 0)\\n\\n val_data = np.stack(val_data, 0)\\n val_data = np.concatenate(val_data, 0)\\n\\n test_data = np.stack(test_data, 0)\\n test_data = np.concatenate(test_data, 0)\\n\\n return train_data, val_data, test_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4276b2fb48b883daaaedb29afa5c784\",\n \"score\": \"0.5187028\",\n \"text\": \"def make_simple_sample_data_2D(data_type='iris'):\\n from iris.cube import Cube\\n from iris.coords import DimCoord,AuxCoord\\n\\n t_0=datetime.datetime(2000,1,1,12,0,0)\\n \\n x=np.arange(0,100e3,1000)\\n y=np.arange(0,50e3,1000)\\n t=t_0+np.arange(0,100,1)*datetime.timedelta(minutes=1)\\n xx,yy=np.meshgrid(x,y) \\n \\n\\n t_temp=np.arange(0,60,1)\\n track1_t=t_0+t_temp*datetime.timedelta(minutes=1)\\n x_0_1=10e3\\n y_0_1=10e3\\n track1_x=x_0_1+30*t_temp*60\\n track1_y=y_0_1+14*t_temp*60\\n track1_magnitude=10*np.ones(track1_x.shape)\\n\\n data=np.zeros((t.shape[0],y.shape[0],x.shape[0]))\\n for i_t,t_i in enumerate(t):\\n if np.any(t_i in track1_t):\\n x_i=track1_x[track1_t==t_i]\\n y_i=track1_y[track1_t==t_i]\\n mag_i=track1_magnitude[track1_t==t_i]\\n data[i_t]=data[i_t]+mag_i*np.exp(-np.power(xx - x_i,2.) / (2 * np.power(10e3, 2.)))*np.exp(-np.power(yy - y_i, 2.) / (2 * np.power(10e3, 2.)))\\n\\n t_start=datetime.datetime(1970,1,1,0,0)\\n t_points=(t-t_start).astype(\\\"timedelta64[ms]\\\").astype(int) / 1000\\n t_coord=DimCoord(t_points,standard_name='time',var_name='time',units='seconds since 1970-01-01 00:00')\\n x_coord=DimCoord(x,standard_name='projection_x_coordinate',var_name='x',units='m')\\n y_coord=DimCoord(y,standard_name='projection_y_coordinate',var_name='y',units='m')\\n lat_coord=AuxCoord(24+1e-5*xx,standard_name='latitude',var_name='latitude',units='degree')\\n lon_coord=AuxCoord(150+1e-5*yy,standard_name='longitude',var_name='longitude',units='degree')\\n sample_data=Cube(data,dim_coords_and_dims=[(t_coord, 0),(y_coord, 1),(x_coord, 2)],aux_coords_and_dims=[(lat_coord, (1,2)),(lon_coord, (1,2))],var_name='w',units='m s-1') \\n\\n if data_type=='xarray':\\n sample_data=DataArray.from_iris(sample_data)\\n \\n return sample_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3bbdab2cf1c3afc491c627f74d59039\",\n \"score\": \"0.51864505\",\n \"text\": \"def wavelet_denoise(array, wavelet, threshold, levels, thrmode='hard'):\\n if not array.ndim==2:\\n raise TypeError('Input array is not a frame or 2d array')\\n \\n WC = pywt.wavedec2(array, wavelet, level=levels)\\n if thrmode=='hard':\\n NWC = map(lambda x: pywt.thresholding.hard(x, threshold), WC)\\n elif thrmode=='soft':\\n NWC = map(lambda x: pywt.thresholding.soft(x, threshold), WC)\\n else:\\n raise ValueError('Threshold mode not recognized')\\n array_filtered = pywt.waverec2(NWC, wavelet)\\n \\n return array_filtered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e07d071ea24f7aa3257dcb7c5792b3\",\n \"score\": \"0.5180787\",\n \"text\": \"def main():\\n\\n\\tpathToResults = \\\"/tmp/results/\\\"\\n\\tname = \\\"_SS_pyNN_\\\"\\n\\tnorm = '1'\\n\\teesAmplitude = '235'\\n\\teesFrequency = '40'\\n\\tdelay = 2\\n\\tweights_1 = np.array([0.091])\\n\\tweights_2 = np.linspace(0.03,0.1,20)\\n\\tweights_3 = np.array([0.046])\\n\\tweights_4 = np.linspace(-.001,-.010,20)\\n\\tweights_5 = np.array([-0.002])\\n\\n\\tfig, ax = plt.subplots(weights_2.size, weights_4.size,figsize=(22,12),sharex='col',sharey='col')\\n\\tfor w1 in weights_1:\\n\\t\\tfor i2,w2 in enumerate(weights_2):\\n\\t\\t\\tfor w3 in weights_3:\\n\\t\\t\\t\\tfor i4,w4 in enumerate(weights_4):\\n\\t\\t\\t\\t\\tfor w5 in weights_5:\\n\\t\\t\\t\\t\\t\\tpattern = \\\"*%suA_%sHz_Delay_%dms%s_w1_%f_w2_%f_w3_%f_w4_%f_w5_%f.p\\\" % (eesAmplitude,eesFrequency,delay,name,w1,w2,w3,w4,w5)\\n\\t\\t\\t\\t\\t\\tresultFile = gt.find(pattern,pathToResults)\\n\\t\\t\\t\\t\\t\\tif not resultFile: continue\\n\\t\\t\\t\\t\\t\\twith open(resultFile[0], 'r') as pickle_file:\\n\\t\\t\\t\\t\\t\\t\\t_ = pickle.load(pickle_file)\\n\\t\\t\\t\\t\\t\\t\\tmeanFr = pickle.load(pickle_file)\\n\\n\\t\\t\\t\\t\\t\\tax[i2,i4].plot(meanFr['TA']['IaInt'],'r')\\n\\t\\t\\t\\t\\t\\tax[i2,i4].plot(meanFr['GM']['IaInt'],'b')\\n\\t\\t\\t\\t\\t\\tax[i2,i4].xaxis.set_ticklabels([])\\n\\t\\t\\t\\t\\t\\tax[i2,i4].yaxis.set_ticklabels([])\\n\\t\\t\\t\\t\\t\\tax[i2,i4].xaxis.set_ticks([])\\n\\t\\t\\t\\t\\t\\tax[i2,i4].yaxis.set_ticks([])\\n\\t\\t\\t\\t\\t\\tif i4==0:ax[i2,i4].set_ylabel(\\\"w2\\\\n%.4f\\\"%(w2),fontsize=6)\\n\\t\\t\\t\\t\\t\\tif i2==weights_2.size-1:ax[i2,i4].set_xlabel(\\\"w4\\\\n%.4f\\\"%(w4),fontsize=6)\\n\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ba7c78dcd1c61cf1bae765b95a8bc89\",\n \"score\": \"0.51759034\",\n \"text\": \"def cwvlt(\\n da,\\n s,\\n dim=None,\\n t0=5 * 365 * 86400,\\n a=1.0,\\n wtype=\\\"morlet\\\",\\n):\\n\\n if dim is None:\\n dim = list(da.dims)\\n else:\\n if isinstance(dim, str):\\n dim = [dim]\\n\\n if len(dim) != 1:\\n raise ValueError(\\\"The transformed dimension should be one-dimensional.\\\")\\n\\n sdim = s.dims[0]\\n\\n # the axes along which to take wavelets\\n axis_num = [da.get_axis_num(d) for d in dim]\\n\\n N = [da.shape[n] for n in axis_num]\\n\\n delta_t = _delta(da, dim)\\n\\n # grid parameters\\n if len(dim) == 1:\\n t = da[da.dims[axis_num[0]]] - da[da.dims[axis_num[0]]].mean()\\n else:\\n raise NotImplementedError(\\n \\\"Only one-dimensional transforms are implemented for now.\\\"\\n )\\n\\n if wtype == \\\"morlet\\\":\\n wavelet = _morlet(t0, a, s, t)\\n else:\\n raise NotImplementedError(\\\"Only the Morlet wavelet is implemented for now.\\\")\\n\\n dawt = (da * np.conj(wavelet)).sum(dim, skipna=True) * delta_t / np.sqrt(np.abs(s))\\n dawt = dawt.drop_vars(sdim)\\n dawt[sdim] = t0 * s\\n\\n return dawt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c3cd67f1c2a62046a165376af04b90\",\n \"score\": \"0.5174303\",\n \"text\": \"def analyze(self):\\n self._setup_datastructs()\\n self._calculate_powers()\\n self._calculate_interpolants()\\n self._calculate_energies()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b21fea069e7ef42c0e42a4c0c93ffe\",\n \"score\": \"0.51703465\",\n \"text\": \"def extract_drums(attack, decay, onset_array, wavefile):\\n read_data = wavio.readwav(wavefile)\\n filename = os.path.splitext(wavefile)[0]\\n read_array = read_data[2] # a list of sample values in sequential order for the wavefile\\n output_csv_filename = '{0}_samples_to_onset.csv'.format(filename)\\n samples_to_onset_array = []\\n output_dir = 'separated_input'\\n for i in range(len(onset_array) - 1): \\n if onset_array[i + 1] >= len(read_array) and onset_array[i] - attack < 0:\\n start = 0 \\n write_array = read_array[0:]\\n elif onset_array[i] - attack < 0:\\n start = 0 \\n write_array = read_array[0: onset_array[i + 1]] \\n elif onset_array[i + 1] >= len(read_array):\\n start = onset_array[i] - attack\\n write_array = read_array[onset_array[i] - attack:]\\n else:\\n start = onset_array[i] - attack\\n write_array = read_array[onset_array[i] - attack: onset_array[i+1]]\\n if len(write_array) - attack >= decay: #if the drumhit file is long enough, write it into the unclassified_drums directory\\n output_filename = '{0}_{1}.wav'.format(filename, \\\"%05d\\\" % i)\\n wavio.writewav24(output_filename, read_data[0], write_array)\\n shutil.move(output_filename, '{0}'.format(output_dir))\\n samples_to_onset = onset_array[i] - start\\n samples_to_onset_array.append([output_filename, samples_to_onset])\\n with open(output_csv_filename, 'w') as csvfile:\\n a = csv.writer(csvfile)\\n a.writerows(samples_to_onset_array)\\n shutil.move(output_csv_filename, '{0}'.format(output_dir))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38682cceb9b9e7403eb5bb0681e0cfd3\",\n \"score\": \"0.51696014\",\n \"text\": \"def analyze(self):\\n self.calculate_FOM()\\n self.generate_FOM_image()\\n self.generate_plot()\\n ##self.generate_spot_mask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a740f2baf7a940f3283e121014541413\",\n \"score\": \"0.51629245\",\n \"text\": \"def run(input_file_path, output_file_path):\\n\\n # 1. Read required data to generate W matrices\\n print \\\"Reading file:\\\", input_file_path\\n sensor_1_name, sensor_2_name, time1, time2, uR1, uR2, scanline_time, weights = read_matchup_data(input_file_path)\\n\\n if sensor_2_name in MHS_sensors:\\n sensor_2_instrument = \\\"MHS\\\"\\n elif sensor_2_name in AMSUB_sensors:\\n sensor_2_instrument = \\\"AMSUB\\\"\\n else:\\n raise ValueError(\\\"Sensor 2 Unknown - \\\"+sensor_2_name)\\n\\n if sensor_1_name in MHS_sensors:\\n sensor_1_instrument = \\\"MHS\\\"\\n elif sensor_1_name in AMSUB_sensors:\\n sensor_1_instrument = \\\"AMSUB\\\"\\n elif sensor_1_name in ref_sensors:\\n sensor_1_instrument = \\\"ref\\\"\\n else:\\n raise ValueError(\\\"Sensor 1 Unknown - \\\"+sensor_1_name)\\n\\n # 2. Generate required W matrix variables\\n print \\\"Generating W Matrix Variables from data...\\\"\\n\\n w_matrix_val, w_matrix_row, w_matrix_col,\\\\\\n w_matrix_nnz, u_matrix_row_count, u_matrix_val, \\\\\\n w_matrix_use1, w_matrix_use2, u_matrix_use1, \\\\\\n u_matrix_use2 = return_w_matrix_variables_MW(time1, time2, scanline_time, uR1, uR2, weights,\\n sensor_1_instrument, sensor_2_instrument)\\n\\n # 3. Update file to include W matrix variables\\n print \\\"Writing to new file:\\\", output_file_path\\n\\n # a. Copy old file to new location\\n copyfile(input_file_path, output_file_path)\\n\\n # b. Amend existing variables\\n dataset = Dataset(output_file_path, 'a')\\n\\n # + uncertainty_type1/2\\n if sensor_1_instrument == \\\"ref\\\":\\n uncertainty_type1 = array([1], dtype=int8)\\n elif sensor_1_instrument == \\\"AMSUB\\\":\\n uncertainty_type1 = array([3, 3, 4, 4, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\\n elif sensor_1_instrument == \\\"MHS\\\":\\n uncertainty_type1 = array([3, 3, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\\n if sensor_2_instrument == \\\"AMSUB\\\":\\n uncertainty_type2 = array([3, 3, 4, 4, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\\n elif sensor_2_instrument == \\\"MHS\\\":\\n uncertainty_type2 = array([3, 3, 4, 4, 4, 4, 4, 1, 2], dtype=int8)\\n\\n dataset.variables['uncertainty_type1'][:] = uncertainty_type1\\n dataset.variables['uncertainty_type2'][:] = uncertainty_type2\\n\\n # + Ur1/2\\n Ur2 = dataset.variables['Ur2'][:]\\n if sensor_2_instrument == \\\"AMSUB\\\":\\n Ur2[:, 0:9] = 0\\n elif sensor_2_instrument == \\\"MHS\\\":\\n Ur2[:, 0:7] = 0\\n dataset.variables['Ur2'][:] = Ur2\\n\\n Ur1 = dataset.variables['Ur1'][:]\\n if sensor_1_instrument == \\\"AMSUB\\\":\\n Ur1[:, :9] = 0\\n elif sensor_1_instrument == \\\"MHS\\\":\\n Ur1[:, :7] = 0\\n dataset.variables['Ur1'][:] = Ur1\\n\\n dataset.close()\\n\\n # c. Add w matrix variables to updated file\\n append_W_to_input_file(output_file_path,\\n w_matrix_val, w_matrix_row, w_matrix_col, w_matrix_nnz,\\n u_matrix_row_count, u_matrix_val,\\n w_matrix_use1, w_matrix_use2, u_matrix_use1, u_matrix_use2)\\n\\n # 4. Test harmonisation\\n test_input_file(output_file_path)\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c71d7a2d7dc7e565ddcb02026dfb6d7\",\n \"score\": \"0.5162724\",\n \"text\": \"def waveletTransform(signalRaw, level=4, wavelet='db2', order='freq'):\\n wp = pywt.WaveletPacket(signalRaw, wavelet, 'symmetric', maxlevel=level)\\n nodes = wp.get_level(level, order=order)\\n yWT = np.array([n.data for n in nodes], 'd')\\n yWT = abs(yWT)\\n return wp.data, yWT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8718568fe6c1e819e0198e3473ae7973\",\n \"score\": \"0.51623017\",\n \"text\": \"def assign_wavelengths(self, input_spectra):\\n\\n # Save these, so we'll know what data type to use for the output.\\n # The types used for accumulating sums and taking averages may not\\n # be the same as these types.\\n self.wavelength_dtype = input_spectra[0].wavelength.dtype\\n self.net_dtype = input_spectra[0].net.dtype\\n self.dq_dtype = input_spectra[0].dq.dtype\\n\\n nwl = 0\\n for in_spec in input_spectra:\\n nwl += in_spec.nelem\\n\\n # Create an array with all the input wavelengths (i.e. the union\\n # of the input wavelengths).\\n wl = np.zeros(nwl, dtype=np.float)\\n i = 0\\n for in_spec in input_spectra:\\n nelem = in_spec.nelem\\n # Concatenate current input wavelengths to wl array.\\n wl[i:i + nelem] = in_spec.wavelength.copy()\\n i += nelem\\n wl.sort()\\n\\n # count_input will be the number of input spectra that cover the\\n # corresponding wavelength in wl.\\n count_input = np.zeros(nwl, dtype=np.int64)\\n for in_spec in input_spectra:\\n input_wl = in_spec.wavelength\\n # wl0 and wl1 will be about a half pixel wider on either side\\n # of the wavelength range for the current input spectrum.\\n if input_wl[1] > input_wl[0]: # wavelengths are increasing\\n wl0 = input_wl[0] - 0.5 * (input_wl[1] - input_wl[0])\\n wl1 = input_wl[-1] + 0.5 * (input_wl[-1] - input_wl[-2])\\n elif input_wl[1] < input_wl[0]: # wavelengths are decreasing\\n wl0 = input_wl[-1] - 0.5 * (input_wl[-2] - input_wl[-1])\\n wl1 = input_wl[0] + 0.5 * (input_wl[0] - input_wl[1])\\n else:\\n raise RuntimeError(\\\"Wavelength increment must not be zero.\\\")\\n temp = np.where(wl >= wl0, 1, 0)\\n temp = np.where(wl >= wl1, 0, temp)\\n count_input += temp\\n del temp\\n # This shouldn't happen.\\n if np.any(count_input <= 0.):\\n raise RuntimeError(\\\"Problem with input wavelengths.\\\")\\n\\n self.wavelength = self.compute_output_wl(wl, count_input)\\n\\n self.wcs = create_spectral_wcs(input_spectra[0].right_ascension[0],\\n input_spectra[0].declination[0],\\n self.wavelength)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd51685fce806c00f93458ce8c34cb21\",\n \"score\": \"0.5159673\",\n \"text\": \"def analyze_spectrum(measurement, background, reference):\\n sample_data = []\\n for isotope in isotope_list:\\n info = analyze_isotope(measurement, background, reference, isotope)\\n sample_data.extend(info)\\n return sample_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a24b332bb4c95c7afdc7f63e51d56b8\",\n \"score\": \"0.51534945\",\n \"text\": \"def __analyze_dataset(self):\\n info_file = os.path.join(self.work_dir, self.data_name + '_info.txt')\\n\\n #if not analyzed before, analyze\\n if os.path.exists(info_file) == False :\\n logging.info('analyze data %s' %(self.data_path))\\n if pysol.analyze_data(self.data_path, self.dtype, info_file) != 0:\\n sys.exit()\\n\\n #parse data num\\n pattern = re.compile(r'data number\\\\s*:\\\\s*(\\\\d+)')\\n result_list = pattern.findall(open(info_file,'r').read())\\n if len(result_list) != 1:\\n logging.error('parse data number failed, result_list is %s' %(str(result_list)))\\n sys.exit()\\n\\n self.data_num = (int)(result_list[0])\\n\\n #parse dimension\\n pattern = re.compile(r'dimension\\\\s*:\\\\s*(\\\\d+)')\\n result_list = pattern.findall(open(info_file,'r').read())\\n if len(result_list) != 1:\\n print result_list\\n print 'parse dimension failed'\\n sys.exit()\\n\\n self.dim = (int)(result_list[0])\\n\\n #parse class number\\n pattern = re.compile(r'class num\\\\s*:\\\\s*(\\\\d+)')\\n result_list = pattern.findall(open(info_file,'r').read())\\n if len(result_list) != 1:\\n logging.error('parse class num failed, result_list is %s' %(str(result_list)))\\n sys.exit()\\n\\n self.class_num = (int)(result_list[0])\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1582,"cells":{"query_id":{"kind":"string","value":"3730c0df8cc293579f9a43739d4d8bd5"},"query":{"kind":"string","value":"The schedule revision is a hash of all information pertaining to scheduling. Information unrelated to scheduling, such as the content being sent at each event, is excluded. This is mainly used to determine when a TimedScheduleInstance should recalculate its schedule."},"positive_passages":{"kind":"list like","value":[{"docid":"00de47933eb7aa28f72bfee4adb6acfc","score":"0.67214155","text":"def memoized_schedule_revision(self):\n schedule_info = json.dumps([\n self.schedule_length,\n self.total_iterations,\n self.start_offset,\n self.start_day_of_week,\n [[e.day, e.time.strftime('%H:%M:%S')] for e in self.memoized_events],\n ])\n return hashlib.md5(schedule_info).hexdigest()","title":""}],"string":"[\n {\n \"docid\": \"00de47933eb7aa28f72bfee4adb6acfc\",\n \"score\": \"0.67214155\",\n \"text\": \"def memoized_schedule_revision(self):\\n schedule_info = json.dumps([\\n self.schedule_length,\\n self.total_iterations,\\n self.start_offset,\\n self.start_day_of_week,\\n [[e.day, e.time.strftime('%H:%M:%S')] for e in self.memoized_events],\\n ])\\n return hashlib.md5(schedule_info).hexdigest()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"377bccb579075d99456edaf07e00a004","score":"0.5524533","text":"def getSchedule(self):\n self.lock.acquire()\n a = copy.deepcopy(self.db['schedule'])\n self.lock.release()\n return a","title":""},{"docid":"f30551ea8dab794a14118979c8ffc5da","score":"0.5376334","text":"def zen_schedule(self):\n\n if self.header.old_version is True:\n return MTime(self.header.schedule)\n\n return self.schedule.initial_start","title":""},{"docid":"38f5fffb20fb87333771b4c0f10d3554","score":"0.5254526","text":"def schedule(self):\n return self._schedule","title":""},{"docid":"38f5fffb20fb87333771b4c0f10d3554","score":"0.5254526","text":"def schedule(self):\n return self._schedule","title":""},{"docid":"2cf2669c1b81452dc03f439975329e16","score":"0.5119087","text":"def get_schedule_id(self):\n return self.schedule_id","title":""},{"docid":"974bdae55935aceb6137d9690fe8c1da","score":"0.51003516","text":"def revision(self):\n return self._revision","title":""},{"docid":"0ea2674b3e920ab38b88888b7c5cbd39","score":"0.5064663","text":"def schedule_id(self) -> int:\n return self._schedule_id","title":""},{"docid":"fd185e29b9fe2370bdfb45d16918391c","score":"0.5052048","text":"def get_schedule_metadata(self):\n raise errors.Unimplemented()","title":""},{"docid":"5f835b637337e470077219cf106cc077","score":"0.50403345","text":"def schedule_info(self) -> 'outputs.GoogleCloudDatapipelinesV1ScheduleSpecResponse':\n return pulumi.get(self, \"schedule_info\")","title":""},{"docid":"59442ba4a9607099ec7333f247184236","score":"0.50277865","text":"def hw_revision(self):\n return self._info['rev']","title":""},{"docid":"63b43c02e9feec96ba7f5c69cd16ae3e","score":"0.49852806","text":"def revision_data(self):\n return {'rev': self._xml.get('rev'), 'srcmd5': self._xml.get('srcmd5')}","title":""},{"docid":"a078f811c6fbebe03a6989aaac47d209","score":"0.49707952","text":"def revision(self):\n r = ''\n if 'revision' in self.m.properties:\n r = self.m.properties['revision']\n if self._revision:\n r = self._revision\n return r","title":""},{"docid":"b6d81def50d0d419ba1e2338618fa138","score":"0.49697927","text":"def revision(self):\n return self.record['assay']['descr']['revision']","title":""},{"docid":"9658d77e63dba8f9ee3d2b352798d5bb","score":"0.49624103","text":"def re_scheduled_on(self):\n return self._re_scheduled_on","title":""},{"docid":"80db1a723075c32bb4a222bd329540bb","score":"0.4960503","text":"def get_schedule_id(self):\n raise errors.Unimplemented()","title":""},{"docid":"145caaaeaf8ccc8192d5f85f907e1826","score":"0.4926376","text":"def configuration(self):\n if self._configuration is None:\n # need to reconstruct configuration from dictionary in event\n self._configuration = SchedulerConfigBuilder.configuration_from_dict(self._event[\"configuration\"])\n # for large configurations the schedules are not passed in the event, need to reload these here\n if len(self._configuration.schedules) == 0:\n loaded_config = configuration.get_scheduler_configuration(self._logger)\n self._configuration.schedules = loaded_config.schedules\n return self._configuration","title":""},{"docid":"a7bcf01806b2762ee336c9f48092522a","score":"0.49199614","text":"def revision(self):","title":""},{"docid":"a0aa80a09e7df6077a39ca9175f8513e","score":"0.49128786","text":"def _update_schedule(self):\n #Initialize the hyper period\n self._update_hyper_period()\n #Initialize pending job queue\n pending_job_queue = []\n #Initialize released_times\n released_times = {}\n #Initialize the scheduled tasks\n scheduled_tasks = []\n #Initialize release_time_list with the ordered list of all release times in a hyper period\n release_time_list = []\n\n for i in range(len(self.tasks)):\n temp = 0\n while temp <= self.hyper:\n release_time_list.append(temp)\n temp += self.tasks[i].period\n release_time_list = list(set(release_time_list))\n release_time_list.sort()\n #Cacluate the deadlines for all jobs\n self._calculate_deadlines()\n\n #Add all newly released jobs at time to pending job queue in EDF order\n for x in release_time_list:\n pending_queue = []\n for i in range(len(self.tasks)):\n if x % self.tasks[i].period == 0:\n pending_queue.append(self.tasks[i].task_id)\n released_times[x] = pending_queue\n\n #Beginning of the main algorithm\n current_time = 0\n while current_time < self.hyper:\n #Get the next scheduling time to pending-job-queue from rt-list and ft list\n if len(release_time_list) > 0:\n current_time = release_time_list.pop(0)\n #Repeat until hyper period ends\n if current_time == self.hyper:\n break\n #Initialize the temporary scheduled tasks in the current moment\n temp_scheduled_tasks = []\n\n #Add all newly released jobs at time to pending job queue in EDF order\n for task_id in scheduled_tasks:\n for i in range(len(self.tasks)):\n if self.tasks[i].task_id == task_id:\n if self.tasks[i].jobs[self.tasks[i].pointer - 1].end_time <= current_time:\n temp_scheduled_tasks.append(task_id)\n break\n for x in temp_scheduled_tasks:\n scheduled_tasks.remove(x)\n if released_times.has_key(current_time):\n pending_job_queue += released_times[current_time]\n temp_pjq = []\n temp_pjq_in_ordered_priority = []\n\n pending_job_queue_tasks = []\n for task_id in pending_job_queue:\n current_task = self._get_task_by_id(task_id)\n pending_job_queue_tasks.append(current_task)\n sorted(pending_job_queue_tasks, key=lambda task: task.priority)\n for task in pending_job_queue_tasks:\n temp_pjq_in_ordered_priority.append(task.task_id)\n #print task.task_id, task.priority\n pending_job_queue = temp_pjq_in_ordered_priority\n\n #For each job in pending job queue in EDF order\n for task_id in pending_job_queue:\n #If the current job does not conflict with any of already scheduled jobs and does not violate MLA\n if self._no_conflict(task_id, scheduled_tasks) and self.mla_ok(scheduled_tasks,\n task_id):\n #Find the location of the job that for the task_id\n num_of_jobs = 0\n for i in range(len(self.tasks)):\n if self.tasks[i].task_id == task_id:\n num_of_jobs = i\n break\n pointer = self.tasks[num_of_jobs].pointer\n\n #If current job meets the deadline\n if current_time + self.tasks[num_of_jobs].metric.duration <= self.tasks[num_of_jobs].jobs[\n pointer].deadline:\n #Schedule this job and update start_time and end_time\n self.tasks[num_of_jobs].jobs[pointer].start_time = current_time\n self.tasks[num_of_jobs].jobs[pointer].end_time = current_time + self.tasks[\n num_of_jobs].metric.duration\n end_time = self.tasks[num_of_jobs].jobs[pointer].end_time\n self.tasks[num_of_jobs].pointer += 1\n\n #Update the scheduled tasks\n scheduled_tasks.append(task_id)\n #Add the task_id in the temporary pending_job_queue for the current moment\n temp_pjq.append(task_id)\n #Update released time\n release_time_list.append(end_time)\n release_time_list = list(set(release_time_list))\n release_time_list.sort()\n else:\n temp_pjq.append(task_id)\n self.tasks[num_of_jobs].pointer += 1\n #Remove the scheduled jobs from pending_job_queue\n for x in temp_pjq:\n pending_job_queue.remove(x)\n\n #Check if any of the job start time is none, if so raise an exception\n for i in range(len(self.tasks)):\n num_of_jobs = len(self.tasks[i].jobs)\n for j in range(num_of_jobs):\n if self.tasks[i].jobs[j].start_time is None:\n raise Exception(\"Some of the tasks can not be scheduled completely\")\n\n #Format all the jobs to required objects schedule_line\n for i in range(len(self.tasks)):\n num_of_jobs = len(self.tasks[i].jobs)\n for j in range(num_of_jobs):\n job = self.tasks[i].jobs[j]\n task_id = self.tasks[i].task_id\n metric_id = self.tasks[i].metric.id\n src = self.tasks[i].src\n dst = self.tasks[i].dst\n start = self.tasks[i].jobs[j].start_time\n duration = self.tasks[i].metric.duration\n period = self.tasks[i].period\n priority = self.tasks[i].priority\n schedule_line = ScheduleLine(task_id, metric_id, src, dst, start, duration, period, priority)\n self.schedules.append(schedule_line)\n\n #Update the table\n for i in range(len(self.tasks)):\n num_of_jobs = len(self.tasks[i].jobs)\n for j in range(num_of_jobs):\n current_time = self.tasks[i].jobs[j].start_time\n end_time = self.tasks[i].jobs[j].end_time\n while current_time < end_time:\n if self.time_table.has_key(current_time):\n self.time_table[current_time].append(self.tasks[i].task_id)\n else:\n self.time_table[current_time] = []\n self.time_table[current_time].append(self.tasks[i].task_id)\n current_time += 1","title":""},{"docid":"2e903d6595f9b8da1c52ab159f04d3a0","score":"0.48892772","text":"def configuration(self):\n if self._configuration is None:\n # need to reconstruct configuration from dictionary in event\n self._configuration = SchedulerConfigBuilder.configuration_from_dict(\n self._event[\"configuration\"]\n )\n # for large configurations the schedules are not passed in the event, need to reload these here\n if len(self._configuration.schedules) == 0:\n loaded_config = configuration.get_scheduler_configuration(self._logger)\n self._configuration.schedules = loaded_config.schedules\n return self._configuration","title":""},{"docid":"00e5ff674e530cd56624dce53befe1af","score":"0.48657018","text":"def change_log(self, num_changelogs=None) -> Dict[date, List[ScheduleChange]]:\n length = len(list(self.sched_versions_by_date))\n if num_changelogs is None:\n change_slice = slice(1, length)\n else:\n change_slice = slice(max(1, length - num_changelogs), length)\n return {\n date_: self.get_changes_for_date(date_)\n for date_ in list(self.sched_versions_by_date.keys())[change_slice]\n }","title":""},{"docid":"b0c3ace354d821fdf7f4067671565d3e","score":"0.48582017","text":"def hash_config(self, config):\n self.normalize(config)\n try:\n schedule = self.halide_tuner.cfg_to_schedule(config)\n return hashlib.sha256(schedule).hexdigest()\n except:\n log.warning('error hashing config', exc_info=True)\n return super(HalideConfigurationManipulator, self).hash_config(config)","title":""},{"docid":"789fa15083d2d8abed95ba2bfcaf53c4","score":"0.48124906","text":"def schedule_name(self):\n return self._schedule_name","title":""},{"docid":"52a9259bb0ddda8efbdd60044f8f19bc","score":"0.48075727","text":"def revision_date(self):\n return self._revision_date","title":""},{"docid":"32c14e00c121f2ab19bbd054bd11b37c","score":"0.4763667","text":"def _get_n_schedule(self):\n return _modeller.mod_schedule_nsch_get(self._get_sched())","title":""},{"docid":"252ec6c191ac7e96267d133e76f3203a","score":"0.47334898","text":"def schedule(self) -> TimeRange:\n return self._schedule","title":""},{"docid":"13bef1c780b6f55f6a5f79bafa4e8561","score":"0.47327968","text":"def __hash__(self) -> int:\n return self._datetime.__hash__()","title":""},{"docid":"39e34285cb332d28750b7f432d039eb4","score":"0.47262037","text":"def get_special_schedule(self, legend=0):\n return self.call_api('sched', 'special', l=legend)","title":""},{"docid":"18da201de79ae871c23a93cc5ea029e0","score":"0.47179618","text":"def __eq__(self, other):\n if not isinstance(other, RecoveryBackupSchedule):\n return False\n\n return self.__dict__ == other.__dict__","title":""},{"docid":"28a14ba6304b48cdaa9811aa4d5ed40c","score":"0.47112602","text":"def schedule(self):\n if self.periodic:\n return self._schedule\n return","title":""},{"docid":"2cb69244fda0e69550291fddd6da19d7","score":"0.46966428","text":"def schedule(self) -> str:\n return pulumi.get(self, \"schedule\")","title":""},{"docid":"c7e8b3253f1e4eff2eac94d6d2a56c23","score":"0.46862197","text":"def compare_content(self, fileName='schedule_log.csv', content=None):\n if content is None:\n content = self.schedule_data\n \n # If log file doesn't exist, create it\n if not os.path.exists(SCRIPTPATH + fileName):\n print('No log file present. ', end='')\n self.update_log(fileName, content)\n return []\n\n # Read the log file\n print(f'Comparing schedule changes... ', flush=True, end='')\n logList = []\n logDictFile = csv.DictReader(open(SCRIPTPATH + fileName))\n for lines in logDictFile:\n listDict = dict(lines)\n logList.append(listDict.copy())\n\n # Convert datetime.date to string date for comparison\n for lines in content:\n lines['date'] = lines['date'].strftime('%Y-%m-%d')\n\n # If dates don't match, i.e. new quarter, create a new log\n if logList[0]['date'] != content[0]['date']:\n print('New date data found. ', end='')\n self.update_log(fileName, content)\n return []\n\n # Populate list allDiff with formatted differences\n allDiff = []\n chngCntr = 0\n checkRange = len(logList)\n if len(content) < len(logList): #Always iterate over the smallest of the two\n checkRange = len(content)\n for i in range(checkRange):\n diffList = []\n # If there has been a change in the content:\n if logList[i] != content[i]:\n date = reformat_str_date(logList[i]['date'])\n log_description = list(logList[i]['description'].split('|'))\n current_description = list(content[i]['description'].split('|'))\n for j in range(len(log_description)):\n if log_description[j] != current_description[j]:\n chngCntr += 1\n current_role = current_description[j].split(' - ', 1)[0]\n current_name = current_description[j].split(' - ', 1)[1]\n log_name = log_description[j].split(' - ', 1)[1]\n if current_name == 'None ':\n current_name = 'No one '\n elif log_name == 'None ':\n log_name = 'no one '\n diffList.append('- ' + current_name + 'is now on' + current_role + ' instead of ' + log_name)\n d = '\\nTeam changes for ' + date + ':\\n'\n for diff in diffList:\n d += diff +'\\n'\n allDiff.append(d)\n if chngCntr is not 0:\n print(f'{chngCntr} change(s) found')\n else:\n print('No changes found')\n return allDiff","title":""},{"docid":"685339b36eba8db66306ac18ed586524","score":"0.46636668","text":"def change_times(self):\n return [t for t,g in self.snapshots().items() if len(g.edges()) > 0]","title":""},{"docid":"d723a1610fb0512a607058552ea8209d","score":"0.466244","text":"def hw_revision(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"hw_revision\")","title":""},{"docid":"c871ec2d8b5e29e87b855375bc2cce23","score":"0.46542224","text":"def __eq__(self, other):\n if not isinstance(other, RepositoryScheduled):\n return False\n\n return self.__dict__ == other.__dict__","title":""},{"docid":"96699c249f0e3e7700f7f4a2e59f4f6b","score":"0.46428698","text":"async def save_schedule(self, schedule: Schedule):\n if self._paused or not self._ready:\n raise NotReadyError()\n\n # TODO should these checks be moved to the storage layer?\n if schedule.name is None or len(schedule.name) == 0:\n raise ValueError(\"name can not be empty\")\n\n if schedule.repeat is not None and not isinstance(schedule.repeat, datetime.timedelta):\n raise ValueError('repeat must be of type datetime.time')\n\n if schedule.exclusive is None:\n raise ValueError('exclusive can not be None')\n\n if isinstance(schedule, TimedSchedule):\n schedule_time = schedule.time\n\n if schedule_time is not None and not isinstance(schedule_time, datetime.time):\n raise ValueError('time must be of type datetime.time')\n\n day = schedule.day\n\n # TODO Remove this check when the database has constraint\n if day is not None and (day < 1 or day > 7):\n raise ValueError('day must be between 1 and 7')\n else:\n day = None\n schedule_time = None\n\n prev_schedule_row = None\n\n if schedule.schedule_id is None:\n is_new_schedule = True\n schedule.schedule_id = uuid.uuid4()\n else:\n try:\n prev_schedule_row = self._schedules[schedule.schedule_id]\n is_new_schedule = False\n except KeyError:\n is_new_schedule = True\n\n if not is_new_schedule:\n update = self._schedules_tbl.update()\n update = update.where(self._schedules_tbl.c.id == str(schedule.schedule_id))\n update = update.values(schedule_name=schedule.name,\n schedule_type=int(schedule.schedule_type),\n schedule_interval=schedule.repeat,\n schedule_day=day,\n schedule_time=schedule_time,\n exclusive=schedule.exclusive,\n process_name=schedule.process_name)\n\n self._logger.debug('Database command: %s', update)\n\n try:\n async with (await self._get_connection_pool()).acquire() as conn:\n result = await conn.execute(update)\n\n if result.rowcount == 0:\n is_new_schedule = True\n except Exception:\n self._logger.debug('Update failed: %s', update)\n raise\n\n if is_new_schedule:\n insert = self._schedules_tbl.insert()\n insert = insert.values(id=str(schedule.schedule_id),\n schedule_type=int(schedule.schedule_type),\n schedule_name=schedule.name,\n schedule_interval=schedule.repeat,\n schedule_day=day,\n schedule_time=schedule_time,\n exclusive=schedule.exclusive,\n process_name=schedule.process_name)\n\n self._logger.debug('Database command: %s', insert)\n\n try:\n async with (await self._get_connection_pool()).acquire() as conn:\n await conn.execute(insert)\n except Exception:\n self._logger.exception('Insert failed: %s', insert)\n raise\n\n repeat_seconds = None\n if schedule.repeat is not None:\n repeat_seconds = schedule.repeat.total_seconds()\n\n schedule_row = self._ScheduleRow(\n id=schedule.schedule_id,\n name=schedule.name,\n type=schedule.schedule_type,\n time=schedule_time,\n day=day,\n repeat=schedule.repeat,\n repeat_seconds=repeat_seconds,\n exclusive=schedule.exclusive,\n process_name=schedule.process_name)\n\n self._schedules[schedule.schedule_id] = schedule_row\n\n # Did the schedule change in a way that will affect task scheduling?\n if schedule.schedule_type in [Schedule.Type.INTERVAL, Schedule.Type.TIMED] and (\n is_new_schedule or\n prev_schedule_row.time != schedule_row.time or\n prev_schedule_row.day != schedule_row.day or\n prev_schedule_row.repeat_seconds != schedule_row.repeat_seconds or\n prev_schedule_row.exclusive != schedule_row.exclusive):\n\n now = self.current_time if self.current_time else time.time()\n self._schedule_first_task(schedule_row, now)\n self._resume_check_schedules()","title":""},{"docid":"7cfd6114bbced7b85d847e2d8819bb7c","score":"0.463927","text":"def get_crashed(self, vid):\n return self.plexe.get_crashed(vid)","title":""},{"docid":"7d23243c6a217c67a1067c27e0dc324c","score":"0.46383563","text":"def getIntervalSchedule(self):\n return self._IntervalSchedule","title":""},{"docid":"1027d1ecae4b76b836540477bfbb5298","score":"0.46195728","text":"def history(self) -> \"unsigned int\":\n return _isdbt_swig.time_deinterleaver_sptr_history(self)","title":""},{"docid":"d9508e7c5a2db8937730b368b07bd0a3","score":"0.4615141","text":"def get_course_schedule(self):\n\n return self.__courseSchedule.get_schedule()","title":""},{"docid":"f055140ec77bcbace1109a01e8a67051","score":"0.4610143","text":"def revision_id(self):\n return self.getattr('revision_id')","title":""},{"docid":"aa5e510449f3c524567fd72310c58179","score":"0.4599056","text":"def revision_id(self):\n return ''.join(('%s:' % self.revision_number\n if self.revision_number else '',\n '%s' % self.changeset_revision\n if self.changeset_revision else ''))","title":""},{"docid":"5ec4eb56ee2c730a8b59569f4b1edb45","score":"0.45788485","text":"def schedule():\n try:\n schedule = get_maintenance_schedule()\n except HTTPError:\n raise HTTPError(\"Error getting maintenance schedule.\")\n return schedule.text","title":""},{"docid":"21424a41db086725725762c4f1e981c1","score":"0.45663548","text":"def __eq__(self, other):\n if not isinstance(other, TaskTemplateSchedule):\n return False\n\n return self.__dict__ == other.__dict__","title":""},{"docid":"8ea05bd73bdc9a4ca7f901e17d55e4a5","score":"0.4558546","text":"def revision(self) -> Optional[str]:\n return pulumi.get(self, \"revision\")","title":""},{"docid":"003c8b7939aa74c0de57634e39007605","score":"0.45539713","text":"def get_schedule_names(self) -> Sequence[str]:\n return self._schedules.get_definition_names()","title":""},{"docid":"d7ff69ba82fe0bca0ea7bc8edcecb4c3","score":"0.45451325","text":"def validate_scheduling_data(self, schedule_data):\r\n \"\"\"\r\n if self.parse_and_check_for_recycled_folders(schedule_data[\"folders\"], schedule_data[\"folder_root_id\"]):\r\n result = {\"error\":\"Error: \" + schedule_data[\"schedule_name\"] + \" - Submitted folders may already exist in recyle bin. Please empty recycle bin and try again.\"}\r\n logging.error(result[\"error\"])\r\n return result\r\n \"\"\"\r\n\r\n if schedule_data[\"channel_include\"] and schedule_data[\"channel_name\"] == \"\":\r\n result = {\"error\": \"Error: \" + schedule_data[\"schedule_name\"] + \" - Submitted schedule data has no channel name.\"}\r\n logging.error(result[\"error\"])\r\n return result\r\n\r\n if self.schedule.schedule_data_has_0_occurrences(schedule_data):\r\n result = {\"error\": \"Error: \" + schedule_data[\"schedule_name\"] + \" - Submitted schedule data does not contain at least one occurrence.\"}\r\n logging.error(result[\"error\"])\r\n return result\r\n\r\n if schedule_data[\"module_include\"] and schedule_data[\"module_id\"] == \"\":\r\n result = {\"error\": \"Error: \" + schedule_data[\"schedule_name\"] + \" - No module Id specified.\"}\r\n logging.error(result[\"error\"])\r\n return result\r\n\r\n if schedule_data[\"module_include\"] and self.module.module_moduleid_already_exists(schedule_data[\"module_id\"]):\r\n result = {\"error\": \"Error: \" + schedule_data[\"schedule_name\"] + \" - Submitted ModuleId already exists.\"}\r\n logging.error(result[\"error\"])\r\n return result\r\n\r\n if schedule_data[\"schedule_template\"] in self.model.get_templates():\r\n result = {\"error\": \"Error: \" + schedule_data[\"schedule_name\"] + \" - Submitted template name does not exist.\"}\r\n logging.error(result[\"error\"])\r\n return result\r\n\r\n return {\"Success\": \"\"}","title":""},{"docid":"e5eff6807ad8f43983a1e6a16611595d","score":"0.4539271","text":"def clone(self) -> \"ScheduleRule\":\n return _ffi_api.ScheduleRuleClone(self) # type: ignore # pylint: disable=no-member","title":""},{"docid":"b79fa9a50bf03682fb53f000d71ab53b","score":"0.45272434","text":"def testNotScheduled(self):\n error = \"^The specification status has no top-level 'scheduledAt' key$\"\n assertRaisesRegex(self, SpecificationError, error, SlurmPipelineStatus,\n {})","title":""},{"docid":"e40bbcc04d4f44f2ef2791264014584d","score":"0.4513189","text":"def scheduling(self) -> Optional[pulumi.Input['JobSchedulingArgs']]:\n return pulumi.get(self, \"scheduling\")","title":""},{"docid":"5ba017704426f78a7c2b1746e2158a29","score":"0.45130363","text":"def __eq__(self, other):\n if not isinstance(other, ScanBlackoutSchedule):\n return False\n\n return self.__dict__ == other.__dict__","title":""},{"docid":"5c5fdad53d3de561454e6bcd0055e9d2","score":"0.4510989","text":"def pretty_print(self):\n from pprint import pprint\n pprint(self.schedule, indent=4)","title":""},{"docid":"3bb2196ae7aa8fe6544326be161ea6f2","score":"0.44995096","text":"def build_schedule(self):\n\n skipped_keys, non_skipped_keys = set(), set()\n\n one_exec_scheduled = False\n eta_avail_job = None\n for exec_n in xrange(self.config.N_EXECUTIONS):\n for vm_name, vm_info in self.config.VMS.items():\n for bmark, param in self.config.BENCHMARKS.items():\n for variant in vm_info[\"variants\"]:\n job = ExecutionJob(self, vm_name, vm_info, bmark, variant, param)\n if not self.config.should_skip(job.key):\n non_skipped_keys |= set([job.key])\n if one_exec_scheduled and not eta_avail_job:\n # first job of second executions eta becomes known.\n eta_avail_job = job\n self.set_eta_avail()\n self.add_job(job)\n else:\n skipped_keys |= set([job.key])\n if not one_exec_scheduled:\n debug(\"%s is in skip list. Not scheduling.\" %\n job.key)\n one_exec_scheduled = True\n self.expected_reboots = len(self)\n # Resume mode: if previous results are available, remove the\n # jobs from the schedule which have already been executed, and\n # add the results to this object, ready to be saved to a Json file.\n if self.resume:\n self._remove_previous_execs_from_schedule()\n # Sanity check ETA estimates\n for key, exec_data in self.results.data.iteritems():\n got_len = len(self.results.eta_estimates[key])\n expect_len = len(exec_data)\n if expect_len != got_len:\n msg = \"ETA estimates didn't tally with results: \"\n msg += \"key=%s, expect_len=%d, got_len=%d \" % \\\n (key, expect_len, got_len)\n msg += \"data[%s]=%s; \" % (key, str(self.results.data[key]))\n msg += \"eta[%s]=%s\" % \\\n (key, str(self.results.eta_estimates[key]))\n util.log_and_mail(self.mailer, error,\n \"Fatal Krun Error\",\n msg, bypass_limiter=True, exit=True)\n return non_skipped_keys, skipped_keys","title":""},{"docid":"881a7e30700c9711ab834cb668f48764","score":"0.44989118","text":"def __ne__(self, other):\n if not isinstance(other, ScheduleA):\n return True\n\n return self.to_dict() != other.to_dict()","title":""},{"docid":"c28e51e53ad62fa044f90036aba47da5","score":"0.4488338","text":"def getSchedParams(self):\n return self._params","title":""},{"docid":"e9ca153236f9df9a255fa818258608d9","score":"0.4478361","text":"def timestamp_modified(self):\n return self._timestamp_modified","title":""},{"docid":"1b72577aac3ddf863644102da4abcefd","score":"0.44751355","text":"def __eq__(self, other):\n if not isinstance(other, ScheduledTask):\n return False\n\n return self.__dict__ == other.__dict__","title":""},{"docid":"25fe5c20775d5e40047ffcfb40bba1c4","score":"0.44724897","text":"def get_board_revision():\n return _hexdump('/proc/device-tree/system/linux,revision')","title":""},{"docid":"a0e29ee52d239cfaf0f621d670175225","score":"0.4471077","text":"def getSwitchingSchedules(self):\n return self._SwitchingSchedules","title":""},{"docid":"061098cbd3df98529ce346104808627d","score":"0.44693238","text":"def scheduled_timestamp(self) -> datetime:\n return self._scheduled_timestamp","title":""},{"docid":"88a91d504b362e0e9076d15011cf1e06","score":"0.4442092","text":"def history(self) -> \"unsigned int\":\n return _isdbt_swig.subset_of_carriers_sptr_history(self)","title":""},{"docid":"375b3803d58277e30c4f536e84311699","score":"0.44311902","text":"def history(self) -> \"unsigned int\":\n return _isdbt_swig.time_interleaver_sptr_history(self)","title":""},{"docid":"a668c640032e306d584c5cfc4047398f","score":"0.44277912","text":"def scheduled_for(self):\n return self._scheduled_for","title":""},{"docid":"1f924e7f3faabc8c9b02aebdcc1e6235","score":"0.44259647","text":"def schedule(self):\n schedule = list()\n for location in self.locations:\n matches = [match for match in self.matches\n if match.location == location]\n # skip locations that do not have any matches\n if matches:\n schedule.append([match for match in self.matches\n if match.location == location])\n\n return schedule","title":""},{"docid":"f4fde6bb2db35f60e33f696664c0fb19","score":"0.44207513","text":"def __ne__(self, other: 'SchedulerInfo') -> bool:\n return not self == other","title":""},{"docid":"04dcb982cf358d10fdf1118d0012a3d0","score":"0.44200706","text":"def is_ingest_pipeline_schedule(self):\n\n result = False\n if self.event is not None and 'detail-type' in self.event and 'Scheduled Event' in self.event['detail-type']:\n self.log('triggered by Scheduled Event')\n result = True\n\n self.log('is_ingest_pipeline_schedule is {}', result)\n return result","title":""},{"docid":"99c973c3a55d0eb37b83c2b250b6781d","score":"0.44150746","text":"def __eq__(self, other: 'SchedulerInfo') -> bool:\n if not isinstance(other, self.__class__):\n return False\n return self.__dict__ == other.__dict__","title":""},{"docid":"831aa0f15ad341555157204a05e1aaa5","score":"0.4405369","text":"def draft_changes(self):\n return self._impl.getDraftChanges(self.critic)","title":""},{"docid":"a07d6d249770f8d19a0d620274bf70e4","score":"0.4401185","text":"def _get_schedule(row, budget):\n\n # {instance name -> (list of) tuples [algo, bugdet]}\n ret = {\n row['instance_id']: [tuple([row['solver'], budget])]\n }\n \n return ret","title":""},{"docid":"f095284eb9a8615956a1b14a06d6d922","score":"0.4400271","text":"def _decode_ens_public_resolver_content_hash(self, context: DecoderContext) -> DecodingOutput:\n node = context.tx_log.topics[1] # node is a hash of the name used by ens internals\n contract = self.ethereum.contracts.contract_by_address(address=context.tx_log.address)\n if contract is None:\n self.msg_aggregator.add_error(\n f'Failed to find ENS public resolver contract with address '\n f'{context.tx_log.address}. This should never happen. Please, '\n f\"open an issue in rotki's github repository.\",\n )\n return DEFAULT_DECODING_OUTPUT\n\n result = contract.decode_event(context.tx_log, 'ContenthashChanged', argument_names=None) # noqa: E501\n node = result[0][0]\n new_hash = result[1][0].hex()\n name_to_show = self._get_name_to_show(node=node, resolver_address=context.tx_log.address)\n\n try:\n codec = content_hash.get_codec(new_hash)\n value_hash = content_hash.decode(new_hash)\n value = f'{codec}://{value_hash}'\n except (TypeError, KeyError, ValueError) as e:\n msg = str(e)\n if isinstance(e, KeyError):\n msg = f'Inability to find key {msg}'\n log.error(f'Failed to decode content hash {new_hash} due to {msg}')\n value = f'unknown type hash {new_hash}'\n\n notes = f'Change ENS content hash to {value}'\n if name_to_show is not None:\n notes += f' for {name_to_show}'\n context.decoded_events.append(self.base.make_event_from_transaction(\n transaction=context.transaction,\n tx_log=context.tx_log,\n event_type=HistoryEventType.INFORMATIONAL,\n event_subtype=HistoryEventSubType.NONE,\n asset=A_ETH,\n balance=Balance(),\n location_label=context.transaction.from_address,\n notes=notes,\n counterparty=CPT_ENS,\n address=context.transaction.to_address,\n ))\n return DEFAULT_DECODING_OUTPUT","title":""},{"docid":"d76865d5cdbae8b9e51700ecf5116b7d","score":"0.43970165","text":"def zen_schedule(self, schedule_dt):\n if not isinstance(schedule_dt, MTime):\n schedule_dt = MTime(schedule_dt)\n raise TypeError(\n \"New schedule datetime must be type datetime.datetime\"\n )\n self.schedule.initial_start = schedule_dt","title":""},{"docid":"b2926c4c05686f557a1dce6cab66eb1e","score":"0.43919498","text":"def scheduling(self) -> pulumi.Output['outputs.JobSchedulingResponse']:\n return pulumi.get(self, \"scheduling\")","title":""},{"docid":"424405d2f0fee480ce74742388640417","score":"0.43899283","text":"def _get_sched(self):\n mdl = self._topcmds.get_mdl(1)\n return _modeller.mod_model_sched_get(mdl)","title":""},{"docid":"ba6b70773ec27eee79b64193c783e2f3","score":"0.43722165","text":"def _get_sched_step(self):\n return _modeller.mod_schedule_step_get(self._get_sched())","title":""},{"docid":"1965ed9bc1d06bf8367a00237addffc9","score":"0.4365871","text":"def getSubjectHash(self):\n s = self.getSubject()\n if s == None:\n return '(no subject)'\n return re.sub(r'^(re|fwd?):\\s*', '', s.lower().strip())","title":""},{"docid":"47bb7bcbc83c7e4616d6814a1d95765b","score":"0.43640184","text":"def revision_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"revision_id\")","title":""},{"docid":"67d533c079c376f416ff3a9d3a3d5b25","score":"0.43583137","text":"def evict(self):\n now = datetime.now()\n\n del_list = []\n for airport_id, t in self._missing_entries.items():\n if t + self._missing_timeout < now:\n del_list.append(airport_id)\n for airport_id in del_list:\n del self._missing_entries[airport_id]\n\n del_list = []\n for airport_id, (t, metar) in self._present_entries.items():\n if not metar.report_time.valid:\n if t + self._refresh < now:\n del_list.append(airport_id)\n continue\n if metar.report_time.data + self._min_replacement < now and t + self._refresh < now:\n del_list.append(airport_id)\n for airport_id in del_list:\n del self._present_entries[airport_id]","title":""},{"docid":"744253324e51b59dd4b3f54863eb7414","score":"0.4356006","text":"def schedule_fired(self, botengine, schedule_id):\n return","title":""},{"docid":"744253324e51b59dd4b3f54863eb7414","score":"0.4356006","text":"def schedule_fired(self, botengine, schedule_id):\n return","title":""},{"docid":"744253324e51b59dd4b3f54863eb7414","score":"0.4356006","text":"def schedule_fired(self, botengine, schedule_id):\n return","title":""},{"docid":"05ef2cbcc890e70724fcae7e3c923d2a","score":"0.435405","text":"def mirror_timestamp(self):\n return self._mirror_timestamp","title":""},{"docid":"4219c1ac967e227f7cb93b69955ef3d2","score":"0.43507192","text":"def cache_schedule(self, schedule, cache):\n\n cache.append({\n 'id': schedule['id'],\n 'name': schedule['name']\n })\n return cache","title":""},{"docid":"4d316bc3d92e7fb2cabad8d45ce46568","score":"0.43442193","text":"def revisionHistory():","title":""},{"docid":"4d316bc3d92e7fb2cabad8d45ce46568","score":"0.43442193","text":"def revisionHistory():","title":""},{"docid":"6fdfd56e24aab43d93e60e7267bf2f84","score":"0.43441814","text":"def get_snapshot_schedules_list(self):\n\n try:\n LOG.info('Getting snapshot schedules list ')\n snapshot_schedules = utils.snap_schedule.UnitySnapScheduleList \\\n .get(cli=self.unity._cli)\n return result_list(snapshot_schedules)\n\n except Exception as e:\n msg = 'Get snapshot schedules list from unity array failed with' \\\n ' error %s' % (str(e))\n LOG.error(msg)\n self.module.fail_json(msg=msg)","title":""},{"docid":"f6c7ce0dca6b7deb64acb2d6367caf45","score":"0.43395317","text":"def history(self) -> \"unsigned int\":\n return _isdbt_swig.ofdm_synchronization_sptr_history(self)","title":""},{"docid":"604f693cbc46cdcd133be4df2855fd12","score":"0.43337807","text":"def schedule_expression(self) -> str:\n return pulumi.get(self, \"schedule_expression\")","title":""},{"docid":"218311a7f38c7eac9b65b7fff8f7de2b","score":"0.43308884","text":"def get_schedule(self, schedule_name: str) -> ScheduleDefinition:\n schedules_with_name = [\n schedule for schedule in self.get_all_schedules() if schedule.name == schedule_name\n ]\n if not schedules_with_name:\n raise DagsterInvariantViolationError(\n f\"Could not find schedule {schedule_name} in repository\"\n )\n return schedules_with_name[0]","title":""},{"docid":"f29d56417183d929b9846452a634e4d1","score":"0.43299526","text":"def is_effective_by_schedule(self):\n raise errors.Unimplemented()","title":""},{"docid":"f3a8dad820a2ea3dd810dcfcabc6092e","score":"0.4325257","text":"def get_previous_changed_time(self):\n if not self.replication_details.HasField('timestamp'):\n self.merge_config_protobuf()\n return self.replication_details.timestamp","title":""},{"docid":"454f93cb21437b58a37189fa73e985f0","score":"0.43153313","text":"def __eq__(self, other):\n if not isinstance(other, ScheduleA):\n return False\n\n return self.to_dict() == other.to_dict()","title":""},{"docid":"15272c19457cbaf9d38d940111984786","score":"0.43132976","text":"def __hash__(self):\n return hash(self._sub_conditions) - hash(self._operator)","title":""},{"docid":"f7b86c92074b1fe87817a218b910f195","score":"0.43003207","text":"async def get_schedule(self) -> Dict[str, Task]:\n return self._tasks","title":""},{"docid":"bf3b8c9e911a931adafd6a5f52a39f19","score":"0.42968905","text":"def setSchedule(self):\n self._setShiftNumbers()\n self._createLayout()\n self._display()\n return None","title":""},{"docid":"9cc1b2a14eb21c1d1ff9b0343b634f07","score":"0.42953873","text":"def git_revision(self):\n return dftbplus_step.__git_revision__","title":""},{"docid":"17c84735fd5b51b401db9a08697c47ac","score":"0.42914334","text":"def update_schedule():\n\n # default bucket is in this format: project-id.appspot.com\n # https://cloud.google.com/appengine/docs/standard/python3/using-cloud-storage\n filename = \"py3_schedule_\" + VERSION\n updated_filename = \"py3_updated_schedule_\" + VERSION\n\n logging.info(f\"Using filename {filename} and updated_filename {updated_filename}\")\n\n ####\n\n [totalgames, jsondata] = fetch_upstream_schedule(URL)\n\n if not jsondata:\n return (\n render_template(\n \"update_schedule.html\",\n version=VERSION,\n filename=filename,\n totalgames=totalgames,\n last_updated=False,\n changes=False,\n ),\n 500,\n )\n\n changes = False\n\n if totalgames == 0:\n pass\n else:\n [teamdates] = parse_schedule(jsondata)\n content = make_data_json(teamdates)\n try:\n old_content = read_file(filename)\n except NotFound:\n create_file(filename, content)\n changes = \"just created\"\n logging.info(\"No schedule found, created it\")\n return (\n render_template(\n \"update_schedule.html\",\n version=VERSION,\n filename=filename,\n totalgames=totalgames,\n last_updated=FOR_UPDATED,\n changes=changes,\n ),\n 202,\n )\n if old_content == content:\n changes = \"No changes needed\"\n try:\n last_updated = read_file(updated_filename)\n _msg = \"Not updating schedule - it is current.\"\n logging.info(_msg)\n except NotFound:\n create_file(updated_filename, FOR_UPDATED)\n last_updated = read_file(updated_filename)\n logging.info(f\"Last updated: {last_updated}\")\n else:\n changes = diff(json.loads(old_content), json.loads(content))\n logging.info(f\"Changes: {changes}\")\n create_file(filename, content)\n create_file(updated_filename, FOR_UPDATED)\n last_updated = read_file(updated_filename)\n # Only send notifications outside playoffs\n # (potential spoilers - games are removed from the schedule)\n if CURRENT_MONTH < 4 or CURRENT_MONTH > 6:\n logging.info(\"Sending an update notification\")\n send_an_email(diff(json.loads(old_content), json.loads(content)), True)\n else:\n logging.info(\n \"Would have sent an update notification, but it might be playoff folks!\"\n )\n return (\n render_template(\n \"update_schedule.html\",\n version=VERSION,\n filename=filename,\n totalgames=totalgames,\n last_updated=last_updated,\n changes=changes,\n ),\n 202,\n )\n\n return render_template(\n \"update_schedule.html\",\n version=VERSION,\n filename=filename,\n totalgames=totalgames,\n last_updated=last_updated,\n changes=changes,\n )","title":""},{"docid":"cea374f5f7e8337acdbb461ce62638dc","score":"0.42887723","text":"def get_all_schedules(self) -> Sequence[ScheduleDefinition]:\n return []","title":""},{"docid":"287cb706cd65e2cad00b6a439ba4352d","score":"0.4286509","text":"def __eq__(self, other):\n if not isinstance(other, ScheduledTaskDetail):\n return False\n\n return self.__dict__ == other.__dict__","title":""},{"docid":"93d7d12768aafe08dae9e594a351389d","score":"0.42857674","text":"def serialize(self):\n snaps = {str(_[0]): _[1] for _ in self.snapshots()}\n props = {'n_snaps': len(snaps)}\n return dict(properties=props, task=self.task, ranking=self.ranking, race=snaps)","title":""},{"docid":"1cb9b86e5aebe77087659d892d2f2014","score":"0.42845753","text":"def snapshot(self):\n self.annotate_(base.Event.SAVED)\n return {\n 'condition': self.condition_,\n 'params': self.params_,\n 'tags': self.tags_,\n 'touched': self.touched_,\n 'created': self.created_,\n 'history': self.history_,\n 'stage': self.stage_,\n 'worker': None,\n }","title":""},{"docid":"78a5f39ad0650a3cc8102e17584d40b7","score":"0.42827222","text":"def output_dict(self):\n output_dict = super(ScheduleFormat, self).output_dict()\n output_dict.update({\n \"jobs\": self.synapp_data\n })\n\n # add prologue/epilogue if they exist\n if self.prologue:\n output_dict.update({\"prologue\": self.prologue})\n\n if self.epilogue:\n output_dict.update({\"epilogue\": self.epilogue})\n\n return output_dict","title":""}],"string":"[\n {\n \"docid\": \"377bccb579075d99456edaf07e00a004\",\n \"score\": \"0.5524533\",\n \"text\": \"def getSchedule(self):\\n self.lock.acquire()\\n a = copy.deepcopy(self.db['schedule'])\\n self.lock.release()\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30551ea8dab794a14118979c8ffc5da\",\n \"score\": \"0.5376334\",\n \"text\": \"def zen_schedule(self):\\n\\n if self.header.old_version is True:\\n return MTime(self.header.schedule)\\n\\n return self.schedule.initial_start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38f5fffb20fb87333771b4c0f10d3554\",\n \"score\": \"0.5254526\",\n \"text\": \"def schedule(self):\\n return self._schedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38f5fffb20fb87333771b4c0f10d3554\",\n \"score\": \"0.5254526\",\n \"text\": \"def schedule(self):\\n return self._schedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cf2669c1b81452dc03f439975329e16\",\n \"score\": \"0.5119087\",\n \"text\": \"def get_schedule_id(self):\\n return self.schedule_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"974bdae55935aceb6137d9690fe8c1da\",\n \"score\": \"0.51003516\",\n \"text\": \"def revision(self):\\n return self._revision\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea2674b3e920ab38b88888b7c5cbd39\",\n \"score\": \"0.5064663\",\n \"text\": \"def schedule_id(self) -> int:\\n return self._schedule_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd185e29b9fe2370bdfb45d16918391c\",\n \"score\": \"0.5052048\",\n \"text\": \"def get_schedule_metadata(self):\\n raise errors.Unimplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f835b637337e470077219cf106cc077\",\n \"score\": \"0.50403345\",\n \"text\": \"def schedule_info(self) -> 'outputs.GoogleCloudDatapipelinesV1ScheduleSpecResponse':\\n return pulumi.get(self, \\\"schedule_info\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59442ba4a9607099ec7333f247184236\",\n \"score\": \"0.50277865\",\n \"text\": \"def hw_revision(self):\\n return self._info['rev']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63b43c02e9feec96ba7f5c69cd16ae3e\",\n \"score\": \"0.49852806\",\n \"text\": \"def revision_data(self):\\n return {'rev': self._xml.get('rev'), 'srcmd5': self._xml.get('srcmd5')}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a078f811c6fbebe03a6989aaac47d209\",\n \"score\": \"0.49707952\",\n \"text\": \"def revision(self):\\n r = ''\\n if 'revision' in self.m.properties:\\n r = self.m.properties['revision']\\n if self._revision:\\n r = self._revision\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d81def50d0d419ba1e2338618fa138\",\n \"score\": \"0.49697927\",\n \"text\": \"def revision(self):\\n return self.record['assay']['descr']['revision']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9658d77e63dba8f9ee3d2b352798d5bb\",\n \"score\": \"0.49624103\",\n \"text\": \"def re_scheduled_on(self):\\n return self._re_scheduled_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80db1a723075c32bb4a222bd329540bb\",\n \"score\": \"0.4960503\",\n \"text\": \"def get_schedule_id(self):\\n raise errors.Unimplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"145caaaeaf8ccc8192d5f85f907e1826\",\n \"score\": \"0.4926376\",\n \"text\": \"def configuration(self):\\n if self._configuration is None:\\n # need to reconstruct configuration from dictionary in event\\n self._configuration = SchedulerConfigBuilder.configuration_from_dict(self._event[\\\"configuration\\\"])\\n # for large configurations the schedules are not passed in the event, need to reload these here\\n if len(self._configuration.schedules) == 0:\\n loaded_config = configuration.get_scheduler_configuration(self._logger)\\n self._configuration.schedules = loaded_config.schedules\\n return self._configuration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7bcf01806b2762ee336c9f48092522a\",\n \"score\": \"0.49199614\",\n \"text\": \"def revision(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0aa80a09e7df6077a39ca9175f8513e\",\n \"score\": \"0.49128786\",\n \"text\": \"def _update_schedule(self):\\n #Initialize the hyper period\\n self._update_hyper_period()\\n #Initialize pending job queue\\n pending_job_queue = []\\n #Initialize released_times\\n released_times = {}\\n #Initialize the scheduled tasks\\n scheduled_tasks = []\\n #Initialize release_time_list with the ordered list of all release times in a hyper period\\n release_time_list = []\\n\\n for i in range(len(self.tasks)):\\n temp = 0\\n while temp <= self.hyper:\\n release_time_list.append(temp)\\n temp += self.tasks[i].period\\n release_time_list = list(set(release_time_list))\\n release_time_list.sort()\\n #Cacluate the deadlines for all jobs\\n self._calculate_deadlines()\\n\\n #Add all newly released jobs at time to pending job queue in EDF order\\n for x in release_time_list:\\n pending_queue = []\\n for i in range(len(self.tasks)):\\n if x % self.tasks[i].period == 0:\\n pending_queue.append(self.tasks[i].task_id)\\n released_times[x] = pending_queue\\n\\n #Beginning of the main algorithm\\n current_time = 0\\n while current_time < self.hyper:\\n #Get the next scheduling time to pending-job-queue from rt-list and ft list\\n if len(release_time_list) > 0:\\n current_time = release_time_list.pop(0)\\n #Repeat until hyper period ends\\n if current_time == self.hyper:\\n break\\n #Initialize the temporary scheduled tasks in the current moment\\n temp_scheduled_tasks = []\\n\\n #Add all newly released jobs at time to pending job queue in EDF order\\n for task_id in scheduled_tasks:\\n for i in range(len(self.tasks)):\\n if self.tasks[i].task_id == task_id:\\n if self.tasks[i].jobs[self.tasks[i].pointer - 1].end_time <= current_time:\\n temp_scheduled_tasks.append(task_id)\\n break\\n for x in temp_scheduled_tasks:\\n scheduled_tasks.remove(x)\\n if released_times.has_key(current_time):\\n pending_job_queue += released_times[current_time]\\n temp_pjq = []\\n temp_pjq_in_ordered_priority = []\\n\\n pending_job_queue_tasks = []\\n for task_id in pending_job_queue:\\n current_task = self._get_task_by_id(task_id)\\n pending_job_queue_tasks.append(current_task)\\n sorted(pending_job_queue_tasks, key=lambda task: task.priority)\\n for task in pending_job_queue_tasks:\\n temp_pjq_in_ordered_priority.append(task.task_id)\\n #print task.task_id, task.priority\\n pending_job_queue = temp_pjq_in_ordered_priority\\n\\n #For each job in pending job queue in EDF order\\n for task_id in pending_job_queue:\\n #If the current job does not conflict with any of already scheduled jobs and does not violate MLA\\n if self._no_conflict(task_id, scheduled_tasks) and self.mla_ok(scheduled_tasks,\\n task_id):\\n #Find the location of the job that for the task_id\\n num_of_jobs = 0\\n for i in range(len(self.tasks)):\\n if self.tasks[i].task_id == task_id:\\n num_of_jobs = i\\n break\\n pointer = self.tasks[num_of_jobs].pointer\\n\\n #If current job meets the deadline\\n if current_time + self.tasks[num_of_jobs].metric.duration <= self.tasks[num_of_jobs].jobs[\\n pointer].deadline:\\n #Schedule this job and update start_time and end_time\\n self.tasks[num_of_jobs].jobs[pointer].start_time = current_time\\n self.tasks[num_of_jobs].jobs[pointer].end_time = current_time + self.tasks[\\n num_of_jobs].metric.duration\\n end_time = self.tasks[num_of_jobs].jobs[pointer].end_time\\n self.tasks[num_of_jobs].pointer += 1\\n\\n #Update the scheduled tasks\\n scheduled_tasks.append(task_id)\\n #Add the task_id in the temporary pending_job_queue for the current moment\\n temp_pjq.append(task_id)\\n #Update released time\\n release_time_list.append(end_time)\\n release_time_list = list(set(release_time_list))\\n release_time_list.sort()\\n else:\\n temp_pjq.append(task_id)\\n self.tasks[num_of_jobs].pointer += 1\\n #Remove the scheduled jobs from pending_job_queue\\n for x in temp_pjq:\\n pending_job_queue.remove(x)\\n\\n #Check if any of the job start time is none, if so raise an exception\\n for i in range(len(self.tasks)):\\n num_of_jobs = len(self.tasks[i].jobs)\\n for j in range(num_of_jobs):\\n if self.tasks[i].jobs[j].start_time is None:\\n raise Exception(\\\"Some of the tasks can not be scheduled completely\\\")\\n\\n #Format all the jobs to required objects schedule_line\\n for i in range(len(self.tasks)):\\n num_of_jobs = len(self.tasks[i].jobs)\\n for j in range(num_of_jobs):\\n job = self.tasks[i].jobs[j]\\n task_id = self.tasks[i].task_id\\n metric_id = self.tasks[i].metric.id\\n src = self.tasks[i].src\\n dst = self.tasks[i].dst\\n start = self.tasks[i].jobs[j].start_time\\n duration = self.tasks[i].metric.duration\\n period = self.tasks[i].period\\n priority = self.tasks[i].priority\\n schedule_line = ScheduleLine(task_id, metric_id, src, dst, start, duration, period, priority)\\n self.schedules.append(schedule_line)\\n\\n #Update the table\\n for i in range(len(self.tasks)):\\n num_of_jobs = len(self.tasks[i].jobs)\\n for j in range(num_of_jobs):\\n current_time = self.tasks[i].jobs[j].start_time\\n end_time = self.tasks[i].jobs[j].end_time\\n while current_time < end_time:\\n if self.time_table.has_key(current_time):\\n self.time_table[current_time].append(self.tasks[i].task_id)\\n else:\\n self.time_table[current_time] = []\\n self.time_table[current_time].append(self.tasks[i].task_id)\\n current_time += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e903d6595f9b8da1c52ab159f04d3a0\",\n \"score\": \"0.48892772\",\n \"text\": \"def configuration(self):\\n if self._configuration is None:\\n # need to reconstruct configuration from dictionary in event\\n self._configuration = SchedulerConfigBuilder.configuration_from_dict(\\n self._event[\\\"configuration\\\"]\\n )\\n # for large configurations the schedules are not passed in the event, need to reload these here\\n if len(self._configuration.schedules) == 0:\\n loaded_config = configuration.get_scheduler_configuration(self._logger)\\n self._configuration.schedules = loaded_config.schedules\\n return self._configuration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e5ff674e530cd56624dce53befe1af\",\n \"score\": \"0.48657018\",\n \"text\": \"def change_log(self, num_changelogs=None) -> Dict[date, List[ScheduleChange]]:\\n length = len(list(self.sched_versions_by_date))\\n if num_changelogs is None:\\n change_slice = slice(1, length)\\n else:\\n change_slice = slice(max(1, length - num_changelogs), length)\\n return {\\n date_: self.get_changes_for_date(date_)\\n for date_ in list(self.sched_versions_by_date.keys())[change_slice]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c3ace354d821fdf7f4067671565d3e\",\n \"score\": \"0.48582017\",\n \"text\": \"def hash_config(self, config):\\n self.normalize(config)\\n try:\\n schedule = self.halide_tuner.cfg_to_schedule(config)\\n return hashlib.sha256(schedule).hexdigest()\\n except:\\n log.warning('error hashing config', exc_info=True)\\n return super(HalideConfigurationManipulator, self).hash_config(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789fa15083d2d8abed95ba2bfcaf53c4\",\n \"score\": \"0.48124906\",\n \"text\": \"def schedule_name(self):\\n return self._schedule_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a9259bb0ddda8efbdd60044f8f19bc\",\n \"score\": \"0.48075727\",\n \"text\": \"def revision_date(self):\\n return self._revision_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c14e00c121f2ab19bbd054bd11b37c\",\n \"score\": \"0.4763667\",\n \"text\": \"def _get_n_schedule(self):\\n return _modeller.mod_schedule_nsch_get(self._get_sched())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252ec6c191ac7e96267d133e76f3203a\",\n \"score\": \"0.47334898\",\n \"text\": \"def schedule(self) -> TimeRange:\\n return self._schedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13bef1c780b6f55f6a5f79bafa4e8561\",\n \"score\": \"0.47327968\",\n \"text\": \"def __hash__(self) -> int:\\n return self._datetime.__hash__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e34285cb332d28750b7f432d039eb4\",\n \"score\": \"0.47262037\",\n \"text\": \"def get_special_schedule(self, legend=0):\\n return self.call_api('sched', 'special', l=legend)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18da201de79ae871c23a93cc5ea029e0\",\n \"score\": \"0.47179618\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, RecoveryBackupSchedule):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a14ba6304b48cdaa9811aa4d5ed40c\",\n \"score\": \"0.47112602\",\n \"text\": \"def schedule(self):\\n if self.periodic:\\n return self._schedule\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb69244fda0e69550291fddd6da19d7\",\n \"score\": \"0.46966428\",\n \"text\": \"def schedule(self) -> str:\\n return pulumi.get(self, \\\"schedule\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e8b3253f1e4eff2eac94d6d2a56c23\",\n \"score\": \"0.46862197\",\n \"text\": \"def compare_content(self, fileName='schedule_log.csv', content=None):\\n if content is None:\\n content = self.schedule_data\\n \\n # If log file doesn't exist, create it\\n if not os.path.exists(SCRIPTPATH + fileName):\\n print('No log file present. ', end='')\\n self.update_log(fileName, content)\\n return []\\n\\n # Read the log file\\n print(f'Comparing schedule changes... ', flush=True, end='')\\n logList = []\\n logDictFile = csv.DictReader(open(SCRIPTPATH + fileName))\\n for lines in logDictFile:\\n listDict = dict(lines)\\n logList.append(listDict.copy())\\n\\n # Convert datetime.date to string date for comparison\\n for lines in content:\\n lines['date'] = lines['date'].strftime('%Y-%m-%d')\\n\\n # If dates don't match, i.e. new quarter, create a new log\\n if logList[0]['date'] != content[0]['date']:\\n print('New date data found. ', end='')\\n self.update_log(fileName, content)\\n return []\\n\\n # Populate list allDiff with formatted differences\\n allDiff = []\\n chngCntr = 0\\n checkRange = len(logList)\\n if len(content) < len(logList): #Always iterate over the smallest of the two\\n checkRange = len(content)\\n for i in range(checkRange):\\n diffList = []\\n # If there has been a change in the content:\\n if logList[i] != content[i]:\\n date = reformat_str_date(logList[i]['date'])\\n log_description = list(logList[i]['description'].split('|'))\\n current_description = list(content[i]['description'].split('|'))\\n for j in range(len(log_description)):\\n if log_description[j] != current_description[j]:\\n chngCntr += 1\\n current_role = current_description[j].split(' - ', 1)[0]\\n current_name = current_description[j].split(' - ', 1)[1]\\n log_name = log_description[j].split(' - ', 1)[1]\\n if current_name == 'None ':\\n current_name = 'No one '\\n elif log_name == 'None ':\\n log_name = 'no one '\\n diffList.append('- ' + current_name + 'is now on' + current_role + ' instead of ' + log_name)\\n d = '\\\\nTeam changes for ' + date + ':\\\\n'\\n for diff in diffList:\\n d += diff +'\\\\n'\\n allDiff.append(d)\\n if chngCntr is not 0:\\n print(f'{chngCntr} change(s) found')\\n else:\\n print('No changes found')\\n return allDiff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"685339b36eba8db66306ac18ed586524\",\n \"score\": \"0.46636668\",\n \"text\": \"def change_times(self):\\n return [t for t,g in self.snapshots().items() if len(g.edges()) > 0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d723a1610fb0512a607058552ea8209d\",\n \"score\": \"0.466244\",\n \"text\": \"def hw_revision(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"hw_revision\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c871ec2d8b5e29e87b855375bc2cce23\",\n \"score\": \"0.46542224\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, RepositoryScheduled):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96699c249f0e3e7700f7f4a2e59f4f6b\",\n \"score\": \"0.46428698\",\n \"text\": \"async def save_schedule(self, schedule: Schedule):\\n if self._paused or not self._ready:\\n raise NotReadyError()\\n\\n # TODO should these checks be moved to the storage layer?\\n if schedule.name is None or len(schedule.name) == 0:\\n raise ValueError(\\\"name can not be empty\\\")\\n\\n if schedule.repeat is not None and not isinstance(schedule.repeat, datetime.timedelta):\\n raise ValueError('repeat must be of type datetime.time')\\n\\n if schedule.exclusive is None:\\n raise ValueError('exclusive can not be None')\\n\\n if isinstance(schedule, TimedSchedule):\\n schedule_time = schedule.time\\n\\n if schedule_time is not None and not isinstance(schedule_time, datetime.time):\\n raise ValueError('time must be of type datetime.time')\\n\\n day = schedule.day\\n\\n # TODO Remove this check when the database has constraint\\n if day is not None and (day < 1 or day > 7):\\n raise ValueError('day must be between 1 and 7')\\n else:\\n day = None\\n schedule_time = None\\n\\n prev_schedule_row = None\\n\\n if schedule.schedule_id is None:\\n is_new_schedule = True\\n schedule.schedule_id = uuid.uuid4()\\n else:\\n try:\\n prev_schedule_row = self._schedules[schedule.schedule_id]\\n is_new_schedule = False\\n except KeyError:\\n is_new_schedule = True\\n\\n if not is_new_schedule:\\n update = self._schedules_tbl.update()\\n update = update.where(self._schedules_tbl.c.id == str(schedule.schedule_id))\\n update = update.values(schedule_name=schedule.name,\\n schedule_type=int(schedule.schedule_type),\\n schedule_interval=schedule.repeat,\\n schedule_day=day,\\n schedule_time=schedule_time,\\n exclusive=schedule.exclusive,\\n process_name=schedule.process_name)\\n\\n self._logger.debug('Database command: %s', update)\\n\\n try:\\n async with (await self._get_connection_pool()).acquire() as conn:\\n result = await conn.execute(update)\\n\\n if result.rowcount == 0:\\n is_new_schedule = True\\n except Exception:\\n self._logger.debug('Update failed: %s', update)\\n raise\\n\\n if is_new_schedule:\\n insert = self._schedules_tbl.insert()\\n insert = insert.values(id=str(schedule.schedule_id),\\n schedule_type=int(schedule.schedule_type),\\n schedule_name=schedule.name,\\n schedule_interval=schedule.repeat,\\n schedule_day=day,\\n schedule_time=schedule_time,\\n exclusive=schedule.exclusive,\\n process_name=schedule.process_name)\\n\\n self._logger.debug('Database command: %s', insert)\\n\\n try:\\n async with (await self._get_connection_pool()).acquire() as conn:\\n await conn.execute(insert)\\n except Exception:\\n self._logger.exception('Insert failed: %s', insert)\\n raise\\n\\n repeat_seconds = None\\n if schedule.repeat is not None:\\n repeat_seconds = schedule.repeat.total_seconds()\\n\\n schedule_row = self._ScheduleRow(\\n id=schedule.schedule_id,\\n name=schedule.name,\\n type=schedule.schedule_type,\\n time=schedule_time,\\n day=day,\\n repeat=schedule.repeat,\\n repeat_seconds=repeat_seconds,\\n exclusive=schedule.exclusive,\\n process_name=schedule.process_name)\\n\\n self._schedules[schedule.schedule_id] = schedule_row\\n\\n # Did the schedule change in a way that will affect task scheduling?\\n if schedule.schedule_type in [Schedule.Type.INTERVAL, Schedule.Type.TIMED] and (\\n is_new_schedule or\\n prev_schedule_row.time != schedule_row.time or\\n prev_schedule_row.day != schedule_row.day or\\n prev_schedule_row.repeat_seconds != schedule_row.repeat_seconds or\\n prev_schedule_row.exclusive != schedule_row.exclusive):\\n\\n now = self.current_time if self.current_time else time.time()\\n self._schedule_first_task(schedule_row, now)\\n self._resume_check_schedules()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfd6114bbced7b85d847e2d8819bb7c\",\n \"score\": \"0.463927\",\n \"text\": \"def get_crashed(self, vid):\\n return self.plexe.get_crashed(vid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d23243c6a217c67a1067c27e0dc324c\",\n \"score\": \"0.46383563\",\n \"text\": \"def getIntervalSchedule(self):\\n return self._IntervalSchedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1027d1ecae4b76b836540477bfbb5298\",\n \"score\": \"0.46195728\",\n \"text\": \"def history(self) -> \\\"unsigned int\\\":\\n return _isdbt_swig.time_deinterleaver_sptr_history(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9508e7c5a2db8937730b368b07bd0a3\",\n \"score\": \"0.4615141\",\n \"text\": \"def get_course_schedule(self):\\n\\n return self.__courseSchedule.get_schedule()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f055140ec77bcbace1109a01e8a67051\",\n \"score\": \"0.4610143\",\n \"text\": \"def revision_id(self):\\n return self.getattr('revision_id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5e510449f3c524567fd72310c58179\",\n \"score\": \"0.4599056\",\n \"text\": \"def revision_id(self):\\n return ''.join(('%s:' % self.revision_number\\n if self.revision_number else '',\\n '%s' % self.changeset_revision\\n if self.changeset_revision else ''))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec4eb56ee2c730a8b59569f4b1edb45\",\n \"score\": \"0.45788485\",\n \"text\": \"def schedule():\\n try:\\n schedule = get_maintenance_schedule()\\n except HTTPError:\\n raise HTTPError(\\\"Error getting maintenance schedule.\\\")\\n return schedule.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21424a41db086725725762c4f1e981c1\",\n \"score\": \"0.45663548\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, TaskTemplateSchedule):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea05bd73bdc9a4ca7f901e17d55e4a5\",\n \"score\": \"0.4558546\",\n \"text\": \"def revision(self) -> Optional[str]:\\n return pulumi.get(self, \\\"revision\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"003c8b7939aa74c0de57634e39007605\",\n \"score\": \"0.45539713\",\n \"text\": \"def get_schedule_names(self) -> Sequence[str]:\\n return self._schedules.get_definition_names()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ff69ba82fe0bca0ea7bc8edcecb4c3\",\n \"score\": \"0.45451325\",\n \"text\": \"def validate_scheduling_data(self, schedule_data):\\r\\n \\\"\\\"\\\"\\r\\n if self.parse_and_check_for_recycled_folders(schedule_data[\\\"folders\\\"], schedule_data[\\\"folder_root_id\\\"]):\\r\\n result = {\\\"error\\\":\\\"Error: \\\" + schedule_data[\\\"schedule_name\\\"] + \\\" - Submitted folders may already exist in recyle bin. Please empty recycle bin and try again.\\\"}\\r\\n logging.error(result[\\\"error\\\"])\\r\\n return result\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n if schedule_data[\\\"channel_include\\\"] and schedule_data[\\\"channel_name\\\"] == \\\"\\\":\\r\\n result = {\\\"error\\\": \\\"Error: \\\" + schedule_data[\\\"schedule_name\\\"] + \\\" - Submitted schedule data has no channel name.\\\"}\\r\\n logging.error(result[\\\"error\\\"])\\r\\n return result\\r\\n\\r\\n if self.schedule.schedule_data_has_0_occurrences(schedule_data):\\r\\n result = {\\\"error\\\": \\\"Error: \\\" + schedule_data[\\\"schedule_name\\\"] + \\\" - Submitted schedule data does not contain at least one occurrence.\\\"}\\r\\n logging.error(result[\\\"error\\\"])\\r\\n return result\\r\\n\\r\\n if schedule_data[\\\"module_include\\\"] and schedule_data[\\\"module_id\\\"] == \\\"\\\":\\r\\n result = {\\\"error\\\": \\\"Error: \\\" + schedule_data[\\\"schedule_name\\\"] + \\\" - No module Id specified.\\\"}\\r\\n logging.error(result[\\\"error\\\"])\\r\\n return result\\r\\n\\r\\n if schedule_data[\\\"module_include\\\"] and self.module.module_moduleid_already_exists(schedule_data[\\\"module_id\\\"]):\\r\\n result = {\\\"error\\\": \\\"Error: \\\" + schedule_data[\\\"schedule_name\\\"] + \\\" - Submitted ModuleId already exists.\\\"}\\r\\n logging.error(result[\\\"error\\\"])\\r\\n return result\\r\\n\\r\\n if schedule_data[\\\"schedule_template\\\"] in self.model.get_templates():\\r\\n result = {\\\"error\\\": \\\"Error: \\\" + schedule_data[\\\"schedule_name\\\"] + \\\" - Submitted template name does not exist.\\\"}\\r\\n logging.error(result[\\\"error\\\"])\\r\\n return result\\r\\n\\r\\n return {\\\"Success\\\": \\\"\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5eff6807ad8f43983a1e6a16611595d\",\n \"score\": \"0.4539271\",\n \"text\": \"def clone(self) -> \\\"ScheduleRule\\\":\\n return _ffi_api.ScheduleRuleClone(self) # type: ignore # pylint: disable=no-member\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79fa9a50bf03682fb53f000d71ab53b\",\n \"score\": \"0.45272434\",\n \"text\": \"def testNotScheduled(self):\\n error = \\\"^The specification status has no top-level 'scheduledAt' key$\\\"\\n assertRaisesRegex(self, SpecificationError, error, SlurmPipelineStatus,\\n {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40bbcc04d4f44f2ef2791264014584d\",\n \"score\": \"0.4513189\",\n \"text\": \"def scheduling(self) -> Optional[pulumi.Input['JobSchedulingArgs']]:\\n return pulumi.get(self, \\\"scheduling\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba017704426f78a7c2b1746e2158a29\",\n \"score\": \"0.45130363\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, ScanBlackoutSchedule):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5fdad53d3de561454e6bcd0055e9d2\",\n \"score\": \"0.4510989\",\n \"text\": \"def pretty_print(self):\\n from pprint import pprint\\n pprint(self.schedule, indent=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb2196ae7aa8fe6544326be161ea6f2\",\n \"score\": \"0.44995096\",\n \"text\": \"def build_schedule(self):\\n\\n skipped_keys, non_skipped_keys = set(), set()\\n\\n one_exec_scheduled = False\\n eta_avail_job = None\\n for exec_n in xrange(self.config.N_EXECUTIONS):\\n for vm_name, vm_info in self.config.VMS.items():\\n for bmark, param in self.config.BENCHMARKS.items():\\n for variant in vm_info[\\\"variants\\\"]:\\n job = ExecutionJob(self, vm_name, vm_info, bmark, variant, param)\\n if not self.config.should_skip(job.key):\\n non_skipped_keys |= set([job.key])\\n if one_exec_scheduled and not eta_avail_job:\\n # first job of second executions eta becomes known.\\n eta_avail_job = job\\n self.set_eta_avail()\\n self.add_job(job)\\n else:\\n skipped_keys |= set([job.key])\\n if not one_exec_scheduled:\\n debug(\\\"%s is in skip list. Not scheduling.\\\" %\\n job.key)\\n one_exec_scheduled = True\\n self.expected_reboots = len(self)\\n # Resume mode: if previous results are available, remove the\\n # jobs from the schedule which have already been executed, and\\n # add the results to this object, ready to be saved to a Json file.\\n if self.resume:\\n self._remove_previous_execs_from_schedule()\\n # Sanity check ETA estimates\\n for key, exec_data in self.results.data.iteritems():\\n got_len = len(self.results.eta_estimates[key])\\n expect_len = len(exec_data)\\n if expect_len != got_len:\\n msg = \\\"ETA estimates didn't tally with results: \\\"\\n msg += \\\"key=%s, expect_len=%d, got_len=%d \\\" % \\\\\\n (key, expect_len, got_len)\\n msg += \\\"data[%s]=%s; \\\" % (key, str(self.results.data[key]))\\n msg += \\\"eta[%s]=%s\\\" % \\\\\\n (key, str(self.results.eta_estimates[key]))\\n util.log_and_mail(self.mailer, error,\\n \\\"Fatal Krun Error\\\",\\n msg, bypass_limiter=True, exit=True)\\n return non_skipped_keys, skipped_keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"881a7e30700c9711ab834cb668f48764\",\n \"score\": \"0.44989118\",\n \"text\": \"def __ne__(self, other):\\n if not isinstance(other, ScheduleA):\\n return True\\n\\n return self.to_dict() != other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28e51e53ad62fa044f90036aba47da5\",\n \"score\": \"0.4488338\",\n \"text\": \"def getSchedParams(self):\\n return self._params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ca153236f9df9a255fa818258608d9\",\n \"score\": \"0.4478361\",\n \"text\": \"def timestamp_modified(self):\\n return self._timestamp_modified\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b72577aac3ddf863644102da4abcefd\",\n \"score\": \"0.44751355\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, ScheduledTask):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25fe5c20775d5e40047ffcfb40bba1c4\",\n \"score\": \"0.44724897\",\n \"text\": \"def get_board_revision():\\n return _hexdump('/proc/device-tree/system/linux,revision')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0e29ee52d239cfaf0f621d670175225\",\n \"score\": \"0.4471077\",\n \"text\": \"def getSwitchingSchedules(self):\\n return self._SwitchingSchedules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061098cbd3df98529ce346104808627d\",\n \"score\": \"0.44693238\",\n \"text\": \"def scheduled_timestamp(self) -> datetime:\\n return self._scheduled_timestamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a91d504b362e0e9076d15011cf1e06\",\n \"score\": \"0.4442092\",\n \"text\": \"def history(self) -> \\\"unsigned int\\\":\\n return _isdbt_swig.subset_of_carriers_sptr_history(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"375b3803d58277e30c4f536e84311699\",\n \"score\": \"0.44311902\",\n \"text\": \"def history(self) -> \\\"unsigned int\\\":\\n return _isdbt_swig.time_interleaver_sptr_history(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a668c640032e306d584c5cfc4047398f\",\n \"score\": \"0.44277912\",\n \"text\": \"def scheduled_for(self):\\n return self._scheduled_for\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f924e7f3faabc8c9b02aebdcc1e6235\",\n \"score\": \"0.44259647\",\n \"text\": \"def schedule(self):\\n schedule = list()\\n for location in self.locations:\\n matches = [match for match in self.matches\\n if match.location == location]\\n # skip locations that do not have any matches\\n if matches:\\n schedule.append([match for match in self.matches\\n if match.location == location])\\n\\n return schedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4fde6bb2db35f60e33f696664c0fb19\",\n \"score\": \"0.44207513\",\n \"text\": \"def __ne__(self, other: 'SchedulerInfo') -> bool:\\n return not self == other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04dcb982cf358d10fdf1118d0012a3d0\",\n \"score\": \"0.44200706\",\n \"text\": \"def is_ingest_pipeline_schedule(self):\\n\\n result = False\\n if self.event is not None and 'detail-type' in self.event and 'Scheduled Event' in self.event['detail-type']:\\n self.log('triggered by Scheduled Event')\\n result = True\\n\\n self.log('is_ingest_pipeline_schedule is {}', result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c973c3a55d0eb37b83c2b250b6781d\",\n \"score\": \"0.44150746\",\n \"text\": \"def __eq__(self, other: 'SchedulerInfo') -> bool:\\n if not isinstance(other, self.__class__):\\n return False\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831aa0f15ad341555157204a05e1aaa5\",\n \"score\": \"0.4405369\",\n \"text\": \"def draft_changes(self):\\n return self._impl.getDraftChanges(self.critic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a07d6d249770f8d19a0d620274bf70e4\",\n \"score\": \"0.4401185\",\n \"text\": \"def _get_schedule(row, budget):\\n\\n # {instance name -> (list of) tuples [algo, bugdet]}\\n ret = {\\n row['instance_id']: [tuple([row['solver'], budget])]\\n }\\n \\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f095284eb9a8615956a1b14a06d6d922\",\n \"score\": \"0.4400271\",\n \"text\": \"def _decode_ens_public_resolver_content_hash(self, context: DecoderContext) -> DecodingOutput:\\n node = context.tx_log.topics[1] # node is a hash of the name used by ens internals\\n contract = self.ethereum.contracts.contract_by_address(address=context.tx_log.address)\\n if contract is None:\\n self.msg_aggregator.add_error(\\n f'Failed to find ENS public resolver contract with address '\\n f'{context.tx_log.address}. This should never happen. Please, '\\n f\\\"open an issue in rotki's github repository.\\\",\\n )\\n return DEFAULT_DECODING_OUTPUT\\n\\n result = contract.decode_event(context.tx_log, 'ContenthashChanged', argument_names=None) # noqa: E501\\n node = result[0][0]\\n new_hash = result[1][0].hex()\\n name_to_show = self._get_name_to_show(node=node, resolver_address=context.tx_log.address)\\n\\n try:\\n codec = content_hash.get_codec(new_hash)\\n value_hash = content_hash.decode(new_hash)\\n value = f'{codec}://{value_hash}'\\n except (TypeError, KeyError, ValueError) as e:\\n msg = str(e)\\n if isinstance(e, KeyError):\\n msg = f'Inability to find key {msg}'\\n log.error(f'Failed to decode content hash {new_hash} due to {msg}')\\n value = f'unknown type hash {new_hash}'\\n\\n notes = f'Change ENS content hash to {value}'\\n if name_to_show is not None:\\n notes += f' for {name_to_show}'\\n context.decoded_events.append(self.base.make_event_from_transaction(\\n transaction=context.transaction,\\n tx_log=context.tx_log,\\n event_type=HistoryEventType.INFORMATIONAL,\\n event_subtype=HistoryEventSubType.NONE,\\n asset=A_ETH,\\n balance=Balance(),\\n location_label=context.transaction.from_address,\\n notes=notes,\\n counterparty=CPT_ENS,\\n address=context.transaction.to_address,\\n ))\\n return DEFAULT_DECODING_OUTPUT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76865d5cdbae8b9e51700ecf5116b7d\",\n \"score\": \"0.43970165\",\n \"text\": \"def zen_schedule(self, schedule_dt):\\n if not isinstance(schedule_dt, MTime):\\n schedule_dt = MTime(schedule_dt)\\n raise TypeError(\\n \\\"New schedule datetime must be type datetime.datetime\\\"\\n )\\n self.schedule.initial_start = schedule_dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2926c4c05686f557a1dce6cab66eb1e\",\n \"score\": \"0.43919498\",\n \"text\": \"def scheduling(self) -> pulumi.Output['outputs.JobSchedulingResponse']:\\n return pulumi.get(self, \\\"scheduling\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424405d2f0fee480ce74742388640417\",\n \"score\": \"0.43899283\",\n \"text\": \"def _get_sched(self):\\n mdl = self._topcmds.get_mdl(1)\\n return _modeller.mod_model_sched_get(mdl)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba6b70773ec27eee79b64193c783e2f3\",\n \"score\": \"0.43722165\",\n \"text\": \"def _get_sched_step(self):\\n return _modeller.mod_schedule_step_get(self._get_sched())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1965ed9bc1d06bf8367a00237addffc9\",\n \"score\": \"0.4365871\",\n \"text\": \"def getSubjectHash(self):\\n s = self.getSubject()\\n if s == None:\\n return '(no subject)'\\n return re.sub(r'^(re|fwd?):\\\\s*', '', s.lower().strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47bb7bcbc83c7e4616d6814a1d95765b\",\n \"score\": \"0.43640184\",\n \"text\": \"def revision_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"revision_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d533c079c376f416ff3a9d3a3d5b25\",\n \"score\": \"0.43583137\",\n \"text\": \"def evict(self):\\n now = datetime.now()\\n\\n del_list = []\\n for airport_id, t in self._missing_entries.items():\\n if t + self._missing_timeout < now:\\n del_list.append(airport_id)\\n for airport_id in del_list:\\n del self._missing_entries[airport_id]\\n\\n del_list = []\\n for airport_id, (t, metar) in self._present_entries.items():\\n if not metar.report_time.valid:\\n if t + self._refresh < now:\\n del_list.append(airport_id)\\n continue\\n if metar.report_time.data + self._min_replacement < now and t + self._refresh < now:\\n del_list.append(airport_id)\\n for airport_id in del_list:\\n del self._present_entries[airport_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744253324e51b59dd4b3f54863eb7414\",\n \"score\": \"0.4356006\",\n \"text\": \"def schedule_fired(self, botengine, schedule_id):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744253324e51b59dd4b3f54863eb7414\",\n \"score\": \"0.4356006\",\n \"text\": \"def schedule_fired(self, botengine, schedule_id):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744253324e51b59dd4b3f54863eb7414\",\n \"score\": \"0.4356006\",\n \"text\": \"def schedule_fired(self, botengine, schedule_id):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05ef2cbcc890e70724fcae7e3c923d2a\",\n \"score\": \"0.435405\",\n \"text\": \"def mirror_timestamp(self):\\n return self._mirror_timestamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4219c1ac967e227f7cb93b69955ef3d2\",\n \"score\": \"0.43507192\",\n \"text\": \"def cache_schedule(self, schedule, cache):\\n\\n cache.append({\\n 'id': schedule['id'],\\n 'name': schedule['name']\\n })\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d316bc3d92e7fb2cabad8d45ce46568\",\n \"score\": \"0.43442193\",\n \"text\": \"def revisionHistory():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d316bc3d92e7fb2cabad8d45ce46568\",\n \"score\": \"0.43442193\",\n \"text\": \"def revisionHistory():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fdfd56e24aab43d93e60e7267bf2f84\",\n \"score\": \"0.43441814\",\n \"text\": \"def get_snapshot_schedules_list(self):\\n\\n try:\\n LOG.info('Getting snapshot schedules list ')\\n snapshot_schedules = utils.snap_schedule.UnitySnapScheduleList \\\\\\n .get(cli=self.unity._cli)\\n return result_list(snapshot_schedules)\\n\\n except Exception as e:\\n msg = 'Get snapshot schedules list from unity array failed with' \\\\\\n ' error %s' % (str(e))\\n LOG.error(msg)\\n self.module.fail_json(msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c7ce0dca6b7deb64acb2d6367caf45\",\n \"score\": \"0.43395317\",\n \"text\": \"def history(self) -> \\\"unsigned int\\\":\\n return _isdbt_swig.ofdm_synchronization_sptr_history(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604f693cbc46cdcd133be4df2855fd12\",\n \"score\": \"0.43337807\",\n \"text\": \"def schedule_expression(self) -> str:\\n return pulumi.get(self, \\\"schedule_expression\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"218311a7f38c7eac9b65b7fff8f7de2b\",\n \"score\": \"0.43308884\",\n \"text\": \"def get_schedule(self, schedule_name: str) -> ScheduleDefinition:\\n schedules_with_name = [\\n schedule for schedule in self.get_all_schedules() if schedule.name == schedule_name\\n ]\\n if not schedules_with_name:\\n raise DagsterInvariantViolationError(\\n f\\\"Could not find schedule {schedule_name} in repository\\\"\\n )\\n return schedules_with_name[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f29d56417183d929b9846452a634e4d1\",\n \"score\": \"0.43299526\",\n \"text\": \"def is_effective_by_schedule(self):\\n raise errors.Unimplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a8dad820a2ea3dd810dcfcabc6092e\",\n \"score\": \"0.4325257\",\n \"text\": \"def get_previous_changed_time(self):\\n if not self.replication_details.HasField('timestamp'):\\n self.merge_config_protobuf()\\n return self.replication_details.timestamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454f93cb21437b58a37189fa73e985f0\",\n \"score\": \"0.43153313\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, ScheduleA):\\n return False\\n\\n return self.to_dict() == other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15272c19457cbaf9d38d940111984786\",\n \"score\": \"0.43132976\",\n \"text\": \"def __hash__(self):\\n return hash(self._sub_conditions) - hash(self._operator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7b86c92074b1fe87817a218b910f195\",\n \"score\": \"0.43003207\",\n \"text\": \"async def get_schedule(self) -> Dict[str, Task]:\\n return self._tasks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3b8c9e911a931adafd6a5f52a39f19\",\n \"score\": \"0.42968905\",\n \"text\": \"def setSchedule(self):\\n self._setShiftNumbers()\\n self._createLayout()\\n self._display()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc1b2a14eb21c1d1ff9b0343b634f07\",\n \"score\": \"0.42953873\",\n \"text\": \"def git_revision(self):\\n return dftbplus_step.__git_revision__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17c84735fd5b51b401db9a08697c47ac\",\n \"score\": \"0.42914334\",\n \"text\": \"def update_schedule():\\n\\n # default bucket is in this format: project-id.appspot.com\\n # https://cloud.google.com/appengine/docs/standard/python3/using-cloud-storage\\n filename = \\\"py3_schedule_\\\" + VERSION\\n updated_filename = \\\"py3_updated_schedule_\\\" + VERSION\\n\\n logging.info(f\\\"Using filename {filename} and updated_filename {updated_filename}\\\")\\n\\n ####\\n\\n [totalgames, jsondata] = fetch_upstream_schedule(URL)\\n\\n if not jsondata:\\n return (\\n render_template(\\n \\\"update_schedule.html\\\",\\n version=VERSION,\\n filename=filename,\\n totalgames=totalgames,\\n last_updated=False,\\n changes=False,\\n ),\\n 500,\\n )\\n\\n changes = False\\n\\n if totalgames == 0:\\n pass\\n else:\\n [teamdates] = parse_schedule(jsondata)\\n content = make_data_json(teamdates)\\n try:\\n old_content = read_file(filename)\\n except NotFound:\\n create_file(filename, content)\\n changes = \\\"just created\\\"\\n logging.info(\\\"No schedule found, created it\\\")\\n return (\\n render_template(\\n \\\"update_schedule.html\\\",\\n version=VERSION,\\n filename=filename,\\n totalgames=totalgames,\\n last_updated=FOR_UPDATED,\\n changes=changes,\\n ),\\n 202,\\n )\\n if old_content == content:\\n changes = \\\"No changes needed\\\"\\n try:\\n last_updated = read_file(updated_filename)\\n _msg = \\\"Not updating schedule - it is current.\\\"\\n logging.info(_msg)\\n except NotFound:\\n create_file(updated_filename, FOR_UPDATED)\\n last_updated = read_file(updated_filename)\\n logging.info(f\\\"Last updated: {last_updated}\\\")\\n else:\\n changes = diff(json.loads(old_content), json.loads(content))\\n logging.info(f\\\"Changes: {changes}\\\")\\n create_file(filename, content)\\n create_file(updated_filename, FOR_UPDATED)\\n last_updated = read_file(updated_filename)\\n # Only send notifications outside playoffs\\n # (potential spoilers - games are removed from the schedule)\\n if CURRENT_MONTH < 4 or CURRENT_MONTH > 6:\\n logging.info(\\\"Sending an update notification\\\")\\n send_an_email(diff(json.loads(old_content), json.loads(content)), True)\\n else:\\n logging.info(\\n \\\"Would have sent an update notification, but it might be playoff folks!\\\"\\n )\\n return (\\n render_template(\\n \\\"update_schedule.html\\\",\\n version=VERSION,\\n filename=filename,\\n totalgames=totalgames,\\n last_updated=last_updated,\\n changes=changes,\\n ),\\n 202,\\n )\\n\\n return render_template(\\n \\\"update_schedule.html\\\",\\n version=VERSION,\\n filename=filename,\\n totalgames=totalgames,\\n last_updated=last_updated,\\n changes=changes,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea374f5f7e8337acdbb461ce62638dc\",\n \"score\": \"0.42887723\",\n \"text\": \"def get_all_schedules(self) -> Sequence[ScheduleDefinition]:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287cb706cd65e2cad00b6a439ba4352d\",\n \"score\": \"0.4286509\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, ScheduledTaskDetail):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d7d12768aafe08dae9e594a351389d\",\n \"score\": \"0.42857674\",\n \"text\": \"def serialize(self):\\n snaps = {str(_[0]): _[1] for _ in self.snapshots()}\\n props = {'n_snaps': len(snaps)}\\n return dict(properties=props, task=self.task, ranking=self.ranking, race=snaps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb9b86e5aebe77087659d892d2f2014\",\n \"score\": \"0.42845753\",\n \"text\": \"def snapshot(self):\\n self.annotate_(base.Event.SAVED)\\n return {\\n 'condition': self.condition_,\\n 'params': self.params_,\\n 'tags': self.tags_,\\n 'touched': self.touched_,\\n 'created': self.created_,\\n 'history': self.history_,\\n 'stage': self.stage_,\\n 'worker': None,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78a5f39ad0650a3cc8102e17584d40b7\",\n \"score\": \"0.42827222\",\n \"text\": \"def output_dict(self):\\n output_dict = super(ScheduleFormat, self).output_dict()\\n output_dict.update({\\n \\\"jobs\\\": self.synapp_data\\n })\\n\\n # add prologue/epilogue if they exist\\n if self.prologue:\\n output_dict.update({\\\"prologue\\\": self.prologue})\\n\\n if self.epilogue:\\n output_dict.update({\\\"epilogue\\\": self.epilogue})\\n\\n return output_dict\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1583,"cells":{"query_id":{"kind":"string","value":"7732ef5dc803d85b05c82625d85d48f2"},"query":{"kind":"string","value":"prepare OR context and execute expression"},"positive_passages":{"kind":"list like","value":[{"docid":"6361845e59c9b253bf001a7d73df29d2","score":"0.0","text":"def eval_expression(row, position, exp, context=None, names=None):\n if names is None:\n names = [str(i) for i in range(len(row))]\n\n context = context or {}\n if exp.startswith(\"jython:\"):\n grow = PythonRow(row, names)\n elif exp.startswith(\"closure:\"):\n raise NotImplementedError(\"closure context isn't exists\")\n else:\n grow = GRELRow(row, names)\n context.update({\n \"row\": grow,\n \"cells\": grow.cells,\n \"cell\": grow.cells[names[position]],\n \"value\": grow.cells[names[position]].value,\n \"recon\": None,\n \"record\": None\n })\n if exp.startswith(\"jython:\"):\n return eval_python(exp, context)\n else:\n return to_python_object(eval_grel(exp, context))","title":""}],"string":"[\n {\n \"docid\": \"6361845e59c9b253bf001a7d73df29d2\",\n \"score\": \"0.0\",\n \"text\": \"def eval_expression(row, position, exp, context=None, names=None):\\n if names is None:\\n names = [str(i) for i in range(len(row))]\\n\\n context = context or {}\\n if exp.startswith(\\\"jython:\\\"):\\n grow = PythonRow(row, names)\\n elif exp.startswith(\\\"closure:\\\"):\\n raise NotImplementedError(\\\"closure context isn't exists\\\")\\n else:\\n grow = GRELRow(row, names)\\n context.update({\\n \\\"row\\\": grow,\\n \\\"cells\\\": grow.cells,\\n \\\"cell\\\": grow.cells[names[position]],\\n \\\"value\\\": grow.cells[names[position]].value,\\n \\\"recon\\\": None,\\n \\\"record\\\": None\\n })\\n if exp.startswith(\\\"jython:\\\"):\\n return eval_python(exp, context)\\n else:\\n return to_python_object(eval_grel(exp, context))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"48f23340b21889fa9170acc5a7849bab","score":"0.648527","text":"def or_op():\n pass","title":""},{"docid":"304ac77c9b814aabb7da3a229df37c6c","score":"0.59999174","text":"def OrOp(query1, query2):\n return or_(query1, query2)","title":""},{"docid":"c554c24b17bc529bd20809c6e3ec9c03","score":"0.5889918","text":"def Evaluate(self, obj):\n if not self.backend.supported_op('OR'):\n return None\n left = self._left.Evaluate(obj)\n right = self._right.Evaluate(obj)\n # None OR x => None\n # x OR None => None\n if left is None or right is None:\n return None\n if not self.backend.left_paren:\n if ' AND ' in left or ' AND ' in right:\n # With no (...) LF bass-ackwards OR >> AND precedence cannot be\n # overridden.\n return None\n return '{left_paren}{left} OR {right}{right_paren}'.format(\n left_paren=self.backend.left_paren, left=left,\n right=right, right_paren=self.backend.right_paren)","title":""},{"docid":"9688f5f44891dbdb0c4bc546ff99b080","score":"0.58842623","text":"def run_expression(expr, x, y):\n return expr(x, y)","title":""},{"docid":"842276a675673ed1cff181abb667b490","score":"0.5873746","text":"def process_single_or(cond, next_ind, num_or):\n cond = copy.deepcopy(cond)\n # print('single pass processing OR')\n # print_sql_as_tree(cond)\n # print(cond)\n def test_cond_boundary(ci):\n boundary_keywords = ['AND', 'OR', '(', ')', '', 'NOT', 'SELECT', 'WHERE', '[OR]']\n if(ci in boundary_keywords): \n # print('D1', ci)\n return True\n if(ci[-3:] == 'OR]'): \n # print('D2', ci)\n return True \n return False\n\n def contain_or_left(left):\n for ci in left:\n if(ci[-3:] == 'OR]'): return True\n return False\n\n cond_len = len(cond)\n\n # if assigned index, directly try the assigned index \n\n for i in range(next_ind, len(cond)):\n x = cond[i]\n if(x == 'OR'): break \n ind = i\n if(ind == len(cond) - 1): \n next_ind = -1\n return None, None, cond, next_ind, False\n\n or_left = []\n i = ind - 1\n if(cond[i] != ')'):\n while(i >= 0 and not test_cond_boundary(cond[i])):\n or_left.append(cond[i])\n i -= 1\n else:\n # print(cond)\n bracket = 1\n or_left.append(cond[i])\n i -= 1\n while(bracket > 0):\n or_left.append(cond[i])\n if(cond[i] == '('): bracket -= 1\n if(cond[i] == ')'): bracket += 1\n i -= 1\n if(cond[i] == 'NOT'):\n or_left.append(cond[i])\n i -= 1\n left_boundary = i + 1\n \n or_right = [] \n i = ind + 1\n # print(ind)\n # print(cond[ind])\n # print_sql_as_tree(cond, relax=True)\n if(cond[i] == 'NOT'):\n or_right.append(cond[i])\n i += 1\n if(cond[i] != '('):\n while(i < cond_len and not test_cond_boundary(cond[i])):\n or_right.append(cond[i])\n i += 1\n right_boundary = i - 1\n else:\n bracket = 1\n or_right.append(cond[i])\n i += 1\n while(bracket > 0):\n # print(i)\n # print(cond[i])\n or_right.append(cond[i])\n if(cond[i] == '('): bracket += 1\n if(cond[i] == ')'): bracket -= 1\n i += 1\n right_boundary = i - 1\n\n # trial success\n if('OR' not in or_right):\n if(cond[left_boundary - 1] == '(' and cond[right_boundary + 1] == ')'):\n left_boundary -= 1\n right_boundary += 1\n cond_simple = cond[: left_boundary]\\\n + ['S1', '[%d:OR]' % num_or, 'S2']\\\n + cond[right_boundary + 1: ]\n or_left.reverse()\n\n if(contain_or_left(or_left)): left_nest = True\n else: left_nest = False\n\n if('NOT' not in or_left): or_left = remove_parenthesis(or_left).split()\n if('NOT' not in or_right): or_right = remove_parenthesis(or_right).split()\n return or_left, or_right, cond_simple, 0, left_nest\n else: \n cond_simple = cond\n return None, None, cond_simple, ind + 1, False","title":""},{"docid":"1480324f40c8c271123bc8b5654f02a7","score":"0.58717304","text":"def __or__(self, other):\n logger.debug(\"from __or__: self:{}, other{}\".format(self.scope_req, other.scope_req))\n if other.history:\n self.history.append(other.history)\n self.history.append(\"{} or {}\".format(self.scope_req, other.scope_req))\n\n # TODO: Merge result_in and scope_req by always rewriting scope_req but losing history ?\n # self.result_in = self.lambda_any_in(self.result_in or self.scope_req, other.scope_req)\n self.result_in = self.func_operator_in(any, self.result_in or self.scope_req,\n other.result_in or other.scope_req)\n self.scope_req = (\"{} or {}\".format(self.scope_req, other.scope_req))\n\n return self","title":""},{"docid":"d449763bfbd315cc4b08b36febf20d00","score":"0.58574176","text":"def _or(*args):\n return \" OR \".join(args)","title":""},{"docid":"1267900cb33d0db233bdb2ece16f7358","score":"0.5724028","text":"def __or__(self, other: Any) -> \"Model\":\n if \"|\" not in self._context_operators.get():\n raise TypeError(\"Undefined operator: |\")\n return self._context_operators.get()[\"|\"](self, other)","title":""},{"docid":"581a00b9fef997f3ab43eb9581cd228b","score":"0.5713856","text":"def __or__(self, eq):\n if eq.is_Exists:\n if self.limits == eq.limits:\n return self.func(self.expr | eq.expr, *self.limits)\n \n if self.expr == eq.expr:\n limits = self.limits_union(eq)\n return self.func(self.expr, *limits).simplify()\n \n return Quantifier.__or__(self, eq)","title":""},{"docid":"b24908f8a6033a0ae30ec6ffed3ef8d1","score":"0.56975394","text":"def _template_to_where_clause(self, t):\n w_clause = None\n args = None\n\n ####### Your Code Goes Here #########\n pass\n\n ####### Your Code Goes Here #########\n\n return w_clause, args","title":""},{"docid":"41fb8dcb70f646e2eb3af3dc73f5ad58","score":"0.56881934","text":"def _or(self, _or):\n\n self.__or = _or","title":""},{"docid":"41fb8dcb70f646e2eb3af3dc73f5ad58","score":"0.56881934","text":"def _or(self, _or):\n\n self.__or = _or","title":""},{"docid":"41fb8dcb70f646e2eb3af3dc73f5ad58","score":"0.56881934","text":"def _or(self, _or):\n\n self.__or = _or","title":""},{"docid":"6ec8c4a9a82025bd46e689b40c2974b9","score":"0.5666606","text":"def do_execute(self):\n expr = str(self.resolve_option(\"expression\"))\n expr = expr.replace(\"{X}\", str(self.input.payload))\n self._output.append(Token(eval(expr)))\n return None","title":""},{"docid":"db77dc6463ca02d89f2f8e35ab429424","score":"0.5657103","text":"def check_for_or(sql_str):\n # print(rex.search(\"WHERE\", \"SELECT * FROM T1 WHERE\", rex.IGNORECASE))\n # print(rex.search(\"or\",\"FROM T1 WHERE ID = 1 or 1 = 1\".split('WHERE')[1], rex.IGNORECASE))\n try:\n if rex.search(\"WHERE\", sql_str, rex.IGNORECASE): # If it has a Where clause\n if rex.search(' or ', sql_str.split('WHERE')[1], rex.IGNORECASE) is not None: # injected OR?\n raise sqlErr(\"OR Detected!\")\n except Exception as e:\n raise e","title":""},{"docid":"df26ff0b9b1cfd0fa70514f261254912","score":"0.5649804","text":"def distribute_and_over_or(expr):\r\n return _distribute((expr, And, Or))","title":""},{"docid":"fdf7fb0ea073473bf8df5fea12e2a86a","score":"0.56125396","text":"def commit_operands():\n nonlocal operand_stack, op, seq\n if op and operand_stack:\n klass = (\n StringGenerator.SequenceOR\n if op == \"|\"\n else StringGenerator.SequenceAND\n )\n seq.append(klass(operand_stack[:]))\n operand_stack = list()\n op = None","title":""},{"docid":"14df0b410d725ec4e54e044f0b559573","score":"0.55921173","text":"def test_or(self):\n\n xy = self.builder._or([self.builder.exactly(\"x\"),\n self.builder.exactly(\"y\")])\n self.assertEqual(writePython(xy),\n dd(\"\"\"\n def _G_or_1():\n _G_exactly_1, lastError = self.exactly('x')\n self.considerError(lastError)\n return (_G_exactly_1, self.currentError)\n def _G_or_2():\n _G_exactly_1, lastError = self.exactly('y')\n self.considerError(lastError)\n return (_G_exactly_1, self.currentError)\n _G_or_3, lastError = self._or([_G_or_1, _G_or_2])\n self.considerError(lastError)\n _G_or_3\n \"\"\"))","title":""},{"docid":"cfe5d4e240a60680ab39e25a19002c39","score":"0.5541906","text":"def cursor_execute(\n self, execute, cursor, statement, parameters, context, executemany\n ):\n\n return execute(cursor, statement, parameters, context)","title":""},{"docid":"ce8bbe6d8d4304cf5a9e3e745ff16aac","score":"0.55245453","text":"def compile(self, node, *args, **kwargs):\n if isinstance(node, WhereNode):\n return where_node_as_ldap(node, self, self.connection)\n return super(SQLCompiler, self).compile(node, *args, **kwargs)","title":""},{"docid":"4f3ba1c1976195206e26b0cf1daccc0b","score":"0.55213875","text":"def expression():\n\t\n\t#Implemented as a binaryExpr using the \"or\" as the operator\n\ttok = tokens.peek()\n\tif debug: print(\"expression: \", tok)\n\tleft = andExpr()\n\ttok = tokens.peek()\n\twhile tok == \"or\": \n\t\ttokens.next()\n\t\tright = term()\n\t\tleft = BinaryExpr(tok, left, right)\n\t\ttok = tokens.peek()\n\treturn left","title":""},{"docid":"2cfaf1647382206a4fbae12b4e1fd4ac","score":"0.55181986","text":"def or_(self, **kwargs):\n raise ApiError(\".or_() cannot be called on Endpoint Standard queries.\")","title":""},{"docid":"d83603a3b1662fed9fbf85302b3c2bc1","score":"0.5504685","text":"def run_expression(expr, x, y):\n return expr.evaluate(x, y)","title":""},{"docid":"51167cf36e6e84e96ceccda35ab61d57","score":"0.54890287","text":"def test_singleOr(self):\n\n x1 = self.builder._or([self.builder.exactly(\"x\")])\n x = self.builder.exactly(\"x\")\n self.assertEqual(writePython(x), writePython(x1))","title":""},{"docid":"1d64771df3cde6841b82e373739eef44","score":"0.5487864","text":"def _adapt_expression(self, op, other_comparator):\n\n return op, self.type","title":""},{"docid":"7aebbf653e93b55a528303143b861321","score":"0.5451838","text":"def or_elements(self, num):\n if num > 1:\n filters = []\n for _ in range(num):\n filters.append(self.get_stack())\n or_clause = lambda: '\\'$or\\' : [%s]' % ', '.join('{ ' + x() + ' }' for x in filters)\n self.put_stack(or_clause)","title":""},{"docid":"ab0b7a5f2d185a11cdc6b2ef51b54d5d","score":"0.54473996","text":"def disjuncts(expr):\r\n return Or.make_args(expr)","title":""},{"docid":"e5c2fa8aae88e52825cae69430078688","score":"0.5436772","text":"def execute(self, context):","title":""},{"docid":"674f30fd7596d98c0c8f8eb7c1825c6c","score":"0.54173076","text":"def or_(*args):\n ret = args[0]\n for i in range(1, len(args)):\n ret = _make.Or(ret, args[i])\n return ret","title":""},{"docid":"d7dda4f6ebbfbcad6479a6a9ba3cebbe","score":"0.5416297","text":"def _eval_or_leave( expr ):\n \n value = _try_eval( expr )\n \n if value is NONE : \n value = expr\n\n return value","title":""},{"docid":"995aeb6bad98a784d74da426036aa79d","score":"0.5406116","text":"def expr_or(self, child):\n return False","title":""},{"docid":"e808ec51129f9a710815389cc023d865","score":"0.5398967","text":"def __or__(self, other):\n return Or(*chain(self.predicates, other.predicates if isinstance(other, Or) else (other,)))","title":""},{"docid":"29a3eb09275b6fd7abe86447ac846d86","score":"0.5396787","text":"def test_extra_expr_ctx(self):\n self.adapted.tal_condition = \"python: value == '122'\"\n self.assertFalse(evaluateExpressionFor(self.adapted))\n self.assertTrue(evaluateExpressionFor(self.adapted, {'value': '122'}))","title":""},{"docid":"8d4b7e284de41113c78d1fbc419ff38c","score":"0.5370747","text":"def distribute_and_over_or_withbug(s):\n s = expr(s)\n if not isinstance(s, Expr):\n return s\n #firstParseArgs = tuple([distribute_and_over_or(arg) for arg in s.args])\n #s = Expr(s.op, firstParseArgs)\n if s.op == '|':\n for subarg in s.args:\n if subarg.op == '&':\n otherArgs = [\n otherArg for otherArg in s.args if otherArg != subarg]\n\n newArgs = []\n if len(otherArgs) > 1:\n otherExpr = Expr('|', tuple(otherArgs))\n for t in subarg.args:\n newArgs.append(Expr('|', t, otherExpr))\n else:\n otherExpr = otherArgs[0]\n for t in subarg.args:\n newArgs.append(Expr('|', t, otherExpr))\n\n toReturn = Expr('&')\n toReturn.args = tuple(newArgs)\n return toReturn\n else:\n newArgs = []\n for subarg in s.args:\n newArgs.append(distribute_and_over_or(subarg))\n s.args = tuple(newArgs)\n return s","title":""},{"docid":"443bb7273394c70d9bd66bda782e79ac","score":"0.53582454","text":"def stmt():\n from ..expressions import arithmetic, objects, call\n from ..statements import conditional, assignment, loop, return_, skip, write\n\n return assignment.assign_stmt() | \\\n conditional.if_stmt() | \\\n loop.while_stmt() | \\\n loop.repeat_stmt() | \\\n loop.for_stmt() | \\\n return_.return_stmt() | \\\n write.write_stmt() | \\\n call.fun_call_stmt() | \\\n objects.object_method() | \\\n skip.skip_stmt() | \\\n arithmetic.aexp()","title":""},{"docid":"c7099e49a74f786590e4bb1fd837f840","score":"0.531756","text":"def distribute_or_over_and(expr):\r\n return _distribute((expr, Or, And))","title":""},{"docid":"503cbdf2c275ec7c0afebf4043419405","score":"0.53091586","text":"def _evaluate_expression(self, expression, models):\n if expression:\n return Expression(str(expression), models).evaluate()","title":""},{"docid":"df1127462315edcbad9edbfef77a41be","score":"0.53061074","text":"def renderOr(self, or_):\n sb = StringBuilder()\n sb.append(\"( or \")\n i = 0\n while i < or_.arity():\n sb.append(self.renderLiteral(or_.get(i)) + \" \")\n i += 1\n sb.append(\")\")\n return sb.__str__()","title":""},{"docid":"72d0ea2e88b8e2ae655ffadc6ae940db","score":"0.5300033","text":"def or_expr(self, size, a, b, flags=None):\n\t\treturn self.expr(LowLevelILOperation.LLIL_OR, a.index, b.index, size=size, flags=flags)","title":""},{"docid":"49e78ca7c26d9f590355f76d3cfe8349","score":"0.52967244","text":"def or_(*clauses):\n return _compound_clause('OR', *clauses)","title":""},{"docid":"a0e942393b613af7022e98886c0fcdf6","score":"0.52910167","text":"def _or(self, args):\n return any(args)","title":""},{"docid":"fead92873a6a07b711d85b30626594c3","score":"0.5281905","text":"def eval_statement(stmt, ctx):\n # TODO implement precedence parenthesis\n stmt_truth = eval_expr(stmt.firstExpr, ctx)\n for op, expr in zip(stmt.operators, stmt.exprs):\n if op == 'and':\n # and\n stmt_truth = stmt_truth and eval_expr(expr, ctx)\n else:\n # or\n stmt_truth = stmt_truth or eval_expr(expr, ctx)\n return stmt_truth","title":""},{"docid":"b1eb09fdfdda47d864c0434c2539133c","score":"0.5265996","text":"def distribute_and_over_or(s):\n if s.op == '|':\n s = NaryExpr('|', *s.args)\n if len(s.args) == 0:\n return FALSE\n if len(s.args) == 1:\n return distribute_and_over_or(s.args[0])\n conj = find_if((lambda d: d.op == '&'), s.args)\n if not conj:\n return NaryExpr(s.op, *s.args)\n others = [a for a in s.args if a is not conj]\n if len(others) == 1:\n rest = others[0]\n else:\n rest = NaryExpr('|', *others)\n return NaryExpr('&', *map(distribute_and_over_or,\n [(c|rest) for c in conj.args]))\n elif s.op == '&':\n return NaryExpr('&', *map(distribute_and_over_or, s.args))\n else:\n return s","title":""},{"docid":"ab94878c66a76f1c9fdbbff3b4c8f03d","score":"0.52267694","text":"def do_orm_execute(self, orm_execute_state: ORMExecuteState) -> None:","title":""},{"docid":"09bfb19820c000892c1be6a116495fde","score":"0.52195036","text":"def OR(*column_specs):\n bind_matchers = as_binding_functions(column_specs)\n return lambda indices: or_matcher(bind_matchers, indices)","title":""},{"docid":"7a613367f384fd6f2e5bd62a713cbba3","score":"0.5217877","text":"def __or__(self, other):\n return self._BinaryOperatorVariable(lambda a,b: a | b, other, canInline=False)","title":""},{"docid":"f57c550c4f462a62545691bfa9b98d93","score":"0.52029693","text":"def cond_eval(cond, expr, df):\n return maybe_eval(df, expr) if cond else None","title":""},{"docid":"5f6fac178f781edfa11354691ce98259","score":"0.5188609","text":"def Evaluate(self, obj):\n left = self._left.Evaluate(obj)\n right = self._right.Evaluate(obj)\n # None AND x => x\n if left is None:\n return right\n # x AND None => x\n if right is None:\n return left\n if self.backend.supported_op('AND'):\n op = ' AND '\n elif self.backend.supported_op(''):\n op = ' '\n else:\n return None\n # Defer to LF bass-ackwards OR >> AND precedence.\n return '{left_paren}{left}{op}{right}{right_paren}'.format(\n left_paren=self.backend.left_paren, left=left, op=op, right=right,\n right_paren=self.backend.right_paren)","title":""},{"docid":"7f81894ec9fde4df63e75a8aa2190180","score":"0.51879466","text":"def __or__(self, other):\n return self._BinaryOperatorVariable(lambda a, b: a | b, other, canInline=False)","title":""},{"docid":"18a56d81504c6e8335626930527c90d3","score":"0.5185744","text":"def or_(self):\n self._add(Or(self, '||'))\n return self","title":""},{"docid":"1f8e862fc515dfb73d2295ea795eccec","score":"0.51842684","text":"def visitOr(cls, or_, visitor):\n visitor.visitOr(or_)\n i = 0\n while i < or_.arity():\n cls.visitAll(or_.get(i), visitor)\n i += 1","title":""},{"docid":"02bda4ee1e9fa469c40c0e359209d2a1","score":"0.518324","text":"def expression(self, ast: jack_ast.Expression, context: Context) -> Optional[Tuple[List[jack_ast.VarDec], List[jack_ast.Statement], jack_ast.Expression]]:\n return None","title":""},{"docid":"b64e4d4e7071d87ee3a147a18bcc04e4","score":"0.5181312","text":"def call(self, expr):\n if isinstance(expr, Express):\n if expr not in self._exec_expr:\n self._exec_expr.append(expr)\n return expr\n else:\n if isinstance(expr, dict):\n res = self._make_dict_expr(expr)\n elif isinstance(expr, list) or isinstance(expr, tuple):\n res = self._make_array_expr(expr)\n elif isinstance(expr, Object):\n res = Result(expr.parent, expr.name, True)\n else:\n res = expr\n\n _expr = Express(None, res, None)\n self._exec_expr.append(_expr)\n return _expr","title":""},{"docid":"dcc16bd131da7600f55b43fefcf3367a","score":"0.518064","text":"def __or__(self, other):\n if isinstance(other, Not):\n return Not(And(self.predicate, other.predicate))\n else:\n return Or(self, other)","title":""},{"docid":"670a8b854fbac67a7cd78cb859d82b38","score":"0.51604015","text":"def condition_or(cls, *conditions: \"ICfnConditionExpression\") -> \"ICfnConditionExpression\":\n return jsii.sinvoke(cls, \"conditionOr\", [*conditions])","title":""},{"docid":"383be42e5b8185041d3fc658a89af58a","score":"0.51588553","text":"def prepare(expr, strategy):\n graph, expr_ctx = expr\n f, values = ir.from_expr(graph, expr_ctx, current_ctx())\n\n env = {'strategy': strategy}\n func, env = pipeline.run_pipeline(f, env, passes)\n\n return func, env","title":""},{"docid":"8f91490c9ec346d14bb13c3fea575073","score":"0.5155812","text":"def execute(self, expression: Expression) -> Any:\n if isinstance(expression, Literal):\n return expression.value\n if isinstance(expression, BinaryOp):\n left = self.execute(expression.left)\n right = self.execute(expression.right)\n binary_op = self.binary_ops[expression.op_type]\n return binary_op(left, right)\n if isinstance(expression, UnaryOp):\n operand = self.execute(expression.operand)\n unary_op = self.unary_ops[expression.op_type]\n return unary_op(operand)\n if isinstance(expression, Function):\n function = self.functions.get(expression.name)\n if function is None:\n raise ExecutorError(f\"Function '{expression.name}' is not defined\")\n args = [self.execute(arg) for arg in expression.args]\n return function(*args)","title":""},{"docid":"946addc9d253665b1c0535a008a6fb41","score":"0.5142207","text":"def execute(self, *a, **kw):\n with self.get_cursor(*a, **kw):\n pass","title":""},{"docid":"ddbd582c6b0dcdb01660020320f0c9d3","score":"0.5141592","text":"def __call__(self, *args):\n assert is_symbol(self.op) and not self.args\n return Expr(self.op, *args)","title":""},{"docid":"a9c31cea851c7a6d25f82cc5996fe1fc","score":"0.5127808","text":"def apply_logic_operator(op, arguments):\n \n running_value = op(arguments[0], arguments[1])\n \n if len(arguments) == 2:\n return running_value\n \n index = 2\n while running_value and index < len(arguments):\n running_value = running_value and op(arguments[index - 1], arguments[index])\n index += 1\n \n return running_value","title":""},{"docid":"526def2674ca7365c023a1ed9d4b1861","score":"0.50934106","text":"def make_atom(self, child, qn, connection):\n lvalue, lookup_type, value_annot, params_or_value = child\n sql, params = super(SphinxWhereNode, self).make_atom(child, qn, connection)\n if lookup_type == 'search':\n if hasattr(lvalue, 'process'):\n try:\n lvalue, params = lvalue.process(lookup_type, params_or_value, connection)\n except EmptyShortCircuit:\n raise EmptyResultSet\n if isinstance(lvalue, tuple):\n # A direct database column lookup.\n field_sql = self.sql_for_columns(lvalue, qn, connection)\n else:\n # A smart object with an as_sql() method.\n field_sql = lvalue.as_sql(qn, connection)\n # TODO: There are a couple problems here.\n # 1. The user _might_ want to search only a specific field.\n # 2. However, since Django requires a field name to use the __search operator\n # There is no way to do a search in _all_ fields.\n # 3. Because, using multiple __search operators is not supported.\n # So, we need to merge multiped __search operators into a single MATCH(), we\n # can't do that here, we have to do that one level up...\n # Ignore the field name, search all fields:\n params = ('@* %s' % params[0], )\n # _OR_ respect the field name, and search on it:\n #params = ('@%s %s' % (field_sql, params[0]), )\n if self._real_negated:\n col = lvalue.col\n if lookup_type == 'exact':\n sql = '%s <> %%s' % col\n elif lookup_type == 'in':\n params_placeholder = '(%s)' % (', '.join(['%s'] * len(params)))\n sql = '%s NOT IN %s' % (col, params_placeholder)\n else:\n raise ValueError(\"Negative '%s' lookup not supported\" % lookup_type)\n return sql, params","title":""},{"docid":"7beecdce324d288585a8273181e8ce04","score":"0.50806934","text":"def _execute(self, operation, data):\n if data and not self.dry_run:\n engine = db.engine\n engine.execute(operation, data)\n db.session.commit()","title":""},{"docid":"b7feb230e67acadecbb6db46d1e79621","score":"0.5073616","text":"def _execute(self, stmt, *values):\n c = self._cursor()\n try:\n return c.execute(stmt, values).fetchone()\n finally:\n c.close()","title":""},{"docid":"b7feb230e67acadecbb6db46d1e79621","score":"0.5073616","text":"def _execute(self, stmt, *values):\n c = self._cursor()\n try:\n return c.execute(stmt, values).fetchone()\n finally:\n c.close()","title":""},{"docid":"f5faf8cb350596b12d3768b0cb161260","score":"0.50549746","text":"def sql(ctx):","title":""},{"docid":"f4a69bcf13a016d994f40884c9e8b690","score":"0.50501764","text":"def run_eqs_query(cls, world_context_object, query_template, querier, run_mode, wrapper_class):\n return None","title":""},{"docid":"60b9908467a3445ab0dfdb62e2c29b93","score":"0.5026602","text":"def expression(self, column, value):\n strategy_name, values = value\n\n if strategy_name in self.STRATEGIES:\n strategy = self.STRATEGIES[strategy_name]\n else:\n raise errors.InvalidValue('Unknown strategy specified.', self.item)\n\n if strategy_name in ['in', '~in']:\n return strategy(column, values)\n elif len(values) == 1:\n return strategy(column, values[0])\n\n expressions = [strategy(column, value) for value in values]\n return or_(*expressions)","title":""},{"docid":"ccf78caac6399629c7ab00f1aa96c50d","score":"0.502654","text":"def __ror__(self, other):\n return Or(other, *self.predicates)","title":""},{"docid":"bb7538ce4b4e78b9c1c534230194b386","score":"0.5023896","text":"def evaluate(self, local_env, expression):\n if isinstance(expression, int):\n return expression\n elif isinstance(expression, str): # symbol\n return self.get(local_env, expression)\n elif isinstance(expression, list):\n if expression[0] in self.commands:\n # special forms evaluate (or not) their args\n command = self.commands[expression[0]]\n args = expression[1:]\n check_args(command, args, ['self', 'local_env'])\n return command(local_env, *args)\n elif expression[0] in self.user_functions:\n function = self.user_functions[expression[0]]\n args = [self.evaluate(local_env, exp) for exp in expression[1:]]\n local_env = function.bind_args(args)\n return self.evaluate(local_env, function.body)\n else:\n # evaluate operator and args\n exps = [self.evaluate(local_env, exp) for exp in expression]\n if len(exps) == 0:\n raise NullExpression()\n operator = exps[0]\n if callable(operator):\n args = exps[1:]\n check_args(operator, args)\n return operator(*args) # apply operator to args\n else:\n raise InvalidOperator(operator)\n else:\n msg = 'Cannot evaluate {0!r}'.format(expression)\n raise InterpreterError(msg)","title":""},{"docid":"deb39b05da339441152d4da1395f84b9","score":"0.5020141","text":"def _or(self, fns):\n err = self.input.nullError()\n for f in fns:\n try:\n m = self.input\n ret, err = f()\n return ret, err\n except ParseError:\n self.input = m\n raise err","title":""},{"docid":"f30fbb0eeaef31a11ca9dec5505f7ca0","score":"0.5013862","text":"def __or__(self, other):\n return Or(self, other)","title":""},{"docid":"f30fbb0eeaef31a11ca9dec5505f7ca0","score":"0.5013862","text":"def __or__(self, other):\n return Or(self, other)","title":""},{"docid":"f30fbb0eeaef31a11ca9dec5505f7ca0","score":"0.5013862","text":"def __or__(self, other):\n return Or(self, other)","title":""},{"docid":"f30fbb0eeaef31a11ca9dec5505f7ca0","score":"0.5013862","text":"def __or__(self, other):\n return Or(self, other)","title":""},{"docid":"f30fbb0eeaef31a11ca9dec5505f7ca0","score":"0.5013862","text":"def __or__(self, other):\n return Or(self, other)","title":""},{"docid":"7e8eb66bc68252f4981daa4ee5cfe180","score":"0.50098497","text":"def exec_compat(expression, globals, locals=None):\n exec(expression, globals, locals)","title":""},{"docid":"61835cfa29aa749a65274c0c01d9c861","score":"0.49924752","text":"def __and__(self, other):\n logger.debug(\"from __or__: self:{}, other{}\".format(self.scope_req, other.scope_req))\n if other.history:\n self.history.append(other.history)\n self.history.append(\"{} and {}\".format(self.scope_req, other.scope_req))\n\n self.result_in = self.func_operator_in(all, self.result_in or self.scope_req,\n other.result_in or other.scope_req)\n self.scope_req = (\"{} and {}\".format(self.scope_req, other.scope_req))\n\n return self","title":""},{"docid":"1884a8927819a61d27f85d773f874d0c","score":"0.4992243","text":"def __or__(self, other):\n return OrPredicate(self, other)","title":""},{"docid":"19e0ca5fff0ec42e90bea91e409a171a","score":"0.49780267","text":"def _do_function(self,e1,e2):\n\t\treturn e1|e2","title":""},{"docid":"4ab11a0b46b452b6b37e645dff60396f","score":"0.49749222","text":"def OR_Query(arglist):\n line = \"','\".join(arglist)\n Data = \"('\" + line + \"')\"\n if CON=='ALL':\n Query = \"select distinct FileName from jsoncallworddata where Keyword in \" + Data\n elif CON=='START50WORD':\n Query = \"select distinct FileName from jsoncallworddata where wordSeq<=50 and Keyword in \" + Data\n elif CON=='LAST50WORD':\n Query = \"select distinct T2.FileName from (select FileName,max(wordSeq) m from \" \\\n \"jsoncallworddata group by FileName) T1 inner join (select * from jsoncallworddata) as T2 where \" \\\n \"T1.FileName=T2.FileName and T2.wordSeq>(T1.m-50) and T2.Keyword in \" + Data\n elif CON=='START50SECOND':\n Query = \"select distinct FileName from jsoncallworddata where startTime<=50 and Keyword in \" + Data\n elif CON=='LAST50SECOND':\n Query = \"select distinct T2.FileName from \" \\\n \"(select FileName,max(startTime) m from jsoncallworddata group by FileName) T1 inner join \" \\\n \"(select * from jsoncallworddata) as T2 where T1.FileName=T2.FileName and T2.startTime>(T1.m-50) and T2.Keyword in \" + Data\n else:\n print('No Operation.')\n datalist=Get_Result(Query)\n return datalist","title":""},{"docid":"6e913f50d0d4f4b7b712d881ef185ad0","score":"0.49704707","text":"def distribute_and_over_or(s):\n s = expr(s)\n if s.op == '|':\n s = associate('|', s.args)\n if s.op != '|':\n return distribute_and_over_or(s)\n if len(s.args) == 0:\n return False\n if len(s.args) == 1:\n return distribute_and_over_or(s.args[0])\n conj = first(arg for arg in s.args if arg.op == '&')\n if not conj:\n return s\n others = [a for a in s.args if a is not conj]\n rest = associate('|', others)\n return associate('&', [distribute_and_over_or(c | rest)\n for c in conj.args])\n elif s.op == '&':\n return associate('&', list(map(distribute_and_over_or, s.args)))\n else:\n return s","title":""},{"docid":"6e913f50d0d4f4b7b712d881ef185ad0","score":"0.49704707","text":"def distribute_and_over_or(s):\n s = expr(s)\n if s.op == '|':\n s = associate('|', s.args)\n if s.op != '|':\n return distribute_and_over_or(s)\n if len(s.args) == 0:\n return False\n if len(s.args) == 1:\n return distribute_and_over_or(s.args[0])\n conj = first(arg for arg in s.args if arg.op == '&')\n if not conj:\n return s\n others = [a for a in s.args if a is not conj]\n rest = associate('|', others)\n return associate('&', [distribute_and_over_or(c | rest)\n for c in conj.args])\n elif s.op == '&':\n return associate('&', list(map(distribute_and_over_or, s.args)))\n else:\n return s","title":""},{"docid":"efa72570cc7bc2f66625322d1c6e1833","score":"0.49690124","text":"def _execute(self, clause):\n return self.db.conn.conn.execute(clause)","title":""},{"docid":"66cae33317c00974ec8a5634218014b2","score":"0.49661663","text":"def and_op():\n pass","title":""},{"docid":"5d0fac5f604d5aae620f8bc952364892","score":"0.49613786","text":"def odoo_execute_kw(self, bind, model, method, *args, **kwargs):\n config = self.binds[bind]\n\n if '__uid' not in config.const:\n common = xmlrpc.client.ServerProxy(f'{config.url}/xmlrpc/2/common')\n config.const['__uid'] = common.authenticate(config.dbname, config.user, config.passwd, {})\n\n models = xmlrpc.client.ServerProxy(f'{config.url}/xmlrpc/2/object')\n return models.execute_kw(config.dbname, config.const['__uid'], config.passwd, model, method, *args, **kwargs)","title":""},{"docid":"1ebbfc46edd8157dc626803c8119d361","score":"0.49523783","text":"def try_eval( self ) :\n\n # same could be achieved via \" .convert( { '*' : < myeval(expr) or expr >} ) \" \n \n result = self.__class__() \n result.args = self.args[:] # copy positional arguments \n\n for key, value in self.iteritems() : \n \n result[ key ] = _eval_or_leave( value )\n \n return result\n \n \n \"\"\"\n for key, raw_value in self.iteritems() : \n \n evaluated = _try_eval( raw_value )\n \n if evaluated is not NONE :\n result[ key ] = evaluated \n else :\n result[ key ] = raw_value\n \n return result\n \"\"\"","title":""},{"docid":"d0f50197716d79eab319a80b36e9f301","score":"0.4950666","text":"def or_gate(x, y):\n if x or y:\n return 1\n return 0","title":""},{"docid":"33442fde88cf7a8968b93d9de770b169","score":"0.49466044","text":"def _combine(self, operator, field_or_clause):\n self._add_any(operator, is_conjunction=True)\n self._add_new(field_or_clause)","title":""},{"docid":"3cebf9100f8100ba646bf448e5df52e1","score":"0.49435163","text":"def do_execute(self):\n if self.storagehandler is None:\n return \"No storage handler available!\"\n expr = str(self.resolve_option(\"expression\")).replace(\n \"{X}\", str(self.storagehandler.storage[str(self.resolve_option(\"storage_name\"))]))\n expr = self.storagehandler.expand(expr)\n self.storagehandler.storage[self.resolve_option(\"storage_name\")] = eval(expr)\n self._output.append(self.input)\n return None","title":""},{"docid":"2e3e4574eee85e26fd27895e492fb2a1","score":"0.49372405","text":"def _exec(self, query: str, args: Optional[Mapping[str, SupportedTypes]] = None) -> psycopg2.extensions.cursor:\n assert self.db is not None\n\n logging.debug('_exec(): query=\"%s\", args=\"%s\"', query, args)\n cur = self.db.cursor()\n # logging.debug('Executing: %s' % (query,))\n cur.execute(query, args)\n\n return cur","title":""},{"docid":"69e1db18a71d095492d916498464ec7e","score":"0.4919036","text":"def visit_Exec(self, node):\n \n raise NotImplementedError, \"Exec statements are not supported at this time\"","title":""},{"docid":"1ef88fab206a3b4cfedfc65158688d18","score":"0.49117392","text":"def eval(expr):\n df = pipein()\n pipeout(df.eval(expr))","title":""},{"docid":"6326151f4f239c57433c18fa44a9b8a7","score":"0.4896938","text":"def prepare(self, query, args):\n bind_args = []\n for arg in args:\n if arg is None:\n bind_args.append('NULL')\n elif arg is False:\n bind_args.append('0')\n elif arg is True:\n bind_args.append('1')\n else:\n bind_args.append(self.mysql.escape(arg, self.mysql.encoders))\n run_query = query % tuple(bind_args)\n self.last_query = run_query\n return run_query","title":""},{"docid":"1b97664a2ed410445c12f0383bb2b395","score":"0.48957047","text":"def execute_if_bound(self, *args):\n pass","title":""},{"docid":"446a2a39a249f56faeebb438a8d0c683","score":"0.4895654","text":"def evaluate(self, *args, **kwargs):","title":""},{"docid":"ed41558aeb58a5331087f33d52e164db","score":"0.48855513","text":"def execute_xpath_query(tree, xpath, context_node = None, **variables):\n if context_node is None: # explicitly check for None rather than using \"or\", because it is treated as a list\n context_node = tree\n result = xpath(context_node, **variables)\n if isinstance(result, list):\n return result\n else:\n return [result]","title":""},{"docid":"16d758d9a9b12d1d0cd73b498812718d","score":"0.4878246","text":"def _add_expression(self, *values):\n for v in values:\n self._add_any(v)\n self._isdirty = False","title":""},{"docid":"9b294be2eddc6d920e034728dde21df7","score":"0.48777825","text":"def reduce(expression):\n print('reduced called:')\n print(expression)\n #Check expression is single keyword fetch or with algebric expression.\n if '(' in expression:\n #Extract Outer Boolean Operator of the expression.\n BooleanOperator = expression.split('(')[0]\n print(BooleanOperator)\n arg_list=[]\n #Check single parameter or not.\n if ',' in expression :\n num_arguments = expression.count(',') + 1\n flag = 0\n count = 0\n arg_index = []\n for i in range(len(expression)):\n if expression[i] == '(':\n flag += 1\n if expression[i] == ')':\n flag -= 1\n if flag == 1 and expression[i] == ',':\n count += 1\n arg_index.append(i)\n for i in range(len(expression)):\n if expression[i] == '(':\n break\n get_arg_from = i + 1\n get_arg_to = len(expression) - 1\n arg_index = [x - get_arg_from for x in arg_index]\n arg_string = expression[get_arg_from:get_arg_to]\n if count != 0:\n arg_index.insert(0, 0)\n indices = arg_index\n parts = [arg_string[i:j] for i, j in zip(indices, indices[1:] + [None])]\n removeComma=[]\n for i in parts:\n if i[0]==',':\n removeComma.append(i.strip(i[0]))\n else:\n removeComma.append(i)\n for i in removeComma:\n if '(' not in i:\n arg_list.append(i.strip(\"'\"))\n else:\n arg_list.append(i)\n else:\n arg_list.append(arg_string)\n else:\n #if Expression signle parameter then it must be NOT Operation.\n print('Not Expression Breaking....')\n expression = expression.replace(\"NOT('\", \"\")\n expression = expression.replace(\"')\", \"\")\n arg_list.append(expression)\n print('arg_List:')\n print(arg_list)\n else:\n #Single Keyword search Operation.\n print('Single Keyword search Operation.')\n search_word = []\n search_word.append(expression)\n Result=OR_Query(search_word)\n return Result\n\n final_arg_List=[]\n List = []\n ListVisit=0\n\n for each in arg_list:\n print(each)\n print(List)\n #Check item is expression or not.\n if '(' in each:\n if BooleanOperator == 'AND':\n ListVisit=1\n if List:\n print('Inside AND,List NonEmpty,call Reduce')\n temp=reduce(each)\n List = list(set(List) & set(temp))\n else:\n print('Inside AND,List Empty,call Reduce')\n List = reduce(each)\n elif BooleanOperator == 'OR':\n ListVisit=1\n print('Inside OR, call Reduce')\n temp=reduce(each)\n List = list(set(List) | set(temp))\n elif BooleanOperator == 'NOT':\n ListVisit=1\n print('Inside NOT call Reduce.')\n temp = reduce(each)\n NotList = list(set(GetAll_Query()) - set(temp))\n print('Not Operation Result:')\n print(NotList)\n if List:\n List = list(set(List) & set(NotList))\n else:\n List=NotList\n print('After NOT Operation:')\n print(List)\n else:\n print('Do Nothing Reduce .')\n else:\n #if item is not expression then stored in Final_arg_List.\n final_arg_List.append(each)\n\n if final_arg_List:\n final_List=[]\n if BooleanOperator == 'AND':\n if List:\n print('Inside AND,List NonEmpty,call AND Function:')\n temp = AND_Query(final_arg_List)\n final_List = list(set(List) & set(temp))\n elif ListVisit==1:\n final_List=[]\n else:\n final_List= AND_Query(final_arg_List)\n elif BooleanOperator == 'OR':\n print('Inside OR,List NonEmpty,call OR Function:')\n temp = OR_Query(final_arg_List)\n final_List = list(set(List) | set(temp))\n elif BooleanOperator == 'NOT':\n temp = OR_Query(final_arg_List)\n print(temp)\n print(set(GetAll_Query()))\n NotList = list(set(GetAll_Query()) - set(temp))\n if List:\n final_List = list(set(List) & set(NotList))\n else:\n final_List=NotList\n else:\n print('Do Nothing.')\n return final_List\n else:\n return List","title":""},{"docid":"44431a573280a2df667679a422a587ff","score":"0.48730105","text":"def simplify_condition(cond):\n # TODO: handle the case of NOT(cond)\n if(('OR' not in cond) and ('NOT' not in cond)):\n # simplify AND statements\n cond_simple = remove_parenthesis(cond)\n cond_simple = sort_simple_condition(cond_simple)\n ret_code = 1\n elif('OR' in cond): \n # simplify OR statements\n cond_simple, or_states, chained_or, nested_or = process_all_or_cond(cond)\n # simplify AND statements\n cond_simple = remove_parenthesis(cond_simple)\n if((not chained_or) and (not nested_or)):\n cond_simple = sort_simple_condition(cond_simple)\n cond_simple = {'cond_simple': cond_simple, 'or_states': or_states}\n ret_code = 2\n\n if(chained_or or nested_or): ret_code = 3\n # print('!!!')\n # print(ret_code)\n else: \n ret_code = 0\n cond_simple = cond\n return ret_code, cond_simple","title":""},{"docid":"96e5b89badc89cd55c2bf1a2dd7e15a0","score":"0.48714125","text":"def _execute(code, glb, exc_func):\n if glb is None:\n glb = {}\n if '__builtins__' not in glb:\n glb['__builtins__'] = RestrictedPython.Guards.safe_builtins.copy()\n if exc_func == 'eval':\n return eval(code, glb)\n else:\n exec(code, glb)\n return glb","title":""},{"docid":"54049061a710b2d2e16b14f1224b8bb0","score":"0.48679867","text":"def or_parser(\n\tfirst_parser: Callable[[str], Optional[Tuple[str, str]]],\n\tsecond_parser: Callable[[str], Optional[Tuple[str, str]]],\n\ttext: str,\n) -> Optional[Tuple[str, str]]:\n\tresult = first_parser(text)\n\tif result is None:\n\t\tresult_2 = second_parser(text)\n\t\treturn result_2\n\telse:\n\t\treturn result","title":""}],"string":"[\n {\n \"docid\": \"48f23340b21889fa9170acc5a7849bab\",\n \"score\": \"0.648527\",\n \"text\": \"def or_op():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304ac77c9b814aabb7da3a229df37c6c\",\n \"score\": \"0.59999174\",\n \"text\": \"def OrOp(query1, query2):\\n return or_(query1, query2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c554c24b17bc529bd20809c6e3ec9c03\",\n \"score\": \"0.5889918\",\n \"text\": \"def Evaluate(self, obj):\\n if not self.backend.supported_op('OR'):\\n return None\\n left = self._left.Evaluate(obj)\\n right = self._right.Evaluate(obj)\\n # None OR x => None\\n # x OR None => None\\n if left is None or right is None:\\n return None\\n if not self.backend.left_paren:\\n if ' AND ' in left or ' AND ' in right:\\n # With no (...) LF bass-ackwards OR >> AND precedence cannot be\\n # overridden.\\n return None\\n return '{left_paren}{left} OR {right}{right_paren}'.format(\\n left_paren=self.backend.left_paren, left=left,\\n right=right, right_paren=self.backend.right_paren)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9688f5f44891dbdb0c4bc546ff99b080\",\n \"score\": \"0.58842623\",\n \"text\": \"def run_expression(expr, x, y):\\n return expr(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842276a675673ed1cff181abb667b490\",\n \"score\": \"0.5873746\",\n \"text\": \"def process_single_or(cond, next_ind, num_or):\\n cond = copy.deepcopy(cond)\\n # print('single pass processing OR')\\n # print_sql_as_tree(cond)\\n # print(cond)\\n def test_cond_boundary(ci):\\n boundary_keywords = ['AND', 'OR', '(', ')', '', 'NOT', 'SELECT', 'WHERE', '[OR]']\\n if(ci in boundary_keywords): \\n # print('D1', ci)\\n return True\\n if(ci[-3:] == 'OR]'): \\n # print('D2', ci)\\n return True \\n return False\\n\\n def contain_or_left(left):\\n for ci in left:\\n if(ci[-3:] == 'OR]'): return True\\n return False\\n\\n cond_len = len(cond)\\n\\n # if assigned index, directly try the assigned index \\n\\n for i in range(next_ind, len(cond)):\\n x = cond[i]\\n if(x == 'OR'): break \\n ind = i\\n if(ind == len(cond) - 1): \\n next_ind = -1\\n return None, None, cond, next_ind, False\\n\\n or_left = []\\n i = ind - 1\\n if(cond[i] != ')'):\\n while(i >= 0 and not test_cond_boundary(cond[i])):\\n or_left.append(cond[i])\\n i -= 1\\n else:\\n # print(cond)\\n bracket = 1\\n or_left.append(cond[i])\\n i -= 1\\n while(bracket > 0):\\n or_left.append(cond[i])\\n if(cond[i] == '('): bracket -= 1\\n if(cond[i] == ')'): bracket += 1\\n i -= 1\\n if(cond[i] == 'NOT'):\\n or_left.append(cond[i])\\n i -= 1\\n left_boundary = i + 1\\n \\n or_right = [] \\n i = ind + 1\\n # print(ind)\\n # print(cond[ind])\\n # print_sql_as_tree(cond, relax=True)\\n if(cond[i] == 'NOT'):\\n or_right.append(cond[i])\\n i += 1\\n if(cond[i] != '('):\\n while(i < cond_len and not test_cond_boundary(cond[i])):\\n or_right.append(cond[i])\\n i += 1\\n right_boundary = i - 1\\n else:\\n bracket = 1\\n or_right.append(cond[i])\\n i += 1\\n while(bracket > 0):\\n # print(i)\\n # print(cond[i])\\n or_right.append(cond[i])\\n if(cond[i] == '('): bracket += 1\\n if(cond[i] == ')'): bracket -= 1\\n i += 1\\n right_boundary = i - 1\\n\\n # trial success\\n if('OR' not in or_right):\\n if(cond[left_boundary - 1] == '(' and cond[right_boundary + 1] == ')'):\\n left_boundary -= 1\\n right_boundary += 1\\n cond_simple = cond[: left_boundary]\\\\\\n + ['S1', '[%d:OR]' % num_or, 'S2']\\\\\\n + cond[right_boundary + 1: ]\\n or_left.reverse()\\n\\n if(contain_or_left(or_left)): left_nest = True\\n else: left_nest = False\\n\\n if('NOT' not in or_left): or_left = remove_parenthesis(or_left).split()\\n if('NOT' not in or_right): or_right = remove_parenthesis(or_right).split()\\n return or_left, or_right, cond_simple, 0, left_nest\\n else: \\n cond_simple = cond\\n return None, None, cond_simple, ind + 1, False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1480324f40c8c271123bc8b5654f02a7\",\n \"score\": \"0.58717304\",\n \"text\": \"def __or__(self, other):\\n logger.debug(\\\"from __or__: self:{}, other{}\\\".format(self.scope_req, other.scope_req))\\n if other.history:\\n self.history.append(other.history)\\n self.history.append(\\\"{} or {}\\\".format(self.scope_req, other.scope_req))\\n\\n # TODO: Merge result_in and scope_req by always rewriting scope_req but losing history ?\\n # self.result_in = self.lambda_any_in(self.result_in or self.scope_req, other.scope_req)\\n self.result_in = self.func_operator_in(any, self.result_in or self.scope_req,\\n other.result_in or other.scope_req)\\n self.scope_req = (\\\"{} or {}\\\".format(self.scope_req, other.scope_req))\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d449763bfbd315cc4b08b36febf20d00\",\n \"score\": \"0.58574176\",\n \"text\": \"def _or(*args):\\n return \\\" OR \\\".join(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1267900cb33d0db233bdb2ece16f7358\",\n \"score\": \"0.5724028\",\n \"text\": \"def __or__(self, other: Any) -> \\\"Model\\\":\\n if \\\"|\\\" not in self._context_operators.get():\\n raise TypeError(\\\"Undefined operator: |\\\")\\n return self._context_operators.get()[\\\"|\\\"](self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581a00b9fef997f3ab43eb9581cd228b\",\n \"score\": \"0.5713856\",\n \"text\": \"def __or__(self, eq):\\n if eq.is_Exists:\\n if self.limits == eq.limits:\\n return self.func(self.expr | eq.expr, *self.limits)\\n \\n if self.expr == eq.expr:\\n limits = self.limits_union(eq)\\n return self.func(self.expr, *limits).simplify()\\n \\n return Quantifier.__or__(self, eq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24908f8a6033a0ae30ec6ffed3ef8d1\",\n \"score\": \"0.56975394\",\n \"text\": \"def _template_to_where_clause(self, t):\\n w_clause = None\\n args = None\\n\\n ####### Your Code Goes Here #########\\n pass\\n\\n ####### Your Code Goes Here #########\\n\\n return w_clause, args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41fb8dcb70f646e2eb3af3dc73f5ad58\",\n \"score\": \"0.56881934\",\n \"text\": \"def _or(self, _or):\\n\\n self.__or = _or\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41fb8dcb70f646e2eb3af3dc73f5ad58\",\n \"score\": \"0.56881934\",\n \"text\": \"def _or(self, _or):\\n\\n self.__or = _or\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41fb8dcb70f646e2eb3af3dc73f5ad58\",\n \"score\": \"0.56881934\",\n \"text\": \"def _or(self, _or):\\n\\n self.__or = _or\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec8c4a9a82025bd46e689b40c2974b9\",\n \"score\": \"0.5666606\",\n \"text\": \"def do_execute(self):\\n expr = str(self.resolve_option(\\\"expression\\\"))\\n expr = expr.replace(\\\"{X}\\\", str(self.input.payload))\\n self._output.append(Token(eval(expr)))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db77dc6463ca02d89f2f8e35ab429424\",\n \"score\": \"0.5657103\",\n \"text\": \"def check_for_or(sql_str):\\n # print(rex.search(\\\"WHERE\\\", \\\"SELECT * FROM T1 WHERE\\\", rex.IGNORECASE))\\n # print(rex.search(\\\"or\\\",\\\"FROM T1 WHERE ID = 1 or 1 = 1\\\".split('WHERE')[1], rex.IGNORECASE))\\n try:\\n if rex.search(\\\"WHERE\\\", sql_str, rex.IGNORECASE): # If it has a Where clause\\n if rex.search(' or ', sql_str.split('WHERE')[1], rex.IGNORECASE) is not None: # injected OR?\\n raise sqlErr(\\\"OR Detected!\\\")\\n except Exception as e:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df26ff0b9b1cfd0fa70514f261254912\",\n \"score\": \"0.5649804\",\n \"text\": \"def distribute_and_over_or(expr):\\r\\n return _distribute((expr, And, Or))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdf7fb0ea073473bf8df5fea12e2a86a\",\n \"score\": \"0.56125396\",\n \"text\": \"def commit_operands():\\n nonlocal operand_stack, op, seq\\n if op and operand_stack:\\n klass = (\\n StringGenerator.SequenceOR\\n if op == \\\"|\\\"\\n else StringGenerator.SequenceAND\\n )\\n seq.append(klass(operand_stack[:]))\\n operand_stack = list()\\n op = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14df0b410d725ec4e54e044f0b559573\",\n \"score\": \"0.55921173\",\n \"text\": \"def test_or(self):\\n\\n xy = self.builder._or([self.builder.exactly(\\\"x\\\"),\\n self.builder.exactly(\\\"y\\\")])\\n self.assertEqual(writePython(xy),\\n dd(\\\"\\\"\\\"\\n def _G_or_1():\\n _G_exactly_1, lastError = self.exactly('x')\\n self.considerError(lastError)\\n return (_G_exactly_1, self.currentError)\\n def _G_or_2():\\n _G_exactly_1, lastError = self.exactly('y')\\n self.considerError(lastError)\\n return (_G_exactly_1, self.currentError)\\n _G_or_3, lastError = self._or([_G_or_1, _G_or_2])\\n self.considerError(lastError)\\n _G_or_3\\n \\\"\\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe5d4e240a60680ab39e25a19002c39\",\n \"score\": \"0.5541906\",\n \"text\": \"def cursor_execute(\\n self, execute, cursor, statement, parameters, context, executemany\\n ):\\n\\n return execute(cursor, statement, parameters, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8bbe6d8d4304cf5a9e3e745ff16aac\",\n \"score\": \"0.55245453\",\n \"text\": \"def compile(self, node, *args, **kwargs):\\n if isinstance(node, WhereNode):\\n return where_node_as_ldap(node, self, self.connection)\\n return super(SQLCompiler, self).compile(node, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3ba1c1976195206e26b0cf1daccc0b\",\n \"score\": \"0.55213875\",\n \"text\": \"def expression():\\n\\t\\n\\t#Implemented as a binaryExpr using the \\\"or\\\" as the operator\\n\\ttok = tokens.peek()\\n\\tif debug: print(\\\"expression: \\\", tok)\\n\\tleft = andExpr()\\n\\ttok = tokens.peek()\\n\\twhile tok == \\\"or\\\": \\n\\t\\ttokens.next()\\n\\t\\tright = term()\\n\\t\\tleft = BinaryExpr(tok, left, right)\\n\\t\\ttok = tokens.peek()\\n\\treturn left\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cfaf1647382206a4fbae12b4e1fd4ac\",\n \"score\": \"0.55181986\",\n \"text\": \"def or_(self, **kwargs):\\n raise ApiError(\\\".or_() cannot be called on Endpoint Standard queries.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83603a3b1662fed9fbf85302b3c2bc1\",\n \"score\": \"0.5504685\",\n \"text\": \"def run_expression(expr, x, y):\\n return expr.evaluate(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51167cf36e6e84e96ceccda35ab61d57\",\n \"score\": \"0.54890287\",\n \"text\": \"def test_singleOr(self):\\n\\n x1 = self.builder._or([self.builder.exactly(\\\"x\\\")])\\n x = self.builder.exactly(\\\"x\\\")\\n self.assertEqual(writePython(x), writePython(x1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d64771df3cde6841b82e373739eef44\",\n \"score\": \"0.5487864\",\n \"text\": \"def _adapt_expression(self, op, other_comparator):\\n\\n return op, self.type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aebbf653e93b55a528303143b861321\",\n \"score\": \"0.5451838\",\n \"text\": \"def or_elements(self, num):\\n if num > 1:\\n filters = []\\n for _ in range(num):\\n filters.append(self.get_stack())\\n or_clause = lambda: '\\\\'$or\\\\' : [%s]' % ', '.join('{ ' + x() + ' }' for x in filters)\\n self.put_stack(or_clause)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0b7a5f2d185a11cdc6b2ef51b54d5d\",\n \"score\": \"0.54473996\",\n \"text\": \"def disjuncts(expr):\\r\\n return Or.make_args(expr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c2fa8aae88e52825cae69430078688\",\n \"score\": \"0.5436772\",\n \"text\": \"def execute(self, context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"674f30fd7596d98c0c8f8eb7c1825c6c\",\n \"score\": \"0.54173076\",\n \"text\": \"def or_(*args):\\n ret = args[0]\\n for i in range(1, len(args)):\\n ret = _make.Or(ret, args[i])\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7dda4f6ebbfbcad6479a6a9ba3cebbe\",\n \"score\": \"0.5416297\",\n \"text\": \"def _eval_or_leave( expr ):\\n \\n value = _try_eval( expr )\\n \\n if value is NONE : \\n value = expr\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995aeb6bad98a784d74da426036aa79d\",\n \"score\": \"0.5406116\",\n \"text\": \"def expr_or(self, child):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e808ec51129f9a710815389cc023d865\",\n \"score\": \"0.5398967\",\n \"text\": \"def __or__(self, other):\\n return Or(*chain(self.predicates, other.predicates if isinstance(other, Or) else (other,)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a3eb09275b6fd7abe86447ac846d86\",\n \"score\": \"0.5396787\",\n \"text\": \"def test_extra_expr_ctx(self):\\n self.adapted.tal_condition = \\\"python: value == '122'\\\"\\n self.assertFalse(evaluateExpressionFor(self.adapted))\\n self.assertTrue(evaluateExpressionFor(self.adapted, {'value': '122'}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4b7e284de41113c78d1fbc419ff38c\",\n \"score\": \"0.5370747\",\n \"text\": \"def distribute_and_over_or_withbug(s):\\n s = expr(s)\\n if not isinstance(s, Expr):\\n return s\\n #firstParseArgs = tuple([distribute_and_over_or(arg) for arg in s.args])\\n #s = Expr(s.op, firstParseArgs)\\n if s.op == '|':\\n for subarg in s.args:\\n if subarg.op == '&':\\n otherArgs = [\\n otherArg for otherArg in s.args if otherArg != subarg]\\n\\n newArgs = []\\n if len(otherArgs) > 1:\\n otherExpr = Expr('|', tuple(otherArgs))\\n for t in subarg.args:\\n newArgs.append(Expr('|', t, otherExpr))\\n else:\\n otherExpr = otherArgs[0]\\n for t in subarg.args:\\n newArgs.append(Expr('|', t, otherExpr))\\n\\n toReturn = Expr('&')\\n toReturn.args = tuple(newArgs)\\n return toReturn\\n else:\\n newArgs = []\\n for subarg in s.args:\\n newArgs.append(distribute_and_over_or(subarg))\\n s.args = tuple(newArgs)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443bb7273394c70d9bd66bda782e79ac\",\n \"score\": \"0.53582454\",\n \"text\": \"def stmt():\\n from ..expressions import arithmetic, objects, call\\n from ..statements import conditional, assignment, loop, return_, skip, write\\n\\n return assignment.assign_stmt() | \\\\\\n conditional.if_stmt() | \\\\\\n loop.while_stmt() | \\\\\\n loop.repeat_stmt() | \\\\\\n loop.for_stmt() | \\\\\\n return_.return_stmt() | \\\\\\n write.write_stmt() | \\\\\\n call.fun_call_stmt() | \\\\\\n objects.object_method() | \\\\\\n skip.skip_stmt() | \\\\\\n arithmetic.aexp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7099e49a74f786590e4bb1fd837f840\",\n \"score\": \"0.531756\",\n \"text\": \"def distribute_or_over_and(expr):\\r\\n return _distribute((expr, Or, And))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"503cbdf2c275ec7c0afebf4043419405\",\n \"score\": \"0.53091586\",\n \"text\": \"def _evaluate_expression(self, expression, models):\\n if expression:\\n return Expression(str(expression), models).evaluate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1127462315edcbad9edbfef77a41be\",\n \"score\": \"0.53061074\",\n \"text\": \"def renderOr(self, or_):\\n sb = StringBuilder()\\n sb.append(\\\"( or \\\")\\n i = 0\\n while i < or_.arity():\\n sb.append(self.renderLiteral(or_.get(i)) + \\\" \\\")\\n i += 1\\n sb.append(\\\")\\\")\\n return sb.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d0ea2e88b8e2ae655ffadc6ae940db\",\n \"score\": \"0.5300033\",\n \"text\": \"def or_expr(self, size, a, b, flags=None):\\n\\t\\treturn self.expr(LowLevelILOperation.LLIL_OR, a.index, b.index, size=size, flags=flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e78ca7c26d9f590355f76d3cfe8349\",\n \"score\": \"0.52967244\",\n \"text\": \"def or_(*clauses):\\n return _compound_clause('OR', *clauses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0e942393b613af7022e98886c0fcdf6\",\n \"score\": \"0.52910167\",\n \"text\": \"def _or(self, args):\\n return any(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fead92873a6a07b711d85b30626594c3\",\n \"score\": \"0.5281905\",\n \"text\": \"def eval_statement(stmt, ctx):\\n # TODO implement precedence parenthesis\\n stmt_truth = eval_expr(stmt.firstExpr, ctx)\\n for op, expr in zip(stmt.operators, stmt.exprs):\\n if op == 'and':\\n # and\\n stmt_truth = stmt_truth and eval_expr(expr, ctx)\\n else:\\n # or\\n stmt_truth = stmt_truth or eval_expr(expr, ctx)\\n return stmt_truth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1eb09fdfdda47d864c0434c2539133c\",\n \"score\": \"0.5265996\",\n \"text\": \"def distribute_and_over_or(s):\\n if s.op == '|':\\n s = NaryExpr('|', *s.args)\\n if len(s.args) == 0:\\n return FALSE\\n if len(s.args) == 1:\\n return distribute_and_over_or(s.args[0])\\n conj = find_if((lambda d: d.op == '&'), s.args)\\n if not conj:\\n return NaryExpr(s.op, *s.args)\\n others = [a for a in s.args if a is not conj]\\n if len(others) == 1:\\n rest = others[0]\\n else:\\n rest = NaryExpr('|', *others)\\n return NaryExpr('&', *map(distribute_and_over_or,\\n [(c|rest) for c in conj.args]))\\n elif s.op == '&':\\n return NaryExpr('&', *map(distribute_and_over_or, s.args))\\n else:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab94878c66a76f1c9fdbbff3b4c8f03d\",\n \"score\": \"0.52267694\",\n \"text\": \"def do_orm_execute(self, orm_execute_state: ORMExecuteState) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09bfb19820c000892c1be6a116495fde\",\n \"score\": \"0.52195036\",\n \"text\": \"def OR(*column_specs):\\n bind_matchers = as_binding_functions(column_specs)\\n return lambda indices: or_matcher(bind_matchers, indices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a613367f384fd6f2e5bd62a713cbba3\",\n \"score\": \"0.5217877\",\n \"text\": \"def __or__(self, other):\\n return self._BinaryOperatorVariable(lambda a,b: a | b, other, canInline=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f57c550c4f462a62545691bfa9b98d93\",\n \"score\": \"0.52029693\",\n \"text\": \"def cond_eval(cond, expr, df):\\n return maybe_eval(df, expr) if cond else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6fac178f781edfa11354691ce98259\",\n \"score\": \"0.5188609\",\n \"text\": \"def Evaluate(self, obj):\\n left = self._left.Evaluate(obj)\\n right = self._right.Evaluate(obj)\\n # None AND x => x\\n if left is None:\\n return right\\n # x AND None => x\\n if right is None:\\n return left\\n if self.backend.supported_op('AND'):\\n op = ' AND '\\n elif self.backend.supported_op(''):\\n op = ' '\\n else:\\n return None\\n # Defer to LF bass-ackwards OR >> AND precedence.\\n return '{left_paren}{left}{op}{right}{right_paren}'.format(\\n left_paren=self.backend.left_paren, left=left, op=op, right=right,\\n right_paren=self.backend.right_paren)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f81894ec9fde4df63e75a8aa2190180\",\n \"score\": \"0.51879466\",\n \"text\": \"def __or__(self, other):\\n return self._BinaryOperatorVariable(lambda a, b: a | b, other, canInline=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a56d81504c6e8335626930527c90d3\",\n \"score\": \"0.5185744\",\n \"text\": \"def or_(self):\\n self._add(Or(self, '||'))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8e862fc515dfb73d2295ea795eccec\",\n \"score\": \"0.51842684\",\n \"text\": \"def visitOr(cls, or_, visitor):\\n visitor.visitOr(or_)\\n i = 0\\n while i < or_.arity():\\n cls.visitAll(or_.get(i), visitor)\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02bda4ee1e9fa469c40c0e359209d2a1\",\n \"score\": \"0.518324\",\n \"text\": \"def expression(self, ast: jack_ast.Expression, context: Context) -> Optional[Tuple[List[jack_ast.VarDec], List[jack_ast.Statement], jack_ast.Expression]]:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64e4d4e7071d87ee3a147a18bcc04e4\",\n \"score\": \"0.5181312\",\n \"text\": \"def call(self, expr):\\n if isinstance(expr, Express):\\n if expr not in self._exec_expr:\\n self._exec_expr.append(expr)\\n return expr\\n else:\\n if isinstance(expr, dict):\\n res = self._make_dict_expr(expr)\\n elif isinstance(expr, list) or isinstance(expr, tuple):\\n res = self._make_array_expr(expr)\\n elif isinstance(expr, Object):\\n res = Result(expr.parent, expr.name, True)\\n else:\\n res = expr\\n\\n _expr = Express(None, res, None)\\n self._exec_expr.append(_expr)\\n return _expr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc16bd131da7600f55b43fefcf3367a\",\n \"score\": \"0.518064\",\n \"text\": \"def __or__(self, other):\\n if isinstance(other, Not):\\n return Not(And(self.predicate, other.predicate))\\n else:\\n return Or(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670a8b854fbac67a7cd78cb859d82b38\",\n \"score\": \"0.51604015\",\n \"text\": \"def condition_or(cls, *conditions: \\\"ICfnConditionExpression\\\") -> \\\"ICfnConditionExpression\\\":\\n return jsii.sinvoke(cls, \\\"conditionOr\\\", [*conditions])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383be42e5b8185041d3fc658a89af58a\",\n \"score\": \"0.51588553\",\n \"text\": \"def prepare(expr, strategy):\\n graph, expr_ctx = expr\\n f, values = ir.from_expr(graph, expr_ctx, current_ctx())\\n\\n env = {'strategy': strategy}\\n func, env = pipeline.run_pipeline(f, env, passes)\\n\\n return func, env\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f91490c9ec346d14bb13c3fea575073\",\n \"score\": \"0.5155812\",\n \"text\": \"def execute(self, expression: Expression) -> Any:\\n if isinstance(expression, Literal):\\n return expression.value\\n if isinstance(expression, BinaryOp):\\n left = self.execute(expression.left)\\n right = self.execute(expression.right)\\n binary_op = self.binary_ops[expression.op_type]\\n return binary_op(left, right)\\n if isinstance(expression, UnaryOp):\\n operand = self.execute(expression.operand)\\n unary_op = self.unary_ops[expression.op_type]\\n return unary_op(operand)\\n if isinstance(expression, Function):\\n function = self.functions.get(expression.name)\\n if function is None:\\n raise ExecutorError(f\\\"Function '{expression.name}' is not defined\\\")\\n args = [self.execute(arg) for arg in expression.args]\\n return function(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"946addc9d253665b1c0535a008a6fb41\",\n \"score\": \"0.5142207\",\n \"text\": \"def execute(self, *a, **kw):\\n with self.get_cursor(*a, **kw):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddbd582c6b0dcdb01660020320f0c9d3\",\n \"score\": \"0.5141592\",\n \"text\": \"def __call__(self, *args):\\n assert is_symbol(self.op) and not self.args\\n return Expr(self.op, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c31cea851c7a6d25f82cc5996fe1fc\",\n \"score\": \"0.5127808\",\n \"text\": \"def apply_logic_operator(op, arguments):\\n \\n running_value = op(arguments[0], arguments[1])\\n \\n if len(arguments) == 2:\\n return running_value\\n \\n index = 2\\n while running_value and index < len(arguments):\\n running_value = running_value and op(arguments[index - 1], arguments[index])\\n index += 1\\n \\n return running_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526def2674ca7365c023a1ed9d4b1861\",\n \"score\": \"0.50934106\",\n \"text\": \"def make_atom(self, child, qn, connection):\\n lvalue, lookup_type, value_annot, params_or_value = child\\n sql, params = super(SphinxWhereNode, self).make_atom(child, qn, connection)\\n if lookup_type == 'search':\\n if hasattr(lvalue, 'process'):\\n try:\\n lvalue, params = lvalue.process(lookup_type, params_or_value, connection)\\n except EmptyShortCircuit:\\n raise EmptyResultSet\\n if isinstance(lvalue, tuple):\\n # A direct database column lookup.\\n field_sql = self.sql_for_columns(lvalue, qn, connection)\\n else:\\n # A smart object with an as_sql() method.\\n field_sql = lvalue.as_sql(qn, connection)\\n # TODO: There are a couple problems here.\\n # 1. The user _might_ want to search only a specific field.\\n # 2. However, since Django requires a field name to use the __search operator\\n # There is no way to do a search in _all_ fields.\\n # 3. Because, using multiple __search operators is not supported.\\n # So, we need to merge multiped __search operators into a single MATCH(), we\\n # can't do that here, we have to do that one level up...\\n # Ignore the field name, search all fields:\\n params = ('@* %s' % params[0], )\\n # _OR_ respect the field name, and search on it:\\n #params = ('@%s %s' % (field_sql, params[0]), )\\n if self._real_negated:\\n col = lvalue.col\\n if lookup_type == 'exact':\\n sql = '%s <> %%s' % col\\n elif lookup_type == 'in':\\n params_placeholder = '(%s)' % (', '.join(['%s'] * len(params)))\\n sql = '%s NOT IN %s' % (col, params_placeholder)\\n else:\\n raise ValueError(\\\"Negative '%s' lookup not supported\\\" % lookup_type)\\n return sql, params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7beecdce324d288585a8273181e8ce04\",\n \"score\": \"0.50806934\",\n \"text\": \"def _execute(self, operation, data):\\n if data and not self.dry_run:\\n engine = db.engine\\n engine.execute(operation, data)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7feb230e67acadecbb6db46d1e79621\",\n \"score\": \"0.5073616\",\n \"text\": \"def _execute(self, stmt, *values):\\n c = self._cursor()\\n try:\\n return c.execute(stmt, values).fetchone()\\n finally:\\n c.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7feb230e67acadecbb6db46d1e79621\",\n \"score\": \"0.5073616\",\n \"text\": \"def _execute(self, stmt, *values):\\n c = self._cursor()\\n try:\\n return c.execute(stmt, values).fetchone()\\n finally:\\n c.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5faf8cb350596b12d3768b0cb161260\",\n \"score\": \"0.50549746\",\n \"text\": \"def sql(ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a69bcf13a016d994f40884c9e8b690\",\n \"score\": \"0.50501764\",\n \"text\": \"def run_eqs_query(cls, world_context_object, query_template, querier, run_mode, wrapper_class):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60b9908467a3445ab0dfdb62e2c29b93\",\n \"score\": \"0.5026602\",\n \"text\": \"def expression(self, column, value):\\n strategy_name, values = value\\n\\n if strategy_name in self.STRATEGIES:\\n strategy = self.STRATEGIES[strategy_name]\\n else:\\n raise errors.InvalidValue('Unknown strategy specified.', self.item)\\n\\n if strategy_name in ['in', '~in']:\\n return strategy(column, values)\\n elif len(values) == 1:\\n return strategy(column, values[0])\\n\\n expressions = [strategy(column, value) for value in values]\\n return or_(*expressions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf78caac6399629c7ab00f1aa96c50d\",\n \"score\": \"0.502654\",\n \"text\": \"def __ror__(self, other):\\n return Or(other, *self.predicates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7538ce4b4e78b9c1c534230194b386\",\n \"score\": \"0.5023896\",\n \"text\": \"def evaluate(self, local_env, expression):\\n if isinstance(expression, int):\\n return expression\\n elif isinstance(expression, str): # symbol\\n return self.get(local_env, expression)\\n elif isinstance(expression, list):\\n if expression[0] in self.commands:\\n # special forms evaluate (or not) their args\\n command = self.commands[expression[0]]\\n args = expression[1:]\\n check_args(command, args, ['self', 'local_env'])\\n return command(local_env, *args)\\n elif expression[0] in self.user_functions:\\n function = self.user_functions[expression[0]]\\n args = [self.evaluate(local_env, exp) for exp in expression[1:]]\\n local_env = function.bind_args(args)\\n return self.evaluate(local_env, function.body)\\n else:\\n # evaluate operator and args\\n exps = [self.evaluate(local_env, exp) for exp in expression]\\n if len(exps) == 0:\\n raise NullExpression()\\n operator = exps[0]\\n if callable(operator):\\n args = exps[1:]\\n check_args(operator, args)\\n return operator(*args) # apply operator to args\\n else:\\n raise InvalidOperator(operator)\\n else:\\n msg = 'Cannot evaluate {0!r}'.format(expression)\\n raise InterpreterError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb39b05da339441152d4da1395f84b9\",\n \"score\": \"0.5020141\",\n \"text\": \"def _or(self, fns):\\n err = self.input.nullError()\\n for f in fns:\\n try:\\n m = self.input\\n ret, err = f()\\n return ret, err\\n except ParseError:\\n self.input = m\\n raise err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30fbb0eeaef31a11ca9dec5505f7ca0\",\n \"score\": \"0.5013862\",\n \"text\": \"def __or__(self, other):\\n return Or(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30fbb0eeaef31a11ca9dec5505f7ca0\",\n \"score\": \"0.5013862\",\n \"text\": \"def __or__(self, other):\\n return Or(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30fbb0eeaef31a11ca9dec5505f7ca0\",\n \"score\": \"0.5013862\",\n \"text\": \"def __or__(self, other):\\n return Or(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30fbb0eeaef31a11ca9dec5505f7ca0\",\n \"score\": \"0.5013862\",\n \"text\": \"def __or__(self, other):\\n return Or(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30fbb0eeaef31a11ca9dec5505f7ca0\",\n \"score\": \"0.5013862\",\n \"text\": \"def __or__(self, other):\\n return Or(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e8eb66bc68252f4981daa4ee5cfe180\",\n \"score\": \"0.50098497\",\n \"text\": \"def exec_compat(expression, globals, locals=None):\\n exec(expression, globals, locals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61835cfa29aa749a65274c0c01d9c861\",\n \"score\": \"0.49924752\",\n \"text\": \"def __and__(self, other):\\n logger.debug(\\\"from __or__: self:{}, other{}\\\".format(self.scope_req, other.scope_req))\\n if other.history:\\n self.history.append(other.history)\\n self.history.append(\\\"{} and {}\\\".format(self.scope_req, other.scope_req))\\n\\n self.result_in = self.func_operator_in(all, self.result_in or self.scope_req,\\n other.result_in or other.scope_req)\\n self.scope_req = (\\\"{} and {}\\\".format(self.scope_req, other.scope_req))\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1884a8927819a61d27f85d773f874d0c\",\n \"score\": \"0.4992243\",\n \"text\": \"def __or__(self, other):\\n return OrPredicate(self, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e0ca5fff0ec42e90bea91e409a171a\",\n \"score\": \"0.49780267\",\n \"text\": \"def _do_function(self,e1,e2):\\n\\t\\treturn e1|e2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab11a0b46b452b6b37e645dff60396f\",\n \"score\": \"0.49749222\",\n \"text\": \"def OR_Query(arglist):\\n line = \\\"','\\\".join(arglist)\\n Data = \\\"('\\\" + line + \\\"')\\\"\\n if CON=='ALL':\\n Query = \\\"select distinct FileName from jsoncallworddata where Keyword in \\\" + Data\\n elif CON=='START50WORD':\\n Query = \\\"select distinct FileName from jsoncallworddata where wordSeq<=50 and Keyword in \\\" + Data\\n elif CON=='LAST50WORD':\\n Query = \\\"select distinct T2.FileName from (select FileName,max(wordSeq) m from \\\" \\\\\\n \\\"jsoncallworddata group by FileName) T1 inner join (select * from jsoncallworddata) as T2 where \\\" \\\\\\n \\\"T1.FileName=T2.FileName and T2.wordSeq>(T1.m-50) and T2.Keyword in \\\" + Data\\n elif CON=='START50SECOND':\\n Query = \\\"select distinct FileName from jsoncallworddata where startTime<=50 and Keyword in \\\" + Data\\n elif CON=='LAST50SECOND':\\n Query = \\\"select distinct T2.FileName from \\\" \\\\\\n \\\"(select FileName,max(startTime) m from jsoncallworddata group by FileName) T1 inner join \\\" \\\\\\n \\\"(select * from jsoncallworddata) as T2 where T1.FileName=T2.FileName and T2.startTime>(T1.m-50) and T2.Keyword in \\\" + Data\\n else:\\n print('No Operation.')\\n datalist=Get_Result(Query)\\n return datalist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e913f50d0d4f4b7b712d881ef185ad0\",\n \"score\": \"0.49704707\",\n \"text\": \"def distribute_and_over_or(s):\\n s = expr(s)\\n if s.op == '|':\\n s = associate('|', s.args)\\n if s.op != '|':\\n return distribute_and_over_or(s)\\n if len(s.args) == 0:\\n return False\\n if len(s.args) == 1:\\n return distribute_and_over_or(s.args[0])\\n conj = first(arg for arg in s.args if arg.op == '&')\\n if not conj:\\n return s\\n others = [a for a in s.args if a is not conj]\\n rest = associate('|', others)\\n return associate('&', [distribute_and_over_or(c | rest)\\n for c in conj.args])\\n elif s.op == '&':\\n return associate('&', list(map(distribute_and_over_or, s.args)))\\n else:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e913f50d0d4f4b7b712d881ef185ad0\",\n \"score\": \"0.49704707\",\n \"text\": \"def distribute_and_over_or(s):\\n s = expr(s)\\n if s.op == '|':\\n s = associate('|', s.args)\\n if s.op != '|':\\n return distribute_and_over_or(s)\\n if len(s.args) == 0:\\n return False\\n if len(s.args) == 1:\\n return distribute_and_over_or(s.args[0])\\n conj = first(arg for arg in s.args if arg.op == '&')\\n if not conj:\\n return s\\n others = [a for a in s.args if a is not conj]\\n rest = associate('|', others)\\n return associate('&', [distribute_and_over_or(c | rest)\\n for c in conj.args])\\n elif s.op == '&':\\n return associate('&', list(map(distribute_and_over_or, s.args)))\\n else:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa72570cc7bc2f66625322d1c6e1833\",\n \"score\": \"0.49690124\",\n \"text\": \"def _execute(self, clause):\\n return self.db.conn.conn.execute(clause)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66cae33317c00974ec8a5634218014b2\",\n \"score\": \"0.49661663\",\n \"text\": \"def and_op():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0fac5f604d5aae620f8bc952364892\",\n \"score\": \"0.49613786\",\n \"text\": \"def odoo_execute_kw(self, bind, model, method, *args, **kwargs):\\n config = self.binds[bind]\\n\\n if '__uid' not in config.const:\\n common = xmlrpc.client.ServerProxy(f'{config.url}/xmlrpc/2/common')\\n config.const['__uid'] = common.authenticate(config.dbname, config.user, config.passwd, {})\\n\\n models = xmlrpc.client.ServerProxy(f'{config.url}/xmlrpc/2/object')\\n return models.execute_kw(config.dbname, config.const['__uid'], config.passwd, model, method, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ebbfc46edd8157dc626803c8119d361\",\n \"score\": \"0.49523783\",\n \"text\": \"def try_eval( self ) :\\n\\n # same could be achieved via \\\" .convert( { '*' : < myeval(expr) or expr >} ) \\\" \\n \\n result = self.__class__() \\n result.args = self.args[:] # copy positional arguments \\n\\n for key, value in self.iteritems() : \\n \\n result[ key ] = _eval_or_leave( value )\\n \\n return result\\n \\n \\n \\\"\\\"\\\"\\n for key, raw_value in self.iteritems() : \\n \\n evaluated = _try_eval( raw_value )\\n \\n if evaluated is not NONE :\\n result[ key ] = evaluated \\n else :\\n result[ key ] = raw_value\\n \\n return result\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f50197716d79eab319a80b36e9f301\",\n \"score\": \"0.4950666\",\n \"text\": \"def or_gate(x, y):\\n if x or y:\\n return 1\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33442fde88cf7a8968b93d9de770b169\",\n \"score\": \"0.49466044\",\n \"text\": \"def _combine(self, operator, field_or_clause):\\n self._add_any(operator, is_conjunction=True)\\n self._add_new(field_or_clause)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cebf9100f8100ba646bf448e5df52e1\",\n \"score\": \"0.49435163\",\n \"text\": \"def do_execute(self):\\n if self.storagehandler is None:\\n return \\\"No storage handler available!\\\"\\n expr = str(self.resolve_option(\\\"expression\\\")).replace(\\n \\\"{X}\\\", str(self.storagehandler.storage[str(self.resolve_option(\\\"storage_name\\\"))]))\\n expr = self.storagehandler.expand(expr)\\n self.storagehandler.storage[self.resolve_option(\\\"storage_name\\\")] = eval(expr)\\n self._output.append(self.input)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3e4574eee85e26fd27895e492fb2a1\",\n \"score\": \"0.49372405\",\n \"text\": \"def _exec(self, query: str, args: Optional[Mapping[str, SupportedTypes]] = None) -> psycopg2.extensions.cursor:\\n assert self.db is not None\\n\\n logging.debug('_exec(): query=\\\"%s\\\", args=\\\"%s\\\"', query, args)\\n cur = self.db.cursor()\\n # logging.debug('Executing: %s' % (query,))\\n cur.execute(query, args)\\n\\n return cur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e1db18a71d095492d916498464ec7e\",\n \"score\": \"0.4919036\",\n \"text\": \"def visit_Exec(self, node):\\n \\n raise NotImplementedError, \\\"Exec statements are not supported at this time\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef88fab206a3b4cfedfc65158688d18\",\n \"score\": \"0.49117392\",\n \"text\": \"def eval(expr):\\n df = pipein()\\n pipeout(df.eval(expr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6326151f4f239c57433c18fa44a9b8a7\",\n \"score\": \"0.4896938\",\n \"text\": \"def prepare(self, query, args):\\n bind_args = []\\n for arg in args:\\n if arg is None:\\n bind_args.append('NULL')\\n elif arg is False:\\n bind_args.append('0')\\n elif arg is True:\\n bind_args.append('1')\\n else:\\n bind_args.append(self.mysql.escape(arg, self.mysql.encoders))\\n run_query = query % tuple(bind_args)\\n self.last_query = run_query\\n return run_query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b97664a2ed410445c12f0383bb2b395\",\n \"score\": \"0.48957047\",\n \"text\": \"def execute_if_bound(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"446a2a39a249f56faeebb438a8d0c683\",\n \"score\": \"0.4895654\",\n \"text\": \"def evaluate(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed41558aeb58a5331087f33d52e164db\",\n \"score\": \"0.48855513\",\n \"text\": \"def execute_xpath_query(tree, xpath, context_node = None, **variables):\\n if context_node is None: # explicitly check for None rather than using \\\"or\\\", because it is treated as a list\\n context_node = tree\\n result = xpath(context_node, **variables)\\n if isinstance(result, list):\\n return result\\n else:\\n return [result]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d758d9a9b12d1d0cd73b498812718d\",\n \"score\": \"0.4878246\",\n \"text\": \"def _add_expression(self, *values):\\n for v in values:\\n self._add_any(v)\\n self._isdirty = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b294be2eddc6d920e034728dde21df7\",\n \"score\": \"0.48777825\",\n \"text\": \"def reduce(expression):\\n print('reduced called:')\\n print(expression)\\n #Check expression is single keyword fetch or with algebric expression.\\n if '(' in expression:\\n #Extract Outer Boolean Operator of the expression.\\n BooleanOperator = expression.split('(')[0]\\n print(BooleanOperator)\\n arg_list=[]\\n #Check single parameter or not.\\n if ',' in expression :\\n num_arguments = expression.count(',') + 1\\n flag = 0\\n count = 0\\n arg_index = []\\n for i in range(len(expression)):\\n if expression[i] == '(':\\n flag += 1\\n if expression[i] == ')':\\n flag -= 1\\n if flag == 1 and expression[i] == ',':\\n count += 1\\n arg_index.append(i)\\n for i in range(len(expression)):\\n if expression[i] == '(':\\n break\\n get_arg_from = i + 1\\n get_arg_to = len(expression) - 1\\n arg_index = [x - get_arg_from for x in arg_index]\\n arg_string = expression[get_arg_from:get_arg_to]\\n if count != 0:\\n arg_index.insert(0, 0)\\n indices = arg_index\\n parts = [arg_string[i:j] for i, j in zip(indices, indices[1:] + [None])]\\n removeComma=[]\\n for i in parts:\\n if i[0]==',':\\n removeComma.append(i.strip(i[0]))\\n else:\\n removeComma.append(i)\\n for i in removeComma:\\n if '(' not in i:\\n arg_list.append(i.strip(\\\"'\\\"))\\n else:\\n arg_list.append(i)\\n else:\\n arg_list.append(arg_string)\\n else:\\n #if Expression signle parameter then it must be NOT Operation.\\n print('Not Expression Breaking....')\\n expression = expression.replace(\\\"NOT('\\\", \\\"\\\")\\n expression = expression.replace(\\\"')\\\", \\\"\\\")\\n arg_list.append(expression)\\n print('arg_List:')\\n print(arg_list)\\n else:\\n #Single Keyword search Operation.\\n print('Single Keyword search Operation.')\\n search_word = []\\n search_word.append(expression)\\n Result=OR_Query(search_word)\\n return Result\\n\\n final_arg_List=[]\\n List = []\\n ListVisit=0\\n\\n for each in arg_list:\\n print(each)\\n print(List)\\n #Check item is expression or not.\\n if '(' in each:\\n if BooleanOperator == 'AND':\\n ListVisit=1\\n if List:\\n print('Inside AND,List NonEmpty,call Reduce')\\n temp=reduce(each)\\n List = list(set(List) & set(temp))\\n else:\\n print('Inside AND,List Empty,call Reduce')\\n List = reduce(each)\\n elif BooleanOperator == 'OR':\\n ListVisit=1\\n print('Inside OR, call Reduce')\\n temp=reduce(each)\\n List = list(set(List) | set(temp))\\n elif BooleanOperator == 'NOT':\\n ListVisit=1\\n print('Inside NOT call Reduce.')\\n temp = reduce(each)\\n NotList = list(set(GetAll_Query()) - set(temp))\\n print('Not Operation Result:')\\n print(NotList)\\n if List:\\n List = list(set(List) & set(NotList))\\n else:\\n List=NotList\\n print('After NOT Operation:')\\n print(List)\\n else:\\n print('Do Nothing Reduce .')\\n else:\\n #if item is not expression then stored in Final_arg_List.\\n final_arg_List.append(each)\\n\\n if final_arg_List:\\n final_List=[]\\n if BooleanOperator == 'AND':\\n if List:\\n print('Inside AND,List NonEmpty,call AND Function:')\\n temp = AND_Query(final_arg_List)\\n final_List = list(set(List) & set(temp))\\n elif ListVisit==1:\\n final_List=[]\\n else:\\n final_List= AND_Query(final_arg_List)\\n elif BooleanOperator == 'OR':\\n print('Inside OR,List NonEmpty,call OR Function:')\\n temp = OR_Query(final_arg_List)\\n final_List = list(set(List) | set(temp))\\n elif BooleanOperator == 'NOT':\\n temp = OR_Query(final_arg_List)\\n print(temp)\\n print(set(GetAll_Query()))\\n NotList = list(set(GetAll_Query()) - set(temp))\\n if List:\\n final_List = list(set(List) & set(NotList))\\n else:\\n final_List=NotList\\n else:\\n print('Do Nothing.')\\n return final_List\\n else:\\n return List\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44431a573280a2df667679a422a587ff\",\n \"score\": \"0.48730105\",\n \"text\": \"def simplify_condition(cond):\\n # TODO: handle the case of NOT(cond)\\n if(('OR' not in cond) and ('NOT' not in cond)):\\n # simplify AND statements\\n cond_simple = remove_parenthesis(cond)\\n cond_simple = sort_simple_condition(cond_simple)\\n ret_code = 1\\n elif('OR' in cond): \\n # simplify OR statements\\n cond_simple, or_states, chained_or, nested_or = process_all_or_cond(cond)\\n # simplify AND statements\\n cond_simple = remove_parenthesis(cond_simple)\\n if((not chained_or) and (not nested_or)):\\n cond_simple = sort_simple_condition(cond_simple)\\n cond_simple = {'cond_simple': cond_simple, 'or_states': or_states}\\n ret_code = 2\\n\\n if(chained_or or nested_or): ret_code = 3\\n # print('!!!')\\n # print(ret_code)\\n else: \\n ret_code = 0\\n cond_simple = cond\\n return ret_code, cond_simple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e5b89badc89cd55c2bf1a2dd7e15a0\",\n \"score\": \"0.48714125\",\n \"text\": \"def _execute(code, glb, exc_func):\\n if glb is None:\\n glb = {}\\n if '__builtins__' not in glb:\\n glb['__builtins__'] = RestrictedPython.Guards.safe_builtins.copy()\\n if exc_func == 'eval':\\n return eval(code, glb)\\n else:\\n exec(code, glb)\\n return glb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54049061a710b2d2e16b14f1224b8bb0\",\n \"score\": \"0.48679867\",\n \"text\": \"def or_parser(\\n\\tfirst_parser: Callable[[str], Optional[Tuple[str, str]]],\\n\\tsecond_parser: Callable[[str], Optional[Tuple[str, str]]],\\n\\ttext: str,\\n) -> Optional[Tuple[str, str]]:\\n\\tresult = first_parser(text)\\n\\tif result is None:\\n\\t\\tresult_2 = second_parser(text)\\n\\t\\treturn result_2\\n\\telse:\\n\\t\\treturn result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1584,"cells":{"query_id":{"kind":"string","value":"e454e8481461c2be828dba7a7a98f306"},"query":{"kind":"string","value":"Stemming removes morphological affixes from words, leaving only the word stem."},"positive_passages":{"kind":"list like","value":[{"docid":"567150460c8a9ff3eecc454545e03755","score":"0.0","text":"def stemming_list(text, lang='en', static=False, file_name='stemming'):\n input_check.input_check_list(text)\n # stemmed word list\n stem_words = []\n # original word list which is affected\n org_words = []\n # affected word list\n aff_words = []\n language = pipe_token.code_convert(lang).lower()\n stemmer = SnowballStemmer(language)\n for word in text:\n stem_word = stemmer.stem(word)\n stem_words.append(stem_word)\n # check if the word changed\n if word != stem_word:\n org_words.append(word)\n aff_words.append(stem_word)\n # check whether or not need statistic\n if static:\n statistic.do_statistic(org_words, aff_words, file_name)\n return stem_words","title":""}],"string":"[\n {\n \"docid\": \"567150460c8a9ff3eecc454545e03755\",\n \"score\": \"0.0\",\n \"text\": \"def stemming_list(text, lang='en', static=False, file_name='stemming'):\\n input_check.input_check_list(text)\\n # stemmed word list\\n stem_words = []\\n # original word list which is affected\\n org_words = []\\n # affected word list\\n aff_words = []\\n language = pipe_token.code_convert(lang).lower()\\n stemmer = SnowballStemmer(language)\\n for word in text:\\n stem_word = stemmer.stem(word)\\n stem_words.append(stem_word)\\n # check if the word changed\\n if word != stem_word:\\n org_words.append(word)\\n aff_words.append(stem_word)\\n # check whether or not need statistic\\n if static:\\n statistic.do_statistic(org_words, aff_words, file_name)\\n return stem_words\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6eb9d5543e2474464062b5a21ffa97f7","score":"0.73459184","text":"def stemmatize(self,word):\n lemma = self.lemmatizer.lemmatize(word)\n stem = (SnowballStemmer(self.language).stem(lemma))\n return stem","title":""},{"docid":"5aaa75487e993bb8da339404ce8b7328","score":"0.70459604","text":"def stem(self, token):\n token = self.norm(\n token, 1\n ) # remove diacritics which representing Arabic short vowels\n if token in self.stop_words:\n return token # exclude stop words from being processed\n token = self.pre32(\n token\n ) # remove length three and length two prefixes in this order\n token = self.suf32(\n token\n ) # remove length three and length two suffixes in this order\n token = self.waw(\n token\n ) # remove connective ‘و’ if it precedes a word beginning with ‘و’\n token = self.norm(token, 2) # normalize initial hamza to bare alif\n # if 4 <= word length <= 7, then stem; otherwise, no stemming\n if len(token) == 4: # length 4 word\n token = self.pro_w4(token)\n elif len(token) == 5: # length 5 word\n token = self.pro_w53(token)\n token = self.end_w5(token)\n elif len(token) == 6: # length 6 word\n token = self.pro_w6(token)\n token = self.end_w6(token)\n elif len(token) == 7: # length 7 word\n token = self.suf1(token)\n if len(token) == 7:\n token = self.pre1(token)\n if len(token) == 6:\n token = self.pro_w6(token)\n token = self.end_w6(token)\n return token","title":""},{"docid":"f5077efe8dac814cd5c98d95599a24de","score":"0.69975543","text":"def normalize_stemming(line):\n words = line.split(' ')\n words = [snowball.stem(word) for word in words]\n line = ' '.join(words)\n return line","title":""},{"docid":"39c4a9f4c300152c70743a4ff6fcdb63","score":"0.6982842","text":"def stem(self, word):\n \n rules = self.rules\n match = self.stem_expr.match(word)\n if not match: return word\n stem = self.strip_prefix(match.group(0))\n \n is_intact = True\n continuing = True\n while continuing:\n pfv = self.first_vowel(stem)\n rulelist = rules.get(stem[-1])\n if not rulelist: break\n \n continuing = False\n for ending, intact, num, append, cont in rulelist:\n if stem.endswith(ending):\n if intact and not is_intact: continue\n newlen = len(stem) - num + len(append)\n \n if ((pfv == 0 and newlen < 2)\n or (pfv > 0 and newlen < 3)):\n # If word starts with vowel, minimum stem length is 2.\n # If word starts with consonant, minimum stem length is\n # 3.\n continue\n \n is_intact = False\n stem = stem[:0-num] + append\n \n continuing = cont\n break\n \n return stem","title":""},{"docid":"a05158755495e5df26240008c4e55535","score":"0.69822466","text":"def stemWords(self, words):\n stemmed = []\n for w in words:\n stemmed.append(self.stemmer.stemWord(w))\n return stemmed","title":""},{"docid":"9e457c6e3ecb88be69c667d44f4f006c","score":"0.6907822","text":"def stem(word):\n stemmer = WordNetLemmatizer()\n return stemmer.lemmatize(word)","title":""},{"docid":"3159f424ce04070f6f48cb56adaa9a67","score":"0.6886361","text":"def stem_word(word):\n\treturn stemmer.stem(word)","title":""},{"docid":"7ed35d5fb3a1836a1ae94f1b7853138e","score":"0.6872106","text":"def stemmingTest():\n t1 = Test(None, \"alpha\", 0)\n\n usControlDemo, usControlConvo, usDementiaDemo, usDementiaConvo,\\\n usFeatures, usVocab, usLabels = makeData(t1)\n\n t2 = Test(None, \"alpha\", 1)\n\n sControlDemo, sControlConvo, sDementiaDemo, sDementiaConvo,\\\n sFeatures, sVocab, sLabels = makeData(t2)\n\n l = []\n l.append(usFeatures)\n l.append(usLabels)\n l.append(usVocab)\n l.append(sFeatures)\n l.append(sLabels)\n l.append(sVocab)\n\n runTest(\"nb\", \"nb\", t1, t2, l,(\"Unstemmed\",\"Stemmed\"))","title":""},{"docid":"fc0ba1dc388e6c38d2a2f4ca53790da1","score":"0.6821889","text":"def stem(self):\n ps = PorterStemmer()\n self.processed = [\n ps.stem(word) for word in self.processed\n ]","title":""},{"docid":"f1cabf483b6d5abc80e0de9fe5994b96","score":"0.6812056","text":"def stem_text(self, text):\n stemmer = SnowballStemmer(language='english')\n text_stemmed = [stemmer.stem(word) for word in self.text.split(' ')]\n text_stemmed = ' '.join(text_stemmed)\n return text_stemmed","title":""},{"docid":"cc929a5ab875a9a5396045e41a152c43","score":"0.6777241","text":"def stem_words(words):\r\n stemmer = PorterStemmer()\r\n return [stemmer.stem(w) for w in words]","title":""},{"docid":"a218f2ac850df319080e8b8ddc9addc6","score":"0.67754483","text":"def stemmedWords(text):\n stemmer = SnowballStemmer(\"english\") # Stemmer instance\n\n # Stem each word that is not punctuation, and store in list\n stemmedText = [stemmer.stem(word) for word in text.split()\n if word not in string.punctuation]\n\n # Join each stemmed word with a space\n return \" \".join(stemmedText)","title":""},{"docid":"e9862013fd462023d06f154dd4abdea0","score":"0.67519945","text":"def normalize(word):\n if word not in STOP and word not in punctuation:\n return STEM.stem(word)","title":""},{"docid":"7902e16d0dfbadbcc118118dc45a845d","score":"0.6750222","text":"def raw_stem(self, word):\n raise NotImplementedError()","title":""},{"docid":"e8f00165cd0ed958b9471523db54dd10","score":"0.673429","text":"def text_stemmer(text):\n\n porter = PorterStemmer()\n stemmed_words = [porter.stem(word) for word in word_tokenize(text)]\n\n return(' '.join(stemmed_words))","title":""},{"docid":"bdffa40cafca4baea9769c149ad1fe91","score":"0.66919714","text":"def stem(word):\n return word.lower().rstrip(\",.!:;'-\\\"\").lstrip(\"'\\\"\")","title":""},{"docid":"d82a576b261c3b084d8070db945dc3fe","score":"0.6614961","text":"def __stem(self, txt: str):\n print('stemming...')\n from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n factory = StemmerFactory()\n stm = factory.create_stemmer()\n result = stm.stem(txt)\n print(result)\n return result","title":""},{"docid":"ffb66dab8c3ff68d23c5d4b6e2294fb4","score":"0.6556869","text":"def stem_concepts(self, concept):\n concept_break = concept.split(\" \")\n concept_break[-1] = snowball_stemmer.stem(concept_break[-1])\n find_word = \" \" + \" \".join(concept_break)\n return find_word","title":""},{"docid":"fa8f9137a3d644795234ea81a9972289","score":"0.65401185","text":"def stem(self, word):\r\n word = word.lower()\r\n\r\n if len(word) <= 2:\r\n return word\r\n\r\n elif word in self.__special_words:\r\n return self.__special_words[word]\r\n\r\n # Map the different apostrophe characters to a single consistent one\r\n word = (word.replace(\"\\u2019\", \"\\x27\")\r\n .replace(\"\\u2018\", \"\\x27\")\r\n .replace(\"\\u201B\", \"\\x27\"))\r\n\r\n if word.startswith(\"\\x27\"):\r\n word = word[1:]\r\n\r\n if word.startswith(\"y\"):\r\n word = \"\".join((\"Y\", word[1:]))\r\n\r\n for i in range(1, len(word)):\r\n if word[i-1] in self.__vowels and word[i] == \"y\":\r\n word = \"\".join((word[:i], \"Y\", word[i+1:]))\r\n\r\n step1a_vowel_found = False\r\n step1b_vowel_found = False\r\n\r\n r1 = \"\"\r\n r2 = \"\"\r\n\r\n if word.startswith((\"gener\", \"commun\", \"arsen\")):\r\n if word.startswith((\"gener\", \"arsen\")):\r\n r1 = word[5:]\r\n else:\r\n r1 = word[6:]\r\n\r\n for i in range(1, len(r1)):\r\n if r1[i] not in self.__vowels and r1[i-1] in self.__vowels:\r\n r2 = r1[i+1:]\r\n break\r\n else:\r\n r1, r2 = self._r1r2_standard(word, self.__vowels)\r\n\r\n\r\n # STEP 0\r\n for suffix in self.__step0_suffixes:\r\n if word.endswith(suffix):\r\n word = word[:-len(suffix)]\r\n r1 = r1[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n break\r\n\r\n # STEP 1a\r\n for suffix in self.__step1a_suffixes:\r\n if word.endswith(suffix):\r\n\r\n if suffix == \"sses\":\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n\r\n elif suffix in (\"ied\", \"ies\"):\r\n if len(word[:-len(suffix)]) > 1:\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n else:\r\n word = word[:-1]\r\n r1 = r1[:-1]\r\n r2 = r2[:-1]\r\n\r\n elif suffix == \"s\":\r\n for letter in word[:-2]:\r\n if letter in self.__vowels:\r\n step1a_vowel_found = True\r\n break\r\n\r\n if step1a_vowel_found:\r\n word = word[:-1]\r\n r1 = r1[:-1]\r\n r2 = r2[:-1]\r\n break\r\n\r\n # STEP 1b\r\n for suffix in self.__step1b_suffixes:\r\n if word.endswith(suffix):\r\n if suffix in (\"eed\", \"eedly\"):\r\n\r\n if r1.endswith(suffix):\r\n word = \"\".join((word[:-len(suffix)], \"ee\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ee\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ee\"))\r\n else:\r\n r2 = \"\"\r\n else:\r\n for letter in word[:-len(suffix)]:\r\n if letter in self.__vowels:\r\n step1b_vowel_found = True\r\n break\r\n\r\n if step1b_vowel_found:\r\n word = word[:-len(suffix)]\r\n r1 = r1[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n\r\n if word.endswith((\"at\", \"bl\", \"iz\")):\r\n word = \"\".join((word, \"e\"))\r\n r1 = \"\".join((r1, \"e\"))\r\n\r\n if len(word) > 5 or len(r1) >=3:\r\n r2 = \"\".join((r2, \"e\"))\r\n\r\n elif word.endswith(self.__double_consonants):\r\n word = word[:-1]\r\n r1 = r1[:-1]\r\n r2 = r2[:-1]\r\n\r\n elif ((r1 == \"\" and len(word) >= 3 and\r\n word[-1] not in self.__vowels and\r\n word[-1] not in \"wxY\" and\r\n word[-2] in self.__vowels and\r\n word[-3] not in self.__vowels)\r\n or\r\n (r1 == \"\" and len(word) == 2 and\r\n word[0] in self.__vowels and\r\n word[1] not in self.__vowels)):\r\n\r\n word = \"\".join((word, \"e\"))\r\n\r\n if len(r1) > 0:\r\n r1 = \"\".join((r1, \"e\"))\r\n\r\n if len(r2) > 0:\r\n r2 = \"\".join((r2, \"e\"))\r\n break\r\n\r\n # STEP 1c\r\n if len(word) > 2 and word[-1] in \"yY\" and word[-2] not in self.__vowels:\r\n word = \"\".join((word[:-1], \"i\"))\r\n if len(r1) >= 1:\r\n r1 = \"\".join((r1[:-1], \"i\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= 1:\r\n r2 = \"\".join((r2[:-1], \"i\"))\r\n else:\r\n r2 = \"\"\r\n\r\n # STEP 2\r\n for suffix in self.__step2_suffixes:\r\n if word.endswith(suffix):\r\n if r1.endswith(suffix):\r\n if suffix == \"tional\":\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n\r\n elif suffix in (\"enci\", \"anci\", \"abli\"):\r\n word = \"\".join((word[:-1], \"e\"))\r\n\r\n if len(r1) >= 1:\r\n r1 = \"\".join((r1[:-1], \"e\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= 1:\r\n r2 = \"\".join((r2[:-1], \"e\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix == \"entli\":\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n\r\n elif suffix in (\"izer\", \"ization\"):\r\n word = \"\".join((word[:-len(suffix)], \"ize\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ize\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ize\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix in (\"ational\", \"ation\", \"ator\"):\r\n word = \"\".join((word[:-len(suffix)], \"ate\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ate\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ate\"))\r\n else:\r\n r2 = \"e\"\r\n\r\n elif suffix in (\"alism\", \"aliti\", \"alli\"):\r\n word = \"\".join((word[:-len(suffix)], \"al\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"al\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"al\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix == \"fulness\":\r\n word = word[:-4]\r\n r1 = r1[:-4]\r\n r2 = r2[:-4]\r\n\r\n elif suffix in (\"ousli\", \"ousness\"):\r\n word = \"\".join((word[:-len(suffix)], \"ous\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ous\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ous\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix in (\"iveness\", \"iviti\"):\r\n word = \"\".join((word[:-len(suffix)], \"ive\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ive\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ive\"))\r\n else:\r\n r2 = \"e\"\r\n\r\n elif suffix in (\"biliti\", \"bli\"):\r\n word = \"\".join((word[:-len(suffix)], \"ble\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ble\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ble\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix == \"ogi\" and word[-4] == \"l\":\r\n word = word[:-1]\r\n r1 = r1[:-1]\r\n r2 = r2[:-1]\r\n\r\n elif suffix in (\"fulli\", \"lessli\"):\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n\r\n elif suffix == \"li\" and word[-3] in self.__li_ending:\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n break\r\n\r\n # STEP 3\r\n for suffix in self.__step3_suffixes:\r\n if word.endswith(suffix):\r\n if r1.endswith(suffix):\r\n if suffix == \"tional\":\r\n word = word[:-2]\r\n r1 = r1[:-2]\r\n r2 = r2[:-2]\r\n\r\n elif suffix == \"ational\":\r\n word = \"\".join((word[:-len(suffix)], \"ate\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ate\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ate\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix == \"alize\":\r\n word = word[:-3]\r\n r1 = r1[:-3]\r\n r2 = r2[:-3]\r\n\r\n elif suffix in (\"icate\", \"iciti\", \"ical\"):\r\n word = \"\".join((word[:-len(suffix)], \"ic\"))\r\n\r\n if len(r1) >= len(suffix):\r\n r1 = \"\".join((r1[:-len(suffix)], \"ic\"))\r\n else:\r\n r1 = \"\"\r\n\r\n if len(r2) >= len(suffix):\r\n r2 = \"\".join((r2[:-len(suffix)], \"ic\"))\r\n else:\r\n r2 = \"\"\r\n\r\n elif suffix in (\"ful\", \"ness\"):\r\n word = word[:-len(suffix)]\r\n r1 = r1[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n\r\n elif suffix == \"ative\" and r2.endswith(suffix):\r\n word = word[:-5]\r\n r1 = r1[:-5]\r\n r2 = r2[:-5]\r\n break\r\n\r\n # STEP 4\r\n for suffix in self.__step4_suffixes:\r\n if word.endswith(suffix):\r\n if r2.endswith(suffix):\r\n if suffix == \"ion\":\r\n if word[-4] in \"st\":\r\n word = word[:-3]\r\n r1 = r1[:-3]\r\n r2 = r2[:-3]\r\n else:\r\n word = word[:-len(suffix)]\r\n r1 = r1[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n break\r\n\r\n # STEP 5\r\n if r2.endswith(\"l\") and word[-2] == \"l\":\r\n word = word[:-1]\r\n elif r2.endswith(\"e\"):\r\n word = word[:-1]\r\n elif r1.endswith(\"e\"):\r\n if len(word) >= 4 and (word[-2] in self.__vowels or\r\n word[-2] in \"wxY\" or\r\n word[-3] not in self.__vowels or\r\n word[-4] in self.__vowels):\r\n word = word[:-1]\r\n\r\n\r\n word = word.replace(\"Y\", \"y\")\r\n\r\n\r\n return word","title":""},{"docid":"3806a2976bca9ed225a919cd0da42cba","score":"0.65348256","text":"def stem_words(words):\r\n stemmer = LancasterStemmer()\r\n stems = []\r\n for word in words:\r\n stem = stemmer.stem(word)\r\n stems.append(stem)\r\n return stems","title":""},{"docid":"6c9051179fbc54cfcca42d27661f0c9f","score":"0.65312177","text":"def stem_words(words):\n stemmer = LancasterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"81e5d32efcea07078a4b21e085f42fb4","score":"0.6514022","text":"def stem_sentence(sentence):\r\n\r\n sentence = sentence.strip().replace('\\n',' ').replace('..',' ')\r\n new_sentence = tag_and_lem(sentence.lower()).replace('eec ','eecs ').replace(' c ',' cs ')\r\n \r\n \r\n return new_sentence","title":""},{"docid":"7f84ecf767332b277a7808eedc9cd610","score":"0.65032065","text":"def stemmerize(review):\n stemmer = SnowballStemmer(\"english\") # we are dealing with english words/reviews\n\n review = review.split()\n words = [stemmer.stem(w) for w in review]\n\n review = \" \".join(words) # joins the words array back to text\n return review","title":""},{"docid":"a627c678c6ccc292ef9acb5b514befdd","score":"0.649629","text":"def stem(s):\n s = s.lower()\n prefix =['inter', 'super', 'trans', 'under', 'anti', 'fore', 'over', 'semi', 'dis', 'mid', 'mis', 'non', 'pre', 'sub', 'de', 'en', 'em', 'in', 'im', 'il', 'ir', 're', 'un']\n suffix = ['ation', 'ition', 'ative', 'itive', 'able', 'ible', 'tion', 'less', 'ment', 'ness', 'eous', 'ious', 'ies', 'ial', 'est', 'ful', 'ing', 'ion', 'ity', 'ive', 'ous', 'al', 'ed', 'en', 'er', 'ic', 'ty', 'ly', 'es', 'e', 's', 'y']\n for pre in prefix:\n if pre == s[0:len(pre)]:\n s = s[len(pre):]\n for suf in suffix:\n if suf == s[-len(suf):]:\n s = s[0:-len(suf)]\n return s","title":""},{"docid":"4097f809c4e1857758025930ae62c335","score":"0.6495639","text":"def stem_words(words):\n stemmer = LancasterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"4097f809c4e1857758025930ae62c335","score":"0.6495639","text":"def stem_words(words):\n stemmer = LancasterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"4097f809c4e1857758025930ae62c335","score":"0.6495639","text":"def stem_words(words):\n stemmer = LancasterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"4097f809c4e1857758025930ae62c335","score":"0.6495639","text":"def stem_words(words):\n stemmer = LancasterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"4097f809c4e1857758025930ae62c335","score":"0.6495639","text":"def stem_words(words):\n stemmer = LancasterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"eeee308d671faf9661928a7d28276381","score":"0.6487098","text":"def stem_words(words):\r\n stemmer = nltk.LancasterStemmer()\r\n stems = []\r\n for word in words:\r\n stem = stemmer.stem(word)\r\n stems.append(stem)\r\n return stems","title":""},{"docid":"f26fe8ad5175f5b366d3fdde4ba0e8e8","score":"0.6475088","text":"def stem_text(text):\n\tp = PorterStemmer()\n# stemmer = nltk.stem.snowball.SnowballStemmer('english')\n\treturn ' '.join(p.stem(word) for word in text.lower().split()) # lowercasing required by the stemmer","title":""},{"docid":"4afab5dfd533758394b01e1f038b1c66","score":"0.6430942","text":"def lowercase_and_stem(_words):\n return [PorterStemmer().stem(w.lower()) for w in _words]","title":""},{"docid":"d46d6ba27359e2fd71e1e6ed8d0aedce","score":"0.63907695","text":"def _stemmer_multi_word(self, inString):\n \n stems = self._tokenize_custom_and_stem(inString)\n return ' '.join(stems)","title":""},{"docid":"5420e86a4e924f822635bd14740b35ff","score":"0.6382402","text":"def stem_words(words):\r\n stemmer = PorterStemmer()\r\n stems = []\r\n for word in words:\r\n stem = stemmer.stem(word)\r\n stems.append(stem)\r\n return stems","title":""},{"docid":"e3e942843a155b61d1bb18cc42d25aeb","score":"0.6378274","text":"def stem(review: list) -> list:\n stemmer = EnglishStemmer()\n stemmed_review = [stemmer.stem(s) for s in review]\n return stemmed_review","title":""},{"docid":"8bd40090d87c054b1f1d713e6750ed20","score":"0.6358115","text":"def apply_stemming(data):\n\n stemmer = SnowballStemmer('english')\n pattrn = re.compile(r\"[-/,\\.\\\\\\s_]\")\n\n output = []\n\n for sequence in data:\n stemmed_seq = []\n for title in sequence:\n stemmed = \"\"\n for token in re.split(pattrn, title):\n if stemmed:\n stemmed += \" \" + stemmer.stem(token)\n else:\n stemmed += stemmer.stem(token)\n stemmed_seq.append(stemmed)\n\n output.append(stemmed_seq)\n\n return [\" \".join(seq) for seq in output]","title":""},{"docid":"669777709c27c08afe8fe9daed09bbe7","score":"0.63510525","text":"def get_words_stem(self, email_body: str):\n try:\n stop_word = stopwords.words('english')\n except LookupError:\n try:\n _create_unverified_https_context = ssl._create_unverified_context\n except AttributeError:\n pass\n else:\n ssl._create_default_https_context = _create_unverified_https_context\n download(\"stopwords\")\n stop_word = stopwords.words('english')\n\n word_list = [word for word in email_body.split() if word.lower() not in stop_word]\n return \" \".join(list(map(lambda x: self.email_words_stem.stem(x), word_list)))","title":""},{"docid":"a0ff13781d72ffff06a3101a7a0cfec7","score":"0.6344794","text":"def stem_and_lower(str_):\n stemmer = snowballstemmer.stemmer(config.LANGUAGE_FULL)\n str_no_punctuation = str_.translate(REMOVE_PUNCTUATION_MAP)\n str_stemmed = stemmer.stemWords(\n map(\n lambda x: x.lower(),\n set(str_no_punctuation.split())\n )\n )\n return ' '.join(str_stemmed)","title":""},{"docid":"6fce16cc9b99d26bdf516d2da87f5de4","score":"0.633084","text":"def stem_words(words):\n stemmer = PorterStemmer()\n stems = []\n for word in words:\n stem = stemmer.stem(word)\n stems.append(stem)\n return stems","title":""},{"docid":"6c60ace48ea83e46fd5f483121903e4e","score":"0.6318394","text":"def stem_tokenizer(doc):\n\ttokens = word_tokenize(doc)\n\ttokens = [stem_word(t) for t in tokens]\n\treturn tokens","title":""},{"docid":"c3550642bdd1c0fae8c24db3538e2dfa","score":"0.63104707","text":"def process_word(word):\n # Normalize word\n normalized_word = LinguisticModules().normalize(word)\n # Stem word\n stemmed_word = LinguisticModules().stem(word)\n # Remove if stopword\n cleaned_word = \"\" if stemmed_word in LinguisticModules.stopwords else stemmed_word\n return cleaned_word","title":""},{"docid":"3de39a6f237d555073f43911e14db73e","score":"0.6308012","text":"def clean(text):\n cleaned_txt = \"\"\n if isinstance(text, str):\n ps = PorterStemmer()\n cleaned_txt = re.sub('[^a-zA-Z]', ' ', text).lower() # Remove non-ASCII characters\n cleaned_txt = [ps.stem(word) for word in cleaned_txt.split() # Remove the root of non-stop words\n if word not in set(stopwords.words('english'))]\n cleaned_txt = [word for word in cleaned_txt if wordnet.synsets(word)] # Filter out outliers\n cleaned_txt = ' '.join(cleaned_txt) # Reassign cleaned_text to a string representation\n return(cleaned_txt)","title":""},{"docid":"cef3b49efb9aa51a55ed13c9bb3b56b1","score":"0.63060194","text":"def stem_words(word_list):\n\n stemmer = PorterStemmer()\n return [stemmer.stem(word) for word in word_list]","title":""},{"docid":"c0a96fd41f52311c02199523de9ddd36","score":"0.6303913","text":"def stem(tt):\n return (nltk.stem.lancaster.LancasterStemmer().stem(tt[0]), tt[1])","title":""},{"docid":"6d8abea4bdfa4c45c069d0b91ac15c41","score":"0.6298413","text":"def normalise(word):\n word = word.lower()\n #word = stemmer.stem_word(word)\n #word = lemmatizer.lemmatize(word)\n return word","title":""},{"docid":"a5f69409bda76e13620b88e2a64f0ea8","score":"0.62947077","text":"def stemmer(self):\n\n return self._stemmer","title":""},{"docid":"a5f69409bda76e13620b88e2a64f0ea8","score":"0.62947077","text":"def stemmer(self):\n\n return self._stemmer","title":""},{"docid":"a5f69409bda76e13620b88e2a64f0ea8","score":"0.62947077","text":"def stemmer(self):\n\n return self._stemmer","title":""},{"docid":"f179ede8db7c6ec9f3d230df0d29c6d8","score":"0.6279247","text":"def stem(s: TextSeries, stem=\"snowball\", language=\"english\") -> TextSeries:\n\n if stem == \"porter\":\n stemmer = PorterStemmer()\n elif stem == \"snowball\":\n stemmer = SnowballStemmer(language)\n else:\n raise ValueError(\"stem argument must be either 'porter' of 'stemmer'\")\n\n def _stem(text):\n return \" \".join([stemmer.stem(word) for word in text])\n\n return s.str.split().apply(_stem)","title":""},{"docid":"a8951611ebf9e2d2c00e781508c46b22","score":"0.6271257","text":"def stem(self, token):\n raise NotImplementedError()","title":""},{"docid":"da111b157351e5dceac2930d0cf7c4ee","score":"0.6266199","text":"def get_stemmer(self):\n #return PorterStemmer()\n return SnowballStemmer(\"english\")","title":""},{"docid":"f86a6da983bef8dc464d94b48090b403","score":"0.6232497","text":"def noun_stem(s):\n vowels = \"aeiou\"\n\n stem = \"\"\n\n # Checks if word is an unchanging plurals\n if s in unchanging_plurals_list:\n return s\n # Checks if prefix is \"men\" (e.g. men, women)\n elif re.match(\".*men$\", s):\n stem = stem[:-3] + \"man\"\n # Else follow same rules as verb_stem (without corpus cross check)\n else:\n if re.match(\".*ies$\", s):\n if len(s) == 4 and not s[0] in vowels:\n stem = s[:-1]\n else:\n stem = s[:-3] + 'y'\n elif re.match(\".*es$\", s):\n if re.match(\".*(o|x|ch|sh|ss|zz)es$\", s):\n stem = s[:-2]\n elif re.match(\".*[^(sxyz)]es$\", s) and s[-4:-2] != 'sh' and s[-4:-2] != 'ch':\n stem = s[:-1]\n elif re.match(\".*(s|z)es$\", s) and s[-4:-1] != (\"sse\" and \"zze\"):\n stem = s[:-1]\n elif re.match(\".*s$\", s):\n if s == \"has\":\n stem = \"have\"\n elif s[-2] == 'y' and s[-3] in vowels:\n stem = s[:-1]\n elif re.match(\".*[^(sxyz)]s$\", s) and s[-4:-2] != 'sh' and s[-4:-2] != 'ch':\n stem = s[:-1]\n # if it doesn't end in \"s\"\n else:\n return s\n\n return stem","title":""},{"docid":"7795d92c4a67fd54141912b3963fa3a1","score":"0.6231186","text":"def tokenize_stem(tweet):\n tweet = \" \".join(re.split(\"[^a-zA-Z]*\", tweet.lower())).strip()\n tokens = [stemmer.stem(t) for t in tweet.split()]\n return tokens","title":""},{"docid":"387b53b83cc920cc5b941e247df58cef","score":"0.6227053","text":"def clean_text(self, text, tokenizer, stemmer):\n cleaned_text = []\n for post in text:\n cleaned_words = []\n for word in tokenizer(post):\n low_word = word.lower()\n if stemmer:\n low_word = stemmer.stem(low_word)\n cleaned_words.append(low_word)\n cleaned_text.append(\" \".join(cleaned_words))\n return cleaned_text","title":""},{"docid":"f0e09dd0ac72932d23f24813a52d4952","score":"0.6213908","text":"def stem_en_words(words):\n stemmer = LancasterStemmer()\n stems = words\n for item in range(len(stems)): # each product\n for col in range(1,3): # long- and short description\n for word in range(len(stems[item][col])): # each word\n stems[item][col][word] = stemmer.stem(stems[item][col][word])\n return stems","title":""},{"docid":"150f909b2b913f5c9474ae5f12e152e4","score":"0.6212482","text":"def verb_stem(s):\n ok = 0\n\n if (re.match(\"\\w*([^aeiousxyzh]|[^cs]h)s$\", s)):\n stem = s[:-1]\n elif (re.match(\"(\\w*)[aeiou]ys$\", s)):\n stem = s[:-1]\n elif (re.match(\"\\w+[^aeiou]ies$\", s)):\n stem = s[:-3]+'y'\n elif (re.match(\"[^aeiou]ies$\", s)):\n stem = s[:-1]\n elif (re.match(\"\\w*([ox]|ch|sh|ss|zz)es$\", s)):\n stem = s[:-2]\n elif (re.match(\"\\w*(([^s]se)|([^z]ze))s$\", s)):\n stem = s[:-1]\n elif (re.match(\"has\", s)):\n stem = \"have\"\n elif (re.match(\"\\w*([^iosxzh]|[^cs]h)es$\", s)):\n stem = s[:-1]\n else:\n stem = \"\"\n\n if (stem != \"\" and ok != 1):\n for (word, tag) in brown.tagged_words():\n if word == stem and tag in ('VB', 'VBZ'):\n return stem\n ok = 1\n break\n\n if (ok == 0):\n return \"\"","title":""},{"docid":"23c3b1dbf62191cd6deca9bfc7dae62b","score":"0.6186194","text":"def stem(s):\n if s[:3] == 'pre':\n return s[3:]\n elif s[-3:] == 'ing':\n return s[:-3]\n elif s[-3:] == 'ers':\n return s[:-3] \n elif s[-2:] == 'er':\n return s[:-2]\n elif s[-2:] == 'es':\n return s[:-2] \n elif s[-1] == 's':\n return s[:-1]\n elif s[:3] == 'anti':\n return s[4:]\n elif s[-2:] == 'en':\n return s[:-2] \n else:\n return s","title":""},{"docid":"38648c284058b3bd17a7ff491368e451","score":"0.6136485","text":"def preprocess_text(text):\n \n # Access stop word dictionary\n stop_words = set(nltk.corpus.stopwords.words('english'))\n # Initialize the Porter stemmer\n porter = nltk.PorterStemmer() \n # Remove punctuation and lowercase each word\n text = remove_punc(text).lower() \n # Remove stop words and stem each word\n return ' '.join(porter.stem(term )\n for term in text.split()\n if term not in set(stop_words))","title":""},{"docid":"14627df1341f700cb5ceb0c4c9aac8e0","score":"0.6106635","text":"def extract_words(input_string) :\n stop_words = set(stopwords.words('english'))\n #input_string = input_string.encode(\"utf-8\")\n input_string = unicode(input_string, \"utf-8\")\n input_string = input_string.lower()\n #input_string = input_string.replace(\"donald\", \"trump\")\n #input_string = input_string.replace(\"hillary\", \"clinton\")\n tokenizer = RegexpTokenizer(pattern = \"\\w+\")\n words = tokenizer.tokenize(input_string)\n stemmer = SnowballStemmer(\"english\")\n #stemmer = PorterStemmer() \n #wnl = WordNetLemmatizer()\n\n filtered_words = [w for w in words if not w in stop_words]\n filtered_words = []\n\n #filtered_words = [stemmer.stem(word) for word in words]\n\n list_of_crap = ['utf8', 'http', 'ws', 'abcn', 'www', 'com', '2d5ogyb', '2cndgea', 'ly', '2cygp9w', '2d6hx9w', 'de', 'lz', '2ccpesl', '00', 'et', '2cyazvp', '2ck2xe1', '2d2iuqb']\n \n for w in words:\n if w not in stop_words and len(w) > 1 and w not in list_of_crap:\n filtered_words = filtered_words + [stemmer.stem(w)] \n\n filtered_words = np.unique(filtered_words)\n filtered_words = filtered_words.tolist()\n #filtered_words = stemmer.stemWords(filtered_words)\n return filtered_words","title":""},{"docid":"088118e8eec24b3cc189faae81fe46a8","score":"0.6100795","text":"def stem(text, stopwords=None):\n stemmer = GreekStemmer()\n tokens = tokenize(text, stopwords)\n stems = [stemmer.stem(token.upper()).lower()\n for token in tokens]\n return stems","title":""},{"docid":"6aecdcf2ef8386085889e6b82fd2d771","score":"0.608585","text":"def stem_ge_words(words):\n stemmer = snowball.GermanStemmer()\n stems = words\n for item in range(len(stems)): # each product\n for col in range(1,3): # long- and short description\n for word in range(len(stems[item][col])): # each word\n stems[item][col][word] = stemmer.stem(stems[item][col][word])\n return stems","title":""},{"docid":"6e3f89e9b3015fd5ac62cc9fad14ce16","score":"0.6079906","text":"def stem_lem(words, documents, stem_or_lem: bool = False):\n stop_words = stopwords.words('danish')\n stop_words.extend(list(utility.load_vector_file(\"word_datasets/stopwords.csv\").values()))\n if stem_or_lem:\n # Stemming\n stemmer = DanishStemmer()\n # Update word list to use stemmed words\n translator = {}\n add = []\n remove = []\n for word in tqdm(words):\n stem = stemmer.stem(word)\n if stem != word:\n if word not in remove:\n remove.append(word)\n if stem not in add and stem not in stop_words:\n add.append(stem)\n if word not in translator and stem not in stop_words:\n translator[word] = stem\n words = [x for x in words if x not in remove]\n words.extend([x for x in add if x not in words])\n else:\n lemmer = lemmy.load(\"da\")\n # build up dictionary that translates old words into their new versions\n translator = {}\n add = []\n remove = []\n for word in tqdm(words):\n lem = lemmer.lemmatize(\"\", word)\n other = [x for x in lem if x != word]\n if len(other) > 0:\n if word not in lem and word not in remove:\n remove.append(word)\n # add all lem options if they are not stopwords\n add.extend([x for x in lem if x not in stop_words and x not in add])\n if word not in translator and lem not in stop_words:\n lem = \" \".join(lem)\n translator[word] = lem\n words = [x for x in words if x not in remove]\n words.extend([x for x in add if x not in words])\n\n # update corpus to use stemmed words\n for x in tqdm(range(len(documents))):\n sentence = documents[x]\n for i in range(len(sentence)):\n word = sentence[i]\n if word in translator:\n sentence[i] = translator[word]\n sentence = ' '.join(sentence)\n sentence = sentence.split(' ')\n documents[x] = sentence\n\n diction = gensim.corpora.Dictionary(documents)\n d_words = diction.token2id\n good_ids = [d_words[x] for x in words]\n diction.filter_tokens(good_ids=good_ids)\n diction.compactify()\n\n return diction, documents","title":""},{"docid":"9fbc026121833fe6a41ed5a36f42db00","score":"0.6079558","text":"def normalise(word):\n word = word.lower()\n word = lemmatizer.lemmatize(word, 'v')\n word = lemmatizer.lemmatize(word)\n word = stemmer.stem(word)\n return word","title":""},{"docid":"b8c3e09e5cea9e37473a96fbf10d6963","score":"0.60634375","text":"def stem(s):\n if s[-2:] == 'es':\n s = s[:-2]\n elif s[-2:] == 'ed':\n s = s[:-2]\n elif s[-2:] == 'er':\n s = s[:-2]\n elif s[-2:] == 'ly':\n s = s[:-2]\n elif s[-3:] == 'ing':\n if len(s) > 4 and s[-4] == s[-5]:\n s = s[:-4]\n elif len(s) == 4:\n s = s\n else:\n s = s[:-3]\n elif s[-1:] == 'y':\n s = s[:-1] + 'i'\n elif s[-1:] == 'e':\n s = s[:-1]\n elif s[-1:] == 's':\n s = s[:-1]\n else:\n s = s\n \n suffix_1 = ['s','y','e']\n suffix_2 = ['es','ed','er','ly']\n suffix_3 = ['ing']\n if s[-1:] in suffix_1:\n stem_rest = stem(s[:-1])\n return stem_rest\n elif s[-2:] in suffix_2:\n stem_rest = stem(s[:-2])\n return stem_rest\n elif s[-3:] in suffix_3:\n stem_rest = stem(s[:-3])\n return stem_rest\n return s","title":""},{"docid":"d64b742202ca8142f546705a5bc1dfbc","score":"0.6048927","text":"def stem(self, string, lang):\n pass","title":""},{"docid":"ad550a39ed4c8f609f37081f0405c7b8","score":"0.60383326","text":"def stem(self, word):\r\n chr_exceeded = False\r\n for i in range(len(word)):\r\n if ord(word[i]) > 255:\r\n chr_exceeded = True\r\n break\r\n\r\n if chr_exceeded:\r\n word = self.__cyrillic_to_roman(word)\r\n\r\n step1_success = False\r\n adjectival_removed = False\r\n verb_removed = False\r\n undouble_success = False\r\n superlative_removed = False\r\n\r\n rv, r2 = self.__regions_russian(word)\r\n\r\n # Step 1\r\n for suffix in self.__perfective_gerund_suffixes:\r\n if rv.endswith(suffix):\r\n if suffix in (\"v\", \"vshi\", \"vshis'\"):\r\n if (rv[-len(suffix) - 3:-len(suffix)] == \"i^a\" or\r\n rv[-len(suffix) - 1:-len(suffix)] == \"a\"):\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n step1_success = True\r\n break\r\n else:\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n step1_success = True\r\n break\r\n\r\n if not step1_success:\r\n for suffix in self.__reflexive_suffixes:\r\n if rv.endswith(suffix):\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n break\r\n\r\n for suffix in self.__adjectival_suffixes:\r\n if rv.endswith(suffix):\r\n if suffix in ('i^ushchi^ui^u', 'i^ushchi^ai^a',\r\n 'i^ushchui^u', 'i^ushchai^a', 'i^ushchoi^u',\r\n 'i^ushchei^u', 'i^ushchimi', 'i^ushchymi',\r\n 'i^ushchego', 'i^ushchogo', 'i^ushchemu',\r\n 'i^ushchomu', 'i^ushchikh', 'i^ushchykh',\r\n 'shchi^ui^u', 'shchi^ai^a', 'i^ushchee',\r\n 'i^ushchie', 'i^ushchye', 'i^ushchoe',\r\n 'i^ushchei`', 'i^ushchii`', 'i^ushchyi`',\r\n 'i^ushchoi`', 'i^ushchem', 'i^ushchim',\r\n 'i^ushchym', 'i^ushchom', 'vshi^ui^u',\r\n 'vshi^ai^a', 'shchui^u', 'shchai^a',\r\n 'shchoi^u', 'shchei^u', 'emi^ui^u',\r\n 'emi^ai^a', 'nni^ui^u', 'nni^ai^a',\r\n 'shchimi', 'shchymi', 'shchego', 'shchogo',\r\n 'shchemu', 'shchomu', 'shchikh', 'shchykh',\r\n 'vshui^u', 'vshai^a', 'vshoi^u', 'vshei^u',\r\n 'shchee', 'shchie', 'shchye', 'shchoe',\r\n 'shchei`', 'shchii`', 'shchyi`', 'shchoi`',\r\n 'shchem', 'shchim', 'shchym', 'shchom',\r\n 'vshimi', 'vshymi', 'vshego', 'vshogo',\r\n 'vshemu', 'vshomu', 'vshikh', 'vshykh',\r\n 'emui^u', 'emai^a', 'emoi^u', 'emei^u',\r\n 'nnui^u', 'nnai^a', 'nnoi^u', 'nnei^u',\r\n 'vshee', 'vshie', 'vshye', 'vshoe',\r\n 'vshei`', 'vshii`', 'vshyi`', 'vshoi`',\r\n 'vshem', 'vshim', 'vshym', 'vshom',\r\n 'emimi', 'emymi', 'emego', 'emogo',\r\n 'ememu', 'emomu', 'emikh', 'emykh',\r\n 'nnimi', 'nnymi', 'nnego', 'nnogo',\r\n 'nnemu', 'nnomu', 'nnikh', 'nnykh',\r\n 'emee', 'emie', 'emye', 'emoe', 'emei`',\r\n 'emii`', 'emyi`', 'emoi`', 'emem', 'emim',\r\n 'emym', 'emom', 'nnee', 'nnie', 'nnye',\r\n 'nnoe', 'nnei`', 'nnii`', 'nnyi`', 'nnoi`',\r\n 'nnem', 'nnim', 'nnym', 'nnom'):\r\n if (rv[-len(suffix) - 3:-len(suffix)] == \"i^a\" or\r\n rv[-len(suffix) - 1:-len(suffix)] == \"a\"):\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n adjectival_removed = True\r\n break\r\n else:\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n adjectival_removed = True\r\n break\r\n\r\n if not adjectival_removed:\r\n for suffix in self.__verb_suffixes:\r\n if rv.endswith(suffix):\r\n if suffix in (\"la\", \"na\", \"ete\", \"i`te\", \"li\",\r\n \"i`\", \"l\", \"em\", \"n\", \"lo\", \"no\",\r\n \"et\", \"i^ut\", \"ny\", \"t'\", \"esh'\",\r\n \"nno\"):\r\n if (rv[-len(suffix) - 3:-len(suffix)] == \"i^a\" or\r\n rv[-len(suffix) - 1:-len(suffix)] == \"a\"):\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n verb_removed = True\r\n break\r\n else:\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n verb_removed = True\r\n break\r\n\r\n if not adjectival_removed and not verb_removed:\r\n for suffix in self.__noun_suffixes:\r\n if rv.endswith(suffix):\r\n word = word[:-len(suffix)]\r\n r2 = r2[:-len(suffix)]\r\n rv = rv[:-len(suffix)]\r\n break\r\n\r\n # Step 2\r\n if rv.endswith(\"i\"):\r\n word = word[:-1]\r\n r2 = r2[:-1]\r\n\r\n # Step 3\r\n for suffix in self.__derivational_suffixes:\r\n if r2.endswith(suffix):\r\n word = word[:-len(suffix)]\r\n break\r\n\r\n # Step 4\r\n if word.endswith(\"nn\"):\r\n word = word[:-1]\r\n undouble_success = True\r\n\r\n if not undouble_success:\r\n for suffix in self.__superlative_suffixes:\r\n if word.endswith(suffix):\r\n word = word[:-len(suffix)]\r\n superlative_removed = True\r\n break\r\n if word.endswith(\"nn\"):\r\n word = word[:-1]\r\n\r\n if not undouble_success and not superlative_removed:\r\n if word.endswith(\"'\"):\r\n word = word[:-1]\r\n\r\n if chr_exceeded:\r\n word = self.__roman_to_cyrillic(word)\r\n\r\n return word","title":""},{"docid":"c84b66005fb115e4cccae2816dd718fd","score":"0.6032574","text":"def tokenizer_steam_words(words):\n tokenizer = RegexpTokenizer(r\"\\w+\")\n words = tokenizer.tokenize(words)\n stop = get_stop_words()\n if len(words) > 1:\n tokens = [x for x in words if x.lower() not in stop]\n else:\n tokens = words\n stemer = SpanishStemmer()\n steamed = []\n for token in tokens:\n steamed.append(stemer.stem(token))\n return \" \".join(steamed)","title":""},{"docid":"b94ac5c548d8747414561d497e832205","score":"0.6026801","text":"def stem(self, w):\n\n if len(w) < 3:\n return w\n\n first_is_y = w[0] == \"y\"\n if first_is_y:\n w = \"Y\" + w[1:]\n\n # Step 1a\n if w.endswith(\"s\"):\n if w.endswith(\"sses\"):\n w = w[:-2]\n elif w.endswith(\"ies\"):\n w = w[:-2]\n elif w[-2] != \"s\":\n w = w[:-1]\n\n # Step 1b\n\n if w.endswith(\"eed\"):\n s = w[:-3]\n if self._mgr0.match(s):\n w = w[:-1]\n else:\n m = self._ed_ing.match(w)\n if m:\n stem = m.group(1)\n if self._s_v.match(stem):\n w = stem\n if self._at_bl_iz.match(w):\n w += \"e\"\n elif self._step1b.match(w):\n w = w[:-1]\n elif self._c_v.match(w):\n w += \"e\"\n\n # Step 1c\n\n if w.endswith(\"y\"):\n stem = w[:-1]\n if self._s_v.match(stem):\n w = stem + \"i\"\n\n # Step 2\n\n m = self._step2.match(w)\n if m:\n stem = m.group(1)\n suffix = m.group(2)\n if self._mgr0.match(stem):\n w = stem + self._step2list[suffix]\n\n # Step 3\n\n m = self._step3.match(w)\n if m:\n stem = m.group(1)\n suffix = m.group(2)\n if self._mgr0.match(stem):\n w = stem + self._step3list[suffix]\n\n # Step 4\n\n m = self._step4_1.match(w)\n if m:\n stem = m.group(1)\n if self._mgr1.match(stem):\n w = stem\n else:\n m = self._step4_2.match(w)\n if m:\n stem = m.group(1) + m.group(2)\n if self._mgr1.match(stem):\n w = stem\n\n # Step 5\n\n m = self._step5.match(w)\n if m:\n stem = m.group(1)\n if self._mgr1.match(stem) or (self._meq1.match(stem) and not self._c_v.match(stem)):\n w = stem\n\n if w.endswith(\"ll\") and self._mgr1.match(w):\n w = w[:-1]\n\n if first_is_y:\n w = \"y\" + w[1:]\n\n return w","title":""},{"docid":"de3cd466043555f889ea4ad97c2e24d5","score":"0.6021961","text":"def stemmer(method,data):\n\tdata = parse_input(data)\n\tif data == False:\n\t\treturn ret_failure(703)\n\telse:\n\t\tres=[]\n\t\tif method == \"lancaster\":\n\t\t\tfor word in data:\n\t\t\t\ttry:\n\t\t\t\t\tres.append([word,LancasterSt.stem(word)])\n\t\t\t\texcept:\n\t\t\t\t\treturn ret_failure(702)\n\t\telif method == \"porter\":\n\t\t\tfor word in data:\n\t\t\t\ttry:\n\t\t\t\t\tres.append([word,PorterSt.stem(word)])\n\t\t\t\texcept:\n\t\t\t\t\treturn ret_failure(702)\n\t\telif method == 'snowball':\n\t\t\tfor word in data:\n\t\t\t\ttry:\n\t\t\t\t\tres.append([word,SnowballSt.stem(word)])\n\t\t\t\texcept:\n\t\t\t\t\treturn ret_failure(702)\n\t\telse:\n\t\t\tabort(404)\n\t\treturn ret_success(res)","title":""},{"docid":"4a253eb60f1d331287cd3c6bda3d0462","score":"0.5999327","text":"def verb_stem(s):\n # add code here\n stem = \"\"\n if (re.match(\"[a-z]*[^sxyzaeiou]s$\", s) and not s.endswith(\"chs\") and not s.endswith(\"shs\")):\n stem = s[:-1]\n elif (re.match(\"[a-z]*[aeiou]ys$\", s)):\n stem = s[:-1]\n elif (re.match(\"[a-z][a-z]*[^aeiou]ies$\", s)):\n stem = s[:-3] + 'y'\n elif (re.match(\"[^aeiou]ies$\", s)):\n stem = s[:-1]\n elif (re.match(\"[a-z]*(oes|xes|ches|shes|sses|zzes)$\", s)):\n stem = s[:-2]\n elif (re.match(\"[a-z]*[^s]ses$\", s)):\n stem = s[:-1]\n elif (re.match(\"has$\", s)):\n stem = \"have\"\n elif (re.match(\"[a-z]*[^iosxz]es$\", s) and not s.endswith(\"ches\") and not s.endswith(\"shes\")):\n stem = s[:-1]\n else:\n return \"\"\n\n if (s == \"has\" or s == \"does\" or s == \"is\"):\n return stem\n elif (stem in VB_list or stem in VBZ_list):\n return stem\n else:\n return \"\"","title":""},{"docid":"3d0695680fe1e21d2e01ae70a946c181","score":"0.59965307","text":"def process_text(text, stem=True):\n if stem:\n stemmer = PorterStemmer()\n return [stemmer.stem(word).lower() for word in word_tokenize(text) if word not in stopwords]\n else:\n return [word for word in word_tokenize(text) if word not in stopwords]","title":""},{"docid":"ebbb45295f54956bf891aa8429894080","score":"0.5963661","text":"def normalise(word):\n word = word.lower()\n word = stemmer.stem_word(word)\n word = lemmatizer.lemmatize(word)\n return word","title":""},{"docid":"fa795a74afe32ebc90df7711be4f76e3","score":"0.5960452","text":"def set_stemmer(self, stemmer):\n\n self._stemmer = stemmer","title":""},{"docid":"fa795a74afe32ebc90df7711be4f76e3","score":"0.5960452","text":"def set_stemmer(self, stemmer):\n\n self._stemmer = stemmer","title":""},{"docid":"fa795a74afe32ebc90df7711be4f76e3","score":"0.5960452","text":"def set_stemmer(self, stemmer):\n\n self._stemmer = stemmer","title":""},{"docid":"45de6bb3ff2e14579b5a8f8d17132327","score":"0.5947376","text":"def verb_stem(s):\r\n # add code here\r\n\r\n stem = extract_3s(s)\r\n\r\n if stem in verbs_singular or s in verbs_plural:\r\n return stem\r\n else:\r\n return \"\"","title":""},{"docid":"67b05ea4aa045028dd06fcf3dc717709","score":"0.5946516","text":"def stem_and_untag_phrase(phrase, tag_separator, stemmer):\n\n stemmed_phrase = \"\"\n\n for tagged_word in phrase.split():\n word = tagged_word.rsplit(tag_separator, 1)[0]\n\n if stemmed_phrase != \"\":\n stemmed_phrase += \" \"\n stemmed_phrase += stemmer.stem(word)\n\n return stemmed_phrase","title":""},{"docid":"03f7de513eec43c1fdc8daf534035c1c","score":"0.5940161","text":"def singularize(self):\n temp = [TextBlob(word) for word in self.text]\n self.text = [list(word.words.singularize())[0] for word in temp]\n tempv = [Word(word) for word in self.text]\n self.text = [word.lemmatize(\"v\") for word in tempv]","title":""},{"docid":"4c98b4a9fd251c705ed740a94d9c2abd","score":"0.5935856","text":"def _tokenize_custom_and_stem(self, text):\n \n tokens = nltk.word_tokenize(text)\n filtered = [token.lower().replace('-', ' ') for token in tokens\n if re.search('[a-zA-Z]', token) is not None]\n stems = self._stemmer_custom(filtered, self._stemmer)\n return stems","title":""},{"docid":"25a16ea39d155b45846641a0b879fba0","score":"0.59340197","text":"def process_title(title, stpwds, stemmer):\n # Convert article title to lowercase and tokenize it\n title = title.lower().split(\" \")\n # Remove the stopwords\n title = [token for token in title if token not in stpwds]\n return [stemmer.stem(token) for token in title]","title":""},{"docid":"1358427a355a56abd727541dc1c69aa9","score":"0.592576","text":"def clean_up_sentence(sentence):\n # tokenize the pattern\n sentence_words = nltk.word_tokenize(sentence)\n # stem each word\n sentence_words = clean_sequence(sentence_words)\n return sentence_words","title":""},{"docid":"e6169e61746df6cc44d12949f1411d6a","score":"0.5913184","text":"def remove_stopwords(self,text):\n stop_words = set(stopwords.words('english'))\n clean_sent = []\n for w in word_tokenize(text):\n if not w in stop_words:\n clean_sent.append(w)\n text= \" \".join(clean_sent)\n return text","title":""},{"docid":"179b626312fa297c81f83da217b807ff","score":"0.59073144","text":"def _find_stem(arr):\n # Determine size of the array\n n_items_in_array = len(arr)\n\n # Take first word from array as reference\n reference_string = arr[0]\n n_chars_in_first_item = len(reference_string)\n\n res = \"\"\n for i_char in range(n_chars_in_first_item):\n # Generate all starting substrings of our reference string\n stem = reference_string[:i_char]\n\n j_item = 1 # Retained in case of an array with only one item\n for j_item in range(1, n_items_in_array):\n # Check if the generated stem is common to to all words\n if not arr[j_item].startswith(stem):\n break\n\n # If current substring is present in all strings and its length is\n # greater than current result\n if (j_item + 1 == n_items_in_array) and (len(res) < len(stem)):\n res = stem\n\n return res","title":""},{"docid":"179b626312fa297c81f83da217b807ff","score":"0.59073144","text":"def _find_stem(arr):\n # Determine size of the array\n n_items_in_array = len(arr)\n\n # Take first word from array as reference\n reference_string = arr[0]\n n_chars_in_first_item = len(reference_string)\n\n res = \"\"\n for i_char in range(n_chars_in_first_item):\n # Generate all starting substrings of our reference string\n stem = reference_string[:i_char]\n\n j_item = 1 # Retained in case of an array with only one item\n for j_item in range(1, n_items_in_array):\n # Check if the generated stem is common to to all words\n if not arr[j_item].startswith(stem):\n break\n\n # If current substring is present in all strings and its length is\n # greater than current result\n if (j_item + 1 == n_items_in_array) and (len(res) < len(stem)):\n res = stem\n\n return res","title":""},{"docid":"0f4691c0868c52b605594026452a3f0a","score":"0.5896456","text":"def morphy_words(words):\n result = []\n\n from nltk.stem.snowball import SnowballStemmer\n stemmer = SnowballStemmer(\"english\")\n\n# words = [stemmer.stem(word) for word in words]\n for word in words:\n morphed = wn.morphy(word)\n if morphed == None:\n result.append(word)\n else:\n result.append(morphed)\n# result.append(morphed)\n\n return result","title":""},{"docid":"4b35880f1322b19ae95e27b4ac82365f","score":"0.5894465","text":"def _stemmer_custom(self, tokens, stemmer):\n \n stems = [stemmer.stem(item) for item in tokens]\n \n return stems","title":""},{"docid":"d0a78579b452c617215fa12e82d3f257","score":"0.588811","text":"def stem(w):\n \n if len(w) < 3: return w\n \n first_is_y = w[0] == \"y\"\n if first_is_y:\n w = \"Y\" + w[1:]\n \n # Step 1a\n if w.endswith(\"s\"):\n if w.endswith(\"sses\"):\n w = w[:-2]\n elif w.endswith(\"ies\"):\n w = w[:-2]\n elif w[-2] != \"s\":\n w = w[:-1]\n \n # Step 1b\n \n if w.endswith(\"eed\"):\n s = w[:-3]\n if _mgr0.match(s):\n w = w[:-1]\n else:\n m = _ed_ing.match(w)\n if m:\n stem = m.group(1)\n if _s_v.match(stem):\n w = stem\n if _at_bl_iz.match(w):\n w += \"e\"\n elif _step1b.match(w):\n w = w[:-1]\n elif _c_v.match(w):\n w += \"e\"\n \n # Step 1c\n \n if w.endswith(\"y\"):\n stem = w[:-1]\n if _s_v.match(stem):\n w = stem + \"i\"\n \n # Step 2\n \n m = _step2.match(w)\n if m:\n stem = m.group(1)\n suffix = m.group(2)\n if _mgr0.match(stem):\n w = stem + _step2list[suffix]\n \n # Step 3\n \n m = _step3.match(w)\n if m:\n stem = m.group(1)\n suffix = m.group(2)\n if _mgr0.match(stem):\n w = stem + _step3list[suffix]\n\n # Step 4\n \n m = _step4_1.match(w)\n if m:\n stem = m.group(1)\n if _mgr1.match(stem):\n w = stem\n else:\n m = _step4_2.match(w)\n if m:\n stem = m.group(1) + m.group(2)\n if _mgr1.match(stem):\n w = stem\n \n # Step 5\n \n m = _step5.match(w)\n if m:\n stem = m.group(1)\n if _mgr1.match(stem) or (_meq1.match(stem) and not _c_v.match(stem)):\n w = stem\n \n if w.endswith(\"ll\") and _mgr1.match(w):\n w = w[:-1]\n \n if first_is_y:\n w = \"y\" + w[1:]\n\n return w","title":""},{"docid":"c04a8aa707f3651e05ede39b654fbafa","score":"0.58791685","text":"def stem(self, tokens):\n return [re.sub('^(did|does)$', 'do', t) for t in tokens]","title":""},{"docid":"c0d0650434deddb7a6af5a2252a680f5","score":"0.58764225","text":"def embers_stem(x):\n x = x.lower()\n if isinstance(x, unicode) == False:\n x = x.decode('utf-8', 'ignore')\n try:\n stemmer = SnowballStemmer('spanish')\n x1 = FeatureCountVectorizer.preprocess_unicode_text(x,stemmer.stem)\n if(x1 == ''):\n x1 = x\n \n stemmer = SnowballStemmer('english')\n x2 = FeatureCountVectorizer.preprocess_unicode_text(x,stemmer.stem)\n if(x2 == ''):\n x2 = x\n \n stemmer = SnowballStemmer('portuguese')\n x3 = FeatureCountVectorizer.preprocess_unicode_text(x,stemmer.stem)\n if(x3 == ''):\n x3 = x\n # print 'success'\n return min(x1, x2, x3, key = lambda x: len(x))\n except:\n return x","title":""},{"docid":"3ab903c027b65101fb1da5fffb702bae","score":"0.5867515","text":"def getTerms(tweet, stops):\n stemming = PorterStemmer()\n tweet = tweet.replace(\"RT \", \"\").strip() # drop RT tag\n tweet = tweet.lower() # lower the characters\n tweet = re.sub(r'[^\\w\\s]','',tweet).strip() #remove puntiation\n tweet = tweet.split() # split by blank spaces\n tweet = [x for x in tweet if x not in stops] # ignore stops \n tweet = [stemming.stem(word) for word in tweet] # stem the terms\n return tweet","title":""},{"docid":"43d2fb921fb910a9491d216a459240d6","score":"0.5860012","text":"def set_stemmed(self, urit, urim, measuretype, measure, stemmed):\n \n self.initialize_scoremodel_for_keys(urit, urim, measuretype, measure)\n self.scoremodel[urit][urim][measuretype][measure][\"stemmed\"] = stemmed","title":""},{"docid":"acaf7997b552f678e70a14e2e1d517f6","score":"0.5851889","text":"def normalise(word):\n word = word.lower()\n # word = stemmer.stem_word(word) #if we consider stemmer then results comes with stemmed word, but in this case word will not match with comment\n word = lemmatizer.lemmatize(word)\n return word","title":""},{"docid":"519ec3041a438c03b1ab8ea988f3c233","score":"0.58423316","text":"def remove_stop_words(text_blob):\r\n # Get words from original text, remove the stop words and combine the\r\n # words again\r\n words = text_blob.words\r\n\r\n stop_words = [stop_word.lower()\r\n for stop_word in stopwords.words(\"english\")]\r\n\r\n words = filter(lambda word: not word.lower() in stop_words, words)\r\n\r\n return TextBlob(\" \".join(words))","title":""},{"docid":"9367e3a9de815d10c97e9d5d3bbdfe0e","score":"0.58420134","text":"def tokenize(text):\n\n text = re.sub('[^(a-zA-Z0-9)]', ' ', text.lower())\n words = word_tokenize(text)\n clean = [word for word in words if word not in stopwords.words(\"english\")]\n lemmed = [WordNetLemmatizer().lemmatize(w) for w in clean]\n stemmed = [PorterStemmer().stem(w) for w in lemmed]\n\n return stemmed","title":""},{"docid":"7ed84323938cd53010f16a82acfd4c60","score":"0.58395886","text":"def normalize_text(string, remove_stopwords=False, stem_words=False):\n strip_special_chars = re.compile(\"[^A-Za-z0-9 ]+\")\n string = string.lower()\n string = string.replace(\"<br />\", \" \")\n string = string.replace(r\"(\\().*(\\))|([^a-zA-Z'])\",' ')\n string = string.replace('&', 'and')\n string = string.replace('@', 'at')\n string = string.replace('0', 'zero')\n string = string.replace('1', 'one')\n string = string.replace('2', 'two')\n string = string.replace('3', 'three')\n string = string.replace('4', 'four')\n string = string.replace('5', 'five')\n string = string.replace('6', 'six')\n string = string.replace('7', 'seven')\n string = string.replace('8', 'eight')\n string = string.replace('9', 'nine')\n string = string.split()\n if remove_stopwords:\n stop_words = stopwords.words('english')\n string = [w for w in string if w not in stop_words]\n if stem_words:\n ps = PorterStemmer()\n string = [ps.stem(w) for w in string]\n string = ' '.join(string)\n return re.sub(strip_special_chars, \"\", string)","title":""},{"docid":"8710054be0456c0d11beff400872bcdf","score":"0.5819634","text":"def build_stemmer(self):\n if not self.stemming:\n return lambda tokens: tokens\n german_stemmer = SnowballStemmer('german')\n return lambda tokens: (german_stemmer.stem(token)\n for token in tokens if ClassifierFeatureTokenizer._pattern_url.match(token) or\n ClassifierFeatureTokenizer._pattern_word.match(token) or\n ClassifierFeatureTokenizer._pattern_entire_text.match(token))","title":""},{"docid":"30d4bff740736b951926fc8068ee479a","score":"0.581764","text":"def get_stem(self):\n raise NotImplementedError()","title":""},{"docid":"e4f573715a8147ba3ba044937aa75efe","score":"0.57977813","text":"def tokenizar_con_stemming(text: str, stemm_algorithm: str = \"snowball\") -> List[str]:\n stemmer = stemmers.get(stemm_algorithm, stemmer_lancaster)\n return [stemmer.stem(token) for token in tokenizar(text)]","title":""},{"docid":"47ef9a347402c2b6cf9e7fbc2e0ddc3c","score":"0.5794814","text":"def lemmatize_and_stem(text):\n stemmer = PorterStemmer()\n return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos=\"v\"))","title":""},{"docid":"eba3ef37352c3aa2566395853a3badc3","score":"0.5792212","text":"def normalize(words):\n words = to_lowercase(words)\n words = remove_stopwords(words)\n lemmatize_words(words)\n return words","title":""},{"docid":"e669b3452907b4f6965dc397036cff36","score":"0.5788771","text":"def get_stemmed_form(idx, word):\n result = indices.IndicesClient(_es()).analyze(index=idx, text=word, analyzer=_STEMMING_ANALYZER)\n return result['tokens'][0]['token']","title":""}],"string":"[\n {\n \"docid\": \"6eb9d5543e2474464062b5a21ffa97f7\",\n \"score\": \"0.73459184\",\n \"text\": \"def stemmatize(self,word):\\n lemma = self.lemmatizer.lemmatize(word)\\n stem = (SnowballStemmer(self.language).stem(lemma))\\n return stem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aaa75487e993bb8da339404ce8b7328\",\n \"score\": \"0.70459604\",\n \"text\": \"def stem(self, token):\\n token = self.norm(\\n token, 1\\n ) # remove diacritics which representing Arabic short vowels\\n if token in self.stop_words:\\n return token # exclude stop words from being processed\\n token = self.pre32(\\n token\\n ) # remove length three and length two prefixes in this order\\n token = self.suf32(\\n token\\n ) # remove length three and length two suffixes in this order\\n token = self.waw(\\n token\\n ) # remove connective ‘و’ if it precedes a word beginning with ‘و’\\n token = self.norm(token, 2) # normalize initial hamza to bare alif\\n # if 4 <= word length <= 7, then stem; otherwise, no stemming\\n if len(token) == 4: # length 4 word\\n token = self.pro_w4(token)\\n elif len(token) == 5: # length 5 word\\n token = self.pro_w53(token)\\n token = self.end_w5(token)\\n elif len(token) == 6: # length 6 word\\n token = self.pro_w6(token)\\n token = self.end_w6(token)\\n elif len(token) == 7: # length 7 word\\n token = self.suf1(token)\\n if len(token) == 7:\\n token = self.pre1(token)\\n if len(token) == 6:\\n token = self.pro_w6(token)\\n token = self.end_w6(token)\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5077efe8dac814cd5c98d95599a24de\",\n \"score\": \"0.69975543\",\n \"text\": \"def normalize_stemming(line):\\n words = line.split(' ')\\n words = [snowball.stem(word) for word in words]\\n line = ' '.join(words)\\n return line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c4a9f4c300152c70743a4ff6fcdb63\",\n \"score\": \"0.6982842\",\n \"text\": \"def stem(self, word):\\n \\n rules = self.rules\\n match = self.stem_expr.match(word)\\n if not match: return word\\n stem = self.strip_prefix(match.group(0))\\n \\n is_intact = True\\n continuing = True\\n while continuing:\\n pfv = self.first_vowel(stem)\\n rulelist = rules.get(stem[-1])\\n if not rulelist: break\\n \\n continuing = False\\n for ending, intact, num, append, cont in rulelist:\\n if stem.endswith(ending):\\n if intact and not is_intact: continue\\n newlen = len(stem) - num + len(append)\\n \\n if ((pfv == 0 and newlen < 2)\\n or (pfv > 0 and newlen < 3)):\\n # If word starts with vowel, minimum stem length is 2.\\n # If word starts with consonant, minimum stem length is\\n # 3.\\n continue\\n \\n is_intact = False\\n stem = stem[:0-num] + append\\n \\n continuing = cont\\n break\\n \\n return stem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05158755495e5df26240008c4e55535\",\n \"score\": \"0.69822466\",\n \"text\": \"def stemWords(self, words):\\n stemmed = []\\n for w in words:\\n stemmed.append(self.stemmer.stemWord(w))\\n return stemmed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e457c6e3ecb88be69c667d44f4f006c\",\n \"score\": \"0.6907822\",\n \"text\": \"def stem(word):\\n stemmer = WordNetLemmatizer()\\n return stemmer.lemmatize(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3159f424ce04070f6f48cb56adaa9a67\",\n \"score\": \"0.6886361\",\n \"text\": \"def stem_word(word):\\n\\treturn stemmer.stem(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed35d5fb3a1836a1ae94f1b7853138e\",\n \"score\": \"0.6872106\",\n \"text\": \"def stemmingTest():\\n t1 = Test(None, \\\"alpha\\\", 0)\\n\\n usControlDemo, usControlConvo, usDementiaDemo, usDementiaConvo,\\\\\\n usFeatures, usVocab, usLabels = makeData(t1)\\n\\n t2 = Test(None, \\\"alpha\\\", 1)\\n\\n sControlDemo, sControlConvo, sDementiaDemo, sDementiaConvo,\\\\\\n sFeatures, sVocab, sLabels = makeData(t2)\\n\\n l = []\\n l.append(usFeatures)\\n l.append(usLabels)\\n l.append(usVocab)\\n l.append(sFeatures)\\n l.append(sLabels)\\n l.append(sVocab)\\n\\n runTest(\\\"nb\\\", \\\"nb\\\", t1, t2, l,(\\\"Unstemmed\\\",\\\"Stemmed\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0ba1dc388e6c38d2a2f4ca53790da1\",\n \"score\": \"0.6821889\",\n \"text\": \"def stem(self):\\n ps = PorterStemmer()\\n self.processed = [\\n ps.stem(word) for word in self.processed\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1cabf483b6d5abc80e0de9fe5994b96\",\n \"score\": \"0.6812056\",\n \"text\": \"def stem_text(self, text):\\n stemmer = SnowballStemmer(language='english')\\n text_stemmed = [stemmer.stem(word) for word in self.text.split(' ')]\\n text_stemmed = ' '.join(text_stemmed)\\n return text_stemmed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc929a5ab875a9a5396045e41a152c43\",\n \"score\": \"0.6777241\",\n \"text\": \"def stem_words(words):\\r\\n stemmer = PorterStemmer()\\r\\n return [stemmer.stem(w) for w in words]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a218f2ac850df319080e8b8ddc9addc6\",\n \"score\": \"0.67754483\",\n \"text\": \"def stemmedWords(text):\\n stemmer = SnowballStemmer(\\\"english\\\") # Stemmer instance\\n\\n # Stem each word that is not punctuation, and store in list\\n stemmedText = [stemmer.stem(word) for word in text.split()\\n if word not in string.punctuation]\\n\\n # Join each stemmed word with a space\\n return \\\" \\\".join(stemmedText)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9862013fd462023d06f154dd4abdea0\",\n \"score\": \"0.67519945\",\n \"text\": \"def normalize(word):\\n if word not in STOP and word not in punctuation:\\n return STEM.stem(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7902e16d0dfbadbcc118118dc45a845d\",\n \"score\": \"0.6750222\",\n \"text\": \"def raw_stem(self, word):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f00165cd0ed958b9471523db54dd10\",\n \"score\": \"0.673429\",\n \"text\": \"def text_stemmer(text):\\n\\n porter = PorterStemmer()\\n stemmed_words = [porter.stem(word) for word in word_tokenize(text)]\\n\\n return(' '.join(stemmed_words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdffa40cafca4baea9769c149ad1fe91\",\n \"score\": \"0.66919714\",\n \"text\": \"def stem(word):\\n return word.lower().rstrip(\\\",.!:;'-\\\\\\\"\\\").lstrip(\\\"'\\\\\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82a576b261c3b084d8070db945dc3fe\",\n \"score\": \"0.6614961\",\n \"text\": \"def __stem(self, txt: str):\\n print('stemming...')\\n from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\\n factory = StemmerFactory()\\n stm = factory.create_stemmer()\\n result = stm.stem(txt)\\n print(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb66dab8c3ff68d23c5d4b6e2294fb4\",\n \"score\": \"0.6556869\",\n \"text\": \"def stem_concepts(self, concept):\\n concept_break = concept.split(\\\" \\\")\\n concept_break[-1] = snowball_stemmer.stem(concept_break[-1])\\n find_word = \\\" \\\" + \\\" \\\".join(concept_break)\\n return find_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8f9137a3d644795234ea81a9972289\",\n \"score\": \"0.65401185\",\n \"text\": \"def stem(self, word):\\r\\n word = word.lower()\\r\\n\\r\\n if len(word) <= 2:\\r\\n return word\\r\\n\\r\\n elif word in self.__special_words:\\r\\n return self.__special_words[word]\\r\\n\\r\\n # Map the different apostrophe characters to a single consistent one\\r\\n word = (word.replace(\\\"\\\\u2019\\\", \\\"\\\\x27\\\")\\r\\n .replace(\\\"\\\\u2018\\\", \\\"\\\\x27\\\")\\r\\n .replace(\\\"\\\\u201B\\\", \\\"\\\\x27\\\"))\\r\\n\\r\\n if word.startswith(\\\"\\\\x27\\\"):\\r\\n word = word[1:]\\r\\n\\r\\n if word.startswith(\\\"y\\\"):\\r\\n word = \\\"\\\".join((\\\"Y\\\", word[1:]))\\r\\n\\r\\n for i in range(1, len(word)):\\r\\n if word[i-1] in self.__vowels and word[i] == \\\"y\\\":\\r\\n word = \\\"\\\".join((word[:i], \\\"Y\\\", word[i+1:]))\\r\\n\\r\\n step1a_vowel_found = False\\r\\n step1b_vowel_found = False\\r\\n\\r\\n r1 = \\\"\\\"\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n if word.startswith((\\\"gener\\\", \\\"commun\\\", \\\"arsen\\\")):\\r\\n if word.startswith((\\\"gener\\\", \\\"arsen\\\")):\\r\\n r1 = word[5:]\\r\\n else:\\r\\n r1 = word[6:]\\r\\n\\r\\n for i in range(1, len(r1)):\\r\\n if r1[i] not in self.__vowels and r1[i-1] in self.__vowels:\\r\\n r2 = r1[i+1:]\\r\\n break\\r\\n else:\\r\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\r\\n\\r\\n\\r\\n # STEP 0\\r\\n for suffix in self.__step0_suffixes:\\r\\n if word.endswith(suffix):\\r\\n word = word[:-len(suffix)]\\r\\n r1 = r1[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n break\\r\\n\\r\\n # STEP 1a\\r\\n for suffix in self.__step1a_suffixes:\\r\\n if word.endswith(suffix):\\r\\n\\r\\n if suffix == \\\"sses\\\":\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n\\r\\n elif suffix in (\\\"ied\\\", \\\"ies\\\"):\\r\\n if len(word[:-len(suffix)]) > 1:\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n else:\\r\\n word = word[:-1]\\r\\n r1 = r1[:-1]\\r\\n r2 = r2[:-1]\\r\\n\\r\\n elif suffix == \\\"s\\\":\\r\\n for letter in word[:-2]:\\r\\n if letter in self.__vowels:\\r\\n step1a_vowel_found = True\\r\\n break\\r\\n\\r\\n if step1a_vowel_found:\\r\\n word = word[:-1]\\r\\n r1 = r1[:-1]\\r\\n r2 = r2[:-1]\\r\\n break\\r\\n\\r\\n # STEP 1b\\r\\n for suffix in self.__step1b_suffixes:\\r\\n if word.endswith(suffix):\\r\\n if suffix in (\\\"eed\\\", \\\"eedly\\\"):\\r\\n\\r\\n if r1.endswith(suffix):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ee\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ee\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ee\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n else:\\r\\n for letter in word[:-len(suffix)]:\\r\\n if letter in self.__vowels:\\r\\n step1b_vowel_found = True\\r\\n break\\r\\n\\r\\n if step1b_vowel_found:\\r\\n word = word[:-len(suffix)]\\r\\n r1 = r1[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n\\r\\n if word.endswith((\\\"at\\\", \\\"bl\\\", \\\"iz\\\")):\\r\\n word = \\\"\\\".join((word, \\\"e\\\"))\\r\\n r1 = \\\"\\\".join((r1, \\\"e\\\"))\\r\\n\\r\\n if len(word) > 5 or len(r1) >=3:\\r\\n r2 = \\\"\\\".join((r2, \\\"e\\\"))\\r\\n\\r\\n elif word.endswith(self.__double_consonants):\\r\\n word = word[:-1]\\r\\n r1 = r1[:-1]\\r\\n r2 = r2[:-1]\\r\\n\\r\\n elif ((r1 == \\\"\\\" and len(word) >= 3 and\\r\\n word[-1] not in self.__vowels and\\r\\n word[-1] not in \\\"wxY\\\" and\\r\\n word[-2] in self.__vowels and\\r\\n word[-3] not in self.__vowels)\\r\\n or\\r\\n (r1 == \\\"\\\" and len(word) == 2 and\\r\\n word[0] in self.__vowels and\\r\\n word[1] not in self.__vowels)):\\r\\n\\r\\n word = \\\"\\\".join((word, \\\"e\\\"))\\r\\n\\r\\n if len(r1) > 0:\\r\\n r1 = \\\"\\\".join((r1, \\\"e\\\"))\\r\\n\\r\\n if len(r2) > 0:\\r\\n r2 = \\\"\\\".join((r2, \\\"e\\\"))\\r\\n break\\r\\n\\r\\n # STEP 1c\\r\\n if len(word) > 2 and word[-1] in \\\"yY\\\" and word[-2] not in self.__vowels:\\r\\n word = \\\"\\\".join((word[:-1], \\\"i\\\"))\\r\\n if len(r1) >= 1:\\r\\n r1 = \\\"\\\".join((r1[:-1], \\\"i\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= 1:\\r\\n r2 = \\\"\\\".join((r2[:-1], \\\"i\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n # STEP 2\\r\\n for suffix in self.__step2_suffixes:\\r\\n if word.endswith(suffix):\\r\\n if r1.endswith(suffix):\\r\\n if suffix == \\\"tional\\\":\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n\\r\\n elif suffix in (\\\"enci\\\", \\\"anci\\\", \\\"abli\\\"):\\r\\n word = \\\"\\\".join((word[:-1], \\\"e\\\"))\\r\\n\\r\\n if len(r1) >= 1:\\r\\n r1 = \\\"\\\".join((r1[:-1], \\\"e\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= 1:\\r\\n r2 = \\\"\\\".join((r2[:-1], \\\"e\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix == \\\"entli\\\":\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n\\r\\n elif suffix in (\\\"izer\\\", \\\"ization\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ize\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ize\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ize\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix in (\\\"ational\\\", \\\"ation\\\", \\\"ator\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ate\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ate\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ate\\\"))\\r\\n else:\\r\\n r2 = \\\"e\\\"\\r\\n\\r\\n elif suffix in (\\\"alism\\\", \\\"aliti\\\", \\\"alli\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"al\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"al\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"al\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix == \\\"fulness\\\":\\r\\n word = word[:-4]\\r\\n r1 = r1[:-4]\\r\\n r2 = r2[:-4]\\r\\n\\r\\n elif suffix in (\\\"ousli\\\", \\\"ousness\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ous\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ous\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ous\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix in (\\\"iveness\\\", \\\"iviti\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ive\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ive\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ive\\\"))\\r\\n else:\\r\\n r2 = \\\"e\\\"\\r\\n\\r\\n elif suffix in (\\\"biliti\\\", \\\"bli\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ble\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ble\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ble\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix == \\\"ogi\\\" and word[-4] == \\\"l\\\":\\r\\n word = word[:-1]\\r\\n r1 = r1[:-1]\\r\\n r2 = r2[:-1]\\r\\n\\r\\n elif suffix in (\\\"fulli\\\", \\\"lessli\\\"):\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n\\r\\n elif suffix == \\\"li\\\" and word[-3] in self.__li_ending:\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n break\\r\\n\\r\\n # STEP 3\\r\\n for suffix in self.__step3_suffixes:\\r\\n if word.endswith(suffix):\\r\\n if r1.endswith(suffix):\\r\\n if suffix == \\\"tional\\\":\\r\\n word = word[:-2]\\r\\n r1 = r1[:-2]\\r\\n r2 = r2[:-2]\\r\\n\\r\\n elif suffix == \\\"ational\\\":\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ate\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ate\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ate\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix == \\\"alize\\\":\\r\\n word = word[:-3]\\r\\n r1 = r1[:-3]\\r\\n r2 = r2[:-3]\\r\\n\\r\\n elif suffix in (\\\"icate\\\", \\\"iciti\\\", \\\"ical\\\"):\\r\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ic\\\"))\\r\\n\\r\\n if len(r1) >= len(suffix):\\r\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ic\\\"))\\r\\n else:\\r\\n r1 = \\\"\\\"\\r\\n\\r\\n if len(r2) >= len(suffix):\\r\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ic\\\"))\\r\\n else:\\r\\n r2 = \\\"\\\"\\r\\n\\r\\n elif suffix in (\\\"ful\\\", \\\"ness\\\"):\\r\\n word = word[:-len(suffix)]\\r\\n r1 = r1[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n\\r\\n elif suffix == \\\"ative\\\" and r2.endswith(suffix):\\r\\n word = word[:-5]\\r\\n r1 = r1[:-5]\\r\\n r2 = r2[:-5]\\r\\n break\\r\\n\\r\\n # STEP 4\\r\\n for suffix in self.__step4_suffixes:\\r\\n if word.endswith(suffix):\\r\\n if r2.endswith(suffix):\\r\\n if suffix == \\\"ion\\\":\\r\\n if word[-4] in \\\"st\\\":\\r\\n word = word[:-3]\\r\\n r1 = r1[:-3]\\r\\n r2 = r2[:-3]\\r\\n else:\\r\\n word = word[:-len(suffix)]\\r\\n r1 = r1[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n break\\r\\n\\r\\n # STEP 5\\r\\n if r2.endswith(\\\"l\\\") and word[-2] == \\\"l\\\":\\r\\n word = word[:-1]\\r\\n elif r2.endswith(\\\"e\\\"):\\r\\n word = word[:-1]\\r\\n elif r1.endswith(\\\"e\\\"):\\r\\n if len(word) >= 4 and (word[-2] in self.__vowels or\\r\\n word[-2] in \\\"wxY\\\" or\\r\\n word[-3] not in self.__vowels or\\r\\n word[-4] in self.__vowels):\\r\\n word = word[:-1]\\r\\n\\r\\n\\r\\n word = word.replace(\\\"Y\\\", \\\"y\\\")\\r\\n\\r\\n\\r\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3806a2976bca9ed225a919cd0da42cba\",\n \"score\": \"0.65348256\",\n \"text\": \"def stem_words(words):\\r\\n stemmer = LancasterStemmer()\\r\\n stems = []\\r\\n for word in words:\\r\\n stem = stemmer.stem(word)\\r\\n stems.append(stem)\\r\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9051179fbc54cfcca42d27661f0c9f\",\n \"score\": \"0.65312177\",\n \"text\": \"def stem_words(words):\\n stemmer = LancasterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e5d32efcea07078a4b21e085f42fb4\",\n \"score\": \"0.6514022\",\n \"text\": \"def stem_sentence(sentence):\\r\\n\\r\\n sentence = sentence.strip().replace('\\\\n',' ').replace('..',' ')\\r\\n new_sentence = tag_and_lem(sentence.lower()).replace('eec ','eecs ').replace(' c ',' cs ')\\r\\n \\r\\n \\r\\n return new_sentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f84ecf767332b277a7808eedc9cd610\",\n \"score\": \"0.65032065\",\n \"text\": \"def stemmerize(review):\\n stemmer = SnowballStemmer(\\\"english\\\") # we are dealing with english words/reviews\\n\\n review = review.split()\\n words = [stemmer.stem(w) for w in review]\\n\\n review = \\\" \\\".join(words) # joins the words array back to text\\n return review\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a627c678c6ccc292ef9acb5b514befdd\",\n \"score\": \"0.649629\",\n \"text\": \"def stem(s):\\n s = s.lower()\\n prefix =['inter', 'super', 'trans', 'under', 'anti', 'fore', 'over', 'semi', 'dis', 'mid', 'mis', 'non', 'pre', 'sub', 'de', 'en', 'em', 'in', 'im', 'il', 'ir', 're', 'un']\\n suffix = ['ation', 'ition', 'ative', 'itive', 'able', 'ible', 'tion', 'less', 'ment', 'ness', 'eous', 'ious', 'ies', 'ial', 'est', 'ful', 'ing', 'ion', 'ity', 'ive', 'ous', 'al', 'ed', 'en', 'er', 'ic', 'ty', 'ly', 'es', 'e', 's', 'y']\\n for pre in prefix:\\n if pre == s[0:len(pre)]:\\n s = s[len(pre):]\\n for suf in suffix:\\n if suf == s[-len(suf):]:\\n s = s[0:-len(suf)]\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4097f809c4e1857758025930ae62c335\",\n \"score\": \"0.6495639\",\n \"text\": \"def stem_words(words):\\n stemmer = LancasterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4097f809c4e1857758025930ae62c335\",\n \"score\": \"0.6495639\",\n \"text\": \"def stem_words(words):\\n stemmer = LancasterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4097f809c4e1857758025930ae62c335\",\n \"score\": \"0.6495639\",\n \"text\": \"def stem_words(words):\\n stemmer = LancasterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4097f809c4e1857758025930ae62c335\",\n \"score\": \"0.6495639\",\n \"text\": \"def stem_words(words):\\n stemmer = LancasterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4097f809c4e1857758025930ae62c335\",\n \"score\": \"0.6495639\",\n \"text\": \"def stem_words(words):\\n stemmer = LancasterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeee308d671faf9661928a7d28276381\",\n \"score\": \"0.6487098\",\n \"text\": \"def stem_words(words):\\r\\n stemmer = nltk.LancasterStemmer()\\r\\n stems = []\\r\\n for word in words:\\r\\n stem = stemmer.stem(word)\\r\\n stems.append(stem)\\r\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26fe8ad5175f5b366d3fdde4ba0e8e8\",\n \"score\": \"0.6475088\",\n \"text\": \"def stem_text(text):\\n\\tp = PorterStemmer()\\n# stemmer = nltk.stem.snowball.SnowballStemmer('english')\\n\\treturn ' '.join(p.stem(word) for word in text.lower().split()) # lowercasing required by the stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4afab5dfd533758394b01e1f038b1c66\",\n \"score\": \"0.6430942\",\n \"text\": \"def lowercase_and_stem(_words):\\n return [PorterStemmer().stem(w.lower()) for w in _words]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46d6ba27359e2fd71e1e6ed8d0aedce\",\n \"score\": \"0.63907695\",\n \"text\": \"def _stemmer_multi_word(self, inString):\\n \\n stems = self._tokenize_custom_and_stem(inString)\\n return ' '.join(stems)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5420e86a4e924f822635bd14740b35ff\",\n \"score\": \"0.6382402\",\n \"text\": \"def stem_words(words):\\r\\n stemmer = PorterStemmer()\\r\\n stems = []\\r\\n for word in words:\\r\\n stem = stemmer.stem(word)\\r\\n stems.append(stem)\\r\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e942843a155b61d1bb18cc42d25aeb\",\n \"score\": \"0.6378274\",\n \"text\": \"def stem(review: list) -> list:\\n stemmer = EnglishStemmer()\\n stemmed_review = [stemmer.stem(s) for s in review]\\n return stemmed_review\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd40090d87c054b1f1d713e6750ed20\",\n \"score\": \"0.6358115\",\n \"text\": \"def apply_stemming(data):\\n\\n stemmer = SnowballStemmer('english')\\n pattrn = re.compile(r\\\"[-/,\\\\.\\\\\\\\\\\\s_]\\\")\\n\\n output = []\\n\\n for sequence in data:\\n stemmed_seq = []\\n for title in sequence:\\n stemmed = \\\"\\\"\\n for token in re.split(pattrn, title):\\n if stemmed:\\n stemmed += \\\" \\\" + stemmer.stem(token)\\n else:\\n stemmed += stemmer.stem(token)\\n stemmed_seq.append(stemmed)\\n\\n output.append(stemmed_seq)\\n\\n return [\\\" \\\".join(seq) for seq in output]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669777709c27c08afe8fe9daed09bbe7\",\n \"score\": \"0.63510525\",\n \"text\": \"def get_words_stem(self, email_body: str):\\n try:\\n stop_word = stopwords.words('english')\\n except LookupError:\\n try:\\n _create_unverified_https_context = ssl._create_unverified_context\\n except AttributeError:\\n pass\\n else:\\n ssl._create_default_https_context = _create_unverified_https_context\\n download(\\\"stopwords\\\")\\n stop_word = stopwords.words('english')\\n\\n word_list = [word for word in email_body.split() if word.lower() not in stop_word]\\n return \\\" \\\".join(list(map(lambda x: self.email_words_stem.stem(x), word_list)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ff13781d72ffff06a3101a7a0cfec7\",\n \"score\": \"0.6344794\",\n \"text\": \"def stem_and_lower(str_):\\n stemmer = snowballstemmer.stemmer(config.LANGUAGE_FULL)\\n str_no_punctuation = str_.translate(REMOVE_PUNCTUATION_MAP)\\n str_stemmed = stemmer.stemWords(\\n map(\\n lambda x: x.lower(),\\n set(str_no_punctuation.split())\\n )\\n )\\n return ' '.join(str_stemmed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fce16cc9b99d26bdf516d2da87f5de4\",\n \"score\": \"0.633084\",\n \"text\": \"def stem_words(words):\\n stemmer = PorterStemmer()\\n stems = []\\n for word in words:\\n stem = stemmer.stem(word)\\n stems.append(stem)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c60ace48ea83e46fd5f483121903e4e\",\n \"score\": \"0.6318394\",\n \"text\": \"def stem_tokenizer(doc):\\n\\ttokens = word_tokenize(doc)\\n\\ttokens = [stem_word(t) for t in tokens]\\n\\treturn tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3550642bdd1c0fae8c24db3538e2dfa\",\n \"score\": \"0.63104707\",\n \"text\": \"def process_word(word):\\n # Normalize word\\n normalized_word = LinguisticModules().normalize(word)\\n # Stem word\\n stemmed_word = LinguisticModules().stem(word)\\n # Remove if stopword\\n cleaned_word = \\\"\\\" if stemmed_word in LinguisticModules.stopwords else stemmed_word\\n return cleaned_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de39a6f237d555073f43911e14db73e\",\n \"score\": \"0.6308012\",\n \"text\": \"def clean(text):\\n cleaned_txt = \\\"\\\"\\n if isinstance(text, str):\\n ps = PorterStemmer()\\n cleaned_txt = re.sub('[^a-zA-Z]', ' ', text).lower() # Remove non-ASCII characters\\n cleaned_txt = [ps.stem(word) for word in cleaned_txt.split() # Remove the root of non-stop words\\n if word not in set(stopwords.words('english'))]\\n cleaned_txt = [word for word in cleaned_txt if wordnet.synsets(word)] # Filter out outliers\\n cleaned_txt = ' '.join(cleaned_txt) # Reassign cleaned_text to a string representation\\n return(cleaned_txt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cef3b49efb9aa51a55ed13c9bb3b56b1\",\n \"score\": \"0.63060194\",\n \"text\": \"def stem_words(word_list):\\n\\n stemmer = PorterStemmer()\\n return [stemmer.stem(word) for word in word_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a96fd41f52311c02199523de9ddd36\",\n \"score\": \"0.6303913\",\n \"text\": \"def stem(tt):\\n return (nltk.stem.lancaster.LancasterStemmer().stem(tt[0]), tt[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8abea4bdfa4c45c069d0b91ac15c41\",\n \"score\": \"0.6298413\",\n \"text\": \"def normalise(word):\\n word = word.lower()\\n #word = stemmer.stem_word(word)\\n #word = lemmatizer.lemmatize(word)\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f69409bda76e13620b88e2a64f0ea8\",\n \"score\": \"0.62947077\",\n \"text\": \"def stemmer(self):\\n\\n return self._stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f69409bda76e13620b88e2a64f0ea8\",\n \"score\": \"0.62947077\",\n \"text\": \"def stemmer(self):\\n\\n return self._stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f69409bda76e13620b88e2a64f0ea8\",\n \"score\": \"0.62947077\",\n \"text\": \"def stemmer(self):\\n\\n return self._stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f179ede8db7c6ec9f3d230df0d29c6d8\",\n \"score\": \"0.6279247\",\n \"text\": \"def stem(s: TextSeries, stem=\\\"snowball\\\", language=\\\"english\\\") -> TextSeries:\\n\\n if stem == \\\"porter\\\":\\n stemmer = PorterStemmer()\\n elif stem == \\\"snowball\\\":\\n stemmer = SnowballStemmer(language)\\n else:\\n raise ValueError(\\\"stem argument must be either 'porter' of 'stemmer'\\\")\\n\\n def _stem(text):\\n return \\\" \\\".join([stemmer.stem(word) for word in text])\\n\\n return s.str.split().apply(_stem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8951611ebf9e2d2c00e781508c46b22\",\n \"score\": \"0.6271257\",\n \"text\": \"def stem(self, token):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da111b157351e5dceac2930d0cf7c4ee\",\n \"score\": \"0.6266199\",\n \"text\": \"def get_stemmer(self):\\n #return PorterStemmer()\\n return SnowballStemmer(\\\"english\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f86a6da983bef8dc464d94b48090b403\",\n \"score\": \"0.6232497\",\n \"text\": \"def noun_stem(s):\\n vowels = \\\"aeiou\\\"\\n\\n stem = \\\"\\\"\\n\\n # Checks if word is an unchanging plurals\\n if s in unchanging_plurals_list:\\n return s\\n # Checks if prefix is \\\"men\\\" (e.g. men, women)\\n elif re.match(\\\".*men$\\\", s):\\n stem = stem[:-3] + \\\"man\\\"\\n # Else follow same rules as verb_stem (without corpus cross check)\\n else:\\n if re.match(\\\".*ies$\\\", s):\\n if len(s) == 4 and not s[0] in vowels:\\n stem = s[:-1]\\n else:\\n stem = s[:-3] + 'y'\\n elif re.match(\\\".*es$\\\", s):\\n if re.match(\\\".*(o|x|ch|sh|ss|zz)es$\\\", s):\\n stem = s[:-2]\\n elif re.match(\\\".*[^(sxyz)]es$\\\", s) and s[-4:-2] != 'sh' and s[-4:-2] != 'ch':\\n stem = s[:-1]\\n elif re.match(\\\".*(s|z)es$\\\", s) and s[-4:-1] != (\\\"sse\\\" and \\\"zze\\\"):\\n stem = s[:-1]\\n elif re.match(\\\".*s$\\\", s):\\n if s == \\\"has\\\":\\n stem = \\\"have\\\"\\n elif s[-2] == 'y' and s[-3] in vowels:\\n stem = s[:-1]\\n elif re.match(\\\".*[^(sxyz)]s$\\\", s) and s[-4:-2] != 'sh' and s[-4:-2] != 'ch':\\n stem = s[:-1]\\n # if it doesn't end in \\\"s\\\"\\n else:\\n return s\\n\\n return stem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7795d92c4a67fd54141912b3963fa3a1\",\n \"score\": \"0.6231186\",\n \"text\": \"def tokenize_stem(tweet):\\n tweet = \\\" \\\".join(re.split(\\\"[^a-zA-Z]*\\\", tweet.lower())).strip()\\n tokens = [stemmer.stem(t) for t in tweet.split()]\\n return tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387b53b83cc920cc5b941e247df58cef\",\n \"score\": \"0.6227053\",\n \"text\": \"def clean_text(self, text, tokenizer, stemmer):\\n cleaned_text = []\\n for post in text:\\n cleaned_words = []\\n for word in tokenizer(post):\\n low_word = word.lower()\\n if stemmer:\\n low_word = stemmer.stem(low_word)\\n cleaned_words.append(low_word)\\n cleaned_text.append(\\\" \\\".join(cleaned_words))\\n return cleaned_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e09dd0ac72932d23f24813a52d4952\",\n \"score\": \"0.6213908\",\n \"text\": \"def stem_en_words(words):\\n stemmer = LancasterStemmer()\\n stems = words\\n for item in range(len(stems)): # each product\\n for col in range(1,3): # long- and short description\\n for word in range(len(stems[item][col])): # each word\\n stems[item][col][word] = stemmer.stem(stems[item][col][word])\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"150f909b2b913f5c9474ae5f12e152e4\",\n \"score\": \"0.6212482\",\n \"text\": \"def verb_stem(s):\\n ok = 0\\n\\n if (re.match(\\\"\\\\w*([^aeiousxyzh]|[^cs]h)s$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"(\\\\w*)[aeiou]ys$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"\\\\w+[^aeiou]ies$\\\", s)):\\n stem = s[:-3]+'y'\\n elif (re.match(\\\"[^aeiou]ies$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"\\\\w*([ox]|ch|sh|ss|zz)es$\\\", s)):\\n stem = s[:-2]\\n elif (re.match(\\\"\\\\w*(([^s]se)|([^z]ze))s$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"has\\\", s)):\\n stem = \\\"have\\\"\\n elif (re.match(\\\"\\\\w*([^iosxzh]|[^cs]h)es$\\\", s)):\\n stem = s[:-1]\\n else:\\n stem = \\\"\\\"\\n\\n if (stem != \\\"\\\" and ok != 1):\\n for (word, tag) in brown.tagged_words():\\n if word == stem and tag in ('VB', 'VBZ'):\\n return stem\\n ok = 1\\n break\\n\\n if (ok == 0):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c3b1dbf62191cd6deca9bfc7dae62b\",\n \"score\": \"0.6186194\",\n \"text\": \"def stem(s):\\n if s[:3] == 'pre':\\n return s[3:]\\n elif s[-3:] == 'ing':\\n return s[:-3]\\n elif s[-3:] == 'ers':\\n return s[:-3] \\n elif s[-2:] == 'er':\\n return s[:-2]\\n elif s[-2:] == 'es':\\n return s[:-2] \\n elif s[-1] == 's':\\n return s[:-1]\\n elif s[:3] == 'anti':\\n return s[4:]\\n elif s[-2:] == 'en':\\n return s[:-2] \\n else:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38648c284058b3bd17a7ff491368e451\",\n \"score\": \"0.6136485\",\n \"text\": \"def preprocess_text(text):\\n \\n # Access stop word dictionary\\n stop_words = set(nltk.corpus.stopwords.words('english'))\\n # Initialize the Porter stemmer\\n porter = nltk.PorterStemmer() \\n # Remove punctuation and lowercase each word\\n text = remove_punc(text).lower() \\n # Remove stop words and stem each word\\n return ' '.join(porter.stem(term )\\n for term in text.split()\\n if term not in set(stop_words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14627df1341f700cb5ceb0c4c9aac8e0\",\n \"score\": \"0.6106635\",\n \"text\": \"def extract_words(input_string) :\\n stop_words = set(stopwords.words('english'))\\n #input_string = input_string.encode(\\\"utf-8\\\")\\n input_string = unicode(input_string, \\\"utf-8\\\")\\n input_string = input_string.lower()\\n #input_string = input_string.replace(\\\"donald\\\", \\\"trump\\\")\\n #input_string = input_string.replace(\\\"hillary\\\", \\\"clinton\\\")\\n tokenizer = RegexpTokenizer(pattern = \\\"\\\\w+\\\")\\n words = tokenizer.tokenize(input_string)\\n stemmer = SnowballStemmer(\\\"english\\\")\\n #stemmer = PorterStemmer() \\n #wnl = WordNetLemmatizer()\\n\\n filtered_words = [w for w in words if not w in stop_words]\\n filtered_words = []\\n\\n #filtered_words = [stemmer.stem(word) for word in words]\\n\\n list_of_crap = ['utf8', 'http', 'ws', 'abcn', 'www', 'com', '2d5ogyb', '2cndgea', 'ly', '2cygp9w', '2d6hx9w', 'de', 'lz', '2ccpesl', '00', 'et', '2cyazvp', '2ck2xe1', '2d2iuqb']\\n \\n for w in words:\\n if w not in stop_words and len(w) > 1 and w not in list_of_crap:\\n filtered_words = filtered_words + [stemmer.stem(w)] \\n\\n filtered_words = np.unique(filtered_words)\\n filtered_words = filtered_words.tolist()\\n #filtered_words = stemmer.stemWords(filtered_words)\\n return filtered_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088118e8eec24b3cc189faae81fe46a8\",\n \"score\": \"0.6100795\",\n \"text\": \"def stem(text, stopwords=None):\\n stemmer = GreekStemmer()\\n tokens = tokenize(text, stopwords)\\n stems = [stemmer.stem(token.upper()).lower()\\n for token in tokens]\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aecdcf2ef8386085889e6b82fd2d771\",\n \"score\": \"0.608585\",\n \"text\": \"def stem_ge_words(words):\\n stemmer = snowball.GermanStemmer()\\n stems = words\\n for item in range(len(stems)): # each product\\n for col in range(1,3): # long- and short description\\n for word in range(len(stems[item][col])): # each word\\n stems[item][col][word] = stemmer.stem(stems[item][col][word])\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3f89e9b3015fd5ac62cc9fad14ce16\",\n \"score\": \"0.6079906\",\n \"text\": \"def stem_lem(words, documents, stem_or_lem: bool = False):\\n stop_words = stopwords.words('danish')\\n stop_words.extend(list(utility.load_vector_file(\\\"word_datasets/stopwords.csv\\\").values()))\\n if stem_or_lem:\\n # Stemming\\n stemmer = DanishStemmer()\\n # Update word list to use stemmed words\\n translator = {}\\n add = []\\n remove = []\\n for word in tqdm(words):\\n stem = stemmer.stem(word)\\n if stem != word:\\n if word not in remove:\\n remove.append(word)\\n if stem not in add and stem not in stop_words:\\n add.append(stem)\\n if word not in translator and stem not in stop_words:\\n translator[word] = stem\\n words = [x for x in words if x not in remove]\\n words.extend([x for x in add if x not in words])\\n else:\\n lemmer = lemmy.load(\\\"da\\\")\\n # build up dictionary that translates old words into their new versions\\n translator = {}\\n add = []\\n remove = []\\n for word in tqdm(words):\\n lem = lemmer.lemmatize(\\\"\\\", word)\\n other = [x for x in lem if x != word]\\n if len(other) > 0:\\n if word not in lem and word not in remove:\\n remove.append(word)\\n # add all lem options if they are not stopwords\\n add.extend([x for x in lem if x not in stop_words and x not in add])\\n if word not in translator and lem not in stop_words:\\n lem = \\\" \\\".join(lem)\\n translator[word] = lem\\n words = [x for x in words if x not in remove]\\n words.extend([x for x in add if x not in words])\\n\\n # update corpus to use stemmed words\\n for x in tqdm(range(len(documents))):\\n sentence = documents[x]\\n for i in range(len(sentence)):\\n word = sentence[i]\\n if word in translator:\\n sentence[i] = translator[word]\\n sentence = ' '.join(sentence)\\n sentence = sentence.split(' ')\\n documents[x] = sentence\\n\\n diction = gensim.corpora.Dictionary(documents)\\n d_words = diction.token2id\\n good_ids = [d_words[x] for x in words]\\n diction.filter_tokens(good_ids=good_ids)\\n diction.compactify()\\n\\n return diction, documents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fbc026121833fe6a41ed5a36f42db00\",\n \"score\": \"0.6079558\",\n \"text\": \"def normalise(word):\\n word = word.lower()\\n word = lemmatizer.lemmatize(word, 'v')\\n word = lemmatizer.lemmatize(word)\\n word = stemmer.stem(word)\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c3e09e5cea9e37473a96fbf10d6963\",\n \"score\": \"0.60634375\",\n \"text\": \"def stem(s):\\n if s[-2:] == 'es':\\n s = s[:-2]\\n elif s[-2:] == 'ed':\\n s = s[:-2]\\n elif s[-2:] == 'er':\\n s = s[:-2]\\n elif s[-2:] == 'ly':\\n s = s[:-2]\\n elif s[-3:] == 'ing':\\n if len(s) > 4 and s[-4] == s[-5]:\\n s = s[:-4]\\n elif len(s) == 4:\\n s = s\\n else:\\n s = s[:-3]\\n elif s[-1:] == 'y':\\n s = s[:-1] + 'i'\\n elif s[-1:] == 'e':\\n s = s[:-1]\\n elif s[-1:] == 's':\\n s = s[:-1]\\n else:\\n s = s\\n \\n suffix_1 = ['s','y','e']\\n suffix_2 = ['es','ed','er','ly']\\n suffix_3 = ['ing']\\n if s[-1:] in suffix_1:\\n stem_rest = stem(s[:-1])\\n return stem_rest\\n elif s[-2:] in suffix_2:\\n stem_rest = stem(s[:-2])\\n return stem_rest\\n elif s[-3:] in suffix_3:\\n stem_rest = stem(s[:-3])\\n return stem_rest\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64b742202ca8142f546705a5bc1dfbc\",\n \"score\": \"0.6048927\",\n \"text\": \"def stem(self, string, lang):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad550a39ed4c8f609f37081f0405c7b8\",\n \"score\": \"0.60383326\",\n \"text\": \"def stem(self, word):\\r\\n chr_exceeded = False\\r\\n for i in range(len(word)):\\r\\n if ord(word[i]) > 255:\\r\\n chr_exceeded = True\\r\\n break\\r\\n\\r\\n if chr_exceeded:\\r\\n word = self.__cyrillic_to_roman(word)\\r\\n\\r\\n step1_success = False\\r\\n adjectival_removed = False\\r\\n verb_removed = False\\r\\n undouble_success = False\\r\\n superlative_removed = False\\r\\n\\r\\n rv, r2 = self.__regions_russian(word)\\r\\n\\r\\n # Step 1\\r\\n for suffix in self.__perfective_gerund_suffixes:\\r\\n if rv.endswith(suffix):\\r\\n if suffix in (\\\"v\\\", \\\"vshi\\\", \\\"vshis'\\\"):\\r\\n if (rv[-len(suffix) - 3:-len(suffix)] == \\\"i^a\\\" or\\r\\n rv[-len(suffix) - 1:-len(suffix)] == \\\"a\\\"):\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n step1_success = True\\r\\n break\\r\\n else:\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n step1_success = True\\r\\n break\\r\\n\\r\\n if not step1_success:\\r\\n for suffix in self.__reflexive_suffixes:\\r\\n if rv.endswith(suffix):\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n break\\r\\n\\r\\n for suffix in self.__adjectival_suffixes:\\r\\n if rv.endswith(suffix):\\r\\n if suffix in ('i^ushchi^ui^u', 'i^ushchi^ai^a',\\r\\n 'i^ushchui^u', 'i^ushchai^a', 'i^ushchoi^u',\\r\\n 'i^ushchei^u', 'i^ushchimi', 'i^ushchymi',\\r\\n 'i^ushchego', 'i^ushchogo', 'i^ushchemu',\\r\\n 'i^ushchomu', 'i^ushchikh', 'i^ushchykh',\\r\\n 'shchi^ui^u', 'shchi^ai^a', 'i^ushchee',\\r\\n 'i^ushchie', 'i^ushchye', 'i^ushchoe',\\r\\n 'i^ushchei`', 'i^ushchii`', 'i^ushchyi`',\\r\\n 'i^ushchoi`', 'i^ushchem', 'i^ushchim',\\r\\n 'i^ushchym', 'i^ushchom', 'vshi^ui^u',\\r\\n 'vshi^ai^a', 'shchui^u', 'shchai^a',\\r\\n 'shchoi^u', 'shchei^u', 'emi^ui^u',\\r\\n 'emi^ai^a', 'nni^ui^u', 'nni^ai^a',\\r\\n 'shchimi', 'shchymi', 'shchego', 'shchogo',\\r\\n 'shchemu', 'shchomu', 'shchikh', 'shchykh',\\r\\n 'vshui^u', 'vshai^a', 'vshoi^u', 'vshei^u',\\r\\n 'shchee', 'shchie', 'shchye', 'shchoe',\\r\\n 'shchei`', 'shchii`', 'shchyi`', 'shchoi`',\\r\\n 'shchem', 'shchim', 'shchym', 'shchom',\\r\\n 'vshimi', 'vshymi', 'vshego', 'vshogo',\\r\\n 'vshemu', 'vshomu', 'vshikh', 'vshykh',\\r\\n 'emui^u', 'emai^a', 'emoi^u', 'emei^u',\\r\\n 'nnui^u', 'nnai^a', 'nnoi^u', 'nnei^u',\\r\\n 'vshee', 'vshie', 'vshye', 'vshoe',\\r\\n 'vshei`', 'vshii`', 'vshyi`', 'vshoi`',\\r\\n 'vshem', 'vshim', 'vshym', 'vshom',\\r\\n 'emimi', 'emymi', 'emego', 'emogo',\\r\\n 'ememu', 'emomu', 'emikh', 'emykh',\\r\\n 'nnimi', 'nnymi', 'nnego', 'nnogo',\\r\\n 'nnemu', 'nnomu', 'nnikh', 'nnykh',\\r\\n 'emee', 'emie', 'emye', 'emoe', 'emei`',\\r\\n 'emii`', 'emyi`', 'emoi`', 'emem', 'emim',\\r\\n 'emym', 'emom', 'nnee', 'nnie', 'nnye',\\r\\n 'nnoe', 'nnei`', 'nnii`', 'nnyi`', 'nnoi`',\\r\\n 'nnem', 'nnim', 'nnym', 'nnom'):\\r\\n if (rv[-len(suffix) - 3:-len(suffix)] == \\\"i^a\\\" or\\r\\n rv[-len(suffix) - 1:-len(suffix)] == \\\"a\\\"):\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n adjectival_removed = True\\r\\n break\\r\\n else:\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n adjectival_removed = True\\r\\n break\\r\\n\\r\\n if not adjectival_removed:\\r\\n for suffix in self.__verb_suffixes:\\r\\n if rv.endswith(suffix):\\r\\n if suffix in (\\\"la\\\", \\\"na\\\", \\\"ete\\\", \\\"i`te\\\", \\\"li\\\",\\r\\n \\\"i`\\\", \\\"l\\\", \\\"em\\\", \\\"n\\\", \\\"lo\\\", \\\"no\\\",\\r\\n \\\"et\\\", \\\"i^ut\\\", \\\"ny\\\", \\\"t'\\\", \\\"esh'\\\",\\r\\n \\\"nno\\\"):\\r\\n if (rv[-len(suffix) - 3:-len(suffix)] == \\\"i^a\\\" or\\r\\n rv[-len(suffix) - 1:-len(suffix)] == \\\"a\\\"):\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n verb_removed = True\\r\\n break\\r\\n else:\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n verb_removed = True\\r\\n break\\r\\n\\r\\n if not adjectival_removed and not verb_removed:\\r\\n for suffix in self.__noun_suffixes:\\r\\n if rv.endswith(suffix):\\r\\n word = word[:-len(suffix)]\\r\\n r2 = r2[:-len(suffix)]\\r\\n rv = rv[:-len(suffix)]\\r\\n break\\r\\n\\r\\n # Step 2\\r\\n if rv.endswith(\\\"i\\\"):\\r\\n word = word[:-1]\\r\\n r2 = r2[:-1]\\r\\n\\r\\n # Step 3\\r\\n for suffix in self.__derivational_suffixes:\\r\\n if r2.endswith(suffix):\\r\\n word = word[:-len(suffix)]\\r\\n break\\r\\n\\r\\n # Step 4\\r\\n if word.endswith(\\\"nn\\\"):\\r\\n word = word[:-1]\\r\\n undouble_success = True\\r\\n\\r\\n if not undouble_success:\\r\\n for suffix in self.__superlative_suffixes:\\r\\n if word.endswith(suffix):\\r\\n word = word[:-len(suffix)]\\r\\n superlative_removed = True\\r\\n break\\r\\n if word.endswith(\\\"nn\\\"):\\r\\n word = word[:-1]\\r\\n\\r\\n if not undouble_success and not superlative_removed:\\r\\n if word.endswith(\\\"'\\\"):\\r\\n word = word[:-1]\\r\\n\\r\\n if chr_exceeded:\\r\\n word = self.__roman_to_cyrillic(word)\\r\\n\\r\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c84b66005fb115e4cccae2816dd718fd\",\n \"score\": \"0.6032574\",\n \"text\": \"def tokenizer_steam_words(words):\\n tokenizer = RegexpTokenizer(r\\\"\\\\w+\\\")\\n words = tokenizer.tokenize(words)\\n stop = get_stop_words()\\n if len(words) > 1:\\n tokens = [x for x in words if x.lower() not in stop]\\n else:\\n tokens = words\\n stemer = SpanishStemmer()\\n steamed = []\\n for token in tokens:\\n steamed.append(stemer.stem(token))\\n return \\\" \\\".join(steamed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94ac5c548d8747414561d497e832205\",\n \"score\": \"0.6026801\",\n \"text\": \"def stem(self, w):\\n\\n if len(w) < 3:\\n return w\\n\\n first_is_y = w[0] == \\\"y\\\"\\n if first_is_y:\\n w = \\\"Y\\\" + w[1:]\\n\\n # Step 1a\\n if w.endswith(\\\"s\\\"):\\n if w.endswith(\\\"sses\\\"):\\n w = w[:-2]\\n elif w.endswith(\\\"ies\\\"):\\n w = w[:-2]\\n elif w[-2] != \\\"s\\\":\\n w = w[:-1]\\n\\n # Step 1b\\n\\n if w.endswith(\\\"eed\\\"):\\n s = w[:-3]\\n if self._mgr0.match(s):\\n w = w[:-1]\\n else:\\n m = self._ed_ing.match(w)\\n if m:\\n stem = m.group(1)\\n if self._s_v.match(stem):\\n w = stem\\n if self._at_bl_iz.match(w):\\n w += \\\"e\\\"\\n elif self._step1b.match(w):\\n w = w[:-1]\\n elif self._c_v.match(w):\\n w += \\\"e\\\"\\n\\n # Step 1c\\n\\n if w.endswith(\\\"y\\\"):\\n stem = w[:-1]\\n if self._s_v.match(stem):\\n w = stem + \\\"i\\\"\\n\\n # Step 2\\n\\n m = self._step2.match(w)\\n if m:\\n stem = m.group(1)\\n suffix = m.group(2)\\n if self._mgr0.match(stem):\\n w = stem + self._step2list[suffix]\\n\\n # Step 3\\n\\n m = self._step3.match(w)\\n if m:\\n stem = m.group(1)\\n suffix = m.group(2)\\n if self._mgr0.match(stem):\\n w = stem + self._step3list[suffix]\\n\\n # Step 4\\n\\n m = self._step4_1.match(w)\\n if m:\\n stem = m.group(1)\\n if self._mgr1.match(stem):\\n w = stem\\n else:\\n m = self._step4_2.match(w)\\n if m:\\n stem = m.group(1) + m.group(2)\\n if self._mgr1.match(stem):\\n w = stem\\n\\n # Step 5\\n\\n m = self._step5.match(w)\\n if m:\\n stem = m.group(1)\\n if self._mgr1.match(stem) or (self._meq1.match(stem) and not self._c_v.match(stem)):\\n w = stem\\n\\n if w.endswith(\\\"ll\\\") and self._mgr1.match(w):\\n w = w[:-1]\\n\\n if first_is_y:\\n w = \\\"y\\\" + w[1:]\\n\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3cd466043555f889ea4ad97c2e24d5\",\n \"score\": \"0.6021961\",\n \"text\": \"def stemmer(method,data):\\n\\tdata = parse_input(data)\\n\\tif data == False:\\n\\t\\treturn ret_failure(703)\\n\\telse:\\n\\t\\tres=[]\\n\\t\\tif method == \\\"lancaster\\\":\\n\\t\\t\\tfor word in data:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tres.append([word,LancasterSt.stem(word)])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\treturn ret_failure(702)\\n\\t\\telif method == \\\"porter\\\":\\n\\t\\t\\tfor word in data:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tres.append([word,PorterSt.stem(word)])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\treturn ret_failure(702)\\n\\t\\telif method == 'snowball':\\n\\t\\t\\tfor word in data:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tres.append([word,SnowballSt.stem(word)])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\treturn ret_failure(702)\\n\\t\\telse:\\n\\t\\t\\tabort(404)\\n\\t\\treturn ret_success(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a253eb60f1d331287cd3c6bda3d0462\",\n \"score\": \"0.5999327\",\n \"text\": \"def verb_stem(s):\\n # add code here\\n stem = \\\"\\\"\\n if (re.match(\\\"[a-z]*[^sxyzaeiou]s$\\\", s) and not s.endswith(\\\"chs\\\") and not s.endswith(\\\"shs\\\")):\\n stem = s[:-1]\\n elif (re.match(\\\"[a-z]*[aeiou]ys$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"[a-z][a-z]*[^aeiou]ies$\\\", s)):\\n stem = s[:-3] + 'y'\\n elif (re.match(\\\"[^aeiou]ies$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"[a-z]*(oes|xes|ches|shes|sses|zzes)$\\\", s)):\\n stem = s[:-2]\\n elif (re.match(\\\"[a-z]*[^s]ses$\\\", s)):\\n stem = s[:-1]\\n elif (re.match(\\\"has$\\\", s)):\\n stem = \\\"have\\\"\\n elif (re.match(\\\"[a-z]*[^iosxz]es$\\\", s) and not s.endswith(\\\"ches\\\") and not s.endswith(\\\"shes\\\")):\\n stem = s[:-1]\\n else:\\n return \\\"\\\"\\n\\n if (s == \\\"has\\\" or s == \\\"does\\\" or s == \\\"is\\\"):\\n return stem\\n elif (stem in VB_list or stem in VBZ_list):\\n return stem\\n else:\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0695680fe1e21d2e01ae70a946c181\",\n \"score\": \"0.59965307\",\n \"text\": \"def process_text(text, stem=True):\\n if stem:\\n stemmer = PorterStemmer()\\n return [stemmer.stem(word).lower() for word in word_tokenize(text) if word not in stopwords]\\n else:\\n return [word for word in word_tokenize(text) if word not in stopwords]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebbb45295f54956bf891aa8429894080\",\n \"score\": \"0.5963661\",\n \"text\": \"def normalise(word):\\n word = word.lower()\\n word = stemmer.stem_word(word)\\n word = lemmatizer.lemmatize(word)\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa795a74afe32ebc90df7711be4f76e3\",\n \"score\": \"0.5960452\",\n \"text\": \"def set_stemmer(self, stemmer):\\n\\n self._stemmer = stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa795a74afe32ebc90df7711be4f76e3\",\n \"score\": \"0.5960452\",\n \"text\": \"def set_stemmer(self, stemmer):\\n\\n self._stemmer = stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa795a74afe32ebc90df7711be4f76e3\",\n \"score\": \"0.5960452\",\n \"text\": \"def set_stemmer(self, stemmer):\\n\\n self._stemmer = stemmer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45de6bb3ff2e14579b5a8f8d17132327\",\n \"score\": \"0.5947376\",\n \"text\": \"def verb_stem(s):\\r\\n # add code here\\r\\n\\r\\n stem = extract_3s(s)\\r\\n\\r\\n if stem in verbs_singular or s in verbs_plural:\\r\\n return stem\\r\\n else:\\r\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b05ea4aa045028dd06fcf3dc717709\",\n \"score\": \"0.5946516\",\n \"text\": \"def stem_and_untag_phrase(phrase, tag_separator, stemmer):\\n\\n stemmed_phrase = \\\"\\\"\\n\\n for tagged_word in phrase.split():\\n word = tagged_word.rsplit(tag_separator, 1)[0]\\n\\n if stemmed_phrase != \\\"\\\":\\n stemmed_phrase += \\\" \\\"\\n stemmed_phrase += stemmer.stem(word)\\n\\n return stemmed_phrase\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03f7de513eec43c1fdc8daf534035c1c\",\n \"score\": \"0.5940161\",\n \"text\": \"def singularize(self):\\n temp = [TextBlob(word) for word in self.text]\\n self.text = [list(word.words.singularize())[0] for word in temp]\\n tempv = [Word(word) for word in self.text]\\n self.text = [word.lemmatize(\\\"v\\\") for word in tempv]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c98b4a9fd251c705ed740a94d9c2abd\",\n \"score\": \"0.5935856\",\n \"text\": \"def _tokenize_custom_and_stem(self, text):\\n \\n tokens = nltk.word_tokenize(text)\\n filtered = [token.lower().replace('-', ' ') for token in tokens\\n if re.search('[a-zA-Z]', token) is not None]\\n stems = self._stemmer_custom(filtered, self._stemmer)\\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a16ea39d155b45846641a0b879fba0\",\n \"score\": \"0.59340197\",\n \"text\": \"def process_title(title, stpwds, stemmer):\\n # Convert article title to lowercase and tokenize it\\n title = title.lower().split(\\\" \\\")\\n # Remove the stopwords\\n title = [token for token in title if token not in stpwds]\\n return [stemmer.stem(token) for token in title]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1358427a355a56abd727541dc1c69aa9\",\n \"score\": \"0.592576\",\n \"text\": \"def clean_up_sentence(sentence):\\n # tokenize the pattern\\n sentence_words = nltk.word_tokenize(sentence)\\n # stem each word\\n sentence_words = clean_sequence(sentence_words)\\n return sentence_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6169e61746df6cc44d12949f1411d6a\",\n \"score\": \"0.5913184\",\n \"text\": \"def remove_stopwords(self,text):\\n stop_words = set(stopwords.words('english'))\\n clean_sent = []\\n for w in word_tokenize(text):\\n if not w in stop_words:\\n clean_sent.append(w)\\n text= \\\" \\\".join(clean_sent)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179b626312fa297c81f83da217b807ff\",\n \"score\": \"0.59073144\",\n \"text\": \"def _find_stem(arr):\\n # Determine size of the array\\n n_items_in_array = len(arr)\\n\\n # Take first word from array as reference\\n reference_string = arr[0]\\n n_chars_in_first_item = len(reference_string)\\n\\n res = \\\"\\\"\\n for i_char in range(n_chars_in_first_item):\\n # Generate all starting substrings of our reference string\\n stem = reference_string[:i_char]\\n\\n j_item = 1 # Retained in case of an array with only one item\\n for j_item in range(1, n_items_in_array):\\n # Check if the generated stem is common to to all words\\n if not arr[j_item].startswith(stem):\\n break\\n\\n # If current substring is present in all strings and its length is\\n # greater than current result\\n if (j_item + 1 == n_items_in_array) and (len(res) < len(stem)):\\n res = stem\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179b626312fa297c81f83da217b807ff\",\n \"score\": \"0.59073144\",\n \"text\": \"def _find_stem(arr):\\n # Determine size of the array\\n n_items_in_array = len(arr)\\n\\n # Take first word from array as reference\\n reference_string = arr[0]\\n n_chars_in_first_item = len(reference_string)\\n\\n res = \\\"\\\"\\n for i_char in range(n_chars_in_first_item):\\n # Generate all starting substrings of our reference string\\n stem = reference_string[:i_char]\\n\\n j_item = 1 # Retained in case of an array with only one item\\n for j_item in range(1, n_items_in_array):\\n # Check if the generated stem is common to to all words\\n if not arr[j_item].startswith(stem):\\n break\\n\\n # If current substring is present in all strings and its length is\\n # greater than current result\\n if (j_item + 1 == n_items_in_array) and (len(res) < len(stem)):\\n res = stem\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4691c0868c52b605594026452a3f0a\",\n \"score\": \"0.5896456\",\n \"text\": \"def morphy_words(words):\\n result = []\\n\\n from nltk.stem.snowball import SnowballStemmer\\n stemmer = SnowballStemmer(\\\"english\\\")\\n\\n# words = [stemmer.stem(word) for word in words]\\n for word in words:\\n morphed = wn.morphy(word)\\n if morphed == None:\\n result.append(word)\\n else:\\n result.append(morphed)\\n# result.append(morphed)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b35880f1322b19ae95e27b4ac82365f\",\n \"score\": \"0.5894465\",\n \"text\": \"def _stemmer_custom(self, tokens, stemmer):\\n \\n stems = [stemmer.stem(item) for item in tokens]\\n \\n return stems\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0a78579b452c617215fa12e82d3f257\",\n \"score\": \"0.588811\",\n \"text\": \"def stem(w):\\n \\n if len(w) < 3: return w\\n \\n first_is_y = w[0] == \\\"y\\\"\\n if first_is_y:\\n w = \\\"Y\\\" + w[1:]\\n \\n # Step 1a\\n if w.endswith(\\\"s\\\"):\\n if w.endswith(\\\"sses\\\"):\\n w = w[:-2]\\n elif w.endswith(\\\"ies\\\"):\\n w = w[:-2]\\n elif w[-2] != \\\"s\\\":\\n w = w[:-1]\\n \\n # Step 1b\\n \\n if w.endswith(\\\"eed\\\"):\\n s = w[:-3]\\n if _mgr0.match(s):\\n w = w[:-1]\\n else:\\n m = _ed_ing.match(w)\\n if m:\\n stem = m.group(1)\\n if _s_v.match(stem):\\n w = stem\\n if _at_bl_iz.match(w):\\n w += \\\"e\\\"\\n elif _step1b.match(w):\\n w = w[:-1]\\n elif _c_v.match(w):\\n w += \\\"e\\\"\\n \\n # Step 1c\\n \\n if w.endswith(\\\"y\\\"):\\n stem = w[:-1]\\n if _s_v.match(stem):\\n w = stem + \\\"i\\\"\\n \\n # Step 2\\n \\n m = _step2.match(w)\\n if m:\\n stem = m.group(1)\\n suffix = m.group(2)\\n if _mgr0.match(stem):\\n w = stem + _step2list[suffix]\\n \\n # Step 3\\n \\n m = _step3.match(w)\\n if m:\\n stem = m.group(1)\\n suffix = m.group(2)\\n if _mgr0.match(stem):\\n w = stem + _step3list[suffix]\\n\\n # Step 4\\n \\n m = _step4_1.match(w)\\n if m:\\n stem = m.group(1)\\n if _mgr1.match(stem):\\n w = stem\\n else:\\n m = _step4_2.match(w)\\n if m:\\n stem = m.group(1) + m.group(2)\\n if _mgr1.match(stem):\\n w = stem\\n \\n # Step 5\\n \\n m = _step5.match(w)\\n if m:\\n stem = m.group(1)\\n if _mgr1.match(stem) or (_meq1.match(stem) and not _c_v.match(stem)):\\n w = stem\\n \\n if w.endswith(\\\"ll\\\") and _mgr1.match(w):\\n w = w[:-1]\\n \\n if first_is_y:\\n w = \\\"y\\\" + w[1:]\\n\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04a8aa707f3651e05ede39b654fbafa\",\n \"score\": \"0.58791685\",\n \"text\": \"def stem(self, tokens):\\n return [re.sub('^(did|does)$', 'do', t) for t in tokens]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d0650434deddb7a6af5a2252a680f5\",\n \"score\": \"0.58764225\",\n \"text\": \"def embers_stem(x):\\n x = x.lower()\\n if isinstance(x, unicode) == False:\\n x = x.decode('utf-8', 'ignore')\\n try:\\n stemmer = SnowballStemmer('spanish')\\n x1 = FeatureCountVectorizer.preprocess_unicode_text(x,stemmer.stem)\\n if(x1 == ''):\\n x1 = x\\n \\n stemmer = SnowballStemmer('english')\\n x2 = FeatureCountVectorizer.preprocess_unicode_text(x,stemmer.stem)\\n if(x2 == ''):\\n x2 = x\\n \\n stemmer = SnowballStemmer('portuguese')\\n x3 = FeatureCountVectorizer.preprocess_unicode_text(x,stemmer.stem)\\n if(x3 == ''):\\n x3 = x\\n # print 'success'\\n return min(x1, x2, x3, key = lambda x: len(x))\\n except:\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab903c027b65101fb1da5fffb702bae\",\n \"score\": \"0.5867515\",\n \"text\": \"def getTerms(tweet, stops):\\n stemming = PorterStemmer()\\n tweet = tweet.replace(\\\"RT \\\", \\\"\\\").strip() # drop RT tag\\n tweet = tweet.lower() # lower the characters\\n tweet = re.sub(r'[^\\\\w\\\\s]','',tweet).strip() #remove puntiation\\n tweet = tweet.split() # split by blank spaces\\n tweet = [x for x in tweet if x not in stops] # ignore stops \\n tweet = [stemming.stem(word) for word in tweet] # stem the terms\\n return tweet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d2fb921fb910a9491d216a459240d6\",\n \"score\": \"0.5860012\",\n \"text\": \"def set_stemmed(self, urit, urim, measuretype, measure, stemmed):\\n \\n self.initialize_scoremodel_for_keys(urit, urim, measuretype, measure)\\n self.scoremodel[urit][urim][measuretype][measure][\\\"stemmed\\\"] = stemmed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acaf7997b552f678e70a14e2e1d517f6\",\n \"score\": \"0.5851889\",\n \"text\": \"def normalise(word):\\n word = word.lower()\\n # word = stemmer.stem_word(word) #if we consider stemmer then results comes with stemmed word, but in this case word will not match with comment\\n word = lemmatizer.lemmatize(word)\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"519ec3041a438c03b1ab8ea988f3c233\",\n \"score\": \"0.58423316\",\n \"text\": \"def remove_stop_words(text_blob):\\r\\n # Get words from original text, remove the stop words and combine the\\r\\n # words again\\r\\n words = text_blob.words\\r\\n\\r\\n stop_words = [stop_word.lower()\\r\\n for stop_word in stopwords.words(\\\"english\\\")]\\r\\n\\r\\n words = filter(lambda word: not word.lower() in stop_words, words)\\r\\n\\r\\n return TextBlob(\\\" \\\".join(words))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9367e3a9de815d10c97e9d5d3bbdfe0e\",\n \"score\": \"0.58420134\",\n \"text\": \"def tokenize(text):\\n\\n text = re.sub('[^(a-zA-Z0-9)]', ' ', text.lower())\\n words = word_tokenize(text)\\n clean = [word for word in words if word not in stopwords.words(\\\"english\\\")]\\n lemmed = [WordNetLemmatizer().lemmatize(w) for w in clean]\\n stemmed = [PorterStemmer().stem(w) for w in lemmed]\\n\\n return stemmed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed84323938cd53010f16a82acfd4c60\",\n \"score\": \"0.58395886\",\n \"text\": \"def normalize_text(string, remove_stopwords=False, stem_words=False):\\n strip_special_chars = re.compile(\\\"[^A-Za-z0-9 ]+\\\")\\n string = string.lower()\\n string = string.replace(\\\"<br />\\\", \\\" \\\")\\n string = string.replace(r\\\"(\\\\().*(\\\\))|([^a-zA-Z'])\\\",' ')\\n string = string.replace('&', 'and')\\n string = string.replace('@', 'at')\\n string = string.replace('0', 'zero')\\n string = string.replace('1', 'one')\\n string = string.replace('2', 'two')\\n string = string.replace('3', 'three')\\n string = string.replace('4', 'four')\\n string = string.replace('5', 'five')\\n string = string.replace('6', 'six')\\n string = string.replace('7', 'seven')\\n string = string.replace('8', 'eight')\\n string = string.replace('9', 'nine')\\n string = string.split()\\n if remove_stopwords:\\n stop_words = stopwords.words('english')\\n string = [w for w in string if w not in stop_words]\\n if stem_words:\\n ps = PorterStemmer()\\n string = [ps.stem(w) for w in string]\\n string = ' '.join(string)\\n return re.sub(strip_special_chars, \\\"\\\", string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8710054be0456c0d11beff400872bcdf\",\n \"score\": \"0.5819634\",\n \"text\": \"def build_stemmer(self):\\n if not self.stemming:\\n return lambda tokens: tokens\\n german_stemmer = SnowballStemmer('german')\\n return lambda tokens: (german_stemmer.stem(token)\\n for token in tokens if ClassifierFeatureTokenizer._pattern_url.match(token) or\\n ClassifierFeatureTokenizer._pattern_word.match(token) or\\n ClassifierFeatureTokenizer._pattern_entire_text.match(token))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d4bff740736b951926fc8068ee479a\",\n \"score\": \"0.581764\",\n \"text\": \"def get_stem(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f573715a8147ba3ba044937aa75efe\",\n \"score\": \"0.57977813\",\n \"text\": \"def tokenizar_con_stemming(text: str, stemm_algorithm: str = \\\"snowball\\\") -> List[str]:\\n stemmer = stemmers.get(stemm_algorithm, stemmer_lancaster)\\n return [stemmer.stem(token) for token in tokenizar(text)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47ef9a347402c2b6cf9e7fbc2e0ddc3c\",\n \"score\": \"0.5794814\",\n \"text\": \"def lemmatize_and_stem(text):\\n stemmer = PorterStemmer()\\n return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos=\\\"v\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba3ef37352c3aa2566395853a3badc3\",\n \"score\": \"0.5792212\",\n \"text\": \"def normalize(words):\\n words = to_lowercase(words)\\n words = remove_stopwords(words)\\n lemmatize_words(words)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e669b3452907b4f6965dc397036cff36\",\n \"score\": \"0.5788771\",\n \"text\": \"def get_stemmed_form(idx, word):\\n result = indices.IndicesClient(_es()).analyze(index=idx, text=word, analyzer=_STEMMING_ANALYZER)\\n return result['tokens'][0]['token']\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1585,"cells":{"query_id":{"kind":"string","value":"27e06f08a255f180d74f118ba04c4cff"},"query":{"kind":"string","value":"Construct a set from an optional iterable."},"positive_passages":{"kind":"list like","value":[{"docid":"169e10ec180b8373d94d59cbe9025b7e","score":"0.0","text":"def __init__(self, iterable=None):\n self._data = {}","title":""}],"string":"[\n {\n \"docid\": \"169e10ec180b8373d94d59cbe9025b7e\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, iterable=None):\\n self._data = {}\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e895aaf55199175e96e8bcf24c88bfc8","score":"0.58696115","text":"def _set_converter(itr):\n if isinstance(itr, (list, tuple, set)):\n itr = FiniteSet(*itr)\n if not isinstance(itr, Set):\n raise TypeError(\"%s is not an instance of list/tuple/set.\"%(itr))\n return itr","title":""},{"docid":"0e6511af9b7ea55a92d8eee793b94879","score":"0.5843371","text":"def as_set(l: Iterable) -> Set:\n return set(flatten(l))","title":""},{"docid":"9b1d1951073a0ec9f2e35c5d58139628","score":"0.5729345","text":"def ipset(*ips):\n return IPSet(map(IP, ips))","title":""},{"docid":"6668fa7efdcf6b9115cb63cdb152fb81","score":"0.5728509","text":"def fromkeys(cls, iterable, value=None):\n d = cls()\n for key in iterable:\n d[key] = value\n return d","title":""},{"docid":"3eea4fffa89d8c70b2210ff29bf0b9b4","score":"0.56494296","text":"def old_dynamodb_stringset_fix(Set: set) -> ty.Optional[set]:\n if all(map(lambda x: isinstance(x, str) or x is None, Set)):\n # DynamoDB will not accept the empty string or None in a StringSet\n return {s for s in Set if s} or None # don't ever return the empty set\n return Set","title":""},{"docid":"788094472cbdc5aa295ae5d25ba2ab89","score":"0.5647453","text":"def Mutabelle_Set(*args):\n return invoke_mutabelle('Set', *args)","title":""},{"docid":"30f8983efd4c5165fc362d6ad684ed93","score":"0.5630211","text":"def add_to_set(set_: AbstractSet[str] | None, new: str) -> set[str]:\n return set(set_).union([new]) if set_ is not None else {new}","title":""},{"docid":"be4f3cb49dd83a9d20de859567d1b266","score":"0.55680054","text":"def make_iterable(v):\n return v if non_string_iterable(v) else () if v is None else (v,)","title":""},{"docid":"2ddaa2747e5b56d14b6860bad3ee55c5","score":"0.551168","text":"def powerset(iterable):\n xs = list(iterable)\n # note we return an iterator rather than a list\n return chain.from_iterable(combinations(xs,n) for n in range(len(xs)+1))","title":""},{"docid":"a105824fc487b4f74fb24e43b665e837","score":"0.549409","text":"def __init__(self, iterable=None, **kwds):\n self.update(iterable, **kwds)","title":""},{"docid":"763ffd8adc2bba88f7a019bfc495652a","score":"0.54893726","text":"def __init__(self, packages=None):\n if packages is None:\n packages = installed()\n\n set.__init__(self, packages)","title":""},{"docid":"7550492e137ed6215d0d76891d4c3ecd","score":"0.541415","text":"def set_or_callable(value):\n if value is None:\n return frozenset()\n if callable(value):\n return value\n if isinstance(value, (frozenset, set, list)):\n return frozenset(value)\n return frozenset([str(value)])","title":""},{"docid":"cf5a6b41ec2bcca7562904c2c0df97fa","score":"0.5396762","text":"def powerset(iterable):\n s = list(iterable)\n return itertools.chain.from_iterable(itertools.combinations(s, r) for r in range(0, len(s)))","title":""},{"docid":"e054b4dd93015c497143660a77cc9596","score":"0.5394259","text":"def test_invalid_to_set():\n with pytest.raises(ValueError):\n to_set(['A', 'B'])","title":""},{"docid":"8be5f8b206c34079be9a99bb37919daf","score":"0.53648406","text":"def _init(self, init: Set) -> None:\n pass","title":""},{"docid":"56b47b71bcf9c2409cdd744eab5e302d","score":"0.5356826","text":"def powerset(iterable): \n \"\"\"By directly call the itertools library\"\"\"\n s = list(iterable)\n return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))\n #if you want to avoide 0 length list, just change the range to (1,len(s)+1)\n # return chain.from_iterable(combinations(s, r) for r in range(1,len(s)+1))","title":""},{"docid":"fbc06cc90991f54b293a5edcd6623f52","score":"0.53527087","text":"def powerset(iterable):\n s = list(iterable)\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))","title":""},{"docid":"fbc06cc90991f54b293a5edcd6623f52","score":"0.53527087","text":"def powerset(iterable):\n s = list(iterable)\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))","title":""},{"docid":"fbc06cc90991f54b293a5edcd6623f52","score":"0.53527087","text":"def powerset(iterable):\n s = list(iterable)\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))","title":""},{"docid":"f05164e628e163d37118590b596cd78e","score":"0.53399616","text":"def powerset(iterable):\n s = list(iterable)\n return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))","title":""},{"docid":"2d76fbad0f54edde3dd3df7fb469fc53","score":"0.53169245","text":"def EmptySet(s):\n ctx = s.ctx\n return ArrayRef(Z3_mk_empty_set(ctx.ref(), s.ast), ctx)","title":""},{"docid":"fac32a867b3328fbaa34a3d5c91f1b6b","score":"0.53031904","text":"def __init__(self, values=None):\n self.dict = {} # each instance of Set has its own dict property used to track memberships\n\n if values is not None:\n for value in values:\n self.add(value)\n\n def __repr__(self):\n \"\"\"\n This is the string representation of a Set object if you type it at the prompt or pass it to a string.\n \"\"\"\n return \"Set: \" + str(self.dict.keys())\n\n # we can represent memebership by being a key in self.dict with value True\n def add(self, value):\n self.dict[value] = True\n\n # value is in the set if it's a key in the dictionary\n def contains(self, value):\n return value in self.dict\n\n def remove(self, value):\n del self.dict[value]","title":""},{"docid":"dabda29b5c2454b29c7a027e3e293773","score":"0.52893233","text":"def _dedupe_iterables(iterables: List[Iterable]) -> Iterable:\n return set(chain.from_iterable(iterables))","title":""},{"docid":"8b6c9b270f1d6fe24a11aba3740cb8d4","score":"0.5285147","text":"def to_python(self, value):\n if value is None:\n return set()\n return set(value)","title":""},{"docid":"6e574b3cfd23b10862c5dda636a85aa1","score":"0.5282033","text":"def tpowerset(iterable):\n return tuple(powerset(iterable))","title":""},{"docid":"47b4b62938bac9b9e5304c15badc9966","score":"0.5281798","text":"def powerset(iterable):\n s = list(iterable)\n return (list(t) for t in\n chain.from_iterable(combinations(s, r) for r in range(1, len(s))))","title":""},{"docid":"e482777dd33cd021372f6871ff260743","score":"0.527905","text":"def powerset(iterable):\n s = list(iterable)\n return itertools.chain.from_iterable(itertools.combinations(s, r) for r in range(len(s) + 1))","title":""},{"docid":"9b7a5c7fe0f2dd1f3347abb7f114086b","score":"0.5254273","text":"def compact(iterable):\n return exclude(iterable, (None,))","title":""},{"docid":"ac3f95e5409b91904aa424a1ee4c92b9","score":"0.52502936","text":"def __or__(self, other):\n return set()","title":""},{"docid":"6a498a616a9e1e5798a1ac7e0d50bfea","score":"0.5192773","text":"def convert_to_set(var):\n result = set()\n if type(var) in [int, float, str]:\n result = {var,}\n elif type(var) == set:\n result = var\n elif type(var) in [list, tuple]:\n result = set(var)\n else:\n result = var\n return result","title":""},{"docid":"e73a5a306a2206805a7bb1d42d9ee8d1","score":"0.5178643","text":"def powerset_alt(s):\r\n tuples = cartesian(*[s for _ in range(len(s))])\r\n return set([frozenset(item) for item in tuples] + [frozenset()])","title":""},{"docid":"fce7d391322e38217374d201fe8753c2","score":"0.5172331","text":"def uniquify(*items) -> tx.Tuple[tx.Any, ...]:\n return tuple(frozenset(item for item in items if item is not None))","title":""},{"docid":"eaf6c1b4222423b221603ce4845421d8","score":"0.51502204","text":"def __init__(self, iterable=None):\n self._length = 0\n self.head = None\n if isinstance(iterable, (str, list, tuple)):\n for item in iterable:\n self.push(item)\n self._length = len(iterable)\n elif iterable is not None:\n raise TypeError('Acceptable arguments: str, list, tuple, None')","title":""},{"docid":"03de9dfd97e3ad1f2790b5517f7273bf","score":"0.5137166","text":"def union(self: _TAbstractOrderedSet, *others: Iterable[T]) -> _TAbstractOrderedSet:\n # Differences with AbstractSet: our set union forces \"other\" to have the same type. That\n # is, while AbstractSet allows {1, 2, 3} | {(True, False)} resulting in\n # set[int | tuple[bool, bool]], the analogous for descendants of _TAbstractOrderedSet is\n # not allowed.\n #\n # GOTCHA: given _TAbstractOrderedSet[S]:\n # if T is a subclass of S => _TAbstractOrderedSet[S] => *appears* to perform\n # unification but it doesn't\n # if S is a subclass of T => type error (while AbstractSet would resolve to\n # AbstractSet[T])\n merged_iterables = itertools.chain([cast(Iterable[T], self)], others)\n return self.__class__(itertools.chain.from_iterable(merged_iterables))","title":""},{"docid":"b77cfbfe27b32f46d5d834024f63bc9d","score":"0.5130836","text":"def powerset(iterable):\r\n set_list = list(iterable)\r\n return list(chain.from_iterable(combinations(set_list, r)\r\n for r in range(len(set_list) + 1)))","title":""},{"docid":"22f7fd9e26dfe1e982c6069ea241b11e","score":"0.51252264","text":"def __call__(self, *args, **kwargs):\n\n if len(args) > 0 or len(kwargs) > 0:\n self.set(*args, **kwargs)\n return self\n else:\n r = self.get(*args, **kwargs)\n if self.multiple:\n return set(r)\n else:\n try:\n return next(iter(r))\n except StopIteration:\n return None","title":""},{"docid":"31be8047cecbdf193d7ee93677576c7e","score":"0.51020294","text":"def _initialize(self, value):\n if value is not UNASSIGNED:\n # Make a copy of the set.\n self._initial = set(value)\n else:\n self._initial = value\n self._value = value","title":""},{"docid":"31be8047cecbdf193d7ee93677576c7e","score":"0.51020294","text":"def _initialize(self, value):\n if value is not UNASSIGNED:\n # Make a copy of the set.\n self._initial = set(value)\n else:\n self._initial = value\n self._value = value","title":""},{"docid":"1ac347793b94946880fbb461cb76f765","score":"0.5077516","text":"def make_iterable(x, ctor=lambda x: list([x])):\n if x is None:\n return None\n return x if is_iterable(x) else ctor(x)","title":""},{"docid":"0f50eb0420ea39c86530b48dbbd8737e","score":"0.505946","text":"def toSet(self) -> Set[T]:\n return set(self)","title":""},{"docid":"1acce48f46fd05bafb335104494d69f6","score":"0.5036294","text":"def set_of(cls):\n return lambda l: isinstance(l, set) and all(isinstance(x, cls) for x in l)","title":""},{"docid":"d99320c460e0859fc6c32554b0bf5677","score":"0.50029236","text":"def __init__(self, iterable=None):\r\n if iterable is None:\r\n iterable = []\r\n self._data = list(iterable)","title":""},{"docid":"e89959907d81c82ffad216c1a040710e","score":"0.50012875","text":"def _get_any_element_from_set(s):\n e = None\n for e in s:\n break\n return e","title":""},{"docid":"b9c7be0dc0e348535fca781b0e4d9b17","score":"0.49939597","text":"def __init__(self, id_, init_value=[]):\n kSetBase.__init__(self, id_, init_value)","title":""},{"docid":"b9c7be0dc0e348535fca781b0e4d9b17","score":"0.49939597","text":"def __init__(self, id_, init_value=[]):\n kSetBase.__init__(self, id_, init_value)","title":""},{"docid":"d02a479c2f7ea003bf06e817d68613b6","score":"0.49891502","text":"def from_dict(cls, d):\n return oset._from_dict(d.copy())","title":""},{"docid":"26748c87da597166d3d04a6f6ed1cfd8","score":"0.4980046","text":"def make_into_set(iter_of_dict):\n return set(frozenset(dict_.items()) for dict_ in iter_of_dict)","title":""},{"docid":"ed62b36254633ab1e2c69831f524789e","score":"0.4974972","text":"def duplicates(iterable):\n seen = set()\n return set(x for x in iterable if x in seen or seen.add(x))","title":""},{"docid":"efe7af4afcaa019487f6114208fa2851","score":"0.4969169","text":"def _compile_empty_set(typ: ast.TSet) -> Tuple[OrderedSet, z3.ExprRef]:\n set_sort = compile_type(typ)\n empty = z3.Const(f'{typ}.empty_set', set_sort)\n x = z3.Const(f'{typ}.empty_set_x', set_sort.domain())\n forall = z3.ForAll(x, z3.Select(empty, x) == z3.BoolVal(False))\n return OrderedSet([forall]), empty","title":""},{"docid":"ed63297e3e36ec52207f664c0ea2dc51","score":"0.49264035","text":"def distinct(iterable):\n distincts = set()\n for item in iterable:\n if item not in distincts:\n distincts.add(item)\n yield item","title":""},{"docid":"12e6adcf70c834bb2558cc18fcfe536d","score":"0.4920598","text":"def list_to_set( _list ):\n return set( _list )","title":""},{"docid":"d480a93aedcd3c497243a100e8765ecc","score":"0.4916546","text":"def lpowerset(iterable):\n return list(powerset(iterable))","title":""},{"docid":"84e68f87e01b045a5b7250b8a23854e1","score":"0.4912465","text":"def _listToSet(self, lst):\n return set(itertools.chain([tuple(l) for l in lst]))","title":""},{"docid":"270e9424d29e766b9ce5f786fced8dbb","score":"0.4872453","text":"def __init__(self, iterable=()):\n self.head = None\n self._count = 0\n if isinstance(iterable, (str, tuple, list)):\n for item in iterable:\n self.push(item)","title":""},{"docid":"040c977da6655646cb86d9de0acc47cc","score":"0.4859901","text":"def ParamInSet(required, my_set):\n fn = lambda x: x in my_set\n err = (\"The value must be one of: %s\" % utils.CommaJoin(my_set))\n return (required, fn, err, None, None)","title":""},{"docid":"5cb40bbaa7cca8dd78e7d509955fb871","score":"0.48571056","text":"def uniq(iterable):\r\n s, l = set(), 0\r\n for i in iterable:\r\n s.add(i)\r\n if len(s) > l:\r\n yield i\r\n l = len(s)","title":""},{"docid":"94ab6701736adbe6dfd8baf032363b58","score":"0.4853331","text":"def union(self, *largs):\n return set(self).union(*largs)","title":""},{"docid":"1f38b8984d5dc0cea42ebbec14d21c2e","score":"0.4846958","text":"def __init__(self, iterable=()):\n self.heap = BinaryHeap(iterable=())\n for item in iterable:\n try:\n is_container = len(item) == 2\n except TypeError: # Case of QNode or non-iterable item\n self.insert(item)\n else:\n if is_container: # Case of value, iterable\n self.insert(item[0], item[1])\n else:\n raise TypeError(\"More than two args: instantiation supports\\\n non-iter value or iter of value, priority\")","title":""},{"docid":"83645ced839567cf75c0ba1ec79d3c7b","score":"0.48339474","text":"def intersection(self: _TAbstractOrderedSet, *others: Iterable[T]) -> _TAbstractOrderedSet:\n cls = self.__class__\n if not others:\n return cls(self)\n common = set.intersection(*(set(other) for other in others))\n return cls(item for item in self if item in common)","title":""},{"docid":"14715e8234330d8d8a736cbf09225e12","score":"0.48232284","text":"def create_C1(data_set):\n C1 = set()\n for t in data_set:\n for item in t:\n item_set = frozenset([item])\n C1.add(item_set)\n return C1","title":""},{"docid":"3fd4cbfd0468c2a5bd93ecc6a47e210b","score":"0.48191273","text":"def __init__(self, iterable):\n self.iterable = iterable\n self.iter = None\n self._on_error = None\n self._skip = {} # Unique, non-comparable element","title":""},{"docid":"797180617de8c33966f9b9959393f099","score":"0.48015398","text":"def __init__(self, iterable=None):\n self.head = None\n self.length = 0\n if isinstance(iterable, (str, list, tuple)):\n for item in iterable:\n self.push(item)","title":""},{"docid":"f3219178046bc00871b1693b781e01f2","score":"0.47998527","text":"def dynamodb_prewrite_set_transform(Set: set) -> ty.Optional[set]:\n if not Set:\n # DynamoDB expects None if your set is empty\n return None\n # no guarantees your set is well-formed, but this covers some of the simple cases\n return Set","title":""},{"docid":"79a2d17356d03165d625ce9cb8bbe91c","score":"0.4790501","text":"def set(expr, *args):","title":""},{"docid":"411737fd0fbc54765f571cedd948836d","score":"0.478703","text":"def _get_set_from_many_lists(lists):\n all_vals = []\n for l in lists:\n all_vals += l\n return set(all_vals)","title":""},{"docid":"bb2a2150aeae62d392425652fe27e861","score":"0.47842762","text":"def test_init_bad_arg(self):\n self.assertRaises(\n ValueError,\n convstructs.TwoWaySetDict,\n [('a', set([1])), ('b', 2)]\n )","title":""},{"docid":"2c5e7ee6eeca93d7ca57e01764234a08","score":"0.4773844","text":"def SetUnion(*args):\n args = _get_args(args)\n ctx = _ctx_from_ast_arg_list(args)\n _args, sz = _to_ast_array(args)\n return ArrayRef(Z3_mk_set_union(ctx.ref(), sz, _args), ctx)","title":""},{"docid":"15e9a77d30c849a4c2f11f4452329068","score":"0.47660893","text":"def make_set(self, elements=None):\n if not elements:\n return\n\n if isinstance(elements, (list, tuple)):\n elements = [elements]\n\n for element in elements:\n self.parent[element] = element\n self.rank[element] = 0","title":""},{"docid":"9993e2d1d68d3df759a13425fb7fc6ea","score":"0.47584745","text":"def powersetLib(seq):\n # s = list(iterable)\n return chain.from_iterable(combinations(seq, r) for r in range(len(seq)+1))","title":""},{"docid":"7f9dcbd10f75d77f504e7d958ca72148","score":"0.47416165","text":"def tcompact(iterable):\n return tuple(compact(iterable))","title":""},{"docid":"830e8f74a1c1e28e3e910b7bc88e01db","score":"0.47329658","text":"def identity(s):\n if not isinstance(s, Set):\n raise TypeError(\"s must be a set\")\n return Function(s, s, lambda x: x)","title":""},{"docid":"0e07f3a8107d53aec3750456cf3b2057","score":"0.47249615","text":"def __and__(self, other):\n return set()","title":""},{"docid":"ae76d3e93b6c464573eba829031571b0","score":"0.47243547","text":"def union_of_distinct_sets(*sets):\n assert sets_exclusive(*sets), \"Sets not mutually exclusive: %s\" % repr(sets)\n return set.union(*sets)","title":""},{"docid":"6cab5241e5efc6c5f02c4938f5ad22e2","score":"0.47179326","text":"def multi_union(sets: Iterable[Set]) -> Set:\n return set().union(*sets)","title":""},{"docid":"c9dd0eaa6b2fdeb28f8bdf86e3722616","score":"0.4717884","text":"def from_iterable(cls, target_types, iterable):\n inst = cls(target_types)\n for hydrated_target in iterable:\n inst.inject_target(hydrated_target)\n return inst","title":""},{"docid":"069ad25e27874f94ed13868faa37cdd3","score":"0.4714997","text":"def add_to_set(s, value):\n if value is not None and value not in s:\n s.append(value)\n return s, True\n return s, False","title":""},{"docid":"3f55dab2df2d4553a3f44e85ebc6af45","score":"0.4711718","text":"def _handle_simple_function_set(self, node, pos_args):\r\n arg_count = len(pos_args)\r\n if arg_count == 0:\r\n return ExprNodes.SetNode(node.pos, args=[],\r\n type=Builtin.set_type)\r\n if arg_count > 1:\r\n return node\r\n iterable = pos_args[0]\r\n if isinstance(iterable, (ExprNodes.ListNode, ExprNodes.TupleNode)):\r\n return ExprNodes.SetNode(node.pos, args=iterable.args)\r\n elif isinstance(iterable, ExprNodes.ComprehensionNode) and \\\r\n isinstance(iterable.target, (ExprNodes.ListNode,\r\n ExprNodes.SetNode)):\r\n iterable.target = ExprNodes.SetNode(node.pos, args=[])\r\n iterable.pos = node.pos\r\n return iterable\r\n else:\r\n return node","title":""},{"docid":"bbd5cdd9db595ef5f633344e566e2e89","score":"0.4705913","text":"def get_cls_kwargs(\n cls: type, *, _set: Optional[Set[str]] = None, raiseerr: bool = False\n) -> Optional[Set[str]]:\n toplevel = _set is None\n if toplevel:\n _set = set()\n assert _set is not None\n\n ctr = cls.__dict__.get(\"__init__\", False)\n\n has_init = (\n ctr\n and isinstance(ctr, types.FunctionType)\n and isinstance(ctr.__code__, types.CodeType)\n )\n\n if has_init:\n names, has_kw = _inspect_func_args(ctr)\n _set.update(names)\n\n if not has_kw and not toplevel:\n if raiseerr:\n raise TypeError(\n f\"given cls {cls} doesn't have an __init__ method\"\n )\n else:\n return None\n else:\n has_kw = False\n\n if not has_init or has_kw:\n for c in cls.__bases__:\n if get_cls_kwargs(c, _set=_set) is None:\n break\n\n _set.discard(\"self\")\n return _set","title":""},{"docid":"c70e410ffcb7587ce9ea5ac253334182","score":"0.47027665","text":"def union(self, trieset):\n\n if self._null_element != trieset._null_element:\n raise ValueError\n\n return TrieSet(set(self)|set(trieset), null_element=self._null_element)","title":""},{"docid":"99c940632ab8cc5b74ba659bfa9340fc","score":"0.4701978","text":"def owlSetInteger(*args):\n return _OWL.owlSetInteger(*args)","title":""},{"docid":"51bdc264accc53a9b29c8bff98361ee3","score":"0.47000635","text":"def isMember(*choices):\n from .Set import Set\n return Set(*choices)","title":""},{"docid":"25292aad77f36bb3d163d564a6e296bb","score":"0.46921727","text":"def fromkeys(cls, seq, value=None, **kwargs):\n values = ((item, value) for item in seq)\n return cls(values, **kwargs)","title":""},{"docid":"6a078bb1185d29688238a0ce91e051d7","score":"0.4690395","text":"def powerset(iterable, min_size, max_size, with_replacement=False):\n combiner = (\n itertools.combinations_with_replacement if with_replacement else itertools.combinations\n )\n sizes = range(min_size, max_size + 1)\n return itertools.chain.from_iterable(combiner(list(iterable), size) for size in sizes)","title":""},{"docid":"d3af41db125556eea66735443db19721","score":"0.46835575","text":"def InSet(options, msg=None):\n options = options if isinstance(options, (list, tuple, dict)) else [options]\n msg = msg or \"value (%(value)s) must be either: %(options)s\"\n def f(v):\n if v in options:\n return v\n else:\n raise Invalid(msg % {'value': v, 'options': options})","title":""},{"docid":"260f49b818497fb7ab0bbb45970fbef3","score":"0.46825135","text":"def test_init_bad_kwargs(self):\n try:\n convstructs.TwoWaySetDict(a=set([1]), b=2)\n except ValueError:\n pass\n else:\n raise AssertionError(\"ValueError not raised\")","title":""},{"docid":"b737e763a60a10aad3ed33000add7013","score":"0.4671833","text":"def __init__(self, iterable=None):\n self.root = None\n if iterable:\n for i in iterable:\n self.insert(i)","title":""},{"docid":"bab0d8855848423959a6a75b435d91b4","score":"0.46690267","text":"def check_iterable(value, name):\n if not isinstance(value, (list, tuple, set)):\n raise ValueError(\n \"Wrong datatype. {} should be list, tuple or set\".format(name)\n )\n return None","title":""},{"docid":"77cbbcdb856c2e502c555eb8ede26138","score":"0.4667971","text":"def make_iterable(arg):\n try:\n iterator = iter(arg)\n return arg\n except TypeError:\n return [arg]","title":""},{"docid":"ed7e530c1c6807b85e7108894bce112b","score":"0.46601167","text":"def power_set(i, cuts):\n if i == len(cuts[0]):\n new_ps = OrderedSet()\n for c in cuts:\n new_c = tuple()\n for c2 in c:\n if c2 != \"ε\":\n new_c += (c2,)\n if len(new_c) > 0:\n new_ps.add(new_c)\n return new_ps\n\n if cuts[0][i] in nullables:\n to_add = OrderedSet()\n for s in cuts:\n to_add.add(s[:i] + (\"ε\",) + s[i+1:])\n cuts.update(to_add)\n return power_set(i+1, cuts)","title":""},{"docid":"5f52a6f4ee295fda52d241e0946542c6","score":"0.46508005","text":"def owlSetIntegerv(*args):\n return _OWL.owlSetIntegerv(*args)","title":""},{"docid":"4b6e80f17440c88ce386672a17b79644","score":"0.46480626","text":"def powerset(self, iterable):\n s = list(iterable)\n ps = [i for i in chain.from_iterable(combinations(s, r) for r in range(len(s)+1))] \n #print(\"ps\",ps)\n\n return ps","title":""},{"docid":"dc65f89a6511df06b118d1c80a76e7aa","score":"0.46445188","text":"def __init__(self, **kwargs):\n for key in kwargs:\n self.set(key, kwargs[key])","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""},{"docid":"f877bfdd173e0b66a35ef64c8e4bf6e5","score":"0.46373764","text":"def set(*args, **kwargs):\n \n pass","title":""}],"string":"[\n {\n \"docid\": \"e895aaf55199175e96e8bcf24c88bfc8\",\n \"score\": \"0.58696115\",\n \"text\": \"def _set_converter(itr):\\n if isinstance(itr, (list, tuple, set)):\\n itr = FiniteSet(*itr)\\n if not isinstance(itr, Set):\\n raise TypeError(\\\"%s is not an instance of list/tuple/set.\\\"%(itr))\\n return itr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6511af9b7ea55a92d8eee793b94879\",\n \"score\": \"0.5843371\",\n \"text\": \"def as_set(l: Iterable) -> Set:\\n return set(flatten(l))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1d1951073a0ec9f2e35c5d58139628\",\n \"score\": \"0.5729345\",\n \"text\": \"def ipset(*ips):\\n return IPSet(map(IP, ips))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6668fa7efdcf6b9115cb63cdb152fb81\",\n \"score\": \"0.5728509\",\n \"text\": \"def fromkeys(cls, iterable, value=None):\\n d = cls()\\n for key in iterable:\\n d[key] = value\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eea4fffa89d8c70b2210ff29bf0b9b4\",\n \"score\": \"0.56494296\",\n \"text\": \"def old_dynamodb_stringset_fix(Set: set) -> ty.Optional[set]:\\n if all(map(lambda x: isinstance(x, str) or x is None, Set)):\\n # DynamoDB will not accept the empty string or None in a StringSet\\n return {s for s in Set if s} or None # don't ever return the empty set\\n return Set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"788094472cbdc5aa295ae5d25ba2ab89\",\n \"score\": \"0.5647453\",\n \"text\": \"def Mutabelle_Set(*args):\\n return invoke_mutabelle('Set', *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f8983efd4c5165fc362d6ad684ed93\",\n \"score\": \"0.5630211\",\n \"text\": \"def add_to_set(set_: AbstractSet[str] | None, new: str) -> set[str]:\\n return set(set_).union([new]) if set_ is not None else {new}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4f3cb49dd83a9d20de859567d1b266\",\n \"score\": \"0.55680054\",\n \"text\": \"def make_iterable(v):\\n return v if non_string_iterable(v) else () if v is None else (v,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ddaa2747e5b56d14b6860bad3ee55c5\",\n \"score\": \"0.551168\",\n \"text\": \"def powerset(iterable):\\n xs = list(iterable)\\n # note we return an iterator rather than a list\\n return chain.from_iterable(combinations(xs,n) for n in range(len(xs)+1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a105824fc487b4f74fb24e43b665e837\",\n \"score\": \"0.549409\",\n \"text\": \"def __init__(self, iterable=None, **kwds):\\n self.update(iterable, **kwds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763ffd8adc2bba88f7a019bfc495652a\",\n \"score\": \"0.54893726\",\n \"text\": \"def __init__(self, packages=None):\\n if packages is None:\\n packages = installed()\\n\\n set.__init__(self, packages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7550492e137ed6215d0d76891d4c3ecd\",\n \"score\": \"0.541415\",\n \"text\": \"def set_or_callable(value):\\n if value is None:\\n return frozenset()\\n if callable(value):\\n return value\\n if isinstance(value, (frozenset, set, list)):\\n return frozenset(value)\\n return frozenset([str(value)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5a6b41ec2bcca7562904c2c0df97fa\",\n \"score\": \"0.5396762\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return itertools.chain.from_iterable(itertools.combinations(s, r) for r in range(0, len(s)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e054b4dd93015c497143660a77cc9596\",\n \"score\": \"0.5394259\",\n \"text\": \"def test_invalid_to_set():\\n with pytest.raises(ValueError):\\n to_set(['A', 'B'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be5f8b206c34079be9a99bb37919daf\",\n \"score\": \"0.53648406\",\n \"text\": \"def _init(self, init: Set) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56b47b71bcf9c2409cdd744eab5e302d\",\n \"score\": \"0.5356826\",\n \"text\": \"def powerset(iterable): \\n \\\"\\\"\\\"By directly call the itertools library\\\"\\\"\\\"\\n s = list(iterable)\\n return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))\\n #if you want to avoide 0 length list, just change the range to (1,len(s)+1)\\n # return chain.from_iterable(combinations(s, r) for r in range(1,len(s)+1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc06cc90991f54b293a5edcd6623f52\",\n \"score\": \"0.53527087\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc06cc90991f54b293a5edcd6623f52\",\n \"score\": \"0.53527087\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc06cc90991f54b293a5edcd6623f52\",\n \"score\": \"0.53527087\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05164e628e163d37118590b596cd78e\",\n \"score\": \"0.53399616\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d76fbad0f54edde3dd3df7fb469fc53\",\n \"score\": \"0.53169245\",\n \"text\": \"def EmptySet(s):\\n ctx = s.ctx\\n return ArrayRef(Z3_mk_empty_set(ctx.ref(), s.ast), ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac32a867b3328fbaa34a3d5c91f1b6b\",\n \"score\": \"0.53031904\",\n \"text\": \"def __init__(self, values=None):\\n self.dict = {} # each instance of Set has its own dict property used to track memberships\\n\\n if values is not None:\\n for value in values:\\n self.add(value)\\n\\n def __repr__(self):\\n \\\"\\\"\\\"\\n This is the string representation of a Set object if you type it at the prompt or pass it to a string.\\n \\\"\\\"\\\"\\n return \\\"Set: \\\" + str(self.dict.keys())\\n\\n # we can represent memebership by being a key in self.dict with value True\\n def add(self, value):\\n self.dict[value] = True\\n\\n # value is in the set if it's a key in the dictionary\\n def contains(self, value):\\n return value in self.dict\\n\\n def remove(self, value):\\n del self.dict[value]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dabda29b5c2454b29c7a027e3e293773\",\n \"score\": \"0.52893233\",\n \"text\": \"def _dedupe_iterables(iterables: List[Iterable]) -> Iterable:\\n return set(chain.from_iterable(iterables))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6c9b270f1d6fe24a11aba3740cb8d4\",\n \"score\": \"0.5285147\",\n \"text\": \"def to_python(self, value):\\n if value is None:\\n return set()\\n return set(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e574b3cfd23b10862c5dda636a85aa1\",\n \"score\": \"0.5282033\",\n \"text\": \"def tpowerset(iterable):\\n return tuple(powerset(iterable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47b4b62938bac9b9e5304c15badc9966\",\n \"score\": \"0.5281798\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return (list(t) for t in\\n chain.from_iterable(combinations(s, r) for r in range(1, len(s))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e482777dd33cd021372f6871ff260743\",\n \"score\": \"0.527905\",\n \"text\": \"def powerset(iterable):\\n s = list(iterable)\\n return itertools.chain.from_iterable(itertools.combinations(s, r) for r in range(len(s) + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7a5c7fe0f2dd1f3347abb7f114086b\",\n \"score\": \"0.5254273\",\n \"text\": \"def compact(iterable):\\n return exclude(iterable, (None,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3f95e5409b91904aa424a1ee4c92b9\",\n \"score\": \"0.52502936\",\n \"text\": \"def __or__(self, other):\\n return set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a498a616a9e1e5798a1ac7e0d50bfea\",\n \"score\": \"0.5192773\",\n \"text\": \"def convert_to_set(var):\\n result = set()\\n if type(var) in [int, float, str]:\\n result = {var,}\\n elif type(var) == set:\\n result = var\\n elif type(var) in [list, tuple]:\\n result = set(var)\\n else:\\n result = var\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73a5a306a2206805a7bb1d42d9ee8d1\",\n \"score\": \"0.5178643\",\n \"text\": \"def powerset_alt(s):\\r\\n tuples = cartesian(*[s for _ in range(len(s))])\\r\\n return set([frozenset(item) for item in tuples] + [frozenset()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce7d391322e38217374d201fe8753c2\",\n \"score\": \"0.5172331\",\n \"text\": \"def uniquify(*items) -> tx.Tuple[tx.Any, ...]:\\n return tuple(frozenset(item for item in items if item is not None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf6c1b4222423b221603ce4845421d8\",\n \"score\": \"0.51502204\",\n \"text\": \"def __init__(self, iterable=None):\\n self._length = 0\\n self.head = None\\n if isinstance(iterable, (str, list, tuple)):\\n for item in iterable:\\n self.push(item)\\n self._length = len(iterable)\\n elif iterable is not None:\\n raise TypeError('Acceptable arguments: str, list, tuple, None')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03de9dfd97e3ad1f2790b5517f7273bf\",\n \"score\": \"0.5137166\",\n \"text\": \"def union(self: _TAbstractOrderedSet, *others: Iterable[T]) -> _TAbstractOrderedSet:\\n # Differences with AbstractSet: our set union forces \\\"other\\\" to have the same type. That\\n # is, while AbstractSet allows {1, 2, 3} | {(True, False)} resulting in\\n # set[int | tuple[bool, bool]], the analogous for descendants of _TAbstractOrderedSet is\\n # not allowed.\\n #\\n # GOTCHA: given _TAbstractOrderedSet[S]:\\n # if T is a subclass of S => _TAbstractOrderedSet[S] => *appears* to perform\\n # unification but it doesn't\\n # if S is a subclass of T => type error (while AbstractSet would resolve to\\n # AbstractSet[T])\\n merged_iterables = itertools.chain([cast(Iterable[T], self)], others)\\n return self.__class__(itertools.chain.from_iterable(merged_iterables))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77cfbfe27b32f46d5d834024f63bc9d\",\n \"score\": \"0.5130836\",\n \"text\": \"def powerset(iterable):\\r\\n set_list = list(iterable)\\r\\n return list(chain.from_iterable(combinations(set_list, r)\\r\\n for r in range(len(set_list) + 1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f7fd9e26dfe1e982c6069ea241b11e\",\n \"score\": \"0.51252264\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n\\n if len(args) > 0 or len(kwargs) > 0:\\n self.set(*args, **kwargs)\\n return self\\n else:\\n r = self.get(*args, **kwargs)\\n if self.multiple:\\n return set(r)\\n else:\\n try:\\n return next(iter(r))\\n except StopIteration:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31be8047cecbdf193d7ee93677576c7e\",\n \"score\": \"0.51020294\",\n \"text\": \"def _initialize(self, value):\\n if value is not UNASSIGNED:\\n # Make a copy of the set.\\n self._initial = set(value)\\n else:\\n self._initial = value\\n self._value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31be8047cecbdf193d7ee93677576c7e\",\n \"score\": \"0.51020294\",\n \"text\": \"def _initialize(self, value):\\n if value is not UNASSIGNED:\\n # Make a copy of the set.\\n self._initial = set(value)\\n else:\\n self._initial = value\\n self._value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ac347793b94946880fbb461cb76f765\",\n \"score\": \"0.5077516\",\n \"text\": \"def make_iterable(x, ctor=lambda x: list([x])):\\n if x is None:\\n return None\\n return x if is_iterable(x) else ctor(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f50eb0420ea39c86530b48dbbd8737e\",\n \"score\": \"0.505946\",\n \"text\": \"def toSet(self) -> Set[T]:\\n return set(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1acce48f46fd05bafb335104494d69f6\",\n \"score\": \"0.5036294\",\n \"text\": \"def set_of(cls):\\n return lambda l: isinstance(l, set) and all(isinstance(x, cls) for x in l)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d99320c460e0859fc6c32554b0bf5677\",\n \"score\": \"0.50029236\",\n \"text\": \"def __init__(self, iterable=None):\\r\\n if iterable is None:\\r\\n iterable = []\\r\\n self._data = list(iterable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89959907d81c82ffad216c1a040710e\",\n \"score\": \"0.50012875\",\n \"text\": \"def _get_any_element_from_set(s):\\n e = None\\n for e in s:\\n break\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c7be0dc0e348535fca781b0e4d9b17\",\n \"score\": \"0.49939597\",\n \"text\": \"def __init__(self, id_, init_value=[]):\\n kSetBase.__init__(self, id_, init_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c7be0dc0e348535fca781b0e4d9b17\",\n \"score\": \"0.49939597\",\n \"text\": \"def __init__(self, id_, init_value=[]):\\n kSetBase.__init__(self, id_, init_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02a479c2f7ea003bf06e817d68613b6\",\n \"score\": \"0.49891502\",\n \"text\": \"def from_dict(cls, d):\\n return oset._from_dict(d.copy())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26748c87da597166d3d04a6f6ed1cfd8\",\n \"score\": \"0.4980046\",\n \"text\": \"def make_into_set(iter_of_dict):\\n return set(frozenset(dict_.items()) for dict_ in iter_of_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed62b36254633ab1e2c69831f524789e\",\n \"score\": \"0.4974972\",\n \"text\": \"def duplicates(iterable):\\n seen = set()\\n return set(x for x in iterable if x in seen or seen.add(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efe7af4afcaa019487f6114208fa2851\",\n \"score\": \"0.4969169\",\n \"text\": \"def _compile_empty_set(typ: ast.TSet) -> Tuple[OrderedSet, z3.ExprRef]:\\n set_sort = compile_type(typ)\\n empty = z3.Const(f'{typ}.empty_set', set_sort)\\n x = z3.Const(f'{typ}.empty_set_x', set_sort.domain())\\n forall = z3.ForAll(x, z3.Select(empty, x) == z3.BoolVal(False))\\n return OrderedSet([forall]), empty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed63297e3e36ec52207f664c0ea2dc51\",\n \"score\": \"0.49264035\",\n \"text\": \"def distinct(iterable):\\n distincts = set()\\n for item in iterable:\\n if item not in distincts:\\n distincts.add(item)\\n yield item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e6adcf70c834bb2558cc18fcfe536d\",\n \"score\": \"0.4920598\",\n \"text\": \"def list_to_set( _list ):\\n return set( _list )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d480a93aedcd3c497243a100e8765ecc\",\n \"score\": \"0.4916546\",\n \"text\": \"def lpowerset(iterable):\\n return list(powerset(iterable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e68f87e01b045a5b7250b8a23854e1\",\n \"score\": \"0.4912465\",\n \"text\": \"def _listToSet(self, lst):\\n return set(itertools.chain([tuple(l) for l in lst]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270e9424d29e766b9ce5f786fced8dbb\",\n \"score\": \"0.4872453\",\n \"text\": \"def __init__(self, iterable=()):\\n self.head = None\\n self._count = 0\\n if isinstance(iterable, (str, tuple, list)):\\n for item in iterable:\\n self.push(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"040c977da6655646cb86d9de0acc47cc\",\n \"score\": \"0.4859901\",\n \"text\": \"def ParamInSet(required, my_set):\\n fn = lambda x: x in my_set\\n err = (\\\"The value must be one of: %s\\\" % utils.CommaJoin(my_set))\\n return (required, fn, err, None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb40bbaa7cca8dd78e7d509955fb871\",\n \"score\": \"0.48571056\",\n \"text\": \"def uniq(iterable):\\r\\n s, l = set(), 0\\r\\n for i in iterable:\\r\\n s.add(i)\\r\\n if len(s) > l:\\r\\n yield i\\r\\n l = len(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94ab6701736adbe6dfd8baf032363b58\",\n \"score\": \"0.4853331\",\n \"text\": \"def union(self, *largs):\\n return set(self).union(*largs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f38b8984d5dc0cea42ebbec14d21c2e\",\n \"score\": \"0.4846958\",\n \"text\": \"def __init__(self, iterable=()):\\n self.heap = BinaryHeap(iterable=())\\n for item in iterable:\\n try:\\n is_container = len(item) == 2\\n except TypeError: # Case of QNode or non-iterable item\\n self.insert(item)\\n else:\\n if is_container: # Case of value, iterable\\n self.insert(item[0], item[1])\\n else:\\n raise TypeError(\\\"More than two args: instantiation supports\\\\\\n non-iter value or iter of value, priority\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83645ced839567cf75c0ba1ec79d3c7b\",\n \"score\": \"0.48339474\",\n \"text\": \"def intersection(self: _TAbstractOrderedSet, *others: Iterable[T]) -> _TAbstractOrderedSet:\\n cls = self.__class__\\n if not others:\\n return cls(self)\\n common = set.intersection(*(set(other) for other in others))\\n return cls(item for item in self if item in common)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14715e8234330d8d8a736cbf09225e12\",\n \"score\": \"0.48232284\",\n \"text\": \"def create_C1(data_set):\\n C1 = set()\\n for t in data_set:\\n for item in t:\\n item_set = frozenset([item])\\n C1.add(item_set)\\n return C1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd4cbfd0468c2a5bd93ecc6a47e210b\",\n \"score\": \"0.48191273\",\n \"text\": \"def __init__(self, iterable):\\n self.iterable = iterable\\n self.iter = None\\n self._on_error = None\\n self._skip = {} # Unique, non-comparable element\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797180617de8c33966f9b9959393f099\",\n \"score\": \"0.48015398\",\n \"text\": \"def __init__(self, iterable=None):\\n self.head = None\\n self.length = 0\\n if isinstance(iterable, (str, list, tuple)):\\n for item in iterable:\\n self.push(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3219178046bc00871b1693b781e01f2\",\n \"score\": \"0.47998527\",\n \"text\": \"def dynamodb_prewrite_set_transform(Set: set) -> ty.Optional[set]:\\n if not Set:\\n # DynamoDB expects None if your set is empty\\n return None\\n # no guarantees your set is well-formed, but this covers some of the simple cases\\n return Set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a2d17356d03165d625ce9cb8bbe91c\",\n \"score\": \"0.4790501\",\n \"text\": \"def set(expr, *args):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411737fd0fbc54765f571cedd948836d\",\n \"score\": \"0.478703\",\n \"text\": \"def _get_set_from_many_lists(lists):\\n all_vals = []\\n for l in lists:\\n all_vals += l\\n return set(all_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2a2150aeae62d392425652fe27e861\",\n \"score\": \"0.47842762\",\n \"text\": \"def test_init_bad_arg(self):\\n self.assertRaises(\\n ValueError,\\n convstructs.TwoWaySetDict,\\n [('a', set([1])), ('b', 2)]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c5e7ee6eeca93d7ca57e01764234a08\",\n \"score\": \"0.4773844\",\n \"text\": \"def SetUnion(*args):\\n args = _get_args(args)\\n ctx = _ctx_from_ast_arg_list(args)\\n _args, sz = _to_ast_array(args)\\n return ArrayRef(Z3_mk_set_union(ctx.ref(), sz, _args), ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e9a77d30c849a4c2f11f4452329068\",\n \"score\": \"0.47660893\",\n \"text\": \"def make_set(self, elements=None):\\n if not elements:\\n return\\n\\n if isinstance(elements, (list, tuple)):\\n elements = [elements]\\n\\n for element in elements:\\n self.parent[element] = element\\n self.rank[element] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9993e2d1d68d3df759a13425fb7fc6ea\",\n \"score\": \"0.47584745\",\n \"text\": \"def powersetLib(seq):\\n # s = list(iterable)\\n return chain.from_iterable(combinations(seq, r) for r in range(len(seq)+1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f9dcbd10f75d77f504e7d958ca72148\",\n \"score\": \"0.47416165\",\n \"text\": \"def tcompact(iterable):\\n return tuple(compact(iterable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"830e8f74a1c1e28e3e910b7bc88e01db\",\n \"score\": \"0.47329658\",\n \"text\": \"def identity(s):\\n if not isinstance(s, Set):\\n raise TypeError(\\\"s must be a set\\\")\\n return Function(s, s, lambda x: x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e07f3a8107d53aec3750456cf3b2057\",\n \"score\": \"0.47249615\",\n \"text\": \"def __and__(self, other):\\n return set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae76d3e93b6c464573eba829031571b0\",\n \"score\": \"0.47243547\",\n \"text\": \"def union_of_distinct_sets(*sets):\\n assert sets_exclusive(*sets), \\\"Sets not mutually exclusive: %s\\\" % repr(sets)\\n return set.union(*sets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cab5241e5efc6c5f02c4938f5ad22e2\",\n \"score\": \"0.47179326\",\n \"text\": \"def multi_union(sets: Iterable[Set]) -> Set:\\n return set().union(*sets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9dd0eaa6b2fdeb28f8bdf86e3722616\",\n \"score\": \"0.4717884\",\n \"text\": \"def from_iterable(cls, target_types, iterable):\\n inst = cls(target_types)\\n for hydrated_target in iterable:\\n inst.inject_target(hydrated_target)\\n return inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069ad25e27874f94ed13868faa37cdd3\",\n \"score\": \"0.4714997\",\n \"text\": \"def add_to_set(s, value):\\n if value is not None and value not in s:\\n s.append(value)\\n return s, True\\n return s, False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f55dab2df2d4553a3f44e85ebc6af45\",\n \"score\": \"0.4711718\",\n \"text\": \"def _handle_simple_function_set(self, node, pos_args):\\r\\n arg_count = len(pos_args)\\r\\n if arg_count == 0:\\r\\n return ExprNodes.SetNode(node.pos, args=[],\\r\\n type=Builtin.set_type)\\r\\n if arg_count > 1:\\r\\n return node\\r\\n iterable = pos_args[0]\\r\\n if isinstance(iterable, (ExprNodes.ListNode, ExprNodes.TupleNode)):\\r\\n return ExprNodes.SetNode(node.pos, args=iterable.args)\\r\\n elif isinstance(iterable, ExprNodes.ComprehensionNode) and \\\\\\r\\n isinstance(iterable.target, (ExprNodes.ListNode,\\r\\n ExprNodes.SetNode)):\\r\\n iterable.target = ExprNodes.SetNode(node.pos, args=[])\\r\\n iterable.pos = node.pos\\r\\n return iterable\\r\\n else:\\r\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbd5cdd9db595ef5f633344e566e2e89\",\n \"score\": \"0.4705913\",\n \"text\": \"def get_cls_kwargs(\\n cls: type, *, _set: Optional[Set[str]] = None, raiseerr: bool = False\\n) -> Optional[Set[str]]:\\n toplevel = _set is None\\n if toplevel:\\n _set = set()\\n assert _set is not None\\n\\n ctr = cls.__dict__.get(\\\"__init__\\\", False)\\n\\n has_init = (\\n ctr\\n and isinstance(ctr, types.FunctionType)\\n and isinstance(ctr.__code__, types.CodeType)\\n )\\n\\n if has_init:\\n names, has_kw = _inspect_func_args(ctr)\\n _set.update(names)\\n\\n if not has_kw and not toplevel:\\n if raiseerr:\\n raise TypeError(\\n f\\\"given cls {cls} doesn't have an __init__ method\\\"\\n )\\n else:\\n return None\\n else:\\n has_kw = False\\n\\n if not has_init or has_kw:\\n for c in cls.__bases__:\\n if get_cls_kwargs(c, _set=_set) is None:\\n break\\n\\n _set.discard(\\\"self\\\")\\n return _set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70e410ffcb7587ce9ea5ac253334182\",\n \"score\": \"0.47027665\",\n \"text\": \"def union(self, trieset):\\n\\n if self._null_element != trieset._null_element:\\n raise ValueError\\n\\n return TrieSet(set(self)|set(trieset), null_element=self._null_element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c940632ab8cc5b74ba659bfa9340fc\",\n \"score\": \"0.4701978\",\n \"text\": \"def owlSetInteger(*args):\\n return _OWL.owlSetInteger(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51bdc264accc53a9b29c8bff98361ee3\",\n \"score\": \"0.47000635\",\n \"text\": \"def isMember(*choices):\\n from .Set import Set\\n return Set(*choices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25292aad77f36bb3d163d564a6e296bb\",\n \"score\": \"0.46921727\",\n \"text\": \"def fromkeys(cls, seq, value=None, **kwargs):\\n values = ((item, value) for item in seq)\\n return cls(values, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a078bb1185d29688238a0ce91e051d7\",\n \"score\": \"0.4690395\",\n \"text\": \"def powerset(iterable, min_size, max_size, with_replacement=False):\\n combiner = (\\n itertools.combinations_with_replacement if with_replacement else itertools.combinations\\n )\\n sizes = range(min_size, max_size + 1)\\n return itertools.chain.from_iterable(combiner(list(iterable), size) for size in sizes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3af41db125556eea66735443db19721\",\n \"score\": \"0.46835575\",\n \"text\": \"def InSet(options, msg=None):\\n options = options if isinstance(options, (list, tuple, dict)) else [options]\\n msg = msg or \\\"value (%(value)s) must be either: %(options)s\\\"\\n def f(v):\\n if v in options:\\n return v\\n else:\\n raise Invalid(msg % {'value': v, 'options': options})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260f49b818497fb7ab0bbb45970fbef3\",\n \"score\": \"0.46825135\",\n \"text\": \"def test_init_bad_kwargs(self):\\n try:\\n convstructs.TwoWaySetDict(a=set([1]), b=2)\\n except ValueError:\\n pass\\n else:\\n raise AssertionError(\\\"ValueError not raised\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b737e763a60a10aad3ed33000add7013\",\n \"score\": \"0.4671833\",\n \"text\": \"def __init__(self, iterable=None):\\n self.root = None\\n if iterable:\\n for i in iterable:\\n self.insert(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab0d8855848423959a6a75b435d91b4\",\n \"score\": \"0.46690267\",\n \"text\": \"def check_iterable(value, name):\\n if not isinstance(value, (list, tuple, set)):\\n raise ValueError(\\n \\\"Wrong datatype. {} should be list, tuple or set\\\".format(name)\\n )\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77cbbcdb856c2e502c555eb8ede26138\",\n \"score\": \"0.4667971\",\n \"text\": \"def make_iterable(arg):\\n try:\\n iterator = iter(arg)\\n return arg\\n except TypeError:\\n return [arg]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7e530c1c6807b85e7108894bce112b\",\n \"score\": \"0.46601167\",\n \"text\": \"def power_set(i, cuts):\\n if i == len(cuts[0]):\\n new_ps = OrderedSet()\\n for c in cuts:\\n new_c = tuple()\\n for c2 in c:\\n if c2 != \\\"ε\\\":\\n new_c += (c2,)\\n if len(new_c) > 0:\\n new_ps.add(new_c)\\n return new_ps\\n\\n if cuts[0][i] in nullables:\\n to_add = OrderedSet()\\n for s in cuts:\\n to_add.add(s[:i] + (\\\"ε\\\",) + s[i+1:])\\n cuts.update(to_add)\\n return power_set(i+1, cuts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52a6f4ee295fda52d241e0946542c6\",\n \"score\": \"0.46508005\",\n \"text\": \"def owlSetIntegerv(*args):\\n return _OWL.owlSetIntegerv(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6e80f17440c88ce386672a17b79644\",\n \"score\": \"0.46480626\",\n \"text\": \"def powerset(self, iterable):\\n s = list(iterable)\\n ps = [i for i in chain.from_iterable(combinations(s, r) for r in range(len(s)+1))] \\n #print(\\\"ps\\\",ps)\\n\\n return ps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc65f89a6511df06b118d1c80a76e7aa\",\n \"score\": \"0.46445188\",\n \"text\": \"def __init__(self, **kwargs):\\n for key in kwargs:\\n self.set(key, kwargs[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f877bfdd173e0b66a35ef64c8e4bf6e5\",\n \"score\": \"0.46373764\",\n \"text\": \"def set(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1586,"cells":{"query_id":{"kind":"string","value":"2512f4358d650d9a6b61cfdee9732344"},"query":{"kind":"string","value":"Sets self.shows[sid] to a new Show instance, or sets the data"},"positive_passages":{"kind":"list like","value":[{"docid":"f4ab11d734e8689914c9c62d1598b696","score":"0.7529713","text":"def _setShowData(self, sid, key, value):\n if sid not in self.shows:\n self.shows[sid] = Show()\n self.shows[sid].data[key] = value","title":""}],"string":"[\n {\n \"docid\": \"f4ab11d734e8689914c9c62d1598b696\",\n \"score\": \"0.7529713\",\n \"text\": \"def _setShowData(self, sid, key, value):\\n if sid not in self.shows:\\n self.shows[sid] = Show()\\n self.shows[sid].data[key] = value\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"93f78bc096cb794dcae5cf286618c245","score":"0.7073067","text":"def _setItem(self, sid, seas, ep, attrib, value):\n if sid not in self.shows:\n self.shows[sid] = Show()\n if seas not in self.shows[sid]:\n self.shows[sid][seas] = Season(show=self.shows[sid])\n if ep not in self.shows[sid][seas]:\n self.shows[sid][seas][ep] = Episode(season=self.shows[sid][seas])\n self.shows[sid][seas][ep][attrib] = value","title":""},{"docid":"ef72452d6a191b01cfec9aa730d72994","score":"0.5894516","text":"def put_show(self, show):\n query = (\"INSERT INTO series \"\n \" (title, episodes, poster, rating, year, runtime, img, title_iso) \"\n \" VALUES (%s, %s, %s, %s, %s, %s, %s, %s) \"\n \" ON DUPLICATE KEY UPDATE \"\n \" title=VALUES(title), \"\n \" episodes=VALUES(episodes), \"\n \" rating=VALUES(rating), \"\n \" poster=VALUES(poster), \"\n \" year=VALUES(year), \"\n \" runtime=VALUES(runtime), \"\n \" img=VALUES(img), \"\n \" title_iso=VALUES(title_iso) \")\n params = (\n show['title'], \n show['episodes'], \n show['poster'], \n show['rating'], \n show[\"year\"], \n show[\"runtime\"],\n requests.get(show['poster']).content.encode('base64'),\n ''.join(re.findall('(\\w+|\\d)', \n show['title'].lower() + str(show['year']))))\n self.cursor.execute(query, params)\n self.cnx.commit()\n return True","title":""},{"docid":"6de7100d71a65f4af6811d32b443abfb","score":"0.5264651","text":"def restore(self, sid):\r\n for key, val in self.sdb[sid].items():\r\n setattr(self, key, val)","title":""},{"docid":"6de7100d71a65f4af6811d32b443abfb","score":"0.5264651","text":"def restore(self, sid):\r\n for key, val in self.sdb[sid].items():\r\n setattr(self, key, val)","title":""},{"docid":"0fbc692c1f6d3008d6546f11827f79cc","score":"0.52347046","text":"def _print_shows(self, shows, fields=None):\n if fields is None:\n fields = [\n 'show_name',\n 'status',\n 'network',\n 'quality',\n ]\n shows = shows['data'].values()\n self.print_objects(shows, fields, cls=Show)","title":""},{"docid":"8eba74991585c2d424c35e74107a2458","score":"0.5166133","text":"def edit_show():\n\n if request.method == 'GET':\n form = ShowForm()\n showid = request.args.get('id')\n\n # Edit Song\n if showid is not None:\n show = Show.query.get(int(showid))\n\n if not is_current_user(show.user_id):\n return render_template(\"not-authorized.html\")\n\n form.showid.data = show.id\n form.bandid.data = show.band_id\n form.name.data = show.name\n form.start.data = show.start\n form.end.data = show.end\n form.address.data = show.address\n form.contact.data = show.contact\n form.pay.data = show.pay\n form.notes.data = show.notes\n\n return render_template(\"edit-show.html\", show_form=form)\n\n else:\n form = ShowForm(request.form)\n form.start.format = '%d/%m/%Y %H:%M'\n\n if form.validate():\n form.errors['error'] = False\n\n if form.showid.data == '':\n # New Show\n show = Show(name=form.name.data, start=form.start.data, end=form.end.data, contact=form.contact.data,\n pay=form.pay.data, notes=form.notes.data, address=form.address.data,\n band_id=form.bandid.data, user_id=current_user.id)\n db.session.add(show)\n db.session.commit()\n form.errors['msg'] = gettext(\n 'Show successfully added! Now you can start building your setlist (see below).')\n form.errors['addedid'] = show.id\n else:\n # Edit Show\n show = Show.query.get(int(form.showid.data))\n show.band_id = form.bandid.data\n show.name = form.name.data\n show.start = form.start.data\n show.end = form.end.data\n show.address = form.address.data\n show.contact = form.contact.data\n show.pay = form.pay.data\n show.notes = form.notes.data\n db.session.add(show)\n form.errors['msg'] = gettext('Show info updated!')\n\n else:\n form.errors['error'] = True\n form.errors['msg'] = gettext('Your request was not successful. Please, check the errors below.')\n\n return jsonify(form.errors)","title":""},{"docid":"d7df12bc56648500b6c05a273887d6a9","score":"0.5105316","text":"def set(id, s):\n raise NotImplementedError","title":""},{"docid":"e747dea749a8d1b3eacabed75f0179c7","score":"0.50841355","text":"def _getShowData(self, sid, language):\n\n if self.config['language'] is None:\n log().debug('Config language is none, using show language')\n if language is None:\n raise tvdb_error(\"config['language'] was None, this should not happen\")\n else:\n log().debug(\n 'Configured language %s override show language of %s' % (\n self.config['language'],\n language\n )\n )\n\n # Parse show information\n log().debug('Getting all series data for %s' % (sid))\n seriesInfoEt = self._getetsrc(\n self.config['url_seriesInfo'] % sid\n )\n for curInfo in seriesInfoEt.keys():\n tag = curInfo\n value = seriesInfoEt[curInfo]\n\n if value is not None:\n if tag in ['banner', 'fanart', 'poster']:\n value = self.config['url_artworkPrefix'] % (value)\n\n self._setShowData(sid, tag, value)\n # set language\n self._setShowData(sid, u'language', self.config['language'])\n\n # Parse banners\n if self.config['banners_enabled']:\n self._parseBanners(sid)\n\n # Parse actors\n if self.config['actors_enabled']:\n self._parseActors(sid)\n\n # Parse episode data\n log().debug('Getting all episodes of %s' % (sid))\n\n url = self.config['url_epInfo'] % sid\n\n epsEt = self._getetsrc(url, language=language)\n\n for cur_ep in epsEt:\n\n if self.config['dvdorder']:\n log().debug('Using DVD ordering.')\n use_dvd = cur_ep.get('dvdSeason') is not None and cur_ep.get('dvdEpisodeNumber') is not None\n else:\n use_dvd = False\n\n if use_dvd:\n elem_seasnum, elem_epno = cur_ep.get('dvdSeason'), cur_ep.get('dvdEpisodeNumber')\n else:\n elem_seasnum, elem_epno = cur_ep['airedSeason'], cur_ep['airedEpisodeNumber']\n\n if elem_seasnum is None or elem_epno is None:\n log().warning(\"An episode has incomplete season/episode number (season: %r, episode: %r)\" % (\n elem_seasnum, elem_epno))\n #log().debug(\n # \" \".join(\n # \"%r is %r\" % (child.tag, child.text) for child in cur_ep.getchildren()))\n # TODO: Should this happen?\n continue # Skip to next episode\n\n # float() is because https://github.com/dbr/tvnamer/issues/95 - should probably be fixed in TVDB data\n seas_no = elem_seasnum\n ep_no = elem_epno\n\n for cur_item in cur_ep.keys():\n tag = cur_item\n value = cur_ep[cur_item]\n if value is not None:\n if tag == 'filename':\n value = self.config['url_artworkPrefix'] % (value)\n self._setItem(sid, seas_no, ep_no, tag, value)","title":""},{"docid":"58ae9969a4280712c963e5da586e9f62","score":"0.5045996","text":"def set(id, s):\r\n raise NotImplementedError","title":""},{"docid":"18171ef9b9aacbef8b79e59306868fd5","score":"0.50167745","text":"def tv_show(self, tv_show):\n\n self._tv_show = tv_show","title":""},{"docid":"a1659b28dc6c257bfb67564a1c6abffd","score":"0.5008146","text":"def add_show(cls, show_info):\n\n show = Show(guidebox_id=show_info[\"id\"],\n title=show_info[\"title\"],\n artwork_urls=show_info[\"artwork_608x342\"],\n first_aired=show_info[\"first_aired\"],\n description=show_info[\"overview\"])\n db.session.add(show)\n db.session.commit()","title":""},{"docid":"6a5db803d6164da9f0b295769f16a22f","score":"0.4977533","text":"def _backup(self, sid):\r\n self.sdb[sid] = self.dictionary()","title":""},{"docid":"00f6c605ef584541a049e24f36373977","score":"0.49677423","text":"def set_show(self, show):\n if show is None:\n self._del_sub('show')\n elif show in self.showtypes:\n self._set_sub_text('show', text=show)\n return self","title":""},{"docid":"a7bc088f5e3a4e5b53db9cd0d5e7bf0a","score":"0.48500535","text":"def shows_setlists_get(self, showid=None, showdate=None):\n params = {'method': 'pnet.shows.setlists.get', 'format': self.FORMAT}\n if showid:\n params['showid'] = showid\n if showdate:\n params['showdate'] = str(self.parse_date(showdate))\n return self.get(params=params)","title":""},{"docid":"eb8b1e6951a5455a76155aaaa73ecd44","score":"0.48346978","text":"def get_shows(offSet,channel,totals):\n #web_pdb.set_trace()\n shows = []\n finalpg = True\n # \n if channel=='VSO':\n\t\turl = '%svoot-web/content/specific/editorial-clone?query=include:366ef23c7cac2b3d5c7b315744f12a8b&&page=%s&responseType=common'%(apiUrl,offSet)\n\t\tjd = requests.get(url,headers=headers).json()\n\t\titems = jd['result']\n\t\tfor item in items:\n\t\t\ttitle = item.get('name')\n\t\t\ttcount = item.get('season') if item.get('season') else 1\n\t\t\tsbu = item.get('SBU')\n\t\t\titem_id= item.get('id')\n\t\t\ticon = 'https://v3img.voot.com/resizeMedium,w_451,h_275/'+item['showImage']\n\t\t\tlabels = {'title': title,\n\t\t\t 'genre': item.get('genres'),\n\t\t\t 'season': item.get('season'),\n\t\t\t 'plot': item['fullSynopsis'],\n\t\t\t 'mediatype': 'tvshow',\n\t\t\t 'year': item['releaseYear']} \n\t\t\tshows.append((title,icon,sbu,item_id,tcount,labels))\n else:\n\t\turl = '%svoot-web/content/generic/shows-by-sbu?sbu=include:%s&sort=mostpopular:desc&&page=%s'%(apiUrl,channel,offSet)\n\t\tjd = requests.get(url,headers=headers).json() \n\t\titems = jd['result']\n\t\tfor item in items:\n\t\t\ttitle = item.get('name')\n\t\t\ttcount = item['meta'].get('season') if item['meta'].get('season') else 1\n\t\t\tsbu = item['meta'].get('SBU')\n\t\t\titem_id= item.get('id')\n\t\t\ticon = item['details']['image'].get('base')+item['details']['image'].get('id')+'.'+item['details']['image'].get('type')\n\t\t\tlabels = {'title': title,\n\t\t\t 'genre': item['meta'].get('genre'),\n\t\t\t 'season': item['meta'].get('season'),\n\t\t\t 'plot': item['meta']['synopsis'].get('full'),\n\t\t\t 'mediatype': 'tvshow',\n\t\t\t 'year': item['meta'].get('releaseYear')} \n\t\t\tshows.append((title,icon,sbu,item_id,tcount,labels))\n\n offSet = int(offSet)\n totals = int(jd['totalAsset'])\n itemsLeft = totals - offSet * 10\n #web_pdb.set_trace()\n if itemsLeft > 0:\n finalpg = False\n pages = int(math.ceil(totals/10.0))\n\n if not finalpg:\n title = 'Next Page.. (Currently in Page %s of %s)' % (offSet,pages)\n offSet += 1\n\n labels = {}\n shows.append((title,_icon,sbu,offSet,tcount,labels))\n\n return shows","title":""},{"docid":"7efd6796ee8d1fdd3c9736acab5ce67b","score":"0.4781474","text":"def update(self, sid):\r\n for key, val in self.sdb[sid].items():\r\n _val = getattr(self, key)\r\n if not _val and val:\r\n setattr(self, key, val)\r\n elif key == \"grant\" and val:\r\n val.update(_val)\r\n setattr(self, key, val)","title":""},{"docid":"32bb59333708b0e1ee2e7034fcb7056a","score":"0.47793517","text":"def update_show(self, show, year, field, value):\n query = (\"\"\"\n UPDATE series\n SET {0:}=%s\n WHERE title=%s AND year=%s;\n \"\"\".format(field))\n params = (value, show, year)\n self.cursor.execute(query, params)\n self.cnx.commit()\n return True","title":""},{"docid":"465d81254ec26620b3f8c81fcc03de74","score":"0.47330478","text":"def __getitem__(self, key):\n if isinstance(key, int_types):\n # Item is integer, treat as show id\n if key not in self.shows:\n self._getShowData(key, self.config['language'])\n return self.shows[key]\n\n sid = self._nameToSid(key)\n log().debug('Got series id %s' % sid)\n return self.shows[sid]","title":""},{"docid":"5db7ca6935ff1f86ee68ddae8f2c6d07","score":"0.47216412","text":"def update(self, sid):\r\n for key, val in self.sdb[sid].items():\r\n _val = getattr(self, key)\r\n if not _val and val:\r\n setattr(self, key, val)\r\n elif key == \"grant\" and val:\r\n _tmp = {}\r\n for state, info in _val.items():\r\n try:\r\n info.join(val[state])\r\n except KeyError:\r\n pass\r\n\r\n _tmp[state] = info\r\n setattr(self, key, _tmp)\r\n\r\n return self","title":""},{"docid":"dc270e6646f72af688ae0692c3f1287b","score":"0.4709362","text":"def shows(self):\n from models.show import Show, ShowArtist\n # catch the artist id that is equeal to id in the table showArtist\n showArtists = models.storage.session.query(\n ShowArtist).filter_by(artist_id=self.id).all()\n listShows = []\n # create a list with an id show\n for sa in showArtists:\n listShows.append(sa.show_id)\n shows = []\n # and returns a list with the shows of this artist\n for show in listShows:\n shows.append(models.storage.session.query(\n Show).filter_by(id=show).first())\n return shows","title":""},{"docid":"1a4f5877f0d3a4647382eea4a74ff0ce","score":"0.4664723","text":"def open(self):\n if self.sid not in self.subs.values():\n self.subs[self.sid] = self","title":""},{"docid":"bb28e18f0d172d88cd4e3137dd17136d","score":"0.4662663","text":"def create_test_show(self):\n self.show_name = \"Test Show\"\n self.show_description = \"This is a fake show for testing.\"\n self.image = \"/podcasts/fake.png\"\n self.show = Show(rss=self.rss, name=self.show_name,\n description=self.show_description, image=self.image)\n return self.show","title":""},{"docid":"c0419c48281610e133cf30fbc3fb1a89","score":"0.4658723","text":"def __init__(self, sid):\n\t\tself.sid = sid","title":""},{"docid":"ea899ddf0425bff8f744aa3e0e7140c5","score":"0.46278617","text":"def set_strava_id(self, strava_id):\n self.strava_id = strava_id","title":""},{"docid":"7b4734f1f03544c07d85f3c9253c8a39","score":"0.46221206","text":"def load_tv_show_data(self):\n soup = BeautifulSoup(self.content, 'html.parser')\n\n self._season_and_episode = {}\n episodes = str(soup('a', {'class': 'magnet'})).split('</a>')\n\n for epi in episodes:\n for pat in self._patterns:\n data = self._match_pattern(pat, epi)\n if data is None:\n continue\n\n name = re.findall('title=\"(.*)\"', epi)[0]\n self.add_season_and_episode(name, data[0], data[1], data[2])\n\n return self._instance","title":""},{"docid":"de109edac05ac6d773d03b0ef4e6833a","score":"0.46181056","text":"def _set_datas(self, datas, attr=None):\r\n dataattr = attr or self.defaultkey\r\n self.datas[dataattr] = list(datas)","title":""},{"docid":"e7616f164e6ce25a95e4ded403393ea4","score":"0.45558167","text":"def on_show(self):\n self.populate_data()","title":""},{"docid":"8663de0ee0c6116d806d5410139481cc","score":"0.4547266","text":"def initialize(self, wavelength, display_pts=5000):\n\n self.data = np.ones(display_pts) * wavelength\n\n # if self.sp_data already exists, keep its last value so that the\n # clear data functionality doesn't override the setpoint\n if len(self.sp_data) > 0:\n self.sp_data = np.ones(display_pts) * self.sp_data[-1]\n else:\n self.sp_data = np.ones(display_pts) * self.data[-1]\n\n self.setpoint = self.sp_data[-1]","title":""},{"docid":"02d3efd159958f430b53ccba5a390411","score":"0.45432544","text":"def fetch_shows():\n show_list = current_user.shows.order_by(desc(Show.start)).all()\n return_data = []\n\n for show in show_list:\n band = Band.query.get(int(show.band_id))\n if show.start:\n start = format_datetime(show.start, get_date_format())\n else:\n start = ''\n return_data.append(dict(id=show.id, name=show.name, start=start, band=band.name))\n\n return jsonify(data=return_data)","title":""},{"docid":"fbaa20f82e9712cc60e999efa6c926d6","score":"0.45369145","text":"def converttostandard(show, season = None, showfactory = SeasonCharts.Show):\n if not isinstance(show,Show):\n raise TypeError(\"converttostandard requires an AniChart Show Object.\")\n chartsource = [(CHARTNAME, show.livechartid),]\n\n if not season:\n season = show.season\n japanese_title = show.japanese\n romaji_title = show.romaji\n english_title = show.english\n additional_titles = show.alternatetitles\n medium = show.showtype\n continuing = \"leftovers\" in [note.lower() for note in show.notes]\n summary = f\"(From {CHARTNAME})\\n{show.summary}\"\n\n tags = [(tag[1],False) for tag in show.tags]\n \n startdate = show.startdate.strftime(SeasonCharts.DTFORMAT)\n\n episodes = show.episodes\n images = [show.img,]\n studios = show.studios\n ## Livechart has default/null value links\n links = [link for link in show.links if link[0] and link[1]]\n\n return showfactory(chartsource = chartsource, season = season,\n japanese_title = japanese_title, romaji_title = romaji_title,\n english_title = english_title, additional_titles = additional_titles,\n medium = medium, continuing = continuing, summary = summary,\n tags = tags, startdate = startdate, episodes = episodes, images = images,\n studios = studios, links = links)","title":""},{"docid":"98fd2cc32f08009f13130d2f6573aa81","score":"0.45247036","text":"def get_shows(self):\n query = (\"\"\"\n SELECT title, year, poster, episodes, ytcount, \n (episodes - ytLicensed)/episodes*100 as ppay, \n ytcount/episodes as ytrep, \n runtime, title_iso, img\n FROM series\n ORDER BY ytrep DESC\n \"\"\")\n self.cursor.execute(query)\n shows = [s for s in self.cursor]\n # for s in self.cursor:\n # imgblob = s[9].decode('base64')\n # s = list(s)\n # s[9] = imgblob\n # shows.append(tuple(s))\n return shows","title":""},{"docid":"15039a442b4405c0f9df4e0dc57cbf26","score":"0.45124647","text":"def add_instance(instance):\n data_store = getattr(DataStore, type(instance).__name__)\n data_store[instance.id] = instance","title":""},{"docid":"42e34bf6399ae994ed11fc4b95d45ea9","score":"0.44905293","text":"def test_add_show_via_api_request_to_existing_tracker_with_watchlist(self):\n expected_tracked_show = tracker.TrackedShow('Narcos')\n with TemporaryDirectory() as dirname:\n # Some setup\n shutil.copy(self.path_to_tracker, dirname)\n shutil.copy(self.path_to_showdb, dirname)\n\n sdb, tdb = tracker.load_all_dbs(dirname)\n sdb.path_to_db = os.path.join(dirname, '.showdb.json')\n tdb.path_to_db = os.path.join(dirname, '.tracker.json')\n sdb.write_db()\n tdb.write_db()\n\n with NamedTemporaryFile('w+t') as f:\n f.write('narcos S01E01')\n f.seek(0)\n watchlist_path = f.name\n args = self.parser.parse_args(\n [\n '--database-dir={}'.format(dirname),\n '--watchlist={}'.format(watchlist_path),\n ]\n )\n tracker.tracker(args)\n showdb, trackerdb = tracker.load_all_dbs(dirname)\n expected_tracked_show._set_next_prev(showdb)\n self.assertEqual(trackerdb._shows['narcos'], expected_tracked_show)","title":""},{"docid":"fc09d92532f70cce2a167e47312eeb9f","score":"0.4465032","text":"def put_episodes(self, show, year, episodes):\n query = (\"INSERT INTO episodes \"\n \" (series_title, series_year, season, episode, title) \"\n \" VALUES (%s, %s, %s, %s, %s) \"\n \" ON DUPLICATE KEY UPDATE \"\n \" series_title=VALUES(series_title), \"\n \" series_year=VALUES(series_year), \"\n \" episode=VALUES(episode), \"\n \" season=VALUES(season), \"\n \" title=VALUES(title) \")\n params = [\n (show, year, \n key[0], key[1], \n name) for key, name in episodes.items()]\n self.cursor.executemany(query, params)\n self.cnx.commit()\n return True","title":""},{"docid":"af773166dc8458e102d467c17eaacef6","score":"0.44532737","text":"def save_setlist():\n show = Show.query.get(int(request.form.get('showid')))\n\n # First we delete the previous songs, so we don't have to deal with updates\n show.remove_all_songs()\n\n # Now we add the new, sorted, setlist\n song_ids = request.form.get('songIds').split(',')\n\n pos = 1\n\n for song_id in song_ids:\n song = Song.query.get(song_id)\n show.add_song(song)\n db.session.add(show)\n db.session.commit()\n show.assign_position(song, pos)\n pos += 1\n\n return jsonify(dict())","title":""},{"docid":"3d36d8fa76b072eb76673d078ad2519f","score":"0.44515738","text":"def __init__(self, create_url=None, **kwargs):\n if create_url:\n try:\n episode = make_request(create_url)\n except ApiError as e:\n raise e\n else:\n self.airdate = episode[\"airdate\"]\n self.airstamp = episode[\"airstamp\"]\n self.number = episode[\"number\"]\n self.id = episode[\"id\"]\n self.season = episode[\"season\"]\n self.runtime = episode[\"runtime\"]\n self.name = episode[\"name\"]\n self.image = episode[\"image\"]\n self.summary = episode[\"summary\"]\n self.airtime = episode[\"airtime\"]\n self.url = episode[\"url\"]\n self._links = episode[\"_links\"]\n for key, val in kwargs.items():\n setattr(self, key, val)\n try:\n self.show = Show(**kwargs['show'])\n except KeyError:\n self.show = None","title":""},{"docid":"c48a34812c8eb44e11c30a7ea10cef89","score":"0.44489646","text":"def setSFData(self, entity, data):\r\n id = data.get('Id')\r\n if id: \r\n obj = self.cxn.retrieve(entity, id)\r\n for k,v in data.items():\r\n obj[k] = v\r\n saveResult = obj.update() \r\n else: \r\n newObj = SObjectClassFactory(self.cxn, entity)\r\n saveResult = self.cxn.create(newObj, data)\r\n if saveResult in BAD_INFO_LIST:\r\n log.info('ERROR: could not set %s with %s got: %s'%(entity,data,saveResult))\r\n else:\r\n log.info('Set %s with %s got: %s'%(entity,data,saveResult))\r\n return saveResult.get('Id','')","title":""},{"docid":"2d972317c6588980085d712f742f5c4e","score":"0.44478175","text":"def test_got_film_with_same_name(self):\n show = tracker.Show(title='Fargo')\n show.populate_seasons()\n self.assertTrue(show._seasons)","title":""},{"docid":"ea57f931db408f8ee3ae61b7bd3b815f","score":"0.44226292","text":"def fetch_setlist(show_id):\n show = Show.query.get(show_id)\n setlist = show.songs\n\n return_data = []\n for song in setlist:\n return_data.append(\n dict(id=song.id, title=song.title, tempo=song.tempo, artist=song.artist, duration=song.pretty_duration(),\n lyrics=song.lyrics))\n\n return jsonify(data=return_data)","title":""},{"docid":"6435a3774e137b15339792da1769ae2f","score":"0.44166067","text":"def setID(self, sid:str) -> None:\n try:\n assert len(sid) == 6, \"Student ID must be 6 characters long.\"\n assert sid.isnumeric(), \"Student ID must be a valid integer.\"\n int(sid)\n except ValueError:\n print(\"Student ID must be a valid integer in string format\")\n except AssertionError as e:\n print(e.args[0]) \n else:\n self.id = sid","title":""},{"docid":"cf2cb329cfe4bcadccb68c9ae4995a0a","score":"0.43960917","text":"def suid(self, suid):\n\n self._suid = suid","title":""},{"docid":"231ad80d3ec73ce56cb38691bd0d2fe2","score":"0.43893096","text":"def show_venue(venue_id):\n venue = Venue.query.get(venue_id)\n currentTime = datetime.now()\n\n upcoming_shows = db.session.query(Show).join(Venue).filter(Show.venue_id == venue_id, Show.start_time >= currentTime).all()\n past_shows = db.session.query(Show).join(Venue).filter(Show.venue_id == venue_id, Show.start_time <= currentTime).all()\n\n #upcoming_shows = Show.query.filter(Show.venue_id==venue_id, Show.start_time >= currentTime).all()\n #past_shows = Show.query.filter(Show.venue_id==venue_id,Show.start_time < currentTime).all()\n #For filtering, I referenced the following forum articles:\n #https://knowledge.udacity.com/questions/261355\n #https://knowledge.udacity.com/questions/452402\n #I originally included the joins here but once I found the documentation for the lazy=\"joined\" I was able to refine the query here just by going through Show\n\n data = {\n 'id': venue.id,\n 'name': venue.name,\n 'city': venue.city,\n 'state': venue.state,\n 'address': venue.address,\n 'phone': venue.phone,\n 'image_link': venue.image_link,\n 'facebook_link': venue.facebook_link,\n 'genres': venue.genres,\n 'website_link': venue.website_link,\n 'seeking_talent': venue.seeking_talent,\n 'seeking_description': venue.seeking_description,\n 'upcoming_shows': [{\n 'artist_id': show.artist.id,\n 'artist_image_link': show.artist.image_link,\n 'start_time': show.start_time\n } for show in upcoming_shows],\n 'past_shows': [{\n 'artist_id': show.artist.id,\n 'artist_image_link': show.artist.image_link,\n 'start_time': show.start_time\n }for show in past_shows]\n } \n return render_template('pages/show_venue.html', venue = data)","title":""},{"docid":"42cb2493261862570bedfef65337e973","score":"0.43657145","text":"def instances(self, instances):\n\n self._instances = instances","title":""},{"docid":"3fa9b7bd9aef8dcb7efcfd833e261f05","score":"0.43641263","text":"def __init__(self, season_id=None, with_episodes=False, **kwargs):\n self.episode_list = []\n if season_id:\n self.season_id = season_id\n self.api_url = \"{}{}{}\".format(SEASON, self.season_id, \"?embed=episodes\" if with_episodes else '')\n response = requests.get(self.api_url)\n if response.status_code > 400:\n raise ValueError('{}'.format(response.json()['message']))\n season = response.json()\n if with_episodes:\n self.episode_list = [Episode(**episode) for episode in season['_embedded']['episodes']]\n self.name = season[\"name\"]\n self._links = season[\"_links\"]\n self.url = season[\"url\"]\n self.premiereDate = season[\"premiereDate\"]\n self.id = season[\"id\"]\n self.episodeOrder = season[\"episodeOrder\"]\n self.number = season[\"number\"]\n self.endDate = season[\"endDate\"]\n self.webChannel = season[\"webChannel\"]\n self.summary = season[\"summary\"]\n self.network = season[\"network\"]\n self.image = season[\"image\"]\n else:\n self.season_id = kwargs.get('id')\n for key, val in kwargs.items():\n setattr(self, key, val)\n self.base_url = \"{}{}\".format(SEASON, self.season_id)","title":""},{"docid":"08be60b1d2d74d5f1ae123339139d188","score":"0.43441167","text":"def test_add_show_in_showdb_to_existing_tracker_with_watchlist(self):\n expected_tracked_show = tracker.TrackedShow('House', _next_episode='S08E01')\n with TemporaryDirectory() as dirname:\n # Some setup\n shutil.copy(self.path_to_tracker, dirname)\n shutil.copy(self.path_to_showdb, dirname)\n\n sdb, tdb = tracker.load_all_dbs(dirname)\n sdb.path_to_db = os.path.join(dirname, '.showdb.json')\n tdb.path_to_db = os.path.join(dirname, '.tracker.json')\n sdb.write_db()\n tdb.write_db()\n\n with NamedTemporaryFile('w+t') as f:\n f.write('house s08e01')\n f.seek(0)\n watchlist_path = f.name\n args = self.parser.parse_args(\n [\n '--database-dir={}'.format(dirname),\n '--watchlist={}'.format(watchlist_path),\n ]\n )\n tracker.tracker(args)\n showdb, trackerdb = tracker.load_all_dbs(dirname)\n expected_tracked_show._set_next_prev(showdb)\n self.assertEqual(trackerdb._shows['house'], expected_tracked_show)","title":""},{"docid":"903762e7044895dea191ba7a5bf241aa","score":"0.43434078","text":"def _set_s_keep(self, s, svs):\n if svs is not None:\n self.svs = svs\n if self.svs is None:\n self.svs = _np.arange(s.size)\n \n self.s_keep = 0*s\n self.s_keep[self.svs] = s[self.svs]\n # print(self.s_keep)\n # self.s_keep = s[svs]","title":""},{"docid":"66764ef2b7c99c1b0726a34546ce3e23","score":"0.43225002","text":"def __init__(self, ds_fid):\n self.ds = ASDFDataSet(ds_fid)\n # Initiate an empty Manager to get access to its config\n self.mgmt = Manager()\n self.info = Info()","title":""},{"docid":"776881832c681cbf3bb1e252ac5ab5d1","score":"0.4319444","text":"def findmissing_showstats(show, url = None, session = None, pipe = None):\n if pipe is None:\n def pipe(output, verbose = False):\n pass\n if not url:\n ids = [url for link in show.links if (url := myanimelist.parse_siteid(link[1]))]\n malinfo = None \n for link in ids:\n try:\n pipe(f\">>> {show.english_title or show.romaji_title or show.japanese_title}({link})\")\n malinfo = getshowstats(link, session = session)\n except Exception as e:\n pipe(f\">>> Failed to get MAL info for: {show.romaji_title if show.romaji_title else show.english_title}({link})\")\n pipe(traceback.format_exc(), verbose = True)\n if malinfo: break\n else:\n malinfo = getshowstats(url)\n\n if not malinfo: return\n\n if not show.japanese_title and malinfo.get(\"japanese_title\"):\n show.japanese_title = malinfo['japanese_title']\n\n for name in malinfo.get(\"additional_titles\",[]):\n if name not in show.additional_titles:\n show.additional_titles.append(name)\n\n if not show.startdate and malinfo.get(\"first_episode\"):\n show.startdate = malinfo['first_episode'].replace(tzinfo = WebModules.JST)\n\n ## NOTE: for both continuing and renewal, we always take True if possible\n if (premiered := malinfo.get(\"premiered\")) or (first_episode := malinfo.get(\"first_episode\")):\n if not premiered:\n premiered = getseason(first_episode) + f\"-{first_episode.year}\"\n if (continuing := parseanimeseason_toobject(premiered) < parseanimeseason_toobject(show.season)):\n show.continuing = continuing\n\n if (renewal := bool(malinfo['relations'].get(\"Prequel\"))):\n show.renewal = renewal\n\n if not show.runtime and malinfo.get(\"runtime\"):\n show.runtime = malinfo['runtime']\n\n if not show.medium and malinfo['type']:\n show.medium = malinfo['type']\n\n ## Hard overriding the next two medium types\n if (runtime := malinfo.get('runtime')) and ((runtime.total_seconds() / 60 ) < 17):\n show.medium = show.medium + \" SHORT\"\n\n if (\"rating\" in malinfo and \"hentai\" in malinfo['rating'].lower()) or \"hentai\" in [genre.lower() for genre in malinfo['genres']]:\n show.medium = \"Hentai\"","title":""},{"docid":"90685209141581b33fd907375ff5bba7","score":"0.43094745","text":"def stores_info(self, stores_info):\n\n self._stores_info = stores_info","title":""},{"docid":"1ef50d72ca613cc0977db0b33a27a06b","score":"0.43009055","text":"def get_client_advert_media_stations_by_id(self, sid):\n pass","title":""},{"docid":"a56ac111120b25722b8740574de8afc9","score":"0.43005788","text":"def test_show_displays_episodes(self):\n create_test_rss(self).save()\n create_test_show(self).save()\n url = reverse(\"api_show\")\n json_response = self.client.get(url, format='json')\n old_count = len(json_response.content)\n create_test_episode(self).save()\n json_response = self.client.get(url, format='json')\n new_count = len(json_response.content)\n self.assertNotEqual(old_count, new_count)","title":""},{"docid":"0c658564110d7864a57f3fedced41564","score":"0.42969975","text":"def set_session_record(self, track, xclip, ident, value = None):\n\tif IS_LIVE_9:\n\t if value in KEYWORDS:\n\t\tself.song().session_record = KEYWORDS[value]\n\t else:\n\t\tself.song().session_record = not(self.song().session_record)","title":""},{"docid":"b020566a81676d4d1fe5e08d920482ce","score":"0.4293776","text":"def set_advert_fingerprinted(self, sid):\n pass","title":""},{"docid":"1b9b8b9ca1a659f49d770bb09ba8245f","score":"0.42876467","text":"def setSessionDataSetId(self, sId):\n if sId is not None and len(sId) > 0:\n self.__sessionDataSetId = str(sId).upper()\n return True\n else:\n return False","title":""},{"docid":"eb1ac796103c58b4cf1ad99239d09f76","score":"0.42812908","text":"def set(name, instance):\n REDIS.hset(name, 'data', pickle.dumps(instance))\n REDIS.expire(name, REDIS_EXPIRE_TIME)","title":""},{"docid":"6fa7bdcb33a0613b9ee53092bab68cb7","score":"0.4277697","text":"def displays(self, displays):\n\n self._displays = displays","title":""},{"docid":"177e91236dd2e530ebf23fd52ad77107","score":"0.42761114","text":"def add_season_db(self):\n result = self.db_handle.fetchdata('add_season', [self.season_name,\n self.ss_date.date(True),\n self.se_date.date(True),\n ]\n )\n self.said = result[0][0]","title":""},{"docid":"510a9a6bc88a4feb6b45f8693038c9f8","score":"0.4264196","text":"def cublasSetStream(handle, id):\r\n\r\n status = _libcublas.cublasSetStream_v2(handle, id)\r\n cublasCheckStatus(status)","title":""},{"docid":"08e572a33e070171a7f6571e53b5160f","score":"0.42459482","text":"def serializeshows(file,shows):\n with open(file,'w', encoding = 'utf-8') as f:\n json.dump(shows,f,default = Show.serialize)","title":""},{"docid":"334ce1f7710d794b3209866ab726890d","score":"0.42396113","text":"def get_advert_by_id(self, sid):\n pass","title":""},{"docid":"444696acae6c33d4a2f3c8b5f91bfd47","score":"0.42315274","text":"def list_shows(offSet,channel,totals):\n shows = cache.cacheFunction(get_shows,offSet,channel,totals)\n listing = []\n for title,icon,sid,item_id,tcount,labels in shows:\n if 'Next Page' not in title:\n list_item = xbmcgui.ListItem(label='[COLOR yellow]%s [COLOR cyan](%s Seasons)[/COLOR]'%(title,tcount))\n url = '{0}?action=list_season&offSet=1&show={1}&totals={2}'.format(_url,item_id,tcount)\n else:\n list_item = xbmcgui.ListItem(label='[COLOR yellow]%s[/COLOR]'%(title))\n url = '{0}?action=list_channel&offSet={1}&channel={2}&totals={3}'.format(_url, item_id,sid, tcount)\n\n \n list_item.setArt({'poster': icon,\n 'thumb': icon,\n 'icon': icon,\n 'fanart': icon})\n\n list_item.setInfo('video', labels)\n\n \n is_folder = True\n listing.append((url, list_item, is_folder))\n xbmcplugin.addDirectoryItems(_handle, listing, len(listing))\n #xbmcplugin.setContent(_handle, 'tvshows')\n xbmcplugin.endOfDirectory(_handle)","title":""},{"docid":"c588f08e600745fde129ae24f486fa09","score":"0.4231","text":"def _set_item(self, key, value, ds_attr=True):\n if ds_attr:\n key = self._get_ds_attr(key)\n try:\n serialised_data = self._to_json(value)\n except TypeError:\n raise NukeDataStoreError('Data not serialisable')\n try:\n self.store[key].setValue(serialised_data)\n except NameError:\n self.store.addKnob(self._create_knob(key))\n self.store[key].setValue(serialised_data)","title":""},{"docid":"403b1820a8fee08fed697a522bef69a8","score":"0.4228602","text":"def remove_show(self, show, year):\n # First delite episodes\n query = (\"\"\"\n DELETE FROM episodes\n WHERE series_title=%s AND series_year=%s;\n \"\"\")\n params = (show, year)\n self.cursor.execute(query, params)\n self.cnx.commit()\n # Then delete show info\n query = (\"\"\"\n DELETE FROM series\n WHERE title=%s AND year=%s;\n \"\"\")\n params = (show, year)\n self.cursor.execute(query, params)\n self.cnx.commit()\n return True","title":""},{"docid":"3d01f7415fe6044b817e777ee57a3637","score":"0.4224558","text":"def load_details(self):\n the_id = self.data[\"id\"][\"value\"]\n self.data.update(self.data_from_id(the_id))\n if isinstance(self.data[\"events\"], (list)):\n self[\"events\"] = EventManager(self.data[\"events\"])\n self.data[\"id\"][\"value\"] = the_id\n\n return self","title":""},{"docid":"ab909116380dcab3f4f4c0bf9939b7df","score":"0.42201987","text":"def retrieve_instance(\n self,\n study_instance_uid: str,\n series_instance_uid: str,\n sop_instance_uid: str,\n media_types: Optional[Tuple[Union[str, Tuple[str, str]], ...]] = None,\n ) -> pydicom.dataset.Dataset: # noqa: E501\n if study_instance_uid is None:\n raise ValueError(\n 'Study Instance UID is required for retrieval of instance.'\n )\n self._assert_uid_format(study_instance_uid)\n if series_instance_uid is None:\n raise ValueError(\n 'Series Instance UID is required for retrieval of instance.'\n )\n self._assert_uid_format(series_instance_uid)\n if sop_instance_uid is None:\n raise ValueError(\n 'SOP Instance UID is required for retrieval of instance.'\n )\n logger.info(\n f'retrieve instance \"{sop_instance_uid}\" '\n f'of series \"{series_instance_uid}\" '\n f'of study \"{study_instance_uid}\"'\n )\n self._assert_uid_format(sop_instance_uid)\n url = self._get_instances_url(\n _Transaction.RETRIEVE,\n study_instance_uid,\n series_instance_uid,\n sop_instance_uid\n )\n if media_types is None:\n media_types = (('application/dicom', '*'), )\n\n common_media_types = self._get_common_media_types(media_types)\n if len(common_media_types) > 1:\n raise ValueError(\n 'Only media type \"application/dicom\" is supported for '\n 'retrieval of an instance.'\n )\n common_media_type = common_media_types[0]\n if common_media_type != 'application/dicom':\n raise ValueError(\n f'Media type \"{common_media_type}\" is not supported for '\n 'retrieval of an instance. It must be \"application/dicom\".'\n )\n iterator = self._http_get_multipart_application_dicom(url, media_types)\n instances = list(iterator)\n if len(instances) > 1:\n # This should not occur, but safety first.\n raise ValueError('More than one instance returned by origin server')\n return instances[0]","title":""},{"docid":"277118f15e81e0ce7feb1a2cb67508e0","score":"0.42200792","text":"def setId(self, novoId):\r\n self.__id = novoId","title":""},{"docid":"59b3fc2a6fdc858fbbdfc88c02200747","score":"0.4218609","text":"def list_season(show,offSet,totals):\n season = cache.cacheFunction(get_season,show,offSet,totals)\n listing = []\n for title,icon,sid,item_id,labels,tcount in season:\n list_item = xbmcgui.ListItem(label=title)\n list_item.setArt({'poster': icon,\n 'thumb': icon,\n 'icon': icon,\n 'fanart': icon})\n list_item.setInfo('video', labels)\n if 'Next Page' not in title:\n url = '{0}?action=list_show&show={1}&offSet={2}&icon={3}'.format(_url,item_id,offSet,icon)\n else:\n url = '{0}?action=list_season&offSet={1}&show={2}&totals={3}'.format(_url,sid,item_id,tcount)\n\n is_folder = True \n listing.append((url, list_item, is_folder))\n xbmcplugin.addDirectoryItems(_handle, listing, len(listing))\n #xbmcplugin.setContent(_handle, 'tvshows')\n xbmcplugin.endOfDirectory(_handle)","title":""},{"docid":"09b90bbb375e2f1957c0c49549507866","score":"0.42185313","text":"def set_instance(self, model, **kwds):\n return self._set_instance(model, kwds)","title":""},{"docid":"198d698e6faa43e7242491a162c68127","score":"0.42112955","text":"def create_show(conn, shows):\n sql = ''' INSERT INTO shows(name,location,month)\n VALUES(?,?,?) '''\n cur = conn.cursor()\n cur.execute(sql, shows)\n return cur.lastrowid","title":""},{"docid":"5460b4cc7da79f658a714c15fd0db01b","score":"0.42094952","text":"def show_as(self, show_as):\n if show_as is None:\n raise ValueError(\"Invalid value for `show_as`, must not be `None`\") # noqa: E501\n\n self._show_as = show_as","title":""},{"docid":"4459ff75a534b9fb6845d57fe4b70d5b","score":"0.4196828","text":"def get_shows(self, person_id):\r\n result = []\r\n for show in ShowsPeopleMapping.query.filter_by(PersonID=person_id).all():\r\n s = {\r\n \"showID\": show.ShowID,\r\n \"instruments\": show.Instruments\r\n }\r\n result.append(s)\r\n return result","title":""},{"docid":"a8be145da6c118cd69c995f9f3218f84","score":"0.419064","text":"def make_episode(show_id, seasons, factor, debug=False):\n\n if debug:\n print(\"begin make_episode()\")\n print(seasons, factor)\n\n #cultivate list of episodes ratings\n episode_list = make_episode_dict(show_id, seasons, factor, debug)\n\n #pick an episode\n episode = pick_episode(episode_list, debug)\n\n #gather episode information & return\n return create_episode(episode, debug)","title":""},{"docid":"ee4d2d58582cc1dd8a290e3aa9bc3c38","score":"0.4188351","text":"async def get_multiple_shows(\n self, ids: List[str], market: Optional[str] = \"US\"\n ) -> List[Show]:\n\n data = await self.http.get_multiple_shows(ids, market)\n return list(Show(self, show) for show in data[\"shows\"])","title":""},{"docid":"58f5d7637a008e76c08791c32c7a58b3","score":"0.41719517","text":"def create_show_submission():\n\n form = ShowForm()\n if form.validate():\n try:\n data = Show(\n venue_id = form.venue_id.data,\n artist_id = form.artist_id.data,\n start_time = form.start_time.data\n )\n db.session.add(data)\n db.session.commit()\n flash('Show was successfully listed!')\n except:\n db.session.rollback()\n flash('An error occurred. Show could not be listed.')\n finally:\n db.session.close()\n return render_template('pages/home.html')\n else:\n flash(f\"{form.errors.items()}\")\n return render_template('pages/home.html')","title":""},{"docid":"b4a90c82f91ef7d13789ba37ee7cb95c","score":"0.41702455","text":"def insertShow(self, username, showID):\n conn = self.getConnection()\n\n cur_check = conn.cursor()\n cur_check.execute(\"SELECT * FROM WATCHLIST WHERE username=? AND \\\n showid=?\", (username, showID))\n\n # check if entry already exists\n if not cur_check.fetchone():\n cur = conn.cursor()\n date = datetime.date.today()\n\n cur.execute(\"INSERT INTO WATCHLIST(USERNAME, SHOWID, LASTWATCHED) \\\n VALUES(?, ?, ?);\", (username, showID, date))\n conn.commit()\n flash(\"Entry added\")\n else:\n flash(\"Entry already exists\")\n\n conn.close()","title":""},{"docid":"5a139c6175af82a5b4a2caf23433d0fe","score":"0.41687134","text":"def work(self):\n if self.options.client and self.options.show:\n client = Client.objects.get(slug=self.options.client)\n show = Show.objects.get(client=client, slug=self.options.show)\n self.one_show(show)\n\n return","title":""},{"docid":"fd906ceeb12e01835ccf931d028211d9","score":"0.41641414","text":"def setSpectrums(self, spectrums):\n self._spectrums = spectrums\n self._currentSpectrumName = None\n self._zincContext = spectrums.getZincContext()\n self._ui.sceneviewerWidgetPreview.setContext(self._zincContext)\n self._previewZincRegion = self._zincContext.createRegion()\n self._previewZincRegion.setName(\"Spectrum editor preview region\")\n self._previewZincScene = self._previewZincRegion.getScene()\n sceneviewer = self._ui.sceneviewerWidgetPreview.getSceneviewer()\n if sceneviewer:\n sceneviewer.setScene(self._previewZincScene)\n spectrummodule = self._zincContext.getSpectrummodule()\n self._spectrummodulenotifier = spectrummodule.createSpectrummodulenotifier()\n self._spectrummodulenotifier.setCallback(self._spectrummoduleCallback)\n self._buildSpectrumList()","title":""},{"docid":"36ce2b12c1b3c4c002def1194ab7328e","score":"0.41609138","text":"def __init__(self, show=None):\n self.show = show","title":""},{"docid":"0172da04cdb286add9345ef399ed0292","score":"0.4149757","text":"def do_add(self):\n tv = load_info_source('tv', self.config)\n search = raw_input('Enter Show Title: ')\n tv.find_show(search)\n print search","title":""},{"docid":"787932b4bbfae7480e59c77fcc307013","score":"0.41489017","text":"def __init__(self,\n show_id,\n show_params,\n root,\n canvas,\n showlist,\n pp_dir,\n pp_home,\n pp_profile,\n command_callback):\n\n # init the common bits\n Show.base__init__(self,\n show_id,\n show_params,\n root,\n canvas,\n showlist,\n pp_dir,\n pp_home,\n pp_profile,\n command_callback)\n\n\n # instatiatate the screen driver - used only to access enable and hide click areas\n self.sr=ScreenDriver()\n\n # create a path stack and control path debugging\n if self.show_params['debug-path']=='yes':\n self.debug=True\n else:\n self.debug=False\n self.path = PathManager()\n \n self.allowed_links=('return','home','call','null','exit','goto','play','jump','repeat','pause','no-command','stop','pause-on','pause-off','mute','unmute','go')\n \n # init variables\n self.track_timeout_timer=None\n self.show_timeout_timer=None\n self.next_track_signal=False\n self.next_track_ref=''\n self.current_track_ref=''\n self.current_track_type=''\n self.req_next=''","title":""},{"docid":"f5c8151291be89fead30f83f5c99b342","score":"0.4140898","text":"def test_request_show_info(self):\n show = tracker.Show(title='Game of Thrones')\n with open('got_s01_response.json', 'r') as f:\n expected_response = json.load(f)\n show.imdb_id = 'tt0944947'\n self.assertDictEqual(\n show.request_show_info(season=1),\n expected_response,\n )","title":""},{"docid":"f90a6f37043b1cf29600ca7776a0e657","score":"0.41408703","text":"def setMovie(self, mode, npcId, avId, extraArgs, timestamp):\n timeStamp = ClockDelta.globalClockDelta.localElapsedTime(timestamp)\n self.remain = NPCToons.CLERK_COUNTDOWN_TIME - timeStamp\n\n self.npcId = npcId\n\n # See if this is the local toon\n self.isLocalToon = (avId == base.localAvatar.doId)\n \n assert(self.notify.debug(\"setMovie: %s %s %s %s\" %\n (mode, avId, timeStamp, self.isLocalToon)))\n\n # This is an old movie in the server ram that has been cleared.\n # Just return and do nothing\n if (mode == NPCToons.SELL_MOVIE_CLEAR):\n assert self.notify.debug('SELL_MOVIE_CLEAR')\n return\n\n if (mode == NPCToons.SELL_MOVIE_TIMEOUT):\n assert self.notify.debug('SELL_MOVIE_TIMEOUT')\n # In case the GUI hasn't popped up yet\n taskMgr.remove(self.uniqueName('lerpCamera'))\n # Stop listening for the GUI\n if (self.isLocalToon):\n self.ignoreEventDict()\n # hide the popupInfo\n if self.popupInfo:\n self.popupInfo.reparentTo(hidden)\n if self.petshopGui:\n self.petshopGui.destroy()\n self.petshopGui = None\n\n self.setChatAbsolute(TTLocalizer.STOREOWNER_TOOKTOOLONG,\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_START):\n assert self.notify.debug('SELL_MOVIE_START')\n self.av = base.cr.doId2do.get(avId)\n if self.av is None:\n self.notify.warning(\"Avatar %d not found in doId\" % (avId))\n return\n else:\n self.accept(self.av.uniqueName('disable'),\n self.__handleUnexpectedExit)\n\n self.setupAvatars(self.av)\n\n if (self.isLocalToon):\n camera.wrtReparentTo(render)\n camera.lerpPosHpr(-5, 9, base.localAvatar.getHeight()-0.5,\n -150, -2, 0,\n 1,\n other=self,\n blendType=\"easeOut\",\n task=self.uniqueName('lerpCamera'))\n\n if (self.isLocalToon):\n taskMgr.doMethodLater(1.0, self.popupPetshopGUI,\n self.uniqueName('popupPetshopGUI'))\n \n elif (mode == NPCToons.SELL_MOVIE_COMPLETE):\n assert self.notify.debug('SELL_MOVIE_COMPLETE')\n self.setChatAbsolute(TTLocalizer.STOREOWNER_THANKSFISH_PETSHOP,\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_PETRETURNED):\n assert self.notify.debug('SELL_MOVIE_PETRETURNED')\n self.setChatAbsolute(TTLocalizer.STOREOWNER_PETRETURNED,\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_PETADOPTED):\n assert self.notify.debug('SELL_MOVIE_PETADOPTED')\n self.setChatAbsolute(TTLocalizer.STOREOWNER_PETADOPTED,\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_PETCANCELED):\n assert self.notify.debug('SELL_MOVIE_PETCANCELED')\n self.setChatAbsolute(TTLocalizer.STOREOWNER_PETCANCELED,\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_TROPHY):\n assert self.notify.debug('SELL_MOVIE_TROPHY')\n\n self.av = base.cr.doId2do.get(avId)\n if self.av is None:\n self.notify.warning(\"Avatar %d not found in doId\" % (avId))\n return\n else:\n numFish, totalNumFish = extraArgs\n self.setChatAbsolute(TTLocalizer.STOREOWNER_TROPHY % (numFish, totalNumFish),\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_NOFISH):\n assert self.notify.debug('SELL_MOVIE_NOFISH')\n self.setChatAbsolute(TTLocalizer.STOREOWNER_NOFISH,\n CFSpeech | CFTimeout)\n self.resetPetshopClerk()\n\n elif (mode == NPCToons.SELL_MOVIE_NO_MONEY):\n self.notify.warning('SELL_MOVIE_NO_MONEY should not be called')\n self.resetPetshopClerk()\n\n return","title":""},{"docid":"c780b97c09e983e35ed77d15be679067","score":"0.41306224","text":"def get_details(self):\n if not self.rss:\n self._get_rss_feed()\n logging.debug(\"Getting details for %s...\" % self)\n if not self.rss['entries']:\n raise ShowFeedNoEpisodesError\n # Determine human title. We are assuming here that the first episode\n # in the feed has a useful description. This may cause problems\n r = re.compile('Show Name\\s*: (.*?);')\n name_match = r.search(self.rss['entries'][0].description)\n if not name_match:\n raise ShowDetailsError, \"Could not determine show name for %s.\" \\\n % self\n human_name = name_match.group(1)\n # Determine show type\n title_re = re.compile('Show\\s*Title\\s*:\\s*(.*?);')\n season_re = re.compile('Season\\s*:\\s*([0-9]*?);')\n episode_re = re.compile('Episode\\s*:\\s*([0-9]*?)$')\n date_re = re.compile('Episode\\s*Date:\\s*([0-9\\-]+)$')\n d = {\n 'seasonepisode': 0,\n 'date': 0,\n 'title': 0\n }\n for episode in self.rss['entries']:\n title_match = title_re.search(episode.description)\n season_match = season_re.search(episode.description)\n episode_match = episode_re.search(episode.description)\n date_match = date_re.search(episode.description)\n if season_match and episode_match:\n d['seasonepisode'] += 1\n elif date_match:\n d['date'] += 1\n elif title_match and title_match.group(1) != 'n/a':\n d['title'] += 1\n # Nothing could be found, fall back to \"time\" type\n if d.values() == [0, 0, 0]:\n show_type = \"time\"\n else:\n # Sort keys based on values\n e = d.keys()\n e.sort(cmp=lambda a,b: cmp(d[a], d[b]))\n show_type = e[-1]\n self.human_name = human_name\n self.show_type = show_type\n return {'show_type': show_type, 'human_name': human_name}","title":""},{"docid":"17e97e5c46eca0ece25076ad13dbcf5d","score":"0.41293922","text":"def insert(self, hash, sid, offset):\n # 1. update song detail\n song_details = self._get_song_details(sid)\n assert song_details is not None\n if song_details[SONG_FINGERPRINTS].count((hash, offset)) == 0:\n # new fingerprint\n song_details[SONG_FINGERPRINTS].append((hash, offset))\n self._set_song_details(song_details)\n\n # 2. update fingerprints\n track_id = song_details[SONG_SONG_NAME]\n fingerprint_details = self._get_fingerprint_details(hash)\n fingerprint_record = (sid, track_id, offset)\n if fingerprint_details.count(fingerprint_record) == 0:\n # new fingerprint record\n fingerprint_details.append(fingerprint_record)\n self._set_fingerprint_details(hash, fingerprint_details)","title":""},{"docid":"938a6649c32c764802c21a2246a060b4","score":"0.4120176","text":"def __init__( self,data,season='None' ):\n self.data = data\n self.season = season","title":""},{"docid":"edf6ed1485d43ffe2c8831f0d9a70564","score":"0.41155356","text":"def add_spouse (self, idx):\n curr = self.get_suspect(idx)\n spouse, rel = curr.create_spouse()\n spouse.set_relative(idx, rel)\n curr.set_relative(self.no_of_suspects(), rel)\n self.suspects.append(spouse)","title":""},{"docid":"948c4f5a451e85f1caa052104bf370bc","score":"0.41123363","text":"def test_all_shows_multiple(self):\n create_test_rss(self).save()\n create_test_show(self).save()\n url = reverse(\"api_show\")\n json_response = self.client.get(url, format='json')\n old_count = len(json_response.content)\n create_test_rss(self).save()\n create_test_show(self).save()\n json_response = self.client.get(url, format='json')\n new_count = len(json_response.content)\n self.assertNotEqual(old_count, new_count)","title":""},{"docid":"44335382d6bb77b50154c42023c844d5","score":"0.4093641","text":"def test_create_retrieve_netflix_show(self, model_instance: database_models.NetflixShowModel, db):\n result = database_interface.create_netflix_show(db_session=db, show=model_instance)\n self.assertIsNotNone(result)\n self.assertTrue(isinstance(result, orm.NetflixShow))\n\n show_query_result = database_interface.get_netflix_show(db_session=db, show_id=result.show_id)\n self.assertIsNotNone(show_query_result)\n self.assertTrue(isinstance(result, orm.NetflixShow))","title":""},{"docid":"d1de1df2c8a0e03bf05a3ca222516cff","score":"0.40931043","text":"def set_show_saveas(self, show):\n self.dlconfig['showsaveas'] = show","title":""},{"docid":"1d830f05908e8569ace495805d221d8c","score":"0.4087999","text":"def show_tracks(self, show = True):\n self.__show_tracks = show\n self.__draw_once()","title":""},{"docid":"d758ec637337fe11645c9da56b5ed2e6","score":"0.40877974","text":"def set_track_id(self,id_):\n if self.track_id == -1:\n self.track_id = id_","title":""},{"docid":"19f0b1982bb586dd2fad02da3c9c6d7f","score":"0.40875012","text":"def set_spectra(self, new_spectra):\n self._spectra = list(new_spectra)\n self._update_properties()","title":""},{"docid":"27e11772b260eb8e03604b81727db144","score":"0.40870816","text":"def _post_setattr_kind(self, old, new):\n for s in self._series.values():\n s.kind = new\n super(ManufacturerInfos, self)._post_setattr_kind(old, new)","title":""},{"docid":"a53a819890306985fafb4e012e374a16","score":"0.40869394","text":"def update_data(self, index, sib):\n try:\n index = int(index) # Conversion to int\n index = str(index) # index as a string type\n oldsib = self[index] # Get actual sib (test if index is OK)\n self[index] = sib # Set updated sib\n return True\n except ValueError:\n return False\n except KeyError:\n return False","title":""},{"docid":"a5f6ff30b61f8e524ec57b36a72c57ee","score":"0.40820292","text":"def set(self, data):\n self._data, self._instant = self._raw_to_data_structure(data)","title":""},{"docid":"e8e482d4ba0fffc294677a0137dd5cb7","score":"0.4080258","text":"def testSeriallyShare(self):\n vp_id, ep_ids = self._tester.ShareNew(self._cookie, [(self._episode_id, self._photo_ids)],\n [self._user2.user_id],\n **self._CreateViewpointDict(self._cookie))\n\n self._tester.ShareNew(self._cookie2, [(ep_ids[0], self._photo_ids)],\n [self._user3.user_id],\n **self._CreateViewpointDict(self._cookie2))","title":""},{"docid":"316372d9c50eaa507f5d599fed7a4b10","score":"0.40778536","text":"def test_note_preserved_update_existing_tracked_show(self):\n expected_tracked_show = tracker.TrackedShow(\n title='Person of Interest',\n notes='new season',\n _next_episode='S02E08',\n )\n with TemporaryDirectory() as dirname:\n # Some setup\n shutil.copy(self.path_to_tracker, dirname)\n shutil.copy(self.path_to_showdb, dirname)\n\n sdb, tdb = tracker.load_all_dbs(dirname)\n sdb.path_to_db = os.path.join(dirname, '.showdb.json')\n tdb.path_to_db = os.path.join(dirname, '.tracker.json')\n sdb.write_db()\n tdb.write_db()\n\n with NamedTemporaryFile('w+t') as f:\n f.write('person of interest s02e08')\n f.seek(0)\n watchlist_path = f.name\n args = self.parser.parse_args(\n [\n '--database-dir={}'.format(dirname),\n '--watchlist={}'.format(watchlist_path),\n ]\n )\n tracker.tracker(args)\n showdb, trackerdb = tracker.load_all_dbs(dirname)\n expected_tracked_show._set_next_prev(showdb)\n self.assertEqual(trackerdb._shows['person_of_interest'], expected_tracked_show)","title":""},{"docid":"014ada712079b8e5a72209fe245d002a","score":"0.4076399","text":"def _story_from_md(self, s: StoryInfo, ao: Author,\n eso: Story = None):\n ds = self.ds\n if eso:\n so = eso\n else:\n so = self.get_story(s.site, s.id)\n if not so:\n so = Story(archive=s.site, site_id=s.id)\n ds.add(so)\n if self._check_update(so, s):\n so.title = s.title\n so.words = s.words\n so.chapters = s.chapters\n so.category = s.category\n so.summary = s.summary\n so.characters = s.characters\n so.complete = s.complete\n so.genre = s.genre\n so.published = s.published\n so.updated = s.updated\n self._handle_tags_for(s)\n so.author = ao\n return so","title":""},{"docid":"7f28884be2994e09b8c287273b09091e","score":"0.40762347","text":"def set_from_admdataset(self,admdataset):\n self.m_strShortName = admdataset.m_strShortName\n self.m_strLongName = admdataset.m_strLongName\n self.m_strSource = admdataset.m_strSource\n self.m_strDefaultVersion = admdataset.m_strDefaultVersion\n self.m_vecDatasetVersions = []","title":""},{"docid":"ec5db3376344a3a02a413e9b4bc86773","score":"0.40730843","text":"def _backup(self, sid):\r\n\r\n res = {\r\n \"state\": self.state,\r\n \"grant\": self.grant,\r\n \"seed\": self.seed,\r\n \"redirect_uris\": self.redirect_uris,\r\n }\r\n\r\n for endpoint in ENDPOINTS:\r\n res[endpoint] = getattr(self, endpoint, None)\r\n\r\n self.sdb[sid] = res","title":""}],"string":"[\n {\n \"docid\": \"93f78bc096cb794dcae5cf286618c245\",\n \"score\": \"0.7073067\",\n \"text\": \"def _setItem(self, sid, seas, ep, attrib, value):\\n if sid not in self.shows:\\n self.shows[sid] = Show()\\n if seas not in self.shows[sid]:\\n self.shows[sid][seas] = Season(show=self.shows[sid])\\n if ep not in self.shows[sid][seas]:\\n self.shows[sid][seas][ep] = Episode(season=self.shows[sid][seas])\\n self.shows[sid][seas][ep][attrib] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef72452d6a191b01cfec9aa730d72994\",\n \"score\": \"0.5894516\",\n \"text\": \"def put_show(self, show):\\n query = (\\\"INSERT INTO series \\\"\\n \\\" (title, episodes, poster, rating, year, runtime, img, title_iso) \\\"\\n \\\" VALUES (%s, %s, %s, %s, %s, %s, %s, %s) \\\"\\n \\\" ON DUPLICATE KEY UPDATE \\\"\\n \\\" title=VALUES(title), \\\"\\n \\\" episodes=VALUES(episodes), \\\"\\n \\\" rating=VALUES(rating), \\\"\\n \\\" poster=VALUES(poster), \\\"\\n \\\" year=VALUES(year), \\\"\\n \\\" runtime=VALUES(runtime), \\\"\\n \\\" img=VALUES(img), \\\"\\n \\\" title_iso=VALUES(title_iso) \\\")\\n params = (\\n show['title'], \\n show['episodes'], \\n show['poster'], \\n show['rating'], \\n show[\\\"year\\\"], \\n show[\\\"runtime\\\"],\\n requests.get(show['poster']).content.encode('base64'),\\n ''.join(re.findall('(\\\\w+|\\\\d)', \\n show['title'].lower() + str(show['year']))))\\n self.cursor.execute(query, params)\\n self.cnx.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de7100d71a65f4af6811d32b443abfb\",\n \"score\": \"0.5264651\",\n \"text\": \"def restore(self, sid):\\r\\n for key, val in self.sdb[sid].items():\\r\\n setattr(self, key, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de7100d71a65f4af6811d32b443abfb\",\n \"score\": \"0.5264651\",\n \"text\": \"def restore(self, sid):\\r\\n for key, val in self.sdb[sid].items():\\r\\n setattr(self, key, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fbc692c1f6d3008d6546f11827f79cc\",\n \"score\": \"0.52347046\",\n \"text\": \"def _print_shows(self, shows, fields=None):\\n if fields is None:\\n fields = [\\n 'show_name',\\n 'status',\\n 'network',\\n 'quality',\\n ]\\n shows = shows['data'].values()\\n self.print_objects(shows, fields, cls=Show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eba74991585c2d424c35e74107a2458\",\n \"score\": \"0.5166133\",\n \"text\": \"def edit_show():\\n\\n if request.method == 'GET':\\n form = ShowForm()\\n showid = request.args.get('id')\\n\\n # Edit Song\\n if showid is not None:\\n show = Show.query.get(int(showid))\\n\\n if not is_current_user(show.user_id):\\n return render_template(\\\"not-authorized.html\\\")\\n\\n form.showid.data = show.id\\n form.bandid.data = show.band_id\\n form.name.data = show.name\\n form.start.data = show.start\\n form.end.data = show.end\\n form.address.data = show.address\\n form.contact.data = show.contact\\n form.pay.data = show.pay\\n form.notes.data = show.notes\\n\\n return render_template(\\\"edit-show.html\\\", show_form=form)\\n\\n else:\\n form = ShowForm(request.form)\\n form.start.format = '%d/%m/%Y %H:%M'\\n\\n if form.validate():\\n form.errors['error'] = False\\n\\n if form.showid.data == '':\\n # New Show\\n show = Show(name=form.name.data, start=form.start.data, end=form.end.data, contact=form.contact.data,\\n pay=form.pay.data, notes=form.notes.data, address=form.address.data,\\n band_id=form.bandid.data, user_id=current_user.id)\\n db.session.add(show)\\n db.session.commit()\\n form.errors['msg'] = gettext(\\n 'Show successfully added! Now you can start building your setlist (see below).')\\n form.errors['addedid'] = show.id\\n else:\\n # Edit Show\\n show = Show.query.get(int(form.showid.data))\\n show.band_id = form.bandid.data\\n show.name = form.name.data\\n show.start = form.start.data\\n show.end = form.end.data\\n show.address = form.address.data\\n show.contact = form.contact.data\\n show.pay = form.pay.data\\n show.notes = form.notes.data\\n db.session.add(show)\\n form.errors['msg'] = gettext('Show info updated!')\\n\\n else:\\n form.errors['error'] = True\\n form.errors['msg'] = gettext('Your request was not successful. Please, check the errors below.')\\n\\n return jsonify(form.errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7df12bc56648500b6c05a273887d6a9\",\n \"score\": \"0.5105316\",\n \"text\": \"def set(id, s):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e747dea749a8d1b3eacabed75f0179c7\",\n \"score\": \"0.50841355\",\n \"text\": \"def _getShowData(self, sid, language):\\n\\n if self.config['language'] is None:\\n log().debug('Config language is none, using show language')\\n if language is None:\\n raise tvdb_error(\\\"config['language'] was None, this should not happen\\\")\\n else:\\n log().debug(\\n 'Configured language %s override show language of %s' % (\\n self.config['language'],\\n language\\n )\\n )\\n\\n # Parse show information\\n log().debug('Getting all series data for %s' % (sid))\\n seriesInfoEt = self._getetsrc(\\n self.config['url_seriesInfo'] % sid\\n )\\n for curInfo in seriesInfoEt.keys():\\n tag = curInfo\\n value = seriesInfoEt[curInfo]\\n\\n if value is not None:\\n if tag in ['banner', 'fanart', 'poster']:\\n value = self.config['url_artworkPrefix'] % (value)\\n\\n self._setShowData(sid, tag, value)\\n # set language\\n self._setShowData(sid, u'language', self.config['language'])\\n\\n # Parse banners\\n if self.config['banners_enabled']:\\n self._parseBanners(sid)\\n\\n # Parse actors\\n if self.config['actors_enabled']:\\n self._parseActors(sid)\\n\\n # Parse episode data\\n log().debug('Getting all episodes of %s' % (sid))\\n\\n url = self.config['url_epInfo'] % sid\\n\\n epsEt = self._getetsrc(url, language=language)\\n\\n for cur_ep in epsEt:\\n\\n if self.config['dvdorder']:\\n log().debug('Using DVD ordering.')\\n use_dvd = cur_ep.get('dvdSeason') is not None and cur_ep.get('dvdEpisodeNumber') is not None\\n else:\\n use_dvd = False\\n\\n if use_dvd:\\n elem_seasnum, elem_epno = cur_ep.get('dvdSeason'), cur_ep.get('dvdEpisodeNumber')\\n else:\\n elem_seasnum, elem_epno = cur_ep['airedSeason'], cur_ep['airedEpisodeNumber']\\n\\n if elem_seasnum is None or elem_epno is None:\\n log().warning(\\\"An episode has incomplete season/episode number (season: %r, episode: %r)\\\" % (\\n elem_seasnum, elem_epno))\\n #log().debug(\\n # \\\" \\\".join(\\n # \\\"%r is %r\\\" % (child.tag, child.text) for child in cur_ep.getchildren()))\\n # TODO: Should this happen?\\n continue # Skip to next episode\\n\\n # float() is because https://github.com/dbr/tvnamer/issues/95 - should probably be fixed in TVDB data\\n seas_no = elem_seasnum\\n ep_no = elem_epno\\n\\n for cur_item in cur_ep.keys():\\n tag = cur_item\\n value = cur_ep[cur_item]\\n if value is not None:\\n if tag == 'filename':\\n value = self.config['url_artworkPrefix'] % (value)\\n self._setItem(sid, seas_no, ep_no, tag, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ae9969a4280712c963e5da586e9f62\",\n \"score\": \"0.5045996\",\n \"text\": \"def set(id, s):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18171ef9b9aacbef8b79e59306868fd5\",\n \"score\": \"0.50167745\",\n \"text\": \"def tv_show(self, tv_show):\\n\\n self._tv_show = tv_show\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1659b28dc6c257bfb67564a1c6abffd\",\n \"score\": \"0.5008146\",\n \"text\": \"def add_show(cls, show_info):\\n\\n show = Show(guidebox_id=show_info[\\\"id\\\"],\\n title=show_info[\\\"title\\\"],\\n artwork_urls=show_info[\\\"artwork_608x342\\\"],\\n first_aired=show_info[\\\"first_aired\\\"],\\n description=show_info[\\\"overview\\\"])\\n db.session.add(show)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5db803d6164da9f0b295769f16a22f\",\n \"score\": \"0.4977533\",\n \"text\": \"def _backup(self, sid):\\r\\n self.sdb[sid] = self.dictionary()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f6c605ef584541a049e24f36373977\",\n \"score\": \"0.49677423\",\n \"text\": \"def set_show(self, show):\\n if show is None:\\n self._del_sub('show')\\n elif show in self.showtypes:\\n self._set_sub_text('show', text=show)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7bc088f5e3a4e5b53db9cd0d5e7bf0a\",\n \"score\": \"0.48500535\",\n \"text\": \"def shows_setlists_get(self, showid=None, showdate=None):\\n params = {'method': 'pnet.shows.setlists.get', 'format': self.FORMAT}\\n if showid:\\n params['showid'] = showid\\n if showdate:\\n params['showdate'] = str(self.parse_date(showdate))\\n return self.get(params=params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8b1e6951a5455a76155aaaa73ecd44\",\n \"score\": \"0.48346978\",\n \"text\": \"def get_shows(offSet,channel,totals):\\n #web_pdb.set_trace()\\n shows = []\\n finalpg = True\\n # \\n if channel=='VSO':\\n\\t\\turl = '%svoot-web/content/specific/editorial-clone?query=include:366ef23c7cac2b3d5c7b315744f12a8b&&page=%s&responseType=common'%(apiUrl,offSet)\\n\\t\\tjd = requests.get(url,headers=headers).json()\\n\\t\\titems = jd['result']\\n\\t\\tfor item in items:\\n\\t\\t\\ttitle = item.get('name')\\n\\t\\t\\ttcount = item.get('season') if item.get('season') else 1\\n\\t\\t\\tsbu = item.get('SBU')\\n\\t\\t\\titem_id= item.get('id')\\n\\t\\t\\ticon = 'https://v3img.voot.com/resizeMedium,w_451,h_275/'+item['showImage']\\n\\t\\t\\tlabels = {'title': title,\\n\\t\\t\\t 'genre': item.get('genres'),\\n\\t\\t\\t 'season': item.get('season'),\\n\\t\\t\\t 'plot': item['fullSynopsis'],\\n\\t\\t\\t 'mediatype': 'tvshow',\\n\\t\\t\\t 'year': item['releaseYear']} \\n\\t\\t\\tshows.append((title,icon,sbu,item_id,tcount,labels))\\n else:\\n\\t\\turl = '%svoot-web/content/generic/shows-by-sbu?sbu=include:%s&sort=mostpopular:desc&&page=%s'%(apiUrl,channel,offSet)\\n\\t\\tjd = requests.get(url,headers=headers).json() \\n\\t\\titems = jd['result']\\n\\t\\tfor item in items:\\n\\t\\t\\ttitle = item.get('name')\\n\\t\\t\\ttcount = item['meta'].get('season') if item['meta'].get('season') else 1\\n\\t\\t\\tsbu = item['meta'].get('SBU')\\n\\t\\t\\titem_id= item.get('id')\\n\\t\\t\\ticon = item['details']['image'].get('base')+item['details']['image'].get('id')+'.'+item['details']['image'].get('type')\\n\\t\\t\\tlabels = {'title': title,\\n\\t\\t\\t 'genre': item['meta'].get('genre'),\\n\\t\\t\\t 'season': item['meta'].get('season'),\\n\\t\\t\\t 'plot': item['meta']['synopsis'].get('full'),\\n\\t\\t\\t 'mediatype': 'tvshow',\\n\\t\\t\\t 'year': item['meta'].get('releaseYear')} \\n\\t\\t\\tshows.append((title,icon,sbu,item_id,tcount,labels))\\n\\n offSet = int(offSet)\\n totals = int(jd['totalAsset'])\\n itemsLeft = totals - offSet * 10\\n #web_pdb.set_trace()\\n if itemsLeft > 0:\\n finalpg = False\\n pages = int(math.ceil(totals/10.0))\\n\\n if not finalpg:\\n title = 'Next Page.. (Currently in Page %s of %s)' % (offSet,pages)\\n offSet += 1\\n\\n labels = {}\\n shows.append((title,_icon,sbu,offSet,tcount,labels))\\n\\n return shows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efd6796ee8d1fdd3c9736acab5ce67b\",\n \"score\": \"0.4781474\",\n \"text\": \"def update(self, sid):\\r\\n for key, val in self.sdb[sid].items():\\r\\n _val = getattr(self, key)\\r\\n if not _val and val:\\r\\n setattr(self, key, val)\\r\\n elif key == \\\"grant\\\" and val:\\r\\n val.update(_val)\\r\\n setattr(self, key, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32bb59333708b0e1ee2e7034fcb7056a\",\n \"score\": \"0.47793517\",\n \"text\": \"def update_show(self, show, year, field, value):\\n query = (\\\"\\\"\\\"\\n UPDATE series\\n SET {0:}=%s\\n WHERE title=%s AND year=%s;\\n \\\"\\\"\\\".format(field))\\n params = (value, show, year)\\n self.cursor.execute(query, params)\\n self.cnx.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465d81254ec26620b3f8c81fcc03de74\",\n \"score\": \"0.47330478\",\n \"text\": \"def __getitem__(self, key):\\n if isinstance(key, int_types):\\n # Item is integer, treat as show id\\n if key not in self.shows:\\n self._getShowData(key, self.config['language'])\\n return self.shows[key]\\n\\n sid = self._nameToSid(key)\\n log().debug('Got series id %s' % sid)\\n return self.shows[sid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5db7ca6935ff1f86ee68ddae8f2c6d07\",\n \"score\": \"0.47216412\",\n \"text\": \"def update(self, sid):\\r\\n for key, val in self.sdb[sid].items():\\r\\n _val = getattr(self, key)\\r\\n if not _val and val:\\r\\n setattr(self, key, val)\\r\\n elif key == \\\"grant\\\" and val:\\r\\n _tmp = {}\\r\\n for state, info in _val.items():\\r\\n try:\\r\\n info.join(val[state])\\r\\n except KeyError:\\r\\n pass\\r\\n\\r\\n _tmp[state] = info\\r\\n setattr(self, key, _tmp)\\r\\n\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc270e6646f72af688ae0692c3f1287b\",\n \"score\": \"0.4709362\",\n \"text\": \"def shows(self):\\n from models.show import Show, ShowArtist\\n # catch the artist id that is equeal to id in the table showArtist\\n showArtists = models.storage.session.query(\\n ShowArtist).filter_by(artist_id=self.id).all()\\n listShows = []\\n # create a list with an id show\\n for sa in showArtists:\\n listShows.append(sa.show_id)\\n shows = []\\n # and returns a list with the shows of this artist\\n for show in listShows:\\n shows.append(models.storage.session.query(\\n Show).filter_by(id=show).first())\\n return shows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4f5877f0d3a4647382eea4a74ff0ce\",\n \"score\": \"0.4664723\",\n \"text\": \"def open(self):\\n if self.sid not in self.subs.values():\\n self.subs[self.sid] = self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb28e18f0d172d88cd4e3137dd17136d\",\n \"score\": \"0.4662663\",\n \"text\": \"def create_test_show(self):\\n self.show_name = \\\"Test Show\\\"\\n self.show_description = \\\"This is a fake show for testing.\\\"\\n self.image = \\\"/podcasts/fake.png\\\"\\n self.show = Show(rss=self.rss, name=self.show_name,\\n description=self.show_description, image=self.image)\\n return self.show\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0419c48281610e133cf30fbc3fb1a89\",\n \"score\": \"0.4658723\",\n \"text\": \"def __init__(self, sid):\\n\\t\\tself.sid = sid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea899ddf0425bff8f744aa3e0e7140c5\",\n \"score\": \"0.46278617\",\n \"text\": \"def set_strava_id(self, strava_id):\\n self.strava_id = strava_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4734f1f03544c07d85f3c9253c8a39\",\n \"score\": \"0.46221206\",\n \"text\": \"def load_tv_show_data(self):\\n soup = BeautifulSoup(self.content, 'html.parser')\\n\\n self._season_and_episode = {}\\n episodes = str(soup('a', {'class': 'magnet'})).split('</a>')\\n\\n for epi in episodes:\\n for pat in self._patterns:\\n data = self._match_pattern(pat, epi)\\n if data is None:\\n continue\\n\\n name = re.findall('title=\\\"(.*)\\\"', epi)[0]\\n self.add_season_and_episode(name, data[0], data[1], data[2])\\n\\n return self._instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de109edac05ac6d773d03b0ef4e6833a\",\n \"score\": \"0.46181056\",\n \"text\": \"def _set_datas(self, datas, attr=None):\\r\\n dataattr = attr or self.defaultkey\\r\\n self.datas[dataattr] = list(datas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7616f164e6ce25a95e4ded403393ea4\",\n \"score\": \"0.45558167\",\n \"text\": \"def on_show(self):\\n self.populate_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8663de0ee0c6116d806d5410139481cc\",\n \"score\": \"0.4547266\",\n \"text\": \"def initialize(self, wavelength, display_pts=5000):\\n\\n self.data = np.ones(display_pts) * wavelength\\n\\n # if self.sp_data already exists, keep its last value so that the\\n # clear data functionality doesn't override the setpoint\\n if len(self.sp_data) > 0:\\n self.sp_data = np.ones(display_pts) * self.sp_data[-1]\\n else:\\n self.sp_data = np.ones(display_pts) * self.data[-1]\\n\\n self.setpoint = self.sp_data[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d3efd159958f430b53ccba5a390411\",\n \"score\": \"0.45432544\",\n \"text\": \"def fetch_shows():\\n show_list = current_user.shows.order_by(desc(Show.start)).all()\\n return_data = []\\n\\n for show in show_list:\\n band = Band.query.get(int(show.band_id))\\n if show.start:\\n start = format_datetime(show.start, get_date_format())\\n else:\\n start = ''\\n return_data.append(dict(id=show.id, name=show.name, start=start, band=band.name))\\n\\n return jsonify(data=return_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbaa20f82e9712cc60e999efa6c926d6\",\n \"score\": \"0.45369145\",\n \"text\": \"def converttostandard(show, season = None, showfactory = SeasonCharts.Show):\\n if not isinstance(show,Show):\\n raise TypeError(\\\"converttostandard requires an AniChart Show Object.\\\")\\n chartsource = [(CHARTNAME, show.livechartid),]\\n\\n if not season:\\n season = show.season\\n japanese_title = show.japanese\\n romaji_title = show.romaji\\n english_title = show.english\\n additional_titles = show.alternatetitles\\n medium = show.showtype\\n continuing = \\\"leftovers\\\" in [note.lower() for note in show.notes]\\n summary = f\\\"(From {CHARTNAME})\\\\n{show.summary}\\\"\\n\\n tags = [(tag[1],False) for tag in show.tags]\\n \\n startdate = show.startdate.strftime(SeasonCharts.DTFORMAT)\\n\\n episodes = show.episodes\\n images = [show.img,]\\n studios = show.studios\\n ## Livechart has default/null value links\\n links = [link for link in show.links if link[0] and link[1]]\\n\\n return showfactory(chartsource = chartsource, season = season,\\n japanese_title = japanese_title, romaji_title = romaji_title,\\n english_title = english_title, additional_titles = additional_titles,\\n medium = medium, continuing = continuing, summary = summary,\\n tags = tags, startdate = startdate, episodes = episodes, images = images,\\n studios = studios, links = links)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fd2cc32f08009f13130d2f6573aa81\",\n \"score\": \"0.45247036\",\n \"text\": \"def get_shows(self):\\n query = (\\\"\\\"\\\"\\n SELECT title, year, poster, episodes, ytcount, \\n (episodes - ytLicensed)/episodes*100 as ppay, \\n ytcount/episodes as ytrep, \\n runtime, title_iso, img\\n FROM series\\n ORDER BY ytrep DESC\\n \\\"\\\"\\\")\\n self.cursor.execute(query)\\n shows = [s for s in self.cursor]\\n # for s in self.cursor:\\n # imgblob = s[9].decode('base64')\\n # s = list(s)\\n # s[9] = imgblob\\n # shows.append(tuple(s))\\n return shows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15039a442b4405c0f9df4e0dc57cbf26\",\n \"score\": \"0.45124647\",\n \"text\": \"def add_instance(instance):\\n data_store = getattr(DataStore, type(instance).__name__)\\n data_store[instance.id] = instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e34bf6399ae994ed11fc4b95d45ea9\",\n \"score\": \"0.44905293\",\n \"text\": \"def test_add_show_via_api_request_to_existing_tracker_with_watchlist(self):\\n expected_tracked_show = tracker.TrackedShow('Narcos')\\n with TemporaryDirectory() as dirname:\\n # Some setup\\n shutil.copy(self.path_to_tracker, dirname)\\n shutil.copy(self.path_to_showdb, dirname)\\n\\n sdb, tdb = tracker.load_all_dbs(dirname)\\n sdb.path_to_db = os.path.join(dirname, '.showdb.json')\\n tdb.path_to_db = os.path.join(dirname, '.tracker.json')\\n sdb.write_db()\\n tdb.write_db()\\n\\n with NamedTemporaryFile('w+t') as f:\\n f.write('narcos S01E01')\\n f.seek(0)\\n watchlist_path = f.name\\n args = self.parser.parse_args(\\n [\\n '--database-dir={}'.format(dirname),\\n '--watchlist={}'.format(watchlist_path),\\n ]\\n )\\n tracker.tracker(args)\\n showdb, trackerdb = tracker.load_all_dbs(dirname)\\n expected_tracked_show._set_next_prev(showdb)\\n self.assertEqual(trackerdb._shows['narcos'], expected_tracked_show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc09d92532f70cce2a167e47312eeb9f\",\n \"score\": \"0.4465032\",\n \"text\": \"def put_episodes(self, show, year, episodes):\\n query = (\\\"INSERT INTO episodes \\\"\\n \\\" (series_title, series_year, season, episode, title) \\\"\\n \\\" VALUES (%s, %s, %s, %s, %s) \\\"\\n \\\" ON DUPLICATE KEY UPDATE \\\"\\n \\\" series_title=VALUES(series_title), \\\"\\n \\\" series_year=VALUES(series_year), \\\"\\n \\\" episode=VALUES(episode), \\\"\\n \\\" season=VALUES(season), \\\"\\n \\\" title=VALUES(title) \\\")\\n params = [\\n (show, year, \\n key[0], key[1], \\n name) for key, name in episodes.items()]\\n self.cursor.executemany(query, params)\\n self.cnx.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af773166dc8458e102d467c17eaacef6\",\n \"score\": \"0.44532737\",\n \"text\": \"def save_setlist():\\n show = Show.query.get(int(request.form.get('showid')))\\n\\n # First we delete the previous songs, so we don't have to deal with updates\\n show.remove_all_songs()\\n\\n # Now we add the new, sorted, setlist\\n song_ids = request.form.get('songIds').split(',')\\n\\n pos = 1\\n\\n for song_id in song_ids:\\n song = Song.query.get(song_id)\\n show.add_song(song)\\n db.session.add(show)\\n db.session.commit()\\n show.assign_position(song, pos)\\n pos += 1\\n\\n return jsonify(dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d36d8fa76b072eb76673d078ad2519f\",\n \"score\": \"0.44515738\",\n \"text\": \"def __init__(self, create_url=None, **kwargs):\\n if create_url:\\n try:\\n episode = make_request(create_url)\\n except ApiError as e:\\n raise e\\n else:\\n self.airdate = episode[\\\"airdate\\\"]\\n self.airstamp = episode[\\\"airstamp\\\"]\\n self.number = episode[\\\"number\\\"]\\n self.id = episode[\\\"id\\\"]\\n self.season = episode[\\\"season\\\"]\\n self.runtime = episode[\\\"runtime\\\"]\\n self.name = episode[\\\"name\\\"]\\n self.image = episode[\\\"image\\\"]\\n self.summary = episode[\\\"summary\\\"]\\n self.airtime = episode[\\\"airtime\\\"]\\n self.url = episode[\\\"url\\\"]\\n self._links = episode[\\\"_links\\\"]\\n for key, val in kwargs.items():\\n setattr(self, key, val)\\n try:\\n self.show = Show(**kwargs['show'])\\n except KeyError:\\n self.show = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48a34812c8eb44e11c30a7ea10cef89\",\n \"score\": \"0.44489646\",\n \"text\": \"def setSFData(self, entity, data):\\r\\n id = data.get('Id')\\r\\n if id: \\r\\n obj = self.cxn.retrieve(entity, id)\\r\\n for k,v in data.items():\\r\\n obj[k] = v\\r\\n saveResult = obj.update() \\r\\n else: \\r\\n newObj = SObjectClassFactory(self.cxn, entity)\\r\\n saveResult = self.cxn.create(newObj, data)\\r\\n if saveResult in BAD_INFO_LIST:\\r\\n log.info('ERROR: could not set %s with %s got: %s'%(entity,data,saveResult))\\r\\n else:\\r\\n log.info('Set %s with %s got: %s'%(entity,data,saveResult))\\r\\n return saveResult.get('Id','')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d972317c6588980085d712f742f5c4e\",\n \"score\": \"0.44478175\",\n \"text\": \"def test_got_film_with_same_name(self):\\n show = tracker.Show(title='Fargo')\\n show.populate_seasons()\\n self.assertTrue(show._seasons)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea57f931db408f8ee3ae61b7bd3b815f\",\n \"score\": \"0.44226292\",\n \"text\": \"def fetch_setlist(show_id):\\n show = Show.query.get(show_id)\\n setlist = show.songs\\n\\n return_data = []\\n for song in setlist:\\n return_data.append(\\n dict(id=song.id, title=song.title, tempo=song.tempo, artist=song.artist, duration=song.pretty_duration(),\\n lyrics=song.lyrics))\\n\\n return jsonify(data=return_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6435a3774e137b15339792da1769ae2f\",\n \"score\": \"0.44166067\",\n \"text\": \"def setID(self, sid:str) -> None:\\n try:\\n assert len(sid) == 6, \\\"Student ID must be 6 characters long.\\\"\\n assert sid.isnumeric(), \\\"Student ID must be a valid integer.\\\"\\n int(sid)\\n except ValueError:\\n print(\\\"Student ID must be a valid integer in string format\\\")\\n except AssertionError as e:\\n print(e.args[0]) \\n else:\\n self.id = sid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2cb329cfe4bcadccb68c9ae4995a0a\",\n \"score\": \"0.43960917\",\n \"text\": \"def suid(self, suid):\\n\\n self._suid = suid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"231ad80d3ec73ce56cb38691bd0d2fe2\",\n \"score\": \"0.43893096\",\n \"text\": \"def show_venue(venue_id):\\n venue = Venue.query.get(venue_id)\\n currentTime = datetime.now()\\n\\n upcoming_shows = db.session.query(Show).join(Venue).filter(Show.venue_id == venue_id, Show.start_time >= currentTime).all()\\n past_shows = db.session.query(Show).join(Venue).filter(Show.venue_id == venue_id, Show.start_time <= currentTime).all()\\n\\n #upcoming_shows = Show.query.filter(Show.venue_id==venue_id, Show.start_time >= currentTime).all()\\n #past_shows = Show.query.filter(Show.venue_id==venue_id,Show.start_time < currentTime).all()\\n #For filtering, I referenced the following forum articles:\\n #https://knowledge.udacity.com/questions/261355\\n #https://knowledge.udacity.com/questions/452402\\n #I originally included the joins here but once I found the documentation for the lazy=\\\"joined\\\" I was able to refine the query here just by going through Show\\n\\n data = {\\n 'id': venue.id,\\n 'name': venue.name,\\n 'city': venue.city,\\n 'state': venue.state,\\n 'address': venue.address,\\n 'phone': venue.phone,\\n 'image_link': venue.image_link,\\n 'facebook_link': venue.facebook_link,\\n 'genres': venue.genres,\\n 'website_link': venue.website_link,\\n 'seeking_talent': venue.seeking_talent,\\n 'seeking_description': venue.seeking_description,\\n 'upcoming_shows': [{\\n 'artist_id': show.artist.id,\\n 'artist_image_link': show.artist.image_link,\\n 'start_time': show.start_time\\n } for show in upcoming_shows],\\n 'past_shows': [{\\n 'artist_id': show.artist.id,\\n 'artist_image_link': show.artist.image_link,\\n 'start_time': show.start_time\\n }for show in past_shows]\\n } \\n return render_template('pages/show_venue.html', venue = data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42cb2493261862570bedfef65337e973\",\n \"score\": \"0.43657145\",\n \"text\": \"def instances(self, instances):\\n\\n self._instances = instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa9b7bd9aef8dcb7efcfd833e261f05\",\n \"score\": \"0.43641263\",\n \"text\": \"def __init__(self, season_id=None, with_episodes=False, **kwargs):\\n self.episode_list = []\\n if season_id:\\n self.season_id = season_id\\n self.api_url = \\\"{}{}{}\\\".format(SEASON, self.season_id, \\\"?embed=episodes\\\" if with_episodes else '')\\n response = requests.get(self.api_url)\\n if response.status_code > 400:\\n raise ValueError('{}'.format(response.json()['message']))\\n season = response.json()\\n if with_episodes:\\n self.episode_list = [Episode(**episode) for episode in season['_embedded']['episodes']]\\n self.name = season[\\\"name\\\"]\\n self._links = season[\\\"_links\\\"]\\n self.url = season[\\\"url\\\"]\\n self.premiereDate = season[\\\"premiereDate\\\"]\\n self.id = season[\\\"id\\\"]\\n self.episodeOrder = season[\\\"episodeOrder\\\"]\\n self.number = season[\\\"number\\\"]\\n self.endDate = season[\\\"endDate\\\"]\\n self.webChannel = season[\\\"webChannel\\\"]\\n self.summary = season[\\\"summary\\\"]\\n self.network = season[\\\"network\\\"]\\n self.image = season[\\\"image\\\"]\\n else:\\n self.season_id = kwargs.get('id')\\n for key, val in kwargs.items():\\n setattr(self, key, val)\\n self.base_url = \\\"{}{}\\\".format(SEASON, self.season_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08be60b1d2d74d5f1ae123339139d188\",\n \"score\": \"0.43441167\",\n \"text\": \"def test_add_show_in_showdb_to_existing_tracker_with_watchlist(self):\\n expected_tracked_show = tracker.TrackedShow('House', _next_episode='S08E01')\\n with TemporaryDirectory() as dirname:\\n # Some setup\\n shutil.copy(self.path_to_tracker, dirname)\\n shutil.copy(self.path_to_showdb, dirname)\\n\\n sdb, tdb = tracker.load_all_dbs(dirname)\\n sdb.path_to_db = os.path.join(dirname, '.showdb.json')\\n tdb.path_to_db = os.path.join(dirname, '.tracker.json')\\n sdb.write_db()\\n tdb.write_db()\\n\\n with NamedTemporaryFile('w+t') as f:\\n f.write('house s08e01')\\n f.seek(0)\\n watchlist_path = f.name\\n args = self.parser.parse_args(\\n [\\n '--database-dir={}'.format(dirname),\\n '--watchlist={}'.format(watchlist_path),\\n ]\\n )\\n tracker.tracker(args)\\n showdb, trackerdb = tracker.load_all_dbs(dirname)\\n expected_tracked_show._set_next_prev(showdb)\\n self.assertEqual(trackerdb._shows['house'], expected_tracked_show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903762e7044895dea191ba7a5bf241aa\",\n \"score\": \"0.43434078\",\n \"text\": \"def _set_s_keep(self, s, svs):\\n if svs is not None:\\n self.svs = svs\\n if self.svs is None:\\n self.svs = _np.arange(s.size)\\n \\n self.s_keep = 0*s\\n self.s_keep[self.svs] = s[self.svs]\\n # print(self.s_keep)\\n # self.s_keep = s[svs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66764ef2b7c99c1b0726a34546ce3e23\",\n \"score\": \"0.43225002\",\n \"text\": \"def __init__(self, ds_fid):\\n self.ds = ASDFDataSet(ds_fid)\\n # Initiate an empty Manager to get access to its config\\n self.mgmt = Manager()\\n self.info = Info()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776881832c681cbf3bb1e252ac5ab5d1\",\n \"score\": \"0.4319444\",\n \"text\": \"def findmissing_showstats(show, url = None, session = None, pipe = None):\\n if pipe is None:\\n def pipe(output, verbose = False):\\n pass\\n if not url:\\n ids = [url for link in show.links if (url := myanimelist.parse_siteid(link[1]))]\\n malinfo = None \\n for link in ids:\\n try:\\n pipe(f\\\">>> {show.english_title or show.romaji_title or show.japanese_title}({link})\\\")\\n malinfo = getshowstats(link, session = session)\\n except Exception as e:\\n pipe(f\\\">>> Failed to get MAL info for: {show.romaji_title if show.romaji_title else show.english_title}({link})\\\")\\n pipe(traceback.format_exc(), verbose = True)\\n if malinfo: break\\n else:\\n malinfo = getshowstats(url)\\n\\n if not malinfo: return\\n\\n if not show.japanese_title and malinfo.get(\\\"japanese_title\\\"):\\n show.japanese_title = malinfo['japanese_title']\\n\\n for name in malinfo.get(\\\"additional_titles\\\",[]):\\n if name not in show.additional_titles:\\n show.additional_titles.append(name)\\n\\n if not show.startdate and malinfo.get(\\\"first_episode\\\"):\\n show.startdate = malinfo['first_episode'].replace(tzinfo = WebModules.JST)\\n\\n ## NOTE: for both continuing and renewal, we always take True if possible\\n if (premiered := malinfo.get(\\\"premiered\\\")) or (first_episode := malinfo.get(\\\"first_episode\\\")):\\n if not premiered:\\n premiered = getseason(first_episode) + f\\\"-{first_episode.year}\\\"\\n if (continuing := parseanimeseason_toobject(premiered) < parseanimeseason_toobject(show.season)):\\n show.continuing = continuing\\n\\n if (renewal := bool(malinfo['relations'].get(\\\"Prequel\\\"))):\\n show.renewal = renewal\\n\\n if not show.runtime and malinfo.get(\\\"runtime\\\"):\\n show.runtime = malinfo['runtime']\\n\\n if not show.medium and malinfo['type']:\\n show.medium = malinfo['type']\\n\\n ## Hard overriding the next two medium types\\n if (runtime := malinfo.get('runtime')) and ((runtime.total_seconds() / 60 ) < 17):\\n show.medium = show.medium + \\\" SHORT\\\"\\n\\n if (\\\"rating\\\" in malinfo and \\\"hentai\\\" in malinfo['rating'].lower()) or \\\"hentai\\\" in [genre.lower() for genre in malinfo['genres']]:\\n show.medium = \\\"Hentai\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90685209141581b33fd907375ff5bba7\",\n \"score\": \"0.43094745\",\n \"text\": \"def stores_info(self, stores_info):\\n\\n self._stores_info = stores_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef50d72ca613cc0977db0b33a27a06b\",\n \"score\": \"0.43009055\",\n \"text\": \"def get_client_advert_media_stations_by_id(self, sid):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a56ac111120b25722b8740574de8afc9\",\n \"score\": \"0.43005788\",\n \"text\": \"def test_show_displays_episodes(self):\\n create_test_rss(self).save()\\n create_test_show(self).save()\\n url = reverse(\\\"api_show\\\")\\n json_response = self.client.get(url, format='json')\\n old_count = len(json_response.content)\\n create_test_episode(self).save()\\n json_response = self.client.get(url, format='json')\\n new_count = len(json_response.content)\\n self.assertNotEqual(old_count, new_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c658564110d7864a57f3fedced41564\",\n \"score\": \"0.42969975\",\n \"text\": \"def set_session_record(self, track, xclip, ident, value = None):\\n\\tif IS_LIVE_9:\\n\\t if value in KEYWORDS:\\n\\t\\tself.song().session_record = KEYWORDS[value]\\n\\t else:\\n\\t\\tself.song().session_record = not(self.song().session_record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b020566a81676d4d1fe5e08d920482ce\",\n \"score\": \"0.4293776\",\n \"text\": \"def set_advert_fingerprinted(self, sid):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b9b8b9ca1a659f49d770bb09ba8245f\",\n \"score\": \"0.42876467\",\n \"text\": \"def setSessionDataSetId(self, sId):\\n if sId is not None and len(sId) > 0:\\n self.__sessionDataSetId = str(sId).upper()\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1ac796103c58b4cf1ad99239d09f76\",\n \"score\": \"0.42812908\",\n \"text\": \"def set(name, instance):\\n REDIS.hset(name, 'data', pickle.dumps(instance))\\n REDIS.expire(name, REDIS_EXPIRE_TIME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa7bdcb33a0613b9ee53092bab68cb7\",\n \"score\": \"0.4277697\",\n \"text\": \"def displays(self, displays):\\n\\n self._displays = displays\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177e91236dd2e530ebf23fd52ad77107\",\n \"score\": \"0.42761114\",\n \"text\": \"def add_season_db(self):\\n result = self.db_handle.fetchdata('add_season', [self.season_name,\\n self.ss_date.date(True),\\n self.se_date.date(True),\\n ]\\n )\\n self.said = result[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"510a9a6bc88a4feb6b45f8693038c9f8\",\n \"score\": \"0.4264196\",\n \"text\": \"def cublasSetStream(handle, id):\\r\\n\\r\\n status = _libcublas.cublasSetStream_v2(handle, id)\\r\\n cublasCheckStatus(status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08e572a33e070171a7f6571e53b5160f\",\n \"score\": \"0.42459482\",\n \"text\": \"def serializeshows(file,shows):\\n with open(file,'w', encoding = 'utf-8') as f:\\n json.dump(shows,f,default = Show.serialize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334ce1f7710d794b3209866ab726890d\",\n \"score\": \"0.42396113\",\n \"text\": \"def get_advert_by_id(self, sid):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444696acae6c33d4a2f3c8b5f91bfd47\",\n \"score\": \"0.42315274\",\n \"text\": \"def list_shows(offSet,channel,totals):\\n shows = cache.cacheFunction(get_shows,offSet,channel,totals)\\n listing = []\\n for title,icon,sid,item_id,tcount,labels in shows:\\n if 'Next Page' not in title:\\n list_item = xbmcgui.ListItem(label='[COLOR yellow]%s [COLOR cyan](%s Seasons)[/COLOR]'%(title,tcount))\\n url = '{0}?action=list_season&offSet=1&show={1}&totals={2}'.format(_url,item_id,tcount)\\n else:\\n list_item = xbmcgui.ListItem(label='[COLOR yellow]%s[/COLOR]'%(title))\\n url = '{0}?action=list_channel&offSet={1}&channel={2}&totals={3}'.format(_url, item_id,sid, tcount)\\n\\n \\n list_item.setArt({'poster': icon,\\n 'thumb': icon,\\n 'icon': icon,\\n 'fanart': icon})\\n\\n list_item.setInfo('video', labels)\\n\\n \\n is_folder = True\\n listing.append((url, list_item, is_folder))\\n xbmcplugin.addDirectoryItems(_handle, listing, len(listing))\\n #xbmcplugin.setContent(_handle, 'tvshows')\\n xbmcplugin.endOfDirectory(_handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c588f08e600745fde129ae24f486fa09\",\n \"score\": \"0.4231\",\n \"text\": \"def _set_item(self, key, value, ds_attr=True):\\n if ds_attr:\\n key = self._get_ds_attr(key)\\n try:\\n serialised_data = self._to_json(value)\\n except TypeError:\\n raise NukeDataStoreError('Data not serialisable')\\n try:\\n self.store[key].setValue(serialised_data)\\n except NameError:\\n self.store.addKnob(self._create_knob(key))\\n self.store[key].setValue(serialised_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403b1820a8fee08fed697a522bef69a8\",\n \"score\": \"0.4228602\",\n \"text\": \"def remove_show(self, show, year):\\n # First delite episodes\\n query = (\\\"\\\"\\\"\\n DELETE FROM episodes\\n WHERE series_title=%s AND series_year=%s;\\n \\\"\\\"\\\")\\n params = (show, year)\\n self.cursor.execute(query, params)\\n self.cnx.commit()\\n # Then delete show info\\n query = (\\\"\\\"\\\"\\n DELETE FROM series\\n WHERE title=%s AND year=%s;\\n \\\"\\\"\\\")\\n params = (show, year)\\n self.cursor.execute(query, params)\\n self.cnx.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d01f7415fe6044b817e777ee57a3637\",\n \"score\": \"0.4224558\",\n \"text\": \"def load_details(self):\\n the_id = self.data[\\\"id\\\"][\\\"value\\\"]\\n self.data.update(self.data_from_id(the_id))\\n if isinstance(self.data[\\\"events\\\"], (list)):\\n self[\\\"events\\\"] = EventManager(self.data[\\\"events\\\"])\\n self.data[\\\"id\\\"][\\\"value\\\"] = the_id\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab909116380dcab3f4f4c0bf9939b7df\",\n \"score\": \"0.42201987\",\n \"text\": \"def retrieve_instance(\\n self,\\n study_instance_uid: str,\\n series_instance_uid: str,\\n sop_instance_uid: str,\\n media_types: Optional[Tuple[Union[str, Tuple[str, str]], ...]] = None,\\n ) -> pydicom.dataset.Dataset: # noqa: E501\\n if study_instance_uid is None:\\n raise ValueError(\\n 'Study Instance UID is required for retrieval of instance.'\\n )\\n self._assert_uid_format(study_instance_uid)\\n if series_instance_uid is None:\\n raise ValueError(\\n 'Series Instance UID is required for retrieval of instance.'\\n )\\n self._assert_uid_format(series_instance_uid)\\n if sop_instance_uid is None:\\n raise ValueError(\\n 'SOP Instance UID is required for retrieval of instance.'\\n )\\n logger.info(\\n f'retrieve instance \\\"{sop_instance_uid}\\\" '\\n f'of series \\\"{series_instance_uid}\\\" '\\n f'of study \\\"{study_instance_uid}\\\"'\\n )\\n self._assert_uid_format(sop_instance_uid)\\n url = self._get_instances_url(\\n _Transaction.RETRIEVE,\\n study_instance_uid,\\n series_instance_uid,\\n sop_instance_uid\\n )\\n if media_types is None:\\n media_types = (('application/dicom', '*'), )\\n\\n common_media_types = self._get_common_media_types(media_types)\\n if len(common_media_types) > 1:\\n raise ValueError(\\n 'Only media type \\\"application/dicom\\\" is supported for '\\n 'retrieval of an instance.'\\n )\\n common_media_type = common_media_types[0]\\n if common_media_type != 'application/dicom':\\n raise ValueError(\\n f'Media type \\\"{common_media_type}\\\" is not supported for '\\n 'retrieval of an instance. It must be \\\"application/dicom\\\".'\\n )\\n iterator = self._http_get_multipart_application_dicom(url, media_types)\\n instances = list(iterator)\\n if len(instances) > 1:\\n # This should not occur, but safety first.\\n raise ValueError('More than one instance returned by origin server')\\n return instances[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277118f15e81e0ce7feb1a2cb67508e0\",\n \"score\": \"0.42200792\",\n \"text\": \"def setId(self, novoId):\\r\\n self.__id = novoId\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59b3fc2a6fdc858fbbdfc88c02200747\",\n \"score\": \"0.4218609\",\n \"text\": \"def list_season(show,offSet,totals):\\n season = cache.cacheFunction(get_season,show,offSet,totals)\\n listing = []\\n for title,icon,sid,item_id,labels,tcount in season:\\n list_item = xbmcgui.ListItem(label=title)\\n list_item.setArt({'poster': icon,\\n 'thumb': icon,\\n 'icon': icon,\\n 'fanart': icon})\\n list_item.setInfo('video', labels)\\n if 'Next Page' not in title:\\n url = '{0}?action=list_show&show={1}&offSet={2}&icon={3}'.format(_url,item_id,offSet,icon)\\n else:\\n url = '{0}?action=list_season&offSet={1}&show={2}&totals={3}'.format(_url,sid,item_id,tcount)\\n\\n is_folder = True \\n listing.append((url, list_item, is_folder))\\n xbmcplugin.addDirectoryItems(_handle, listing, len(listing))\\n #xbmcplugin.setContent(_handle, 'tvshows')\\n xbmcplugin.endOfDirectory(_handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b90bbb375e2f1957c0c49549507866\",\n \"score\": \"0.42185313\",\n \"text\": \"def set_instance(self, model, **kwds):\\n return self._set_instance(model, kwds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198d698e6faa43e7242491a162c68127\",\n \"score\": \"0.42112955\",\n \"text\": \"def create_show(conn, shows):\\n sql = ''' INSERT INTO shows(name,location,month)\\n VALUES(?,?,?) '''\\n cur = conn.cursor()\\n cur.execute(sql, shows)\\n return cur.lastrowid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5460b4cc7da79f658a714c15fd0db01b\",\n \"score\": \"0.42094952\",\n \"text\": \"def show_as(self, show_as):\\n if show_as is None:\\n raise ValueError(\\\"Invalid value for `show_as`, must not be `None`\\\") # noqa: E501\\n\\n self._show_as = show_as\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4459ff75a534b9fb6845d57fe4b70d5b\",\n \"score\": \"0.4196828\",\n \"text\": \"def get_shows(self, person_id):\\r\\n result = []\\r\\n for show in ShowsPeopleMapping.query.filter_by(PersonID=person_id).all():\\r\\n s = {\\r\\n \\\"showID\\\": show.ShowID,\\r\\n \\\"instruments\\\": show.Instruments\\r\\n }\\r\\n result.append(s)\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8be145da6c118cd69c995f9f3218f84\",\n \"score\": \"0.419064\",\n \"text\": \"def make_episode(show_id, seasons, factor, debug=False):\\n\\n if debug:\\n print(\\\"begin make_episode()\\\")\\n print(seasons, factor)\\n\\n #cultivate list of episodes ratings\\n episode_list = make_episode_dict(show_id, seasons, factor, debug)\\n\\n #pick an episode\\n episode = pick_episode(episode_list, debug)\\n\\n #gather episode information & return\\n return create_episode(episode, debug)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee4d2d58582cc1dd8a290e3aa9bc3c38\",\n \"score\": \"0.4188351\",\n \"text\": \"async def get_multiple_shows(\\n self, ids: List[str], market: Optional[str] = \\\"US\\\"\\n ) -> List[Show]:\\n\\n data = await self.http.get_multiple_shows(ids, market)\\n return list(Show(self, show) for show in data[\\\"shows\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f5d7637a008e76c08791c32c7a58b3\",\n \"score\": \"0.41719517\",\n \"text\": \"def create_show_submission():\\n\\n form = ShowForm()\\n if form.validate():\\n try:\\n data = Show(\\n venue_id = form.venue_id.data,\\n artist_id = form.artist_id.data,\\n start_time = form.start_time.data\\n )\\n db.session.add(data)\\n db.session.commit()\\n flash('Show was successfully listed!')\\n except:\\n db.session.rollback()\\n flash('An error occurred. Show could not be listed.')\\n finally:\\n db.session.close()\\n return render_template('pages/home.html')\\n else:\\n flash(f\\\"{form.errors.items()}\\\")\\n return render_template('pages/home.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a90c82f91ef7d13789ba37ee7cb95c\",\n \"score\": \"0.41702455\",\n \"text\": \"def insertShow(self, username, showID):\\n conn = self.getConnection()\\n\\n cur_check = conn.cursor()\\n cur_check.execute(\\\"SELECT * FROM WATCHLIST WHERE username=? AND \\\\\\n showid=?\\\", (username, showID))\\n\\n # check if entry already exists\\n if not cur_check.fetchone():\\n cur = conn.cursor()\\n date = datetime.date.today()\\n\\n cur.execute(\\\"INSERT INTO WATCHLIST(USERNAME, SHOWID, LASTWATCHED) \\\\\\n VALUES(?, ?, ?);\\\", (username, showID, date))\\n conn.commit()\\n flash(\\\"Entry added\\\")\\n else:\\n flash(\\\"Entry already exists\\\")\\n\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a139c6175af82a5b4a2caf23433d0fe\",\n \"score\": \"0.41687134\",\n \"text\": \"def work(self):\\n if self.options.client and self.options.show:\\n client = Client.objects.get(slug=self.options.client)\\n show = Show.objects.get(client=client, slug=self.options.show)\\n self.one_show(show)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd906ceeb12e01835ccf931d028211d9\",\n \"score\": \"0.41641414\",\n \"text\": \"def setSpectrums(self, spectrums):\\n self._spectrums = spectrums\\n self._currentSpectrumName = None\\n self._zincContext = spectrums.getZincContext()\\n self._ui.sceneviewerWidgetPreview.setContext(self._zincContext)\\n self._previewZincRegion = self._zincContext.createRegion()\\n self._previewZincRegion.setName(\\\"Spectrum editor preview region\\\")\\n self._previewZincScene = self._previewZincRegion.getScene()\\n sceneviewer = self._ui.sceneviewerWidgetPreview.getSceneviewer()\\n if sceneviewer:\\n sceneviewer.setScene(self._previewZincScene)\\n spectrummodule = self._zincContext.getSpectrummodule()\\n self._spectrummodulenotifier = spectrummodule.createSpectrummodulenotifier()\\n self._spectrummodulenotifier.setCallback(self._spectrummoduleCallback)\\n self._buildSpectrumList()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ce2b12c1b3c4c002def1194ab7328e\",\n \"score\": \"0.41609138\",\n \"text\": \"def __init__(self, show=None):\\n self.show = show\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0172da04cdb286add9345ef399ed0292\",\n \"score\": \"0.4149757\",\n \"text\": \"def do_add(self):\\n tv = load_info_source('tv', self.config)\\n search = raw_input('Enter Show Title: ')\\n tv.find_show(search)\\n print search\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787932b4bbfae7480e59c77fcc307013\",\n \"score\": \"0.41489017\",\n \"text\": \"def __init__(self,\\n show_id,\\n show_params,\\n root,\\n canvas,\\n showlist,\\n pp_dir,\\n pp_home,\\n pp_profile,\\n command_callback):\\n\\n # init the common bits\\n Show.base__init__(self,\\n show_id,\\n show_params,\\n root,\\n canvas,\\n showlist,\\n pp_dir,\\n pp_home,\\n pp_profile,\\n command_callback)\\n\\n\\n # instatiatate the screen driver - used only to access enable and hide click areas\\n self.sr=ScreenDriver()\\n\\n # create a path stack and control path debugging\\n if self.show_params['debug-path']=='yes':\\n self.debug=True\\n else:\\n self.debug=False\\n self.path = PathManager()\\n \\n self.allowed_links=('return','home','call','null','exit','goto','play','jump','repeat','pause','no-command','stop','pause-on','pause-off','mute','unmute','go')\\n \\n # init variables\\n self.track_timeout_timer=None\\n self.show_timeout_timer=None\\n self.next_track_signal=False\\n self.next_track_ref=''\\n self.current_track_ref=''\\n self.current_track_type=''\\n self.req_next=''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c8151291be89fead30f83f5c99b342\",\n \"score\": \"0.4140898\",\n \"text\": \"def test_request_show_info(self):\\n show = tracker.Show(title='Game of Thrones')\\n with open('got_s01_response.json', 'r') as f:\\n expected_response = json.load(f)\\n show.imdb_id = 'tt0944947'\\n self.assertDictEqual(\\n show.request_show_info(season=1),\\n expected_response,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90a6f37043b1cf29600ca7776a0e657\",\n \"score\": \"0.41408703\",\n \"text\": \"def setMovie(self, mode, npcId, avId, extraArgs, timestamp):\\n timeStamp = ClockDelta.globalClockDelta.localElapsedTime(timestamp)\\n self.remain = NPCToons.CLERK_COUNTDOWN_TIME - timeStamp\\n\\n self.npcId = npcId\\n\\n # See if this is the local toon\\n self.isLocalToon = (avId == base.localAvatar.doId)\\n \\n assert(self.notify.debug(\\\"setMovie: %s %s %s %s\\\" %\\n (mode, avId, timeStamp, self.isLocalToon)))\\n\\n # This is an old movie in the server ram that has been cleared.\\n # Just return and do nothing\\n if (mode == NPCToons.SELL_MOVIE_CLEAR):\\n assert self.notify.debug('SELL_MOVIE_CLEAR')\\n return\\n\\n if (mode == NPCToons.SELL_MOVIE_TIMEOUT):\\n assert self.notify.debug('SELL_MOVIE_TIMEOUT')\\n # In case the GUI hasn't popped up yet\\n taskMgr.remove(self.uniqueName('lerpCamera'))\\n # Stop listening for the GUI\\n if (self.isLocalToon):\\n self.ignoreEventDict()\\n # hide the popupInfo\\n if self.popupInfo:\\n self.popupInfo.reparentTo(hidden)\\n if self.petshopGui:\\n self.petshopGui.destroy()\\n self.petshopGui = None\\n\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_TOOKTOOLONG,\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_START):\\n assert self.notify.debug('SELL_MOVIE_START')\\n self.av = base.cr.doId2do.get(avId)\\n if self.av is None:\\n self.notify.warning(\\\"Avatar %d not found in doId\\\" % (avId))\\n return\\n else:\\n self.accept(self.av.uniqueName('disable'),\\n self.__handleUnexpectedExit)\\n\\n self.setupAvatars(self.av)\\n\\n if (self.isLocalToon):\\n camera.wrtReparentTo(render)\\n camera.lerpPosHpr(-5, 9, base.localAvatar.getHeight()-0.5,\\n -150, -2, 0,\\n 1,\\n other=self,\\n blendType=\\\"easeOut\\\",\\n task=self.uniqueName('lerpCamera'))\\n\\n if (self.isLocalToon):\\n taskMgr.doMethodLater(1.0, self.popupPetshopGUI,\\n self.uniqueName('popupPetshopGUI'))\\n \\n elif (mode == NPCToons.SELL_MOVIE_COMPLETE):\\n assert self.notify.debug('SELL_MOVIE_COMPLETE')\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_THANKSFISH_PETSHOP,\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_PETRETURNED):\\n assert self.notify.debug('SELL_MOVIE_PETRETURNED')\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_PETRETURNED,\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_PETADOPTED):\\n assert self.notify.debug('SELL_MOVIE_PETADOPTED')\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_PETADOPTED,\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_PETCANCELED):\\n assert self.notify.debug('SELL_MOVIE_PETCANCELED')\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_PETCANCELED,\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_TROPHY):\\n assert self.notify.debug('SELL_MOVIE_TROPHY')\\n\\n self.av = base.cr.doId2do.get(avId)\\n if self.av is None:\\n self.notify.warning(\\\"Avatar %d not found in doId\\\" % (avId))\\n return\\n else:\\n numFish, totalNumFish = extraArgs\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_TROPHY % (numFish, totalNumFish),\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_NOFISH):\\n assert self.notify.debug('SELL_MOVIE_NOFISH')\\n self.setChatAbsolute(TTLocalizer.STOREOWNER_NOFISH,\\n CFSpeech | CFTimeout)\\n self.resetPetshopClerk()\\n\\n elif (mode == NPCToons.SELL_MOVIE_NO_MONEY):\\n self.notify.warning('SELL_MOVIE_NO_MONEY should not be called')\\n self.resetPetshopClerk()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c780b97c09e983e35ed77d15be679067\",\n \"score\": \"0.41306224\",\n \"text\": \"def get_details(self):\\n if not self.rss:\\n self._get_rss_feed()\\n logging.debug(\\\"Getting details for %s...\\\" % self)\\n if not self.rss['entries']:\\n raise ShowFeedNoEpisodesError\\n # Determine human title. We are assuming here that the first episode\\n # in the feed has a useful description. This may cause problems\\n r = re.compile('Show Name\\\\s*: (.*?);')\\n name_match = r.search(self.rss['entries'][0].description)\\n if not name_match:\\n raise ShowDetailsError, \\\"Could not determine show name for %s.\\\" \\\\\\n % self\\n human_name = name_match.group(1)\\n # Determine show type\\n title_re = re.compile('Show\\\\s*Title\\\\s*:\\\\s*(.*?);')\\n season_re = re.compile('Season\\\\s*:\\\\s*([0-9]*?);')\\n episode_re = re.compile('Episode\\\\s*:\\\\s*([0-9]*?)$')\\n date_re = re.compile('Episode\\\\s*Date:\\\\s*([0-9\\\\-]+)$')\\n d = {\\n 'seasonepisode': 0,\\n 'date': 0,\\n 'title': 0\\n }\\n for episode in self.rss['entries']:\\n title_match = title_re.search(episode.description)\\n season_match = season_re.search(episode.description)\\n episode_match = episode_re.search(episode.description)\\n date_match = date_re.search(episode.description)\\n if season_match and episode_match:\\n d['seasonepisode'] += 1\\n elif date_match:\\n d['date'] += 1\\n elif title_match and title_match.group(1) != 'n/a':\\n d['title'] += 1\\n # Nothing could be found, fall back to \\\"time\\\" type\\n if d.values() == [0, 0, 0]:\\n show_type = \\\"time\\\"\\n else:\\n # Sort keys based on values\\n e = d.keys()\\n e.sort(cmp=lambda a,b: cmp(d[a], d[b]))\\n show_type = e[-1]\\n self.human_name = human_name\\n self.show_type = show_type\\n return {'show_type': show_type, 'human_name': human_name}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e97e5c46eca0ece25076ad13dbcf5d\",\n \"score\": \"0.41293922\",\n \"text\": \"def insert(self, hash, sid, offset):\\n # 1. update song detail\\n song_details = self._get_song_details(sid)\\n assert song_details is not None\\n if song_details[SONG_FINGERPRINTS].count((hash, offset)) == 0:\\n # new fingerprint\\n song_details[SONG_FINGERPRINTS].append((hash, offset))\\n self._set_song_details(song_details)\\n\\n # 2. update fingerprints\\n track_id = song_details[SONG_SONG_NAME]\\n fingerprint_details = self._get_fingerprint_details(hash)\\n fingerprint_record = (sid, track_id, offset)\\n if fingerprint_details.count(fingerprint_record) == 0:\\n # new fingerprint record\\n fingerprint_details.append(fingerprint_record)\\n self._set_fingerprint_details(hash, fingerprint_details)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938a6649c32c764802c21a2246a060b4\",\n \"score\": \"0.4120176\",\n \"text\": \"def __init__( self,data,season='None' ):\\n self.data = data\\n self.season = season\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edf6ed1485d43ffe2c8831f0d9a70564\",\n \"score\": \"0.41155356\",\n \"text\": \"def add_spouse (self, idx):\\n curr = self.get_suspect(idx)\\n spouse, rel = curr.create_spouse()\\n spouse.set_relative(idx, rel)\\n curr.set_relative(self.no_of_suspects(), rel)\\n self.suspects.append(spouse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"948c4f5a451e85f1caa052104bf370bc\",\n \"score\": \"0.41123363\",\n \"text\": \"def test_all_shows_multiple(self):\\n create_test_rss(self).save()\\n create_test_show(self).save()\\n url = reverse(\\\"api_show\\\")\\n json_response = self.client.get(url, format='json')\\n old_count = len(json_response.content)\\n create_test_rss(self).save()\\n create_test_show(self).save()\\n json_response = self.client.get(url, format='json')\\n new_count = len(json_response.content)\\n self.assertNotEqual(old_count, new_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44335382d6bb77b50154c42023c844d5\",\n \"score\": \"0.4093641\",\n \"text\": \"def test_create_retrieve_netflix_show(self, model_instance: database_models.NetflixShowModel, db):\\n result = database_interface.create_netflix_show(db_session=db, show=model_instance)\\n self.assertIsNotNone(result)\\n self.assertTrue(isinstance(result, orm.NetflixShow))\\n\\n show_query_result = database_interface.get_netflix_show(db_session=db, show_id=result.show_id)\\n self.assertIsNotNone(show_query_result)\\n self.assertTrue(isinstance(result, orm.NetflixShow))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1de1df2c8a0e03bf05a3ca222516cff\",\n \"score\": \"0.40931043\",\n \"text\": \"def set_show_saveas(self, show):\\n self.dlconfig['showsaveas'] = show\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d830f05908e8569ace495805d221d8c\",\n \"score\": \"0.4087999\",\n \"text\": \"def show_tracks(self, show = True):\\n self.__show_tracks = show\\n self.__draw_once()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d758ec637337fe11645c9da56b5ed2e6\",\n \"score\": \"0.40877974\",\n \"text\": \"def set_track_id(self,id_):\\n if self.track_id == -1:\\n self.track_id = id_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19f0b1982bb586dd2fad02da3c9c6d7f\",\n \"score\": \"0.40875012\",\n \"text\": \"def set_spectra(self, new_spectra):\\n self._spectra = list(new_spectra)\\n self._update_properties()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27e11772b260eb8e03604b81727db144\",\n \"score\": \"0.40870816\",\n \"text\": \"def _post_setattr_kind(self, old, new):\\n for s in self._series.values():\\n s.kind = new\\n super(ManufacturerInfos, self)._post_setattr_kind(old, new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a53a819890306985fafb4e012e374a16\",\n \"score\": \"0.40869394\",\n \"text\": \"def update_data(self, index, sib):\\n try:\\n index = int(index) # Conversion to int\\n index = str(index) # index as a string type\\n oldsib = self[index] # Get actual sib (test if index is OK)\\n self[index] = sib # Set updated sib\\n return True\\n except ValueError:\\n return False\\n except KeyError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f6ff30b61f8e524ec57b36a72c57ee\",\n \"score\": \"0.40820292\",\n \"text\": \"def set(self, data):\\n self._data, self._instant = self._raw_to_data_structure(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e482d4ba0fffc294677a0137dd5cb7\",\n \"score\": \"0.4080258\",\n \"text\": \"def testSeriallyShare(self):\\n vp_id, ep_ids = self._tester.ShareNew(self._cookie, [(self._episode_id, self._photo_ids)],\\n [self._user2.user_id],\\n **self._CreateViewpointDict(self._cookie))\\n\\n self._tester.ShareNew(self._cookie2, [(ep_ids[0], self._photo_ids)],\\n [self._user3.user_id],\\n **self._CreateViewpointDict(self._cookie2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"316372d9c50eaa507f5d599fed7a4b10\",\n \"score\": \"0.40778536\",\n \"text\": \"def test_note_preserved_update_existing_tracked_show(self):\\n expected_tracked_show = tracker.TrackedShow(\\n title='Person of Interest',\\n notes='new season',\\n _next_episode='S02E08',\\n )\\n with TemporaryDirectory() as dirname:\\n # Some setup\\n shutil.copy(self.path_to_tracker, dirname)\\n shutil.copy(self.path_to_showdb, dirname)\\n\\n sdb, tdb = tracker.load_all_dbs(dirname)\\n sdb.path_to_db = os.path.join(dirname, '.showdb.json')\\n tdb.path_to_db = os.path.join(dirname, '.tracker.json')\\n sdb.write_db()\\n tdb.write_db()\\n\\n with NamedTemporaryFile('w+t') as f:\\n f.write('person of interest s02e08')\\n f.seek(0)\\n watchlist_path = f.name\\n args = self.parser.parse_args(\\n [\\n '--database-dir={}'.format(dirname),\\n '--watchlist={}'.format(watchlist_path),\\n ]\\n )\\n tracker.tracker(args)\\n showdb, trackerdb = tracker.load_all_dbs(dirname)\\n expected_tracked_show._set_next_prev(showdb)\\n self.assertEqual(trackerdb._shows['person_of_interest'], expected_tracked_show)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014ada712079b8e5a72209fe245d002a\",\n \"score\": \"0.4076399\",\n \"text\": \"def _story_from_md(self, s: StoryInfo, ao: Author,\\n eso: Story = None):\\n ds = self.ds\\n if eso:\\n so = eso\\n else:\\n so = self.get_story(s.site, s.id)\\n if not so:\\n so = Story(archive=s.site, site_id=s.id)\\n ds.add(so)\\n if self._check_update(so, s):\\n so.title = s.title\\n so.words = s.words\\n so.chapters = s.chapters\\n so.category = s.category\\n so.summary = s.summary\\n so.characters = s.characters\\n so.complete = s.complete\\n so.genre = s.genre\\n so.published = s.published\\n so.updated = s.updated\\n self._handle_tags_for(s)\\n so.author = ao\\n return so\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f28884be2994e09b8c287273b09091e\",\n \"score\": \"0.40762347\",\n \"text\": \"def set_from_admdataset(self,admdataset):\\n self.m_strShortName = admdataset.m_strShortName\\n self.m_strLongName = admdataset.m_strLongName\\n self.m_strSource = admdataset.m_strSource\\n self.m_strDefaultVersion = admdataset.m_strDefaultVersion\\n self.m_vecDatasetVersions = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5db3376344a3a02a413e9b4bc86773\",\n \"score\": \"0.40730843\",\n \"text\": \"def _backup(self, sid):\\r\\n\\r\\n res = {\\r\\n \\\"state\\\": self.state,\\r\\n \\\"grant\\\": self.grant,\\r\\n \\\"seed\\\": self.seed,\\r\\n \\\"redirect_uris\\\": self.redirect_uris,\\r\\n }\\r\\n\\r\\n for endpoint in ENDPOINTS:\\r\\n res[endpoint] = getattr(self, endpoint, None)\\r\\n\\r\\n self.sdb[sid] = res\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1587,"cells":{"query_id":{"kind":"string","value":"ededc25806c7e733b395325eb53820d9"},"query":{"kind":"string","value":"Return the annotation identifier of a voxel"},"positive_passages":{"kind":"list like","value":[{"docid":"4502f434a8b56ae53b07b3473d7b6610","score":"0.753712","text":"def annId ( imageargs, dbcfg, proj ):\n\n # Perform argument processing\n (resolution, voxel) = restargs.voxel ( imageargs, dbcfg )\n\n # Get the identifier\n db = emcadb.EMCADB ( dbcfg, proj )\n return db.getVoxel ( resolution, voxel )","title":""}],"string":"[\n {\n \"docid\": \"4502f434a8b56ae53b07b3473d7b6610\",\n \"score\": \"0.753712\",\n \"text\": \"def annId ( imageargs, dbcfg, proj ):\\n\\n # Perform argument processing\\n (resolution, voxel) = restargs.voxel ( imageargs, dbcfg )\\n\\n # Get the identifier\\n db = emcadb.EMCADB ( dbcfg, proj )\\n return db.getVoxel ( resolution, voxel )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9fde52d6bec0b855d2448c514c6d2fda","score":"0.7325891","text":"def annotation_id():","title":""},{"docid":"128cf272ffe0aac6ae2b2d999125c768","score":"0.6396383","text":"def get_idx(self, idx):\n return self._annotations[idx]","title":""},{"docid":"18aea42ddd1f220075c10643f4fe8444","score":"0.62244123","text":"def get_annotation(self, index):\n image_id, image, boxes, labels = self._getitem(index)\n is_difficult = np.zeros(boxes.shape[0], dtype=np.uint8)\n return image_id, (boxes, labels, is_difficult)","title":""},{"docid":"3aa56ff6c0bb640d86bd43aed4f5ada4","score":"0.6220405","text":"def get_id(self):\n return self.get_roi_feature('id')","title":""},{"docid":"9865dfaa49eaa23cb681ba4e3081898f","score":"0.62099105","text":"def imgId(idx):\n return idx / (self.args.num_rows * self.args.num_cols)","title":""},{"docid":"d64420ee52c7d4b4d3fd8df05f3f1f77","score":"0.6199614","text":"def image_id_at(self, i):\n return i","title":""},{"docid":"efc361733781eef5fed94e0a2f028c4e","score":"0.60940516","text":"def image_id_at(self, i):\n # print('----------image_index---------------')\n # print(self.image_index[i])\n return self._image_index[i]","title":""},{"docid":"57bc10bfb66131f53817112d57efc51b","score":"0.6036816","text":"def getVoxelxyzindexfromGlobal(self, voxeli):\r\n pass","title":""},{"docid":"91959ca19f53df399d7566f357f28d17","score":"0.59682924","text":"def _region_id(self, region):\n return '%d,%d' % (region.getX(), region.getY())","title":""},{"docid":"6652d5ceb688204ecde91737306d58de","score":"0.59467643","text":"def image_id_at(self, i):\n return i","title":""},{"docid":"6652d5ceb688204ecde91737306d58de","score":"0.59467643","text":"def image_id_at(self, i):\n return i","title":""},{"docid":"6652d5ceb688204ecde91737306d58de","score":"0.59467643","text":"def image_id_at(self, i):\n return i","title":""},{"docid":"9980933f4360483b91a4eb87eb820f2f","score":"0.5920132","text":"def get_tagged_id(building, json_file):\n\n building[\"tagged_id\"] = 0\n building[\"tagged_id_coords\"] = 0\n # loop through the JSON annotations file\n for idx, v in enumerate(json_file.values()):\n annos = v[\"regions\"]\n\n if v[\"filename\"] == building[\"file_name\"]:\n try:\n for annotation in annos:\n anno = annotation[\"shape_attributes\"]\n # if the annotation is not a point, go to the next annotation\n if anno[\"name\"] != \"point\":\n continue\n\n if anno[\"name\"] == \"point\":\n tagged_id = annotation[\"region_attributes\"][\"tagged_id\"]\n px = anno[\"cx\"]\n py = anno[\"cy\"]\n point = [py, px]\n # if the point location matches with the building mask, add the\n # id to the building data\n if building[\"mask\"][py][px]:\n building[\"tagged_id\"] = tagged_id\n building[\"tagged_id_coords\"] = point\n\n except KeyError as e:\n print(\"Error:\", e)\n return building\n\n return building","title":""},{"docid":"8114ca4bf92a860af8b017f1cf681516","score":"0.58780146","text":"def to_image_id(self, vid):\n return self._cur_graph.index_to_id[vid]","title":""},{"docid":"12c73873bc721cd3eaf8f140de9aed77","score":"0.5818742","text":"def _create_img_id_to_idx(self):\n with h5py.File(self.image_features_path, 'r') as features_file:\n coco_ids = features_file['ids'][()]\n coco_id_to_index = {id: i for i, id in enumerate(coco_ids)}\n return coco_id_to_index","title":""},{"docid":"b6d6bd2b032186e3609b11b9957507d8","score":"0.57647324","text":"def getID(x, y):\n return network_coord_id[ (x, y) ]","title":""},{"docid":"29cf813da762dc6bdab47e794a586dda","score":"0.57575125","text":"def identify_patchid(indiv_xy_position, patchid_map): \n \n row=int(indiv_xy_position[0])\n col=int(indiv_xy_position[1])\n patchid=patchid_map[row][col]\n \n return patchid","title":""},{"docid":"6cad6f89d4a67a0b03894e5c8a879d02","score":"0.5751664","text":"def id(self, image):\n # bbox = self.data_processing.detect_faces(image)\n # face_image = self.data_processing.crop(image, bbox)\n face_image = self.data_processing.process(image)\n embedding = self.model_engineering.encode([face_image])[0]\n uid = self.model_engineering.knn_classify(embedding)\n return uid","title":""},{"docid":"eba1fbbaf38276872991fbd9b3d818a5","score":"0.5747769","text":"def img_id(self):\n tokens = self.idname.split(\"-\")\n return tokens[-1]","title":""},{"docid":"621aa7f3e0c0fbc506d71e9886981530","score":"0.5744122","text":"def _extractDetectorName(self, dataId):\n return int(\"%(ccd)d\" % dataId)","title":""},{"docid":"82d00b15df3ad253c322b67075471de1","score":"0.57416075","text":"def annotation_name(self) -> str:\n return self.split()[0]","title":""},{"docid":"c7ffb26df49183119c0bbc7db7f41b50","score":"0.5711817","text":"def get_gInd_XYZ(self,x,y,z):\n return x+y*self.Nx + z*self.Nx*self.Ny","title":""},{"docid":"0560ee35dbe66c483cdc06b522f9ea61","score":"0.5710416","text":"def annotation_at_pos(self, pos) -> Any:\n return self.params_list[pos].annotation","title":""},{"docid":"a5e26b97f330a9159879a9ac6416c543","score":"0.56902236","text":"def annotate ( self, annid, offset, locations ):\n\n # For now first label for a voxel wins\n\n exceptions = []\n\n # xyz coordinates get stored as zyx to be more\n # efficient when converting to images\n for voxel in locations:\n # label unlabeled voxels\n if ( self.data [ voxel[2]-offset[2], voxel[1]-offset[1], voxel[0]-offset[0]] == 0 ):\n self.data [ voxel[2]-offset[2], voxel[1]-offset[1], voxel[0]-offset[0] ] = annid\n\n # already labelled voxels are exceptions, unless they are the same value\n elif (self.data [ voxel[2]-offset[2], voxel[1]-offset[1], voxel[0]-offset[0]] != annid ):\n exceptions.append ( voxel )\n\n #RBTODO remove this after testing\n else:\n print \"Not an exception\"\n \n print \"List of exceptions = \", exceptions\n return exceptions","title":""},{"docid":"fc2c1f6909dec029366b9c52ef993282","score":"0.56885666","text":"def get_object_ray_cast_hit_id(index: int) -> int:\n pass","title":""},{"docid":"60426ecbbc5a6d0b8fc0e2c23600ee51","score":"0.56803745","text":"def getTagId():","title":""},{"docid":"fb176e13c797178140d14ad22986ceeb","score":"0.5665059","text":"def extract_variable1_index(self, data):\n return np.where(data.coords['features'].values==self.variable1)[0][0]","title":""},{"docid":"cf10fe78da2b0570ea3f540dccccf13e","score":"0.5643946","text":"def get_index(self, map: OccupancyGrid):\n width = map.info.width\n x = self.pose.position.x \n y = self.pose.position.y \n x_index = (x - map.info.origin.position.x) // map.info.resolution\n y_index = (y - map.info.origin.position.y) // map.info.resolution\n ret = x_index + y_index * width\n return int(ret)","title":""},{"docid":"6e6ffcf62d6dca67c4439a229b2e75d2","score":"0.56408346","text":"def material_get_identact(dataline_material):\r\n return tokenize(dataline_material)[9]","title":""},{"docid":"28adf67559ff70560be915535b03ba42","score":"0.5629065","text":"def _make_array_identifier(self, entry):\n # make up a random 32 bit int as the identifier\n # TODO: change to simple counting\n identifier = np.random.randint(-(2 ** 31), 2 ** 31 - 1, 1, dtype=np.int32)[0]\n # '@{some_number}_{bytes_per_pixel}'\n # if its a numpy array, include bytes per pixel, otherwise just interpret it as raw byts\n # TODO : I thinkg its always raw binary and the argument deserialization types handles conversion to java arrays\n # This definitely could use some cleanup and simplification. Probably best to encode the data type here and remove\n # argument deserialization types\n return identifier, \"@\" + str(int(identifier)) + \"_\" + str(\n 0 if isinstance(entry, bytes) else entry.dtype.itemsize\n )","title":""},{"docid":"f685c8bb3b8ab7b4ba6362f6950ce10c","score":"0.56138176","text":"def get_identifier_from_fileinfo(oc, f):\n return '%s-%s-%s-%s' % (\n get_ancestor_fileinfo(oc, f, 3).get_name(),\n get_ancestor_fileinfo(oc, f, 2).get_name(),\n get_ancestor_fileinfo(oc, f, 1).get_name(),\n f.get_name()\n )","title":""},{"docid":"4f676aeae233593d20a183ebf014f84d","score":"0.5590139","text":"def identifier(self):\n first_data_source = next(iter(self._train_data.values()))\n input_tensors = first_data_source.output_tensors\n if self._data_format == 'NHWC':\n _, eh, ew, _ = input_tensors['eye'].shape.as_list()\n else:\n _, _, eh, ew = input_tensors['eye'].shape.as_list()\n return 'ELG_i%dx%d_f%dx%d_n%d_m%d' % (\n ew, eh,\n int(ew / self._hg_first_layer_stride),\n int(eh / self._hg_first_layer_stride),\n self._hg_num_feature_maps, self._hg_num_modules,\n )","title":""},{"docid":"230375753bc192b1d7648108700c1ff6","score":"0.5582777","text":"def ident(self):\n return int(self.v[SWC.I])","title":""},{"docid":"5848a41305d1202cab24bd15f602e149","score":"0.5563336","text":"def get_pixel_index(image, x, y):\n return (image['width'] * x) + y","title":""},{"docid":"e16935ed843ca3870f8a91c9292b1380","score":"0.55585855","text":"def get_ann_info(self, idx):\n return self.tile_dataset.get_ann_info(idx)","title":""},{"docid":"eac5d536cdb41607949443b2538192dc","score":"0.5550746","text":"def __getitem__(self, index): \n coco = self.coco\n vocab = self.vocab\n # ann_id = self.ids[index] \n # img_id = coco.anns[ann_id]['image_id']\n img_id = self.ids[index] \n path = coco.loadImgs(img_id)[0]['file_name'] \n if path not in self.namelist:\n return 'None', 0 \n image = Image.open(os.path.join(self.root, path)).convert('RGB') \n if self.transform is not None:\n image = self.transform(image) \n return image, img_id","title":""},{"docid":"c57aae8c8159d7fffc7ce1d2bd55b293","score":"0.5521607","text":"def get_raster_file_number(self):\n _raster_coords = pd.read_csv('data/bounding_boxes_coordinates')\n _raster_coords.set_index('index', inplace=True)\n try:\n _x, _y = self.polygons[0].bounds[:2]\n except IndexError:\n print('no polygon found')\n print(self.polygons)\n _x, _y = self.polygons[0].bounds[:2]\n ind = find_index(_y, _raster_coords.index)\n col = find_index(_x, _raster_coords.columns)\n self.raster_file_number = str(_raster_coords.loc[ind, col])","title":""},{"docid":"61f3bdb6d8945912ea50bce174ed68ec","score":"0.5519245","text":"def _get_idx(self, joint: str) -> int:\n joint_id = MPII_JOINT_INDICES[joint]\n # XXX: For some reason there are annotations where the same joint ID\n # appears more than once (?!)\n correct_id = int(np.nonzero(self.point['id'] == joint_id)[0][0])\n return correct_id","title":""},{"docid":"cbd3beb12dc4d1cd82fb9a64daff9792","score":"0.54989094","text":"def get_img_annotation(self, idx):\r\n img_id = self.img_ids[idx]\r\n ann_ids = self.coco_api.getAnnIds([img_id])\r\n anns = self.coco_api.loadAnns(ann_ids)\r\n gt_bboxes = []\r\n gt_labels = []\r\n gt_bboxes_ignore = []\r\n if self.use_instance_mask:\r\n gt_masks = []\r\n if self.use_keypoint:\r\n gt_keypoints = []\r\n for ann in anns:\r\n if ann.get('ignore', False):\r\n continue\r\n x1, y1, w, h = ann['bbox']\r\n if ann['area'] <= 0 or w < 1 or h < 1:\r\n continue\r\n if ann['category_id'] not in self.cat_ids:\r\n continue\r\n bbox = [x1, y1, x1 + w, y1 + h]\r\n if ann['iscrowd']:\r\n gt_bboxes_ignore.append(bbox)\r\n else:\r\n gt_bboxes.append(bbox)\r\n gt_labels.append(self.cat2label[ann['category_id']])\r\n if self.use_instance_mask:\r\n gt_masks.append(self.coco_api.annToMask(ann))\r\n if self.use_keypoint:\r\n gt_keypoints.append(ann['keypoints'])\r\n if gt_bboxes:\r\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\r\n gt_labels = np.array(gt_labels, dtype=np.int64)\r\n else:\r\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\r\n gt_labels = np.array([], dtype=np.int64)\r\n if gt_bboxes_ignore:\r\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\r\n else:\r\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\r\n annotation = dict(\r\n bboxes=gt_bboxes, labels=gt_labels, bboxes_ignore=gt_bboxes_ignore)\r\n if self.use_instance_mask:\r\n annotation['masks'] = gt_masks\r\n if self.use_keypoint:\r\n if gt_keypoints:\r\n annotation['keypoints'] = np.array(gt_keypoints, dtype=np.float32)\r\n else:\r\n annotation['keypoints'] = np.zeros((0, 51), dtype=np.float32)\r\n return annotation","title":""},{"docid":"dbe32aea9526cc138f07c3d85cfa2960","score":"0.54935855","text":"def _get_unique_annotation_id(self):\n\n # Incrementer row keys start with an \"i\"\n row_key = serialize_key(\"iannotations\")\n append_row = self.table.row(row_key, append=True)\n append_row.increment_cell_value(self.incrementer_family_id,\n serialize_key(\"counter\"), 1)\n\n # This increments the row entry and returns the value AFTER incrementing\n latest_row = append_row.commit()\n annotation_id = int.from_bytes(\n latest_row[self.incrementer_family_id][serialize_key('counter')][0][\n 0], byteorder=\"big\")\n\n return np.uint64(annotation_id)","title":""},{"docid":"3e184e7ae5d3296e0d1f6fe3944f2bbf","score":"0.54926604","text":"def coord_to_index_code(self, coord_prefix):","title":""},{"docid":"7c553a9d50f57a78f07bcbe705ebbb94","score":"0.5476703","text":"def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"glomerulus\":\n return info[\"id\"]\n else:\n super(self.__class__, self).image_reference(image_id)","title":""},{"docid":"8e2aaf91d4fd6676be534db16b8c8026","score":"0.54695815","text":"def get_ray_cast_sprite_id() -> int:\n pass","title":""},{"docid":"974ddf590414be989f5dc26f49452374","score":"0.54620486","text":"def _coord2id(self, i, j):\n return (i - 1) * self.width + (j - 1)","title":""},{"docid":"70693d011d4fc5cfc5373e5608bde000","score":"0.54561096","text":"def grid_to_index(mapdata, x, y):\n ### REQUIRED CREDIT\n return int((y * mapdata.info.width) + x)","title":""},{"docid":"6ecd238d5b85d247b452910c6f86ab63","score":"0.5451302","text":"def image_id_at(self, i,f=None):\n if f is None:\n return i\n return self.image_id_at(self.video_index_to_image_index(i,f))","title":""},{"docid":"8b874658eb76cefdf9d8ede6ab42d7be","score":"0.5443176","text":"def label(self, image_np):\n pass","title":""},{"docid":"8a873340d9085c6cf5ebcff676dc2075","score":"0.54279727","text":"def getIdentifierFromNVDAObject(self,obj):\r\n\t\traise NotImplementedError","title":""},{"docid":"a82e56c53106d77960a43e20d2717a74","score":"0.54276735","text":"def get_objid(lordloc, rordloc, islit, iobj, trc_img, ypos=0.5):\n yidx = int(np.round(ypos*lordloc.shape[0]))\n pixl_slit = lordloc[yidx, islit]\n pixr_slit = rordloc[yidx, islit]\n #\n xobj = (trc_img[islit]['traces'][yidx,iobj]-pixl_slit) / (pixr_slit-pixl_slit)\n objid= int(np.round(xobj*1e3))\n # Return\n return objid, xobj","title":""},{"docid":"223b7ed3acc6c5d0aa574af60ac4c211","score":"0.54264766","text":"def _get_identifier(human_gene: HumanGene) -> str:\n return str(human_gene.identifier)","title":""},{"docid":"8019d81b5a1fe02b9e0c5687a2e95139","score":"0.5421047","text":"def scene_id(self):\n return self['name'].split('.')[0]","title":""},{"docid":"04c72f44a56575cadaf1b379d465d466","score":"0.5419409","text":"def as_index(self):\n return self.variable.to_coord().as_index","title":""},{"docid":"eb4ceb54beb7f2569d22d237f9762d72","score":"0.54190063","text":"def get_index(filename, attr):\r\n try:\r\n tag_name = np.load(filename)\r\n tag_name = [tag.lower() for tag in tag_name]\r\n try:\r\n return tag_name.index(attr.lower())\r\n except ValueError:\r\n print('No such attribute:', attr)\r\n except Exception:\r\n print('No such file:', filename)\r\n return -1","title":""},{"docid":"70e8d3f117f01b83ff313ccaf8b52a69","score":"0.5409619","text":"def get_image_id(image_id: int, x: Union[float, int], y: Union[float, int], width: Union[float,\n int], height: Union[float, int]) -> None:\n pass","title":""},{"docid":"aa1d4524ec4ed5d3772b58fdcda50b43","score":"0.5405024","text":"def getMapId(self, vis_params):\n return self.mosaic().getMapId(vis_params)","title":""},{"docid":"6ecba7aed8ed91092ba74a1917634ba3","score":"0.5404897","text":"def hexdigest(self):\n return generate_uid_from_semantic_segmentation_2d_annotation(self._segmentation_image)","title":""},{"docid":"d179c867e0b94a7dce35c738dd9d1fd6","score":"0.5397746","text":"def _get_col_id(self):\n c = getattr(self.layer, 'classes_', 1)\n p = len(self.layer.cases) * self.layer.indexer.n_partitions\n return _get_col_idx(self.e, p, c)","title":""},{"docid":"6d3661e7825a3dc48ce96c18861d5af6","score":"0.539485","text":"def identifier(self):\n if \"identifier\" in self.h5.attrs:\n return self.h5.attrs[\"identifier\"]\n else:\n return None","title":""},{"docid":"1184f5557003d75421c433fb3851ac4a","score":"0.5392987","text":"def pix_idx(x, y, level):\n\ti, j = xy_to_ij(x, y, level)\n\twh = width(level)\n\ti = i + wh/2; j = j + wh/2;\n\treturn j * wh + i","title":""},{"docid":"9603f0a377b12bdd797a622eafb510c8","score":"0.5377663","text":"def find_image_row_col_label_coordinate(raster, lat, lon, image_size):\n row, col = raster.index(lat, lon, precision=23)\n return round(row / image_size), round(col / image_size)","title":""},{"docid":"8fca25d9c5053ecdaa76d11719e510df","score":"0.537179","text":"def image_id():\n return GLOBAL.metadata.get('ami-id')","title":""},{"docid":"5253b4494ff77dc082a13a304b339999","score":"0.536078","text":"def col_to_id(column, z_cell):\n return z_cell + column * ncells_tall","title":""},{"docid":"5253b4494ff77dc082a13a304b339999","score":"0.536078","text":"def col_to_id(column, z_cell):\n return z_cell + column * ncells_tall","title":""},{"docid":"6f232da10614702fc1edae998b7b8f03","score":"0.5359232","text":"def getPinIdentifier(self, line: Any) -> str:","title":""},{"docid":"4785d15a793770ab87bdf9ba97e28a9f","score":"0.53589034","text":"def GetIdentifier(self):","title":""},{"docid":"e12210f31c4867a89afb14680cbc5b10","score":"0.53578925","text":"def get_image_id(self, game_id):\n return self.image_index_training[game_id]","title":""},{"docid":"17cb068ca96f79238621463369f5b8da","score":"0.5356314","text":"def find_vertex_name(self, id):\n vertex_type = \"\"\n if not id.isdigit():\n # print(id)\n id = self.id_map.get(id, \"0\")\n # print(id)\n \n vertex_type = self.id_map.get(id, \"apriltag\")\n # print(vertex_type)\n else:\n vertex_type = self.id_map.get(id, \"apriltag\")\n\n if (vertex_type == \"Vehicle\"):\n id = \"duckiebot_%s\" % id\n else:\n id = \"apriltag_%s\" % id\n\n return id","title":""},{"docid":"edd3f6a6b3b7b7aab215154e505d975f","score":"0.5355522","text":"def check_annotated(tree):\n if (isinstance(tree, ast.Subscript) and type(tree.slice) is ast.Index and\n type(tree.value) is ast.Name): # noqa: E129\n return tree.value.id, tree.slice.value","title":""},{"docid":"04227df40502a8097f34f0613f41c13f","score":"0.5347402","text":"def id(self):\n\n\t\treturn struct.unpack_from('<I', self.instrument.data, self.offset + 8)[0]","title":""},{"docid":"a0313649632af006ec7fb44a81767bb6","score":"0.5342543","text":"def get_annotation(self, key):\n return self._annotations.get(key, None)","title":""},{"docid":"2a68d51b2547cebd7369d090f4099d53","score":"0.5332197","text":"def get_annotations(self, img_id):\n # Some images don't have any annotations. Return empty list instead.\n return self._img_id2annotations.get(img_id, [])","title":""},{"docid":"2a68d51b2547cebd7369d090f4099d53","score":"0.5332197","text":"def get_annotations(self, img_id):\n # Some images don't have any annotations. Return empty list instead.\n return self._img_id2annotations.get(img_id, [])","title":""},{"docid":"37868bd42cb920b8741a16c6636ca8b3","score":"0.53313947","text":"def get_contact_sprite_id1() -> int:\n pass","title":""},{"docid":"9cb6e44a5b62fcf289642e63a96619a9","score":"0.5323577","text":"def get_max_annotation_id(self):\n\n # Incrementer row keys start with an \"i\"\n row_key = serialize_key(\"iannotations\")\n row = self.table.read_row(row_key)\n\n # Read incrementer value\n annotation_id = int.from_bytes(row.cells[self.incrementer_family_id][serialize_key('counter')][0].value, byteorder=\"big\")\n\n return np.uint64(annotation_id)","title":""},{"docid":"d873639e7458c8ba638c202fa6ada534","score":"0.53218627","text":"def get_id(index: int):\n return 'subarray_{:02d}'.format(index)","title":""},{"docid":"1e432af0a05365d1f20d6996e74b316a","score":"0.5319183","text":"def _get_annotation(self):\n \n return self._annotation","title":""},{"docid":"bb1a1d487cf860b39a87b6acb2f909a7","score":"0.53161883","text":"def printable_map(self) -> np.ndarray:\n map_identifier = np.zeros(shape=(self.settings[\"map_size\"], self.settings[\"map_size\"]), dtype=np.int)\n\n for entity in self.animals():\n map_identifier[entity.position] += entity.identifier\n\n for entity in self.foods():\n map_identifier[entity.position] += entity.identifier\n\n return map_identifier","title":""},{"docid":"bcaaa24525efac9aa985b18e00710b9e","score":"0.5307999","text":"def get_sprite_image_id(sprite_id: int) -> int:\n pass","title":""},{"docid":"9395e989b51063f57a3c1ccf13d15fe9","score":"0.5300224","text":"def id_to_label(self, idx):\n pass","title":""},{"docid":"59a6c1dd87062fcb15b870ba6e5db8d2","score":"0.52978706","text":"def uidx(self, grid_tag='default'):\r\n return (self.princeidx + 1) * self.grid_dims[grid_tag]","title":""},{"docid":"834de234113bb6c14399c0113fa124ab","score":"0.52912325","text":"def id(self):\n return self.metadata[4]","title":""},{"docid":"bef913645b6c45286e4aedabea5ce119","score":"0.5288406","text":"def xlabel(self):\n return 'x' if self.dimensions[0] == 'i' else 'i'","title":""},{"docid":"a41706de95a8e4ea6ab8e830a928e97d","score":"0.5286877","text":"def get_identifier(self):\n return getattr(self, self.IDENTIFIER_FIELD)","title":""},{"docid":"08f42a198cf47df09d317f0bdddf85b6","score":"0.5281084","text":"def at(self, num):\n for i,key in enumerate(self.labelMap):\n if(i==num):\n return key\n return \"\"","title":""},{"docid":"c5bc28e522bab14f713f678c3c462768","score":"0.5280401","text":"def get_ann_info(self, idx: int):\n dataset_item = self.otx_dataset[idx]\n labels = self.labels\n return get_annotation_mmdet_format(dataset_item, labels, self.domain)","title":""},{"docid":"81b9de441f2c9c0b902c10efc7f223c9","score":"0.5280239","text":"def image_metadata(img):\n src = rasterio.open(img)\n arr = src.read(1)\n name = src.name.split('.')[0]\n y = int(name.split('-')[0])\n x = int(name.split('-')[1])\n z = int(name.split('-')[2])\n width = int(arr.shape[0])\n height =int(arr.shape[1])\n\n return name, x, y, z, width, height","title":""},{"docid":"62459c19bdb62667a15437a3408258d2","score":"0.52722496","text":"def _posToId(self, row, col):\n return self.posToId[row, col]","title":""},{"docid":"b4432bb9d8a7d06a3ff82d7a1fca3dd5","score":"0.5270191","text":"def get_id(self):\n return ','.join(map(str, self.pos)) + ':' + '.'.join(map(str, self.vel))","title":""},{"docid":"8ec1adc2126913ed02f01b0ef7ab36b1","score":"0.5268546","text":"def getItemIdentifier(item, position):","title":""},{"docid":"a890f01e965139f32a886c4ae1bb0574","score":"0.52662337","text":"def image_identifier(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"image_identifier\")","title":""},{"docid":"6f75a524c7dad75c2039791d9b5d9d58","score":"0.5264078","text":"def __getitem__(self, index: int) -> Tuple[Any, Any]:\n img = Image.open(self.images[index]).convert(\"RGB\")\n if self.transform:\n img = self.transform(img)\n target = self._parse_voc_xml(ET.parse(self.targets[index]).getroot())\n return img, target[\"annotation\"]","title":""},{"docid":"ec7a5a722cca6164224c2835d14885a5","score":"0.5262701","text":"def object_id(self):\r\n return self._feature.attributes.get(self._schema.object_id)","title":""},{"docid":"f14da1ddbfc9737944d561d39f1f3ae3","score":"0.5256659","text":"def __call__(self, img):\n return self.annotations(img)","title":""},{"docid":"8a2c1c942896d0daf8f4e79e10142e46","score":"0.5256637","text":"def get_labels(ab_img):\n pass","title":""},{"docid":"cf4223a48c5c52cadf30c10bc5c455b0","score":"0.5255165","text":"def get_true_tip_index(row):\n for i, pixel in enumerate(row):\n b, g, r = pixel\n if r > 100:\n return i","title":""},{"docid":"5df8a4e55dd2b2f5ec2bbbb72ceff47a","score":"0.52473456","text":"def get_XYZ_index(self,g_nd): # this will depend upon a global geometry structure like a brick\n z = g_nd/(self.Nx*self.Ny)\n y = (g_nd - z*self.Nx*self.Ny)/self.Nx\n x = (g_nd - z*self.Nx*self.Ny - y*self.Nx)\n return (x,y,z)","title":""},{"docid":"0c249f76969d703f5d4597be5d618565","score":"0.52439","text":"def GetIdent(self):\n return _itkGeometricalQuadEdgePython.itkGeometricalQuadEdgeULULBBF_GetIdent(self)","title":""},{"docid":"b90f4792d3ec16d4cb2c05d31236d92d","score":"0.5242144","text":"def build_image_id(line, row_name_to_id_mapper):\n season = line[row_name_to_id_mapper['Season']]\n season = build_season_id(season)\n site = line[row_name_to_id_mapper[' Site']]\n roll = fix_roll_id(line[row_name_to_id_mapper[' Roll']])\n imgname = line[row_name_to_id_mapper[' PathFilename']]\n image_name = os.path.basename(imgname)\n return '#'.join([season, site, roll, image_name])","title":""},{"docid":"750dd9e3c0cd0a05d2906985cc8fdb64","score":"0.5241895","text":"def image_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"image_id\")","title":""},{"docid":"ba10886170fb41969aeef5a4ce937965","score":"0.52332395","text":"def get_id(self):\n return self.name #sidlir.type_id(self.data)","title":""}],"string":"[\n {\n \"docid\": \"9fde52d6bec0b855d2448c514c6d2fda\",\n \"score\": \"0.7325891\",\n \"text\": \"def annotation_id():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128cf272ffe0aac6ae2b2d999125c768\",\n \"score\": \"0.6396383\",\n \"text\": \"def get_idx(self, idx):\\n return self._annotations[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18aea42ddd1f220075c10643f4fe8444\",\n \"score\": \"0.62244123\",\n \"text\": \"def get_annotation(self, index):\\n image_id, image, boxes, labels = self._getitem(index)\\n is_difficult = np.zeros(boxes.shape[0], dtype=np.uint8)\\n return image_id, (boxes, labels, is_difficult)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa56ff6c0bb640d86bd43aed4f5ada4\",\n \"score\": \"0.6220405\",\n \"text\": \"def get_id(self):\\n return self.get_roi_feature('id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9865dfaa49eaa23cb681ba4e3081898f\",\n \"score\": \"0.62099105\",\n \"text\": \"def imgId(idx):\\n return idx / (self.args.num_rows * self.args.num_cols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64420ee52c7d4b4d3fd8df05f3f1f77\",\n \"score\": \"0.6199614\",\n \"text\": \"def image_id_at(self, i):\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc361733781eef5fed94e0a2f028c4e\",\n \"score\": \"0.60940516\",\n \"text\": \"def image_id_at(self, i):\\n # print('----------image_index---------------')\\n # print(self.image_index[i])\\n return self._image_index[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57bc10bfb66131f53817112d57efc51b\",\n \"score\": \"0.6036816\",\n \"text\": \"def getVoxelxyzindexfromGlobal(self, voxeli):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91959ca19f53df399d7566f357f28d17\",\n \"score\": \"0.59682924\",\n \"text\": \"def _region_id(self, region):\\n return '%d,%d' % (region.getX(), region.getY())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6652d5ceb688204ecde91737306d58de\",\n \"score\": \"0.59467643\",\n \"text\": \"def image_id_at(self, i):\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6652d5ceb688204ecde91737306d58de\",\n \"score\": \"0.59467643\",\n \"text\": \"def image_id_at(self, i):\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6652d5ceb688204ecde91737306d58de\",\n \"score\": \"0.59467643\",\n \"text\": \"def image_id_at(self, i):\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9980933f4360483b91a4eb87eb820f2f\",\n \"score\": \"0.5920132\",\n \"text\": \"def get_tagged_id(building, json_file):\\n\\n building[\\\"tagged_id\\\"] = 0\\n building[\\\"tagged_id_coords\\\"] = 0\\n # loop through the JSON annotations file\\n for idx, v in enumerate(json_file.values()):\\n annos = v[\\\"regions\\\"]\\n\\n if v[\\\"filename\\\"] == building[\\\"file_name\\\"]:\\n try:\\n for annotation in annos:\\n anno = annotation[\\\"shape_attributes\\\"]\\n # if the annotation is not a point, go to the next annotation\\n if anno[\\\"name\\\"] != \\\"point\\\":\\n continue\\n\\n if anno[\\\"name\\\"] == \\\"point\\\":\\n tagged_id = annotation[\\\"region_attributes\\\"][\\\"tagged_id\\\"]\\n px = anno[\\\"cx\\\"]\\n py = anno[\\\"cy\\\"]\\n point = [py, px]\\n # if the point location matches with the building mask, add the\\n # id to the building data\\n if building[\\\"mask\\\"][py][px]:\\n building[\\\"tagged_id\\\"] = tagged_id\\n building[\\\"tagged_id_coords\\\"] = point\\n\\n except KeyError as e:\\n print(\\\"Error:\\\", e)\\n return building\\n\\n return building\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8114ca4bf92a860af8b017f1cf681516\",\n \"score\": \"0.58780146\",\n \"text\": \"def to_image_id(self, vid):\\n return self._cur_graph.index_to_id[vid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12c73873bc721cd3eaf8f140de9aed77\",\n \"score\": \"0.5818742\",\n \"text\": \"def _create_img_id_to_idx(self):\\n with h5py.File(self.image_features_path, 'r') as features_file:\\n coco_ids = features_file['ids'][()]\\n coco_id_to_index = {id: i for i, id in enumerate(coco_ids)}\\n return coco_id_to_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d6bd2b032186e3609b11b9957507d8\",\n \"score\": \"0.57647324\",\n \"text\": \"def getID(x, y):\\n return network_coord_id[ (x, y) ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29cf813da762dc6bdab47e794a586dda\",\n \"score\": \"0.57575125\",\n \"text\": \"def identify_patchid(indiv_xy_position, patchid_map): \\n \\n row=int(indiv_xy_position[0])\\n col=int(indiv_xy_position[1])\\n patchid=patchid_map[row][col]\\n \\n return patchid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cad6f89d4a67a0b03894e5c8a879d02\",\n \"score\": \"0.5751664\",\n \"text\": \"def id(self, image):\\n # bbox = self.data_processing.detect_faces(image)\\n # face_image = self.data_processing.crop(image, bbox)\\n face_image = self.data_processing.process(image)\\n embedding = self.model_engineering.encode([face_image])[0]\\n uid = self.model_engineering.knn_classify(embedding)\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba1fbbaf38276872991fbd9b3d818a5\",\n \"score\": \"0.5747769\",\n \"text\": \"def img_id(self):\\n tokens = self.idname.split(\\\"-\\\")\\n return tokens[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"621aa7f3e0c0fbc506d71e9886981530\",\n \"score\": \"0.5744122\",\n \"text\": \"def _extractDetectorName(self, dataId):\\n return int(\\\"%(ccd)d\\\" % dataId)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d00b15df3ad253c322b67075471de1\",\n \"score\": \"0.57416075\",\n \"text\": \"def annotation_name(self) -> str:\\n return self.split()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ffb26df49183119c0bbc7db7f41b50\",\n \"score\": \"0.5711817\",\n \"text\": \"def get_gInd_XYZ(self,x,y,z):\\n return x+y*self.Nx + z*self.Nx*self.Ny\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0560ee35dbe66c483cdc06b522f9ea61\",\n \"score\": \"0.5710416\",\n \"text\": \"def annotation_at_pos(self, pos) -> Any:\\n return self.params_list[pos].annotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e26b97f330a9159879a9ac6416c543\",\n \"score\": \"0.56902236\",\n \"text\": \"def annotate ( self, annid, offset, locations ):\\n\\n # For now first label for a voxel wins\\n\\n exceptions = []\\n\\n # xyz coordinates get stored as zyx to be more\\n # efficient when converting to images\\n for voxel in locations:\\n # label unlabeled voxels\\n if ( self.data [ voxel[2]-offset[2], voxel[1]-offset[1], voxel[0]-offset[0]] == 0 ):\\n self.data [ voxel[2]-offset[2], voxel[1]-offset[1], voxel[0]-offset[0] ] = annid\\n\\n # already labelled voxels are exceptions, unless they are the same value\\n elif (self.data [ voxel[2]-offset[2], voxel[1]-offset[1], voxel[0]-offset[0]] != annid ):\\n exceptions.append ( voxel )\\n\\n #RBTODO remove this after testing\\n else:\\n print \\\"Not an exception\\\"\\n \\n print \\\"List of exceptions = \\\", exceptions\\n return exceptions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2c1f6909dec029366b9c52ef993282\",\n \"score\": \"0.56885666\",\n \"text\": \"def get_object_ray_cast_hit_id(index: int) -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60426ecbbc5a6d0b8fc0e2c23600ee51\",\n \"score\": \"0.56803745\",\n \"text\": \"def getTagId():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb176e13c797178140d14ad22986ceeb\",\n \"score\": \"0.5665059\",\n \"text\": \"def extract_variable1_index(self, data):\\n return np.where(data.coords['features'].values==self.variable1)[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf10fe78da2b0570ea3f540dccccf13e\",\n \"score\": \"0.5643946\",\n \"text\": \"def get_index(self, map: OccupancyGrid):\\n width = map.info.width\\n x = self.pose.position.x \\n y = self.pose.position.y \\n x_index = (x - map.info.origin.position.x) // map.info.resolution\\n y_index = (y - map.info.origin.position.y) // map.info.resolution\\n ret = x_index + y_index * width\\n return int(ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6ffcf62d6dca67c4439a229b2e75d2\",\n \"score\": \"0.56408346\",\n \"text\": \"def material_get_identact(dataline_material):\\r\\n return tokenize(dataline_material)[9]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28adf67559ff70560be915535b03ba42\",\n \"score\": \"0.5629065\",\n \"text\": \"def _make_array_identifier(self, entry):\\n # make up a random 32 bit int as the identifier\\n # TODO: change to simple counting\\n identifier = np.random.randint(-(2 ** 31), 2 ** 31 - 1, 1, dtype=np.int32)[0]\\n # '@{some_number}_{bytes_per_pixel}'\\n # if its a numpy array, include bytes per pixel, otherwise just interpret it as raw byts\\n # TODO : I thinkg its always raw binary and the argument deserialization types handles conversion to java arrays\\n # This definitely could use some cleanup and simplification. Probably best to encode the data type here and remove\\n # argument deserialization types\\n return identifier, \\\"@\\\" + str(int(identifier)) + \\\"_\\\" + str(\\n 0 if isinstance(entry, bytes) else entry.dtype.itemsize\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f685c8bb3b8ab7b4ba6362f6950ce10c\",\n \"score\": \"0.56138176\",\n \"text\": \"def get_identifier_from_fileinfo(oc, f):\\n return '%s-%s-%s-%s' % (\\n get_ancestor_fileinfo(oc, f, 3).get_name(),\\n get_ancestor_fileinfo(oc, f, 2).get_name(),\\n get_ancestor_fileinfo(oc, f, 1).get_name(),\\n f.get_name()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f676aeae233593d20a183ebf014f84d\",\n \"score\": \"0.5590139\",\n \"text\": \"def identifier(self):\\n first_data_source = next(iter(self._train_data.values()))\\n input_tensors = first_data_source.output_tensors\\n if self._data_format == 'NHWC':\\n _, eh, ew, _ = input_tensors['eye'].shape.as_list()\\n else:\\n _, _, eh, ew = input_tensors['eye'].shape.as_list()\\n return 'ELG_i%dx%d_f%dx%d_n%d_m%d' % (\\n ew, eh,\\n int(ew / self._hg_first_layer_stride),\\n int(eh / self._hg_first_layer_stride),\\n self._hg_num_feature_maps, self._hg_num_modules,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230375753bc192b1d7648108700c1ff6\",\n \"score\": \"0.5582777\",\n \"text\": \"def ident(self):\\n return int(self.v[SWC.I])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5848a41305d1202cab24bd15f602e149\",\n \"score\": \"0.5563336\",\n \"text\": \"def get_pixel_index(image, x, y):\\n return (image['width'] * x) + y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e16935ed843ca3870f8a91c9292b1380\",\n \"score\": \"0.55585855\",\n \"text\": \"def get_ann_info(self, idx):\\n return self.tile_dataset.get_ann_info(idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac5d536cdb41607949443b2538192dc\",\n \"score\": \"0.5550746\",\n \"text\": \"def __getitem__(self, index): \\n coco = self.coco\\n vocab = self.vocab\\n # ann_id = self.ids[index] \\n # img_id = coco.anns[ann_id]['image_id']\\n img_id = self.ids[index] \\n path = coco.loadImgs(img_id)[0]['file_name'] \\n if path not in self.namelist:\\n return 'None', 0 \\n image = Image.open(os.path.join(self.root, path)).convert('RGB') \\n if self.transform is not None:\\n image = self.transform(image) \\n return image, img_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57aae8c8159d7fffc7ce1d2bd55b293\",\n \"score\": \"0.5521607\",\n \"text\": \"def get_raster_file_number(self):\\n _raster_coords = pd.read_csv('data/bounding_boxes_coordinates')\\n _raster_coords.set_index('index', inplace=True)\\n try:\\n _x, _y = self.polygons[0].bounds[:2]\\n except IndexError:\\n print('no polygon found')\\n print(self.polygons)\\n _x, _y = self.polygons[0].bounds[:2]\\n ind = find_index(_y, _raster_coords.index)\\n col = find_index(_x, _raster_coords.columns)\\n self.raster_file_number = str(_raster_coords.loc[ind, col])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f3bdb6d8945912ea50bce174ed68ec\",\n \"score\": \"0.5519245\",\n \"text\": \"def _get_idx(self, joint: str) -> int:\\n joint_id = MPII_JOINT_INDICES[joint]\\n # XXX: For some reason there are annotations where the same joint ID\\n # appears more than once (?!)\\n correct_id = int(np.nonzero(self.point['id'] == joint_id)[0][0])\\n return correct_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbd3beb12dc4d1cd82fb9a64daff9792\",\n \"score\": \"0.54989094\",\n \"text\": \"def get_img_annotation(self, idx):\\r\\n img_id = self.img_ids[idx]\\r\\n ann_ids = self.coco_api.getAnnIds([img_id])\\r\\n anns = self.coco_api.loadAnns(ann_ids)\\r\\n gt_bboxes = []\\r\\n gt_labels = []\\r\\n gt_bboxes_ignore = []\\r\\n if self.use_instance_mask:\\r\\n gt_masks = []\\r\\n if self.use_keypoint:\\r\\n gt_keypoints = []\\r\\n for ann in anns:\\r\\n if ann.get('ignore', False):\\r\\n continue\\r\\n x1, y1, w, h = ann['bbox']\\r\\n if ann['area'] <= 0 or w < 1 or h < 1:\\r\\n continue\\r\\n if ann['category_id'] not in self.cat_ids:\\r\\n continue\\r\\n bbox = [x1, y1, x1 + w, y1 + h]\\r\\n if ann['iscrowd']:\\r\\n gt_bboxes_ignore.append(bbox)\\r\\n else:\\r\\n gt_bboxes.append(bbox)\\r\\n gt_labels.append(self.cat2label[ann['category_id']])\\r\\n if self.use_instance_mask:\\r\\n gt_masks.append(self.coco_api.annToMask(ann))\\r\\n if self.use_keypoint:\\r\\n gt_keypoints.append(ann['keypoints'])\\r\\n if gt_bboxes:\\r\\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\\r\\n gt_labels = np.array(gt_labels, dtype=np.int64)\\r\\n else:\\r\\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\\r\\n gt_labels = np.array([], dtype=np.int64)\\r\\n if gt_bboxes_ignore:\\r\\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\\r\\n else:\\r\\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\\r\\n annotation = dict(\\r\\n bboxes=gt_bboxes, labels=gt_labels, bboxes_ignore=gt_bboxes_ignore)\\r\\n if self.use_instance_mask:\\r\\n annotation['masks'] = gt_masks\\r\\n if self.use_keypoint:\\r\\n if gt_keypoints:\\r\\n annotation['keypoints'] = np.array(gt_keypoints, dtype=np.float32)\\r\\n else:\\r\\n annotation['keypoints'] = np.zeros((0, 51), dtype=np.float32)\\r\\n return annotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe32aea9526cc138f07c3d85cfa2960\",\n \"score\": \"0.54935855\",\n \"text\": \"def _get_unique_annotation_id(self):\\n\\n # Incrementer row keys start with an \\\"i\\\"\\n row_key = serialize_key(\\\"iannotations\\\")\\n append_row = self.table.row(row_key, append=True)\\n append_row.increment_cell_value(self.incrementer_family_id,\\n serialize_key(\\\"counter\\\"), 1)\\n\\n # This increments the row entry and returns the value AFTER incrementing\\n latest_row = append_row.commit()\\n annotation_id = int.from_bytes(\\n latest_row[self.incrementer_family_id][serialize_key('counter')][0][\\n 0], byteorder=\\\"big\\\")\\n\\n return np.uint64(annotation_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e184e7ae5d3296e0d1f6fe3944f2bbf\",\n \"score\": \"0.54926604\",\n \"text\": \"def coord_to_index_code(self, coord_prefix):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c553a9d50f57a78f07bcbe705ebbb94\",\n \"score\": \"0.5476703\",\n \"text\": \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"glomerulus\\\":\\n return info[\\\"id\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e2aaf91d4fd6676be534db16b8c8026\",\n \"score\": \"0.54695815\",\n \"text\": \"def get_ray_cast_sprite_id() -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"974ddf590414be989f5dc26f49452374\",\n \"score\": \"0.54620486\",\n \"text\": \"def _coord2id(self, i, j):\\n return (i - 1) * self.width + (j - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70693d011d4fc5cfc5373e5608bde000\",\n \"score\": \"0.54561096\",\n \"text\": \"def grid_to_index(mapdata, x, y):\\n ### REQUIRED CREDIT\\n return int((y * mapdata.info.width) + x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ecd238d5b85d247b452910c6f86ab63\",\n \"score\": \"0.5451302\",\n \"text\": \"def image_id_at(self, i,f=None):\\n if f is None:\\n return i\\n return self.image_id_at(self.video_index_to_image_index(i,f))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b874658eb76cefdf9d8ede6ab42d7be\",\n \"score\": \"0.5443176\",\n \"text\": \"def label(self, image_np):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a873340d9085c6cf5ebcff676dc2075\",\n \"score\": \"0.54279727\",\n \"text\": \"def getIdentifierFromNVDAObject(self,obj):\\r\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a82e56c53106d77960a43e20d2717a74\",\n \"score\": \"0.54276735\",\n \"text\": \"def get_objid(lordloc, rordloc, islit, iobj, trc_img, ypos=0.5):\\n yidx = int(np.round(ypos*lordloc.shape[0]))\\n pixl_slit = lordloc[yidx, islit]\\n pixr_slit = rordloc[yidx, islit]\\n #\\n xobj = (trc_img[islit]['traces'][yidx,iobj]-pixl_slit) / (pixr_slit-pixl_slit)\\n objid= int(np.round(xobj*1e3))\\n # Return\\n return objid, xobj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223b7ed3acc6c5d0aa574af60ac4c211\",\n \"score\": \"0.54264766\",\n \"text\": \"def _get_identifier(human_gene: HumanGene) -> str:\\n return str(human_gene.identifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8019d81b5a1fe02b9e0c5687a2e95139\",\n \"score\": \"0.5421047\",\n \"text\": \"def scene_id(self):\\n return self['name'].split('.')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c72f44a56575cadaf1b379d465d466\",\n \"score\": \"0.5419409\",\n \"text\": \"def as_index(self):\\n return self.variable.to_coord().as_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4ceb54beb7f2569d22d237f9762d72\",\n \"score\": \"0.54190063\",\n \"text\": \"def get_index(filename, attr):\\r\\n try:\\r\\n tag_name = np.load(filename)\\r\\n tag_name = [tag.lower() for tag in tag_name]\\r\\n try:\\r\\n return tag_name.index(attr.lower())\\r\\n except ValueError:\\r\\n print('No such attribute:', attr)\\r\\n except Exception:\\r\\n print('No such file:', filename)\\r\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e8d3f117f01b83ff313ccaf8b52a69\",\n \"score\": \"0.5409619\",\n \"text\": \"def get_image_id(image_id: int, x: Union[float, int], y: Union[float, int], width: Union[float,\\n int], height: Union[float, int]) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1d4524ec4ed5d3772b58fdcda50b43\",\n \"score\": \"0.5405024\",\n \"text\": \"def getMapId(self, vis_params):\\n return self.mosaic().getMapId(vis_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ecba7aed8ed91092ba74a1917634ba3\",\n \"score\": \"0.5404897\",\n \"text\": \"def hexdigest(self):\\n return generate_uid_from_semantic_segmentation_2d_annotation(self._segmentation_image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d179c867e0b94a7dce35c738dd9d1fd6\",\n \"score\": \"0.5397746\",\n \"text\": \"def _get_col_id(self):\\n c = getattr(self.layer, 'classes_', 1)\\n p = len(self.layer.cases) * self.layer.indexer.n_partitions\\n return _get_col_idx(self.e, p, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3661e7825a3dc48ce96c18861d5af6\",\n \"score\": \"0.539485\",\n \"text\": \"def identifier(self):\\n if \\\"identifier\\\" in self.h5.attrs:\\n return self.h5.attrs[\\\"identifier\\\"]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1184f5557003d75421c433fb3851ac4a\",\n \"score\": \"0.5392987\",\n \"text\": \"def pix_idx(x, y, level):\\n\\ti, j = xy_to_ij(x, y, level)\\n\\twh = width(level)\\n\\ti = i + wh/2; j = j + wh/2;\\n\\treturn j * wh + i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9603f0a377b12bdd797a622eafb510c8\",\n \"score\": \"0.5377663\",\n \"text\": \"def find_image_row_col_label_coordinate(raster, lat, lon, image_size):\\n row, col = raster.index(lat, lon, precision=23)\\n return round(row / image_size), round(col / image_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fca25d9c5053ecdaa76d11719e510df\",\n \"score\": \"0.537179\",\n \"text\": \"def image_id():\\n return GLOBAL.metadata.get('ami-id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5253b4494ff77dc082a13a304b339999\",\n \"score\": \"0.536078\",\n \"text\": \"def col_to_id(column, z_cell):\\n return z_cell + column * ncells_tall\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5253b4494ff77dc082a13a304b339999\",\n \"score\": \"0.536078\",\n \"text\": \"def col_to_id(column, z_cell):\\n return z_cell + column * ncells_tall\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f232da10614702fc1edae998b7b8f03\",\n \"score\": \"0.5359232\",\n \"text\": \"def getPinIdentifier(self, line: Any) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4785d15a793770ab87bdf9ba97e28a9f\",\n \"score\": \"0.53589034\",\n \"text\": \"def GetIdentifier(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e12210f31c4867a89afb14680cbc5b10\",\n \"score\": \"0.53578925\",\n \"text\": \"def get_image_id(self, game_id):\\n return self.image_index_training[game_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17cb068ca96f79238621463369f5b8da\",\n \"score\": \"0.5356314\",\n \"text\": \"def find_vertex_name(self, id):\\n vertex_type = \\\"\\\"\\n if not id.isdigit():\\n # print(id)\\n id = self.id_map.get(id, \\\"0\\\")\\n # print(id)\\n \\n vertex_type = self.id_map.get(id, \\\"apriltag\\\")\\n # print(vertex_type)\\n else:\\n vertex_type = self.id_map.get(id, \\\"apriltag\\\")\\n\\n if (vertex_type == \\\"Vehicle\\\"):\\n id = \\\"duckiebot_%s\\\" % id\\n else:\\n id = \\\"apriltag_%s\\\" % id\\n\\n return id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd3f6a6b3b7b7aab215154e505d975f\",\n \"score\": \"0.5355522\",\n \"text\": \"def check_annotated(tree):\\n if (isinstance(tree, ast.Subscript) and type(tree.slice) is ast.Index and\\n type(tree.value) is ast.Name): # noqa: E129\\n return tree.value.id, tree.slice.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04227df40502a8097f34f0613f41c13f\",\n \"score\": \"0.5347402\",\n \"text\": \"def id(self):\\n\\n\\t\\treturn struct.unpack_from('<I', self.instrument.data, self.offset + 8)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0313649632af006ec7fb44a81767bb6\",\n \"score\": \"0.5342543\",\n \"text\": \"def get_annotation(self, key):\\n return self._annotations.get(key, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a68d51b2547cebd7369d090f4099d53\",\n \"score\": \"0.5332197\",\n \"text\": \"def get_annotations(self, img_id):\\n # Some images don't have any annotations. Return empty list instead.\\n return self._img_id2annotations.get(img_id, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a68d51b2547cebd7369d090f4099d53\",\n \"score\": \"0.5332197\",\n \"text\": \"def get_annotations(self, img_id):\\n # Some images don't have any annotations. Return empty list instead.\\n return self._img_id2annotations.get(img_id, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37868bd42cb920b8741a16c6636ca8b3\",\n \"score\": \"0.53313947\",\n \"text\": \"def get_contact_sprite_id1() -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb6e44a5b62fcf289642e63a96619a9\",\n \"score\": \"0.5323577\",\n \"text\": \"def get_max_annotation_id(self):\\n\\n # Incrementer row keys start with an \\\"i\\\"\\n row_key = serialize_key(\\\"iannotations\\\")\\n row = self.table.read_row(row_key)\\n\\n # Read incrementer value\\n annotation_id = int.from_bytes(row.cells[self.incrementer_family_id][serialize_key('counter')][0].value, byteorder=\\\"big\\\")\\n\\n return np.uint64(annotation_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d873639e7458c8ba638c202fa6ada534\",\n \"score\": \"0.53218627\",\n \"text\": \"def get_id(index: int):\\n return 'subarray_{:02d}'.format(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e432af0a05365d1f20d6996e74b316a\",\n \"score\": \"0.5319183\",\n \"text\": \"def _get_annotation(self):\\n \\n return self._annotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb1a1d487cf860b39a87b6acb2f909a7\",\n \"score\": \"0.53161883\",\n \"text\": \"def printable_map(self) -> np.ndarray:\\n map_identifier = np.zeros(shape=(self.settings[\\\"map_size\\\"], self.settings[\\\"map_size\\\"]), dtype=np.int)\\n\\n for entity in self.animals():\\n map_identifier[entity.position] += entity.identifier\\n\\n for entity in self.foods():\\n map_identifier[entity.position] += entity.identifier\\n\\n return map_identifier\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcaaa24525efac9aa985b18e00710b9e\",\n \"score\": \"0.5307999\",\n \"text\": \"def get_sprite_image_id(sprite_id: int) -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9395e989b51063f57a3c1ccf13d15fe9\",\n \"score\": \"0.5300224\",\n \"text\": \"def id_to_label(self, idx):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a6c1dd87062fcb15b870ba6e5db8d2\",\n \"score\": \"0.52978706\",\n \"text\": \"def uidx(self, grid_tag='default'):\\r\\n return (self.princeidx + 1) * self.grid_dims[grid_tag]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"834de234113bb6c14399c0113fa124ab\",\n \"score\": \"0.52912325\",\n \"text\": \"def id(self):\\n return self.metadata[4]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef913645b6c45286e4aedabea5ce119\",\n \"score\": \"0.5288406\",\n \"text\": \"def xlabel(self):\\n return 'x' if self.dimensions[0] == 'i' else 'i'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41706de95a8e4ea6ab8e830a928e97d\",\n \"score\": \"0.5286877\",\n \"text\": \"def get_identifier(self):\\n return getattr(self, self.IDENTIFIER_FIELD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f42a198cf47df09d317f0bdddf85b6\",\n \"score\": \"0.5281084\",\n \"text\": \"def at(self, num):\\n for i,key in enumerate(self.labelMap):\\n if(i==num):\\n return key\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5bc28e522bab14f713f678c3c462768\",\n \"score\": \"0.5280401\",\n \"text\": \"def get_ann_info(self, idx: int):\\n dataset_item = self.otx_dataset[idx]\\n labels = self.labels\\n return get_annotation_mmdet_format(dataset_item, labels, self.domain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81b9de441f2c9c0b902c10efc7f223c9\",\n \"score\": \"0.5280239\",\n \"text\": \"def image_metadata(img):\\n src = rasterio.open(img)\\n arr = src.read(1)\\n name = src.name.split('.')[0]\\n y = int(name.split('-')[0])\\n x = int(name.split('-')[1])\\n z = int(name.split('-')[2])\\n width = int(arr.shape[0])\\n height =int(arr.shape[1])\\n\\n return name, x, y, z, width, height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62459c19bdb62667a15437a3408258d2\",\n \"score\": \"0.52722496\",\n \"text\": \"def _posToId(self, row, col):\\n return self.posToId[row, col]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4432bb9d8a7d06a3ff82d7a1fca3dd5\",\n \"score\": \"0.5270191\",\n \"text\": \"def get_id(self):\\n return ','.join(map(str, self.pos)) + ':' + '.'.join(map(str, self.vel))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec1adc2126913ed02f01b0ef7ab36b1\",\n \"score\": \"0.5268546\",\n \"text\": \"def getItemIdentifier(item, position):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a890f01e965139f32a886c4ae1bb0574\",\n \"score\": \"0.52662337\",\n \"text\": \"def image_identifier(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"image_identifier\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f75a524c7dad75c2039791d9b5d9d58\",\n \"score\": \"0.5264078\",\n \"text\": \"def __getitem__(self, index: int) -> Tuple[Any, Any]:\\n img = Image.open(self.images[index]).convert(\\\"RGB\\\")\\n if self.transform:\\n img = self.transform(img)\\n target = self._parse_voc_xml(ET.parse(self.targets[index]).getroot())\\n return img, target[\\\"annotation\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7a5a722cca6164224c2835d14885a5\",\n \"score\": \"0.5262701\",\n \"text\": \"def object_id(self):\\r\\n return self._feature.attributes.get(self._schema.object_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f14da1ddbfc9737944d561d39f1f3ae3\",\n \"score\": \"0.5256659\",\n \"text\": \"def __call__(self, img):\\n return self.annotations(img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2c1c942896d0daf8f4e79e10142e46\",\n \"score\": \"0.5256637\",\n \"text\": \"def get_labels(ab_img):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4223a48c5c52cadf30c10bc5c455b0\",\n \"score\": \"0.5255165\",\n \"text\": \"def get_true_tip_index(row):\\n for i, pixel in enumerate(row):\\n b, g, r = pixel\\n if r > 100:\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5df8a4e55dd2b2f5ec2bbbb72ceff47a\",\n \"score\": \"0.52473456\",\n \"text\": \"def get_XYZ_index(self,g_nd): # this will depend upon a global geometry structure like a brick\\n z = g_nd/(self.Nx*self.Ny)\\n y = (g_nd - z*self.Nx*self.Ny)/self.Nx\\n x = (g_nd - z*self.Nx*self.Ny - y*self.Nx)\\n return (x,y,z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c249f76969d703f5d4597be5d618565\",\n \"score\": \"0.52439\",\n \"text\": \"def GetIdent(self):\\n return _itkGeometricalQuadEdgePython.itkGeometricalQuadEdgeULULBBF_GetIdent(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90f4792d3ec16d4cb2c05d31236d92d\",\n \"score\": \"0.5242144\",\n \"text\": \"def build_image_id(line, row_name_to_id_mapper):\\n season = line[row_name_to_id_mapper['Season']]\\n season = build_season_id(season)\\n site = line[row_name_to_id_mapper[' Site']]\\n roll = fix_roll_id(line[row_name_to_id_mapper[' Roll']])\\n imgname = line[row_name_to_id_mapper[' PathFilename']]\\n image_name = os.path.basename(imgname)\\n return '#'.join([season, site, roll, image_name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"750dd9e3c0cd0a05d2906985cc8fdb64\",\n \"score\": \"0.5241895\",\n \"text\": \"def image_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"image_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba10886170fb41969aeef5a4ce937965\",\n \"score\": \"0.52332395\",\n \"text\": \"def get_id(self):\\n return self.name #sidlir.type_id(self.data)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1588,"cells":{"query_id":{"kind":"string","value":"610f4e95b64e6e0bd1051cb22ac0bad4"},"query":{"kind":"string","value":"Updates qtable entries using the temporal difference formula. This allows the AI to learn from its experiences"},"positive_passages":{"kind":"list like","value":[{"docid":"9946043d3d89ddc6196c7da07911d7ff","score":"0.0","text":"def update(qtable, state, next_state, next_turn):\n\n # Calculate unique index for the given state\n state_idx = state_idx_calc(state)\n size = state.shape[0]\n\n # Given the two input states, calculates what action was taken\n # by taking the difference between the two and finding where\n # the value is non-zero.\n action = state - next_state\n move_location = np.nonzero(action)\n\n # Given what the action is, finds the x and y coordinates on the\n # board of where the action took place.\n move_x = move_location[0][0]\n move_y = move_location[1][0]\n\n # action[move_x, move_y] is where the action took place, and thus\n # if the value at this location is 2, then that means an `o` must\n # have been played. That's because the underlying board has\n # `blank` == 2\n # `x` == 1\n # `o` == 0\n # Thus, if the difference is 2, then it must be that it went from\n # a `blank` == 2 to an `o` == 0.\n #\n # Now, we know that, for a given player, the available actions they\n # can make is changing any blank square to a square that has their\n # symbol in it; namely, an `o` or an `x`. Thus, they have a maximum\n # of `size * size` actions they can make. This calculates a unique\n # index for each action. Specifically, if a 3x3 board is used then\n # it calculates a number from 0-8 for `o` moves, and number from\n # 9-17 for `x` moves. Thus, the first half of the columns of the\n # qtable has `o` moves, while the second half has `x` moves.\n if action[move_x, move_y] == 2:\n action_idx = size * move_x + move_y\n else:\n action_idx = size * move_x + move_y + size*size\n\n # retreive qtable value for taking this action in the given\n # board configuration state.\n qtable_value = qtable[state_idx, action_idx]\n\n # Player `x` actions are stored in the 2nd half of the columns\n # of the qtable. Thus, if it's player `x` turn next, then send\n # in only these columns of the qtable, and calculate the best\n # action that can be taken among these columns. If it's player\n # `o` turn next, then send in the first half.\n #\n # Double divide symbol used to force qtable_half to be integer type.\n qtable_half = qtable.shape[1] // 2\n if next_turn == 1:\n next_reward = best_next_action(qtable[:, qtable_half:], next_state)\n else:\n next_reward = best_next_action(qtable[:, :qtable_half], next_state)\n\n # Computes temporal difference formula.\n # We calculate reward of next_state and next_turn, as next_state is the\n # configuration of the board after having made the move we decided to make.\n qtable_update = qtable_value + ETA * (reward(next_state, next_turn) + GAMMA * next_reward - qtable_value)\n\n # Updates qtable with the temporal difference.\n qtable[state_idx, action_idx] = qtable_update\n return","title":""}],"string":"[\n {\n \"docid\": \"9946043d3d89ddc6196c7da07911d7ff\",\n \"score\": \"0.0\",\n \"text\": \"def update(qtable, state, next_state, next_turn):\\n\\n # Calculate unique index for the given state\\n state_idx = state_idx_calc(state)\\n size = state.shape[0]\\n\\n # Given the two input states, calculates what action was taken\\n # by taking the difference between the two and finding where\\n # the value is non-zero.\\n action = state - next_state\\n move_location = np.nonzero(action)\\n\\n # Given what the action is, finds the x and y coordinates on the\\n # board of where the action took place.\\n move_x = move_location[0][0]\\n move_y = move_location[1][0]\\n\\n # action[move_x, move_y] is where the action took place, and thus\\n # if the value at this location is 2, then that means an `o` must\\n # have been played. That's because the underlying board has\\n # `blank` == 2\\n # `x` == 1\\n # `o` == 0\\n # Thus, if the difference is 2, then it must be that it went from\\n # a `blank` == 2 to an `o` == 0.\\n #\\n # Now, we know that, for a given player, the available actions they\\n # can make is changing any blank square to a square that has their\\n # symbol in it; namely, an `o` or an `x`. Thus, they have a maximum\\n # of `size * size` actions they can make. This calculates a unique\\n # index for each action. Specifically, if a 3x3 board is used then\\n # it calculates a number from 0-8 for `o` moves, and number from\\n # 9-17 for `x` moves. Thus, the first half of the columns of the\\n # qtable has `o` moves, while the second half has `x` moves.\\n if action[move_x, move_y] == 2:\\n action_idx = size * move_x + move_y\\n else:\\n action_idx = size * move_x + move_y + size*size\\n\\n # retreive qtable value for taking this action in the given\\n # board configuration state.\\n qtable_value = qtable[state_idx, action_idx]\\n\\n # Player `x` actions are stored in the 2nd half of the columns\\n # of the qtable. Thus, if it's player `x` turn next, then send\\n # in only these columns of the qtable, and calculate the best\\n # action that can be taken among these columns. If it's player\\n # `o` turn next, then send in the first half.\\n #\\n # Double divide symbol used to force qtable_half to be integer type.\\n qtable_half = qtable.shape[1] // 2\\n if next_turn == 1:\\n next_reward = best_next_action(qtable[:, qtable_half:], next_state)\\n else:\\n next_reward = best_next_action(qtable[:, :qtable_half], next_state)\\n\\n # Computes temporal difference formula.\\n # We calculate reward of next_state and next_turn, as next_state is the\\n # configuration of the board after having made the move we decided to make.\\n qtable_update = qtable_value + ETA * (reward(next_state, next_turn) + GAMMA * next_reward - qtable_value)\\n\\n # Updates qtable with the temporal difference.\\n qtable[state_idx, action_idx] = qtable_update\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"62aaf30d5e9e5dc5a7234e336139c895","score":"0.6334579","text":"def updateQTableFromTerminatingState( self, reward ):\n \n # retrieve the old action value from the Q-table (indexed by the previous state and the previous action)\n old_q = self.q_table[self.prev_s][self.prev_a]\n \n # TODO: what should the new action value be?\n new_q = old_q\n \n # assign the new action value to the Q-table\n self.q_table[self.prev_s][self.prev_a] = new_q","title":""},{"docid":"6f4bc77c86426f1b1d62c426855a27e6","score":"0.62391174","text":"def updateQTable( self, reward, current_state ):\n \n # retrieve the old action value from the Q-table (indexed by the previous state and the previous action)\n old_q = self.q_table[self.prev_s][self.prev_a]\n \n # TODO: what should the new action value be?\n new_q = old_q\n \n # assign the new action value to the Q-table\n self.q_table[self.prev_s][self.prev_a] = new_q","title":""},{"docid":"166d8782e8b0e29d49316946dcf04922","score":"0.62018937","text":"def _compute_deltas(self):\n db.session.execute(text(\n \"\"\"\n update okresy_metriky t\n set rezervace_celkem_zmena_den = t0.rezervace_celkem - t1.rezervace_celkem,\n rezervace_cekajici_zmena_den = t0.rezervace_cekajici - t1.rezervace_cekajici,\n rezervace_cekajici_1_zmena_den = t0.rezervace_cekajici_1 - t1.rezervace_cekajici_1,\n rezervace_cekajici_2_zmena_den = t0.rezervace_cekajici_2 - t1.rezervace_cekajici_2,\n rezervace_kapacita_zmena_den = t0.rezervace_kapacita - t1.rezervace_kapacita,\n rezervace_kapacita_1_zmena_den = t0.rezervace_kapacita_1 - t1.rezervace_kapacita_1,\n rezervace_kapacita_2_zmena_den = t0.rezervace_kapacita_2 - t1.rezervace_kapacita_2,\n registrace_celkem_zmena_den = t0.registrace_celkem - t1.registrace_celkem,\n registrace_fronta_zmena_den = t0.registrace_fronta - t1.registrace_fronta,\n registrace_rezervace_prumer_zmena_den = t0.registrace_rezervace_prumer - t1.registrace_rezervace_prumer, \n registrace_tydenni_uspesnost_zmena_den = t0.registrace_tydenni_uspesnost - t1.registrace_tydenni_uspesnost,\n registrace_14denni_uspesnost_zmena_den = t0.registrace_14denni_uspesnost - t1.registrace_14denni_uspesnost,\n registrace_30denni_uspesnost_zmena_den = t0.registrace_30denni_uspesnost - t1.registrace_30denni_uspesnost,\n registrace_prumer_cekani_zmena_den = t0.registrace_prumer_cekani - t1.registrace_prumer_cekani,\n registrace_fronta_prumer_cekani_zmena_den = t0.registrace_fronta_prumer_cekani - t1.registrace_fronta_prumer_cekani,\n vakciny_prijate_pocet_zmena_den = t0.vakciny_prijate_pocet - t1.vakciny_prijate_pocet,\n vakciny_ockovane_pocet_zmena_den = t0.vakciny_ockovane_pocet - t1.vakciny_ockovane_pocet,\n vakciny_znicene_pocet_zmena_den = t0.vakciny_znicene_pocet - t1.vakciny_znicene_pocet\n from okresy_metriky t0 \n join okresy_metriky t1\n on t0.okres_id = t1.okres_id\n where t.okres_id = t0.okres_id and t.datum = :datum and t0.datum = :datum and t1.datum = :datum_1 \n \"\"\"\n ), {'datum': self._date, 'datum_1': self._date - timedelta(1)})\n\n db.session.execute(text(\n \"\"\"\n update okresy_metriky t\n set rezervace_celkem_zmena_tyden = t0.rezervace_celkem - t7.rezervace_celkem,\n rezervace_cekajici_zmena_tyden = t0.rezervace_cekajici - t7.rezervace_cekajici,\n rezervace_cekajici_1_zmena_tyden = t0.rezervace_cekajici_1 - t7.rezervace_cekajici_1,\n rezervace_cekajici_2_zmena_tyden = t0.rezervace_cekajici_2 - t7.rezervace_cekajici_2,\n rezervace_kapacita_zmena_tyden = t0.rezervace_kapacita - t7.rezervace_kapacita,\n rezervace_kapacita_1_zmena_tyden = t0.rezervace_kapacita_1 - t7.rezervace_kapacita_1,\n rezervace_kapacita_2_zmena_tyden = t0.rezervace_kapacita_2 - t7.rezervace_kapacita_2,\n registrace_celkem_zmena_tyden = t0.registrace_celkem - t7.registrace_celkem,\n registrace_fronta_zmena_tyden = t0.registrace_fronta - t7.registrace_fronta,\n registrace_rezervace_prumer_zmena_tyden = t0.registrace_rezervace_prumer - t7.registrace_rezervace_prumer, \n registrace_tydenni_uspesnost_zmena_tyden = t0.registrace_tydenni_uspesnost - t7.registrace_tydenni_uspesnost,\n registrace_14denni_uspesnost_zmena_tyden = t0.registrace_14denni_uspesnost - t7.registrace_14denni_uspesnost,\n registrace_30denni_uspesnost_zmena_tyden = t0.registrace_30denni_uspesnost - t7.registrace_30denni_uspesnost,\n registrace_prumer_cekani_zmena_tyden = t0.registrace_prumer_cekani - t7.registrace_prumer_cekani,\n registrace_fronta_prumer_cekani_zmena_tyden = t0.registrace_fronta_prumer_cekani - t7.registrace_fronta_prumer_cekani,\n vakciny_prijate_pocet_zmena_tyden = t0.vakciny_prijate_pocet - t7.vakciny_prijate_pocet,\n vakciny_ockovane_pocet_zmena_tyden = t0.vakciny_ockovane_pocet - t7.vakciny_ockovane_pocet,\n vakciny_znicene_pocet_zmena_tyden = t0.vakciny_znicene_pocet - t7.vakciny_znicene_pocet\n from okresy_metriky t0 \n join okresy_metriky t7\n on t0.okres_id = t7.okres_id\n where t.okres_id = t0.okres_id and t.datum = :datum and t0.datum = :datum and t7.datum = :datum_7 \n \"\"\"\n ), {'datum': self._date, 'datum_7': self._date - timedelta(7)})\n\n app.logger.info('Computing okres metrics - deltas finished.')","title":""},{"docid":"1b79c3c014aa37635c68870e4c5b10e6","score":"0.6111744","text":"def update_q_table(self, state, action, reward):\n\n q_key = self.q_key(state, action)\n inputs = self.env.sense(self)\n self.next_waypoint = self.planner.next_waypoint()\n new_state = self.set_current_state(inputs)\n\n #update q_value in q_table according to learning formula\n x = self.q_value(state, action)\n V = reward + (self.gamma * self.max_q_value(new_state))\n new_q_value = x + (self.alpha * (V - x))\n self.q_table[q_key] = new_q_value","title":""},{"docid":"f8699e853b356f0c81cc74866cb03acf","score":"0.5995451","text":"def update_qTable(q_table, state, action, reward, next_state_value, gamma_discount = 0.9, alpha = 0.5):\n update_q_value = q_table[action, state] + alpha * (reward + (gamma_discount * next_state_value) - q_table[action, state])\n q_table[action, state] = update_q_value\n\n return q_table","title":""},{"docid":"21ebd2f4754e0478c5f9b9f691ec46c2","score":"0.59950894","text":"def __refreshMeasurementsTables__(self):\n key = self.logic.currentStentType\n # for key in self.logic.getStentKeys():\n measures = self.logic.currentMeasurements[key]\n model = self.measurementsTableModels[key]\n for row in range(len(measures)):\n for col in range(2):\n item = qt.QStandardItem()\n item.setData(measures[row][col], qt.Qt.DisplayRole)\n item.setEditable(False)\n model.setItem(row, col, item)\n\n model2 = self.anglesTableModels[key]\n item = qt.QStandardItem()\n item.setData(self.logic.currentAngles[key][0], qt.Qt.DisplayRole)\n item.setEditable(False)\n model2.setItem(0, 0, item)\n\n item = qt.QStandardItem()\n item.setData(self.logic.currentAngles[key][1], qt.Qt.DisplayRole)\n item.setEditable(False)\n model2.setItem(1, 0, item)","title":""},{"docid":"5e42cbe44b1751ede245519032135b8f","score":"0.59406656","text":"def update_Qtable(self, r, action, next_state):\n if self.learning:\n # TODO 8. When learning, update the q table according\n # to the given rules\n from_self = (1-self.alphas[self.state])*self.Qtable[self.state][action]\n from_update = self.alphas[self.state]*(r+self.gamma*(max(self.Qtable[next_state].values())))\n #from_self = (1-self.alpha)*self.Qtable[self.state][action]\n #from_update = self.alpha*(r+self.gamma*(max(self.Qtable[next_state].values())))\n self.Qtable[self.state][action] = from_self + from_update # 此处应该是根据公式来更新，没这么简单....","title":""},{"docid":"c2d68d9d70ac8b43d853441f2e1f977e","score":"0.5885301","text":"def update_q_table(self, state, action, reward, next_state):\n # Q(s, a) += \n # alpha * (reward + gamma * max_a' Q(s', a') - Q(s, a))\n q_value = self.gamma * np.amax(self.q_table[next_state])\n q_value += reward\n q_value -= self.q_table[state, action]\n q_value *= self.learning_rate\n q_value += self.q_table[state, action]\n self.q_table[state, action] = q_value","title":""},{"docid":"db45671af1a536fda11de6ca3cd88d2d","score":"0.5873049","text":"def _update_metrics():\n data_list = expr_data['data'][expr.fmtd_sub_expr_str]['metrics']\n start_time = t_now - (float(expr.period)) * int(expr.periods)\n while (len(data_list) != 0\n and data_list[0]['timestamp'] < start_time):\n data_list.popleft()","title":""},{"docid":"a6989a584386c5bafd7fb313b7e0dd63","score":"0.5822668","text":"def update_q(self, new_state: State, old_state: State, action: int, reward: float, end_of_race: bool) -> None:\n\n \n \n if(self.i == 99):\n self.q_table.save(\"in.txt\")\n\n alpha = 0.3\n gamma = 0.999\n newQ = (1-alpha)*self.q_table.get_q_value(old_state,action) + alpha * (reward + gamma * self.maxQ(new_state))\n self.q_table.set_q_value(old_state,action,newQ)","title":""},{"docid":"126a7c2cffa4604288e92cdaf064712c","score":"0.57783085","text":"def update_detrend_factor_table():\n#\n#--- clean up temp_dir so that we can put new fits files\n#\n mcf.mk_empty_dir(temp_dir)\n#\n#--- extract stat fits files\n#\n new_entry = get_new_entry()\n#\n#--- if files are extracted, compute amvp_avg values for the obsid\n#\n if len(new_entry) > 0:\n processed_list = update_amp_avg_list(new_entry)\n#\n#--- clean up \"keep_entry\" and amp_avg_list \n#\n update_holding_list(new_entry, processed_list)\n cleanup_amp_list()","title":""},{"docid":"617c3c2a2540b4e81cc8061a591b9ad2","score":"0.56813276","text":"def updateQ(self, action, next_state):\n\n # Find the max value in the Q row (this is the best next Q following the action)\n max_future_q = self.Q.maxValue(next_state) \n \n current_r = self.R.getValue(self.current_state, action, next_state)\n current_q = self.Q.getValue(self.current_state, action, next_state)\n\n # Sometimes we get nulls. We can't do anything with them\n if np.isnan(current_r):\n return\n\n # Q learning formula - update the Q table current state with a little of the reward from the future\n\n # Simple version\n #new_q = int(current_r + self.gamma * max_future_q)\n\n # Complex version\n new_q = ((1-self.alpha) * current_q + self.alpha * (current_r + self.gamma * max_future_q))\n\n # Update q table with the new value\n self.Q.setValue(self.current_state, action, next_state, new_q)","title":""},{"docid":"14842c83fdeb770ecc6e62eda7a611d5","score":"0.56682324","text":"def _adjustTime(self):\n for exam in range(len(self.exams)):\n stdTime = self._getStudy(self.exams[exam])\n self.studyTime[exam] = self.studyTime[exam] - stdTime","title":""},{"docid":"53ee899b7c22cf10e030e389f30d328f","score":"0.5633692","text":"def update_time_difference(query: str, **query_params) -> str:\r\n logger.info(\"Changing date_part subquery on query.\")\r\n\r\n try:\r\n # Check input variables\r\n assert \"time_difference\" in query_params.keys(), \"Time difference is not set on the yaml source\"\r\n assert type(query) == str, \"The variable query is not a string\"\r\n # Dict with the correspondence between date_part and its respective interval\r\n valid_interval_dif = {\"MONTH\": \"month\", \"WEEK\": \"days\", \"DAY\": \"day\", \"DOY\": \"day\"}\r\n # Possible variable units in interval function\r\n possible_intervals = ['year', 'month', 'day', 'hour', 'minute', 'second', 'years', 'months', 'days',\r\n 'hours', 'minutes', 'seconds']\r\n\r\n # Check input variables\r\n assert \"interval_dif\" in query_params['time_difference'].keys(), \\\r\n \"The interval_dif is not set on the time_difference on the yaml source\"\r\n assert \"interval_t\" in query_params['time_difference'].keys(), \\\r\n \"The interval_t is not set on the time_difference on the yaml source\"\r\n\r\n # Get interval difference\r\n interval_dif = query_params['time_difference']['interval_dif']\r\n assert type(interval_dif) == str, \"The variable interval_dif is not a string\"\r\n\r\n # Get time constant that will be multiplied by the interval difference\r\n interval_t = query_params['time_difference']['interval_t']\r\n assert type(interval_t) == int, \"The variable interval_dif is not an integer\"\r\n\r\n # Check if the interval difference corresponds to the date_part\r\n if interval_dif == 'date_part':\r\n assert \"time_groups\" in query_params.keys(), \"Time groups is not set on the yaml source\"\r\n assert type(query) == str, \"The variable query is not a string\"\r\n valid_date_parts = [\"MONTH\", \"WEEK\", \"DAY\", 'DOY']\r\n assert \"date_part\" in query_params['time_groups'].keys(), \\\r\n \"Date Part is not set on the time_groups on the yaml source\"\r\n date_part = query_params['time_groups']['date_part']\r\n\r\n interval_dif = valid_interval_dif[date_part]\r\n assert date_part in valid_date_parts, \"The given date_part is not valid\"\r\n\r\n # In case of 'WEEK' than the number of days should be multiplied by 7 which corresponds to 1 week\r\n if date_part == 'WEEK':\r\n interval_t = interval_t * 7\r\n assert interval_dif in possible_intervals, \"The given interval_dif is not valid\"\r\n\r\n # Replace the interval difference\r\n tag = \"[{}]\".format(\"interval_dif\")\r\n query = query.replace(tag, \"'{} {}'\".format(interval_t, interval_dif))\r\n\r\n return query\r\n except AssertionError as e:\r\n logger.error(e)","title":""},{"docid":"35be5bd7f791f49d081f72dfb3e45e04","score":"0.56207436","text":"def update(self, exp):\n\t\ts=exp[0]\n\t\ta=exp[1]\n\t\ts_=exp[2]\n\t\tr=exp[3]\n\t\tdone=exp[4]\n\n\t\tself.t=self.t+1\n\n\t\t# q update\n\t\tn_keys=[(s_,act) for act in self.actions]\n\t\tq_n=[self.get_vals(k[0],k[1],self.Q) for k in n_keys]\n\t\t\n\t\tq_target=(r+self.gamma*max(q_n)) if not done else r\n\t\t# print('prev q '+str((s,a))+' : '+str(self.get_vals(s,a,self.Q)))\n\t\tself.Q[s,a]=self.get_vals(s,a,self.Q)+self.alpha(self.t)*(q_target-self.get_vals(s,a,self.Q))\n\t\t# print('new q '+str((s,a))+' : '+str(self.get_vals(s,a,self.Q)))\n\t\tkeys=[(s,act) for act in self.actions]\n\t\ttot_q=sum([math.exp(self.get_vals(k[0],k[1],self.Q)) for k in keys])\n\t\tfor k in keys:\n\t\t\tself.pi[k]=math.exp(self.get_vals(k[0],k[1],self.Q))/tot_q","title":""},{"docid":"39ae845767c2c70b1cf49492ffa3b326","score":"0.55660784","text":"def update(self, q):\r\n self.repeat += 1\r\n\r\n newEF = self.EF+(0.1-(5-q)*(0.08+(5-q)*0.02))\r\n if newEF < 1.3:\r\n self.EF = 1.3\r\n elif newEF > 2.5:\r\n self.EF = 2.5\r\n else:\r\n self.EF = newEF\r\n\r\n self.interval = self.get_interval(self.repeat, self.interval, self.EF)\r\n self.nextDate = self.startDate + timedelta(days=self.interval)","title":""},{"docid":"9dde9bee8f848f23572943603697d185","score":"0.5544781","text":"def _update_state():\n data_sub = expr_data['data'][expr.fmtd_sub_expr_str]\n data_list = data_sub['metrics']\n period = float(expr.period)\n periods = int(expr.periods)\n right = t_now\n left = right - period\n temp_data = []\n value_in_periods = []\n i = len(data_list) - 1\n while i >= 0:\n if data_list[i]['timestamp'] >= left:\n temp_data.append(float(data_list[i]['value']))\n else:\n value = calculator.calc_value(\n expr.normalized_func, temp_data)\n value_in_periods.append(value)\n right = left\n left = right - period\n temp_data = []\n i += 1\n i -= 1\n value = calculator.calc_value(\n expr.normalized_func, temp_data)\n value_in_periods.append(value)\n for i in range(len(value_in_periods), periods, 1):\n value_in_periods.append(\n calculator.calc_value(expr.normalized_func, []))\n expr_data['data'][expr.fmtd_sub_expr_str]['values'] = (\n value_in_periods)\n expr_data['data'][expr.fmtd_sub_expr_str]['state'] = (\n calculator.compare_thresh(\n value_in_periods,\n expr.normalized_operator,\n float(expr.threshold)))","title":""},{"docid":"160f1689783c16ce1c9a732e0909e420","score":"0.5463045","text":"def update(self, t):\n raise NotImplementedError","title":""},{"docid":"746d7b7d77bd2722c164d2cebab6e578","score":"0.5441705","text":"def test_update_timestep(self):\n\n mps = self.model.update_timestep(self.modelparams, self.expparams)\n\n assert(mps.shape == (\n self.n_models,\n self.model.n_modelparams,\n self.n_expparams\n ))\n mps = mps.transpose((2,0,1)).reshape(self.n_models * self.n_expparams, -1)\n \n assert(np.all(self.model.are_models_valid(mps)))","title":""},{"docid":"90319f983b244a5e82588b53b7d862da","score":"0.54386264","text":"def update_T(self, sq):\n for state, r_action, h_action, state_star in sq:\n self.T[self.action_idx[r_action]][self.state_idx[state]][self.state_idx[state_star]] += 1.0\n self.T[self.action_idx[r_action]][self.state_idx[state]] = self.T[self.action_idx[r_action]][self.state_idx[state]] / sum(self.T[self.action_idx[r_action]][self.state_idx[state]])\n return","title":""},{"docid":"02c87b6f0becf91d53073f6626fc1e50","score":"0.5431382","text":"def apply_proper_motion(star_records: pd.DataFrame, new_time: Union[Real, datetime.datetime],\n copy: bool = True) -> pd.DataFrame:\n\n if copy:\n out = star_records.copy()\n\n else:\n out = star_records\n\n # convert the ra and dec values into radians\n ra0 = out['ra'].values * DEG2RAD # type: np.ndarray\n dec0 = out['dec'].values * DEG2RAD # type: np.ndarray\n\n # assume linear motion based on Hipparcos Vol 1\n # compute the unit vector for each star\n r_unit = radec_to_unit(ra0, dec0).reshape(3, -1)\n\n # compute the unit vector in the direction of increasing right ascension\n p_unit = np.vstack([-np.sin(ra0), np.cos(ra0), np.zeros(ra0.shape)])\n\n # compute the unit vector in the direction of increasing declination\n q_unit = np.vstack([-np.sin(dec0) * np.cos(ra0), -np.sin(dec0) * np.sin(ra0), np.cos(dec0)])\n\n # compute the change in distance per year (future expansion)\n zeta0 = 0\n\n start_time = out['epoch'].values\n\n # compute the time delta\n if isinstance(new_time, datetime.datetime):\n\n new_time = timedelta_to_si_years(new_time - datetime.datetime(1, 1, 1))\n\n timedelta = new_time - start_time\n\n # compute the new direction vector\n r_unit_new = r_unit * (1 + zeta0 * timedelta) + (p_unit * out['ra_proper_motion'].values * DEG2RAD +\n q_unit * out['dec_proper_motion'].values * DEG2RAD) * timedelta\n r_unit_new /= np.linalg.norm(r_unit_new, axis=0, keepdims=True)\n\n # compute the new ra/dec and store it\n ra, dec = unit_to_radec(r_unit_new)\n\n out['ra'] = ra * RAD2DEG\n out['dec'] = dec * RAD2DEG\n\n # compute the new uncertainties for ra and dec\n out['ra_sigma'] = np.sqrt(out['ra_sigma'] ** 2 + timedelta ** 2 * out['ra_pm_sigma'] ** 2)\n out['dec_sigma'] = np.sqrt(out['dec_sigma'] ** 2 + timedelta ** 2 * out['dec_pm_sigma'] ** 2)\n\n # update the epochs\n out[\"epoch\"] = new_time\n\n return out","title":""},{"docid":"8c2599bedc51975e9b7ff104b997ad89","score":"0.54156566","text":"def update(self, delta_time):\n pass","title":""},{"docid":"8c2599bedc51975e9b7ff104b997ad89","score":"0.54156566","text":"def update(self, delta_time):\n pass","title":""},{"docid":"3972721a1a91ceee7b1052ad72454d68","score":"0.5413627","text":"def update_Q_sim(\n choice, reward,\n Qs, _,\n alpha, nalpha, calpha, cnalpha,\n n_subj, verbose):\n\n # if n_trials_back == 0:\n index = np.arange(n_subj), choice\n cindex = np.arange(n_subj), 1 - choice\n\n # Get reward prediction errors (RPEs)\n RPE = (1 - Qs[index]) * reward # RPEs for positive outcomes (reward == 1)\n nRPE = (0 - Qs[index]) * (1 - reward) # RPEs for negative outcomes (reward == 0)\n\n # Get counterfactual prediction errors (cRPEs)\n cRPE = (0 - Qs[cindex]) * reward # actual reward was 1; I think I would have gotten 0 for the other action\n cnRPE = (1 - Qs[cindex]) * (1 - reward) # actual reward 0; would have gotten 1 for the other action\n\n # Update action taken\n Qs[index] += alpha * RPE + nalpha * nRPE # add RPE at all pos. outcomes, and nRPE at all neg. outcomes\n\n # Update counterfactual action\n Qs[cindex] += calpha * cRPE + cnalpha * cnRPE # add cRPE at all pos. outcomes, and cnRPE at all neg. outcomes\n\n if verbose:\n print('upd_Q - index: ', index)\n print('upd_Q - cindex: ', cindex)\n print('upd_Q - RPE: ', np.round(RPE, 2))\n print('upd_Q - nRPE: ', np.round(nRPE, 2))\n print('upd_Q - cRPE: ', np.round(cRPE, 2))\n print('upd_Q - cnRPE: ', np.round(cnRPE, 2))\n print('upd_Q - new Qs:\\n', np.round(Qs, 2))\n\n return Qs, _","title":""},{"docid":"34cc0dd6199255acca876ef93e35db32","score":"0.5406927","text":"def update_qval(q_values,action_count, reward, action):\n q_values[action] = q_values[action] + ((reward[action]-q_values[action])/action_count[action])\n return q_values","title":""},{"docid":"3b7b5d64cd4427140b2e798c56b48b0c","score":"0.53899384","text":"def end_of_timestep_update(self, i_episode, timestep, done):\n if timestep == 0:\n return\n S_old, A_old, R = self.episode_database[i_episode][timestep - 1]\n\n S_new, A_new, _ = self.episode_database[i_episode][timestep]\n\n S_old_disc = self.__discretize_observation(S_old)\n S_new_disc = self.__discretize_observation(S_new)\n\n self.Q[tuple(S_old_disc + [A_old])] += self.alpha * (\n R + self.discount_rate * self.Q[tuple(S_new_disc + [A_new])] - self.Q[tuple(S_old_disc + [A_old])])\n\n # EXTREMELY IMPORTANT\n if done and timestep < 199:\n # we have to set the reward for the last-terminal observation manually, since the above update can sometimes\n # adjusts it -> WHY????\n # The same continuous state sometimes leads to done and sometimes not -> Because we discretized our\n # sample space - so it is really important to just manually make sure that its 0\n\n # Maybe a more elegant solution is to select threshold points such that the bins correspond\n # To the actual values that lead to failure (x /notin (-2.4,2.4) and theta /notin ()\n\n # It works like an induction, these terminal states will backpropagate to the earlier states\n self.Q[tuple(S_new_disc + [A_new])] = 0","title":""},{"docid":"420961c503869e3fbc0fe2a84d6f8bff","score":"0.5385878","text":"def _update_expressions(self, time):\n self.params.time_update_parameters(time)\n self.bcs.time_update_bcs(time, kind='dirichlet')\n self.bcs.time_update_bcs(time, kind='von-neumann')","title":""},{"docid":"7e19e36bb873cd7a18f30e9bcd22e1d6","score":"0.5352648","text":"def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n\n # the qvalue is given by our getQValue function, \n qvalue = self.getQValue(state, action)\n # the learning rate, alpha\n a = self.alpha\n # discount \n discount = self.discount\n # next state's value\n nextQ = self.computeValueFromQValues(nextState)\n\n # use formula Qt+1 = ((reward + discount * future value) - qvalue) * a\n self.qv[(state,action)] = ((reward + discount * nextQ) - qvalue) * a + qvalue","title":""},{"docid":"0a3181dc81f3afd6e14d4194477471ec","score":"0.53433126","text":"def update(self, delta_time):\n pass","title":""},{"docid":"275a6b6b6270f9100b272ebf70a1035f","score":"0.53428423","text":"def temp_update(self) -> None:\r\n\r\n self.update_values()\r\n self.after(300000, self.temp_update)","title":""},{"docid":"248c10aa5d1a556126bfea19cc21a88c","score":"0.53053635","text":"def test(self):\n testData = SrtDataSet.fromLines(\n \"1041\\n01:37:25,200 --> 01:37:30,700\\nTraduction : Attila0375\\nhttp://attilaswebsite.free.fr\".split(\"\\n\"))\n print(testData.toString())\n testData.applyDelta(timedelta(seconds=-5))\n print(testData.toString())","title":""},{"docid":"da18c1345805564d4d80724d8bb6d853","score":"0.5297251","text":"def update(self, dt):\n pass","title":""},{"docid":"da18c1345805564d4d80724d8bb6d853","score":"0.5297251","text":"def update(self, dt):\n pass","title":""},{"docid":"d1d7d3d846aed8e436f1e86d5278ada6","score":"0.5295477","text":"def update_comparison_table(lr_report_1:str , lr_report_2:str):\n lr_report_data_1 = {} # values = [(names,scores)]\n lr_report_data_2 = {}\n db_entry_1 = []\n db_entry_2 = []\n\n transaction_names = []\n extra_names = []\n extra_lr_1_ninety = []\n extra_lr_2_ninety = []\n diff = []\n \n for report in db_data:\n if lr_report_1 in report.lr_name:\n lr_report_data_1[report.lr_name] = []\n db_entry_1 = report\n if lr_report_2 in report.lr_name:\n lr_report_data_2[report.lr_name] = []\n db_entry_2 = report\n \n for transaction in db_entry_1.lr_transactions:\n lr_report_data_1[db_entry_1.lr_name].append((transaction.transaction_name, \n transaction.nintieth_percentile)\n )\n for transaction in db_entry_2.lr_transactions:\n lr_report_data_2[db_entry_2.lr_name].append((transaction.transaction_name, \n transaction.nintieth_percentile)\n )\n \n \n for transaction_1 in lr_report_data_1.values(): # This finds the transactions common between versions.\n for transaction_2 in lr_report_data_2.values():\n if transaction_1[0] == transaction_2[0] and transaction_1[0] not in transaction_names:\n transaction_names.append(transaction_1[0])\n diff.append(transaction_1[1] - transaction_2[1])\n transaction_1[1] = 0\n transaction_2[1] = 0\n \n ## all transactions which are NOT zeroes will be appended to their respective 'extra' arrays ##\n for transaction in lr_report_data_1.values():\n if transaction[1] !=0:\n extra_names.append(transaction[0])\n extra_lr_1_ninety.append(transaction[1])\n extra_lr_2_ninety.append('<b>N/A</b>') \n\n for transaction in lr_report_data_2.values():\n if transaction[1] !=0:\n extra_names.append(transaction[0])\n extra_lr_2_ninety.append(transaction[1])\n extra_lr_1_ninety.append('<b>N/A</b>')\n\n\n ## apply color to diff corresponding to test that has higher runtime for runtimes greater than 1 second difference ##\n for i in range(len(diff)):\n if float(diff[i]) > 1.0:\n diff[i] = \"<span style='color:deeppink'><b>%s</b></span>\" %str(round(diff[i], 3))\n elif float(diff[i]) < -1.0:\n diff[i] = \"<span style='color:blue'><b>%s</b></span>\" %str(round(diff[i], 3))\n else:\n diff[i] = str(round(diff[i], 3))\n\n ## CREATE THE TABLE\n table = go.Figure(data =[go.Table(columnwidth = [150, 70, 70, 70], \n header = dict(values = (\"Transaction Name\", \"R1 :Ninetieth Percentile \" \n + lr_report_1, \"R2: Ninetieth Percentile \" \n + lr_report_2, \"Diff (R1 - R2)\"\n ), \n fill_color = ['lightgray', colors[0], colors[1], 'lightgray'], \n font_color = ['black', 'black', 'black', 'black'] , align ='left'\n ),\n cells = dict(values = [transaction_names + extra_names, \n lr_report_data_1.values()[0] + lr_report_data_1.values()[1], \n lr_report_data_2.values()[0] + lr_report_data_2.values()[1], diff\n ], \n align = 'left')\n )\n ])\n\n return table","title":""},{"docid":"5f8c47a812d5718dac22ccc9f43a4243","score":"0.5293238","text":"def do_reverse_q_updates(self, state_seq, reward_seq, action_seq):\n assert(len(state_seq) == len(action_seq) + 1)\n assert(len(state_seq) == len(reward_seq))\n\n for i in reversed(range(len(action_seq))):\n state = state_seq[i]\n action = action_seq[i]\n new_state = state_seq[i+1]\n reward = reward_seq[i]\n self.qtable[state, action] = self.compute_q_update(state, action, new_state, reward)","title":""},{"docid":"4d29d35e7e32efd6ec2a17d49f0a62a3","score":"0.52792513","text":"def update_quantities(self):\n\n t = self.get_time()\n self.dt = self.get_timestep()\n \n if sum(self.ind) > 0: \n \n quant = self.domain.quantities['elevation']\n quant.compute_gradients()\n\n S = num.maximum(num.abs(quant.x_gradient), num.abs(quant.y_gradient))\n \n self.u_star = num.zeros_like(self.depth)\n \n self.u_star[self.ind] = num.sqrt(g * S[self.ind] * self.depth[self.ind])\n \n self.edot = self.erosion()\n self.ddot = self.deposition()\n\n self.dzdt = (self.ddot - self.edot) / (1 - self.porosity)\n \n self.dChdt = (self.edot - self.ddot)\n \n self.update_concentration(self.dChdt)\n self.sediment_flux()\n \n self.update_bed(self.dzdt)","title":""},{"docid":"34fbcd0f10334b95cffab8b8f4eb8453","score":"0.52701086","text":"def test_updateColByInterpolation(self):\n table = self.InterpolationTableWithDuplicate\n obs = table.updateColByInterpolation('GrowthRate','Temp').delimitedSelf()\n exp =\\\n [\"#SampleID\\tTemp\\tGrowthRate\\tDescription\\n\",\\\n \"#Comment line, full of crazy comments\\n\",\\\n \"#Another comment line\\n\",\\\n \"S.1\\t1.0\\t3.0\\tSample.1.Description\\n\",\\\n \"S.2\\t2.0\\t2.0\\tSample.2.Description\\n\",\\\n \"S.3\\t3.0\\t0.0\\tSample.3.Description\\n\",\\\n \"S.4\\t0.0\\t3.0\\tSample.4.Description\\n\",\\\n \"S.5\\t1.0\\t3.0\\tSample.5.Description\\n\",\\\n \"S.5b\\t1.0\\t3.0\\tSample.5.Description\\n\",\\\n \"S.6\\t1.5\\t2.5\\tSample.6.Description\\n\",\\\n \"S.7\\t2.72\\t0.56\\tSample.7.Description\\n\",\\\n \"S.8\\t3.14\\t0.0\\tSample.7.Description\\n\"] \n self.assertEqualItems(obs,exp)","title":""},{"docid":"d15e2a1d9d602ffdefa14f68ed8ca6e8","score":"0.52600527","text":"def update(self, reward):\n # the following are for most recent played state-action pair:\n # calculate TD error\n if self.new_action is not None: # non-terminal new state state\n td_error = (\n reward\n + self.agent.action_value_function[\n self.new_state.dealer_first_card,\n self.new_state.hand_value,\n self.new_action.value,\n ]\n - self.agent.action_value_function[\n self.old_state.dealer_first_card,\n self.old_state.hand_value,\n self.old_action.value,\n ]\n )\n else: # terminal new state, new Q = 0\n td_error = (\n reward\n - self.agent.action_value_function[\n self.old_state.dealer_first_card,\n self.old_state.hand_value,\n self.old_action.value,\n ]\n )\n\n # bump E eligibility trace for old state by 1\n self.eligibility_trace[\n self.old_state.dealer_first_card,\n self.old_state.hand_value,\n self.old_action.value,\n ] += 1\n # bump N(s,a) state-action visit count\n self.agent.state_action_count[\n self.old_state.dealer_first_card,\n self.old_state.hand_value,\n self.old_action.value,\n ] += 1\n # bump N(s) state visit count\n self.agent.state_count[\n self.old_state.dealer_first_card,\n self.old_state.hand_value,\n ] += 1\n # the following are for all state-action pairs:\n # update Q\n self.agent.action_value_function += (\n self.agent.alpha(self.old_state, self.old_action)\n * td_error\n * self.eligibility_trace\n )\n # update E\n self.eligibility_trace = self.agent.lamda * self.eligibility_trace","title":""},{"docid":"a47cefc426717737d6aa72bc947a35f2","score":"0.5253603","text":"def set_t(self):\n if ('t' not in self.keys()) & ('datetime' in self.input):\n t0 = min(self['datetime'])\n t = ma.array([dt.total_seconds() for dt in self['datetime'] - t0])\n #t_median = ma.median(t)\n #self.t_ref = t0+timedelta(seconds = dt_median)\n #self.data['t'] = t - t_median\n self.data['t'] = t\n #self.data['t'] = self.input['t'] - np.median(self.input['t'])\n\n #self.ref['datetime'] = t0 + timedelta(seconds=t_median)","title":""},{"docid":"10ab4bc012a2ca702d4267d7a2ad575c","score":"0.5240644","text":"def update(self, state, action, nextState, reward):\n # return\n # We need calculate Q value by own\n next_q_value = 0.0\n next_move = (nextState, self.getPolicy(nextState))\n if next_move in self.q_values:\n next_q_value = self.q_values[next_move]\n\n expected_value_in_next_state = reward + self.discount * next_q_value\n value_in_current_state = self.q_values[(state, action)]\n\n # If values changed - update it\n if value_in_current_state != expected_value_in_next_state:\n # Update weights\n for (key, value) in self.weights.items():\n self.weights[key] = value + self.alpha * (expected_value_in_next_state - value_in_current_state) * self.featureVector[key]\n\n # Update QTable\n value_in_current_state = value_in_current_state + self.alpha * (expected_value_in_next_state - value_in_current_state)\n\n # Update epsilon in learning state\n eps = 0.05\n self.epsilon = max(0.0, -eps / 4500 * self.episodesSoFar + eps)","title":""},{"docid":"847d9f909127d26a05a40a190b6b67c6","score":"0.5223851","text":"def update_timeindex(self, event):\n bars = {}\n for sym in self.symbol_list:\n bars[sym] = self.bars.get_n_bars(sym, self.index_)\n \n self.index_ += 1\n #Update position\n dp = dict((k,v) for k,v in [(s,0) for s in self.symbol_list])\n dp['datetime'] = bars[self.symbol_list[0]]['date']\n \n for s in self.symbol_list:\n dp[s] = self.current_positions[s]\n \n self.all_positions.append(dp)\n \n #Update holdings\n dh = dict((k,v) for k,v in [(s,0) for s in self.symbol_list])\n dh['datetime'] = bars[self.symbol_list[0]]['date']\n dh['cash']= self.current_holdings['cash']\n dh['commission'] = self.current_holdings['commission']\n dh['total'] = self.current_holdings['cash']\n \n for s in self.symbol_list:\n \n #Approximation to the real value \n if bars[s]['close']!=0:\n market_value = self.current_positions[s]*bars[s]['close']\n dh[s] = market_value\n dh['total'] += market_value\n else:\n market_value = self.all_holdings[-1][s]\n dh[s] = market_value\n dh['total']+=market_value\n \n self.all_holdings.append(dh)\n \n \"\"\"\n Creates a pandas DataFrame from the all_holdings\n list of dictionaries.\n \"\"\"\n curve = pd.DataFrame(self.all_holdings)\n ##make datetime as index\n curve.set_index('datetime', inplace=True)\n curve['returns'] = curve['total'].pct_change()\n curve['equity_curve'] = (1.0+curve['returns']).cumprod()\n \n self.equity_curve = curve","title":""},{"docid":"780da0c5c5ef433d009843c28caa7a41","score":"0.52154934","text":"def computeDeltaT(self):\n\t\t\n\t\tself.dt=[]\n\t\t\n\t\tfor i in range(len(self.date)-1):\n\t\t\tt=self.date[i+1]-self.date[i]\n\t\t\tself.dt.append(t.seconds)\n\t\t\n\t\tself.t=[self.dt[0]]\n\t\t\n\t\tfor d in self.dt[1:]:\n\t\t\tself.t.append(self.t[-1]+d)\n\t\t\n\t\tself.dt=np.array(self.dt)\n\t\tself.t=np.array(self.t)","title":""},{"docid":"345f8814917eef3571f6e3ba1720c8cc","score":"0.5210371","text":"def correct(self, update_time):\n if (len(self.main_filter.ledger_update_times) > 1) and (update_time <= self.main_filter.ledger_update_times[-1]):\n raise ValueError(\"update time must be later in time than last update time\")\n\n self.main_filter.correct(update_time)\n for key in self.delta_tiers.keys():\n self.delta_tiers[key].correct(update_time)","title":""},{"docid":"f2fc1fc36ef858d4b9bb7e9770206809","score":"0.52055526","text":"def test_fickett_table10_1(self):\r\n\r\n def compare_to_table(self, dataray):\r\n\r\n sigfigs = 6 # Number of significant figures in table\r\n\r\n time = dataray[:, 0]\r\n position = dataray[:, 1]\r\n pressure = dataray[:, 2]\r\n velocity = dataray[:, 3]\r\n density = dataray[:, 4]\r\n sound_speed = dataray[:, 5]\r\n shock_velocity = dataray[:, 6]\r\n reaction_progress = dataray[:, 7]\r\n\r\n solution = SteadyDetonationReactionZone()\r\n\r\n result = solution.run_tvec(time) # run_tvec method expects vector of times\r\n\r\n np.testing.assert_allclose(result['position_relative'], position, rtol=1.0e-6)\r\n np.testing.assert_allclose(result['pressure'], pressure, rtol=1.0e-6)\r\n np.testing.assert_allclose(result['velocity'], velocity, rtol=1.0e-6)\r\n np.testing.assert_allclose(result['density'], density, rtol=1.0e-6)\r\n np.testing.assert_allclose(result['sound_speed'], sound_speed, rtol=1.0e-6)\r\n np.testing.assert_allclose(result['reaction_progress'],\r\n reaction_progress, rtol=1.0e-6)\r\n\r\n # Table 10.1\r\n\r\n # 't', 'x', 'pressure', 'velocity', 'density', 'sound_speed',\r\n # shockVel', 'reaction_progress'\r\n\r\n dataray=np.array([\r\n [0.0, 0.0000000, 0.57800000, 0.4250000, 3.200000, 0.7361216, \r\n 0.85, 0.000],\r\n [0.1, 0.0435625, 0.54910000, 0.4037500, 3.047619, 0.7352009,\r\n 0.85, 0.19],\r\n [0.2, 0.0892500, 0.52020000, 0.3825000, 2.909091, 0.7324317,\r\n 0.85, 0.36],\r\n [0.3, 0.1370625, 0.49130000, 0.3612500, 2.782609, 0.7277931,\r\n 0.85, 0.51],\r\n [0.4, 0.1870000, 0.46240000, 0.3400000, 2.666667, 0.7212489,\r\n 0.85, 0.64],\r\n [0.5, 0.2390625, 0.43350000, 0.3187500, 2.560000, 0.7127467,\r\n 0.85, 0.75],\r\n [0.6, 0.2932500, 0.40460000, 0.2975000, 2.461538, 0.7022152,\r\n 0.85, 0.84],\r\n [0.7, 0.3495625, 0.37570000, 0.2762500, 2.370370, 0.6895617,\r\n 0.85, 0.91],\r\n [0.8, 0.4080000, 0.34680000, 0.2550000, 2.285714, 0.6746666,\r\n 0.85, 0.96],\r\n [0.9, 0.4685625, 0.31790000, 0.2337500, 2.206897, 0.6573776,\r\n 0.85, 0.99],\r\n [1.0, 0.5312500, 0.28900000, 0.2125000, 2.133333, 0.6375000,\r\n 0.85, 1.00],\r\n [1.2, 0.6587500, 0.28900000, 0.2125000, 2.133333, 0.6375000,\r\n 0.85, 1.00]\r\n ])\r\n\r\n # NOTE: CORRECTED VALUE 0.70125 to 0.65875 for x value at t=1.2\r\n # (Error in original table and formula)\r\n\r\n compare_to_table(self, dataray)","title":""},{"docid":"c0b387a728228cb26709b35bde3898b9","score":"0.5190317","text":"def update_table(var: []):\n new_table = var['table_new']\n old_table = var['table_old']\n date_col = var['date_col']\n start_date = grab_date(new_table, date_col)\n client = bigquery.Client()\n union_query = update_query(new_table, old_table, date_col, start_date)\n query_job = client.query(union_query)\n query_job.result()\n cloud_logger.info(f\"{old_table} merged with {new_table} with cutoff date {start_date}\")\n return","title":""},{"docid":"4fd2c88378b611ef708fc8201d15a16b","score":"0.51787657","text":"def _update_hfield(self):\n h_t1 = self._hfield[:-1]\n h_t2 = self._coeff_h1 * (self._efield[1:] - self._efield[:-1])\n self._hfield[:-1] = h_t1 - h_t2","title":""},{"docid":"0d3d34fd8c6d63195ee19fc8dcbbd5d0","score":"0.51699024","text":"def update(self, state, action, value, alpha=0.1):\n # TODO: Encode state to get tile indices\n discrete_states = tile_encode(state, self.tilings)\n \n # TODO: Update q-value for each tiling by update factor alpha\n for q_table, state in zip(self.q_tables, discrete_states):\n q_table[tuple(state) + (action,)] += alpha * (value - q_table[tuple(state) + (action,)])","title":""},{"docid":"de16971274fa208a01707fd8a2fb1974","score":"0.51628643","text":"def update(self, state, action, reward, next_state) -> None:\n # select next action from policy.\n next_action = self.compute_action(next_state, self.action.episode)\n\n state = self.q_table.digitize(state)\n next_state = self.q_table.digitize(next_state)\n\n cur_q = self.q_table[state, action]\n next_q = self.q_table[next_state, next_action]\n td_target = reward + self.gamma * next_q\n self.q_table[state, action] = cur_q + self.alpha * (td_target - cur_q)","title":""},{"docid":"3a2f6ee689eecb02532bc56cc2169f59","score":"0.5155594","text":"def updateCurrentValues(self):\n \n percent = 0.001\n self.currentValues = {}\n for row in range(len(self.pvs)):\n pv = self.pvs[row]\n self.currentValues[pv] = self.epicsGet.caget(pv)\n self.ui.tableWidget.setItem(row,2,QtGui.QTableWidgetItem(str(self.currentValues[pv])))\n\n tol = abs(self.startValues[pv]*percent)\n diff = abs(abs(self.startValues[pv]) - abs(self.currentValues[pv]))\n if diff > tol:\n self.ui.tableWidget.item(row,2).setForeground(QtGui.QColor(255,101,101))#red\n else:\n self.ui.tableWidget.item(row,2).setForeground(QtGui.QColor(255,255,255))#white\n QApplication.processEvents()","title":""},{"docid":"6504c798ba4e87073850e0f78cc3bde1","score":"0.5154043","text":"def test_time_history_not_enough_columns(self):\n time_history = {'type': 'pressure', 'quantity': {'units': 'bar', 'column': 1}}\n time_history['time'] = {'units': 'second', 'column': 0}\n time_history['values'] = [[0], [1]]\n assert not v.validate({'datapoints': [{'time-histories': [time_history]}]}, update=True)","title":""},{"docid":"880a861a5036e0aae73dcd5ce3058e2e","score":"0.51435024","text":"def test_agent_updates_Q_properly():\n #constants\n training_steps = 24 #max q learning steps\n training_cohort = 3\n testing_steps = 6\n testing_cohort = 10\n\n #conduct deep q learning\n agent = QAgent(discount=0.95, exploration_rate=1.0, exploration_rate_decay=0.9)\n simulator = AbbcEnvironment(patients=training_cohort, validation_fraction=.3)\n rewardlog, actionlog = run_simulation(agent, simulator, training_steps, enroll_rate=1)\n print(\"dql training\")\n print(actionlog)\n print(rewardlog)\n\n #simulate trained dql agent with fixed policy\n simulator = AbbcEnvironment(patients=testing_cohort)\n rewardlog, actionlog = run_simulation(agent, simulator, testing_steps, update=False)\n print(\"dql testing\")\n print(actionlog)\n print(rewardlog)\n\n #Takes any drug more than 80% of the time\n assert np.mean(actionlog>0) > .80","title":""},{"docid":"6060390d1dab2dea0f9c9c979afc68a9","score":"0.51340073","text":"def update_dense_time(self, i):\n start = self.time.data[i]\n \n self.dense_time.start = start\n self.dense_time = TimeAxis(self.dense_time.start, \n self.dense_time.length,\n self.dense_time.step)","title":""},{"docid":"6244cb3edaea7556c151013b7757c5b0","score":"0.5131417","text":"def updateReference(self):\n self.ui.updateReference.setText(\"Getting vals...\")\n self.getStartValues()\n for row in range(len(self.pvs)):\n pv = self.pvs[row]\n self.ui.tableWidget.setItem(row,1,QtGui.QTableWidgetItem(str(self.startValues[pv])))\n self.ui.updateReference.setText(\"Update Reference\")","title":""},{"docid":"380d6d62abc71003126e7d26d4bdf320","score":"0.512841","text":"def update_quick(self, t_subtracted, gamma):\n p = np.pi\n t = t_subtracted.reshape(1, -1)\n s, l = self.scale.reshape(-1, 1), self.loc.reshape(-1, 1)\n self.weight = ne.evaluate('gamma / (((t-l)**2 + s**2) * p)')\n gamma_tilde = self.weight * self.scale.reshape(-1, 1)\n self.retain()\n self.loc = self.center(t_subtracted, gamma_tilde)\n self.scale = self.__scaling_param(gamma, gamma_tilde)\n self.pi = self.mixing_coef(gamma)","title":""},{"docid":"6d3e37f21c4a31db9841ee19996995bc","score":"0.512704","text":"def update_q(self, reward, index):\n self.q_value[index] = (self.q_value[index] * self.n_value[index] + reward) / (self.n_value[index] + 1)\n self.n_value[index] += 1","title":""},{"docid":"7f313d81e1725f7a29a5a03f23a005b8","score":"0.51114494","text":"def update(self, t, ot, at, r, ot1, at1):\n # Build the staggered [[ot1, at0], [ot2, at1], ...] action-observation\n # sequences, using the input `ot` as the latest observation. Note that\n # `at` will be None. This will be used to compute the current Q values.\n seqt, ht = self._stagger_action_obs_seq(t, self.history, ot)\n\n # Build staggered sequence at next time stamp. This will be used to\n # Compute the \"G\" Q-values.\n seqt1, ht1 = self._stagger_action_obs_seq(t, ht, ot1)\n ht1[-1, 1] = at\n\n if t > self.seq_len + 1:\n\n if None in seqt:\n display(seqt)\n raise ValueError('seqt contains Nones at time {}'.format(t))\n\n # Transform seqt, seqt1\n seqt_t = self.feature_transformer.transform(seqt)\n seqt1_t = self.feature_transformer.transform(seqt1)\n\n # Compute alpha (if there's nonzero alpha decay)\n alpha = self.initial_alpha / (self._n_updates+1)**self._alpha_decay\n\n # TD(0) update using observation-action sequences as states.\n Qt = self.Q[seqt_t, at]\n Qt1 = self.Q[seqt1_t, at1]\n G = r + self.gamma * Qt1\n self.Q[seqt_t, at] += alpha * (G - Qt)\n self._n_updates += 1\n self.Q_update_counts_[seqt_t, at] += 1\n\n # Store observation-action pair in history attribute.\n self._add_history(t, ot, at)","title":""},{"docid":"e96ec46a1a9ee042c2bdf2d14ef77454","score":"0.51103103","text":"def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n self.q_values[(state, action)] = self.getQValue(state, action) + self.alpha * (reward + self.discount * self.computeValueFromQValues(nextState) - self.getQValue(state, action))","title":""},{"docid":"2aa0af4b96470dcc0ce63a05aba8ae17","score":"0.51000136","text":"def do_updates(self):\n if self.time % self.params['pg']['update_every'] == 0 or self.time == 1:\n plan_time = 0\n H, num_rollouts = self.H, self.params['pg']['num_rollouts']\n for i in range(self.params['pg']['num_iter']):\n # Sample rollouts using ground truth model\n check = time.time()\n rollouts = self.sample_rollouts(H, num_rollouts)\n plan_time += time.time() - check\n \n # Performs value updates alongside advantage calculation\n rews = self.update_pol(rollouts)\n\n # Time spent generating rollouts should be considered planning time\n self.hist['plan_time'][self.time-1] += plan_time\n self.hist['update_time'][self.time-1] -= plan_time","title":""},{"docid":"975f2567de18decc740826c05c79905a","score":"0.50955653","text":"def test_time_history_too_many_columns(self):\n time_history = {'type': 'pressure', 'quantity': {'units': 'bar', 'column': 1}}\n time_history['time'] = {'units': 'second', 'column': 0}\n time_history['values'] = [[0, 1, 2], [1, 2, 3]]\n assert not v.validate({'datapoints': [{'time-histories': [time_history]}]}, update=True)","title":""},{"docid":"a0ed6999ede5e8be906f09996a4ced9d","score":"0.509117","text":"def update_data_table(conf, pos_terms):\n conn, domain, model, step = conf['db'], conf['domain'], conf['model'], conf['step']\n dbc = conn.cursor()\n\n dbc.execute(\"SELECT count(*) FROM data WHERE domain='%s' AND model='%s' AND step=%d AND relevant IS NULL\" % (domain, model, step))\n num_unj = dbc.fetchone()[0]\n print \"Number of unjudged terms before update_data_table:\", num_unj\n\n for term in pos_terms:\n dbc.execute(\"UPDATE data SET relevant=1 WHERE domain=? AND model=? AND term=? AND step=?\", (domain, model, term, step))\n conn.commit()\n\n # get terms which are obviously not relevant (which were not in the pos_terms)\n dbc.execute(\"SELECT term FROM data WHERE domain='%s' AND model='%s' AND step=%d AND relevant IS NULL\" % (domain, model, step))\n res = dbc.fetchall()\n\n neg_terms = [t[0] for t in res] \n # update in DB\n for term in neg_terms:\n dbc.execute(\"UPDATE data SET relevant=0 WHERE domain=? AND model=? AND term=? AND step=?\", (domain, model, term, step))\n conn.commit()\n\n #get missing terms \n dbc.execute(\"SELECT count(*) FROM data WHERE domain='%s' AND model='%s' AND relevant IS NULL\" % (domain, model))\n num_missing = dbc.fetchone()[0]\n print \"Number of unjudged terms after update_data_table:\", num_missing\n\n return pos_terms, neg_terms, num_missing","title":""},{"docid":"ac8dc1a1ea6492c1928562c840ae3ff1","score":"0.5087421","text":"def update(self, rt):\n a = self.alpha\n d1 = self.d1\n d2 = self.d2\n \n e = [] # error terms\n \n if self.selected:\n oldq = self.go\n #newq = oldq + a * ((rt * d1) - oldq)\n newq = oldq + a * d1 * (rt - oldq)\n e = [a * ((rt * d1) - oldq), 0]\n self.go = newq\n \n if self.anticorrelated:\n oldq = self.nogo\n #newq = oldq + a * ((rt * d2 * -1) - oldq)\n newq = oldq + a * d2 * (rt * -1 - oldq)\n e[1] = a * ((rt * d2 * -1) - oldq)\n self.nogo = newq\n else:\n oldq = self.nogo\n #newq = oldq + a * ((rt * d2) - oldq)\n newq = oldq + a * d2 * (rt - oldq)\n e = [0, a * ((rt * d2) - oldq)]\n self.nogo = newq\n \n if self.anticorrelated:\n oldq = self.go\n #newq = oldq + a * ((rt * d1 * -1) - oldq)\n newq = oldq + a * d1 * (rt * -1 - oldq)\n e[0] = a * ((rt * d1 * -1) - oldq)\n self.go = newq\n \n return tuple(e)","title":""},{"docid":"3c697c2f1872aa934558eb9de38d178c","score":"0.50863653","text":"def update_quick(self, t_subtracted, gamma):\n self.weight = gamma\n self.retain()\n self.loc = self.center(t_subtracted, gamma)\n self.scale = self.std(t_subtracted, gamma, self.loc)\n self.pi = self.mixing_coef(gamma)","title":""},{"docid":"0cbd3abbc78c6b2a2afd6a792cb67681","score":"0.5080429","text":"def update_timeindex(self, event):\n default_symbol = self.instruments[self.exchange][0]\n default_exchange = self.exchange\n latest_datetime = self.data.get_latest_bar_datetime(default_exchange, default_symbol)\n\n # Update positions\n # ================\n dp = dict( (k,v) for k,v in [(e, {}) for e in self.instruments])\n for e in dp:\n dp[e] = dict( (k,v) for k,v in [(s, 0) for s in self.instruments[e]])\n\n dp['datetime'] = latest_datetime\n\n for e in self.instruments:\n for s in self.instruments[e]:\n dp[e][s] = self.current_positions[e][s]\n\n # Append the current positions\n self.all_positions.append(dp)\n\n # Update holdings\n # ===============\n dh = dict( (k,v) for k, v in [(e, {}) for e in self.instruments] )\n for e in dh:\n dh[e] = dict( (k,v) for k, v in [(s, 0) for s in self.instruments[e]])\n\n dh['datetime'] = latest_datetime\n dh['cash'] = self.current_holdings['cash']\n dh['fee'] = self.current_holdings['fee']\n dh['total'] = self.current_holdings['total']\n\n # NOTE This does seem to cover only the case where all the assets are traded against a similar quote currency.\n for e in self.instruments:\n for s in self.instruments[e]:\n # Approximation to the real value\n close_price = self.data.get_latest_bar_value(e, s, \"close\")\n market_value = self.current_positions[e][s] * close_price\n dh[e][s] = market_value\n dh['{}-{}-fill'.format(e,s)] = self.current_holdings['{}-{}-fill'.format(e,s)]\n dh['{}-{}-price'.format(e,s)] = close_price\n dh['total'] += market_value\n\n # Append the current holdings\n self.all_holdings.append(dh)\n\n # Reset the fill variable\n for e in self.instruments:\n for s in self.instruments[e]:\n self.current_holdings['{}-{}-fill'.format(e,s)] = ''","title":""},{"docid":"092827def335f353464950e99e638f2e","score":"0.50782835","text":"def changes(\n self,\n ti: Union[float, int],\n tf: Union[float, int],\n time_scale: float = 1.0,\n ) -> Generator[tuple[float, float], None, None]:\n start, end = np.searchsorted(\n self._times, (ti * time_scale, tf * time_scale), side=\"right\"\n )\n for i in range(start, end):\n change = self._phases[i] - self._phases[i - 1]\n yield (self._times[i] / time_scale, change)","title":""},{"docid":"86d5f7eb7bad6f9b58fe7f0e550ca3ca","score":"0.5075755","text":"def update(self, indexes, td_error):\n pass","title":""},{"docid":"3190c1cb7605fe07024e0dbebfc62f0d","score":"0.5075188","text":"def update(self, gameState, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n\n def multiplyAll(counter, multiplier): # Function to multiply all features by the corresponding (alpha * difference)\n multiplier = float(multiplier)\n for key in counter.keys():\n counter[key] *= multiplier\n\n difference = reward + self.discount * self.getValue(nextState) - self.getQValue(gameState, action) # Difference formula from Approximate Q-learning\n features = self.featExtractor.getFeatures(gameState, action) # Get the features of the environment\n multiplyAll(features, (self.alpha * difference))\n self.weights += features","title":""},{"docid":"97712adb3ee38c362ab145fcfa6e3b86","score":"0.50688374","text":"def test_add():\n dates1 = np.array([datetime(2021, 12, 18, 7, n, 0) for n in range(20, 30)])\n data1 = np.array([5, 5, 5, 9, 9, 9, 9, 9, 2, 2])\n tab = Table()\n tab[\"dates\"] = dates1\n tab[\"euvalues\"] = data1\n blocks = [0, 3, 8]\n info = {}\n info['unit'] = 'V'\n info['tlmMnemonic'] = 'TEST_VOLTAGE'\n mnemonic1 = ed.EdbMnemonic('TEST_VOLTAGE', Time('2021-12-18T07:20:00'), Time('2021-12-18T07:30:00'), tab, {}, info, blocks=blocks)\n\n dates2 = np.array([datetime(2021, 12, 18, 7, n, 0) for n in range(27, 37)])\n data2 = np.array([9, 2, 2, 2, 19, 19, 19, 19, 12, 12])\n tab = Table()\n tab[\"dates\"] = dates2\n tab[\"euvalues\"] = data2\n blocks = [0, 1, 4, 8]\n info = {}\n info['unit'] = 'V'\n info['tlmMnemonic'] = 'TEST_VOLTAGE'\n mnemonic2 = ed.EdbMnemonic('TEST_VOLTAGE', Time('2021-12-18T07:27:00'), Time('2021-12-18T07:33:00'), tab, {}, info, blocks=blocks)\n\n added = mnemonic1 + mnemonic2\n assert all(added.data[\"euvalues\"] == np.array([5, 5, 5, 9, 9, 9, 9, 9, 2, 2, 2, 19, 19, 19, 19, 12, 12]))\n assert all(added.data[\"dates\"] == np.append(dates1, dates2[3:]))\n assert added.info['unit'] == 'V'","title":""},{"docid":"ee7d85d437b5c7ea3a031ea781dfa1d2","score":"0.505619","text":"def cal_q_from_ind_mea(\n df_option, deltat=0, hx_name='hx', alpha=0.95\n):\n\n # check inputs\n df_option.check_type(\n pd.core.frame.DataFrame, inspect.stack()[0][3]\n )\n df = df_option.get()\n if deltat < 0:\n raise ValueError(\n \"deltat to \"+inspect.stack()[0][3]+\"() is negative.\"\n )\n data_col_name = prod_str+\"_\"+hx_name\n uncer_col_name = data_col_name+uncer_str_end\n if data_col_name not in df.columns.values:\n raise IndexError(\n inspect.stack()[0][3]+\"() cannot find column\" +\n data_col_name+\". Please run cal_mdotdeltah_per_time()\" +\n \" to generate the column before using \"+inspect.stack()[0][3]+\"()\"\n )\n # need code to check column names\n\n # identify where the integration is needed\n q_ind = []\n q_uncer_ind = []\n if deltat > 0: # integration by trapezoidal rule required\n time_str = df.details[time_col_name]\n end_ind = df.shape[0]-1\n beg_ind = 0\n acc = 0.0\n acc_uncer_sq = 0.0\n acc_time = 0.0\n while beg_ind < end_ind:\n # begin integration\n beg_time = df[time_str][df.index[beg_ind]]\n end_time = beg_time+deltat\n acc = 0.5*df[data_col_name][df.index[beg_ind]].get()\n if beg_ind < end_ind:\n deltaT = df[time_str][df.index[beg_ind+1]] -\\\n df[time_str][df.index[beg_ind]]\n else:\n deltaT = df[time_str][df.index[beg_ind]] -\\\n df[time_str][df.index[beg_ind-1]]\n acc = acc*deltaT\n acc_uncer_sq = (\n 0.5*df[uncer_col_name][df.index[beg_ind]].get()*deltaT\n )**2\n while beg_ind < end_ind:\n beg_ind = beg_ind+1\n if beg_ind < end_ind and \\\n df[time_str][df.index[beg_ind+1]] <= end_time:\n deltaT = df[time_str][df.index[beg_ind+1]] -\\\n df[time_str][df.index[beg_ind-1]]\n acc = acc +\\\n df[data_col_name][df.index[beg_ind]].get()*deltaT/2.\n acc_uncer_sq = acc_uncer_sq+(\n df[uncer_col_name][df.index[beg_ind]].get()*deltaT/2.\n )**2\n else:\n deltaT = df[time_str][df.index[beg_ind]] -\\\n df[time_str][df.index[beg_ind-1]]\n acc = acc +\\\n df[data_col_name][df.index[beg_ind]].get()*deltaT/2.\n acc_uncer_sq = acc_uncer_sq+(\n df[uncer_col_name][df.index[beg_ind]].get()*deltaT/2.\n )**2\n break\n\n # divide the integrated heat transfer by time\n acc_time = df[time_str][df.index[beg_ind]]-beg_time\n q_ind.append(acc/acc_time)\n q_uncer_ind.append(sqrt(acc_uncer_sq)/acc_time)\n\n else: # mean of instantaneous heat transfer rate is needed\n q_ind = [df[data_col_name][ind].get() for ind in df.index]\n q_uncer_ind = [df[uncer_col_name][ind].get() for ind in df.index]\n\n # taking the mean values for result\n if deltat == 0:\n time_std_end = '_inst'\n else:\n time_std_end = '_'+str(int(deltat))+'s'\n len_q_ind = len(q_ind)\n df.details['q_'+hx_name+'_mean'+time_std_end] = sum(q_ind)/len_q_ind\n\n # getting the variation of samples as environmental noise effect\n zero_order_sq = sum([(value/len_q_ind)**2 for value in q_uncer_ind])\n first_order_sq = (\n stdev(q_ind)*t.interval(alpha, len_q_ind-1)[1]/len_q_ind\n )**2 # give NaN for zero degree of freedom\n df.details['q_uncer_'+hx_name+'_mean'+time_std_end] = sqrt(\n zero_order_sq+first_order_sq\n )\n df.details['q_uncer_'+hx_name+'_mean'+time_std_end+'_zero'] = sqrt(\n zero_order_sq\n )\n df.details['q_uncer_'+hx_name+'_mean'+time_std_end+'_first'] = sqrt(\n first_order_sq\n )\n\n # return values\n df_option.set(df)\n return df_option","title":""},{"docid":"c968a2922713609a288085bfb9fcbceb","score":"0.5051216","text":"def updateTreasures(self):\n u = self.pf_uncertainty()\n treasures = np.array(self.Treasures).reshape(len(self.Treasures), 3)\n print(\"debug: \", treasures[:,:2].T)\n d = treasures[:,:2].T - self.bestParticle.reshape(-1, 1)\n d = np.hypot(d[0], d[1])\n print(\"distances \", d)\n print(\"min d: \", np.amin(d))\n if np.amin(d) < self.min_dis_treasures and treasures[np.argmin(d),2] > u:\n treasures[np.argmin(d),:] = list(self.bestParticle) + [u]\n treasures = treasures.reshape(len(self.Treasures), 3)\n self.Treasures = list(treasures)\n if np.amin(d) > self.min_dis_treasures:\n rospy.loginfo(\"Added new treasure!\")\n self.Treasures.append(list(self.bestParticle)+[u])","title":""},{"docid":"b3d6d8191df2f854d7c180917956fae3","score":"0.5050302","text":"def mutate_update_deltas(self, network, update_deltas):","title":""},{"docid":"54f428e5aaa866c1e3de3c0006fc8d2d","score":"0.5041094","text":"def update_obs_table(self, s_set: list = None, e_set: list = None):\n\n update_S = s_set if s_set else list(self.S) + list(self.s_dot_a())\n update_E = e_set if e_set else self.E\n\n # This could save few queries\n update_S.reverse()\n\n for s in update_S:\n for e in update_E:\n if len(self.T[s]) != len(self.E):\n output = self.sul.query(s + e)\n self.T[s] += (output[-1],)","title":""},{"docid":"293a1bc163d0e798d0bb92adbebb23f9","score":"0.5036949","text":"def update(self, delta_time):\n \n for entity in self:\n entity.update(delta_time)","title":""},{"docid":"04a3649af4fdfc7b98188bc8d33c56d6","score":"0.5033723","text":"def cti_detrend_factor():\n#\n#--- update detrend factor table (amp_avg_list)\n#\n update_detrend_factor_table()\n#\n#--- update detrend data tables \n#\n make_detrended_data_table()","title":""},{"docid":"a462251fa4b018f9ff11d2ef26ff479f","score":"0.50200844","text":"def on_t_update(self, old_state, action, new_state, reward, done):\n pass","title":""},{"docid":"e395cb3c4fd903dd1618306d319a3d58","score":"0.50170076","text":"def run_update(self, **kw):\n\n for qb in self.qubits:\n if qb.flux_parking() != 0 and np.abs(qb.flux_parking()) != 0.5:\n log.warning((f\"{qb.name}.flux_parking() is not 0, 0.5\"\n \"nor -0.5. Nothing will be updated.\"))\n else:\n sweet_spot = self.analysis.fit_res[\n qb.name][f'{qb.name}_sweet_spot']\n opposite_sweet_spot = self.analysis.fit_res[\n qb.name][f'{qb.name}_opposite_sweet_spot']\n sweet_spot_RO_freq = self.analysis.fit_res[\n qb.name][f'{qb.name}_sweet_spot_RO_frequency']\n\n self.fluxlines_dict[qb.name](sweet_spot)\n qb.ro_freq(sweet_spot_RO_freq)\n # Update V_per_phi0 key in each qubit. From this, it is possible\n # to retrieve the opposite sweet spot\n V_per_phi0 = np.abs(2 * (sweet_spot - opposite_sweet_spot))\n qb.fit_ge_freq_from_dc_offset()['V_per_phi0'] = V_per_phi0\n\n # Store the USS in 'dac_sweet_spot'\n if qb.flux_parking() == 0:\n qb.fit_ge_freq_from_dc_offset(\n )['dac_sweet_spot'] = sweet_spot\n elif qb.flux_parking() == 0.5:\n qb.fit_ge_freq_from_dc_offset(\n )['dac_sweet_spot'] = sweet_spot - V_per_phi0/2\n elif qb.flux_parking() == -0.5:\n qb.fit_ge_freq_from_dc_offset(\n )['dac_sweet_spot'] = sweet_spot + V_per_phi0/2","title":""},{"docid":"82feefcbb6a71df8fed103043aacbc6d","score":"0.5015755","text":"def difference_update(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"51e24c1e80f8de78d2a0d3b8471bd926","score":"0.50108117","text":"def update(self, acceleration, quaternion, DT):\r\n an = quaternion.vecTransformation(acceleration) \r\n self.values += DT * (an + G)","title":""},{"docid":"5d6395e8804261fa4cbd98a604f85db0","score":"0.50057286","text":"def update_timestep_data_no_conditioned_area(tsd):\n\n zero_fields = ['Qhs_lat_sys',\n 'Qhs_sen_sys',\n 'Qcs_lat_sys',\n 'Qcs_sen_sys',\n 'Qhs_sen',\n 'Qcs_sen',\n 'x_int',\n 'Qhs_em_ls',\n 'Qcs_em_ls',\n 'Qhpro_sys',\n 'Qcpro_sys',\n 'ma_sup_hs',\n 'ma_sup_cs',\n 'Ta_sup_hs',\n 'Ta_re_hs',\n 'Ta_sup_cs',\n 'Ta_re_cs',\n 'NG_hs',\n 'COAL_hs',\n 'OIL_hs',\n 'WOOD_hs',\n 'NG_ww',\n 'COAL_ww',\n 'OIL_ww',\n 'WOOD_ww',\n 'vfw_m3perh',\n 'DH_hs',\n 'Qhs_sys', 'Qhs',\n 'DH_ww',\n 'Qww_sys',\n 'Qww',\n 'DC_cs',\n 'DC_cs_lat',\n 'Qcs_sys',\n 'Qcs',\n 'DC_cdata',\n 'Qcdata_sys',\n 'Qcdata',\n 'DC_cre',\n 'Qcre_sys',\n 'Qcre',\n 'Eaux',\n 'Ehs_lat_aux',\n 'Eaux_hs',\n 'Eaux_cs',\n 'Eaux_ve',\n 'Eaux_ww',\n 'Eaux_fw',\n 'E_sys',\n 'GRID',\n 'E_ww',\n 'E_hs',\n 'E_cs',\n 'E_cre',\n 'E_cdata',\n 'E_pro',\n 'Epro',\n 'Edata',\n 'Ea',\n 'El',\n 'Eal',\n 'Ev',\n 'Eve',\n 'mcphs_sys',\n 'mcpcs_sys',\n 'mcptw'\n 'mcpww_sys',\n 'mcpcdata_sys',\n 'mcpcre_sys',\n 'Tcdata_sys_re', 'Tcdata_sys_sup',\n 'Tcre_sys_re', 'Tcre_sys_sup',\n 'Tww_sys_sup', 'Tww_sys_re',\n 'Ths_sys_sup', 'Ths_sys_re',\n 'Tcs_sys_sup', 'Tcs_sys_re',\n 'DH_ww', 'Qww_sys', 'Qww',\n 'mcptw', 'I_sol', 'I_rad',\n 'Qgain_light', 'Qgain_app', 'Qgain_pers', 'Qgain_data', 'Qgain_wall', 'Qgain_base', 'Qgain_roof',\n 'Qgain_wind', 'Qgain_vent'\n 'Q_cool_ref', 'q_cs_lat_peop']\n\n tsd.update(dict((x, np.zeros(HOURS_IN_YEAR)) for x in zero_fields))\n\n tsd['T_int'] = tsd['T_ext'].copy()\n\n return tsd","title":""},{"docid":"99bf023d8d0ae5802687fd54571c2ac8","score":"0.49942693","text":"def computeQValues(self):\n for i in range(self.numTraining):\n states, actions, rewards, termination = self.runEpisode()\n n = len(states)\n q = np.copy(self.q_values)\n\n # print(\"Iteration\", i)\n # print(\"states\", states)\n # print(\"actions\", actions)\n # print(\"rewards\", rewards)\n # print(termination)\n # input()\n\n for t in range(len(states) - 1):\n # if states[t] == states[t+1]:\n # continue\n sx1, sy1 = states[t]\n sx2, sy2 = states[t+1]\n \n q[sx1][sy1][actions[t]] = rewards[t] + self.discount*self.getV(sx2, sy2)\n\n # print(states[t], states[t+1], actions[t], self.getV(sx2, sy2))\n # print(np.moveaxis(q, 2, 0))\n # input()\n \n if termination.value == 0:\n sx1, sy1 = states[n-1] \n q[sx1][sy1][actions[n-1]] = rewards[n-1]\n\n self.q_values = q\n \n # print(self.q_values)\n # print(self.q_values.reshape(4,6,8))\n # print(np.moveaxis(self.q_values, 2, 0))\n # input()","title":""},{"docid":"4b65bee7229157308278dd6c6a3961d5","score":"0.49833325","text":"def get_update_info(self, molsys: Molsys, f: np.ndarray, q: np.ndarray, step: Step):\n f_old = step.forces\n x_old = step.geom\n\n old_molsys = copy.deepcopy(molsys)\n old_molsys.geom = x_old\n q_old = old_molsys.q_array()\n\n dq = q - q_old\n dg = f_old - f # gradients -- not forces!\n dqdg = np.dot(dq, dg)\n dqdq = np.dot(dq, dq)\n max_change = abs_max(dq)\n return dq, dg, dqdg, dqdq, max_change","title":""},{"docid":"a321ba4d46e6b21f5cec8562397ba358","score":"0.49813497","text":"def test_updateCol_functions_with_valid_data(self):\n table = self.InterpolationTableWithDuplicate\n obs = table.updateCol('GrowthRate','SampleID',{'S.5b':3.01,'S.5':2.59}).delimitedSelf()\n \n exp =\\\n [\"#SampleID\\tTemp\\tGrowthRate\\tDescription\\n\",\\\n \"#Comment line, full of crazy comments\\n\",\\\n \"#Another comment line\\n\",\\\n \"S.1\\t1.0\\t3.0\\tSample.1.Description\\n\",\\\n \"S.2\\t2.0\\t2.0\\tSample.2.Description\\n\",\\\n \"S.3\\t3.0\\t0.0\\tSample.3.Description\\n\",\\\n \"S.4\\t0.0\\tUnknown\\tSample.4.Description\\n\",\\\n \"S.5\\t1.0\\t2.59\\tSample.5.Description\\n\",\\\n \"S.5b\\t1.0\\t3.01\\tSample.5.Description\\n\",\\\n \"S.6\\t1.5\\tUnknown\\tSample.6.Description\\n\",\\\n \"S.7\\t2.72\\tUnknown\\tSample.7.Description\\n\",\\\n \"S.8\\t3.14\\tUnknown\\tSample.7.Description\\n\"] \n self.assertEqualItems(obs,exp)","title":""},{"docid":"57ae2cdf084334f347eb5cd5e8e4a5c9","score":"0.49800757","text":"def delta(self, time: tf.Tensor, instrument: tf.Tensor,\n numeraire: tf.Tensor):","title":""},{"docid":"3d7ba90294344ce7499b92e9922b9eb7","score":"0.49799034","text":"def update(table, id_):\n list_labels = ['Month',\n 'Day',\n 'Year',\n 'Type',\n 'Amount']\n type_list = ['month',\n 'str',\n 'int',\n 'in',\n 'int']\n # your code\n common.update_table(table, id_, list_labels, type_list)\n\n return table","title":""},{"docid":"feeee493db033eb42113147bdfbc4eb6","score":"0.4976679","text":"def increment_time(self):\n self.timesteps += 1\n for atom in self.atoms:\n VelocityVerlet.update_position(atom)\n # shift atom to within the boundaries\n atom.position = self.shift_position(atom.position)\n\n # reset energy\n self.energy_history.append(self.energy)\n self.energy = 0.","title":""},{"docid":"33c79c39aad0f5b94e8af72a2df96285","score":"0.49760443","text":"def run_update(self, **kw):\n for qb in self.qubits:\n f0 = self.analysis.fit_res[qb.name].params['f0'].value\n qb.ge_freq(f0)\n if self.analysis.analyze_ef:\n f0_ef = 2 * self.analysis.fit_res[\n qb.name].params['f0_gf_over_2'] - f0\n qb.ef_freq(f0_ef)","title":""},{"docid":"362d53fc57fabea6dec3b26cba442c25","score":"0.49696663","text":"def update(self, table, criteria, values):\n return","title":""},{"docid":"fa41ca72d797a8c3cf243c363f0a72e6","score":"0.49670866","text":"def _update_hematocrit(self):\n\n\tG=self._G\n\tdt = self._dt\n vrbc = self._P.rbc_volume()\n htt2htd = self._P.tube_to_discharge_hematocrit\n\tinvivo=self._invivo\n\n\t#Calculate number of RBCs after dt\n\tfor e in G.es:\n e['nRBC']=e['nRBC']+dt*e['flowRBCIn']-dt*e['htt']*0.25*np.pi*e['diameter']**2*e['v']/vrbc\n\n\t#Update tube and discharge hematocrit\n\tfor e in G.es:\n\t e['htt']=e['nRBC']*vrbc/(0.25*np.pi*e['diameter']**2*e['length'])\n e['htd']=htt2htd(e['htt'], e['diameter'], invivo)\n\n\t if e['htt'] > 0.99:\n\t\tprint('WARNING')\n\t\tvgm.write_pkl(G,'G_warning.pkl')\n\t\tprint('htt > 0.99 in vessel')\n\t\tprint(e.index)","title":""},{"docid":"9bce9c8c4a9136045cddb02cc9d277a8","score":"0.496367","text":"def test_time_history_bad_time_units(self):\n time_history = {'type': 'pressure', 'quantity': {'units': 'bar', 'column': 1}}\n time_history['time'] = {'units': 'candela*ampere', 'column': 0}\n time_history['values'] = [[0, 1], [1, 2]]\n assert not v.validate({'datapoints': [{'time-histories': [time_history]}]}, update=True)","title":""},{"docid":"07ab824e84a2b521be914ab3053292ca","score":"0.4962276","text":"def _update_timestamps(self):\n end_time_str = iso8601.format_date(self.end*0.001, precision=3)\n if 'end_time' in self.das.parent:\n self.das.parent['end_time'][:] = util.str_to_bytes(end_time_str)\n self.das.parent['duration'][0] = (self.end-self.start)*0.001\n self.das.parent['collection_time'][0] = self.collection_time\n else:\n h5nexus.field(self.das.parent,'end_time',\n data=end_time_str, dtype='|S',\n label='measurement end time')\n h5nexus.field(self.das.parent,'duration',\n data=[(self.end-self.start)*0.001],\n units='s', dtype='float32',\n label='total measurement duration')\n h5nexus.field(self.das.parent,'collection_time',\n data=[self.collection_time],\n units='s', dtype='float32',\n label='total time detectors were active')","title":""},{"docid":"e0e1d4ff55569aa074704a3df416a8de","score":"0.49615124","text":"def calculate_deltas(train_toks, classifier, unattested, ffreq_empirical, nfmap, nfarray, nftranspose, encoding):\n ...","title":""},{"docid":"8bc3ce8666525922d82ee4a22a8a8fb4","score":"0.49611092","text":"def test_update(self):\n dataset1 = DataSet(data={'description': 'dataset 1'}, )\n self.assertEqual(dataset1.description, \"dataset 1\")\n dataset1.update(rerun=5, description=\"new dataset\")\n self.database.cursor.execute(\n \"SELECT rerun, description FROM dataset WHERE id=%s\", (dataset1.id,))\n results = self.database.cursor.fetchone()\n self.assertEqual(results[0], 5)\n self.assertEqual(results[1], \"new dataset\")\n self.assertEqual(dataset1.description, \"new dataset\")\n self.assertEqual(dataset1.rerun, 5)\n dataset1.update(process_end_ts=datetime.datetime(1970, 1, 1))\n self.assertEqual(\n dataset1.process_end_ts, datetime.datetime(1970, 1, 1)\n )","title":""},{"docid":"d68bbd9cf0c989022f6f3c95f1cb9f4a","score":"0.49560022","text":"def _update_t(self, num_samples):\n self.ts = self.t_start + xp.linspace(0., \n num_samples * self.dt,\n num_samples,\n endpoint=False)\n self.t_start += num_samples * self.dt\n self.v = xp.zeros(num_samples)","title":""},{"docid":"3972ccea4f54de27097dc7317e010346","score":"0.49540737","text":"def _measupdate(self):\n pass","title":""},{"docid":"94d77757c7bfb1aa95c7801f198263fa","score":"0.49522328","text":"def ModifyDFs(self):\r\n # Add columns to plays DF for time elapsed.\r\n # Relevant for adding line markers for goals in plots.\r\n self.plays['Time'] = pd.to_datetime(\r\n self.plays.period_time, format=\"%M:%S\"\r\n )\r\n # Then separate out the Hour, Minute, and Second for each play\r\n self.plays['Hour'] = self.plays['Time'].dt.hour\r\n self.plays['Minute'] = self.plays['Time'].dt.minute\r\n self.plays['Second'] = self.plays['Time'].dt.second\r\n \r\n # Change Time column to the game time elapsed\r\n for i in range(2, self.plays.period.unique().max() + 1):\r\n self.plays.loc[self.plays.period == i, 'Time'] = \\\r\n self.plays[self.plays.period == i].Time + \\\r\n dt.timedelta(minutes= 20 * (i - 1))\r\n \r\n self.plays.loc[self.plays.period == i, 'Minute'] = \\\r\n self.plays[self.plays.period == i].Minute + (20 * (i - 1))\r\n \r\n if i >= 4:\r\n self.plays.loc[self.plays.period == i, 'Hour'] = \\\r\n self.plays[self.plays.period == i].Hour + (floor((i - 3) / 3))\r\n \r\n # Create a time index that with intervals of 5 seconds for entire game\r\n secns = [int(s) for s in range(0, 60, 5)] * 60\r\n mints = [floor(len(secns[:s]) / 12) for s in range(len(secns))]\r\n hours = [int(0) for m in mints]\r\n \r\n # Determine if the index needs to be adjusted for OT\r\n ## Additional adjustments will need to be made for playoff OT\r\n if 4 in self.plays.period.unique():\r\n OTsec = [int(s) for s in range(0, 60, 5)] * 5\r\n secns += OTsec\r\n mints += [floor(len(OTsec[:s]) / 12) for s in range(len(OTsec))]\r\n hours += [int(1) for s in OTsec]\r\n \r\n index = [dt.time(h, m, s) for h, m, s in zip(hours, mints, secns)]\r\n s = pd.Series(index, name='Time')\r\n \r\n # Clean Time column format\r\n self.plays.Time = pd.Series(\r\n [dt.time(d.hour, d.minute, d.second) for d in self.plays.Time]\r\n )\r\n \r\n # Add in player_4 column in case the JSON file does not contain one\r\n if 'player_4' not in self.plays.columns:\r\n self.plays.loc[:, 'player_4'] = ''\r\n \r\n # Merge the new index with the plays, shots, and shifts df's\r\n # (This expands the dataframe to allow for prettier P5 charts)\r\n self.plays = pd.concat(\r\n [self.plays, pd.DataFrame(s)],\r\n ignore_index=True, join='outer', keys='Time'\r\n )\r\n self.plays.sort_values(by='Time', inplace=True, ascending=True)\r\n self.plays['datetime'] = pd.to_datetime(self.plays['datetime'])","title":""},{"docid":"da034bbda9022a8c6c52a9fdcbf82d87","score":"0.4935009","text":"def update_performance(self):\n # update the last_price field for all ideas with null closed_date.\n ideas = IdeaModel.query.all()\n for idea in ideas:\n if not idea.closed_date:\n idea.last_price = Stock.fetch_stock_quote(idea.symbol)[\"latestPrice\"]\n self.save_changes(idea)\n\n analysts = UserModel.query.filter(UserModel.num_ideas > 0).all()\n for analyst in analysts:\n ideas_by_analyst = analyst.ideas.all()\n\n cumulative_return = reduce((lambda x, y: x + y), map(calc_idea_return, ideas_by_analyst))\n analyst.avg_return = cumulative_return / analyst.num_ideas\n\n total_price_target_capture = reduce((lambda x, y: x + y), map(calc_pt_capture, ideas_by_analyst))\n analyst.avg_price_target_capture = total_price_target_capture / analyst.num_ideas\n\n successful_total = reduce((lambda x, y: x + y), map(calc_idea_profitability, ideas_by_analyst))\n analyst.success_rate = successful_total / analyst.num_ideas\n\n cumulative_holding_period = reduce((lambda x, y: x + y), map(calc_holding_period, ideas_by_analyst))\n analyst.avg_holding_period = cumulative_holding_period / analyst.num_ideas\n\n num_analysts = len(analysts)\n all_avg_returns = [analyst.avg_return for analyst in analysts]\n avg_return_percentiles = stats.rankdata(all_avg_returns, \"average\") / num_analysts\n all_pt_captures = [analyst.avg_price_target_capture for analyst in analysts]\n avg_price_target_capture_percentiles = stats.rankdata(all_pt_captures, \"average\") / num_analysts\n all_success_rates = [analyst.success_rate for analyst in analysts]\n success_rate_percentiles = stats.rankdata(all_success_rates, \"average\") / num_analysts\n all_avg_holding_periods = [analyst.avg_holding_period for analyst in analysts]\n avg_holding_period_percentiles = stats.rankdata(all_avg_holding_periods, \"average\") / num_analysts\n all_num_ideas = [analyst.num_ideas for analyst in analysts]\n num_ideas_percentiles = stats.rankdata(all_num_ideas, \"average\") / num_analysts\n\n for idx, analyst in enumerate(analysts):\n analyst.avg_return_percentile = avg_return_percentiles[idx]\n analyst.avg_price_target_capture_percentile = avg_price_target_capture_percentiles[idx]\n analyst.success_rate_percentile = success_rate_percentiles[idx]\n analyst.avg_holding_period_percentile = avg_holding_period_percentiles[idx]\n analyst.num_ideas_percentile = num_ideas_percentiles[idx]\n\n # temporary score to keep track of analyst ranking\n analyst.analyst_rank = analyst.avg_return_percentile + 0.5 * analyst.avg_price_target_capture_percentile\n\n # sort list of analysts by ranking score, update percentile ranking, and save analyst changes\n analysts.sort(key=lambda x: x.analyst_rank, reverse=True)\n all_analyst_ranks = [analyst.analyst_rank for analyst in analysts]\n analyst_rank_percentiles = stats.rankdata(all_analyst_ranks, \"average\") / num_analysts\n\n for idx, analyst in enumerate(analysts):\n analyst.analyst_rank = idx + 1\n analyst.analyst_rank_percentile = analyst_rank_percentiles[idx]\n self.save_changes(analyst)","title":""},{"docid":"47a3d1fbd72b5dbf1c5769fdfa5c21f3","score":"0.49230352","text":"def update_params(dt, idx_now, idx_next, arrays1, arrays2, arrays3, arrays4, arrays5, arrays6, arrays7, arrays8, time_sim, time_ext, sim_nH):\n # Set the times as parameters inside function\n t_now = time_sim[idx_now]\n t_next = time_ext[idx_next]\n t_up = time_sim[idx_now]+dt\n t_mid = t_now+((t_next-t_now)/2)\n \n if t_up <t_mid:\n \"\"\"\n If the updated time is closer to the current time than the end of the external timestep, stick with the ISM parameters from the previous timestep\n \"\"\"\n nH_next = arrays1[idx_now]\n nHe_next = arrays2[idx_now]\n xH_next = arrays3[idx_now]\n xHe_next = arrays4[idx_now]\n xHe2_next = arrays5[idx_now]\n T_next = arrays6[idx_now]\n B_next = arrays7[idx_now]\n rho_next = arrays8[idx_now]\n elif t_up > t_mid:\n \"\"\"\n If the updated time is closer to the end of the external timestep than the beginning, use the ISM parameters from the beginning of the next external timestep\n \"\"\"\n nH_next = sim_nH[idx_next]\n nHe_next = arr_nHesim[idx_next]\n xH_next = arr_xHsim[idx_next]\n xHe_next = arr_xHesim[idx_next]\n xHe2_next = arr_xHe2sim[idx_next]\n T_next = arr_temperaturesim[idx_next]\n B_next = arr_Bsim[idx_next]\n rho_next = arr_rhosim[idx_next]\n elif t_up == t_mid:\n \"\"\"\n If the updated time is right in the middle, use the ISM parameters from the beginning of the next external timestep. This is a separate elif statememt because for some reason having it in the same elif statement as either of the others (>= or <=) seems to break the simulation. Probably some Python thing?\n \"\"\"\n nH_next = sim_nH[idx_next]\n nHe_next = arr_nHesim[idx_next]\n xH_next = arr_xHsim[idx_next]\n xHe_next = arr_xHesim[idx_next]\n xHe2_next = arr_xHe2sim[idx_next]\n T_next = arr_temperaturesim[idx_next]\n B_next = arr_Bsim[idx_next]\n rho_next = arr_rhosim[idx_now]\n\n return arrays1.append(float(nH_next)), arrays2.append(float(nHe_next)), arrays3.append(float(xH_next)), arrays4.append(float(xHe_next)), arrays5.append(float(xHe2_next)), arrays6.append(float(T_next)), arrays7.append(float(B_next)), arrays8.append(float(rho_next))","title":""},{"docid":"1fddf2daa507927d291fd161885be8a5","score":"0.49225911","text":"def update_temporal_coverage(self):\n\n update_target_temporal_coverage(self)","title":""},{"docid":"cc46d08514c3f674d81af2c6167dd365","score":"0.4922514","text":"def up_delta(self, time: tf.Tensor, instrument: tf.Tensor,\n numeraire: tf.Tensor):","title":""},{"docid":"af5ae0a57565b71cf70af04b52b542f4","score":"0.49203718","text":"def update_target(self, tau):\n if tau == 1.0:\n self.target_qmodel.set_weights(self.qmodel.get_weights())\n else:\n tws = self.target_qmodel.trainable_variables\n ws = self.qmodel.trainable_variables\n for ndx in range(len(tws)):\n tws[ndx] = ws[ndx] * tau + tws[ndx] * (1 - tau)","title":""},{"docid":"5c7a67360f7b138a09dfff1a487d25ff","score":"0.49193344","text":"def update(self, state, action, reward, next_state, next_action, eoe):\r\n # transform the state into index\r\n i = state[0]*self.h + state[1]\r\n j = next_state[0]*self.h + next_state[1]\r\n self.Q[i, action] = self.Q[i, action] + \\\r\n self.alpha*(reward + self.gamma*(1-eoe)*self.Q[j, next_action] -self.Q[i, action])","title":""},{"docid":"bf6f5c8112b3929a924f8ef8a2265fa3","score":"0.49171758","text":"def update_history_dist(H, n):\n\n new_inds = np.where(~H[\"known_to_aposmm\"])[0]\n\n p = np.logical_and.reduce((H[\"sim_ended\"], ~np.isnan(H[\"f\"])))\n\n for new_ind in new_inds:\n # Loop over new returned points and update their distances\n if p[new_ind]:\n H[\"known_to_aposmm\"][new_ind] = True\n\n # Compute distance to boundary\n H[\"dist_to_unit_bounds\"][new_ind] = min(\n min(np.ones(n) - H[\"x_on_cube\"][new_ind]), min(H[\"x_on_cube\"][new_ind] - np.zeros(n))\n )\n\n dist_to_all = cdist(H[\"x_on_cube\"][[new_ind]], H[\"x_on_cube\"][p], \"euclidean\").flatten()\n new_better_than = H[\"f\"][new_ind] < H[\"f\"][p]\n\n # Update any other points if new_ind is closer and better\n if H[\"local_pt\"][new_ind]:\n inds_of_p = np.logical_and(dist_to_all < H[\"dist_to_better_l\"][p], new_better_than)\n updates = np.where(p)[0][inds_of_p]\n H[\"dist_to_better_l\"][updates] = dist_to_all[inds_of_p]\n H[\"ind_of_better_l\"][updates] = new_ind\n else:\n inds_of_p = np.logical_and(dist_to_all < H[\"dist_to_better_s\"][p], new_better_than)\n updates = np.where(p)[0][inds_of_p]\n H[\"dist_to_better_s\"][updates] = dist_to_all[inds_of_p]\n H[\"ind_of_better_s\"][updates] = new_ind\n\n # Since we allow equality when deciding better_than_new_l and\n # better_than_new_s, we have to prevent new_ind from being its own\n # better point.\n better_than_new_l = np.logical_and.reduce((~new_better_than, H[\"local_pt\"][p], H[\"sim_id\"][p] != new_ind))\n better_than_new_s = np.logical_and.reduce((~new_better_than, ~H[\"local_pt\"][p], H[\"sim_id\"][p] != new_ind))\n\n # Who is closest to ind and better\n if np.any(better_than_new_l):\n ind = dist_to_all[better_than_new_l].argmin()\n H[\"ind_of_better_l\"][new_ind] = H[\"sim_id\"][p][np.nonzero(better_than_new_l)[0][ind]]\n H[\"dist_to_better_l\"][new_ind] = dist_to_all[better_than_new_l][ind]\n\n if np.any(better_than_new_s):\n ind = dist_to_all[better_than_new_s].argmin()\n H[\"ind_of_better_s\"][new_ind] = H[\"sim_id\"][p][np.nonzero(better_than_new_s)[0][ind]]\n H[\"dist_to_better_s\"][new_ind] = dist_to_all[better_than_new_s][ind]\n\n # if not ignore_L8:\n # r_k = calc_rk(len(H['x_on_cube'][0]), n_s, rk_const, lhs_divisions)\n # H['worse_within_rk'][new_ind][p] = np.logical_and.reduce((H['f'][new_ind] <= H['f'][p], dist_to_all <= r_k))\n\n # # Add trues if new point is 'worse_within_rk'\n # inds_to_change = np.logical_and.reduce((H['dist_to_all'][p, new_ind] <= r_k, H['f'][new_ind] >= H['f'][p], H['sim_id'][p] != new_ind))\n # H['worse_within_rk'][inds_to_change, new_ind] = True\n\n # if not H['local_pt'][new_ind]:\n # H['worse_within_rk'][H['dist_to_all'] > r_k] = False\n\n if np.any(~H[\"local_pt\"]) and not np.any(np.isinf(H[\"dist_to_better_s\"][~H[\"local_pt\"]])):\n # Our best sample point was not identified because the min was not unique.\n min_inds = H[\"f\"][~H[\"local_pt\"]] == np.min(H[\"f\"][~H[\"local_pt\"]])\n assert len(min_inds) >= 2, \"Check this\"\n # Take the first point with this value to be the best sample point\n best_samp = H[\"sim_id\"][~H[\"local_pt\"]][min_inds][0]\n H[\"dist_to_better_s\"][best_samp] = np.inf\n H[\"ind_of_better_s\"][best_samp] = -1\n\n # if np.any(H['local_pt']) and not np.any(np.isinf(H['dist_to_better_l'][H['local_pt']])):\n # # Our best sample point was not identified because the min was not unique.\n # min_inds = H['f'][H['local_pt']] == np.min(H['f'][H['local_pt']])\n # assert len(min_inds) >= 2, \"Check this\"\n # # Take the first point with this value to be the best sample point\n # best_local = H['sim_id'][H['local_pt']][min_inds][0]\n # H['dist_to_better_l'][best_local] = np.inf\n # H['ind_of_better_l'][best_local] = -1","title":""}],"string":"[\n {\n \"docid\": \"62aaf30d5e9e5dc5a7234e336139c895\",\n \"score\": \"0.6334579\",\n \"text\": \"def updateQTableFromTerminatingState( self, reward ):\\n \\n # retrieve the old action value from the Q-table (indexed by the previous state and the previous action)\\n old_q = self.q_table[self.prev_s][self.prev_a]\\n \\n # TODO: what should the new action value be?\\n new_q = old_q\\n \\n # assign the new action value to the Q-table\\n self.q_table[self.prev_s][self.prev_a] = new_q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4bc77c86426f1b1d62c426855a27e6\",\n \"score\": \"0.62391174\",\n \"text\": \"def updateQTable( self, reward, current_state ):\\n \\n # retrieve the old action value from the Q-table (indexed by the previous state and the previous action)\\n old_q = self.q_table[self.prev_s][self.prev_a]\\n \\n # TODO: what should the new action value be?\\n new_q = old_q\\n \\n # assign the new action value to the Q-table\\n self.q_table[self.prev_s][self.prev_a] = new_q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166d8782e8b0e29d49316946dcf04922\",\n \"score\": \"0.62018937\",\n \"text\": \"def _compute_deltas(self):\\n db.session.execute(text(\\n \\\"\\\"\\\"\\n update okresy_metriky t\\n set rezervace_celkem_zmena_den = t0.rezervace_celkem - t1.rezervace_celkem,\\n rezervace_cekajici_zmena_den = t0.rezervace_cekajici - t1.rezervace_cekajici,\\n rezervace_cekajici_1_zmena_den = t0.rezervace_cekajici_1 - t1.rezervace_cekajici_1,\\n rezervace_cekajici_2_zmena_den = t0.rezervace_cekajici_2 - t1.rezervace_cekajici_2,\\n rezervace_kapacita_zmena_den = t0.rezervace_kapacita - t1.rezervace_kapacita,\\n rezervace_kapacita_1_zmena_den = t0.rezervace_kapacita_1 - t1.rezervace_kapacita_1,\\n rezervace_kapacita_2_zmena_den = t0.rezervace_kapacita_2 - t1.rezervace_kapacita_2,\\n registrace_celkem_zmena_den = t0.registrace_celkem - t1.registrace_celkem,\\n registrace_fronta_zmena_den = t0.registrace_fronta - t1.registrace_fronta,\\n registrace_rezervace_prumer_zmena_den = t0.registrace_rezervace_prumer - t1.registrace_rezervace_prumer, \\n registrace_tydenni_uspesnost_zmena_den = t0.registrace_tydenni_uspesnost - t1.registrace_tydenni_uspesnost,\\n registrace_14denni_uspesnost_zmena_den = t0.registrace_14denni_uspesnost - t1.registrace_14denni_uspesnost,\\n registrace_30denni_uspesnost_zmena_den = t0.registrace_30denni_uspesnost - t1.registrace_30denni_uspesnost,\\n registrace_prumer_cekani_zmena_den = t0.registrace_prumer_cekani - t1.registrace_prumer_cekani,\\n registrace_fronta_prumer_cekani_zmena_den = t0.registrace_fronta_prumer_cekani - t1.registrace_fronta_prumer_cekani,\\n vakciny_prijate_pocet_zmena_den = t0.vakciny_prijate_pocet - t1.vakciny_prijate_pocet,\\n vakciny_ockovane_pocet_zmena_den = t0.vakciny_ockovane_pocet - t1.vakciny_ockovane_pocet,\\n vakciny_znicene_pocet_zmena_den = t0.vakciny_znicene_pocet - t1.vakciny_znicene_pocet\\n from okresy_metriky t0 \\n join okresy_metriky t1\\n on t0.okres_id = t1.okres_id\\n where t.okres_id = t0.okres_id and t.datum = :datum and t0.datum = :datum and t1.datum = :datum_1 \\n \\\"\\\"\\\"\\n ), {'datum': self._date, 'datum_1': self._date - timedelta(1)})\\n\\n db.session.execute(text(\\n \\\"\\\"\\\"\\n update okresy_metriky t\\n set rezervace_celkem_zmena_tyden = t0.rezervace_celkem - t7.rezervace_celkem,\\n rezervace_cekajici_zmena_tyden = t0.rezervace_cekajici - t7.rezervace_cekajici,\\n rezervace_cekajici_1_zmena_tyden = t0.rezervace_cekajici_1 - t7.rezervace_cekajici_1,\\n rezervace_cekajici_2_zmena_tyden = t0.rezervace_cekajici_2 - t7.rezervace_cekajici_2,\\n rezervace_kapacita_zmena_tyden = t0.rezervace_kapacita - t7.rezervace_kapacita,\\n rezervace_kapacita_1_zmena_tyden = t0.rezervace_kapacita_1 - t7.rezervace_kapacita_1,\\n rezervace_kapacita_2_zmena_tyden = t0.rezervace_kapacita_2 - t7.rezervace_kapacita_2,\\n registrace_celkem_zmena_tyden = t0.registrace_celkem - t7.registrace_celkem,\\n registrace_fronta_zmena_tyden = t0.registrace_fronta - t7.registrace_fronta,\\n registrace_rezervace_prumer_zmena_tyden = t0.registrace_rezervace_prumer - t7.registrace_rezervace_prumer, \\n registrace_tydenni_uspesnost_zmena_tyden = t0.registrace_tydenni_uspesnost - t7.registrace_tydenni_uspesnost,\\n registrace_14denni_uspesnost_zmena_tyden = t0.registrace_14denni_uspesnost - t7.registrace_14denni_uspesnost,\\n registrace_30denni_uspesnost_zmena_tyden = t0.registrace_30denni_uspesnost - t7.registrace_30denni_uspesnost,\\n registrace_prumer_cekani_zmena_tyden = t0.registrace_prumer_cekani - t7.registrace_prumer_cekani,\\n registrace_fronta_prumer_cekani_zmena_tyden = t0.registrace_fronta_prumer_cekani - t7.registrace_fronta_prumer_cekani,\\n vakciny_prijate_pocet_zmena_tyden = t0.vakciny_prijate_pocet - t7.vakciny_prijate_pocet,\\n vakciny_ockovane_pocet_zmena_tyden = t0.vakciny_ockovane_pocet - t7.vakciny_ockovane_pocet,\\n vakciny_znicene_pocet_zmena_tyden = t0.vakciny_znicene_pocet - t7.vakciny_znicene_pocet\\n from okresy_metriky t0 \\n join okresy_metriky t7\\n on t0.okres_id = t7.okres_id\\n where t.okres_id = t0.okres_id and t.datum = :datum and t0.datum = :datum and t7.datum = :datum_7 \\n \\\"\\\"\\\"\\n ), {'datum': self._date, 'datum_7': self._date - timedelta(7)})\\n\\n app.logger.info('Computing okres metrics - deltas finished.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b79c3c014aa37635c68870e4c5b10e6\",\n \"score\": \"0.6111744\",\n \"text\": \"def update_q_table(self, state, action, reward):\\n\\n q_key = self.q_key(state, action)\\n inputs = self.env.sense(self)\\n self.next_waypoint = self.planner.next_waypoint()\\n new_state = self.set_current_state(inputs)\\n\\n #update q_value in q_table according to learning formula\\n x = self.q_value(state, action)\\n V = reward + (self.gamma * self.max_q_value(new_state))\\n new_q_value = x + (self.alpha * (V - x))\\n self.q_table[q_key] = new_q_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8699e853b356f0c81cc74866cb03acf\",\n \"score\": \"0.5995451\",\n \"text\": \"def update_qTable(q_table, state, action, reward, next_state_value, gamma_discount = 0.9, alpha = 0.5):\\n update_q_value = q_table[action, state] + alpha * (reward + (gamma_discount * next_state_value) - q_table[action, state])\\n q_table[action, state] = update_q_value\\n\\n return q_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ebd2f4754e0478c5f9b9f691ec46c2\",\n \"score\": \"0.59950894\",\n \"text\": \"def __refreshMeasurementsTables__(self):\\n key = self.logic.currentStentType\\n # for key in self.logic.getStentKeys():\\n measures = self.logic.currentMeasurements[key]\\n model = self.measurementsTableModels[key]\\n for row in range(len(measures)):\\n for col in range(2):\\n item = qt.QStandardItem()\\n item.setData(measures[row][col], qt.Qt.DisplayRole)\\n item.setEditable(False)\\n model.setItem(row, col, item)\\n\\n model2 = self.anglesTableModels[key]\\n item = qt.QStandardItem()\\n item.setData(self.logic.currentAngles[key][0], qt.Qt.DisplayRole)\\n item.setEditable(False)\\n model2.setItem(0, 0, item)\\n\\n item = qt.QStandardItem()\\n item.setData(self.logic.currentAngles[key][1], qt.Qt.DisplayRole)\\n item.setEditable(False)\\n model2.setItem(1, 0, item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e42cbe44b1751ede245519032135b8f\",\n \"score\": \"0.59406656\",\n \"text\": \"def update_Qtable(self, r, action, next_state):\\n if self.learning:\\n # TODO 8. When learning, update the q table according\\n # to the given rules\\n from_self = (1-self.alphas[self.state])*self.Qtable[self.state][action]\\n from_update = self.alphas[self.state]*(r+self.gamma*(max(self.Qtable[next_state].values())))\\n #from_self = (1-self.alpha)*self.Qtable[self.state][action]\\n #from_update = self.alpha*(r+self.gamma*(max(self.Qtable[next_state].values())))\\n self.Qtable[self.state][action] = from_self + from_update # 此处应该是根据公式来更新，没这么简单....\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d68d9d70ac8b43d853441f2e1f977e\",\n \"score\": \"0.5885301\",\n \"text\": \"def update_q_table(self, state, action, reward, next_state):\\n # Q(s, a) += \\n # alpha * (reward + gamma * max_a' Q(s', a') - Q(s, a))\\n q_value = self.gamma * np.amax(self.q_table[next_state])\\n q_value += reward\\n q_value -= self.q_table[state, action]\\n q_value *= self.learning_rate\\n q_value += self.q_table[state, action]\\n self.q_table[state, action] = q_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db45671af1a536fda11de6ca3cd88d2d\",\n \"score\": \"0.5873049\",\n \"text\": \"def _update_metrics():\\n data_list = expr_data['data'][expr.fmtd_sub_expr_str]['metrics']\\n start_time = t_now - (float(expr.period)) * int(expr.periods)\\n while (len(data_list) != 0\\n and data_list[0]['timestamp'] < start_time):\\n data_list.popleft()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6989a584386c5bafd7fb313b7e0dd63\",\n \"score\": \"0.5822668\",\n \"text\": \"def update_q(self, new_state: State, old_state: State, action: int, reward: float, end_of_race: bool) -> None:\\n\\n \\n \\n if(self.i == 99):\\n self.q_table.save(\\\"in.txt\\\")\\n\\n alpha = 0.3\\n gamma = 0.999\\n newQ = (1-alpha)*self.q_table.get_q_value(old_state,action) + alpha * (reward + gamma * self.maxQ(new_state))\\n self.q_table.set_q_value(old_state,action,newQ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"126a7c2cffa4604288e92cdaf064712c\",\n \"score\": \"0.57783085\",\n \"text\": \"def update_detrend_factor_table():\\n#\\n#--- clean up temp_dir so that we can put new fits files\\n#\\n mcf.mk_empty_dir(temp_dir)\\n#\\n#--- extract stat fits files\\n#\\n new_entry = get_new_entry()\\n#\\n#--- if files are extracted, compute amvp_avg values for the obsid\\n#\\n if len(new_entry) > 0:\\n processed_list = update_amp_avg_list(new_entry)\\n#\\n#--- clean up \\\"keep_entry\\\" and amp_avg_list \\n#\\n update_holding_list(new_entry, processed_list)\\n cleanup_amp_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"617c3c2a2540b4e81cc8061a591b9ad2\",\n \"score\": \"0.56813276\",\n \"text\": \"def updateQ(self, action, next_state):\\n\\n # Find the max value in the Q row (this is the best next Q following the action)\\n max_future_q = self.Q.maxValue(next_state) \\n \\n current_r = self.R.getValue(self.current_state, action, next_state)\\n current_q = self.Q.getValue(self.current_state, action, next_state)\\n\\n # Sometimes we get nulls. We can't do anything with them\\n if np.isnan(current_r):\\n return\\n\\n # Q learning formula - update the Q table current state with a little of the reward from the future\\n\\n # Simple version\\n #new_q = int(current_r + self.gamma * max_future_q)\\n\\n # Complex version\\n new_q = ((1-self.alpha) * current_q + self.alpha * (current_r + self.gamma * max_future_q))\\n\\n # Update q table with the new value\\n self.Q.setValue(self.current_state, action, next_state, new_q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14842c83fdeb770ecc6e62eda7a611d5\",\n \"score\": \"0.56682324\",\n \"text\": \"def _adjustTime(self):\\n for exam in range(len(self.exams)):\\n stdTime = self._getStudy(self.exams[exam])\\n self.studyTime[exam] = self.studyTime[exam] - stdTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ee899b7c22cf10e030e389f30d328f\",\n \"score\": \"0.5633692\",\n \"text\": \"def update_time_difference(query: str, **query_params) -> str:\\r\\n logger.info(\\\"Changing date_part subquery on query.\\\")\\r\\n\\r\\n try:\\r\\n # Check input variables\\r\\n assert \\\"time_difference\\\" in query_params.keys(), \\\"Time difference is not set on the yaml source\\\"\\r\\n assert type(query) == str, \\\"The variable query is not a string\\\"\\r\\n # Dict with the correspondence between date_part and its respective interval\\r\\n valid_interval_dif = {\\\"MONTH\\\": \\\"month\\\", \\\"WEEK\\\": \\\"days\\\", \\\"DAY\\\": \\\"day\\\", \\\"DOY\\\": \\\"day\\\"}\\r\\n # Possible variable units in interval function\\r\\n possible_intervals = ['year', 'month', 'day', 'hour', 'minute', 'second', 'years', 'months', 'days',\\r\\n 'hours', 'minutes', 'seconds']\\r\\n\\r\\n # Check input variables\\r\\n assert \\\"interval_dif\\\" in query_params['time_difference'].keys(), \\\\\\r\\n \\\"The interval_dif is not set on the time_difference on the yaml source\\\"\\r\\n assert \\\"interval_t\\\" in query_params['time_difference'].keys(), \\\\\\r\\n \\\"The interval_t is not set on the time_difference on the yaml source\\\"\\r\\n\\r\\n # Get interval difference\\r\\n interval_dif = query_params['time_difference']['interval_dif']\\r\\n assert type(interval_dif) == str, \\\"The variable interval_dif is not a string\\\"\\r\\n\\r\\n # Get time constant that will be multiplied by the interval difference\\r\\n interval_t = query_params['time_difference']['interval_t']\\r\\n assert type(interval_t) == int, \\\"The variable interval_dif is not an integer\\\"\\r\\n\\r\\n # Check if the interval difference corresponds to the date_part\\r\\n if interval_dif == 'date_part':\\r\\n assert \\\"time_groups\\\" in query_params.keys(), \\\"Time groups is not set on the yaml source\\\"\\r\\n assert type(query) == str, \\\"The variable query is not a string\\\"\\r\\n valid_date_parts = [\\\"MONTH\\\", \\\"WEEK\\\", \\\"DAY\\\", 'DOY']\\r\\n assert \\\"date_part\\\" in query_params['time_groups'].keys(), \\\\\\r\\n \\\"Date Part is not set on the time_groups on the yaml source\\\"\\r\\n date_part = query_params['time_groups']['date_part']\\r\\n\\r\\n interval_dif = valid_interval_dif[date_part]\\r\\n assert date_part in valid_date_parts, \\\"The given date_part is not valid\\\"\\r\\n\\r\\n # In case of 'WEEK' than the number of days should be multiplied by 7 which corresponds to 1 week\\r\\n if date_part == 'WEEK':\\r\\n interval_t = interval_t * 7\\r\\n assert interval_dif in possible_intervals, \\\"The given interval_dif is not valid\\\"\\r\\n\\r\\n # Replace the interval difference\\r\\n tag = \\\"[{}]\\\".format(\\\"interval_dif\\\")\\r\\n query = query.replace(tag, \\\"'{} {}'\\\".format(interval_t, interval_dif))\\r\\n\\r\\n return query\\r\\n except AssertionError as e:\\r\\n logger.error(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35be5bd7f791f49d081f72dfb3e45e04\",\n \"score\": \"0.56207436\",\n \"text\": \"def update(self, exp):\\n\\t\\ts=exp[0]\\n\\t\\ta=exp[1]\\n\\t\\ts_=exp[2]\\n\\t\\tr=exp[3]\\n\\t\\tdone=exp[4]\\n\\n\\t\\tself.t=self.t+1\\n\\n\\t\\t# q update\\n\\t\\tn_keys=[(s_,act) for act in self.actions]\\n\\t\\tq_n=[self.get_vals(k[0],k[1],self.Q) for k in n_keys]\\n\\t\\t\\n\\t\\tq_target=(r+self.gamma*max(q_n)) if not done else r\\n\\t\\t# print('prev q '+str((s,a))+' : '+str(self.get_vals(s,a,self.Q)))\\n\\t\\tself.Q[s,a]=self.get_vals(s,a,self.Q)+self.alpha(self.t)*(q_target-self.get_vals(s,a,self.Q))\\n\\t\\t# print('new q '+str((s,a))+' : '+str(self.get_vals(s,a,self.Q)))\\n\\t\\tkeys=[(s,act) for act in self.actions]\\n\\t\\ttot_q=sum([math.exp(self.get_vals(k[0],k[1],self.Q)) for k in keys])\\n\\t\\tfor k in keys:\\n\\t\\t\\tself.pi[k]=math.exp(self.get_vals(k[0],k[1],self.Q))/tot_q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ae845767c2c70b1cf49492ffa3b326\",\n \"score\": \"0.55660784\",\n \"text\": \"def update(self, q):\\r\\n self.repeat += 1\\r\\n\\r\\n newEF = self.EF+(0.1-(5-q)*(0.08+(5-q)*0.02))\\r\\n if newEF < 1.3:\\r\\n self.EF = 1.3\\r\\n elif newEF > 2.5:\\r\\n self.EF = 2.5\\r\\n else:\\r\\n self.EF = newEF\\r\\n\\r\\n self.interval = self.get_interval(self.repeat, self.interval, self.EF)\\r\\n self.nextDate = self.startDate + timedelta(days=self.interval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dde9bee8f848f23572943603697d185\",\n \"score\": \"0.5544781\",\n \"text\": \"def _update_state():\\n data_sub = expr_data['data'][expr.fmtd_sub_expr_str]\\n data_list = data_sub['metrics']\\n period = float(expr.period)\\n periods = int(expr.periods)\\n right = t_now\\n left = right - period\\n temp_data = []\\n value_in_periods = []\\n i = len(data_list) - 1\\n while i >= 0:\\n if data_list[i]['timestamp'] >= left:\\n temp_data.append(float(data_list[i]['value']))\\n else:\\n value = calculator.calc_value(\\n expr.normalized_func, temp_data)\\n value_in_periods.append(value)\\n right = left\\n left = right - period\\n temp_data = []\\n i += 1\\n i -= 1\\n value = calculator.calc_value(\\n expr.normalized_func, temp_data)\\n value_in_periods.append(value)\\n for i in range(len(value_in_periods), periods, 1):\\n value_in_periods.append(\\n calculator.calc_value(expr.normalized_func, []))\\n expr_data['data'][expr.fmtd_sub_expr_str]['values'] = (\\n value_in_periods)\\n expr_data['data'][expr.fmtd_sub_expr_str]['state'] = (\\n calculator.compare_thresh(\\n value_in_periods,\\n expr.normalized_operator,\\n float(expr.threshold)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"160f1689783c16ce1c9a732e0909e420\",\n \"score\": \"0.5463045\",\n \"text\": \"def update(self, t):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746d7b7d77bd2722c164d2cebab6e578\",\n \"score\": \"0.5441705\",\n \"text\": \"def test_update_timestep(self):\\n\\n mps = self.model.update_timestep(self.modelparams, self.expparams)\\n\\n assert(mps.shape == (\\n self.n_models,\\n self.model.n_modelparams,\\n self.n_expparams\\n ))\\n mps = mps.transpose((2,0,1)).reshape(self.n_models * self.n_expparams, -1)\\n \\n assert(np.all(self.model.are_models_valid(mps)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90319f983b244a5e82588b53b7d862da\",\n \"score\": \"0.54386264\",\n \"text\": \"def update_T(self, sq):\\n for state, r_action, h_action, state_star in sq:\\n self.T[self.action_idx[r_action]][self.state_idx[state]][self.state_idx[state_star]] += 1.0\\n self.T[self.action_idx[r_action]][self.state_idx[state]] = self.T[self.action_idx[r_action]][self.state_idx[state]] / sum(self.T[self.action_idx[r_action]][self.state_idx[state]])\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c87b6f0becf91d53073f6626fc1e50\",\n \"score\": \"0.5431382\",\n \"text\": \"def apply_proper_motion(star_records: pd.DataFrame, new_time: Union[Real, datetime.datetime],\\n copy: bool = True) -> pd.DataFrame:\\n\\n if copy:\\n out = star_records.copy()\\n\\n else:\\n out = star_records\\n\\n # convert the ra and dec values into radians\\n ra0 = out['ra'].values * DEG2RAD # type: np.ndarray\\n dec0 = out['dec'].values * DEG2RAD # type: np.ndarray\\n\\n # assume linear motion based on Hipparcos Vol 1\\n # compute the unit vector for each star\\n r_unit = radec_to_unit(ra0, dec0).reshape(3, -1)\\n\\n # compute the unit vector in the direction of increasing right ascension\\n p_unit = np.vstack([-np.sin(ra0), np.cos(ra0), np.zeros(ra0.shape)])\\n\\n # compute the unit vector in the direction of increasing declination\\n q_unit = np.vstack([-np.sin(dec0) * np.cos(ra0), -np.sin(dec0) * np.sin(ra0), np.cos(dec0)])\\n\\n # compute the change in distance per year (future expansion)\\n zeta0 = 0\\n\\n start_time = out['epoch'].values\\n\\n # compute the time delta\\n if isinstance(new_time, datetime.datetime):\\n\\n new_time = timedelta_to_si_years(new_time - datetime.datetime(1, 1, 1))\\n\\n timedelta = new_time - start_time\\n\\n # compute the new direction vector\\n r_unit_new = r_unit * (1 + zeta0 * timedelta) + (p_unit * out['ra_proper_motion'].values * DEG2RAD +\\n q_unit * out['dec_proper_motion'].values * DEG2RAD) * timedelta\\n r_unit_new /= np.linalg.norm(r_unit_new, axis=0, keepdims=True)\\n\\n # compute the new ra/dec and store it\\n ra, dec = unit_to_radec(r_unit_new)\\n\\n out['ra'] = ra * RAD2DEG\\n out['dec'] = dec * RAD2DEG\\n\\n # compute the new uncertainties for ra and dec\\n out['ra_sigma'] = np.sqrt(out['ra_sigma'] ** 2 + timedelta ** 2 * out['ra_pm_sigma'] ** 2)\\n out['dec_sigma'] = np.sqrt(out['dec_sigma'] ** 2 + timedelta ** 2 * out['dec_pm_sigma'] ** 2)\\n\\n # update the epochs\\n out[\\\"epoch\\\"] = new_time\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2599bedc51975e9b7ff104b997ad89\",\n \"score\": \"0.54156566\",\n \"text\": \"def update(self, delta_time):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2599bedc51975e9b7ff104b997ad89\",\n \"score\": \"0.54156566\",\n \"text\": \"def update(self, delta_time):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3972721a1a91ceee7b1052ad72454d68\",\n \"score\": \"0.5413627\",\n \"text\": \"def update_Q_sim(\\n choice, reward,\\n Qs, _,\\n alpha, nalpha, calpha, cnalpha,\\n n_subj, verbose):\\n\\n # if n_trials_back == 0:\\n index = np.arange(n_subj), choice\\n cindex = np.arange(n_subj), 1 - choice\\n\\n # Get reward prediction errors (RPEs)\\n RPE = (1 - Qs[index]) * reward # RPEs for positive outcomes (reward == 1)\\n nRPE = (0 - Qs[index]) * (1 - reward) # RPEs for negative outcomes (reward == 0)\\n\\n # Get counterfactual prediction errors (cRPEs)\\n cRPE = (0 - Qs[cindex]) * reward # actual reward was 1; I think I would have gotten 0 for the other action\\n cnRPE = (1 - Qs[cindex]) * (1 - reward) # actual reward 0; would have gotten 1 for the other action\\n\\n # Update action taken\\n Qs[index] += alpha * RPE + nalpha * nRPE # add RPE at all pos. outcomes, and nRPE at all neg. outcomes\\n\\n # Update counterfactual action\\n Qs[cindex] += calpha * cRPE + cnalpha * cnRPE # add cRPE at all pos. outcomes, and cnRPE at all neg. outcomes\\n\\n if verbose:\\n print('upd_Q - index: ', index)\\n print('upd_Q - cindex: ', cindex)\\n print('upd_Q - RPE: ', np.round(RPE, 2))\\n print('upd_Q - nRPE: ', np.round(nRPE, 2))\\n print('upd_Q - cRPE: ', np.round(cRPE, 2))\\n print('upd_Q - cnRPE: ', np.round(cnRPE, 2))\\n print('upd_Q - new Qs:\\\\n', np.round(Qs, 2))\\n\\n return Qs, _\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34cc0dd6199255acca876ef93e35db32\",\n \"score\": \"0.5406927\",\n \"text\": \"def update_qval(q_values,action_count, reward, action):\\n q_values[action] = q_values[action] + ((reward[action]-q_values[action])/action_count[action])\\n return q_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b7b5d64cd4427140b2e798c56b48b0c\",\n \"score\": \"0.53899384\",\n \"text\": \"def end_of_timestep_update(self, i_episode, timestep, done):\\n if timestep == 0:\\n return\\n S_old, A_old, R = self.episode_database[i_episode][timestep - 1]\\n\\n S_new, A_new, _ = self.episode_database[i_episode][timestep]\\n\\n S_old_disc = self.__discretize_observation(S_old)\\n S_new_disc = self.__discretize_observation(S_new)\\n\\n self.Q[tuple(S_old_disc + [A_old])] += self.alpha * (\\n R + self.discount_rate * self.Q[tuple(S_new_disc + [A_new])] - self.Q[tuple(S_old_disc + [A_old])])\\n\\n # EXTREMELY IMPORTANT\\n if done and timestep < 199:\\n # we have to set the reward for the last-terminal observation manually, since the above update can sometimes\\n # adjusts it -> WHY????\\n # The same continuous state sometimes leads to done and sometimes not -> Because we discretized our\\n # sample space - so it is really important to just manually make sure that its 0\\n\\n # Maybe a more elegant solution is to select threshold points such that the bins correspond\\n # To the actual values that lead to failure (x /notin (-2.4,2.4) and theta /notin ()\\n\\n # It works like an induction, these terminal states will backpropagate to the earlier states\\n self.Q[tuple(S_new_disc + [A_new])] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420961c503869e3fbc0fe2a84d6f8bff\",\n \"score\": \"0.5385878\",\n \"text\": \"def _update_expressions(self, time):\\n self.params.time_update_parameters(time)\\n self.bcs.time_update_bcs(time, kind='dirichlet')\\n self.bcs.time_update_bcs(time, kind='von-neumann')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e19e36bb873cd7a18f30e9bcd22e1d6\",\n \"score\": \"0.5352648\",\n \"text\": \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n # the qvalue is given by our getQValue function, \\n qvalue = self.getQValue(state, action)\\n # the learning rate, alpha\\n a = self.alpha\\n # discount \\n discount = self.discount\\n # next state's value\\n nextQ = self.computeValueFromQValues(nextState)\\n\\n # use formula Qt+1 = ((reward + discount * future value) - qvalue) * a\\n self.qv[(state,action)] = ((reward + discount * nextQ) - qvalue) * a + qvalue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3181dc81f3afd6e14d4194477471ec\",\n \"score\": \"0.53433126\",\n \"text\": \"def update(self, delta_time):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"275a6b6b6270f9100b272ebf70a1035f\",\n \"score\": \"0.53428423\",\n \"text\": \"def temp_update(self) -> None:\\r\\n\\r\\n self.update_values()\\r\\n self.after(300000, self.temp_update)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"248c10aa5d1a556126bfea19cc21a88c\",\n \"score\": \"0.53053635\",\n \"text\": \"def test(self):\\n testData = SrtDataSet.fromLines(\\n \\\"1041\\\\n01:37:25,200 --> 01:37:30,700\\\\nTraduction : Attila0375\\\\nhttp://attilaswebsite.free.fr\\\".split(\\\"\\\\n\\\"))\\n print(testData.toString())\\n testData.applyDelta(timedelta(seconds=-5))\\n print(testData.toString())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da18c1345805564d4d80724d8bb6d853\",\n \"score\": \"0.5297251\",\n \"text\": \"def update(self, dt):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da18c1345805564d4d80724d8bb6d853\",\n \"score\": \"0.5297251\",\n \"text\": \"def update(self, dt):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d7d3d846aed8e436f1e86d5278ada6\",\n \"score\": \"0.5295477\",\n \"text\": \"def update_comparison_table(lr_report_1:str , lr_report_2:str):\\n lr_report_data_1 = {} # values = [(names,scores)]\\n lr_report_data_2 = {}\\n db_entry_1 = []\\n db_entry_2 = []\\n\\n transaction_names = []\\n extra_names = []\\n extra_lr_1_ninety = []\\n extra_lr_2_ninety = []\\n diff = []\\n \\n for report in db_data:\\n if lr_report_1 in report.lr_name:\\n lr_report_data_1[report.lr_name] = []\\n db_entry_1 = report\\n if lr_report_2 in report.lr_name:\\n lr_report_data_2[report.lr_name] = []\\n db_entry_2 = report\\n \\n for transaction in db_entry_1.lr_transactions:\\n lr_report_data_1[db_entry_1.lr_name].append((transaction.transaction_name, \\n transaction.nintieth_percentile)\\n )\\n for transaction in db_entry_2.lr_transactions:\\n lr_report_data_2[db_entry_2.lr_name].append((transaction.transaction_name, \\n transaction.nintieth_percentile)\\n )\\n \\n \\n for transaction_1 in lr_report_data_1.values(): # This finds the transactions common between versions.\\n for transaction_2 in lr_report_data_2.values():\\n if transaction_1[0] == transaction_2[0] and transaction_1[0] not in transaction_names:\\n transaction_names.append(transaction_1[0])\\n diff.append(transaction_1[1] - transaction_2[1])\\n transaction_1[1] = 0\\n transaction_2[1] = 0\\n \\n ## all transactions which are NOT zeroes will be appended to their respective 'extra' arrays ##\\n for transaction in lr_report_data_1.values():\\n if transaction[1] !=0:\\n extra_names.append(transaction[0])\\n extra_lr_1_ninety.append(transaction[1])\\n extra_lr_2_ninety.append('<b>N/A</b>') \\n\\n for transaction in lr_report_data_2.values():\\n if transaction[1] !=0:\\n extra_names.append(transaction[0])\\n extra_lr_2_ninety.append(transaction[1])\\n extra_lr_1_ninety.append('<b>N/A</b>')\\n\\n\\n ## apply color to diff corresponding to test that has higher runtime for runtimes greater than 1 second difference ##\\n for i in range(len(diff)):\\n if float(diff[i]) > 1.0:\\n diff[i] = \\\"<span style='color:deeppink'><b>%s</b></span>\\\" %str(round(diff[i], 3))\\n elif float(diff[i]) < -1.0:\\n diff[i] = \\\"<span style='color:blue'><b>%s</b></span>\\\" %str(round(diff[i], 3))\\n else:\\n diff[i] = str(round(diff[i], 3))\\n\\n ## CREATE THE TABLE\\n table = go.Figure(data =[go.Table(columnwidth = [150, 70, 70, 70], \\n header = dict(values = (\\\"Transaction Name\\\", \\\"R1 :Ninetieth Percentile \\\" \\n + lr_report_1, \\\"R2: Ninetieth Percentile \\\" \\n + lr_report_2, \\\"Diff (R1 - R2)\\\"\\n ), \\n fill_color = ['lightgray', colors[0], colors[1], 'lightgray'], \\n font_color = ['black', 'black', 'black', 'black'] , align ='left'\\n ),\\n cells = dict(values = [transaction_names + extra_names, \\n lr_report_data_1.values()[0] + lr_report_data_1.values()[1], \\n lr_report_data_2.values()[0] + lr_report_data_2.values()[1], diff\\n ], \\n align = 'left')\\n )\\n ])\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8c47a812d5718dac22ccc9f43a4243\",\n \"score\": \"0.5293238\",\n \"text\": \"def do_reverse_q_updates(self, state_seq, reward_seq, action_seq):\\n assert(len(state_seq) == len(action_seq) + 1)\\n assert(len(state_seq) == len(reward_seq))\\n\\n for i in reversed(range(len(action_seq))):\\n state = state_seq[i]\\n action = action_seq[i]\\n new_state = state_seq[i+1]\\n reward = reward_seq[i]\\n self.qtable[state, action] = self.compute_q_update(state, action, new_state, reward)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d29d35e7e32efd6ec2a17d49f0a62a3\",\n \"score\": \"0.52792513\",\n \"text\": \"def update_quantities(self):\\n\\n t = self.get_time()\\n self.dt = self.get_timestep()\\n \\n if sum(self.ind) > 0: \\n \\n quant = self.domain.quantities['elevation']\\n quant.compute_gradients()\\n\\n S = num.maximum(num.abs(quant.x_gradient), num.abs(quant.y_gradient))\\n \\n self.u_star = num.zeros_like(self.depth)\\n \\n self.u_star[self.ind] = num.sqrt(g * S[self.ind] * self.depth[self.ind])\\n \\n self.edot = self.erosion()\\n self.ddot = self.deposition()\\n\\n self.dzdt = (self.ddot - self.edot) / (1 - self.porosity)\\n \\n self.dChdt = (self.edot - self.ddot)\\n \\n self.update_concentration(self.dChdt)\\n self.sediment_flux()\\n \\n self.update_bed(self.dzdt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34fbcd0f10334b95cffab8b8f4eb8453\",\n \"score\": \"0.52701086\",\n \"text\": \"def test_updateColByInterpolation(self):\\n table = self.InterpolationTableWithDuplicate\\n obs = table.updateColByInterpolation('GrowthRate','Temp').delimitedSelf()\\n exp =\\\\\\n [\\\"#SampleID\\\\tTemp\\\\tGrowthRate\\\\tDescription\\\\n\\\",\\\\\\n \\\"#Comment line, full of crazy comments\\\\n\\\",\\\\\\n \\\"#Another comment line\\\\n\\\",\\\\\\n \\\"S.1\\\\t1.0\\\\t3.0\\\\tSample.1.Description\\\\n\\\",\\\\\\n \\\"S.2\\\\t2.0\\\\t2.0\\\\tSample.2.Description\\\\n\\\",\\\\\\n \\\"S.3\\\\t3.0\\\\t0.0\\\\tSample.3.Description\\\\n\\\",\\\\\\n \\\"S.4\\\\t0.0\\\\t3.0\\\\tSample.4.Description\\\\n\\\",\\\\\\n \\\"S.5\\\\t1.0\\\\t3.0\\\\tSample.5.Description\\\\n\\\",\\\\\\n \\\"S.5b\\\\t1.0\\\\t3.0\\\\tSample.5.Description\\\\n\\\",\\\\\\n \\\"S.6\\\\t1.5\\\\t2.5\\\\tSample.6.Description\\\\n\\\",\\\\\\n \\\"S.7\\\\t2.72\\\\t0.56\\\\tSample.7.Description\\\\n\\\",\\\\\\n \\\"S.8\\\\t3.14\\\\t0.0\\\\tSample.7.Description\\\\n\\\"] \\n self.assertEqualItems(obs,exp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15e2a1d9d602ffdefa14f68ed8ca6e8\",\n \"score\": \"0.52600527\",\n \"text\": \"def update(self, reward):\\n # the following are for most recent played state-action pair:\\n # calculate TD error\\n if self.new_action is not None: # non-terminal new state state\\n td_error = (\\n reward\\n + self.agent.action_value_function[\\n self.new_state.dealer_first_card,\\n self.new_state.hand_value,\\n self.new_action.value,\\n ]\\n - self.agent.action_value_function[\\n self.old_state.dealer_first_card,\\n self.old_state.hand_value,\\n self.old_action.value,\\n ]\\n )\\n else: # terminal new state, new Q = 0\\n td_error = (\\n reward\\n - self.agent.action_value_function[\\n self.old_state.dealer_first_card,\\n self.old_state.hand_value,\\n self.old_action.value,\\n ]\\n )\\n\\n # bump E eligibility trace for old state by 1\\n self.eligibility_trace[\\n self.old_state.dealer_first_card,\\n self.old_state.hand_value,\\n self.old_action.value,\\n ] += 1\\n # bump N(s,a) state-action visit count\\n self.agent.state_action_count[\\n self.old_state.dealer_first_card,\\n self.old_state.hand_value,\\n self.old_action.value,\\n ] += 1\\n # bump N(s) state visit count\\n self.agent.state_count[\\n self.old_state.dealer_first_card,\\n self.old_state.hand_value,\\n ] += 1\\n # the following are for all state-action pairs:\\n # update Q\\n self.agent.action_value_function += (\\n self.agent.alpha(self.old_state, self.old_action)\\n * td_error\\n * self.eligibility_trace\\n )\\n # update E\\n self.eligibility_trace = self.agent.lamda * self.eligibility_trace\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47cefc426717737d6aa72bc947a35f2\",\n \"score\": \"0.5253603\",\n \"text\": \"def set_t(self):\\n if ('t' not in self.keys()) & ('datetime' in self.input):\\n t0 = min(self['datetime'])\\n t = ma.array([dt.total_seconds() for dt in self['datetime'] - t0])\\n #t_median = ma.median(t)\\n #self.t_ref = t0+timedelta(seconds = dt_median)\\n #self.data['t'] = t - t_median\\n self.data['t'] = t\\n #self.data['t'] = self.input['t'] - np.median(self.input['t'])\\n\\n #self.ref['datetime'] = t0 + timedelta(seconds=t_median)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ab4bc012a2ca702d4267d7a2ad575c\",\n \"score\": \"0.5240644\",\n \"text\": \"def update(self, state, action, nextState, reward):\\n # return\\n # We need calculate Q value by own\\n next_q_value = 0.0\\n next_move = (nextState, self.getPolicy(nextState))\\n if next_move in self.q_values:\\n next_q_value = self.q_values[next_move]\\n\\n expected_value_in_next_state = reward + self.discount * next_q_value\\n value_in_current_state = self.q_values[(state, action)]\\n\\n # If values changed - update it\\n if value_in_current_state != expected_value_in_next_state:\\n # Update weights\\n for (key, value) in self.weights.items():\\n self.weights[key] = value + self.alpha * (expected_value_in_next_state - value_in_current_state) * self.featureVector[key]\\n\\n # Update QTable\\n value_in_current_state = value_in_current_state + self.alpha * (expected_value_in_next_state - value_in_current_state)\\n\\n # Update epsilon in learning state\\n eps = 0.05\\n self.epsilon = max(0.0, -eps / 4500 * self.episodesSoFar + eps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847d9f909127d26a05a40a190b6b67c6\",\n \"score\": \"0.5223851\",\n \"text\": \"def update_timeindex(self, event):\\n bars = {}\\n for sym in self.symbol_list:\\n bars[sym] = self.bars.get_n_bars(sym, self.index_)\\n \\n self.index_ += 1\\n #Update position\\n dp = dict((k,v) for k,v in [(s,0) for s in self.symbol_list])\\n dp['datetime'] = bars[self.symbol_list[0]]['date']\\n \\n for s in self.symbol_list:\\n dp[s] = self.current_positions[s]\\n \\n self.all_positions.append(dp)\\n \\n #Update holdings\\n dh = dict((k,v) for k,v in [(s,0) for s in self.symbol_list])\\n dh['datetime'] = bars[self.symbol_list[0]]['date']\\n dh['cash']= self.current_holdings['cash']\\n dh['commission'] = self.current_holdings['commission']\\n dh['total'] = self.current_holdings['cash']\\n \\n for s in self.symbol_list:\\n \\n #Approximation to the real value \\n if bars[s]['close']!=0:\\n market_value = self.current_positions[s]*bars[s]['close']\\n dh[s] = market_value\\n dh['total'] += market_value\\n else:\\n market_value = self.all_holdings[-1][s]\\n dh[s] = market_value\\n dh['total']+=market_value\\n \\n self.all_holdings.append(dh)\\n \\n \\\"\\\"\\\"\\n Creates a pandas DataFrame from the all_holdings\\n list of dictionaries.\\n \\\"\\\"\\\"\\n curve = pd.DataFrame(self.all_holdings)\\n ##make datetime as index\\n curve.set_index('datetime', inplace=True)\\n curve['returns'] = curve['total'].pct_change()\\n curve['equity_curve'] = (1.0+curve['returns']).cumprod()\\n \\n self.equity_curve = curve\",\n \"title\": \"\"\n },\n {\n \"docid\": \"780da0c5c5ef433d009843c28caa7a41\",\n \"score\": \"0.52154934\",\n \"text\": \"def computeDeltaT(self):\\n\\t\\t\\n\\t\\tself.dt=[]\\n\\t\\t\\n\\t\\tfor i in range(len(self.date)-1):\\n\\t\\t\\tt=self.date[i+1]-self.date[i]\\n\\t\\t\\tself.dt.append(t.seconds)\\n\\t\\t\\n\\t\\tself.t=[self.dt[0]]\\n\\t\\t\\n\\t\\tfor d in self.dt[1:]:\\n\\t\\t\\tself.t.append(self.t[-1]+d)\\n\\t\\t\\n\\t\\tself.dt=np.array(self.dt)\\n\\t\\tself.t=np.array(self.t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"345f8814917eef3571f6e3ba1720c8cc\",\n \"score\": \"0.5210371\",\n \"text\": \"def correct(self, update_time):\\n if (len(self.main_filter.ledger_update_times) > 1) and (update_time <= self.main_filter.ledger_update_times[-1]):\\n raise ValueError(\\\"update time must be later in time than last update time\\\")\\n\\n self.main_filter.correct(update_time)\\n for key in self.delta_tiers.keys():\\n self.delta_tiers[key].correct(update_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2fc1fc36ef858d4b9bb7e9770206809\",\n \"score\": \"0.52055526\",\n \"text\": \"def test_fickett_table10_1(self):\\r\\n\\r\\n def compare_to_table(self, dataray):\\r\\n\\r\\n sigfigs = 6 # Number of significant figures in table\\r\\n\\r\\n time = dataray[:, 0]\\r\\n position = dataray[:, 1]\\r\\n pressure = dataray[:, 2]\\r\\n velocity = dataray[:, 3]\\r\\n density = dataray[:, 4]\\r\\n sound_speed = dataray[:, 5]\\r\\n shock_velocity = dataray[:, 6]\\r\\n reaction_progress = dataray[:, 7]\\r\\n\\r\\n solution = SteadyDetonationReactionZone()\\r\\n\\r\\n result = solution.run_tvec(time) # run_tvec method expects vector of times\\r\\n\\r\\n np.testing.assert_allclose(result['position_relative'], position, rtol=1.0e-6)\\r\\n np.testing.assert_allclose(result['pressure'], pressure, rtol=1.0e-6)\\r\\n np.testing.assert_allclose(result['velocity'], velocity, rtol=1.0e-6)\\r\\n np.testing.assert_allclose(result['density'], density, rtol=1.0e-6)\\r\\n np.testing.assert_allclose(result['sound_speed'], sound_speed, rtol=1.0e-6)\\r\\n np.testing.assert_allclose(result['reaction_progress'],\\r\\n reaction_progress, rtol=1.0e-6)\\r\\n\\r\\n # Table 10.1\\r\\n\\r\\n # 't', 'x', 'pressure', 'velocity', 'density', 'sound_speed',\\r\\n # shockVel', 'reaction_progress'\\r\\n\\r\\n dataray=np.array([\\r\\n [0.0, 0.0000000, 0.57800000, 0.4250000, 3.200000, 0.7361216, \\r\\n 0.85, 0.000],\\r\\n [0.1, 0.0435625, 0.54910000, 0.4037500, 3.047619, 0.7352009,\\r\\n 0.85, 0.19],\\r\\n [0.2, 0.0892500, 0.52020000, 0.3825000, 2.909091, 0.7324317,\\r\\n 0.85, 0.36],\\r\\n [0.3, 0.1370625, 0.49130000, 0.3612500, 2.782609, 0.7277931,\\r\\n 0.85, 0.51],\\r\\n [0.4, 0.1870000, 0.46240000, 0.3400000, 2.666667, 0.7212489,\\r\\n 0.85, 0.64],\\r\\n [0.5, 0.2390625, 0.43350000, 0.3187500, 2.560000, 0.7127467,\\r\\n 0.85, 0.75],\\r\\n [0.6, 0.2932500, 0.40460000, 0.2975000, 2.461538, 0.7022152,\\r\\n 0.85, 0.84],\\r\\n [0.7, 0.3495625, 0.37570000, 0.2762500, 2.370370, 0.6895617,\\r\\n 0.85, 0.91],\\r\\n [0.8, 0.4080000, 0.34680000, 0.2550000, 2.285714, 0.6746666,\\r\\n 0.85, 0.96],\\r\\n [0.9, 0.4685625, 0.31790000, 0.2337500, 2.206897, 0.6573776,\\r\\n 0.85, 0.99],\\r\\n [1.0, 0.5312500, 0.28900000, 0.2125000, 2.133333, 0.6375000,\\r\\n 0.85, 1.00],\\r\\n [1.2, 0.6587500, 0.28900000, 0.2125000, 2.133333, 0.6375000,\\r\\n 0.85, 1.00]\\r\\n ])\\r\\n\\r\\n # NOTE: CORRECTED VALUE 0.70125 to 0.65875 for x value at t=1.2\\r\\n # (Error in original table and formula)\\r\\n\\r\\n compare_to_table(self, dataray)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b387a728228cb26709b35bde3898b9\",\n \"score\": \"0.5190317\",\n \"text\": \"def update_table(var: []):\\n new_table = var['table_new']\\n old_table = var['table_old']\\n date_col = var['date_col']\\n start_date = grab_date(new_table, date_col)\\n client = bigquery.Client()\\n union_query = update_query(new_table, old_table, date_col, start_date)\\n query_job = client.query(union_query)\\n query_job.result()\\n cloud_logger.info(f\\\"{old_table} merged with {new_table} with cutoff date {start_date}\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd2c88378b611ef708fc8201d15a16b\",\n \"score\": \"0.51787657\",\n \"text\": \"def _update_hfield(self):\\n h_t1 = self._hfield[:-1]\\n h_t2 = self._coeff_h1 * (self._efield[1:] - self._efield[:-1])\\n self._hfield[:-1] = h_t1 - h_t2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3d34fd8c6d63195ee19fc8dcbbd5d0\",\n \"score\": \"0.51699024\",\n \"text\": \"def update(self, state, action, value, alpha=0.1):\\n # TODO: Encode state to get tile indices\\n discrete_states = tile_encode(state, self.tilings)\\n \\n # TODO: Update q-value for each tiling by update factor alpha\\n for q_table, state in zip(self.q_tables, discrete_states):\\n q_table[tuple(state) + (action,)] += alpha * (value - q_table[tuple(state) + (action,)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de16971274fa208a01707fd8a2fb1974\",\n \"score\": \"0.51628643\",\n \"text\": \"def update(self, state, action, reward, next_state) -> None:\\n # select next action from policy.\\n next_action = self.compute_action(next_state, self.action.episode)\\n\\n state = self.q_table.digitize(state)\\n next_state = self.q_table.digitize(next_state)\\n\\n cur_q = self.q_table[state, action]\\n next_q = self.q_table[next_state, next_action]\\n td_target = reward + self.gamma * next_q\\n self.q_table[state, action] = cur_q + self.alpha * (td_target - cur_q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2f6ee689eecb02532bc56cc2169f59\",\n \"score\": \"0.5155594\",\n \"text\": \"def updateCurrentValues(self):\\n \\n percent = 0.001\\n self.currentValues = {}\\n for row in range(len(self.pvs)):\\n pv = self.pvs[row]\\n self.currentValues[pv] = self.epicsGet.caget(pv)\\n self.ui.tableWidget.setItem(row,2,QtGui.QTableWidgetItem(str(self.currentValues[pv])))\\n\\n tol = abs(self.startValues[pv]*percent)\\n diff = abs(abs(self.startValues[pv]) - abs(self.currentValues[pv]))\\n if diff > tol:\\n self.ui.tableWidget.item(row,2).setForeground(QtGui.QColor(255,101,101))#red\\n else:\\n self.ui.tableWidget.item(row,2).setForeground(QtGui.QColor(255,255,255))#white\\n QApplication.processEvents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6504c798ba4e87073850e0f78cc3bde1\",\n \"score\": \"0.5154043\",\n \"text\": \"def test_time_history_not_enough_columns(self):\\n time_history = {'type': 'pressure', 'quantity': {'units': 'bar', 'column': 1}}\\n time_history['time'] = {'units': 'second', 'column': 0}\\n time_history['values'] = [[0], [1]]\\n assert not v.validate({'datapoints': [{'time-histories': [time_history]}]}, update=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880a861a5036e0aae73dcd5ce3058e2e\",\n \"score\": \"0.51435024\",\n \"text\": \"def test_agent_updates_Q_properly():\\n #constants\\n training_steps = 24 #max q learning steps\\n training_cohort = 3\\n testing_steps = 6\\n testing_cohort = 10\\n\\n #conduct deep q learning\\n agent = QAgent(discount=0.95, exploration_rate=1.0, exploration_rate_decay=0.9)\\n simulator = AbbcEnvironment(patients=training_cohort, validation_fraction=.3)\\n rewardlog, actionlog = run_simulation(agent, simulator, training_steps, enroll_rate=1)\\n print(\\\"dql training\\\")\\n print(actionlog)\\n print(rewardlog)\\n\\n #simulate trained dql agent with fixed policy\\n simulator = AbbcEnvironment(patients=testing_cohort)\\n rewardlog, actionlog = run_simulation(agent, simulator, testing_steps, update=False)\\n print(\\\"dql testing\\\")\\n print(actionlog)\\n print(rewardlog)\\n\\n #Takes any drug more than 80% of the time\\n assert np.mean(actionlog>0) > .80\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6060390d1dab2dea0f9c9c979afc68a9\",\n \"score\": \"0.51340073\",\n \"text\": \"def update_dense_time(self, i):\\n start = self.time.data[i]\\n \\n self.dense_time.start = start\\n self.dense_time = TimeAxis(self.dense_time.start, \\n self.dense_time.length,\\n self.dense_time.step)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6244cb3edaea7556c151013b7757c5b0\",\n \"score\": \"0.5131417\",\n \"text\": \"def updateReference(self):\\n self.ui.updateReference.setText(\\\"Getting vals...\\\")\\n self.getStartValues()\\n for row in range(len(self.pvs)):\\n pv = self.pvs[row]\\n self.ui.tableWidget.setItem(row,1,QtGui.QTableWidgetItem(str(self.startValues[pv])))\\n self.ui.updateReference.setText(\\\"Update Reference\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380d6d62abc71003126e7d26d4bdf320\",\n \"score\": \"0.512841\",\n \"text\": \"def update_quick(self, t_subtracted, gamma):\\n p = np.pi\\n t = t_subtracted.reshape(1, -1)\\n s, l = self.scale.reshape(-1, 1), self.loc.reshape(-1, 1)\\n self.weight = ne.evaluate('gamma / (((t-l)**2 + s**2) * p)')\\n gamma_tilde = self.weight * self.scale.reshape(-1, 1)\\n self.retain()\\n self.loc = self.center(t_subtracted, gamma_tilde)\\n self.scale = self.__scaling_param(gamma, gamma_tilde)\\n self.pi = self.mixing_coef(gamma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3e37f21c4a31db9841ee19996995bc\",\n \"score\": \"0.512704\",\n \"text\": \"def update_q(self, reward, index):\\n self.q_value[index] = (self.q_value[index] * self.n_value[index] + reward) / (self.n_value[index] + 1)\\n self.n_value[index] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f313d81e1725f7a29a5a03f23a005b8\",\n \"score\": \"0.51114494\",\n \"text\": \"def update(self, t, ot, at, r, ot1, at1):\\n # Build the staggered [[ot1, at0], [ot2, at1], ...] action-observation\\n # sequences, using the input `ot` as the latest observation. Note that\\n # `at` will be None. This will be used to compute the current Q values.\\n seqt, ht = self._stagger_action_obs_seq(t, self.history, ot)\\n\\n # Build staggered sequence at next time stamp. This will be used to\\n # Compute the \\\"G\\\" Q-values.\\n seqt1, ht1 = self._stagger_action_obs_seq(t, ht, ot1)\\n ht1[-1, 1] = at\\n\\n if t > self.seq_len + 1:\\n\\n if None in seqt:\\n display(seqt)\\n raise ValueError('seqt contains Nones at time {}'.format(t))\\n\\n # Transform seqt, seqt1\\n seqt_t = self.feature_transformer.transform(seqt)\\n seqt1_t = self.feature_transformer.transform(seqt1)\\n\\n # Compute alpha (if there's nonzero alpha decay)\\n alpha = self.initial_alpha / (self._n_updates+1)**self._alpha_decay\\n\\n # TD(0) update using observation-action sequences as states.\\n Qt = self.Q[seqt_t, at]\\n Qt1 = self.Q[seqt1_t, at1]\\n G = r + self.gamma * Qt1\\n self.Q[seqt_t, at] += alpha * (G - Qt)\\n self._n_updates += 1\\n self.Q_update_counts_[seqt_t, at] += 1\\n\\n # Store observation-action pair in history attribute.\\n self._add_history(t, ot, at)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96ec46a1a9ee042c2bdf2d14ef77454\",\n \"score\": \"0.51103103\",\n \"text\": \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n self.q_values[(state, action)] = self.getQValue(state, action) + self.alpha * (reward + self.discount * self.computeValueFromQValues(nextState) - self.getQValue(state, action))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa0af4b96470dcc0ce63a05aba8ae17\",\n \"score\": \"0.51000136\",\n \"text\": \"def do_updates(self):\\n if self.time % self.params['pg']['update_every'] == 0 or self.time == 1:\\n plan_time = 0\\n H, num_rollouts = self.H, self.params['pg']['num_rollouts']\\n for i in range(self.params['pg']['num_iter']):\\n # Sample rollouts using ground truth model\\n check = time.time()\\n rollouts = self.sample_rollouts(H, num_rollouts)\\n plan_time += time.time() - check\\n \\n # Performs value updates alongside advantage calculation\\n rews = self.update_pol(rollouts)\\n\\n # Time spent generating rollouts should be considered planning time\\n self.hist['plan_time'][self.time-1] += plan_time\\n self.hist['update_time'][self.time-1] -= plan_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"975f2567de18decc740826c05c79905a\",\n \"score\": \"0.50955653\",\n \"text\": \"def test_time_history_too_many_columns(self):\\n time_history = {'type': 'pressure', 'quantity': {'units': 'bar', 'column': 1}}\\n time_history['time'] = {'units': 'second', 'column': 0}\\n time_history['values'] = [[0, 1, 2], [1, 2, 3]]\\n assert not v.validate({'datapoints': [{'time-histories': [time_history]}]}, update=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ed6999ede5e8be906f09996a4ced9d\",\n \"score\": \"0.509117\",\n \"text\": \"def update_data_table(conf, pos_terms):\\n conn, domain, model, step = conf['db'], conf['domain'], conf['model'], conf['step']\\n dbc = conn.cursor()\\n\\n dbc.execute(\\\"SELECT count(*) FROM data WHERE domain='%s' AND model='%s' AND step=%d AND relevant IS NULL\\\" % (domain, model, step))\\n num_unj = dbc.fetchone()[0]\\n print \\\"Number of unjudged terms before update_data_table:\\\", num_unj\\n\\n for term in pos_terms:\\n dbc.execute(\\\"UPDATE data SET relevant=1 WHERE domain=? AND model=? AND term=? AND step=?\\\", (domain, model, term, step))\\n conn.commit()\\n\\n # get terms which are obviously not relevant (which were not in the pos_terms)\\n dbc.execute(\\\"SELECT term FROM data WHERE domain='%s' AND model='%s' AND step=%d AND relevant IS NULL\\\" % (domain, model, step))\\n res = dbc.fetchall()\\n\\n neg_terms = [t[0] for t in res] \\n # update in DB\\n for term in neg_terms:\\n dbc.execute(\\\"UPDATE data SET relevant=0 WHERE domain=? AND model=? AND term=? AND step=?\\\", (domain, model, term, step))\\n conn.commit()\\n\\n #get missing terms \\n dbc.execute(\\\"SELECT count(*) FROM data WHERE domain='%s' AND model='%s' AND relevant IS NULL\\\" % (domain, model))\\n num_missing = dbc.fetchone()[0]\\n print \\\"Number of unjudged terms after update_data_table:\\\", num_missing\\n\\n return pos_terms, neg_terms, num_missing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8dc1a1ea6492c1928562c840ae3ff1\",\n \"score\": \"0.5087421\",\n \"text\": \"def update(self, rt):\\n a = self.alpha\\n d1 = self.d1\\n d2 = self.d2\\n \\n e = [] # error terms\\n \\n if self.selected:\\n oldq = self.go\\n #newq = oldq + a * ((rt * d1) - oldq)\\n newq = oldq + a * d1 * (rt - oldq)\\n e = [a * ((rt * d1) - oldq), 0]\\n self.go = newq\\n \\n if self.anticorrelated:\\n oldq = self.nogo\\n #newq = oldq + a * ((rt * d2 * -1) - oldq)\\n newq = oldq + a * d2 * (rt * -1 - oldq)\\n e[1] = a * ((rt * d2 * -1) - oldq)\\n self.nogo = newq\\n else:\\n oldq = self.nogo\\n #newq = oldq + a * ((rt * d2) - oldq)\\n newq = oldq + a * d2 * (rt - oldq)\\n e = [0, a * ((rt * d2) - oldq)]\\n self.nogo = newq\\n \\n if self.anticorrelated:\\n oldq = self.go\\n #newq = oldq + a * ((rt * d1 * -1) - oldq)\\n newq = oldq + a * d1 * (rt * -1 - oldq)\\n e[0] = a * ((rt * d1 * -1) - oldq)\\n self.go = newq\\n \\n return tuple(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c697c2f1872aa934558eb9de38d178c\",\n \"score\": \"0.50863653\",\n \"text\": \"def update_quick(self, t_subtracted, gamma):\\n self.weight = gamma\\n self.retain()\\n self.loc = self.center(t_subtracted, gamma)\\n self.scale = self.std(t_subtracted, gamma, self.loc)\\n self.pi = self.mixing_coef(gamma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cbd3abbc78c6b2a2afd6a792cb67681\",\n \"score\": \"0.5080429\",\n \"text\": \"def update_timeindex(self, event):\\n default_symbol = self.instruments[self.exchange][0]\\n default_exchange = self.exchange\\n latest_datetime = self.data.get_latest_bar_datetime(default_exchange, default_symbol)\\n\\n # Update positions\\n # ================\\n dp = dict( (k,v) for k,v in [(e, {}) for e in self.instruments])\\n for e in dp:\\n dp[e] = dict( (k,v) for k,v in [(s, 0) for s in self.instruments[e]])\\n\\n dp['datetime'] = latest_datetime\\n\\n for e in self.instruments:\\n for s in self.instruments[e]:\\n dp[e][s] = self.current_positions[e][s]\\n\\n # Append the current positions\\n self.all_positions.append(dp)\\n\\n # Update holdings\\n # ===============\\n dh = dict( (k,v) for k, v in [(e, {}) for e in self.instruments] )\\n for e in dh:\\n dh[e] = dict( (k,v) for k, v in [(s, 0) for s in self.instruments[e]])\\n\\n dh['datetime'] = latest_datetime\\n dh['cash'] = self.current_holdings['cash']\\n dh['fee'] = self.current_holdings['fee']\\n dh['total'] = self.current_holdings['total']\\n\\n # NOTE This does seem to cover only the case where all the assets are traded against a similar quote currency.\\n for e in self.instruments:\\n for s in self.instruments[e]:\\n # Approximation to the real value\\n close_price = self.data.get_latest_bar_value(e, s, \\\"close\\\")\\n market_value = self.current_positions[e][s] * close_price\\n dh[e][s] = market_value\\n dh['{}-{}-fill'.format(e,s)] = self.current_holdings['{}-{}-fill'.format(e,s)]\\n dh['{}-{}-price'.format(e,s)] = close_price\\n dh['total'] += market_value\\n\\n # Append the current holdings\\n self.all_holdings.append(dh)\\n\\n # Reset the fill variable\\n for e in self.instruments:\\n for s in self.instruments[e]:\\n self.current_holdings['{}-{}-fill'.format(e,s)] = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"092827def335f353464950e99e638f2e\",\n \"score\": \"0.50782835\",\n \"text\": \"def changes(\\n self,\\n ti: Union[float, int],\\n tf: Union[float, int],\\n time_scale: float = 1.0,\\n ) -> Generator[tuple[float, float], None, None]:\\n start, end = np.searchsorted(\\n self._times, (ti * time_scale, tf * time_scale), side=\\\"right\\\"\\n )\\n for i in range(start, end):\\n change = self._phases[i] - self._phases[i - 1]\\n yield (self._times[i] / time_scale, change)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d5f7eb7bad6f9b58fe7f0e550ca3ca\",\n \"score\": \"0.5075755\",\n \"text\": \"def update(self, indexes, td_error):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3190c1cb7605fe07024e0dbebfc62f0d\",\n \"score\": \"0.5075188\",\n \"text\": \"def update(self, gameState, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n def multiplyAll(counter, multiplier): # Function to multiply all features by the corresponding (alpha * difference)\\n multiplier = float(multiplier)\\n for key in counter.keys():\\n counter[key] *= multiplier\\n\\n difference = reward + self.discount * self.getValue(nextState) - self.getQValue(gameState, action) # Difference formula from Approximate Q-learning\\n features = self.featExtractor.getFeatures(gameState, action) # Get the features of the environment\\n multiplyAll(features, (self.alpha * difference))\\n self.weights += features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97712adb3ee38c362ab145fcfa6e3b86\",\n \"score\": \"0.50688374\",\n \"text\": \"def test_add():\\n dates1 = np.array([datetime(2021, 12, 18, 7, n, 0) for n in range(20, 30)])\\n data1 = np.array([5, 5, 5, 9, 9, 9, 9, 9, 2, 2])\\n tab = Table()\\n tab[\\\"dates\\\"] = dates1\\n tab[\\\"euvalues\\\"] = data1\\n blocks = [0, 3, 8]\\n info = {}\\n info['unit'] = 'V'\\n info['tlmMnemonic'] = 'TEST_VOLTAGE'\\n mnemonic1 = ed.EdbMnemonic('TEST_VOLTAGE', Time('2021-12-18T07:20:00'), Time('2021-12-18T07:30:00'), tab, {}, info, blocks=blocks)\\n\\n dates2 = np.array([datetime(2021, 12, 18, 7, n, 0) for n in range(27, 37)])\\n data2 = np.array([9, 2, 2, 2, 19, 19, 19, 19, 12, 12])\\n tab = Table()\\n tab[\\\"dates\\\"] = dates2\\n tab[\\\"euvalues\\\"] = data2\\n blocks = [0, 1, 4, 8]\\n info = {}\\n info['unit'] = 'V'\\n info['tlmMnemonic'] = 'TEST_VOLTAGE'\\n mnemonic2 = ed.EdbMnemonic('TEST_VOLTAGE', Time('2021-12-18T07:27:00'), Time('2021-12-18T07:33:00'), tab, {}, info, blocks=blocks)\\n\\n added = mnemonic1 + mnemonic2\\n assert all(added.data[\\\"euvalues\\\"] == np.array([5, 5, 5, 9, 9, 9, 9, 9, 2, 2, 2, 19, 19, 19, 19, 12, 12]))\\n assert all(added.data[\\\"dates\\\"] == np.append(dates1, dates2[3:]))\\n assert added.info['unit'] == 'V'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee7d85d437b5c7ea3a031ea781dfa1d2\",\n \"score\": \"0.505619\",\n \"text\": \"def cal_q_from_ind_mea(\\n df_option, deltat=0, hx_name='hx', alpha=0.95\\n):\\n\\n # check inputs\\n df_option.check_type(\\n pd.core.frame.DataFrame, inspect.stack()[0][3]\\n )\\n df = df_option.get()\\n if deltat < 0:\\n raise ValueError(\\n \\\"deltat to \\\"+inspect.stack()[0][3]+\\\"() is negative.\\\"\\n )\\n data_col_name = prod_str+\\\"_\\\"+hx_name\\n uncer_col_name = data_col_name+uncer_str_end\\n if data_col_name not in df.columns.values:\\n raise IndexError(\\n inspect.stack()[0][3]+\\\"() cannot find column\\\" +\\n data_col_name+\\\". Please run cal_mdotdeltah_per_time()\\\" +\\n \\\" to generate the column before using \\\"+inspect.stack()[0][3]+\\\"()\\\"\\n )\\n # need code to check column names\\n\\n # identify where the integration is needed\\n q_ind = []\\n q_uncer_ind = []\\n if deltat > 0: # integration by trapezoidal rule required\\n time_str = df.details[time_col_name]\\n end_ind = df.shape[0]-1\\n beg_ind = 0\\n acc = 0.0\\n acc_uncer_sq = 0.0\\n acc_time = 0.0\\n while beg_ind < end_ind:\\n # begin integration\\n beg_time = df[time_str][df.index[beg_ind]]\\n end_time = beg_time+deltat\\n acc = 0.5*df[data_col_name][df.index[beg_ind]].get()\\n if beg_ind < end_ind:\\n deltaT = df[time_str][df.index[beg_ind+1]] -\\\\\\n df[time_str][df.index[beg_ind]]\\n else:\\n deltaT = df[time_str][df.index[beg_ind]] -\\\\\\n df[time_str][df.index[beg_ind-1]]\\n acc = acc*deltaT\\n acc_uncer_sq = (\\n 0.5*df[uncer_col_name][df.index[beg_ind]].get()*deltaT\\n )**2\\n while beg_ind < end_ind:\\n beg_ind = beg_ind+1\\n if beg_ind < end_ind and \\\\\\n df[time_str][df.index[beg_ind+1]] <= end_time:\\n deltaT = df[time_str][df.index[beg_ind+1]] -\\\\\\n df[time_str][df.index[beg_ind-1]]\\n acc = acc +\\\\\\n df[data_col_name][df.index[beg_ind]].get()*deltaT/2.\\n acc_uncer_sq = acc_uncer_sq+(\\n df[uncer_col_name][df.index[beg_ind]].get()*deltaT/2.\\n )**2\\n else:\\n deltaT = df[time_str][df.index[beg_ind]] -\\\\\\n df[time_str][df.index[beg_ind-1]]\\n acc = acc +\\\\\\n df[data_col_name][df.index[beg_ind]].get()*deltaT/2.\\n acc_uncer_sq = acc_uncer_sq+(\\n df[uncer_col_name][df.index[beg_ind]].get()*deltaT/2.\\n )**2\\n break\\n\\n # divide the integrated heat transfer by time\\n acc_time = df[time_str][df.index[beg_ind]]-beg_time\\n q_ind.append(acc/acc_time)\\n q_uncer_ind.append(sqrt(acc_uncer_sq)/acc_time)\\n\\n else: # mean of instantaneous heat transfer rate is needed\\n q_ind = [df[data_col_name][ind].get() for ind in df.index]\\n q_uncer_ind = [df[uncer_col_name][ind].get() for ind in df.index]\\n\\n # taking the mean values for result\\n if deltat == 0:\\n time_std_end = '_inst'\\n else:\\n time_std_end = '_'+str(int(deltat))+'s'\\n len_q_ind = len(q_ind)\\n df.details['q_'+hx_name+'_mean'+time_std_end] = sum(q_ind)/len_q_ind\\n\\n # getting the variation of samples as environmental noise effect\\n zero_order_sq = sum([(value/len_q_ind)**2 for value in q_uncer_ind])\\n first_order_sq = (\\n stdev(q_ind)*t.interval(alpha, len_q_ind-1)[1]/len_q_ind\\n )**2 # give NaN for zero degree of freedom\\n df.details['q_uncer_'+hx_name+'_mean'+time_std_end] = sqrt(\\n zero_order_sq+first_order_sq\\n )\\n df.details['q_uncer_'+hx_name+'_mean'+time_std_end+'_zero'] = sqrt(\\n zero_order_sq\\n )\\n df.details['q_uncer_'+hx_name+'_mean'+time_std_end+'_first'] = sqrt(\\n first_order_sq\\n )\\n\\n # return values\\n df_option.set(df)\\n return df_option\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c968a2922713609a288085bfb9fcbceb\",\n \"score\": \"0.5051216\",\n \"text\": \"def updateTreasures(self):\\n u = self.pf_uncertainty()\\n treasures = np.array(self.Treasures).reshape(len(self.Treasures), 3)\\n print(\\\"debug: \\\", treasures[:,:2].T)\\n d = treasures[:,:2].T - self.bestParticle.reshape(-1, 1)\\n d = np.hypot(d[0], d[1])\\n print(\\\"distances \\\", d)\\n print(\\\"min d: \\\", np.amin(d))\\n if np.amin(d) < self.min_dis_treasures and treasures[np.argmin(d),2] > u:\\n treasures[np.argmin(d),:] = list(self.bestParticle) + [u]\\n treasures = treasures.reshape(len(self.Treasures), 3)\\n self.Treasures = list(treasures)\\n if np.amin(d) > self.min_dis_treasures:\\n rospy.loginfo(\\\"Added new treasure!\\\")\\n self.Treasures.append(list(self.bestParticle)+[u])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d6d8191df2f854d7c180917956fae3\",\n \"score\": \"0.5050302\",\n \"text\": \"def mutate_update_deltas(self, network, update_deltas):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f428e5aaa866c1e3de3c0006fc8d2d\",\n \"score\": \"0.5041094\",\n \"text\": \"def update_obs_table(self, s_set: list = None, e_set: list = None):\\n\\n update_S = s_set if s_set else list(self.S) + list(self.s_dot_a())\\n update_E = e_set if e_set else self.E\\n\\n # This could save few queries\\n update_S.reverse()\\n\\n for s in update_S:\\n for e in update_E:\\n if len(self.T[s]) != len(self.E):\\n output = self.sul.query(s + e)\\n self.T[s] += (output[-1],)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293a1bc163d0e798d0bb92adbebb23f9\",\n \"score\": \"0.5036949\",\n \"text\": \"def update(self, delta_time):\\n \\n for entity in self:\\n entity.update(delta_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a3649af4fdfc7b98188bc8d33c56d6\",\n \"score\": \"0.5033723\",\n \"text\": \"def cti_detrend_factor():\\n#\\n#--- update detrend factor table (amp_avg_list)\\n#\\n update_detrend_factor_table()\\n#\\n#--- update detrend data tables \\n#\\n make_detrended_data_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a462251fa4b018f9ff11d2ef26ff479f\",\n \"score\": \"0.50200844\",\n \"text\": \"def on_t_update(self, old_state, action, new_state, reward, done):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e395cb3c4fd903dd1618306d319a3d58\",\n \"score\": \"0.50170076\",\n \"text\": \"def run_update(self, **kw):\\n\\n for qb in self.qubits:\\n if qb.flux_parking() != 0 and np.abs(qb.flux_parking()) != 0.5:\\n log.warning((f\\\"{qb.name}.flux_parking() is not 0, 0.5\\\"\\n \\\"nor -0.5. Nothing will be updated.\\\"))\\n else:\\n sweet_spot = self.analysis.fit_res[\\n qb.name][f'{qb.name}_sweet_spot']\\n opposite_sweet_spot = self.analysis.fit_res[\\n qb.name][f'{qb.name}_opposite_sweet_spot']\\n sweet_spot_RO_freq = self.analysis.fit_res[\\n qb.name][f'{qb.name}_sweet_spot_RO_frequency']\\n\\n self.fluxlines_dict[qb.name](sweet_spot)\\n qb.ro_freq(sweet_spot_RO_freq)\\n # Update V_per_phi0 key in each qubit. From this, it is possible\\n # to retrieve the opposite sweet spot\\n V_per_phi0 = np.abs(2 * (sweet_spot - opposite_sweet_spot))\\n qb.fit_ge_freq_from_dc_offset()['V_per_phi0'] = V_per_phi0\\n\\n # Store the USS in 'dac_sweet_spot'\\n if qb.flux_parking() == 0:\\n qb.fit_ge_freq_from_dc_offset(\\n )['dac_sweet_spot'] = sweet_spot\\n elif qb.flux_parking() == 0.5:\\n qb.fit_ge_freq_from_dc_offset(\\n )['dac_sweet_spot'] = sweet_spot - V_per_phi0/2\\n elif qb.flux_parking() == -0.5:\\n qb.fit_ge_freq_from_dc_offset(\\n )['dac_sweet_spot'] = sweet_spot + V_per_phi0/2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82feefcbb6a71df8fed103043aacbc6d\",\n \"score\": \"0.5015755\",\n \"text\": \"def difference_update(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e24c1e80f8de78d2a0d3b8471bd926\",\n \"score\": \"0.50108117\",\n \"text\": \"def update(self, acceleration, quaternion, DT):\\r\\n an = quaternion.vecTransformation(acceleration) \\r\\n self.values += DT * (an + G)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d6395e8804261fa4cbd98a604f85db0\",\n \"score\": \"0.50057286\",\n \"text\": \"def update_timestep_data_no_conditioned_area(tsd):\\n\\n zero_fields = ['Qhs_lat_sys',\\n 'Qhs_sen_sys',\\n 'Qcs_lat_sys',\\n 'Qcs_sen_sys',\\n 'Qhs_sen',\\n 'Qcs_sen',\\n 'x_int',\\n 'Qhs_em_ls',\\n 'Qcs_em_ls',\\n 'Qhpro_sys',\\n 'Qcpro_sys',\\n 'ma_sup_hs',\\n 'ma_sup_cs',\\n 'Ta_sup_hs',\\n 'Ta_re_hs',\\n 'Ta_sup_cs',\\n 'Ta_re_cs',\\n 'NG_hs',\\n 'COAL_hs',\\n 'OIL_hs',\\n 'WOOD_hs',\\n 'NG_ww',\\n 'COAL_ww',\\n 'OIL_ww',\\n 'WOOD_ww',\\n 'vfw_m3perh',\\n 'DH_hs',\\n 'Qhs_sys', 'Qhs',\\n 'DH_ww',\\n 'Qww_sys',\\n 'Qww',\\n 'DC_cs',\\n 'DC_cs_lat',\\n 'Qcs_sys',\\n 'Qcs',\\n 'DC_cdata',\\n 'Qcdata_sys',\\n 'Qcdata',\\n 'DC_cre',\\n 'Qcre_sys',\\n 'Qcre',\\n 'Eaux',\\n 'Ehs_lat_aux',\\n 'Eaux_hs',\\n 'Eaux_cs',\\n 'Eaux_ve',\\n 'Eaux_ww',\\n 'Eaux_fw',\\n 'E_sys',\\n 'GRID',\\n 'E_ww',\\n 'E_hs',\\n 'E_cs',\\n 'E_cre',\\n 'E_cdata',\\n 'E_pro',\\n 'Epro',\\n 'Edata',\\n 'Ea',\\n 'El',\\n 'Eal',\\n 'Ev',\\n 'Eve',\\n 'mcphs_sys',\\n 'mcpcs_sys',\\n 'mcptw'\\n 'mcpww_sys',\\n 'mcpcdata_sys',\\n 'mcpcre_sys',\\n 'Tcdata_sys_re', 'Tcdata_sys_sup',\\n 'Tcre_sys_re', 'Tcre_sys_sup',\\n 'Tww_sys_sup', 'Tww_sys_re',\\n 'Ths_sys_sup', 'Ths_sys_re',\\n 'Tcs_sys_sup', 'Tcs_sys_re',\\n 'DH_ww', 'Qww_sys', 'Qww',\\n 'mcptw', 'I_sol', 'I_rad',\\n 'Qgain_light', 'Qgain_app', 'Qgain_pers', 'Qgain_data', 'Qgain_wall', 'Qgain_base', 'Qgain_roof',\\n 'Qgain_wind', 'Qgain_vent'\\n 'Q_cool_ref', 'q_cs_lat_peop']\\n\\n tsd.update(dict((x, np.zeros(HOURS_IN_YEAR)) for x in zero_fields))\\n\\n tsd['T_int'] = tsd['T_ext'].copy()\\n\\n return tsd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bf023d8d0ae5802687fd54571c2ac8\",\n \"score\": \"0.49942693\",\n \"text\": \"def computeQValues(self):\\n for i in range(self.numTraining):\\n states, actions, rewards, termination = self.runEpisode()\\n n = len(states)\\n q = np.copy(self.q_values)\\n\\n # print(\\\"Iteration\\\", i)\\n # print(\\\"states\\\", states)\\n # print(\\\"actions\\\", actions)\\n # print(\\\"rewards\\\", rewards)\\n # print(termination)\\n # input()\\n\\n for t in range(len(states) - 1):\\n # if states[t] == states[t+1]:\\n # continue\\n sx1, sy1 = states[t]\\n sx2, sy2 = states[t+1]\\n \\n q[sx1][sy1][actions[t]] = rewards[t] + self.discount*self.getV(sx2, sy2)\\n\\n # print(states[t], states[t+1], actions[t], self.getV(sx2, sy2))\\n # print(np.moveaxis(q, 2, 0))\\n # input()\\n \\n if termination.value == 0:\\n sx1, sy1 = states[n-1] \\n q[sx1][sy1][actions[n-1]] = rewards[n-1]\\n\\n self.q_values = q\\n \\n # print(self.q_values)\\n # print(self.q_values.reshape(4,6,8))\\n # print(np.moveaxis(self.q_values, 2, 0))\\n # input()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b65bee7229157308278dd6c6a3961d5\",\n \"score\": \"0.49833325\",\n \"text\": \"def get_update_info(self, molsys: Molsys, f: np.ndarray, q: np.ndarray, step: Step):\\n f_old = step.forces\\n x_old = step.geom\\n\\n old_molsys = copy.deepcopy(molsys)\\n old_molsys.geom = x_old\\n q_old = old_molsys.q_array()\\n\\n dq = q - q_old\\n dg = f_old - f # gradients -- not forces!\\n dqdg = np.dot(dq, dg)\\n dqdq = np.dot(dq, dq)\\n max_change = abs_max(dq)\\n return dq, dg, dqdg, dqdq, max_change\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a321ba4d46e6b21f5cec8562397ba358\",\n \"score\": \"0.49813497\",\n \"text\": \"def test_updateCol_functions_with_valid_data(self):\\n table = self.InterpolationTableWithDuplicate\\n obs = table.updateCol('GrowthRate','SampleID',{'S.5b':3.01,'S.5':2.59}).delimitedSelf()\\n \\n exp =\\\\\\n [\\\"#SampleID\\\\tTemp\\\\tGrowthRate\\\\tDescription\\\\n\\\",\\\\\\n \\\"#Comment line, full of crazy comments\\\\n\\\",\\\\\\n \\\"#Another comment line\\\\n\\\",\\\\\\n \\\"S.1\\\\t1.0\\\\t3.0\\\\tSample.1.Description\\\\n\\\",\\\\\\n \\\"S.2\\\\t2.0\\\\t2.0\\\\tSample.2.Description\\\\n\\\",\\\\\\n \\\"S.3\\\\t3.0\\\\t0.0\\\\tSample.3.Description\\\\n\\\",\\\\\\n \\\"S.4\\\\t0.0\\\\tUnknown\\\\tSample.4.Description\\\\n\\\",\\\\\\n \\\"S.5\\\\t1.0\\\\t2.59\\\\tSample.5.Description\\\\n\\\",\\\\\\n \\\"S.5b\\\\t1.0\\\\t3.01\\\\tSample.5.Description\\\\n\\\",\\\\\\n \\\"S.6\\\\t1.5\\\\tUnknown\\\\tSample.6.Description\\\\n\\\",\\\\\\n \\\"S.7\\\\t2.72\\\\tUnknown\\\\tSample.7.Description\\\\n\\\",\\\\\\n \\\"S.8\\\\t3.14\\\\tUnknown\\\\tSample.7.Description\\\\n\\\"] \\n self.assertEqualItems(obs,exp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ae2cdf084334f347eb5cd5e8e4a5c9\",\n \"score\": \"0.49800757\",\n \"text\": \"def delta(self, time: tf.Tensor, instrument: tf.Tensor,\\n numeraire: tf.Tensor):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7ba90294344ce7499b92e9922b9eb7\",\n \"score\": \"0.49799034\",\n \"text\": \"def update(table, id_):\\n list_labels = ['Month',\\n 'Day',\\n 'Year',\\n 'Type',\\n 'Amount']\\n type_list = ['month',\\n 'str',\\n 'int',\\n 'in',\\n 'int']\\n # your code\\n common.update_table(table, id_, list_labels, type_list)\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feeee493db033eb42113147bdfbc4eb6\",\n \"score\": \"0.4976679\",\n \"text\": \"def increment_time(self):\\n self.timesteps += 1\\n for atom in self.atoms:\\n VelocityVerlet.update_position(atom)\\n # shift atom to within the boundaries\\n atom.position = self.shift_position(atom.position)\\n\\n # reset energy\\n self.energy_history.append(self.energy)\\n self.energy = 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c79c39aad0f5b94e8af72a2df96285\",\n \"score\": \"0.49760443\",\n \"text\": \"def run_update(self, **kw):\\n for qb in self.qubits:\\n f0 = self.analysis.fit_res[qb.name].params['f0'].value\\n qb.ge_freq(f0)\\n if self.analysis.analyze_ef:\\n f0_ef = 2 * self.analysis.fit_res[\\n qb.name].params['f0_gf_over_2'] - f0\\n qb.ef_freq(f0_ef)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"362d53fc57fabea6dec3b26cba442c25\",\n \"score\": \"0.49696663\",\n \"text\": \"def update(self, table, criteria, values):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa41ca72d797a8c3cf243c363f0a72e6\",\n \"score\": \"0.49670866\",\n \"text\": \"def _update_hematocrit(self):\\n\\n\\tG=self._G\\n\\tdt = self._dt\\n vrbc = self._P.rbc_volume()\\n htt2htd = self._P.tube_to_discharge_hematocrit\\n\\tinvivo=self._invivo\\n\\n\\t#Calculate number of RBCs after dt\\n\\tfor e in G.es:\\n e['nRBC']=e['nRBC']+dt*e['flowRBCIn']-dt*e['htt']*0.25*np.pi*e['diameter']**2*e['v']/vrbc\\n\\n\\t#Update tube and discharge hematocrit\\n\\tfor e in G.es:\\n\\t e['htt']=e['nRBC']*vrbc/(0.25*np.pi*e['diameter']**2*e['length'])\\n e['htd']=htt2htd(e['htt'], e['diameter'], invivo)\\n\\n\\t if e['htt'] > 0.99:\\n\\t\\tprint('WARNING')\\n\\t\\tvgm.write_pkl(G,'G_warning.pkl')\\n\\t\\tprint('htt > 0.99 in vessel')\\n\\t\\tprint(e.index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bce9c8c4a9136045cddb02cc9d277a8\",\n \"score\": \"0.496367\",\n \"text\": \"def test_time_history_bad_time_units(self):\\n time_history = {'type': 'pressure', 'quantity': {'units': 'bar', 'column': 1}}\\n time_history['time'] = {'units': 'candela*ampere', 'column': 0}\\n time_history['values'] = [[0, 1], [1, 2]]\\n assert not v.validate({'datapoints': [{'time-histories': [time_history]}]}, update=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ab824e84a2b521be914ab3053292ca\",\n \"score\": \"0.4962276\",\n \"text\": \"def _update_timestamps(self):\\n end_time_str = iso8601.format_date(self.end*0.001, precision=3)\\n if 'end_time' in self.das.parent:\\n self.das.parent['end_time'][:] = util.str_to_bytes(end_time_str)\\n self.das.parent['duration'][0] = (self.end-self.start)*0.001\\n self.das.parent['collection_time'][0] = self.collection_time\\n else:\\n h5nexus.field(self.das.parent,'end_time',\\n data=end_time_str, dtype='|S',\\n label='measurement end time')\\n h5nexus.field(self.das.parent,'duration',\\n data=[(self.end-self.start)*0.001],\\n units='s', dtype='float32',\\n label='total measurement duration')\\n h5nexus.field(self.das.parent,'collection_time',\\n data=[self.collection_time],\\n units='s', dtype='float32',\\n label='total time detectors were active')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0e1d4ff55569aa074704a3df416a8de\",\n \"score\": \"0.49615124\",\n \"text\": \"def calculate_deltas(train_toks, classifier, unattested, ffreq_empirical, nfmap, nfarray, nftranspose, encoding):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc3ce8666525922d82ee4a22a8a8fb4\",\n \"score\": \"0.49611092\",\n \"text\": \"def test_update(self):\\n dataset1 = DataSet(data={'description': 'dataset 1'}, )\\n self.assertEqual(dataset1.description, \\\"dataset 1\\\")\\n dataset1.update(rerun=5, description=\\\"new dataset\\\")\\n self.database.cursor.execute(\\n \\\"SELECT rerun, description FROM dataset WHERE id=%s\\\", (dataset1.id,))\\n results = self.database.cursor.fetchone()\\n self.assertEqual(results[0], 5)\\n self.assertEqual(results[1], \\\"new dataset\\\")\\n self.assertEqual(dataset1.description, \\\"new dataset\\\")\\n self.assertEqual(dataset1.rerun, 5)\\n dataset1.update(process_end_ts=datetime.datetime(1970, 1, 1))\\n self.assertEqual(\\n dataset1.process_end_ts, datetime.datetime(1970, 1, 1)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68bbd9cf0c989022f6f3c95f1cb9f4a\",\n \"score\": \"0.49560022\",\n \"text\": \"def _update_t(self, num_samples):\\n self.ts = self.t_start + xp.linspace(0., \\n num_samples * self.dt,\\n num_samples,\\n endpoint=False)\\n self.t_start += num_samples * self.dt\\n self.v = xp.zeros(num_samples)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3972ccea4f54de27097dc7317e010346\",\n \"score\": \"0.49540737\",\n \"text\": \"def _measupdate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d77757c7bfb1aa95c7801f198263fa\",\n \"score\": \"0.49522328\",\n \"text\": \"def ModifyDFs(self):\\r\\n # Add columns to plays DF for time elapsed.\\r\\n # Relevant for adding line markers for goals in plots.\\r\\n self.plays['Time'] = pd.to_datetime(\\r\\n self.plays.period_time, format=\\\"%M:%S\\\"\\r\\n )\\r\\n # Then separate out the Hour, Minute, and Second for each play\\r\\n self.plays['Hour'] = self.plays['Time'].dt.hour\\r\\n self.plays['Minute'] = self.plays['Time'].dt.minute\\r\\n self.plays['Second'] = self.plays['Time'].dt.second\\r\\n \\r\\n # Change Time column to the game time elapsed\\r\\n for i in range(2, self.plays.period.unique().max() + 1):\\r\\n self.plays.loc[self.plays.period == i, 'Time'] = \\\\\\r\\n self.plays[self.plays.period == i].Time + \\\\\\r\\n dt.timedelta(minutes= 20 * (i - 1))\\r\\n \\r\\n self.plays.loc[self.plays.period == i, 'Minute'] = \\\\\\r\\n self.plays[self.plays.period == i].Minute + (20 * (i - 1))\\r\\n \\r\\n if i >= 4:\\r\\n self.plays.loc[self.plays.period == i, 'Hour'] = \\\\\\r\\n self.plays[self.plays.period == i].Hour + (floor((i - 3) / 3))\\r\\n \\r\\n # Create a time index that with intervals of 5 seconds for entire game\\r\\n secns = [int(s) for s in range(0, 60, 5)] * 60\\r\\n mints = [floor(len(secns[:s]) / 12) for s in range(len(secns))]\\r\\n hours = [int(0) for m in mints]\\r\\n \\r\\n # Determine if the index needs to be adjusted for OT\\r\\n ## Additional adjustments will need to be made for playoff OT\\r\\n if 4 in self.plays.period.unique():\\r\\n OTsec = [int(s) for s in range(0, 60, 5)] * 5\\r\\n secns += OTsec\\r\\n mints += [floor(len(OTsec[:s]) / 12) for s in range(len(OTsec))]\\r\\n hours += [int(1) for s in OTsec]\\r\\n \\r\\n index = [dt.time(h, m, s) for h, m, s in zip(hours, mints, secns)]\\r\\n s = pd.Series(index, name='Time')\\r\\n \\r\\n # Clean Time column format\\r\\n self.plays.Time = pd.Series(\\r\\n [dt.time(d.hour, d.minute, d.second) for d in self.plays.Time]\\r\\n )\\r\\n \\r\\n # Add in player_4 column in case the JSON file does not contain one\\r\\n if 'player_4' not in self.plays.columns:\\r\\n self.plays.loc[:, 'player_4'] = ''\\r\\n \\r\\n # Merge the new index with the plays, shots, and shifts df's\\r\\n # (This expands the dataframe to allow for prettier P5 charts)\\r\\n self.plays = pd.concat(\\r\\n [self.plays, pd.DataFrame(s)],\\r\\n ignore_index=True, join='outer', keys='Time'\\r\\n )\\r\\n self.plays.sort_values(by='Time', inplace=True, ascending=True)\\r\\n self.plays['datetime'] = pd.to_datetime(self.plays['datetime'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da034bbda9022a8c6c52a9fdcbf82d87\",\n \"score\": \"0.4935009\",\n \"text\": \"def update_performance(self):\\n # update the last_price field for all ideas with null closed_date.\\n ideas = IdeaModel.query.all()\\n for idea in ideas:\\n if not idea.closed_date:\\n idea.last_price = Stock.fetch_stock_quote(idea.symbol)[\\\"latestPrice\\\"]\\n self.save_changes(idea)\\n\\n analysts = UserModel.query.filter(UserModel.num_ideas > 0).all()\\n for analyst in analysts:\\n ideas_by_analyst = analyst.ideas.all()\\n\\n cumulative_return = reduce((lambda x, y: x + y), map(calc_idea_return, ideas_by_analyst))\\n analyst.avg_return = cumulative_return / analyst.num_ideas\\n\\n total_price_target_capture = reduce((lambda x, y: x + y), map(calc_pt_capture, ideas_by_analyst))\\n analyst.avg_price_target_capture = total_price_target_capture / analyst.num_ideas\\n\\n successful_total = reduce((lambda x, y: x + y), map(calc_idea_profitability, ideas_by_analyst))\\n analyst.success_rate = successful_total / analyst.num_ideas\\n\\n cumulative_holding_period = reduce((lambda x, y: x + y), map(calc_holding_period, ideas_by_analyst))\\n analyst.avg_holding_period = cumulative_holding_period / analyst.num_ideas\\n\\n num_analysts = len(analysts)\\n all_avg_returns = [analyst.avg_return for analyst in analysts]\\n avg_return_percentiles = stats.rankdata(all_avg_returns, \\\"average\\\") / num_analysts\\n all_pt_captures = [analyst.avg_price_target_capture for analyst in analysts]\\n avg_price_target_capture_percentiles = stats.rankdata(all_pt_captures, \\\"average\\\") / num_analysts\\n all_success_rates = [analyst.success_rate for analyst in analysts]\\n success_rate_percentiles = stats.rankdata(all_success_rates, \\\"average\\\") / num_analysts\\n all_avg_holding_periods = [analyst.avg_holding_period for analyst in analysts]\\n avg_holding_period_percentiles = stats.rankdata(all_avg_holding_periods, \\\"average\\\") / num_analysts\\n all_num_ideas = [analyst.num_ideas for analyst in analysts]\\n num_ideas_percentiles = stats.rankdata(all_num_ideas, \\\"average\\\") / num_analysts\\n\\n for idx, analyst in enumerate(analysts):\\n analyst.avg_return_percentile = avg_return_percentiles[idx]\\n analyst.avg_price_target_capture_percentile = avg_price_target_capture_percentiles[idx]\\n analyst.success_rate_percentile = success_rate_percentiles[idx]\\n analyst.avg_holding_period_percentile = avg_holding_period_percentiles[idx]\\n analyst.num_ideas_percentile = num_ideas_percentiles[idx]\\n\\n # temporary score to keep track of analyst ranking\\n analyst.analyst_rank = analyst.avg_return_percentile + 0.5 * analyst.avg_price_target_capture_percentile\\n\\n # sort list of analysts by ranking score, update percentile ranking, and save analyst changes\\n analysts.sort(key=lambda x: x.analyst_rank, reverse=True)\\n all_analyst_ranks = [analyst.analyst_rank for analyst in analysts]\\n analyst_rank_percentiles = stats.rankdata(all_analyst_ranks, \\\"average\\\") / num_analysts\\n\\n for idx, analyst in enumerate(analysts):\\n analyst.analyst_rank = idx + 1\\n analyst.analyst_rank_percentile = analyst_rank_percentiles[idx]\\n self.save_changes(analyst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47a3d1fbd72b5dbf1c5769fdfa5c21f3\",\n \"score\": \"0.49230352\",\n \"text\": \"def update_params(dt, idx_now, idx_next, arrays1, arrays2, arrays3, arrays4, arrays5, arrays6, arrays7, arrays8, time_sim, time_ext, sim_nH):\\n # Set the times as parameters inside function\\n t_now = time_sim[idx_now]\\n t_next = time_ext[idx_next]\\n t_up = time_sim[idx_now]+dt\\n t_mid = t_now+((t_next-t_now)/2)\\n \\n if t_up <t_mid:\\n \\\"\\\"\\\"\\n If the updated time is closer to the current time than the end of the external timestep, stick with the ISM parameters from the previous timestep\\n \\\"\\\"\\\"\\n nH_next = arrays1[idx_now]\\n nHe_next = arrays2[idx_now]\\n xH_next = arrays3[idx_now]\\n xHe_next = arrays4[idx_now]\\n xHe2_next = arrays5[idx_now]\\n T_next = arrays6[idx_now]\\n B_next = arrays7[idx_now]\\n rho_next = arrays8[idx_now]\\n elif t_up > t_mid:\\n \\\"\\\"\\\"\\n If the updated time is closer to the end of the external timestep than the beginning, use the ISM parameters from the beginning of the next external timestep\\n \\\"\\\"\\\"\\n nH_next = sim_nH[idx_next]\\n nHe_next = arr_nHesim[idx_next]\\n xH_next = arr_xHsim[idx_next]\\n xHe_next = arr_xHesim[idx_next]\\n xHe2_next = arr_xHe2sim[idx_next]\\n T_next = arr_temperaturesim[idx_next]\\n B_next = arr_Bsim[idx_next]\\n rho_next = arr_rhosim[idx_next]\\n elif t_up == t_mid:\\n \\\"\\\"\\\"\\n If the updated time is right in the middle, use the ISM parameters from the beginning of the next external timestep. This is a separate elif statememt because for some reason having it in the same elif statement as either of the others (>= or <=) seems to break the simulation. Probably some Python thing?\\n \\\"\\\"\\\"\\n nH_next = sim_nH[idx_next]\\n nHe_next = arr_nHesim[idx_next]\\n xH_next = arr_xHsim[idx_next]\\n xHe_next = arr_xHesim[idx_next]\\n xHe2_next = arr_xHe2sim[idx_next]\\n T_next = arr_temperaturesim[idx_next]\\n B_next = arr_Bsim[idx_next]\\n rho_next = arr_rhosim[idx_now]\\n\\n return arrays1.append(float(nH_next)), arrays2.append(float(nHe_next)), arrays3.append(float(xH_next)), arrays4.append(float(xHe_next)), arrays5.append(float(xHe2_next)), arrays6.append(float(T_next)), arrays7.append(float(B_next)), arrays8.append(float(rho_next))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fddf2daa507927d291fd161885be8a5\",\n \"score\": \"0.49225911\",\n \"text\": \"def update_temporal_coverage(self):\\n\\n update_target_temporal_coverage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc46d08514c3f674d81af2c6167dd365\",\n \"score\": \"0.4922514\",\n \"text\": \"def up_delta(self, time: tf.Tensor, instrument: tf.Tensor,\\n numeraire: tf.Tensor):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5ae0a57565b71cf70af04b52b542f4\",\n \"score\": \"0.49203718\",\n \"text\": \"def update_target(self, tau):\\n if tau == 1.0:\\n self.target_qmodel.set_weights(self.qmodel.get_weights())\\n else:\\n tws = self.target_qmodel.trainable_variables\\n ws = self.qmodel.trainable_variables\\n for ndx in range(len(tws)):\\n tws[ndx] = ws[ndx] * tau + tws[ndx] * (1 - tau)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c7a67360f7b138a09dfff1a487d25ff\",\n \"score\": \"0.49193344\",\n \"text\": \"def update(self, state, action, reward, next_state, next_action, eoe):\\r\\n # transform the state into index\\r\\n i = state[0]*self.h + state[1]\\r\\n j = next_state[0]*self.h + next_state[1]\\r\\n self.Q[i, action] = self.Q[i, action] + \\\\\\r\\n self.alpha*(reward + self.gamma*(1-eoe)*self.Q[j, next_action] -self.Q[i, action])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6f5c8112b3929a924f8ef8a2265fa3\",\n \"score\": \"0.49171758\",\n \"text\": \"def update_history_dist(H, n):\\n\\n new_inds = np.where(~H[\\\"known_to_aposmm\\\"])[0]\\n\\n p = np.logical_and.reduce((H[\\\"sim_ended\\\"], ~np.isnan(H[\\\"f\\\"])))\\n\\n for new_ind in new_inds:\\n # Loop over new returned points and update their distances\\n if p[new_ind]:\\n H[\\\"known_to_aposmm\\\"][new_ind] = True\\n\\n # Compute distance to boundary\\n H[\\\"dist_to_unit_bounds\\\"][new_ind] = min(\\n min(np.ones(n) - H[\\\"x_on_cube\\\"][new_ind]), min(H[\\\"x_on_cube\\\"][new_ind] - np.zeros(n))\\n )\\n\\n dist_to_all = cdist(H[\\\"x_on_cube\\\"][[new_ind]], H[\\\"x_on_cube\\\"][p], \\\"euclidean\\\").flatten()\\n new_better_than = H[\\\"f\\\"][new_ind] < H[\\\"f\\\"][p]\\n\\n # Update any other points if new_ind is closer and better\\n if H[\\\"local_pt\\\"][new_ind]:\\n inds_of_p = np.logical_and(dist_to_all < H[\\\"dist_to_better_l\\\"][p], new_better_than)\\n updates = np.where(p)[0][inds_of_p]\\n H[\\\"dist_to_better_l\\\"][updates] = dist_to_all[inds_of_p]\\n H[\\\"ind_of_better_l\\\"][updates] = new_ind\\n else:\\n inds_of_p = np.logical_and(dist_to_all < H[\\\"dist_to_better_s\\\"][p], new_better_than)\\n updates = np.where(p)[0][inds_of_p]\\n H[\\\"dist_to_better_s\\\"][updates] = dist_to_all[inds_of_p]\\n H[\\\"ind_of_better_s\\\"][updates] = new_ind\\n\\n # Since we allow equality when deciding better_than_new_l and\\n # better_than_new_s, we have to prevent new_ind from being its own\\n # better point.\\n better_than_new_l = np.logical_and.reduce((~new_better_than, H[\\\"local_pt\\\"][p], H[\\\"sim_id\\\"][p] != new_ind))\\n better_than_new_s = np.logical_and.reduce((~new_better_than, ~H[\\\"local_pt\\\"][p], H[\\\"sim_id\\\"][p] != new_ind))\\n\\n # Who is closest to ind and better\\n if np.any(better_than_new_l):\\n ind = dist_to_all[better_than_new_l].argmin()\\n H[\\\"ind_of_better_l\\\"][new_ind] = H[\\\"sim_id\\\"][p][np.nonzero(better_than_new_l)[0][ind]]\\n H[\\\"dist_to_better_l\\\"][new_ind] = dist_to_all[better_than_new_l][ind]\\n\\n if np.any(better_than_new_s):\\n ind = dist_to_all[better_than_new_s].argmin()\\n H[\\\"ind_of_better_s\\\"][new_ind] = H[\\\"sim_id\\\"][p][np.nonzero(better_than_new_s)[0][ind]]\\n H[\\\"dist_to_better_s\\\"][new_ind] = dist_to_all[better_than_new_s][ind]\\n\\n # if not ignore_L8:\\n # r_k = calc_rk(len(H['x_on_cube'][0]), n_s, rk_const, lhs_divisions)\\n # H['worse_within_rk'][new_ind][p] = np.logical_and.reduce((H['f'][new_ind] <= H['f'][p], dist_to_all <= r_k))\\n\\n # # Add trues if new point is 'worse_within_rk'\\n # inds_to_change = np.logical_and.reduce((H['dist_to_all'][p, new_ind] <= r_k, H['f'][new_ind] >= H['f'][p], H['sim_id'][p] != new_ind))\\n # H['worse_within_rk'][inds_to_change, new_ind] = True\\n\\n # if not H['local_pt'][new_ind]:\\n # H['worse_within_rk'][H['dist_to_all'] > r_k] = False\\n\\n if np.any(~H[\\\"local_pt\\\"]) and not np.any(np.isinf(H[\\\"dist_to_better_s\\\"][~H[\\\"local_pt\\\"]])):\\n # Our best sample point was not identified because the min was not unique.\\n min_inds = H[\\\"f\\\"][~H[\\\"local_pt\\\"]] == np.min(H[\\\"f\\\"][~H[\\\"local_pt\\\"]])\\n assert len(min_inds) >= 2, \\\"Check this\\\"\\n # Take the first point with this value to be the best sample point\\n best_samp = H[\\\"sim_id\\\"][~H[\\\"local_pt\\\"]][min_inds][0]\\n H[\\\"dist_to_better_s\\\"][best_samp] = np.inf\\n H[\\\"ind_of_better_s\\\"][best_samp] = -1\\n\\n # if np.any(H['local_pt']) and not np.any(np.isinf(H['dist_to_better_l'][H['local_pt']])):\\n # # Our best sample point was not identified because the min was not unique.\\n # min_inds = H['f'][H['local_pt']] == np.min(H['f'][H['local_pt']])\\n # assert len(min_inds) >= 2, \\\"Check this\\\"\\n # # Take the first point with this value to be the best sample point\\n # best_local = H['sim_id'][H['local_pt']][min_inds][0]\\n # H['dist_to_better_l'][best_local] = np.inf\\n # H['ind_of_better_l'][best_local] = -1\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1589,"cells":{"query_id":{"kind":"string","value":"957d25b4cc0857853323e278b3053c9d"},"query":{"kind":"string","value":"Get determinant coefficient with appropriate Fermi anticommutation factor"},"positive_passages":{"kind":"list like","value":[{"docid":"d5354811821ddff15942473cad0e8efa","score":"0.6345779","text":"def get_det_coeff(self, det):\n\t\tif det in self.det_coeffs:\n\t\t\treturn self.det_coeffs[det] * self.dets[det].parity(det)\n\t\treturn 0","title":""}],"string":"[\n {\n \"docid\": \"d5354811821ddff15942473cad0e8efa\",\n \"score\": \"0.6345779\",\n \"text\": \"def get_det_coeff(self, det):\\n\\t\\tif det in self.det_coeffs:\\n\\t\\t\\treturn self.det_coeffs[det] * self.dets[det].parity(det)\\n\\t\\treturn 0\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"413fc5941756b2ca7da1865bf4e82869","score":"0.7301002","text":"def __determinant(a, b, c, d, e, f, g, h, i):\n return (a * e * i - a * f * h - b * d * i + b * f * g + c * d * h - c * e * g)","title":""},{"docid":"c318f71da8bb3c2e134b577fed78eed9","score":"0.7147995","text":"def determinant(self):\n return np.linalg.det(self.xform_)","title":""},{"docid":"fa5891acbdfb80910d3635e70c5089f9","score":"0.6982245","text":"def det(self):\n return self.a*self.c - self.b*self.b/4","title":""},{"docid":"014eea6bf02dfbe3f08951397e486c22","score":"0.6915586","text":"def determinant(self):\n if not self.is_square():\n raise(ValueError, \"Cannot calculate determinant of non-square matrix.\")\n if self.h > 2:\n raise(NotImplementedError, \"Calculating determinant not implemented for matrices largerer than 2x2.\")\n \n # TODO - your code here\n if self.h == 1:\n selfDeterminant = self.g[0][0]\n else:\n selfDeterminant = (self.g[0][0]*self.g[1][1] - self.g[0][1]*self.g[1][0]) \n return selfDeterminant","title":""},{"docid":"e972d84aa0ff6ce5e1548353ebe94961","score":"0.69117075","text":"def calculate_determinant(matrix):\n return np.linalg.det(matrix)","title":""},{"docid":"feddc998875fc4aa65f719c4682cc5aa","score":"0.68923783","text":"def determinant(*args):\n return _almath.determinant(*args)","title":""},{"docid":"81ec2dfa5a19961ce03fdd600277ebf3","score":"0.6889683","text":"def determinant(A):\n n = len(A)\n AM = copy_matrix(A)\n\n for fd in range(n):\n if AM[fd][fd] == 0:\n AM[fd][fd] = 1.0e-18\n for i in range(fd + 1, n):\n cr_scaler = AM[i][fd] / AM[fd][fd]\n for j in range(n):\n AM[i][j] = AM[i][j] - cr_scaler * AM[fd][j]\n\n product = 1.0\n for i in range(n):\n product *= AM[i][i]\n\n return product","title":""},{"docid":"79f7ea5e55596dff39d6fa8a5a7e135a","score":"0.6796189","text":"def determinant(self):\r\n if not self.is_square():\r\n raise(ValueError, \"Cannot calculate determinant of non-square matrix.\")\r\n if self.h > 2:\r\n raise(NotImplementedError, \"Calculating determinant not implemented for matrices largerer than 2x2.\")\r\n \r\n # TODO - your code here ------------------------------------------------------------------------------------\r\n \r\n if self.h == 2:\r\n a = self.g[0][0] \r\n b = self.g[0][1]\r\n c = self.g[1][0]\r\n d = self.g[1][1]\r\n # ex.)∣A∣=1*1 - 0*0\r\n \r\n #determinant value mult. diagonal elements of 2x2 matrix. subtract multiplied elements of the diagonals. \r\n determVal = a*d - b*c\r\n return determVal\r\n \r\n if self.h == 1:\r\n determVal = self.g[0][0]\r\n return determVal\r\n \r\n \r\n \r\n #END---------------------------------------------------------------------------------------------------------\r","title":""},{"docid":"c97a4ddea4023de8305fe0df10105d2a","score":"0.67368466","text":"def determinant(A):\n\n if A.shape == (1, 1):\n return(A[0, 0])\n if A.shape == (0, 0):\n return(1.)\n\n cofacsum = 0.\n for j in range(A.shape[1]):\n cofacsum += (-1)**(0 + j) * A[0, j] * determinant(minor(A, 0, j))\n return(cofacsum)","title":""},{"docid":"6455eba632244d9b6be2f312c2c8d5d9","score":"0.66929084","text":"def determinant(self, name=\"det\"): # pylint: disable=unused-argument\n if self.is_square is False: # pylint: disable=g-bool-id-comparison\n raise NotImplementedError(\n \"Determinant not implemented for an operator that is expected to \"\n \"not be square.\")\n return self._determinant()","title":""},{"docid":"4b1da2b5cd6da19a0c4ec8d557738770","score":"0.668395","text":"def determinant(self) -> Union[float, int]:\n if self.row_count == 0 and self.col_count == 0:\n return 1\n elif self.row_count == 1 and self.col_count == 1:\n return self._matrix[0][0]\n else:\n determinant = 0\n for j in range(self.col_count):\n determinant += self._matrix[0][j] * ((-1) ** j) * self.minor(0, j).determinant()\n return determinant","title":""},{"docid":"932f587c9970ef70cdf00758999244e3","score":"0.6636221","text":"def determinant(self):\n if not self.is_square():\n raise MatrixDimensionError(\"matrix must be a square matrix\")\n det = 0\n if self._rows == 1:\n det = self.get(1, 1)\n elif self._rows == 2:\n det = self.get(1, 1)*self.get(2, 2) - self.get(1, 2)*self.get(2, 1)\n else:\n for cindex in range(self._cols):\n if self.get(1, cindex+1) != 0:\n det += self.get(1, cindex+1) * self.cofactor(1, cindex+1)\n return det","title":""},{"docid":"afa78590edf564c83d82e27b0e3e991a","score":"0.66086936","text":"def detomega(self):\n return scipy.linalg.det(self.sigma_u)\n # TODO: Does this have degrees of freedom correction like\n # the docstring claims?","title":""},{"docid":"6225a0fc6cc0054c055ea48d098f84ff","score":"0.6593563","text":"def tf(self):\n\n A, B, C, D = self.ABCD\n Aprime = polyDiag([np.poly1d([1, 0])] * self.order) - A\n det = determinant(Aprime)\n nout = self.shape[0]\n\n nominator = C * cofactorMat(Aprime).T * B + polyDiag([det] * nout) * D\n denominator = det\n\n return(nominator, denominator)","title":""},{"docid":"8ef397a516307da179c9f2642f7ec40e","score":"0.65463823","text":"def determinantValue(self):\n if self.matrix.determinant == None:\n if self.isSquareMatrix():\n determinant = self.__determinantHelper(Copy(self.matrix.data))\n self.matrix.determinant = determinant\n if determinant == 0:\n self.matrix.invertibility = False\n self.matrix.singular = True\n return determinant\n else:\n raise incompaitableTypeException\n else:\n return self.matrix.determinant","title":""},{"docid":"6b896fb201d2e96e31d590d04ea66235","score":"0.6501958","text":"def det(self):\n assert self.sq()\n if self.m() == 1:\n return self[0, 0]\n else:\n total = 0\n positive = 1\n for j in range(self.n()):\n total += self[0, j]*self.minor(0, j).det()*positive\n positive = -positive\n return total","title":""},{"docid":"25a15d3102f80061bdfa1b048cef2777","score":"0.64121246","text":"def determinant(L):\n return (L[:,:,0] * L[:,:,2])**2","title":""},{"docid":"34e207c5aab42ef1fd3172ae76631ab7","score":"0.6383526","text":"def determinant_a(self):\n matrix = self.get_values_from_matrix_a()\n self.matrix_service.return_values_to_service_a(matrix)\n det = self.matrix_service.determinant_matrix_a()\n self.show_det(det)","title":""},{"docid":"d97bdb39bfc3ede9a0b7d8a056c5d0c6","score":"0.6382183","text":"def det(A):\n return flow._C.det(A)","title":""},{"docid":"35374bcc6e45d403ee2e5137809c44f0","score":"0.6309721","text":"def determinant(self):\n if not self.is_square():\n raise(ValueError, \"Cannot calculate determinant of non-square matrix.\")\n if self.h > 2:\n raise(NotImplementedError, \"Calculating determinant not implemented for matrices largerer than 2x2.\")\n\n if self.h == 1 and self.w == 0:\n return self.g[0][0]\n\n determinant = ( (self.g[0][0] * self.g[1][1]) - (self.g[0][1] * self.g[1][0]) )\n return determinant","title":""},{"docid":"6bd33fbcc09012faeb15a95e710a80d6","score":"0.6268462","text":"def determinant(self):\r\n if not self.isSquare():\r\n raise MatrixDeterminantError()\r\n # Calculate 2x2 determinants directly.\r\n if self.rows() == 2:\r\n return self[(0, 0)]*self[(1, 1)] - self[(0, 1)]*self[(1, 0)]\r\n # Expand by minors for larger matricies.\r\n return self.expandByMinorsOnRow(0)","title":""},{"docid":"056af41dcf4a771d324a889a9f90485d","score":"0.62210584","text":"def discriminant(self):\n g = self.gens()\n M = Matrix(ZZ,len(g), [ (g[i]*g[j]).trace()\n for i in range(len(g)) for j in range(len(g)) ])\n return M.determinant()","title":""},{"docid":"b7333bf14c7283f3b12237cb136d24a4","score":"0.61693656","text":"def det(m):\n if len(m)==1: return m[0][0]\n a,mnr=alt,minor # speedup: local lookup\n return sum(a(e*det(mnr(m,0,c)),c) for c,e in enumerate(m[0]) if e!=0)","title":""},{"docid":"911e1f23c2dbd22a01257d78f4e7b0e0","score":"0.61652964","text":"def det_tres (matriz): \n soma = matriz[0][0] * matriz[1][1] * matriz[2][2]\n soma += matriz[0][1] * matriz[1][2] * matriz[2][0]\n soma += matriz[0][2] * matriz[1][0] * matriz[2][1]\n soma_negativa = matriz[0][0] * matriz[1][2] * matriz[2][1]\n soma_negativa += matriz[0][1] * matriz[1][0] * matriz[2][2]\n soma_negativa += matriz[0][2] * matriz[1][1] * matriz[2][0]\n det = soma - soma_negativa\n return det","title":""},{"docid":"50b30f2055d773d73d7451197416c8ad","score":"0.6147022","text":"def determinant(v1, v2):\r\n return v1.x * v2.y - v1.y * v2.x","title":""},{"docid":"918abcce4205b70c7027ace109e3f965","score":"0.61363083","text":"def discriminant(self,_latex=False):\n\n a = self.a\n b = self.b\n c = self.c\n d = self.d\n\n if (_latex):\n return latex(sympify(b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d))\n else:\n return b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d","title":""},{"docid":"860543217e77a411ee65e15f69a76170","score":"0.612526","text":"def __determinantHelper(self, x, sum=0):\n count = list(range(len(x)))\n if len(x) == 2 and len(x[0]) == 2:\n v = x[0][0] * x[1][1] - x[0][1] * x[1][0]\n return v\n for i in count:\n cp = copy.deepcopy(x)\n cp = cp[1:]\n size = len(cp)\n for h in range(size):\n cp[h] = cp[h][0:i] + cp[h][i + 1 :]\n sign = pow((-1), (i % 2))\n subdet = self.__determinantHelper(cp)\n sum += x[0][i] * sign * subdet\n return sum","title":""},{"docid":"6e6ef8751198aa0003d85e495dab1db5","score":"0.6091034","text":"def determinant(matrix):\n if len(matrix.shape) != 2 or matrix.shape[0] != matrix.shape[1]:\n raise ValueError('Matrix not square')\n\n if matrix.shape[0] == 1:\n return matrix[0]\n elif matrix.shape[0] == 2:\n return matrix[0, 0] * matrix[1, 1] - matrix[0, 1] * matrix[1, 0]\n elif matrix.shape[0] == 3:\n return matrix[0, 0] * matrix[1, 1] * matrix[2, 2] +\\\n matrix[1, 0] * matrix[2, 1] * matrix[0, 2] +\\\n matrix[2, 0] * matrix[0, 1] * matrix[1, 2] -\\\n matrix[0, 0] * matrix[2, 1] * matrix[1, 2] -\\\n matrix[1, 0] * matrix[0, 1] * matrix[2, 2] -\\\n matrix[2, 0] * matrix[1, 1] * matrix[0, 2]\n else:\n l, r = gaussian_decomposition(matrix)\n return functools.reduce(operator.mul, (l[i, i] * r[i, i] for i in range(matrix.shape[0])))","title":""},{"docid":"1f74dba4add7259892497acf98ad4eb9","score":"0.6028011","text":"def log_abs_determinant(self, name=\"log_abs_det\"): # pylint: disable=unused-argument\n if self.is_square is False: # pylint: disable=g-bool-id-comparison\n raise NotImplementedError(\n \"Determinant not implemented for an operator that is expected to \"\n \"not be square.\")\n return self._log_abs_determinant()","title":""},{"docid":"42f14c54d3d937dd45153d2152afe88e","score":"0.6022667","text":"def determinant(self): \n if not self.is_square():\n raise ValueError(\"Cannot calculate determinant of non-square matrix.\")\n if self.h > 2:\n raise NotImplementedError(\"Calculating determinant not implemented for matrices largerer than 2x2.\")\n det_g = [] \n if self.h == 1:\n det_g = self.g[0][0]\n else:\n det_g = self.g[0][0] * self.g[1][1] - self.g[0][1] * self.g[1][0]\n return det_g","title":""},{"docid":"a35bc0e5699c70e9b0398deca942ecc2","score":"0.6021113","text":"def det(self):\n r = self.rows\n terms = [\n r[0][0] * (r[1][1] * r[2][2] - r[1][2] * r[2][1]),\n r[0][1] * (r[1][2] * r[2][0] - r[1][0] * r[2][2]),\n r[0][2] * (r[1][0] * r[2][1] - r[1][1] * r[2][0]),\n ]\n return sum(terms)","title":""},{"docid":"af5cd1a241651f64c88253fe02b52f81","score":"0.59775287","text":"def discriminant(self,_latex=False):\n\n a = self.a\n b = self.b\n c = self.c\n d = self.d\n\n if (_latex):\n return latex(sympify(b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d))\n else:\n return sympify(b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d)","title":""},{"docid":"c50a0c299730f59553945319d62983f8","score":"0.5964027","text":"def calcula_det(matriz):\n linhas_matriz = len(matriz)\n colunas_matriz = len(matriz[0])\n if linhas_matriz < 4:\n det = det_tres(matriz)\n return det \n else:\n soma = 0\n for j in range(colunas_matriz):\n matriz_temp = remove_linha_coluna(matriz, j)\n soma += matriz[0][j] * (-1)**(j+2) * calcula_det(matriz_temp)\n\n return soma","title":""},{"docid":"e55052e762999f3ad7d0a3daeaaa95bb","score":"0.59525865","text":"def det(A):\n if not A.is_matrix() or A.shape[0] != A.shape[1]:\n raise ValueError('can only compute determinant of square matrices')\n n = A.shape[0]\n if n == 0:\n return ConstExpr(1)\n elif n == 1:\n return A[0,0]\n else:\n return reduce(operator.add,\n ((-1)**j * (A[0,j] * minor(A, 0, j))\n for j in range(n)))","title":""},{"docid":"15eb6a1b63ec22d1824e7b442b24b5c4","score":"0.59524906","text":"def discriminant(self):\n return self.gram_matrix().determinant()","title":""},{"docid":"06d0e19c5d7232b0036b93f300af4e85","score":"0.59410274","text":"def differentiation_rate_em(self, current_infection):\n\n if current_infection == 0:\n return self.effector * self.effector_differentiation_rate\n else:\n return 0","title":""},{"docid":"39f81a719f16fa19595c97bba13a103e","score":"0.5881098","text":"def jacobian_det(self):\n from utilities import simple_determinant\n if self._jacob_det is None: \n if (self.nf != self.nc):\n raise ValueError(\"The Jacobian matrix is not square.\")\n self.jacobian() # to force the computation of self._jacob_matrix\n #!# the following is a workaround for a bug in Sage (cf. trac ticket #14403)\n self._jacob_det = FunctionChart(self.chart, \n simplify_chain(simple_determinant(self._jacob_matrix)) )\n # the proper writing should be this:\n # self._jacob_det = FunctionChart(self.chart, simplify_chain(self._jacob_matrix.det()) )\n return self._jacob_det","title":""},{"docid":"6673a787561e503154c03c197cfcbd6c","score":"0.5867356","text":"def get_dielec_factor(self):\n\n return self.dielec_cons","title":""},{"docid":"fd915b5f616331fc1263b72db8e64a19","score":"0.58593017","text":"def det2x2(a):\n return a[..., 0, 0] * a[..., 1, 1] - a[..., 1, 0] * a[..., 0, 1]","title":""},{"docid":"f4f42a2e12d3060376a7ddbe076eaee7","score":"0.5856611","text":"def determinant_b(self):\n matrix = self.get_values_from_matrix_b()\n self.matrix_service.return_values_to_service_b(matrix)\n det = self.matrix_service.determinant_matrix_b()\n self.show_det(det)","title":""},{"docid":"ebed5b91e7b5e44e9371938ac03b1bde","score":"0.5842995","text":"def det3(r11,r12,r13,r21,r22,r23,r31,r32,r33):\n return r11*(r22*r33-r23*r32)-r12*(r21*r33-r23*r31)+r13*(r21*r32-r22*r31)","title":""},{"docid":"b5a3c1feedceac3bf623c43dabcd4d94","score":"0.5822633","text":"def dudc(self, x):\n\n de_dl = (-1./self.lamb**2) * (np.exp(self.lamb*x) - 1) + (x * np.exp(self.lamb*x))/(self.lamb)\n\n return de_dl","title":""},{"docid":"5eb560c2b96b56962e7b90b6054b545a","score":"0.58017063","text":"def cofactor(self):\n assert self.sq()\n result = Matrix.zero(self.m(), self.m())\n for i in range(self.m()):\n for j in range(self.m()):\n result[i, j] = self.minor(i, j).det() * (-1) ** (i + j)\n return result","title":""},{"docid":"d932f18717a897127a3672a7cfa327ed","score":"0.578442","text":"def det(var):\n if torch.is_tensor(var):\n raise ValueError(\"det is currently only supported on Variable\")\n return var.det()","title":""},{"docid":"dd6e08d41e5570ffc512e9de76e7042d","score":"0.57699466","text":"def dof():\n return 26","title":""},{"docid":"32c5f138a7e3658483d4dbcc9d163ef5","score":"0.5731186","text":"def discriminant(self,_latex=False):\n\n if (_latex):\n return latex(sympify(self.b**2-4*self.a*self.c))\n else:\n return self.b**2-4*self.a*self.c","title":""},{"docid":"21b389d284a1c065d750e6db7e9ce50f","score":"0.5712141","text":"def determinant(vec1, vec2):\n return vec1.x * vec2.y - vec1.y * vec2.x","title":""},{"docid":"b47da173d8a555f8cd01a15d9efee141","score":"0.5704865","text":"def euler_characteristic(self):\r\n chi = len(self.phi) - len(self.alpha) + len(self.sigma)\r\n return (chi)","title":""},{"docid":"09188ac64c29e5323c79656290e49d3b","score":"0.568815","text":"def calcDistortionCoef(designInfo, config, layout, offx, offy):\n mdf, tlist, xarcs, yarcs, targetX, targetY, slitXYs = project(designInfo, config, layout, offx, offy)\n return mdf, fitModel(xarcs, yarcs, targetX, targetY)","title":""},{"docid":"e460d30942669286d04cd0a8ff854e8e","score":"0.56631863","text":"def det(a, b):\n return a[0]*b[1]-a[1]*b[0]","title":""},{"docid":"6591e1df02b0e2ce3a52067bb001f84b","score":"0.5625979","text":"def vandermonde(self, fermionic_sector):\n X = self.x_vars()\n xij = [X[i] - X[j]\n for j in range(fermionic_sector)\n for i in range(j)]\n delta_m = prod(xij)\n return delta_m","title":""},{"docid":"a49bc7c7f942b99009cc620832c43d46","score":"0.557825","text":"def determinant(self, a, mul=1):\n\n if self.shape(a)[0] != self.shape(a)[1]:\n raise ValueError('Matrix is not square')\n\n if self.shape(a)[0] == 1:\n return mul * a[0][0]\n\n width = len(a)\n\n if width == 1:\n return mul * a[0][0]\n else:\n sign = -1\n sum_i = 0\n\n for i in range(width):\n m = []\n for j in range(1, width):\n buff = []\n for k in range(width):\n if k != i:\n buff.append(a[j][k])\n m.append(buff)\n sign *= -1\n sum_i += mul * self.determinant(m, sign * a[0][i])\n\n return sum_i","title":""},{"docid":"b2d9e8a9a23af9523af054222f7e68a2","score":"0.5570632","text":"def getDet_X(self):\n return self.det_X","title":""},{"docid":"03fbf8d124c32c359ca9c9015d0f4fc3","score":"0.5545594","text":"def get_derivada(self):\n self.derivar()\n return self.derivada","title":""},{"docid":"c2975308ad9f16f811542eeda4d6a983","score":"0.554318","text":"def discriminant(self,_latex=False):\n\n if (_latex):\n return latex(sympify(self.b**2-4*self.a*self.c))\n else:\n return sympify(self.b**2-4*self.a*self.c)","title":""},{"docid":"87adb4be6fdf0f5c5aaa5afe1ea0e051","score":"0.55386597","text":"def DEP(self,CMFactor=1,relativePermittivity=81,radius=1,gradESquared=array([0,0])):\n return (2*pi*CMFactor*relativePermittivity*self.vacuumPermittivity)*(radius**3)*(gradESquared)*(self.includeDEP)","title":""},{"docid":"c9ef188e54124cdea58dc7d96b71ef13","score":"0.5528655","text":"def harmcoeff(n,m):\n result = sqrt(((2*n+1)*factorial(n-m))/((4*pi)*factorial(n+m)))\n return result","title":""},{"docid":"4b8ad4ba196d2ba1ec1dbc0a6d280c81","score":"0.55258083","text":"def distance_factors(self):\n d1 = np.exp(-((np.arange(self.k) + 1.) / self.sigma) ** 2)\n\n return d1 / d1.sum()","title":""},{"docid":"55bfdce48b59901c7f38d887de644f42","score":"0.551927","text":"def calc_eff_diffusivity(self):\n d_normal = self._calc_eff_prop()\n self._eff_property = d_normal / sp.mean(self._phase['pore.molar_density'])\n return self._eff_property","title":""},{"docid":"e63d428f016787fd3e72728e0194084a","score":"0.55178005","text":"def LU_det(A):\n U = LU(A)[1]\n det = 1\n for i in range(U.shape[0]):\n det *= U[i,i]\n return det","title":""},{"docid":"4004fa19c8fb57d86142451affdc935f","score":"0.5509748","text":"def _get_log_determinant(D, B):\n log_det_D = 2*np.sum(D)\n _,log_det_IpDBBT = np.linalg.slogdet(np.eye(len(D)) + B @ B.T/np.exp(2*D[:,np.newaxis]))\n log_det_M = log_det_D + log_det_IpDBBT\n return log_det_M","title":""},{"docid":"cd7200bdaf22573737208de84137152c","score":"0.55050236","text":"def minor_det_4x4(a, i = 0, j = 1, k = 2):\n return a[i,i] * (a[j,j]*a[k,k] - a[j,k]*a[k,j]) - a[i,j] * (a[j,i]*a[k,k] - a[j,k]*a[k,i]) + a[i,k] * (a[j,i]*a[k,j] - a[j,j]*a[k,i])","title":""},{"docid":"d52f292bfc7a9270843c688bfdf177ef","score":"0.5504556","text":"def det(matrix):\n index_x=0\n result = 0\n try:\n if is_square_matrix(matrix):\n pass\n else:\n raise SyntaxError\n except:\n return \"matrix is not a square matrix\"\n \n if is_square_matrix(matrix) and order_of_matrix(matrix)==(2,2) :\n x = 0\n y = 0\n a = matrix[x][y]*matrix[x+1][y+1]\n b = matrix[x][y+1]*matrix[x+1][y]\n result= a-b\n return result\n else:\n for i in range(get_col_count(matrix)):\n if i%2 != 0:\n result+= (-matrix[index_x][i])*det(submatrix(matrix,index=(index_x,i)))\n else:\n result+= (matrix[index_x][i])*det(submatrix(matrix,index=(index_x,i)))\n \n return result","title":""},{"docid":"0a0e746ae2791b1ac0acbd031e5377d4","score":"0.5480466","text":"def estimate_dnu(self, method='empirical', numax=None):\n if numax is None:\n numax, _ = self.estimate_numax(method='simple')\n\n if method == 'empirical':\n #Calcluate dnu using the method by Stello et al. 2009\n dnu = u.Quantity(0.294 * numax.value ** 0.772, numax.unit)\n dnu_err = 0.15 * dnu\n return dnu, dnu_err\n\n if method == 'autocorrelate':\n dnu, dnu_err, _, _, _, _, _, _ = self._dnu_autocorrelate(numax)\n return dnu, dnu_err","title":""},{"docid":"96051b30fc71771ef6f83e329b970cd1","score":"0.5474057","text":"def FPdenom(n, stoch, cap, N, delta): #MAB\n return np.exp(-2*n/(1.0-2.0*stoch))*((-cap*(1.0+delta)-n*(1.0-2.0*stoch))**(2.*cap*(2.+delta-2.*stoch)/(1.-2.*stoch)**2))","title":""},{"docid":"1c098aa42c13bdb0fc058f1afc9ed0d3","score":"0.54677427","text":"def information_of_sum_log_det_covs(sum_log_det_covs, D):\n return -0.5 * (D + 2) * sum_log_det_covs","title":""},{"docid":"c523174930a8d8520ed16d844c19cf68","score":"0.5455172","text":"def get_DAF(self):\n # Figure out which state is the derived allele\n if self.anc == self.ref:\n derived = '1'\n elif self.anc == self.alt:\n derived = '0'\n else:\n derived = 'NA'\n if derived == 'NA':\n self.DAF = 'NA'\n else:\n # Then, how many derived alleles do we observe?\n total = 0\n der = 0\n for i in self.genotypes:\n if derived in i:\n der += i.count(derived)\n if not '.' in i:\n total += 2\n self.DAF = float(der)/total","title":""},{"docid":"ce645821628c4e79d610d0d3027ba718","score":"0.54538196","text":"def diffusion_coeff(t, sigma):\n #return torch.tensor(sigma**t, device=device)\n return sigma**t","title":""},{"docid":"4da9f1da9493e704479891fd54741bc9","score":"0.54443055","text":"def plane(theMat):\n d = np.linalg.det(minor(theMat,0,0))\n a = -np.linalg.det(minor(theMat,0,1))\n b = np.linalg.det(minor(theMat,0,2))\n c = -np.linalg.det(minor(theMat,0,3))\n return d,a,b,c","title":""},{"docid":"07eb164ad8c354c5f2b3bc7a534e18e5","score":"0.5440912","text":"def nDEc(self):\n nDEc = -(3.**(5./6.)/2.**(5./3.) *\n (self.wc**2. * self._Gamma *\n (self._DEphat[self._idmin])**2./self.bvec)**(1./3.))\n return nDEc","title":""},{"docid":"87add7029fe16e5dc0527aac9f27e088","score":"0.5438731","text":"def solve(deriv,perturb_max_log_Teff = 0.03,perturb_max_log_L = 0.1,perturb_max_log_T_center = 0.01,perturb_max_log_rho_center = 0.1):\n\tdelmax = np.zeros(4)\n\tdelce = np.zeros(4)\n\tdelmax[0] = perturb_max_log_Teff\n\tdelmax[1] = perturb_max_log_L\n\tdelmax[2] = perturb_max_log_T_center\n\tdelmax[3] = perturb_max_log_rho_center\n\tn=3\n\tnm=n-1\n\tnp_=n+1\n\tfor k in range(0,nm+1):\n\t kp=k+1\n\t fac1=deriv[k,k]\n\t for i in range(kp,n+1):\n\t\t fac2=deriv[i,k]\n\t\t for j in range(kp,np_+1):\n\t\t \tderiv[i,j]=deriv[i,j]*fac1-deriv[k,j]*fac2\n\tdelce[n] = -deriv[n,np_]/deriv[n,n]\n\tfor i in range(1,n+1):\n\t\ti1 = n-i\n\t\ti2 = i1+1\n\t\tdelce[i1] = -deriv[i1,np_]\n\t\tfor j in range(i2, n+1):\n\t\t\tdelce[i1] = delce[i1] - deriv[i1,j]*delce[j]\n\t\tdelce[i1] = delce[i1]/deriv[i1,i1]\n\tdm = 0.\n\tfor i in range(0, n+1):\n\t\td = np.abs(delce[i]/delmax[i])\n\t\tif dm < d:\n\t\t\tdm = d\n\tfacdel = 1.\n\tif dm > 1.:\n\t\tfacdel = dm\n\tfor i in range(0, n+1):\n\t\tdelce[i] = delce[i]/facdel\n\tfacdel = dm\n\treturn delce, facdel","title":""},{"docid":"4d5b57e3311db09aa44a3d521295c4de","score":"0.54373616","text":"def guess_f_diode_initial_value(ps, guess_fc, guess_D):\n f_nyquist = ps.sample_rate / 2\n P_aliased_nyq = (guess_D / (math.pi ** 2)) / (f_nyquist ** 2 + guess_fc ** 2)\n if ps.power[-1] < P_aliased_nyq:\n dif = ps.power[-1] / P_aliased_nyq\n return math.sqrt(dif * f_nyquist ** 2 / (1.0 - dif))\n else:\n return 2 * f_nyquist","title":""},{"docid":"674bb2743d769504f5b065308b45af55","score":"0.5423342","text":"def zfact(Tr, Pr):\n #'Tr reduced temperatue\n #'Pr reduced pressure","title":""},{"docid":"35e3aa324f02804b98a4f309e691c9ac","score":"0.54203725","text":"def cofactorMat(A):\n\n C = np.zeros(A.shape, dtype=object)\n for i in range(A.shape[0]):\n for j in range(A.shape[1]):\n C[i, j] = (-1)**(i + j) * determinant(minor(A, i, j))\n return(C)","title":""},{"docid":"6be89b464a9b45f9cab6244388ecda51","score":"0.54184896","text":"def tanp_to_det(self, x, y):\n if self._imwcs is None:\n raise RuntimeError(\"WCS has not been set\")\n return self._imwcs.tanp_to_det(x, y)","title":""},{"docid":"be845729d7c29117461de78673e4f6d0","score":"0.54067904","text":"def do_DetourMatrix(self, mol_dir):\n\n from padelpy import padeldescriptor\n import glob\n\n xml_files = glob.glob(\"functions/descriptors/bidimensional_descriptors/*.xml\")\n xml_files.sort() \n\n FP_list = ['AcidicGroupCount',\n 'ALOGP',\n 'AminoAcidCount',\n 'APol',\n 'AromaticAtomsCount',\n 'AromaticBondsCount',\n 'AtomCount',\n 'Autocorrelation',\n 'BaryszMatrix',\n 'BasicGroupCount',\n 'BCUT',\n 'BondCount',\n 'BPol',\n 'BurdenModifiedEigenvalues',\n 'CarbonTypes',\n 'ChiChain',\n 'ChiCluster',\n 'ChiPathCluster',\n 'ChiPath',\n 'Constitutional',\n 'Crippen',\n 'DetourMatrix',\n 'EccentricConnectivityIndex',\n 'EStateAtomType',\n 'ExtendedTopochemicalAtom',\n 'FMF',\n 'FragmentComplexity',\n 'HBondAcceptorCount',\n 'HBondDonorCount',\n 'HybridizationRatio',\n 'InformationContent',\n 'IPMolecularLearning',\n 'KappaShapeIndices',\n 'KierHallSmarts',\n 'LargestChain',\n 'LargestPiSystem',\n 'LongestAliphaticChain',\n 'MannholdLogP',\n 'McGowanVolume',\n 'MDE',\n 'MLFER',\n 'PathCount',\n 'PetitjeanNumber',\n 'RingCount',\n 'RotatableBondsCount',\n 'RuleOfFive',\n 'Topological',\n 'TopologicalCharge',\n 'TopologicalDistanceMatrix',\n 'TPSA',\n 'VABC',\n 'VAdjMa',\n 'WalkCount',\n 'Weight',\n 'WeightedPath',\n 'WienerNumbers',\n 'XLogP',\n 'ZagrebIndex']\n\n fp = dict(zip(FP_list, xml_files))\n\n fingerprint = 'DetourMatrix'\n\n fingerprint_output_file = ''.join([fingerprint,'.csv'])\n fingerprint_descriptortypes = fp[fingerprint]\n\n descriptor = padeldescriptor(mol_dir='dataset.smi', \n d_file=fingerprint_output_file, #'Substructure.csv'\n #descriptortypes='SubstructureFingerprint.xml', \n descriptortypes= fingerprint_descriptortypes,\n detectaromaticity=True,\n standardizenitro=True,\n standardizetautomers=True,\n threads=2,\n removesalt=True,\n log=True,\n fingerprints=True,\n d_2d=True) \n\n return False","title":""},{"docid":"60eb7714db8f6c3f5b002ab3fc0f2a26","score":"0.5388723","text":"def getCoalDecom():\n return 1.65","title":""},{"docid":"ae92212f6bbcc43879256a3e717e558f","score":"0.5385383","text":"def d_denom(af1, af2, af3, af4):\n return (np.einsum('ij,ik,il,im->jklm',af1, 1-af2, 1-af3, af4) \\\n + np.einsum('ij,ik,il,im->jklm',1-af1, af2, 1-af3, af4) \\\n + np.einsum('ij,ik,il,im->jklm',1-af1, af2, af3, 1-af4) \\\n + np.einsum('ij,ik,il,im->jklm',af1, 1-af2, af3, 1-af4))","title":""},{"docid":"1e8e355c96398e4e239b27bf82e5340b","score":"0.53806424","text":"def Dc(self, z):\r\n if z == 0:\r\n return 0\r\n else:\r\n f = lambda z: 1.0 / self._hinv(z)\r\n I = integrate.quad(f, 0, z)\r\n return self.Dh * I[0]","title":""},{"docid":"81437ff10bb6f8bedee4d4b429138440","score":"0.53765965","text":"def compute_det_dcv(self, batch_positions):\n pass","title":""},{"docid":"144900fdf4a07f2567789eaee437ac5f","score":"0.53648454","text":"def cdf(mu_no):\n \n return scipy.integrate.quad(pdf, 1, mu_no)[0]","title":""},{"docid":"e2231fe946b52fcaca1abd0cc1f2e4ea","score":"0.53556025","text":"def cdf(self, x):\r\n return (1 + special.erf((x - self.mu) / (self.sigma * np.sqrt(2)))) / 2","title":""},{"docid":"f1bb66e8f5e195b324e87ee27dd05563","score":"0.5346724","text":"def c_factor(n) :\n return 2.0*(np.log(n-1)+0.5772156649) - (2.0*(n-1.)/(n*1.0))","title":""},{"docid":"31b1e3ba83b691c01ce6fff3b449128e","score":"0.5337875","text":"def _Fn_Gonthier_etal(self, delta, _dq_n):\n # hysteresis damping factor\n self.ksi = self._hysteresis_damping_factor_Gonthier_etal()\n # contact force\n _Fn = self._Fn(delta, _dq_n)\n return _Fn","title":""},{"docid":"082e11d272fa232703cd559cfe4b8937","score":"0.53361696","text":"def der(x, pars):\r\n n = len(pars)\r\n fdx = 0\r\n for i in range(n-1):\r\n fdx += (n-i)*pars[i]*x**(n-i-1)\r\n return fdx","title":""},{"docid":"baf02f2d13c5206114344d542c73847f","score":"0.5335477","text":"def cdf_distance(equalization_factors,i,j,x):\n \n # compute temporary variable\n scaling_factor = sum([pdf_distance_unscaled(equalization_factors,i,j,t) for t in range(1, n_motor_units+1)])\n \n # discrete case\n x = (int)(np.round(x))\n value = 0\n for t in range(1,x+1):\n value += pdf_distance(equalization_factors,scaling_factor,i,j,t)\n return value","title":""},{"docid":"278041f0691ca6179160c9af7567aaea","score":"0.533488","text":"def cdf(self, x):\n lamb = self.lambtha\n e = 2.7182818285\n if x < 0:\n return 0\n return 1 - e**((-1 * lamb) * x)","title":""},{"docid":"4c8e4a934619abae70c74f978e39771e","score":"0.53291386","text":"def nDEb(self):\n return -(4.*np.sqrt(2.) - 1.)/3. * self.nDEc","title":""},{"docid":"595d59bcfc640d3f049f4db3068a00b3","score":"0.5328781","text":"def solver(a, b, c):\n D = b * b - 4 * a * c\n if D >= 0:\n x1 = (-b + sqrt(D)) / (2 * a)\n x2 = (-b - sqrt(D)) / (2 * a)\n text = \"The discriminant is: %s 783107\\n X1 is: %s \\n X2 is: %s \\n\" % (D, x1, x2)\n else:\n text = \"The discriminant is: %s \\n This equation has no solutions\" % D\n return text","title":""},{"docid":"8fb820fc171211cee8f0ce056a5204eb","score":"0.5314531","text":"def torsion(self, p: tuple) -> float:\n der1, der2, der3 = self.find_derivatives(p)\n\n numerator = Matrix([\n [der1[0], der1[1], der1[2]],\n [der2[0], der2[1], der2[2]],\n [der3[0], der3[1], der3[2]]\n ]).det()\n denominator = np.linalg.norm(np.cross(der1, der2)) ** 2\n torsion = numerator / denominator\n return torsion","title":""},{"docid":"fde256b834c2768a4bcd565ead4375e3","score":"0.5308959","text":"def cuspide(self):\n dcl_dalpha1 = derivative(self.cl_aoa, x0=self.alpha_cl_max, dx=0.001, n=1)\n dcl_dalpha2 = derivative(self.cl_aoa, x0=self.alpha_cl_max, dx=0.001, n=2)\n if dcl_dalpha2 == 0:\n return float('Inf')\n return float((1+dcl_dalpha1**2)**1.5/abs(dcl_dalpha2))","title":""},{"docid":"75c75bfb0d9ea82d5bda380384cc1265","score":"0.5307266","text":"def altitude_mass_factor(q):\n factor = ((q / 2.0) +\n ((q ** 2) / 6.0) +\n ((q ** 3) / 12.0) +\n ((q ** 4) / 20.0) +\n ((q ** 5) / 30.0))\n return factor","title":""},{"docid":"682403106ee168187163ef94d1a41782","score":"0.5295419","text":"def main():\n answer = (math.factorial(20)/(16*15*12*math.factorial(10)))\n print answer","title":""},{"docid":"eebbf0714392de31c52611510ecaf586","score":"0.5294235","text":"def factor(self):\n # factor first 3*3 part\n K,R = scipy.linalg.rq(self.P[:,:3])\n # make diagonal of K positive\n T = np.diag(np.sign(np.diag(K)))\n if linalg.det(T) < 0:\n T[1,1] *= -1\n self.K = np.dot(K,T)\n self.R = np.dot(T,R) # T is its own inverse\n self.t = np.dot(linalg.inv(self.K),self.P[:,3])\n return self.K, self.R, self.t","title":""},{"docid":"cf632fd79018a3b82c848739b5d7bc76","score":"0.529137","text":"def cdf(x, mu, sigma):\n #x = sy.sympify(x)\n return (1+sy.erf((x-mu)/(sigma*sy.sqrt(2))))/2","title":""},{"docid":"1a84b295c1bf7db159054fd98814d177","score":"0.527984","text":"def dvdy(x, y, mu):\n return -(1 - mu) * y / pow(r1(x, y, mu), 3) - mu * y / pow(r2(x, y, mu), 3) + y","title":""},{"docid":"c5da723adeffda94f430b679b61b3fa4","score":"0.52747357","text":"def jacDet(self,star):\n\n # Make local copies\n scale = self.scale\n \n jacobian = star[:,0]/\\\n ((1.-(np.tanh(star[:,1]/scale[1]))**2.)/scale[1])/\\\n ((1.-(np.tanh(star[:,2]/scale[2]))**2.)/scale[2])/\\\n ((1.-(np.tanh(star[:,3]/scale[3]))**2.)/scale[3])\n \n return(np.abs(jacobian))","title":""},{"docid":"9d71e178f2e8d3e55676fada9915467d","score":"0.52735037","text":"def index(self):\n if not self.mat_repr.ishnf:\n mat_repr = Submodule.fromMatrix(self.mat_repr.copy())\n mat_repr.toHNF()\n else:\n mat_repr = self.mat_repr\n n = self.base.column\n if mat_repr.column < n:\n raise ValueError(\"index is infinite\")\n det = 1\n for i in range(1, n + 1):\n # for HNF, determinant = product of diagonal elements\n det *= mat_repr[i, i]\n if self.denominator != 1:\n return rational.Rational(det, self.denominator ** n)\n else:\n return det","title":""},{"docid":"c1dcc7460f767c739e7a207bac418448","score":"0.52700096","text":"def QCD_correction(self):\n alpha_s = ROOT.TGraph( os.path.expandvars('$FAIRSHIP/python/alpha_s.dat') )\n a_s = alpha_s.Eval(self.MN)\n qcd_corr = a_s / math.pi\n qcd_corr += 5.2 * (a_s / math.pi)**2.\n qcd_corr += 26.4 * (a_s / math.pi)**3.\n return qcd_corr","title":""},{"docid":"abcef5be58bf710596f0d8dd2b3fa071","score":"0.5269141","text":"def get_eff_mass_factor(self):\n\n return self.eff_mass_e","title":""}],"string":"[\n {\n \"docid\": \"413fc5941756b2ca7da1865bf4e82869\",\n \"score\": \"0.7301002\",\n \"text\": \"def __determinant(a, b, c, d, e, f, g, h, i):\\n return (a * e * i - a * f * h - b * d * i + b * f * g + c * d * h - c * e * g)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c318f71da8bb3c2e134b577fed78eed9\",\n \"score\": \"0.7147995\",\n \"text\": \"def determinant(self):\\n return np.linalg.det(self.xform_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5891acbdfb80910d3635e70c5089f9\",\n \"score\": \"0.6982245\",\n \"text\": \"def det(self):\\n return self.a*self.c - self.b*self.b/4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014eea6bf02dfbe3f08951397e486c22\",\n \"score\": \"0.6915586\",\n \"text\": \"def determinant(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Cannot calculate determinant of non-square matrix.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"Calculating determinant not implemented for matrices largerer than 2x2.\\\")\\n \\n # TODO - your code here\\n if self.h == 1:\\n selfDeterminant = self.g[0][0]\\n else:\\n selfDeterminant = (self.g[0][0]*self.g[1][1] - self.g[0][1]*self.g[1][0]) \\n return selfDeterminant\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e972d84aa0ff6ce5e1548353ebe94961\",\n \"score\": \"0.69117075\",\n \"text\": \"def calculate_determinant(matrix):\\n return np.linalg.det(matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feddc998875fc4aa65f719c4682cc5aa\",\n \"score\": \"0.68923783\",\n \"text\": \"def determinant(*args):\\n return _almath.determinant(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ec2dfa5a19961ce03fdd600277ebf3\",\n \"score\": \"0.6889683\",\n \"text\": \"def determinant(A):\\n n = len(A)\\n AM = copy_matrix(A)\\n\\n for fd in range(n):\\n if AM[fd][fd] == 0:\\n AM[fd][fd] = 1.0e-18\\n for i in range(fd + 1, n):\\n cr_scaler = AM[i][fd] / AM[fd][fd]\\n for j in range(n):\\n AM[i][j] = AM[i][j] - cr_scaler * AM[fd][j]\\n\\n product = 1.0\\n for i in range(n):\\n product *= AM[i][i]\\n\\n return product\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f7ea5e55596dff39d6fa8a5a7e135a\",\n \"score\": \"0.6796189\",\n \"text\": \"def determinant(self):\\r\\n if not self.is_square():\\r\\n raise(ValueError, \\\"Cannot calculate determinant of non-square matrix.\\\")\\r\\n if self.h > 2:\\r\\n raise(NotImplementedError, \\\"Calculating determinant not implemented for matrices largerer than 2x2.\\\")\\r\\n \\r\\n # TODO - your code here ------------------------------------------------------------------------------------\\r\\n \\r\\n if self.h == 2:\\r\\n a = self.g[0][0] \\r\\n b = self.g[0][1]\\r\\n c = self.g[1][0]\\r\\n d = self.g[1][1]\\r\\n # ex.)∣A∣=1*1 - 0*0\\r\\n \\r\\n #determinant value mult. diagonal elements of 2x2 matrix. subtract multiplied elements of the diagonals. \\r\\n determVal = a*d - b*c\\r\\n return determVal\\r\\n \\r\\n if self.h == 1:\\r\\n determVal = self.g[0][0]\\r\\n return determVal\\r\\n \\r\\n \\r\\n \\r\\n #END---------------------------------------------------------------------------------------------------------\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97a4ddea4023de8305fe0df10105d2a\",\n \"score\": \"0.67368466\",\n \"text\": \"def determinant(A):\\n\\n if A.shape == (1, 1):\\n return(A[0, 0])\\n if A.shape == (0, 0):\\n return(1.)\\n\\n cofacsum = 0.\\n for j in range(A.shape[1]):\\n cofacsum += (-1)**(0 + j) * A[0, j] * determinant(minor(A, 0, j))\\n return(cofacsum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6455eba632244d9b6be2f312c2c8d5d9\",\n \"score\": \"0.66929084\",\n \"text\": \"def determinant(self, name=\\\"det\\\"): # pylint: disable=unused-argument\\n if self.is_square is False: # pylint: disable=g-bool-id-comparison\\n raise NotImplementedError(\\n \\\"Determinant not implemented for an operator that is expected to \\\"\\n \\\"not be square.\\\")\\n return self._determinant()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b1da2b5cd6da19a0c4ec8d557738770\",\n \"score\": \"0.668395\",\n \"text\": \"def determinant(self) -> Union[float, int]:\\n if self.row_count == 0 and self.col_count == 0:\\n return 1\\n elif self.row_count == 1 and self.col_count == 1:\\n return self._matrix[0][0]\\n else:\\n determinant = 0\\n for j in range(self.col_count):\\n determinant += self._matrix[0][j] * ((-1) ** j) * self.minor(0, j).determinant()\\n return determinant\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932f587c9970ef70cdf00758999244e3\",\n \"score\": \"0.6636221\",\n \"text\": \"def determinant(self):\\n if not self.is_square():\\n raise MatrixDimensionError(\\\"matrix must be a square matrix\\\")\\n det = 0\\n if self._rows == 1:\\n det = self.get(1, 1)\\n elif self._rows == 2:\\n det = self.get(1, 1)*self.get(2, 2) - self.get(1, 2)*self.get(2, 1)\\n else:\\n for cindex in range(self._cols):\\n if self.get(1, cindex+1) != 0:\\n det += self.get(1, cindex+1) * self.cofactor(1, cindex+1)\\n return det\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa78590edf564c83d82e27b0e3e991a\",\n \"score\": \"0.66086936\",\n \"text\": \"def detomega(self):\\n return scipy.linalg.det(self.sigma_u)\\n # TODO: Does this have degrees of freedom correction like\\n # the docstring claims?\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6225a0fc6cc0054c055ea48d098f84ff\",\n \"score\": \"0.6593563\",\n \"text\": \"def tf(self):\\n\\n A, B, C, D = self.ABCD\\n Aprime = polyDiag([np.poly1d([1, 0])] * self.order) - A\\n det = determinant(Aprime)\\n nout = self.shape[0]\\n\\n nominator = C * cofactorMat(Aprime).T * B + polyDiag([det] * nout) * D\\n denominator = det\\n\\n return(nominator, denominator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef397a516307da179c9f2642f7ec40e\",\n \"score\": \"0.65463823\",\n \"text\": \"def determinantValue(self):\\n if self.matrix.determinant == None:\\n if self.isSquareMatrix():\\n determinant = self.__determinantHelper(Copy(self.matrix.data))\\n self.matrix.determinant = determinant\\n if determinant == 0:\\n self.matrix.invertibility = False\\n self.matrix.singular = True\\n return determinant\\n else:\\n raise incompaitableTypeException\\n else:\\n return self.matrix.determinant\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b896fb201d2e96e31d590d04ea66235\",\n \"score\": \"0.6501958\",\n \"text\": \"def det(self):\\n assert self.sq()\\n if self.m() == 1:\\n return self[0, 0]\\n else:\\n total = 0\\n positive = 1\\n for j in range(self.n()):\\n total += self[0, j]*self.minor(0, j).det()*positive\\n positive = -positive\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a15d3102f80061bdfa1b048cef2777\",\n \"score\": \"0.64121246\",\n \"text\": \"def determinant(L):\\n return (L[:,:,0] * L[:,:,2])**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34e207c5aab42ef1fd3172ae76631ab7\",\n \"score\": \"0.6383526\",\n \"text\": \"def determinant_a(self):\\n matrix = self.get_values_from_matrix_a()\\n self.matrix_service.return_values_to_service_a(matrix)\\n det = self.matrix_service.determinant_matrix_a()\\n self.show_det(det)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97bdb39bfc3ede9a0b7d8a056c5d0c6\",\n \"score\": \"0.6382183\",\n \"text\": \"def det(A):\\n return flow._C.det(A)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35374bcc6e45d403ee2e5137809c44f0\",\n \"score\": \"0.6309721\",\n \"text\": \"def determinant(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Cannot calculate determinant of non-square matrix.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"Calculating determinant not implemented for matrices largerer than 2x2.\\\")\\n\\n if self.h == 1 and self.w == 0:\\n return self.g[0][0]\\n\\n determinant = ( (self.g[0][0] * self.g[1][1]) - (self.g[0][1] * self.g[1][0]) )\\n return determinant\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd33fbcc09012faeb15a95e710a80d6\",\n \"score\": \"0.6268462\",\n \"text\": \"def determinant(self):\\r\\n if not self.isSquare():\\r\\n raise MatrixDeterminantError()\\r\\n # Calculate 2x2 determinants directly.\\r\\n if self.rows() == 2:\\r\\n return self[(0, 0)]*self[(1, 1)] - self[(0, 1)]*self[(1, 0)]\\r\\n # Expand by minors for larger matricies.\\r\\n return self.expandByMinorsOnRow(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"056af41dcf4a771d324a889a9f90485d\",\n \"score\": \"0.62210584\",\n \"text\": \"def discriminant(self):\\n g = self.gens()\\n M = Matrix(ZZ,len(g), [ (g[i]*g[j]).trace()\\n for i in range(len(g)) for j in range(len(g)) ])\\n return M.determinant()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7333bf14c7283f3b12237cb136d24a4\",\n \"score\": \"0.61693656\",\n \"text\": \"def det(m):\\n if len(m)==1: return m[0][0]\\n a,mnr=alt,minor # speedup: local lookup\\n return sum(a(e*det(mnr(m,0,c)),c) for c,e in enumerate(m[0]) if e!=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911e1f23c2dbd22a01257d78f4e7b0e0\",\n \"score\": \"0.61652964\",\n \"text\": \"def det_tres (matriz): \\n soma = matriz[0][0] * matriz[1][1] * matriz[2][2]\\n soma += matriz[0][1] * matriz[1][2] * matriz[2][0]\\n soma += matriz[0][2] * matriz[1][0] * matriz[2][1]\\n soma_negativa = matriz[0][0] * matriz[1][2] * matriz[2][1]\\n soma_negativa += matriz[0][1] * matriz[1][0] * matriz[2][2]\\n soma_negativa += matriz[0][2] * matriz[1][1] * matriz[2][0]\\n det = soma - soma_negativa\\n return det\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b30f2055d773d73d7451197416c8ad\",\n \"score\": \"0.6147022\",\n \"text\": \"def determinant(v1, v2):\\r\\n return v1.x * v2.y - v1.y * v2.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918abcce4205b70c7027ace109e3f965\",\n \"score\": \"0.61363083\",\n \"text\": \"def discriminant(self,_latex=False):\\n\\n a = self.a\\n b = self.b\\n c = self.c\\n d = self.d\\n\\n if (_latex):\\n return latex(sympify(b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d))\\n else:\\n return b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860543217e77a411ee65e15f69a76170\",\n \"score\": \"0.612526\",\n \"text\": \"def __determinantHelper(self, x, sum=0):\\n count = list(range(len(x)))\\n if len(x) == 2 and len(x[0]) == 2:\\n v = x[0][0] * x[1][1] - x[0][1] * x[1][0]\\n return v\\n for i in count:\\n cp = copy.deepcopy(x)\\n cp = cp[1:]\\n size = len(cp)\\n for h in range(size):\\n cp[h] = cp[h][0:i] + cp[h][i + 1 :]\\n sign = pow((-1), (i % 2))\\n subdet = self.__determinantHelper(cp)\\n sum += x[0][i] * sign * subdet\\n return sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6ef8751198aa0003d85e495dab1db5\",\n \"score\": \"0.6091034\",\n \"text\": \"def determinant(matrix):\\n if len(matrix.shape) != 2 or matrix.shape[0] != matrix.shape[1]:\\n raise ValueError('Matrix not square')\\n\\n if matrix.shape[0] == 1:\\n return matrix[0]\\n elif matrix.shape[0] == 2:\\n return matrix[0, 0] * matrix[1, 1] - matrix[0, 1] * matrix[1, 0]\\n elif matrix.shape[0] == 3:\\n return matrix[0, 0] * matrix[1, 1] * matrix[2, 2] +\\\\\\n matrix[1, 0] * matrix[2, 1] * matrix[0, 2] +\\\\\\n matrix[2, 0] * matrix[0, 1] * matrix[1, 2] -\\\\\\n matrix[0, 0] * matrix[2, 1] * matrix[1, 2] -\\\\\\n matrix[1, 0] * matrix[0, 1] * matrix[2, 2] -\\\\\\n matrix[2, 0] * matrix[1, 1] * matrix[0, 2]\\n else:\\n l, r = gaussian_decomposition(matrix)\\n return functools.reduce(operator.mul, (l[i, i] * r[i, i] for i in range(matrix.shape[0])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f74dba4add7259892497acf98ad4eb9\",\n \"score\": \"0.6028011\",\n \"text\": \"def log_abs_determinant(self, name=\\\"log_abs_det\\\"): # pylint: disable=unused-argument\\n if self.is_square is False: # pylint: disable=g-bool-id-comparison\\n raise NotImplementedError(\\n \\\"Determinant not implemented for an operator that is expected to \\\"\\n \\\"not be square.\\\")\\n return self._log_abs_determinant()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f14c54d3d937dd45153d2152afe88e\",\n \"score\": \"0.6022667\",\n \"text\": \"def determinant(self): \\n if not self.is_square():\\n raise ValueError(\\\"Cannot calculate determinant of non-square matrix.\\\")\\n if self.h > 2:\\n raise NotImplementedError(\\\"Calculating determinant not implemented for matrices largerer than 2x2.\\\")\\n det_g = [] \\n if self.h == 1:\\n det_g = self.g[0][0]\\n else:\\n det_g = self.g[0][0] * self.g[1][1] - self.g[0][1] * self.g[1][0]\\n return det_g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35bc0e5699c70e9b0398deca942ecc2\",\n \"score\": \"0.6021113\",\n \"text\": \"def det(self):\\n r = self.rows\\n terms = [\\n r[0][0] * (r[1][1] * r[2][2] - r[1][2] * r[2][1]),\\n r[0][1] * (r[1][2] * r[2][0] - r[1][0] * r[2][2]),\\n r[0][2] * (r[1][0] * r[2][1] - r[1][1] * r[2][0]),\\n ]\\n return sum(terms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5cd1a241651f64c88253fe02b52f81\",\n \"score\": \"0.59775287\",\n \"text\": \"def discriminant(self,_latex=False):\\n\\n a = self.a\\n b = self.b\\n c = self.c\\n d = self.d\\n\\n if (_latex):\\n return latex(sympify(b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d))\\n else:\\n return sympify(b*b*c*c-4*a*c*c*c-4*b*b*b*d-27*a*a*d*d+18*a*b*c*d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50a0c299730f59553945319d62983f8\",\n \"score\": \"0.5964027\",\n \"text\": \"def calcula_det(matriz):\\n linhas_matriz = len(matriz)\\n colunas_matriz = len(matriz[0])\\n if linhas_matriz < 4:\\n det = det_tres(matriz)\\n return det \\n else:\\n soma = 0\\n for j in range(colunas_matriz):\\n matriz_temp = remove_linha_coluna(matriz, j)\\n soma += matriz[0][j] * (-1)**(j+2) * calcula_det(matriz_temp)\\n\\n return soma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55052e762999f3ad7d0a3daeaaa95bb\",\n \"score\": \"0.59525865\",\n \"text\": \"def det(A):\\n if not A.is_matrix() or A.shape[0] != A.shape[1]:\\n raise ValueError('can only compute determinant of square matrices')\\n n = A.shape[0]\\n if n == 0:\\n return ConstExpr(1)\\n elif n == 1:\\n return A[0,0]\\n else:\\n return reduce(operator.add,\\n ((-1)**j * (A[0,j] * minor(A, 0, j))\\n for j in range(n)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15eb6a1b63ec22d1824e7b442b24b5c4\",\n \"score\": \"0.59524906\",\n \"text\": \"def discriminant(self):\\n return self.gram_matrix().determinant()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d0e19c5d7232b0036b93f300af4e85\",\n \"score\": \"0.59410274\",\n \"text\": \"def differentiation_rate_em(self, current_infection):\\n\\n if current_infection == 0:\\n return self.effector * self.effector_differentiation_rate\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f81a719f16fa19595c97bba13a103e\",\n \"score\": \"0.5881098\",\n \"text\": \"def jacobian_det(self):\\n from utilities import simple_determinant\\n if self._jacob_det is None: \\n if (self.nf != self.nc):\\n raise ValueError(\\\"The Jacobian matrix is not square.\\\")\\n self.jacobian() # to force the computation of self._jacob_matrix\\n #!# the following is a workaround for a bug in Sage (cf. trac ticket #14403)\\n self._jacob_det = FunctionChart(self.chart, \\n simplify_chain(simple_determinant(self._jacob_matrix)) )\\n # the proper writing should be this:\\n # self._jacob_det = FunctionChart(self.chart, simplify_chain(self._jacob_matrix.det()) )\\n return self._jacob_det\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6673a787561e503154c03c197cfcbd6c\",\n \"score\": \"0.5867356\",\n \"text\": \"def get_dielec_factor(self):\\n\\n return self.dielec_cons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd915b5f616331fc1263b72db8e64a19\",\n \"score\": \"0.58593017\",\n \"text\": \"def det2x2(a):\\n return a[..., 0, 0] * a[..., 1, 1] - a[..., 1, 0] * a[..., 0, 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f42a2e12d3060376a7ddbe076eaee7\",\n \"score\": \"0.5856611\",\n \"text\": \"def determinant_b(self):\\n matrix = self.get_values_from_matrix_b()\\n self.matrix_service.return_values_to_service_b(matrix)\\n det = self.matrix_service.determinant_matrix_b()\\n self.show_det(det)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebed5b91e7b5e44e9371938ac03b1bde\",\n \"score\": \"0.5842995\",\n \"text\": \"def det3(r11,r12,r13,r21,r22,r23,r31,r32,r33):\\n return r11*(r22*r33-r23*r32)-r12*(r21*r33-r23*r31)+r13*(r21*r32-r22*r31)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a3c1feedceac3bf623c43dabcd4d94\",\n \"score\": \"0.5822633\",\n \"text\": \"def dudc(self, x):\\n\\n de_dl = (-1./self.lamb**2) * (np.exp(self.lamb*x) - 1) + (x * np.exp(self.lamb*x))/(self.lamb)\\n\\n return de_dl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb560c2b96b56962e7b90b6054b545a\",\n \"score\": \"0.58017063\",\n \"text\": \"def cofactor(self):\\n assert self.sq()\\n result = Matrix.zero(self.m(), self.m())\\n for i in range(self.m()):\\n for j in range(self.m()):\\n result[i, j] = self.minor(i, j).det() * (-1) ** (i + j)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d932f18717a897127a3672a7cfa327ed\",\n \"score\": \"0.578442\",\n \"text\": \"def det(var):\\n if torch.is_tensor(var):\\n raise ValueError(\\\"det is currently only supported on Variable\\\")\\n return var.det()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6e08d41e5570ffc512e9de76e7042d\",\n \"score\": \"0.57699466\",\n \"text\": \"def dof():\\n return 26\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c5f138a7e3658483d4dbcc9d163ef5\",\n \"score\": \"0.5731186\",\n \"text\": \"def discriminant(self,_latex=False):\\n\\n if (_latex):\\n return latex(sympify(self.b**2-4*self.a*self.c))\\n else:\\n return self.b**2-4*self.a*self.c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b389d284a1c065d750e6db7e9ce50f\",\n \"score\": \"0.5712141\",\n \"text\": \"def determinant(vec1, vec2):\\n return vec1.x * vec2.y - vec1.y * vec2.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b47da173d8a555f8cd01a15d9efee141\",\n \"score\": \"0.5704865\",\n \"text\": \"def euler_characteristic(self):\\r\\n chi = len(self.phi) - len(self.alpha) + len(self.sigma)\\r\\n return (chi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09188ac64c29e5323c79656290e49d3b\",\n \"score\": \"0.568815\",\n \"text\": \"def calcDistortionCoef(designInfo, config, layout, offx, offy):\\n mdf, tlist, xarcs, yarcs, targetX, targetY, slitXYs = project(designInfo, config, layout, offx, offy)\\n return mdf, fitModel(xarcs, yarcs, targetX, targetY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e460d30942669286d04cd0a8ff854e8e\",\n \"score\": \"0.56631863\",\n \"text\": \"def det(a, b):\\n return a[0]*b[1]-a[1]*b[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6591e1df02b0e2ce3a52067bb001f84b\",\n \"score\": \"0.5625979\",\n \"text\": \"def vandermonde(self, fermionic_sector):\\n X = self.x_vars()\\n xij = [X[i] - X[j]\\n for j in range(fermionic_sector)\\n for i in range(j)]\\n delta_m = prod(xij)\\n return delta_m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a49bc7c7f942b99009cc620832c43d46\",\n \"score\": \"0.557825\",\n \"text\": \"def determinant(self, a, mul=1):\\n\\n if self.shape(a)[0] != self.shape(a)[1]:\\n raise ValueError('Matrix is not square')\\n\\n if self.shape(a)[0] == 1:\\n return mul * a[0][0]\\n\\n width = len(a)\\n\\n if width == 1:\\n return mul * a[0][0]\\n else:\\n sign = -1\\n sum_i = 0\\n\\n for i in range(width):\\n m = []\\n for j in range(1, width):\\n buff = []\\n for k in range(width):\\n if k != i:\\n buff.append(a[j][k])\\n m.append(buff)\\n sign *= -1\\n sum_i += mul * self.determinant(m, sign * a[0][i])\\n\\n return sum_i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d9e8a9a23af9523af054222f7e68a2\",\n \"score\": \"0.5570632\",\n \"text\": \"def getDet_X(self):\\n return self.det_X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03fbf8d124c32c359ca9c9015d0f4fc3\",\n \"score\": \"0.5545594\",\n \"text\": \"def get_derivada(self):\\n self.derivar()\\n return self.derivada\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2975308ad9f16f811542eeda4d6a983\",\n \"score\": \"0.554318\",\n \"text\": \"def discriminant(self,_latex=False):\\n\\n if (_latex):\\n return latex(sympify(self.b**2-4*self.a*self.c))\\n else:\\n return sympify(self.b**2-4*self.a*self.c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87adb4be6fdf0f5c5aaa5afe1ea0e051\",\n \"score\": \"0.55386597\",\n \"text\": \"def DEP(self,CMFactor=1,relativePermittivity=81,radius=1,gradESquared=array([0,0])):\\n return (2*pi*CMFactor*relativePermittivity*self.vacuumPermittivity)*(radius**3)*(gradESquared)*(self.includeDEP)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ef188e54124cdea58dc7d96b71ef13\",\n \"score\": \"0.5528655\",\n \"text\": \"def harmcoeff(n,m):\\n result = sqrt(((2*n+1)*factorial(n-m))/((4*pi)*factorial(n+m)))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8ad4ba196d2ba1ec1dbc0a6d280c81\",\n \"score\": \"0.55258083\",\n \"text\": \"def distance_factors(self):\\n d1 = np.exp(-((np.arange(self.k) + 1.) / self.sigma) ** 2)\\n\\n return d1 / d1.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55bfdce48b59901c7f38d887de644f42\",\n \"score\": \"0.551927\",\n \"text\": \"def calc_eff_diffusivity(self):\\n d_normal = self._calc_eff_prop()\\n self._eff_property = d_normal / sp.mean(self._phase['pore.molar_density'])\\n return self._eff_property\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63d428f016787fd3e72728e0194084a\",\n \"score\": \"0.55178005\",\n \"text\": \"def LU_det(A):\\n U = LU(A)[1]\\n det = 1\\n for i in range(U.shape[0]):\\n det *= U[i,i]\\n return det\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4004fa19c8fb57d86142451affdc935f\",\n \"score\": \"0.5509748\",\n \"text\": \"def _get_log_determinant(D, B):\\n log_det_D = 2*np.sum(D)\\n _,log_det_IpDBBT = np.linalg.slogdet(np.eye(len(D)) + B @ B.T/np.exp(2*D[:,np.newaxis]))\\n log_det_M = log_det_D + log_det_IpDBBT\\n return log_det_M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7200bdaf22573737208de84137152c\",\n \"score\": \"0.55050236\",\n \"text\": \"def minor_det_4x4(a, i = 0, j = 1, k = 2):\\n return a[i,i] * (a[j,j]*a[k,k] - a[j,k]*a[k,j]) - a[i,j] * (a[j,i]*a[k,k] - a[j,k]*a[k,i]) + a[i,k] * (a[j,i]*a[k,j] - a[j,j]*a[k,i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d52f292bfc7a9270843c688bfdf177ef\",\n \"score\": \"0.5504556\",\n \"text\": \"def det(matrix):\\n index_x=0\\n result = 0\\n try:\\n if is_square_matrix(matrix):\\n pass\\n else:\\n raise SyntaxError\\n except:\\n return \\\"matrix is not a square matrix\\\"\\n \\n if is_square_matrix(matrix) and order_of_matrix(matrix)==(2,2) :\\n x = 0\\n y = 0\\n a = matrix[x][y]*matrix[x+1][y+1]\\n b = matrix[x][y+1]*matrix[x+1][y]\\n result= a-b\\n return result\\n else:\\n for i in range(get_col_count(matrix)):\\n if i%2 != 0:\\n result+= (-matrix[index_x][i])*det(submatrix(matrix,index=(index_x,i)))\\n else:\\n result+= (matrix[index_x][i])*det(submatrix(matrix,index=(index_x,i)))\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a0e746ae2791b1ac0acbd031e5377d4\",\n \"score\": \"0.5480466\",\n \"text\": \"def estimate_dnu(self, method='empirical', numax=None):\\n if numax is None:\\n numax, _ = self.estimate_numax(method='simple')\\n\\n if method == 'empirical':\\n #Calcluate dnu using the method by Stello et al. 2009\\n dnu = u.Quantity(0.294 * numax.value ** 0.772, numax.unit)\\n dnu_err = 0.15 * dnu\\n return dnu, dnu_err\\n\\n if method == 'autocorrelate':\\n dnu, dnu_err, _, _, _, _, _, _ = self._dnu_autocorrelate(numax)\\n return dnu, dnu_err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96051b30fc71771ef6f83e329b970cd1\",\n \"score\": \"0.5474057\",\n \"text\": \"def FPdenom(n, stoch, cap, N, delta): #MAB\\n return np.exp(-2*n/(1.0-2.0*stoch))*((-cap*(1.0+delta)-n*(1.0-2.0*stoch))**(2.*cap*(2.+delta-2.*stoch)/(1.-2.*stoch)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c098aa42c13bdb0fc058f1afc9ed0d3\",\n \"score\": \"0.54677427\",\n \"text\": \"def information_of_sum_log_det_covs(sum_log_det_covs, D):\\n return -0.5 * (D + 2) * sum_log_det_covs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c523174930a8d8520ed16d844c19cf68\",\n \"score\": \"0.5455172\",\n \"text\": \"def get_DAF(self):\\n # Figure out which state is the derived allele\\n if self.anc == self.ref:\\n derived = '1'\\n elif self.anc == self.alt:\\n derived = '0'\\n else:\\n derived = 'NA'\\n if derived == 'NA':\\n self.DAF = 'NA'\\n else:\\n # Then, how many derived alleles do we observe?\\n total = 0\\n der = 0\\n for i in self.genotypes:\\n if derived in i:\\n der += i.count(derived)\\n if not '.' in i:\\n total += 2\\n self.DAF = float(der)/total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce645821628c4e79d610d0d3027ba718\",\n \"score\": \"0.54538196\",\n \"text\": \"def diffusion_coeff(t, sigma):\\n #return torch.tensor(sigma**t, device=device)\\n return sigma**t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da9f1da9493e704479891fd54741bc9\",\n \"score\": \"0.54443055\",\n \"text\": \"def plane(theMat):\\n d = np.linalg.det(minor(theMat,0,0))\\n a = -np.linalg.det(minor(theMat,0,1))\\n b = np.linalg.det(minor(theMat,0,2))\\n c = -np.linalg.det(minor(theMat,0,3))\\n return d,a,b,c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07eb164ad8c354c5f2b3bc7a534e18e5\",\n \"score\": \"0.5440912\",\n \"text\": \"def nDEc(self):\\n nDEc = -(3.**(5./6.)/2.**(5./3.) *\\n (self.wc**2. * self._Gamma *\\n (self._DEphat[self._idmin])**2./self.bvec)**(1./3.))\\n return nDEc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87add7029fe16e5dc0527aac9f27e088\",\n \"score\": \"0.5438731\",\n \"text\": \"def solve(deriv,perturb_max_log_Teff = 0.03,perturb_max_log_L = 0.1,perturb_max_log_T_center = 0.01,perturb_max_log_rho_center = 0.1):\\n\\tdelmax = np.zeros(4)\\n\\tdelce = np.zeros(4)\\n\\tdelmax[0] = perturb_max_log_Teff\\n\\tdelmax[1] = perturb_max_log_L\\n\\tdelmax[2] = perturb_max_log_T_center\\n\\tdelmax[3] = perturb_max_log_rho_center\\n\\tn=3\\n\\tnm=n-1\\n\\tnp_=n+1\\n\\tfor k in range(0,nm+1):\\n\\t kp=k+1\\n\\t fac1=deriv[k,k]\\n\\t for i in range(kp,n+1):\\n\\t\\t fac2=deriv[i,k]\\n\\t\\t for j in range(kp,np_+1):\\n\\t\\t \\tderiv[i,j]=deriv[i,j]*fac1-deriv[k,j]*fac2\\n\\tdelce[n] = -deriv[n,np_]/deriv[n,n]\\n\\tfor i in range(1,n+1):\\n\\t\\ti1 = n-i\\n\\t\\ti2 = i1+1\\n\\t\\tdelce[i1] = -deriv[i1,np_]\\n\\t\\tfor j in range(i2, n+1):\\n\\t\\t\\tdelce[i1] = delce[i1] - deriv[i1,j]*delce[j]\\n\\t\\tdelce[i1] = delce[i1]/deriv[i1,i1]\\n\\tdm = 0.\\n\\tfor i in range(0, n+1):\\n\\t\\td = np.abs(delce[i]/delmax[i])\\n\\t\\tif dm < d:\\n\\t\\t\\tdm = d\\n\\tfacdel = 1.\\n\\tif dm > 1.:\\n\\t\\tfacdel = dm\\n\\tfor i in range(0, n+1):\\n\\t\\tdelce[i] = delce[i]/facdel\\n\\tfacdel = dm\\n\\treturn delce, facdel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d5b57e3311db09aa44a3d521295c4de\",\n \"score\": \"0.54373616\",\n \"text\": \"def guess_f_diode_initial_value(ps, guess_fc, guess_D):\\n f_nyquist = ps.sample_rate / 2\\n P_aliased_nyq = (guess_D / (math.pi ** 2)) / (f_nyquist ** 2 + guess_fc ** 2)\\n if ps.power[-1] < P_aliased_nyq:\\n dif = ps.power[-1] / P_aliased_nyq\\n return math.sqrt(dif * f_nyquist ** 2 / (1.0 - dif))\\n else:\\n return 2 * f_nyquist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"674bb2743d769504f5b065308b45af55\",\n \"score\": \"0.5423342\",\n \"text\": \"def zfact(Tr, Pr):\\n #'Tr reduced temperatue\\n #'Pr reduced pressure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e3aa324f02804b98a4f309e691c9ac\",\n \"score\": \"0.54203725\",\n \"text\": \"def cofactorMat(A):\\n\\n C = np.zeros(A.shape, dtype=object)\\n for i in range(A.shape[0]):\\n for j in range(A.shape[1]):\\n C[i, j] = (-1)**(i + j) * determinant(minor(A, i, j))\\n return(C)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6be89b464a9b45f9cab6244388ecda51\",\n \"score\": \"0.54184896\",\n \"text\": \"def tanp_to_det(self, x, y):\\n if self._imwcs is None:\\n raise RuntimeError(\\\"WCS has not been set\\\")\\n return self._imwcs.tanp_to_det(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be845729d7c29117461de78673e4f6d0\",\n \"score\": \"0.54067904\",\n \"text\": \"def do_DetourMatrix(self, mol_dir):\\n\\n from padelpy import padeldescriptor\\n import glob\\n\\n xml_files = glob.glob(\\\"functions/descriptors/bidimensional_descriptors/*.xml\\\")\\n xml_files.sort() \\n\\n FP_list = ['AcidicGroupCount',\\n 'ALOGP',\\n 'AminoAcidCount',\\n 'APol',\\n 'AromaticAtomsCount',\\n 'AromaticBondsCount',\\n 'AtomCount',\\n 'Autocorrelation',\\n 'BaryszMatrix',\\n 'BasicGroupCount',\\n 'BCUT',\\n 'BondCount',\\n 'BPol',\\n 'BurdenModifiedEigenvalues',\\n 'CarbonTypes',\\n 'ChiChain',\\n 'ChiCluster',\\n 'ChiPathCluster',\\n 'ChiPath',\\n 'Constitutional',\\n 'Crippen',\\n 'DetourMatrix',\\n 'EccentricConnectivityIndex',\\n 'EStateAtomType',\\n 'ExtendedTopochemicalAtom',\\n 'FMF',\\n 'FragmentComplexity',\\n 'HBondAcceptorCount',\\n 'HBondDonorCount',\\n 'HybridizationRatio',\\n 'InformationContent',\\n 'IPMolecularLearning',\\n 'KappaShapeIndices',\\n 'KierHallSmarts',\\n 'LargestChain',\\n 'LargestPiSystem',\\n 'LongestAliphaticChain',\\n 'MannholdLogP',\\n 'McGowanVolume',\\n 'MDE',\\n 'MLFER',\\n 'PathCount',\\n 'PetitjeanNumber',\\n 'RingCount',\\n 'RotatableBondsCount',\\n 'RuleOfFive',\\n 'Topological',\\n 'TopologicalCharge',\\n 'TopologicalDistanceMatrix',\\n 'TPSA',\\n 'VABC',\\n 'VAdjMa',\\n 'WalkCount',\\n 'Weight',\\n 'WeightedPath',\\n 'WienerNumbers',\\n 'XLogP',\\n 'ZagrebIndex']\\n\\n fp = dict(zip(FP_list, xml_files))\\n\\n fingerprint = 'DetourMatrix'\\n\\n fingerprint_output_file = ''.join([fingerprint,'.csv'])\\n fingerprint_descriptortypes = fp[fingerprint]\\n\\n descriptor = padeldescriptor(mol_dir='dataset.smi', \\n d_file=fingerprint_output_file, #'Substructure.csv'\\n #descriptortypes='SubstructureFingerprint.xml', \\n descriptortypes= fingerprint_descriptortypes,\\n detectaromaticity=True,\\n standardizenitro=True,\\n standardizetautomers=True,\\n threads=2,\\n removesalt=True,\\n log=True,\\n fingerprints=True,\\n d_2d=True) \\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60eb7714db8f6c3f5b002ab3fc0f2a26\",\n \"score\": \"0.5388723\",\n \"text\": \"def getCoalDecom():\\n return 1.65\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae92212f6bbcc43879256a3e717e558f\",\n \"score\": \"0.5385383\",\n \"text\": \"def d_denom(af1, af2, af3, af4):\\n return (np.einsum('ij,ik,il,im->jklm',af1, 1-af2, 1-af3, af4) \\\\\\n + np.einsum('ij,ik,il,im->jklm',1-af1, af2, 1-af3, af4) \\\\\\n + np.einsum('ij,ik,il,im->jklm',1-af1, af2, af3, 1-af4) \\\\\\n + np.einsum('ij,ik,il,im->jklm',af1, 1-af2, af3, 1-af4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8e355c96398e4e239b27bf82e5340b\",\n \"score\": \"0.53806424\",\n \"text\": \"def Dc(self, z):\\r\\n if z == 0:\\r\\n return 0\\r\\n else:\\r\\n f = lambda z: 1.0 / self._hinv(z)\\r\\n I = integrate.quad(f, 0, z)\\r\\n return self.Dh * I[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81437ff10bb6f8bedee4d4b429138440\",\n \"score\": \"0.53765965\",\n \"text\": \"def compute_det_dcv(self, batch_positions):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144900fdf4a07f2567789eaee437ac5f\",\n \"score\": \"0.53648454\",\n \"text\": \"def cdf(mu_no):\\n \\n return scipy.integrate.quad(pdf, 1, mu_no)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2231fe946b52fcaca1abd0cc1f2e4ea\",\n \"score\": \"0.53556025\",\n \"text\": \"def cdf(self, x):\\r\\n return (1 + special.erf((x - self.mu) / (self.sigma * np.sqrt(2)))) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1bb66e8f5e195b324e87ee27dd05563\",\n \"score\": \"0.5346724\",\n \"text\": \"def c_factor(n) :\\n return 2.0*(np.log(n-1)+0.5772156649) - (2.0*(n-1.)/(n*1.0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b1e3ba83b691c01ce6fff3b449128e\",\n \"score\": \"0.5337875\",\n \"text\": \"def _Fn_Gonthier_etal(self, delta, _dq_n):\\n # hysteresis damping factor\\n self.ksi = self._hysteresis_damping_factor_Gonthier_etal()\\n # contact force\\n _Fn = self._Fn(delta, _dq_n)\\n return _Fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082e11d272fa232703cd559cfe4b8937\",\n \"score\": \"0.53361696\",\n \"text\": \"def der(x, pars):\\r\\n n = len(pars)\\r\\n fdx = 0\\r\\n for i in range(n-1):\\r\\n fdx += (n-i)*pars[i]*x**(n-i-1)\\r\\n return fdx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf02f2d13c5206114344d542c73847f\",\n \"score\": \"0.5335477\",\n \"text\": \"def cdf_distance(equalization_factors,i,j,x):\\n \\n # compute temporary variable\\n scaling_factor = sum([pdf_distance_unscaled(equalization_factors,i,j,t) for t in range(1, n_motor_units+1)])\\n \\n # discrete case\\n x = (int)(np.round(x))\\n value = 0\\n for t in range(1,x+1):\\n value += pdf_distance(equalization_factors,scaling_factor,i,j,t)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278041f0691ca6179160c9af7567aaea\",\n \"score\": \"0.533488\",\n \"text\": \"def cdf(self, x):\\n lamb = self.lambtha\\n e = 2.7182818285\\n if x < 0:\\n return 0\\n return 1 - e**((-1 * lamb) * x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c8e4a934619abae70c74f978e39771e\",\n \"score\": \"0.53291386\",\n \"text\": \"def nDEb(self):\\n return -(4.*np.sqrt(2.) - 1.)/3. * self.nDEc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595d59bcfc640d3f049f4db3068a00b3\",\n \"score\": \"0.5328781\",\n \"text\": \"def solver(a, b, c):\\n D = b * b - 4 * a * c\\n if D >= 0:\\n x1 = (-b + sqrt(D)) / (2 * a)\\n x2 = (-b - sqrt(D)) / (2 * a)\\n text = \\\"The discriminant is: %s 783107\\\\n X1 is: %s \\\\n X2 is: %s \\\\n\\\" % (D, x1, x2)\\n else:\\n text = \\\"The discriminant is: %s \\\\n This equation has no solutions\\\" % D\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb820fc171211cee8f0ce056a5204eb\",\n \"score\": \"0.5314531\",\n \"text\": \"def torsion(self, p: tuple) -> float:\\n der1, der2, der3 = self.find_derivatives(p)\\n\\n numerator = Matrix([\\n [der1[0], der1[1], der1[2]],\\n [der2[0], der2[1], der2[2]],\\n [der3[0], der3[1], der3[2]]\\n ]).det()\\n denominator = np.linalg.norm(np.cross(der1, der2)) ** 2\\n torsion = numerator / denominator\\n return torsion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde256b834c2768a4bcd565ead4375e3\",\n \"score\": \"0.5308959\",\n \"text\": \"def cuspide(self):\\n dcl_dalpha1 = derivative(self.cl_aoa, x0=self.alpha_cl_max, dx=0.001, n=1)\\n dcl_dalpha2 = derivative(self.cl_aoa, x0=self.alpha_cl_max, dx=0.001, n=2)\\n if dcl_dalpha2 == 0:\\n return float('Inf')\\n return float((1+dcl_dalpha1**2)**1.5/abs(dcl_dalpha2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c75bfb0d9ea82d5bda380384cc1265\",\n \"score\": \"0.5307266\",\n \"text\": \"def altitude_mass_factor(q):\\n factor = ((q / 2.0) +\\n ((q ** 2) / 6.0) +\\n ((q ** 3) / 12.0) +\\n ((q ** 4) / 20.0) +\\n ((q ** 5) / 30.0))\\n return factor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682403106ee168187163ef94d1a41782\",\n \"score\": \"0.5295419\",\n \"text\": \"def main():\\n answer = (math.factorial(20)/(16*15*12*math.factorial(10)))\\n print answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eebbf0714392de31c52611510ecaf586\",\n \"score\": \"0.5294235\",\n \"text\": \"def factor(self):\\n # factor first 3*3 part\\n K,R = scipy.linalg.rq(self.P[:,:3])\\n # make diagonal of K positive\\n T = np.diag(np.sign(np.diag(K)))\\n if linalg.det(T) < 0:\\n T[1,1] *= -1\\n self.K = np.dot(K,T)\\n self.R = np.dot(T,R) # T is its own inverse\\n self.t = np.dot(linalg.inv(self.K),self.P[:,3])\\n return self.K, self.R, self.t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf632fd79018a3b82c848739b5d7bc76\",\n \"score\": \"0.529137\",\n \"text\": \"def cdf(x, mu, sigma):\\n #x = sy.sympify(x)\\n return (1+sy.erf((x-mu)/(sigma*sy.sqrt(2))))/2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a84b295c1bf7db159054fd98814d177\",\n \"score\": \"0.527984\",\n \"text\": \"def dvdy(x, y, mu):\\n return -(1 - mu) * y / pow(r1(x, y, mu), 3) - mu * y / pow(r2(x, y, mu), 3) + y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5da723adeffda94f430b679b61b3fa4\",\n \"score\": \"0.52747357\",\n \"text\": \"def jacDet(self,star):\\n\\n # Make local copies\\n scale = self.scale\\n \\n jacobian = star[:,0]/\\\\\\n ((1.-(np.tanh(star[:,1]/scale[1]))**2.)/scale[1])/\\\\\\n ((1.-(np.tanh(star[:,2]/scale[2]))**2.)/scale[2])/\\\\\\n ((1.-(np.tanh(star[:,3]/scale[3]))**2.)/scale[3])\\n \\n return(np.abs(jacobian))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d71e178f2e8d3e55676fada9915467d\",\n \"score\": \"0.52735037\",\n \"text\": \"def index(self):\\n if not self.mat_repr.ishnf:\\n mat_repr = Submodule.fromMatrix(self.mat_repr.copy())\\n mat_repr.toHNF()\\n else:\\n mat_repr = self.mat_repr\\n n = self.base.column\\n if mat_repr.column < n:\\n raise ValueError(\\\"index is infinite\\\")\\n det = 1\\n for i in range(1, n + 1):\\n # for HNF, determinant = product of diagonal elements\\n det *= mat_repr[i, i]\\n if self.denominator != 1:\\n return rational.Rational(det, self.denominator ** n)\\n else:\\n return det\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1dcc7460f767c739e7a207bac418448\",\n \"score\": \"0.52700096\",\n \"text\": \"def QCD_correction(self):\\n alpha_s = ROOT.TGraph( os.path.expandvars('$FAIRSHIP/python/alpha_s.dat') )\\n a_s = alpha_s.Eval(self.MN)\\n qcd_corr = a_s / math.pi\\n qcd_corr += 5.2 * (a_s / math.pi)**2.\\n qcd_corr += 26.4 * (a_s / math.pi)**3.\\n return qcd_corr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abcef5be58bf710596f0d8dd2b3fa071\",\n \"score\": \"0.5269141\",\n \"text\": \"def get_eff_mass_factor(self):\\n\\n return self.eff_mass_e\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1590,"cells":{"query_id":{"kind":"string","value":"76d126eea30c50235a8ef21afcddc67d"},"query":{"kind":"string","value":"Generate patch files from git"},"positive_passages":{"kind":"list like","value":[{"docid":"75757dfcc998005c155116c28200b707","score":"0.69303894","text":"def generate_patches(repo, start, end, outdir, options):\n gbp.log.info(\"Generating patches from git (%s..%s)\" % (start, end))\n patches = []\n commands = {}\n for treeish in [start, end]:\n if not repo.has_treeish(treeish):\n raise GbpError('Invalid treeish object %s' % treeish)\n\n start_sha1 = repo.rev_parse(\"%s^0\" % start)\n try:\n end_commit = end\n except GitRepositoryError:\n # In case of plain tree-ish objects, assume current branch head is the\n # last commit\n end_commit = \"HEAD\"\n end_commit_sha1 = repo.rev_parse(\"%s^0\" % end_commit)\n\n start_sha1 = repo.rev_parse(\"%s^0\" % start)\n\n if not is_ancestor(repo, start_sha1, end_commit_sha1):\n raise GbpError(\"Start commit '%s' not an ancestor of end commit \"\n \"'%s'\" % (start, end_commit))\n # Check for merge commits, squash if merges found\n merges = repo.get_commits(start, end_commit, options=['--merges'])\n if merges:\n # Shorten SHA1s\n start_sha1 = repo.rev_parse(start, short=7)\n merge_sha1 = repo.rev_parse(merges[0], short=7)\n patch_fn = format_diff(outdir, None, repo, start_sha1, merge_sha1)\n if patch_fn:\n gbp.log.info(\"Merge commits found! Diff between %s..%s written \"\n \"into one monolithic diff\" % (start_sha1, merge_sha1))\n patches.append(patch_fn)\n start = merge_sha1\n\n # Generate patches\n for commit in reversed(repo.get_commits(start, end_commit)):\n info = repo.get_commit_info(commit)\n (cmds, info['body']) = parse_gbp_commands(info,\n 'gbp-rpm',\n ('ignore'),\n ('if', 'ifarch'))\n if 'ignore' not in cmds:\n patch_fn = format_patch(outdir, repo, info, patches,\n options.patch_numbers)\n if patch_fn:\n commands[os.path.basename(patch_fn)] = cmds\n else:\n gbp.log.info('Ignoring commit %s' % info['id'])\n\n # Generate diff to the tree-ish object\n if end_commit != end:\n gbp.log.info(\"Generating diff file %s..%s\" % (end_commit, end))\n patch_fn = format_diff(outdir, None, repo, end_commit, end,\n options.patch_export_ignore_path)\n if patch_fn:\n patches.append(patch_fn)\n\n return [os.path.relpath(p) for p in patches], commands","title":""}],"string":"[\n {\n \"docid\": \"75757dfcc998005c155116c28200b707\",\n \"score\": \"0.69303894\",\n \"text\": \"def generate_patches(repo, start, end, outdir, options):\\n gbp.log.info(\\\"Generating patches from git (%s..%s)\\\" % (start, end))\\n patches = []\\n commands = {}\\n for treeish in [start, end]:\\n if not repo.has_treeish(treeish):\\n raise GbpError('Invalid treeish object %s' % treeish)\\n\\n start_sha1 = repo.rev_parse(\\\"%s^0\\\" % start)\\n try:\\n end_commit = end\\n except GitRepositoryError:\\n # In case of plain tree-ish objects, assume current branch head is the\\n # last commit\\n end_commit = \\\"HEAD\\\"\\n end_commit_sha1 = repo.rev_parse(\\\"%s^0\\\" % end_commit)\\n\\n start_sha1 = repo.rev_parse(\\\"%s^0\\\" % start)\\n\\n if not is_ancestor(repo, start_sha1, end_commit_sha1):\\n raise GbpError(\\\"Start commit '%s' not an ancestor of end commit \\\"\\n \\\"'%s'\\\" % (start, end_commit))\\n # Check for merge commits, squash if merges found\\n merges = repo.get_commits(start, end_commit, options=['--merges'])\\n if merges:\\n # Shorten SHA1s\\n start_sha1 = repo.rev_parse(start, short=7)\\n merge_sha1 = repo.rev_parse(merges[0], short=7)\\n patch_fn = format_diff(outdir, None, repo, start_sha1, merge_sha1)\\n if patch_fn:\\n gbp.log.info(\\\"Merge commits found! Diff between %s..%s written \\\"\\n \\\"into one monolithic diff\\\" % (start_sha1, merge_sha1))\\n patches.append(patch_fn)\\n start = merge_sha1\\n\\n # Generate patches\\n for commit in reversed(repo.get_commits(start, end_commit)):\\n info = repo.get_commit_info(commit)\\n (cmds, info['body']) = parse_gbp_commands(info,\\n 'gbp-rpm',\\n ('ignore'),\\n ('if', 'ifarch'))\\n if 'ignore' not in cmds:\\n patch_fn = format_patch(outdir, repo, info, patches,\\n options.patch_numbers)\\n if patch_fn:\\n commands[os.path.basename(patch_fn)] = cmds\\n else:\\n gbp.log.info('Ignoring commit %s' % info['id'])\\n\\n # Generate diff to the tree-ish object\\n if end_commit != end:\\n gbp.log.info(\\\"Generating diff file %s..%s\\\" % (end_commit, end))\\n patch_fn = format_diff(outdir, None, repo, end_commit, end,\\n options.patch_export_ignore_path)\\n if patch_fn:\\n patches.append(patch_fn)\\n\\n return [os.path.relpath(p) for p in patches], commands\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"13e1bf6c03a59fdbfb8b61d2bcf63c17","score":"0.64661807","text":"def main_git():\n proc = subprocess.Popen([\"git\", \"diff\", \"scala\"], stdout=subprocess.PIPE)\n out = proc.communicate()[0].decode()\n\n RE = re.compile(\"([-+])Subproject commit (.*)\")\n frm = None\n to = None\n for mode, commit in RE.findall(out):\n if mode == \"-\":\n frm = commit\n elif mode == \"+\":\n to = commit\n else:\n raise ValueError(\"Unkown mode: %s\" % mode)\n if frm is None or to is None:\n raise ValueError(\"Cannot calculate diff from %s to %s\" % (frm, to))\n\n if \"dirty\" in to:\n to = []\n else:\n to = [to]\n proc = subprocess.Popen([\"git\", \"-C\", \"scala\", \"diff\", \"-U1\", frm] + to,\n stdout=subprocess.PIPE)\n diff = proc.communicate()[0].decode()\n fname = None\n\n for path, start1, lines1, start2, lines2 in DIFFRE.findall(diff):\n if path:\n fname = path\n else:\n # We already applied previous changes, so the new start value\n # (that is, start2) is the one we are interested in.\n apply_hunk(\"scala/\" + fname, int(start2),\n int(start2) + int(lines1),\n int(start2) + int(lines2))","title":""},{"docid":"e065e59077e63ec1449a4733c313eaf6","score":"0.60689807","text":"def GenerateDiff(self):\n branch = upload.RunShell(['git', 'cl', 'upstream']).strip()\n diff = upload.RunShell(['git', 'diff-tree', '-p', '--no-prefix',\n branch, 'HEAD']).splitlines(True)\n for i in range(len(diff)):\n # In the case of added files, replace /dev/null with the path to the\n # file being added.\n if diff[i].startswith('--- /dev/null'):\n diff[i] = '--- %s' % diff[i+1][4:]\n return ''.join(diff)","title":""},{"docid":"8b48919f473be215b3c4ae98ac0f4bee","score":"0.6018004","text":"def git_repo(\n tmp_path_factory: pytest.TempPathFactory,\n request: pytest.FixtureRequest,\n) -> Iterator[Path]:\n tmp_path = tmp_path_factory.mktemp(\"git_changelog\")\n tmp_path.mkdir(exist_ok=True, parents=True)\n git = partial(_git, \"-C\", str(tmp_path))\n commit = partial(_commit, tmp_path, \"dummy\")\n git(\"init\")\n git(\"config\", \"user.name\", \"dummy\")\n git(\"config\", \"user.email\", \"dummy@example.com\")\n git(\"remote\", \"add\", \"origin\", \"git@github.com:example/example\")\n versions = request.param\n for version in versions:\n for section in AngularConvention.TYPES:\n commit(section)\n commit(section)\n if version:\n git(\"tag\", version)\n yield tmp_path\n shutil.rmtree(tmp_path, ignore_errors=True)","title":""},{"docid":"398b97d6d0a5709090ba9238c804ceff","score":"0.6002166","text":"def apply_svn_patch(patch_root, patches, whitelist=None, blacklist=None):\n if whitelist:\n patches = [(name, diff) for name, diff in patches if name in whitelist]\n elif blacklist:\n patches = [(name, diff) for name, diff in patches if name not in blacklist]\n diffs = [diff for _, diff in patches]\n patch = ''.join(diffs)\n\n if patch:\n print '===Patching files==='\n for filename, _ in patches:\n print 'Patching %s' % filename\n try:\n call(PATCH_TOOL, '-p0', '--remove-empty-files', '--force', '--forward',\n stdin_data=patch, cwd=patch_root, tries=1)\n for filename, _ in patches:\n full_filename = path.abspath(path.join(patch_root, filename))\n git('add', full_filename, cwd=path.dirname(full_filename))\n except SubprocessFailed as e:\n raise PatchFailed(e.message, e.code, e.output)","title":""},{"docid":"976c3e0ba83cd74bdd2286fb911204b4","score":"0.5962196","text":"def blame_patch(patch):\n return subprocess.check_output((\"git\", \"log\",\n \"--follow\", \"--find-renames\", \"--diff-filter=A\",\n \"--format=%s (%aN <%aE>)\",\n \"--\", patch)).decode(\"utf-8\").splitlines()","title":""},{"docid":"8d264daaa5015848f9b650cd874538a5","score":"0.59295815","text":"def format_diffs(commit, paths):\n\n diffs = []\n # We are ignoring merges so all commits should have a single parent\n for diff in commit.tree.diff(commit.parents[0], paths=paths, create_patch=True):\n if diff.a_path is not None:\n # The patch commit diffs are stored as \"(filename1)\\n(diff1)\\n(filename2)\\n(diff2)...\"\n lines = \"\\n\".join(\n line\n for line in diff.diff.decode(\"utf-8\").splitlines()\n if line.startswith((\"+\", \"-\"))\n )\n diffs.append(f\"{diff.a_path}\\n{lines}\")\n\n return \"\\n\".join(diffs)","title":""},{"docid":"d06e4367d6465ffe16fe5a3e44bfd061","score":"0.590408","text":"def git_diff(*args):\n command = (['git', '--no-pager', 'diff'] + list(args) + [\n '--', 'requirements.txt', 'misc/requirements/requirements-*.txt'])\n proc = subprocess.run(command,\n stdout=subprocess.PIPE,\n encoding='utf-8',\n check=True)\n return proc.stdout.splitlines()","title":""},{"docid":"f0f8f407c5c46be94b8656f98660ebae","score":"0.58514106","text":"def patch_project(\n hw_prefix: str = typer.Argument(default=..., help=\"prefix of the homework title\"),\n patch_branch: str = typer.Argument(\n default=..., help=\"source bracnch to patch to the main branch\"\n ),\n source_repo: Optional[str] = typer.Option(\n default=None, help=\"default to tmpl-{hw-prefix}-revise\"\n ),\n only_repo: Optional[str] = typer.Option(default=None, help=\"only repo to patch\"),\n dry: bool = typer.Option(\n False, \"--dry\", help=\"dry run, do not publish result to the remote\"\n ),\n yes: bool = opt_all_yes,\n token: str = opt_github_token,\n org: str = opt_gh_org,\n):\n ensure_config_exists()\n\n def fallback(val, fallback_value):\n return val if val else fallback_value\n\n # Handle default value manually because we'll change our config after app starts up\n token: str = fallback(token, app_context.config.github.personal_access_token)\n org: str = fallback(org, app_context.config.github.organization)\n\n ensure_git_cached()\n ensure_gh_token(token)\n\n if not (\n yes or typer.confirm(f\"Patch homework:{hw_prefix} from branch:{patch_branch}?\")\n ):\n raise typer.Abort()\n # init\n spinner = Halo(stream=sys.stderr)\n\n if not source_repo:\n source_repo = f\"tmpl-{hw_prefix}-revise\"\n\n # Check if repo already contains the patched branch. Skip if so.\n # api : https://developer.github.com/v3/git/refs/#get-a-reference\n res = requests.get(\n f\"https://api.github.com/repos/{org}/{source_repo}/git/refs/heads/{patch_branch}\",\n headers=github_headers(token),\n )\n if res.status_code != 200: # this branch not exists on the remote\n spinner.fail(\n f\"branch : `{patch_branch}` doesn't exist on repo:{org}/{source_repo} \"\n )\n return\n\n cur = Path(\".\")\n for d in cur.glob(\"patch-*\"):\n shutil.rmtree(d)\n spinner.info(\"delete dated folder\")\n\n spinner.start(f\"Fetch issue template {patch_branch} from {source_repo}\")\n # Fetch patch template on the source repo\n issues = get_github_endpoint_paged_list(\n endpoint=f\"repos/{org}/{source_repo}/issues\", github_token=token, verbose=False\n )\n\n def find_target_issue() -> Optional[Dict]:\n for issue in issues:\n if issue[\"title\"].strip() == patch_branch.strip():\n return issue\n return None\n\n target_issue = find_target_issue()\n if not target_issue:\n raise Exception(f\"cannot found issue tmpl `{patch_branch}` on `{source_repo}`\")\n issue_tmpl_body = target_issue[\"body\"]\n spinner.succeed()\n\n root_folder = Path(\n tempfile.mkdtemp(\n prefix=\"patch-{}-{}-\".format(\n patch_branch, datetime.now().strftime(\"%b%d%H%M%S\")\n ),\n dir=\".\",\n )\n )\n\n spinner.succeed(f\"Create tmp folder: {root_folder}\")\n spinner.info(f\"Fetch source repo {source_repo} from Github\")\n src_repo_path = root_folder / \"source_repo\"\n sp.run(\n [\n \"git\",\n \"clone\",\n f\"https://github.com/{org}/{source_repo}.git\",\n src_repo_path.name,\n ],\n cwd=root_folder,\n )\n\n src_repo = Repo(src_repo_path)\n sp.run(\n [\"git\", \"checkout\", \"--track\", f\"origin/{patch_branch}\"],\n cwd=src_repo_path,\n stdout=sp.DEVNULL,\n stderr=sp.DEVNULL,\n )\n spinner.succeed()\n\n # Pasting changed files into students repo\n src_repo_git = src_repo.git\n src_repo_git.checkout(patch_branch)\n changed_files, renamed_files = get_changed_files(\n master_commit=src_repo.heads[\"master\"].commit,\n patch_commit=src_repo.heads[patch_branch].commit,\n )\n\n spinner.start(\"Fetch information for homework repo\")\n spinner.succeed()\n if only_repo is not None:\n repos = [\n re\n for re in query_matching_repos(\n org, github_repo_prefix=only_repo, github_token=token, verbose=False\n )\n if re[\"name\"] == only_repo\n ]\n repo = next(iter(repos), None)\n if repo:\n spinner.info(\"Only patch to repo : \" + repo[\"name\"])\n repos = [repo]\n else:\n repos = query_matching_repos(\n org, github_repo_prefix=hw_prefix, github_token=token, verbose=False\n )\n spinner.succeed()\n\n # Patch to student repos\n student_path = root_folder / \"student_repos\"\n student_path.mkdir()\n for repo_idx, r in enumerate(repos, start=1):\n pre_prompt_str = f\"({repo_idx}/{len(repos)}) \" + r[\"name\"]\n spinner.start()\n\n # Check if repo already contains the patched branch. Skip if so.\n # api : https://developer.github.com/v3/git/refs/#get-a-reference\n res = requests.get(\n f\"https://api.github.com/repos/{org}/{r['name']}/git/refs/heads/{patch_branch}\",\n headers=github_headers(token),\n )\n if res.status_code == 200: # this branch exists in the remote\n spinner.text = pre_prompt_str + \" Skip \" + \" already patched\"\n\n spinner.succeed()\n continue\n\n spinner.text = pre_prompt_str + \"cloning repo\"\n\n sp.run(\n [\"git\", \"clone\", \"--depth=1\", r[\"html_url\"]],\n cwd=student_path,\n stdout=sp.DEVNULL,\n stderr=sp.DEVNULL,\n )\n\n hw_repo_name = r[\"html_url\"].rsplit(\"/\")[-1]\n\n # open a new branch & checkout to that branch\n sp.run(\n [\"git\", \"checkout\", \"-b\", patch_branch],\n cwd=student_path / hw_repo_name,\n stdout=sp.DEVNULL,\n stderr=sp.DEVNULL,\n )\n\n # copy file to student repo\n for f in changed_files.keys():\n (student_path / hw_repo_name / f).parent.mkdir(parents=True, exist_ok=True)\n shutil.copyfile(src=src_repo_path / f, dst=student_path / hw_repo_name / f)\n for f in renamed_files.keys():\n os.remove(student_path / hw_repo_name / f)\n\n # changed_files = get_changed_files(\n # master_commit = src_repo.heads['master'].commit,\n # patch_commit = src_repo.heads[patch_branch].commit\n # )\n # push (publish) that branch to student repo\n sp.run(\n [\"git\", \"add\", \".\"],\n cwd=student_path / hw_repo_name,\n stdout=sp.DEVNULL,\n stderr=sp.DEVNULL,\n )\n\n # Pass if no changed\n student_repo = Repo(student_path / hw_repo_name)\n if len(student_repo.index.diff(\"HEAD\")) == 0:\n spinner.text = pre_prompt_str + \" Passed \" + \" no changes in repo\"\n\n spinner.succeed()\n continue\n\n sp.run(\n [\"git\", \"commit\", \"-m\", f\":construction_worker: Patch: {patch_branch}\"],\n cwd=student_path / hw_repo_name,\n stdout=sp.DEVNULL,\n stderr=sp.DEVNULL,\n )\n\n spinner.text = pre_prompt_str + \" publish patch to remote...\"\n if dry:\n spinner.succeed(pre_prompt_str + \" Patched \")\n continue\n res = sp.run(\n [\"git\", \"push\", \"-u\", \"origin\", patch_branch],\n cwd=student_path / hw_repo_name,\n stdout=sp.DEVNULL,\n stderr=sp.DEVNULL,\n )\n if res.returncode != 0:\n spinner.fail(\n pre_prompt_str\n + \" Failed \"\n + f\"Cannot push branch {patch_branch} to_origin\"\n )\n continue\n\n # open an pull-request on students repo\n # student_repo/patch-branch -> student_repo/master\n body = {\n \"title\": f\"[PATCH] {patch_branch}\",\n \"body\": issue_tmpl_body,\n \"head\": patch_branch,\n \"base\": \"master\",\n }\n res = requests.post(\n f\"https://api.github.com/repos/{org}/{r['name']}/pulls\",\n headers=github_headers(token),\n json=body,\n )\n if res.status_code == 201:\n spinner.succeed(f\"{pre_prompt_str} Patched \")\n else:\n spinner.fail(\n pre_prompt_str\n + \" Failed \"\n + f\"Cannot create PR {patch_branch} to origin/master\"\n )\n try:\n print(res.json()[\"errors\"][0][\"message\"])\n except:\n pass\n continue\n\n # TODO : print summary after patch\n # how many success, skiped, failed","title":""},{"docid":"79de92318a130aab284532646d3a4078","score":"0.583339","text":"def get_svn_patch(patch_url):\n svn_exe = 'svn.bat' if sys.platform.startswith('win') else 'svn'\n patch_data = call(svn_exe, 'cat', patch_url)\n return parse_diff(patch_data)","title":""},{"docid":"18916286b2fb0b6bdbadca962603a4a9","score":"0.5827033","text":"def create_patch(self, from_tag, to_tag):\n return str(self._git.diff(f'{from_tag}..{to_tag}', _tty_out=False))","title":""},{"docid":"9a464bf5ec3d006372b26f6445ec66cb","score":"0.57943296","text":"def smart_git():\r\n pass","title":""},{"docid":"f257b853ef6347d7ded14c9163cd2ab2","score":"0.57583135","text":"def bootstrap():\n local('git config commit.template templates/git-commit-template.txt')","title":""},{"docid":"1236bd55cd3510edf29c68f851085e1c","score":"0.57125634","text":"def svn_diff_open_patch_file(*args) -> \"svn_patch_file_t **\":\n return _diff.svn_diff_open_patch_file(*args)","title":""},{"docid":"c6970e3296197d71fa03c3bf2d4af83d","score":"0.56949264","text":"def GenerateDiff(self, files, root):\n previous_cwd = os.getcwd()\n if root is None:\n os.chdir(gcl.GetRepositoryRoot())\n else:\n os.chdir(root)\n\n # Directories will return None so filter them out.\n diff = filter(None, [scm.SVN.DiffItem(f) for f in files])\n os.chdir(previous_cwd)\n return \"\".join(diff)","title":""},{"docid":"66a1ab5e5d6f0905c214385ef964f01e","score":"0.5685457","text":"def git_files():\n process = subprocess.Popen(\n [\"git\", \"diff\", \"--cached\", \"--name-only\"],\n stdout = subprocess.PIPE,\n stderr = subprocess.PIPE)\n\n while True:\n ret = process.poll()\n line = process.stdout.readline()\n yield line\n if ret is not None: return","title":""},{"docid":"abff0cfacb17155cbbc1e53efd0e4e3f","score":"0.567108","text":"def patch_sleuthkit(self):\n for filename, rules in iter(self.files.items()):\n filename = os.path.join(*filename.split(\"/\"))\n\n with open(filename, \"r\") as file_object:\n data = file_object.read()\n\n for search, replace in rules:\n data = re.sub(search, replace, data)\n\n with open(filename, \"w\") as fd:\n fd.write(data)\n\n patch_files = [\n \"sleuthkit-{0:s}-configure.ac\".format(self._SLEUTHKIT_GIT_TAG)]\n\n for patch_file in patch_files:\n patch_file = os.path.join(\"patches\", patch_file)\n if not os.path.exists(patch_file):\n print(\"No such patch file: {0:s}\".format(patch_file))\n continue\n\n patch_file = os.path.join(\"..\", patch_file)\n subprocess.check_call([\"git\", \"apply\", patch_file], cwd=\"sleuthkit\")","title":""},{"docid":"f2b83c3d422c86825157831db37f899d","score":"0.566736","text":"def create_patches_and_update_specfile(self, upstream_ref) -> None:\n if self.with_action(action=ActionName.create_patches):\n patches = self.create_patches(\n upstream=upstream_ref, destination=str(self.absolute_specfile_dir)\n )\n self.specfile_add_patches(patches)","title":""},{"docid":"602ea1cc7571d107cd6f83656bb3b4b3","score":"0.5663216","text":"def apply_patch(self, patch=None):\n if not any([patch, self.patch]):\n return\n if not patch:\n patch = self.patch\n self.cmd(f'patch -p1 < {self.project.patch}/{self.ver}/{patch}')","title":""},{"docid":"36be65994e3895fa4808be7568981633","score":"0.5660939","text":"def lint_patch(clang_format, infile):\n files = git.get_files_to_check_from_patch(infile, is_interesting_file)\n\n # Patch may have files that we do not want to check which is fine\n if files:\n _lint_files(clang_format, files)","title":""},{"docid":"166154c314f2253f77154fdae4b622ac","score":"0.5627981","text":"def svn_diff_parse_next_patch(*args) -> \"svn_patch_t **\":\n return _diff.svn_diff_parse_next_patch(*args)","title":""},{"docid":"4ee880f2631918f9630f49523913cac2","score":"0.56246203","text":"def apply_patch(self, patch=None):\n if not any([patch, self.patch]):\n return\n if not patch:\n patch = self.patch\n self.cmd(f\"patch -p1 < {self.project.patch}/{self.ver}/{patch}\")","title":""},{"docid":"2a5fe70141006e2e169ed3dabb9cb6a4","score":"0.5591653","text":"def export_patches(repo, options):\n current = repo.get_branch()\n if is_pq_branch(current):\n base = pq_branch_base(current)\n gbp.log.info(\"On branch '%s', switching to '%s'\" % (current, base))\n repo.set_branch(base)\n pq_branch = current\n else:\n pq_branch = pq_branch_name(current)\n spec = parse_spec(options, repo)\n upstream_commit = find_upstream_commit(repo, spec, options.upstream_tag)\n export_treeish = pq_branch\n\n update_patch_series(repo, spec, upstream_commit, export_treeish, options)\n\n GitCommand('status')(['--', spec.specdir])","title":""},{"docid":"901d2ab96b417cef1952a4f46c1cd7eb","score":"0.55826074","text":"def update_patch_series(repo, spec, start, end, options):\n # Unlink old patch files and generate new patches\n rm_patch_files(spec)\n\n patches, commands = generate_patches(repo, start, end,\n spec.specdir, options)\n spec.update_patches(patches, commands)\n spec.write_spec_file()\n return patches","title":""},{"docid":"600937c8e185385a75d24486501ac9d1","score":"0.5572914","text":"def gitext_diff(external):\n from git_externals import iter_externals\n for _ in iter_externals(external):\n click.echo(git('diff'))","title":""},{"docid":"a2ab9448dbb2cecb9c4ae2f017905fb5","score":"0.5562694","text":"def git_diff_pipeline(output_name, master_path, prev_commit):\n log = logging.getLogger(__name__)\n\n current_path = inline_current(master_path)\n log.debug(\"current_path {0}\".format(current_path))\n prev_path = inline_prev(prev_commit, master_path)\n log.debug(\"prev_path {0}\".format(prev_path))\n\n # Run latexmk\n diff_path = os.path.splitext(output_name)[0]\n ldiff_cmd = \"latexdiff --type=CTRADITIONAL {prev} {current} > {diff}.tex\".\\\n format(prev=prev_path, current=current_path, diff=diff_path)\n subprocess.call(ldiff_cmd, shell=True)\n\n # Compile the diff document with latexmk\n ltmk_cmd = \"latexmk -f -pdf -bibtex-cond {0}.tex\".format(diff_path)\n subprocess.call(ltmk_cmd, shell=True)\n\n # Copy to build directory\n if not os.path.exists(\"build\"):\n os.makedirs(\"build\")\n pdf_path = \"{0}.pdf\".format(output_name)\n if os.path.exists(pdf_path):\n shutil.move(pdf_path, os.path.join(\"build\", pdf_path))\n\n # Clean up\n ltmk_cmd = \"latexmk -f -pdf -bibtex-cond -c {0}.tex\".format(diff_path)\n subprocess.call(ltmk_cmd, shell=True)\n build_exts = ['Notes.bib', '.bbl', '.tex']\n for ext in build_exts:\n path = \"\".join((output_name, ext))\n if os.path.exists(path):\n os.remove(path)\n os.remove(prev_path)\n os.remove(current_path)","title":""},{"docid":"cc8b19dc79400baf298170a3edede9d5","score":"0.55483615","text":"def rietveld_to_git(lines):\n state = NORMAL\n new_name = None\n\n for line in lines:\n if state is NORMAL:\n if line.startswith('Index: '):\n new_name = line[7:].strip('\\n')\n state = INDEX\n else:\n yield line\n elif state is INDEX:\n if line.startswith(SVN_SEPARATOR):\n state = METAINFO\n else:\n yield 'Index: {}\\n'.format(new_name)\n yield line\n state = NORMAL\n elif state is METAINFO:\n if line.startswith('rename from '):\n # File renamed.\n old_name = line[12:].strip('\\n')\n yield GIT_PART_HEAD.format(old_name, new_name)\n elif line.startswith('copy from '):\n # File copied.\n old_name = line[10:].strip('\\n')\n yield GIT_PART_HEAD.format(old_name, new_name)\n else:\n # File added or removed or changed.\n yield GIT_PART_HEAD.format(new_name, new_name)\n yield line\n state = NORMAL","title":""},{"docid":"3b6d139540fcb44acbec9757f2a4f60c","score":"0.551302","text":"def diff_files(self):\n files = dict()\n # get the git diff stat without the last summary line\n lines = self.repo.git.diff('--stat', '--no-color').splitlines()[:-1]\n for line in lines:\n match = re.match(r' (.*\\S) *\\| *([0-9]+) ', line)\n fname = match[1]\n #changes = int(match[2])\n diff = self.get_diff_raw(fname)\n\n files[fname] = Patch(\n files=[fname],\n size=len(diff),\n hunks=diff.count('@@')/2,\n test_size=False)\n\n return files","title":""},{"docid":"cedafc37dc5bb82e083413480684eebe","score":"0.54810876","text":"def create_patches(\n self, upstream: str = None, destination: str = None\n ) -> List[PatchMetadata]:\n upstream = upstream or self.get_specfile_version()\n destination = destination or self.local_project.working_dir\n\n sync_files_to_ignore = [\n str(sf.src.relative_to(self.local_project.working_dir))\n for sf in self.package_config.get_all_files_to_sync().get_raw_files_to_sync(\n self.local_project.working_dir,\n Path(\n # dest (downstream) is not important, we only care about src (upstream)\n destination\n ),\n )\n ]\n files_to_ignore = (\n self.package_config.patch_generation_ignore_paths + sync_files_to_ignore\n )\n\n pg = PatchGenerator(self.local_project)\n return pg.create_patches(upstream, destination, files_to_ignore=files_to_ignore)","title":""},{"docid":"ddea2affe8a06b2c1c391f81d8706f19","score":"0.5479714","text":"def extract_json_from_git(json_files, path_to_files, days_to_generate):\n\n time_24h_ago = datetime.utcnow() - timedelta(days=days_to_generate)\n _time_24h_ago = datetime.strftime(time_24h_ago, \"%Y-%m-%d %H:%M:%S\")\n time_24h_ago = datetime.strptime(_time_24h_ago, \"%Y-%m-%d %H:%M:%S\")\n\n for file in json_files:\n file_path = \"{}/\".format(path_to_files) + file\n count_pushes = 0\n with open(file_path) as json_file:\n data = json.load(json_file)\n base_link = \"https://github.com/mozilla-releng/firefox-infra-\" \\\n \"changelog/blob/master/{}/\"\\\n .format(path_to_files)\n repository_url = base_link + file\\\n .rstrip()\\\n .replace(\" \", \"%20\")\\\n .rstrip()\\\n .replace(\".json\", \".md\")\n repository_json = base_link + file\\\n .rstrip()\\\n .replace(\" \", \"%20\")\n repository_title = file.replace(\".json\", \"\")\n try:\n generate_markdown_header(\"changelog.md\",\n repository_title,\n repository_url,\n repository_json)\n if \"0\" in data:\n del data[\"0\"]\n for commit_iterator in data:\n commit_number = str(commit_iterator)\n commit_date = data.get(commit_number).get(\"commit_date\")\n is_it_under_24 = datetime.strptime(commit_date,\n \"%Y-%m-%d %H:%M:%S\")\n if is_it_under_24 > time_24h_ago:\n count_pushes = count_pushes + 1\n commit_description = data\\\n .get(commit_number)\\\n .get(\"commit_message\")\n commit_description = remove_chars(commit_description,\n \"\\U0001f60b\")\n commit_url = data.get(commit_number).get(\"url\")\n commit_url = \"[Link](\" + commit_url + \")\"\n author = data.get(commit_number).get(\"commiter_name\")\n review = \"N/A\"\n write_main_md_table(\"changelog.md\",\n commit_url,\n commit_description,\n author,\n review,\n commit_date)\n if count_pushes == 0:\n commit_url = \" \"\n if days_to_generate == 1:\n commit_description = \"No push in the last day.. \" \\\n \"[see the history of MD \" \\\n \"commits](\" + \\\n repository_url + \\\n \")\"\n else:\n commit_description = \"No push in the last \" + \\\n str(days_to_generate) + \\\n \" days.. [see the history of MD\" \\\n \" commits](\" + \\\n repository_url + \\\n \")\"\n author = \"FIC - BOT\"\n review = \"Self Generated\"\n commit_date = \" - \"\n write_main_md_table(\"changelog.md\",\n commit_url,\n commit_description,\n author,\n review,\n commit_date)\n except KeyError:\n if LOGGER:\n print(\"File \", file, \" is empty. \\n\",\n \"Please check:\",\n repository_url,\n \" for more details.\\n\")","title":""},{"docid":"e945ab5d994dfb4dba06ea4f29c7b41d","score":"0.5460745","text":"def get_diffing_files(commit, parent, repo):\n diff = repo.diff(parent, commit)\n\n patches = [p for p in diff]\n\n files = set()\n\n for patch in patches:\n if patch.delta.is_binary:\n continue\n nfile = patch.delta.new_file\n files.add((nfile.id, nfile.path, patch.delta.status))\n\n return files","title":""},{"docid":"9b78f0ea1ec63108ca294aec109dd64f","score":"0.54456115","text":"def patch(ctx, tool, dir, remove):\n from nf_core.modules import ModulePatch\n\n try:\n module_patch = ModulePatch(\n dir,\n ctx.obj[\"modules_repo_url\"],\n ctx.obj[\"modules_repo_branch\"],\n ctx.obj[\"modules_repo_no_pull\"],\n )\n if remove:\n module_patch.remove(tool)\n else:\n module_patch.patch(tool)\n except (UserWarning, LookupError) as e:\n log.error(e)\n sys.exit(1)","title":""},{"docid":"ea276e0999e5527dd6de0b0699e23bd1","score":"0.54398674","text":"def build_git_commits(project, update=True, include_patch=False,\n use_pydiff=True, skip=-1, limit=-1, verbose=False):\n\n repo_name = get_repo_name(project)\n repo = git.Repo(repo_name)\n assert repo.bare is False\n\n if update:\n try:\n # Commits will include pruned entries\n commits = load_git_commits(project)\n except Exception:\n update = False\n\n if not update:\n commits = collections.defaultdict(list)\n\n commits = process_commits(repo, commits, get_filter_config(project),\n use_pydiff=use_pydiff, skip=skip, limit=limit,\n project=project, verbose=verbose)\n print\n print 'total commits:', len(commits)\n \"\"\"\n if include_patch:\n print 'Augment Git data with patch info'\n commits = update_commits_with_patch_data(commits, project)\n print\n \"\"\"\n print 'Other commit message post-processing'\n print ' Blueprints'\n annotate_blueprints(commits)\n print ' Identifying cherry-pick commits'\n annotate_cherry_pick(commits)\n\n master_commit = get_git_master_commit(repo_name)\n print 'Master Commit:', master_commit\n commits = annotate_mainline(commits, master_commit)\n\n \"\"\"\n print 'Consolidate Git Merge related commits'\n commits = consolidate_merge_commits(commits,\n get_git_master_commit(repo_name))\n \"\"\"\n\n annotate_children(commits) # note parent/child relationships\n save_git_commits(commits, project)","title":""},{"docid":"c8b5844d5fd8b60edbdc983fe710b1a8","score":"0.5417832","text":"def test_multi_patch_parsing(self):\n with open(_get_path_to_asset('c.patch')) as f_hand:\n patches = patchit.PatchSet.from_stream(f_hand)\n\n self.assertEqual(patches[0].source_filename, 'a')\n self.assertEqual(patches[0].target_filename, 'b')\n self.assertEqual(len(patches[0].hunks), 2)\n\n self.assertEqual(patches[1].source_filename, 'abc')\n self.assertEqual(patches[1].target_filename, 'def')\n self.assertEqual(len(patches[1].hunks), 1)","title":""},{"docid":"a689df5c5bee6d4511b34cf5ff9c4e7a","score":"0.5400419","text":"def git_prebuild(pkg_dir, build_cmd=build_py):\n\n class MyBuildPy(build_cmd):\n ''' Subclass to write commit data into installation tree '''\n def run(self):\n # loose as `.dev` is suppose to be invalid\n print(\"check version number\")\n loose_pep440re = re.compile(r'^(\\d+)\\.(\\d+)\\.(\\d+((a|b|rc)\\d+)?)(\\.post\\d+)?(\\.dev\\d*)?$')\n if not loose_pep440re.match(version):\n raise ValueError(\"Version number '%s' is not valid (should match [N!]N(.N)*[{a|b|rc}N][.postN][.devN])\" % version)\n\n\n build_cmd.run(self)\n # this one will only fire for build commands\n if hasattr(self, 'build_lib'):\n self._record_commit(self.build_lib)\n\n def make_release_tree(self, base_dir, files):\n # this one will fire for sdist\n build_cmd.make_release_tree(self, base_dir, files)\n self._record_commit(base_dir)\n\n def _record_commit(self, base_dir):\n import subprocess\n proc = subprocess.Popen('git rev-parse --short HEAD',\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True)\n repo_commit, _ = proc.communicate()\n repo_commit = repo_commit.strip().decode(\"ascii\")\n\n out_pth = pjoin(base_dir, pkg_dir, 'utils', '_sysinfo.py')\n if os.path.isfile(out_pth) and not repo_commit:\n # nothing to write, don't clobber\n return\n\n print(\"writing git commit '%s' to %s\" % (repo_commit, out_pth))\n\n # remove to avoid overwriting original via hard link\n try:\n os.remove(out_pth)\n except (IOError, OSError):\n pass\n with open(out_pth, \"w\", encoding=\"utf-8\") as out_file:\n out_file.writelines(\n [\n \"# GENERATED BY setup.py\\n\",\n 'commit = \"%s\"\\n' % repo_commit,\n ]\n )\n\n return MyBuildPy","title":""},{"docid":"9dc9c0498a1a56fcc3fa756ddcb56db6","score":"0.53959465","text":"def _do_apply(self, patch: PatchedFile):\n result = subprocess.run(self.GIT_APPLY.split(' '), input=str(patch), text=True, capture_output=True, check=True)\n if result.returncode > 0:\n print(result.stderr)\n raise PluginException(\"Failed to apply patch.\")","title":""},{"docid":"742087bdf1e62f5f78e5b53b289d309c","score":"0.5385519","text":"def getGitReport(self, path='.'):\n if not path.endswith('/'): path += '/'\n checkForDiffs(path)\n checkForUnpushedCommits(path)","title":""},{"docid":"b2fed180ab66727334ce9522ba6830a8","score":"0.5363858","text":"def main():\n assert len(argv) == 2, \"Usage: to_rawgit 'https://github.com/...'\"\n git_link = argv[1]\n print('Link to convert:' + git_link)\n parsed = urlparse(git_link)\n content = parsed.path.split('/')\n content.remove('blob')\n new_path = '/'.join(content)\n new_link = parsed._replace(netloc='cdn.rawgit.com', path=new_path)\n print(new_link.geturl())","title":""},{"docid":"e4c28b2cbbc459bcc7312ac4506a2a96","score":"0.53553474","text":"def patch(self, patch_path):\n \n with open(patch_path) as f:\n for i, line in enumerate(f):\n self.execute(line.decode(u\"utf-8\"), i + 1)","title":""},{"docid":"25b2e5dcfaa4d822db3f183f4d552e62","score":"0.5342589","text":"def git(args):\n cmd = GIT_BIN + ' ' + args\n out = process(cmd)\n return out","title":""},{"docid":"b99d87d00efc455842d0f50e31b13296","score":"0.5323858","text":"def test_get_changed_files_disparate_histories(in_git_dir):\n git_commit()\n in_git_dir.join('a.txt').ensure()\n cmd_output('git', 'add', '.')\n git_commit()\n cmd_output('git', 'branch', '-m', 'branch1')\n\n cmd_output('git', 'checkout', '--orphan', 'branch2')\n cmd_output('git', 'rm', '-rf', '.')\n in_git_dir.join('a.txt').ensure()\n in_git_dir.join('b.txt').ensure()\n cmd_output('git', 'add', '.')\n git_commit()\n\n assert git.get_changed_files('branch1', 'branch2') == ['b.txt']","title":""},{"docid":"064acdaef423223176cca30264b298b0","score":"0.5321807","text":"def _get_diff(self, changeset):\n patch_file = io.BytesIO()\n try:\n dulwich.patch.write_object_diff(patch_file,\n self.repo.object_store,\n changeset.old, changeset.new)\n except UnicodeDecodeError as e:\n logger.debug(e)\n return b''\n\n return patch_file.getvalue()","title":""},{"docid":"9b5dff9e4a5a9a5902d22a9a50870d2a","score":"0.53185433","text":"def git_hook(complexity=-1, strict=False, ignore=None, lazy=False):\r\n gitcmd = \"git diff-index --cached --name-only --diff-filter=ACMRTUXB HEAD\"\r\n if lazy:\r\n # Catch all files, including those not added to the index\r\n gitcmd = gitcmd.replace('--cached ', '')\r\n\r\n if hasattr(ignore, 'split'):\r\n ignore = ignore.split(',')\r\n\r\n # Returns the exit code, list of files modified, list of error messages\r\n _, files_modified, _ = run(gitcmd)\r\n\r\n # We only want to pass ignore and max_complexity if they differ from the\r\n # defaults so that we don't override a local configuration file\r\n options = {}\r\n if ignore:\r\n options['ignore'] = ignore\r\n if complexity > -1:\r\n options['max_complexity'] = complexity\r\n\r\n files_modified = [f for f in files_modified if f.endswith('.py')]\r\n\r\n flake8_style = get_style_guide(\r\n parse_argv=True,\r\n config_file=DEFAULT_CONFIG,\r\n **options\r\n )\r\n\r\n # Copy staged versions to temporary directory\r\n tmpdir = mkdtemp()\r\n files_to_check = []\r\n try:\r\n for file_ in files_modified:\r\n # get the staged version of the file\r\n gitcmd_getstaged = \"git show :%s\" % file_\r\n _, out, _ = run(gitcmd_getstaged, raw_output=True, decode=False)\r\n # write the staged version to temp dir with its full path to\r\n # avoid overwriting files with the same name\r\n dirname, filename = os.path.split(os.path.abspath(file_))\r\n prefix = os.path.commonprefix([dirname, tmpdir])\r\n dirname = os.path.relpath(dirname, start=prefix)\r\n dirname = os.path.join(tmpdir, dirname)\r\n if not os.path.isdir(dirname):\r\n os.makedirs(dirname)\r\n filename = os.path.join(dirname, filename)\r\n # write staged version of file to temporary directory\r\n with open(filename, \"wb\") as fh:\r\n fh.write(out)\r\n files_to_check.append(filename)\r\n # Run the checks\r\n report = flake8_style.check_files(files_to_check)\r\n # remove temporary directory\r\n finally:\r\n shutil.rmtree(tmpdir, ignore_errors=True)\r\n\r\n if strict:\r\n return report.total_errors\r\n\r\n return 0","title":""},{"docid":"f23d1213d1f3a093054342c6bf1ed1ec","score":"0.5312993","text":"def collect_diff_files(root, versions):\n command = ['git', 'diff', '--name-only', versions]\n os.chdir(root)\n p = subprocess.Popen(command,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p.communicate()\n if err:\n raise DiffCollectionFailed(\"Unable to find diff files to examine \"\n \"in %s: %s\" % (root, err))\n for filename in out.splitlines():\n if not os.path.basename(filename).startswith('.'):\n yield filename","title":""},{"docid":"a91581996c62e88b24fc0823fc180d1b","score":"0.5305009","text":"def solutions_to_git(input_solutions):\n assert input_solutions\n solutions = copy.deepcopy(input_solutions)\n first_solution = True\n buildspec = None\n for solution in solutions:\n original_url = solution['url']\n parsed_url = urlparse.urlparse(original_url)\n parsed_path = parsed_url.path\n\n # Rewrite SVN urls into Git urls.\n buildspec_m = re.match(BUILDSPEC_RE, parsed_path)\n if first_solution and buildspec_m:\n solution['url'] = GIT_BUILDSPEC_PATH\n buildspec = BUILDSPEC_TYPE(\n container=buildspec_m.group(1),\n version=buildspec_m.group(2),\n )\n solution['deps_file'] = path.join(buildspec.container, buildspec.version,\n 'DEPS')\n elif parsed_path in RECOGNIZED_PATHS:\n solution['url'] = RECOGNIZED_PATHS[parsed_path]\n solution['deps_file'] = '.DEPS.git'\n elif parsed_url.scheme == 'https' and 'googlesource' in parsed_url.netloc:\n pass\n else:\n print 'Warning: %s' % ('path %r not recognized' % parsed_path,)\n\n # Strip out deps containing $$V8_REV$$, etc.\n if 'custom_deps' in solution:\n new_custom_deps = {}\n for deps_name, deps_value in solution['custom_deps'].iteritems():\n if deps_value and '$$' in deps_value:\n print 'Dropping %s:%s from custom deps' % (deps_name, deps_value)\n else:\n new_custom_deps[deps_name] = deps_value\n solution['custom_deps'] = new_custom_deps\n\n if first_solution:\n root = parsed_path\n first_solution = False\n\n solution['managed'] = False\n # We don't want gclient to be using a safesync URL. Instead it should\n # using the lkgr/lkcr branch/tags.\n if 'safesync_url' in solution:\n print 'Removing safesync url %s from %s' % (solution['safesync_url'],\n parsed_path)\n del solution['safesync_url']\n return solutions, root, buildspec","title":""},{"docid":"09c32a931ba6cf66b5f17e43fb7ae0b6","score":"0.5301984","text":"def git_hook(strict=False, lazy=True):\n gitcmd = \"git diff-index --cached --name-only --diff-filter=ACMRTUXB HEAD\"\n if lazy:\n # Catch all files, including those not added to the index.\n gitcmd = gitcmd.replace('--cached ', '')\n\n p = Popen(gitcmd.split(), stdout=PIPE, stderr=PIPE)\n (stdout, stderr) = p.communicate()\n files_modified = [line.strip() for line in stdout.splitlines()]\n\n # Run the checks.\n errors = 0\n for file in files_modified:\n errors += Parser(file).check()\n\n return errors if strict else 0","title":""},{"docid":"3bacbc761639cf6a2010a7f53fafe36c","score":"0.5293779","text":"def test_patch_parsing(self):\n with open(_get_path_to_asset('a.patch')) as f_hand:\n patches = patchit.PatchSet.from_stream(f_hand)\n self.assertEqual(patches[0].source_filename,\n 'telefonliste2007.txt')\n self.assertEqual(patches[0].target_filename,\n 'telefonliste2008.txt')\n self.assertEqual(len(patches[0].hunks), 1)","title":""},{"docid":"67046b28b0044423430c547899b32a21","score":"0.52921116","text":"def main(git_log):\n df = pd.read_csv(git_log, sep = '|', names = ['commit', 'message', 'author', 'email'])\n df['area'] = df['message'].apply(define_area)\n df['message'] = df['message'].apply(delete_prefix)\n\n # Split commits by areas\n core = df[df['area']==Area.core.value]\n tests = df[df['area']==Area.tests.value]\n build = df[df['area']==Area.build.value]\n apps = df[df['area']==Area.apps.value]\n docs = df[df['area']==Area.docs.value]\n other = df[df['area'].isna()]\n\n # Define individual contributors\n contributors = df.groupby(['author', 'email'])\n contributors = list(contributors.groups.keys())\n\n with open('release-notes.md', 'w') as f:\n f.write('# Release Notes\\n')\n\n f.write('\\n## API / ABI / Integration Changes\\n')\n f.write('\\n**API/ABI version: 1.x.**\\n')\n\n f.write('\\n## New Features and Improvements\\n')\n f.write('\\n## Important Bug Fixes\\n')\n f.write('\\n## Build\\n')\n f.write('\\n## Documentation\\n')\n\n f.write('\\n## Contributors\\n')\n for name, email in contributors:\n f.write(f'\\n{name} <{email}>')\n f.write('\\n')\n\n f.write('\\n## Changelog\\n')\n f.write('\\n<details><summary>Click to expand/collapse</summary>')\n f.write('\\n<p>')\n f.write('\\n')\n\n if not core.empty:\n f.write('\\n### Core Functionality')\n write_into_changelog(core, f)\n\n if not tests.empty:\n f.write('\\n### Unit Tests')\n write_into_changelog(tests, f)\n\n if not build.empty:\n f.write('\\n### Build Scripts (CMake, etc.)')\n write_into_changelog(build, f)\n\n if not apps.empty:\n f.write('\\n### Sample Applications')\n write_into_changelog(apps, f)\n\n if not docs.empty:\n f.write('\\n### Documentation')\n write_into_changelog(docs, f)\n\n if not other.empty:\n f.write('\\n### Other')\n write_into_changelog(other, f)\n\n f.write('\\n</p>')\n f.write('\\n</details>')","title":""},{"docid":"c64ef06a3b57563f29b57e4475bb4ef4","score":"0.52858096","text":"def import_spec_patches(repo, options):\n current = repo.get_branch()\n # Get spec and related information\n if is_pq_branch(current):\n base = pq_branch_base(current)\n if options.force:\n spec = parse_spec(options, repo, base)\n spec_treeish = base\n else:\n raise GbpError(\"Already on a patch-queue branch '%s' - doing \"\n \"nothing.\" % current)\n else:\n spec = parse_spec(options, repo)\n spec_treeish = None\n base = current\n upstream_commit = find_upstream_commit(repo, spec, options.upstream_tag)\n packager = get_packager(spec)\n pq_branch = pq_branch_name(base)\n\n # Create pq-branch\n if repo.has_branch(pq_branch) and not options.force:\n raise GbpError(\"Patch-queue branch '%s' already exists. \"\n \"Try 'switch' instead.\" % pq_branch)\n try:\n if repo.get_branch() == pq_branch:\n repo.force_head(upstream_commit, hard=True)\n else:\n repo.create_branch(pq_branch, upstream_commit, force=True)\n except GitRepositoryError as err:\n raise GbpError(\"Cannot create patch-queue branch '%s': %s\" %\n (pq_branch, err))\n\n # Put patches in a safe place\n if spec_treeish:\n packaging_tmp = tempfile.mkdtemp(prefix='dump_')\n packaging_tree = '%s:%s' % (spec_treeish, options.packaging_dir)\n dump_tree(repo, packaging_tmp, packaging_tree, with_submodules=False,\n recursive=False)\n spec.specdir = packaging_tmp\n in_queue = spec.patchseries()\n queue = safe_patches(in_queue)\n # Do import\n try:\n gbp.log.info(\"Switching to branch '%s'\" % pq_branch)\n repo.set_branch(pq_branch)\n\n if not queue:\n return\n gbp.log.info(\"Trying to apply patches from branch '%s' onto '%s'\" %\n (base, upstream_commit))\n for patch in queue:\n gbp.log.debug(\"Applying %s\" % patch.path)\n apply_and_commit_patch(repo, patch, packager)\n except (GbpError, GitRepositoryError) as err:\n repo.set_branch(base)\n repo.delete_branch(pq_branch)\n raise GbpError('Import failed: %s' % err)\n\n gbp.log.info(\"%d patches listed in '%s' imported on '%s'\" % (len(queue), spec.specfile,\n pq_branch))","title":""},{"docid":"e026b5185f7be8af240f49b94cf94507","score":"0.5285126","text":"def copy_p4_changes_to_git(ctx, start_at, stop_at, new_git_branches):\n\n p2g = P2G(ctx)\n\n if MEM_CAPPED:\n p2g_mc = P2GMemcapped(p2g=p2g)\n p2g_mc.copy(start_at, stop_at, new_git_branches)\n else:\n p2g.copy(start_at, stop_at, new_git_branches)","title":""},{"docid":"f8d9bcafd871e35d71a0476355737288","score":"0.5284944","text":"def test_patch_apply_simple(self):\n with open(_get_path_to_asset('a.patch')) as patch_hand:\n with open(_get_path_to_asset('original_a.txt')) as source_hand:\n patches = patchit.PatchSet.from_stream(patch_hand)\n file_iter = (x.strip('\\n') for x in iter(source_hand.readline, ''))\n output = list(patches[0].merge(patchit.LineEnumerator(file_iter)))\n\n self.assertEqual(output, ['Mayer, Susanne, Lager, -212',\n 'Schmid, Carola, Geschäftsleitung, -435',\n 'Schmitt, Marie, Labor, -804',\n 'Waldmann, Ernst, Labor, -805',\n 'Zundel, Walter, Vertrieb, -476', ])","title":""},{"docid":"97b952b3492fc1e600f483fa8d5a839d","score":"0.52734655","text":"def get_changed_files(github_dir, pr_id, base_ref, head_ref):\n\n response = github_graphql_client.make_request(GRAPHQL_GET_PR_COMMIT_COUNT, {\"nodeId\": pr_id})\n commit_count = response[\"data\"][\"node\"][\"commits\"][\"totalCount\"]\n\n # fetch all latest commits\n _ = subprocess.run([\"git\", \"-C\", github_dir, \"-c\", \"protocol.version=2\", \"fetch\", \"--deepen\", str(commit_count)])\n\n output = subprocess.getoutput(\n f\"git -C {github_dir} diff --name-only {base_ref}...{head_ref}\")\n\n return [changed_file for changed_file in output.split(\"\\n\") if changed_file]","title":""},{"docid":"eb81206a2498ab4d4a872094679ca53c","score":"0.5272389","text":"def gitext_freeze(externals, messages):\n from git_externals import load_gitexts, dump_gitexts, foreach_externals_dir, root_path, resolve_revision\n git_externals = load_gitexts()\n repo_root = root_path()\n re_from_git_svn_id = re.compile(\"git-svn-id:.*@(\\d+)\")\n re_from_svnversion = re.compile(\"(\\d+):(\\d+)\")\n\n def get_version(rel_url, ext_path, refs):\n if 'tag' in refs:\n return\n\n bare_svn = False\n if git_externals[rel_url][\"vcs\"] == \"svn\":\n revision = command('svnversion', '-c').strip()\n match = re_from_svnversion.search(revision)\n if match:\n revision = \"svn:r\" + match.group(2) # 565:56555 -> svn:r56555\n bare_svn = True\n else:\n message = git(\"log\", \"--format=%b\", \"--grep\", \"git-svn-id:\", \"-1\")\n match = re_from_git_svn_id.search(message)\n if match:\n revision = \"svn:r\" + match.group(1)\n else:\n here = os.path.relpath(os.getcwd(), repo_root)\n error(\"Unsupported external format, svn or git-svn repo expected:\\n\\t{}\".format(here))\n else:\n branch_name = current_branch()\n remote_name = git(\"config\", \"branch.%s.remote\" % branch_name)\n revision = git(\"log\", \"%s/%s\" % (remote_name, branch_name), \"-1\", \"--format=%H\")\n\n info(\"Freeze {0} at {1}\".format(rel_url, revision))\n if messages and not bare_svn:\n old = resolve_revision(git_externals[rel_url][\"ref\"])\n new = resolve_revision(revision)\n git(\"log\", \"--format=- %h %s\", \"{}..{}\".format(old, new), capture=False)\n git_externals[rel_url][\"ref\"] = revision\n\n foreach_externals_dir(repo_root, get_version, only=externals)\n\n dump_gitexts(git_externals)","title":""},{"docid":"16f1f0031a9244e9c1de1428c16aa0f8","score":"0.52708477","text":"def make_git_diff_header(target_path, repos_relpath,\n old_tag, new_tag, add=False, src_label=None,\n dst_label=None, delete=False, text_changes=True,\n cp=False, mv=False, copyfrom_path=None,\n copyfrom_rev=None):\n\n path_as_shown = target_path.replace('\\\\', '/')\n if src_label:\n src_label = src_label.replace('\\\\', '/')\n src_label = '\\t(.../' + src_label + ')'\n else:\n src_label = ''\n if dst_label:\n dst_label = dst_label.replace('\\\\', '/')\n dst_label = '\\t(.../' + dst_label + ')'\n else:\n dst_label = ''\n\n output = [\n \"Index: \" + path_as_shown + \"\\n\",\n \"===================================================================\\n\"\n ]\n if add:\n output.extend([\n \"diff --git a/\" + repos_relpath + \" b/\" + repos_relpath + \"\\n\",\n \"new file mode 100644\\n\",\n ])\n if text_changes:\n output.extend([\n \"--- a/\" + repos_relpath + src_label + \"\\t(\" + old_tag + \")\\n\",\n \"+++ b/\" + repos_relpath + dst_label + \"\\t(\" + new_tag + \")\\n\"\n ])\n elif delete:\n output.extend([\n \"diff --git a/\" + repos_relpath + \" b/\" + repos_relpath + \"\\n\",\n \"deleted file mode 100644\\n\",\n ])\n if text_changes:\n output.extend([\n \"--- a/\" + repos_relpath + src_label + \"\\t(\" + old_tag + \")\\n\",\n \"+++ b/\" + repos_relpath + dst_label + \"\\t(\" + new_tag + \")\\n\"\n ])\n elif cp:\n if copyfrom_rev:\n copyfrom_rev = '@' + copyfrom_rev\n else:\n copyfrom_rev = ''\n output.extend([\n \"diff --git a/\" + copyfrom_path + \" b/\" + repos_relpath + \"\\n\",\n \"copy from \" + copyfrom_path + copyfrom_rev + \"\\n\",\n \"copy to \" + repos_relpath + \"\\n\",\n ])\n if text_changes:\n output.extend([\n \"--- a/\" + copyfrom_path + src_label + \"\\t(\" + old_tag + \")\\n\",\n \"+++ b/\" + repos_relpath + \"\\t(\" + new_tag + \")\\n\"\n ])\n elif mv:\n output.extend([\n \"diff --git a/\" + copyfrom_path + \" b/\" + path_as_shown + \"\\n\",\n \"rename from \" + copyfrom_path + \"\\n\",\n \"rename to \" + repos_relpath + \"\\n\",\n ])\n if text_changes:\n output.extend([\n \"--- a/\" + copyfrom_path + src_label + \"\\t(\" + old_tag + \")\\n\",\n \"+++ b/\" + repos_relpath + \"\\t(\" + new_tag + \")\\n\"\n ])\n else:\n output.extend([\n \"diff --git a/\" + repos_relpath + \" b/\" + repos_relpath + \"\\n\",\n \"--- a/\" + repos_relpath + src_label + \"\\t(\" + old_tag + \")\\n\",\n \"+++ b/\" + repos_relpath + dst_label + \"\\t(\" + new_tag + \")\\n\",\n ])\n return output","title":""},{"docid":"058af8cc9d5e6bba800df78029b755c6","score":"0.5267223","text":"def collect_diffs_for_files(root, versions, source_files, context_lines):\n os.chdir(root)\n for filename in source_files:\n command = ['git', 'diff', '-U%d' % context_lines,\n '-w', versions, '--', filename]\n p = subprocess.Popen(command,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n diff_lines, err = p.communicate()\n if err:\n raise DiffCollectionFailed(\n \"Unable to collect diffs for file %s/%s: %s\" %\n (root, filename, err))\n yield diff_lines","title":""},{"docid":"69c3386ca19edb2e3e6913e8bab15448","score":"0.52605367","text":"def rm_patch_files(spec):\n # Remove all old patches from the spec dir\n for patch in spec.patchseries(unapplied=True):\n gbp.log.debug(\"Removing '%s'\" % patch.path)\n try:\n os.unlink(patch.path)\n except OSError as err:\n if err.errno != errno.ENOENT:\n raise GbpError(\"Failed to remove patch: %s\" % err)\n else:\n gbp.log.debug(\"Patch %s does not exist.\" % patch.path)","title":""},{"docid":"2b03578a9baf0b27736bb75f7b385028","score":"0.525592","text":"def _get_patchset(self, client, project, repo, sha):\n\n key = f\"get_changelist:{md5_text(project + repo).hexdigest()}:{sha}\"\n commit_files = cache.get(key)\n if commit_files is None:\n commit_files = client.get_commit_filechanges(project, repo, sha)\n cache.set(key, commit_files, 900)\n\n return self._transform_patchset(commit_files)","title":""},{"docid":"299dd3eb218670fe7f07db55511cbeba","score":"0.52544034","text":"def create_new_patch_release():\n try:\n last_version_number = get_last_version()\n except subprocess.CalledProcessError as err:\n if err.stderr.decode(\"utf8\").startswith(\"HTTP 404:\"):\n # The project doesn't have any releases yet.\n new_version_number = \"0.0.1\"\n else:\n raise\n else:\n new_version_number = bump_patch_number(last_version_number)\n\n subprocess.run(\n [\"gh\", \"release\", \"create\", \"--generate-notes\", new_version_number],\n check=True,\n )","title":""},{"docid":"7dbd37223789a3f4e40eeaeb0fed2f4f","score":"0.52522826","text":"def _create_branch(repo, from_branch, to_branch, dry_run, force, patches):\n env = os.environ.copy()\n\n if git.branch_exists(repo, to_branch, env):\n click.echo(f\"{to_branch} already exists, skipping...\")\n sys.exit(0)\n\n snap_basename = urlparse(repo)\n snap_basename = Path(snap_basename.path).name\n if snap_basename.endswith(\".git\"):\n snap_basename = snap_basename.rstrip(\".git\")\n sh.rm(\"-rf\", snap_basename)\n sh.git.clone(repo, branch=from_branch, _env=env)\n sh.git.config(\"user.email\", \"cdkbot@gmail.com\", _cwd=snap_basename)\n sh.git.config(\"user.name\", \"cdkbot\", _cwd=snap_basename)\n sh.git.checkout(\"-b\", to_branch, _cwd=snap_basename)\n\n snapcraft_fn = Path(snap_basename) / \"snapcraft.yaml\"\n snapcraft_fn_tpl = Path(snap_basename) / \"snapcraft.yaml.in\"\n if not snapcraft_fn_tpl.exists():\n click.echo(f\"{snapcraft_fn_tpl} not found\")\n sys.exit(1)\n\n # Apply patches\n patches_list = []\n if patches:\n patches_path = Path(patches)\n if patches_path.exists():\n click.echo(\"Patches found, applying.\")\n patches_map = yaml.safe_load(patches_path.read_text(encoding=\"utf8\"))\n # TODO: cleanup\n if \"all\" in patches_map:\n for patch_fn in patches_map[\"all\"]:\n patch_fn = Path(patch_fn).absolute()\n shared_path = str(Path(\"shared\") / patch_fn.parts[-1])\n sh.cp(str(patch_fn), str(shared_path), _cwd=snap_basename)\n patches_list.append(shared_path)\n sh.git.add(shared_path, _cwd=snap_basename)\n if to_branch.lstrip(\"v\") in patches_map:\n for patch_fn in patches_map[to_branch.lstrip(\"v\")]:\n patch_fn = Path(patch_fn).absolute()\n shared_path = str(Path(\"shared\") / patch_fn.parts[-1])\n sh.cp(str(patch_fn), str(shared_path), _cwd=snap_basename)\n patches_list.append(shared_path)\n sh.git.add(shared_path, _cwd=snap_basename)\n\n snapcraft_yml = snapcraft_fn_tpl.read_text()\n snapcraft_yml = _render(\n snapcraft_fn_tpl,\n {\"snap_version\": to_branch.lstrip(\"v\"), \"patches\": patches_list},\n )\n snapcraft_fn.write_text(snapcraft_yml)\n if not dry_run:\n sh.git.add(\".\", _cwd=snap_basename)\n sh.git.commit(\"-m\", f\"Creating branch {to_branch}\", _cwd=snap_basename)\n sh.git.push(repo, to_branch, _cwd=snap_basename, _env=env)","title":""},{"docid":"f20eb563c5f655e0c0c0627531095ad3","score":"0.5240313","text":"def commitChanges():\n\n # Get a reference to the Git repo (in this folder)\n repo = Repo(os.path.dirname(__file__))\n\n # Commit the new version\n repo.git.commit(\"pymed/version.py\", message=\"Bumped the release version\")\n\n # Push any changes\n repo.git.push(\"origin\", \"master\")","title":""},{"docid":"ddc298a348be63640dd7d7b211cdd290","score":"0.5237253","text":"def normalize_patch(self, patch, filename, revision=HEAD):\n if revision != PRE_CREATION and has_expanded_svn_keywords(patch):\n keywords = self.client.get_keywords(filename, revision)\n\n if keywords:\n return collapse_svn_keywords(patch, force_bytes(keywords))\n\n return patch","title":""},{"docid":"0cff0667fe18b92330637e917c8fc896","score":"0.5234486","text":"def prep_commit(self, new_rev, subdir=None):\n if not subdir:\n subdir = self.subdir\n self.scmlogs.annotate(\"cwd is %s\" % os.getcwd())\n newly_created = []\n\n if not os.path.exists('a'):\n newly_created.append('a')\n\n if not os.path.exists(subdir):\n os.mkdir(subdir)\n # This will take care of adding subdir/b too\n newly_created.append(subdir)\n\n for fn in ('a', subdir + '/b'):\n f = open(fn, 'w')\n f.write(str(new_rev))\n f.close()\n self.scmlogs.annotate(\"Wrote %s to %s\" % (new_rev, fn))\n\n # we never commit through symlink 'c' but instead see the updated\n # revision through the symlink\n if not os.path.lexists('c'):\n os.symlink('a', 'c')\n newly_created.append('c')\n\n return newly_created","title":""},{"docid":"fb61c053da4aefe2a45e2576d51a1f99","score":"0.52327067","text":"def _show_patch(sha256):\n patches = spack.repo.path.patch_index.index\n data = patches.get(sha256)\n\n if not data:\n candidates = [k for k in patches if k.startswith(sha256)]\n if not candidates:\n tty.die(\"no such resource: %s\" % sha256)\n elif len(candidates) > 1:\n tty.die(\"%s: ambiguous hash prefix. Options are:\", *candidates)\n\n sha256 = candidates[0]\n data = patches.get(sha256)\n\n color.cprint(\"@c{%s}\" % sha256)\n for package, rec in data.items():\n owner = rec[\"owner\"]\n\n if \"relative_path\" in rec:\n pkg_dir = spack.repo.path.get_pkg_class(owner).package_dir\n path = os.path.join(pkg_dir, rec[\"relative_path\"])\n print(\" path: %s\" % path)\n else:\n print(\" url: %s\" % rec[\"url\"])\n\n print(\" applies to: %s\" % package)\n if owner != package:\n print(\" patched by: %s\" % owner)","title":""},{"docid":"244e1f213591defc71f71505b255b14a","score":"0.5184749","text":"def test_all_version_notes_in_a_single_file(self):\n runner = CliRunner()\n\n def do_build_once_with(version, fragment_file, fragment):\n with open(f\"newsfragments/{fragment_file}\", \"w\") as f:\n f.write(fragment)\n\n result = runner.invoke(\n _main,\n [\n \"--version\",\n version,\n \"--name\",\n \"foo\",\n \"--date\",\n \"01-01-2001\",\n \"--yes\",\n ],\n catch_exceptions=False,\n )\n # not git repository, manually remove fragment file\n Path(f\"newsfragments/{fragment_file}\").unlink()\n return result\n\n results = []\n with runner.isolated_filesystem():\n with open(\"pyproject.toml\", \"w\") as f:\n f.write(\n \"\\n\".join(\n [\n \"[tool.towncrier]\",\n \" single_file=true\",\n \" # The `filename` variable is fixed and not formated in any way.\",\n ' filename=\"{version}-notes.rst\"',\n ]\n )\n )\n os.mkdir(\"newsfragments\")\n results.append(\n do_build_once_with(\"7.8.9\", \"123.feature\", \"Adds levitation\")\n )\n results.append(do_build_once_with(\"7.9.0\", \"456.bugfix\", \"Adds catapult\"))\n\n self.assertEqual(0, results[0].exit_code, results[0].output)\n self.assertEqual(0, results[1].exit_code, results[1].output)\n self.assertEqual(\n 1,\n len(list(Path.cwd().glob(\"*-notes.rst\"))),\n \"single newfile for multiple builds\",\n )\n self.assertTrue(os.path.exists(\"{version}-notes.rst\"), os.listdir(\".\"))\n\n output = read(\"{version}-notes.rst\")\n\n self.assertEqual(\n output,\n dedent(\n \"\"\"\n foo 7.9.0 (01-01-2001)\n ======================\n\n Bugfixes\n --------\n\n - Adds catapult (#456)\n\n\n foo 7.8.9 (01-01-2001)\n ======================\n\n Features\n --------\n\n - Adds levitation (#123)\n \"\"\"\n ).lstrip(),\n )","title":""},{"docid":"4afcca0962f0edcb95a86c52873763b5","score":"0.5162349","text":"def main(argv):\n retval = 0\n\n (options, args) = parse_args(argv)\n if not options:\n return ExitCodes.parse_error\n\n gbp.log.setup(options.color, options.verbose, options.color_scheme)\n\n if len(args) < 2:\n gbp.log.err(\"No action given.\")\n return 1\n else:\n action = args[1]\n\n if args[1] in [\"export\", \"import\", \"rebase\", \"drop\", \"switch\", \"convert\"]:\n pass\n elif args[1] in [\"apply\"]:\n if len(args) != 3:\n gbp.log.err(\"No patch name given.\")\n return 1\n else:\n patchfile = args[2]\n else:\n gbp.log.err(\"Unknown action '%s'.\" % args[1])\n return 1\n\n try:\n repo = RpmGitRepository(os.path.curdir)\n except GitRepositoryError:\n gbp.log.err(\"%s is not a git repository\" % (os.path.abspath('.')))\n return 1\n\n try:\n # Create base temporary directory for this run\n init_tmpdir(options.tmp_dir, prefix='pq-rpm_')\n current = repo.get_branch()\n if action == \"export\":\n export_patches(repo, options)\n elif action == \"import\":\n import_spec_patches(repo, options)\n elif action == \"drop\":\n drop_pq(repo, current)\n elif action == \"rebase\":\n rebase_pq(repo, options)\n elif action == \"apply\":\n patch = Patch(patchfile)\n apply_single_patch(repo, current, patch, fallback_author=None)\n elif action == \"switch\":\n switch_pq(repo, current)\n except KeyboardInterrupt:\n retval = 1\n gbp.log.err(\"Interrupted. Aborting.\")\n except CommandExecFailed:\n retval = 1\n except GitRepositoryError as err:\n gbp.log.err(\"Git command failed: %s\" % err)\n retval = 1\n except GbpError as err:\n if str(err):\n gbp.log.err(err)\n retval = 1\n finally:\n del_tmpdir()\n\n return retval","title":""},{"docid":"7b11d82e4550b6f743ea76fd7ac9f97b","score":"0.5159089","text":"def _pack():\n pass # p4gf_proc.popen_no_throw([\"git\", \"gc\"])","title":""},{"docid":"60728e99874c474a5e80f7091433459d","score":"0.51312226","text":"def create_files_for_git(repositories_holder, onerepo):\n if onerepo:\n repository_team = REPOSITORIES\\\n .get(\"Github\")\\\n .get(repositories_holder)\\\n .get(\"team\")\n repository_type = REPOSITORIES \\\n .get(\"Github\") \\\n .get(repositories_holder) \\\n .get(\"configuration\") \\\n .get(\"type\")\n if LOGGER:\n print(\"\\nWorking on repo: {}\".format(repositories_holder))\n folders_to_check = [folder for folder in REPOSITORIES\n .get(\"Github\")\n .get(repositories_holder)\n .get(\"configuration\")\n .get(\"folders-to-check\")]\n filter_git_commit_data(repositories_holder,\n repository_team,\n repository_type,\n folders_to_check)\n if repositories_holder == \"build-puppet\":\n create_git_md_table(repositories_holder, \"git_files\")\n create_md_table_for_scriptworkers(repositories_holder)\n else:\n create_git_md_table(repositories_holder, \"git_files\")\n if LOGGER:\n print(\"MD table generated successfully\")\n print(\"Finished working on {}\".format(repositories_holder))\n else:\n for repo in repositories_holder[\"Github\"]:\n repository_name = repo\n repository_team = repositories_holder\\\n .get(\"Github\")\\\n .get(repo)\\\n .get(\"team\")\n repository_type = repositories_holder \\\n .get(\"Github\") \\\n .get(repo) \\\n .get(\"configuration\") \\\n .get(\"type\")\n if LOGGER:\n print(\"\\nWorking on repo: {}\".format(repository_name))\n folders_to_check = [folder for folder in repositories_holder\n .get(\"Github\")\n .get(repo)\n .get(\"configuration\")\n .get(\"folders-to-check\")]\n filter_git_commit_data(repository_name,\n repository_team,\n repository_type,\n folders_to_check)\n if repository_name == \"build-puppet\":\n create_git_md_table(repository_name, \"git_files\")\n create_md_table_for_scriptworkers(repository_name)\n else:\n create_git_md_table(repository_name, \"git_files\")\n if LOGGER:\n print(\"MD table generated successfully\")\n print(\"Finished working on {}\".format(repository_name))","title":""},{"docid":"328d4132a2d25da1699d1bf125d2c01a","score":"0.5126164","text":"def code_prep():\n rev = hg_revision()\n assert not rev.endswith('+'), 'Client has pending changes, cannot install.'\n fprint('Preparing local code tarball (rev %s)' % rev)\n filename = 'viewfinder.%s.tar.gz' % rev\n\n local('hg identify -i > hg_revision.txt')\n local('tar czf %s --exclude \"*.o\" --exclude \"*~\" --exclude \"*.pyc\" __init__.py scripts/ marketing/ backend/ resources/ secrets/viewfinder.co hg_revision.txt' % filename)\n return rev","title":""},{"docid":"951b8ebff062560453981384d4cd52c5","score":"0.5125932","text":"def generate_patch_from_img(img,coor,patch_size):\n \n return extract_patch(img, coor, patch_size)","title":""},{"docid":"c29a54b5210d4569cd7c2cf866846ca2","score":"0.51184845","text":"def test_release_notes_in_separate_files(self):\n runner = CliRunner()\n\n def do_build_once_with(version, fragment_file, fragment):\n with open(f\"newsfragments/{fragment_file}\", \"w\") as f:\n f.write(fragment)\n\n result = runner.invoke(\n _main,\n [\n \"--version\",\n version,\n \"--name\",\n \"foo\",\n \"--date\",\n \"01-01-2001\",\n \"--yes\",\n ],\n )\n # not git repository, manually remove fragment file\n Path(f\"newsfragments/{fragment_file}\").unlink()\n return result\n\n results = []\n with runner.isolated_filesystem():\n with open(\"pyproject.toml\", \"w\") as f:\n f.write(\n \"\\n\".join(\n [\n \"[tool.towncrier]\",\n \" single_file=false\",\n ' filename=\"{version}-notes.rst\"',\n ]\n )\n )\n os.mkdir(\"newsfragments\")\n results.append(\n do_build_once_with(\"7.8.9\", \"123.feature\", \"Adds levitation\")\n )\n results.append(do_build_once_with(\"7.9.0\", \"456.bugfix\", \"Adds catapult\"))\n\n self.assertEqual(0, results[0].exit_code, results[0].output)\n self.assertEqual(0, results[1].exit_code, results[1].output)\n self.assertEqual(\n 2,\n len(list(Path.cwd().glob(\"*-notes.rst\"))),\n \"one newfile for each build\",\n )\n self.assertTrue(os.path.exists(\"7.8.9-notes.rst\"), os.listdir(\".\"))\n self.assertTrue(os.path.exists(\"7.9.0-notes.rst\"), os.listdir(\".\"))\n\n outputs = []\n outputs.append(read(\"7.8.9-notes.rst\"))\n outputs.append(read(\"7.9.0-notes.rst\"))\n\n self.assertEqual(\n outputs[0],\n dedent(\n \"\"\"\n foo 7.8.9 (01-01-2001)\n ======================\n\n Features\n --------\n\n - Adds levitation (#123)\n \"\"\"\n ).lstrip(),\n )\n self.assertEqual(\n outputs[1],\n dedent(\n \"\"\"\n foo 7.9.0 (01-01-2001)\n ======================\n\n Bugfixes\n --------\n\n - Adds catapult (#456)\n \"\"\"\n ).lstrip(),\n )","title":""},{"docid":"5a153219714060dc97593244ce5e62a5","score":"0.51050234","text":"def bump_patch(self):\n self.commit()\n self._version = self._version.bump_patch()\n self._last_digest = self._digest()","title":""},{"docid":"3fa38f2c9c62542d619815a527e48bfb","score":"0.5102615","text":"def _get_diff(self, changeset):\n patch_file = io.StringIO()\n dulwich.patch.write_object_diff(patch_file,\n self.repo.object_store,\n changeset.old, changeset.new)\n return patch_file.getvalue()","title":""},{"docid":"8432eb9f04c09da9f46d4b0366fcb473","score":"0.50958365","text":"def filter_non_code(commit_flow):\n for (commit_id, commit_message, commit_patch) in commit_flow:\n new_commit_patch = list()\n keep_skipping = False\n for line in commit_patch:\n if (\"diff --git\" in line):\n # So... this is a change to a file\n # collect everything that looks like a file extension\n matches = EXTENSIONS_FINDER.findall(line)\n # Does this line mention a filetype we are interested in ?\n if is_code_file(matches):\n keep_skipping = False\n new_commit_patch.append(line)\n continue\n else:\n keep_skipping = True\n continue\n if keep_skipping:\n continue\n else:\n new_commit_patch.append(line)\n continue\n yield (commit_id, commit_message, new_commit_patch)","title":""},{"docid":"da70249685cac957e844f4b072040bb1","score":"0.5064239","text":"def git_commit():\n\n os.system('''\n echo \"Checking current working dir\"\n pwd\n ls -lah\n echo \"Configuring git username and email address\"\n git config --global user.email \"actions.public.prismaconfig@github.com/uts-itd/\"\n git config --global user.name \"Prisma IAC config GitHub Action\"\n echo \"Running 'git add'\"\n git add --all .\n git diff --cached\n echo \"Commiting changes...\"\n git commit -m 'Adding prisma IAC scan config'\n echo \"Pushing changes...\"\n git push\n echo \"Show git log (may truncate)\"\n git log\n ''')","title":""},{"docid":"777625327140746c4a71c4f8f5739817","score":"0.50620663","text":"def get_files_affected_by_patch(self, relative_to='src/'):\n patch_root = self.m.gclient.calculate_patch_root(\n self.m.properties.get('patch_project'))\n cwd = self._working_dir.join(patch_root) if self._working_dir else None\n files = self.m.tryserver.get_files_affected_by_patch(patch_root, cwd=cwd)\n for i, path in enumerate(files):\n path = str(path)\n assert path.startswith(relative_to)\n files[i] = path[len(relative_to):]\n return files","title":""},{"docid":"619e60696ebf0a14487c95b2d4e40571","score":"0.50588316","text":"def _files_changed_in_git_diff(self, hash_1, hash_2):\n\n os.chdir(self.repo_path)\n out, __ = self._os_cmd(\n \"git diff {} {} --name-only\".format(hash_1.id.hex, hash_2.id.hex))\n files_changed = []\n for file in out.splitlines():\n for wanted in [\".py\", \".c\", \".cpp\", \".F90\", \".f90\", \".java\"]:\n if wanted in str(file) and \"__init__.py\" not in str(file):\n files_changed.append(Path(str(file)).name[:-1])\n\n # A work around for FORTRAN file extensions.\n if self.repo_lang == \"fortran\":\n files_changed = list(map(lambda x: x[:-4]+\".f90\", files_changed))\n\n return files_changed","title":""},{"docid":"4d8f194343089da907675c0a9fa50dec","score":"0.50580674","text":"def test_main(\n petsc_dir: Path,\n test_path: Path,\n output_dir: Path,\n patch_list: list[PathDiffPair],\n errors_fixed: list[CondensedDiags],\n errors_left: list[CondensedDiags],\n replace: bool = False,\n) -> ReturnCode:\n def test(generated_output: list[str], reference_file: Path) -> str:\n short_ref_name = reference_file.relative_to(petsc_dir)\n if replace:\n pl.sync_print('\\tREPLACE', short_ref_name)\n reference_file.write_text(''.join(generated_output))\n return ''\n if not reference_file.exists():\n return f'Missing reference file \\'{reference_file}\\'\\n'\n return ''.join(\n difflib.unified_diff(\n reference_file.read_text().splitlines(True), generated_output,\n fromfile=str(short_ref_name), tofile='Generated Output', n=0\n )\n )\n\n # sanitize the output so that it will be equal across systems\n def sanitize_output_file(text: Optional[str]) -> list[str]:\n return [] if text is None else [l.replace(str(petsc_dir), '.') for l in text.splitlines(True)]\n\n def sanitize_patch_file(text: Optional[str]) -> list[str]:\n # skip the diff header with file names\n return [] if text is None else text.splitlines(True)[2:]\n\n def rename_patch_file_target(text: str, new_path: Path) -> str:\n lines = text.splitlines(True)\n out_file = lines[0].split()[1]\n lines[0] = lines[0].replace(out_file, str(new_path))\n lines[1] = lines[1].replace(out_file, str(new_path))\n return ''.join(lines)\n\n FIXED_MARKER = '<--- FIXED --->'\n LEFT_MARKER = '<--- LEFT --->'\n patch_error = {}\n root_dir = f'--directory={petsc_dir.anchor}'\n patches = dict(patch_list)\n\n tmp_output = {\n p : [FIXED_MARKER, '\\n'.join(s), LEFT_MARKER]\n for diags in errors_fixed\n for p, s in diags.items()\n }\n for diags in errors_left:\n for path, strlist in diags.items():\n if path not in tmp_output:\n tmp_output[path] = [f'{FIXED_MARKER}\\n{LEFT_MARKER}']\n tmp_output[path].extend(strlist)\n\n # ensure that each output ends with a newline\n output = {\n path : '\\n'.join(strlist if strlist[-1].endswith('\\n') else strlist + [''])\n for path, strlist in tmp_output.items()\n }\n # output = {\n # path : '\\n'.join(strlist if len(strlist) == 4 else strlist + ['']) for path, strlist in tmp_output.items()\n # }\n #output = {key : '\\n'.join(val if len(val) == 4 else val + ['']) for key, val in output.items()}\n if test_path.is_dir():\n c_suffixes = (r'*.c', r'*.cxx', r'*.cpp', r'*.cc', r'*.CC')\n file_list = [item for sublist in map(test_path.glob, c_suffixes) for item in sublist]\n else:\n file_list = [test_path]\n for test_file in file_list:\n output_base = output_dir / test_file.stem\n output_file = output_base.with_suffix('.out')\n patch_file = output_base.with_suffix('.patch')\n short_name = test_file.relative_to(petsc_dir)\n\n pl.sync_print('\\tTEST ', short_name)\n\n output_errors = [\n test(sanitize_output_file(output.get(test_file)), output_file),\n test(sanitize_patch_file(patches.get(test_file)), patch_file)\n ]\n\n # no point in checking the patch, we have already replaced\n if not replace:\n # make sure the patch can be applied\n with TemporaryCopy(test_file) as tmp_src, \\\n tempfile.NamedTemporaryFile(delete=True, suffix='.patch') as temp_patch:\n tmp_patch_path = Path(temp_patch.name).resolve(strict=True)\n try:\n tmp_patch_path.write_text(rename_patch_file_target(patches[test_file], tmp_src.temp_path))\n pl.util.subprocess_capture_output(\n ['patch', root_dir, '--strip=0', '--unified', f'--input={tmp_patch_path}']\n )\n except Exception as exc:\n exception = ''.join(traceback_format_exception(exc))\n emess = f'Application of patch based on {test_file} failed:\\n{exception}\\n'\n rej = tmp_src.rej_file()\n if rej.exists():\n emess += f'\\n{rej}:\\n{rej.read_text()}'\n output_errors.append(emess)\n\n output_errors = [e for e in output_errors if e]\n if output_errors:\n pl.sync_print('\\tNOT OK ', short_name)\n patch_error[test_file] = '\\n'.join(output_errors)\n else:\n pl.sync_print('\\tOK ', short_name)\n if patch_error:\n err_str = f\"[ERROR] {85 * '-'} [ERROR]\"\n err_bars = (err_str + '\\n', err_str)\n for err_file in patch_error:\n pl.sync_print(patch_error[err_file].join(err_bars))\n return ReturnCode.ERROR_TEST_FAILED\n return ReturnCode.SUCCESS","title":""},{"docid":"0bde0963bfc3274895a54efc5bcef7d8","score":"0.50537014","text":"def resource_list(args):\n patches = spack.repo.path.patch_index.index\n for sha256 in patches:\n if args.only_hashes:\n print(sha256)\n else:\n _show_patch(sha256)","title":""},{"docid":"fa8d082181a53dfd44c5d1ce8dbfe6a4","score":"0.5053243","text":"def test_multiple(self):\n [dump, log] = self.make_repo((\n dict(nodes=(\n dict(action=\"add\", path=\"file1\", kind=\"file\", content=b\"\"),\n dict(action=\"add\", path=\"file2\", kind=\"file\", content=b\"\"),\n )),\n dict(nodes=(\n dict(action=\"change\", path=\"file1\", content=b\"mod 1\\n\"),\n dict(action=\"change\", path=\"file2\", content=b\"mod 2\\n\"),\n )),\n ))\n git = os.path.join(self.dir, \"git\")\n subprocess.check_call((\"git\", \"init\", \"--quiet\", \"--\", git))\n script = 'cd \"$1\" && git fast-import --quiet'\n importer = (\"sh\", \"-c\", script, \"--\", git)\n with svnex.FastExportPipe(importer) as importer, log:\n exporter = svnex.Exporter(dump, importer, root=\"\", git_svn=True, quiet=True)\n exporter.export(\"refs/heads/master\")\n cmd = (\"git\", \"log\", \"--reverse\",\n \"--format=format:%H%n%B\", \"--shortstat\", \"refs/heads/master\")\n log = subprocess.check_output(cmd, cwd=git).decode(\"ascii\")\n self.assertMultiLineEqual('''\\\n3047a82f20bef2648a67a90912d8d755353d9e9e\n\n\ngit-svn-id: @1 00000000-0000-0000-0000-000000000000\n\n 2 files changed, 0 insertions(+), 0 deletions(-)\n\n82aeb20279a1269f048243a603b141ee0ea204e9\n\n\ngit-svn-id: @2 00000000-0000-0000-0000-000000000000\n\n 2 files changed, 2 insertions(+)\n''',\n log)","title":""},{"docid":"3fe058dc1061c0e628334519da1ca8af","score":"0.50501686","text":"def test_patch_parsing_multi_hunks(self):\n with open(_get_path_to_asset('b.patch')) as f_hand:\n patches = patchit.PatchSet.from_stream(f_hand)\n self.assertEqual(patches[0].source_filename, 'a')\n self.assertEqual(patches[0].target_filename, 'b')\n self.assertEqual(len(patches[0].hunks), 2)","title":""},{"docid":"84ade01a88075a68badd1eccb02ab909","score":"0.50476944","text":"def munge_spec(path, build):\n spec = os.path.join(path, '%s.spec' % PACKAGE)\n m = re.match(r'\\d+', build['release'])\n if not m:\n raise ValueError('unable to parse Release %s' % build['release'])\n releaseint = int(m.group())\n release = '%d.1.coverage%%{dist}' % releaseint\n with open(spec) as fileh:\n lines = fileh.readlines()\n print('rewriting %s with coverage changes' % spec)\n with open(spec, 'w') as fileh:\n for line in lines:\n if re.match(r'\\s*Release:', line):\n fileh.write('Release: %s\\n' % release)\n elif re.match(r'%prep', line):\n text = read_fragment('package')\n fileh.write(text)\n fileh.write(line)\n elif re.match(r'cmake ', line):\n fileh.write(line)\n text = read_fragment('cmake')\n fileh.write(text)\n elif re.match(r'%install', line):\n fileh.write(line)\n text = read_fragment('install')\n fileh.write(text)\n elif re.match(r'%files base', line):\n text = read_fragment('files')\n fileh.write(text)\n fileh.write(line)\n else:\n fileh.write(line)","title":""},{"docid":"3f6f124a35b2fa9d44e5810d5d2f63b6","score":"0.50471437","text":"def test_diff_with_changelist(self):\n client = self.build_client(needs_diff=True)\n tmpfiles = self.precreate_tempfiles(4)\n\n self.write_workarea_file('README', b'new line\\n')\n self.write_workarea_file(os.path.join('src', 'main.c'),\n b'new content\\n')\n self.write_workarea_file('newfile', b'new file!\\n')\n self.write_workarea_file(os.path.join('doc', 'index.md'),\n b'# new header line\\n')\n\n self.spy_on(run_process_exec, op=kgb.SpyOpMatchInOrder([\n self.rule_query_wa_root,\n self.make_rule_stash_selection([\n b'README',\n b'src/main.c',\n b'doc/index.md',\n ]),\n self.make_rule_list_changelist(\n name='my_changelist',\n results=[\n 'Modifying ./README\\n',\n 'Modifying ./src/main.c\\n',\n 'Adding ./newfile\\n',\n 'Deleting ./delfile\\n',\n ]),\n self.make_rule_status(\n [\n b'F\\tO\\tM\\t./README',\n b'F\\tO\\tM\\t./src/main.c',\n b'F\\t-\\t-\\t./ignore-me',\n b'F\\t?\\t?\\t./newfile',\n b'F\\t!\\t-\\t./delfile',\n ],\n selection=['-sor', '-scm', '-sunm', '-sne']),\n self.make_rule_nobjstatus(\n sos_paths=[\n './README',\n './src/main.c',\n ],\n flags=['-gaRevision', '-gaRevId'],\n results=[\n '!nObjStatus! 1',\n '!Record!',\n './README',\n '3',\n '1',\n 'Revision',\n '1',\n '1',\n 'RevId',\n '1',\n '5',\n '!Record!',\n './src/main.c',\n '3',\n '1',\n 'Revision',\n '1',\n '3',\n 'RevId',\n '2',\n '26',\n ]),\n self.rule_query_project,\n self.rule_query_server,\n self.rule_query_rso,\n self.make_rule_exportrev(sos_path='./README/#/1',\n out_filename=tmpfiles[1],\n content=b'old line\\n'),\n self.make_rule_diff(client, tmpfiles[1], 'README'),\n self.make_rule_diff(client, tmpfiles[2], 'newfile'),\n self.make_rule_exportrev(sos_path='./src/main.c/#/3',\n out_filename=tmpfiles[3],\n content=b'old content\\n'),\n self.make_rule_diff(client, tmpfiles[3],\n os.path.join('src', 'main.c')),\n self.make_rule_restore_selection(tmpfiles[0]),\n ]))\n\n result = client.diff(revisions={\n 'base': None,\n 'extra': {\n 'sos_changelist': 'my_changelist',\n },\n 'tip': None,\n })\n\n self.assertDiffEqual(\n result['diff'],\n b'#diffx: encoding=utf-8, version=1.0\\n'\n b'#.meta: format=json, length=354\\n'\n b'{\\n'\n b' \"scm\": \"sos\",\\n'\n b' \"sos\": {\\n'\n b' \"changelist\": \"my_changelist\",\\n'\n b' \"project\": \"test-project\",\\n'\n b' \"rso\": [\\n'\n b' \"main\",\\n'\n b' \"test\"\\n'\n b' ],\\n'\n b' \"server\": \"test-server\"\\n'\n b' },\\n'\n b' \"stats\": {\\n'\n b' \"changes\": 1,\\n'\n b' \"deletions\": 2,\\n'\n b' \"files\": 3,\\n'\n b' \"insertions\": 3,\\n'\n b' \"lines changed\": 5\\n'\n b' }\\n'\n b'}\\n'\n b'#.change:\\n'\n b'#..meta: format=json, length=121\\n'\n b'{\\n'\n b' \"stats\": {\\n'\n b' \"deletions\": 2,\\n'\n b' \"files\": 3,\\n'\n b' \"insertions\": 3,\\n'\n b' \"lines changed\": 5\\n'\n b' }\\n'\n b'}\\n'\n b'#..file:\\n'\n b'#...meta: format=json, length=258\\n'\n b'{\\n'\n b' \"op\": \"modify\",\\n'\n b' \"path\": \"README\",\\n'\n b' \"revision\": {\\n'\n b' \"old\": \"1\"\\n'\n b' },\\n'\n b' \"sos\": {\\n'\n b' \"rev_id\": {\\n'\n b' \"old\": 5\\n'\n b' }\\n'\n b' },\\n'\n b' \"stats\": {\\n'\n b' \"deletions\": 1,\\n'\n b' \"insertions\": 1,\\n'\n b' \"lines changed\": 2\\n'\n b' }\\n'\n b'}\\n'\n b'#...diff: length=54, line_endings=unix\\n'\n b'--- README\\n'\n b'+++ README\\n'\n b'@@ -1 +1 @@\\n'\n b'-old line\\n'\n b'+new line\\n'\n b'#..file:\\n'\n b'#...meta: format=json, length=144\\n'\n b'{\\n'\n b' \"op\": \"create\",\\n'\n b' \"path\": \"newfile\",\\n'\n b' \"stats\": {\\n'\n b' \"deletions\": 0,\\n'\n b' \"insertions\": 1,\\n'\n b' \"lines changed\": 1\\n'\n b' }\\n'\n b'}\\n'\n b'#...diff: length=51, line_endings=unix\\n'\n b'--- /dev/null\\n'\n b'+++ newfile\\n'\n b'@@ -0,0 +1 @@\\n'\n b'+new file!\\n'\n b'#..file:\\n'\n b'#...meta: format=json, length=263\\n'\n b'{\\n'\n b' \"op\": \"modify\",\\n'\n b' \"path\": \"src/main.c\",\\n'\n b' \"revision\": {\\n'\n b' \"old\": \"3\"\\n'\n b' },\\n'\n b' \"sos\": {\\n'\n b' \"rev_id\": {\\n'\n b' \"old\": 26\\n'\n b' }\\n'\n b' },\\n'\n b' \"stats\": {\\n'\n b' \"deletions\": 1,\\n'\n b' \"insertions\": 1,\\n'\n b' \"lines changed\": 2\\n'\n b' }\\n'\n b'}\\n'\n b'#...diff: length=68, line_endings=unix\\n'\n b'--- src/main.c\\n'\n b'+++ src/main.c\\n'\n b'@@ -1 +1 @@\\n'\n b'-old content\\n'\n b'+new content\\n')\n\n self.assertEqual(result.get('review_request_extra_data'), {\n 'sos_changelist': 'my_changelist',\n 'sos_project': 'test-project',\n 'sos_server': 'test-server',\n 'sos_workarea': '1234567890',\n })","title":""},{"docid":"d9f8eb67cdb3c4563794333c41010ba3","score":"0.5045795","text":"def get_git_changeset(path=None):\n if path is None:\n path = os.path.normpath(os.path.join(controlbeast.__path__[0], \"..\"))\n\n # run `git show` in ControlBeast's root directory and grab its output from stdout\n try:\n with subprocess.Popen(\n 'git show --pretty=format:%ct --quiet HEAD',\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True,\n cwd=path,\n universal_newlines=True\n ) as git_show:\n timestamp = to_str(git_show.communicate()[0]).partition('\\n')[0]\n except OSError:\n timestamp = None\n\n try:\n timestamp = datetime.datetime.utcfromtimestamp(int(timestamp))\n except (ValueError, TypeError):\n return 'GIT-unknown'\n\n return 'GIT-{0:>s}'.format(timestamp.strftime('%Y%m%d%H%M%S'))","title":""},{"docid":"32c4eaddd5f9cef8039f530288385ffa","score":"0.5036955","text":"def hooks() -> None:\n proj().setup_git_hooks()","title":""},{"docid":"332cf4c7e9cc4aa4edca2843855ab3aa","score":"0.50354874","text":"def svn_diff_close_patch_file(*args) -> \"svn_error_t *\":\n return _diff.svn_diff_close_patch_file(*args)","title":""},{"docid":"e2831cbe40d08f54072b24c85e2c1bb8","score":"0.5034028","text":"def apply_patches_to_MANN_files() -> None:\n\n # Original MANN files\n filenames = [\"AdamW.py\", \"AdamWParameter.py\", \"ExpertWeights.py\", \"Gating.py\", \"MANN.py\"]\n\n # Path configuration\n MANN_folder = \"../src/adherent/MANN/\"\n patches_MANN_folder = \"../src/adherent/MANN/patches/\"\n\n # Apply the patches\n for filename in filenames:\n original_file = MANN_folder + filename\n patch_file = patches_MANN_folder + filename[:-2] + \"patch\"\n os.system(\"patch \" + original_file + \" \" + patch_file)","title":""},{"docid":"2796d9e81b19147be0d3eded6a5a43fd","score":"0.5033925","text":"def test_separate_modality_change(tmp_path):\n\n toprepo = tmp_path / 'repo'\n with git.Repo.init(toprepo, expand_vars=False) as repo:\n with open(toprepo / 'hopic-ci-config.yaml', 'w') as f:\n f.write(dedent('''\\\n version:\n bump: no\n\n modality-source-preparation:\n CHANGE:\n - sh: touch new-file.txt\n changed-files:\n - new-file.txt\n commit-message: Add new file\n '''))\n repo.index.add(('hopic-ci-config.yaml',))\n base_commit = repo.index.commit(message='Initial commit', **_commitargs)\n\n result = run(\n ('--workspace', str(toprepo),\n 'prepare-source-tree',\n '--author-name', _author.name,\n '--author-email', _author.email,\n '--author-date', f\"@{_git_time}\",\n '--commit-date', f\"@{_git_time}\",\n 'apply-modality-change', 'CHANGE'),\n )\n assert result.exit_code == 0\n\n with git.Repo(toprepo, expand_vars=False) as repo:\n assert repo.head.commit != base_commit\n assert repo.head.commit.parents == (base_commit,)","title":""},{"docid":"1ed084d91789571a07abe4398e0b3c24","score":"0.5027947","text":"def get_changed_files(repo_path: str, parent_branch: str, include_merges: bool) -> Sequence[str]:\n file_regex_str = r\"^(?P<op>[AM])\\s+(?P<path>[a-zA-Z0-9_/\\\\]+\\.py)$\"\n file_regex = re.compile(file_regex_str)\n\n # fetch changed files from previous commits\n logging.info(\"Fetching files from previous commits\\n\")\n cmd = f\"git log {'' if include_merges else '--no-merges --first-parent'} --name-status {parent_branch}..HEAD\"\n logging.debug(f\"Executing: {cmd}\")\n all_files = subprocess.check_output(cmd.split(), cwd=repo_path).decode(\"utf-8\")\n logging.debug(f\"Result:\\n{all_files}\")\n\n # fetch changed files that are potentionally not committed yet\n logging.info(\"Fetching uncommitted files\\n\")\n cmd = f\"git diff --name-status\"\n logging.debug(f\"Executing: {cmd}\")\n uncommitted = subprocess.check_output(cmd.split(), cwd=repo_path).decode(\"utf-8\")\n logging.debug(f\"Result:\\n{uncommitted}\")\n all_files += uncommitted\n\n # fetch staged new files\n logging.info(\"Fetching new files... those need to be stagged\\n\")\n cmd = f\"git diff --name-status --cached\"\n logging.debug(f\"Executing: {cmd}\")\n staged = subprocess.check_output(cmd.split(), cwd=repo_path).decode(\"utf-8\")\n logging.debug(f\"Result:\\n{staged}\")\n all_files += staged\n\n filtered = []\n for item in all_files.split(\"\\n\"):\n match = file_regex.match(item)\n if match:\n filtered.append(match.group(\"path\"))\n # remove duplicates\n filtered = list(set(filtered))\n logging.debug(f\"Files to consider: {len(filtered)}: {filtered}\")\n return list(set(filtered))","title":""},{"docid":"36ce70c09915d144157f15c9bc17916a","score":"0.5024164","text":"def apply_patch(self, patch_file, base_path, base_dir, p=None,\n revert=False):\n if not is_valid_version(self.subversion_client_version,\n self.PATCH_MIN_VERSION):\n raise MinimumVersionError(\n 'Using \"rbt patch\" with the SVN backend requires at least '\n 'svn 1.7.0')\n\n if base_dir and not base_dir.startswith(base_path):\n # The patch was created in either a higher level directory or a\n # directory not under this one. We should exclude files from the\n # patch that are not under this directory.\n excluded, empty = self._exclude_files_not_in_tree(patch_file,\n base_path)\n\n if excluded:\n logging.warn('This patch was generated in a different '\n 'directory. To prevent conflicts, all files '\n 'not under the current directory have been '\n 'excluded. To apply all files in this '\n 'patch, apply this patch from the %s directory.',\n base_dir)\n\n if empty:\n logging.warn('All files were excluded from the patch.')\n\n cmd = ['patch']\n p_num = p or self._get_p_number(base_path, base_dir)\n\n if p_num >= 0:\n cmd.append('--strip=%s' % p_num)\n\n if revert:\n cmd.append('--reverse-diff')\n\n cmd.append(six.text_type(patch_file))\n\n rc, patch_output = self._run_svn(cmd, return_error_code=True)\n\n if self.supports_empty_files():\n try:\n with open(patch_file, 'rb') as f:\n patch = f.read()\n except IOError as e:\n logging.error('Unable to read file %s: %s', patch_file, e)\n return\n\n self.apply_patch_for_empty_files(patch, p_num, revert=revert)\n\n # TODO: What is svn's equivalent of a garbage patch message?\n\n return PatchResult(applied=(rc == 0), patch_output=patch_output)","title":""},{"docid":"13f9a51593fc0e83a257cad0399a1326","score":"0.5021405","text":"def build(self, manager):\n lite_dir = manager.lite_dir\n output_dir = manager.output_dir\n\n for jupyterlite_file in self.lite_files:\n rel = jupyterlite_file.relative_to(lite_dir)\n dest = output_dir / rel\n yield self.task(\n name=f\"patch:{rel}\",\n file_dep=[jupyterlite_file],\n actions=[\n (self.merge_one_jupyterlite, [dest, [dest, jupyterlite_file]]),\n (self.maybe_timestamp, [dest]),\n ],\n )","title":""},{"docid":"23cc3b64c14ea0ae236a7eb9927166e8","score":"0.5018992","text":"def test_projectless_changelog(self):\n runner = CliRunner()\n\n with runner.isolated_filesystem():\n with open(\"pyproject.toml\", \"w\") as f:\n f.write(\"[tool.towncrier]\\n\" 'package = \"foo\"\\n')\n os.mkdir(\"foo\")\n os.mkdir(\"foo/newsfragments\")\n with open(\"foo/newsfragments/123.feature\", \"w\") as f:\n f.write(\"Adds levitation\")\n # Towncrier ignores .rst extension\n with open(\"foo/newsfragments/124.feature.rst\", \"w\") as f:\n f.write(\"Extends levitation\")\n\n result = runner.invoke(\n _main,\n [\n \"--name\",\n \"FooBarBaz\",\n \"--version\",\n \"7.8.9\",\n \"--date\",\n \"01-01-2001\",\n \"--draft\",\n ],\n )\n\n self.assertEqual(0, result.exit_code)\n self.assertEqual(\n result.output,\n dedent(\n \"\"\"\n Loading template...\n Finding news fragments...\n Rendering news fragments...\n Draft only -- nothing has been written.\n What is seen below is what would be written.\n\n FooBarBaz 7.8.9 (01-01-2001)\n ============================\n\n Features\n --------\n\n - Adds levitation (#123)\n - Extends levitation (#124)\n\n \"\"\"\n ).lstrip(),\n )","title":""},{"docid":"ef62ccd75c5fcf6d5363c80c60ccf946","score":"0.501486","text":"def build_message(\n commit: str,\n commit_github_url: str,\n changed_files: List[str],\n diffs: dict,\n commit_author: str,\n) -> List[Dict[str, Any]]:\n\n title = f\"Commit {commit[0:7]} Modified Schema Struct Files\"\n changes: List[Dict[str, Any]] = [{\"value\": f\"Changed by: {commit_author}\"}]\n for changed_file in changed_files:\n changes.append(\n {\n \"title\": f\"{changed_file}\",\n \"value\": f\"```{diffs[changed_file]}```\",\n \"short\": False,\n }\n )\n\n message = [\n {\n \"fallback\": title,\n \"color\": \"warning\",\n \"title\": title,\n \"title_link\": commit_github_url,\n \"fields\": changes,\n }\n ]\n return message","title":""},{"docid":"2e1c079cc7c16bd70c74ca9e25b4115f","score":"0.5012719","text":"def post_pr_merge_sync(context):\n context.run(\"git switch main\")\n context.run(\"git pull\")","title":""},{"docid":"e73df4999e4a14e903672e473723192b","score":"0.50117606","text":"def files_in_commit(commit: Optional[str] = None) -> Iterable[str]:\n cmd = ['git', 'diff', '--name-status']\n if commit is not None:\n cmd.append(f'{commit}..{commit}~1')\n\n output = subprocess.check_output(cmd)\n\n for line in output.decode('utf-8').split('\\n'):\n if line == '':\n break\n\n path = line.split('\\t')[-1]\n\n if not path.endswith('.py'):\n continue\n\n abs_path = os.path.abspath(path)\n\n yield abs_path","title":""},{"docid":"e1b2b32d7dc0ff276503ffe6e1b50b17","score":"0.5009937","text":"def _create_patch(self, file: Path, start_line_no: int) -> PatchedFile:\n diff = self._do_diff(file)\n patch_set = PatchSet(diff)\n\n if len(patch_set) != 1:\n raise PluginException(\"Expected one patch in the patch_set\")\n\n patched_file = patch_set[0]\n to_remove = []\n for hunk in patched_file:\n if not self._hunk_contains_line(hunk, start_line_no):\n to_remove.append(hunk)\n\n # Remove hunks that we are not interested in\n for hunk in to_remove:\n patched_file.remove(hunk)\n\n if len(patched_file) == 0:\n raise PluginException(\"Found no hunks to commit\")\n\n if len(patched_file) != 1:\n raise PluginException(\"Found more than one hunk\")\n\n return patched_file","title":""},{"docid":"9c286eef01193bd6dc827a9b51b4dd04","score":"0.50011075","text":"def test_get_open_refspecs():\n open_prs = {\n \"pr_strings\": [\"pr1_this\", \"pr2_that\"],\n \"merge_commit_shas\": [\"aaaaaaaa\", \"bbbbbbbb\"],\n \"base_shas\": [\"shafoo\", \"shabar\"],\n \"head_shas\": [\"shabaz\", \"shagah\"],\n \"backlogged\": [False, False]\n }\n bridge = SpackCIBridge.SpackCIBridge()\n open_refspecs, fetch_refspecs = bridge.get_open_refspecs(open_prs)\n assert open_refspecs == [\n \"github/pr1_this:github/pr1_this\",\n \"github/pr2_that:github/pr2_that\"\n ]\n assert fetch_refspecs == [\n \"+aaaaaaaa:refs/remotes/github/pr1_this\",\n \"+bbbbbbbb:refs/remotes/github/pr2_that\"\n ]\n\n protected_branches = [\"develop\", \"master\"]\n bridge.update_refspecs_for_protected_branches(protected_branches, open_refspecs, fetch_refspecs)\n assert open_refspecs == [\n \"github/pr1_this:github/pr1_this\",\n \"github/pr2_that:github/pr2_that\",\n \"github/develop:github/develop\",\n \"github/master:github/master\",\n ]\n assert fetch_refspecs == [\n \"+aaaaaaaa:refs/remotes/github/pr1_this\",\n \"+bbbbbbbb:refs/remotes/github/pr2_that\",\n \"+refs/heads/develop:refs/remotes/github/develop\",\n \"+refs/heads/master:refs/remotes/github/master\"\n ]","title":""},{"docid":"3cae22ccb15643ccf5b7aee329d27cdd","score":"0.4999196","text":"def patch():\n pass","title":""},{"docid":"c8884f6340bcfd867ca9635ba147c23e","score":"0.49979344","text":"def git_add(filename):\n subprocess.call(['git', 'add', filename])","title":""},{"docid":"434dfaa0a6312e3ad8440e4c72f86b9e","score":"0.49963978","text":"def git_add(args):\n \n # Check if initialized git\n if not os.path.exists('.git/objects'):\n print('fatal: not a git repository (or any of the parent directories): .git')\n return\n\n # check if args is empty directory\n if not len(args):\n return\n \n ##############################\n for file in args:\n update_git_object(file)\n update_git_index(file)","title":""},{"docid":"67c8e84c251e1d8f91fb4c468f55997a","score":"0.49961337","text":"def build_pr(args):\n branches = [\"pull/{0}/head:pr\".format(args.pr)]\n build_commit(args, branches)","title":""},{"docid":"f67bd1ca0cd5a732d1483b18c2c183e4","score":"0.49954492","text":"def GetGitDiffFiles(git_dir, logger):\n result = dict()\n result['has_diff'] = False\n result['add_list'] = list()\n result['del_list'] = list()\n result['unsvn_list'] = list()\n \n try:\n cmd = 'cd %s && git status --short' % (git_dir)\n (status, msg) = Function.RunCommand(cmd, ignore_stderr_when_ok=True)\n if status != 0:\n logger.LevPrint(\"ERROR\", \"run command %s error!\" % (cmd), False)\n return None\n\n file_list = msg.split('\\n')\n for file_stat in file_list:\n tmp_list = file_stat.split()\n if len(tmp_list) <= 1:\n continue\n file_status = tmp_list[0]\n file_name = tmp_list[-1]\n if file_status == 'M':\n result['add_list'].append(file_name)\n result['has_diff'] = True\n elif file_status == \"D\":\n result['del_list'].append(file_name)\n result['has_diff'] = True\n else:\n result['unsvn_list'].append(file_name)\n except BaseException as ex:\n logger.LevPrint(\"ERROR\", \"get git diff file error, ex : %s!\" % (str(ex)), False)\n return None\n result['result'] = 0\n return result","title":""}],"string":"[\n {\n \"docid\": \"13e1bf6c03a59fdbfb8b61d2bcf63c17\",\n \"score\": \"0.64661807\",\n \"text\": \"def main_git():\\n proc = subprocess.Popen([\\\"git\\\", \\\"diff\\\", \\\"scala\\\"], stdout=subprocess.PIPE)\\n out = proc.communicate()[0].decode()\\n\\n RE = re.compile(\\\"([-+])Subproject commit (.*)\\\")\\n frm = None\\n to = None\\n for mode, commit in RE.findall(out):\\n if mode == \\\"-\\\":\\n frm = commit\\n elif mode == \\\"+\\\":\\n to = commit\\n else:\\n raise ValueError(\\\"Unkown mode: %s\\\" % mode)\\n if frm is None or to is None:\\n raise ValueError(\\\"Cannot calculate diff from %s to %s\\\" % (frm, to))\\n\\n if \\\"dirty\\\" in to:\\n to = []\\n else:\\n to = [to]\\n proc = subprocess.Popen([\\\"git\\\", \\\"-C\\\", \\\"scala\\\", \\\"diff\\\", \\\"-U1\\\", frm] + to,\\n stdout=subprocess.PIPE)\\n diff = proc.communicate()[0].decode()\\n fname = None\\n\\n for path, start1, lines1, start2, lines2 in DIFFRE.findall(diff):\\n if path:\\n fname = path\\n else:\\n # We already applied previous changes, so the new start value\\n # (that is, start2) is the one we are interested in.\\n apply_hunk(\\\"scala/\\\" + fname, int(start2),\\n int(start2) + int(lines1),\\n int(start2) + int(lines2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e065e59077e63ec1449a4733c313eaf6\",\n \"score\": \"0.60689807\",\n \"text\": \"def GenerateDiff(self):\\n branch = upload.RunShell(['git', 'cl', 'upstream']).strip()\\n diff = upload.RunShell(['git', 'diff-tree', '-p', '--no-prefix',\\n branch, 'HEAD']).splitlines(True)\\n for i in range(len(diff)):\\n # In the case of added files, replace /dev/null with the path to the\\n # file being added.\\n if diff[i].startswith('--- /dev/null'):\\n diff[i] = '--- %s' % diff[i+1][4:]\\n return ''.join(diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b48919f473be215b3c4ae98ac0f4bee\",\n \"score\": \"0.6018004\",\n \"text\": \"def git_repo(\\n tmp_path_factory: pytest.TempPathFactory,\\n request: pytest.FixtureRequest,\\n) -> Iterator[Path]:\\n tmp_path = tmp_path_factory.mktemp(\\\"git_changelog\\\")\\n tmp_path.mkdir(exist_ok=True, parents=True)\\n git = partial(_git, \\\"-C\\\", str(tmp_path))\\n commit = partial(_commit, tmp_path, \\\"dummy\\\")\\n git(\\\"init\\\")\\n git(\\\"config\\\", \\\"user.name\\\", \\\"dummy\\\")\\n git(\\\"config\\\", \\\"user.email\\\", \\\"dummy@example.com\\\")\\n git(\\\"remote\\\", \\\"add\\\", \\\"origin\\\", \\\"git@github.com:example/example\\\")\\n versions = request.param\\n for version in versions:\\n for section in AngularConvention.TYPES:\\n commit(section)\\n commit(section)\\n if version:\\n git(\\\"tag\\\", version)\\n yield tmp_path\\n shutil.rmtree(tmp_path, ignore_errors=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398b97d6d0a5709090ba9238c804ceff\",\n \"score\": \"0.6002166\",\n \"text\": \"def apply_svn_patch(patch_root, patches, whitelist=None, blacklist=None):\\n if whitelist:\\n patches = [(name, diff) for name, diff in patches if name in whitelist]\\n elif blacklist:\\n patches = [(name, diff) for name, diff in patches if name not in blacklist]\\n diffs = [diff for _, diff in patches]\\n patch = ''.join(diffs)\\n\\n if patch:\\n print '===Patching files==='\\n for filename, _ in patches:\\n print 'Patching %s' % filename\\n try:\\n call(PATCH_TOOL, '-p0', '--remove-empty-files', '--force', '--forward',\\n stdin_data=patch, cwd=patch_root, tries=1)\\n for filename, _ in patches:\\n full_filename = path.abspath(path.join(patch_root, filename))\\n git('add', full_filename, cwd=path.dirname(full_filename))\\n except SubprocessFailed as e:\\n raise PatchFailed(e.message, e.code, e.output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976c3e0ba83cd74bdd2286fb911204b4\",\n \"score\": \"0.5962196\",\n \"text\": \"def blame_patch(patch):\\n return subprocess.check_output((\\\"git\\\", \\\"log\\\",\\n \\\"--follow\\\", \\\"--find-renames\\\", \\\"--diff-filter=A\\\",\\n \\\"--format=%s (%aN <%aE>)\\\",\\n \\\"--\\\", patch)).decode(\\\"utf-8\\\").splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d264daaa5015848f9b650cd874538a5\",\n \"score\": \"0.59295815\",\n \"text\": \"def format_diffs(commit, paths):\\n\\n diffs = []\\n # We are ignoring merges so all commits should have a single parent\\n for diff in commit.tree.diff(commit.parents[0], paths=paths, create_patch=True):\\n if diff.a_path is not None:\\n # The patch commit diffs are stored as \\\"(filename1)\\\\n(diff1)\\\\n(filename2)\\\\n(diff2)...\\\"\\n lines = \\\"\\\\n\\\".join(\\n line\\n for line in diff.diff.decode(\\\"utf-8\\\").splitlines()\\n if line.startswith((\\\"+\\\", \\\"-\\\"))\\n )\\n diffs.append(f\\\"{diff.a_path}\\\\n{lines}\\\")\\n\\n return \\\"\\\\n\\\".join(diffs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06e4367d6465ffe16fe5a3e44bfd061\",\n \"score\": \"0.590408\",\n \"text\": \"def git_diff(*args):\\n command = (['git', '--no-pager', 'diff'] + list(args) + [\\n '--', 'requirements.txt', 'misc/requirements/requirements-*.txt'])\\n proc = subprocess.run(command,\\n stdout=subprocess.PIPE,\\n encoding='utf-8',\\n check=True)\\n return proc.stdout.splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f8f407c5c46be94b8656f98660ebae\",\n \"score\": \"0.58514106\",\n \"text\": \"def patch_project(\\n hw_prefix: str = typer.Argument(default=..., help=\\\"prefix of the homework title\\\"),\\n patch_branch: str = typer.Argument(\\n default=..., help=\\\"source bracnch to patch to the main branch\\\"\\n ),\\n source_repo: Optional[str] = typer.Option(\\n default=None, help=\\\"default to tmpl-{hw-prefix}-revise\\\"\\n ),\\n only_repo: Optional[str] = typer.Option(default=None, help=\\\"only repo to patch\\\"),\\n dry: bool = typer.Option(\\n False, \\\"--dry\\\", help=\\\"dry run, do not publish result to the remote\\\"\\n ),\\n yes: bool = opt_all_yes,\\n token: str = opt_github_token,\\n org: str = opt_gh_org,\\n):\\n ensure_config_exists()\\n\\n def fallback(val, fallback_value):\\n return val if val else fallback_value\\n\\n # Handle default value manually because we'll change our config after app starts up\\n token: str = fallback(token, app_context.config.github.personal_access_token)\\n org: str = fallback(org, app_context.config.github.organization)\\n\\n ensure_git_cached()\\n ensure_gh_token(token)\\n\\n if not (\\n yes or typer.confirm(f\\\"Patch homework:{hw_prefix} from branch:{patch_branch}?\\\")\\n ):\\n raise typer.Abort()\\n # init\\n spinner = Halo(stream=sys.stderr)\\n\\n if not source_repo:\\n source_repo = f\\\"tmpl-{hw_prefix}-revise\\\"\\n\\n # Check if repo already contains the patched branch. Skip if so.\\n # api : https://developer.github.com/v3/git/refs/#get-a-reference\\n res = requests.get(\\n f\\\"https://api.github.com/repos/{org}/{source_repo}/git/refs/heads/{patch_branch}\\\",\\n headers=github_headers(token),\\n )\\n if res.status_code != 200: # this branch not exists on the remote\\n spinner.fail(\\n f\\\"branch : `{patch_branch}` doesn't exist on repo:{org}/{source_repo} \\\"\\n )\\n return\\n\\n cur = Path(\\\".\\\")\\n for d in cur.glob(\\\"patch-*\\\"):\\n shutil.rmtree(d)\\n spinner.info(\\\"delete dated folder\\\")\\n\\n spinner.start(f\\\"Fetch issue template {patch_branch} from {source_repo}\\\")\\n # Fetch patch template on the source repo\\n issues = get_github_endpoint_paged_list(\\n endpoint=f\\\"repos/{org}/{source_repo}/issues\\\", github_token=token, verbose=False\\n )\\n\\n def find_target_issue() -> Optional[Dict]:\\n for issue in issues:\\n if issue[\\\"title\\\"].strip() == patch_branch.strip():\\n return issue\\n return None\\n\\n target_issue = find_target_issue()\\n if not target_issue:\\n raise Exception(f\\\"cannot found issue tmpl `{patch_branch}` on `{source_repo}`\\\")\\n issue_tmpl_body = target_issue[\\\"body\\\"]\\n spinner.succeed()\\n\\n root_folder = Path(\\n tempfile.mkdtemp(\\n prefix=\\\"patch-{}-{}-\\\".format(\\n patch_branch, datetime.now().strftime(\\\"%b%d%H%M%S\\\")\\n ),\\n dir=\\\".\\\",\\n )\\n )\\n\\n spinner.succeed(f\\\"Create tmp folder: {root_folder}\\\")\\n spinner.info(f\\\"Fetch source repo {source_repo} from Github\\\")\\n src_repo_path = root_folder / \\\"source_repo\\\"\\n sp.run(\\n [\\n \\\"git\\\",\\n \\\"clone\\\",\\n f\\\"https://github.com/{org}/{source_repo}.git\\\",\\n src_repo_path.name,\\n ],\\n cwd=root_folder,\\n )\\n\\n src_repo = Repo(src_repo_path)\\n sp.run(\\n [\\\"git\\\", \\\"checkout\\\", \\\"--track\\\", f\\\"origin/{patch_branch}\\\"],\\n cwd=src_repo_path,\\n stdout=sp.DEVNULL,\\n stderr=sp.DEVNULL,\\n )\\n spinner.succeed()\\n\\n # Pasting changed files into students repo\\n src_repo_git = src_repo.git\\n src_repo_git.checkout(patch_branch)\\n changed_files, renamed_files = get_changed_files(\\n master_commit=src_repo.heads[\\\"master\\\"].commit,\\n patch_commit=src_repo.heads[patch_branch].commit,\\n )\\n\\n spinner.start(\\\"Fetch information for homework repo\\\")\\n spinner.succeed()\\n if only_repo is not None:\\n repos = [\\n re\\n for re in query_matching_repos(\\n org, github_repo_prefix=only_repo, github_token=token, verbose=False\\n )\\n if re[\\\"name\\\"] == only_repo\\n ]\\n repo = next(iter(repos), None)\\n if repo:\\n spinner.info(\\\"Only patch to repo : \\\" + repo[\\\"name\\\"])\\n repos = [repo]\\n else:\\n repos = query_matching_repos(\\n org, github_repo_prefix=hw_prefix, github_token=token, verbose=False\\n )\\n spinner.succeed()\\n\\n # Patch to student repos\\n student_path = root_folder / \\\"student_repos\\\"\\n student_path.mkdir()\\n for repo_idx, r in enumerate(repos, start=1):\\n pre_prompt_str = f\\\"({repo_idx}/{len(repos)}) \\\" + r[\\\"name\\\"]\\n spinner.start()\\n\\n # Check if repo already contains the patched branch. Skip if so.\\n # api : https://developer.github.com/v3/git/refs/#get-a-reference\\n res = requests.get(\\n f\\\"https://api.github.com/repos/{org}/{r['name']}/git/refs/heads/{patch_branch}\\\",\\n headers=github_headers(token),\\n )\\n if res.status_code == 200: # this branch exists in the remote\\n spinner.text = pre_prompt_str + \\\" Skip \\\" + \\\" already patched\\\"\\n\\n spinner.succeed()\\n continue\\n\\n spinner.text = pre_prompt_str + \\\"cloning repo\\\"\\n\\n sp.run(\\n [\\\"git\\\", \\\"clone\\\", \\\"--depth=1\\\", r[\\\"html_url\\\"]],\\n cwd=student_path,\\n stdout=sp.DEVNULL,\\n stderr=sp.DEVNULL,\\n )\\n\\n hw_repo_name = r[\\\"html_url\\\"].rsplit(\\\"/\\\")[-1]\\n\\n # open a new branch & checkout to that branch\\n sp.run(\\n [\\\"git\\\", \\\"checkout\\\", \\\"-b\\\", patch_branch],\\n cwd=student_path / hw_repo_name,\\n stdout=sp.DEVNULL,\\n stderr=sp.DEVNULL,\\n )\\n\\n # copy file to student repo\\n for f in changed_files.keys():\\n (student_path / hw_repo_name / f).parent.mkdir(parents=True, exist_ok=True)\\n shutil.copyfile(src=src_repo_path / f, dst=student_path / hw_repo_name / f)\\n for f in renamed_files.keys():\\n os.remove(student_path / hw_repo_name / f)\\n\\n # changed_files = get_changed_files(\\n # master_commit = src_repo.heads['master'].commit,\\n # patch_commit = src_repo.heads[patch_branch].commit\\n # )\\n # push (publish) that branch to student repo\\n sp.run(\\n [\\\"git\\\", \\\"add\\\", \\\".\\\"],\\n cwd=student_path / hw_repo_name,\\n stdout=sp.DEVNULL,\\n stderr=sp.DEVNULL,\\n )\\n\\n # Pass if no changed\\n student_repo = Repo(student_path / hw_repo_name)\\n if len(student_repo.index.diff(\\\"HEAD\\\")) == 0:\\n spinner.text = pre_prompt_str + \\\" Passed \\\" + \\\" no changes in repo\\\"\\n\\n spinner.succeed()\\n continue\\n\\n sp.run(\\n [\\\"git\\\", \\\"commit\\\", \\\"-m\\\", f\\\":construction_worker: Patch: {patch_branch}\\\"],\\n cwd=student_path / hw_repo_name,\\n stdout=sp.DEVNULL,\\n stderr=sp.DEVNULL,\\n )\\n\\n spinner.text = pre_prompt_str + \\\" publish patch to remote...\\\"\\n if dry:\\n spinner.succeed(pre_prompt_str + \\\" Patched \\\")\\n continue\\n res = sp.run(\\n [\\\"git\\\", \\\"push\\\", \\\"-u\\\", \\\"origin\\\", patch_branch],\\n cwd=student_path / hw_repo_name,\\n stdout=sp.DEVNULL,\\n stderr=sp.DEVNULL,\\n )\\n if res.returncode != 0:\\n spinner.fail(\\n pre_prompt_str\\n + \\\" Failed \\\"\\n + f\\\"Cannot push branch {patch_branch} to_origin\\\"\\n )\\n continue\\n\\n # open an pull-request on students repo\\n # student_repo/patch-branch -> student_repo/master\\n body = {\\n \\\"title\\\": f\\\"[PATCH] {patch_branch}\\\",\\n \\\"body\\\": issue_tmpl_body,\\n \\\"head\\\": patch_branch,\\n \\\"base\\\": \\\"master\\\",\\n }\\n res = requests.post(\\n f\\\"https://api.github.com/repos/{org}/{r['name']}/pulls\\\",\\n headers=github_headers(token),\\n json=body,\\n )\\n if res.status_code == 201:\\n spinner.succeed(f\\\"{pre_prompt_str} Patched \\\")\\n else:\\n spinner.fail(\\n pre_prompt_str\\n + \\\" Failed \\\"\\n + f\\\"Cannot create PR {patch_branch} to origin/master\\\"\\n )\\n try:\\n print(res.json()[\\\"errors\\\"][0][\\\"message\\\"])\\n except:\\n pass\\n continue\\n\\n # TODO : print summary after patch\\n # how many success, skiped, failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79de92318a130aab284532646d3a4078\",\n \"score\": \"0.583339\",\n \"text\": \"def get_svn_patch(patch_url):\\n svn_exe = 'svn.bat' if sys.platform.startswith('win') else 'svn'\\n patch_data = call(svn_exe, 'cat', patch_url)\\n return parse_diff(patch_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18916286b2fb0b6bdbadca962603a4a9\",\n \"score\": \"0.5827033\",\n \"text\": \"def create_patch(self, from_tag, to_tag):\\n return str(self._git.diff(f'{from_tag}..{to_tag}', _tty_out=False))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a464bf5ec3d006372b26f6445ec66cb\",\n \"score\": \"0.57943296\",\n \"text\": \"def smart_git():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f257b853ef6347d7ded14c9163cd2ab2\",\n \"score\": \"0.57583135\",\n \"text\": \"def bootstrap():\\n local('git config commit.template templates/git-commit-template.txt')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1236bd55cd3510edf29c68f851085e1c\",\n \"score\": \"0.57125634\",\n \"text\": \"def svn_diff_open_patch_file(*args) -> \\\"svn_patch_file_t **\\\":\\n return _diff.svn_diff_open_patch_file(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6970e3296197d71fa03c3bf2d4af83d\",\n \"score\": \"0.56949264\",\n \"text\": \"def GenerateDiff(self, files, root):\\n previous_cwd = os.getcwd()\\n if root is None:\\n os.chdir(gcl.GetRepositoryRoot())\\n else:\\n os.chdir(root)\\n\\n # Directories will return None so filter them out.\\n diff = filter(None, [scm.SVN.DiffItem(f) for f in files])\\n os.chdir(previous_cwd)\\n return \\\"\\\".join(diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a1ab5e5d6f0905c214385ef964f01e\",\n \"score\": \"0.5685457\",\n \"text\": \"def git_files():\\n process = subprocess.Popen(\\n [\\\"git\\\", \\\"diff\\\", \\\"--cached\\\", \\\"--name-only\\\"],\\n stdout = subprocess.PIPE,\\n stderr = subprocess.PIPE)\\n\\n while True:\\n ret = process.poll()\\n line = process.stdout.readline()\\n yield line\\n if ret is not None: return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abff0cfacb17155cbbc1e53efd0e4e3f\",\n \"score\": \"0.567108\",\n \"text\": \"def patch_sleuthkit(self):\\n for filename, rules in iter(self.files.items()):\\n filename = os.path.join(*filename.split(\\\"/\\\"))\\n\\n with open(filename, \\\"r\\\") as file_object:\\n data = file_object.read()\\n\\n for search, replace in rules:\\n data = re.sub(search, replace, data)\\n\\n with open(filename, \\\"w\\\") as fd:\\n fd.write(data)\\n\\n patch_files = [\\n \\\"sleuthkit-{0:s}-configure.ac\\\".format(self._SLEUTHKIT_GIT_TAG)]\\n\\n for patch_file in patch_files:\\n patch_file = os.path.join(\\\"patches\\\", patch_file)\\n if not os.path.exists(patch_file):\\n print(\\\"No such patch file: {0:s}\\\".format(patch_file))\\n continue\\n\\n patch_file = os.path.join(\\\"..\\\", patch_file)\\n subprocess.check_call([\\\"git\\\", \\\"apply\\\", patch_file], cwd=\\\"sleuthkit\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b83c3d422c86825157831db37f899d\",\n \"score\": \"0.566736\",\n \"text\": \"def create_patches_and_update_specfile(self, upstream_ref) -> None:\\n if self.with_action(action=ActionName.create_patches):\\n patches = self.create_patches(\\n upstream=upstream_ref, destination=str(self.absolute_specfile_dir)\\n )\\n self.specfile_add_patches(patches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602ea1cc7571d107cd6f83656bb3b4b3\",\n \"score\": \"0.5663216\",\n \"text\": \"def apply_patch(self, patch=None):\\n if not any([patch, self.patch]):\\n return\\n if not patch:\\n patch = self.patch\\n self.cmd(f'patch -p1 < {self.project.patch}/{self.ver}/{patch}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36be65994e3895fa4808be7568981633\",\n \"score\": \"0.5660939\",\n \"text\": \"def lint_patch(clang_format, infile):\\n files = git.get_files_to_check_from_patch(infile, is_interesting_file)\\n\\n # Patch may have files that we do not want to check which is fine\\n if files:\\n _lint_files(clang_format, files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166154c314f2253f77154fdae4b622ac\",\n \"score\": \"0.5627981\",\n \"text\": \"def svn_diff_parse_next_patch(*args) -> \\\"svn_patch_t **\\\":\\n return _diff.svn_diff_parse_next_patch(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee880f2631918f9630f49523913cac2\",\n \"score\": \"0.56246203\",\n \"text\": \"def apply_patch(self, patch=None):\\n if not any([patch, self.patch]):\\n return\\n if not patch:\\n patch = self.patch\\n self.cmd(f\\\"patch -p1 < {self.project.patch}/{self.ver}/{patch}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a5fe70141006e2e169ed3dabb9cb6a4\",\n \"score\": \"0.5591653\",\n \"text\": \"def export_patches(repo, options):\\n current = repo.get_branch()\\n if is_pq_branch(current):\\n base = pq_branch_base(current)\\n gbp.log.info(\\\"On branch '%s', switching to '%s'\\\" % (current, base))\\n repo.set_branch(base)\\n pq_branch = current\\n else:\\n pq_branch = pq_branch_name(current)\\n spec = parse_spec(options, repo)\\n upstream_commit = find_upstream_commit(repo, spec, options.upstream_tag)\\n export_treeish = pq_branch\\n\\n update_patch_series(repo, spec, upstream_commit, export_treeish, options)\\n\\n GitCommand('status')(['--', spec.specdir])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901d2ab96b417cef1952a4f46c1cd7eb\",\n \"score\": \"0.55826074\",\n \"text\": \"def update_patch_series(repo, spec, start, end, options):\\n # Unlink old patch files and generate new patches\\n rm_patch_files(spec)\\n\\n patches, commands = generate_patches(repo, start, end,\\n spec.specdir, options)\\n spec.update_patches(patches, commands)\\n spec.write_spec_file()\\n return patches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600937c8e185385a75d24486501ac9d1\",\n \"score\": \"0.5572914\",\n \"text\": \"def gitext_diff(external):\\n from git_externals import iter_externals\\n for _ in iter_externals(external):\\n click.echo(git('diff'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ab9448dbb2cecb9c4ae2f017905fb5\",\n \"score\": \"0.5562694\",\n \"text\": \"def git_diff_pipeline(output_name, master_path, prev_commit):\\n log = logging.getLogger(__name__)\\n\\n current_path = inline_current(master_path)\\n log.debug(\\\"current_path {0}\\\".format(current_path))\\n prev_path = inline_prev(prev_commit, master_path)\\n log.debug(\\\"prev_path {0}\\\".format(prev_path))\\n\\n # Run latexmk\\n diff_path = os.path.splitext(output_name)[0]\\n ldiff_cmd = \\\"latexdiff --type=CTRADITIONAL {prev} {current} > {diff}.tex\\\".\\\\\\n format(prev=prev_path, current=current_path, diff=diff_path)\\n subprocess.call(ldiff_cmd, shell=True)\\n\\n # Compile the diff document with latexmk\\n ltmk_cmd = \\\"latexmk -f -pdf -bibtex-cond {0}.tex\\\".format(diff_path)\\n subprocess.call(ltmk_cmd, shell=True)\\n\\n # Copy to build directory\\n if not os.path.exists(\\\"build\\\"):\\n os.makedirs(\\\"build\\\")\\n pdf_path = \\\"{0}.pdf\\\".format(output_name)\\n if os.path.exists(pdf_path):\\n shutil.move(pdf_path, os.path.join(\\\"build\\\", pdf_path))\\n\\n # Clean up\\n ltmk_cmd = \\\"latexmk -f -pdf -bibtex-cond -c {0}.tex\\\".format(diff_path)\\n subprocess.call(ltmk_cmd, shell=True)\\n build_exts = ['Notes.bib', '.bbl', '.tex']\\n for ext in build_exts:\\n path = \\\"\\\".join((output_name, ext))\\n if os.path.exists(path):\\n os.remove(path)\\n os.remove(prev_path)\\n os.remove(current_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8b19dc79400baf298170a3edede9d5\",\n \"score\": \"0.55483615\",\n \"text\": \"def rietveld_to_git(lines):\\n state = NORMAL\\n new_name = None\\n\\n for line in lines:\\n if state is NORMAL:\\n if line.startswith('Index: '):\\n new_name = line[7:].strip('\\\\n')\\n state = INDEX\\n else:\\n yield line\\n elif state is INDEX:\\n if line.startswith(SVN_SEPARATOR):\\n state = METAINFO\\n else:\\n yield 'Index: {}\\\\n'.format(new_name)\\n yield line\\n state = NORMAL\\n elif state is METAINFO:\\n if line.startswith('rename from '):\\n # File renamed.\\n old_name = line[12:].strip('\\\\n')\\n yield GIT_PART_HEAD.format(old_name, new_name)\\n elif line.startswith('copy from '):\\n # File copied.\\n old_name = line[10:].strip('\\\\n')\\n yield GIT_PART_HEAD.format(old_name, new_name)\\n else:\\n # File added or removed or changed.\\n yield GIT_PART_HEAD.format(new_name, new_name)\\n yield line\\n state = NORMAL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b6d139540fcb44acbec9757f2a4f60c\",\n \"score\": \"0.551302\",\n \"text\": \"def diff_files(self):\\n files = dict()\\n # get the git diff stat without the last summary line\\n lines = self.repo.git.diff('--stat', '--no-color').splitlines()[:-1]\\n for line in lines:\\n match = re.match(r' (.*\\\\S) *\\\\| *([0-9]+) ', line)\\n fname = match[1]\\n #changes = int(match[2])\\n diff = self.get_diff_raw(fname)\\n\\n files[fname] = Patch(\\n files=[fname],\\n size=len(diff),\\n hunks=diff.count('@@')/2,\\n test_size=False)\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cedafc37dc5bb82e083413480684eebe\",\n \"score\": \"0.54810876\",\n \"text\": \"def create_patches(\\n self, upstream: str = None, destination: str = None\\n ) -> List[PatchMetadata]:\\n upstream = upstream or self.get_specfile_version()\\n destination = destination or self.local_project.working_dir\\n\\n sync_files_to_ignore = [\\n str(sf.src.relative_to(self.local_project.working_dir))\\n for sf in self.package_config.get_all_files_to_sync().get_raw_files_to_sync(\\n self.local_project.working_dir,\\n Path(\\n # dest (downstream) is not important, we only care about src (upstream)\\n destination\\n ),\\n )\\n ]\\n files_to_ignore = (\\n self.package_config.patch_generation_ignore_paths + sync_files_to_ignore\\n )\\n\\n pg = PatchGenerator(self.local_project)\\n return pg.create_patches(upstream, destination, files_to_ignore=files_to_ignore)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddea2affe8a06b2c1c391f81d8706f19\",\n \"score\": \"0.5479714\",\n \"text\": \"def extract_json_from_git(json_files, path_to_files, days_to_generate):\\n\\n time_24h_ago = datetime.utcnow() - timedelta(days=days_to_generate)\\n _time_24h_ago = datetime.strftime(time_24h_ago, \\\"%Y-%m-%d %H:%M:%S\\\")\\n time_24h_ago = datetime.strptime(_time_24h_ago, \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n for file in json_files:\\n file_path = \\\"{}/\\\".format(path_to_files) + file\\n count_pushes = 0\\n with open(file_path) as json_file:\\n data = json.load(json_file)\\n base_link = \\\"https://github.com/mozilla-releng/firefox-infra-\\\" \\\\\\n \\\"changelog/blob/master/{}/\\\"\\\\\\n .format(path_to_files)\\n repository_url = base_link + file\\\\\\n .rstrip()\\\\\\n .replace(\\\" \\\", \\\"%20\\\")\\\\\\n .rstrip()\\\\\\n .replace(\\\".json\\\", \\\".md\\\")\\n repository_json = base_link + file\\\\\\n .rstrip()\\\\\\n .replace(\\\" \\\", \\\"%20\\\")\\n repository_title = file.replace(\\\".json\\\", \\\"\\\")\\n try:\\n generate_markdown_header(\\\"changelog.md\\\",\\n repository_title,\\n repository_url,\\n repository_json)\\n if \\\"0\\\" in data:\\n del data[\\\"0\\\"]\\n for commit_iterator in data:\\n commit_number = str(commit_iterator)\\n commit_date = data.get(commit_number).get(\\\"commit_date\\\")\\n is_it_under_24 = datetime.strptime(commit_date,\\n \\\"%Y-%m-%d %H:%M:%S\\\")\\n if is_it_under_24 > time_24h_ago:\\n count_pushes = count_pushes + 1\\n commit_description = data\\\\\\n .get(commit_number)\\\\\\n .get(\\\"commit_message\\\")\\n commit_description = remove_chars(commit_description,\\n \\\"\\\\U0001f60b\\\")\\n commit_url = data.get(commit_number).get(\\\"url\\\")\\n commit_url = \\\"[Link](\\\" + commit_url + \\\")\\\"\\n author = data.get(commit_number).get(\\\"commiter_name\\\")\\n review = \\\"N/A\\\"\\n write_main_md_table(\\\"changelog.md\\\",\\n commit_url,\\n commit_description,\\n author,\\n review,\\n commit_date)\\n if count_pushes == 0:\\n commit_url = \\\" \\\"\\n if days_to_generate == 1:\\n commit_description = \\\"No push in the last day.. \\\" \\\\\\n \\\"[see the history of MD \\\" \\\\\\n \\\"commits](\\\" + \\\\\\n repository_url + \\\\\\n \\\")\\\"\\n else:\\n commit_description = \\\"No push in the last \\\" + \\\\\\n str(days_to_generate) + \\\\\\n \\\" days.. [see the history of MD\\\" \\\\\\n \\\" commits](\\\" + \\\\\\n repository_url + \\\\\\n \\\")\\\"\\n author = \\\"FIC - BOT\\\"\\n review = \\\"Self Generated\\\"\\n commit_date = \\\" - \\\"\\n write_main_md_table(\\\"changelog.md\\\",\\n commit_url,\\n commit_description,\\n author,\\n review,\\n commit_date)\\n except KeyError:\\n if LOGGER:\\n print(\\\"File \\\", file, \\\" is empty. \\\\n\\\",\\n \\\"Please check:\\\",\\n repository_url,\\n \\\" for more details.\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e945ab5d994dfb4dba06ea4f29c7b41d\",\n \"score\": \"0.5460745\",\n \"text\": \"def get_diffing_files(commit, parent, repo):\\n diff = repo.diff(parent, commit)\\n\\n patches = [p for p in diff]\\n\\n files = set()\\n\\n for patch in patches:\\n if patch.delta.is_binary:\\n continue\\n nfile = patch.delta.new_file\\n files.add((nfile.id, nfile.path, patch.delta.status))\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b78f0ea1ec63108ca294aec109dd64f\",\n \"score\": \"0.54456115\",\n \"text\": \"def patch(ctx, tool, dir, remove):\\n from nf_core.modules import ModulePatch\\n\\n try:\\n module_patch = ModulePatch(\\n dir,\\n ctx.obj[\\\"modules_repo_url\\\"],\\n ctx.obj[\\\"modules_repo_branch\\\"],\\n ctx.obj[\\\"modules_repo_no_pull\\\"],\\n )\\n if remove:\\n module_patch.remove(tool)\\n else:\\n module_patch.patch(tool)\\n except (UserWarning, LookupError) as e:\\n log.error(e)\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea276e0999e5527dd6de0b0699e23bd1\",\n \"score\": \"0.54398674\",\n \"text\": \"def build_git_commits(project, update=True, include_patch=False,\\n use_pydiff=True, skip=-1, limit=-1, verbose=False):\\n\\n repo_name = get_repo_name(project)\\n repo = git.Repo(repo_name)\\n assert repo.bare is False\\n\\n if update:\\n try:\\n # Commits will include pruned entries\\n commits = load_git_commits(project)\\n except Exception:\\n update = False\\n\\n if not update:\\n commits = collections.defaultdict(list)\\n\\n commits = process_commits(repo, commits, get_filter_config(project),\\n use_pydiff=use_pydiff, skip=skip, limit=limit,\\n project=project, verbose=verbose)\\n print\\n print 'total commits:', len(commits)\\n \\\"\\\"\\\"\\n if include_patch:\\n print 'Augment Git data with patch info'\\n commits = update_commits_with_patch_data(commits, project)\\n print\\n \\\"\\\"\\\"\\n print 'Other commit message post-processing'\\n print ' Blueprints'\\n annotate_blueprints(commits)\\n print ' Identifying cherry-pick commits'\\n annotate_cherry_pick(commits)\\n\\n master_commit = get_git_master_commit(repo_name)\\n print 'Master Commit:', master_commit\\n commits = annotate_mainline(commits, master_commit)\\n\\n \\\"\\\"\\\"\\n print 'Consolidate Git Merge related commits'\\n commits = consolidate_merge_commits(commits,\\n get_git_master_commit(repo_name))\\n \\\"\\\"\\\"\\n\\n annotate_children(commits) # note parent/child relationships\\n save_git_commits(commits, project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b5844d5fd8b60edbdc983fe710b1a8\",\n \"score\": \"0.5417832\",\n \"text\": \"def test_multi_patch_parsing(self):\\n with open(_get_path_to_asset('c.patch')) as f_hand:\\n patches = patchit.PatchSet.from_stream(f_hand)\\n\\n self.assertEqual(patches[0].source_filename, 'a')\\n self.assertEqual(patches[0].target_filename, 'b')\\n self.assertEqual(len(patches[0].hunks), 2)\\n\\n self.assertEqual(patches[1].source_filename, 'abc')\\n self.assertEqual(patches[1].target_filename, 'def')\\n self.assertEqual(len(patches[1].hunks), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a689df5c5bee6d4511b34cf5ff9c4e7a\",\n \"score\": \"0.5400419\",\n \"text\": \"def git_prebuild(pkg_dir, build_cmd=build_py):\\n\\n class MyBuildPy(build_cmd):\\n ''' Subclass to write commit data into installation tree '''\\n def run(self):\\n # loose as `.dev` is suppose to be invalid\\n print(\\\"check version number\\\")\\n loose_pep440re = re.compile(r'^(\\\\d+)\\\\.(\\\\d+)\\\\.(\\\\d+((a|b|rc)\\\\d+)?)(\\\\.post\\\\d+)?(\\\\.dev\\\\d*)?$')\\n if not loose_pep440re.match(version):\\n raise ValueError(\\\"Version number '%s' is not valid (should match [N!]N(.N)*[{a|b|rc}N][.postN][.devN])\\\" % version)\\n\\n\\n build_cmd.run(self)\\n # this one will only fire for build commands\\n if hasattr(self, 'build_lib'):\\n self._record_commit(self.build_lib)\\n\\n def make_release_tree(self, base_dir, files):\\n # this one will fire for sdist\\n build_cmd.make_release_tree(self, base_dir, files)\\n self._record_commit(base_dir)\\n\\n def _record_commit(self, base_dir):\\n import subprocess\\n proc = subprocess.Popen('git rev-parse --short HEAD',\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=True)\\n repo_commit, _ = proc.communicate()\\n repo_commit = repo_commit.strip().decode(\\\"ascii\\\")\\n\\n out_pth = pjoin(base_dir, pkg_dir, 'utils', '_sysinfo.py')\\n if os.path.isfile(out_pth) and not repo_commit:\\n # nothing to write, don't clobber\\n return\\n\\n print(\\\"writing git commit '%s' to %s\\\" % (repo_commit, out_pth))\\n\\n # remove to avoid overwriting original via hard link\\n try:\\n os.remove(out_pth)\\n except (IOError, OSError):\\n pass\\n with open(out_pth, \\\"w\\\", encoding=\\\"utf-8\\\") as out_file:\\n out_file.writelines(\\n [\\n \\\"# GENERATED BY setup.py\\\\n\\\",\\n 'commit = \\\"%s\\\"\\\\n' % repo_commit,\\n ]\\n )\\n\\n return MyBuildPy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dc9c0498a1a56fcc3fa756ddcb56db6\",\n \"score\": \"0.53959465\",\n \"text\": \"def _do_apply(self, patch: PatchedFile):\\n result = subprocess.run(self.GIT_APPLY.split(' '), input=str(patch), text=True, capture_output=True, check=True)\\n if result.returncode > 0:\\n print(result.stderr)\\n raise PluginException(\\\"Failed to apply patch.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"742087bdf1e62f5f78e5b53b289d309c\",\n \"score\": \"0.5385519\",\n \"text\": \"def getGitReport(self, path='.'):\\n if not path.endswith('/'): path += '/'\\n checkForDiffs(path)\\n checkForUnpushedCommits(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fed180ab66727334ce9522ba6830a8\",\n \"score\": \"0.5363858\",\n \"text\": \"def main():\\n assert len(argv) == 2, \\\"Usage: to_rawgit 'https://github.com/...'\\\"\\n git_link = argv[1]\\n print('Link to convert:' + git_link)\\n parsed = urlparse(git_link)\\n content = parsed.path.split('/')\\n content.remove('blob')\\n new_path = '/'.join(content)\\n new_link = parsed._replace(netloc='cdn.rawgit.com', path=new_path)\\n print(new_link.geturl())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c28b2cbbc459bcc7312ac4506a2a96\",\n \"score\": \"0.53553474\",\n \"text\": \"def patch(self, patch_path):\\n \\n with open(patch_path) as f:\\n for i, line in enumerate(f):\\n self.execute(line.decode(u\\\"utf-8\\\"), i + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25b2e5dcfaa4d822db3f183f4d552e62\",\n \"score\": \"0.5342589\",\n \"text\": \"def git(args):\\n cmd = GIT_BIN + ' ' + args\\n out = process(cmd)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b99d87d00efc455842d0f50e31b13296\",\n \"score\": \"0.5323858\",\n \"text\": \"def test_get_changed_files_disparate_histories(in_git_dir):\\n git_commit()\\n in_git_dir.join('a.txt').ensure()\\n cmd_output('git', 'add', '.')\\n git_commit()\\n cmd_output('git', 'branch', '-m', 'branch1')\\n\\n cmd_output('git', 'checkout', '--orphan', 'branch2')\\n cmd_output('git', 'rm', '-rf', '.')\\n in_git_dir.join('a.txt').ensure()\\n in_git_dir.join('b.txt').ensure()\\n cmd_output('git', 'add', '.')\\n git_commit()\\n\\n assert git.get_changed_files('branch1', 'branch2') == ['b.txt']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064acdaef423223176cca30264b298b0\",\n \"score\": \"0.5321807\",\n \"text\": \"def _get_diff(self, changeset):\\n patch_file = io.BytesIO()\\n try:\\n dulwich.patch.write_object_diff(patch_file,\\n self.repo.object_store,\\n changeset.old, changeset.new)\\n except UnicodeDecodeError as e:\\n logger.debug(e)\\n return b''\\n\\n return patch_file.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b5dff9e4a5a9a5902d22a9a50870d2a\",\n \"score\": \"0.53185433\",\n \"text\": \"def git_hook(complexity=-1, strict=False, ignore=None, lazy=False):\\r\\n gitcmd = \\\"git diff-index --cached --name-only --diff-filter=ACMRTUXB HEAD\\\"\\r\\n if lazy:\\r\\n # Catch all files, including those not added to the index\\r\\n gitcmd = gitcmd.replace('--cached ', '')\\r\\n\\r\\n if hasattr(ignore, 'split'):\\r\\n ignore = ignore.split(',')\\r\\n\\r\\n # Returns the exit code, list of files modified, list of error messages\\r\\n _, files_modified, _ = run(gitcmd)\\r\\n\\r\\n # We only want to pass ignore and max_complexity if they differ from the\\r\\n # defaults so that we don't override a local configuration file\\r\\n options = {}\\r\\n if ignore:\\r\\n options['ignore'] = ignore\\r\\n if complexity > -1:\\r\\n options['max_complexity'] = complexity\\r\\n\\r\\n files_modified = [f for f in files_modified if f.endswith('.py')]\\r\\n\\r\\n flake8_style = get_style_guide(\\r\\n parse_argv=True,\\r\\n config_file=DEFAULT_CONFIG,\\r\\n **options\\r\\n )\\r\\n\\r\\n # Copy staged versions to temporary directory\\r\\n tmpdir = mkdtemp()\\r\\n files_to_check = []\\r\\n try:\\r\\n for file_ in files_modified:\\r\\n # get the staged version of the file\\r\\n gitcmd_getstaged = \\\"git show :%s\\\" % file_\\r\\n _, out, _ = run(gitcmd_getstaged, raw_output=True, decode=False)\\r\\n # write the staged version to temp dir with its full path to\\r\\n # avoid overwriting files with the same name\\r\\n dirname, filename = os.path.split(os.path.abspath(file_))\\r\\n prefix = os.path.commonprefix([dirname, tmpdir])\\r\\n dirname = os.path.relpath(dirname, start=prefix)\\r\\n dirname = os.path.join(tmpdir, dirname)\\r\\n if not os.path.isdir(dirname):\\r\\n os.makedirs(dirname)\\r\\n filename = os.path.join(dirname, filename)\\r\\n # write staged version of file to temporary directory\\r\\n with open(filename, \\\"wb\\\") as fh:\\r\\n fh.write(out)\\r\\n files_to_check.append(filename)\\r\\n # Run the checks\\r\\n report = flake8_style.check_files(files_to_check)\\r\\n # remove temporary directory\\r\\n finally:\\r\\n shutil.rmtree(tmpdir, ignore_errors=True)\\r\\n\\r\\n if strict:\\r\\n return report.total_errors\\r\\n\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23d1213d1f3a093054342c6bf1ed1ec\",\n \"score\": \"0.5312993\",\n \"text\": \"def collect_diff_files(root, versions):\\n command = ['git', 'diff', '--name-only', versions]\\n os.chdir(root)\\n p = subprocess.Popen(command,\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n out, err = p.communicate()\\n if err:\\n raise DiffCollectionFailed(\\\"Unable to find diff files to examine \\\"\\n \\\"in %s: %s\\\" % (root, err))\\n for filename in out.splitlines():\\n if not os.path.basename(filename).startswith('.'):\\n yield filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a91581996c62e88b24fc0823fc180d1b\",\n \"score\": \"0.5305009\",\n \"text\": \"def solutions_to_git(input_solutions):\\n assert input_solutions\\n solutions = copy.deepcopy(input_solutions)\\n first_solution = True\\n buildspec = None\\n for solution in solutions:\\n original_url = solution['url']\\n parsed_url = urlparse.urlparse(original_url)\\n parsed_path = parsed_url.path\\n\\n # Rewrite SVN urls into Git urls.\\n buildspec_m = re.match(BUILDSPEC_RE, parsed_path)\\n if first_solution and buildspec_m:\\n solution['url'] = GIT_BUILDSPEC_PATH\\n buildspec = BUILDSPEC_TYPE(\\n container=buildspec_m.group(1),\\n version=buildspec_m.group(2),\\n )\\n solution['deps_file'] = path.join(buildspec.container, buildspec.version,\\n 'DEPS')\\n elif parsed_path in RECOGNIZED_PATHS:\\n solution['url'] = RECOGNIZED_PATHS[parsed_path]\\n solution['deps_file'] = '.DEPS.git'\\n elif parsed_url.scheme == 'https' and 'googlesource' in parsed_url.netloc:\\n pass\\n else:\\n print 'Warning: %s' % ('path %r not recognized' % parsed_path,)\\n\\n # Strip out deps containing $$V8_REV$$, etc.\\n if 'custom_deps' in solution:\\n new_custom_deps = {}\\n for deps_name, deps_value in solution['custom_deps'].iteritems():\\n if deps_value and '$$' in deps_value:\\n print 'Dropping %s:%s from custom deps' % (deps_name, deps_value)\\n else:\\n new_custom_deps[deps_name] = deps_value\\n solution['custom_deps'] = new_custom_deps\\n\\n if first_solution:\\n root = parsed_path\\n first_solution = False\\n\\n solution['managed'] = False\\n # We don't want gclient to be using a safesync URL. Instead it should\\n # using the lkgr/lkcr branch/tags.\\n if 'safesync_url' in solution:\\n print 'Removing safesync url %s from %s' % (solution['safesync_url'],\\n parsed_path)\\n del solution['safesync_url']\\n return solutions, root, buildspec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c32a931ba6cf66b5f17e43fb7ae0b6\",\n \"score\": \"0.5301984\",\n \"text\": \"def git_hook(strict=False, lazy=True):\\n gitcmd = \\\"git diff-index --cached --name-only --diff-filter=ACMRTUXB HEAD\\\"\\n if lazy:\\n # Catch all files, including those not added to the index.\\n gitcmd = gitcmd.replace('--cached ', '')\\n\\n p = Popen(gitcmd.split(), stdout=PIPE, stderr=PIPE)\\n (stdout, stderr) = p.communicate()\\n files_modified = [line.strip() for line in stdout.splitlines()]\\n\\n # Run the checks.\\n errors = 0\\n for file in files_modified:\\n errors += Parser(file).check()\\n\\n return errors if strict else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bacbc761639cf6a2010a7f53fafe36c\",\n \"score\": \"0.5293779\",\n \"text\": \"def test_patch_parsing(self):\\n with open(_get_path_to_asset('a.patch')) as f_hand:\\n patches = patchit.PatchSet.from_stream(f_hand)\\n self.assertEqual(patches[0].source_filename,\\n 'telefonliste2007.txt')\\n self.assertEqual(patches[0].target_filename,\\n 'telefonliste2008.txt')\\n self.assertEqual(len(patches[0].hunks), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67046b28b0044423430c547899b32a21\",\n \"score\": \"0.52921116\",\n \"text\": \"def main(git_log):\\n df = pd.read_csv(git_log, sep = '|', names = ['commit', 'message', 'author', 'email'])\\n df['area'] = df['message'].apply(define_area)\\n df['message'] = df['message'].apply(delete_prefix)\\n\\n # Split commits by areas\\n core = df[df['area']==Area.core.value]\\n tests = df[df['area']==Area.tests.value]\\n build = df[df['area']==Area.build.value]\\n apps = df[df['area']==Area.apps.value]\\n docs = df[df['area']==Area.docs.value]\\n other = df[df['area'].isna()]\\n\\n # Define individual contributors\\n contributors = df.groupby(['author', 'email'])\\n contributors = list(contributors.groups.keys())\\n\\n with open('release-notes.md', 'w') as f:\\n f.write('# Release Notes\\\\n')\\n\\n f.write('\\\\n## API / ABI / Integration Changes\\\\n')\\n f.write('\\\\n**API/ABI version: 1.x.**\\\\n')\\n\\n f.write('\\\\n## New Features and Improvements\\\\n')\\n f.write('\\\\n## Important Bug Fixes\\\\n')\\n f.write('\\\\n## Build\\\\n')\\n f.write('\\\\n## Documentation\\\\n')\\n\\n f.write('\\\\n## Contributors\\\\n')\\n for name, email in contributors:\\n f.write(f'\\\\n{name} <{email}>')\\n f.write('\\\\n')\\n\\n f.write('\\\\n## Changelog\\\\n')\\n f.write('\\\\n<details><summary>Click to expand/collapse</summary>')\\n f.write('\\\\n<p>')\\n f.write('\\\\n')\\n\\n if not core.empty:\\n f.write('\\\\n### Core Functionality')\\n write_into_changelog(core, f)\\n\\n if not tests.empty:\\n f.write('\\\\n### Unit Tests')\\n write_into_changelog(tests, f)\\n\\n if not build.empty:\\n f.write('\\\\n### Build Scripts (CMake, etc.)')\\n write_into_changelog(build, f)\\n\\n if not apps.empty:\\n f.write('\\\\n### Sample Applications')\\n write_into_changelog(apps, f)\\n\\n if not docs.empty:\\n f.write('\\\\n### Documentation')\\n write_into_changelog(docs, f)\\n\\n if not other.empty:\\n f.write('\\\\n### Other')\\n write_into_changelog(other, f)\\n\\n f.write('\\\\n</p>')\\n f.write('\\\\n</details>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64ef06a3b57563f29b57e4475bb4ef4\",\n \"score\": \"0.52858096\",\n \"text\": \"def import_spec_patches(repo, options):\\n current = repo.get_branch()\\n # Get spec and related information\\n if is_pq_branch(current):\\n base = pq_branch_base(current)\\n if options.force:\\n spec = parse_spec(options, repo, base)\\n spec_treeish = base\\n else:\\n raise GbpError(\\\"Already on a patch-queue branch '%s' - doing \\\"\\n \\\"nothing.\\\" % current)\\n else:\\n spec = parse_spec(options, repo)\\n spec_treeish = None\\n base = current\\n upstream_commit = find_upstream_commit(repo, spec, options.upstream_tag)\\n packager = get_packager(spec)\\n pq_branch = pq_branch_name(base)\\n\\n # Create pq-branch\\n if repo.has_branch(pq_branch) and not options.force:\\n raise GbpError(\\\"Patch-queue branch '%s' already exists. \\\"\\n \\\"Try 'switch' instead.\\\" % pq_branch)\\n try:\\n if repo.get_branch() == pq_branch:\\n repo.force_head(upstream_commit, hard=True)\\n else:\\n repo.create_branch(pq_branch, upstream_commit, force=True)\\n except GitRepositoryError as err:\\n raise GbpError(\\\"Cannot create patch-queue branch '%s': %s\\\" %\\n (pq_branch, err))\\n\\n # Put patches in a safe place\\n if spec_treeish:\\n packaging_tmp = tempfile.mkdtemp(prefix='dump_')\\n packaging_tree = '%s:%s' % (spec_treeish, options.packaging_dir)\\n dump_tree(repo, packaging_tmp, packaging_tree, with_submodules=False,\\n recursive=False)\\n spec.specdir = packaging_tmp\\n in_queue = spec.patchseries()\\n queue = safe_patches(in_queue)\\n # Do import\\n try:\\n gbp.log.info(\\\"Switching to branch '%s'\\\" % pq_branch)\\n repo.set_branch(pq_branch)\\n\\n if not queue:\\n return\\n gbp.log.info(\\\"Trying to apply patches from branch '%s' onto '%s'\\\" %\\n (base, upstream_commit))\\n for patch in queue:\\n gbp.log.debug(\\\"Applying %s\\\" % patch.path)\\n apply_and_commit_patch(repo, patch, packager)\\n except (GbpError, GitRepositoryError) as err:\\n repo.set_branch(base)\\n repo.delete_branch(pq_branch)\\n raise GbpError('Import failed: %s' % err)\\n\\n gbp.log.info(\\\"%d patches listed in '%s' imported on '%s'\\\" % (len(queue), spec.specfile,\\n pq_branch))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e026b5185f7be8af240f49b94cf94507\",\n \"score\": \"0.5285126\",\n \"text\": \"def copy_p4_changes_to_git(ctx, start_at, stop_at, new_git_branches):\\n\\n p2g = P2G(ctx)\\n\\n if MEM_CAPPED:\\n p2g_mc = P2GMemcapped(p2g=p2g)\\n p2g_mc.copy(start_at, stop_at, new_git_branches)\\n else:\\n p2g.copy(start_at, stop_at, new_git_branches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8d9bcafd871e35d71a0476355737288\",\n \"score\": \"0.5284944\",\n \"text\": \"def test_patch_apply_simple(self):\\n with open(_get_path_to_asset('a.patch')) as patch_hand:\\n with open(_get_path_to_asset('original_a.txt')) as source_hand:\\n patches = patchit.PatchSet.from_stream(patch_hand)\\n file_iter = (x.strip('\\\\n') for x in iter(source_hand.readline, ''))\\n output = list(patches[0].merge(patchit.LineEnumerator(file_iter)))\\n\\n self.assertEqual(output, ['Mayer, Susanne, Lager, -212',\\n 'Schmid, Carola, Geschäftsleitung, -435',\\n 'Schmitt, Marie, Labor, -804',\\n 'Waldmann, Ernst, Labor, -805',\\n 'Zundel, Walter, Vertrieb, -476', ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b952b3492fc1e600f483fa8d5a839d\",\n \"score\": \"0.52734655\",\n \"text\": \"def get_changed_files(github_dir, pr_id, base_ref, head_ref):\\n\\n response = github_graphql_client.make_request(GRAPHQL_GET_PR_COMMIT_COUNT, {\\\"nodeId\\\": pr_id})\\n commit_count = response[\\\"data\\\"][\\\"node\\\"][\\\"commits\\\"][\\\"totalCount\\\"]\\n\\n # fetch all latest commits\\n _ = subprocess.run([\\\"git\\\", \\\"-C\\\", github_dir, \\\"-c\\\", \\\"protocol.version=2\\\", \\\"fetch\\\", \\\"--deepen\\\", str(commit_count)])\\n\\n output = subprocess.getoutput(\\n f\\\"git -C {github_dir} diff --name-only {base_ref}...{head_ref}\\\")\\n\\n return [changed_file for changed_file in output.split(\\\"\\\\n\\\") if changed_file]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb81206a2498ab4d4a872094679ca53c\",\n \"score\": \"0.5272389\",\n \"text\": \"def gitext_freeze(externals, messages):\\n from git_externals import load_gitexts, dump_gitexts, foreach_externals_dir, root_path, resolve_revision\\n git_externals = load_gitexts()\\n repo_root = root_path()\\n re_from_git_svn_id = re.compile(\\\"git-svn-id:.*@(\\\\d+)\\\")\\n re_from_svnversion = re.compile(\\\"(\\\\d+):(\\\\d+)\\\")\\n\\n def get_version(rel_url, ext_path, refs):\\n if 'tag' in refs:\\n return\\n\\n bare_svn = False\\n if git_externals[rel_url][\\\"vcs\\\"] == \\\"svn\\\":\\n revision = command('svnversion', '-c').strip()\\n match = re_from_svnversion.search(revision)\\n if match:\\n revision = \\\"svn:r\\\" + match.group(2) # 565:56555 -> svn:r56555\\n bare_svn = True\\n else:\\n message = git(\\\"log\\\", \\\"--format=%b\\\", \\\"--grep\\\", \\\"git-svn-id:\\\", \\\"-1\\\")\\n match = re_from_git_svn_id.search(message)\\n if match:\\n revision = \\\"svn:r\\\" + match.group(1)\\n else:\\n here = os.path.relpath(os.getcwd(), repo_root)\\n error(\\\"Unsupported external format, svn or git-svn repo expected:\\\\n\\\\t{}\\\".format(here))\\n else:\\n branch_name = current_branch()\\n remote_name = git(\\\"config\\\", \\\"branch.%s.remote\\\" % branch_name)\\n revision = git(\\\"log\\\", \\\"%s/%s\\\" % (remote_name, branch_name), \\\"-1\\\", \\\"--format=%H\\\")\\n\\n info(\\\"Freeze {0} at {1}\\\".format(rel_url, revision))\\n if messages and not bare_svn:\\n old = resolve_revision(git_externals[rel_url][\\\"ref\\\"])\\n new = resolve_revision(revision)\\n git(\\\"log\\\", \\\"--format=- %h %s\\\", \\\"{}..{}\\\".format(old, new), capture=False)\\n git_externals[rel_url][\\\"ref\\\"] = revision\\n\\n foreach_externals_dir(repo_root, get_version, only=externals)\\n\\n dump_gitexts(git_externals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f1f0031a9244e9c1de1428c16aa0f8\",\n \"score\": \"0.52708477\",\n \"text\": \"def make_git_diff_header(target_path, repos_relpath,\\n old_tag, new_tag, add=False, src_label=None,\\n dst_label=None, delete=False, text_changes=True,\\n cp=False, mv=False, copyfrom_path=None,\\n copyfrom_rev=None):\\n\\n path_as_shown = target_path.replace('\\\\\\\\', '/')\\n if src_label:\\n src_label = src_label.replace('\\\\\\\\', '/')\\n src_label = '\\\\t(.../' + src_label + ')'\\n else:\\n src_label = ''\\n if dst_label:\\n dst_label = dst_label.replace('\\\\\\\\', '/')\\n dst_label = '\\\\t(.../' + dst_label + ')'\\n else:\\n dst_label = ''\\n\\n output = [\\n \\\"Index: \\\" + path_as_shown + \\\"\\\\n\\\",\\n \\\"===================================================================\\\\n\\\"\\n ]\\n if add:\\n output.extend([\\n \\\"diff --git a/\\\" + repos_relpath + \\\" b/\\\" + repos_relpath + \\\"\\\\n\\\",\\n \\\"new file mode 100644\\\\n\\\",\\n ])\\n if text_changes:\\n output.extend([\\n \\\"--- a/\\\" + repos_relpath + src_label + \\\"\\\\t(\\\" + old_tag + \\\")\\\\n\\\",\\n \\\"+++ b/\\\" + repos_relpath + dst_label + \\\"\\\\t(\\\" + new_tag + \\\")\\\\n\\\"\\n ])\\n elif delete:\\n output.extend([\\n \\\"diff --git a/\\\" + repos_relpath + \\\" b/\\\" + repos_relpath + \\\"\\\\n\\\",\\n \\\"deleted file mode 100644\\\\n\\\",\\n ])\\n if text_changes:\\n output.extend([\\n \\\"--- a/\\\" + repos_relpath + src_label + \\\"\\\\t(\\\" + old_tag + \\\")\\\\n\\\",\\n \\\"+++ b/\\\" + repos_relpath + dst_label + \\\"\\\\t(\\\" + new_tag + \\\")\\\\n\\\"\\n ])\\n elif cp:\\n if copyfrom_rev:\\n copyfrom_rev = '@' + copyfrom_rev\\n else:\\n copyfrom_rev = ''\\n output.extend([\\n \\\"diff --git a/\\\" + copyfrom_path + \\\" b/\\\" + repos_relpath + \\\"\\\\n\\\",\\n \\\"copy from \\\" + copyfrom_path + copyfrom_rev + \\\"\\\\n\\\",\\n \\\"copy to \\\" + repos_relpath + \\\"\\\\n\\\",\\n ])\\n if text_changes:\\n output.extend([\\n \\\"--- a/\\\" + copyfrom_path + src_label + \\\"\\\\t(\\\" + old_tag + \\\")\\\\n\\\",\\n \\\"+++ b/\\\" + repos_relpath + \\\"\\\\t(\\\" + new_tag + \\\")\\\\n\\\"\\n ])\\n elif mv:\\n output.extend([\\n \\\"diff --git a/\\\" + copyfrom_path + \\\" b/\\\" + path_as_shown + \\\"\\\\n\\\",\\n \\\"rename from \\\" + copyfrom_path + \\\"\\\\n\\\",\\n \\\"rename to \\\" + repos_relpath + \\\"\\\\n\\\",\\n ])\\n if text_changes:\\n output.extend([\\n \\\"--- a/\\\" + copyfrom_path + src_label + \\\"\\\\t(\\\" + old_tag + \\\")\\\\n\\\",\\n \\\"+++ b/\\\" + repos_relpath + \\\"\\\\t(\\\" + new_tag + \\\")\\\\n\\\"\\n ])\\n else:\\n output.extend([\\n \\\"diff --git a/\\\" + repos_relpath + \\\" b/\\\" + repos_relpath + \\\"\\\\n\\\",\\n \\\"--- a/\\\" + repos_relpath + src_label + \\\"\\\\t(\\\" + old_tag + \\\")\\\\n\\\",\\n \\\"+++ b/\\\" + repos_relpath + dst_label + \\\"\\\\t(\\\" + new_tag + \\\")\\\\n\\\",\\n ])\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058af8cc9d5e6bba800df78029b755c6\",\n \"score\": \"0.5267223\",\n \"text\": \"def collect_diffs_for_files(root, versions, source_files, context_lines):\\n os.chdir(root)\\n for filename in source_files:\\n command = ['git', 'diff', '-U%d' % context_lines,\\n '-w', versions, '--', filename]\\n p = subprocess.Popen(command,\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n diff_lines, err = p.communicate()\\n if err:\\n raise DiffCollectionFailed(\\n \\\"Unable to collect diffs for file %s/%s: %s\\\" %\\n (root, filename, err))\\n yield diff_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c3386ca19edb2e3e6913e8bab15448\",\n \"score\": \"0.52605367\",\n \"text\": \"def rm_patch_files(spec):\\n # Remove all old patches from the spec dir\\n for patch in spec.patchseries(unapplied=True):\\n gbp.log.debug(\\\"Removing '%s'\\\" % patch.path)\\n try:\\n os.unlink(patch.path)\\n except OSError as err:\\n if err.errno != errno.ENOENT:\\n raise GbpError(\\\"Failed to remove patch: %s\\\" % err)\\n else:\\n gbp.log.debug(\\\"Patch %s does not exist.\\\" % patch.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b03578a9baf0b27736bb75f7b385028\",\n \"score\": \"0.525592\",\n \"text\": \"def _get_patchset(self, client, project, repo, sha):\\n\\n key = f\\\"get_changelist:{md5_text(project + repo).hexdigest()}:{sha}\\\"\\n commit_files = cache.get(key)\\n if commit_files is None:\\n commit_files = client.get_commit_filechanges(project, repo, sha)\\n cache.set(key, commit_files, 900)\\n\\n return self._transform_patchset(commit_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"299dd3eb218670fe7f07db55511cbeba\",\n \"score\": \"0.52544034\",\n \"text\": \"def create_new_patch_release():\\n try:\\n last_version_number = get_last_version()\\n except subprocess.CalledProcessError as err:\\n if err.stderr.decode(\\\"utf8\\\").startswith(\\\"HTTP 404:\\\"):\\n # The project doesn't have any releases yet.\\n new_version_number = \\\"0.0.1\\\"\\n else:\\n raise\\n else:\\n new_version_number = bump_patch_number(last_version_number)\\n\\n subprocess.run(\\n [\\\"gh\\\", \\\"release\\\", \\\"create\\\", \\\"--generate-notes\\\", new_version_number],\\n check=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dbd37223789a3f4e40eeaeb0fed2f4f\",\n \"score\": \"0.52522826\",\n \"text\": \"def _create_branch(repo, from_branch, to_branch, dry_run, force, patches):\\n env = os.environ.copy()\\n\\n if git.branch_exists(repo, to_branch, env):\\n click.echo(f\\\"{to_branch} already exists, skipping...\\\")\\n sys.exit(0)\\n\\n snap_basename = urlparse(repo)\\n snap_basename = Path(snap_basename.path).name\\n if snap_basename.endswith(\\\".git\\\"):\\n snap_basename = snap_basename.rstrip(\\\".git\\\")\\n sh.rm(\\\"-rf\\\", snap_basename)\\n sh.git.clone(repo, branch=from_branch, _env=env)\\n sh.git.config(\\\"user.email\\\", \\\"cdkbot@gmail.com\\\", _cwd=snap_basename)\\n sh.git.config(\\\"user.name\\\", \\\"cdkbot\\\", _cwd=snap_basename)\\n sh.git.checkout(\\\"-b\\\", to_branch, _cwd=snap_basename)\\n\\n snapcraft_fn = Path(snap_basename) / \\\"snapcraft.yaml\\\"\\n snapcraft_fn_tpl = Path(snap_basename) / \\\"snapcraft.yaml.in\\\"\\n if not snapcraft_fn_tpl.exists():\\n click.echo(f\\\"{snapcraft_fn_tpl} not found\\\")\\n sys.exit(1)\\n\\n # Apply patches\\n patches_list = []\\n if patches:\\n patches_path = Path(patches)\\n if patches_path.exists():\\n click.echo(\\\"Patches found, applying.\\\")\\n patches_map = yaml.safe_load(patches_path.read_text(encoding=\\\"utf8\\\"))\\n # TODO: cleanup\\n if \\\"all\\\" in patches_map:\\n for patch_fn in patches_map[\\\"all\\\"]:\\n patch_fn = Path(patch_fn).absolute()\\n shared_path = str(Path(\\\"shared\\\") / patch_fn.parts[-1])\\n sh.cp(str(patch_fn), str(shared_path), _cwd=snap_basename)\\n patches_list.append(shared_path)\\n sh.git.add(shared_path, _cwd=snap_basename)\\n if to_branch.lstrip(\\\"v\\\") in patches_map:\\n for patch_fn in patches_map[to_branch.lstrip(\\\"v\\\")]:\\n patch_fn = Path(patch_fn).absolute()\\n shared_path = str(Path(\\\"shared\\\") / patch_fn.parts[-1])\\n sh.cp(str(patch_fn), str(shared_path), _cwd=snap_basename)\\n patches_list.append(shared_path)\\n sh.git.add(shared_path, _cwd=snap_basename)\\n\\n snapcraft_yml = snapcraft_fn_tpl.read_text()\\n snapcraft_yml = _render(\\n snapcraft_fn_tpl,\\n {\\\"snap_version\\\": to_branch.lstrip(\\\"v\\\"), \\\"patches\\\": patches_list},\\n )\\n snapcraft_fn.write_text(snapcraft_yml)\\n if not dry_run:\\n sh.git.add(\\\".\\\", _cwd=snap_basename)\\n sh.git.commit(\\\"-m\\\", f\\\"Creating branch {to_branch}\\\", _cwd=snap_basename)\\n sh.git.push(repo, to_branch, _cwd=snap_basename, _env=env)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20eb563c5f655e0c0c0627531095ad3\",\n \"score\": \"0.5240313\",\n \"text\": \"def commitChanges():\\n\\n # Get a reference to the Git repo (in this folder)\\n repo = Repo(os.path.dirname(__file__))\\n\\n # Commit the new version\\n repo.git.commit(\\\"pymed/version.py\\\", message=\\\"Bumped the release version\\\")\\n\\n # Push any changes\\n repo.git.push(\\\"origin\\\", \\\"master\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc298a348be63640dd7d7b211cdd290\",\n \"score\": \"0.5237253\",\n \"text\": \"def normalize_patch(self, patch, filename, revision=HEAD):\\n if revision != PRE_CREATION and has_expanded_svn_keywords(patch):\\n keywords = self.client.get_keywords(filename, revision)\\n\\n if keywords:\\n return collapse_svn_keywords(patch, force_bytes(keywords))\\n\\n return patch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cff0667fe18b92330637e917c8fc896\",\n \"score\": \"0.5234486\",\n \"text\": \"def prep_commit(self, new_rev, subdir=None):\\n if not subdir:\\n subdir = self.subdir\\n self.scmlogs.annotate(\\\"cwd is %s\\\" % os.getcwd())\\n newly_created = []\\n\\n if not os.path.exists('a'):\\n newly_created.append('a')\\n\\n if not os.path.exists(subdir):\\n os.mkdir(subdir)\\n # This will take care of adding subdir/b too\\n newly_created.append(subdir)\\n\\n for fn in ('a', subdir + '/b'):\\n f = open(fn, 'w')\\n f.write(str(new_rev))\\n f.close()\\n self.scmlogs.annotate(\\\"Wrote %s to %s\\\" % (new_rev, fn))\\n\\n # we never commit through symlink 'c' but instead see the updated\\n # revision through the symlink\\n if not os.path.lexists('c'):\\n os.symlink('a', 'c')\\n newly_created.append('c')\\n\\n return newly_created\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb61c053da4aefe2a45e2576d51a1f99\",\n \"score\": \"0.52327067\",\n \"text\": \"def _show_patch(sha256):\\n patches = spack.repo.path.patch_index.index\\n data = patches.get(sha256)\\n\\n if not data:\\n candidates = [k for k in patches if k.startswith(sha256)]\\n if not candidates:\\n tty.die(\\\"no such resource: %s\\\" % sha256)\\n elif len(candidates) > 1:\\n tty.die(\\\"%s: ambiguous hash prefix. Options are:\\\", *candidates)\\n\\n sha256 = candidates[0]\\n data = patches.get(sha256)\\n\\n color.cprint(\\\"@c{%s}\\\" % sha256)\\n for package, rec in data.items():\\n owner = rec[\\\"owner\\\"]\\n\\n if \\\"relative_path\\\" in rec:\\n pkg_dir = spack.repo.path.get_pkg_class(owner).package_dir\\n path = os.path.join(pkg_dir, rec[\\\"relative_path\\\"])\\n print(\\\" path: %s\\\" % path)\\n else:\\n print(\\\" url: %s\\\" % rec[\\\"url\\\"])\\n\\n print(\\\" applies to: %s\\\" % package)\\n if owner != package:\\n print(\\\" patched by: %s\\\" % owner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244e1f213591defc71f71505b255b14a\",\n \"score\": \"0.5184749\",\n \"text\": \"def test_all_version_notes_in_a_single_file(self):\\n runner = CliRunner()\\n\\n def do_build_once_with(version, fragment_file, fragment):\\n with open(f\\\"newsfragments/{fragment_file}\\\", \\\"w\\\") as f:\\n f.write(fragment)\\n\\n result = runner.invoke(\\n _main,\\n [\\n \\\"--version\\\",\\n version,\\n \\\"--name\\\",\\n \\\"foo\\\",\\n \\\"--date\\\",\\n \\\"01-01-2001\\\",\\n \\\"--yes\\\",\\n ],\\n catch_exceptions=False,\\n )\\n # not git repository, manually remove fragment file\\n Path(f\\\"newsfragments/{fragment_file}\\\").unlink()\\n return result\\n\\n results = []\\n with runner.isolated_filesystem():\\n with open(\\\"pyproject.toml\\\", \\\"w\\\") as f:\\n f.write(\\n \\\"\\\\n\\\".join(\\n [\\n \\\"[tool.towncrier]\\\",\\n \\\" single_file=true\\\",\\n \\\" # The `filename` variable is fixed and not formated in any way.\\\",\\n ' filename=\\\"{version}-notes.rst\\\"',\\n ]\\n )\\n )\\n os.mkdir(\\\"newsfragments\\\")\\n results.append(\\n do_build_once_with(\\\"7.8.9\\\", \\\"123.feature\\\", \\\"Adds levitation\\\")\\n )\\n results.append(do_build_once_with(\\\"7.9.0\\\", \\\"456.bugfix\\\", \\\"Adds catapult\\\"))\\n\\n self.assertEqual(0, results[0].exit_code, results[0].output)\\n self.assertEqual(0, results[1].exit_code, results[1].output)\\n self.assertEqual(\\n 1,\\n len(list(Path.cwd().glob(\\\"*-notes.rst\\\"))),\\n \\\"single newfile for multiple builds\\\",\\n )\\n self.assertTrue(os.path.exists(\\\"{version}-notes.rst\\\"), os.listdir(\\\".\\\"))\\n\\n output = read(\\\"{version}-notes.rst\\\")\\n\\n self.assertEqual(\\n output,\\n dedent(\\n \\\"\\\"\\\"\\n foo 7.9.0 (01-01-2001)\\n ======================\\n\\n Bugfixes\\n --------\\n\\n - Adds catapult (#456)\\n\\n\\n foo 7.8.9 (01-01-2001)\\n ======================\\n\\n Features\\n --------\\n\\n - Adds levitation (#123)\\n \\\"\\\"\\\"\\n ).lstrip(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4afcca0962f0edcb95a86c52873763b5\",\n \"score\": \"0.5162349\",\n \"text\": \"def main(argv):\\n retval = 0\\n\\n (options, args) = parse_args(argv)\\n if not options:\\n return ExitCodes.parse_error\\n\\n gbp.log.setup(options.color, options.verbose, options.color_scheme)\\n\\n if len(args) < 2:\\n gbp.log.err(\\\"No action given.\\\")\\n return 1\\n else:\\n action = args[1]\\n\\n if args[1] in [\\\"export\\\", \\\"import\\\", \\\"rebase\\\", \\\"drop\\\", \\\"switch\\\", \\\"convert\\\"]:\\n pass\\n elif args[1] in [\\\"apply\\\"]:\\n if len(args) != 3:\\n gbp.log.err(\\\"No patch name given.\\\")\\n return 1\\n else:\\n patchfile = args[2]\\n else:\\n gbp.log.err(\\\"Unknown action '%s'.\\\" % args[1])\\n return 1\\n\\n try:\\n repo = RpmGitRepository(os.path.curdir)\\n except GitRepositoryError:\\n gbp.log.err(\\\"%s is not a git repository\\\" % (os.path.abspath('.')))\\n return 1\\n\\n try:\\n # Create base temporary directory for this run\\n init_tmpdir(options.tmp_dir, prefix='pq-rpm_')\\n current = repo.get_branch()\\n if action == \\\"export\\\":\\n export_patches(repo, options)\\n elif action == \\\"import\\\":\\n import_spec_patches(repo, options)\\n elif action == \\\"drop\\\":\\n drop_pq(repo, current)\\n elif action == \\\"rebase\\\":\\n rebase_pq(repo, options)\\n elif action == \\\"apply\\\":\\n patch = Patch(patchfile)\\n apply_single_patch(repo, current, patch, fallback_author=None)\\n elif action == \\\"switch\\\":\\n switch_pq(repo, current)\\n except KeyboardInterrupt:\\n retval = 1\\n gbp.log.err(\\\"Interrupted. Aborting.\\\")\\n except CommandExecFailed:\\n retval = 1\\n except GitRepositoryError as err:\\n gbp.log.err(\\\"Git command failed: %s\\\" % err)\\n retval = 1\\n except GbpError as err:\\n if str(err):\\n gbp.log.err(err)\\n retval = 1\\n finally:\\n del_tmpdir()\\n\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b11d82e4550b6f743ea76fd7ac9f97b\",\n \"score\": \"0.5159089\",\n \"text\": \"def _pack():\\n pass # p4gf_proc.popen_no_throw([\\\"git\\\", \\\"gc\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60728e99874c474a5e80f7091433459d\",\n \"score\": \"0.51312226\",\n \"text\": \"def create_files_for_git(repositories_holder, onerepo):\\n if onerepo:\\n repository_team = REPOSITORIES\\\\\\n .get(\\\"Github\\\")\\\\\\n .get(repositories_holder)\\\\\\n .get(\\\"team\\\")\\n repository_type = REPOSITORIES \\\\\\n .get(\\\"Github\\\") \\\\\\n .get(repositories_holder) \\\\\\n .get(\\\"configuration\\\") \\\\\\n .get(\\\"type\\\")\\n if LOGGER:\\n print(\\\"\\\\nWorking on repo: {}\\\".format(repositories_holder))\\n folders_to_check = [folder for folder in REPOSITORIES\\n .get(\\\"Github\\\")\\n .get(repositories_holder)\\n .get(\\\"configuration\\\")\\n .get(\\\"folders-to-check\\\")]\\n filter_git_commit_data(repositories_holder,\\n repository_team,\\n repository_type,\\n folders_to_check)\\n if repositories_holder == \\\"build-puppet\\\":\\n create_git_md_table(repositories_holder, \\\"git_files\\\")\\n create_md_table_for_scriptworkers(repositories_holder)\\n else:\\n create_git_md_table(repositories_holder, \\\"git_files\\\")\\n if LOGGER:\\n print(\\\"MD table generated successfully\\\")\\n print(\\\"Finished working on {}\\\".format(repositories_holder))\\n else:\\n for repo in repositories_holder[\\\"Github\\\"]:\\n repository_name = repo\\n repository_team = repositories_holder\\\\\\n .get(\\\"Github\\\")\\\\\\n .get(repo)\\\\\\n .get(\\\"team\\\")\\n repository_type = repositories_holder \\\\\\n .get(\\\"Github\\\") \\\\\\n .get(repo) \\\\\\n .get(\\\"configuration\\\") \\\\\\n .get(\\\"type\\\")\\n if LOGGER:\\n print(\\\"\\\\nWorking on repo: {}\\\".format(repository_name))\\n folders_to_check = [folder for folder in repositories_holder\\n .get(\\\"Github\\\")\\n .get(repo)\\n .get(\\\"configuration\\\")\\n .get(\\\"folders-to-check\\\")]\\n filter_git_commit_data(repository_name,\\n repository_team,\\n repository_type,\\n folders_to_check)\\n if repository_name == \\\"build-puppet\\\":\\n create_git_md_table(repository_name, \\\"git_files\\\")\\n create_md_table_for_scriptworkers(repository_name)\\n else:\\n create_git_md_table(repository_name, \\\"git_files\\\")\\n if LOGGER:\\n print(\\\"MD table generated successfully\\\")\\n print(\\\"Finished working on {}\\\".format(repository_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328d4132a2d25da1699d1bf125d2c01a\",\n \"score\": \"0.5126164\",\n \"text\": \"def code_prep():\\n rev = hg_revision()\\n assert not rev.endswith('+'), 'Client has pending changes, cannot install.'\\n fprint('Preparing local code tarball (rev %s)' % rev)\\n filename = 'viewfinder.%s.tar.gz' % rev\\n\\n local('hg identify -i > hg_revision.txt')\\n local('tar czf %s --exclude \\\"*.o\\\" --exclude \\\"*~\\\" --exclude \\\"*.pyc\\\" __init__.py scripts/ marketing/ backend/ resources/ secrets/viewfinder.co hg_revision.txt' % filename)\\n return rev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"951b8ebff062560453981384d4cd52c5\",\n \"score\": \"0.5125932\",\n \"text\": \"def generate_patch_from_img(img,coor,patch_size):\\n \\n return extract_patch(img, coor, patch_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29a54b5210d4569cd7c2cf866846ca2\",\n \"score\": \"0.51184845\",\n \"text\": \"def test_release_notes_in_separate_files(self):\\n runner = CliRunner()\\n\\n def do_build_once_with(version, fragment_file, fragment):\\n with open(f\\\"newsfragments/{fragment_file}\\\", \\\"w\\\") as f:\\n f.write(fragment)\\n\\n result = runner.invoke(\\n _main,\\n [\\n \\\"--version\\\",\\n version,\\n \\\"--name\\\",\\n \\\"foo\\\",\\n \\\"--date\\\",\\n \\\"01-01-2001\\\",\\n \\\"--yes\\\",\\n ],\\n )\\n # not git repository, manually remove fragment file\\n Path(f\\\"newsfragments/{fragment_file}\\\").unlink()\\n return result\\n\\n results = []\\n with runner.isolated_filesystem():\\n with open(\\\"pyproject.toml\\\", \\\"w\\\") as f:\\n f.write(\\n \\\"\\\\n\\\".join(\\n [\\n \\\"[tool.towncrier]\\\",\\n \\\" single_file=false\\\",\\n ' filename=\\\"{version}-notes.rst\\\"',\\n ]\\n )\\n )\\n os.mkdir(\\\"newsfragments\\\")\\n results.append(\\n do_build_once_with(\\\"7.8.9\\\", \\\"123.feature\\\", \\\"Adds levitation\\\")\\n )\\n results.append(do_build_once_with(\\\"7.9.0\\\", \\\"456.bugfix\\\", \\\"Adds catapult\\\"))\\n\\n self.assertEqual(0, results[0].exit_code, results[0].output)\\n self.assertEqual(0, results[1].exit_code, results[1].output)\\n self.assertEqual(\\n 2,\\n len(list(Path.cwd().glob(\\\"*-notes.rst\\\"))),\\n \\\"one newfile for each build\\\",\\n )\\n self.assertTrue(os.path.exists(\\\"7.8.9-notes.rst\\\"), os.listdir(\\\".\\\"))\\n self.assertTrue(os.path.exists(\\\"7.9.0-notes.rst\\\"), os.listdir(\\\".\\\"))\\n\\n outputs = []\\n outputs.append(read(\\\"7.8.9-notes.rst\\\"))\\n outputs.append(read(\\\"7.9.0-notes.rst\\\"))\\n\\n self.assertEqual(\\n outputs[0],\\n dedent(\\n \\\"\\\"\\\"\\n foo 7.8.9 (01-01-2001)\\n ======================\\n\\n Features\\n --------\\n\\n - Adds levitation (#123)\\n \\\"\\\"\\\"\\n ).lstrip(),\\n )\\n self.assertEqual(\\n outputs[1],\\n dedent(\\n \\\"\\\"\\\"\\n foo 7.9.0 (01-01-2001)\\n ======================\\n\\n Bugfixes\\n --------\\n\\n - Adds catapult (#456)\\n \\\"\\\"\\\"\\n ).lstrip(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a153219714060dc97593244ce5e62a5\",\n \"score\": \"0.51050234\",\n \"text\": \"def bump_patch(self):\\n self.commit()\\n self._version = self._version.bump_patch()\\n self._last_digest = self._digest()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa38f2c9c62542d619815a527e48bfb\",\n \"score\": \"0.5102615\",\n \"text\": \"def _get_diff(self, changeset):\\n patch_file = io.StringIO()\\n dulwich.patch.write_object_diff(patch_file,\\n self.repo.object_store,\\n changeset.old, changeset.new)\\n return patch_file.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8432eb9f04c09da9f46d4b0366fcb473\",\n \"score\": \"0.50958365\",\n \"text\": \"def filter_non_code(commit_flow):\\n for (commit_id, commit_message, commit_patch) in commit_flow:\\n new_commit_patch = list()\\n keep_skipping = False\\n for line in commit_patch:\\n if (\\\"diff --git\\\" in line):\\n # So... this is a change to a file\\n # collect everything that looks like a file extension\\n matches = EXTENSIONS_FINDER.findall(line)\\n # Does this line mention a filetype we are interested in ?\\n if is_code_file(matches):\\n keep_skipping = False\\n new_commit_patch.append(line)\\n continue\\n else:\\n keep_skipping = True\\n continue\\n if keep_skipping:\\n continue\\n else:\\n new_commit_patch.append(line)\\n continue\\n yield (commit_id, commit_message, new_commit_patch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da70249685cac957e844f4b072040bb1\",\n \"score\": \"0.5064239\",\n \"text\": \"def git_commit():\\n\\n os.system('''\\n echo \\\"Checking current working dir\\\"\\n pwd\\n ls -lah\\n echo \\\"Configuring git username and email address\\\"\\n git config --global user.email \\\"actions.public.prismaconfig@github.com/uts-itd/\\\"\\n git config --global user.name \\\"Prisma IAC config GitHub Action\\\"\\n echo \\\"Running 'git add'\\\"\\n git add --all .\\n git diff --cached\\n echo \\\"Commiting changes...\\\"\\n git commit -m 'Adding prisma IAC scan config'\\n echo \\\"Pushing changes...\\\"\\n git push\\n echo \\\"Show git log (may truncate)\\\"\\n git log\\n ''')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"777625327140746c4a71c4f8f5739817\",\n \"score\": \"0.50620663\",\n \"text\": \"def get_files_affected_by_patch(self, relative_to='src/'):\\n patch_root = self.m.gclient.calculate_patch_root(\\n self.m.properties.get('patch_project'))\\n cwd = self._working_dir.join(patch_root) if self._working_dir else None\\n files = self.m.tryserver.get_files_affected_by_patch(patch_root, cwd=cwd)\\n for i, path in enumerate(files):\\n path = str(path)\\n assert path.startswith(relative_to)\\n files[i] = path[len(relative_to):]\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"619e60696ebf0a14487c95b2d4e40571\",\n \"score\": \"0.50588316\",\n \"text\": \"def _files_changed_in_git_diff(self, hash_1, hash_2):\\n\\n os.chdir(self.repo_path)\\n out, __ = self._os_cmd(\\n \\\"git diff {} {} --name-only\\\".format(hash_1.id.hex, hash_2.id.hex))\\n files_changed = []\\n for file in out.splitlines():\\n for wanted in [\\\".py\\\", \\\".c\\\", \\\".cpp\\\", \\\".F90\\\", \\\".f90\\\", \\\".java\\\"]:\\n if wanted in str(file) and \\\"__init__.py\\\" not in str(file):\\n files_changed.append(Path(str(file)).name[:-1])\\n\\n # A work around for FORTRAN file extensions.\\n if self.repo_lang == \\\"fortran\\\":\\n files_changed = list(map(lambda x: x[:-4]+\\\".f90\\\", files_changed))\\n\\n return files_changed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8f194343089da907675c0a9fa50dec\",\n \"score\": \"0.50580674\",\n \"text\": \"def test_main(\\n petsc_dir: Path,\\n test_path: Path,\\n output_dir: Path,\\n patch_list: list[PathDiffPair],\\n errors_fixed: list[CondensedDiags],\\n errors_left: list[CondensedDiags],\\n replace: bool = False,\\n) -> ReturnCode:\\n def test(generated_output: list[str], reference_file: Path) -> str:\\n short_ref_name = reference_file.relative_to(petsc_dir)\\n if replace:\\n pl.sync_print('\\\\tREPLACE', short_ref_name)\\n reference_file.write_text(''.join(generated_output))\\n return ''\\n if not reference_file.exists():\\n return f'Missing reference file \\\\'{reference_file}\\\\'\\\\n'\\n return ''.join(\\n difflib.unified_diff(\\n reference_file.read_text().splitlines(True), generated_output,\\n fromfile=str(short_ref_name), tofile='Generated Output', n=0\\n )\\n )\\n\\n # sanitize the output so that it will be equal across systems\\n def sanitize_output_file(text: Optional[str]) -> list[str]:\\n return [] if text is None else [l.replace(str(petsc_dir), '.') for l in text.splitlines(True)]\\n\\n def sanitize_patch_file(text: Optional[str]) -> list[str]:\\n # skip the diff header with file names\\n return [] if text is None else text.splitlines(True)[2:]\\n\\n def rename_patch_file_target(text: str, new_path: Path) -> str:\\n lines = text.splitlines(True)\\n out_file = lines[0].split()[1]\\n lines[0] = lines[0].replace(out_file, str(new_path))\\n lines[1] = lines[1].replace(out_file, str(new_path))\\n return ''.join(lines)\\n\\n FIXED_MARKER = '<--- FIXED --->'\\n LEFT_MARKER = '<--- LEFT --->'\\n patch_error = {}\\n root_dir = f'--directory={petsc_dir.anchor}'\\n patches = dict(patch_list)\\n\\n tmp_output = {\\n p : [FIXED_MARKER, '\\\\n'.join(s), LEFT_MARKER]\\n for diags in errors_fixed\\n for p, s in diags.items()\\n }\\n for diags in errors_left:\\n for path, strlist in diags.items():\\n if path not in tmp_output:\\n tmp_output[path] = [f'{FIXED_MARKER}\\\\n{LEFT_MARKER}']\\n tmp_output[path].extend(strlist)\\n\\n # ensure that each output ends with a newline\\n output = {\\n path : '\\\\n'.join(strlist if strlist[-1].endswith('\\\\n') else strlist + [''])\\n for path, strlist in tmp_output.items()\\n }\\n # output = {\\n # path : '\\\\n'.join(strlist if len(strlist) == 4 else strlist + ['']) for path, strlist in tmp_output.items()\\n # }\\n #output = {key : '\\\\n'.join(val if len(val) == 4 else val + ['']) for key, val in output.items()}\\n if test_path.is_dir():\\n c_suffixes = (r'*.c', r'*.cxx', r'*.cpp', r'*.cc', r'*.CC')\\n file_list = [item for sublist in map(test_path.glob, c_suffixes) for item in sublist]\\n else:\\n file_list = [test_path]\\n for test_file in file_list:\\n output_base = output_dir / test_file.stem\\n output_file = output_base.with_suffix('.out')\\n patch_file = output_base.with_suffix('.patch')\\n short_name = test_file.relative_to(petsc_dir)\\n\\n pl.sync_print('\\\\tTEST ', short_name)\\n\\n output_errors = [\\n test(sanitize_output_file(output.get(test_file)), output_file),\\n test(sanitize_patch_file(patches.get(test_file)), patch_file)\\n ]\\n\\n # no point in checking the patch, we have already replaced\\n if not replace:\\n # make sure the patch can be applied\\n with TemporaryCopy(test_file) as tmp_src, \\\\\\n tempfile.NamedTemporaryFile(delete=True, suffix='.patch') as temp_patch:\\n tmp_patch_path = Path(temp_patch.name).resolve(strict=True)\\n try:\\n tmp_patch_path.write_text(rename_patch_file_target(patches[test_file], tmp_src.temp_path))\\n pl.util.subprocess_capture_output(\\n ['patch', root_dir, '--strip=0', '--unified', f'--input={tmp_patch_path}']\\n )\\n except Exception as exc:\\n exception = ''.join(traceback_format_exception(exc))\\n emess = f'Application of patch based on {test_file} failed:\\\\n{exception}\\\\n'\\n rej = tmp_src.rej_file()\\n if rej.exists():\\n emess += f'\\\\n{rej}:\\\\n{rej.read_text()}'\\n output_errors.append(emess)\\n\\n output_errors = [e for e in output_errors if e]\\n if output_errors:\\n pl.sync_print('\\\\tNOT OK ', short_name)\\n patch_error[test_file] = '\\\\n'.join(output_errors)\\n else:\\n pl.sync_print('\\\\tOK ', short_name)\\n if patch_error:\\n err_str = f\\\"[ERROR] {85 * '-'} [ERROR]\\\"\\n err_bars = (err_str + '\\\\n', err_str)\\n for err_file in patch_error:\\n pl.sync_print(patch_error[err_file].join(err_bars))\\n return ReturnCode.ERROR_TEST_FAILED\\n return ReturnCode.SUCCESS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bde0963bfc3274895a54efc5bcef7d8\",\n \"score\": \"0.50537014\",\n \"text\": \"def resource_list(args):\\n patches = spack.repo.path.patch_index.index\\n for sha256 in patches:\\n if args.only_hashes:\\n print(sha256)\\n else:\\n _show_patch(sha256)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8d082181a53dfd44c5d1ce8dbfe6a4\",\n \"score\": \"0.5053243\",\n \"text\": \"def test_multiple(self):\\n [dump, log] = self.make_repo((\\n dict(nodes=(\\n dict(action=\\\"add\\\", path=\\\"file1\\\", kind=\\\"file\\\", content=b\\\"\\\"),\\n dict(action=\\\"add\\\", path=\\\"file2\\\", kind=\\\"file\\\", content=b\\\"\\\"),\\n )),\\n dict(nodes=(\\n dict(action=\\\"change\\\", path=\\\"file1\\\", content=b\\\"mod 1\\\\n\\\"),\\n dict(action=\\\"change\\\", path=\\\"file2\\\", content=b\\\"mod 2\\\\n\\\"),\\n )),\\n ))\\n git = os.path.join(self.dir, \\\"git\\\")\\n subprocess.check_call((\\\"git\\\", \\\"init\\\", \\\"--quiet\\\", \\\"--\\\", git))\\n script = 'cd \\\"$1\\\" && git fast-import --quiet'\\n importer = (\\\"sh\\\", \\\"-c\\\", script, \\\"--\\\", git)\\n with svnex.FastExportPipe(importer) as importer, log:\\n exporter = svnex.Exporter(dump, importer, root=\\\"\\\", git_svn=True, quiet=True)\\n exporter.export(\\\"refs/heads/master\\\")\\n cmd = (\\\"git\\\", \\\"log\\\", \\\"--reverse\\\",\\n \\\"--format=format:%H%n%B\\\", \\\"--shortstat\\\", \\\"refs/heads/master\\\")\\n log = subprocess.check_output(cmd, cwd=git).decode(\\\"ascii\\\")\\n self.assertMultiLineEqual('''\\\\\\n3047a82f20bef2648a67a90912d8d755353d9e9e\\n\\n\\ngit-svn-id: @1 00000000-0000-0000-0000-000000000000\\n\\n 2 files changed, 0 insertions(+), 0 deletions(-)\\n\\n82aeb20279a1269f048243a603b141ee0ea204e9\\n\\n\\ngit-svn-id: @2 00000000-0000-0000-0000-000000000000\\n\\n 2 files changed, 2 insertions(+)\\n''',\\n log)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe058dc1061c0e628334519da1ca8af\",\n \"score\": \"0.50501686\",\n \"text\": \"def test_patch_parsing_multi_hunks(self):\\n with open(_get_path_to_asset('b.patch')) as f_hand:\\n patches = patchit.PatchSet.from_stream(f_hand)\\n self.assertEqual(patches[0].source_filename, 'a')\\n self.assertEqual(patches[0].target_filename, 'b')\\n self.assertEqual(len(patches[0].hunks), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ade01a88075a68badd1eccb02ab909\",\n \"score\": \"0.50476944\",\n \"text\": \"def munge_spec(path, build):\\n spec = os.path.join(path, '%s.spec' % PACKAGE)\\n m = re.match(r'\\\\d+', build['release'])\\n if not m:\\n raise ValueError('unable to parse Release %s' % build['release'])\\n releaseint = int(m.group())\\n release = '%d.1.coverage%%{dist}' % releaseint\\n with open(spec) as fileh:\\n lines = fileh.readlines()\\n print('rewriting %s with coverage changes' % spec)\\n with open(spec, 'w') as fileh:\\n for line in lines:\\n if re.match(r'\\\\s*Release:', line):\\n fileh.write('Release: %s\\\\n' % release)\\n elif re.match(r'%prep', line):\\n text = read_fragment('package')\\n fileh.write(text)\\n fileh.write(line)\\n elif re.match(r'cmake ', line):\\n fileh.write(line)\\n text = read_fragment('cmake')\\n fileh.write(text)\\n elif re.match(r'%install', line):\\n fileh.write(line)\\n text = read_fragment('install')\\n fileh.write(text)\\n elif re.match(r'%files base', line):\\n text = read_fragment('files')\\n fileh.write(text)\\n fileh.write(line)\\n else:\\n fileh.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f6f124a35b2fa9d44e5810d5d2f63b6\",\n \"score\": \"0.50471437\",\n \"text\": \"def test_diff_with_changelist(self):\\n client = self.build_client(needs_diff=True)\\n tmpfiles = self.precreate_tempfiles(4)\\n\\n self.write_workarea_file('README', b'new line\\\\n')\\n self.write_workarea_file(os.path.join('src', 'main.c'),\\n b'new content\\\\n')\\n self.write_workarea_file('newfile', b'new file!\\\\n')\\n self.write_workarea_file(os.path.join('doc', 'index.md'),\\n b'# new header line\\\\n')\\n\\n self.spy_on(run_process_exec, op=kgb.SpyOpMatchInOrder([\\n self.rule_query_wa_root,\\n self.make_rule_stash_selection([\\n b'README',\\n b'src/main.c',\\n b'doc/index.md',\\n ]),\\n self.make_rule_list_changelist(\\n name='my_changelist',\\n results=[\\n 'Modifying ./README\\\\n',\\n 'Modifying ./src/main.c\\\\n',\\n 'Adding ./newfile\\\\n',\\n 'Deleting ./delfile\\\\n',\\n ]),\\n self.make_rule_status(\\n [\\n b'F\\\\tO\\\\tM\\\\t./README',\\n b'F\\\\tO\\\\tM\\\\t./src/main.c',\\n b'F\\\\t-\\\\t-\\\\t./ignore-me',\\n b'F\\\\t?\\\\t?\\\\t./newfile',\\n b'F\\\\t!\\\\t-\\\\t./delfile',\\n ],\\n selection=['-sor', '-scm', '-sunm', '-sne']),\\n self.make_rule_nobjstatus(\\n sos_paths=[\\n './README',\\n './src/main.c',\\n ],\\n flags=['-gaRevision', '-gaRevId'],\\n results=[\\n '!nObjStatus! 1',\\n '!Record!',\\n './README',\\n '3',\\n '1',\\n 'Revision',\\n '1',\\n '1',\\n 'RevId',\\n '1',\\n '5',\\n '!Record!',\\n './src/main.c',\\n '3',\\n '1',\\n 'Revision',\\n '1',\\n '3',\\n 'RevId',\\n '2',\\n '26',\\n ]),\\n self.rule_query_project,\\n self.rule_query_server,\\n self.rule_query_rso,\\n self.make_rule_exportrev(sos_path='./README/#/1',\\n out_filename=tmpfiles[1],\\n content=b'old line\\\\n'),\\n self.make_rule_diff(client, tmpfiles[1], 'README'),\\n self.make_rule_diff(client, tmpfiles[2], 'newfile'),\\n self.make_rule_exportrev(sos_path='./src/main.c/#/3',\\n out_filename=tmpfiles[3],\\n content=b'old content\\\\n'),\\n self.make_rule_diff(client, tmpfiles[3],\\n os.path.join('src', 'main.c')),\\n self.make_rule_restore_selection(tmpfiles[0]),\\n ]))\\n\\n result = client.diff(revisions={\\n 'base': None,\\n 'extra': {\\n 'sos_changelist': 'my_changelist',\\n },\\n 'tip': None,\\n })\\n\\n self.assertDiffEqual(\\n result['diff'],\\n b'#diffx: encoding=utf-8, version=1.0\\\\n'\\n b'#.meta: format=json, length=354\\\\n'\\n b'{\\\\n'\\n b' \\\"scm\\\": \\\"sos\\\",\\\\n'\\n b' \\\"sos\\\": {\\\\n'\\n b' \\\"changelist\\\": \\\"my_changelist\\\",\\\\n'\\n b' \\\"project\\\": \\\"test-project\\\",\\\\n'\\n b' \\\"rso\\\": [\\\\n'\\n b' \\\"main\\\",\\\\n'\\n b' \\\"test\\\"\\\\n'\\n b' ],\\\\n'\\n b' \\\"server\\\": \\\"test-server\\\"\\\\n'\\n b' },\\\\n'\\n b' \\\"stats\\\": {\\\\n'\\n b' \\\"changes\\\": 1,\\\\n'\\n b' \\\"deletions\\\": 2,\\\\n'\\n b' \\\"files\\\": 3,\\\\n'\\n b' \\\"insertions\\\": 3,\\\\n'\\n b' \\\"lines changed\\\": 5\\\\n'\\n b' }\\\\n'\\n b'}\\\\n'\\n b'#.change:\\\\n'\\n b'#..meta: format=json, length=121\\\\n'\\n b'{\\\\n'\\n b' \\\"stats\\\": {\\\\n'\\n b' \\\"deletions\\\": 2,\\\\n'\\n b' \\\"files\\\": 3,\\\\n'\\n b' \\\"insertions\\\": 3,\\\\n'\\n b' \\\"lines changed\\\": 5\\\\n'\\n b' }\\\\n'\\n b'}\\\\n'\\n b'#..file:\\\\n'\\n b'#...meta: format=json, length=258\\\\n'\\n b'{\\\\n'\\n b' \\\"op\\\": \\\"modify\\\",\\\\n'\\n b' \\\"path\\\": \\\"README\\\",\\\\n'\\n b' \\\"revision\\\": {\\\\n'\\n b' \\\"old\\\": \\\"1\\\"\\\\n'\\n b' },\\\\n'\\n b' \\\"sos\\\": {\\\\n'\\n b' \\\"rev_id\\\": {\\\\n'\\n b' \\\"old\\\": 5\\\\n'\\n b' }\\\\n'\\n b' },\\\\n'\\n b' \\\"stats\\\": {\\\\n'\\n b' \\\"deletions\\\": 1,\\\\n'\\n b' \\\"insertions\\\": 1,\\\\n'\\n b' \\\"lines changed\\\": 2\\\\n'\\n b' }\\\\n'\\n b'}\\\\n'\\n b'#...diff: length=54, line_endings=unix\\\\n'\\n b'--- README\\\\n'\\n b'+++ README\\\\n'\\n b'@@ -1 +1 @@\\\\n'\\n b'-old line\\\\n'\\n b'+new line\\\\n'\\n b'#..file:\\\\n'\\n b'#...meta: format=json, length=144\\\\n'\\n b'{\\\\n'\\n b' \\\"op\\\": \\\"create\\\",\\\\n'\\n b' \\\"path\\\": \\\"newfile\\\",\\\\n'\\n b' \\\"stats\\\": {\\\\n'\\n b' \\\"deletions\\\": 0,\\\\n'\\n b' \\\"insertions\\\": 1,\\\\n'\\n b' \\\"lines changed\\\": 1\\\\n'\\n b' }\\\\n'\\n b'}\\\\n'\\n b'#...diff: length=51, line_endings=unix\\\\n'\\n b'--- /dev/null\\\\n'\\n b'+++ newfile\\\\n'\\n b'@@ -0,0 +1 @@\\\\n'\\n b'+new file!\\\\n'\\n b'#..file:\\\\n'\\n b'#...meta: format=json, length=263\\\\n'\\n b'{\\\\n'\\n b' \\\"op\\\": \\\"modify\\\",\\\\n'\\n b' \\\"path\\\": \\\"src/main.c\\\",\\\\n'\\n b' \\\"revision\\\": {\\\\n'\\n b' \\\"old\\\": \\\"3\\\"\\\\n'\\n b' },\\\\n'\\n b' \\\"sos\\\": {\\\\n'\\n b' \\\"rev_id\\\": {\\\\n'\\n b' \\\"old\\\": 26\\\\n'\\n b' }\\\\n'\\n b' },\\\\n'\\n b' \\\"stats\\\": {\\\\n'\\n b' \\\"deletions\\\": 1,\\\\n'\\n b' \\\"insertions\\\": 1,\\\\n'\\n b' \\\"lines changed\\\": 2\\\\n'\\n b' }\\\\n'\\n b'}\\\\n'\\n b'#...diff: length=68, line_endings=unix\\\\n'\\n b'--- src/main.c\\\\n'\\n b'+++ src/main.c\\\\n'\\n b'@@ -1 +1 @@\\\\n'\\n b'-old content\\\\n'\\n b'+new content\\\\n')\\n\\n self.assertEqual(result.get('review_request_extra_data'), {\\n 'sos_changelist': 'my_changelist',\\n 'sos_project': 'test-project',\\n 'sos_server': 'test-server',\\n 'sos_workarea': '1234567890',\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f8eb67cdb3c4563794333c41010ba3\",\n \"score\": \"0.5045795\",\n \"text\": \"def get_git_changeset(path=None):\\n if path is None:\\n path = os.path.normpath(os.path.join(controlbeast.__path__[0], \\\"..\\\"))\\n\\n # run `git show` in ControlBeast's root directory and grab its output from stdout\\n try:\\n with subprocess.Popen(\\n 'git show --pretty=format:%ct --quiet HEAD',\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=True,\\n cwd=path,\\n universal_newlines=True\\n ) as git_show:\\n timestamp = to_str(git_show.communicate()[0]).partition('\\\\n')[0]\\n except OSError:\\n timestamp = None\\n\\n try:\\n timestamp = datetime.datetime.utcfromtimestamp(int(timestamp))\\n except (ValueError, TypeError):\\n return 'GIT-unknown'\\n\\n return 'GIT-{0:>s}'.format(timestamp.strftime('%Y%m%d%H%M%S'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c4eaddd5f9cef8039f530288385ffa\",\n \"score\": \"0.5036955\",\n \"text\": \"def hooks() -> None:\\n proj().setup_git_hooks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332cf4c7e9cc4aa4edca2843855ab3aa\",\n \"score\": \"0.50354874\",\n \"text\": \"def svn_diff_close_patch_file(*args) -> \\\"svn_error_t *\\\":\\n return _diff.svn_diff_close_patch_file(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2831cbe40d08f54072b24c85e2c1bb8\",\n \"score\": \"0.5034028\",\n \"text\": \"def apply_patches_to_MANN_files() -> None:\\n\\n # Original MANN files\\n filenames = [\\\"AdamW.py\\\", \\\"AdamWParameter.py\\\", \\\"ExpertWeights.py\\\", \\\"Gating.py\\\", \\\"MANN.py\\\"]\\n\\n # Path configuration\\n MANN_folder = \\\"../src/adherent/MANN/\\\"\\n patches_MANN_folder = \\\"../src/adherent/MANN/patches/\\\"\\n\\n # Apply the patches\\n for filename in filenames:\\n original_file = MANN_folder + filename\\n patch_file = patches_MANN_folder + filename[:-2] + \\\"patch\\\"\\n os.system(\\\"patch \\\" + original_file + \\\" \\\" + patch_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2796d9e81b19147be0d3eded6a5a43fd\",\n \"score\": \"0.5033925\",\n \"text\": \"def test_separate_modality_change(tmp_path):\\n\\n toprepo = tmp_path / 'repo'\\n with git.Repo.init(toprepo, expand_vars=False) as repo:\\n with open(toprepo / 'hopic-ci-config.yaml', 'w') as f:\\n f.write(dedent('''\\\\\\n version:\\n bump: no\\n\\n modality-source-preparation:\\n CHANGE:\\n - sh: touch new-file.txt\\n changed-files:\\n - new-file.txt\\n commit-message: Add new file\\n '''))\\n repo.index.add(('hopic-ci-config.yaml',))\\n base_commit = repo.index.commit(message='Initial commit', **_commitargs)\\n\\n result = run(\\n ('--workspace', str(toprepo),\\n 'prepare-source-tree',\\n '--author-name', _author.name,\\n '--author-email', _author.email,\\n '--author-date', f\\\"@{_git_time}\\\",\\n '--commit-date', f\\\"@{_git_time}\\\",\\n 'apply-modality-change', 'CHANGE'),\\n )\\n assert result.exit_code == 0\\n\\n with git.Repo(toprepo, expand_vars=False) as repo:\\n assert repo.head.commit != base_commit\\n assert repo.head.commit.parents == (base_commit,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed084d91789571a07abe4398e0b3c24\",\n \"score\": \"0.5027947\",\n \"text\": \"def get_changed_files(repo_path: str, parent_branch: str, include_merges: bool) -> Sequence[str]:\\n file_regex_str = r\\\"^(?P<op>[AM])\\\\s+(?P<path>[a-zA-Z0-9_/\\\\\\\\]+\\\\.py)$\\\"\\n file_regex = re.compile(file_regex_str)\\n\\n # fetch changed files from previous commits\\n logging.info(\\\"Fetching files from previous commits\\\\n\\\")\\n cmd = f\\\"git log {'' if include_merges else '--no-merges --first-parent'} --name-status {parent_branch}..HEAD\\\"\\n logging.debug(f\\\"Executing: {cmd}\\\")\\n all_files = subprocess.check_output(cmd.split(), cwd=repo_path).decode(\\\"utf-8\\\")\\n logging.debug(f\\\"Result:\\\\n{all_files}\\\")\\n\\n # fetch changed files that are potentionally not committed yet\\n logging.info(\\\"Fetching uncommitted files\\\\n\\\")\\n cmd = f\\\"git diff --name-status\\\"\\n logging.debug(f\\\"Executing: {cmd}\\\")\\n uncommitted = subprocess.check_output(cmd.split(), cwd=repo_path).decode(\\\"utf-8\\\")\\n logging.debug(f\\\"Result:\\\\n{uncommitted}\\\")\\n all_files += uncommitted\\n\\n # fetch staged new files\\n logging.info(\\\"Fetching new files... those need to be stagged\\\\n\\\")\\n cmd = f\\\"git diff --name-status --cached\\\"\\n logging.debug(f\\\"Executing: {cmd}\\\")\\n staged = subprocess.check_output(cmd.split(), cwd=repo_path).decode(\\\"utf-8\\\")\\n logging.debug(f\\\"Result:\\\\n{staged}\\\")\\n all_files += staged\\n\\n filtered = []\\n for item in all_files.split(\\\"\\\\n\\\"):\\n match = file_regex.match(item)\\n if match:\\n filtered.append(match.group(\\\"path\\\"))\\n # remove duplicates\\n filtered = list(set(filtered))\\n logging.debug(f\\\"Files to consider: {len(filtered)}: {filtered}\\\")\\n return list(set(filtered))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ce70c09915d144157f15c9bc17916a\",\n \"score\": \"0.5024164\",\n \"text\": \"def apply_patch(self, patch_file, base_path, base_dir, p=None,\\n revert=False):\\n if not is_valid_version(self.subversion_client_version,\\n self.PATCH_MIN_VERSION):\\n raise MinimumVersionError(\\n 'Using \\\"rbt patch\\\" with the SVN backend requires at least '\\n 'svn 1.7.0')\\n\\n if base_dir and not base_dir.startswith(base_path):\\n # The patch was created in either a higher level directory or a\\n # directory not under this one. We should exclude files from the\\n # patch that are not under this directory.\\n excluded, empty = self._exclude_files_not_in_tree(patch_file,\\n base_path)\\n\\n if excluded:\\n logging.warn('This patch was generated in a different '\\n 'directory. To prevent conflicts, all files '\\n 'not under the current directory have been '\\n 'excluded. To apply all files in this '\\n 'patch, apply this patch from the %s directory.',\\n base_dir)\\n\\n if empty:\\n logging.warn('All files were excluded from the patch.')\\n\\n cmd = ['patch']\\n p_num = p or self._get_p_number(base_path, base_dir)\\n\\n if p_num >= 0:\\n cmd.append('--strip=%s' % p_num)\\n\\n if revert:\\n cmd.append('--reverse-diff')\\n\\n cmd.append(six.text_type(patch_file))\\n\\n rc, patch_output = self._run_svn(cmd, return_error_code=True)\\n\\n if self.supports_empty_files():\\n try:\\n with open(patch_file, 'rb') as f:\\n patch = f.read()\\n except IOError as e:\\n logging.error('Unable to read file %s: %s', patch_file, e)\\n return\\n\\n self.apply_patch_for_empty_files(patch, p_num, revert=revert)\\n\\n # TODO: What is svn's equivalent of a garbage patch message?\\n\\n return PatchResult(applied=(rc == 0), patch_output=patch_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13f9a51593fc0e83a257cad0399a1326\",\n \"score\": \"0.5021405\",\n \"text\": \"def build(self, manager):\\n lite_dir = manager.lite_dir\\n output_dir = manager.output_dir\\n\\n for jupyterlite_file in self.lite_files:\\n rel = jupyterlite_file.relative_to(lite_dir)\\n dest = output_dir / rel\\n yield self.task(\\n name=f\\\"patch:{rel}\\\",\\n file_dep=[jupyterlite_file],\\n actions=[\\n (self.merge_one_jupyterlite, [dest, [dest, jupyterlite_file]]),\\n (self.maybe_timestamp, [dest]),\\n ],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23cc3b64c14ea0ae236a7eb9927166e8\",\n \"score\": \"0.5018992\",\n \"text\": \"def test_projectless_changelog(self):\\n runner = CliRunner()\\n\\n with runner.isolated_filesystem():\\n with open(\\\"pyproject.toml\\\", \\\"w\\\") as f:\\n f.write(\\\"[tool.towncrier]\\\\n\\\" 'package = \\\"foo\\\"\\\\n')\\n os.mkdir(\\\"foo\\\")\\n os.mkdir(\\\"foo/newsfragments\\\")\\n with open(\\\"foo/newsfragments/123.feature\\\", \\\"w\\\") as f:\\n f.write(\\\"Adds levitation\\\")\\n # Towncrier ignores .rst extension\\n with open(\\\"foo/newsfragments/124.feature.rst\\\", \\\"w\\\") as f:\\n f.write(\\\"Extends levitation\\\")\\n\\n result = runner.invoke(\\n _main,\\n [\\n \\\"--name\\\",\\n \\\"FooBarBaz\\\",\\n \\\"--version\\\",\\n \\\"7.8.9\\\",\\n \\\"--date\\\",\\n \\\"01-01-2001\\\",\\n \\\"--draft\\\",\\n ],\\n )\\n\\n self.assertEqual(0, result.exit_code)\\n self.assertEqual(\\n result.output,\\n dedent(\\n \\\"\\\"\\\"\\n Loading template...\\n Finding news fragments...\\n Rendering news fragments...\\n Draft only -- nothing has been written.\\n What is seen below is what would be written.\\n\\n FooBarBaz 7.8.9 (01-01-2001)\\n ============================\\n\\n Features\\n --------\\n\\n - Adds levitation (#123)\\n - Extends levitation (#124)\\n\\n \\\"\\\"\\\"\\n ).lstrip(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef62ccd75c5fcf6d5363c80c60ccf946\",\n \"score\": \"0.501486\",\n \"text\": \"def build_message(\\n commit: str,\\n commit_github_url: str,\\n changed_files: List[str],\\n diffs: dict,\\n commit_author: str,\\n) -> List[Dict[str, Any]]:\\n\\n title = f\\\"Commit {commit[0:7]} Modified Schema Struct Files\\\"\\n changes: List[Dict[str, Any]] = [{\\\"value\\\": f\\\"Changed by: {commit_author}\\\"}]\\n for changed_file in changed_files:\\n changes.append(\\n {\\n \\\"title\\\": f\\\"{changed_file}\\\",\\n \\\"value\\\": f\\\"```{diffs[changed_file]}```\\\",\\n \\\"short\\\": False,\\n }\\n )\\n\\n message = [\\n {\\n \\\"fallback\\\": title,\\n \\\"color\\\": \\\"warning\\\",\\n \\\"title\\\": title,\\n \\\"title_link\\\": commit_github_url,\\n \\\"fields\\\": changes,\\n }\\n ]\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e1c079cc7c16bd70c74ca9e25b4115f\",\n \"score\": \"0.5012719\",\n \"text\": \"def post_pr_merge_sync(context):\\n context.run(\\\"git switch main\\\")\\n context.run(\\\"git pull\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73df4999e4a14e903672e473723192b\",\n \"score\": \"0.50117606\",\n \"text\": \"def files_in_commit(commit: Optional[str] = None) -> Iterable[str]:\\n cmd = ['git', 'diff', '--name-status']\\n if commit is not None:\\n cmd.append(f'{commit}..{commit}~1')\\n\\n output = subprocess.check_output(cmd)\\n\\n for line in output.decode('utf-8').split('\\\\n'):\\n if line == '':\\n break\\n\\n path = line.split('\\\\t')[-1]\\n\\n if not path.endswith('.py'):\\n continue\\n\\n abs_path = os.path.abspath(path)\\n\\n yield abs_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b2b32d7dc0ff276503ffe6e1b50b17\",\n \"score\": \"0.5009937\",\n \"text\": \"def _create_patch(self, file: Path, start_line_no: int) -> PatchedFile:\\n diff = self._do_diff(file)\\n patch_set = PatchSet(diff)\\n\\n if len(patch_set) != 1:\\n raise PluginException(\\\"Expected one patch in the patch_set\\\")\\n\\n patched_file = patch_set[0]\\n to_remove = []\\n for hunk in patched_file:\\n if not self._hunk_contains_line(hunk, start_line_no):\\n to_remove.append(hunk)\\n\\n # Remove hunks that we are not interested in\\n for hunk in to_remove:\\n patched_file.remove(hunk)\\n\\n if len(patched_file) == 0:\\n raise PluginException(\\\"Found no hunks to commit\\\")\\n\\n if len(patched_file) != 1:\\n raise PluginException(\\\"Found more than one hunk\\\")\\n\\n return patched_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c286eef01193bd6dc827a9b51b4dd04\",\n \"score\": \"0.50011075\",\n \"text\": \"def test_get_open_refspecs():\\n open_prs = {\\n \\\"pr_strings\\\": [\\\"pr1_this\\\", \\\"pr2_that\\\"],\\n \\\"merge_commit_shas\\\": [\\\"aaaaaaaa\\\", \\\"bbbbbbbb\\\"],\\n \\\"base_shas\\\": [\\\"shafoo\\\", \\\"shabar\\\"],\\n \\\"head_shas\\\": [\\\"shabaz\\\", \\\"shagah\\\"],\\n \\\"backlogged\\\": [False, False]\\n }\\n bridge = SpackCIBridge.SpackCIBridge()\\n open_refspecs, fetch_refspecs = bridge.get_open_refspecs(open_prs)\\n assert open_refspecs == [\\n \\\"github/pr1_this:github/pr1_this\\\",\\n \\\"github/pr2_that:github/pr2_that\\\"\\n ]\\n assert fetch_refspecs == [\\n \\\"+aaaaaaaa:refs/remotes/github/pr1_this\\\",\\n \\\"+bbbbbbbb:refs/remotes/github/pr2_that\\\"\\n ]\\n\\n protected_branches = [\\\"develop\\\", \\\"master\\\"]\\n bridge.update_refspecs_for_protected_branches(protected_branches, open_refspecs, fetch_refspecs)\\n assert open_refspecs == [\\n \\\"github/pr1_this:github/pr1_this\\\",\\n \\\"github/pr2_that:github/pr2_that\\\",\\n \\\"github/develop:github/develop\\\",\\n \\\"github/master:github/master\\\",\\n ]\\n assert fetch_refspecs == [\\n \\\"+aaaaaaaa:refs/remotes/github/pr1_this\\\",\\n \\\"+bbbbbbbb:refs/remotes/github/pr2_that\\\",\\n \\\"+refs/heads/develop:refs/remotes/github/develop\\\",\\n \\\"+refs/heads/master:refs/remotes/github/master\\\"\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cae22ccb15643ccf5b7aee329d27cdd\",\n \"score\": \"0.4999196\",\n \"text\": \"def patch():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8884f6340bcfd867ca9635ba147c23e\",\n \"score\": \"0.49979344\",\n \"text\": \"def git_add(filename):\\n subprocess.call(['git', 'add', filename])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"434dfaa0a6312e3ad8440e4c72f86b9e\",\n \"score\": \"0.49963978\",\n \"text\": \"def git_add(args):\\n \\n # Check if initialized git\\n if not os.path.exists('.git/objects'):\\n print('fatal: not a git repository (or any of the parent directories): .git')\\n return\\n\\n # check if args is empty directory\\n if not len(args):\\n return\\n \\n ##############################\\n for file in args:\\n update_git_object(file)\\n update_git_index(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c8e84c251e1d8f91fb4c468f55997a\",\n \"score\": \"0.49961337\",\n \"text\": \"def build_pr(args):\\n branches = [\\\"pull/{0}/head:pr\\\".format(args.pr)]\\n build_commit(args, branches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f67bd1ca0cd5a732d1483b18c2c183e4\",\n \"score\": \"0.49954492\",\n \"text\": \"def GetGitDiffFiles(git_dir, logger):\\n result = dict()\\n result['has_diff'] = False\\n result['add_list'] = list()\\n result['del_list'] = list()\\n result['unsvn_list'] = list()\\n \\n try:\\n cmd = 'cd %s && git status --short' % (git_dir)\\n (status, msg) = Function.RunCommand(cmd, ignore_stderr_when_ok=True)\\n if status != 0:\\n logger.LevPrint(\\\"ERROR\\\", \\\"run command %s error!\\\" % (cmd), False)\\n return None\\n\\n file_list = msg.split('\\\\n')\\n for file_stat in file_list:\\n tmp_list = file_stat.split()\\n if len(tmp_list) <= 1:\\n continue\\n file_status = tmp_list[0]\\n file_name = tmp_list[-1]\\n if file_status == 'M':\\n result['add_list'].append(file_name)\\n result['has_diff'] = True\\n elif file_status == \\\"D\\\":\\n result['del_list'].append(file_name)\\n result['has_diff'] = True\\n else:\\n result['unsvn_list'].append(file_name)\\n except BaseException as ex:\\n logger.LevPrint(\\\"ERROR\\\", \\\"get git diff file error, ex : %s!\\\" % (str(ex)), False)\\n return None\\n result['result'] = 0\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1591,"cells":{"query_id":{"kind":"string","value":"e18251726218e5936eb8605f9f04d110"},"query":{"kind":"string","value":"give me a user_id in the form of 'uXXXX'"},"positive_passages":{"kind":"list like","value":[{"docid":"ec4e0b8244530275a791e03288448b60","score":"0.0","text":"def get_userinfo(uid):\n \n eid = uid2eid[uid]\n e = employees[eid]\n print e","title":""}],"string":"[\n {\n \"docid\": \"ec4e0b8244530275a791e03288448b60\",\n \"score\": \"0.0\",\n \"text\": \"def get_userinfo(uid):\\n \\n eid = uid2eid[uid]\\n e = employees[eid]\\n print e\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4f71559f237c6bd50f41ad164cdfc301","score":"0.7902427","text":"def get_id(self):\n return unichr(self.user_id)","title":""},{"docid":"5e2591c6a2c9484be5577e4c7b5c3b36","score":"0.7891965","text":"def get_id(self):\n\n if self.user_id is None:\n return ''\n if version_info >= (3, 0):\n return str(self.user_id)\n return unicode(self.user_id)","title":""},{"docid":"8fa47d4f25257555f421e13d29718ad9","score":"0.7748601","text":"def get_user_id(self):\n return str(self.user_id.id)","title":""},{"docid":"154a0052102aad84e57ada50b55bcf7c","score":"0.7662898","text":"def user_id(self):\n return unicode(self._id)","title":""},{"docid":"b3d87ab94c849aff68415dcf143e6718","score":"0.7645075","text":"def get_uid() -> int:\n return USER_ID","title":""},{"docid":"90e7dd04a2b94d41a18fc10ba815e4ee","score":"0.75860614","text":"def user_id(self) -> str:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"414816f45da9844a57ce6043d58d5109","score":"0.75139123","text":"def get_user_id(user_name):\n return name_dict[user_name]","title":""},{"docid":"721f478e2a8c2c0593a680740896b4f5","score":"0.7483968","text":"def GetUserId():","title":""},{"docid":"d2ee62b9ee1abd5c5ca815bc757838ad","score":"0.7476764","text":"def user_to_uid(self, user):\n pass","title":""},{"docid":"8670ddf41c73d2d789cdf184deabdf57","score":"0.7399731","text":"def user_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"8670ddf41c73d2d789cdf184deabdf57","score":"0.7399731","text":"def user_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"452684f99db1ee430cd2af6c48a7a7ec","score":"0.7333088","text":"def get_user_id_by_name(self, user_name):\n assert isinstance(user_name, str), \"`user_name` must be a valid username rather than \\\"{}\\\"\".format(user_name)\n user_name = user_name.strip().lstrip(\"@\")\n return \"U{}\".format(user_name)","title":""},{"docid":"efae77dcfd5d108f1c8beae312171410","score":"0.7276379","text":"def author_id_to_username(user_id=None):\n return","title":""},{"docid":"85df049f0a3fd7fa8a9ffb67e32ed12e","score":"0.7224942","text":"def user_id(self) -> str:\n return self._user_id","title":""},{"docid":"85df049f0a3fd7fa8a9ffb67e32ed12e","score":"0.7224942","text":"def user_id(self) -> str:\n return self._user_id","title":""},{"docid":"82682a60ef66e0e8d323f4a74eead598","score":"0.7216896","text":"def resolve_user_id(user_id):\n # TODO (neitsch): This method is not implemented yet. Use the user_id\n # (format: 12345678@facebook.com) to get the actual name of the user\n return user_id","title":""},{"docid":"583592a4e74afda820c27fdcbf9a354e","score":"0.72047234","text":"def to_key_id(user_key):\n return \"col_user__{}\".format(user_key.urlsafe())","title":""},{"docid":"de9e6c2c44a3070385b7353bfdb286b1","score":"0.7190837","text":"def user_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"de9e6c2c44a3070385b7353bfdb286b1","score":"0.7190837","text":"def user_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"fe0eb91e98f5de51cd4322a89bf83cba","score":"0.71809566","text":"def user_id(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"a9eaa003df1854bd982aff83d8a07296","score":"0.7171286","text":"def get_userid(user):\n database = get_db()\n cur = database.execute('SELECT id from users where user = ?', [user])\n\n userid = cur.fetchone()\n if userid is None:\n return -1\n return userid['id']","title":""},{"docid":"e1981fb3fd41d760429423b215173843","score":"0.71576446","text":"def user_id(self):\n return self._parsed_req.get('user_id')","title":""},{"docid":"4169e0349fd763f274a24f746ff6d274","score":"0.713344","text":"def get_userId(username):\n return make_query(f\"\"\"SELECT id FROM user where username='{username}'\"\"\", 0, 0)","title":""},{"docid":"4de7f4277561182d45cba2f5c3842073","score":"0.71233433","text":"def get_user(id):\n return \"%s\" % users[id]","title":""},{"docid":"21da64b0657eb54209ddffaea3c4efa8","score":"0.7121699","text":"def encode_type2_user_id(user_id):\n return u\"{0:04d}-{1:02d}\".format(user_id, Type2Code.calculate(user_id))","title":""},{"docid":"b43db0a92e0b15cdaf8c7d3c6819f902","score":"0.71001905","text":"def format_id_user(id_value):\n id_str = str(id_value)\n while len(id_str) < 6:\n id_str = u'0' + id_str\n return id_str","title":""},{"docid":"92b8cfe1bfed5cd0a331cd25a56fb93d","score":"0.70772094","text":"def id(self):\n return str(self._user.id)","title":""},{"docid":"fab17f778626802a6867bcbc87edf50b","score":"0.7054668","text":"def get_uid():\n return uuid.uuid4().hex[:7]","title":""},{"docid":"5054dd7f64c73367b6d45493a31dbc47","score":"0.7032906","text":"def extract_uid(self, data):\n return str(data[\"username\"])","title":""},{"docid":"60dc880a7869710c3a5153bdf8097bae","score":"0.7029802","text":"def __id_cleanse(user_id):\n if regex.match(r'^<@!\\d\\d\\d+>$', user_id):\n return user_id.replace('!', '')\n return user_id","title":""},{"docid":"c5e6f022e2851a31f7201dcf16c4b791","score":"0.70205665","text":"def get_user_name_by_id(self, user_id):\n assert isinstance(user_id, str) and user_id[0] == \"U\", \"`user_id` must be a valid user ID rather than \\\"{}\\\"\".format(user_id)\n return user_id[1:]","title":""},{"docid":"29951fe7b8d07eaaf43176bb031bedfb","score":"0.7006138","text":"def user_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"29951fe7b8d07eaaf43176bb031bedfb","score":"0.7006138","text":"def user_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"563c14638ea79b3fc39178463c249b28","score":"0.69851923","text":"def get_user_id(headers: dict) -> str:\n response = requests.get(f'{url_base}/me', headers=headers)\n error_handle('user info', 200, 'GET', response=response)\n return json.loads(response.content.decode('utf-8'))['id']","title":""},{"docid":"a66ba1701da6e110335413e6dd7303e7","score":"0.69787747","text":"def get_id():\r\n if auth.user:\r\n return auth.user.id\r\n else:\r\n return 'None'","title":""},{"docid":"6dce095dd091e09de8e5b1df7c67e64d","score":"0.69711435","text":"def get_user_id(self, email):","title":""},{"docid":"a6e74dab2aa21c7675051c741006e2c7","score":"0.6914548","text":"def encode_type1_user_id(user_id):\n return u\"{0:04d}-{1:d}\".format(user_id, Type1Code.calculate(user_id))","title":""},{"docid":"961f38ef1496058584d1e4a32ec430af","score":"0.6912966","text":"def get_user_id(self, name: str) -> str:\n users = self.client.fetchAllUsers()\n for user in users:\n if user.name == name:\n return str(user.uid)\n return ''","title":""},{"docid":"bbaea34943f2cfee01d16810c649071d","score":"0.6888811","text":"def get_userid():\n return os.getuid()","title":""},{"docid":"f8e99b6e6b17e516000c5a8a70d12a85","score":"0.6885755","text":"def build_google_user_id(user):\n\n try:\n return user.emails.get(email__endswith='@'+settings.MI_GOOGLE_EMAIL_DOMAIN).email\n except EmailAddress.DoesNotExist:\n return '{}@{}'.format(\n user.email.split('@')[0],\n settings.MI_GOOGLE_EMAIL_DOMAIN)","title":""},{"docid":"faf3cb061f8fc078c0b88b83ecb5a6b7","score":"0.6881131","text":"def user_id(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"faf3cb061f8fc078c0b88b83ecb5a6b7","score":"0.6881131","text":"def user_id(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"4337087a63c270ac8ce8c7a47fa29531","score":"0.68805474","text":"def get_id(self):\n return self.username","title":""},{"docid":"aa1ac163ba67fbd3b6856057cf62cc99","score":"0.6874144","text":"def _get_device_unique_id(eight: EightSleep, user_obj: EightUser | None = None) -> str:\n unique_id = eight.deviceid\n if user_obj:\n unique_id = f\"{unique_id}.{user_obj.userid}.{user_obj.side}\"\n return unique_id","title":""},{"docid":"5ae5abefc2c5526840abe9af31ec48d2","score":"0.6855741","text":"def get_id(self):\n \t\treturn self.username","title":""},{"docid":"ef8ab76f38af0669c7ddd2f745798f17","score":"0.68553174","text":"def uid(self):\n return int((self._token or self.token).split(\"-\")[0])","title":""},{"docid":"e727a76264816a82ac49f2627c8f24e3","score":"0.6850257","text":"def uid2username(userid):\n return pwd.getpwuid(userid)[0]","title":""},{"docid":"71e2aa4ad773b210692c16cc821ddc96","score":"0.6844965","text":"def _get_device_unique_id(eight: EightSleep, user_obj: EightUser | None = None) -> str:\n unique_id = eight.device_id\n assert unique_id\n if user_obj:\n unique_id = f\"{unique_id}.{user_obj.user_id}.{user_obj.side}\"\n return unique_id","title":""},{"docid":"89650304e74c5836fd3878ecc0888ab4","score":"0.6819636","text":"def userIdGenerator():\n return str(uuid4())","title":""},{"docid":"7d9414019c23fa9080e4693a6d4139b0","score":"0.681149","text":"def get_user_id(conn, cur, username):\n query = \"SELECT id FROM users WHERE name = (\\\"%s\\\");\" % username\n cur.execute(query)\n answer = cur.fetchone()[0]\n return answer","title":""},{"docid":"7445cc90b63dda363c5f3b0055f4949f","score":"0.68106097","text":"def getuserid(twitSN):\r\n getid=api.get_user(twitSN)\r\n testid=getid.id\r\n return testid","title":""},{"docid":"c999afc372a291820085bb79762d79b6","score":"0.6798213","text":"def get_user_id(self, frame):\n try:\n user_card = frame.find(class_=self.userurl_pattern)['usercard']\n find = re.search(r'id=(\\d{1,11})&', user_card)\n if find:\n user_id = find.group(1)\n else:\n user_id = 'NO_USERID'\n except (KeyError, TypeError, AttributeError) as e:\n user_id = 'NO_USERID'\n return user_id","title":""},{"docid":"c851ea71f29cbfaf93da82978550c034","score":"0.67966425","text":"def get_uid() -> str:\r\n if 'INTEGRATION_TEST' in os.environ:\r\n return \"dummy_test_uid\"\r\n if not 'idtoken' in flask.request.json:\r\n raise oauth2client.crypt.AppIdentityError('No OAauth2 token.')\r\n token = flask.request.json['idtoken']\r\n idinfo = oauth2client.client.verify_id_token(token, None)\r\n if idinfo['aud'] not in ACCEPTED_OAUTH_CLIENTS:\r\n raise oauth2client.crypt.AppIdentityError('Unrecognized client.')\r\n if idinfo['iss'] not in ['accounts.google.com',\r\n 'https://accounts.google.com']:\r\n raise oauth2client.crypt.AppIdentityError('Wrong issuer.')\r\n return idinfo['sub']","title":""},{"docid":"7f4fbd7151a752bc2e8389c227f8e3fa","score":"0.6794962","text":"def user_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"7f4fbd7151a752bc2e8389c227f8e3fa","score":"0.6794962","text":"def user_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"7f4fbd7151a752bc2e8389c227f8e3fa","score":"0.6794962","text":"def user_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"user_id\")","title":""},{"docid":"cae7ed4e863e61e9aeb9f874a98cecf9","score":"0.679323","text":"def get_id(self):\n return self.user_id","title":""},{"docid":"cdba85115759b1cb5e0202721a53f7ee","score":"0.67575896","text":"def get_user_id():\n uid = os.environ.get(\"SYNDICATE_USER_ID\")\n if uid is None:\n raise Exception(\"BUG: SYNDICATE_USER_ID is not set\")\n\n try:\n uid = int(uid)\n except:\n raise Exception(\"BUG: SYNDICATE_USER_ID is '%s', expected int\" % uid)\n\n return uid","title":""},{"docid":"8e5fca7c23e1eba36a62579fadf55c13","score":"0.6741697","text":"def id(self):\n safe_username = self.username[:-3] + ('*' * 3)\n institution_name = self.__class__.__name__\n\n return \"%s@%s\" % (safe_username, institution_name)","title":""},{"docid":"47a1fe2a3847cd40bd9587f678d5773d","score":"0.6711067","text":"def store_id(self, user): # pylint: disable=no-self-use\n if isinstance(user, int):\n return f\"{user}-suggestions\"\n return f\"{user.id}-suggestions\"","title":""},{"docid":"9f7b9c19e662e4a27cbb88990f67eb42","score":"0.67085725","text":"def username_to_id(self, username):\n users = self.users\n if username[0] == \"@\":\n username = username[1:]\n for user in users:\n if user[\"name\"] == username:\n return user[\"id\"]\n return None","title":""},{"docid":"ffa4ef3abcc36e5dcd20e35565b6a5b4","score":"0.6707774","text":"def grab_user_id(row):\r\n\r\n return row.split('\\t')[0]","title":""},{"docid":"15422d231237209fb3ec0990b359f675","score":"0.670044","text":"def get_user_id(username):\n r = query_db('select user_id from user where user_name = ?',\n [username], one=True)\n return r if r else None","title":""},{"docid":"1072d825d4b8dfb3a69c36da72bf4434","score":"0.6699767","text":"async def discord_id(self, ctx: commands.Context, user: Optional[discord.User]):\n if user is None:\n user = ctx.author\n\n return await ctx.send(user.id)","title":""},{"docid":"1c11538ce747ff6819f93fcea63c7b81","score":"0.6698008","text":"def get_user_id(username):\n rv = query_db('select user_id from user where username = ?',\n [username], one=True)\n return rv[0] if rv else None","title":""},{"docid":"9f8ba663e8976bc459f5c26d7c7d3924","score":"0.66786426","text":"def get_discourse_user_id(user):\n user_url = '{0}/u/by-external/{1}.json'.format(settings.DISCOURSE_BASE_URL, user.id)\n resp = requests.get(user_url, params=discourse_auth())\n return resp.json().get('user',{}).get('id')","title":""},{"docid":"e5d1266ee8af9cc6497236722375ced9","score":"0.6667512","text":"def get_user_id(self, username):\n\n\n self.cursor.execute(\"SELECT uid FROM users WHERE username=?\", [username])\n result = self.cursor.fetchone()\n if result == None:\n return False\n return int(result[0])","title":""},{"docid":"44d010132a770f745e00f27ffa835527","score":"0.66531116","text":"def get_authid(self, _user: int):\n link = self.users.filter_by(user=_user).first()\n return link.authid if link is not None else None","title":""},{"docid":"04d7d04b560bb244ea58ec54d26c24d9","score":"0.66461784","text":"def userid(self, username):\n reposql = s.sql.text('SELECT users.id FROM users WHERE users.login = :username')\n userid = 0\n result = self.db.execute(reposql, username=username)\n for row in result:\n userid = row[0]\n return userid","title":""},{"docid":"b9cff38411f95fe741123845cd255d22","score":"0.66454273","text":"def get_user_id(self):\n\n return self.__get_property('id')","title":""},{"docid":"ab3310bc1524a0556f34952c5bb97e0a","score":"0.6626989","text":"def get_user_id(gi,email):\n try:\n return get_user(gi,email).id\n except AttributeError:\n return None","title":""},{"docid":"842fb69c9a963393c0c8b145a4e120ff","score":"0.66264874","text":"def get_user_id(username):\n conn, cursor = connect()\n\n cursor.execute(f'SELECT id FROM USERS WHERE username=\"{username}\"')\n user_id = cursor.fetchone()[0]\n disconnect(conn)\n\n return user_id","title":""},{"docid":"0a56328166c9c2fe8ea2dce3b96dcf06","score":"0.6612601","text":"def user_id(self):\r\n return self._user_id","title":""},{"docid":"91046d0b354f2c95579b554bd0d0595e","score":"0.66031104","text":"def user_id(self) -> str:\n return USPSClientEnums.USPSUserId.value","title":""},{"docid":"a0516cbf748afe58a27f2f59eaead3c9","score":"0.65983355","text":"async def myid(self, ctx, user: discord.Member):\n if user == None:\n user=ctx.message.author\n await self.bot.say(\"ID: {}\".format(user.id))","title":""},{"docid":"cbe07d9bbe7ff298d0455e67c286b561","score":"0.65872085","text":"def generate_uid(salt=settings.SECRET_KEY):\n min_length = 20\n alphabet = 'abcdefghijklmnopqrstuvwxyz0123456789'\n hashids = Hashids(salt=salt, min_length=min_length, alphabet=alphabet)\n uid = hashids.encode(int(time() * 1000))\n return uid","title":""},{"docid":"3fdfebc92592f9906e0360ff168ee78a","score":"0.658529","text":"def get_user_id(email_address):\n user = db.select_one('users', 'email_address', email_address) # selects a specific row of the users table\n return user[0] # picks the value in the 1st column of that row","title":""},{"docid":"0cb15dba92c212ba843fa7029294e83b","score":"0.65849525","text":"def get_user_id(self, obj):\n\n if obj[\"specialist_id\"]:\n return obj[\"specialist_id\"]\n return obj[\"query__client_id\"]","title":""},{"docid":"f89f5c9e14e176178e1ebfcd10ef68e7","score":"0.6584642","text":"def _get_my_user_id(self) -> int:\n # Does it need an email to resolve?\n url = f\"{self.url}/users/me.json\"\n response = self._get_api_call(url=url).json()\n my_id = response.get(\"user\", {}).get(\"id\")\n return my_id","title":""},{"docid":"6d7b56a759e2b7ac8202b7e4aba373cd","score":"0.658401","text":"def _user_oid(token) -> str:\n data = _get_parsed_token_data(token)\n return data.get(\"oid\")","title":""},{"docid":"4d6a7bfbf3c56b958c4e56793f15a233","score":"0.6582805","text":"def genuuid():\n return str(uuid.uuid4())","title":""},{"docid":"0998ca71d3f1c576eb8164c21894a661","score":"0.6574318","text":"def UID():","title":""},{"docid":"e227d8846ea0b872ea27d17faa0a5d33","score":"0.65665585","text":"def get_user_identifier(self):\n value = self.config.get(\"user\", \"identifier\", fallback=None)\n if value is None:\n value = str(random.randint(1000, 9999)) + \"-\" + str(random.randint(100, 999)) + \"-\" + str(random.randint(10, 99))\n self.config[\"user\"][\"identifier\"] = value\n self._flush()\n self.logger.info(\"No user identifier stored, generated new identifier: \" + value)\n return value","title":""},{"docid":"1908b0724b5d19e1e969f50ea8257000","score":"0.6561779","text":"def get_user_id(self, details, response):\n return self._user_id(response)","title":""},{"docid":"c74a19f18f403de56b6126c7137fb927","score":"0.6556574","text":"def get_id(self):\r\n if self.username is not None:\r\n # 应修改为自己电脑上.db文件的地址\r\n conn = sqlite3.connect(\"../../RUCCA.db\")\r\n cursor = conn.cursor()\r\n cursor.execute('''\r\n SELECT id\r\n FROM person_info\r\n WHERE username = ?\r\n ''', (self.username,))\r\n value = cursor.fetchone()\r\n conn.commit()\r\n conn.close()\r\n if value is not None:\r\n return str(value[0])\r\n return \"0\"","title":""},{"docid":"e27d09f5b03a8956e80ac2d31130ee06","score":"0.6555434","text":"def get_id(self):\n\n return self.user_id","title":""},{"docid":"4fa3f8d700c2fb9c7df4b6481d6ee9ad","score":"0.65534794","text":"def convert_id(author):\n return str(author).split(\"#\")[1]","title":""},{"docid":"24f6ed36b4130fb70fd123f4266ef353","score":"0.65501165","text":"def get_client_id(username):\n base_str = '23456789ABCDEFGHJKMNPQRSTUVWXYZ' \n client_id = username + ''.join(random.choice(base_str) for x in range(10)) \n return client_id","title":""},{"docid":"ccb3e7d3faba9094029c4c49d4d33523","score":"0.65471417","text":"def user_id(self):\n return self._user_id","title":""},{"docid":"ccb3e7d3faba9094029c4c49d4d33523","score":"0.65471417","text":"def user_id(self):\n return self._user_id","title":""},{"docid":"ccb3e7d3faba9094029c4c49d4d33523","score":"0.65471417","text":"def user_id(self):\n return self._user_id","title":""},{"docid":"ccb3e7d3faba9094029c4c49d4d33523","score":"0.65471417","text":"def user_id(self):\n return self._user_id","title":""},{"docid":"7e1bf328578ff7124612ec0f1b5bf4f3","score":"0.654646","text":"def generate_uid():\n return f\"{datetime.datetime.now():%y-%-m-%-d-%-H-%M-%S}\"","title":""},{"docid":"cdea76a56f15b2dfe41296a7bbc3ba21","score":"0.6531067","text":"def game_key(self, user_id):\n return user_id.lstrip('+')","title":""},{"docid":"258d34ad371a17a747a01b299621e7a3","score":"0.65310234","text":"def create_uid(length = 8) :\n length = ((length <= 0) and 8) or length\n n = create_valid_id((36 ** length) - 1)\n return(((\"0\" * length) + base_36_encode(n).lower())[-length:])","title":""},{"docid":"63eac36a20e38c15ac92ef5fc5062a6a","score":"0.6525266","text":"def get_user_id(self):\n return self._user_id","title":""},{"docid":"3b928f9c1708f4e1da0e182658a471f2","score":"0.6517217","text":"def uid(self) -> Optional[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"b2606b594b09618f0f4fa8991d2e6ba8","score":"0.6514275","text":"def username2uid(username):\n return pwd.getpwnam(username)[2]","title":""},{"docid":"16d2d5793c3f1c2345e30f45b2182006","score":"0.65139365","text":"def get_user_id(self, username):\n for user in Users.users:\n if user['username'] == username:\n return user['user_id']","title":""},{"docid":"57ea510acaf87ef6c718e46e2d269f1d","score":"0.6506556","text":"def get_user_id(username):\n try:\n user = User.select().where(User.username == username).get();\n return user.id\n except User.DoesNotExist:\n user = None\n return None","title":""},{"docid":"8b65d7cac789da5345a2e640f7b6b61a","score":"0.65065217","text":"def _generate_job_id(self, user_id, job_type):\n return \"%s-%s\" % (job_type, user_id)","title":""}],"string":"[\n {\n \"docid\": \"4f71559f237c6bd50f41ad164cdfc301\",\n \"score\": \"0.7902427\",\n \"text\": \"def get_id(self):\\n return unichr(self.user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2591c6a2c9484be5577e4c7b5c3b36\",\n \"score\": \"0.7891965\",\n \"text\": \"def get_id(self):\\n\\n if self.user_id is None:\\n return ''\\n if version_info >= (3, 0):\\n return str(self.user_id)\\n return unicode(self.user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa47d4f25257555f421e13d29718ad9\",\n \"score\": \"0.7748601\",\n \"text\": \"def get_user_id(self):\\n return str(self.user_id.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154a0052102aad84e57ada50b55bcf7c\",\n \"score\": \"0.7662898\",\n \"text\": \"def user_id(self):\\n return unicode(self._id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d87ab94c849aff68415dcf143e6718\",\n \"score\": \"0.7645075\",\n \"text\": \"def get_uid() -> int:\\n return USER_ID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e7dd04a2b94d41a18fc10ba815e4ee\",\n \"score\": \"0.75860614\",\n \"text\": \"def user_id(self) -> str:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"414816f45da9844a57ce6043d58d5109\",\n \"score\": \"0.75139123\",\n \"text\": \"def get_user_id(user_name):\\n return name_dict[user_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"721f478e2a8c2c0593a680740896b4f5\",\n \"score\": \"0.7483968\",\n \"text\": \"def GetUserId():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ee62b9ee1abd5c5ca815bc757838ad\",\n \"score\": \"0.7476764\",\n \"text\": \"def user_to_uid(self, user):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8670ddf41c73d2d789cdf184deabdf57\",\n \"score\": \"0.7399731\",\n \"text\": \"def user_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8670ddf41c73d2d789cdf184deabdf57\",\n \"score\": \"0.7399731\",\n \"text\": \"def user_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"452684f99db1ee430cd2af6c48a7a7ec\",\n \"score\": \"0.7333088\",\n \"text\": \"def get_user_id_by_name(self, user_name):\\n assert isinstance(user_name, str), \\\"`user_name` must be a valid username rather than \\\\\\\"{}\\\\\\\"\\\".format(user_name)\\n user_name = user_name.strip().lstrip(\\\"@\\\")\\n return \\\"U{}\\\".format(user_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efae77dcfd5d108f1c8beae312171410\",\n \"score\": \"0.7276379\",\n \"text\": \"def author_id_to_username(user_id=None):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85df049f0a3fd7fa8a9ffb67e32ed12e\",\n \"score\": \"0.7224942\",\n \"text\": \"def user_id(self) -> str:\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85df049f0a3fd7fa8a9ffb67e32ed12e\",\n \"score\": \"0.7224942\",\n \"text\": \"def user_id(self) -> str:\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82682a60ef66e0e8d323f4a74eead598\",\n \"score\": \"0.7216896\",\n \"text\": \"def resolve_user_id(user_id):\\n # TODO (neitsch): This method is not implemented yet. Use the user_id\\n # (format: 12345678@facebook.com) to get the actual name of the user\\n return user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"583592a4e74afda820c27fdcbf9a354e\",\n \"score\": \"0.72047234\",\n \"text\": \"def to_key_id(user_key):\\n return \\\"col_user__{}\\\".format(user_key.urlsafe())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9e6c2c44a3070385b7353bfdb286b1\",\n \"score\": \"0.7190837\",\n \"text\": \"def user_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9e6c2c44a3070385b7353bfdb286b1\",\n \"score\": \"0.7190837\",\n \"text\": \"def user_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe0eb91e98f5de51cd4322a89bf83cba\",\n \"score\": \"0.71809566\",\n \"text\": \"def user_id(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9eaa003df1854bd982aff83d8a07296\",\n \"score\": \"0.7171286\",\n \"text\": \"def get_userid(user):\\n database = get_db()\\n cur = database.execute('SELECT id from users where user = ?', [user])\\n\\n userid = cur.fetchone()\\n if userid is None:\\n return -1\\n return userid['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1981fb3fd41d760429423b215173843\",\n \"score\": \"0.71576446\",\n \"text\": \"def user_id(self):\\n return self._parsed_req.get('user_id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4169e0349fd763f274a24f746ff6d274\",\n \"score\": \"0.713344\",\n \"text\": \"def get_userId(username):\\n return make_query(f\\\"\\\"\\\"SELECT id FROM user where username='{username}'\\\"\\\"\\\", 0, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de7f4277561182d45cba2f5c3842073\",\n \"score\": \"0.71233433\",\n \"text\": \"def get_user(id):\\n return \\\"%s\\\" % users[id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21da64b0657eb54209ddffaea3c4efa8\",\n \"score\": \"0.7121699\",\n \"text\": \"def encode_type2_user_id(user_id):\\n return u\\\"{0:04d}-{1:02d}\\\".format(user_id, Type2Code.calculate(user_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43db0a92e0b15cdaf8c7d3c6819f902\",\n \"score\": \"0.71001905\",\n \"text\": \"def format_id_user(id_value):\\n id_str = str(id_value)\\n while len(id_str) < 6:\\n id_str = u'0' + id_str\\n return id_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b8cfe1bfed5cd0a331cd25a56fb93d\",\n \"score\": \"0.70772094\",\n \"text\": \"def id(self):\\n return str(self._user.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fab17f778626802a6867bcbc87edf50b\",\n \"score\": \"0.7054668\",\n \"text\": \"def get_uid():\\n return uuid.uuid4().hex[:7]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5054dd7f64c73367b6d45493a31dbc47\",\n \"score\": \"0.7032906\",\n \"text\": \"def extract_uid(self, data):\\n return str(data[\\\"username\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60dc880a7869710c3a5153bdf8097bae\",\n \"score\": \"0.7029802\",\n \"text\": \"def __id_cleanse(user_id):\\n if regex.match(r'^<@!\\\\d\\\\d\\\\d+>$', user_id):\\n return user_id.replace('!', '')\\n return user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e6f022e2851a31f7201dcf16c4b791\",\n \"score\": \"0.70205665\",\n \"text\": \"def get_user_name_by_id(self, user_id):\\n assert isinstance(user_id, str) and user_id[0] == \\\"U\\\", \\\"`user_id` must be a valid user ID rather than \\\\\\\"{}\\\\\\\"\\\".format(user_id)\\n return user_id[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29951fe7b8d07eaaf43176bb031bedfb\",\n \"score\": \"0.7006138\",\n \"text\": \"def user_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29951fe7b8d07eaaf43176bb031bedfb\",\n \"score\": \"0.7006138\",\n \"text\": \"def user_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"563c14638ea79b3fc39178463c249b28\",\n \"score\": \"0.69851923\",\n \"text\": \"def get_user_id(headers: dict) -> str:\\n response = requests.get(f'{url_base}/me', headers=headers)\\n error_handle('user info', 200, 'GET', response=response)\\n return json.loads(response.content.decode('utf-8'))['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66ba1701da6e110335413e6dd7303e7\",\n \"score\": \"0.69787747\",\n \"text\": \"def get_id():\\r\\n if auth.user:\\r\\n return auth.user.id\\r\\n else:\\r\\n return 'None'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dce095dd091e09de8e5b1df7c67e64d\",\n \"score\": \"0.69711435\",\n \"text\": \"def get_user_id(self, email):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e74dab2aa21c7675051c741006e2c7\",\n \"score\": \"0.6914548\",\n \"text\": \"def encode_type1_user_id(user_id):\\n return u\\\"{0:04d}-{1:d}\\\".format(user_id, Type1Code.calculate(user_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961f38ef1496058584d1e4a32ec430af\",\n \"score\": \"0.6912966\",\n \"text\": \"def get_user_id(self, name: str) -> str:\\n users = self.client.fetchAllUsers()\\n for user in users:\\n if user.name == name:\\n return str(user.uid)\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbaea34943f2cfee01d16810c649071d\",\n \"score\": \"0.6888811\",\n \"text\": \"def get_userid():\\n return os.getuid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e99b6e6b17e516000c5a8a70d12a85\",\n \"score\": \"0.6885755\",\n \"text\": \"def build_google_user_id(user):\\n\\n try:\\n return user.emails.get(email__endswith='@'+settings.MI_GOOGLE_EMAIL_DOMAIN).email\\n except EmailAddress.DoesNotExist:\\n return '{}@{}'.format(\\n user.email.split('@')[0],\\n settings.MI_GOOGLE_EMAIL_DOMAIN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf3cb061f8fc078c0b88b83ecb5a6b7\",\n \"score\": \"0.6881131\",\n \"text\": \"def user_id(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf3cb061f8fc078c0b88b83ecb5a6b7\",\n \"score\": \"0.6881131\",\n \"text\": \"def user_id(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4337087a63c270ac8ce8c7a47fa29531\",\n \"score\": \"0.68805474\",\n \"text\": \"def get_id(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1ac163ba67fbd3b6856057cf62cc99\",\n \"score\": \"0.6874144\",\n \"text\": \"def _get_device_unique_id(eight: EightSleep, user_obj: EightUser | None = None) -> str:\\n unique_id = eight.deviceid\\n if user_obj:\\n unique_id = f\\\"{unique_id}.{user_obj.userid}.{user_obj.side}\\\"\\n return unique_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae5abefc2c5526840abe9af31ec48d2\",\n \"score\": \"0.6855741\",\n \"text\": \"def get_id(self):\\n \\t\\treturn self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef8ab76f38af0669c7ddd2f745798f17\",\n \"score\": \"0.68553174\",\n \"text\": \"def uid(self):\\n return int((self._token or self.token).split(\\\"-\\\")[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e727a76264816a82ac49f2627c8f24e3\",\n \"score\": \"0.6850257\",\n \"text\": \"def uid2username(userid):\\n return pwd.getpwuid(userid)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e2aa4ad773b210692c16cc821ddc96\",\n \"score\": \"0.6844965\",\n \"text\": \"def _get_device_unique_id(eight: EightSleep, user_obj: EightUser | None = None) -> str:\\n unique_id = eight.device_id\\n assert unique_id\\n if user_obj:\\n unique_id = f\\\"{unique_id}.{user_obj.user_id}.{user_obj.side}\\\"\\n return unique_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89650304e74c5836fd3878ecc0888ab4\",\n \"score\": \"0.6819636\",\n \"text\": \"def userIdGenerator():\\n return str(uuid4())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d9414019c23fa9080e4693a6d4139b0\",\n \"score\": \"0.681149\",\n \"text\": \"def get_user_id(conn, cur, username):\\n query = \\\"SELECT id FROM users WHERE name = (\\\\\\\"%s\\\\\\\");\\\" % username\\n cur.execute(query)\\n answer = cur.fetchone()[0]\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7445cc90b63dda363c5f3b0055f4949f\",\n \"score\": \"0.68106097\",\n \"text\": \"def getuserid(twitSN):\\r\\n getid=api.get_user(twitSN)\\r\\n testid=getid.id\\r\\n return testid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c999afc372a291820085bb79762d79b6\",\n \"score\": \"0.6798213\",\n \"text\": \"def get_user_id(self, frame):\\n try:\\n user_card = frame.find(class_=self.userurl_pattern)['usercard']\\n find = re.search(r'id=(\\\\d{1,11})&', user_card)\\n if find:\\n user_id = find.group(1)\\n else:\\n user_id = 'NO_USERID'\\n except (KeyError, TypeError, AttributeError) as e:\\n user_id = 'NO_USERID'\\n return user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c851ea71f29cbfaf93da82978550c034\",\n \"score\": \"0.67966425\",\n \"text\": \"def get_uid() -> str:\\r\\n if 'INTEGRATION_TEST' in os.environ:\\r\\n return \\\"dummy_test_uid\\\"\\r\\n if not 'idtoken' in flask.request.json:\\r\\n raise oauth2client.crypt.AppIdentityError('No OAauth2 token.')\\r\\n token = flask.request.json['idtoken']\\r\\n idinfo = oauth2client.client.verify_id_token(token, None)\\r\\n if idinfo['aud'] not in ACCEPTED_OAUTH_CLIENTS:\\r\\n raise oauth2client.crypt.AppIdentityError('Unrecognized client.')\\r\\n if idinfo['iss'] not in ['accounts.google.com',\\r\\n 'https://accounts.google.com']:\\r\\n raise oauth2client.crypt.AppIdentityError('Wrong issuer.')\\r\\n return idinfo['sub']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f4fbd7151a752bc2e8389c227f8e3fa\",\n \"score\": \"0.6794962\",\n \"text\": \"def user_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f4fbd7151a752bc2e8389c227f8e3fa\",\n \"score\": \"0.6794962\",\n \"text\": \"def user_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f4fbd7151a752bc2e8389c227f8e3fa\",\n \"score\": \"0.6794962\",\n \"text\": \"def user_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae7ed4e863e61e9aeb9f874a98cecf9\",\n \"score\": \"0.679323\",\n \"text\": \"def get_id(self):\\n return self.user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdba85115759b1cb5e0202721a53f7ee\",\n \"score\": \"0.67575896\",\n \"text\": \"def get_user_id():\\n uid = os.environ.get(\\\"SYNDICATE_USER_ID\\\")\\n if uid is None:\\n raise Exception(\\\"BUG: SYNDICATE_USER_ID is not set\\\")\\n\\n try:\\n uid = int(uid)\\n except:\\n raise Exception(\\\"BUG: SYNDICATE_USER_ID is '%s', expected int\\\" % uid)\\n\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5fca7c23e1eba36a62579fadf55c13\",\n \"score\": \"0.6741697\",\n \"text\": \"def id(self):\\n safe_username = self.username[:-3] + ('*' * 3)\\n institution_name = self.__class__.__name__\\n\\n return \\\"%s@%s\\\" % (safe_username, institution_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47a1fe2a3847cd40bd9587f678d5773d\",\n \"score\": \"0.6711067\",\n \"text\": \"def store_id(self, user): # pylint: disable=no-self-use\\n if isinstance(user, int):\\n return f\\\"{user}-suggestions\\\"\\n return f\\\"{user.id}-suggestions\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f7b9c19e662e4a27cbb88990f67eb42\",\n \"score\": \"0.67085725\",\n \"text\": \"def username_to_id(self, username):\\n users = self.users\\n if username[0] == \\\"@\\\":\\n username = username[1:]\\n for user in users:\\n if user[\\\"name\\\"] == username:\\n return user[\\\"id\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa4ef3abcc36e5dcd20e35565b6a5b4\",\n \"score\": \"0.6707774\",\n \"text\": \"def grab_user_id(row):\\r\\n\\r\\n return row.split('\\\\t')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15422d231237209fb3ec0990b359f675\",\n \"score\": \"0.670044\",\n \"text\": \"def get_user_id(username):\\n r = query_db('select user_id from user where user_name = ?',\\n [username], one=True)\\n return r if r else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1072d825d4b8dfb3a69c36da72bf4434\",\n \"score\": \"0.6699767\",\n \"text\": \"async def discord_id(self, ctx: commands.Context, user: Optional[discord.User]):\\n if user is None:\\n user = ctx.author\\n\\n return await ctx.send(user.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c11538ce747ff6819f93fcea63c7b81\",\n \"score\": \"0.6698008\",\n \"text\": \"def get_user_id(username):\\n rv = query_db('select user_id from user where username = ?',\\n [username], one=True)\\n return rv[0] if rv else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8ba663e8976bc459f5c26d7c7d3924\",\n \"score\": \"0.66786426\",\n \"text\": \"def get_discourse_user_id(user):\\n user_url = '{0}/u/by-external/{1}.json'.format(settings.DISCOURSE_BASE_URL, user.id)\\n resp = requests.get(user_url, params=discourse_auth())\\n return resp.json().get('user',{}).get('id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d1266ee8af9cc6497236722375ced9\",\n \"score\": \"0.6667512\",\n \"text\": \"def get_user_id(self, username):\\n\\n\\n self.cursor.execute(\\\"SELECT uid FROM users WHERE username=?\\\", [username])\\n result = self.cursor.fetchone()\\n if result == None:\\n return False\\n return int(result[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d010132a770f745e00f27ffa835527\",\n \"score\": \"0.66531116\",\n \"text\": \"def get_authid(self, _user: int):\\n link = self.users.filter_by(user=_user).first()\\n return link.authid if link is not None else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d7d04b560bb244ea58ec54d26c24d9\",\n \"score\": \"0.66461784\",\n \"text\": \"def userid(self, username):\\n reposql = s.sql.text('SELECT users.id FROM users WHERE users.login = :username')\\n userid = 0\\n result = self.db.execute(reposql, username=username)\\n for row in result:\\n userid = row[0]\\n return userid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cff38411f95fe741123845cd255d22\",\n \"score\": \"0.66454273\",\n \"text\": \"def get_user_id(self):\\n\\n return self.__get_property('id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab3310bc1524a0556f34952c5bb97e0a\",\n \"score\": \"0.6626989\",\n \"text\": \"def get_user_id(gi,email):\\n try:\\n return get_user(gi,email).id\\n except AttributeError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842fb69c9a963393c0c8b145a4e120ff\",\n \"score\": \"0.66264874\",\n \"text\": \"def get_user_id(username):\\n conn, cursor = connect()\\n\\n cursor.execute(f'SELECT id FROM USERS WHERE username=\\\"{username}\\\"')\\n user_id = cursor.fetchone()[0]\\n disconnect(conn)\\n\\n return user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a56328166c9c2fe8ea2dce3b96dcf06\",\n \"score\": \"0.6612601\",\n \"text\": \"def user_id(self):\\r\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91046d0b354f2c95579b554bd0d0595e\",\n \"score\": \"0.66031104\",\n \"text\": \"def user_id(self) -> str:\\n return USPSClientEnums.USPSUserId.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0516cbf748afe58a27f2f59eaead3c9\",\n \"score\": \"0.65983355\",\n \"text\": \"async def myid(self, ctx, user: discord.Member):\\n if user == None:\\n user=ctx.message.author\\n await self.bot.say(\\\"ID: {}\\\".format(user.id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe07d9bbe7ff298d0455e67c286b561\",\n \"score\": \"0.65872085\",\n \"text\": \"def generate_uid(salt=settings.SECRET_KEY):\\n min_length = 20\\n alphabet = 'abcdefghijklmnopqrstuvwxyz0123456789'\\n hashids = Hashids(salt=salt, min_length=min_length, alphabet=alphabet)\\n uid = hashids.encode(int(time() * 1000))\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fdfebc92592f9906e0360ff168ee78a\",\n \"score\": \"0.658529\",\n \"text\": \"def get_user_id(email_address):\\n user = db.select_one('users', 'email_address', email_address) # selects a specific row of the users table\\n return user[0] # picks the value in the 1st column of that row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb15dba92c212ba843fa7029294e83b\",\n \"score\": \"0.65849525\",\n \"text\": \"def get_user_id(self, obj):\\n\\n if obj[\\\"specialist_id\\\"]:\\n return obj[\\\"specialist_id\\\"]\\n return obj[\\\"query__client_id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f89f5c9e14e176178e1ebfcd10ef68e7\",\n \"score\": \"0.6584642\",\n \"text\": \"def _get_my_user_id(self) -> int:\\n # Does it need an email to resolve?\\n url = f\\\"{self.url}/users/me.json\\\"\\n response = self._get_api_call(url=url).json()\\n my_id = response.get(\\\"user\\\", {}).get(\\\"id\\\")\\n return my_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7b56a759e2b7ac8202b7e4aba373cd\",\n \"score\": \"0.658401\",\n \"text\": \"def _user_oid(token) -> str:\\n data = _get_parsed_token_data(token)\\n return data.get(\\\"oid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d6a7bfbf3c56b958c4e56793f15a233\",\n \"score\": \"0.6582805\",\n \"text\": \"def genuuid():\\n return str(uuid.uuid4())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0998ca71d3f1c576eb8164c21894a661\",\n \"score\": \"0.6574318\",\n \"text\": \"def UID():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e227d8846ea0b872ea27d17faa0a5d33\",\n \"score\": \"0.65665585\",\n \"text\": \"def get_user_identifier(self):\\n value = self.config.get(\\\"user\\\", \\\"identifier\\\", fallback=None)\\n if value is None:\\n value = str(random.randint(1000, 9999)) + \\\"-\\\" + str(random.randint(100, 999)) + \\\"-\\\" + str(random.randint(10, 99))\\n self.config[\\\"user\\\"][\\\"identifier\\\"] = value\\n self._flush()\\n self.logger.info(\\\"No user identifier stored, generated new identifier: \\\" + value)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1908b0724b5d19e1e969f50ea8257000\",\n \"score\": \"0.6561779\",\n \"text\": \"def get_user_id(self, details, response):\\n return self._user_id(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c74a19f18f403de56b6126c7137fb927\",\n \"score\": \"0.6556574\",\n \"text\": \"def get_id(self):\\r\\n if self.username is not None:\\r\\n # 应修改为自己电脑上.db文件的地址\\r\\n conn = sqlite3.connect(\\\"../../RUCCA.db\\\")\\r\\n cursor = conn.cursor()\\r\\n cursor.execute('''\\r\\n SELECT id\\r\\n FROM person_info\\r\\n WHERE username = ?\\r\\n ''', (self.username,))\\r\\n value = cursor.fetchone()\\r\\n conn.commit()\\r\\n conn.close()\\r\\n if value is not None:\\r\\n return str(value[0])\\r\\n return \\\"0\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27d09f5b03a8956e80ac2d31130ee06\",\n \"score\": \"0.6555434\",\n \"text\": \"def get_id(self):\\n\\n return self.user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa3f8d700c2fb9c7df4b6481d6ee9ad\",\n \"score\": \"0.65534794\",\n \"text\": \"def convert_id(author):\\n return str(author).split(\\\"#\\\")[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f6ed36b4130fb70fd123f4266ef353\",\n \"score\": \"0.65501165\",\n \"text\": \"def get_client_id(username):\\n base_str = '23456789ABCDEFGHJKMNPQRSTUVWXYZ' \\n client_id = username + ''.join(random.choice(base_str) for x in range(10)) \\n return client_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb3e7d3faba9094029c4c49d4d33523\",\n \"score\": \"0.65471417\",\n \"text\": \"def user_id(self):\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb3e7d3faba9094029c4c49d4d33523\",\n \"score\": \"0.65471417\",\n \"text\": \"def user_id(self):\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb3e7d3faba9094029c4c49d4d33523\",\n \"score\": \"0.65471417\",\n \"text\": \"def user_id(self):\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb3e7d3faba9094029c4c49d4d33523\",\n \"score\": \"0.65471417\",\n \"text\": \"def user_id(self):\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e1bf328578ff7124612ec0f1b5bf4f3\",\n \"score\": \"0.654646\",\n \"text\": \"def generate_uid():\\n return f\\\"{datetime.datetime.now():%y-%-m-%-d-%-H-%M-%S}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdea76a56f15b2dfe41296a7bbc3ba21\",\n \"score\": \"0.6531067\",\n \"text\": \"def game_key(self, user_id):\\n return user_id.lstrip('+')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258d34ad371a17a747a01b299621e7a3\",\n \"score\": \"0.65310234\",\n \"text\": \"def create_uid(length = 8) :\\n length = ((length <= 0) and 8) or length\\n n = create_valid_id((36 ** length) - 1)\\n return(((\\\"0\\\" * length) + base_36_encode(n).lower())[-length:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63eac36a20e38c15ac92ef5fc5062a6a\",\n \"score\": \"0.6525266\",\n \"text\": \"def get_user_id(self):\\n return self._user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b928f9c1708f4e1da0e182658a471f2\",\n \"score\": \"0.6517217\",\n \"text\": \"def uid(self) -> Optional[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2606b594b09618f0f4fa8991d2e6ba8\",\n \"score\": \"0.6514275\",\n \"text\": \"def username2uid(username):\\n return pwd.getpwnam(username)[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d2d5793c3f1c2345e30f45b2182006\",\n \"score\": \"0.65139365\",\n \"text\": \"def get_user_id(self, username):\\n for user in Users.users:\\n if user['username'] == username:\\n return user['user_id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ea510acaf87ef6c718e46e2d269f1d\",\n \"score\": \"0.6506556\",\n \"text\": \"def get_user_id(username):\\n try:\\n user = User.select().where(User.username == username).get();\\n return user.id\\n except User.DoesNotExist:\\n user = None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b65d7cac789da5345a2e640f7b6b61a\",\n \"score\": \"0.65065217\",\n \"text\": \"def _generate_job_id(self, user_id, job_type):\\n return \\\"%s-%s\\\" % (job_type, user_id)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1592,"cells":{"query_id":{"kind":"string","value":"877bc523dd420b983205c4724c8c77cc"},"query":{"kind":"string","value":"[Lib] [Execution] End special bonfire_ignition"},"positive_passages":{"kind":"list like","value":[{"docid":"f7310469a480ff478ee01856da1f055a","score":"0.0","text":"def event_m10_14_x34(z132=10141800, z133=100461):\n \"\"\"State 0,6: Bonfire light action\"\"\"\n PlayerActionRequest(5)\n assert PlayerIsInEventAction(5) != 0\n \"\"\"State 7: Wait for action to finish\"\"\"\n CompareStateTime(0, 1.5, 2, 1.5)\n IsPlayerPlayingMotion(1, 5, 0)\n if ConditionGroup(1):\n pass\n elif ConditionGroup(0):\n \"\"\"State 5: Bonfire ignition\"\"\"\n ChangeObjState(z132, 70)\n \"\"\"State 3: Lighting completion flag ON\"\"\"\n SetEventFlag(z133, 1)\n \"\"\"State 1: Production FE display\"\"\"\n OpenPresentationWindow(20)\n assert PresentationWindowDisplay() != 0\n \"\"\"State 4: Waiting for non-display of production FE\"\"\"\n assert PresentationWindowDisplay() != 1\n \"\"\"State 2: Event message display\"\"\"\n # action:2022:\"A primal bonfire was rekindled\"\n DisplayEventMessage(2022, 0, 0, 0)\n assert EventMessageDisplay() != 0\n \"\"\"State 8: End state\"\"\"\n return 0","title":""}],"string":"[\n {\n \"docid\": \"f7310469a480ff478ee01856da1f055a\",\n \"score\": \"0.0\",\n \"text\": \"def event_m10_14_x34(z132=10141800, z133=100461):\\n \\\"\\\"\\\"State 0,6: Bonfire light action\\\"\\\"\\\"\\n PlayerActionRequest(5)\\n assert PlayerIsInEventAction(5) != 0\\n \\\"\\\"\\\"State 7: Wait for action to finish\\\"\\\"\\\"\\n CompareStateTime(0, 1.5, 2, 1.5)\\n IsPlayerPlayingMotion(1, 5, 0)\\n if ConditionGroup(1):\\n pass\\n elif ConditionGroup(0):\\n \\\"\\\"\\\"State 5: Bonfire ignition\\\"\\\"\\\"\\n ChangeObjState(z132, 70)\\n \\\"\\\"\\\"State 3: Lighting completion flag ON\\\"\\\"\\\"\\n SetEventFlag(z133, 1)\\n \\\"\\\"\\\"State 1: Production FE display\\\"\\\"\\\"\\n OpenPresentationWindow(20)\\n assert PresentationWindowDisplay() != 0\\n \\\"\\\"\\\"State 4: Waiting for non-display of production FE\\\"\\\"\\\"\\n assert PresentationWindowDisplay() != 1\\n \\\"\\\"\\\"State 2: Event message display\\\"\\\"\\\"\\n # action:2022:\\\"A primal bonfire was rekindled\\\"\\n DisplayEventMessage(2022, 0, 0, 0)\\n assert EventMessageDisplay() != 0\\n \\\"\\\"\\\"State 8: End state\\\"\\\"\\\"\\n return 0\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"09fa8d184abc69524635a80b17e528e2","score":"0.7245591","text":"def end(self):","title":""},{"docid":"09fa8d184abc69524635a80b17e528e2","score":"0.7245591","text":"def end(self):","title":""},{"docid":"09fa8d184abc69524635a80b17e528e2","score":"0.7245591","text":"def end(self):","title":""},{"docid":"c1ffe01db1c03d1f942c0609715e1f4f","score":"0.7142117","text":"def end_run(self):\n pass","title":""},{"docid":"52f4ccb8651d30d269370523da65d76e","score":"0.6991322","text":"def end_sim():\n\n pass","title":""},{"docid":"93ac33199a765243de1f8d68126a3182","score":"0.6988841","text":"def end():\n with Session() as lib:\n lib.call_module(module=\"end\", args=\"\")","title":""},{"docid":"f2b80e0dbb26b6b5d04ffd6a9c4de3f2","score":"0.6897577","text":"def _end(self):\n pass","title":""},{"docid":"061ddb5b209f48731abde40a73a0e013","score":"0.6816187","text":"def end(self):\n\n core = callback.getCore()\n core.endRequest(self, False)","title":""},{"docid":"061ddb5b209f48731abde40a73a0e013","score":"0.6816187","text":"def end(self):\n\n core = callback.getCore()\n core.endRequest(self, False)","title":""},{"docid":"a3166254e7ac8006193cd867a41a9a6a","score":"0.68160975","text":"def end(self):\n pass","title":""},{"docid":"a3166254e7ac8006193cd867a41a9a6a","score":"0.68160975","text":"def end(self):\n pass","title":""},{"docid":"243edc7cd5194cc3b3db0c8115ac267c","score":"0.67762774","text":"def stop(self):\r\n self.ctx.bot.dispatch(\"trivia_end\", self)","title":""},{"docid":"b2b77b4a7fd2478f16c53c864def3aff","score":"0.67745626","text":"def end(self):\n\n pass","title":""},{"docid":"9c87574d3442b869981d3bb3d2de3478","score":"0.6753734","text":"def _finish():","title":""},{"docid":"9c87574d3442b869981d3bb3d2de3478","score":"0.6753734","text":"def _finish():","title":""},{"docid":"34e16aca7af496dd32ee994152711e5f","score":"0.6727069","text":"def finish():","title":""},{"docid":"c84ac910e34665fe175d7bd3157ecef0","score":"0.6694841","text":"def end_workunit(self, workunit):\n pass","title":""},{"docid":"36dffba7d008b2714f0fd1002b063de1","score":"0.6636289","text":"def leave(self, callsign):","title":""},{"docid":"5847c6dc74d157c86ea2b0faffa12bf8","score":"0.6612259","text":"def end():\n logging.info(\"Ending DAG\")","title":""},{"docid":"1f765266360f7c6a9b1fadd68016f0fc","score":"0.65996623","text":"def end(self, apiInstance):\n pass","title":""},{"docid":"6282853e3ada1f65c6c262293dbe1ea8","score":"0.6587956","text":"def finish(self):","title":""},{"docid":"6282853e3ada1f65c6c262293dbe1ea8","score":"0.6587956","text":"def finish(self):","title":""},{"docid":"6282853e3ada1f65c6c262293dbe1ea8","score":"0.6587956","text":"def finish(self):","title":""},{"docid":"6282853e3ada1f65c6c262293dbe1ea8","score":"0.6587956","text":"def finish(self):","title":""},{"docid":"6282853e3ada1f65c6c262293dbe1ea8","score":"0.6587956","text":"def finish(self):","title":""},{"docid":"4214f8f2b69ee0c48294418c2fec1b03","score":"0.6568385","text":"def on_end(self):\n pass","title":""},{"docid":"4214f8f2b69ee0c48294418c2fec1b03","score":"0.6568385","text":"def on_end(self):\n pass","title":""},{"docid":"81631cf6a45e950cdf98e5d47fcc3eff","score":"0.6547691","text":"def end_run(self, run_id, end_time, message, traceback, stats):","title":""},{"docid":"9836312794f7eeb619a2d26eafd2decb","score":"0.65342546","text":"def end_call_manual(self):\n self.log_message(\n 'Chiamata terminata',\n 'Fine chiamata telefonica o con strumenti social')\n\n duration = datetime.now() - datetime.strptime(\n self.start_datetime, DEFAULT_SERVER_DATETIME_FORMAT)\n\n return self.write({\n 'duration': duration.total_seconds() / 3600.0,\n 'state': 'done',\n })","title":""},{"docid":"f42ed45cdee8d0f48393e8c47b8823c0","score":"0.65288144","text":"def onStop(signum, stackframe):\n\t\n\tsys.exit(0)","title":""},{"docid":"080d7d7cb2e924951778b41797fc484b","score":"0.65230626","text":"def end(self):\r\n self.com(self.sourceS.Standby)\r\n self.com(self.sourceX.Standby)\r\n self.logfile.close()\r\n self.wb.save(filename = str(self.raw_file_name+'.xlsx'))\r\n wx.CallAfter(self.Analysis_file_name.SetValue,self.raw_file_name+'.xlsx')\r\n wx.PostEvent(self._notify_window, stuff.ResultEvent(self.EVT, 'GPIB ended'))","title":""},{"docid":"7237b0ec202d3592084e8b98c3c67540","score":"0.6508037","text":"def end_turn(self):\n pass","title":""},{"docid":"a120e08ecb96d603a8ca04803a7f7022","score":"0.6501057","text":"def end(self) -> None:\n pass","title":""},{"docid":"b3b1fea27f822aaffd1b907ea67a0976","score":"0.6490894","text":"def endState(self):","title":""},{"docid":"9e3db8044b4f88cb23b255d26d5dfc19","score":"0.6464023","text":"def fncEndBreak(self): \n \n # stopper le timer\n self.statut = self.END_BREAK; \n self.timerPomodoroBreak.stop()\n \n # stop\n self.fncStop(False); # argShowMessage=False","title":""},{"docid":"108b5ffbd5dc30acf7579234bd002b37","score":"0.64493465","text":"def terminate(self):","title":""},{"docid":"108b5ffbd5dc30acf7579234bd002b37","score":"0.64493465","text":"def terminate(self):","title":""},{"docid":"108b5ffbd5dc30acf7579234bd002b37","score":"0.64493465","text":"def terminate(self):","title":""},{"docid":"cf8f40b2d2b226de134e2a9f625d95fe","score":"0.6447654","text":"def finish(self):\n # type: (...) -> None\n pass","title":""},{"docid":"c278a1a8f30d8e8c6259d82ab3244504","score":"0.6417753","text":"def exit(self):\n pass","title":""},{"docid":"c278a1a8f30d8e8c6259d82ab3244504","score":"0.6417753","text":"def exit(self):\n pass","title":""},{"docid":"2b8ff1c49575f85150fe16ccffaefaa5","score":"0.64132386","text":"def end(self, session):","title":""},{"docid":"a01867e6fa57f5475614f3508056fd4c","score":"0.6407513","text":"def exit(self, context):","title":""},{"docid":"22ab517b0d56ad0fc367eeea8afb8426","score":"0.6400376","text":"def end(self):\n\n self.COMM.bcast(None, root=0)","title":""},{"docid":"d9b6b4df4da9789b4d699072387c6af3","score":"0.6390285","text":"def finish(self) -> None:\n ...","title":""},{"docid":"1380485ec0e44bbf8189c117ddd7b1ea","score":"0.63847995","text":"def Finish(self):","title":""},{"docid":"b8935e1e827cb9f1562a0c8c872f34f5","score":"0.63633627","text":"def terminate(self):\n ...","title":""},{"docid":"b8935e1e827cb9f1562a0c8c872f34f5","score":"0.63633627","text":"def terminate(self):\n ...","title":""},{"docid":"b8935e1e827cb9f1562a0c8c872f34f5","score":"0.63633627","text":"def terminate(self):\n ...","title":""},{"docid":"9d60d7c941ed7102fa6c93128493449f","score":"0.63441384","text":"def finish(self):\n pass","title":""},{"docid":"9d60d7c941ed7102fa6c93128493449f","score":"0.63441384","text":"def finish(self):\n pass","title":""},{"docid":"9d60d7c941ed7102fa6c93128493449f","score":"0.63441384","text":"def finish(self):\n pass","title":""},{"docid":"76d6ca6f52d75f63a06be78f9ce5325f","score":"0.63341725","text":"def task_end():\n print(\"[Task_End]\")","title":""},{"docid":"76d6ca6f52d75f63a06be78f9ce5325f","score":"0.63341725","text":"def task_end():\n print(\"[Task_End]\")","title":""},{"docid":"dee72128815698bb30b02c7aece6a6d2","score":"0.6324315","text":"def backend_exit(self):\n\t\tpass","title":""},{"docid":"1f5168f9d0a3cb904cc11cee08c7d4ae","score":"0.63174754","text":"def end_game(self):\n exit()","title":""},{"docid":"1f5168f9d0a3cb904cc11cee08c7d4ae","score":"0.63174754","text":"def end_game(self):\n exit()","title":""},{"docid":"1f5168f9d0a3cb904cc11cee08c7d4ae","score":"0.63174754","text":"def end_game(self):\n exit()","title":""},{"docid":"3bd50e51a658b9cb0de83a07115b66ac","score":"0.63145185","text":"def end(self) -> int:\n\n pass","title":""},{"docid":"686003964ab1d223891accac9d978f02","score":"0.6297495","text":"def stop(self):\n self.end()\n self.unhook()","title":""},{"docid":"9db44f582a834c860950af64f8ab8ecb","score":"0.62790793","text":"def end_game(self):\n raise NotImplementedError","title":""},{"docid":"c2a6922c3fc61515c150a5feb540ec79","score":"0.62717587","text":"def quit(a):\r\n\t\t_trigger(\"on_quit\")","title":""},{"docid":"937bdba5aacb40132f6281f51c53c174","score":"0.62461257","text":"def task_end():\n print(\"[ Task_End ]\")","title":""},{"docid":"4fb1de31e5932a5cdf2f56c836f27841","score":"0.62455726","text":"def notify_endbinary(self, ok):\n pass","title":""},{"docid":"144ceb876509c0782c62808408ced1e3","score":"0.62409174","text":"def STOP(event):\n raise Boom()","title":""},{"docid":"e1c4d57633bef8021ed51363033e3f98","score":"0.62359035","text":"def task_end():\n print(\"[ Task_End ]\")","title":""},{"docid":"e1c4d57633bef8021ed51363033e3f98","score":"0.62359035","text":"def task_end():\n print(\"[ Task_End ]\")","title":""},{"docid":"fdb7199091c93aa7b42ebdf1ce131ea1","score":"0.6221306","text":"def _end_integration(self):\n\t\tSimulation._end_integration(self)\n\t\tself._extract_results()","title":""},{"docid":"2840be4b1253d2f9cff52215d6c47383","score":"0.6212405","text":"def end_callback(self, sim):\n\n self.logger.debug(\"TimeRunner.end_callback() called.\")\n self.end_flag = True","title":""},{"docid":"ebeb1b510fc967040ed63c829ffe2eb5","score":"0.62033415","text":"def defend(self, *args):\n\n return self.create_mission(mission.DEFEND, *args)","title":""},{"docid":"f68ede547dc186ffb53be7cb306b17a0","score":"0.6200459","text":"def end2(self, *args, **kwargs) -> Any:\n pass","title":""},{"docid":"f2baac175804ed4d6ac3378e8054c45c","score":"0.6193898","text":"def payload_end(self):\n ...","title":""},{"docid":"48a603a1dce751e8279f8ea99c9ee327","score":"0.6189355","text":"def stopRun(self):\n start = False\n comm.isend(obj=start,dest=0, tag=12)","title":""},{"docid":"239657c0a6a2724c20e6f72d41b27f40","score":"0.6187598","text":"def end_game(self) -> None:\n pass","title":""},{"docid":"fdbe624c5374cb191a480d739245fedd","score":"0.6187444","text":"def close_cmd(self):\n end_func = \"\"\"\n\n SendKeepActive(\"\")\n\n EndFunc\n \"\"\"\n\n return end_func","title":""},{"docid":"d7c50bbed8d6e24557cdd15c3d0c9705","score":"0.6177088","text":"def quitIt(self):\n comm.isend(obj=False,dest=0, tag=13)\n self.master.quit()","title":""},{"docid":"8a1eff26ba01b77f6e1e7bcbd61872ed","score":"0.61730427","text":"def test_end_job(self):\n pass","title":""},{"docid":"1db732a82c25f01cfe9d061ee5f1f071","score":"0.6166997","text":"def end_test():\n print(\"This is the end of the test.\")","title":""},{"docid":"0a2c09e928a191828cf451d75fef1f20","score":"0.61503536","text":"def close(self):\n if self.isalive():\n self.send_expect('exit', '# ')\n super(IxiaPacketGenerator, self).close()","title":""},{"docid":"bcc5f51f2988aa1cfe641b62540e6b15","score":"0.6146544","text":"def abort(self):","title":""},{"docid":"9ea5b2d4587178affab8292271614ab9","score":"0.6125412","text":"def END_FINALLY(self):\n raise self.vm.PyVMError(\"END_FINALLY not implemented yet\")","title":""},{"docid":"fb54e049f584fa0537369030560bd85f","score":"0.61225295","text":"def battle_end(self, result, unit=None):\n print('battle_end', result, unit)","title":""},{"docid":"602b1d2d62c5ee3b0157599b72f59998","score":"0.61205095","text":"def end(self):\n return self._bb.endEA","title":""},{"docid":"e81d39152b4c45d5971311988bc11b03","score":"0.61172056","text":"def stop(self):","title":""},{"docid":"e81d39152b4c45d5971311988bc11b03","score":"0.61172056","text":"def stop(self):","title":""},{"docid":"e81d39152b4c45d5971311988bc11b03","score":"0.61172056","text":"def stop(self):","title":""},{"docid":"ace0c9e85b620d03bbe156d66048048b","score":"0.610946","text":"def stop(self) -> None:","title":""},{"docid":"e16dbff93697f3d86b8c80f4104f0908","score":"0.6092453","text":"def _exit_program(self) -> None:","title":""},{"docid":"a420d38de496d475b1c7b9167b509ec1","score":"0.60894203","text":"def end(compatible_output=True):\n raise NotImplementedError","title":""},{"docid":"1dc127a347965d4041d73c57c98331a9","score":"0.60763466","text":"def x_end(self):\n raise Exception(\"Not yet implemented.\")","title":""},{"docid":"d408ce1f5fb0020db1482c841ade1e70","score":"0.6075274","text":"def interrupted(self):\n self.end()","title":""},{"docid":"1d1da1970d0328518bdaa9e8c341bf6b","score":"0.6074713","text":"def VoiceLogicEffectExcelEnd(builder):\n return End(builder)","title":""},{"docid":"5cde33a025241895441641e39eaaf2c6","score":"0.6064631","text":"def abort():","title":""},{"docid":"5cde33a025241895441641e39eaaf2c6","score":"0.6064631","text":"def abort():","title":""},{"docid":"09a1af82d602c821b032b2d69a19eb72","score":"0.6063927","text":"def process_TERMINATE(self):\n pass","title":""},{"docid":"08e40477e232acc72e13e4395f764f4e","score":"0.6051262","text":"def quitter(self):\n sys.exit()","title":""},{"docid":"ee87f5dbd00efc24df73f723a07cf5c4","score":"0.60491663","text":"def OnEndBody(self, cmd):\n return self.Continue()","title":""},{"docid":"f888e6cc897b25356694fe18a36e43b4","score":"0.60469526","text":"def __exit__(self, *_):\n\n self.stop()","title":""},{"docid":"c0b152f3ef2593869e83d258d75a9341","score":"0.6046716","text":"def TraceQuit():\n sendTrace({'type':'TraceQuit'})\n _TraceQuit()","title":""},{"docid":"86de57e9503675b5261e15a3181970fa","score":"0.6036147","text":"def abort_program(self):\n self.send('AB')","title":""},{"docid":"c0eab6c38a869634b2530198f93c67a5","score":"0.603556","text":"def end_to_end_test_1_4(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"09fa8d184abc69524635a80b17e528e2\",\n \"score\": \"0.7245591\",\n \"text\": \"def end(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09fa8d184abc69524635a80b17e528e2\",\n \"score\": \"0.7245591\",\n \"text\": \"def end(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09fa8d184abc69524635a80b17e528e2\",\n \"score\": \"0.7245591\",\n \"text\": \"def end(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1ffe01db1c03d1f942c0609715e1f4f\",\n \"score\": \"0.7142117\",\n \"text\": \"def end_run(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f4ccb8651d30d269370523da65d76e\",\n \"score\": \"0.6991322\",\n \"text\": \"def end_sim():\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ac33199a765243de1f8d68126a3182\",\n \"score\": \"0.6988841\",\n \"text\": \"def end():\\n with Session() as lib:\\n lib.call_module(module=\\\"end\\\", args=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b80e0dbb26b6b5d04ffd6a9c4de3f2\",\n \"score\": \"0.6897577\",\n \"text\": \"def _end(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061ddb5b209f48731abde40a73a0e013\",\n \"score\": \"0.6816187\",\n \"text\": \"def end(self):\\n\\n core = callback.getCore()\\n core.endRequest(self, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061ddb5b209f48731abde40a73a0e013\",\n \"score\": \"0.6816187\",\n \"text\": \"def end(self):\\n\\n core = callback.getCore()\\n core.endRequest(self, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3166254e7ac8006193cd867a41a9a6a\",\n \"score\": \"0.68160975\",\n \"text\": \"def end(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3166254e7ac8006193cd867a41a9a6a\",\n \"score\": \"0.68160975\",\n \"text\": \"def end(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243edc7cd5194cc3b3db0c8115ac267c\",\n \"score\": \"0.67762774\",\n \"text\": \"def stop(self):\\r\\n self.ctx.bot.dispatch(\\\"trivia_end\\\", self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b77b4a7fd2478f16c53c864def3aff\",\n \"score\": \"0.67745626\",\n \"text\": \"def end(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c87574d3442b869981d3bb3d2de3478\",\n \"score\": \"0.6753734\",\n \"text\": \"def _finish():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c87574d3442b869981d3bb3d2de3478\",\n \"score\": \"0.6753734\",\n \"text\": \"def _finish():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34e16aca7af496dd32ee994152711e5f\",\n \"score\": \"0.6727069\",\n \"text\": \"def finish():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c84ac910e34665fe175d7bd3157ecef0\",\n \"score\": \"0.6694841\",\n \"text\": \"def end_workunit(self, workunit):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36dffba7d008b2714f0fd1002b063de1\",\n \"score\": \"0.6636289\",\n \"text\": \"def leave(self, callsign):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5847c6dc74d157c86ea2b0faffa12bf8\",\n \"score\": \"0.6612259\",\n \"text\": \"def end():\\n logging.info(\\\"Ending DAG\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f765266360f7c6a9b1fadd68016f0fc\",\n \"score\": \"0.65996623\",\n \"text\": \"def end(self, apiInstance):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6282853e3ada1f65c6c262293dbe1ea8\",\n \"score\": \"0.6587956\",\n \"text\": \"def finish(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6282853e3ada1f65c6c262293dbe1ea8\",\n \"score\": \"0.6587956\",\n \"text\": \"def finish(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6282853e3ada1f65c6c262293dbe1ea8\",\n \"score\": \"0.6587956\",\n \"text\": \"def finish(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6282853e3ada1f65c6c262293dbe1ea8\",\n \"score\": \"0.6587956\",\n \"text\": \"def finish(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6282853e3ada1f65c6c262293dbe1ea8\",\n \"score\": \"0.6587956\",\n \"text\": \"def finish(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4214f8f2b69ee0c48294418c2fec1b03\",\n \"score\": \"0.6568385\",\n \"text\": \"def on_end(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4214f8f2b69ee0c48294418c2fec1b03\",\n \"score\": \"0.6568385\",\n \"text\": \"def on_end(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81631cf6a45e950cdf98e5d47fcc3eff\",\n \"score\": \"0.6547691\",\n \"text\": \"def end_run(self, run_id, end_time, message, traceback, stats):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9836312794f7eeb619a2d26eafd2decb\",\n \"score\": \"0.65342546\",\n \"text\": \"def end_call_manual(self):\\n self.log_message(\\n 'Chiamata terminata',\\n 'Fine chiamata telefonica o con strumenti social')\\n\\n duration = datetime.now() - datetime.strptime(\\n self.start_datetime, DEFAULT_SERVER_DATETIME_FORMAT)\\n\\n return self.write({\\n 'duration': duration.total_seconds() / 3600.0,\\n 'state': 'done',\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f42ed45cdee8d0f48393e8c47b8823c0\",\n \"score\": \"0.65288144\",\n \"text\": \"def onStop(signum, stackframe):\\n\\t\\n\\tsys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080d7d7cb2e924951778b41797fc484b\",\n \"score\": \"0.65230626\",\n \"text\": \"def end(self):\\r\\n self.com(self.sourceS.Standby)\\r\\n self.com(self.sourceX.Standby)\\r\\n self.logfile.close()\\r\\n self.wb.save(filename = str(self.raw_file_name+'.xlsx'))\\r\\n wx.CallAfter(self.Analysis_file_name.SetValue,self.raw_file_name+'.xlsx')\\r\\n wx.PostEvent(self._notify_window, stuff.ResultEvent(self.EVT, 'GPIB ended'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7237b0ec202d3592084e8b98c3c67540\",\n \"score\": \"0.6508037\",\n \"text\": \"def end_turn(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a120e08ecb96d603a8ca04803a7f7022\",\n \"score\": \"0.6501057\",\n \"text\": \"def end(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3b1fea27f822aaffd1b907ea67a0976\",\n \"score\": \"0.6490894\",\n \"text\": \"def endState(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3db8044b4f88cb23b255d26d5dfc19\",\n \"score\": \"0.6464023\",\n \"text\": \"def fncEndBreak(self): \\n \\n # stopper le timer\\n self.statut = self.END_BREAK; \\n self.timerPomodoroBreak.stop()\\n \\n # stop\\n self.fncStop(False); # argShowMessage=False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108b5ffbd5dc30acf7579234bd002b37\",\n \"score\": \"0.64493465\",\n \"text\": \"def terminate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108b5ffbd5dc30acf7579234bd002b37\",\n \"score\": \"0.64493465\",\n \"text\": \"def terminate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108b5ffbd5dc30acf7579234bd002b37\",\n \"score\": \"0.64493465\",\n \"text\": \"def terminate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf8f40b2d2b226de134e2a9f625d95fe\",\n \"score\": \"0.6447654\",\n \"text\": \"def finish(self):\\n # type: (...) -> None\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c278a1a8f30d8e8c6259d82ab3244504\",\n \"score\": \"0.6417753\",\n \"text\": \"def exit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c278a1a8f30d8e8c6259d82ab3244504\",\n \"score\": \"0.6417753\",\n \"text\": \"def exit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8ff1c49575f85150fe16ccffaefaa5\",\n \"score\": \"0.64132386\",\n \"text\": \"def end(self, session):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01867e6fa57f5475614f3508056fd4c\",\n \"score\": \"0.6407513\",\n \"text\": \"def exit(self, context):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22ab517b0d56ad0fc367eeea8afb8426\",\n \"score\": \"0.6400376\",\n \"text\": \"def end(self):\\n\\n self.COMM.bcast(None, root=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b6b4df4da9789b4d699072387c6af3\",\n \"score\": \"0.6390285\",\n \"text\": \"def finish(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1380485ec0e44bbf8189c117ddd7b1ea\",\n \"score\": \"0.63847995\",\n \"text\": \"def Finish(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8935e1e827cb9f1562a0c8c872f34f5\",\n \"score\": \"0.63633627\",\n \"text\": \"def terminate(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8935e1e827cb9f1562a0c8c872f34f5\",\n \"score\": \"0.63633627\",\n \"text\": \"def terminate(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8935e1e827cb9f1562a0c8c872f34f5\",\n \"score\": \"0.63633627\",\n \"text\": \"def terminate(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d60d7c941ed7102fa6c93128493449f\",\n \"score\": \"0.63441384\",\n \"text\": \"def finish(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d60d7c941ed7102fa6c93128493449f\",\n \"score\": \"0.63441384\",\n \"text\": \"def finish(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d60d7c941ed7102fa6c93128493449f\",\n \"score\": \"0.63441384\",\n \"text\": \"def finish(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76d6ca6f52d75f63a06be78f9ce5325f\",\n \"score\": \"0.63341725\",\n \"text\": \"def task_end():\\n print(\\\"[Task_End]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76d6ca6f52d75f63a06be78f9ce5325f\",\n \"score\": \"0.63341725\",\n \"text\": \"def task_end():\\n print(\\\"[Task_End]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee72128815698bb30b02c7aece6a6d2\",\n \"score\": \"0.6324315\",\n \"text\": \"def backend_exit(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5168f9d0a3cb904cc11cee08c7d4ae\",\n \"score\": \"0.63174754\",\n \"text\": \"def end_game(self):\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5168f9d0a3cb904cc11cee08c7d4ae\",\n \"score\": \"0.63174754\",\n \"text\": \"def end_game(self):\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5168f9d0a3cb904cc11cee08c7d4ae\",\n \"score\": \"0.63174754\",\n \"text\": \"def end_game(self):\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd50e51a658b9cb0de83a07115b66ac\",\n \"score\": \"0.63145185\",\n \"text\": \"def end(self) -> int:\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"686003964ab1d223891accac9d978f02\",\n \"score\": \"0.6297495\",\n \"text\": \"def stop(self):\\n self.end()\\n self.unhook()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db44f582a834c860950af64f8ab8ecb\",\n \"score\": \"0.62790793\",\n \"text\": \"def end_game(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a6922c3fc61515c150a5feb540ec79\",\n \"score\": \"0.62717587\",\n \"text\": \"def quit(a):\\r\\n\\t\\t_trigger(\\\"on_quit\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937bdba5aacb40132f6281f51c53c174\",\n \"score\": \"0.62461257\",\n \"text\": \"def task_end():\\n print(\\\"[ Task_End ]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb1de31e5932a5cdf2f56c836f27841\",\n \"score\": \"0.62455726\",\n \"text\": \"def notify_endbinary(self, ok):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144ceb876509c0782c62808408ced1e3\",\n \"score\": \"0.62409174\",\n \"text\": \"def STOP(event):\\n raise Boom()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c4d57633bef8021ed51363033e3f98\",\n \"score\": \"0.62359035\",\n \"text\": \"def task_end():\\n print(\\\"[ Task_End ]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c4d57633bef8021ed51363033e3f98\",\n \"score\": \"0.62359035\",\n \"text\": \"def task_end():\\n print(\\\"[ Task_End ]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb7199091c93aa7b42ebdf1ce131ea1\",\n \"score\": \"0.6221306\",\n \"text\": \"def _end_integration(self):\\n\\t\\tSimulation._end_integration(self)\\n\\t\\tself._extract_results()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2840be4b1253d2f9cff52215d6c47383\",\n \"score\": \"0.6212405\",\n \"text\": \"def end_callback(self, sim):\\n\\n self.logger.debug(\\\"TimeRunner.end_callback() called.\\\")\\n self.end_flag = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebeb1b510fc967040ed63c829ffe2eb5\",\n \"score\": \"0.62033415\",\n \"text\": \"def defend(self, *args):\\n\\n return self.create_mission(mission.DEFEND, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f68ede547dc186ffb53be7cb306b17a0\",\n \"score\": \"0.6200459\",\n \"text\": \"def end2(self, *args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2baac175804ed4d6ac3378e8054c45c\",\n \"score\": \"0.6193898\",\n \"text\": \"def payload_end(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48a603a1dce751e8279f8ea99c9ee327\",\n \"score\": \"0.6189355\",\n \"text\": \"def stopRun(self):\\n start = False\\n comm.isend(obj=start,dest=0, tag=12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"239657c0a6a2724c20e6f72d41b27f40\",\n \"score\": \"0.6187598\",\n \"text\": \"def end_game(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbe624c5374cb191a480d739245fedd\",\n \"score\": \"0.6187444\",\n \"text\": \"def close_cmd(self):\\n end_func = \\\"\\\"\\\"\\n\\n SendKeepActive(\\\"\\\")\\n\\n EndFunc\\n \\\"\\\"\\\"\\n\\n return end_func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7c50bbed8d6e24557cdd15c3d0c9705\",\n \"score\": \"0.6177088\",\n \"text\": \"def quitIt(self):\\n comm.isend(obj=False,dest=0, tag=13)\\n self.master.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a1eff26ba01b77f6e1e7bcbd61872ed\",\n \"score\": \"0.61730427\",\n \"text\": \"def test_end_job(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db732a82c25f01cfe9d061ee5f1f071\",\n \"score\": \"0.6166997\",\n \"text\": \"def end_test():\\n print(\\\"This is the end of the test.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2c09e928a191828cf451d75fef1f20\",\n \"score\": \"0.61503536\",\n \"text\": \"def close(self):\\n if self.isalive():\\n self.send_expect('exit', '# ')\\n super(IxiaPacketGenerator, self).close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc5f51f2988aa1cfe641b62540e6b15\",\n \"score\": \"0.6146544\",\n \"text\": \"def abort(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea5b2d4587178affab8292271614ab9\",\n \"score\": \"0.6125412\",\n \"text\": \"def END_FINALLY(self):\\n raise self.vm.PyVMError(\\\"END_FINALLY not implemented yet\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb54e049f584fa0537369030560bd85f\",\n \"score\": \"0.61225295\",\n \"text\": \"def battle_end(self, result, unit=None):\\n print('battle_end', result, unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602b1d2d62c5ee3b0157599b72f59998\",\n \"score\": \"0.61205095\",\n \"text\": \"def end(self):\\n return self._bb.endEA\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81d39152b4c45d5971311988bc11b03\",\n \"score\": \"0.61172056\",\n \"text\": \"def stop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81d39152b4c45d5971311988bc11b03\",\n \"score\": \"0.61172056\",\n \"text\": \"def stop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81d39152b4c45d5971311988bc11b03\",\n \"score\": \"0.61172056\",\n \"text\": \"def stop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace0c9e85b620d03bbe156d66048048b\",\n \"score\": \"0.610946\",\n \"text\": \"def stop(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e16dbff93697f3d86b8c80f4104f0908\",\n \"score\": \"0.6092453\",\n \"text\": \"def _exit_program(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a420d38de496d475b1c7b9167b509ec1\",\n \"score\": \"0.60894203\",\n \"text\": \"def end(compatible_output=True):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc127a347965d4041d73c57c98331a9\",\n \"score\": \"0.60763466\",\n \"text\": \"def x_end(self):\\n raise Exception(\\\"Not yet implemented.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d408ce1f5fb0020db1482c841ade1e70\",\n \"score\": \"0.6075274\",\n \"text\": \"def interrupted(self):\\n self.end()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d1da1970d0328518bdaa9e8c341bf6b\",\n \"score\": \"0.6074713\",\n \"text\": \"def VoiceLogicEffectExcelEnd(builder):\\n return End(builder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cde33a025241895441641e39eaaf2c6\",\n \"score\": \"0.6064631\",\n \"text\": \"def abort():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cde33a025241895441641e39eaaf2c6\",\n \"score\": \"0.6064631\",\n \"text\": \"def abort():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a1af82d602c821b032b2d69a19eb72\",\n \"score\": \"0.6063927\",\n \"text\": \"def process_TERMINATE(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08e40477e232acc72e13e4395f764f4e\",\n \"score\": \"0.6051262\",\n \"text\": \"def quitter(self):\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee87f5dbd00efc24df73f723a07cf5c4\",\n \"score\": \"0.60491663\",\n \"text\": \"def OnEndBody(self, cmd):\\n return self.Continue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f888e6cc897b25356694fe18a36e43b4\",\n \"score\": \"0.60469526\",\n \"text\": \"def __exit__(self, *_):\\n\\n self.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b152f3ef2593869e83d258d75a9341\",\n \"score\": \"0.6046716\",\n \"text\": \"def TraceQuit():\\n sendTrace({'type':'TraceQuit'})\\n _TraceQuit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86de57e9503675b5261e15a3181970fa\",\n \"score\": \"0.6036147\",\n \"text\": \"def abort_program(self):\\n self.send('AB')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0eab6c38a869634b2530198f93c67a5\",\n \"score\": \"0.603556\",\n \"text\": \"def end_to_end_test_1_4(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1593,"cells":{"query_id":{"kind":"string","value":"3e349e869d2d23310c3da45b57fb9122"},"query":{"kind":"string","value":"Guarda la ruta provista en un archivo"},"positive_passages":{"kind":"list like","value":[{"docid":"ae18d5c30da4ea6513ccecd13d3e7907","score":"0.69561684","text":"def guardar_ruta(archivo, texto):\n with open(archivo, \"w\") as ruta:\n ruta.write(f'{texto}')","title":""}],"string":"[\n {\n \"docid\": \"ae18d5c30da4ea6513ccecd13d3e7907\",\n \"score\": \"0.69561684\",\n \"text\": \"def guardar_ruta(archivo, texto):\\n with open(archivo, \\\"w\\\") as ruta:\\n ruta.write(f'{texto}')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a0db7e7417ae67d4ecf2a8d426768e28","score":"0.67659104","text":"def save(self, path):","title":""},{"docid":"ee03039bec37e652f2d5f497ae526009","score":"0.62812024","text":"def guardar(self):\n self.imagen_actual.save(\"imagen_nueva.png\")","title":""},{"docid":"89207a4b3175d203f1ca7352a4b7f833","score":"0.61607206","text":"def guardar(nombre_archivo, diccionario_a_guardar):\n\n try:\n # Abrimos nuestro archivo\n archivo= open(nombre_archivo,\"w\")\n\n #convertimos nuestro diccionario en un archivo .json\n convertido= json.dumps(diccionario_a_guardar)\n\n # guardamos el archivo\n archivo.write(convertido)\n\n #Cerramos el archivo\n archivo.close()\n\n return True\n except:\n\n #si ocurre algun error retorna falso\n return False","title":""},{"docid":"56e423a3cc2517c280d8b37c459baa69","score":"0.60031474","text":"def save_to_file(self, path):\r\n raise NotImplementedError","title":""},{"docid":"e21fbfbd9b2cae490321dbdce2fa664f","score":"0.59755576","text":"def guardar_okada( dtopo, nombre_archivo, tipo = 3 ):\n\n # revisar si nombre_archivo es del tipo de string\n if not isinstance(nombre_archivo, basestring):\n nombre_archivo = str( nombre_archivo )\n else:\n nombre_archivo = nombre_archivo\n\n # se guarda el archivo \n dtopo.write(nombre_archivo, dtopo_type = tipo)","title":""},{"docid":"cc99b3495178215213ad58879eb3e0fc","score":"0.5969014","text":"def put_file(self, key, location):\n pass","title":""},{"docid":"3b22621211b3db7cd9f1d0b1b03b109e","score":"0.595592","text":"def save(self, path):\n self.src.save(path)","title":""},{"docid":"40660d439c6a0819c28fb976185b924d","score":"0.592659","text":"def Save(self):\n chaine = \"\"\n for x in self.grille:\n chaine+=''.join(x)+\"\\n\"\n if self.nom.endswith(\".\"):\n chemin = os.path.join(\"cartes\",self.nom+\"txt\")\n else:\n chemin = os.path.join(\"cartes\",self.nom+\".txt\")\n with open(chemin, 'w') as fichier:\n fichier.write(chaine)","title":""},{"docid":"f4ef76d10775c1b34b918e23a10cea4a","score":"0.5908202","text":"def fileWrite(path, fileName, chaineAEcrire):\n\n # %% Repertoire # os.path.join(path, fileName) si besoin\n os.chdir(path)\n\n # %% Creation et Ecriture\n f = open(fileName, 'w')\n f.writelines(chaineAEcrire)\n f.close()","title":""},{"docid":"ee4a931c4bd7b202bf14a23ef9e26b9a","score":"0.58741575","text":"def save(self, filename):\r\n pass","title":""},{"docid":"bc96bf3c72142190ed112661d5965ee8","score":"0.58739156","text":"def saveFile(self, path):\n\t\tfobj = self.allFiles[path]\n\t\tself.allFiles[path].selfCopy()","title":""},{"docid":"67b449bf2a488eb230dc92ee6ff06724","score":"0.5850641","text":"def guardar_gris(self, img, cont, ruta):\n\n k = ruta+'/imagen' + str(cont) + '.png'\n cv2.imwrite(k, img)","title":""},{"docid":"c84a2ea393aed5425ea555354b01e286","score":"0.5847585","text":"def save_data(self, path):\n\t\tpass","title":""},{"docid":"ca19471442995615a611ce60af79b44e","score":"0.5802713","text":"def file_writer(strings, lokacija_ind):\r\n obcina_ind = improved_search(strings, 'Občina:', 23)\r\n obcina = data_nicefier(strings[obcina_ind + 1]).upper()\r\n data = data_stringyfier(strings, lokacija_ind)\r\n ime, tekst, datacija, lokacija = data\r\n ime = ime.strip()\r\n ime = eliminator(ime)\r\n path = 'TEKSTI/' + obcina + '/' + ime.strip() + '.txt'\r\n if not os.path.exists('TEKSTI/' + obcina):\r\n os.makedirs('TEKSTI/' + obcina)\r\n with open(path, 'w', encoding=\"utf-8\") as file:\r\n file.write(ime + tekst + datacija + lokacija)","title":""},{"docid":"e5afc7e2c0068caa58e604fe9d7ae29c","score":"0.57936144","text":"def ingreso():\n fecha = time.asctime()\n f = open(\"registros.txt\", \"a\")\n f.write(\"IN \" + fecha + \" \" + \"Encargad@ \" + nombre + \"\\n\")\n f.close()\n print(\"Registro salvado\")","title":""},{"docid":"96ac18526cee3b22d73ffc4b54722163","score":"0.5767469","text":"def pos_save_file():\n try:\n save_file_prompt = asksaveasfilename(defaultextension=\".json\")\n save_file_name = open(save_file_prompt, \"w\")\n if save_file_name is not None:\n # Write the file to disk\n obj_locations = copy.deepcopy(get_dict_rect_positions())\n save_file_name.write(str(obj_locations))\n save_file_name.close()\n log.info(\"File Saved Successfully.\")\n else:\n log.info(\"Error! Need king to save!\")\n except IOError:\n log.info(\"Save File Error (IOError)\")","title":""},{"docid":"2386b87050843e4fb5d3a744164f4627","score":"0.57370955","text":"def save(self,path):\n raise NotImplementedError","title":""},{"docid":"42838b09a04c64bf875dbf7ab0736ca2","score":"0.5646486","text":"def save_by_replacing(self):\n self.save_converted_file_0(1)","title":""},{"docid":"e1bf470ad4e9fbf7a151d4e1dd23a517","score":"0.5642181","text":"def save():","title":""},{"docid":"1bd563dd3d67febfae6deb9d6b9db628","score":"0.56404704","text":"def save_fp(self, fp, filename, directory=''):","title":""},{"docid":"e9e36f091819c95f2e183b04095a541f","score":"0.5639669","text":"def save_file(filename, content):\r\n\r\n create_dir(os.path.dirname(filename))\r\n with open(filename, 'w') as f:\r\n f.write(content)","title":""},{"docid":"d23c808d44e137d36a78829fbbe735eb","score":"0.56320095","text":"def save(self, directory):\n pass","title":""},{"docid":"d23c808d44e137d36a78829fbbe735eb","score":"0.56320095","text":"def save(self, directory):\n pass","title":""},{"docid":"90b345aabe09fb6bf2a07f02174cbd2d","score":"0.56312644","text":"def save_paths(source_file: str, new_path: str):\r\n with open(source_file, encoding='utf-8', mode='w') as f:\r\n f.write(new_path)","title":""},{"docid":"3ead2e30d6786d6ed24cfc7f9d6962f2","score":"0.56306994","text":"def save(self, destinationName):","title":""},{"docid":"f83c7e7b38f41ada0ffecc97882ebf68","score":"0.56101125","text":"def fileReplace(path, fileName, urlSiteAAspirer):\n\n # %%Recuperation du contenu de la page\n htmlSoup = sW.extractHTML(urlSiteAAspirer)\n\n # %% Enregistrement du fichier une fois le contenu de la page extrait\n fileWrite(path, fileName, htmlSoup.prettify())\n\n fileLinksReplace(path, fileName, urlSiteAAspirer, htmlSoup)\n\n filePictureLinksReplace(path, fileName, urlSiteAAspirer, htmlSoup)","title":""},{"docid":"30336c21bc85ed18953e74ee9352b933","score":"0.56064665","text":"def salida():\n fecha = time.asctime()\n f = open(\"registros.txt\", \"a\")\n f.write(\"OUT \" + fecha + \" \" + \"Encargad@ \" +\n nombre + \" $\" + str(totalturno)+\"\\n\")\n f.close()\n print(\"Registro salvado\")","title":""},{"docid":"454e06f31947ec9beec014a7ff9459b4","score":"0.5603385","text":"def save(self):\r\n if not self.dirty:\r\n return\r\n\r\n data = '\\n'.join(map(self.make_relative,self.paths))\r\n if data:\r\n log.debug(\"Saving %s\", self.filename)\r\n data = (\r\n \"import sys; sys.__plen = len(sys.path)\\n\"\r\n \"%s\\n\"\r\n \"import sys; new=sys.path[sys.__plen:];\"\r\n \" del sys.path[sys.__plen:];\"\r\n \" p=getattr(sys,'__egginsert',len(os.environ.get('PYTHONPATH','').split(os.pathsep))); sys.path[p:p]=new;\"\r\n \" sys.__egginsert = p+len(new)\\n\"\r\n ) % data\r\n\r\n if os.path.islink(self.filename):\r\n os.unlink(self.filename)\r\n f = open(self.filename,'wb')\r\n f.write(data); f.close()\r\n\r\n elif os.path.exists(self.filename):\r\n log.debug(\"Deleting empty %s\", self.filename)\r\n os.unlink(self.filename)\r\n\r\n self.dirty = False","title":""},{"docid":"1a5cda49ea66d44cb3e8537e5e374a71","score":"0.56016964","text":"def save(self, saved_path: str):\n pass","title":""},{"docid":"ebcb220a88e43ed30464259a23544118","score":"0.5594327","text":"def save(self):\n rval = save_data(self)\n if not rval == '':\n self.tmpsrc = rval\n print \"Updated storage path of file: %s\" % rval\n else:\n print 'There is nothing to save.'","title":""},{"docid":"1863a7ec5272f103f044e560eb198115","score":"0.5573114","text":"def excelfile(cls):\n\n d = datetime.today().date().strftime('%d/%m/%Y')\n c = datetime.fromtimestamp(path.getmtime(\n cls._nombrearchivo)).strftime('%d/%m/%Y')\n\n if c != d:\n print('\\n removiendo el archivo')\n remove(cls._nombrearchivo)","title":""},{"docid":"2ffd4d7404f30b49615e4e6c70815fe6","score":"0.5565485","text":"def _save(self, data, path):\n with open(path, 'wb') as f:\n f.write(data)","title":""},{"docid":"137857edc66d641c4cc03c446e9e033b","score":"0.5542466","text":"def backup(self) -> Path:","title":""},{"docid":"ea3f20433b28d9a649ae0331f4f9d95a","score":"0.5532166","text":"def put_file(full_path, file_obj, overwrite=False, parent_rev=None):","title":""},{"docid":"f9b4c2e105f6a0e8cf0eebcc61dda4ae","score":"0.5526791","text":"def sauvegarde():\n with open(\"saverobot\", \"wb\") as f:\n cb = canvas.coords(robots[0])\n cr = canvas.coords(robots[1])\n cg = canvas.coords(robots[2])\n cy = canvas.coords(robots[3])\n bt = canvas.coords(pos_target[0])\n rt = canvas.coords(pos_target[1])\n gt = canvas.coords(pos_target[2])\n yt = canvas.coords(pos_target[3])\n save = [cb, cr, cg, cy, bt, rt, gt, yt, cpt]\n pickle.dump(save, f)","title":""},{"docid":"2652c7f1cdfec26a805e9e423f2f110c","score":"0.5523489","text":"def safeSave(self):\n self.fileInfo.makeBackup()\n filePath = os.path.join(self.fileInfo.dir, self.fileInfo.name)\n tempPath = filePath + '.tmp'\n self.save(tempPath)\n renameFile(tempPath, filePath)\n self.fileInfo.setMTime()\n self.fileInfo.extras.clear()","title":""},{"docid":"38376f44a2b150fcacf24f14828ff145","score":"0.5510586","text":"def save_as(self, filepath):\n self.filepath = filepath\n self.save()","title":""},{"docid":"acc2ca0f102ece53d77142cf8fe6c88f","score":"0.5509352","text":"def save_file(cls, pg, xo, yo, dx, dy, rot, tr, ipj, file):\n gxapi_cy.WrapPGU._save_file(GXContext._get_tls_geo(), pg, xo, yo, dx, dy, rot, tr, ipj, file.encode())","title":""},{"docid":"140c2d8dcdb26cf49771e5b34dece5a8","score":"0.55054504","text":"def save_metadata(self) -> Path:","title":""},{"docid":"b56104c66a0ec4a3a290a3c7eaa6e757","score":"0.54986435","text":"def saveFile(self):\n if self.outputMask is None:\n print \"Noting to save\"\n return\n if self.spSegments is not None:\n path = config.outputFile(self.filename)\n dirSplit = path.split('.')\n np.savetxt(dirSplit[0] + \".csv\", self.spSegments, delimiter=\",\", fmt=\"%d\")\n \n output = cv2.cvtColor(self.outputMask, cv2.COLOR_RGB2BGR)\n cv2.imwrite(config.outputFile(self.filename).decode('utf-8').encode('gbk'), output)\n self.updateStatus(\"Save to %s\" % config.outputFile(self.filename))\n self.setClean()","title":""},{"docid":"c1d0dc211d6e7690d5d23ecb24033968","score":"0.54742175","text":"def save(data, path, enc):\n import json\n import codecs\n import os\n\n if os.access(os.path.dirname(os.path.abspath(path)), os.W_OK):\n with codecs.open(path, 'w', enc) as fo:\n json.dump(data, fo, sort_keys=True, indent=4, ensure_ascii=False)\n else:\n print('Cat not craete file!')","title":""},{"docid":"bd32dfa4805acc8cccbdaecbccf56d4b","score":"0.5465589","text":"def save(self, path):\r\n write_mrk(path, self.points)","title":""},{"docid":"44425f439250dc8ec448d6919c6223df","score":"0.5462761","text":"def save(self, directory):\n raise NotImplementedError","title":""},{"docid":"8677c63afcc5d081f79188dff1efab4a","score":"0.54616195","text":"def save_in_backup(self):\r\n now = datetime.datetime.now()\r\n filename = now.strftime('%Y-%m-%d_%H-%M-%S_LocalLeaderboards.dat')\r\n if not os.path.exists(Backup.DEFAULT_FOLDER):\r\n os.mkdir(Backup.DEFAULT_FOLDER)\r\n self.save(os.path.join(Backup.DEFAULT_FOLDER, filename), must_be_valid=False)","title":""},{"docid":"eee985f4557f5454b3f7302d485e6432","score":"0.5453255","text":"def _save(self,data):\n file_name = re.sub('[\\/:*?\"<>| \\n]','_',self.title,re.S)\n if self.path :\n file_name = os.join(self.path,file_name+'.pdf')\n else:\n file_name = os.path.join(\".\",file_name+'.pdf')\n with open(file_name, 'wb') as f:\n f.write(data)","title":""},{"docid":"c8c8112a82a4d3e55747a2ac18f5a3a8","score":"0.5450561","text":"def move_file(self):\n pass","title":""},{"docid":"f62a22caa9fb53100cabdc6d52fdb712","score":"0.5448746","text":"def save_path(self):\n with open(\"path.json\", \"w\", encoding=\"utf-8\") as file:\n json.dump(self.path, file, ensure_ascii=False)","title":""},{"docid":"fb8de173ed669d3a80d4c08970433e52","score":"0.5402648","text":"def saveFile(self):\n\n file = tkFileDialog.asksaveasfile(mode='w',defaultextension = '.life')\n if file:\n self.guiToPatternCounter += 1\n guiToPatternQueue.put(['saveFile',self.guiToPatternCounter,file])","title":""},{"docid":"0d3460c54a6fba163a1abb2b5d83f9a3","score":"0.5399128","text":"def _put(self, name, document):\n # Let's go back to the origi of file\n document.seek(0)\n\n # Let's open the destination, it will be created if needed\n # And let's write the origin in it by block of 4096bytes\n with open(os.path.join(self.path, unicode(name)), 'wb') as destination, document as origin:\n destination.write(origin.read())","title":""},{"docid":"aa6116f46d9dd9dd4a7947a05d1dfd2e","score":"0.5393509","text":"def sauve_fichier(self):\n with open(self.fichier_out, 'w', encoding = 'utf8') as fichier:\n fichier.write(self.chaine)","title":""},{"docid":"dc72e8ed331951e786ba32112993eef0","score":"0.5384045","text":"def test_save(self):\n fileobj = self.storage.open( u\"content.txt\" )\n self.storage.save( u\"test file.bin\", File( fileobj ) )\n fileobj.close( )\n assert self.storage.exists( u\"test file.bin\" ) == True\n f = self.storage.open( u\"test file.bin\" )\n content = f.read( )\n f.close( )\n assert content.strip( ) == u\"Test line in file\"\n self.storage.remove( u\"test file.bin\" )","title":""},{"docid":"bbcb465dd94288fc1b2b5ce56c27b4bb","score":"0.5378622","text":"def on_save(self):\r\n self.__savefile()","title":""},{"docid":"454f012e7f133c8efb42dcd8e46d9e79","score":"0.53777385","text":"def save(self):\n filename = self.netloc\n filename = re.sub(errorchars, '', filename) # Entferne alle Sonderzeichen\n if not self.debug:\n outfile = open(filename, 'wb')\n pickle.dump(self, outfile) # Speichere in Datei\n outfile.close()\n return True","title":""},{"docid":"f7dc022a025b2b918feb08cd005dc544","score":"0.5363813","text":"def save_path(self, path, filename):\n if not path:\n print('The path is empty. No path saved.')\n return\n\n with open(filename, 'wb') as f:\n f.write(','.join('(%s, %s)' % x for x in path))","title":""},{"docid":"8d57237de0ccbe237fa4f89b3debbd1c","score":"0.53574413","text":"def save_file(self):\n try:\n file_types = [('Text files', '*.txt'), ('All Files', '*')]\n file_info = asksaveasfilename(title=\"Save file\",\n defaultextension=\".txt\",\n filetypes=file_types)\n self.file = FileManager(file_info)\n if self.coasters.get_radius() != 0.0:\n self.file.save(self.coasters.__str__())\n else:\n self.compute_action()\n self.file.close()\n except FileNotFoundError:\n messagebox.showinfo(\"Invalid file name\",\n \"Please write a valid name\")","title":""},{"docid":"6ec9fa876234a804afbf0e1254a422f1","score":"0.5351124","text":"def __save_to_file(self):\n self.set_id(self.makeid())\n directory_name = dates.now_str(FMT_DATE_STD)\n full_dir = os.path.join(self.SAVE_BASE_DIRECTORY, directory_name)\n full_path = os.path.join(full_dir, \"{0}.json\".format(self.id))\n\n if not os.path.isdir(full_dir):\n os.makedirs(full_dir)\n\n with open(full_path, 'w') as o:\n o.write(json.dumps(self.data, indent=4))","title":""},{"docid":"726052076c7514ece7ed2d021803542e","score":"0.53477","text":"def save(self, path=None):\n if not path:\n path = self.path\n if path != self.path:\n self.path = path\n with open(path,'w+') as index:\n for line in self.export():\n index.write(\"%s%s\" % (line, os.linesep))","title":""},{"docid":"4b0d3178a8a911cda1e054640e2225d3","score":"0.5343568","text":"def guardar_ingesta_localmente(file_to_upload, data):\n # Cambiar datos de formato json a objetos binario\n pickle.dump(data, file_to_upload)\n logging.info(\"pkl guardado exitosamente en local.\")\n file_to_upload.close()","title":""},{"docid":"b43d933eb69c6987627694e36edcab87","score":"0.5339175","text":"def write_file(path, text):\n with open(path, 'w+') as f:\n f.write(text)\n print('Fichero %s generado' % path)","title":""},{"docid":"8ec1913458b1bd3871b40d229f5ee185","score":"0.5336342","text":"def save(self):\n if self.hasChanged:\n filePath = self.path\n tempPath = filePath + '.tmp'\n fileDir = os.path.split(filePath)[0]\n if not os.path.exists(fileDir):\n os.makedirs(fileDir)\n cPickle.dump(self.data, open(tempPath, 'w'))\n renameFile(tempPath, filePath, True)\n self.hasChanged = False","title":""},{"docid":"20954d477066719bc3e8a28d9d0bcb9f","score":"0.5316111","text":"def save_file(file_, path=''):\n filename = file_.name\n file_path = os.path.join(path, filename)\n if os.path.isfile(file_path):\n os.remove(file_path)\n fd = open(str(path) + \"/\" + str(filename), 'wb')\n for chunk in file_.chunks():\n fd.write(chunk)\n fd.close()\n return str(path) + \"/\" + str(filename)","title":""},{"docid":"4972a92dfcf5d9dc684368e467a58b9a","score":"0.5314942","text":"def writeDoeFile():\n pass","title":""},{"docid":"060c31d346fa9ea5e26bc974b1a3d040","score":"0.5313048","text":"def handle_project_save(self, file_name: str):","title":""},{"docid":"b3432dcf6e7762305098d06bd146de12","score":"0.53118926","text":"def save(self, path):\n path = self.conform(path)\n print \"Save path : \" + path\n nuke.scriptSaveAs(path)","title":""},{"docid":"705a7b365b465c9f7a9490b81836a070","score":"0.53108317","text":"def escribirArchivo(self):\n\t\tf = open('db.txt','w')\n\t\tf2f = []\n\t\tfor r in self.bd:\n\t\t\tf.seek(0,2)\n\t\t\tf.write(\"\\n\\n> Registro [\" + str(r)+ \"]\")\n\t\t\tfor i in range(len(self.bd[r])):\n\t\t\t\tf.seek(0,2)\n\t\t\t\tf.write(\"\\n\\t\" + str(self.bd[r][i-1][0]) + \" => \" + str(self.bd[r][i-1][1]))","title":""},{"docid":"424df3b833e4d72798075afec5b3765e","score":"0.5290365","text":"def write(self, makeBackup = True):\n\n if makeBackup:\n bakfilepath = self.filepath + '~'\n try: os.remove(bakfilepath)\n except: pass\n os.rename(self.filepath, bakfilepath)\n else:\n try: os.remove(self.filepath)\n except: pass\n fout = open(self.filepath, 'w')\n fout.writelines(self.lines)\n fout.close()","title":""},{"docid":"ce8c761a97108828b813c90ac3256a3b","score":"0.528623","text":"def test_put_file_to_existing_directory(self):\r\n text = \"foo!\"\r\n local = self.mkfile('foo.txt', text)\r\n local2 = self.path('foo2.txt')\r\n with hide('everything'):\r\n put(local, '/')\r\n get('/foo.txt', local2)\r\n eq_contents(local2, text)","title":""},{"docid":"8c80413f00e05eba1b35b9fd8c3a7b6a","score":"0.5285937","text":"def writeToDisk(self, filename):\n\t\tp = UrmasPersist.UrmasPersist(self)\n\t\tp.persist(filename)","title":""},{"docid":"b63f5089ca3744408af877fbaaaaa409","score":"0.52767","text":"def put_file(self, model_file_info, model_file_location):\n self.remove_if_exists(model_file_info) # backup old revision\n cache_key = self.__get_cache_key(model_file_info)\n cache_full_path = os.path.join(\n self.cache_root_location,\n cache_key['Path']) # Branch and Tag do not have same name.\n cache_file_dir = os.path.dirname(cache_full_path)\n if not os.path.exists(cache_file_dir):\n os.makedirs(cache_file_dir, exist_ok=True)\n # We can't make operation transaction\n move(model_file_location, cache_full_path)\n self.cached_files.append(cache_key)\n self.save_cached_files()\n return cache_full_path","title":""},{"docid":"97b99706170f74f0bbc16f41640bb6e7","score":"0.52761835","text":"def save(self, userfile):\n try:\n global id_item_actual\n item = DBSession.query(Item).get(id_item_actual[0])\n id_item = item.id_item\n version_anterior = item.version\n version_nueva = int(item.version) + 1\n id_tipo_item = item.id_tipo_item\n tipo_item = DBSession.query(TipoItem).get(id_tipo_item)\n id_fase = tipo_item.id_fase\n fase = DBSession.query(Fase).get(id_fase)\n id_proyecto = fase.id_proyecto\n linea_bases_item = item.linea_bases\n\n #Comprobamos que el Item no se encuentre en una Linea Base\n if linea_bases_item != []:\n for linea_base_item in linea_bases_item:\n flash(_(\"No se puede agregar el archivo! El Item se encuentra en una Linea Base...\"), 'error')\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\n\n if userfile == \"\":\n flash(_(\"Introduzca una ruta de archivo!\"), 'error')\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\n\n forbidden_files = [\".js\", \".htm\", \".html\", \".mp3\"]\n for forbidden_file in forbidden_files:\n if userfile.filename.find(forbidden_file) != -1:\n flash(_(\"No se puede guardar el contenido...\"), 'error')\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, \n id_tipo_item=id_tipo_item, id_item=id_item)\n\n #Guardamos el archivo nuevo\n nombre_archivo = userfile.filename\n contenido_archivo = userfile.file.read()\n nuevo_archivo = ItemArchivo()\n nuevo_archivo.id_item = item.id_item\n nuevo_archivo.version_item = version_nueva\n nuevo_archivo.nombre_archivo = nombre_archivo\n nuevo_archivo.contenido_archivo = contenido_archivo\n DBSession.add(nuevo_archivo)\n\n #El item actual cambia de version a tener un nuevo archivo adjunto\n itemHistorial = ItemHistorial()\n itemHistorial.id_item = item.id_item\n itemHistorial.id_tipo_item = item.id_tipo_item\n itemHistorial.codigo = item.codigo\n itemHistorial.descripcion = item.descripcion\n itemHistorial.complejidad = item.complejidad\n itemHistorial.prioridad = item.prioridad\n itemHistorial.estado = \"Desarrollo\"\n itemHistorial.version = version_anterior\n itemHistorial.observacion = item.observacion\n itemHistorial.fecha_modificacion = item.fecha_modificacion\n item.version = version_nueva\n item.estado = \"Desarrollo\"\n DBSession.add(itemHistorial)\n DBSession.flush()\n\n #Consultamos los detalles que tiene el Item a ser editado y tambien\n #los atributos actuales de su Tipo de Item correspondiente\n detalles = DBSession.query(ItemDetalle).filter_by(id_item=id_item)\n atributos = DBSession.query(Atributo).filter_by(id_tipo_item=id_tipo_item)\n lista_id_atributo = []\n\n if atributos != None:\n for atributo in atributos:\n lista_id_atributo.append(atributo.id_atributo)\n\n #Enviamos al historial los detalles del item a ser editado\n if detalles != None:\n for detalle in detalles:\n if lista_id_atributo.count(detalle.id_atributo) >= 1: \n lista_id_atributo.remove(detalle.id_atributo)\n itemDetalleHistorial = ItemDetalleHistorial()\n itemDetalleHistorial.id_item = id_item\n itemDetalleHistorial.id_item_detalle = detalle.id_item_detalle\n itemDetalleHistorial.id_atributo = detalle.id_atributo\n itemDetalleHistorial.nombre_atributo = detalle.nombre_atributo\n itemDetalleHistorial.valor = detalle.valor\n itemDetalleHistorial.version = version_anterior\n DBSession.add(itemDetalleHistorial)\n DBSession.flush()\n\n #Cargamos a vacio los atributos que no contemplaban los detalles actuales\n if lista_id_atributo != None:\n for id_atributo in lista_id_atributo:\n atributo = DBSession.query(Atributo).get(id_atributo)\n itemDetalle = ItemDetalle()\n itemDetalle.id_item = id_item\n itemDetalle.id_atributo = atributo.id_atributo\n itemDetalle.nombre_atributo = atributo.nombre\n itemDetalle.valor = \"\"\n DBSession.add(itemDetalle)\n DBSession.flush()\n\n #Enviamos sus relaciones actuales al historial de relaciones\n hijos = DBSession.query(RelacionItem).filter_by(id_item1=id_item)\n antecesores = DBSession.query(RelacionItem).filter_by(id_item2=id_item)\n if hijos != None:\n for hijo in hijos:\n relacion_historial = RelacionHistorial()\n relacion_historial.tipo = hijo.tipo\n relacion_historial.id_item1 = hijo.id_item1\n relacion_historial.id_item2 = hijo.id_item2\n relacion_historial.version_modif = version_anterior\n DBSession.add(relacion_historial)\n DBSession.flush()\n if antecesores != None:\n for antecesor in antecesores:\n relacion_historial = RelacionHistorial()\n relacion_historial.tipo = antecesor.tipo\n relacion_historial.id_item1 = antecesor.id_item1\n relacion_historial.id_item2 = antecesor.id_item2\n relacion_historial.version_modif = version_anterior\n DBSession.add(relacion_historial)\n DBSession.flush()\n\n #Ponemos a revision todos los items afectados por el Item editado\n #Tambien colocamos a \"Revision\" las Lineas Bases correspondientes\n global itemsAfectados\n global listaRelaciones\n itemsAfectados = []\n listaRelaciones = []\n \n itemsAfectados.append(id_item)\n\n for item_afectado in itemsAfectados:\n self.buscarRelaciones(item_afectado)\n\n for item_afectado in itemsAfectados:\n item_cambio = DBSession.query(Item).get(item_afectado)\n item_cambio.estado = \"Revision\"\n linea_bases_item = item_cambio.linea_bases\n if linea_bases_item != None:\n for linea_base_item in linea_bases_item:\n if linea_base_item.estado == \"Aprobado\":\n id_linea_base = linea_base_item.id_linea_base \n linea_base = DBSession.query(LineaBase).get(id_linea_base)\n linea_base.estado = \"Revision\"\n fase = DBSession.query(Fase).get(linea_base.id_fase)\n if fase.relacion_estado_fase.nombre == \"Finalizado\":\n fase.id_estado_fase = '4'\n DBSession.flush()\n DBSession.flush() \n\n #Los archivos adjuntos del item a se editado, se copian\n #para tener el registro de estos archivos con esa version de item\n archivos_item_editado = DBSession.query(ItemArchivo).filter_by(id_item=id_item).filter_by(version_item=version_anterior)\n if archivos_item_editado != None:\n for archivo in archivos_item_editado:\n nuevo_archivo = ItemArchivo()\n nuevo_archivo.id_item = archivo.id_item\n nuevo_archivo.version_item = version_anterior\n nuevo_archivo.nombre_archivo = archivo.nombre_archivo\n nuevo_archivo.contenido_archivo = archivo.contenido_archivo\n archivo.version_item = version_nueva\n DBSession.add(nuevo_archivo)\n DBSession.flush()\n\n DBSession.flush()\n transaction.commit()\n except IntegrityError:\n transaction.abort()\n flash(_(\"No se ha guardado! Hay Problemas con el servidor...\"), 'error')\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\n except SQLAlchemyError:\n flash(_(\"No se ha guardado! SQLAlchemyError...\"), 'error')\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\n except (AttributeError, NameError):\n flash(_(\"No se ha guardado! Hay Problemas con el servidor...\"), 'error')\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\n else:\n flash(_(\"Archivo guardado!\"), 'ok')\n\n redirect(\"/item/archivos_adjuntos\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)","title":""},{"docid":"937c72169e4d5fdcb2ace90b8b343f28","score":"0.52759737","text":"def cargar_archivo(self, widget, archivo):\n \n self.abrir( archivo)","title":""},{"docid":"01ab94d59800ebbe45e8cfebfa020ba2","score":"0.5263835","text":"def _copy_file_to_backup_dir(self):\n file_path = vim.eval('expand(\"<afile>:p:h\")')\n filename = vim.eval('expand(\"<afile>:t\")')\n full_path = \"%s/%s\" % (file_path,filename)\n backup_filename = vim.eval(\\\n 'expand(\"<afile>:p\")').replace('/','%')\n backup_file_path = \"%s/%s\" % (os.path.expanduser(self.backup_dir),\\\n backup_filename)\n if os.path.exists(full_path) and full_path not in self._leave_cleanup:\n self._leave_cleanup.append(backup_file_path)\n shutil.copy2(full_path,backup_file_path)","title":""},{"docid":"77e78d20dc151133b258e9e9ab38e480","score":"0.52637804","text":"def save_file(self, file_path=\"\"):\r\n print(\"Saving data to\", file_path)\r\n file_helper = filesource.FileSource(file_path)\r\n file_helper.save_file(self.data, self.header)","title":""},{"docid":"ca952cbc1cd56e37b4d3f12cf1774823","score":"0.52624744","text":"def save_to_file(path, content, mode=\"w\"):\n with open(path, mode) as dest_file:\n dest_file.write(content.encode('utf-8'))","title":""},{"docid":"3a79ba5ae69b8c35e5513a9ad92e3aff","score":"0.5250527","text":"def OnSaveAs(self, event):\n self.historyFile = self.QueryForFile()\n if self.historyFile is not None:\n self.SaveFile()","title":""},{"docid":"c31c5bb9af1e0652665a07509124e5a5","score":"0.52498627","text":"def save_game(self):\n get_file_path = ReadConfigFile(self.file_config).get_output_file() \n print get_file_path\n # in this section we should include the code to invoke save game class in a file","title":""},{"docid":"bc59aceeb8d132e477b1107b906d1e6e","score":"0.52479094","text":"def quicksave(self, prim, sec, f):\n if not isinstance(f, pathlib.Path):\n f = pathlib.Path(f)\n logging.info(\"Writing to {!s}\".format(f))\n if not f.parent.exists():\n f.parent.mkdir(parents=True)\n with f.open('wb') as fp:\n numpy.savez_compressed(fp, prim=prim, sec=sec)","title":""},{"docid":"2daac18524eb39db388d5643ec71980b","score":"0.52420634","text":"def write(self, filename):\n pass","title":""},{"docid":"2daac18524eb39db388d5643ec71980b","score":"0.52420634","text":"def write(self, filename):\n pass","title":""},{"docid":"da02f9254bcdf58046a3d4d2ee65b4e1","score":"0.52365375","text":"def savingMaze(file, pixels):\n write_img(file, pixels)","title":""},{"docid":"0c34797a3f9746e40ede3ad41d553b9d","score":"0.5236356","text":"def save_to_file(filepath, content):\r\n f = open(filepath, 'w', encoding='utf-8') \r\n f.write(content)\r\n f.close()","title":""},{"docid":"e44bc2da63cc8e2291ebab2db48c3fa7","score":"0.52209544","text":"def save():\n \n # CHECK UNTITLED\n do_save = xsf.checkUntitled(hou.hipFile.basename())\n \n # CHECK PREVIOUS OWNER\n if do_save:\n do_save = xsf.checkOwner(hou.hipFile.path())\n \n # IF ALLOWED, SAVE FILE\n if do_save:\n hou.hipFile.save()\n status_message = \"Successfully saved {0} ({1})\".format(hou.hipFile.basename(), time.strftime(\"%c\"))\n hou.ui.setStatusMessage(status_message)","title":""},{"docid":"ff3fbc9827d8dd0c1646ab7a7455b665","score":"0.5220212","text":"def save_file(filename, content, mode='w'):\n file_handle = xbmcvfs.File(\n xbmc.translatePath(os.path.join(g.DATA_PATH, filename)), mode)\n try:\n file_handle.write(content.encode('utf-8'))\n finally:\n file_handle.close()","title":""},{"docid":"51205b974d643f2f563c1920db63bd77","score":"0.52104366","text":"def new_entry_or_replace(self,the_path):\n\n if os.path.exists(the_path):\n os.remove(the_path)\n return open(the_path,'a')","title":""},{"docid":"0d83845dd6a65e429ff65063683c1aea","score":"0.52052265","text":"def _write_file(self, path, content):\n with open(path, 'w') as fh:\n fh.write(content)","title":""},{"docid":"f71b61eb3e0962a3b503587c9221025e","score":"0.5204771","text":"def save_upload(f, path):\n if not os.path.exists(os.path.dirname(path)):\n os.makedirs(os.path.dirname(path))\n with open(path, 'wb+') as destination:\n destination.write(f.read())","title":""},{"docid":"eeaf79167c5f5b9a5bece6a093600313","score":"0.5204335","text":"def save(self, path):\n sk_io.imsave(path, self.image)","title":""},{"docid":"123d3a4dbc7e8ee64d8ba9416dececa3","score":"0.52042913","text":"def disk_write(self):\n # save tracklist to file\n for i, tracklist in enumerate(self.tracklist_2_str(to_file=True), 1):\n fname = self.debug_folder / f\"tracklist_{i}.txt\"\n fname.write_text(tracklist, encoding=\"utf-8\")\n\n # save found personel to file\n fname = self.debug_folder / \"personnel.txt\"\n fname.write_text(self.personnel_2_str(), encoding=\"utf8\")","title":""},{"docid":"262227ba848035b63282e495d1645462","score":"0.5200625","text":"def save_file(self):\n np.savez(self.savefile,table=self.table)","title":""},{"docid":"7ccb0f5b1a56b8c944b24fd3f9e0974b","score":"0.5185998","text":"def save_to(self,fp):\n\t\tresponsible = False\n\t\tif type(fp)==str:\n\t\t\tresponsible = True\n\t\t\tfp = open(fp,\"wb\")\n\t\tfor block in self.blocks:\n\t\t\tblock.save_to(fp)\n\t\tif responsible:\n\t\t\tfp.close()","title":""},{"docid":"b8387e762fc4d9f28bfe68858740921a","score":"0.5185139","text":"def save_as(self, event=None):\n\n # Open a file dialog to get target\n df = dialogs.save_as(self.directory, extension='.cad')\n\n if df[1] != '':\n self.directory, self.filename = df\n self.save()","title":""},{"docid":"8830d1476a12075a797f6a8219380029","score":"0.5184838","text":"def save(self):\n with open(f'players/{self.idt}.txt', 'w') as fichier:\n fichier.write(self.__repr__())","title":""},{"docid":"897d62a7bae7dca3c26c59af12dcea68","score":"0.5180214","text":"def save_uploaded_file_new(uploaded, fname):\n ufile = None\n try:\n split = fname.rsplit('.', 1)\n file = uploaded\n dt = datetime.datetime.now()\n name = '%s-%s' % (translit.slugify(split[0]), (int(time.mktime(dt.timetuple()))))\n name = 'maket/' + name + '.' + split[1]\n path = default_storage.save(name, ContentFile(file.read()))\n except Exception as e:\n print e\n if os.path.exists(name.decode('utf-8')):\n os.unlink(name.decode('utf-8'))\n return path","title":""},{"docid":"86e5a479c573997bc9d52f1dc1ee6029","score":"0.5180209","text":"def save_to_file(self, path, index, format = \"xml\"):\r\n f = open(path, 'w')\r\n if format == \"xml\":\r\n self.get_scans()[index].write_xml(f)\r\n self.filenames[self.get_scans()[index]] = f\r\n else:\r\n self.get_scans()[index].write_text(f)\r\n f.close()","title":""},{"docid":"66924e92f421dcb552a012b560dcadef","score":"0.5157146","text":"def save(filename,data,path,command):\n\ttry:\t\t\n\t\toutputfile = open(os.path.join(path,filename),\"w\")\t\t\t\t\n\t\tprint (\"Saving: \" + command).ljust(50),\n\t\toutputfile.write(data)\t\t\t\n\t\toutputfile.close()\n\t\tprint \"...... Success\"\n\t\treturn 0\n\texcept Exception as ex:\n\t\tprint \"...... Error\", ex\n\t\treturn 1","title":""},{"docid":"912a0b57b28b56350b6d12b7e83454e4","score":"0.51536906","text":"def setFile(self):\n print('+--------------------------------+')\n print('| Carregando Documento |')\n print('+--------------------------------+')\n try:\n self.file = open(self.filePath, 'rb').read() #ainda não estou pegando o arquivo inteiro\n print('| Documento Carregado! |')\n print('+--------------------------------+')\n except:\n print('| Falha ao carregar Documento |')\n print('+--------------------------------+')","title":""},{"docid":"0f91b62414a91da5f7f515f3c9b26443","score":"0.51526207","text":"def save(self, folder, name):\n pass","title":""},{"docid":"0f91b62414a91da5f7f515f3c9b26443","score":"0.51526207","text":"def save(self, folder, name):\n pass","title":""},{"docid":"0f91b62414a91da5f7f515f3c9b26443","score":"0.51526207","text":"def save(self, folder, name):\n pass","title":""},{"docid":"0f91b62414a91da5f7f515f3c9b26443","score":"0.51526207","text":"def save(self, folder, name):\n pass","title":""}],"string":"[\n {\n \"docid\": \"a0db7e7417ae67d4ecf2a8d426768e28\",\n \"score\": \"0.67659104\",\n \"text\": \"def save(self, path):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee03039bec37e652f2d5f497ae526009\",\n \"score\": \"0.62812024\",\n \"text\": \"def guardar(self):\\n self.imagen_actual.save(\\\"imagen_nueva.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89207a4b3175d203f1ca7352a4b7f833\",\n \"score\": \"0.61607206\",\n \"text\": \"def guardar(nombre_archivo, diccionario_a_guardar):\\n\\n try:\\n # Abrimos nuestro archivo\\n archivo= open(nombre_archivo,\\\"w\\\")\\n\\n #convertimos nuestro diccionario en un archivo .json\\n convertido= json.dumps(diccionario_a_guardar)\\n\\n # guardamos el archivo\\n archivo.write(convertido)\\n\\n #Cerramos el archivo\\n archivo.close()\\n\\n return True\\n except:\\n\\n #si ocurre algun error retorna falso\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e423a3cc2517c280d8b37c459baa69\",\n \"score\": \"0.60031474\",\n \"text\": \"def save_to_file(self, path):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e21fbfbd9b2cae490321dbdce2fa664f\",\n \"score\": \"0.59755576\",\n \"text\": \"def guardar_okada( dtopo, nombre_archivo, tipo = 3 ):\\n\\n # revisar si nombre_archivo es del tipo de string\\n if not isinstance(nombre_archivo, basestring):\\n nombre_archivo = str( nombre_archivo )\\n else:\\n nombre_archivo = nombre_archivo\\n\\n # se guarda el archivo \\n dtopo.write(nombre_archivo, dtopo_type = tipo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc99b3495178215213ad58879eb3e0fc\",\n \"score\": \"0.5969014\",\n \"text\": \"def put_file(self, key, location):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b22621211b3db7cd9f1d0b1b03b109e\",\n \"score\": \"0.595592\",\n \"text\": \"def save(self, path):\\n self.src.save(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40660d439c6a0819c28fb976185b924d\",\n \"score\": \"0.592659\",\n \"text\": \"def Save(self):\\n chaine = \\\"\\\"\\n for x in self.grille:\\n chaine+=''.join(x)+\\\"\\\\n\\\"\\n if self.nom.endswith(\\\".\\\"):\\n chemin = os.path.join(\\\"cartes\\\",self.nom+\\\"txt\\\")\\n else:\\n chemin = os.path.join(\\\"cartes\\\",self.nom+\\\".txt\\\")\\n with open(chemin, 'w') as fichier:\\n fichier.write(chaine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ef76d10775c1b34b918e23a10cea4a\",\n \"score\": \"0.5908202\",\n \"text\": \"def fileWrite(path, fileName, chaineAEcrire):\\n\\n # %% Repertoire # os.path.join(path, fileName) si besoin\\n os.chdir(path)\\n\\n # %% Creation et Ecriture\\n f = open(fileName, 'w')\\n f.writelines(chaineAEcrire)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee4a931c4bd7b202bf14a23ef9e26b9a\",\n \"score\": \"0.58741575\",\n \"text\": \"def save(self, filename):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc96bf3c72142190ed112661d5965ee8\",\n \"score\": \"0.58739156\",\n \"text\": \"def saveFile(self, path):\\n\\t\\tfobj = self.allFiles[path]\\n\\t\\tself.allFiles[path].selfCopy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b449bf2a488eb230dc92ee6ff06724\",\n \"score\": \"0.5850641\",\n \"text\": \"def guardar_gris(self, img, cont, ruta):\\n\\n k = ruta+'/imagen' + str(cont) + '.png'\\n cv2.imwrite(k, img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c84a2ea393aed5425ea555354b01e286\",\n \"score\": \"0.5847585\",\n \"text\": \"def save_data(self, path):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca19471442995615a611ce60af79b44e\",\n \"score\": \"0.5802713\",\n \"text\": \"def file_writer(strings, lokacija_ind):\\r\\n obcina_ind = improved_search(strings, 'Občina:', 23)\\r\\n obcina = data_nicefier(strings[obcina_ind + 1]).upper()\\r\\n data = data_stringyfier(strings, lokacija_ind)\\r\\n ime, tekst, datacija, lokacija = data\\r\\n ime = ime.strip()\\r\\n ime = eliminator(ime)\\r\\n path = 'TEKSTI/' + obcina + '/' + ime.strip() + '.txt'\\r\\n if not os.path.exists('TEKSTI/' + obcina):\\r\\n os.makedirs('TEKSTI/' + obcina)\\r\\n with open(path, 'w', encoding=\\\"utf-8\\\") as file:\\r\\n file.write(ime + tekst + datacija + lokacija)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5afc7e2c0068caa58e604fe9d7ae29c\",\n \"score\": \"0.57936144\",\n \"text\": \"def ingreso():\\n fecha = time.asctime()\\n f = open(\\\"registros.txt\\\", \\\"a\\\")\\n f.write(\\\"IN \\\" + fecha + \\\" \\\" + \\\"Encargad@ \\\" + nombre + \\\"\\\\n\\\")\\n f.close()\\n print(\\\"Registro salvado\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96ac18526cee3b22d73ffc4b54722163\",\n \"score\": \"0.5767469\",\n \"text\": \"def pos_save_file():\\n try:\\n save_file_prompt = asksaveasfilename(defaultextension=\\\".json\\\")\\n save_file_name = open(save_file_prompt, \\\"w\\\")\\n if save_file_name is not None:\\n # Write the file to disk\\n obj_locations = copy.deepcopy(get_dict_rect_positions())\\n save_file_name.write(str(obj_locations))\\n save_file_name.close()\\n log.info(\\\"File Saved Successfully.\\\")\\n else:\\n log.info(\\\"Error! Need king to save!\\\")\\n except IOError:\\n log.info(\\\"Save File Error (IOError)\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2386b87050843e4fb5d3a744164f4627\",\n \"score\": \"0.57370955\",\n \"text\": \"def save(self,path):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42838b09a04c64bf875dbf7ab0736ca2\",\n \"score\": \"0.5646486\",\n \"text\": \"def save_by_replacing(self):\\n self.save_converted_file_0(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1bf470ad4e9fbf7a151d4e1dd23a517\",\n \"score\": \"0.5642181\",\n \"text\": \"def save():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd563dd3d67febfae6deb9d6b9db628\",\n \"score\": \"0.56404704\",\n \"text\": \"def save_fp(self, fp, filename, directory=''):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e36f091819c95f2e183b04095a541f\",\n \"score\": \"0.5639669\",\n \"text\": \"def save_file(filename, content):\\r\\n\\r\\n create_dir(os.path.dirname(filename))\\r\\n with open(filename, 'w') as f:\\r\\n f.write(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23c808d44e137d36a78829fbbe735eb\",\n \"score\": \"0.56320095\",\n \"text\": \"def save(self, directory):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23c808d44e137d36a78829fbbe735eb\",\n \"score\": \"0.56320095\",\n \"text\": \"def save(self, directory):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b345aabe09fb6bf2a07f02174cbd2d\",\n \"score\": \"0.56312644\",\n \"text\": \"def save_paths(source_file: str, new_path: str):\\r\\n with open(source_file, encoding='utf-8', mode='w') as f:\\r\\n f.write(new_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ead2e30d6786d6ed24cfc7f9d6962f2\",\n \"score\": \"0.56306994\",\n \"text\": \"def save(self, destinationName):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83c7e7b38f41ada0ffecc97882ebf68\",\n \"score\": \"0.56101125\",\n \"text\": \"def fileReplace(path, fileName, urlSiteAAspirer):\\n\\n # %%Recuperation du contenu de la page\\n htmlSoup = sW.extractHTML(urlSiteAAspirer)\\n\\n # %% Enregistrement du fichier une fois le contenu de la page extrait\\n fileWrite(path, fileName, htmlSoup.prettify())\\n\\n fileLinksReplace(path, fileName, urlSiteAAspirer, htmlSoup)\\n\\n filePictureLinksReplace(path, fileName, urlSiteAAspirer, htmlSoup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30336c21bc85ed18953e74ee9352b933\",\n \"score\": \"0.56064665\",\n \"text\": \"def salida():\\n fecha = time.asctime()\\n f = open(\\\"registros.txt\\\", \\\"a\\\")\\n f.write(\\\"OUT \\\" + fecha + \\\" \\\" + \\\"Encargad@ \\\" +\\n nombre + \\\" $\\\" + str(totalturno)+\\\"\\\\n\\\")\\n f.close()\\n print(\\\"Registro salvado\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454e06f31947ec9beec014a7ff9459b4\",\n \"score\": \"0.5603385\",\n \"text\": \"def save(self):\\r\\n if not self.dirty:\\r\\n return\\r\\n\\r\\n data = '\\\\n'.join(map(self.make_relative,self.paths))\\r\\n if data:\\r\\n log.debug(\\\"Saving %s\\\", self.filename)\\r\\n data = (\\r\\n \\\"import sys; sys.__plen = len(sys.path)\\\\n\\\"\\r\\n \\\"%s\\\\n\\\"\\r\\n \\\"import sys; new=sys.path[sys.__plen:];\\\"\\r\\n \\\" del sys.path[sys.__plen:];\\\"\\r\\n \\\" p=getattr(sys,'__egginsert',len(os.environ.get('PYTHONPATH','').split(os.pathsep))); sys.path[p:p]=new;\\\"\\r\\n \\\" sys.__egginsert = p+len(new)\\\\n\\\"\\r\\n ) % data\\r\\n\\r\\n if os.path.islink(self.filename):\\r\\n os.unlink(self.filename)\\r\\n f = open(self.filename,'wb')\\r\\n f.write(data); f.close()\\r\\n\\r\\n elif os.path.exists(self.filename):\\r\\n log.debug(\\\"Deleting empty %s\\\", self.filename)\\r\\n os.unlink(self.filename)\\r\\n\\r\\n self.dirty = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a5cda49ea66d44cb3e8537e5e374a71\",\n \"score\": \"0.56016964\",\n \"text\": \"def save(self, saved_path: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcb220a88e43ed30464259a23544118\",\n \"score\": \"0.5594327\",\n \"text\": \"def save(self):\\n rval = save_data(self)\\n if not rval == '':\\n self.tmpsrc = rval\\n print \\\"Updated storage path of file: %s\\\" % rval\\n else:\\n print 'There is nothing to save.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1863a7ec5272f103f044e560eb198115\",\n \"score\": \"0.5573114\",\n \"text\": \"def excelfile(cls):\\n\\n d = datetime.today().date().strftime('%d/%m/%Y')\\n c = datetime.fromtimestamp(path.getmtime(\\n cls._nombrearchivo)).strftime('%d/%m/%Y')\\n\\n if c != d:\\n print('\\\\n removiendo el archivo')\\n remove(cls._nombrearchivo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ffd4d7404f30b49615e4e6c70815fe6\",\n \"score\": \"0.5565485\",\n \"text\": \"def _save(self, data, path):\\n with open(path, 'wb') as f:\\n f.write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137857edc66d641c4cc03c446e9e033b\",\n \"score\": \"0.5542466\",\n \"text\": \"def backup(self) -> Path:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3f20433b28d9a649ae0331f4f9d95a\",\n \"score\": \"0.5532166\",\n \"text\": \"def put_file(full_path, file_obj, overwrite=False, parent_rev=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b4c2e105f6a0e8cf0eebcc61dda4ae\",\n \"score\": \"0.5526791\",\n \"text\": \"def sauvegarde():\\n with open(\\\"saverobot\\\", \\\"wb\\\") as f:\\n cb = canvas.coords(robots[0])\\n cr = canvas.coords(robots[1])\\n cg = canvas.coords(robots[2])\\n cy = canvas.coords(robots[3])\\n bt = canvas.coords(pos_target[0])\\n rt = canvas.coords(pos_target[1])\\n gt = canvas.coords(pos_target[2])\\n yt = canvas.coords(pos_target[3])\\n save = [cb, cr, cg, cy, bt, rt, gt, yt, cpt]\\n pickle.dump(save, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2652c7f1cdfec26a805e9e423f2f110c\",\n \"score\": \"0.5523489\",\n \"text\": \"def safeSave(self):\\n self.fileInfo.makeBackup()\\n filePath = os.path.join(self.fileInfo.dir, self.fileInfo.name)\\n tempPath = filePath + '.tmp'\\n self.save(tempPath)\\n renameFile(tempPath, filePath)\\n self.fileInfo.setMTime()\\n self.fileInfo.extras.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38376f44a2b150fcacf24f14828ff145\",\n \"score\": \"0.5510586\",\n \"text\": \"def save_as(self, filepath):\\n self.filepath = filepath\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc2ca0f102ece53d77142cf8fe6c88f\",\n \"score\": \"0.5509352\",\n \"text\": \"def save_file(cls, pg, xo, yo, dx, dy, rot, tr, ipj, file):\\n gxapi_cy.WrapPGU._save_file(GXContext._get_tls_geo(), pg, xo, yo, dx, dy, rot, tr, ipj, file.encode())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140c2d8dcdb26cf49771e5b34dece5a8\",\n \"score\": \"0.55054504\",\n \"text\": \"def save_metadata(self) -> Path:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56104c66a0ec4a3a290a3c7eaa6e757\",\n \"score\": \"0.54986435\",\n \"text\": \"def saveFile(self):\\n if self.outputMask is None:\\n print \\\"Noting to save\\\"\\n return\\n if self.spSegments is not None:\\n path = config.outputFile(self.filename)\\n dirSplit = path.split('.')\\n np.savetxt(dirSplit[0] + \\\".csv\\\", self.spSegments, delimiter=\\\",\\\", fmt=\\\"%d\\\")\\n \\n output = cv2.cvtColor(self.outputMask, cv2.COLOR_RGB2BGR)\\n cv2.imwrite(config.outputFile(self.filename).decode('utf-8').encode('gbk'), output)\\n self.updateStatus(\\\"Save to %s\\\" % config.outputFile(self.filename))\\n self.setClean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d0dc211d6e7690d5d23ecb24033968\",\n \"score\": \"0.54742175\",\n \"text\": \"def save(data, path, enc):\\n import json\\n import codecs\\n import os\\n\\n if os.access(os.path.dirname(os.path.abspath(path)), os.W_OK):\\n with codecs.open(path, 'w', enc) as fo:\\n json.dump(data, fo, sort_keys=True, indent=4, ensure_ascii=False)\\n else:\\n print('Cat not craete file!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd32dfa4805acc8cccbdaecbccf56d4b\",\n \"score\": \"0.5465589\",\n \"text\": \"def save(self, path):\\r\\n write_mrk(path, self.points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44425f439250dc8ec448d6919c6223df\",\n \"score\": \"0.5462761\",\n \"text\": \"def save(self, directory):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8677c63afcc5d081f79188dff1efab4a\",\n \"score\": \"0.54616195\",\n \"text\": \"def save_in_backup(self):\\r\\n now = datetime.datetime.now()\\r\\n filename = now.strftime('%Y-%m-%d_%H-%M-%S_LocalLeaderboards.dat')\\r\\n if not os.path.exists(Backup.DEFAULT_FOLDER):\\r\\n os.mkdir(Backup.DEFAULT_FOLDER)\\r\\n self.save(os.path.join(Backup.DEFAULT_FOLDER, filename), must_be_valid=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee985f4557f5454b3f7302d485e6432\",\n \"score\": \"0.5453255\",\n \"text\": \"def _save(self,data):\\n file_name = re.sub('[\\\\/:*?\\\"<>| \\\\n]','_',self.title,re.S)\\n if self.path :\\n file_name = os.join(self.path,file_name+'.pdf')\\n else:\\n file_name = os.path.join(\\\".\\\",file_name+'.pdf')\\n with open(file_name, 'wb') as f:\\n f.write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c8112a82a4d3e55747a2ac18f5a3a8\",\n \"score\": \"0.5450561\",\n \"text\": \"def move_file(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62a22caa9fb53100cabdc6d52fdb712\",\n \"score\": \"0.5448746\",\n \"text\": \"def save_path(self):\\n with open(\\\"path.json\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as file:\\n json.dump(self.path, file, ensure_ascii=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8de173ed669d3a80d4c08970433e52\",\n \"score\": \"0.5402648\",\n \"text\": \"def saveFile(self):\\n\\n file = tkFileDialog.asksaveasfile(mode='w',defaultextension = '.life')\\n if file:\\n self.guiToPatternCounter += 1\\n guiToPatternQueue.put(['saveFile',self.guiToPatternCounter,file])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3460c54a6fba163a1abb2b5d83f9a3\",\n \"score\": \"0.5399128\",\n \"text\": \"def _put(self, name, document):\\n # Let's go back to the origi of file\\n document.seek(0)\\n\\n # Let's open the destination, it will be created if needed\\n # And let's write the origin in it by block of 4096bytes\\n with open(os.path.join(self.path, unicode(name)), 'wb') as destination, document as origin:\\n destination.write(origin.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa6116f46d9dd9dd4a7947a05d1dfd2e\",\n \"score\": \"0.5393509\",\n \"text\": \"def sauve_fichier(self):\\n with open(self.fichier_out, 'w', encoding = 'utf8') as fichier:\\n fichier.write(self.chaine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc72e8ed331951e786ba32112993eef0\",\n \"score\": \"0.5384045\",\n \"text\": \"def test_save(self):\\n fileobj = self.storage.open( u\\\"content.txt\\\" )\\n self.storage.save( u\\\"test file.bin\\\", File( fileobj ) )\\n fileobj.close( )\\n assert self.storage.exists( u\\\"test file.bin\\\" ) == True\\n f = self.storage.open( u\\\"test file.bin\\\" )\\n content = f.read( )\\n f.close( )\\n assert content.strip( ) == u\\\"Test line in file\\\"\\n self.storage.remove( u\\\"test file.bin\\\" )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbcb465dd94288fc1b2b5ce56c27b4bb\",\n \"score\": \"0.5378622\",\n \"text\": \"def on_save(self):\\r\\n self.__savefile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454f012e7f133c8efb42dcd8e46d9e79\",\n \"score\": \"0.53777385\",\n \"text\": \"def save(self):\\n filename = self.netloc\\n filename = re.sub(errorchars, '', filename) # Entferne alle Sonderzeichen\\n if not self.debug:\\n outfile = open(filename, 'wb')\\n pickle.dump(self, outfile) # Speichere in Datei\\n outfile.close()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7dc022a025b2b918feb08cd005dc544\",\n \"score\": \"0.5363813\",\n \"text\": \"def save_path(self, path, filename):\\n if not path:\\n print('The path is empty. No path saved.')\\n return\\n\\n with open(filename, 'wb') as f:\\n f.write(','.join('(%s, %s)' % x for x in path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d57237de0ccbe237fa4f89b3debbd1c\",\n \"score\": \"0.53574413\",\n \"text\": \"def save_file(self):\\n try:\\n file_types = [('Text files', '*.txt'), ('All Files', '*')]\\n file_info = asksaveasfilename(title=\\\"Save file\\\",\\n defaultextension=\\\".txt\\\",\\n filetypes=file_types)\\n self.file = FileManager(file_info)\\n if self.coasters.get_radius() != 0.0:\\n self.file.save(self.coasters.__str__())\\n else:\\n self.compute_action()\\n self.file.close()\\n except FileNotFoundError:\\n messagebox.showinfo(\\\"Invalid file name\\\",\\n \\\"Please write a valid name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec9fa876234a804afbf0e1254a422f1\",\n \"score\": \"0.5351124\",\n \"text\": \"def __save_to_file(self):\\n self.set_id(self.makeid())\\n directory_name = dates.now_str(FMT_DATE_STD)\\n full_dir = os.path.join(self.SAVE_BASE_DIRECTORY, directory_name)\\n full_path = os.path.join(full_dir, \\\"{0}.json\\\".format(self.id))\\n\\n if not os.path.isdir(full_dir):\\n os.makedirs(full_dir)\\n\\n with open(full_path, 'w') as o:\\n o.write(json.dumps(self.data, indent=4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"726052076c7514ece7ed2d021803542e\",\n \"score\": \"0.53477\",\n \"text\": \"def save(self, path=None):\\n if not path:\\n path = self.path\\n if path != self.path:\\n self.path = path\\n with open(path,'w+') as index:\\n for line in self.export():\\n index.write(\\\"%s%s\\\" % (line, os.linesep))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0d3178a8a911cda1e054640e2225d3\",\n \"score\": \"0.5343568\",\n \"text\": \"def guardar_ingesta_localmente(file_to_upload, data):\\n # Cambiar datos de formato json a objetos binario\\n pickle.dump(data, file_to_upload)\\n logging.info(\\\"pkl guardado exitosamente en local.\\\")\\n file_to_upload.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43d933eb69c6987627694e36edcab87\",\n \"score\": \"0.5339175\",\n \"text\": \"def write_file(path, text):\\n with open(path, 'w+') as f:\\n f.write(text)\\n print('Fichero %s generado' % path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec1913458b1bd3871b40d229f5ee185\",\n \"score\": \"0.5336342\",\n \"text\": \"def save(self):\\n if self.hasChanged:\\n filePath = self.path\\n tempPath = filePath + '.tmp'\\n fileDir = os.path.split(filePath)[0]\\n if not os.path.exists(fileDir):\\n os.makedirs(fileDir)\\n cPickle.dump(self.data, open(tempPath, 'w'))\\n renameFile(tempPath, filePath, True)\\n self.hasChanged = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20954d477066719bc3e8a28d9d0bcb9f\",\n \"score\": \"0.5316111\",\n \"text\": \"def save_file(file_, path=''):\\n filename = file_.name\\n file_path = os.path.join(path, filename)\\n if os.path.isfile(file_path):\\n os.remove(file_path)\\n fd = open(str(path) + \\\"/\\\" + str(filename), 'wb')\\n for chunk in file_.chunks():\\n fd.write(chunk)\\n fd.close()\\n return str(path) + \\\"/\\\" + str(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4972a92dfcf5d9dc684368e467a58b9a\",\n \"score\": \"0.5314942\",\n \"text\": \"def writeDoeFile():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"060c31d346fa9ea5e26bc974b1a3d040\",\n \"score\": \"0.5313048\",\n \"text\": \"def handle_project_save(self, file_name: str):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3432dcf6e7762305098d06bd146de12\",\n \"score\": \"0.53118926\",\n \"text\": \"def save(self, path):\\n path = self.conform(path)\\n print \\\"Save path : \\\" + path\\n nuke.scriptSaveAs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705a7b365b465c9f7a9490b81836a070\",\n \"score\": \"0.53108317\",\n \"text\": \"def escribirArchivo(self):\\n\\t\\tf = open('db.txt','w')\\n\\t\\tf2f = []\\n\\t\\tfor r in self.bd:\\n\\t\\t\\tf.seek(0,2)\\n\\t\\t\\tf.write(\\\"\\\\n\\\\n> Registro [\\\" + str(r)+ \\\"]\\\")\\n\\t\\t\\tfor i in range(len(self.bd[r])):\\n\\t\\t\\t\\tf.seek(0,2)\\n\\t\\t\\t\\tf.write(\\\"\\\\n\\\\t\\\" + str(self.bd[r][i-1][0]) + \\\" => \\\" + str(self.bd[r][i-1][1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424df3b833e4d72798075afec5b3765e\",\n \"score\": \"0.5290365\",\n \"text\": \"def write(self, makeBackup = True):\\n\\n if makeBackup:\\n bakfilepath = self.filepath + '~'\\n try: os.remove(bakfilepath)\\n except: pass\\n os.rename(self.filepath, bakfilepath)\\n else:\\n try: os.remove(self.filepath)\\n except: pass\\n fout = open(self.filepath, 'w')\\n fout.writelines(self.lines)\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8c761a97108828b813c90ac3256a3b\",\n \"score\": \"0.528623\",\n \"text\": \"def test_put_file_to_existing_directory(self):\\r\\n text = \\\"foo!\\\"\\r\\n local = self.mkfile('foo.txt', text)\\r\\n local2 = self.path('foo2.txt')\\r\\n with hide('everything'):\\r\\n put(local, '/')\\r\\n get('/foo.txt', local2)\\r\\n eq_contents(local2, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c80413f00e05eba1b35b9fd8c3a7b6a\",\n \"score\": \"0.5285937\",\n \"text\": \"def writeToDisk(self, filename):\\n\\t\\tp = UrmasPersist.UrmasPersist(self)\\n\\t\\tp.persist(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b63f5089ca3744408af877fbaaaaa409\",\n \"score\": \"0.52767\",\n \"text\": \"def put_file(self, model_file_info, model_file_location):\\n self.remove_if_exists(model_file_info) # backup old revision\\n cache_key = self.__get_cache_key(model_file_info)\\n cache_full_path = os.path.join(\\n self.cache_root_location,\\n cache_key['Path']) # Branch and Tag do not have same name.\\n cache_file_dir = os.path.dirname(cache_full_path)\\n if not os.path.exists(cache_file_dir):\\n os.makedirs(cache_file_dir, exist_ok=True)\\n # We can't make operation transaction\\n move(model_file_location, cache_full_path)\\n self.cached_files.append(cache_key)\\n self.save_cached_files()\\n return cache_full_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b99706170f74f0bbc16f41640bb6e7\",\n \"score\": \"0.52761835\",\n \"text\": \"def save(self, userfile):\\n try:\\n global id_item_actual\\n item = DBSession.query(Item).get(id_item_actual[0])\\n id_item = item.id_item\\n version_anterior = item.version\\n version_nueva = int(item.version) + 1\\n id_tipo_item = item.id_tipo_item\\n tipo_item = DBSession.query(TipoItem).get(id_tipo_item)\\n id_fase = tipo_item.id_fase\\n fase = DBSession.query(Fase).get(id_fase)\\n id_proyecto = fase.id_proyecto\\n linea_bases_item = item.linea_bases\\n\\n #Comprobamos que el Item no se encuentre en una Linea Base\\n if linea_bases_item != []:\\n for linea_base_item in linea_bases_item:\\n flash(_(\\\"No se puede agregar el archivo! El Item se encuentra en una Linea Base...\\\"), 'error')\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\\n\\n if userfile == \\\"\\\":\\n flash(_(\\\"Introduzca una ruta de archivo!\\\"), 'error')\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\\n\\n forbidden_files = [\\\".js\\\", \\\".htm\\\", \\\".html\\\", \\\".mp3\\\"]\\n for forbidden_file in forbidden_files:\\n if userfile.filename.find(forbidden_file) != -1:\\n flash(_(\\\"No se puede guardar el contenido...\\\"), 'error')\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, \\n id_tipo_item=id_tipo_item, id_item=id_item)\\n\\n #Guardamos el archivo nuevo\\n nombre_archivo = userfile.filename\\n contenido_archivo = userfile.file.read()\\n nuevo_archivo = ItemArchivo()\\n nuevo_archivo.id_item = item.id_item\\n nuevo_archivo.version_item = version_nueva\\n nuevo_archivo.nombre_archivo = nombre_archivo\\n nuevo_archivo.contenido_archivo = contenido_archivo\\n DBSession.add(nuevo_archivo)\\n\\n #El item actual cambia de version a tener un nuevo archivo adjunto\\n itemHistorial = ItemHistorial()\\n itemHistorial.id_item = item.id_item\\n itemHistorial.id_tipo_item = item.id_tipo_item\\n itemHistorial.codigo = item.codigo\\n itemHistorial.descripcion = item.descripcion\\n itemHistorial.complejidad = item.complejidad\\n itemHistorial.prioridad = item.prioridad\\n itemHistorial.estado = \\\"Desarrollo\\\"\\n itemHistorial.version = version_anterior\\n itemHistorial.observacion = item.observacion\\n itemHistorial.fecha_modificacion = item.fecha_modificacion\\n item.version = version_nueva\\n item.estado = \\\"Desarrollo\\\"\\n DBSession.add(itemHistorial)\\n DBSession.flush()\\n\\n #Consultamos los detalles que tiene el Item a ser editado y tambien\\n #los atributos actuales de su Tipo de Item correspondiente\\n detalles = DBSession.query(ItemDetalle).filter_by(id_item=id_item)\\n atributos = DBSession.query(Atributo).filter_by(id_tipo_item=id_tipo_item)\\n lista_id_atributo = []\\n\\n if atributos != None:\\n for atributo in atributos:\\n lista_id_atributo.append(atributo.id_atributo)\\n\\n #Enviamos al historial los detalles del item a ser editado\\n if detalles != None:\\n for detalle in detalles:\\n if lista_id_atributo.count(detalle.id_atributo) >= 1: \\n lista_id_atributo.remove(detalle.id_atributo)\\n itemDetalleHistorial = ItemDetalleHistorial()\\n itemDetalleHistorial.id_item = id_item\\n itemDetalleHistorial.id_item_detalle = detalle.id_item_detalle\\n itemDetalleHistorial.id_atributo = detalle.id_atributo\\n itemDetalleHistorial.nombre_atributo = detalle.nombre_atributo\\n itemDetalleHistorial.valor = detalle.valor\\n itemDetalleHistorial.version = version_anterior\\n DBSession.add(itemDetalleHistorial)\\n DBSession.flush()\\n\\n #Cargamos a vacio los atributos que no contemplaban los detalles actuales\\n if lista_id_atributo != None:\\n for id_atributo in lista_id_atributo:\\n atributo = DBSession.query(Atributo).get(id_atributo)\\n itemDetalle = ItemDetalle()\\n itemDetalle.id_item = id_item\\n itemDetalle.id_atributo = atributo.id_atributo\\n itemDetalle.nombre_atributo = atributo.nombre\\n itemDetalle.valor = \\\"\\\"\\n DBSession.add(itemDetalle)\\n DBSession.flush()\\n\\n #Enviamos sus relaciones actuales al historial de relaciones\\n hijos = DBSession.query(RelacionItem).filter_by(id_item1=id_item)\\n antecesores = DBSession.query(RelacionItem).filter_by(id_item2=id_item)\\n if hijos != None:\\n for hijo in hijos:\\n relacion_historial = RelacionHistorial()\\n relacion_historial.tipo = hijo.tipo\\n relacion_historial.id_item1 = hijo.id_item1\\n relacion_historial.id_item2 = hijo.id_item2\\n relacion_historial.version_modif = version_anterior\\n DBSession.add(relacion_historial)\\n DBSession.flush()\\n if antecesores != None:\\n for antecesor in antecesores:\\n relacion_historial = RelacionHistorial()\\n relacion_historial.tipo = antecesor.tipo\\n relacion_historial.id_item1 = antecesor.id_item1\\n relacion_historial.id_item2 = antecesor.id_item2\\n relacion_historial.version_modif = version_anterior\\n DBSession.add(relacion_historial)\\n DBSession.flush()\\n\\n #Ponemos a revision todos los items afectados por el Item editado\\n #Tambien colocamos a \\\"Revision\\\" las Lineas Bases correspondientes\\n global itemsAfectados\\n global listaRelaciones\\n itemsAfectados = []\\n listaRelaciones = []\\n \\n itemsAfectados.append(id_item)\\n\\n for item_afectado in itemsAfectados:\\n self.buscarRelaciones(item_afectado)\\n\\n for item_afectado in itemsAfectados:\\n item_cambio = DBSession.query(Item).get(item_afectado)\\n item_cambio.estado = \\\"Revision\\\"\\n linea_bases_item = item_cambio.linea_bases\\n if linea_bases_item != None:\\n for linea_base_item in linea_bases_item:\\n if linea_base_item.estado == \\\"Aprobado\\\":\\n id_linea_base = linea_base_item.id_linea_base \\n linea_base = DBSession.query(LineaBase).get(id_linea_base)\\n linea_base.estado = \\\"Revision\\\"\\n fase = DBSession.query(Fase).get(linea_base.id_fase)\\n if fase.relacion_estado_fase.nombre == \\\"Finalizado\\\":\\n fase.id_estado_fase = '4'\\n DBSession.flush()\\n DBSession.flush() \\n\\n #Los archivos adjuntos del item a se editado, se copian\\n #para tener el registro de estos archivos con esa version de item\\n archivos_item_editado = DBSession.query(ItemArchivo).filter_by(id_item=id_item).filter_by(version_item=version_anterior)\\n if archivos_item_editado != None:\\n for archivo in archivos_item_editado:\\n nuevo_archivo = ItemArchivo()\\n nuevo_archivo.id_item = archivo.id_item\\n nuevo_archivo.version_item = version_anterior\\n nuevo_archivo.nombre_archivo = archivo.nombre_archivo\\n nuevo_archivo.contenido_archivo = archivo.contenido_archivo\\n archivo.version_item = version_nueva\\n DBSession.add(nuevo_archivo)\\n DBSession.flush()\\n\\n DBSession.flush()\\n transaction.commit()\\n except IntegrityError:\\n transaction.abort()\\n flash(_(\\\"No se ha guardado! Hay Problemas con el servidor...\\\"), 'error')\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\\n except SQLAlchemyError:\\n flash(_(\\\"No se ha guardado! SQLAlchemyError...\\\"), 'error')\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\\n except (AttributeError, NameError):\\n flash(_(\\\"No se ha guardado! Hay Problemas con el servidor...\\\"), 'error')\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\\n else:\\n flash(_(\\\"Archivo guardado!\\\"), 'ok')\\n\\n redirect(\\\"/item/archivos_adjuntos\\\", id_proyecto=id_proyecto, id_fase=id_fase, id_tipo_item=id_tipo_item, id_item=id_item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937c72169e4d5fdcb2ace90b8b343f28\",\n \"score\": \"0.52759737\",\n \"text\": \"def cargar_archivo(self, widget, archivo):\\n \\n self.abrir( archivo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ab94d59800ebbe45e8cfebfa020ba2\",\n \"score\": \"0.5263835\",\n \"text\": \"def _copy_file_to_backup_dir(self):\\n file_path = vim.eval('expand(\\\"<afile>:p:h\\\")')\\n filename = vim.eval('expand(\\\"<afile>:t\\\")')\\n full_path = \\\"%s/%s\\\" % (file_path,filename)\\n backup_filename = vim.eval(\\\\\\n 'expand(\\\"<afile>:p\\\")').replace('/','%')\\n backup_file_path = \\\"%s/%s\\\" % (os.path.expanduser(self.backup_dir),\\\\\\n backup_filename)\\n if os.path.exists(full_path) and full_path not in self._leave_cleanup:\\n self._leave_cleanup.append(backup_file_path)\\n shutil.copy2(full_path,backup_file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e78d20dc151133b258e9e9ab38e480\",\n \"score\": \"0.52637804\",\n \"text\": \"def save_file(self, file_path=\\\"\\\"):\\r\\n print(\\\"Saving data to\\\", file_path)\\r\\n file_helper = filesource.FileSource(file_path)\\r\\n file_helper.save_file(self.data, self.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca952cbc1cd56e37b4d3f12cf1774823\",\n \"score\": \"0.52624744\",\n \"text\": \"def save_to_file(path, content, mode=\\\"w\\\"):\\n with open(path, mode) as dest_file:\\n dest_file.write(content.encode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a79ba5ae69b8c35e5513a9ad92e3aff\",\n \"score\": \"0.5250527\",\n \"text\": \"def OnSaveAs(self, event):\\n self.historyFile = self.QueryForFile()\\n if self.historyFile is not None:\\n self.SaveFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31c5bb9af1e0652665a07509124e5a5\",\n \"score\": \"0.52498627\",\n \"text\": \"def save_game(self):\\n get_file_path = ReadConfigFile(self.file_config).get_output_file() \\n print get_file_path\\n # in this section we should include the code to invoke save game class in a file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc59aceeb8d132e477b1107b906d1e6e\",\n \"score\": \"0.52479094\",\n \"text\": \"def quicksave(self, prim, sec, f):\\n if not isinstance(f, pathlib.Path):\\n f = pathlib.Path(f)\\n logging.info(\\\"Writing to {!s}\\\".format(f))\\n if not f.parent.exists():\\n f.parent.mkdir(parents=True)\\n with f.open('wb') as fp:\\n numpy.savez_compressed(fp, prim=prim, sec=sec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2daac18524eb39db388d5643ec71980b\",\n \"score\": \"0.52420634\",\n \"text\": \"def write(self, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2daac18524eb39db388d5643ec71980b\",\n \"score\": \"0.52420634\",\n \"text\": \"def write(self, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da02f9254bcdf58046a3d4d2ee65b4e1\",\n \"score\": \"0.52365375\",\n \"text\": \"def savingMaze(file, pixels):\\n write_img(file, pixels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c34797a3f9746e40ede3ad41d553b9d\",\n \"score\": \"0.5236356\",\n \"text\": \"def save_to_file(filepath, content):\\r\\n f = open(filepath, 'w', encoding='utf-8') \\r\\n f.write(content)\\r\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44bc2da63cc8e2291ebab2db48c3fa7\",\n \"score\": \"0.52209544\",\n \"text\": \"def save():\\n \\n # CHECK UNTITLED\\n do_save = xsf.checkUntitled(hou.hipFile.basename())\\n \\n # CHECK PREVIOUS OWNER\\n if do_save:\\n do_save = xsf.checkOwner(hou.hipFile.path())\\n \\n # IF ALLOWED, SAVE FILE\\n if do_save:\\n hou.hipFile.save()\\n status_message = \\\"Successfully saved {0} ({1})\\\".format(hou.hipFile.basename(), time.strftime(\\\"%c\\\"))\\n hou.ui.setStatusMessage(status_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3fbc9827d8dd0c1646ab7a7455b665\",\n \"score\": \"0.5220212\",\n \"text\": \"def save_file(filename, content, mode='w'):\\n file_handle = xbmcvfs.File(\\n xbmc.translatePath(os.path.join(g.DATA_PATH, filename)), mode)\\n try:\\n file_handle.write(content.encode('utf-8'))\\n finally:\\n file_handle.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51205b974d643f2f563c1920db63bd77\",\n \"score\": \"0.52104366\",\n \"text\": \"def new_entry_or_replace(self,the_path):\\n\\n if os.path.exists(the_path):\\n os.remove(the_path)\\n return open(the_path,'a')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d83845dd6a65e429ff65063683c1aea\",\n \"score\": \"0.52052265\",\n \"text\": \"def _write_file(self, path, content):\\n with open(path, 'w') as fh:\\n fh.write(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71b61eb3e0962a3b503587c9221025e\",\n \"score\": \"0.5204771\",\n \"text\": \"def save_upload(f, path):\\n if not os.path.exists(os.path.dirname(path)):\\n os.makedirs(os.path.dirname(path))\\n with open(path, 'wb+') as destination:\\n destination.write(f.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeaf79167c5f5b9a5bece6a093600313\",\n \"score\": \"0.5204335\",\n \"text\": \"def save(self, path):\\n sk_io.imsave(path, self.image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123d3a4dbc7e8ee64d8ba9416dececa3\",\n \"score\": \"0.52042913\",\n \"text\": \"def disk_write(self):\\n # save tracklist to file\\n for i, tracklist in enumerate(self.tracklist_2_str(to_file=True), 1):\\n fname = self.debug_folder / f\\\"tracklist_{i}.txt\\\"\\n fname.write_text(tracklist, encoding=\\\"utf-8\\\")\\n\\n # save found personel to file\\n fname = self.debug_folder / \\\"personnel.txt\\\"\\n fname.write_text(self.personnel_2_str(), encoding=\\\"utf8\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262227ba848035b63282e495d1645462\",\n \"score\": \"0.5200625\",\n \"text\": \"def save_file(self):\\n np.savez(self.savefile,table=self.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ccb0f5b1a56b8c944b24fd3f9e0974b\",\n \"score\": \"0.5185998\",\n \"text\": \"def save_to(self,fp):\\n\\t\\tresponsible = False\\n\\t\\tif type(fp)==str:\\n\\t\\t\\tresponsible = True\\n\\t\\t\\tfp = open(fp,\\\"wb\\\")\\n\\t\\tfor block in self.blocks:\\n\\t\\t\\tblock.save_to(fp)\\n\\t\\tif responsible:\\n\\t\\t\\tfp.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8387e762fc4d9f28bfe68858740921a\",\n \"score\": \"0.5185139\",\n \"text\": \"def save_as(self, event=None):\\n\\n # Open a file dialog to get target\\n df = dialogs.save_as(self.directory, extension='.cad')\\n\\n if df[1] != '':\\n self.directory, self.filename = df\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8830d1476a12075a797f6a8219380029\",\n \"score\": \"0.5184838\",\n \"text\": \"def save(self):\\n with open(f'players/{self.idt}.txt', 'w') as fichier:\\n fichier.write(self.__repr__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897d62a7bae7dca3c26c59af12dcea68\",\n \"score\": \"0.5180214\",\n \"text\": \"def save_uploaded_file_new(uploaded, fname):\\n ufile = None\\n try:\\n split = fname.rsplit('.', 1)\\n file = uploaded\\n dt = datetime.datetime.now()\\n name = '%s-%s' % (translit.slugify(split[0]), (int(time.mktime(dt.timetuple()))))\\n name = 'maket/' + name + '.' + split[1]\\n path = default_storage.save(name, ContentFile(file.read()))\\n except Exception as e:\\n print e\\n if os.path.exists(name.decode('utf-8')):\\n os.unlink(name.decode('utf-8'))\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e5a479c573997bc9d52f1dc1ee6029\",\n \"score\": \"0.5180209\",\n \"text\": \"def save_to_file(self, path, index, format = \\\"xml\\\"):\\r\\n f = open(path, 'w')\\r\\n if format == \\\"xml\\\":\\r\\n self.get_scans()[index].write_xml(f)\\r\\n self.filenames[self.get_scans()[index]] = f\\r\\n else:\\r\\n self.get_scans()[index].write_text(f)\\r\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66924e92f421dcb552a012b560dcadef\",\n \"score\": \"0.5157146\",\n \"text\": \"def save(filename,data,path,command):\\n\\ttry:\\t\\t\\n\\t\\toutputfile = open(os.path.join(path,filename),\\\"w\\\")\\t\\t\\t\\t\\n\\t\\tprint (\\\"Saving: \\\" + command).ljust(50),\\n\\t\\toutputfile.write(data)\\t\\t\\t\\n\\t\\toutputfile.close()\\n\\t\\tprint \\\"...... Success\\\"\\n\\t\\treturn 0\\n\\texcept Exception as ex:\\n\\t\\tprint \\\"...... Error\\\", ex\\n\\t\\treturn 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"912a0b57b28b56350b6d12b7e83454e4\",\n \"score\": \"0.51536906\",\n \"text\": \"def setFile(self):\\n print('+--------------------------------+')\\n print('| Carregando Documento |')\\n print('+--------------------------------+')\\n try:\\n self.file = open(self.filePath, 'rb').read() #ainda não estou pegando o arquivo inteiro\\n print('| Documento Carregado! |')\\n print('+--------------------------------+')\\n except:\\n print('| Falha ao carregar Documento |')\\n print('+--------------------------------+')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f91b62414a91da5f7f515f3c9b26443\",\n \"score\": \"0.51526207\",\n \"text\": \"def save(self, folder, name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f91b62414a91da5f7f515f3c9b26443\",\n \"score\": \"0.51526207\",\n \"text\": \"def save(self, folder, name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f91b62414a91da5f7f515f3c9b26443\",\n \"score\": \"0.51526207\",\n \"text\": \"def save(self, folder, name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f91b62414a91da5f7f515f3c9b26443\",\n \"score\": \"0.51526207\",\n \"text\": \"def save(self, folder, name):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1594,"cells":{"query_id":{"kind":"string","value":"5f1819f5b4ebc4f2621b3a11660cf2bf"},"query":{"kind":"string","value":"Show a count of blogs in this category"},"positive_passages":{"kind":"list like","value":[{"docid":"8fb1cdd1839cf8a11498e093eaf91fb3","score":"0.0","text":"def post_count(self, obj):\n\n # Note: This is purposefully not optimized\n return obj.post_set.count()","title":""}],"string":"[\n {\n \"docid\": \"8fb1cdd1839cf8a11498e093eaf91fb3\",\n \"score\": \"0.0\",\n \"text\": \"def post_count(self, obj):\\n\\n # Note: This is purposefully not optimized\\n return obj.post_set.count()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4b2c11de80b88c2e074f7ce853f05876","score":"0.6901813","text":"def get_count(self) -> int:\n return self.category_stats[\"count\"]","title":""},{"docid":"d279e5aabcf4bd97da404d7b701cf755","score":"0.65567595","text":"def get_category_count(self):\n return Category.objects.count()","title":""},{"docid":"223f09e6ce6f44339340f8226b5d4652","score":"0.65446323","text":"def category_count(sender, instance, signal, *args, **kwargs):\n from coltrane.models import Category\n\n for cat in Category.objects.all():\n cat.post_count = cat.get_live_post_count()\n cat.save()","title":""},{"docid":"2be1a0be810291234bb6a1b8f10afb1c","score":"0.625777","text":"def count():\n click.echo('articles: {}'.format(Article.query.count()))\n click.echo('events: {}'.format(Event.query.count()))\n click.echo('stories: {}'.format(Story.query.count()))","title":""},{"docid":"442dcada50aef5317ddad277f347ccef","score":"0.62186295","text":"def count(self):\n return self.action('GET', 'count')[\"count\"]","title":""},{"docid":"044c49640b437451c14c1d911434f173","score":"0.60837793","text":"def total_posts():\n\n return Post.published.count()","title":""},{"docid":"b37c5178717b0fd2e92b8a646f217f74","score":"0.5902415","text":"def getCategoryCount(self, **kwargs):\n\n allParams = []\n\n params = locals()\n for (key, val) in list(params['kwargs'].items()):\n if key not in allParams:\n raise TypeError(\"Got an unexpected keyword argument '%s' to method getCategoryCount\" % key)\n params[key] = val\n del params['kwargs']\n\n resourcePath = '/category/count'\n resourcePath = resourcePath.replace('{format}', 'json')\n method = 'GET'\n\n queryParams = {}\n headerParams = {}\n formParams = {}\n files = {}\n bodyParam = None\n\n headerParams['Accept'] = 'application/json'\n headerParams['Content-Type'] = 'application/json'\n\n\n\n\n\n\n\n\n\n\n\n postData = (formParams if formParams else bodyParam)\n\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\n postData, headerParams, files=files)\n\n\n if not response:\n return None\n\n responseObject = self.apiClient.deserialize(response, 'CountResult')\n return responseObject","title":""},{"docid":"df879aa75aa7154494e2ef4a43c0d395","score":"0.58602196","text":"def home(request):\n num_bloggers = Blogger.objects.count()\n num_blogposts = BlogPost.objects.count()\n num_comments = Comment.objects.count()\n\n context = {\n 'num_bloggers': num_bloggers,\n 'num_blogposts': num_blogposts,\n 'num_comments': num_comments,\n }\n\n return render(request, 'home.html', context=context)","title":""},{"docid":"4c1b6cb35c5f14c3622c8a0ed3740d56","score":"0.5823932","text":"def countPubPerCat():\n result = db.session.query(Categories.id, func.count(Categories_publications.id))\\\n .filter(Categories_publications.category_id == Categories.id)\\\n .group_by(Categories.id)\n result = {r[0]: r[1] for r in result}\n\n db.session.close()\n return result","title":""},{"docid":"877677d6568ef08652a2d84a7c59b97e","score":"0.5809214","text":"def index(request):\n\n # Generates counts of some of the main objects\n num_bloggers = Blogger.objects.all().count()\n num_posts = BlogPost.objects.all().count()\n\n context = {\n 'num_bloggers': num_bloggers,\n 'num_posts': num_posts,\n }\n\n # Render the HTML template index.html with the data in the context variable\n return render(request, 'index.html', context=context)","title":""},{"docid":"187c3cd4629825a543865798970c8830","score":"0.58088964","text":"async def count(request: Request, **kwargs):\n return await piccolo_crud.get_count(request=request)","title":""},{"docid":"4c3a98f8ab08debee6f8f7cfa2bdc716","score":"0.58078915","text":"def index(request):\n\n # Generate counts of some of the main objects\n num_blogs = Blog.objects.all().count()\n num_bloggers = Blogger.objects.count()\n\n context = {\n 'num_blogs': num_blogs,\n 'num_bloggers': num_bloggers,\n }\n\n # Render the HTML template index.html with teh date in the context variable\n return render(request, 'index.html', context=context)","title":""},{"docid":"80bdc07240f7f38443f01a406e0405a9","score":"0.57955503","text":"def count(self):\n\n\t\treturn self._pagination['count']","title":""},{"docid":"313c6150b105a65e3684c3522934e91d","score":"0.5752313","text":"def count_objects(self):\r\n count = {}\r\n for obj in self.trackableObjects.values():\r\n cat = obj.category.split(':')[0]\r\n if cat in count:\r\n count[cat] += 1\r\n else:\r\n count[cat] = 1\r\n return count","title":""},{"docid":"9642de721029b2a329f78c9ed8fd131b","score":"0.57494557","text":"def count(self) -> str:\n return pulumi.get(self, \"count\")","title":""},{"docid":"9896e0fa579064594bf1b46afe303967","score":"0.5636535","text":"def index(request):\n\n\n num_books = Book.objects.all().count()\n num_categories = Category.objects.all().count()\n book_list = Book.objects.all()\n\n\n added_books = Book.objects.order_by('date_added') [:3]\n categories = Category.objects.all()\n\n context = {\n 'num_books': num_books,\n 'num_categories': num_categories,\n 'added_books': added_books,\n 'categories': categories,\n 'book_list': book_list,\n }\n\n return render(request, 'index.html', context=context)","title":""},{"docid":"3e8a293de37dde9b5e05479697afcb60","score":"0.56134665","text":"def citedby_count(self):\n return int(self._json['coredata']['citedby-count'])","title":""},{"docid":"a50b5605e2b37691440e5f53c43edcaf","score":"0.5611485","text":"def display_document_count(\n self,\n queries: List[str],\n query_tokenizer: Callable[[str], List[str]] = str.split,\n ):\n return self._render_bar_chart(\n self.search_document_count, \"Number of documents\", queries, query_tokenizer\n )","title":""},{"docid":"c35de997524e47beafc384320d419fc9","score":"0.5602637","text":"def citedby_count(self):\n return int(get_encoded_text(self.coredata, 'citedby-count'))","title":""},{"docid":"d9e6b5b3261b104ac0d85c36712d1533","score":"0.5596192","text":"def count(self):\n resp, page = self.request(\"GET\", self.uri)\n return page[\"total\"]","title":""},{"docid":"efd91ec78abb1f875c6db41e92f0223e","score":"0.55925333","text":"def category_view(self, request, cat_slug):\n context = self.get_context(request)\n\n try:\n category = BlogCategory.objects.get(slug=cat_slug)\n except Exception:\n category = None\n\n if category is None:\n pass\n\n context[\"posts\"] = BlogPostPage.objects.live().public().filter(categories__in=[category]).order_by(\"-first_published_at\")\n context[\"cat_slug\"] = cat_slug\n return render(request, \"blog/list.html\", context)","title":""},{"docid":"4fb3412299a2cce703f75680d7f9fe61","score":"0.5585604","text":"def print_categories(self):\n ctr = -1\n for cat in self.categories.keys():\n ctr += 1\n print ctr, ': ', cat","title":""},{"docid":"bfde7a0c4e3e928c8f7195a70d62b486","score":"0.55691737","text":"def categories_show(self):\n\n cursor = DatabaseManager.connection_to_database(self)\n\n cursor.execute(\"SELECT * FROM category\")\n\n my_results = cursor.fetchall()\n\n i = 1\n cat_list = []\n for cat_tuples in my_results:\n for cat_str in cat_tuples:\n cat_list2 = []\n cat_list2.append(i)\n cat_list2.append(cat_str)\n i += 1\n cat_list.append(cat_list2)\n\n for cat_list2 in cat_list:\n print(cat_list2)","title":""},{"docid":"b0525522676b94720229902c386dc08f","score":"0.55573046","text":"def count_term(count):\n return ngettext('{0} post', '{0} posts', count).format(count)","title":""},{"docid":"2f0188a5be3b95ece98e8dccf21efa1f","score":"0.5556652","text":"def month_count(self):\n if self.month_count_ is not None:\n return self.month_count_\n engine = settings.DATABASES['default']['ENGINE']\n sql = '''SELECT COUNT(*) as c, %s AS month\n FROM blogging_article AS a\n WHERE a.author_id = %%s AND a.public\n GROUP BY month''' % month_sql_[engine]\n self.month_count_ = dict([(row[1], row[0]) for row in\n fetch_rows(sql, self.owner.id)])\n return self.month_count_","title":""},{"docid":"c491e1f948c591b710a9ff7c8a36baf0","score":"0.5551819","text":"def popular_articles():\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n c.execute(\"SELECT title, count(path) AS num FROM articles, log \\\n WHERE articles.slug = substring(path from 10 for 100) \\\n GROUP BY title ORDER BY num DESC;\")\n print(\"Most popular articles:\")\n for (title, num) in c.fetchall():\n print(\" {} - {} views\".format(title, num))\n print(\"-\" * 70)\n db.close()","title":""},{"docid":"d25b6b8535d4a71fa80a26837947c2b7","score":"0.55429345","text":"def getCataloguesCount(self):\n\n content = dict(\n event='getCataloguesCount'\n )\n return self._sendAwaitCallback(content)","title":""},{"docid":"772235d8aa1679c8f12050f0e757003b","score":"0.55358","text":"def Count(self):\n return self._get_attribute('count')","title":""},{"docid":"772235d8aa1679c8f12050f0e757003b","score":"0.55358","text":"def Count(self):\n return self._get_attribute('count')","title":""},{"docid":"772235d8aa1679c8f12050f0e757003b","score":"0.55358","text":"def Count(self):\n return self._get_attribute('count')","title":""},{"docid":"0cead2beda6288b2929db2bafc4fb196","score":"0.55339324","text":"def Count(self) -> int:","title":""},{"docid":"0cead2beda6288b2929db2bafc4fb196","score":"0.55339324","text":"def Count(self) -> int:","title":""},{"docid":"d27fc4f1bee3de944f24b8ef2843e5ee","score":"0.5526031","text":"def getCount(self):\n return self.base.get(\"count\", [])","title":""},{"docid":"fc209ea5889b6cc889b6ba6d3079d214","score":"0.55257595","text":"def popular_articles():\n context = \"(1) The 3 Most Popular Articles are:\"\n popular_articles = query_news(\"\"\"SELECT articles.title,\n COUNT(articles.title) AS t FROM articles\n JOIN log ON log.path = CONCAT('/article/', articles.slug)\n GROUP BY articles.title ORDER BY t\n DESC\n LIMIT 3\"\"\")\n format_output(context, popular_articles)","title":""},{"docid":"b58ae79237fa4a51b774532a03377837","score":"0.5513208","text":"def blog_list_by_category(request, cat_id):\n blogentries = BlogEntry.objects.filter(category=cat_id)\n categories = Category.objects.all()\n filtered = True\n context = {\n 'blogentries': blogentries,\n 'categories': categories,\n 'cat_id': cat_id,\n 'filtered' : filtered,\n }\n template = 'blog/blog_list.html'\n return render(request, template, context)","title":""},{"docid":"9db036217784c9653df0587fed20f41b","score":"0.5495344","text":"def popular_articles():\n\n results = fetch_query(\n \"\"\"select articles.title, count(log.path)\n from articles, log\n where log.path = '/article/' || articles.slug\n and log.status = '200 OK'\n group by articles.title\n order by count(log.path) desc limit 3;\"\"\"\n )\n print('\\n\\n' + \"Most popular three articles of all time:\" + '\\n')\n for item in results:\n print(\"\\\"\" + item[0].title() + \"\\\": \" + str(\"{:,}\".format(item[1])) +\n \" views\")","title":""},{"docid":"92bb80c117efad5dee4f7cd5a2bd0d10","score":"0.549002","text":"def Count(self):\r\n\t\treturn self._get_attribute('count')","title":""},{"docid":"fe8c3fd950a51484a8eea67d1028e41d","score":"0.5489271","text":"def count(cls, slug):\n from .models import Content\n # Gets the count for a tag, hopefully form an in-memory cache.\n cnt = cls._cache.get(slug)\n if cnt is None:\n cnt = Content.search_objects.search(tags=[slug]).count()\n cls._cache[slug] = cnt\n return cnt","title":""},{"docid":"6e32f140d5241e5e8007c6d00ee210fa","score":"0.5467922","text":"def count(self) -> int:\n return pulumi.get(self, \"count\")","title":""},{"docid":"6e32f140d5241e5e8007c6d00ee210fa","score":"0.5467922","text":"def count(self) -> int:\n return pulumi.get(self, \"count\")","title":""},{"docid":"0b683329a8b627cbe9784faa4b9ff117","score":"0.54478216","text":"def counts(self):\n return resource.Counts(self)","title":""},{"docid":"7a26bcfc61fe920a523373d7ff6186e8","score":"0.5435155","text":"def count_publishers(url):\n params = {'rows': 0}\n resp = requests.get(url=url, params=params)\n data = json.loads(resp.text)\n return data['message']['total-results']","title":""},{"docid":"13e4987b67d996859fea574bf4a158cc","score":"0.5422333","text":"def blog_list(request):\n blogentries = BlogEntry.objects.all()\n categories = Category.objects.all()\n comments = Comment.objects.all()\n context = {\n 'blogentries': blogentries,\n 'categories': categories,\n }\n template_name = 'blog/blog_list.html'\n return render(request, template_name, context)","title":""},{"docid":"085e17a3dc5eb573fc35f677a84828f6","score":"0.5409633","text":"def getCatalogObjectCount(url, user, pWd):\n\n print(\"getting object count for catalog resource:-\" + url + \" user=\" + user)\n apiURL = url + \"/access/2/catalog/data/objects\"\n return callGETRestEndpoint(apiURL, user, pWd)","title":""},{"docid":"77bb963c3157c6ca9b0eff73990d9b96","score":"0.5409083","text":"def nested_count(self):\n num_catalogs = self.run_sql(\"SELECT count(*) FROM nested_catalogs;\")\n return num_catalogs[0][0]","title":""},{"docid":"c39236c48fb15537cc573f9b68c85e0a","score":"0.5401059","text":"def doc_count(client, index):\n return int(CatClient(client).count(index=[index], format='json')[0]['count'])","title":""},{"docid":"08206f29f29b18ba6826a4c3877183c9","score":"0.5396827","text":"def get_number_of_categories(self) -> int:\n # 'unknown' not tracked anymore...\n # return len([c for c in self.node_stats.keys() if c != 'unknown'])\n return len(self.node_stats.keys())","title":""},{"docid":"56271949d115c23179d8f50e078f20e7","score":"0.5392975","text":"def count(self):\n return int(self._shelf_dict.get(\"book_count\", {}).get(\"#text\"))","title":""},{"docid":"fb4391ce3924e246d8d4572357cec69f","score":"0.5384941","text":"def plot_category_occurrences(df):\n\n # Isolate category columns\n cat_cols = [\n col for col in df.columns if col not in [\"id\", \"message\", \"original\", \"genre\"]\n ]\n\n # Retrieve sums of category occurrences\n dist = df[cat_cols].sum().sort_values(ascending=False)\n\n # Format category names\n names = dist.index.str.title().str.replace(\"_\", \" \").to_list()\n\n # Get list of color\n colors = [\"#E1396C\" for i in range(len(dist))]\n\n # Define graph\n graph = {\n \"data\": [\n Bar(\n x=names,\n y=dist.values.tolist(),\n opacity=0.7,\n marker={\"color\": colors, \"line\": {\"color\": colors, \"width\": 1.5}},\n )\n ],\n \"layout\": {\n \"title\": \"Occurrence of Message Categories\",\n \"xaxis\": {\"title\": \"Category\", \"automargin\": True},\n \"yaxis\": {\"title\": \"Count\", \"automargin\": True},\n \"autosize\": True,\n },\n }\n\n return graph","title":""},{"docid":"cfba0d5e76659e151394c6b5481c8a2f","score":"0.5376637","text":"def blog_category(request, category):\n posts = Post.objects.filter(\n categories__name__contains=category\n ).order_by(\n '-created_on'\n )\n context = {\n \"category\": category,\n \"posts\": posts\n }\n return render(request, \"blog/blog_category.html\", context)","title":""},{"docid":"fa6295ca17867d5300b6d00142716933","score":"0.5371939","text":"def _count(self, path: str) -> int:\n result = self._http.get(path, params={\"per_page\": 1})\n json_data = result.json()\n return json_data[\"meta\"][\"pagination\"][\"total\"]","title":""},{"docid":"ffcb53a7c204333aa6ad3ba78ae574ef","score":"0.53686446","text":"def load_total_posts(self):\n self.execute(\n f\"\"\"\n SELECT COUNT(post.id)\n FROM folder\n JOIN thread on folder.id = thread.folder_id\n JOIN post on thread.id = post.thread_id\n WHERE folder.course_id = '{self.id}';\n \"\"\")\n return self.fetchone()[0]","title":""},{"docid":"856e1d7077d0e4b8e53d528ae1303804","score":"0.53585666","text":"def count_total_docs():\n query_url = 'http://www.dgidb.org/api/v2/interactions?count=1&page=1'\n return requests.get(query_url).json()['_meta']['total_count']","title":""},{"docid":"38a45089dc68077a2f8c875255d5a37a","score":"0.535592","text":"def like_counts(self):\n return self.likes.all().count","title":""},{"docid":"31d01d70f6a55cf61964bfc799a23abe","score":"0.5355158","text":"def display_occurrence_count(\n self,\n queries: List[str],\n query_tokenizer: Callable[[str], List[str]] = str.split,\n ):\n return self._render_bar_chart(\n self.search_occurrence_count, \"Number of matches\", queries, query_tokenizer\n )","title":""},{"docid":"3c2bda31b424969c68ada6a4eddbdd06","score":"0.5351696","text":"def file_count():\n\n corpus = Corpus.from_env()\n click.echo(corpus.file_count)","title":""},{"docid":"d9f07fdfa929236bd9ae4055e90dfb36","score":"0.53275245","text":"def count(self) -> int:\n pass","title":""},{"docid":"bccf8a629fae7e9c2383bc0ffdb3b706","score":"0.53264743","text":"def count_categories(self, tcount=True, ecount=True):\n ncat = 0\n for category in self.categories:\n ctasks = [t for t in self.tasks if t.category is category]\n cevents = [e for e in self.events if e.category is category]\n if tcount * len(ctasks) or ecount * len(cevents):\n ncat += 1\n return ncat","title":""},{"docid":"0d499d940ac2aa935842aa2ee1f56804","score":"0.5310689","text":"def count() -> int:\n return _api_calls.get(_ENDPOINT + \"count\").json()","title":""},{"docid":"0d499d940ac2aa935842aa2ee1f56804","score":"0.5310689","text":"def count() -> int:\n return _api_calls.get(_ENDPOINT + \"count\").json()","title":""},{"docid":"0b98f8e34ae49750920df516941755ca","score":"0.53073746","text":"def get_total_node_counts_across_categories(self) -> int:\n count = 0\n for category in self.node_stats.values():\n count += category.get_count()\n return count","title":""},{"docid":"a970ed3d4731566a728aeadf8e4136dc","score":"0.53067493","text":"def count(self, **kwargs) -> int:\n result = self._client.head(self._api_url, params=kwargs)\n\n return int(result.headers['X-Total-Count'])","title":""},{"docid":"723ca67472cb1bbbe71dd2ecc0ea0fdc","score":"0.53032935","text":"def format_counts():\n\n t = PrettyTable(['File Type', 'Doc Count'])\n t.align = 'l'\n\n for c in Document_Format.format_counts():\n t.add_row(c)\n\n click.echo(t)","title":""},{"docid":"6378b47ab471de3f2af022ee23f0a9ba","score":"0.5294795","text":"def get_count(cls, proxy):\n\n fetch = build_fetch(proxy=proxy)\n front_page = fetch(OOXX_URL)\n cls.count = int(cls.regexes['count'].findall(front_page)[0])","title":""},{"docid":"28d42758636ae12b93e668dcf941de30","score":"0.52944005","text":"def list_blog_posts():\n\n posts = BlogPost.query.order_by(BlogPost.id.desc())\n return render_template('index.html', items=posts)","title":""},{"docid":"1ac7668d240dbc5e537ad2e6fb4263e0","score":"0.5285426","text":"def count(self):\n return self.count_value","title":""},{"docid":"2704af13df305d572a01e504ecb7245c","score":"0.52756625","text":"def index(request):\n blogs = BlogPost.objects.order_by('date_added')\n context = {'blogs': blogs}\n return render(request, 'blogs/index.html', context)","title":""},{"docid":"ad830f221c67e15605d4a27ab71d5d07","score":"0.52741045","text":"def product(request, category_slug=None):\n if category_slug != None:\n categories = get_object_or_404(Category, slug=category_slug)\n products = Product.objects.filter(category=categories)\n product_count = products.count()\n else:\n products = Product.objects.all()\n product_count=products.count()\n\n data = {\n 'products': products,\n 'product_count':product_count\n }\n return render(request, 'product.html', context=data)","title":""},{"docid":"e39741c9b6b97ebb5bdeee954aaded93","score":"0.52686375","text":"def calculate_word_count_stats(articles: pd.DataFrame):\n by_source = articles.groupby(['base_url'])['word_count']\n by_source = by_source.agg(['count', 'mean', 'std'])\n by_source.sort_values('count', ascending=False, inplace=True)\n print_full(by_source)\n\n top_sources = by_source.head(10).index\n top_counts = by_source.reset_index()[by_source.index.isin(top_sources)]\n sns.barplot(x='base_url', y='count', data=top_counts)\n sns.plt.show()\n sns.boxplot(x='base_url', y='word_count',\n data=articles[articles['base_url'].isin(top_sources)])\n sns.plt.show()","title":""},{"docid":"58a2ec64ee4ddae586cd412108a9fe2b","score":"0.52621716","text":"def count_likes(cls, post):\n count = 0\n likes = cls.all().filter(\"post = \", post)\n for like in likes:\n if like.do_like:\n count += 1\n return count","title":""},{"docid":"1cfaeac668df7996f4d3bad253c267b5","score":"0.5261962","text":"def count():","title":""},{"docid":"1cfaeac668df7996f4d3bad253c267b5","score":"0.5261962","text":"def count():","title":""},{"docid":"39fd1c8f3006dd4bbee0b08a4eb763b6","score":"0.5254831","text":"def get_counts(self, ) -> int:\n return self.count","title":""},{"docid":"f8dbe95c19a33b9e76888722613e38c3","score":"0.52503103","text":"def get_count_newsletters(self, obj):\n return obj.newsletter_set.count()","title":""},{"docid":"f8dbe95c19a33b9e76888722613e38c3","score":"0.52503103","text":"def get_count_newsletters(self, obj):\n return obj.newsletter_set.count()","title":""},{"docid":"e69587cb4cc7762a92c5c264ad406c20","score":"0.52437675","text":"def feed(request):\n # Generate counts of some of the main objects\n num_posts=Post.objects.all().count()\n num_users=User.objects.count() # The 'all()' is implied by default.\n \n # Render the HTML template home.html with the data in the context variable\n return render(\n request,\n 'feed.html',\n # context={'num_posts':num_posts},\n )","title":""},{"docid":"e9f9ee2a7238a159d6ed860c0f6aa338","score":"0.5240591","text":"def count_images_per_category(self):\n if not self.index:\n self.reindex()\n\n return {\n self.cats[cat_id][\"name\"]: len(set(imgs_list))\n for cat_id, imgs_list in self.index.catidx_to_imgidxs.items()\n }","title":""},{"docid":"a29da1839a7370a907927e7610638e11","score":"0.52370936","text":"def view_status(budget):\n print()\n print(\"Your Budget limit: $\" + \"{0:.2f}\".format(budget.get_limit()))\n print()\n list_of_categories = budget.categories.all()\n for i in range(len(list_of_categories)):\n category = list_of_categories[i]\n category_name = category['name']\n category_limit = category['limit']\n print(\"{}'s limit: $\".format(category_name) +\n \"{0:.2f}\".format(category_limit))\n category_total = budget.category_total(category_name)\n print(\"Total spending in {}'s category: \".format(category_name) +\n \"${0:.2f}\".format(float(category_total)))\n print()\n print(\"Total spending amounts to: \" + \"${0:.2f}\".format(budget.total_spent()))","title":""},{"docid":"2dec5f0a3187e3871e756acfa1fb5390","score":"0.523425","text":"def liwc_counts(the_text):\n # Construct a counter of the words and return as JSON\n text = re.sub(u'\\u2019', \"'\", the_text).lower()\n the_dict = {cat: len(re.findall(regex_list[cat], text)) for cat in categories}\n return json.dumps(the_dict)","title":""},{"docid":"265ffa2031f03ec3d6be4827f8c83fbb","score":"0.5229807","text":"def database_count():\n a = len(_os.listdir(datapath(True))) - 3\n b = _os.walk(datapath(True)) # generator\n c = [1]\n c = len([c[0] + 1 for root, dirs, files in b for _ in files]) - 6\n print(\"Total number of companies contained: {}\".format(a))\n print(\"Total number of detailed sheets: {}\".format(c))\n _gc.collect()","title":""},{"docid":"74eacd21f6bb5eee91ec343a3a375686","score":"0.5226196","text":"def count() -> int:\n return 0","title":""},{"docid":"7a7c5461fbafc690f37e0db7c4539cb3","score":"0.522224","text":"def count_restaurants_by_category(data, categories):\n catcounts = categories.assign(n=0)\n for d in data:\n for c in d['categories']:\n if c['alias'] in catcounts['alias'].values:\n catcounts.loc[catcounts['alias'] == c['alias'], 'n'] += 1\n return catcounts","title":""},{"docid":"6eebcff797c54575d840de441ab80bde","score":"0.52208436","text":"def get_categories():\n mycursor.execute(\"USE {}\".format(DB_NAME))\n mycursor.execute(QUERY_GET_CATS)\n myresult = mycursor.fetchall()\n\n print(tabulate(myresult, headers = mycursor.column_names, tablefmt='fancy_grid'))","title":""},{"docid":"51ba1c513f070a473152379e1c77dc40","score":"0.5220193","text":"def show_categories():\n categories = service.get_categories()\n latest_items = service.get_latest_items()\n\n return render_template(\"categories.html\", categories=categories,\n items=latest_items)","title":""},{"docid":"976b3bccd160305b1290186136b1f723","score":"0.52196765","text":"def daily_counts(self, account):\n Memo = apps.get_model(\"diary\", \"Memo\")\n memo = Memo.objects.filter(date=date.today(), author=account.user).first()\n\n # If there is no memo object, we cannot create the daily_counts - no error\n if not memo:\n return\n\n # Associate the article counts with the memo\n if not hasattr(memo, \"article_counts\"):\n counts = self.create(memo=memo)\n else:\n counts = memo.article_counts\n\n # Compute the number of articles read today\n counts.read = memo.articles.count()\n\n # Create a queryset for the instapaper account\n Article = apps.get_model(\"reading\", \"Article\")\n qs = Article.instapaper.account(account)\n\n # Add the current unread count for today\n counts.unread = qs.unread().count()\n\n # Filter for year to date counts\n qs = qs.ytd(memo.date.year)\n counts.archived= qs.archived().count()\n counts.starred = qs.starred().count()\n counts.save()\n\n return counts","title":""},{"docid":"9fed01428870e3399ff07f72005cd3f4","score":"0.5217082","text":"def posts_count(self) -> int:\n return len(self.posts)","title":""},{"docid":"81d48e97b08af145bb46f5fc24772ac7","score":"0.52164453","text":"def category_list(request):\n return render_to_response('charleston/category_list.html', {'object_list': Category.live_entry_set()})","title":""},{"docid":"9502cfea08fc4efc345c606aa5734d26","score":"0.52156436","text":"def list_of_categories(request):\n all_categories = Category.objects.all()\n\n context = {\n 'all_categories' : all_categories\n }\n\n return render(request=request, template_name=\"pages/category.html\", context=context)","title":""},{"docid":"d0f361bd4d88aef57b021379373aa49c","score":"0.5213993","text":"def cmd_show_tags(self):\n tag_count = defaultdict(int)\n if self.args.verbose:\n # Consider tags of (filtered) entries.\n for entry in self.generate_entries():\n for tag in entry.get_tags().as_strings():\n tag_count[tag] += 1\n else:\n # Consider feed tags only.\n for feed in self.generate_feeds():\n for tag in feed.get_tags().as_strings():\n tag_count[tag] += 1\n for tag, cnt in sorted(tag_count.items()):\n messager.msg(f'{cnt:7} {tag}')","title":""},{"docid":"f506e69c9d86aed6090eda2e479ad1fe","score":"0.5211928","text":"def get_site_count(controller):\n url = controller.url.rstrip('/')+\"/dna/intent/api/v1/site/count\"\n get_intent_api(url, controller)\n return","title":""},{"docid":"89ece5b6c269c662559019c0618f21a5","score":"0.52067983","text":"def list_baru(request):\n terbaru = Gigs.objects.order_by('-buat')\n count_terbaru = Gigs.objects.all().count()\n context = {'baru': terbaru, 'count': count_terbaru }\n return render(request, 'child/list_baru.html', context)","title":""},{"docid":"c76934f6ec5398c9518a9d738d9c64cf","score":"0.5206008","text":"def popular_authors():\n\n results = fetch_query(\n \"\"\"select author_slug.name, count(log.path)\n from author_slug, log\n where log.path = '/article/' || author_slug.slug\n and log.status = '200 OK'\n group by author_slug.name\n order by count(log.path) desc;\"\"\"\n )\n print('\\n\\n' + \"Authors listed by popularity as defined by \"\n \"total article views:\" + '\\n')\n for item in results:\n print(item[0] + \": \" + str(\"{:,}\".format(item[1])) + \" views\")","title":""},{"docid":"ab6862ef9116e45de1b2ab0802a7128e","score":"0.52027315","text":"def category_index(request):\n categories = Category.objects.values_list('name')\n categories = [c[0] for c in categories]\n return render(request, 'coco/category_index.html', {'categories': categories})","title":""},{"docid":"d03c630d1d71e42adf7d71ce86e3fbb2","score":"0.52023655","text":"def getCatalogResourceCount(url, user, pWd):\n\n apiURL = url + \"/access/2/catalog/data/objects?q=core.allclassTypes:core.Resource\"\n print(\"getting object count for catalog resource:-\" + apiURL + \" user=\" + user)\n return callGETRestEndpoint(apiURL, user, pWd)","title":""},{"docid":"7edb8100e13e238a8fb0a4a6a9c7c310","score":"0.52000797","text":"def _count(self,):\n return self.count","title":""},{"docid":"91d07c492abfe2df23dddd1a83964367","score":"0.5195354","text":"def blog(request):\n\n blogs = Blog.objects.all().order_by('-date_posted')\n paginator = Paginator(blogs, 3)\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n\n context = {\n 'blogs': page_obj,\n }\n return render(request, 'blog/blog.html', context)","title":""},{"docid":"304e4dc8f8c35c305b0cba28f08626b0","score":"0.51829916","text":"def tally(self, category):\n try:\n bayes_category = self.categories.get_category(category)\n except KeyError:\n return 0\n\n return bayes_category.get_tally()","title":""},{"docid":"3f6643c387d340eb0172df374dbb0843","score":"0.51784873","text":"def view_count(self) -> int :\n\n return int(self._statistics.get('viewCount'))","title":""},{"docid":"a3f3568e02fb51cc72d0bb04f5360157","score":"0.5177821","text":"def get_counts(workspace=None):\n results = {}\n results['counts'], results['subcounts'] = counts, subcounts = {}, {}\n cache_file = os.path.join(cache_dir, f'.counts_{workspace or \"all\"}.json')\n try:\n with requests.Session() as session:\n session.headers.update(headers)\n # Counts by workspace\n response = session.get(f'{base_url}/api/front/link', params=dict(\n workspace__in=all_workspaces,\n company__startup__isnull=False,\n group_by='workspace_id',\n count='id',\n all=1,\n ))\n logger.debug(f\"[{response.elapsed}] {response.url}\")\n for result in response.json():\n counts[result['workspace_id']] = result['id_count']\n # Counts by category in workspace\n if workspace:\n response = session.get(f'{base_url}/api/front/link', params=dict(\n workspace=workspace,\n company__startup__isnull=False,\n group_by=f'extra_data__{category_field}',\n count='id',\n all=1,\n ))\n logger.debug(f\"[{response.elapsed}] {response.url}\")\n for result in response.json():\n subpage = result[f'extra_data__{category_field}']\n subcounts[subpage] = result['id_count']\n # Save results in cache\n with open(cache_file, 'w') as file:\n json.dump(results, file)\n except:\n if not os.path.exists(cache_file):\n return {}, {}\n # Get results from cache\n with open(cache_file, 'r') as file:\n results = json.load(file)\n return results['counts'], results['subcounts']","title":""},{"docid":"5aa4f6ba5e0f32db3aa56c8fcf4930be","score":"0.51772106","text":"def get_num_posts():\r\n\tnum_posts = mc.get('num_posts')\r\n\tif(num_posts == None):\r\n\t\tcursor = db_execute('SELECT COUNT(*) FROM news')\r\n\t\tnum_posts = cursor.fetchone()[0]\r\n\t\tmc.set('num_posts', num_posts)\r\n\treturn num_posts","title":""},{"docid":"cebd32501935d0955a8b91eae1834b84","score":"0.5175497","text":"def index(request):\n '''\n # Generate counts of some of the main objects\n num_books = Book.objects.all().count()\n num_instances = BookInstance.objects.all().count()\n \n # Available books (status = 'a')\n num_instances_available = BookInstance.objects.filter(status__exact='a').count()\n \n # The 'all()' is implied by default. \n num_authors = Author.objects.count()\n \n context = {\n 'num_books': num_books,\n 'num_instances': num_instances,\n 'num_instances_available': num_instances_available,\n 'num_authors': num_authors,\n }\n '''\n # Render the HTML template index.html with the data in the context variable\n return render(request, 'index.html')","title":""}],"string":"[\n {\n \"docid\": \"4b2c11de80b88c2e074f7ce853f05876\",\n \"score\": \"0.6901813\",\n \"text\": \"def get_count(self) -> int:\\n return self.category_stats[\\\"count\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d279e5aabcf4bd97da404d7b701cf755\",\n \"score\": \"0.65567595\",\n \"text\": \"def get_category_count(self):\\n return Category.objects.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223f09e6ce6f44339340f8226b5d4652\",\n \"score\": \"0.65446323\",\n \"text\": \"def category_count(sender, instance, signal, *args, **kwargs):\\n from coltrane.models import Category\\n\\n for cat in Category.objects.all():\\n cat.post_count = cat.get_live_post_count()\\n cat.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be1a0be810291234bb6a1b8f10afb1c\",\n \"score\": \"0.625777\",\n \"text\": \"def count():\\n click.echo('articles: {}'.format(Article.query.count()))\\n click.echo('events: {}'.format(Event.query.count()))\\n click.echo('stories: {}'.format(Story.query.count()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442dcada50aef5317ddad277f347ccef\",\n \"score\": \"0.62186295\",\n \"text\": \"def count(self):\\n return self.action('GET', 'count')[\\\"count\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044c49640b437451c14c1d911434f173\",\n \"score\": \"0.60837793\",\n \"text\": \"def total_posts():\\n\\n return Post.published.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b37c5178717b0fd2e92b8a646f217f74\",\n \"score\": \"0.5902415\",\n \"text\": \"def getCategoryCount(self, **kwargs):\\n\\n allParams = []\\n\\n params = locals()\\n for (key, val) in list(params['kwargs'].items()):\\n if key not in allParams:\\n raise TypeError(\\\"Got an unexpected keyword argument '%s' to method getCategoryCount\\\" % key)\\n params[key] = val\\n del params['kwargs']\\n\\n resourcePath = '/category/count'\\n resourcePath = resourcePath.replace('{format}', 'json')\\n method = 'GET'\\n\\n queryParams = {}\\n headerParams = {}\\n formParams = {}\\n files = {}\\n bodyParam = None\\n\\n headerParams['Accept'] = 'application/json'\\n headerParams['Content-Type'] = 'application/json'\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n postData = (formParams if formParams else bodyParam)\\n\\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\\n postData, headerParams, files=files)\\n\\n\\n if not response:\\n return None\\n\\n responseObject = self.apiClient.deserialize(response, 'CountResult')\\n return responseObject\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df879aa75aa7154494e2ef4a43c0d395\",\n \"score\": \"0.58602196\",\n \"text\": \"def home(request):\\n num_bloggers = Blogger.objects.count()\\n num_blogposts = BlogPost.objects.count()\\n num_comments = Comment.objects.count()\\n\\n context = {\\n 'num_bloggers': num_bloggers,\\n 'num_blogposts': num_blogposts,\\n 'num_comments': num_comments,\\n }\\n\\n return render(request, 'home.html', context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1b6cb35c5f14c3622c8a0ed3740d56\",\n \"score\": \"0.5823932\",\n \"text\": \"def countPubPerCat():\\n result = db.session.query(Categories.id, func.count(Categories_publications.id))\\\\\\n .filter(Categories_publications.category_id == Categories.id)\\\\\\n .group_by(Categories.id)\\n result = {r[0]: r[1] for r in result}\\n\\n db.session.close()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877677d6568ef08652a2d84a7c59b97e\",\n \"score\": \"0.5809214\",\n \"text\": \"def index(request):\\n\\n # Generates counts of some of the main objects\\n num_bloggers = Blogger.objects.all().count()\\n num_posts = BlogPost.objects.all().count()\\n\\n context = {\\n 'num_bloggers': num_bloggers,\\n 'num_posts': num_posts,\\n }\\n\\n # Render the HTML template index.html with the data in the context variable\\n return render(request, 'index.html', context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187c3cd4629825a543865798970c8830\",\n \"score\": \"0.58088964\",\n \"text\": \"async def count(request: Request, **kwargs):\\n return await piccolo_crud.get_count(request=request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3a98f8ab08debee6f8f7cfa2bdc716\",\n \"score\": \"0.58078915\",\n \"text\": \"def index(request):\\n\\n # Generate counts of some of the main objects\\n num_blogs = Blog.objects.all().count()\\n num_bloggers = Blogger.objects.count()\\n\\n context = {\\n 'num_blogs': num_blogs,\\n 'num_bloggers': num_bloggers,\\n }\\n\\n # Render the HTML template index.html with teh date in the context variable\\n return render(request, 'index.html', context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bdc07240f7f38443f01a406e0405a9\",\n \"score\": \"0.57955503\",\n \"text\": \"def count(self):\\n\\n\\t\\treturn self._pagination['count']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313c6150b105a65e3684c3522934e91d\",\n \"score\": \"0.5752313\",\n \"text\": \"def count_objects(self):\\r\\n count = {}\\r\\n for obj in self.trackableObjects.values():\\r\\n cat = obj.category.split(':')[0]\\r\\n if cat in count:\\r\\n count[cat] += 1\\r\\n else:\\r\\n count[cat] = 1\\r\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9642de721029b2a329f78c9ed8fd131b\",\n \"score\": \"0.57494557\",\n \"text\": \"def count(self) -> str:\\n return pulumi.get(self, \\\"count\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9896e0fa579064594bf1b46afe303967\",\n \"score\": \"0.5636535\",\n \"text\": \"def index(request):\\n\\n\\n num_books = Book.objects.all().count()\\n num_categories = Category.objects.all().count()\\n book_list = Book.objects.all()\\n\\n\\n added_books = Book.objects.order_by('date_added') [:3]\\n categories = Category.objects.all()\\n\\n context = {\\n 'num_books': num_books,\\n 'num_categories': num_categories,\\n 'added_books': added_books,\\n 'categories': categories,\\n 'book_list': book_list,\\n }\\n\\n return render(request, 'index.html', context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8a293de37dde9b5e05479697afcb60\",\n \"score\": \"0.56134665\",\n \"text\": \"def citedby_count(self):\\n return int(self._json['coredata']['citedby-count'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a50b5605e2b37691440e5f53c43edcaf\",\n \"score\": \"0.5611485\",\n \"text\": \"def display_document_count(\\n self,\\n queries: List[str],\\n query_tokenizer: Callable[[str], List[str]] = str.split,\\n ):\\n return self._render_bar_chart(\\n self.search_document_count, \\\"Number of documents\\\", queries, query_tokenizer\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c35de997524e47beafc384320d419fc9\",\n \"score\": \"0.5602637\",\n \"text\": \"def citedby_count(self):\\n return int(get_encoded_text(self.coredata, 'citedby-count'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9e6b5b3261b104ac0d85c36712d1533\",\n \"score\": \"0.5596192\",\n \"text\": \"def count(self):\\n resp, page = self.request(\\\"GET\\\", self.uri)\\n return page[\\\"total\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efd91ec78abb1f875c6db41e92f0223e\",\n \"score\": \"0.55925333\",\n \"text\": \"def category_view(self, request, cat_slug):\\n context = self.get_context(request)\\n\\n try:\\n category = BlogCategory.objects.get(slug=cat_slug)\\n except Exception:\\n category = None\\n\\n if category is None:\\n pass\\n\\n context[\\\"posts\\\"] = BlogPostPage.objects.live().public().filter(categories__in=[category]).order_by(\\\"-first_published_at\\\")\\n context[\\\"cat_slug\\\"] = cat_slug\\n return render(request, \\\"blog/list.html\\\", context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb3412299a2cce703f75680d7f9fe61\",\n \"score\": \"0.5585604\",\n \"text\": \"def print_categories(self):\\n ctr = -1\\n for cat in self.categories.keys():\\n ctr += 1\\n print ctr, ': ', cat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfde7a0c4e3e928c8f7195a70d62b486\",\n \"score\": \"0.55691737\",\n \"text\": \"def categories_show(self):\\n\\n cursor = DatabaseManager.connection_to_database(self)\\n\\n cursor.execute(\\\"SELECT * FROM category\\\")\\n\\n my_results = cursor.fetchall()\\n\\n i = 1\\n cat_list = []\\n for cat_tuples in my_results:\\n for cat_str in cat_tuples:\\n cat_list2 = []\\n cat_list2.append(i)\\n cat_list2.append(cat_str)\\n i += 1\\n cat_list.append(cat_list2)\\n\\n for cat_list2 in cat_list:\\n print(cat_list2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0525522676b94720229902c386dc08f\",\n \"score\": \"0.55573046\",\n \"text\": \"def count_term(count):\\n return ngettext('{0} post', '{0} posts', count).format(count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f0188a5be3b95ece98e8dccf21efa1f\",\n \"score\": \"0.5556652\",\n \"text\": \"def month_count(self):\\n if self.month_count_ is not None:\\n return self.month_count_\\n engine = settings.DATABASES['default']['ENGINE']\\n sql = '''SELECT COUNT(*) as c, %s AS month\\n FROM blogging_article AS a\\n WHERE a.author_id = %%s AND a.public\\n GROUP BY month''' % month_sql_[engine]\\n self.month_count_ = dict([(row[1], row[0]) for row in\\n fetch_rows(sql, self.owner.id)])\\n return self.month_count_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c491e1f948c591b710a9ff7c8a36baf0\",\n \"score\": \"0.5551819\",\n \"text\": \"def popular_articles():\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n c.execute(\\\"SELECT title, count(path) AS num FROM articles, log \\\\\\n WHERE articles.slug = substring(path from 10 for 100) \\\\\\n GROUP BY title ORDER BY num DESC;\\\")\\n print(\\\"Most popular articles:\\\")\\n for (title, num) in c.fetchall():\\n print(\\\" {} - {} views\\\".format(title, num))\\n print(\\\"-\\\" * 70)\\n db.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25b6b8535d4a71fa80a26837947c2b7\",\n \"score\": \"0.55429345\",\n \"text\": \"def getCataloguesCount(self):\\n\\n content = dict(\\n event='getCataloguesCount'\\n )\\n return self._sendAwaitCallback(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772235d8aa1679c8f12050f0e757003b\",\n \"score\": \"0.55358\",\n \"text\": \"def Count(self):\\n return self._get_attribute('count')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772235d8aa1679c8f12050f0e757003b\",\n \"score\": \"0.55358\",\n \"text\": \"def Count(self):\\n return self._get_attribute('count')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772235d8aa1679c8f12050f0e757003b\",\n \"score\": \"0.55358\",\n \"text\": \"def Count(self):\\n return self._get_attribute('count')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cead2beda6288b2929db2bafc4fb196\",\n \"score\": \"0.55339324\",\n \"text\": \"def Count(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cead2beda6288b2929db2bafc4fb196\",\n \"score\": \"0.55339324\",\n \"text\": \"def Count(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27fc4f1bee3de944f24b8ef2843e5ee\",\n \"score\": \"0.5526031\",\n \"text\": \"def getCount(self):\\n return self.base.get(\\\"count\\\", [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc209ea5889b6cc889b6ba6d3079d214\",\n \"score\": \"0.55257595\",\n \"text\": \"def popular_articles():\\n context = \\\"(1) The 3 Most Popular Articles are:\\\"\\n popular_articles = query_news(\\\"\\\"\\\"SELECT articles.title,\\n COUNT(articles.title) AS t FROM articles\\n JOIN log ON log.path = CONCAT('/article/', articles.slug)\\n GROUP BY articles.title ORDER BY t\\n DESC\\n LIMIT 3\\\"\\\"\\\")\\n format_output(context, popular_articles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b58ae79237fa4a51b774532a03377837\",\n \"score\": \"0.5513208\",\n \"text\": \"def blog_list_by_category(request, cat_id):\\n blogentries = BlogEntry.objects.filter(category=cat_id)\\n categories = Category.objects.all()\\n filtered = True\\n context = {\\n 'blogentries': blogentries,\\n 'categories': categories,\\n 'cat_id': cat_id,\\n 'filtered' : filtered,\\n }\\n template = 'blog/blog_list.html'\\n return render(request, template, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db036217784c9653df0587fed20f41b\",\n \"score\": \"0.5495344\",\n \"text\": \"def popular_articles():\\n\\n results = fetch_query(\\n \\\"\\\"\\\"select articles.title, count(log.path)\\n from articles, log\\n where log.path = '/article/' || articles.slug\\n and log.status = '200 OK'\\n group by articles.title\\n order by count(log.path) desc limit 3;\\\"\\\"\\\"\\n )\\n print('\\\\n\\\\n' + \\\"Most popular three articles of all time:\\\" + '\\\\n')\\n for item in results:\\n print(\\\"\\\\\\\"\\\" + item[0].title() + \\\"\\\\\\\": \\\" + str(\\\"{:,}\\\".format(item[1])) +\\n \\\" views\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bb80c117efad5dee4f7cd5a2bd0d10\",\n \"score\": \"0.549002\",\n \"text\": \"def Count(self):\\r\\n\\t\\treturn self._get_attribute('count')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8c3fd950a51484a8eea67d1028e41d\",\n \"score\": \"0.5489271\",\n \"text\": \"def count(cls, slug):\\n from .models import Content\\n # Gets the count for a tag, hopefully form an in-memory cache.\\n cnt = cls._cache.get(slug)\\n if cnt is None:\\n cnt = Content.search_objects.search(tags=[slug]).count()\\n cls._cache[slug] = cnt\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e32f140d5241e5e8007c6d00ee210fa\",\n \"score\": \"0.5467922\",\n \"text\": \"def count(self) -> int:\\n return pulumi.get(self, \\\"count\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e32f140d5241e5e8007c6d00ee210fa\",\n \"score\": \"0.5467922\",\n \"text\": \"def count(self) -> int:\\n return pulumi.get(self, \\\"count\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b683329a8b627cbe9784faa4b9ff117\",\n \"score\": \"0.54478216\",\n \"text\": \"def counts(self):\\n return resource.Counts(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a26bcfc61fe920a523373d7ff6186e8\",\n \"score\": \"0.5435155\",\n \"text\": \"def count_publishers(url):\\n params = {'rows': 0}\\n resp = requests.get(url=url, params=params)\\n data = json.loads(resp.text)\\n return data['message']['total-results']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13e4987b67d996859fea574bf4a158cc\",\n \"score\": \"0.5422333\",\n \"text\": \"def blog_list(request):\\n blogentries = BlogEntry.objects.all()\\n categories = Category.objects.all()\\n comments = Comment.objects.all()\\n context = {\\n 'blogentries': blogentries,\\n 'categories': categories,\\n }\\n template_name = 'blog/blog_list.html'\\n return render(request, template_name, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085e17a3dc5eb573fc35f677a84828f6\",\n \"score\": \"0.5409633\",\n \"text\": \"def getCatalogObjectCount(url, user, pWd):\\n\\n print(\\\"getting object count for catalog resource:-\\\" + url + \\\" user=\\\" + user)\\n apiURL = url + \\\"/access/2/catalog/data/objects\\\"\\n return callGETRestEndpoint(apiURL, user, pWd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77bb963c3157c6ca9b0eff73990d9b96\",\n \"score\": \"0.5409083\",\n \"text\": \"def nested_count(self):\\n num_catalogs = self.run_sql(\\\"SELECT count(*) FROM nested_catalogs;\\\")\\n return num_catalogs[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39236c48fb15537cc573f9b68c85e0a\",\n \"score\": \"0.5401059\",\n \"text\": \"def doc_count(client, index):\\n return int(CatClient(client).count(index=[index], format='json')[0]['count'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08206f29f29b18ba6826a4c3877183c9\",\n \"score\": \"0.5396827\",\n \"text\": \"def get_number_of_categories(self) -> int:\\n # 'unknown' not tracked anymore...\\n # return len([c for c in self.node_stats.keys() if c != 'unknown'])\\n return len(self.node_stats.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56271949d115c23179d8f50e078f20e7\",\n \"score\": \"0.5392975\",\n \"text\": \"def count(self):\\n return int(self._shelf_dict.get(\\\"book_count\\\", {}).get(\\\"#text\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb4391ce3924e246d8d4572357cec69f\",\n \"score\": \"0.5384941\",\n \"text\": \"def plot_category_occurrences(df):\\n\\n # Isolate category columns\\n cat_cols = [\\n col for col in df.columns if col not in [\\\"id\\\", \\\"message\\\", \\\"original\\\", \\\"genre\\\"]\\n ]\\n\\n # Retrieve sums of category occurrences\\n dist = df[cat_cols].sum().sort_values(ascending=False)\\n\\n # Format category names\\n names = dist.index.str.title().str.replace(\\\"_\\\", \\\" \\\").to_list()\\n\\n # Get list of color\\n colors = [\\\"#E1396C\\\" for i in range(len(dist))]\\n\\n # Define graph\\n graph = {\\n \\\"data\\\": [\\n Bar(\\n x=names,\\n y=dist.values.tolist(),\\n opacity=0.7,\\n marker={\\\"color\\\": colors, \\\"line\\\": {\\\"color\\\": colors, \\\"width\\\": 1.5}},\\n )\\n ],\\n \\\"layout\\\": {\\n \\\"title\\\": \\\"Occurrence of Message Categories\\\",\\n \\\"xaxis\\\": {\\\"title\\\": \\\"Category\\\", \\\"automargin\\\": True},\\n \\\"yaxis\\\": {\\\"title\\\": \\\"Count\\\", \\\"automargin\\\": True},\\n \\\"autosize\\\": True,\\n },\\n }\\n\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfba0d5e76659e151394c6b5481c8a2f\",\n \"score\": \"0.5376637\",\n \"text\": \"def blog_category(request, category):\\n posts = Post.objects.filter(\\n categories__name__contains=category\\n ).order_by(\\n '-created_on'\\n )\\n context = {\\n \\\"category\\\": category,\\n \\\"posts\\\": posts\\n }\\n return render(request, \\\"blog/blog_category.html\\\", context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa6295ca17867d5300b6d00142716933\",\n \"score\": \"0.5371939\",\n \"text\": \"def _count(self, path: str) -> int:\\n result = self._http.get(path, params={\\\"per_page\\\": 1})\\n json_data = result.json()\\n return json_data[\\\"meta\\\"][\\\"pagination\\\"][\\\"total\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcb53a7c204333aa6ad3ba78ae574ef\",\n \"score\": \"0.53686446\",\n \"text\": \"def load_total_posts(self):\\n self.execute(\\n f\\\"\\\"\\\"\\n SELECT COUNT(post.id)\\n FROM folder\\n JOIN thread on folder.id = thread.folder_id\\n JOIN post on thread.id = post.thread_id\\n WHERE folder.course_id = '{self.id}';\\n \\\"\\\"\\\")\\n return self.fetchone()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856e1d7077d0e4b8e53d528ae1303804\",\n \"score\": \"0.53585666\",\n \"text\": \"def count_total_docs():\\n query_url = 'http://www.dgidb.org/api/v2/interactions?count=1&page=1'\\n return requests.get(query_url).json()['_meta']['total_count']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a45089dc68077a2f8c875255d5a37a\",\n \"score\": \"0.535592\",\n \"text\": \"def like_counts(self):\\n return self.likes.all().count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d01d70f6a55cf61964bfc799a23abe\",\n \"score\": \"0.5355158\",\n \"text\": \"def display_occurrence_count(\\n self,\\n queries: List[str],\\n query_tokenizer: Callable[[str], List[str]] = str.split,\\n ):\\n return self._render_bar_chart(\\n self.search_occurrence_count, \\\"Number of matches\\\", queries, query_tokenizer\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2bda31b424969c68ada6a4eddbdd06\",\n \"score\": \"0.5351696\",\n \"text\": \"def file_count():\\n\\n corpus = Corpus.from_env()\\n click.echo(corpus.file_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f07fdfa929236bd9ae4055e90dfb36\",\n \"score\": \"0.53275245\",\n \"text\": \"def count(self) -> int:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccf8a629fae7e9c2383bc0ffdb3b706\",\n \"score\": \"0.53264743\",\n \"text\": \"def count_categories(self, tcount=True, ecount=True):\\n ncat = 0\\n for category in self.categories:\\n ctasks = [t for t in self.tasks if t.category is category]\\n cevents = [e for e in self.events if e.category is category]\\n if tcount * len(ctasks) or ecount * len(cevents):\\n ncat += 1\\n return ncat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d499d940ac2aa935842aa2ee1f56804\",\n \"score\": \"0.5310689\",\n \"text\": \"def count() -> int:\\n return _api_calls.get(_ENDPOINT + \\\"count\\\").json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d499d940ac2aa935842aa2ee1f56804\",\n \"score\": \"0.5310689\",\n \"text\": \"def count() -> int:\\n return _api_calls.get(_ENDPOINT + \\\"count\\\").json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b98f8e34ae49750920df516941755ca\",\n \"score\": \"0.53073746\",\n \"text\": \"def get_total_node_counts_across_categories(self) -> int:\\n count = 0\\n for category in self.node_stats.values():\\n count += category.get_count()\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a970ed3d4731566a728aeadf8e4136dc\",\n \"score\": \"0.53067493\",\n \"text\": \"def count(self, **kwargs) -> int:\\n result = self._client.head(self._api_url, params=kwargs)\\n\\n return int(result.headers['X-Total-Count'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723ca67472cb1bbbe71dd2ecc0ea0fdc\",\n \"score\": \"0.53032935\",\n \"text\": \"def format_counts():\\n\\n t = PrettyTable(['File Type', 'Doc Count'])\\n t.align = 'l'\\n\\n for c in Document_Format.format_counts():\\n t.add_row(c)\\n\\n click.echo(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6378b47ab471de3f2af022ee23f0a9ba\",\n \"score\": \"0.5294795\",\n \"text\": \"def get_count(cls, proxy):\\n\\n fetch = build_fetch(proxy=proxy)\\n front_page = fetch(OOXX_URL)\\n cls.count = int(cls.regexes['count'].findall(front_page)[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d42758636ae12b93e668dcf941de30\",\n \"score\": \"0.52944005\",\n \"text\": \"def list_blog_posts():\\n\\n posts = BlogPost.query.order_by(BlogPost.id.desc())\\n return render_template('index.html', items=posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ac7668d240dbc5e537ad2e6fb4263e0\",\n \"score\": \"0.5285426\",\n \"text\": \"def count(self):\\n return self.count_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2704af13df305d572a01e504ecb7245c\",\n \"score\": \"0.52756625\",\n \"text\": \"def index(request):\\n blogs = BlogPost.objects.order_by('date_added')\\n context = {'blogs': blogs}\\n return render(request, 'blogs/index.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad830f221c67e15605d4a27ab71d5d07\",\n \"score\": \"0.52741045\",\n \"text\": \"def product(request, category_slug=None):\\n if category_slug != None:\\n categories = get_object_or_404(Category, slug=category_slug)\\n products = Product.objects.filter(category=categories)\\n product_count = products.count()\\n else:\\n products = Product.objects.all()\\n product_count=products.count()\\n\\n data = {\\n 'products': products,\\n 'product_count':product_count\\n }\\n return render(request, 'product.html', context=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e39741c9b6b97ebb5bdeee954aaded93\",\n \"score\": \"0.52686375\",\n \"text\": \"def calculate_word_count_stats(articles: pd.DataFrame):\\n by_source = articles.groupby(['base_url'])['word_count']\\n by_source = by_source.agg(['count', 'mean', 'std'])\\n by_source.sort_values('count', ascending=False, inplace=True)\\n print_full(by_source)\\n\\n top_sources = by_source.head(10).index\\n top_counts = by_source.reset_index()[by_source.index.isin(top_sources)]\\n sns.barplot(x='base_url', y='count', data=top_counts)\\n sns.plt.show()\\n sns.boxplot(x='base_url', y='word_count',\\n data=articles[articles['base_url'].isin(top_sources)])\\n sns.plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a2ec64ee4ddae586cd412108a9fe2b\",\n \"score\": \"0.52621716\",\n \"text\": \"def count_likes(cls, post):\\n count = 0\\n likes = cls.all().filter(\\\"post = \\\", post)\\n for like in likes:\\n if like.do_like:\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfaeac668df7996f4d3bad253c267b5\",\n \"score\": \"0.5261962\",\n \"text\": \"def count():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfaeac668df7996f4d3bad253c267b5\",\n \"score\": \"0.5261962\",\n \"text\": \"def count():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fd1c8f3006dd4bbee0b08a4eb763b6\",\n \"score\": \"0.5254831\",\n \"text\": \"def get_counts(self, ) -> int:\\n return self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8dbe95c19a33b9e76888722613e38c3\",\n \"score\": \"0.52503103\",\n \"text\": \"def get_count_newsletters(self, obj):\\n return obj.newsletter_set.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8dbe95c19a33b9e76888722613e38c3\",\n \"score\": \"0.52503103\",\n \"text\": \"def get_count_newsletters(self, obj):\\n return obj.newsletter_set.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69587cb4cc7762a92c5c264ad406c20\",\n \"score\": \"0.52437675\",\n \"text\": \"def feed(request):\\n # Generate counts of some of the main objects\\n num_posts=Post.objects.all().count()\\n num_users=User.objects.count() # The 'all()' is implied by default.\\n \\n # Render the HTML template home.html with the data in the context variable\\n return render(\\n request,\\n 'feed.html',\\n # context={'num_posts':num_posts},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f9ee2a7238a159d6ed860c0f6aa338\",\n \"score\": \"0.5240591\",\n \"text\": \"def count_images_per_category(self):\\n if not self.index:\\n self.reindex()\\n\\n return {\\n self.cats[cat_id][\\\"name\\\"]: len(set(imgs_list))\\n for cat_id, imgs_list in self.index.catidx_to_imgidxs.items()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a29da1839a7370a907927e7610638e11\",\n \"score\": \"0.52370936\",\n \"text\": \"def view_status(budget):\\n print()\\n print(\\\"Your Budget limit: $\\\" + \\\"{0:.2f}\\\".format(budget.get_limit()))\\n print()\\n list_of_categories = budget.categories.all()\\n for i in range(len(list_of_categories)):\\n category = list_of_categories[i]\\n category_name = category['name']\\n category_limit = category['limit']\\n print(\\\"{}'s limit: $\\\".format(category_name) +\\n \\\"{0:.2f}\\\".format(category_limit))\\n category_total = budget.category_total(category_name)\\n print(\\\"Total spending in {}'s category: \\\".format(category_name) +\\n \\\"${0:.2f}\\\".format(float(category_total)))\\n print()\\n print(\\\"Total spending amounts to: \\\" + \\\"${0:.2f}\\\".format(budget.total_spent()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dec5f0a3187e3871e756acfa1fb5390\",\n \"score\": \"0.523425\",\n \"text\": \"def liwc_counts(the_text):\\n # Construct a counter of the words and return as JSON\\n text = re.sub(u'\\\\u2019', \\\"'\\\", the_text).lower()\\n the_dict = {cat: len(re.findall(regex_list[cat], text)) for cat in categories}\\n return json.dumps(the_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"265ffa2031f03ec3d6be4827f8c83fbb\",\n \"score\": \"0.5229807\",\n \"text\": \"def database_count():\\n a = len(_os.listdir(datapath(True))) - 3\\n b = _os.walk(datapath(True)) # generator\\n c = [1]\\n c = len([c[0] + 1 for root, dirs, files in b for _ in files]) - 6\\n print(\\\"Total number of companies contained: {}\\\".format(a))\\n print(\\\"Total number of detailed sheets: {}\\\".format(c))\\n _gc.collect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74eacd21f6bb5eee91ec343a3a375686\",\n \"score\": \"0.5226196\",\n \"text\": \"def count() -> int:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7c5461fbafc690f37e0db7c4539cb3\",\n \"score\": \"0.522224\",\n \"text\": \"def count_restaurants_by_category(data, categories):\\n catcounts = categories.assign(n=0)\\n for d in data:\\n for c in d['categories']:\\n if c['alias'] in catcounts['alias'].values:\\n catcounts.loc[catcounts['alias'] == c['alias'], 'n'] += 1\\n return catcounts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eebcff797c54575d840de441ab80bde\",\n \"score\": \"0.52208436\",\n \"text\": \"def get_categories():\\n mycursor.execute(\\\"USE {}\\\".format(DB_NAME))\\n mycursor.execute(QUERY_GET_CATS)\\n myresult = mycursor.fetchall()\\n\\n print(tabulate(myresult, headers = mycursor.column_names, tablefmt='fancy_grid'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51ba1c513f070a473152379e1c77dc40\",\n \"score\": \"0.5220193\",\n \"text\": \"def show_categories():\\n categories = service.get_categories()\\n latest_items = service.get_latest_items()\\n\\n return render_template(\\\"categories.html\\\", categories=categories,\\n items=latest_items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976b3bccd160305b1290186136b1f723\",\n \"score\": \"0.52196765\",\n \"text\": \"def daily_counts(self, account):\\n Memo = apps.get_model(\\\"diary\\\", \\\"Memo\\\")\\n memo = Memo.objects.filter(date=date.today(), author=account.user).first()\\n\\n # If there is no memo object, we cannot create the daily_counts - no error\\n if not memo:\\n return\\n\\n # Associate the article counts with the memo\\n if not hasattr(memo, \\\"article_counts\\\"):\\n counts = self.create(memo=memo)\\n else:\\n counts = memo.article_counts\\n\\n # Compute the number of articles read today\\n counts.read = memo.articles.count()\\n\\n # Create a queryset for the instapaper account\\n Article = apps.get_model(\\\"reading\\\", \\\"Article\\\")\\n qs = Article.instapaper.account(account)\\n\\n # Add the current unread count for today\\n counts.unread = qs.unread().count()\\n\\n # Filter for year to date counts\\n qs = qs.ytd(memo.date.year)\\n counts.archived= qs.archived().count()\\n counts.starred = qs.starred().count()\\n counts.save()\\n\\n return counts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fed01428870e3399ff07f72005cd3f4\",\n \"score\": \"0.5217082\",\n \"text\": \"def posts_count(self) -> int:\\n return len(self.posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d48e97b08af145bb46f5fc24772ac7\",\n \"score\": \"0.52164453\",\n \"text\": \"def category_list(request):\\n return render_to_response('charleston/category_list.html', {'object_list': Category.live_entry_set()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9502cfea08fc4efc345c606aa5734d26\",\n \"score\": \"0.52156436\",\n \"text\": \"def list_of_categories(request):\\n all_categories = Category.objects.all()\\n\\n context = {\\n 'all_categories' : all_categories\\n }\\n\\n return render(request=request, template_name=\\\"pages/category.html\\\", context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f361bd4d88aef57b021379373aa49c\",\n \"score\": \"0.5213993\",\n \"text\": \"def cmd_show_tags(self):\\n tag_count = defaultdict(int)\\n if self.args.verbose:\\n # Consider tags of (filtered) entries.\\n for entry in self.generate_entries():\\n for tag in entry.get_tags().as_strings():\\n tag_count[tag] += 1\\n else:\\n # Consider feed tags only.\\n for feed in self.generate_feeds():\\n for tag in feed.get_tags().as_strings():\\n tag_count[tag] += 1\\n for tag, cnt in sorted(tag_count.items()):\\n messager.msg(f'{cnt:7} {tag}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f506e69c9d86aed6090eda2e479ad1fe\",\n \"score\": \"0.5211928\",\n \"text\": \"def get_site_count(controller):\\n url = controller.url.rstrip('/')+\\\"/dna/intent/api/v1/site/count\\\"\\n get_intent_api(url, controller)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ece5b6c269c662559019c0618f21a5\",\n \"score\": \"0.52067983\",\n \"text\": \"def list_baru(request):\\n terbaru = Gigs.objects.order_by('-buat')\\n count_terbaru = Gigs.objects.all().count()\\n context = {'baru': terbaru, 'count': count_terbaru }\\n return render(request, 'child/list_baru.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c76934f6ec5398c9518a9d738d9c64cf\",\n \"score\": \"0.5206008\",\n \"text\": \"def popular_authors():\\n\\n results = fetch_query(\\n \\\"\\\"\\\"select author_slug.name, count(log.path)\\n from author_slug, log\\n where log.path = '/article/' || author_slug.slug\\n and log.status = '200 OK'\\n group by author_slug.name\\n order by count(log.path) desc;\\\"\\\"\\\"\\n )\\n print('\\\\n\\\\n' + \\\"Authors listed by popularity as defined by \\\"\\n \\\"total article views:\\\" + '\\\\n')\\n for item in results:\\n print(item[0] + \\\": \\\" + str(\\\"{:,}\\\".format(item[1])) + \\\" views\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6862ef9116e45de1b2ab0802a7128e\",\n \"score\": \"0.52027315\",\n \"text\": \"def category_index(request):\\n categories = Category.objects.values_list('name')\\n categories = [c[0] for c in categories]\\n return render(request, 'coco/category_index.html', {'categories': categories})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03c630d1d71e42adf7d71ce86e3fbb2\",\n \"score\": \"0.52023655\",\n \"text\": \"def getCatalogResourceCount(url, user, pWd):\\n\\n apiURL = url + \\\"/access/2/catalog/data/objects?q=core.allclassTypes:core.Resource\\\"\\n print(\\\"getting object count for catalog resource:-\\\" + apiURL + \\\" user=\\\" + user)\\n return callGETRestEndpoint(apiURL, user, pWd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edb8100e13e238a8fb0a4a6a9c7c310\",\n \"score\": \"0.52000797\",\n \"text\": \"def _count(self,):\\n return self.count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d07c492abfe2df23dddd1a83964367\",\n \"score\": \"0.5195354\",\n \"text\": \"def blog(request):\\n\\n blogs = Blog.objects.all().order_by('-date_posted')\\n paginator = Paginator(blogs, 3)\\n page_number = request.GET.get('page')\\n page_obj = paginator.get_page(page_number)\\n\\n context = {\\n 'blogs': page_obj,\\n }\\n return render(request, 'blog/blog.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304e4dc8f8c35c305b0cba28f08626b0\",\n \"score\": \"0.51829916\",\n \"text\": \"def tally(self, category):\\n try:\\n bayes_category = self.categories.get_category(category)\\n except KeyError:\\n return 0\\n\\n return bayes_category.get_tally()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f6643c387d340eb0172df374dbb0843\",\n \"score\": \"0.51784873\",\n \"text\": \"def view_count(self) -> int :\\n\\n return int(self._statistics.get('viewCount'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f3568e02fb51cc72d0bb04f5360157\",\n \"score\": \"0.5177821\",\n \"text\": \"def get_counts(workspace=None):\\n results = {}\\n results['counts'], results['subcounts'] = counts, subcounts = {}, {}\\n cache_file = os.path.join(cache_dir, f'.counts_{workspace or \\\"all\\\"}.json')\\n try:\\n with requests.Session() as session:\\n session.headers.update(headers)\\n # Counts by workspace\\n response = session.get(f'{base_url}/api/front/link', params=dict(\\n workspace__in=all_workspaces,\\n company__startup__isnull=False,\\n group_by='workspace_id',\\n count='id',\\n all=1,\\n ))\\n logger.debug(f\\\"[{response.elapsed}] {response.url}\\\")\\n for result in response.json():\\n counts[result['workspace_id']] = result['id_count']\\n # Counts by category in workspace\\n if workspace:\\n response = session.get(f'{base_url}/api/front/link', params=dict(\\n workspace=workspace,\\n company__startup__isnull=False,\\n group_by=f'extra_data__{category_field}',\\n count='id',\\n all=1,\\n ))\\n logger.debug(f\\\"[{response.elapsed}] {response.url}\\\")\\n for result in response.json():\\n subpage = result[f'extra_data__{category_field}']\\n subcounts[subpage] = result['id_count']\\n # Save results in cache\\n with open(cache_file, 'w') as file:\\n json.dump(results, file)\\n except:\\n if not os.path.exists(cache_file):\\n return {}, {}\\n # Get results from cache\\n with open(cache_file, 'r') as file:\\n results = json.load(file)\\n return results['counts'], results['subcounts']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa4f6ba5e0f32db3aa56c8fcf4930be\",\n \"score\": \"0.51772106\",\n \"text\": \"def get_num_posts():\\r\\n\\tnum_posts = mc.get('num_posts')\\r\\n\\tif(num_posts == None):\\r\\n\\t\\tcursor = db_execute('SELECT COUNT(*) FROM news')\\r\\n\\t\\tnum_posts = cursor.fetchone()[0]\\r\\n\\t\\tmc.set('num_posts', num_posts)\\r\\n\\treturn num_posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cebd32501935d0955a8b91eae1834b84\",\n \"score\": \"0.5175497\",\n \"text\": \"def index(request):\\n '''\\n # Generate counts of some of the main objects\\n num_books = Book.objects.all().count()\\n num_instances = BookInstance.objects.all().count()\\n \\n # Available books (status = 'a')\\n num_instances_available = BookInstance.objects.filter(status__exact='a').count()\\n \\n # The 'all()' is implied by default. \\n num_authors = Author.objects.count()\\n \\n context = {\\n 'num_books': num_books,\\n 'num_instances': num_instances,\\n 'num_instances_available': num_instances_available,\\n 'num_authors': num_authors,\\n }\\n '''\\n # Render the HTML template index.html with the data in the context variable\\n return render(request, 'index.html')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1595,"cells":{"query_id":{"kind":"string","value":"4b405ac332f6951a2c37f3ee664e8eb7"},"query":{"kind":"string","value":"Should provide a temp file that's cleared on exit."},"positive_passages":{"kind":"list like","value":[{"docid":"233240173585dcf5e01fbf13e8ccc91d","score":"0.6800151","text":"def test_get_temp_file(self):\n with get_temp_file() as temp_file:\n self.assertTrue(os.path.exists(temp_file))\n self.assertTrue(os.path.isfile(temp_file))\n self.assertFalse(os.path.exists(temp_file))","title":""}],"string":"[\n {\n \"docid\": \"233240173585dcf5e01fbf13e8ccc91d\",\n \"score\": \"0.6800151\",\n \"text\": \"def test_get_temp_file(self):\\n with get_temp_file() as temp_file:\\n self.assertTrue(os.path.exists(temp_file))\\n self.assertTrue(os.path.isfile(temp_file))\\n self.assertFalse(os.path.exists(temp_file))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1087f27ba0104a5b15f7422f68647085","score":"0.76812315","text":"def __reset_file(self):\n portable.remove(self.test_fn)\n self.test_fh, self.test_fn = tempfile.mkstemp(\n dir=self.test_path)","title":""},{"docid":"06f7b7f967bee77e6ab436ac0307dcae","score":"0.76368517","text":"def managed_temp_file():\n import tempfile\n _, temp_file = tempfile.mkstemp()\n try:\n yield temp_file\n finally:\n os.remove(temp_file)","title":""},{"docid":"985faf310852242161a80b426ac94194","score":"0.7625453","text":"def temporary_file():\n tempfile = mktemp()\n try:\n yield tempfile\n finally:\n os.remove(tempfile)","title":""},{"docid":"106efee85c41fbb04065b41db5b413aa","score":"0.75366324","text":"def get_temp_file():\n file_handle = tempfile.TemporaryFile(mode='w+')\n return file_handle","title":""},{"docid":"5223ad2628c24675f1e50ad50bdb2ae1","score":"0.7455795","text":"def get_tempfile():\n return tempfile.TemporaryFile()","title":""},{"docid":"bc2f1ff04afec657eb91e26493350562","score":"0.7318907","text":"def create_tmp_file(self):\n with open(self.path, \"wb\") as out:\n out.truncate(self.file_size)","title":""},{"docid":"97d1ce34c141a3018d95297f95d341d8","score":"0.72976667","text":"def __init__(self):\n fd, self.filename = tempfile.mkstemp()\n os.close(fd)\n logging.debug('Created temp file: %s', self.filename)","title":""},{"docid":"582fb03cfef2f81ecd19cc26b0ebe70f","score":"0.7278696","text":"def remove_temp_file(self):\r\n os.remove('temp_holdings.txt')","title":""},{"docid":"a88f1a6b32da5f834792a797dddfb619","score":"0.72448707","text":"def stop(cls):\n tempfile.mkdtemp = tempfile._original_mkdtemp\n tempfile.mkstemp = tempfile._original_mkstemp\n tempfile.NamedTemporaryFile = tempfile._original_NamedTemporaryFile\n\n tempfile._is_patched = False\n\n shutil.rmtree(cls.TEMP_DIR)\n\n cls.TEMP_DIR = None","title":""},{"docid":"532b50ac8dc01a774dab6c531c2c0d6e","score":"0.72360104","text":"def new_temp_file(self, **kwargs):\n # We don't want the file to be deleted when closed, but only when the kernel stops\n kwargs['delete'] = False\n kwargs['mode'] = 'w'\n file = tempfile.NamedTemporaryFile(**kwargs)\n self.files.append(file.name)\n return file","title":""},{"docid":"eca58c1f4e0f2c5c5e01138ac22e799c","score":"0.72134817","text":"def tempf():\n _, tempf = tempfile.mkstemp()\n tempf = Path(tempf)\n # make a temp file with 5 bytes\n tempf.write_bytes(b\"tempo\")\n yield tempf\n tempf.unlink() if tempf.exists() else None","title":""},{"docid":"9f343cb5b3b63f9492964ebd6cbeef34","score":"0.72133356","text":"def GetTempFileName():\r\n pass","title":""},{"docid":"b4d69f86789e6a2b75a873099a733c1b","score":"0.72114295","text":"def MakeTempfile(self):\n fd, filename = tempfile.mkstemp()\n os.close(fd)\n self.tempfiles.append(filename)\n return filename","title":""},{"docid":"f51487dfe83a418dc3eab8bd15f3e2cf","score":"0.7203604","text":"def reset(self):\n self.clean_up()\n self.closed = False\n self.working_path = tempfile.mkdtemp(prefix=\"ffplogs_\")","title":""},{"docid":"b5a3f495da312818f2d102c1f71dbfc7","score":"0.7195213","text":"def remove_temp_file(self):\n if self._temp_file is None:\n return\n if not os.path.isfile(self._temp_file):\n return\n os.remove(self._temp_file)\n print(f\"removed {self._temp_file}\")\n self._temp_file = None","title":""},{"docid":"df912dbfca8df54d6a2272d99be82cab","score":"0.7158964","text":"def fake_clean_tmp_dir(self):\n pass","title":""},{"docid":"5f819d4dcaa2f8b3b446dc7a33166c36","score":"0.7154565","text":"def get_temporary_file(self):\n temp = NamedTemporaryFile(delete=False)\n self.addCleanup(os.unlink, temp.name)\n\n return temp","title":""},{"docid":"dcf3bd13627cff58cf1bcf664b6dd3d9","score":"0.70627695","text":"def empty(self):\n os_handle, filename = tempfile.mkstemp(dir=self.temp_dir)\n os.close(os_handle)\n return filename","title":""},{"docid":"b5ed777ba0e96d1bb6b3cf83a89e3532","score":"0.7030533","text":"def mktmpfile(self):\n return tempfile.mkstemp(dir=self.path)","title":""},{"docid":"1dcaed25e20724ff7091e0810f75c2aa","score":"0.70288587","text":"def NamedTemporaryFile(cls, *args, **kwargs):\n kwargs['dir'] = MonitorTempFile.TEMP_DIR\n return tempfile._original_NamedTemporaryFile(*args, **kwargs)","title":""},{"docid":"89f57c18fb0cb8f5aa7fe7723106868c","score":"0.7009181","text":"def tmpfile(request):\n _, path = tempfile.mkstemp()\n\n def fin():\n print('tmpfile finalizer')\n\n request.addfinalizer(fin)\n return path","title":""},{"docid":"bc7b12957fba3f60d5ce7468d82bd257","score":"0.6991771","text":"def GetTempPath():\r\n pass","title":""},{"docid":"eb0b0fd69100fc1df23d222df16adebe","score":"0.69748926","text":"def make_file(self):\r\n return tempfile.TemporaryFile()","title":""},{"docid":"57dd9fe441ef1f8557f4f66edcb74718","score":"0.6942737","text":"def __init__(self):\n\t\tself.tmp = tempfile.mkdtemp()","title":""},{"docid":"5a7a2166404e4a2b5a670df628135957","score":"0.69285464","text":"def tmpfname(request):\n\n (fd,fname) = tempfile.mkstemp()\n\n def fin():\n try:\n os.close(fd)\n except OSError:\n pass\n if os.path.isfile(fname):\n os.remove(fname)\n\n request.addfinalizer(fin)\n\n return fname","title":""},{"docid":"7921cb7cb1415fe83ce18396674109c5","score":"0.6817242","text":"def tearDown(self): \n # Delete the temporary file\n os.remove(self.filename)","title":""},{"docid":"d00c884e14dd2f301448c760824a8336","score":"0.6792506","text":"def clean_temp_files(class_instance):\n log.info(\"Deleting temporary files\")\n if os.path.isfile(bdi_config.temp_yaml_configure.name):\n exec_cmd(cmd=f\"rm -f {bdi_config.temp_yaml_configure.name}\")\n if os.path.isfile(bdi_config.temp_yaml_data.name):\n exec_cmd(cmd=f\"rm -f {bdi_config.temp_yaml_data.name}\")\n if os.path.isfile(bdi_config.temp_yaml_run.name):\n exec_cmd(cmd=f\"rm -f {bdi_config.temp_yaml_run.name}\")\n exec_cmd(cmd=\"rm -f tmp*\")\n os.environ[\"TILLER_NAMESPACE\"] = \"\"","title":""},{"docid":"8f970ffd88d3e6dcd136554fb3f6bba4","score":"0.6742224","text":"def getTempFile(suffix=\"\", rootDir=None):\n if rootDir is None:\n handle, tmpFile = tempfile.mkstemp(suffix)\n os.close(handle)\n return tmpFile\n else:\n tmpFile = os.path.join(rootDir, \"tmp_\" + getRandomAlphaNumericString() + suffix)\n open(tmpFile, 'w').close()\n os.chmod(tmpFile, 0o777) #Ensure everyone has access to the file.\n return tmpFile","title":""},{"docid":"803055bf8632685c9cbc2b63176866fd","score":"0.6739965","text":"def tearDown(self):\n remove(\"tmp.txt\")","title":""},{"docid":"1f3df2b544e76e6b54939dcdccc88e22","score":"0.67203075","text":"def setUp(self):\n # Get a temporary file object using tempfile\n (file_object, file_path) = tempfile.mkstemp()\n self.filename = file_path\n \n # Close the file descriptor.\n # Note that using mkstemp() instead tempfile.TemporaryFile means the \n # file remains in the filesystem even after it is closed.\n os.close(file_object)","title":""},{"docid":"065000afc8beb566891dc1446cd19e2f","score":"0.67115736","text":"def _make_temp_file():\n temp_file = tempfile.NamedTemporaryFile()\n temp_file.write('this is a test')\n temp_file.seek(0)\n test_md5 = hashlib.md5('this is a test').hexdigest()\n return temp_file, test_md5","title":""},{"docid":"b43e22bcd37c227bb226b7acb5a90028","score":"0.6698032","text":"def UnopenedTemporaryFile(**kwargs):\n path = CreateTemporaryFile(**kwargs)\n try:\n yield path\n finally:\n if os.path.exists(path):\n os.unlink(path)","title":""},{"docid":"737aec5317a9570bc8155486e836c4eb","score":"0.6694874","text":"def __del__(self):\n os.remove(sys.argv[1] + '.tmp')","title":""},{"docid":"a1aa9380f50e34515564ee5601a8dc30","score":"0.6689624","text":"def temp_file(suffix):\n return tempfile.NamedTemporaryFile(prefix=\"cuneiform_\", suffix=suffix)","title":""},{"docid":"a806181fcafbacc30631c95b9feaad11","score":"0.6685215","text":"def createLoggerTemporaryFile(self):\n\t\ttry:\n\t\t\tself._logger_file = tempfile.NamedTemporaryFile()\n\t\t\tos.system(\"cmsStage -f \"+self.dataset_details['LFN'] + \"/Logger.tgz \"+self._logger_file.name)\n\t\t\tself._logger_tar_object = tarfile.open(fileobj=self._logger_file)\n\t\t\tif len(self._logger_tar_object.getmembers())==0: \n\t\t\t\tprint \"\\nERROR: Failed to stage logger file\"\n\t\t\t\tself._logger_tar_object = None\n\t\t\t\tself._logger_file = None\n\t\texcept:\n\t\t\tprint \"\\nERROR: Failed to stage logger file\"\n\t\t\tself._logger_tar_object = None\n\t\t\tself._logger_file = None","title":""},{"docid":"2d9e7251dfc2dfed2f87237b8472ea57","score":"0.6676475","text":"def _create_temp_dir():\r\n tmpdir = tempfile.mkdtemp(prefix='wppm_')\r\n atexit.register(\r\n lambda path: shutil.rmtree(path, onerror=onerror),\r\n tmpdir,\r\n )\r\n return tmpdir","title":""},{"docid":"c4adbc164a489a9aa665edf137833c03","score":"0.66644645","text":"def get_safe_tempfile():\r\n\timport tempfile\r\n\tfd, path = tempfile.mkstemp()\r\n\treturn (os.fdopen(fd, \"wb\"), path)","title":""},{"docid":"b9944fa98d7861317c37eab1e8694690","score":"0.66557175","text":"def create_temp_file(data):\n features_file = tempfile.NamedTemporaryFile(mode=\"w+\", delete=False)\n features_file.write(data)\n features_file.seek(0)\n return features_file","title":""},{"docid":"7ebe9c5259a628c15b36a3ae37e3fa2e","score":"0.66484386","text":"def __enter__(self):\n self.temporary_directory = tempfile.mkdtemp(**self.options)\n logger.debug(\"Created temporary directory: %s\", self.temporary_directory)\n return self.temporary_directory","title":""},{"docid":"a6cd001ee9a79b82d93a27fa1f6e7f80","score":"0.66468143","text":"def cleanup_temp():\n if AlienSessionInfo['templist']:\n for f in AlienSessionInfo['templist']:\n if os.path.isfile(f): os.remove(f)","title":""},{"docid":"47939160d94ea1debe79356d084bb695","score":"0.6644578","text":"def tmpfile(ext=''):\n fd, out = tempfile.mkstemp(suffix=ext)\n os.close(fd) # http://www.logilab.org/blogentry/17873\n return out","title":""},{"docid":"f874a61a6af307921975f9d95251d822","score":"0.6639228","text":"def getLocalTempFile(self):\n handle, tmpFile = tempfile.mkstemp(prefix=\"tmp\", suffix=\".tmp\", dir=self.localTempDir)\n os.close(handle)\n return os.path.abspath(tmpFile)","title":""},{"docid":"ec739facc5e9e54e980bde814caecd3d","score":"0.66197217","text":"def temporary_directory():\n return \"/tmp/\"","title":""},{"docid":"fa7ef632a0a1949c285880e9af7058ab","score":"0.6618681","text":"def __del__(self):\n if not os.environ.has_key('NEXTEST_SAVE_TEMP') and self.tmpConfig:\n os.remove(self.tmpConfig)","title":""},{"docid":"34643869f9d4a39440ca221936b55680","score":"0.6612205","text":"def deleteTemporaryFile(self, filePath):\n ...","title":""},{"docid":"042425ca6c699aed56c2d39bf0484f10","score":"0.6601878","text":"def mkdtemp():\n import os\n import sys\n import tempfile\n import test_xpedite.test_profiler\n tempDir = tempfile.mkdtemp()\n for dataFile in os.listdir(tempDir):\n os.remove(dataFile)\n return tempDir","title":""},{"docid":"1bb7e87859fda25f5c9a327c807139a2","score":"0.66004926","text":"def tearDown(self):\n os.remove(self.tmpfilepath)\n if path.exists(\"file.txt\"):\n os.remove(\"file.txt\")","title":""},{"docid":"5e40d9a17946c03d82200bdea9b8ea14","score":"0.6599002","text":"def setup_method(self):\n self.temp_dir = tempfile.TemporaryDirectory()","title":""},{"docid":"5e40d9a17946c03d82200bdea9b8ea14","score":"0.6599002","text":"def setup_method(self):\n self.temp_dir = tempfile.TemporaryDirectory()","title":""},{"docid":"5b5dfc15f076a236448f7a867e08f011","score":"0.65962636","text":"def createTempFile():\n TEMP_FILE = mc.file(q=True, sceneName=True).replace('.ma', 'TEMP.ma')\n mc.file(rename=TEMP_FILE)\n mc.file(save=True, force=True)\n mc.file(new=True, force=True)\n mc.file(TEMP_FILE, open=True, force=True)\n mc.file(renameToSave=True)","title":""},{"docid":"c49f8a2dd296fa171f8ee2937dbbcc0a","score":"0.6578688","text":"def temptext():\n\n fd, path = tempfile.mkstemp()\n try:\n yield (fd, path)\n finally:\n # Close the file descriptor and ignore errors\n try:\n os.close(fd)\n except OSError:\n pass\n\n # delete the path\n shutil.rmtree(path, ignore_errors=True)","title":""},{"docid":"0bb75c7e5b49d4cc084af00b229ac4f7","score":"0.6576771","text":"def _tmp(self):\n tmpfn = tempfile.NamedTemporaryFile(prefix='tmp',\n suffix='.json',\n delete=False)\n return tmpfn.name","title":""},{"docid":"402b7181e9ce4360e6fe45738d3dc639","score":"0.65728176","text":"def temppath(*args, **kwargs):\n fd, path = mkstemp(*args, **kwargs)\n os.close(fd)\n try:\n yield path\n finally:\n os.remove(path)","title":""},{"docid":"71d6bedc6dae2f6f93992fa508d98ca5","score":"0.6564314","text":"def delete_temporary_file(tmp_file):\n os.remove(tmp_file)\n return","title":""},{"docid":"b3f718ffc24699d3e6975875ea8271b0","score":"0.65639985","text":"def tmpFile(string):\n r = tempfile.NamedTemporaryFile('w+b', -1, None, None, '.sf', string, None, False)\n r.close()\n return r","title":""},{"docid":"0e574a73775f60733cdf4f825ddb6ba7","score":"0.6563742","text":"def __enter__(self):\n self.file = open(os.path.join(self.temp_dir, self.filename), \"w\")\n return self.file","title":""},{"docid":"2c98b49d82bdaaff53400ed3408bbf10","score":"0.65553457","text":"def clean_temp(config, args):\n\n log = logging.getLogger('markdoc.clean-temp')\n\n if p.exists(config.temp_dir):\n log.debug('rm -Rf %s' % config.temp_dir)\n shutil.rmtree(config.temp_dir)\n\n log.debug('makedirs %s' % config.temp_dir)\n os.makedirs(config.temp_dir)","title":""},{"docid":"09abc2cd9da41f3240f397520d4d31a8","score":"0.65532285","text":"def __del__(self):\n if self._tmp:\n os.unlink(self._file)","title":""},{"docid":"ed562918c1ce1a2e14336bc82a6359ab","score":"0.65480757","text":"def tmp_directory(self):","title":""},{"docid":"8c2799da484afe07db8a8edeb5d15dc6","score":"0.6546528","text":"def tmp_dir():\n output_dir = mkdtemp()\n yield output_dir\n shutil.rmtree(output_dir)","title":""},{"docid":"9d1f5b2e059ea3c8c8faf025761a64bc","score":"0.65365726","text":"def temporary_filename(**kwargs):\n filedescriptor, filename = tempfile.mkstemp(**kwargs)\n os.close(filedescriptor)\n\n try:\n yield filename\n finally:\n if os.path.exists(filename):\n os.remove(filename)","title":""},{"docid":"50ea69857283023b19e9782f2b5586c8","score":"0.6533918","text":"def __init__(self):\n self.path = tempfile.mkdtemp()","title":""},{"docid":"49d1444f12c488d8292fc5fb796e0913","score":"0.6527179","text":"def on_closing():\n delete_temp_files()\n app.destroy()","title":""},{"docid":"5e3c36139a944f515aeb82236656e711","score":"0.6519217","text":"def tempFile(name):\n\n path = os.path.join(localDir(\"_test_\"), name)\n\n try:\n os.remove(path)\n except FileNotFoundError:\n pass\n if not os.path.exists(os.path.dirname(path)):\n os.mkdir(os.path.dirname(path))\n return path","title":""},{"docid":"aa68845923c9d9ab966e5146f5d6f705","score":"0.6518461","text":"def setUp(self):\n with open(self.tmpfilepath, \"x\", encoding=\"utf8\") as file:\n file.write(\"Delete me!\")","title":""},{"docid":"58578f9c32c2f8c1ca0db18223c80db3","score":"0.6510267","text":"def get_temp_file(keep=False, autoext=\"\", fd=False):\n f = tempfile.NamedTemporaryFile(prefix=\"scapy\", suffix=autoext,\n delete=False)\n if not keep:\n conf.temp_files.append(f.name)\n\n if fd:\n return f\n else:\n # Close the file so something else can take it.\n f.close()\n return f.name","title":""},{"docid":"98c6a74fb6da55917d529d624c36e629","score":"0.65069336","text":"def purge_temp_file(path=''):\n try:\n _fo = open(path, 'w')\n _fo.seek(0, SEEK_END)\n _fs = _fo.tell()\n _fo.seek(-1 * _fs, SEEK_END)\n _repeat = (_fs // 120) + 1\n _fo.writelines([hashlib.sha512(path).hexdigest()] * _repeat)\n finally:\n _fo.close()\n remove(path)","title":""},{"docid":"6a6c725bd8ca93e8caf8983d73fd6a8a","score":"0.6497294","text":"def setUp(self):\n self.tmpdir = tempfile.mkdtemp()\n self.tmpfile = os.path.join(self.tmpdir, \"test1\")\n\n # Touch the file\n open(self.tmpfile, \"w\").close()","title":""},{"docid":"de8fd749d7756e9dc3691fa1f95b09c5","score":"0.6491572","text":"def temporary_file(mode):\r\n temporary_directory = tempfile.mkdtemp()\r\n basename = 'tmpfile-%s-%s' % (int(time.time()), random.randint(1, 1000))\r\n full_filename = os.path.join(temporary_directory, basename)\r\n open(full_filename, 'w').close()\r\n try:\r\n with open(full_filename, mode) as f:\r\n yield f\r\n finally:\r\n shutil.rmtree(temporary_directory)","title":""},{"docid":"6ddffde289e575044fdff98881c8c541","score":"0.6459435","text":"def __load_temp_file(self):\r\n if not osp.isfile(self.TEMPFILE_PATH):\r\n # Creating temporary file\r\n default = ['# -*- coding: utf-8 -*-',\r\n '\"\"\"', self.tr(\"Spyder Editor\"), '',\r\n self.tr(\"This temporary script file is located here:\"),\r\n self.TEMPFILE_PATH,\r\n '\"\"\"', '', '']\r\n text = os.linesep.join([encoding.to_unicode(qstr)\r\n for qstr in default])\r\n encoding.write(unicode(text), self.TEMPFILE_PATH, 'utf-8')\r\n self.load(self.TEMPFILE_PATH)","title":""},{"docid":"d8967f48f4ad167b9fa71b9c1eeeb439","score":"0.6449689","text":"def tempfile(self, filename=\"\"):\n\n try:\n self.temp_directory.mkdir(parents=True)\n except FileExistsError:\n pass\n\n if len(filename):\n suffix = \".\" + filename\n else:\n suffix = \"\"\n\n fd, path = tempfile.mkstemp(prefix=\"\", suffix=suffix,\n dir=str(self.temp_directory))\n os.close(fd)\n path = pathlib.Path(path)\n\n try:\n yield path\n finally:\n if path.exists():\n path.unlink()","title":""},{"docid":"bdc589455603e3b78cbb86ad0ceb549d","score":"0.6446526","text":"def temporary_file(mode):\n temporary_directory = tempfile.mkdtemp()\n basename = 'tmpfile-%s-%s' % (int(time.time()), random.randint(1, 1000))\n full_filename = os.path.join(temporary_directory, basename)\n open(full_filename, 'w').close()\n try:\n with open(full_filename, mode) as f:\n yield f\n finally:\n shutil.rmtree(temporary_directory)","title":""},{"docid":"07dffbc33276fda673da33cbf78a208b","score":"0.6432617","text":"def getLocalTempFileName(self):\n # Create, and then delete a temp file. Creating will guarantee you a unique, unused\n # file name. There is a very, very, very low chance that another job will create the\n # same file name in the span of this one being deleted and then being used by the user.\n tempFile = self.getLocalTempFile()\n os.remove(tempFile)\n return tempFile","title":""},{"docid":"3289b68863a069365f98c39522a82284","score":"0.6420551","text":"def tearDown(self):\n for fname in self.tempfiles:\n if os.path.exists(fname):\n os.unlink(fname)","title":""},{"docid":"6bce4b0f275fce3631df4f34541b7400","score":"0.6419176","text":"def writeTmpF(data):\n fileuuid = uuid.uuid4().hex # gen rand uuid to handle file conflicts\n fpath = os.path.join('/tmp', fileuuid)\n with open(fpath, \"w\") as f:\n f.write(data)\n return fpath","title":""},{"docid":"f06943f22bb77f93bda11c82aadb1850","score":"0.6416767","text":"def tempdir():\n td = tempfile.mkdtemp()\n td = Path(td)\n yield td\n shutil.rmtree(td) if td.exists() else None","title":""},{"docid":"9a0d160f9c863f5ba3655e040dcf7434","score":"0.6416265","text":"def __init__(self, out_file=None, verbose=True):\n if out_file is not None:\n try:\n mafi = open(out_file, 'w')\n# osfi = os.open(out_file, os.O_RDWR, os.O_CREAT)\n# self.temp_files = [osfi, osfi]\n self.out_null = mafi\n except:\n # raise\n out_file = None\n if out_file is None:\n self.temp_files = [os.open(os.devnull, os.O_RDWR)\n for x in range(2)]\n self.out_null = open(os.devnull, 'w')\n self.save_dup = (os.dup(1), os.dup(2))","title":""},{"docid":"a25b508a9ce4257c17ba5ab107f95a14","score":"0.64077693","text":"def temporary_file(prefix=\"tmp\", suffix=\"\"):\n try:\n _, filepath = tempfile.mkstemp(prefix=prefix, suffix=suffix)\n yield filepath\n finally:\n try:\n os.remove(filepath)\n except PermissionError:\n pass","title":""},{"docid":"be9dd48ae521eded146e66065c64e0d7","score":"0.6389609","text":"def mktemp(self):\n # XXX: Should we provide a cleaner interface for people to use? One\n # that returns FilePath? One that returns a directory?\n\n # XXX: Actually belongs in a mixin or something, not actually specific\n # to async.\n return make_temporary_directory(self).child('temp').path","title":""},{"docid":"36b3da36dc73ca46f2d675759e7b1915","score":"0.6386423","text":"def tmpfile(tmp_dir=None, suffix=''):\n dir = tmp_dir or os.getenv('TMPDIR', '/tmp')\n tmp_file = tempfile.NamedTemporaryFile(dir=dir, suffix=suffix)\n yield tmp_file\n tmp_file.close()","title":""},{"docid":"f802d3c4c7f3781e6e165ef84c3ba4fa","score":"0.63823247","text":"def tear_method(self):\n self.temp_dir.cleanup()","title":""},{"docid":"f802d3c4c7f3781e6e165ef84c3ba4fa","score":"0.63823247","text":"def tear_method(self):\n self.temp_dir.cleanup()","title":""},{"docid":"153356d7054bf1fdcacda87c71b570cc","score":"0.63593787","text":"def temp_dir():\n\treturn conf['TEMP_DIR']","title":""},{"docid":"17ed99f9cc95dabf4c5a47a3e4bd42ff","score":"0.6356306","text":"def WriteToNamedTemporaryFile(self):\n f = tempfile.NamedTemporaryFile(prefix='chromite.binpkg.pkgidx.')\n self.Write(f)\n f.flush()\n f.seek(0)\n return f","title":""},{"docid":"386244197ac70cf5c89e905e094c072f","score":"0.6352557","text":"def _tmp(self):\n tmpfn = tempfile.NamedTemporaryFile(prefix='tmp',\n suffix='.csv',\n delete=False)\n return tmpfn.name","title":""},{"docid":"d84236140158be900cde5de2c241a3a2","score":"0.6350381","text":"def get_temp_name():\n fp = tempfile.NamedTemporaryFile(delete=True, suffix='.h5')\n file_name = fp.name\n fp.close()\n del fp\n return file_name","title":""},{"docid":"165367c0ac05694cd663c96294a0bd68","score":"0.6347124","text":"def write_temp_file(content=''):\n _fpath = '/tmp/%12d' % (randint(1, 999999999999))\n _fo = open(_fpath, 'w')\n _fo.write(content)\n _fo.flush()\n _fo.close()\n\n return _fpath","title":""},{"docid":"bc9f7615df2e16f01e623925aeb1182e","score":"0.63353306","text":"def __enter__(self):\n self._save_dir = os.getcwd()\n self._tmp_dir = mkdtemp(dir=\"/tmp/\")\n if self._tmp_dir == None:\n raise FatalError('Cannot obtain temp directory')\n os.chdir(self._tmp_dir)\n return self","title":""},{"docid":"d98008cb2aa9b7bbc4ceffba62955e55","score":"0.6332841","text":"def make_temp_file(self, prefix='tmp', suffix=''):\n fd, file_name = tempfile.mkstemp(prefix=prefix, suffix=suffix)\n os.close(fd)\n self._temp_files.append(file_name)\n return file_name","title":""},{"docid":"729cbef3b9184bc2a2edfb0b3ac9a9f1","score":"0.6332112","text":"def empty_temp_dir():\r\n d = catalog.default_dir()\r\n for i in range(10000):\r\n new_d = os.path.join(d,tempfile.gettempprefix()[1:-1]+repr(i))\r\n if not os.path.exists(new_d):\r\n os.mkdir(new_d)\r\n break\r\n return new_d","title":""},{"docid":"4a8956c52d7e3f6e7fd00a77fa5b39f3","score":"0.63316613","text":"def temp_filename(suffix=None):\n filename = tempfile.mkstemp(suffix=suffix)[1]\n if os.path.exists(filename):\n os.remove(filename)\n return filename","title":""},{"docid":"a6af0a5f82b0211f2fc0fe99202bc0bc","score":"0.6324579","text":"def _removeTemporary(temp_file):\n try:\n os.remove(temp_file)\n except:\n Logger.log(\"w\", \"Couldn't remove temporary file %s\", temp_file)","title":""},{"docid":"2704ce54d891b4a047d817387e3f4686","score":"0.63204163","text":"def discard(self):\n if not self._is_finalized:\n self._real_close()\n os.unlink(self._temp_fp)\n self._is_finalized = True","title":""},{"docid":"9947a524bc754b2d67e89952ecbee29c","score":"0.6319844","text":"def start(cls):\n\n if hasattr(tempfile, '_is_patched') and tempfile._is_patched:\n raise Exception(\"need tempfile to be in unpatched state!\")\n\n cls.TEMP_DIR = tempfile.mkdtemp(prefix='isign-test-run-')\n\n tempfile._original_mkdtemp = tempfile.mkdtemp\n tempfile.mkdtemp = MonitorTempFile.mkdtemp\n\n tempfile._original_mkstemp = tempfile.mkstemp\n tempfile.mkstemp = MonitorTempFile.mkstemp\n\n tempfile._original_NamedTemporaryFile = tempfile.NamedTemporaryFile\n tempfile.NamedTemporaryFile = MonitorTempFile.NamedTemporaryFile\n\n tempfile._is_patched = True","title":""},{"docid":"20416db7378aeddd757f377a272de58b","score":"0.63179153","text":"def del_temp_files(self):\n if os.path.exists(self.CLONED_DIR):\n shutil.rmtree(self.CLONED_DIR)","title":""},{"docid":"704dd8627da9b7cfa240bbe709089e0d","score":"0.63163733","text":"def delete_temp_file(file):\n file.close()\n os.unlink(file.name)","title":""},{"docid":"6174d986d7223da69dd831a566e4edd2","score":"0.6307793","text":"def close(self):\n # type: () -> None\n if self._auto_clean:\n self.clean()\n super(TempFS, self).close()","title":""},{"docid":"734f423bb7448ccc456e457e8fae254e","score":"0.62781996","text":"def tempdir():\n directory = tempfile.mkdtemp()\n yield Directory(directory)\n shutil.rmtree(directory)","title":""},{"docid":"22386b7061f840562e1d5b8a4787c657","score":"0.6273169","text":"def setUp(self):\n self.temp_directory = tempfile.mkdtemp()","title":""},{"docid":"f2d51824a287027d0bb72ad441cb5b9d","score":"0.62590915","text":"def __exit__(self, exc_type, exc_value, traceback):\n logger.debug(\"Cleaning up temporary directory: %s\", self.temporary_directory)\n shutil.rmtree(self.temporary_directory)\n del self.temporary_directory","title":""}],"string":"[\n {\n \"docid\": \"1087f27ba0104a5b15f7422f68647085\",\n \"score\": \"0.76812315\",\n \"text\": \"def __reset_file(self):\\n portable.remove(self.test_fn)\\n self.test_fh, self.test_fn = tempfile.mkstemp(\\n dir=self.test_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06f7b7f967bee77e6ab436ac0307dcae\",\n \"score\": \"0.76368517\",\n \"text\": \"def managed_temp_file():\\n import tempfile\\n _, temp_file = tempfile.mkstemp()\\n try:\\n yield temp_file\\n finally:\\n os.remove(temp_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985faf310852242161a80b426ac94194\",\n \"score\": \"0.7625453\",\n \"text\": \"def temporary_file():\\n tempfile = mktemp()\\n try:\\n yield tempfile\\n finally:\\n os.remove(tempfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106efee85c41fbb04065b41db5b413aa\",\n \"score\": \"0.75366324\",\n \"text\": \"def get_temp_file():\\n file_handle = tempfile.TemporaryFile(mode='w+')\\n return file_handle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5223ad2628c24675f1e50ad50bdb2ae1\",\n \"score\": \"0.7455795\",\n \"text\": \"def get_tempfile():\\n return tempfile.TemporaryFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc2f1ff04afec657eb91e26493350562\",\n \"score\": \"0.7318907\",\n \"text\": \"def create_tmp_file(self):\\n with open(self.path, \\\"wb\\\") as out:\\n out.truncate(self.file_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97d1ce34c141a3018d95297f95d341d8\",\n \"score\": \"0.72976667\",\n \"text\": \"def __init__(self):\\n fd, self.filename = tempfile.mkstemp()\\n os.close(fd)\\n logging.debug('Created temp file: %s', self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"582fb03cfef2f81ecd19cc26b0ebe70f\",\n \"score\": \"0.7278696\",\n \"text\": \"def remove_temp_file(self):\\r\\n os.remove('temp_holdings.txt')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a88f1a6b32da5f834792a797dddfb619\",\n \"score\": \"0.72448707\",\n \"text\": \"def stop(cls):\\n tempfile.mkdtemp = tempfile._original_mkdtemp\\n tempfile.mkstemp = tempfile._original_mkstemp\\n tempfile.NamedTemporaryFile = tempfile._original_NamedTemporaryFile\\n\\n tempfile._is_patched = False\\n\\n shutil.rmtree(cls.TEMP_DIR)\\n\\n cls.TEMP_DIR = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"532b50ac8dc01a774dab6c531c2c0d6e\",\n \"score\": \"0.72360104\",\n \"text\": \"def new_temp_file(self, **kwargs):\\n # We don't want the file to be deleted when closed, but only when the kernel stops\\n kwargs['delete'] = False\\n kwargs['mode'] = 'w'\\n file = tempfile.NamedTemporaryFile(**kwargs)\\n self.files.append(file.name)\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca58c1f4e0f2c5c5e01138ac22e799c\",\n \"score\": \"0.72134817\",\n \"text\": \"def tempf():\\n _, tempf = tempfile.mkstemp()\\n tempf = Path(tempf)\\n # make a temp file with 5 bytes\\n tempf.write_bytes(b\\\"tempo\\\")\\n yield tempf\\n tempf.unlink() if tempf.exists() else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f343cb5b3b63f9492964ebd6cbeef34\",\n \"score\": \"0.72133356\",\n \"text\": \"def GetTempFileName():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d69f86789e6a2b75a873099a733c1b\",\n \"score\": \"0.72114295\",\n \"text\": \"def MakeTempfile(self):\\n fd, filename = tempfile.mkstemp()\\n os.close(fd)\\n self.tempfiles.append(filename)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51487dfe83a418dc3eab8bd15f3e2cf\",\n \"score\": \"0.7203604\",\n \"text\": \"def reset(self):\\n self.clean_up()\\n self.closed = False\\n self.working_path = tempfile.mkdtemp(prefix=\\\"ffplogs_\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a3f495da312818f2d102c1f71dbfc7\",\n \"score\": \"0.7195213\",\n \"text\": \"def remove_temp_file(self):\\n if self._temp_file is None:\\n return\\n if not os.path.isfile(self._temp_file):\\n return\\n os.remove(self._temp_file)\\n print(f\\\"removed {self._temp_file}\\\")\\n self._temp_file = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df912dbfca8df54d6a2272d99be82cab\",\n \"score\": \"0.7158964\",\n \"text\": \"def fake_clean_tmp_dir(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f819d4dcaa2f8b3b446dc7a33166c36\",\n \"score\": \"0.7154565\",\n \"text\": \"def get_temporary_file(self):\\n temp = NamedTemporaryFile(delete=False)\\n self.addCleanup(os.unlink, temp.name)\\n\\n return temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf3bd13627cff58cf1bcf664b6dd3d9\",\n \"score\": \"0.70627695\",\n \"text\": \"def empty(self):\\n os_handle, filename = tempfile.mkstemp(dir=self.temp_dir)\\n os.close(os_handle)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5ed777ba0e96d1bb6b3cf83a89e3532\",\n \"score\": \"0.7030533\",\n \"text\": \"def mktmpfile(self):\\n return tempfile.mkstemp(dir=self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dcaed25e20724ff7091e0810f75c2aa\",\n \"score\": \"0.70288587\",\n \"text\": \"def NamedTemporaryFile(cls, *args, **kwargs):\\n kwargs['dir'] = MonitorTempFile.TEMP_DIR\\n return tempfile._original_NamedTemporaryFile(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f57c18fb0cb8f5aa7fe7723106868c\",\n \"score\": \"0.7009181\",\n \"text\": \"def tmpfile(request):\\n _, path = tempfile.mkstemp()\\n\\n def fin():\\n print('tmpfile finalizer')\\n\\n request.addfinalizer(fin)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7b12957fba3f60d5ce7468d82bd257\",\n \"score\": \"0.6991771\",\n \"text\": \"def GetTempPath():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0b0fd69100fc1df23d222df16adebe\",\n \"score\": \"0.69748926\",\n \"text\": \"def make_file(self):\\r\\n return tempfile.TemporaryFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57dd9fe441ef1f8557f4f66edcb74718\",\n \"score\": \"0.6942737\",\n \"text\": \"def __init__(self):\\n\\t\\tself.tmp = tempfile.mkdtemp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7a2166404e4a2b5a670df628135957\",\n \"score\": \"0.69285464\",\n \"text\": \"def tmpfname(request):\\n\\n (fd,fname) = tempfile.mkstemp()\\n\\n def fin():\\n try:\\n os.close(fd)\\n except OSError:\\n pass\\n if os.path.isfile(fname):\\n os.remove(fname)\\n\\n request.addfinalizer(fin)\\n\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7921cb7cb1415fe83ce18396674109c5\",\n \"score\": \"0.6817242\",\n \"text\": \"def tearDown(self): \\n # Delete the temporary file\\n os.remove(self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00c884e14dd2f301448c760824a8336\",\n \"score\": \"0.6792506\",\n \"text\": \"def clean_temp_files(class_instance):\\n log.info(\\\"Deleting temporary files\\\")\\n if os.path.isfile(bdi_config.temp_yaml_configure.name):\\n exec_cmd(cmd=f\\\"rm -f {bdi_config.temp_yaml_configure.name}\\\")\\n if os.path.isfile(bdi_config.temp_yaml_data.name):\\n exec_cmd(cmd=f\\\"rm -f {bdi_config.temp_yaml_data.name}\\\")\\n if os.path.isfile(bdi_config.temp_yaml_run.name):\\n exec_cmd(cmd=f\\\"rm -f {bdi_config.temp_yaml_run.name}\\\")\\n exec_cmd(cmd=\\\"rm -f tmp*\\\")\\n os.environ[\\\"TILLER_NAMESPACE\\\"] = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f970ffd88d3e6dcd136554fb3f6bba4\",\n \"score\": \"0.6742224\",\n \"text\": \"def getTempFile(suffix=\\\"\\\", rootDir=None):\\n if rootDir is None:\\n handle, tmpFile = tempfile.mkstemp(suffix)\\n os.close(handle)\\n return tmpFile\\n else:\\n tmpFile = os.path.join(rootDir, \\\"tmp_\\\" + getRandomAlphaNumericString() + suffix)\\n open(tmpFile, 'w').close()\\n os.chmod(tmpFile, 0o777) #Ensure everyone has access to the file.\\n return tmpFile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803055bf8632685c9cbc2b63176866fd\",\n \"score\": \"0.6739965\",\n \"text\": \"def tearDown(self):\\n remove(\\\"tmp.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3df2b544e76e6b54939dcdccc88e22\",\n \"score\": \"0.67203075\",\n \"text\": \"def setUp(self):\\n # Get a temporary file object using tempfile\\n (file_object, file_path) = tempfile.mkstemp()\\n self.filename = file_path\\n \\n # Close the file descriptor.\\n # Note that using mkstemp() instead tempfile.TemporaryFile means the \\n # file remains in the filesystem even after it is closed.\\n os.close(file_object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065000afc8beb566891dc1446cd19e2f\",\n \"score\": \"0.67115736\",\n \"text\": \"def _make_temp_file():\\n temp_file = tempfile.NamedTemporaryFile()\\n temp_file.write('this is a test')\\n temp_file.seek(0)\\n test_md5 = hashlib.md5('this is a test').hexdigest()\\n return temp_file, test_md5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43e22bcd37c227bb226b7acb5a90028\",\n \"score\": \"0.6698032\",\n \"text\": \"def UnopenedTemporaryFile(**kwargs):\\n path = CreateTemporaryFile(**kwargs)\\n try:\\n yield path\\n finally:\\n if os.path.exists(path):\\n os.unlink(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737aec5317a9570bc8155486e836c4eb\",\n \"score\": \"0.6694874\",\n \"text\": \"def __del__(self):\\n os.remove(sys.argv[1] + '.tmp')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1aa9380f50e34515564ee5601a8dc30\",\n \"score\": \"0.6689624\",\n \"text\": \"def temp_file(suffix):\\n return tempfile.NamedTemporaryFile(prefix=\\\"cuneiform_\\\", suffix=suffix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a806181fcafbacc30631c95b9feaad11\",\n \"score\": \"0.6685215\",\n \"text\": \"def createLoggerTemporaryFile(self):\\n\\t\\ttry:\\n\\t\\t\\tself._logger_file = tempfile.NamedTemporaryFile()\\n\\t\\t\\tos.system(\\\"cmsStage -f \\\"+self.dataset_details['LFN'] + \\\"/Logger.tgz \\\"+self._logger_file.name)\\n\\t\\t\\tself._logger_tar_object = tarfile.open(fileobj=self._logger_file)\\n\\t\\t\\tif len(self._logger_tar_object.getmembers())==0: \\n\\t\\t\\t\\tprint \\\"\\\\nERROR: Failed to stage logger file\\\"\\n\\t\\t\\t\\tself._logger_tar_object = None\\n\\t\\t\\t\\tself._logger_file = None\\n\\t\\texcept:\\n\\t\\t\\tprint \\\"\\\\nERROR: Failed to stage logger file\\\"\\n\\t\\t\\tself._logger_tar_object = None\\n\\t\\t\\tself._logger_file = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9e7251dfc2dfed2f87237b8472ea57\",\n \"score\": \"0.6676475\",\n \"text\": \"def _create_temp_dir():\\r\\n tmpdir = tempfile.mkdtemp(prefix='wppm_')\\r\\n atexit.register(\\r\\n lambda path: shutil.rmtree(path, onerror=onerror),\\r\\n tmpdir,\\r\\n )\\r\\n return tmpdir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4adbc164a489a9aa665edf137833c03\",\n \"score\": \"0.66644645\",\n \"text\": \"def get_safe_tempfile():\\r\\n\\timport tempfile\\r\\n\\tfd, path = tempfile.mkstemp()\\r\\n\\treturn (os.fdopen(fd, \\\"wb\\\"), path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9944fa98d7861317c37eab1e8694690\",\n \"score\": \"0.66557175\",\n \"text\": \"def create_temp_file(data):\\n features_file = tempfile.NamedTemporaryFile(mode=\\\"w+\\\", delete=False)\\n features_file.write(data)\\n features_file.seek(0)\\n return features_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ebe9c5259a628c15b36a3ae37e3fa2e\",\n \"score\": \"0.66484386\",\n \"text\": \"def __enter__(self):\\n self.temporary_directory = tempfile.mkdtemp(**self.options)\\n logger.debug(\\\"Created temporary directory: %s\\\", self.temporary_directory)\\n return self.temporary_directory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cd001ee9a79b82d93a27fa1f6e7f80\",\n \"score\": \"0.66468143\",\n \"text\": \"def cleanup_temp():\\n if AlienSessionInfo['templist']:\\n for f in AlienSessionInfo['templist']:\\n if os.path.isfile(f): os.remove(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47939160d94ea1debe79356d084bb695\",\n \"score\": \"0.6644578\",\n \"text\": \"def tmpfile(ext=''):\\n fd, out = tempfile.mkstemp(suffix=ext)\\n os.close(fd) # http://www.logilab.org/blogentry/17873\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f874a61a6af307921975f9d95251d822\",\n \"score\": \"0.6639228\",\n \"text\": \"def getLocalTempFile(self):\\n handle, tmpFile = tempfile.mkstemp(prefix=\\\"tmp\\\", suffix=\\\".tmp\\\", dir=self.localTempDir)\\n os.close(handle)\\n return os.path.abspath(tmpFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec739facc5e9e54e980bde814caecd3d\",\n \"score\": \"0.66197217\",\n \"text\": \"def temporary_directory():\\n return \\\"/tmp/\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa7ef632a0a1949c285880e9af7058ab\",\n \"score\": \"0.6618681\",\n \"text\": \"def __del__(self):\\n if not os.environ.has_key('NEXTEST_SAVE_TEMP') and self.tmpConfig:\\n os.remove(self.tmpConfig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34643869f9d4a39440ca221936b55680\",\n \"score\": \"0.6612205\",\n \"text\": \"def deleteTemporaryFile(self, filePath):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042425ca6c699aed56c2d39bf0484f10\",\n \"score\": \"0.6601878\",\n \"text\": \"def mkdtemp():\\n import os\\n import sys\\n import tempfile\\n import test_xpedite.test_profiler\\n tempDir = tempfile.mkdtemp()\\n for dataFile in os.listdir(tempDir):\\n os.remove(dataFile)\\n return tempDir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bb7e87859fda25f5c9a327c807139a2\",\n \"score\": \"0.66004926\",\n \"text\": \"def tearDown(self):\\n os.remove(self.tmpfilepath)\\n if path.exists(\\\"file.txt\\\"):\\n os.remove(\\\"file.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e40d9a17946c03d82200bdea9b8ea14\",\n \"score\": \"0.6599002\",\n \"text\": \"def setup_method(self):\\n self.temp_dir = tempfile.TemporaryDirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e40d9a17946c03d82200bdea9b8ea14\",\n \"score\": \"0.6599002\",\n \"text\": \"def setup_method(self):\\n self.temp_dir = tempfile.TemporaryDirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b5dfc15f076a236448f7a867e08f011\",\n \"score\": \"0.65962636\",\n \"text\": \"def createTempFile():\\n TEMP_FILE = mc.file(q=True, sceneName=True).replace('.ma', 'TEMP.ma')\\n mc.file(rename=TEMP_FILE)\\n mc.file(save=True, force=True)\\n mc.file(new=True, force=True)\\n mc.file(TEMP_FILE, open=True, force=True)\\n mc.file(renameToSave=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49f8a2dd296fa171f8ee2937dbbcc0a\",\n \"score\": \"0.6578688\",\n \"text\": \"def temptext():\\n\\n fd, path = tempfile.mkstemp()\\n try:\\n yield (fd, path)\\n finally:\\n # Close the file descriptor and ignore errors\\n try:\\n os.close(fd)\\n except OSError:\\n pass\\n\\n # delete the path\\n shutil.rmtree(path, ignore_errors=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb75c7e5b49d4cc084af00b229ac4f7\",\n \"score\": \"0.6576771\",\n \"text\": \"def _tmp(self):\\n tmpfn = tempfile.NamedTemporaryFile(prefix='tmp',\\n suffix='.json',\\n delete=False)\\n return tmpfn.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402b7181e9ce4360e6fe45738d3dc639\",\n \"score\": \"0.65728176\",\n \"text\": \"def temppath(*args, **kwargs):\\n fd, path = mkstemp(*args, **kwargs)\\n os.close(fd)\\n try:\\n yield path\\n finally:\\n os.remove(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d6bedc6dae2f6f93992fa508d98ca5\",\n \"score\": \"0.6564314\",\n \"text\": \"def delete_temporary_file(tmp_file):\\n os.remove(tmp_file)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3f718ffc24699d3e6975875ea8271b0\",\n \"score\": \"0.65639985\",\n \"text\": \"def tmpFile(string):\\n r = tempfile.NamedTemporaryFile('w+b', -1, None, None, '.sf', string, None, False)\\n r.close()\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e574a73775f60733cdf4f825ddb6ba7\",\n \"score\": \"0.6563742\",\n \"text\": \"def __enter__(self):\\n self.file = open(os.path.join(self.temp_dir, self.filename), \\\"w\\\")\\n return self.file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c98b49d82bdaaff53400ed3408bbf10\",\n \"score\": \"0.65553457\",\n \"text\": \"def clean_temp(config, args):\\n\\n log = logging.getLogger('markdoc.clean-temp')\\n\\n if p.exists(config.temp_dir):\\n log.debug('rm -Rf %s' % config.temp_dir)\\n shutil.rmtree(config.temp_dir)\\n\\n log.debug('makedirs %s' % config.temp_dir)\\n os.makedirs(config.temp_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09abc2cd9da41f3240f397520d4d31a8\",\n \"score\": \"0.65532285\",\n \"text\": \"def __del__(self):\\n if self._tmp:\\n os.unlink(self._file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed562918c1ce1a2e14336bc82a6359ab\",\n \"score\": \"0.65480757\",\n \"text\": \"def tmp_directory(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2799da484afe07db8a8edeb5d15dc6\",\n \"score\": \"0.6546528\",\n \"text\": \"def tmp_dir():\\n output_dir = mkdtemp()\\n yield output_dir\\n shutil.rmtree(output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1f5b2e059ea3c8c8faf025761a64bc\",\n \"score\": \"0.65365726\",\n \"text\": \"def temporary_filename(**kwargs):\\n filedescriptor, filename = tempfile.mkstemp(**kwargs)\\n os.close(filedescriptor)\\n\\n try:\\n yield filename\\n finally:\\n if os.path.exists(filename):\\n os.remove(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ea69857283023b19e9782f2b5586c8\",\n \"score\": \"0.6533918\",\n \"text\": \"def __init__(self):\\n self.path = tempfile.mkdtemp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d1444f12c488d8292fc5fb796e0913\",\n \"score\": \"0.6527179\",\n \"text\": \"def on_closing():\\n delete_temp_files()\\n app.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3c36139a944f515aeb82236656e711\",\n \"score\": \"0.6519217\",\n \"text\": \"def tempFile(name):\\n\\n path = os.path.join(localDir(\\\"_test_\\\"), name)\\n\\n try:\\n os.remove(path)\\n except FileNotFoundError:\\n pass\\n if not os.path.exists(os.path.dirname(path)):\\n os.mkdir(os.path.dirname(path))\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa68845923c9d9ab966e5146f5d6f705\",\n \"score\": \"0.6518461\",\n \"text\": \"def setUp(self):\\n with open(self.tmpfilepath, \\\"x\\\", encoding=\\\"utf8\\\") as file:\\n file.write(\\\"Delete me!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58578f9c32c2f8c1ca0db18223c80db3\",\n \"score\": \"0.6510267\",\n \"text\": \"def get_temp_file(keep=False, autoext=\\\"\\\", fd=False):\\n f = tempfile.NamedTemporaryFile(prefix=\\\"scapy\\\", suffix=autoext,\\n delete=False)\\n if not keep:\\n conf.temp_files.append(f.name)\\n\\n if fd:\\n return f\\n else:\\n # Close the file so something else can take it.\\n f.close()\\n return f.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c6a74fb6da55917d529d624c36e629\",\n \"score\": \"0.65069336\",\n \"text\": \"def purge_temp_file(path=''):\\n try:\\n _fo = open(path, 'w')\\n _fo.seek(0, SEEK_END)\\n _fs = _fo.tell()\\n _fo.seek(-1 * _fs, SEEK_END)\\n _repeat = (_fs // 120) + 1\\n _fo.writelines([hashlib.sha512(path).hexdigest()] * _repeat)\\n finally:\\n _fo.close()\\n remove(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a6c725bd8ca93e8caf8983d73fd6a8a\",\n \"score\": \"0.6497294\",\n \"text\": \"def setUp(self):\\n self.tmpdir = tempfile.mkdtemp()\\n self.tmpfile = os.path.join(self.tmpdir, \\\"test1\\\")\\n\\n # Touch the file\\n open(self.tmpfile, \\\"w\\\").close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de8fd749d7756e9dc3691fa1f95b09c5\",\n \"score\": \"0.6491572\",\n \"text\": \"def temporary_file(mode):\\r\\n temporary_directory = tempfile.mkdtemp()\\r\\n basename = 'tmpfile-%s-%s' % (int(time.time()), random.randint(1, 1000))\\r\\n full_filename = os.path.join(temporary_directory, basename)\\r\\n open(full_filename, 'w').close()\\r\\n try:\\r\\n with open(full_filename, mode) as f:\\r\\n yield f\\r\\n finally:\\r\\n shutil.rmtree(temporary_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ddffde289e575044fdff98881c8c541\",\n \"score\": \"0.6459435\",\n \"text\": \"def __load_temp_file(self):\\r\\n if not osp.isfile(self.TEMPFILE_PATH):\\r\\n # Creating temporary file\\r\\n default = ['# -*- coding: utf-8 -*-',\\r\\n '\\\"\\\"\\\"', self.tr(\\\"Spyder Editor\\\"), '',\\r\\n self.tr(\\\"This temporary script file is located here:\\\"),\\r\\n self.TEMPFILE_PATH,\\r\\n '\\\"\\\"\\\"', '', '']\\r\\n text = os.linesep.join([encoding.to_unicode(qstr)\\r\\n for qstr in default])\\r\\n encoding.write(unicode(text), self.TEMPFILE_PATH, 'utf-8')\\r\\n self.load(self.TEMPFILE_PATH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8967f48f4ad167b9fa71b9c1eeeb439\",\n \"score\": \"0.6449689\",\n \"text\": \"def tempfile(self, filename=\\\"\\\"):\\n\\n try:\\n self.temp_directory.mkdir(parents=True)\\n except FileExistsError:\\n pass\\n\\n if len(filename):\\n suffix = \\\".\\\" + filename\\n else:\\n suffix = \\\"\\\"\\n\\n fd, path = tempfile.mkstemp(prefix=\\\"\\\", suffix=suffix,\\n dir=str(self.temp_directory))\\n os.close(fd)\\n path = pathlib.Path(path)\\n\\n try:\\n yield path\\n finally:\\n if path.exists():\\n path.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc589455603e3b78cbb86ad0ceb549d\",\n \"score\": \"0.6446526\",\n \"text\": \"def temporary_file(mode):\\n temporary_directory = tempfile.mkdtemp()\\n basename = 'tmpfile-%s-%s' % (int(time.time()), random.randint(1, 1000))\\n full_filename = os.path.join(temporary_directory, basename)\\n open(full_filename, 'w').close()\\n try:\\n with open(full_filename, mode) as f:\\n yield f\\n finally:\\n shutil.rmtree(temporary_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07dffbc33276fda673da33cbf78a208b\",\n \"score\": \"0.6432617\",\n \"text\": \"def getLocalTempFileName(self):\\n # Create, and then delete a temp file. Creating will guarantee you a unique, unused\\n # file name. There is a very, very, very low chance that another job will create the\\n # same file name in the span of this one being deleted and then being used by the user.\\n tempFile = self.getLocalTempFile()\\n os.remove(tempFile)\\n return tempFile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3289b68863a069365f98c39522a82284\",\n \"score\": \"0.6420551\",\n \"text\": \"def tearDown(self):\\n for fname in self.tempfiles:\\n if os.path.exists(fname):\\n os.unlink(fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bce4b0f275fce3631df4f34541b7400\",\n \"score\": \"0.6419176\",\n \"text\": \"def writeTmpF(data):\\n fileuuid = uuid.uuid4().hex # gen rand uuid to handle file conflicts\\n fpath = os.path.join('/tmp', fileuuid)\\n with open(fpath, \\\"w\\\") as f:\\n f.write(data)\\n return fpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06943f22bb77f93bda11c82aadb1850\",\n \"score\": \"0.6416767\",\n \"text\": \"def tempdir():\\n td = tempfile.mkdtemp()\\n td = Path(td)\\n yield td\\n shutil.rmtree(td) if td.exists() else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a0d160f9c863f5ba3655e040dcf7434\",\n \"score\": \"0.6416265\",\n \"text\": \"def __init__(self, out_file=None, verbose=True):\\n if out_file is not None:\\n try:\\n mafi = open(out_file, 'w')\\n# osfi = os.open(out_file, os.O_RDWR, os.O_CREAT)\\n# self.temp_files = [osfi, osfi]\\n self.out_null = mafi\\n except:\\n # raise\\n out_file = None\\n if out_file is None:\\n self.temp_files = [os.open(os.devnull, os.O_RDWR)\\n for x in range(2)]\\n self.out_null = open(os.devnull, 'w')\\n self.save_dup = (os.dup(1), os.dup(2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25b508a9ce4257c17ba5ab107f95a14\",\n \"score\": \"0.64077693\",\n \"text\": \"def temporary_file(prefix=\\\"tmp\\\", suffix=\\\"\\\"):\\n try:\\n _, filepath = tempfile.mkstemp(prefix=prefix, suffix=suffix)\\n yield filepath\\n finally:\\n try:\\n os.remove(filepath)\\n except PermissionError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9dd48ae521eded146e66065c64e0d7\",\n \"score\": \"0.6389609\",\n \"text\": \"def mktemp(self):\\n # XXX: Should we provide a cleaner interface for people to use? One\\n # that returns FilePath? One that returns a directory?\\n\\n # XXX: Actually belongs in a mixin or something, not actually specific\\n # to async.\\n return make_temporary_directory(self).child('temp').path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b3da36dc73ca46f2d675759e7b1915\",\n \"score\": \"0.6386423\",\n \"text\": \"def tmpfile(tmp_dir=None, suffix=''):\\n dir = tmp_dir or os.getenv('TMPDIR', '/tmp')\\n tmp_file = tempfile.NamedTemporaryFile(dir=dir, suffix=suffix)\\n yield tmp_file\\n tmp_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f802d3c4c7f3781e6e165ef84c3ba4fa\",\n \"score\": \"0.63823247\",\n \"text\": \"def tear_method(self):\\n self.temp_dir.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f802d3c4c7f3781e6e165ef84c3ba4fa\",\n \"score\": \"0.63823247\",\n \"text\": \"def tear_method(self):\\n self.temp_dir.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153356d7054bf1fdcacda87c71b570cc\",\n \"score\": \"0.63593787\",\n \"text\": \"def temp_dir():\\n\\treturn conf['TEMP_DIR']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ed99f9cc95dabf4c5a47a3e4bd42ff\",\n \"score\": \"0.6356306\",\n \"text\": \"def WriteToNamedTemporaryFile(self):\\n f = tempfile.NamedTemporaryFile(prefix='chromite.binpkg.pkgidx.')\\n self.Write(f)\\n f.flush()\\n f.seek(0)\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"386244197ac70cf5c89e905e094c072f\",\n \"score\": \"0.6352557\",\n \"text\": \"def _tmp(self):\\n tmpfn = tempfile.NamedTemporaryFile(prefix='tmp',\\n suffix='.csv',\\n delete=False)\\n return tmpfn.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d84236140158be900cde5de2c241a3a2\",\n \"score\": \"0.6350381\",\n \"text\": \"def get_temp_name():\\n fp = tempfile.NamedTemporaryFile(delete=True, suffix='.h5')\\n file_name = fp.name\\n fp.close()\\n del fp\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"165367c0ac05694cd663c96294a0bd68\",\n \"score\": \"0.6347124\",\n \"text\": \"def write_temp_file(content=''):\\n _fpath = '/tmp/%12d' % (randint(1, 999999999999))\\n _fo = open(_fpath, 'w')\\n _fo.write(content)\\n _fo.flush()\\n _fo.close()\\n\\n return _fpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9f7615df2e16f01e623925aeb1182e\",\n \"score\": \"0.63353306\",\n \"text\": \"def __enter__(self):\\n self._save_dir = os.getcwd()\\n self._tmp_dir = mkdtemp(dir=\\\"/tmp/\\\")\\n if self._tmp_dir == None:\\n raise FatalError('Cannot obtain temp directory')\\n os.chdir(self._tmp_dir)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d98008cb2aa9b7bbc4ceffba62955e55\",\n \"score\": \"0.6332841\",\n \"text\": \"def make_temp_file(self, prefix='tmp', suffix=''):\\n fd, file_name = tempfile.mkstemp(prefix=prefix, suffix=suffix)\\n os.close(fd)\\n self._temp_files.append(file_name)\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729cbef3b9184bc2a2edfb0b3ac9a9f1\",\n \"score\": \"0.6332112\",\n \"text\": \"def empty_temp_dir():\\r\\n d = catalog.default_dir()\\r\\n for i in range(10000):\\r\\n new_d = os.path.join(d,tempfile.gettempprefix()[1:-1]+repr(i))\\r\\n if not os.path.exists(new_d):\\r\\n os.mkdir(new_d)\\r\\n break\\r\\n return new_d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a8956c52d7e3f6e7fd00a77fa5b39f3\",\n \"score\": \"0.63316613\",\n \"text\": \"def temp_filename(suffix=None):\\n filename = tempfile.mkstemp(suffix=suffix)[1]\\n if os.path.exists(filename):\\n os.remove(filename)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6af0a5f82b0211f2fc0fe99202bc0bc\",\n \"score\": \"0.6324579\",\n \"text\": \"def _removeTemporary(temp_file):\\n try:\\n os.remove(temp_file)\\n except:\\n Logger.log(\\\"w\\\", \\\"Couldn't remove temporary file %s\\\", temp_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2704ce54d891b4a047d817387e3f4686\",\n \"score\": \"0.63204163\",\n \"text\": \"def discard(self):\\n if not self._is_finalized:\\n self._real_close()\\n os.unlink(self._temp_fp)\\n self._is_finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9947a524bc754b2d67e89952ecbee29c\",\n \"score\": \"0.6319844\",\n \"text\": \"def start(cls):\\n\\n if hasattr(tempfile, '_is_patched') and tempfile._is_patched:\\n raise Exception(\\\"need tempfile to be in unpatched state!\\\")\\n\\n cls.TEMP_DIR = tempfile.mkdtemp(prefix='isign-test-run-')\\n\\n tempfile._original_mkdtemp = tempfile.mkdtemp\\n tempfile.mkdtemp = MonitorTempFile.mkdtemp\\n\\n tempfile._original_mkstemp = tempfile.mkstemp\\n tempfile.mkstemp = MonitorTempFile.mkstemp\\n\\n tempfile._original_NamedTemporaryFile = tempfile.NamedTemporaryFile\\n tempfile.NamedTemporaryFile = MonitorTempFile.NamedTemporaryFile\\n\\n tempfile._is_patched = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20416db7378aeddd757f377a272de58b\",\n \"score\": \"0.63179153\",\n \"text\": \"def del_temp_files(self):\\n if os.path.exists(self.CLONED_DIR):\\n shutil.rmtree(self.CLONED_DIR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"704dd8627da9b7cfa240bbe709089e0d\",\n \"score\": \"0.63163733\",\n \"text\": \"def delete_temp_file(file):\\n file.close()\\n os.unlink(file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6174d986d7223da69dd831a566e4edd2\",\n \"score\": \"0.6307793\",\n \"text\": \"def close(self):\\n # type: () -> None\\n if self._auto_clean:\\n self.clean()\\n super(TempFS, self).close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734f423bb7448ccc456e457e8fae254e\",\n \"score\": \"0.62781996\",\n \"text\": \"def tempdir():\\n directory = tempfile.mkdtemp()\\n yield Directory(directory)\\n shutil.rmtree(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22386b7061f840562e1d5b8a4787c657\",\n \"score\": \"0.6273169\",\n \"text\": \"def setUp(self):\\n self.temp_directory = tempfile.mkdtemp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d51824a287027d0bb72ad441cb5b9d\",\n \"score\": \"0.62590915\",\n \"text\": \"def __exit__(self, exc_type, exc_value, traceback):\\n logger.debug(\\\"Cleaning up temporary directory: %s\\\", self.temporary_directory)\\n shutil.rmtree(self.temporary_directory)\\n del self.temporary_directory\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1596,"cells":{"query_id":{"kind":"string","value":"70b836134d58047b1b9f8f729a58ab4a"},"query":{"kind":"string","value":"See examples/listusers for example usage."},"positive_passages":{"kind":"list like","value":[{"docid":"98fb1c0d9c35be01ffde49e865713388","score":"0.0","text":"def get_users(self, request: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:\n return self.call_endpoint(\n url=\"users\",\n method=\"GET\",\n request=request,\n )","title":""}],"string":"[\n {\n \"docid\": \"98fb1c0d9c35be01ffde49e865713388\",\n \"score\": \"0.0\",\n \"text\": \"def get_users(self, request: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:\\n return self.call_endpoint(\\n url=\\\"users\\\",\\n method=\\\"GET\\\",\\n request=request,\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"628df88ba64f2c1e56b7ec98c4ae02e3","score":"0.8584034","text":"def get_users():\n api.list_users()","title":""},{"docid":"810984be17a4b999aff0f666e87351b7","score":"0.8257032","text":"def list_users():\n users = User.query.all()\n\n return render_template('list-users.html', users=users)","title":""},{"docid":"f880018108f8be5943b8424dcabb91b1","score":"0.81302005","text":"def get_users():","title":""},{"docid":"62ac668fee7b9be8e7520de9b950b32f","score":"0.80949366","text":"def list_users():\n users = User.query.all()\n return render_template('users.html',users=users)","title":""},{"docid":"05c8256aa16ca1bdf7eb08026ccfea4d","score":"0.8080177","text":"def list_users():\n\n users = User.query.all()\n return render_template(\"list.html\", users=users)","title":""},{"docid":"464814c1223defdd1f13e10870ce27cf","score":"0.80530787","text":"def user_list():\n\n\tusers = User.query.all()\n\treturn render_template(\"user_list.html\", users=users)","title":""},{"docid":"c6da3ca238cc1a6b433b0adae334c388","score":"0.8010558","text":"def list_users():\n users = User.query.order_by(User.last_name).all()\n return render_template('base.html', users=users)","title":""},{"docid":"205e302f72777db49edba4e8793abb4e","score":"0.79931337","text":"def list_users(self):\n self.__cursor.execute(''' SELECT * FROM people ORDER by uid ''')\n print(\"Printing all users\")\n for i in self.__cursor.fetchall():\n print(i)","title":""},{"docid":"32867a3d7cd70e82bce89395e889c9de","score":"0.7954761","text":"def getUsers():","title":""},{"docid":"51ef2a3fae310cce2c72f5a76b0cd5f0","score":"0.7953423","text":"def list(self):\n return self._list(\"/users\")","title":""},{"docid":"5a865fd19de528a3fba6efa1cd3815de","score":"0.7902285","text":"def cmd_list(self):\n\n db_dir = prop.get_prop('Local', 'db-dir')\n local_users_db_name = \"%s/%s/%s\" % (prop.TOP, db_dir, USERS_DB_NAME)\n\n users_db = UserDb.UserDb(local_users_db_name, safe=True)\n\n print \"User Id | Hex Id | User Name\"\n\n for user in users_db.getUsers():\n\n user_id = int(user['id'])\n user_name = user['name']\n print \"%7d | %6s |%15s\" % (user_id, hex(user_id), user_name)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"0f960ebfa2d56fe1e2da6558b9f42d23","score":"0.78816026","text":"def user_list():\n\n users = User.query.all()\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"078fb3f0b08fb4c0b7d1a0377543ed36","score":"0.7844623","text":"def fetch_users(db: Dict[str, Dict[User, Talks]], args: Namespace) -> None:\n\n if args.list:\n # find users / find users -ls / find users --list\n print('Available users are:')\n\n # Print usernames in columns\n for chunk in split_list(list(db['talks'].keys()), 3):\n for user in chunk:\n print(f\" # {user:20}\", end=\"\")\n print()","title":""},{"docid":"322210ab694f61eaf8fbaf7846145363","score":"0.78255814","text":"def list_users():\n users = User.query.order_by(User.first_name, User.last_name).all()\n return render_template(\"/user_templates/home.html\", users=users)","title":""},{"docid":"369e5e3a27fd0c1d3b6499b69d1d34ae","score":"0.78175634","text":"def list_users():\n users = User.query.all()\n return render_template('index.html', users=users)","title":""},{"docid":"742cbc0f3084d905854b8916a6cb497c","score":"0.78159624","text":"def list_users():\n received = request.get_json()\n if received:\n page = received.get('page', 1)\n else:\n page = 1\n\n # Query matches\n per_page = current_app.config['USERS_PER_PAGE']\n\n users = (\n User\n .query\n .filter(\n User.is_deleted == False,\n )\n .order_by(User.username.asc())\n .paginate(page, per_page, error_out=False)\n )\n\n response = [u.username for u in users.items]\n\n return api_success(**util.paginated(page, users.pages, response)), 200","title":""},{"docid":"f1502594b5b207fb692528c772bd6f4d","score":"0.78036636","text":"def test_list_users(self):\n pass","title":""},{"docid":"493c951eb47fcbfd737e20ba0e9a4ce6","score":"0.779449","text":"def get_all_users():\n pass","title":""},{"docid":"41f4351ee2781d30cc3af625bff42d67","score":"0.77541614","text":"def get_users():\n return []","title":""},{"docid":"5a614eb71f267ccf7365719e83faff56","score":"0.77005094","text":"def list_users():\r\n\r\n users = User.query.order_by(User.last_name, User.first_name).all()\r\n\r\n return render_template(\"index.html\", users=users)","title":""},{"docid":"9a0cd9e95054bc1d60f4943f0a87b5cc","score":"0.76975864","text":"def list_users():\n def _json_processor(output):\n return [ll['user'] for ll in output]\n\n def _raw_processor(output):\n lines = output.split('\\n')[1:]\n return [line.split('\\t')[0] for line in lines]\n\n return query_rabbit(\n ['list_users'],\n raw_processor=_raw_processor,\n json_processor=_json_processor)","title":""},{"docid":"677a3a811b34b59f7c5467efdc3c1e66","score":"0.76902163","text":"def user_list():\n\n users = User.query.order_by(User.email).all()\n\n return render_template('user_view.html', jinja_user=users)","title":""},{"docid":"1c5e7a913581e00eb3c14e0af04a748d","score":"0.7650209","text":"def gather_all_users(self):\n data = self.gsi.return_list_of_users()\n if type(data) == list:\n for i in data:\n self.user_list.append(i)\n else:\n print \"[!] Error user list in wrong format\"","title":""},{"docid":"8d30353ad7201cc2225f46c08a4ef8f9","score":"0.76000553","text":"def show_users():\n users = User.query.all()\n\n return render_template(\"user_list.html\", users=users)","title":""},{"docid":"ea468450447aa41f5a37d331a7d77b34","score":"0.7583099","text":"def list_users(self):\n list_func = getattr(self.storage, 'list_users')\n return list_func()","title":""},{"docid":"cb919f2941242e9e2aa0040faf080972","score":"0.756967","text":"def list_users(self):\n\n resp = self._http_request(\n method='GET',\n url_suffix='/auth/users',\n auth=self.auth\n )\n\n self._check_for_error(resp)\n\n return resp","title":""},{"docid":"738f506832688514cd871bcb89734ec9","score":"0.75627697","text":"def user_list() -> list:\r\n\r\n request_info = requests.get(\r\n \"https://cypher-fitness-app.herokuapp.com/api/user_list/\"\r\n )\r\n\r\n return request_info.json()","title":""},{"docid":"7580cb2f594429b2a9f40e20e522c995","score":"0.7552619","text":"def test_list_users_endpoint(self):\n user_object = Users(api_version_users)\n\n assert user_object.list_users(session=self.test_session,\n baseurl=self.baseurl,\n limit=100), 'List users did not return correct response code'","title":""},{"docid":"aa49668771bf84c4e01d09be8c61bd48","score":"0.75389916","text":"def print_users(users_list, users):\r\n print(users_list, users)","title":""},{"docid":"618f4e8ba341917bae00ae04b1e91696","score":"0.74988544","text":"def usersList(self):\n req_url = self.baseurl + \"/users\"\n response_list = self.session.get(req_url)\n\n if response_list.status_code == 200:\n return response_list.json()\n elif response_list.status_code == 500:\n raise GogsServerErrorException()","title":""},{"docid":"007516a6772584559d891072339dea8a","score":"0.74795824","text":"def retrieve_users(request):\n res = []\n for user_id in server.list_user_ids():\n res.append(server.check_user_details(user_id))\n\n return res","title":""},{"docid":"6fa2f14524938ff2376bac767a601b1c","score":"0.7465165","text":"def list_users_command(client):\n\n data = client.list_users()\n users = []\n\n for user in data.get('result'):\n my_user = {\n 'Name': user.get('name'),\n 'Email': user.get('email'),\n 'Admin': user.get('admin'),\n 'Iden': user.get('iden'),\n 'Rules': user_rules_helper(user.get('rules')),\n 'Roles': user_roles_helper(client, user.get('roles'))\n }\n users.append(my_user)\n\n name = 'Synapse Users'\n headers = ['Name', 'Email', 'Admin', 'Rules', 'Roles']\n readable_output = tableToMarkdown(name, users, headers=headers, removeNull=False)\n\n results = CommandResults(\n outputs_prefix='Synapse.Users',\n outputs_key_field='Iden',\n outputs=users,\n readable_output=readable_output,\n raw_response=data\n )\n\n return results","title":""},{"docid":"fd6c1a5ab0aea863270c79186c0f8492","score":"0.74631035","text":"def get_users():\n users = User.query.all()\n \n user_output = [] \n for user in users:\n user_output.append(user.username) \n return make_response({'users' : user_output}, 200)","title":""},{"docid":"07fade16f14e9091bf21e078150acf03","score":"0.7462443","text":"def get_users(session):\n resp = session.get('/openapi/v1/users')\n user_list = []\n\n for entry in resp.json():\n user_list.append((entry['id'], entry['first_name'], entry['last_name'], entry['email']))\n\n return user_list","title":""},{"docid":"083762122fdcce517f44f34df07b7023","score":"0.7454718","text":"async def list_users(engine, offset=0, limit=5):\n return []","title":""},{"docid":"bf30e8b036121567f5c3547d286c7493","score":"0.74346817","text":"def list_users(db_conn, params):\n\n query = \"\"\"\n SELECT *\n FROM users\n ORDER BY created DESC;\n /* TODO OFFSET LIMIT */\n \"\"\"\n params = {}\n return list_rows(db_conn, query, params)","title":""},{"docid":"3d288e7aa5ab05270c8714a51deb26a3","score":"0.7428012","text":"def display_users(cls):\n return cls.userslist","title":""},{"docid":"7fe825c60f37fb5d69ea2b8406ccc602","score":"0.7416266","text":"def test_list_users(client, users, admin_headers):\n resp = client.get('/dashboard/users/', headers=admin_headers)\n assert resp.status_code == 200\n data = resp.get_data(as_text=True)\n\n for u in users:\n assert u.username in data\n assert u.email in data","title":""},{"docid":"6bff828b5ed9b18a566eb1a0f1c9cd0b","score":"0.74108696","text":"def user_list_page():\n\n users = User.query.all()\n return render_template(\"user-list.html\", users=users)","title":""},{"docid":"f79ec2d1b3b9a5bde4b74dde78befe1e","score":"0.7402986","text":"def all_users_list(self) -> List[Union[User, None]]:\n users = User.load_all(self.cursor)\n for user in users:\n print(\"User ID: {user_id}\\nUsername: {username}\\nE-mail: {email}\\n\".format(user_id=user.id, username=user.username, email=user.email))\n #raise NotImplementedError","title":""},{"docid":"8e1eef0eca3b6e93ccde79d149a4219f","score":"0.7402794","text":"def ListUsers(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""},{"docid":"6aea72923b28f1f4785e25dff241f990","score":"0.7398049","text":"def test_users_list(self):\n pass","title":""},{"docid":"8cf19c8966a2212b15004c126e29092c","score":"0.73961437","text":"def users(ctx):\n client = boto3.client('iam')\n marker = None\n while True:\n if marker:\n res = client.list_users(Marker=marker)\n else:\n res = client.list_users()\n print(\"user\\ttibanna_usergroup\")\n for r in res['Users']:\n res_groups = client.list_groups_for_user(\n UserName=r['UserName']\n )\n groups = [rg['GroupName'] for rg in res_groups['Groups']]\n groups = filter(lambda x: 'tibanna_' in x, groups)\n groups = [x.replace('tibanna_', '') for x in groups]\n print(\"%s\\t%s\" % (r['UserName'], ','.join(groups)))\n marker = res.get('Marker', '')\n if not marker:\n break","title":""},{"docid":"187af2fa77d146130d42948fe15bf83f","score":"0.73927766","text":"def test_listusers_get(self):\n response = self.client.open('/v1/listusers',\n method='GET')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","title":""},{"docid":"832ba7bf117f7542b80a3d828101c1cc","score":"0.7384407","text":"def listUsers( self, sock, words ):\r\n \r\n users = self.room.getAcl().listUsers()\r\n\r\n users.sort()\r\n\r\n msg = \"Users: \" + string.join( users, \", \" )\r\n self.sendMessage( sock, msg )","title":""},{"docid":"6f9b7af1ee913100d84ee2511e6866e7","score":"0.7362483","text":"def ViewUserLists(self, buffer = None, index = None):\r\n\r\n who = buffer.get_all_screen_names(index)\r\n dlg = gui.UserListDialog(parent = self.session.frame, title = _(\"Select user\"), users = who)\r\n dlg.setup_users()\r\n dlg.finish_setup()\r\n if dlg.ShowModal() != wx.ID_OK:\r\n return output.speak(_(\"Canceled.\"), True)\r\n user = dlg.users.GetValue()\r\n dlg.Destroy()\r\n call_threaded(self.session.list_manager(screen_name = user))","title":""},{"docid":"64541b785fb2f95163aac06a7169453d","score":"0.73588324","text":"def getUsersList():\n\tuser_list = db_fUser.get_all_registers(sort=True, value='username')\n\tusername_list = []\n\t\n\tfor user in user_list:\n\t\tusername_list.append(str(db_fUser.get_username(user)))\n\t\n\treturn username_list","title":""},{"docid":"cf881ec6f2c8d63495fd4aeff3d6728e","score":"0.73427016","text":"def load_user_list():\n users=User.query.all()\n return render_template(\"user_list.html\", users = users)","title":""},{"docid":"f9bfcf360b3bacdbdb20a990f49897a8","score":"0.7340618","text":"def getUserList(self) -> List[ghidra.framework.remote.User]:\n ...","title":""},{"docid":"1006241907afff1a8e640a24ad1898a0","score":"0.7295648","text":"def get_users():\n response = \"\"\n users_list = app.config['db'].get_users()\n for user in users_list:\n response += UserTemplate(user[\"value\"])\n return response","title":""},{"docid":"301e8c2f37ae4877490fab2b923b968d","score":"0.72939193","text":"def list_users(state, org_uid, role_name, active, inactive, format):\n if inactive:\n active = False\n role_id = _get_role_id(state.sdk, role_name) if role_name else None\n columns = (\n [\"userUid\", \"status\", \"username\", \"orgUid\"]\n if format == OutputFormat.TABLE\n else None\n )\n df = _get_users_dataframe(state.sdk, columns, org_uid, role_id, active)\n if df.empty:\n click.echo(\"No results found.\")\n else:\n formatter = DataFrameOutputFormatter(format)\n formatter.echo_formatted_dataframe(df)","title":""},{"docid":"4d7a7671579fe7a3a013d0da024c6323","score":"0.7282874","text":"def list_all_users(self):\n qry = \"select * from \" + self.userTable\n self.cursor.execute(qry)\n results = self.cursor.fetchall()\n\n if results:\n print(\"Identifier, User-Name, Password\")\n for row in results:\n print('{}, {}, {}'.format(row[\"identifier\"], row[\"username\"], row[\"token\"]))","title":""},{"docid":"bb7f50930c78bf4534df8f29de75e9d2","score":"0.72816885","text":"def display_users(cls):\n return cls.user_list","title":""},{"docid":"bb7f50930c78bf4534df8f29de75e9d2","score":"0.72816885","text":"def display_users(cls):\n return cls.user_list","title":""},{"docid":"0c902fdb34362c9ba81de51f4d03c1e2","score":"0.7280245","text":"def test_api_users_list_users(flaskclient, auth_headers):\n\n url = url_for(\"user.Users\")\n rep = flaskclient.get(url, headers=auth_headers)\n assert rep.status_code == 200","title":""},{"docid":"5435477043b55c9b348dab1aae4564d5","score":"0.7278144","text":"def get_users():\n check_admin()\n users = User.query.all()\n\n return render_template('admin/users/users.html', users=users, title=\"All Users\")","title":""},{"docid":"c053f86bd562f08b2f7e91c4c6fbaa4d","score":"0.7265345","text":"def list_users(self, conn, limit=20, offset=0, sort=\"+timestamp\", email=None):\n path = urls.USERS[\"LIST\"]\n params = {\n \"limit\": limit,\n \"offset\": offset,\n \"sort\": sort\n }\n if email:\n params[\"email\"] = email\n\n resp = conn.command(apiMethod=\"GET\", apiPath=path, apiParams=params)\n return resp","title":""},{"docid":"1f603477f9903770b1d77850b797e852","score":"0.72638667","text":"def list_users(current_user, roles):\n users = User.query.all()\n\n resp = {\n \"data\": [\n\n ]\n }\n for user in users:\n resp['data'].append({'id': user.id, 'name': user.username})\n\n return jsonify(resp)","title":""},{"docid":"36cad7346e902f79cb8c3dda9b68175f","score":"0.72597456","text":"async def user_list(_ctx):\n\n # return {'users': list(user.username for user in online_users.values() if user is not ctx.ws.user)}\n return {'users': list(user.username for user in online_users.values())}","title":""},{"docid":"1565d182b464110544dca211fe8ea391","score":"0.7251618","text":"def get_users(self):\n data = self._api_call('homes/%i/users' % self.id)\n return data","title":""},{"docid":"9f1f809c62188af7d3e755e4d991bc76","score":"0.7244264","text":"def list_users(self):\n\n response = self.slack.users.list()\n users = [username['name'] for username in response.body['members']]\n return users","title":""},{"docid":"be8cf7ca8a57067685e5493467f5f955","score":"0.7203822","text":"def achievements_user_list(context, users, skip_group=False):\n request = context['request']\n\n response_format = 'html'\n if 'response_format' in context:\n response_format = context['response_format']\n\n return Markup(render_to_string('achievements/tags/user_list',\n {'users': users, 'skip_group': skip_group},\n context_instance=RequestContext(request),\n response_format=response_format))","title":""},{"docid":"31bf19418398c06919adc00390ea8f6e","score":"0.71923274","text":"def BuildUserList(requested_users):\n if len(requested_users) == 1 and requested_users[0] == 'all':\n return USERNAMES + ['root']\n print requested_users\n return requested_users","title":""},{"docid":"9b3ba55b9a2c75ef9b86fe3f8cba68b1","score":"0.71768975","text":"def user_listing():\n username = session[\"username\"]\n user = mongo.db.user.find_one({\"username\": username})\n return render_template(\n \"userlisting.html\", property=mongo.db.property.find({\"user\": user[\"_id\"]})\n )","title":""},{"docid":"efba2d2b665b874d0347d43d2231753f","score":"0.7165167","text":"def get_users():\n\n url = env.recommendations_api + '/users'\n \n resp = requests.get(url,\n timeout=env.timeout,\n headers=env.header)\n\n env.set_return_header(resp.headers)\n\n if resp.status_code != 200:\n LOG.debug(\"Request returned with \" + (str(resp.status_code)))\n error = resp.text\n return False, error\n\n users = json.loads(resp.text)\n\n return True, users","title":""},{"docid":"78def17220e51bf2e72a99b9e3e23565","score":"0.7163498","text":"def get_users():\n response = []\n try:\n query_to_search_users = \"SELECT * FROM users\"\n connection.cursor.execute(query_to_search_users)\n rows = connection.cursor.fetchall()\n if not rows:\n return \"No Users\"\n for eachrow in rows:\n response.append({\n 'id':eachrow[0],\n 'name':eachrow[1], \n 'email':eachrow[2],\n 'role' :eachrow[4]\n })\n return response\n except Exception as exc:\n print(exc)","title":""},{"docid":"07fc4a005e57b6608d729a47fbfb01b5","score":"0.7158014","text":"def show_all_users():\n\n users = User.query.all()\n\n return render_template('/users.html',\n users=users)","title":""},{"docid":"c778660076fef01ac23c287dad313f25","score":"0.71517056","text":"def cli(ctx, omit_empty_organisms=False):\n return ctx.gi.users.get_users(omit_empty_organisms=omit_empty_organisms)","title":""},{"docid":"51c7d9ecc6032768aefeafeb850d1534","score":"0.71491456","text":"def all_users():\n return 'allUsers'","title":""},{"docid":"5704a562bd6381479b16c3efd8c31e83","score":"0.71486807","text":"def all_users():\n r = service.users().list(domain= SERVICE_DOMAIN).execute()\n users = []\n for user in r[\"users\"]:\n users.append(user[\"primaryEmail\"])\n return users","title":""},{"docid":"1788821e27f6465659661b5f46db9ebc","score":"0.7145525","text":"def test_user_list(self):\n utils.login(self)\n response = self.client.get(reverse('spirit:admin:user:index'))\n self.assertEqual(list(response.context['users']), [self.user, ])","title":""},{"docid":"f62f6893995296def8e85ce684032dd3","score":"0.7142263","text":"def getListUser(self , **kargs):","title":""},{"docid":"88d73efae3c0d909f28b96f657261118","score":"0.71260136","text":"def list_users(self):\n resp, resp_body = self.method_get(\"users\", admin=True)\n if resp.status_code in (401, 403, 404):\n raise exc.AuthorizationFailure(\"You are not authorized to list \"\n \"users.\")\n # The API is inconsistent; if only one user exists, it will not return\n # a list.\n if \"users\" in resp_body:\n users = resp_body[\"users\"]\n else:\n users = [resp_body]\n # The returned values may contain password data. Strip that out.\n for user in users:\n bad_keys = [key for key in user.keys()\n if \"password\" in key.lower()]\n for bad_key in bad_keys:\n user.pop(bad_key)\n return [User(self, user) for user in users]","title":""},{"docid":"4cd0ec31008801fa5128056507f3e09a","score":"0.71128654","text":"def get_users(self):\n req_obj = self.rest_get(\"/users\")\n if req_obj.success:\n return req_obj.data","title":""},{"docid":"ce523c04e8f4a930470a8d3327af040e","score":"0.7108909","text":"def list_users(self):\n raise StoreMethodNotImplemented","title":""},{"docid":"3b03209415414ba7ed7979b7f74975fa","score":"0.70905995","text":"def showUsers(conn):\n cursor = conn.cursor()\n os.system(\"cls\")\n cursor.execute(\"SELECT user_id,username FROM users;\")\n users = cursor.fetchall()\n print(\"Here are all the currently registered users: \")\n for user in users:\n user_id = user[0]\n name = user[1]\n\n print(f\"{user_id}. {name}\")\n \n os.system(\"pause\")\n menu(conn)","title":""},{"docid":"34394cee3fb56bd568c23f23a52aed25","score":"0.70898676","text":"def test_list_users(self, authenticated_client):\n r = authenticated_client.api.list_users()\n assert \"admin\" in r[\"data\"]","title":""},{"docid":"69a985e619c460ff282923db214acfce","score":"0.70889974","text":"def user_list():\n if request.method == \"POST\":\n data = request.get_json()\n result = userValidator.validator.validate(data)\n\n if not result:\n return userValidator.validator.errors, status.HTTP_400_BAD_REQUEST\n if \"id\" in data:\n user = userCollection.getUserById(data[\"id\"])\n if user is not None:\n return \"User Exist with same Id\", status.HTTP_400_BAD_REQUEST\n success = userCollection.createUser(data)\n if success:\n return \"User Created\", status.HTTP_201_CREATED\n return \"Error in Creating User\", status.HTTP_500_INTERNAL_SERVER_ERROR\n else:\n page = request.args.get(\"page\", 1, int)\n limit = request.args.get(\"limit\", 5, int)\n name = request.args.get(\"name\", \"\", str)\n sort = request.args.get(\"sort\", \"\", str)\n return getUsers(page, limit, name, sort), status.HTTP_200_OK","title":""},{"docid":"15bc784d3aeebd44800af289bec31526","score":"0.70862436","text":"def get_all():\n\treturn users.all()","title":""},{"docid":"3aba21298a25cccedaa54685edd4174e","score":"0.70735425","text":"def get_users():\n\n with CON:\n CUR.execute(\"SELECT * FROM users\")\n return CUR.fetchall()","title":""},{"docid":"58b9556d0d8b9469b552d2fc94b554b6","score":"0.706808","text":"def test_get(self):\n rsp = self.apiGet(get_user_list_url(),\n expected_mimetype=user_list_mimetype)\n self.assertEqual(rsp['stat'], 'ok')\n self.assertEqual(len(rsp['users']), User.objects.count())","title":""},{"docid":"40ae5e33491e91ad28cd2cb8c2eda138","score":"0.7067798","text":"def list_all_users():\n all_users = storage.all(\"User\")\n users = [u.to_dict() for u in all_users.values()]\n return jsonify(users)","title":""},{"docid":"245e000903dda52f0311b3df3af588fa","score":"0.70663947","text":"def bf_get_users(selected_bfaccount):\n try:\n # def get_users_list():\n bf = Pennsieve(selected_bfaccount)\n # organization_name = bf.context.name\n organization_id = bf.context.id\n list_users = bf._api._get('/organizations/' + str(organization_id) + '/members')\n list_users_first_last = []\n for i in range(len(list_users)):\n #first_last = list_users[i]['firstName'] + ' ' + list_users[i]['lastName']\n first_last = list_users[i]['firstName'].capitalize() + \" \" + list_users[i]['lastName'].capitalize() + \" (\" + list_users[i]['email'] + \") !|**|!\" + list_users[i]['id']\n list_users_first_last.append(first_last)\n list_users_first_last.sort() # Returning the list of users in alphabetical order\n return list_users_first_last\n # list_users_first_last = gevent.spawn(get_users_list())\n # gevent.sleep(0)\n # return list_users_first_last\n except Exception as e:\n raise e","title":""},{"docid":"7a54082f28d9bf399aa145cd502513b0","score":"0.7065335","text":"def list_all_users(db: Session = Depends(get_db)):\n return db.query(models.User).all()","title":""},{"docid":"8a82e404522b80797db5da0174386391","score":"0.70538914","text":"def getUsers(self):\n return [user[0] for user in self.db.execute(\"select user from users\")]","title":""},{"docid":"2123ae20f4760b10e88137f38f01f6a5","score":"0.70524645","text":"def users(request):\n session = DBSESSION()\n users = session.query(User).all()\n return dict(title='UserList', users=users)","title":""},{"docid":"4bc5457da80d7df8887140b93b75add6","score":"0.70440155","text":"def list(config):\n _exit_if_user_is_logged_out(config.token)\n\n _list_resource('{}/api/v1/users/'.format(SHAREDCLOUD_CLI_URL),\n config.token,\n ['UUID', 'EMAIL', 'USERNAME', 'BALANCE', 'DATE_JOINED', 'LAST_LOGIN'],\n ['uuid', 'email', 'username', 'balance', 'date_joined', 'last_login'],\n mappers={\n 'balance': _map_non_formatted_money_to_version_with_currency,\n 'date_joined': _map_datetime_obj_to_human_representation,\n 'last_login': _map_datetime_obj_to_human_representation\n })","title":""},{"docid":"a2b371fc8056a68ac18dfd3152c7820b","score":"0.704112","text":"def getUserNames():","title":""},{"docid":"03b6120b6ef439643796241188ed5f99","score":"0.70355904","text":"def view_users():\n return render_template(\"view_users.html\", data=User.query.order_by(User.username))","title":""},{"docid":"49a0858d3cf5ebf39fa44d6eb820d219","score":"0.70348805","text":"def list(request):\n\n users = NormalUser.objects.all()\n return render(request, 'list.html', {'users':users})","title":""},{"docid":"623e6f36366933e64dd87e186935c425","score":"0.7034246","text":"def users():\n\n\t# Check if this is the master administrator\n\tif g.user.user_name != gen_app.config['ADMIN_USER']:\n\t\tflash(\"You should not be here\", \"danger\")\n\t\treturn redirect(url_for('index'))\n\n\n\tusrs = models.User.query.all()\n\n\n\treturn render_template('users.html', title=\"Users\", rows=usrs)","title":""},{"docid":"b64322dbc7ea33be24ddedbda4f0b46c","score":"0.7031841","text":"def loading_users():\n users = get_data(config.api_endpoints[\"users\"])\n return users","title":""}],"string":"[\n {\n \"docid\": \"628df88ba64f2c1e56b7ec98c4ae02e3\",\n \"score\": \"0.8584034\",\n \"text\": \"def get_users():\\n api.list_users()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810984be17a4b999aff0f666e87351b7\",\n \"score\": \"0.8257032\",\n \"text\": \"def list_users():\\n users = User.query.all()\\n\\n return render_template('list-users.html', users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f880018108f8be5943b8424dcabb91b1\",\n \"score\": \"0.81302005\",\n \"text\": \"def get_users():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ac668fee7b9be8e7520de9b950b32f\",\n \"score\": \"0.80949366\",\n \"text\": \"def list_users():\\n users = User.query.all()\\n return render_template('users.html',users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c8256aa16ca1bdf7eb08026ccfea4d\",\n \"score\": \"0.8080177\",\n \"text\": \"def list_users():\\n\\n users = User.query.all()\\n return render_template(\\\"list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464814c1223defdd1f13e10870ce27cf\",\n \"score\": \"0.80530787\",\n \"text\": \"def user_list():\\n\\n\\tusers = User.query.all()\\n\\treturn render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6da3ca238cc1a6b433b0adae334c388\",\n \"score\": \"0.8010558\",\n \"text\": \"def list_users():\\n users = User.query.order_by(User.last_name).all()\\n return render_template('base.html', users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"205e302f72777db49edba4e8793abb4e\",\n \"score\": \"0.79931337\",\n \"text\": \"def list_users(self):\\n self.__cursor.execute(''' SELECT * FROM people ORDER by uid ''')\\n print(\\\"Printing all users\\\")\\n for i in self.__cursor.fetchall():\\n print(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32867a3d7cd70e82bce89395e889c9de\",\n \"score\": \"0.7954761\",\n \"text\": \"def getUsers():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51ef2a3fae310cce2c72f5a76b0cd5f0\",\n \"score\": \"0.7953423\",\n \"text\": \"def list(self):\\n return self._list(\\\"/users\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a865fd19de528a3fba6efa1cd3815de\",\n \"score\": \"0.7902285\",\n \"text\": \"def cmd_list(self):\\n\\n db_dir = prop.get_prop('Local', 'db-dir')\\n local_users_db_name = \\\"%s/%s/%s\\\" % (prop.TOP, db_dir, USERS_DB_NAME)\\n\\n users_db = UserDb.UserDb(local_users_db_name, safe=True)\\n\\n print \\\"User Id | Hex Id | User Name\\\"\\n\\n for user in users_db.getUsers():\\n\\n user_id = int(user['id'])\\n user_name = user['name']\\n print \\\"%7d | %6s |%15s\\\" % (user_id, hex(user_id), user_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f960ebfa2d56fe1e2da6558b9f42d23\",\n \"score\": \"0.78816026\",\n \"text\": \"def user_list():\\n\\n users = User.query.all()\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078fb3f0b08fb4c0b7d1a0377543ed36\",\n \"score\": \"0.7844623\",\n \"text\": \"def fetch_users(db: Dict[str, Dict[User, Talks]], args: Namespace) -> None:\\n\\n if args.list:\\n # find users / find users -ls / find users --list\\n print('Available users are:')\\n\\n # Print usernames in columns\\n for chunk in split_list(list(db['talks'].keys()), 3):\\n for user in chunk:\\n print(f\\\" # {user:20}\\\", end=\\\"\\\")\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322210ab694f61eaf8fbaf7846145363\",\n \"score\": \"0.78255814\",\n \"text\": \"def list_users():\\n users = User.query.order_by(User.first_name, User.last_name).all()\\n return render_template(\\\"/user_templates/home.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369e5e3a27fd0c1d3b6499b69d1d34ae\",\n \"score\": \"0.78175634\",\n \"text\": \"def list_users():\\n users = User.query.all()\\n return render_template('index.html', users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"742cbc0f3084d905854b8916a6cb497c\",\n \"score\": \"0.78159624\",\n \"text\": \"def list_users():\\n received = request.get_json()\\n if received:\\n page = received.get('page', 1)\\n else:\\n page = 1\\n\\n # Query matches\\n per_page = current_app.config['USERS_PER_PAGE']\\n\\n users = (\\n User\\n .query\\n .filter(\\n User.is_deleted == False,\\n )\\n .order_by(User.username.asc())\\n .paginate(page, per_page, error_out=False)\\n )\\n\\n response = [u.username for u in users.items]\\n\\n return api_success(**util.paginated(page, users.pages, response)), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1502594b5b207fb692528c772bd6f4d\",\n \"score\": \"0.78036636\",\n \"text\": \"def test_list_users(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"493c951eb47fcbfd737e20ba0e9a4ce6\",\n \"score\": \"0.779449\",\n \"text\": \"def get_all_users():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f4351ee2781d30cc3af625bff42d67\",\n \"score\": \"0.77541614\",\n \"text\": \"def get_users():\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a614eb71f267ccf7365719e83faff56\",\n \"score\": \"0.77005094\",\n \"text\": \"def list_users():\\r\\n\\r\\n users = User.query.order_by(User.last_name, User.first_name).all()\\r\\n\\r\\n return render_template(\\\"index.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a0cd9e95054bc1d60f4943f0a87b5cc\",\n \"score\": \"0.76975864\",\n \"text\": \"def list_users():\\n def _json_processor(output):\\n return [ll['user'] for ll in output]\\n\\n def _raw_processor(output):\\n lines = output.split('\\\\n')[1:]\\n return [line.split('\\\\t')[0] for line in lines]\\n\\n return query_rabbit(\\n ['list_users'],\\n raw_processor=_raw_processor,\\n json_processor=_json_processor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"677a3a811b34b59f7c5467efdc3c1e66\",\n \"score\": \"0.76902163\",\n \"text\": \"def user_list():\\n\\n users = User.query.order_by(User.email).all()\\n\\n return render_template('user_view.html', jinja_user=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c5e7a913581e00eb3c14e0af04a748d\",\n \"score\": \"0.7650209\",\n \"text\": \"def gather_all_users(self):\\n data = self.gsi.return_list_of_users()\\n if type(data) == list:\\n for i in data:\\n self.user_list.append(i)\\n else:\\n print \\\"[!] Error user list in wrong format\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d30353ad7201cc2225f46c08a4ef8f9\",\n \"score\": \"0.76000553\",\n \"text\": \"def show_users():\\n users = User.query.all()\\n\\n return render_template(\\\"user_list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea468450447aa41f5a37d331a7d77b34\",\n \"score\": \"0.7583099\",\n \"text\": \"def list_users(self):\\n list_func = getattr(self.storage, 'list_users')\\n return list_func()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb919f2941242e9e2aa0040faf080972\",\n \"score\": \"0.756967\",\n \"text\": \"def list_users(self):\\n\\n resp = self._http_request(\\n method='GET',\\n url_suffix='/auth/users',\\n auth=self.auth\\n )\\n\\n self._check_for_error(resp)\\n\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"738f506832688514cd871bcb89734ec9\",\n \"score\": \"0.75627697\",\n \"text\": \"def user_list() -> list:\\r\\n\\r\\n request_info = requests.get(\\r\\n \\\"https://cypher-fitness-app.herokuapp.com/api/user_list/\\\"\\r\\n )\\r\\n\\r\\n return request_info.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7580cb2f594429b2a9f40e20e522c995\",\n \"score\": \"0.7552619\",\n \"text\": \"def test_list_users_endpoint(self):\\n user_object = Users(api_version_users)\\n\\n assert user_object.list_users(session=self.test_session,\\n baseurl=self.baseurl,\\n limit=100), 'List users did not return correct response code'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa49668771bf84c4e01d09be8c61bd48\",\n \"score\": \"0.75389916\",\n \"text\": \"def print_users(users_list, users):\\r\\n print(users_list, users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"618f4e8ba341917bae00ae04b1e91696\",\n \"score\": \"0.74988544\",\n \"text\": \"def usersList(self):\\n req_url = self.baseurl + \\\"/users\\\"\\n response_list = self.session.get(req_url)\\n\\n if response_list.status_code == 200:\\n return response_list.json()\\n elif response_list.status_code == 500:\\n raise GogsServerErrorException()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"007516a6772584559d891072339dea8a\",\n \"score\": \"0.74795824\",\n \"text\": \"def retrieve_users(request):\\n res = []\\n for user_id in server.list_user_ids():\\n res.append(server.check_user_details(user_id))\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa2f14524938ff2376bac767a601b1c\",\n \"score\": \"0.7465165\",\n \"text\": \"def list_users_command(client):\\n\\n data = client.list_users()\\n users = []\\n\\n for user in data.get('result'):\\n my_user = {\\n 'Name': user.get('name'),\\n 'Email': user.get('email'),\\n 'Admin': user.get('admin'),\\n 'Iden': user.get('iden'),\\n 'Rules': user_rules_helper(user.get('rules')),\\n 'Roles': user_roles_helper(client, user.get('roles'))\\n }\\n users.append(my_user)\\n\\n name = 'Synapse Users'\\n headers = ['Name', 'Email', 'Admin', 'Rules', 'Roles']\\n readable_output = tableToMarkdown(name, users, headers=headers, removeNull=False)\\n\\n results = CommandResults(\\n outputs_prefix='Synapse.Users',\\n outputs_key_field='Iden',\\n outputs=users,\\n readable_output=readable_output,\\n raw_response=data\\n )\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6c1a5ab0aea863270c79186c0f8492\",\n \"score\": \"0.74631035\",\n \"text\": \"def get_users():\\n users = User.query.all()\\n \\n user_output = [] \\n for user in users:\\n user_output.append(user.username) \\n return make_response({'users' : user_output}, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fade16f14e9091bf21e078150acf03\",\n \"score\": \"0.7462443\",\n \"text\": \"def get_users(session):\\n resp = session.get('/openapi/v1/users')\\n user_list = []\\n\\n for entry in resp.json():\\n user_list.append((entry['id'], entry['first_name'], entry['last_name'], entry['email']))\\n\\n return user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083762122fdcce517f44f34df07b7023\",\n \"score\": \"0.7454718\",\n \"text\": \"async def list_users(engine, offset=0, limit=5):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf30e8b036121567f5c3547d286c7493\",\n \"score\": \"0.74346817\",\n \"text\": \"def list_users(db_conn, params):\\n\\n query = \\\"\\\"\\\"\\n SELECT *\\n FROM users\\n ORDER BY created DESC;\\n /* TODO OFFSET LIMIT */\\n \\\"\\\"\\\"\\n params = {}\\n return list_rows(db_conn, query, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d288e7aa5ab05270c8714a51deb26a3\",\n \"score\": \"0.7428012\",\n \"text\": \"def display_users(cls):\\n return cls.userslist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe825c60f37fb5d69ea2b8406ccc602\",\n \"score\": \"0.7416266\",\n \"text\": \"def test_list_users(client, users, admin_headers):\\n resp = client.get('/dashboard/users/', headers=admin_headers)\\n assert resp.status_code == 200\\n data = resp.get_data(as_text=True)\\n\\n for u in users:\\n assert u.username in data\\n assert u.email in data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bff828b5ed9b18a566eb1a0f1c9cd0b\",\n \"score\": \"0.74108696\",\n \"text\": \"def user_list_page():\\n\\n users = User.query.all()\\n return render_template(\\\"user-list.html\\\", users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f79ec2d1b3b9a5bde4b74dde78befe1e\",\n \"score\": \"0.7402986\",\n \"text\": \"def all_users_list(self) -> List[Union[User, None]]:\\n users = User.load_all(self.cursor)\\n for user in users:\\n print(\\\"User ID: {user_id}\\\\nUsername: {username}\\\\nE-mail: {email}\\\\n\\\".format(user_id=user.id, username=user.username, email=user.email))\\n #raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1eef0eca3b6e93ccde79d149a4219f\",\n \"score\": \"0.7402794\",\n \"text\": \"def ListUsers(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aea72923b28f1f4785e25dff241f990\",\n \"score\": \"0.7398049\",\n \"text\": \"def test_users_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf19c8966a2212b15004c126e29092c\",\n \"score\": \"0.73961437\",\n \"text\": \"def users(ctx):\\n client = boto3.client('iam')\\n marker = None\\n while True:\\n if marker:\\n res = client.list_users(Marker=marker)\\n else:\\n res = client.list_users()\\n print(\\\"user\\\\ttibanna_usergroup\\\")\\n for r in res['Users']:\\n res_groups = client.list_groups_for_user(\\n UserName=r['UserName']\\n )\\n groups = [rg['GroupName'] for rg in res_groups['Groups']]\\n groups = filter(lambda x: 'tibanna_' in x, groups)\\n groups = [x.replace('tibanna_', '') for x in groups]\\n print(\\\"%s\\\\t%s\\\" % (r['UserName'], ','.join(groups)))\\n marker = res.get('Marker', '')\\n if not marker:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187af2fa77d146130d42948fe15bf83f\",\n \"score\": \"0.73927766\",\n \"text\": \"def test_listusers_get(self):\\n response = self.client.open('/v1/listusers',\\n method='GET')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832ba7bf117f7542b80a3d828101c1cc\",\n \"score\": \"0.7384407\",\n \"text\": \"def listUsers( self, sock, words ):\\r\\n \\r\\n users = self.room.getAcl().listUsers()\\r\\n\\r\\n users.sort()\\r\\n\\r\\n msg = \\\"Users: \\\" + string.join( users, \\\", \\\" )\\r\\n self.sendMessage( sock, msg )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9b7af1ee913100d84ee2511e6866e7\",\n \"score\": \"0.7362483\",\n \"text\": \"def ViewUserLists(self, buffer = None, index = None):\\r\\n\\r\\n who = buffer.get_all_screen_names(index)\\r\\n dlg = gui.UserListDialog(parent = self.session.frame, title = _(\\\"Select user\\\"), users = who)\\r\\n dlg.setup_users()\\r\\n dlg.finish_setup()\\r\\n if dlg.ShowModal() != wx.ID_OK:\\r\\n return output.speak(_(\\\"Canceled.\\\"), True)\\r\\n user = dlg.users.GetValue()\\r\\n dlg.Destroy()\\r\\n call_threaded(self.session.list_manager(screen_name = user))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64541b785fb2f95163aac06a7169453d\",\n \"score\": \"0.73588324\",\n \"text\": \"def getUsersList():\\n\\tuser_list = db_fUser.get_all_registers(sort=True, value='username')\\n\\tusername_list = []\\n\\t\\n\\tfor user in user_list:\\n\\t\\tusername_list.append(str(db_fUser.get_username(user)))\\n\\t\\n\\treturn username_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf881ec6f2c8d63495fd4aeff3d6728e\",\n \"score\": \"0.73427016\",\n \"text\": \"def load_user_list():\\n users=User.query.all()\\n return render_template(\\\"user_list.html\\\", users = users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9bfcf360b3bacdbdb20a990f49897a8\",\n \"score\": \"0.7340618\",\n \"text\": \"def getUserList(self) -> List[ghidra.framework.remote.User]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1006241907afff1a8e640a24ad1898a0\",\n \"score\": \"0.7295648\",\n \"text\": \"def get_users():\\n response = \\\"\\\"\\n users_list = app.config['db'].get_users()\\n for user in users_list:\\n response += UserTemplate(user[\\\"value\\\"])\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301e8c2f37ae4877490fab2b923b968d\",\n \"score\": \"0.72939193\",\n \"text\": \"def list_users(state, org_uid, role_name, active, inactive, format):\\n if inactive:\\n active = False\\n role_id = _get_role_id(state.sdk, role_name) if role_name else None\\n columns = (\\n [\\\"userUid\\\", \\\"status\\\", \\\"username\\\", \\\"orgUid\\\"]\\n if format == OutputFormat.TABLE\\n else None\\n )\\n df = _get_users_dataframe(state.sdk, columns, org_uid, role_id, active)\\n if df.empty:\\n click.echo(\\\"No results found.\\\")\\n else:\\n formatter = DataFrameOutputFormatter(format)\\n formatter.echo_formatted_dataframe(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d7a7671579fe7a3a013d0da024c6323\",\n \"score\": \"0.7282874\",\n \"text\": \"def list_all_users(self):\\n qry = \\\"select * from \\\" + self.userTable\\n self.cursor.execute(qry)\\n results = self.cursor.fetchall()\\n\\n if results:\\n print(\\\"Identifier, User-Name, Password\\\")\\n for row in results:\\n print('{}, {}, {}'.format(row[\\\"identifier\\\"], row[\\\"username\\\"], row[\\\"token\\\"]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7f50930c78bf4534df8f29de75e9d2\",\n \"score\": \"0.72816885\",\n \"text\": \"def display_users(cls):\\n return cls.user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7f50930c78bf4534df8f29de75e9d2\",\n \"score\": \"0.72816885\",\n \"text\": \"def display_users(cls):\\n return cls.user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c902fdb34362c9ba81de51f4d03c1e2\",\n \"score\": \"0.7280245\",\n \"text\": \"def test_api_users_list_users(flaskclient, auth_headers):\\n\\n url = url_for(\\\"user.Users\\\")\\n rep = flaskclient.get(url, headers=auth_headers)\\n assert rep.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5435477043b55c9b348dab1aae4564d5\",\n \"score\": \"0.7278144\",\n \"text\": \"def get_users():\\n check_admin()\\n users = User.query.all()\\n\\n return render_template('admin/users/users.html', users=users, title=\\\"All Users\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c053f86bd562f08b2f7e91c4c6fbaa4d\",\n \"score\": \"0.7265345\",\n \"text\": \"def list_users(self, conn, limit=20, offset=0, sort=\\\"+timestamp\\\", email=None):\\n path = urls.USERS[\\\"LIST\\\"]\\n params = {\\n \\\"limit\\\": limit,\\n \\\"offset\\\": offset,\\n \\\"sort\\\": sort\\n }\\n if email:\\n params[\\\"email\\\"] = email\\n\\n resp = conn.command(apiMethod=\\\"GET\\\", apiPath=path, apiParams=params)\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f603477f9903770b1d77850b797e852\",\n \"score\": \"0.72638667\",\n \"text\": \"def list_users(current_user, roles):\\n users = User.query.all()\\n\\n resp = {\\n \\\"data\\\": [\\n\\n ]\\n }\\n for user in users:\\n resp['data'].append({'id': user.id, 'name': user.username})\\n\\n return jsonify(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36cad7346e902f79cb8c3dda9b68175f\",\n \"score\": \"0.72597456\",\n \"text\": \"async def user_list(_ctx):\\n\\n # return {'users': list(user.username for user in online_users.values() if user is not ctx.ws.user)}\\n return {'users': list(user.username for user in online_users.values())}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1565d182b464110544dca211fe8ea391\",\n \"score\": \"0.7251618\",\n \"text\": \"def get_users(self):\\n data = self._api_call('homes/%i/users' % self.id)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1f809c62188af7d3e755e4d991bc76\",\n \"score\": \"0.7244264\",\n \"text\": \"def list_users(self):\\n\\n response = self.slack.users.list()\\n users = [username['name'] for username in response.body['members']]\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8cf7ca8a57067685e5493467f5f955\",\n \"score\": \"0.7203822\",\n \"text\": \"def achievements_user_list(context, users, skip_group=False):\\n request = context['request']\\n\\n response_format = 'html'\\n if 'response_format' in context:\\n response_format = context['response_format']\\n\\n return Markup(render_to_string('achievements/tags/user_list',\\n {'users': users, 'skip_group': skip_group},\\n context_instance=RequestContext(request),\\n response_format=response_format))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31bf19418398c06919adc00390ea8f6e\",\n \"score\": \"0.71923274\",\n \"text\": \"def BuildUserList(requested_users):\\n if len(requested_users) == 1 and requested_users[0] == 'all':\\n return USERNAMES + ['root']\\n print requested_users\\n return requested_users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3ba55b9a2c75ef9b86fe3f8cba68b1\",\n \"score\": \"0.71768975\",\n \"text\": \"def user_listing():\\n username = session[\\\"username\\\"]\\n user = mongo.db.user.find_one({\\\"username\\\": username})\\n return render_template(\\n \\\"userlisting.html\\\", property=mongo.db.property.find({\\\"user\\\": user[\\\"_id\\\"]})\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efba2d2b665b874d0347d43d2231753f\",\n \"score\": \"0.7165167\",\n \"text\": \"def get_users():\\n\\n url = env.recommendations_api + '/users'\\n \\n resp = requests.get(url,\\n timeout=env.timeout,\\n headers=env.header)\\n\\n env.set_return_header(resp.headers)\\n\\n if resp.status_code != 200:\\n LOG.debug(\\\"Request returned with \\\" + (str(resp.status_code)))\\n error = resp.text\\n return False, error\\n\\n users = json.loads(resp.text)\\n\\n return True, users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78def17220e51bf2e72a99b9e3e23565\",\n \"score\": \"0.7163498\",\n \"text\": \"def get_users():\\n response = []\\n try:\\n query_to_search_users = \\\"SELECT * FROM users\\\"\\n connection.cursor.execute(query_to_search_users)\\n rows = connection.cursor.fetchall()\\n if not rows:\\n return \\\"No Users\\\"\\n for eachrow in rows:\\n response.append({\\n 'id':eachrow[0],\\n 'name':eachrow[1], \\n 'email':eachrow[2],\\n 'role' :eachrow[4]\\n })\\n return response\\n except Exception as exc:\\n print(exc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fc4a005e57b6608d729a47fbfb01b5\",\n \"score\": \"0.7158014\",\n \"text\": \"def show_all_users():\\n\\n users = User.query.all()\\n\\n return render_template('/users.html',\\n users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c778660076fef01ac23c287dad313f25\",\n \"score\": \"0.71517056\",\n \"text\": \"def cli(ctx, omit_empty_organisms=False):\\n return ctx.gi.users.get_users(omit_empty_organisms=omit_empty_organisms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c7d9ecc6032768aefeafeb850d1534\",\n \"score\": \"0.71491456\",\n \"text\": \"def all_users():\\n return 'allUsers'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5704a562bd6381479b16c3efd8c31e83\",\n \"score\": \"0.71486807\",\n \"text\": \"def all_users():\\n r = service.users().list(domain= SERVICE_DOMAIN).execute()\\n users = []\\n for user in r[\\\"users\\\"]:\\n users.append(user[\\\"primaryEmail\\\"])\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1788821e27f6465659661b5f46db9ebc\",\n \"score\": \"0.7145525\",\n \"text\": \"def test_user_list(self):\\n utils.login(self)\\n response = self.client.get(reverse('spirit:admin:user:index'))\\n self.assertEqual(list(response.context['users']), [self.user, ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62f6893995296def8e85ce684032dd3\",\n \"score\": \"0.7142263\",\n \"text\": \"def getListUser(self , **kargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d73efae3c0d909f28b96f657261118\",\n \"score\": \"0.71260136\",\n \"text\": \"def list_users(self):\\n resp, resp_body = self.method_get(\\\"users\\\", admin=True)\\n if resp.status_code in (401, 403, 404):\\n raise exc.AuthorizationFailure(\\\"You are not authorized to list \\\"\\n \\\"users.\\\")\\n # The API is inconsistent; if only one user exists, it will not return\\n # a list.\\n if \\\"users\\\" in resp_body:\\n users = resp_body[\\\"users\\\"]\\n else:\\n users = [resp_body]\\n # The returned values may contain password data. Strip that out.\\n for user in users:\\n bad_keys = [key for key in user.keys()\\n if \\\"password\\\" in key.lower()]\\n for bad_key in bad_keys:\\n user.pop(bad_key)\\n return [User(self, user) for user in users]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cd0ec31008801fa5128056507f3e09a\",\n \"score\": \"0.71128654\",\n \"text\": \"def get_users(self):\\n req_obj = self.rest_get(\\\"/users\\\")\\n if req_obj.success:\\n return req_obj.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce523c04e8f4a930470a8d3327af040e\",\n \"score\": \"0.7108909\",\n \"text\": \"def list_users(self):\\n raise StoreMethodNotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b03209415414ba7ed7979b7f74975fa\",\n \"score\": \"0.70905995\",\n \"text\": \"def showUsers(conn):\\n cursor = conn.cursor()\\n os.system(\\\"cls\\\")\\n cursor.execute(\\\"SELECT user_id,username FROM users;\\\")\\n users = cursor.fetchall()\\n print(\\\"Here are all the currently registered users: \\\")\\n for user in users:\\n user_id = user[0]\\n name = user[1]\\n\\n print(f\\\"{user_id}. {name}\\\")\\n \\n os.system(\\\"pause\\\")\\n menu(conn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34394cee3fb56bd568c23f23a52aed25\",\n \"score\": \"0.70898676\",\n \"text\": \"def test_list_users(self, authenticated_client):\\n r = authenticated_client.api.list_users()\\n assert \\\"admin\\\" in r[\\\"data\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a985e619c460ff282923db214acfce\",\n \"score\": \"0.70889974\",\n \"text\": \"def user_list():\\n if request.method == \\\"POST\\\":\\n data = request.get_json()\\n result = userValidator.validator.validate(data)\\n\\n if not result:\\n return userValidator.validator.errors, status.HTTP_400_BAD_REQUEST\\n if \\\"id\\\" in data:\\n user = userCollection.getUserById(data[\\\"id\\\"])\\n if user is not None:\\n return \\\"User Exist with same Id\\\", status.HTTP_400_BAD_REQUEST\\n success = userCollection.createUser(data)\\n if success:\\n return \\\"User Created\\\", status.HTTP_201_CREATED\\n return \\\"Error in Creating User\\\", status.HTTP_500_INTERNAL_SERVER_ERROR\\n else:\\n page = request.args.get(\\\"page\\\", 1, int)\\n limit = request.args.get(\\\"limit\\\", 5, int)\\n name = request.args.get(\\\"name\\\", \\\"\\\", str)\\n sort = request.args.get(\\\"sort\\\", \\\"\\\", str)\\n return getUsers(page, limit, name, sort), status.HTTP_200_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15bc784d3aeebd44800af289bec31526\",\n \"score\": \"0.70862436\",\n \"text\": \"def get_all():\\n\\treturn users.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aba21298a25cccedaa54685edd4174e\",\n \"score\": \"0.70735425\",\n \"text\": \"def get_users():\\n\\n with CON:\\n CUR.execute(\\\"SELECT * FROM users\\\")\\n return CUR.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58b9556d0d8b9469b552d2fc94b554b6\",\n \"score\": \"0.706808\",\n \"text\": \"def test_get(self):\\n rsp = self.apiGet(get_user_list_url(),\\n expected_mimetype=user_list_mimetype)\\n self.assertEqual(rsp['stat'], 'ok')\\n self.assertEqual(len(rsp['users']), User.objects.count())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ae5e33491e91ad28cd2cb8c2eda138\",\n \"score\": \"0.7067798\",\n \"text\": \"def list_all_users():\\n all_users = storage.all(\\\"User\\\")\\n users = [u.to_dict() for u in all_users.values()]\\n return jsonify(users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"245e000903dda52f0311b3df3af588fa\",\n \"score\": \"0.70663947\",\n \"text\": \"def bf_get_users(selected_bfaccount):\\n try:\\n # def get_users_list():\\n bf = Pennsieve(selected_bfaccount)\\n # organization_name = bf.context.name\\n organization_id = bf.context.id\\n list_users = bf._api._get('/organizations/' + str(organization_id) + '/members')\\n list_users_first_last = []\\n for i in range(len(list_users)):\\n #first_last = list_users[i]['firstName'] + ' ' + list_users[i]['lastName']\\n first_last = list_users[i]['firstName'].capitalize() + \\\" \\\" + list_users[i]['lastName'].capitalize() + \\\" (\\\" + list_users[i]['email'] + \\\") !|**|!\\\" + list_users[i]['id']\\n list_users_first_last.append(first_last)\\n list_users_first_last.sort() # Returning the list of users in alphabetical order\\n return list_users_first_last\\n # list_users_first_last = gevent.spawn(get_users_list())\\n # gevent.sleep(0)\\n # return list_users_first_last\\n except Exception as e:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a54082f28d9bf399aa145cd502513b0\",\n \"score\": \"0.7065335\",\n \"text\": \"def list_all_users(db: Session = Depends(get_db)):\\n return db.query(models.User).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a82e404522b80797db5da0174386391\",\n \"score\": \"0.70538914\",\n \"text\": \"def getUsers(self):\\n return [user[0] for user in self.db.execute(\\\"select user from users\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2123ae20f4760b10e88137f38f01f6a5\",\n \"score\": \"0.70524645\",\n \"text\": \"def users(request):\\n session = DBSESSION()\\n users = session.query(User).all()\\n return dict(title='UserList', users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc5457da80d7df8887140b93b75add6\",\n \"score\": \"0.70440155\",\n \"text\": \"def list(config):\\n _exit_if_user_is_logged_out(config.token)\\n\\n _list_resource('{}/api/v1/users/'.format(SHAREDCLOUD_CLI_URL),\\n config.token,\\n ['UUID', 'EMAIL', 'USERNAME', 'BALANCE', 'DATE_JOINED', 'LAST_LOGIN'],\\n ['uuid', 'email', 'username', 'balance', 'date_joined', 'last_login'],\\n mappers={\\n 'balance': _map_non_formatted_money_to_version_with_currency,\\n 'date_joined': _map_datetime_obj_to_human_representation,\\n 'last_login': _map_datetime_obj_to_human_representation\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b371fc8056a68ac18dfd3152c7820b\",\n \"score\": \"0.704112\",\n \"text\": \"def getUserNames():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b6120b6ef439643796241188ed5f99\",\n \"score\": \"0.70355904\",\n \"text\": \"def view_users():\\n return render_template(\\\"view_users.html\\\", data=User.query.order_by(User.username))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a0858d3cf5ebf39fa44d6eb820d219\",\n \"score\": \"0.70348805\",\n \"text\": \"def list(request):\\n\\n users = NormalUser.objects.all()\\n return render(request, 'list.html', {'users':users})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"623e6f36366933e64dd87e186935c425\",\n \"score\": \"0.7034246\",\n \"text\": \"def users():\\n\\n\\t# Check if this is the master administrator\\n\\tif g.user.user_name != gen_app.config['ADMIN_USER']:\\n\\t\\tflash(\\\"You should not be here\\\", \\\"danger\\\")\\n\\t\\treturn redirect(url_for('index'))\\n\\n\\n\\tusrs = models.User.query.all()\\n\\n\\n\\treturn render_template('users.html', title=\\\"Users\\\", rows=usrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64322dbc7ea33be24ddedbda4f0b46c\",\n \"score\": \"0.7031841\",\n \"text\": \"def loading_users():\\n users = get_data(config.api_endpoints[\\\"users\\\"])\\n return users\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1597,"cells":{"query_id":{"kind":"string","value":"d3f45a02137a2a5293cd77ddd7b22297"},"query":{"kind":"string","value":"This function can be used to verify that .npy file contains the right labels, by regenerating the semantic segmentation array from the .npy file."},"positive_passages":{"kind":"list like","value":[{"docid":"bcc81de761596d5852d5140baa0784cd","score":"0.0","text":"def regen_img_from_npy(path_npy, path_img='/home/js/PycharmProjects/gym-duckietown-generate-fp/sample/test4.png'):\n\n color_map = {'white': np.array([255, 255, 255]), 'yellow': np.array([255, 255, 0]), 'left': np.array([255, 0, 0]),\n 'right': np.array([0, 255, 0]), 'obstacle': np.array([255, 50, 255]), 'background': np.array([0, 0, 0])}\n\n ss_arr = np.load(path_npy)\n # ss_2d = ss_arr.reshape((240,320))\n # img = np.zeros((240,320,3))\n\n ss_2d = ss_arr.reshape((120,160))\n img = np.zeros((120,160,3))\n\n img[(ss_2d == 2)] = np.array([255, 255, 255]).astype(int)\n img[(ss_2d == 1)] = np.array([255, 255, 0]).astype(int)\n img[(ss_2d == 3)] = np.array([255, 0, 255]).astype(int)\n img[(ss_2d == 4)] = np.array([255, 0, 0]).astype(int)\n img[(ss_2d == 5)] = np.array([0, 255, 100]).astype(int)\n img[(ss_2d == 6)] = np.array([100, 100, 100]).astype(int)\n img[(ss_2d == 7)] = np.array([0, 100, 100]).astype(int)\n\n imsave(path_img, img)\n #\n # pil_image = PIL.Image.open(\n # '/home/js/PycharmProjects/gym-duckietown-generate-fp/datasets/image_2020_12_03_15_16_10/bezier_only/bezier.png')\n # img = np.array(pil_image)\n # height, width, _ = img.shape\n # colors = set()\n # for i in range(height):\n # for j in range(width):\n # colors.add(str(img[i,j]))\n # print(colors)\n return","title":""}],"string":"[\n {\n \"docid\": \"bcc81de761596d5852d5140baa0784cd\",\n \"score\": \"0.0\",\n \"text\": \"def regen_img_from_npy(path_npy, path_img='/home/js/PycharmProjects/gym-duckietown-generate-fp/sample/test4.png'):\\n\\n color_map = {'white': np.array([255, 255, 255]), 'yellow': np.array([255, 255, 0]), 'left': np.array([255, 0, 0]),\\n 'right': np.array([0, 255, 0]), 'obstacle': np.array([255, 50, 255]), 'background': np.array([0, 0, 0])}\\n\\n ss_arr = np.load(path_npy)\\n # ss_2d = ss_arr.reshape((240,320))\\n # img = np.zeros((240,320,3))\\n\\n ss_2d = ss_arr.reshape((120,160))\\n img = np.zeros((120,160,3))\\n\\n img[(ss_2d == 2)] = np.array([255, 255, 255]).astype(int)\\n img[(ss_2d == 1)] = np.array([255, 255, 0]).astype(int)\\n img[(ss_2d == 3)] = np.array([255, 0, 255]).astype(int)\\n img[(ss_2d == 4)] = np.array([255, 0, 0]).astype(int)\\n img[(ss_2d == 5)] = np.array([0, 255, 100]).astype(int)\\n img[(ss_2d == 6)] = np.array([100, 100, 100]).astype(int)\\n img[(ss_2d == 7)] = np.array([0, 100, 100]).astype(int)\\n\\n imsave(path_img, img)\\n #\\n # pil_image = PIL.Image.open(\\n # '/home/js/PycharmProjects/gym-duckietown-generate-fp/datasets/image_2020_12_03_15_16_10/bezier_only/bezier.png')\\n # img = np.array(pil_image)\\n # height, width, _ = img.shape\\n # colors = set()\\n # for i in range(height):\\n # for j in range(width):\\n # colors.add(str(img[i,j]))\\n # print(colors)\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"181990efde8f09ae8a740812134a80ed","score":"0.65193164","text":"def test_segmentation_save_load(self):\n\n segmentation = Segmentation('test.annot', [1, 1, 2, 2])\n segmentation.add_label(0, Label('no label', (0, 0, 0, 255)))\n segmentation.add_label(1, Label('label 0', (255, 0, 0, 255)))\n segmentation.add_label(2, Label('label 1', (0, 255, 0, 255)))\n\n # Work in a temporary directory. This guarantees cleanup even on error.\n with tempfile.TemporaryDirectory() as directory:\n\n # Save and reload the test data.\n filename = os.path.join(directory, segmentation.name)\n nimesh.io.freesurfer.save_segmentation(filename, segmentation)\n loaded = nimesh.io.freesurfer.load_segmentation(filename)\n\n np.testing.assert_array_almost_equal(segmentation.keys,\n loaded.keys)\n self.assertEqual(segmentation.name, loaded.name)\n\n # The labels should also match.\n items = zip(segmentation.labels.items(), loaded.labels.items())\n for (k1, l1), (k2, l2) in items:\n self.assertEqual(k1, k2)\n self.assertEqual(l1.color, l2.color)","title":""},{"docid":"d5a3929f12317235b4507194fb9d9ae3","score":"0.6114336","text":"def load_data_and_labels(positive_data_file, negative_data_file):\n # Load data from files\n '''positive_examples = list(open(positive_data_file, \"r\").readlines())\n positive_examples = [s.strip() for s in positive_examples]\n negative_examples = list(open(negative_data_file, \"r\").readlines())\n negative_examples = [s.strip() for s in negative_examples]'''\n\n tempString = \"\"\n tempList = []\n positive_examples = []\n negative_examples = []\n x_text = []\n \n #A = np.array([1,2,3,4,5,6])\n '''a = [1,2,3,4]\n b = [5,6,7,8]\n tempList.append(a)\n tempList.append(b)\n \n A = np.array(tempList)\n print (A[0])\n B = np.reshape(A, (2, 4))\n print(B[0][0])\n #B = np.reshape(A, (-1, 4))\n #print(B[0])'''\n\n\n with open(\"./data/rt-polaritydata/malicious.pos\", \"r\") as f:\n for line in f:\n if line != \"\\n\":\n tempString += line\n else:\n tempString = tempString[:-1] #remove \\n\n tempString = tempString.replace(\" \", \"\") #size 20000\n for x in tempString:\n tempList.append(int(x))\n positive_examples.append(tempList[:])\n x_text.append(tempList[:]) #append copy of list instead of reference\n tempList[:] = []\n tempString = \"\"\n\n tempList[:] = []\n tempString = \"\"\n\n with open(\"./data/rt-polaritydata/non_malicious.neg\", \"r\") as f:\n for line in f:\n if line != \"\\n\":\n tempString += line\n else:\n tempString = tempString[:-1] #remove \\n\n tempString = tempString.replace(\" \", \"\")\n for x in tempString:\n tempList.append(int(x))\n negative_examples.append(tempList[:])\n x_text.append(tempList[:])\n tempList[:] = []\n tempString = \"\"\n\n # Generate labels\n positive_labels = [[0, 1] for _ in positive_examples]\n negative_labels = [[1, 0] for _ in negative_examples]\n y = np.concatenate([positive_labels, negative_labels], 0)\n return [x_text, y]","title":""},{"docid":"e842b339e19168c33c70d9fdc2caee28","score":"0.6067623","text":"def test_rf_label_reader():\n labels, image_cuts, empty_count, null_count, no_count, yes_count = \\\n rf.rf_label_reader(SAMPLE_TXT_FILE,SAMPLE_IMAGE_FILE)\n assert len(labels) == np.asanyarray(image_cuts).shape[0]\n assert empty_count + null_count + no_count + yes_count > 0","title":""},{"docid":"7ecd7df4a16f32f02d0ab292fc53c6cb","score":"0.59799004","text":"def pre_process_labeled_data():\n neg = open(r\"data\\neg_A0201.txt\", \"r\")\n pos = open(r\"data\\pos_A0201.txt\", \"r\")\n\n data_neg, labels_neg = represent_data_as_vector(neg, 0)\n data_pos, labels_pos = represent_data_as_vector(pos, 1)\n\n all_data = np.concatenate((data_neg, data_pos))\n labels = np.concatenate((labels_neg, labels_pos))\n\n np.savetxt(fname=r\"data\\data.txt\", X=all_data)\n np.savetxt(fname=r\"data\\labels.txt\", X=labels)\n\n x_train, x_test, y_train, y_test = train_test_split(all_data, labels, test_size=0.01)\n\n np.savetxt(fname=r\"data\\x_train.txt\", X=x_train)\n np.savetxt(fname=r\"data\\x_test.txt\", X=x_test)\n np.savetxt(fname=r\"data\\y_train.txt\", X=y_train.reshape(-1, 1))\n np.savetxt(fname=r\"data\\y_test.txt\", X=y_test.reshape(-1, 1))","title":""},{"docid":"f06f2dca23721917af7f035e87246788","score":"0.59588283","text":"def check_labels_file_header(filename):\r\n with tf.gfile.Open(filename, \"rb\") as f:\r\n magic = read32(f)\r\n read32(f) # num_items, unused\r\n if magic != 2049:\r\n raise ValueError(\"Invalid magic number %d in MNIST file %s\" % (magic,\r\n f.name))","title":""},{"docid":"616c731611b2bb5a29acf44abffa6cdf","score":"0.5851225","text":"def build_segmentation_dataset(list_of_files):\n temp = []\n train_labels = []\n for f_cf in list_of_files:\n with open(f_cf) as file:\n lines = file.read().splitlines()\n temp.append(lines)\n #label_id = [f_ind for f_ind, filt in enumerate(filter) if filt in f_cf][0]\n #train_labels.append(len(temp[-1]) * [label_id])\n train_filter = [item for l in temp for item in l]\n image_folder = os.path.join(voc_root_folder, \"VOC2009/SegmentationClass/\")\n image_filenames = [os.path.join(image_folder, file) for f in train_filter for file in os.listdir(image_folder) if\n f in file]\n x = np.array([resize(cv2.cvtColor(cv2.imread(img_f),cv2.COLOR_BGR2RGB), (image_size, image_size, 3)) for img_f in image_filenames]).astype(\n 'float32')\n # changed y to an array of shape (num_examples, num_classes) with 0 if class is not present and 1 if class is present\n y_temp = []\n for tf in train_filter:\n y_temp.append([1 if tf in l else 0 for l in temp])\n y = np.array(y_temp)\n return x, y","title":""},{"docid":"de0e7635bfec69ad0b63eff012bd5e6c","score":"0.58441633","text":"def __init__(self) :\n #Opens up labels\n for i in range(len(filenames)):\n filehandle = open(filenames[i], 'rb')\n filehandle.seek(0)\n if filenames[i][-10:] == \"idx1-ubyte\" :\n info1 = filehandle.read(8)\n magic,size= st.unpack(\">II\",info1)\n if filenames[i][:5] == \"train\" :\n self.train_label = (st.unpack('>'+'B'*size,filehandle.read(size)))\n else :\n self.test_label = (st.unpack('>'+'B'*size,filehandle.read(size))) \n else:\n info2 = filehandle.read(16)\n self.magic, self.size,self.nrow,self.ncol = st.unpack(\">IIII\", info2) \n nBytesTotal = self.size*self.nrow*self.ncol*1 #since each pixel data is 1 byte\n if filenames[i][:5] == \"train\" :\n self.train_img = np.asarray(st.unpack('>'+'B'*nBytesTotal,filehandle.read(nBytesTotal))).reshape((self.size,self.nrow,self.ncol))\n else :\n self.test_img = np.asarray(st.unpack('>'+'B'*nBytesTotal,filehandle.read(nBytesTotal))).reshape((self.size,self.nrow,self.ncol))","title":""},{"docid":"02ba7abea738f9cb7931cdad81139ecb","score":"0.5843663","text":"def checkArrays(self):\n\n fnames = np.array([self.name+'/teff.npy',\n self.name+'/logg.npy',\n self.name+'/fe_h.npy',\n self.name+'/c_h.npy',\n self.name+'/n_h.npy',\n self.name+'/o_h.npy',\n self.name+'/fib.npy',\n self.name+'/spectra.npy',\n self.name+'/spectra_errs.npy',\n self.name+'/bitmasks.npy'])\n fexist = True\n for f in fnames:\n fexist *= os.path.isfile(f)\n # If all files exist, read data from file for increased initialization speed\n if fexist:\n self.teff = np.load(self.name+'/teff.npy')\n self.logg = np.load(self.name+'/logg.npy')\n self.fe_h = np.load(self.name+'/fe_h.npy')\n self.c_h = np.load(self.name+'/c_h.npy')\n self.n_h = np.load(self.name+'/n_h.npy')\n self.o_h = np.load(self.name+'/o_h.npy')\n self.fib = np.load(self.name+'/fib.npy')\n self.spectra = np.ma.masked_array(np.load(self.name+'/spectra.npy'))\n self.spectra_errs = np.ma.masked_array(np.load(self.name+'/spectra_errs.npy'))\n self._bitmasks = np.load(self.name+'/bitmasks.npy')\n # If any file is missing, generate arrays and write to file\n elif not fexist:\n self.makeArrays(self.matchingData)\n np.save(self.name+'/teff.npy',self.teff.data)\n np.save(self.name+'/logg.npy',self.logg.data)\n np.save(self.name+'/fe_h.npy',self.fe_h.data)\n np.save(self.name+'/c_h.npy',self.c_h.data)\n np.save(self.name+'/n_h.npy',self.n_h.data)\n np.save(self.name+'/o_h.npy',self.o_h.data)\n np.save(self.name+'/fib.npy',self.fib.data)\n np.save(self.name+'/spectra.npy',self.spectra.data)\n np.save(self.name+'/spectra_errs.npy',self.spectra_errs.data)\n np.save(self.name+'/bitmasks.npy',self._bitmasks)","title":""},{"docid":"fafb88654e163b3dd32a78b81ac4256a","score":"0.5812097","text":"def load_MNIST_labels(filename):\r\n\twith open(filename, 'r') as f:\r\n\t\tmagic = np.fromfile(f, dtype=np.dtype('>i4'), count=1)\r\n\t\tn_labels = np.fromfile(f, dtype=np.dtype('>i4'), count=1)\r\n\t\tlabels = np.fromfile(f, dtype=np.uint8)\r\n\t\t\r\n\t\tf.close()\r\n\t\t\r\n\t\treturn labels","title":""},{"docid":"7a372951ac0442f110cf4b7b613037b8","score":"0.57573295","text":"def test_unique_label():\n np.random.seed(10)\n Nshape = ground_truth.file_path.values.shape\n is_relevant = np.zeros(Nshape).astype(int)\n\n idx = np.arange(len(is_relevant), dtype='int')\n\n categorization_score(idx, is_relevant, idx, np.random.rand(*Nshape))","title":""},{"docid":"004f61a7e578b5f266c77fb34258ca06","score":"0.5749477","text":"def load_data(path_X,path_Y,n_data):\n\n with np.load(path_X, allow_pickle=True) as f:\n X = f['X']\n print (\"input X shape\",X.shape)\n X = X#.transpose(1,0,2,3) # use the transpose when loading from Data_XYlabel_fimg_120_new.npz\n st1_x = 0 \n ed1_x = 54000 \n st2_x = 54000 \n ed2_x = 60000 \n x_train = np.concatenate([X[st1_x:ed1_x],X[60000:114000]],axis=0)\n x_test = np.concatenate([X[st2_x:ed2_x],X[114000:120000]],axis=0) \n\n with np.load(path_Y, allow_pickle=True) as f:\n Y,l_p = f['label'],f['Y']\n Y = Y#.transpose(1,0)\n l_p = l_p.transpose(1,0)\n st1_x = 0 \n ed1_x = 54000\n st2_x = 54000 \n ed2_x = 60000 \n y_train = np.expand_dims(np.concatenate([Y[st1_x:ed1_x],Y[60000:114000]],axis=0),axis=1)\n y_test = np.expand_dims(np.concatenate([Y[st2_x:ed2_x],Y[114000:120000]],axis=0),axis=1)\n\n l_p_train = np.concatenate([l_p[st1_x:ed1_x],l_p[60000:114000]],axis=0)\n l_p_test = np.concatenate([l_p[st2_x:ed2_x],l_p[114000:120000]],axis=0) \n\n print (\"Loading Signal to Noise Ratio - information\")\n path_SNR = \"/KDD_Submission/Data/Data_X_snr_Ylabel_120.npz\"\n x_snr_r = np.load(path_SNR, allow_pickle=True)['X'][1,:]\n boolArr_thresh_20 = x_snr_r > 20\n x_snr_ind = boolArr_thresh_20\n x_snr_ind_test = np.concatenate([x_snr_ind[st2_x:ed2_x],x_snr_ind[114000:120000]],axis=0)\n self.x_snr_r_test = np.concatenate([x_snr_r[st2_x:ed2_x],x_snr_r[114000:120000]],axis=0)\n\n path_mu = \"/KDD_Submission/Data/Data_X_mu_Ylabel_120.npz\"\n x_mu = np.load(path_mu, allow_pickle=True)['X'][0,:]\n self.x_mu_test = np.concatenate([x_mu[st2_x:ed2_x],x_mu[114000:120000]],axis=0) \n return (x_train, y_train,l_p_train), (x_test, y_test,l_p_test,x_snr_ind_test)","title":""},{"docid":"ab9b969dd4691f0d157f95701c94824c","score":"0.5748243","text":"def test_parse_annotations() -> None:\n filepath = get_test_file(\"motchallenge_labels.txt\")\n result = parse_annotations(filepath)\n\n assert list(result.keys()) == [0, 1, 3]\n\n for _, labels in result.items():\n for label in labels:\n assert label.attributes is not None\n assert label.category == \"pedestrian\"\n assert label.id == \"1\"\n assert label.attributes[\"visibility\"] == 1.0\n assert label.box2d is not None\n assert label.box2d.x1 in [458, 460]\n assert label.box2d.x2 in [587, 589]","title":""},{"docid":"6a96ab87c1338a1b28eed79f00dd7d9b","score":"0.5733115","text":"def test_encode_and_decode(self):\n test_image_height = 20\n test_image_width = 15\n filename = 'dummy'\n\n image = self._create_test_image(test_image_height, test_image_width)\n # Take the last channel as dummy label.\n label = image[..., 0]\n\n example = data_utils.create_tfexample(\n image_data=_encode_png_image(image),\n image_format='png', filename=filename,\n label_data=_encode_png_image(label), label_format='png')\n\n # Parse created example, expect getting identical results.\n parser = data_utils.SegmentationDecoder(is_panoptic_dataset=False)\n parsed_tensors = parser(example.SerializeToString())\n\n self.assertIn('image', parsed_tensors)\n self.assertIn('image_name', parsed_tensors)\n self.assertIn('label', parsed_tensors)\n self.assertEqual(filename, parsed_tensors['image_name'])\n np.testing.assert_array_equal(image, parsed_tensors['image'].numpy())\n # Decoded label is a 3-D array with last dimension of 1.\n decoded_label = parsed_tensors['label'].numpy()\n np.testing.assert_array_equal(label, decoded_label[..., 0])","title":""},{"docid":"7337b39f81c808aba15458c127d8011b","score":"0.5732981","text":"def getPenDigitData():\n path = raw_input(\"Enter the path of Train Data: \")\n data = np.genfromtxt(path, delimiter=',', dtype=int)\n labels = data[:, -1]\n\n unwantedLabels = [4, 5, 6, 7, 8, 9]\n listToDelete = []\n for i, line in enumerate(range(len(data))):\n if labels[i] in unwantedLabels:\n listToDelete.append(i)\n\n actualData = np.delete(data, listToDelete, axis=0)\n\n print(actualData.shape)\n # Separating the labels and data into different arrays\n actualLabels = actualData[:, -1]\n actualData = actualData[:, :-1]\n\n actualData = pre.scale(actualData)\n\n # Change the label vector to label matrix\n # If Label is 2 then it becomes [0, 1, 0]\n labelMatrix = np.zeros((actualLabels.shape[0], 4))\n for j in range(len(actualLabels)):\n if actualLabels[j] == 0:\n labelMatrix[j][0] = 1\n if actualLabels[j] == 1:\n labelMatrix[j][1] = 1\n if actualLabels[j] == 2:\n labelMatrix[j][2] = 1\n if actualLabels[j] == 3:\n labelMatrix[j][3] = 1\n\n # print(labelMatrix)\n testData, testLabels = tpd.loadTestData()\n\n tpd.trainUsingNNOnPenDigitData(actualData, labelMatrix, testData, testLabels)\n\n return actualData, actualLabels","title":""},{"docid":"0d86a31ffb3c8a45f7194b3e4246c073","score":"0.572113","text":"def _load_kitti_annotation(self, index):\n if self._image_set == 'test':\n image_name = os.path.join(self._devkit_path, 'testing/image_2', index+'.png')\n img = cv2.imread(image_name)\n width = img.shape[0]\n height = img.shape[1]\n\n return {\n 'image' : image_name,\n 'height' : height,\n 'width' : width,\n 'flipped' : False,\n 'is_train' : False\n }\n filename = os.path.join(self._devkit_path, 'training/label_2', index+'.txt')\n imagename = os.path.join(self._devkit_path, 'training/label_2', index+'.png')\n img = cv2.imread(imagename)\n width = img.shape[0]\n height = img.shape[1]\n f = open(filename)\n lines = f.readlines()\n num_objs = 0\n for l in lines:\n str_cls = l.split()\n cls = str(str_cls[0])\n if cls in self.classes or cls in ['Truck', 'Tram', 'Van']:\n num_objs += 1\n num_objs = num_objs\n\n boxes_2d = np.zeros((num_objs, 4), dtype=np.uint16)\n dimensions = np.zeros((num_objs, 3), dtype=np.unit16)\n translations = np.zeros((num_objs, 3), dtype=np.float32)\n ryaws = np.zeros((num_objs,), dtype=np.float32)\n gt_classes = np.zeros((num_objs), dtype=np.int32)\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n seg_areas = np.zeros((num_objs), dtype=np.float32)\n\n ix = 0\n\n for line in lines:\n data = line.split(' ')\n data[1:] = [float(x) for x in data[1:]]\n\n # extract label, truncation, occlusion\n if str(data[0]) in ['Truck', 'Tram', 'Van']:\n data[0] = 'Car'\n if str(data[0]) not in self._classes:\n continue\n\n type = data[0] # 'Car', 'Pedestrian', ...\n truncation = data[1] # truncated pixel ratio [0..1]\n occlusion = int(data[2]) # 0=visible, 1=partly occluded, 2=fully occluded, 3=unknown\n alpha = data[3] # object observation angle [-pi..pi]\n\n # extract 2d bounding box in 0-based coordinates\n xmin = int(data[4])\n ymin = int(data[5])\n xmax = int(data[6])\n ymax = int(data[7])\n cls = self._class_to_ind[data[0]] # class 0, 1, 2 for car, pedestrian and cyclist\n\n # extract 3d bounding box information\n h = data[8] # box height\n w = data[9] # box width\n l = data[10] # box length (in meters)\n t = (data[11], data[12], data[13]) # location (x,y,z) in camera coord.\n ry = data[14] # yaw angle (around Y-axis in camera coordinates) [-pi..pi]\n\n boxes_2d[ix, :] = [xmin,ymin, xmax, ymax]\n dimensions[ix, :] = [h, w, l]\n translations[ix, :] = t\n ryaws[ix, :] = ry\n gt_classes[ix] = cls\n overlaps[ix, cls] = 1.0\n seg_areas[ix] = (xmax-xmin+1)*(ymax-ymin+1)\n\n ix = ix + 1\n\n return [\n ('image', imagename),\n ('height', height),\n ('width', width),\n ('boxes_2d', boxes_2d),\n ('dimensions', dimensions),\n ('yaws', ryaws),\n ('gt_classes', gt_classes),\n ('gt_overlaps', overlaps),\n ('max_overlaps', overlaps.max(axis=1)),\n ('max_classes', overlaps.argmax(axis=1)),\n ('flipped', False),\n ('seg_areas', seg_areas),\n ('is_train', True)\n ]","title":""},{"docid":"0c8865521ca74c62121aeeac62c5dd23","score":"0.5705019","text":"def convert_txt_to_np_with_label(file, label):\n a=[]\n with open(file, 'r') as f:\n data = f.readlines() \n for line in data:\n if len(line)>11:\n b = line[:-1].split(' ')\n c = []\n for i in b:\n if i != '':\n c.append(i)\n c = map(float, c)\n a.append(list(c))\n x = np.array(a).reshape((-1, 2, 16))\n y = np.array([label] * x.shape[0])\n return x, y","title":""},{"docid":"20915231ebcac3cce067114b34105d64","score":"0.56915194","text":"def test_count_labels(\n data_dir,\n):\n annotation_format = \"kitti\"\n annotation_file_path = os.path.join(str(data_dir), annotation_format, \"kitti_1.txt\")\n label_counts = analyze.count_labels(annotation_file_path, annotation_format)\n assert label_counts[\"person\"] == 4\n assert label_counts[\"truck\"] == 1\n assert label_counts[\"car\"] == 1\n annotation_format = \"pascal\"\n annotation_file_path = os.path.join(str(data_dir), annotation_format, \"pascal_1.xml\")\n label_counts = analyze.count_labels(annotation_file_path, annotation_format)\n assert label_counts[\"person\"] == 2\n assert label_counts[\"car\"] == 1\n annotation_format = \"darknet\"\n annotation_file_path = os.path.join(str(data_dir), annotation_format, \"darknet_1.txt\")\n label_counts = analyze.count_labels(annotation_file_path, annotation_format)\n assert label_counts[\"1\"] == 2\n assert label_counts[\"2\"] == 1\n assert label_counts[\"3\"] == 1","title":""},{"docid":"5403c76750ff8e2072c81e5bb85c5b1e","score":"0.56765985","text":"def load_labels(path, filename):\n print 'Loading MNIST labels...'\n with gzip.open(os.path.join(path, filename)) as f:\n magic = struct.unpack('>i', f.read(4))\n assert(magic[0] == 2049)\n num_labels = struct.unpack('>i', f.read(4))\n data = np.fromstring(f.read(), np.dtype('>u1'))\n return data","title":""},{"docid":"60a14995142eff77a230a9fbb69cfba0","score":"0.56188756","text":"def prune_labels(data_file, file_label_path, ver_batch, voxel_width, n_class, data_folder, version=\"\"):\n i_rows = 0\n xyz = np.zeros((0, 3), dtype='float32')\n rgb = np.zeros((0, 3), dtype='uint8')\n labels = np.zeros((0, n_class+1), dtype='uint32')\n path_dem = data_folder + \"Generic/\"\n\n # ---the clouds can potentially be too big to parse directly---\n # ---they are cut in batches in the order they are stored---\n while True:\n try:\n vertices = np.genfromtxt(data_file, delimiter=' ', max_rows=ver_batch, skip_header=i_rows)\n # print(np.shape(vertices))\n # input(\"SDF\")\n except StopIteration:\n # end of file\n break\n # if rgb == False\n print(\"*\")\n if np.shape(vertices)[1] <= 6: # xyz-rgb\n vertices_tem = np.copy(vertices)\n rows, _ = np.shape(vertices)\n vertices = np.ones((rows, 6))\n vertices[:, :3] = np.copy(vertices_tem[:,:3])\n\n xyz_full = np.array(vertices[:, 0:3], dtype='float32')\n rgb_full = np.array(vertices[:, 4:7], dtype='uint8')\n\n del vertices\n labels_full = np.genfromtxt(file_label_path, dtype=\"u1\", delimiter=' ', max_rows=ver_batch, skip_header=i_rows)\n xyz_sub, rgb_sub, labels_sub = libply_c.prune(xyz_full, voxel_width, rgb_full, labels_full, n_class)\n\n del xyz_full, rgb_full\n xyz = np.vstack((xyz, xyz_sub))\n rgb = np.vstack((rgb, rgb_sub))\n labels = np.vstack((labels, labels_sub))\n i_rows = i_rows + ver_batch\n\n ####################################################################################################################\n \n file_name = os.path.splitext(os.path.basename(data_file))[0]\n path_short = path_dem + file_name + version\n xyz_rgb = np.hstack((xyz, rgb))\n np.savetxt(path_short + \".txt\", xyz_rgb, delimiter=\",\", header=\"X,Y,Z,R,G,B\", comments='')\n\n #dem_array = build_dem(path_short, xyz_rgb)\n\n nr, nc = np.shape(xyz_rgb)\n dem_array = np.ones((nr, nc + 1), dtype=np.float32)\n dem_array[:, :3] = xyz_rgb[:, :3]\n dem_array[:, 3] = xyz_rgb[:, 2]\n dem_array[:, 4:] = xyz_rgb[:, 3:]\n\n ####################################################################################################################\n\n return xyz, rgb, labels, dem_array","title":""},{"docid":"168c116042f30e143e7d351bf2138a5f","score":"0.56104684","text":"def read_label_txt(label_txt, action):\n\n f = open(label_txt, 'r')\n # filenames = []\n # labels = []\n concat = []\n x = 0\n for line in f:\n x += 1\n if x < 61:\n filename, label = line[:-1].split(' ')\n # print(filename)\n # print(label)\n im = Image.open(z_settings.PROJECT_ROOT + 'lsdata/' + action + '/' + filename)\n im = (np.array(im))\n # print(im.shape)\n \n r = im.flatten()\n label = [int(label)]\n concat += list(label) + list(r)\n \n out = np.array(concat, np.uint8)\n out.tofile(z_settings.PROJECT_ROOT + \"lsdata/bin/\" + action + \".bin\")","title":""},{"docid":"c79ae33e8d853a2aa9542d3891a0f85b","score":"0.5583995","text":"def recover_3darrays(emotions_dir, neutral_instances):\r\n\r\n labels = sorted(os.listdir(emotions_dir))\r\n\r\n for index1, label in enumerate(labels):\r\n if index1 == 0:\r\n print(\"Recovering arrays for label\", label)\r\n for index2, npy in enumerate(\r\n os.listdir(emotions_dir + label)[:neutral_instances]):\r\n im = np.load(emotions_dir + label + '/' + npy)\r\n if index1 == 0 and index2 == 0:\r\n X = np.zeros((0, im.shape[0], im.shape[1], im.shape[2],\r\n im.shape[3]))\r\n y = np.zeros((0, len(labels)))\r\n X = np.append(X, [im], axis=0)\r\n\r\n y_temp = [0] * len(labels)\r\n for index, lab in enumerate(labels):\r\n if int(label) == int(lab):\r\n y_temp[index] = 1.0\r\n break\r\n y = np.append(y, [y_temp], axis=0)\r\n else:\r\n print(\"Recovering arrays for label\", label)\r\n for index2, npy in enumerate(os.listdir(emotions_dir + label)):\r\n im = np.load(emotions_dir + label + '/' + npy)\r\n X = np.append(X, [im], axis=0)\r\n\r\n y_temp = [0] * len(labels)\r\n for index, lab in enumerate(labels):\r\n if int(label) == int(lab):\r\n y_temp[index] = 1.0\r\n break\r\n y = np.append(y, [y_temp], axis=0)\r\n\r\n print(\"\\nShape of X array:\", X.shape)\r\n print(\"Shape of y array:\", y.shape)\r\n return X, y","title":""},{"docid":"aac201955cdd4fcf0af56c81f1070a06","score":"0.5568087","text":"def decode_label_file(file_path):\r\n bin_data = open(file_path, 'rb').read()\r\n offset = 0\r\n fmt_header = '>2i'\r\n magic_number, num_images = struct.unpack_from(fmt_header, bin_data, offset)\r\n print('magic number:%d, the number of the images: %d' % (magic_number, num_images))\r\n offset += struct.calcsize(fmt_header)\r\n fmt_image = '>B'\r\n labels = np.empty(num_images)\r\n for i in range(num_images):\r\n labels[i] = struct.unpack_from(fmt_image, bin_data, offset)[0]\r\n offset += struct.calcsize(fmt_image)\r\n return labels","title":""},{"docid":"f3921e99a56cd4cf7dcde6ba10c1484a","score":"0.55643845","text":"def process_dataset(labels, word2idx, read = False): \n \n \"\"\"\n \n Helper functions\n \n \"\"\"\n \n def pickle_matrix(matrix, path):\n \n with open(path,\"wb\") as output_file:\n\n pickle.dump(matrix, output_file)\n \n def unpickle_matrix(path):\n \n with open(path,\"rb\") as input_file:\n\n matrix = pickle.load(input_file)\n \n return matrix\n \n \"\"\"\n \n main code of process_dataset\n \n \"\"\"\n \n input_path = \"agg_data\"\n \n output_path = \"processed_data\"\n \n path_sentence_matrices = join(output_path, \"all_sentence_matrices.pkl\")\n\n path_labels_matrices = join(output_path, \"all_label_matrices.pkl\")\n \n if isfile(path_sentence_matrices) and isfile(path_labels_matrices) and read:\n \n print(\"Loading from \" + path_sentence_matrices + \" and \" + path_labels_matrices) \n \n sentence_matrices = unpickle_matrix(path_sentence_matrices)\n\n labels_matrices = unpickle_matrix(path_labels_matrices)\n\n return sentence_matrices, labels_matrices \n \n else:\n \n print(\"Processing dataset ...\")\n \n with open(\"agg_data/agg_data.pkl\",'rb') as dataframe_file:\n\n opened_dataframe = pickle.load(dataframe_file)\n\n num_records = len(opened_dataframe)\n\n print('Num of unique segments segments: {}'.format(num_records))\n\n num_labels = len(opened_dataframe[\"label\"].iloc[0])\n\n sentence_matrices = np.zeros(num_records, dtype = 'object')\n\n labels_matrices = np.zeros((num_records, num_labels))\n\n for index, row in opened_dataframe.iterrows():\n\n segment = row[\"segment\"]\n\n label = row[\"label\"]\n\n sentence_matrices[index] = sentence_serialization(segment, word2idx)\n\n labels_matrices[index] = label\n\n path_sentence_matrices = join(output_path, \"all_sentence_matrices.pkl\")\n\n path_labels_matrices = join(output_path, \"all_label_matrices.pkl\")\n\n pickle_matrix(sentence_matrices, path_sentence_matrices)\n\n pickle_matrix(labels_matrices, path_labels_matrices)\n\n return sentence_matrices, labels_matrices","title":""},{"docid":"a6cc8b0a7464ddfe0a6157c815820d7a","score":"0.55637866","text":"def test_classifyarray(self):\n pass","title":""},{"docid":"57846793464da6e1e597ac30658fe373","score":"0.5562969","text":"def recover_3darrays(emotions_dir, neutral_instances):\n\n labels = sorted(os.listdir(emotions_dir))\n\n for index1, label in enumerate(labels):\n if index1 == 0:\n print(\"Recovering arrays for label\", label)\n for index2, npy in enumerate(\n os.listdir(emotions_dir + label)[:neutral_instances]):\n im = np.load(emotions_dir + label + '/' + npy)\n if index1 == 0 and index2 == 0:\n X = np.zeros((0, im.shape[0], im.shape[1], im.shape[2],\n im.shape[3]))\n y = np.zeros((0, len(labels)))\n X = np.append(X, [im], axis=0)\n\n y_temp = [0] * len(labels)\n for index, lab in enumerate(labels):\n if int(label) == int(lab):\n y_temp[index] = 1.0\n break\n y = np.append(y, [y_temp], axis=0)\n else:\n print(\"Recovering arrays for label\", label)\n for index2, npy in enumerate(os.listdir(emotions_dir + label)):\n im = np.load(emotions_dir + label + '/' + npy)\n X = np.append(X, [im], axis=0)\n\n y_temp = [0] * len(labels)\n for index, lab in enumerate(labels):\n if int(label) == int(lab):\n y_temp[index] = 1.0\n break\n y = np.append(y, [y_temp], axis=0)\n\n print(\"\\nShape of X array:\", X.shape)\n print(\"Shape of y array:\", y.shape)\n return X, y","title":""},{"docid":"81b566c43e6d3b55265226c490c05986","score":"0.5554325","text":"def preprocess_mnist(data_path='data/data.txt', label_path='data/labels.txt',\n hash_path='data/hash.mat', name='Rodrigo Pena'):\n\n # Load in data and labels\n data = np.array(np.loadtxt(data_path))\n labels = np.loadtxt(label_path)\n hash_path = np\n\n # Select the 4 digits that should be used\n digits = name2digits(name, hash_path=hash_path)\n\n # Restrict full dataset to subset of selected digits\n rows_mask = np.logical_or.reduce([labels == x for x in digits])\n data = data[rows_mask, :]\n labels = labels[rows_mask].astype('int')\n\n return data, digits, labels","title":""},{"docid":"c72d2ad1a6f19c217b8e6140546ac314","score":"0.5552485","text":"def _extract_labels(filename, num_labels):\n print('Extracting labels from: ', filename)\n with gzip.open(filename) as bytestream:\n bytestream.read(8)\n buf = bytestream.read(1 * num_labels)\n labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)\n return labels","title":""},{"docid":"1fb8c8627fb16e5f21ea2475f5a57fce","score":"0.55489475","text":"def _load_labels(self, label_dir: Path, dataset: str, subset: str) -> np.array:\n with open(label_dir / f\"label_{subset}.pt\") as f:\n labels = [line.rstrip() for line in f]\n labels = [labels[i] for i in self.ind_list]\n return np.asarray(labels, dtype=np.string_)","title":""},{"docid":"b1dbfe46c2ab82fc64920cb4a455ccee","score":"0.5548237","text":"def __init__(self, inputDir, labelsFile, fake_data=False, one_hot=False):\n self.indexInEpoch = 0\n self.epochsCompleted = 0\n self.inputDir = inputDir\n output = np.genfromtxt(labelsFile, skip_header=1, dtype=[('image', 'S10'), ('label', 'S11')], delimiter=',')\n\n labels = [x[1] for x in output]\n\n self.numberOfClasses = len(set(labels))\n self.images = []\n self.sizes = []\n for file in os.listdir(inputDir):\n if(file.endswith('.jpg')):\n self.images.append(file)\n im = Image.open(os.path.join(inputDir, file))\n w,h = im.size\n self.sizes.append((w,h))\n self.images = np.array(self.images)\n\n maxSize = np.min(self.sizes,axis=0)\n self.maxW = int(maxSize[0])\n self.maxH = int(maxSize[1])\n self.maxSize = int(self.maxW * self.maxH)\n\n self.labelsDict = {int(re.search(\"w_(\\\\d+)\\.jpg\", x[0]).group(1)):int(re.search(\"whale_(\\\\d+)\", x[1]).group(1)) for x in output}\n\n # to provide default values\n self.labelsDict = defaultdict(lambda:0, self.labelsDict)\n\n self.examples = [int(re.search(\"w_(\\\\d+).jpg\",x).group(1)) for x in self.images]\n self.labels = [self.labelsDict[x] for x in self.examples]\n self.labels = np.array(self.labels)\n\n self.imagesIds = np.array(self.examples)\n self.train = self.images[self.labels > 0]\n self.origTrainLabels = self.labels[self.labels > 0]\n\n l = np.zeros(max(self.origTrainLabels))\n i = 0\n for k in sorted(set(self.origTrainLabels)):\n l[k-1] = i\n i += 1\n self.trainLabels = np.array([l[x-1] for x in self.origTrainLabels])\n self.test = self.images[self.labels == 0]","title":""},{"docid":"e43a276c829139e5b07ed2488fb04f29","score":"0.5537566","text":"def test_save_npy(self):\n npyf = open(self.npy_fname, \"wb\")\n utils.save1D(npyf, self.x, self.y,\n xlabel=self.xlab, ylabels=self.ylabs)\n npyf.close()\n\n npy_recarray = numpy.load(self.npy_fname)\n\n self.assertEqual(npy_recarray.shape, (3,))\n self.assertTrue(numpy.array_equal(npy_recarray['Ordinate1'],\n numpy.array((4, 5, 6))))","title":""},{"docid":"5367553b4c01ecddf82e9f218d7c45f1","score":"0.5536063","text":"def test_072(self):\n images = Images('foo',\n ['files/1.jpg', 'files/2.jpg', 'files/3.jpg', 'files/8rgb.jpg',\n 'files/8rgb.png', 'files/1.jpg', 'files/2.jpg', 'files/3.jpg'],\n [0, 0, 0, 0, 0, 0, 1, 1])\n images.split = 0.25, 12\n X_train, X_test, Y_train, Y_test = images.split\n self.assertEqual(len(images._train), 5)\n self.assertEqual(len(X_train), 5)\n self.assertEqual(len(X_test), 3)\n self.assertEqual(len(Y_train), 5)\n self.assertEqual(len(Y_test), 3)\n for _ in range(len(images._train)):\n # this is the label index\n y = images._train[_][0]\n self.assertEqual(Y_train[_][y], 1)","title":""},{"docid":"f532154d130870bff4254a03d70f1365","score":"0.5530023","text":"def part4():\n dal_path = \"dict_of_affect.txt\"\n dal = load_dal(dal_path)\n\n '''\n Training the model.\n '''\n # Initialize vocabulary.\n corpus_path = \"train.txt\"\n build_vocab(corpus_path)\n\n # Initialize X_train and Y_train arrays.\n list_snippet_label_tuple_train = load_corpus(corpus_path)\n m = len(list_snippet_label_tuple_train)\n V = len(vocab)\n X_train = numpy.empty([m, V + 3], dtype=float)\n Y_train = numpy.empty(m, dtype=float)\n\n # Build X_train and Y_train arrays.\n i = 0\n while i < len(list_snippet_label_tuple_train):\n snippet, label = list_snippet_label_tuple_train[i]\n list_quotes_separated = tokenize(snippet)\n list_edit_tags = tag_edits(list_quotes_separated)\n list_negation_pos_tuple = tag_negation(\" \".join(list_edit_tags))\n feature_vector = modified_get_features(list_negation_pos_tuple, dal)\n X_train[i] = feature_vector\n Y_train[i] = label\n i = i + 1\n\n # Normalizing the values for the trained model.\n X_normalized_train = normalize(X_train)\n\n '''\n Testing the model.\n '''\n test_corpus = \"test.txt\"\n list_snippet_label_tuple_test = load_corpus(test_corpus)\n\n m = len(list_snippet_label_tuple_test)\n X_test = numpy.empty([m, V + 3], dtype=float)\n Y_true = numpy.empty(m, dtype=float)\n\n # Build X_test and Y_true arrays.\n i = 0\n while i < len(list_snippet_label_tuple_test):\n snippet, label = list_snippet_label_tuple_test[i]\n list_quotes_separated = tokenize(snippet)\n list_edit_tags = tag_edits(list_quotes_separated)\n list_negation_pos_tuple_test = tag_negation(\" \".join(list_edit_tags))\n feature_vector = modified_get_features(list_negation_pos_tuple_test, dal)\n X_test[i] = feature_vector\n Y_true[i] = label\n i = i + 1\n\n # Normalizing the values for the trained model.\n X_test_normalized = normalize(X_test)\n print(\"Testing Completed\")\n\n '''\n Logistic Regression Model.\n '''\n lr_obj = LogisticRegression()\n lr_obj.fit(X_normalized_train, Y_train)\n Y_lr_pred = lr_obj.predict(X_test_normalized)\n\n p, r, f = evaluate_predictions(Y_lr_pred, Y_true)\n print(\"For Logistic: \")\n print(\"Precision:\" + str(p))\n print(\"Recall:\" + str(r))\n print(\"F-measure:\" + str(f))\n '''\n Top k features from the Logistic Regression.\n '''\n l = top_features(lr_obj, 10)\n print(str(l))","title":""},{"docid":"792ed2ac517be520f564adb319dad6f9","score":"0.5527436","text":"def _load_imagenet3d_annotation(self, index):\n\n if self._image_set == 'test' or self._image_set == 'test_1' or self._image_set == 'test_2':\n lines = []\n else:\n filename = os.path.join(self._imagenet3d_path, 'Labels', index + '.txt')\n lines = []\n with open(filename) as f:\n for line in f:\n lines.append(line)\n\n num_objs = len(lines)\n\n boxes = np.zeros((num_objs, 4), dtype=np.float32)\n viewpoints = np.zeros((num_objs, 3), dtype=np.float32) # azimuth, elevation, in-plane rotation\n viewpoints_flipped = np.zeros((num_objs, 3), dtype=np.float32) # azimuth, elevation, in-plane rotation\n gt_classes = np.zeros((num_objs), dtype=np.int32)\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n for ix, line in enumerate(lines):\n words = line.split()\n assert len(words) == 5 or len(words) == 8, 'Wrong label format: {}'.format(index)\n cls = self._class_to_ind[words[0]]\n boxes[ix, :] = [float(n) for n in words[1:5]]\n gt_classes[ix] = cls\n overlaps[ix, cls] = 1.0\n if len(words) == 8:\n viewpoints[ix, :] = [float(n) for n in words[5:8]]\n # flip the viewpoint\n viewpoints_flipped[ix, 0] = -viewpoints[ix, 0] # azimuth\n viewpoints_flipped[ix, 1] = viewpoints[ix, 1] # elevation\n viewpoints_flipped[ix, 2] = -viewpoints[ix, 2] # in-plane rotation\n else:\n viewpoints[ix, :] = np.inf\n viewpoints_flipped[ix, :] = np.inf\n\n gt_subclasses = np.zeros((num_objs), dtype=np.int32)\n gt_subclasses_flipped = np.zeros((num_objs), dtype=np.int32)\n subindexes = np.zeros((num_objs, self.num_classes), dtype=np.int32)\n subindexes_flipped = np.zeros((num_objs, self.num_classes), dtype=np.int32)\n viewindexes_azimuth = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n viewindexes_azimuth_flipped = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n viewindexes_elevation = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n viewindexes_elevation_flipped = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n viewindexes_rotation = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n viewindexes_rotation_flipped = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n overlaps = scipy.sparse.csr_matrix(overlaps)\n subindexes = scipy.sparse.csr_matrix(subindexes)\n subindexes_flipped = scipy.sparse.csr_matrix(subindexes_flipped)\n viewindexes_azimuth = scipy.sparse.csr_matrix(viewindexes_azimuth)\n viewindexes_azimuth_flipped = scipy.sparse.csr_matrix(viewindexes_azimuth_flipped)\n viewindexes_elevation = scipy.sparse.csr_matrix(viewindexes_elevation)\n viewindexes_elevation_flipped = scipy.sparse.csr_matrix(viewindexes_elevation_flipped)\n viewindexes_rotation = scipy.sparse.csr_matrix(viewindexes_rotation)\n viewindexes_rotation_flipped = scipy.sparse.csr_matrix(viewindexes_rotation_flipped)\n\n if cfg.IS_RPN:\n if cfg.IS_MULTISCALE:\n # compute overlaps between grid boxes and gt boxes in multi-scales\n # rescale the gt boxes\n boxes_all = np.zeros((0, 4), dtype=np.float32)\n for scale in cfg.TRAIN.SCALES:\n boxes_all = np.vstack((boxes_all, boxes * scale))\n gt_classes_all = np.tile(gt_classes, len(cfg.TRAIN.SCALES))\n\n # compute grid boxes\n s = PIL.Image.open(self.image_path_from_index(index)).size\n image_height = s[1]\n image_width = s[0]\n boxes_grid, _, _ = get_boxes_grid(image_height, image_width)\n\n # compute overlap\n overlaps_grid = bbox_overlaps(boxes_grid.astype(np.float), boxes_all.astype(np.float))\n \n # check how many gt boxes are covered by grids\n if num_objs != 0:\n index = np.tile(range(num_objs), len(cfg.TRAIN.SCALES))\n max_overlaps = overlaps_grid.max(axis = 0)\n fg_inds = []\n for k in xrange(1, self.num_classes):\n fg_inds.extend(np.where((gt_classes_all == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])\n index_covered = np.unique(index[fg_inds])\n\n for i in xrange(self.num_classes):\n self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])\n self._num_boxes_covered[i] += len(np.where(gt_classes[index_covered] == i)[0])\n else:\n assert len(cfg.TRAIN.SCALES_BASE) == 1\n scale = cfg.TRAIN.SCALES_BASE[0]\n feat_stride = 16\n # faster rcnn region proposal\n base_size = 16\n ratios = cfg.TRAIN.RPN_ASPECTS\n scales = cfg.TRAIN.RPN_SCALES\n anchors = generate_anchors(base_size, ratios, scales)\n num_anchors = anchors.shape[0]\n\n # image size\n s = PIL.Image.open(self.image_path_from_index(index)).size\n image_height = s[1]\n image_width = s[0]\n\n # height and width of the heatmap\n height = np.round((image_height * scale - 1) / 4.0 + 1)\n height = np.floor((height - 1) / 2 + 1 + 0.5)\n height = np.floor((height - 1) / 2 + 1 + 0.5)\n\n width = np.round((image_width * scale - 1) / 4.0 + 1)\n width = np.floor((width - 1) / 2.0 + 1 + 0.5)\n width = np.floor((width - 1) / 2.0 + 1 + 0.5)\n\n # gt boxes\n gt_boxes = boxes * scale\n\n # 1. Generate proposals from bbox deltas and shifted anchors\n shift_x = np.arange(0, width) * feat_stride\n shift_y = np.arange(0, height) * feat_stride\n shift_x, shift_y = np.meshgrid(shift_x, shift_y)\n shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),\n shift_x.ravel(), shift_y.ravel())).transpose()\n # add A anchors (1, A, 4) to\n # cell K shifts (K, 1, 4) to get\n # shift anchors (K, A, 4)\n # reshape to (K*A, 4) shifted anchors\n A = num_anchors\n K = shifts.shape[0]\n all_anchors = (anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)))\n all_anchors = all_anchors.reshape((K * A, 4))\n\n # compute overlap\n overlaps_grid = bbox_overlaps(all_anchors.astype(np.float), gt_boxes.astype(np.float))\n \n # check how many gt boxes are covered by anchors\n if num_objs != 0:\n max_overlaps = overlaps_grid.max(axis = 0)\n fg_inds = []\n for k in xrange(1, self.num_classes):\n fg_inds.extend(np.where((gt_classes == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])\n\n for i in xrange(self.num_classes):\n self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])\n self._num_boxes_covered[i] += len(np.where(gt_classes[fg_inds] == i)[0])\n\n return {'boxes' : boxes,\n 'gt_classes': gt_classes,\n 'gt_viewpoints': viewpoints,\n 'gt_viewpoints_flipped': viewpoints_flipped,\n 'gt_viewindexes_azimuth': viewindexes_azimuth,\n 'gt_viewindexes_azimuth_flipped': viewindexes_azimuth_flipped,\n 'gt_viewindexes_elevation': viewindexes_elevation,\n 'gt_viewindexes_elevation_flipped': viewindexes_elevation_flipped,\n 'gt_viewindexes_rotation': viewindexes_rotation,\n 'gt_viewindexes_rotation_flipped': viewindexes_rotation_flipped,\n 'gt_subclasses': gt_subclasses,\n 'gt_subclasses_flipped': gt_subclasses_flipped,\n 'gt_overlaps' : overlaps,\n 'gt_subindexes': subindexes,\n 'gt_subindexes_flipped': subindexes_flipped,\n 'flipped' : False}","title":""},{"docid":"c16d251eae3552013d8337f6a652dbf1","score":"0.55223763","text":"def load_data_and_labels():\n # Load data from files\n with open('./data/authors.json') as data_file:\n data = json.load(data_file)\n\n # pprint(data)\n\n list_examples = list()\n labels_lenght = len(data)\n\n list_labels = []\n for d in data:\n index = d['index']\n temp_label = []\n i = 0\n while i < labels_lenght: # inzializzi la label temporanea con tutti 0\n if i == index:\n temp_label.append(1)\n else:\n temp_label.append(0)\n i += 1\n # temp_label[d['index']] = 1\n temp_examples = list(open('data/stemmed_data/' + d['file_name']).readlines())\n temp_examples = [s.strip() for s in temp_examples]\n for _ in temp_examples:\n list_labels.append(temp_label)\n list_examples.append(temp_examples)\n\n x_text = []\n for exa in list_examples:\n x_text = x_text + exa\n x_text = [clean_str(sent) for sent in x_text]\n x_text = [s.split(\" \") for s in x_text]\n\n # y = np.concatenate(list_labels, 0)\n return [x_text, list_labels]","title":""},{"docid":"82b13df0fdb06fed810d7df1529abbe3","score":"0.5521459","text":"def gen_occ_labels(mode, check_order=False):\n print('dataset_out_dir:', gen_data_dir)\n if not os.path.exists(gen_data_dir): os.makedirs(gen_data_dir)\n scenes_root = org_dataset_root # root for scene subdirs\n org_rgb_dir = 'cam0'\n org_depth_dir = 'depth0'\n org_normal_dir = 'normal0'\n org_label_dir = 'label0'\n mask_gt_type = 'instance'\n\n thr_depth = 25. # thresholds: depth_diff(mm)\n ROI_sz = 3 # region of interest size in images\n version_n = 'raycasting_{}mm_debug'.format(int(thr_depth)) # version name\n if mode == 'normal': # all scenes for dataset generation\n sel_scene_list = sorted([fn for fn in os.listdir(scenes_root)])\n elif mode == 'debug': # one scene dir for debug\n # sel_scene_list = ['3FO4IDP1HR4R_Balcony']\n sel_scene_list = ['3FO4MMLCRTM9_Living_room']\n\n for scene_id, scene_name in enumerate(tqdm(sel_scene_list)):\n cur_in_scene_dir = os.path.join(scenes_root, scene_name)\n cur_in_rgb_dir = os.path.join(cur_in_scene_dir, org_rgb_dir, 'data')\n cur_in_depth_dir = os.path.join(cur_in_scene_dir, org_depth_dir, 'data')\n cur_in_normal_dir = os.path.join(cur_in_scene_dir, org_normal_dir, 'data')\n cur_in_label_dir = os.path.join(cur_in_scene_dir, org_label_dir, 'data')\n\n curr_out_data_dir = os.path.join(gen_data_dir, '{}_{}'.format(scene_name, version_n))\n if not os.path.exists(curr_out_data_dir): os.makedirs(curr_out_data_dir)\n print('write data to {}'.format(curr_out_data_dir))\n\n file_ind = [name.replace('.png', '') for name in os.listdir(cur_in_rgb_dir)]\n file_ind.sort(key=int)\n file_names = ['{}'.format(idx) for idx in file_ind]\n\n for idx, file_name in enumerate(tqdm(file_names)):\n in_rgb_path = os.path.join(cur_in_rgb_dir, '{}.png'.format(file_name))\n in_depth_path = os.path.join(cur_in_depth_dir, '{}.png'.format(file_name))\n in_normal_path = os.path.join(cur_in_normal_dir, '{}.png'.format(file_name))\n in_label_path = os.path.join(cur_in_label_dir, '{}_{}.png'.format(file_name, mask_gt_type))\n\n out_depth_vis_path = os.path.join(curr_out_data_dir, '{:04d}-depth_vis.png'.format(idx))\n out_depth_path = os.path.join(curr_out_data_dir, '{:04d}-depth.png'.format(idx))\n out_rgb_path = os.path.join(curr_out_data_dir, '{:04d}-rgb.png'.format(idx))\n out_normal_path = os.path.join(curr_out_data_dir, '{:04d}-normal.png'.format(idx))\n out_occ_edge_path = os.path.join(curr_out_data_dir, '{:04d}-edge.png'.format(idx))\n out_occ_edge_fg_path = os.path.join(curr_out_data_dir, '{:04d}-edge_fg.png'.format(idx))\n out_occ_ori_path = os.path.join(curr_out_data_dir, '{:04d}-ori.png'.format(idx))\n out_pixwise_order_path = os.path.join(curr_out_data_dir, '{:04d}-order-pix.npz'.format(idx))\n out_delta_abs_depth_path = os.path.join(curr_out_data_dir, '{:04d}-delta_abs_depth_check.png'.format(idx))\n out_delta_adj_depth_path = os.path.join(curr_out_data_dir, '{:04d}-delta_adj_depth_check.png'.format(idx))\n\n rgb = cv2.imread(in_rgb_path, cv2.IMREAD_COLOR)\n depth_org = cv2.imread(in_depth_path, cv2.IMREAD_UNCHANGED) # uint16\n normal_org = cv2.imread(in_normal_path, cv2.IMREAD_UNCHANGED) # z,y,x; point-to-camera\n mask_gt = cv2.imread(in_label_path, cv2.IMREAD_UNCHANGED) # instance-wise mask (not accurate)\n mask_invalid = np.zeros(rgb.shape[:2]) # all pixels are valid in interiornet\n\n depth = np.copy(depth_org).astype(np.float32)\n normal = normal_org[:, :, [2, 1, 0]] # z,y,x => x,y,z\n\n # gen occlusion order for each pixel w.r.t. its neighbor pixels\n _, occ_order_pix, delta_abs_depth, delta_adj_depth, _ = gen_occ_order(K, depth, mask_gt, mask_invalid, ROI_sz,\n thr_depth, normal, lbl_type='mask',\n dataset='interiornet', fast_abs_diff=False)\n if check_order: # vis occ order\n occ_order_vis_path = os.path.join(curr_out_data_dir, '{:04d}-order-check.png'.format(idx))\n viz_occ_order(rgb[:, :, [2, 1, 0]], occ_order_pix, occ_order_vis_path)\n # viz_delta_depth(rgb[:, :, [2, 1, 0]], delta_abs_depth, out_delta_abs_depth_path, thr_depth * 2)\n # viz_delta_depth(rgb[:, :, [2, 1, 0]], delta_adj_depth, out_delta_adj_depth_path, thr_depth * 2)\n\n # gen occlusion edge from occlusion order\n occ_edge_fg, occ_edge_bg, occ_edge = occ_order_to_edge(occ_order_pix, connectivity=8)\n\n # gen occlusion orientation from occlusion order\n occ_ori = occ_order_connect8_to_ori(occ_order_pix[:, :, 1:])\n occ_ori[occ_edge_fg == 0] = 0 # only preserve occ_ori on occ_edge_fg\n occ_ori_vis = ((occ_ori + 1) / 2. * 255).astype(np.uint8) # H,W\n\n # vis depth\n depth[depth == 0] = np.inf # interiornet, inf for unknown region\n depth_vis = (depth.min() / depth) * 255.0 # save inverse depth for better vis\n\n # save img, depth, normal, occ edge, occ ori, occ order\n cv2.imwrite(out_rgb_path, rgb)\n cv2.imwrite(out_depth_path, depth_org)\n plt.imsave(out_depth_vis_path, depth_vis, vmin=depth_vis.min(), vmax=depth_vis.max())\n cv2.imwrite(out_normal_path, normal_org)\n cv2.imwrite(out_occ_edge_path, (occ_edge * 255.0).astype('uint8'))\n cv2.imwrite(out_occ_edge_fg_path, (occ_edge_fg * 255.0).astype('uint8'))\n cv2.imwrite(out_occ_ori_path, occ_ori_vis)\n np.savez_compressed(out_pixwise_order_path, order=occ_order_pix.astype('int8'))","title":""},{"docid":"fc7baaed3f7f1d8d0a2daee7b585b7d4","score":"0.55199367","text":"def test_labeled_binary(self):\n tf1 = Nations().training\n self.assert_binary_io(tf1, TriplesFactory)","title":""},{"docid":"c7fcdc96a02ae42f5498425d4764da69","score":"0.55173224","text":"def load_PA100K_10():\n # Loading the file that contains the name of the images.\n mat = scipy.io.loadmat('annotation.mat')\n\n # Getting the train validation and test data names and labels.\n test_images_name = mat['test_images_name']\n test_label = mat['test_label']\n train_images_name = mat['train_images_name']\n train_label = mat['train_label']\n \n train_label = train_label[:1000]\n test_label = test_label[:1000]\n\n train_images = []\n for i in range(len(train_images_name)):\n train_images.append(plt.imread('/Users/aymene/Desktop/comp-internship/data/release_data/release_data/' + train_images_name[i][0][0]))\n if (i+1)>=1000:\n break\n\n test_images = []\n for i in range(len(test_images_name)):\n test_images.append(plt.imread('/Users/aymene/Desktop/comp-internship/data/release_data/release_data/' + test_images_name[i][0][0]))\n if (i+1)>=1000:\n break\n \n \"\"\"\n train_images,train_label,test_images,test_label : are np arrays.\n \"\"\"\n return train_images,train_label,test_images,test_label","title":""},{"docid":"7b877e2e0ddb931775c65d3066aade00","score":"0.5515445","text":"def load_bbox_annotation(self, label,img_path):\n imname = os.path.join(img_path)\n im = cv2.imread(imname)\n\n h_ratio = 1.0 * self.image_size / im.shape[0]\n w_ratio = 1.0 * self.image_size / im.shape[1]\n\n label_data = pickle.load(open(label,'r'))\n\n num_objs = label_data['num_labels']\n\n label = np.zeros((self.cell_size, self.cell_size, 5+cfg.NUM_LABELS))\n\n for objs in label_data['objects']:\n box_ind = objs['box_index']\n class_label = objs['num_class_label']\n\n\n x1 = max(min((float(box_ind[0]) - 1) * w_ratio, self.image_size - 1), 0)\n y1 = max(min((float(box_ind[1]) - 1) * h_ratio, self.image_size - 1), 0)\n x2 = max(min((float(box_ind[2]) - 1) * w_ratio, self.image_size - 1), 0)\n y2 = max(min((float(box_ind[3]) - 1) * h_ratio, self.image_size - 1), 0)\n\n boxes = [(x2 + x1) / 2.0, (y2 + y1) / 2.0, x2 - x1, y2 - y1]\n \n x_ind = int(box_ind[0] * self.cell_size / self.image_size)\n y_ind = int(box_ind[1] * self.cell_size / self.image_size)\n\n if label[y_ind, x_ind, 0] == 1:\n continue\n label[y_ind, x_ind, 0] = 1\n label[y_ind, x_ind, 1:5] = boxes\n label[y_ind, x_ind, 5 + class_label] = 1\n\n return label, num_objs","title":""},{"docid":"280bba1244d98d2557ba2c7c46d2cf05","score":"0.5502933","text":"def voc_eval(detpath,\n annopath,\n classname,\n ovthresh=0.5):\n # assumes detections are in detpath.format(classname)\n # assumes annotations are in annopath.format(imagename)\n # assumes imagesetfile is a text file with each line an image name\n # cachedir caches the annotations in a pickle file\n npos = 0\n gt_boxes = {}\n for line in open(os.path.join('../PascalVOC/bonus_ground_truth.txt')):\n s = re.split(' ', line)\n cls = int(s[1])\n if (cls != classname):\n continue\n if s[0] in gt_boxes.keys():\n boxes = np.array(s[2:]).astype(np.float)\n boxes = np.array([boxes[0], boxes[1], boxes[0] + boxes[2], boxes[1] + boxes[3]])\n gt_boxes[s[0]]['bbox'].append(boxes)\n gt_boxes[s[0]]['det'].append(False)\n else:\n gt_boxes[s[0]] = {}\n gt_boxes[s[0]]['bbox'] = []\n gt_boxes[s[0]]['det'] = []\n boxes = np.array(s[2:]).astype(np.float)\n boxes = np.array([boxes[0], boxes[1], boxes[0] + boxes[2], boxes[1] + boxes[3]])\n gt_boxes[s[0]]['bbox'].append(boxes)\n gt_boxes[s[0]]['det'].append(False)\n npos = npos + 1\n \n\n # read dets\n # detfile = detpath.format(classname)\n detfile = detpath\n # print(detpath)\n with open(detfile, 'r') as f:\n lines = f.readlines()\n\n splitlines = [x.strip().split(' ') for x in lines]\n image_ids = [x[0] for x in splitlines]\n confidence = np.array([float(x[1]) for x in splitlines])\n BB = np.array([[float(z) for z in x[2:]] for x in splitlines])\n # print(BB, '@@@',confidence)\n # sort by confidence\n sorted_ind = np.argsort(-confidence)\n sorted_scores = np.sort(-confidence)\n BB = BB[sorted_ind, :]\n image_ids = [image_ids[x] for x in sorted_ind]\n\n # go down dets and mark TPs and FPs\n nd = len(image_ids)\n tp = np.zeros(nd)\n fp = np.zeros(nd)\n for d in range(nd):\n if image_ids[d] not in gt_boxes.keys():\n fp[d] = 1.\n continue\n R = gt_boxes[image_ids[d]]\n bb = BB[d, :].astype(float)\n ovmax = -np.inf\n BBGT = np.array(R['bbox']).astype(float)\n\n if BBGT.size > 0:\n # compute overlaps\n # intersection\n ixmin = np.maximum(BBGT[:, 0], bb[0])\n iymin = np.maximum(BBGT[:, 1], bb[1])\n ixmax = np.minimum(BBGT[:, 2], bb[2])\n iymax = np.minimum(BBGT[:, 3], bb[3])\n iw = np.maximum(ixmax - ixmin + 1., 0.)\n ih = np.maximum(iymax - iymin + 1., 0.)\n inters = iw * ih\n\n # union\n uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +\n (BBGT[:, 2] - BBGT[:, 0] + 1.) *\n (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)\n\n overlaps = inters / uni\n ovmax = np.max(overlaps)\n jmax = np.argmax(overlaps)\n\n if ovmax > ovthresh:\n if not R['det'][jmax]:\n tp[d] = 1.\n R['det'][jmax] = 1\n else:\n fp[d] = 1.\n else:\n fp[d] = 1.\n\n # compute precision recall\n fp = np.cumsum(fp)\n tp = np.cumsum(tp)\n rec = tp / float(npos)\n # avoid divide by zero in case the first detection matches a difficult\n # ground truth\n prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)\n ap = voc_ap(rec, prec, True)\n\n return rec, prec, ap","title":""},{"docid":"842a2f5e8663fc72f6c0b51d0b864189","score":"0.54969317","text":"def create_VOC_label_file(label_file_root_path, image_name, image_size, bboxs):\n\n objIndex = 0\n for bbox in bboxs:\n objIndex += 1\n #data = data.strip('\\n')\n #datas = data.split(' ')\n datas = bboxs\n if 4 != len(bbox):\n print 'bounding box information error'\n continue\n #pictureName = each.replace('.txt', '.jpg')\n #imageFile = imgpath + pictureName\n #img = cv2.imread(imageFile)\n #imgSize = img.shape\n if objIndex == 1:\n xmlName = image_name.replace('.jpg', '.xml')\n f = open(os.path.join(label_file_root_path, xmlName), \"w\")\n doc = Document()\n annotation = doc.createElement('annotation')\n doc.appendChild(annotation)\n\n folder = doc.createElement('folder')\n folder.appendChild(doc.createTextNode(foldername))\n annotation.appendChild(folder)\n\n filename = doc.createElement('filename')\n filename.appendChild(doc.createTextNode(image_name))\n annotation.appendChild(filename)\n\n source = doc.createElement('source')\n database = doc.createElement('database')\n database.appendChild(doc.createTextNode('My Database'))\n source.appendChild(database)\n source_annotation = doc.createElement('annotation')\n source_annotation.appendChild(doc.createTextNode(foldername))\n source.appendChild(source_annotation)\n image = doc.createElement('image')\n image.appendChild(doc.createTextNode('flickr'))\n source.appendChild(image)\n flickrid = doc.createElement('flickrid')\n flickrid.appendChild(doc.createTextNode('NULL'))\n source.appendChild(flickrid)\n annotation.appendChild(source)\n\n owner = doc.createElement('owner')\n flickrid = doc.createElement('flickrid')\n flickrid.appendChild(doc.createTextNode('NULL'))\n owner.appendChild(flickrid)\n name = doc.createElement('name')\n name.appendChild(doc.createTextNode('idaneel'))\n owner.appendChild(name)\n annotation.appendChild(owner)\n\n size = doc.createElement('size')\n width = doc.createElement('width')\n width.appendChild(doc.createTextNode(str(image_size[1])))\n size.appendChild(width)\n height = doc.createElement('height')\n height.appendChild(doc.createTextNode(str(image_size[0])))\n size.appendChild(height)\n depth = doc.createElement('depth')\n depth.appendChild(doc.createTextNode(str(image_size[2])))\n size.appendChild(depth)\n annotation.appendChild(size)\n\n segmented = doc.createElement('segmented')\n segmented.appendChild(doc.createTextNode(str(0)))\n annotation.appendChild(segmented)\n annotation.appendChild(insertObject(doc, bbox))\n else:\n annotation.appendChild(insertObject(doc, bbox))\n try:\n f.write(doc.toprettyxml(indent=' '))\n f.close()\n except:\n pass","title":""},{"docid":"f4b7eb49e01c40834abaf65d9ad2ec02","score":"0.54937774","text":"def load_labels(file: str) -> np.ndarray:\n rows = []\n with open(file, 'r', encoding='utf-8') as f:\n for row in f:\n rows.append(row.strip())\n return np.array(rows)","title":""},{"docid":"25dbfa95e2ada78f416b554e3a47e5ad","score":"0.54923767","text":"def _data_check(self, datalist):\n \n feat_n_check = set([])\n label_n_check = set([])\n label_item_set = set([])\n label_row_set = set([])\n for each_data in datalist:\n for each_x_path in each_data['x']:\n if os.path.exists(each_x_path) is False:\n raise Exception('episode file not exist')\n feat_n_check.add(each_data['feat_n'])\n for each_y_path in each_data['y']:\n label_n_check.add(len(np.array(each_y_path)))\n label_item_set.update(np.array(each_y_path).astype(int).tolist())\n label_row_set.add(sum(np.array(each_y_path).astype(int)))\n \n if len(feat_n_check) != 1:\n raise Exception('feat_n is inconformity in data')\n if len(label_n_check) != 1:\n raise Exception('label_n is inconformity in data')\n \n if len(label_item_set) <= 1:\n raise Exception('value space size <=1 is unvalid')\n elif len(label_item_set) == 2:\n if 0 in label_item_set and 1 in label_item_set:\n if list(label_n_check)[0] == 1:\n self.task = 'binaryclass'\n else:\n if max(label_row_set) == 1:\n self.task = 'multiclass'\n else:\n self.task = 'multilabel'\n else:\n raise Exception('odd value exist in label value space')\n else:\n if list(label_n_check)[0] == 1:\n self.task = 'regression'\n else:\n raise Exception('odd value exist in label value space')\n print ('current task can beed seen as {0}'.format(self.task))\n self.input_size = list(feat_n_check)[0]\n self.label_size = list(label_n_check)[0]\n self.loss_name = self._get_lossname(self.loss_name)","title":""},{"docid":"36f9c70e5ad54a07dd895d98ad0454bb","score":"0.54920715","text":"def filter_label():\n # first walk label_source folder to get label_merge.txt\n label_source_path = '/mnt/data/LINEdevkit/txt/label_source'\n label_merge = []\n for label_source in os.listdir(label_source_path):\n with open(os.path.join(label_source_path, label_source)) as f:\n lines = [line.strip() for line in f.readlines()]\n label_merge.extend(lines)\n print 'all image is %d'%len(label_merge)\n\n # label_path = '/media/data/LINEdevkit/txt/label_merge.txt'\n label_path_filter = '/mnt/data/LINEdevkit/txt/label_merge_filter.txt'\n label_filter_file = open(label_path_filter, 'w')\n label_filter_val_ls = []\n label_filter_train_ls = []\n st = set()\n for line in label_merge:\n try:\n line = line.decode('utf-8')\n label = line.split(' ', 1)[1]\n except:\n continue\n print line\n label = re.sub(ur'[^\\u4e00-\\u9fa5a-zA-Z0-9]{1}', '*', label)\n st = st | set(label)\n tmp = line.split(' ', 1)[0] + ' ' + label + '\\n'\n label_filter_train_ls.append(tmp.encode('utf-8')) \\\n if random.uniform(0, 1) < 0.9 else label_filter_val_ls.append(tmp.encode('utf-8'))\n label_filter_file.write(tmp.encode('utf-8'))\n label_filter_file.close()\n\n random.shuffle(label_filter_train_ls)\n random.shuffle(label_filter_val_ls)\n with open('/mnt/data/LINEdevkit/txt/label_train.txt', 'w') as f_train:\n for file_train in label_filter_train_ls:\n f_train.write(file_train)\n\n with open('/mnt/data/LINEdevkit/txt/label_val.txt', 'w') as f_val:\n for file_val in label_filter_val_ls:\n f_val.write(file_val)\n\n with open('/mnt/data/LINEdevkit/txt/merge_map.txt', 'w') as merge_map_file:\n for (ind, label) in enumerate(st):\n merge_map_file.write('%d %s\\n' % (ind, label.encode('utf-8')))\n merge_map_file.close()","title":""},{"docid":"dabd5862c67b2263e935bf0bac9b5e95","score":"0.5489686","text":"def parse_data(dir_path='tiny-imagenet-200', n_classes_limit=N_CLASSES_LIMIT):\n\n def _read_wnids(filename='wnids.txt'):\n wnids_file = os.path.join(dir_path, filename)\n assert os.path.exists(wnids_file)\n wnids = OrderedDict()\n c = 0\n with open(wnids_file) as f:\n for line in f:\n wnids[line.strip()] = c\n c += 1\n\n # only take 50 labels\n if c == n_classes_limit:\n break\n\n print(\"%d labels\" % c)\n return wnids\n\n def _enumerate_train_imgs(train_dir, wnids):\n assert os.path.isdir(train_dir)\n subdirs = os.listdir(train_dir)\n\n train_imgs, train_labels = [], []\n test_imgs, test_labels = [], []\n\n for subdir in subdirs:\n if subdir != \".DS_Store\": # On Mac only\n c = 0 # for each subdir (i.e. label), take first 50 samples for test\n subdir_path = os.path.join(train_dir, subdir, \"images\")\n imgs = os.listdir(subdir_path)\n\n try:\n label_idx = wnids[subdir]\n except:\n continue\n\n for img in imgs:\n full_img_path = os.path.join(subdir_path, img)\n if not os.path.exists(full_img_path):\n print(\"%s not existed ...\" % full_img_path)\n continue\n\n if c < 50:\n test_imgs.append(full_img_path)\n test_labels.append(label_idx)\n else:\n train_imgs.append(full_img_path)\n train_labels.append(label_idx)\n\n c += 1\n\n return train_imgs, train_labels, test_imgs, test_labels\n\n def _read_val_annotations(val_annotation, wnids):\n val_imgs, val_labels = [], []\n with open(val_annotation) as f:\n for line in f:\n fields = line.strip().split()\n img, label = fields[0], fields[1]\n full_img_path = os.path.join(dir_path, \"val\", \"images\", img)\n if not os.path.exists(full_img_path):\n print(\"%s not existed ...\" % full_img_path)\n continue\n\n try:\n label_idx = wnids[label]\n except:\n continue\n\n val_imgs.append(full_img_path)\n val_labels.append(label_idx)\n\n return val_imgs, val_labels\n\n wnids = _read_wnids()\n train_dir = os.path.join(dir_path, \"train\")\n val_annotation = os.path.join(dir_path, \"val\", \"val_annotations.txt\")\n\n # take train and test images\n train_imgs, train_labels, test_imgs, test_labels = \\\n _enumerate_train_imgs(train_dir, wnids)\n\n # take val images\n val_imgs, val_labels = _read_val_annotations(val_annotation, wnids)\n\n data_dict = {\n \"train_imgs\": train_imgs,\n \"train_labels\": train_labels,\n \"val_imgs\": val_imgs,\n \"val_labels\": val_labels,\n \"test_imgs\": test_imgs,\n \"test_labels\": test_labels\n }\n\n return data_dict # A dictionary of lists of data paths and labels","title":""},{"docid":"5d204dde7ecf754033f76e7a8342641f","score":"0.54848415","text":"def load_data_and_labels_new(positive_data_file=None, negative_data_file=None, data_dir=None):\n\n # Load data from files\n \"\"\"\n positive_examples = list(open(positive_data_file, \"r\").readlines())\n positive_examples = [s.strip() for s in positive_examples]\n negative_examples = list(open(negative_data_file, \"r\").readlines())\n negative_examples = [s.strip() for s in negative_examples]\n \"\"\"\n positive_examples = list()\n negative_examples = list()\n\n if data_dir:\n data_folder = data_dir\n else:\n data_folder = './data'\n\n train_data = {'00': list(), '01': list()}\n\n for current_cat in ['00', '01']:\n print(\"{}/{}/\".format(data_folder, current_cat))\n file_list = list()\n for root, dirs, f_list in os.walk(\"{}/{}/\".format(data_folder, current_cat)):\n for oo in f_list:\n if 'class.nfo' != oo:\n file_list.append('{}/{}/{}'.format(data_folder, current_cat, oo))\n\n print(\"Готовим категорию: {} - {} сообщений\".format(current_cat, len(file_list)))\n\n data = list()\n for ff in file_list:\n f = open(ff, 'r')\n ss = re.split(r'[\\r|\\n]+', f.read())\n train_data[current_cat].append(\" \".join(ss))\n\n positive_examples = train_data['00']\n negative_examples = train_data['01']\n\n # Split by words\n x_text = positive_examples + negative_examples\n x_text = [clean_str_new(sent) for sent in x_text]\n # Generate labels\n positive_labels = [[0, 1] for _ in positive_examples]\n negative_labels = [[1, 0] for _ in negative_examples]\n y = np.concatenate([positive_labels, negative_labels], 0)\n\n #print(x_text)\n #print(y)\n #raw_input()\n\n return [x_text, y]","title":""},{"docid":"4b6d74220b6f38ee0220fb190ac52bec","score":"0.54772806","text":"def test_gh_issue_3025():\n d = np.zeros((60, 320))\n d[:, :257] = 1\n d[:, 260:] = 1\n d[36, 257] = 1\n d[35, 258] = 1\n d[35, 259] = 1\n assert ndimage.label(d, np.ones((3, 3)))[1] == 1","title":""},{"docid":"be06a1b2207b7ca714224eaeb8fc7d56","score":"0.5472245","text":"def read_igor_marginals_txt(marginals_file_name , dim_names=False):\n with open(marginals_file_name,'r') as file:\n #Model parameters are stored inside a dictionary of ndarrays\n model_dict = {}\n dimension_names_dict = {}\n element_name=\"\"\n first = True\n first_dim_line = False\n element_marginal_array = []\n indices_array = []\n\n for line in file:\n strip_line = line.rstrip('\\n') #Remove end of line character\n if strip_line[0]=='@':\n first_dim_line = True\n if not(first):\n #Add the previous to the dictionnary\n model_dict[element_name] = element_marginal_array\n else:\n first = False\n\t\t\t\t\n element_name = strip_line[1:]\n\n if strip_line[0]=='$':\n #define array dimensions\n coma_index = strip_line.find(',')\n dimensions = []\n\n #Get rid of $Dim[\n previous_coma_index = 4\n while coma_index != -1:\n dimensions.append(int(strip_line[previous_coma_index+1:coma_index]))\n previous_coma_index = coma_index\n coma_index = strip_line.find(',',coma_index+1)\n\t\t\t\n #Add last dimension and get rid of the closing bracket \n dimensions.append(int(strip_line[previous_coma_index+1:-1]))\n\n element_marginal_array = np.ndarray(shape=dimensions)\n\n if strip_line[0]=='#':\n if first_dim_line:\n dimensions_names = []\n if len(dimensions) > 1:\n comma_index = strip_line.find(',')\n opening_bracket_index = strip_line.find('[')\n while opening_bracket_index != -1:\n dimensions_names.append(strip_line[opening_bracket_index+1:comma_index])\n opening_bracket_index = strip_line.find('[',comma_index) \n comma_index = strip_line.find(',',opening_bracket_index)\n first_dim_line = False\n dimensions_names.append(element_name)\n dimension_names_dict[element_name] = dimensions_names\n \n \n #update indices\n indices_array = []\n if len(dimensions) > 1:\n comma_index = strip_line.find(',')\n closing_brack_index = strip_line.find(']')\t\t\t\t\t\n while closing_brack_index != -1:\n indices_array.append(int(strip_line[comma_index+1:closing_brack_index]))\n opening_bracket_index = strip_line.find('[',closing_brack_index) \n comma_index = strip_line.find(',',opening_bracket_index)\n closing_brack_index = strip_line.find(']',closing_brack_index+1)\n\t\t\t\t\n\n if strip_line[0]=='%':\n #read doubles\n coma_index = strip_line.find(',')\n marginals_values = []\n\n #Get rid of the %\n previous_coma_index = 0\n while coma_index != -1:\n marginals_values.append(float(strip_line[previous_coma_index+1:coma_index]))\n previous_coma_index = coma_index\n coma_index = strip_line.find(',',coma_index+1)\n\t\t\t\n #Add last dimension and get rid of the closing bracket \n marginals_values.append(float(strip_line[previous_coma_index+1:]))\n if len(marginals_values)!=dimensions[-1]:\n print(\"problem\")\n element_marginal_array[tuple(indices_array)] = marginals_values\n model_dict[element_name] = element_marginal_array\t\t\t\t\n \n \n return [model_dict,dimension_names_dict]","title":""},{"docid":"87565b232c2006c5c1be18dd388db773","score":"0.5469901","text":"def load_data_and_labels(positive_data_file, negative_data_file, data_dir=None):\n\n # Load data from files\n \"\"\"\n positive_examples = list(open(positive_data_file, \"r\").readlines())\n positive_examples = [s.strip() for s in positive_examples]\n negative_examples = list(open(negative_data_file, \"r\").readlines())\n negative_examples = [s.strip() for s in negative_examples]\n \"\"\"\n positive_examples = list()\n negative_examples = list()\n\n if data_dir:\n data_folder = data_dir\n else:\n data_folder = './data'\n\n train_data = {'00': list(), '01': list()}\n\n for current_cat in ['00', '01']:\n print(\"{}/{}/\".format(data_folder, current_cat))\n file_list = list()\n for root, dirs, f_list in os.walk(\"{}/{}/\".format(data_folder, current_cat)):\n for oo in f_list:\n if 'class.nfo' != oo:\n file_list.append('{}/{}/{}'.format(data_folder, current_cat, oo))\n\n print(\"Готовим категорию: {} - {} сообщений\".format(current_cat, len(file_list)))\n\n for ff in file_list:\n f = open(ff, 'r')\n ss = f.read()\n train_data[current_cat].append(ss)\n\n positive_examples = train_data['00']\n negative_examples = train_data['01']\n\n # Split by words\n x_text = positive_examples + negative_examples\n x_text = [clean_str(sent) for sent in x_text]\n # Generate labels\n positive_labels = [[0, 1] for _ in positive_examples]\n negative_labels = [[1, 0] for _ in negative_examples]\n y = np.concatenate([positive_labels, negative_labels], 0)\n\n print(x_text)\n print(y)\n\n raw_input()\n return [x_text, y]","title":""},{"docid":"d23f36150e6c8152bdd649124bc64ce8","score":"0.5463556","text":"def segmentation_images(imagepath=\"./images/\", outpath=\"./OUTPUT/\"):\n files = sorted([f for f in os.listdir(imagepath) if f.endswith('.png')])\n for i in range(len(files)):\n image = mpimg.imread(imagepath + files[i])[:, :, :3]\n print(files[i], f\"{i}/{len(files)}\")\n\n # nuclei\n nuclei = mpimg.imread(outpath + \"nuclei_predictions/\" + files[i])\n nuclei = color.rgb2gray(nuclei)\n nuclei = nuclei > filters.threshold_otsu(nuclei)\n nuclei_only = np.copy(image)\n nuclei_only[nuclei == False] = 0\n mpimg.imsave(outpath + \"nuclei/\" + files[i], nuclei_only)\n del nuclei_only\n # print(\"nuclei saved\")\n\n # acini\n acini = mpimg.imread(outpath + \"acini_predictions/\" + files[i])\n acini = color.rgb2gray(acini)\n acini = acini > filters.threshold_otsu(acini)\n acini_only = np.copy(image)\n acini_only[acini == False] = 0\n mpimg.imsave(outpath + \"acini/\" + files[i], acini_only)\n del acini_only\n # print(\"acini saved\")\n\n # acinar_nuclei\n acinar_nuclei = np.logical_and(acini == True, nuclei == True)\n acinar_nuclei_only = np.copy(image)\n acinar_nuclei_only[acinar_nuclei == False] = 0\n mpimg.imsave(outpath + \"acinar_nuclei/\" + files[i], acinar_nuclei_only)\n # print(\"acinar_nuclei saved\")\n del acinar_nuclei\n del acinar_nuclei_only\n\n # stromal_nuclei\n stromal_nuclei = np.logical_and(acini == False, nuclei == True)\n stromal_nuclei_only = np.copy(image)\n stromal_nuclei_only[stromal_nuclei == False] = 0\n mpimg.imsave(outpath + \"stromal_nuclei/\" + files[i], stromal_nuclei_only)\n # print(\"stromal_nuclei saved\")\n del stromal_nuclei\n del stromal_nuclei_only\n\n # core - not saved, just for stroma calculation\n core = mpimg.imread(outpath + \"tissue_predictions/\" + files[i])\n core = color.rgb2gray(core)\n core = rescale(core, 23.4375, multichannel=False) # scale factor is 6000/256 = 23.4375\n core = core > filters.threshold_mean(core) # threshold the prediction\n\n # stroma\n stroma_only = np.copy(image)\n stroma_only[core == False] = 0\n del core\n stroma_only[acini == True] = 0 # removes acini from the mask\n mpimg.imsave(outpath + \"stroma/\" + files[i], stroma_only)\n # print(\"acinar_nuclei saved\")\n del stroma_only\n print(\"all segmentation done\")","title":""},{"docid":"c58b92aef414cdce2237e52638af85b3","score":"0.54634035","text":"def get_data(input_path):\r\n found_bg = False\r\n all_imgs = {}\r\n\r\n classes_count = {}\r\n\r\n class_mapping = {}\r\n\r\n i = 1\r\n\r\n with open(input_path, 'r') as f:\r\n\r\n print('Parsing annotation files')\r\n\r\n for line in f:\r\n\r\n # Print process\r\n sys.stdout.write('\\r' + 'idx=' + str(i))\r\n i += 1\r\n\r\n # line_split = line.strip().split(',')\r\n\r\n # (filename, y1, x1, y2, x2, class_name) = line_split\r\n print(line)\r\n filename = line.split()[0]\r\n y1, x1, y2, x2, class_name = line.split()[1].split(',')\r\n\r\n if class_name == '':\r\n # wrong labelling\r\n continue\r\n\r\n if class_name not in classes_count:\r\n classes_count[class_name] = 1\r\n else:\r\n classes_count[class_name] += 1\r\n\r\n if class_name not in class_mapping:\r\n if class_name == 'bg' and found_bg == False:\r\n print(\r\n 'Found class name bg.Will be treated as background (this is usually for hard negative mining).')\r\n found_bg = True\r\n class_mapping[class_name] = len(class_mapping)\r\n\r\n if filename not in all_imgs:\r\n all_imgs[filename] = {}\r\n\r\n img = cv2.imread(filename)\r\n (rows, cols) = img.shape[:2]\r\n all_imgs[filename]['filepath'] = filename\r\n all_imgs[filename]['width'] = cols\r\n all_imgs[filename]['height'] = rows\r\n all_imgs[filename]['bboxes'] = []\r\n # if np.random.randint(0,6) > 0:\r\n # \tall_imgs[filename]['imageset'] = 'trainval'\r\n # else:\r\n # \tall_imgs[filename]['imageset'] = 'test'\r\n\r\n all_imgs[filename]['bboxes'].append(\r\n {'class': class_name, 'x1': int(x1), 'x2': int(x2), 'y1': int(y1), 'y2': int(y2)})\r\n\r\n all_data = []\r\n for key in all_imgs:\r\n all_data.append(all_imgs[key])\r\n\r\n # make sure the bg class is last in the list\r\n if found_bg:\r\n if class_mapping['bg'] != len(class_mapping) - 1:\r\n key_to_switch = [key for key in class_mapping.keys() if class_mapping[key] == len(class_mapping) - 1][0]\r\n val_to_switch = class_mapping['bg']\r\n class_mapping['bg'] = len(class_mapping) - 1\r\n class_mapping[key_to_switch] = val_to_switch\r\n\r\n return all_data, classes_count, class_mapping","title":""},{"docid":"c5fc046e2590354d242e648dfd4ff844","score":"0.5460302","text":"def load_PA100K():\n # Loading the file that contains the name of the images.\n mat = scipy.io.loadmat('annotation.mat')\n\n # Getting the train validation and test data names and labels.\n test_images_name = mat['test_images_name']\n test_label = mat['test_label']\n train_images_name = mat['train_images_name']\n train_label = mat['train_label']\n val_images_name = mat['val_images_name']\n val_label = mat['val_label']\n\n # Retrieving the train images and putting them into a list and not an array because they do not have the same shape.\n train_images = []\n for i in range(len(train_images_name)):\n train_images.append(plt.imread('./data/release_data/release_data/'+train_images_name[i][0][0]))\n\n # Doing the same thing with the test and validation images. \n # I am considering the validation images with the test images because on the website of the competition \n # I found that the the test images contain almost 20000 images which is equivallent to the length of the test and validation datasets.\n test_images = []\n for i in range(len(test_images_name)):\n test_images.append(plt.imread('./data/release_data/release_data/'+test_images_name[i][0][0]))\n\n for i in range(len(val_images_name)):\n test_images.append(plt.imread('./data/release_data/release_data/'+val_images_name[i][0][0]))\n\n # Concatenating the test and validation labels.\n test_label = test_label + val_label\n\n # Returning everything.\n \"\"\"\n train_images,test_images : are lists.\n train_label,test_label : are np arrays.\n \"\"\"\n return train_images,train_label,test_images,test_label","title":""},{"docid":"c0d27b4f0d17beb16aef900b37b025e4","score":"0.5443519","text":"def load_data(name):\r\n with open(name + '-labels.idx1-ubyte', 'rb') as label_path:\r\n\r\n magic, n = struct.unpack('>II', label_path.read(8))\r\n label = np.fromfile(label_path, dtype=np.uint8)\r\n\r\n with open(name + '-images.idx3-ubyte', 'rb') as image_path:\r\n\r\n magic, num, row, col = struct.unpack('>IIII', image_path.read(16))\r\n image = np.fromfile(image_path, dtype = np.uint8).reshape(len(label), 784)\r\n image = ((image/255) - 0.5) * 2\r\n\r\n return image, label","title":""},{"docid":"75f9f919e51527fbfadb828fb4b3b2ac","score":"0.5442328","text":"def load_CIFAR10(ROOT):\n np_names = ['np_train_data', 'np_train_labels', 'np_test_data', 'np_test_labels']\n print(all([os.path.isfile(os.path.join(ROOT, name)) for name in np_names]))\n if all([os.path.isfile(os.path.join(ROOT, name)) for name in np_names]):\n print(\"Np files were found\")\n # load np arrays from given files\n xt, yt, xte, yte = [np.fromfile(os.path.join(ROOT, name), dtype=np.uint8) for name in np_names]\n xt = xt.reshape(-1, 32, 32, 3).astype(\"float\")\n xte = xte.reshape(-1, 32, 32, 3).astype(\"float\")\n print(xt.shape, yt.shape, xte.shape, yte.shape)\n return xt, yt, xte, yte\n else:\n xs = []\n ys = []\n for b in range(1,6):\n f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\n X, Y = load_CIFAR_batch(f)\n xs.append(X)\n ys.append(Y)\n size_estimator = sum(x.shape[0] for x in xs)\n x_all = np.zeros((size_estimator,) + xs[0].shape[1:], dtype=float)\n y_all = np.zeros(size_estimator, dtype=int)\n cur_size = 0\n for x, y in zip(xs, ys):\n x_all[cur_size:cur_size + x.shape[0]] = x\n y_all[cur_size:cur_size + x.shape[0]] = y\n cur_size += x.shape[0]\n del x\n del y\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\n for arr, fname in zip([x_all, y_all, Xte, Yte], np_names):\n arr.astype(np.uint8).tofile(os.path.join(ROOT, fname))\n return x_all, y_all, Xte, Yte","title":""},{"docid":"9330beea82b5ffe64f1f61e208280973","score":"0.543412","text":"def data_info(labels, mode_label, logger, config, hidden_test=False):\n\n zeros, zeros_index, ones, ones_index, indices = util.count_classes_gender(labels)\n\n zeros_index_f, zeros_index_m = zeros_index\n ones_index_f, ones_index_m = ones_index\n if mode_label == 'train' and config.EXPERIMENT_DETAILS['SUB_SAMPLE_ND_CLASS']:\n min_set = min(len(zeros_index_f), len(zeros_index_m),\n len(ones_index_f), len(ones_index_m))\n min_set *= 2\n\n zeros, zeros_index, ones, ones_index, indices = util.count_classes(labels)\n\n if config.EXPERIMENT_DETAILS['OVERSAMPLE']:\n zeros, zeros_index = re_distribute(zeros)\n ones, ones_index = re_distribute(ones)\n\n if mode_label == 'train' and config.EXPERIMENT_DETAILS[\n 'SUB_SAMPLE_ND_CLASS']:\n zeros_index = random.sample(zeros_index, min_set)\n ones_index = random.sample(ones_index, min_set)\n update_zeros = {}\n for i in zeros_index:\n tmp_folder = indices[i]\n if tmp_folder not in update_zeros:\n update_zeros[tmp_folder] = [i]\n else:\n update_zeros[tmp_folder].append(i)\n zeros = update_zeros\n update_ones = {}\n for i in ones_index:\n tmp_folder = indices[i]\n if tmp_folder not in update_ones:\n update_ones[tmp_folder] = [i]\n else:\n update_ones[tmp_folder].append(i)\n ones = update_ones\n\n print(f\"The number of class zero and one files in the {mode_label} \"\n f\"split after segmentation are {len(zeros_index)}, {len(ones_index)}\")\n logger.info(f\"The number of class zero files in the {mode_label} split \"\n f\"after segmentation are {len(zeros_index)}\")\n logger.info(f\"The number of class one files in the {mode_label} split \"\n f\"after segmentation are {len(ones_index)}\")\n\n use_class_weights = config.EXPERIMENT_DETAILS['CLASS_WEIGHTS']\n if use_class_weights:\n weights, class_weights = find_weight(zeros,\n ones,\n config)\n else:\n class_weights = weights = [1, 1]\n\n logger.info(f\"{config.WEIGHT_TYPE} Weights: {weights}\")\n\n return zeros, zeros_index, ones, ones_index, weights, class_weights","title":""},{"docid":"94b83ec9d278be27cb096e48fc9acfdb","score":"0.5428598","text":"def read_train(input_path, output_path, max_len, padding=True):\n labels, entity_poses, clean_texts = [], [], []\n with open(input_path) as f:\n example_lines = []\n for line in f:\n if line == '\\n':\n label, raw_text = read_one_example(example_lines)\n labels.append(label)\n e1_pos, e2_pos, clean_text = parse_text(raw_text)\n entity_poses.append((e1_pos, e2_pos))\n clean_texts.append(clean_text)\n example_lines.clear()\n else:\n example_lines.append(line.strip())\n label2id_filename = os.path.join(os.path.split(output_path)[0], 'label2id.json')\n if os.path.exists(label2id_filename):\n with open(label2id_filename) as f:\n label_index = json.loads(f.readline().strip())\n else:\n label_index = {}\n labels_unique = list(set(labels))\n for i in range(len(labels_unique)):\n label_index[labels_unique[i]] = i\n with open(label2id_filename, 'w') as f:\n f.write(json.dumps(label_index, ensure_ascii=False))\n id2label_filename = os.path.join(os.path.split(output_path)[0], 'id2label.json')\n if not os.path.exists(id2label_filename):\n id2label = dict()\n for key, value in label_index.items():\n id2label[value] = key\n with open(id2label_filename, 'w') as f:\n f.write(json.dumps(id2label, ensure_ascii=False))\n pos_filename = os.path.join(os.path.split(output_path)[0], 'pos2id.json')\n if os.path.exists(pos_filename):\n with open(pos_filename) as f:\n pos_index = json.loads(f.readline())\n else:\n pos_index = pos2id(max_len)\n with open(pos_filename, 'w') as f:\n f.write(json.dumps(pos_index, ensure_ascii=False))\n tokenizer = Tokenizer()\n tokenizer.fit_on_texts(clean_texts)\n # save tokenizer\n filename = os.path.join(os.path.split(output_path)[0], 'tokenizer.json')\n tokenizer.to_json(filename)\n with tf.io.TFRecordWriter(output_path) as writer:\n for label, (e1_pos, e2_pos), text in zip(labels, entity_poses, clean_texts):\n example = parse_train_example(label_index, pos_index, tokenizer, label, text, e1_pos, e2_pos, max_len,\n padding)\n writer.write(example.SerializeToString())","title":""},{"docid":"7dca4b3ca777777110ed00b4015a7c2e","score":"0.5427852","text":"def load_mnist_fail(dataset=\"training\", digits=np.arange(10), path=\".\"):\n if dataset == \"training\":\n fname_img = path + '/train-images-idx3-ubyte' #os.path.join(path, '/train-images-idx3-ubyte')\n fname_lbl = path + '/train-images-idx3-ubyte' #os.path.join(path, '/train-labels-idx1-ubyte')\n elif dataset == \"testing\":\n fname_img = path + '/t10k-images-idx3-ubyte' #os.path.join(path, '/t10k-images-idx3-ubyte')\n fname_lbl = path + '/t10k-labels-idx1-ubyte' #os.path.join(path, '/t10k-labels-idx1-ubyte')\n else:\n raise ValueError(\"dataset must be 'testing' or 'training'\")\n flbl = open(fname_lbl, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n lbl = pyarray(\"b\", flbl.read())\n flbl.close()\n\n fimg = open(fname_img, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = pyarray(\"B\", fimg.read())\n fimg.close()\n\n ind = [ k for k in range(size) if lbl[k] in digits ]\n N = len(ind)\n\n images = zeros((N, 1, rows, cols), dtype=uint8)\n labels = zeros(N, dtype=int8)\n for i in range(len(ind)):\n images[i][0] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\n labels[i] = lbl[ind[i]]\n for i in range(10):\n print i, len([x for x in range(size) if lbl[x] == i])\n print \"WHEE\", images.shape, labels.shape, N, digits, len(lbl)\n return images, labels","title":""},{"docid":"d01cc27a78c7836d32272fd5b6319738","score":"0.54223394","text":"def load_data():\r\n train_files = ['data/mnist/train%d.txt' % (i,) for i in range(10)]\r\n test_files = ['data/mnist/test%d.txt' % (i,) for i in range(10)]\r\n tmp = []\r\n for i in train_files:\r\n with open(i, 'r') as fp:\r\n tmp += fp.readlines()\r\n # load train data in N*D array (60000x784 for MNIST) \r\n # divided by 255 to achieve normalization\r\n train_data = np.array([[j for j in i.split(\" \")] for i in tmp], dtype='int') / 255\r\n print \"Train data array size: \", train_data.shape\r\n tmp = []\r\n for i in test_files:\r\n with open(i, 'r') as fp:\r\n tmp += fp.readlines()\r\n # load test data in N*D array (10000x784 for MNIST) \r\n # divided by 255 to achieve normalization\r\n test_data = np.array([[j for j in i.split(\" \")] for i in tmp], dtype='int') / 255\r\n print \"Test data array size: \", test_data.shape\r\n tmp = []\r\n for i, _file in enumerate(train_files):\r\n with open(_file, 'r') as fp:\r\n for line in fp:\r\n tmp.append([1 if j == i else 0 for j in range(0, 10)])\r\n train_truth = np.array(tmp, dtype='int')\r\n del tmp[:]\r\n for i, _file in enumerate(test_files):\r\n with open(_file, 'r') as fp:\r\n for _ in fp:\r\n tmp.append([1 if j == i else 0 for j in range(0, 10)])\r\n test_truth = np.array(tmp, dtype='int')\r\n print \"Train truth array size: \", train_truth.shape\r\n print \"Test truth array size: \", test_truth.shape\r\n return train_data, test_data, train_truth, test_truth","title":""},{"docid":"fffa8b6b68a7c8f058e183e624690427","score":"0.5421787","text":"def extract_labels(filename, num_images):\n print('Extracting', filename)\n with gzip.open(filename) as bytestream:\n # Skip the magic number and count; we know these values.\n bytestream.read(8)\n buf = bytestream.read(1 * num_images)\n labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n # Convert to dense 1-hot representation.\n return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)","title":""},{"docid":"b02350c0ae2b372400828a9a5e2b4dac","score":"0.54205894","text":"def proprocessing(self):\r\n \r\n if not self.are_labeled:\r\n pos = self.label_raw_dat(self.text1, self.class1) #Labels data \r\n neg = self.label_raw_dat(self.text2, self.class2) #Labels data\r\n #print(pos.head())\r\n #print(neg.head())\r\n \r\n dat = pan.concat([pos, neg]) #Concatenates 2 input data sets into one large one\r\n dat['Text'].str.lower() #Converts all words into lowercase characters\r\n dat.replace(to_replace = r'[^a-zA-Z]', value = ' ', regex = True, inplace = True) #Get help w/ Regex!!\r\n \r\n #dat.replace(to_replace = r'[^a-zA-Z0-9]', value = ' ', regex = True, inplace = True)\r\n #dat.replace(r'[^a-zA-Z0-9]', ' ',line)\r\n \r\n self.dat = dat.sample(frac=1).reset_index(drop=True)\r\n \r\n #print(self.dat.head())\r\n \r\n self.labels = np.array(self.dat['Label'])\r\n self.words = np.array(self.dat['Text']) \r\n \r\n self.train_txt, self.test_txt, self.train_label, self.test_label = train_test_split(self.words[:len(self.words)//1], self.labels[:len(self.labels)//1], test_size= .20, train_size = .80, shuffle = True)\r\n \r\n self.num_docs = self.train_txt.shape[0]\r\n \r\n print('Number of total documents w/ in train set', self.num_docs)\r\n \r\n #print(train_txt[1:10])\r\n #print(train_label[1:10])\r\n \r\n return (self.train_txt, self.train_label, self.test_txt, self.test_label) #Return sparse matrix of word frequencies w/ in document\r\n \r\n else: \r\n \r\n print('Must not include labels')\r\n return False","title":""},{"docid":"f1f0e91b7b54d16fa4ed14b67fc8e89b","score":"0.5418564","text":"def read_train():\n # if color images needed instead of grayscale, use cv2.IMREAD_COLOR instead of cv2.IMREAD_GRAYSCALE\n\n uppercase = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n\n xtrain = []\n ytrain = []\n\n path = \"asl_alphabet_train/\"\n for letter in range(len(uppercase)):\n for number in range(1, 1001):\n let = uppercase[letter]\n ytrain.append(letter)\n xtrain.append(cv2.imread(path + let + \"/\" + let + str(number) + \".jpg\", cv2.IMREAD_GRAYSCALE))\n\n path = \"asl_alphabet_train/del/\"\n for number in range(1, 1001):\n ytrain.append(26)\n xtrain.append(cv2.imread(path + \"del\" + str(number) + \".jpg\", cv2.IMREAD_GRAYSCALE))\n\n path = \"asl_alphabet_train/nothing/\"\n for number in range(1, 1001):\n ytrain.append(27)\n xtrain.append(cv2.imread(path + \"nothing\" + str(number) + \".jpg\", cv2.IMREAD_GRAYSCALE))\n\n path = \"asl_alphabet_train/space/\"\n for number in range(1, 1001):\n ytrain.append(28)\n xtrain.append(cv2.imread(path + \"space\" + str(number) + \".jpg\", cv2.IMREAD_GRAYSCALE))\n\n xtrain = np.array(xtrain).astype(np.float64)\n ytrain = np.array(ytrain)\n\n # for color images, reshape to 87000 x 120000\n # for all grayscale images, reshape to 29000 x 120000\n # for one-third grayscale images, reshape to 29000 x 40000\n xtrain = xtrain.reshape(29000, 40000)\n\n #mean_train = np.mean(xtrain)\n #sd_train = np.std(xtrain)\n #xtrain -= mean_train\n #xtrain /= sd_train\n\n with open('xtrain_gray_nonnorm.npy', mode=\"wb\") as f:\n np.save(f, xtrain)\n with open('ytrain_gray_nonnorm.npy', mode=\"wb\") as f:\n np.save(f, ytrain)","title":""},{"docid":"bc72a0fa4080c939932c3e4991696168","score":"0.54178965","text":"def load_segmentation_labels(data: UndistortedDataSet, shot):\n if data.undistorted_segmentation_exists(shot.id):\n return data.load_undistorted_segmentation(shot.id)\n else:\n size = shot.camera.height, shot.camera.width\n return np.zeros(size, dtype=np.uint8)","title":""},{"docid":"29cc167bfe74dcb92bb409491e5a93fc","score":"0.54063666","text":"def rectify(data_root, sub_dir):\n with open(os.path.join(data_root, 'label_new.txt'), 'w') as label_new_txt:\n with open(os.path.join(data_root, 'label.txt'), 'r') as label_txt:\n frs = label_txt.readlines()\n for line in frs:\n line = line.strip().split(' ', 1) # split only two elements , in case of there is space in line\n label = line[1]\n u_label = label.decode('utf-8')\n new_label = rectify_line(u_label)\n label_new_txt.write('%s %s\\n'%(os.path.join(data_root, sub_dir, line[0]), new_label.encode('utf-8')))\n\n with open(os.path.join(data_root, 'map_new.txt'), 'w') as map_new_txt:\n with open(os.path.join(data_root, 'map.txt'), 'r') as map_txt:\n frs = map_txt.readlines()\n cnt = 0\n for line in frs:\n line = line.strip().split(' ', 1)\n if len(line) <=1: continue\n label = line[1]\n u_label = label.decode('utf-8')\n if is_regular_ch(u_label):\n map_new_txt.write('%d %s\\n'%(cnt,u_label.encode('utf-8')))\n cnt += 1\n map_new_txt.write('%d %s'%(cnt, '*'))\n print 'all classes is %d'%cnt\n label_new_txt.close()\n label_txt.close()\n map_new_txt.close()\n map_txt.close()","title":""},{"docid":"d73a8ac0838137d679c4ab6fd6db2afd","score":"0.54051083","text":"def prune(data_file, ver_batch, voxel_width, n_class, data_folder, rgb_intensity_index, version=\"\", gt_index=3, label_dict=None):\n i_rows = 0\n xyz = np.zeros((0, 3), dtype='float32')\n rgb = np.zeros((0, 3), dtype='uint8')\n labels = np.zeros((0, n_class + 1), dtype='uint32')\n path_dem = data_folder + \"Generic/\"\n\n # ---the clouds can potentially be too big to parse directly---\n # ---they are cut in batches in the order they are stored---\n while True:\n #if True:\n try:\n vertices = np.genfromtxt(data_file, delimiter=' ', max_rows=ver_batch, skip_header=i_rows)\n #vertices[:, 0] += -610000.00\n #vertices[:, 1] += -5700000.00\n\n\n #vertices = np.load(data_file)\n except StopIteration:\n #break # end of file\n print(\"finish\")\n break\n\n if np.shape(vertices)[0]<1:\n break\n \n print(np.shape(vertices)[1])\n if np.shape(vertices)[1] == 3: # if has labels\n labels_full = np.zeros(1, dtype='uint8')\n labels = np.zeros(1, dtype='uint8')\n n_class = 0\n if i_rows==0:\n print(\"has not label on Data\")\n else:\n if np.shape(vertices)[1] > gt_index:\n labels_full = np.copy(vertices[:, gt_index]) \n labels_full = labels_full.astype(\"uint8\")\n print(\"-- \\t has label on Data \")\n else:\n labels_full = np.copy(vertices[:, 3]) \n labels_full = labels_full.astype(\"uint8\")\n if i_rows==0:\n print(\"-- \\t warning -- gt_index = {0} out of bounds for {1} columns, used instead gt_index = 3\".format(gt_index, np.shape(vertices)[1]))\n \n\n if not label_dict==None:\n labels_full_t = []\n for item in labels_full:\n labels_full_t.append(label_dict[str(item)])\n labels_full = np.array(labels_full_t)\n labels_full = labels_full.astype(\"uint8\")\n if i_rows==0:\n print(\"-- \\t \\t has label on Data used directory\")\n\n xyz_full = np.array(vertices[:, 0:3], dtype='float32')\n \n if rgb_intensity_index is None: \n if np.shape(vertices)[1] >= 6:\n if gt_index==3:\n rgb_full = np.array(vertices[:, 4:7], dtype='float32')\n elif gt_index==6:\n rgb_full = np.array(vertices[:, 3:6], dtype='float32')\n else:\n rgb_full = np.ones_like(vertices[:, :3], dtype='float32')\n\n else:\n \n if np.shape(vertices)[1] >= np.max(rgb_intensity_index):\n rows, _ = np.shape(vertices)\n rgb_full = np.zeros((rows, 3))\n rgb_full[:, 0] = np.copy(vertices[:, rgb_intensity_index[0]])\n rgb_full[:, 1] = np.copy(vertices[:, rgb_intensity_index[1]])\n rgb_full[:, 2] = np.copy(vertices[:, rgb_intensity_index[2]])\n if i_rows==0:\n print(\"has intensity or color \")\n else:\n rgb_full = np.ones_like(vertices[:, :3], dtype='float32')\n if i_rows==0:\n print(\"has not intensity or color \")\n \n\n del vertices\n\n print(\"--labels in file section \", np.unique(labels_full))\n\n xyz_sub, rgb_sub, labels_sub = libply_c.prune(xyz_full, voxel_width, rgb_full, labels_full, n_class)\n\n del xyz_full, rgb_full\n xyz = np.vstack((xyz, xyz_sub))\n rgb = np.vstack((rgb, rgb_sub))\n labels = np.vstack((labels, labels_sub))\n i_rows = i_rows + ver_batch\n\n if np.shape(labels_full)[0] < 2:\n labels = []\n\n ###########################################################################################################\n # Statistical Outlier Removal\n ###########################################################################################################\n if False:\n mean_k = 30 # 50# 50 10\n std_dev = 1.0\n array = xyz[:, :3]\n from sklearn.neighbors import KDTree\n\n # KDTree object (sklearn)\n kDTree = KDTree(array, leaf_size=50)\n dx, idx_knn = kDTree.query(array[:, :], k=mean_k + 1)\n dx, idx_knn = dx[:, 1:], idx_knn[:, 1:]\n\n distances = np.sum(dx, axis=1) / (mean_k - 1.0)\n valid_distances = np.shape(distances)[0]\n\n # Estimate the mean and the standard deviation of the distance vector\n sum = np.sum(distances)\n sq_sum = np.sum(distances ** 2)\n\n mean = sum / float(valid_distances)\n variance = (sq_sum - sum * sum / float(valid_distances)) / (float(valid_distances) - 1)\n stddev = np.sqrt(variance)\n\n # a distance that is bigger than this signals an outlier\n distance_threshold = mean + std_dev * stddev\n idx = np.nonzero(distances < distance_threshold)\n \n xyz = np.copy(xyz[idx])\n rgb = np.copy(rgb[idx])\n labels = np.copy(labels[idx])\n\n ####################################################################################################################\n\n ####################################################################################################################\n xyz_rgb = np.hstack((xyz, rgb))\n file_name = os.path.splitext(os.path.basename(data_file))[0]\n path_short = path_dem + file_name + version\n\n np.savetxt(path_short + \".txt\", xyz_rgb, delimiter=\",\", header=\"X,Y,Z,R,G,B\", comments='', fmt='%f,%f,%f,%d,%d,%d')\n dem_array = build_dem(path_short, xyz_rgb)\n\n return xyz, rgb, labels, dem_array","title":""},{"docid":"73873614ca194a33f678f97a01a03d88","score":"0.5392996","text":"def extract_img_label(names, path):\n train_img = os.path.join(path, 'origin/')\n train_label = os.path.join(path, 'annotation/')\n num_imgs = len(names)\n data = []\n labels = []\n print('^^^^^^^^^^ start img_set for sycle')\n for i in names:\n name = os.path.splitext(i)[0]\n print(name)\n coder = ImageCoder((224, 224))\n image_buffer, height, width = Ofrecord._process_image(\n os.path.join(train_img, i), coder)\n\n data += [image_buffer]\n\n if os.path.exists(os.path.join(train_label, name)):\n\n with open(os.path.join(train_label, name), \"r\", encoding='utf-8') as jsonFile:\n la = json.load(jsonFile)\n if la:\n labels += [la[0]['category_id']]\n else:\n data.pop()\n num_imgs -= 1\n else:\n print('File is not found')\n print('^^^^^^^^^ img_set for end')\n data = np.array(data)\n labels = np.array(labels)\n print(data.shape, labels.shape)\n return num_imgs, data, labels","title":""},{"docid":"d54eb88a7aafd21062e2ca944d335301","score":"0.5392445","text":"def postprocess(label_file_out: str, label_file_in: str, min_size: int = 20):\n label_in = plt.imread(label_file_in)[..., 3] > 0\n label_out = remove_small_objects(label_in, min_size=min_size)\n layer_out = 255 * label_out.astype(np.uint8)\n arr_out = np.zeros((*label_out.shape, 4), dtype=np.uint8)\n arr_out[..., 2] = layer_out\n arr_out[..., 3] = layer_out\n plt.imsave(label_file_out, arr_out)\n pass","title":""},{"docid":"da44eb77b1cb573a5785a9e0b2425adc","score":"0.5384938","text":"def load_label(self, idx):\n im = Image.open('{}/SegmentationClass/{}.png'.format(self.train_dir, idx))\n im = im.resize((500, 500))\n label = np.array(im, dtype=np.uint8)\n label = label[np.newaxis, ...]\n return label","title":""},{"docid":"149b8164192cfe163454a6a9c4c19377","score":"0.53794116","text":"def load_label(self, idx):\n im = Image.open('{}/SegmentationClass/{}.png'.format(self.val_dir, idx))\n im = im.resize((500, 500))\n label = np.array(im, dtype=np.uint8)\n label = label[np.newaxis, ...]\n return label","title":""},{"docid":"d417ab94cd7a3a57f103c0913525236c","score":"0.5364689","text":"def generate_labels(self, label_path):\n labels = pd.read_csv(label_path)\n labels = labels[pd.notna(labels['EncodedPixels'])]\n num_objects = labels.pivot_table(index=['ImageId'], aggfunc='size')\n valid_images = num_objects[(self.min_objects_per_image <= num_objects) & \n (num_objects <= self.max_objects_per_image)].keys()\n valid_labels = labels[labels['ImageId'].isin(valid_images)].reset_index(drop=True)\n\n if not self.already_labeled:\n valid_labels['BoundingBox'] = valid_labels['EncodedPixels'].apply(self.rle_to_bbox)\n valid_labels['ObjectClass'] = valid_labels[['ImageId', 'BoundingBox']].apply(self.label_object, axis=1)\n else:\n valid_labels['BoundingBox'] = valid_labels['BoundingBox'].apply(lambda bbx: [float(i) for i in bbx[1:-1].split()])\n return valid_labels","title":""},{"docid":"5ae4b00f38e1e2ffe9b27180089c2f8e","score":"0.5363863","text":"def load_bio_data(file_info, res=21):\n \n \"\"\"print(\"Importing tanslation vectors \\n======================\")\n shiftx = np.array(np.loadtxt(main_path+data_index+\"/shiftx.txt\", delimiter=\"\\t\"))\n shifty = np.array(np.loadtxt(main_path+data_index+\"/shifty.txt\", delimiter=\"\\t\"))\n\n # translation vectors in pixels\n vx = [shiftx[i]/dx for i in range(14)]\n vy = [shifty[i]/dx for i in range(14)]\n print(\"Translation vectors successfully imported \\n======================\")\"\"\"\n\n main_path, data_index, n = file_info\n path2 = main_path+data_index+\"/fullx.txt\"\n print(\"Importing bio data from\", path2, \"\\n======================\")\n data2 = np.loadtxt(path2, delimiter=\"\\t\")\n print(\"Data successfully imported. Shape of data:\", data2.shape, \n \"\\n======================\")\n\n\n s = int(res**2)\n\n scans = []\n for j in range(14):\n start = n\n end = (s+1)*n-1\n gap = end-start\n x = []\n for i in range(9):\n #print(i, start, end)\n x.append(data2[j,start:end+1])\n start = end + 2*n + 1\n end = start + gap\n\n x = np.array(x)\n if j==0:\n print(\"Shape of one detector chunk before reshaping:\", x.shape)\n X = x.reshape(3,3,21,21,2000).transpose(4,0,2,1,3)\n Z = X.reshape(2000,63,63).transpose(0,2,1)\n scans.append(Z)\n if j==0:\n print(\"Final shape of chunk after reshaping:\", Z.shape, \"\\n====================\\n\")\n \"\"\"Zmean = np.mean(Z,axis=0)\n plt.imshow(Zmean)\n plt.colorbar()\n plt.show()\"\"\"\n\n scans = np.array(scans)\n # adding extra dimension so it matches non-bio data structure\n scans = np.expand_dims(scans,axis=0)\n scans = scans.transpose(0,1,3,4,2)\n scans = scans.transpose(0,4,2,3,1)\n print(\"Final shape of bio data (number of scans, frame number, height, width, detectors):\", scans.shape)\n \n return scans","title":""},{"docid":"42f7479c4c8cd48dfd7fcf197015205a","score":"0.53570545","text":"def load_data_and_labels(data_file):\n # Load data from files\n examples = list(open(data_file, \"r\").readlines())\n examples = [s.strip() for s in examples]\n\n # Split by words\n x_text = [clean_str(sent) for sent in examples]\n # Generate labels\n y = np.loadtxt('./data/label.txt')\n labellist = y.tolist()\n # Make one-hot vectors of labels\n arr = np.empty((0, 47))\n for line in labellist:\n i = scoreid(line, labellist)\n a = one_hot(i)\n arr = np.append(arr, a.reshape((1,47)), axis=0)\n return [x_text, arr]","title":""},{"docid":"6ebf3607776ca892af30aa7981eb0722","score":"0.53474957","text":"def interpolate_labels(data_file, data_gt, xyz, labels, false_color, unary, segment_id, n_classes, ver_batch, train=True, gt_index=3, label_dict=None):\n\n if len(labels.shape) > 1 and labels.shape[1] > 1:\n labels = np.argmax(labels, axis = 1)\n i_rows = 0\n labels_f = np.zeros((0, ), dtype='uint8')\n #---the clouds can potentially be too big to parse directly---\n #---they are cut in batches in the order they are stored---\n nn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(xyz)\n xyz_txt = np.zeros((0, 4), dtype=np.float32)\n \n label_file = data_gt\n false_color_txt = np.zeros((0, ), dtype='float32')\n segment_id_full = np.zeros((0, ), dtype='float32')\n\n unary_full = np.zeros((0, np.shape(unary)[1]), dtype='float32') # z min, z max, z mean\n train = False\n while True: \n try:\n vertices = np.genfromtxt(data_file, delimiter=' ', max_rows=ver_batch, skip_header=i_rows) \n if train:\n real_label = np.genfromtxt(label_file, max_rows=ver_batch, skip_header=i_rows)\n else:\n real_label = np.copy(vertices[:, gt_index]) \n except:\n break\n print(\"*\")\n if np.shape(vertices)[0]<1:\n break\n\n if not label_dict==None:\n labels_full_t = []\n for item in real_label:\n labels_full_t.append(label_dict[str(int(item))])\n labels_full = np.array(labels_full_t)\n labels_full = labels_full.astype(\"uint8\")\n real_label = labels_full.astype(\"uint8\")\n\n if np.shape(vertices)[0]>2:\n xyz_full = np.array(vertices[:, 0:3], dtype='float32') \n distances, neighbor = nn.kneighbors(xyz_full)\n del vertices, distances\n\n labels_f = np.hstack((labels_f, labels[neighbor].flatten())) # Output Net\n i_rows = i_rows + ver_batch\n\n false_color_txt = np.hstack((false_color_txt, false_color[neighbor].flatten())) # false color composition\n segment_id_full = np.hstack((segment_id_full, segment_id[neighbor].flatten())) # saving segment id\n\n xyz_full = np.concatenate((xyz_full, np.expand_dims(real_label, axis=1)), axis=1) # current XYZ + GT batch\n xyz_txt = np.concatenate((xyz_txt, xyz_full), axis=0) # Contain XYZ coordinates + GT accumulative\n\n unary_temp = np.squeeze(unary[neighbor])\n unary_full = np.vstack((unary_full, unary_temp)) # saving unary\n del unary_temp\n else:\n break\n \n false_color_txt = np.concatenate((xyz_txt, np.expand_dims(false_color_txt, axis=1)), axis=1) # XYZ + GT + Falsecolor\n xyz_txt = np.concatenate((xyz_txt, np.expand_dims(labels_f, axis=1)), axis=1) # XYZ + GT + Output\n\n def first_nonzero(arr, axis, invalid_val=-1):\n mask = arr != 0\n return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val)\n ##################################################################################################################\n # smoothing\n # nn_xyz = NearestNeighbors(n_neighbors=100, algorithm='kd_tree').fit(xyz_txt[:, :3])\n # _, neighbor_xyz = nn_xyz.kneighbors(xyz_txt[:, :3])\n # i = 0\n # while i < 2:\n # indices_non_zeros = np.where(labels_f == 0)[0]\n # # print (\"a\")\n # new_neighbour = np.isin(neighbor_xyz, indices_non_zeros)\n # # print (\"b\")\n # new_neighbour = np.logical_not(new_neighbour) * neighbor_xyz\n # # print (\"c\")\n # near_neighbour = np.squeeze(first_nonzero(new_neighbour, 1))\n # # print (\"d\")\n # idx = 0\n # value_near_neighbour = np.zeros(np.shape(near_neighbour), dtype=np.int32)\n # # print (\"e\")\n # for value in neighbor_xyz:\n # value_near_neighbour[idx] = value[near_neighbour[idx]] # the near neighbour not zero value\n # idx += 1\n # # print (\"f\")\n # labels_f[indices_non_zeros] = labels_f[value_near_neighbour[indices_non_zeros]]\n # i += 1\n # print(\"iteration : %d\" % i)\n #\n #xyz_txt[:, 4] = labels_f\n ##################################################################################################################\n\n xyz_GT_out_id_prob = np.concatenate((xyz_txt, np.expand_dims(segment_id_full, axis=1), unary_full), axis=1)\n # XYZ + GT + Output + id + probabilities + z min +z max + z_mean\n \n return labels_f, xyz_txt, false_color_txt, xyz_GT_out_id_prob","title":""},{"docid":"5352c2ba307b4d12326f1e2e2b68fce5","score":"0.5342152","text":"def read_data(path):\n l_words = []\n l_labels = []\n\n with open(path) as file:\n for i, line in enumerate(file):\n try:\n pairs = line.split('\\t')\n word, ner = pairs[0], re.sub(r'\\W', '', pairs[1])\n l_words.append(word), l_labels.append(ner)\n except IndexError:\n pass\n\n arr_labels = np.array(l_labels)\n condlist = [arr_labels == 'LOC', arr_labels == 'PER',\n arr_labels == 'O', arr_labels == 'ORG', arr_labels == 'MISC']\n choicelist = [0, 1, 2, 3, 4]\n labels = np.select(condlist, choicelist)\n\n vocab = set(l_words)\n word_to_num = {word: i for i, word in enumerate(vocab, 2)}\n word_to_num['__PADDING__'] = 0\n return (l_words, labels, word_to_num, vocab)","title":""},{"docid":"b175e411d014a00c00ab0e637b0f3406","score":"0.5338252","text":"def test_iteration(self):\n with open('../../extra-files/molecule.xyz', 'r') as file1:\n mol1 = Molecule(file1.read())\n \n newlabels = []\n for atom in mol1:\n newlabels += atom[0]\n\n self.assertEqual(mol1.labels, newlabels, 'checking labels')\n #TODO: assert that the coordinates are also correct","title":""},{"docid":"0f965b5ca6c4e7f0041a4fc6c69969bf","score":"0.53346336","text":"def validate_vocabulary_file_labels(self):\n valid_labels = True\n vocab_labels = [RDFS_LABEL, COMMENT, DESCRIPTION, TITLE]\n for label in vocab_labels:\n all_labels = self.find_labels_in_vocabulary_file(label)\n\n for current_label in all_labels:\n if not self.check_label(label, current_label):\n valid_labels = False\n\n return valid_labels","title":""},{"docid":"9bc584779311693a4364b91afd1154fe","score":"0.5320594","text":"def labels_from_file(fname):\n with open(fname, 'r') as fin:\n data = fin.readlines()\n return np.array([int(line[0]) for line in data])","title":""},{"docid":"c4ae4bc62e84f1ce7584992453569072","score":"0.53170663","text":"def get_valid_label(self):\n if self.valid_label.size == 0:\n labels = []\n with io.open(self.args.valid_data_dir, \"r\", encoding=\"utf8\") as f:\n for line in f:\n labels.append([int(line.strip().split(\"\\t\")[-1])])\n self.valid_label = np.array(labels)\n return self.valid_label","title":""},{"docid":"8c5b385088d90ff8f1e90d9f22730050","score":"0.53170544","text":"def load_labels(data='/Users/kailinlu/Documents/emotional-speech/data/'):\n files = os.listdir(data)\n labels = [file.split('_')[2].split('.')[0] for file in files]\n labels_map = defaultdict(int)\n i = 0\n for label in list(set(labels)):\n labels_map[label] = i\n i += 1\n int_labels = np.array([labels_map[label] for label in labels])\n onehot = np.zeros((len(int_labels), max(int_labels) + 1))\n onehot[np.arange(len(int_labels)), int_labels] = 1\n return onehot, int_labels, labels_map","title":""},{"docid":"876149994312f9c1f1ba48072c2ab3be","score":"0.5312809","text":"def _load_txt_annotation(self, index):\n\n filename = os.path.join(self._annoPath, index + '.txt')\n annos = []\n with open(filename,\"r\") as f:\n annos = f.readlines()\n\n num_objs = 0 \n if self._cleanRegex is not None:\n for idx,line in enumerate(annos):\n m = re.match(self._cleanRegex,line)\n if m is not None:\n if self._find_cls(m.groupdict()) != -1:\n num_objs += 1\n else:\n num_objs = len(annos)\n \n # reformat into the dictionary\n boxes = np.zeros((num_objs, 4), dtype=np.int16)\n gt_classes = np.zeros((num_objs), dtype=np.int32)\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n # \"Seg\" area for pascal is just the box area\n seg_areas = np.zeros((num_objs), dtype=np.float32)\n ix = 0\n\n for line in annos:\n m = re.match(self._cleanRegex,line)\n if m is not None:\n mgd = m.groupdict()\n x1,y1,x2,y2 = self._extract_bounding_box(mgd)\n cls = self._find_cls(mgd)\n if cls == -1:\n continue\n boxes[ix, :] = [x1, y1, x2, y2]\n gt_classes[ix] = cls\n overlaps[ix, cls] = 1.0\n seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)\n ix += 1\n\n overlaps = scipy.sparse.csr_matrix(overlaps)\n\n if cfg.OBJ_DET.BBOX_VERBOSE:\n bbox = {'boxes' : boxes,\n 'gt_classes': gt_classes,\n 'gt_overlaps' : overlaps,\n 'flipped' : False,\n 'seg_areas' : seg_areas,\n 'set' : self._setID}\n else:\n bbox = {'boxes' : boxes,\n 'gt_classes': gt_classes,\n 'flipped': False,\n 'set' : self._setID}\n return bbox","title":""},{"docid":"26099aa9ede256c1b0705f8205c1607b","score":"0.5300784","text":"def _load_data_annotation(self, name):\n filename = os.path.join(self._data_path, name + '.xml')\n tree = ET.parse(filename)\n objs = tree.findall('object')\n # if not self.config['use_diff']:\n # # Exclude the samples labeled as difficult\n # non_diff_objs = [\n # obj for obj in objs if int(obj.find('difficult').text) == 0]\n # # if len(non_diff_objs) != len(objs):\n # # print 'Removed {} difficult objects'.format(\n # # len(objs) - len(non_diff_objs))\n # objs = non_diff_objs\n num_objs = len(objs)\n\n boxes = np.empty(shape=[0, 4], dtype=np.uint16)\n gt_classes = np.empty(shape=[0], dtype=np.int32)\n overlaps = np.empty(shape=[0, self.num_classes], dtype=np.float32)\n seg_areas = np.empty(shape=[0], dtype=np.float32)\n ishards = np.empty(shape=[0], dtype=np.int32)\n num_HSIL = 0\n num_abnormal = 0\n\n # Load object bounding boxes into a data frame.\n # clean data\n for obj in objs:\n cls_name = obj.find('name').text.lower().strip()\n if not (cls_name in self._classes):\n if cls_name=='hsil':\n print(\"change \"+ cls_name + \" to HSIL\")\n cls_name = \"HSIL\"\n else:\n print(\"change \"+ cls_name + \" to abnormal\")\n cls_name = \"abnormal\"\n ##xiugai 2019 01 20 benlai zhiyou yiju ruxia \n ##continue\n ##\n if cls_name == \"HSIL\":\n num_HSIL = num_HSIL + 1\n if cls_name == \"abnormal\":\n num_abnormal = num_abnormal + 1\n\n cls = self._class_to_ind[cls_name]\n gt_classes = np.append(gt_classes, cls)\n\n bbox = obj.find('bndbox')\n # labelImage pixel indexes 1-based\n x1 = float(bbox.find('xmin').text) - 1 if float(bbox.find('xmin').text)>0 else 0\n y1 = float(bbox.find('ymin').text) - 1 if float(bbox.find('ymin').text)>0 else 0\n x2 = float(bbox.find('xmax').text) - 1\n y2 = float(bbox.find('ymax').text) - 1\n if x1 >= x2 or y1>=y2:\n continue\n\n diffc = obj.find('difficult')\n difficult = 0 if diffc == None else int(diffc.text)\n ishards = np.append(ishards, difficult)\n\n boxes = np.append(boxes, np.expand_dims([x1, y1, x2, y2], axis=0), axis=0)\n\n overlap_vect = np.zeros(self.num_classes, dtype=np.float32)\n overlap_vect[cls] = 1.0\n overlaps = np.append(overlaps, np.expand_dims(overlap_vect, axis=0), axis=0)\n seg_areas = np.append(seg_areas, (x2 - x1 + 1) * (y2 - y1 + 1)) \n\n print(\"HISL has \" + str(num_HSIL) + \" bndboxes\")\n print(\"abnormal has \" + str(num_abnormal) + \" bndboxes\")\n overlaps = scipy.sparse.csr_matrix(overlaps)\n\n return {'boxes': boxes,\n 'gt_classes': gt_classes,\n 'gt_ishard': ishards,\n 'gt_overlaps': overlaps,\n 'flipped': False,\n 'seg_areas': seg_areas}","title":""},{"docid":"e09db3211c0270d9331f057e63d8a255","score":"0.5300199","text":"def create_train_data(data_path, masks_path):\n\n image_rows = 420\n image_cols = 580\n\n images = os.listdir(data_path)\n masks = os.listdir(masks_path)\n total = len(images)\n\n imgs = np.ndarray((total, image_rows, image_cols, 1), dtype=np.uint8)\n imgs_mask = np.ndarray((total,image_rows, image_cols, 1), dtype=np.uint8)\n i = 0\n\n for image_name in images:\n if image_name.endswith(\"jpg\"):\n img = cv2.imread(os.path.join(data_path, image_name), cv2.IMREAD_GRAYSCALE)\n else:\n continue\n try:\n img = cv2.resize(img, (image_cols, image_rows), interpolation=cv2.INTER_CUBIC)\n img = np.asarray(img).reshape((image_rows, image_cols, 1))\n imgs[i] = img\n i+=1\n except Exception as e:\n print(\"file name:\", image_name)\n print(\"index:\", i)\n print(e)\n # print(img.shape)\n print(\"images done, masks now....\")\n i=0\n\n for image_mask_name in masks:\n if image_name.endswith(\"jpg\"):\n img_mask = cv2.imread(os.path.join(masks_path, image_mask_name), cv2.IMREAD_GRAYSCALE)\n else:\n continue\n img_mask = cv2.resize(img_mask, (image_cols, image_rows), interpolation=cv2.INTER_CUBIC)\n img_mask = np.asarray(img_mask).reshape((image_rows, image_cols, 1))\n imgs_mask[i] = img_mask\n i+=1\n\n print(\"Successfully done!\")\n\n np.save('imgs_train.npy', imgs)\n np.save('imgs_mask_train.npy', imgs_mask)","title":""},{"docid":"53ade76812ee78c6951d6406ae475c28","score":"0.52991676","text":"def test_encode_target(self):\n data = pd.read_csv(self.train_data_path)\n datapipeline = self.datapipeline(self.max_sequence_len, self.label_mapping)\n output = datapipeline.encode_target(data)\n\n unique_labels = set(np.unique(output))\n print(\"u\", unique_labels)\n\n self.assertIsInstance(output, np.ndarray)\n self.assertTrue(unique_labels == set([0, 1, 2]))","title":""},{"docid":"f239abbf51696e55119dcc47a4f659d9","score":"0.5295375","text":"def build_classification_dataset(list_of_files):\n temp = []\n train_labels = []\n for f_cf in list_of_files:\n with open(f_cf) as file:\n lines = file.read().splitlines()\n #print(lines)\n temp.append([line.split()[0] for line in lines if int(line.split()[-1]) == 1])\n #print(temp)\n label_id = [f_ind for f_ind, filt in enumerate(filter) if filt in f_cf][0]\n print(label_id)\n train_labels.append(len(temp[-1]) * [label_id])\n #print(train_labels)\n train_filter = [item for l in temp for item in l]\n print(train_filter)\n image_folder = os.path.join(voc_root_folder, \"VOC2009/JPEGImages/\")\n image_filenames = [os.path.join(image_folder, file) for f in train_filter for file in os.listdir(image_folder) if\n f in file]\n x = np.array([resize(io.imread(img_f), (image_size, image_size, 3)) for img_f in image_filenames]).astype(\n 'float32')\n # changed y to an array of shape (num_examples, num_classes) with 0 if class is not present and 1 if class is present\n y_temp = []\n for tf in train_filter:\n y_temp.append([1 if tf in l else 0 for l in temp])\n y = np.array(y_temp)\n\n return x, y","title":""},{"docid":"ec677a82fa0238647c9909d982a54324","score":"0.52901566","text":"def getDermatologyData():\n path = raw_input(\"Enter the path of data: \")\n data = np.genfromtxt(path, delimiter=',', dtype='U')\n\n # Removing the rows with '?' in them\n char = '?'\n listToDelete = []\n for i, line in enumerate(range(len(data))):\n if char in data[i]:\n listToDelete.append(i)\n\n data = np.delete(data, listToDelete, axis=0)\n\n # Removing the rows with labels 4, 5, 6 since\n # we are considering only 1, 2, 3 labels\n data = data.astype(int)\n labels = data[:, -1]\n\n unwantedLabels = [4, 5, 6]\n listToDelete = []\n for i, line in enumerate(range(len(data))):\n if labels[i] in unwantedLabels:\n listToDelete.append(i)\n\n actualData = np.delete(data, listToDelete, axis=0)\n\n # Separating the labels and data into different arrays\n actualLabels = actualData[:, -1]\n actualData = actualData[:, :-1]\n\n actualData = pre.scale(actualData)\n\n # Change the label vector to label matrix\n # If Label is 2 then it becomes [0, 1, 0]\n labelMatrix = np.zeros((labels.shape[0], 3))\n for j in range(labels.shape[0]):\n if labels[j] == 1:\n labelMatrix[j][0] = 1\n if labels[j] == 2:\n labelMatrix[j][1] = 1\n if labels[j] == 3:\n labelMatrix[j][2] = 1\n\n trainWithVanillaNN(actualData, labelMatrix)\n\n return actualData, actualLabels","title":""},{"docid":"d22d0be8758237dd98e594419e219277","score":"0.52868456","text":"def loadData(self):\n self.image = np.load(self.dataImg)\n self.label = np.load(self.dataLabel)\n self.image_totalImgs = self.image.shape[0]\n\n try:\n self.image_depth = self.image[0].shape[2]\n except IndexError:\n self.image_depth = 1\n\n self.num_eval = int((self.eval_ratio * self.image_totalImgs))\n print(\"This is the total images\", self.image_totalImgs)\n print(\"This is the ratio\", self.num_eval)\n\n\n np.random.seed(2845) #45600\n\n if (len(self.image) == len(self.label)):\n p = np.random.permutation(len(self.image))\n self.image = self.image[p]\n self.label = self.label[p]\n else:\n print(\"Image data and heading data are not the same size\")\n return 0\n\n self.train_images = self.image[:-self.num_eval, :]\n print(\"This is the len of train images after it has been divided\", len(self.train_images))\n self.eval_images = self.image[-self.num_eval:, :]\n\n print(\"THIS IS THE TOTAL SIZE BEFORE DIVIDING THE DATA\", len(self.label))\n self.train_labels = self.label[:-self.num_eval, :]\n print(\"This is cutting the labels!!!!!\", len(self.train_labels))\n self.eval_labels = self.label[-self.num_eval:, :]","title":""},{"docid":"cd6cf3809e05e277307609b88f25157b","score":"0.5286005","text":"def label_learner():\n\n pkls = ['IncepV3_0506_iM2.pickle',\n 'Xception_iM3.pickle',\n 'IncepV3+drop_0514_iM7.pickle',\n 'IncepV3_0520_iM10.pickle',\n 'DenseNet169_0524_iM12.pickle',\n 'Resnet50_0524_iM13.pickle'\n ]\n\n label = pickle.load(open(vali_label, 'rb'))\n\n weight = [0.35, 0.05, 0.05, 0.3, 0.05, 0.2]\n\n mat = numpy.zeros(label[:, 1:].shape)\n for i, pkl in enumerate(pkls):\n predict = pickle.load(open(ENSEMBLE_PATH+pkl, 'rb')) # check if img id match\n if numpy.all(predict[:, 0] == label[:, 0]):\n mat += predict[:, 1:]*weight[i]\n else:\n raise ValueError('{0}: label id does not match'.format(pkl))\n\n reg = DecisionTreeRegressor(max_depth=10, criterion='mse')\n reg.fit(mat, label[:, 1:])\n\n threshold = 0.99\n with open('TreeRegressor.csv', 'w') as f:\n f.write('image_id,label_id\\n')\n for n, row in enumerate(reg.predict(mat)):\n imgid = int(predict[n, 0])\n labels = numpy.where(row > threshold)[0] + 1 # note that index+1 = actual labels\n f.write(str(int(imgid)) + ',' +\n ''.join(str(label) + ' ' for label in labels) +\n '\\n'\n )\n\n threshold = 0.01\n with open('TreeRegressor_negtive.csv', 'w') as f:\n f.write('image_id,label_id\\n')\n for n, row in enumerate(reg.predict(mat)):\n imgid = int(predict[n, 0])\n labels = numpy.where(\n numpy.logical_and(row < threshold, mat[n, :] > 0.2)\n )[0] + 1 # note that ndex+1 = actual labels\n f.write(str(int(imgid)) + ',' +\n ''.join(str(label) + ' ' for label in labels) +\n '\\n'\n )","title":""},{"docid":"29ed9fa0e46af94c7776ebfcae2d618c","score":"0.5281635","text":"def generate_labels_for_negative_images(img_path):\n if not img_path.endswith('.JPG') and not img_path.endswith('.jpg'):\n print(\"This image path does not end with .jpg or .JPG, aborting check again!\")\n quit()\n img = io.imread(img_path)\n if np.shape(img)[0] == 2:\n lbl = np.zeros(img[0].shape[:-1])\n else:\n lbl = np.zeros(img.shape[:-1])\n print('The shape of the label file is', np.shape(lbl))\n lbl_im= Image.fromarray(lbl).convert('RGB')\n save_name = img_path.replace('jpg','png').replace('JPG','png')\n lbl_im.save(save_name)","title":""},{"docid":"298d9ef77fe9a7c94f9d624e170740af","score":"0.5281074","text":"def read_seg_dataset_multiclass(example):\n if N_DATA_BANDS==4:\n image, nir, label = tf.py_function(func=load_npz, inp=[example], Tout=[tf.uint8, tf.uint8, tf.uint8])\n nir = tf.cast(nir, tf.float32)/ 255.0\n else:\n image, label = tf.py_function(func=load_npz, inp=[example], Tout=[tf.uint8, tf.uint8])\n\n image = tf.cast(image, tf.float32)/ 255.0\n label = tf.cast(label, tf.uint8)\n\n if NCLASSES==1:\n label = tf.expand_dims(label,-1)\n\n if N_DATA_BANDS==4:\n image = tf.concat([image, tf.expand_dims(nir,-1)],-1)\n return image, label\n else:\n return image, label","title":""},{"docid":"8d2a4eba0bef50d575e963358f5e5ffb","score":"0.5279427","text":"def MyToSegmentDATA(data, dest='./content/VsegDATA', histeq=False):\r\n\r\n idx = 1\r\n if os.path.isdir(dest):\r\n while os.path.isdir(dest + str(idx)):\r\n idx += 1\r\n dest = dest + str(idx)\r\n \r\n os.mkdir(dest)\r\n\r\n excess = 20\r\n\r\n label_dict = dict()\r\n label_file_path = os.path.join(dest, 'label.json')\r\n\r\n path_selector = dict()\r\n cat_list = ['normal', 'compre', 'burst', 'unsure']\r\n img_dirs = [os.path.join(dest, 'image', c) for c in cat_list]\r\n msk_dirs = [os.path.join(dest, 'mask', c) for c in cat_list]\r\n for cat, img_dir, msk_dir in zip(cat_list, img_dirs, msk_dirs):\r\n path_selector[cat] = (img_dir, msk_dir)\r\n os.makedirs(img_dir)\r\n os.makedirs(msk_dir)\r\n label_dict[cat] = dict()\r\n\r\n for d in data:\r\n \r\n img = Image.open(d['filepath'])\r\n img_width, img_height = img.size\r\n img_filename = d['filename'].split('.')[0]\r\n\r\n for idx in range(len(d['x_label'])):\r\n\r\n # pass all S\r\n if d['position'][idx] == 'S':\r\n continue\r\n\r\n # check all corp image are in correct range\r\n x_min = max(0, min(d['x_label'][idx]) - excess)\r\n x_max = min(img_width, max(d['x_label'][idx]) + excess)\r\n y_min = max(0, min(d['y_label'][idx]) - excess)\r\n y_max = min(img_height, max(d['y_label'][idx]) + excess)\r\n\r\n # save the new coordinate x,y after crop the image\r\n # (left top as (0,0))\r\n temp_dict = dict()\r\n temp_dict['xs'] = [k - x_min for k in d['x_label'][idx]]\r\n temp_dict['ys'] = [k - y_min for k in d['y_label'][idx]]\r\n\r\n box = (x_min, y_min, x_max, y_max)\r\n\r\n seg_img = img.crop(box)\r\n seg_img_filename = img_filename + '_{}.bmp'.format(d['position'][idx])\r\n seg_mask_filename = img_filename + '_{}.txt'.format(d['position'][idx])\r\n\r\n # draw mask\r\n mask = Image.new('1', seg_img.size)\r\n ImageDraw.Draw(mask).polygon([(x,y) for x, y in zip(temp_dict['xs'], temp_dict['ys'])], outline=1, fill=1)\r\n mask = np.array(mask)\r\n \r\n # ex: label_dict[__filename__][xs] = [0, 4, 5, ....]\r\n # label_dict[__filename__][ys] = [2, 5, 7, ....] \r\n label_dict[d['type'][idx]][seg_img_filename] = temp_dict\r\n\r\n seg_img_dirpath, seg_mask_dirpath = path_selector[d['type'][idx]]\r\n\r\n seg_img_filepath = os.path.join(seg_img_dirpath, seg_img_filename)\r\n # do histogram equalization\r\n if histeq == True:\r\n seg_img_array = np.array(seg_img)\r\n seg_img_array = equalizeHist(seg_img_array)\r\n seg_img = Image.fromarray(seg_img_array)\r\n seg_img.save(seg_img_filepath)\r\n\r\n seg_mask_filepath = os.path.join(seg_mask_dirpath, seg_mask_filename)\r\n np.savetxt(seg_mask_filepath, mask, fmt='%d')\r\n\r\n with open(label_file_path, mode='w', encoding='utf-8') as f:\r\n json.dump(label_dict, f)","title":""},{"docid":"1ddba5eb050d178439ee854ad6e9ca48","score":"0.52779764","text":"def load_pred(data_basedir, splits_path, class_path, split_name, results_path, results_name, \n\t\t\t\t\t\ttarget_classnum = 9, total_num = 10432, origin_test_batch = 200, exp_name = 'autoencoder'):\n\t## Load class info\n\tinstance_num, class_dic, data_class, fileid_list = class_counter(data_basedir = data_basedir, splits_path = splits_path, \n\t\t\t\t\t\t\t\t\t\tclass_path = class_path, split_name= split_name)\n\t## find N classes which have most instances\n\tsorted_class = sorted(class_dic.items(),key=lambda item:item[1],reverse = True)[:target_classnum]\n\tsorted_class = [item[0] for item in sorted_class]\n\t## get the class name \n\tsorted_class.sort()\n\tprint(sorted_class)\n\tprint(len(data_class))\n\t## find the sample index of desired class sample\n\ttarget_index = [] \t\t\t\t\t\t# all the desired sample index list\n\ttarget_index_dic = {}\t\t\t\t\t\t\t\t\t\t\t\t\t\t# desired sample dictionary {class_name: []}\n\t## iterate through the class name list\n\tfor target_class in sorted_class:\t\t\t\t\t\t\t\t\t\t\t# initialize the dictionary\n\t\ttarget_index_dic[target_class] = []\t\t\t\t\t\t\t\t\t\t\n\t## load the desired sample index by iterate through class name\n\tfor target_class in sorted_class: \t\t\t\t\t\t\n\t\ttarget_index += [i for i,x in enumerate(data_class) if x == target_class]\t \n\t\ttarget_index_dic[target_class] += [i for i,x in enumerate(data_class) if x == target_class]\n\n\t## LOAD the file(npy format) from disk\n\t### find the number of file should be loaded \n\tnum_of_file = math.ceil(total_num/origin_test_batch)\n\t### iterate through all the npy file \n\tfor idx in tqdm.tqdm(range(num_of_file)):\n\t\tif exp_name == 'autoencoder':\n\t\t\tpostidx = '%03d'%idx + '.npy'\n\t\t\ttmp_pred = np.load(os.path.join(results_path, results_name + postidx))\n\t\telif exp_name == 'clustering':\n\t\t\ttmp_pred = np.load(os.path.join(results_path, results_name))\n\t\tif idx == 0:\n\t\t\tpred = tmp_pred\n\t\telse:\n\t\t\tpred = np.concatenate((pred, tmp_pred), axis=0)\n\tprint(\"Load prediction Shape\",pred.shape)\n\treturn pred, target_index, sorted_class, target_index_dic","title":""},{"docid":"a2676876379fa13a7aecb714b5f4f113","score":"0.5277411","text":"def load_cifar():\n # Loading the data.\n (x_train_cifar, y_train_cifar), (x_test_cifar, y_test_cifar) = keras.datasets.cifar100.load_data(label_mode='fine')\n \n # Creating arrays of the same length as the labels vectors.\n test_label_cifar = np.empty([y_test_cifar.shape[0]], dtype=int)\n train_label_cifar = np.empty([y_train_cifar.shape[0]], dtype=int)\n\n # Puting the labels which are integers into the new vectors.\n for i in range(len(y_train_cifar)):\n # Since le labels are in vectors and they are floats they have to be extracted then casted to integers.\n train_label_cifar[i] = int(y_train_cifar[i][0])\n\n # Doing the same thing for the test labels.\n for i in range(len(y_test_cifar)):\n test_label_cifar[i] = int(y_train_cifar[i][0])\n\n # Preprocessing the labels vectors.\n train_label_cifar = preprocess_labels(train_label_cifar)\n train_label_cifar = train_label_cifar.astype('float32')\n test_label_cifar = preprocess_labels(test_label_cifar)\n test_label_cifar = test_label_cifar.astype('float32')\n\n \"\"\"\n x_train_cifar, train_label_cifar, x_test_cifar, test_label_cifar : are np arrays.\n \"\"\"\n \n return x_train_cifar, train_label_cifar, x_test_cifar, test_label_cifar","title":""},{"docid":"c5f193049cf1845ea307e077d8b1573b","score":"0.5274266","text":"def check_labels_bin(arr):\n try:\n if len(set(arr)) == 1:\n elem = list(set(arr))[0]\n assert(elem==1 or elem==-1)\n else:\n assert(len(set(arr)) == 2)\n assert( sorted(list(set(arr)))[0] == -1 and sorted(list(set(arr)))[1] == 1 )\n except:\n traceback.print_exc()\n raise Exception(\"Class labels (both true and predicted) can only take values -1 and 1... Exiting...\")","title":""},{"docid":"fefa855f7084cdc7b92a9d3c509c7653","score":"0.52609277","text":"def load_labels(which_norb, which_set):\n memmap_path = get_memmap_path(which_norb, 'labels')\n dtype = numpy.dtype('int32')\n row_size = 5 if which_norb == 'small' else 11\n shape = (training_set_size + testing_set_size, row_size)\n\n def make_memmap():\n cat_files, info_files = [get_norb_file_paths(which_norb,\n 'both',\n x)\n for x in ('cat', 'info')]\n\n memmap_dir = os.path.split(memmap_path)[0]\n if not os.path.isdir(memmap_dir):\n os.mkdir(memmap_dir)\n\n print(\"allocating labels' memmap...\")\n writeable_memmap = numpy.memmap(filename=memmap_path,\n dtype=dtype,\n mode='w+',\n shape=shape)\n print(\"... done.\")\n\n cat_memmap = writeable_memmap[:, :1] # 1st column\n info_memmap = writeable_memmap[:, 1:] # remaining columns\n\n for norb_files, memmap in safe_zip((cat_files, info_files),\n (cat_memmap, info_memmap)):\n read_norb_files(norb_files, memmap)\n\n if not os.path.isfile(memmap_path):\n print(\"Caching images to memmap file %s.\\n\"\n \"This will only be done once.\" % memmap_path)\n make_memmap()\n\n labels = numpy.memmap(filename=memmap_path,\n dtype=dtype,\n mode='r',\n shape=shape)\n\n if which_set == 'train':\n labels = labels[:training_set_size, :]\n elif which_set == 'test':\n labels = labels[training_set_size:, :]\n\n return labels","title":""},{"docid":"a64d78c2291532ff0beeede38ba3a3fb","score":"0.5252109","text":"def load_data_and_labels():\n texts = list(open('./data/tekst.csv').readlines())\n texts = [clean_str(sentence) for sentence in texts]\n print(\"removed punctuation\")\n texts = [s.strip() for s in texts]\n print(\"\")\n x_text = texts\n codes = list(open('./data/var7.csv').readlines())\n codes = [s.strip() for s in codes]\n global real_codes\n real_codes = codes\n global dictionary_of_codes\n dictionary_of_codes_reverse = {}\n token_codes = [dictionary_of_codes_reverse[i] for i in codes if dictionary_of_codes_reverse.setdefault(i,len(dictionary_of_codes_reverse)+1)] ##Create one_hot vector\n dictionary_of_codes = dict((v,k) for k,v in dictionary_of_codes_reverse.iteritems())\n token_codes_vector = np.eye(5)[token_codes].tolist()\n y = token_codes_vector\n return [x_text, y, real_codes, dictionary_of_codes]","title":""},{"docid":"c54afbbc4700292e5fb2d1c3cc2d5351","score":"0.5250636","text":"def _label_read(self, batch_filenames):\n label = []\n for file in batch_filenames:\n with open(self.label_path) as fp:\n for line in fp:\n if os.path.splitext(file)[0] in line:\n # int convert is conflict?\n label.append(line.split(' ')[-1].rstrip())\n break\n return label","title":""},{"docid":"b8f1dbf5dd8b8288ffc05acd6cea028f","score":"0.5244824","text":"def detection(source_path, target_path, model_weights_path, prob_thre = 0.3, iou_thre = False):\n \n pbb_dir = os.path.join(target_path, 'pbb')\n path_uid_csv = os.path.join(source_path, 'preprocess3D.csv')\n npy2csv_output_file = os.path.join(target_path, 'pred3D.csv')\n delt_dir = os.path.join(source_path, '3D')\n if not os.path.exists(pbb_dir):\n os.makedirs(pbb_dir)\n \n###########################################################################################\n \n \n predict_3D(delt_dir, pbb_dir, model_weights_path) # write to pbb_output_path\n\n \n###########################################################################################\n #pbb_dir = '/data/model_weights/crt/detect_result/tianchi_luna/20180507_model/'\n #delt_dir = '/data/model_weights/tlc/nodule_prep/tianchi_v3/'\n #path_uid_csv = '/home/crt/nodule_detector/annotations_tianchi_luna_pixel.csv'\n\n\n cads = pd.read_csv(path_uid_csv)[[u'path', u'uid']]\n path_list, uid_list = cads.values[:, 0], cads.values[:, 1]\n name_list = get_pbb_namelist(pbb_dir)\n\n new_data = []\n for name in name_list:\n #try:\n pbb = np.load(os.path.join(pbb_dir,\"%s_pbb.npy\"%name))\n delt_zyx = np.load(os.path.join(delt_dir, \"%s_delta_zyx.npy\"%name))\n ps, zs, ys, xs, ds = pbb[:,0], pbb[:,1], pbb[:,2], pbb[:,3], pbb[:,4]\n ps = ps[-ps.argsort()]\n ps = logit2prob(ps)\n zs = (zs + delt_zyx[0]) / delt_zyx[3] * delt_zyx[6]\n ys = (ys + delt_zyx[1]) / delt_zyx[4] * delt_zyx[7]\n xs = (xs + delt_zyx[2]) / delt_zyx[5] * delt_zyx[8]\n ds = ds * delt_zyx[7]\n candidates = np.column_stack((zs, ys, xs, ds, ps))\n candidates = candidates[candidates[:,4] > 0.3] # if comment, use all data\n clusters = bbox_dbscan(candidates, iou_eps=0.3, minPts=1)\n print 'cluster',clusters.shape\n cluster_coords = clusters[:, :5]\n cluster_ids = clusters[:, -1]\n candidates = []\n for i in np.arange(1, max(cluster_ids)+1):\n idxs = np.where(cluster_ids==i)[0]\n pts = np.array([cluster_coords[i] for i in idxs])\n #print 'pts',len(pts)\n centroid = get_centroid(pts)\n candidates.append(centroid)\n index = np.where(np.array(uid_list)==name)[0][0]\n image_path = path_list[index].encode('utf-8')\n name_dup = np.array([name] * len(candidates))\n image_path_dup = np.array([image_path] * len(candidates))\n candidates = np.column_stack((image_path_dup, name_dup, candidates))\n new_data.append(candidates)\n #except:\n #pass\n if new_data != []:\n new_data = np.vstack(new_data)\n df = pd.DataFrame(new_data, columns=[u'path', u'uid', u'coordZ', u'coordY', u'coordX', u'diameter', u'probability'])\n else:\n df = pd.DataFrame(columns=[u'path', u'uid', u'coordZ', u'coordY', u'coordX', u'diameter', u'probability'])\n df.to_csv(npy2csv_output_file, index=False)","title":""},{"docid":"bd029706b44c58d644a4402cd18b604c","score":"0.52441216","text":"def read_mnist_labels(split) -> np.ndarray:\n assert split in ['train', 'val']\n\n # read data as numpy array. The data structure is specified in Yann LeCun\n # site.\n fd = LABELS[split].open('rb')\n magic, size, = struct.unpack('>ii', fd.read(2 * 4))\n data = np.frombuffer(fd.read(), 'u1').reshape(size, 1)\n fd.close()\n\n return data","title":""},{"docid":"3398a783466b58a91d8a20d39cd65362","score":"0.5235401","text":"def read_labels(data_folder, label_files): \n data_folder = Path(data_folder)\n blinks = []\n corrupt = []\n interval_corrupt = []\n n_corrupt = 0\n aux = []\n \n for file in label_files:\n interval_corrupt = []\n with open(data_folder / file) as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n for row in readCSV:\n if row[0]==\"corrupt\":\n n_corrupt = int(row[1])\n elif n_corrupt > 0:\n if float(row[1]) == -1:\n print('Corrupted file: ' + file)\n else:\n interval_corrupt.append([float(row[0]), float(row[1])])\n n_corrupt -= 1\n elif row[0]==\"blinks\":\n if n_corrupt != 0:\n print('Error: ' + file)\n else:\n aux.append(float(row[0]))\n \n blinks.append(aux)\n corrupt.append(interval_corrupt)\n aux = []\n \n return np.array(blinks, dtype=object), corrupt","title":""},{"docid":"b27b7a546cb2e0f07017b570ff840868","score":"0.5232381","text":"def save_predictions(filename, y):\n np.save(filename, y)# Load the data","title":""},{"docid":"6144be1bfe551df6f53d02df1ede82b9","score":"0.52305365","text":"def process_batch(self, detections, labels):\n detections = detections[detections[:, 4] > self.CONF_THRESHOLD]\n gt_classes = labels[:, 0].astype(np.int16)\n detection_classes = detections[:, 5].astype(np.int16)\n\n all_ious = box_iou_calc(labels[:, 1:], detections[:, :4])\n want_idx = np.where(all_ious > self.IOU_THRESHOLD)\n\n all_matches = []\n for i in range(want_idx[0].shape[0]):\n all_matches.append([want_idx[0][i], want_idx[1][i], all_ious[want_idx[0][i], want_idx[1][i]]])\n\n all_matches = np.array(all_matches)\n if all_matches.shape[0] > 0: # if there is match\n all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]\n all_matches = all_matches[np.unique(all_matches[:, 1], return_index=True)[1]]\n all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]\n all_matches = all_matches[np.unique(all_matches[:, 0], return_index=True)[1]]\n\n for i, label in enumerate(labels):\n if all_matches.shape[0] > 0 and all_matches[all_matches[:, 0] == i].shape[0] == 1:\n gt_class = gt_classes[i]\n detection_class = detection_classes[int(all_matches[all_matches[:, 0] == i, 1][0])]\n self.matrix[(gt_class), detection_class] += 1\n else:\n gt_class = gt_classes[i]\n self.matrix[self.nc, (gt_class)] += 1\n\n for i, detection in enumerate(detections):\n if all_matches.shape[0] and all_matches[all_matches[:, 1] == i].shape[0] == 0:\n detection_class = detection_classes[i]\n self.matrix[detection_class, self.nc] += 1","title":""},{"docid":"7078826e3918e0075fb1cb308d45fc4e","score":"0.52277213","text":"def load_labels(self):\n labels_path = './models/coco.names'\n self.labels = open(labels_path).read().strip().split('\\n')","title":""},{"docid":"7be355509ab695506f497596a2d1799f","score":"0.5224367","text":"def detect_labels(path):\n from google.cloud import vision\n import io\n client = vision.ImageAnnotatorClient()\n\n with io.open('photo.jpg', 'rb') as image_file:\n content = image_file.read()\n\n image = vision.types.Image(content=content)\n response = client.label_detection(image=image)\n objects = response.label_annotations\n\n labelList = [translate_label(object_info.description).lower() for object_info in objects]\n return labelList","title":""}],"string":"[\n {\n \"docid\": \"181990efde8f09ae8a740812134a80ed\",\n \"score\": \"0.65193164\",\n \"text\": \"def test_segmentation_save_load(self):\\n\\n segmentation = Segmentation('test.annot', [1, 1, 2, 2])\\n segmentation.add_label(0, Label('no label', (0, 0, 0, 255)))\\n segmentation.add_label(1, Label('label 0', (255, 0, 0, 255)))\\n segmentation.add_label(2, Label('label 1', (0, 255, 0, 255)))\\n\\n # Work in a temporary directory. This guarantees cleanup even on error.\\n with tempfile.TemporaryDirectory() as directory:\\n\\n # Save and reload the test data.\\n filename = os.path.join(directory, segmentation.name)\\n nimesh.io.freesurfer.save_segmentation(filename, segmentation)\\n loaded = nimesh.io.freesurfer.load_segmentation(filename)\\n\\n np.testing.assert_array_almost_equal(segmentation.keys,\\n loaded.keys)\\n self.assertEqual(segmentation.name, loaded.name)\\n\\n # The labels should also match.\\n items = zip(segmentation.labels.items(), loaded.labels.items())\\n for (k1, l1), (k2, l2) in items:\\n self.assertEqual(k1, k2)\\n self.assertEqual(l1.color, l2.color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a3929f12317235b4507194fb9d9ae3\",\n \"score\": \"0.6114336\",\n \"text\": \"def load_data_and_labels(positive_data_file, negative_data_file):\\n # Load data from files\\n '''positive_examples = list(open(positive_data_file, \\\"r\\\").readlines())\\n positive_examples = [s.strip() for s in positive_examples]\\n negative_examples = list(open(negative_data_file, \\\"r\\\").readlines())\\n negative_examples = [s.strip() for s in negative_examples]'''\\n\\n tempString = \\\"\\\"\\n tempList = []\\n positive_examples = []\\n negative_examples = []\\n x_text = []\\n \\n #A = np.array([1,2,3,4,5,6])\\n '''a = [1,2,3,4]\\n b = [5,6,7,8]\\n tempList.append(a)\\n tempList.append(b)\\n \\n A = np.array(tempList)\\n print (A[0])\\n B = np.reshape(A, (2, 4))\\n print(B[0][0])\\n #B = np.reshape(A, (-1, 4))\\n #print(B[0])'''\\n\\n\\n with open(\\\"./data/rt-polaritydata/malicious.pos\\\", \\\"r\\\") as f:\\n for line in f:\\n if line != \\\"\\\\n\\\":\\n tempString += line\\n else:\\n tempString = tempString[:-1] #remove \\\\n\\n tempString = tempString.replace(\\\" \\\", \\\"\\\") #size 20000\\n for x in tempString:\\n tempList.append(int(x))\\n positive_examples.append(tempList[:])\\n x_text.append(tempList[:]) #append copy of list instead of reference\\n tempList[:] = []\\n tempString = \\\"\\\"\\n\\n tempList[:] = []\\n tempString = \\\"\\\"\\n\\n with open(\\\"./data/rt-polaritydata/non_malicious.neg\\\", \\\"r\\\") as f:\\n for line in f:\\n if line != \\\"\\\\n\\\":\\n tempString += line\\n else:\\n tempString = tempString[:-1] #remove \\\\n\\n tempString = tempString.replace(\\\" \\\", \\\"\\\")\\n for x in tempString:\\n tempList.append(int(x))\\n negative_examples.append(tempList[:])\\n x_text.append(tempList[:])\\n tempList[:] = []\\n tempString = \\\"\\\"\\n\\n # Generate labels\\n positive_labels = [[0, 1] for _ in positive_examples]\\n negative_labels = [[1, 0] for _ in negative_examples]\\n y = np.concatenate([positive_labels, negative_labels], 0)\\n return [x_text, y]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e842b339e19168c33c70d9fdc2caee28\",\n \"score\": \"0.6067623\",\n \"text\": \"def test_rf_label_reader():\\n labels, image_cuts, empty_count, null_count, no_count, yes_count = \\\\\\n rf.rf_label_reader(SAMPLE_TXT_FILE,SAMPLE_IMAGE_FILE)\\n assert len(labels) == np.asanyarray(image_cuts).shape[0]\\n assert empty_count + null_count + no_count + yes_count > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ecd7df4a16f32f02d0ab292fc53c6cb\",\n \"score\": \"0.59799004\",\n \"text\": \"def pre_process_labeled_data():\\n neg = open(r\\\"data\\\\neg_A0201.txt\\\", \\\"r\\\")\\n pos = open(r\\\"data\\\\pos_A0201.txt\\\", \\\"r\\\")\\n\\n data_neg, labels_neg = represent_data_as_vector(neg, 0)\\n data_pos, labels_pos = represent_data_as_vector(pos, 1)\\n\\n all_data = np.concatenate((data_neg, data_pos))\\n labels = np.concatenate((labels_neg, labels_pos))\\n\\n np.savetxt(fname=r\\\"data\\\\data.txt\\\", X=all_data)\\n np.savetxt(fname=r\\\"data\\\\labels.txt\\\", X=labels)\\n\\n x_train, x_test, y_train, y_test = train_test_split(all_data, labels, test_size=0.01)\\n\\n np.savetxt(fname=r\\\"data\\\\x_train.txt\\\", X=x_train)\\n np.savetxt(fname=r\\\"data\\\\x_test.txt\\\", X=x_test)\\n np.savetxt(fname=r\\\"data\\\\y_train.txt\\\", X=y_train.reshape(-1, 1))\\n np.savetxt(fname=r\\\"data\\\\y_test.txt\\\", X=y_test.reshape(-1, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06f2dca23721917af7f035e87246788\",\n \"score\": \"0.59588283\",\n \"text\": \"def check_labels_file_header(filename):\\r\\n with tf.gfile.Open(filename, \\\"rb\\\") as f:\\r\\n magic = read32(f)\\r\\n read32(f) # num_items, unused\\r\\n if magic != 2049:\\r\\n raise ValueError(\\\"Invalid magic number %d in MNIST file %s\\\" % (magic,\\r\\n f.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616c731611b2bb5a29acf44abffa6cdf\",\n \"score\": \"0.5851225\",\n \"text\": \"def build_segmentation_dataset(list_of_files):\\n temp = []\\n train_labels = []\\n for f_cf in list_of_files:\\n with open(f_cf) as file:\\n lines = file.read().splitlines()\\n temp.append(lines)\\n #label_id = [f_ind for f_ind, filt in enumerate(filter) if filt in f_cf][0]\\n #train_labels.append(len(temp[-1]) * [label_id])\\n train_filter = [item for l in temp for item in l]\\n image_folder = os.path.join(voc_root_folder, \\\"VOC2009/SegmentationClass/\\\")\\n image_filenames = [os.path.join(image_folder, file) for f in train_filter for file in os.listdir(image_folder) if\\n f in file]\\n x = np.array([resize(cv2.cvtColor(cv2.imread(img_f),cv2.COLOR_BGR2RGB), (image_size, image_size, 3)) for img_f in image_filenames]).astype(\\n 'float32')\\n # changed y to an array of shape (num_examples, num_classes) with 0 if class is not present and 1 if class is present\\n y_temp = []\\n for tf in train_filter:\\n y_temp.append([1 if tf in l else 0 for l in temp])\\n y = np.array(y_temp)\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0e7635bfec69ad0b63eff012bd5e6c\",\n \"score\": \"0.58441633\",\n \"text\": \"def __init__(self) :\\n #Opens up labels\\n for i in range(len(filenames)):\\n filehandle = open(filenames[i], 'rb')\\n filehandle.seek(0)\\n if filenames[i][-10:] == \\\"idx1-ubyte\\\" :\\n info1 = filehandle.read(8)\\n magic,size= st.unpack(\\\">II\\\",info1)\\n if filenames[i][:5] == \\\"train\\\" :\\n self.train_label = (st.unpack('>'+'B'*size,filehandle.read(size)))\\n else :\\n self.test_label = (st.unpack('>'+'B'*size,filehandle.read(size))) \\n else:\\n info2 = filehandle.read(16)\\n self.magic, self.size,self.nrow,self.ncol = st.unpack(\\\">IIII\\\", info2) \\n nBytesTotal = self.size*self.nrow*self.ncol*1 #since each pixel data is 1 byte\\n if filenames[i][:5] == \\\"train\\\" :\\n self.train_img = np.asarray(st.unpack('>'+'B'*nBytesTotal,filehandle.read(nBytesTotal))).reshape((self.size,self.nrow,self.ncol))\\n else :\\n self.test_img = np.asarray(st.unpack('>'+'B'*nBytesTotal,filehandle.read(nBytesTotal))).reshape((self.size,self.nrow,self.ncol))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02ba7abea738f9cb7931cdad81139ecb\",\n \"score\": \"0.5843663\",\n \"text\": \"def checkArrays(self):\\n\\n fnames = np.array([self.name+'/teff.npy',\\n self.name+'/logg.npy',\\n self.name+'/fe_h.npy',\\n self.name+'/c_h.npy',\\n self.name+'/n_h.npy',\\n self.name+'/o_h.npy',\\n self.name+'/fib.npy',\\n self.name+'/spectra.npy',\\n self.name+'/spectra_errs.npy',\\n self.name+'/bitmasks.npy'])\\n fexist = True\\n for f in fnames:\\n fexist *= os.path.isfile(f)\\n # If all files exist, read data from file for increased initialization speed\\n if fexist:\\n self.teff = np.load(self.name+'/teff.npy')\\n self.logg = np.load(self.name+'/logg.npy')\\n self.fe_h = np.load(self.name+'/fe_h.npy')\\n self.c_h = np.load(self.name+'/c_h.npy')\\n self.n_h = np.load(self.name+'/n_h.npy')\\n self.o_h = np.load(self.name+'/o_h.npy')\\n self.fib = np.load(self.name+'/fib.npy')\\n self.spectra = np.ma.masked_array(np.load(self.name+'/spectra.npy'))\\n self.spectra_errs = np.ma.masked_array(np.load(self.name+'/spectra_errs.npy'))\\n self._bitmasks = np.load(self.name+'/bitmasks.npy')\\n # If any file is missing, generate arrays and write to file\\n elif not fexist:\\n self.makeArrays(self.matchingData)\\n np.save(self.name+'/teff.npy',self.teff.data)\\n np.save(self.name+'/logg.npy',self.logg.data)\\n np.save(self.name+'/fe_h.npy',self.fe_h.data)\\n np.save(self.name+'/c_h.npy',self.c_h.data)\\n np.save(self.name+'/n_h.npy',self.n_h.data)\\n np.save(self.name+'/o_h.npy',self.o_h.data)\\n np.save(self.name+'/fib.npy',self.fib.data)\\n np.save(self.name+'/spectra.npy',self.spectra.data)\\n np.save(self.name+'/spectra_errs.npy',self.spectra_errs.data)\\n np.save(self.name+'/bitmasks.npy',self._bitmasks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafb88654e163b3dd32a78b81ac4256a\",\n \"score\": \"0.5812097\",\n \"text\": \"def load_MNIST_labels(filename):\\r\\n\\twith open(filename, 'r') as f:\\r\\n\\t\\tmagic = np.fromfile(f, dtype=np.dtype('>i4'), count=1)\\r\\n\\t\\tn_labels = np.fromfile(f, dtype=np.dtype('>i4'), count=1)\\r\\n\\t\\tlabels = np.fromfile(f, dtype=np.uint8)\\r\\n\\t\\t\\r\\n\\t\\tf.close()\\r\\n\\t\\t\\r\\n\\t\\treturn labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a372951ac0442f110cf4b7b613037b8\",\n \"score\": \"0.57573295\",\n \"text\": \"def test_unique_label():\\n np.random.seed(10)\\n Nshape = ground_truth.file_path.values.shape\\n is_relevant = np.zeros(Nshape).astype(int)\\n\\n idx = np.arange(len(is_relevant), dtype='int')\\n\\n categorization_score(idx, is_relevant, idx, np.random.rand(*Nshape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004f61a7e578b5f266c77fb34258ca06\",\n \"score\": \"0.5749477\",\n \"text\": \"def load_data(path_X,path_Y,n_data):\\n\\n with np.load(path_X, allow_pickle=True) as f:\\n X = f['X']\\n print (\\\"input X shape\\\",X.shape)\\n X = X#.transpose(1,0,2,3) # use the transpose when loading from Data_XYlabel_fimg_120_new.npz\\n st1_x = 0 \\n ed1_x = 54000 \\n st2_x = 54000 \\n ed2_x = 60000 \\n x_train = np.concatenate([X[st1_x:ed1_x],X[60000:114000]],axis=0)\\n x_test = np.concatenate([X[st2_x:ed2_x],X[114000:120000]],axis=0) \\n\\n with np.load(path_Y, allow_pickle=True) as f:\\n Y,l_p = f['label'],f['Y']\\n Y = Y#.transpose(1,0)\\n l_p = l_p.transpose(1,0)\\n st1_x = 0 \\n ed1_x = 54000\\n st2_x = 54000 \\n ed2_x = 60000 \\n y_train = np.expand_dims(np.concatenate([Y[st1_x:ed1_x],Y[60000:114000]],axis=0),axis=1)\\n y_test = np.expand_dims(np.concatenate([Y[st2_x:ed2_x],Y[114000:120000]],axis=0),axis=1)\\n\\n l_p_train = np.concatenate([l_p[st1_x:ed1_x],l_p[60000:114000]],axis=0)\\n l_p_test = np.concatenate([l_p[st2_x:ed2_x],l_p[114000:120000]],axis=0) \\n\\n print (\\\"Loading Signal to Noise Ratio - information\\\")\\n path_SNR = \\\"/KDD_Submission/Data/Data_X_snr_Ylabel_120.npz\\\"\\n x_snr_r = np.load(path_SNR, allow_pickle=True)['X'][1,:]\\n boolArr_thresh_20 = x_snr_r > 20\\n x_snr_ind = boolArr_thresh_20\\n x_snr_ind_test = np.concatenate([x_snr_ind[st2_x:ed2_x],x_snr_ind[114000:120000]],axis=0)\\n self.x_snr_r_test = np.concatenate([x_snr_r[st2_x:ed2_x],x_snr_r[114000:120000]],axis=0)\\n\\n path_mu = \\\"/KDD_Submission/Data/Data_X_mu_Ylabel_120.npz\\\"\\n x_mu = np.load(path_mu, allow_pickle=True)['X'][0,:]\\n self.x_mu_test = np.concatenate([x_mu[st2_x:ed2_x],x_mu[114000:120000]],axis=0) \\n return (x_train, y_train,l_p_train), (x_test, y_test,l_p_test,x_snr_ind_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9b969dd4691f0d157f95701c94824c\",\n \"score\": \"0.5748243\",\n \"text\": \"def test_parse_annotations() -> None:\\n filepath = get_test_file(\\\"motchallenge_labels.txt\\\")\\n result = parse_annotations(filepath)\\n\\n assert list(result.keys()) == [0, 1, 3]\\n\\n for _, labels in result.items():\\n for label in labels:\\n assert label.attributes is not None\\n assert label.category == \\\"pedestrian\\\"\\n assert label.id == \\\"1\\\"\\n assert label.attributes[\\\"visibility\\\"] == 1.0\\n assert label.box2d is not None\\n assert label.box2d.x1 in [458, 460]\\n assert label.box2d.x2 in [587, 589]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a96ab87c1338a1b28eed79f00dd7d9b\",\n \"score\": \"0.5733115\",\n \"text\": \"def test_encode_and_decode(self):\\n test_image_height = 20\\n test_image_width = 15\\n filename = 'dummy'\\n\\n image = self._create_test_image(test_image_height, test_image_width)\\n # Take the last channel as dummy label.\\n label = image[..., 0]\\n\\n example = data_utils.create_tfexample(\\n image_data=_encode_png_image(image),\\n image_format='png', filename=filename,\\n label_data=_encode_png_image(label), label_format='png')\\n\\n # Parse created example, expect getting identical results.\\n parser = data_utils.SegmentationDecoder(is_panoptic_dataset=False)\\n parsed_tensors = parser(example.SerializeToString())\\n\\n self.assertIn('image', parsed_tensors)\\n self.assertIn('image_name', parsed_tensors)\\n self.assertIn('label', parsed_tensors)\\n self.assertEqual(filename, parsed_tensors['image_name'])\\n np.testing.assert_array_equal(image, parsed_tensors['image'].numpy())\\n # Decoded label is a 3-D array with last dimension of 1.\\n decoded_label = parsed_tensors['label'].numpy()\\n np.testing.assert_array_equal(label, decoded_label[..., 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7337b39f81c808aba15458c127d8011b\",\n \"score\": \"0.5732981\",\n \"text\": \"def getPenDigitData():\\n path = raw_input(\\\"Enter the path of Train Data: \\\")\\n data = np.genfromtxt(path, delimiter=',', dtype=int)\\n labels = data[:, -1]\\n\\n unwantedLabels = [4, 5, 6, 7, 8, 9]\\n listToDelete = []\\n for i, line in enumerate(range(len(data))):\\n if labels[i] in unwantedLabels:\\n listToDelete.append(i)\\n\\n actualData = np.delete(data, listToDelete, axis=0)\\n\\n print(actualData.shape)\\n # Separating the labels and data into different arrays\\n actualLabels = actualData[:, -1]\\n actualData = actualData[:, :-1]\\n\\n actualData = pre.scale(actualData)\\n\\n # Change the label vector to label matrix\\n # If Label is 2 then it becomes [0, 1, 0]\\n labelMatrix = np.zeros((actualLabels.shape[0], 4))\\n for j in range(len(actualLabels)):\\n if actualLabels[j] == 0:\\n labelMatrix[j][0] = 1\\n if actualLabels[j] == 1:\\n labelMatrix[j][1] = 1\\n if actualLabels[j] == 2:\\n labelMatrix[j][2] = 1\\n if actualLabels[j] == 3:\\n labelMatrix[j][3] = 1\\n\\n # print(labelMatrix)\\n testData, testLabels = tpd.loadTestData()\\n\\n tpd.trainUsingNNOnPenDigitData(actualData, labelMatrix, testData, testLabels)\\n\\n return actualData, actualLabels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d86a31ffb3c8a45f7194b3e4246c073\",\n \"score\": \"0.572113\",\n \"text\": \"def _load_kitti_annotation(self, index):\\n if self._image_set == 'test':\\n image_name = os.path.join(self._devkit_path, 'testing/image_2', index+'.png')\\n img = cv2.imread(image_name)\\n width = img.shape[0]\\n height = img.shape[1]\\n\\n return {\\n 'image' : image_name,\\n 'height' : height,\\n 'width' : width,\\n 'flipped' : False,\\n 'is_train' : False\\n }\\n filename = os.path.join(self._devkit_path, 'training/label_2', index+'.txt')\\n imagename = os.path.join(self._devkit_path, 'training/label_2', index+'.png')\\n img = cv2.imread(imagename)\\n width = img.shape[0]\\n height = img.shape[1]\\n f = open(filename)\\n lines = f.readlines()\\n num_objs = 0\\n for l in lines:\\n str_cls = l.split()\\n cls = str(str_cls[0])\\n if cls in self.classes or cls in ['Truck', 'Tram', 'Van']:\\n num_objs += 1\\n num_objs = num_objs\\n\\n boxes_2d = np.zeros((num_objs, 4), dtype=np.uint16)\\n dimensions = np.zeros((num_objs, 3), dtype=np.unit16)\\n translations = np.zeros((num_objs, 3), dtype=np.float32)\\n ryaws = np.zeros((num_objs,), dtype=np.float32)\\n gt_classes = np.zeros((num_objs), dtype=np.int32)\\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n seg_areas = np.zeros((num_objs), dtype=np.float32)\\n\\n ix = 0\\n\\n for line in lines:\\n data = line.split(' ')\\n data[1:] = [float(x) for x in data[1:]]\\n\\n # extract label, truncation, occlusion\\n if str(data[0]) in ['Truck', 'Tram', 'Van']:\\n data[0] = 'Car'\\n if str(data[0]) not in self._classes:\\n continue\\n\\n type = data[0] # 'Car', 'Pedestrian', ...\\n truncation = data[1] # truncated pixel ratio [0..1]\\n occlusion = int(data[2]) # 0=visible, 1=partly occluded, 2=fully occluded, 3=unknown\\n alpha = data[3] # object observation angle [-pi..pi]\\n\\n # extract 2d bounding box in 0-based coordinates\\n xmin = int(data[4])\\n ymin = int(data[5])\\n xmax = int(data[6])\\n ymax = int(data[7])\\n cls = self._class_to_ind[data[0]] # class 0, 1, 2 for car, pedestrian and cyclist\\n\\n # extract 3d bounding box information\\n h = data[8] # box height\\n w = data[9] # box width\\n l = data[10] # box length (in meters)\\n t = (data[11], data[12], data[13]) # location (x,y,z) in camera coord.\\n ry = data[14] # yaw angle (around Y-axis in camera coordinates) [-pi..pi]\\n\\n boxes_2d[ix, :] = [xmin,ymin, xmax, ymax]\\n dimensions[ix, :] = [h, w, l]\\n translations[ix, :] = t\\n ryaws[ix, :] = ry\\n gt_classes[ix] = cls\\n overlaps[ix, cls] = 1.0\\n seg_areas[ix] = (xmax-xmin+1)*(ymax-ymin+1)\\n\\n ix = ix + 1\\n\\n return [\\n ('image', imagename),\\n ('height', height),\\n ('width', width),\\n ('boxes_2d', boxes_2d),\\n ('dimensions', dimensions),\\n ('yaws', ryaws),\\n ('gt_classes', gt_classes),\\n ('gt_overlaps', overlaps),\\n ('max_overlaps', overlaps.max(axis=1)),\\n ('max_classes', overlaps.argmax(axis=1)),\\n ('flipped', False),\\n ('seg_areas', seg_areas),\\n ('is_train', True)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8865521ca74c62121aeeac62c5dd23\",\n \"score\": \"0.5705019\",\n \"text\": \"def convert_txt_to_np_with_label(file, label):\\n a=[]\\n with open(file, 'r') as f:\\n data = f.readlines() \\n for line in data:\\n if len(line)>11:\\n b = line[:-1].split(' ')\\n c = []\\n for i in b:\\n if i != '':\\n c.append(i)\\n c = map(float, c)\\n a.append(list(c))\\n x = np.array(a).reshape((-1, 2, 16))\\n y = np.array([label] * x.shape[0])\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20915231ebcac3cce067114b34105d64\",\n \"score\": \"0.56915194\",\n \"text\": \"def test_count_labels(\\n data_dir,\\n):\\n annotation_format = \\\"kitti\\\"\\n annotation_file_path = os.path.join(str(data_dir), annotation_format, \\\"kitti_1.txt\\\")\\n label_counts = analyze.count_labels(annotation_file_path, annotation_format)\\n assert label_counts[\\\"person\\\"] == 4\\n assert label_counts[\\\"truck\\\"] == 1\\n assert label_counts[\\\"car\\\"] == 1\\n annotation_format = \\\"pascal\\\"\\n annotation_file_path = os.path.join(str(data_dir), annotation_format, \\\"pascal_1.xml\\\")\\n label_counts = analyze.count_labels(annotation_file_path, annotation_format)\\n assert label_counts[\\\"person\\\"] == 2\\n assert label_counts[\\\"car\\\"] == 1\\n annotation_format = \\\"darknet\\\"\\n annotation_file_path = os.path.join(str(data_dir), annotation_format, \\\"darknet_1.txt\\\")\\n label_counts = analyze.count_labels(annotation_file_path, annotation_format)\\n assert label_counts[\\\"1\\\"] == 2\\n assert label_counts[\\\"2\\\"] == 1\\n assert label_counts[\\\"3\\\"] == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5403c76750ff8e2072c81e5bb85c5b1e\",\n \"score\": \"0.56765985\",\n \"text\": \"def load_labels(path, filename):\\n print 'Loading MNIST labels...'\\n with gzip.open(os.path.join(path, filename)) as f:\\n magic = struct.unpack('>i', f.read(4))\\n assert(magic[0] == 2049)\\n num_labels = struct.unpack('>i', f.read(4))\\n data = np.fromstring(f.read(), np.dtype('>u1'))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a14995142eff77a230a9fbb69cfba0\",\n \"score\": \"0.56188756\",\n \"text\": \"def prune_labels(data_file, file_label_path, ver_batch, voxel_width, n_class, data_folder, version=\\\"\\\"):\\n i_rows = 0\\n xyz = np.zeros((0, 3), dtype='float32')\\n rgb = np.zeros((0, 3), dtype='uint8')\\n labels = np.zeros((0, n_class+1), dtype='uint32')\\n path_dem = data_folder + \\\"Generic/\\\"\\n\\n # ---the clouds can potentially be too big to parse directly---\\n # ---they are cut in batches in the order they are stored---\\n while True:\\n try:\\n vertices = np.genfromtxt(data_file, delimiter=' ', max_rows=ver_batch, skip_header=i_rows)\\n # print(np.shape(vertices))\\n # input(\\\"SDF\\\")\\n except StopIteration:\\n # end of file\\n break\\n # if rgb == False\\n print(\\\"*\\\")\\n if np.shape(vertices)[1] <= 6: # xyz-rgb\\n vertices_tem = np.copy(vertices)\\n rows, _ = np.shape(vertices)\\n vertices = np.ones((rows, 6))\\n vertices[:, :3] = np.copy(vertices_tem[:,:3])\\n\\n xyz_full = np.array(vertices[:, 0:3], dtype='float32')\\n rgb_full = np.array(vertices[:, 4:7], dtype='uint8')\\n\\n del vertices\\n labels_full = np.genfromtxt(file_label_path, dtype=\\\"u1\\\", delimiter=' ', max_rows=ver_batch, skip_header=i_rows)\\n xyz_sub, rgb_sub, labels_sub = libply_c.prune(xyz_full, voxel_width, rgb_full, labels_full, n_class)\\n\\n del xyz_full, rgb_full\\n xyz = np.vstack((xyz, xyz_sub))\\n rgb = np.vstack((rgb, rgb_sub))\\n labels = np.vstack((labels, labels_sub))\\n i_rows = i_rows + ver_batch\\n\\n ####################################################################################################################\\n \\n file_name = os.path.splitext(os.path.basename(data_file))[0]\\n path_short = path_dem + file_name + version\\n xyz_rgb = np.hstack((xyz, rgb))\\n np.savetxt(path_short + \\\".txt\\\", xyz_rgb, delimiter=\\\",\\\", header=\\\"X,Y,Z,R,G,B\\\", comments='')\\n\\n #dem_array = build_dem(path_short, xyz_rgb)\\n\\n nr, nc = np.shape(xyz_rgb)\\n dem_array = np.ones((nr, nc + 1), dtype=np.float32)\\n dem_array[:, :3] = xyz_rgb[:, :3]\\n dem_array[:, 3] = xyz_rgb[:, 2]\\n dem_array[:, 4:] = xyz_rgb[:, 3:]\\n\\n ####################################################################################################################\\n\\n return xyz, rgb, labels, dem_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"168c116042f30e143e7d351bf2138a5f\",\n \"score\": \"0.56104684\",\n \"text\": \"def read_label_txt(label_txt, action):\\n\\n f = open(label_txt, 'r')\\n # filenames = []\\n # labels = []\\n concat = []\\n x = 0\\n for line in f:\\n x += 1\\n if x < 61:\\n filename, label = line[:-1].split(' ')\\n # print(filename)\\n # print(label)\\n im = Image.open(z_settings.PROJECT_ROOT + 'lsdata/' + action + '/' + filename)\\n im = (np.array(im))\\n # print(im.shape)\\n \\n r = im.flatten()\\n label = [int(label)]\\n concat += list(label) + list(r)\\n \\n out = np.array(concat, np.uint8)\\n out.tofile(z_settings.PROJECT_ROOT + \\\"lsdata/bin/\\\" + action + \\\".bin\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c79ae33e8d853a2aa9542d3891a0f85b\",\n \"score\": \"0.5583995\",\n \"text\": \"def recover_3darrays(emotions_dir, neutral_instances):\\r\\n\\r\\n labels = sorted(os.listdir(emotions_dir))\\r\\n\\r\\n for index1, label in enumerate(labels):\\r\\n if index1 == 0:\\r\\n print(\\\"Recovering arrays for label\\\", label)\\r\\n for index2, npy in enumerate(\\r\\n os.listdir(emotions_dir + label)[:neutral_instances]):\\r\\n im = np.load(emotions_dir + label + '/' + npy)\\r\\n if index1 == 0 and index2 == 0:\\r\\n X = np.zeros((0, im.shape[0], im.shape[1], im.shape[2],\\r\\n im.shape[3]))\\r\\n y = np.zeros((0, len(labels)))\\r\\n X = np.append(X, [im], axis=0)\\r\\n\\r\\n y_temp = [0] * len(labels)\\r\\n for index, lab in enumerate(labels):\\r\\n if int(label) == int(lab):\\r\\n y_temp[index] = 1.0\\r\\n break\\r\\n y = np.append(y, [y_temp], axis=0)\\r\\n else:\\r\\n print(\\\"Recovering arrays for label\\\", label)\\r\\n for index2, npy in enumerate(os.listdir(emotions_dir + label)):\\r\\n im = np.load(emotions_dir + label + '/' + npy)\\r\\n X = np.append(X, [im], axis=0)\\r\\n\\r\\n y_temp = [0] * len(labels)\\r\\n for index, lab in enumerate(labels):\\r\\n if int(label) == int(lab):\\r\\n y_temp[index] = 1.0\\r\\n break\\r\\n y = np.append(y, [y_temp], axis=0)\\r\\n\\r\\n print(\\\"\\\\nShape of X array:\\\", X.shape)\\r\\n print(\\\"Shape of y array:\\\", y.shape)\\r\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac201955cdd4fcf0af56c81f1070a06\",\n \"score\": \"0.5568087\",\n \"text\": \"def decode_label_file(file_path):\\r\\n bin_data = open(file_path, 'rb').read()\\r\\n offset = 0\\r\\n fmt_header = '>2i'\\r\\n magic_number, num_images = struct.unpack_from(fmt_header, bin_data, offset)\\r\\n print('magic number:%d, the number of the images: %d' % (magic_number, num_images))\\r\\n offset += struct.calcsize(fmt_header)\\r\\n fmt_image = '>B'\\r\\n labels = np.empty(num_images)\\r\\n for i in range(num_images):\\r\\n labels[i] = struct.unpack_from(fmt_image, bin_data, offset)[0]\\r\\n offset += struct.calcsize(fmt_image)\\r\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3921e99a56cd4cf7dcde6ba10c1484a\",\n \"score\": \"0.55643845\",\n \"text\": \"def process_dataset(labels, word2idx, read = False): \\n \\n \\\"\\\"\\\"\\n \\n Helper functions\\n \\n \\\"\\\"\\\"\\n \\n def pickle_matrix(matrix, path):\\n \\n with open(path,\\\"wb\\\") as output_file:\\n\\n pickle.dump(matrix, output_file)\\n \\n def unpickle_matrix(path):\\n \\n with open(path,\\\"rb\\\") as input_file:\\n\\n matrix = pickle.load(input_file)\\n \\n return matrix\\n \\n \\\"\\\"\\\"\\n \\n main code of process_dataset\\n \\n \\\"\\\"\\\"\\n \\n input_path = \\\"agg_data\\\"\\n \\n output_path = \\\"processed_data\\\"\\n \\n path_sentence_matrices = join(output_path, \\\"all_sentence_matrices.pkl\\\")\\n\\n path_labels_matrices = join(output_path, \\\"all_label_matrices.pkl\\\")\\n \\n if isfile(path_sentence_matrices) and isfile(path_labels_matrices) and read:\\n \\n print(\\\"Loading from \\\" + path_sentence_matrices + \\\" and \\\" + path_labels_matrices) \\n \\n sentence_matrices = unpickle_matrix(path_sentence_matrices)\\n\\n labels_matrices = unpickle_matrix(path_labels_matrices)\\n\\n return sentence_matrices, labels_matrices \\n \\n else:\\n \\n print(\\\"Processing dataset ...\\\")\\n \\n with open(\\\"agg_data/agg_data.pkl\\\",'rb') as dataframe_file:\\n\\n opened_dataframe = pickle.load(dataframe_file)\\n\\n num_records = len(opened_dataframe)\\n\\n print('Num of unique segments segments: {}'.format(num_records))\\n\\n num_labels = len(opened_dataframe[\\\"label\\\"].iloc[0])\\n\\n sentence_matrices = np.zeros(num_records, dtype = 'object')\\n\\n labels_matrices = np.zeros((num_records, num_labels))\\n\\n for index, row in opened_dataframe.iterrows():\\n\\n segment = row[\\\"segment\\\"]\\n\\n label = row[\\\"label\\\"]\\n\\n sentence_matrices[index] = sentence_serialization(segment, word2idx)\\n\\n labels_matrices[index] = label\\n\\n path_sentence_matrices = join(output_path, \\\"all_sentence_matrices.pkl\\\")\\n\\n path_labels_matrices = join(output_path, \\\"all_label_matrices.pkl\\\")\\n\\n pickle_matrix(sentence_matrices, path_sentence_matrices)\\n\\n pickle_matrix(labels_matrices, path_labels_matrices)\\n\\n return sentence_matrices, labels_matrices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cc8b0a7464ddfe0a6157c815820d7a\",\n \"score\": \"0.55637866\",\n \"text\": \"def test_classifyarray(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57846793464da6e1e597ac30658fe373\",\n \"score\": \"0.5562969\",\n \"text\": \"def recover_3darrays(emotions_dir, neutral_instances):\\n\\n labels = sorted(os.listdir(emotions_dir))\\n\\n for index1, label in enumerate(labels):\\n if index1 == 0:\\n print(\\\"Recovering arrays for label\\\", label)\\n for index2, npy in enumerate(\\n os.listdir(emotions_dir + label)[:neutral_instances]):\\n im = np.load(emotions_dir + label + '/' + npy)\\n if index1 == 0 and index2 == 0:\\n X = np.zeros((0, im.shape[0], im.shape[1], im.shape[2],\\n im.shape[3]))\\n y = np.zeros((0, len(labels)))\\n X = np.append(X, [im], axis=0)\\n\\n y_temp = [0] * len(labels)\\n for index, lab in enumerate(labels):\\n if int(label) == int(lab):\\n y_temp[index] = 1.0\\n break\\n y = np.append(y, [y_temp], axis=0)\\n else:\\n print(\\\"Recovering arrays for label\\\", label)\\n for index2, npy in enumerate(os.listdir(emotions_dir + label)):\\n im = np.load(emotions_dir + label + '/' + npy)\\n X = np.append(X, [im], axis=0)\\n\\n y_temp = [0] * len(labels)\\n for index, lab in enumerate(labels):\\n if int(label) == int(lab):\\n y_temp[index] = 1.0\\n break\\n y = np.append(y, [y_temp], axis=0)\\n\\n print(\\\"\\\\nShape of X array:\\\", X.shape)\\n print(\\\"Shape of y array:\\\", y.shape)\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81b566c43e6d3b55265226c490c05986\",\n \"score\": \"0.5554325\",\n \"text\": \"def preprocess_mnist(data_path='data/data.txt', label_path='data/labels.txt',\\n hash_path='data/hash.mat', name='Rodrigo Pena'):\\n\\n # Load in data and labels\\n data = np.array(np.loadtxt(data_path))\\n labels = np.loadtxt(label_path)\\n hash_path = np\\n\\n # Select the 4 digits that should be used\\n digits = name2digits(name, hash_path=hash_path)\\n\\n # Restrict full dataset to subset of selected digits\\n rows_mask = np.logical_or.reduce([labels == x for x in digits])\\n data = data[rows_mask, :]\\n labels = labels[rows_mask].astype('int')\\n\\n return data, digits, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72d2ad1a6f19c217b8e6140546ac314\",\n \"score\": \"0.5552485\",\n \"text\": \"def _extract_labels(filename, num_labels):\\n print('Extracting labels from: ', filename)\\n with gzip.open(filename) as bytestream:\\n bytestream.read(8)\\n buf = bytestream.read(1 * num_labels)\\n labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb8c8627fb16e5f21ea2475f5a57fce\",\n \"score\": \"0.55489475\",\n \"text\": \"def _load_labels(self, label_dir: Path, dataset: str, subset: str) -> np.array:\\n with open(label_dir / f\\\"label_{subset}.pt\\\") as f:\\n labels = [line.rstrip() for line in f]\\n labels = [labels[i] for i in self.ind_list]\\n return np.asarray(labels, dtype=np.string_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1dbfe46c2ab82fc64920cb4a455ccee\",\n \"score\": \"0.5548237\",\n \"text\": \"def __init__(self, inputDir, labelsFile, fake_data=False, one_hot=False):\\n self.indexInEpoch = 0\\n self.epochsCompleted = 0\\n self.inputDir = inputDir\\n output = np.genfromtxt(labelsFile, skip_header=1, dtype=[('image', 'S10'), ('label', 'S11')], delimiter=',')\\n\\n labels = [x[1] for x in output]\\n\\n self.numberOfClasses = len(set(labels))\\n self.images = []\\n self.sizes = []\\n for file in os.listdir(inputDir):\\n if(file.endswith('.jpg')):\\n self.images.append(file)\\n im = Image.open(os.path.join(inputDir, file))\\n w,h = im.size\\n self.sizes.append((w,h))\\n self.images = np.array(self.images)\\n\\n maxSize = np.min(self.sizes,axis=0)\\n self.maxW = int(maxSize[0])\\n self.maxH = int(maxSize[1])\\n self.maxSize = int(self.maxW * self.maxH)\\n\\n self.labelsDict = {int(re.search(\\\"w_(\\\\\\\\d+)\\\\.jpg\\\", x[0]).group(1)):int(re.search(\\\"whale_(\\\\\\\\d+)\\\", x[1]).group(1)) for x in output}\\n\\n # to provide default values\\n self.labelsDict = defaultdict(lambda:0, self.labelsDict)\\n\\n self.examples = [int(re.search(\\\"w_(\\\\\\\\d+).jpg\\\",x).group(1)) for x in self.images]\\n self.labels = [self.labelsDict[x] for x in self.examples]\\n self.labels = np.array(self.labels)\\n\\n self.imagesIds = np.array(self.examples)\\n self.train = self.images[self.labels > 0]\\n self.origTrainLabels = self.labels[self.labels > 0]\\n\\n l = np.zeros(max(self.origTrainLabels))\\n i = 0\\n for k in sorted(set(self.origTrainLabels)):\\n l[k-1] = i\\n i += 1\\n self.trainLabels = np.array([l[x-1] for x in self.origTrainLabels])\\n self.test = self.images[self.labels == 0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43a276c829139e5b07ed2488fb04f29\",\n \"score\": \"0.5537566\",\n \"text\": \"def test_save_npy(self):\\n npyf = open(self.npy_fname, \\\"wb\\\")\\n utils.save1D(npyf, self.x, self.y,\\n xlabel=self.xlab, ylabels=self.ylabs)\\n npyf.close()\\n\\n npy_recarray = numpy.load(self.npy_fname)\\n\\n self.assertEqual(npy_recarray.shape, (3,))\\n self.assertTrue(numpy.array_equal(npy_recarray['Ordinate1'],\\n numpy.array((4, 5, 6))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5367553b4c01ecddf82e9f218d7c45f1\",\n \"score\": \"0.5536063\",\n \"text\": \"def test_072(self):\\n images = Images('foo',\\n ['files/1.jpg', 'files/2.jpg', 'files/3.jpg', 'files/8rgb.jpg',\\n 'files/8rgb.png', 'files/1.jpg', 'files/2.jpg', 'files/3.jpg'],\\n [0, 0, 0, 0, 0, 0, 1, 1])\\n images.split = 0.25, 12\\n X_train, X_test, Y_train, Y_test = images.split\\n self.assertEqual(len(images._train), 5)\\n self.assertEqual(len(X_train), 5)\\n self.assertEqual(len(X_test), 3)\\n self.assertEqual(len(Y_train), 5)\\n self.assertEqual(len(Y_test), 3)\\n for _ in range(len(images._train)):\\n # this is the label index\\n y = images._train[_][0]\\n self.assertEqual(Y_train[_][y], 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f532154d130870bff4254a03d70f1365\",\n \"score\": \"0.5530023\",\n \"text\": \"def part4():\\n dal_path = \\\"dict_of_affect.txt\\\"\\n dal = load_dal(dal_path)\\n\\n '''\\n Training the model.\\n '''\\n # Initialize vocabulary.\\n corpus_path = \\\"train.txt\\\"\\n build_vocab(corpus_path)\\n\\n # Initialize X_train and Y_train arrays.\\n list_snippet_label_tuple_train = load_corpus(corpus_path)\\n m = len(list_snippet_label_tuple_train)\\n V = len(vocab)\\n X_train = numpy.empty([m, V + 3], dtype=float)\\n Y_train = numpy.empty(m, dtype=float)\\n\\n # Build X_train and Y_train arrays.\\n i = 0\\n while i < len(list_snippet_label_tuple_train):\\n snippet, label = list_snippet_label_tuple_train[i]\\n list_quotes_separated = tokenize(snippet)\\n list_edit_tags = tag_edits(list_quotes_separated)\\n list_negation_pos_tuple = tag_negation(\\\" \\\".join(list_edit_tags))\\n feature_vector = modified_get_features(list_negation_pos_tuple, dal)\\n X_train[i] = feature_vector\\n Y_train[i] = label\\n i = i + 1\\n\\n # Normalizing the values for the trained model.\\n X_normalized_train = normalize(X_train)\\n\\n '''\\n Testing the model.\\n '''\\n test_corpus = \\\"test.txt\\\"\\n list_snippet_label_tuple_test = load_corpus(test_corpus)\\n\\n m = len(list_snippet_label_tuple_test)\\n X_test = numpy.empty([m, V + 3], dtype=float)\\n Y_true = numpy.empty(m, dtype=float)\\n\\n # Build X_test and Y_true arrays.\\n i = 0\\n while i < len(list_snippet_label_tuple_test):\\n snippet, label = list_snippet_label_tuple_test[i]\\n list_quotes_separated = tokenize(snippet)\\n list_edit_tags = tag_edits(list_quotes_separated)\\n list_negation_pos_tuple_test = tag_negation(\\\" \\\".join(list_edit_tags))\\n feature_vector = modified_get_features(list_negation_pos_tuple_test, dal)\\n X_test[i] = feature_vector\\n Y_true[i] = label\\n i = i + 1\\n\\n # Normalizing the values for the trained model.\\n X_test_normalized = normalize(X_test)\\n print(\\\"Testing Completed\\\")\\n\\n '''\\n Logistic Regression Model.\\n '''\\n lr_obj = LogisticRegression()\\n lr_obj.fit(X_normalized_train, Y_train)\\n Y_lr_pred = lr_obj.predict(X_test_normalized)\\n\\n p, r, f = evaluate_predictions(Y_lr_pred, Y_true)\\n print(\\\"For Logistic: \\\")\\n print(\\\"Precision:\\\" + str(p))\\n print(\\\"Recall:\\\" + str(r))\\n print(\\\"F-measure:\\\" + str(f))\\n '''\\n Top k features from the Logistic Regression.\\n '''\\n l = top_features(lr_obj, 10)\\n print(str(l))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792ed2ac517be520f564adb319dad6f9\",\n \"score\": \"0.5527436\",\n \"text\": \"def _load_imagenet3d_annotation(self, index):\\n\\n if self._image_set == 'test' or self._image_set == 'test_1' or self._image_set == 'test_2':\\n lines = []\\n else:\\n filename = os.path.join(self._imagenet3d_path, 'Labels', index + '.txt')\\n lines = []\\n with open(filename) as f:\\n for line in f:\\n lines.append(line)\\n\\n num_objs = len(lines)\\n\\n boxes = np.zeros((num_objs, 4), dtype=np.float32)\\n viewpoints = np.zeros((num_objs, 3), dtype=np.float32) # azimuth, elevation, in-plane rotation\\n viewpoints_flipped = np.zeros((num_objs, 3), dtype=np.float32) # azimuth, elevation, in-plane rotation\\n gt_classes = np.zeros((num_objs), dtype=np.int32)\\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n\\n for ix, line in enumerate(lines):\\n words = line.split()\\n assert len(words) == 5 or len(words) == 8, 'Wrong label format: {}'.format(index)\\n cls = self._class_to_ind[words[0]]\\n boxes[ix, :] = [float(n) for n in words[1:5]]\\n gt_classes[ix] = cls\\n overlaps[ix, cls] = 1.0\\n if len(words) == 8:\\n viewpoints[ix, :] = [float(n) for n in words[5:8]]\\n # flip the viewpoint\\n viewpoints_flipped[ix, 0] = -viewpoints[ix, 0] # azimuth\\n viewpoints_flipped[ix, 1] = viewpoints[ix, 1] # elevation\\n viewpoints_flipped[ix, 2] = -viewpoints[ix, 2] # in-plane rotation\\n else:\\n viewpoints[ix, :] = np.inf\\n viewpoints_flipped[ix, :] = np.inf\\n\\n gt_subclasses = np.zeros((num_objs), dtype=np.int32)\\n gt_subclasses_flipped = np.zeros((num_objs), dtype=np.int32)\\n subindexes = np.zeros((num_objs, self.num_classes), dtype=np.int32)\\n subindexes_flipped = np.zeros((num_objs, self.num_classes), dtype=np.int32)\\n viewindexes_azimuth = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n viewindexes_azimuth_flipped = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n viewindexes_elevation = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n viewindexes_elevation_flipped = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n viewindexes_rotation = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n viewindexes_rotation_flipped = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n subindexes = scipy.sparse.csr_matrix(subindexes)\\n subindexes_flipped = scipy.sparse.csr_matrix(subindexes_flipped)\\n viewindexes_azimuth = scipy.sparse.csr_matrix(viewindexes_azimuth)\\n viewindexes_azimuth_flipped = scipy.sparse.csr_matrix(viewindexes_azimuth_flipped)\\n viewindexes_elevation = scipy.sparse.csr_matrix(viewindexes_elevation)\\n viewindexes_elevation_flipped = scipy.sparse.csr_matrix(viewindexes_elevation_flipped)\\n viewindexes_rotation = scipy.sparse.csr_matrix(viewindexes_rotation)\\n viewindexes_rotation_flipped = scipy.sparse.csr_matrix(viewindexes_rotation_flipped)\\n\\n if cfg.IS_RPN:\\n if cfg.IS_MULTISCALE:\\n # compute overlaps between grid boxes and gt boxes in multi-scales\\n # rescale the gt boxes\\n boxes_all = np.zeros((0, 4), dtype=np.float32)\\n for scale in cfg.TRAIN.SCALES:\\n boxes_all = np.vstack((boxes_all, boxes * scale))\\n gt_classes_all = np.tile(gt_classes, len(cfg.TRAIN.SCALES))\\n\\n # compute grid boxes\\n s = PIL.Image.open(self.image_path_from_index(index)).size\\n image_height = s[1]\\n image_width = s[0]\\n boxes_grid, _, _ = get_boxes_grid(image_height, image_width)\\n\\n # compute overlap\\n overlaps_grid = bbox_overlaps(boxes_grid.astype(np.float), boxes_all.astype(np.float))\\n \\n # check how many gt boxes are covered by grids\\n if num_objs != 0:\\n index = np.tile(range(num_objs), len(cfg.TRAIN.SCALES))\\n max_overlaps = overlaps_grid.max(axis = 0)\\n fg_inds = []\\n for k in xrange(1, self.num_classes):\\n fg_inds.extend(np.where((gt_classes_all == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])\\n index_covered = np.unique(index[fg_inds])\\n\\n for i in xrange(self.num_classes):\\n self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])\\n self._num_boxes_covered[i] += len(np.where(gt_classes[index_covered] == i)[0])\\n else:\\n assert len(cfg.TRAIN.SCALES_BASE) == 1\\n scale = cfg.TRAIN.SCALES_BASE[0]\\n feat_stride = 16\\n # faster rcnn region proposal\\n base_size = 16\\n ratios = cfg.TRAIN.RPN_ASPECTS\\n scales = cfg.TRAIN.RPN_SCALES\\n anchors = generate_anchors(base_size, ratios, scales)\\n num_anchors = anchors.shape[0]\\n\\n # image size\\n s = PIL.Image.open(self.image_path_from_index(index)).size\\n image_height = s[1]\\n image_width = s[0]\\n\\n # height and width of the heatmap\\n height = np.round((image_height * scale - 1) / 4.0 + 1)\\n height = np.floor((height - 1) / 2 + 1 + 0.5)\\n height = np.floor((height - 1) / 2 + 1 + 0.5)\\n\\n width = np.round((image_width * scale - 1) / 4.0 + 1)\\n width = np.floor((width - 1) / 2.0 + 1 + 0.5)\\n width = np.floor((width - 1) / 2.0 + 1 + 0.5)\\n\\n # gt boxes\\n gt_boxes = boxes * scale\\n\\n # 1. Generate proposals from bbox deltas and shifted anchors\\n shift_x = np.arange(0, width) * feat_stride\\n shift_y = np.arange(0, height) * feat_stride\\n shift_x, shift_y = np.meshgrid(shift_x, shift_y)\\n shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),\\n shift_x.ravel(), shift_y.ravel())).transpose()\\n # add A anchors (1, A, 4) to\\n # cell K shifts (K, 1, 4) to get\\n # shift anchors (K, A, 4)\\n # reshape to (K*A, 4) shifted anchors\\n A = num_anchors\\n K = shifts.shape[0]\\n all_anchors = (anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)))\\n all_anchors = all_anchors.reshape((K * A, 4))\\n\\n # compute overlap\\n overlaps_grid = bbox_overlaps(all_anchors.astype(np.float), gt_boxes.astype(np.float))\\n \\n # check how many gt boxes are covered by anchors\\n if num_objs != 0:\\n max_overlaps = overlaps_grid.max(axis = 0)\\n fg_inds = []\\n for k in xrange(1, self.num_classes):\\n fg_inds.extend(np.where((gt_classes == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])\\n\\n for i in xrange(self.num_classes):\\n self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])\\n self._num_boxes_covered[i] += len(np.where(gt_classes[fg_inds] == i)[0])\\n\\n return {'boxes' : boxes,\\n 'gt_classes': gt_classes,\\n 'gt_viewpoints': viewpoints,\\n 'gt_viewpoints_flipped': viewpoints_flipped,\\n 'gt_viewindexes_azimuth': viewindexes_azimuth,\\n 'gt_viewindexes_azimuth_flipped': viewindexes_azimuth_flipped,\\n 'gt_viewindexes_elevation': viewindexes_elevation,\\n 'gt_viewindexes_elevation_flipped': viewindexes_elevation_flipped,\\n 'gt_viewindexes_rotation': viewindexes_rotation,\\n 'gt_viewindexes_rotation_flipped': viewindexes_rotation_flipped,\\n 'gt_subclasses': gt_subclasses,\\n 'gt_subclasses_flipped': gt_subclasses_flipped,\\n 'gt_overlaps' : overlaps,\\n 'gt_subindexes': subindexes,\\n 'gt_subindexes_flipped': subindexes_flipped,\\n 'flipped' : False}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16d251eae3552013d8337f6a652dbf1\",\n \"score\": \"0.55223763\",\n \"text\": \"def load_data_and_labels():\\n # Load data from files\\n with open('./data/authors.json') as data_file:\\n data = json.load(data_file)\\n\\n # pprint(data)\\n\\n list_examples = list()\\n labels_lenght = len(data)\\n\\n list_labels = []\\n for d in data:\\n index = d['index']\\n temp_label = []\\n i = 0\\n while i < labels_lenght: # inzializzi la label temporanea con tutti 0\\n if i == index:\\n temp_label.append(1)\\n else:\\n temp_label.append(0)\\n i += 1\\n # temp_label[d['index']] = 1\\n temp_examples = list(open('data/stemmed_data/' + d['file_name']).readlines())\\n temp_examples = [s.strip() for s in temp_examples]\\n for _ in temp_examples:\\n list_labels.append(temp_label)\\n list_examples.append(temp_examples)\\n\\n x_text = []\\n for exa in list_examples:\\n x_text = x_text + exa\\n x_text = [clean_str(sent) for sent in x_text]\\n x_text = [s.split(\\\" \\\") for s in x_text]\\n\\n # y = np.concatenate(list_labels, 0)\\n return [x_text, list_labels]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b13df0fdb06fed810d7df1529abbe3\",\n \"score\": \"0.5521459\",\n \"text\": \"def gen_occ_labels(mode, check_order=False):\\n print('dataset_out_dir:', gen_data_dir)\\n if not os.path.exists(gen_data_dir): os.makedirs(gen_data_dir)\\n scenes_root = org_dataset_root # root for scene subdirs\\n org_rgb_dir = 'cam0'\\n org_depth_dir = 'depth0'\\n org_normal_dir = 'normal0'\\n org_label_dir = 'label0'\\n mask_gt_type = 'instance'\\n\\n thr_depth = 25. # thresholds: depth_diff(mm)\\n ROI_sz = 3 # region of interest size in images\\n version_n = 'raycasting_{}mm_debug'.format(int(thr_depth)) # version name\\n if mode == 'normal': # all scenes for dataset generation\\n sel_scene_list = sorted([fn for fn in os.listdir(scenes_root)])\\n elif mode == 'debug': # one scene dir for debug\\n # sel_scene_list = ['3FO4IDP1HR4R_Balcony']\\n sel_scene_list = ['3FO4MMLCRTM9_Living_room']\\n\\n for scene_id, scene_name in enumerate(tqdm(sel_scene_list)):\\n cur_in_scene_dir = os.path.join(scenes_root, scene_name)\\n cur_in_rgb_dir = os.path.join(cur_in_scene_dir, org_rgb_dir, 'data')\\n cur_in_depth_dir = os.path.join(cur_in_scene_dir, org_depth_dir, 'data')\\n cur_in_normal_dir = os.path.join(cur_in_scene_dir, org_normal_dir, 'data')\\n cur_in_label_dir = os.path.join(cur_in_scene_dir, org_label_dir, 'data')\\n\\n curr_out_data_dir = os.path.join(gen_data_dir, '{}_{}'.format(scene_name, version_n))\\n if not os.path.exists(curr_out_data_dir): os.makedirs(curr_out_data_dir)\\n print('write data to {}'.format(curr_out_data_dir))\\n\\n file_ind = [name.replace('.png', '') for name in os.listdir(cur_in_rgb_dir)]\\n file_ind.sort(key=int)\\n file_names = ['{}'.format(idx) for idx in file_ind]\\n\\n for idx, file_name in enumerate(tqdm(file_names)):\\n in_rgb_path = os.path.join(cur_in_rgb_dir, '{}.png'.format(file_name))\\n in_depth_path = os.path.join(cur_in_depth_dir, '{}.png'.format(file_name))\\n in_normal_path = os.path.join(cur_in_normal_dir, '{}.png'.format(file_name))\\n in_label_path = os.path.join(cur_in_label_dir, '{}_{}.png'.format(file_name, mask_gt_type))\\n\\n out_depth_vis_path = os.path.join(curr_out_data_dir, '{:04d}-depth_vis.png'.format(idx))\\n out_depth_path = os.path.join(curr_out_data_dir, '{:04d}-depth.png'.format(idx))\\n out_rgb_path = os.path.join(curr_out_data_dir, '{:04d}-rgb.png'.format(idx))\\n out_normal_path = os.path.join(curr_out_data_dir, '{:04d}-normal.png'.format(idx))\\n out_occ_edge_path = os.path.join(curr_out_data_dir, '{:04d}-edge.png'.format(idx))\\n out_occ_edge_fg_path = os.path.join(curr_out_data_dir, '{:04d}-edge_fg.png'.format(idx))\\n out_occ_ori_path = os.path.join(curr_out_data_dir, '{:04d}-ori.png'.format(idx))\\n out_pixwise_order_path = os.path.join(curr_out_data_dir, '{:04d}-order-pix.npz'.format(idx))\\n out_delta_abs_depth_path = os.path.join(curr_out_data_dir, '{:04d}-delta_abs_depth_check.png'.format(idx))\\n out_delta_adj_depth_path = os.path.join(curr_out_data_dir, '{:04d}-delta_adj_depth_check.png'.format(idx))\\n\\n rgb = cv2.imread(in_rgb_path, cv2.IMREAD_COLOR)\\n depth_org = cv2.imread(in_depth_path, cv2.IMREAD_UNCHANGED) # uint16\\n normal_org = cv2.imread(in_normal_path, cv2.IMREAD_UNCHANGED) # z,y,x; point-to-camera\\n mask_gt = cv2.imread(in_label_path, cv2.IMREAD_UNCHANGED) # instance-wise mask (not accurate)\\n mask_invalid = np.zeros(rgb.shape[:2]) # all pixels are valid in interiornet\\n\\n depth = np.copy(depth_org).astype(np.float32)\\n normal = normal_org[:, :, [2, 1, 0]] # z,y,x => x,y,z\\n\\n # gen occlusion order for each pixel w.r.t. its neighbor pixels\\n _, occ_order_pix, delta_abs_depth, delta_adj_depth, _ = gen_occ_order(K, depth, mask_gt, mask_invalid, ROI_sz,\\n thr_depth, normal, lbl_type='mask',\\n dataset='interiornet', fast_abs_diff=False)\\n if check_order: # vis occ order\\n occ_order_vis_path = os.path.join(curr_out_data_dir, '{:04d}-order-check.png'.format(idx))\\n viz_occ_order(rgb[:, :, [2, 1, 0]], occ_order_pix, occ_order_vis_path)\\n # viz_delta_depth(rgb[:, :, [2, 1, 0]], delta_abs_depth, out_delta_abs_depth_path, thr_depth * 2)\\n # viz_delta_depth(rgb[:, :, [2, 1, 0]], delta_adj_depth, out_delta_adj_depth_path, thr_depth * 2)\\n\\n # gen occlusion edge from occlusion order\\n occ_edge_fg, occ_edge_bg, occ_edge = occ_order_to_edge(occ_order_pix, connectivity=8)\\n\\n # gen occlusion orientation from occlusion order\\n occ_ori = occ_order_connect8_to_ori(occ_order_pix[:, :, 1:])\\n occ_ori[occ_edge_fg == 0] = 0 # only preserve occ_ori on occ_edge_fg\\n occ_ori_vis = ((occ_ori + 1) / 2. * 255).astype(np.uint8) # H,W\\n\\n # vis depth\\n depth[depth == 0] = np.inf # interiornet, inf for unknown region\\n depth_vis = (depth.min() / depth) * 255.0 # save inverse depth for better vis\\n\\n # save img, depth, normal, occ edge, occ ori, occ order\\n cv2.imwrite(out_rgb_path, rgb)\\n cv2.imwrite(out_depth_path, depth_org)\\n plt.imsave(out_depth_vis_path, depth_vis, vmin=depth_vis.min(), vmax=depth_vis.max())\\n cv2.imwrite(out_normal_path, normal_org)\\n cv2.imwrite(out_occ_edge_path, (occ_edge * 255.0).astype('uint8'))\\n cv2.imwrite(out_occ_edge_fg_path, (occ_edge_fg * 255.0).astype('uint8'))\\n cv2.imwrite(out_occ_ori_path, occ_ori_vis)\\n np.savez_compressed(out_pixwise_order_path, order=occ_order_pix.astype('int8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7baaed3f7f1d8d0a2daee7b585b7d4\",\n \"score\": \"0.55199367\",\n \"text\": \"def test_labeled_binary(self):\\n tf1 = Nations().training\\n self.assert_binary_io(tf1, TriplesFactory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fcdc96a02ae42f5498425d4764da69\",\n \"score\": \"0.55173224\",\n \"text\": \"def load_PA100K_10():\\n # Loading the file that contains the name of the images.\\n mat = scipy.io.loadmat('annotation.mat')\\n\\n # Getting the train validation and test data names and labels.\\n test_images_name = mat['test_images_name']\\n test_label = mat['test_label']\\n train_images_name = mat['train_images_name']\\n train_label = mat['train_label']\\n \\n train_label = train_label[:1000]\\n test_label = test_label[:1000]\\n\\n train_images = []\\n for i in range(len(train_images_name)):\\n train_images.append(plt.imread('/Users/aymene/Desktop/comp-internship/data/release_data/release_data/' + train_images_name[i][0][0]))\\n if (i+1)>=1000:\\n break\\n\\n test_images = []\\n for i in range(len(test_images_name)):\\n test_images.append(plt.imread('/Users/aymene/Desktop/comp-internship/data/release_data/release_data/' + test_images_name[i][0][0]))\\n if (i+1)>=1000:\\n break\\n \\n \\\"\\\"\\\"\\n train_images,train_label,test_images,test_label : are np arrays.\\n \\\"\\\"\\\"\\n return train_images,train_label,test_images,test_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b877e2e0ddb931775c65d3066aade00\",\n \"score\": \"0.5515445\",\n \"text\": \"def load_bbox_annotation(self, label,img_path):\\n imname = os.path.join(img_path)\\n im = cv2.imread(imname)\\n\\n h_ratio = 1.0 * self.image_size / im.shape[0]\\n w_ratio = 1.0 * self.image_size / im.shape[1]\\n\\n label_data = pickle.load(open(label,'r'))\\n\\n num_objs = label_data['num_labels']\\n\\n label = np.zeros((self.cell_size, self.cell_size, 5+cfg.NUM_LABELS))\\n\\n for objs in label_data['objects']:\\n box_ind = objs['box_index']\\n class_label = objs['num_class_label']\\n\\n\\n x1 = max(min((float(box_ind[0]) - 1) * w_ratio, self.image_size - 1), 0)\\n y1 = max(min((float(box_ind[1]) - 1) * h_ratio, self.image_size - 1), 0)\\n x2 = max(min((float(box_ind[2]) - 1) * w_ratio, self.image_size - 1), 0)\\n y2 = max(min((float(box_ind[3]) - 1) * h_ratio, self.image_size - 1), 0)\\n\\n boxes = [(x2 + x1) / 2.0, (y2 + y1) / 2.0, x2 - x1, y2 - y1]\\n \\n x_ind = int(box_ind[0] * self.cell_size / self.image_size)\\n y_ind = int(box_ind[1] * self.cell_size / self.image_size)\\n\\n if label[y_ind, x_ind, 0] == 1:\\n continue\\n label[y_ind, x_ind, 0] = 1\\n label[y_ind, x_ind, 1:5] = boxes\\n label[y_ind, x_ind, 5 + class_label] = 1\\n\\n return label, num_objs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280bba1244d98d2557ba2c7c46d2cf05\",\n \"score\": \"0.5502933\",\n \"text\": \"def voc_eval(detpath,\\n annopath,\\n classname,\\n ovthresh=0.5):\\n # assumes detections are in detpath.format(classname)\\n # assumes annotations are in annopath.format(imagename)\\n # assumes imagesetfile is a text file with each line an image name\\n # cachedir caches the annotations in a pickle file\\n npos = 0\\n gt_boxes = {}\\n for line in open(os.path.join('../PascalVOC/bonus_ground_truth.txt')):\\n s = re.split(' ', line)\\n cls = int(s[1])\\n if (cls != classname):\\n continue\\n if s[0] in gt_boxes.keys():\\n boxes = np.array(s[2:]).astype(np.float)\\n boxes = np.array([boxes[0], boxes[1], boxes[0] + boxes[2], boxes[1] + boxes[3]])\\n gt_boxes[s[0]]['bbox'].append(boxes)\\n gt_boxes[s[0]]['det'].append(False)\\n else:\\n gt_boxes[s[0]] = {}\\n gt_boxes[s[0]]['bbox'] = []\\n gt_boxes[s[0]]['det'] = []\\n boxes = np.array(s[2:]).astype(np.float)\\n boxes = np.array([boxes[0], boxes[1], boxes[0] + boxes[2], boxes[1] + boxes[3]])\\n gt_boxes[s[0]]['bbox'].append(boxes)\\n gt_boxes[s[0]]['det'].append(False)\\n npos = npos + 1\\n \\n\\n # read dets\\n # detfile = detpath.format(classname)\\n detfile = detpath\\n # print(detpath)\\n with open(detfile, 'r') as f:\\n lines = f.readlines()\\n\\n splitlines = [x.strip().split(' ') for x in lines]\\n image_ids = [x[0] for x in splitlines]\\n confidence = np.array([float(x[1]) for x in splitlines])\\n BB = np.array([[float(z) for z in x[2:]] for x in splitlines])\\n # print(BB, '@@@',confidence)\\n # sort by confidence\\n sorted_ind = np.argsort(-confidence)\\n sorted_scores = np.sort(-confidence)\\n BB = BB[sorted_ind, :]\\n image_ids = [image_ids[x] for x in sorted_ind]\\n\\n # go down dets and mark TPs and FPs\\n nd = len(image_ids)\\n tp = np.zeros(nd)\\n fp = np.zeros(nd)\\n for d in range(nd):\\n if image_ids[d] not in gt_boxes.keys():\\n fp[d] = 1.\\n continue\\n R = gt_boxes[image_ids[d]]\\n bb = BB[d, :].astype(float)\\n ovmax = -np.inf\\n BBGT = np.array(R['bbox']).astype(float)\\n\\n if BBGT.size > 0:\\n # compute overlaps\\n # intersection\\n ixmin = np.maximum(BBGT[:, 0], bb[0])\\n iymin = np.maximum(BBGT[:, 1], bb[1])\\n ixmax = np.minimum(BBGT[:, 2], bb[2])\\n iymax = np.minimum(BBGT[:, 3], bb[3])\\n iw = np.maximum(ixmax - ixmin + 1., 0.)\\n ih = np.maximum(iymax - iymin + 1., 0.)\\n inters = iw * ih\\n\\n # union\\n uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +\\n (BBGT[:, 2] - BBGT[:, 0] + 1.) *\\n (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)\\n\\n overlaps = inters / uni\\n ovmax = np.max(overlaps)\\n jmax = np.argmax(overlaps)\\n\\n if ovmax > ovthresh:\\n if not R['det'][jmax]:\\n tp[d] = 1.\\n R['det'][jmax] = 1\\n else:\\n fp[d] = 1.\\n else:\\n fp[d] = 1.\\n\\n # compute precision recall\\n fp = np.cumsum(fp)\\n tp = np.cumsum(tp)\\n rec = tp / float(npos)\\n # avoid divide by zero in case the first detection matches a difficult\\n # ground truth\\n prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)\\n ap = voc_ap(rec, prec, True)\\n\\n return rec, prec, ap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842a2f5e8663fc72f6c0b51d0b864189\",\n \"score\": \"0.54969317\",\n \"text\": \"def create_VOC_label_file(label_file_root_path, image_name, image_size, bboxs):\\n\\n objIndex = 0\\n for bbox in bboxs:\\n objIndex += 1\\n #data = data.strip('\\\\n')\\n #datas = data.split(' ')\\n datas = bboxs\\n if 4 != len(bbox):\\n print 'bounding box information error'\\n continue\\n #pictureName = each.replace('.txt', '.jpg')\\n #imageFile = imgpath + pictureName\\n #img = cv2.imread(imageFile)\\n #imgSize = img.shape\\n if objIndex == 1:\\n xmlName = image_name.replace('.jpg', '.xml')\\n f = open(os.path.join(label_file_root_path, xmlName), \\\"w\\\")\\n doc = Document()\\n annotation = doc.createElement('annotation')\\n doc.appendChild(annotation)\\n\\n folder = doc.createElement('folder')\\n folder.appendChild(doc.createTextNode(foldername))\\n annotation.appendChild(folder)\\n\\n filename = doc.createElement('filename')\\n filename.appendChild(doc.createTextNode(image_name))\\n annotation.appendChild(filename)\\n\\n source = doc.createElement('source')\\n database = doc.createElement('database')\\n database.appendChild(doc.createTextNode('My Database'))\\n source.appendChild(database)\\n source_annotation = doc.createElement('annotation')\\n source_annotation.appendChild(doc.createTextNode(foldername))\\n source.appendChild(source_annotation)\\n image = doc.createElement('image')\\n image.appendChild(doc.createTextNode('flickr'))\\n source.appendChild(image)\\n flickrid = doc.createElement('flickrid')\\n flickrid.appendChild(doc.createTextNode('NULL'))\\n source.appendChild(flickrid)\\n annotation.appendChild(source)\\n\\n owner = doc.createElement('owner')\\n flickrid = doc.createElement('flickrid')\\n flickrid.appendChild(doc.createTextNode('NULL'))\\n owner.appendChild(flickrid)\\n name = doc.createElement('name')\\n name.appendChild(doc.createTextNode('idaneel'))\\n owner.appendChild(name)\\n annotation.appendChild(owner)\\n\\n size = doc.createElement('size')\\n width = doc.createElement('width')\\n width.appendChild(doc.createTextNode(str(image_size[1])))\\n size.appendChild(width)\\n height = doc.createElement('height')\\n height.appendChild(doc.createTextNode(str(image_size[0])))\\n size.appendChild(height)\\n depth = doc.createElement('depth')\\n depth.appendChild(doc.createTextNode(str(image_size[2])))\\n size.appendChild(depth)\\n annotation.appendChild(size)\\n\\n segmented = doc.createElement('segmented')\\n segmented.appendChild(doc.createTextNode(str(0)))\\n annotation.appendChild(segmented)\\n annotation.appendChild(insertObject(doc, bbox))\\n else:\\n annotation.appendChild(insertObject(doc, bbox))\\n try:\\n f.write(doc.toprettyxml(indent=' '))\\n f.close()\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b7eb49e01c40834abaf65d9ad2ec02\",\n \"score\": \"0.54937774\",\n \"text\": \"def load_labels(file: str) -> np.ndarray:\\n rows = []\\n with open(file, 'r', encoding='utf-8') as f:\\n for row in f:\\n rows.append(row.strip())\\n return np.array(rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25dbfa95e2ada78f416b554e3a47e5ad\",\n \"score\": \"0.54923767\",\n \"text\": \"def _data_check(self, datalist):\\n \\n feat_n_check = set([])\\n label_n_check = set([])\\n label_item_set = set([])\\n label_row_set = set([])\\n for each_data in datalist:\\n for each_x_path in each_data['x']:\\n if os.path.exists(each_x_path) is False:\\n raise Exception('episode file not exist')\\n feat_n_check.add(each_data['feat_n'])\\n for each_y_path in each_data['y']:\\n label_n_check.add(len(np.array(each_y_path)))\\n label_item_set.update(np.array(each_y_path).astype(int).tolist())\\n label_row_set.add(sum(np.array(each_y_path).astype(int)))\\n \\n if len(feat_n_check) != 1:\\n raise Exception('feat_n is inconformity in data')\\n if len(label_n_check) != 1:\\n raise Exception('label_n is inconformity in data')\\n \\n if len(label_item_set) <= 1:\\n raise Exception('value space size <=1 is unvalid')\\n elif len(label_item_set) == 2:\\n if 0 in label_item_set and 1 in label_item_set:\\n if list(label_n_check)[0] == 1:\\n self.task = 'binaryclass'\\n else:\\n if max(label_row_set) == 1:\\n self.task = 'multiclass'\\n else:\\n self.task = 'multilabel'\\n else:\\n raise Exception('odd value exist in label value space')\\n else:\\n if list(label_n_check)[0] == 1:\\n self.task = 'regression'\\n else:\\n raise Exception('odd value exist in label value space')\\n print ('current task can beed seen as {0}'.format(self.task))\\n self.input_size = list(feat_n_check)[0]\\n self.label_size = list(label_n_check)[0]\\n self.loss_name = self._get_lossname(self.loss_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36f9c70e5ad54a07dd895d98ad0454bb\",\n \"score\": \"0.54920715\",\n \"text\": \"def filter_label():\\n # first walk label_source folder to get label_merge.txt\\n label_source_path = '/mnt/data/LINEdevkit/txt/label_source'\\n label_merge = []\\n for label_source in os.listdir(label_source_path):\\n with open(os.path.join(label_source_path, label_source)) as f:\\n lines = [line.strip() for line in f.readlines()]\\n label_merge.extend(lines)\\n print 'all image is %d'%len(label_merge)\\n\\n # label_path = '/media/data/LINEdevkit/txt/label_merge.txt'\\n label_path_filter = '/mnt/data/LINEdevkit/txt/label_merge_filter.txt'\\n label_filter_file = open(label_path_filter, 'w')\\n label_filter_val_ls = []\\n label_filter_train_ls = []\\n st = set()\\n for line in label_merge:\\n try:\\n line = line.decode('utf-8')\\n label = line.split(' ', 1)[1]\\n except:\\n continue\\n print line\\n label = re.sub(ur'[^\\\\u4e00-\\\\u9fa5a-zA-Z0-9]{1}', '*', label)\\n st = st | set(label)\\n tmp = line.split(' ', 1)[0] + ' ' + label + '\\\\n'\\n label_filter_train_ls.append(tmp.encode('utf-8')) \\\\\\n if random.uniform(0, 1) < 0.9 else label_filter_val_ls.append(tmp.encode('utf-8'))\\n label_filter_file.write(tmp.encode('utf-8'))\\n label_filter_file.close()\\n\\n random.shuffle(label_filter_train_ls)\\n random.shuffle(label_filter_val_ls)\\n with open('/mnt/data/LINEdevkit/txt/label_train.txt', 'w') as f_train:\\n for file_train in label_filter_train_ls:\\n f_train.write(file_train)\\n\\n with open('/mnt/data/LINEdevkit/txt/label_val.txt', 'w') as f_val:\\n for file_val in label_filter_val_ls:\\n f_val.write(file_val)\\n\\n with open('/mnt/data/LINEdevkit/txt/merge_map.txt', 'w') as merge_map_file:\\n for (ind, label) in enumerate(st):\\n merge_map_file.write('%d %s\\\\n' % (ind, label.encode('utf-8')))\\n merge_map_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dabd5862c67b2263e935bf0bac9b5e95\",\n \"score\": \"0.5489686\",\n \"text\": \"def parse_data(dir_path='tiny-imagenet-200', n_classes_limit=N_CLASSES_LIMIT):\\n\\n def _read_wnids(filename='wnids.txt'):\\n wnids_file = os.path.join(dir_path, filename)\\n assert os.path.exists(wnids_file)\\n wnids = OrderedDict()\\n c = 0\\n with open(wnids_file) as f:\\n for line in f:\\n wnids[line.strip()] = c\\n c += 1\\n\\n # only take 50 labels\\n if c == n_classes_limit:\\n break\\n\\n print(\\\"%d labels\\\" % c)\\n return wnids\\n\\n def _enumerate_train_imgs(train_dir, wnids):\\n assert os.path.isdir(train_dir)\\n subdirs = os.listdir(train_dir)\\n\\n train_imgs, train_labels = [], []\\n test_imgs, test_labels = [], []\\n\\n for subdir in subdirs:\\n if subdir != \\\".DS_Store\\\": # On Mac only\\n c = 0 # for each subdir (i.e. label), take first 50 samples for test\\n subdir_path = os.path.join(train_dir, subdir, \\\"images\\\")\\n imgs = os.listdir(subdir_path)\\n\\n try:\\n label_idx = wnids[subdir]\\n except:\\n continue\\n\\n for img in imgs:\\n full_img_path = os.path.join(subdir_path, img)\\n if not os.path.exists(full_img_path):\\n print(\\\"%s not existed ...\\\" % full_img_path)\\n continue\\n\\n if c < 50:\\n test_imgs.append(full_img_path)\\n test_labels.append(label_idx)\\n else:\\n train_imgs.append(full_img_path)\\n train_labels.append(label_idx)\\n\\n c += 1\\n\\n return train_imgs, train_labels, test_imgs, test_labels\\n\\n def _read_val_annotations(val_annotation, wnids):\\n val_imgs, val_labels = [], []\\n with open(val_annotation) as f:\\n for line in f:\\n fields = line.strip().split()\\n img, label = fields[0], fields[1]\\n full_img_path = os.path.join(dir_path, \\\"val\\\", \\\"images\\\", img)\\n if not os.path.exists(full_img_path):\\n print(\\\"%s not existed ...\\\" % full_img_path)\\n continue\\n\\n try:\\n label_idx = wnids[label]\\n except:\\n continue\\n\\n val_imgs.append(full_img_path)\\n val_labels.append(label_idx)\\n\\n return val_imgs, val_labels\\n\\n wnids = _read_wnids()\\n train_dir = os.path.join(dir_path, \\\"train\\\")\\n val_annotation = os.path.join(dir_path, \\\"val\\\", \\\"val_annotations.txt\\\")\\n\\n # take train and test images\\n train_imgs, train_labels, test_imgs, test_labels = \\\\\\n _enumerate_train_imgs(train_dir, wnids)\\n\\n # take val images\\n val_imgs, val_labels = _read_val_annotations(val_annotation, wnids)\\n\\n data_dict = {\\n \\\"train_imgs\\\": train_imgs,\\n \\\"train_labels\\\": train_labels,\\n \\\"val_imgs\\\": val_imgs,\\n \\\"val_labels\\\": val_labels,\\n \\\"test_imgs\\\": test_imgs,\\n \\\"test_labels\\\": test_labels\\n }\\n\\n return data_dict # A dictionary of lists of data paths and labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d204dde7ecf754033f76e7a8342641f\",\n \"score\": \"0.54848415\",\n \"text\": \"def load_data_and_labels_new(positive_data_file=None, negative_data_file=None, data_dir=None):\\n\\n # Load data from files\\n \\\"\\\"\\\"\\n positive_examples = list(open(positive_data_file, \\\"r\\\").readlines())\\n positive_examples = [s.strip() for s in positive_examples]\\n negative_examples = list(open(negative_data_file, \\\"r\\\").readlines())\\n negative_examples = [s.strip() for s in negative_examples]\\n \\\"\\\"\\\"\\n positive_examples = list()\\n negative_examples = list()\\n\\n if data_dir:\\n data_folder = data_dir\\n else:\\n data_folder = './data'\\n\\n train_data = {'00': list(), '01': list()}\\n\\n for current_cat in ['00', '01']:\\n print(\\\"{}/{}/\\\".format(data_folder, current_cat))\\n file_list = list()\\n for root, dirs, f_list in os.walk(\\\"{}/{}/\\\".format(data_folder, current_cat)):\\n for oo in f_list:\\n if 'class.nfo' != oo:\\n file_list.append('{}/{}/{}'.format(data_folder, current_cat, oo))\\n\\n print(\\\"Готовим категорию: {} - {} сообщений\\\".format(current_cat, len(file_list)))\\n\\n data = list()\\n for ff in file_list:\\n f = open(ff, 'r')\\n ss = re.split(r'[\\\\r|\\\\n]+', f.read())\\n train_data[current_cat].append(\\\" \\\".join(ss))\\n\\n positive_examples = train_data['00']\\n negative_examples = train_data['01']\\n\\n # Split by words\\n x_text = positive_examples + negative_examples\\n x_text = [clean_str_new(sent) for sent in x_text]\\n # Generate labels\\n positive_labels = [[0, 1] for _ in positive_examples]\\n negative_labels = [[1, 0] for _ in negative_examples]\\n y = np.concatenate([positive_labels, negative_labels], 0)\\n\\n #print(x_text)\\n #print(y)\\n #raw_input()\\n\\n return [x_text, y]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6d74220b6f38ee0220fb190ac52bec\",\n \"score\": \"0.54772806\",\n \"text\": \"def test_gh_issue_3025():\\n d = np.zeros((60, 320))\\n d[:, :257] = 1\\n d[:, 260:] = 1\\n d[36, 257] = 1\\n d[35, 258] = 1\\n d[35, 259] = 1\\n assert ndimage.label(d, np.ones((3, 3)))[1] == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be06a1b2207b7ca714224eaeb8fc7d56\",\n \"score\": \"0.5472245\",\n \"text\": \"def read_igor_marginals_txt(marginals_file_name , dim_names=False):\\n with open(marginals_file_name,'r') as file:\\n #Model parameters are stored inside a dictionary of ndarrays\\n model_dict = {}\\n dimension_names_dict = {}\\n element_name=\\\"\\\"\\n first = True\\n first_dim_line = False\\n element_marginal_array = []\\n indices_array = []\\n\\n for line in file:\\n strip_line = line.rstrip('\\\\n') #Remove end of line character\\n if strip_line[0]=='@':\\n first_dim_line = True\\n if not(first):\\n #Add the previous to the dictionnary\\n model_dict[element_name] = element_marginal_array\\n else:\\n first = False\\n\\t\\t\\t\\t\\n element_name = strip_line[1:]\\n\\n if strip_line[0]=='$':\\n #define array dimensions\\n coma_index = strip_line.find(',')\\n dimensions = []\\n\\n #Get rid of $Dim[\\n previous_coma_index = 4\\n while coma_index != -1:\\n dimensions.append(int(strip_line[previous_coma_index+1:coma_index]))\\n previous_coma_index = coma_index\\n coma_index = strip_line.find(',',coma_index+1)\\n\\t\\t\\t\\n #Add last dimension and get rid of the closing bracket \\n dimensions.append(int(strip_line[previous_coma_index+1:-1]))\\n\\n element_marginal_array = np.ndarray(shape=dimensions)\\n\\n if strip_line[0]=='#':\\n if first_dim_line:\\n dimensions_names = []\\n if len(dimensions) > 1:\\n comma_index = strip_line.find(',')\\n opening_bracket_index = strip_line.find('[')\\n while opening_bracket_index != -1:\\n dimensions_names.append(strip_line[opening_bracket_index+1:comma_index])\\n opening_bracket_index = strip_line.find('[',comma_index) \\n comma_index = strip_line.find(',',opening_bracket_index)\\n first_dim_line = False\\n dimensions_names.append(element_name)\\n dimension_names_dict[element_name] = dimensions_names\\n \\n \\n #update indices\\n indices_array = []\\n if len(dimensions) > 1:\\n comma_index = strip_line.find(',')\\n closing_brack_index = strip_line.find(']')\\t\\t\\t\\t\\t\\n while closing_brack_index != -1:\\n indices_array.append(int(strip_line[comma_index+1:closing_brack_index]))\\n opening_bracket_index = strip_line.find('[',closing_brack_index) \\n comma_index = strip_line.find(',',opening_bracket_index)\\n closing_brack_index = strip_line.find(']',closing_brack_index+1)\\n\\t\\t\\t\\t\\n\\n if strip_line[0]=='%':\\n #read doubles\\n coma_index = strip_line.find(',')\\n marginals_values = []\\n\\n #Get rid of the %\\n previous_coma_index = 0\\n while coma_index != -1:\\n marginals_values.append(float(strip_line[previous_coma_index+1:coma_index]))\\n previous_coma_index = coma_index\\n coma_index = strip_line.find(',',coma_index+1)\\n\\t\\t\\t\\n #Add last dimension and get rid of the closing bracket \\n marginals_values.append(float(strip_line[previous_coma_index+1:]))\\n if len(marginals_values)!=dimensions[-1]:\\n print(\\\"problem\\\")\\n element_marginal_array[tuple(indices_array)] = marginals_values\\n model_dict[element_name] = element_marginal_array\\t\\t\\t\\t\\n \\n \\n return [model_dict,dimension_names_dict]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87565b232c2006c5c1be18dd388db773\",\n \"score\": \"0.5469901\",\n \"text\": \"def load_data_and_labels(positive_data_file, negative_data_file, data_dir=None):\\n\\n # Load data from files\\n \\\"\\\"\\\"\\n positive_examples = list(open(positive_data_file, \\\"r\\\").readlines())\\n positive_examples = [s.strip() for s in positive_examples]\\n negative_examples = list(open(negative_data_file, \\\"r\\\").readlines())\\n negative_examples = [s.strip() for s in negative_examples]\\n \\\"\\\"\\\"\\n positive_examples = list()\\n negative_examples = list()\\n\\n if data_dir:\\n data_folder = data_dir\\n else:\\n data_folder = './data'\\n\\n train_data = {'00': list(), '01': list()}\\n\\n for current_cat in ['00', '01']:\\n print(\\\"{}/{}/\\\".format(data_folder, current_cat))\\n file_list = list()\\n for root, dirs, f_list in os.walk(\\\"{}/{}/\\\".format(data_folder, current_cat)):\\n for oo in f_list:\\n if 'class.nfo' != oo:\\n file_list.append('{}/{}/{}'.format(data_folder, current_cat, oo))\\n\\n print(\\\"Готовим категорию: {} - {} сообщений\\\".format(current_cat, len(file_list)))\\n\\n for ff in file_list:\\n f = open(ff, 'r')\\n ss = f.read()\\n train_data[current_cat].append(ss)\\n\\n positive_examples = train_data['00']\\n negative_examples = train_data['01']\\n\\n # Split by words\\n x_text = positive_examples + negative_examples\\n x_text = [clean_str(sent) for sent in x_text]\\n # Generate labels\\n positive_labels = [[0, 1] for _ in positive_examples]\\n negative_labels = [[1, 0] for _ in negative_examples]\\n y = np.concatenate([positive_labels, negative_labels], 0)\\n\\n print(x_text)\\n print(y)\\n\\n raw_input()\\n return [x_text, y]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23f36150e6c8152bdd649124bc64ce8\",\n \"score\": \"0.5463556\",\n \"text\": \"def segmentation_images(imagepath=\\\"./images/\\\", outpath=\\\"./OUTPUT/\\\"):\\n files = sorted([f for f in os.listdir(imagepath) if f.endswith('.png')])\\n for i in range(len(files)):\\n image = mpimg.imread(imagepath + files[i])[:, :, :3]\\n print(files[i], f\\\"{i}/{len(files)}\\\")\\n\\n # nuclei\\n nuclei = mpimg.imread(outpath + \\\"nuclei_predictions/\\\" + files[i])\\n nuclei = color.rgb2gray(nuclei)\\n nuclei = nuclei > filters.threshold_otsu(nuclei)\\n nuclei_only = np.copy(image)\\n nuclei_only[nuclei == False] = 0\\n mpimg.imsave(outpath + \\\"nuclei/\\\" + files[i], nuclei_only)\\n del nuclei_only\\n # print(\\\"nuclei saved\\\")\\n\\n # acini\\n acini = mpimg.imread(outpath + \\\"acini_predictions/\\\" + files[i])\\n acini = color.rgb2gray(acini)\\n acini = acini > filters.threshold_otsu(acini)\\n acini_only = np.copy(image)\\n acini_only[acini == False] = 0\\n mpimg.imsave(outpath + \\\"acini/\\\" + files[i], acini_only)\\n del acini_only\\n # print(\\\"acini saved\\\")\\n\\n # acinar_nuclei\\n acinar_nuclei = np.logical_and(acini == True, nuclei == True)\\n acinar_nuclei_only = np.copy(image)\\n acinar_nuclei_only[acinar_nuclei == False] = 0\\n mpimg.imsave(outpath + \\\"acinar_nuclei/\\\" + files[i], acinar_nuclei_only)\\n # print(\\\"acinar_nuclei saved\\\")\\n del acinar_nuclei\\n del acinar_nuclei_only\\n\\n # stromal_nuclei\\n stromal_nuclei = np.logical_and(acini == False, nuclei == True)\\n stromal_nuclei_only = np.copy(image)\\n stromal_nuclei_only[stromal_nuclei == False] = 0\\n mpimg.imsave(outpath + \\\"stromal_nuclei/\\\" + files[i], stromal_nuclei_only)\\n # print(\\\"stromal_nuclei saved\\\")\\n del stromal_nuclei\\n del stromal_nuclei_only\\n\\n # core - not saved, just for stroma calculation\\n core = mpimg.imread(outpath + \\\"tissue_predictions/\\\" + files[i])\\n core = color.rgb2gray(core)\\n core = rescale(core, 23.4375, multichannel=False) # scale factor is 6000/256 = 23.4375\\n core = core > filters.threshold_mean(core) # threshold the prediction\\n\\n # stroma\\n stroma_only = np.copy(image)\\n stroma_only[core == False] = 0\\n del core\\n stroma_only[acini == True] = 0 # removes acini from the mask\\n mpimg.imsave(outpath + \\\"stroma/\\\" + files[i], stroma_only)\\n # print(\\\"acinar_nuclei saved\\\")\\n del stroma_only\\n print(\\\"all segmentation done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c58b92aef414cdce2237e52638af85b3\",\n \"score\": \"0.54634035\",\n \"text\": \"def get_data(input_path):\\r\\n found_bg = False\\r\\n all_imgs = {}\\r\\n\\r\\n classes_count = {}\\r\\n\\r\\n class_mapping = {}\\r\\n\\r\\n i = 1\\r\\n\\r\\n with open(input_path, 'r') as f:\\r\\n\\r\\n print('Parsing annotation files')\\r\\n\\r\\n for line in f:\\r\\n\\r\\n # Print process\\r\\n sys.stdout.write('\\\\r' + 'idx=' + str(i))\\r\\n i += 1\\r\\n\\r\\n # line_split = line.strip().split(',')\\r\\n\\r\\n # (filename, y1, x1, y2, x2, class_name) = line_split\\r\\n print(line)\\r\\n filename = line.split()[0]\\r\\n y1, x1, y2, x2, class_name = line.split()[1].split(',')\\r\\n\\r\\n if class_name == '':\\r\\n # wrong labelling\\r\\n continue\\r\\n\\r\\n if class_name not in classes_count:\\r\\n classes_count[class_name] = 1\\r\\n else:\\r\\n classes_count[class_name] += 1\\r\\n\\r\\n if class_name not in class_mapping:\\r\\n if class_name == 'bg' and found_bg == False:\\r\\n print(\\r\\n 'Found class name bg.Will be treated as background (this is usually for hard negative mining).')\\r\\n found_bg = True\\r\\n class_mapping[class_name] = len(class_mapping)\\r\\n\\r\\n if filename not in all_imgs:\\r\\n all_imgs[filename] = {}\\r\\n\\r\\n img = cv2.imread(filename)\\r\\n (rows, cols) = img.shape[:2]\\r\\n all_imgs[filename]['filepath'] = filename\\r\\n all_imgs[filename]['width'] = cols\\r\\n all_imgs[filename]['height'] = rows\\r\\n all_imgs[filename]['bboxes'] = []\\r\\n # if np.random.randint(0,6) > 0:\\r\\n # \\tall_imgs[filename]['imageset'] = 'trainval'\\r\\n # else:\\r\\n # \\tall_imgs[filename]['imageset'] = 'test'\\r\\n\\r\\n all_imgs[filename]['bboxes'].append(\\r\\n {'class': class_name, 'x1': int(x1), 'x2': int(x2), 'y1': int(y1), 'y2': int(y2)})\\r\\n\\r\\n all_data = []\\r\\n for key in all_imgs:\\r\\n all_data.append(all_imgs[key])\\r\\n\\r\\n # make sure the bg class is last in the list\\r\\n if found_bg:\\r\\n if class_mapping['bg'] != len(class_mapping) - 1:\\r\\n key_to_switch = [key for key in class_mapping.keys() if class_mapping[key] == len(class_mapping) - 1][0]\\r\\n val_to_switch = class_mapping['bg']\\r\\n class_mapping['bg'] = len(class_mapping) - 1\\r\\n class_mapping[key_to_switch] = val_to_switch\\r\\n\\r\\n return all_data, classes_count, class_mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fc046e2590354d242e648dfd4ff844\",\n \"score\": \"0.5460302\",\n \"text\": \"def load_PA100K():\\n # Loading the file that contains the name of the images.\\n mat = scipy.io.loadmat('annotation.mat')\\n\\n # Getting the train validation and test data names and labels.\\n test_images_name = mat['test_images_name']\\n test_label = mat['test_label']\\n train_images_name = mat['train_images_name']\\n train_label = mat['train_label']\\n val_images_name = mat['val_images_name']\\n val_label = mat['val_label']\\n\\n # Retrieving the train images and putting them into a list and not an array because they do not have the same shape.\\n train_images = []\\n for i in range(len(train_images_name)):\\n train_images.append(plt.imread('./data/release_data/release_data/'+train_images_name[i][0][0]))\\n\\n # Doing the same thing with the test and validation images. \\n # I am considering the validation images with the test images because on the website of the competition \\n # I found that the the test images contain almost 20000 images which is equivallent to the length of the test and validation datasets.\\n test_images = []\\n for i in range(len(test_images_name)):\\n test_images.append(plt.imread('./data/release_data/release_data/'+test_images_name[i][0][0]))\\n\\n for i in range(len(val_images_name)):\\n test_images.append(plt.imread('./data/release_data/release_data/'+val_images_name[i][0][0]))\\n\\n # Concatenating the test and validation labels.\\n test_label = test_label + val_label\\n\\n # Returning everything.\\n \\\"\\\"\\\"\\n train_images,test_images : are lists.\\n train_label,test_label : are np arrays.\\n \\\"\\\"\\\"\\n return train_images,train_label,test_images,test_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d27b4f0d17beb16aef900b37b025e4\",\n \"score\": \"0.5443519\",\n \"text\": \"def load_data(name):\\r\\n with open(name + '-labels.idx1-ubyte', 'rb') as label_path:\\r\\n\\r\\n magic, n = struct.unpack('>II', label_path.read(8))\\r\\n label = np.fromfile(label_path, dtype=np.uint8)\\r\\n\\r\\n with open(name + '-images.idx3-ubyte', 'rb') as image_path:\\r\\n\\r\\n magic, num, row, col = struct.unpack('>IIII', image_path.read(16))\\r\\n image = np.fromfile(image_path, dtype = np.uint8).reshape(len(label), 784)\\r\\n image = ((image/255) - 0.5) * 2\\r\\n\\r\\n return image, label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f9f919e51527fbfadb828fb4b3b2ac\",\n \"score\": \"0.5442328\",\n \"text\": \"def load_CIFAR10(ROOT):\\n np_names = ['np_train_data', 'np_train_labels', 'np_test_data', 'np_test_labels']\\n print(all([os.path.isfile(os.path.join(ROOT, name)) for name in np_names]))\\n if all([os.path.isfile(os.path.join(ROOT, name)) for name in np_names]):\\n print(\\\"Np files were found\\\")\\n # load np arrays from given files\\n xt, yt, xte, yte = [np.fromfile(os.path.join(ROOT, name), dtype=np.uint8) for name in np_names]\\n xt = xt.reshape(-1, 32, 32, 3).astype(\\\"float\\\")\\n xte = xte.reshape(-1, 32, 32, 3).astype(\\\"float\\\")\\n print(xt.shape, yt.shape, xte.shape, yte.shape)\\n return xt, yt, xte, yte\\n else:\\n xs = []\\n ys = []\\n for b in range(1,6):\\n f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\\n X, Y = load_CIFAR_batch(f)\\n xs.append(X)\\n ys.append(Y)\\n size_estimator = sum(x.shape[0] for x in xs)\\n x_all = np.zeros((size_estimator,) + xs[0].shape[1:], dtype=float)\\n y_all = np.zeros(size_estimator, dtype=int)\\n cur_size = 0\\n for x, y in zip(xs, ys):\\n x_all[cur_size:cur_size + x.shape[0]] = x\\n y_all[cur_size:cur_size + x.shape[0]] = y\\n cur_size += x.shape[0]\\n del x\\n del y\\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\\n for arr, fname in zip([x_all, y_all, Xte, Yte], np_names):\\n arr.astype(np.uint8).tofile(os.path.join(ROOT, fname))\\n return x_all, y_all, Xte, Yte\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9330beea82b5ffe64f1f61e208280973\",\n \"score\": \"0.543412\",\n \"text\": \"def data_info(labels, mode_label, logger, config, hidden_test=False):\\n\\n zeros, zeros_index, ones, ones_index, indices = util.count_classes_gender(labels)\\n\\n zeros_index_f, zeros_index_m = zeros_index\\n ones_index_f, ones_index_m = ones_index\\n if mode_label == 'train' and config.EXPERIMENT_DETAILS['SUB_SAMPLE_ND_CLASS']:\\n min_set = min(len(zeros_index_f), len(zeros_index_m),\\n len(ones_index_f), len(ones_index_m))\\n min_set *= 2\\n\\n zeros, zeros_index, ones, ones_index, indices = util.count_classes(labels)\\n\\n if config.EXPERIMENT_DETAILS['OVERSAMPLE']:\\n zeros, zeros_index = re_distribute(zeros)\\n ones, ones_index = re_distribute(ones)\\n\\n if mode_label == 'train' and config.EXPERIMENT_DETAILS[\\n 'SUB_SAMPLE_ND_CLASS']:\\n zeros_index = random.sample(zeros_index, min_set)\\n ones_index = random.sample(ones_index, min_set)\\n update_zeros = {}\\n for i in zeros_index:\\n tmp_folder = indices[i]\\n if tmp_folder not in update_zeros:\\n update_zeros[tmp_folder] = [i]\\n else:\\n update_zeros[tmp_folder].append(i)\\n zeros = update_zeros\\n update_ones = {}\\n for i in ones_index:\\n tmp_folder = indices[i]\\n if tmp_folder not in update_ones:\\n update_ones[tmp_folder] = [i]\\n else:\\n update_ones[tmp_folder].append(i)\\n ones = update_ones\\n\\n print(f\\\"The number of class zero and one files in the {mode_label} \\\"\\n f\\\"split after segmentation are {len(zeros_index)}, {len(ones_index)}\\\")\\n logger.info(f\\\"The number of class zero files in the {mode_label} split \\\"\\n f\\\"after segmentation are {len(zeros_index)}\\\")\\n logger.info(f\\\"The number of class one files in the {mode_label} split \\\"\\n f\\\"after segmentation are {len(ones_index)}\\\")\\n\\n use_class_weights = config.EXPERIMENT_DETAILS['CLASS_WEIGHTS']\\n if use_class_weights:\\n weights, class_weights = find_weight(zeros,\\n ones,\\n config)\\n else:\\n class_weights = weights = [1, 1]\\n\\n logger.info(f\\\"{config.WEIGHT_TYPE} Weights: {weights}\\\")\\n\\n return zeros, zeros_index, ones, ones_index, weights, class_weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b83ec9d278be27cb096e48fc9acfdb\",\n \"score\": \"0.5428598\",\n \"text\": \"def read_train(input_path, output_path, max_len, padding=True):\\n labels, entity_poses, clean_texts = [], [], []\\n with open(input_path) as f:\\n example_lines = []\\n for line in f:\\n if line == '\\\\n':\\n label, raw_text = read_one_example(example_lines)\\n labels.append(label)\\n e1_pos, e2_pos, clean_text = parse_text(raw_text)\\n entity_poses.append((e1_pos, e2_pos))\\n clean_texts.append(clean_text)\\n example_lines.clear()\\n else:\\n example_lines.append(line.strip())\\n label2id_filename = os.path.join(os.path.split(output_path)[0], 'label2id.json')\\n if os.path.exists(label2id_filename):\\n with open(label2id_filename) as f:\\n label_index = json.loads(f.readline().strip())\\n else:\\n label_index = {}\\n labels_unique = list(set(labels))\\n for i in range(len(labels_unique)):\\n label_index[labels_unique[i]] = i\\n with open(label2id_filename, 'w') as f:\\n f.write(json.dumps(label_index, ensure_ascii=False))\\n id2label_filename = os.path.join(os.path.split(output_path)[0], 'id2label.json')\\n if not os.path.exists(id2label_filename):\\n id2label = dict()\\n for key, value in label_index.items():\\n id2label[value] = key\\n with open(id2label_filename, 'w') as f:\\n f.write(json.dumps(id2label, ensure_ascii=False))\\n pos_filename = os.path.join(os.path.split(output_path)[0], 'pos2id.json')\\n if os.path.exists(pos_filename):\\n with open(pos_filename) as f:\\n pos_index = json.loads(f.readline())\\n else:\\n pos_index = pos2id(max_len)\\n with open(pos_filename, 'w') as f:\\n f.write(json.dumps(pos_index, ensure_ascii=False))\\n tokenizer = Tokenizer()\\n tokenizer.fit_on_texts(clean_texts)\\n # save tokenizer\\n filename = os.path.join(os.path.split(output_path)[0], 'tokenizer.json')\\n tokenizer.to_json(filename)\\n with tf.io.TFRecordWriter(output_path) as writer:\\n for label, (e1_pos, e2_pos), text in zip(labels, entity_poses, clean_texts):\\n example = parse_train_example(label_index, pos_index, tokenizer, label, text, e1_pos, e2_pos, max_len,\\n padding)\\n writer.write(example.SerializeToString())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dca4b3ca777777110ed00b4015a7c2e\",\n \"score\": \"0.5427852\",\n \"text\": \"def load_mnist_fail(dataset=\\\"training\\\", digits=np.arange(10), path=\\\".\\\"):\\n if dataset == \\\"training\\\":\\n fname_img = path + '/train-images-idx3-ubyte' #os.path.join(path, '/train-images-idx3-ubyte')\\n fname_lbl = path + '/train-images-idx3-ubyte' #os.path.join(path, '/train-labels-idx1-ubyte')\\n elif dataset == \\\"testing\\\":\\n fname_img = path + '/t10k-images-idx3-ubyte' #os.path.join(path, '/t10k-images-idx3-ubyte')\\n fname_lbl = path + '/t10k-labels-idx1-ubyte' #os.path.join(path, '/t10k-labels-idx1-ubyte')\\n else:\\n raise ValueError(\\\"dataset must be 'testing' or 'training'\\\")\\n flbl = open(fname_lbl, 'rb')\\n magic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n lbl = pyarray(\\\"b\\\", flbl.read())\\n flbl.close()\\n\\n fimg = open(fname_img, 'rb')\\n magic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n img = pyarray(\\\"B\\\", fimg.read())\\n fimg.close()\\n\\n ind = [ k for k in range(size) if lbl[k] in digits ]\\n N = len(ind)\\n\\n images = zeros((N, 1, rows, cols), dtype=uint8)\\n labels = zeros(N, dtype=int8)\\n for i in range(len(ind)):\\n images[i][0] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\\n labels[i] = lbl[ind[i]]\\n for i in range(10):\\n print i, len([x for x in range(size) if lbl[x] == i])\\n print \\\"WHEE\\\", images.shape, labels.shape, N, digits, len(lbl)\\n return images, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01cc27a78c7836d32272fd5b6319738\",\n \"score\": \"0.54223394\",\n \"text\": \"def load_data():\\r\\n train_files = ['data/mnist/train%d.txt' % (i,) for i in range(10)]\\r\\n test_files = ['data/mnist/test%d.txt' % (i,) for i in range(10)]\\r\\n tmp = []\\r\\n for i in train_files:\\r\\n with open(i, 'r') as fp:\\r\\n tmp += fp.readlines()\\r\\n # load train data in N*D array (60000x784 for MNIST) \\r\\n # divided by 255 to achieve normalization\\r\\n train_data = np.array([[j for j in i.split(\\\" \\\")] for i in tmp], dtype='int') / 255\\r\\n print \\\"Train data array size: \\\", train_data.shape\\r\\n tmp = []\\r\\n for i in test_files:\\r\\n with open(i, 'r') as fp:\\r\\n tmp += fp.readlines()\\r\\n # load test data in N*D array (10000x784 for MNIST) \\r\\n # divided by 255 to achieve normalization\\r\\n test_data = np.array([[j for j in i.split(\\\" \\\")] for i in tmp], dtype='int') / 255\\r\\n print \\\"Test data array size: \\\", test_data.shape\\r\\n tmp = []\\r\\n for i, _file in enumerate(train_files):\\r\\n with open(_file, 'r') as fp:\\r\\n for line in fp:\\r\\n tmp.append([1 if j == i else 0 for j in range(0, 10)])\\r\\n train_truth = np.array(tmp, dtype='int')\\r\\n del tmp[:]\\r\\n for i, _file in enumerate(test_files):\\r\\n with open(_file, 'r') as fp:\\r\\n for _ in fp:\\r\\n tmp.append([1 if j == i else 0 for j in range(0, 10)])\\r\\n test_truth = np.array(tmp, dtype='int')\\r\\n print \\\"Train truth array size: \\\", train_truth.shape\\r\\n print \\\"Test truth array size: \\\", test_truth.shape\\r\\n return train_data, test_data, train_truth, test_truth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fffa8b6b68a7c8f058e183e624690427\",\n \"score\": \"0.5421787\",\n \"text\": \"def extract_labels(filename, num_images):\\n print('Extracting', filename)\\n with gzip.open(filename) as bytestream:\\n # Skip the magic number and count; we know these values.\\n bytestream.read(8)\\n buf = bytestream.read(1 * num_images)\\n labels = numpy.frombuffer(buf, dtype=numpy.uint8)\\n # Convert to dense 1-hot representation.\\n return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02350c0ae2b372400828a9a5e2b4dac\",\n \"score\": \"0.54205894\",\n \"text\": \"def proprocessing(self):\\r\\n \\r\\n if not self.are_labeled:\\r\\n pos = self.label_raw_dat(self.text1, self.class1) #Labels data \\r\\n neg = self.label_raw_dat(self.text2, self.class2) #Labels data\\r\\n #print(pos.head())\\r\\n #print(neg.head())\\r\\n \\r\\n dat = pan.concat([pos, neg]) #Concatenates 2 input data sets into one large one\\r\\n dat['Text'].str.lower() #Converts all words into lowercase characters\\r\\n dat.replace(to_replace = r'[^a-zA-Z]', value = ' ', regex = True, inplace = True) #Get help w/ Regex!!\\r\\n \\r\\n #dat.replace(to_replace = r'[^a-zA-Z0-9]', value = ' ', regex = True, inplace = True)\\r\\n #dat.replace(r'[^a-zA-Z0-9]', ' ',line)\\r\\n \\r\\n self.dat = dat.sample(frac=1).reset_index(drop=True)\\r\\n \\r\\n #print(self.dat.head())\\r\\n \\r\\n self.labels = np.array(self.dat['Label'])\\r\\n self.words = np.array(self.dat['Text']) \\r\\n \\r\\n self.train_txt, self.test_txt, self.train_label, self.test_label = train_test_split(self.words[:len(self.words)//1], self.labels[:len(self.labels)//1], test_size= .20, train_size = .80, shuffle = True)\\r\\n \\r\\n self.num_docs = self.train_txt.shape[0]\\r\\n \\r\\n print('Number of total documents w/ in train set', self.num_docs)\\r\\n \\r\\n #print(train_txt[1:10])\\r\\n #print(train_label[1:10])\\r\\n \\r\\n return (self.train_txt, self.train_label, self.test_txt, self.test_label) #Return sparse matrix of word frequencies w/ in document\\r\\n \\r\\n else: \\r\\n \\r\\n print('Must not include labels')\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f0e91b7b54d16fa4ed14b67fc8e89b\",\n \"score\": \"0.5418564\",\n \"text\": \"def read_train():\\n # if color images needed instead of grayscale, use cv2.IMREAD_COLOR instead of cv2.IMREAD_GRAYSCALE\\n\\n uppercase = \\\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\\\"\\n\\n xtrain = []\\n ytrain = []\\n\\n path = \\\"asl_alphabet_train/\\\"\\n for letter in range(len(uppercase)):\\n for number in range(1, 1001):\\n let = uppercase[letter]\\n ytrain.append(letter)\\n xtrain.append(cv2.imread(path + let + \\\"/\\\" + let + str(number) + \\\".jpg\\\", cv2.IMREAD_GRAYSCALE))\\n\\n path = \\\"asl_alphabet_train/del/\\\"\\n for number in range(1, 1001):\\n ytrain.append(26)\\n xtrain.append(cv2.imread(path + \\\"del\\\" + str(number) + \\\".jpg\\\", cv2.IMREAD_GRAYSCALE))\\n\\n path = \\\"asl_alphabet_train/nothing/\\\"\\n for number in range(1, 1001):\\n ytrain.append(27)\\n xtrain.append(cv2.imread(path + \\\"nothing\\\" + str(number) + \\\".jpg\\\", cv2.IMREAD_GRAYSCALE))\\n\\n path = \\\"asl_alphabet_train/space/\\\"\\n for number in range(1, 1001):\\n ytrain.append(28)\\n xtrain.append(cv2.imread(path + \\\"space\\\" + str(number) + \\\".jpg\\\", cv2.IMREAD_GRAYSCALE))\\n\\n xtrain = np.array(xtrain).astype(np.float64)\\n ytrain = np.array(ytrain)\\n\\n # for color images, reshape to 87000 x 120000\\n # for all grayscale images, reshape to 29000 x 120000\\n # for one-third grayscale images, reshape to 29000 x 40000\\n xtrain = xtrain.reshape(29000, 40000)\\n\\n #mean_train = np.mean(xtrain)\\n #sd_train = np.std(xtrain)\\n #xtrain -= mean_train\\n #xtrain /= sd_train\\n\\n with open('xtrain_gray_nonnorm.npy', mode=\\\"wb\\\") as f:\\n np.save(f, xtrain)\\n with open('ytrain_gray_nonnorm.npy', mode=\\\"wb\\\") as f:\\n np.save(f, ytrain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc72a0fa4080c939932c3e4991696168\",\n \"score\": \"0.54178965\",\n \"text\": \"def load_segmentation_labels(data: UndistortedDataSet, shot):\\n if data.undistorted_segmentation_exists(shot.id):\\n return data.load_undistorted_segmentation(shot.id)\\n else:\\n size = shot.camera.height, shot.camera.width\\n return np.zeros(size, dtype=np.uint8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29cc167bfe74dcb92bb409491e5a93fc\",\n \"score\": \"0.54063666\",\n \"text\": \"def rectify(data_root, sub_dir):\\n with open(os.path.join(data_root, 'label_new.txt'), 'w') as label_new_txt:\\n with open(os.path.join(data_root, 'label.txt'), 'r') as label_txt:\\n frs = label_txt.readlines()\\n for line in frs:\\n line = line.strip().split(' ', 1) # split only two elements , in case of there is space in line\\n label = line[1]\\n u_label = label.decode('utf-8')\\n new_label = rectify_line(u_label)\\n label_new_txt.write('%s %s\\\\n'%(os.path.join(data_root, sub_dir, line[0]), new_label.encode('utf-8')))\\n\\n with open(os.path.join(data_root, 'map_new.txt'), 'w') as map_new_txt:\\n with open(os.path.join(data_root, 'map.txt'), 'r') as map_txt:\\n frs = map_txt.readlines()\\n cnt = 0\\n for line in frs:\\n line = line.strip().split(' ', 1)\\n if len(line) <=1: continue\\n label = line[1]\\n u_label = label.decode('utf-8')\\n if is_regular_ch(u_label):\\n map_new_txt.write('%d %s\\\\n'%(cnt,u_label.encode('utf-8')))\\n cnt += 1\\n map_new_txt.write('%d %s'%(cnt, '*'))\\n print 'all classes is %d'%cnt\\n label_new_txt.close()\\n label_txt.close()\\n map_new_txt.close()\\n map_txt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73a8ac0838137d679c4ab6fd6db2afd\",\n \"score\": \"0.54051083\",\n \"text\": \"def prune(data_file, ver_batch, voxel_width, n_class, data_folder, rgb_intensity_index, version=\\\"\\\", gt_index=3, label_dict=None):\\n i_rows = 0\\n xyz = np.zeros((0, 3), dtype='float32')\\n rgb = np.zeros((0, 3), dtype='uint8')\\n labels = np.zeros((0, n_class + 1), dtype='uint32')\\n path_dem = data_folder + \\\"Generic/\\\"\\n\\n # ---the clouds can potentially be too big to parse directly---\\n # ---they are cut in batches in the order they are stored---\\n while True:\\n #if True:\\n try:\\n vertices = np.genfromtxt(data_file, delimiter=' ', max_rows=ver_batch, skip_header=i_rows)\\n #vertices[:, 0] += -610000.00\\n #vertices[:, 1] += -5700000.00\\n\\n\\n #vertices = np.load(data_file)\\n except StopIteration:\\n #break # end of file\\n print(\\\"finish\\\")\\n break\\n\\n if np.shape(vertices)[0]<1:\\n break\\n \\n print(np.shape(vertices)[1])\\n if np.shape(vertices)[1] == 3: # if has labels\\n labels_full = np.zeros(1, dtype='uint8')\\n labels = np.zeros(1, dtype='uint8')\\n n_class = 0\\n if i_rows==0:\\n print(\\\"has not label on Data\\\")\\n else:\\n if np.shape(vertices)[1] > gt_index:\\n labels_full = np.copy(vertices[:, gt_index]) \\n labels_full = labels_full.astype(\\\"uint8\\\")\\n print(\\\"-- \\\\t has label on Data \\\")\\n else:\\n labels_full = np.copy(vertices[:, 3]) \\n labels_full = labels_full.astype(\\\"uint8\\\")\\n if i_rows==0:\\n print(\\\"-- \\\\t warning -- gt_index = {0} out of bounds for {1} columns, used instead gt_index = 3\\\".format(gt_index, np.shape(vertices)[1]))\\n \\n\\n if not label_dict==None:\\n labels_full_t = []\\n for item in labels_full:\\n labels_full_t.append(label_dict[str(item)])\\n labels_full = np.array(labels_full_t)\\n labels_full = labels_full.astype(\\\"uint8\\\")\\n if i_rows==0:\\n print(\\\"-- \\\\t \\\\t has label on Data used directory\\\")\\n\\n xyz_full = np.array(vertices[:, 0:3], dtype='float32')\\n \\n if rgb_intensity_index is None: \\n if np.shape(vertices)[1] >= 6:\\n if gt_index==3:\\n rgb_full = np.array(vertices[:, 4:7], dtype='float32')\\n elif gt_index==6:\\n rgb_full = np.array(vertices[:, 3:6], dtype='float32')\\n else:\\n rgb_full = np.ones_like(vertices[:, :3], dtype='float32')\\n\\n else:\\n \\n if np.shape(vertices)[1] >= np.max(rgb_intensity_index):\\n rows, _ = np.shape(vertices)\\n rgb_full = np.zeros((rows, 3))\\n rgb_full[:, 0] = np.copy(vertices[:, rgb_intensity_index[0]])\\n rgb_full[:, 1] = np.copy(vertices[:, rgb_intensity_index[1]])\\n rgb_full[:, 2] = np.copy(vertices[:, rgb_intensity_index[2]])\\n if i_rows==0:\\n print(\\\"has intensity or color \\\")\\n else:\\n rgb_full = np.ones_like(vertices[:, :3], dtype='float32')\\n if i_rows==0:\\n print(\\\"has not intensity or color \\\")\\n \\n\\n del vertices\\n\\n print(\\\"--labels in file section \\\", np.unique(labels_full))\\n\\n xyz_sub, rgb_sub, labels_sub = libply_c.prune(xyz_full, voxel_width, rgb_full, labels_full, n_class)\\n\\n del xyz_full, rgb_full\\n xyz = np.vstack((xyz, xyz_sub))\\n rgb = np.vstack((rgb, rgb_sub))\\n labels = np.vstack((labels, labels_sub))\\n i_rows = i_rows + ver_batch\\n\\n if np.shape(labels_full)[0] < 2:\\n labels = []\\n\\n ###########################################################################################################\\n # Statistical Outlier Removal\\n ###########################################################################################################\\n if False:\\n mean_k = 30 # 50# 50 10\\n std_dev = 1.0\\n array = xyz[:, :3]\\n from sklearn.neighbors import KDTree\\n\\n # KDTree object (sklearn)\\n kDTree = KDTree(array, leaf_size=50)\\n dx, idx_knn = kDTree.query(array[:, :], k=mean_k + 1)\\n dx, idx_knn = dx[:, 1:], idx_knn[:, 1:]\\n\\n distances = np.sum(dx, axis=1) / (mean_k - 1.0)\\n valid_distances = np.shape(distances)[0]\\n\\n # Estimate the mean and the standard deviation of the distance vector\\n sum = np.sum(distances)\\n sq_sum = np.sum(distances ** 2)\\n\\n mean = sum / float(valid_distances)\\n variance = (sq_sum - sum * sum / float(valid_distances)) / (float(valid_distances) - 1)\\n stddev = np.sqrt(variance)\\n\\n # a distance that is bigger than this signals an outlier\\n distance_threshold = mean + std_dev * stddev\\n idx = np.nonzero(distances < distance_threshold)\\n \\n xyz = np.copy(xyz[idx])\\n rgb = np.copy(rgb[idx])\\n labels = np.copy(labels[idx])\\n\\n ####################################################################################################################\\n\\n ####################################################################################################################\\n xyz_rgb = np.hstack((xyz, rgb))\\n file_name = os.path.splitext(os.path.basename(data_file))[0]\\n path_short = path_dem + file_name + version\\n\\n np.savetxt(path_short + \\\".txt\\\", xyz_rgb, delimiter=\\\",\\\", header=\\\"X,Y,Z,R,G,B\\\", comments='', fmt='%f,%f,%f,%d,%d,%d')\\n dem_array = build_dem(path_short, xyz_rgb)\\n\\n return xyz, rgb, labels, dem_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73873614ca194a33f678f97a01a03d88\",\n \"score\": \"0.5392996\",\n \"text\": \"def extract_img_label(names, path):\\n train_img = os.path.join(path, 'origin/')\\n train_label = os.path.join(path, 'annotation/')\\n num_imgs = len(names)\\n data = []\\n labels = []\\n print('^^^^^^^^^^ start img_set for sycle')\\n for i in names:\\n name = os.path.splitext(i)[0]\\n print(name)\\n coder = ImageCoder((224, 224))\\n image_buffer, height, width = Ofrecord._process_image(\\n os.path.join(train_img, i), coder)\\n\\n data += [image_buffer]\\n\\n if os.path.exists(os.path.join(train_label, name)):\\n\\n with open(os.path.join(train_label, name), \\\"r\\\", encoding='utf-8') as jsonFile:\\n la = json.load(jsonFile)\\n if la:\\n labels += [la[0]['category_id']]\\n else:\\n data.pop()\\n num_imgs -= 1\\n else:\\n print('File is not found')\\n print('^^^^^^^^^ img_set for end')\\n data = np.array(data)\\n labels = np.array(labels)\\n print(data.shape, labels.shape)\\n return num_imgs, data, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d54eb88a7aafd21062e2ca944d335301\",\n \"score\": \"0.5392445\",\n \"text\": \"def postprocess(label_file_out: str, label_file_in: str, min_size: int = 20):\\n label_in = plt.imread(label_file_in)[..., 3] > 0\\n label_out = remove_small_objects(label_in, min_size=min_size)\\n layer_out = 255 * label_out.astype(np.uint8)\\n arr_out = np.zeros((*label_out.shape, 4), dtype=np.uint8)\\n arr_out[..., 2] = layer_out\\n arr_out[..., 3] = layer_out\\n plt.imsave(label_file_out, arr_out)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da44eb77b1cb573a5785a9e0b2425adc\",\n \"score\": \"0.5384938\",\n \"text\": \"def load_label(self, idx):\\n im = Image.open('{}/SegmentationClass/{}.png'.format(self.train_dir, idx))\\n im = im.resize((500, 500))\\n label = np.array(im, dtype=np.uint8)\\n label = label[np.newaxis, ...]\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149b8164192cfe163454a6a9c4c19377\",\n \"score\": \"0.53794116\",\n \"text\": \"def load_label(self, idx):\\n im = Image.open('{}/SegmentationClass/{}.png'.format(self.val_dir, idx))\\n im = im.resize((500, 500))\\n label = np.array(im, dtype=np.uint8)\\n label = label[np.newaxis, ...]\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d417ab94cd7a3a57f103c0913525236c\",\n \"score\": \"0.5364689\",\n \"text\": \"def generate_labels(self, label_path):\\n labels = pd.read_csv(label_path)\\n labels = labels[pd.notna(labels['EncodedPixels'])]\\n num_objects = labels.pivot_table(index=['ImageId'], aggfunc='size')\\n valid_images = num_objects[(self.min_objects_per_image <= num_objects) & \\n (num_objects <= self.max_objects_per_image)].keys()\\n valid_labels = labels[labels['ImageId'].isin(valid_images)].reset_index(drop=True)\\n\\n if not self.already_labeled:\\n valid_labels['BoundingBox'] = valid_labels['EncodedPixels'].apply(self.rle_to_bbox)\\n valid_labels['ObjectClass'] = valid_labels[['ImageId', 'BoundingBox']].apply(self.label_object, axis=1)\\n else:\\n valid_labels['BoundingBox'] = valid_labels['BoundingBox'].apply(lambda bbx: [float(i) for i in bbx[1:-1].split()])\\n return valid_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae4b00f38e1e2ffe9b27180089c2f8e\",\n \"score\": \"0.5363863\",\n \"text\": \"def load_bio_data(file_info, res=21):\\n \\n \\\"\\\"\\\"print(\\\"Importing tanslation vectors \\\\n======================\\\")\\n shiftx = np.array(np.loadtxt(main_path+data_index+\\\"/shiftx.txt\\\", delimiter=\\\"\\\\t\\\"))\\n shifty = np.array(np.loadtxt(main_path+data_index+\\\"/shifty.txt\\\", delimiter=\\\"\\\\t\\\"))\\n\\n # translation vectors in pixels\\n vx = [shiftx[i]/dx for i in range(14)]\\n vy = [shifty[i]/dx for i in range(14)]\\n print(\\\"Translation vectors successfully imported \\\\n======================\\\")\\\"\\\"\\\"\\n\\n main_path, data_index, n = file_info\\n path2 = main_path+data_index+\\\"/fullx.txt\\\"\\n print(\\\"Importing bio data from\\\", path2, \\\"\\\\n======================\\\")\\n data2 = np.loadtxt(path2, delimiter=\\\"\\\\t\\\")\\n print(\\\"Data successfully imported. Shape of data:\\\", data2.shape, \\n \\\"\\\\n======================\\\")\\n\\n\\n s = int(res**2)\\n\\n scans = []\\n for j in range(14):\\n start = n\\n end = (s+1)*n-1\\n gap = end-start\\n x = []\\n for i in range(9):\\n #print(i, start, end)\\n x.append(data2[j,start:end+1])\\n start = end + 2*n + 1\\n end = start + gap\\n\\n x = np.array(x)\\n if j==0:\\n print(\\\"Shape of one detector chunk before reshaping:\\\", x.shape)\\n X = x.reshape(3,3,21,21,2000).transpose(4,0,2,1,3)\\n Z = X.reshape(2000,63,63).transpose(0,2,1)\\n scans.append(Z)\\n if j==0:\\n print(\\\"Final shape of chunk after reshaping:\\\", Z.shape, \\\"\\\\n====================\\\\n\\\")\\n \\\"\\\"\\\"Zmean = np.mean(Z,axis=0)\\n plt.imshow(Zmean)\\n plt.colorbar()\\n plt.show()\\\"\\\"\\\"\\n\\n scans = np.array(scans)\\n # adding extra dimension so it matches non-bio data structure\\n scans = np.expand_dims(scans,axis=0)\\n scans = scans.transpose(0,1,3,4,2)\\n scans = scans.transpose(0,4,2,3,1)\\n print(\\\"Final shape of bio data (number of scans, frame number, height, width, detectors):\\\", scans.shape)\\n \\n return scans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f7479c4c8cd48dfd7fcf197015205a\",\n \"score\": \"0.53570545\",\n \"text\": \"def load_data_and_labels(data_file):\\n # Load data from files\\n examples = list(open(data_file, \\\"r\\\").readlines())\\n examples = [s.strip() for s in examples]\\n\\n # Split by words\\n x_text = [clean_str(sent) for sent in examples]\\n # Generate labels\\n y = np.loadtxt('./data/label.txt')\\n labellist = y.tolist()\\n # Make one-hot vectors of labels\\n arr = np.empty((0, 47))\\n for line in labellist:\\n i = scoreid(line, labellist)\\n a = one_hot(i)\\n arr = np.append(arr, a.reshape((1,47)), axis=0)\\n return [x_text, arr]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebf3607776ca892af30aa7981eb0722\",\n \"score\": \"0.53474957\",\n \"text\": \"def interpolate_labels(data_file, data_gt, xyz, labels, false_color, unary, segment_id, n_classes, ver_batch, train=True, gt_index=3, label_dict=None):\\n\\n if len(labels.shape) > 1 and labels.shape[1] > 1:\\n labels = np.argmax(labels, axis = 1)\\n i_rows = 0\\n labels_f = np.zeros((0, ), dtype='uint8')\\n #---the clouds can potentially be too big to parse directly---\\n #---they are cut in batches in the order they are stored---\\n nn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(xyz)\\n xyz_txt = np.zeros((0, 4), dtype=np.float32)\\n \\n label_file = data_gt\\n false_color_txt = np.zeros((0, ), dtype='float32')\\n segment_id_full = np.zeros((0, ), dtype='float32')\\n\\n unary_full = np.zeros((0, np.shape(unary)[1]), dtype='float32') # z min, z max, z mean\\n train = False\\n while True: \\n try:\\n vertices = np.genfromtxt(data_file, delimiter=' ', max_rows=ver_batch, skip_header=i_rows) \\n if train:\\n real_label = np.genfromtxt(label_file, max_rows=ver_batch, skip_header=i_rows)\\n else:\\n real_label = np.copy(vertices[:, gt_index]) \\n except:\\n break\\n print(\\\"*\\\")\\n if np.shape(vertices)[0]<1:\\n break\\n\\n if not label_dict==None:\\n labels_full_t = []\\n for item in real_label:\\n labels_full_t.append(label_dict[str(int(item))])\\n labels_full = np.array(labels_full_t)\\n labels_full = labels_full.astype(\\\"uint8\\\")\\n real_label = labels_full.astype(\\\"uint8\\\")\\n\\n if np.shape(vertices)[0]>2:\\n xyz_full = np.array(vertices[:, 0:3], dtype='float32') \\n distances, neighbor = nn.kneighbors(xyz_full)\\n del vertices, distances\\n\\n labels_f = np.hstack((labels_f, labels[neighbor].flatten())) # Output Net\\n i_rows = i_rows + ver_batch\\n\\n false_color_txt = np.hstack((false_color_txt, false_color[neighbor].flatten())) # false color composition\\n segment_id_full = np.hstack((segment_id_full, segment_id[neighbor].flatten())) # saving segment id\\n\\n xyz_full = np.concatenate((xyz_full, np.expand_dims(real_label, axis=1)), axis=1) # current XYZ + GT batch\\n xyz_txt = np.concatenate((xyz_txt, xyz_full), axis=0) # Contain XYZ coordinates + GT accumulative\\n\\n unary_temp = np.squeeze(unary[neighbor])\\n unary_full = np.vstack((unary_full, unary_temp)) # saving unary\\n del unary_temp\\n else:\\n break\\n \\n false_color_txt = np.concatenate((xyz_txt, np.expand_dims(false_color_txt, axis=1)), axis=1) # XYZ + GT + Falsecolor\\n xyz_txt = np.concatenate((xyz_txt, np.expand_dims(labels_f, axis=1)), axis=1) # XYZ + GT + Output\\n\\n def first_nonzero(arr, axis, invalid_val=-1):\\n mask = arr != 0\\n return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val)\\n ##################################################################################################################\\n # smoothing\\n # nn_xyz = NearestNeighbors(n_neighbors=100, algorithm='kd_tree').fit(xyz_txt[:, :3])\\n # _, neighbor_xyz = nn_xyz.kneighbors(xyz_txt[:, :3])\\n # i = 0\\n # while i < 2:\\n # indices_non_zeros = np.where(labels_f == 0)[0]\\n # # print (\\\"a\\\")\\n # new_neighbour = np.isin(neighbor_xyz, indices_non_zeros)\\n # # print (\\\"b\\\")\\n # new_neighbour = np.logical_not(new_neighbour) * neighbor_xyz\\n # # print (\\\"c\\\")\\n # near_neighbour = np.squeeze(first_nonzero(new_neighbour, 1))\\n # # print (\\\"d\\\")\\n # idx = 0\\n # value_near_neighbour = np.zeros(np.shape(near_neighbour), dtype=np.int32)\\n # # print (\\\"e\\\")\\n # for value in neighbor_xyz:\\n # value_near_neighbour[idx] = value[near_neighbour[idx]] # the near neighbour not zero value\\n # idx += 1\\n # # print (\\\"f\\\")\\n # labels_f[indices_non_zeros] = labels_f[value_near_neighbour[indices_non_zeros]]\\n # i += 1\\n # print(\\\"iteration : %d\\\" % i)\\n #\\n #xyz_txt[:, 4] = labels_f\\n ##################################################################################################################\\n\\n xyz_GT_out_id_prob = np.concatenate((xyz_txt, np.expand_dims(segment_id_full, axis=1), unary_full), axis=1)\\n # XYZ + GT + Output + id + probabilities + z min +z max + z_mean\\n \\n return labels_f, xyz_txt, false_color_txt, xyz_GT_out_id_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5352c2ba307b4d12326f1e2e2b68fce5\",\n \"score\": \"0.5342152\",\n \"text\": \"def read_data(path):\\n l_words = []\\n l_labels = []\\n\\n with open(path) as file:\\n for i, line in enumerate(file):\\n try:\\n pairs = line.split('\\\\t')\\n word, ner = pairs[0], re.sub(r'\\\\W', '', pairs[1])\\n l_words.append(word), l_labels.append(ner)\\n except IndexError:\\n pass\\n\\n arr_labels = np.array(l_labels)\\n condlist = [arr_labels == 'LOC', arr_labels == 'PER',\\n arr_labels == 'O', arr_labels == 'ORG', arr_labels == 'MISC']\\n choicelist = [0, 1, 2, 3, 4]\\n labels = np.select(condlist, choicelist)\\n\\n vocab = set(l_words)\\n word_to_num = {word: i for i, word in enumerate(vocab, 2)}\\n word_to_num['__PADDING__'] = 0\\n return (l_words, labels, word_to_num, vocab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b175e411d014a00c00ab0e637b0f3406\",\n \"score\": \"0.5338252\",\n \"text\": \"def test_iteration(self):\\n with open('../../extra-files/molecule.xyz', 'r') as file1:\\n mol1 = Molecule(file1.read())\\n \\n newlabels = []\\n for atom in mol1:\\n newlabels += atom[0]\\n\\n self.assertEqual(mol1.labels, newlabels, 'checking labels')\\n #TODO: assert that the coordinates are also correct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f965b5ca6c4e7f0041a4fc6c69969bf\",\n \"score\": \"0.53346336\",\n \"text\": \"def validate_vocabulary_file_labels(self):\\n valid_labels = True\\n vocab_labels = [RDFS_LABEL, COMMENT, DESCRIPTION, TITLE]\\n for label in vocab_labels:\\n all_labels = self.find_labels_in_vocabulary_file(label)\\n\\n for current_label in all_labels:\\n if not self.check_label(label, current_label):\\n valid_labels = False\\n\\n return valid_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc584779311693a4364b91afd1154fe\",\n \"score\": \"0.5320594\",\n \"text\": \"def labels_from_file(fname):\\n with open(fname, 'r') as fin:\\n data = fin.readlines()\\n return np.array([int(line[0]) for line in data])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ae4bc62e84f1ce7584992453569072\",\n \"score\": \"0.53170663\",\n \"text\": \"def get_valid_label(self):\\n if self.valid_label.size == 0:\\n labels = []\\n with io.open(self.args.valid_data_dir, \\\"r\\\", encoding=\\\"utf8\\\") as f:\\n for line in f:\\n labels.append([int(line.strip().split(\\\"\\\\t\\\")[-1])])\\n self.valid_label = np.array(labels)\\n return self.valid_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c5b385088d90ff8f1e90d9f22730050\",\n \"score\": \"0.53170544\",\n \"text\": \"def load_labels(data='/Users/kailinlu/Documents/emotional-speech/data/'):\\n files = os.listdir(data)\\n labels = [file.split('_')[2].split('.')[0] for file in files]\\n labels_map = defaultdict(int)\\n i = 0\\n for label in list(set(labels)):\\n labels_map[label] = i\\n i += 1\\n int_labels = np.array([labels_map[label] for label in labels])\\n onehot = np.zeros((len(int_labels), max(int_labels) + 1))\\n onehot[np.arange(len(int_labels)), int_labels] = 1\\n return onehot, int_labels, labels_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"876149994312f9c1f1ba48072c2ab3be\",\n \"score\": \"0.5312809\",\n \"text\": \"def _load_txt_annotation(self, index):\\n\\n filename = os.path.join(self._annoPath, index + '.txt')\\n annos = []\\n with open(filename,\\\"r\\\") as f:\\n annos = f.readlines()\\n\\n num_objs = 0 \\n if self._cleanRegex is not None:\\n for idx,line in enumerate(annos):\\n m = re.match(self._cleanRegex,line)\\n if m is not None:\\n if self._find_cls(m.groupdict()) != -1:\\n num_objs += 1\\n else:\\n num_objs = len(annos)\\n \\n # reformat into the dictionary\\n boxes = np.zeros((num_objs, 4), dtype=np.int16)\\n gt_classes = np.zeros((num_objs), dtype=np.int32)\\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n # \\\"Seg\\\" area for pascal is just the box area\\n seg_areas = np.zeros((num_objs), dtype=np.float32)\\n ix = 0\\n\\n for line in annos:\\n m = re.match(self._cleanRegex,line)\\n if m is not None:\\n mgd = m.groupdict()\\n x1,y1,x2,y2 = self._extract_bounding_box(mgd)\\n cls = self._find_cls(mgd)\\n if cls == -1:\\n continue\\n boxes[ix, :] = [x1, y1, x2, y2]\\n gt_classes[ix] = cls\\n overlaps[ix, cls] = 1.0\\n seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)\\n ix += 1\\n\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n if cfg.OBJ_DET.BBOX_VERBOSE:\\n bbox = {'boxes' : boxes,\\n 'gt_classes': gt_classes,\\n 'gt_overlaps' : overlaps,\\n 'flipped' : False,\\n 'seg_areas' : seg_areas,\\n 'set' : self._setID}\\n else:\\n bbox = {'boxes' : boxes,\\n 'gt_classes': gt_classes,\\n 'flipped': False,\\n 'set' : self._setID}\\n return bbox\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26099aa9ede256c1b0705f8205c1607b\",\n \"score\": \"0.5300784\",\n \"text\": \"def _load_data_annotation(self, name):\\n filename = os.path.join(self._data_path, name + '.xml')\\n tree = ET.parse(filename)\\n objs = tree.findall('object')\\n # if not self.config['use_diff']:\\n # # Exclude the samples labeled as difficult\\n # non_diff_objs = [\\n # obj for obj in objs if int(obj.find('difficult').text) == 0]\\n # # if len(non_diff_objs) != len(objs):\\n # # print 'Removed {} difficult objects'.format(\\n # # len(objs) - len(non_diff_objs))\\n # objs = non_diff_objs\\n num_objs = len(objs)\\n\\n boxes = np.empty(shape=[0, 4], dtype=np.uint16)\\n gt_classes = np.empty(shape=[0], dtype=np.int32)\\n overlaps = np.empty(shape=[0, self.num_classes], dtype=np.float32)\\n seg_areas = np.empty(shape=[0], dtype=np.float32)\\n ishards = np.empty(shape=[0], dtype=np.int32)\\n num_HSIL = 0\\n num_abnormal = 0\\n\\n # Load object bounding boxes into a data frame.\\n # clean data\\n for obj in objs:\\n cls_name = obj.find('name').text.lower().strip()\\n if not (cls_name in self._classes):\\n if cls_name=='hsil':\\n print(\\\"change \\\"+ cls_name + \\\" to HSIL\\\")\\n cls_name = \\\"HSIL\\\"\\n else:\\n print(\\\"change \\\"+ cls_name + \\\" to abnormal\\\")\\n cls_name = \\\"abnormal\\\"\\n ##xiugai 2019 01 20 benlai zhiyou yiju ruxia \\n ##continue\\n ##\\n if cls_name == \\\"HSIL\\\":\\n num_HSIL = num_HSIL + 1\\n if cls_name == \\\"abnormal\\\":\\n num_abnormal = num_abnormal + 1\\n\\n cls = self._class_to_ind[cls_name]\\n gt_classes = np.append(gt_classes, cls)\\n\\n bbox = obj.find('bndbox')\\n # labelImage pixel indexes 1-based\\n x1 = float(bbox.find('xmin').text) - 1 if float(bbox.find('xmin').text)>0 else 0\\n y1 = float(bbox.find('ymin').text) - 1 if float(bbox.find('ymin').text)>0 else 0\\n x2 = float(bbox.find('xmax').text) - 1\\n y2 = float(bbox.find('ymax').text) - 1\\n if x1 >= x2 or y1>=y2:\\n continue\\n\\n diffc = obj.find('difficult')\\n difficult = 0 if diffc == None else int(diffc.text)\\n ishards = np.append(ishards, difficult)\\n\\n boxes = np.append(boxes, np.expand_dims([x1, y1, x2, y2], axis=0), axis=0)\\n\\n overlap_vect = np.zeros(self.num_classes, dtype=np.float32)\\n overlap_vect[cls] = 1.0\\n overlaps = np.append(overlaps, np.expand_dims(overlap_vect, axis=0), axis=0)\\n seg_areas = np.append(seg_areas, (x2 - x1 + 1) * (y2 - y1 + 1)) \\n\\n print(\\\"HISL has \\\" + str(num_HSIL) + \\\" bndboxes\\\")\\n print(\\\"abnormal has \\\" + str(num_abnormal) + \\\" bndboxes\\\")\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n return {'boxes': boxes,\\n 'gt_classes': gt_classes,\\n 'gt_ishard': ishards,\\n 'gt_overlaps': overlaps,\\n 'flipped': False,\\n 'seg_areas': seg_areas}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e09db3211c0270d9331f057e63d8a255\",\n \"score\": \"0.5300199\",\n \"text\": \"def create_train_data(data_path, masks_path):\\n\\n image_rows = 420\\n image_cols = 580\\n\\n images = os.listdir(data_path)\\n masks = os.listdir(masks_path)\\n total = len(images)\\n\\n imgs = np.ndarray((total, image_rows, image_cols, 1), dtype=np.uint8)\\n imgs_mask = np.ndarray((total,image_rows, image_cols, 1), dtype=np.uint8)\\n i = 0\\n\\n for image_name in images:\\n if image_name.endswith(\\\"jpg\\\"):\\n img = cv2.imread(os.path.join(data_path, image_name), cv2.IMREAD_GRAYSCALE)\\n else:\\n continue\\n try:\\n img = cv2.resize(img, (image_cols, image_rows), interpolation=cv2.INTER_CUBIC)\\n img = np.asarray(img).reshape((image_rows, image_cols, 1))\\n imgs[i] = img\\n i+=1\\n except Exception as e:\\n print(\\\"file name:\\\", image_name)\\n print(\\\"index:\\\", i)\\n print(e)\\n # print(img.shape)\\n print(\\\"images done, masks now....\\\")\\n i=0\\n\\n for image_mask_name in masks:\\n if image_name.endswith(\\\"jpg\\\"):\\n img_mask = cv2.imread(os.path.join(masks_path, image_mask_name), cv2.IMREAD_GRAYSCALE)\\n else:\\n continue\\n img_mask = cv2.resize(img_mask, (image_cols, image_rows), interpolation=cv2.INTER_CUBIC)\\n img_mask = np.asarray(img_mask).reshape((image_rows, image_cols, 1))\\n imgs_mask[i] = img_mask\\n i+=1\\n\\n print(\\\"Successfully done!\\\")\\n\\n np.save('imgs_train.npy', imgs)\\n np.save('imgs_mask_train.npy', imgs_mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ade76812ee78c6951d6406ae475c28\",\n \"score\": \"0.52991676\",\n \"text\": \"def test_encode_target(self):\\n data = pd.read_csv(self.train_data_path)\\n datapipeline = self.datapipeline(self.max_sequence_len, self.label_mapping)\\n output = datapipeline.encode_target(data)\\n\\n unique_labels = set(np.unique(output))\\n print(\\\"u\\\", unique_labels)\\n\\n self.assertIsInstance(output, np.ndarray)\\n self.assertTrue(unique_labels == set([0, 1, 2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f239abbf51696e55119dcc47a4f659d9\",\n \"score\": \"0.5295375\",\n \"text\": \"def build_classification_dataset(list_of_files):\\n temp = []\\n train_labels = []\\n for f_cf in list_of_files:\\n with open(f_cf) as file:\\n lines = file.read().splitlines()\\n #print(lines)\\n temp.append([line.split()[0] for line in lines if int(line.split()[-1]) == 1])\\n #print(temp)\\n label_id = [f_ind for f_ind, filt in enumerate(filter) if filt in f_cf][0]\\n print(label_id)\\n train_labels.append(len(temp[-1]) * [label_id])\\n #print(train_labels)\\n train_filter = [item for l in temp for item in l]\\n print(train_filter)\\n image_folder = os.path.join(voc_root_folder, \\\"VOC2009/JPEGImages/\\\")\\n image_filenames = [os.path.join(image_folder, file) for f in train_filter for file in os.listdir(image_folder) if\\n f in file]\\n x = np.array([resize(io.imread(img_f), (image_size, image_size, 3)) for img_f in image_filenames]).astype(\\n 'float32')\\n # changed y to an array of shape (num_examples, num_classes) with 0 if class is not present and 1 if class is present\\n y_temp = []\\n for tf in train_filter:\\n y_temp.append([1 if tf in l else 0 for l in temp])\\n y = np.array(y_temp)\\n\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec677a82fa0238647c9909d982a54324\",\n \"score\": \"0.52901566\",\n \"text\": \"def getDermatologyData():\\n path = raw_input(\\\"Enter the path of data: \\\")\\n data = np.genfromtxt(path, delimiter=',', dtype='U')\\n\\n # Removing the rows with '?' in them\\n char = '?'\\n listToDelete = []\\n for i, line in enumerate(range(len(data))):\\n if char in data[i]:\\n listToDelete.append(i)\\n\\n data = np.delete(data, listToDelete, axis=0)\\n\\n # Removing the rows with labels 4, 5, 6 since\\n # we are considering only 1, 2, 3 labels\\n data = data.astype(int)\\n labels = data[:, -1]\\n\\n unwantedLabels = [4, 5, 6]\\n listToDelete = []\\n for i, line in enumerate(range(len(data))):\\n if labels[i] in unwantedLabels:\\n listToDelete.append(i)\\n\\n actualData = np.delete(data, listToDelete, axis=0)\\n\\n # Separating the labels and data into different arrays\\n actualLabels = actualData[:, -1]\\n actualData = actualData[:, :-1]\\n\\n actualData = pre.scale(actualData)\\n\\n # Change the label vector to label matrix\\n # If Label is 2 then it becomes [0, 1, 0]\\n labelMatrix = np.zeros((labels.shape[0], 3))\\n for j in range(labels.shape[0]):\\n if labels[j] == 1:\\n labelMatrix[j][0] = 1\\n if labels[j] == 2:\\n labelMatrix[j][1] = 1\\n if labels[j] == 3:\\n labelMatrix[j][2] = 1\\n\\n trainWithVanillaNN(actualData, labelMatrix)\\n\\n return actualData, actualLabels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22d0be8758237dd98e594419e219277\",\n \"score\": \"0.52868456\",\n \"text\": \"def loadData(self):\\n self.image = np.load(self.dataImg)\\n self.label = np.load(self.dataLabel)\\n self.image_totalImgs = self.image.shape[0]\\n\\n try:\\n self.image_depth = self.image[0].shape[2]\\n except IndexError:\\n self.image_depth = 1\\n\\n self.num_eval = int((self.eval_ratio * self.image_totalImgs))\\n print(\\\"This is the total images\\\", self.image_totalImgs)\\n print(\\\"This is the ratio\\\", self.num_eval)\\n\\n\\n np.random.seed(2845) #45600\\n\\n if (len(self.image) == len(self.label)):\\n p = np.random.permutation(len(self.image))\\n self.image = self.image[p]\\n self.label = self.label[p]\\n else:\\n print(\\\"Image data and heading data are not the same size\\\")\\n return 0\\n\\n self.train_images = self.image[:-self.num_eval, :]\\n print(\\\"This is the len of train images after it has been divided\\\", len(self.train_images))\\n self.eval_images = self.image[-self.num_eval:, :]\\n\\n print(\\\"THIS IS THE TOTAL SIZE BEFORE DIVIDING THE DATA\\\", len(self.label))\\n self.train_labels = self.label[:-self.num_eval, :]\\n print(\\\"This is cutting the labels!!!!!\\\", len(self.train_labels))\\n self.eval_labels = self.label[-self.num_eval:, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6cf3809e05e277307609b88f25157b\",\n \"score\": \"0.5286005\",\n \"text\": \"def label_learner():\\n\\n pkls = ['IncepV3_0506_iM2.pickle',\\n 'Xception_iM3.pickle',\\n 'IncepV3+drop_0514_iM7.pickle',\\n 'IncepV3_0520_iM10.pickle',\\n 'DenseNet169_0524_iM12.pickle',\\n 'Resnet50_0524_iM13.pickle'\\n ]\\n\\n label = pickle.load(open(vali_label, 'rb'))\\n\\n weight = [0.35, 0.05, 0.05, 0.3, 0.05, 0.2]\\n\\n mat = numpy.zeros(label[:, 1:].shape)\\n for i, pkl in enumerate(pkls):\\n predict = pickle.load(open(ENSEMBLE_PATH+pkl, 'rb')) # check if img id match\\n if numpy.all(predict[:, 0] == label[:, 0]):\\n mat += predict[:, 1:]*weight[i]\\n else:\\n raise ValueError('{0}: label id does not match'.format(pkl))\\n\\n reg = DecisionTreeRegressor(max_depth=10, criterion='mse')\\n reg.fit(mat, label[:, 1:])\\n\\n threshold = 0.99\\n with open('TreeRegressor.csv', 'w') as f:\\n f.write('image_id,label_id\\\\n')\\n for n, row in enumerate(reg.predict(mat)):\\n imgid = int(predict[n, 0])\\n labels = numpy.where(row > threshold)[0] + 1 # note that index+1 = actual labels\\n f.write(str(int(imgid)) + ',' +\\n ''.join(str(label) + ' ' for label in labels) +\\n '\\\\n'\\n )\\n\\n threshold = 0.01\\n with open('TreeRegressor_negtive.csv', 'w') as f:\\n f.write('image_id,label_id\\\\n')\\n for n, row in enumerate(reg.predict(mat)):\\n imgid = int(predict[n, 0])\\n labels = numpy.where(\\n numpy.logical_and(row < threshold, mat[n, :] > 0.2)\\n )[0] + 1 # note that ndex+1 = actual labels\\n f.write(str(int(imgid)) + ',' +\\n ''.join(str(label) + ' ' for label in labels) +\\n '\\\\n'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ed9fa0e46af94c7776ebfcae2d618c\",\n \"score\": \"0.5281635\",\n \"text\": \"def generate_labels_for_negative_images(img_path):\\n if not img_path.endswith('.JPG') and not img_path.endswith('.jpg'):\\n print(\\\"This image path does not end with .jpg or .JPG, aborting check again!\\\")\\n quit()\\n img = io.imread(img_path)\\n if np.shape(img)[0] == 2:\\n lbl = np.zeros(img[0].shape[:-1])\\n else:\\n lbl = np.zeros(img.shape[:-1])\\n print('The shape of the label file is', np.shape(lbl))\\n lbl_im= Image.fromarray(lbl).convert('RGB')\\n save_name = img_path.replace('jpg','png').replace('JPG','png')\\n lbl_im.save(save_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298d9ef77fe9a7c94f9d624e170740af\",\n \"score\": \"0.5281074\",\n \"text\": \"def read_seg_dataset_multiclass(example):\\n if N_DATA_BANDS==4:\\n image, nir, label = tf.py_function(func=load_npz, inp=[example], Tout=[tf.uint8, tf.uint8, tf.uint8])\\n nir = tf.cast(nir, tf.float32)/ 255.0\\n else:\\n image, label = tf.py_function(func=load_npz, inp=[example], Tout=[tf.uint8, tf.uint8])\\n\\n image = tf.cast(image, tf.float32)/ 255.0\\n label = tf.cast(label, tf.uint8)\\n\\n if NCLASSES==1:\\n label = tf.expand_dims(label,-1)\\n\\n if N_DATA_BANDS==4:\\n image = tf.concat([image, tf.expand_dims(nir,-1)],-1)\\n return image, label\\n else:\\n return image, label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d2a4eba0bef50d575e963358f5e5ffb\",\n \"score\": \"0.5279427\",\n \"text\": \"def MyToSegmentDATA(data, dest='./content/VsegDATA', histeq=False):\\r\\n\\r\\n idx = 1\\r\\n if os.path.isdir(dest):\\r\\n while os.path.isdir(dest + str(idx)):\\r\\n idx += 1\\r\\n dest = dest + str(idx)\\r\\n \\r\\n os.mkdir(dest)\\r\\n\\r\\n excess = 20\\r\\n\\r\\n label_dict = dict()\\r\\n label_file_path = os.path.join(dest, 'label.json')\\r\\n\\r\\n path_selector = dict()\\r\\n cat_list = ['normal', 'compre', 'burst', 'unsure']\\r\\n img_dirs = [os.path.join(dest, 'image', c) for c in cat_list]\\r\\n msk_dirs = [os.path.join(dest, 'mask', c) for c in cat_list]\\r\\n for cat, img_dir, msk_dir in zip(cat_list, img_dirs, msk_dirs):\\r\\n path_selector[cat] = (img_dir, msk_dir)\\r\\n os.makedirs(img_dir)\\r\\n os.makedirs(msk_dir)\\r\\n label_dict[cat] = dict()\\r\\n\\r\\n for d in data:\\r\\n \\r\\n img = Image.open(d['filepath'])\\r\\n img_width, img_height = img.size\\r\\n img_filename = d['filename'].split('.')[0]\\r\\n\\r\\n for idx in range(len(d['x_label'])):\\r\\n\\r\\n # pass all S\\r\\n if d['position'][idx] == 'S':\\r\\n continue\\r\\n\\r\\n # check all corp image are in correct range\\r\\n x_min = max(0, min(d['x_label'][idx]) - excess)\\r\\n x_max = min(img_width, max(d['x_label'][idx]) + excess)\\r\\n y_min = max(0, min(d['y_label'][idx]) - excess)\\r\\n y_max = min(img_height, max(d['y_label'][idx]) + excess)\\r\\n\\r\\n # save the new coordinate x,y after crop the image\\r\\n # (left top as (0,0))\\r\\n temp_dict = dict()\\r\\n temp_dict['xs'] = [k - x_min for k in d['x_label'][idx]]\\r\\n temp_dict['ys'] = [k - y_min for k in d['y_label'][idx]]\\r\\n\\r\\n box = (x_min, y_min, x_max, y_max)\\r\\n\\r\\n seg_img = img.crop(box)\\r\\n seg_img_filename = img_filename + '_{}.bmp'.format(d['position'][idx])\\r\\n seg_mask_filename = img_filename + '_{}.txt'.format(d['position'][idx])\\r\\n\\r\\n # draw mask\\r\\n mask = Image.new('1', seg_img.size)\\r\\n ImageDraw.Draw(mask).polygon([(x,y) for x, y in zip(temp_dict['xs'], temp_dict['ys'])], outline=1, fill=1)\\r\\n mask = np.array(mask)\\r\\n \\r\\n # ex: label_dict[__filename__][xs] = [0, 4, 5, ....]\\r\\n # label_dict[__filename__][ys] = [2, 5, 7, ....] \\r\\n label_dict[d['type'][idx]][seg_img_filename] = temp_dict\\r\\n\\r\\n seg_img_dirpath, seg_mask_dirpath = path_selector[d['type'][idx]]\\r\\n\\r\\n seg_img_filepath = os.path.join(seg_img_dirpath, seg_img_filename)\\r\\n # do histogram equalization\\r\\n if histeq == True:\\r\\n seg_img_array = np.array(seg_img)\\r\\n seg_img_array = equalizeHist(seg_img_array)\\r\\n seg_img = Image.fromarray(seg_img_array)\\r\\n seg_img.save(seg_img_filepath)\\r\\n\\r\\n seg_mask_filepath = os.path.join(seg_mask_dirpath, seg_mask_filename)\\r\\n np.savetxt(seg_mask_filepath, mask, fmt='%d')\\r\\n\\r\\n with open(label_file_path, mode='w', encoding='utf-8') as f:\\r\\n json.dump(label_dict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ddba5eb050d178439ee854ad6e9ca48\",\n \"score\": \"0.52779764\",\n \"text\": \"def load_pred(data_basedir, splits_path, class_path, split_name, results_path, results_name, \\n\\t\\t\\t\\t\\t\\ttarget_classnum = 9, total_num = 10432, origin_test_batch = 200, exp_name = 'autoencoder'):\\n\\t## Load class info\\n\\tinstance_num, class_dic, data_class, fileid_list = class_counter(data_basedir = data_basedir, splits_path = splits_path, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tclass_path = class_path, split_name= split_name)\\n\\t## find N classes which have most instances\\n\\tsorted_class = sorted(class_dic.items(),key=lambda item:item[1],reverse = True)[:target_classnum]\\n\\tsorted_class = [item[0] for item in sorted_class]\\n\\t## get the class name \\n\\tsorted_class.sort()\\n\\tprint(sorted_class)\\n\\tprint(len(data_class))\\n\\t## find the sample index of desired class sample\\n\\ttarget_index = [] \\t\\t\\t\\t\\t\\t# all the desired sample index list\\n\\ttarget_index_dic = {}\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# desired sample dictionary {class_name: []}\\n\\t## iterate through the class name list\\n\\tfor target_class in sorted_class:\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# initialize the dictionary\\n\\t\\ttarget_index_dic[target_class] = []\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t## load the desired sample index by iterate through class name\\n\\tfor target_class in sorted_class: \\t\\t\\t\\t\\t\\t\\n\\t\\ttarget_index += [i for i,x in enumerate(data_class) if x == target_class]\\t \\n\\t\\ttarget_index_dic[target_class] += [i for i,x in enumerate(data_class) if x == target_class]\\n\\n\\t## LOAD the file(npy format) from disk\\n\\t### find the number of file should be loaded \\n\\tnum_of_file = math.ceil(total_num/origin_test_batch)\\n\\t### iterate through all the npy file \\n\\tfor idx in tqdm.tqdm(range(num_of_file)):\\n\\t\\tif exp_name == 'autoencoder':\\n\\t\\t\\tpostidx = '%03d'%idx + '.npy'\\n\\t\\t\\ttmp_pred = np.load(os.path.join(results_path, results_name + postidx))\\n\\t\\telif exp_name == 'clustering':\\n\\t\\t\\ttmp_pred = np.load(os.path.join(results_path, results_name))\\n\\t\\tif idx == 0:\\n\\t\\t\\tpred = tmp_pred\\n\\t\\telse:\\n\\t\\t\\tpred = np.concatenate((pred, tmp_pred), axis=0)\\n\\tprint(\\\"Load prediction Shape\\\",pred.shape)\\n\\treturn pred, target_index, sorted_class, target_index_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2676876379fa13a7aecb714b5f4f113\",\n \"score\": \"0.5277411\",\n \"text\": \"def load_cifar():\\n # Loading the data.\\n (x_train_cifar, y_train_cifar), (x_test_cifar, y_test_cifar) = keras.datasets.cifar100.load_data(label_mode='fine')\\n \\n # Creating arrays of the same length as the labels vectors.\\n test_label_cifar = np.empty([y_test_cifar.shape[0]], dtype=int)\\n train_label_cifar = np.empty([y_train_cifar.shape[0]], dtype=int)\\n\\n # Puting the labels which are integers into the new vectors.\\n for i in range(len(y_train_cifar)):\\n # Since le labels are in vectors and they are floats they have to be extracted then casted to integers.\\n train_label_cifar[i] = int(y_train_cifar[i][0])\\n\\n # Doing the same thing for the test labels.\\n for i in range(len(y_test_cifar)):\\n test_label_cifar[i] = int(y_train_cifar[i][0])\\n\\n # Preprocessing the labels vectors.\\n train_label_cifar = preprocess_labels(train_label_cifar)\\n train_label_cifar = train_label_cifar.astype('float32')\\n test_label_cifar = preprocess_labels(test_label_cifar)\\n test_label_cifar = test_label_cifar.astype('float32')\\n\\n \\\"\\\"\\\"\\n x_train_cifar, train_label_cifar, x_test_cifar, test_label_cifar : are np arrays.\\n \\\"\\\"\\\"\\n \\n return x_train_cifar, train_label_cifar, x_test_cifar, test_label_cifar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f193049cf1845ea307e077d8b1573b\",\n \"score\": \"0.5274266\",\n \"text\": \"def check_labels_bin(arr):\\n try:\\n if len(set(arr)) == 1:\\n elem = list(set(arr))[0]\\n assert(elem==1 or elem==-1)\\n else:\\n assert(len(set(arr)) == 2)\\n assert( sorted(list(set(arr)))[0] == -1 and sorted(list(set(arr)))[1] == 1 )\\n except:\\n traceback.print_exc()\\n raise Exception(\\\"Class labels (both true and predicted) can only take values -1 and 1... Exiting...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fefa855f7084cdc7b92a9d3c509c7653\",\n \"score\": \"0.52609277\",\n \"text\": \"def load_labels(which_norb, which_set):\\n memmap_path = get_memmap_path(which_norb, 'labels')\\n dtype = numpy.dtype('int32')\\n row_size = 5 if which_norb == 'small' else 11\\n shape = (training_set_size + testing_set_size, row_size)\\n\\n def make_memmap():\\n cat_files, info_files = [get_norb_file_paths(which_norb,\\n 'both',\\n x)\\n for x in ('cat', 'info')]\\n\\n memmap_dir = os.path.split(memmap_path)[0]\\n if not os.path.isdir(memmap_dir):\\n os.mkdir(memmap_dir)\\n\\n print(\\\"allocating labels' memmap...\\\")\\n writeable_memmap = numpy.memmap(filename=memmap_path,\\n dtype=dtype,\\n mode='w+',\\n shape=shape)\\n print(\\\"... done.\\\")\\n\\n cat_memmap = writeable_memmap[:, :1] # 1st column\\n info_memmap = writeable_memmap[:, 1:] # remaining columns\\n\\n for norb_files, memmap in safe_zip((cat_files, info_files),\\n (cat_memmap, info_memmap)):\\n read_norb_files(norb_files, memmap)\\n\\n if not os.path.isfile(memmap_path):\\n print(\\\"Caching images to memmap file %s.\\\\n\\\"\\n \\\"This will only be done once.\\\" % memmap_path)\\n make_memmap()\\n\\n labels = numpy.memmap(filename=memmap_path,\\n dtype=dtype,\\n mode='r',\\n shape=shape)\\n\\n if which_set == 'train':\\n labels = labels[:training_set_size, :]\\n elif which_set == 'test':\\n labels = labels[training_set_size:, :]\\n\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a64d78c2291532ff0beeede38ba3a3fb\",\n \"score\": \"0.5252109\",\n \"text\": \"def load_data_and_labels():\\n texts = list(open('./data/tekst.csv').readlines())\\n texts = [clean_str(sentence) for sentence in texts]\\n print(\\\"removed punctuation\\\")\\n texts = [s.strip() for s in texts]\\n print(\\\"\\\")\\n x_text = texts\\n codes = list(open('./data/var7.csv').readlines())\\n codes = [s.strip() for s in codes]\\n global real_codes\\n real_codes = codes\\n global dictionary_of_codes\\n dictionary_of_codes_reverse = {}\\n token_codes = [dictionary_of_codes_reverse[i] for i in codes if dictionary_of_codes_reverse.setdefault(i,len(dictionary_of_codes_reverse)+1)] ##Create one_hot vector\\n dictionary_of_codes = dict((v,k) for k,v in dictionary_of_codes_reverse.iteritems())\\n token_codes_vector = np.eye(5)[token_codes].tolist()\\n y = token_codes_vector\\n return [x_text, y, real_codes, dictionary_of_codes]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c54afbbc4700292e5fb2d1c3cc2d5351\",\n \"score\": \"0.5250636\",\n \"text\": \"def _label_read(self, batch_filenames):\\n label = []\\n for file in batch_filenames:\\n with open(self.label_path) as fp:\\n for line in fp:\\n if os.path.splitext(file)[0] in line:\\n # int convert is conflict?\\n label.append(line.split(' ')[-1].rstrip())\\n break\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f1dbf5dd8b8288ffc05acd6cea028f\",\n \"score\": \"0.5244824\",\n \"text\": \"def detection(source_path, target_path, model_weights_path, prob_thre = 0.3, iou_thre = False):\\n \\n pbb_dir = os.path.join(target_path, 'pbb')\\n path_uid_csv = os.path.join(source_path, 'preprocess3D.csv')\\n npy2csv_output_file = os.path.join(target_path, 'pred3D.csv')\\n delt_dir = os.path.join(source_path, '3D')\\n if not os.path.exists(pbb_dir):\\n os.makedirs(pbb_dir)\\n \\n###########################################################################################\\n \\n \\n predict_3D(delt_dir, pbb_dir, model_weights_path) # write to pbb_output_path\\n\\n \\n###########################################################################################\\n #pbb_dir = '/data/model_weights/crt/detect_result/tianchi_luna/20180507_model/'\\n #delt_dir = '/data/model_weights/tlc/nodule_prep/tianchi_v3/'\\n #path_uid_csv = '/home/crt/nodule_detector/annotations_tianchi_luna_pixel.csv'\\n\\n\\n cads = pd.read_csv(path_uid_csv)[[u'path', u'uid']]\\n path_list, uid_list = cads.values[:, 0], cads.values[:, 1]\\n name_list = get_pbb_namelist(pbb_dir)\\n\\n new_data = []\\n for name in name_list:\\n #try:\\n pbb = np.load(os.path.join(pbb_dir,\\\"%s_pbb.npy\\\"%name))\\n delt_zyx = np.load(os.path.join(delt_dir, \\\"%s_delta_zyx.npy\\\"%name))\\n ps, zs, ys, xs, ds = pbb[:,0], pbb[:,1], pbb[:,2], pbb[:,3], pbb[:,4]\\n ps = ps[-ps.argsort()]\\n ps = logit2prob(ps)\\n zs = (zs + delt_zyx[0]) / delt_zyx[3] * delt_zyx[6]\\n ys = (ys + delt_zyx[1]) / delt_zyx[4] * delt_zyx[7]\\n xs = (xs + delt_zyx[2]) / delt_zyx[5] * delt_zyx[8]\\n ds = ds * delt_zyx[7]\\n candidates = np.column_stack((zs, ys, xs, ds, ps))\\n candidates = candidates[candidates[:,4] > 0.3] # if comment, use all data\\n clusters = bbox_dbscan(candidates, iou_eps=0.3, minPts=1)\\n print 'cluster',clusters.shape\\n cluster_coords = clusters[:, :5]\\n cluster_ids = clusters[:, -1]\\n candidates = []\\n for i in np.arange(1, max(cluster_ids)+1):\\n idxs = np.where(cluster_ids==i)[0]\\n pts = np.array([cluster_coords[i] for i in idxs])\\n #print 'pts',len(pts)\\n centroid = get_centroid(pts)\\n candidates.append(centroid)\\n index = np.where(np.array(uid_list)==name)[0][0]\\n image_path = path_list[index].encode('utf-8')\\n name_dup = np.array([name] * len(candidates))\\n image_path_dup = np.array([image_path] * len(candidates))\\n candidates = np.column_stack((image_path_dup, name_dup, candidates))\\n new_data.append(candidates)\\n #except:\\n #pass\\n if new_data != []:\\n new_data = np.vstack(new_data)\\n df = pd.DataFrame(new_data, columns=[u'path', u'uid', u'coordZ', u'coordY', u'coordX', u'diameter', u'probability'])\\n else:\\n df = pd.DataFrame(columns=[u'path', u'uid', u'coordZ', u'coordY', u'coordX', u'diameter', u'probability'])\\n df.to_csv(npy2csv_output_file, index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd029706b44c58d644a4402cd18b604c\",\n \"score\": \"0.52441216\",\n \"text\": \"def read_mnist_labels(split) -> np.ndarray:\\n assert split in ['train', 'val']\\n\\n # read data as numpy array. The data structure is specified in Yann LeCun\\n # site.\\n fd = LABELS[split].open('rb')\\n magic, size, = struct.unpack('>ii', fd.read(2 * 4))\\n data = np.frombuffer(fd.read(), 'u1').reshape(size, 1)\\n fd.close()\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3398a783466b58a91d8a20d39cd65362\",\n \"score\": \"0.5235401\",\n \"text\": \"def read_labels(data_folder, label_files): \\n data_folder = Path(data_folder)\\n blinks = []\\n corrupt = []\\n interval_corrupt = []\\n n_corrupt = 0\\n aux = []\\n \\n for file in label_files:\\n interval_corrupt = []\\n with open(data_folder / file) as csvfile:\\n readCSV = csv.reader(csvfile, delimiter=',')\\n for row in readCSV:\\n if row[0]==\\\"corrupt\\\":\\n n_corrupt = int(row[1])\\n elif n_corrupt > 0:\\n if float(row[1]) == -1:\\n print('Corrupted file: ' + file)\\n else:\\n interval_corrupt.append([float(row[0]), float(row[1])])\\n n_corrupt -= 1\\n elif row[0]==\\\"blinks\\\":\\n if n_corrupt != 0:\\n print('Error: ' + file)\\n else:\\n aux.append(float(row[0]))\\n \\n blinks.append(aux)\\n corrupt.append(interval_corrupt)\\n aux = []\\n \\n return np.array(blinks, dtype=object), corrupt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b27b7a546cb2e0f07017b570ff840868\",\n \"score\": \"0.5232381\",\n \"text\": \"def save_predictions(filename, y):\\n np.save(filename, y)# Load the data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6144be1bfe551df6f53d02df1ede82b9\",\n \"score\": \"0.52305365\",\n \"text\": \"def process_batch(self, detections, labels):\\n detections = detections[detections[:, 4] > self.CONF_THRESHOLD]\\n gt_classes = labels[:, 0].astype(np.int16)\\n detection_classes = detections[:, 5].astype(np.int16)\\n\\n all_ious = box_iou_calc(labels[:, 1:], detections[:, :4])\\n want_idx = np.where(all_ious > self.IOU_THRESHOLD)\\n\\n all_matches = []\\n for i in range(want_idx[0].shape[0]):\\n all_matches.append([want_idx[0][i], want_idx[1][i], all_ious[want_idx[0][i], want_idx[1][i]]])\\n\\n all_matches = np.array(all_matches)\\n if all_matches.shape[0] > 0: # if there is match\\n all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]\\n all_matches = all_matches[np.unique(all_matches[:, 1], return_index=True)[1]]\\n all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]\\n all_matches = all_matches[np.unique(all_matches[:, 0], return_index=True)[1]]\\n\\n for i, label in enumerate(labels):\\n if all_matches.shape[0] > 0 and all_matches[all_matches[:, 0] == i].shape[0] == 1:\\n gt_class = gt_classes[i]\\n detection_class = detection_classes[int(all_matches[all_matches[:, 0] == i, 1][0])]\\n self.matrix[(gt_class), detection_class] += 1\\n else:\\n gt_class = gt_classes[i]\\n self.matrix[self.nc, (gt_class)] += 1\\n\\n for i, detection in enumerate(detections):\\n if all_matches.shape[0] and all_matches[all_matches[:, 1] == i].shape[0] == 0:\\n detection_class = detection_classes[i]\\n self.matrix[detection_class, self.nc] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7078826e3918e0075fb1cb308d45fc4e\",\n \"score\": \"0.52277213\",\n \"text\": \"def load_labels(self):\\n labels_path = './models/coco.names'\\n self.labels = open(labels_path).read().strip().split('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be355509ab695506f497596a2d1799f\",\n \"score\": \"0.5224367\",\n \"text\": \"def detect_labels(path):\\n from google.cloud import vision\\n import io\\n client = vision.ImageAnnotatorClient()\\n\\n with io.open('photo.jpg', 'rb') as image_file:\\n content = image_file.read()\\n\\n image = vision.types.Image(content=content)\\n response = client.label_detection(image=image)\\n objects = response.label_annotations\\n\\n labelList = [translate_label(object_info.description).lower() for object_info in objects]\\n return labelList\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1598,"cells":{"query_id":{"kind":"string","value":"72193bd1b65736259484f9f7739c5321"},"query":{"kind":"string","value":"Automatically removes code blocks from the code."},"positive_passages":{"kind":"list like","value":[{"docid":"a2cd6552c0b31eb32886d54a47d3c8c7","score":"0.6670364","text":"def cleanup_code(self, content):\n # remove ```py\\n```\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n\n # remove `foo`\n return content.strip('` \\n')","title":""}],"string":"[\n {\n \"docid\": \"a2cd6552c0b31eb32886d54a47d3c8c7\",\n \"score\": \"0.6670364\",\n \"text\": \"def cleanup_code(self, content):\\n # remove ```py\\\\n```\\n if content.startswith('```') and content.endswith('```'):\\n return '\\\\n'.join(content.split('\\\\n')[1:-1])\\n\\n # remove `foo`\\n return content.strip('` \\\\n')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f6761198124dad601935ec29835a76c1","score":"0.66694766","text":"def cleanup_code(self, content):\n # remove ```py\\n```\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n # remove `foo`\n return content.strip('` \\n')","title":""},{"docid":"68e50218b5f1922621c9b8a45d711716","score":"0.66460794","text":"def eliminate_dead_code(self):\n used_set = {op.i\n for block in self\n for instr in block.instrs\n for op in instr.ops\n if isinstance(op, InstructionOp)}\n side_effect_free = {\"let\", \"neg\", \"add\", \"sub\", \"mul\", \"div\", \"cmp\",\n \"adda\", \"load\", \"phi\"}\n for block in self:\n for i, instr in enumerate(block.instrs):\n if instr.instr in side_effect_free and instr.i not in used_set:\n del block.instrs[i]","title":""},{"docid":"d6451d9e95d514290d7e0c624b9bf7af","score":"0.66187984","text":"def cleanup_code(self, content: str) -> str:\n # remove ```py\\n```\n if content.startswith(\"```\") and content.endswith(\"```\"):\n return \"\\n\".join(content.split(\"\\n\")[1:-1])\n\n # remove `foo`\n return content.strip(\"` \\n\")","title":""},{"docid":"775e7bbcaad12e2f56d71ace8a6030bc","score":"0.6581413","text":"def cleanup_code(content):\n # remove ```py\\n```\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n\n # remove `foo`\n return content.strip('` \\n')","title":""},{"docid":"62be328f17cd5509ef9d96be393b9c83","score":"0.656567","text":"def cleanup_code(content):\n # remove ```py\\n```\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n\n # remove `foo`\n return content.strip('` \\n')","title":""},{"docid":"47431968794098bce659c9684f69d8f9","score":"0.65363586","text":"def cleanup_code(content):\r\n # remove ```py\\n```\r\n if content.startswith('```') and content.endswith('```'):\r\n return '\\n'.join(content.split('\\n')[1:-1])\r\n\r\n # remove `foo`\r\n return content.strip('` \\n')","title":""},{"docid":"07865e389850e496351b1a69c1c3a6b1","score":"0.6508453","text":"def _do_code_blocks(self, text):\n code_block_re = re.compile(r'''\n (?:\\n\\n|\\A\\n?)\n ( # $1 = the code block -- one or more lines, starting with a space/tab\n (?:\n (?:[ ]{%d} | \\t) # Lines must start with a tab or a tab-width of spaces\n .*\\n+\n )+\n )\n ((?=^[ ]{0,%d}\\S)|\\Z) # Lookahead for non-space at line-start, or end of doc\n # Lookahead to make sure this block isn't already in a code block.\n # Needed when syntax highlighting is being used.\n (?![^<]*\\</code\\>)\n ''' % (self.tab_width, self.tab_width),\n re.M | re.X)\n return code_block_re.sub(self._code_block_sub, text)","title":""},{"docid":"2cea18a828af65f23e29bb4cd6acb7a4","score":"0.6495373","text":"def cleanup_code(self, content):\n # remove `foo`\n return content.strip('\\n')","title":""},{"docid":"f293574a8605e92a8e5a2ae29e969cb6","score":"0.61948645","text":"def depart_literal_block(self, node: literal_block):\n self.body.append(\"</code></pre>\\n\")","title":""},{"docid":"e7391172acfad66b4ecd9313929c6eec","score":"0.6174976","text":"def _do_fenced_code_blocks(self, text):\n return self._fenced_code_block_re.sub(self._fenced_code_block_sub, text)","title":""},{"docid":"e7391172acfad66b4ecd9313929c6eec","score":"0.6174976","text":"def _do_fenced_code_blocks(self, text):\n return self._fenced_code_block_re.sub(self._fenced_code_block_sub, text)","title":""},{"docid":"cabe23a92530821b8945528d302f7c6a","score":"0.6039041","text":"def _strip_without_code(self, line_list):\n\n new_line_list = []\n\n code_flag = False\n\n for line in line_list:\n\n if \"@code\" in line and not code_flag:\n leading_spaces = line.find(\"@code\")\n new_line_list.append(line.strip())\n code_flag = True\n elif \"@code\" in line and code_flag:\n new_line_list.append(line.strip())\n code_flag = False\n elif (not \"@code\" in line) and code_flag:\n new_line_list.append(line[leading_spaces:])\n else:\n new_line_list.append(line.strip())\n\n return new_line_list","title":""},{"docid":"e3a231d508a42747b8a3015a76d421df","score":"0.6036036","text":"def edited_code_trim(lines):\n # Not using a list comprehension, so I can look for \"markers\".\n trimmed = []\n cancel_markers = set(('//cancel', '/*cancel', '/*\\ncancel'))\n in_block_comment = False\n for line in lines:\n stripped = line.lstrip()\n if in_block_comment:\n if '*/' in stripped:\n in_block_comment = False\n debug(line, align=True)\n continue\n elif stripped.startswith('/*'):\n in_block_comment = True\n debug('Starting block comment:')\n debug(line, align=True)\n continue\n # A comment line, look for markers.\n if stripped.startswith('//'):\n if stripped.replace(' ', '').lower() in cancel_markers:\n debug(f'Found cancel marker: {line}')\n return None\n debug('Skipping comment:')\n debug(line, align=True)\n continue\n # Usable code.\n trimmed.append(line)\n\n # Remove leading blank lines.\n while trimmed and (not trimmed[0].strip()):\n trimmed.pop(0)\n\n if trimmed and (trimmed[0].lower().startswith('cancel')):\n # Just wrote 'cancel'/'CANCEL' at the top of the file.\n debug(f'Found plain cancel marker: {trimmed[0]!r}')\n return None\n return trimmed or None","title":""},{"docid":"c9fcfb573009f6c28d2166b3641c1d2d","score":"0.60329205","text":"def test_codeblocks_with_added_removed_lines_awesome_theme(app: Sphinx) -> None:\n app.build()\n tree = parse_html(Path(app.outdir) / \"index.html\")\n code_blocks = tree(\"div\", class_=\"highlight\")\n block = code_blocks[6]\n code = block.select(\"pre > code\")\n assert len(code) == 1\n assert str(code[0]).replace(\"\\n\", \"\") == (\n '<code><ins><span class=\"nb\">print</span>'\n '<span class=\"p\">(</span><span class=\"s2\">\"Added\"</span>'\n '<span class=\"p\">)</span></ins>'\n '<del><span class=\"nb\">print</span><span class=\"p\">'\n '(</span><span class=\"s2\">\"Removed\"</span>'\n '<span class=\"p\">)</span></del></code>'\n )","title":""},{"docid":"3047ee37f81340c44c262a529fdfd7f0","score":"0.6022532","text":"def comment_remover_py(code):\n code = str(code)\n return re.sub(r'(?m)^ *#.*\\n?', ' ', code)","title":""},{"docid":"2c03353f55f546b0afe177ec0d9c188b","score":"0.5988622","text":"def code_gen(blocks):\n decl = \"\"\n head = \"\"\n tail = \"\"\n for block in blocks:\n decl += block.declare\n head = f\"{head}\\n{{\\n{block.behavior}\"\n tail = f\"{block.cleanup}\\n}}\\n{tail}\"\n return decl + head + tail","title":""},{"docid":"41fdf9e416a093b380a7bf95c51a66cf","score":"0.5955663","text":"def remove_comments(self):\n self.main = remove_comments(self.main)","title":""},{"docid":"43b5621a4bd17611f5399055b4b5fc24","score":"0.59371006","text":"def beautify(self, code):\n pass","title":""},{"docid":"c1430821e494870545441c8059f19e8b","score":"0.5900719","text":"def remove_comments_and_spaces(cls, code):\n new_code_list = list()\n for line in code.split('\\n'):\n line = line.split('//', 1)[0]\n new_code_list.append(line)\n new_code = ''.join(new_code_list)\n new_code.replace('\\t', ' ')\n new_code.replace('\\n', ' ')\n new_code.replace('\\r', ' ')\n while True:\n backup_code = new_code\n new_code = new_code.replace(' ', ' ')\n if new_code == backup_code:\n break\n return new_code","title":""},{"docid":"c6e557db7385c8ae3010d0d7c40c715d","score":"0.5865519","text":"def run(self, root):\r\n blocks = root.getiterator('pre')\r\n for block in blocks:\r\n children = block.getchildren()\r\n if len(children) == 1 and children[0].tag == 'code':\r\n code = CodeHilite(children[0].text,\r\n linenums=self.config['linenums'],\r\n guess_lang=self.config['guess_lang'],\r\n css_class=self.config['css_class'],\r\n style=self.config['pygments_style'],\r\n noclasses=self.config['noclasses'],\r\n tab_length=self.markdown.tab_length)\r\n placeholder = self.markdown.htmlStash.store(code.hilite(),\r\n safe=True)\r\n # Clear codeblock in etree instance\r\n block.clear()\r\n # Change to p element which will later\r\n # be removed when inserting raw html\r\n block.tag = 'p'\r\n block.text = placeholder","title":""},{"docid":"c5812811acab3521e474f79ad4b30e95","score":"0.5842561","text":"def remove_code_punc(code):\n\n sec = code\n together = set([\"==\", \"&&\", \"<>\", \"||\"])\n spacing = set([\"+\", \"-\", \"*\", \"/\", \"!\", \"^\"])\n exclude = set(\n [\"=\", \"|\", \"&\", \"[\", \"]\", \"\\r\", \"\\n\", \"(\", \")\", \"{\", \"}\", \":\", \",\", \";\", \".\", '\"', \"'\", \">\", \"<\", \"#\", \"%\", \"$\",\n \"~\", \"\\\\\", \"?\"])\n new_sec = \"\"\n i = 0\n while i < len(sec):\n try:\n if sec[i:i + 1] in together:\n new_sec += \" \" + sec[i:i + 1] + \" \"\n i += 2\n continue\n except:\n print \"last\"\n if sec[i] in exclude:\n new_sec += \" \"\n elif sec[i] in spacing:\n new_sec += \" \" + sec[i] + \" \"\n else:\n new_sec += sec[i]\n i += 1\n new_sec = new_sec.replace(\" \", \" \")\n return new_sec","title":""},{"docid":"a0a5e0f37bfb779c7fa6e2372b570fa2","score":"0.5837798","text":"def _unfold_blocks_for_jump(self, lineno):\r\n for line in self._sidebarWidget.foldedBlocks:\r\n if lineno >= line:\r\n self._sidebarWidget.code_folding_event(line + 1)\r\n else:\r\n break","title":""},{"docid":"f5778c2cc2b0727e5746a6f1a6e35ef3","score":"0.5831914","text":"def compressCodeBlocks(self, text) :\r\n\r\n lines = text.split(\"\\n\")\r\n buffer = \"\"\r\n numLines = len(lines)\r\n for i in range(numLines) :\r\n \r\n if (lines[i].strip() or i == numLines-1 or i == 0 or\r\n not ( lines[i-1].startswith(\" \")\r\n and lines[i+1].startswith(\" \") ) ):\r\n buffer += \"\\n\" + lines[i]\r\n\r\n return buffer","title":""},{"docid":"70e0b48041820ce66295c8a4b1914775","score":"0.5830408","text":"def Code(self, block):\n # Break the code block into each line of code\n lines = block.split('\\n')\n\n for line in lines:\n # Right-strip the line to avoid trailing white space. We do this on the\n # full string so that tabbing will be removed if a blank line was added.\n new_line = (_TAB_CHARACTER * self._tab_level) + line\n self._lines.append(new_line.rstrip())","title":""},{"docid":"af0b30721aadeb7530b6a6cdd6bba532","score":"0.5828867","text":"def cleaning():\n local(\"find . -name '*.orig' -exec rm -i {} \\;\")\n local(\"find . -type f -name '*.pyc' -exec rm {} \\;\")","title":""},{"docid":"fbd3cb028d39baef6277dc1753cff64f","score":"0.5772482","text":"def test_codegen_clean_visitor_functions(self) -> None:\n new_code = clean_generated_code(\"\\n\".join(visitor_codegen.generated_code))\n new_file = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"visitor_codegen.deleteme.py\"\n )\n with open(new_file, \"w\") as fp:\n fp.write(new_code)\n try:\n format_file(new_file)\n except Exception:\n # We failed to format, but this is probably due to invalid code that\n # black doesn't like. This test will still fail and report to run codegen.\n pass\n with open(new_file, \"r\") as fp:\n new_code = fp.read()\n os.remove(new_file)\n with open(\n os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"../../_typed_visitor.py\"\n ),\n \"r\",\n ) as fp:\n old_code = fp.read()\n\n # Now that we've done simple codegen, verify that it matches.\n self.assert_code_matches(old_code, new_code, \"libcst._typed_visitor\")","title":""},{"docid":"1a5fe3c684d374ee8ccfaf16c041cc4c","score":"0.5769642","text":"def cleanupState(self):\n self.commitStrConst()\n\n self._indentLev = 2\n mainBodyChunks = self._methodBodyChunks\n self._methodBodyChunks = []\n self._addAutoSetupCode()\n self._methodBodyChunks.extend(mainBodyChunks)\n self._addAutoCleanupCode()","title":""},{"docid":"b43d25d3a73c2583cb727baa1a0fedc0","score":"0.5706498","text":"def delts(self,code):\n###################################################################\n \n delattr(self, code)","title":""},{"docid":"b43bcd91bfc997d74589ae124f49464d","score":"0.5679468","text":"def add_code_final(self):\n pass","title":""},{"docid":"a2786b0c6cb5d8bc52743da1310dc163","score":"0.5641561","text":"def itercodeblocks(self):\r\n codeblock = CodeBlock(0)\r\n last_line_has_decorator = False\r\n for codeline in self.itercodelines(): \r\n if codeline.isnewblock() and not last_line_has_decorator :\r\n if codeblock.string:\r\n self.options.update(codeblock.options)\r\n codeblock.options.update(self.options)\r\n yield codeblock\r\n codeblock = CodeBlock(codeline.start_row)\r\n codeblock.append(codeline)\r\n line_start = codeline.string.lstrip('\\n')\r\n if line_start and line_start[0] == '@':\r\n last_line_has_decorator = True\r\n continue\r\n# FIXME: I don't understand the purpose of this code. Until I don't have\r\n# a test case that fail, I leave it commented out.\r\n# line_end = codeline.string.rstrip(\" \\n\")\r\n# if line_end and line_end == ':' : \r\n# if codeblock.string:\r\n# self.options.update(codeblock.options)\r\n# codeblock.options.update(self.options)\r\n# yield codeblock\r\n# codeblock = CodeBlock(codeline.start_row)\r\n else:\r\n codeblock.append(codeline)\r\n last_line_has_decorator = False\r\n else:\r\n self.options.update(codeblock.options)\r\n codeblock.options.update(self.options)\r\n yield codeblock","title":""},{"docid":"aa5cbc6f49d88f0b02cda32a4f570a41","score":"0.5625681","text":"def rm_inline_comment(area):\n\n comment = TABLE.get(os.path.splitext(area.filename)[1], DEFAULT)\n area.replace_ranges('sel', '^ *%s ?|^\\t*%s ?' % (comment, comment), \n lambda data, index0, index1: data.replace(\n '%s ' % comment, '').replace(comment, ''))\n area.clear_selection()\n area.chmode('NORMAL')","title":""},{"docid":"3aff3ce73414e8cdfbd18ee8be5c6e54","score":"0.56242514","text":"def Unindent():\n pass","title":""},{"docid":"39a9d3e4b5403390f4c6349d6942185b","score":"0.5619939","text":"def _simple_clean_clauses(self):\n pass","title":""},{"docid":"52813e652e020aacc305df66d621aa6c","score":"0.55901533","text":"def _strip_comments(code):\n return re.sub(r'(?m)^ *#.*\\n?', '', code)","title":""},{"docid":"8b751fb14112d5942a73ab8cc952150f","score":"0.5574364","text":"def sniff_code_in_comments():\n\t#unimplementable as long as comments are ignored","title":""},{"docid":"16a1a163a48ffa139455159ab85dfbfb","score":"0.5539005","text":"def sniff_unused_code():","title":""},{"docid":"9273ef3c297371966e40f05950e7ea81","score":"0.5484809","text":"def dedent(self):\n if self.level == 0:\n raise SyntaxError, \"internal error in code generator\"\n self.level = self.level - 1","title":""},{"docid":"ebfc5ad9104b3a2a00e008c3592af154","score":"0.54624426","text":"def test_no_permalinks_on_codeblocks(app: Sphinx) -> None:\n app.config.html_permalinks = False # type: ignore[attr-defined]\n app.build()\n tree = parse_html(Path(app.outdir) / \"index.html\")\n code_blocks = tree(\"div\", class_=\"highlight\")\n assert len(code_blocks) == 7\n code_headers = tree(\"div\", class_=\"code-header\")\n assert len(code_headers) == 7\n headerlinks = tree(\"a\", class_=\"headerlink\")\n assert len(headerlinks) == 0","title":""},{"docid":"cf273e6cf14594dbe7ab76e92750e79f","score":"0.5449824","text":"def remove_its(self):\n i = 0\n while i < len(self.instrs):\n ins = self.instrs[i]\n if get_loc(ins).loc_addr > self.itremlimit: break\n if ins[0].upper().startswith('IT'):\n j = len(ins[0].strip()) + 1\n self.instrs[i:i+j] = inlining.translate_it_block(self.instrs[i:i+j])\n i += 1","title":""},{"docid":"c5866458c3546ae2223ac2c564045a7e","score":"0.5438617","text":"def test_remove_hidden_test_region_code(self, preprocessor):\n cell = create_code_cell()\n cell.source = dedent(\n \"\"\"\n assert True\n ### BEGIN HIDDEN TESTS\n assert True\n ### END HIDDEN TESTS\n \"\"\"\n ).strip()\n removed_test = preprocessor._remove_hidden_test_region(cell)\n assert removed_test\n assert cell.source == \"assert True\"","title":""},{"docid":"1c366f04273d8de2e4b723e2d0348ac6","score":"0.54213065","text":"def onCodeChange():\n if ed.getContents() != module.current_buffer:\n ed.setContents(module.current_buffer)","title":""},{"docid":"8b7e07213769075ca75cf01d0e18e62b","score":"0.54151255","text":"def _wrap_code(self, inner):\n yield 0, \"<code>\"\n for tup in inner:\n yield tup\n yield 0, \"</code>\"","title":""},{"docid":"f002587547ae16e13b46ce8b974e9247","score":"0.5411101","text":"def neighbor_loop_code_end(self):\n\n return \"\"\" \"\"\"","title":""},{"docid":"9a494984bf089c3c497d3fe8ab556d8c","score":"0.5356163","text":"def dedent_code(code):\r\n dedent_code = dedent(code)\r\n offset = firstlinelen(code) - firstlinelen(dedent_code)\r\n code=dedent_code\r\n return [code, offset]","title":""},{"docid":"549e4c9058fd63ac5fd2a3199bb18726","score":"0.53493077","text":"def _processCodeBlock(self, parent_elem, lines, inList):\r\n\r\n detabbed, theRest = self.blockGuru.detectTabbed(lines)\r\n\r\n pre = self.doc.createElement('pre')\r\n code = self.doc.createElement('code')\r\n parent_elem.appendChild(pre)\r\n pre.appendChild(code)\r\n text = \"\\n\".join(detabbed).rstrip()+\"\\n\"\r\n #text = text.replace(\"&\", \"&\")\r\n code.appendChild(self.doc.createTextNode(text))\r\n self._processSection(parent_elem, theRest, inList)","title":""},{"docid":"3dcd72e4b18e7afb92b5a0d34b0e8c54","score":"0.53459704","text":"def clean_highlighted_code(html):\n cleaner = clean.Cleaner(allow_tags=['pre'], remove_unknown_tags=False)\n for el in html.findall('.//pre'):\n p = el.getparent()\n cleaned = cleaner.clean_html(el)\n p.replace(el, cleaned)","title":""},{"docid":"eecff36fad0708dbeadd7196283fab49","score":"0.53390694","text":"def preprocess(self, source, name, filename=None):\n if not _extends_re.search(source):\n return source\n\n if not _extends_end_re.search(source):\n source += '\\n{% enddjextends %}'\n\n source = _block_re.sub(r'{% djblock \\1 %}', source)\n source = _block_end_re.sub(r'{% enddjblock %}', source)\n\n source = _super_re.sub(\"{{ '{{' }} block.super {{ '}}' }}\", source)\n\n return source","title":""},{"docid":"db69093826dfd589a80601b22aa247e8","score":"0.53290784","text":"def _process(self, template):\n code_blk = re.compile(r\"<%(.*?)%>\\n?\", re.DOTALL)\n indent, n = 0, 0\n code = []\n for n, blk in enumerate(code_blk.split(template)):\n # Replace '<\\%' and '%\\>' escapes\n blk = re.sub(r\"<\\\\%\", \"<%\", re.sub(r\"%\\\\>\", \"%>\", blk))\n # Unescape '%{}' characters\n blk = re.sub(r\"\\\\(%|{|})\", r\"\\g<1>\", blk)\n\n if not (n % 2):\n if not blk: continue\n # Escape backslash characters\n blk = re.sub(r'\\\\', r'\\\\\\\\', blk)\n # Escape double-quote characters\n blk = re.sub(r'\"', r'\\\\\"', blk)\n blk = (\" \" * (indent*4)) + 'echo(\"\"\"{0}\"\"\")'.format(blk)\n else:\n blk = blk.rstrip()\n if blk.lstrip().startswith(\":\"):\n if not indent:\n err = \"unexpected block ending\"\n raise SyntaxError(\"Line {0}: {1}\".format(n, err))\n indent -= 1\n if blk.startswith(\":end\"):\n continue\n blk = blk.lstrip()[1:]\n\n blk = re.sub(\"(?m)^\", \" \" * (indent * 4), blk)\n if blk.endswith(\":\"):\n indent += 1\n\n code.append(blk)\n\n if indent:\n err = \"Reached EOF before closing block\"\n raise EOFError(\"Line {0}: {1}\".format(n, err))\n\n return \"\\n\".join(code)","title":""},{"docid":"1a591c2c43f0f8c4e41d0a4a524ceb4a","score":"0.53279907","text":"def visit_CodeBlock(self, code_block: CodeBlock):\n for elm in code_block.code_elements:\n self.visit(elm.code_elm)","title":""},{"docid":"2a65336f3a2798f23f8dbcea1be29425","score":"0.53097695","text":"def _trim_end(self, tokens: List[Token]) -> 'Block':\n i = last_token = self.end - 1\n while tokens[i].name in NON_CODING_TOKENS | {'DEDENT', 'NEWLINE'}:\n # if we find an indented comment inside our block, keep it\n if (\n tokens[i].name in {'NL', 'NEWLINE'} and\n tokens[i + 1].name == UNIMPORTANT_WS and\n len(tokens[i + 1].src) > self._initial_indent(tokens)\n ):\n break\n # otherwise we've found another line to remove\n elif tokens[i].name in {'NL', 'NEWLINE'}:\n last_token = i\n i -= 1\n return self._replace(end=last_token + 1)","title":""},{"docid":"491510b41b3d6f54e4ce7550a993753e","score":"0.5303578","text":"def decompile_code(code_object):\n # init vars\n showasm = False\n showast = False\n out = StringIO()\n scanner = scanner27.Scanner27()\n scanner.setShowAsm(showasm, out)\n tokens, customize = scanner.disassemble(code_object)\n\n # Build AST from disassembly.\n walk = walker.Walker(out, scanner, showast=showast)\n try:\n ast = walk.build_ast(tokens, customize)\n except walker.ParserError as ex: # parser failed, dump disassembly\n print >> out, ex\n raise\n del tokens # save memory\n\n # convert leading '__doc__ = \"...\" into doc string\n assert ast == 'stmts'\n try:\n if ast[0][0] == walker.ASSIGN_DOC_STRING(code_object.co_consts[0]):\n walk.print_docstring('', code_object.co_consts[0])\n del ast[0]\n if ast[-1] == walker.RETURN_NONE:\n ast.pop() # remove last node\n #todo: if empty, add 'pass'\n except:\n pass\n walk.mod_globs = walker.find_globals(ast, set())\n walk.gen_source(ast, customize)\n if walk.ERROR:\n raise Exception(walk.ERROR)\n return out.getvalue()","title":""},{"docid":"af65e5ae4610ef3efc26df305cad5976","score":"0.5300249","text":"def sanitize_code(code, logging_function=None):\n # sanitize code\n tree = ast.parse(code)\n parsed_code = CodeSanitizer(logging_function).visit(tree)\n\n # fix code\n ast.fix_missing_locations(parsed_code)\n\n return parsed_code","title":""},{"docid":"d9aa8566b2e6b251704d5a652fddcca4","score":"0.5290873","text":"def codeblock_cmd(self, task, step):\n\n name = self.get_command_params(step.command, \"codeblock\")[0]\n name = self.replace_variables(name)\n self.process_codeblock(task, name.upper())","title":""},{"docid":"8f12b85e9cba7dac1e42ac5c90b4596a","score":"0.52869046","text":"def delete_lines(code_edit):\n textCursor = code_edit.textCursor()\n\n start = textCursor.selectionStart()\n end = textCursor.selectionEnd()\n textCursor.setPosition(start, QtGui.QTextCursor.MoveAnchor)\n textCursor.movePosition(QtGui.QTextCursor.StartOfLine)\n new_start = textCursor.position()\n\n textCursor.setPosition(end, QtGui.QTextCursor.MoveAnchor)\n textCursor.movePosition(QtGui.QTextCursor.EndOfLine)\n\n new_end = textCursor.position()\n\n textCursor.setPosition(new_start, QtGui.QTextCursor.KeepAnchor)\n\n if textCursor.selectedText() == '':\n textCursor.setPosition(start, QtGui.QTextCursor.MoveAnchor)\n next_line = new_end+1\n if 0 < next_line >= code_edit.document().characterCount():\n next_line = next_line-2\n if next_line == -1:\n return\n textCursor.setPosition(next_line, QtGui.QTextCursor.KeepAnchor)\n\n textCursor.insertText('')","title":""},{"docid":"adcf01f582f839e630e4c10dda4a6b69","score":"0.52864176","text":"def clean_python(c):\n c.run(\"find . -name '*.pyc' -exec rm -f {} +\")\n c.run(\"find . -name '*.pyo' -exec rm -f {} +\")\n c.run(\"find . -name '*~' -exec rm -f {} +\")\n c.run(\"find . -name '__pycache__' -exec rm -fr {} +\")","title":""},{"docid":"dbe2fe2e899c24b5e2938a5aee4c20e9","score":"0.526342","text":"def test_malindented_code_line(self):\n data1 = [\".. #!/usr/bin/env python3\\n\", # indent == 4 * \" \"\n \"\\n\",\n \" print('hello world')\"] # indent == 2 * \" \"\n data2 = [\"..\\t#!/usr/bin/env python3\\n\", # indent == 8 * \" \"\n \"\\n\",\n \" print('hello world')\"] # indent == 2 * \" \"\n for data in (data1, data2):\n try:\n blocks = Text2Code(data)()\n self.fail( \"wrong indent did not raise ValueError\" )\n except ValueError:\n pass","title":""},{"docid":"c0f15e5e95f7a77e9a2d872c000b90c6","score":"0.5263146","text":"def undisplay(self):\n for line in self.lines: line.undraw()","title":""},{"docid":"6b904065a327e2d32c16108996ab5bd9","score":"0.5248634","text":"def pep8mod_remove_ignore(ignore_code):\r\n if ignore_code in pep8mod.options.ignore:\r\n pep8mod.options.ignore.remove(ignore_code)","title":""},{"docid":"2ba69c9ddf6e1190953f0174ef77288b","score":"0.5247638","text":"def _ignore_somecode(text):\n text = re.sub('\\r', '', text)\n text = re.sub('\\f', '', text)\n text = re.sub('\\0', '', text)\n return text","title":""},{"docid":"1603d24e4446fc97fae193914da21c72","score":"0.52285105","text":"def normalize_python(code, remove_docstrings=True):\n try:\n tree = ast.parse(code, \"<<code>>\")\n except SyntaxError:\n return None\n\n if remove_docstrings:\n tree = RemoveDocstring().visit(tree)\n\n return astunparse.unparse(tree)","title":""},{"docid":"091e727d47148b34acf9f777c16015e3","score":"0.52285033","text":"def special_parse_process_python_code(sourcecode):\n import ast\n import astor\n #sourcecode = 'from __future__ import print_function\\n' + sourcecode\n sourcecode_ = sourcecode.encode('utf8')\n pt = ast.parse(sourcecode_, 'testfile')\n\n generator = astor.codegen.SourceGenerator(' ' * 4)\n generator.visit(pt)\n resturctured_source = (''.join(generator.result))\n print(resturctured_source)\n\n visitor = ast.NodeVisitor()\n visitor.visit(pt)\n\n import redbaron\n # Pares a FULL syntax tree that keeps blockcomments\n baron = redbaron.RedBaron(sourcecode)\n #fst = baron.fst()\n node = (baron.node_list[54]) # NOQA\n [n.type for n in baron.node_list]\n\n #class SpecialVisitor(ast.NodeVisitor):","title":""},{"docid":"72a83802653d8c0537bd83f86d93428f","score":"0.5226368","text":"def wrap_code(self, code):\n if not self.do_wrap:\n debug('Not doing auto includes/macros.')\n return code\n line_table = set(line.strip() for line in code.splitlines())\n lines = []\n for defname in sorted(config['defines']):\n if self.has_define(defname, lines=line_table):\n debug(f'Not defining {defname}')\n continue\n defline = f'#define {defname} {config[\"defines\"][defname]}'\n lines.append(f'#ifndef {defname}')\n lines.append(f' {defline}')\n lines.append(f'#endif // {defname}')\n debug(f'Defined {defname} {config[\"defines\"][defname]}')\n\n includes = config['includes']\n for includetype in sorted(includes):\n for includename in includes[includetype]:\n includedef = f'#include \"{includename}\"'\n if includedef in line_table:\n debug(f'Not including {includetype} {includename}')\n continue\n lines.append(includedef)\n debug(f'Including {includetype} {includename}')\n\n for macroname in sorted(config['macros']):\n deflines = config['macros'][macroname]['define']\n if isinstance(deflines, str):\n deflines = [deflines]\n if self.has_define(macroname, lines=line_table):\n debug(f'Not including macro: {macroname}')\n continue\n lines.append(f'#ifndef {macroname}')\n lines.extend(self.fix_macro_lines(deflines, indent=4))\n lines.append(f'#endif // ifdef {macroname}')\n debug(f'Including macro: {macroname}')\n\n main_sigs = [s for s in line_table if self.is_main_sig(s)]\n main_sig = main_sigs[0] if main_sigs else None\n if main_sig:\n debug('No main() needed.')\n lines.append(code)\n else:\n indent = 4 if code.lstrip()[0] == code[0] else 0\n lines.append(self.wrap_main(code.rstrip(), indent=indent))\n return '\\n'.join(lines)","title":""},{"docid":"f6197007625db8b7f443307cd8a7996a","score":"0.5223137","text":"def clear_compiled_python_files():\n __exec_cmd('find %s/ -name \"*.pyc\" -exec rm -rf {} \\;' % env.role['django_root'])","title":""},{"docid":"ceca0376b28544e527928f5748976e49","score":"0.5204748","text":"def uncompyle(version, co, out = None, showasm = 0, showast = 0, deob = 0):\n assert type(co) == types.CodeType\n __real_out = out or sys.stdout\n if co.co_filename:\n print >> __real_out, '#Embedded file name: %s' % co.co_filename\n scanner = getscanner(version)\n scanner.setShowAsm(showasm, out)\n tokens, customize = scanner.disassemble(co, deob=deob)\n walker = Walker(out, scanner, showast=showast)\n try:\n ast = walker.build_ast(tokens, customize)\n except ParserError as e:\n print >> __real_out, e\n raise\n\n del tokens\n assert ast == 'stmts'\n try:\n if ast[0][0] == ASSIGN_DOC_STRING(co.co_consts[0]):\n walker.print_docstring('', co.co_consts[0])\n del ast[0]\n if ast[-1] == RETURN_NONE:\n ast.pop()\n except:\n pass\n\n walker.mod_globs = find_globals(ast, set())\n walker.gen_source(ast, customize)\n for g in walker.mod_globs:\n walker.write('global %s ## Warning: Unused global\\n' % g)\n\n if walker.pending_newlines:\n print >> __real_out\n if walker.ERROR:\n raise walker.ERROR","title":""},{"docid":"42e5ffdec939a14a21ad0edc45836fea","score":"0.52047116","text":"def clean():\n local('find . -name \"*.pyc\" -exec rm {} \\;')","title":""},{"docid":"214b4cd46a9f44791d2f335dd46cf787","score":"0.51985526","text":"def remove_outputs(nb):\n for cell in nb.cells:\n if cell.cell_type == 'code':\n cell.outputs = []","title":""},{"docid":"8359ab440581d7cb07f11cb4507f64f0","score":"0.51881707","text":"def example():\n print \"\"\"\n \"\"\"","title":""},{"docid":"8359ab440581d7cb07f11cb4507f64f0","score":"0.51881707","text":"def example():\n print \"\"\"\n \"\"\"","title":""},{"docid":"0215ffa08e46d4fa445f1dfe58d2d847","score":"0.5186604","text":"def codegen(self, code: IndentedBuffer):\n pass","title":""},{"docid":"6f524b52b0814a58e5b677c60a98f7d1","score":"0.51857674","text":"def handle_cleanup(self):\n # XXX This is a lot slower than it needs to be, because\n # we don't have a \"cdef int i\" in scope here.\n return je(\"\"\"\nfor i in range(self._n_{{ myself.name }}):\n Py_CLEAR(self._{{ myself.name }}[i])\n\"\"\", myself=self)","title":""},{"docid":"ad499a7566306c30c0f5abb44892a2e4","score":"0.5172536","text":"def remove_commentlines(self):\n\n tmp = self.main.splitlines()\n tmp = list(itertools.filterfalse(re.compile(r\"^\\s*%.*$\").match, tmp))\n self.main = \"\\n\".join(tmp)","title":""},{"docid":"6ae1ce654eca01afce5fc0bf45e44441","score":"0.51699215","text":"def delete_pyc():\n run(\"find . -name '*.pyc' -delete\")","title":""},{"docid":"b1f902367121c48b7853d84adb904853","score":"0.516638","text":"def remove_inline_comments(_code):\n lines = _code.split('\\n')\n counter = 0\n for i in range(len(lines)):\n inline_comment = parse_line_comment(lines[i])\n if inline_comment != \"\": counter += 1\n lines[i] = lines[i].replace(inline_comment, \"\")\n\n return '\\n'.join(lines), counter","title":""},{"docid":"34bd024009c5015b33337518b20598a5","score":"0.51623744","text":"def test_codegen_clean_matcher_classes(self) -> None:\n new_code = clean_generated_code(\"\\n\".join(matcher_codegen.generated_code))\n new_file = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"matcher_codegen.deleteme.py\"\n )\n with open(new_file, \"w\") as fp:\n fp.write(new_code)\n try:\n format_file(new_file)\n except Exception:\n # We failed to format, but this is probably due to invalid code that\n # black doesn't like. This test will still fail and report to run codegen.\n pass\n with open(new_file, \"r\") as fp:\n new_code = fp.read()\n os.remove(new_file)\n with open(\n os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"../../matchers/__init__.py\"\n ),\n \"r\",\n ) as fp:\n old_code = fp.read()\n\n # Now that we've done simple codegen, verify that it matches.\n self.assert_code_matches(old_code, new_code, \"libcst.matchers.__init__\")","title":""},{"docid":"95322519d145e01d5e4a0375dc1d9a8f","score":"0.5153222","text":"def UnComment(self):\n sel = self.GetSelection()\n start = self.LineFromPosition(sel[0])\n end = self.LineFromPosition(sel[1])\n if start>end: #swap around\n start,end=end,start\n #start an undo mark\n self.BeginUndoAction()\n for ln in range(start, end + 1):\n linestart = self.PositionFromLine(ln)\n if chr(self.GetCharAt(linestart)) == '#':\n #set cursor to the right of the #\n self.SetCurrentPos(linestart + 1)\n #delete to the beginning of th line\n self.DelLineLeft()\n #finish the undo mark\n self.EndUndoAction()","title":""},{"docid":"6f9b228c09e7877c5b93c1543596830c","score":"0.5150296","text":"def make_code(self):\n self.node = self.heap.pop()\n code = \"\"\n self._make_codes_recur(self.node, code)","title":""},{"docid":"861db482a889846cff0016d34d74d7b4","score":"0.51480126","text":"def _format_code(text: str) -> str:\n if text:\n return re.sub(r'`([^`]+)`', '\\code{\\g<1>}', text)\n else:\n return ''","title":""},{"docid":"860fa5f5f49acda764de4eea09ce6218","score":"0.5141483","text":"def cleanup_rst(rst_file):\n lines_out = []\n lines = open(rst_file, \"r\").readlines()\n for i, line in enumerate(lines[:]):\n # Change ipython3 code blocks to bash\n if line == \".. code:: ipython3\\n\":\n lines_out.append(\".. code:: bash\\n\")\n # Change image paths to correct directory\n elif \".. image::\" in line:\n new_line = line.replace(\".. image:: \", \".. image:: images/\")\n lines_out.append(new_line)\n # Get rid of magic command '%'\n elif line.startswith(\" %\"):\n lines_out.append(line.replace(\" %\", \" \"))\n # Get rid of magic command '!'\n elif line.startswith(\" !\"):\n lines_out.append(line.replace(\" !\", \" \"))\n else:\n lines_out.append(line)\n\n return lines_out","title":""},{"docid":"9dad94588b4e863cc3c87f032a97f916","score":"0.5136101","text":"def genScriptCode(self, topBlock):\n\n codeObj = CodeAndCb() # Holds all the code that is generated.\n\n opcode = topBlock.getOpcode()\n if opcode == 'event_whenflagclicked':\n self.whenFlagClicked(codeObj, topBlock)\n elif opcode == 'control_start_as_clone':\n self.whenSpriteCloned(codeObj, topBlock)\n elif opcode == 'event_whenthisspriteclicked':\n self.whenClicked(codeObj, topBlock)\n elif opcode == 'event_whenkeypressed':\n self.whenKeyPressed(codeObj, topBlock)\n elif opcode == 'event_whenbroadcastreceived':\n self.whenIReceive(codeObj, topBlock)\n elif opcode == 'procedures_definition':\n self.genProcDefCode(codeObj, topBlock)\n elif opcode == 'event_whenbackdropswitchesto':\n self.whenSwitchToBackdrop(codeObj, topBlock)\n\n # If not in one of the above \"hat blocks\", then it is an\n # orphaned bit of code that will not be run in either Scratch\n # or ScratchFoot.\n\n return codeObj","title":""},{"docid":"c07861dfd699f6c86ea973c3173e29c2","score":"0.5129188","text":"def cleanup(self):\r\n self._remove_all_validated_scripts()\r\n return","title":""},{"docid":"e0056383895567d382553d68e4e85145","score":"0.51286703","text":"def ignore_somecode(text):\n text = re.sub('\\r', '', text)\n text = re.sub('\\f', '', text)\n text = re.sub('\\0', '', text)\n return text","title":""},{"docid":"fba790ce85f5d0fe5e073b524ecc7d32","score":"0.51139766","text":"def strip_stripped_code(wiki):\n stripped = wiki.strip_code()\n cleaned = []\n kw = ('Category:', 'thumb')\n for line in stripped.split('\\n'):\n if line.startswith(kw):\n continue\n cleaned.append(line)\n return cleaned","title":""},{"docid":"a48bc5c1f07d5d1faf39923cba11fd5c","score":"0.5112562","text":"def test_codeblocks_with_linenumbers_awesome_theme(app: Sphinx) -> None:\n app.build()\n tree = parse_html(Path(app.outdir) / \"index.html\")\n code_blocks = tree(\"div\", class_=\"highlight\")\n block = code_blocks[4]\n code = block.select(\"pre > code\")\n assert len(code) == 1\n assert str(code[0]).replace(\"\\n\", \"\") == (\n '<code><span class=\"code-line\" '\n 'id=\"line-1\"><span class=\"linenos\">1</span>'\n '<span class=\"nb\">print</span><span class=\"p\">'\n '(</span><span class=\"s2\">\"Hello\"</span>'\n '<span class=\"p\">)</span></span></code>'\n )","title":""},{"docid":"c649466e42550da046b1d5e99797dae8","score":"0.5111278","text":"async def clear_code(self, position: int) -> None:\n await self.send_command(CMD_DELETE_CODE, position)","title":""},{"docid":"4b6a7c596f462ce67cb1b9623296b127","score":"0.50969344","text":"def test_replace_itself(self):\n original = textwrap.dedent(\n \"\"\"\\\n name = \"whatever\"\n\n @early()\n def help():\n return [[\"foo\", \"bar\"], [\"Some Existing\", \"stuff\"]]\n \"\"\"\n )\n\n code_block = textwrap.dedent(\n \"\"\"\n @early()\n def help():\n return [[\"foo\", \"bar\"], [\"Some Existing\", \"stuff\"]]\n \"\"\"\n )\n\n expected = textwrap.dedent(\n \"\"\"\\\n name = \"whatever\"\n\n @early()\n def help():\n return [[\"foo\", \"bar\"], [\"Some Existing\", \"stuff\"]]\n \"\"\"\n )\n\n self._test(expected, original, parso.parse(code_block))","title":""},{"docid":"d2a0606d81d55818a67e8d657633936c","score":"0.50889146","text":"def run(self, text):\r\n for i in range(self.markdown.htmlStash.html_counter):\r\n html, safe = self.markdown.htmlStash.rawHtmlBlocks[i]\r\n if self.markdown.safeMode and not safe:\r\n if str(self.markdown.safeMode).lower() == 'escape':\r\n html = self.escape(html)\r\n elif str(self.markdown.safeMode).lower() == 'remove':\r\n html = ''\r\n else:\r\n html = self.markdown.html_replacement_text\r\n if self.isblocklevel(html) and (safe or not self.markdown.safeMode):\r\n text = text.replace(\"<p>%s</p>\" % \r\n (self.markdown.htmlStash.get_placeholder(i)),\r\n html + \"\\n\")\r\n text = text.replace(self.markdown.htmlStash.get_placeholder(i), \r\n html)\r\n return text","title":""},{"docid":"378b971d2080986eee4caff4c7aa46f0","score":"0.5088259","text":"def remove(language):","title":""},{"docid":"4351cc1970b4c807278bb5c85675435b","score":"0.5085091","text":"def remove_not_executed_definitions(self, filename, cov_result):\n # Methods that weren't executed aren't relevant, so we remove them here.\n for line in self.method_definition_lines:\n if not self.was_executed(line, filename, cov_result):\n self.method_definition_lines.remove(line)","title":""},{"docid":"8e68bc66db7fadab9fc56c1b52bda5e4","score":"0.5085032","text":"def run(self, lines):\r\n\r\n # Check for code hilite extension\r\n if not self.checked_for_codehilite:\r\n for ext in self.markdown.registeredExtensions:\r\n if isinstance(ext, CodeHiliteExtension):\r\n self.codehilite_conf = ext.config\r\n break\r\n\r\n self.checked_for_codehilite = True\r\n\r\n text = \"\\n\".join(lines)\r\n while 1:\r\n m = self.FENCED_BLOCK_RE.search(text)\r\n if m:\r\n lang = ''\r\n if m.group('lang'):\r\n lang = self.LANG_TAG % m.group('lang')\r\n\r\n # If config is not empty, then the codehighlite extension\r\n # is enabled, so we call it to highlite the code\r\n if self.codehilite_conf:\r\n highliter = CodeHilite(m.group('code'),\r\n linenums=self.codehilite_conf['linenums'][0],\r\n guess_lang=self.codehilite_conf['guess_lang'][0],\r\n css_class=self.codehilite_conf['css_class'][0],\r\n style=self.codehilite_conf['pygments_style'][0],\r\n lang=(m.group('lang') or None),\r\n noclasses=self.codehilite_conf['noclasses'][0])\r\n\r\n code = highliter.hilite()\r\n else:\r\n code = self.CODE_WRAP % (lang, self._escape(m.group('code')))\r\n\r\n placeholder = self.markdown.htmlStash.store(code, safe=True)\r\n text = '%s\\n%s\\n%s'% (text[:m.start()], placeholder, text[m.end():])\r\n else:\r\n break\r\n return text.split(\"\\n\")","title":""},{"docid":"f02d3bf43fa689e1ce2d07b5fb75f703","score":"0.5084823","text":"def clear(source=None):\n if source == None:\n globals = sys._getframe(1).f_globals\n In = globals[\"In\"]\n source = In[len(In)-2]\n print_func = lambda line:sys.stdout.write(line+\"\\n\")\n clear_comment(source, print_func)","title":""},{"docid":"75c2f52afd15a9a01de5fb8b6655fe23","score":"0.50844","text":"def _executeCleanBlocks(self, destination):\n # Set difference: clean = existing - dirty\n clean_block_starts = set( self._cacheFiles.keys() ) - self._dirtyBlocks\n \n inputShape = self.Input.meta.shape\n clean_block_rois = map( partial( getBlockBounds, inputShape, self._blockshape ),\n clean_block_starts )\n destination[0] = map( partial(map, TinyVector), clean_block_rois )\n return destination","title":""},{"docid":"f192b2eab50e95e843c83269a27fefb4","score":"0.5082369","text":"def run(self, lines):\n text = \"\\n\".join(lines)\n while 1:\n m = FENCED_BLOCK_RE.search(text)\n if not m:\n break\n lang = m.group('lang')\n linenums = bool(m.group('linenums'))\n html = highlight_syntax(m.group('code'), lang, linenums=linenums)\n placeholder = self.markdown.htmlStash.store(html, safe=True)\n text = '{}\\n{}\\n{}'.format(\n text[:m.start()],\n placeholder,\n text[m.end():]\n )\n\n return text.split(\"\\n\")","title":""},{"docid":"733a496a60d21c486d09a140e2342172","score":"0.50760186","text":"def find_included_code_with_mistune(features, text):\n if \"code\" not in features: features[\"code\"] = []\n if (\"```\" in text):\n md = markdowner(text)\n parsed = BeautifulSoup(md, \"html.parser\")\n for codeblock in parsed.findAll(\"code\"):\n if codeblock.has_attr(\"class\"):\n features[\"code\"].append(codeblock[\"class\"][0])\n else:\n features[\"code\"].append(\"unspecified\")\n codeblock.string = \"\"\n features[\"code\"] = sorted(list(set(features[\"code\"])))\n return parsed.text\n else:\n return text","title":""},{"docid":"cf296860d002da5211f8b39634637c36","score":"0.50738317","text":"def clean():\n for i in range(6):\n # move cursor one line up, then delete line\n sys.stdout.write('\\033[1A\\033[K')\n sys.stdout.flush()","title":""},{"docid":"4abcf049cd41d3b05966c0863f7e1e98","score":"0.50691843","text":"def clean_source(self):\n src_len = len(self.source)\n source_clean = []\n for i in range(src_len):\n line = self.source[i]\n if not (line.startswith(\"#requires\") or line.startswith(\"#provides\")\n or line.startswith(\"#return\") or line.startswith(\"#param\")):\n source_clean.append(line)\n return source_clean","title":""},{"docid":"4f606dcf2b7a5854fb24c147a72382c7","score":"0.5062167","text":"def dis_code(code):\n for const in code.co_consts:\n if isinstance(const, types.CodeType):\n dis_code(const)\n\n print(\"\")\n print(code)\n dis.dis(code)","title":""},{"docid":"30b161752b6ba3e04dcfcbc5872c6a44","score":"0.50545734","text":"def remove_all_ip_blocks(self):\n raise NotImplementedError()","title":""}],"string":"[\n {\n \"docid\": \"f6761198124dad601935ec29835a76c1\",\n \"score\": \"0.66694766\",\n \"text\": \"def cleanup_code(self, content):\\n # remove ```py\\\\n```\\n if content.startswith('```') and content.endswith('```'):\\n return '\\\\n'.join(content.split('\\\\n')[1:-1])\\n # remove `foo`\\n return content.strip('` \\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e50218b5f1922621c9b8a45d711716\",\n \"score\": \"0.66460794\",\n \"text\": \"def eliminate_dead_code(self):\\n used_set = {op.i\\n for block in self\\n for instr in block.instrs\\n for op in instr.ops\\n if isinstance(op, InstructionOp)}\\n side_effect_free = {\\\"let\\\", \\\"neg\\\", \\\"add\\\", \\\"sub\\\", \\\"mul\\\", \\\"div\\\", \\\"cmp\\\",\\n \\\"adda\\\", \\\"load\\\", \\\"phi\\\"}\\n for block in self:\\n for i, instr in enumerate(block.instrs):\\n if instr.instr in side_effect_free and instr.i not in used_set:\\n del block.instrs[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6451d9e95d514290d7e0c624b9bf7af\",\n \"score\": \"0.66187984\",\n \"text\": \"def cleanup_code(self, content: str) -> str:\\n # remove ```py\\\\n```\\n if content.startswith(\\\"```\\\") and content.endswith(\\\"```\\\"):\\n return \\\"\\\\n\\\".join(content.split(\\\"\\\\n\\\")[1:-1])\\n\\n # remove `foo`\\n return content.strip(\\\"` \\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"775e7bbcaad12e2f56d71ace8a6030bc\",\n \"score\": \"0.6581413\",\n \"text\": \"def cleanup_code(content):\\n # remove ```py\\\\n```\\n if content.startswith('```') and content.endswith('```'):\\n return '\\\\n'.join(content.split('\\\\n')[1:-1])\\n\\n # remove `foo`\\n return content.strip('` \\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62be328f17cd5509ef9d96be393b9c83\",\n \"score\": \"0.656567\",\n \"text\": \"def cleanup_code(content):\\n # remove ```py\\\\n```\\n if content.startswith('```') and content.endswith('```'):\\n return '\\\\n'.join(content.split('\\\\n')[1:-1])\\n\\n # remove `foo`\\n return content.strip('` \\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47431968794098bce659c9684f69d8f9\",\n \"score\": \"0.65363586\",\n \"text\": \"def cleanup_code(content):\\r\\n # remove ```py\\\\n```\\r\\n if content.startswith('```') and content.endswith('```'):\\r\\n return '\\\\n'.join(content.split('\\\\n')[1:-1])\\r\\n\\r\\n # remove `foo`\\r\\n return content.strip('` \\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07865e389850e496351b1a69c1c3a6b1\",\n \"score\": \"0.6508453\",\n \"text\": \"def _do_code_blocks(self, text):\\n code_block_re = re.compile(r'''\\n (?:\\\\n\\\\n|\\\\A\\\\n?)\\n ( # $1 = the code block -- one or more lines, starting with a space/tab\\n (?:\\n (?:[ ]{%d} | \\\\t) # Lines must start with a tab or a tab-width of spaces\\n .*\\\\n+\\n )+\\n )\\n ((?=^[ ]{0,%d}\\\\S)|\\\\Z) # Lookahead for non-space at line-start, or end of doc\\n # Lookahead to make sure this block isn't already in a code block.\\n # Needed when syntax highlighting is being used.\\n (?![^<]*\\\\</code\\\\>)\\n ''' % (self.tab_width, self.tab_width),\\n re.M | re.X)\\n return code_block_re.sub(self._code_block_sub, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cea18a828af65f23e29bb4cd6acb7a4\",\n \"score\": \"0.6495373\",\n \"text\": \"def cleanup_code(self, content):\\n # remove `foo`\\n return content.strip('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f293574a8605e92a8e5a2ae29e969cb6\",\n \"score\": \"0.61948645\",\n \"text\": \"def depart_literal_block(self, node: literal_block):\\n self.body.append(\\\"</code></pre>\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7391172acfad66b4ecd9313929c6eec\",\n \"score\": \"0.6174976\",\n \"text\": \"def _do_fenced_code_blocks(self, text):\\n return self._fenced_code_block_re.sub(self._fenced_code_block_sub, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7391172acfad66b4ecd9313929c6eec\",\n \"score\": \"0.6174976\",\n \"text\": \"def _do_fenced_code_blocks(self, text):\\n return self._fenced_code_block_re.sub(self._fenced_code_block_sub, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabe23a92530821b8945528d302f7c6a\",\n \"score\": \"0.6039041\",\n \"text\": \"def _strip_without_code(self, line_list):\\n\\n new_line_list = []\\n\\n code_flag = False\\n\\n for line in line_list:\\n\\n if \\\"@code\\\" in line and not code_flag:\\n leading_spaces = line.find(\\\"@code\\\")\\n new_line_list.append(line.strip())\\n code_flag = True\\n elif \\\"@code\\\" in line and code_flag:\\n new_line_list.append(line.strip())\\n code_flag = False\\n elif (not \\\"@code\\\" in line) and code_flag:\\n new_line_list.append(line[leading_spaces:])\\n else:\\n new_line_list.append(line.strip())\\n\\n return new_line_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a231d508a42747b8a3015a76d421df\",\n \"score\": \"0.6036036\",\n \"text\": \"def edited_code_trim(lines):\\n # Not using a list comprehension, so I can look for \\\"markers\\\".\\n trimmed = []\\n cancel_markers = set(('//cancel', '/*cancel', '/*\\\\ncancel'))\\n in_block_comment = False\\n for line in lines:\\n stripped = line.lstrip()\\n if in_block_comment:\\n if '*/' in stripped:\\n in_block_comment = False\\n debug(line, align=True)\\n continue\\n elif stripped.startswith('/*'):\\n in_block_comment = True\\n debug('Starting block comment:')\\n debug(line, align=True)\\n continue\\n # A comment line, look for markers.\\n if stripped.startswith('//'):\\n if stripped.replace(' ', '').lower() in cancel_markers:\\n debug(f'Found cancel marker: {line}')\\n return None\\n debug('Skipping comment:')\\n debug(line, align=True)\\n continue\\n # Usable code.\\n trimmed.append(line)\\n\\n # Remove leading blank lines.\\n while trimmed and (not trimmed[0].strip()):\\n trimmed.pop(0)\\n\\n if trimmed and (trimmed[0].lower().startswith('cancel')):\\n # Just wrote 'cancel'/'CANCEL' at the top of the file.\\n debug(f'Found plain cancel marker: {trimmed[0]!r}')\\n return None\\n return trimmed or None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9fcfb573009f6c28d2166b3641c1d2d\",\n \"score\": \"0.60329205\",\n \"text\": \"def test_codeblocks_with_added_removed_lines_awesome_theme(app: Sphinx) -> None:\\n app.build()\\n tree = parse_html(Path(app.outdir) / \\\"index.html\\\")\\n code_blocks = tree(\\\"div\\\", class_=\\\"highlight\\\")\\n block = code_blocks[6]\\n code = block.select(\\\"pre > code\\\")\\n assert len(code) == 1\\n assert str(code[0]).replace(\\\"\\\\n\\\", \\\"\\\") == (\\n '<code><ins><span class=\\\"nb\\\">print</span>'\\n '<span class=\\\"p\\\">(</span><span class=\\\"s2\\\">\\\"Added\\\"</span>'\\n '<span class=\\\"p\\\">)</span></ins>'\\n '<del><span class=\\\"nb\\\">print</span><span class=\\\"p\\\">'\\n '(</span><span class=\\\"s2\\\">\\\"Removed\\\"</span>'\\n '<span class=\\\"p\\\">)</span></del></code>'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3047ee37f81340c44c262a529fdfd7f0\",\n \"score\": \"0.6022532\",\n \"text\": \"def comment_remover_py(code):\\n code = str(code)\\n return re.sub(r'(?m)^ *#.*\\\\n?', ' ', code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c03353f55f546b0afe177ec0d9c188b\",\n \"score\": \"0.5988622\",\n \"text\": \"def code_gen(blocks):\\n decl = \\\"\\\"\\n head = \\\"\\\"\\n tail = \\\"\\\"\\n for block in blocks:\\n decl += block.declare\\n head = f\\\"{head}\\\\n{{\\\\n{block.behavior}\\\"\\n tail = f\\\"{block.cleanup}\\\\n}}\\\\n{tail}\\\"\\n return decl + head + tail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41fdf9e416a093b380a7bf95c51a66cf\",\n \"score\": \"0.5955663\",\n \"text\": \"def remove_comments(self):\\n self.main = remove_comments(self.main)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b5621a4bd17611f5399055b4b5fc24\",\n \"score\": \"0.59371006\",\n \"text\": \"def beautify(self, code):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1430821e494870545441c8059f19e8b\",\n \"score\": \"0.5900719\",\n \"text\": \"def remove_comments_and_spaces(cls, code):\\n new_code_list = list()\\n for line in code.split('\\\\n'):\\n line = line.split('//', 1)[0]\\n new_code_list.append(line)\\n new_code = ''.join(new_code_list)\\n new_code.replace('\\\\t', ' ')\\n new_code.replace('\\\\n', ' ')\\n new_code.replace('\\\\r', ' ')\\n while True:\\n backup_code = new_code\\n new_code = new_code.replace(' ', ' ')\\n if new_code == backup_code:\\n break\\n return new_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6e557db7385c8ae3010d0d7c40c715d\",\n \"score\": \"0.5865519\",\n \"text\": \"def run(self, root):\\r\\n blocks = root.getiterator('pre')\\r\\n for block in blocks:\\r\\n children = block.getchildren()\\r\\n if len(children) == 1 and children[0].tag == 'code':\\r\\n code = CodeHilite(children[0].text,\\r\\n linenums=self.config['linenums'],\\r\\n guess_lang=self.config['guess_lang'],\\r\\n css_class=self.config['css_class'],\\r\\n style=self.config['pygments_style'],\\r\\n noclasses=self.config['noclasses'],\\r\\n tab_length=self.markdown.tab_length)\\r\\n placeholder = self.markdown.htmlStash.store(code.hilite(),\\r\\n safe=True)\\r\\n # Clear codeblock in etree instance\\r\\n block.clear()\\r\\n # Change to p element which will later\\r\\n # be removed when inserting raw html\\r\\n block.tag = 'p'\\r\\n block.text = placeholder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5812811acab3521e474f79ad4b30e95\",\n \"score\": \"0.5842561\",\n \"text\": \"def remove_code_punc(code):\\n\\n sec = code\\n together = set([\\\"==\\\", \\\"&&\\\", \\\"<>\\\", \\\"||\\\"])\\n spacing = set([\\\"+\\\", \\\"-\\\", \\\"*\\\", \\\"/\\\", \\\"!\\\", \\\"^\\\"])\\n exclude = set(\\n [\\\"=\\\", \\\"|\\\", \\\"&\\\", \\\"[\\\", \\\"]\\\", \\\"\\\\r\\\", \\\"\\\\n\\\", \\\"(\\\", \\\")\\\", \\\"{\\\", \\\"}\\\", \\\":\\\", \\\",\\\", \\\";\\\", \\\".\\\", '\\\"', \\\"'\\\", \\\">\\\", \\\"<\\\", \\\"#\\\", \\\"%\\\", \\\"$\\\",\\n \\\"~\\\", \\\"\\\\\\\\\\\", \\\"?\\\"])\\n new_sec = \\\"\\\"\\n i = 0\\n while i < len(sec):\\n try:\\n if sec[i:i + 1] in together:\\n new_sec += \\\" \\\" + sec[i:i + 1] + \\\" \\\"\\n i += 2\\n continue\\n except:\\n print \\\"last\\\"\\n if sec[i] in exclude:\\n new_sec += \\\" \\\"\\n elif sec[i] in spacing:\\n new_sec += \\\" \\\" + sec[i] + \\\" \\\"\\n else:\\n new_sec += sec[i]\\n i += 1\\n new_sec = new_sec.replace(\\\" \\\", \\\" \\\")\\n return new_sec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a5e0f37bfb779c7fa6e2372b570fa2\",\n \"score\": \"0.5837798\",\n \"text\": \"def _unfold_blocks_for_jump(self, lineno):\\r\\n for line in self._sidebarWidget.foldedBlocks:\\r\\n if lineno >= line:\\r\\n self._sidebarWidget.code_folding_event(line + 1)\\r\\n else:\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5778c2cc2b0727e5746a6f1a6e35ef3\",\n \"score\": \"0.5831914\",\n \"text\": \"def compressCodeBlocks(self, text) :\\r\\n\\r\\n lines = text.split(\\\"\\\\n\\\")\\r\\n buffer = \\\"\\\"\\r\\n numLines = len(lines)\\r\\n for i in range(numLines) :\\r\\n \\r\\n if (lines[i].strip() or i == numLines-1 or i == 0 or\\r\\n not ( lines[i-1].startswith(\\\" \\\")\\r\\n and lines[i+1].startswith(\\\" \\\") ) ):\\r\\n buffer += \\\"\\\\n\\\" + lines[i]\\r\\n\\r\\n return buffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e0b48041820ce66295c8a4b1914775\",\n \"score\": \"0.5830408\",\n \"text\": \"def Code(self, block):\\n # Break the code block into each line of code\\n lines = block.split('\\\\n')\\n\\n for line in lines:\\n # Right-strip the line to avoid trailing white space. We do this on the\\n # full string so that tabbing will be removed if a blank line was added.\\n new_line = (_TAB_CHARACTER * self._tab_level) + line\\n self._lines.append(new_line.rstrip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0b30721aadeb7530b6a6cdd6bba532\",\n \"score\": \"0.5828867\",\n \"text\": \"def cleaning():\\n local(\\\"find . -name '*.orig' -exec rm -i {} \\\\;\\\")\\n local(\\\"find . -type f -name '*.pyc' -exec rm {} \\\\;\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd3cb028d39baef6277dc1753cff64f\",\n \"score\": \"0.5772482\",\n \"text\": \"def test_codegen_clean_visitor_functions(self) -> None:\\n new_code = clean_generated_code(\\\"\\\\n\\\".join(visitor_codegen.generated_code))\\n new_file = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"visitor_codegen.deleteme.py\\\"\\n )\\n with open(new_file, \\\"w\\\") as fp:\\n fp.write(new_code)\\n try:\\n format_file(new_file)\\n except Exception:\\n # We failed to format, but this is probably due to invalid code that\\n # black doesn't like. This test will still fail and report to run codegen.\\n pass\\n with open(new_file, \\\"r\\\") as fp:\\n new_code = fp.read()\\n os.remove(new_file)\\n with open(\\n os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"../../_typed_visitor.py\\\"\\n ),\\n \\\"r\\\",\\n ) as fp:\\n old_code = fp.read()\\n\\n # Now that we've done simple codegen, verify that it matches.\\n self.assert_code_matches(old_code, new_code, \\\"libcst._typed_visitor\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a5fe3c684d374ee8ccfaf16c041cc4c\",\n \"score\": \"0.5769642\",\n \"text\": \"def cleanupState(self):\\n self.commitStrConst()\\n\\n self._indentLev = 2\\n mainBodyChunks = self._methodBodyChunks\\n self._methodBodyChunks = []\\n self._addAutoSetupCode()\\n self._methodBodyChunks.extend(mainBodyChunks)\\n self._addAutoCleanupCode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43d25d3a73c2583cb727baa1a0fedc0\",\n \"score\": \"0.5706498\",\n \"text\": \"def delts(self,code):\\n###################################################################\\n \\n delattr(self, code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43bcd91bfc997d74589ae124f49464d\",\n \"score\": \"0.5679468\",\n \"text\": \"def add_code_final(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2786b0c6cb5d8bc52743da1310dc163\",\n \"score\": \"0.5641561\",\n \"text\": \"def itercodeblocks(self):\\r\\n codeblock = CodeBlock(0)\\r\\n last_line_has_decorator = False\\r\\n for codeline in self.itercodelines(): \\r\\n if codeline.isnewblock() and not last_line_has_decorator :\\r\\n if codeblock.string:\\r\\n self.options.update(codeblock.options)\\r\\n codeblock.options.update(self.options)\\r\\n yield codeblock\\r\\n codeblock = CodeBlock(codeline.start_row)\\r\\n codeblock.append(codeline)\\r\\n line_start = codeline.string.lstrip('\\\\n')\\r\\n if line_start and line_start[0] == '@':\\r\\n last_line_has_decorator = True\\r\\n continue\\r\\n# FIXME: I don't understand the purpose of this code. Until I don't have\\r\\n# a test case that fail, I leave it commented out.\\r\\n# line_end = codeline.string.rstrip(\\\" \\\\n\\\")\\r\\n# if line_end and line_end == ':' : \\r\\n# if codeblock.string:\\r\\n# self.options.update(codeblock.options)\\r\\n# codeblock.options.update(self.options)\\r\\n# yield codeblock\\r\\n# codeblock = CodeBlock(codeline.start_row)\\r\\n else:\\r\\n codeblock.append(codeline)\\r\\n last_line_has_decorator = False\\r\\n else:\\r\\n self.options.update(codeblock.options)\\r\\n codeblock.options.update(self.options)\\r\\n yield codeblock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5cbc6f49d88f0b02cda32a4f570a41\",\n \"score\": \"0.5625681\",\n \"text\": \"def rm_inline_comment(area):\\n\\n comment = TABLE.get(os.path.splitext(area.filename)[1], DEFAULT)\\n area.replace_ranges('sel', '^ *%s ?|^\\\\t*%s ?' % (comment, comment), \\n lambda data, index0, index1: data.replace(\\n '%s ' % comment, '').replace(comment, ''))\\n area.clear_selection()\\n area.chmode('NORMAL')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aff3ce73414e8cdfbd18ee8be5c6e54\",\n \"score\": \"0.56242514\",\n \"text\": \"def Unindent():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39a9d3e4b5403390f4c6349d6942185b\",\n \"score\": \"0.5619939\",\n \"text\": \"def _simple_clean_clauses(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52813e652e020aacc305df66d621aa6c\",\n \"score\": \"0.55901533\",\n \"text\": \"def _strip_comments(code):\\n return re.sub(r'(?m)^ *#.*\\\\n?', '', code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b751fb14112d5942a73ab8cc952150f\",\n \"score\": \"0.5574364\",\n \"text\": \"def sniff_code_in_comments():\\n\\t#unimplementable as long as comments are ignored\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a1a163a48ffa139455159ab85dfbfb\",\n \"score\": \"0.5539005\",\n \"text\": \"def sniff_unused_code():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9273ef3c297371966e40f05950e7ea81\",\n \"score\": \"0.5484809\",\n \"text\": \"def dedent(self):\\n if self.level == 0:\\n raise SyntaxError, \\\"internal error in code generator\\\"\\n self.level = self.level - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebfc5ad9104b3a2a00e008c3592af154\",\n \"score\": \"0.54624426\",\n \"text\": \"def test_no_permalinks_on_codeblocks(app: Sphinx) -> None:\\n app.config.html_permalinks = False # type: ignore[attr-defined]\\n app.build()\\n tree = parse_html(Path(app.outdir) / \\\"index.html\\\")\\n code_blocks = tree(\\\"div\\\", class_=\\\"highlight\\\")\\n assert len(code_blocks) == 7\\n code_headers = tree(\\\"div\\\", class_=\\\"code-header\\\")\\n assert len(code_headers) == 7\\n headerlinks = tree(\\\"a\\\", class_=\\\"headerlink\\\")\\n assert len(headerlinks) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf273e6cf14594dbe7ab76e92750e79f\",\n \"score\": \"0.5449824\",\n \"text\": \"def remove_its(self):\\n i = 0\\n while i < len(self.instrs):\\n ins = self.instrs[i]\\n if get_loc(ins).loc_addr > self.itremlimit: break\\n if ins[0].upper().startswith('IT'):\\n j = len(ins[0].strip()) + 1\\n self.instrs[i:i+j] = inlining.translate_it_block(self.instrs[i:i+j])\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5866458c3546ae2223ac2c564045a7e\",\n \"score\": \"0.5438617\",\n \"text\": \"def test_remove_hidden_test_region_code(self, preprocessor):\\n cell = create_code_cell()\\n cell.source = dedent(\\n \\\"\\\"\\\"\\n assert True\\n ### BEGIN HIDDEN TESTS\\n assert True\\n ### END HIDDEN TESTS\\n \\\"\\\"\\\"\\n ).strip()\\n removed_test = preprocessor._remove_hidden_test_region(cell)\\n assert removed_test\\n assert cell.source == \\\"assert True\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c366f04273d8de2e4b723e2d0348ac6\",\n \"score\": \"0.54213065\",\n \"text\": \"def onCodeChange():\\n if ed.getContents() != module.current_buffer:\\n ed.setContents(module.current_buffer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7e07213769075ca75cf01d0e18e62b\",\n \"score\": \"0.54151255\",\n \"text\": \"def _wrap_code(self, inner):\\n yield 0, \\\"<code>\\\"\\n for tup in inner:\\n yield tup\\n yield 0, \\\"</code>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f002587547ae16e13b46ce8b974e9247\",\n \"score\": \"0.5411101\",\n \"text\": \"def neighbor_loop_code_end(self):\\n\\n return \\\"\\\"\\\" \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a494984bf089c3c497d3fe8ab556d8c\",\n \"score\": \"0.5356163\",\n \"text\": \"def dedent_code(code):\\r\\n dedent_code = dedent(code)\\r\\n offset = firstlinelen(code) - firstlinelen(dedent_code)\\r\\n code=dedent_code\\r\\n return [code, offset]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549e4c9058fd63ac5fd2a3199bb18726\",\n \"score\": \"0.53493077\",\n \"text\": \"def _processCodeBlock(self, parent_elem, lines, inList):\\r\\n\\r\\n detabbed, theRest = self.blockGuru.detectTabbed(lines)\\r\\n\\r\\n pre = self.doc.createElement('pre')\\r\\n code = self.doc.createElement('code')\\r\\n parent_elem.appendChild(pre)\\r\\n pre.appendChild(code)\\r\\n text = \\\"\\\\n\\\".join(detabbed).rstrip()+\\\"\\\\n\\\"\\r\\n #text = text.replace(\\\"&\\\", \\\"&\\\")\\r\\n code.appendChild(self.doc.createTextNode(text))\\r\\n self._processSection(parent_elem, theRest, inList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dcd72e4b18e7afb92b5a0d34b0e8c54\",\n \"score\": \"0.53459704\",\n \"text\": \"def clean_highlighted_code(html):\\n cleaner = clean.Cleaner(allow_tags=['pre'], remove_unknown_tags=False)\\n for el in html.findall('.//pre'):\\n p = el.getparent()\\n cleaned = cleaner.clean_html(el)\\n p.replace(el, cleaned)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eecff36fad0708dbeadd7196283fab49\",\n \"score\": \"0.53390694\",\n \"text\": \"def preprocess(self, source, name, filename=None):\\n if not _extends_re.search(source):\\n return source\\n\\n if not _extends_end_re.search(source):\\n source += '\\\\n{% enddjextends %}'\\n\\n source = _block_re.sub(r'{% djblock \\\\1 %}', source)\\n source = _block_end_re.sub(r'{% enddjblock %}', source)\\n\\n source = _super_re.sub(\\\"{{ '{{' }} block.super {{ '}}' }}\\\", source)\\n\\n return source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db69093826dfd589a80601b22aa247e8\",\n \"score\": \"0.53290784\",\n \"text\": \"def _process(self, template):\\n code_blk = re.compile(r\\\"<%(.*?)%>\\\\n?\\\", re.DOTALL)\\n indent, n = 0, 0\\n code = []\\n for n, blk in enumerate(code_blk.split(template)):\\n # Replace '<\\\\%' and '%\\\\>' escapes\\n blk = re.sub(r\\\"<\\\\\\\\%\\\", \\\"<%\\\", re.sub(r\\\"%\\\\\\\\>\\\", \\\"%>\\\", blk))\\n # Unescape '%{}' characters\\n blk = re.sub(r\\\"\\\\\\\\(%|{|})\\\", r\\\"\\\\g<1>\\\", blk)\\n\\n if not (n % 2):\\n if not blk: continue\\n # Escape backslash characters\\n blk = re.sub(r'\\\\\\\\', r'\\\\\\\\\\\\\\\\', blk)\\n # Escape double-quote characters\\n blk = re.sub(r'\\\"', r'\\\\\\\\\\\"', blk)\\n blk = (\\\" \\\" * (indent*4)) + 'echo(\\\"\\\"\\\"{0}\\\"\\\"\\\")'.format(blk)\\n else:\\n blk = blk.rstrip()\\n if blk.lstrip().startswith(\\\":\\\"):\\n if not indent:\\n err = \\\"unexpected block ending\\\"\\n raise SyntaxError(\\\"Line {0}: {1}\\\".format(n, err))\\n indent -= 1\\n if blk.startswith(\\\":end\\\"):\\n continue\\n blk = blk.lstrip()[1:]\\n\\n blk = re.sub(\\\"(?m)^\\\", \\\" \\\" * (indent * 4), blk)\\n if blk.endswith(\\\":\\\"):\\n indent += 1\\n\\n code.append(blk)\\n\\n if indent:\\n err = \\\"Reached EOF before closing block\\\"\\n raise EOFError(\\\"Line {0}: {1}\\\".format(n, err))\\n\\n return \\\"\\\\n\\\".join(code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a591c2c43f0f8c4e41d0a4a524ceb4a\",\n \"score\": \"0.53279907\",\n \"text\": \"def visit_CodeBlock(self, code_block: CodeBlock):\\n for elm in code_block.code_elements:\\n self.visit(elm.code_elm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a65336f3a2798f23f8dbcea1be29425\",\n \"score\": \"0.53097695\",\n \"text\": \"def _trim_end(self, tokens: List[Token]) -> 'Block':\\n i = last_token = self.end - 1\\n while tokens[i].name in NON_CODING_TOKENS | {'DEDENT', 'NEWLINE'}:\\n # if we find an indented comment inside our block, keep it\\n if (\\n tokens[i].name in {'NL', 'NEWLINE'} and\\n tokens[i + 1].name == UNIMPORTANT_WS and\\n len(tokens[i + 1].src) > self._initial_indent(tokens)\\n ):\\n break\\n # otherwise we've found another line to remove\\n elif tokens[i].name in {'NL', 'NEWLINE'}:\\n last_token = i\\n i -= 1\\n return self._replace(end=last_token + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"491510b41b3d6f54e4ce7550a993753e\",\n \"score\": \"0.5303578\",\n \"text\": \"def decompile_code(code_object):\\n # init vars\\n showasm = False\\n showast = False\\n out = StringIO()\\n scanner = scanner27.Scanner27()\\n scanner.setShowAsm(showasm, out)\\n tokens, customize = scanner.disassemble(code_object)\\n\\n # Build AST from disassembly.\\n walk = walker.Walker(out, scanner, showast=showast)\\n try:\\n ast = walk.build_ast(tokens, customize)\\n except walker.ParserError as ex: # parser failed, dump disassembly\\n print >> out, ex\\n raise\\n del tokens # save memory\\n\\n # convert leading '__doc__ = \\\"...\\\" into doc string\\n assert ast == 'stmts'\\n try:\\n if ast[0][0] == walker.ASSIGN_DOC_STRING(code_object.co_consts[0]):\\n walk.print_docstring('', code_object.co_consts[0])\\n del ast[0]\\n if ast[-1] == walker.RETURN_NONE:\\n ast.pop() # remove last node\\n #todo: if empty, add 'pass'\\n except:\\n pass\\n walk.mod_globs = walker.find_globals(ast, set())\\n walk.gen_source(ast, customize)\\n if walk.ERROR:\\n raise Exception(walk.ERROR)\\n return out.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af65e5ae4610ef3efc26df305cad5976\",\n \"score\": \"0.5300249\",\n \"text\": \"def sanitize_code(code, logging_function=None):\\n # sanitize code\\n tree = ast.parse(code)\\n parsed_code = CodeSanitizer(logging_function).visit(tree)\\n\\n # fix code\\n ast.fix_missing_locations(parsed_code)\\n\\n return parsed_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9aa8566b2e6b251704d5a652fddcca4\",\n \"score\": \"0.5290873\",\n \"text\": \"def codeblock_cmd(self, task, step):\\n\\n name = self.get_command_params(step.command, \\\"codeblock\\\")[0]\\n name = self.replace_variables(name)\\n self.process_codeblock(task, name.upper())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f12b85e9cba7dac1e42ac5c90b4596a\",\n \"score\": \"0.52869046\",\n \"text\": \"def delete_lines(code_edit):\\n textCursor = code_edit.textCursor()\\n\\n start = textCursor.selectionStart()\\n end = textCursor.selectionEnd()\\n textCursor.setPosition(start, QtGui.QTextCursor.MoveAnchor)\\n textCursor.movePosition(QtGui.QTextCursor.StartOfLine)\\n new_start = textCursor.position()\\n\\n textCursor.setPosition(end, QtGui.QTextCursor.MoveAnchor)\\n textCursor.movePosition(QtGui.QTextCursor.EndOfLine)\\n\\n new_end = textCursor.position()\\n\\n textCursor.setPosition(new_start, QtGui.QTextCursor.KeepAnchor)\\n\\n if textCursor.selectedText() == '':\\n textCursor.setPosition(start, QtGui.QTextCursor.MoveAnchor)\\n next_line = new_end+1\\n if 0 < next_line >= code_edit.document().characterCount():\\n next_line = next_line-2\\n if next_line == -1:\\n return\\n textCursor.setPosition(next_line, QtGui.QTextCursor.KeepAnchor)\\n\\n textCursor.insertText('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adcf01f582f839e630e4c10dda4a6b69\",\n \"score\": \"0.52864176\",\n \"text\": \"def clean_python(c):\\n c.run(\\\"find . -name '*.pyc' -exec rm -f {} +\\\")\\n c.run(\\\"find . -name '*.pyo' -exec rm -f {} +\\\")\\n c.run(\\\"find . -name '*~' -exec rm -f {} +\\\")\\n c.run(\\\"find . -name '__pycache__' -exec rm -fr {} +\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe2fe2e899c24b5e2938a5aee4c20e9\",\n \"score\": \"0.526342\",\n \"text\": \"def test_malindented_code_line(self):\\n data1 = [\\\".. #!/usr/bin/env python3\\\\n\\\", # indent == 4 * \\\" \\\"\\n \\\"\\\\n\\\",\\n \\\" print('hello world')\\\"] # indent == 2 * \\\" \\\"\\n data2 = [\\\"..\\\\t#!/usr/bin/env python3\\\\n\\\", # indent == 8 * \\\" \\\"\\n \\\"\\\\n\\\",\\n \\\" print('hello world')\\\"] # indent == 2 * \\\" \\\"\\n for data in (data1, data2):\\n try:\\n blocks = Text2Code(data)()\\n self.fail( \\\"wrong indent did not raise ValueError\\\" )\\n except ValueError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0f15e5e95f7a77e9a2d872c000b90c6\",\n \"score\": \"0.5263146\",\n \"text\": \"def undisplay(self):\\n for line in self.lines: line.undraw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b904065a327e2d32c16108996ab5bd9\",\n \"score\": \"0.5248634\",\n \"text\": \"def pep8mod_remove_ignore(ignore_code):\\r\\n if ignore_code in pep8mod.options.ignore:\\r\\n pep8mod.options.ignore.remove(ignore_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba69c9ddf6e1190953f0174ef77288b\",\n \"score\": \"0.5247638\",\n \"text\": \"def _ignore_somecode(text):\\n text = re.sub('\\\\r', '', text)\\n text = re.sub('\\\\f', '', text)\\n text = re.sub('\\\\0', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1603d24e4446fc97fae193914da21c72\",\n \"score\": \"0.52285105\",\n \"text\": \"def normalize_python(code, remove_docstrings=True):\\n try:\\n tree = ast.parse(code, \\\"<<code>>\\\")\\n except SyntaxError:\\n return None\\n\\n if remove_docstrings:\\n tree = RemoveDocstring().visit(tree)\\n\\n return astunparse.unparse(tree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091e727d47148b34acf9f777c16015e3\",\n \"score\": \"0.52285033\",\n \"text\": \"def special_parse_process_python_code(sourcecode):\\n import ast\\n import astor\\n #sourcecode = 'from __future__ import print_function\\\\n' + sourcecode\\n sourcecode_ = sourcecode.encode('utf8')\\n pt = ast.parse(sourcecode_, 'testfile')\\n\\n generator = astor.codegen.SourceGenerator(' ' * 4)\\n generator.visit(pt)\\n resturctured_source = (''.join(generator.result))\\n print(resturctured_source)\\n\\n visitor = ast.NodeVisitor()\\n visitor.visit(pt)\\n\\n import redbaron\\n # Pares a FULL syntax tree that keeps blockcomments\\n baron = redbaron.RedBaron(sourcecode)\\n #fst = baron.fst()\\n node = (baron.node_list[54]) # NOQA\\n [n.type for n in baron.node_list]\\n\\n #class SpecialVisitor(ast.NodeVisitor):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a83802653d8c0537bd83f86d93428f\",\n \"score\": \"0.5226368\",\n \"text\": \"def wrap_code(self, code):\\n if not self.do_wrap:\\n debug('Not doing auto includes/macros.')\\n return code\\n line_table = set(line.strip() for line in code.splitlines())\\n lines = []\\n for defname in sorted(config['defines']):\\n if self.has_define(defname, lines=line_table):\\n debug(f'Not defining {defname}')\\n continue\\n defline = f'#define {defname} {config[\\\"defines\\\"][defname]}'\\n lines.append(f'#ifndef {defname}')\\n lines.append(f' {defline}')\\n lines.append(f'#endif // {defname}')\\n debug(f'Defined {defname} {config[\\\"defines\\\"][defname]}')\\n\\n includes = config['includes']\\n for includetype in sorted(includes):\\n for includename in includes[includetype]:\\n includedef = f'#include \\\"{includename}\\\"'\\n if includedef in line_table:\\n debug(f'Not including {includetype} {includename}')\\n continue\\n lines.append(includedef)\\n debug(f'Including {includetype} {includename}')\\n\\n for macroname in sorted(config['macros']):\\n deflines = config['macros'][macroname]['define']\\n if isinstance(deflines, str):\\n deflines = [deflines]\\n if self.has_define(macroname, lines=line_table):\\n debug(f'Not including macro: {macroname}')\\n continue\\n lines.append(f'#ifndef {macroname}')\\n lines.extend(self.fix_macro_lines(deflines, indent=4))\\n lines.append(f'#endif // ifdef {macroname}')\\n debug(f'Including macro: {macroname}')\\n\\n main_sigs = [s for s in line_table if self.is_main_sig(s)]\\n main_sig = main_sigs[0] if main_sigs else None\\n if main_sig:\\n debug('No main() needed.')\\n lines.append(code)\\n else:\\n indent = 4 if code.lstrip()[0] == code[0] else 0\\n lines.append(self.wrap_main(code.rstrip(), indent=indent))\\n return '\\\\n'.join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6197007625db8b7f443307cd8a7996a\",\n \"score\": \"0.5223137\",\n \"text\": \"def clear_compiled_python_files():\\n __exec_cmd('find %s/ -name \\\"*.pyc\\\" -exec rm -rf {} \\\\;' % env.role['django_root'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceca0376b28544e527928f5748976e49\",\n \"score\": \"0.5204748\",\n \"text\": \"def uncompyle(version, co, out = None, showasm = 0, showast = 0, deob = 0):\\n assert type(co) == types.CodeType\\n __real_out = out or sys.stdout\\n if co.co_filename:\\n print >> __real_out, '#Embedded file name: %s' % co.co_filename\\n scanner = getscanner(version)\\n scanner.setShowAsm(showasm, out)\\n tokens, customize = scanner.disassemble(co, deob=deob)\\n walker = Walker(out, scanner, showast=showast)\\n try:\\n ast = walker.build_ast(tokens, customize)\\n except ParserError as e:\\n print >> __real_out, e\\n raise\\n\\n del tokens\\n assert ast == 'stmts'\\n try:\\n if ast[0][0] == ASSIGN_DOC_STRING(co.co_consts[0]):\\n walker.print_docstring('', co.co_consts[0])\\n del ast[0]\\n if ast[-1] == RETURN_NONE:\\n ast.pop()\\n except:\\n pass\\n\\n walker.mod_globs = find_globals(ast, set())\\n walker.gen_source(ast, customize)\\n for g in walker.mod_globs:\\n walker.write('global %s ## Warning: Unused global\\\\n' % g)\\n\\n if walker.pending_newlines:\\n print >> __real_out\\n if walker.ERROR:\\n raise walker.ERROR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e5ffdec939a14a21ad0edc45836fea\",\n \"score\": \"0.52047116\",\n \"text\": \"def clean():\\n local('find . -name \\\"*.pyc\\\" -exec rm {} \\\\;')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214b4cd46a9f44791d2f335dd46cf787\",\n \"score\": \"0.51985526\",\n \"text\": \"def remove_outputs(nb):\\n for cell in nb.cells:\\n if cell.cell_type == 'code':\\n cell.outputs = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8359ab440581d7cb07f11cb4507f64f0\",\n \"score\": \"0.51881707\",\n \"text\": \"def example():\\n print \\\"\\\"\\\"\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8359ab440581d7cb07f11cb4507f64f0\",\n \"score\": \"0.51881707\",\n \"text\": \"def example():\\n print \\\"\\\"\\\"\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0215ffa08e46d4fa445f1dfe58d2d847\",\n \"score\": \"0.5186604\",\n \"text\": \"def codegen(self, code: IndentedBuffer):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f524b52b0814a58e5b677c60a98f7d1\",\n \"score\": \"0.51857674\",\n \"text\": \"def handle_cleanup(self):\\n # XXX This is a lot slower than it needs to be, because\\n # we don't have a \\\"cdef int i\\\" in scope here.\\n return je(\\\"\\\"\\\"\\nfor i in range(self._n_{{ myself.name }}):\\n Py_CLEAR(self._{{ myself.name }}[i])\\n\\\"\\\"\\\", myself=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad499a7566306c30c0f5abb44892a2e4\",\n \"score\": \"0.5172536\",\n \"text\": \"def remove_commentlines(self):\\n\\n tmp = self.main.splitlines()\\n tmp = list(itertools.filterfalse(re.compile(r\\\"^\\\\s*%.*$\\\").match, tmp))\\n self.main = \\\"\\\\n\\\".join(tmp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae1ce654eca01afce5fc0bf45e44441\",\n \"score\": \"0.51699215\",\n \"text\": \"def delete_pyc():\\n run(\\\"find . -name '*.pyc' -delete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1f902367121c48b7853d84adb904853\",\n \"score\": \"0.516638\",\n \"text\": \"def remove_inline_comments(_code):\\n lines = _code.split('\\\\n')\\n counter = 0\\n for i in range(len(lines)):\\n inline_comment = parse_line_comment(lines[i])\\n if inline_comment != \\\"\\\": counter += 1\\n lines[i] = lines[i].replace(inline_comment, \\\"\\\")\\n\\n return '\\\\n'.join(lines), counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34bd024009c5015b33337518b20598a5\",\n \"score\": \"0.51623744\",\n \"text\": \"def test_codegen_clean_matcher_classes(self) -> None:\\n new_code = clean_generated_code(\\\"\\\\n\\\".join(matcher_codegen.generated_code))\\n new_file = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"matcher_codegen.deleteme.py\\\"\\n )\\n with open(new_file, \\\"w\\\") as fp:\\n fp.write(new_code)\\n try:\\n format_file(new_file)\\n except Exception:\\n # We failed to format, but this is probably due to invalid code that\\n # black doesn't like. This test will still fail and report to run codegen.\\n pass\\n with open(new_file, \\\"r\\\") as fp:\\n new_code = fp.read()\\n os.remove(new_file)\\n with open(\\n os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"../../matchers/__init__.py\\\"\\n ),\\n \\\"r\\\",\\n ) as fp:\\n old_code = fp.read()\\n\\n # Now that we've done simple codegen, verify that it matches.\\n self.assert_code_matches(old_code, new_code, \\\"libcst.matchers.__init__\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95322519d145e01d5e4a0375dc1d9a8f\",\n \"score\": \"0.5153222\",\n \"text\": \"def UnComment(self):\\n sel = self.GetSelection()\\n start = self.LineFromPosition(sel[0])\\n end = self.LineFromPosition(sel[1])\\n if start>end: #swap around\\n start,end=end,start\\n #start an undo mark\\n self.BeginUndoAction()\\n for ln in range(start, end + 1):\\n linestart = self.PositionFromLine(ln)\\n if chr(self.GetCharAt(linestart)) == '#':\\n #set cursor to the right of the #\\n self.SetCurrentPos(linestart + 1)\\n #delete to the beginning of th line\\n self.DelLineLeft()\\n #finish the undo mark\\n self.EndUndoAction()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9b228c09e7877c5b93c1543596830c\",\n \"score\": \"0.5150296\",\n \"text\": \"def make_code(self):\\n self.node = self.heap.pop()\\n code = \\\"\\\"\\n self._make_codes_recur(self.node, code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861db482a889846cff0016d34d74d7b4\",\n \"score\": \"0.51480126\",\n \"text\": \"def _format_code(text: str) -> str:\\n if text:\\n return re.sub(r'`([^`]+)`', '\\\\code{\\\\g<1>}', text)\\n else:\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860fa5f5f49acda764de4eea09ce6218\",\n \"score\": \"0.5141483\",\n \"text\": \"def cleanup_rst(rst_file):\\n lines_out = []\\n lines = open(rst_file, \\\"r\\\").readlines()\\n for i, line in enumerate(lines[:]):\\n # Change ipython3 code blocks to bash\\n if line == \\\".. code:: ipython3\\\\n\\\":\\n lines_out.append(\\\".. code:: bash\\\\n\\\")\\n # Change image paths to correct directory\\n elif \\\".. image::\\\" in line:\\n new_line = line.replace(\\\".. image:: \\\", \\\".. image:: images/\\\")\\n lines_out.append(new_line)\\n # Get rid of magic command '%'\\n elif line.startswith(\\\" %\\\"):\\n lines_out.append(line.replace(\\\" %\\\", \\\" \\\"))\\n # Get rid of magic command '!'\\n elif line.startswith(\\\" !\\\"):\\n lines_out.append(line.replace(\\\" !\\\", \\\" \\\"))\\n else:\\n lines_out.append(line)\\n\\n return lines_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dad94588b4e863cc3c87f032a97f916\",\n \"score\": \"0.5136101\",\n \"text\": \"def genScriptCode(self, topBlock):\\n\\n codeObj = CodeAndCb() # Holds all the code that is generated.\\n\\n opcode = topBlock.getOpcode()\\n if opcode == 'event_whenflagclicked':\\n self.whenFlagClicked(codeObj, topBlock)\\n elif opcode == 'control_start_as_clone':\\n self.whenSpriteCloned(codeObj, topBlock)\\n elif opcode == 'event_whenthisspriteclicked':\\n self.whenClicked(codeObj, topBlock)\\n elif opcode == 'event_whenkeypressed':\\n self.whenKeyPressed(codeObj, topBlock)\\n elif opcode == 'event_whenbroadcastreceived':\\n self.whenIReceive(codeObj, topBlock)\\n elif opcode == 'procedures_definition':\\n self.genProcDefCode(codeObj, topBlock)\\n elif opcode == 'event_whenbackdropswitchesto':\\n self.whenSwitchToBackdrop(codeObj, topBlock)\\n\\n # If not in one of the above \\\"hat blocks\\\", then it is an\\n # orphaned bit of code that will not be run in either Scratch\\n # or ScratchFoot.\\n\\n return codeObj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07861dfd699f6c86ea973c3173e29c2\",\n \"score\": \"0.5129188\",\n \"text\": \"def cleanup(self):\\r\\n self._remove_all_validated_scripts()\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0056383895567d382553d68e4e85145\",\n \"score\": \"0.51286703\",\n \"text\": \"def ignore_somecode(text):\\n text = re.sub('\\\\r', '', text)\\n text = re.sub('\\\\f', '', text)\\n text = re.sub('\\\\0', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fba790ce85f5d0fe5e073b524ecc7d32\",\n \"score\": \"0.51139766\",\n \"text\": \"def strip_stripped_code(wiki):\\n stripped = wiki.strip_code()\\n cleaned = []\\n kw = ('Category:', 'thumb')\\n for line in stripped.split('\\\\n'):\\n if line.startswith(kw):\\n continue\\n cleaned.append(line)\\n return cleaned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48bc5c1f07d5d1faf39923cba11fd5c\",\n \"score\": \"0.5112562\",\n \"text\": \"def test_codeblocks_with_linenumbers_awesome_theme(app: Sphinx) -> None:\\n app.build()\\n tree = parse_html(Path(app.outdir) / \\\"index.html\\\")\\n code_blocks = tree(\\\"div\\\", class_=\\\"highlight\\\")\\n block = code_blocks[4]\\n code = block.select(\\\"pre > code\\\")\\n assert len(code) == 1\\n assert str(code[0]).replace(\\\"\\\\n\\\", \\\"\\\") == (\\n '<code><span class=\\\"code-line\\\" '\\n 'id=\\\"line-1\\\"><span class=\\\"linenos\\\">1</span>'\\n '<span class=\\\"nb\\\">print</span><span class=\\\"p\\\">'\\n '(</span><span class=\\\"s2\\\">\\\"Hello\\\"</span>'\\n '<span class=\\\"p\\\">)</span></span></code>'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c649466e42550da046b1d5e99797dae8\",\n \"score\": \"0.5111278\",\n \"text\": \"async def clear_code(self, position: int) -> None:\\n await self.send_command(CMD_DELETE_CODE, position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6a7c596f462ce67cb1b9623296b127\",\n \"score\": \"0.50969344\",\n \"text\": \"def test_replace_itself(self):\\n original = textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n name = \\\"whatever\\\"\\n\\n @early()\\n def help():\\n return [[\\\"foo\\\", \\\"bar\\\"], [\\\"Some Existing\\\", \\\"stuff\\\"]]\\n \\\"\\\"\\\"\\n )\\n\\n code_block = textwrap.dedent(\\n \\\"\\\"\\\"\\n @early()\\n def help():\\n return [[\\\"foo\\\", \\\"bar\\\"], [\\\"Some Existing\\\", \\\"stuff\\\"]]\\n \\\"\\\"\\\"\\n )\\n\\n expected = textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n name = \\\"whatever\\\"\\n\\n @early()\\n def help():\\n return [[\\\"foo\\\", \\\"bar\\\"], [\\\"Some Existing\\\", \\\"stuff\\\"]]\\n \\\"\\\"\\\"\\n )\\n\\n self._test(expected, original, parso.parse(code_block))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a0606d81d55818a67e8d657633936c\",\n \"score\": \"0.50889146\",\n \"text\": \"def run(self, text):\\r\\n for i in range(self.markdown.htmlStash.html_counter):\\r\\n html, safe = self.markdown.htmlStash.rawHtmlBlocks[i]\\r\\n if self.markdown.safeMode and not safe:\\r\\n if str(self.markdown.safeMode).lower() == 'escape':\\r\\n html = self.escape(html)\\r\\n elif str(self.markdown.safeMode).lower() == 'remove':\\r\\n html = ''\\r\\n else:\\r\\n html = self.markdown.html_replacement_text\\r\\n if self.isblocklevel(html) and (safe or not self.markdown.safeMode):\\r\\n text = text.replace(\\\"<p>%s</p>\\\" % \\r\\n (self.markdown.htmlStash.get_placeholder(i)),\\r\\n html + \\\"\\\\n\\\")\\r\\n text = text.replace(self.markdown.htmlStash.get_placeholder(i), \\r\\n html)\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"378b971d2080986eee4caff4c7aa46f0\",\n \"score\": \"0.5088259\",\n \"text\": \"def remove(language):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4351cc1970b4c807278bb5c85675435b\",\n \"score\": \"0.5085091\",\n \"text\": \"def remove_not_executed_definitions(self, filename, cov_result):\\n # Methods that weren't executed aren't relevant, so we remove them here.\\n for line in self.method_definition_lines:\\n if not self.was_executed(line, filename, cov_result):\\n self.method_definition_lines.remove(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e68bc66db7fadab9fc56c1b52bda5e4\",\n \"score\": \"0.5085032\",\n \"text\": \"def run(self, lines):\\r\\n\\r\\n # Check for code hilite extension\\r\\n if not self.checked_for_codehilite:\\r\\n for ext in self.markdown.registeredExtensions:\\r\\n if isinstance(ext, CodeHiliteExtension):\\r\\n self.codehilite_conf = ext.config\\r\\n break\\r\\n\\r\\n self.checked_for_codehilite = True\\r\\n\\r\\n text = \\\"\\\\n\\\".join(lines)\\r\\n while 1:\\r\\n m = self.FENCED_BLOCK_RE.search(text)\\r\\n if m:\\r\\n lang = ''\\r\\n if m.group('lang'):\\r\\n lang = self.LANG_TAG % m.group('lang')\\r\\n\\r\\n # If config is not empty, then the codehighlite extension\\r\\n # is enabled, so we call it to highlite the code\\r\\n if self.codehilite_conf:\\r\\n highliter = CodeHilite(m.group('code'),\\r\\n linenums=self.codehilite_conf['linenums'][0],\\r\\n guess_lang=self.codehilite_conf['guess_lang'][0],\\r\\n css_class=self.codehilite_conf['css_class'][0],\\r\\n style=self.codehilite_conf['pygments_style'][0],\\r\\n lang=(m.group('lang') or None),\\r\\n noclasses=self.codehilite_conf['noclasses'][0])\\r\\n\\r\\n code = highliter.hilite()\\r\\n else:\\r\\n code = self.CODE_WRAP % (lang, self._escape(m.group('code')))\\r\\n\\r\\n placeholder = self.markdown.htmlStash.store(code, safe=True)\\r\\n text = '%s\\\\n%s\\\\n%s'% (text[:m.start()], placeholder, text[m.end():])\\r\\n else:\\r\\n break\\r\\n return text.split(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f02d3bf43fa689e1ce2d07b5fb75f703\",\n \"score\": \"0.5084823\",\n \"text\": \"def clear(source=None):\\n if source == None:\\n globals = sys._getframe(1).f_globals\\n In = globals[\\\"In\\\"]\\n source = In[len(In)-2]\\n print_func = lambda line:sys.stdout.write(line+\\\"\\\\n\\\")\\n clear_comment(source, print_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c2f52afd15a9a01de5fb8b6655fe23\",\n \"score\": \"0.50844\",\n \"text\": \"def _executeCleanBlocks(self, destination):\\n # Set difference: clean = existing - dirty\\n clean_block_starts = set( self._cacheFiles.keys() ) - self._dirtyBlocks\\n \\n inputShape = self.Input.meta.shape\\n clean_block_rois = map( partial( getBlockBounds, inputShape, self._blockshape ),\\n clean_block_starts )\\n destination[0] = map( partial(map, TinyVector), clean_block_rois )\\n return destination\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f192b2eab50e95e843c83269a27fefb4\",\n \"score\": \"0.5082369\",\n \"text\": \"def run(self, lines):\\n text = \\\"\\\\n\\\".join(lines)\\n while 1:\\n m = FENCED_BLOCK_RE.search(text)\\n if not m:\\n break\\n lang = m.group('lang')\\n linenums = bool(m.group('linenums'))\\n html = highlight_syntax(m.group('code'), lang, linenums=linenums)\\n placeholder = self.markdown.htmlStash.store(html, safe=True)\\n text = '{}\\\\n{}\\\\n{}'.format(\\n text[:m.start()],\\n placeholder,\\n text[m.end():]\\n )\\n\\n return text.split(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"733a496a60d21c486d09a140e2342172\",\n \"score\": \"0.50760186\",\n \"text\": \"def find_included_code_with_mistune(features, text):\\n if \\\"code\\\" not in features: features[\\\"code\\\"] = []\\n if (\\\"```\\\" in text):\\n md = markdowner(text)\\n parsed = BeautifulSoup(md, \\\"html.parser\\\")\\n for codeblock in parsed.findAll(\\\"code\\\"):\\n if codeblock.has_attr(\\\"class\\\"):\\n features[\\\"code\\\"].append(codeblock[\\\"class\\\"][0])\\n else:\\n features[\\\"code\\\"].append(\\\"unspecified\\\")\\n codeblock.string = \\\"\\\"\\n features[\\\"code\\\"] = sorted(list(set(features[\\\"code\\\"])))\\n return parsed.text\\n else:\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf296860d002da5211f8b39634637c36\",\n \"score\": \"0.50738317\",\n \"text\": \"def clean():\\n for i in range(6):\\n # move cursor one line up, then delete line\\n sys.stdout.write('\\\\033[1A\\\\033[K')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abcf049cd41d3b05966c0863f7e1e98\",\n \"score\": \"0.50691843\",\n \"text\": \"def clean_source(self):\\n src_len = len(self.source)\\n source_clean = []\\n for i in range(src_len):\\n line = self.source[i]\\n if not (line.startswith(\\\"#requires\\\") or line.startswith(\\\"#provides\\\")\\n or line.startswith(\\\"#return\\\") or line.startswith(\\\"#param\\\")):\\n source_clean.append(line)\\n return source_clean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f606dcf2b7a5854fb24c147a72382c7\",\n \"score\": \"0.5062167\",\n \"text\": \"def dis_code(code):\\n for const in code.co_consts:\\n if isinstance(const, types.CodeType):\\n dis_code(const)\\n\\n print(\\\"\\\")\\n print(code)\\n dis.dis(code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b161752b6ba3e04dcfcbc5872c6a44\",\n \"score\": \"0.50545734\",\n \"text\": \"def remove_all_ip_blocks(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1599,"cells":{"query_id":{"kind":"string","value":"16a46ab61728cbb4b559837c838ffb03"},"query":{"kind":"string","value":"Check if all direct descendents are terminal."},"positive_passages":{"kind":"list like","value":[{"docid":"46ef1e20bf80b2309cf7fc7348befd97","score":"0.6559002","text":"def is_preterminal(self):\n if self.root.is_terminal():\n return False\n for clade in self.root.clades:\n if not clade.is_terminal():\n return False\n return True","title":""}],"string":"[\n {\n \"docid\": \"46ef1e20bf80b2309cf7fc7348befd97\",\n \"score\": \"0.6559002\",\n \"text\": \"def is_preterminal(self):\\n if self.root.is_terminal():\\n return False\\n for clade in self.root.clades:\\n if not clade.is_terminal():\\n return False\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b4fc0b37e187cf20daa0bbb2097879ed","score":"0.71356946","text":"def is_terminal(self):\n return len(self.children) == 0","title":""},{"docid":"063bf8fb9e98b51b606c0a21d8fb4539","score":"0.69620866","text":"def is_terminal(self):\n return not bool(self.left_child and self.right_child)","title":""},{"docid":"435fd709a6b9903d5dd1d53272e50629","score":"0.69018537","text":"def is_terminal(self):\n return not self.root.clades","title":""},{"docid":"e09786243d43e7a5abc104383e238ac2","score":"0.65404665","text":"def is_terminal(self):\n return not self.clades","title":""},{"docid":"4518dda0c4ad2e81f03ebf95eb14fed4","score":"0.64568436","text":"def is_terminal(self) -> bool:\n pass","title":""},{"docid":"a6befb086d939699500ef4f978cf6863","score":"0.63691103","text":"def is_terminal(self):\n\n\t\tfor colour in self.COLOURS:\n\t\t\tif self.exit_counts[colour] == self.WIN_EXIT_COUNT:\n\t\t\t\treturn True\n\t\t\n\t\treturn False","title":""},{"docid":"6dc95680d7f5c87b6957b6a9e062e770","score":"0.6273354","text":"def is_terminal(self) -> bool:\n return self._is_terminal","title":""},{"docid":"9aaae7b47651b3ab3a7b5126f0e8f2f5","score":"0.6226771","text":"def terminal(node):\n return node[L] == ''","title":""},{"docid":"f8ad3bac42aab40b2d3db7c62f3efdd8","score":"0.61867374","text":"def terminals(self):\n terminals = []\n # The two following lines are not the same as\n # descendants = self.children\n # which would extend also to self.children!\n descendants = []\n descendants.extend(self.children)\n for child in descendants:\n if child.terminal:\n terminals.append(child)\n else:\n descendants.extend(child.children)\n return terminals","title":""},{"docid":"a3e2a49aec5da19feb64d70dc01337ad","score":"0.6147368","text":"def is_terminal(self):\n return self._type == SyntacticNode.TERMINAL","title":""},{"docid":"b01b9da0db8b6f61d21741fd61fed5c9","score":"0.60720414","text":"def is_terminal(self):\n state = self.get_current_state()\n actions = self.get_possible_actions(state)\n return len(actions) == 0","title":""},{"docid":"4bdab7cd2c69bedbf60ec4ae2d9c7a7b","score":"0.5987419","text":"def is_terminal(self):\n if len(self.traj) <= 3:\n # This means that no more than 1 player has played, game will continue\n return False\n else:\n play_seqs = self.get_play_sequences()\n return play_seqs in self.terminal_nodes","title":""},{"docid":"e9e6f1081dd8524045a41cf29f7e0fac","score":"0.59822595","text":"def isTerminal(self, symbol):\n return symbol in self.terminalKeys()","title":""},{"docid":"284edc3d8963477335ad1f7e72725400","score":"0.59796685","text":"def is_terminal(self, parent_node_number):\n y = self.successors(parent_node_number)\n try:\n next(y)\n except:\n return True","title":""},{"docid":"ea94c8b101308837181270d281c5dbdf","score":"0.59137374","text":"def is_terminal(self):\n if self.is_victory():\n return True\n elif not self.is_victory() and self.count_of_game_tokens_in_game() == self.get_maximal_amount_of_game_tokens():\n return True\n else:\n return False","title":""},{"docid":"62089f81bfe82a1f86507a3f441090d2","score":"0.5846326","text":"def is_terminal(self, state):\n return len(self.get_possible_actions(state)) == 0","title":""},{"docid":"926e2916a3c1d965b03878b042d2ba42","score":"0.58219135","text":"def is_terminal(self, state):\n return False","title":""},{"docid":"394f6ff14f4a6463cbf47afac13b8d82","score":"0.5809742","text":"def matches(self, terminal):\n return self.kind == terminal.name","title":""},{"docid":"760090eb1163ae62f9bacb554573d7cb","score":"0.5749353","text":"def is_terminal(self, state):\n return state.cachedWin or len(state.blanks) == 0","title":""},{"docid":"69312dba31242146f33b0bb755ca198a","score":"0.57290226","text":"def is_terminal(self):\n return self.__terminal_hand is not None","title":""},{"docid":"609d3bc819d400682c0d70fb0b852d1d","score":"0.5712845","text":"def is_monophyletic(self, terminals, *more_terminals):\n target_set = set(_combine_args(terminals, *more_terminals))\n current = self.root\n while True:\n if set(current.get_terminals()) == target_set:\n return current\n # Try a narrower subclade\n for subclade in current.clades:\n if set(subclade.get_terminals()).issuperset(target_set):\n current = subclade\n break\n else:\n return False","title":""},{"docid":"da4662b27ec5b67ed4ec9298b60cc993","score":"0.56929684","text":"def is_terminal(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"is_terminal\")","title":""},{"docid":"e844fbbcef279da34eb9f4166d8202aa","score":"0.56607735","text":"def is_terminal(self, state: D.T_state) -> D.T_agent[D.T_predicate]:\n return self._is_terminal(state)","title":""},{"docid":"c936ebcda86aa4b7133ba981ba6d7c67","score":"0.56542295","text":"def isTerminal(self, state):\n return True if state is 100 or state is 0 else False","title":""},{"docid":"e3e647ce7a8f337c2bb762135d55bab4","score":"0.5625877","text":"def check_terminal(self):\n if self.has_crashed():\n return True\n elif self.current_episode_length> self.max_episode_length:\n return True\n else:\n return False","title":""},{"docid":"1c9d4e04b433439b763d19381bb38593","score":"0.5604717","text":"def is_terminal(state):\n return state not in STATES_TRANSITIONS","title":""},{"docid":"760339ad5aa8dc900a24587b26eb8099","score":"0.5590825","text":"def is_terminal(self):\n return self.task.is_terminal(self.state)","title":""},{"docid":"1a85845d78a3f39cfc7c77a5ee5d1fdd","score":"0.55575275","text":"def is_terminal(depth, board):\n return depth <= 0 or board.is_game_over()","title":""},{"docid":"b93e8407c7b75a19b1edf32d46dff65a","score":"0.55431694","text":"def is_terminal(state, maxdepth = None):\n board = state[0]\n turn = state[1]\n depth = state[2]\n (moves, captures) = crank.get_hints(board, turn)\n if maxdepth is not None:\n return ((not moves) and (not captures)) or depth >= maxdepth\n else:\n return ((not moves) and (not captures))","title":""},{"docid":"9218ef43b8588ae782393d4ce92d1c72","score":"0.5534429","text":"def is_terminal_state(self, state):\n pass","title":""},{"docid":"b1adb0478375f1864ccb139d71600133","score":"0.55309165","text":"def isTerminal(cls, state: TicTacToeState) -> bool:\n is_terminal = TicTacToeState.isGoal(state)\n\n if is_terminal != None:\n if is_terminal < 4 and is_terminal > -1:\n return True\n\n # end loop\n return False","title":""},{"docid":"00670f4e207665c353afe83bb79e5963","score":"0.55282474","text":"def _is_terminal(self, sim: Simulation, state: NamedTuple) -> bool:\n ...","title":""},{"docid":"7fd1a017ac2fe8a27f5340147379ed60","score":"0.55239075","text":"def ShouldWalk(self):\n return self._subentries is None","title":""},{"docid":"87bf9d84a95ae7ecf6103579c8c7ab6d","score":"0.5505575","text":"def _is_terminal(self, state: D.T_state) -> D.T_agent[D.T_predicate]:\n raise NotImplementedError","title":""},{"docid":"29a751ad4c175737a94e2fa54569b73d","score":"0.5442403","text":"def is_terminal(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"is_terminal\")","title":""},{"docid":"a62cc6ac5118caba1509fcfe9e7ff828","score":"0.54037803","text":"def is_terminal(self):\n return self._user_state.time_budget <= 0","title":""},{"docid":"1d8b729523345adec6d50ac717396744","score":"0.5383512","text":"def _is_terminal(self):\n return self.vehicle.crashed or self.steps/self.POLICY_FREQUENCY >= self.config[\"duration\"]","title":""},{"docid":"29b81bb99abeb5b5ca561b18108c4649","score":"0.5372268","text":"def _is_terminal(self):\n return self.vehicle.crashed or self.steps > self.config[\"duration\"]\n\n # return self.vehicle.crashed or self.vehicle.out or self.steps > self.config[\"duration\"]","title":""},{"docid":"b8ac27598ba8bb59b28a45d75e7b13fd","score":"0.53466034","text":"def is_terminal_state(state):\n return not Heuristic.winned(state) == \"\"","title":""},{"docid":"dd1761e84d138c4f2d86380b67fb1223","score":"0.53423077","text":"def terminal_test(state, player):\n dirs = othello_constants.DIRECTIONS\n\n if state.count(0) >= state.count(player):\n if not Othello.has_forward_actions(state.board, player, dirs):\n if not Othello.has_forward_actions(state.board, -player, dirs):\n return True\n else:\n if not Othello.has_reverse_actions(state.board, player, dirs):\n if not Othello.has_reverse_actions(state.board, -player, dirs):\n return True\n\n return False","title":""},{"docid":"ad7c166ff1aca8670543280df09298a5","score":"0.5335042","text":"def is_terminal(self):\n if self._is_terminal is None:\n self._is_terminal = self.is_full or self.winner\n return self._is_terminal","title":""},{"docid":"0344aba8af38dd6683c1a16b0a76d62c","score":"0.52913547","text":"def terminal(self):\n\t\tside_term = self.ego_state[1] < self.lane_loc[0] or self.ego_state[1] > self.lane_loc[-1]\n\t\tend_term = self.ego_state[0] > self.max_distance\n\t\ttime_term = self.curr_step >= self.max_steps\n\t\treturn side_term or end_term or time_term","title":""},{"docid":"c1b1e93ebb1179f139b1c46772a636fa","score":"0.52865857","text":"def terminal(board):\n boardelements = []\n for row in board:\n for element in row:\n boardelements.append(element)\n\n myset = set(boardelements)\n\n if len(myset) > 1:\n\n # for checking rows\n for (i, row) in enumerate(board):\n rowset = set(row)\n if None not in rowset and len(rowset) == 1:\n return True\n\n # for checking columns\n transpose_of_board = [[board[j][i] for j in range(len(board))] for i in range(len(board[0]))]\n for (i, col) in enumerate(transpose_of_board):\n colset = set(col)\n if None not in colset and len(colset) == 1:\n return True\n\n # for checking diagonals\n # Main Diagonal\n maindiagonal = []\n for (i, row) in enumerate(board):\n for (j, element) in enumerate(row):\n if i == j:\n maindiagonal.append(element)\n\n maindiagonalset = set(maindiagonal)\n\n if (None not in maindiagonalset) and (len(maindiagonalset) == 1):\n return True\n\n # Second Diagonal\n seconddiagonal = []\n for (i, row) in enumerate(board):\n for (j, element) in enumerate(row):\n if i + j == 2:\n seconddiagonal.append(element)\n\n seconddiagonalset = set(seconddiagonal)\n\n if (None not in seconddiagonalset) and (len(seconddiagonalset) == 1):\n return True\n\n # if draw\n s = set()\n for row in board:\n for element in row:\n s.add(element)\n\n if None not in s:\n return True\n\n return False","title":""},{"docid":"dcd0ca75ca278f1d7e39e1892973fa0d","score":"0.5270652","text":"def terminal_test(self, state):\n return state.utility != 0 or len(state.moves) == 0","title":""},{"docid":"0717955694e61c467047470bc6798f33","score":"0.52613366","text":"def is_terminal(self):\n return self.has_max_won in [0, 1, -1]","title":""},{"docid":"d1e8060316953ba1f2a84b164c2770be","score":"0.52457374","text":"def is_terminal(self, section):\n return section.value() in self.CHAR_DELIM","title":""},{"docid":"328c5bfe9cb3914d242f757cf41c7bc9","score":"0.5224005","text":"def dashed_children_exist(self):\n for child in self.children.values():\n if child.state == State.DASHED_CIRCLE or child.state == State.DASHED_BOX:\n return True\n if len(child.children) == 0 or not child.dashed_children_exist():\n continue\n return True\n return False","title":""},{"docid":"013ca4cc168d161f28d441f839e2b14c","score":"0.5202275","text":"def __AcceptTerminal(self):\n if self.__next_symbol < len(self.__symbols):\n self.__Error('Expected no additional symbols')\n return True","title":""},{"docid":"c2be06023954ced74dec573fc384453e","score":"0.5194339","text":"def is_terminal(state: list):\n\n win_states = [\n (state[0], state[1], state[2]),\n (state[3], state[4], state[5]),\n (state[6], state[7], state[8]),\n (state[0], state[3], state[6]),\n (state[1], state[4], state[7]),\n (state[2], state[5], state[8]),\n (state[0], state[4], state[8]),\n (state[2], state[4], state[6])\n ]\n for line in win_states:\n if line[0] == line[1] and line[2] == line[1]:\n return True\n for field in state:\n if field in numbers:\n return False\n return True","title":""},{"docid":"3bfbeb15a591612c62761a436b5a6875","score":"0.5181404","text":"def is_terminal(state, player=True):\n if utility_of(state, player) != 0:\n return True\n else:\n return False","title":""},{"docid":"00c7acd158b6d7a7e0a1deead49129f5","score":"0.5166754","text":"def is_single_child(context: TreeContext) -> bool:\n return len(context[-2]._children) == 1","title":""},{"docid":"73eaf88dadebd4e4a31e1fe9165c5a71","score":"0.51603746","text":"def terminals(self):\n\n return self._terminals.getSlice(0)","title":""},{"docid":"22e4292b9c22d411020347781e316d2c","score":"0.5150798","text":"def is_child_node(self):\n return not self.is_root_node()","title":""},{"docid":"3cc7c490be10f4165b7dc6304fa48c02","score":"0.5143967","text":"def terminal_test(self, state):\n return not self.actions(state)","title":""},{"docid":"3cc7c490be10f4165b7dc6304fa48c02","score":"0.5143967","text":"def terminal_test(self, state):\n return not self.actions(state)","title":""},{"docid":"e6ed2f9f7208bd2b0c6e6f04319fad49","score":"0.51373184","text":"def isatty(self):\n try:\n return self.stdout.isatty()\n except AttributeError:\n return False","title":""},{"docid":"5003a9047af2f294616cd21efbcb6ffe","score":"0.51274043","text":"def isatty (self): # File-like object.\n return os.isatty(self.child_fd)","title":""},{"docid":"bd7cea35a6623d5062d8be77e9a00b53","score":"0.51235384","text":"def is_terminal(state):\n\n is_done = True\n\n for i in range(len(state)):\n if i in state:\n is_done = False\n\n for i in [0, 3, 6]:\n if state[i] == state[i+1] == state[i+2]:\n return True\n\n for i in [0, 1, 2]:\n if state[i] == state[i+3] == state[i+6]:\n return True\n\n if state[0] == state[4] == state[8]:\n return True\n\n if state[2] == state[4] == state[6]:\n return True\n\n return is_done","title":""},{"docid":"c7653a8b64743274a6304e9aaa72685f","score":"0.5109323","text":"def isDescendantOf(self, node):\n return (self in node.pathToRoot())","title":""},{"docid":"8accd629fb977e9292e4877050dd91e1","score":"0.50864804","text":"def terminal(board):\n if winner(board) != None:\n return True\n\n # Draw\n for line in board:\n for element in line:\n if element == EMPTY:\n return False\n\n return True","title":""},{"docid":"c143d59aa90acabde47db18c335add44","score":"0.50778186","text":"def IsTerminal(self):\n if not self._ready_condition:\n raise NotImplementedError()\n if not self._fresh:\n return False\n if self._ready_condition not in self._conditions:\n return False\n return self._conditions[self._ready_condition]['status'] is not None","title":""},{"docid":"5e96959df1c77cae69dc0bc69f375165","score":"0.5065302","text":"def terminal_performatives(self) -> FrozenSet[Message.Performative]:\n return self._terminal_performatives","title":""},{"docid":"fbad2e84ac82729f3bbbe018aea24847","score":"0.5050743","text":"def count_terminals(self):\n count = 0\n for node in self:\n if node.is_terminal():\n count += 1\n return count","title":""},{"docid":"c6fecf3e6b61981e1b59226b7acd049a","score":"0.50498414","text":"def count_terminals(self):\n return _utils.iterlen(self.find_clades(terminal=True))","title":""},{"docid":"091c0350dc6b841408afa5363d1871f1","score":"0.504946","text":"def hasTerminalStops(self, gc=None):\n stops = []\n aligned = isinstance(self, Alignment)\n for seq_name in self.Names:\n if aligned:\n seq = self.NamedSeqs[seq_name].data\n else:\n seq = self.NamedSeqs[seq_name]\n stops.append(seq.hasTerminalStop(gc=gc))\n return max(stops)","title":""},{"docid":"0a5152cace8fbccc92cc998ce34f15b0","score":"0.50401443","text":"def terminal(board):\n \n if winner(board) is not None:\n return True\n \n all_moves = [cell for row in board for cell in row]\n if not any(move == EMPTY for move in all_moves):\n return True\n\n return False","title":""},{"docid":"3d3b357b8481c7828b3964a2a993f8cb","score":"0.5021967","text":"def is_tty(self):\n return stream_is_tty(self.stream)","title":""},{"docid":"f2c55a641410a095d7bb3aaf10f5d3db","score":"0.50217664","text":"def terminal_test(gameState):\n return not bool(gameState.get_legal_moves()) # No more moves","title":""},{"docid":"6c11c72edf7e125cdd42e21203af1868","score":"0.5021019","text":"def is_non_terminal(self):\n return not self.state.game_over()","title":""},{"docid":"a94cabe437e0d6bd86b564de00d8ea87","score":"0.501648","text":"def get_all_terminal_nodes(root):\n\n node_checker(root)\n terminal_nodes = []\n\n def add_children_to_nodes(current_node):\n children = current_node.children\n nonlocal terminal_nodes\n if current_node.is_terminal:\n terminal_nodes.append(current_node)\n return\n for c in children:\n add_children_to_nodes(c)\n\n add_children_to_nodes(root)\n\n return terminal_nodes","title":""},{"docid":"39bfcd6d19b2df1e911c8162564de8fb","score":"0.49777797","text":"def all_of_validator(self, verbose=1):\n assert self.children\n result = True\n for child in self.children:\n if not child.validate(verbose):\n result = False\n return result","title":""},{"docid":"bdc4655bf85fb2bff89aee5e9d1300b1","score":"0.49763197","text":"def stream_is_tty(stream):\n isatty = getattr(stream, 'isatty', None)\n return isatty and isatty()","title":""},{"docid":"8ace2801861e5082b9470a613820164c","score":"0.49727628","text":"def tree_is_separable(tree):\n return separable.is_separable(tree.evaluate(OPERATORS))","title":""},{"docid":"29540f92b2cda1ac9c1a958b5dc2e5ea","score":"0.493346","text":"def is_terminal(self, limit):\n if limit == 0:\n if not self.start:\n return True\n elif (self.score >= limit) or (not self.start):\n return True\n return False","title":""},{"docid":"d1405bf99fba3df334eb68859fdeb8dd","score":"0.4923727","text":"def terminal(board):\n if winner(board) or not any(None in sublist for sublist in board):\n return True\n\n return False","title":""},{"docid":"1f2eb481226a51ba4dcc661b3cde2a13","score":"0.491552","text":"def terminal(board, position):\n return not _valid_position(position) or not _no_crash(board, position)","title":""},{"docid":"69053f3e65565726c3fa1e60f55f3cc1","score":"0.4911571","text":"def headNonTerminal(self):\n if self.dot==len(self.rhs):\n return False\n else:\n return self.rhs[self.dot][0].isupper()","title":""},{"docid":"e11c2eca95c35e4688b44d5245e0f703","score":"0.48989522","text":"def terminated(self):\n # if any of the leaves are not terminated, the tree is not terminated\n for leaf in self.leaves:\n if not leaf.terminated:\n return False\n return True","title":""},{"docid":"76a16f9e8e6649dc17e344098d3a4bb7","score":"0.48979142","text":"def terminal(board): \n if winner(board) is not None:\n return True\n \n cellsArr = [cells for row in board for cells in row]\n if not any(cell == EMPTY for cell in cellsArr):\n return True\n \n return False","title":""},{"docid":"2e53fe9894d86810087757c2ed12e203","score":"0.48958004","text":"def is_terminal_state(self, board):\n return len(available_cols_to_move(board)) == 0","title":""},{"docid":"028e9ee9f5608c3f683c729df24b1204","score":"0.489348","text":"def has_children(context: TreeContext) -> bool:\n return bool(context[-1]._children)","title":""},{"docid":"fb0f274b7c6680f93a2fcda1cbe12ec2","score":"0.48883444","text":"def has_children(self):\n\n return bool(self.children)","title":""},{"docid":"4583b915ae996de45ee39b83a3ff3b8d","score":"0.48826888","text":"def is_leaf_node(self):\n return not self.get_descendant_count()","title":""},{"docid":"823c11ca427c49b7555dc8b4db67650a","score":"0.48740843","text":"def terminal_test(gameState):\n if gameState.get_legal_moves(): # If there are legal moves left then return False\n return False\n\n return True","title":""},{"docid":"c0de026886c232cf4e3c3bb2fbd60aeb","score":"0.4872136","text":"def is_root(self) -> bool:\n return len(self.escaped) == 0","title":""},{"docid":"1f9e644f69d217b9c0d99752947de5f9","score":"0.48679084","text":"def terminal(board):\n\n # See if the board has a winner\n if winner(board) is not None:\n return True\n\n # Check if the board is filled\n if not any(EMPTY in row for row in board):\n return True\n\n return False","title":""},{"docid":"06eba9cf6ab7f701f6ba5fd43f99cc06","score":"0.48576152","text":"def aterm_is(self, v):\n\n raise Exception(\"Is this used? SHould probably be deleted\")\n\n if isinstance(v, six.string_types):\n\n if '.' not in v:\n v = 'root.' + v\n\n v_p, v_r = self.split_term_lower(v)\n\n if self.join_lc == v.lower():\n\n return True\n elif v_r == '*' and v_p == self.parent_term_lc:\n return True\n elif v_p == '*' and v_r == self.record_term_lc:\n return True\n elif v_p == '*' and v_r == '*':\n return True\n else:\n return False\n\n else:\n\n return any(self.term_is(e) for e in v)","title":""},{"docid":"5975072ba113729bc859b503efb1f12d","score":"0.48500377","text":"def is_nested(self):\n return bool([1 for path, alias in self._dotted_as_names()\n if alias is None and len(path) > 1])","title":""},{"docid":"0ec1f04c5e557618aa6f2077ba2eee45","score":"0.4846948","text":"def terminal_test(gameState):\n return not gameState.get_legal_moves()","title":""},{"docid":"2c22d8e48ef256667663c9722b788cd4","score":"0.48352483","text":"def is_attendants(user):\r\n\r\n if Attendants.objects.filter(user=user):\r\n return True\r\n return is_administrator(user)","title":""},{"docid":"d9a3018791b6d9b286581639b9b1cc81","score":"0.48334625","text":"def isCompleteTree(self, root):\n _, ans, _ = self.dfs(root, 0)\n return ans","title":""},{"docid":"d03735dbb55e861cd174b513a9ded01a","score":"0.4821981","text":"def has_child(context: TreeContext, tag_name_set: AbstractSet[str]) -> bool:\n return has_descendant(context, tag_name_set, 1)","title":""},{"docid":"70cda77bb875d44f4366ad10e13b059b","score":"0.48204812","text":"def terminal(board):\n if winner(board) != None:\n return True\n if any(None in row for row in board):\n return False\n else:\n return True","title":""},{"docid":"f9275a97a68c3d1f837d288b2f911444","score":"0.4811534","text":"def _isAncestor(self, ancestorPath, child):\n \n while child is not None:\n if child.path != ancestorPath:\n child = child.parent\n else:\n return True\n\n return False","title":""},{"docid":"b7d2ecc2f1c27428f6c1a34de3deeb68","score":"0.48085862","text":"def is_single_path(self) -> bool:\n if not self.children:\n return_value = True\n elif len(self.children) > 1:\n return_value = False\n else:\n child = self.children[0] # guaranteed to have exactly 1 by if and elif clauses above\n return_value = child.is_single_path()\n\n return return_value","title":""},{"docid":"40c8d226cb9906b03abad337e3c2273c","score":"0.48084387","text":"def isSinglePathTree(self):\n for item in self.header_table:\n if self.header_table[item].node_link is not None:\n return False\n return True","title":""},{"docid":"23b2bd29c430db82580ff6166742c8cd","score":"0.48081723","text":"def is_root(self):\n return len(self.parents) == 0","title":""},{"docid":"ffc413aff7378625d977c6f25d5298b1","score":"0.4792206","text":"def has_visible_parents(self, node):\n parent = node\n if parent is None:\n return True \n else:\n style = self.get_style(parent)\n if style is not None and style.replace(\" \",\"\").find(\"display:none\") >= 0:\n return False\n return self.has_visible_parents(parent.getparent())","title":""},{"docid":"88c7676286446723a88ae6e157661beb","score":"0.47855246","text":"def isNonTerminal(self, symbol):\n return symbol in self.rule_keys","title":""},{"docid":"7e79c179dc7ebb9352b2a3edd747e091","score":"0.47793627","text":"def has_root():\n return False","title":""}],"string":"[\n {\n \"docid\": \"b4fc0b37e187cf20daa0bbb2097879ed\",\n \"score\": \"0.71356946\",\n \"text\": \"def is_terminal(self):\\n return len(self.children) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063bf8fb9e98b51b606c0a21d8fb4539\",\n \"score\": \"0.69620866\",\n \"text\": \"def is_terminal(self):\\n return not bool(self.left_child and self.right_child)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"435fd709a6b9903d5dd1d53272e50629\",\n \"score\": \"0.69018537\",\n \"text\": \"def is_terminal(self):\\n return not self.root.clades\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e09786243d43e7a5abc104383e238ac2\",\n \"score\": \"0.65404665\",\n \"text\": \"def is_terminal(self):\\n return not self.clades\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4518dda0c4ad2e81f03ebf95eb14fed4\",\n \"score\": \"0.64568436\",\n \"text\": \"def is_terminal(self) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6befb086d939699500ef4f978cf6863\",\n \"score\": \"0.63691103\",\n \"text\": \"def is_terminal(self):\\n\\n\\t\\tfor colour in self.COLOURS:\\n\\t\\t\\tif self.exit_counts[colour] == self.WIN_EXIT_COUNT:\\n\\t\\t\\t\\treturn True\\n\\t\\t\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc95680d7f5c87b6957b6a9e062e770\",\n \"score\": \"0.6273354\",\n \"text\": \"def is_terminal(self) -> bool:\\n return self._is_terminal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aaae7b47651b3ab3a7b5126f0e8f2f5\",\n \"score\": \"0.6226771\",\n \"text\": \"def terminal(node):\\n return node[L] == ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8ad3bac42aab40b2d3db7c62f3efdd8\",\n \"score\": \"0.61867374\",\n \"text\": \"def terminals(self):\\n terminals = []\\n # The two following lines are not the same as\\n # descendants = self.children\\n # which would extend also to self.children!\\n descendants = []\\n descendants.extend(self.children)\\n for child in descendants:\\n if child.terminal:\\n terminals.append(child)\\n else:\\n descendants.extend(child.children)\\n return terminals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e2a49aec5da19feb64d70dc01337ad\",\n \"score\": \"0.6147368\",\n \"text\": \"def is_terminal(self):\\n return self._type == SyntacticNode.TERMINAL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b01b9da0db8b6f61d21741fd61fed5c9\",\n \"score\": \"0.60720414\",\n \"text\": \"def is_terminal(self):\\n state = self.get_current_state()\\n actions = self.get_possible_actions(state)\\n return len(actions) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bdab7cd2c69bedbf60ec4ae2d9c7a7b\",\n \"score\": \"0.5987419\",\n \"text\": \"def is_terminal(self):\\n if len(self.traj) <= 3:\\n # This means that no more than 1 player has played, game will continue\\n return False\\n else:\\n play_seqs = self.get_play_sequences()\\n return play_seqs in self.terminal_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e6f1081dd8524045a41cf29f7e0fac\",\n \"score\": \"0.59822595\",\n \"text\": \"def isTerminal(self, symbol):\\n return symbol in self.terminalKeys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284edc3d8963477335ad1f7e72725400\",\n \"score\": \"0.59796685\",\n \"text\": \"def is_terminal(self, parent_node_number):\\n y = self.successors(parent_node_number)\\n try:\\n next(y)\\n except:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea94c8b101308837181270d281c5dbdf\",\n \"score\": \"0.59137374\",\n \"text\": \"def is_terminal(self):\\n if self.is_victory():\\n return True\\n elif not self.is_victory() and self.count_of_game_tokens_in_game() == self.get_maximal_amount_of_game_tokens():\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62089f81bfe82a1f86507a3f441090d2\",\n \"score\": \"0.5846326\",\n \"text\": \"def is_terminal(self, state):\\n return len(self.get_possible_actions(state)) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926e2916a3c1d965b03878b042d2ba42\",\n \"score\": \"0.58219135\",\n \"text\": \"def is_terminal(self, state):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"394f6ff14f4a6463cbf47afac13b8d82\",\n \"score\": \"0.5809742\",\n \"text\": \"def matches(self, terminal):\\n return self.kind == terminal.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760090eb1163ae62f9bacb554573d7cb\",\n \"score\": \"0.5749353\",\n \"text\": \"def is_terminal(self, state):\\n return state.cachedWin or len(state.blanks) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69312dba31242146f33b0bb755ca198a\",\n \"score\": \"0.57290226\",\n \"text\": \"def is_terminal(self):\\n return self.__terminal_hand is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"609d3bc819d400682c0d70fb0b852d1d\",\n \"score\": \"0.5712845\",\n \"text\": \"def is_monophyletic(self, terminals, *more_terminals):\\n target_set = set(_combine_args(terminals, *more_terminals))\\n current = self.root\\n while True:\\n if set(current.get_terminals()) == target_set:\\n return current\\n # Try a narrower subclade\\n for subclade in current.clades:\\n if set(subclade.get_terminals()).issuperset(target_set):\\n current = subclade\\n break\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4662b27ec5b67ed4ec9298b60cc993\",\n \"score\": \"0.56929684\",\n \"text\": \"def is_terminal(self) -> pulumi.Output[Optional[bool]]:\\n return pulumi.get(self, \\\"is_terminal\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e844fbbcef279da34eb9f4166d8202aa\",\n \"score\": \"0.56607735\",\n \"text\": \"def is_terminal(self, state: D.T_state) -> D.T_agent[D.T_predicate]:\\n return self._is_terminal(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c936ebcda86aa4b7133ba981ba6d7c67\",\n \"score\": \"0.56542295\",\n \"text\": \"def isTerminal(self, state):\\n return True if state is 100 or state is 0 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e647ce7a8f337c2bb762135d55bab4\",\n \"score\": \"0.5625877\",\n \"text\": \"def check_terminal(self):\\n if self.has_crashed():\\n return True\\n elif self.current_episode_length> self.max_episode_length:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9d4e04b433439b763d19381bb38593\",\n \"score\": \"0.5604717\",\n \"text\": \"def is_terminal(state):\\n return state not in STATES_TRANSITIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760339ad5aa8dc900a24587b26eb8099\",\n \"score\": \"0.5590825\",\n \"text\": \"def is_terminal(self):\\n return self.task.is_terminal(self.state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a85845d78a3f39cfc7c77a5ee5d1fdd\",\n \"score\": \"0.55575275\",\n \"text\": \"def is_terminal(depth, board):\\n return depth <= 0 or board.is_game_over()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93e8407c7b75a19b1edf32d46dff65a\",\n \"score\": \"0.55431694\",\n \"text\": \"def is_terminal(state, maxdepth = None):\\n board = state[0]\\n turn = state[1]\\n depth = state[2]\\n (moves, captures) = crank.get_hints(board, turn)\\n if maxdepth is not None:\\n return ((not moves) and (not captures)) or depth >= maxdepth\\n else:\\n return ((not moves) and (not captures))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9218ef43b8588ae782393d4ce92d1c72\",\n \"score\": \"0.5534429\",\n \"text\": \"def is_terminal_state(self, state):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1adb0478375f1864ccb139d71600133\",\n \"score\": \"0.55309165\",\n \"text\": \"def isTerminal(cls, state: TicTacToeState) -> bool:\\n is_terminal = TicTacToeState.isGoal(state)\\n\\n if is_terminal != None:\\n if is_terminal < 4 and is_terminal > -1:\\n return True\\n\\n # end loop\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00670f4e207665c353afe83bb79e5963\",\n \"score\": \"0.55282474\",\n \"text\": \"def _is_terminal(self, sim: Simulation, state: NamedTuple) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd1a017ac2fe8a27f5340147379ed60\",\n \"score\": \"0.55239075\",\n \"text\": \"def ShouldWalk(self):\\n return self._subentries is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87bf9d84a95ae7ecf6103579c8c7ab6d\",\n \"score\": \"0.5505575\",\n \"text\": \"def _is_terminal(self, state: D.T_state) -> D.T_agent[D.T_predicate]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a751ad4c175737a94e2fa54569b73d\",\n \"score\": \"0.5442403\",\n \"text\": \"def is_terminal(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"is_terminal\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62cc6ac5118caba1509fcfe9e7ff828\",\n \"score\": \"0.54037803\",\n \"text\": \"def is_terminal(self):\\n return self._user_state.time_budget <= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d8b729523345adec6d50ac717396744\",\n \"score\": \"0.5383512\",\n \"text\": \"def _is_terminal(self):\\n return self.vehicle.crashed or self.steps/self.POLICY_FREQUENCY >= self.config[\\\"duration\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b81bb99abeb5b5ca561b18108c4649\",\n \"score\": \"0.5372268\",\n \"text\": \"def _is_terminal(self):\\n return self.vehicle.crashed or self.steps > self.config[\\\"duration\\\"]\\n\\n # return self.vehicle.crashed or self.vehicle.out or self.steps > self.config[\\\"duration\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ac27598ba8bb59b28a45d75e7b13fd\",\n \"score\": \"0.53466034\",\n \"text\": \"def is_terminal_state(state):\\n return not Heuristic.winned(state) == \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd1761e84d138c4f2d86380b67fb1223\",\n \"score\": \"0.53423077\",\n \"text\": \"def terminal_test(state, player):\\n dirs = othello_constants.DIRECTIONS\\n\\n if state.count(0) >= state.count(player):\\n if not Othello.has_forward_actions(state.board, player, dirs):\\n if not Othello.has_forward_actions(state.board, -player, dirs):\\n return True\\n else:\\n if not Othello.has_reverse_actions(state.board, player, dirs):\\n if not Othello.has_reverse_actions(state.board, -player, dirs):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7c166ff1aca8670543280df09298a5\",\n \"score\": \"0.5335042\",\n \"text\": \"def is_terminal(self):\\n if self._is_terminal is None:\\n self._is_terminal = self.is_full or self.winner\\n return self._is_terminal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0344aba8af38dd6683c1a16b0a76d62c\",\n \"score\": \"0.52913547\",\n \"text\": \"def terminal(self):\\n\\t\\tside_term = self.ego_state[1] < self.lane_loc[0] or self.ego_state[1] > self.lane_loc[-1]\\n\\t\\tend_term = self.ego_state[0] > self.max_distance\\n\\t\\ttime_term = self.curr_step >= self.max_steps\\n\\t\\treturn side_term or end_term or time_term\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1b1e93ebb1179f139b1c46772a636fa\",\n \"score\": \"0.52865857\",\n \"text\": \"def terminal(board):\\n boardelements = []\\n for row in board:\\n for element in row:\\n boardelements.append(element)\\n\\n myset = set(boardelements)\\n\\n if len(myset) > 1:\\n\\n # for checking rows\\n for (i, row) in enumerate(board):\\n rowset = set(row)\\n if None not in rowset and len(rowset) == 1:\\n return True\\n\\n # for checking columns\\n transpose_of_board = [[board[j][i] for j in range(len(board))] for i in range(len(board[0]))]\\n for (i, col) in enumerate(transpose_of_board):\\n colset = set(col)\\n if None not in colset and len(colset) == 1:\\n return True\\n\\n # for checking diagonals\\n # Main Diagonal\\n maindiagonal = []\\n for (i, row) in enumerate(board):\\n for (j, element) in enumerate(row):\\n if i == j:\\n maindiagonal.append(element)\\n\\n maindiagonalset = set(maindiagonal)\\n\\n if (None not in maindiagonalset) and (len(maindiagonalset) == 1):\\n return True\\n\\n # Second Diagonal\\n seconddiagonal = []\\n for (i, row) in enumerate(board):\\n for (j, element) in enumerate(row):\\n if i + j == 2:\\n seconddiagonal.append(element)\\n\\n seconddiagonalset = set(seconddiagonal)\\n\\n if (None not in seconddiagonalset) and (len(seconddiagonalset) == 1):\\n return True\\n\\n # if draw\\n s = set()\\n for row in board:\\n for element in row:\\n s.add(element)\\n\\n if None not in s:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd0ca75ca278f1d7e39e1892973fa0d\",\n \"score\": \"0.5270652\",\n \"text\": \"def terminal_test(self, state):\\n return state.utility != 0 or len(state.moves) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0717955694e61c467047470bc6798f33\",\n \"score\": \"0.52613366\",\n \"text\": \"def is_terminal(self):\\n return self.has_max_won in [0, 1, -1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e8060316953ba1f2a84b164c2770be\",\n \"score\": \"0.52457374\",\n \"text\": \"def is_terminal(self, section):\\n return section.value() in self.CHAR_DELIM\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328c5bfe9cb3914d242f757cf41c7bc9\",\n \"score\": \"0.5224005\",\n \"text\": \"def dashed_children_exist(self):\\n for child in self.children.values():\\n if child.state == State.DASHED_CIRCLE or child.state == State.DASHED_BOX:\\n return True\\n if len(child.children) == 0 or not child.dashed_children_exist():\\n continue\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013ca4cc168d161f28d441f839e2b14c\",\n \"score\": \"0.5202275\",\n \"text\": \"def __AcceptTerminal(self):\\n if self.__next_symbol < len(self.__symbols):\\n self.__Error('Expected no additional symbols')\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2be06023954ced74dec573fc384453e\",\n \"score\": \"0.5194339\",\n \"text\": \"def is_terminal(state: list):\\n\\n win_states = [\\n (state[0], state[1], state[2]),\\n (state[3], state[4], state[5]),\\n (state[6], state[7], state[8]),\\n (state[0], state[3], state[6]),\\n (state[1], state[4], state[7]),\\n (state[2], state[5], state[8]),\\n (state[0], state[4], state[8]),\\n (state[2], state[4], state[6])\\n ]\\n for line in win_states:\\n if line[0] == line[1] and line[2] == line[1]:\\n return True\\n for field in state:\\n if field in numbers:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bfbeb15a591612c62761a436b5a6875\",\n \"score\": \"0.5181404\",\n \"text\": \"def is_terminal(state, player=True):\\n if utility_of(state, player) != 0:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c7acd158b6d7a7e0a1deead49129f5\",\n \"score\": \"0.5166754\",\n \"text\": \"def is_single_child(context: TreeContext) -> bool:\\n return len(context[-2]._children) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73eaf88dadebd4e4a31e1fe9165c5a71\",\n \"score\": \"0.51603746\",\n \"text\": \"def terminals(self):\\n\\n return self._terminals.getSlice(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22e4292b9c22d411020347781e316d2c\",\n \"score\": \"0.5150798\",\n \"text\": \"def is_child_node(self):\\n return not self.is_root_node()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc7c490be10f4165b7dc6304fa48c02\",\n \"score\": \"0.5143967\",\n \"text\": \"def terminal_test(self, state):\\n return not self.actions(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc7c490be10f4165b7dc6304fa48c02\",\n \"score\": \"0.5143967\",\n \"text\": \"def terminal_test(self, state):\\n return not self.actions(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ed2f9f7208bd2b0c6e6f04319fad49\",\n \"score\": \"0.51373184\",\n \"text\": \"def isatty(self):\\n try:\\n return self.stdout.isatty()\\n except AttributeError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5003a9047af2f294616cd21efbcb6ffe\",\n \"score\": \"0.51274043\",\n \"text\": \"def isatty (self): # File-like object.\\n return os.isatty(self.child_fd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7cea35a6623d5062d8be77e9a00b53\",\n \"score\": \"0.51235384\",\n \"text\": \"def is_terminal(state):\\n\\n is_done = True\\n\\n for i in range(len(state)):\\n if i in state:\\n is_done = False\\n\\n for i in [0, 3, 6]:\\n if state[i] == state[i+1] == state[i+2]:\\n return True\\n\\n for i in [0, 1, 2]:\\n if state[i] == state[i+3] == state[i+6]:\\n return True\\n\\n if state[0] == state[4] == state[8]:\\n return True\\n\\n if state[2] == state[4] == state[6]:\\n return True\\n\\n return is_done\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7653a8b64743274a6304e9aaa72685f\",\n \"score\": \"0.5109323\",\n \"text\": \"def isDescendantOf(self, node):\\n return (self in node.pathToRoot())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8accd629fb977e9292e4877050dd91e1\",\n \"score\": \"0.50864804\",\n \"text\": \"def terminal(board):\\n if winner(board) != None:\\n return True\\n\\n # Draw\\n for line in board:\\n for element in line:\\n if element == EMPTY:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c143d59aa90acabde47db18c335add44\",\n \"score\": \"0.50778186\",\n \"text\": \"def IsTerminal(self):\\n if not self._ready_condition:\\n raise NotImplementedError()\\n if not self._fresh:\\n return False\\n if self._ready_condition not in self._conditions:\\n return False\\n return self._conditions[self._ready_condition]['status'] is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e96959df1c77cae69dc0bc69f375165\",\n \"score\": \"0.5065302\",\n \"text\": \"def terminal_performatives(self) -> FrozenSet[Message.Performative]:\\n return self._terminal_performatives\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbad2e84ac82729f3bbbe018aea24847\",\n \"score\": \"0.5050743\",\n \"text\": \"def count_terminals(self):\\n count = 0\\n for node in self:\\n if node.is_terminal():\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6fecf3e6b61981e1b59226b7acd049a\",\n \"score\": \"0.50498414\",\n \"text\": \"def count_terminals(self):\\n return _utils.iterlen(self.find_clades(terminal=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091c0350dc6b841408afa5363d1871f1\",\n \"score\": \"0.504946\",\n \"text\": \"def hasTerminalStops(self, gc=None):\\n stops = []\\n aligned = isinstance(self, Alignment)\\n for seq_name in self.Names:\\n if aligned:\\n seq = self.NamedSeqs[seq_name].data\\n else:\\n seq = self.NamedSeqs[seq_name]\\n stops.append(seq.hasTerminalStop(gc=gc))\\n return max(stops)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5152cace8fbccc92cc998ce34f15b0\",\n \"score\": \"0.50401443\",\n \"text\": \"def terminal(board):\\n \\n if winner(board) is not None:\\n return True\\n \\n all_moves = [cell for row in board for cell in row]\\n if not any(move == EMPTY for move in all_moves):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3b357b8481c7828b3964a2a993f8cb\",\n \"score\": \"0.5021967\",\n \"text\": \"def is_tty(self):\\n return stream_is_tty(self.stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c55a641410a095d7bb3aaf10f5d3db\",\n \"score\": \"0.50217664\",\n \"text\": \"def terminal_test(gameState):\\n return not bool(gameState.get_legal_moves()) # No more moves\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c11c72edf7e125cdd42e21203af1868\",\n \"score\": \"0.5021019\",\n \"text\": \"def is_non_terminal(self):\\n return not self.state.game_over()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94cabe437e0d6bd86b564de00d8ea87\",\n \"score\": \"0.501648\",\n \"text\": \"def get_all_terminal_nodes(root):\\n\\n node_checker(root)\\n terminal_nodes = []\\n\\n def add_children_to_nodes(current_node):\\n children = current_node.children\\n nonlocal terminal_nodes\\n if current_node.is_terminal:\\n terminal_nodes.append(current_node)\\n return\\n for c in children:\\n add_children_to_nodes(c)\\n\\n add_children_to_nodes(root)\\n\\n return terminal_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39bfcd6d19b2df1e911c8162564de8fb\",\n \"score\": \"0.49777797\",\n \"text\": \"def all_of_validator(self, verbose=1):\\n assert self.children\\n result = True\\n for child in self.children:\\n if not child.validate(verbose):\\n result = False\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc4655bf85fb2bff89aee5e9d1300b1\",\n \"score\": \"0.49763197\",\n \"text\": \"def stream_is_tty(stream):\\n isatty = getattr(stream, 'isatty', None)\\n return isatty and isatty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ace2801861e5082b9470a613820164c\",\n \"score\": \"0.49727628\",\n \"text\": \"def tree_is_separable(tree):\\n return separable.is_separable(tree.evaluate(OPERATORS))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29540f92b2cda1ac9c1a958b5dc2e5ea\",\n \"score\": \"0.493346\",\n \"text\": \"def is_terminal(self, limit):\\n if limit == 0:\\n if not self.start:\\n return True\\n elif (self.score >= limit) or (not self.start):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1405bf99fba3df334eb68859fdeb8dd\",\n \"score\": \"0.4923727\",\n \"text\": \"def terminal(board):\\n if winner(board) or not any(None in sublist for sublist in board):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2eb481226a51ba4dcc661b3cde2a13\",\n \"score\": \"0.491552\",\n \"text\": \"def terminal(board, position):\\n return not _valid_position(position) or not _no_crash(board, position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69053f3e65565726c3fa1e60f55f3cc1\",\n \"score\": \"0.4911571\",\n \"text\": \"def headNonTerminal(self):\\n if self.dot==len(self.rhs):\\n return False\\n else:\\n return self.rhs[self.dot][0].isupper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e11c2eca95c35e4688b44d5245e0f703\",\n \"score\": \"0.48989522\",\n \"text\": \"def terminated(self):\\n # if any of the leaves are not terminated, the tree is not terminated\\n for leaf in self.leaves:\\n if not leaf.terminated:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a16f9e8e6649dc17e344098d3a4bb7\",\n \"score\": \"0.48979142\",\n \"text\": \"def terminal(board): \\n if winner(board) is not None:\\n return True\\n \\n cellsArr = [cells for row in board for cells in row]\\n if not any(cell == EMPTY for cell in cellsArr):\\n return True\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e53fe9894d86810087757c2ed12e203\",\n \"score\": \"0.48958004\",\n \"text\": \"def is_terminal_state(self, board):\\n return len(available_cols_to_move(board)) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028e9ee9f5608c3f683c729df24b1204\",\n \"score\": \"0.489348\",\n \"text\": \"def has_children(context: TreeContext) -> bool:\\n return bool(context[-1]._children)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb0f274b7c6680f93a2fcda1cbe12ec2\",\n \"score\": \"0.48883444\",\n \"text\": \"def has_children(self):\\n\\n return bool(self.children)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4583b915ae996de45ee39b83a3ff3b8d\",\n \"score\": \"0.48826888\",\n \"text\": \"def is_leaf_node(self):\\n return not self.get_descendant_count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"823c11ca427c49b7555dc8b4db67650a\",\n \"score\": \"0.48740843\",\n \"text\": \"def terminal_test(gameState):\\n if gameState.get_legal_moves(): # If there are legal moves left then return False\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0de026886c232cf4e3c3bb2fbd60aeb\",\n \"score\": \"0.4872136\",\n \"text\": \"def is_root(self) -> bool:\\n return len(self.escaped) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9e644f69d217b9c0d99752947de5f9\",\n \"score\": \"0.48679084\",\n \"text\": \"def terminal(board):\\n\\n # See if the board has a winner\\n if winner(board) is not None:\\n return True\\n\\n # Check if the board is filled\\n if not any(EMPTY in row for row in board):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06eba9cf6ab7f701f6ba5fd43f99cc06\",\n \"score\": \"0.48576152\",\n \"text\": \"def aterm_is(self, v):\\n\\n raise Exception(\\\"Is this used? SHould probably be deleted\\\")\\n\\n if isinstance(v, six.string_types):\\n\\n if '.' not in v:\\n v = 'root.' + v\\n\\n v_p, v_r = self.split_term_lower(v)\\n\\n if self.join_lc == v.lower():\\n\\n return True\\n elif v_r == '*' and v_p == self.parent_term_lc:\\n return True\\n elif v_p == '*' and v_r == self.record_term_lc:\\n return True\\n elif v_p == '*' and v_r == '*':\\n return True\\n else:\\n return False\\n\\n else:\\n\\n return any(self.term_is(e) for e in v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5975072ba113729bc859b503efb1f12d\",\n \"score\": \"0.48500377\",\n \"text\": \"def is_nested(self):\\n return bool([1 for path, alias in self._dotted_as_names()\\n if alias is None and len(path) > 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec1f04c5e557618aa6f2077ba2eee45\",\n \"score\": \"0.4846948\",\n \"text\": \"def terminal_test(gameState):\\n return not gameState.get_legal_moves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c22d8e48ef256667663c9722b788cd4\",\n \"score\": \"0.48352483\",\n \"text\": \"def is_attendants(user):\\r\\n\\r\\n if Attendants.objects.filter(user=user):\\r\\n return True\\r\\n return is_administrator(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a3018791b6d9b286581639b9b1cc81\",\n \"score\": \"0.48334625\",\n \"text\": \"def isCompleteTree(self, root):\\n _, ans, _ = self.dfs(root, 0)\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03735dbb55e861cd174b513a9ded01a\",\n \"score\": \"0.4821981\",\n \"text\": \"def has_child(context: TreeContext, tag_name_set: AbstractSet[str]) -> bool:\\n return has_descendant(context, tag_name_set, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70cda77bb875d44f4366ad10e13b059b\",\n \"score\": \"0.48204812\",\n \"text\": \"def terminal(board):\\n if winner(board) != None:\\n return True\\n if any(None in row for row in board):\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9275a97a68c3d1f837d288b2f911444\",\n \"score\": \"0.4811534\",\n \"text\": \"def _isAncestor(self, ancestorPath, child):\\n \\n while child is not None:\\n if child.path != ancestorPath:\\n child = child.parent\\n else:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d2ecc2f1c27428f6c1a34de3deeb68\",\n \"score\": \"0.48085862\",\n \"text\": \"def is_single_path(self) -> bool:\\n if not self.children:\\n return_value = True\\n elif len(self.children) > 1:\\n return_value = False\\n else:\\n child = self.children[0] # guaranteed to have exactly 1 by if and elif clauses above\\n return_value = child.is_single_path()\\n\\n return return_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c8d226cb9906b03abad337e3c2273c\",\n \"score\": \"0.48084387\",\n \"text\": \"def isSinglePathTree(self):\\n for item in self.header_table:\\n if self.header_table[item].node_link is not None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b2bd29c430db82580ff6166742c8cd\",\n \"score\": \"0.48081723\",\n \"text\": \"def is_root(self):\\n return len(self.parents) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc413aff7378625d977c6f25d5298b1\",\n \"score\": \"0.4792206\",\n \"text\": \"def has_visible_parents(self, node):\\n parent = node\\n if parent is None:\\n return True \\n else:\\n style = self.get_style(parent)\\n if style is not None and style.replace(\\\" \\\",\\\"\\\").find(\\\"display:none\\\") >= 0:\\n return False\\n return self.has_visible_parents(parent.getparent())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c7676286446723a88ae6e157661beb\",\n \"score\": \"0.47855246\",\n \"text\": \"def isNonTerminal(self, symbol):\\n return symbol in self.rule_keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e79c179dc7ebb9352b2a3edd747e091\",\n \"score\": \"0.47793627\",\n \"text\": \"def has_root():\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":15,"numItemsPerPage":100,"numTotalItems":994482,"offset":1500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjgzOGE5MTA0YTNhNzFhOTE3OTU0Y2YwIl0sImlhdCI6MTc5MTc1NDk0MywianRpIjoiZjkyOGNmYjEtOGNhMi00ODJkLWExMmMtM2FlNWUxMzRiZTBhIiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc5MTc1ODU0MywiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.IlsdVZwDLVwfZpHwAOESxrL17ek7XcLQiaoipRuXRKnjnJovtKkzQ9z_-Qvt9aBQ1Ru3oQiwrMlmH0Qhdqo6AQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
5c2d9f1fda55127723a3f0c6cfae90fe | Wrap only RA values into 02pi (using mod). | [
{
"docid": "660be432713cdf21713be305e718a483",
"score": "0.8059379",
"text": "def wrapRA(ra):\n ra = ra % (2.0 * np.pi)\n return ra",
"title": ""
}
] | [
{
"docid": "d75e38f81b0fdf8586733ca2cabbad7a",
"score": "0.7120562",
"text": "def wrapRADec(ra, dec):\n # Wrap dec.\n low = np.where(dec < -np.pi / 2.0)[0]\n dec[low] = -1 * (np.pi + dec[low])\n ra[low] = ra[low] - np.pi\n high = np.where(dec > np.pi / 2.0)[0]\n dec[high] = np.pi - dec... |
3f0a1346fb8bb373c8c5262b87589d11 | Parses the command line for arguments. A few different potential arguments. One indicates if it is the second tweet of the day (assumes that is not the case). Another marks whether we should be tweeting in a particular language. | [
{
"docid": "3c02e8494598f6ccc01def7632eed796",
"score": "0.71433055",
"text": "def parse_args(argv=None):\n argv = sys.argv[1:] if argv is None else argv\n parser = argparse.ArgumentParser(description=__doc__)\n\n parser.add_argument('-t', '--two', dest='day_two', action='store',\n ... | [
{
"docid": "876e2006fc429b6aafb42e4b55c625f5",
"score": "0.6255365",
"text": "def parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-s\", \"--search_query\", help=\"Search Query\",\n type=str, required=True)\n parser.add_argument(\"-n\", \"--... |
6639be396edb40a5fa5ca95801fe78fc | Append layer and layer.param (if exists) to self.layer and self._parameters, respectively. | [
{
"docid": "82b0ab34a1c5afdfbf2b726314c1205a",
"score": "0.7053847",
"text": "def add(self, layer):\n self.layers.append(layer)\n\n # FCLayer object has Parameter object stored as layer.param\n try:\n self._parameters.extend(layer.param)\n # no parameters in Activa... | [
{
"docid": "d2e3bfc8f30468dfb3748ac981d3f522",
"score": "0.66861075",
"text": "def make_param_list(self):\n self.params, self.bn_layers = {}, {}\n\n for key in list(self.layers.keys()):\n self.params[key] = []\n self.bn_layers[key] = []\n for layer in self.... |
fc522dc6bf113654f13dd2beef05993a | show a blocking warning | [
{
"docid": "bfa4a64a0e3ef5e421b2f4c4a1e6e76c",
"score": "0.0",
"text": "def _notify_error(self, message: str):\n warn_msg = [\"Errors were encountered while running the playbook:\"]\n messages = remove_ansi(message).splitlines()\n messages[-1] += \"...\"\n warn_msg.extend(mes... | [
{
"docid": "e83198fc2dfe50eb93366a8f8d7b4a26",
"score": "0.7087826",
"text": "def warning(msg):\n if not QUIET:\n print(f\"WARNING [gldserver {datetime.datetime.now()}]: {msg}\",file=sys.stderr,flush=True)",
"title": ""
},
{
"docid": "40e62695e960695e3148dd3bef51cee6",
"score":... |
f556f8b23192e8efb675129f7cb130ab | Calculate the constant part of LogDistance Path Loss equation. We assume a fixed carrier frequency for all communications in our simulation. This means that only the distance and antenna gain parts of the LogDistance PL equation will be changing, so we can memoize the freq + speed of light part to save computation. | [
{
"docid": "03d0bd3ffe512d39d0459c9475acdff4",
"score": "0.631338",
"text": "def pl_constant_dB(carrier_freq_GHz: float, ple: float) -> float:\n return 10 * ple * log10(carrier_freq_GHz * 1e9) + 10 * ple * log10((4 * pi) / SPEED_OF_LIGHT)",
"title": ""
}
] | [
{
"docid": "0d20475941b77e0e4f3887cbbfdaad5e",
"score": "0.63087827",
"text": "def _L(w):\n return 1 / w * log(1 + w)",
"title": ""
},
{
"docid": "acabf88daa46c5a515d4c32f0140e8ff",
"score": "0.6266826",
"text": "def loglike(self, params):\n nobs2 = self.nobs / ... |
9caa817a98f6542e2746de1fc6224521 | u"""Size of the file, in bytes. | [
{
"docid": "bcef0da332db7373fff8c9912efa1be2",
"score": "0.78664887",
"text": "def size(self):\n return os.path.getsize(self)",
"title": ""
}
] | [
{
"docid": "f121bac4c8a59e36ec4142002fdac271",
"score": "0.8228751",
"text": "def get_file_size(self):\n return self.file_size",
"title": ""
},
{
"docid": "a99dd2cfc8551833e5dee35c244a59f7",
"score": "0.8152326",
"text": "def size_in_bytes(fname):\n return os.stat(fname).st... |
6e49063aaba15bf4fb06655800b296ed | Validates that the old_password field is correct. | [
{
"docid": "8f7c2d5d80ecb0cf5e24a3c21e424134",
"score": "0.68561393",
"text": "def clean_old_password(self):\n if not self.isEmployeePwd():\n super(PasswordChangeForm1, self).clean_old_password()",
"title": ""
}
] | [
{
"docid": "489cfc3f2b388198b9ee89b3fdd47df4",
"score": "0.8666899",
"text": "def clean_old_password(self):\r\n old_password = self.cleaned_data[\"old_password\"]\r\n if not self.user.check_password(old_password):\r\n raise forms.ValidationError(\r\n self.error_me... |
dc26b21abe31760b535bc59c39a92288 | removes member from register (and all associated register entries) | [
{
"docid": "73761206adc87a7fb837840e648aa7c3",
"score": "0.0",
"text": "def supprimer_membre(self, m):\n if m in self.membres:\n self.membres.remove(m)\n for c in self.certificats:\n del self.registre[m, c]\n duplicates = []\n for n in se... | [
{
"docid": "63d785cc7c2908fe2d4820a5b1fffb0f",
"score": "0.71147346",
"text": "def removeMember(self, member):\n self.members.pop([member])",
"title": ""
},
{
"docid": "e60878bb2479cca35ad45053306660ef",
"score": "0.70818776",
"text": "def unregister(registration):",
"titl... |
586ac2d04d6274f7a9502b4c31836148 | Append games to the season list. | [
{
"docid": "d21ce55288c877629f8af68999b49530",
"score": "0.8398314",
"text": "def add_to_season(self, *games):\n for item in games:\n item = item.convert_dict()\n self.season.append(item)\n self._gather_stats()",
"title": ""
}
] | [
{
"docid": "056b89b377ba104102bc06499e37d152",
"score": "0.6881717",
"text": "def games(self):\n games = []\n for season in self.seasons:\n games += season.games\n return games",
"title": ""
},
{
"docid": "c781ae5ff97616fb91bbb35c2bf4a767",
"score": "0.636... |
9723809074f89660f17a631d91fb74c0 | Gets the identity matching the given identity id. | [
{
"docid": "f56995282bcbdc0b0ebf2e2d7cfcd7b0",
"score": "0.7341148",
"text": "def get_identity(self, identity_id, identity_to_retrieve=None):\n response = self.api_client.get_identity(\n identity_id,\n identity_to_retrieve if identity_to_retrieve else identity_id)\n\n ... | [
{
"docid": "bbda155689deac87303c3837ccae62a0",
"score": "0.70973593",
"text": "def get_identity(self, identity_to_retrieve=None):\n return self.parent.get_identity(self.identity_id, identity_to_retrieve)",
"title": ""
},
{
"docid": "d22f73dd8b7edee0b69c688e377e4b21",
"score": "0.6... |
028730c2d50419c63bd52b91e89fccc0 | Return the number of items in the control. | [
{
"docid": "fb21f40f2b6bfc76f132942b00a6c4ad",
"score": "0.72718483",
"text": "def GetCount(self):\n return len(self.__buttons)",
"title": ""
}
] | [
{
"docid": "fbf9eeb7487493aa082c1c6ee5763ee0",
"score": "0.8328919",
"text": "def getNumItems(self):\n\n return self.item_list.getSize()",
"title": ""
},
{
"docid": "fbf9eeb7487493aa082c1c6ee5763ee0",
"score": "0.8328919",
"text": "def getNumItems(self):\n\n return self.item_li... |
f2394ca0b91b86f4d4f82589fef6d772 | Return whether the queue is full or all data has been read. | [
{
"docid": "66c090d50ff9f7ec4a3eab241ea62c94",
"score": "0.0",
"text": "def is_ready(self):\n\n return self.__is_ready.is_set()",
"title": ""
}
] | [
{
"docid": "f6130d575c952183de83d49dc9df8901",
"score": "0.82898295",
"text": "def isFull(self):\n return len(self.queue) == self.size",
"title": ""
},
{
"docid": "c6d682e8bc53b83c6706adcc0e357585",
"score": "0.8242448",
"text": "def isFull(self):\n for i in self.queue:... |
d0c4765c05bfe01cddc61f5e9265a156 | r""" Returns a dictionary in a format compatible with that of the suffix trie transition function. | [
{
"docid": "178fc0d596485577bf265886aa8fd866",
"score": "0.736864",
"text": "def trie_type_dict(self):\n d = {}\n new_node = len(self._transition_function)\n for (u, dd) in iteritems(self._transition_function):\n for (sl, v) in iteritems(dd):\n w = self._wo... | [
{
"docid": "be82dd23635531b7ffeb67a8908c0505",
"score": "0.61743563",
"text": "def transition_function_dictionary(self):\n d = {}\n for (u,v,(i,j)) in self.edge_iterator():\n d.setdefault(u, {})[v] = (i,j)\n return d",
"title": ""
},
{
"docid": "e03170e774abc3... |
3e494b74512ea70aba76d57ec72e4c7a | gzip encodes a given stream of data. | [
{
"docid": "5df9e4e191d338372114aba38cafa637",
"score": "0.62311316",
"text": "def gzip_media(self, filedata):\n gzip_data = StringIO()\n gzf = GzipFile(fileobj=gzip_data, mode='wb')\n gzf.write(filedata)\n gzf.close()\n return gzip_data.getvalue()",
"title": ""
... | [
{
"docid": "009faba2db9d1af7503d42b7b4f6b98e",
"score": "0.67226607",
"text": "def compress_gzip(foo: bytes) -> bytes:\n c = zlib.compressobj(wbits=zlib.MAX_WBITS + 16)\n return c.compress(foo) + c.flush()",
"title": ""
},
{
"docid": "122d29aa625e68324f6eaf6882315e87",
"score": "0.... |
ffec06ba450c5873d6158be16b23598a | ID of the node. | [
{
"docid": "b7dd188ffe33cf43b36f7ec18b01d0a2",
"score": "0.0",
"text": "def id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"id\")",
"title": ""
}
] | [
{
"docid": "bfeb088ae137488a8657dc480af877ba",
"score": "0.8696809",
"text": "def node_id(self):\n return self._node_id",
"title": ""
},
{
"docid": "af4cdb945ea4d59ec0983cfce7870708",
"score": "0.8684843",
"text": "def nodeid(self) :\n\t\ttry :\n\t\t\treturn self._nodeid\n\t\t... |
7e500c63b119ab4dee6e8bf57086809c | Returns the JaaSJwtBuilder with the id claim set. | [
{
"docid": "29e62392b294cb2c58d33106396db73f",
"score": "0.4470215",
"text": "def withUserId(self, userId):\n self.userClaims['id'] = userId\n return self",
"title": ""
}
] | [
{
"docid": "7d529b031159cdcfd2a3bc4658d59ba2",
"score": "0.48859382",
"text": "def _generate_jwt(iat, exp, key_file_path, kms_project_id, key_ring, crypto_key, target_audience):\n\n key_content = gcs_ext.get_gcs_encrypted_file_as_string(\n key_file_path, kms_project_id, key_ring, crypt... |
fc04656f3f0d17081f07c74c64c9b2b8 | The threshold for the type of the warning. | [
{
"docid": "cca26f537659a9a75eb14e218e17d12d",
"score": "0.0",
"text": "def value(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"value\")",
"title": ""
}
] | [
{
"docid": "7d0c13f9191020955b9b5dbe5396dcbe",
"score": "0.7387244",
"text": "def _get_threshold_type(self):\n return self.__threshold_type",
"title": ""
},
{
"docid": "7d0c13f9191020955b9b5dbe5396dcbe",
"score": "0.7387244",
"text": "def _get_threshold_type(self):\n return sel... |
621d64470b8b17674af88ea7e53257da | Reads the config from file | [
{
"docid": "face8b430c2a812140a37a1b19d99c2c",
"score": "0.7190561",
"text": "def read_config(config):\n conf_file = pathlib.Path(config)\n contents = conf_file.read_text(encoding='utf-8')\n print(\"Config read successfully!\")\n return yaml.safe_load(contents)",
"title":... | [
{
"docid": "70096a34b46229d5eee21d62d532536c",
"score": "0.84541",
"text": "def readconfig(file):\n if not os.path.isfile(file):\n print(\"WARNING: Could not load config file '%s'\" % file)\n return\n config.read(file)",
"title": ""
},
{
"docid": "87bc93f528e4a9a33087ece1... |
4602a83890fcac8d593b358b8baa1855 | See base class for details. | [
{
"docid": "39c5a89cfbd4fcbffafcc95d9e21c232",
"score": "0.0",
"text": "def _flatten(self, x):\n if x and not isinstance(x, (dict, FeaturesDict)):\n raise ValueError(\n 'Error while flattening dict: FeaturesDict received a non dict item: '\n '{}'.f... | [
{
"docid": "944fb2df87fb227ccee6471df3301cdd",
"score": "0.7688483",
"text": "def __call__(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "9674a53f41c819786d78160c50d1decc",
"score": "0.74180526",
"text": "def __call__(self):\n raise NotImplementedError(... |
830dd5a397bc6f2367506d0bd68cb134 | Q Learning Algorithm with epsilon greedy | [
{
"docid": "9768e62d5bff19a3f1b6225cb034f01c",
"score": "0.0",
"text": "def Q_learning_train_gamma(env, alpha, gamma, episodes, dataset_name, epsilons=0.1, plot=True):\n\n \"\"\"Training the agent\"\"\"\n\n # For plotting metrics\n metrics = {}\n epsilon_decay = 0.99\n ... | [
{
"docid": "bfb6f7425cb806d95696eea8510ae30f",
"score": "0.74683756",
"text": "def epsilonGreedy(self, Q, state, nA, eps):\n if random.random() > eps :\n return np.argmax(Q[state])\n else:\n return random.choice(np.arange(self.env.action_space.n))",
"title": ""
... |
b7b7d6c6a0ad03360d2371d941d8ff4f | Buildpacks in the buildpack group | [
{
"docid": "09483f5db9e24e2f6790a05c68457916",
"score": "0.67055935",
"text": "def buildpacks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['BuildpackPropertiesArgs']]]]:\n return pulumi.get(self, \"buildpacks\")",
"title": ""
}
] | [
{
"docid": "4c03b68da59eb6dec5c38d6587aba15c",
"score": "0.5979639",
"text": "def getBuildSets():",
"title": ""
},
{
"docid": "d9420e6f100d7dd0bb86c6d33ad5bb33",
"score": "0.54672205",
"text": "def gather() -> None:\n # pylint: disable=too-many-locals\n\n # First off, clear out... |
7a908d5d1a54d2fc0324eeb01d0c9658 | Overwritten method Calls super().end_state() and stops listening to keyboard input | [
{
"docid": "705a2b164ecce56f390e244860e9ca81",
"score": "0.5866146",
"text": "def end_state(self):\n super().end_state()\n self.game_objects = []",
"title": ""
}
] | [
{
"docid": "eda64dfefdf5a138b9a895a3d5043f79",
"score": "0.6791873",
"text": "def end(self):\n curses.nocbreak()\n self.screen.keypad(0)\n curses.echo()\n curses.endwin()",
"title": ""
},
{
"docid": "da2e99d8b27cec68fadf7ad0d67b0f2a",
"score": "0.661081",
... |
19073137a0624bd0a26fd62d59db54b8 | Create service with some type, port and protocol. | [
{
"docid": "9ba208b0d60fe4b5ac8a92ca5e2c0302",
"score": "0.76180995",
"text": "def create_service(self, name, namespace, port, protocol, type,\n labels=None):\n manifest = {\n \"apiVersion\": \"v1\",\n \"kind\": \"Service\",\n \"metadata\": {... | [
{
"docid": "48c34a1cf7181fc3d55af8733cec73fa",
"score": "0.73876977",
"text": "def create_service(self, name):",
"title": ""
},
{
"docid": "917ec64a492b8e95019f0245dc3597b1",
"score": "0.6845694",
"text": "def create_service(kube_host, kube_port, namespace, service_name, pod_name,\n ... |
6604067e06569c15bc31c6a79add24dc | Yield the neighbor vertices of `vertex`. | [
{
"docid": "34ac0938b785041db790bb5e3dbcecb8",
"score": "0.770574",
"text": "def _get_neighbors(self, vertex, vertex_edges):\n\n for edge in vertex_edges:\n neighbor_id = (\n edge.get_vertex1_id()\n if vertex.get_id() != edge.get_vertex1_id()\n ... | [
{
"docid": "9b86843226fdbca52b066c870e0185fe",
"score": "0.75119525",
"text": "def neighbors_for_vertex(self, vertex):\n return self.neighbors_for_index(self.index_of(vertex))",
"title": ""
},
{
"docid": "f8a2e858c9076cff7f30cd63001347af",
"score": "0.71118885",
"text": "def g... |
f0e5fd2b46ed8f94bda08f6aeae66bb9 | Filter recruitments based on the event (online/inperson) status. | [
{
"docid": "77c9760a05b23b12e95dc8f64affb005",
"score": "0.69496536",
"text": "def filter_status(self, queryset: QuerySet, name: str, value: str) -> QuerySet:\n if value == \"online\":\n return queryset.filter(event__tags__name=\"online\")\n elif value == \"inperson\":\n ... | [
{
"docid": "2802b1c73bdf561e22b75ed88fc1cf80",
"score": "0.5941553",
"text": "def filter_events(self, **kwargs) -> list:\n raise NotImplementedError()",
"title": ""
},
{
"docid": "8410484cd809fc2c529e62b466cafd9b",
"score": "0.58687305",
"text": "def filter_by_status():\n a... |
0aff7249fa642f7cdf0aae4a0e36e5a9 | Outputs a plot of the predicted and realized carbon intensities for a given date. | [
{
"docid": "2201bd47b6015d8a7e192576ee9f71e9",
"score": "0.6886646",
"text": "def plot_carbon(date=get_current_day()):\n\n data_dict = query_carbon(date)\n t = np.linspace(0, 24, num=len(data_dict['data']), endpoint=False)\n actual = []\n forecast = []\n\n for i in range(len(data_dict['da... | [
{
"docid": "8701f8c218d75b294e8383deb96a9905",
"score": "0.63622636",
"text": "def show_prediction(self, test_day_id, predictor, columns=None):\n x, y = self.get_test_day_feature_vectors(test_day_id, columns)\n y_pred = self.predict_day_from_features(x, predictor)\n fig = plt.figure... |
4efbd3d16acd0dbf2db7b94b532a587d | Sets the domain name of the given dnssec_name structure. | [
{
"docid": "5ccb377ebfff79f9f312cc011ddd6221",
"score": "0.76919276",
"text": "def set_name(self,dname):\n _ldns.ldns_dnssec_name_set_name(self,dname)",
"title": ""
}
] | [
{
"docid": "27744b65159e841ab281b289653e8674",
"score": "0.7482131",
"text": "def domain_name(self, value):\n self._domain_name = value",
"title": ""
},
{
"docid": "82a4901269714c116b62d81fd72a380b",
"score": "0.674498",
"text": "def user_domain_name(self, value):\n sel... |
6966f4f326edbcede2735d03ebac20a2 | Create and return new C++ Triangulation object | [
{
"docid": "d5c51257fe762369c93a541775abb2fa",
"score": "0.71772313",
"text": "def Triangulation(*args, **kwargs): # real signature unknown\n pass",
"title": ""
}
] | [
{
"docid": "bf4994234014c47bcf4212439e3a09a2",
"score": "0.62678784",
"text": "def get_triangulation(self):\n return self.__triangulation",
"title": ""
},
{
"docid": "7167e37bce34a2803ad9c85551ed8149",
"score": "0.5697933",
"text": "def New(*args, **kargs):\n obj = itkT... |
1dec5831a0a5d1991be74a4d4edd8dae | Convert a data item of any type into a string such that we can deserialize it later. | [
{
"docid": "08851705468b3dfe57f5ffe685cc9516",
"score": "0.6217311",
"text": "def serialize_serializedata(data):\n\n # this is essentially one huge case statement...\n\n # None\n if type(data) == type(None):\n return 'N'\n\n # Boolean\n elif type(data) == type(True):\n if data == True:\n ... | [
{
"docid": "fd54bee57d5fd73584804963147052ac",
"score": "0.75119156",
"text": "def itemstostr(data):\n if data is False:\n return 'False'\n\n if not isinstance(data, numbers.Number):\n if not data:\n return 'None'\n\n msg = ''\n if isinstance(data, dict):\n fo... |
efccf0b7ac25f1ad6fc528e9f037b850 | Initialize a LocationSet Object If only a single location is given, a set is still created | [
{
"docid": "d17b8fcabfff0be6f2a9be261e0c426d",
"score": "0.6724767",
"text": "def __init__(self, locations, srs=4326, _skip_check=False):\n if not _skip_check:\n if isinstance(locations, ogr.Geometry) or isinstance(locations, Location):\n self._locations = np.array(\n ... | [
{
"docid": "25268a877307d3d8599f57bc0619e3c3",
"score": "0.6706208",
"text": "def location_set(self):\n location_list = []\n for location in self.location.all():\n location_list.append(location)\n location_set = set(location_list)\n return location_set",
"title... |
eeb1271439bdf9ce826a91f948de1899 | a nice thing for "modify 'random' field instead of the 'next' field in the copied list" is that the original list's next structure is never changed, so you can write a helper generator to visit the original list with a nice for loop encapsulating | [
{
"docid": "e847fdf694f0668a5899c76644e2d1b5",
"score": "0.73104393",
"text": "def copyRandomList(self, head: 'Node') -> 'Node':\n def nodes():\n node = head\n while node:\n yield node\n node = node.next\n\n # create new nodes\n fo... | [
{
"docid": "d3f31e4a4b05f7dbafadd393ed49c8a9",
"score": "0.70639145",
"text": "def copy_random_list(head):\n pstep = qstep = head\n new_nodes = {}\n while pstep:\n new_nodes[pstep] = RandomListNode(pstep.label)\n pstep = pstep.next\n while qstep:\n new_node = new_nodes[q... |
1f70d7ea39e8ac5e252f91c8377606cf | return yahoo option chain given maturity | [
{
"docid": "e02e080d2a4d270ee4aa4c56241627e9",
"score": "0.56962997",
"text": "def fromYahoo(sym, maturity, time, rate):\n #print \"Downloading Data ... \"\n year = maturity.strftime(\"%Y\")\n month = maturity.strftime(\"%m\")\n day = maturity.strftime(\"%d\")\n url = \"http://finance.yah... | [
{
"docid": "f0746a83c3928ae60345943b469a9c13",
"score": "0.6662822",
"text": "def get_option_chain(symbol: str, expiry: str):\n\n yf_ticker = yf.Ticker(symbol)\n try:\n chain = yf_ticker.option_chain(expiry)\n except Exception:\n console.print(f\"[red]Error: Expiration {expiry} ca... |
e4726b3c6cb0743db7ba8d2ffffaa906 | Sets the reasoner_id of this Result. | [
{
"docid": "5940c71f7421141dadd11ba6da532904",
"score": "0.850189",
"text": "def reasoner_id(self, reasoner_id):\n\n self._reasoner_id = reasoner_id",
"title": ""
}
] | [
{
"docid": "34f1dbea12f0b0bd947fab04ac4a25b0",
"score": "0.74012935",
"text": "def reasoner_id(self):\n return self._reasoner_id",
"title": ""
},
{
"docid": "34f1dbea12f0b0bd947fab04ac4a25b0",
"score": "0.74012935",
"text": "def reasoner_id(self):\n return self._reasone... |
d00999fdadb6b51a7fcb626139b8f8bc | Init the backbone using clip. | [
{
"docid": "596cbed0660c6eff651be5bcdb30e484",
"score": "0.6292251",
"text": "def __init__(self, model_name: str, vis_mode: bool = False):\n super().__init__()\n self.vis_mode = vis_mode\n self.model, _ = clip.load(name=model_name, jit=not vis_mode)",
"title": ""
}
] | [
{
"docid": "9575e2b7120f2d4976342070423f53e4",
"score": "0.6526476",
"text": "def setClip(self, clip):\n\n self._clip = clip\n\n ## @todo Check the below comment is still the goal, as we're not doing it\n ## right now...\n # If this clip has an entityRefrence instead of a path, we need to re... |
8a7594f645da73cebc353b2ff1467ee4 | A simple function that does all the default stuff | [
{
"docid": "a82b578b7207c650484c89d239a9e0e7",
"score": "0.0",
"text": "def download_all_separate(urls, name=None):\n\n for url in urls:\n if name == None:\n name = get_min_page_title(url)\n \n print('Fetching list of urls...')\n suburls = get_pdf_urls_rec(url)\n\n ... | [
{
"docid": "b2779071b59a5faf77ac8454d83306a0",
"score": "0.79775506",
"text": "def default():",
"title": ""
},
{
"docid": "58363b93134f33109676d3efd90e9d70",
"score": "0.6877584",
"text": "def _helper_function(self):\n pass",
"title": ""
},
{
"docid": "a41334c5822a... |
30163eae35a86014433d7a8a699735ef | Get a Trusted pyodbc connection to the SQL Server database on server. Try several connection strings. | [
{
"docid": "c2c65b3f5394edcf16f8e26629907525",
"score": "0.6705438",
"text": "def get_connection_or_die(server, database):\n drivers = [\n \"{ODBC Driver 17 for SQL Server}\", # supports SQL Server 2008 through 2017\n \"{ODBC Driver 13.1 for SQL Server}\", # supports SQL Server 2008 t... | [
{
"docid": "62fbed0138fad3ba0554c892c4b35462",
"score": "0.76228654",
"text": "def getSqldatabaseODBCconn(databasename : str, servername : str) -> pyodbc.connect:\r\n\r\n odbcconnstring = getsqldatabaseconnectionstring(databasename=databasename, client='odbc', servername=servername)\r\n \r\n \"... |
b4e70fd34a2744dd2216fe0b46fb77a8 | Ensure that the specified user cannot update his or her own profile with invalid data | [
{
"docid": "27df4f58b7fb421387ea7a3d58a810eb",
"score": "0.68820053",
"text": "def test_user_invalid_update_user(self):\n\n invalid_payload = {\n 'uid': 'invalid_uid',\n }\n\n self.client.force_authenticate(self.user)\n response = self.client.put(self.user_url, dat... | [
{
"docid": "e9d1bc7b7e12c15b021f15f4bac13f1a",
"score": "0.7023483",
"text": "def test_users_can_not_update_profiles(self):\n url = reverse('profile-update-view', kwargs={'pk': self.user.profile.pk})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 403)",
... |
63c34b2c518eb225d89c8f7693dc8400 | Returns custom_profile field of FullUser object by Peer object | [
{
"docid": "2f39e32aa6c3af37ab882a42c15bddf0",
"score": "0.75907224",
"text": "def get_user_custom_profile_by_peer(self, peer):\n outpeer = self.manager.get_outpeer(peer)\n\n full_profile = self.internal.users.LoadFullUsers(\n users_pb2.RequestLoadFullUsers(\n use... | [
{
"docid": "22d617ffdf6ac975494caaca64b9a99f",
"score": "0.67983615",
"text": "def get_user_custom_profile_by_nick(self, nick):\n full_profile = self.get_user_full_profile_by_nick(nick)\n\n if hasattr(full_profile, 'custom_profile'):\n return str(full_profile.custom_profile)",
... |
b1147b9bb84787bc2b3b5126079ce8f0 | displays letters on mdt coordinator form | [
{
"docid": "c02d6c2992950278d49b5fcd49d90d67",
"score": "0.6999842",
"text": "def letters_for_mdt(self,button_no):\n\n self.root10 = Toplevel(self.master)\n self.root10.title(\"Letters\")\n self.letter_for_mdt = PopupLetter(self.root10)\n self.letter_for_mdt.setvariables(self... | [
{
"docid": "4d8a5a5beb909e56a6262a10b106609b",
"score": "0.6428827",
"text": "def clinic_letter(self):\n self.root3 = Toplevel(self.master)\n self.root3.title(\"Clinic letter\")\n self.clinic_letter = ClinicLetter(self.root3)",
"title": ""
},
{
"docid": "d95c2c2d5cef2cdf... |
2828a3d15f4a1fe1c18cedb903fa30db | return entire catalog data set in JSON format | [
{
"docid": "fad07c3d745ad89f5c953d48ca838c7f",
"score": "0.67597187",
"text": "def api_catalog():\n catalog = []\n for cat in list_category():\n category = {'category': cat.serialize, 'subcategories': [], 'items': []}\n for sub_cat in list_subcategory(cat):\n sub_cat_items... | [
{
"docid": "9affbef7981af7ff998262a2635cdd73",
"score": "0.78417176",
"text": "def jsonCatalog():\n output = {}\n # Get all the categories from the database\n categories = session.query(CatalogCategory).all()\n for category in categories:\n # Get all items from the category\n i... |
f806af227faf183fed349f2dc85d0766 | return H0 in m/s/Mpc | [
{
"docid": "f84acf596d86d3df53f1292bd2fde37e",
"score": "0.72144634",
"text": "def H0_def(h):\n return H0_over_h*h",
"title": ""
}
] | [
{
"docid": "4cb2dbcb92095d087dd3edd03e44efe7",
"score": "0.66041595",
"text": "def Hubble_convert(H_0):\r\n result = H_0*1000.0*3.1536*10**13/(3.09*10**16)/10**6 #This formula convert the Hubble parameter from\r\n #km/s/Mpc to Myr^-1 in order to match the unit convention in this program\r\n ret... |
5c20a8e0547bb340204ea5a71ab668a0 | Construct error message and append to redirect url | [
{
"docid": "4bc1ce0780e53187319db83df141416b",
"score": "0.7947118",
"text": "def create_error_url(error_number, message, request):\n values = [('error', str(error_number)), ('message', message)]\n query = six.moves.urllib.parse.urlencode(values)\n return request.form['redirect'] + '?' + query"... | [
{
"docid": "c9f42fde6177f8384dc6429d7c8ee809",
"score": "0.7033951",
"text": "def error(message, url=None):\n flash_error(message)\n return flask.redirect(url or referrer_or_home())",
"title": ""
},
{
"docid": "85652d7d86f1f0b34e624aa0ee28da25",
"score": "0.69093055",
"text": "... |
31a441f6568cb17b7ee308f1652aea69 | Plot the schedule. Implements the .loc accessor on the series object. | [
{
"docid": "5f5f31221ad69873dd07e7afe4f49fdf",
"score": "0.63736933",
"text": "def plot(self, **kwargs):\n return self.series.plot(**kwargs)",
"title": ""
}
] | [
{
"docid": "7212461e919a1cd33045f3afe4e7ee51",
"score": "0.6391678",
"text": "def plot_row(self, loc):\n # Print data informations\n print(self.data_names, self.columns)\n if len(self.data_names) == 1:\n self.axes = (self.axes,)\n # Plot all plots\n for ind1... |
0b639d9f0a7fa07b672f55ff5afd1133 | Returns distance between two nodes and first step to go from source to destination | [
{
"docid": "95786797b20cf69827e472958313cf11",
"score": "0.67382646",
"text": "def get_path_between(self, source_name, destination_name):\n node1 = self.__graph[source_name]\n return node1.distance_vectors[destination_name]",
"title": ""
}
] | [
{
"docid": "3b3b6720761679f51722429445644824",
"score": "0.7749197",
"text": "def node_distance(self, node1, node2):\n if node1 == node2:\n return 0.0\n for i, (n1, n2) in enumerate(zip(self.paths[node1], self.paths[node2])):\n if n1 != n2:\n break\n ... |
f352adeebc77029d897aa03c00285628 | Compute classic statistical analysis on series | [
{
"docid": "25aa946e4412dba0d2208b26bb3df64b",
"score": "0.5817848",
"text": "def compute_stats_by_categories(series):\r\n n = float(series.shape[0])\r\n counts = series.value_counts()\r\n\r\n nb_unique = np.float(counts.count())\r\n high_freq = np.float(counts[0] / n)\r\n ... | [
{
"docid": "95e11421f31a566c07b9394fac79b018",
"score": "0.66061056",
"text": "def stata(series):\r\n if series == -1:\r\n return 0\r\n else:\r\n return 1",
"title": ""
},
{
"docid": "108d8cb0bc174afcaf87178ce3e0f4f8",
"score": "0.64251745",
"text": "def ensemble_... |
c82eec161ed334cab973605365dadc31 | Get a dictionary of a standard 1D or 2D topology, such as a line or square lattice. Pass boundary_conditions as a kwarg to specifiy the boundary condition of the standard topology. | [
{
"docid": "6e2ac803a0f63bdd2ba12054d96b3470",
"score": "0.69452333",
"text": "def standard_topologies(L, topology: str, **kwargs) -> dict:\n assert topology in ['line', 'rect_lattice'], \"topology must be 'line', 'rect_lattice', received {}\".format(\n topology)\n top = {}\n M = kwargs.... | [
{
"docid": "aeb2a3b546b375b810521669116d544b",
"score": "0.60054445",
"text": "def generate_boundary_conditions(boundary_conditions, schematised):\n bcdct = {}\n\n for bndcnd in boundary_conditions.itertuples():\n\n # Find nearest branch for geometry\n # extended_line = geometry.exte... |
619c64831216fbcf72d85962ac5164f8 | Integrate a system of ordinary differential equations using Euler's 2ndorder method. x_next = euler_integration(xdot,x,h) | [
{
"docid": "4d7f46e4872f719f5099495c9734dc52",
"score": "0.8602326",
"text": "def euler_integration(xdot,x,h):\r\n a = np.array(x)\r\n b = np.array(xdot)\r\n return a + (h*b)",
"title": ""
}
] | [
{
"docid": "9e7275c2be8a969e53e3d494052527ae",
"score": "0.65760505",
"text": "def heun_integrate_loop(\n dynamics, x0,\n ts):\n x = x0\n xs = []\n for t1, t2 in tqdm(zip(ts[:-1], ts[1:])):\n xdot = dynamics(x, t1)\n xp = x + (t2 - t1) * xdot\n x = x + (t2 - t1) * (dynamics(xp, t2) + x... |
f2b86c969a4696a0164ffec80b0e5621 | Display a list of the products which have sold the most | [
{
"docid": "c5daf769df655ca7eaf290de7838cb96",
"score": "0.6261755",
"text": "def display_bestsellers(request, count=0, template='product/best_sellers.html'):\n if count == 0:\n count = config_value('PRODUCT','NUM_PAGINATED')\n \n ctx = RequestContext(request, {\n 'products' : bes... | [
{
"docid": "2576064d6203b67c608d5e62b15464e4",
"score": "0.72025245",
"text": "def product_bestseller_view(request, id):\n bestsellers = Product.objects.annotate(\n count_ordered=Count('orderlineitem')).order_by('-count_ordered')[:4]\n\n return render(request, \"product_detail.html\", {\"be... |
39efd8ff6b848d3b1328abd0051783e8 | Default console logger for simple usage | [
{
"docid": "8af795098dfa4d63914b541f324f4b54",
"score": "0.69834226",
"text": "def get_default_logger():\r\n\r\n _formatter = logging.Formatter(\r\n \"%(asctime)s [%(levelname)s] %(message)s\")\r\n\r\n _ch = logging.StreamHandler()\r\n _ch.setLevel(logging.DEBUG)\r\n _ch.setFormatter(... | [
{
"docid": "133798773bb61379d57dad966dc72b20",
"score": "0.6992316",
"text": "def get_logger():",
"title": ""
},
{
"docid": "8a655a85729cd0d8ff18911030603041",
"score": "0.69453794",
"text": "def define_logging():\n logging.basicConfig(filename=\"my_program.log\", level=logging.DE... |
a95a18dbab5770da45b02ac0a15a2d72 | Returns the other player on the same team as this player | [
{
"docid": "b9a51946966823c44e7a466f8406dc6d",
"score": "0.6965718",
"text": "def teammate(game_info, player):\n colour_to_match = game_info['team colours'][game_info['players'].index(player)]\n for second_player, colour in zip(game_info['players'], game_info['team colours']):\n if colour_t... | [
{
"docid": "d1af0b45767943b841a97ce27fa7ac37",
"score": "0.78770065",
"text": "def get_other_player(self):\n pass",
"title": ""
},
{
"docid": "c8147f3750d3fed225a9123b365a5d6d",
"score": "0.74182016",
"text": "def get_opponent(self, player: str) -> str:\n if player == s... |
d6bcfeec3a3c0beed1f5cf9c4156f613 | Adds one junction in table net["junction"]. Junctions are the nodes of the network that all other elements connect to. | [
{
"docid": "5773062c0ad3dc6162e3175f9e37670c",
"score": "0.63616616",
"text": "def create_junction(net, pn_bar, tfluid_k, height_m=0, name=None, index=None, in_service=True,\n type=\"junction\", geodata=None, **kwargs):\n add_new_component(net, Junction)\n\n if index and index i... | [
{
"docid": "d73e1c16d7d1852b40da56a9280208b8",
"score": "0.6284319",
"text": "def junctions(self):\n for name, node in self._junctions.items():\n yield name, node",
"title": ""
},
{
"docid": "1caaf9e0c50d08b2eb189e935764c45e",
"score": "0.6082226",
"text": "def add_... |
ddccab44f01cfd1272c3e1d3b1d74a2f | Returns a creds dictionary filled with parsed from env Keystone API version used is 3; v2 was deprecated in 2014 (Icehouse). Along with this deprecation, environment variable 'OS_TENANT_NAME' is replaced by 'OS_PROJECT_NAME'. | [
{
"docid": "930d77d9271154908bc8ceb0d75f9d39",
"score": "0.7885217",
"text": "def get_credentials():\n creds = {'username': os.environ.get('OS_USERNAME'),\n 'password': os.environ.get('OS_PASSWORD'),\n 'auth_url': os.environ.get('OS_AUTH_URL'),\n 'project_name': os... | [
{
"docid": "bed505a02aaa58a3b3f4c21e66231637",
"score": "0.82458955",
"text": "def get_keystone_creds():\n d = {}\n d['username'] = environ['OS_USERNAME']\n d['password'] = environ['OS_PASSWORD']\n d['auth_url'] = environ['OS_AUTH_URL']\n d['tenant_name'] = environ['OS_TENANT_NAME']\n ... |
8570cead08dbc65508c2b57b69d9fc70 | Return the filepath with the written walks \ [from_tag, to_tag, weight] = (str, str, int) | [
{
"docid": "b2655a6d2d49e6bc8748e5279a736a92",
"score": "0.49370837",
"text": "def tag_walker(params, info, pre_res, **kwargs):\n \n res = params_handler(params,info,pre_res)\n\n prefix = \"simrank_iter(%d)_eps(%d)_r(%d)\" % (params[\"max_iter\"], params[\"eps\"],params[\"r\"])\n res[\"walk_... | [
{
"docid": "b96c9655ee19bc5d85c4a9b6f306179c",
"score": "0.57588494",
"text": "def get_weighted_file(self, term_char):\n if term_char in self.char_range(\"a\",\"f\"):\n return (\"models/mergedWeighted/a-f.txt\", (\"a\", \"f\"))\n elif term_char in self.char_range(\"g\",\"p\"):\n... |
b97fa14019e5127d89808594d6b98a51 | Function to read the username | [
{
"docid": "3b48750a894c6313f3f93a7b4cd12694",
"score": "0.0",
"text": "def get_user(self, username):\n\n\t\tself.username = username",
"title": ""
}
] | [
{
"docid": "c9ba6875370818c1d0ced1bfcad2af80",
"score": "0.8053713",
"text": "def getUserName() -> unicode:\n ...",
"title": ""
},
{
"docid": "5a84c606cbc209a3b0f8b6682b7e7ee9",
"score": "0.7873471",
"text": "def get_username():\n username = input(\"Username: \")\n retur... |
22f0ff35d9ea8f9da35203356dd04f0f | Function that calculates the last person location of the josephus problem | [
{
"docid": "cfbac83164862aa9ecb785fd65188313",
"score": "0.0",
"text": "def josephus(n, k):\n # special case, k = 1\n if k == 1:\n return n - 1\n # base case, if only one person left, they win\n elif n <= 1:\n return 0\n if k <= n:\n num_dead = n / k\n else:\n ... | [
{
"docid": "584fe4eb5bc1473944b252949263b46a",
"score": "0.56745833",
"text": "def last_location(self, user_id):\n user_ref = self.get(user_id)\n return user_ref.last_location",
"title": ""
},
{
"docid": "30fff688eaa73a699831d0961728c923",
"score": "0.56577885",
"text": "def ge... |
4747fb4c562a3f8a407485fb9c766a41 | Return dictionary of phase angles for all EVSEs in the network. | [
{
"docid": "525ceb20148896a6f3672097518e9f27",
"score": "0.74022955",
"text": "def phase_angles(self) -> Dict[str, float]:\n return {\n self.station_ids[i]: self._phase_angles[i]\n for i in range(len(self._phase_angles))\n }",
"title": ""
}
] | [
{
"docid": "776da258a9c29fe5afb1e90ee8672a47",
"score": "0.5651817",
"text": "def get_degree(self):\n u_degree = {}\n for edge_tp in config.edge_type:\n u_degree[edge_tp] = {}\n node_list = self.node_type[edge_tp[0]]\n for u in node_list:\n u... |
3f787ca4a821fd432142397a8795ba06 | Read a publisher by its href. | [
{
"docid": "41de1becc31fd486b203d8fdaf0dc7f5",
"score": "0.6052781",
"text": "def test_02_read_publisher(self):\n publisher = self.client.get(self.publisher['_href'])\n for key, val in self.publisher.items():\n with self.subTest(key=key):\n self.assertEqual(publis... | [
{
"docid": "a7f1e55f5aeb78cb808fb3b652ebcdc4",
"score": "0.67622393",
"text": "def read(self, href):\n return self._read(href)",
"title": ""
},
{
"docid": "a7f1e55f5aeb78cb808fb3b652ebcdc4",
"score": "0.67622393",
"text": "def read(self, href):\n return self._read(href)... |
b3eb43241168d6d93da0694621b098c1 | Get number of stars given from GitHub users to repositories created by this user | [
{
"docid": "37a09208876bd7a9925f5f9a43726e11",
"score": "0.5487575",
"text": "def getStars(self):\n return self._stars",
"title": ""
}
] | [
{
"docid": "d22cbb26a191059849243c4fc9e0d882",
"score": "0.638393",
"text": "def rankContributors(list_contributor_logins):\n token_from_user = input('token :')\n connection_string = {'Authorization': 'token ' + token_from_user}\n baseUrl = 'https://api.github.com'\n average_star_ranking = [... |
e4a891cf8eed1800dc1d44c125870cc1 | Split a dataset into train, validation and test sets | [
{
"docid": "b0f701fd35dce87a92a1f8f81cd26d34",
"score": "0.0",
"text": "def train_val_test_split(\n reframed, n_hours, n_features, target_var=\"temp\", ascending_sampling=False\n):\n\n n_train_hours = int(reframed.shape[0] * 0.6)\n n_val_hours = int(reframed.shape[0] * 0.2)\n\n if ascending_... | [
{
"docid": "c80892f3a5b6d0c68e2fae68d7dd7c2f",
"score": "0.8654461",
"text": "def __split_dataset(self):\n self.train, self.valid, _, _ = train_test_split(self.data, self.data, test_size=0.2)\n self.valid, self.test, _, _ = train_test_split(self.valid, self.valid, test_size=0.5)",
"tit... |
f9d5746b3e0e7fa99c8bd158eef3fb12 | >>> lnk = Link(1, Link(1, Link(1, Link(5)))) >>> unique = remove_duplicates(lnk) >>> len(unique) 2 >>> len(lnk) 2 | [
{
"docid": "9a46b7d1b94081f5f4118fff7b3d3919",
"score": "0.7803838",
"text": "def remove_duplicates(lnk):\n\tif lnk is Link.empty or lnk.rest is Link.empty:\n\t\treturn lnk \n\tif lnk.first == lnk.second:\n\t\tlnk.rest = lnk.rest.rest\n\t\tremove_duplicates(lnk) \n\t\treturn lnk \n\tremove_duplicates(ln... | [
{
"docid": "18466a6c14eb2387e8dc585a041098c1",
"score": "0.7703053",
"text": "def remove_duplicates(lnk):\r\n\tif lnk is Link.empty or lnk.rest is Link.empty:\r\n\t\treturn lnk\r\n\telif lnk.first == lnk.rest.first:\r\n\t\tlnk.rest = lnk.rest.rest\r\n\t\tremove_duplicates(lnk)\r\n\t\treturn lnk\r\n\tels... |
ce60256e482d643823f4598155fe8c52 | Fuzzy AND operator is minimum of two numbers. | [
{
"docid": "8964abb2e36534b12ef3e0f4f939f44a",
"score": "0.81514597",
"text": "def FuzzyAND(aNumber, bNumber):\n if IsNumber(aNumber) and IsNumber(bNumber):\n return min(aNumber, bNumber)\n\n else:\n return None # return None if errors",
"title": ""
}
] | [
{
"docid": "a778de176172150762eb45800c58ec0b",
"score": "0.66924685",
"text": "def min_and(a, b, c, d, w):\n m = 1 << (w - 1)\n while m != 0:\n if (~a & ~c & m) != 0:\n temp = (a | m) & -m\n if temp <= b:\n a = temp\n ... |
dcc35b007b0168e36cccb1ebb33e1236 | Use an existing TCA decomposition to fit trials from disengaged and sated days. Compare ratio of trials disengaged vs engaged using a ramp index. | [
{
"docid": "4f987da9e3070edce8658ed69d46ea60",
"score": "0.4711248",
"text": "def fit_disengaged_sated(\n mouse,\n trace_type='zscore_day',\n method='mncp_hals',\n cs='',\n warp=False,\n word=None,\n group_by='all',\n nan_thresh=0.85,\n scor... | [
{
"docid": "bd17fa02a4dbdbc1712c5f672969e866",
"score": "0.5465664",
"text": "def calibrateTAC(self,data):\n # scan through data and get mean peak positions in a fairly crude search\n peakpos=[]\n N=len(data)\n x=np.arange(N)\n i=2\n while i<N-6:\n if... |
7f9d5418820abd5a3fb1eeb16d43f7e5 | calculates geometric mean longitude of the sun | [
{
"docid": "4b5b203e8fb4d48ba68d05372223bb63",
"score": "0.0",
"text": "def get_geomean_long(ajc: FlexNum):\n\n geomean_long = (280.46646 + ajc * (36000.76983 + ajc * 0.0003032)) % 360\n return geomean_long",
"title": ""
}
] | [
{
"docid": "f4bee6dfdd1c5255c98874e24413ec49",
"score": "0.6482157",
"text": "def area(lat,lon):\n lat_r = np.radians(lat)\n lon_r = np.radians(lon)\n f=2*omega*np.sin(lat_r)\n grad_lon=lon_r.copy()\n grad_lon.data=np.gradient(lon_r)\n\n dx=grad_lon*earth_radius*np.cos(lat_r)\n dy=n... |
429176e432616849a696358c7ab89eec | import data domain on different dc during the operation restart vdsm | [
{
"docid": "bbda6735a49e3ea4a6f969b35f1e4e48",
"score": "0.73865354",
"text": "def test_restart_vdsm_during_import_domain(self):\n self._restart_component(\n test_utils.restartVdsmd, config.HOSTS[0], config.HOSTS_PW\n )",
"title": ""
}
] | [
{
"docid": "ff2d46360f0b78ddce8577fd44178d94",
"score": "0.61138797",
"text": "def test_restart_engine_during_import_domain(self):\n self._restart_component(\n test_utils.restart_engine, config.ENGINE, 10, 300\n )",
"title": ""
},
{
"docid": "769d079ef98b133b3b0fa6bd... |
e40aaf68aab09718ffdf709621911853 | Returns Kusto data type string equivalent of python object | [
{
"docid": "46a4950c3c3147c40727eb3871eb97e8",
"score": "0.5660054",
"text": "def GetColumnType(self,item):\n if isinstance(item, str):\n return \"string\"\n elif isinstance(item, bool):\n return \"bool\"\n elif isinstance(item, datetime):\n return \... | [
{
"docid": "3c802d2918cfa947df478be773cad2df",
"score": "0.6989038",
"text": "def value_type_str(self):\r\n return self._vkrecord.data_type_str()",
"title": ""
},
{
"docid": "6dc7ff1670080c39bb4a26ecef2c5299",
"score": "0.6857374",
"text": "def dataType(self):\n return io.d... |
7182873bb7fa9aeaedc833522527959a | Start process for periodical metrics publishing. | [
{
"docid": "9dc9198b33cbaeaac343fa91ee46e10e",
"score": "0.6862031",
"text": "def run(self):\n print(f\"Start publishing metrics each {self.check_timeout} seconds\")\n try:\n while True:\n self._step()\n time.sleep(self.check_timeout)\n excep... | [
{
"docid": "f447ae54301e15a0407b756c5941b2e1",
"score": "0.62567204",
"text": "def start_publishing(self):\r\n self.LOGGER.info(\"Issuing consumer related RPC commands\")\r\n self.enable_delivery_confirmations()\r\n self.schedule_next_message()",
"title": ""
},
{
"docid"... |
11d22fe9a3cc442fd63806cfe92823a9 | Inferences DeepLab model and visualizes result. | [
{
"docid": "b2b6c84a58a39de71fb3f094ecbdb6e1",
"score": "0.0",
"text": "def run_visualization(SAMPLE_IMAGE, MODEL):\n original_im = Image.open(SAMPLE_IMAGE)\n seg_map = MODEL.run(original_im)\n vis_segmentation(original_im, seg_map)",
"title": ""
}
] | [
{
"docid": "1439180aba664f6d439d7964ba78802a",
"score": "0.6432368",
"text": "def Inference(self):\n p = self.params\n subgraphs = {}\n with tf.name_scope('inference'):\n input_placeholders = self._Placeholders()\n predictions = self.ComputePredictions(self.theta, input_placeholders)\... |
c8306e2273058966a234d835b7e1a3bc | Called when a connection is retrieved from the Pool. | [
{
"docid": "5681a542d64da8c118b62e792bcbcb8b",
"score": "0.0",
"text": "def connection_checked_out(self, dic):",
"title": ""
}
] | [
{
"docid": "30b14013ba3808f88d7851d47d0eca76",
"score": "0.7331757",
"text": "def handle_establishing_connection(self):\r\n pass",
"title": ""
},
{
"docid": "5aa9447772e95aecfb8117b137e3462f",
"score": "0.69465435",
"text": "def return_connection_to_pool(pool, conn):\n\n\tpool... |
430c32fd50beaf87187b11238c48ff2e | Compress a directory history into a new one with at most 20 entries. Return a new list made from the first and last 10 elements of dhist after removal of duplicates. | [
{
"docid": "8fdb423663e59bdafa76039572b781d1",
"score": "0.7217732",
"text": "def compress_dhist(dh):\n head, tail = dh[:-10], dh[-10:]\n\n newhead = []\n done = set()\n for h in head:\n if h in done:\n continue\n newhead.append(h)\n done.add(h)\n\n return ... | [
{
"docid": "bec98a3ef0a4d69b35bd258fae83c107",
"score": "0.5557128",
"text": "def make_history(self):\n hist.create_history()",
"title": ""
},
{
"docid": "ddafa5286413b46971343bdc88582d91",
"score": "0.5521702",
"text": "def sorted_histogram(d):\n\n\tlisted_histogram = dictionary_... |
a4e03b79f4bca472bc5d55a46f02d91a | Calculate the maximum of (self.y, self.left.max, self.right.max) | [
{
"docid": "cb6643a0cbd1f47373fee401e0f44e63",
"score": "0.84987015",
"text": "def maximum(self):\n t1 = self.y\n t2 = self.left.max if self.left is not None else 0\n t3 = self.right.max if self.right is not None else 0\n if t2 > t1:\n t1 = t2\n if t3 > t1:\... | [
{
"docid": "8645674f1401514dff619e4bd5cd2d62",
"score": "0.76793355",
"text": "def get_max(self):\n current = self\n while current.right is not None:\n current = current.right\n return current.val",
"title": ""
},
{
"docid": "9e0226fd16b1e0e69bc62a65761ac880",... |
fe15ac4f58fda9fc416b73632a43a992 | Load a benchmarking dataset. | [
{
"docid": "9dbfb2ad6a3d33a44ec1a3dd2a278eeb",
"score": "0.63846135",
"text": "def load_dataset(name, version):\n dataset_dir = os.path.join(DATA_DIR, name)\n dataset_ver_dir = os.path.join(dataset_dir, version)\n\n if not os.path.isdir(dataset_dir):\n raise FileNotFoundError(\"Dataset d... | [
{
"docid": "7cfee186bf8f388df2bc0aed803dbe78",
"score": "0.6830046",
"text": "def load_dataset():\n\n\n train_dd_loader = DailyDialogLoader(PATH_TO_TRAIN_DATA, load=False)\n train_dataloader = DataLoader(train_dd_loader, batch_size=16, shuffle=True, num_workers=0,\n coll... |
95db07da0ed40d43a308444d43d99a7b | Push the current branch to upstream. | [
{
"docid": "8c862767a70a436fa1af6befe0354bf3",
"score": "0.7993709",
"text": "def push_upstream(self, remote_branch):\n raise NotImplementedError",
"title": ""
}
] | [
{
"docid": "e11013d3cd4286379a7ffac73991c3fc",
"score": "0.7736781",
"text": "def push_branch(self, branch: str) -> None:\n self.git(\"push\", \"origin\", branch)",
"title": ""
},
{
"docid": "bb11a6094d9e5e453461617be6de1d1c",
"score": "0.7610119",
"text": "def push(self):\n ... |
9e8dd24d36b31110b38f0807a9141d3d | Test if self and `other` are equivalent. | [
{
"docid": "5b117e18d148dcdd3502a5108507265c",
"score": "0.0",
"text": "def is_equivalent(self, other, name, logger, tolerance=0.):\r\n if not isinstance(other, Vector):\r\n logger.debug('other is not a Vector object.')\r\n return False\r\n for component in ('x', 'y',... | [
{
"docid": "a2616b8d27fad6adfe1fba7498c9baef",
"score": "0.86753935",
"text": "def equivalent(self, other):\n if not isinstance(other, type(self)):\n return False\n\n return self.value == other.value",
"title": ""
},
{
"docid": "5603ab4689d39104367b5bb58c292f37",
"score... |
0923e54fbef2198369ff49b9b46e4e07 | Writes the KPC log file from the buffer of operations self.operationList According to self.writeMode, will append to existing log file or overwrite it. Returns True if anything was OK, false in case of problem in writing, undefined kpc path or empty operations buffer. File format KPC logs are tsv (comma separated value... | [
{
"docid": "a80052cac2dc7cb8d8b37d040ef5640a",
"score": "0.762399",
"text": "def writeKpc ( self ) :\r\n\r\n # no kpc file\r\n \r\n if self.isEmpty( self.kpcPath ) : return False\r\n\r\n # builds the log of KPC operations in memory\r\n\r\n ok = self.buildKpc()\r\n\r\n ... | [
{
"docid": "1b9584388ed2797e3ac4493b47c5e194",
"score": "0.5652837",
"text": "def write_log(self, log):\n # Write the logged data.\n print 'Writing logged data from operation:', self.name, '...'\n\n # Create sub-group.\n oplog = log.create_group(self.name)\n\n # Make s... |
2f55ef1f4fca4ad91ec1ff63856ee00c | The names of compiler plugins to use when compiling this target with javac. | [
{
"docid": "4607f0ad0d24f053e4feb607762f548f",
"score": "0.762797",
"text": "def javac_plugins(self):\n return self.payload.javac_plugins",
"title": ""
}
] | [
{
"docid": "4d9b710546bd7b1d9c92e1e084820ad8",
"score": "0.68274903",
"text": "def javac_plugin_args(self):\n return self.payload.javac_plugin_args",
"title": ""
},
{
"docid": "9cb35a40dadcbcd80804765b9d7d0e98",
"score": "0.6647237",
"text": "def scalac_plugins(self):\n return ... |
8e0cc9102fb9d52e13b29eef1560b904 | Target is a Dataset object | [
{
"docid": "bea4b49d375501378354a28312b06e60",
"score": "0.6221573",
"text": "def test_migration_target_dataset_dataset(self, Create):\n Create.side_effect = fake_migration_create\n\n source = self.client.Dataset(1)\n target = self.client.Dataset(2)\n migration = source.migra... | [
{
"docid": "cbefba46a5381909814bc503a7964e60",
"score": "0.7230855",
"text": "def dataset(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "26031e4d686dcb61088a13967b562361",
"score": "0.6850486",
"text": "def get_dataset(self):\n \n return self.d... |
01b1f2664d447a7c59a1bff41407a3fb | Deinterlace all frames rather than just the frames identified as interlaced. The default is `false`. | [
{
"docid": "05394e7df54450c959e3f4feef784be2",
"score": "0.8239616",
"text": "def deinterlace_all_frames(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"deinterlace_all_frames\")",
"title": ""
}
] | [
{
"docid": "3f58a6e2dd8475dc3c90e5e374d5eb26",
"score": "0.6910056",
"text": "def deinterlace(self) -> Optional[pulumi.Input['DeinterlaceArgs']]:\n return pulumi.get(self, \"deinterlace\")",
"title": ""
},
{
"docid": "d7b29547aae938a385937022cd3b51da",
"score": "0.6562976",
"t... |
be52a3ae4258344a6cd99b72a6d1dccd | Using challenge id and user_id from the session, get all attributes that matched for that user to complete the challenge | [
{
"docid": "0de95ad9ee49b4d82546a66c4d033dbf",
"score": "0.565714",
"text": "def matched_attributes():\n user_challenge_id = request.args.get('user_challenge_id')\n categories = UserChallengeCategory.query.filter(UserChallengeCategory.user_challenge_id==user_challenge_id).all()\n winning_tags_t... | [
{
"docid": "2a68dcc3efe7a2dbc0131b7093d361ce",
"score": "0.5634904",
"text": "def example_data():\n import datetime\n\n # Amazing. A dimension where all proper nouns begin with \"Schmla\":\n u1 = User(username='Shmlony', password='123', email='shmlony@email.com', \n phone='111-22... |
ed0eee55e45052cf7e06916c07afd9bc | Helps to list all specified files in package including files in directories since `package_data` ignores directories. | [
{
"docid": "09bad0013a0dd4d825a9a8815b823eca",
"score": "0.822155",
"text": "def get_package_data_files(package, data, package_dir=None):\n if package_dir is None:\n package_dir = os.path.join(*package.split('.'))\n all_files = []\n for f in data:\n path = os.path.join(package_dir... | [
{
"docid": "9b9c693e91e50e7f0a9168a1f2600086",
"score": "0.76190716",
"text": "def find_package_data(data_root, package_root):\n files = []\n for root, dirnames, filenames in os.walk(data_root):\n for fn in filenames:\n files.append(relpath(join(root, fn), package_root))\n ret... |
a4f43756e38427187545affc135b545a | Scripted pipeline to process FluoXAS data | [
{
"docid": "f548e1d390f916c338f7efeac824c7ee",
"score": "0.0",
"text": "def tasks(**parameters):\n tasks = []\n\n # Common parameters\n commonparams = task_parameters(parameters, \"common\")\n sinstrument = require_pop(commonparams, \"instrument\")\n coutputparent = commonparams.pop(\"out... | [
{
"docid": "229a4bdd48a9e744c6b38a6794e0beb5",
"score": "0.7169673",
"text": "def pipeline():\n indexTargetsProcesses()\n #Start the mapping operations\n mappingProcesses()\n extractingProcesses()\n graphingProcesses()\n formatOutput()",
"title": ""
},
{
"docid": "20e842fa3... |
46a7ecaa379a016942a993503469a8bd | Sets up _client as a test emulation of DynamoDB. Creates the full database schema, a test user, and two devices (one for mobile, one for webapplication). | [
{
"docid": "b8c6dbd3d74a7ed974009238bb7c1f6d",
"score": "0.0",
"text": "def setUp(self):\n super(LocalClientTestCase, self).setUp()\n options.options.localdb_dir = ''\n self._client = LocalClient(test_SCHEMA)\n test_SCHEMA.VerifyOrCreate(self._client, self.stop)\n self.wait()",
"title... | [
{
"docid": "215dfd1ba1c84a029e7042c351b19542",
"score": "0.7453459",
"text": "def setUp(self):\r\n super(DynamoDBClientTestCase, self).setUp()\r\n options.options.domain = 'goviewfinder.com'\r\n secrets.InitSecretsForTest()\r\n self._client = dynamodb_client.DynamoDBClient(schema=vf_schema.S... |
e5f2f01e2cb575c68541ea2684aa4630 | Performs wavelet analysis on a dataset | [
{
"docid": "9374964ad45102ff7b307bf9cb0a8173",
"score": "0.646736",
"text": "def analyze(data, wavelet=DEFAULT_WAVELET, scales=None, dt=1., dj=0.125, \n mask_coi=False, frequency=False, axis=-1):\n n = data.shape[axis]\n if scales is None:\n s0 = find_s0(wavelet, dt)\n scales = fi... | [
{
"docid": "9ac1013ef45a01eb02ad55a7f60cad55",
"score": "0.65809494",
"text": "def model_data(self,wav,dt,t0,minf,maxf,vel,ref,jf=1,nrmax=3,eps=0.,dtmax=5e-05,time=True,\n ntx=0,nty=0,px=0,py=0,nthrds=1,sverb=True,wverb=False) -> np.ndarray:\n # Save wavelet temporal parameters\n n... |
3730c0df8cc293579f9a43739d4d8bd5 | The schedule revision is a hash of all information pertaining to scheduling. Information unrelated to scheduling, such as the content being sent at each event, is excluded. This is mainly used to determine when a TimedScheduleInstance should recalculate its schedule. | [
{
"docid": "00de47933eb7aa28f72bfee4adb6acfc",
"score": "0.67214155",
"text": "def memoized_schedule_revision(self):\n schedule_info = json.dumps([\n self.schedule_length,\n self.total_iterations,\n self.start_offset,\n self.start_day_of_week,\n ... | [
{
"docid": "377bccb579075d99456edaf07e00a004",
"score": "0.5524533",
"text": "def getSchedule(self):\n self.lock.acquire()\n a = copy.deepcopy(self.db['schedule'])\n self.lock.release()\n return a",
"title": ""
},
{
"docid": "f30551ea8dab794a14118979c8ffc5da",
... |
7732ef5dc803d85b05c82625d85d48f2 | prepare OR context and execute expression | [
{
"docid": "6361845e59c9b253bf001a7d73df29d2",
"score": "0.0",
"text": "def eval_expression(row, position, exp, context=None, names=None):\n if names is None:\n names = [str(i) for i in range(len(row))]\n\n context = context or {}\n if exp.startswith(\"jython:\"):\n grow = PythonR... | [
{
"docid": "48f23340b21889fa9170acc5a7849bab",
"score": "0.648527",
"text": "def or_op():\n pass",
"title": ""
},
{
"docid": "304ac77c9b814aabb7da3a229df37c6c",
"score": "0.59999174",
"text": "def OrOp(query1, query2):\n return or_(query1, query2)",
"title": ""
},
{
... |
e454e8481461c2be828dba7a7a98f306 | Stemming removes morphological affixes from words, leaving only the word stem. | [
{
"docid": "567150460c8a9ff3eecc454545e03755",
"score": "0.0",
"text": "def stemming_list(text, lang='en', static=False, file_name='stemming'):\n input_check.input_check_list(text)\n # stemmed word list\n stem_words = []\n # original word list which is affected\n org_words = []\n # aff... | [
{
"docid": "6eb9d5543e2474464062b5a21ffa97f7",
"score": "0.73459184",
"text": "def stemmatize(self,word):\n lemma = self.lemmatizer.lemmatize(word)\n stem = (SnowballStemmer(self.language).stem(lemma))\n return stem",
"title": ""
},
{
"docid": "5aaa75487e993bb8da339404ce... |
27e06f08a255f180d74f118ba04c4cff | Construct a set from an optional iterable. | [
{
"docid": "169e10ec180b8373d94d59cbe9025b7e",
"score": "0.0",
"text": "def __init__(self, iterable=None):\n self._data = {}",
"title": ""
}
] | [
{
"docid": "e895aaf55199175e96e8bcf24c88bfc8",
"score": "0.58696115",
"text": "def _set_converter(itr):\n if isinstance(itr, (list, tuple, set)):\n itr = FiniteSet(*itr)\n if not isinstance(itr, Set):\n raise TypeError(\"%s is not an instance of list/tuple/set.\"%(itr))\n return i... |
2512f4358d650d9a6b61cfdee9732344 | Sets self.shows[sid] to a new Show instance, or sets the data | [
{
"docid": "f4ab11d734e8689914c9c62d1598b696",
"score": "0.7529713",
"text": "def _setShowData(self, sid, key, value):\n if sid not in self.shows:\n self.shows[sid] = Show()\n self.shows[sid].data[key] = value",
"title": ""
}
] | [
{
"docid": "93f78bc096cb794dcae5cf286618c245",
"score": "0.7073067",
"text": "def _setItem(self, sid, seas, ep, attrib, value):\n if sid not in self.shows:\n self.shows[sid] = Show()\n if seas not in self.shows[sid]:\n self.shows[sid][seas] = Season(show=self.shows[si... |
ededc25806c7e733b395325eb53820d9 | Return the annotation identifier of a voxel | [
{
"docid": "4502f434a8b56ae53b07b3473d7b6610",
"score": "0.753712",
"text": "def annId ( imageargs, dbcfg, proj ):\n\n # Perform argument processing\n (resolution, voxel) = restargs.voxel ( imageargs, dbcfg )\n\n # Get the identifier\n db = emcadb.EMCADB ( dbcfg, proj )\n return db.getVoxel ( resol... | [
{
"docid": "9fde52d6bec0b855d2448c514c6d2fda",
"score": "0.7325891",
"text": "def annotation_id():",
"title": ""
},
{
"docid": "128cf272ffe0aac6ae2b2d999125c768",
"score": "0.6396383",
"text": "def get_idx(self, idx):\n return self._annotations[idx]",
"title": ""
},
{
... |
610f4e95b64e6e0bd1051cb22ac0bad4 | Updates qtable entries using the temporal difference formula. This allows the AI to learn from its experiences | [
{
"docid": "9946043d3d89ddc6196c7da07911d7ff",
"score": "0.0",
"text": "def update(qtable, state, next_state, next_turn):\n\n # Calculate unique index for the given state\n state_idx = state_idx_calc(state)\n size = state.shape[0]\n\n # Given the two input states, calculates what action was ... | [
{
"docid": "62aaf30d5e9e5dc5a7234e336139c895",
"score": "0.6334579",
"text": "def updateQTableFromTerminatingState( self, reward ):\n \n # retrieve the old action value from the Q-table (indexed by the previous state and the previous action)\n old_q = self.q_table[self.prev_s][self.... |
957d25b4cc0857853323e278b3053c9d | Get determinant coefficient with appropriate Fermi anticommutation factor | [
{
"docid": "d5354811821ddff15942473cad0e8efa",
"score": "0.6345779",
"text": "def get_det_coeff(self, det):\n\t\tif det in self.det_coeffs:\n\t\t\treturn self.det_coeffs[det] * self.dets[det].parity(det)\n\t\treturn 0",
"title": ""
}
] | [
{
"docid": "413fc5941756b2ca7da1865bf4e82869",
"score": "0.7301002",
"text": "def __determinant(a, b, c, d, e, f, g, h, i):\n return (a * e * i - a * f * h - b * d * i + b * f * g + c * d * h - c * e * g)",
"title": ""
},
{
"docid": "c318f71da8bb3c2e134b577fed78eed9",
"score": "0.7147... |
76d126eea30c50235a8ef21afcddc67d | Generate patch files from git | [
{
"docid": "75757dfcc998005c155116c28200b707",
"score": "0.69303894",
"text": "def generate_patches(repo, start, end, outdir, options):\n gbp.log.info(\"Generating patches from git (%s..%s)\" % (start, end))\n patches = []\n commands = {}\n for treeish in [start, end]:\n if not repo.h... | [
{
"docid": "13e1bf6c03a59fdbfb8b61d2bcf63c17",
"score": "0.64661807",
"text": "def main_git():\n proc = subprocess.Popen([\"git\", \"diff\", \"scala\"], stdout=subprocess.PIPE)\n out = proc.communicate()[0].decode()\n\n RE = re.compile(\"([-+])Subproject commit (.*)\")\n frm = None\n to =... |
e18251726218e5936eb8605f9f04d110 | give me a user_id in the form of 'uXXXX' | [
{
"docid": "ec4e0b8244530275a791e03288448b60",
"score": "0.0",
"text": "def get_userinfo(uid):\n \n eid = uid2eid[uid]\n e = employees[eid]\n print e",
"title": ""
}
] | [
{
"docid": "4f71559f237c6bd50f41ad164cdfc301",
"score": "0.7902427",
"text": "def get_id(self):\n return unichr(self.user_id)",
"title": ""
},
{
"docid": "5e2591c6a2c9484be5577e4c7b5c3b36",
"score": "0.7891965",
"text": "def get_id(self):\n\n if self.user_id is None:\n ... |
877bc523dd420b983205c4724c8c77cc | [Lib] [Execution] End special bonfire_ignition | [
{
"docid": "f7310469a480ff478ee01856da1f055a",
"score": "0.0",
"text": "def event_m10_14_x34(z132=10141800, z133=100461):\n \"\"\"State 0,6: Bonfire light action\"\"\"\n PlayerActionRequest(5)\n assert PlayerIsInEventAction(5) != 0\n \"\"\"State 7: Wait for action to finish\"\"\"\n Compar... | [
{
"docid": "09fa8d184abc69524635a80b17e528e2",
"score": "0.7245591",
"text": "def end(self):",
"title": ""
},
{
"docid": "09fa8d184abc69524635a80b17e528e2",
"score": "0.7245591",
"text": "def end(self):",
"title": ""
},
{
"docid": "09fa8d184abc69524635a80b17e528e2",
"... |
3e349e869d2d23310c3da45b57fb9122 | Guarda la ruta provista en un archivo | [
{
"docid": "ae18d5c30da4ea6513ccecd13d3e7907",
"score": "0.69561684",
"text": "def guardar_ruta(archivo, texto):\n with open(archivo, \"w\") as ruta:\n ruta.write(f'{texto}')",
"title": ""
}
] | [
{
"docid": "a0db7e7417ae67d4ecf2a8d426768e28",
"score": "0.67659104",
"text": "def save(self, path):",
"title": ""
},
{
"docid": "ee03039bec37e652f2d5f497ae526009",
"score": "0.62812024",
"text": "def guardar(self):\n self.imagen_actual.save(\"imagen_nueva.png\")",
"title"... |
5f1819f5b4ebc4f2621b3a11660cf2bf | Show a count of blogs in this category | [
{
"docid": "8fb1cdd1839cf8a11498e093eaf91fb3",
"score": "0.0",
"text": "def post_count(self, obj):\n\n # Note: This is purposefully not optimized\n return obj.post_set.count()",
"title": ""
}
] | [
{
"docid": "4b2c11de80b88c2e074f7ce853f05876",
"score": "0.6901813",
"text": "def get_count(self) -> int:\n return self.category_stats[\"count\"]",
"title": ""
},
{
"docid": "d279e5aabcf4bd97da404d7b701cf755",
"score": "0.65567595",
"text": "def get_category_count(self):\n... |
4b405ac332f6951a2c37f3ee664e8eb7 | Should provide a temp file that's cleared on exit. | [
{
"docid": "233240173585dcf5e01fbf13e8ccc91d",
"score": "0.6800151",
"text": "def test_get_temp_file(self):\n with get_temp_file() as temp_file:\n self.assertTrue(os.path.exists(temp_file))\n self.assertTrue(os.path.isfile(temp_file))\n self.assertFalse(os.path.exists... | [
{
"docid": "1087f27ba0104a5b15f7422f68647085",
"score": "0.76812315",
"text": "def __reset_file(self):\n portable.remove(self.test_fn)\n self.test_fh, self.test_fn = tempfile.mkstemp(\n dir=self.test_path)",
"title": ""
},
{
"docid": "06f7b7f9... |
70b836134d58047b1b9f8f729a58ab4a | See examples/listusers for example usage. | [
{
"docid": "98fb1c0d9c35be01ffde49e865713388",
"score": "0.0",
"text": "def get_users(self, request: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:\n return self.call_endpoint(\n url=\"users\",\n method=\"GET\",\n request=request,\n )",
"title": ""... | [
{
"docid": "628df88ba64f2c1e56b7ec98c4ae02e3",
"score": "0.8584034",
"text": "def get_users():\n api.list_users()",
"title": ""
},
{
"docid": "810984be17a4b999aff0f666e87351b7",
"score": "0.8257032",
"text": "def list_users():\n users = User.query.all()\n\n return render_tem... |
d3f45a02137a2a5293cd77ddd7b22297 | This function can be used to verify that .npy file contains the right labels, by regenerating the semantic segmentation array from the .npy file. | [
{
"docid": "bcc81de761596d5852d5140baa0784cd",
"score": "0.0",
"text": "def regen_img_from_npy(path_npy, path_img='/home/js/PycharmProjects/gym-duckietown-generate-fp/sample/test4.png'):\n\n color_map = {'white': np.array([255, 255, 255]), 'yellow': np.array([255, 255, 0]), 'left': np.array([255, 0, ... | [
{
"docid": "181990efde8f09ae8a740812134a80ed",
"score": "0.65193164",
"text": "def test_segmentation_save_load(self):\n\n segmentation = Segmentation('test.annot', [1, 1, 2, 2])\n segmentation.add_label(0, Label('no label', (0, 0, 0, 255)))\n segmentation.add_label(1, Label('label 0... |
72193bd1b65736259484f9f7739c5321 | Automatically removes code blocks from the code. | [
{
"docid": "a2cd6552c0b31eb32886d54a47d3c8c7",
"score": "0.6670364",
"text": "def cleanup_code(self, content):\n # remove ```py\\n```\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n\n # remove `foo`\n re... | [
{
"docid": "f6761198124dad601935ec29835a76c1",
"score": "0.66694766",
"text": "def cleanup_code(self, content):\n # remove ```py\\n```\n if content.startswith('```') and content.endswith('```'):\n return '\\n'.join(content.split('\\n')[1:-1])\n # remove `foo`\n ret... |
16a46ab61728cbb4b559837c838ffb03 | Check if all direct descendents are terminal. | [
{
"docid": "46ef1e20bf80b2309cf7fc7348befd97",
"score": "0.6559002",
"text": "def is_preterminal(self):\n if self.root.is_terminal():\n return False\n for clade in self.root.clades:\n if not clade.is_terminal():\n return False\n return True",
... | [
{
"docid": "b4fc0b37e187cf20daa0bbb2097879ed",
"score": "0.71356946",
"text": "def is_terminal(self):\n return len(self.children) == 0",
"title": ""
},
{
"docid": "063bf8fb9e98b51b606c0a21d8fb4539",
"score": "0.69620866",
"text": "def is_terminal(self):\n return not boo... |